diff --git a/TODO.md b/TODO.md
index aa1b58e5..da7726bc 100644
--- a/TODO.md
+++ b/TODO.md
@@ -20,25 +20,33 @@ N/A
 
 <hr><br>
 
-## Known Issues
+## Known Issues & Limitations
 
 ### Face with Attention
 
 `FaceMesh-Attention` is not supported when using `WASM` backend due to missing kernel op in **TFJS**  
+No issues with default model `FaceMesh`  
 
 ### Object Detection
 
 `NanoDet` model is not supported when using `WASM` backend due to missing kernel op in **TFJS**  
+No issues with default model `MB3-CenterNet`  
 
 ### WebGPU
 
 Experimental support only until support is officially added in Chromium  
-Enable via <chrome://flags/#enable-unsafe-webgpu>
+Enable via <chrome://flags/#enable-unsafe-webgpu>  
 
 ### Firefox
 
 Running in **web workers** requires `OffscreenCanvas` which is still disabled by default in **Firefox**  
-Enable via `about:config` -> `gfx.offscreencanvas.enabled`
+Enable via `about:config` -> `gfx.offscreencanvas.enabled`  
+[Details](https://developer.mozilla.org/en-US/docs/Web/API/OffscreenCanvas#browser_compatibility)  
+
+### Safari
+
+No support for running in **web workers** as Safari still does not support `OffscreenCanvas`  
+[Details](https://developer.mozilla.org/en-US/docs/Web/API/OffscreenCanvas#browser_compatibility)  
 
 <hr><br>
 
@@ -49,7 +57,13 @@ Enable via `about:config` -> `gfx.offscreencanvas.enabled`
 - New method [`human.video()`](https://vladmandic.github.io/human/typedoc/classes/Human.html#video)  
   Runs continous detection of an input **video**  
   instead of processing each frame manually using `human.detect()`  
-- New simple demo [*Live*](https://vladmandic.github.io/human/demo/video/index.html) | [*Code*](https://github.com/vladmandic/human/blob/main/demo/video/index.html)  
+- New demo for **webcam** and **video** methods [*Live*](https://vladmandic.github.io/human/demo/video/index.html) | [*Code*](https://github.com/vladmandic/human/blob/main/demo/video/index.html)  
+  *Full HTML and JavaScript code in less than a screen*  
+- Redesigned [`human.segmentation`](https://vladmandic.github.io/human/typedoc/classes/Human.html#segmentation)  
+  *Breaking changes*  
+- New model `rvm` for high-quality body segmentation in real-time  
+  *Not part of default deployment, download from [human-models](https://github.com/vladmandic/human-models/tree/main/models)*  
+- New demo for **segmentation** methods [*Live*](https://vladmandic.github.io/human/demo/segmentation/index.html) | [*Code*](https://github.com/vladmandic/human/blob/main/demo/segmentation/index.html)  
   *Full HTML and JavaScript code in less than a screen*  
 - New advanced demo using **BabylonJS and VRM** [*Live*](https://vladmandic.github.io/human-bjs-vrm) | [*Code*](https://github.com/vladmandic/human-bjs-vrm)
 - Update **TypeDoc** generation [*Link*](https://vladmandic.github.io/human/typedoc)  
diff --git a/demo/faceid/index.js b/demo/faceid/index.js
index 2cd259dd..63fee5da 100644
--- a/demo/faceid/index.js
+++ b/demo/faceid/index.js
@@ -4,6 +4,353 @@
   author: <https://github.com/vladmandic>'
 */
 
-import*as S from"../../dist/human.esm.js";var l,L="human",f="person",v=(...a)=>console.log("indexdb",...a);async function h(){return l?!0:new Promise(a=>{let n=indexedDB.open(L,1);n.onerror=o=>v("error:",o),n.onupgradeneeded=o=>{v("create:",o.target),l=o.target.result,l.createObjectStore(f,{keyPath:"id",autoIncrement:!0})},n.onsuccess=o=>{l=o.target.result,v("open:",l),a(!0)}})}async function C(){let a=[];return l||await h(),new Promise(n=>{let o=l.transaction([f],"readwrite").objectStore(f).openCursor(null,"next");o.onerror=i=>v("load error:",i),o.onsuccess=i=>{i.target.result?(a.push(i.target.result.value),i.target.result.continue()):n(a)}})}async function b(){return l||await h(),new Promise(a=>{let n=l.transaction([f],"readwrite").objectStore(f).count();n.onerror=o=>v("count error:",o),n.onsuccess=()=>a(n.result)})}async function x(a){l||await h();let n={name:a.name,descriptor:a.descriptor,image:a.image};l.transaction([f],"readwrite").objectStore(f).put(n),v("save:",n)}async function D(a){l||await h(),l.transaction([f],"readwrite").objectStore(f).delete(a.id),v("delete:",a)}var g={cacheSensitivity:0,modelBasePath:"../../models",filter:{equalization:!0},face:{enabled:!0,detector:{rotation:!0,return:!0,cropFactor:1.6,mask:!1},description:{enabled:!0},iris:{enabled:!0},emotion:{enabled:!1},antispoof:{enabled:!0},liveness:{enabled:!0}},body:{enabled:!1},hand:{enabled:!1},object:{enabled:!1},gesture:{enabled:!0}},B={order:2,multiplier:25,min:.2,max:.8},d={minConfidence:.6,minSize:224,maxTime:3e4,blinkMin:10,blinkMax:800,threshold:.5,mask:g.face.detector.mask,rotation:g.face.detector.rotation,cropFactor:g.face.detector.cropFactor,...B},e={faceCount:{status:!1,val:0},faceConfidence:{status:!1,val:0},facingCenter:{status:!1,val:0},lookingCenter:{status:!1,val:0},blinkDetected:{status:!1,val:0},faceSize:{status:!1,val:0},antispoofCheck:{status:!1,val:0},livenessCheck:{status:!1,val:0},age:{status:!1,val:0},gender:{status:!1,val:0},timeout:{status:!0,val:0},descriptor:{status:!1,val:0},elapsedMs:{status:void 0,val:0},detectFPS:{status:void 0,val:0},drawFPS:{status:void 0,val:0}},E=()=>e.faceCount.status&&e.faceSize.status&&e.blinkDetected.status&&e.facingCenter.status&&e.lookingCenter.status&&e.faceConfidence.status&&e.antispoofCheck.status&&e.livenessCheck.status&&e.descriptor.status&&e.age.status&&e.gender.status,c={face:null,record:null},u={start:0,end:0,time:0},s=new S.Human(g);s.env.perfadd=!1;s.draw.options.font='small-caps 18px "Lato"';s.draw.options.lineHeight=20;var t={video:document.getElementById("video"),canvas:document.getElementById("canvas"),log:document.getElementById("log"),fps:document.getElementById("fps"),match:document.getElementById("match"),name:document.getElementById("name"),save:document.getElementById("save"),delete:document.getElementById("delete"),retry:document.getElementById("retry"),source:document.getElementById("source"),ok:document.getElementById("ok")},y={detect:0,draw:0},I=0,r=(...a)=>{t.log.innerText+=a.join(" ")+`
-`,console.log(...a)};async function H(){let a={audio:!1,video:{facingMode:"user",resizeMode:"none",width:{ideal:document.body.clientWidth}}},n=await navigator.mediaDevices.getUserMedia(a),o=new Promise(i=>{t.video.onloadeddata=()=>i(!0)});t.video.srcObject=n,t.video.play(),await o,t.canvas.width=t.video.videoWidth,t.canvas.height=t.video.videoHeight,t.canvas.style.width="50%",t.canvas.style.height="50%",s.env.initial&&r("video:",t.video.videoWidth,t.video.videoHeight,"|",n.getVideoTracks()[0].label),t.canvas.onclick=()=>{t.video.paused?t.video.play():t.video.pause()}}async function T(){var a;if(!t.video.paused){(a=c.face)!=null&&a.tensor&&s.tf.dispose(c.face.tensor),await s.detect(t.video);let n=s.now();e.detectFPS.val=Math.round(1e4/(n-y.detect))/10,y.detect=n,requestAnimationFrame(T)}}function P(){let a=32;for(let[n,o]of Object.entries(e)){let i=document.getElementById(`ok-${n}`);i||(i=document.createElement("div"),i.id=`ok-${n}`,i.innerText=n,i.className="ok",i.style.top=`${a}px`,t.ok.appendChild(i)),typeof o.status=="boolean"&&(i.style.backgroundColor=o.status?"lightgreen":"lightcoral");let m=o.status?"ok":"fail";i.innerText=`${n}: ${o.val===0?m:o.val}`,a+=28}}async function R(){var o;let a=s.next(s.result);s.draw.canvas(t.video,t.canvas),await s.draw.all(t.canvas,a);let n=s.now();if(e.drawFPS.val=Math.round(1e4/(n-y.draw))/10,y.draw=n,e.faceCount.val=s.result.face.length,e.faceCount.status=e.faceCount.val===1,e.faceCount.status){let i=Object.values(s.result.gesture).map(m=>m.gesture);(i.includes("blink left eye")||i.includes("blink right eye"))&&(u.start=s.now()),u.start>0&&!i.includes("blink left eye")&&!i.includes("blink right eye")&&(u.end=s.now()),e.blinkDetected.status=e.blinkDetected.status||Math.abs(u.end-u.start)>d.blinkMin&&Math.abs(u.end-u.start)<d.blinkMax,e.blinkDetected.status&&u.time===0&&(u.time=Math.trunc(u.end-u.start)),e.facingCenter.status=i.includes("facing center"),e.lookingCenter.status=i.includes("looking center"),e.faceConfidence.val=s.result.face[0].faceScore||s.result.face[0].boxScore||0,e.faceConfidence.status=e.faceConfidence.val>=d.minConfidence,e.antispoofCheck.val=s.result.face[0].real||0,e.antispoofCheck.status=e.antispoofCheck.val>=d.minConfidence,e.livenessCheck.val=s.result.face[0].live||0,e.livenessCheck.status=e.livenessCheck.val>=d.minConfidence,e.faceSize.val=Math.min(s.result.face[0].box[2],s.result.face[0].box[3]),e.faceSize.status=e.faceSize.val>=d.minSize,e.descriptor.val=((o=s.result.face[0].embedding)==null?void 0:o.length)||0,e.descriptor.status=e.descriptor.val>0,e.age.val=s.result.face[0].age||0,e.age.status=e.age.val>0,e.gender.val=s.result.face[0].genderScore||0,e.gender.status=e.gender.val>=d.minConfidence}return e.timeout.status=e.elapsedMs.val<=d.maxTime,P(),E()||!e.timeout.status?(t.video.pause(),s.result.face[0]):(e.elapsedMs.val=Math.trunc(s.now()-I),new Promise(i=>{setTimeout(async()=>{await R(),i(s.result.face[0])},30)}))}async function z(){var a,n,o,i;if(t.name.value.length>0){let m=(a=t.canvas.getContext("2d"))==null?void 0:a.getImageData(0,0,t.canvas.width,t.canvas.height),p={id:0,name:t.name.value,descriptor:(n=c.face)==null?void 0:n.embedding,image:m};await x(p),r("saved face record:",p.name,"descriptor length:",(i=(o=c.face)==null?void 0:o.embedding)==null?void 0:i.length),r("known face records:",await b())}else r("invalid name")}async function j(){c.record&&c.record.id>0&&await D(c.record)}async function $(){var i,m,p,k;if(t.canvas.style.height="",(i=t.canvas.getContext("2d"))==null||i.clearRect(0,0,d.minSize,d.minSize),!((m=c==null?void 0:c.face)!=null&&m.tensor)||!((p=c==null?void 0:c.face)!=null&&p.embedding))return!1;if(console.log("face record:",c.face),r(`detected face: ${c.face.gender} ${c.face.age||0}y distance ${c.face.iris||0}cm/${Math.round(100*(c.face.iris||0)/2.54)/100}in`),s.tf.browser.toPixels(c.face.tensor,t.canvas),await b()===0)return r("face database is empty: nothing to compare face with"),document.body.style.background="black",t.delete.style.display="none",!1;let a=await C(),n=a.map(w=>w.descriptor).filter(w=>w.length>0),o=s.match(c.face.embedding,n,B);return c.record=a[o.index]||null,c.record&&(r(`best match: ${c.record.name} | id: ${c.record.id} | similarity: ${Math.round(1e3*o.similarity)/10}%`),t.name.value=c.record.name,t.source.style.display="",(k=t.source.getContext("2d"))==null||k.putImageData(c.record.image,0,0)),document.body.style.background=o.similarity>d.threshold?"darkgreen":"maroon",o.similarity>d.threshold}async function M(){var a,n;return e.faceCount.status=!1,e.faceConfidence.status=!1,e.facingCenter.status=!1,e.blinkDetected.status=!1,e.faceSize.status=!1,e.antispoofCheck.status=!1,e.livenessCheck.status=!1,e.age.status=!1,e.gender.status=!1,e.elapsedMs.val=0,t.match.style.display="none",t.retry.style.display="none",t.source.style.display="none",t.canvas.style.height="50%",document.body.style.background="black",await H(),await T(),I=s.now(),c.face=await R(),t.canvas.width=((a=c.face.tensor)==null?void 0:a.shape[1])||d.minSize,t.canvas.height=((n=c.face.tensor)==null?void 0:n.shape[0])||d.minSize,t.source.width=t.canvas.width,t.source.height=t.canvas.height,t.canvas.style.width="",t.match.style.display="flex",t.save.style.display="flex",t.delete.style.display="flex",t.retry.style.display="block",E()?$():(r("did not find valid face"),!1)}async function q(){var a,n;r("human version:",s.version,"| tfjs version:",s.tf.version["tfjs-core"]),r("options:",JSON.stringify(d).replace(/{|}|"|\[|\]/g,"").replace(/,/g," ")),r("initializing webcam..."),await H(),r("loading human models..."),await s.load(),r("initializing human..."),r("face embedding model:",g.face.description.enabled?"faceres":"",(a=g.face.mobilefacenet)!=null&&a.enabled?"mobilefacenet":"",(n=g.face.insightface)!=null&&n.enabled?"insightface":""),r("loading face database..."),r("known face records:",await b()),t.retry.addEventListener("click",M),t.save.addEventListener("click",z),t.delete.addEventListener("click",j),await s.warmup(),await M()}window.onload=q;
+
+// demo/faceid/index.ts
+import * as H from "../../dist/human.esm.js";
+
+// demo/faceid/indexdb.ts
+var db;
+var database = "human";
+var table = "person";
+var log = (...msg) => console.log("indexdb", ...msg);
+async function open() {
+  if (db)
+    return true;
+  return new Promise((resolve) => {
+    const request = indexedDB.open(database, 1);
+    request.onerror = (evt) => log("error:", evt);
+    request.onupgradeneeded = (evt) => {
+      log("create:", evt.target);
+      db = evt.target.result;
+      db.createObjectStore(table, { keyPath: "id", autoIncrement: true });
+    };
+    request.onsuccess = (evt) => {
+      db = evt.target.result;
+      log("open:", db);
+      resolve(true);
+    };
+  });
+}
+async function load() {
+  const faceDB = [];
+  if (!db)
+    await open();
+  return new Promise((resolve) => {
+    const cursor = db.transaction([table], "readwrite").objectStore(table).openCursor(null, "next");
+    cursor.onerror = (evt) => log("load error:", evt);
+    cursor.onsuccess = (evt) => {
+      if (evt.target.result) {
+        faceDB.push(evt.target.result.value);
+        evt.target.result.continue();
+      } else {
+        resolve(faceDB);
+      }
+    };
+  });
+}
+async function count() {
+  if (!db)
+    await open();
+  return new Promise((resolve) => {
+    const store = db.transaction([table], "readwrite").objectStore(table).count();
+    store.onerror = (evt) => log("count error:", evt);
+    store.onsuccess = () => resolve(store.result);
+  });
+}
+async function save(faceRecord) {
+  if (!db)
+    await open();
+  const newRecord = { name: faceRecord.name, descriptor: faceRecord.descriptor, image: faceRecord.image };
+  db.transaction([table], "readwrite").objectStore(table).put(newRecord);
+  log("save:", newRecord);
+}
+async function remove(faceRecord) {
+  if (!db)
+    await open();
+  db.transaction([table], "readwrite").objectStore(table).delete(faceRecord.id);
+  log("delete:", faceRecord);
+}
+
+// demo/faceid/index.ts
+var humanConfig = {
+  cacheSensitivity: 0,
+  modelBasePath: "../../models",
+  filter: { equalization: true },
+  face: {
+    enabled: true,
+    detector: { rotation: true, return: true, cropFactor: 1.6, mask: false },
+    description: { enabled: true },
+    iris: { enabled: true },
+    emotion: { enabled: false },
+    antispoof: { enabled: true },
+    liveness: { enabled: true }
+  },
+  body: { enabled: false },
+  hand: { enabled: false },
+  object: { enabled: false },
+  gesture: { enabled: true }
+};
+var matchOptions = { order: 2, multiplier: 25, min: 0.2, max: 0.8 };
+var options = {
+  minConfidence: 0.6,
+  minSize: 224,
+  maxTime: 3e4,
+  blinkMin: 10,
+  blinkMax: 800,
+  threshold: 0.5,
+  mask: humanConfig.face.detector.mask,
+  rotation: humanConfig.face.detector.rotation,
+  cropFactor: humanConfig.face.detector.cropFactor,
+  ...matchOptions
+};
+var ok = {
+  faceCount: { status: false, val: 0 },
+  faceConfidence: { status: false, val: 0 },
+  facingCenter: { status: false, val: 0 },
+  lookingCenter: { status: false, val: 0 },
+  blinkDetected: { status: false, val: 0 },
+  faceSize: { status: false, val: 0 },
+  antispoofCheck: { status: false, val: 0 },
+  livenessCheck: { status: false, val: 0 },
+  age: { status: false, val: 0 },
+  gender: { status: false, val: 0 },
+  timeout: { status: true, val: 0 },
+  descriptor: { status: false, val: 0 },
+  elapsedMs: { status: void 0, val: 0 },
+  detectFPS: { status: void 0, val: 0 },
+  drawFPS: { status: void 0, val: 0 }
+};
+var allOk = () => ok.faceCount.status && ok.faceSize.status && ok.blinkDetected.status && ok.facingCenter.status && ok.lookingCenter.status && ok.faceConfidence.status && ok.antispoofCheck.status && ok.livenessCheck.status && ok.descriptor.status && ok.age.status && ok.gender.status;
+var current = { face: null, record: null };
+var blink = {
+  start: 0,
+  end: 0,
+  time: 0
+};
+var human = new H.Human(humanConfig);
+human.env.perfadd = false;
+human.draw.options.font = 'small-caps 18px "Lato"';
+human.draw.options.lineHeight = 20;
+var dom = {
+  video: document.getElementById("video"),
+  canvas: document.getElementById("canvas"),
+  log: document.getElementById("log"),
+  fps: document.getElementById("fps"),
+  match: document.getElementById("match"),
+  name: document.getElementById("name"),
+  save: document.getElementById("save"),
+  delete: document.getElementById("delete"),
+  retry: document.getElementById("retry"),
+  source: document.getElementById("source"),
+  ok: document.getElementById("ok")
+};
+var timestamp = { detect: 0, draw: 0 };
+var startTime = 0;
+var log2 = (...msg) => {
+  dom.log.innerText += msg.join(" ") + "\n";
+  console.log(...msg);
+};
+async function webCam() {
+  const cameraOptions = { audio: false, video: { facingMode: "user", resizeMode: "none", width: { ideal: document.body.clientWidth } } };
+  const stream = await navigator.mediaDevices.getUserMedia(cameraOptions);
+  const ready = new Promise((resolve) => {
+    dom.video.onloadeddata = () => resolve(true);
+  });
+  dom.video.srcObject = stream;
+  void dom.video.play();
+  await ready;
+  dom.canvas.width = dom.video.videoWidth;
+  dom.canvas.height = dom.video.videoHeight;
+  dom.canvas.style.width = "50%";
+  dom.canvas.style.height = "50%";
+  if (human.env.initial)
+    log2("video:", dom.video.videoWidth, dom.video.videoHeight, "|", stream.getVideoTracks()[0].label);
+  dom.canvas.onclick = () => {
+    if (dom.video.paused)
+      void dom.video.play();
+    else
+      dom.video.pause();
+  };
+}
+async function detectionLoop() {
+  var _a;
+  if (!dom.video.paused) {
+    if ((_a = current.face) == null ? void 0 : _a.tensor)
+      human.tf.dispose(current.face.tensor);
+    await human.detect(dom.video);
+    const now = human.now();
+    ok.detectFPS.val = Math.round(1e4 / (now - timestamp.detect)) / 10;
+    timestamp.detect = now;
+    requestAnimationFrame(detectionLoop);
+  }
+}
+function drawValidationTests() {
+  let y = 32;
+  for (const [key, val] of Object.entries(ok)) {
+    let el = document.getElementById(`ok-${key}`);
+    if (!el) {
+      el = document.createElement("div");
+      el.id = `ok-${key}`;
+      el.innerText = key;
+      el.className = "ok";
+      el.style.top = `${y}px`;
+      dom.ok.appendChild(el);
+    }
+    if (typeof val.status === "boolean")
+      el.style.backgroundColor = val.status ? "lightgreen" : "lightcoral";
+    const status = val.status ? "ok" : "fail";
+    el.innerText = `${key}: ${val.val === 0 ? status : val.val}`;
+    y += 28;
+  }
+}
+async function validationLoop() {
+  var _a;
+  const interpolated = human.next(human.result);
+  human.draw.canvas(dom.video, dom.canvas);
+  await human.draw.all(dom.canvas, interpolated);
+  const now = human.now();
+  ok.drawFPS.val = Math.round(1e4 / (now - timestamp.draw)) / 10;
+  timestamp.draw = now;
+  ok.faceCount.val = human.result.face.length;
+  ok.faceCount.status = ok.faceCount.val === 1;
+  if (ok.faceCount.status) {
+    const gestures = Object.values(human.result.gesture).map((gesture) => gesture.gesture);
+    if (gestures.includes("blink left eye") || gestures.includes("blink right eye"))
+      blink.start = human.now();
+    if (blink.start > 0 && !gestures.includes("blink left eye") && !gestures.includes("blink right eye"))
+      blink.end = human.now();
+    ok.blinkDetected.status = ok.blinkDetected.status || Math.abs(blink.end - blink.start) > options.blinkMin && Math.abs(blink.end - blink.start) < options.blinkMax;
+    if (ok.blinkDetected.status && blink.time === 0)
+      blink.time = Math.trunc(blink.end - blink.start);
+    ok.facingCenter.status = gestures.includes("facing center");
+    ok.lookingCenter.status = gestures.includes("looking center");
+    ok.faceConfidence.val = human.result.face[0].faceScore || human.result.face[0].boxScore || 0;
+    ok.faceConfidence.status = ok.faceConfidence.val >= options.minConfidence;
+    ok.antispoofCheck.val = human.result.face[0].real || 0;
+    ok.antispoofCheck.status = ok.antispoofCheck.val >= options.minConfidence;
+    ok.livenessCheck.val = human.result.face[0].live || 0;
+    ok.livenessCheck.status = ok.livenessCheck.val >= options.minConfidence;
+    ok.faceSize.val = Math.min(human.result.face[0].box[2], human.result.face[0].box[3]);
+    ok.faceSize.status = ok.faceSize.val >= options.minSize;
+    ok.descriptor.val = ((_a = human.result.face[0].embedding) == null ? void 0 : _a.length) || 0;
+    ok.descriptor.status = ok.descriptor.val > 0;
+    ok.age.val = human.result.face[0].age || 0;
+    ok.age.status = ok.age.val > 0;
+    ok.gender.val = human.result.face[0].genderScore || 0;
+    ok.gender.status = ok.gender.val >= options.minConfidence;
+  }
+  ok.timeout.status = ok.elapsedMs.val <= options.maxTime;
+  drawValidationTests();
+  if (allOk() || !ok.timeout.status) {
+    dom.video.pause();
+    return human.result.face[0];
+  }
+  ok.elapsedMs.val = Math.trunc(human.now() - startTime);
+  return new Promise((resolve) => {
+    setTimeout(async () => {
+      await validationLoop();
+      resolve(human.result.face[0]);
+    }, 30);
+  });
+}
+async function saveRecords() {
+  var _a, _b, _c, _d;
+  if (dom.name.value.length > 0) {
+    const image = (_a = dom.canvas.getContext("2d")) == null ? void 0 : _a.getImageData(0, 0, dom.canvas.width, dom.canvas.height);
+    const rec = { id: 0, name: dom.name.value, descriptor: (_b = current.face) == null ? void 0 : _b.embedding, image };
+    await save(rec);
+    log2("saved face record:", rec.name, "descriptor length:", (_d = (_c = current.face) == null ? void 0 : _c.embedding) == null ? void 0 : _d.length);
+    log2("known face records:", await count());
+  } else {
+    log2("invalid name");
+  }
+}
+async function deleteRecord() {
+  if (current.record && current.record.id > 0) {
+    await remove(current.record);
+  }
+}
+async function detectFace() {
+  var _a, _b, _c, _d;
+  dom.canvas.style.height = "";
+  (_a = dom.canvas.getContext("2d")) == null ? void 0 : _a.clearRect(0, 0, options.minSize, options.minSize);
+  if (!((_b = current == null ? void 0 : current.face) == null ? void 0 : _b.tensor) || !((_c = current == null ? void 0 : current.face) == null ? void 0 : _c.embedding))
+    return false;
+  console.log("face record:", current.face);
+  log2(`detected face: ${current.face.gender} ${current.face.age || 0}y distance ${current.face.iris || 0}cm/${Math.round(100 * (current.face.iris || 0) / 2.54) / 100}in`);
+  human.tf.browser.toPixels(current.face.tensor, dom.canvas);
+  if (await count() === 0) {
+    log2("face database is empty: nothing to compare face with");
+    document.body.style.background = "black";
+    dom.delete.style.display = "none";
+    return false;
+  }
+  const db2 = await load();
+  const descriptors = db2.map((rec) => rec.descriptor).filter((desc) => desc.length > 0);
+  const res = human.match(current.face.embedding, descriptors, matchOptions);
+  current.record = db2[res.index] || null;
+  if (current.record) {
+    log2(`best match: ${current.record.name} | id: ${current.record.id} | similarity: ${Math.round(1e3 * res.similarity) / 10}%`);
+    dom.name.value = current.record.name;
+    dom.source.style.display = "";
+    (_d = dom.source.getContext("2d")) == null ? void 0 : _d.putImageData(current.record.image, 0, 0);
+  }
+  document.body.style.background = res.similarity > options.threshold ? "darkgreen" : "maroon";
+  return res.similarity > options.threshold;
+}
+async function main() {
+  var _a, _b;
+  ok.faceCount.status = false;
+  ok.faceConfidence.status = false;
+  ok.facingCenter.status = false;
+  ok.blinkDetected.status = false;
+  ok.faceSize.status = false;
+  ok.antispoofCheck.status = false;
+  ok.livenessCheck.status = false;
+  ok.age.status = false;
+  ok.gender.status = false;
+  ok.elapsedMs.val = 0;
+  dom.match.style.display = "none";
+  dom.retry.style.display = "none";
+  dom.source.style.display = "none";
+  dom.canvas.style.height = "50%";
+  document.body.style.background = "black";
+  await webCam();
+  await detectionLoop();
+  startTime = human.now();
+  current.face = await validationLoop();
+  dom.canvas.width = ((_a = current.face.tensor) == null ? void 0 : _a.shape[1]) || options.minSize;
+  dom.canvas.height = ((_b = current.face.tensor) == null ? void 0 : _b.shape[0]) || options.minSize;
+  dom.source.width = dom.canvas.width;
+  dom.source.height = dom.canvas.height;
+  dom.canvas.style.width = "";
+  dom.match.style.display = "flex";
+  dom.save.style.display = "flex";
+  dom.delete.style.display = "flex";
+  dom.retry.style.display = "block";
+  if (!allOk()) {
+    log2("did not find valid face");
+    return false;
+  }
+  return detectFace();
+}
+async function init() {
+  var _a, _b;
+  log2("human version:", human.version, "| tfjs version:", human.tf.version["tfjs-core"]);
+  log2("options:", JSON.stringify(options).replace(/{|}|"|\[|\]/g, "").replace(/,/g, " "));
+  log2("initializing webcam...");
+  await webCam();
+  log2("loading human models...");
+  await human.load();
+  log2("initializing human...");
+  log2("face embedding model:", humanConfig.face.description.enabled ? "faceres" : "", ((_a = humanConfig.face["mobilefacenet"]) == null ? void 0 : _a.enabled) ? "mobilefacenet" : "", ((_b = humanConfig.face["insightface"]) == null ? void 0 : _b.enabled) ? "insightface" : "");
+  log2("loading face database...");
+  log2("known face records:", await count());
+  dom.retry.addEventListener("click", main);
+  dom.save.addEventListener("click", saveRecords);
+  dom.delete.addEventListener("click", deleteRecord);
+  await human.warmup();
+  await main();
+}
+window.onload = init;
 //# sourceMappingURL=index.js.map
diff --git a/demo/faceid/index.js.map b/demo/faceid/index.js.map
index ba7ac80d..a2e25b78 100644
--- a/demo/faceid/index.js.map
+++ b/demo/faceid/index.js.map
@@ -2,6 +2,6 @@
   "version": 3,
   "sources": ["index.ts", "indexdb.ts"],
   "sourcesContent": ["/**\n * Human demo for browsers\n * @default Human Library\n * @summary <https://github.com/vladmandic/human>\n * @author <https://github.com/vladmandic>\n * @copyright <https://github.com/vladmandic>\n * @license MIT\n */\n\nimport * as H from '../../dist/human.esm.js'; // equivalent of @vladmandic/Human\nimport * as indexDb from './indexdb'; // methods to deal with indexdb\n\nconst humanConfig = { // user configuration for human, used to fine-tune behavior\n  cacheSensitivity: 0,\n  modelBasePath: '../../models',\n  filter: { equalization: true }, // lets run with histogram equilizer\n  face: {\n    enabled: true,\n    detector: { rotation: true, return: true, cropFactor: 1.6, mask: false }, // return tensor is used to get detected face image\n    description: { enabled: true }, // default model for face descriptor extraction is faceres\n    // mobilefacenet: { enabled: true, modelPath: 'https://vladmandic.github.io/human-models/models/mobilefacenet.json' }, // alternative model\n    // insightface: { enabled: true, modelPath: 'https://vladmandic.github.io/insightface/models/insightface-mobilenet-swish.json' }, // alternative model\n    iris: { enabled: true }, // needed to determine gaze direction\n    emotion: { enabled: false }, // not needed\n    antispoof: { enabled: true }, // enable optional antispoof module\n    liveness: { enabled: true }, // enable optional liveness module\n  },\n  body: { enabled: false },\n  hand: { enabled: false },\n  object: { enabled: false },\n  gesture: { enabled: true }, // parses face and iris gestures\n};\n\n// const matchOptions = { order: 2, multiplier: 1000, min: 0.0, max: 1.0 }; // for embedding model\nconst matchOptions = { order: 2, multiplier: 25, min: 0.2, max: 0.8 }; // for faceres model\n\nconst options = {\n  minConfidence: 0.6, // overal face confidence for box, face, gender, real, live\n  minSize: 224, // min input to face descriptor model before degradation\n  maxTime: 30000, // max time before giving up\n  blinkMin: 10, // minimum duration of a valid blink\n  blinkMax: 800, // maximum duration of a valid blink\n  threshold: 0.5, // minimum similarity\n  mask: humanConfig.face.detector.mask,\n  rotation: humanConfig.face.detector.rotation,\n  cropFactor: humanConfig.face.detector.cropFactor,\n  ...matchOptions,\n};\n\nconst ok: Record<string, { status: boolean | undefined, val: number }> = { // must meet all rules\n  faceCount: { status: false, val: 0 },\n  faceConfidence: { status: false, val: 0 },\n  facingCenter: { status: false, val: 0 },\n  lookingCenter: { status: false, val: 0 },\n  blinkDetected: { status: false, val: 0 },\n  faceSize: { status: false, val: 0 },\n  antispoofCheck: { status: false, val: 0 },\n  livenessCheck: { status: false, val: 0 },\n  age: { status: false, val: 0 },\n  gender: { status: false, val: 0 },\n  timeout: { status: true, val: 0 },\n  descriptor: { status: false, val: 0 },\n  elapsedMs: { status: undefined, val: 0 }, // total time while waiting for valid face\n  detectFPS: { status: undefined, val: 0 }, // mark detection fps performance\n  drawFPS: { status: undefined, val: 0 }, // mark redraw fps performance\n};\n\nconst allOk = () => ok.faceCount.status\n  && ok.faceSize.status\n  && ok.blinkDetected.status\n  && ok.facingCenter.status\n  && ok.lookingCenter.status\n  && ok.faceConfidence.status\n  && ok.antispoofCheck.status\n  && ok.livenessCheck.status\n  && ok.descriptor.status\n  && ok.age.status\n  && ok.gender.status;\n\nconst current: { face: H.FaceResult | null, record: indexDb.FaceRecord | null } = { face: null, record: null }; // current face record and matched database record\n\nconst blink = { // internal timers for blink start/end/duration\n  start: 0,\n  end: 0,\n  time: 0,\n};\n\n// let db: Array<{ name: string, source: string, embedding: number[] }> = []; // holds loaded face descriptor database\nconst human = new H.Human(humanConfig); // create instance of human with overrides from user configuration\n\nhuman.env.perfadd = false; // is performance data showing instant or total values\nhuman.draw.options.font = 'small-caps 18px \"Lato\"'; // set font used to draw labels when using draw methods\nhuman.draw.options.lineHeight = 20;\n\nconst dom = { // grab instances of dom objects so we dont have to look them up later\n  video: document.getElementById('video') as HTMLVideoElement,\n  canvas: document.getElementById('canvas') as HTMLCanvasElement,\n  log: document.getElementById('log') as HTMLPreElement,\n  fps: document.getElementById('fps') as HTMLPreElement,\n  match: document.getElementById('match') as HTMLDivElement,\n  name: document.getElementById('name') as HTMLInputElement,\n  save: document.getElementById('save') as HTMLSpanElement,\n  delete: document.getElementById('delete') as HTMLSpanElement,\n  retry: document.getElementById('retry') as HTMLDivElement,\n  source: document.getElementById('source') as HTMLCanvasElement,\n  ok: document.getElementById('ok') as HTMLDivElement,\n};\nconst timestamp = { detect: 0, draw: 0 }; // holds information used to calculate performance and possible memory leaks\nlet startTime = 0;\n\nconst log = (...msg) => { // helper method to output messages\n  dom.log.innerText += msg.join(' ') + '\\n';\n  console.log(...msg); // eslint-disable-line no-console\n};\n\nasync function webCam() { // initialize webcam\n  // @ts-ignore resizeMode is not yet defined in tslib\n  const cameraOptions: MediaStreamConstraints = { audio: false, video: { facingMode: 'user', resizeMode: 'none', width: { ideal: document.body.clientWidth } } };\n  const stream: MediaStream = await navigator.mediaDevices.getUserMedia(cameraOptions);\n  const ready = new Promise((resolve) => { dom.video.onloadeddata = () => resolve(true); });\n  dom.video.srcObject = stream;\n  void dom.video.play();\n  await ready;\n  dom.canvas.width = dom.video.videoWidth;\n  dom.canvas.height = dom.video.videoHeight;\n  dom.canvas.style.width = '50%';\n  dom.canvas.style.height = '50%';\n  if (human.env.initial) log('video:', dom.video.videoWidth, dom.video.videoHeight, '|', stream.getVideoTracks()[0].label);\n  dom.canvas.onclick = () => { // pause when clicked on screen and resume on next click\n    if (dom.video.paused) void dom.video.play();\n    else dom.video.pause();\n  };\n}\n\nasync function detectionLoop() { // main detection loop\n  if (!dom.video.paused) {\n    if (current.face?.tensor) human.tf.dispose(current.face.tensor); // dispose previous tensor\n    await human.detect(dom.video); // actual detection; were not capturing output in a local variable as it can also be reached via human.result\n    const now = human.now();\n    ok.detectFPS.val = Math.round(10000 / (now - timestamp.detect)) / 10;\n    timestamp.detect = now;\n    requestAnimationFrame(detectionLoop); // start new frame immediately\n  }\n}\n\nfunction drawValidationTests() {\n  let y = 32;\n  for (const [key, val] of Object.entries(ok)) {\n    let el = document.getElementById(`ok-${key}`);\n    if (!el) {\n      el = document.createElement('div');\n      el.id = `ok-${key}`;\n      el.innerText = key;\n      el.className = 'ok';\n      el.style.top = `${y}px`;\n      dom.ok.appendChild(el);\n    }\n    if (typeof val.status === 'boolean') el.style.backgroundColor = val.status ? 'lightgreen' : 'lightcoral';\n    const status = val.status ? 'ok' : 'fail';\n    el.innerText = `${key}: ${val.val === 0 ? status : val.val}`;\n    y += 28;\n  }\n}\n\nasync function validationLoop(): Promise<H.FaceResult> { // main screen refresh loop\n  const interpolated = human.next(human.result); // smoothen result using last-known results\n  human.draw.canvas(dom.video, dom.canvas); // draw canvas to screen\n  await human.draw.all(dom.canvas, interpolated); // draw labels, boxes, lines, etc.\n  const now = human.now();\n  ok.drawFPS.val = Math.round(10000 / (now - timestamp.draw)) / 10;\n  timestamp.draw = now;\n  ok.faceCount.val = human.result.face.length;\n  ok.faceCount.status = ok.faceCount.val === 1; // must be exactly detected face\n  if (ok.faceCount.status) { // skip the rest if no face\n    const gestures: string[] = Object.values(human.result.gesture).map((gesture: H.GestureResult) => gesture.gesture); // flatten all gestures\n    if (gestures.includes('blink left eye') || gestures.includes('blink right eye')) blink.start = human.now(); // blink starts when eyes get closed\n    if (blink.start > 0 && !gestures.includes('blink left eye') && !gestures.includes('blink right eye')) blink.end = human.now(); // if blink started how long until eyes are back open\n    ok.blinkDetected.status = ok.blinkDetected.status || (Math.abs(blink.end - blink.start) > options.blinkMin && Math.abs(blink.end - blink.start) < options.blinkMax);\n    if (ok.blinkDetected.status && blink.time === 0) blink.time = Math.trunc(blink.end - blink.start);\n    ok.facingCenter.status = gestures.includes('facing center');\n    ok.lookingCenter.status = gestures.includes('looking center'); // must face camera and look at camera\n    ok.faceConfidence.val = human.result.face[0].faceScore || human.result.face[0].boxScore || 0;\n    ok.faceConfidence.status = ok.faceConfidence.val >= options.minConfidence;\n    ok.antispoofCheck.val = human.result.face[0].real || 0;\n    ok.antispoofCheck.status = ok.antispoofCheck.val >= options.minConfidence;\n    ok.livenessCheck.val = human.result.face[0].live || 0;\n    ok.livenessCheck.status = ok.livenessCheck.val >= options.minConfidence;\n    ok.faceSize.val = Math.min(human.result.face[0].box[2], human.result.face[0].box[3]);\n    ok.faceSize.status = ok.faceSize.val >= options.minSize;\n    ok.descriptor.val = human.result.face[0].embedding?.length || 0;\n    ok.descriptor.status = ok.descriptor.val > 0;\n    ok.age.val = human.result.face[0].age || 0;\n    ok.age.status = ok.age.val > 0;\n    ok.gender.val = human.result.face[0].genderScore || 0;\n    ok.gender.status = ok.gender.val >= options.minConfidence;\n  }\n  // run again\n  ok.timeout.status = ok.elapsedMs.val <= options.maxTime;\n  drawValidationTests();\n  if (allOk() || !ok.timeout.status) { // all criteria met\n    dom.video.pause();\n    return human.result.face[0];\n  }\n  ok.elapsedMs.val = Math.trunc(human.now() - startTime);\n  return new Promise((resolve) => {\n    setTimeout(async () => {\n      await validationLoop(); // run validation loop until conditions are met\n      resolve(human.result.face[0]); // recursive promise resolve\n    }, 30); // use to slow down refresh from max refresh rate to target of 30 fps\n  });\n}\n\nasync function saveRecords() {\n  if (dom.name.value.length > 0) {\n    const image = dom.canvas.getContext('2d')?.getImageData(0, 0, dom.canvas.width, dom.canvas.height) as ImageData;\n    const rec = { id: 0, name: dom.name.value, descriptor: current.face?.embedding as number[], image };\n    await indexDb.save(rec);\n    log('saved face record:', rec.name, 'descriptor length:', current.face?.embedding?.length);\n    log('known face records:', await indexDb.count());\n  } else {\n    log('invalid name');\n  }\n}\n\nasync function deleteRecord() {\n  if (current.record && current.record.id > 0) {\n    await indexDb.remove(current.record);\n  }\n}\n\nasync function detectFace() {\n  dom.canvas.style.height = '';\n  dom.canvas.getContext('2d')?.clearRect(0, 0, options.minSize, options.minSize);\n  if (!current?.face?.tensor || !current?.face?.embedding) return false;\n  console.log('face record:', current.face); // eslint-disable-line no-console\n  log(`detected face: ${current.face.gender} ${current.face.age || 0}y distance ${current.face.iris || 0}cm/${Math.round(100 * (current.face.iris || 0) / 2.54) / 100}in`);\n  human.tf.browser.toPixels(current.face.tensor as unknown as H.TensorLike, dom.canvas);\n  if (await indexDb.count() === 0) {\n    log('face database is empty: nothing to compare face with');\n    document.body.style.background = 'black';\n    dom.delete.style.display = 'none';\n    return false;\n  }\n  const db = await indexDb.load();\n  const descriptors = db.map((rec) => rec.descriptor).filter((desc) => desc.length > 0);\n  const res = human.match(current.face.embedding, descriptors, matchOptions);\n  current.record = db[res.index] || null;\n  if (current.record) {\n    log(`best match: ${current.record.name} | id: ${current.record.id} | similarity: ${Math.round(1000 * res.similarity) / 10}%`);\n    dom.name.value = current.record.name;\n    dom.source.style.display = '';\n    dom.source.getContext('2d')?.putImageData(current.record.image, 0, 0);\n  }\n  document.body.style.background = res.similarity > options.threshold ? 'darkgreen' : 'maroon';\n  return res.similarity > options.threshold;\n}\n\nasync function main() { // main entry point\n  ok.faceCount.status = false;\n  ok.faceConfidence.status = false;\n  ok.facingCenter.status = false;\n  ok.blinkDetected.status = false;\n  ok.faceSize.status = false;\n  ok.antispoofCheck.status = false;\n  ok.livenessCheck.status = false;\n  ok.age.status = false;\n  ok.gender.status = false;\n  ok.elapsedMs.val = 0;\n  dom.match.style.display = 'none';\n  dom.retry.style.display = 'none';\n  dom.source.style.display = 'none';\n  dom.canvas.style.height = '50%';\n  document.body.style.background = 'black';\n  await webCam();\n  await detectionLoop(); // start detection loop\n  startTime = human.now();\n  current.face = await validationLoop(); // start validation loop\n  dom.canvas.width = current.face.tensor?.shape[1] || options.minSize;\n  dom.canvas.height = current.face.tensor?.shape[0] || options.minSize;\n  dom.source.width = dom.canvas.width;\n  dom.source.height = dom.canvas.height;\n  dom.canvas.style.width = '';\n  dom.match.style.display = 'flex';\n  dom.save.style.display = 'flex';\n  dom.delete.style.display = 'flex';\n  dom.retry.style.display = 'block';\n  if (!allOk()) { // is all criteria met?\n    log('did not find valid face');\n    return false;\n  }\n  return detectFace();\n}\n\nasync function init() {\n  log('human version:', human.version, '| tfjs version:', human.tf.version['tfjs-core']);\n  log('options:', JSON.stringify(options).replace(/{|}|\"|\\[|\\]/g, '').replace(/,/g, ' '));\n  log('initializing webcam...');\n  await webCam(); // start webcam\n  log('loading human models...');\n  await human.load(); // preload all models\n  log('initializing human...');\n  log('face embedding model:', humanConfig.face.description.enabled ? 'faceres' : '', humanConfig.face['mobilefacenet']?.enabled ? 'mobilefacenet' : '', humanConfig.face['insightface']?.enabled ? 'insightface' : '');\n  log('loading face database...');\n  log('known face records:', await indexDb.count());\n  dom.retry.addEventListener('click', main);\n  dom.save.addEventListener('click', saveRecords);\n  dom.delete.addEventListener('click', deleteRecord);\n  await human.warmup(); // warmup function to initialize backend for future faster detection\n  await main();\n}\n\nwindow.onload = init;\n", "let db: IDBDatabase; // instance of indexdb\n\nconst database = 'human';\nconst table = 'person';\n\nexport interface FaceRecord { id: number, name: string, descriptor: number[], image: ImageData }\n\nconst log = (...msg) => console.log('indexdb', ...msg); // eslint-disable-line no-console\n\nexport async function open() {\n  if (db) return true;\n  return new Promise((resolve) => {\n    const request: IDBOpenDBRequest = indexedDB.open(database, 1);\n    request.onerror = (evt) => log('error:', evt);\n    request.onupgradeneeded = (evt: IDBVersionChangeEvent) => { // create if doesnt exist\n      log('create:', evt.target);\n      db = (evt.target as IDBOpenDBRequest).result;\n      db.createObjectStore(table, { keyPath: 'id', autoIncrement: true });\n    };\n    request.onsuccess = (evt) => { // open\n      db = (evt.target as IDBOpenDBRequest).result;\n      log('open:', db);\n      resolve(true);\n    };\n  });\n}\n\nexport async function load(): Promise<FaceRecord[]> {\n  const faceDB: FaceRecord[] = [];\n  if (!db) await open(); // open or create if not already done\n  return new Promise((resolve) => {\n    const cursor: IDBRequest = db.transaction([table], 'readwrite').objectStore(table).openCursor(null, 'next');\n    cursor.onerror = (evt) => log('load error:', evt);\n    cursor.onsuccess = (evt) => {\n      if ((evt.target as IDBRequest).result) {\n        faceDB.push((evt.target as IDBRequest).result.value);\n        (evt.target as IDBRequest).result.continue();\n      } else {\n        resolve(faceDB);\n      }\n    };\n  });\n}\n\nexport async function count(): Promise<number> {\n  if (!db) await open(); // open or create if not already done\n  return new Promise((resolve) => {\n    const store: IDBRequest = db.transaction([table], 'readwrite').objectStore(table).count();\n    store.onerror = (evt) => log('count error:', evt);\n    store.onsuccess = () => resolve(store.result);\n  });\n}\n\nexport async function save(faceRecord: FaceRecord) {\n  if (!db) await open(); // open or create if not already done\n  const newRecord = { name: faceRecord.name, descriptor: faceRecord.descriptor, image: faceRecord.image }; // omit id as its autoincrement\n  db.transaction([table], 'readwrite').objectStore(table).put(newRecord);\n  log('save:', newRecord);\n}\n\nexport async function remove(faceRecord: FaceRecord) {\n  if (!db) await open(); // open or create if not already done\n  db.transaction([table], 'readwrite').objectStore(table).delete(faceRecord.id); // delete based on id\n  log('delete:', faceRecord);\n}\n"],
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+  "names": ["log", "db"]
 }
diff --git a/demo/index.html b/demo/index.html
index f1af65a9..afabf9c6 100644
--- a/demo/index.html
+++ b/demo/index.html
@@ -110,10 +110,6 @@
       <canvas id="compare-canvas" width="200" height="200"></canvas>
       <div id="similarity"></div>
     </div>
-    <div id="segmentation-container" class="compare-image">
-      <canvas id="segmentation-mask" width="256" height="256" style="width: 256px; height: 256px;"></canvas>
-      <canvas id="segmentation-canvas" width="256" height="256" style="width: 256px; height: 256px;"></canvas>
-    </div>
     <div id="samples-container" class="samples-container"></div>
     <div id="hint" class="hint"></div>
     <div id="log" class="log"></div>
diff --git a/demo/index.js b/demo/index.js
index 616cf881..6d5142df 100644
--- a/demo/index.js
+++ b/demo/index.js
@@ -111,7 +111,6 @@ const ui = {
   results: false, // show results tree
   lastFrame: 0, // time of last frame processing
   viewportSet: false, // internal, has custom viewport been set
-  background: null, // holds instance of segmentation background image
   transferCanvas: null, // canvas used to transfer data to and from worker
 
   // webrtc
@@ -263,21 +262,7 @@ async function drawResults(input) {
   // draw fps chart
   await menu.process.updateChart('FPS', ui.detectFPS);
 
-  document.getElementById('segmentation-container').style.display = userConfig.segmentation.enabled ? 'block' : 'none';
-  if (userConfig.segmentation.enabled && ui.buffered) { // refresh segmentation if using buffered output
-    const seg = await human.segmentation(input, ui.background);
-    if (seg.alpha) {
-      const canvasSegMask = document.getElementById('segmentation-mask');
-      const ctxSegMask = canvasSegMask.getContext('2d');
-      ctxSegMask.clearRect(0, 0, canvasSegMask.width, canvasSegMask.height); // need to clear as seg.alpha is alpha based canvas so it adds
-      ctxSegMask.drawImage(seg.alpha, 0, 0, seg.alpha.width, seg.alpha.height, 0, 0, canvasSegMask.width, canvasSegMask.height);
-      const canvasSegCanvas = document.getElementById('segmentation-canvas');
-      const ctxSegCanvas = canvasSegCanvas.getContext('2d');
-      ctxSegCanvas.clearRect(0, 0, canvasSegCanvas.width, canvasSegCanvas.height); // need to clear as seg.alpha is alpha based canvas so it adds
-      ctxSegCanvas.drawImage(seg.canvas, 0, 0, seg.alpha.width, seg.alpha.height, 0, 0, canvasSegCanvas.width, canvasSegCanvas.height);
-    }
-    // result.canvas = seg.alpha;
-  } else if (!result.canvas || ui.buffered) { // refresh with input if using buffered output or if missing canvas
+  if (!result.canvas || ui.buffered) { // refresh with input if using buffered output or if missing canvas
     const image = await human.image(input, false);
     result.canvas = image.canvas;
     human.tf.dispose(image.tensor);
@@ -743,7 +728,6 @@ function setupMenu() {
   menu.image.addBool('technicolor', userConfig.filter, 'technicolor', (val) => userConfig.filter.technicolor = val);
   menu.image.addBool('polaroid', userConfig.filter, 'polaroid', (val) => userConfig.filter.polaroid = val);
   menu.image.addHTML('<input type="file" id="file-input" class="input-file"></input> &nbsp input');
-  menu.image.addHTML('<input type="file" id="file-background" class="input-file"></input> &nbsp background');
 
   menu.process = new Menu(document.body, '', { top, left: x[2] });
   menu.process.addList('backend', ['cpu', 'webgl', 'wasm', 'humangl'], userConfig.backend, (val) => userConfig.backend = val);
@@ -791,8 +775,6 @@ function setupMenu() {
   menu.models.addHTML('<hr style="border-style: inset; border-color: dimgray">');
   menu.models.addBool('gestures', userConfig.gesture, 'enabled', (val) => userConfig.gesture.enabled = val);
   menu.models.addHTML('<hr style="border-style: inset; border-color: dimgray">');
-  menu.models.addBool('body segmentation', userConfig.segmentation, 'enabled', (val) => userConfig.segmentation.enabled = val);
-  menu.models.addHTML('<hr style="border-style: inset; border-color: dimgray">');
   menu.models.addBool('object detection', userConfig.object, 'enabled', (val) => userConfig.object.enabled = val);
   menu.models.addHTML('<hr style="border-style: inset; border-color: dimgray">');
   menu.models.addBool('face compare', compare, 'enabled', (val) => {
@@ -860,42 +842,12 @@ async function processDataURL(f, action) {
         if (e.target.result.startsWith('data:video')) await processVideo(e.target.result, f.name);
         document.getElementById('canvas').style.display = 'none';
       }
-      if (action === 'background') {
-        const image = new Image();
-        image.onerror = async () => status('image loading error');
-        image.onload = async () => {
-          ui.background = image;
-          if (document.getElementById('canvas').style.display === 'block') { // replace canvas used for video
-            const canvas = document.getElementById('canvas');
-            const ctx = canvas.getContext('2d');
-            const seg = await human.segmentation(canvas, ui.background, userConfig);
-            if (seg.canvas) ctx.drawImage(seg.canvas, 0, 0);
-          } else {
-            const canvases = document.getElementById('samples-container').children; // replace loaded images
-            for (const canvas of canvases) {
-              const ctx = canvas.getContext('2d');
-              const seg = await human.segmentation(canvas, ui.background, userConfig);
-              if (seg.canvas) ctx.drawImage(seg.canvas, 0, 0);
-            }
-          }
-        };
-        image.src = e.target.result;
-      }
       resolve(true);
     };
     reader.readAsDataURL(f);
   });
 }
 
-async function runSegmentation() {
-  document.getElementById('file-background').onchange = async (evt) => {
-    userConfig.segmentation.enabled = true;
-    evt.preventDefault();
-    if (evt.target.files.length < 2) ui.columns = 1;
-    for (const f of evt.target.files) await processDataURL(f, 'background');
-  };
-}
-
 async function dragAndDrop() {
   document.body.addEventListener('dragenter', (evt) => evt.preventDefault());
   document.body.addEventListener('dragleave', (evt) => evt.preventDefault());
@@ -1071,9 +1023,6 @@ async function main() {
   // init drag & drop
   await dragAndDrop();
 
-  // init segmentation
-  await runSegmentation();
-
   if (params.has('image')) {
     try {
       const image = JSON.parse(params.get('image'));
diff --git a/demo/nodejs/node-canvas.js b/demo/nodejs/node-canvas.js
index 71bcb26c..ca250c0b 100644
--- a/demo/nodejs/node-canvas.js
+++ b/demo/nodejs/node-canvas.js
@@ -54,9 +54,6 @@ async function main() {
 
     // run detection
     const result = await human.detect(imageData);
-    // run segmentation
-    // const seg = await human.segmentation(inputCanvas);
-    // log.data('Segmentation:', { data: seg.data.length, alpha: typeof seg.alpha, canvas: typeof seg.canvas });
 
     // print results summary
     const persons = result.persons; // invoke persons getter, only used to print summary on console
diff --git a/demo/segmentation/index.html b/demo/segmentation/index.html
new file mode 100644
index 00000000..d0907d58
--- /dev/null
+++ b/demo/segmentation/index.html
@@ -0,0 +1,61 @@
+<!DOCTYPE html>
+<html lang="en">
+  <head>
+    <meta charset="utf-8">
+    <meta http-equiv="content-type" content="text/html; charset=utf-8">
+    <title>Human Demo</title>
+    <meta name="viewport" content="width=device-width, shrink-to-fit=yes">
+    <meta name="mobile-web-app-capable" content="yes">
+    <meta name="application-name" content="Human Demo">
+    <meta name="keywords" content="Human Demo">
+    <meta name="description" content="Human Demo; Author: Vladimir Mandic <mandic00@live.com>">
+    <link rel="manifest" href="../manifest.webmanifest">
+    <link rel="shortcut icon" href="../favicon.ico" type="image/x-icon">
+    <link rel="icon" sizes="256x256" href="../assets/icons/dash-256.png">
+    <link rel="apple-touch-icon" href="../assets/icons/dash-256.png">
+    <link rel="apple-touch-startup-image" href="../assets/icons/dash-256.png">
+    <style>
+      @font-face { font-family: 'CenturyGothic'; font-display: swap; font-style: normal; font-weight: 400; src: local('CenturyGothic'), url('../assets/century-gothic.ttf') format('truetype'); }
+      html { font-size: 18px; }
+      body { font-size: 1rem; font-family: "CenturyGothic", "Segoe UI", sans-serif; font-variant: small-caps; width: -webkit-fill-available; height: 100%; background: black; color: white; overflow: hidden; margin: 0; }
+      select { font-size: 1rem; font-family: "CenturyGothic", "Segoe UI", sans-serif; font-variant: small-caps; background: gray; color: white; border: none; }
+    </style>
+  <script src="index.js" type="module"></script>
+  </head>
+  <body>
+    <noscript><h1>javascript is required</h1></noscript>
+    <nav>
+      <div id="nav" class="nav"></div>
+    </nav>
+    <header>
+      <div id="header" class="header" style="position: fixed; top: 0; right: 0; padding: 4px; margin: 16px; background: rgba(0, 0, 0, 0.5); z-index: 10; line-height: 2rem;">
+        <label for="mode">mode</label>
+        <select id="mode" name="mode">
+          <option value="default">remove background</option>
+          <option value="alpha">draw alpha channel</option>
+          <option value="foreground">full foreground</option>
+          <option value="state">recurrent state</option>
+        </select><br>
+        <label for="composite">composite</label>
+        <select id="composite" name="composite"></select><br>
+        <label for="ratio">downsample ratio</label>
+        <input type="range" name="ratio" id="ratio" min="0.1" max="1" value="0.5" step="0.05">
+        <div id="fps" style="margin-top: 8px"></div>
+      </div>
+    </header>
+    <main>
+      <div id="main" class="main">
+        <video id="webcam" style="position: fixed; top: 0; left: 0; width: 50vw; height: 50vh"></video>
+        <video id="video" style="position: fixed; top: 0; right: 0; width: 50vw; height: 50vh" controls></video>
+        <canvas id="output" style="position: fixed; bottom: 0; left: 0; width: 50vw; height: 50vh"></canvas>
+        <canvas id="merge" style="position: fixed; bottom: 0; right: 0; width: 50vw; height: 50vh"></canvas>
+      </div>
+    </main>
+    <footer>
+      <div id="footer" class="footer"></div>
+    </footer>
+    <aside>
+      <div id="aside" class="aside"></div>
+    </aside>
+  </body>
+</html>
diff --git a/demo/segmentation/index.js b/demo/segmentation/index.js
new file mode 100644
index 00000000..8c7854be
--- /dev/null
+++ b/demo/segmentation/index.js
@@ -0,0 +1,98 @@
+/**
+ * Human demo for browsers
+ * @default Human Library
+ * @summary <https://github.com/vladmandic/human>
+ * @author <https://github.com/vladmandic>
+ * @copyright <https://github.com/vladmandic>
+ * @license MIT
+ */
+
+import * as H from '../../dist/human.esm.js'; // equivalent of @vladmandic/Human
+
+const humanConfig = { // user configuration for human, used to fine-tune behavior
+  // backend: 'wasm',
+  // wasmPath: 'https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-backend-wasm@3.20.0/dist/',
+  modelBasePath: 'https://vladmandic.github.io/human-models/models/',
+  filter: { enabled: true, equalization: false, flip: false },
+  face: { enabled: false },
+  body: { enabled: false },
+  hand: { enabled: false },
+  object: { enabled: false },
+  gesture: { enabled: false },
+  segmentation: {
+    enabled: true,
+    modelPath: 'rvm.json', // can use rvm, selfie or meet
+    ratio: 0.5,
+    mode: 'default',
+  },
+};
+
+const human = new H.Human(humanConfig); // create instance of human with overrides from user configuration
+
+const log = (...msg) => console.log(...msg); // eslint-disable-line no-console
+
+async function main() {
+  // gather dom elements
+  const dom = {
+    video: document.getElementById('video'),
+    webcam: document.getElementById('webcam'),
+    output: document.getElementById('output'),
+    merge: document.getElementById('merge'),
+    mode: document.getElementById('mode'),
+    composite: document.getElementById('composite'),
+    ratio: document.getElementById('ratio'),
+    fps: document.getElementById('fps'),
+  };
+  // set defaults
+  dom.fps.innerText = 'initializing';
+  dom.ratio.valueAsNumber = human.config.segmentation.ratio;
+  dom.video.src = '../assets/rijeka.mp4';
+  dom.composite.innerHTML = ['source-atop', 'color', 'color-burn', 'color-dodge', 'copy', 'darken', 'destination-atop', 'destination-in', 'destination-out', 'destination-over', 'difference', 'exclusion', 'hard-light', 'hue', 'lighten', 'lighter', 'luminosity', 'multiply', 'overlay', 'saturation', 'screen', 'soft-light', 'source-in', 'source-out', 'source-over', 'xor'].map((gco) => `<option value="${gco}">${gco}</option>`).join(''); // eslint-disable-line max-len
+  const ctxMerge = dom.merge.getContext('2d');
+
+  log('human version:', human.version, '| tfjs version:', human.tf.version['tfjs-core']);
+  log('platform:', human.env.platform, '| agent:', human.env.agent);
+  await human.load(); // preload all models
+  log('backend:', human.tf.getBackend(), '| available:', human.env.backends);
+  log('models stats:', human.getModelStats());
+  log('models loaded:', Object.values(human.models).filter((model) => model !== null).length);
+  await human.warmup(); // warmup function to initialize backend for future faster detection
+  const numTensors = human.tf.engine().state.numTensors;
+
+  // initialize webcam
+  dom.webcam.onplay = () => { // start processing on video play
+    log('start processing');
+    dom.output.width = human.webcam.width;
+    dom.output.height = human.webcam.height;
+    dom.merge.width = human.webcam.width;
+    dom.merge.height = human.webcam.height;
+    loop(); // eslint-disable-line no-use-before-define
+  };
+  await human.webcam.start({ element: dom.webcam, crop: true, width: 960, height: 720 }); // use human webcam helper methods and associate webcam stream with a dom element
+  if (!human.webcam.track) dom.fps.innerText = 'webcam error';
+
+  // processing loop
+  async function loop() {
+    if (!human.webcam.element || human.webcam.paused) return; // check if webcam is valid and playing
+    human.config.segmentation.mode = dom.mode.value; // get segmentation mode from ui
+    human.config.segmentation.ratio = dom.ratio.valueAsNumber; // get segmentation downsample ratio from ui
+    const t0 = Date.now();
+    const rgba = await human.segmentation(human.webcam.element, human.config); // run model and process results
+    const t1 = Date.now();
+    if (!rgba) {
+      dom.fps.innerText = 'error';
+      return;
+    }
+    dom.fps.innerText = `fps: ${Math.round(10000 / (t1 - t0)) / 10}`; // mark performance
+    human.tf.browser.toPixels(rgba, dom.output); // draw raw output
+    human.tf.dispose(rgba); // dispose tensors
+    ctxMerge.globalCompositeOperation = 'source-over';
+    ctxMerge.drawImage(dom.video, 0, 0); // draw original video to first stacked canvas
+    ctxMerge.globalCompositeOperation = dom.composite.value;
+    ctxMerge.drawImage(dom.output, 0, 0); // draw processed output to second stacked canvas
+    if (numTensors !== human.tf.engine().state.numTensors) log({ leak: human.tf.engine().state.numTensors - numTensors }); // check for memory leaks
+    requestAnimationFrame(loop);
+  }
+}
+
+window.onload = main;
diff --git a/demo/typescript/index.js b/demo/typescript/index.js
index 50a2ccfe..c35e7f49 100644
--- a/demo/typescript/index.js
+++ b/demo/typescript/index.js
@@ -4,6 +4,96 @@
   author: <https://github.com/vladmandic>'
 */
 
-import*as i from"../../dist/human.esm.js";var m={modelBasePath:"../../models",filter:{enabled:!0,equalization:!1,flip:!1},face:{enabled:!0,detector:{rotation:!1},mesh:{enabled:!0},attention:{enabled:!1},iris:{enabled:!0},description:{enabled:!0},emotion:{enabled:!0}},body:{enabled:!0},hand:{enabled:!0},object:{enabled:!1},segmentation:{enabled:!1},gesture:{enabled:!0}},e=new i.Human(m);e.env.perfadd=!1;e.draw.options.font='small-caps 18px "Lato"';e.draw.options.lineHeight=20;var a={video:document.getElementById("video"),canvas:document.getElementById("canvas"),log:document.getElementById("log"),fps:document.getElementById("status"),perf:document.getElementById("performance")},n={detect:0,draw:0,tensors:0,start:0},s={detectFPS:0,drawFPS:0,frames:0,averageMs:0},o=(...t)=>{a.log.innerText+=t.join(" ")+`
-`,console.log(...t)},d=t=>a.fps.innerText=t,f=t=>a.perf.innerText="tensors:"+e.tf.memory().numTensors.toString()+" | performance: "+JSON.stringify(t).replace(/"|{|}/g,"").replace(/,/g," | ");async function l(){if(!a.video.paused){n.start===0&&(n.start=e.now()),await e.detect(a.video);let t=e.tf.memory().numTensors;t-n.tensors!==0&&o("allocated tensors:",t-n.tensors),n.tensors=t,s.detectFPS=Math.round(1e3*1e3/(e.now()-n.detect))/1e3,s.frames++,s.averageMs=Math.round(1e3*(e.now()-n.start)/s.frames)/1e3,s.frames%100===0&&!a.video.paused&&o("performance",{...s,tensors:n.tensors})}n.detect=e.now(),requestAnimationFrame(l)}async function c(){if(!a.video.paused){let r=e.next(e.result);e.config.filter.flip?e.draw.canvas(r.canvas,a.canvas):e.draw.canvas(a.video,a.canvas),await e.draw.all(a.canvas,r),f(r.performance)}let t=e.now();s.drawFPS=Math.round(1e3*1e3/(t-n.draw))/1e3,n.draw=t,d(a.video.paused?"paused":`fps: ${s.detectFPS.toFixed(1).padStart(5," ")} detect | ${s.drawFPS.toFixed(1).padStart(5," ")} draw`),setTimeout(c,30)}async function u(){await e.webcam.start({element:a.video,crop:!0}),a.canvas.width=e.webcam.width,a.canvas.height=e.webcam.height,a.canvas.onclick=async()=>{e.webcam.paused?await e.webcam.play():e.webcam.pause()}}async function w(){o("human version:",e.version,"| tfjs version:",e.tf.version["tfjs-core"]),o("platform:",e.env.platform,"| agent:",e.env.agent),d("loading..."),await e.load(),o("backend:",e.tf.getBackend(),"| available:",e.env.backends),o("models stats:",e.getModelStats()),o("models loaded:",Object.values(e.models).filter(t=>t!==null).length),d("initializing..."),await e.warmup(),await u(),await l(),await c()}window.onload=w;
+
+// demo/typescript/index.ts
+import * as H from "../../dist/human.esm.js";
+var humanConfig = {
+  modelBasePath: "../../models",
+  filter: { enabled: true, equalization: false, flip: false },
+  face: { enabled: true, detector: { rotation: false }, mesh: { enabled: true }, attention: { enabled: false }, iris: { enabled: true }, description: { enabled: true }, emotion: { enabled: true } },
+  body: { enabled: true },
+  hand: { enabled: true },
+  object: { enabled: false },
+  segmentation: { enabled: false },
+  gesture: { enabled: true }
+};
+var human = new H.Human(humanConfig);
+human.env.perfadd = false;
+human.draw.options.font = 'small-caps 18px "Lato"';
+human.draw.options.lineHeight = 20;
+var dom = {
+  video: document.getElementById("video"),
+  canvas: document.getElementById("canvas"),
+  log: document.getElementById("log"),
+  fps: document.getElementById("status"),
+  perf: document.getElementById("performance")
+};
+var timestamp = { detect: 0, draw: 0, tensors: 0, start: 0 };
+var fps = { detectFPS: 0, drawFPS: 0, frames: 0, averageMs: 0 };
+var log = (...msg) => {
+  dom.log.innerText += msg.join(" ") + "\n";
+  console.log(...msg);
+};
+var status = (msg) => dom.fps.innerText = msg;
+var perf = (msg) => dom.perf.innerText = "tensors:" + human.tf.memory().numTensors.toString() + " | performance: " + JSON.stringify(msg).replace(/"|{|}/g, "").replace(/,/g, " | ");
+async function detectionLoop() {
+  if (!dom.video.paused) {
+    if (timestamp.start === 0)
+      timestamp.start = human.now();
+    await human.detect(dom.video);
+    const tensors = human.tf.memory().numTensors;
+    if (tensors - timestamp.tensors !== 0)
+      log("allocated tensors:", tensors - timestamp.tensors);
+    timestamp.tensors = tensors;
+    fps.detectFPS = Math.round(1e3 * 1e3 / (human.now() - timestamp.detect)) / 1e3;
+    fps.frames++;
+    fps.averageMs = Math.round(1e3 * (human.now() - timestamp.start) / fps.frames) / 1e3;
+    if (fps.frames % 100 === 0 && !dom.video.paused)
+      log("performance", { ...fps, tensors: timestamp.tensors });
+  }
+  timestamp.detect = human.now();
+  requestAnimationFrame(detectionLoop);
+}
+async function drawLoop() {
+  if (!dom.video.paused) {
+    const interpolated = human.next(human.result);
+    if (human.config.filter.flip)
+      human.draw.canvas(interpolated.canvas, dom.canvas);
+    else
+      human.draw.canvas(dom.video, dom.canvas);
+    await human.draw.all(dom.canvas, interpolated);
+    perf(interpolated.performance);
+  }
+  const now = human.now();
+  fps.drawFPS = Math.round(1e3 * 1e3 / (now - timestamp.draw)) / 1e3;
+  timestamp.draw = now;
+  status(dom.video.paused ? "paused" : `fps: ${fps.detectFPS.toFixed(1).padStart(5, " ")} detect | ${fps.drawFPS.toFixed(1).padStart(5, " ")} draw`);
+  setTimeout(drawLoop, 30);
+}
+async function webCam() {
+  await human.webcam.start({ element: dom.video, crop: true });
+  dom.canvas.width = human.webcam.width;
+  dom.canvas.height = human.webcam.height;
+  dom.canvas.onclick = async () => {
+    if (human.webcam.paused)
+      await human.webcam.play();
+    else
+      human.webcam.pause();
+  };
+}
+async function main() {
+  log("human version:", human.version, "| tfjs version:", human.tf.version["tfjs-core"]);
+  log("platform:", human.env.platform, "| agent:", human.env.agent);
+  status("loading...");
+  await human.load();
+  log("backend:", human.tf.getBackend(), "| available:", human.env.backends);
+  log("models stats:", human.getModelStats());
+  log("models loaded:", Object.values(human.models).filter((model) => model !== null).length);
+  status("initializing...");
+  await human.warmup();
+  await webCam();
+  await detectionLoop();
+  await drawLoop();
+}
+window.onload = main;
 //# sourceMappingURL=index.js.map
diff --git a/demo/typescript/index.js.map b/demo/typescript/index.js.map
index 0f131def..605a670d 100644
--- a/demo/typescript/index.js.map
+++ b/demo/typescript/index.js.map
@@ -2,6 +2,6 @@
   "version": 3,
   "sources": ["index.ts"],
   "sourcesContent": ["/**\n * Human demo for browsers\n * @default Human Library\n * @summary <https://github.com/vladmandic/human>\n * @author <https://github.com/vladmandic>\n * @copyright <https://github.com/vladmandic>\n * @license MIT\n */\n\nimport * as H from '../../dist/human.esm.js'; // equivalent of @vladmandic/Human\n\nconst humanConfig: Partial<H.Config> = { // user configuration for human, used to fine-tune behavior\n  // backend: 'wasm',\n  // wasmPath: 'https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-backend-wasm@3.20.0/dist/',\n  // cacheSensitivity: 0,\n  // async: false,\n  modelBasePath: '../../models',\n  filter: { enabled: true, equalization: false, flip: false },\n  face: { enabled: true, detector: { rotation: false }, mesh: { enabled: true }, attention: { enabled: false }, iris: { enabled: true }, description: { enabled: true }, emotion: { enabled: true } },\n  body: { enabled: true },\n  hand: { enabled: true },\n  object: { enabled: false },\n  segmentation: { enabled: false },\n  gesture: { enabled: true },\n};\n\nconst human = new H.Human(humanConfig); // create instance of human with overrides from user configuration\n\nhuman.env.perfadd = false; // is performance data showing instant or total values\nhuman.draw.options.font = 'small-caps 18px \"Lato\"'; // set font used to draw labels when using draw methods\nhuman.draw.options.lineHeight = 20;\n// human.draw.options.fillPolygons = true;\n\nconst dom = { // grab instances of dom objects so we dont have to look them up later\n  video: document.getElementById('video') as HTMLVideoElement,\n  canvas: document.getElementById('canvas') as HTMLCanvasElement,\n  log: document.getElementById('log') as HTMLPreElement,\n  fps: document.getElementById('status') as HTMLPreElement,\n  perf: document.getElementById('performance') as HTMLDivElement,\n};\nconst timestamp = { detect: 0, draw: 0, tensors: 0, start: 0 }; // holds information used to calculate performance and possible memory leaks\nconst fps = { detectFPS: 0, drawFPS: 0, frames: 0, averageMs: 0 }; // holds calculated fps information for both detect and screen refresh\n\nconst log = (...msg) => { // helper method to output messages\n  dom.log.innerText += msg.join(' ') + '\\n';\n  console.log(...msg); // eslint-disable-line no-console\n};\nconst status = (msg) => dom.fps.innerText = msg; // print status element\nconst perf = (msg) => dom.perf.innerText = 'tensors:' + (human.tf.memory().numTensors as number).toString() + ' | performance: ' + JSON.stringify(msg).replace(/\"|{|}/g, '').replace(/,/g, ' | '); // print performance element\n\nasync function detectionLoop() { // main detection loop\n  if (!dom.video.paused) {\n    if (timestamp.start === 0) timestamp.start = human.now();\n    // log('profiling data:', await human.profile(dom.video));\n    await human.detect(dom.video); // actual detection; were not capturing output in a local variable as it can also be reached via human.result\n    const tensors = human.tf.memory().numTensors; // check current tensor usage for memory leaks\n    if (tensors - timestamp.tensors !== 0) log('allocated tensors:', tensors - timestamp.tensors); // printed on start and each time there is a tensor leak\n    timestamp.tensors = tensors;\n    fps.detectFPS = Math.round(1000 * 1000 / (human.now() - timestamp.detect)) / 1000;\n    fps.frames++;\n    fps.averageMs = Math.round(1000 * (human.now() - timestamp.start) / fps.frames) / 1000;\n    if (fps.frames % 100 === 0 && !dom.video.paused) log('performance', { ...fps, tensors: timestamp.tensors });\n  }\n  timestamp.detect = human.now();\n  requestAnimationFrame(detectionLoop); // start new frame immediately\n}\n\nasync function drawLoop() { // main screen refresh loop\n  if (!dom.video.paused) {\n    const interpolated = human.next(human.result); // smoothen result using last-known results\n    if (human.config.filter.flip) human.draw.canvas(interpolated.canvas as HTMLCanvasElement, dom.canvas); // draw processed image to screen canvas\n    else human.draw.canvas(dom.video, dom.canvas); // draw original video to screen canvas // better than using procesed image as this loop happens faster than processing loop\n    await human.draw.all(dom.canvas, interpolated); // draw labels, boxes, lines, etc.\n    perf(interpolated.performance); // write performance data\n  }\n  const now = human.now();\n  fps.drawFPS = Math.round(1000 * 1000 / (now - timestamp.draw)) / 1000;\n  timestamp.draw = now;\n  status(dom.video.paused ? 'paused' : `fps: ${fps.detectFPS.toFixed(1).padStart(5, ' ')} detect | ${fps.drawFPS.toFixed(1).padStart(5, ' ')} draw`); // write status\n  setTimeout(drawLoop, 30); // use to slow down refresh from max refresh rate to target of 30 fps\n}\n\nasync function webCam() {\n  await human.webcam.start({ element: dom.video, crop: true }); // use human webcam helper methods and associate webcam stream with a dom element\n  dom.canvas.width = human.webcam.width;\n  dom.canvas.height = human.webcam.height;\n  dom.canvas.onclick = async () => { // pause when clicked on screen and resume on next click\n    if (human.webcam.paused) await human.webcam.play();\n    else human.webcam.pause();\n  };\n}\n\nasync function main() { // main entry point\n  log('human version:', human.version, '| tfjs version:', human.tf.version['tfjs-core']);\n  log('platform:', human.env.platform, '| agent:', human.env.agent);\n  status('loading...');\n  await human.load(); // preload all models\n  log('backend:', human.tf.getBackend(), '| available:', human.env.backends);\n  log('models stats:', human.getModelStats());\n  log('models loaded:', Object.values(human.models).filter((model) => model !== null).length);\n  status('initializing...');\n  await human.warmup(); // warmup function to initialize backend for future faster detection\n  await webCam(); // start webcam\n  await detectionLoop(); // start detection loop\n  await drawLoop(); // start draw loop\n}\n\nwindow.onload = main;\n"],
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+  "names": []
 }
diff --git a/dist/human.esm-nobundle.js b/dist/human.esm-nobundle.js
index ccf8b289..c67d923a 100644
--- a/dist/human.esm-nobundle.js
+++ b/dist/human.esm-nobundle.js
@@ -4,7 +4,267 @@
   author: <https://github.com/vladmandic>'
 */
 
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+    __defProp(target, name, { get: all2[name], enumerable: true });
+};
+var __copyProps = (to, from, except, desc) => {
+  if (from && typeof from === "object" || typeof from === "function") {
+    for (let key of __getOwnPropNames(from))
+      if (!__hasOwnProp.call(to, key) && key !== except)
+        __defProp(to, key, { get: () => from[key], enumerable: !(desc = __getOwnPropDesc(from, key)) || desc.enumerable });
+  }
+  return to;
+};
+var __reExport = (target, mod3, secondTarget) => (__copyProps(target, mod3, "default"), secondTarget && __copyProps(secondTarget, mod3, "default"));
+var __publicField = (obj, key, value) => {
+  __defNormalProp(obj, typeof key !== "symbol" ? key + "" : key, value);
+  return value;
+};
+var __accessCheck = (obj, member, msg) => {
+  if (!member.has(obj))
+    throw TypeError("Cannot " + msg);
+};
+var __privateGet = (obj, member, getter) => {
+  __accessCheck(obj, member, "read from private field");
+  return getter ? getter.call(obj) : member.get(obj);
+};
+var __privateAdd = (obj, member, value) => {
+  if (member.has(obj))
+    throw TypeError("Cannot add the same private member more than once");
+  member instanceof WeakSet ? member.add(obj) : member.set(obj, value);
+};
+var __privateSet = (obj, member, value, setter) => {
+  __accessCheck(obj, member, "write to private field");
+  setter ? setter.call(obj, value) : member.set(obj, value);
+  return value;
+};
+
+// src/util/util.ts
+function log(...msg) {
+  const dt = new Date();
+  const ts = `${dt.getHours().toString().padStart(2, "0")}:${dt.getMinutes().toString().padStart(2, "0")}:${dt.getSeconds().toString().padStart(2, "0")}.${dt.getMilliseconds().toString().padStart(3, "0")}`;
+  if (msg)
+    console.log(ts, "Human:", ...msg);
+}
+function join(folder, file) {
+  const separator = folder.endsWith("/") ? "" : "/";
+  const skipJoin = file.startsWith(".") || file.startsWith("/") || file.startsWith("http:") || file.startsWith("https:") || file.startsWith("file:");
+  const path = skipJoin ? `${file}` : `${folder}${separator}${file}`;
+  if (!path.toLocaleLowerCase().includes(".json"))
+    throw new Error(`modelpath error: expecting json file: ${path}`);
+  return path;
+}
+var now = () => {
+  if (typeof performance !== "undefined")
+    return performance.now();
+  return parseInt((Number(process.hrtime.bigint()) / 1e3 / 1e3).toString());
+};
+function validate(defaults, config3, parent = "config", msgs = []) {
+  for (const key of Object.keys(config3)) {
+    if (typeof config3[key] === "object") {
+      validate(defaults[key], config3[key], key, msgs);
+    } else {
+      const defined = defaults && typeof defaults[key] !== "undefined";
+      if (!defined)
+        msgs.push({ reason: "unknown property", where: `${parent}.${key} = ${config3[key]}` });
+      const same = defaults && typeof defaults[key] === typeof config3[key];
+      if (defined && !same)
+        msgs.push({ reason: "property type mismatch", where: `${parent}.${key} = ${config3[key]}`, expected: typeof defaults[key] });
+    }
+  }
+  if (config3.debug && parent === "config" && msgs.length > 0)
+    log("invalid configuration", msgs);
+  return msgs;
+}
+function mergeDeep(...objects) {
+  const isObject = (obj) => obj && typeof obj === "object";
+  return objects.reduce((prev, obj) => {
+    Object.keys(obj || {}).forEach((key) => {
+      const pVal = prev[key];
+      const oVal = obj[key];
+      if (Array.isArray(pVal) && Array.isArray(oVal))
+        prev[key] = pVal.concat(...oVal);
+      else if (isObject(pVal) && isObject(oVal))
+        prev[key] = mergeDeep(pVal, oVal);
+      else
+        prev[key] = oVal;
+    });
+    return prev;
+  }, {});
+}
+
+// src/config.ts
+var config = {
+  backend: "",
+  modelBasePath: "",
+  cacheModels: true,
+  validateModels: true,
+  wasmPath: "",
+  wasmPlatformFetch: false,
+  debug: false,
+  async: true,
+  warmup: "full",
+  cacheSensitivity: 0.7,
+  skipAllowed: false,
+  deallocate: false,
+  flags: {},
+  softwareKernels: false,
+  filter: {
+    enabled: true,
+    equalization: false,
+    width: 0,
+    height: 0,
+    flip: false,
+    return: true,
+    brightness: 0,
+    contrast: 0,
+    sharpness: 0,
+    blur: 0,
+    saturation: 0,
+    hue: 0,
+    negative: false,
+    sepia: false,
+    vintage: false,
+    kodachrome: false,
+    technicolor: false,
+    polaroid: false,
+    pixelate: 0
+  },
+  gesture: {
+    enabled: true
+  },
+  face: {
+    enabled: true,
+    detector: {
+      modelPath: "blazeface.json",
+      rotation: true,
+      maxDetected: 1,
+      skipFrames: 99,
+      skipTime: 2500,
+      minConfidence: 0.2,
+      iouThreshold: 0.1,
+      mask: false,
+      return: false
+    },
+    mesh: {
+      enabled: true,
+      modelPath: "facemesh.json",
+      keepInvalid: false
+    },
+    attention: {
+      enabled: false,
+      modelPath: "facemesh-attention.json"
+    },
+    iris: {
+      enabled: true,
+      modelPath: "iris.json"
+    },
+    emotion: {
+      enabled: true,
+      minConfidence: 0.1,
+      skipFrames: 99,
+      skipTime: 1500,
+      modelPath: "emotion.json"
+    },
+    description: {
+      enabled: true,
+      modelPath: "faceres.json",
+      skipFrames: 99,
+      skipTime: 3e3,
+      minConfidence: 0.1
+    },
+    antispoof: {
+      enabled: false,
+      skipFrames: 99,
+      skipTime: 4e3,
+      modelPath: "antispoof.json"
+    },
+    liveness: {
+      enabled: false,
+      skipFrames: 99,
+      skipTime: 4e3,
+      modelPath: "liveness.json"
+    }
+  },
+  body: {
+    enabled: true,
+    modelPath: "movenet-lightning.json",
+    maxDetected: -1,
+    minConfidence: 0.3,
+    skipFrames: 1,
+    skipTime: 200
+  },
+  hand: {
+    enabled: true,
+    rotation: true,
+    skipFrames: 99,
+    skipTime: 1e3,
+    minConfidence: 0.5,
+    iouThreshold: 0.2,
+    maxDetected: -1,
+    landmarks: true,
+    detector: {
+      modelPath: "handtrack.json"
+    },
+    skeleton: {
+      modelPath: "handlandmark-full.json"
+    }
+  },
+  object: {
+    enabled: false,
+    modelPath: "mb3-centernet.json",
+    minConfidence: 0.2,
+    iouThreshold: 0.4,
+    maxDetected: 10,
+    skipFrames: 99,
+    skipTime: 2e3
+  },
+  segmentation: {
+    enabled: false,
+    modelPath: "rvm.json",
+    ratio: 0.5,
+    mode: "default"
+  }
+};
+
+// dist/tfjs.esm.js
+var tfjs_esm_exports = {};
+__export(tfjs_esm_exports, {
+  GraphModel: () => GraphModel,
+  Tensor: () => Tensor,
+  version: () => version8
+});
+__reExport(tfjs_esm_exports, dist_star);
+__reExport(tfjs_esm_exports, dist_star2);
+import * as dist_star from "@tensorflow/tfjs/dist/index.js";
+import * as dist_star2 from "@tensorflow/tfjs-backend-webgl/dist/index.js";
+import { Tensor } from "@tensorflow/tfjs/dist/index.js";
+import { GraphModel } from "@tensorflow/tfjs-converter/dist/index";
+var version = "3.20.0";
+var version2 = "3.20.0";
+var version3 = "3.20.0";
+var version4 = "3.20.0";
+var version5 = "3.20.0";
+var version6 = "3.20.0";
+var version7 = "3.20.0";
+var version8 = {
+  tfjs: version,
+  "tfjs-core": version2,
+  "tfjs-data": version3,
+  "tfjs-layers": version4,
+  "tfjs-converter": version5,
+  "tfjs-backend-webgl": version6,
+  "tfjs-backend-wasm": version7
+};
+
+// src/image/imagefxshaders.ts
+var vertexIdentity = `
   precision highp float;
   attribute vec2 pos;
   attribute vec2 uv;
@@ -14,7 +274,8 @@ var A5=Object.defineProperty;var Ln=Object.getOwnPropertyDescriptor;var Wn=Objec
     vUv = uv;
     gl_Position = vec4(pos.x, pos.y*flipY, 0.0, 1.);
   }
-`;var Zt=`
+`;
+var colorMatrixWithAlpha = `
   precision highp float;
   varying vec2 vUv;
   uniform sampler2D texture;
@@ -26,7 +287,8 @@ var A5=Object.defineProperty;var Ln=Object.getOwnPropertyDescriptor;var Wn=Objec
     gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[13] * c.a + m[14];
     gl_FragColor.a = m[15] * c.r + m[16] * c.g + m[17] * c.b + m[18] * c.a + m[19];
   }
-`,Xt=`
+`;
+var colorMatrixWithoutAlpha = `
   precision highp float;
   varying vec2 vUv;
   uniform sampler2D texture;
@@ -38,7 +300,8 @@ var A5=Object.defineProperty;var Ln=Object.getOwnPropertyDescriptor;var Wn=Objec
     gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[14];
     gl_FragColor.a = c.a;
   }
-`,qt=`
+`;
+var pixelate = `
   precision highp float;
   varying vec2 vUv;
   uniform vec2 size;
@@ -51,7 +314,8 @@ var A5=Object.defineProperty;var Ln=Object.getOwnPropertyDescriptor;var Wn=Objec
     vec2 coord = pixelate(vUv, size);
     gl_FragColor += texture2D(texture, coord);
   }
-`,Ut=`
+`;
+var blur = `
   precision highp float;
   varying vec2 vUv;
   uniform sampler2D texture;
@@ -74,7 +338,8 @@ var A5=Object.defineProperty;var Ln=Object.getOwnPropertyDescriptor;var Wn=Objec
     gl_FragColor += texture2D(texture, vUv + vec2( 6.0*px.x,  6.0*px.y))*0.00895781211794;
     gl_FragColor += texture2D(texture, vUv + vec2( 7.0*px.x,  7.0*px.y))*0.0044299121055113265;
   }
-`,Yt=`
+`;
+var convolution = `
   precision highp float;
   varying vec2 vUv;
   uniform sampler2D texture;
@@ -96,19 +361,13394 @@ var A5=Object.defineProperty;var Ln=Object.getOwnPropertyDescriptor;var Wn=Objec
     c31 * m[6] + c32 * m[7] + c33 * m[8];
     gl_FragColor.a = c22.a;
   }
-`;var i5=(e,t,n)=>{let o=new RegExp("\\b"+t+" \\w+ (\\w+)","ig");e.replace(o,(r,s)=>(n[s]=0,r))},l5=class{constructor(t,n,o){k(this,"uniform",{});k(this,"attribute",{});k(this,"gl");k(this,"id");k(this,"compile",(t,n)=>{let o=this.gl.createShader(n);return o?(this.gl.shaderSource(o,t),this.gl.compileShader(o),this.gl.getShaderParameter(o,this.gl.COMPILE_STATUS)?o:(h(`filter: gl compile failed: ${this.gl.getShaderInfoLog(o)||"unknown"}`),null)):(h("filter: could not create shader"),null)});this.gl=t;let r=this.compile(n,this.gl.VERTEX_SHADER),s=this.compile(o,this.gl.FRAGMENT_SHADER);if(this.id=this.gl.createProgram(),!(!r||!s)){if(!this.id){h("filter: could not create webgl program");return}if(this.gl.attachShader(this.id,r),this.gl.attachShader(this.id,s),this.gl.linkProgram(this.id),!this.gl.getProgramParameter(this.id,this.gl.LINK_STATUS)){h(`filter: gl link failed: ${this.gl.getProgramInfoLog(this.id)||"unknown"}`);return}this.gl.useProgram(this.id),i5(n,"attribute",this.attribute);for(let a in this.attribute)this.attribute[a]=this.gl.getAttribLocation(this.id,a);i5(n,"uniform",this.uniform),i5(o,"uniform",this.uniform);for(let a in this.uniform)this.uniform[a]=this.gl.getUniformLocation(this.id,a)}}};function Kt(){let e=0,t=null,n=!1,o=-1,r=[null,null],s=[],a=null,i=null,c=y0(100,100),d={},y={INTERMEDIATE:1},l=c.getContext("webgl");if(!l){h("filter: cannot get webgl context");return}this.gl=l;function f(P,p){if(!(P===c.width&&p===c.height)){if(c.width=P,c.height=p,!a){let g=new Float32Array([-1,-1,0,1,1,-1,1,1,-1,1,0,0,-1,1,0,0,1,-1,1,1,1,1,1,0]);a=l.createBuffer(),l.bindBuffer(l.ARRAY_BUFFER,a),l.bufferData(l.ARRAY_BUFFER,g,l.STATIC_DRAW),l.pixelStorei(l.UNPACK_PREMULTIPLY_ALPHA_WEBGL,!0)}l.viewport(0,0,c.width,c.height),r=[null,null]}}function x(P,p){let g=l.createFramebuffer();l.bindFramebuffer(l.FRAMEBUFFER,g);let S=l.createRenderbuffer();l.bindRenderbuffer(l.RENDERBUFFER,S);let E=l.createTexture();return l.bindTexture(l.TEXTURE_2D,E),l.texImage2D(l.TEXTURE_2D,0,l.RGBA,P,p,0,l.RGBA,l.UNSIGNED_BYTE,null),l.texParameteri(l.TEXTURE_2D,l.TEXTURE_MAG_FILTER,l.LINEAR),l.texParameteri(l.TEXTURE_2D,l.TEXTURE_MIN_FILTER,l.LINEAR),l.texParameteri(l.TEXTURE_2D,l.TEXTURE_WRAP_S,l.CLAMP_TO_EDGE),l.texParameteri(l.TEXTURE_2D,l.TEXTURE_WRAP_T,l.CLAMP_TO_EDGE),l.framebufferTexture2D(l.FRAMEBUFFER,l.COLOR_ATTACHMENT0,l.TEXTURE_2D,E,0),l.bindTexture(l.TEXTURE_2D,null),l.bindFramebuffer(l.FRAMEBUFFER,null),{fbo:g,texture:E}}function m(P){return r[P]=r[P]||x(c.width,c.height),r[P]}function u(P=0){if(!i)return;let p=null,g=null,S=!1;e===0?p=t:p=m(o).texture||null,e++,n&&!(P&y.INTERMEDIATE)?(g=null,S=e%2===0):(o=(o+1)%2,g=m(o).fbo||null),l.bindTexture(l.TEXTURE_2D,p),l.bindFramebuffer(l.FRAMEBUFFER,g),l.uniform1f(i.uniform.flipY,S?-1:1),l.drawArrays(l.TRIANGLES,0,6)}function M(P){if(d[P])return i=d[P],l.useProgram((i?i.id:null)||null),i;if(i=new l5(l,Dt,P),!i)return h("filter: could not get webgl program"),null;let p=Float32Array.BYTES_PER_ELEMENT,g=4*p;return l.enableVertexAttribArray(i.attribute.pos),l.vertexAttribPointer(i.attribute.pos,2,l.FLOAT,!1,g,0*p),l.enableVertexAttribArray(i.attribute.uv),l.vertexAttribPointer(i.attribute.uv,2,l.FLOAT,!1,g,2*p),d[P]=i,i}let v={colorMatrix:P=>{let p=new Float32Array(P);p[4]/=255,p[9]/=255,p[14]/=255,p[19]/=255;let g=p[18]===1&&p[3]===0&&p[8]===0&&p[13]===0&&p[15]===0&&p[16]===0&&p[17]===0&&p[19]===0?Xt:Zt,S=M(g);!S||(l.uniform1fv(S.uniform.m,p),u())},brightness:P=>{let p=(P||0)+1;v.colorMatrix([p,0,0,0,0,0,p,0,0,0,0,0,p,0,0,0,0,0,1,0])},saturation:P=>{let p=(P||0)*2/3+1,g=(p-1)*-.5;v.colorMatrix([p,g,g,0,0,g,p,g,0,0,g,g,p,0,0,0,0,0,1,0])},desaturate:()=>{v.saturation(-1)},contrast:P=>{let p=(P||0)+1,g=-128*(p-1);v.colorMatrix([p,0,0,0,g,0,p,0,0,g,0,0,p,0,g,0,0,0,1,0])},negative:()=>{v.contrast(-2)},hue:P=>{P=(P||0)/180*Math.PI;let p=Math.cos(P),g=Math.sin(P),S=.213,E=.715,I=.072;v.colorMatrix([S+p*(1-S)+g*-S,E+p*-E+g*-E,I+p*-I+g*(1-I),0,0,S+p*-S+g*.143,E+p*(1-E)+g*.14,I+p*-I+g*-.283,0,0,S+p*-S+g*-(1-S),E+p*-E+g*E,I+p*(1-I)+g*I,0,0,0,0,0,1,0])},desaturateLuminance:()=>{v.colorMatrix([.2764723,.929708,.0938197,0,-37.1,.2764723,.929708,.0938197,0,-37.1,.2764723,.929708,.0938197,0,-37.1,0,0,0,1,0])},sepia:()=>{v.colorMatrix([.393,.7689999,.18899999,0,0,.349,.6859999,.16799999,0,0,.272,.5339999,.13099999,0,0,0,0,0,1,0])},brownie:()=>{v.colorMatrix([.5997023498159715,.34553243048391263,-.2708298674538042,0,47.43192855600873,-.037703249837783157,.8609577587992641,.15059552388459913,0,-36.96841498319127,.24113635128153335,-.07441037908422492,.44972182064877153,0,-7.562075277591283,0,0,0,1,0])},vintagePinhole:()=>{v.colorMatrix([.6279345635605994,.3202183420819367,-.03965408211312453,0,9.651285835294123,.02578397704808868,.6441188644374771,.03259127616149294,0,7.462829176470591,.0466055556782719,-.0851232987247891,.5241648018700465,0,5.159190588235296,0,0,0,1,0])},kodachrome:()=>{v.colorMatrix([1.1285582396593525,-.3967382283601348,-.03992559172921793,0,63.72958762196502,-.16404339962244616,1.0835251566291304,-.05498805115633132,0,24.732407896706203,-.16786010706155763,-.5603416277695248,1.6014850761964943,0,35.62982807460946,0,0,0,1,0])},technicolor:()=>{v.colorMatrix([1.9125277891456083,-.8545344976951645,-.09155508482755585,0,11.793603434377337,-.3087833385928097,1.7658908555458428,-.10601743074722245,0,-70.35205161461398,-.231103377548616,-.7501899197440212,1.847597816108189,0,30.950940869491138,0,0,0,1,0])},polaroid:()=>{v.colorMatrix([1.438,-.062,-.062,0,0,-.122,1.378,-.122,0,0,-.016,-.016,1.483,0,0,0,0,0,1,0])},shiftToBGR:()=>{v.colorMatrix([0,0,1,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,1,0])},convolution:P=>{let 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function k1(e){var t;return w.initial&&(C0=null),C0?e.debug&&h("cached model:",C0.modelUrl):C0=await O((t=e.face.detector)==null?void 0:t.modelPath),ee=C0.executor&&C0.inputs[0].shape?C0.inputs[0].shape[2]:256,n2=A.scalar(ee,"int32"),R1=A.tensor2d(g1(ee)),C0}function vr(e){let t={};t.boxStarts=A.slice(e,[0,1],[-1,2]),t.centers=A.add(t.boxStarts,R1),t.boxSizes=A.slice(e,[0,3],[-1,2]),t.boxSizesNormalized=A.div(t.boxSizes,n2),t.centersNormalized=A.div(t.centers,n2),t.halfBoxSize=A.div(t.boxSizesNormalized,W.tf2),t.starts=A.sub(t.centersNormalized,t.halfBoxSize),t.ends=A.add(t.centersNormalized,t.halfBoxSize),t.startNormalized=A.mul(t.starts,n2),t.endNormalized=A.mul(t.ends,n2);let n=A.concat2d([t.startNormalized,t.endNormalized],1);return Object.keys(t).forEach(o=>A.dispose(t[o])),n}async function w1(e,t){var i,c,d,y;if(!e||e.isDisposedInternal||e.shape.length!==4||e.shape[1]<1||e.shape[2]<1)return[];let 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z1=224,Tr,Pr=5,S2=[8,16,32,32,32];function S1(){let e=[],t=0;for(;t<Pr;){let n=0,o=t;for(;o<S2.length&&S2[o]===S2[t];)n+=2,o++;let r=S2[t],s=Math.ceil(z1/r),a=Math.ceil(z1/r);for(let i=0;i<s;++i)for(let c=0;c<a;++c)for(let d=0;d<n;++d)e.push({x:(c+.5)/a,y:(i+.5)/s});t=o}Tr={x:A.tensor1d(e.map(n=>n.x)),y:A.tensor1d(e.map(n=>n.y))}}function Z0(e,t=[1,1]){let n=[e.map(i=>i[0]),e.map(i=>i[1])],o=[Math.min(...n[0]),Math.min(...n[1])],r=[Math.max(...n[0]),Math.max(...n[1])],s=[o[0],o[1],r[0]-o[0],r[1]-o[1]],a=[s[0]/t[0],s[1]/t[1],s[2]/t[0],s[3]/t[1]];return{box:s,boxRaw:a}}function j1(e,t=[1,1]){let n=[e.map(d=>d[0]),e.map(d=>d[1])],o=[Math.min(...n[0]),Math.min(...n[1])],r=[Math.max(...n[0]),Math.max(...n[1])],s=[(o[0]+r[0])/2,(o[1]+r[1])/2],a=Math.max(s[0]-o[0],s[1]-o[1],-s[0]+r[0],-s[1]+r[1]),i=[Math.trunc(s[0]-a),Math.trunc(s[1]-a),Math.trunc(2*a),Math.trunc(2*a)],c=[i[0]/t[0],i[1]/t[1],i[2]/t[0],i[3]/t[1]];return{box:i,boxRaw:c}}function j2(e,t){let n=[e[2]*t,e[3]*t];return[e[0]-(n[0]-e[2])/2,e[1]-(n[1]-e[3])/2,n[0],n[1]]}var O1={initial:!0},c0={detector:null,landmarks:null},Oe={detector:[224,224],landmarks:[256,256]},S5=Number.MAX_SAFE_INTEGER,kr={landmarks:["ld_3d","activation_segmentation","activation_heatmap","world_3d","output_poseflag"],detector:[]},I2=null,o2,te=[[0,0],[0,0],[0,0],[0,0]],N1=0,I1=e=>1-1/(1+Math.exp(e));async function C1(e){var t;if(O1.initial&&(c0.detector=null),!c0.detector&&e.body.detector&&e.body.detector.modelPath){c0.detector=await O(e.body.detector.modelPath);let n=(t=c0.detector)!=null&&t.executor?Object.values(c0.detector.modelSignature.inputs):void 0;Oe.detector[0]=Array.isArray(n)?parseInt(n[0].tensorShape.dim[1].size):0,Oe.detector[1]=Array.isArray(n)?parseInt(n[0].tensorShape.dim[2].size):0}else e.debug&&c0.detector&&h("cached model:",c0.detector.modelUrl);return S1(),c0.detector}async function L1(e){var t;if(O1.initial&&(c0.landmarks=null),c0.landmarks)e.debug&&h("cached 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e.shape[1]!==t?(n.resize=A.image.resizeBilinear(n.cropped||e,[t,t]),o=A.div(n.resize,W.tf255)):o=A.div(n.cropped||e,W.tf255);return Object.keys(n).forEach(a=>A.dispose(n[a])),o}function Er(e,t){for(let n of e)n.position=[Math.trunc(n.position[0]*(t[0]+te[2][0]+te[2][1])/t[0]-te[2][0]),Math.trunc(n.position[1]*(t[1]+te[1][0]+te[1][1])/t[1]-te[1][0]),n.position[2]],n.positionRaw=[n.position[0]/t[0],n.position[1]/t[1],2*n.position[2]/(t[0]+t[1])];if(o2)for(let n of e)n.positionRaw=[n.positionRaw[0]+o2[1],n.positionRaw[1]+o2[0],n.positionRaw[2]],n.position=[Math.trunc(n.positionRaw[0]*t[0]),Math.trunc(n.positionRaw[1]*t[1]),n.positionRaw[2]];return e}function zr(e){let t=e.find(i=>i.part==="leftPalm"),n=e.find(i=>i.part==="leftWrist"),o=e.find(i=>i.part==="leftIndex");t.position[2]=((n.position[2]||0)+(o.position[2]||0))/2;let r=e.find(i=>i.part==="rightPalm"),s=e.find(i=>i.part==="rightWrist"),a=e.find(i=>i.part==="rightIndex");r.position[2]=((s.position[2]||0)+(a.position[2]||0))/2}async function Sr(e,t,n){var m,u;if(!((m=c0.landmarks)!=null&&m.executor))return null;let o={};[o.ld,o.segmentation,o.heatmap,o.world,o.poseflag]=(u=c0.landmarks)==null?void 0:u.execute(e,kr.landmarks);let r=(await o.poseflag.data())[0],s=await o.ld.data(),a=await o.world.data();Object.keys(o).forEach(M=>A.dispose(o[M]));let i=[],c=5;for(let M=0;M<s.length/c;M++){let v=I1(s[c*M+3]),P=I1(s[c*M+4]),p=Math.trunc(100*v*P*r)/100,g=[s[c*M+0]/Oe.landmarks[0],s[c*M+1]/Oe.landmarks[1],s[c*M+2]+0],S=[Math.trunc(n[0]*g[0]),Math.trunc(n[1]*g[1]),g[2]],E=[a[c*M+0],a[c*M+1],a[c*M+2]+0];i.push({part:E5[M],positionRaw:g,position:S,distance:E,score:p})}if(r<(t.body.minConfidence||0))return null;zr(i);let d=Er(i,n),y=d.map(M=>M.position),l=Z0(y,[n[0],n[1]]),f={};for(let[M,v]of Object.entries(z5)){let P=[];for(let p=0;p<v.length-1;p++){let g=d.find(E=>E.part===v[p]),S=d.find(E=>E.part===v[p+1]);g&&S&&P.push([g.position,S.position])}f[M]=P}return{id:0,score:Math.trunc(100*r)/100,box:l.box,boxRaw:l.boxRaw,keypoints:d,annotations:f}}async function j5(e,t){let n=[e.shape[2]||0,e.shape[1]||0],o=(t.body.skipTime||0)>T()-N1,r=S5<(t.body.skipFrames||0);if(t.skipAllowed&&o&&r&&I2!==null)S5++;else{let s={};s.landmarks=wr(e,256),I2=await Sr(s.landmarks,t,n),Object.keys(s).forEach(a=>A.dispose(s[a])),N1=T(),S5=0}return I2?[I2]:[]}var Ce=[{class:1,label:"person"},{class:2,label:"bicycle"},{class:3,label:"car"},{class:4,label:"motorcycle"},{class:5,label:"airplane"},{class:6,label:"bus"},{class:7,label:"train"},{class:8,label:"truck"},{class:9,label:"boat"},{class:10,label:"traffic light"},{class:11,label:"fire hydrant"},{class:12,label:"stop sign"},{class:13,label:"parking 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Lr=[[61,146],[146,91],[91,181],[181,84],[84,17],[17,314],[314,405],[405,321],[321,375],[375,291],[61,185],[185,40],[40,39],[39,37],[37,0],[0,267],[267,269],[269,270],[270,409],[409,291],[78,95],[95,88],[88,178],[178,87],[87,14],[14,317],[317,402],[402,318],[318,324],[324,308],[78,191],[191,80],[80,81],[81,82],[82,13],[13,312],[312,311],[311,310],[310,415],[415,308]],Wr=[[263,249],[249,390],[390,373],[373,374],[374,380],[380,381],[381,382],[382,362],[263,466],[466,388],[388,387],[387,386],[386,385],[385,384],[384,398],[398,362]],Fr=[[276,283],[283,282],[282,295],[295,285],[300,293],[293,334],[334,296],[296,336]],Gr=[[474,475],[475,476],[476,477],[477,474]],Br=[[33,7],[7,163],[163,144],[144,145],[145,153],[153,154],[154,155],[155,133],[33,246],[246,161],[161,160],[160,159],[159,158],[158,157],[157,173],[173,133]],Hr=[[46,53],[53,52],[52,65],[65,55],[70,63],[63,105],[105,66],[66,107]],Vr=[[469,470],[470,471],[471,472],[472,469]],Dr=[[10,338],[338,297],[297,332],[332,284],[284,251],[251,389],[389,356],[356,454],[454,323],[323,361],[361,288],[288,397],[397,365],[365,379],[379,378],[378,400],[400,377],[377,152],[152,148],[148,176],[176,149],[149,150],[150,136],[136,172],[172,58],[58,132],[132,93],[93,234],[234,127],[127,162],[162,21],[21,54],[54,103],[103,67],[67,109],[109,10]];function oe(e){let t=e.map(n=>n[0]);return t.push(e[e.length-1][1]),t}var Zr={lips:oe(Lr),leftEye:oe(Wr),leftEyebrow:oe(Fr),leftIris:oe(Gr),rightEye:oe(Br),rightEyebrow:oe(Hr),rightIris:oe(Vr),faceOval:oe(Dr)},Xr=Object.entries(Zr).map(([e,t])=>t.map(n=>[n,e])).flat(),d7=new 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0:a.data()),irisL:await((c=(i=t.filter(u=>u.size===10))==null?void 0:i[0])==null?void 0:c.data()),eyeL:await((y=(d=t.filter(u=>u.size===142))==null?void 0:d[0])==null?void 0:y.data()),irisR:await((f=(l=t.filter(u=>u.size===10))==null?void 0:l[1])==null?void 0:f.data()),eyeR:await((m=(x=t.filter(u=>u.size===142))==null?void 0:x[1])==null?void 0:m.data())};for(let u of Object.values(n))if(!u)return e;let o=he.reduce((u,M)=>u+=e[M][2],0)/he.length;for(let u=0;u<n.irisL.length/2;u++)e.push([n.irisL[2*u+0],n.irisL[2*u+1],o]);let r=be.reduce((u,M)=>u+=e[M][2],0)/be.length;for(let u=0;u<n.irisR.length/2;u++)e.push([n.irisR[2*u+0],n.irisR[2*u+1],r]);for(let u=0;u<n.eyeL.length/2;u++)e[he[u]]=[n.eyeL[2*u+0],n.eyeL[2*u+1],e[he[u]][2]];for(let u=0;u<n.eyeR.length/2;u++)e[be[u]]=[n.eyeR[2*u+0],n.eyeR[2*u+1],e[be[u]][2]];for(let u=0;u<n.lips.length/2;u++)e[r2[u]]=[n.lips[2*u+0],n.lips[2*u+1],e[r2[u]][2]];return e}var B0={boxes:[],skipped:Number.MAX_SAFE_INTEGER,timestamp:0},Y=null,A2=0;async function x3(e,t){var c,d,y,l,f,x,m,u,M,v;if(!(Y!=null&&Y.executor))return[];let n=(((c=t.face.detector)==null?void 0:c.skipTime)||0)>T()-B0.timestamp,o=B0.skipped<(((d=t.face.detector)==null?void 0:d.skipFrames)||0);!t.skipAllowed||!n||!o||B0.boxes.length===0?(B0.boxes=await w1(e,t),B0.timestamp=T(),B0.skipped=0):B0.skipped++;let r=[],s=[],a=0,i=A2;for(let P=0;P<B0.boxes.length;P++){let p=B0.boxes[P],g=0,S,E={id:a++,mesh:[],meshRaw:[],box:[0,0,0,0],boxRaw:[0,0,0,0],score:0,boxScore:0,faceScore:0,annotations:{}};if([g,S,E.tensor]=v1((y=t.face.detector)==null?void 0:y.rotation,p,e,(l=t.face.mesh)!=null&&l.enabled?A2:Ie()),t.filter.equalization){let I=E.tensor?await p2(E.tensor):void 0;A.dispose(E.tensor),I&&(E.tensor=I)}if(E.boxScore=Math.round(100*p.confidence)/100,(f=t.face.mesh)!=null&&f.enabled)if(!Y)t.debug&&h("face mesh detection requested, but model is not loaded");else{if(((x=t.face.attention)==null?void 0:x.enabled)&&!w.kernels.includes("atan2"))return t.face.attention.enabled=!1,A.dispose(E.tensor),r;let I=Y.execute(E.tensor),U=await I.find(F=>F.shape[F.shape.length-1]===1).data();if(E.faceScore=Math.round(100*U[0])/100,E.faceScore<(((m=t.face.detector)==null?void 0:m.minConfidence)||1)){if(p.confidence=E.faceScore,t.face.mesh.keepInvalid){E.box=R2(p,e),E.boxRaw=k2(p,e),E.score=E.boxScore,E.mesh=p.landmarks.map(F=>[(p.startPoint[0]+p.endPoint[0])/2+(p.endPoint[0]+p.startPoint[0])*F[0]/Ie(),(p.startPoint[1]+p.endPoint[1])/2+(p.endPoint[1]+p.startPoint[1])*F[1]/Ie()]),E.meshRaw=E.mesh.map(F=>[F[0]/(e.shape[2]||1),F[1]/(e.shape[1]||1),(F[2]||0)/i]);for(let F of Object.keys(fe))E.annotations[F]=[E.mesh[fe[F]]]}}else{let F=I.find(C=>C.shape[C.shape.length-1]===1404),B=A.reshape(F,[-1,3]),$=await B.array();A.dispose(B),(u=t.face.attention)!=null&&u.enabled?$=await d3($,I):(M=t.face.iris)!=null&&M.enabled&&($=await 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o=A.concat2d([n.startPoints,n.endPoints],1);return Object.keys(n).forEach(r=>A.dispose(n[r])),o}normalizeLandmarks(t,n){let o={};o.reshape=A.reshape(t,[-1,7,2]),o.div=A.div(o.reshape,this.inputSizeTensor),o.landmarks=A.add(o.div,this.anchors[n]?this.anchors[n]:0);let r=A.mul(o.landmarks,this.inputSizeTensor);return Object.keys(o).forEach(s=>A.dispose(o[s])),r}async predict(t,n){var i;let o={};o.resize=A.image.resizeBilinear(t,[this.inputSize,this.inputSize]),o.div=A.div(o.resize,W.tf127),o.image=A.sub(o.div,W.tf1),o.batched=this.model.execute(o.image),o.predictions=A.squeeze(o.batched),o.slice=A.slice(o.predictions,[0,0],[-1,1]),o.sigmoid=A.sigmoid(o.slice),o.scores=A.squeeze(o.sigmoid);let r=await o.scores.data();o.boxes=A.slice(o.predictions,[0,1],[-1,4]),o.norm=this.normalizeBoxes(o.boxes),o.nms=await A.image.nonMaxSuppressionAsync(o.norm,o.scores,3*(((i=n.hand)==null?void 0:i.maxDetected)||1),n.hand.iouThreshold,n.hand.minConfidence);let s=await o.nms.array(),a=[];for(let c of s){let d={};d.box=A.slice(o.norm,[c,0],[1,-1]),d.slice=A.slice(o.predictions,[c,5],[1,14]),d.norm=this.normalizeLandmarks(d.slice,c),d.palmLandmarks=A.reshape(d.norm,[-1,2]);let y=await d.box.data(),l=y.slice(0,2),f=y.slice(2,4),x=await d.palmLandmarks.array(),m={startPoint:l,endPoint:f,palmLandmarks:x,confidence:r[c]},u=v3(m,[(t.shape[2]||1)/this.inputSize,(t.shape[1]||0)/this.inputSize]);a.push(u),Object.keys(d).forEach(M=>A.dispose(d[M]))}return Object.keys(o).forEach(c=>A.dispose(o[c])),a}};var Qr=5,w3=1.65,E3=[0,5,9,13,17,1,2],_r=0,$r=2,z3=0,H2=class{constructor(t,n){k(this,"handDetector");k(this,"handPoseModel");k(this,"inputSize");k(this,"storedBoxes");k(this,"skipped");k(this,"detectedHands");var o,r,s;this.handDetector=t,this.handPoseModel=n,this.inputSize=((s=(r=(o=this.handPoseModel)==null?void 0:o.inputs)==null?void 0:r[0].shape)==null?void 0:s[2])||0,this.storedBoxes=[],this.skipped=Number.MAX_SAFE_INTEGER,this.detectedHands=0}calculateLandmarksBoundingBox(t){let 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function K3(e){var t;if(w.initial&&(_[1]=null),_[1])e.debug&&h("cached model:",_[1].modelUrl);else{_[1]=await O((t=e.hand.skeleton)==null?void 0:t.modelPath);let n=_[1].executor?Object.values(_[1].modelSignature.inputs):void 0;de[1][0]=Array.isArray(n)?parseInt(n[0].tensorShape.dim[1].size):0,de[1][1]=Array.isArray(n)?parseInt(n[0].tensorShape.dim[2].size):0}return _[1]}async function fA(e,t){let n=[];if(!e||!_[0])return n;let o={},r=(e.shape[2]||1)/(e.shape[1]||1),s=Math.min(Math.round((e.shape[1]||0)/8)*8,xA),a=Math.round(s*r/8)*8;o.resize=A.image.resizeBilinear(e,[s,a]),o.cast=A.cast(o.resize,"int32"),[o.rawScores,o.rawBoxes]=await _[0].executeAsync(o.cast,cA),o.boxes=A.squeeze(o.rawBoxes,[0,2]),o.scores=A.squeeze(o.rawScores,[0]);let i=A.unstack(o.scores,1);A.dispose(i[X3]),i.splice(X3,1),o.filtered=A.stack(i,1),A.dispose(i),o.max=A.max(o.filtered,1),o.argmax=A.argMax(o.filtered,1);let c=0;o.nms=await 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s=e[0][r],a=Math.round(100*s[51+4])/100;if(a>t.body.minConfidence){let i=[];for(let l=0;l<17;l++){let f=s[3*l+2];if(f>t.body.minConfidence){let x=[s[3*l+1],s[3*l+0]];i.push({part:U2[l],score:Math.round(100*f)/100,positionRaw:x,position:[Math.round((n.shape[2]||0)*x[0]),Math.round((n.shape[1]||0)*x[1])]})}}let c=Z0(i.map(l=>l.position),[n.shape[2],n.shape[1]]),d={};for(let[l,f]of Object.entries(Y2)){let x=[];for(let m=0;m<f.length-1;m++){let u=i.find(v=>v.part===f[m]),M=i.find(v=>v.part===f[m+1]);u&&M&&u.score>(t.body.minConfidence||0)&&M.score>(t.body.minConfidence||0)&&x.push([u.position,M.position])}d[l]=x}let y={id:r,score:a,box:c.box,boxRaw:c.boxRaw,keypoints:[...i],annotations:d};xt(y),o.push(y)}}return o.sort((r,s)=>s.score-r.score),o.length>t.body.maxDetected&&(o.length=t.body.maxDetected),o}async function ft(e,t){var r;if(!(n0!=null&&n0.executor)||!((r=n0==null?void 0:n0.inputs)!=null&&r[0].shape))return[];t.skipAllowed||(Re.boxes.length=0),yt++;let n=(t.body.skipTime||0)>T()-Re.last,o=yt<(t.body.skipFrames||0);return t.skipAllowed&&n&&o?Re.bodies:new Promise(async s=>{let a={};yt=0,a.input=on(e,K2),a.res=n0==null?void 0:n0.execute(a.input),Re.last=T();let i=await a.res.array();Re.bodies=a.res.shape[2]===17?pA(i,t,e):uA(i,t,e);for(let c of Re.bodies)rn(c,[e.shape[2]||1,e.shape[1]||1]),nn(c.keypoints);Object.keys(a).forEach(c=>A.dispose(a[c])),s(Re.bodies)})}var I0,J2=[],an=0,mt=Number.MAX_SAFE_INTEGER,_2=0,Q2=2.5;async function ln(e){if(!I0||w.initial){I0=await O(e.object.modelPath);let t=I0!=null&&I0.executor?Object.values(I0.modelSignature.inputs):void 0;_2=Array.isArray(t)?parseInt(t[0].tensorShape.dim[2].size):416}else e.debug&&h("cached model:",I0.modelUrl);return I0}async function hA(e,t,n){let o=0,r=[],s=_2;for(let d of[1,2,4]){let y=d*13,l=A.squeeze(e.find(v=>v.shape[1]===y**2&&(v.shape[2]||0)===Ce.length)),f=await 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e.moveTo(t+s.roundRect,n),e.lineTo(t+o-s.roundRect,n),e.quadraticCurveTo(t+o,n,t+o,n+s.roundRect),e.lineTo(t+o,n+r-s.roundRect),e.quadraticCurveTo(t+o,n+r,t+o-s.roundRect,n+r),e.lineTo(t+s.roundRect,n+r),e.quadraticCurveTo(t,n+r,t,n+r-s.roundRect),e.lineTo(t,n+s.roundRect),e.quadraticCurveTo(t,n,t+s.roundRect,n),e.closePath();e.stroke()}function wt(e,t,n){if(!(t.length<2)){e.beginPath(),e.moveTo(t[0][0],t[0][1]);for(let o of t)e.strokeStyle=Y0(o[2]||0,n),e.lineTo(Math.trunc(o[0]),Math.trunc(o[1]));e.stroke(),n.fillPolygons&&(e.closePath(),e.fill())}}function vn(e,t,n){if(!(t.length<2)){if(e.lineWidth=n.lineWidth,!n.useCurves||t.length<=2){wt(e,t,n);return}e.moveTo(t[0][0],t[0][1]);for(let o=0;o<t.length-2;o++){let r=(t[o][0]+t[o+1][0])/2,s=(t[o][1]+t[o+1][1])/2;e.quadraticCurveTo(t[o][0],t[o][1],r,s)}e.quadraticCurveTo(t[t.length-2][0],t[t.length-2][1],t[t.length-1][0],t[t.length-1][1]),e.stroke(),n.fillPolygons&&(e.closePath(),e.fill())}}function Et(e,t,n,o=5){let r,s,a;e.beginPath(),e.moveTo(t[0],t[1]),e.lineTo(n[0],n[1]),r=Math.atan2(n[1]-t[1],n[0]-t[0]),s=o*Math.cos(r)+n[0],a=o*Math.sin(r)+n[1],e.moveTo(s,a),r+=1/3*(2*Math.PI),s=o*Math.cos(r)+n[0],a=o*Math.sin(r)+n[1],e.lineTo(s,a),r+=1/3*(2*Math.PI),s=o*Math.cos(r)+n[0],a=o*Math.sin(r)+n[1],e.lineTo(s,a),e.closePath(),e.stroke(),e.fill()}var x0={color:"rgba(173, 216, 230, 0.6)",labelColor:"rgba(173, 216, 230, 1)",shadowColor:"black",alpha:.5,font:'small-caps 16px "Segoe UI"',lineHeight:18,lineWidth:4,pointSize:2,roundRect:8,drawPoints:!1,drawLabels:!0,drawBoxes:!0,drawAttention:!0,drawGestures:!0,drawPolygons:!0,drawGaze:!0,fillPolygons:!1,useDepth:!0,useCurves:!1};var V;function NA(e,t){var n,o;if(V.drawLabels){let r=[];if(r.push(`face: ${Math.trunc(100*e.score)}%`),e.genderScore&&r.push(`${e.gender||""} ${Math.trunc(100*e.genderScore)}%`),e.age&&r.push(`age: ${e.age||""}`),e.iris&&r.push(`distance: ${e.iris}`),e.real&&r.push(`real: ${Math.trunc(100*e.real)}%`),e.live&&r.push(`live: ${Math.trunc(100*e.live)}%`),e.emotion&&e.emotion.length>0){let s=e.emotion.map(a=>`${Math.trunc(100*a.score)}% ${a.emotion}`);s.length>3&&(s.length=3),r.push(s.join(" "))}((n=e.rotation)==null?void 0:n.angle)&&((o=e.rotation)==null?void 0:o.gaze)&&(e.rotation.angle.roll&&r.push(`roll: ${ke(e.rotation.angle.roll)}\xB0 yaw:${ke(e.rotation.angle.yaw)}\xB0 pitch:${ke(e.rotation.angle.pitch)}\xB0`),e.rotation.gaze.bearing&&r.push(`gaze: ${ke(e.rotation.gaze.bearing)}\xB0`)),r.length===0&&r.push("face"),t.fillStyle=V.color;for(let s=r.length-1;s>=0;s--){let a=Math.max(e.box[0],0),i=s*V.lineHeight+e.box[1];V.shadowColor&&V.shadowColor!==""&&(t.fillStyle=V.shadowColor,t.fillText(r[s],a+5,i+16)),t.fillStyle=V.labelColor,t.fillText(r[s],a+4,i+15)}}}function IA(e,t){var n,o,r,s;if(((n=e.annotations)==null?void 0:n.leftEyeIris)&&((o=e.annotations)==null?void 0:o.leftEyeIris[0])){t.strokeStyle=V.useDepth?"rgba(255, 200, 255, 0.3)":V.color,t.beginPath();let a=Math.abs(e.annotations.leftEyeIris[3][0]-e.annotations.leftEyeIris[1][0])/2,i=Math.abs(e.annotations.leftEyeIris[4][1]-e.annotations.leftEyeIris[2][1])/2;t.ellipse(e.annotations.leftEyeIris[0][0],e.annotations.leftEyeIris[0][1],a,i,0,0,2*Math.PI),t.stroke(),V.fillPolygons&&(t.fillStyle=V.useDepth?"rgba(255, 255, 200, 0.3)":V.color,t.fill())}if(((r=e.annotations)==null?void 0:r.rightEyeIris)&&((s=e.annotations)==null?void 0:s.rightEyeIris[0])){t.strokeStyle=V.useDepth?"rgba(255, 200, 255, 0.3)":V.color,t.beginPath();let a=Math.abs(e.annotations.rightEyeIris[3][0]-e.annotations.rightEyeIris[1][0])/2,i=Math.abs(e.annotations.rightEyeIris[4][1]-e.annotations.rightEyeIris[2][1])/2;t.ellipse(e.annotations.rightEyeIris[0][0],e.annotations.rightEyeIris[0][1],a,i,0,0,2*Math.PI),t.stroke(),V.fillPolygons&&(t.fillStyle=V.useDepth?"rgba(255, 255, 200, 0.3)":V.color,t.fill())}}function OA(e,t){var n;if(V.drawGaze&&((n=e.rotation)==null?void 0:n.angle)&&typeof Path2D!="undefined"){t.strokeStyle="pink";let o=e.box[0]+e.box[2]/2-e.box[3]*ke(e.rotation.angle.yaw)/90,r=e.box[1]+e.box[3]/2+e.box[2]*ke(e.rotation.angle.pitch)/90,s=new Path2D(`
-      M ${e.box[0]+e.box[2]/2} ${e.box[1]}
+`;
+
+// src/image/imagefx.ts
+var collect = (source, prefix, collection) => {
+  const r = new RegExp("\\b" + prefix + " \\w+ (\\w+)", "ig");
+  source.replace(r, (match3, name) => {
+    collection[name] = 0;
+    return match3;
+  });
+};
+var GLProgram = class {
+  constructor(gl, vertexSource, fragmentSource) {
+    __publicField(this, "uniform", {});
+    __publicField(this, "attribute", {});
+    __publicField(this, "gl");
+    __publicField(this, "id");
+    __publicField(this, "compile", (source, type) => {
+      const shader = this.gl.createShader(type);
+      if (!shader) {
+        log("filter: could not create shader");
+        return null;
+      }
+      this.gl.shaderSource(shader, source);
+      this.gl.compileShader(shader);
+      if (!this.gl.getShaderParameter(shader, this.gl.COMPILE_STATUS)) {
+        log(`filter: gl compile failed: ${this.gl.getShaderInfoLog(shader) || "unknown"}`);
+        return null;
+      }
+      return shader;
+    });
+    this.gl = gl;
+    const vertexShader = this.compile(vertexSource, this.gl.VERTEX_SHADER);
+    const fragmentShader = this.compile(fragmentSource, this.gl.FRAGMENT_SHADER);
+    this.id = this.gl.createProgram();
+    if (!vertexShader || !fragmentShader)
+      return;
+    if (!this.id) {
+      log("filter: could not create webgl program");
+      return;
+    }
+    this.gl.attachShader(this.id, vertexShader);
+    this.gl.attachShader(this.id, fragmentShader);
+    this.gl.linkProgram(this.id);
+    if (!this.gl.getProgramParameter(this.id, this.gl.LINK_STATUS)) {
+      log(`filter: gl link failed: ${this.gl.getProgramInfoLog(this.id) || "unknown"}`);
+      return;
+    }
+    this.gl.useProgram(this.id);
+    collect(vertexSource, "attribute", this.attribute);
+    for (const a in this.attribute)
+      this.attribute[a] = this.gl.getAttribLocation(this.id, a);
+    collect(vertexSource, "uniform", this.uniform);
+    collect(fragmentSource, "uniform", this.uniform);
+    for (const u in this.uniform)
+      this.uniform[u] = this.gl.getUniformLocation(this.id, u);
+  }
+};
+function GLImageFilter() {
+  let drawCount = 0;
+  let sourceTexture = null;
+  let lastInChain = false;
+  let currentFramebufferIndex = -1;
+  let tempFramebuffers = [null, null];
+  let filterChain = [];
+  let vertexBuffer = null;
+  let currentProgram = null;
+  const fxcanvas = canvas(100, 100);
+  const shaderProgramCache = {};
+  const DRAW = { INTERMEDIATE: 1 };
+  const gl = fxcanvas.getContext("webgl");
+  if (!gl) {
+    log("filter: cannot get webgl context");
+    return;
+  }
+  this.gl = gl;
+  function resize(width, height) {
+    if (width === fxcanvas.width && height === fxcanvas.height)
+      return;
+    fxcanvas.width = width;
+    fxcanvas.height = height;
+    if (!vertexBuffer) {
+      const vertices = new Float32Array([-1, -1, 0, 1, 1, -1, 1, 1, -1, 1, 0, 0, -1, 1, 0, 0, 1, -1, 1, 1, 1, 1, 1, 0]);
+      vertexBuffer = gl.createBuffer();
+      gl.bindBuffer(gl.ARRAY_BUFFER, vertexBuffer);
+      gl.bufferData(gl.ARRAY_BUFFER, vertices, gl.STATIC_DRAW);
+      gl.pixelStorei(gl.UNPACK_PREMULTIPLY_ALPHA_WEBGL, true);
+    }
+    gl.viewport(0, 0, fxcanvas.width, fxcanvas.height);
+    tempFramebuffers = [null, null];
+  }
+  function createFramebufferTexture(width, height) {
+    const fbo = gl.createFramebuffer();
+    gl.bindFramebuffer(gl.FRAMEBUFFER, fbo);
+    const renderbuffer = gl.createRenderbuffer();
+    gl.bindRenderbuffer(gl.RENDERBUFFER, renderbuffer);
+    const texture = gl.createTexture();
+    gl.bindTexture(gl.TEXTURE_2D, texture);
+    gl.texImage2D(gl.TEXTURE_2D, 0, gl.RGBA, width, height, 0, gl.RGBA, gl.UNSIGNED_BYTE, null);
+    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MAG_FILTER, gl.LINEAR);
+    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MIN_FILTER, gl.LINEAR);
+    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_S, gl.CLAMP_TO_EDGE);
+    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_T, gl.CLAMP_TO_EDGE);
+    gl.framebufferTexture2D(gl.FRAMEBUFFER, gl.COLOR_ATTACHMENT0, gl.TEXTURE_2D, texture, 0);
+    gl.bindTexture(gl.TEXTURE_2D, null);
+    gl.bindFramebuffer(gl.FRAMEBUFFER, null);
+    return { fbo, texture };
+  }
+  function getTempFramebuffer(index2) {
+    tempFramebuffers[index2] = tempFramebuffers[index2] || createFramebufferTexture(fxcanvas.width, fxcanvas.height);
+    return tempFramebuffers[index2];
+  }
+  function draw(flags = 0) {
+    if (!currentProgram)
+      return;
+    let source = null;
+    let target = null;
+    let flipY = false;
+    if (drawCount === 0)
+      source = sourceTexture;
+    else
+      source = getTempFramebuffer(currentFramebufferIndex).texture || null;
+    drawCount++;
+    if (lastInChain && !(flags & DRAW.INTERMEDIATE)) {
+      target = null;
+      flipY = drawCount % 2 === 0;
+    } else {
+      currentFramebufferIndex = (currentFramebufferIndex + 1) % 2;
+      target = getTempFramebuffer(currentFramebufferIndex).fbo || null;
+    }
+    gl.bindTexture(gl.TEXTURE_2D, source);
+    gl.bindFramebuffer(gl.FRAMEBUFFER, target);
+    gl.uniform1f(currentProgram.uniform["flipY"], flipY ? -1 : 1);
+    gl.drawArrays(gl.TRIANGLES, 0, 6);
+  }
+  function compileShader(fragmentSource) {
+    if (shaderProgramCache[fragmentSource]) {
+      currentProgram = shaderProgramCache[fragmentSource];
+      gl.useProgram((currentProgram ? currentProgram.id : null) || null);
+      return currentProgram;
+    }
+    currentProgram = new GLProgram(gl, vertexIdentity, fragmentSource);
+    if (!currentProgram) {
+      log("filter: could not get webgl program");
+      return null;
+    }
+    const floatSize = Float32Array.BYTES_PER_ELEMENT;
+    const vertSize = 4 * floatSize;
+    gl.enableVertexAttribArray(currentProgram.attribute["pos"]);
+    gl.vertexAttribPointer(currentProgram.attribute["pos"], 2, gl.FLOAT, false, vertSize, 0 * floatSize);
+    gl.enableVertexAttribArray(currentProgram.attribute["uv"]);
+    gl.vertexAttribPointer(currentProgram.attribute["uv"], 2, gl.FLOAT, false, vertSize, 2 * floatSize);
+    shaderProgramCache[fragmentSource] = currentProgram;
+    return currentProgram;
+  }
+  const filter = {
+    colorMatrix: (matrix) => {
+      const m = new Float32Array(matrix);
+      m[4] /= 255;
+      m[9] /= 255;
+      m[14] /= 255;
+      m[19] /= 255;
+      const shader = m[18] === 1 && m[3] === 0 && m[8] === 0 && m[13] === 0 && m[15] === 0 && m[16] === 0 && m[17] === 0 && m[19] === 0 ? colorMatrixWithoutAlpha : colorMatrixWithAlpha;
+      const program = compileShader(shader);
+      if (!program)
+        return;
+      gl.uniform1fv(program.uniform["m"], m);
+      draw();
+    },
+    brightness: (brightness) => {
+      const b = (brightness || 0) + 1;
+      filter.colorMatrix([
+        b,
+        0,
+        0,
+        0,
+        0,
+        0,
+        b,
+        0,
+        0,
+        0,
+        0,
+        0,
+        b,
+        0,
+        0,
+        0,
+        0,
+        0,
+        1,
+        0
+      ]);
+    },
+    saturation: (amount) => {
+      const x = (amount || 0) * 2 / 3 + 1;
+      const y = (x - 1) * -0.5;
+      filter.colorMatrix([
+        x,
+        y,
+        y,
+        0,
+        0,
+        y,
+        x,
+        y,
+        0,
+        0,
+        y,
+        y,
+        x,
+        0,
+        0,
+        0,
+        0,
+        0,
+        1,
+        0
+      ]);
+    },
+    desaturate: () => {
+      filter.saturation(-1);
+    },
+    contrast: (amount) => {
+      const v = (amount || 0) + 1;
+      const o = -128 * (v - 1);
+      filter.colorMatrix([
+        v,
+        0,
+        0,
+        0,
+        o,
+        0,
+        v,
+        0,
+        0,
+        o,
+        0,
+        0,
+        v,
+        0,
+        o,
+        0,
+        0,
+        0,
+        1,
+        0
+      ]);
+    },
+    negative: () => {
+      filter.contrast(-2);
+    },
+    hue: (rotation) => {
+      rotation = (rotation || 0) / 180 * Math.PI;
+      const cos = Math.cos(rotation);
+      const sin = Math.sin(rotation);
+      const lumR = 0.213;
+      const lumG = 0.715;
+      const lumB = 0.072;
+      filter.colorMatrix([
+        lumR + cos * (1 - lumR) + sin * -lumR,
+        lumG + cos * -lumG + sin * -lumG,
+        lumB + cos * -lumB + sin * (1 - lumB),
+        0,
+        0,
+        lumR + cos * -lumR + sin * 0.143,
+        lumG + cos * (1 - lumG) + sin * 0.14,
+        lumB + cos * -lumB + sin * -0.283,
+        0,
+        0,
+        lumR + cos * -lumR + sin * -(1 - lumR),
+        lumG + cos * -lumG + sin * lumG,
+        lumB + cos * (1 - lumB) + sin * lumB,
+        0,
+        0,
+        0,
+        0,
+        0,
+        1,
+        0
+      ]);
+    },
+    desaturateLuminance: () => {
+      filter.colorMatrix([
+        0.2764723,
+        0.929708,
+        0.0938197,
+        0,
+        -37.1,
+        0.2764723,
+        0.929708,
+        0.0938197,
+        0,
+        -37.1,
+        0.2764723,
+        0.929708,
+        0.0938197,
+        0,
+        -37.1,
+        0,
+        0,
+        0,
+        1,
+        0
+      ]);
+    },
+    sepia: () => {
+      filter.colorMatrix([
+        0.393,
+        0.7689999,
+        0.18899999,
+        0,
+        0,
+        0.349,
+        0.6859999,
+        0.16799999,
+        0,
+        0,
+        0.272,
+        0.5339999,
+        0.13099999,
+        0,
+        0,
+        0,
+        0,
+        0,
+        1,
+        0
+      ]);
+    },
+    brownie: () => {
+      filter.colorMatrix([
+        0.5997023498159715,
+        0.34553243048391263,
+        -0.2708298674538042,
+        0,
+        47.43192855600873,
+        -0.037703249837783157,
+        0.8609577587992641,
+        0.15059552388459913,
+        0,
+        -36.96841498319127,
+        0.24113635128153335,
+        -0.07441037908422492,
+        0.44972182064877153,
+        0,
+        -7.562075277591283,
+        0,
+        0,
+        0,
+        1,
+        0
+      ]);
+    },
+    vintagePinhole: () => {
+      filter.colorMatrix([
+        0.6279345635605994,
+        0.3202183420819367,
+        -0.03965408211312453,
+        0,
+        9.651285835294123,
+        0.02578397704808868,
+        0.6441188644374771,
+        0.03259127616149294,
+        0,
+        7.462829176470591,
+        0.0466055556782719,
+        -0.0851232987247891,
+        0.5241648018700465,
+        0,
+        5.159190588235296,
+        0,
+        0,
+        0,
+        1,
+        0
+      ]);
+    },
+    kodachrome: () => {
+      filter.colorMatrix([
+        1.1285582396593525,
+        -0.3967382283601348,
+        -0.03992559172921793,
+        0,
+        63.72958762196502,
+        -0.16404339962244616,
+        1.0835251566291304,
+        -0.05498805115633132,
+        0,
+        24.732407896706203,
+        -0.16786010706155763,
+        -0.5603416277695248,
+        1.6014850761964943,
+        0,
+        35.62982807460946,
+        0,
+        0,
+        0,
+        1,
+        0
+      ]);
+    },
+    technicolor: () => {
+      filter.colorMatrix([
+        1.9125277891456083,
+        -0.8545344976951645,
+        -0.09155508482755585,
+        0,
+        11.793603434377337,
+        -0.3087833385928097,
+        1.7658908555458428,
+        -0.10601743074722245,
+        0,
+        -70.35205161461398,
+        -0.231103377548616,
+        -0.7501899197440212,
+        1.847597816108189,
+        0,
+        30.950940869491138,
+        0,
+        0,
+        0,
+        1,
+        0
+      ]);
+    },
+    polaroid: () => {
+      filter.colorMatrix([
+        1.438,
+        -0.062,
+        -0.062,
+        0,
+        0,
+        -0.122,
+        1.378,
+        -0.122,
+        0,
+        0,
+        -0.016,
+        -0.016,
+        1.483,
+        0,
+        0,
+        0,
+        0,
+        0,
+        1,
+        0
+      ]);
+    },
+    shiftToBGR: () => {
+      filter.colorMatrix([
+        0,
+        0,
+        1,
+        0,
+        0,
+        0,
+        1,
+        0,
+        0,
+        0,
+        1,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        1,
+        0
+      ]);
+    },
+    convolution: (matrix) => {
+      const m = new Float32Array(matrix);
+      const pixelSizeX = 1 / fxcanvas.width;
+      const pixelSizeY = 1 / fxcanvas.height;
+      const program = compileShader(convolution);
+      if (!program)
+        return;
+      gl.uniform1fv(program.uniform["m"], m);
+      gl.uniform2f(program.uniform["px"], pixelSizeX, pixelSizeY);
+      draw();
+    },
+    detectEdges: () => {
+      filter.convolution.call(this, [
+        0,
+        1,
+        0,
+        1,
+        -4,
+        1,
+        0,
+        1,
+        0
+      ]);
+    },
+    sobelX: () => {
+      filter.convolution.call(this, [
+        -1,
+        0,
+        1,
+        -2,
+        0,
+        2,
+        -1,
+        0,
+        1
+      ]);
+    },
+    sobelY: () => {
+      filter.convolution.call(this, [
+        -1,
+        -2,
+        -1,
+        0,
+        0,
+        0,
+        1,
+        2,
+        1
+      ]);
+    },
+    sharpen: (amount) => {
+      const a = amount || 1;
+      filter.convolution.call(this, [
+        0,
+        -1 * a,
+        0,
+        -1 * a,
+        1 + 4 * a,
+        -1 * a,
+        0,
+        -1 * a,
+        0
+      ]);
+    },
+    emboss: (size2) => {
+      const s = size2 || 1;
+      filter.convolution.call(this, [
+        -2 * s,
+        -1 * s,
+        0,
+        -1 * s,
+        1,
+        1 * s,
+        0,
+        1 * s,
+        2 * s
+      ]);
+    },
+    blur: (size2) => {
+      const blurSizeX = size2 / 7 / fxcanvas.width;
+      const blurSizeY = size2 / 7 / fxcanvas.height;
+      const program = compileShader(blur);
+      if (!program)
+        return;
+      gl.uniform2f(program.uniform["px"], 0, blurSizeY);
+      draw(DRAW.INTERMEDIATE);
+      gl.uniform2f(program.uniform["px"], blurSizeX, 0);
+      draw();
+    },
+    pixelate: (size2) => {
+      const blurSizeX = size2 / fxcanvas.width;
+      const blurSizeY = size2 / fxcanvas.height;
+      const program = compileShader(pixelate);
+      if (!program)
+        return;
+      gl.uniform2f(program.uniform["size"], blurSizeX, blurSizeY);
+      draw();
+    }
+  };
+  this.add = function(name) {
+    const args = Array.prototype.slice.call(arguments, 1);
+    const func = filter[name];
+    filterChain.push({ func, args });
+  };
+  this.reset = function() {
+    filterChain = [];
+  };
+  this.get = function() {
+    return filterChain;
+  };
+  this.apply = function(image27) {
+    resize(image27.width, image27.height);
+    drawCount = 0;
+    if (!sourceTexture)
+      sourceTexture = gl.createTexture();
+    gl.bindTexture(gl.TEXTURE_2D, sourceTexture);
+    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_S, gl.CLAMP_TO_EDGE);
+    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_T, gl.CLAMP_TO_EDGE);
+    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MIN_FILTER, gl.NEAREST);
+    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MAG_FILTER, gl.NEAREST);
+    gl.texImage2D(gl.TEXTURE_2D, 0, gl.RGBA, gl.RGBA, gl.UNSIGNED_BYTE, image27);
+    for (let i = 0; i < filterChain.length; i++) {
+      lastInChain = i === filterChain.length - 1;
+      const f = filterChain[i];
+      f.func.apply(this, f.args || []);
+    }
+    return fxcanvas;
+  };
+  this.draw = function(image27) {
+    this.add("brightness", 0);
+    return this.apply(image27);
+  };
+}
+
+// src/image/enhance.ts
+async function histogramEqualization(inputImage) {
+  const squeeze14 = inputImage.shape.length === 4 ? tfjs_esm_exports.squeeze(inputImage) : inputImage;
+  const channels = tfjs_esm_exports.split(squeeze14, 3, 2);
+  const min2 = [tfjs_esm_exports.min(channels[0]), tfjs_esm_exports.min(channels[1]), tfjs_esm_exports.min(channels[2])];
+  const max4 = [tfjs_esm_exports.max(channels[0]), tfjs_esm_exports.max(channels[1]), tfjs_esm_exports.max(channels[2])];
+  const absMax = await Promise.all(max4.map((channel) => channel.data()));
+  const maxValue = 0.99 * Math.max(absMax[0][0], absMax[1][0], absMax[2][0]);
+  const sub11 = [tfjs_esm_exports.sub(channels[0], min2[0]), tfjs_esm_exports.sub(channels[1], min2[1]), tfjs_esm_exports.sub(channels[2], min2[2])];
+  const range = [tfjs_esm_exports.sub(max4[0], min2[0]), tfjs_esm_exports.sub(max4[1], min2[1]), tfjs_esm_exports.sub(max4[2], min2[2])];
+  const fact = [tfjs_esm_exports.div(maxValue, range[0]), tfjs_esm_exports.div(maxValue, range[1]), tfjs_esm_exports.div(maxValue, range[2])];
+  const enh = [tfjs_esm_exports.mul(sub11[0], fact[0]), tfjs_esm_exports.mul(sub11[1], fact[1]), tfjs_esm_exports.mul(sub11[2], fact[2])];
+  const rgb2 = tfjs_esm_exports.stack([enh[0], enh[1], enh[2]], 2);
+  const reshape8 = tfjs_esm_exports.reshape(rgb2, [1, squeeze14.shape[0], squeeze14.shape[1], 3]);
+  tfjs_esm_exports.dispose([...channels, ...min2, ...max4, ...sub11, ...range, ...fact, ...enh, rgb2, squeeze14]);
+  return reshape8;
+}
+
+// src/image/image.ts
+var maxSize = 3840;
+var inCanvas = null;
+var outCanvas = null;
+var tmpCanvas = null;
+var fx;
+var last = {
+  inputSum: 0,
+  cacheDiff: 1,
+  sumMethod: 0,
+  inputTensor: void 0
+};
+function reset() {
+  last.inputSum = 0;
+  last.cacheDiff = 1;
+  last.sumMethod = 0;
+  last.inputTensor = void 0;
+}
+function canvas(width, height) {
+  let c;
+  if (env.browser) {
+    if (env.worker) {
+      if (typeof OffscreenCanvas === "undefined")
+        throw new Error("canvas error: attempted to run in web worker but OffscreenCanvas is not supported");
+      c = new OffscreenCanvas(width, height);
+    } else {
+      if (typeof document === "undefined")
+        throw new Error("canvas error: attempted to run in browser but DOM is not defined");
+      c = document.createElement("canvas");
+      c.width = width;
+      c.height = height;
+    }
+  } else {
+    if (typeof env.Canvas !== "undefined")
+      c = new env.Canvas(width, height);
+    else if (typeof globalThis.Canvas !== "undefined")
+      c = new globalThis.Canvas(width, height);
+  }
+  return c;
+}
+function copy(input, output) {
+  const outputCanvas = output || canvas(input.width, input.height);
+  const ctx = outputCanvas.getContext("2d");
+  ctx.drawImage(input, 0, 0);
+  return outputCanvas;
+}
+async function process2(input, config3, getTensor = true) {
+  var _a, _b;
+  if (!input) {
+    if (config3.debug)
+      log("input error: input is missing");
+    return { tensor: null, canvas: null };
+  }
+  if (!(input instanceof Tensor) && !(typeof Image !== "undefined" && input instanceof Image) && !(typeof env.Canvas !== "undefined" && input instanceof env.Canvas) && !(typeof globalThis.Canvas !== "undefined" && input instanceof globalThis.Canvas) && !(typeof ImageData !== "undefined" && input instanceof ImageData) && !(typeof ImageBitmap !== "undefined" && input instanceof ImageBitmap) && !(typeof HTMLImageElement !== "undefined" && input instanceof HTMLImageElement) && !(typeof HTMLMediaElement !== "undefined" && input instanceof HTMLMediaElement) && !(typeof HTMLVideoElement !== "undefined" && input instanceof HTMLVideoElement) && !(typeof HTMLCanvasElement !== "undefined" && input instanceof HTMLCanvasElement) && !(typeof OffscreenCanvas !== "undefined" && input instanceof OffscreenCanvas)) {
+    throw new Error("input error: type is not recognized");
+  }
+  if (input instanceof Tensor) {
+    let tensor7 = null;
+    if (input["isDisposedInternal"])
+      throw new Error("input error: attempted to use tensor but it is disposed");
+    if (!input.shape)
+      throw new Error("input error: attempted to use tensor without a shape");
+    if (input.shape.length === 3) {
+      if (input.shape[2] === 3) {
+        tensor7 = tfjs_esm_exports.expandDims(input, 0);
+      } else if (input.shape[2] === 4) {
+        const rgb2 = tfjs_esm_exports.slice3d(input, [0, 0, 0], [-1, -1, 3]);
+        tensor7 = tfjs_esm_exports.expandDims(rgb2, 0);
+        tfjs_esm_exports.dispose(rgb2);
+      }
+    } else if (input.shape.length === 4) {
+      if (input.shape[3] === 3) {
+        tensor7 = tfjs_esm_exports.clone(input);
+      } else if (input.shape[3] === 4) {
+        tensor7 = tfjs_esm_exports.slice4d(input, [0, 0, 0, 0], [-1, -1, -1, 3]);
+      }
+    }
+    if (tensor7 == null || tensor7.shape.length !== 4 || tensor7.shape[0] !== 1 || tensor7.shape[3] !== 3)
+      throw new Error(`input error: attempted to use tensor with unrecognized shape: ${input.shape.toString()}`);
+    if (tensor7.dtype === "int32") {
+      const cast8 = tfjs_esm_exports.cast(tensor7, "float32");
+      tfjs_esm_exports.dispose(tensor7);
+      tensor7 = cast8;
+    }
+    return { tensor: tensor7, canvas: config3.filter.return ? outCanvas : null };
+  }
+  if (typeof input["readyState"] !== "undefined" && input.readyState <= 2) {
+    if (config3.debug)
+      log("input stream is not ready");
+    return { tensor: null, canvas: inCanvas };
+  }
+  const originalWidth = input["naturalWidth"] || input["videoWidth"] || input["width"] || input["shape"] && input["shape"][1] > 0;
+  const originalHeight = input["naturalHeight"] || input["videoHeight"] || input["height"] || input["shape"] && input["shape"][2] > 0;
+  if (!originalWidth || !originalHeight) {
+    if (config3.debug)
+      log("cannot determine input dimensions");
+    return { tensor: null, canvas: inCanvas };
+  }
+  let targetWidth = originalWidth;
+  let targetHeight = originalHeight;
+  if (targetWidth > maxSize) {
+    targetWidth = maxSize;
+    targetHeight = Math.trunc(targetWidth * originalHeight / originalWidth);
+  }
+  if (targetHeight > maxSize) {
+    targetHeight = maxSize;
+    targetWidth = Math.trunc(targetHeight * originalWidth / originalHeight);
+  }
+  if ((((_a = config3.filter) == null ? void 0 : _a.width) || 0) > 0)
+    targetWidth = config3.filter.width;
+  else if ((((_b = config3.filter) == null ? void 0 : _b.height) || 0) > 0)
+    targetWidth = originalWidth * ((config3.filter.height || 0) / originalHeight);
+  if ((config3.filter.height || 0) > 0)
+    targetHeight = config3.filter.height;
+  else if ((config3.filter.width || 0) > 0)
+    targetHeight = originalHeight * ((config3.filter.width || 0) / originalWidth);
+  if (!targetWidth || !targetHeight)
+    throw new Error("input error: cannot determine dimension");
+  if (!inCanvas || inCanvas.width !== targetWidth || inCanvas.height !== targetHeight)
+    inCanvas = canvas(targetWidth, targetHeight);
+  const inCtx = inCanvas.getContext("2d");
+  if (typeof ImageData !== "undefined" && input instanceof ImageData) {
+    inCtx.putImageData(input, 0, 0);
+  } else {
+    if (config3.filter.flip && typeof inCtx.translate !== "undefined") {
+      inCtx.translate(originalWidth, 0);
+      inCtx.scale(-1, 1);
+      inCtx.drawImage(input, 0, 0, originalWidth, originalHeight, 0, 0, inCanvas.width, inCanvas.height);
+      inCtx.setTransform(1, 0, 0, 1, 0, 0);
+    } else {
+      inCtx.drawImage(input, 0, 0, originalWidth, originalHeight, 0, 0, inCanvas.width, inCanvas.height);
+    }
+  }
+  if (!outCanvas || inCanvas.width !== outCanvas.width || inCanvas.height !== outCanvas.height)
+    outCanvas = canvas(inCanvas.width, inCanvas.height);
+  if (config3.filter.enabled && env.webgl.supported) {
+    if (!fx)
+      fx = env.browser ? new GLImageFilter() : null;
+    env.filter = !!fx;
+    if (!(fx == null ? void 0 : fx.add)) {
+      if (config3.debug)
+        log("input process error: cannot initialize filters");
+      env.webgl.supported = false;
+      config3.filter.enabled = false;
+      copy(inCanvas, outCanvas);
+    } else {
+      fx.reset();
+      if (config3.filter.brightness !== 0)
+        fx.add("brightness", config3.filter.brightness);
+      if (config3.filter.contrast !== 0)
+        fx.add("contrast", config3.filter.contrast);
+      if (config3.filter.sharpness !== 0)
+        fx.add("sharpen", config3.filter.sharpness);
+      if (config3.filter.blur !== 0)
+        fx.add("blur", config3.filter.blur);
+      if (config3.filter.saturation !== 0)
+        fx.add("saturation", config3.filter.saturation);
+      if (config3.filter.hue !== 0)
+        fx.add("hue", config3.filter.hue);
+      if (config3.filter.negative)
+        fx.add("negative");
+      if (config3.filter.sepia)
+        fx.add("sepia");
+      if (config3.filter.vintage)
+        fx.add("brownie");
+      if (config3.filter.sepia)
+        fx.add("sepia");
+      if (config3.filter.kodachrome)
+        fx.add("kodachrome");
+      if (config3.filter.technicolor)
+        fx.add("technicolor");
+      if (config3.filter.polaroid)
+        fx.add("polaroid");
+      if (config3.filter.pixelate !== 0)
+        fx.add("pixelate", config3.filter.pixelate);
+      if (fx.get() > 0)
+        outCanvas = fx.apply(inCanvas);
+      else
+        outCanvas = fx.draw(inCanvas);
+    }
+  } else {
+    copy(inCanvas, outCanvas);
+    if (fx)
+      fx = null;
+    env.filter = !!fx;
+  }
+  if (!getTensor)
+    return { tensor: null, canvas: outCanvas };
+  if (!outCanvas)
+    throw new Error("canvas error: cannot create output");
+  let pixels;
+  let depth = 3;
+  if (typeof ImageData !== "undefined" && input instanceof ImageData || input.data && input.width && input.height) {
+    if (env.browser && tfjs_esm_exports.browser) {
+      pixels = tfjs_esm_exports.browser ? tfjs_esm_exports.browser.fromPixels(input) : null;
+    } else {
+      depth = input.data.length / input.height / input.width;
+      const arr = new Uint8Array(input.data.buffer);
+      pixels = tfjs_esm_exports.tensor(arr, [input.height, input.width, depth], "int32");
+    }
+  } else {
+    if (!tmpCanvas || outCanvas.width !== tmpCanvas.width || outCanvas.height !== tmpCanvas.height)
+      tmpCanvas = canvas(outCanvas.width, outCanvas.height);
+    if (tfjs_esm_exports.browser && env.browser) {
+      if (config3.backend === "webgl" || config3.backend === "humangl" || config3.backend === "webgpu") {
+        pixels = tfjs_esm_exports.browser.fromPixels(outCanvas);
+      } else {
+        tmpCanvas = copy(outCanvas);
+        pixels = tfjs_esm_exports.browser.fromPixels(tmpCanvas);
+      }
+    } else {
+      const tempCanvas = copy(outCanvas);
+      const tempCtx = tempCanvas.getContext("2d");
+      const tempData = tempCtx.getImageData(0, 0, targetWidth, targetHeight);
+      depth = tempData.data.length / targetWidth / targetHeight;
+      const arr = new Uint8Array(tempData.data.buffer);
+      pixels = tfjs_esm_exports.tensor(arr, [targetWidth, targetHeight, depth]);
+    }
+  }
+  if (depth === 4) {
+    const rgb2 = tfjs_esm_exports.slice3d(pixels, [0, 0, 0], [-1, -1, 3]);
+    tfjs_esm_exports.dispose(pixels);
+    pixels = rgb2;
+  }
+  if (!pixels)
+    throw new Error("input error: cannot create tensor");
+  const casted = tfjs_esm_exports.cast(pixels, "float32");
+  const tensor6 = config3.filter.equalization ? await histogramEqualization(casted) : tfjs_esm_exports.expandDims(casted, 0);
+  tfjs_esm_exports.dispose([pixels, casted]);
+  return { tensor: tensor6, canvas: config3.filter.return ? outCanvas : null };
+}
+async function skip(config3, input) {
+  let skipFrame = false;
+  if (config3.cacheSensitivity === 0 || !input.shape || input.shape.length !== 4 || input.shape[1] > 2048 || input.shape[2] > 2048)
+    return skipFrame;
+  if (!last.inputTensor) {
+    last.inputTensor = tfjs_esm_exports.clone(input);
+  } else if (last.inputTensor.shape[1] !== input.shape[1] || last.inputTensor.shape[2] !== input.shape[2]) {
+    tfjs_esm_exports.dispose(last.inputTensor);
+    last.inputTensor = tfjs_esm_exports.clone(input);
+  } else {
+    const t2 = {};
+    t2.diff = tfjs_esm_exports.sub(input, last.inputTensor);
+    t2.squared = tfjs_esm_exports.mul(t2.diff, t2.diff);
+    t2.sum = tfjs_esm_exports.sum(t2.squared);
+    const diffSum = await t2.sum.data();
+    const diffRelative = diffSum[0] / (input.shape[1] || 1) / (input.shape[2] || 1) / 255 / 3;
+    tfjs_esm_exports.dispose([last.inputTensor, t2.diff, t2.squared, t2.sum]);
+    last.inputTensor = tfjs_esm_exports.clone(input);
+    skipFrame = diffRelative <= (config3.cacheSensitivity || 0);
+  }
+  return skipFrame;
+}
+async function compare(config3, input1, input2) {
+  const t2 = {};
+  if (!input1 || !input2 || input1.shape.length !== 4 || input1.shape.length !== input2.shape.length) {
+    if (!config3.debug)
+      log("invalid input tensor or tensor shapes do not match:", input1.shape, input2.shape);
+    return 0;
+  }
+  if (input1.shape[0] !== 1 || input2.shape[0] !== 1 || input1.shape[3] !== 3 || input2.shape[3] !== 3) {
+    if (!config3.debug)
+      log("input tensors must be of shape [1, height, width, 3]:", input1.shape, input2.shape);
+    return 0;
+  }
+  t2.input1 = tfjs_esm_exports.clone(input1);
+  t2.input2 = input1.shape[1] !== input2.shape[1] || input1.shape[2] !== input2.shape[2] ? tfjs_esm_exports.image.resizeBilinear(input2, [input1.shape[1], input1.shape[2]]) : tfjs_esm_exports.clone(input2);
+  t2.diff = tfjs_esm_exports.sub(t2.input1, t2.input2);
+  t2.squared = tfjs_esm_exports.mul(t2.diff, t2.diff);
+  t2.sum = tfjs_esm_exports.sum(t2.squared);
+  const diffSum = await t2.sum.data();
+  const diffRelative = diffSum[0] / (input1.shape[1] || 1) / (input1.shape[2] || 1) / 255 / 3;
+  tfjs_esm_exports.dispose([t2.input1, t2.input2, t2.diff, t2.squared, t2.sum]);
+  return diffRelative;
+}
+
+// src/util/env.ts
+var Env = class {
+  constructor() {
+    __publicField(this, "browser");
+    __publicField(this, "node");
+    __publicField(this, "worker");
+    __publicField(this, "platform", "");
+    __publicField(this, "agent", "");
+    __publicField(this, "backends", []);
+    __publicField(this, "initial");
+    __publicField(this, "filter");
+    __publicField(this, "tfjs");
+    __publicField(this, "offscreen");
+    __publicField(this, "perfadd", false);
+    __publicField(this, "tensorflow", {
+      version: void 0,
+      gpu: void 0
+    });
+    __publicField(this, "wasm", {
+      supported: void 0,
+      backend: void 0,
+      simd: void 0,
+      multithread: void 0
+    });
+    __publicField(this, "webgl", {
+      supported: void 0,
+      backend: void 0,
+      version: void 0,
+      renderer: void 0
+    });
+    __publicField(this, "webgpu", {
+      supported: void 0,
+      backend: void 0,
+      adapter: void 0
+    });
+    __publicField(this, "cpu", {
+      model: void 0,
+      flags: []
+    });
+    __publicField(this, "kernels", []);
+    __publicField(this, "Canvas");
+    __publicField(this, "Image");
+    __publicField(this, "ImageData");
+    this.browser = typeof navigator !== "undefined";
+    this.node = typeof process !== "undefined" && typeof process.versions !== "undefined" && typeof process.versions.node !== "undefined";
+    this.tfjs = { version: version8["tfjs-core"] };
+    this.offscreen = typeof OffscreenCanvas !== "undefined";
+    this.initial = true;
+    this.worker = this.browser && this.offscreen ? typeof WorkerGlobalScope !== "undefined" : void 0;
+    if (typeof navigator !== "undefined") {
+      const raw = navigator.userAgent.match(/\(([^()]+)\)/g);
+      if (raw == null ? void 0 : raw[0]) {
+        const platformMatch = raw[0].match(/\(([^()]+)\)/g);
+        this.platform = (platformMatch == null ? void 0 : platformMatch[0]) ? platformMatch[0].replace(/\(|\)/g, "") : "";
+        this.agent = navigator.userAgent.replace(raw[0], "");
+        if (this.platform[1])
+          this.agent = this.agent.replace(raw[1], "");
+        this.agent = this.agent.replace(/  /g, " ");
+      }
+    } else if (typeof process !== "undefined") {
+      this.platform = `${process.platform} ${process.arch}`;
+      this.agent = `NodeJS ${process.version}`;
+    }
+  }
+  async updateBackend() {
+    this.backends = Object.keys(tfjs_esm_exports.engine().registryFactory);
+    this.tensorflow = {
+      version: tfjs_esm_exports.backend().binding ? tfjs_esm_exports.backend().binding.TF_Version : void 0,
+      gpu: tfjs_esm_exports.backend().binding ? tfjs_esm_exports.backend().binding.isUsingGpuDevice() : void 0
+    };
+    this.wasm.supported = typeof WebAssembly !== "undefined";
+    this.wasm.backend = this.backends.includes("wasm");
+    if (this.wasm.supported && this.wasm.backend && tfjs_esm_exports.getBackend() === "wasm") {
+      this.wasm.simd = tfjs_esm_exports.env().get("WASM_HAS_SIMD_SUPPORT");
+      this.wasm.multithread = tfjs_esm_exports.env().get("WASM_HAS_MULTITHREAD_SUPPORT");
+    }
+    const c = canvas(100, 100);
+    const ctx = c ? c.getContext("webgl2") : void 0;
+    this.webgl.supported = typeof ctx !== "undefined";
+    this.webgl.backend = this.backends.includes("webgl");
+    if (this.webgl.supported && this.webgl.backend && (tfjs_esm_exports.getBackend() === "webgl" || tfjs_esm_exports.getBackend() === "humangl")) {
+      const gl = tfjs_esm_exports.backend().gpgpu !== "undefined" ? await tfjs_esm_exports.backend().getGPGPUContext().gl : null;
+      if (gl) {
+        this.webgl.version = gl.getParameter(gl.VERSION);
+        this.webgl.renderer = gl.getParameter(gl.RENDERER);
+      }
+    }
+    this.webgpu.supported = this.browser && typeof navigator.gpu !== "undefined";
+    this.webgpu.backend = this.backends.includes("webgpu");
+    try {
+      if (this.webgpu.supported) {
+        const adapter = await navigator.gpu.requestAdapter();
+        this.webgpu.adapter = adapter ? adapter.name : void 0;
+      }
+    } catch (e) {
+      this.webgpu.supported = false;
+    }
+    try {
+      this.kernels = tfjs_esm_exports.getKernelsForBackend(tfjs_esm_exports.getBackend()).map((kernel) => kernel.kernelName.toLowerCase());
+    } catch (e) {
+    }
+  }
+  updateCPU() {
+    const cpu = { model: "", flags: [] };
+    if (this.node && this.platform.startsWith("linux")) {
+    }
+    if (!this.cpu)
+      Object.defineProperty(this, "cpu", { value: cpu });
+    else
+      this.cpu = cpu;
+  }
+};
+var env = new Env();
+
+// src/util/webcam.ts
+var WebCam = class {
+  constructor() {
+    __publicField(this, "config");
+    __publicField(this, "element");
+    __publicField(this, "stream");
+    __publicField(this, "start", async (webcamConfig) => {
+      if (webcamConfig == null ? void 0 : webcamConfig.debug)
+        this.config.debug = webcamConfig == null ? void 0 : webcamConfig.debug;
+      if (webcamConfig == null ? void 0 : webcamConfig.crop)
+        this.config.crop = webcamConfig == null ? void 0 : webcamConfig.crop;
+      if (webcamConfig == null ? void 0 : webcamConfig.mode)
+        this.config.mode = webcamConfig == null ? void 0 : webcamConfig.mode;
+      if (webcamConfig == null ? void 0 : webcamConfig.width)
+        this.config.width = webcamConfig == null ? void 0 : webcamConfig.width;
+      if (webcamConfig == null ? void 0 : webcamConfig.height)
+        this.config.height = webcamConfig == null ? void 0 : webcamConfig.height;
+      if (webcamConfig == null ? void 0 : webcamConfig.element) {
+        if (typeof webcamConfig.element === "string") {
+          const el = document.getElementById(webcamConfig.element);
+          if (el && el instanceof HTMLVideoElement) {
+            this.element = el;
+          } else {
+            if (this.config.debug)
+              log("webcam", "cannot get dom element", webcamConfig.element);
+            return;
+          }
+        } else if (webcamConfig.element instanceof HTMLVideoElement) {
+          this.element = webcamConfig.element;
+        } else {
+          if (this.config.debug)
+            log("webcam", "unknown dom element", webcamConfig.element);
+          return;
+        }
+      } else {
+        this.element = document.createElement("video");
+      }
+      const requestedConstraints = {
+        audio: false,
+        video: {
+          facingMode: this.config.mode === "front" ? "user" : "environment",
+          resizeMode: this.config.crop ? "crop-and-scale" : "none",
+          width: { ideal: this.config.width > 0 ? this.config.width : window.innerWidth },
+          height: { ideal: this.config.height > 0 ? this.config.height : window.innerHeight }
+        }
+      };
+      this.element.addEventListener("play", () => {
+        if (this.config.debug)
+          log("webcam", "play");
+      });
+      this.element.addEventListener("pause", () => {
+        if (this.config.debug)
+          log("webcam", "pause");
+      });
+      this.element.addEventListener("click", async () => {
+        if (!this.element || !this.stream)
+          return;
+        if (this.element.paused)
+          await this.element.play();
+        else
+          this.element.pause();
+      });
+      if (!(navigator == null ? void 0 : navigator.mediaDevices)) {
+        if (this.config.debug)
+          log("webcam", "no devices");
+        return;
+      }
+      try {
+        this.stream = await navigator.mediaDevices.getUserMedia(requestedConstraints);
+      } catch (err) {
+        log("webcam", err);
+        return;
+      }
+      if (!this.stream) {
+        if (this.config.debug)
+          log("webcam", "no stream");
+        return;
+      }
+      this.element.srcObject = this.stream;
+      const ready3 = new Promise((resolve) => {
+        if (!this.element)
+          resolve(false);
+        else
+          this.element.onloadeddata = () => resolve(true);
+      });
+      await ready3;
+      await this.element.play();
+      if (this.config.debug) {
+        log("webcam", {
+          width: this.width,
+          height: this.height,
+          label: this.label,
+          stream: this.stream,
+          track: this.track,
+          settings: this.settings,
+          constraints: this.constraints,
+          capabilities: this.capabilities
+        });
+      }
+    });
+    __publicField(this, "pause", () => {
+      if (this.element)
+        this.element.pause();
+    });
+    __publicField(this, "play", async () => {
+      if (this.element)
+        await this.element.play();
+    });
+    __publicField(this, "stop", () => {
+      if (this.config.debug)
+        log("webcam", "stop");
+      if (this.track)
+        this.track.stop();
+    });
+    this.config = {
+      element: void 0,
+      debug: true,
+      mode: "front",
+      crop: false,
+      width: 0,
+      height: 0
+    };
+  }
+  get track() {
+    if (!this.stream)
+      return void 0;
+    return this.stream.getVideoTracks()[0];
+  }
+  get capabilities() {
+    if (!this.track)
+      return void 0;
+    return this.track.getCapabilities ? this.track.getCapabilities() : void 0;
+  }
+  get constraints() {
+    if (!this.track)
+      return void 0;
+    return this.track.getConstraints ? this.track.getConstraints() : void 0;
+  }
+  get settings() {
+    if (!this.stream)
+      return void 0;
+    const track = this.stream.getVideoTracks()[0];
+    return track.getSettings ? track.getSettings() : void 0;
+  }
+  get label() {
+    if (!this.track)
+      return "";
+    return this.track.label;
+  }
+  get paused() {
+    var _a;
+    return ((_a = this.element) == null ? void 0 : _a.paused) || false;
+  }
+  get width() {
+    var _a;
+    return ((_a = this.element) == null ? void 0 : _a.videoWidth) || 0;
+  }
+  get height() {
+    var _a;
+    return ((_a = this.element) == null ? void 0 : _a.videoHeight) || 0;
+  }
+};
+
+// models/models.json
+var models_exports = {};
+__export(models_exports, {
+  age: () => age,
+  "anti-spoofing": () => anti_spoofing,
+  antispoof: () => antispoof,
+  blazeface: () => blazeface,
+  "blazeface-back": () => blazeface_back,
+  "blazeface-front": () => blazeface_front,
+  "blazepose-detect": () => blazepose_detect,
+  "blazepose-detector2d": () => blazepose_detector2d,
+  "blazepose-detector3d": () => blazepose_detector3d,
+  "blazepose-full": () => blazepose_full,
+  "blazepose-heavy": () => blazepose_heavy,
+  "blazepose-lite": () => blazepose_lite,
+  default: () => models_default,
+  efficientpose: () => efficientpose,
+  "efficientpose-i-lite": () => efficientpose_i_lite,
+  "efficientpose-ii-lite": () => efficientpose_ii_lite,
+  "efficientpose-iv": () => efficientpose_iv,
+  emotion: () => emotion,
+  faceboxes: () => faceboxes,
+  facemesh: () => facemesh,
+  "facemesh-attention": () => facemesh_attention,
+  "facemesh-attention-alt": () => facemesh_attention_alt,
+  "facemesh-detection-full": () => facemesh_detection_full,
+  "facemesh-detection-short": () => facemesh_detection_short,
+  "facemesh-orig": () => facemesh_orig,
+  faceres: () => faceres,
+  "faceres-deep": () => faceres_deep,
+  gear: () => gear,
+  gender: () => gender,
+  "gender-ssrnet-imdb": () => gender_ssrnet_imdb,
+  handdetect: () => handdetect,
+  "handlandmark-full": () => handlandmark_full,
+  "handlandmark-lite": () => handlandmark_lite,
+  "handlandmark-sparse": () => handlandmark_sparse,
+  handskeleton: () => handskeleton,
+  handtrack: () => handtrack,
+  "insightface-efficientnet-b0": () => insightface_efficientnet_b0,
+  "insightface-ghostnet-strides1": () => insightface_ghostnet_strides1,
+  "insightface-ghostnet-strides2": () => insightface_ghostnet_strides2,
+  "insightface-mobilenet-emore": () => insightface_mobilenet_emore,
+  "insightface-mobilenet-swish": () => insightface_mobilenet_swish,
+  iris: () => iris,
+  liveness: () => liveness,
+  "mb3-centernet": () => mb3_centernet,
+  meet: () => meet,
+  mobileface: () => mobileface,
+  mobilefacenet: () => mobilefacenet,
+  models: () => models,
+  "movenet-lightning": () => movenet_lightning,
+  "movenet-multipose": () => movenet_multipose,
+  "movenet-thunder": () => movenet_thunder,
+  nanodet: () => nanodet,
+  "nanodet-e": () => nanodet_e,
+  "nanodet-g": () => nanodet_g,
+  "nanodet-m": () => nanodet_m,
+  "nanodet-t": () => nanodet_t,
+  posenet: () => posenet,
+  selfie: () => selfie
+});
+var antispoof = 853098;
+var blazeface = 538928;
+var emotion = 820516;
+var facemesh = 1477958;
+var faceres = 6978814;
+var handlandmark_full = 5431368;
+var handtrack = 2964837;
+var iris = 2599092;
+var liveness = 592976;
+var mb3_centernet = 4030290;
+var models = 0;
+var movenet_lightning = 4650216;
+var selfie = 212886;
+var age = 161240;
+var blazeface_back = 538928;
+var blazeface_front = 402048;
+var blazepose_detector2d = 7499400;
+var blazepose_detector3d = 5928856;
+var blazepose_full = 6338290;
+var blazepose_heavy = 27501554;
+var blazepose_lite = 2725490;
+var efficientpose = 5651240;
+var faceboxes = 2013002;
+var facemesh_attention_alt = 2387598;
+var facemesh_attention = 2382414;
+var facemesh_detection_full = 1026192;
+var facemesh_detection_short = 201268;
+var facemesh_orig = 2955780;
+var faceres_deep = 13957620;
+var gear = 1498916;
+var gender_ssrnet_imdb = 161236;
+var gender = 201808;
+var handdetect = 3515612;
+var handlandmark_lite = 2023432;
+var handlandmark_sparse = 5286322;
+var handskeleton = 5502280;
+var meet = 372228;
+var mobileface = 2183192;
+var mobilefacenet = 5171976;
+var movenet_multipose = 9448838;
+var movenet_thunder = 12477112;
+var nanodet = 7574558;
+var posenet = 5032780;
+var blazepose_detect = 5928804;
+var anti_spoofing = 853098;
+var efficientpose_i_lite = 2269064;
+var efficientpose_ii_lite = 5651240;
+var efficientpose_iv = 25643252;
+var insightface_efficientnet_b0 = 13013224;
+var insightface_ghostnet_strides1 = 8093408;
+var insightface_ghostnet_strides2 = 8049584;
+var insightface_mobilenet_emore = 6938536;
+var insightface_mobilenet_swish = 12168584;
+var nanodet_e = 12319156;
+var nanodet_g = 7574558;
+var nanodet_m = 1887474;
+var nanodet_t = 5294216;
+var models_default = {
+  antispoof,
+  blazeface,
+  emotion,
+  facemesh,
+  faceres,
+  "handlandmark-full": handlandmark_full,
+  handtrack,
+  iris,
+  liveness,
+  "mb3-centernet": mb3_centernet,
+  models,
+  "movenet-lightning": movenet_lightning,
+  selfie,
+  age,
+  "blazeface-back": blazeface_back,
+  "blazeface-front": blazeface_front,
+  "blazepose-detector2d": blazepose_detector2d,
+  "blazepose-detector3d": blazepose_detector3d,
+  "blazepose-full": blazepose_full,
+  "blazepose-heavy": blazepose_heavy,
+  "blazepose-lite": blazepose_lite,
+  efficientpose,
+  faceboxes,
+  "facemesh-attention-alt": facemesh_attention_alt,
+  "facemesh-attention": facemesh_attention,
+  "facemesh-detection-full": facemesh_detection_full,
+  "facemesh-detection-short": facemesh_detection_short,
+  "facemesh-orig": facemesh_orig,
+  "faceres-deep": faceres_deep,
+  gear,
+  "gender-ssrnet-imdb": gender_ssrnet_imdb,
+  gender,
+  handdetect,
+  "handlandmark-lite": handlandmark_lite,
+  "handlandmark-sparse": handlandmark_sparse,
+  handskeleton,
+  meet,
+  mobileface,
+  mobilefacenet,
+  "movenet-multipose": movenet_multipose,
+  "movenet-thunder": movenet_thunder,
+  nanodet,
+  posenet,
+  "blazepose-detect": blazepose_detect,
+  "anti-spoofing": anti_spoofing,
+  "efficientpose-i-lite": efficientpose_i_lite,
+  "efficientpose-ii-lite": efficientpose_ii_lite,
+  "efficientpose-iv": efficientpose_iv,
+  "insightface-efficientnet-b0": insightface_efficientnet_b0,
+  "insightface-ghostnet-strides1": insightface_ghostnet_strides1,
+  "insightface-ghostnet-strides2": insightface_ghostnet_strides2,
+  "insightface-mobilenet-emore": insightface_mobilenet_emore,
+  "insightface-mobilenet-swish": insightface_mobilenet_swish,
+  "nanodet-e": nanodet_e,
+  "nanodet-g": nanodet_g,
+  "nanodet-m": nanodet_m,
+  "nanodet-t": nanodet_t
+};
+
+// src/tfjs/load.ts
+var options = {
+  cacheModels: true,
+  cacheSupported: true,
+  verbose: true,
+  debug: false,
+  modelBasePath: ""
+};
+var modelStats = {};
+async function httpHandler(url, init3) {
+  if (options.debug)
+    log("load model fetch:", url, init3);
+  return fetch(url, init3);
+}
+function setModelLoadOptions(config3) {
+  options.cacheModels = config3.cacheModels;
+  options.verbose = config3.debug;
+  options.modelBasePath = config3.modelBasePath;
+}
+async function loadModel(modelPath) {
+  var _a, _b, _c, _d;
+  let modelUrl = join(options.modelBasePath, modelPath || "");
+  if (!modelUrl.toLowerCase().endsWith(".json"))
+    modelUrl += ".json";
+  const modelPathSegments = modelUrl.includes("/") ? modelUrl.split("/") : modelUrl.split("\\");
+  const shortModelName = modelPathSegments[modelPathSegments.length - 1].replace(".json", "");
+  const cachedModelName = "indexeddb://" + shortModelName;
+  modelStats[shortModelName] = {
+    name: shortModelName,
+    sizeFromManifest: 0,
+    sizeLoadedWeights: 0,
+    sizeDesired: models_exports[shortModelName],
+    inCache: false
+  };
+  options.cacheSupported = typeof indexedDB !== "undefined";
+  let cachedModels = {};
+  try {
+    cachedModels = options.cacheSupported && options.cacheModels ? await tfjs_esm_exports.io.listModels() : {};
+  } catch (e) {
+    options.cacheSupported = false;
+  }
+  modelStats[shortModelName].inCache = options.cacheSupported && options.cacheModels && Object.keys(cachedModels).includes(cachedModelName);
+  const tfLoadOptions = typeof fetch === "undefined" ? {} : { fetchFunc: (url, init3) => httpHandler(url, init3) };
+  let model21 = new GraphModel(modelStats[shortModelName].inCache ? cachedModelName : modelUrl, tfLoadOptions);
+  let loaded = false;
+  try {
+    model21.findIOHandler();
+    if (options.debug)
+      log("model load handler:", model21["handler"]);
+  } catch (err) {
+    log("error finding model i/o handler:", modelUrl, err);
+  }
+  try {
+    const artifacts = await ((_a = model21.handler) == null ? void 0 : _a.load()) || null;
+    modelStats[shortModelName].sizeFromManifest = ((_b = artifacts == null ? void 0 : artifacts.weightData) == null ? void 0 : _b.byteLength) || 0;
+    if (artifacts)
+      model21.loadSync(artifacts);
+    else
+      model21 = await tfjs_esm_exports.loadGraphModel(modelStats[shortModelName].inCache ? cachedModelName : modelUrl, tfLoadOptions);
+    modelStats[shortModelName].sizeLoadedWeights = ((_d = (_c = model21.artifacts) == null ? void 0 : _c.weightData) == null ? void 0 : _d.byteLength) || 0;
+    if (options.verbose)
+      log("load:", { model: shortModelName, url: model21["modelUrl"], bytes: modelStats[shortModelName].sizeLoadedWeights });
+    loaded = true;
+  } catch (err) {
+    log("error loading model:", modelUrl, err);
+  }
+  if (loaded && options.cacheModels && options.cacheSupported && !modelStats[shortModelName].inCache) {
+    try {
+      const saveResult = await model21.save(cachedModelName);
+      if (options.debug)
+        log("model saved:", cachedModelName, saveResult);
+    } catch (err) {
+      log("error saving model:", modelUrl, err);
+    }
+  }
+  return model21;
+}
+
+// package.json
+var version9 = "2.11.0";
+
+// src/models.ts
+var models_exports2 = {};
+__export(models_exports2, {
+  Models: () => Models,
+  getModelStats: () => getModelStats,
+  load: () => load22,
+  reset: () => reset2,
+  validate: () => validate2,
+  validateModel: () => validateModel
+});
+
+// src/face/antispoof.ts
+var model;
+var cached = [];
+var skipped = Number.MAX_SAFE_INTEGER;
+var lastCount = 0;
+var lastTime = 0;
+async function load(config3) {
+  var _a;
+  if (env.initial)
+    model = null;
+  if (!model)
+    model = await loadModel((_a = config3.face.antispoof) == null ? void 0 : _a.modelPath);
+  else if (config3.debug)
+    log("cached model:", model["modelUrl"]);
+  return model;
+}
+async function predict(image27, config3, idx, count2) {
+  var _a, _b;
+  if (!model || !(model == null ? void 0 : model["executor"]))
+    return 0;
+  const skipTime = (((_a = config3.face.antispoof) == null ? void 0 : _a.skipTime) || 0) > now() - lastTime;
+  const skipFrame = skipped < (((_b = config3.face.antispoof) == null ? void 0 : _b.skipFrames) || 0);
+  if (config3.skipAllowed && skipTime && skipFrame && lastCount === count2 && cached[idx]) {
+    skipped++;
+    return cached[idx];
+  }
+  skipped = 0;
+  return new Promise(async (resolve) => {
+    const resize = tfjs_esm_exports.image.resizeBilinear(image27, [(model == null ? void 0 : model.inputs[0].shape) ? model.inputs[0].shape[2] : 0, (model == null ? void 0 : model.inputs[0].shape) ? model.inputs[0].shape[1] : 0], false);
+    const res = model == null ? void 0 : model.execute(resize);
+    const num = (await res.data())[0];
+    cached[idx] = Math.round(100 * num) / 100;
+    lastCount = count2;
+    lastTime = now();
+    tfjs_esm_exports.dispose([resize, res]);
+    resolve(cached[idx]);
+  });
+}
+
+// src/face/facemeshcoords.ts
+var meshAnnotations = {
+  silhouette: [
+    10,
+    338,
+    297,
+    332,
+    284,
+    251,
+    389,
+    356,
+    454,
+    323,
+    361,
+    288,
+    397,
+    365,
+    379,
+    378,
+    400,
+    377,
+    152,
+    148,
+    176,
+    149,
+    150,
+    136,
+    172,
+    58,
+    132,
+    93,
+    234,
+    127,
+    162,
+    21,
+    54,
+    103,
+    67,
+    109
+  ],
+  lipsUpperOuter: [185, 40, 39, 37, 0, 267, 269, 270, 409],
+  lipsLowerOuter: [61, 146, 91, 181, 84, 17, 314, 405, 321, 375, 291],
+  lipsUpperInner: [191, 80, 81, 82, 13, 312, 311, 310, 415],
+  lipsLowerInner: [78, 95, 88, 178, 87, 14, 317, 402, 318, 324, 308],
+  lipsLowerSemiOuter: [76, 77, 90, 180, 85, 16, 315, 404, 320, 307, 306],
+  lipsUpperSemiOuter: [184, 74, 73, 72, 11, 302, 303, 304, 408],
+  lipsLowerSemiInner: [62, 96, 89, 179, 86, 15, 316, 403, 319, 325, 292],
+  lipsUpperSemiInner: [183, 42, 41, 38, 12, 268, 271, 272, 407],
+  rightEyeUpper0: [246, 161, 160, 159, 158, 157, 173],
+  rightEyeLower0: [33, 7, 163, 144, 145, 153, 154, 155, 133],
+  rightEyeUpper1: [247, 30, 29, 27, 28, 56, 190],
+  rightEyeLower1: [130, 25, 110, 24, 23, 22, 26, 112, 243],
+  rightEyeUpper2: [113, 225, 224, 223, 222, 221, 189],
+  rightEyeLower2: [226, 31, 228, 229, 230, 231, 232, 233, 244],
+  rightEyeLower3: [143, 111, 117, 118, 119, 120, 121, 128, 245],
+  rightEyebrowUpper: [156, 70, 63, 105, 66, 107, 55, 193],
+  rightEyebrowLower: [35, 124, 46, 53, 52, 65],
+  rightEyeIris: [473, 474, 475, 476, 477],
+  leftEyeUpper0: [466, 388, 387, 386, 385, 384, 398],
+  leftEyeLower0: [263, 249, 390, 373, 374, 380, 381, 382, 362],
+  leftEyeUpper1: [467, 260, 259, 257, 258, 286, 414],
+  leftEyeLower1: [359, 255, 339, 254, 253, 252, 256, 341, 463],
+  leftEyeUpper2: [342, 445, 444, 443, 442, 441, 413],
+  leftEyeLower2: [446, 261, 448, 449, 450, 451, 452, 453, 464],
+  leftEyeLower3: [372, 340, 346, 347, 348, 349, 350, 357, 465],
+  leftEyebrowUpper: [383, 300, 293, 334, 296, 336, 285, 417],
+  leftEyebrowLower: [265, 353, 276, 283, 282, 295],
+  leftEyeIris: [468, 469, 470, 471, 472],
+  midwayBetweenEyes: [168],
+  noseTip: [1],
+  noseBottom: [2],
+  noseRightCorner: [98],
+  noseLeftCorner: [327],
+  rightCheek: [205],
+  leftCheek: [425]
+};
+var meshLandmarks = {
+  count: 468,
+  mouth: 13,
+  symmetryLine: [13, meshAnnotations.midwayBetweenEyes[0]]
+};
+var blazeFaceLandmarks = {
+  leftEye: 0,
+  rightEye: 1,
+  nose: 2,
+  mouth: 3,
+  leftEar: 4,
+  rightEar: 5,
+  symmetryLine: [3, 2]
+};
+var irisIndices = [
+  { key: "EyeUpper0", indices: [9, 10, 11, 12, 13, 14, 15] },
+  { key: "EyeUpper1", indices: [25, 26, 27, 28, 29, 30, 31] },
+  { key: "EyeUpper2", indices: [41, 42, 43, 44, 45, 46, 47] },
+  { key: "EyeLower0", indices: [0, 1, 2, 3, 4, 5, 6, 7, 8] },
+  { key: "EyeLower1", indices: [16, 17, 18, 19, 20, 21, 22, 23, 24] },
+  { key: "EyeLower2", indices: [32, 33, 34, 35, 36, 37, 38, 39, 40] },
+  { key: "EyeLower3", indices: [54, 55, 56, 57, 58, 59, 60, 61, 62] },
+  { key: "EyebrowUpper", indices: [63, 64, 65, 66, 67, 68, 69, 70] },
+  { key: "EyebrowLower", indices: [48, 49, 50, 51, 52, 53] }
+];
+var UV468 = [
+  [0.499976992607117, 0.652534008026123],
+  [0.500025987625122, 0.547487020492554],
+  [0.499974012374878, 0.602371990680695],
+  [0.482113003730774, 0.471979022026062],
+  [0.500150978565216, 0.527155995368958],
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+];
+var VTX68 = [
+  127,
+  234,
+  132,
+  58,
+  172,
+  150,
+  149,
+  148,
+  152,
+  377,
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+  380,
+  57,
+  40,
+  37,
+  0,
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+  314,
+  17,
+  84,
+  91,
+  78,
+  81,
+  13,
+  311,
+  308,
+  402,
+  14,
+  178
+];
+var VTX33 = [33, 133, 362, 263, 1, 62, 308, 159, 145, 386, 374, 6, 102, 331, 2, 13, 14, 70, 105, 107, 336, 334, 300, 54, 10, 284, 50, 280, 234, 454, 58, 288, 152];
+var VTX7 = [33, 133, 362, 263, 1, 78, 308];
+var UV68 = VTX68.map((x) => UV468[x]);
+var UV33 = VTX33.map((x) => UV468[x]);
+var UV7 = VTX7.map((x) => UV468[x]);
+function connectionsToIndices(connections) {
+  const indices = connections.map((connection) => connection[0]);
+  indices.push(connections[connections.length - 1][1]);
+  return indices;
+}
+var pairsLips = [
+  [61, 146],
+  [146, 91],
+  [91, 181],
+  [181, 84],
+  [84, 17],
+  [17, 314],
+  [314, 405],
+  [405, 321],
+  [321, 375],
+  [375, 291],
+  [61, 185],
+  [185, 40],
+  [40, 39],
+  [39, 37],
+  [37, 0],
+  [0, 267],
+  [267, 269],
+  [269, 270],
+  [270, 409],
+  [409, 291],
+  [78, 95],
+  [95, 88],
+  [88, 178],
+  [178, 87],
+  [87, 14],
+  [14, 317],
+  [317, 402],
+  [402, 318],
+  [318, 324],
+  [324, 308],
+  [78, 191],
+  [191, 80],
+  [80, 81],
+  [81, 82],
+  [82, 13],
+  [13, 312],
+  [312, 311],
+  [311, 310],
+  [310, 415],
+  [415, 308]
+];
+var pairsLeftEye = [[263, 249], [249, 390], [390, 373], [373, 374], [374, 380], [380, 381], [381, 382], [382, 362], [263, 466], [466, 388], [388, 387], [387, 386], [386, 385], [385, 384], [384, 398], [398, 362]];
+var pairsLeftEyebrow = [[276, 283], [283, 282], [282, 295], [295, 285], [300, 293], [293, 334], [334, 296], [296, 336]];
+var pairsLeftIris = [[474, 475], [475, 476], [476, 477], [477, 474]];
+var pairsRightEye = [[33, 7], [7, 163], [163, 144], [144, 145], [145, 153], [153, 154], [154, 155], [155, 133], [33, 246], [246, 161], [161, 160], [160, 159], [159, 158], [158, 157], [157, 173], [173, 133]];
+var pairsRightEyebrow = [[46, 53], [53, 52], [52, 65], [65, 55], [70, 63], [63, 105], [105, 66], [66, 107]];
+var pairsRightIris = [[469, 470], [470, 471], [471, 472], [472, 469]];
+var pairsFaceContour = [
+  [10, 338],
+  [338, 297],
+  [297, 332],
+  [332, 284],
+  [284, 251],
+  [251, 389],
+  [389, 356],
+  [356, 454],
+  [454, 323],
+  [323, 361],
+  [361, 288],
+  [288, 397],
+  [397, 365],
+  [365, 379],
+  [379, 378],
+  [378, 400],
+  [400, 377],
+  [377, 152],
+  [152, 148],
+  [148, 176],
+  [176, 149],
+  [149, 150],
+  [150, 136],
+  [136, 172],
+  [172, 58],
+  [58, 132],
+  [132, 93],
+  [93, 234],
+  [234, 127],
+  [127, 162],
+  [162, 21],
+  [21, 54],
+  [54, 103],
+  [103, 67],
+  [67, 109],
+  [109, 10]
+];
+var contourKeypoints = {
+  lips: connectionsToIndices(pairsLips),
+  leftEye: connectionsToIndices(pairsLeftEye),
+  leftEyebrow: connectionsToIndices(pairsLeftEyebrow),
+  leftIris: connectionsToIndices(pairsLeftIris),
+  rightEye: connectionsToIndices(pairsRightEye),
+  rightEyebrow: connectionsToIndices(pairsRightEyebrow),
+  rightIris: connectionsToIndices(pairsRightIris),
+  faceOval: connectionsToIndices(pairsFaceContour)
+};
+
+// src/tfjs/constants.ts
+var constants = {
+  tf255: 255,
+  tf1: 1,
+  tf2: 2,
+  tf05: 0.5,
+  tf127: 127.5,
+  rgb: [0.2989, 0.587, 0.114]
+};
+function init() {
+  constants.tf255 = tfjs_esm_exports.scalar(255, "float32");
+  constants.tf1 = tfjs_esm_exports.scalar(1, "float32");
+  constants.tf2 = tfjs_esm_exports.scalar(2, "float32");
+  constants.tf05 = tfjs_esm_exports.scalar(0.5, "float32");
+  constants.tf127 = tfjs_esm_exports.scalar(127.5, "float32");
+  constants.rgb = tfjs_esm_exports.tensor1d([0.2989, 0.587, 0.114], "float32");
+}
+
+// src/face/facemeshutil.ts
+var getBoxSize = (box) => [Math.abs(box.endPoint[0] - box.startPoint[0]), Math.abs(box.endPoint[1] - box.startPoint[1])];
+var getBoxCenter = (box) => [box.startPoint[0] + (box.endPoint[0] - box.startPoint[0]) / 2, box.startPoint[1] + (box.endPoint[1] - box.startPoint[1]) / 2, 1];
+var clampBox = (box, input) => box ? [
+  Math.trunc(Math.max(0, box.startPoint[0])),
+  Math.trunc(Math.max(0, box.startPoint[1])),
+  Math.trunc(Math.min(input.shape[2] || 0, box.endPoint[0]) - Math.max(0, box.startPoint[0])),
+  Math.trunc(Math.min(input.shape[1] || 0, box.endPoint[1]) - Math.max(0, box.startPoint[1]))
+] : [0, 0, 0, 0];
+var getRawBox = (box, input) => box ? [
+  box.startPoint[0] / (input.shape[2] || 0),
+  box.startPoint[1] / (input.shape[1] || 0),
+  (box.endPoint[0] - box.startPoint[0]) / (input.shape[2] || 0),
+  (box.endPoint[1] - box.startPoint[1]) / (input.shape[1] || 0)
+] : [0, 0, 0, 0];
+var scaleBoxCoordinates = (box, factor) => {
+  const startPoint = [box.startPoint[0] * factor[0], box.startPoint[1] * factor[1]];
+  const endPoint = [box.endPoint[0] * factor[0], box.endPoint[1] * factor[1]];
+  return { startPoint, endPoint, landmarks: box.landmarks, confidence: box.confidence };
+};
+var cutAndResize = (box, image27, cropSize) => {
+  const h = image27.shape[1];
+  const w = image27.shape[2];
+  const cutBox = [box.startPoint[1] / h, box.startPoint[0] / w, box.endPoint[1] / h, box.endPoint[0] / w];
+  const crop = tfjs_esm_exports.image.cropAndResize(image27, [cutBox], [0], cropSize);
+  const norm = tfjs_esm_exports.div(crop, constants.tf255);
+  tfjs_esm_exports.dispose(crop);
+  return norm;
+};
+var enlargeBox = (box, factor) => {
+  const center = getBoxCenter(box);
+  const size2 = getBoxSize(box);
+  const halfSize = [factor * size2[0] / 2, factor * size2[1] / 2];
+  return { startPoint: [center[0] - halfSize[0], center[1] - halfSize[1]], endPoint: [center[0] + halfSize[0], center[1] + halfSize[1]], landmarks: box.landmarks, confidence: box.confidence };
+};
+var squarifyBox = (box) => {
+  const centers = getBoxCenter(box);
+  const size2 = getBoxSize(box);
+  const halfSize = Math.max(...size2) / 2;
+  return { startPoint: [Math.round(centers[0] - halfSize), Math.round(centers[1] - halfSize)], endPoint: [Math.round(centers[0] + halfSize), Math.round(centers[1] + halfSize)], landmarks: box.landmarks, confidence: box.confidence };
+};
+var calculateLandmarksBoundingBox = (landmarks) => {
+  const x = landmarks.map((d) => d[0]);
+  const y = landmarks.map((d) => d[1]);
+  return { startPoint: [Math.min(...x), Math.min(...y)], endPoint: [Math.max(...x), Math.max(...y)], landmarks };
+};
+var fixedRotationMatrix = [[1, 0, 0], [0, 1, 0], [0, 0, 1]];
+var normalizeRadians = (angle) => angle - 2 * Math.PI * Math.floor((angle + Math.PI) / (2 * Math.PI));
+var computeRotation = (point1, point2) => normalizeRadians(Math.PI / 2 - Math.atan2(-(point2[1] - point1[1]), point2[0] - point1[0]));
+var buildTranslationMatrix = (x, y) => [[1, 0, x], [0, 1, y], [0, 0, 1]];
+var dot = (v1, v2) => {
+  let product = 0;
+  for (let i = 0; i < v1.length; i++)
+    product += v1[i] * v2[i];
+  return product;
+};
+var getColumnFrom2DArr = (arr, columnIndex) => {
+  const column = [];
+  for (let i = 0; i < arr.length; i++)
+    column.push(arr[i][columnIndex]);
+  return column;
+};
+var multiplyTransformMatrices = (mat1, mat2) => {
+  const product = [];
+  const size2 = mat1.length;
+  for (let row = 0; row < size2; row++) {
+    product.push([]);
+    for (let col = 0; col < size2; col++)
+      product[row].push(dot(mat1[row], getColumnFrom2DArr(mat2, col)));
+  }
+  return product;
+};
+var buildRotationMatrix = (rotation, center) => {
+  const cosA = Math.cos(rotation);
+  const sinA = Math.sin(rotation);
+  const rotationMatrix = [[cosA, -sinA, 0], [sinA, cosA, 0], [0, 0, 1]];
+  const translationMatrix = buildTranslationMatrix(center[0], center[1]);
+  const translationTimesRotation = multiplyTransformMatrices(translationMatrix, rotationMatrix);
+  const negativeTranslationMatrix = buildTranslationMatrix(-center[0], -center[1]);
+  return multiplyTransformMatrices(translationTimesRotation, negativeTranslationMatrix);
+};
+var invertTransformMatrix = (matrix) => {
+  const rotationComponent = [[matrix[0][0], matrix[1][0]], [matrix[0][1], matrix[1][1]]];
+  const translationComponent = [matrix[0][2], matrix[1][2]];
+  const invertedTranslation = [-dot(rotationComponent[0], translationComponent), -dot(rotationComponent[1], translationComponent)];
+  return [rotationComponent[0].concat(invertedTranslation[0]), rotationComponent[1].concat(invertedTranslation[1]), [0, 0, 1]];
+};
+var rotatePoint = (homogeneousCoordinate, rotationMatrix) => [dot(homogeneousCoordinate, rotationMatrix[0]), dot(homogeneousCoordinate, rotationMatrix[1])];
+function generateAnchors(inputSize10) {
+  const spec = inputSize10 === 192 ? { strides: [4], anchors: [1] } : { strides: [inputSize10 / 16, inputSize10 / 8], anchors: [2, 6] };
+  const anchors3 = [];
+  for (let i = 0; i < spec.strides.length; i++) {
+    const stride = spec.strides[i];
+    const gridRows = Math.floor((inputSize10 + stride - 1) / stride);
+    const gridCols = Math.floor((inputSize10 + stride - 1) / stride);
+    const anchorsNum = spec.anchors[i];
+    for (let gridY = 0; gridY < gridRows; gridY++) {
+      const anchorY = stride * (gridY + 0.5);
+      for (let gridX = 0; gridX < gridCols; gridX++) {
+        const anchorX = stride * (gridX + 0.5);
+        for (let n = 0; n < anchorsNum; n++)
+          anchors3.push([anchorX, anchorY]);
+      }
+    }
+  }
+  return anchors3;
+}
+function transformRawCoords(coordsRaw, box, angle, rotationMatrix, inputSize10) {
+  const boxSize = getBoxSize(box);
+  const coordsScaled = coordsRaw.map((coord) => [
+    boxSize[0] / inputSize10 * (coord[0] - inputSize10 / 2),
+    boxSize[1] / inputSize10 * (coord[1] - inputSize10 / 2),
+    coord[2] || 0
+  ]);
+  const largeAngle = angle && angle !== 0 && Math.abs(angle) > 0.2;
+  const coordsRotationMatrix = largeAngle ? buildRotationMatrix(angle, [0, 0]) : fixedRotationMatrix;
+  const coordsRotated = largeAngle ? coordsScaled.map((coord) => [...rotatePoint(coord, coordsRotationMatrix), coord[2]]) : coordsScaled;
+  const inverseRotationMatrix = largeAngle ? invertTransformMatrix(rotationMatrix) : fixedRotationMatrix;
+  const boxCenter = getBoxCenter(box);
+  const offsets = [dot(boxCenter, inverseRotationMatrix[0]), dot(boxCenter, inverseRotationMatrix[1])];
+  return coordsRotated.map((coord) => [
+    Math.trunc(coord[0] + offsets[0]),
+    Math.trunc(coord[1] + offsets[1]),
+    Math.trunc(coord[2] || 0)
+  ]);
+}
+function correctFaceRotation(rotate, box, input, inputSize10) {
+  const symmetryLine = box.landmarks.length >= meshLandmarks.count ? meshLandmarks.symmetryLine : blazeFaceLandmarks.symmetryLine;
+  let angle = 0;
+  let rotationMatrix = fixedRotationMatrix;
+  let face4;
+  if (rotate && env.kernels.includes("rotatewithoffset")) {
+    angle = computeRotation(box.landmarks[symmetryLine[0]], box.landmarks[symmetryLine[1]]);
+    const largeAngle = angle && angle !== 0 && Math.abs(angle) > 0.2;
+    if (largeAngle) {
+      const center = getBoxCenter(box);
+      const centerRaw = [center[0] / input.shape[2], center[1] / input.shape[1]];
+      const rotated = tfjs_esm_exports.image.rotateWithOffset(input, angle, 0, centerRaw);
+      rotationMatrix = buildRotationMatrix(-angle, center);
+      face4 = cutAndResize(box, rotated, [inputSize10, inputSize10]);
+      tfjs_esm_exports.dispose(rotated);
+    } else {
+      face4 = cutAndResize(box, input, [inputSize10, inputSize10]);
+    }
+  } else {
+    face4 = cutAndResize(box, input, [inputSize10, inputSize10]);
+  }
+  return [angle, rotationMatrix, face4];
+}
+var findFaceCenter = (mesh) => {
+  const x = mesh.map((m) => m[0]);
+  const y = mesh.map((m) => m[1]);
+  return [Math.min(...x) + (Math.max(...x) - Math.min(...x)) / 2, Math.min(...y) + (Math.max(...y) - Math.min(...y)) / 2];
+};
+var calculateFaceBox = (mesh, previousBox) => {
+  const center = findFaceCenter(mesh);
+  const boxSize = getBoxSize(previousBox);
+  const calculatedBox = {
+    startPoint: [center[0] - boxSize[0] / 2, center[1] - boxSize[1] / 2],
+    endPoint: [center[0] + boxSize[0] / 2, center[1] + boxSize[1] / 2]
+  };
+  return calculatedBox;
+};
+
+// src/face/blazeface.ts
+var keypointsCount = 6;
+var faceBoxScaleFactor = 1.4;
+var model2;
+var anchors = null;
+var inputSize = 0;
+var inputSizeT = null;
+var size = () => inputSize;
+async function load2(config3) {
+  var _a;
+  if (env.initial)
+    model2 = null;
+  if (!model2)
+    model2 = await loadModel((_a = config3.face.detector) == null ? void 0 : _a.modelPath);
+  else if (config3.debug)
+    log("cached model:", model2["modelUrl"]);
+  inputSize = model2["executor"] && model2.inputs[0].shape ? model2.inputs[0].shape[2] : 256;
+  inputSizeT = tfjs_esm_exports.scalar(inputSize, "int32");
+  anchors = tfjs_esm_exports.tensor2d(generateAnchors(inputSize));
+  return model2;
+}
+function decodeBoxes(boxOutputs) {
+  const t2 = {};
+  t2.boxStarts = tfjs_esm_exports.slice(boxOutputs, [0, 1], [-1, 2]);
+  t2.centers = tfjs_esm_exports.add(t2.boxStarts, anchors);
+  t2.boxSizes = tfjs_esm_exports.slice(boxOutputs, [0, 3], [-1, 2]);
+  t2.boxSizesNormalized = tfjs_esm_exports.div(t2.boxSizes, inputSizeT);
+  t2.centersNormalized = tfjs_esm_exports.div(t2.centers, inputSizeT);
+  t2.halfBoxSize = tfjs_esm_exports.div(t2.boxSizesNormalized, constants.tf2);
+  t2.starts = tfjs_esm_exports.sub(t2.centersNormalized, t2.halfBoxSize);
+  t2.ends = tfjs_esm_exports.add(t2.centersNormalized, t2.halfBoxSize);
+  t2.startNormalized = tfjs_esm_exports.mul(t2.starts, inputSizeT);
+  t2.endNormalized = tfjs_esm_exports.mul(t2.ends, inputSizeT);
+  const boxes = tfjs_esm_exports.concat2d([t2.startNormalized, t2.endNormalized], 1);
+  Object.keys(t2).forEach((tensor6) => tfjs_esm_exports.dispose(t2[tensor6]));
+  return boxes;
+}
+async function getBoxes(inputImage, config3) {
+  var _a, _b, _c, _d;
+  if (!inputImage || inputImage["isDisposedInternal"] || inputImage.shape.length !== 4 || inputImage.shape[1] < 1 || inputImage.shape[2] < 1)
+    return [];
+  const t2 = {};
+  t2.resized = tfjs_esm_exports.image.resizeBilinear(inputImage, [inputSize, inputSize]);
+  t2.div = tfjs_esm_exports.div(t2.resized, constants.tf127);
+  t2.normalized = tfjs_esm_exports.sub(t2.div, constants.tf05);
+  const res = model2 == null ? void 0 : model2.execute(t2.normalized);
+  if (Array.isArray(res) && res.length > 2) {
+    const sorted = res.sort((a, b) => a.size - b.size);
+    t2.concat384 = tfjs_esm_exports.concat([sorted[0], sorted[2]], 2);
+    t2.concat512 = tfjs_esm_exports.concat([sorted[1], sorted[3]], 2);
+    t2.concat = tfjs_esm_exports.concat([t2.concat512, t2.concat384], 1);
+    t2.batch = tfjs_esm_exports.squeeze(t2.concat, 0);
+  } else if (Array.isArray(res)) {
+    t2.batch = tfjs_esm_exports.squeeze(res[0]);
+  } else {
+    t2.batch = tfjs_esm_exports.squeeze(res);
+  }
+  tfjs_esm_exports.dispose(res);
+  t2.boxes = decodeBoxes(t2.batch);
+  t2.logits = tfjs_esm_exports.slice(t2.batch, [0, 0], [-1, 1]);
+  t2.sigmoid = tfjs_esm_exports.sigmoid(t2.logits);
+  t2.scores = tfjs_esm_exports.squeeze(t2.sigmoid);
+  t2.nms = await tfjs_esm_exports.image.nonMaxSuppressionAsync(t2.boxes, t2.scores, ((_a = config3.face.detector) == null ? void 0 : _a.maxDetected) || 0, ((_b = config3.face.detector) == null ? void 0 : _b.iouThreshold) || 0, ((_c = config3.face.detector) == null ? void 0 : _c.minConfidence) || 0);
+  const nms = await t2.nms.array();
+  const boxes = [];
+  const scores = await t2.scores.data();
+  for (let i = 0; i < nms.length; i++) {
+    const confidence = scores[nms[i]];
+    if (confidence > (((_d = config3.face.detector) == null ? void 0 : _d.minConfidence) || 0)) {
+      const b = {};
+      b.bbox = tfjs_esm_exports.slice(t2.boxes, [nms[i], 0], [1, -1]);
+      b.slice = tfjs_esm_exports.slice(t2.batch, [nms[i], keypointsCount - 1], [1, -1]);
+      b.squeeze = tfjs_esm_exports.squeeze(b.slice);
+      b.landmarks = tfjs_esm_exports.reshape(b.squeeze, [keypointsCount, -1]);
+      const points = await b.bbox.data();
+      const rawBox = {
+        startPoint: [points[0], points[1]],
+        endPoint: [points[2], points[3]],
+        landmarks: await b.landmarks.array(),
+        confidence
+      };
+      const scaledBox = scaleBoxCoordinates(rawBox, [(inputImage.shape[2] || 0) / inputSize, (inputImage.shape[1] || 0) / inputSize]);
+      const enlargedBox = enlargeBox(scaledBox, config3.face["scale"] || faceBoxScaleFactor);
+      const squaredBox = squarifyBox(enlargedBox);
+      boxes.push(squaredBox);
+      Object.keys(b).forEach((tensor6) => tfjs_esm_exports.dispose(b[tensor6]));
+    }
+  }
+  Object.keys(t2).forEach((tensor6) => tfjs_esm_exports.dispose(t2[tensor6]));
+  return boxes;
+}
+
+// src/body/blazeposecoords.ts
+var blazeposecoords_exports = {};
+__export(blazeposecoords_exports, {
+  connected: () => connected,
+  kpt: () => kpt
+});
+var kpt = [
+  "nose",
+  "leftEyeInside",
+  "leftEye",
+  "leftEyeOutside",
+  "rightEyeInside",
+  "rightEye",
+  "rightEyeOutside",
+  "leftEar",
+  "rightEar",
+  "leftMouth",
+  "rightMouth",
+  "leftShoulder",
+  "rightShoulder",
+  "leftElbow",
+  "rightElbow",
+  "leftWrist",
+  "rightWrist",
+  "leftPinky",
+  "rightPinky",
+  "leftIndex",
+  "rightIndex",
+  "leftThumb",
+  "rightThumb",
+  "leftHip",
+  "rightHip",
+  "leftKnee",
+  "rightKnee",
+  "leftAnkle",
+  "rightAnkle",
+  "leftHeel",
+  "rightHeel",
+  "leftFoot",
+  "rightFoot",
+  "bodyCenter",
+  "bodyTop",
+  "leftPalm",
+  "leftHand",
+  "rightPalm",
+  "rightHand"
+];
+var connected = {
+  shoulders: ["leftShoulder", "rightShoulder"],
+  hips: ["rightHip", "leftHip"],
+  mouth: ["leftMouth", "rightMouth"],
+  leftLegUpper: ["leftHip", "leftKnee"],
+  leftLegLower: ["leftKnee", "leftAnkle"],
+  leftFoot: ["leftAnkle", "leftHeel", "leftFoot"],
+  leftTorso: ["leftShoulder", "leftHip"],
+  leftArmUpper: ["leftShoulder", "leftElbow"],
+  leftArmLower: ["leftElbow", "leftWrist"],
+  leftHand: ["leftWrist", "leftPalm"],
+  leftHandPinky: ["leftPalm", "leftPinky"],
+  leftHandIndex: ["leftPalm", "leftIndex"],
+  leftHandThumb: ["leftPalm", "leftThumb"],
+  leftEyeOutline: ["leftEyeInside", "leftEyeOutside"],
+  rightLegUpper: ["rightHip", "rightKnee"],
+  rightLegLower: ["rightKnee", "rightAnkle"],
+  rightFoot: ["rightAnkle", "rightHeel", "rightFoot"],
+  rightTorso: ["rightShoulder", "rightHip"],
+  rightArmUpper: ["rightShoulder", "rightElbow"],
+  rightArmLower: ["rightElbow", "rightWrist"],
+  rightHand: ["rightWrist", "rightPalm"],
+  rightHandPinky: ["rightPalm", "rightPinky"],
+  rightHandIndex: ["rightPalm", "rightIndex"],
+  rightHandThumb: ["rightPalm", "rightThumb"],
+  rightEyeOutline: ["rightEyeInside", "rightEyeOutside"]
+};
+
+// src/body/blazeposedetector.ts
+var inputSize2 = 224;
+var anchorTensor;
+var numLayers = 5;
+var strides = [8, 16, 32, 32, 32];
+function createAnchors() {
+  const anchors3 = [];
+  let layerId = 0;
+  while (layerId < numLayers) {
+    let anchorCount = 0;
+    let lastSameStrideLayer = layerId;
+    while (lastSameStrideLayer < strides.length && strides[lastSameStrideLayer] === strides[layerId]) {
+      anchorCount += 2;
+      lastSameStrideLayer++;
+    }
+    const stride = strides[layerId];
+    const featureMapHeight = Math.ceil(inputSize2 / stride);
+    const featureMapWidth = Math.ceil(inputSize2 / stride);
+    for (let y = 0; y < featureMapHeight; ++y) {
+      for (let x = 0; x < featureMapWidth; ++x) {
+        for (let anchorId = 0; anchorId < anchorCount; ++anchorId) {
+          anchors3.push({ x: (x + 0.5) / featureMapWidth, y: (y + 0.5) / featureMapHeight });
+        }
+      }
+    }
+    layerId = lastSameStrideLayer;
+  }
+  anchorTensor = { x: tfjs_esm_exports.tensor1d(anchors3.map((a) => a.x)), y: tfjs_esm_exports.tensor1d(anchors3.map((a) => a.y)) };
+}
+
+// src/util/box.ts
+function calc(keypoints, outputSize2 = [1, 1]) {
+  const coords = [keypoints.map((pt) => pt[0]), keypoints.map((pt) => pt[1])];
+  const min2 = [Math.min(...coords[0]), Math.min(...coords[1])];
+  const max4 = [Math.max(...coords[0]), Math.max(...coords[1])];
+  const box = [min2[0], min2[1], max4[0] - min2[0], max4[1] - min2[1]];
+  const boxRaw = [box[0] / outputSize2[0], box[1] / outputSize2[1], box[2] / outputSize2[0], box[3] / outputSize2[1]];
+  return { box, boxRaw };
+}
+function square(keypoints, outputSize2 = [1, 1]) {
+  const coords = [keypoints.map((pt) => pt[0]), keypoints.map((pt) => pt[1])];
+  const min2 = [Math.min(...coords[0]), Math.min(...coords[1])];
+  const max4 = [Math.max(...coords[0]), Math.max(...coords[1])];
+  const center = [(min2[0] + max4[0]) / 2, (min2[1] + max4[1]) / 2];
+  const dist = Math.max(center[0] - min2[0], center[1] - min2[1], -center[0] + max4[0], -center[1] + max4[1]);
+  const box = [Math.trunc(center[0] - dist), Math.trunc(center[1] - dist), Math.trunc(2 * dist), Math.trunc(2 * dist)];
+  const boxRaw = [box[0] / outputSize2[0], box[1] / outputSize2[1], box[2] / outputSize2[0], box[3] / outputSize2[1]];
+  return { box, boxRaw };
+}
+function scale(box, scaleFact) {
+  const dist = [box[2] * scaleFact, box[3] * scaleFact];
+  const newBox = [
+    box[0] - (dist[0] - box[2]) / 2,
+    box[1] - (dist[1] - box[3]) / 2,
+    dist[0],
+    dist[1]
+  ];
+  return newBox;
+}
+
+// src/body/blazepose.ts
+var env3 = { initial: true };
+var models2 = { detector: null, landmarks: null };
+var inputSize3 = { detector: [224, 224], landmarks: [256, 256] };
+var skipped2 = Number.MAX_SAFE_INTEGER;
+var outputNodes = {
+  landmarks: ["ld_3d", "activation_segmentation", "activation_heatmap", "world_3d", "output_poseflag"],
+  detector: []
+};
+var cache = null;
+var cropBox;
+var padding = [[0, 0], [0, 0], [0, 0], [0, 0]];
+var lastTime2 = 0;
+var sigmoid3 = (x) => 1 - 1 / (1 + Math.exp(x));
+async function loadDetect(config3) {
+  var _a;
+  if (env3.initial)
+    models2.detector = null;
+  if (!models2.detector && config3.body["detector"] && config3.body["detector"].modelPath || "") {
+    models2.detector = await loadModel(config3.body["detector"].modelPath);
+    const inputs = ((_a = models2.detector) == null ? void 0 : _a["executor"]) ? Object.values(models2.detector.modelSignature["inputs"]) : void 0;
+    inputSize3.detector[0] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[1].size) : 0;
+    inputSize3.detector[1] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 0;
+  } else if (config3.debug && models2.detector)
+    log("cached model:", models2.detector["modelUrl"]);
+  createAnchors();
+  return models2.detector;
+}
+async function loadPose(config3) {
+  var _a;
+  if (env3.initial)
+    models2.landmarks = null;
+  if (!models2.landmarks) {
+    models2.landmarks = await loadModel(config3.body.modelPath);
+    const inputs = ((_a = models2.landmarks) == null ? void 0 : _a["executor"]) ? Object.values(models2.landmarks.modelSignature["inputs"]) : void 0;
+    inputSize3.landmarks[0] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[1].size) : 0;
+    inputSize3.landmarks[1] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 0;
+  } else if (config3.debug)
+    log("cached model:", models2.landmarks["modelUrl"]);
+  return models2.landmarks;
+}
+function prepareImage(input, size2) {
+  var _a, _b;
+  const t2 = {};
+  if (!((_a = input == null ? void 0 : input.shape) == null ? void 0 : _a[1]) || !((_b = input == null ? void 0 : input.shape) == null ? void 0 : _b[2]))
+    return input;
+  let final;
+  if (cropBox) {
+    t2.cropped = tfjs_esm_exports.image.cropAndResize(input, [cropBox], [0], [input.shape[1], input.shape[2]]);
+  }
+  if (input.shape[1] !== input.shape[2]) {
+    const height = [
+      input.shape[2] > input.shape[1] ? Math.trunc((input.shape[2] - input.shape[1]) / 2) : 0,
+      input.shape[2] > input.shape[1] ? Math.trunc((input.shape[2] - input.shape[1]) / 2) : 0
+    ];
+    const width = [
+      input.shape[1] > input.shape[2] ? Math.trunc((input.shape[1] - input.shape[2]) / 2) : 0,
+      input.shape[1] > input.shape[2] ? Math.trunc((input.shape[1] - input.shape[2]) / 2) : 0
+    ];
+    padding = [
+      [0, 0],
+      height,
+      width,
+      [0, 0]
+    ];
+    t2.pad = tfjs_esm_exports.pad(t2.cropped || input, padding);
+    t2.resize = tfjs_esm_exports.image.resizeBilinear(t2.pad, [size2, size2]);
+    final = tfjs_esm_exports.div(t2.resize, constants.tf255);
+  } else if (input.shape[1] !== size2) {
+    t2.resize = tfjs_esm_exports.image.resizeBilinear(t2.cropped || input, [size2, size2]);
+    final = tfjs_esm_exports.div(t2.resize, constants.tf255);
+  } else {
+    final = tfjs_esm_exports.div(t2.cropped || input, constants.tf255);
+  }
+  Object.keys(t2).forEach((tensor6) => tfjs_esm_exports.dispose(t2[tensor6]));
+  return final;
+}
+function rescaleKeypoints(keypoints, outputSize2) {
+  for (const kpt4 of keypoints) {
+    kpt4.position = [
+      Math.trunc(kpt4.position[0] * (outputSize2[0] + padding[2][0] + padding[2][1]) / outputSize2[0] - padding[2][0]),
+      Math.trunc(kpt4.position[1] * (outputSize2[1] + padding[1][0] + padding[1][1]) / outputSize2[1] - padding[1][0]),
+      kpt4.position[2]
+    ];
+    kpt4.positionRaw = [kpt4.position[0] / outputSize2[0], kpt4.position[1] / outputSize2[1], 2 * kpt4.position[2] / (outputSize2[0] + outputSize2[1])];
+  }
+  if (cropBox) {
+    for (const kpt4 of keypoints) {
+      kpt4.positionRaw = [
+        kpt4.positionRaw[0] + cropBox[1],
+        kpt4.positionRaw[1] + cropBox[0],
+        kpt4.positionRaw[2]
+      ];
+      kpt4.position = [
+        Math.trunc(kpt4.positionRaw[0] * outputSize2[0]),
+        Math.trunc(kpt4.positionRaw[1] * outputSize2[1]),
+        kpt4.positionRaw[2]
+      ];
+    }
+  }
+  return keypoints;
+}
+function fixKeypoints(keypoints) {
+  const leftPalm = keypoints.find((k) => k.part === "leftPalm");
+  const leftWrist = keypoints.find((k) => k.part === "leftWrist");
+  const leftIndex = keypoints.find((k) => k.part === "leftIndex");
+  leftPalm.position[2] = ((leftWrist.position[2] || 0) + (leftIndex.position[2] || 0)) / 2;
+  const rightPalm = keypoints.find((k) => k.part === "rightPalm");
+  const rightWrist = keypoints.find((k) => k.part === "rightWrist");
+  const rightIndex = keypoints.find((k) => k.part === "rightIndex");
+  rightPalm.position[2] = ((rightWrist.position[2] || 0) + (rightIndex.position[2] || 0)) / 2;
+}
+async function detectLandmarks(input, config3, outputSize2) {
+  var _a, _b;
+  if (!((_a = models2.landmarks) == null ? void 0 : _a["executor"]))
+    return null;
+  const t2 = {};
+  [t2.ld, t2.segmentation, t2.heatmap, t2.world, t2.poseflag] = (_b = models2.landmarks) == null ? void 0 : _b.execute(input, outputNodes.landmarks);
+  const poseScore = (await t2.poseflag.data())[0];
+  const points = await t2.ld.data();
+  const distances = await t2.world.data();
+  Object.keys(t2).forEach((tensor6) => tfjs_esm_exports.dispose(t2[tensor6]));
+  const keypointsRelative = [];
+  const depth = 5;
+  for (let i = 0; i < points.length / depth; i++) {
+    const score = sigmoid3(points[depth * i + 3]);
+    const presence = sigmoid3(points[depth * i + 4]);
+    const adjScore = Math.trunc(100 * score * presence * poseScore) / 100;
+    const positionRaw = [points[depth * i + 0] / inputSize3.landmarks[0], points[depth * i + 1] / inputSize3.landmarks[1], points[depth * i + 2] + 0];
+    const position = [Math.trunc(outputSize2[0] * positionRaw[0]), Math.trunc(outputSize2[1] * positionRaw[1]), positionRaw[2]];
+    const distance2 = [distances[depth * i + 0], distances[depth * i + 1], distances[depth * i + 2] + 0];
+    keypointsRelative.push({ part: kpt[i], positionRaw, position, distance: distance2, score: adjScore });
+  }
+  if (poseScore < (config3.body.minConfidence || 0))
+    return null;
+  fixKeypoints(keypointsRelative);
+  const keypoints = rescaleKeypoints(keypointsRelative, outputSize2);
+  const kpts = keypoints.map((k) => k.position);
+  const boxes = calc(kpts, [outputSize2[0], outputSize2[1]]);
+  const annotations2 = {};
+  for (const [name, indexes] of Object.entries(connected)) {
+    const pt = [];
+    for (let i = 0; i < indexes.length - 1; i++) {
+      const pt0 = keypoints.find((kpt4) => kpt4.part === indexes[i]);
+      const pt1 = keypoints.find((kpt4) => kpt4.part === indexes[i + 1]);
+      if (pt0 && pt1)
+        pt.push([pt0.position, pt1.position]);
+    }
+    annotations2[name] = pt;
+  }
+  const body4 = { id: 0, score: Math.trunc(100 * poseScore) / 100, box: boxes.box, boxRaw: boxes.boxRaw, keypoints, annotations: annotations2 };
+  return body4;
+}
+async function predict2(input, config3) {
+  const outputSize2 = [input.shape[2] || 0, input.shape[1] || 0];
+  const skipTime = (config3.body.skipTime || 0) > now() - lastTime2;
+  const skipFrame = skipped2 < (config3.body.skipFrames || 0);
+  if (config3.skipAllowed && skipTime && skipFrame && cache !== null) {
+    skipped2++;
+  } else {
+    const t2 = {};
+    t2.landmarks = prepareImage(input, 256);
+    cache = await detectLandmarks(t2.landmarks, config3, outputSize2);
+    Object.keys(t2).forEach((tensor6) => tfjs_esm_exports.dispose(t2[tensor6]));
+    lastTime2 = now();
+    skipped2 = 0;
+  }
+  return cache ? [cache] : [];
+}
+
+// src/object/labels.ts
+var labels = [
+  { class: 1, label: "person" },
+  { class: 2, label: "bicycle" },
+  { class: 3, label: "car" },
+  { class: 4, label: "motorcycle" },
+  { class: 5, label: "airplane" },
+  { class: 6, label: "bus" },
+  { class: 7, label: "train" },
+  { class: 8, label: "truck" },
+  { class: 9, label: "boat" },
+  { class: 10, label: "traffic light" },
+  { class: 11, label: "fire hydrant" },
+  { class: 12, label: "stop sign" },
+  { class: 13, label: "parking meter" },
+  { class: 14, label: "bench" },
+  { class: 15, label: "bird" },
+  { class: 16, label: "cat" },
+  { class: 17, label: "dog" },
+  { class: 18, label: "horse" },
+  { class: 19, label: "sheep" },
+  { class: 20, label: "cow" },
+  { class: 21, label: "elephant" },
+  { class: 22, label: "bear" },
+  { class: 23, label: "zebra" },
+  { class: 24, label: "giraffe" },
+  { class: 25, label: "backpack" },
+  { class: 26, label: "umbrella" },
+  { class: 27, label: "handbag" },
+  { class: 28, label: "tie" },
+  { class: 29, label: "suitcase" },
+  { class: 30, label: "frisbee" },
+  { class: 31, label: "skis" },
+  { class: 32, label: "snowboard" },
+  { class: 33, label: "sports ball" },
+  { class: 34, label: "kite" },
+  { class: 35, label: "baseball bat" },
+  { class: 36, label: "baseball glove" },
+  { class: 37, label: "skateboard" },
+  { class: 38, label: "surfboard" },
+  { class: 39, label: "tennis racket" },
+  { class: 40, label: "bottle" },
+  { class: 41, label: "wine glass" },
+  { class: 42, label: "cup" },
+  { class: 43, label: "fork" },
+  { class: 44, label: "knife" },
+  { class: 45, label: "spoon" },
+  { class: 46, label: "bowl" },
+  { class: 47, label: "banana" },
+  { class: 48, label: "apple" },
+  { class: 49, label: "sandwich" },
+  { class: 50, label: "orange" },
+  { class: 51, label: "broccoli" },
+  { class: 52, label: "carrot" },
+  { class: 53, label: "hot dog" },
+  { class: 54, label: "pizza" },
+  { class: 55, label: "donut" },
+  { class: 56, label: "cake" },
+  { class: 57, label: "chair" },
+  { class: 58, label: "couch" },
+  { class: 59, label: "potted plant" },
+  { class: 60, label: "bed" },
+  { class: 61, label: "dining table" },
+  { class: 62, label: "toilet" },
+  { class: 63, label: "tv" },
+  { class: 64, label: "laptop" },
+  { class: 65, label: "mouse" },
+  { class: 66, label: "remote" },
+  { class: 67, label: "keyboard" },
+  { class: 68, label: "cell phone" },
+  { class: 69, label: "microwave" },
+  { class: 70, label: "oven" },
+  { class: 71, label: "toaster" },
+  { class: 72, label: "sink" },
+  { class: 73, label: "refrigerator" },
+  { class: 74, label: "book" },
+  { class: 75, label: "clock" },
+  { class: 76, label: "vase" },
+  { class: 77, label: "scissors" },
+  { class: 78, label: "teddy bear" },
+  { class: 79, label: "hair drier" },
+  { class: 80, label: "toothbrush" }
+];
+
+// src/object/centernet.ts
+var model3;
+var inputSize4 = 0;
+var last2 = [];
+var lastTime3 = 0;
+var skipped3 = Number.MAX_SAFE_INTEGER;
+async function load3(config3) {
+  if (env.initial)
+    model3 = null;
+  if (!model3) {
+    model3 = await loadModel(config3.object.modelPath);
+    const inputs = (model3 == null ? void 0 : model3["executor"]) ? Object.values(model3.modelSignature["inputs"]) : void 0;
+    inputSize4 = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 0;
+  } else if (config3.debug)
+    log("cached model:", model3["modelUrl"]);
+  return model3;
+}
+async function process3(res, outputShape, config3) {
+  if (!res)
+    return [];
+  const t2 = {};
+  const results = [];
+  const detections = await res.array();
+  t2.squeeze = tfjs_esm_exports.squeeze(res);
+  const arr = tfjs_esm_exports.split(t2.squeeze, 6, 1);
+  t2.stack = tfjs_esm_exports.stack([arr[1], arr[0], arr[3], arr[2]], 1);
+  t2.boxes = tfjs_esm_exports.squeeze(t2.stack);
+  t2.scores = tfjs_esm_exports.squeeze(arr[4]);
+  t2.classes = tfjs_esm_exports.squeeze(arr[5]);
+  tfjs_esm_exports.dispose([res, ...arr]);
+  t2.nms = await tfjs_esm_exports.image.nonMaxSuppressionAsync(t2.boxes, t2.scores, config3.object.maxDetected, config3.object.iouThreshold, config3.object.minConfidence || 0);
+  const nms = await t2.nms.data();
+  let i = 0;
+  for (const id of Array.from(nms)) {
+    const score = Math.trunc(100 * detections[0][id][4]) / 100;
+    const classVal = detections[0][id][5];
+    if (Number.isNaN(classVal))
+      continue;
+    const label = labels[classVal].label;
+    const [x, y] = [
+      detections[0][id][0] / inputSize4,
+      detections[0][id][1] / inputSize4
+    ];
+    const boxRaw = [
+      x,
+      y,
+      detections[0][id][2] / inputSize4 - x,
+      detections[0][id][3] / inputSize4 - y
+    ];
+    const box = [
+      Math.trunc(boxRaw[0] * outputShape[0]),
+      Math.trunc(boxRaw[1] * outputShape[1]),
+      Math.trunc(boxRaw[2] * outputShape[0]),
+      Math.trunc(boxRaw[3] * outputShape[1])
+    ];
+    results.push({ id: i++, score, class: classVal, label, box, boxRaw });
+  }
+  Object.keys(t2).forEach((tensor6) => tfjs_esm_exports.dispose(t2[tensor6]));
+  return results;
+}
+async function predict3(input, config3) {
+  if (!(model3 == null ? void 0 : model3["executor"]))
+    return [];
+  const skipTime = (config3.object.skipTime || 0) > now() - lastTime3;
+  const skipFrame = skipped3 < (config3.object.skipFrames || 0);
+  if (config3.skipAllowed && skipTime && skipFrame && last2.length > 0) {
+    skipped3++;
+    return last2;
+  }
+  skipped3 = 0;
+  return new Promise(async (resolve) => {
+    const outputSize2 = [input.shape[2] || 0, input.shape[1] || 0];
+    const resize = tfjs_esm_exports.image.resizeBilinear(input, [inputSize4, inputSize4]);
+    const objectT = config3.object.enabled ? model3 == null ? void 0 : model3.execute(resize, ["tower_0/detections"]) : null;
+    lastTime3 = now();
+    tfjs_esm_exports.dispose(resize);
+    const obj = await process3(objectT, outputSize2, config3);
+    last2 = obj;
+    resolve(obj);
+  });
+}
+
+// src/body/efficientposecoords.ts
+var efficientposecoords_exports = {};
+__export(efficientposecoords_exports, {
+  connected: () => connected2,
+  kpt: () => kpt2
+});
+var kpt2 = [
+  "head",
+  "neck",
+  "rightShoulder",
+  "rightElbow",
+  "rightWrist",
+  "chest",
+  "leftShoulder",
+  "leftElbow",
+  "leftWrist",
+  "bodyCenter",
+  "rightHip",
+  "rightKnee",
+  "rightAnkle",
+  "leftHip",
+  "leftKnee",
+  "leftAnkle"
+];
+var connected2 = {
+  leftLeg: ["leftHip", "leftKnee", "leftAnkle"],
+  rightLeg: ["rightHip", "rightKnee", "rightAnkle"],
+  torso: ["leftShoulder", "rightShoulder", "rightHip", "leftHip", "leftShoulder"],
+  leftArm: ["leftShoulder", "leftElbow", "leftWrist"],
+  rightArm: ["rightShoulder", "rightElbow", "rightWrist"],
+  head: []
+};
+
+// src/body/efficientpose.ts
+var model4;
+var lastTime4 = 0;
+var cache2 = { id: 0, keypoints: [], box: [0, 0, 0, 0], boxRaw: [0, 0, 0, 0], score: 0, annotations: {} };
+var skipped4 = Number.MAX_SAFE_INTEGER;
+async function load4(config3) {
+  if (env.initial)
+    model4 = null;
+  if (!model4)
+    model4 = await loadModel(config3.body.modelPath);
+  else if (config3.debug)
+    log("cached model:", model4["modelUrl"]);
+  return model4;
+}
+async function max2d(inputs, minScore) {
+  const [width, height] = inputs.shape;
+  const reshaped = tfjs_esm_exports.reshape(inputs, [height * width]);
+  const max4 = tfjs_esm_exports.max(reshaped, 0);
+  const newScore = (await max4.data())[0];
+  if (newScore > minScore) {
+    const coordinates = tfjs_esm_exports.argMax(reshaped, 0);
+    const mod3 = tfjs_esm_exports.mod(coordinates, width);
+    const x = (await mod3.data())[0];
+    const div16 = tfjs_esm_exports.div(coordinates, width);
+    const y = (await div16.data())[0];
+    tfjs_esm_exports.dispose([reshaped, max4, coordinates, mod3, div16]);
+    return [x, y, newScore];
+  }
+  tfjs_esm_exports.dispose([reshaped, max4]);
+  return [0, 0, newScore];
+}
+async function predict4(image27, config3) {
+  if (!(model4 == null ? void 0 : model4["executor"]))
+    return [];
+  const skipTime = (config3.body.skipTime || 0) > now() - lastTime4;
+  const skipFrame = skipped4 < (config3.body.skipFrames || 0);
+  if (config3.skipAllowed && skipTime && skipFrame && Object.keys(cache2.keypoints).length > 0) {
+    skipped4++;
+    return [cache2];
+  }
+  skipped4 = 0;
+  return new Promise(async (resolve) => {
+    const tensor6 = tfjs_esm_exports.tidy(() => {
+      if (!(model4 == null ? void 0 : model4.inputs[0].shape))
+        return null;
+      const resize = tfjs_esm_exports.image.resizeBilinear(image27, [model4.inputs[0].shape[2], model4.inputs[0].shape[1]], false);
+      const enhance2 = tfjs_esm_exports.mul(resize, constants.tf2);
+      const norm = tfjs_esm_exports.sub(enhance2, constants.tf1);
+      return norm;
+    });
+    let resT;
+    if (config3.body.enabled)
+      resT = model4 == null ? void 0 : model4.execute(tensor6);
+    lastTime4 = now();
+    tfjs_esm_exports.dispose(tensor6);
+    if (resT) {
+      cache2.keypoints.length = 0;
+      const squeeze14 = tfjs_esm_exports.squeeze(resT);
+      tfjs_esm_exports.dispose(resT);
+      const stack5 = tfjs_esm_exports.unstack(squeeze14, 2);
+      tfjs_esm_exports.dispose(squeeze14);
+      for (let id = 0; id < stack5.length; id++) {
+        const [x2, y2, partScore] = await max2d(stack5[id], config3.body.minConfidence);
+        if (partScore > (config3.body.minConfidence || 0)) {
+          cache2.keypoints.push({
+            score: Math.round(100 * partScore) / 100,
+            part: kpt2[id],
+            positionRaw: [
+              x2 / model4.inputs[0].shape[2],
+              y2 / model4.inputs[0].shape[1]
+            ],
+            position: [
+              Math.round(image27.shape[2] * x2 / model4.inputs[0].shape[2]),
+              Math.round(image27.shape[1] * y2 / model4.inputs[0].shape[1])
+            ]
+          });
+        }
+      }
+      stack5.forEach((s) => tfjs_esm_exports.dispose(s));
+    }
+    cache2.score = cache2.keypoints.reduce((prev, curr) => curr.score > prev ? curr.score : prev, 0);
+    const x = cache2.keypoints.map((a) => a.position[0]);
+    const y = cache2.keypoints.map((a) => a.position[1]);
+    cache2.box = [
+      Math.min(...x),
+      Math.min(...y),
+      Math.max(...x) - Math.min(...x),
+      Math.max(...y) - Math.min(...y)
+    ];
+    const xRaw = cache2.keypoints.map((a) => a.positionRaw[0]);
+    const yRaw = cache2.keypoints.map((a) => a.positionRaw[1]);
+    cache2.boxRaw = [
+      Math.min(...xRaw),
+      Math.min(...yRaw),
+      Math.max(...xRaw) - Math.min(...xRaw),
+      Math.max(...yRaw) - Math.min(...yRaw)
+    ];
+    for (const [name, indexes] of Object.entries(connected2)) {
+      const pt = [];
+      for (let i = 0; i < indexes.length - 1; i++) {
+        const pt0 = cache2.keypoints.find((kpt4) => kpt4.part === indexes[i]);
+        const pt1 = cache2.keypoints.find((kpt4) => kpt4.part === indexes[i + 1]);
+        if (pt0 && pt1 && pt0.score > (config3.body.minConfidence || 0) && pt1.score > (config3.body.minConfidence || 0))
+          pt.push([pt0.position, pt1.position]);
+      }
+      cache2.annotations[name] = pt;
+    }
+    resolve([cache2]);
+  });
+}
+
+// src/gear/emotion.ts
+var annotations = ["angry", "disgust", "fear", "happy", "sad", "surprise", "neutral"];
+var model5;
+var last3 = [];
+var lastCount2 = 0;
+var lastTime5 = 0;
+var skipped5 = Number.MAX_SAFE_INTEGER;
+async function load5(config3) {
+  var _a;
+  if (env.initial)
+    model5 = null;
+  if (!model5)
+    model5 = await loadModel((_a = config3.face.emotion) == null ? void 0 : _a.modelPath);
+  else if (config3.debug)
+    log("cached model:", model5["modelUrl"]);
+  return model5;
+}
+async function predict5(image27, config3, idx, count2) {
+  var _a, _b;
+  if (!model5)
+    return [];
+  const skipFrame = skipped5 < (((_a = config3.face.emotion) == null ? void 0 : _a.skipFrames) || 0);
+  const skipTime = (((_b = config3.face.emotion) == null ? void 0 : _b.skipTime) || 0) > now() - lastTime5;
+  if (config3.skipAllowed && skipTime && skipFrame && lastCount2 === count2 && last3[idx] && last3[idx].length > 0) {
+    skipped5++;
+    return last3[idx];
+  }
+  skipped5 = 0;
+  return new Promise(async (resolve) => {
+    var _a2;
+    const obj = [];
+    if ((_a2 = config3.face.emotion) == null ? void 0 : _a2.enabled) {
+      const t2 = {};
+      const inputSize10 = (model5 == null ? void 0 : model5.inputs[0].shape) ? model5.inputs[0].shape[2] : 0;
+      t2.resize = tfjs_esm_exports.image.resizeBilinear(image27, [inputSize10, inputSize10], false);
+      t2.channels = tfjs_esm_exports.mul(t2.resize, constants.rgb);
+      t2.grayscale = tfjs_esm_exports.sum(t2.channels, 3, true);
+      t2.grayscaleSub = tfjs_esm_exports.sub(t2.grayscale, constants.tf05);
+      t2.grayscaleMul = tfjs_esm_exports.mul(t2.grayscaleSub, constants.tf2);
+      t2.emotion = model5 == null ? void 0 : model5.execute(t2.grayscaleMul);
+      lastTime5 = now();
+      const data = await t2.emotion.data();
+      for (let i = 0; i < data.length; i++) {
+        if (data[i] > (config3.face.emotion.minConfidence || 0))
+          obj.push({ score: Math.min(0.99, Math.trunc(100 * data[i]) / 100), emotion: annotations[i] });
+      }
+      obj.sort((a, b) => b.score - a.score);
+      Object.keys(t2).forEach((tensor6) => tfjs_esm_exports.dispose(t2[tensor6]));
+    }
+    last3[idx] = obj;
+    lastCount2 = count2;
+    resolve(obj);
+  });
+}
+
+// src/face/iris.ts
+var model6;
+var inputSize5 = 0;
+var irisEnlarge = 2.3;
+var leftOutline = meshAnnotations.leftEyeLower0;
+var rightOutline = meshAnnotations.rightEyeLower0;
+var eyeLandmarks = {
+  leftBounds: [leftOutline[0], leftOutline[leftOutline.length - 1]],
+  rightBounds: [rightOutline[0], rightOutline[rightOutline.length - 1]]
+};
+var irisLandmarks = {
+  upperCenter: 3,
+  lowerCenter: 4,
+  index: 71,
+  numCoordinates: 76
+};
+async function load6(config3) {
+  var _a, _b;
+  if (env.initial)
+    model6 = null;
+  if (!model6)
+    model6 = await loadModel((_a = config3.face.iris) == null ? void 0 : _a.modelPath);
+  else if (config3.debug)
+    log("cached model:", model6["modelUrl"]);
+  inputSize5 = (model6 == null ? void 0 : model6["executor"]) && ((_b = model6.inputs) == null ? void 0 : _b[0].shape) ? model6.inputs[0].shape[2] : 0;
+  if (inputSize5 === -1)
+    inputSize5 = 64;
+  return model6;
+}
+function replaceIrisCoords(rawCoords, newCoords, prefix, keys) {
+  for (let i = 0; i < irisIndices.length; i++) {
+    const { key, indices } = irisIndices[i];
+    const originalIndices = meshAnnotations[`${prefix}${key}`];
+    if (!keys || keys.includes(key)) {
+      for (let j = 0; j < indices.length; j++) {
+        const index2 = indices[j];
+        rawCoords[originalIndices[j]] = [
+          newCoords[index2][0],
+          newCoords[index2][1],
+          (newCoords[index2][2] + rawCoords[originalIndices[j]][2]) / 2
+        ];
+      }
+    }
+  }
+}
+var getLeftToRightEyeDepthDifference = (rawCoords) => {
+  const leftEyeZ = rawCoords[eyeLandmarks.leftBounds[0]][2];
+  const rightEyeZ = rawCoords[eyeLandmarks.rightBounds[0]][2];
+  return leftEyeZ - rightEyeZ;
+};
+var getEyeBox = (rawCoords, face4, eyeInnerCornerIndex, eyeOuterCornerIndex, meshSize, flip = false) => {
+  const box = squarifyBox(enlargeBox(calculateLandmarksBoundingBox([rawCoords[eyeInnerCornerIndex], rawCoords[eyeOuterCornerIndex]]), irisEnlarge));
+  const boxSize = getBoxSize(box);
+  let crop = tfjs_esm_exports.image.cropAndResize(face4, [[
+    box.startPoint[1] / meshSize,
+    box.startPoint[0] / meshSize,
+    box.endPoint[1] / meshSize,
+    box.endPoint[0] / meshSize
+  ]], [0], [inputSize5, inputSize5]);
+  if (flip && env.kernels.includes("flipleftright")) {
+    const flipped = tfjs_esm_exports.image.flipLeftRight(crop);
+    tfjs_esm_exports.dispose(crop);
+    crop = flipped;
+  }
+  return { box, boxSize, crop };
+};
+var getEyeCoords = (eyeData, eyeBox, eyeBoxSize, flip = false) => {
+  const eyeRawCoords = [];
+  for (let i = 0; i < irisLandmarks.numCoordinates; i++) {
+    const x = eyeData[i * 3];
+    const y = eyeData[i * 3 + 1];
+    const z = eyeData[i * 3 + 2];
+    eyeRawCoords.push([
+      (flip ? 1 - x / inputSize5 : x / inputSize5) * eyeBoxSize[0] + eyeBox.startPoint[0],
+      y / inputSize5 * eyeBoxSize[1] + eyeBox.startPoint[1],
+      z
+    ]);
+  }
+  return { rawCoords: eyeRawCoords, iris: eyeRawCoords.slice(irisLandmarks.index) };
+};
+var getAdjustedIrisCoords = (rawCoords, irisCoords, direction) => {
+  const upperCenterZ = rawCoords[meshAnnotations[`${direction}EyeUpper0`][irisLandmarks.upperCenter]][2];
+  const lowerCenterZ = rawCoords[meshAnnotations[`${direction}EyeLower0`][irisLandmarks.lowerCenter]][2];
+  const averageZ = (upperCenterZ + lowerCenterZ) / 2;
+  return irisCoords.map((coord, i) => {
+    let z = averageZ;
+    if (i === 2) {
+      z = upperCenterZ;
+    } else if (i === 4) {
+      z = lowerCenterZ;
+    }
+    return [coord[0], coord[1], z];
+  });
+};
+async function augmentIris(rawCoords, face4, meshSize) {
+  if (!(model6 == null ? void 0 : model6["executor"]))
+    return rawCoords;
+  const { box: leftEyeBox, boxSize: leftEyeBoxSize, crop: leftEyeCrop } = getEyeBox(rawCoords, face4, eyeLandmarks.leftBounds[0], eyeLandmarks.leftBounds[1], meshSize, true);
+  const { box: rightEyeBox, boxSize: rightEyeBoxSize, crop: rightEyeCrop } = getEyeBox(rawCoords, face4, eyeLandmarks.rightBounds[0], eyeLandmarks.rightBounds[1], meshSize, true);
+  const combined = tfjs_esm_exports.concat([leftEyeCrop, rightEyeCrop]);
+  tfjs_esm_exports.dispose(leftEyeCrop);
+  tfjs_esm_exports.dispose(rightEyeCrop);
+  const eyePredictions = model6.execute(combined);
+  tfjs_esm_exports.dispose(combined);
+  const eyePredictionsData = await eyePredictions.data();
+  tfjs_esm_exports.dispose(eyePredictions);
+  const leftEyeData = eyePredictionsData.slice(0, irisLandmarks.numCoordinates * 3);
+  const { rawCoords: leftEyeRawCoords, iris: leftIrisRawCoords } = getEyeCoords(leftEyeData, leftEyeBox, leftEyeBoxSize, true);
+  const rightEyeData = eyePredictionsData.slice(irisLandmarks.numCoordinates * 3);
+  const { rawCoords: rightEyeRawCoords, iris: rightIrisRawCoords } = getEyeCoords(rightEyeData, rightEyeBox, rightEyeBoxSize, false);
+  const leftToRightEyeDepthDifference = getLeftToRightEyeDepthDifference(rawCoords);
+  if (Math.abs(leftToRightEyeDepthDifference) < 30) {
+    replaceIrisCoords(rawCoords, leftEyeRawCoords, "left", null);
+    replaceIrisCoords(rawCoords, rightEyeRawCoords, "right", null);
+  } else if (leftToRightEyeDepthDifference < 1) {
+    replaceIrisCoords(rawCoords, leftEyeRawCoords, "left", ["EyeUpper0", "EyeLower0"]);
+  } else {
+    replaceIrisCoords(rawCoords, rightEyeRawCoords, "right", ["EyeUpper0", "EyeLower0"]);
+  }
+  const adjustedLeftIrisCoords = getAdjustedIrisCoords(rawCoords, leftIrisRawCoords, "left");
+  const adjustedRightIrisCoords = getAdjustedIrisCoords(rawCoords, rightIrisRawCoords, "right");
+  const newCoords = rawCoords.concat(adjustedLeftIrisCoords).concat(adjustedRightIrisCoords);
+  return newCoords;
+}
+
+// src/face/constants.ts
+var LIPS_CONNECTIONS = [
+  [61, 146],
+  [146, 91],
+  [91, 181],
+  [181, 84],
+  [84, 17],
+  [17, 314],
+  [314, 405],
+  [405, 321],
+  [321, 375],
+  [375, 291],
+  [61, 185],
+  [185, 40],
+  [40, 39],
+  [39, 37],
+  [37, 0],
+  [0, 267],
+  [267, 269],
+  [269, 270],
+  [270, 409],
+  [409, 291],
+  [78, 95],
+  [95, 88],
+  [88, 178],
+  [178, 87],
+  [87, 14],
+  [14, 317],
+  [317, 402],
+  [402, 318],
+  [318, 324],
+  [324, 308],
+  [78, 191],
+  [191, 80],
+  [80, 81],
+  [81, 82],
+  [82, 13],
+  [13, 312],
+  [312, 311],
+  [311, 310],
+  [310, 415],
+  [415, 308]
+];
+var LEFT_EYE_CONNECTIONS = [[263, 249], [249, 390], [390, 373], [373, 374], [374, 380], [380, 381], [381, 382], [382, 362], [263, 466], [466, 388], [388, 387], [387, 386], [386, 385], [385, 384], [384, 398], [398, 362]];
+var LEFT_EYEBROW_CONNECTIONS = [[276, 283], [283, 282], [282, 295], [295, 285], [300, 293], [293, 334], [334, 296], [296, 336]];
+var LEFT_IRIS_CONNECTIONS = [[474, 475], [475, 476], [476, 477], [477, 474]];
+var RIGHT_EYE_CONNECTIONS = [[33, 7], [7, 163], [163, 144], [144, 145], [145, 153], [153, 154], [154, 155], [155, 133], [33, 246], [246, 161], [161, 160], [160, 159], [159, 158], [158, 157], [157, 173], [173, 133]];
+var RIGHT_EYEBROW_CONNECTIONS = [[46, 53], [53, 52], [52, 65], [65, 55], [70, 63], [63, 105], [105, 66], [66, 107]];
+var RIGHT_IRIS_CONNECTIONS = [[469, 470], [470, 471], [471, 472], [472, 469]];
+var FACE_OVAL_CONNECTIONS = [
+  [10, 338],
+  [338, 297],
+  [297, 332],
+  [332, 284],
+  [284, 251],
+  [251, 389],
+  [389, 356],
+  [356, 454],
+  [454, 323],
+  [323, 361],
+  [361, 288],
+  [288, 397],
+  [397, 365],
+  [365, 379],
+  [379, 378],
+  [378, 400],
+  [400, 377],
+  [377, 152],
+  [152, 148],
+  [148, 176],
+  [176, 149],
+  [149, 150],
+  [150, 136],
+  [136, 172],
+  [172, 58],
+  [58, 132],
+  [132, 93],
+  [93, 234],
+  [234, 127],
+  [127, 162],
+  [162, 21],
+  [21, 54],
+  [54, 103],
+  [103, 67],
+  [67, 109],
+  [109, 10]
+];
+function connectionsToIndices2(connections) {
+  const indices = connections.map((connection) => connection[0]);
+  indices.push(connections[connections.length - 1][1]);
+  return indices;
+}
+var MEDIAPIPE_FACE_MESH_KEYPOINTS_BY_CONTOUR = {
+  lips: connectionsToIndices2(LIPS_CONNECTIONS),
+  leftEye: connectionsToIndices2(LEFT_EYE_CONNECTIONS),
+  leftEyebrow: connectionsToIndices2(LEFT_EYEBROW_CONNECTIONS),
+  leftIris: connectionsToIndices2(LEFT_IRIS_CONNECTIONS),
+  rightEye: connectionsToIndices2(RIGHT_EYE_CONNECTIONS),
+  rightEyebrow: connectionsToIndices2(RIGHT_EYEBROW_CONNECTIONS),
+  rightIris: connectionsToIndices2(RIGHT_IRIS_CONNECTIONS),
+  faceOval: connectionsToIndices2(FACE_OVAL_CONNECTIONS)
+};
+var indexLabelPairs = Object.entries(MEDIAPIPE_FACE_MESH_KEYPOINTS_BY_CONTOUR).map(([label, indices]) => indices.map((index2) => [index2, label])).flat();
+var MEDIAPIPE_FACE_MESH_KEYPOINTS = new Map(indexLabelPairs);
+var LANDMARKS_REFINEMENT_LIPS_CONFIG = [
+  61,
+  146,
+  91,
+  181,
+  84,
+  17,
+  314,
+  405,
+  321,
+  375,
+  291,
+  185,
+  40,
+  39,
+  37,
+  0,
+  267,
+  269,
+  270,
+  409,
+  78,
+  95,
+  88,
+  178,
+  87,
+  14,
+  317,
+  402,
+  318,
+  324,
+  308,
+  191,
+  80,
+  81,
+  82,
+  13,
+  312,
+  311,
+  310,
+  415,
+  76,
+  77,
+  90,
+  180,
+  85,
+  16,
+  315,
+  404,
+  320,
+  307,
+  306,
+  184,
+  74,
+  73,
+  72,
+  11,
+  302,
+  303,
+  304,
+  408,
+  62,
+  96,
+  89,
+  179,
+  86,
+  15,
+  316,
+  403,
+  319,
+  325,
+  292,
+  183,
+  42,
+  41,
+  38,
+  12,
+  268,
+  271,
+  272,
+  407
+];
+var LANDMARKS_REFINEMENT_LEFT_EYE_CONFIG = [
+  33,
+  7,
+  163,
+  144,
+  145,
+  153,
+  154,
+  155,
+  133,
+  246,
+  161,
+  160,
+  159,
+  158,
+  157,
+  173,
+  130,
+  25,
+  110,
+  24,
+  23,
+  22,
+  26,
+  112,
+  243,
+  247,
+  30,
+  29,
+  27,
+  28,
+  56,
+  190,
+  226,
+  31,
+  228,
+  229,
+  230,
+  231,
+  232,
+  233,
+  244,
+  113,
+  225,
+  224,
+  223,
+  222,
+  221,
+  189,
+  35,
+  124,
+  46,
+  53,
+  52,
+  65,
+  143,
+  111,
+  117,
+  118,
+  119,
+  120,
+  121,
+  128,
+  245,
+  156,
+  70,
+  63,
+  105,
+  66,
+  107,
+  55,
+  193
+];
+var LANDMARKS_REFINEMENT_RIGHT_EYE_CONFIG = [
+  263,
+  249,
+  390,
+  373,
+  374,
+  380,
+  381,
+  382,
+  362,
+  466,
+  388,
+  387,
+  386,
+  385,
+  384,
+  398,
+  359,
+  255,
+  339,
+  254,
+  253,
+  252,
+  256,
+  341,
+  463,
+  467,
+  260,
+  259,
+  257,
+  258,
+  286,
+  414,
+  446,
+  261,
+  448,
+  449,
+  450,
+  451,
+  452,
+  453,
+  464,
+  342,
+  445,
+  444,
+  443,
+  442,
+  441,
+  413,
+  265,
+  353,
+  276,
+  283,
+  282,
+  295,
+  372,
+  340,
+  346,
+  347,
+  348,
+  349,
+  350,
+  357,
+  465,
+  383,
+  300,
+  293,
+  334,
+  296,
+  336,
+  285,
+  417
+];
+
+// src/face/attention.ts
+async function augment(rawCoords, results) {
+  var _a, _b, _c, _d, _e, _f, _g, _h, _i, _j;
+  const t2 = {
+    lips: await ((_b = (_a = results.filter((r) => r.size === 160)) == null ? void 0 : _a[0]) == null ? void 0 : _b.data()),
+    irisL: await ((_d = (_c = results.filter((r) => r.size === 10)) == null ? void 0 : _c[0]) == null ? void 0 : _d.data()),
+    eyeL: await ((_f = (_e = results.filter((r) => r.size === 142)) == null ? void 0 : _e[0]) == null ? void 0 : _f.data()),
+    irisR: await ((_h = (_g = results.filter((r) => r.size === 10)) == null ? void 0 : _g[1]) == null ? void 0 : _h.data()),
+    eyeR: await ((_j = (_i = results.filter((r) => r.size === 142)) == null ? void 0 : _i[1]) == null ? void 0 : _j.data())
+  };
+  for (const val of Object.values(t2)) {
+    if (!val)
+      return rawCoords;
+  }
+  const irisLDepth = LANDMARKS_REFINEMENT_LEFT_EYE_CONFIG.reduce((prev, curr) => prev += rawCoords[curr][2], 0) / LANDMARKS_REFINEMENT_LEFT_EYE_CONFIG.length;
+  for (let i = 0; i < t2.irisL.length / 2; i++)
+    rawCoords.push([t2.irisL[2 * i + 0], t2.irisL[2 * i + 1], irisLDepth]);
+  const irisRDepth = LANDMARKS_REFINEMENT_RIGHT_EYE_CONFIG.reduce((prev, curr) => prev += rawCoords[curr][2], 0) / LANDMARKS_REFINEMENT_RIGHT_EYE_CONFIG.length;
+  for (let i = 0; i < t2.irisR.length / 2; i++)
+    rawCoords.push([t2.irisR[2 * i + 0], t2.irisR[2 * i + 1], irisRDepth]);
+  for (let i = 0; i < t2.eyeL.length / 2; i++)
+    rawCoords[LANDMARKS_REFINEMENT_LEFT_EYE_CONFIG[i]] = [t2.eyeL[2 * i + 0], t2.eyeL[2 * i + 1], rawCoords[LANDMARKS_REFINEMENT_LEFT_EYE_CONFIG[i]][2]];
+  for (let i = 0; i < t2.eyeR.length / 2; i++)
+    rawCoords[LANDMARKS_REFINEMENT_RIGHT_EYE_CONFIG[i]] = [t2.eyeR[2 * i + 0], t2.eyeR[2 * i + 1], rawCoords[LANDMARKS_REFINEMENT_RIGHT_EYE_CONFIG[i]][2]];
+  for (let i = 0; i < t2.lips.length / 2; i++)
+    rawCoords[LANDMARKS_REFINEMENT_LIPS_CONFIG[i]] = [t2.lips[2 * i + 0], t2.lips[2 * i + 1], rawCoords[LANDMARKS_REFINEMENT_LIPS_CONFIG[i]][2]];
+  return rawCoords;
+}
+
+// src/face/facemesh.ts
+var cache3 = {
+  boxes: [],
+  skipped: Number.MAX_SAFE_INTEGER,
+  timestamp: 0
+};
+var model7 = null;
+var inputSize6 = 0;
+async function predict6(input, config3) {
+  var _a, _b, _c, _d, _e, _f, _g, _h, _i, _j;
+  if (!(model7 == null ? void 0 : model7["executor"]))
+    return [];
+  const skipTime = (((_a = config3.face.detector) == null ? void 0 : _a.skipTime) || 0) > now() - cache3.timestamp;
+  const skipFrame = cache3.skipped < (((_b = config3.face.detector) == null ? void 0 : _b.skipFrames) || 0);
+  if (!config3.skipAllowed || !skipTime || !skipFrame || cache3.boxes.length === 0) {
+    cache3.boxes = await getBoxes(input, config3);
+    cache3.timestamp = now();
+    cache3.skipped = 0;
+  } else {
+    cache3.skipped++;
+  }
+  const faces = [];
+  const newCache = [];
+  let id = 0;
+  const size2 = inputSize6;
+  for (let i = 0; i < cache3.boxes.length; i++) {
+    const box = cache3.boxes[i];
+    let angle = 0;
+    let rotationMatrix;
+    const face4 = {
+      id: id++,
+      mesh: [],
+      meshRaw: [],
+      box: [0, 0, 0, 0],
+      boxRaw: [0, 0, 0, 0],
+      score: 0,
+      boxScore: 0,
+      faceScore: 0,
+      annotations: {}
+    };
+    [angle, rotationMatrix, face4.tensor] = correctFaceRotation((_c = config3.face.detector) == null ? void 0 : _c.rotation, box, input, ((_d = config3.face.mesh) == null ? void 0 : _d.enabled) ? inputSize6 : size());
+    if (config3.filter.equalization) {
+      const equilized = face4.tensor ? await histogramEqualization(face4.tensor) : void 0;
+      tfjs_esm_exports.dispose(face4.tensor);
+      if (equilized)
+        face4.tensor = equilized;
+    }
+    face4.boxScore = Math.round(100 * box.confidence) / 100;
+    if (!((_e = config3.face.mesh) == null ? void 0 : _e.enabled)) {
+      face4.box = clampBox(box, input);
+      face4.boxRaw = getRawBox(box, input);
+      face4.score = face4.boxScore;
+      face4.mesh = box.landmarks.map((pt) => [
+        (box.startPoint[0] + box.endPoint[0]) / 2 + (box.endPoint[0] + box.startPoint[0]) * pt[0] / size(),
+        (box.startPoint[1] + box.endPoint[1]) / 2 + (box.endPoint[1] + box.startPoint[1]) * pt[1] / size()
+      ]);
+      face4.meshRaw = face4.mesh.map((pt) => [pt[0] / (input.shape[2] || 0), pt[1] / (input.shape[1] || 0), (pt[2] || 0) / size2]);
+      for (const key of Object.keys(blazeFaceLandmarks)) {
+        face4.annotations[key] = [face4.mesh[blazeFaceLandmarks[key]]];
+      }
+    } else if (!model7) {
+      if (config3.debug)
+        log("face mesh detection requested, but model is not loaded");
+    } else {
+      if (((_f = config3.face.attention) == null ? void 0 : _f.enabled) && !env.kernels.includes("atan2")) {
+        config3.face.attention.enabled = false;
+        tfjs_esm_exports.dispose(face4.tensor);
+        return faces;
+      }
+      const results = model7.execute(face4.tensor);
+      const confidenceT = results.find((t2) => t2.shape[t2.shape.length - 1] === 1);
+      const faceConfidence = await confidenceT.data();
+      face4.faceScore = Math.round(100 * faceConfidence[0]) / 100;
+      if (face4.faceScore < (((_g = config3.face.detector) == null ? void 0 : _g.minConfidence) || 1)) {
+        box.confidence = face4.faceScore;
+        if (config3.face.mesh.keepInvalid) {
+          face4.box = clampBox(box, input);
+          face4.boxRaw = getRawBox(box, input);
+          face4.score = face4.boxScore;
+          face4.mesh = box.landmarks.map((pt) => [
+            (box.startPoint[0] + box.endPoint[0]) / 2 + (box.endPoint[0] + box.startPoint[0]) * pt[0] / size(),
+            (box.startPoint[1] + box.endPoint[1]) / 2 + (box.endPoint[1] + box.startPoint[1]) * pt[1] / size()
+          ]);
+          face4.meshRaw = face4.mesh.map((pt) => [pt[0] / (input.shape[2] || 1), pt[1] / (input.shape[1] || 1), (pt[2] || 0) / size2]);
+          for (const key of Object.keys(blazeFaceLandmarks)) {
+            face4.annotations[key] = [face4.mesh[blazeFaceLandmarks[key]]];
+          }
+        }
+      } else {
+        const meshT = results.find((t2) => t2.shape[t2.shape.length - 1] === 1404);
+        const coordsReshaped = tfjs_esm_exports.reshape(meshT, [-1, 3]);
+        let rawCoords = await coordsReshaped.array();
+        tfjs_esm_exports.dispose(coordsReshaped);
+        if ((_h = config3.face.attention) == null ? void 0 : _h.enabled) {
+          rawCoords = await augment(rawCoords, results);
+        } else if ((_i = config3.face.iris) == null ? void 0 : _i.enabled) {
+          rawCoords = await augmentIris(rawCoords, face4.tensor, inputSize6);
+        }
+        face4.mesh = transformRawCoords(rawCoords, box, angle, rotationMatrix, inputSize6);
+        face4.meshRaw = face4.mesh.map((pt) => [pt[0] / (input.shape[2] || 0), pt[1] / (input.shape[1] || 0), (pt[2] || 0) / size2]);
+        for (const key of Object.keys(meshAnnotations))
+          face4.annotations[key] = meshAnnotations[key].map((index2) => face4.mesh[index2]);
+        face4.score = face4.faceScore;
+        const calculatedBox = { ...calculateFaceBox(face4.mesh, box), confidence: box.confidence, landmarks: box.landmarks };
+        face4.box = clampBox(calculatedBox, input);
+        face4.boxRaw = getRawBox(calculatedBox, input);
+        newCache.push(calculatedBox);
+      }
+      tfjs_esm_exports.dispose(results);
+    }
+    if (face4.score > (((_j = config3.face.detector) == null ? void 0 : _j.minConfidence) || 1))
+      faces.push(face4);
+    else
+      tfjs_esm_exports.dispose(face4.tensor);
+  }
+  cache3.boxes = newCache;
+  return faces;
+}
+async function load7(config3) {
+  var _a, _b, _c, _d, _e, _f;
+  if (env.initial)
+    model7 = null;
+  if (((_a = config3.face.attention) == null ? void 0 : _a.enabled) && (model7 == null ? void 0 : model7["signature"])) {
+    if (Object.keys(((_b = model7 == null ? void 0 : model7["signature"]) == null ? void 0 : _b.outputs) || {}).length < 6)
+      model7 = null;
+  }
+  if (!model7) {
+    if ((_c = config3.face.attention) == null ? void 0 : _c.enabled)
+      model7 = await loadModel(config3.face.attention.modelPath);
+    else
+      model7 = await loadModel((_d = config3.face.mesh) == null ? void 0 : _d.modelPath);
+  } else if (config3.debug) {
+    log("cached model:", model7["modelUrl"]);
+  }
+  inputSize6 = model7["executor"] && ((_e = model7 == null ? void 0 : model7.inputs) == null ? void 0 : _e[0].shape) ? (_f = model7 == null ? void 0 : model7.inputs) == null ? void 0 : _f[0].shape[2] : 256;
+  return model7;
+}
+var triangulation = TRI468;
+var uvmap = UV468;
+
+// src/face/faceres.ts
+var model8;
+var last4 = [];
+var lastTime6 = 0;
+var lastCount3 = 0;
+var skipped6 = Number.MAX_SAFE_INTEGER;
+async function load8(config3) {
+  var _a;
+  if (env.initial)
+    model8 = null;
+  if (!model8)
+    model8 = await loadModel((_a = config3.face.description) == null ? void 0 : _a.modelPath);
+  else if (config3.debug)
+    log("cached model:", model8["modelUrl"]);
+  return model8;
+}
+function enhance(input) {
+  const tensor6 = input.image || input.tensor || input;
+  if (!(model8 == null ? void 0 : model8.inputs[0].shape))
+    return tensor6;
+  const crop = tfjs_esm_exports.image.resizeBilinear(tensor6, [model8.inputs[0].shape[2], model8.inputs[0].shape[1]], false);
+  const norm = tfjs_esm_exports.mul(crop, constants.tf255);
+  tfjs_esm_exports.dispose(crop);
+  return norm;
+}
+async function predict7(image27, config3, idx, count2) {
+  var _a, _b, _c, _d;
+  const obj = {
+    age: 0,
+    gender: "unknown",
+    genderScore: 0,
+    descriptor: []
+  };
+  if (!(model8 == null ? void 0 : model8["executor"]))
+    return obj;
+  const skipFrame = skipped6 < (((_a = config3.face.description) == null ? void 0 : _a.skipFrames) || 0);
+  const skipTime = (((_b = config3.face.description) == null ? void 0 : _b.skipTime) || 0) > now() - lastTime6;
+  if (config3.skipAllowed && skipFrame && skipTime && lastCount3 === count2 && ((_c = last4 == null ? void 0 : last4[idx]) == null ? void 0 : _c.age) > 0 && ((_d = last4 == null ? void 0 : last4[idx]) == null ? void 0 : _d.genderScore) > 0) {
+    skipped6++;
+    return last4[idx];
+  }
+  skipped6 = 0;
+  return new Promise(async (resolve) => {
+    var _a2;
+    if ((_a2 = config3.face.description) == null ? void 0 : _a2.enabled) {
+      const enhanced = enhance(image27);
+      const resT = model8 == null ? void 0 : model8.execute(enhanced);
+      lastTime6 = now();
+      tfjs_esm_exports.dispose(enhanced);
+      const genderT = resT.find((t2) => t2.shape[1] === 1);
+      const gender2 = await genderT.data();
+      const confidence = Math.trunc(200 * Math.abs(gender2[0] - 0.5)) / 100;
+      if (confidence > (config3.face.description.minConfidence || 0)) {
+        obj.gender = gender2[0] <= 0.5 ? "female" : "male";
+        obj.genderScore = Math.min(0.99, confidence);
+      }
+      const argmax = tfjs_esm_exports.argMax(resT.find((t2) => t2.shape[1] === 100), 1);
+      const ageIdx = (await argmax.data())[0];
+      tfjs_esm_exports.dispose(argmax);
+      const ageT = resT.find((t2) => t2.shape[1] === 100);
+      const all2 = await ageT.data();
+      obj.age = Math.round(all2[ageIdx - 1] > all2[ageIdx + 1] ? 10 * ageIdx - 100 * all2[ageIdx - 1] : 10 * ageIdx + 100 * all2[ageIdx + 1]) / 10;
+      if (Number.isNaN(gender2[0]) || Number.isNaN(all2[0]))
+        log("faceres error:", { model: model8, result: resT });
+      const desc = resT.find((t2) => t2.shape[1] === 1024);
+      const descriptor = desc ? await desc.data() : [];
+      obj.descriptor = Array.from(descriptor);
+      resT.forEach((t2) => tfjs_esm_exports.dispose(t2));
+    }
+    last4[idx] = obj;
+    lastCount3 = count2;
+    resolve(obj);
+  });
+}
+
+// src/gear/gear.ts
+var model9;
+var last5 = [];
+var raceNames = ["white", "black", "asian", "indian", "other"];
+var ageWeights = [15, 23, 28, 35.5, 45.5, 55.5, 65];
+var lastCount4 = 0;
+var lastTime7 = 0;
+var skipped7 = Number.MAX_SAFE_INTEGER;
+async function load9(config3) {
+  var _a;
+  if (env.initial)
+    model9 = null;
+  if (!model9)
+    model9 = await loadModel((_a = config3.face.gear) == null ? void 0 : _a.modelPath);
+  else if (config3.debug)
+    log("cached model:", model9["modelUrl"]);
+  return model9;
+}
+async function predict8(image27, config3, idx, count2) {
+  var _a, _b;
+  if (!model9)
+    return { age: 0, gender: "unknown", genderScore: 0, race: [] };
+  const skipFrame = skipped7 < (((_a = config3.face.gear) == null ? void 0 : _a.skipFrames) || 0);
+  const skipTime = (((_b = config3.face.gear) == null ? void 0 : _b.skipTime) || 0) > now() - lastTime7;
+  if (config3.skipAllowed && skipTime && skipFrame && lastCount4 === count2 && last5[idx]) {
+    skipped7++;
+    return last5[idx];
+  }
+  skipped7 = 0;
+  return new Promise(async (resolve) => {
+    var _a2, _b2;
+    if (!(model9 == null ? void 0 : model9.inputs[0].shape))
+      return;
+    const t2 = {};
+    const box = [[0, 0.1, 0.9, 0.9]];
+    t2.resize = tfjs_esm_exports.image.cropAndResize(image27, box, [0], [model9.inputs[0].shape[2], model9.inputs[0].shape[1]]);
+    const obj = { age: 0, gender: "unknown", genderScore: 0, race: [] };
+    if ((_a2 = config3.face.gear) == null ? void 0 : _a2.enabled)
+      [t2.age, t2.gender, t2.race] = model9.execute(t2.resize, ["age_output", "gender_output", "race_output"]);
+    const gender2 = await t2.gender.data();
+    obj.gender = gender2[0] > gender2[1] ? "male" : "female";
+    obj.genderScore = Math.round(100 * (gender2[0] > gender2[1] ? gender2[0] : gender2[1])) / 100;
+    const race = await t2.race.data();
+    for (let i = 0; i < race.length; i++) {
+      if (race[i] > (((_b2 = config3.face.gear) == null ? void 0 : _b2.minConfidence) || 0.2))
+        obj.race.push({ score: Math.round(100 * race[i]) / 100, race: raceNames[i] });
+    }
+    obj.race.sort((a, b) => b.score - a.score);
+    const ageDistribution = Array.from(await t2.age.data());
+    const ageSorted = ageDistribution.map((a, i) => [ageWeights[i], a]).sort((a, b) => b[1] - a[1]);
+    let age2 = ageSorted[0][0];
+    for (let i = 1; i < ageSorted.length; i++)
+      age2 += ageSorted[i][1] * (ageSorted[i][0] - age2);
+    obj.age = Math.round(10 * age2) / 10;
+    Object.keys(t2).forEach((tensor6) => tfjs_esm_exports.dispose(t2[tensor6]));
+    last5[idx] = obj;
+    lastCount4 = count2;
+    lastTime7 = now();
+    resolve(obj);
+  });
+}
+
+// src/hand/handposeutil.ts
+function getBoxSize2(box) {
+  return [
+    Math.abs(box.endPoint[0] - box.startPoint[0]),
+    Math.abs(box.endPoint[1] - box.startPoint[1])
+  ];
+}
+function getBoxCenter2(box) {
+  return [
+    box.startPoint[0] + (box.endPoint[0] - box.startPoint[0]) / 2,
+    box.startPoint[1] + (box.endPoint[1] - box.startPoint[1]) / 2
+  ];
+}
+function cutBoxFromImageAndResize(box, image27, cropSize) {
+  const h = image27.shape[1];
+  const w = image27.shape[2];
+  const boxes = [[
+    box.startPoint[1] / h,
+    box.startPoint[0] / w,
+    box.endPoint[1] / h,
+    box.endPoint[0] / w
+  ]];
+  return tfjs_esm_exports.image.cropAndResize(image27, boxes, [0], cropSize);
+}
+function scaleBoxCoordinates2(box, factor) {
+  const startPoint = [box.startPoint[0] * factor[0], box.startPoint[1] * factor[1]];
+  const endPoint = [box.endPoint[0] * factor[0], box.endPoint[1] * factor[1]];
+  const palmLandmarks = box.palmLandmarks.map((coord) => {
+    const scaledCoord = [coord[0] * factor[0], coord[1] * factor[1]];
+    return scaledCoord;
+  });
+  return { startPoint, endPoint, palmLandmarks, confidence: box.confidence };
+}
+function enlargeBox2(box, factor = 1.5) {
+  const center = getBoxCenter2(box);
+  const size2 = getBoxSize2(box);
+  const newHalfSize = [factor * size2[0] / 2, factor * size2[1] / 2];
+  const startPoint = [center[0] - newHalfSize[0], center[1] - newHalfSize[1]];
+  const endPoint = [center[0] + newHalfSize[0], center[1] + newHalfSize[1]];
+  return { startPoint, endPoint, palmLandmarks: box.palmLandmarks };
+}
+function squarifyBox2(box) {
+  const centers = getBoxCenter2(box);
+  const size2 = getBoxSize2(box);
+  const maxEdge = Math.max(...size2);
+  const halfSize = maxEdge / 2;
+  const startPoint = [centers[0] - halfSize, centers[1] - halfSize];
+  const endPoint = [centers[0] + halfSize, centers[1] + halfSize];
+  return { startPoint, endPoint, palmLandmarks: box.palmLandmarks };
+}
+function normalizeRadians2(angle) {
+  return angle - 2 * Math.PI * Math.floor((angle + Math.PI) / (2 * Math.PI));
+}
+function computeRotation2(point1, point2) {
+  const radians = Math.PI / 2 - Math.atan2(-(point2[1] - point1[1]), point2[0] - point1[0]);
+  return normalizeRadians2(radians);
+}
+var buildTranslationMatrix2 = (x, y) => [[1, 0, x], [0, 1, y], [0, 0, 1]];
+function dot2(v1, v2) {
+  let product = 0;
+  for (let i = 0; i < v1.length; i++) {
+    product += v1[i] * v2[i];
+  }
+  return product;
+}
+function getColumnFrom2DArr2(arr, columnIndex) {
+  const column = [];
+  for (let i = 0; i < arr.length; i++) {
+    column.push(arr[i][columnIndex]);
+  }
+  return column;
+}
+function multiplyTransformMatrices2(mat1, mat2) {
+  const product = [];
+  const size2 = mat1.length;
+  for (let row = 0; row < size2; row++) {
+    product.push([]);
+    for (let col = 0; col < size2; col++) {
+      product[row].push(dot2(mat1[row], getColumnFrom2DArr2(mat2, col)));
+    }
+  }
+  return product;
+}
+function buildRotationMatrix2(rotation, center) {
+  const cosA = Math.cos(rotation);
+  const sinA = Math.sin(rotation);
+  const rotationMatrix = [[cosA, -sinA, 0], [sinA, cosA, 0], [0, 0, 1]];
+  const translationMatrix = buildTranslationMatrix2(center[0], center[1]);
+  const translationTimesRotation = multiplyTransformMatrices2(translationMatrix, rotationMatrix);
+  const negativeTranslationMatrix = buildTranslationMatrix2(-center[0], -center[1]);
+  return multiplyTransformMatrices2(translationTimesRotation, negativeTranslationMatrix);
+}
+function invertTransformMatrix2(matrix) {
+  const rotationComponent = [[matrix[0][0], matrix[1][0]], [matrix[0][1], matrix[1][1]]];
+  const translationComponent = [matrix[0][2], matrix[1][2]];
+  const invertedTranslation = [
+    -dot2(rotationComponent[0], translationComponent),
+    -dot2(rotationComponent[1], translationComponent)
+  ];
+  return [
+    rotationComponent[0].concat(invertedTranslation[0]),
+    rotationComponent[1].concat(invertedTranslation[1]),
+    [0, 0, 1]
+  ];
+}
+function rotatePoint2(homogeneousCoordinate, rotationMatrix) {
+  return [
+    dot2(homogeneousCoordinate, rotationMatrix[0]),
+    dot2(homogeneousCoordinate, rotationMatrix[1])
+  ];
+}
+
+// src/hand/handposeanchors.ts
+var anchors2 = [
+  { x: 0.015625, y: 0.015625 },
+  { x: 0.015625, y: 0.015625 },
+  { x: 0.046875, y: 0.015625 },
+  { x: 0.046875, y: 0.015625 },
+  { x: 0.078125, y: 0.015625 },
+  { x: 0.078125, y: 0.015625 },
+  { x: 0.109375, y: 0.015625 },
+  { x: 0.109375, y: 0.015625 },
+  { x: 0.140625, y: 0.015625 },
+  { x: 0.140625, y: 0.015625 },
+  { x: 0.171875, y: 0.015625 },
+  { x: 0.171875, y: 0.015625 },
+  { x: 0.203125, y: 0.015625 },
+  { x: 0.203125, y: 0.015625 },
+  { x: 0.234375, y: 0.015625 },
+  { x: 0.234375, y: 0.015625 },
+  { x: 0.265625, y: 0.015625 },
+  { x: 0.265625, y: 0.015625 },
+  { x: 0.296875, y: 0.015625 },
+  { x: 0.296875, y: 0.015625 },
+  { x: 0.328125, y: 0.015625 },
+  { x: 0.328125, y: 0.015625 },
+  { x: 0.359375, y: 0.015625 },
+  { x: 0.359375, y: 0.015625 },
+  { x: 0.390625, y: 0.015625 },
+  { x: 0.390625, y: 0.015625 },
+  { x: 0.421875, y: 0.015625 },
+  { x: 0.421875, y: 0.015625 },
+  { x: 0.453125, y: 0.015625 },
+  { x: 0.453125, y: 0.015625 },
+  { x: 0.484375, y: 0.015625 },
+  { x: 0.484375, y: 0.015625 },
+  { x: 0.515625, y: 0.015625 },
+  { x: 0.515625, y: 0.015625 },
+  { x: 0.546875, y: 0.015625 },
+  { x: 0.546875, y: 0.015625 },
+  { x: 0.578125, y: 0.015625 },
+  { x: 0.578125, y: 0.015625 },
+  { x: 0.609375, y: 0.015625 },
+  { x: 0.609375, y: 0.015625 },
+  { x: 0.640625, y: 0.015625 },
+  { x: 0.640625, y: 0.015625 },
+  { x: 0.671875, y: 0.015625 },
+  { x: 0.671875, y: 0.015625 },
+  { x: 0.703125, y: 0.015625 },
+  { x: 0.703125, y: 0.015625 },
+  { x: 0.734375, y: 0.015625 },
+  { x: 0.734375, y: 0.015625 },
+  { x: 0.765625, y: 0.015625 },
+  { x: 0.765625, y: 0.015625 },
+  { x: 0.796875, y: 0.015625 },
+  { x: 0.796875, y: 0.015625 },
+  { x: 0.828125, y: 0.015625 },
+  { x: 0.828125, y: 0.015625 },
+  { x: 0.859375, y: 0.015625 },
+  { x: 0.859375, y: 0.015625 },
+  { x: 0.890625, y: 0.015625 },
+  { x: 0.890625, y: 0.015625 },
+  { x: 0.921875, y: 0.015625 },
+  { x: 0.921875, y: 0.015625 },
+  { x: 0.953125, y: 0.015625 },
+  { x: 0.953125, y: 0.015625 },
+  { x: 0.984375, y: 0.015625 },
+  { x: 0.984375, y: 0.015625 },
+  { x: 0.015625, y: 0.046875 },
+  { x: 0.015625, y: 0.046875 },
+  { x: 0.046875, y: 0.046875 },
+  { x: 0.046875, y: 0.046875 },
+  { x: 0.078125, y: 0.046875 },
+  { x: 0.078125, y: 0.046875 },
+  { x: 0.109375, y: 0.046875 },
+  { x: 0.109375, y: 0.046875 },
+  { x: 0.140625, y: 0.046875 },
+  { x: 0.140625, y: 0.046875 },
+  { x: 0.171875, y: 0.046875 },
+  { x: 0.171875, y: 0.046875 },
+  { x: 0.203125, y: 0.046875 },
+  { x: 0.203125, y: 0.046875 },
+  { x: 0.234375, y: 0.046875 },
+  { x: 0.234375, y: 0.046875 },
+  { x: 0.265625, y: 0.046875 },
+  { x: 0.265625, y: 0.046875 },
+  { x: 0.296875, y: 0.046875 },
+  { x: 0.296875, y: 0.046875 },
+  { x: 0.328125, y: 0.046875 },
+  { x: 0.328125, y: 0.046875 },
+  { x: 0.359375, y: 0.046875 },
+  { x: 0.359375, y: 0.046875 },
+  { x: 0.390625, y: 0.046875 },
+  { x: 0.390625, y: 0.046875 },
+  { x: 0.421875, y: 0.046875 },
+  { x: 0.421875, y: 0.046875 },
+  { x: 0.453125, y: 0.046875 },
+  { x: 0.453125, y: 0.046875 },
+  { x: 0.484375, y: 0.046875 },
+  { x: 0.484375, y: 0.046875 },
+  { x: 0.515625, y: 0.046875 },
+  { x: 0.515625, y: 0.046875 },
+  { x: 0.546875, y: 0.046875 },
+  { x: 0.546875, y: 0.046875 },
+  { x: 0.578125, y: 0.046875 },
+  { x: 0.578125, y: 0.046875 },
+  { x: 0.609375, y: 0.046875 },
+  { x: 0.609375, y: 0.046875 },
+  { x: 0.640625, y: 0.046875 },
+  { x: 0.640625, y: 0.046875 },
+  { x: 0.671875, y: 0.046875 },
+  { x: 0.671875, y: 0.046875 },
+  { x: 0.703125, y: 0.046875 },
+  { x: 0.703125, y: 0.046875 },
+  { x: 0.734375, y: 0.046875 },
+  { x: 0.734375, y: 0.046875 },
+  { x: 0.765625, y: 0.046875 },
+  { x: 0.765625, y: 0.046875 },
+  { x: 0.796875, y: 0.046875 },
+  { x: 0.796875, y: 0.046875 },
+  { x: 0.828125, y: 0.046875 },
+  { x: 0.828125, y: 0.046875 },
+  { x: 0.859375, y: 0.046875 },
+  { x: 0.859375, y: 0.046875 },
+  { x: 0.890625, y: 0.046875 },
+  { x: 0.890625, y: 0.046875 },
+  { x: 0.921875, y: 0.046875 },
+  { x: 0.921875, y: 0.046875 },
+  { x: 0.953125, y: 0.046875 },
+  { x: 0.953125, y: 0.046875 },
+  { x: 0.984375, y: 0.046875 },
+  { x: 0.984375, y: 0.046875 },
+  { x: 0.015625, y: 0.078125 },
+  { x: 0.015625, y: 0.078125 },
+  { x: 0.046875, y: 0.078125 },
+  { x: 0.046875, y: 0.078125 },
+  { x: 0.078125, y: 0.078125 },
+  { x: 0.078125, y: 0.078125 },
+  { x: 0.109375, y: 0.078125 },
+  { x: 0.109375, y: 0.078125 },
+  { x: 0.140625, y: 0.078125 },
+  { x: 0.140625, y: 0.078125 },
+  { x: 0.171875, y: 0.078125 },
+  { x: 0.171875, y: 0.078125 },
+  { x: 0.203125, y: 0.078125 },
+  { x: 0.203125, y: 0.078125 },
+  { x: 0.234375, y: 0.078125 },
+  { x: 0.234375, y: 0.078125 },
+  { x: 0.265625, y: 0.078125 },
+  { x: 0.265625, y: 0.078125 },
+  { x: 0.296875, y: 0.078125 },
+  { x: 0.296875, y: 0.078125 },
+  { x: 0.328125, y: 0.078125 },
+  { x: 0.328125, y: 0.078125 },
+  { x: 0.359375, y: 0.078125 },
+  { x: 0.359375, y: 0.078125 },
+  { x: 0.390625, y: 0.078125 },
+  { x: 0.390625, y: 0.078125 },
+  { x: 0.421875, y: 0.078125 },
+  { x: 0.421875, y: 0.078125 },
+  { x: 0.453125, y: 0.078125 },
+  { x: 0.453125, y: 0.078125 },
+  { x: 0.484375, y: 0.078125 },
+  { x: 0.484375, y: 0.078125 },
+  { x: 0.515625, y: 0.078125 },
+  { x: 0.515625, y: 0.078125 },
+  { x: 0.546875, y: 0.078125 },
+  { x: 0.546875, y: 0.078125 },
+  { x: 0.578125, y: 0.078125 },
+  { x: 0.578125, y: 0.078125 },
+  { x: 0.609375, y: 0.078125 },
+  { x: 0.609375, y: 0.078125 },
+  { x: 0.640625, y: 0.078125 },
+  { x: 0.640625, y: 0.078125 },
+  { x: 0.671875, y: 0.078125 },
+  { x: 0.671875, y: 0.078125 },
+  { x: 0.703125, y: 0.078125 },
+  { x: 0.703125, y: 0.078125 },
+  { x: 0.734375, y: 0.078125 },
+  { x: 0.734375, y: 0.078125 },
+  { x: 0.765625, y: 0.078125 },
+  { x: 0.765625, y: 0.078125 },
+  { x: 0.796875, y: 0.078125 },
+  { x: 0.796875, y: 0.078125 },
+  { x: 0.828125, y: 0.078125 },
+  { x: 0.828125, y: 0.078125 },
+  { x: 0.859375, y: 0.078125 },
+  { x: 0.859375, y: 0.078125 },
+  { x: 0.890625, y: 0.078125 },
+  { x: 0.890625, y: 0.078125 },
+  { x: 0.921875, y: 0.078125 },
+  { x: 0.921875, y: 0.078125 },
+  { x: 0.953125, y: 0.078125 },
+  { x: 0.953125, y: 0.078125 },
+  { x: 0.984375, y: 0.078125 },
+  { x: 0.984375, y: 0.078125 },
+  { x: 0.015625, y: 0.109375 },
+  { x: 0.015625, y: 0.109375 },
+  { x: 0.046875, y: 0.109375 },
+  { x: 0.046875, y: 0.109375 },
+  { x: 0.078125, y: 0.109375 },
+  { x: 0.078125, y: 0.109375 },
+  { x: 0.109375, y: 0.109375 },
+  { x: 0.109375, y: 0.109375 },
+  { x: 0.140625, y: 0.109375 },
+  { x: 0.140625, y: 0.109375 },
+  { x: 0.171875, y: 0.109375 },
+  { x: 0.171875, y: 0.109375 },
+  { x: 0.203125, y: 0.109375 },
+  { x: 0.203125, y: 0.109375 },
+  { x: 0.234375, y: 0.109375 },
+  { x: 0.234375, y: 0.109375 },
+  { x: 0.265625, y: 0.109375 },
+  { x: 0.265625, y: 0.109375 },
+  { x: 0.296875, y: 0.109375 },
+  { x: 0.296875, y: 0.109375 },
+  { x: 0.328125, y: 0.109375 },
+  { x: 0.328125, y: 0.109375 },
+  { x: 0.359375, y: 0.109375 },
+  { x: 0.359375, y: 0.109375 },
+  { x: 0.390625, y: 0.109375 },
+  { x: 0.390625, y: 0.109375 },
+  { x: 0.421875, y: 0.109375 },
+  { x: 0.421875, y: 0.109375 },
+  { x: 0.453125, y: 0.109375 },
+  { x: 0.453125, y: 0.109375 },
+  { x: 0.484375, y: 0.109375 },
+  { x: 0.484375, y: 0.109375 },
+  { x: 0.515625, y: 0.109375 },
+  { x: 0.515625, y: 0.109375 },
+  { x: 0.546875, y: 0.109375 },
+  { x: 0.546875, y: 0.109375 },
+  { x: 0.578125, y: 0.109375 },
+  { x: 0.578125, y: 0.109375 },
+  { x: 0.609375, y: 0.109375 },
+  { x: 0.609375, y: 0.109375 },
+  { x: 0.640625, y: 0.109375 },
+  { x: 0.640625, y: 0.109375 },
+  { x: 0.671875, y: 0.109375 },
+  { x: 0.671875, y: 0.109375 },
+  { x: 0.703125, y: 0.109375 },
+  { x: 0.703125, y: 0.109375 },
+  { x: 0.734375, y: 0.109375 },
+  { x: 0.734375, y: 0.109375 },
+  { x: 0.765625, y: 0.109375 },
+  { x: 0.765625, y: 0.109375 },
+  { x: 0.796875, y: 0.109375 },
+  { x: 0.796875, y: 0.109375 },
+  { x: 0.828125, y: 0.109375 },
+  { x: 0.828125, y: 0.109375 },
+  { x: 0.859375, y: 0.109375 },
+  { x: 0.859375, y: 0.109375 },
+  { x: 0.890625, y: 0.109375 },
+  { x: 0.890625, y: 0.109375 },
+  { x: 0.921875, y: 0.109375 },
+  { x: 0.921875, y: 0.109375 },
+  { x: 0.953125, y: 0.109375 },
+  { x: 0.953125, y: 0.109375 },
+  { x: 0.984375, y: 0.109375 },
+  { x: 0.984375, y: 0.109375 },
+  { x: 0.015625, y: 0.140625 },
+  { x: 0.015625, y: 0.140625 },
+  { x: 0.046875, y: 0.140625 },
+  { x: 0.046875, y: 0.140625 },
+  { x: 0.078125, y: 0.140625 },
+  { x: 0.078125, y: 0.140625 },
+  { x: 0.109375, y: 0.140625 },
+  { x: 0.109375, y: 0.140625 },
+  { x: 0.140625, y: 0.140625 },
+  { x: 0.140625, y: 0.140625 },
+  { x: 0.171875, y: 0.140625 },
+  { x: 0.171875, y: 0.140625 },
+  { x: 0.203125, y: 0.140625 },
+  { x: 0.203125, y: 0.140625 },
+  { x: 0.234375, y: 0.140625 },
+  { x: 0.234375, y: 0.140625 },
+  { x: 0.265625, y: 0.140625 },
+  { x: 0.265625, y: 0.140625 },
+  { x: 0.296875, y: 0.140625 },
+  { x: 0.296875, y: 0.140625 },
+  { x: 0.328125, y: 0.140625 },
+  { x: 0.328125, y: 0.140625 },
+  { x: 0.359375, y: 0.140625 },
+  { x: 0.359375, y: 0.140625 },
+  { x: 0.390625, y: 0.140625 },
+  { x: 0.390625, y: 0.140625 },
+  { x: 0.421875, y: 0.140625 },
+  { x: 0.421875, y: 0.140625 },
+  { x: 0.453125, y: 0.140625 },
+  { x: 0.453125, y: 0.140625 },
+  { x: 0.484375, y: 0.140625 },
+  { x: 0.484375, y: 0.140625 },
+  { x: 0.515625, y: 0.140625 },
+  { x: 0.515625, y: 0.140625 },
+  { x: 0.546875, y: 0.140625 },
+  { x: 0.546875, y: 0.140625 },
+  { x: 0.578125, y: 0.140625 },
+  { x: 0.578125, y: 0.140625 },
+  { x: 0.609375, y: 0.140625 },
+  { x: 0.609375, y: 0.140625 },
+  { x: 0.640625, y: 0.140625 },
+  { x: 0.640625, y: 0.140625 },
+  { x: 0.671875, y: 0.140625 },
+  { x: 0.671875, y: 0.140625 },
+  { x: 0.703125, y: 0.140625 },
+  { x: 0.703125, y: 0.140625 },
+  { x: 0.734375, y: 0.140625 },
+  { x: 0.734375, y: 0.140625 },
+  { x: 0.765625, y: 0.140625 },
+  { x: 0.765625, y: 0.140625 },
+  { x: 0.796875, y: 0.140625 },
+  { x: 0.796875, y: 0.140625 },
+  { x: 0.828125, y: 0.140625 },
+  { x: 0.828125, y: 0.140625 },
+  { x: 0.859375, y: 0.140625 },
+  { x: 0.859375, y: 0.140625 },
+  { x: 0.890625, y: 0.140625 },
+  { x: 0.890625, y: 0.140625 },
+  { x: 0.921875, y: 0.140625 },
+  { x: 0.921875, y: 0.140625 },
+  { x: 0.953125, y: 0.140625 },
+  { x: 0.953125, y: 0.140625 },
+  { x: 0.984375, y: 0.140625 },
+  { x: 0.984375, y: 0.140625 },
+  { x: 0.015625, y: 0.171875 },
+  { x: 0.015625, y: 0.171875 },
+  { x: 0.046875, y: 0.171875 },
+  { x: 0.046875, y: 0.171875 },
+  { x: 0.078125, y: 0.171875 },
+  { x: 0.078125, y: 0.171875 },
+  { x: 0.109375, y: 0.171875 },
+  { x: 0.109375, y: 0.171875 },
+  { x: 0.140625, y: 0.171875 },
+  { x: 0.140625, y: 0.171875 },
+  { x: 0.171875, y: 0.171875 },
+  { x: 0.171875, y: 0.171875 },
+  { x: 0.203125, y: 0.171875 },
+  { x: 0.203125, y: 0.171875 },
+  { x: 0.234375, y: 0.171875 },
+  { x: 0.234375, y: 0.171875 },
+  { x: 0.265625, y: 0.171875 },
+  { x: 0.265625, y: 0.171875 },
+  { x: 0.296875, y: 0.171875 },
+  { x: 0.296875, y: 0.171875 },
+  { x: 0.328125, y: 0.171875 },
+  { x: 0.328125, y: 0.171875 },
+  { x: 0.359375, y: 0.171875 },
+  { x: 0.359375, y: 0.171875 },
+  { x: 0.390625, y: 0.171875 },
+  { x: 0.390625, y: 0.171875 },
+  { x: 0.421875, y: 0.171875 },
+  { x: 0.421875, y: 0.171875 },
+  { x: 0.453125, y: 0.171875 },
+  { x: 0.453125, y: 0.171875 },
+  { x: 0.484375, y: 0.171875 },
+  { x: 0.484375, y: 0.171875 },
+  { x: 0.515625, y: 0.171875 },
+  { x: 0.515625, y: 0.171875 },
+  { x: 0.546875, y: 0.171875 },
+  { x: 0.546875, y: 0.171875 },
+  { x: 0.578125, y: 0.171875 },
+  { x: 0.578125, y: 0.171875 },
+  { x: 0.609375, y: 0.171875 },
+  { x: 0.609375, y: 0.171875 },
+  { x: 0.640625, y: 0.171875 },
+  { x: 0.640625, y: 0.171875 },
+  { x: 0.671875, y: 0.171875 },
+  { x: 0.671875, y: 0.171875 },
+  { x: 0.703125, y: 0.171875 },
+  { x: 0.703125, y: 0.171875 },
+  { x: 0.734375, y: 0.171875 },
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+  { x: 0.6875, y: 0.6875 },
+  { x: 0.6875, y: 0.6875 },
+  { x: 0.6875, y: 0.6875 },
+  { x: 0.6875, y: 0.6875 },
+  { x: 0.6875, y: 0.6875 },
+  { x: 0.6875, y: 0.6875 },
+  { x: 0.8125, y: 0.6875 },
+  { x: 0.8125, y: 0.6875 },
+  { x: 0.8125, y: 0.6875 },
+  { x: 0.8125, y: 0.6875 },
+  { x: 0.8125, y: 0.6875 },
+  { x: 0.8125, y: 0.6875 },
+  { x: 0.9375, y: 0.6875 },
+  { x: 0.9375, y: 0.6875 },
+  { x: 0.9375, y: 0.6875 },
+  { x: 0.9375, y: 0.6875 },
+  { x: 0.9375, y: 0.6875 },
+  { x: 0.9375, y: 0.6875 },
+  { x: 0.0625, y: 0.8125 },
+  { x: 0.0625, y: 0.8125 },
+  { x: 0.0625, y: 0.8125 },
+  { x: 0.0625, y: 0.8125 },
+  { x: 0.0625, y: 0.8125 },
+  { x: 0.0625, y: 0.8125 },
+  { x: 0.1875, y: 0.8125 },
+  { x: 0.1875, y: 0.8125 },
+  { x: 0.1875, y: 0.8125 },
+  { x: 0.1875, y: 0.8125 },
+  { x: 0.1875, y: 0.8125 },
+  { x: 0.1875, y: 0.8125 },
+  { x: 0.3125, y: 0.8125 },
+  { x: 0.3125, y: 0.8125 },
+  { x: 0.3125, y: 0.8125 },
+  { x: 0.3125, y: 0.8125 },
+  { x: 0.3125, y: 0.8125 },
+  { x: 0.3125, y: 0.8125 },
+  { x: 0.4375, y: 0.8125 },
+  { x: 0.4375, y: 0.8125 },
+  { x: 0.4375, y: 0.8125 },
+  { x: 0.4375, y: 0.8125 },
+  { x: 0.4375, y: 0.8125 },
+  { x: 0.4375, y: 0.8125 },
+  { x: 0.5625, y: 0.8125 },
+  { x: 0.5625, y: 0.8125 },
+  { x: 0.5625, y: 0.8125 },
+  { x: 0.5625, y: 0.8125 },
+  { x: 0.5625, y: 0.8125 },
+  { x: 0.5625, y: 0.8125 },
+  { x: 0.6875, y: 0.8125 },
+  { x: 0.6875, y: 0.8125 },
+  { x: 0.6875, y: 0.8125 },
+  { x: 0.6875, y: 0.8125 },
+  { x: 0.6875, y: 0.8125 },
+  { x: 0.6875, y: 0.8125 },
+  { x: 0.8125, y: 0.8125 },
+  { x: 0.8125, y: 0.8125 },
+  { x: 0.8125, y: 0.8125 },
+  { x: 0.8125, y: 0.8125 },
+  { x: 0.8125, y: 0.8125 },
+  { x: 0.8125, y: 0.8125 },
+  { x: 0.9375, y: 0.8125 },
+  { x: 0.9375, y: 0.8125 },
+  { x: 0.9375, y: 0.8125 },
+  { x: 0.9375, y: 0.8125 },
+  { x: 0.9375, y: 0.8125 },
+  { x: 0.9375, y: 0.8125 },
+  { x: 0.0625, y: 0.9375 },
+  { x: 0.0625, y: 0.9375 },
+  { x: 0.0625, y: 0.9375 },
+  { x: 0.0625, y: 0.9375 },
+  { x: 0.0625, y: 0.9375 },
+  { x: 0.0625, y: 0.9375 },
+  { x: 0.1875, y: 0.9375 },
+  { x: 0.1875, y: 0.9375 },
+  { x: 0.1875, y: 0.9375 },
+  { x: 0.1875, y: 0.9375 },
+  { x: 0.1875, y: 0.9375 },
+  { x: 0.1875, y: 0.9375 },
+  { x: 0.3125, y: 0.9375 },
+  { x: 0.3125, y: 0.9375 },
+  { x: 0.3125, y: 0.9375 },
+  { x: 0.3125, y: 0.9375 },
+  { x: 0.3125, y: 0.9375 },
+  { x: 0.3125, y: 0.9375 },
+  { x: 0.4375, y: 0.9375 },
+  { x: 0.4375, y: 0.9375 },
+  { x: 0.4375, y: 0.9375 },
+  { x: 0.4375, y: 0.9375 },
+  { x: 0.4375, y: 0.9375 },
+  { x: 0.4375, y: 0.9375 },
+  { x: 0.5625, y: 0.9375 },
+  { x: 0.5625, y: 0.9375 },
+  { x: 0.5625, y: 0.9375 },
+  { x: 0.5625, y: 0.9375 },
+  { x: 0.5625, y: 0.9375 },
+  { x: 0.5625, y: 0.9375 },
+  { x: 0.6875, y: 0.9375 },
+  { x: 0.6875, y: 0.9375 },
+  { x: 0.6875, y: 0.9375 },
+  { x: 0.6875, y: 0.9375 },
+  { x: 0.6875, y: 0.9375 },
+  { x: 0.6875, y: 0.9375 },
+  { x: 0.8125, y: 0.9375 },
+  { x: 0.8125, y: 0.9375 },
+  { x: 0.8125, y: 0.9375 },
+  { x: 0.8125, y: 0.9375 },
+  { x: 0.8125, y: 0.9375 },
+  { x: 0.8125, y: 0.9375 },
+  { x: 0.9375, y: 0.9375 },
+  { x: 0.9375, y: 0.9375 },
+  { x: 0.9375, y: 0.9375 },
+  { x: 0.9375, y: 0.9375 },
+  { x: 0.9375, y: 0.9375 },
+  { x: 0.9375, y: 0.9375 }
+];
+
+// src/hand/handposedetector.ts
+var HandDetector = class {
+  constructor(model21) {
+    __publicField(this, "model");
+    __publicField(this, "anchors");
+    __publicField(this, "anchorsTensor");
+    __publicField(this, "inputSize");
+    __publicField(this, "inputSizeTensor");
+    __publicField(this, "doubleInputSizeTensor");
+    var _a, _b, _c, _d;
+    this.model = model21;
+    this.anchors = anchors2.map((anchor) => [anchor.x, anchor.y]);
+    this.anchorsTensor = tfjs_esm_exports.tensor2d(this.anchors);
+    this.inputSize = ((_d = (_c = (_b = (_a = this == null ? void 0 : this.model) == null ? void 0 : _a.inputs) == null ? void 0 : _b[0]) == null ? void 0 : _c.shape) == null ? void 0 : _d[2]) || 0;
+    this.inputSizeTensor = tfjs_esm_exports.tensor1d([this.inputSize, this.inputSize]);
+    this.doubleInputSizeTensor = tfjs_esm_exports.tensor1d([this.inputSize * 2, this.inputSize * 2]);
+  }
+  normalizeBoxes(boxes) {
+    const t2 = {};
+    t2.boxOffsets = tfjs_esm_exports.slice(boxes, [0, 0], [-1, 2]);
+    t2.boxSizes = tfjs_esm_exports.slice(boxes, [0, 2], [-1, 2]);
+    t2.div = tfjs_esm_exports.div(t2.boxOffsets, this.inputSizeTensor);
+    t2.boxCenterPoints = tfjs_esm_exports.add(t2.div, this.anchorsTensor);
+    t2.halfBoxSizes = tfjs_esm_exports.div(t2.boxSizes, this.doubleInputSizeTensor);
+    t2.sub = tfjs_esm_exports.sub(t2.boxCenterPoints, t2.halfBoxSizes);
+    t2.startPoints = tfjs_esm_exports.mul(t2.sub, this.inputSizeTensor);
+    t2.add = tfjs_esm_exports.add(t2.boxCenterPoints, t2.halfBoxSizes);
+    t2.endPoints = tfjs_esm_exports.mul(t2.add, this.inputSizeTensor);
+    const res = tfjs_esm_exports.concat2d([t2.startPoints, t2.endPoints], 1);
+    Object.keys(t2).forEach((tensor6) => tfjs_esm_exports.dispose(t2[tensor6]));
+    return res;
+  }
+  normalizeLandmarks(rawPalmLandmarks, index2) {
+    const t2 = {};
+    t2.reshape = tfjs_esm_exports.reshape(rawPalmLandmarks, [-1, 7, 2]);
+    t2.div = tfjs_esm_exports.div(t2.reshape, this.inputSizeTensor);
+    t2.landmarks = tfjs_esm_exports.add(t2.div, this.anchors[index2] ? this.anchors[index2] : 0);
+    const res = tfjs_esm_exports.mul(t2.landmarks, this.inputSizeTensor);
+    Object.keys(t2).forEach((tensor6) => tfjs_esm_exports.dispose(t2[tensor6]));
+    return res;
+  }
+  async predict(input, config3) {
+    var _a;
+    const t2 = {};
+    t2.resize = tfjs_esm_exports.image.resizeBilinear(input, [this.inputSize, this.inputSize]);
+    t2.div = tfjs_esm_exports.div(t2.resize, constants.tf127);
+    t2.image = tfjs_esm_exports.sub(t2.div, constants.tf1);
+    t2.batched = this.model.execute(t2.image);
+    t2.predictions = tfjs_esm_exports.squeeze(t2.batched);
+    t2.slice = tfjs_esm_exports.slice(t2.predictions, [0, 0], [-1, 1]);
+    t2.sigmoid = tfjs_esm_exports.sigmoid(t2.slice);
+    t2.scores = tfjs_esm_exports.squeeze(t2.sigmoid);
+    const scores = await t2.scores.data();
+    t2.boxes = tfjs_esm_exports.slice(t2.predictions, [0, 1], [-1, 4]);
+    t2.norm = this.normalizeBoxes(t2.boxes);
+    t2.nms = await tfjs_esm_exports.image.nonMaxSuppressionAsync(t2.norm, t2.scores, 3 * (((_a = config3.hand) == null ? void 0 : _a.maxDetected) || 1), config3.hand.iouThreshold, config3.hand.minConfidence);
+    const nms = await t2.nms.array();
+    const hands = [];
+    for (const index2 of nms) {
+      const p = {};
+      p.box = tfjs_esm_exports.slice(t2.norm, [index2, 0], [1, -1]);
+      p.slice = tfjs_esm_exports.slice(t2.predictions, [index2, 5], [1, 14]);
+      p.norm = this.normalizeLandmarks(p.slice, index2);
+      p.palmLandmarks = tfjs_esm_exports.reshape(p.norm, [-1, 2]);
+      const box = await p.box.data();
+      const startPoint = box.slice(0, 2);
+      const endPoint = box.slice(2, 4);
+      const palmLandmarks = await p.palmLandmarks.array();
+      const hand3 = { startPoint, endPoint, palmLandmarks, confidence: scores[index2] };
+      const scaled = scaleBoxCoordinates2(hand3, [(input.shape[2] || 1) / this.inputSize, (input.shape[1] || 0) / this.inputSize]);
+      hands.push(scaled);
+      Object.keys(p).forEach((tensor6) => tfjs_esm_exports.dispose(p[tensor6]));
+    }
+    Object.keys(t2).forEach((tensor6) => tfjs_esm_exports.dispose(t2[tensor6]));
+    return hands;
+  }
+};
+
+// src/hand/handposepipeline.ts
+var palmBoxEnlargeFactor = 5;
+var handBoxEnlargeFactor = 1.65;
+var palmLandmarkIds = [0, 5, 9, 13, 17, 1, 2];
+var palmLandmarksPalmBase = 0;
+var palmLandmarksMiddleFingerBase = 2;
+var lastTime8 = 0;
+var HandPipeline = class {
+  constructor(handDetector, handPoseModel2) {
+    __publicField(this, "handDetector");
+    __publicField(this, "handPoseModel");
+    __publicField(this, "inputSize");
+    __publicField(this, "storedBoxes");
+    __publicField(this, "skipped");
+    __publicField(this, "detectedHands");
+    var _a, _b, _c;
+    this.handDetector = handDetector;
+    this.handPoseModel = handPoseModel2;
+    this.inputSize = ((_c = (_b = (_a = this.handPoseModel) == null ? void 0 : _a.inputs) == null ? void 0 : _b[0].shape) == null ? void 0 : _c[2]) || 0;
+    this.storedBoxes = [];
+    this.skipped = Number.MAX_SAFE_INTEGER;
+    this.detectedHands = 0;
+  }
+  calculateLandmarksBoundingBox(landmarks) {
+    const xs = landmarks.map((d) => d[0]);
+    const ys = landmarks.map((d) => d[1]);
+    const startPoint = [Math.min(...xs), Math.min(...ys)];
+    const endPoint = [Math.max(...xs), Math.max(...ys)];
+    return { startPoint, endPoint };
+  }
+  getBoxForPalmLandmarks(palmLandmarks, rotationMatrix) {
+    const rotatedPalmLandmarks = palmLandmarks.map((coord) => rotatePoint2([...coord, 1], rotationMatrix));
+    const boxAroundPalm = this.calculateLandmarksBoundingBox(rotatedPalmLandmarks);
+    return enlargeBox2(squarifyBox2(boxAroundPalm), palmBoxEnlargeFactor);
+  }
+  getBoxForHandLandmarks(landmarks) {
+    const boundingBox = this.calculateLandmarksBoundingBox(landmarks);
+    const boxAroundHand = enlargeBox2(squarifyBox2(boundingBox), handBoxEnlargeFactor);
+    boxAroundHand.palmLandmarks = [];
+    for (let i = 0; i < palmLandmarkIds.length; i++) {
+      boxAroundHand.palmLandmarks.push(landmarks[palmLandmarkIds[i]].slice(0, 2));
+    }
+    return boxAroundHand;
+  }
+  transformRawCoords(rawCoords, box2, angle, rotationMatrix) {
+    const boxSize = getBoxSize2(box2);
+    const scaleFactor = [boxSize[0] / this.inputSize, boxSize[1] / this.inputSize, (boxSize[0] + boxSize[1]) / this.inputSize / 2];
+    const coordsScaled = rawCoords.map((coord) => [
+      scaleFactor[0] * (coord[0] - this.inputSize / 2),
+      scaleFactor[1] * (coord[1] - this.inputSize / 2),
+      scaleFactor[2] * coord[2]
+    ]);
+    const coordsRotationMatrix = buildRotationMatrix2(angle, [0, 0]);
+    const coordsRotated = coordsScaled.map((coord) => {
+      const rotated = rotatePoint2(coord, coordsRotationMatrix);
+      return [...rotated, coord[2]];
+    });
+    const inverseRotationMatrix = invertTransformMatrix2(rotationMatrix);
+    const boxCenter = [...getBoxCenter2(box2), 1];
+    const originalBoxCenter = [
+      dot2(boxCenter, inverseRotationMatrix[0]),
+      dot2(boxCenter, inverseRotationMatrix[1])
+    ];
+    return coordsRotated.map((coord) => [
+      Math.trunc(coord[0] + originalBoxCenter[0]),
+      Math.trunc(coord[1] + originalBoxCenter[1]),
+      Math.trunc(coord[2])
+    ]);
+  }
+  async estimateHands(image27, config3) {
+    let useFreshBox = false;
+    let boxes;
+    const skipTime = (config3.hand.skipTime || 0) > now() - lastTime8;
+    const skipFrame = this.skipped < (config3.hand.skipFrames || 0);
+    if (config3.skipAllowed && skipTime && skipFrame) {
+      boxes = await this.handDetector.predict(image27, config3);
+      this.skipped = 0;
+    }
+    if (config3.skipAllowed)
+      this.skipped++;
+    if (boxes && boxes.length > 0 && (boxes.length !== this.detectedHands && this.detectedHands !== config3.hand.maxDetected || !config3.hand.landmarks)) {
+      this.detectedHands = 0;
+      this.storedBoxes = [...boxes];
+      if (this.storedBoxes.length > 0)
+        useFreshBox = true;
+    }
+    const hands = [];
+    for (let i = 0; i < this.storedBoxes.length; i++) {
+      const currentBox = this.storedBoxes[i];
+      if (!currentBox)
+        continue;
+      if (config3.hand.landmarks) {
+        const angle = config3.hand.rotation ? computeRotation2(currentBox.palmLandmarks[palmLandmarksPalmBase], currentBox.palmLandmarks[palmLandmarksMiddleFingerBase]) : 0;
+        const palmCenter = getBoxCenter2(currentBox);
+        const palmCenterNormalized = [palmCenter[0] / image27.shape[2], palmCenter[1] / image27.shape[1]];
+        const rotatedImage = config3.hand.rotation && env.kernels.includes("rotatewithoffset") ? tfjs_esm_exports.image.rotateWithOffset(image27, angle, 0, palmCenterNormalized) : image27.clone();
+        const rotationMatrix = buildRotationMatrix2(-angle, palmCenter);
+        const newBox = useFreshBox ? this.getBoxForPalmLandmarks(currentBox.palmLandmarks, rotationMatrix) : currentBox;
+        const croppedInput = cutBoxFromImageAndResize(newBox, rotatedImage, [this.inputSize, this.inputSize]);
+        const handImage = tfjs_esm_exports.div(croppedInput, constants.tf255);
+        tfjs_esm_exports.dispose(croppedInput);
+        tfjs_esm_exports.dispose(rotatedImage);
+        const [confidenceT, keypoints] = this.handPoseModel.execute(handImage);
+        lastTime8 = now();
+        tfjs_esm_exports.dispose(handImage);
+        const confidence = (await confidenceT.data())[0];
+        tfjs_esm_exports.dispose(confidenceT);
+        if (confidence >= config3.hand.minConfidence / 4) {
+          const keypointsReshaped = tfjs_esm_exports.reshape(keypoints, [-1, 3]);
+          const rawCoords = await keypointsReshaped.array();
+          tfjs_esm_exports.dispose(keypoints);
+          tfjs_esm_exports.dispose(keypointsReshaped);
+          const coords = this.transformRawCoords(rawCoords, newBox, angle, rotationMatrix);
+          const nextBoundingBox = this.getBoxForHandLandmarks(coords);
+          this.storedBoxes[i] = { ...nextBoundingBox, confidence };
+          const result = {
+            landmarks: coords,
+            confidence,
+            boxConfidence: currentBox.confidence,
+            fingerConfidence: confidence,
+            box: { topLeft: nextBoundingBox.startPoint, bottomRight: nextBoundingBox.endPoint }
+          };
+          hands.push(result);
+        } else {
+          this.storedBoxes[i] = null;
+        }
+        tfjs_esm_exports.dispose(keypoints);
+      } else {
+        const enlarged = enlargeBox2(squarifyBox2(currentBox), handBoxEnlargeFactor);
+        const result = {
+          confidence: currentBox.confidence,
+          boxConfidence: currentBox.confidence,
+          fingerConfidence: 0,
+          box: { topLeft: enlarged.startPoint, bottomRight: enlarged.endPoint },
+          landmarks: []
+        };
+        hands.push(result);
+      }
+    }
+    this.storedBoxes = this.storedBoxes.filter((a) => a !== null);
+    this.detectedHands = hands.length;
+    if (hands.length > config3.hand.maxDetected)
+      hands.length = config3.hand.maxDetected;
+    return hands;
+  }
+};
+
+// src/hand/fingerdef.ts
+var Finger = {
+  thumb: 0,
+  index: 1,
+  middle: 2,
+  ring: 3,
+  pinky: 4,
+  all: [0, 1, 2, 3, 4],
+  nameMapping: { 0: "thumb", 1: "index", 2: "middle", 3: "ring", 4: "pinky" },
+  pointsMapping: {
+    0: [[0, 1], [1, 2], [2, 3], [3, 4]],
+    1: [[0, 5], [5, 6], [6, 7], [7, 8]],
+    2: [[0, 9], [9, 10], [10, 11], [11, 12]],
+    3: [[0, 13], [13, 14], [14, 15], [15, 16]],
+    4: [[0, 17], [17, 18], [18, 19], [19, 20]]
+  },
+  getName: (value) => Finger.nameMapping[value],
+  getPoints: (value) => Finger.pointsMapping[value]
+};
+var FingerCurl = {
+  none: 0,
+  half: 1,
+  full: 2,
+  nameMapping: { 0: "none", 1: "half", 2: "full" },
+  getName: (value) => FingerCurl.nameMapping[value]
+};
+var FingerDirection = {
+  verticalUp: 0,
+  verticalDown: 1,
+  horizontalLeft: 2,
+  horizontalRight: 3,
+  diagonalUpRight: 4,
+  diagonalUpLeft: 5,
+  diagonalDownRight: 6,
+  diagonalDownLeft: 7,
+  nameMapping: { 0: "verticalUp", 1: "verticalDown", 2: "horizontalLeft", 3: "horizontalRight", 4: "diagonalUpRight", 5: "diagonalUpLeft", 6: "diagonalDownRight", 7: "diagonalDownLeft" },
+  getName: (value) => FingerDirection.nameMapping[value]
+};
+var FingerGesture = class {
+  constructor(name) {
+    __publicField(this, "name");
+    __publicField(this, "curls");
+    __publicField(this, "directions");
+    __publicField(this, "weights");
+    __publicField(this, "weightsRelative");
+    this.name = name;
+    this.curls = {};
+    this.directions = {};
+    this.weights = [1, 1, 1, 1, 1];
+    this.weightsRelative = [1, 1, 1, 1, 1];
+  }
+  curl(finger, curl, confidence) {
+    if (typeof this.curls[finger] === "undefined")
+      this.curls[finger] = [];
+    this.curls[finger].push([curl, confidence]);
+  }
+  direction(finger, position, confidence) {
+    if (!this.directions[finger])
+      this.directions[finger] = [];
+    this.directions[finger].push([position, confidence]);
+  }
+  weight(finger, weight) {
+    this.weights[finger] = weight;
+    const total = this.weights.reduce((a, b) => a + b, 0);
+    this.weightsRelative = this.weights.map((el) => el * 5 / total);
+  }
+  matchAgainst(detectedCurls, detectedDirections) {
+    let confidence = 0;
+    for (const fingerIdx in detectedCurls) {
+      const detectedCurl = detectedCurls[fingerIdx];
+      const expectedCurls = this.curls[fingerIdx];
+      if (typeof expectedCurls === "undefined") {
+        confidence += this.weightsRelative[fingerIdx];
+        continue;
+      }
+      for (const [expectedCurl, score] of expectedCurls) {
+        if (detectedCurl === expectedCurl) {
+          confidence += score * this.weightsRelative[fingerIdx];
+          break;
+        }
+      }
+    }
+    for (const fingerIdx in detectedDirections) {
+      const detectedDirection = detectedDirections[fingerIdx];
+      const expectedDirections = this.directions[fingerIdx];
+      if (typeof expectedDirections === "undefined") {
+        confidence += this.weightsRelative[fingerIdx];
+        continue;
+      }
+      for (const [expectedDirection, score] of expectedDirections) {
+        if (detectedDirection === expectedDirection) {
+          confidence += score * this.weightsRelative[fingerIdx];
+          break;
+        }
+      }
+    }
+    return confidence / 10;
+  }
+};
+
+// src/hand/fingergesture.ts
+var { thumb, index, middle, ring, pinky } = Finger;
+var { none, half, full } = FingerCurl;
+var { verticalUp, verticalDown, horizontalLeft, horizontalRight, diagonalUpRight, diagonalUpLeft, diagonalDownRight, diagonalDownLeft } = FingerDirection;
+var ThumbsUp = new FingerGesture("thumbs up");
+ThumbsUp.curl(thumb, none, 1);
+ThumbsUp.direction(thumb, verticalUp, 1);
+ThumbsUp.direction(thumb, diagonalUpLeft, 0.25);
+ThumbsUp.direction(thumb, diagonalUpRight, 0.25);
+for (const finger of [Finger.index, Finger.middle, Finger.ring, Finger.pinky]) {
+  ThumbsUp.curl(finger, full, 1);
+  ThumbsUp.direction(finger, horizontalLeft, 1);
+  ThumbsUp.direction(finger, horizontalRight, 1);
+}
+var Victory = new FingerGesture("victory");
+Victory.curl(thumb, half, 0.5);
+Victory.curl(thumb, none, 0.5);
+Victory.direction(thumb, verticalUp, 1);
+Victory.direction(thumb, diagonalUpLeft, 1);
+Victory.curl(index, none, 1);
+Victory.direction(index, verticalUp, 0.75);
+Victory.direction(index, diagonalUpLeft, 1);
+Victory.curl(middle, none, 1);
+Victory.direction(middle, verticalUp, 1);
+Victory.direction(middle, diagonalUpLeft, 0.75);
+Victory.curl(ring, full, 1);
+Victory.direction(ring, verticalUp, 0.2);
+Victory.direction(ring, diagonalUpLeft, 1);
+Victory.direction(ring, horizontalLeft, 0.2);
+Victory.curl(pinky, full, 1);
+Victory.direction(pinky, verticalUp, 0.2);
+Victory.direction(pinky, diagonalUpLeft, 1);
+Victory.direction(pinky, horizontalLeft, 0.2);
+Victory.weight(index, 2);
+Victory.weight(middle, 2);
+var Point = new FingerGesture("point");
+Point.curl(thumb, full, 1);
+Point.curl(index, none, 0.5);
+Point.curl(middle, full, 0.5);
+Point.curl(ring, full, 0.5);
+Point.curl(pinky, full, 0.5);
+Point.weight(index, 2);
+Point.weight(middle, 2);
+var MiddleFinger = new FingerGesture("middle finger");
+MiddleFinger.curl(thumb, none, 1);
+MiddleFinger.curl(index, full, 0.5);
+MiddleFinger.curl(middle, full, 0.5);
+MiddleFinger.curl(ring, full, 0.5);
+MiddleFinger.curl(pinky, full, 0.5);
+MiddleFinger.weight(index, 2);
+MiddleFinger.weight(middle, 2);
+var OpenPalm = new FingerGesture("open palm");
+OpenPalm.curl(thumb, none, 0.75);
+OpenPalm.curl(index, none, 0.75);
+OpenPalm.curl(middle, none, 0.75);
+OpenPalm.curl(ring, none, 0.75);
+OpenPalm.curl(pinky, none, 0.75);
+var fingergesture_default = [ThumbsUp, Victory, Point, MiddleFinger, OpenPalm];
+
+// src/hand/fingerpose.ts
+var minConfidence = 0.7;
+var options2 = {
+  HALF_CURL_START_LIMIT: 60,
+  NO_CURL_START_LIMIT: 130,
+  DISTANCE_VOTE_POWER: 1.1,
+  SINGLE_ANGLE_VOTE_POWER: 0.9,
+  TOTAL_ANGLE_VOTE_POWER: 1.6
+};
+function calculateSlope(point1x, point1y, point2x, point2y) {
+  const value = (point1y - point2y) / (point1x - point2x);
+  let slope = Math.atan(value) * 180 / Math.PI;
+  if (slope <= 0)
+    slope = -slope;
+  else if (slope > 0)
+    slope = 180 - slope;
+  return slope;
+}
+function getSlopes(point1, point2) {
+  if (!point1 || !point2)
+    return [0, 0];
+  const slopeXY = calculateSlope(point1[0], point1[1], point2[0], point2[1]);
+  if (point1.length === 2)
+    return slopeXY;
+  const slopeYZ = calculateSlope(point1[1], point1[2], point2[1], point2[2]);
+  return [slopeXY, slopeYZ];
+}
+function angleOrientationAt(angle, weightageAt = 1) {
+  let isVertical = 0;
+  let isDiagonal = 0;
+  let isHorizontal = 0;
+  if (angle >= 75 && angle <= 105)
+    isVertical = 1 * weightageAt;
+  else if (angle >= 25 && angle <= 155)
+    isDiagonal = 1 * weightageAt;
+  else
+    isHorizontal = 1 * weightageAt;
+  return [isVertical, isDiagonal, isHorizontal];
+}
+function estimateFingerCurl(startPoint, midPoint, endPoint) {
+  const start_mid_x_dist = startPoint[0] - midPoint[0];
+  const start_end_x_dist = startPoint[0] - endPoint[0];
+  const mid_end_x_dist = midPoint[0] - endPoint[0];
+  const start_mid_y_dist = startPoint[1] - midPoint[1];
+  const start_end_y_dist = startPoint[1] - endPoint[1];
+  const mid_end_y_dist = midPoint[1] - endPoint[1];
+  const start_mid_z_dist = startPoint[2] - midPoint[2];
+  const start_end_z_dist = startPoint[2] - endPoint[2];
+  const mid_end_z_dist = midPoint[2] - endPoint[2];
+  const start_mid_dist = Math.sqrt(start_mid_x_dist * start_mid_x_dist + start_mid_y_dist * start_mid_y_dist + start_mid_z_dist * start_mid_z_dist);
+  const start_end_dist = Math.sqrt(start_end_x_dist * start_end_x_dist + start_end_y_dist * start_end_y_dist + start_end_z_dist * start_end_z_dist);
+  const mid_end_dist = Math.sqrt(mid_end_x_dist * mid_end_x_dist + mid_end_y_dist * mid_end_y_dist + mid_end_z_dist * mid_end_z_dist);
+  let cos_in = (mid_end_dist * mid_end_dist + start_mid_dist * start_mid_dist - start_end_dist * start_end_dist) / (2 * mid_end_dist * start_mid_dist);
+  if (cos_in > 1)
+    cos_in = 1;
+  else if (cos_in < -1)
+    cos_in = -1;
+  let angleOfCurve = Math.acos(cos_in);
+  angleOfCurve = 57.2958 * angleOfCurve % 180;
+  let fingerCurl;
+  if (angleOfCurve > options2.NO_CURL_START_LIMIT)
+    fingerCurl = FingerCurl.none;
+  else if (angleOfCurve > options2.HALF_CURL_START_LIMIT)
+    fingerCurl = FingerCurl.half;
+  else
+    fingerCurl = FingerCurl.full;
+  return fingerCurl;
+}
+function estimateHorizontalDirection(start_end_x_dist, start_mid_x_dist, mid_end_x_dist, max_dist_x) {
+  let estimatedDirection;
+  if (max_dist_x === Math.abs(start_end_x_dist)) {
+    if (start_end_x_dist > 0)
+      estimatedDirection = FingerDirection.horizontalLeft;
+    else
+      estimatedDirection = FingerDirection.horizontalRight;
+  } else if (max_dist_x === Math.abs(start_mid_x_dist)) {
+    if (start_mid_x_dist > 0)
+      estimatedDirection = FingerDirection.horizontalLeft;
+    else
+      estimatedDirection = FingerDirection.horizontalRight;
+  } else {
+    if (mid_end_x_dist > 0)
+      estimatedDirection = FingerDirection.horizontalLeft;
+    else
+      estimatedDirection = FingerDirection.horizontalRight;
+  }
+  return estimatedDirection;
+}
+function estimateVerticalDirection(start_end_y_dist, start_mid_y_dist, mid_end_y_dist, max_dist_y) {
+  let estimatedDirection;
+  if (max_dist_y === Math.abs(start_end_y_dist)) {
+    if (start_end_y_dist < 0)
+      estimatedDirection = FingerDirection.verticalDown;
+    else
+      estimatedDirection = FingerDirection.verticalUp;
+  } else if (max_dist_y === Math.abs(start_mid_y_dist)) {
+    if (start_mid_y_dist < 0)
+      estimatedDirection = FingerDirection.verticalDown;
+    else
+      estimatedDirection = FingerDirection.verticalUp;
+  } else {
+    if (mid_end_y_dist < 0)
+      estimatedDirection = FingerDirection.verticalDown;
+    else
+      estimatedDirection = FingerDirection.verticalUp;
+  }
+  return estimatedDirection;
+}
+function estimateDiagonalDirection(start_end_y_dist, start_mid_y_dist, mid_end_y_dist, max_dist_y, start_end_x_dist, start_mid_x_dist, mid_end_x_dist, max_dist_x) {
+  let estimatedDirection;
+  const reqd_vertical_direction = estimateVerticalDirection(start_end_y_dist, start_mid_y_dist, mid_end_y_dist, max_dist_y);
+  const reqd_horizontal_direction = estimateHorizontalDirection(start_end_x_dist, start_mid_x_dist, mid_end_x_dist, max_dist_x);
+  if (reqd_vertical_direction === FingerDirection.verticalUp) {
+    if (reqd_horizontal_direction === FingerDirection.horizontalLeft)
+      estimatedDirection = FingerDirection.diagonalUpLeft;
+    else
+      estimatedDirection = FingerDirection.diagonalUpRight;
+  } else {
+    if (reqd_horizontal_direction === FingerDirection.horizontalLeft)
+      estimatedDirection = FingerDirection.diagonalDownLeft;
+    else
+      estimatedDirection = FingerDirection.diagonalDownRight;
+  }
+  return estimatedDirection;
+}
+function calculateFingerDirection(startPoint, midPoint, endPoint, fingerSlopes) {
+  const start_mid_x_dist = startPoint[0] - midPoint[0];
+  const start_end_x_dist = startPoint[0] - endPoint[0];
+  const mid_end_x_dist = midPoint[0] - endPoint[0];
+  const start_mid_y_dist = startPoint[1] - midPoint[1];
+  const start_end_y_dist = startPoint[1] - endPoint[1];
+  const mid_end_y_dist = midPoint[1] - endPoint[1];
+  const max_dist_x = Math.max(Math.abs(start_mid_x_dist), Math.abs(start_end_x_dist), Math.abs(mid_end_x_dist));
+  const max_dist_y = Math.max(Math.abs(start_mid_y_dist), Math.abs(start_end_y_dist), Math.abs(mid_end_y_dist));
+  let voteVertical = 0;
+  let voteDiagonal = 0;
+  let voteHorizontal = 0;
+  const start_end_x_y_dist_ratio = max_dist_y / (max_dist_x + 1e-5);
+  if (start_end_x_y_dist_ratio > 1.5)
+    voteVertical += options2.DISTANCE_VOTE_POWER;
+  else if (start_end_x_y_dist_ratio > 0.66)
+    voteDiagonal += options2.DISTANCE_VOTE_POWER;
+  else
+    voteHorizontal += options2.DISTANCE_VOTE_POWER;
+  const start_mid_dist = Math.sqrt(start_mid_x_dist * start_mid_x_dist + start_mid_y_dist * start_mid_y_dist);
+  const start_end_dist = Math.sqrt(start_end_x_dist * start_end_x_dist + start_end_y_dist * start_end_y_dist);
+  const mid_end_dist = Math.sqrt(mid_end_x_dist * mid_end_x_dist + mid_end_y_dist * mid_end_y_dist);
+  const max_dist = Math.max(start_mid_dist, start_end_dist, mid_end_dist);
+  let calc_start_point_x = startPoint[0];
+  let calc_start_point_y = startPoint[1];
+  let calc_end_point_x = endPoint[0];
+  let calc_end_point_y = endPoint[1];
+  if (max_dist === start_mid_dist) {
+    calc_end_point_x = endPoint[0];
+    calc_end_point_y = endPoint[1];
+  } else if (max_dist === mid_end_dist) {
+    calc_start_point_x = midPoint[0];
+    calc_start_point_y = midPoint[1];
+  }
+  const calcStartPoint = [calc_start_point_x, calc_start_point_y];
+  const calcEndPoint = [calc_end_point_x, calc_end_point_y];
+  const totalAngle = getSlopes(calcStartPoint, calcEndPoint);
+  const votes = angleOrientationAt(totalAngle, options2.TOTAL_ANGLE_VOTE_POWER);
+  voteVertical += votes[0];
+  voteDiagonal += votes[1];
+  voteHorizontal += votes[2];
+  for (const fingerSlope of fingerSlopes) {
+    const fingerVotes = angleOrientationAt(fingerSlope, options2.SINGLE_ANGLE_VOTE_POWER);
+    voteVertical += fingerVotes[0];
+    voteDiagonal += fingerVotes[1];
+    voteHorizontal += fingerVotes[2];
+  }
+  let estimatedDirection;
+  if (voteVertical === Math.max(voteVertical, voteDiagonal, voteHorizontal)) {
+    estimatedDirection = estimateVerticalDirection(start_end_y_dist, start_mid_y_dist, mid_end_y_dist, max_dist_y);
+  } else if (voteHorizontal === Math.max(voteDiagonal, voteHorizontal)) {
+    estimatedDirection = estimateHorizontalDirection(start_end_x_dist, start_mid_x_dist, mid_end_x_dist, max_dist_x);
+  } else {
+    estimatedDirection = estimateDiagonalDirection(start_end_y_dist, start_mid_y_dist, mid_end_y_dist, max_dist_y, start_end_x_dist, start_mid_x_dist, mid_end_x_dist, max_dist_x);
+  }
+  return estimatedDirection;
+}
+function estimate(landmarks) {
+  const slopesXY = [];
+  const slopesYZ = [];
+  const fingerCurls = [];
+  const fingerDirections = [];
+  if (!landmarks)
+    return { curls: fingerCurls, directions: fingerDirections };
+  for (const finger of Finger.all) {
+    const points = Finger.getPoints(finger);
+    const slopeAtXY = [];
+    const slopeAtYZ = [];
+    for (const point2 of points) {
+      const point1 = landmarks[point2[0]];
+      const point22 = landmarks[point2[1]];
+      const slopes = getSlopes(point1, point22);
+      const slopeXY = slopes[0];
+      const slopeYZ = slopes[1];
+      slopeAtXY.push(slopeXY);
+      slopeAtYZ.push(slopeYZ);
+    }
+    slopesXY.push(slopeAtXY);
+    slopesYZ.push(slopeAtYZ);
+  }
+  for (const finger of Finger.all) {
+    const pointIndexAt = finger === Finger.thumb ? 1 : 0;
+    const fingerPointsAt = Finger.getPoints(finger);
+    const startPoint = landmarks[fingerPointsAt[pointIndexAt][0]];
+    const midPoint = landmarks[fingerPointsAt[pointIndexAt + 1][1]];
+    const endPoint = landmarks[fingerPointsAt[3][1]];
+    const fingerCurled = estimateFingerCurl(startPoint, midPoint, endPoint);
+    const fingerPosition = calculateFingerDirection(startPoint, midPoint, endPoint, slopesXY[finger].slice(pointIndexAt));
+    fingerCurls[finger] = fingerCurled;
+    fingerDirections[finger] = fingerPosition;
+  }
+  return { curls: fingerCurls, directions: fingerDirections };
+}
+function analyze(keypoints) {
+  if (!keypoints || keypoints.length === 0)
+    return null;
+  const estimatorRes = estimate(keypoints);
+  const landmarks = {};
+  for (const fingerIdx of Finger.all) {
+    landmarks[Finger.getName(fingerIdx)] = {
+      curl: FingerCurl.getName(estimatorRes.curls[fingerIdx]),
+      direction: FingerDirection.getName(estimatorRes.directions[fingerIdx])
+    };
+  }
+  return landmarks;
+}
+function match(keypoints) {
+  const poses = [];
+  if (!keypoints || keypoints.length === 0)
+    return poses;
+  const estimatorRes = estimate(keypoints);
+  for (const gesture2 of fingergesture_default) {
+    const confidence = gesture2.matchAgainst(estimatorRes.curls, estimatorRes.directions);
+    if (confidence >= minConfidence)
+      poses.push({ name: gesture2.name, confidence });
+  }
+  return poses;
+}
+
+// src/hand/handpose.ts
+var meshAnnotations2 = {
+  thumb: [1, 2, 3, 4],
+  index: [5, 6, 7, 8],
+  middle: [9, 10, 11, 12],
+  ring: [13, 14, 15, 16],
+  pinky: [17, 18, 19, 20],
+  palm: [0]
+};
+var handDetectorModel;
+var handPoseModel;
+var handPipeline;
+async function predict9(input, config3) {
+  const predictions = await handPipeline.estimateHands(input, config3);
+  if (!predictions)
+    return [];
+  const hands = [];
+  for (let i = 0; i < predictions.length; i++) {
+    const annotations2 = {};
+    if (predictions[i].landmarks) {
+      for (const key of Object.keys(meshAnnotations2)) {
+        annotations2[key] = meshAnnotations2[key].map((index2) => predictions[i].landmarks[index2]);
+      }
+    }
+    const keypoints = predictions[i].landmarks;
+    let box = [Number.MAX_SAFE_INTEGER, Number.MAX_SAFE_INTEGER, 0, 0];
+    let boxRaw = [0, 0, 0, 0];
+    if (keypoints && keypoints.length > 0) {
+      for (const pt of keypoints) {
+        if (pt[0] < box[0])
+          box[0] = pt[0];
+        if (pt[1] < box[1])
+          box[1] = pt[1];
+        if (pt[0] > box[2])
+          box[2] = pt[0];
+        if (pt[1] > box[3])
+          box[3] = pt[1];
+      }
+      box[2] -= box[0];
+      box[3] -= box[1];
+      boxRaw = [box[0] / (input.shape[2] || 0), box[1] / (input.shape[1] || 0), box[2] / (input.shape[2] || 0), box[3] / (input.shape[1] || 0)];
+    } else {
+      box = predictions[i].box ? [
+        Math.trunc(Math.max(0, predictions[i].box.topLeft[0])),
+        Math.trunc(Math.max(0, predictions[i].box.topLeft[1])),
+        Math.trunc(Math.min(input.shape[2] || 0, predictions[i].box.bottomRight[0]) - Math.max(0, predictions[i].box.topLeft[0])),
+        Math.trunc(Math.min(input.shape[1] || 0, predictions[i].box.bottomRight[1]) - Math.max(0, predictions[i].box.topLeft[1]))
+      ] : [0, 0, 0, 0];
+      boxRaw = [
+        predictions[i].box.topLeft[0] / (input.shape[2] || 0),
+        predictions[i].box.topLeft[1] / (input.shape[1] || 0),
+        (predictions[i].box.bottomRight[0] - predictions[i].box.topLeft[0]) / (input.shape[2] || 0),
+        (predictions[i].box.bottomRight[1] - predictions[i].box.topLeft[1]) / (input.shape[1] || 0)
+      ];
+    }
+    const landmarks = analyze(keypoints);
+    hands.push({
+      id: i,
+      score: Math.round(100 * predictions[i].confidence) / 100,
+      boxScore: Math.round(100 * predictions[i].boxConfidence) / 100,
+      fingerScore: Math.round(100 * predictions[i].fingerConfidence) / 100,
+      label: "hand",
+      box,
+      boxRaw,
+      keypoints,
+      annotations: annotations2,
+      landmarks
+    });
+  }
+  return hands;
+}
+async function load10(config3) {
+  var _a, _b;
+  if (env.initial) {
+    handDetectorModel = null;
+    handPoseModel = null;
+  }
+  if (!handDetectorModel || !handPoseModel) {
+    [handDetectorModel, handPoseModel] = await Promise.all([
+      config3.hand.enabled ? loadModel((_a = config3.hand.detector) == null ? void 0 : _a.modelPath) : null,
+      config3.hand.landmarks ? loadModel((_b = config3.hand.skeleton) == null ? void 0 : _b.modelPath) : null
+    ]);
+  } else {
+    if (config3.debug)
+      log("cached model:", handDetectorModel["modelUrl"]);
+    if (config3.debug)
+      log("cached model:", handPoseModel["modelUrl"]);
+  }
+  const handDetector = handDetectorModel ? new HandDetector(handDetectorModel) : void 0;
+  if (handDetector && handPoseModel)
+    handPipeline = new HandPipeline(handDetector, handPoseModel);
+  return [handDetectorModel, handPoseModel];
+}
+
+// src/hand/handtrack.ts
+var models3 = [null, null];
+var modelOutputNodes = ["StatefulPartitionedCall/Postprocessor/Slice", "StatefulPartitionedCall/Postprocessor/ExpandDims_1"];
+var inputSize7 = [[0, 0], [0, 0]];
+var classes = ["hand", "fist", "pinch", "point", "face", "tip", "pinchtip"];
+var faceIndex = 4;
+var boxExpandFact = 1.6;
+var maxDetectorResolution = 512;
+var detectorExpandFact = 1.4;
+var skipped8 = Number.MAX_SAFE_INTEGER;
+var lastTime9 = 0;
+var outputSize = [0, 0];
+var cache4 = {
+  boxes: [],
+  hands: []
+};
+var fingerMap = {
+  thumb: [1, 2, 3, 4],
+  index: [5, 6, 7, 8],
+  middle: [9, 10, 11, 12],
+  ring: [13, 14, 15, 16],
+  pinky: [17, 18, 19, 20],
+  base: [0],
+  palm: [0, 17, 13, 9, 5, 1, 0]
+};
+async function loadDetect2(config3) {
+  var _a;
+  if (env.initial)
+    models3[0] = null;
+  if (!models3[0]) {
+    fakeOps(["tensorlistreserve", "enter", "tensorlistfromtensor", "merge", "loopcond", "switch", "exit", "tensorliststack", "nextiteration", "tensorlistsetitem", "tensorlistgetitem", "reciprocal", "shape", "split", "where"], config3);
+    models3[0] = await loadModel((_a = config3.hand.detector) == null ? void 0 : _a.modelPath);
+    const inputs = models3[0]["executor"] ? Object.values(models3[0].modelSignature["inputs"]) : void 0;
+    inputSize7[0][0] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[1].size) : 0;
+    inputSize7[0][1] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 0;
+  } else if (config3.debug)
+    log("cached model:", models3[0]["modelUrl"]);
+  return models3[0];
+}
+async function loadSkeleton(config3) {
+  var _a;
+  if (env.initial)
+    models3[1] = null;
+  if (!models3[1]) {
+    models3[1] = await loadModel((_a = config3.hand.skeleton) == null ? void 0 : _a.modelPath);
+    const inputs = models3[1]["executor"] ? Object.values(models3[1].modelSignature["inputs"]) : void 0;
+    inputSize7[1][0] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[1].size) : 0;
+    inputSize7[1][1] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 0;
+  } else if (config3.debug)
+    log("cached model:", models3[1]["modelUrl"]);
+  return models3[1];
+}
+async function detectHands(input, config3) {
+  const hands = [];
+  if (!input || !models3[0])
+    return hands;
+  const t2 = {};
+  const ratio2 = (input.shape[2] || 1) / (input.shape[1] || 1);
+  const height = Math.min(Math.round((input.shape[1] || 0) / 8) * 8, maxDetectorResolution);
+  const width = Math.round(height * ratio2 / 8) * 8;
+  t2.resize = tfjs_esm_exports.image.resizeBilinear(input, [height, width]);
+  t2.cast = tfjs_esm_exports.cast(t2.resize, "int32");
+  [t2.rawScores, t2.rawBoxes] = await models3[0].executeAsync(t2.cast, modelOutputNodes);
+  t2.boxes = tfjs_esm_exports.squeeze(t2.rawBoxes, [0, 2]);
+  t2.scores = tfjs_esm_exports.squeeze(t2.rawScores, [0]);
+  const classScores = tfjs_esm_exports.unstack(t2.scores, 1);
+  tfjs_esm_exports.dispose(classScores[faceIndex]);
+  classScores.splice(faceIndex, 1);
+  t2.filtered = tfjs_esm_exports.stack(classScores, 1);
+  tfjs_esm_exports.dispose(classScores);
+  t2.max = tfjs_esm_exports.max(t2.filtered, 1);
+  t2.argmax = tfjs_esm_exports.argMax(t2.filtered, 1);
+  let id = 0;
+  t2.nms = await tfjs_esm_exports.image.nonMaxSuppressionAsync(t2.boxes, t2.max, (config3.hand.maxDetected || 0) + 1, config3.hand.iouThreshold || 0, config3.hand.minConfidence || 1);
+  const nms = await t2.nms.data();
+  const scores = await t2.max.data();
+  const classNum = await t2.argmax.data();
+  for (const nmsIndex of Array.from(nms)) {
+    const boxSlice = tfjs_esm_exports.slice(t2.boxes, nmsIndex, 1);
+    const boxYX = await boxSlice.data();
+    tfjs_esm_exports.dispose(boxSlice);
+    const boxData = [boxYX[1], boxYX[0], boxYX[3] - boxYX[1], boxYX[2] - boxYX[0]];
+    const boxRaw = scale(boxData, detectorExpandFact);
+    const boxFull = [Math.trunc(boxData[0] * outputSize[0]), Math.trunc(boxData[1] * outputSize[1]), Math.trunc(boxData[2] * outputSize[0]), Math.trunc(boxData[3] * outputSize[1])];
+    const score = scores[nmsIndex];
+    const label = classes[classNum[nmsIndex]];
+    const hand3 = { id: id++, score, box: boxFull, boxRaw, label };
+    hands.push(hand3);
+  }
+  Object.keys(t2).forEach((tensor6) => tfjs_esm_exports.dispose(t2[tensor6]));
+  hands.sort((a, b) => b.score - a.score);
+  if (hands.length > (config3.hand.maxDetected || 1))
+    hands.length = config3.hand.maxDetected || 1;
+  return hands;
+}
+async function detectFingers(input, h, config3) {
+  const hand3 = {
+    id: h.id,
+    score: Math.round(100 * h.score) / 100,
+    boxScore: Math.round(100 * h.score) / 100,
+    fingerScore: 0,
+    box: h.box,
+    boxRaw: h.boxRaw,
+    label: h.label,
+    keypoints: [],
+    landmarks: {},
+    annotations: {}
+  };
+  if (input && models3[1] && config3.hand.landmarks && h.score > (config3.hand.minConfidence || 0)) {
+    const t2 = {};
+    const boxCrop = [h.boxRaw[1], h.boxRaw[0], h.boxRaw[3] + h.boxRaw[1], h.boxRaw[2] + h.boxRaw[0]];
+    t2.crop = tfjs_esm_exports.image.cropAndResize(input, [boxCrop], [0], [inputSize7[1][0], inputSize7[1][1]], "bilinear");
+    t2.div = tfjs_esm_exports.div(t2.crop, constants.tf255);
+    [t2.score, t2.keypoints] = models3[1].execute(t2.div, ["Identity_1", "Identity"]);
+    const rawScore = (await t2.score.data())[0];
+    const score = (100 - Math.trunc(100 / (1 + Math.exp(rawScore)))) / 100;
+    if (score >= (config3.hand.minConfidence || 0)) {
+      hand3.fingerScore = score;
+      t2.reshaped = tfjs_esm_exports.reshape(t2.keypoints, [-1, 3]);
+      const coordsData = await t2.reshaped.array();
+      const coordsRaw = coordsData.map((kpt4) => [kpt4[0] / inputSize7[1][1], kpt4[1] / inputSize7[1][0], kpt4[2] || 0]);
+      const coordsNorm = coordsRaw.map((kpt4) => [kpt4[0] * h.boxRaw[2], kpt4[1] * h.boxRaw[3], kpt4[2] || 0]);
+      hand3.keypoints = coordsNorm.map((kpt4) => [outputSize[0] * (kpt4[0] + h.boxRaw[0]), outputSize[1] * (kpt4[1] + h.boxRaw[1]), kpt4[2] || 0]);
+      hand3.landmarks = analyze(hand3.keypoints);
+      for (const key of Object.keys(fingerMap)) {
+        hand3.annotations[key] = fingerMap[key].map((index2) => hand3.landmarks && hand3.keypoints[index2] ? hand3.keypoints[index2] : null);
+      }
+    }
+    Object.keys(t2).forEach((tensor6) => tfjs_esm_exports.dispose(t2[tensor6]));
+  }
+  return hand3;
+}
+async function predict10(input, config3) {
+  var _a, _b;
+  if (!((_a = models3[0]) == null ? void 0 : _a["executor"]) || !((_b = models3[1]) == null ? void 0 : _b["executor"]) || !models3[0].inputs[0].shape || !models3[1].inputs[0].shape)
+    return [];
+  outputSize = [input.shape[2] || 0, input.shape[1] || 0];
+  skipped8++;
+  const skipTime = (config3.hand.skipTime || 0) > now() - lastTime9;
+  const skipFrame = skipped8 < (config3.hand.skipFrames || 0);
+  if (config3.skipAllowed && skipTime && skipFrame) {
+    return cache4.hands;
+  }
+  return new Promise(async (resolve) => {
+    const skipTimeExtended = 3 * (config3.hand.skipTime || 0) > now() - lastTime9;
+    const skipFrameExtended = skipped8 < 3 * (config3.hand.skipFrames || 0);
+    if (config3.skipAllowed && cache4.hands.length === config3.hand.maxDetected) {
+      cache4.hands = await Promise.all(cache4.boxes.map((handBox) => detectFingers(input, handBox, config3)));
+    } else if (config3.skipAllowed && skipTimeExtended && skipFrameExtended && cache4.hands.length > 0) {
+      cache4.hands = await Promise.all(cache4.boxes.map((handBox) => detectFingers(input, handBox, config3)));
+    } else {
+      cache4.boxes = await detectHands(input, config3);
+      lastTime9 = now();
+      cache4.hands = await Promise.all(cache4.boxes.map((handBox) => detectFingers(input, handBox, config3)));
+      skipped8 = 0;
+    }
+    const oldCache = [...cache4.boxes];
+    cache4.boxes.length = 0;
+    if (config3.cacheSensitivity > 0) {
+      for (let i = 0; i < cache4.hands.length; i++) {
+        const boxKpt = square(cache4.hands[i].keypoints, outputSize);
+        if (boxKpt.box[2] / (input.shape[2] || 1) > 0.05 && boxKpt.box[3] / (input.shape[1] || 1) > 0.05 && cache4.hands[i].fingerScore && cache4.hands[i].fingerScore > (config3.hand.minConfidence || 0)) {
+          const boxScale = scale(boxKpt.box, boxExpandFact);
+          const boxScaleRaw = scale(boxKpt.boxRaw, boxExpandFact);
+          cache4.boxes.push({ ...oldCache[i], box: boxScale, boxRaw: boxScaleRaw });
+        }
+      }
+    }
+    for (let i = 0; i < cache4.hands.length; i++) {
+      const bbox = calc(cache4.hands[i].keypoints, outputSize);
+      cache4.hands[i].box = bbox.box;
+      cache4.hands[i].boxRaw = bbox.boxRaw;
+    }
+    resolve(cache4.hands);
+  });
+}
+
+// src/face/insightface.ts
+var model10;
+var last6 = [];
+var lastCount5 = 0;
+var lastTime10 = 0;
+var skipped9 = Number.MAX_SAFE_INTEGER;
+async function load11(config3) {
+  if (env.initial)
+    model10 = null;
+  if (!model10)
+    model10 = await loadModel(config3.face["insightface"].modelPath);
+  else if (config3.debug)
+    log("cached model:", model10["modelUrl"]);
+  return model10;
+}
+async function predict11(input, config3, idx, count2) {
+  var _a, _b;
+  if (!(model10 == null ? void 0 : model10["executor"]))
+    return [];
+  const skipFrame = skipped9 < (((_a = config3.face["insightface"]) == null ? void 0 : _a.skipFrames) || 0);
+  const skipTime = (((_b = config3.face["insightface"]) == null ? void 0 : _b.skipTime) || 0) > now() - lastTime10;
+  if (config3.skipAllowed && skipTime && skipFrame && lastCount5 === count2 && last6[idx]) {
+    skipped9++;
+    return last6[idx];
+  }
+  return new Promise(async (resolve) => {
+    var _a2;
+    let data = [];
+    if (((_a2 = config3.face["insightface"]) == null ? void 0 : _a2.enabled) && (model10 == null ? void 0 : model10.inputs[0].shape)) {
+      const t2 = {};
+      t2.crop = tfjs_esm_exports.image.resizeBilinear(input, [model10.inputs[0].shape[2], model10.inputs[0].shape[1]], false);
+      t2.data = model10.execute(t2.crop);
+      const output = await t2.data.data();
+      data = Array.from(output);
+      Object.keys(t2).forEach((tensor6) => tfjs_esm_exports.dispose(t2[tensor6]));
+    }
+    last6[idx] = data;
+    lastCount5 = count2;
+    lastTime10 = now();
+    resolve(data);
+  });
+}
+
+// src/face/liveness.ts
+var model11;
+var cached2 = [];
+var skipped10 = Number.MAX_SAFE_INTEGER;
+var lastCount6 = 0;
+var lastTime11 = 0;
+async function load12(config3) {
+  var _a;
+  if (env.initial)
+    model11 = null;
+  if (!model11)
+    model11 = await loadModel((_a = config3.face.liveness) == null ? void 0 : _a.modelPath);
+  else if (config3.debug)
+    log("cached model:", model11["modelUrl"]);
+  return model11;
+}
+async function predict12(image27, config3, idx, count2) {
+  var _a, _b;
+  if (!(model11 == null ? void 0 : model11["executor"]))
+    return 0;
+  const skipTime = (((_a = config3.face.liveness) == null ? void 0 : _a.skipTime) || 0) > now() - lastTime11;
+  const skipFrame = skipped10 < (((_b = config3.face.liveness) == null ? void 0 : _b.skipFrames) || 0);
+  if (config3.skipAllowed && skipTime && skipFrame && lastCount6 === count2 && cached2[idx]) {
+    skipped10++;
+    return cached2[idx];
+  }
+  skipped10 = 0;
+  return new Promise(async (resolve) => {
+    const resize = tfjs_esm_exports.image.resizeBilinear(image27, [(model11 == null ? void 0 : model11.inputs[0].shape) ? model11.inputs[0].shape[2] : 0, (model11 == null ? void 0 : model11.inputs[0].shape) ? model11.inputs[0].shape[1] : 0], false);
+    const res = model11 == null ? void 0 : model11.execute(resize);
+    const num = (await res.data())[0];
+    cached2[idx] = Math.round(100 * num) / 100;
+    lastCount6 = count2;
+    lastTime11 = now();
+    tfjs_esm_exports.dispose([resize, res]);
+    resolve(cached2[idx]);
+  });
+}
+
+// src/segmentation/meet.ts
+var model12;
+async function load13(config3) {
+  if (!model12 || env.initial)
+    model12 = await loadModel(config3.segmentation.modelPath);
+  else if (config3.debug)
+    log("cached model:", model12["modelUrl"]);
+  return model12;
+}
+async function predict13(input, config3) {
+  var _a;
+  if (!model12)
+    model12 = await load13(config3);
+  if (!(model12 == null ? void 0 : model12["executor"]) || !((_a = model12 == null ? void 0 : model12.inputs) == null ? void 0 : _a[0].shape))
+    return null;
+  const t2 = {};
+  t2.resize = tfjs_esm_exports.image.resizeBilinear(input, [model12.inputs[0].shape ? model12.inputs[0].shape[1] : 0, model12.inputs[0].shape ? model12.inputs[0].shape[2] : 0], false);
+  t2.norm = tfjs_esm_exports.div(t2.resize, constants.tf255);
+  t2.res = model12.execute(t2.norm);
+  t2.squeeze = tfjs_esm_exports.squeeze(t2.res, 0);
+  [t2.bgRaw, t2.fgRaw] = tfjs_esm_exports.unstack(t2.squeeze, 2);
+  t2.fg = tfjs_esm_exports.softmax(t2.fgRaw);
+  t2.mul = tfjs_esm_exports.mul(t2.fg, constants.tf255);
+  t2.expand = tfjs_esm_exports.expandDims(t2.mul, 2);
+  t2.output = tfjs_esm_exports.image.resizeBilinear(t2.expand, [input.shape[1], input.shape[2]]);
+  let rgba;
+  switch (config3.segmentation.mode || "default") {
+    case "default":
+      t2.input = tfjs_esm_exports.squeeze(input);
+      t2.concat = tfjs_esm_exports.concat([t2.input, t2.output], -1);
+      rgba = tfjs_esm_exports.cast(t2.concat, "int32");
+      break;
+    case "alpha":
+      rgba = tfjs_esm_exports.cast(t2.output, "int32");
+      break;
+    default:
+      rgba = tfjs_esm_exports.tensor(0);
+  }
+  Object.keys(t2).forEach((tensor6) => tfjs_esm_exports.dispose(t2[tensor6]));
+  return rgba;
+}
+
+// src/face/mobilefacenet.ts
+var model13;
+var last7 = [];
+var lastCount7 = 0;
+var lastTime12 = 0;
+var skipped11 = Number.MAX_SAFE_INTEGER;
+async function load14(config3) {
+  var _a;
+  if (env.initial)
+    model13 = null;
+  if (!model13)
+    model13 = await loadModel((_a = config3.face["mobilefacenet"]) == null ? void 0 : _a.modelPath);
+  else if (config3.debug)
+    log("cached model:", model13["modelUrl"]);
+  return model13;
+}
+async function predict14(input, config3, idx, count2) {
+  var _a, _b;
+  if (!(model13 == null ? void 0 : model13["executor"]))
+    return [];
+  const skipFrame = skipped11 < (((_a = config3.face["mobilefacenet"]) == null ? void 0 : _a.skipFrames) || 0);
+  const skipTime = (((_b = config3.face["mobilefacenet"]) == null ? void 0 : _b.skipTime) || 0) > now() - lastTime12;
+  if (config3.skipAllowed && skipTime && skipFrame && lastCount7 === count2 && last7[idx]) {
+    skipped11++;
+    return last7[idx];
+  }
+  return new Promise(async (resolve) => {
+    var _a2;
+    let data = [];
+    if (((_a2 = config3.face["mobilefacenet"]) == null ? void 0 : _a2.enabled) && (model13 == null ? void 0 : model13.inputs[0].shape)) {
+      const t2 = {};
+      t2.crop = tfjs_esm_exports.image.resizeBilinear(input, [model13.inputs[0].shape[2], model13.inputs[0].shape[1]], false);
+      t2.data = model13.execute(t2.crop);
+      const output = await t2.data.data();
+      data = Array.from(output);
+      Object.keys(t2).forEach((tensor6) => tfjs_esm_exports.dispose(t2[tensor6]));
+    }
+    last7[idx] = data;
+    lastCount7 = count2;
+    lastTime12 = now();
+    resolve(data);
+  });
+}
+
+// src/body/movenetcoords.ts
+var movenetcoords_exports = {};
+__export(movenetcoords_exports, {
+  connected: () => connected3,
+  horizontal: () => horizontal,
+  kpt: () => kpt3,
+  relative: () => relative,
+  vertical: () => vertical
+});
+var kpt3 = [
+  "nose",
+  "leftEye",
+  "rightEye",
+  "leftEar",
+  "rightEar",
+  "leftShoulder",
+  "rightShoulder",
+  "leftElbow",
+  "rightElbow",
+  "leftWrist",
+  "rightWrist",
+  "leftHip",
+  "rightHip",
+  "leftKnee",
+  "rightKnee",
+  "leftAnkle",
+  "rightAnkle"
+];
+var horizontal = [
+  ["leftEye", "rightEye"],
+  ["leftEar", "rightEar"],
+  ["leftShoulder", "rightShoulder"],
+  ["leftElbow", "rightElbow"],
+  ["leftWrist", "rightWrist"],
+  ["leftHip", "rightHip"],
+  ["leftKnee", "rightKnee"],
+  ["leftAnkle", "rightAnkle"]
+];
+var vertical = [
+  ["leftKnee", "leftShoulder"],
+  ["rightKnee", "rightShoulder"],
+  ["leftAnkle", "leftKnee"],
+  ["rightAnkle", "rightKnee"]
+];
+var relative = [
+  [["leftHip", "rightHip"], ["leftShoulder", "rightShoulder"]],
+  [["leftElbow", "rightElbow"], ["leftShoulder", "rightShoulder"]]
+];
+var connected3 = {
+  leftLeg: ["leftHip", "leftKnee", "leftAnkle"],
+  rightLeg: ["rightHip", "rightKnee", "rightAnkle"],
+  torso: ["leftShoulder", "rightShoulder", "rightHip", "leftHip", "leftShoulder"],
+  leftArm: ["leftShoulder", "leftElbow", "leftWrist"],
+  rightArm: ["rightShoulder", "rightElbow", "rightWrist"],
+  head: []
+};
+
+// src/body/movenetfix.ts
+var maxJitter = 5e-3;
+var cache5 = {
+  keypoints: [],
+  padding: [[0, 0], [0, 0], [0, 0], [0, 0]]
+};
+function bodyParts(body4) {
+  for (const pair of horizontal) {
+    const left = body4.keypoints.findIndex((kp) => kp.part === pair[0]);
+    const right = body4.keypoints.findIndex((kp) => kp.part === pair[1]);
+    if (body4.keypoints[left] && body4.keypoints[right]) {
+      if (body4.keypoints[left].position[0] < body4.keypoints[right].position[0]) {
+        const tmp = body4.keypoints[left];
+        body4.keypoints[left] = body4.keypoints[right];
+        body4.keypoints[right] = tmp;
+      }
+    }
+  }
+  for (const pair of vertical) {
+    const lower = body4.keypoints.findIndex((kp) => kp && kp.part === pair[0]);
+    const higher = body4.keypoints.findIndex((kp) => kp && kp.part === pair[1]);
+    if (body4.keypoints[lower] && body4.keypoints[higher]) {
+      if (body4.keypoints[lower].position[1] < body4.keypoints[higher].position[1]) {
+        body4.keypoints.splice(lower, 1);
+      }
+    }
+  }
+  for (const [pair, compare2] of relative) {
+    const left = body4.keypoints.findIndex((kp) => kp && kp.part === pair[0]);
+    const right = body4.keypoints.findIndex((kp) => kp && kp.part === pair[1]);
+    const leftTo = body4.keypoints.findIndex((kp) => kp && kp.part === compare2[0]);
+    const rightTo = body4.keypoints.findIndex((kp) => kp && kp.part === compare2[1]);
+    if (!body4.keypoints[leftTo] || !body4.keypoints[rightTo])
+      continue;
+    const distanceLeft = body4.keypoints[left] ? [
+      Math.abs(body4.keypoints[leftTo].position[0] - body4.keypoints[left].position[0]),
+      Math.abs(body4.keypoints[rightTo].position[0] - body4.keypoints[left].position[0])
+    ] : [0, 0];
+    const distanceRight = body4.keypoints[right] ? [
+      Math.abs(body4.keypoints[rightTo].position[0] - body4.keypoints[right].position[0]),
+      Math.abs(body4.keypoints[leftTo].position[0] - body4.keypoints[right].position[0])
+    ] : [0, 0];
+    if (distanceLeft[0] > distanceLeft[1] || distanceRight[0] > distanceRight[1]) {
+      const tmp = body4.keypoints[left];
+      body4.keypoints[left] = body4.keypoints[right];
+      body4.keypoints[right] = tmp;
+    }
+  }
+}
+function jitter(keypoints) {
+  for (let i = 0; i < keypoints.length; i++) {
+    if (keypoints[i] && cache5.keypoints[i]) {
+      const diff = [Math.abs(keypoints[i].positionRaw[0] - cache5.keypoints[i].positionRaw[0]), Math.abs(keypoints[i].positionRaw[1] - cache5.keypoints[i].positionRaw[1])];
+      if (diff[0] < maxJitter && diff[1] < maxJitter) {
+        keypoints[i] = cache5.keypoints[i];
+      } else {
+        cache5.keypoints[i] = keypoints[i];
+      }
+    } else {
+      cache5.keypoints[i] = keypoints[i];
+    }
+  }
+  return keypoints;
+}
+function padInput(input, inputSize10) {
+  var _a, _b;
+  const t2 = {};
+  if (!((_a = input == null ? void 0 : input.shape) == null ? void 0 : _a[1]) || !((_b = input == null ? void 0 : input.shape) == null ? void 0 : _b[2]))
+    return input;
+  cache5.padding = [
+    [0, 0],
+    [input.shape[2] > input.shape[1] ? Math.trunc((input.shape[2] - input.shape[1]) / 2) : 0, input.shape[2] > input.shape[1] ? Math.trunc((input.shape[2] - input.shape[1]) / 2) : 0],
+    [input.shape[1] > input.shape[2] ? Math.trunc((input.shape[1] - input.shape[2]) / 2) : 0, input.shape[1] > input.shape[2] ? Math.trunc((input.shape[1] - input.shape[2]) / 2) : 0],
+    [0, 0]
+  ];
+  t2.pad = tfjs_esm_exports.pad(input, cache5.padding);
+  t2.resize = tfjs_esm_exports.image.resizeBilinear(t2.pad, [inputSize10, inputSize10]);
+  const final = tfjs_esm_exports.cast(t2.resize, "int32");
+  Object.keys(t2).forEach((tensor6) => tfjs_esm_exports.dispose(t2[tensor6]));
+  return final;
+}
+function rescaleBody(body4, outputSize2) {
+  body4.keypoints = body4.keypoints.filter((kpt4) => kpt4 == null ? void 0 : kpt4.position);
+  for (const kpt4 of body4.keypoints) {
+    kpt4.position = [
+      kpt4.position[0] * (outputSize2[0] + cache5.padding[2][0] + cache5.padding[2][1]) / outputSize2[0] - cache5.padding[2][0],
+      kpt4.position[1] * (outputSize2[1] + cache5.padding[1][0] + cache5.padding[1][1]) / outputSize2[1] - cache5.padding[1][0]
+    ];
+    kpt4.positionRaw = [
+      kpt4.position[0] / outputSize2[0],
+      kpt4.position[1] / outputSize2[1]
+    ];
+  }
+  const rescaledBoxes = calc(body4.keypoints.map((pt) => pt.position), outputSize2);
+  body4.box = rescaledBoxes.box;
+  body4.boxRaw = rescaledBoxes.boxRaw;
+  return body4;
+}
+
+// src/body/movenet.ts
+var model14;
+var inputSize8 = 0;
+var skipped12 = Number.MAX_SAFE_INTEGER;
+var cache6 = {
+  boxes: [],
+  bodies: [],
+  last: 0
+};
+async function load15(config3) {
+  var _a;
+  if (env.initial)
+    model14 = null;
+  if (!model14) {
+    fakeOps(["size"], config3);
+    model14 = await loadModel(config3.body.modelPath);
+  } else if (config3.debug)
+    log("cached model:", model14["modelUrl"]);
+  inputSize8 = (model14 == null ? void 0 : model14["executor"]) && ((_a = model14 == null ? void 0 : model14.inputs) == null ? void 0 : _a[0].shape) ? model14.inputs[0].shape[2] : 0;
+  if (inputSize8 < 64)
+    inputSize8 = 256;
+  return model14;
+}
+function parseSinglePose(res, config3, image27) {
+  const kpt4 = res[0][0];
+  const keypoints = [];
+  let score = 0;
+  for (let id = 0; id < kpt4.length; id++) {
+    score = kpt4[id][2];
+    if (score > config3.body.minConfidence) {
+      const positionRaw = [kpt4[id][1], kpt4[id][0]];
+      keypoints.push({
+        score: Math.round(100 * score) / 100,
+        part: kpt3[id],
+        positionRaw,
+        position: [
+          Math.round((image27.shape[2] || 0) * positionRaw[0]),
+          Math.round((image27.shape[1] || 0) * positionRaw[1])
+        ]
+      });
+    }
+  }
+  score = keypoints.reduce((prev, curr) => curr.score > prev ? curr.score : prev, 0);
+  const bodies = [];
+  const newBox = calc(keypoints.map((pt) => pt.position), [image27.shape[2], image27.shape[1]]);
+  const annotations2 = {};
+  for (const [name, indexes] of Object.entries(connected3)) {
+    const pt = [];
+    for (let i = 0; i < indexes.length - 1; i++) {
+      const pt0 = keypoints.find((kp) => kp.part === indexes[i]);
+      const pt1 = keypoints.find((kp) => kp.part === indexes[i + 1]);
+      if (pt0 && pt1 && pt0.score > (config3.body.minConfidence || 0) && pt1.score > (config3.body.minConfidence || 0))
+        pt.push([pt0.position, pt1.position]);
+    }
+    annotations2[name] = pt;
+  }
+  const body4 = { id: 0, score, box: newBox.box, boxRaw: newBox.boxRaw, keypoints, annotations: annotations2 };
+  bodyParts(body4);
+  bodies.push(body4);
+  return bodies;
+}
+function parseMultiPose(res, config3, image27) {
+  const bodies = [];
+  for (let id = 0; id < res[0].length; id++) {
+    const kpt4 = res[0][id];
+    const totalScore = Math.round(100 * kpt4[51 + 4]) / 100;
+    if (totalScore > config3.body.minConfidence) {
+      const keypoints = [];
+      for (let i = 0; i < 17; i++) {
+        const score = kpt4[3 * i + 2];
+        if (score > config3.body.minConfidence) {
+          const positionRaw = [kpt4[3 * i + 1], kpt4[3 * i + 0]];
+          keypoints.push({
+            part: kpt3[i],
+            score: Math.round(100 * score) / 100,
+            positionRaw,
+            position: [Math.round((image27.shape[2] || 0) * positionRaw[0]), Math.round((image27.shape[1] || 0) * positionRaw[1])]
+          });
+        }
+      }
+      const newBox = calc(keypoints.map((pt) => pt.position), [image27.shape[2], image27.shape[1]]);
+      const annotations2 = {};
+      for (const [name, indexes] of Object.entries(connected3)) {
+        const pt = [];
+        for (let i = 0; i < indexes.length - 1; i++) {
+          const pt0 = keypoints.find((kp) => kp.part === indexes[i]);
+          const pt1 = keypoints.find((kp) => kp.part === indexes[i + 1]);
+          if (pt0 && pt1 && pt0.score > (config3.body.minConfidence || 0) && pt1.score > (config3.body.minConfidence || 0))
+            pt.push([pt0.position, pt1.position]);
+        }
+        annotations2[name] = pt;
+      }
+      const body4 = { id, score: totalScore, box: newBox.box, boxRaw: newBox.boxRaw, keypoints: [...keypoints], annotations: annotations2 };
+      bodyParts(body4);
+      bodies.push(body4);
+    }
+  }
+  bodies.sort((a, b) => b.score - a.score);
+  if (bodies.length > config3.body.maxDetected)
+    bodies.length = config3.body.maxDetected;
+  return bodies;
+}
+async function predict15(input, config3) {
+  var _a;
+  if (!(model14 == null ? void 0 : model14["executor"]) || !((_a = model14 == null ? void 0 : model14.inputs) == null ? void 0 : _a[0].shape))
+    return [];
+  if (!config3.skipAllowed)
+    cache6.boxes.length = 0;
+  skipped12++;
+  const skipTime = (config3.body.skipTime || 0) > now() - cache6.last;
+  const skipFrame = skipped12 < (config3.body.skipFrames || 0);
+  if (config3.skipAllowed && skipTime && skipFrame) {
+    return cache6.bodies;
+  }
+  return new Promise(async (resolve) => {
+    const t2 = {};
+    skipped12 = 0;
+    t2.input = padInput(input, inputSize8);
+    t2.res = model14 == null ? void 0 : model14.execute(t2.input);
+    cache6.last = now();
+    const res = await t2.res.array();
+    cache6.bodies = t2.res.shape[2] === 17 ? parseSinglePose(res, config3, input) : parseMultiPose(res, config3, input);
+    for (const body4 of cache6.bodies) {
+      rescaleBody(body4, [input.shape[2] || 1, input.shape[1] || 1]);
+      jitter(body4.keypoints);
+    }
+    Object.keys(t2).forEach((tensor6) => tfjs_esm_exports.dispose(t2[tensor6]));
+    resolve(cache6.bodies);
+  });
+}
+
+// src/object/nanodet.ts
+var model15;
+var last8 = [];
+var lastTime13 = 0;
+var skipped13 = Number.MAX_SAFE_INTEGER;
+var inputSize9 = 0;
+var scaleBox = 2.5;
+async function load16(config3) {
+  if (!model15 || env.initial) {
+    model15 = await loadModel(config3.object.modelPath);
+    const inputs = (model15 == null ? void 0 : model15["executor"]) ? Object.values(model15.modelSignature["inputs"]) : void 0;
+    inputSize9 = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 416;
+  } else if (config3.debug)
+    log("cached model:", model15["modelUrl"]);
+  return model15;
+}
+async function process4(res, outputShape, config3) {
+  let id = 0;
+  let results = [];
+  const size2 = inputSize9;
+  for (const strideSize of [1, 2, 4]) {
+    const baseSize = strideSize * 13;
+    const scoresT = tfjs_esm_exports.squeeze(res.find((a) => a.shape[1] === baseSize ** 2 && (a.shape[2] || 0) === labels.length));
+    const scores = await scoresT.array();
+    const featuresT = tfjs_esm_exports.squeeze(res.find((a) => a.shape[1] === baseSize ** 2 && (a.shape[2] || 0) < labels.length));
+    const boxesMaxT = featuresT.reshape([-1, 4, featuresT.shape[1] / 4]);
+    const boxIdxT = boxesMaxT.argMax(2);
+    const boxIdx = await boxIdxT.array();
+    for (let i = 0; i < scoresT.shape[0]; i++) {
+      for (let j = 0; j < scoresT.shape[1]; j++) {
+        const score = scores[i][j];
+        if (score > (config3.object.minConfidence || 0) && j !== 61) {
+          const cx = (0.5 + Math.trunc(i % baseSize)) / baseSize;
+          const cy = (0.5 + Math.trunc(i / baseSize)) / baseSize;
+          const boxOffset = boxIdx[i].map((a) => a * (baseSize / strideSize / size2));
+          const [x, y] = [
+            cx - scaleBox / strideSize * boxOffset[0],
+            cy - scaleBox / strideSize * boxOffset[1]
+          ];
+          const [w, h] = [
+            cx + scaleBox / strideSize * boxOffset[2] - x,
+            cy + scaleBox / strideSize * boxOffset[3] - y
+          ];
+          let boxRaw = [x, y, w, h];
+          boxRaw = boxRaw.map((a) => Math.max(0, Math.min(a, 1)));
+          const box = [
+            boxRaw[0] * outputShape[0],
+            boxRaw[1] * outputShape[1],
+            boxRaw[2] * outputShape[0],
+            boxRaw[3] * outputShape[1]
+          ];
+          const result = {
+            id: id++,
+            score: Math.round(100 * score) / 100,
+            class: j + 1,
+            label: labels[j].label,
+            box: box.map((a) => Math.trunc(a)),
+            boxRaw
+          };
+          results.push(result);
+        }
+      }
+    }
+    tfjs_esm_exports.dispose([scoresT, featuresT, boxesMaxT, boxIdxT]);
+  }
+  const nmsBoxes = results.map((a) => [a.boxRaw[1], a.boxRaw[0], a.boxRaw[3], a.boxRaw[2]]);
+  const nmsScores = results.map((a) => a.score);
+  let nmsIdx = [];
+  if (nmsBoxes && nmsBoxes.length > 0) {
+    const nms = await tfjs_esm_exports.image.nonMaxSuppressionAsync(nmsBoxes, nmsScores, config3.object.maxDetected, config3.object.iouThreshold, config3.object.minConfidence);
+    nmsIdx = await nms.data();
+    tfjs_esm_exports.dispose(nms);
+  }
+  results = results.filter((_val, idx) => nmsIdx.includes(idx)).sort((a, b) => b.score - a.score);
+  return results;
+}
+async function predict16(image27, config3) {
+  if (!(model15 == null ? void 0 : model15["executor"]))
+    return [];
+  const skipTime = (config3.object.skipTime || 0) > now() - lastTime13;
+  const skipFrame = skipped13 < (config3.object.skipFrames || 0);
+  if (config3.skipAllowed && skipTime && skipFrame && last8.length > 0) {
+    skipped13++;
+    return last8;
+  }
+  skipped13 = 0;
+  if (!env.kernels.includes("mod") || !env.kernels.includes("sparsetodense"))
+    return last8;
+  return new Promise(async (resolve) => {
+    const outputSize2 = [image27.shape[2] || 0, image27.shape[1] || 0];
+    const resizeT = tfjs_esm_exports.image.resizeBilinear(image27, [inputSize9, inputSize9], false);
+    const normT = tfjs_esm_exports.div(resizeT, constants.tf255);
+    const transposeT = tfjs_esm_exports.transpose(normT, [0, 3, 1, 2]);
+    let objectT;
+    if (config3.object.enabled)
+      objectT = model15.execute(transposeT);
+    lastTime13 = now();
+    const obj = await process4(objectT, outputSize2, config3);
+    last8 = obj;
+    tfjs_esm_exports.dispose([resizeT, normT, transposeT, ...objectT]);
+    resolve(obj);
+  });
+}
+
+// src/body/posenetutils.ts
+var partNames = [
+  "nose",
+  "leftEye",
+  "rightEye",
+  "leftEar",
+  "rightEar",
+  "leftShoulder",
+  "rightShoulder",
+  "leftElbow",
+  "rightElbow",
+  "leftWrist",
+  "rightWrist",
+  "leftHip",
+  "rightHip",
+  "leftKnee",
+  "rightKnee",
+  "leftAnkle",
+  "rightAnkle"
+];
+var count = partNames.length;
+var partIds = partNames.reduce((result, jointName, i) => {
+  result[jointName] = i;
+  return result;
+}, {});
+var connectedPartNames = [
+  ["leftHip", "leftShoulder"],
+  ["leftElbow", "leftShoulder"],
+  ["leftElbow", "leftWrist"],
+  ["leftHip", "leftKnee"],
+  ["leftKnee", "leftAnkle"],
+  ["rightHip", "rightShoulder"],
+  ["rightElbow", "rightShoulder"],
+  ["rightElbow", "rightWrist"],
+  ["rightHip", "rightKnee"],
+  ["rightKnee", "rightAnkle"],
+  ["leftShoulder", "rightShoulder"],
+  ["leftHip", "rightHip"]
+];
+var connectedPartIndices = connectedPartNames.map(([jointNameA, jointNameB]) => [partIds[jointNameA], partIds[jointNameB]]);
+var poseChain = [
+  ["nose", "leftEye"],
+  ["leftEye", "leftEar"],
+  ["nose", "rightEye"],
+  ["rightEye", "rightEar"],
+  ["nose", "leftShoulder"],
+  ["leftShoulder", "leftElbow"],
+  ["leftElbow", "leftWrist"],
+  ["leftShoulder", "leftHip"],
+  ["leftHip", "leftKnee"],
+  ["leftKnee", "leftAnkle"],
+  ["nose", "rightShoulder"],
+  ["rightShoulder", "rightElbow"],
+  ["rightElbow", "rightWrist"],
+  ["rightShoulder", "rightHip"],
+  ["rightHip", "rightKnee"],
+  ["rightKnee", "rightAnkle"]
+];
+function getBoundingBox(keypoints) {
+  const coord = keypoints.reduce(({ maxX, maxY, minX, minY }, { position: { x, y } }) => ({
+    maxX: Math.max(maxX, x),
+    maxY: Math.max(maxY, y),
+    minX: Math.min(minX, x),
+    minY: Math.min(minY, y)
+  }), {
+    maxX: Number.NEGATIVE_INFINITY,
+    maxY: Number.NEGATIVE_INFINITY,
+    minX: Number.POSITIVE_INFINITY,
+    minY: Number.POSITIVE_INFINITY
+  });
+  return [coord.minX, coord.minY, coord.maxX - coord.minX, coord.maxY - coord.minY];
+}
+function scalePoses(poses, [height, width], [inputResolutionHeight, inputResolutionWidth]) {
+  const scaleY = height / inputResolutionHeight;
+  const scaleX = width / inputResolutionWidth;
+  const scalePose = (pose, i) => ({
+    id: i,
+    score: pose.score,
+    boxRaw: [pose.box[0] / inputResolutionWidth, pose.box[1] / inputResolutionHeight, pose.box[2] / inputResolutionWidth, pose.box[3] / inputResolutionHeight],
+    box: [Math.trunc(pose.box[0] * scaleX), Math.trunc(pose.box[1] * scaleY), Math.trunc(pose.box[2] * scaleX), Math.trunc(pose.box[3] * scaleY)],
+    keypoints: pose.keypoints.map(({ score, part, position }) => ({
+      score,
+      part,
+      position: [Math.trunc(position.x * scaleX), Math.trunc(position.y * scaleY)],
+      positionRaw: [position.x / inputResolutionHeight, position.y / inputResolutionHeight]
+    })),
+    annotations: {}
+  });
+  const scaledPoses = poses.map((pose, i) => scalePose(pose, i));
+  return scaledPoses;
+}
+var MaxHeap = class {
+  constructor(maxSize2, getElementValue) {
+    __publicField(this, "priorityQueue");
+    __publicField(this, "numberOfElements");
+    __publicField(this, "getElementValue");
+    this.priorityQueue = new Array(maxSize2);
+    this.numberOfElements = -1;
+    this.getElementValue = getElementValue;
+  }
+  enqueue(x) {
+    this.priorityQueue[++this.numberOfElements] = x;
+    this.swim(this.numberOfElements);
+  }
+  dequeue() {
+    const max4 = this.priorityQueue[0];
+    this.exchange(0, this.numberOfElements--);
+    this.sink(0);
+    this.priorityQueue[this.numberOfElements + 1] = null;
+    return max4;
+  }
+  empty() {
+    return this.numberOfElements === -1;
+  }
+  size() {
+    return this.numberOfElements + 1;
+  }
+  all() {
+    return this.priorityQueue.slice(0, this.numberOfElements + 1);
+  }
+  max() {
+    return this.priorityQueue[0];
+  }
+  swim(k) {
+    while (k > 0 && this.less(Math.floor(k / 2), k)) {
+      this.exchange(k, Math.floor(k / 2));
+      k = Math.floor(k / 2);
+    }
+  }
+  sink(k) {
+    while (2 * k <= this.numberOfElements) {
+      let j = 2 * k;
+      if (j < this.numberOfElements && this.less(j, j + 1))
+        j++;
+      if (!this.less(k, j))
+        break;
+      this.exchange(k, j);
+      k = j;
+    }
+  }
+  getValueAt(i) {
+    return this.getElementValue(this.priorityQueue[i]);
+  }
+  less(i, j) {
+    return this.getValueAt(i) < this.getValueAt(j);
+  }
+  exchange(i, j) {
+    const t2 = this.priorityQueue[i];
+    this.priorityQueue[i] = this.priorityQueue[j];
+    this.priorityQueue[j] = t2;
+  }
+};
+function getOffsetPoint(y, x, keypoint, offsets) {
+  return {
+    y: offsets.get(y, x, keypoint),
+    x: offsets.get(y, x, keypoint + count)
+  };
+}
+function getImageCoords(part, outputStride2, offsets) {
+  const { heatmapY, heatmapX, id: keypoint } = part;
+  const { y, x } = getOffsetPoint(heatmapY, heatmapX, keypoint, offsets);
+  return {
+    x: part.heatmapX * outputStride2 + x,
+    y: part.heatmapY * outputStride2 + y
+  };
+}
+function clamp(a, min2, max4) {
+  if (a < min2)
+    return min2;
+  if (a > max4)
+    return max4;
+  return a;
+}
+function squaredDistance(y1, x1, y2, x2) {
+  const dy = y2 - y1;
+  const dx = x2 - x1;
+  return dy * dy + dx * dx;
+}
+function addVectors(a, b) {
+  return { x: a.x + b.x, y: a.y + b.y };
+}
+
+// src/body/posenet.ts
+var model16;
+var poseNetOutputs = ["MobilenetV1/offset_2/BiasAdd", "MobilenetV1/heatmap_2/BiasAdd", "MobilenetV1/displacement_fwd_2/BiasAdd", "MobilenetV1/displacement_bwd_2/BiasAdd"];
+var localMaximumRadius = 1;
+var outputStride = 16;
+var squaredNmsRadius = 50 ** 2;
+function traverse(edgeId, sourceKeypoint, targetId, scores, offsets, displacements, offsetRefineStep = 2) {
+  const getDisplacement = (point2) => ({
+    y: displacements.get(point2.y, point2.x, edgeId),
+    x: displacements.get(point2.y, point2.x, displacements.shape[2] / 2 + edgeId)
+  });
+  const getStridedIndexNearPoint = (point2, height2, width2) => ({
+    y: clamp(Math.round(point2.y / outputStride), 0, height2 - 1),
+    x: clamp(Math.round(point2.x / outputStride), 0, width2 - 1)
+  });
+  const [height, width] = scores.shape;
+  const sourceKeypointIndices = getStridedIndexNearPoint(sourceKeypoint.position, height, width);
+  const displacement = getDisplacement(sourceKeypointIndices);
+  const displacedPoint = addVectors(sourceKeypoint.position, displacement);
+  let targetKeypoint = displacedPoint;
+  for (let i = 0; i < offsetRefineStep; i++) {
+    const targetKeypointIndices = getStridedIndexNearPoint(targetKeypoint, height, width);
+    const offsetPoint = getOffsetPoint(targetKeypointIndices.y, targetKeypointIndices.x, targetId, offsets);
+    targetKeypoint = addVectors(
+      { x: targetKeypointIndices.x * outputStride, y: targetKeypointIndices.y * outputStride },
+      { x: offsetPoint.x, y: offsetPoint.y }
+    );
+  }
+  const targetKeyPointIndices = getStridedIndexNearPoint(targetKeypoint, height, width);
+  const score = scores.get(targetKeyPointIndices.y, targetKeyPointIndices.x, targetId);
+  return { position: targetKeypoint, part: partNames[targetId], score };
+}
+function decodePose(root, scores, offsets, displacementsFwd, displacementsBwd) {
+  const tuples = poseChain.map(([parentJoinName, childJoinName]) => [partIds[parentJoinName], partIds[childJoinName]]);
+  const edgesFwd = tuples.map(([, childJointId]) => childJointId);
+  const edgesBwd = tuples.map(([parentJointId]) => parentJointId);
+  const numParts = scores.shape[2];
+  const numEdges = edgesFwd.length;
+  const keypoints = new Array(numParts);
+  const rootPoint = getImageCoords(root.part, outputStride, offsets);
+  keypoints[root.part.id] = {
+    score: root.score,
+    part: partNames[root.part.id],
+    position: rootPoint
+  };
+  for (let edge = numEdges - 1; edge >= 0; --edge) {
+    const sourceId = edgesFwd[edge];
+    const targetId = edgesBwd[edge];
+    if (keypoints[sourceId] && !keypoints[targetId]) {
+      keypoints[targetId] = traverse(edge, keypoints[sourceId], targetId, scores, offsets, displacementsBwd);
+    }
+  }
+  for (let edge = 0; edge < numEdges; ++edge) {
+    const sourceId = edgesBwd[edge];
+    const targetId = edgesFwd[edge];
+    if (keypoints[sourceId] && !keypoints[targetId]) {
+      keypoints[targetId] = traverse(edge, keypoints[sourceId], targetId, scores, offsets, displacementsFwd);
+    }
+  }
+  return keypoints;
+}
+function scoreIsMaximumInLocalWindow(keypointId, score, heatmapY, heatmapX, scores) {
+  const [height, width] = scores.shape;
+  let localMaximum = true;
+  const yStart = Math.max(heatmapY - localMaximumRadius, 0);
+  const yEnd = Math.min(heatmapY + localMaximumRadius + 1, height);
+  for (let yCurrent = yStart; yCurrent < yEnd; ++yCurrent) {
+    const xStart = Math.max(heatmapX - localMaximumRadius, 0);
+    const xEnd = Math.min(heatmapX + localMaximumRadius + 1, width);
+    for (let xCurrent = xStart; xCurrent < xEnd; ++xCurrent) {
+      if (scores.get(yCurrent, xCurrent, keypointId) > score) {
+        localMaximum = false;
+        break;
+      }
+    }
+    if (!localMaximum)
+      break;
+  }
+  return localMaximum;
+}
+function buildPartWithScoreQueue(minConfidence2, scores) {
+  const [height, width, numKeypoints] = scores.shape;
+  const queue = new MaxHeap(height * width * numKeypoints, ({ score }) => score);
+  for (let heatmapY = 0; heatmapY < height; ++heatmapY) {
+    for (let heatmapX = 0; heatmapX < width; ++heatmapX) {
+      for (let keypointId = 0; keypointId < numKeypoints; ++keypointId) {
+        const score = scores.get(heatmapY, heatmapX, keypointId);
+        if (score < minConfidence2)
+          continue;
+        if (scoreIsMaximumInLocalWindow(keypointId, score, heatmapY, heatmapX, scores))
+          queue.enqueue({ score, part: { heatmapY, heatmapX, id: keypointId } });
+      }
+    }
+  }
+  return queue;
+}
+function withinRadius(poses, { x, y }, keypointId) {
+  return poses.some(({ keypoints }) => {
+    var _a;
+    const correspondingKeypoint = (_a = keypoints[keypointId]) == null ? void 0 : _a.position;
+    if (!correspondingKeypoint)
+      return false;
+    return squaredDistance(y, x, correspondingKeypoint.y, correspondingKeypoint.x) <= squaredNmsRadius;
+  });
+}
+function getInstanceScore(existingPoses, keypoints) {
+  const notOverlappedKeypointScores = keypoints.reduce((result, { position, score }, keypointId) => {
+    if (!withinRadius(existingPoses, position, keypointId))
+      result += score;
+    return result;
+  }, 0);
+  return notOverlappedKeypointScores / keypoints.length;
+}
+function decode(offsets, scores, displacementsFwd, displacementsBwd, maxDetected, minConfidence2) {
+  const poses = [];
+  const queue = buildPartWithScoreQueue(minConfidence2, scores);
+  while (poses.length < maxDetected && !queue.empty()) {
+    const root = queue.dequeue();
+    const rootImageCoords = getImageCoords(root.part, outputStride, offsets);
+    if (withinRadius(poses, rootImageCoords, root.part.id))
+      continue;
+    let keypoints = decodePose(root, scores, offsets, displacementsFwd, displacementsBwd);
+    keypoints = keypoints.filter((a) => a.score > minConfidence2);
+    const score = getInstanceScore(poses, keypoints);
+    const box = getBoundingBox(keypoints);
+    if (score > minConfidence2)
+      poses.push({ keypoints, box, score: Math.round(100 * score) / 100 });
+  }
+  return poses;
+}
+async function predict17(input, config3) {
+  if (!(model16 == null ? void 0 : model16["executor"]))
+    return [];
+  const res = tfjs_esm_exports.tidy(() => {
+    if (!model16.inputs[0].shape)
+      return [];
+    const resized = tfjs_esm_exports.image.resizeBilinear(input, [model16.inputs[0].shape[2], model16.inputs[0].shape[1]]);
+    const normalized = tfjs_esm_exports.sub(tfjs_esm_exports.div(tfjs_esm_exports.cast(resized, "float32"), 127.5), 1);
+    const results = model16.execute(normalized, poseNetOutputs);
+    const results3d = results.map((y) => tfjs_esm_exports.squeeze(y, [0]));
+    results3d[1] = tfjs_esm_exports.sigmoid(results3d[1]);
+    return results3d;
+  });
+  const buffers = await Promise.all(res.map((tensor6) => tensor6.buffer()));
+  for (const t2 of res)
+    tfjs_esm_exports.dispose(t2);
+  const decoded = decode(buffers[0], buffers[1], buffers[2], buffers[3], config3.body.maxDetected, config3.body.minConfidence);
+  if (!model16.inputs[0].shape)
+    return [];
+  const scaled = scalePoses(decoded, [input.shape[1], input.shape[2]], [model16.inputs[0].shape[2], model16.inputs[0].shape[1]]);
+  return scaled;
+}
+async function load17(config3) {
+  if (!model16 || env.initial)
+    model16 = await loadModel(config3.body.modelPath);
+  else if (config3.debug)
+    log("cached model:", model16["modelUrl"]);
+  return model16;
+}
+
+// src/segmentation/rvm.ts
+var model17;
+var outputNodes2 = ["fgr", "pha", "r1o", "r2o", "r3o", "r4o"];
+var t = {};
+var ratio = 0;
+function init2(config3) {
+  tfjs_esm_exports.dispose([t.r1i, t.r2i, t.r3i, t.r4i, t.downsample_ratio]);
+  t.r1i = tfjs_esm_exports.tensor(0);
+  t.r2i = tfjs_esm_exports.tensor(0);
+  t.r3i = tfjs_esm_exports.tensor(0);
+  t.r4i = tfjs_esm_exports.tensor(0);
+  ratio = config3.segmentation.ratio || 0.5;
+  t.downsample_ratio = tfjs_esm_exports.tensor(ratio);
+}
+async function load18(config3) {
+  if (!model17 || env.initial)
+    model17 = await loadModel(config3.segmentation.modelPath);
+  else if (config3.debug)
+    log("cached model:", model17["modelUrl"]);
+  init2(config3);
+  return model17;
+}
+var normalize = (r) => tfjs_esm_exports.tidy(() => {
+  const squeeze14 = tfjs_esm_exports.squeeze(r, [0]);
+  const mul15 = tfjs_esm_exports.mul(squeeze14, constants.tf255);
+  const cast8 = tfjs_esm_exports.cast(mul15, "int32");
+  return cast8;
+});
+function getRGBA(fgr, pha) {
+  const rgb2 = fgr ? normalize(fgr) : tfjs_esm_exports.fill([pha.shape[1] || 0, pha.shape[2] || 0, 3], 255, "int32");
+  const a = pha ? normalize(pha) : tfjs_esm_exports.fill([fgr.shape[1] || 0, fgr.shape[2] || 0, 1], 255, "int32");
+  const rgba = tfjs_esm_exports.concat([rgb2, a], -1);
+  tfjs_esm_exports.dispose([rgb2, a]);
+  return rgba;
+}
+function getState(state) {
+  return tfjs_esm_exports.tidy(() => {
+    const r = {};
+    r.unstack = tfjs_esm_exports.unstack(state, -1);
+    r.concat = tfjs_esm_exports.concat(r.unstack, 1);
+    r.split = tfjs_esm_exports.split(r.concat, 4, 1);
+    r.stack = tfjs_esm_exports.concat(r.split, 2);
+    r.squeeze = tfjs_esm_exports.squeeze(r.stack, [0]);
+    r.expand = tfjs_esm_exports.expandDims(r.squeeze, -1);
+    r.add = tfjs_esm_exports.add(r.expand, 1);
+    r.mul = tfjs_esm_exports.mul(r.add, 127.5);
+    r.cast = tfjs_esm_exports.cast(r.mul, "int32");
+    r.tile = tfjs_esm_exports.tile(r.cast, [1, 1, 3]);
+    r.alpha = tfjs_esm_exports.fill([r.tile.shape[0] || 0, r.tile.shape[1] || 0, 1], 255, "int32");
+    return tfjs_esm_exports.concat([r.tile, r.alpha], -1);
+  });
+}
+async function predict18(input, config3) {
+  if (!model17)
+    model17 = await load18(config3);
+  if (!(model17 == null ? void 0 : model17["executor"]))
+    return null;
+  t.src = tfjs_esm_exports.div(input, 255);
+  if (ratio !== config3.segmentation.ratio)
+    init2(config3);
+  const [fgr, pha, r1o, r2o, r3o, r4o] = await model17.executeAsync(t, outputNodes2);
+  let rgba;
+  switch (config3.segmentation.mode || "default") {
+    case "default":
+      rgba = getRGBA(fgr, pha);
+      break;
+    case "alpha":
+      rgba = getRGBA(null, pha);
+      break;
+    case "foreground":
+      rgba = getRGBA(fgr, null);
+      break;
+    case "state":
+      rgba = getState(r1o);
+      break;
+    default:
+      rgba = tfjs_esm_exports.tensor(0);
+  }
+  tfjs_esm_exports.dispose([t.src, fgr, pha, t.r1i, t.r2i, t.r3i, t.r4i]);
+  [t.r1i, t.r2i, t.r3i, t.r4i] = [r1o, r2o, r3o, r4o];
+  return rgba;
+}
+
+// src/segmentation/selfie.ts
+var model18;
+async function load19(config3) {
+  if (!model18 || env.initial)
+    model18 = await loadModel(config3.segmentation.modelPath);
+  else if (config3.debug)
+    log("cached model:", model18["modelUrl"]);
+  return model18;
+}
+async function predict19(input, config3) {
+  var _a;
+  if (!model18)
+    model18 = await load19(config3);
+  if (!(model18 == null ? void 0 : model18["executor"]) || !((_a = model18 == null ? void 0 : model18.inputs) == null ? void 0 : _a[0].shape))
+    return null;
+  const t2 = {};
+  t2.resize = tfjs_esm_exports.image.resizeBilinear(input, [model18.inputs[0].shape ? model18.inputs[0].shape[1] : 0, model18.inputs[0].shape ? model18.inputs[0].shape[2] : 0], false);
+  t2.norm = tfjs_esm_exports.div(t2.resize, constants.tf255);
+  t2.res = model18.execute(t2.norm);
+  t2.squeeze = tfjs_esm_exports.squeeze(t2.res, 0);
+  t2.alpha = tfjs_esm_exports.image.resizeBilinear(t2.squeeze, [input.shape[1], input.shape[2]]);
+  t2.mul = tfjs_esm_exports.mul(t2.alpha, constants.tf255);
+  let rgba;
+  switch (config3.segmentation.mode || "default") {
+    case "default":
+      t2.input = tfjs_esm_exports.squeeze(input);
+      t2.concat = tfjs_esm_exports.concat([t2.input, t2.mul], -1);
+      rgba = tfjs_esm_exports.cast(t2.concat, "int32");
+      break;
+    case "alpha":
+      rgba = tfjs_esm_exports.cast(t2.mul, "int32");
+      break;
+    default:
+      rgba = tfjs_esm_exports.tensor(0);
+  }
+  Object.keys(t2).forEach((tensor6) => tfjs_esm_exports.dispose(t2[tensor6]));
+  return rgba;
+}
+
+// src/gear/ssrnet-age.ts
+var model19;
+var last9 = [];
+var lastCount8 = 0;
+var lastTime14 = 0;
+var skipped14 = Number.MAX_SAFE_INTEGER;
+async function load20(config3) {
+  if (env.initial)
+    model19 = null;
+  if (!model19)
+    model19 = await loadModel(config3.face["ssrnet"].modelPathAge);
+  else if (config3.debug)
+    log("cached model:", model19["modelUrl"]);
+  return model19;
+}
+async function predict20(image27, config3, idx, count2) {
+  var _a, _b, _c, _d;
+  if (!model19)
+    return { age: 0 };
+  const skipFrame = skipped14 < (((_a = config3.face["ssrnet"]) == null ? void 0 : _a.skipFrames) || 0);
+  const skipTime = (((_b = config3.face["ssrnet"]) == null ? void 0 : _b.skipTime) || 0) > now() - lastTime14;
+  if (config3.skipAllowed && skipFrame && skipTime && lastCount8 === count2 && ((_c = last9[idx]) == null ? void 0 : _c.age) && ((_d = last9[idx]) == null ? void 0 : _d.age) > 0) {
+    skipped14++;
+    return last9[idx];
+  }
+  skipped14 = 0;
+  return new Promise(async (resolve) => {
+    var _a2;
+    if (!(model19 == null ? void 0 : model19.inputs) || !model19.inputs[0] || !model19.inputs[0].shape)
+      return;
+    const t2 = {};
+    t2.resize = tfjs_esm_exports.image.resizeBilinear(image27, [model19.inputs[0].shape[2], model19.inputs[0].shape[1]], false);
+    t2.enhance = tfjs_esm_exports.mul(t2.resize, constants.tf255);
+    const obj = { age: 0 };
+    if ((_a2 = config3.face["ssrnet"]) == null ? void 0 : _a2.enabled)
+      t2.age = model19.execute(t2.enhance);
+    if (t2.age) {
+      const data = await t2.age.data();
+      obj.age = Math.trunc(10 * data[0]) / 10;
+    }
+    Object.keys(t2).forEach((tensor6) => tfjs_esm_exports.dispose(t2[tensor6]));
+    last9[idx] = obj;
+    lastCount8 = count2;
+    lastTime14 = now();
+    resolve(obj);
+  });
+}
+
+// src/gear/ssrnet-gender.ts
+var model20;
+var last10 = [];
+var lastCount9 = 0;
+var lastTime15 = 0;
+var skipped15 = Number.MAX_SAFE_INTEGER;
+var rgb = [0.2989, 0.587, 0.114];
+async function load21(config3) {
+  var _a;
+  if (env.initial)
+    model20 = null;
+  if (!model20)
+    model20 = await loadModel((_a = config3.face["ssrnet"]) == null ? void 0 : _a.modelPathGender);
+  else if (config3.debug)
+    log("cached model:", model20["modelUrl"]);
+  return model20;
+}
+async function predict21(image27, config3, idx, count2) {
+  var _a, _b, _c, _d;
+  if (!model20)
+    return { gender: "unknown", genderScore: 0 };
+  const skipFrame = skipped15 < (((_a = config3.face["ssrnet"]) == null ? void 0 : _a.skipFrames) || 0);
+  const skipTime = (((_b = config3.face["ssrnet"]) == null ? void 0 : _b.skipTime) || 0) > now() - lastTime15;
+  if (config3.skipAllowed && skipFrame && skipTime && lastCount9 === count2 && ((_c = last10[idx]) == null ? void 0 : _c.gender) && ((_d = last10[idx]) == null ? void 0 : _d.genderScore) > 0) {
+    skipped15++;
+    return last10[idx];
+  }
+  skipped15 = 0;
+  return new Promise(async (resolve) => {
+    var _a2;
+    if (!(model20 == null ? void 0 : model20.inputs[0].shape))
+      return;
+    const t2 = {};
+    t2.resize = tfjs_esm_exports.image.resizeBilinear(image27, [model20.inputs[0].shape[2], model20.inputs[0].shape[1]], false);
+    t2.enhance = tfjs_esm_exports.tidy(() => {
+      const [red, green, blue] = tfjs_esm_exports.split(t2.resize, 3, 3);
+      const redNorm = tfjs_esm_exports.mul(red, rgb[0]);
+      const greenNorm = tfjs_esm_exports.mul(green, rgb[1]);
+      const blueNorm = tfjs_esm_exports.mul(blue, rgb[2]);
+      const grayscale = tfjs_esm_exports.addN([redNorm, greenNorm, blueNorm]);
+      const normalize2 = tfjs_esm_exports.mul(tfjs_esm_exports.sub(grayscale, constants.tf05), 2);
+      return normalize2;
+    });
+    const obj = { gender: "unknown", genderScore: 0 };
+    if ((_a2 = config3.face["ssrnet"]) == null ? void 0 : _a2.enabled)
+      t2.gender = model20.execute(t2.enhance);
+    const data = await t2.gender.data();
+    obj.gender = data[0] > data[1] ? "female" : "male";
+    obj.genderScore = data[0] > data[1] ? Math.trunc(100 * data[0]) / 100 : Math.trunc(100 * data[1]) / 100;
+    Object.keys(t2).forEach((tensor6) => tfjs_esm_exports.dispose(t2[tensor6]));
+    last10[idx] = obj;
+    lastCount9 = count2;
+    lastTime15 = now();
+    resolve(obj);
+  });
+}
+
+// src/models.ts
+var Models = class {
+  constructor() {
+    __publicField(this, "ssrnetage", null);
+    __publicField(this, "gear", null);
+    __publicField(this, "blazeposedetect", null);
+    __publicField(this, "blazepose", null);
+    __publicField(this, "centernet", null);
+    __publicField(this, "efficientpose", null);
+    __publicField(this, "mobilefacenet", null);
+    __publicField(this, "insightface", null);
+    __publicField(this, "emotion", null);
+    __publicField(this, "facedetect", null);
+    __publicField(this, "faceiris", null);
+    __publicField(this, "facemesh", null);
+    __publicField(this, "faceres", null);
+    __publicField(this, "ssrnetgender", null);
+    __publicField(this, "handpose", null);
+    __publicField(this, "handskeleton", null);
+    __publicField(this, "handtrack", null);
+    __publicField(this, "liveness", null);
+    __publicField(this, "meet", null);
+    __publicField(this, "movenet", null);
+    __publicField(this, "nanodet", null);
+    __publicField(this, "posenet", null);
+    __publicField(this, "selfie", null);
+    __publicField(this, "rvm", null);
+    __publicField(this, "antispoof", null);
+  }
+};
+var instance;
+var getModelStats = (currentInstance) => {
+  if (currentInstance)
+    instance = currentInstance;
+  if (!instance)
+    log("instance not registred");
+  let totalSizeFromManifest = 0;
+  let totalSizeWeights = 0;
+  let totalSizeLoading = 0;
+  for (const m of Object.values(modelStats)) {
+    totalSizeFromManifest += m.sizeFromManifest;
+    totalSizeWeights += m.sizeLoadedWeights;
+    totalSizeLoading += m.sizeDesired;
+  }
+  const percentageLoaded = totalSizeLoading > 0 ? totalSizeWeights / totalSizeLoading : 0;
+  return {
+    numLoadedModels: Object.values(modelStats).length,
+    numDefinedModels: Object.keys(instance.models).length,
+    percentageLoaded,
+    totalSizeFromManifest,
+    totalSizeWeights,
+    totalSizeLoading,
+    totalSizeEnabled: void 0,
+    modelStats: Object.values(modelStats)
+  };
+};
+function reset2(currentInstance) {
+  if (currentInstance)
+    instance = currentInstance;
+  for (const model21 of Object.keys(instance.models))
+    instance.models[model21] = null;
+}
+async function load22(currentInstance) {
+  var _a, _b, _c, _d, _e, _f, _g, _h, _i, _j, _k, _l, _m, _n, _o, _p, _q, _r, _s, _t, _u, _v, _w, _x, _y, _z, _A, _B, _C;
+  if (currentInstance)
+    instance = currentInstance;
+  if (!instance)
+    log("instance not registred");
+  if (env.initial)
+    reset2(instance);
+  if (instance.config.hand.enabled) {
+    if (!instance.models.handpose && ((_b = (_a = instance.config.hand.detector) == null ? void 0 : _a.modelPath) == null ? void 0 : _b.includes("handdetect"))) {
+      [instance.models.handpose, instance.models.handskeleton] = await load10(instance.config);
+    }
+    if (!instance.models.handskeleton && instance.config.hand.landmarks && ((_d = (_c = instance.config.hand.detector) == null ? void 0 : _c.modelPath) == null ? void 0 : _d.includes("handdetect"))) {
+      [instance.models.handpose, instance.models.handskeleton] = await load10(instance.config);
+    }
+  }
+  if (instance.config.body.enabled && !instance.models.blazepose && ((_e = instance.config.body.modelPath) == null ? void 0 : _e.includes("blazepose")))
+    instance.models.blazepose = loadPose(instance.config);
+  if (instance.config.body.enabled && !instance.models.blazeposedetect && instance.config.body["detector"] && instance.config.body["detector"].modelPath)
+    instance.models.blazeposedetect = loadDetect(instance.config);
+  if (instance.config.body.enabled && !instance.models.efficientpose && ((_f = instance.config.body.modelPath) == null ? void 0 : _f.includes("efficientpose")))
+    instance.models.efficientpose = load4(instance.config);
+  if (instance.config.body.enabled && !instance.models.movenet && ((_g = instance.config.body.modelPath) == null ? void 0 : _g.includes("movenet")))
+    instance.models.movenet = load15(instance.config);
+  if (instance.config.body.enabled && !instance.models.posenet && ((_h = instance.config.body.modelPath) == null ? void 0 : _h.includes("posenet")))
+    instance.models.posenet = load17(instance.config);
+  if (instance.config.face.enabled && !instance.models.facedetect)
+    instance.models.facedetect = load2(instance.config);
+  if (instance.config.face.enabled && ((_i = instance.config.face.antispoof) == null ? void 0 : _i.enabled) && !instance.models.antispoof)
+    instance.models.antispoof = load(instance.config);
+  if (instance.config.face.enabled && ((_j = instance.config.face.liveness) == null ? void 0 : _j.enabled) && !instance.models.liveness)
+    instance.models.liveness = load12(instance.config);
+  if (instance.config.face.enabled && ((_k = instance.config.face.description) == null ? void 0 : _k.enabled) && !instance.models.faceres)
+    instance.models.faceres = load8(instance.config);
+  if (instance.config.face.enabled && ((_l = instance.config.face.emotion) == null ? void 0 : _l.enabled) && !instance.models.emotion)
+    instance.models.emotion = load5(instance.config);
+  if (instance.config.face.enabled && ((_m = instance.config.face.iris) == null ? void 0 : _m.enabled) && !((_n = instance.config.face.attention) == null ? void 0 : _n.enabled) && !instance.models.faceiris)
+    instance.models.faceiris = load6(instance.config);
+  if (instance.config.face.enabled && ((_o = instance.config.face.mesh) == null ? void 0 : _o.enabled) && !instance.models.facemesh)
+    instance.models.facemesh = load7(instance.config);
+  if (instance.config.face.enabled && ((_p = instance.config.face["gear"]) == null ? void 0 : _p.enabled) && !instance.models.gear)
+    instance.models.gear = load9(instance.config);
+  if (instance.config.face.enabled && ((_q = instance.config.face["ssrnet"]) == null ? void 0 : _q.enabled) && !instance.models.ssrnetage)
+    instance.models.ssrnetage = load20(instance.config);
+  if (instance.config.face.enabled && ((_r = instance.config.face["ssrnet"]) == null ? void 0 : _r.enabled) && !instance.models.ssrnetgender)
+    instance.models.ssrnetgender = load21(instance.config);
+  if (instance.config.face.enabled && ((_s = instance.config.face["mobilefacenet"]) == null ? void 0 : _s.enabled) && !instance.models.mobilefacenet)
+    instance.models.mobilefacenet = load14(instance.config);
+  if (instance.config.face.enabled && ((_t = instance.config.face["insightface"]) == null ? void 0 : _t.enabled) && !instance.models.insightface)
+    instance.models.insightface = load11(instance.config);
+  if (instance.config.hand.enabled && !instance.models.handtrack && ((_v = (_u = instance.config.hand.detector) == null ? void 0 : _u.modelPath) == null ? void 0 : _v.includes("handtrack")))
+    instance.models.handtrack = loadDetect2(instance.config);
+  if (instance.config.hand.enabled && instance.config.hand.landmarks && !instance.models.handskeleton && ((_x = (_w = instance.config.hand.detector) == null ? void 0 : _w.modelPath) == null ? void 0 : _x.includes("handtrack")))
+    instance.models.handskeleton = loadSkeleton(instance.config);
+  if (instance.config.object.enabled && !instance.models.centernet && ((_y = instance.config.object.modelPath) == null ? void 0 : _y.includes("centernet")))
+    instance.models.centernet = load3(instance.config);
+  if (instance.config.object.enabled && !instance.models.nanodet && ((_z = instance.config.object.modelPath) == null ? void 0 : _z.includes("nanodet")))
+    instance.models.nanodet = load16(instance.config);
+  if (instance.config.segmentation.enabled && !instance.models.selfie && ((_A = instance.config.segmentation.modelPath) == null ? void 0 : _A.includes("selfie")))
+    instance.models.selfie = load19(instance.config);
+  if (instance.config.segmentation.enabled && !instance.models.meet && ((_B = instance.config.segmentation.modelPath) == null ? void 0 : _B.includes("meet")))
+    instance.models.meet = load13(instance.config);
+  if (instance.config.segmentation.enabled && !instance.models.rvm && ((_C = instance.config.segmentation.modelPath) == null ? void 0 : _C.includes("rvm")))
+    instance.models.rvm = load18(instance.config);
+  for await (const model21 of Object.keys(instance.models)) {
+    if (instance.models[model21] && typeof instance.models[model21] !== "undefined") {
+      instance.models[model21] = await instance.models[model21];
+    }
+  }
+}
+function validateModel(currentInstance, model21, name) {
+  var _a, _b;
+  if (!model21)
+    return null;
+  if (currentInstance)
+    instance = currentInstance;
+  if (!instance)
+    log("instance not registred");
+  if (!((_a = instance == null ? void 0 : instance.config) == null ? void 0 : _a.validateModels))
+    return null;
+  const simpleOps = ["const", "placeholder", "noop", "pad", "squeeze", "add", "sub", "mul", "div"];
+  const ignoreOps = ["biasadd", "fusedbatchnormv3", "matmul", "switch", "shape", "merge", "split", "broadcastto"];
+  const ops = [];
+  const missing = [];
+  const url = model21["modelUrl"];
+  const executor = model21["executor"];
+  if ((_b = executor == null ? void 0 : executor.graph) == null ? void 0 : _b.nodes) {
+    for (const kernel of Object.values(executor.graph.nodes)) {
+      const op = kernel.op.toLowerCase();
+      if (!ops.includes(op))
+        ops.push(op);
+    }
+  } else {
+    if (!executor && instance.config.debug) {
+      log("model not loaded", name);
+    }
+  }
+  for (const op of ops) {
+    if (!simpleOps.includes(op) && !ignoreOps.includes(op) && !instance.env.kernels.includes(op) && !instance.env.kernels.includes(op.replace("_", "")) && !instance.env.kernels.includes(op.replace("native", "")) && !instance.env.kernels.includes(op.replace("v2", ""))) {
+      missing.push(op);
+    }
+  }
+  if (instance.config.debug && missing.length > 0)
+    log("model validation failed:", name, missing);
+  return missing.length > 0 ? { name, missing, ops, url } : null;
+}
+function validate2(currentInstance) {
+  if (currentInstance)
+    instance = currentInstance;
+  if (!instance)
+    log("instance not registred");
+  const missing = [];
+  for (const defined of Object.keys(currentInstance.models)) {
+    const model21 = currentInstance.models[defined];
+    if (!model21)
+      continue;
+    const res = validateModel(currentInstance, model21, defined);
+    if (res)
+      missing.push(res);
+  }
+  return missing;
+}
+
+// src/tfjs/humangl.ts
+var config2 = {
+  name: "humangl",
+  priority: 999,
+  canvas: null,
+  gl: null,
+  extensions: [],
+  webGLattr: {
+    alpha: false,
+    antialias: false,
+    premultipliedAlpha: false,
+    preserveDrawingBuffer: false,
+    depth: false,
+    stencil: false,
+    failIfMajorPerformanceCaveat: false,
+    desynchronized: true
+  }
+};
+function extensions() {
+  const gl = config2.gl;
+  if (!gl)
+    return;
+  config2.extensions = gl.getSupportedExtensions();
+}
+function register(instance2) {
+  var _a;
+  if (instance2.config.backend !== "humangl")
+    return;
+  if (config2.name in tfjs_esm_exports.engine().registry && !((_a = config2 == null ? void 0 : config2.gl) == null ? void 0 : _a.getParameter(config2.gl.VERSION))) {
+    log("humangl error: backend invalid context");
+    reset2(instance2);
+  }
+  if (!tfjs_esm_exports.findBackend(config2.name)) {
+    try {
+      config2.canvas = canvas(100, 100);
+    } catch (err) {
+      log("humangl error: cannot create canvas:", err);
+      return;
+    }
+    try {
+      config2.gl = config2.canvas.getContext("webgl2", config2.webGLattr);
+      if (!config2.gl) {
+        log("humangl error: cannot get webgl context");
+        return;
+      }
+      const glv2 = config2.gl.getParameter(config2.gl.VERSION).includes("2.0");
+      if (!glv2) {
+        log("backend override: using fallback webgl backend as webgl 2.0 is not detected");
+        instance2.config.backend = "webgl";
+        return;
+      }
+      if (config2.canvas) {
+        config2.canvas.addEventListener("webglcontextlost", (e) => {
+          log("humangl error:", e.type);
+          log("possible browser memory leak using webgl or conflict with multiple backend registrations");
+          instance2.emit("error");
+          throw new Error("backend error: webgl context lost");
+        });
+        config2.canvas.addEventListener("webglcontextrestored", (e) => {
+          log("humangl error: context restored:", e);
+        });
+        config2.canvas.addEventListener("webglcontextcreationerror", (e) => {
+          log("humangl error: context create:", e);
+        });
+      }
+    } catch (err) {
+      log("humangl error: cannot get webgl context:", err);
+      return;
+    }
+    try {
+      tfjs_esm_exports.setWebGLContext(2, config2.gl);
+    } catch (err) {
+      log("humangl error: cannot set webgl context:", err);
+      return;
+    }
+    try {
+      const ctx = new tfjs_esm_exports.GPGPUContext(config2.gl);
+      tfjs_esm_exports.registerBackend(config2.name, () => new tfjs_esm_exports.MathBackendWebGL(ctx), config2.priority);
+    } catch (err) {
+      log("humangl error: cannot register webgl backend:", err);
+      return;
+    }
+    try {
+      const kernels = tfjs_esm_exports.getKernelsForBackend("webgl");
+      kernels.forEach((kernelConfig) => {
+        const newKernelConfig = { ...kernelConfig, backendName: config2.name };
+        tfjs_esm_exports.registerKernel(newKernelConfig);
+      });
+    } catch (err) {
+      log("humangl error: cannot update webgl backend registration:", err);
+      return;
+    }
+    try {
+      if (tfjs_esm_exports.env().flagRegistry.WEBGL_VERSION)
+        tfjs_esm_exports.env().set("WEBGL_VERSION", 2);
+    } catch (err) {
+      log("humangl error: cannot set WebGL backend flags:", err);
+      return;
+    }
+    extensions();
+    const current = tfjs_esm_exports.backend().getGPGPUContext ? tfjs_esm_exports.backend().getGPGPUContext().gl : null;
+    if (current) {
+      if (instance2.config.debug)
+        log("humangl backend registered:", { webgl: current.getParameter(current.VERSION), renderer: current.getParameter(current.RENDERER) });
+    } else {
+      log("humangl error: no current gl context:", current, config2.gl);
+    }
+  }
+}
+
+// src/tfjs/backend.ts
+function registerCustomOps(config3) {
+  const newKernels = [];
+  if (!env.kernels.includes("mod")) {
+    const kernelMod = {
+      kernelName: "Mod",
+      backendName: tfjs_esm_exports.getBackend(),
+      kernelFunc: (op) => tfjs_esm_exports.tidy(() => tfjs_esm_exports.sub(op.inputs.a, tfjs_esm_exports.mul(tfjs_esm_exports.div(op.inputs.a, op.inputs.b), op.inputs.b)))
+    };
+    tfjs_esm_exports.registerKernel(kernelMod);
+    env.kernels.push("mod");
+    newKernels.push("mod");
+  }
+  if (!env.kernels.includes("floormod")) {
+    const kernelFloorMod = {
+      kernelName: "FloorMod",
+      backendName: tfjs_esm_exports.getBackend(),
+      kernelFunc: (op) => tfjs_esm_exports.tidy(() => tfjs_esm_exports.add(tfjs_esm_exports.mul(tfjs_esm_exports.floorDiv(op.inputs.a / op.inputs.b), op.inputs.b), tfjs_esm_exports.mod(op.inputs.a, op.inputs.b)))
+    };
+    tfjs_esm_exports.registerKernel(kernelFloorMod);
+    env.kernels.push("floormod");
+    newKernels.push("floormod");
+  }
+  if (!env.kernels.includes("rotatewithoffset") && config3.softwareKernels) {
+    const kernelRotateWithOffset = {
+      kernelName: "RotateWithOffset",
+      backendName: tfjs_esm_exports.getBackend(),
+      kernelFunc: (op) => tfjs_esm_exports.tidy(() => {
+        const backend4 = tfjs_esm_exports.getBackend();
+        tfjs_esm_exports.setBackend("cpu");
+        const t2 = tfjs_esm_exports.image.rotateWithOffset(op.inputs.image, op.attrs.radians, op.attrs.fillValue, op.attrs.center);
+        tfjs_esm_exports.setBackend(backend4);
+        return t2;
+      })
+    };
+    tfjs_esm_exports.registerKernel(kernelRotateWithOffset);
+    env.kernels.push("rotatewithoffset");
+    newKernels.push("rotatewithoffset");
+  }
+  if (newKernels.length > 0 && config3.debug)
+    log("registered kernels:", newKernels);
+}
+var defaultFlags = {};
+async function check(instance2, force = false) {
+  instance2.state = "backend";
+  if (force || env.initial || instance2.config.backend && instance2.config.backend.length > 0 && tfjs_esm_exports.getBackend() !== instance2.config.backend) {
+    const timeStamp = now();
+    if (instance2.config.backend && instance2.config.backend.length > 0) {
+      if (typeof window === "undefined" && typeof WorkerGlobalScope !== "undefined" && instance2.config.debug) {
+        if (instance2.config.debug)
+          log("running inside web worker");
+      }
+      if (env.browser && instance2.config.backend === "tensorflow") {
+        if (instance2.config.debug)
+          log("override: backend set to tensorflow while running in browser");
+        instance2.config.backend = "webgl";
+      }
+      if (env.node && (instance2.config.backend === "webgl" || instance2.config.backend === "humangl")) {
+        if (instance2.config.debug)
+          log(`override: backend set to ${instance2.config.backend} while running in nodejs`);
+        instance2.config.backend = "tensorflow";
+      }
+      if (env.browser && instance2.config.backend === "webgpu") {
+        if (typeof navigator === "undefined" || typeof navigator.gpu === "undefined") {
+          log("override: backend set to webgpu but browser does not support webgpu");
+          instance2.config.backend = "webgl";
+        } else {
+          const adapter = await navigator.gpu.requestAdapter();
+          if (instance2.config.debug)
+            log("enumerated webgpu adapter:", adapter);
+          if (!adapter) {
+            log("override: backend set to webgpu but browser reports no available gpu");
+            instance2.config.backend = "webgl";
+          } else {
+            const adapterInfo = "requestAdapterInfo" in adapter ? await adapter.requestAdapterInfo() : void 0;
+            log("webgpu adapter info:", adapterInfo);
+          }
+        }
+      }
+      let available = Object.keys(tfjs_esm_exports.engine().registryFactory);
+      if (instance2.config.backend === "humangl" && !available.includes("humangl")) {
+        register(instance2);
+        available = Object.keys(tfjs_esm_exports.engine().registryFactory);
+      }
+      if (instance2.config.debug)
+        log("available backends:", available);
+      if (!available.includes(instance2.config.backend)) {
+        log(`error: backend ${instance2.config.backend} not found in registry`);
+        instance2.config.backend = env.node ? "tensorflow" : "webgl";
+        if (instance2.config.debug)
+          log(`override: setting backend ${instance2.config.backend}`);
+      }
+      if (instance2.config.debug)
+        log("setting backend:", [instance2.config.backend]);
+      if (instance2.config.backend === "wasm") {
+        if (tfjs_esm_exports.env().flagRegistry.CANVAS2D_WILL_READ_FREQUENTLY)
+          tfjs_esm_exports.env().set("CANVAS2D_WILL_READ_FREQUENTLY", true);
+        if (instance2.config.debug)
+          log("wasm path:", instance2.config.wasmPath);
+        if (typeof tfjs_esm_exports.setWasmPaths !== "undefined")
+          tfjs_esm_exports.setWasmPaths(instance2.config.wasmPath, instance2.config.wasmPlatformFetch);
+        else
+          throw new Error("backend error: attempting to use wasm backend but wasm path is not set");
+        let mt = false;
+        let simd = false;
+        try {
+          mt = await tfjs_esm_exports.env().getAsync("WASM_HAS_MULTITHREAD_SUPPORT");
+          simd = await tfjs_esm_exports.env().getAsync("WASM_HAS_SIMD_SUPPORT");
+          if (instance2.config.debug)
+            log(`wasm execution: ${simd ? "simd" : "no simd"} ${mt ? "multithreaded" : "singlethreaded"}`);
+          if (instance2.config.debug && !simd)
+            log("warning: wasm simd support is not enabled");
+        } catch (e) {
+          log("wasm detection failed");
+        }
+      }
+      try {
+        await tfjs_esm_exports.setBackend(instance2.config.backend);
+        await tfjs_esm_exports.ready();
+      } catch (err) {
+        log("error: cannot set backend:", instance2.config.backend, err);
+        return false;
+      }
+      if (instance2.config.debug)
+        defaultFlags = JSON.parse(JSON.stringify(tfjs_esm_exports.env().flags));
+    }
+    if (tfjs_esm_exports.getBackend() === "humangl" || tfjs_esm_exports.getBackend() === "webgl") {
+      if (tfjs_esm_exports.env().flagRegistry.WEBGL_USE_SHAPES_UNIFORMS)
+        tfjs_esm_exports.env().set("WEBGL_USE_SHAPES_UNIFORMS", true);
+      if (tfjs_esm_exports.env().flagRegistry.WEBGL_EXP_CONV)
+        tfjs_esm_exports.env().set("WEBGL_EXP_CONV", true);
+      if (instance2.config.debug && typeof instance2.config.deallocate !== "undefined" && instance2.config.deallocate) {
+        log("changing webgl: WEBGL_DELETE_TEXTURE_THRESHOLD:", true);
+        tfjs_esm_exports.env().set("WEBGL_DELETE_TEXTURE_THRESHOLD", 0);
+      }
+    }
+    if (tfjs_esm_exports.getBackend() === "webgpu") {
+    }
+    if (instance2.config.debug) {
+      const newFlags = tfjs_esm_exports.env().flags;
+      const updatedFlags = {};
+      for (const key of Object.keys(newFlags)) {
+        if (defaultFlags[key] === newFlags[key])
+          continue;
+        updatedFlags[key] = newFlags[key];
+      }
+      if (instance2.config.debug && Object.keys(updatedFlags).length > 0)
+        log("backend:", tfjs_esm_exports.getBackend(), "flags:", updatedFlags);
+    }
+    if (instance2.config.flags && Object.keys(instance2.config.flags).length > 0) {
+      if (instance2.config.debug)
+        log("flags:", instance2.config["flags"]);
+      for (const [key, val] of Object.entries(instance2.config.flags)) {
+        tfjs_esm_exports.env().set(key, val);
+      }
+    }
+    tfjs_esm_exports.enableProdMode();
+    init();
+    instance2.performance.initBackend = Math.trunc(now() - timeStamp);
+    instance2.config.backend = tfjs_esm_exports.getBackend();
+    await env.updateBackend();
+    registerCustomOps(instance2.config);
+    env.initial = false;
+  }
+  return true;
+}
+function fakeOps(kernelNames, config3) {
+  for (const kernelName of kernelNames) {
+    const kernelConfig = {
+      kernelName,
+      backendName: config3.backend,
+      kernelFunc: () => {
+        if (config3.debug)
+          log("kernelFunc", kernelName, config3.backend);
+      }
+    };
+    tfjs_esm_exports.registerKernel(kernelConfig);
+  }
+  env.kernels = tfjs_esm_exports.getKernelsForBackend(tfjs_esm_exports.getBackend()).map((kernel) => kernel.kernelName.toLowerCase());
+}
+
+// src/draw/draw.ts
+var draw_exports = {};
+__export(draw_exports, {
+  all: () => all,
+  body: () => body,
+  canvas: () => canvas2,
+  face: () => face,
+  gesture: () => gesture,
+  hand: () => hand,
+  object: () => object,
+  options: () => options3,
+  person: () => person
+});
+
+// src/draw/primitives.ts
+var getCanvasContext = (input) => {
+  if (!input)
+    log("draw error: invalid canvas");
+  else if (!input.getContext)
+    log("draw error: canvas context not defined");
+  else {
+    const ctx = input.getContext("2d");
+    if (!ctx)
+      log("draw error: cannot get canvas context");
+    else
+      return ctx;
+  }
+  return null;
+};
+var rad2deg = (theta) => Math.round(theta * 180 / Math.PI);
+var colorDepth = (z, opt2) => {
+  if (!opt2.useDepth || typeof z === "undefined")
+    return opt2.color;
+  const rgb2 = Uint8ClampedArray.from([127 + 2 * z, 127 - 2 * z, 255]);
+  return `rgba(${rgb2[0]}, ${rgb2[1]}, ${rgb2[2]}, ${opt2.alpha})`;
+};
+function point(ctx, x, y, z, localOptions) {
+  ctx.fillStyle = colorDepth(z, localOptions);
+  ctx.beginPath();
+  ctx.arc(x, y, localOptions.pointSize, 0, 2 * Math.PI);
+  ctx.fill();
+}
+function rect(ctx, x, y, width, height, localOptions) {
+  ctx.beginPath();
+  ctx.lineWidth = localOptions.lineWidth;
+  if (localOptions.useCurves) {
+    const cx = (x + x + width) / 2;
+    const cy = (y + y + height) / 2;
+    ctx.ellipse(cx, cy, width / 2, height / 2, 0, 0, 2 * Math.PI);
+  } else {
+    ctx.moveTo(x + localOptions.roundRect, y);
+    ctx.lineTo(x + width - localOptions.roundRect, y);
+    ctx.quadraticCurveTo(x + width, y, x + width, y + localOptions.roundRect);
+    ctx.lineTo(x + width, y + height - localOptions.roundRect);
+    ctx.quadraticCurveTo(x + width, y + height, x + width - localOptions.roundRect, y + height);
+    ctx.lineTo(x + localOptions.roundRect, y + height);
+    ctx.quadraticCurveTo(x, y + height, x, y + height - localOptions.roundRect);
+    ctx.lineTo(x, y + localOptions.roundRect);
+    ctx.quadraticCurveTo(x, y, x + localOptions.roundRect, y);
+    ctx.closePath();
+  }
+  ctx.stroke();
+}
+function lines(ctx, points, localOptions) {
+  if (points.length < 2)
+    return;
+  ctx.beginPath();
+  ctx.moveTo(points[0][0], points[0][1]);
+  for (const pt of points) {
+    ctx.strokeStyle = colorDepth(pt[2] || 0, localOptions);
+    ctx.lineTo(Math.trunc(pt[0]), Math.trunc(pt[1]));
+  }
+  ctx.stroke();
+  if (localOptions.fillPolygons) {
+    ctx.closePath();
+    ctx.fill();
+  }
+}
+function curves(ctx, points, localOptions) {
+  if (points.length < 2)
+    return;
+  ctx.lineWidth = localOptions.lineWidth;
+  if (!localOptions.useCurves || points.length <= 2) {
+    lines(ctx, points, localOptions);
+    return;
+  }
+  ctx.moveTo(points[0][0], points[0][1]);
+  for (let i = 0; i < points.length - 2; i++) {
+    const xc = (points[i][0] + points[i + 1][0]) / 2;
+    const yc = (points[i][1] + points[i + 1][1]) / 2;
+    ctx.quadraticCurveTo(points[i][0], points[i][1], xc, yc);
+  }
+  ctx.quadraticCurveTo(points[points.length - 2][0], points[points.length - 2][1], points[points.length - 1][0], points[points.length - 1][1]);
+  ctx.stroke();
+  if (localOptions.fillPolygons) {
+    ctx.closePath();
+    ctx.fill();
+  }
+}
+function arrow(ctx, from, to, radius = 5) {
+  let angle;
+  let x;
+  let y;
+  ctx.beginPath();
+  ctx.moveTo(from[0], from[1]);
+  ctx.lineTo(to[0], to[1]);
+  angle = Math.atan2(to[1] - from[1], to[0] - from[0]);
+  x = radius * Math.cos(angle) + to[0];
+  y = radius * Math.sin(angle) + to[1];
+  ctx.moveTo(x, y);
+  angle += 1 / 3 * (2 * Math.PI);
+  x = radius * Math.cos(angle) + to[0];
+  y = radius * Math.sin(angle) + to[1];
+  ctx.lineTo(x, y);
+  angle += 1 / 3 * (2 * Math.PI);
+  x = radius * Math.cos(angle) + to[0];
+  y = radius * Math.sin(angle) + to[1];
+  ctx.lineTo(x, y);
+  ctx.closePath();
+  ctx.stroke();
+  ctx.fill();
+}
+
+// src/draw/options.ts
+var options3 = {
+  color: "rgba(173, 216, 230, 0.6)",
+  labelColor: "rgba(173, 216, 230, 1)",
+  shadowColor: "black",
+  alpha: 0.5,
+  font: 'small-caps 16px "Segoe UI"',
+  lineHeight: 18,
+  lineWidth: 4,
+  pointSize: 2,
+  roundRect: 8,
+  drawPoints: false,
+  drawLabels: true,
+  drawBoxes: true,
+  drawAttention: true,
+  drawGestures: true,
+  drawPolygons: true,
+  drawGaze: true,
+  fillPolygons: false,
+  useDepth: true,
+  useCurves: false
+};
+
+// src/draw/face.ts
+var opt;
+function drawLabels(f, ctx) {
+  var _a, _b;
+  if (opt.drawLabels) {
+    const labels2 = [];
+    labels2.push(`face: ${Math.trunc(100 * f.score)}%`);
+    if (f.genderScore)
+      labels2.push(`${f.gender || ""} ${Math.trunc(100 * f.genderScore)}%`);
+    if (f.age)
+      labels2.push(`age: ${f.age || ""}`);
+    if (f.iris)
+      labels2.push(`distance: ${f.iris}`);
+    if (f.real)
+      labels2.push(`real: ${Math.trunc(100 * f.real)}%`);
+    if (f.live)
+      labels2.push(`live: ${Math.trunc(100 * f.live)}%`);
+    if (f.emotion && f.emotion.length > 0) {
+      const emotion2 = f.emotion.map((a) => `${Math.trunc(100 * a.score)}% ${a.emotion}`);
+      if (emotion2.length > 3)
+        emotion2.length = 3;
+      labels2.push(emotion2.join(" "));
+    }
+    if (((_a = f.rotation) == null ? void 0 : _a.angle) && ((_b = f.rotation) == null ? void 0 : _b.gaze)) {
+      if (f.rotation.angle.roll)
+        labels2.push(`roll: ${rad2deg(f.rotation.angle.roll)}\xB0 yaw:${rad2deg(f.rotation.angle.yaw)}\xB0 pitch:${rad2deg(f.rotation.angle.pitch)}\xB0`);
+      if (f.rotation.gaze.bearing)
+        labels2.push(`gaze: ${rad2deg(f.rotation.gaze.bearing)}\xB0`);
+    }
+    if (labels2.length === 0)
+      labels2.push("face");
+    ctx.fillStyle = opt.color;
+    for (let i = labels2.length - 1; i >= 0; i--) {
+      const x = Math.max(f.box[0], 0);
+      const y = i * opt.lineHeight + f.box[1];
+      if (opt.shadowColor && opt.shadowColor !== "") {
+        ctx.fillStyle = opt.shadowColor;
+        ctx.fillText(labels2[i], x + 5, y + 16);
+      }
+      ctx.fillStyle = opt.labelColor;
+      ctx.fillText(labels2[i], x + 4, y + 15);
+    }
+  }
+}
+function drawIrisElipse(f, ctx) {
+  var _a, _b, _c, _d;
+  if (((_a = f.annotations) == null ? void 0 : _a.leftEyeIris) && ((_b = f.annotations) == null ? void 0 : _b.leftEyeIris[0])) {
+    ctx.strokeStyle = opt.useDepth ? "rgba(255, 200, 255, 0.3)" : opt.color;
+    ctx.beginPath();
+    const sizeX = Math.abs(f.annotations.leftEyeIris[3][0] - f.annotations.leftEyeIris[1][0]) / 2;
+    const sizeY = Math.abs(f.annotations.leftEyeIris[4][1] - f.annotations.leftEyeIris[2][1]) / 2;
+    ctx.ellipse(f.annotations.leftEyeIris[0][0], f.annotations.leftEyeIris[0][1], sizeX, sizeY, 0, 0, 2 * Math.PI);
+    ctx.stroke();
+    if (opt.fillPolygons) {
+      ctx.fillStyle = opt.useDepth ? "rgba(255, 255, 200, 0.3)" : opt.color;
+      ctx.fill();
+    }
+  }
+  if (((_c = f.annotations) == null ? void 0 : _c.rightEyeIris) && ((_d = f.annotations) == null ? void 0 : _d.rightEyeIris[0])) {
+    ctx.strokeStyle = opt.useDepth ? "rgba(255, 200, 255, 0.3)" : opt.color;
+    ctx.beginPath();
+    const sizeX = Math.abs(f.annotations.rightEyeIris[3][0] - f.annotations.rightEyeIris[1][0]) / 2;
+    const sizeY = Math.abs(f.annotations.rightEyeIris[4][1] - f.annotations.rightEyeIris[2][1]) / 2;
+    ctx.ellipse(f.annotations.rightEyeIris[0][0], f.annotations.rightEyeIris[0][1], sizeX, sizeY, 0, 0, 2 * Math.PI);
+    ctx.stroke();
+    if (opt.fillPolygons) {
+      ctx.fillStyle = opt.useDepth ? "rgba(255, 255, 200, 0.3)" : opt.color;
+      ctx.fill();
+    }
+  }
+}
+function drawGazeSpheres(f, ctx) {
+  var _a;
+  if (opt.drawGaze && ((_a = f.rotation) == null ? void 0 : _a.angle) && typeof Path2D !== "undefined") {
+    ctx.strokeStyle = "pink";
+    const valX = f.box[0] + f.box[2] / 2 - f.box[3] * rad2deg(f.rotation.angle.yaw) / 90;
+    const valY = f.box[1] + f.box[3] / 2 + f.box[2] * rad2deg(f.rotation.angle.pitch) / 90;
+    const pathV = new Path2D(`
+      M ${f.box[0] + f.box[2] / 2} ${f.box[1]}
       C
-        ${o} ${e.box[1]},
-        ${o} ${e.box[1]+e.box[3]},
-        ${e.box[0]+e.box[2]/2} ${e.box[1]+e.box[3]}
-    `),a=new Path2D(`
-      M ${e.box[0]} ${e.box[1]+e.box[3]/2}
+        ${valX} ${f.box[1]},
+        ${valX} ${f.box[1] + f.box[3]},
+        ${f.box[0] + f.box[2] / 2} ${f.box[1] + f.box[3]}
+    `);
+    const pathH = new Path2D(`
+      M ${f.box[0]} ${f.box[1] + f.box[3] / 2}
       C 
-        ${e.box[0]} ${r},
-        ${e.box[0]+e.box[2]} ${r},
-        ${e.box[0]+e.box[2]} ${e.box[1]+e.box[3]/2}
-    `);t.stroke(a),t.stroke(s)}}function CA(e,t){var n;if(V.drawGaze&&((n=e.rotation)==null?void 0:n.gaze.strength)&&e.rotation.gaze.bearing&&e.annotations.leftEyeIris&&e.annotations.rightEyeIris&&e.annotations.leftEyeIris[0]&&e.annotations.rightEyeIris[0]){t.strokeStyle="pink",t.fillStyle="pink";let o=[e.annotations.leftEyeIris[0][0]+Math.sin(e.rotation.gaze.bearing)*e.rotation.gaze.strength*e.box[3],e.annotations.leftEyeIris[0][1]+Math.cos(e.rotation.gaze.bearing)*e.rotation.gaze.strength*e.box[2]];Et(t,[e.annotations.leftEyeIris[0][0],e.annotations.leftEyeIris[0][1]],[o[0],o[1]],4);let r=[e.annotations.rightEyeIris[0][0]+Math.sin(e.rotation.gaze.bearing)*e.rotation.gaze.strength*e.box[3],e.annotations.rightEyeIris[0][1]+Math.cos(e.rotation.gaze.bearing)*e.rotation.gaze.strength*e.box[2]];Et(t,[e.annotations.rightEyeIris[0][0],e.annotations.rightEyeIris[0][1]],[r[0],r[1]],4)}}function LA(e,t){if(V.drawPolygons&&e.mesh.length>=468){t.lineWidth=1;for(let n=0;n<me.length/3;n++){let o=[me[n*3+0],me[n*3+1],me[n*3+2]].map(r=>e.mesh[r]);wt(t,o,V)}IA(e,t)}}function WA(e,t){if(V.drawPoints&&e.mesh.length>=468)for(let n=0;n<e.mesh.length;n++)K0(t,e.mesh[n][0],e.mesh[n][1],e.mesh[n][2],V),V.drawAttention&&(r2.includes(n)&&K0(t,e.mesh[n][0],e.mesh[n][1],e.mesh[n][2]+127,V),he.includes(n)&&K0(t,e.mesh[n][0],e.mesh[n][1],e.mesh[n][2]-127,V),be.includes(n)&&K0(t,e.mesh[n][0],e.mesh[n][1],e.mesh[n][2]-127,V))}function FA(e,t){V.drawBoxes&&V0(t,e.box[0],e.box[1],e.box[2],e.box[3],V)}function De(e,t,n){if(V=J(x0,n),!t||!e)return;let o=k0(e);if(!!o){o.font=V.font,o.strokeStyle=V.color,o.fillStyle=V.color;for(let r of t)FA(r,o),NA(r,o),r.mesh&&r.mesh.length>0&&(WA(r,o),LA(r,o),OA(r,o),CA(r,o))}}function Ze(e,t,n){let o=J(x0,n);if(!t||!e)return;let r=k0(e);if(!!r){r.lineJoin="round";for(let 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hand"}):s&&r&&r.position[1]<s.position[1]&&t.push({body:n,gesture:"raise right hand"});let a=e[n].keypoints.find(c=>c.part==="leftShoulder"),i=e[n].keypoints.find(c=>c.part==="rightShoulder");a&&i&&Math.abs(a.positionRaw[1]-i.positionRaw[1])>.1&&t.push({body:n,gesture:`leaning ${a.position[1]>i.position[1]?"left":"right"}`})}return t},wn=e=>{if(!e)return[];let t=[];for(let n=0;n<e.length;n++)if(e[n].mesh&&e[n].mesh.length>450){let o=(e[n].mesh[33][2]||0)-(e[n].mesh[263][2]||0),r=e[n].mesh[33][0]-e[n].mesh[263][0];Math.abs(o/r)<=.15?t.push({face:n,gesture:"facing center"}):t.push({face:n,gesture:`facing ${o<0?"left":"right"}`}),Math.abs(e[n].mesh[374][1]-e[n].mesh[386][1])/Math.abs(e[n].mesh[443][1]-e[n].mesh[450][1])<.2&&t.push({face:n,gesture:"blink left eye"}),Math.abs(e[n].mesh[145][1]-e[n].mesh[159][1])/Math.abs(e[n].mesh[223][1]-e[n].mesh[230][1])<.2&&t.push({face:n,gesture:"blink right eye"});let i=Math.min(100,500*Math.abs(e[n].mesh[13][1]-e[n].mesh[14][1])/Math.abs(e[n].mesh[10][1]-e[n].mesh[152][1]));i>10&&t.push({face:n,gesture:`mouth ${Math.trunc(i)}% open`});let c=e[n].mesh[152][2]||0;Math.abs(c)>10&&t.push({face:n,gesture:`head ${c<0?"up":"down"}`})}return t},En=e=>{var n,o,r,s;if(!e)return[];let t=[];for(let a=0;a<e.length;a++){if(!((o=(n=e[a].annotations)==null?void 0:n.leftEyeIris)!=null&&o[0])||!((s=(r=e[a].annotations)==null?void 0:r.rightEyeIris)!=null&&s[0]))continue;let i=e[a].annotations.leftEyeIris[3][0]-e[a].annotations.leftEyeIris[1][0],c=e[a].annotations.leftEyeIris[4][1]-e[a].annotations.leftEyeIris[2][1],d=Math.abs(i*c),y=e[a].annotations.rightEyeIris[3][0]-e[a].annotations.rightEyeIris[1][0],l=e[a].annotations.rightEyeIris[4][1]-e[a].annotations.rightEyeIris[2][1],f=Math.abs(y*l),x=!1;Math.abs(d-f)/Math.max(d,f)<.25&&(x=!0,t.push({iris:a,gesture:"facing center"}));let u=Math.abs(e[a].mesh[263][0]-e[a].annotations.leftEyeIris[0][0])/e[a].box[2],M=Math.abs(e[a].mesh[33][0]-e[a].annotations.rightEyeIris[0][0])/e[a].box[2];(u>.06||M>.06)&&(x=!1),u>M?u>.05&&t.push({iris:a,gesture:"looking right"}):M>.05&&t.push({iris:a,gesture:"looking left"});let v=Math.abs(e[a].mesh[145][1]-e[a].annotations.rightEyeIris[0][1])/e[a].box[3],P=Math.abs(e[a].mesh[374][1]-e[a].annotations.leftEyeIris[0][1])/e[a].box[3];(P<.01||v<.01||P>.022||v>.022)&&(x=!1),(P<.01||v<.01)&&t.push({iris:a,gesture:"looking down"}),(P>.022||v>.022)&&t.push({iris:a,gesture:"looking up"}),x&&t.push({iris:a,gesture:"looking center"})}return t},zn=e=>{if(!e)return[];let t=[];for(let n=0;n<e.length;n++){let o=[];if(e[n].annotations)for(let[r,s]of Object.entries(e[n].annotations))r!=="palmBase"&&Array.isArray(s)&&s[0]&&o.push({name:r.toLowerCase(),position:s[0]});if(o&&o.length>0){let r=o.reduce((a,i)=>(a.position[2]||0)<(i.position[2]||0)?a:i);t.push({hand:n,gesture:`${r.name} forward`});let s=o.reduce((a,i)=>a.position[1]<i.position[1]?a:i);t.push({hand:n,gesture:`${s.name} up`})}if(e[n].keypoints){let r=W3(e[n].keypoints);for(let s of r)t.push({hand:n,gesture:s.name})}}return t};var j={face:[],body:[],hand:[],gesture:[],object:[],persons:[],performance:{},timestamp:0,error:null},Ct=0;function Sn(e,t){var a,i,c,d,y,l,f,x,m,u,M,v,P,p,g,S,E,I,H,U,F,B,$;let n=T();if(!e)return{face:[],body:[],hand:[],gesture:[],object:[],persons:[],performance:{},timestamp:0,error:null};let o=Date.now()-e.timestamp,r=o<1e3?8-Math.log(o+1):1;if(e.canvas&&(j.canvas=e.canvas),e.error&&(j.error=e.error),!j.body||e.body.length!==j.body.length)j.body=JSON.parse(JSON.stringify(e.body));else for(let R=0;R<e.body.length;R++){let C=e.body[R].box.map((L,G)=>((r-1)*j.body[R].box[G]+L)/r),l0=e.body[R].boxRaw.map((L,G)=>((r-1)*j.body[R].boxRaw[G]+L)/r),X=e.body[R].keypoints.map((L,G)=>{var w0,E0,J0,Q0,D0,Je,z,Qe,we;return{score:L.score,part:L.part,position:[j.body[R].keypoints[G]?((r-1)*(j.body[R].keypoints[G].position[0]||0)+(L.position[0]||0))/r:L.position[0],j.body[R].keypoints[G]?((r-1)*(j.body[R].keypoints[G].position[1]||0)+(L.position[1]||0))/r:L.position[1],j.body[R].keypoints[G]?((r-1)*(j.body[R].keypoints[G].position[2]||0)+(L.position[2]||0))/r:L.position[2]],positionRaw:[j.body[R].keypoints[G]?((r-1)*(j.body[R].keypoints[G].positionRaw[0]||0)+(L.positionRaw[0]||0))/r:L.positionRaw[0],j.body[R].keypoints[G]?((r-1)*(j.body[R].keypoints[G].positionRaw[1]||0)+(L.positionRaw[1]||0))/r:L.positionRaw[1],j.body[R].keypoints[G]?((r-1)*(j.body[R].keypoints[G].positionRaw[2]||0)+(L.positionRaw[2]||0))/r:L.positionRaw[2]],distance:[j.body[R].keypoints[G]?((r-1)*(((w0=j.body[R].keypoints[G].distance)==null?void 0:w0[0])||0)+(((E0=L.distance)==null?void 0:E0[0])||0))/r:(J0=L.distance)==null?void 0:J0[0],j.body[R].keypoints[G]?((r-1)*(((Q0=j.body[R].keypoints[G].distance)==null?void 0:Q0[1])||0)+(((D0=L.distance)==null?void 0:D0[1])||0))/r:(Je=L.distance)==null?void 0:Je[1],j.body[R].keypoints[G]?((r-1)*(((z=j.body[R].keypoints[G].distance)==null?void 0:z[2])||0)+(((Qe=L.distance)==null?void 0:Qe[2])||0))/r:(we=L.distance)==null?void 0:we[2]]}}),o0={},e0={connected:{}};(a=t.body.modelPath)!=null&&a.includes("efficientpose")?e0=O2:(i=t.body.modelPath)!=null&&i.includes("blazepose")?e0=z2:(c=t.body.modelPath)!=null&&c.includes("movenet")&&(e0=l2);for(let[L,G]of Object.entries(e0.connected)){let w0=[];for(let E0=0;E0<G.length-1;E0++){let J0=X.find(D0=>D0.part===G[E0]),Q0=X.find(D0=>D0.part===G[E0+1]);J0&&Q0&&w0.push([J0.position,Q0.position])}o0[L]=w0}j.body[R]={...e.body[R],box:C,boxRaw:l0,keypoints:X,annotations:o0}}if(!j.hand||e.hand.length!==j.hand.length)j.hand=JSON.parse(JSON.stringify(e.hand));else for(let R=0;R<e.hand.length;R++){let C=e.hand[R].box.map((e0,L)=>((r-1)*j.hand[R].box[L]+e0)/r),l0=e.hand[R].boxRaw.map((e0,L)=>((r-1)*j.hand[R].boxRaw[L]+e0)/r);j.hand[R].keypoints.length!==e.hand[R].keypoints.length&&(j.hand[R].keypoints=e.hand[R].keypoints);let X=e.hand[R].keypoints&&e.hand[R].keypoints.length>0?e.hand[R].keypoints.map((e0,L)=>e0.map((G,w0)=>((r-1)*(j.hand[R].keypoints[L][w0]||1)+(G||0))/r)):[],o0={};if(Object.keys(j.hand[R].annotations).length!==Object.keys(e.hand[R].annotations).length)j.hand[R].annotations=e.hand[R].annotations,o0=j.hand[R].annotations;else if(e.hand[R].annotations)for(let e0 of Object.keys(e.hand[R].annotations))o0[e0]=(l=(y=(d=e.hand[R])==null?void 0:d.annotations)==null?void 0:y[e0])!=null&&l[0]?e.hand[R].annotations[e0].map((L,G)=>L.map((w0,E0)=>((r-1)*j.hand[R].annotations[e0][G][E0]+w0)/r)):null;j.hand[R]={...e.hand[R],box:C,boxRaw:l0,keypoints:X,annotations:o0}}if(!j.face||e.face.length!==j.face.length)j.face=JSON.parse(JSON.stringify(e.face));else for(let R=0;R<e.face.length;R++){let C=e.face[R].box.map((X,o0)=>((r-1)*j.face[R].box[o0]+X)/r),l0=e.face[R].boxRaw.map((X,o0)=>((r-1)*j.face[R].boxRaw[o0]+X)/r);if(e.face[R].rotation){let X={matrix:[0,0,0,0,0,0,0,0,0],angle:{roll:0,yaw:0,pitch:0},gaze:{bearing:0,strength:0}};X.matrix=(f=e.face[R].rotation)==null?void 0:f.matrix,X.angle={roll:((r-1)*(((m=(x=j.face[R].rotation)==null?void 0:x.angle)==null?void 0:m.roll)||0)+(((M=(u=e.face[R].rotation)==null?void 0:u.angle)==null?void 0:M.roll)||0))/r,yaw:((r-1)*(((P=(v=j.face[R].rotation)==null?void 0:v.angle)==null?void 0:P.yaw)||0)+(((g=(p=e.face[R].rotation)==null?void 0:p.angle)==null?void 0:g.yaw)||0))/r,pitch:((r-1)*(((E=(S=j.face[R].rotation)==null?void 0:S.angle)==null?void 0:E.pitch)||0)+(((H=(I=e.face[R].rotation)==null?void 0:I.angle)==null?void 0:H.pitch)||0))/r},X.gaze={bearing:((r-1)*(((U=j.face[R].rotation)==null?void 0:U.gaze.bearing)||0)+(((F=e.face[R].rotation)==null?void 0:F.gaze.bearing)||0))/r,strength:((r-1)*(((B=j.face[R].rotation)==null?void 0:B.gaze.strength)||0)+((($=e.face[R].rotation)==null?void 0:$.gaze.strength)||0))/r},j.face[R]={...e.face[R],rotation:X,box:C,boxRaw:l0}}else j.face[R]={...e.face[R],box:C,boxRaw:l0}}if(!j.object||e.object.length!==j.object.length)j.object=JSON.parse(JSON.stringify(e.object));else for(let R=0;R<e.object.length;R++){let C=e.object[R].box.map((X,o0)=>((r-1)*j.object[R].box[o0]+X)/r),l0=e.object[R].boxRaw.map((X,o0)=>((r-1)*j.object[R].boxRaw[o0]+X)/r);j.object[R]={...e.object[R],box:C,boxRaw:l0}}if(e.persons){let R=e.persons;if(!j.persons||R.length!==j.persons.length)j.persons=JSON.parse(JSON.stringify(R));else for(let C=0;C<R.length;C++)j.persons[C].box=R[C].box.map((l0,X)=>((r-1)*j.persons[C].box[X]+l0)/r)}e.gesture&&(j.gesture=e.gesture);let s=T();return Ct=w.perfadd?Ct+Math.round(s-n):Math.round(s-n),e.performance&&(j.performance={...e.performance,interpolate:Ct}),j}var Nn={};_0(Nn,{distance:()=>y2,match:()=>Wt,similarity:()=>Lt});function y2(e,t,n={order:2,multiplier:25}){if(!e||!e)return Number.MAX_SAFE_INTEGER;let o=0;for(let r=0;r<e.length;r++){let s=!n.order||n.order===2?e[r]-t[r]:Math.abs(e[r]-t[r]);o+=!n.order||n.order===2?s*s:s**n.order}return(n.multiplier||20)*o}var jn=(e,t,n,o)=>{if(e===0)return 1;let r=t===2?Math.sqrt(e):e**(1/t),s=(1-r/100-n)/(o-n);return Math.max(Math.min(s,1),0)};function Lt(e,t,n={order:2,multiplier:25,min:.2,max:.8}){let o=y2(e,t,n);return jn(o,n.order||2,n.min||0,n.max||1)}function Wt(e,t,n={order:2,multiplier:25,threshold:0,min:.2,max:.8}){if(!Array.isArray(e)||!Array.isArray(t)||e.length<64||t.length===0)return{index:-1,distance:Number.POSITIVE_INFINITY,similarity:0};let o=Number.MAX_SAFE_INTEGER,r=-1;for(let a=0;a<t.length;a++){let i=t[a].length===e.length?y2(e,t[a],n):Number.MAX_SAFE_INTEGER;if(i<o&&(o=i,r=a),o<(n.threshold||0))break}let s=jn(o,n.order||2,n.min||0,n.max||1);return{index:r,distance:o,similarity:s}}function In(e,t,n,o,r){var i,c,d,y,l,f;let s=0,a=[];for(let x of e){let m={id:s++,face:x,body:null,hands:{left:null,right:null},gestures:[],box:[0,0,0,0]};for(let g of t)x.box[0]>g.box[0]&&x.box[0]<g.box[0]+g.box[2]&&x.box[1]+x.box[3]>g.box[1]&&x.box[1]+x.box[3]<g.box[1]+g.box[3]&&(m.body=g);if(m.body)for(let g of n)g.box[0]+g.box[2]>m.body.box[0]&&g.box[0]+g.box[2]<m.body.box[0]+m.body.box[2]&&g.box[1]+g.box[3]>m.body.box[1]&&g.box[1]+g.box[3]<m.body.box[1]+m.body.box[3]&&m.hands&&(m.hands.left=g),g.box[0]<m.body.box[0]+m.body.box[2]&&g.box[0]>m.body.box[0]&&g.box[1]+g.box[3]>m.body.box[1]&&g.box[1]+g.box[3]<m.body.box[1]+m.body.box[3]&&m.hands&&(m.hands.right=g);for(let g of o)(g.face!==void 0&&g.face===x.id||g.iris!==void 0&&g.iris===x.id||g.body!==void 0&&g.body===((i=m.body)==null?void 0:i.id)||g.hand!==void 0&&g.hand===((c=m.hands.left)==null?void 0:c.id)||g.hand!==void 0&&g.hand===((d=m.hands.right)==null?void 0:d.id))&&m.gestures.push(g);let u=[],M=[],v=g=>{g&&g.length===4&&(u.push(g[0],g[0]+g[2]),M.push(g[1],g[1]+g[3]))};v(m.face.box),v((y=m.body)==null?void 0:y.box),v((l=m.hands.left)==null?void 0:l.box),v((f=m.hands.right)==null?void 0:f.box);let P=Math.min(...u),p=Math.min(...M);m.box=[P,p,Math.max(...u)-P,Math.max(...M)-p],(r==null?void 0:r[1])&&(r==null?void 0:r[2])&&(m.boxRaw=[m.box[0]/r[2],m.box[1]/r[1],m.box[2]/r[2],m.box[3]/r[1]]),a.push(m)}return a}var n5=`
+        ${f.box[0]} ${valY},
+        ${f.box[0] + f.box[2]} ${valY},
+        ${f.box[0] + f.box[2]} ${f.box[1] + f.box[3] / 2}
+    `);
+    ctx.stroke(pathH);
+    ctx.stroke(pathV);
+  }
+}
+function drawGazeArrows(f, ctx) {
+  var _a;
+  if (opt.drawGaze && ((_a = f.rotation) == null ? void 0 : _a.gaze.strength) && f.rotation.gaze.bearing && f.annotations.leftEyeIris && f.annotations.rightEyeIris && f.annotations.leftEyeIris[0] && f.annotations.rightEyeIris[0]) {
+    ctx.strokeStyle = "pink";
+    ctx.fillStyle = "pink";
+    const leftGaze = [
+      f.annotations.leftEyeIris[0][0] + Math.sin(f.rotation.gaze.bearing) * f.rotation.gaze.strength * f.box[3],
+      f.annotations.leftEyeIris[0][1] + Math.cos(f.rotation.gaze.bearing) * f.rotation.gaze.strength * f.box[2]
+    ];
+    arrow(ctx, [f.annotations.leftEyeIris[0][0], f.annotations.leftEyeIris[0][1]], [leftGaze[0], leftGaze[1]], 4);
+    const rightGaze = [
+      f.annotations.rightEyeIris[0][0] + Math.sin(f.rotation.gaze.bearing) * f.rotation.gaze.strength * f.box[3],
+      f.annotations.rightEyeIris[0][1] + Math.cos(f.rotation.gaze.bearing) * f.rotation.gaze.strength * f.box[2]
+    ];
+    arrow(ctx, [f.annotations.rightEyeIris[0][0], f.annotations.rightEyeIris[0][1]], [rightGaze[0], rightGaze[1]], 4);
+  }
+}
+function drawFacePolygons(f, ctx) {
+  if (opt.drawPolygons && f.mesh.length >= 468) {
+    ctx.lineWidth = 1;
+    for (let i = 0; i < TRI468.length / 3; i++) {
+      const points = [TRI468[i * 3 + 0], TRI468[i * 3 + 1], TRI468[i * 3 + 2]].map((index2) => f.mesh[index2]);
+      lines(ctx, points, opt);
+    }
+    drawIrisElipse(f, ctx);
+  }
+}
+function drawFacePoints(f, ctx) {
+  if (opt.drawPoints && f.mesh.length >= 468) {
+    for (let i = 0; i < f.mesh.length; i++) {
+      point(ctx, f.mesh[i][0], f.mesh[i][1], f.mesh[i][2], opt);
+      if (opt.drawAttention) {
+        if (LANDMARKS_REFINEMENT_LIPS_CONFIG.includes(i))
+          point(ctx, f.mesh[i][0], f.mesh[i][1], f.mesh[i][2] + 127, opt);
+        if (LANDMARKS_REFINEMENT_LEFT_EYE_CONFIG.includes(i))
+          point(ctx, f.mesh[i][0], f.mesh[i][1], f.mesh[i][2] - 127, opt);
+        if (LANDMARKS_REFINEMENT_RIGHT_EYE_CONFIG.includes(i))
+          point(ctx, f.mesh[i][0], f.mesh[i][1], f.mesh[i][2] - 127, opt);
+      }
+    }
+  }
+}
+function drawFaceBoxes(f, ctx) {
+  if (opt.drawBoxes) {
+    rect(ctx, f.box[0], f.box[1], f.box[2], f.box[3], opt);
+  }
+}
+function face(inCanvas2, result, drawOptions) {
+  opt = mergeDeep(options3, drawOptions);
+  if (!result || !inCanvas2)
+    return;
+  const ctx = getCanvasContext(inCanvas2);
+  if (!ctx)
+    return;
+  ctx.font = opt.font;
+  ctx.strokeStyle = opt.color;
+  ctx.fillStyle = opt.color;
+  for (const f of result) {
+    drawFaceBoxes(f, ctx);
+    drawLabels(f, ctx);
+    if (f.mesh && f.mesh.length > 0) {
+      drawFacePoints(f, ctx);
+      drawFacePolygons(f, ctx);
+      drawGazeSpheres(f, ctx);
+      drawGazeArrows(f, ctx);
+    }
+  }
+}
+
+// src/draw/body.ts
+function body(inCanvas2, result, drawOptions) {
+  const localOptions = mergeDeep(options3, drawOptions);
+  if (!result || !inCanvas2)
+    return;
+  const ctx = getCanvasContext(inCanvas2);
+  if (!ctx)
+    return;
+  ctx.lineJoin = "round";
+  for (let i = 0; i < result.length; i++) {
+    ctx.strokeStyle = localOptions.color;
+    ctx.fillStyle = localOptions.color;
+    ctx.lineWidth = localOptions.lineWidth;
+    ctx.font = localOptions.font;
+    if (localOptions.drawBoxes && result[i].box && result[i].box.length === 4) {
+      rect(ctx, result[i].box[0], result[i].box[1], result[i].box[2], result[i].box[3], localOptions);
+      if (localOptions.drawLabels) {
+        if (localOptions.shadowColor && localOptions.shadowColor !== "") {
+          ctx.fillStyle = localOptions.shadowColor;
+          ctx.fillText(`body ${100 * result[i].score}%`, result[i].box[0] + 3, 1 + result[i].box[1] + localOptions.lineHeight, result[i].box[2]);
+        }
+        ctx.fillStyle = localOptions.labelColor;
+        ctx.fillText(`body ${100 * result[i].score}%`, result[i].box[0] + 2, 0 + result[i].box[1] + localOptions.lineHeight, result[i].box[2]);
+      }
+    }
+    if (localOptions.drawPoints && result[i].keypoints) {
+      for (let pt = 0; pt < result[i].keypoints.length; pt++) {
+        if (!result[i].keypoints[pt].score || result[i].keypoints[pt].score === 0)
+          continue;
+        ctx.fillStyle = colorDepth(result[i].keypoints[pt].position[2], localOptions);
+        point(ctx, result[i].keypoints[pt].position[0], result[i].keypoints[pt].position[1], 0, localOptions);
+      }
+    }
+    if (localOptions.drawLabels && result[i].keypoints) {
+      ctx.font = localOptions.font;
+      for (const pt of result[i].keypoints) {
+        if (!pt.score || pt.score === 0)
+          continue;
+        ctx.fillStyle = colorDepth(pt.position[2], localOptions);
+        ctx.fillText(`${pt.part} ${Math.trunc(100 * pt.score)}%`, pt.position[0] + 4, pt.position[1] + 4);
+      }
+    }
+    if (localOptions.drawPolygons && result[i].keypoints && result[i].annotations) {
+      for (const part of Object.values(result[i].annotations)) {
+        for (const connected4 of part)
+          curves(ctx, connected4, localOptions);
+      }
+    }
+  }
+}
+
+// src/draw/hand.ts
+function hand(inCanvas2, result, drawOptions) {
+  const localOptions = mergeDeep(options3, drawOptions);
+  if (!result || !inCanvas2)
+    return;
+  const ctx = getCanvasContext(inCanvas2);
+  if (!ctx)
+    return;
+  ctx.lineJoin = "round";
+  ctx.font = localOptions.font;
+  for (const h of result) {
+    if (localOptions.drawBoxes) {
+      ctx.strokeStyle = localOptions.color;
+      ctx.fillStyle = localOptions.color;
+      rect(ctx, h.box[0], h.box[1], h.box[2], h.box[3], localOptions);
+      if (localOptions.drawLabels) {
+        if (localOptions.shadowColor && localOptions.shadowColor !== "") {
+          ctx.fillStyle = localOptions.shadowColor;
+          ctx.fillText(`hand:${Math.trunc(100 * h.score)}%`, h.box[0] + 3, 1 + h.box[1] + localOptions.lineHeight, h.box[2]);
+        }
+        ctx.fillStyle = localOptions.labelColor;
+        ctx.fillText(`hand:${Math.trunc(100 * h.score)}%`, h.box[0] + 2, 0 + h.box[1] + localOptions.lineHeight, h.box[2]);
+      }
+      ctx.stroke();
+    }
+    if (localOptions.drawPoints) {
+      if (h.keypoints && h.keypoints.length > 0) {
+        for (const pt of h.keypoints) {
+          ctx.fillStyle = colorDepth(pt[2], localOptions);
+          point(ctx, pt[0], pt[1], 0, localOptions);
+        }
+      }
+    }
+    if (localOptions.drawLabels && h.annotations) {
+      const addHandLabel = (part, title) => {
+        if (!part || part.length === 0 || !part[0])
+          return;
+        const z = part[part.length - 1][2] || -256;
+        ctx.fillStyle = colorDepth(z, localOptions);
+        ctx.fillText(title, part[part.length - 1][0] + 4, part[part.length - 1][1] + 4);
+      };
+      ctx.font = localOptions.font;
+      addHandLabel(h.annotations.index, "index");
+      addHandLabel(h.annotations.middle, "middle");
+      addHandLabel(h.annotations.ring, "ring");
+      addHandLabel(h.annotations.pinky, "pinky");
+      addHandLabel(h.annotations.thumb, "thumb");
+      addHandLabel(h.annotations.palm, "palm");
+    }
+    if (localOptions.drawPolygons && h.annotations) {
+      const addHandLine = (part) => {
+        if (!part || part.length === 0 || !part[0])
+          return;
+        for (let i = 0; i < part.length; i++) {
+          ctx.beginPath();
+          const z = part[i][2] || 0;
+          ctx.strokeStyle = colorDepth(i * z, localOptions);
+          ctx.moveTo(part[i > 0 ? i - 1 : 0][0], part[i > 0 ? i - 1 : 0][1]);
+          ctx.lineTo(part[i][0], part[i][1]);
+          ctx.stroke();
+        }
+      };
+      ctx.lineWidth = localOptions.lineWidth;
+      addHandLine(h.annotations.index);
+      addHandLine(h.annotations.middle);
+      addHandLine(h.annotations.ring);
+      addHandLine(h.annotations.pinky);
+      addHandLine(h.annotations.thumb);
+    }
+  }
+}
+
+// src/draw/object.ts
+function object(inCanvas2, result, drawOptions) {
+  const localOptions = mergeDeep(options3, drawOptions);
+  if (!result || !inCanvas2)
+    return;
+  const ctx = getCanvasContext(inCanvas2);
+  if (!ctx)
+    return;
+  ctx.lineJoin = "round";
+  ctx.font = localOptions.font;
+  for (const h of result) {
+    if (localOptions.drawBoxes) {
+      ctx.strokeStyle = localOptions.color;
+      ctx.fillStyle = localOptions.color;
+      rect(ctx, h.box[0], h.box[1], h.box[2], h.box[3], localOptions);
+      if (localOptions.drawLabels) {
+        const label = `${h.label} ${Math.round(100 * h.score)}%`;
+        if (localOptions.shadowColor && localOptions.shadowColor !== "") {
+          ctx.fillStyle = localOptions.shadowColor;
+          ctx.fillText(label, h.box[0] + 3, 1 + h.box[1] + localOptions.lineHeight, h.box[2]);
+        }
+        ctx.fillStyle = localOptions.labelColor;
+        ctx.fillText(label, h.box[0] + 2, 0 + h.box[1] + localOptions.lineHeight, h.box[2]);
+      }
+      ctx.stroke();
+    }
+  }
+}
+
+// src/draw/gesture.ts
+function gesture(inCanvas2, result, drawOptions) {
+  const localOptions = mergeDeep(options3, drawOptions);
+  if (!result || !inCanvas2)
+    return;
+  if (localOptions.drawGestures) {
+    const ctx = getCanvasContext(inCanvas2);
+    if (!ctx)
+      return;
+    ctx.font = localOptions.font;
+    ctx.fillStyle = localOptions.color;
+    let i = 1;
+    for (let j = 0; j < result.length; j++) {
+      let where = [];
+      let what = [];
+      [where, what] = Object.entries(result[j]);
+      if (what.length > 1 && what[1].length > 0) {
+        const who = where[1] > 0 ? `#${where[1]}` : "";
+        const label = `${where[0]} ${who}: ${what[1]}`;
+        if (localOptions.shadowColor && localOptions.shadowColor !== "") {
+          ctx.fillStyle = localOptions.shadowColor;
+          ctx.fillText(label, 8, 2 + i * localOptions.lineHeight);
+        }
+        ctx.fillStyle = localOptions.labelColor;
+        ctx.fillText(label, 6, 0 + i * localOptions.lineHeight);
+        i += 1;
+      }
+    }
+  }
+}
+
+// src/draw/draw.ts
+var drawTime = 0;
+function person(inCanvas2, result, drawOptions) {
+  const localOptions = mergeDeep(options3, drawOptions);
+  if (!result || !inCanvas2)
+    return;
+  const ctx = getCanvasContext(inCanvas2);
+  if (!ctx)
+    return;
+  ctx.lineJoin = "round";
+  ctx.font = localOptions.font;
+  for (let i = 0; i < result.length; i++) {
+    if (localOptions.drawBoxes) {
+      ctx.strokeStyle = localOptions.color;
+      ctx.fillStyle = localOptions.color;
+      rect(ctx, result[i].box[0], result[i].box[1], result[i].box[2], result[i].box[3], localOptions);
+      if (localOptions.drawLabels) {
+        const label = `person #${i}`;
+        if (localOptions.shadowColor && localOptions.shadowColor !== "") {
+          ctx.fillStyle = localOptions.shadowColor;
+          ctx.fillText(label, result[i].box[0] + 3, 1 + result[i].box[1] + localOptions.lineHeight, result[i].box[2]);
+        }
+        ctx.fillStyle = localOptions.labelColor;
+        ctx.fillText(label, result[i].box[0] + 2, 0 + result[i].box[1] + localOptions.lineHeight, result[i].box[2]);
+      }
+      ctx.stroke();
+    }
+  }
+}
+function canvas2(input, output) {
+  if (!input || !output)
+    return;
+  const ctx = getCanvasContext(output);
+  if (!ctx)
+    return;
+  ctx.drawImage(input, 0, 0);
+}
+async function all(inCanvas2, result, drawOptions) {
+  if (!(result == null ? void 0 : result.performance) || !inCanvas2)
+    return null;
+  const timeStamp = now();
+  const localOptions = mergeDeep(options3, drawOptions);
+  const promise = Promise.all([
+    face(inCanvas2, result.face, localOptions),
+    body(inCanvas2, result.body, localOptions),
+    hand(inCanvas2, result.hand, localOptions),
+    object(inCanvas2, result.object, localOptions),
+    gesture(inCanvas2, result.gesture, localOptions)
+  ]);
+  drawTime = env.perfadd ? drawTime + Math.round(now() - timeStamp) : Math.round(now() - timeStamp);
+  result.performance.draw = drawTime;
+  return promise;
+}
+
+// src/face/mask.ts
+var expandFact = 0.1;
+var alpha = 0.5;
+function insidePoly(x, y, polygon) {
+  let inside = false;
+  let j = polygon.length - 1;
+  for (let i = 0; i < polygon.length; j = i++) {
+    if (polygon[i].y > y !== polygon[j].y > y && x < (polygon[j].x - polygon[i].x) * (y - polygon[i].y) / (polygon[j].y - polygon[i].y) + polygon[i].x)
+      inside = !inside;
+  }
+  return inside;
+}
+async function mask(face4) {
+  if (!face4.tensor)
+    return face4.tensor;
+  if (!face4.mesh || face4.mesh.length < 100)
+    return face4.tensor;
+  const width = face4.tensor.shape[2] || 0;
+  const height = face4.tensor.shape[1] || 0;
+  const buffer = await face4.tensor.buffer();
+  let silhouette = [];
+  for (const pt of meshAnnotations.silhouette)
+    silhouette.push({ x: (face4.mesh[pt][0] - face4.box[0]) / face4.box[2], y: (face4.mesh[pt][1] - face4.box[1]) / face4.box[3] });
+  if (expandFact && expandFact > 0)
+    silhouette = silhouette.map((pt) => ({ x: pt.x > 0.5 ? pt.x + expandFact : pt.x - expandFact, y: pt.y > 0.5 ? pt.y + expandFact : pt.y - expandFact }));
+  for (let x = 0; x < width; x++) {
+    for (let y = 0; y < height; y++) {
+      const inside = insidePoly(x / width, y / width, silhouette);
+      if (!inside) {
+        buffer.set(alpha * buffer.get(0, y, x, 0), 0, y, x, 0);
+        buffer.set(alpha * buffer.get(0, y, x, 1), 0, y, x, 1);
+        buffer.set(alpha * buffer.get(0, y, x, 2), 0, y, x, 2);
+      }
+    }
+  }
+  const output = buffer.toTensor();
+  tfjs_esm_exports.dispose(buffer);
+  return output;
+}
+
+// src/face/angles.ts
+var calculateGaze = (face4) => {
+  const radians = (pt1, pt2) => Math.atan2(pt1[1] - pt2[1], pt1[0] - pt2[0]);
+  if (!face4.annotations.rightEyeIris || !face4.annotations.leftEyeIris)
+    return { bearing: 0, strength: 0 };
+  const offsetIris = [0, -0.1];
+  const eyeRatio = 1;
+  const left = (face4.mesh[33][2] || 0) > (face4.mesh[263][2] || 0);
+  const irisCenter = left ? face4.mesh[473] : face4.mesh[468];
+  const eyeCenter = left ? [(face4.mesh[133][0] + face4.mesh[33][0]) / 2, (face4.mesh[133][1] + face4.mesh[33][1]) / 2] : [(face4.mesh[263][0] + face4.mesh[362][0]) / 2, (face4.mesh[263][1] + face4.mesh[362][1]) / 2];
+  const eyeSize = left ? [face4.mesh[133][0] - face4.mesh[33][0], face4.mesh[23][1] - face4.mesh[27][1]] : [face4.mesh[263][0] - face4.mesh[362][0], face4.mesh[253][1] - face4.mesh[257][1]];
+  const eyeDiff = [
+    (eyeCenter[0] - irisCenter[0]) / eyeSize[0] - offsetIris[0],
+    eyeRatio * (irisCenter[1] - eyeCenter[1]) / eyeSize[1] - offsetIris[1]
+  ];
+  let strength = Math.sqrt(eyeDiff[0] * eyeDiff[0] + eyeDiff[1] * eyeDiff[1]);
+  strength = Math.min(strength, face4.boxRaw[2] / 2, face4.boxRaw[3] / 2);
+  const bearing = (radians([0, 0], eyeDiff) + Math.PI / 2) % Math.PI;
+  return { bearing, strength };
+};
+var calculateFaceAngle = (face4, imageSize) => {
+  const normalize2 = (v) => {
+    const length = Math.sqrt(v[0] * v[0] + v[1] * v[1] + v[2] * v[2]);
+    v[0] /= length;
+    v[1] /= length;
+    v[2] /= length;
+    return v;
+  };
+  const subVectors = (a, b) => {
+    const x = a[0] - b[0];
+    const y = a[1] - b[1];
+    const z = a[2] - b[2];
+    return [x, y, z];
+  };
+  const crossVectors = (a, b) => {
+    const x = a[1] * b[2] - a[2] * b[1];
+    const y = a[2] * b[0] - a[0] * b[2];
+    const z = a[0] * b[1] - a[1] * b[0];
+    return [x, y, z];
+  };
+  const rotationMatrixToEulerAngle = (r) => {
+    const [r00, _r01, _r02, r10, r11, r12, r20, r21, r22] = r;
+    let thetaX;
+    let thetaY;
+    let thetaZ;
+    if (r10 < 1) {
+      if (r10 > -1) {
+        thetaZ = Math.asin(r10);
+        thetaY = Math.atan2(-r20, r00);
+        thetaX = Math.atan2(-r12, r11);
+      } else {
+        thetaZ = -Math.PI / 2;
+        thetaY = -Math.atan2(r21, r22);
+        thetaX = 0;
+      }
+    } else {
+      thetaZ = Math.PI / 2;
+      thetaY = Math.atan2(r21, r22);
+      thetaX = 0;
+    }
+    if (Number.isNaN(thetaX))
+      thetaX = 0;
+    if (Number.isNaN(thetaY))
+      thetaY = 0;
+    if (Number.isNaN(thetaZ))
+      thetaZ = 0;
+    return { pitch: 2 * -thetaX, yaw: 2 * -thetaY, roll: 2 * -thetaZ };
+  };
+  const mesh = face4.meshRaw;
+  if (!mesh || mesh.length < 300)
+    return { angle: { pitch: 0, yaw: 0, roll: 0 }, matrix: [1, 0, 0, 0, 1, 0, 0, 0, 1], gaze: { bearing: 0, strength: 0 } };
+  const size2 = Math.max(face4.boxRaw[2] * imageSize[0], face4.boxRaw[3] * imageSize[1]) / 1.5;
+  const pts = [mesh[10], mesh[152], mesh[234], mesh[454]].map((pt) => [pt[0] * imageSize[0] / size2, pt[1] * imageSize[1] / size2, pt[2]]);
+  const yAxis = normalize2(subVectors(pts[1], pts[0]));
+  let xAxis = normalize2(subVectors(pts[3], pts[2]));
+  const zAxis = normalize2(crossVectors(xAxis, yAxis));
+  xAxis = crossVectors(yAxis, zAxis);
+  const matrix = [
+    xAxis[0],
+    xAxis[1],
+    xAxis[2],
+    yAxis[0],
+    yAxis[1],
+    yAxis[2],
+    zAxis[0],
+    zAxis[1],
+    zAxis[2]
+  ];
+  const angle = rotationMatrixToEulerAngle(matrix);
+  const gaze = mesh.length === 478 ? calculateGaze(face4) : { bearing: 0, strength: 0 };
+  return { angle, matrix, gaze };
+};
+
+// src/face/face.ts
+var detectFace = async (instance2, input) => {
+  var _a, _b, _c, _d, _e, _f, _g, _h, _i, _j, _k, _l, _m, _n, _o, _p, _q, _r, _s, _t, _u, _v, _w, _x, _y, _z, _A, _B, _C;
+  let timeStamp = now();
+  let ageRes;
+  let gearRes;
+  let genderRes;
+  let emotionRes;
+  let mobilefacenetRes;
+  let insightfaceRes;
+  let antispoofRes;
+  let livenessRes;
+  let descRes;
+  const faceRes = [];
+  instance2.state = "run:face";
+  const faces = await predict6(input, instance2.config);
+  instance2.performance.face = env.perfadd ? (instance2.performance.face || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);
+  if (!input.shape || input.shape.length !== 4)
+    return [];
+  if (!faces)
+    return [];
+  for (let i = 0; i < faces.length; i++) {
+    instance2.analyze("Get Face");
+    if (!faces[i].tensor || faces[i].tensor.isDisposedInternal) {
+      log("Face object is disposed:", faces[i].tensor);
+      continue;
+    }
+    if ((_a = instance2.config.face.detector) == null ? void 0 : _a.mask) {
+      const masked = await mask(faces[i]);
+      tfjs_esm_exports.dispose(faces[i].tensor);
+      if (masked)
+        faces[i].tensor = masked;
+    }
+    const rotation = faces[i].mesh && faces[i].mesh.length > 200 ? calculateFaceAngle(faces[i], [input.shape[2], input.shape[1]]) : null;
+    instance2.analyze("Start Emotion:");
+    if (instance2.config.async) {
+      emotionRes = ((_b = instance2.config.face.emotion) == null ? void 0 : _b.enabled) ? predict5(faces[i].tensor || tfjs_esm_exports.tensor([]), instance2.config, i, faces.length) : [];
+    } else {
+      instance2.state = "run:emotion";
+      timeStamp = now();
+      emotionRes = ((_c = instance2.config.face.emotion) == null ? void 0 : _c.enabled) ? await predict5(faces[i].tensor || tfjs_esm_exports.tensor([]), instance2.config, i, faces.length) : [];
+      instance2.performance.emotion = env.perfadd ? (instance2.performance.emotion || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);
+    }
+    instance2.analyze("End Emotion:");
+    instance2.analyze("Start AntiSpoof:");
+    if (instance2.config.async) {
+      antispoofRes = ((_d = instance2.config.face.antispoof) == null ? void 0 : _d.enabled) ? predict(faces[i].tensor || tfjs_esm_exports.tensor([]), instance2.config, i, faces.length) : 0;
+    } else {
+      instance2.state = "run:antispoof";
+      timeStamp = now();
+      antispoofRes = ((_e = instance2.config.face.antispoof) == null ? void 0 : _e.enabled) ? await predict(faces[i].tensor || tfjs_esm_exports.tensor([]), instance2.config, i, faces.length) : 0;
+      instance2.performance.antispoof = env.perfadd ? (instance2.performance.antispoof || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);
+    }
+    instance2.analyze("End AntiSpoof:");
+    instance2.analyze("Start Liveness:");
+    if (instance2.config.async) {
+      livenessRes = ((_f = instance2.config.face.liveness) == null ? void 0 : _f.enabled) ? predict12(faces[i].tensor || tfjs_esm_exports.tensor([]), instance2.config, i, faces.length) : 0;
+    } else {
+      instance2.state = "run:liveness";
+      timeStamp = now();
+      livenessRes = ((_g = instance2.config.face.liveness) == null ? void 0 : _g.enabled) ? await predict12(faces[i].tensor || tfjs_esm_exports.tensor([]), instance2.config, i, faces.length) : 0;
+      instance2.performance.liveness = env.perfadd ? (instance2.performance.antispoof || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);
+    }
+    instance2.analyze("End Liveness:");
+    instance2.analyze("Start GEAR:");
+    if (instance2.config.async) {
+      gearRes = ((_h = instance2.config.face.gear) == null ? void 0 : _h.enabled) ? predict8(faces[i].tensor || tfjs_esm_exports.tensor([]), instance2.config, i, faces.length) : null;
+    } else {
+      instance2.state = "run:gear";
+      timeStamp = now();
+      gearRes = ((_i = instance2.config.face.gear) == null ? void 0 : _i.enabled) ? await predict8(faces[i].tensor || tfjs_esm_exports.tensor([]), instance2.config, i, faces.length) : null;
+      instance2.performance.gear = Math.trunc(now() - timeStamp);
+    }
+    instance2.analyze("End GEAR:");
+    instance2.analyze("Start SSRNet:");
+    if (instance2.config.async) {
+      ageRes = ((_j = instance2.config.face["ssrnet"]) == null ? void 0 : _j.enabled) ? predict20(faces[i].tensor || tfjs_esm_exports.tensor([]), instance2.config, i, faces.length) : null;
+      genderRes = ((_k = instance2.config.face["ssrnet"]) == null ? void 0 : _k.enabled) ? predict21(faces[i].tensor || tfjs_esm_exports.tensor([]), instance2.config, i, faces.length) : null;
+    } else {
+      instance2.state = "run:ssrnet";
+      timeStamp = now();
+      ageRes = ((_l = instance2.config.face["ssrnet"]) == null ? void 0 : _l.enabled) ? await predict20(faces[i].tensor || tfjs_esm_exports.tensor([]), instance2.config, i, faces.length) : null;
+      genderRes = ((_m = instance2.config.face["ssrnet"]) == null ? void 0 : _m.enabled) ? await predict21(faces[i].tensor || tfjs_esm_exports.tensor([]), instance2.config, i, faces.length) : null;
+      instance2.performance.ssrnet = Math.trunc(now() - timeStamp);
+    }
+    instance2.analyze("End SSRNet:");
+    instance2.analyze("Start MobileFaceNet:");
+    if (instance2.config.async) {
+      mobilefacenetRes = ((_n = instance2.config.face["mobilefacenet"]) == null ? void 0 : _n.enabled) ? predict14(faces[i].tensor || tfjs_esm_exports.tensor([]), instance2.config, i, faces.length) : null;
+    } else {
+      instance2.state = "run:mobilefacenet";
+      timeStamp = now();
+      mobilefacenetRes = ((_o = instance2.config.face["mobilefacenet"]) == null ? void 0 : _o.enabled) ? await predict14(faces[i].tensor || tfjs_esm_exports.tensor([]), instance2.config, i, faces.length) : null;
+      instance2.performance.mobilefacenet = Math.trunc(now() - timeStamp);
+    }
+    instance2.analyze("End MobileFaceNet:");
+    instance2.analyze("Start InsightFace:");
+    if (instance2.config.async) {
+      insightfaceRes = ((_p = instance2.config.face["insightface"]) == null ? void 0 : _p.enabled) ? predict11(faces[i].tensor || tfjs_esm_exports.tensor([]), instance2.config, i, faces.length) : null;
+    } else {
+      instance2.state = "run:mobilefacenet";
+      timeStamp = now();
+      insightfaceRes = ((_q = instance2.config.face["insightface"]) == null ? void 0 : _q.enabled) ? await predict11(faces[i].tensor || tfjs_esm_exports.tensor([]), instance2.config, i, faces.length) : null;
+      instance2.performance.mobilefacenet = Math.trunc(now() - timeStamp);
+    }
+    instance2.analyze("End InsightFace:");
+    instance2.analyze("Start Description:");
+    if (instance2.config.async) {
+      descRes = predict7(faces[i].tensor || tfjs_esm_exports.tensor([]), instance2.config, i, faces.length);
+    } else {
+      instance2.state = "run:description";
+      timeStamp = now();
+      descRes = await predict7(faces[i].tensor || tfjs_esm_exports.tensor([]), instance2.config, i, faces.length);
+      instance2.performance.description = env.perfadd ? (instance2.performance.description || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);
+    }
+    instance2.analyze("End Description:");
+    if (instance2.config.async) {
+      [ageRes, genderRes, emotionRes, mobilefacenetRes, insightfaceRes, descRes, gearRes, antispoofRes, livenessRes] = await Promise.all([ageRes, genderRes, emotionRes, mobilefacenetRes, insightfaceRes, descRes, gearRes, antispoofRes, livenessRes]);
+    }
+    instance2.analyze("Finish Face:");
+    if (((_r = instance2.config.face["ssrnet"]) == null ? void 0 : _r.enabled) && ageRes && genderRes) {
+      descRes = {
+        ...descRes,
+        age: ageRes.age,
+        gender: genderRes.gender,
+        genderScore: genderRes.genderScore
+      };
+    }
+    if (((_s = instance2.config.face.gear) == null ? void 0 : _s.enabled) && gearRes) {
+      descRes = {
+        ...descRes,
+        age: gearRes.age,
+        gender: gearRes.gender,
+        genderScore: gearRes.genderScore,
+        race: gearRes.race
+      };
+    }
+    if (((_t = instance2.config.face["mobilefacenet"]) == null ? void 0 : _t.enabled) && mobilefacenetRes) {
+      descRes.descriptor = mobilefacenetRes;
+    }
+    if (((_u = instance2.config.face["insightface"]) == null ? void 0 : _u.enabled) && insightfaceRes) {
+      descRes.descriptor = insightfaceRes;
+    }
+    if (!((_v = instance2.config.face.iris) == null ? void 0 : _v.enabled)) {
+    }
+    const irisSize = ((_y = (_x = (_w = faces[i]) == null ? void 0 : _w.annotations) == null ? void 0 : _x.leftEyeIris) == null ? void 0 : _y[0]) && ((_B = (_A = (_z = faces[i]) == null ? void 0 : _z.annotations) == null ? void 0 : _A.rightEyeIris) == null ? void 0 : _B[0]) && faces[i].annotations.leftEyeIris.length > 0 && faces[i].annotations.rightEyeIris.length > 0 && faces[i].annotations.leftEyeIris[0] !== null && faces[i].annotations.rightEyeIris[0] !== null ? Math.max(Math.abs(faces[i].annotations.leftEyeIris[3][0] - faces[i].annotations.leftEyeIris[1][0]), Math.abs(faces[i].annotations.rightEyeIris[4][1] - faces[i].annotations.rightEyeIris[2][1])) / input.shape[2] : 0;
+    const tensor6 = ((_C = instance2.config.face.detector) == null ? void 0 : _C.return) ? tfjs_esm_exports.squeeze(faces[i].tensor) : null;
+    tfjs_esm_exports.dispose(faces[i].tensor);
+    if (faces[i].tensor)
+      delete faces[i].tensor;
+    const res = {
+      ...faces[i],
+      id: i
+    };
+    if (descRes.age)
+      res.age = descRes.age;
+    if (descRes.gender)
+      res.gender = descRes.gender;
+    if (descRes.genderScore)
+      res.genderScore = descRes.genderScore;
+    if (descRes.descriptor)
+      res.embedding = descRes.descriptor;
+    if (descRes.race)
+      res.race = descRes.race;
+    if (emotionRes)
+      res.emotion = emotionRes;
+    if (antispoofRes)
+      res.real = antispoofRes;
+    if (livenessRes)
+      res.live = livenessRes;
+    if (irisSize && irisSize !== 0)
+      res.iris = Math.trunc(500 / irisSize / 11.7) / 100;
+    if (rotation)
+      res.rotation = rotation;
+    if (tensor6)
+      res.tensor = tensor6;
+    faceRes.push(res);
+    instance2.analyze("End Face");
+  }
+  instance2.analyze("End FaceMesh:");
+  if (instance2.config.async) {
+    if (instance2.performance.face)
+      delete instance2.performance.face;
+    if (instance2.performance.age)
+      delete instance2.performance.age;
+    if (instance2.performance.gender)
+      delete instance2.performance.gender;
+    if (instance2.performance.emotion)
+      delete instance2.performance.emotion;
+  }
+  return faceRes;
+};
+
+// src/gesture/gesture.ts
+var body2 = (res) => {
+  if (!res)
+    return [];
+  const gestures = [];
+  for (let i = 0; i < res.length; i++) {
+    const leftWrist = res[i].keypoints.find((a) => a.part === "leftWrist");
+    const rightWrist = res[i].keypoints.find((a) => a.part === "rightWrist");
+    const nose = res[i].keypoints.find((a) => a.part === "nose");
+    if (nose && leftWrist && rightWrist && leftWrist.position[1] < nose.position[1] && rightWrist.position[1] < nose.position[1])
+      gestures.push({ body: i, gesture: "i give up" });
+    else if (nose && leftWrist && leftWrist.position[1] < nose.position[1])
+      gestures.push({ body: i, gesture: "raise left hand" });
+    else if (nose && rightWrist && rightWrist.position[1] < nose.position[1])
+      gestures.push({ body: i, gesture: "raise right hand" });
+    const leftShoulder = res[i].keypoints.find((a) => a.part === "leftShoulder");
+    const rightShoulder = res[i].keypoints.find((a) => a.part === "rightShoulder");
+    if (leftShoulder && rightShoulder && Math.abs(leftShoulder.positionRaw[1] - rightShoulder.positionRaw[1]) > 0.1) {
+      gestures.push({ body: i, gesture: `leaning ${leftShoulder.position[1] > rightShoulder.position[1] ? "left" : "right"}` });
+    }
+  }
+  return gestures;
+};
+var face2 = (res) => {
+  if (!res)
+    return [];
+  const gestures = [];
+  for (let i = 0; i < res.length; i++) {
+    if (res[i].mesh && res[i].mesh.length > 450) {
+      const zDiff = (res[i].mesh[33][2] || 0) - (res[i].mesh[263][2] || 0);
+      const xDiff = res[i].mesh[33][0] - res[i].mesh[263][0];
+      if (Math.abs(zDiff / xDiff) <= 0.15)
+        gestures.push({ face: i, gesture: "facing center" });
+      else
+        gestures.push({ face: i, gesture: `facing ${zDiff < 0 ? "left" : "right"}` });
+      const openLeft = Math.abs(res[i].mesh[374][1] - res[i].mesh[386][1]) / Math.abs(res[i].mesh[443][1] - res[i].mesh[450][1]);
+      if (openLeft < 0.2)
+        gestures.push({ face: i, gesture: "blink left eye" });
+      const openRight = Math.abs(res[i].mesh[145][1] - res[i].mesh[159][1]) / Math.abs(res[i].mesh[223][1] - res[i].mesh[230][1]);
+      if (openRight < 0.2)
+        gestures.push({ face: i, gesture: "blink right eye" });
+      const mouthOpen = Math.min(100, 500 * Math.abs(res[i].mesh[13][1] - res[i].mesh[14][1]) / Math.abs(res[i].mesh[10][1] - res[i].mesh[152][1]));
+      if (mouthOpen > 10)
+        gestures.push({ face: i, gesture: `mouth ${Math.trunc(mouthOpen)}% open` });
+      const chinDepth = res[i].mesh[152][2] || 0;
+      if (Math.abs(chinDepth) > 10)
+        gestures.push({ face: i, gesture: `head ${chinDepth < 0 ? "up" : "down"}` });
+    }
+  }
+  return gestures;
+};
+var iris2 = (res) => {
+  var _a, _b, _c, _d;
+  if (!res)
+    return [];
+  const gestures = [];
+  for (let i = 0; i < res.length; i++) {
+    if (!((_b = (_a = res[i].annotations) == null ? void 0 : _a.leftEyeIris) == null ? void 0 : _b[0]) || !((_d = (_c = res[i].annotations) == null ? void 0 : _c.rightEyeIris) == null ? void 0 : _d[0]))
+      continue;
+    const sizeXLeft = res[i].annotations.leftEyeIris[3][0] - res[i].annotations.leftEyeIris[1][0];
+    const sizeYLeft = res[i].annotations.leftEyeIris[4][1] - res[i].annotations.leftEyeIris[2][1];
+    const areaLeft = Math.abs(sizeXLeft * sizeYLeft);
+    const sizeXRight = res[i].annotations.rightEyeIris[3][0] - res[i].annotations.rightEyeIris[1][0];
+    const sizeYRight = res[i].annotations.rightEyeIris[4][1] - res[i].annotations.rightEyeIris[2][1];
+    const areaRight = Math.abs(sizeXRight * sizeYRight);
+    let center = false;
+    const difference = Math.abs(areaLeft - areaRight) / Math.max(areaLeft, areaRight);
+    if (difference < 0.25) {
+      center = true;
+      gestures.push({ iris: i, gesture: "facing center" });
+    }
+    const leftIrisCenterX = Math.abs(res[i].mesh[263][0] - res[i].annotations.leftEyeIris[0][0]) / res[i].box[2];
+    const rightIrisCenterX = Math.abs(res[i].mesh[33][0] - res[i].annotations.rightEyeIris[0][0]) / res[i].box[2];
+    if (leftIrisCenterX > 0.06 || rightIrisCenterX > 0.06)
+      center = false;
+    if (leftIrisCenterX > rightIrisCenterX) {
+      if (leftIrisCenterX > 0.05)
+        gestures.push({ iris: i, gesture: "looking right" });
+    } else {
+      if (rightIrisCenterX > 0.05)
+        gestures.push({ iris: i, gesture: "looking left" });
+    }
+    const rightIrisCenterY = Math.abs(res[i].mesh[145][1] - res[i].annotations.rightEyeIris[0][1]) / res[i].box[3];
+    const leftIrisCenterY = Math.abs(res[i].mesh[374][1] - res[i].annotations.leftEyeIris[0][1]) / res[i].box[3];
+    if (leftIrisCenterY < 0.01 || rightIrisCenterY < 0.01 || leftIrisCenterY > 0.022 || rightIrisCenterY > 0.022)
+      center = false;
+    if (leftIrisCenterY < 0.01 || rightIrisCenterY < 0.01)
+      gestures.push({ iris: i, gesture: "looking down" });
+    if (leftIrisCenterY > 0.022 || rightIrisCenterY > 0.022)
+      gestures.push({ iris: i, gesture: "looking up" });
+    if (center)
+      gestures.push({ iris: i, gesture: "looking center" });
+  }
+  return gestures;
+};
+var hand2 = (res) => {
+  if (!res)
+    return [];
+  const gestures = [];
+  for (let i = 0; i < res.length; i++) {
+    const fingers = [];
+    if (res[i].annotations) {
+      for (const [finger, pos] of Object.entries(res[i].annotations)) {
+        if (finger !== "palmBase" && Array.isArray(pos) && pos[0])
+          fingers.push({ name: finger.toLowerCase(), position: pos[0] });
+      }
+    }
+    if (fingers && fingers.length > 0) {
+      const closest = fingers.reduce((best, a) => (best.position[2] || 0) < (a.position[2] || 0) ? best : a);
+      gestures.push({ hand: i, gesture: `${closest.name} forward` });
+      const highest = fingers.reduce((best, a) => best.position[1] < a.position[1] ? best : a);
+      gestures.push({ hand: i, gesture: `${highest.name} up` });
+    }
+    if (res[i].keypoints) {
+      const poses = match(res[i].keypoints);
+      for (const pose of poses)
+        gestures.push({ hand: i, gesture: pose.name });
+    }
+  }
+  return gestures;
+};
+
+// src/util/interpolate.ts
+var bufferedResult = { face: [], body: [], hand: [], gesture: [], object: [], persons: [], performance: {}, timestamp: 0, error: null };
+var interpolateTime = 0;
+function calc2(newResult, config3) {
+  var _a, _b, _c, _d, _e, _f, _g, _h, _i, _j, _k, _l, _m, _n, _o, _p, _q, _r, _s, _t, _u, _v, _w;
+  const t0 = now();
+  if (!newResult)
+    return { face: [], body: [], hand: [], gesture: [], object: [], persons: [], performance: {}, timestamp: 0, error: null };
+  const elapsed = Date.now() - newResult.timestamp;
+  const bufferedFactor = elapsed < 1e3 ? 8 - Math.log(elapsed + 1) : 1;
+  if (newResult.canvas)
+    bufferedResult.canvas = newResult.canvas;
+  if (newResult.error)
+    bufferedResult.error = newResult.error;
+  if (!bufferedResult.body || newResult.body.length !== bufferedResult.body.length) {
+    bufferedResult.body = JSON.parse(JSON.stringify(newResult.body));
+  } else {
+    for (let i = 0; i < newResult.body.length; i++) {
+      const box = newResult.body[i].box.map((newBoxCoord, j) => ((bufferedFactor - 1) * bufferedResult.body[i].box[j] + newBoxCoord) / bufferedFactor);
+      const boxRaw = newResult.body[i].boxRaw.map((newBoxCoord, j) => ((bufferedFactor - 1) * bufferedResult.body[i].boxRaw[j] + newBoxCoord) / bufferedFactor);
+      const keypoints = newResult.body[i].keypoints.map((newKpt, j) => {
+        var _a2, _b2, _c2, _d2, _e2, _f2, _g2, _h2, _i2;
+        return {
+          score: newKpt.score,
+          part: newKpt.part,
+          position: [
+            bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].position[0] || 0) + (newKpt.position[0] || 0)) / bufferedFactor : newKpt.position[0],
+            bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].position[1] || 0) + (newKpt.position[1] || 0)) / bufferedFactor : newKpt.position[1],
+            bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].position[2] || 0) + (newKpt.position[2] || 0)) / bufferedFactor : newKpt.position[2]
+          ],
+          positionRaw: [
+            bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].positionRaw[0] || 0) + (newKpt.positionRaw[0] || 0)) / bufferedFactor : newKpt.positionRaw[0],
+            bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].positionRaw[1] || 0) + (newKpt.positionRaw[1] || 0)) / bufferedFactor : newKpt.positionRaw[1],
+            bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].positionRaw[2] || 0) + (newKpt.positionRaw[2] || 0)) / bufferedFactor : newKpt.positionRaw[2]
+          ],
+          distance: [
+            bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (((_a2 = bufferedResult.body[i].keypoints[j].distance) == null ? void 0 : _a2[0]) || 0) + (((_b2 = newKpt.distance) == null ? void 0 : _b2[0]) || 0)) / bufferedFactor : (_c2 = newKpt.distance) == null ? void 0 : _c2[0],
+            bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (((_d2 = bufferedResult.body[i].keypoints[j].distance) == null ? void 0 : _d2[1]) || 0) + (((_e2 = newKpt.distance) == null ? void 0 : _e2[1]) || 0)) / bufferedFactor : (_f2 = newKpt.distance) == null ? void 0 : _f2[1],
+            bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (((_g2 = bufferedResult.body[i].keypoints[j].distance) == null ? void 0 : _g2[2]) || 0) + (((_h2 = newKpt.distance) == null ? void 0 : _h2[2]) || 0)) / bufferedFactor : (_i2 = newKpt.distance) == null ? void 0 : _i2[2]
+          ]
+        };
+      });
+      const annotations2 = {};
+      let coords = { connected: {} };
+      if ((_a = config3.body.modelPath) == null ? void 0 : _a.includes("efficientpose"))
+        coords = efficientposecoords_exports;
+      else if ((_b = config3.body.modelPath) == null ? void 0 : _b.includes("blazepose"))
+        coords = blazeposecoords_exports;
+      else if ((_c = config3.body.modelPath) == null ? void 0 : _c.includes("movenet"))
+        coords = movenetcoords_exports;
+      for (const [name, indexes] of Object.entries(coords.connected)) {
+        const pt = [];
+        for (let j = 0; j < indexes.length - 1; j++) {
+          const pt0 = keypoints.find((kp) => kp.part === indexes[j]);
+          const pt1 = keypoints.find((kp) => kp.part === indexes[j + 1]);
+          if (pt0 && pt1)
+            pt.push([pt0.position, pt1.position]);
+        }
+        annotations2[name] = pt;
+      }
+      bufferedResult.body[i] = { ...newResult.body[i], box, boxRaw, keypoints, annotations: annotations2 };
+    }
+  }
+  if (!bufferedResult.hand || newResult.hand.length !== bufferedResult.hand.length) {
+    bufferedResult.hand = JSON.parse(JSON.stringify(newResult.hand));
+  } else {
+    for (let i = 0; i < newResult.hand.length; i++) {
+      const box = newResult.hand[i].box.map((b, j) => ((bufferedFactor - 1) * bufferedResult.hand[i].box[j] + b) / bufferedFactor);
+      const boxRaw = newResult.hand[i].boxRaw.map((b, j) => ((bufferedFactor - 1) * bufferedResult.hand[i].boxRaw[j] + b) / bufferedFactor);
+      if (bufferedResult.hand[i].keypoints.length !== newResult.hand[i].keypoints.length)
+        bufferedResult.hand[i].keypoints = newResult.hand[i].keypoints;
+      const keypoints = newResult.hand[i].keypoints && newResult.hand[i].keypoints.length > 0 ? newResult.hand[i].keypoints.map((landmark, j) => landmark.map((coord, k) => ((bufferedFactor - 1) * (bufferedResult.hand[i].keypoints[j][k] || 1) + (coord || 0)) / bufferedFactor)) : [];
+      let annotations2 = {};
+      if (Object.keys(bufferedResult.hand[i].annotations).length !== Object.keys(newResult.hand[i].annotations).length) {
+        bufferedResult.hand[i].annotations = newResult.hand[i].annotations;
+        annotations2 = bufferedResult.hand[i].annotations;
+      } else if (newResult.hand[i].annotations) {
+        for (const key of Object.keys(newResult.hand[i].annotations)) {
+          annotations2[key] = ((_f = (_e = (_d = newResult.hand[i]) == null ? void 0 : _d.annotations) == null ? void 0 : _e[key]) == null ? void 0 : _f[0]) ? newResult.hand[i].annotations[key].map((val, j) => val.map((coord, k) => ((bufferedFactor - 1) * bufferedResult.hand[i].annotations[key][j][k] + coord) / bufferedFactor)) : null;
+        }
+      }
+      bufferedResult.hand[i] = { ...newResult.hand[i], box, boxRaw, keypoints, annotations: annotations2 };
+    }
+  }
+  if (!bufferedResult.face || newResult.face.length !== bufferedResult.face.length) {
+    bufferedResult.face = JSON.parse(JSON.stringify(newResult.face));
+  } else {
+    for (let i = 0; i < newResult.face.length; i++) {
+      const box = newResult.face[i].box.map((b, j) => ((bufferedFactor - 1) * bufferedResult.face[i].box[j] + b) / bufferedFactor);
+      const boxRaw = newResult.face[i].boxRaw.map((b, j) => ((bufferedFactor - 1) * bufferedResult.face[i].boxRaw[j] + b) / bufferedFactor);
+      if (newResult.face[i].rotation) {
+        const rotation = { matrix: [0, 0, 0, 0, 0, 0, 0, 0, 0], angle: { roll: 0, yaw: 0, pitch: 0 }, gaze: { bearing: 0, strength: 0 } };
+        rotation.matrix = (_g = newResult.face[i].rotation) == null ? void 0 : _g.matrix;
+        rotation.angle = {
+          roll: ((bufferedFactor - 1) * (((_i = (_h = bufferedResult.face[i].rotation) == null ? void 0 : _h.angle) == null ? void 0 : _i.roll) || 0) + (((_k = (_j = newResult.face[i].rotation) == null ? void 0 : _j.angle) == null ? void 0 : _k.roll) || 0)) / bufferedFactor,
+          yaw: ((bufferedFactor - 1) * (((_m = (_l = bufferedResult.face[i].rotation) == null ? void 0 : _l.angle) == null ? void 0 : _m.yaw) || 0) + (((_o = (_n = newResult.face[i].rotation) == null ? void 0 : _n.angle) == null ? void 0 : _o.yaw) || 0)) / bufferedFactor,
+          pitch: ((bufferedFactor - 1) * (((_q = (_p = bufferedResult.face[i].rotation) == null ? void 0 : _p.angle) == null ? void 0 : _q.pitch) || 0) + (((_s = (_r = newResult.face[i].rotation) == null ? void 0 : _r.angle) == null ? void 0 : _s.pitch) || 0)) / bufferedFactor
+        };
+        rotation.gaze = {
+          bearing: ((bufferedFactor - 1) * (((_t = bufferedResult.face[i].rotation) == null ? void 0 : _t.gaze.bearing) || 0) + (((_u = newResult.face[i].rotation) == null ? void 0 : _u.gaze.bearing) || 0)) / bufferedFactor,
+          strength: ((bufferedFactor - 1) * (((_v = bufferedResult.face[i].rotation) == null ? void 0 : _v.gaze.strength) || 0) + (((_w = newResult.face[i].rotation) == null ? void 0 : _w.gaze.strength) || 0)) / bufferedFactor
+        };
+        bufferedResult.face[i] = { ...newResult.face[i], rotation, box, boxRaw };
+      } else {
+        bufferedResult.face[i] = { ...newResult.face[i], box, boxRaw };
+      }
+    }
+  }
+  if (!bufferedResult.object || newResult.object.length !== bufferedResult.object.length) {
+    bufferedResult.object = JSON.parse(JSON.stringify(newResult.object));
+  } else {
+    for (let i = 0; i < newResult.object.length; i++) {
+      const box = newResult.object[i].box.map((b, j) => ((bufferedFactor - 1) * bufferedResult.object[i].box[j] + b) / bufferedFactor);
+      const boxRaw = newResult.object[i].boxRaw.map((b, j) => ((bufferedFactor - 1) * bufferedResult.object[i].boxRaw[j] + b) / bufferedFactor);
+      bufferedResult.object[i] = { ...newResult.object[i], box, boxRaw };
+    }
+  }
+  if (newResult.persons) {
+    const newPersons = newResult.persons;
+    if (!bufferedResult.persons || newPersons.length !== bufferedResult.persons.length) {
+      bufferedResult.persons = JSON.parse(JSON.stringify(newPersons));
+    } else {
+      for (let i = 0; i < newPersons.length; i++) {
+        bufferedResult.persons[i].box = newPersons[i].box.map((box, j) => ((bufferedFactor - 1) * bufferedResult.persons[i].box[j] + box) / bufferedFactor);
+      }
+    }
+  }
+  if (newResult.gesture)
+    bufferedResult.gesture = newResult.gesture;
+  const t1 = now();
+  interpolateTime = env.perfadd ? interpolateTime + Math.round(t1 - t0) : Math.round(t1 - t0);
+  if (newResult.performance)
+    bufferedResult.performance = { ...newResult.performance, interpolate: interpolateTime };
+  return bufferedResult;
+}
+
+// src/face/match.ts
+var match_exports = {};
+__export(match_exports, {
+  distance: () => distance,
+  match: () => match2,
+  similarity: () => similarity
+});
+function distance(descriptor1, descriptor2, options4 = { order: 2, multiplier: 25 }) {
+  if (!descriptor1 || !descriptor1)
+    return Number.MAX_SAFE_INTEGER;
+  let sum3 = 0;
+  for (let i = 0; i < descriptor1.length; i++) {
+    const diff = !options4.order || options4.order === 2 ? descriptor1[i] - descriptor2[i] : Math.abs(descriptor1[i] - descriptor2[i]);
+    sum3 += !options4.order || options4.order === 2 ? diff * diff : diff ** options4.order;
+  }
+  return (options4.multiplier || 20) * sum3;
+}
+var normalizeDistance = (dist, order, min2, max4) => {
+  if (dist === 0)
+    return 1;
+  const root = order === 2 ? Math.sqrt(dist) : dist ** (1 / order);
+  const norm = (1 - root / 100 - min2) / (max4 - min2);
+  const clamp2 = Math.max(Math.min(norm, 1), 0);
+  return clamp2;
+};
+function similarity(descriptor1, descriptor2, options4 = { order: 2, multiplier: 25, min: 0.2, max: 0.8 }) {
+  const dist = distance(descriptor1, descriptor2, options4);
+  return normalizeDistance(dist, options4.order || 2, options4.min || 0, options4.max || 1);
+}
+function match2(descriptor, descriptors, options4 = { order: 2, multiplier: 25, threshold: 0, min: 0.2, max: 0.8 }) {
+  if (!Array.isArray(descriptor) || !Array.isArray(descriptors) || descriptor.length < 64 || descriptors.length === 0) {
+    return { index: -1, distance: Number.POSITIVE_INFINITY, similarity: 0 };
+  }
+  let lowestDistance = Number.MAX_SAFE_INTEGER;
+  let index2 = -1;
+  for (let i = 0; i < descriptors.length; i++) {
+    const res = descriptors[i].length === descriptor.length ? distance(descriptor, descriptors[i], options4) : Number.MAX_SAFE_INTEGER;
+    if (res < lowestDistance) {
+      lowestDistance = res;
+      index2 = i;
+    }
+    if (lowestDistance < (options4.threshold || 0))
+      break;
+  }
+  const normalizedSimilarity = normalizeDistance(lowestDistance, options4.order || 2, options4.min || 0, options4.max || 1);
+  return { index: index2, distance: lowestDistance, similarity: normalizedSimilarity };
+}
+
+// src/util/persons.ts
+function join2(faces, bodies, hands, gestures, shape) {
+  var _a, _b, _c, _d, _e, _f;
+  let id = 0;
+  const persons = [];
+  for (const face4 of faces) {
+    const person2 = { id: id++, face: face4, body: null, hands: { left: null, right: null }, gestures: [], box: [0, 0, 0, 0] };
+    for (const body4 of bodies) {
+      if (face4.box[0] > body4.box[0] && face4.box[0] < body4.box[0] + body4.box[2] && face4.box[1] + face4.box[3] > body4.box[1] && face4.box[1] + face4.box[3] < body4.box[1] + body4.box[3]) {
+        person2.body = body4;
+      }
+    }
+    if (person2.body) {
+      for (const hand3 of hands) {
+        if (hand3.box[0] + hand3.box[2] > person2.body.box[0] && hand3.box[0] + hand3.box[2] < person2.body.box[0] + person2.body.box[2] && hand3.box[1] + hand3.box[3] > person2.body.box[1] && hand3.box[1] + hand3.box[3] < person2.body.box[1] + person2.body.box[3]) {
+          if (person2.hands)
+            person2.hands.left = hand3;
+        }
+        if (hand3.box[0] < person2.body.box[0] + person2.body.box[2] && hand3.box[0] > person2.body.box[0] && hand3.box[1] + hand3.box[3] > person2.body.box[1] && hand3.box[1] + hand3.box[3] < person2.body.box[1] + person2.body.box[3]) {
+          if (person2.hands)
+            person2.hands.right = hand3;
+        }
+      }
+    }
+    for (const gesture2 of gestures) {
+      if (gesture2["face"] !== void 0 && gesture2["face"] === face4.id)
+        person2.gestures.push(gesture2);
+      else if (gesture2["iris"] !== void 0 && gesture2["iris"] === face4.id)
+        person2.gestures.push(gesture2);
+      else if (gesture2["body"] !== void 0 && gesture2["body"] === ((_a = person2.body) == null ? void 0 : _a.id))
+        person2.gestures.push(gesture2);
+      else if (gesture2["hand"] !== void 0 && gesture2["hand"] === ((_b = person2.hands.left) == null ? void 0 : _b.id))
+        person2.gestures.push(gesture2);
+      else if (gesture2["hand"] !== void 0 && gesture2["hand"] === ((_c = person2.hands.right) == null ? void 0 : _c.id))
+        person2.gestures.push(gesture2);
+    }
+    const x = [];
+    const y = [];
+    const extractXY = (box) => {
+      if (box && box.length === 4) {
+        x.push(box[0], box[0] + box[2]);
+        y.push(box[1], box[1] + box[3]);
+      }
+    };
+    extractXY(person2.face.box);
+    extractXY((_d = person2.body) == null ? void 0 : _d.box);
+    extractXY((_e = person2.hands.left) == null ? void 0 : _e.box);
+    extractXY((_f = person2.hands.right) == null ? void 0 : _f.box);
+    const minX = Math.min(...x);
+    const minY = Math.min(...y);
+    person2.box = [minX, minY, Math.max(...x) - minX, Math.max(...y) - minY];
+    if ((shape == null ? void 0 : shape[1]) && (shape == null ? void 0 : shape[2]))
+      person2.boxRaw = [person2.box[0] / shape[2], person2.box[1] / shape[1], person2.box[2] / shape[2], person2.box[3] / shape[1]];
+    persons.push(person2);
+  }
+  return persons;
+}
+
+// src/sample.ts
+var face3 = `
 /9j/4AAQSkZJRgABAQEAYABgAAD/4QBoRXhpZgAATU0AKgAAAAgABAEaAAUAAAABAAAAPgEbAAUA
 AAABAAAARgEoAAMAAAABAAIAAAExAAIAAAARAAAATgAAAAAAAABgAAAAAQAAAGAAAAABcGFpbnQu
 bmV0IDQuMi4xMwAA/9sAQwAGBAUGBQQGBgUGBwcGCAoQCgoJCQoUDg8MEBcUGBgXFBYWGh0lHxob
@@ -259,7 +13899,8 @@ PQ4GJ+ashuK0MhWaoWcA0AaOmASMK7jRNPWYBmHyiuepO2x10qfcv6vYxCzYqoGK4HVYVTJrmb5l
 c6oaM5TUJ8EgGsG4kLNUHT0M64OaqMMikSRsuKbnFMRLG3zVehOaGNE445NNlnVFpDMu6uie9Vo1
 8z5mOAOST2pDK91cNN+5tsrH3PrW54a06KxT7fdrlh/q1Pc+tJ6IUdZGvHPLezMcnBOWbsPap5r3
 ylFtbdT1xUWNWzU0/Zbwlgfmx8zGsHWtRHmMqE59aAMyNifvHPc1f0gtPdqkY5JosJHeNci2tktY
-euPnNY+oXWZEVJNrZ9aun8SIq/CzodHuriIokhDIR1ronbKZr0o6o8ipoz//2Q==`,o5=`
+euPnNY+oXWZEVJNrZ9aun8SIq/CzodHuriIokhDIR1ronbKZr0o6o8ipoz//2Q==`;
+var body3 = `
 /9j/4AAQSkZJRgABAQAAAQABAAD/2wBDAAsICAoIBwsKCQoNDAsNERwSEQ8PESIZGhQcKSQrKigk
 JyctMkA3LTA9MCcnOEw5PUNFSElIKzZPVU5GVEBHSEX/2wBDAQwNDREPESESEiFFLicuRUVFRUVF
 RUVFRUVFRUVFRUVFRUVFRUVFRUVFRUVFRUVFRUVFRUVFRUVFRUVFRUVFRUX/wAARCASwBLADASIA
@@ -827,4 +14468,579 @@ AAAAAAJAAAAAAAAAAAAAABAJEAAAAAAAAAAAAAAAIEoBKAAAAAAAAAAAAAAABAlAAAAAAAIAAAAA
 BAkBAkBAkBAlACEgMZjdjbFW8bWrEx8YWANb6Fp+bfwab+vLDKMFK9qxH5L0bAr8OPRPKz2AY7J2
 SbAjYZAI2E7AIEgIEgIEgMdkSy2NgY7MdlmyNoBXsxmFuyNgVTVjNV3KjlBRNTlXTVHKCrlIqt5T
 lBhEMohlFerLlBjEMohMVTEARDKCITsAk2AEgAAAkAAAAAAAAAAAAAAAAAAAAAAAASAAAAAAAAD/
-2Q==`;async function UA(e){let t=(r,s="application/octet-stream")=>fetch(`data:${s};base64,${r}`).then(a=>a.blob()),n,o;switch(e.config.warmup){case"face":n=await t(n5);break;case"body":case"full":n=await t(o5);break;default:n=null}if(n){let r=await createImageBitmap(n);o=await e.detect(r,e.config),r.close()}return o}async function YA(e){return new Promise(t=>{let n;switch(e.config.warmup){case"face":n="data:image/jpeg;base64,"+n5;break;case"full":case"body":n="data:image/jpeg;base64,"+o5;break;default:n=""}let o;if(typeof Image!="undefined")o=new Image;else if(w.Image)o=new w.Image;else return;o.onload=async()=>{let r=y0(o.naturalWidth,o.naturalHeight);if(!r)h("Warmup: Canvas not found"),t(void 0);else{let s=r.getContext("2d");s&&s.drawImage(o,0,0);let a=await e.image(r),i=a.tensor?await e.detect(a.tensor,e.config):void 0;t(i)}},n?o.src=n:t(void 0)})}async function KA(e){let t=r=>Buffer.from(r,"base64"),n;e.config.warmup==="face"?n=t(n5):n=t(o5);let o;if("node"in A&&A.getBackend()==="tensorflow"){let r=A.node.decodeJpeg(n),s=A.expandDims(r,0);e.tf.dispose(r),o=await e.detect(s,e.config),e.tf.dispose(s)}else e.config.debug&&h("Warmup tfjs-node not loaded");return o}async function JA(e){let t;return typeof createImageBitmap=="function"?t=await UA(e):typeof Image!="undefined"||w.Canvas!==void 0?t=await YA(e):t=await KA(e),t}async function QA(e){var i,c,d,y;if(!A.env().flagRegistry.ENGINE_COMPILE_ONLY)return;let t=A.getBackend(),n=A.backend();if(t!=="webgl"&&t!=="humangl"||!(n!=null&&n.checkCompileCompletion))return;A.env().set("ENGINE_COMPILE_ONLY",!0);let o=A.engine().state.numTensors,r=[];for(let[l,f]of Object.entries(e.models).filter(([x,m])=>x!==null&&m!==null)){let x=(c=(i=f.inputs)==null?void 0:i[0])!=null&&c.shape?[...f.inputs[0].shape]:[1,64,64,3],m=(y=(d=f.inputs)==null?void 0:d[0])!=null&&y.dtype?f.inputs[0].dtype:"float32";for(let M=0;M<x.length;M++)x[M]===-1&&(x[M]=M===0?1:64);let u=A.zeros(x,m);try{let M=f.execute(u);r.push(l),Array.isArray(M)?M.forEach(v=>A.dispose(v)):A.dispose(M)}catch(M){h("compile fail model:",l)}A.dispose(u)}let s=await n.checkCompileCompletionAsync();n.getUniformLocations(),e.config.debug&&h("compile pass:",{models:r,kernels:s.length}),A.env().set("ENGINE_COMPILE_ONLY",!1);let a=A.engine().state.numTensors;a-o>0&&h("tensor leak:",a-o)}async function On(e,t){await i2(e,!1);let n=T();return e.state="warmup",t&&(e.config=J(e.config,t)),!e.config.warmup||e.config.warmup.length===0||e.config.warmup==="none"?{face:[],body:[],hand:[],gesture:[],object:[],performance:e.performance,timestamp:T(),persons:[],error:null}:new Promise(async o=>{await a2.load(e),await QA(e);let r=await JA(e),s=T();e.config.debug&&h("warmup",e.config.warmup,Math.round(s-n),"ms"),e.emit("warmup"),o(r)})}var Ke,f2,m2,r5,xe,Cn=class{constructor(t){k(this,"version");k(this,"config");k(this,"result");k(this,"state");k(this,"process");k(this,"tf");k(this,"env");k(this,"draw");k(this,"models");k(this,"events");k(this,"faceTriangulation");k(this,"faceUVMap");k(this,"performance");Ee(this,Ke,void 0);Ee(this,f2,void 0);Ee(this,m2,void 0);k(this,"gl");k(this,"analyze",(...t)=>{if(!G0(this,f2))return;let n=this.tf.engine().state.numTensors,o=G0(this,Ke);_e(this,Ke,n);let r=n-o;r!==0&&h(...t,r)});Ee(this,r5,t=>{if(!G0(this,m2))return null;if(!t)return"input is not defined";if(this.env.node&&!(t instanceof ze))return"input must be a tensor";try{this.tf.getBackend()}catch(n){return"backend not loaded"}return null});k(this,"similarity",Lt);k(this,"distance",y2);k(this,"match",Wt);k(this,"webcam",new g2);k(this,"emit",t=>{var n;(n=this.events)!=null&&n.dispatchEvent&&this.events.dispatchEvent(new Event(t))});Ee(this,xe,{});this.env=w;let n=($e.tfjs||A.version_core).replace(/-(.*)/,"");ye.wasmPath=`https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-backend-wasm@${n}/dist/`,ye.modelBasePath=w.browser?"../models/":"file://models/",ye.backend=w.browser?"webgl":"tensorflow",this.version=kt,Object.defineProperty(this,"version",{value:kt}),this.config=JSON.parse(JSON.stringify(ye)),Object.seal(this.config),this.config.cacheModels=typeof indexedDB!="undefined",t&&(this.config=J(this.config,t)),Mn(this.config),this.tf=A,this.state="idle",_e(this,Ke,0),_e(this,f2,!1),_e(this,m2,!1),this.performance={},this.events=typeof EventTarget!="undefined"?new EventTarget:void 0,this.models=new x2,this.draw={options:x0,canvas:(r,s)=>jt(r,s),face:(r,s,a)=>De(r,s,a),body:(r,s,a)=>Ze(r,s,a),hand:(r,s,a)=>Xe(r,s,a),gesture:(r,s,a)=>Ue(r,s,a),object:(r,s,a)=>qe(r,s,a),person:(r,s,a)=>St(r,s,a),all:(r,s,a)=>Nt(r,s,a)},this.result={face:[],body:[],hand:[],gesture:[],object:[],performance:{},timestamp:0,persons:[],error:null},this.process={tensor:null,canvas:null},this.faceTriangulation=f3,this.faceUVMap=m3,this.gl=Z,Ve(this,null,""),this.emit("create"),(this.config.debug||this.env.browser)&&h(`version: ${this.version}`),this.config.debug&&h(`tfjs version: ${this.tf.version["tfjs-core"]}`);let o=JSON.parse(JSON.stringify(this.env));delete o.kernels,delete o.initial,delete o.perfadd,this.config.debug&&h("environment:",o)}reset(){let t=this.config.backend;this.config=JSON.parse(JSON.stringify(ye)),this.config.backend=t,c5(),w.initial=!0}validate(t){let n=s5(ye,t||this.config);return n.length===0&&(this.config=J(this.config,t)),n}check(){return t5(this)}now(){return T()}image(t,n=!0){return je(t,this.config,n)}async segmentation(t,n){return bn(t,n,this.config)}enhance(t){return K5(t)}compare(t,n){return Qt(this.config,t,n)}async init(){await i2(this,!0),await this.tf.ready(),c5()}async load(t){this.state="load";let n=T(),o=Object.values(this.models).filter(a=>a).length;t&&(this.config=J(this.config,t)),this.env.initial&&(await i2(this,!1)||h("error: backend check failed"),await A.ready(),this.env.browser&&(this.config.debug&&h("configuration:",this.config),this.config.debug&&h("tf flags:",this.tf.ENV.flags))),await Rt(this),this.env.initial&&this.config.debug&&h("tf engine state:",this.tf.engine().state.numBytes,"bytes",this.tf.engine().state.numTensors,"tensors"),this.env.initial=!1,Object.values(this.models).filter(a=>a).length!==o&&(t5(this),this.emit("load"));let s=Math.trunc(T()-n);s>(this.performance.loadModels||0)&&(this.performance.loadModels=this.env.perfadd?(this.performance.loadModels||0)+s:s)}next(t=this.result){return Sn(t,this.config)}getModelStats(){return Pt(this)}async warmup(t){let n=T(),o=await On(this,t),r=T();return this.performance.warmup=Math.trunc(r-n),o}async profile(t,n){let o=await this.tf.profile(()=>this.detect(t,n)),r={},s=0;for(let i of o.kernels)r[i.name]?r[i.name]+=i.kernelTimeMs:r[i.name]=i.kernelTimeMs,s+=i.kernelTimeMs;let a=[];Object.entries(r).forEach(i=>a.push({kernel:i[0],time:i[1],perc:0}));for(let i of a)i.perc=Math.round(1e3*i.time/s)/1e3,i.time=Math.round(1e3*i.time)/1e3;return a.sort((i,c)=>c.time-i.time),a.length=20,a}async detect(t,n){return this.state="detect",new Promise(async o=>{var M,v,P,p,g,S,E,I,H,U,F,B,$,R,C,l0,X,o0,e0,L,G;this.state="config";let r;this.config=J(this.config,n),this.state="check";let s=G0(this,r5).call(this,t);s&&(h(s,t),this.emit("error"),o({face:[],body:[],hand:[],gesture:[],object:[],performance:this.performance,timestamp:T(),persons:[],error:s}));let a=T();await this.load(),r=T(),this.state="image";let i=await je(t,this.config);if(this.process=i,this.performance.inputProcess=this.env.perfadd?(this.performance.inputProcess||0)+Math.trunc(T()-r):Math.trunc(T()-r),this.analyze("Get Image:"),!i.tensor){this.config.debug&&h("could not convert input to tensor"),this.emit("error"),o({face:[],body:[],hand:[],gesture:[],object:[],performance:this.performance,timestamp:T(),persons:[],error:"could not convert input to tensor"});return}this.emit("image"),r=T(),this.config.skipAllowed=await Jt(this.config,i.tensor),this.performance.totalFrames||(this.performance.totalFrames=0),this.performance.cachedFrames||(this.performance.cachedFrames=0),this.performance.totalFrames++,this.config.skipAllowed&&this.performance.cachedFrames++,this.performance.cacheCheck=this.env.perfadd?(this.performance.cacheCheck||0)+Math.trunc(T()-r):Math.trunc(T()-r),this.analyze("Check Changed:");let c=[],d=[],y=[],l=[];this.state="detect:face",this.config.async?(c=this.config.face.enabled?Ot(this,i.tensor):[],this.performance.face&&delete this.performance.face):(r=T(),c=this.config.face.enabled?await Ot(this,i.tensor):[],this.performance.face=this.env.perfadd?(this.performance.face||0)+Math.trunc(T()-r):Math.trunc(T()-r)),this.config.async&&(this.config.body.maxDetected===-1||this.config.hand.maxDetected===-1)&&(c=await c),this.analyze("Start Body:"),this.state="detect:body";let f=this.config.body.maxDetected===-1?J(this.config,{body:{maxDetected:this.config.face.enabled?1*c.length:1}}):this.config;this.config.async?((M=this.config.body.modelPath)!=null&&M.includes("posenet")?d=this.config.body.enabled?Mt(i.tensor,f):[]:(v=this.config.body.modelPath)!=null&&v.includes("blazepose")?d=this.config.body.enabled?j5(i.tensor,f):[]:(P=this.config.body.modelPath)!=null&&P.includes("efficientpose")?d=this.config.body.enabled?F5(i.tensor,f):[]:(p=this.config.body.modelPath)!=null&&p.includes("movenet")&&(d=this.config.body.enabled?ft(i.tensor,f):[]),this.performance.body&&delete this.performance.body):(r=T(),(g=this.config.body.modelPath)!=null&&g.includes("posenet")?d=this.config.body.enabled?await Mt(i.tensor,f):[]:(S=this.config.body.modelPath)!=null&&S.includes("blazepose")?d=this.config.body.enabled?await j5(i.tensor,f):[]:(E=this.config.body.modelPath)!=null&&E.includes("efficientpose")?d=this.config.body.enabled?await F5(i.tensor,f):[]:(I=this.config.body.modelPath)!=null&&I.includes("movenet")&&(d=this.config.body.enabled?await ft(i.tensor,f):[]),this.performance.body=this.env.perfadd?(this.performance.body||0)+Math.trunc(T()-r):Math.trunc(T()-r)),this.analyze("End Body:"),this.analyze("Start Hand:"),this.state="detect:hand";let x=this.config.hand.maxDetected===-1?J(this.config,{hand:{maxDetected:this.config.face.enabled?2*c.length:1}}):this.config;this.config.async?((U=(H=this.config.hand.detector)==null?void 0:H.modelPath)!=null&&U.includes("handdetect")?y=this.config.hand.enabled?nt(i.tensor,x):[]:(B=(F=this.config.hand.detector)==null?void 0:F.modelPath)!=null&&B.includes("handtrack")&&(y=this.config.hand.enabled?st(i.tensor,x):[]),this.performance.hand&&delete this.performance.hand):(r=T(),(R=($=this.config.hand.detector)==null?void 0:$.modelPath)!=null&&R.includes("handdetect")?y=this.config.hand.enabled?await nt(i.tensor,x):[]:(l0=(C=this.config.hand.detector)==null?void 0:C.modelPath)!=null&&l0.includes("handtrack")&&(y=this.config.hand.enabled?await st(i.tensor,x):[]),this.performance.hand=this.env.perfadd?(this.performance.hand||0)+Math.trunc(T()-r):Math.trunc(T()-r)),this.analyze("End Hand:"),this.analyze("Start Object:"),this.state="detect:object",this.config.async?((X=this.config.object.modelPath)!=null&&X.includes("nanodet")?l=this.config.object.enabled?pt(i.tensor,this.config):[]:(o0=this.config.object.modelPath)!=null&&o0.includes("centernet")&&(l=this.config.object.enabled?O5(i.tensor,this.config):[]),this.performance.object&&delete this.performance.object):(r=T(),(e0=this.config.object.modelPath)!=null&&e0.includes("nanodet")?l=this.config.object.enabled?await pt(i.tensor,this.config):[]:(L=this.config.object.modelPath)!=null&&L.includes("centernet")&&(l=this.config.object.enabled?await O5(i.tensor,this.config):[]),this.performance.object=this.env.perfadd?(this.performance.object||0)+Math.trunc(T()-r):Math.trunc(T()-r)),this.analyze("End Object:"),this.state="detect:await",this.config.async&&([c,d,y,l]=await Promise.all([c,d,y,l])),this.state="detect:gesture";let m=[];this.config.gesture.enabled&&(r=T(),m=[...wn(c),...kn(d),...zn(y),...En(c)],this.config.async?this.performance.gesture&&delete this.performance.gesture:this.performance.gesture=this.env.perfadd?(this.performance.gesture||0)+Math.trunc(T()-r):Math.trunc(T()-r)),this.performance.total=this.env.perfadd?(this.performance.total||0)+Math.trunc(T()-a):Math.trunc(T()-a);let u=((G=this.process.tensor)==null?void 0:G.shape)||[];this.result={face:c,body:d,hand:y,gesture:m,object:l,performance:this.performance,canvas:this.process.canvas,timestamp:Date.now(),error:null,get persons(){return In(c,d,y,m,u)}},A.dispose(i.tensor),this.emit("detect"),this.state="idle",o(this.result)})}async sleep(t){return new Promise(n=>{setTimeout(n,t)})}async video(t,n=!0,o=0){n?(G0(this,xe)[t.id]||(this.config.debug&&h("video start",t.id),G0(this,xe)[t.id]=!0),!t.paused&&G0(this,xe)[t.id]&&t.readyState>=2&&await this.detect(t),o>0&&await this.sleep(o),G0(this,xe)[t.id]&&requestAnimationFrame(()=>this.video(t,n,o))):(this.config.debug&&h("video stop",t.id),G0(this,xe)[t.id]=!1)}};Ke=new WeakMap,f2=new WeakMap,m2=new WeakMap,r5=new WeakMap,xe=new WeakMap;export{b2 as Env,Cn as Human,Cn as default,ye as defaults,Tn as draw,w as env,Nn as match,a2 as models};
+2Q==`;
+
+// src/warmup.ts
+async function warmupBitmap(instance2) {
+  const b64toBlob = (base64, type = "application/octet-stream") => fetch(`data:${type};base64,${base64}`).then((res2) => res2.blob());
+  let blob;
+  let res;
+  switch (instance2.config.warmup) {
+    case "face":
+      blob = await b64toBlob(face3);
+      break;
+    case "body":
+    case "full":
+      blob = await b64toBlob(body3);
+      break;
+    default:
+      blob = null;
+  }
+  if (blob) {
+    const bitmap = await createImageBitmap(blob);
+    res = await instance2.detect(bitmap, instance2.config);
+    bitmap.close();
+  }
+  return res;
+}
+async function warmupCanvas(instance2) {
+  return new Promise((resolve) => {
+    let src;
+    switch (instance2.config.warmup) {
+      case "face":
+        src = "data:image/jpeg;base64," + face3;
+        break;
+      case "full":
+      case "body":
+        src = "data:image/jpeg;base64," + body3;
+        break;
+      default:
+        src = "";
+    }
+    let img;
+    if (typeof Image !== "undefined")
+      img = new Image();
+    else if (env.Image)
+      img = new env.Image();
+    else
+      return;
+    img.onload = async () => {
+      const canvas3 = canvas(img.naturalWidth, img.naturalHeight);
+      if (!canvas3) {
+        log("Warmup: Canvas not found");
+        resolve(void 0);
+      } else {
+        const ctx = canvas3.getContext("2d");
+        if (ctx)
+          ctx.drawImage(img, 0, 0);
+        const tensor6 = await instance2.image(canvas3);
+        const res = tensor6.tensor ? await instance2.detect(tensor6.tensor, instance2.config) : void 0;
+        resolve(res);
+      }
+    };
+    if (src)
+      img.src = src;
+    else
+      resolve(void 0);
+  });
+}
+async function warmupNode(instance2) {
+  const atob = (str) => Buffer.from(str, "base64");
+  let img;
+  if (instance2.config.warmup === "face")
+    img = atob(face3);
+  else
+    img = atob(body3);
+  let res;
+  if ("node" in tfjs_esm_exports && tfjs_esm_exports.getBackend() === "tensorflow") {
+    const data = tfjs_esm_exports["node"].decodeJpeg(img);
+    const expanded = tfjs_esm_exports.expandDims(data, 0);
+    instance2.tf.dispose(data);
+    res = await instance2.detect(expanded, instance2.config);
+    instance2.tf.dispose(expanded);
+  } else {
+    if (instance2.config.debug)
+      log("Warmup tfjs-node not loaded");
+  }
+  return res;
+}
+async function runInference(instance2) {
+  let res;
+  if (typeof createImageBitmap === "function")
+    res = await warmupBitmap(instance2);
+  else if (typeof Image !== "undefined" || env.Canvas !== void 0)
+    res = await warmupCanvas(instance2);
+  else
+    res = await warmupNode(instance2);
+  return res;
+}
+async function runCompile(instance2) {
+  var _a, _b, _c, _d;
+  if (!tfjs_esm_exports.env().flagRegistry.ENGINE_COMPILE_ONLY)
+    return;
+  const backendType = tfjs_esm_exports.getBackend();
+  const webGLBackend = tfjs_esm_exports.backend();
+  if (backendType !== "webgl" && backendType !== "humangl" || !(webGLBackend == null ? void 0 : webGLBackend.checkCompileCompletion)) {
+    return;
+  }
+  tfjs_esm_exports.env().set("ENGINE_COMPILE_ONLY", true);
+  const numTensorsStart = tfjs_esm_exports.engine().state.numTensors;
+  const compiledModels = [];
+  for (const [modelName, model21] of Object.entries(instance2.models).filter(([key, val]) => key !== null && val !== null)) {
+    const shape = ((_b = (_a = model21.inputs) == null ? void 0 : _a[0]) == null ? void 0 : _b.shape) ? [...model21.inputs[0].shape] : [1, 64, 64, 3];
+    const dtype = ((_d = (_c = model21.inputs) == null ? void 0 : _c[0]) == null ? void 0 : _d.dtype) ? model21.inputs[0].dtype : "float32";
+    for (let dim = 0; dim < shape.length; dim++) {
+      if (shape[dim] === -1)
+        shape[dim] = dim === 0 ? 1 : 64;
+    }
+    const tensor6 = tfjs_esm_exports.zeros(shape, dtype);
+    try {
+      const res = model21.execute(tensor6);
+      compiledModels.push(modelName);
+      if (Array.isArray(res))
+        res.forEach((t2) => tfjs_esm_exports.dispose(t2));
+      else
+        tfjs_esm_exports.dispose(res);
+    } catch (e) {
+      if (instance2.config.debug)
+        log("compile fail model:", modelName);
+    }
+    tfjs_esm_exports.dispose(tensor6);
+  }
+  const kernels = await webGLBackend.checkCompileCompletionAsync();
+  webGLBackend.getUniformLocations();
+  if (instance2.config.debug)
+    log("compile pass:", { models: compiledModels, kernels: kernels.length });
+  tfjs_esm_exports.env().set("ENGINE_COMPILE_ONLY", false);
+  const numTensorsEnd = tfjs_esm_exports.engine().state.numTensors;
+  if (numTensorsEnd - numTensorsStart > 0)
+    log("tensor leak:", numTensorsEnd - numTensorsStart);
+}
+async function warmup(instance2, userConfig) {
+  await check(instance2, false);
+  const t0 = now();
+  instance2.state = "warmup";
+  if (userConfig)
+    instance2.config = mergeDeep(instance2.config, userConfig);
+  if (!instance2.config.warmup || instance2.config.warmup.length === 0 || instance2.config.warmup === "none") {
+    return { face: [], body: [], hand: [], gesture: [], object: [], performance: instance2.performance, timestamp: now(), persons: [], error: null };
+  }
+  return new Promise(async (resolve) => {
+    await models_exports2.load(instance2);
+    await runCompile(instance2);
+    const res = await runInference(instance2);
+    const t1 = now();
+    if (instance2.config.debug)
+      log("warmup", instance2.config.warmup, Math.round(t1 - t0), "ms");
+    instance2.emit("warmup");
+    resolve(res);
+  });
+}
+
+// src/human.ts
+var _numTensors, _analyzeMemoryLeaks, _checkSanity, _sanity, _loops;
+var Human2 = class {
+  constructor(userConfig) {
+    __publicField(this, "version");
+    __publicField(this, "config");
+    __publicField(this, "result");
+    __publicField(this, "state");
+    __publicField(this, "process");
+    __publicField(this, "tf");
+    __publicField(this, "env");
+    __publicField(this, "draw");
+    __publicField(this, "models");
+    __publicField(this, "events");
+    __publicField(this, "faceTriangulation");
+    __publicField(this, "faceUVMap");
+    __publicField(this, "performance");
+    __privateAdd(this, _numTensors, void 0);
+    __privateAdd(this, _analyzeMemoryLeaks, void 0);
+    __privateAdd(this, _checkSanity, void 0);
+    __publicField(this, "gl");
+    __publicField(this, "analyze", (...msg) => {
+      if (!__privateGet(this, _analyzeMemoryLeaks))
+        return;
+      const currentTensors = this.tf.engine().state.numTensors;
+      const previousTensors = __privateGet(this, _numTensors);
+      __privateSet(this, _numTensors, currentTensors);
+      const leaked = currentTensors - previousTensors;
+      if (leaked !== 0)
+        log(...msg, leaked);
+    });
+    __privateAdd(this, _sanity, (input) => {
+      if (!__privateGet(this, _checkSanity))
+        return null;
+      if (!input)
+        return "input is not defined";
+      if (this.env.node && !(input instanceof Tensor))
+        return "input must be a tensor";
+      try {
+        this.tf.getBackend();
+      } catch (e) {
+        return "backend not loaded";
+      }
+      return null;
+    });
+    __publicField(this, "similarity", similarity);
+    __publicField(this, "distance", distance);
+    __publicField(this, "match", match2);
+    __publicField(this, "webcam", new WebCam());
+    __publicField(this, "emit", (event) => {
+      var _a;
+      if ((_a = this.events) == null ? void 0 : _a.dispatchEvent)
+        this.events.dispatchEvent(new Event(event));
+    });
+    __privateAdd(this, _loops, {});
+    this.env = env;
+    const tfVersion = (version8.tfjs || tfjs_esm_exports.version_core).replace(/-(.*)/, "");
+    config.wasmPath = `https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-backend-wasm@${tfVersion}/dist/`;
+    config.modelBasePath = env.browser ? "../models/" : "file://models/";
+    config.backend = env.browser ? "webgl" : "tensorflow";
+    this.version = version9;
+    Object.defineProperty(this, "version", { value: version9 });
+    this.config = JSON.parse(JSON.stringify(config));
+    Object.seal(this.config);
+    this.config.cacheModels = typeof indexedDB !== "undefined";
+    if (userConfig)
+      this.config = mergeDeep(this.config, userConfig);
+    setModelLoadOptions(this.config);
+    this.tf = tfjs_esm_exports;
+    this.state = "idle";
+    __privateSet(this, _numTensors, 0);
+    __privateSet(this, _analyzeMemoryLeaks, false);
+    __privateSet(this, _checkSanity, false);
+    this.performance = {};
+    this.events = typeof EventTarget !== "undefined" ? new EventTarget() : void 0;
+    this.models = new Models();
+    this.draw = {
+      options: options3,
+      canvas: (input, output) => canvas2(input, output),
+      face: (output, result, options4) => face(output, result, options4),
+      body: (output, result, options4) => body(output, result, options4),
+      hand: (output, result, options4) => hand(output, result, options4),
+      gesture: (output, result, options4) => gesture(output, result, options4),
+      object: (output, result, options4) => object(output, result, options4),
+      person: (output, result, options4) => person(output, result, options4),
+      all: (output, result, options4) => all(output, result, options4)
+    };
+    this.result = { face: [], body: [], hand: [], gesture: [], object: [], performance: {}, timestamp: 0, persons: [], error: null };
+    this.process = { tensor: null, canvas: null };
+    this.faceTriangulation = triangulation;
+    this.faceUVMap = uvmap;
+    this.gl = config2;
+    validateModel(this, null, "");
+    this.emit("create");
+    if (this.config.debug || this.env.browser)
+      log(`version: ${this.version}`);
+    if (this.config.debug)
+      log(`tfjs version: ${this.tf.version["tfjs-core"]}`);
+    const envTemp = JSON.parse(JSON.stringify(this.env));
+    delete envTemp.kernels;
+    delete envTemp.initial;
+    delete envTemp.perfadd;
+    if (this.config.debug)
+      log("environment:", envTemp);
+  }
+  reset() {
+    const currentBackend = this.config.backend;
+    this.config = JSON.parse(JSON.stringify(config));
+    this.config.backend = currentBackend;
+    reset();
+    env.initial = true;
+  }
+  validate(userConfig) {
+    const msgs = validate(config, userConfig || this.config);
+    if (msgs.length === 0)
+      this.config = mergeDeep(this.config, userConfig);
+    return msgs;
+  }
+  check() {
+    return validate2(this);
+  }
+  now() {
+    return now();
+  }
+  image(input, getTensor = true) {
+    return process2(input, this.config, getTensor);
+  }
+  async segmentation(input, userConfig) {
+    var _a, _b, _c;
+    if (userConfig)
+      this.config = mergeDeep(this.config, userConfig);
+    if (!this.config.segmentation.enabled)
+      return null;
+    const processed = await process2(input, this.config);
+    if (!processed.tensor)
+      return null;
+    let tensor6 = null;
+    if ((_a = this.config.segmentation.modelPath) == null ? void 0 : _a.includes("rvm"))
+      tensor6 = await predict18(processed.tensor, this.config);
+    if ((_b = this.config.segmentation.modelPath) == null ? void 0 : _b.includes("meet"))
+      tensor6 = await predict13(processed.tensor, this.config);
+    if ((_c = this.config.segmentation.modelPath) == null ? void 0 : _c.includes("selfie"))
+      tensor6 = await predict19(processed.tensor, this.config);
+    tfjs_esm_exports.dispose(processed.tensor);
+    return tensor6;
+  }
+  enhance(input) {
+    return enhance(input);
+  }
+  compare(firstImageTensor, secondImageTensor) {
+    return compare(this.config, firstImageTensor, secondImageTensor);
+  }
+  async init() {
+    await check(this, true);
+    await this.tf.ready();
+    reset();
+  }
+  async load(userConfig) {
+    this.state = "load";
+    const timeStamp = now();
+    const count2 = Object.values(this.models).filter((model21) => model21).length;
+    if (userConfig)
+      this.config = mergeDeep(this.config, userConfig);
+    if (this.env.initial) {
+      if (!await check(this, false))
+        log("error: backend check failed");
+      await tfjs_esm_exports.ready();
+      if (this.env.browser) {
+        if (this.config.debug)
+          log("configuration:", this.config);
+        if (this.config.debug)
+          log("tf flags:", this.tf.ENV.flags);
+      }
+    }
+    await load22(this);
+    if (this.env.initial && this.config.debug)
+      log("tf engine state:", this.tf.engine().state.numBytes, "bytes", this.tf.engine().state.numTensors, "tensors");
+    this.env.initial = false;
+    const loaded = Object.values(this.models).filter((model21) => model21).length;
+    if (loaded !== count2) {
+      validate2(this);
+      this.emit("load");
+    }
+    const current = Math.trunc(now() - timeStamp);
+    if (current > (this.performance.loadModels || 0))
+      this.performance.loadModels = this.env.perfadd ? (this.performance.loadModels || 0) + current : current;
+  }
+  next(result = this.result) {
+    return calc2(result, this.config);
+  }
+  getModelStats() {
+    return getModelStats(this);
+  }
+  async warmup(userConfig) {
+    const t0 = now();
+    const res = await warmup(this, userConfig);
+    const t1 = now();
+    this.performance.warmup = Math.trunc(t1 - t0);
+    return res;
+  }
+  async profile(input, userConfig) {
+    const profile = await this.tf.profile(() => this.detect(input, userConfig));
+    const kernels = {};
+    let total = 0;
+    for (const kernel of profile.kernels) {
+      if (kernels[kernel.name])
+        kernels[kernel.name] += kernel.kernelTimeMs;
+      else
+        kernels[kernel.name] = kernel.kernelTimeMs;
+      total += kernel.kernelTimeMs;
+    }
+    const kernelArr = [];
+    Object.entries(kernels).forEach((key) => kernelArr.push({ kernel: key[0], time: key[1], perc: 0 }));
+    for (const kernel of kernelArr) {
+      kernel.perc = Math.round(1e3 * kernel.time / total) / 1e3;
+      kernel.time = Math.round(1e3 * kernel.time) / 1e3;
+    }
+    kernelArr.sort((a, b) => b.time - a.time);
+    kernelArr.length = 20;
+    return kernelArr;
+  }
+  async detect(input, userConfig) {
+    this.state = "detect";
+    return new Promise(async (resolve) => {
+      var _a, _b, _c, _d, _e, _f, _g, _h, _i, _j, _k, _l, _m, _n, _o, _p, _q, _r, _s, _t, _u;
+      this.state = "config";
+      let timeStamp;
+      this.config = mergeDeep(this.config, userConfig);
+      this.state = "check";
+      const error = __privateGet(this, _sanity).call(this, input);
+      if (error) {
+        log(error, input);
+        this.emit("error");
+        resolve({ face: [], body: [], hand: [], gesture: [], object: [], performance: this.performance, timestamp: now(), persons: [], error });
+      }
+      const timeStart = now();
+      await this.load();
+      timeStamp = now();
+      this.state = "image";
+      const img = await process2(input, this.config);
+      this.process = img;
+      this.performance.inputProcess = this.env.perfadd ? (this.performance.inputProcess || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);
+      this.analyze("Get Image:");
+      if (!img.tensor) {
+        if (this.config.debug)
+          log("could not convert input to tensor");
+        this.emit("error");
+        resolve({ face: [], body: [], hand: [], gesture: [], object: [], performance: this.performance, timestamp: now(), persons: [], error: "could not convert input to tensor" });
+        return;
+      }
+      this.emit("image");
+      timeStamp = now();
+      this.config.skipAllowed = await skip(this.config, img.tensor);
+      if (!this.performance.totalFrames)
+        this.performance.totalFrames = 0;
+      if (!this.performance.cachedFrames)
+        this.performance.cachedFrames = 0;
+      this.performance.totalFrames++;
+      if (this.config.skipAllowed)
+        this.performance.cachedFrames++;
+      this.performance.cacheCheck = this.env.perfadd ? (this.performance.cacheCheck || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);
+      this.analyze("Check Changed:");
+      let faceRes = [];
+      let bodyRes = [];
+      let handRes = [];
+      let objectRes = [];
+      this.state = "detect:face";
+      if (this.config.async) {
+        faceRes = this.config.face.enabled ? detectFace(this, img.tensor) : [];
+        if (this.performance.face)
+          delete this.performance.face;
+      } else {
+        timeStamp = now();
+        faceRes = this.config.face.enabled ? await detectFace(this, img.tensor) : [];
+        this.performance.face = this.env.perfadd ? (this.performance.face || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);
+      }
+      if (this.config.async && (this.config.body.maxDetected === -1 || this.config.hand.maxDetected === -1))
+        faceRes = await faceRes;
+      this.analyze("Start Body:");
+      this.state = "detect:body";
+      const bodyConfig = this.config.body.maxDetected === -1 ? mergeDeep(this.config, { body: { maxDetected: this.config.face.enabled ? 1 * faceRes.length : 1 } }) : this.config;
+      if (this.config.async) {
+        if ((_a = this.config.body.modelPath) == null ? void 0 : _a.includes("posenet"))
+          bodyRes = this.config.body.enabled ? predict17(img.tensor, bodyConfig) : [];
+        else if ((_b = this.config.body.modelPath) == null ? void 0 : _b.includes("blazepose"))
+          bodyRes = this.config.body.enabled ? predict2(img.tensor, bodyConfig) : [];
+        else if ((_c = this.config.body.modelPath) == null ? void 0 : _c.includes("efficientpose"))
+          bodyRes = this.config.body.enabled ? predict4(img.tensor, bodyConfig) : [];
+        else if ((_d = this.config.body.modelPath) == null ? void 0 : _d.includes("movenet"))
+          bodyRes = this.config.body.enabled ? predict15(img.tensor, bodyConfig) : [];
+        if (this.performance.body)
+          delete this.performance.body;
+      } else {
+        timeStamp = now();
+        if ((_e = this.config.body.modelPath) == null ? void 0 : _e.includes("posenet"))
+          bodyRes = this.config.body.enabled ? await predict17(img.tensor, bodyConfig) : [];
+        else if ((_f = this.config.body.modelPath) == null ? void 0 : _f.includes("blazepose"))
+          bodyRes = this.config.body.enabled ? await predict2(img.tensor, bodyConfig) : [];
+        else if ((_g = this.config.body.modelPath) == null ? void 0 : _g.includes("efficientpose"))
+          bodyRes = this.config.body.enabled ? await predict4(img.tensor, bodyConfig) : [];
+        else if ((_h = this.config.body.modelPath) == null ? void 0 : _h.includes("movenet"))
+          bodyRes = this.config.body.enabled ? await predict15(img.tensor, bodyConfig) : [];
+        this.performance.body = this.env.perfadd ? (this.performance.body || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);
+      }
+      this.analyze("End Body:");
+      this.analyze("Start Hand:");
+      this.state = "detect:hand";
+      const handConfig = this.config.hand.maxDetected === -1 ? mergeDeep(this.config, { hand: { maxDetected: this.config.face.enabled ? 2 * faceRes.length : 1 } }) : this.config;
+      if (this.config.async) {
+        if ((_j = (_i = this.config.hand.detector) == null ? void 0 : _i.modelPath) == null ? void 0 : _j.includes("handdetect"))
+          handRes = this.config.hand.enabled ? predict9(img.tensor, handConfig) : [];
+        else if ((_l = (_k = this.config.hand.detector) == null ? void 0 : _k.modelPath) == null ? void 0 : _l.includes("handtrack"))
+          handRes = this.config.hand.enabled ? predict10(img.tensor, handConfig) : [];
+        if (this.performance.hand)
+          delete this.performance.hand;
+      } else {
+        timeStamp = now();
+        if ((_n = (_m = this.config.hand.detector) == null ? void 0 : _m.modelPath) == null ? void 0 : _n.includes("handdetect"))
+          handRes = this.config.hand.enabled ? await predict9(img.tensor, handConfig) : [];
+        else if ((_p = (_o = this.config.hand.detector) == null ? void 0 : _o.modelPath) == null ? void 0 : _p.includes("handtrack"))
+          handRes = this.config.hand.enabled ? await predict10(img.tensor, handConfig) : [];
+        this.performance.hand = this.env.perfadd ? (this.performance.hand || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);
+      }
+      this.analyze("End Hand:");
+      this.analyze("Start Object:");
+      this.state = "detect:object";
+      if (this.config.async) {
+        if ((_q = this.config.object.modelPath) == null ? void 0 : _q.includes("nanodet"))
+          objectRes = this.config.object.enabled ? predict16(img.tensor, this.config) : [];
+        else if ((_r = this.config.object.modelPath) == null ? void 0 : _r.includes("centernet"))
+          objectRes = this.config.object.enabled ? predict3(img.tensor, this.config) : [];
+        if (this.performance.object)
+          delete this.performance.object;
+      } else {
+        timeStamp = now();
+        if ((_s = this.config.object.modelPath) == null ? void 0 : _s.includes("nanodet"))
+          objectRes = this.config.object.enabled ? await predict16(img.tensor, this.config) : [];
+        else if ((_t = this.config.object.modelPath) == null ? void 0 : _t.includes("centernet"))
+          objectRes = this.config.object.enabled ? await predict3(img.tensor, this.config) : [];
+        this.performance.object = this.env.perfadd ? (this.performance.object || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);
+      }
+      this.analyze("End Object:");
+      this.state = "detect:await";
+      if (this.config.async)
+        [faceRes, bodyRes, handRes, objectRes] = await Promise.all([faceRes, bodyRes, handRes, objectRes]);
+      this.state = "detect:gesture";
+      let gestureRes = [];
+      if (this.config.gesture.enabled) {
+        timeStamp = now();
+        gestureRes = [...face2(faceRes), ...body2(bodyRes), ...hand2(handRes), ...iris2(faceRes)];
+        if (!this.config.async)
+          this.performance.gesture = this.env.perfadd ? (this.performance.gesture || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);
+        else if (this.performance.gesture)
+          delete this.performance.gesture;
+      }
+      this.performance.total = this.env.perfadd ? (this.performance.total || 0) + Math.trunc(now() - timeStart) : Math.trunc(now() - timeStart);
+      const shape = ((_u = this.process.tensor) == null ? void 0 : _u.shape) || [];
+      this.result = {
+        face: faceRes,
+        body: bodyRes,
+        hand: handRes,
+        gesture: gestureRes,
+        object: objectRes,
+        performance: this.performance,
+        canvas: this.process.canvas,
+        timestamp: Date.now(),
+        error: null,
+        get persons() {
+          return join2(faceRes, bodyRes, handRes, gestureRes, shape);
+        }
+      };
+      tfjs_esm_exports.dispose(img.tensor);
+      this.emit("detect");
+      this.state = "idle";
+      resolve(this.result);
+    });
+  }
+  async sleep(ms) {
+    return new Promise((resolve) => {
+      setTimeout(resolve, ms);
+    });
+  }
+  async video(element, run = true, delay = 0) {
+    if (run) {
+      if (!__privateGet(this, _loops)[element.id]) {
+        if (this.config.debug)
+          log("video start", element.id);
+        __privateGet(this, _loops)[element.id] = true;
+      }
+      if (!element.paused && __privateGet(this, _loops)[element.id] && element.readyState >= 2)
+        await this.detect(element);
+      if (delay > 0)
+        await this.sleep(delay);
+      if (__privateGet(this, _loops)[element.id])
+        requestAnimationFrame(() => this.video(element, run, delay));
+    } else {
+      if (this.config.debug)
+        log("video stop", element.id);
+      __privateGet(this, _loops)[element.id] = false;
+    }
+  }
+};
+_numTensors = new WeakMap();
+_analyzeMemoryLeaks = new WeakMap();
+_checkSanity = new WeakMap();
+_sanity = new WeakMap();
+_loops = new WeakMap();
+export {
+  Env,
+  Human2 as Human,
+  Human2 as default,
+  config as defaults,
+  draw_exports as draw,
+  env,
+  match_exports as match,
+  models_exports2 as models
+};
diff --git a/dist/human.esm.js b/dist/human.esm.js
index d624977b..44f09525 100644
--- a/dist/human.esm.js
+++ b/dist/human.esm.js
@@ -215,8 +215,9 @@ var config = {
   },
   segmentation: {
     enabled: false,
-    modelPath: "selfie.json",
-    blur: 8
+    modelPath: "rvm.json",
+    ratio: 0.5,
+    mode: "default"
   }
 };
 
@@ -715,7 +716,7 @@ __export(tfjs_esm_exports, {
   valueAndGrads: () => valueAndGrads,
   variable: () => variable,
   variableGrads: () => variableGrads,
-  version: () => V,
+  version: () => version82,
   version_converter: () => version3,
   version_core: () => version,
   version_layers: () => version2,
@@ -1047,7 +1048,7 @@ var require_long = __commonJS({
         167,
         11
       ])), {}).exports;
-    } catch (e2) {
+    } catch (e) {
     }
     function Long2(low, high, unsigned) {
       this.low = low | 0;
@@ -1134,8 +1135,8 @@ var require_long = __commonJS({
       }
       var radixToPower = fromNumber(pow_dbl(radix, 8));
       var result = ZERO;
-      for (var i2 = 0; i2 < str.length; i2 += 8) {
-        var size2 = Math.min(8, str.length - i2), value = parseInt(str.substring(i2, i2 + size2), radix);
+      for (var i = 0; i < str.length; i += 8) {
+        var size2 = Math.min(8, str.length - i), value = parseInt(str.substring(i, i + size2), radix);
         if (size2 < 8) {
           var power = fromNumber(pow_dbl(radix, size2));
           result = result.mul(power).add(fromNumber(value));
@@ -1663,20 +1664,20 @@ var require_alea = __commonJS({
         return prng;
       }
       function Mash() {
-        var n2 = 4022871197;
+        var n = 4022871197;
         var mash = function(data) {
           data = String(data);
-          for (var i2 = 0; i2 < data.length; i2++) {
-            n2 += data.charCodeAt(i2);
-            var h = 0.02519603282416938 * n2;
-            n2 = h >>> 0;
-            h -= n2;
-            h *= n2;
-            n2 = h >>> 0;
-            h -= n2;
-            n2 += h * 4294967296;
+          for (var i = 0; i < data.length; i++) {
+            n += data.charCodeAt(i);
+            var h = 0.02519603282416938 * n;
+            n = h >>> 0;
+            h -= n;
+            h *= n;
+            n = h >>> 0;
+            h -= n;
+            n += h * 4294967296;
           }
-          return (n2 >>> 0) * 23283064365386963e-26;
+          return (n >>> 0) * 23283064365386963e-26;
         };
         return mash;
       }
@@ -1849,21 +1850,21 @@ var require_xorshift7 = __commonJS({
       function XorGen(seed) {
         var me = this;
         me.next = function() {
-          var X = me.x, i2 = me.i, t2, v, w;
-          t2 = X[i2];
+          var X = me.x, i = me.i, t2, v, w;
+          t2 = X[i];
           t2 ^= t2 >>> 7;
           v = t2 ^ t2 << 24;
-          t2 = X[i2 + 1 & 7];
+          t2 = X[i + 1 & 7];
           v ^= t2 ^ t2 >>> 10;
-          t2 = X[i2 + 3 & 7];
+          t2 = X[i + 3 & 7];
           v ^= t2 ^ t2 >>> 3;
-          t2 = X[i2 + 4 & 7];
+          t2 = X[i + 4 & 7];
           v ^= t2 ^ t2 << 7;
-          t2 = X[i2 + 7 & 7];
+          t2 = X[i + 7 & 7];
           t2 = t2 ^ t2 << 13;
           v ^= t2 ^ t2 << 9;
-          X[i2] = v;
-          me.i = i2 + 1 & 7;
+          X[i] = v;
+          me.i = i + 1 & 7;
           return v;
         };
         function init22(me2, seed2) {
@@ -1942,20 +1943,20 @@ var require_xor4096 = __commonJS({
       function XorGen(seed) {
         var me = this;
         me.next = function() {
-          var w = me.w, X = me.X, i2 = me.i, t2, v;
+          var w = me.w, X = me.X, i = me.i, t2, v;
           me.w = w = w + 1640531527 | 0;
-          v = X[i2 + 34 & 127];
-          t2 = X[i2 = i2 + 1 & 127];
+          v = X[i + 34 & 127];
+          t2 = X[i = i + 1 & 127];
           v ^= v << 13;
           t2 ^= t2 << 17;
           v ^= v >>> 15;
           t2 ^= t2 >>> 12;
-          v = X[i2] = v ^ t2;
-          me.i = i2;
+          v = X[i] = v ^ t2;
+          me.i = i;
           return v + (w ^ w >>> 16) | 0;
         };
         function init22(me2, seed2) {
-          var t2, v, i2, j, w, X = [], limit = 128;
+          var t2, v, i, j, w, X = [], limit = 128;
           if (seed2 === (seed2 | 0)) {
             v = seed2;
             seed2 = null;
@@ -1964,7 +1965,7 @@ var require_xor4096 = __commonJS({
             v = 0;
             limit = Math.max(limit, seed2.length);
           }
-          for (i2 = 0, j = -32; j < limit; ++j) {
+          for (i = 0, j = -32; j < limit; ++j) {
             if (seed2)
               v ^= seed2.charCodeAt((j + 32) % seed2.length);
             if (j === 0)
@@ -1976,25 +1977,25 @@ var require_xor4096 = __commonJS({
             if (j >= 0) {
               w = w + 1640531527 | 0;
               t2 = X[j & 127] ^= v + w;
-              i2 = 0 == t2 ? i2 + 1 : 0;
+              i = 0 == t2 ? i + 1 : 0;
             }
           }
-          if (i2 >= 128) {
+          if (i >= 128) {
             X[(seed2 && seed2.length || 0) & 127] = -1;
           }
-          i2 = 127;
+          i = 127;
           for (j = 4 * 128; j > 0; --j) {
-            v = X[i2 + 34 & 127];
-            t2 = X[i2 = i2 + 1 & 127];
+            v = X[i + 34 & 127];
+            t2 = X[i = i + 1 & 127];
             v ^= v << 13;
             t2 ^= t2 << 17;
             v ^= v >>> 15;
             t2 ^= t2 >>> 12;
-            X[i2] = v ^ t2;
+            X[i] = v ^ t2;
           }
           me2.w = w;
           me2.X = X;
-          me2.i = i2;
+          me2.i = i;
         }
         init22(me, seed);
       }
@@ -2137,18 +2138,18 @@ var require_seedrandom = __commonJS({
         ), key);
         var arc4 = new ARC4(key);
         var prng = function() {
-          var n2 = arc4.g(chunks), d = startdenom, x = 0;
-          while (n2 < significance) {
-            n2 = (n2 + x) * width;
+          var n = arc4.g(chunks), d = startdenom, x = 0;
+          while (n < significance) {
+            n = (n + x) * width;
             d *= width;
             x = arc4.g(1);
           }
-          while (n2 >= overflow) {
-            n2 /= 2;
+          while (n >= overflow) {
+            n /= 2;
             d /= 2;
             x >>>= 1;
           }
-          return (n2 + x) / d;
+          return (n + x) / d;
         };
         prng.int32 = function() {
           return arc4.g(4) | 0;
@@ -2180,26 +2181,26 @@ var require_seedrandom = __commonJS({
         );
       }
       function ARC4(key) {
-        var t2, keylen = key.length, me = this, i2 = 0, j = me.i = me.j = 0, s2 = me.S = [];
+        var t2, keylen = key.length, me = this, i = 0, j = me.i = me.j = 0, s = me.S = [];
         if (!keylen) {
           key = [keylen++];
         }
-        while (i2 < width) {
-          s2[i2] = i2++;
+        while (i < width) {
+          s[i] = i++;
         }
-        for (i2 = 0; i2 < width; i2++) {
-          s2[i2] = s2[j = mask2 & j + key[i2 % keylen] + (t2 = s2[i2])];
-          s2[j] = t2;
+        for (i = 0; i < width; i++) {
+          s[i] = s[j = mask2 & j + key[i % keylen] + (t2 = s[i])];
+          s[j] = t2;
         }
         (me.g = function(count22) {
-          var t3, r2 = 0, i3 = me.i, j2 = me.j, s3 = me.S;
+          var t22, r = 0, i2 = me.i, j2 = me.j, s2 = me.S;
           while (count22--) {
-            t3 = s3[i3 = mask2 & i3 + 1];
-            r2 = r2 * width + s3[mask2 & (s3[i3] = s3[j2 = mask2 & j2 + t3]) + (s3[j2] = t3)];
+            t22 = s2[i2 = mask2 & i2 + 1];
+            r = r * width + s2[mask2 & (s2[i2] = s2[j2 = mask2 & j2 + t22]) + (s2[j2] = t22)];
           }
-          me.i = i3;
+          me.i = i2;
           me.j = j2;
-          return r2;
+          return r;
         })(width);
       }
       function copy2(f, t2) {
@@ -2215,7 +2216,7 @@ var require_seedrandom = __commonJS({
           for (prop in obj) {
             try {
               result.push(flatten4(obj[prop], depth - 1));
-            } catch (e2) {
+            } catch (e) {
             }
           }
         }
@@ -2238,7 +2239,7 @@ var require_seedrandom = __commonJS({
             (global2.crypto || global2.msCrypto).getRandomValues(out);
           }
           return tostring(out);
-        } catch (e2) {
+        } catch (e) {
           var browser = global2.navigator, plugins = browser && browser.plugins;
           return [+new Date(), global2, plugins, global2.screen, tostring(pool3)];
         }
@@ -2387,10 +2388,10 @@ var require_tfjs_backend_wasm_threaded_simd = __commonJS({
           return scriptDirectory + path;
         }
         var read_, readAsync, readBinary, setWindowTitle;
-        function logExceptionOnExit(e2) {
-          if (e2 instanceof ExitStatus)
+        function logExceptionOnExit(e) {
+          if (e instanceof ExitStatus)
             return;
-          let toLog = e2;
+          let toLog = e;
           err("exiting due to exception: " + toLog);
         }
         var fs;
@@ -2456,9 +2457,9 @@ var require_tfjs_backend_wasm_threaded_simd = __commonJS({
           let nodeWorkerThreads;
           try {
             nodeWorkerThreads = require_worker_threads();
-          } catch (e2) {
+          } catch (e) {
             console.error('The "worker_threads" module is not supported in this node.js build - perhaps a newer version is needed?');
-            throw e2;
+            throw e;
           }
           global.Worker = nodeWorkerThreads.Worker;
         } else if (ENVIRONMENT_IS_WEB || ENVIRONMENT_IS_WORKER) {
@@ -2544,8 +2545,8 @@ var require_tfjs_backend_wasm_threaded_simd = __commonJS({
           if (typeof WebAssembly.Function === "function") {
             var typeNames = { "i": "i32", "j": "i64", "f": "f32", "d": "f64" };
             var type = { parameters: [], results: sig[0] == "v" ? [] : [typeNames[sig[0]]] };
-            for (var i2 = 1; i2 < sig.length; ++i2) {
-              type.parameters.push(typeNames[sig[i2]]);
+            for (var i = 1; i < sig.length; ++i) {
+              type.parameters.push(typeNames[sig[i]]);
             }
             return new WebAssembly.Function(type, func2);
           }
@@ -2554,8 +2555,8 @@ var require_tfjs_backend_wasm_threaded_simd = __commonJS({
           var sigParam = sig.slice(1);
           var typeCodes = { "i": 127, "j": 126, "f": 125, "d": 124 };
           typeSection.push(sigParam.length);
-          for (var i2 = 0; i2 < sigParam.length; ++i2) {
-            typeSection.push(typeCodes[sigParam[i2]]);
+          for (var i = 0; i < sigParam.length; ++i) {
+            typeSection.push(typeCodes[sigParam[i]]);
           }
           if (sigRet == "v") {
             typeSection.push(0);
@@ -2586,10 +2587,10 @@ var require_tfjs_backend_wasm_threaded_simd = __commonJS({
           return wasmTable.length - 1;
         }
         function updateTableMap(offset, count22) {
-          for (var i2 = offset; i2 < offset + count22; i2++) {
-            var item = getWasmTableEntry(i2);
+          for (var i = offset; i < offset + count22; i++) {
+            var item = getWasmTableEntry(i);
             if (item) {
-              functionsInTableMap.set(item, i2);
+              functionsInTableMap.set(item, i);
             }
           }
         }
@@ -2645,14 +2646,14 @@ var require_tfjs_backend_wasm_threaded_simd = __commonJS({
           var cArgs = [];
           var stack2 = 0;
           if (args) {
-            for (var i2 = 0; i2 < args.length; i2++) {
-              var converter = toC[argTypes[i2]];
+            for (var i = 0; i < args.length; i++) {
+              var converter = toC[argTypes[i]];
               if (converter) {
                 if (stack2 === 0)
                   stack2 = stackSave();
-                cArgs[i2] = converter(args[i2]);
+                cArgs[i] = converter(args[i]);
               } else {
-                cArgs[i2] = args[i2];
+                cArgs[i] = args[i];
               }
             }
           }
@@ -2733,10 +2734,10 @@ var require_tfjs_backend_wasm_threaded_simd = __commonJS({
             return 0;
           var startIdx = outIdx;
           var endIdx = outIdx + maxBytesToWrite - 1;
-          for (var i2 = 0; i2 < str.length; ++i2) {
-            var u = str.charCodeAt(i2);
+          for (var i = 0; i < str.length; ++i) {
+            var u = str.charCodeAt(i);
             if (u >= 55296 && u <= 57343) {
-              var u1 = str.charCodeAt(++i2);
+              var u1 = str.charCodeAt(++i);
               u = 65536 + ((u & 1023) << 10) | u1 & 1023;
             }
             if (u <= 127) {
@@ -2771,10 +2772,10 @@ var require_tfjs_backend_wasm_threaded_simd = __commonJS({
         }
         function lengthBytesUTF8(str) {
           var len = 0;
-          for (var i2 = 0; i2 < str.length; ++i2) {
-            var u = str.charCodeAt(i2);
+          for (var i = 0; i < str.length; ++i) {
+            var u = str.charCodeAt(i);
             if (u >= 55296 && u <= 57343)
-              u = 65536 + ((u & 1023) << 10) | str.charCodeAt(++i2) & 1023;
+              u = 65536 + ((u & 1023) << 10) | str.charCodeAt(++i) & 1023;
             if (u <= 127)
               ++len;
             else if (u <= 2047)
@@ -2791,8 +2792,8 @@ var require_tfjs_backend_wasm_threaded_simd = __commonJS({
           GROWABLE_HEAP_I8().set(array2, buffer3);
         }
         function writeAsciiToMemory(str, buffer3, dontAddNull) {
-          for (var i2 = 0; i2 < str.length; ++i2) {
-            GROWABLE_HEAP_I8()[buffer3++ >> 0] = str.charCodeAt(i2);
+          for (var i = 0; i < str.length; ++i) {
+            GROWABLE_HEAP_I8()[buffer3++ >> 0] = str.charCodeAt(i);
           }
           if (!dontAddNull)
             GROWABLE_HEAP_I8()[buffer3 >> 0] = 0;
@@ -2936,9 +2937,9 @@ var require_tfjs_backend_wasm_threaded_simd = __commonJS({
           ABORT = true;
           EXITSTATUS = 1;
           what += ". Build with -s ASSERTIONS=1 for more info.";
-          var e2 = new WebAssembly.RuntimeError(what);
-          readyPromiseReject(e2);
-          throw e2;
+          var e = new WebAssembly.RuntimeError(what);
+          readyPromiseReject(e);
+          throw e;
         }
         var dataURIPrefix = "data:application/octet-stream;base64,";
         function isDataURI(filename) {
@@ -3044,8 +3045,8 @@ var require_tfjs_backend_wasm_threaded_simd = __commonJS({
             try {
               var exports2 = Module["instantiateWasm"](info, receiveInstance);
               return exports2;
-            } catch (e2) {
-              err("Module.instantiateWasm callback failed with error: " + e2);
+            } catch (e) {
+              err("Module.instantiateWasm callback failed with error: " + e);
               return false;
             }
           }
@@ -3114,11 +3115,11 @@ var require_tfjs_backend_wasm_threaded_simd = __commonJS({
         function _exit(status) {
           exit(status);
         }
-        function handleException(e2) {
-          if (e2 instanceof ExitStatus || e2 == "unwind") {
+        function handleException(e) {
+          if (e instanceof ExitStatus || e == "unwind") {
             return EXITSTATUS;
           }
-          quit_(1, e2);
+          quit_(1, e);
         }
         var PThread = { unusedWorkers: [], runningWorkers: [], tlsInitFunctions: [], init: function() {
           if (ENVIRONMENT_IS_PTHREAD) {
@@ -3128,7 +3129,7 @@ var require_tfjs_backend_wasm_threaded_simd = __commonJS({
           }
         }, initMainThread: function() {
           var pthreadPoolSize = 8;
-          for (var i2 = 0; i2 < pthreadPoolSize; ++i2) {
+          for (var i = 0; i < pthreadPoolSize; ++i) {
             PThread.allocateUnusedWorker();
           }
         }, initWorker: function() {
@@ -3142,8 +3143,8 @@ var require_tfjs_backend_wasm_threaded_simd = __commonJS({
               PThread.returnWorkerToPool(pthread.worker);
             }
           }
-          for (var i2 = 0; i2 < PThread.unusedWorkers.length; ++i2) {
-            var worker = PThread.unusedWorkers[i2];
+          for (var i = 0; i < PThread.unusedWorkers.length; ++i) {
+            var worker = PThread.unusedWorkers[i];
             worker.terminate();
           }
           PThread.unusedWorkers = [];
@@ -3164,12 +3165,12 @@ var require_tfjs_backend_wasm_threaded_simd = __commonJS({
           }
         }, receiveObjectTransfer: function(data) {
         }, threadInit: function() {
-          for (var i2 in PThread.tlsInitFunctions) {
-            PThread.tlsInitFunctions[i2]();
+          for (var i in PThread.tlsInitFunctions) {
+            PThread.tlsInitFunctions[i]();
           }
         }, loadWasmModuleToWorker: function(worker, onFinishedLoading) {
-          worker.onmessage = (e2) => {
-            var d = e2["data"];
+          worker.onmessage = (e) => {
+            var d = e["data"];
             var cmd = d["cmd"];
             if (worker.pthread)
               PThread.currentProxiedOperationCallerThread = worker.pthread.threadInfoStruct;
@@ -3218,17 +3219,17 @@ var require_tfjs_backend_wasm_threaded_simd = __commonJS({
             }
             PThread.currentProxiedOperationCallerThread = void 0;
           };
-          worker.onerror = (e2) => {
+          worker.onerror = (e) => {
             var message = "worker sent an error!";
-            err(message + " " + e2.filename + ":" + e2.lineno + ": " + e2.message);
-            throw e2;
+            err(message + " " + e.filename + ":" + e.lineno + ": " + e.message);
+            throw e;
           };
           if (ENVIRONMENT_IS_NODE) {
             worker.on("message", function(data) {
               worker.onmessage({ data });
             });
-            worker.on("error", function(e2) {
-              worker.onerror(e2);
+            worker.on("error", function(e) {
+              worker.onerror(e);
             });
             worker.on("detachedExit", function() {
             });
@@ -3258,8 +3259,8 @@ var require_tfjs_backend_wasm_threaded_simd = __commonJS({
             return _emscripten_proxy_to_main_thread_js(1, 0, returnCode);
           try {
             _exit(returnCode);
-          } catch (e2) {
-            handleException(e2);
+          } catch (e) {
+            handleException(e);
           }
         }
         var wasmTableMirror = [];
@@ -3281,8 +3282,8 @@ var require_tfjs_backend_wasm_threaded_simd = __commonJS({
           if (!error.stack) {
             try {
               throw new Error();
-            } catch (e2) {
-              error = e2;
+            } catch (e) {
+              error = e;
             }
             if (!error.stack) {
               return "(no stack trace available)";
@@ -3424,9 +3425,9 @@ var require_tfjs_backend_wasm_threaded_simd = __commonJS({
             var serializedNumCallArgs = numCallArgs;
             var args = stackAlloc(serializedNumCallArgs * 8);
             var b = args >> 3;
-            for (var i2 = 0; i2 < numCallArgs; i2++) {
-              var arg = outerArgs[2 + i2];
-              GROWABLE_HEAP_F64()[b + i2] = arg;
+            for (var i = 0; i < numCallArgs; i++) {
+              var arg = outerArgs[2 + i];
+              GROWABLE_HEAP_F64()[b + i] = arg;
             }
             return _emscripten_run_in_main_runtime_thread_js(index2, serializedNumCallArgs, args, sync);
           });
@@ -3435,8 +3436,8 @@ var require_tfjs_backend_wasm_threaded_simd = __commonJS({
         function _emscripten_receive_on_main_thread_js(index2, numCallArgs, args) {
           _emscripten_receive_on_main_thread_js_callArgs.length = numCallArgs;
           var b = args >> 3;
-          for (var i2 = 0; i2 < numCallArgs; i2++) {
-            _emscripten_receive_on_main_thread_js_callArgs[i2] = GROWABLE_HEAP_F64()[b + i2];
+          for (var i = 0; i < numCallArgs; i++) {
+            _emscripten_receive_on_main_thread_js_callArgs[i] = GROWABLE_HEAP_F64()[b + i];
           }
           var isEmAsmConst = index2 < 0;
           var func2 = !isEmAsmConst ? proxiedFunctionTable[index2] : ASM_CONSTS[-index2 - 1];
@@ -3447,7 +3448,7 @@ var require_tfjs_backend_wasm_threaded_simd = __commonJS({
             wasmMemory.grow(size2 - buffer2.byteLength + 65535 >>> 16);
             updateGlobalBufferAndViews(wasmMemory.buffer);
             return 1;
-          } catch (e2) {
+          } catch (e) {
           }
         }
         function _emscripten_resize_heap(requestedSize) {
@@ -3472,8 +3473,8 @@ var require_tfjs_backend_wasm_threaded_simd = __commonJS({
           return false;
         }
         var JSEvents = { inEventHandler: 0, removeAllEventListeners: function() {
-          for (var i2 = JSEvents.eventHandlers.length - 1; i2 >= 0; --i2) {
-            JSEvents._removeHandler(i2);
+          for (var i = JSEvents.eventHandlers.length - 1; i >= 0; --i) {
+            JSEvents._removeHandler(i);
           }
           JSEvents.eventHandlers = [];
           JSEvents.deferredCalls = [];
@@ -3486,14 +3487,14 @@ var require_tfjs_backend_wasm_threaded_simd = __commonJS({
           function arraysHaveEqualContent(arrA, arrB) {
             if (arrA.length != arrB.length)
               return false;
-            for (var i3 in arrA) {
-              if (arrA[i3] != arrB[i3])
+            for (var i2 in arrA) {
+              if (arrA[i2] != arrB[i2])
                 return false;
             }
             return true;
           }
-          for (var i2 in JSEvents.deferredCalls) {
-            var call = JSEvents.deferredCalls[i2];
+          for (var i in JSEvents.deferredCalls) {
+            var call = JSEvents.deferredCalls[i];
             if (call.targetFunction == targetFunction && arraysHaveEqualContent(call.argsList, argsList)) {
               return;
             }
@@ -3503,10 +3504,10 @@ var require_tfjs_backend_wasm_threaded_simd = __commonJS({
             return x.precedence < y.precedence;
           });
         }, removeDeferredCalls: function(targetFunction) {
-          for (var i2 = 0; i2 < JSEvents.deferredCalls.length; ++i2) {
-            if (JSEvents.deferredCalls[i2].targetFunction == targetFunction) {
-              JSEvents.deferredCalls.splice(i2, 1);
-              --i2;
+          for (var i = 0; i < JSEvents.deferredCalls.length; ++i) {
+            if (JSEvents.deferredCalls[i].targetFunction == targetFunction) {
+              JSEvents.deferredCalls.splice(i, 1);
+              --i;
             }
           }
         }, canPerformEventHandlerRequests: function() {
@@ -3515,22 +3516,22 @@ var require_tfjs_backend_wasm_threaded_simd = __commonJS({
           if (!JSEvents.canPerformEventHandlerRequests()) {
             return;
           }
-          for (var i2 = 0; i2 < JSEvents.deferredCalls.length; ++i2) {
-            var call = JSEvents.deferredCalls[i2];
-            JSEvents.deferredCalls.splice(i2, 1);
-            --i2;
+          for (var i = 0; i < JSEvents.deferredCalls.length; ++i) {
+            var call = JSEvents.deferredCalls[i];
+            JSEvents.deferredCalls.splice(i, 1);
+            --i;
             call.targetFunction.apply(null, call.argsList);
           }
         }, eventHandlers: [], removeAllHandlersOnTarget: function(target, eventTypeString) {
-          for (var i2 = 0; i2 < JSEvents.eventHandlers.length; ++i2) {
-            if (JSEvents.eventHandlers[i2].target == target && (!eventTypeString || eventTypeString == JSEvents.eventHandlers[i2].eventTypeString)) {
-              JSEvents._removeHandler(i2--);
+          for (var i = 0; i < JSEvents.eventHandlers.length; ++i) {
+            if (JSEvents.eventHandlers[i].target == target && (!eventTypeString || eventTypeString == JSEvents.eventHandlers[i].eventTypeString)) {
+              JSEvents._removeHandler(i--);
             }
           }
-        }, _removeHandler: function(i2) {
-          var h = JSEvents.eventHandlers[i2];
+        }, _removeHandler: function(i) {
+          var h = JSEvents.eventHandlers[i];
           h.target.removeEventListener(h.eventTypeString, h.eventListenerFunc, h.useCapture);
-          JSEvents.eventHandlers.splice(i2, 1);
+          JSEvents.eventHandlers.splice(i, 1);
         }, registerOrRemoveHandler: function(eventHandler) {
           var jsEventHandler = function jsEventHandler2(event) {
             ++JSEvents.inEventHandler;
@@ -3546,9 +3547,9 @@ var require_tfjs_backend_wasm_threaded_simd = __commonJS({
             JSEvents.eventHandlers.push(eventHandler);
             JSEvents.registerRemoveEventListeners();
           } else {
-            for (var i2 = 0; i2 < JSEvents.eventHandlers.length; ++i2) {
-              if (JSEvents.eventHandlers[i2].target == eventHandler.target && JSEvents.eventHandlers[i2].eventTypeString == eventHandler.eventTypeString) {
-                JSEvents._removeHandler(i2--);
+            for (var i = 0; i < JSEvents.eventHandlers.length; ++i) {
+              if (JSEvents.eventHandlers[i].target == eventHandler.target && JSEvents.eventHandlers[i].eventTypeString == eventHandler.eventTypeString) {
+                JSEvents._removeHandler(i--);
               }
             }
           }
@@ -3697,8 +3698,8 @@ var require_tfjs_backend_wasm_threaded_simd = __commonJS({
         function __webgl_enable_WEBGL_draw_buffers(ctx) {
           var ext = ctx.getExtension("WEBGL_draw_buffers");
           if (ext) {
-            ctx["drawBuffers"] = function(n2, bufs) {
-              ext["drawBuffersWEBGL"](n2, bufs);
+            ctx["drawBuffers"] = function(n, bufs) {
+              ext["drawBuffersWEBGL"](n, bufs);
             };
             return 1;
           }
@@ -3712,15 +3713,15 @@ var require_tfjs_backend_wasm_threaded_simd = __commonJS({
           }
         }, getNewId: function(table) {
           var ret = GL.counter++;
-          for (var i2 = table.length; i2 < ret; i2++) {
-            table[i2] = null;
+          for (var i = table.length; i < ret; i++) {
+            table[i] = null;
           }
           return ret;
         }, getSource: function(shader, count22, string2, length) {
           var source = "";
-          for (var i2 = 0; i2 < count22; ++i2) {
-            var len = length ? GROWABLE_HEAP_I32()[length + i2 * 4 >> 2] : -1;
-            source += UTF8ToString(GROWABLE_HEAP_I32()[string2 + i2 * 4 >> 2], len < 0 ? void 0 : len);
+          for (var i = 0; i < count22; ++i) {
+            var len = length ? GROWABLE_HEAP_I32()[length + i * 4 >> 2] : -1;
+            source += UTF8ToString(GROWABLE_HEAP_I32()[string2 + i * 4 >> 2], len < 0 ? void 0 : len);
           }
           return source;
         }, createContext: function(canvas3, webGLContextAttributes) {
@@ -3832,7 +3833,7 @@ var require_tfjs_backend_wasm_threaded_simd = __commonJS({
           if (ENVIRONMENT_IS_PTHREAD)
             return _emscripten_proxy_to_main_thread_js(5, 1, fd, iov, iovcnt, pnum);
           var num = 0;
-          for (var i2 = 0; i2 < iovcnt; i2++) {
+          for (var i = 0; i < iovcnt; i++) {
             var ptr = GROWABLE_HEAP_I32()[iov >> 2];
             var len = GROWABLE_HEAP_I32()[iov + 4 >> 2];
             iov += 8;
@@ -4385,10 +4386,10 @@ var require_tfjs_backend_wasm = __commonJS({
           return scriptDirectory + path;
         }
         var read_, readAsync, readBinary, setWindowTitle;
-        function logExceptionOnExit(e2) {
-          if (e2 instanceof ExitStatus)
+        function logExceptionOnExit(e) {
+          if (e instanceof ExitStatus)
             return;
-          let toLog = e2;
+          let toLog = e;
           err("exiting due to exception: " + toLog);
         }
         var fs;
@@ -4522,8 +4523,8 @@ var require_tfjs_backend_wasm = __commonJS({
           if (typeof WebAssembly.Function === "function") {
             var typeNames = { "i": "i32", "j": "i64", "f": "f32", "d": "f64" };
             var type = { parameters: [], results: sig[0] == "v" ? [] : [typeNames[sig[0]]] };
-            for (var i2 = 1; i2 < sig.length; ++i2) {
-              type.parameters.push(typeNames[sig[i2]]);
+            for (var i = 1; i < sig.length; ++i) {
+              type.parameters.push(typeNames[sig[i]]);
             }
             return new WebAssembly.Function(type, func2);
           }
@@ -4532,8 +4533,8 @@ var require_tfjs_backend_wasm = __commonJS({
           var sigParam = sig.slice(1);
           var typeCodes = { "i": 127, "j": 126, "f": 125, "d": 124 };
           typeSection.push(sigParam.length);
-          for (var i2 = 0; i2 < sigParam.length; ++i2) {
-            typeSection.push(typeCodes[sigParam[i2]]);
+          for (var i = 0; i < sigParam.length; ++i) {
+            typeSection.push(typeCodes[sigParam[i]]);
           }
           if (sigRet == "v") {
             typeSection.push(0);
@@ -4564,10 +4565,10 @@ var require_tfjs_backend_wasm = __commonJS({
           return wasmTable.length - 1;
         }
         function updateTableMap(offset, count22) {
-          for (var i2 = offset; i2 < offset + count22; i2++) {
-            var item = getWasmTableEntry(i2);
+          for (var i = offset; i < offset + count22; i++) {
+            var item = getWasmTableEntry(i);
             if (item) {
-              functionsInTableMap.set(item, i2);
+              functionsInTableMap.set(item, i);
             }
           }
         }
@@ -4619,14 +4620,14 @@ var require_tfjs_backend_wasm = __commonJS({
           var cArgs = [];
           var stack2 = 0;
           if (args) {
-            for (var i2 = 0; i2 < args.length; i2++) {
-              var converter = toC[argTypes[i2]];
+            for (var i = 0; i < args.length; i++) {
+              var converter = toC[argTypes[i]];
               if (converter) {
                 if (stack2 === 0)
                   stack2 = stackSave();
-                cArgs[i2] = converter(args[i2]);
+                cArgs[i] = converter(args[i]);
               } else {
-                cArgs[i2] = args[i2];
+                cArgs[i] = args[i];
               }
             }
           }
@@ -4698,10 +4699,10 @@ var require_tfjs_backend_wasm = __commonJS({
             return 0;
           var startIdx = outIdx;
           var endIdx = outIdx + maxBytesToWrite - 1;
-          for (var i2 = 0; i2 < str.length; ++i2) {
-            var u = str.charCodeAt(i2);
+          for (var i = 0; i < str.length; ++i) {
+            var u = str.charCodeAt(i);
             if (u >= 55296 && u <= 57343) {
-              var u1 = str.charCodeAt(++i2);
+              var u1 = str.charCodeAt(++i);
               u = 65536 + ((u & 1023) << 10) | u1 & 1023;
             }
             if (u <= 127) {
@@ -4736,10 +4737,10 @@ var require_tfjs_backend_wasm = __commonJS({
         }
         function lengthBytesUTF8(str) {
           var len = 0;
-          for (var i2 = 0; i2 < str.length; ++i2) {
-            var u = str.charCodeAt(i2);
+          for (var i = 0; i < str.length; ++i) {
+            var u = str.charCodeAt(i);
             if (u >= 55296 && u <= 57343)
-              u = 65536 + ((u & 1023) << 10) | str.charCodeAt(++i2) & 1023;
+              u = 65536 + ((u & 1023) << 10) | str.charCodeAt(++i) & 1023;
             if (u <= 127)
               ++len;
             else if (u <= 2047)
@@ -4756,8 +4757,8 @@ var require_tfjs_backend_wasm = __commonJS({
           HEAP8.set(array2, buffer3);
         }
         function writeAsciiToMemory(str, buffer3, dontAddNull) {
-          for (var i2 = 0; i2 < str.length; ++i2) {
-            HEAP8[buffer3++ >> 0] = str.charCodeAt(i2);
+          for (var i = 0; i < str.length; ++i) {
+            HEAP8[buffer3++ >> 0] = str.charCodeAt(i);
           }
           if (!dontAddNull)
             HEAP8[buffer3 >> 0] = 0;
@@ -4866,9 +4867,9 @@ var require_tfjs_backend_wasm = __commonJS({
           ABORT = true;
           EXITSTATUS = 1;
           what += ". Build with -s ASSERTIONS=1 for more info.";
-          var e2 = new WebAssembly.RuntimeError(what);
-          readyPromiseReject(e2);
-          throw e2;
+          var e = new WebAssembly.RuntimeError(what);
+          readyPromiseReject(e);
+          throw e;
         }
         var dataURIPrefix = "data:application/octet-stream;base64,";
         function isDataURI(filename) {
@@ -4964,8 +4965,8 @@ var require_tfjs_backend_wasm = __commonJS({
             try {
               var exports2 = Module["instantiateWasm"](info, receiveInstance);
               return exports2;
-            } catch (e2) {
-              err("Module.instantiateWasm callback failed with error: " + e2);
+            } catch (e) {
+              err("Module.instantiateWasm callback failed with error: " + e);
               return false;
             }
           }
@@ -5018,8 +5019,8 @@ var require_tfjs_backend_wasm = __commonJS({
           if (!error.stack) {
             try {
               throw new Error();
-            } catch (e2) {
-              error = e2;
+            } catch (e) {
+              error = e;
             }
             if (!error.stack) {
               return "(no stack trace available)";
@@ -5045,7 +5046,7 @@ var require_tfjs_backend_wasm = __commonJS({
             wasmMemory.grow(size2 - buffer2.byteLength + 65535 >>> 16);
             updateGlobalBufferAndViews(wasmMemory.buffer);
             return 1;
-          } catch (e2) {
+          } catch (e) {
           }
         }
         function _emscripten_resize_heap(requestedSize) {
@@ -5091,7 +5092,7 @@ var require_tfjs_backend_wasm = __commonJS({
         }
         function _fd_write(fd, iov, iovcnt, pnum) {
           var num = 0;
-          for (var i2 = 0; i2 < iovcnt; i2++) {
+          for (var i = 0; i < iovcnt; i++) {
             var ptr = HEAP32[iov >> 2];
             var len = HEAP32[iov + 4 >> 2];
             iov += 8;
@@ -5645,19 +5646,19 @@ function swap(object2, left, right) {
 }
 function sum(arr) {
   let sum7 = 0;
-  for (let i2 = 0; i2 < arr.length; i2++) {
-    sum7 += arr[i2];
+  for (let i = 0; i < arr.length; i++) {
+    sum7 += arr[i];
   }
   return sum7;
 }
 function randUniform(a, b) {
-  const r2 = Math.random();
-  return b * r2 + (1 - r2) * a;
+  const r = Math.random();
+  return b * r + (1 - r) * a;
 }
 function distSquared(a, b) {
   let result = 0;
-  for (let i2 = 0; i2 < a.length; i2++) {
-    const diff = Number(a[i2]) - Number(b[i2]);
+  for (let i = 0; i < a.length; i++) {
+    const diff = Number(a[i]) - Number(b[i]);
     result += diff * diff;
   }
   return result;
@@ -5678,8 +5679,8 @@ function flatten(arr, result = [], skipTypedArray = false) {
     result = [];
   }
   if (Array.isArray(arr) || isTypedArray(arr) && !skipTypedArray) {
-    for (let i2 = 0; i2 < arr.length; ++i2) {
-      flatten(arr[i2], result, skipTypedArray);
+    for (let i = 0; i < arr.length; ++i) {
+      flatten(arr[i], result, skipTypedArray);
     }
   } else {
     result.push(arr);
@@ -5691,8 +5692,8 @@ function sizeFromShape(shape) {
     return 1;
   }
   let size2 = shape[0];
-  for (let i2 = 1; i2 < shape.length; i2++) {
-    size2 *= shape[i2];
+  for (let i = 1; i < shape.length; i++) {
+    size2 *= shape[i];
   }
   return size2;
 }
@@ -5709,8 +5710,8 @@ function arraysEqual(n1, n2) {
   if (n1.length !== n2.length) {
     return false;
   }
-  for (let i2 = 0; i2 < n1.length; i2++) {
-    if (n1[i2] !== n2[i2]) {
+  for (let i = 0; i < n1.length; i++) {
+    if (n1[i] !== n2[i]) {
       return false;
     }
   }
@@ -5736,10 +5737,10 @@ function sizeToSquarishShape(size2) {
   const width = Math.ceil(Math.sqrt(size2));
   return [width, Math.ceil(size2 / width)];
 }
-function createShuffledIndices(n2) {
-  const shuffledIndices = new Uint32Array(n2);
-  for (let i2 = 0; i2 < n2; ++i2) {
-    shuffledIndices[i2] = i2;
+function createShuffledIndices(n) {
+  const shuffledIndices = new Uint32Array(n);
+  for (let i = 0; i < n; ++i) {
+    shuffledIndices[i] = i;
   }
   shuffle(shuffledIndices);
   return shuffledIndices;
@@ -5772,16 +5773,16 @@ function repeatedTry(checkFn, delayFn = (counter) => 0, maxCounter) {
 function inferFromImplicitShape(shape, size2) {
   let shapeProd = 1;
   let implicitIdx = -1;
-  for (let i2 = 0; i2 < shape.length; ++i2) {
-    if (shape[i2] >= 0) {
-      shapeProd *= shape[i2];
-    } else if (shape[i2] === -1) {
+  for (let i = 0; i < shape.length; ++i) {
+    if (shape[i] >= 0) {
+      shapeProd *= shape[i];
+    } else if (shape[i] === -1) {
       if (implicitIdx !== -1) {
-        throw Error(`Shapes can only have 1 implicit size. Found -1 at dim ${implicitIdx} and dim ${i2}`);
+        throw Error(`Shapes can only have 1 implicit size. Found -1 at dim ${implicitIdx} and dim ${i}`);
       }
-      implicitIdx = i2;
-    } else if (shape[i2] < 0) {
-      throw Error(`Shapes can not be < 0. Found ${shape[i2]} at dim ${i2}`);
+      implicitIdx = i;
+    } else if (shape[i] < 0) {
+      throw Error(`Shapes can not be < 0. Found ${shape[i]} at dim ${i}`);
     }
   }
   if (implicitIdx === -1) {
@@ -5802,7 +5803,7 @@ function inferFromImplicitShape(shape, size2) {
 }
 function parseAxisParam(axis, shape) {
   const rank = shape.length;
-  axis = axis == null ? shape.map((s2, i2) => i2) : [].concat(axis);
+  axis = axis == null ? shape.map((s, i) => i) : [].concat(axis);
   assert(axis.every((ax) => ax >= -rank && ax < rank), () => `All values in axis param must be in range [-${rank}, ${rank}) but got axis ${axis}`);
   assert(axis.every((ax) => isInt(ax)), () => `All values in axis param must be integers but got axis ${axis}`);
   return axis.map((a) => a < 0 ? rank + a : a);
@@ -5813,22 +5814,22 @@ function squeezeShape(shape, axis) {
   const isEmptyArray = axis != null && Array.isArray(axis) && axis.length === 0;
   const axes = axis == null || isEmptyArray ? null : parseAxisParam(axis, shape).sort();
   let j = 0;
-  for (let i2 = 0; i2 < shape.length; ++i2) {
+  for (let i = 0; i < shape.length; ++i) {
     if (axes != null) {
-      if (axes[j] === i2 && shape[i2] !== 1) {
-        throw new Error(`Can't squeeze axis ${i2} since its dim '${shape[i2]}' is not 1`);
+      if (axes[j] === i && shape[i] !== 1) {
+        throw new Error(`Can't squeeze axis ${i} since its dim '${shape[i]}' is not 1`);
       }
-      if ((axes[j] == null || axes[j] > i2) && shape[i2] === 1) {
-        newShape.push(shape[i2]);
-        keptDims.push(i2);
+      if ((axes[j] == null || axes[j] > i) && shape[i] === 1) {
+        newShape.push(shape[i]);
+        keptDims.push(i);
       }
-      if (axes[j] <= i2) {
+      if (axes[j] <= i) {
         j++;
       }
     }
-    if (shape[i2] !== 1) {
-      newShape.push(shape[i2]);
-      keptDims.push(i2);
+    if (shape[i] !== 1) {
+      newShape.push(shape[i]);
+      keptDims.push(i);
     }
   }
   return { newShape, keptDims };
@@ -5862,8 +5863,8 @@ function getArrayFromDType(dtype, size2) {
   return values;
 }
 function checkConversionForErrors(vals, dtype) {
-  for (let i2 = 0; i2 < vals.length; i2++) {
-    const num = vals[i2];
+  for (let i = 0; i < vals.length; i++) {
+    const num = vals[i];
     if (isNaN(num) || !isFinite(num)) {
       throw Error(`A tensor of type ${dtype} being uploaded contains ${num}.`);
     }
@@ -5939,9 +5940,9 @@ function isFunction(f) {
   return !!(f && f.constructor && f.call && f.apply);
 }
 function nearestDivisor(size2, start) {
-  for (let i2 = start; i2 < size2; ++i2) {
-    if (size2 % i2 === 0) {
-      return i2;
+  for (let i = start; i < size2; ++i) {
+    if (size2 % i === 0) {
+      return i;
     }
   }
   return size2;
@@ -5953,8 +5954,8 @@ function computeStrides(shape) {
   }
   const strides2 = new Array(rank - 1);
   strides2[rank - 2] = shape[rank - 1];
-  for (let i2 = rank - 3; i2 >= 0; --i2) {
-    strides2[i2] = strides2[i2 + 1] * shape[i2 + 1];
+  for (let i = rank - 3; i >= 0; --i) {
+    strides2[i] = strides2[i + 1] * shape[i + 1];
   }
   return strides2;
 }
@@ -5962,15 +5963,15 @@ function createNestedArray(offset, shape, a, isComplex = false) {
   const ret = new Array();
   if (shape.length === 1) {
     const d = shape[0] * (isComplex ? 2 : 1);
-    for (let i2 = 0; i2 < d; i2++) {
-      ret[i2] = a[offset + i2];
+    for (let i = 0; i < d; i++) {
+      ret[i] = a[offset + i];
     }
   } else {
     const d = shape[0];
     const rest = shape.slice(1);
     const len = rest.reduce((acc, c) => acc * c) * (isComplex ? 2 : 1);
-    for (let i2 = 0; i2 < d; i2++) {
-      ret[i2] = createNestedArray(offset + i2 * len, rest, a, isComplex);
+    for (let i = 0; i < d; i++) {
+      ret[i] = createNestedArray(offset + i * len, rest, a, isComplex);
     }
   }
   return ret;
@@ -5990,8 +5991,8 @@ function toNestedArray(shape, a, isComplex = false) {
 }
 function makeOnesTypedArray(size2, dtype) {
   const array2 = makeZerosTypedArray(size2, dtype);
-  for (let i2 = 0; i2 < array2.length; i2++) {
-    array2[i2] = 1;
+  for (let i = 0; i < array2.length; i++) {
+    array2[i] = 1;
   }
   return array2;
 }
@@ -6030,8 +6031,8 @@ function locToIndex(locs, rank, strides2) {
     return locs[0];
   }
   let index2 = locs[locs.length - 1];
-  for (let i2 = 0; i2 < locs.length - 1; ++i2) {
-    index2 += strides2[i2] * locs[i2];
+  for (let i = 0; i < locs.length - 1; ++i) {
+    index2 += strides2[i] * locs[i];
   }
   return index2;
 }
@@ -6042,9 +6043,9 @@ function indexToLoc(index2, rank, strides2) {
     return [index2];
   }
   const locs = new Array(rank);
-  for (let i2 = 0; i2 < locs.length - 1; ++i2) {
-    locs[i2] = Math.floor(index2 / strides2[i2]);
-    index2 -= locs[i2] * strides2[i2];
+  for (let i = 0; i < locs.length - 1; ++i) {
+    locs[i] = Math.floor(index2 / strides2[i]);
+    index2 -= locs[i] * strides2[i];
   }
   locs[locs.length - 1] = index2;
   return locs;
@@ -6150,7 +6151,7 @@ var Environment = class {
 };
 function getQueryParams(queryString) {
   const params = {};
-  queryString.replace(/[?&]([^=?&]+)(?:=([^&]*))?/g, (s2, ...t2) => {
+  queryString.replace(/[?&]([^=?&]+)(?:=([^&]*))?/g, (s, ...t2) => {
     decodeParam(params, t2[0], t2[1]);
     return t2.join("=");
   });
@@ -6528,15 +6529,15 @@ var k2 = hexToLong("9ae16a3b2f90404f");
 function shiftMix(val) {
   return val.xor(val.shru(47));
 }
-function fetch2(s2, offset, numBytes) {
-  const bytes = s2.slice(offset, offset + numBytes);
+function fetch2(s, offset, numBytes) {
+  const bytes = s.slice(offset, offset + numBytes);
   return Long.fromBytes(Array.from(bytes), true, true);
 }
-function fetch64(s2, offset) {
-  return fetch2(s2, offset, 8);
+function fetch64(s, offset) {
+  return fetch2(s, offset, 8);
 }
-function fetch32(s2, offset) {
-  return fetch2(s2, offset, 4);
+function fetch32(s, offset) {
+  return fetch2(s, offset, 4);
 }
 function rotate64(val, shift) {
   return shift === 0 ? val : val.shru(shift).or(val.shl(64 - shift));
@@ -6558,83 +6559,83 @@ function weakHashLen32WithSeeds(w, x, y, z, a, b) {
   b = b.add(rotate64(a, 44));
   return [a.add(z), b.add(c)];
 }
-function weakHashLen32WithSeedsStr(s2, offset, a, b) {
-  return weakHashLen32WithSeeds(fetch64(s2, offset), fetch64(s2, offset + 8), fetch64(s2, offset + 16), fetch64(s2, offset + 24), a, b);
+function weakHashLen32WithSeedsStr(s, offset, a, b) {
+  return weakHashLen32WithSeeds(fetch64(s, offset), fetch64(s, offset + 8), fetch64(s, offset + 16), fetch64(s, offset + 24), a, b);
 }
-function hashLen0to16(s2, len = s2.length) {
+function hashLen0to16(s, len = s.length) {
   if (len >= 8) {
     const mul2 = k2.add(len * 2);
-    const a = fetch64(s2, 0).add(k2);
-    const b = fetch64(s2, len - 8);
+    const a = fetch64(s, 0).add(k2);
+    const b = fetch64(s, len - 8);
     const c = rotate64(b, 37).mul(mul2).add(a);
     const d = rotate64(a, 25).add(b).mul(mul2);
     return hashLen16(c, d, mul2);
   }
   if (len >= 4) {
     const mul2 = k2.add(len * 2);
-    const a = fetch32(s2, 0);
-    return hashLen16(a.shl(3).add(len), fetch32(s2, len - 4), mul2);
+    const a = fetch32(s, 0);
+    return hashLen16(a.shl(3).add(len), fetch32(s, len - 4), mul2);
   }
   if (len > 0) {
-    const a = s2[0];
-    const b = s2[len >> 1];
-    const c = s2[len - 1];
+    const a = s[0];
+    const b = s[len >> 1];
+    const c = s[len - 1];
     const y = a + (b << 8);
     const z = len + (c << 2);
     return shiftMix(k2.mul(y).xor(k0.mul(z))).mul(k2);
   }
   return k2;
 }
-function hashLen17to32(s2, len = s2.length) {
+function hashLen17to32(s, len = s.length) {
   const mul2 = k2.add(len * 2);
-  const a = fetch64(s2, 0).mul(k1);
-  const b = fetch64(s2, 8);
-  const c = fetch64(s2, len - 8).mul(mul2);
-  const d = fetch64(s2, len - 16).mul(k2);
+  const a = fetch64(s, 0).mul(k1);
+  const b = fetch64(s, 8);
+  const c = fetch64(s, len - 8).mul(mul2);
+  const d = fetch64(s, len - 16).mul(k2);
   return hashLen16(rotate64(a.add(b), 43).add(rotate64(c, 30)).add(d), a.add(rotate64(b.add(k2), 18)).add(c), mul2);
 }
-function hashLen33to64(s2, len = s2.length) {
+function hashLen33to64(s, len = s.length) {
   const mul2 = k2.add(len * 2);
-  const a = fetch64(s2, 0).mul(k2);
-  const b = fetch64(s2, 8);
-  const c = fetch64(s2, len - 8).mul(mul2);
-  const d = fetch64(s2, len - 16).mul(k2);
+  const a = fetch64(s, 0).mul(k2);
+  const b = fetch64(s, 8);
+  const c = fetch64(s, len - 8).mul(mul2);
+  const d = fetch64(s, len - 16).mul(k2);
   const y = rotate64(a.add(b), 43).add(rotate64(c, 30)).add(d);
   const z = hashLen16(y, a.add(rotate64(b.add(k2), 18)).add(c), mul2);
-  const e2 = fetch64(s2, 16).mul(mul2);
-  const f = fetch64(s2, 24);
-  const g = y.add(fetch64(s2, len - 32)).mul(mul2);
-  const h = z.add(fetch64(s2, len - 24)).mul(mul2);
-  return hashLen16(rotate64(e2.add(f), 43).add(rotate64(g, 30)).add(h), e2.add(rotate64(f.add(a), 18)).add(g), mul2);
+  const e = fetch64(s, 16).mul(mul2);
+  const f = fetch64(s, 24);
+  const g = y.add(fetch64(s, len - 32)).mul(mul2);
+  const h = z.add(fetch64(s, len - 24)).mul(mul2);
+  return hashLen16(rotate64(e.add(f), 43).add(rotate64(g, 30)).add(h), e.add(rotate64(f.add(a), 18)).add(g), mul2);
 }
-function fingerPrint64(s2, len = s2.length) {
+function fingerPrint64(s, len = s.length) {
   const seed = Long.fromNumber(81, true);
   if (len <= 32) {
     if (len <= 16) {
-      return hashLen0to16(s2, len);
+      return hashLen0to16(s, len);
     } else {
-      return hashLen17to32(s2, len);
+      return hashLen17to32(s, len);
     }
   } else if (len <= 64) {
-    return hashLen33to64(s2, len);
+    return hashLen33to64(s, len);
   }
   let x = seed;
   let y = seed.mul(k1).add(113);
   let z = shiftMix(y.mul(k2).add(113)).mul(k2);
   let v = [Long.UZERO, Long.UZERO];
   let w = [Long.UZERO, Long.UZERO];
-  x = x.mul(k2).add(fetch64(s2, 0));
+  x = x.mul(k2).add(fetch64(s, 0));
   let offset = 0;
   const end = (len - 1 >> 6) * 64;
   const last64 = end + (len - 1 & 63) - 63;
   do {
-    x = rotate64(x.add(y).add(v[0]).add(fetch64(s2, offset + 8)), 37).mul(k1);
-    y = rotate64(y.add(v[1]).add(fetch64(s2, offset + 48)), 42).mul(k1);
+    x = rotate64(x.add(y).add(v[0]).add(fetch64(s, offset + 8)), 37).mul(k1);
+    y = rotate64(y.add(v[1]).add(fetch64(s, offset + 48)), 42).mul(k1);
     x = x.xor(w[1]);
-    y = y.add(v[0]).add(fetch64(s2, offset + 40));
+    y = y.add(v[0]).add(fetch64(s, offset + 40));
     z = rotate64(z.add(w[0]), 33).mul(k1);
-    v = weakHashLen32WithSeedsStr(s2, offset, v[1].mul(k1), x.add(w[0]));
-    w = weakHashLen32WithSeedsStr(s2, offset + 32, z.add(w[1]), y.add(fetch64(s2, offset + 16)));
+    v = weakHashLen32WithSeedsStr(s, offset, v[1].mul(k1), x.add(w[0]));
+    w = weakHashLen32WithSeedsStr(s, offset + 32, z.add(w[1]), y.add(fetch64(s, offset + 16)));
     [z, x] = [x, z];
     offset += 64;
   } while (offset !== end);
@@ -6643,13 +6644,13 @@ function fingerPrint64(s2, len = s2.length) {
   w[0] = w[0].add(len - 1 & 63);
   v[0] = v[0].add(w[0]);
   w[0] = w[0].add(v[0]);
-  x = rotate64(x.add(y).add(v[0]).add(fetch64(s2, offset + 8)), 37).mul(mul2);
-  y = rotate64(y.add(v[1]).add(fetch64(s2, offset + 48)), 42).mul(mul2);
+  x = rotate64(x.add(y).add(v[0]).add(fetch64(s, offset + 8)), 37).mul(mul2);
+  y = rotate64(y.add(v[1]).add(fetch64(s, offset + 48)), 42).mul(mul2);
   x = x.xor(w[1].mul(9));
-  y = y.add(v[0].mul(9).add(fetch64(s2, offset + 40)));
+  y = y.add(v[0].mul(9).add(fetch64(s, offset + 40)));
   z = rotate64(z.add(w[0]), 33).mul(mul2);
-  v = weakHashLen32WithSeedsStr(s2, offset, v[1].mul(mul2), x.add(w[0]));
-  w = weakHashLen32WithSeedsStr(s2, offset + 32, z.add(w[1]), y.add(fetch64(s2, offset + 16)));
+  v = weakHashLen32WithSeedsStr(s, offset, v[1].mul(mul2), x.add(w[0]));
+  w = weakHashLen32WithSeedsStr(s, offset + 32, z.add(w[1]), y.add(fetch64(s, offset + 16)));
   [z, x] = [x, z];
   return hashLen16(hashLen16(v[0], w[0], mul2).add(shiftMix(y).mul(k0)).add(z), hashLen16(v[1], w[1], mul2).add(x), mul2);
 }
@@ -6681,9 +6682,9 @@ function toTypedArray(a, dtype) {
     return new Int32Array(a);
   } else if (dtype === "bool") {
     const bool = new Uint8Array(a.length);
-    for (let i2 = 0; i2 < bool.length; ++i2) {
-      if (Math.round(a[i2]) !== 0) {
-        bool[i2] = 1;
+    for (let i = 0; i < bool.length; ++i) {
+      if (Math.round(a[i]) !== 0) {
+        bool[i] = 1;
       }
     }
     return bool;
@@ -6697,9 +6698,9 @@ function now2() {
 function fetch3(path, requestInits) {
   return env().platform.fetch(path, requestInits);
 }
-function encodeString(s2, encoding = "utf-8") {
+function encodeString(s, encoding = "utf-8") {
   encoding = encoding || "utf-8";
-  return env().platform.encode(s2, encoding);
+  return env().platform.encode(s, encoding);
 }
 function decodeString(bytes, encoding = "utf-8") {
   encoding = encoding || "utf-8";
@@ -6730,8 +6731,8 @@ var Profiler = class {
       timer = Promise.resolve({ kernelMs: now2() - start });
     }
     if (env().getBool("CHECK_COMPUTATION_FOR_ERRORS")) {
-      for (let i2 = 0; i2 < outputs.length; i2++) {
-        const output = outputs[i2];
+      for (let i = 0; i < outputs.length; i++) {
+        const output = outputs[i];
         output.data().then((tensorVals) => {
           checkComputationForErrors(tensorVals, output.dtype, kernelName);
         });
@@ -6759,8 +6760,8 @@ function checkComputationForErrors(vals, dtype, kernelName) {
   if (dtype !== "float32") {
     return false;
   }
-  for (let i2 = 0; i2 < vals.length; i2++) {
-    const num = vals[i2];
+  for (let i = 0; i < vals.length; i++) {
+    const num = vals[i];
     if (isNaN(num) || !isFinite(num)) {
       console.warn(`Found ${num} in the result of '${kernelName}'`);
       return true;
@@ -6790,11 +6791,11 @@ var Logger = class {
 function getFilteredNodesXToY(tape, xs, y) {
   const tensorsFromX = {};
   const nodesFromX = {};
-  for (let i2 = 0; i2 < xs.length; i2++) {
-    tensorsFromX[xs[i2].id] = true;
+  for (let i = 0; i < xs.length; i++) {
+    tensorsFromX[xs[i].id] = true;
   }
-  for (let i2 = 0; i2 < tape.length; i2++) {
-    const node2 = tape[i2];
+  for (let i = 0; i < tape.length; i++) {
+    const node2 = tape[i];
     const nodeInputs = node2.inputs;
     for (const inputName in nodeInputs) {
       const input2 = nodeInputs[inputName];
@@ -6815,8 +6816,8 @@ function getFilteredNodesXToY(tape, xs, y) {
   const tensorsLeadToY = {};
   tensorsLeadToY[y.id] = true;
   const nodesToY = {};
-  for (let i2 = tape.length - 1; i2 >= 0; i2--) {
-    const node2 = tape[i2];
+  for (let i = tape.length - 1; i >= 0; i--) {
+    const node2 = tape[i];
     const nodeInputs = node2.inputs;
     for (let j = 0; j < node2.outputs.length; j++) {
       if (tensorsLeadToY[node2.outputs[j].id]) {
@@ -6829,8 +6830,8 @@ function getFilteredNodesXToY(tape, xs, y) {
     }
   }
   const filteredTape = [];
-  for (let i2 = 0; i2 < tape.length; i2++) {
-    const node2 = tape[i2];
+  for (let i = 0; i < tape.length; i++) {
+    const node2 = tape[i];
     if (nodesFromX[node2.id] && nodesToY[node2.id]) {
       const prunedInputs = {};
       for (const inputName in node2.inputs) {
@@ -6848,8 +6849,8 @@ function getFilteredNodesXToY(tape, xs, y) {
   return filteredTape;
 }
 function backpropagateGradients(tensorAccumulatedGradientMap, filteredTape, tidy2, add5) {
-  for (let i2 = filteredTape.length - 1; i2 >= 0; i2--) {
-    const node2 = filteredTape[i2];
+  for (let i = filteredTape.length - 1; i >= 0; i--) {
+    const node2 = filteredTape[i];
     const dys = [];
     node2.outputs.forEach((o) => {
       const gradTensor = tensorAccumulatedGradientMap[o.id];
@@ -6900,17 +6901,17 @@ function tensorToString(vals, shape, dtype, verbose) {
     lines2.push(`  shape: [${shape}]`);
     lines2.push(`  values:`);
   }
-  lines2.push(valsLines.map((l3) => "    " + l3).join("\n"));
+  lines2.push(valsLines.map((l) => "    " + l).join("\n"));
   return lines2.join("\n");
 }
 function computeMaxSizePerColumn(vals, shape, dtype, strides2) {
-  const n2 = sizeFromShape(shape);
+  const n = sizeFromShape(shape);
   const numCols = strides2[strides2.length - 1];
   const padPerCol = new Array(numCols).fill(0);
   const rank = shape.length;
   const valuesOrTuples = dtype === "complex64" ? createComplexTuples(vals) : vals;
   if (rank > 1) {
-    for (let row = 0; row < n2 / numCols; row++) {
+    for (let row = 0; row < n / numCols; row++) {
       const offset = row * numCols;
       for (let j = 0; j < numCols; j++) {
         padPerCol[j] = Math.max(padPerCol[j], valToString(valuesOrTuples[offset + j], 0, dtype).length);
@@ -6959,12 +6960,12 @@ function subTensorToString(vals, shape, dtype, strides2, padPerCol, isLast = tru
         lastVals = createComplexTuples(lastVals);
       }
       return [
-        "[" + firstVals.map((x, i2) => valToString(x, padPerCol[i2], dtype)).join(", ") + ", ..., " + lastVals.map((x, i2) => valToString(x, padPerCol[size2 - FORMAT_NUM_FIRST_LAST_VALS + i2], dtype)).join(", ") + "]"
+        "[" + firstVals.map((x, i) => valToString(x, padPerCol[i], dtype)).join(", ") + ", ..., " + lastVals.map((x, i) => valToString(x, padPerCol[size2 - FORMAT_NUM_FIRST_LAST_VALS + i], dtype)).join(", ") + "]"
       ];
     }
     const displayVals = dtype === "complex64" ? createComplexTuples(vals) : Array.from(vals);
     return [
-      "[" + displayVals.map((x, i2) => valToString(x, padPerCol[i2], dtype)).join(", ") + "]"
+      "[" + displayVals.map((x, i) => valToString(x, padPerCol[i], dtype)).join(", ") + "]"
     ];
   }
   const subshape = shape.slice(1);
@@ -6972,31 +6973,31 @@ function subTensorToString(vals, shape, dtype, strides2, padPerCol, isLast = tru
   const stride = strides2[0] * storagePerElement;
   const lines2 = [];
   if (size2 > FORMAT_LIMIT_NUM_VALS) {
-    for (let i2 = 0; i2 < FORMAT_NUM_FIRST_LAST_VALS; i2++) {
-      const start = i2 * stride;
+    for (let i = 0; i < FORMAT_NUM_FIRST_LAST_VALS; i++) {
+      const start = i * stride;
       const end = start + stride;
       lines2.push(...subTensorToString(vals.slice(start, end), subshape, dtype, substrides, padPerCol, false));
     }
     lines2.push("...");
-    for (let i2 = size2 - FORMAT_NUM_FIRST_LAST_VALS; i2 < size2; i2++) {
-      const start = i2 * stride;
+    for (let i = size2 - FORMAT_NUM_FIRST_LAST_VALS; i < size2; i++) {
+      const start = i * stride;
       const end = start + stride;
-      lines2.push(...subTensorToString(vals.slice(start, end), subshape, dtype, substrides, padPerCol, i2 === size2 - 1));
+      lines2.push(...subTensorToString(vals.slice(start, end), subshape, dtype, substrides, padPerCol, i === size2 - 1));
     }
   } else {
-    for (let i2 = 0; i2 < size2; i2++) {
-      const start = i2 * stride;
+    for (let i = 0; i < size2; i++) {
+      const start = i * stride;
       const end = start + stride;
-      lines2.push(...subTensorToString(vals.slice(start, end), subshape, dtype, substrides, padPerCol, i2 === size2 - 1));
+      lines2.push(...subTensorToString(vals.slice(start, end), subshape, dtype, substrides, padPerCol, i === size2 - 1));
     }
   }
   const sep = rank === 2 ? "," : "";
   lines2[0] = "[" + lines2[0] + sep;
-  for (let i2 = 1; i2 < lines2.length - 1; i2++) {
-    lines2[i2] = " " + lines2[i2] + sep;
+  for (let i = 1; i < lines2.length - 1; i++) {
+    lines2[i] = " " + lines2[i] + sep;
   }
   let newLineSep = ",\n";
-  for (let i2 = 2; i2 < rank; i2++) {
+  for (let i = 2; i < rank; i++) {
     newLineSep += "\n";
   }
   lines2[lines2.length - 1] = " " + lines2[lines2.length - 1] + "]" + (isLast ? "" : newLineSep);
@@ -7004,8 +7005,8 @@ function subTensorToString(vals, shape, dtype, strides2, padPerCol, isLast = tru
 }
 function createComplexTuples(vals) {
   const complexTuples = [];
-  for (let i2 = 0; i2 < vals.length; i2 += 2) {
-    complexTuples.push([vals[i2], vals[i2 + 1]]);
+  for (let i = 0; i < vals.length; i += 2) {
+    complexTuples.push([vals[i], vals[i + 1]]);
   }
   return complexTuples;
 }
@@ -7015,8 +7016,8 @@ var TensorBuffer = class {
     this.shape = shape.slice();
     this.size = sizeFromShape(shape);
     if (values != null) {
-      const n2 = values.length;
-      assert(n2 === this.size, () => `Length of values '${n2}' does not match the size inferred by the shape '${this.size}'.`);
+      const n = values.length;
+      assert(n === this.size, () => `Length of values '${n}' does not match the size inferred by the shape '${this.size}'.`);
     }
     if (dtype === "complex64") {
       throw new Error(`complex64 dtype TensorBuffers are not supported. Please create a TensorBuffer for the real and imaginary parts separately and call tf.complex(real, imag).`);
@@ -7036,17 +7037,17 @@ var TensorBuffer = class {
     if (locs.length === 0) {
       locs = [0];
     }
-    let i2 = 0;
+    let i = 0;
     for (const loc of locs) {
-      if (loc < 0 || loc >= this.shape[i2]) {
+      if (loc < 0 || loc >= this.shape[i]) {
         const msg = `Requested out of range element at ${locs}.   Buffer shape=${this.shape}`;
         throw new Error(msg);
       }
-      i2++;
+      i++;
     }
     let index2 = locs[locs.length - 1];
-    for (let i3 = 0; i3 < locs.length - 1; ++i3) {
-      index2 += this.strides[i3] * locs[i3];
+    for (let i2 = 0; i2 < locs.length - 1; ++i2) {
+      index2 += this.strides[i2] * locs[i2];
     }
     return this.values[index2];
   }
@@ -7057,8 +7058,8 @@ var TensorBuffer = class {
       return locs[0];
     }
     let index2 = locs[locs.length - 1];
-    for (let i2 = 0; i2 < locs.length - 1; ++i2) {
-      index2 += this.strides[i2] * locs[i2];
+    for (let i = 0; i < locs.length - 1; ++i) {
+      index2 += this.strides[i] * locs[i];
     }
     return index2;
   }
@@ -7069,9 +7070,9 @@ var TensorBuffer = class {
       return [index2];
     }
     const locs = new Array(this.shape.length);
-    for (let i2 = 0; i2 < locs.length - 1; ++i2) {
-      locs[i2] = Math.floor(index2 / this.strides[i2]);
-      index2 -= locs[i2] * this.strides[i2];
+    for (let i = 0; i < locs.length - 1; ++i) {
+      locs[i] = Math.floor(index2 / this.strides[i]);
+      index2 -= locs[i] * this.strides[i];
     }
     locs[locs.length - 1] = index2;
     return locs;
@@ -7392,8 +7393,8 @@ var Engine = class {
       return;
     }
     const sortedBackends = this.getSortedBackends();
-    for (let i2 = 0; i2 < sortedBackends.length; i2++) {
-      const backendName = sortedBackends[i2];
+    for (let i = 0; i < sortedBackends.length; i++) {
+      const backendName = sortedBackends[i];
       const success = await this.initializeBackend(backendName).success;
       if (success) {
         await this.setBackend(backendName);
@@ -7545,8 +7546,8 @@ var Engine = class {
   }
   initializeBackendsAndReturnBest() {
     const sortedBackends = this.getSortedBackends();
-    for (let i2 = 0; i2 < sortedBackends.length; i2++) {
-      const backendName = sortedBackends[i2];
+    for (let i = 0; i < sortedBackends.length; i++) {
+      const backendName = sortedBackends[i];
       const { success, asyncInit } = this.initializeBackend(backendName);
       if (asyncInit || success) {
         return { name: backendName, asyncInit };
@@ -7763,7 +7764,7 @@ var Engine = class {
       } else {
         inputTensorsToSave = inputsToSave.map((inputName) => inputs[inputName]);
       }
-      const outputTensorsToSave = outputs.filter((_, i2) => outputsToSave[i2]);
+      const outputTensorsToSave = outputs.filter((_, i) => outputsToSave[i]);
       return inputTensorsToSave.concat(outputTensorsToSave);
     }
     return [];
@@ -7917,9 +7918,9 @@ var Engine = class {
     }
     if (gradientsFunc != null) {
       tapeNode.gradient = (dys) => {
-        dys = dys.map((dy, i2) => {
+        dys = dys.map((dy, i) => {
           if (dy == null) {
-            const output = outputs[i2];
+            const output = outputs[i];
             const vals = makeZerosTypedArray(output.size, output.dtype);
             return this.makeTensor(vals, output.shape, output.dtype);
           }
@@ -7958,8 +7959,8 @@ var Engine = class {
   endScope(result) {
     const tensorsToTrackInParent = getTensorsInContainer(result);
     const tensorsToTrackInParentSet = new Set(tensorsToTrackInParent.map((t2) => t2.id));
-    for (let i2 = 0; i2 < this.state.activeScope.track.length; i2++) {
-      const tensor2 = this.state.activeScope.track[i2];
+    for (let i = 0; i < this.state.activeScope.track.length; i++) {
+      const tensor2 = this.state.activeScope.track[i];
       if (!tensor2.kept && !tensorsToTrackInParentSet.has(tensor2.id)) {
         tensor2.dispose();
       }
@@ -8010,8 +8011,8 @@ var Engine = class {
       assert(inputs.every((t2) => t2 instanceof Tensor), () => "The args passed in customGrad(f)(x1, x2,...) must all be tensors");
       let res;
       const inputMap = {};
-      inputs.forEach((input2, i2) => {
-        inputMap[i2] = input2;
+      inputs.forEach((input2, i) => {
+        inputMap[i] = input2;
       });
       const forwardFunc = (_, save) => {
         res = f(...[...inputs, save]);
@@ -8025,8 +8026,8 @@ var Engine = class {
         assert(grads2.length === inputs.length, () => "The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function that returns the same number of tensors as inputs passed to f(...).");
         assert(grads2.every((t2) => t2 instanceof Tensor), () => "The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function that returns a list of only tensors.");
         const gradMap = {};
-        grads2.forEach((grad2, i2) => {
-          gradMap[i2] = () => grad2;
+        grads2.forEach((grad2, i) => {
+          gradMap[i] = () => grad2;
         });
         return gradMap;
       };
@@ -8181,8 +8182,8 @@ function deepAssertShapeConsistency(val, shape, indices) {
   assert(shape.length > 0, () => `Element arr[${indices.join("][")}] should be a primitive, but is an array of ${val.length} elements`);
   assert(val.length === shape[0], () => `Element arr[${indices.join("][")}] should have ${shape[0]} elements, but has ${val.length} elements`);
   const subShape = shape.slice(1);
-  for (let i2 = 0; i2 < val.length; ++i2) {
-    deepAssertShapeConsistency(val[i2], subShape, indices.concat(i2));
+  for (let i = 0; i < val.length; ++i) {
+    deepAssertShapeConsistency(val[i], subShape, indices.concat(i));
   }
 }
 function assertDtype(expectedDtype, actualDType, argName, functionName) {
@@ -8223,7 +8224,7 @@ function convertToTensorArray(arg, argName, functionName, parseAsDtype = "numeri
     throw new Error(`Argument ${argName} passed to ${functionName} must be a \`Tensor[]\` or \`TensorLike[]\``);
   }
   const tensors = arg;
-  return tensors.map((t2, i2) => convertToTensor(t2, `${argName}[${i2}]`, functionName, parseAsDtype));
+  return tensors.map((t2, i) => convertToTensor(t2, `${argName}[${i}]`, functionName, parseAsDtype));
 }
 var OP_SCOPE_SUFFIX = "__op";
 function op(f) {
@@ -8277,10 +8278,10 @@ function makeTensor(values, shape, inferredShape, dtype) {
     const providedSize = sizeFromShape(shape);
     const inferredSize = sizeFromShape(inferredShape);
     assert(providedSize === inferredSize, () => `Based on the provided shape, [${shape}], the tensor should have ${providedSize} values but has ${inferredSize}`);
-    for (let i2 = 0; i2 < inferredShape.length; ++i2) {
-      const inferred = inferredShape[i2];
-      const flatDimsDontMatch = i2 === inferredShape.length - 1 ? inferred !== sizeFromShape(shape.slice(i2)) : true;
-      assert(inferredShape[i2] === shape[i2] || !flatDimsDontMatch, () => `Error creating a new Tensor. Inferred shape (${inferredShape}) does not match the provided shape (${shape}). `);
+    for (let i = 0; i < inferredShape.length; ++i) {
+      const inferred = inferredShape[i];
+      const flatDimsDontMatch = i === inferredShape.length - 1 ? inferred !== sizeFromShape(shape.slice(i)) : true;
+      assert(inferredShape[i] === shape[i] || !flatDimsDontMatch, () => `Error creating a new Tensor. Inferred shape (${inferredShape}) does not match the provided shape (${shape}). `);
     }
   }
   if (!isTypedArray(values) && !Array.isArray(values)) {
@@ -8308,9 +8309,9 @@ async function encodeWeights(tensors, group) {
   const specs = [];
   const dataPromises = [];
   const names = Array.isArray(tensors) ? tensors.map((tensor2) => tensor2.name) : Object.keys(tensors);
-  for (let i2 = 0; i2 < names.length; ++i2) {
-    const name = names[i2];
-    const t2 = Array.isArray(tensors) ? tensors[i2].tensor : tensors[name];
+  for (let i = 0; i < names.length; ++i) {
+    const name = names[i];
+    const t2 = Array.isArray(tensors) ? tensors[i].tensor : tensors[name];
     if (t2.dtype !== "float32" && t2.dtype !== "int32" && t2.dtype !== "bool" && t2.dtype !== "string" && t2.dtype !== "complex64") {
       throw new Error(`Unsupported dtype in weight '${name}': ${t2.dtype}`);
     }
@@ -8321,8 +8322,8 @@ async function encodeWeights(tensors, group) {
         const totalNumBytes = vals.reduce((p2, c) => p2 + c.length, 0) + NUM_BYTES_STRING_LENGTH * vals.length;
         const bytes = new Uint8Array(totalNumBytes);
         let offset = 0;
-        for (let i3 = 0; i3 < vals.length; i3++) {
-          const val = vals[i3];
+        for (let i2 = 0; i2 < vals.length; i2++) {
+          const val = vals[i2];
           const bytesOfLength = new Uint8Array(new Uint32Array([val.length]).buffer);
           bytes.set(bytesOfLength, offset);
           offset += NUM_BYTES_STRING_LENGTH;
@@ -8372,9 +8373,9 @@ function decodeWeights(buffer2, specs) {
       if (dtype === "float32") {
         if (quantization.dtype === "uint8" || quantization.dtype === "uint16") {
           values = new Float32Array(quantizedArray.length);
-          for (let i2 = 0; i2 < quantizedArray.length; i2++) {
-            const v = quantizedArray[i2];
-            values[i2] = v * quantization.scale + quantization.min;
+          for (let i = 0; i < quantizedArray.length; i++) {
+            const v = quantizedArray[i];
+            values[i] = v * quantization.scale + quantization.min;
           }
         } else if (quantization.dtype === "float16") {
           if (float16Decode === void 0) {
@@ -8389,9 +8390,9 @@ function decodeWeights(buffer2, specs) {
           throw new Error(`Unsupported quantization type ${quantization.dtype} for weight type int32.`);
         }
         values = new Int32Array(quantizedArray.length);
-        for (let i2 = 0; i2 < quantizedArray.length; i2++) {
-          const v = quantizedArray[i2];
-          values[i2] = Math.round(v * quantization.scale + quantization.min);
+        for (let i = 0; i < quantizedArray.length; i++) {
+          const v = quantizedArray[i];
+          values[i] = Math.round(v * quantization.scale + quantization.min);
         }
       } else {
         throw new Error(`Unsupported dtype in weight '${name}': ${dtype}`);
@@ -8400,7 +8401,7 @@ function decodeWeights(buffer2, specs) {
     } else if (dtype === "string") {
       const size22 = sizeFromShape(spec.shape);
       values = [];
-      for (let i2 = 0; i2 < size22; i2++) {
+      for (let i = 0; i < size22; i++) {
         const byteLength = new Uint32Array(buffer2.slice(offset, offset + NUM_BYTES_STRING_LENGTH))[0];
         offset += NUM_BYTES_STRING_LENGTH;
         const bytes = new Uint8Array(buffer2.slice(offset, offset + byteLength));
@@ -8420,9 +8421,9 @@ function decodeWeights(buffer2, specs) {
         values = new Float32Array(byteBuffer);
         const real5 = new Float32Array(values.length / 2);
         const image2 = new Float32Array(values.length / 2);
-        for (let i2 = 0; i2 < real5.length; i2++) {
-          real5[i2] = values[i2 * 2];
-          image2[i2] = values[i2 * 2 + 1];
+        for (let i = 0; i < real5.length; i++) {
+          real5[i] = values[i * 2];
+          image2[i] = values[i * 2 + 1];
         }
         const realTensor = tensor(real5, shape, "float32");
         const imageTensor = tensor(image2, shape, "float32");
@@ -8473,21 +8474,21 @@ function arrayBufferToBase64String(buffer2) {
     return Buffer.from(buffer2).toString("base64");
   }
   const buf = new Uint8Array(buffer2);
-  let s2 = "";
-  for (let i2 = 0, l3 = buf.length; i2 < l3; i2++) {
-    s2 += String.fromCharCode(buf[i2]);
+  let s = "";
+  for (let i = 0, l = buf.length; i < l; i++) {
+    s += String.fromCharCode(buf[i]);
   }
-  return btoa(s2);
+  return btoa(s);
 }
 function base64StringToArrayBuffer(str) {
   if (useNodeBuffer) {
     const buf = Buffer.from(str, "base64");
     return buf.buffer.slice(buf.byteOffset, buf.byteOffset + buf.byteLength);
   }
-  const s2 = atob(str);
-  const buffer2 = new Uint8Array(s2.length);
-  for (let i2 = 0; i2 < s2.length; ++i2) {
-    buffer2.set([s2.charCodeAt(i2)], i2);
+  const s = atob(str);
+  const buffer2 = new Uint8Array(s.length);
+  for (let i = 0; i < s.length; ++i) {
+    buffer2.set([s.charCodeAt(i)], i);
   }
   return buffer2.buffer;
 }
@@ -8577,24 +8578,24 @@ function getModelArtifactsInfoForJSON(modelArtifacts) {
   };
 }
 function computeFloat16MantisaTable() {
-  const convertMantissa = (i2) => {
-    let m = i2 << 13;
-    let e2 = 0;
+  const convertMantissa = (i) => {
+    let m = i << 13;
+    let e = 0;
     while ((m & 8388608) === 0) {
-      e2 -= 8388608;
+      e -= 8388608;
       m <<= 1;
     }
     m &= ~8388608;
-    e2 += 947912704;
-    return m | e2;
+    e += 947912704;
+    return m | e;
   };
   const mantisaTable = new Uint32Array(2048);
   mantisaTable[0] = 0;
-  for (let i2 = 1; i2 < 1024; i2++) {
-    mantisaTable[i2] = convertMantissa(i2);
+  for (let i = 1; i < 1024; i++) {
+    mantisaTable[i] = convertMantissa(i);
   }
-  for (let i2 = 1024; i2 < 2048; i2++) {
-    mantisaTable[i2] = 939524096 + (i2 - 1024 << 13);
+  for (let i = 1024; i < 2048; i++) {
+    mantisaTable[i] = 939524096 + (i - 1024 << 13);
   }
   return mantisaTable;
 }
@@ -8604,18 +8605,18 @@ function computeFloat16ExponentTable() {
   exponentTable[31] = 1199570944;
   exponentTable[32] = 2147483648;
   exponentTable[63] = 3347054592;
-  for (let i2 = 1; i2 < 31; i2++) {
-    exponentTable[i2] = i2 << 23;
+  for (let i = 1; i < 31; i++) {
+    exponentTable[i] = i << 23;
   }
-  for (let i2 = 33; i2 < 63; i2++) {
-    exponentTable[i2] = 2147483648 + (i2 - 32 << 23);
+  for (let i = 33; i < 63; i++) {
+    exponentTable[i] = 2147483648 + (i - 32 << 23);
   }
   return exponentTable;
 }
 function computeFloat16OffsetTable() {
   const offsetTable = new Uint32Array(64);
-  for (let i2 = 0; i2 < 64; i2++) {
-    offsetTable[i2] = 1024;
+  for (let i = 0; i < 64; i++) {
+    offsetTable[i] = 1024;
   }
   offsetTable[0] = offsetTable[32] = 0;
   return offsetTable;
@@ -9021,8 +9022,8 @@ var BrowserLocalStorageManager = class {
     const out = {};
     const prefix = PATH_PREFIX + PATH_SEPARATOR;
     const suffix = PATH_SEPARATOR + INFO_SUFFIX;
-    for (let i2 = 0; i2 < this.LS.length; ++i2) {
-      const key = this.LS.key(i2);
+    for (let i = 0; i < this.LS.length; ++i) {
+      const key = this.LS.key(i);
       if (key.startsWith(prefix) && key.endsWith(suffix)) {
         const modelPath = getModelPathFromKey(key);
         out[modelPath] = JSON.parse(this.LS.getItem(key));
@@ -9488,19 +9489,19 @@ function weightsLoaderFactory(fetchWeightsFunction) {
       });
     });
     if (!weightsFound.every((found) => found)) {
-      const weightsNotFound = weightNames.filter((_, i2) => !weightsFound[i2]);
+      const weightsNotFound = weightNames.filter((_, i) => !weightsFound[i]);
       throw new Error(`Could not find weights in manifest with names: ${weightsNotFound.join(", ")}. 
 Manifest JSON has weights with names: ${allManifestWeightNames.join(", ")}.`);
     }
-    const groupIndicesToFetch = groupIndicesToFetchMap.reduce((accumulator, shouldFetch, i2) => {
+    const groupIndicesToFetch = groupIndicesToFetchMap.reduce((accumulator, shouldFetch, i) => {
       if (shouldFetch) {
-        accumulator.push(i2);
+        accumulator.push(i);
       }
       return accumulator;
     }, []);
     const fetchUrls = [];
-    groupIndicesToFetch.forEach((i2) => {
-      manifest[i2].paths.forEach((filepath) => {
+    groupIndicesToFetch.forEach((i) => {
+      manifest[i].paths.forEach((filepath) => {
         const fetchUrl = filePathPrefix + (!filePathPrefix.endsWith("/") ? "/" : "") + filepath;
         fetchUrls.push(fetchUrl);
       });
@@ -9508,21 +9509,21 @@ Manifest JSON has weights with names: ${allManifestWeightNames.join(", ")}.`);
     const buffers = await fetchWeightsFunction(fetchUrls);
     const weightsTensorMap = {};
     let bufferIndexOffset = 0;
-    groupIndicesToFetch.forEach((i2) => {
-      const numBuffers = manifest[i2].paths.length;
+    groupIndicesToFetch.forEach((i) => {
+      const numBuffers = manifest[i].paths.length;
       let groupBytes = 0;
-      for (let i3 = 0; i3 < numBuffers; i3++) {
-        groupBytes += buffers[bufferIndexOffset + i3].byteLength;
+      for (let i2 = 0; i2 < numBuffers; i2++) {
+        groupBytes += buffers[bufferIndexOffset + i2].byteLength;
       }
       const groupBuffer = new ArrayBuffer(groupBytes);
       const groupByteBuffer = new Uint8Array(groupBuffer);
       let groupBufferOffset = 0;
-      for (let i3 = 0; i3 < numBuffers; i3++) {
-        const buffer2 = new Uint8Array(buffers[bufferIndexOffset + i3]);
+      for (let i2 = 0; i2 < numBuffers; i2++) {
+        const buffer2 = new Uint8Array(buffers[bufferIndexOffset + i2]);
         groupByteBuffer.set(buffer2, groupBufferOffset);
         groupBufferOffset += buffer2.byteLength;
       }
-      const weightsEntries = groupWeightsToFetch[i2];
+      const weightsEntries = groupWeightsToFetch[i];
       weightsEntries.forEach((weightsEntry) => {
         const byteBuffer = groupBuffer.slice(weightsEntry.groupOffset, weightsEntry.groupOffset + weightsEntry.sizeBytes);
         const nameToTensorMap = decodeWeights(byteBuffer, [weightsEntry.manifestEntry]);
@@ -9595,7 +9596,7 @@ var HTTPRequest = class {
     let modelJSON;
     try {
       modelJSON = await modelConfigRequest.json();
-    } catch (e2) {
+    } catch (e) {
       let message = `Failed to parse model JSON of response from ${this.path}.`;
       if (this.path.endsWith(".pb")) {
         message += " Your path contains a .pb file extension. Support for .pb models have been removed in TensorFlow.js 1.0 in favor of .json models. You can re-convert your Python TensorFlow model using the TensorFlow.js 1.0 conversion scripts or you can convert your.pb models with the 'pb2json'NPM script in the tensorflow/tfjs-converter repository.";
@@ -9843,7 +9844,7 @@ var real = op({ real_ });
 function transpose_(x, perm, conjugate) {
   const $x = convertToTensor(x, "x", "transpose");
   if (perm == null) {
-    perm = $x.shape.map((s2, i2) => i2).reverse();
+    perm = $x.shape.map((s, i) => i).reverse();
   }
   assert($x.rank === perm.length, () => `Error in transpose: rank of input ${$x.rank} must match length of perm ${perm}.`);
   perm.forEach((axis) => {
@@ -9893,10 +9894,10 @@ __export2(broadcast_util_exports, {
 function getBroadcastDims(inShape, outShape) {
   const inRank = inShape.length;
   const dims = [];
-  for (let i2 = 0; i2 < inRank; i2++) {
-    const dim = inRank - 1 - i2;
+  for (let i = 0; i < inRank; i++) {
+    const dim = inRank - 1 - i;
     const a = inShape[dim] || 1;
-    const b = outShape[outShape.length - 1 - i2] || 1;
+    const b = outShape[outShape.length - 1 - i] || 1;
     if (b > 1 && a === 1) {
       dims.unshift(dim);
     }
@@ -9905,9 +9906,9 @@ function getBroadcastDims(inShape, outShape) {
 }
 function getReductionAxes(inShape, outShape) {
   const result = [];
-  for (let i2 = 0; i2 < outShape.length; i2++) {
-    const inDim = inShape[inShape.length - i2 - 1];
-    const outAxis = outShape.length - i2 - 1;
+  for (let i = 0; i < outShape.length; i++) {
+    const inDim = inShape[inShape.length - i - 1];
+    const outAxis = outShape.length - i - 1;
     const outDim = outShape[outAxis];
     if (inDim == null || inDim === 1 && outDim > 1) {
       result.unshift(outAxis);
@@ -9917,13 +9918,13 @@ function getReductionAxes(inShape, outShape) {
 }
 function assertAndGetBroadcastShape(shapeA, shapeB) {
   const result = [];
-  const l3 = Math.max(shapeA.length, shapeB.length);
-  for (let i2 = 0; i2 < l3; i2++) {
-    let a = shapeA[shapeA.length - i2 - 1];
+  const l = Math.max(shapeA.length, shapeB.length);
+  for (let i = 0; i < l; i++) {
+    let a = shapeA[shapeA.length - i - 1];
     if (a == null) {
       a = 1;
     }
-    let b = shapeB[shapeB.length - i2 - 1];
+    let b = shapeB[shapeB.length - i - 1];
     if (b == null) {
       b = 1;
     }
@@ -10027,9 +10028,9 @@ function fromPixels_(pixels, numChannels = 3) {
   } else {
     const numPixels = width * height;
     values = new Int32Array(numPixels * numChannels);
-    for (let i2 = 0; i2 < numPixels; i2++) {
+    for (let i = 0; i < numPixels; i++) {
       for (let channel = 0; channel < numChannels; ++channel) {
-        values[i2 * numChannels + channel] = vals[i2 * 4 + channel];
+        values[i * numChannels + channel] = vals[i * 4 + channel];
       }
     }
   }
@@ -10054,7 +10055,7 @@ async function fromPixelsAsync(pixels, numChannels = 3) {
     let imageBitmap;
     try {
       imageBitmap = await createImageBitmap(pixels, { premultiplyAlpha: "none" });
-    } catch (e2) {
+    } catch (e) {
       imageBitmap = null;
     }
     if (imageBitmap != null && imageBitmap.width === pixels.width && imageBitmap.height === pixels.height) {
@@ -10088,10 +10089,10 @@ async function toPixels(img, canvas3) {
   const data = await $img.data();
   const multiplier = $img.dtype === "float32" ? 255 : 1;
   const bytes = new Uint8ClampedArray(width * height * 4);
-  for (let i2 = 0; i2 < height * width; ++i2) {
+  for (let i = 0; i < height * width; ++i) {
     const rgba = [0, 0, 0, 255];
     for (let d = 0; d < depth; d++) {
-      const value = data[i2 * depth + d];
+      const value = data[i * depth + d];
       if ($img.dtype === "float32") {
         if (value < 0 || value > 1) {
           throw new Error(`Tensor values for a float32 Tensor must be in the range [0 - 1] but encountered ${value}.`);
@@ -10109,7 +10110,7 @@ async function toPixels(img, canvas3) {
         rgba[d] = value * multiplier;
       }
     }
-    const j = i2 * 4;
+    const j = i * 4;
     bytes[j + 0] = Math.round(rgba[0]);
     bytes[j + 1] = Math.round(rgba[1]);
     bytes[j + 2] = Math.round(rgba[2]);
@@ -10153,16 +10154,16 @@ function prepareAndValidate(tensor2, indices) {
   const indicesShape = indices.shape;
   const sliceRank = indicesShape[indicesShape.length - 1];
   let nResult = 1;
-  for (let i2 = 0; i2 < indicesShape.length - 1; ++i2) {
-    nResult *= indicesShape[i2];
+  for (let i = 0; i < indicesShape.length - 1; ++i) {
+    nResult *= indicesShape[i];
   }
   const inputShape = tensor2.shape;
   const resultShape = indicesShape.slice();
   resultShape.pop();
   let sliceSize = 1;
-  for (let i2 = sliceRank; i2 < tensorRank; ++i2) {
-    sliceSize *= inputShape[i2];
-    resultShape.push(inputShape[i2]);
+  for (let i = sliceRank; i < tensorRank; ++i) {
+    sliceSize *= inputShape[i];
+    resultShape.push(inputShape[i]);
   }
   const strides2 = [
     ...computeStrides(tensor2.shape).map((stride) => stride / sliceSize),
@@ -10228,8 +10229,8 @@ function calculateShapes(updates, indices, shape) {
   const sliceRank = indicesRank > 1 ? indices.shape[indicesRank - 1] : 1;
   const totalNd = shape.length;
   let sliceSize = 1;
-  for (let i2 = sliceRank; i2 < totalNd; ++i2) {
-    sliceSize *= shape[i2];
+  for (let i = sliceRank; i < totalNd; ++i) {
+    sliceSize *= shape[i];
   }
   const safeSliceDim = sliceRank < 1 ? 1 : sliceRank;
   const numUpdates = sizeFromShape(indices.shape) / safeSliceDim;
@@ -10260,8 +10261,8 @@ function assertParamsValid(input2, begin, size2) {
   const inputRank = input2.shape.length;
   assert(inputRank === begin.length, () => `Error in slice${inputRank}D: Length of begin ${begin} must match the rank of the array (${inputRank}).`);
   assert(inputRank === size2.length, () => `Error in slice${inputRank}D: Length of size ${size2} must match the rank of the array (${inputRank}).`);
-  for (let i2 = 0; i2 < inputRank; ++i2) {
-    assert(begin[i2] + size2[i2] <= input2.shape[i2], () => `Error in slice${inputRank}D: begin[${i2}] + size[${i2}] (${begin[i2] + size2[i2]}) would overflow input.shape[${i2}] (${input2.shape[i2]})`);
+  for (let i = 0; i < inputRank; ++i) {
+    assert(begin[i] + size2[i] <= input2.shape[i], () => `Error in slice${inputRank}D: begin[${i}] + size[${i}] (${begin[i] + size2[i]}) would overflow input.shape[${i}] (${input2.shape[i]})`);
   }
 }
 function maskToAxes(mask2) {
@@ -10285,11 +10286,11 @@ function computeOutShape(begin, end, strides2) {
 }
 function stridesWithElidedDims(strides2, ellipsisInsertionIndex, numElidedAxes, inputShape) {
   const newStrides = [...strides2];
-  for (let i2 = newStrides.length; i2 < inputShape.length; i2++) {
+  for (let i = newStrides.length; i < inputShape.length; i++) {
     newStrides.push(1);
   }
-  for (let i2 = 0; i2 < numElidedAxes; i2++) {
-    if (i2 === 0) {
+  for (let i = 0; i < numElidedAxes; i++) {
+    if (i === 0) {
       newStrides[ellipsisInsertionIndex] = 1;
     } else {
       newStrides.splice(ellipsisInsertionIndex, 0, 1);
@@ -10306,8 +10307,8 @@ function unnormalizeAxis(ellipsisInsertionIndex, numElidedAxes, normalizedAxis)
 }
 function getElidedAxes(numElidedAxes, ellipsisInsertionIndex) {
   const elidedAxes = [];
-  for (let i2 = 0; i2 < numElidedAxes; i2++) {
-    elidedAxes.push(ellipsisInsertionIndex + i2);
+  for (let i = 0; i < numElidedAxes; i++) {
+    elidedAxes.push(ellipsisInsertionIndex + i);
   }
   return elidedAxes;
 }
@@ -10365,12 +10366,12 @@ function stopIndicesWithElidedDims(endMask, ellipsisInsertionIndex, numElidedAxe
       newIndices[axis] = originalValue;
     }
   }
-  for (let i2 = 0; i2 < newIndices.length; i2++) {
-    const axisSize = inputShape[i2];
-    if (newIndices[i2] < 0) {
-      newIndices[i2] += axisSize;
+  for (let i = 0; i < newIndices.length; i++) {
+    const axisSize = inputShape[i];
+    if (newIndices[i] < 0) {
+      newIndices[i] += axisSize;
     }
-    newIndices[i2] = clamp(0, newIndices[i2], inputShape[i2]);
+    newIndices[i] = clamp(0, newIndices[i], inputShape[i]);
   }
   return newIndices;
 }
@@ -10421,14 +10422,14 @@ function stopForAxis(endMask, stopIndices, strides2, inputShape, axis, ellipsisM
 }
 function isSliceContinous(shape, begin, size2) {
   let firstNonOneAxis = size2.length;
-  for (let i2 = 0; i2 < size2.length; i2++) {
-    if (size2[i2] > 1) {
-      firstNonOneAxis = i2;
+  for (let i = 0; i < size2.length; i++) {
+    if (size2[i] > 1) {
+      firstNonOneAxis = i;
       break;
     }
   }
-  for (let i2 = firstNonOneAxis + 1; i2 < size2.length; i2++) {
-    if (begin[i2] > 0 || size2[i2] !== shape[i2]) {
+  for (let i = firstNonOneAxis + 1; i < size2.length; i++) {
+    if (begin[i] > 0 || size2[i] !== shape[i]) {
       return false;
     }
   }
@@ -10436,8 +10437,8 @@ function isSliceContinous(shape, begin, size2) {
 }
 function computeFlatOffset(begin, strides2) {
   let flatOffset = begin.length > 0 ? begin[begin.length - 1] : 1;
-  for (let i2 = 0; i2 < begin.length - 1; i2++) {
-    flatOffset += begin[i2] * strides2[i2];
+  for (let i = 0; i < begin.length - 1; i++) {
+    flatOffset += begin[i] * strides2[i];
   }
   return flatOffset;
 }
@@ -10464,12 +10465,12 @@ function parseSliceParams(x, begin, size2) {
   } else {
     size_ = size2;
   }
-  size_ = size_.map((d, i2) => {
+  size_ = size_.map((d, i) => {
     if (d >= 0) {
       return d;
     } else {
-      assert(d === -1, () => `Negative size values should be exactly -1 but got ${d} for the slice() size at index ${i2}.`);
-      return x.shape[i2] - begin_[i2];
+      assert(d === -1, () => `Negative size values should be exactly -1 but got ${d} for the slice() size at index ${i}.`);
+      return x.shape[i] - begin_[i];
     }
   });
   return [begin_, size_];
@@ -10498,11 +10499,11 @@ function sliceInfo(xShape, begin, end, strides2, beginMask, endMask, ellipsisMas
     newAxisMask,
     shrinkAxisMask
   };
-  for (let i2 = 0; i2 < sparseSpec.dims; i2++) {
-    if (ellipsisSeen && (1 << i2 & newAxisMask) !== 0) {
+  for (let i = 0; i < sparseSpec.dims; i++) {
+    if (ellipsisSeen && (1 << i & newAxisMask) !== 0) {
       sparseSpec.numAddAxisAfterEllipsis++;
     }
-    if (1 << i2 & ellipsisMask) {
+    if (1 << i & ellipsisMask) {
       ellipsisSeen = true;
     }
   }
@@ -10523,56 +10524,56 @@ function sliceInfo(xShape, begin, end, strides2, beginMask, endMask, ellipsisMas
   let isSimpleSlice = true;
   const processingShape = [];
   const finalShape = [];
-  for (let i2 = 0; i2 < xShape.length; ++i2) {
-    if (denseSpec.strides[i2] === 0) {
-      throw Error(`strides[${i2}] must be non-zero`);
+  for (let i = 0; i < xShape.length; ++i) {
+    if (denseSpec.strides[i] === 0) {
+      throw Error(`strides[${i}] must be non-zero`);
     }
-    const shrinkI = !!(denseSpec.shrinkAxisMask & 1 << i2);
-    const dimI = xShape[i2];
+    const shrinkI = !!(denseSpec.shrinkAxisMask & 1 << i);
+    const dimI = xShape[i];
     if (dimI === -1) {
       processingShape.push(shrinkI ? 1 : -1);
       continue;
     }
-    const masks = [denseSpec.beginMask & 1 << i2, denseSpec.endMask & 1 << i2];
+    const masks = [denseSpec.beginMask & 1 << i, denseSpec.endMask & 1 << i];
     const validRange = [
-      denseSpec.strides[i2] > 0 ? 0 : -1,
-      denseSpec.strides[i2] > 0 ? dimI : dimI - 1
+      denseSpec.strides[i] > 0 ? 0 : -1,
+      denseSpec.strides[i] > 0 ? dimI : dimI - 1
     ];
-    if (shrinkI && denseSpec.strides[i2] <= 0) {
+    if (shrinkI && denseSpec.strides[i] <= 0) {
       throw Error("only stride 1 allowed on non-range indexing.");
     }
-    isSimpleSlice = isSimpleSlice && denseSpec.strides[i2] === 1;
-    const beginAndEndMasked = !!(denseSpec.beginMask & 1 << i2 && denseSpec.endMask & 1 << i2);
+    isSimpleSlice = isSimpleSlice && denseSpec.strides[i] === 1;
+    const beginAndEndMasked = !!(denseSpec.beginMask & 1 << i && denseSpec.endMask & 1 << i);
     if (denseSpec.beginValid && denseSpec.endValid) {
       if (shrinkI) {
-        const xFwd = denseSpec.begin[i2] < 0 ? dimI + denseSpec.begin[i2] : denseSpec.begin[i2];
-        denseSpec.begin[i2] = xFwd;
-        denseSpec.end[i2] = denseSpec.begin[i2] + 1;
+        const xFwd = denseSpec.begin[i] < 0 ? dimI + denseSpec.begin[i] : denseSpec.begin[i];
+        denseSpec.begin[i] = xFwd;
+        denseSpec.end[i] = denseSpec.begin[i] + 1;
         if (xFwd < 0 || xFwd >= dimI) {
-          throw Error(`slice index ${denseSpec.begin[i2]} of dimension ${i2} out of bounds.`);
+          throw Error(`slice index ${denseSpec.begin[i]} of dimension ${i} out of bounds.`);
         }
       } else {
-        denseSpec.begin[i2] = canonical(denseSpec.begin[i2], 0, denseSpec.strides[i2], dimI, masks, validRange);
-        denseSpec.end[i2] = canonical(denseSpec.end[i2], 1, denseSpec.strides[i2], dimI, masks, validRange);
+        denseSpec.begin[i] = canonical(denseSpec.begin[i], 0, denseSpec.strides[i], dimI, masks, validRange);
+        denseSpec.end[i] = canonical(denseSpec.end[i], 1, denseSpec.strides[i], dimI, masks, validRange);
       }
-      const takeAllInDimension = denseSpec.strides[i2] === 1 && denseSpec.begin[i2] === 0 && denseSpec.end[i2] === dimI;
+      const takeAllInDimension = denseSpec.strides[i] === 1 && denseSpec.begin[i] === 0 && denseSpec.end[i] === dimI;
       isIdentity = isIdentity && takeAllInDimension;
-      sliceDim0 = sliceDim0 && (i2 === 0 && denseSpec.strides[i2] === 1 || takeAllInDimension);
+      sliceDim0 = sliceDim0 && (i === 0 && denseSpec.strides[i] === 1 || takeAllInDimension);
     } else {
-      isIdentity = isIdentity && (denseSpec.strides[i2] === 1 && beginAndEndMasked);
-      sliceDim0 = sliceDim0 && (i2 === 0 && denseSpec.strides[i2] === 1 || beginAndEndMasked);
+      isIdentity = isIdentity && (denseSpec.strides[i] === 1 && beginAndEndMasked);
+      sliceDim0 = sliceDim0 && (i === 0 && denseSpec.strides[i] === 1 || beginAndEndMasked);
     }
     let intervalLength;
     let knownInterval = false;
     if (denseSpec.beginValid && denseSpec.endValid) {
-      intervalLength = denseSpec.end[i2] - denseSpec.begin[i2];
+      intervalLength = denseSpec.end[i] - denseSpec.begin[i];
       knownInterval = true;
     } else if (shrinkI) {
       intervalLength = 1;
       knownInterval = true;
     } else if (beginAndEndMasked) {
       if (dimI >= 0) {
-        if (denseSpec.strides[i2] < 0) {
+        if (denseSpec.strides[i] < 0) {
           intervalLength = -dimI;
         } else {
           intervalLength = dimI;
@@ -10582,10 +10583,10 @@ function sliceInfo(xShape, begin, end, strides2, beginMask, endMask, ellipsisMas
     }
     if (knownInterval) {
       let sizeI;
-      if (intervalLength === 0 || intervalLength < 0 !== denseSpec.strides[i2] < 0) {
+      if (intervalLength === 0 || intervalLength < 0 !== denseSpec.strides[i] < 0) {
         sizeI = 0;
       } else {
-        sizeI = Math.trunc(intervalLength / denseSpec.strides[i2]) + (intervalLength % denseSpec.strides[i2] !== 0 ? 1 : 0);
+        sizeI = Math.trunc(intervalLength / denseSpec.strides[i]) + (intervalLength % denseSpec.strides[i] !== 0 ? 1 : 0);
       }
       processingShape.push(sizeI);
     } else {
@@ -10600,7 +10601,7 @@ function sliceInfo(xShape, begin, end, strides2, beginMask, endMask, ellipsisMas
       finalShape.push(1);
     }
   }
-  const finalShapeSparse = finalShape.filter((dim, i2) => denseSpec.finalShapeGatherIndices[i2] !== NEW_AXIS);
+  const finalShapeSparse = finalShape.filter((dim, i) => denseSpec.finalShapeGatherIndices[i] !== NEW_AXIS);
   return {
     finalShapeSparse,
     finalShape,
@@ -10625,9 +10626,9 @@ function buildDenseSpec(sparse2, dense2) {
   dense2.finalShapeGatherIndices = [];
   dense2.finalShapeGatherIndicesSparse = [];
   dense2.inputShapeGatherIndicesSparse = new Array(dense2.dims);
-  for (let i2 = 0; i2 < sparse2.dims; i2++) {
-    if (1 << i2 & sparse2.ellipsisMask) {
-      const nextIndex = Math.min(dense2.dims - (sparse2.dims - i2) + 1 + sparse2.numAddAxisAfterEllipsis, dense2.dims);
+  for (let i = 0; i < sparse2.dims; i++) {
+    if (1 << i & sparse2.ellipsisMask) {
+      const nextIndex = Math.min(dense2.dims - (sparse2.dims - i) + 1 + sparse2.numAddAxisAfterEllipsis, dense2.dims);
       for (; fullIndex < nextIndex; fullIndex++) {
         dense2.begin[fullIndex] = 0;
         dense2.end[fullIndex] = 0;
@@ -10636,9 +10637,9 @@ function buildDenseSpec(sparse2, dense2) {
         dense2.endMask |= 1 << fullIndex;
         dense2.finalShapeGatherIndices.push(fullIndex);
         dense2.finalShapeGatherIndicesSparse.push(-1);
-        dense2.inputShapeGatherIndicesSparse[fullIndex] = i2;
+        dense2.inputShapeGatherIndicesSparse[fullIndex] = i;
       }
-    } else if (1 << i2 & sparse2.newAxisMask) {
+    } else if (1 << i & sparse2.newAxisMask) {
       dense2.finalShapeGatherIndices.push(NEW_AXIS);
       dense2.finalShapeGatherIndicesSparse.push(-1);
     } else {
@@ -10646,27 +10647,27 @@ function buildDenseSpec(sparse2, dense2) {
         throw Error(`Index out of range using input dim ${fullIndex}; input has only ${dense2.dims} dims, ${dense2.begin.length}.`);
       }
       if (sparse2.begin != null) {
-        dense2.begin[fullIndex] = sparse2.begin[i2];
+        dense2.begin[fullIndex] = sparse2.begin[i];
       }
       if (sparse2.end != null) {
-        dense2.end[fullIndex] = sparse2.end[i2];
+        dense2.end[fullIndex] = sparse2.end[i];
       }
-      dense2.strides[fullIndex] = sparse2.strides[i2];
-      if (sparse2.beginMask & 1 << i2) {
+      dense2.strides[fullIndex] = sparse2.strides[i];
+      if (sparse2.beginMask & 1 << i) {
         dense2.beginMask |= 1 << fullIndex;
       }
-      if (sparse2.endMask & 1 << i2) {
+      if (sparse2.endMask & 1 << i) {
         dense2.endMask |= 1 << fullIndex;
       }
-      if (sparse2.shrinkAxisMask & 1 << i2) {
+      if (sparse2.shrinkAxisMask & 1 << i) {
         dense2.finalShapeGatherIndices.push(SHRINK_AXIS);
         dense2.finalShapeGatherIndicesSparse.push(-1);
         dense2.shrinkAxisMask |= 1 << fullIndex;
       } else {
         dense2.finalShapeGatherIndices.push(fullIndex);
-        dense2.finalShapeGatherIndicesSparse.push(i2);
+        dense2.finalShapeGatherIndicesSparse.push(i);
       }
-      dense2.inputShapeGatherIndicesSparse[fullIndex] = i2;
+      dense2.inputShapeGatherIndicesSparse[fullIndex] = i;
       fullIndex++;
     }
   }
@@ -10767,11 +10768,11 @@ function expectArraysPredicate(actual, expected, predicate) {
 Actual:   ${actualFlat}.
 Expected: ${expectedFlat}.`);
   }
-  for (let i2 = 0; i2 < expectedFlat.length; ++i2) {
-    const a = actualFlat[i2];
-    const e2 = expectedFlat[i2];
-    if (!predicate(a, e2)) {
-      throw new Error(`Arrays differ: actual[${i2}] = ${a}, expected[${i2}] = ${e2}.
+  for (let i = 0; i < expectedFlat.length; ++i) {
+    const a = actualFlat[i];
+    const e = expectedFlat[i];
+    if (!predicate(a, e)) {
+      throw new Error(`Arrays differ: actual[${i}] = ${a}, expected[${i}] = ${e}.
 Actual:   ${actualFlat}.
 Expected: ${expectedFlat}.`);
     }
@@ -10793,30 +10794,30 @@ function expectArraysEqual(actual, expected) {
   }
   return expectArraysPredicate(actual, expected, (a, b) => areClose(a, b, 0));
 }
-function expectNumbersClose(a, e2, epsilon3) {
+function expectNumbersClose(a, e, epsilon3) {
   if (epsilon3 == null) {
     epsilon3 = testEpsilon();
   }
-  if (!areClose(a, e2, epsilon3)) {
-    throw new Error(`Numbers differ: actual === ${a}, expected === ${e2}`);
+  if (!areClose(a, e, epsilon3)) {
+    throw new Error(`Numbers differ: actual === ${a}, expected === ${e}`);
   }
   if (typeof expect !== "undefined") {
     expect().nothing();
   }
 }
-function areClose(a, e2, epsilon3) {
-  if (!isFinite(a) && !isFinite(e2)) {
+function areClose(a, e, epsilon3) {
+  if (!isFinite(a) && !isFinite(e)) {
     return true;
   }
-  if (isNaN(a) || isNaN(e2) || Math.abs(a - e2) > epsilon3) {
+  if (isNaN(a) || isNaN(e) || Math.abs(a - e) > epsilon3) {
     return false;
   }
   return true;
 }
 function expectValuesInRange(actual, low, high) {
-  for (let i2 = 0; i2 < actual.length; i2++) {
-    if (actual[i2] < low || actual[i2] > high) {
-      throw new Error(`Value out of range:${actual[i2]} low: ${low}, high: ${high}`);
+  for (let i = 0; i < actual.length; i++) {
+    if (actual[i] < low || actual[i] > high) {
+      throw new Error(`Value out of range:${actual[i]} low: ${low}, high: ${high}`);
     }
   }
 }
@@ -10826,19 +10827,19 @@ function expectArrayBuffersEqual(actual, expected) {
   if (actualArray.length !== expectedArray.length) {
     throw new Error(`Expected ArrayBuffer to be of length ${expectedArray.length}, but it was ${actualArray.length}`);
   }
-  for (let i2 = 0; i2 < expectedArray.length; i2++) {
-    if (actualArray[i2] !== expectedArray[i2]) {
-      throw new Error(`Expected ArrayBuffer value at ${i2} to be ${expectedArray[i2]} but got ${actualArray[i2]} instead`);
+  for (let i = 0; i < expectedArray.length; i++) {
+    if (actualArray[i] !== expectedArray[i]) {
+      throw new Error(`Expected ArrayBuffer value at ${i} to be ${expectedArray[i]} but got ${actualArray[i]} instead`);
     }
   }
 }
 function encodeStrings(a) {
-  for (let i2 = 0; i2 < a.length; i2++) {
-    const val = a[i2];
+  for (let i = 0; i < a.length; i++) {
+    const val = a[i];
     if (Array.isArray(val)) {
       encodeStrings(val);
     } else {
-      a[i2] = encodeString(val);
+      a[i] = encodeString(val);
     }
   }
   return a;
@@ -10931,7 +10932,7 @@ var acosh = op({ acosh_ });
 function addN_(tensors) {
   assert(Array.isArray(tensors), () => "The argument passed to tf.addN() must be a list of tensors");
   assert(tensors.length >= 1, () => `Must pass at least one tensor to tf.addN(), but got ${tensors.length}`);
-  const $tensors = tensors.map((t2, i2) => convertToTensor(t2, `tensors${i2}`, "addN"));
+  const $tensors = tensors.map((t2, i) => convertToTensor(t2, `tensors${i}`, "addN"));
   const firstTensor = $tensors[0];
   $tensors.forEach((t2) => {
     if (t2.dtype !== firstTensor.dtype) {
@@ -11423,11 +11424,11 @@ function basicLSTMCell_(forgetBias, lstmKernel, lstmBias, data, c, h) {
   const batchSize = res.shape[0];
   const sliceCols = res.shape[1] / 4;
   const sliceSize = [batchSize, sliceCols];
-  const i2 = slice(res, [0, 0], sliceSize);
+  const i = slice(res, [0, 0], sliceSize);
   const j = slice(res, [0, sliceCols], sliceSize);
   const f = slice(res, [0, sliceCols * 2], sliceSize);
   const o = slice(res, [0, sliceCols * 3], sliceSize);
-  const newC = add2(mul(sigmoid(i2), tanh2(j)), mul($c, sigmoid(add2($forgetBias, f))));
+  const newC = add2(mul(sigmoid(i), tanh2(j)), mul($c, sigmoid(add2($forgetBias, f))));
   const newH = mul(tanh2(newC), sigmoid(o));
   return [newC, newH];
 }
@@ -11601,14 +11602,14 @@ function broadcastTo_(x, shape) {
   }
   const inputShape = input2.shape;
   const reps = Array.from(shape);
-  for (let i2 = shape.length - 1; i2 >= 0; i2--) {
-    if (inputShape[i2] === shape[i2]) {
-      reps[i2] = 1;
-    } else if (input2.shape[i2] !== 1) {
+  for (let i = shape.length - 1; i >= 0; i--) {
+    if (inputShape[i] === shape[i]) {
+      reps[i] = 1;
+    } else if (input2.shape[i] !== 1) {
       throw new Error(`broadcastTo(): [${xShape}] cannot be broadcast to [${shape}].`);
     }
   }
-  const axes = reps.map((n2, i2) => n2 > 1 ? i2 : -1).filter((i2) => i2 >= 0);
+  const axes = reps.map((n, i) => n > 1 ? i : -1).filter((i) => i >= 0);
   if (axes.length === 0) {
     return clone(input2);
   }
@@ -11962,7 +11963,7 @@ function dot_(t1, t2) {
 }
 var dot = op({ dot_ });
 function einsum_(equation, ...tensors) {
-  const $tensors = tensors.map((t2, i2) => convertToTensor(t2, `tensors${i2}`, "einsum"));
+  const $tensors = tensors.map((t2, i) => convertToTensor(t2, `tensors${i}`, "einsum"));
   const attrs = { equation };
   return ENGINE.runKernel(Einsum, $tensors, attrs);
 }
@@ -11984,8 +11985,8 @@ function erf_(x) {
 }
 var erf = op({ erf_ });
 function axesAreInnerMostDims(axes, rank) {
-  for (let i2 = 0; i2 < axes.length; ++i2) {
-    if (axes[axes.length - i2 - 1] !== rank - 1 - i2) {
+  for (let i = 0; i < axes.length; ++i) {
+    if (axes[axes.length - i - 1] !== rank - 1 - i) {
       return false;
     }
   }
@@ -12028,21 +12029,21 @@ function getAxesPermutation(axes, rank) {
     return null;
   }
   const result = [];
-  for (let i2 = 0; i2 < rank; ++i2) {
-    if (axes.indexOf(i2) === -1) {
-      result.push(i2);
+  for (let i = 0; i < rank; ++i) {
+    if (axes.indexOf(i) === -1) {
+      result.push(i);
     }
   }
   axes.forEach((axis) => result.push(axis));
   return result;
 }
 function getUndoAxesPermutation(axes) {
-  return axes.map((axis, i2) => [i2, axis]).sort((a, b) => a[1] - b[1]).map((x) => x[0]);
+  return axes.map((axis, i) => [i, axis]).sort((a, b) => a[1] - b[1]).map((x) => x[0]);
 }
 function getInnerMostAxes(numAxes, rank) {
   const res = [];
-  for (let i2 = rank - numAxes; i2 < rank; ++i2) {
-    res.push(i2);
+  for (let i = rank - numAxes; i < rank; ++i) {
+    res.push(i);
   }
   return res;
 }
@@ -12188,9 +12189,9 @@ function eye_(numRows, numColumns, batchShape, dtype = "float32") {
     numColumns = numRows;
   }
   const buff = buffer([numRows, numColumns], dtype);
-  const n2 = numRows <= numColumns ? numRows : numColumns;
-  for (let i2 = 0; i2 < n2; ++i2) {
-    buff.set(1, i2, i2);
+  const n = numRows <= numColumns ? numRows : numColumns;
+  for (let i = 0; i < n; ++i) {
+    buff.set(1, i, i);
   }
   const out = reshape(buff.toTensor(), [numRows, numColumns]);
   if (batchShape == null) {
@@ -12406,9 +12407,9 @@ function variableGrads(f, varList) {
   assert(grads2.some((g) => g != null), () => "Cannot find a connection between any variable and the result of the loss function y=f(x). Please make sure the operations that use variables are inside the function f passed to minimize().");
   assert(value.rank === 0, () => `The f passed in variableGrads(f) must return a scalar, but it returned a rank-${value.rank} tensor`);
   const namedGrads = {};
-  varList.forEach((v, i2) => {
-    if (grads2[i2] != null) {
-      namedGrads[v.name] = grads2[i2];
+  varList.forEach((v, i) => {
+    if (grads2[i] != null) {
+      namedGrads[v.name] = grads2[i];
     }
   });
   if (specifiedNonTrainable != null) {
@@ -12691,9 +12692,9 @@ function mirrorPad_(x, paddings, mode) {
   }
   assert(paddings.length === $x.rank, () => `Padding doesn't match input. Must be ${$x.rank}. Got ${paddings.length}.`);
   const shapeOffset = mode === "reflect" ? 1 : 0;
-  for (let i2 = 0; i2 < $x.rank; i2++) {
-    assert(paddings[i2].length === 2, () => `Invalid number of paddings. Must be length of 2 each.`);
-    assert(paddings[i2][0] >= 0 && paddings[i2][0] <= $x.shape[i2] - shapeOffset && paddings[i2][1] >= 0 && paddings[i2][1] <= $x.shape[i2] - shapeOffset, () => `Padding in dimension ${i2} cannot be greater than or equal to ${$x.shape[i2] - shapeOffset} or less than 0 for input of shape ${$x.shape}`);
+  for (let i = 0; i < $x.rank; i++) {
+    assert(paddings[i].length === 2, () => `Invalid number of paddings. Must be length of 2 each.`);
+    assert(paddings[i][0] >= 0 && paddings[i][0] <= $x.shape[i] - shapeOffset && paddings[i][1] >= 0 && paddings[i][1] <= $x.shape[i] - shapeOffset, () => `Padding in dimension ${i} cannot be greater than or equal to ${$x.shape[i] - shapeOffset} or less than 0 for input of shape ${$x.shape}`);
   }
   const attrs = { paddings, mode };
   const inputs = { x: $x };
@@ -12727,17 +12728,17 @@ function multiRNNCell_(lstmCells, data, c, h) {
   const $h = convertToTensorArray(h, "h", "multiRNNCell");
   let input2 = $data;
   const newStates = [];
-  for (let i2 = 0; i2 < lstmCells.length; i2++) {
-    const output = lstmCells[i2](input2, $c[i2], $h[i2]);
+  for (let i = 0; i < lstmCells.length; i++) {
+    const output = lstmCells[i](input2, $c[i], $h[i]);
     newStates.push(output[0]);
     newStates.push(output[1]);
     input2 = output[1];
   }
   const newC = [];
   const newH = [];
-  for (let i2 = 0; i2 < newStates.length; i2 += 2) {
-    newC.push(newStates[i2]);
-    newH.push(newStates[i2 + 1]);
+  for (let i = 0; i < newStates.length; i += 2) {
+    newC.push(newStates[i]);
+    newH.push(newStates[i + 1]);
   }
   return [newC, newH];
 }
@@ -12818,9 +12819,9 @@ function spaceToBatchND_(x, blockShape, paddings) {
   const $x = convertToTensor(x, "x", "spaceToBatchND");
   assert($x.rank >= 1 + blockShape.length, () => `input rank ${$x.rank} should be > than [blockShape] ${blockShape.length}`);
   assert(paddings.length === blockShape.length, () => `paddings.shape[0] ${paddings.length} must be equal to [blockShape] ${blockShape.length}`);
-  assert($x.shape.reduce((a, b, i2) => {
-    if (i2 > 0 && i2 <= blockShape.length) {
-      return a && (b + paddings[i2 - 1][0] + paddings[i2 - 1][1]) % blockShape[i2 - 1] === 0;
+  assert($x.shape.reduce((a, b, i) => {
+    if (i > 0 && i <= blockShape.length) {
+      return a && (b + paddings[i - 1][0] + paddings[i - 1][1]) % blockShape[i - 1] === 0;
     }
     return a;
   }, true), () => `input spatial dimensions ${$x.shape.slice(1)} with paddings ${paddings.toString()} must be divisible by blockShapes ${blockShape.toString()}`);
@@ -12871,21 +12872,21 @@ function requiredSpaceToBatchPaddings(inputShape, blockShape, basePadding) {
   const padStart = basePadding.map((b) => b[0]);
   const origPadEnd = basePadding.map((b) => b[1]);
   const fullInputShape = inputShape.concat(padStart, origPadEnd);
-  const padEndExtra = blockShape.map((b, i2) => (b - fullInputShape[i2] % b) % b);
-  const padEnd = origPadEnd.map((s2, i2) => s2 + padEndExtra[i2]);
-  const paddings = blockShape.map((_, i2) => [padStart[i2], padEnd[i2]]);
-  const crops = blockShape.map((_, i2) => [0, padEndExtra[i2]]);
+  const padEndExtra = blockShape.map((b, i) => (b - fullInputShape[i] % b) % b);
+  const padEnd = origPadEnd.map((s, i) => s + padEndExtra[i]);
+  const paddings = blockShape.map((_, i) => [padStart[i], padEnd[i]]);
+  const crops = blockShape.map((_, i) => [0, padEndExtra[i]]);
   return [paddings, crops];
 }
 function withSpaceToBatchBasePaddings(filterShape, dilation) {
-  const dilatedFilterShape = filterShape.map((s2, i2) => {
-    return s2 + (s2 - 1) * (dilation[i2] - 1);
+  const dilatedFilterShape = filterShape.map((s, i) => {
+    return s + (s - 1) * (dilation[i] - 1);
   });
-  const padExtraShape = dilatedFilterShape.map((s2) => s2 - 1);
-  const padExtraStart = padExtraShape.map((s2) => Math.floor(s2 / 2));
-  const padExtraEnd = padExtraShape.map((s2, i2) => s2 - padExtraStart[i2]);
-  return padExtraShape.map((_, i2) => {
-    return [padExtraStart[i2], padExtraEnd[i2]];
+  const padExtraShape = dilatedFilterShape.map((s) => s - 1);
+  const padExtraStart = padExtraShape.map((s) => Math.floor(s / 2));
+  const padExtraEnd = padExtraShape.map((s, i) => s - padExtraStart[i]);
+  return padExtraShape.map((_, i) => {
+    return [padExtraStart[i], padExtraEnd[i]];
   });
 }
 var pool = op({ pool_ });
@@ -12910,7 +12911,7 @@ function raggedTensorToTensor_(shape, values, defaultValue, rowPartitionTensors,
   const $shape = convertToTensor(shape, "shape", "raggedTensorToTensor", "int32");
   const $values = convertToTensor(values, "values", "raggedTensorToTensor");
   const $defaultValue = convertToTensor(defaultValue, "defaultValue", "raggedTensorToTensor", $values.dtype);
-  const $rowPartitionTensors = rowPartitionTensors.map((t2, i2) => convertToTensor(t2, `tensors${i2}`, "raggedTensorToTensor", "int32"));
+  const $rowPartitionTensors = rowPartitionTensors.map((t2, i) => convertToTensor(t2, `tensors${i}`, "raggedTensorToTensor", "int32"));
   const inputs = {
     shape: $shape,
     values: $values,
@@ -12933,8 +12934,8 @@ function rand_(shape, randFunction, dtype) {
   } else {
     throw new Error(`Unknown data type ${dtype}`);
   }
-  for (let i2 = 0; i2 < size2; i2++) {
-    values[i2] = randFunction();
+  for (let i = 0; i < size2; i++) {
+    values[i] = randFunction();
   }
   return ENGINE.makeTensor(values, shape, dtype);
 }
@@ -12963,13 +12964,13 @@ var MPRandGauss = class {
     let resultX, resultY;
     let isValid = false;
     while (!isValid) {
-      let v1, v2, s2;
+      let v1, v2, s;
       do {
         v1 = 2 * this.random() - 1;
         v2 = 2 * this.random() - 1;
-        s2 = v1 * v1 + v2 * v2;
-      } while (s2 >= 1 || s2 === 0);
-      const mul2 = Math.sqrt(-2 * Math.log(s2) / s2);
+        s = v1 * v1 + v2 * v2;
+      } while (s >= 1 || s === 0);
+      const mul2 = Math.sqrt(-2 * Math.log(s) / s);
       resultX = this.mean + this.stdDev * v1 * mul2;
       resultY = this.mean + this.stdDev * v2 * mul2;
       if (!this.truncated || this.isValidTruncated(resultX)) {
@@ -13074,8 +13075,8 @@ function randomGamma_(shape, alpha2, beta = 1, dtype = "float32", seed) {
   }
   const rgamma = new RandGamma(alpha2, beta, dtype, seed);
   const res = buffer(shape, dtype);
-  for (let i2 = 0; i2 < res.values.length; i2++) {
-    res.values[i2] = rgamma.nextValue();
+  for (let i = 0; i < res.values.length; i++) {
+    res.values[i] = rgamma.nextValue();
   }
   return res.toTensor();
 }
@@ -13086,8 +13087,8 @@ function randomNormal_(shape, mean5 = 0, stdDev = 1, dtype, seed) {
   }
   const randGauss = new MPRandGauss(mean5, stdDev, dtype, false, seed);
   const res = buffer(shape, dtype);
-  for (let i2 = 0; i2 < res.values.length; i2++) {
-    res.values[i2] = randGauss.nextValue();
+  for (let i = 0; i < res.values.length; i++) {
+    res.values[i] = randGauss.nextValue();
   }
   return res.toTensor();
 }
@@ -13102,8 +13103,8 @@ var randomStandardNormal = op({ randomStandardNormal_ });
 function randomUniform_(shape, minval = 0, maxval = 1, dtype = "float32", seed) {
   const res = buffer(shape, dtype);
   const random = new UniformRandom(minval, maxval, null, seed);
-  for (let i2 = 0; i2 < res.values.length; i2++) {
-    res.values[i2] = random.nextValue();
+  for (let i = 0; i < res.values.length; i++) {
+    res.values[i] = random.nextValue();
   }
   return res.toTensor();
 }
@@ -13222,17 +13223,17 @@ async function setdiff1dAsync_(x, y) {
   const yVals = await $y.data();
   const ySet = new Set(yVals);
   let outputSize2 = 0;
-  for (let i2 = 0; i2 < xVals.length; i2++) {
-    if (!ySet.has(xVals[i2])) {
+  for (let i = 0; i < xVals.length; i++) {
+    if (!ySet.has(xVals[i])) {
       outputSize2++;
     }
   }
   const buffer2 = new TensorBuffer([outputSize2], $x.dtype);
   const indices = new TensorBuffer([outputSize2], "int32");
-  for (let i2 = 0, p2 = 0; i2 < xVals.length; i2++) {
-    if (!ySet.has(xVals[i2])) {
-      buffer2.values[p2] = xVals[i2];
-      indices.values[p2] = i2;
+  for (let i = 0, p2 = 0; i < xVals.length; i++) {
+    if (!ySet.has(xVals[i])) {
+      buffer2.values[p2] = xVals[i];
+      indices.values[p2] = i;
       p2++;
     }
   }
@@ -13319,9 +13320,9 @@ function irfft_(input2) {
     const imagInput = reshape(imag(input2), [batch, innerDimensionSize]);
     const realConjugate = reverse(slice(realInput, [0, 1], [batch, innerDimensionSize - 2]), 1);
     const imagConjugate = mul(reverse(slice(imagInput, [0, 1], [batch, innerDimensionSize - 2]), 1), scalar(-1));
-    const r2 = concat([realInput, realConjugate], 1);
-    const i2 = concat([imagInput, imagConjugate], 1);
-    const complexInput = reshape(complex(r2, i2), [outputShape[0], outputShape[1]]);
+    const r = concat([realInput, realConjugate], 1);
+    const i = concat([imagInput, imagConjugate], 1);
+    const complexInput = reshape(complex(r, i), [outputShape[0], outputShape[1]]);
     ret = ifft(complexInput);
   }
   ret = real(ret);
@@ -13518,8 +13519,8 @@ function truncatedNormal_(shape, mean5 = 0, stdDev = 1, dtype, seed) {
   }
   const randGauss = new MPRandGauss(mean5, stdDev, dtype, true, seed);
   const res = buffer(shape, dtype);
-  for (let i2 = 0; i2 < res.values.length; i2++) {
-    res.values[i2] = randGauss.nextValue();
+  for (let i = 0; i < res.values.length; i++) {
+    res.values[i] = randGauss.nextValue();
   }
   return res.toTensor();
 }
@@ -13558,16 +13559,16 @@ function variable(initialValue, trainable = true, name, dtype) {
 }
 function whereImpl(condShape, condVals) {
   const indices = [];
-  for (let i2 = 0; i2 < condVals.length; i2++) {
-    if (condVals[i2]) {
-      indices.push(i2);
+  for (let i = 0; i < condVals.length; i++) {
+    if (condVals[i]) {
+      indices.push(i);
     }
   }
   const inBuffer = buffer(condShape, "int32");
   const out = buffer([indices.length, condShape.length], "int32");
-  for (let i2 = 0; i2 < indices.length; i2++) {
-    const loc = inBuffer.indexToLoc(indices[i2]);
-    const offset = i2 * condShape.length;
+  for (let i = 0; i < indices.length; i++) {
+    const loc = inBuffer.indexToLoc(indices[i]);
+    const offset = i * condShape.length;
     out.values.set(loc, offset);
   }
   return out.toTensor();
@@ -13591,8 +13592,8 @@ async function booleanMaskAsync_(tensor2, mask2, axis) {
   assert(maskDim > 0, () => "mask cannot be scalar");
   assertShapesMatch(tensorShape.slice(axisFrom, axisFrom + maskDim), $mask.shape, `mask's shape must match the first K dimensions of tensor's shape,`);
   let leadingSize = 1;
-  for (let i2 = axisFrom; i2 < axisFrom + maskDim; i2++) {
-    leadingSize *= tensorShape[i2];
+  for (let i = axisFrom; i < axisFrom + maskDim; i++) {
+    leadingSize *= tensorShape[i];
   }
   const targetTensorShape = tensorShape.slice(0, axisFrom).concat([leadingSize], tensorShape.slice(axisFrom + maskDim));
   const reshapedTensor = reshape($tensor, targetTensorShape);
@@ -13689,11 +13690,11 @@ function getNoiseShape(x, noiseShape) {
   }
   if (x.shape.length === noiseShape.length) {
     const newDimension = [];
-    for (let i2 = 0; i2 < x.shape.length; i2++) {
-      if (noiseShape[i2] == null && x.shape[i2] != null) {
-        newDimension.push(x.shape[i2]);
+    for (let i = 0; i < x.shape.length; i++) {
+      if (noiseShape[i] == null && x.shape[i] != null) {
+        newDimension.push(x.shape[i]);
       } else {
-        newDimension.push(noiseShape[i2]);
+        newDimension.push(noiseShape[i]);
       }
     }
     return newDimension;
@@ -13719,9 +13720,9 @@ function enclosingPowerOfTwo(value) {
 function cosineWindow(windowLength, a, b) {
   const even = 1 - windowLength % 2;
   const newValues = new Float32Array(windowLength);
-  for (let i2 = 0; i2 < windowLength; ++i2) {
-    const cosArg = 2 * Math.PI * i2 / (windowLength + even - 1);
-    newValues[i2] = a - b * Math.cos(cosArg);
+  for (let i = 0; i < windowLength; ++i) {
+    const cosArg = 2 * Math.PI * i / (windowLength + even - 1);
+    newValues[i] = a - b * Math.cos(cosArg);
   }
   return tensor1d(newValues, "float32");
 }
@@ -13741,13 +13742,13 @@ async function inTopKAsync_(predictions, targets, k = 1) {
     const offset = b * size2;
     const vals = predictionsVals.subarray(offset, offset + size2);
     const valAndInd = [];
-    for (let i2 = 0; i2 < vals.length; i2++) {
-      valAndInd.push({ value: vals[i2], index: i2 });
+    for (let i = 0; i < vals.length; i++) {
+      valAndInd.push({ value: vals[i], index: i });
     }
     valAndInd.sort((a, b2) => b2.value - a.value);
     precision3[b] = 0;
-    for (let i2 = 0; i2 < k; i2++) {
-      if (valAndInd[i2].index === targetsVals[b]) {
+    for (let i = 0; i < k; i++) {
+      if (valAndInd[i].index === targetsVals[b]) {
         precision3[b] = 1;
         break;
       }
@@ -13874,7 +13875,7 @@ function fusedConv2d_({ x, filter, strides: strides2, pad: pad3, dataFormat = "N
     } else if (alphaShape.length === 3) {
       try {
         assertAndGetBroadcastShape(alphaShape, convInfo.outShape);
-      } catch (e2) {
+      } catch (e) {
         const errMsg = `Error in fused conv2d: PReLU activation weights (${alphaShape}) is not compatible with the output shape of the conv2d (${convInfo.outShape}).`;
         throw Error(errMsg);
       }
@@ -14298,9 +14299,9 @@ function nonMaxSuppressionV5Impl(boxes, scores, maxOutputSize, iouThreshold, sco
 }
 function nonMaxSuppressionImpl_(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma, returnScoresTensor = false, padToMaxOutputSize = false, returnValidOutputs = false) {
   const candidates = [];
-  for (let i2 = 0; i2 < scores.length; i2++) {
-    if (scores[i2] > scoreThreshold) {
-      candidates.push({ score: scores[i2], boxIndex: i2, suppressBeginIndex: 0 });
+  for (let i = 0; i < scores.length; i++) {
+    if (scores[i] > scoreThreshold) {
+      candidates.push({ score: scores[i], boxIndex: i, suppressBeginIndex: 0 });
     }
   }
   candidates.sort(ascendingComparator);
@@ -14350,8 +14351,8 @@ function nonMaxSuppressionImpl_(boxes, scores, maxOutputSize, iouThreshold, scor
   }
   return result;
 }
-function intersectionOverUnion(boxes, i2, j) {
-  const iCoord = boxes.subarray(i2 * 4, i2 * 4 + 4);
+function intersectionOverUnion(boxes, i, j) {
+  const iCoord = boxes.subarray(i * 4, i * 4 + 4);
   const jCoord = boxes.subarray(j * 4, j * 4 + 4);
   const yminI = Math.min(iCoord[0], iCoord[2]);
   const xminI = Math.min(iCoord[1], iCoord[3]);
@@ -14527,14 +14528,14 @@ function threshold_(image2, method = "binary", inverted = false, threshValue = 0
   const BLUE_INTENCITY_COEF = 0.114;
   const totalPixelsInImage = $image.shape[0] * $image.shape[1];
   let $threshold = mul(tensor1d([threshValue]), 255);
-  let r2, g, b, grayscale;
+  let r, g, b, grayscale;
   assert($image.rank === 3, () => `Error in threshold: image must be rank 3,but got rank ${$image.rank}.`);
   assert($image.shape[2] === 3 || $image.shape[2] === 1, () => `Error in threshold: image color channel must be equal to 3 or 1but got ${$image.shape[2]}.`);
   assert($image.dtype === "int32" || $image.dtype === "float32", () => `Error in dtype: image dtype must be int32 or float32,but got dtype ${$image.dtype}.`);
   assert(method === "otsu" || method === "binary", () => `Method must be binary or otsu, but was ${method}`);
   if ($image.shape[2] === 3) {
-    [r2, g, b] = split($image, [1, 1, 1], -1);
-    const $r = mul(r2, RED_INTENCITY_COEF);
+    [r, g, b] = split($image, [1, 1, 1], -1);
+    const $r = mul(r, RED_INTENCITY_COEF);
     const $g = mul(g, GREEN_INTENCITY_COEF);
     const $b = mul(b, BLUE_INTENCITY_COEF);
     grayscale = add2(add2($r, $g), $b);
@@ -14606,9 +14607,9 @@ function bandPart_(a, numLower, numUpper) {
   if (numUpper < 0) {
     numUpper = N;
   }
-  const i2 = reshape(range(0, M, 1, "int32"), [-1, 1]);
+  const i = reshape(range(0, M, 1, "int32"), [-1, 1]);
   const j = range(0, N, 1, "int32");
-  const ij = sub(i2, j);
+  const ij = sub(i, j);
   const inBand = logicalAnd(lessEqual(ij, scalar(+numLower, "int32")), greaterEqual(ij, scalar(-numUpper, "int32")));
   const zero = zeros([M, N], $a.dtype);
   return reshape(stack(unstack(reshape($a, [-1, M, N])).map((mat) => where(inBand, mat, zero))), shape);
@@ -14620,8 +14621,8 @@ function gramSchmidt_(xs) {
     inputIsTensor2D = false;
     assert(xs != null && xs.length > 0, () => "Gram-Schmidt process: input must not be null, undefined, or empty");
     const dim = xs[0].shape[0];
-    for (let i2 = 1; i2 < xs.length; ++i2) {
-      assert(xs[i2].shape[0] === dim, () => `Gram-Schmidt: Non-unique lengths found in the input vectors: (${xs[i2].shape[0]} vs. ${dim})`);
+    for (let i = 1; i < xs.length; ++i) {
+      assert(xs[i].shape[0] === dim, () => `Gram-Schmidt: Non-unique lengths found in the input vectors: (${xs[i].shape[0]} vs. ${dim})`);
     }
   } else {
     inputIsTensor2D = true;
@@ -14630,11 +14631,11 @@ function gramSchmidt_(xs) {
   assert(xs.length <= xs[0].shape[0], () => `Gram-Schmidt: Number of vectors (${xs.length}) exceeds number of dimensions (${xs[0].shape[0]}).`);
   const ys = [];
   const xs1d = xs;
-  for (let i2 = 0; i2 < xs.length; ++i2) {
+  for (let i = 0; i < xs.length; ++i) {
     ys.push(ENGINE.tidy(() => {
-      let x = xs1d[i2];
-      if (i2 > 0) {
-        for (let j = 0; j < i2; ++j) {
+      let x = xs1d[i];
+      if (i > 0) {
+        for (let j = 0; j < i; ++j) {
           const proj = mul(sum2(mul(ys[j], x)), ys[j]);
           x = sub(x, proj);
         }
@@ -14668,30 +14669,30 @@ function qr_(x, fullMatrices = false) {
       r2ds.push(r2d);
     });
     const q = reshape(stack(q2ds, 0), x.shape);
-    const r2 = reshape(stack(r2ds, 0), x.shape);
-    return [q, r2];
+    const r = reshape(stack(r2ds, 0), x.shape);
+    return [q, r];
   }
 }
 function qr2d(x, fullMatrices = false) {
   return ENGINE.tidy(() => {
     assert(x.shape.length === 2, () => `qr2d() requires a 2D Tensor, but got a ${x.shape.length}D Tensor.`);
     const m = x.shape[0];
-    const n2 = x.shape[1];
+    const n = x.shape[1];
     let q = eye(m);
-    let r2 = clone(x);
+    let r = clone(x);
     const one2D = tensor2d([[1]], [1, 1]);
     let w = clone(one2D);
-    const iters = m >= n2 ? n2 : m;
+    const iters = m >= n ? n : m;
     for (let j = 0; j < iters; ++j) {
-      const rTemp = r2;
+      const rTemp = r;
       const wTemp = w;
       const qTemp = q;
-      [w, r2, q] = ENGINE.tidy(() => {
-        const rjEnd1 = slice(r2, [j, j], [m - j, 1]);
+      [w, r, q] = ENGINE.tidy(() => {
+        const rjEnd1 = slice(r, [j, j], [m - j, 1]);
         const normX = norm(rjEnd1);
-        const rjj = slice(r2, [j, j], [1, 1]);
-        const s2 = where(greater(rjj, 0), tensor2d([[-1]]), tensor2d([[1]]));
-        const u1 = sub(rjj, mul(s2, normX));
+        const rjj = slice(r, [j, j], [1, 1]);
+        const s = where(greater(rjj, 0), tensor2d([[-1]]), tensor2d([[1]]));
+        const u1 = sub(rjj, mul(s, normX));
         const wPre = div(rjEnd1, u1);
         if (wPre.shape[0] === 1) {
           w = clone(one2D);
@@ -14701,15 +14702,15 @@ function qr2d(x, fullMatrices = false) {
             slice(wPre, [1, 0], [wPre.shape[0] - 1, wPre.shape[1]])
           ], 0);
         }
-        const tau = neg(div(matMul(s2, u1), normX));
-        const rjEndAll = slice(r2, [j, 0], [m - j, n2]);
+        const tau = neg(div(matMul(s, u1), normX));
+        const rjEndAll = slice(r, [j, 0], [m - j, n]);
         const tauTimesW = mul(tau, w);
         const wT = transpose(w);
         if (j === 0) {
-          r2 = sub(rjEndAll, matMul(tauTimesW, matMul(wT, rjEndAll)));
+          r = sub(rjEndAll, matMul(tauTimesW, matMul(wT, rjEndAll)));
         } else {
           const rTimesTau = sub(rjEndAll, matMul(tauTimesW, matMul(wT, rjEndAll)));
-          r2 = concat([slice(r2, [0, 0], [j, n2]), rTimesTau], 0);
+          r = concat([slice(r, [0, 0], [j, n]), rTimesTau], 0);
         }
         const tawTimesWT = transpose(tauTimesW);
         const qAllJEnd = slice(q, [0, j], [m, q.shape[1] - j]);
@@ -14719,15 +14720,15 @@ function qr2d(x, fullMatrices = false) {
           const qTimesTau = sub(qAllJEnd, matMul(matMul(qAllJEnd, w), tawTimesWT));
           q = concat([slice(q, [0, 0], [m, j]), qTimesTau], 1);
         }
-        return [w, r2, q];
+        return [w, r, q];
       });
       dispose([rTemp, wTemp, qTemp]);
     }
-    if (!fullMatrices && m > n2) {
-      q = slice(q, [0, 0], [m, n2]);
-      r2 = slice(r2, [0, 0], [n2, n2]);
+    if (!fullMatrices && m > n) {
+      q = slice(q, [0, 0], [m, n]);
+      r = slice(r, [0, 0], [n, n]);
     }
-    return [q, r2];
+    return [q, r];
   });
 }
 var qr = op({ qr_ });
@@ -15206,27 +15207,27 @@ var AdadeltaOptimizer = class extends Optimizer {
   }
   applyGradients(variableGradients) {
     const variableNames = Array.isArray(variableGradients) ? variableGradients.map((item) => item.name) : Object.keys(variableGradients);
-    variableNames.forEach((name, i2) => {
+    variableNames.forEach((name, i) => {
       const value = ENGINE.registeredVariables[name];
       const trainable = false;
-      if (this.accumulatedGrads[i2] == null) {
-        this.accumulatedGrads[i2] = {
+      if (this.accumulatedGrads[i] == null) {
+        this.accumulatedGrads[i] = {
           originalName: `${name}/accum_grad`,
           variable: tidy(() => zerosLike(value).variable(trainable))
         };
       }
-      if (this.accumulatedUpdates[i2] == null) {
-        this.accumulatedUpdates[i2] = {
+      if (this.accumulatedUpdates[i] == null) {
+        this.accumulatedUpdates[i] = {
           originalName: `${name}/accum_var`,
           variable: tidy(() => zerosLike(value).variable(trainable))
         };
       }
-      const gradient = Array.isArray(variableGradients) ? variableGradients[i2].tensor : variableGradients[name];
+      const gradient = Array.isArray(variableGradients) ? variableGradients[i].tensor : variableGradients[name];
       if (gradient == null) {
         return;
       }
-      const accumulatedGrad = this.accumulatedGrads[i2].variable;
-      const accumulatedUpdate = this.accumulatedUpdates[i2].variable;
+      const accumulatedGrad = this.accumulatedGrads[i].variable;
+      const accumulatedUpdate = this.accumulatedUpdates[i].variable;
       tidy(() => {
         const newAccumulatedGrad = add2(mul(accumulatedGrad, this.rho), mul(square(gradient), 1 - this.rho));
         const updates = mul(div(sqrt(add2(accumulatedUpdate, this.epsilon)), sqrt(add2(accumulatedGrad, this.epsilon))), gradient);
@@ -15284,20 +15285,20 @@ var AdagradOptimizer = class extends Optimizer {
   }
   applyGradients(variableGradients) {
     const variableNames = Array.isArray(variableGradients) ? variableGradients.map((item) => item.name) : Object.keys(variableGradients);
-    variableNames.forEach((name, i2) => {
+    variableNames.forEach((name, i) => {
       const value = ENGINE.registeredVariables[name];
-      if (this.accumulatedGrads[i2] == null) {
+      if (this.accumulatedGrads[i] == null) {
         const trainable = false;
-        this.accumulatedGrads[i2] = {
+        this.accumulatedGrads[i] = {
           originalName: `${name}/accumulator`,
           variable: tidy(() => fill(value.shape, this.initialAccumulatorValue).variable(trainable))
         };
       }
-      const gradient = Array.isArray(variableGradients) ? variableGradients[i2].tensor : variableGradients[name];
+      const gradient = Array.isArray(variableGradients) ? variableGradients[i].tensor : variableGradients[name];
       if (gradient == null) {
         return;
       }
-      const accumulatedGrad = this.accumulatedGrads[i2].variable;
+      const accumulatedGrad = this.accumulatedGrads[i].variable;
       tidy(() => {
         const newAccumulatedGrad = add2(accumulatedGrad, square(gradient));
         accumulatedGrad.assign(newAccumulatedGrad);
@@ -15354,27 +15355,27 @@ var AdamOptimizer = class extends Optimizer {
     tidy(() => {
       const oneMinusAccBeta1 = sub(1, this.accBeta1);
       const oneMinusAccBeta2 = sub(1, this.accBeta2);
-      varNames.forEach((name, i2) => {
+      varNames.forEach((name, i) => {
         const value = ENGINE.registeredVariables[name];
         const trainable = false;
-        if (this.accumulatedFirstMoment[i2] == null) {
-          this.accumulatedFirstMoment[i2] = {
+        if (this.accumulatedFirstMoment[i] == null) {
+          this.accumulatedFirstMoment[i] = {
             originalName: `${name}/m`,
             variable: tidy(() => zerosLike(value).variable(trainable))
           };
         }
-        if (this.accumulatedSecondMoment[i2] == null) {
-          this.accumulatedSecondMoment[i2] = {
+        if (this.accumulatedSecondMoment[i] == null) {
+          this.accumulatedSecondMoment[i] = {
             originalName: `${name}/v`,
             variable: tidy(() => zerosLike(value).variable(trainable))
           };
         }
-        const gradient = Array.isArray(variableGradients) ? variableGradients[i2].tensor : variableGradients[name];
+        const gradient = Array.isArray(variableGradients) ? variableGradients[i].tensor : variableGradients[name];
         if (gradient == null) {
           return;
         }
-        const firstMoment = this.accumulatedFirstMoment[i2].variable;
-        const secondMoment = this.accumulatedSecondMoment[i2].variable;
+        const firstMoment = this.accumulatedFirstMoment[i].variable;
+        const secondMoment = this.accumulatedSecondMoment[i].variable;
         const newFirstMoment = add2(mul(firstMoment, this.beta1), mul(gradient, 1 - this.beta1));
         const newSecondMoment = add2(mul(secondMoment, this.beta2), mul(square(gradient), 1 - this.beta2));
         const biasCorrectedFirstMoment = div(newFirstMoment, oneMinusAccBeta1);
@@ -15457,27 +15458,27 @@ var AdamaxOptimizer = class extends Optimizer {
     tidy(() => {
       const oneMinusAccBeta1 = sub(1, this.accBeta1);
       const lr = div(-this.learningRate, add2(mul(this.iteration, this.decay), 1));
-      variableNames.forEach((name, i2) => {
+      variableNames.forEach((name, i) => {
         const value = ENGINE.registeredVariables[name];
         const trainable = false;
-        if (this.accumulatedFirstMoment[i2] == null) {
-          this.accumulatedFirstMoment[i2] = {
+        if (this.accumulatedFirstMoment[i] == null) {
+          this.accumulatedFirstMoment[i] = {
             originalName: `${name}/m`,
             variable: zerosLike(value).variable(trainable)
           };
         }
-        if (this.accumulatedWeightedInfNorm[i2] == null) {
-          this.accumulatedWeightedInfNorm[i2] = {
+        if (this.accumulatedWeightedInfNorm[i] == null) {
+          this.accumulatedWeightedInfNorm[i] = {
             originalName: `${name}/v`,
             variable: zerosLike(value).variable(trainable)
           };
         }
-        const gradient = Array.isArray(variableGradients) ? variableGradients[i2].tensor : variableGradients[name];
+        const gradient = Array.isArray(variableGradients) ? variableGradients[i].tensor : variableGradients[name];
         if (gradient == null) {
           return;
         }
-        const firstMoment = this.accumulatedFirstMoment[i2].variable;
-        const weightedInfNorm = this.accumulatedWeightedInfNorm[i2].variable;
+        const firstMoment = this.accumulatedFirstMoment[i].variable;
+        const weightedInfNorm = this.accumulatedWeightedInfNorm[i].variable;
         const newFirstMoment = add2(mul(firstMoment, this.beta1), mul(gradient, 1 - this.beta1));
         const ut0 = mul(weightedInfNorm, this.beta2);
         const ut1 = abs(gradient);
@@ -15531,8 +15532,8 @@ var SGDOptimizer = class extends Optimizer {
   }
   applyGradients(variableGradients) {
     const varNames = Array.isArray(variableGradients) ? variableGradients.map((v) => v.name) : Object.keys(variableGradients);
-    varNames.forEach((name, i2) => {
-      const gradient = Array.isArray(variableGradients) ? variableGradients[i2].tensor : variableGradients[name];
+    varNames.forEach((name, i) => {
+      const gradient = Array.isArray(variableGradients) ? variableGradients[i].tensor : variableGradients[name];
       if (gradient == null) {
         return;
       }
@@ -15583,17 +15584,17 @@ var MomentumOptimizer = class extends SGDOptimizer {
   }
   applyGradients(variableGradients) {
     const variableNames = Array.isArray(variableGradients) ? variableGradients.map((item) => item.name) : Object.keys(variableGradients);
-    variableNames.forEach((name, i2) => {
+    variableNames.forEach((name, i) => {
       const value = ENGINE.registeredVariables[name];
-      if (this.accumulations[i2] == null) {
+      if (this.accumulations[i] == null) {
         const trainable = false;
-        this.accumulations[i2] = {
+        this.accumulations[i] = {
           originalName: `${name}/momentum`,
           variable: tidy(() => zerosLike(value).variable(trainable))
         };
       }
-      const accumulation = this.accumulations[i2].variable;
-      const gradient = Array.isArray(variableGradients) ? variableGradients[i2].tensor : variableGradients[name];
+      const accumulation = this.accumulations[i].variable;
+      const gradient = Array.isArray(variableGradients) ? variableGradients[i].tensor : variableGradients[name];
       if (gradient == null) {
         return;
       }
@@ -15661,37 +15662,37 @@ var RMSPropOptimizer = class extends Optimizer {
   }
   applyGradients(variableGradients) {
     const variableNames = Array.isArray(variableGradients) ? variableGradients.map((item) => item.name) : Object.keys(variableGradients);
-    variableNames.forEach((name, i2) => {
+    variableNames.forEach((name, i) => {
       const value = ENGINE.registeredVariables[name];
       const trainable = false;
-      if (this.accumulatedMeanSquares[i2] == null) {
-        this.accumulatedMeanSquares[i2] = {
+      if (this.accumulatedMeanSquares[i] == null) {
+        this.accumulatedMeanSquares[i] = {
           originalName: `${name}/rms`,
           variable: tidy(() => zerosLike(value).variable(trainable))
         };
       }
-      if (this.accumulatedMoments[i2] == null) {
-        this.accumulatedMoments[i2] = {
+      if (this.accumulatedMoments[i] == null) {
+        this.accumulatedMoments[i] = {
           originalName: `${name}/momentum`,
           variable: tidy(() => zerosLike(value).variable(trainable))
         };
       }
-      if (this.accumulatedMeanGrads[i2] == null && this.centered) {
-        this.accumulatedMeanGrads[i2] = {
+      if (this.accumulatedMeanGrads[i] == null && this.centered) {
+        this.accumulatedMeanGrads[i] = {
           originalName: `${name}/mg`,
           variable: tidy(() => zerosLike(value).variable(trainable))
         };
       }
-      const gradient = Array.isArray(variableGradients) ? variableGradients[i2].tensor : variableGradients[name];
+      const gradient = Array.isArray(variableGradients) ? variableGradients[i].tensor : variableGradients[name];
       if (gradient == null) {
         return;
       }
-      const accumulatedMeanSquare = this.accumulatedMeanSquares[i2].variable;
-      const accumulatedMoments = this.accumulatedMoments[i2].variable;
+      const accumulatedMeanSquare = this.accumulatedMeanSquares[i].variable;
+      const accumulatedMoments = this.accumulatedMoments[i].variable;
       tidy(() => {
         const newAccumulatedMeanSquare = add2(mul(accumulatedMeanSquare, this.decay), mul(square(gradient), 1 - this.decay));
         if (this.centered) {
-          const accumulatedMeanGrad = this.accumulatedMeanGrads[i2].variable;
+          const accumulatedMeanGrad = this.accumulatedMeanGrads[i].variable;
           const newAccumulatedMeanGrad = add2(mul(accumulatedMeanGrad, this.decay), mul(gradient, 1 - this.decay));
           const gradContribution = div(mul(gradient, this.learningRate), sqrt(sub(newAccumulatedMeanSquare, add2(square(newAccumulatedMeanGrad), this.epsilon))));
           const newAccumulatedMoments = add2(mul(accumulatedMoments, this.momentum), gradContribution);
@@ -15897,21 +15898,21 @@ __export2(backend_util_exports, {
 });
 function assertParamsConsistent(shapes, axis) {
   const rank = shapes[0].length;
-  shapes.forEach((shape, i2) => {
-    assert(shape.length === rank, () => `Error in concat${rank}D: rank of tensors[${i2}] must be the same as the rank of the rest (${rank})`);
+  shapes.forEach((shape, i) => {
+    assert(shape.length === rank, () => `Error in concat${rank}D: rank of tensors[${i}] must be the same as the rank of the rest (${rank})`);
   });
   assert(axis >= 0 && axis < rank, () => `Error in concat${rank}D: axis must be between 0 and ${rank - 1}.`);
   const firstShape = shapes[0];
-  shapes.forEach((shape, i2) => {
-    for (let r2 = 0; r2 < rank; r2++) {
-      assert(r2 === axis || shape[r2] === firstShape[r2], () => `Error in concat${rank}D: Shape of tensors[${i2}] (${shape}) does not match the shape of the rest (${firstShape}) along the non-concatenated axis ${i2}.`);
+  shapes.forEach((shape, i) => {
+    for (let r = 0; r < rank; r++) {
+      assert(r === axis || shape[r] === firstShape[r], () => `Error in concat${rank}D: Shape of tensors[${i}] (${shape}) does not match the shape of the rest (${firstShape}) along the non-concatenated axis ${i}.`);
     }
   });
 }
 function computeOutShape2(shapes, axis) {
   const outputShape = shapes[0].slice();
-  for (let i2 = 1; i2 < shapes.length; i2++) {
-    outputShape[axis] += shapes[i2][axis];
+  for (let i = 1; i < shapes.length; i++) {
+    outputShape[axis] += shapes[i][axis];
   }
   return outputShape;
 }
@@ -15942,14 +15943,14 @@ function combineRaggedTensorToTensorShapes(raggedRank, shape, valueShape) {
   if (raggedRank + valueShape.length !== outputShape.length) {
     throw new Error(`rt input.shape and shape=${shape} are incompatible: rt input.rank = ${raggedRank + valueShape.length}, but shape.rank = ${outputShape.length}`);
   }
-  for (let i2 = 1; i2 < valueShape.length; ++i2) {
-    const valueDim = valueShape[i2];
-    const outputShapeDimIndex = outputShape[outputShape.length - valueShape.length + i2];
+  for (let i = 1; i < valueShape.length; ++i) {
+    const valueDim = valueShape[i];
+    const outputShapeDimIndex = outputShape[outputShape.length - valueShape.length + i];
     const outputShapeDim = outputShape[outputShapeDimIndex];
     if (valueDim >= 0) {
       if (outputShapeDim >= 0) {
         if (outputShapeDim !== valueDim) {
-          throw new Error(`rt input.shape and shape=${shape} are incompatible: rt input.shape[${i2 + raggedRank}] = ${valueDim} but shape[${i2 + raggedRank}] = ${outputShapeDim}`);
+          throw new Error(`rt input.shape and shape=${shape} are incompatible: rt input.shape[${i + raggedRank}] = ${valueDim} but shape[${i + raggedRank}] = ${outputShapeDim}`);
         }
       } else {
         outputShape[outputShapeDimIndex] = valueDim;
@@ -15995,11 +15996,11 @@ function validateDefaultValueShape(defaultValueShape, valueShape) {
   if (defaultNDims >= valuesNDims) {
     throw new Error(`defaultValue.shape=${defaultValueShape} and ragged tensor flatValues.shape=${valueShape}, are incompatible: defaultValue.rank = ${defaultNDims} must be less than ragged tensor input flatValues.rank = ${valuesNDims})`);
   }
-  for (let i2 = 0; i2 < Math.min(defaultNDims, valuesNDims - 1); ++i2) {
-    const defaultDim = defaultValueShape[i2];
-    const valueDim = valueShape[i2 + 1];
+  for (let i = 0; i < Math.min(defaultNDims, valuesNDims - 1); ++i) {
+    const defaultDim = defaultValueShape[i];
+    const valueDim = valueShape[i + 1];
     if (defaultDim >= 0 && valueDim >= 0 && defaultDim !== 1 && defaultDim !== valueDim) {
-      throw new Error(`defaultValue.shape=${defaultValueShape}, and ragged tensor input flatValues.shape=${valueShape} are incompatible: defaultValue.shape[${i2 - defaultValueShape.length}] = ${defaultDim} but ragged tensor input.flatValues.shape[${i2 - defaultValueShape.length}] = ${valueDim}`);
+      throw new Error(`defaultValue.shape=${defaultValueShape}, and ragged tensor input flatValues.shape=${valueShape} are incompatible: defaultValue.shape[${i - defaultValueShape.length}] = ${defaultDim} but ragged tensor input.flatValues.shape[${i - defaultValueShape.length}] = ${valueDim}`);
     }
   }
 }
@@ -16024,8 +16025,8 @@ function getReshaped(inputShape, blockShape, prod6, batchToSpace = true) {
   } else {
     reshaped = reshaped.concat(inputShape[0]);
     const spatialLength = blockShape.length;
-    for (let i2 = 0; i2 < spatialLength; ++i2) {
-      reshaped = reshaped.concat([inputShape[i2 + 1] / blockShape[i2], blockShape[i2]]);
+    for (let i = 0; i < spatialLength; ++i) {
+      reshaped = reshaped.concat([inputShape[i + 1] / blockShape[i], blockShape[i]]);
     }
     reshaped = reshaped.concat(inputShape.slice(spatialLength + 1));
   }
@@ -16035,22 +16036,22 @@ function getPermuted(reshapedRank, blockShapeRank, batchToSpace = true) {
   const permuted = [];
   if (batchToSpace) {
     permuted.push(blockShapeRank);
-    for (let i2 = blockShapeRank + 1; i2 < reshapedRank; ++i2) {
-      if (i2 <= 2 * blockShapeRank) {
-        permuted.push(i2);
-        permuted.push(i2 - (blockShapeRank + 1));
+    for (let i = blockShapeRank + 1; i < reshapedRank; ++i) {
+      if (i <= 2 * blockShapeRank) {
+        permuted.push(i);
+        permuted.push(i - (blockShapeRank + 1));
       } else {
-        permuted.push(i2);
+        permuted.push(i);
       }
     }
   } else {
     const permutedBeforeBatch = [];
     const permutedAfterBatch = [];
-    for (let i2 = 1; i2 < reshapedRank; ++i2) {
-      if (i2 >= blockShapeRank * 2 + 1 || i2 % 2 === 1) {
-        permutedAfterBatch.push(i2);
+    for (let i = 1; i < reshapedRank; ++i) {
+      if (i >= blockShapeRank * 2 + 1 || i % 2 === 1) {
+        permutedAfterBatch.push(i);
       } else {
-        permutedBeforeBatch.push(i2);
+        permutedBeforeBatch.push(i);
       }
     }
     permuted.push(...permutedBeforeBatch);
@@ -16066,30 +16067,30 @@ function getReshapedPermuted(inputShape, blockShape, prod6, batchToSpace = true)
   } else {
     reshapedPermuted.push(inputShape[0] * prod6);
   }
-  for (let i2 = 1; i2 < inputShape.length; ++i2) {
-    if (i2 <= blockShape.length) {
+  for (let i = 1; i < inputShape.length; ++i) {
+    if (i <= blockShape.length) {
       if (batchToSpace) {
-        reshapedPermuted.push(blockShape[i2 - 1] * inputShape[i2]);
+        reshapedPermuted.push(blockShape[i - 1] * inputShape[i]);
       } else {
-        reshapedPermuted.push(inputShape[i2] / blockShape[i2 - 1]);
+        reshapedPermuted.push(inputShape[i] / blockShape[i - 1]);
       }
     } else {
-      reshapedPermuted.push(inputShape[i2]);
+      reshapedPermuted.push(inputShape[i]);
     }
   }
   return reshapedPermuted;
 }
 function getSliceBeginCoords(crops, blockShape) {
   const sliceBeginCoords = [0];
-  for (let i2 = 0; i2 < blockShape; ++i2) {
-    sliceBeginCoords.push(crops[i2][0]);
+  for (let i = 0; i < blockShape; ++i) {
+    sliceBeginCoords.push(crops[i][0]);
   }
   return sliceBeginCoords;
 }
 function getSliceSize(uncroppedShape, crops, blockShape) {
   const sliceSize = uncroppedShape.slice(0, 1);
-  for (let i2 = 0; i2 < blockShape; ++i2) {
-    sliceSize.push(uncroppedShape[i2 + 1] - crops[i2][0] - crops[i2][1]);
+  for (let i = 0; i < blockShape; ++i) {
+    sliceSize.push(uncroppedShape[i + 1] - crops[i][0] - crops[i][1]);
   }
   return sliceSize;
 }
@@ -16106,18 +16107,18 @@ function mergeRealAndImagArrays(real5, imag5) {
     throw new Error(`Cannot merge real and imag arrays of different lengths. real:${real5.length}, imag: ${imag5.length}.`);
   }
   const result = new Float32Array(real5.length * 2);
-  for (let i2 = 0; i2 < result.length; i2 += 2) {
-    result[i2] = real5[i2 / 2];
-    result[i2 + 1] = imag5[i2 / 2];
+  for (let i = 0; i < result.length; i += 2) {
+    result[i] = real5[i / 2];
+    result[i + 1] = imag5[i / 2];
   }
   return result;
 }
 function splitRealAndImagArrays(complex5) {
   const real5 = new Float32Array(complex5.length / 2);
   const imag5 = new Float32Array(complex5.length / 2);
-  for (let i2 = 0; i2 < complex5.length; i2 += 2) {
-    real5[i2 / 2] = complex5[i2];
-    imag5[i2 / 2] = complex5[i2 + 1];
+  for (let i = 0; i < complex5.length; i += 2) {
+    real5[i / 2] = complex5[i];
+    imag5[i / 2] = complex5[i + 1];
   }
   return { real: real5, imag: imag5 };
 }
@@ -16125,9 +16126,9 @@ function complexWithEvenIndex(complex5) {
   const len = Math.ceil(complex5.length / 4);
   const real5 = new Float32Array(len);
   const imag5 = new Float32Array(len);
-  for (let i2 = 0; i2 < complex5.length; i2 += 4) {
-    real5[Math.floor(i2 / 4)] = complex5[i2];
-    imag5[Math.floor(i2 / 4)] = complex5[i2 + 1];
+  for (let i = 0; i < complex5.length; i += 4) {
+    real5[Math.floor(i / 4)] = complex5[i];
+    imag5[Math.floor(i / 4)] = complex5[i + 1];
   }
   return { real: real5, imag: imag5 };
 }
@@ -16135,9 +16136,9 @@ function complexWithOddIndex(complex5) {
   const len = Math.floor(complex5.length / 4);
   const real5 = new Float32Array(len);
   const imag5 = new Float32Array(len);
-  for (let i2 = 2; i2 < complex5.length; i2 += 4) {
-    real5[Math.floor(i2 / 4)] = complex5[i2];
-    imag5[Math.floor(i2 / 4)] = complex5[i2 + 1];
+  for (let i = 2; i < complex5.length; i += 4) {
+    real5[Math.floor(i / 4)] = complex5[i];
+    imag5[Math.floor(i / 4)] = complex5[i + 1];
   }
   return { real: real5, imag: imag5 };
 }
@@ -16150,18 +16151,18 @@ function assignToTypedArray(data, real5, imag5, index2) {
   data[index2 * 2] = real5;
   data[index2 * 2 + 1] = imag5;
 }
-function exponents(n2, inverse) {
-  const real5 = new Float32Array(n2 / 2);
-  const imag5 = new Float32Array(n2 / 2);
-  for (let i2 = 0; i2 < Math.ceil(n2 / 2); i2++) {
-    const x = (inverse ? 2 : -2) * Math.PI * (i2 / n2);
-    real5[i2] = Math.cos(x);
-    imag5[i2] = Math.sin(x);
+function exponents(n, inverse) {
+  const real5 = new Float32Array(n / 2);
+  const imag5 = new Float32Array(n / 2);
+  for (let i = 0; i < Math.ceil(n / 2); i++) {
+    const x = (inverse ? 2 : -2) * Math.PI * (i / n);
+    real5[i] = Math.cos(x);
+    imag5[i] = Math.sin(x);
   }
   return { real: real5, imag: imag5 };
 }
-function exponent(k, n2, inverse) {
-  const x = (inverse ? 2 : -2) * Math.PI * (k / n2);
+function exponent(k, n, inverse) {
+  const x = (inverse ? 2 : -2) * Math.PI * (k / n);
   const real5 = Math.cos(x);
   const imag5 = Math.sin(x);
   return { real: real5, imag: imag5 };
@@ -16189,8 +16190,8 @@ function decodeEinsumEquation(equation, numTensors) {
     throw new Error("Support for more than 2 input tensors is not implemented yet.");
   }
   const allDims = [];
-  for (let i2 = 0; i2 < outputString.length; ++i2) {
-    const dimName = outputString[i2];
+  for (let i = 0; i < outputString.length; ++i) {
+    const dimName = outputString[i];
     if (!inputTerms.some((inputTerm) => inputTerm.indexOf(dimName) !== -1)) {
       throw new Error(`Output subscripts contain the label ${dimName} not present in the input subscripts.`);
     }
@@ -16198,40 +16199,40 @@ function decodeEinsumEquation(equation, numTensors) {
       allDims.push(dimName);
     }
   }
-  for (let i2 = 0; i2 < inputString.length; ++i2) {
-    const dimName = inputString[i2];
+  for (let i = 0; i < inputString.length; ++i) {
+    const dimName = inputString[i];
     if (allDims.indexOf(dimName) === -1 && dimName !== COMMA) {
       allDims.push(dimName);
     }
   }
   const idDims = new Array(inputTerms.length);
-  for (let i2 = 0; i2 < numInputs; ++i2) {
-    if (new Set(inputTerms[i2].split("")).size !== inputTerms[i2].length) {
-      throw new Error(`Found duplicate axes in input component ${inputTerms[i2]}. Support for duplicate axes in input is not implemented yet.`);
+  for (let i = 0; i < numInputs; ++i) {
+    if (new Set(inputTerms[i].split("")).size !== inputTerms[i].length) {
+      throw new Error(`Found duplicate axes in input component ${inputTerms[i]}. Support for duplicate axes in input is not implemented yet.`);
     }
-    idDims[i2] = [];
-    for (let j = 0; j < inputTerms[i2].length; ++j) {
-      idDims[i2].push(allDims.indexOf(inputTerms[i2][j]));
+    idDims[i] = [];
+    for (let j = 0; j < inputTerms[i].length; ++j) {
+      idDims[i].push(allDims.indexOf(inputTerms[i][j]));
     }
   }
   const numDims = allDims.length;
   const numOutDims = outputString.length;
   const summedDims = [];
-  for (let i2 = numOutDims; i2 < numDims; ++i2) {
-    summedDims.push(i2);
+  for (let i = numOutDims; i < numDims; ++i) {
+    summedDims.push(i);
   }
   return { allDims, summedDims, idDims };
 }
 function getEinsumPermutation(nDims, idDims) {
   let permutationIndices = new Array(nDims);
   permutationIndices.fill(-1);
-  for (let i2 = 0; i2 < idDims.length; ++i2) {
-    permutationIndices[idDims[i2]] = i2;
+  for (let i = 0; i < idDims.length; ++i) {
+    permutationIndices[idDims[i]] = i;
   }
   const expandDims7 = [];
-  for (let i2 = 0; i2 < nDims; ++i2) {
-    if (permutationIndices[i2] === -1) {
-      expandDims7.push(i2);
+  for (let i = 0; i < nDims; ++i) {
+    if (permutationIndices[i] === -1) {
+      expandDims7.push(i);
     }
   }
   permutationIndices = permutationIndices.filter((d) => d !== -1);
@@ -16239,13 +16240,13 @@ function getEinsumPermutation(nDims, idDims) {
 }
 function checkEinsumDimSizes(nDims, idDims, tensors) {
   const dimSizes = new Array(nDims);
-  for (let i2 = 0; i2 < tensors.length; ++i2) {
-    const shape = tensors[i2].shape;
-    for (let j = 0; j < idDims[i2].length; ++j) {
-      if (dimSizes[idDims[i2][j]] === void 0) {
-        dimSizes[idDims[i2][j]] = shape[j];
+  for (let i = 0; i < tensors.length; ++i) {
+    const shape = tensors[i].shape;
+    for (let j = 0; j < idDims[i].length; ++j) {
+      if (dimSizes[idDims[i][j]] === void 0) {
+        dimSizes[idDims[i][j]] = shape[j];
       } else {
-        assert(dimSizes[idDims[i2][j]] === shape[j], () => `Expected dimension ${dimSizes[idDims[i2][j]]} at axis ${j} of input shaped ${JSON.stringify(shape)}, but got dimension ${shape[j]}`);
+        assert(dimSizes[idDims[i][j]] === shape[j], () => `Expected dimension ${dimSizes[idDims[i][j]]} at axis ${j} of input shaped ${JSON.stringify(shape)}, but got dimension ${shape[j]}`);
       }
     }
   }
@@ -16258,16 +16259,16 @@ function getEinsumComputePath(summedDims, idDims) {
     path.push(-1);
   }
   nSteps = summedDims.length + 1;
-  for (let i2 = 0; i2 < nSteps; ++i2) {
+  for (let i = 0; i < nSteps; ++i) {
     steps.push([]);
   }
   const computedTermIndices = [];
-  for (let i2 = 0; i2 < path.length; ++i2) {
-    const summedDim = path[i2];
+  for (let i = 0; i < path.length; ++i) {
+    const summedDim = path[i];
     const termIndices = findTermsWithDim(idDims, summedDim);
     for (const termIndex of termIndices) {
       if (computedTermIndices.indexOf(termIndex) === -1) {
-        steps[i2].push(termIndex);
+        steps[i].push(termIndex);
         computedTermIndices.push(termIndex);
       }
     }
@@ -16279,9 +16280,9 @@ function isIdentityPermutation(perm) {
 }
 function findTermsWithDim(idDims, dim) {
   const termIndices = [];
-  for (let i2 = 0; i2 < idDims.length; ++i2) {
-    if (idDims[i2].length === 0 || idDims[i2].indexOf(dim) !== -1 || dim === -1) {
-      termIndices.push(i2);
+  for (let i = 0; i < idDims.length; ++i) {
+    if (idDims[i].length === 0 || idDims[i].indexOf(dim) !== -1 || dim === -1) {
+      termIndices.push(i);
     }
   }
   return termIndices;
@@ -16405,9 +16406,9 @@ function collectGatherOpShapeInfo(x, indices, axis, batchDims) {
   if (axis < batchDims) {
     throw new Error(`batchDims (${batchDims}) must be less than or equal to axis (${axis}).`);
   }
-  for (let i2 = 0; i2 < batchDims; ++i2) {
-    if (x.shape[i2] !== indices.shape[i2]) {
-      throw new Error(`x.shape[${i2}]: ${x.shape[i2]} should be equal to indices.shape[${i2}]: ${indices.shape[i2]}.`);
+  for (let i = 0; i < batchDims; ++i) {
+    if (x.shape[i] !== indices.shape[i]) {
+      throw new Error(`x.shape[${i}]: ${x.shape[i]} should be equal to indices.shape[${i}]: ${indices.shape[i]}.`);
     }
   }
   const dimSize = x.shape[axis];
@@ -16415,20 +16416,20 @@ function collectGatherOpShapeInfo(x, indices, axis, batchDims) {
   let batchSize = 1;
   let outerSize = 1;
   let sliceSize = 1;
-  for (let i2 = 0; i2 < batchDims; ++i2) {
-    outputShape.push(x.shape[i2]);
-    batchSize *= x.shape[i2];
+  for (let i = 0; i < batchDims; ++i) {
+    outputShape.push(x.shape[i]);
+    batchSize *= x.shape[i];
   }
-  for (let i2 = batchDims; i2 < axis; i2++) {
-    outputShape.push(x.shape[i2]);
-    outerSize *= x.shape[i2];
+  for (let i = batchDims; i < axis; i++) {
+    outputShape.push(x.shape[i]);
+    outerSize *= x.shape[i];
   }
-  for (let i2 = batchDims; i2 < indicesRank; i2++) {
-    outputShape.push(indices.shape[i2]);
+  for (let i = batchDims; i < indicesRank; i++) {
+    outputShape.push(indices.shape[i]);
   }
-  for (let i2 = axis + 1; i2 < xRank; i2++) {
-    outputShape.push(x.shape[i2]);
-    sliceSize *= x.shape[i2];
+  for (let i = axis + 1; i < xRank; i++) {
+    outputShape.push(x.shape[i]);
+    sliceSize *= x.shape[i];
   }
   return { batchSize, sliceSize, outerSize, dimSize, outputShape };
 }
@@ -16440,7 +16441,7 @@ function fromUint8ToStringArray(vals) {
   }
 }
 function fromStringArrayToUint8(strings) {
-  return strings.map((s2) => encodeString(s2));
+  return strings.map((s) => encodeString(s));
 }
 var kernel_impls_exports = {};
 __export2(kernel_impls_exports, {
@@ -16514,8 +16515,8 @@ var addNGradConfig = {
   saveAllInputs: true,
   gradFunc: (dy, saved) => {
     const ders = {};
-    saved.forEach((_, i2) => {
-      ders[i2] = () => dy.clone();
+    saved.forEach((_, i) => {
+      ders[i] = () => dy.clone();
     });
     return ders;
   }
@@ -16715,17 +16716,17 @@ var broadcastToGradConfig = {
     const inputShape = broadCastToAttrs.inputShape;
     const outputShape = broadCastToAttrs.shape;
     const reps = Array.from(outputShape);
-    for (let i2 = inputShape.length - 1; i2 >= 0; i2--) {
-      if (inputShape[i2] === outputShape[i2]) {
-        reps[i2] = 1;
-      } else if (inputShape[i2] !== 1) {
+    for (let i = inputShape.length - 1; i >= 0; i--) {
+      if (inputShape[i] === outputShape[i]) {
+        reps[i] = 1;
+      } else if (inputShape[i] !== 1) {
         throw new Error(`broadcastTo(): [${inputShape}] cannot be broadcast to [${outputShape}].`);
       }
     }
     const axes = [];
-    for (let i2 = 0; i2 < reps.length; i2++) {
-      if (reps[i2] > 1) {
-        axes.push(i2);
+    for (let i = 0; i < reps.length; i++) {
+      if (reps[i] > 1) {
+        axes.push(i);
       }
     }
     return { x: () => sum2(dy, axes, true) };
@@ -16766,7 +16767,7 @@ var concatGradConfig = {
     const shapes = saved.map((t2) => t2.shape);
     const { axis } = attrs;
     const $axis = parseAxisParam(axis, saved[0].shape)[0];
-    const sizeSplits = shapes.map((s2) => s2[$axis]);
+    const sizeSplits = shapes.map((s) => s[$axis]);
     const derTensors = split(dy, sizeSplits, $axis);
     return derTensors.map((t2) => () => t2);
   }
@@ -16978,8 +16979,8 @@ var fusedBatchNormGradConfig = {
     const reductionAxes = getReductionAxes(mean5.shape, x.shape);
     const tileShape = [];
     if (mean5.rank === 1) {
-      for (let i2 = 0; i2 < x.shape.length - 1; ++i2) {
-        tileShape.push(x.shape[i2]);
+      for (let i = 0; i < x.shape.length - 1; ++i) {
+        tileShape.push(x.shape[i]);
       }
       tileShape.push(1);
     }
@@ -17063,16 +17064,16 @@ var gatherGradConfig = {
 };
 function arrayRange(start, stop) {
   const result = [];
-  for (let i2 = start; i2 < stop; ++i2) {
-    result.push(i2);
+  for (let i = start; i < stop; ++i) {
+    result.push(i);
   }
   return result;
 }
 function arrayConcat(arrays) {
   const result = [];
-  for (let i2 = 0; i2 < arrays.length; ++i2) {
-    for (let j = 0; j < arrays[i2].length; ++j) {
-      result.push(arrays[i2][j]);
+  for (let i = 0; i < arrays.length; ++i) {
+    for (let j = 0; j < arrays[i].length; ++j) {
+      result.push(arrays[i][j]);
     }
   }
   return result;
@@ -17522,7 +17523,7 @@ var prodGradConfig = {
     const { axis } = attrs;
     let axisArr = [];
     if (axis === void 0 || axis === null) {
-      axisArr = x.shape.map((_, i2) => i2);
+      axisArr = x.shape.map((_, i) => i);
     } else if (typeof axis === "number") {
       axisArr = [axis];
     } else {
@@ -17700,8 +17701,8 @@ var sliceGradConfig = {
     const inputShape = x.shape;
     const [begin_, size_] = parseSliceParams(x, begin, size2);
     const paddings = [];
-    for (let i2 = 0; i2 < dy.rank; i2++) {
-      paddings.push([begin_[i2], inputShape[i2] - begin_[i2] - size_[i2]]);
+    for (let i = 0; i < dy.rank; i++) {
+      paddings.push([begin_[i], inputShape[i] - begin_[i] - size_[i]]);
     }
     return { x: () => pad(dy, paddings) };
   }
@@ -17840,36 +17841,36 @@ var tileGradConfig = {
     const derX = () => {
       let xGrad = zerosLike(x);
       if (x.rank === 1) {
-        for (let i2 = 0; i2 < reps[0]; ++i2) {
-          xGrad = add2(xGrad, slice(dy, [i2 * x.shape[0]], [x.shape[0]]));
+        for (let i = 0; i < reps[0]; ++i) {
+          xGrad = add2(xGrad, slice(dy, [i * x.shape[0]], [x.shape[0]]));
         }
       } else if (x.rank === 2) {
-        for (let i2 = 0; i2 < reps[0]; ++i2) {
+        for (let i = 0; i < reps[0]; ++i) {
           for (let j = 0; j < reps[1]; ++j) {
-            xGrad = add2(xGrad, slice(dy, [i2 * x.shape[0], j * x.shape[1]], [
+            xGrad = add2(xGrad, slice(dy, [i * x.shape[0], j * x.shape[1]], [
               x.shape[0],
               x.shape[1]
             ]));
           }
         }
       } else if (x.rank === 3) {
-        for (let i2 = 0; i2 < reps[0]; ++i2) {
+        for (let i = 0; i < reps[0]; ++i) {
           for (let j = 0; j < reps[1]; ++j) {
             for (let k = 0; k < reps[2]; ++k) {
-              xGrad = add2(xGrad, slice(dy, [i2 * x.shape[0], j * x.shape[1], k * x.shape[2]], [x.shape[0], x.shape[1], x.shape[2]]));
+              xGrad = add2(xGrad, slice(dy, [i * x.shape[0], j * x.shape[1], k * x.shape[2]], [x.shape[0], x.shape[1], x.shape[2]]));
             }
           }
         }
       } else if (x.rank === 4) {
-        for (let i2 = 0; i2 < reps[0]; ++i2) {
+        for (let i = 0; i < reps[0]; ++i) {
           for (let j = 0; j < reps[1]; ++j) {
             for (let k = 0; k < reps[2]; ++k) {
-              for (let l3 = 0; l3 < reps[3]; ++l3) {
+              for (let l = 0; l < reps[3]; ++l) {
                 xGrad = add2(xGrad, slice(dy, [
-                  i2 * x.shape[0],
+                  i * x.shape[0],
                   j * x.shape[1],
                   k * x.shape[2],
-                  l3 * x.shape[3]
+                  l * x.shape[3]
                 ], [x.shape[0], x.shape[1], x.shape[2], x.shape[3]]));
               }
             }
@@ -17916,8 +17917,8 @@ function gatherDropNegatives(x, indices) {
   const gathered = gather(x, zeroClippedIndices);
   let isPositive = greaterEqual(indices, scalar(0, "int32"));
   const numIters = gathered.rank - isPositive.rank;
-  for (let i2 = 0; i2 < numIters; ++i2) {
-    isPositive = expandDims(isPositive, i2 + 1);
+  for (let i = 0; i < numIters; ++i) {
+    isPositive = expandDims(isPositive, i + 1);
   }
   isPositive = logicalAnd(isPositive, ones2(gathered.shape, "bool"));
   const zeroSlice = zerosLike(gathered);
@@ -18653,7 +18654,7 @@ var LruCache = class {
       throw new Error(`The maxEntries of LRU caches must be at least 0, but got ${maxEntries}.`);
     }
     if (this.maxEntries > maxEntries) {
-      for (let i2 = 0; i2 < this.maxEntries - maxEntries; i2++) {
+      for (let i = 0; i < this.maxEntries - maxEntries; i++) {
         const keyToDelete = this.cache.keys().next().value;
         this.cache.delete(keyToDelete);
       }
@@ -18664,7 +18665,7 @@ var LruCache = class {
 function pyListRepeat(value, numValues) {
   if (Array.isArray(value)) {
     let newArray = [];
-    for (let i2 = 0; i2 < numValues; i2++) {
+    for (let i = 0; i < numValues; i++) {
       newArray = newArray.concat(value);
     }
     return newArray;
@@ -18852,12 +18853,12 @@ function checkStringTypeUnionValue(values, label, value) {
 function checkArrayTypeAndLength(x, expectedType, minLength = 0, maxLength = Infinity) {
   assert2(minLength >= 0);
   assert2(maxLength >= minLength);
-  return Array.isArray(x) && x.length >= minLength && x.length <= maxLength && x.every((e2) => typeof e2 === expectedType);
+  return Array.isArray(x) && x.length >= minLength && x.length <= maxLength && x.every((e) => typeof e === expectedType);
 }
 function assertPositiveInteger(value, name) {
   if (Array.isArray(value)) {
     util_exports.assert(value.length > 0, () => `${name} is unexpectedly an empty array.`);
-    value.forEach((v, i2) => assertPositiveInteger(v, `element ${i2 + 1} of ${name}`));
+    value.forEach((v, i) => assertPositiveInteger(v, `element ${i + 1} of ${name}`));
   } else {
     util_exports.assert(Number.isInteger(value) && value > 0, () => `Expected ${name} to be a positive integer, but got ${formatAsFriendlyString(value)}.`);
   }
@@ -18937,9 +18938,9 @@ function nameScope(name, fn) {
     const val = fn();
     _nameScopeStack.pop();
     return val;
-  } catch (e2) {
+  } catch (e) {
     _nameScopeStack.pop();
-    throw e2;
+    throw e;
   }
 }
 function currentNameScopePrefix() {
@@ -18987,8 +18988,8 @@ function arrayProd(array2, begin, end) {
     end = array2.length;
   }
   let prod6 = 1;
-  for (let i2 = begin; i2 < end; ++i2) {
-    prod6 *= array2[i2];
+  for (let i = begin; i < end; ++i) {
+    prod6 *= array2[i];
   }
   return prod6;
 }
@@ -18997,8 +18998,8 @@ function min2(array2) {
     return Number.NaN;
   }
   let min7 = Number.POSITIVE_INFINITY;
-  for (let i2 = 0; i2 < array2.length; i2++) {
-    const value = array2[i2];
+  for (let i = 0; i < array2.length; i++) {
+    const value = array2[i];
     if (value < min7) {
       min7 = value;
     }
@@ -19010,8 +19011,8 @@ function max2(array2) {
     return Number.NaN;
   }
   let max7 = Number.NEGATIVE_INFINITY;
-  for (let i2 = 0; i2 < array2.length; i2++) {
-    const value = array2[i2];
+  for (let i = 0; i < array2.length; i++) {
+    const value = array2[i];
     if (value > max7) {
       max7 = value;
     }
@@ -19023,8 +19024,8 @@ function range2(begin, end) {
     throw new ValueError(`end (${end}) < begin (${begin}) is forbidden.`);
   }
   const out = [];
-  for (let i2 = begin; i2 < end; ++i2) {
-    out.push(i2);
+  for (let i = begin; i < end; ++i) {
+    out.push(i);
   }
   return out;
 }
@@ -19049,13 +19050,13 @@ function expandDims2(x, axis = -1) {
   outShape.splice(axis, 0, 1);
   return reshape(x, outShape);
 }
-function repeat(x, n2) {
+function repeat(x, n) {
   return tidy(() => {
     if (x.shape.length !== 2) {
       throw new ValueError(`repeat() expects a rank-2 tensor, but received a rank-${x.shape.length} tensor.`);
     }
     const y = expandDims2(x, 1);
-    return tile2(y, [1, n2, 1]);
+    return tile2(y, [1, n, 1]);
   });
 }
 function flatten2(x) {
@@ -19190,14 +19191,14 @@ function concatAlongFirstAxis(a, b) {
       throw new ValueError(`concatAlongFirstAxis() received an unsupported tensor rank: ${a.rank}`);
   }
 }
-function tile2(x, n2) {
-  if (!Array.isArray(n2)) {
-    n2 = [n2];
+function tile2(x, n) {
+  if (!Array.isArray(n)) {
+    n = [n];
   }
-  if (x.rank !== n2.length) {
-    throw new ValueError(`The length of input n (${n2.length}) does not match the number of dimensions in input x (${x.rank})`);
+  if (x.rank !== n.length) {
+    throw new ValueError(`The length of input n (${n.length}) does not match the number of dimensions in input x (${x.rank})`);
   }
-  return tile(x, n2);
+  return tile(x, n);
 }
 function randomNormal2(shape, mean5 = 0, stddev = 1, dtype, seed) {
   return randomNormal(shape, mean5, stddev, dtype, seed);
@@ -19232,13 +19233,13 @@ function dot2(a, b, activation2, bias) {
     const bLastDim = bShape.pop();
     const ySecondLastDim = bShape.pop();
     const yOtherDims = [...bShape, bLastDim];
-    const perm = Array.from({ length: b.rank }, (_, i2) => {
-      if (i2 === 0) {
+    const perm = Array.from({ length: b.rank }, (_, i) => {
+      if (i === 0) {
         return b.rank - 2;
-      } else if (i2 <= b.rank - 2) {
-        return i2 - 1;
+      } else if (i <= b.rank - 2) {
+        return i - 1;
       }
-      return i2;
+      return i;
     });
     b = reshape(transpose(b, perm), [ySecondLastDim, -1]);
     const outputShape = [...aFirstDims, ...yOtherDims];
@@ -20096,9 +20097,9 @@ var Layer = class extends serialization_exports.Serializable {
         }
       }
       if (spec.shape != null) {
-        for (let i2 = 0; i2 < spec.shape.length; ++i2) {
-          const specDim = spec.shape[i2];
-          const dim = x.shape[i2];
+        for (let i = 0; i < spec.shape.length; ++i) {
+          const specDim = spec.shape[i];
+          const dim = x.shape[i];
           if (specDim != null && dim != null) {
             if (specDim !== dim) {
               throw new ValueError(`Input ${inputIndex} is incompatible with layer ${this.name}: expected shape=${spec.shape}, found shape=${x.shape}.`);
@@ -20202,8 +20203,8 @@ var Layer = class extends serialization_exports.Serializable {
       console.warn(`The rank of the input tensor provided (shape: ${JSON.stringify(inputShape)}) does not match that of the batchInputShape (${JSON.stringify(this.batchInputShape)}) of the layer ${this.name}`);
     } else {
       let dimMismatch = false;
-      this.batchInputShape.forEach((dimension, i2) => {
-        if (dimension != null && inputShape[i2] != null && inputShape[i2] !== dimension) {
+      this.batchInputShape.forEach((dimension, i) => {
+        if (dimension != null && inputShape[i] != null && inputShape[i] !== dimension) {
           dimMismatch = true;
         }
       });
@@ -20257,10 +20258,10 @@ var Layer = class extends serialization_exports.Serializable {
       }
       const weightValueTuples = [];
       const paramValues = batchGetValue(params);
-      for (let i2 = 0; i2 < paramValues.length; ++i2) {
-        const pv = paramValues[i2];
-        const p2 = params[i2];
-        const w = weights[i2];
+      for (let i = 0; i < paramValues.length; ++i) {
+        const pv = paramValues[i];
+        const p2 = params[i];
+        const w = weights[i];
         if (!util_exports.arraysEqual(pv.shape, w.shape)) {
           throw new ValueError(`Layer weight shape ${pv.shape} not compatible with provided weight shape ${w.shape}`);
         }
@@ -20355,10 +20356,10 @@ var Layer = class extends serialization_exports.Serializable {
       inputShapes,
       outputShapes
     }, kwargs);
-    for (let i2 = 0; i2 < outputTensors.length; i2++) {
-      outputTensors[i2].sourceLayer = this;
-      outputTensors[i2].nodeIndex = this.inboundNodes.length - 1;
-      outputTensors[i2].tensorIndex = i2;
+    for (let i = 0; i < outputTensors.length; i++) {
+      outputTensors[i].sourceLayer = this;
+      outputTensors[i].nodeIndex = this.inboundNodes.length - 1;
+      outputTensors[i].tensorIndex = i;
     }
   }
   getConfig() {
@@ -20419,10 +20420,10 @@ function getSourceInputs(tensor2, layer, nodeIndex) {
       return node2.inputTensors;
     } else {
       const sourceTensors = [];
-      for (let i2 = 0; i2 < node2.inboundLayers.length; i2++) {
-        const x = node2.inputTensors[i2];
-        const layer2 = node2.inboundLayers[i2];
-        const nodeIndex2 = node2.nodeIndices[i2];
+      for (let i = 0; i < node2.inboundLayers.length; i++) {
+        const x = node2.inputTensors[i];
+        const layer2 = node2.inboundLayers[i];
+        const nodeIndex2 = node2.nodeIndices[i];
         const previousSources = getSourceInputs(x, layer2, nodeIndex2);
         for (const x2 of previousSources) {
           if (sourceTensors.indexOf(x2) === -1) {
@@ -20656,7 +20657,7 @@ function execute(fetches, feedDict, kwargs, probe) {
     Object.assign(recipientCounts, cachedRecipientCounts.get(fetchAndFeedKey));
   }
   const internalFeedDict = new FeedDict(feedDict);
-  for (let i2 = 0; i2 < sorted.length; ++i2) {
+  for (let i = 0; i < sorted.length; ++i) {
     if (probe != null) {
       const numTensors = memory().numTensors;
       if (numTensors > probe.maxNumTensors) {
@@ -20666,7 +20667,7 @@ function execute(fetches, feedDict, kwargs, probe) {
         probe.minNumTensors = numTensors;
       }
     }
-    const symbolic = sorted[i2];
+    const symbolic = sorted[i];
     const srcLayer = symbolic.sourceLayer;
     if (srcLayer instanceof InputLayer) {
       continue;
@@ -20701,13 +20702,13 @@ function execute(fetches, feedDict, kwargs, probe) {
     }
     const layerOutputs = getNodeOutputs(symbolic);
     const outputSymbolicTensors = Array.isArray(layerOutputs) ? layerOutputs : [layerOutputs];
-    for (let i3 = 0; i3 < outputSymbolicTensors.length; ++i3) {
-      if (!internalFeedDict.hasKey(outputSymbolicTensors[i3])) {
-        internalFeedDict.add(outputSymbolicTensors[i3], outputTensors[i3], Array.isArray(outputMask) ? outputMask[0] : outputMask);
+    for (let i2 = 0; i2 < outputSymbolicTensors.length; ++i2) {
+      if (!internalFeedDict.hasKey(outputSymbolicTensors[i2])) {
+        internalFeedDict.add(outputSymbolicTensors[i2], outputTensors[i2], Array.isArray(outputMask) ? outputMask[0] : outputMask);
       }
-      const index2 = outputNames.indexOf(outputSymbolicTensors[i3].name);
+      const index2 = outputNames.indexOf(outputSymbolicTensors[i2].name);
       if (index2 !== -1) {
-        finalOutputs[index2] = outputTensors[i3];
+        finalOutputs[index2] = outputTensors[i2];
       }
     }
     if (!training) {
@@ -20801,10 +20802,10 @@ function getNodeOutputs(fetch4) {
     layerOutputs = fetch4.sourceLayer.output;
   } else {
     let nodeIndex = null;
-    for (let i2 = 0; i2 < fetch4.sourceLayer.inboundNodes.length; ++i2) {
-      for (const outputTensor of fetch4.sourceLayer.inboundNodes[i2].outputTensors) {
+    for (let i = 0; i < fetch4.sourceLayer.inboundNodes.length; ++i) {
+      for (const outputTensor of fetch4.sourceLayer.inboundNodes[i].outputTensors) {
         if (outputTensor.id === fetch4.id) {
-          nodeIndex = i2;
+          nodeIndex = i;
           break;
         }
       }
@@ -21100,8 +21101,8 @@ async function resolveScalarsInLogs(logs) {
   }
   if (promises.length > 0) {
     const values = await Promise.all(promises);
-    for (let i2 = 0; i2 < values.length; ++i2) {
-      logs[keys[i2]] = values[i2][0];
+    for (let i = 0; i < values.length; ++i) {
+      logs[keys[i]] = values[i][0];
     }
     dispose(scalarsToDispose);
   }
@@ -21294,20 +21295,20 @@ var History = class extends BaseCallback {
     const indices = [];
     for (const key in this.history) {
       const valueArray = this.history[key];
-      for (let i2 = 0; i2 < valueArray.length; ++i2) {
-        if (typeof valueArray[i2] !== "number") {
-          const valueScalar = valueArray[i2];
+      for (let i = 0; i < valueArray.length; ++i) {
+        if (typeof valueArray[i] !== "number") {
+          const valueScalar = valueArray[i];
           promises.push(valueScalar.data());
           keys.push(key);
-          indices.push(i2);
+          indices.push(i);
         }
       }
     }
     const values = await Promise.all(promises);
-    for (let n2 = 0; n2 < values.length; ++n2) {
-      const tensorToDispose = this.history[keys[n2]][indices[n2]];
+    for (let n = 0; n < values.length; ++n) {
+      const tensorToDispose = this.history[keys[n]][indices[n]];
       tensorToDispose.dispose();
-      this.history[keys[n2]][indices[n2]] = values[n2][0];
+      this.history[keys[n]][indices[n]] = values[n][0];
     }
   }
 };
@@ -21822,13 +21823,13 @@ function printSummary(model22, lineLength, positions, printFn = console.log) {
   printRow(toDisplay, positions, printFn);
   printFn("=".repeat(lineLength));
   const layers = model22.layers;
-  for (let i2 = 0; i2 < layers.length; ++i2) {
+  for (let i = 0; i < layers.length; ++i) {
     if (sequentialLike) {
-      printLayerSummary(layers[i2], positions, printFn);
+      printLayerSummary(layers[i], positions, printFn);
     } else {
-      printLayerSummaryWithConnections(layers[i2], positions, relevantNodes, printFn);
+      printLayerSummaryWithConnections(layers[i], positions, relevantNodes, printFn);
     }
-    printFn((i2 === layers.length - 1 ? "=" : "_").repeat(lineLength));
+    printFn((i === layers.length - 1 ? "=" : "_").repeat(lineLength));
   }
   model22.checkTrainableWeightsConsistency();
   const trainableCount = countTrainableParams(model22);
@@ -21883,13 +21884,13 @@ function isModelSequentialLike(model22) {
 }
 function printRow(fields, positions, printFn = console.log) {
   let line = "";
-  for (let i2 = 0; i2 < fields.length; ++i2) {
-    if (i2 > 0) {
+  for (let i = 0; i < fields.length; ++i) {
+    if (i > 0) {
       line = line.slice(0, line.length - 1) + " ";
     }
-    line += fields[i2];
-    line = line.slice(0, positions[i2]);
-    line += " ".repeat(positions[i2] - line.length);
+    line += fields[i];
+    line = line.slice(0, positions[i]);
+    line += " ".repeat(positions[i] - line.length);
   }
   printFn(line);
 }
@@ -21934,10 +21935,10 @@ function printLayerSummaryWithConnections(layer, positions, relevantNodes, print
     if (relevantNodes != null && relevantNodes.length > 0 && relevantNodes.indexOf(node2) === -1) {
       continue;
     }
-    for (let i2 = 0; i2 < node2.inboundLayers.length; ++i2) {
-      const inboundLayer = node2.inboundLayers[i2].name;
-      const inboundLayerIndex = node2.nodeIndices[i2];
-      const inboundTensorIndex = node2.tensorIndices[i2];
+    for (let i = 0; i < node2.inboundLayers.length; ++i) {
+      const inboundLayer = node2.inboundLayers[i].name;
+      const inboundLayerIndex = node2.nodeIndices[i];
+      const inboundTensorIndex = node2.tensorIndices[i];
       connections.push(`${inboundLayer}[${inboundLayerIndex}][${inboundTensorIndex}]`);
     }
   }
@@ -21952,8 +21953,8 @@ function printLayerSummaryWithConnections(layer, positions, relevantNodes, print
     firstConnection
   ];
   printRow(fields, positions, printFn);
-  for (let i2 = 1; i2 < connections.length; ++i2) {
-    printRow(["", "", "", "", connections[i2]], positions, printFn);
+  for (let i = 1; i < connections.length; ++i) {
+    printRow(["", "", "", "", connections[i]], positions, printFn);
   }
 }
 function isArrayItemInputOrOutputName(key, index2, value) {
@@ -21969,9 +21970,9 @@ function convertPythonicToTs(pythonicConfig, key) {
   } else if (pythonicConfig instanceof Array) {
     const tsArray = [];
     const arrayLength = pythonicConfig.length;
-    for (let i2 = 0; i2 < arrayLength; ++i2) {
-      const item = pythonicConfig[i2];
-      if (isArrayItemInputOrOutputName(key, i2, item)) {
+    for (let i = 0; i < arrayLength; ++i) {
+      const item = pythonicConfig[i];
+      if (isArrayItemInputOrOutputName(key, i, item)) {
         tsArray.push(item);
       } else {
         tsArray.push(convertPythonicToTs(item, key));
@@ -22002,9 +22003,9 @@ function convertTsToPythonic(tsConfig, key) {
   } else if (tsConfig instanceof Array) {
     const pyArray = [];
     const arrayLength = tsConfig.length;
-    for (let i2 = 0; i2 < arrayLength; ++i2) {
-      const item = tsConfig[i2];
-      if (isArrayItemInputOrOutputName(key, i2, item)) {
+    for (let i = 0; i < arrayLength; ++i) {
+      const item = tsConfig[i];
+      if (isArrayItemInputOrOutputName(key, i, item)) {
         pyArray.push(item);
       } else {
         pyArray.push(convertTsToPythonic(item, key));
@@ -22084,10 +22085,10 @@ var Container = class extends Layer {
     this.feedInputShapes = [];
     this.feedInputNames = [];
     this.feedOutputNames = [];
-    for (let i2 = 0; i2 < this.inputLayers.length; i2++) {
-      const layer = this.inputLayers[i2];
+    for (let i = 0; i < this.inputLayers.length; i++) {
+      const layer = this.inputLayers[i];
       if (!(layer instanceof InputLayer)) {
-        throw new TypeError(`Input layers to a LayersModel must be InputLayer objects. Received inputs: ${args.inputs}. Input ${i2} (0-based) originates from layer type ${layer.getClassName()}.`);
+        throw new TypeError(`Input layers to a LayersModel must be InputLayer objects. Received inputs: ${args.inputs}. Input ${i} (0-based) originates from layer type ${layer.getClassName()}.`);
       }
       this.inputNames.push(layer.name);
       this.feedInputShapes.push(layer.batchInputShape);
@@ -22125,11 +22126,11 @@ var Container = class extends Layer {
         nodesInProgress2.push(node2);
       }
       const numInboundLayers = node2.inboundLayers.length;
-      for (let i2 = 0; i2 < numInboundLayers; i2++) {
-        const x = node2.inputTensors[i2];
-        const layer2 = node2.inboundLayers[i2];
-        const nodeIndex2 = node2.nodeIndices[i2];
-        const tensorIndex2 = node2.tensorIndices[i2];
+      for (let i = 0; i < numInboundLayers; i++) {
+        const x = node2.inputTensors[i];
+        const layer2 = node2.inboundLayers[i];
+        const nodeIndex2 = node2.nodeIndices[i];
+        const tensorIndex2 = node2.tensorIndices[i];
         buildMapOfGraph(x, finishedNodes2, nodesInProgress2, layer2, nodeIndex2, tensorIndex2);
       }
       finishedNodes2.push(node2);
@@ -22155,9 +22156,9 @@ var Container = class extends Layer {
       layersDepths[node2.outboundLayer.id] = depth;
       layerIDToLayer[node2.outboundLayer.id] = node2.outboundLayer;
       nodesDepths[node2.id] = depth;
-      for (let i2 = 0; i2 < node2.inboundLayers.length; i2++) {
-        const inboundLayer = node2.inboundLayers[i2];
-        const nodeIndex = node2.nodeIndices[i2];
+      for (let i = 0; i < node2.inboundLayers.length; i++) {
+        const inboundLayer = node2.inboundLayers[i];
+        const nodeIndex = node2.nodeIndices[i];
         const inboundNode = inboundLayer.inboundNodes[nodeIndex];
         const previousDepth2 = nodesDepths[inboundNode.id] == null ? 0 : nodesDepths[inboundNode.id];
         nodesDepths[inboundNode.id] = Math.max(depth + 1, previousDepth2);
@@ -22360,8 +22361,8 @@ var Container = class extends Layer {
     return tidy(() => {
       inputs = toList(inputs);
       const feedDict = new FeedDict();
-      for (let i2 = 0; i2 < this.inputs.length; ++i2) {
-        feedDict.add(this.inputs[i2], inputs[i2]);
+      for (let i = 0; i < this.inputs.length; ++i) {
+        feedDict.add(this.inputs[i], inputs[i]);
       }
       return execute(this.outputs, feedDict, kwargs);
     });
@@ -22384,9 +22385,9 @@ var Container = class extends Layer {
       throw new ValueError(`Invalid inputShape argument ${inputShape}: model has ${this.inputLayers.length} tensor inputs.`);
     }
     const layersToOutputShapes = {};
-    for (let i2 = 0; i2 < inputShapes.length; i2++) {
-      const layer = this.inputLayers[i2];
-      const inputShape2 = inputShapes[i2];
+    for (let i = 0; i < inputShapes.length; i++) {
+      const layer = this.inputLayers[i];
+      const inputShape2 = inputShapes[i];
       const shapeKey = layer.name + "_0_0";
       layersToOutputShapes[shapeKey] = inputShape2;
     }
@@ -22420,15 +22421,15 @@ var Container = class extends Layer {
     }
     const outputShapes = [];
     const outputShapeKeys = [];
-    for (let i2 = 0; i2 < this.outputLayers.length; i2++) {
-      const layer = this.outputLayers[i2];
-      const nodeIndex = this.outputLayersNodeIndices[i2];
-      const tensorIndex = this.outputLayersTensorIndices[i2];
+    for (let i = 0; i < this.outputLayers.length; i++) {
+      const layer = this.outputLayers[i];
+      const nodeIndex = this.outputLayersNodeIndices[i];
+      const tensorIndex = this.outputLayersTensorIndices[i];
       const shapeKey = `${layer.name}_${nodeIndex}_${tensorIndex}`;
       outputShapeKeys.push(shapeKey);
     }
-    for (let i2 = 0; i2 < outputShapeKeys.length; i2++) {
-      const key = outputShapeKeys[i2];
+    for (let i = 0; i < outputShapeKeys.length; i++) {
+      const key = outputShapeKeys[i];
       assert2(key in layersToOutputShapes);
       outputShapes.push(layersToOutputShapes[key]);
     }
@@ -22439,10 +22440,10 @@ var Container = class extends Layer {
       masks = pyListRepeat(null, inputs.length);
     }
     const tensorMap = {};
-    for (let i2 = 0; i2 < this.inputs.length; ++i2) {
-      const x = this.inputs[i2];
-      const y = inputs[i2];
-      const mask2 = masks[i2];
+    for (let i = 0; i < this.inputs.length; ++i) {
+      const x = this.inputs[i];
+      const y = inputs[i];
+      const mask2 = masks[i];
       tensorMap[x.id] = [y, mask2];
     }
     const depthKeys = Object.keys(this.nodesByDepth).map((x) => parseInt(x, 10)).sort(reverseNumberCompare);
@@ -22488,10 +22489,10 @@ var Container = class extends Layer {
           if (layer.activityRegularizer) {
             throw new NotImplementedError("LayersModel invocation with concrete Tensor value(s) in the presence of activity regularizer(s) is not supported yet.");
           }
-          for (let i2 = 0; i2 < referenceOutputTensors.length; ++i2) {
-            const x = referenceOutputTensors[i2];
-            const y = outputTensors2[i2];
-            const mask2 = outputMasks2[i2];
+          for (let i = 0; i < referenceOutputTensors.length; ++i) {
+            const x = referenceOutputTensors[i];
+            const y = outputTensors2[i];
+            const mask2 = outputMasks2[i];
             tensorMap[x.id] = [y, mask2];
           }
         }
@@ -22581,10 +22582,10 @@ var Container = class extends Layer {
           }
           if (node2.inboundLayers.length > 0) {
             const nodeData = [];
-            for (let i2 = 0; i2 < node2.inboundLayers.length; i2++) {
-              const inboundLayer = node2.inboundLayers[i2];
-              const nodeIndex = node2.nodeIndices[i2];
-              const tensorIndex = node2.tensorIndices[i2];
+            for (let i = 0; i < node2.inboundLayers.length; i++) {
+              const inboundLayer = node2.inboundLayers[i];
+              const nodeIndex = node2.nodeIndices[i];
+              const tensorIndex = node2.tensorIndices[i];
               const nodeKey2 = Container.nodeKey(inboundLayer, nodeIndex);
               let newNodeIndex = nodeConversionMap[nodeKey2];
               if (newNodeIndex == null) {
@@ -22605,9 +22606,9 @@ var Container = class extends Layer {
     }
     config3["layers"] = layerConfigs;
     const modelInputs = [];
-    for (let i2 = 0; i2 < this.inputLayers.length; i2++) {
-      const layer = this.inputLayers[i2];
-      const nodeIndex = this.inputLayersNodeIndices[i2];
+    for (let i = 0; i < this.inputLayers.length; i++) {
+      const layer = this.inputLayers[i];
+      const nodeIndex = this.inputLayersNodeIndices[i];
       const nodeKey = Container.nodeKey(layer, nodeIndex);
       if (!this.containerNodes.has(nodeKey)) {
         continue;
@@ -22616,14 +22617,14 @@ var Container = class extends Layer {
       if (newNodeIndex === null || newNodeIndex === void 0) {
         newNodeIndex = 0;
       }
-      const tensorIndex = this.inputLayersTensorIndices[i2];
+      const tensorIndex = this.inputLayersTensorIndices[i];
       modelInputs.push([layer.name, newNodeIndex, tensorIndex]);
     }
     config3["inputLayers"] = modelInputs;
     const modelOutputs = [];
-    for (let i2 = 0; i2 < this.outputLayers.length; i2++) {
-      const layer = this.outputLayers[i2];
-      const nodeIndex = this.outputLayersNodeIndices[i2];
+    for (let i = 0; i < this.outputLayers.length; i++) {
+      const layer = this.outputLayers[i];
+      const nodeIndex = this.outputLayersNodeIndices[i];
       const nodeKey = Container.nodeKey(layer, nodeIndex);
       if (!this.containerNodes.has(nodeKey)) {
         continue;
@@ -22632,7 +22633,7 @@ var Container = class extends Layer {
       if (newNodeIndex === null || newNodeIndex === void 0) {
         newNodeIndex = 0;
       }
-      const tensorIndex = this.outputLayersTensorIndices[i2];
+      const tensorIndex = this.outputLayersTensorIndices[i];
       modelOutputs.push([layer.name, newNodeIndex, tensorIndex]);
     }
     config3["outputLayers"] = modelOutputs;
@@ -22897,7 +22898,7 @@ async function fitDataset(model22, dataset, args) {
     const outLabels = model22.getDedupedMetricsNames();
     let callbackMetrics;
     if (doValidation) {
-      callbackMetrics = outLabels.slice().concat(outLabels.map((n2) => "val_" + n2));
+      callbackMetrics = outLabels.slice().concat(outLabels.map((n) => "val_" + n));
     } else {
       callbackMetrics = outLabels.slice();
     }
@@ -22943,16 +22944,16 @@ async function fitDataset(model22, dataset, args) {
           const sampleWeights = [];
           if (args.classWeight != null) {
             const standardClassWeights = standardizeClassWeights(args.classWeight, model22.outputNames);
-            for (let i2 = 0; i2 < standardClassWeights.length; ++i2) {
-              sampleWeights.push(await standardizeWeights(ys[i2], null, standardClassWeights[i2]));
+            for (let i = 0; i < standardClassWeights.length; ++i) {
+              sampleWeights.push(await standardizeWeights(ys[i], null, standardClassWeights[i]));
             }
           }
           const ins = xs.concat(ys).concat(sampleWeights);
           const outs = trainFunction(ins);
           dispose(ins);
-          for (let i2 = 0; i2 < outLabels.length; ++i2) {
-            const label = outLabels[i2];
-            const out = outs[i2];
+          for (let i = 0; i < outLabels.length; ++i) {
+            const label = outLabels[i];
+            const out = outs[i];
             batchLogs[label] = out;
             keep(out);
           }
@@ -22972,8 +22973,8 @@ async function fitDataset(model22, dataset, args) {
                 verbose: 0
               }));
             }
-            for (let i2 = 0; i2 < model22.metricsNames.length; ++i2) {
-              epochLogs[`val_${model22.metricsNames[i2]}`] = valOuts[i2];
+            for (let i = 0; i < model22.metricsNames.length; ++i) {
+              epochLogs[`val_${model22.metricsNames[i]}`] = valOuts[i];
             }
           }
           break;
@@ -23031,15 +23032,15 @@ async function evaluateDataset(model22, dataset, args) {
         const batchOuts = tidy(() => f(xsAndYs));
         dispose(xsAndYs);
         if (batch === 0) {
-          for (let i2 = 0; i2 < batchOuts.length; ++i2) {
+          for (let i = 0; i < batchOuts.length; ++i) {
             outs.push(scalar(0));
           }
         }
         const batchSize = xsAndYs[0].shape[0];
-        for (let i2 = 0; i2 < batchOuts.length; ++i2) {
-          const batchOut = batchOuts[i2];
-          const oldScalar = outs[i2];
-          outs[i2] = tidy(() => add2(outs[i2], mul(batchSize, batchOut)));
+        for (let i = 0; i < batchOuts.length; ++i) {
+          const batchOut = batchOuts[i];
+          const oldScalar = outs[i];
+          outs[i] = tidy(() => add2(outs[i], mul(batchSize, batchOut)));
           if (batch > 0) {
             dispose(oldScalar);
           }
@@ -23057,9 +23058,9 @@ async function evaluateDataset(model22, dataset, args) {
       break;
     }
   }
-  for (let i2 = 0; i2 < outs.length; ++i2) {
-    const oldScalar = outs[i2];
-    outs[i2] = div(outs[i2], numExamples);
+  for (let i = 0; i < outs.length; ++i) {
+    const oldScalar = outs[i];
+    outs[i] = div(outs[i], numExamples);
     dispose(oldScalar);
   }
   return singletonOrArray(outs);
@@ -23161,18 +23162,18 @@ async function fitLoop(model22, f, ins, outLabels, batchSize, epochs, verbose, c
           batchLogs["size"] = batchEnd - batchStart;
           const insBatch = sliceArraysByIndices(ins, batchIds);
           const outs = f(insBatch);
-          for (let i2 = 0; i2 < outLabels.length; ++i2) {
-            const label = outLabels[i2];
-            const out = outs[i2];
+          for (let i = 0; i < outLabels.length; ++i) {
+            const label = outLabels[i];
+            const out = outs[i];
             batchLogs[label] = out;
             keep(out);
           }
           if (batchIndex === batches.length - 1) {
             if (doValidation) {
               const valOuts = model22.testLoop(valF, valIns, batchSize);
-              for (let i2 = 0; i2 < outLabels.length; ++i2) {
-                const label = outLabels[i2];
-                const out = valOuts[i2];
+              for (let i = 0; i < outLabels.length; ++i) {
+                const label = outLabels[i];
+                const out = valOuts[i];
                 keep(out);
                 epochLogs["val_" + label] = out;
               }
@@ -23258,7 +23259,7 @@ async function fitTensors(model22, x, y, args = {}) {
     if (doValidation) {
       model22.makeTestFunction();
       valFunction = model22.testFunction;
-      callbackMetrics = outLabels.slice().concat(outLabels.map((n2) => "val_" + n2));
+      callbackMetrics = outLabels.slice().concat(outLabels.map((n) => "val_" + n));
     } else {
       valFunction = null;
       valIns = [];
@@ -23285,8 +23286,8 @@ function ensureTensorsRank2OrHigher(tensors) {
   if (tensors instanceof Tensor) {
     tensors = [tensors];
   }
-  for (let i2 = 0; i2 < tensors.length; ++i2) {
-    const tensor2 = tensors[i2];
+  for (let i = 0; i < tensors.length; ++i) {
+    const tensor2 = tensors[i];
     if (tensor2.rank === 1) {
       outs.push(expandDims2(tensor2, 1));
     } else if (tensor2.rank === 0) {
@@ -23396,22 +23397,22 @@ function standardizeInputData(data, names, shapes, checkBatchAxis = true, except
   }
   arrays = ensureTensorsRank2OrHigher(arrays);
   if (shapes != null) {
-    for (let i2 = 0; i2 < names.length; ++i2) {
-      if (shapes[i2] == null) {
+    for (let i = 0; i < names.length; ++i) {
+      if (shapes[i] == null) {
         continue;
       }
-      const array2 = arrays[i2];
-      if (array2.shape.length !== shapes[i2].length) {
-        throw new ValueError(`Error when checking ${exceptionPrefix}: expected ${names[i2]} to have ${shapes[i2].length} dimension(s). but got array with shape ${array2.shape}`);
+      const array2 = arrays[i];
+      if (array2.shape.length !== shapes[i].length) {
+        throw new ValueError(`Error when checking ${exceptionPrefix}: expected ${names[i]} to have ${shapes[i].length} dimension(s). but got array with shape ${array2.shape}`);
       }
-      for (let j = 0; j < shapes[i2].length; ++j) {
+      for (let j = 0; j < shapes[i].length; ++j) {
         if (j === 0 && !checkBatchAxis) {
           continue;
         }
         const dim = array2.shape[j];
-        const refDim = shapes[i2][j];
+        const refDim = shapes[i][j];
         if (refDim != null && refDim >= 0 && dim !== refDim) {
-          throw new ValueError(`${exceptionPrefix} expected a batch of elements where each example has shape [${shapes[i2].slice(1, shapes[i2].length)}] (i.e.,tensor shape [*,${shapes[i2].slice(1, shapes[i2].length)}]) but the ${exceptionPrefix} received an input with ${array2.shape[0]} examples, each with shape [${array2.shape.slice(1, array2.shape.length)}] (tensor shape [${array2.shape}])`);
+          throw new ValueError(`${exceptionPrefix} expected a batch of elements where each example has shape [${shapes[i].slice(1, shapes[i].length)}] (i.e.,tensor shape [*,${shapes[i].slice(1, shapes[i].length)}]) but the ${exceptionPrefix} received an input with ${array2.shape[0]} examples, each with shape [${array2.shape.slice(1, array2.shape.length)}] (tensor shape [${array2.shape}])`);
         }
       }
     }
@@ -23439,10 +23440,10 @@ function checkLossAndTargetCompatibility(targets, lossFns, outputShapes) {
     binaryCrossentropy,
     categoricalCrossentropy
   ];
-  for (let i2 = 0; i2 < targets.length; ++i2) {
-    const y = targets[i2];
-    const loss = lossFns[i2];
-    const shape = outputShapes[i2];
+  for (let i = 0; i < targets.length; ++i) {
+    const y = targets[i];
+    const loss = lossFns[i];
+    const shape = outputShapes[i];
     if (loss == null) {
       continue;
     }
@@ -23478,23 +23479,23 @@ function checkInputData(data, names, shapes, checkBatchAxis = true, exceptionPre
     arrays = [data];
   }
   if (shapes != null) {
-    for (let i2 = 0; i2 < names.length; ++i2) {
-      if (shapes[i2] == null) {
+    for (let i = 0; i < names.length; ++i) {
+      if (shapes[i] == null) {
         continue;
       }
-      const array2 = arrays[i2];
-      if (array2.shape.length !== shapes[i2].length) {
-        throw new ValueError(`Error when checking ${exceptionPrefix}: expected ${names[i2]} to have ${shapes[i2].length} dimension(s), but got array with shape ${JSON.stringify(array2.shape)}`);
+      const array2 = arrays[i];
+      if (array2.shape.length !== shapes[i].length) {
+        throw new ValueError(`Error when checking ${exceptionPrefix}: expected ${names[i]} to have ${shapes[i].length} dimension(s), but got array with shape ${JSON.stringify(array2.shape)}`);
       }
-      for (let j = 0; j < shapes[i2].length; ++j) {
+      for (let j = 0; j < shapes[i].length; ++j) {
         if (j === 0 && !checkBatchAxis) {
           continue;
         }
         const dim = array2.shape[j];
-        const refDim = shapes[i2][j];
+        const refDim = shapes[i][j];
         if (refDim != null) {
           if (refDim !== dim) {
-            throw new ValueError(`Error when checking ${exceptionPrefix}: expected ${names[i2]} to have shape ${JSON.stringify(shapes[i2])} but got array with shape ${JSON.stringify(array2.shape)}.`);
+            throw new ValueError(`Error when checking ${exceptionPrefix}: expected ${names[i]} to have shape ${JSON.stringify(shapes[i])} but got array with shape ${JSON.stringify(array2.shape)}.`);
           }
         }
       }
@@ -23573,7 +23574,7 @@ var LayersModel = class extends Container {
         throw new ValueError(`When passing an Array as loss, it should have one entry per model output. The model has ${this.outputs.length} output(s), but you passed loss=${args.loss}.`);
       }
       const theLosses = args.loss;
-      lossFunctions = theLosses.map((l3) => get(l3));
+      lossFunctions = theLosses.map((l) => get(l));
     } else {
       const lossFunction = get(args.loss);
       this.outputs.forEach((_) => {
@@ -23584,26 +23585,26 @@ var LayersModel = class extends Container {
     this.feedOutputNames = [];
     this.feedOutputShapes = [];
     this.feedLossFns = [];
-    for (let i2 = 0; i2 < this.outputs.length; ++i2) {
-      const shape = this.internalOutputShapes[i2];
-      const name = this.outputNames[i2];
+    for (let i = 0; i < this.outputs.length; ++i) {
+      const shape = this.internalOutputShapes[i];
+      const name = this.outputNames[i];
       this.feedOutputNames.push(name);
       this.feedOutputShapes.push(shape);
-      this.feedLossFns.push(this.lossFunctions[i2]);
+      this.feedLossFns.push(this.lossFunctions[i]);
     }
     const skipTargetIndices = [];
     this.metrics = args.metrics;
     this.metricsNames = ["loss"];
     this.metricsTensors = [];
     nameScope("loss", () => {
-      for (let i2 = 0; i2 < this.outputs.length; ++i2) {
-        if (skipTargetIndices.indexOf(i2) !== -1) {
+      for (let i = 0; i < this.outputs.length; ++i) {
+        if (skipTargetIndices.indexOf(i) !== -1) {
           continue;
         }
-        const weightedLoss = this.lossFunctions[i2];
+        const weightedLoss = this.lossFunctions[i];
         if (this.outputs.length > 1) {
-          this.metricsTensors.push([weightedLoss, i2]);
-          this.metricsNames.push(this.outputNames[i2] + "_loss");
+          this.metricsTensors.push([weightedLoss, i]);
+          this.metricsNames.push(this.outputNames[i] + "_loss");
         }
       }
     });
@@ -23616,11 +23617,11 @@ var LayersModel = class extends Container {
       this.metricsTensors.push([metricTensor, outputIndex]);
     };
     nameScope("metric", () => {
-      for (let i2 = 0; i2 < this.outputs.length; ++i2) {
-        if (skipTargetIndices.indexOf(i2) !== -1) {
+      for (let i = 0; i < this.outputs.length; ++i) {
+        if (skipTargetIndices.indexOf(i) !== -1) {
           continue;
         }
-        const outputMetrics = nestedMetrics[i2];
+        const outputMetrics = nestedMetrics[i];
         const handleMetrics = (metrics) => {
           const metricNamePrefix = "";
           let metricName;
@@ -23628,14 +23629,14 @@ var LayersModel = class extends Container {
           let weightedMetricFn;
           for (const metric of metrics) {
             if (typeof metric === "string" && ["accuracy", "acc", "crossentropy", "ce"].indexOf(metric) !== -1) {
-              const outputShape = this.internalOutputShapes[i2];
-              if (outputShape[outputShape.length - 1] === 1 || this.lossFunctions[i2] === binaryCrossentropy) {
+              const outputShape = this.internalOutputShapes[i];
+              if (outputShape[outputShape.length - 1] === 1 || this.lossFunctions[i] === binaryCrossentropy) {
                 if (["accuracy", "acc"].indexOf(metric) !== -1) {
                   accFn = binaryAccuracy;
                 } else if (["crossentropy", "ce"].indexOf(metric) !== -1) {
                   accFn = binaryCrossentropy2;
                 }
-              } else if (this.lossFunctions[i2] === sparseCategoricalCrossentropy) {
+              } else if (this.lossFunctions[i] === sparseCategoricalCrossentropy) {
                 if (["accuracy", "acc"].indexOf(metric) !== -1) {
                   accFn = sparseCategoricalAccuracy;
                 } else if (["crossentropy", "ce"].indexOf(metric) !== -1) {
@@ -23665,7 +23666,7 @@ var LayersModel = class extends Container {
             nameScope(metricName, () => {
               metricResult = weightedMetricFn;
             });
-            appendMetric(i2, metricName, metricResult);
+            appendMetric(i, metricName, metricResult);
           }
         };
         handleMetrics(outputMetrics);
@@ -23734,8 +23735,8 @@ var LayersModel = class extends Container {
       if (inputs.length !== this.inputs.length) {
         throw new ValueError(`The number of inputs provided (${inputs.length}) does not match the number of inputs of this model (${this.inputs.length}).`);
       }
-      for (let i2 = 0; i2 < this.inputs.length; ++i2) {
-        feedDict.add(this.inputs[i2], inputs[i2]);
+      for (let i = 0; i < this.inputs.length; ++i) {
+        feedDict.add(this.inputs[i], inputs[i]);
       }
     } else {
       for (const input2 of this.inputs) {
@@ -23755,10 +23756,10 @@ var LayersModel = class extends Container {
     for (const layer of this.layers) {
       const layerOutputs = Array.isArray(layer.output) ? layer.output : [layer.output];
       const layerOutputNames = layerOutputs.map((output) => output.name);
-      for (let i2 = 0; i2 < symbolicTensorNames.length; ++i2) {
-        const index2 = layerOutputNames.indexOf(symbolicTensorNames[i2]);
+      for (let i = 0; i < symbolicTensorNames.length; ++i) {
+        const index2 = layerOutputNames.indexOf(symbolicTensorNames[i]);
         if (index2 !== -1) {
-          outputSymbolicTensors[i2] = layerOutputs[index2];
+          outputSymbolicTensors[i] = layerOutputs[index2];
           outputsRemaining--;
         }
         if (outputsRemaining === 0) {
@@ -23771,9 +23772,9 @@ var LayersModel = class extends Container {
     }
     if (outputsRemaining > 0) {
       const remainingNames = [];
-      outputSymbolicTensors.forEach((tensor2, i2) => {
+      outputSymbolicTensors.forEach((tensor2, i) => {
         if (tensor2 == null) {
-          remainingNames.push(symbolicTensorNames[i2]);
+          remainingNames.push(symbolicTensorNames[i]);
         }
       });
       throw new ValueError(`Cannot find SymbolicTensors for output name(s): ${JSON.stringify(remainingNames)}`);
@@ -23795,8 +23796,8 @@ var LayersModel = class extends Container {
           const insBatch = sliceArrays(ins, batchStart, batchEnd);
           const feeds = [];
           if (Array.isArray(insBatch)) {
-            for (let i2 = 0; i2 < insBatch.length; ++i2) {
-              feeds.push({ key: this.inputs[i2], value: insBatch[i2] });
+            for (let i = 0; i < insBatch.length; ++i) {
+              feeds.push({ key: this.inputs[i], value: insBatch[i] });
             }
           } else {
             feeds.push({ key: this.inputs[0], value: insBatch });
@@ -23804,7 +23805,7 @@ var LayersModel = class extends Container {
           const feedDict = new FeedDict(feeds);
           return execute(this.outputs, feedDict);
         });
-        batchOuts.forEach((batchOut, i2) => outsBatches[i2].push(batchOut));
+        batchOuts.forEach((batchOut, i) => outsBatches[i].push(batchOut));
       }
       return singletonOrArray(outsBatches.map((batches2) => concat(batches2, 0)));
     });
@@ -23830,9 +23831,9 @@ var LayersModel = class extends Container {
       throw new RuntimeError("You must compile a model before training/testing. Use LayersModel.compile(modelCompileArgs).");
     }
     const outputShapes = [];
-    for (let i2 = 0; i2 < this.feedOutputShapes.length; ++i2) {
-      const outputShape = this.feedOutputShapes[i2];
-      const lossFn = this.feedLossFns[i2];
+    for (let i = 0; i < this.feedOutputShapes.length; ++i) {
+      const outputShape = this.feedOutputShapes[i];
+      const lossFn = this.feedLossFns[i];
       if (lossFn === sparseCategoricalCrossentropy) {
         outputShapes.push(outputShape.slice(0, outputShape.length - 1).concat([1]));
       } else {
@@ -23859,8 +23860,8 @@ var LayersModel = class extends Container {
     if (classWeight != null) {
       const classWeights = standardizeClassWeights(classWeight, this.outputNames);
       standardSampleWeights = [];
-      for (let i2 = 0; i2 < classWeights.length; ++i2) {
-        standardSampleWeights.push(await standardizeWeights(standardYs[i2], null, classWeights[i2]));
+      for (let i = 0; i < classWeights.length; ++i) {
+        standardSampleWeights.push(await standardizeWeights(standardYs[i], null, classWeights[i]));
       }
     }
     return [standardXs, standardYs, standardSampleWeights];
@@ -23884,17 +23885,17 @@ var LayersModel = class extends Container {
           const insBatch = sliceArraysByIndices(ins, batchIds);
           const batchOuts = f(insBatch);
           if (batchIndex === 0) {
-            for (let i2 = 0; i2 < batchOuts.length; ++i2) {
+            for (let i = 0; i < batchOuts.length; ++i) {
               outs.push(scalar(0));
             }
           }
-          for (let i2 = 0; i2 < batchOuts.length; ++i2) {
-            const batchOut = batchOuts[i2];
-            outs[i2] = add2(outs[i2], mul(batchEnd - batchStart, batchOut));
+          for (let i = 0; i < batchOuts.length; ++i) {
+            const batchOut = batchOuts[i];
+            outs[i] = add2(outs[i], mul(batchEnd - batchStart, batchOut));
           }
         }
-        for (let i2 = 0; i2 < outs.length; ++i2) {
-          outs[i2] = div(outs[i2], numSamples);
+        for (let i = 0; i < outs.length; ++i) {
+          outs[i] = div(outs[i], numSamples);
         }
       }
       return outs;
@@ -23903,11 +23904,11 @@ var LayersModel = class extends Container {
   getDedupedMetricsNames() {
     const outLabels = this.metricsNames;
     const dedupedOutLabels = [];
-    for (let i2 = 0; i2 < outLabels.length; ++i2) {
-      const label = outLabels[i2];
+    for (let i = 0; i < outLabels.length; ++i) {
+      const label = outLabels[i];
       let newLabel = label;
       if (count(outLabels, label) > 1) {
-        const dupIndex = count(outLabels.slice(0, i2), label);
+        const dupIndex = count(outLabels.slice(0, i), label);
         newLabel += `_${dupIndex}`;
       }
       dedupedOutLabels.push(newLabel);
@@ -23923,33 +23924,33 @@ var LayersModel = class extends Container {
       const metricsValues = [];
       const totalLossFunction = () => {
         const feeds = [];
-        for (let i2 = 0; i2 < this.inputs.length; ++i2) {
-          feeds.push({ key: this.inputs[i2], value: inputs[i2] });
+        for (let i = 0; i < this.inputs.length; ++i) {
+          feeds.push({ key: this.inputs[i], value: inputs[i] });
         }
         const feedDict = new FeedDict(feeds);
         const outputs = execute(this.outputs, feedDict, { "training": true });
         let totalLoss;
-        for (let i2 = 0; i2 < this.lossFunctions.length; ++i2) {
-          const lossFunction = this.lossFunctions[i2];
-          let loss = lossFunction(targets[i2], outputs[i2]);
-          if (sampleWeights[i2] != null) {
-            loss = computeWeightedLoss2(loss, sampleWeights[i2]);
+        for (let i = 0; i < this.lossFunctions.length; ++i) {
+          const lossFunction = this.lossFunctions[i];
+          let loss = lossFunction(targets[i], outputs[i]);
+          if (sampleWeights[i] != null) {
+            loss = computeWeightedLoss2(loss, sampleWeights[i]);
           }
           const meanLoss = mean(loss);
           lossValues.push(meanLoss);
-          if (i2 === 0) {
+          if (i === 0) {
             totalLoss = loss;
           } else {
             totalLoss = add2(totalLoss, loss);
           }
         }
-        for (let i2 = 0; i2 < this.metricsTensors.length; ++i2) {
+        for (let i = 0; i < this.metricsTensors.length; ++i) {
           let weightedMetric;
-          if (this.outputs.length > 1 && i2 < this.outputs.length) {
-            weightedMetric = lossValues[i2];
+          if (this.outputs.length > 1 && i < this.outputs.length) {
+            weightedMetric = lossValues[i];
           } else {
-            const metric = this.metricsTensors[i2][0];
-            const outputIndex = this.metricsTensors[i2][1];
+            const metric = this.metricsTensors[i][0];
+            const outputIndex = this.metricsTensors[i][1];
             weightedMetric = mean(metric(targets[outputIndex], outputs[outputIndex]));
           }
           keep(weightedMetric);
@@ -23975,24 +23976,24 @@ var LayersModel = class extends Container {
         const inputs = data.slice(0, this.inputs.length);
         const targets = data.slice(this.inputs.length, this.inputs.length + this.outputs.length);
         const feeds = [];
-        for (let i2 = 0; i2 < this.inputs.length; ++i2) {
-          feeds.push({ key: this.inputs[i2], value: inputs[i2] });
+        for (let i = 0; i < this.inputs.length; ++i) {
+          feeds.push({ key: this.inputs[i], value: inputs[i] });
         }
         const feedDict = new FeedDict(feeds);
         const outputs = execute(this.outputs, feedDict);
-        for (let i2 = 0; i2 < this.lossFunctions.length; ++i2) {
-          const lossFunction = this.lossFunctions[i2];
-          const loss = mean(lossFunction(targets[i2], outputs[i2]));
-          if (i2 === 0) {
+        for (let i = 0; i < this.lossFunctions.length; ++i) {
+          const lossFunction = this.lossFunctions[i];
+          const loss = mean(lossFunction(targets[i], outputs[i]));
+          if (i === 0) {
             totalLoss = loss;
           } else {
             totalLoss = add2(totalLoss, loss);
           }
           valOutputs.push(totalLoss);
         }
-        for (let i2 = 0; i2 < this.metricsTensors.length; ++i2) {
-          const metric = this.metricsTensors[i2][0];
-          const outputIndex = this.metricsTensors[i2][1];
+        for (let i = 0; i < this.metricsTensors.length; ++i) {
+          const metric = this.metricsTensors[i][0];
+          const outputIndex = this.metricsTensors[i][1];
           const meanMetric = mean(metric(targets[outputIndex], outputs[outputIndex]));
           valOutputs.push(meanMetric);
         }
@@ -24027,11 +24028,11 @@ var LayersModel = class extends Container {
     const trainableOnly = config3 != null && config3.trainableOnly;
     const weights = trainableOnly ? this.trainableWeights : this.weights;
     const weightValues = this.getWeights(trainableOnly);
-    for (let i2 = 0; i2 < weights.length; ++i2) {
-      if (trainableOnly && !weights[i2].trainable) {
+    for (let i = 0; i < weights.length; ++i) {
+      if (trainableOnly && !weights[i].trainable) {
         continue;
       }
-      namedWeights.push({ name: weights[i2].originalName, tensor: weightValues[i2] });
+      namedWeights.push({ name: weights[i].originalName, tensor: weightValues[i] });
     }
     return namedWeights;
   }
@@ -24828,15 +24829,15 @@ var PReLU = class extends Layer {
     inputShape = getExactlyOneShape(inputShape);
     const paramShape = inputShape.slice(1);
     if (this.sharedAxes != null) {
-      for (const i2 of this.sharedAxes) {
-        paramShape[i2 - 1] = 1;
+      for (const i of this.sharedAxes) {
+        paramShape[i - 1] = 1;
       }
     }
     this.alpha = this.addWeight("alpha", paramShape, "float32", this.alphaInitializer, this.alphaRegularizer, true, this.alphaConstraint);
     const axes = {};
     if (this.sharedAxes != null) {
-      for (let i2 = 1; i2 < inputShape.length; ++i2) {
-        axes[i2] = inputShape[i2];
+      for (let i = 1; i < inputShape.length; ++i) {
+        axes[i] = inputShape[i];
       }
     }
     this.inputSpec = [new InputSpec({
@@ -24942,17 +24943,17 @@ var Softmax3 = class extends Layer {
 };
 Softmax3.className = "Softmax";
 serialization_exports.registerClass(Softmax3);
-function normalizeArray(value, n2, name) {
+function normalizeArray(value, n, name) {
   if (typeof value === "number") {
-    return pyListRepeat(value, n2);
+    return pyListRepeat(value, n);
   } else {
-    if (value.length !== n2) {
-      throw new ValueError(`The ${name} argument must be an integer or tuple of ${n2} integers. Received: ${value.length} elements.`);
+    if (value.length !== n) {
+      throw new ValueError(`The ${name} argument must be an integer or tuple of ${n} integers. Received: ${value.length} elements.`);
     }
-    for (let i2 = 0; i2 < n2; ++i2) {
-      const singleValue = value[i2];
+    for (let i = 0; i < n; ++i) {
+      const singleValue = value[i];
       if (!isInteger(singleValue)) {
-        throw new ValueError(`The ${name} argument must be an integer or tuple of ${n2} integers. Received: ${JSON.stringify(value)} including a non-integer number ${singleValue}`);
+        throw new ValueError(`The ${name} argument must be an integer or tuple of ${n} integers. Received: ${JSON.stringify(value)} including a non-integer number ${singleValue}`);
       }
     }
     return value;
@@ -25211,8 +25212,8 @@ var Conv = class extends BaseConv {
     inputShape = getExactlyOneShape(inputShape);
     const newSpace = [];
     const space = this.dataFormat === "channelsLast" ? inputShape.slice(1, inputShape.length - 1) : inputShape.slice(2);
-    for (let i2 = 0; i2 < space.length; ++i2) {
-      const newDim = convOutputLength(space[i2], this.kernelSize[i2], this.padding, this.strides[i2], typeof this.dilationRate === "number" ? this.dilationRate : this.dilationRate[i2]);
+    for (let i = 0; i < space.length; ++i) {
+      const newDim = convOutputLength(space[i], this.kernelSize[i], this.padding, this.strides[i], typeof this.dilationRate === "number" ? this.dilationRate : this.dilationRate[i]);
       newSpace.push(newDim);
     }
     let outputShape = [inputShape[0]];
@@ -25529,7 +25530,7 @@ var SeparableConv = class extends Conv {
     const inputDim = inputShape[channelAxis];
     const depthwiseKernelShape = this.kernelSize.concat([inputDim, this.depthMultiplier]);
     const pointwiseKernelShape = [];
-    for (let i2 = 0; i2 < this.rank; ++i2) {
+    for (let i = 0; i < this.rank; ++i) {
       pointwiseKernelShape.push(1);
     }
     pointwiseKernelShape.push(inputDim * this.depthMultiplier, this.filters);
@@ -25879,8 +25880,8 @@ function rnn(stepFunction, inputs, initialStates, goBackwards = false, mask2, co
           const stepMask = perStepMasks[t2];
           const negStepMask = sub(onesLike(stepMask), stepMask);
           const output = add2(mul(stepOutputs[0], stepMask), mul(states[0], negStepMask));
-          const newStates = states.map((state, i2) => {
-            return add2(mul(stepOutputs[1][i2], stepMask), mul(state, negStepMask));
+          const newStates = states.map((state, i) => {
+            return add2(mul(stepOutputs[1][i], stepMask), mul(state, negStepMask));
           });
           return { output, newStates };
         });
@@ -25970,7 +25971,7 @@ var RNN = class extends Layer {
       }
       const outputMask = this.returnSequences ? mask2 : null;
       if (this.returnState) {
-        const stateMask = this.states.map((s2) => null);
+        const stateMask = this.states.map((s) => null);
         return [outputMask].concat(stateMask);
       } else {
         return outputMask;
@@ -25981,7 +25982,7 @@ var RNN = class extends Layer {
     if (this.states_ == null) {
       const numStates = Array.isArray(this.cell.stateSize) ? this.cell.stateSize.length : 1;
       const output = [];
-      for (let i2 = 0; i2 < numStates; ++i2) {
+      for (let i = 0; i < numStates; ++i) {
         output.push(null);
       }
       return output;
@@ -25989,8 +25990,8 @@ var RNN = class extends Layer {
       return this.states_;
     }
   }
-  set states(s2) {
-    this.states_ = s2;
+  set states(s) {
+    this.states_ = s;
   }
   build(inputShape) {
     const constantShape = null;
@@ -26434,7 +26435,7 @@ var GRUCell = class extends RNNCell {
       const dpMask = this.dropoutMask;
       const recDpMask = this.recurrentDropoutMask;
       let z;
-      let r2;
+      let r;
       let hh;
       if (0 < this.dropout && this.dropout < 1) {
         inputs = mul(inputs, dpMask[0]);
@@ -26452,8 +26453,8 @@ var GRUCell = class extends RNNCell {
       const [xZ, xR, xH] = split(matrixX, 3, matrixX.rank - 1);
       const [recurrentZ, recurrentR] = split(matrixInner, 2, matrixInner.rank - 1);
       z = this.recurrentActivation.apply(add2(xZ, recurrentZ));
-      r2 = this.recurrentActivation.apply(add2(xR, recurrentR));
-      const recurrentH = dot2(mul(r2, hTMinus1), rk2);
+      r = this.recurrentActivation.apply(add2(xR, recurrentR));
+      const recurrentH = dot2(mul(r, hTMinus1), rk2);
       hh = this.activation.apply(add2(xH, recurrentH));
       const h = add2(mul(z, hTMinus1), mul(add2(1, neg(z)), hh));
       return [h, h];
@@ -26611,7 +26612,7 @@ var LSTMCell = class extends RNNCell {
       }
       const dpMask = this.dropoutMask;
       const recDpMask = this.recurrentDropoutMask;
-      let i2;
+      let i;
       let f;
       let c;
       let o;
@@ -26627,9 +26628,9 @@ var LSTMCell = class extends RNNCell {
         z = biasAdd(z, this.bias.read());
       }
       const [z0, z1, z2, z3] = split(z, 4, z.rank - 1);
-      i2 = this.recurrentActivation.apply(z0);
+      i = this.recurrentActivation.apply(z0);
       f = this.recurrentActivation.apply(z1);
-      c = add2(mul(f, cTMinus1), mul(i2, this.activation.apply(z2)));
+      c = add2(mul(f, cTMinus1), mul(i, this.activation.apply(z2)));
       o = this.recurrentActivation.apply(z3);
       const h = mul(o, this.activation.apply(c));
       return [h, h, c];
@@ -26726,10 +26727,10 @@ var StackedRNNCells = class extends RNNCell {
       nestedStates.reverse();
       const newNestedStates = [];
       let callInputs;
-      for (let i2 = 0; i2 < this.cells.length; ++i2) {
-        const cell = this.cells[i2];
-        states = nestedStates[i2];
-        if (i2 === 0) {
+      for (let i = 0; i < this.cells.length; ++i) {
+        const cell = this.cells[i];
+        states = nestedStates[i];
+        if (i === 0) {
           callInputs = [inputs[0]].concat(states);
         } else {
           callInputs = [callInputs[0]].concat(states);
@@ -26750,8 +26751,8 @@ var StackedRNNCells = class extends RNNCell {
     }
     inputShape = inputShape;
     let outputDim;
-    this.cells.forEach((cell, i2) => {
-      nameScope(`RNNCell_${i2}`, () => {
+    this.cells.forEach((cell, i) => {
+      nameScope(`RNNCell_${i}`, () => {
         cell.build(inputShape);
         if (Array.isArray(cell.stateSize)) {
           outputDim = cell.stateSize[0];
@@ -26818,8 +26819,8 @@ var StackedRNNCells = class extends RNNCell {
     for (const cell of this.cells) {
       const numParams = cell.weights.length;
       const inputWeights = weights.splice(numParams);
-      for (let i2 = 0; i2 < cell.weights.length; ++i2) {
-        tuples.push([cell.weights[i2], inputWeights[i2]]);
+      for (let i = 0; i < cell.weights.length; ++i) {
+        tuples.push([cell.weights[i], inputWeights[i]]);
       }
     }
     batchSetValue(tuples);
@@ -26837,15 +26838,15 @@ function generateDropoutMask(args) {
   const masks = Array(count22).fill(void 0).map(createMask);
   return masks.map((m) => keep(m.clone()));
 }
-var __rest = function(s2, e2) {
+var __rest = function(s, e) {
   var t2 = {};
-  for (var p2 in s2)
-    if (Object.prototype.hasOwnProperty.call(s2, p2) && e2.indexOf(p2) < 0)
-      t2[p2] = s2[p2];
-  if (s2 != null && typeof Object.getOwnPropertySymbols === "function")
-    for (var i2 = 0, p2 = Object.getOwnPropertySymbols(s2); i2 < p2.length; i2++) {
-      if (e2.indexOf(p2[i2]) < 0 && Object.prototype.propertyIsEnumerable.call(s2, p2[i2]))
-        t2[p2[i2]] = s2[p2[i2]];
+  for (var p2 in s)
+    if (Object.prototype.hasOwnProperty.call(s, p2) && e.indexOf(p2) < 0)
+      t2[p2] = s[p2];
+  if (s != null && typeof Object.getOwnPropertySymbols === "function")
+    for (var i = 0, p2 = Object.getOwnPropertySymbols(s); i < p2.length; i++) {
+      if (e.indexOf(p2[i]) < 0 && Object.prototype.propertyIsEnumerable.call(s, p2[i]))
+        t2[p2[i]] = s[p2[i]];
     }
   return t2;
 };
@@ -27076,9 +27077,9 @@ var ConvLSTM2DCell = class extends LSTMCell {
       hF = this.recurrentConv(hF, recKernelF);
       hC = this.recurrentConv(hC, recKernelC);
       hO = this.recurrentConv(hO, recKernelO);
-      const i2 = this.recurrentActivation.apply(add2(xI, hI));
+      const i = this.recurrentActivation.apply(add2(xI, hI));
       const f = this.recurrentActivation.apply(add2(xF, hF));
-      const c = add2(mul(f, cTMinus1), mul(i2, this.activation.apply(add2(xC, hC))));
+      const c = add2(mul(f, cTMinus1), mul(i, this.activation.apply(add2(xC, hC))));
       const h = mul(this.recurrentActivation.apply(add2(xO, hO)), this.activation.apply(c));
       return [h, h, c];
     });
@@ -27134,8 +27135,8 @@ var Dropout = class extends Layer {
     }
     const inputShape = input2.shape;
     const noiseShape = [];
-    for (let i2 = 0; i2 < this.noiseShape.length; ++i2) {
-      noiseShape.push(this.noiseShape[i2] == null ? inputShape[i2] : this.noiseShape[i2]);
+    for (let i = 0; i < this.noiseShape.length; ++i) {
+      noiseShape.push(this.noiseShape[i] == null ? inputShape[i] : this.noiseShape[i]);
     }
     return noiseShape;
   }
@@ -27292,8 +27293,8 @@ var Flatten = class extends Layer {
       let input2 = getExactlyOneTensor(inputs);
       if (this.dataFormat === "channelsFirst" && input2.rank > 1) {
         const permutation = [0];
-        for (let i2 = 2; i2 < input2.rank; ++i2) {
-          permutation.push(i2);
+        for (let i = 2; i < input2.rank; ++i) {
+          permutation.push(i);
         }
         permutation.push(1);
         input2 = transpose(input2, permutation);
@@ -27365,9 +27366,9 @@ var Reshape2 = class extends Layer {
   constructor(args) {
     super(args);
     this.targetShape = args.targetShape;
-    for (let i2 = 0; i2 < this.targetShape.length; ++i2) {
-      if (this.isUnknown(this.targetShape[i2])) {
-        this.targetShape[i2] = null;
+    for (let i = 0; i < this.targetShape.length; ++i) {
+      if (this.isUnknown(this.targetShape[i])) {
+        this.targetShape[i] = null;
       }
     }
   }
@@ -27379,11 +27380,11 @@ var Reshape2 = class extends Layer {
     const finalShape = outputShape.slice();
     let known = 1;
     let unknown = null;
-    for (let i2 = 0; i2 < finalShape.length; ++i2) {
-      const dim = finalShape[i2];
+    for (let i = 0; i < finalShape.length; ++i) {
+      const dim = finalShape[i];
       if (this.isUnknown(dim)) {
         if (unknown === null) {
-          unknown = i2;
+          unknown = i;
         } else {
           throw new ValueError("Can only specifiy one unknown dimension.");
         }
@@ -27404,8 +27405,8 @@ var Reshape2 = class extends Layer {
   }
   computeOutputShape(inputShape) {
     let anyUnknownDims = false;
-    for (let i2 = 0; i2 < inputShape.length; ++i2) {
-      if (this.isUnknown(inputShape[i2])) {
+    for (let i = 0; i < inputShape.length; ++i) {
+      if (this.isUnknown(inputShape[i])) {
         anyUnknownDims = true;
         break;
       }
@@ -27456,8 +27457,8 @@ var Permute = class extends Layer {
   computeOutputShape(inputShape) {
     inputShape = getExactlyOneShape(inputShape);
     const outputShape = inputShape.slice();
-    this.dims.forEach((dim, i2) => {
-      outputShape[i2 + 1] = inputShape[dim];
+    this.dims.forEach((dim, i) => {
+      outputShape[i + 1] = inputShape[dim];
     });
     return outputShape;
   }
@@ -27566,16 +27567,16 @@ var Embedding = class extends Layer {
     if (inLens.length !== inputShape.length - 1) {
       throw new ValueError(`"inputLength" is ${this.inputLength}, but received input shape has shape ${inputShape}`);
     } else {
-      let i2 = 0;
+      let i = 0;
       for (let k = 0; k < inLens.length; ++k) {
         const s1 = inLens[k];
         const s2 = inputShape[k + 1];
         if (s1 != null && s2 != null && s1 !== s2) {
           throw new ValueError(`"inputLength" is ${this.inputLength}, but received input shape has shape ${inputShape}`);
         } else if (s1 == null) {
-          inLens[i2] = s2;
+          inLens[i] = s2;
         }
-        i2++;
+        i++;
       }
     }
     return [inputShape[0], ...inLens, this.outputDim];
@@ -27627,19 +27628,19 @@ var Merge = class extends Layer {
     }
     const outputShape = shape1.slice(0, shape1.length - shape2.length);
     for (let k = 0; k < shape2.length; ++k) {
-      const i2 = shape1[shape1.length - shape2.length + k];
+      const i = shape1[shape1.length - shape2.length + k];
       const j = shape2[k];
-      if (i2 == null || j == null || i2 < 0 || j < 0) {
+      if (i == null || j == null || i < 0 || j < 0) {
         outputShape.push(null);
-      } else if (i2 === 1) {
+      } else if (i === 1) {
         outputShape.push(j);
       } else if (j === 1) {
-        outputShape.push(i2);
+        outputShape.push(i);
       } else {
-        if (i2 !== j) {
+        if (i !== j) {
           throw new ValueError("Operands could not be broadcast together with shapes " + JSON.stringify(shape1) + " " + JSON.stringify(shape2));
         }
-        outputShape.push(i2);
+        outputShape.push(i);
       }
     }
     return outputShape;
@@ -27663,8 +27664,8 @@ var Merge = class extends Layer {
       throw new ValueError(`Can not merge tensors with different batch sizes. Got tensors with shapes: ${JSON.stringify(inputShape)}.`);
     }
     let outputShape = inputShape[0] == null ? null : inputShape[0].slice(1);
-    for (let i2 = 1; i2 < inputShape.length; ++i2) {
-      const shape = inputShape[i2] == null ? null : inputShape[i2].slice(1);
+    for (let i = 1; i < inputShape.length; ++i) {
+      const shape = inputShape[i] == null ? null : inputShape[i].slice(1);
       outputShape = this.computeElementwiseOpOutputShape(outputShape, shape);
     }
     const allRanks = inputShape.map((shape) => shape.length);
@@ -27740,8 +27741,8 @@ var Merge = class extends Layer {
     } else {
       outputShape = inputShape[0].slice(1);
     }
-    for (let i2 = 1; i2 < inputShape.length; ++i2) {
-      const shape = inputShape[i2] == null ? null : inputShape[i2].slice(1);
+    for (let i = 1; i < inputShape.length; ++i) {
+      const shape = inputShape[i] == null ? null : inputShape[i].slice(1);
       outputShape = this.computeElementwiseOpOutputShape(outputShape, shape);
     }
     let batchSizes = [];
@@ -27777,8 +27778,8 @@ var Merge = class extends Layer {
       }
       mask2 = mask2.map((m) => m == null ? m : expandDims(m, 0));
       let output = mask2[0];
-      for (let i2 = 1; i2 < mask2.length - 1; ++i2) {
-        output = logicalAnd(output, mask2[i2]);
+      for (let i = 1; i < mask2.length - 1; ++i) {
+        output = logicalAnd(output, mask2[i]);
       }
       return output;
     });
@@ -27791,8 +27792,8 @@ var Add2 = class extends Merge {
   mergeFunction(inputs) {
     return tidy(() => {
       let output = inputs[0].clone();
-      for (let i2 = 1; i2 < inputs.length; ++i2) {
-        output = add2(output, inputs[i2]);
+      for (let i = 1; i < inputs.length; ++i) {
+        output = add2(output, inputs[i]);
       }
       return output;
     });
@@ -27807,8 +27808,8 @@ var Multiply2 = class extends Merge {
   mergeFunction(inputs) {
     return tidy(() => {
       let output = inputs[0].clone();
-      for (let i2 = 1; i2 < inputs.length; ++i2) {
-        output = mul(output, inputs[i2]);
+      for (let i = 1; i < inputs.length; ++i) {
+        output = mul(output, inputs[i]);
       }
       return output;
     });
@@ -27823,8 +27824,8 @@ var Average = class extends Merge {
   mergeFunction(inputs) {
     return tidy(() => {
       let output = inputs[0].clone();
-      for (let i2 = 1; i2 < inputs.length; ++i2) {
-        output = add2(output, inputs[i2]);
+      for (let i = 1; i < inputs.length; ++i) {
+        output = add2(output, inputs[i]);
       }
       return mul(1 / inputs.length, output);
     });
@@ -27839,8 +27840,8 @@ var Maximum2 = class extends Merge {
   mergeFunction(inputs) {
     return tidy(() => {
       let output = inputs[0];
-      for (let i2 = 1; i2 < inputs.length; ++i2) {
-        output = maximum(output, inputs[i2]);
+      for (let i = 1; i < inputs.length; ++i) {
+        output = maximum(output, inputs[i]);
       }
       return output;
     });
@@ -27855,8 +27856,8 @@ var Minimum2 = class extends Merge {
   mergeFunction(inputs) {
     return tidy(() => {
       let output = inputs[0];
-      for (let i2 = 1; i2 < inputs.length; ++i2) {
-        output = minimum(output, inputs[i2]);
+      for (let i = 1; i < inputs.length; ++i) {
+        output = minimum(output, inputs[i]);
       }
       return output;
     });
@@ -27891,8 +27892,8 @@ var Concatenate = class extends Merge {
       return;
     }
     const shapeSet = [];
-    for (let i2 = 0; i2 < inputShape.length; ++i2) {
-      const shapeWithoutConcatAxis = inputShape[i2].slice();
+    for (let i = 0; i < inputShape.length; ++i) {
+      const shapeWithoutConcatAxis = inputShape[i].slice();
       shapeWithoutConcatAxis.splice(this.axis, 1);
       let exists = false;
       for (const shape of shapeSet) {
@@ -27955,13 +27956,13 @@ var Concatenate = class extends Merge {
         return null;
       }
       const outputMasks = [];
-      for (let i2 = 0; i2 < inputs.length; ++i2) {
-        if (mask2[i2] == null) {
-          outputMasks.push(cast(onesLike(inputs[i2]), "bool"));
-        } else if (mask2[i2].rank < inputs[i2].rank) {
-          outputMasks.push(expandDims(mask2[i2], -1));
+      for (let i = 0; i < inputs.length; ++i) {
+        if (mask2[i] == null) {
+          outputMasks.push(cast(onesLike(inputs[i]), "bool"));
+        } else if (mask2[i].rank < inputs[i].rank) {
+          outputMasks.push(expandDims(mask2[i], -1));
         } else {
-          outputMasks.push(mask2[i2]);
+          outputMasks.push(mask2[i]);
         }
       }
       const concatenatedMasks = concat(outputMasks, this.axis);
@@ -28008,14 +28009,14 @@ function batchDot(x, y, axes) {
     if (xNDim > yNDim) {
       diff = xNDim - yNDim;
       const diffShape = [];
-      for (let i2 = 0; i2 < diff; ++i2) {
+      for (let i = 0; i < diff; ++i) {
         diffShape.push(1);
       }
       y = reshape(y, y.shape.concat(diffShape));
     } else if (yNDim > xNDim) {
       diff = yNDim - xNDim;
       const diffShape = [];
-      for (let i2 = 0; i2 < diff; ++i2) {
+      for (let i = 0; i < diff; ++i) {
         diffShape.push(1);
       }
       x = reshape(x, x.shape.concat(diffShape));
@@ -28042,8 +28043,8 @@ function batchDot(x, y, axes) {
         idx = xNDim - 1;
       }
       const squeezeAxes = [];
-      for (let i2 = idx; i2 < idx + diff; ++i2) {
-        squeezeAxes.push(i2);
+      for (let i = idx; i < idx + diff; ++i) {
+        squeezeAxes.push(i);
       }
       out = squeeze(out, squeezeAxes);
     }
@@ -28086,7 +28087,7 @@ var Dot = class extends Merge {
         interpretAxis(this.axes, x2.shape.length)
       ];
     } else {
-      axes = this.axes.map((axis, i2) => interpretAxis(axis, inputs[i2].shape.length));
+      axes = this.axes.map((axis, i) => interpretAxis(axis, inputs[i].shape.length));
     }
     if (this.normalize) {
       x1 = l2Normalize(x1, axes[0]);
@@ -28433,9 +28434,9 @@ var LayerNormalization = class extends Layer {
     if (typeof this.axis === "number") {
       this.axis = [this.axis];
     }
-    for (let i2 = 0; i2 < this.axis.length; ++i2) {
-      if (this.axis[i2] < 0) {
-        this.axis[i2] += nDims;
+    for (let i = 0; i < this.axis.length; ++i) {
+      if (this.axis[i] < 0) {
+        this.axis[i] += nDims;
       }
     }
     for (const axis of this.axis) {
@@ -28482,13 +28483,13 @@ var LayerNormalization = class extends Layer {
       let offset = this.center ? broadcast(this.beta.read()) : null;
       const momentsTiling = [];
       const scaleOffsetTiling = [];
-      for (let i2 = 0; i2 < nDims; ++i2) {
-        if (this.axis.indexOf(i2) !== -1) {
-          momentsTiling.push(inputShape[i2]);
+      for (let i = 0; i < nDims; ++i) {
+        if (this.axis.indexOf(i) !== -1) {
+          momentsTiling.push(inputShape[i]);
           scaleOffsetTiling.push(1);
         } else {
           momentsTiling.push(1);
-          scaleOffsetTiling.push(inputShape[i2]);
+          scaleOffsetTiling.push(inputShape[i]);
         }
       }
       mean5 = tile(mean5, momentsTiling);
@@ -29918,9 +29919,9 @@ function getPadding(node2, tensorMap, context) {
   if (pad3 === "explicit") {
     pad3 = getParamValue("explicitPaddings", node2, tensorMap, context);
     const explicitPadding = [[0, 0], [0, 0], [0, 0], [0, 0]];
-    for (let i2 = 0; i2 < 4; i2++) {
-      explicitPadding[i2][0] = pad3[i2 * 2];
-      explicitPadding[i2][1] = pad3[i2 * 2 + 1];
+    for (let i = 0; i < 4; i++) {
+      explicitPadding[i][0] = pad3[i * 2];
+      explicitPadding[i][1] = pad3[i * 2 + 1];
     }
     return explicitPadding;
   }
@@ -36291,8 +36292,8 @@ function decodeBase64(text) {
     throw new Error("Unable to decode base64 in this environment. Missing built-in atob() or Buffer()");
   }
 }
-function parseStringParam(s2, keepCase) {
-  const value = Array.isArray(s2) ? String.fromCharCode.apply(null, s2) : decodeBase64(s2);
+function parseStringParam(s, keepCase) {
+  const value = Array.isArray(s) ? String.fromCharCode.apply(null, s) : decodeBase64(s);
   return keepCase ? value : value.toLowerCase();
 }
 function getStringParam(attrs, name, def, keepCase = false) {
@@ -36825,9 +36826,9 @@ function assertShapesMatchAllowUndefinedSize(shapeA, shapeB, errorMessagePrefix
     return;
   }
   util_exports.assert(shapeA.length === shapeB.length, () => errorMessagePrefix + ` Shapes ${shapeA} and ${shapeB} must match`);
-  for (let i2 = 0; i2 < shapeA.length; i2++) {
-    const dim0 = shapeA[i2];
-    const dim1 = shapeB[i2];
+  for (let i = 0; i < shapeA.length; i++) {
+    const dim0 = shapeA[i];
+    const dim1 = shapeB[i];
     util_exports.assert(dim0 < 0 || dim1 < 0 || dim0 === dim1, () => errorMessagePrefix + ` Shapes ${shapeA} and ${shapeB} must match`);
   }
 }
@@ -36864,13 +36865,13 @@ function mergeElementShape(elementShapeA, elementShapeB) {
     throw new Error(`Incompatible ranks during merge: ${elementShapeA} vs. ${elementShapeB}`);
   }
   const result = [];
-  for (let i2 = 0; i2 < elementShapeA.length; ++i2) {
-    const dim0 = elementShapeA[i2];
-    const dim1 = elementShapeB[i2];
+  for (let i = 0; i < elementShapeA.length; ++i) {
+    const dim0 = elementShapeA[i];
+    const dim1 = elementShapeB[i];
     if (dim0 >= 0 && dim1 >= 0 && dim0 !== dim1) {
       throw new Error(`Incompatible shape during merge: ${elementShapeA} vs. ${elementShapeB}`);
     }
-    result[i2] = dim0 >= 0 ? dim0 : dim1;
+    result[i] = dim0 >= 0 ? dim0 : dim1;
   }
   return result;
 }
@@ -36958,7 +36959,7 @@ var TensorArray = class {
     if (indices.length !== tensors.length) {
       throw new Error(`TensorArray ${this.name}: could not write multiple tensors,because the index size: ${indices.length} is not the same as tensors size: ${tensors.length}.`);
     }
-    indices.forEach((i2, index2) => this.write(i2, tensors[index2]));
+    indices.forEach((i, index2) => this.write(i, tensors[index2]));
   }
   gather(indices, dtype) {
     if (!!dtype && dtype !== this.dtype) {
@@ -36966,8 +36967,8 @@ var TensorArray = class {
     }
     if (!indices) {
       indices = [];
-      for (let i2 = 0; i2 < this.size(); i2++) {
-        indices.push(i2);
+      for (let i = 0; i < this.size(); i++) {
+        indices.push(i);
       }
     } else {
       indices = indices.slice(0, this.size());
@@ -36987,8 +36988,8 @@ var TensorArray = class {
       return tensor([], [0].concat(this.elementShape));
     }
     const indices = [];
-    for (let i2 = 0; i2 < this.size(); i2++) {
-      indices.push(i2);
+    for (let i = 0; i < this.size(); i++) {
+      indices.push(i);
     }
     const tensors = this.readMany(indices);
     assertShapesMatchAllowUndefinedSize(this.elementShape, tensors[0].shape, `TensorArray shape mismatch: tensor array shape (${this.elementShape}) vs first tensor shape (${tensors[0].shape})`);
@@ -37028,17 +37029,17 @@ var TensorArray = class {
     const tensors = [];
     tidy(() => {
       tensor2 = reshape(tensor2, [1, totalLength, elementPerRow]);
-      for (let i2 = 0; i2 < length.length; ++i2) {
-        const previousLength = i2 === 0 ? 0 : cumulativeLengths[i2 - 1];
+      for (let i = 0; i < length.length; ++i) {
+        const previousLength = i === 0 ? 0 : cumulativeLengths[i - 1];
         const indices2 = [0, previousLength, 0];
-        const sizes = [1, length[i2], elementPerRow];
-        tensors[i2] = reshape(slice(tensor2, indices2, sizes), this.elementShape);
+        const sizes = [1, length[i], elementPerRow];
+        tensors[i] = reshape(slice(tensor2, indices2, sizes), this.elementShape);
       }
       return tensors;
     });
     const indices = [];
-    for (let i2 = 0; i2 < length.length; i2++) {
-      indices[i2] = i2;
+    for (let i = 0; i < length.length; i++) {
+      indices[i] = i;
     }
     this.writeMany(indices, tensors);
   }
@@ -37126,8 +37127,8 @@ var TensorList = class {
     }
     const destTensorList = new TensorList([], this.elementShape, this.elementDtype, this.maxNumElements);
     destTensorList.tensors.length = size2;
-    for (let i2 = 0; i2 < Math.min(this.tensors.length, size2); ++i2) {
-      destTensorList.tensors[i2] = this.tensors[i2];
+    for (let i = 0; i < Math.min(this.tensors.length, size2); ++i) {
+      destTensorList.tensors[i] = this.tensors[i];
     }
     return destTensorList;
   }
@@ -37170,7 +37171,7 @@ var TensorList = class {
       return tensor([], [0].concat(outputElementShape));
     }
     return tidy(() => {
-      const tensors = indices.map((i2) => reshape(this.tensors[i2], outputElementShape));
+      const tensors = indices.map((i) => reshape(this.tensors[i], outputElementShape));
       return stack(tensors, 0);
     });
   }
@@ -37237,18 +37238,18 @@ function split2(tensor2, length, elementShape) {
   const tensors = tidy(() => {
     const tensors2 = [];
     tensor2 = reshape(tensor2, [1, totalLength, elementPerRow]);
-    for (let i2 = 0; i2 < length.length; ++i2) {
-      const previousLength = i2 === 0 ? 0 : cumulativeLengths[i2 - 1];
+    for (let i = 0; i < length.length; ++i) {
+      const previousLength = i === 0 ? 0 : cumulativeLengths[i - 1];
       const indices = [0, previousLength, 0];
-      const sizes = [1, length[i2], elementPerRow];
-      tensors2[i2] = reshape(slice(tensor2, indices, sizes), outputElementShape);
+      const sizes = [1, length[i], elementPerRow];
+      tensors2[i] = reshape(slice(tensor2, indices, sizes), outputElementShape);
     }
     tensor2.dispose();
     return tensors2;
   });
   const list = new TensorList([], elementShape, tensor2.dtype, length.length);
-  for (let i2 = 0; i2 < tensors.length; i2++) {
-    list.setItem(i2, tensors[i2]);
+  for (let i = 0; i < tensors.length; i++) {
+    list.setItem(i, tensors[i]);
   }
   return list;
 }
@@ -37855,8 +37856,8 @@ var executeOp8 = (node2, tensorMap, context, ops = ops_for_converter_exports) =>
       const summarize = getParamValue("summarize", node2, tensorMap, context);
       console.warn("The graph has a tf.print() operation,usually used for debugging, which slows down performance.");
       console.log(message);
-      for (let i2 = 0; i2 < data.length; i2++) {
-        console.log(Array.prototype.slice.call(data[i2].dataSync()).slice(0, summarize));
+      for (let i = 0; i < data.length; i++) {
+        console.log(Array.prototype.slice.call(data[i].dataSync()).slice(0, summarize));
       }
       return [input2];
     default:
@@ -37895,9 +37896,9 @@ var HashTable = class {
       const keysLength = $keys.length;
       const valuesLength = $values.length;
       util_exports.assert(keysLength === valuesLength, () => `The number of elements doesn't match, keys has ${keysLength} elements, the values has ${valuesLength} elements.`);
-      for (let i2 = 0; i2 < keysLength; i2++) {
-        const key = $keys[i2];
-        const value = $values[i2];
+      for (let i = 0; i < keysLength; i++) {
+        const key = $keys[i];
+        const value = $values[i];
         keep(value);
         this.tensorMap.set(key, value);
       }
@@ -37909,8 +37910,8 @@ var HashTable = class {
     const $keys = await keys.data();
     return tidy(() => {
       const result = [];
-      for (let i2 = 0; i2 < $keys.length; i2++) {
-        const key = $keys[i2];
+      for (let i = 0; i < $keys.length; i++) {
+        const key = $keys[i];
         const value = this.findWithDefault(key, defaultValue);
         result.push(value);
       }
@@ -38184,10 +38185,10 @@ var executeOp15 = (node2, tensorMap, context, ops = ops_for_converter_exports) =
   switch (node2.op) {
     case "ConcatV2":
     case "Concat": {
-      const n2 = getParamValue("n", node2, tensorMap, context);
+      const n = getParamValue("n", node2, tensorMap, context);
       const axis = getParamValue("axis", node2, tensorMap, context);
       let inputs = getParamValue("tensors", node2, tensorMap, context);
-      inputs = inputs.slice(0, n2);
+      inputs = inputs.slice(0, n);
       return [ops.concat(inputs, axis)];
     }
     case "Gather": {
@@ -38205,9 +38206,9 @@ var executeOp15 = (node2, tensorMap, context, ops = ops_for_converter_exports) =
     case "Reverse": {
       const dims = getParamValue("dims", node2, tensorMap, context);
       const axis = [];
-      for (let i2 = 0; i2 < dims.length; i2++) {
-        if (dims[i2]) {
-          axis.push(i2);
+      for (let i = 0; i < dims.length; i++) {
+        if (dims[i]) {
+          axis.push(i);
         }
       }
       const input2 = getParamValue("x", node2, tensorMap, context);
@@ -38488,8 +38489,8 @@ var ExecutionContext = class {
   }
   generateCurrentContextIds() {
     const names = [];
-    for (let i2 = 0; i2 < this.contexts.length - 1; i2++) {
-      const contexts2 = this.contexts.slice(0, this.contexts.length - i2);
+    for (let i = 0; i < this.contexts.length - 1; i++) {
+      const contexts2 = this.contexts.slice(0, this.contexts.length - i);
       names.push(this.contextIdforContexts(contexts2));
     }
     names.push("");
@@ -38754,7 +38755,7 @@ var GraphExecutor = class {
       throw new Error(`This execution contains the node '${dynamicNode.name}', which has the dynamic op '${dynamicNode.op}'. Please use model.executeAsync() instead. Alternatively, to avoid the dynamic ops, specify the inputs [${syncInputs}]`);
     }
     if (missingInputs.length > 0) {
-      const outNames = outputs.map((n2) => n2.name);
+      const outNames = outputs.map((n) => n.name);
       const inNames = Object.keys(inputs);
       throw new Error(`Cannot compute the outputs [${outNames}] from the provided inputs [${inNames}]. Missing the following inputs: [${missingInputs}]`);
     }
@@ -38769,15 +38770,15 @@ var GraphExecutor = class {
     this.checkOutputs(outputs);
     const inputNodes = names.map((name) => this.graph.nodes[parseNodeName(name)[0]]);
     const outputNodeNames = outputs.map((name) => parseNodeName(name)[0]);
-    let outputNodes2 = outputNodeNames.map((name) => this.graph.nodes[name]);
+    let outputNodes3 = outputNodeNames.map((name) => this.graph.nodes[name]);
     this.resetIntermediateTensors();
-    if (outputNodes2.length === 0) {
-      outputNodes2 = this._outputs;
+    if (outputNodes3.length === 0) {
+      outputNodes3 = this._outputs;
     }
-    const compilationKey = this.getCompilationKey(inputNodes, outputNodes2);
+    const compilationKey = this.getCompilationKey(inputNodes, outputNodes3);
     let orderedNodes = this.compiledMap.get(compilationKey);
     if (orderedNodes == null) {
-      orderedNodes = this.compile(inputs, outputNodes2);
+      orderedNodes = this.compile(inputs, outputNodes3);
       this.compiledMap.set(compilationKey, orderedNodes);
     }
     const tensorArrayMap = {};
@@ -38793,8 +38794,8 @@ var GraphExecutor = class {
       });
       const tensorsToKeep = this.getFrozenTensorIds(tensorsMap);
       const intermediateTensorConsumerCount = {};
-      for (let i2 = 0; i2 < orderedNodes.length; i2++) {
-        const node2 = orderedNodes[i2];
+      for (let i = 0; i < orderedNodes.length; i++) {
+        const node2 = orderedNodes[i];
         if (!tensorsMap[node2.name]) {
           const tensors = executeOp20(node2, tensorsMap, context, this._resourceManager);
           if (util_exports.isPromise(tensors)) {
@@ -38894,8 +38895,8 @@ var GraphExecutor = class {
     }
     try {
       this.keepTensorForDebug = env().getBool("KEEP_INTERMEDIATE_TENSORS");
-    } catch (e2) {
-      console.warn(e2.message);
+    } catch (e) {
+      console.warn(e.message);
     }
     this.resetIntermediateTensors();
     const context = new ExecutionContext(this.weightMap, tensorArrayMap, tensorListMap, this.functionExecutorMap);
@@ -38923,11 +38924,11 @@ var GraphExecutor = class {
     const names = Object.keys(inputs);
     const inputNodes = names.map((name) => this.graph.nodes[parseNodeName(name)[0]]);
     const outputNodeNames = outputNames.map((name) => parseNodeName(name)[0]);
-    let outputNodes2 = outputNodeNames.map((name) => this.graph.nodes[name]);
-    if (outputNodes2.length === 0) {
-      outputNodes2 = this._outputs;
+    let outputNodes3 = outputNodeNames.map((name) => this.graph.nodes[name]);
+    if (outputNodes3.length === 0) {
+      outputNodes3 = this._outputs;
     }
-    const { usedNodes, missingInputs, dynamicNode, syncInputs } = getExecutionSubgraph(inputs, outputNodes2, this.weightMap, this._initNodes);
+    const { usedNodes, missingInputs, dynamicNode, syncInputs } = getExecutionSubgraph(inputs, outputNodes3, this.weightMap, this._initNodes);
     const stack2 = [
       ...inputNodes,
       ...this.graph.weights,
@@ -38952,7 +38953,7 @@ var GraphExecutor = class {
     if (dynamicNode == null && !isFunctionExecution) {
       console.warn(`This model execution did not contain any nodes with control flow or dynamic output shapes. You can use model.execute() instead.`);
     }
-    const missingOutputs = outputNodes2.filter((node2) => !isControlFlow(node2) && !getTensor(node2.name, tensorsMap, context)).map((node2) => node2.name);
+    const missingOutputs = outputNodes3.filter((node2) => !isControlFlow(node2) && !getTensor(node2.name, tensorsMap, context)).map((node2) => node2.name);
     if (missingOutputs.length > 0) {
       let alternativeMsg = "";
       if (dynamicNode != null) {
@@ -39215,7 +39216,7 @@ var GraphModel = class {
     if (this.structuredOutputKeys) {
       const outputTensorsArray = outputTensors instanceof Tensor ? [outputTensors] : outputTensors;
       const outputTensorMap = {};
-      outputTensorsArray.forEach((outputTensor, i2) => outputTensorMap[this.structuredOutputKeys[i2]] = outputTensor);
+      outputTensorsArray.forEach((outputTensor, i) => outputTensorMap[this.structuredOutputKeys[i]] = outputTensor);
       return outputTensorMap;
     }
     return outputTensors;
@@ -39228,8 +39229,8 @@ var GraphModel = class {
     if (inputs.length !== this.inputNodes.length) {
       throw new Error(`Input tensor count mismatch,the graph model has ${this.inputNodes.length} placeholders, while there are ${inputs.length} input tensors.`);
     }
-    return this.inputNodes.reduce((map, inputName, i2) => {
-      map[inputName] = inputs[i2];
+    return this.inputNodes.reduce((map, inputName, i) => {
+      map[inputName] = inputs[i];
       return map;
     }, {});
   }
@@ -39553,8 +39554,8 @@ var GrowingRingBuffer = class extends RingBuffer {
     const newCapacity = this.capacity * 2;
     const newData = new Array(newCapacity);
     const len = this.length();
-    for (let i2 = 0; i2 < len; i2++) {
-      newData[i2] = this.get(this.wrap(this.begin + i2));
+    for (let i = 0; i < len; i++) {
+      newData[i] = this.get(this.wrap(this.begin + i));
     }
     this.data = newData;
     this.capacity = newCapacity;
@@ -39695,9 +39696,9 @@ var FunctionCallIterator = class extends LazyIterator {
   async next() {
     try {
       return this.nextFn();
-    } catch (e2) {
-      e2.message = `Error thrown while iterating through a dataset: ${e2.message}`;
-      throw e2;
+    } catch (e) {
+      e.message = `Error thrown while iterating through a dataset: ${e.message}`;
+      throw e;
     }
   }
 };
@@ -39859,8 +39860,8 @@ var ErrorHandlingLazyIterator = class extends LazyIterator {
     while (true) {
       try {
         return await this.upstream.next();
-      } catch (e2) {
-        if (!this.handler(e2)) {
+      } catch (e) {
+        if (!this.handler(e)) {
           return { value: null, done: true };
         }
       }
@@ -40250,8 +40251,8 @@ function zip(datasets) {
   }
   let size2;
   if (Array.isArray(datasets)) {
-    for (let i2 = 0; i2 < datasets.length; i2++) {
-      size2 = size2 == null ? datasets[i2].size : Math.min(size2, datasets[i2].size);
+    for (let i = 0; i < datasets.length; i++) {
+      size2 = size2 == null ? datasets[i].size : Math.min(size2, datasets[i].size);
     }
   } else if (datasets instanceof Object) {
     for (const ds in datasets) {
@@ -40402,13 +40403,13 @@ var CSVDataset = class extends Dataset {
     const values = this.parseRow(line);
     const features = {};
     const labels2 = {};
-    for (let i2 = 0; i2 < this.fullColumnNames.length; i2++) {
-      const key = this.fullColumnNames[i2];
+    for (let i = 0; i < this.fullColumnNames.length; i++) {
+      const key = this.fullColumnNames[i];
       const config3 = this.columnConfigs ? this.columnConfigs[key] : null;
       if (this.configuredColumnsOnly && !config3) {
         continue;
       } else {
-        const value = values[i2];
+        const value = values[i];
         let parsedValue = null;
         if (value === "") {
           if (config3 && config3.default !== void 0) {
@@ -40465,16 +40466,16 @@ var CSVDataset = class extends Dataset {
     let readOffset = 0;
     const readLength = line.length;
     let currentState = STATE_OUT;
-    for (let i2 = 0; i2 < readLength; i2++) {
+    for (let i = 0; i < readLength; i++) {
       switch (currentState) {
         case STATE_OUT:
-          switch (line.charAt(i2)) {
+          switch (line.charAt(i)) {
             case CODE_QUOTE:
-              readOffset = i2 + 1;
+              readOffset = i + 1;
               currentState = STATE_QUOTE;
               break;
             case this.delimiter:
-              readOffset = i2 + 1;
+              readOffset = i + 1;
               if (this.delimiter === " " && this.delimWhitespace) {
                 break;
               }
@@ -40483,22 +40484,22 @@ var CSVDataset = class extends Dataset {
               break;
             default:
               currentState = STATE_FIELD;
-              readOffset = i2;
+              readOffset = i;
               break;
           }
           break;
         case STATE_FIELD:
-          switch (line.charAt(i2)) {
+          switch (line.charAt(i)) {
             case this.delimiter:
-              result.push(line.substring(readOffset, i2));
+              result.push(line.substring(readOffset, i));
               currentState = STATE_OUT;
-              readOffset = i2 + 1;
+              readOffset = i + 1;
               break;
             default:
           }
           break;
         case STATE_QUOTE:
-          switch (line.charAt(i2)) {
+          switch (line.charAt(i)) {
             case CODE_QUOTE:
               currentState = STATE_QUOTE_AFTER_QUOTE;
               break;
@@ -40506,11 +40507,11 @@ var CSVDataset = class extends Dataset {
           }
           break;
         case STATE_QUOTE_AFTER_QUOTE:
-          switch (line.charAt(i2)) {
+          switch (line.charAt(i)) {
             case this.delimiter:
-              result.push(line.substring(readOffset, i2 - 1));
+              result.push(line.substring(readOffset, i - 1));
               currentState = STATE_OUT;
-              readOffset = i2 + 1;
+              readOffset = i + 1;
               break;
             case CODE_QUOTE:
               currentState = STATE_QUOTE;
@@ -40521,7 +40522,7 @@ var CSVDataset = class extends Dataset {
           }
           break;
         case STATE_WITHIN_QUOTE_IN_QUOTE:
-          switch (line.charAt(i2)) {
+          switch (line.charAt(i)) {
             case CODE_QUOTE:
               currentState = STATE_QUOTE;
               break;
@@ -40580,8 +40581,8 @@ var MicrophoneIterator = class extends LazyIterator {
         audio: this.audioTrackConstraints == null ? true : this.audioTrackConstraints,
         video: false
       });
-    } catch (e2) {
-      throw new Error(`Error thrown while initializing video stream: ${e2.message}`);
+    } catch (e) {
+      throw new Error(`Error thrown while initializing video stream: ${e.message}`);
     }
     if (!this.stream) {
       throw new Error("Could not obtain audio from microphone.");
@@ -40668,7 +40669,7 @@ var MicrophoneIterator = class extends LazyIterator {
   flattenQueue(queue) {
     const frameSize = queue[0].length;
     const freqData = new Float32Array(queue.length * frameSize);
-    queue.forEach((data, i2) => freqData.set(data, i2 * frameSize));
+    queue.forEach((data, i) => freqData.set(data, i * frameSize));
     return freqData;
   }
   getTensorFromAudioDataArray(freqData, shape) {
@@ -40733,9 +40734,9 @@ var WebcamIterator = class extends LazyIterator {
           height: this.webcamVideoElement.height
         }
       });
-    } catch (e2) {
-      e2.message = `Error thrown while initializing video stream: ${e2.message}`;
-      throw e2;
+    } catch (e) {
+      e.message = `Error thrown while initializing video stream: ${e.message}`;
+      throw e;
     }
     if (!this.stream) {
       throw new Error("Could not obtain video from webcam.");
@@ -40761,14 +40762,14 @@ var WebcamIterator = class extends LazyIterator {
     let img;
     try {
       img = browser_exports.fromPixels(this.webcamVideoElement);
-    } catch (e2) {
-      throw new Error(`Error thrown converting video to pixels: ${JSON.stringify(e2)}`);
+    } catch (e) {
+      throw new Error(`Error thrown converting video to pixels: ${JSON.stringify(e)}`);
     }
     if (this.resize) {
       try {
         return { value: this.cropAndResizeFrame(img), done: false };
-      } catch (e2) {
-        throw new Error(`Error thrown cropping the video: ${e2.message}`);
+      } catch (e) {
+        throw new Error(`Error thrown cropping the video: ${e.message}`);
       } finally {
         img.dispose();
       }
@@ -41224,8 +41225,8 @@ __export2(shared_exports, {
 });
 function simpleAbsImpl(vals) {
   const resultValues = new Float32Array(vals.length);
-  for (let i2 = 0; i2 < vals.length; ++i2) {
-    resultValues[i2] = Math.abs(vals[i2]);
+  for (let i = 0; i < vals.length; ++i) {
+    resultValues[i] = Math.abs(vals[i]);
   }
   return resultValues;
 }
@@ -41257,19 +41258,19 @@ function createSimpleBinaryKernelImpl(op2) {
     const aBroadcastDims = backend_util_exports.getBroadcastDims(aShape, newShape);
     const bBroadcastDims = backend_util_exports.getBroadcastDims(bShape, newShape);
     if (aBroadcastDims.length + bBroadcastDims.length === 0) {
-      for (let i2 = 0; i2 < result.length; ++i2) {
-        result[i2] = op2(aVals[i2 % aVals.length], bVals[i2 % bVals.length]);
+      for (let i = 0; i < result.length; ++i) {
+        result[i] = op2(aVals[i % aVals.length], bVals[i % bVals.length]);
       }
     } else {
-      for (let i2 = 0; i2 < result.length; ++i2) {
-        const loc = util_exports.indexToLoc(i2, resultRank, resultStrides);
+      for (let i = 0; i < result.length; ++i) {
+        const loc = util_exports.indexToLoc(i, resultRank, resultStrides);
         const aLoc = loc.slice(-aRank);
         aBroadcastDims.forEach((d) => aLoc[d] = 0);
         const aIndex = util_exports.locToIndex(aLoc, aRank, aStrides);
         const bLoc = loc.slice(-bRank);
         bBroadcastDims.forEach((d) => bLoc[d] = 0);
         const bIndex = util_exports.locToIndex(bLoc, bRank, bStrides);
-        result[i2] = op2(aVals[aIndex], bVals[bIndex]);
+        result[i] = op2(aVals[aIndex], bVals[bIndex]);
       }
     }
     return [result, newShape];
@@ -41437,16 +41438,16 @@ function createComplexBinaryKernelImpl(op2) {
     const bRank = bShape.length;
     const bStrides = util_exports.computeStrides(bShape);
     if (aBroadcastDims.length + bBroadcastDims.length === 0) {
-      for (let i2 = 0; i2 < resultRealVals.length; i2++) {
-        const aIdx = i2 % aVals.length;
-        const bIdx = i2 % bVals.length;
+      for (let i = 0; i < resultRealVals.length; i++) {
+        const aIdx = i % aVals.length;
+        const bIdx = i % bVals.length;
         const result = op2(aVals[aIdx * 2], aVals[aIdx * 2 + 1], bVals[bIdx * 2], bVals[bIdx * 2 + 1]);
-        resultRealVals[i2] = result.real;
-        resultImagVals[i2] = result.imag;
+        resultRealVals[i] = result.real;
+        resultImagVals[i] = result.imag;
       }
     } else {
-      for (let i2 = 0; i2 < resultRealVals.length; i2++) {
-        const loc = util_exports.indexToLoc(i2, resultRank, resultStrides);
+      for (let i = 0; i < resultRealVals.length; i++) {
+        const loc = util_exports.indexToLoc(i, resultRank, resultStrides);
         const aLoc = loc.slice(-aRank);
         aBroadcastDims.forEach((d) => aLoc[d] = 0);
         const aIndex = util_exports.locToIndex(aLoc, aRank, aStrides);
@@ -41454,8 +41455,8 @@ function createComplexBinaryKernelImpl(op2) {
         bBroadcastDims.forEach((d) => bLoc[d] = 0);
         const bIndex = util_exports.locToIndex(bLoc, bRank, bStrides);
         const opResult = op2(aVals[aIndex * 2], aVals[aIndex * 2 + 1], bVals[bIndex * 2], bVals[bIndex * 2 + 1]);
-        resultRealVals[i2] = opResult.real;
-        resultImagVals[i2] = opResult.imag;
+        resultRealVals[i] = opResult.real;
+        resultImagVals[i] = opResult.imag;
       }
     }
     return [resultRealVals, resultImagVals, resultShape];
@@ -41474,8 +41475,8 @@ var addConfig = {
 function bincountImpl(xVals, weightsVals, weightsDtype, weightsShape, size2) {
   const weightsSize = util_exports.sizeFromShape(weightsShape);
   const outVals = util_exports.makeZerosTypedArray(size2, weightsDtype);
-  for (let i2 = 0; i2 < xVals.length; i2++) {
-    const value = xVals[i2];
+  for (let i = 0; i < xVals.length; i++) {
+    const value = xVals[i];
     if (value < 0) {
       throw new Error("Input x must be non-negative!");
     }
@@ -41483,7 +41484,7 @@ function bincountImpl(xVals, weightsVals, weightsDtype, weightsShape, size2) {
       continue;
     }
     if (weightsSize > 0) {
-      outVals[value] += weightsVals[i2];
+      outVals[value] += weightsVals[i];
     } else {
       outVals[value] += 1;
     }
@@ -41494,9 +41495,9 @@ function bincountReduceImpl(xBuf, weightsBuf, size2, binaryOutput = false) {
   const numRows = xBuf.shape[0];
   const numCols = xBuf.shape[1];
   const outBuf = buffer([numRows, size2], weightsBuf.dtype);
-  for (let i2 = 0; i2 < numRows; i2++) {
+  for (let i = 0; i < numRows; i++) {
     for (let j = 0; j < numCols; j++) {
-      const value = xBuf.get(i2, j);
+      const value = xBuf.get(i, j);
       if (value < 0) {
         throw new Error("Input x must be non-negative!");
       }
@@ -41504,12 +41505,12 @@ function bincountReduceImpl(xBuf, weightsBuf, size2, binaryOutput = false) {
         continue;
       }
       if (binaryOutput) {
-        outBuf.set(1, i2, value);
+        outBuf.set(1, i, value);
       } else {
         if (weightsBuf.size > 0) {
-          outBuf.set(outBuf.get(i2, value) + weightsBuf.get(i2, j), i2, value);
+          outBuf.set(outBuf.get(i, value) + weightsBuf.get(i, j), i, value);
         } else {
-          outBuf.set(outBuf.get(i2, value) + 1, i2, value);
+          outBuf.set(outBuf.get(i, value) + 1, i, value);
         }
       }
     }
@@ -41519,8 +41520,8 @@ function bincountReduceImpl(xBuf, weightsBuf, size2, binaryOutput = false) {
 function createSimpleUnaryImpl(op2) {
   return (values, dtype, attrs) => {
     const newValues = util_exports.getTypedArrayFromDType(dtype, values.length);
-    for (let i2 = 0; i2 < values.length; ++i2) {
-      newValues[i2] = op2(values[i2], attrs);
+    for (let i = 0; i < values.length; ++i) {
+      newValues[i] = op2(values[i], attrs);
     }
     return newValues;
   };
@@ -41537,8 +41538,8 @@ function unaryKernelFunc(name, op2, dtype) {
     const xSize = util_exports.sizeFromShape(x.shape);
     const $dtype = dtype || x.dtype;
     const newValues = util_exports.getArrayFromDType($dtype, xSize);
-    for (let i2 = 0; i2 < xSize; ++i2) {
-      newValues[i2] = op2(values[i2], attrs);
+    for (let i = 0; i < xSize; ++i) {
+      newValues[i] = op2(values[i], attrs);
     }
     return cpuBackend.makeTensorInfo(x.shape, $dtype, newValues);
   };
@@ -41619,11 +41620,11 @@ var floorConfig = {
 };
 function gatherNdImpl(indicesData, paramsBuf, dtype, numSlices, sliceRank, sliceSize, strides2, paramsShape, paramsSize) {
   const outBuf = buffer([numSlices, sliceSize], dtype);
-  for (let i2 = 0; i2 < numSlices; i2++) {
+  for (let i = 0; i < numSlices; i++) {
     const index2 = [];
     let flattenIndex = 0;
     for (let j = 0; j < sliceRank; j++) {
-      const dim = indicesData[i2 * sliceRank + j];
+      const dim = indicesData[i * sliceRank + j];
       flattenIndex += dim * strides2[j];
       index2.push(dim);
     }
@@ -41631,15 +41632,15 @@ function gatherNdImpl(indicesData, paramsBuf, dtype, numSlices, sliceRank, slice
       throw new Error(`Invalid indices: ${index2} does not index into ${paramsShape}`);
     }
     for (let k = 0; k < sliceSize; k++) {
-      outBuf.values[i2 * sliceSize + k] = paramsBuf.get(...paramsBuf.indexToLoc(flattenIndex * sliceSize + k));
+      outBuf.values[i * sliceSize + k] = paramsBuf.get(...paramsBuf.indexToLoc(flattenIndex * sliceSize + k));
     }
   }
   return outBuf;
 }
 function gatherV2Impl(xBuf, indicesBuf, flattenOutputShape) {
   const outBuf = buffer(flattenOutputShape, xBuf.dtype);
-  for (let i2 = 0; i2 < outBuf.size; ++i2) {
-    const newLoc = outBuf.indexToLoc(i2);
+  for (let i = 0; i < outBuf.size; ++i) {
+    const newLoc = outBuf.indexToLoc(i);
     const originalLoc = newLoc.slice();
     const batchIdx = originalLoc[0];
     const indicesIdx = originalLoc[2];
@@ -41647,7 +41648,7 @@ function gatherV2Impl(xBuf, indicesBuf, flattenOutputShape) {
     originalLoc[2] = indicesBuf.values[indicesIndex];
     const originalIndex = xBuf.locToIndex(originalLoc);
     if (0 <= originalIndex && originalIndex < xBuf.values.length) {
-      outBuf.values[i2] = xBuf.values[originalIndex];
+      outBuf.values[i] = xBuf.values[originalIndex];
     }
   }
   return outBuf;
@@ -41684,8 +41685,8 @@ function linSpaceImpl(start, stop, num) {
   const step5 = (stop - start) / (num - 1);
   const values = util_exports.makeZerosTypedArray(num, "float32");
   values[0] = start;
-  for (let i2 = 1; i2 < values.length; i2++) {
-    values[i2] = values[i2 - 1] + step5;
+  for (let i = 1; i < values.length; i++) {
+    values[i] = values[i - 1] + step5;
   }
   return values;
 }
@@ -41698,8 +41699,8 @@ var logConfig = {
 };
 function maxImpl(aVals, reduceSize, outShape, dtype) {
   const vals = util_exports.getTypedArrayFromDType(dtype, util_exports.sizeFromShape(outShape));
-  for (let i2 = 0; i2 < vals.length; ++i2) {
-    const offset = i2 * reduceSize;
+  for (let i = 0; i < vals.length; ++i) {
+    const offset = i * reduceSize;
     let max7 = aVals[offset];
     for (let j = 0; j < reduceSize; ++j) {
       const value = aVals[offset + j];
@@ -41707,7 +41708,7 @@ function maxImpl(aVals, reduceSize, outShape, dtype) {
         max7 = value;
       }
     }
-    vals[i2] = max7;
+    vals[i] = max7;
   }
   return vals;
 }
@@ -41768,14 +41769,14 @@ function transposeImpl(xVals, xShape, dtype, perm, newShape) {
   const xStrides = util_exports.computeStrides(xShape);
   const newStrides = util_exports.computeStrides(newShape);
   const result = util_exports.getTypedArrayFromDType(dtype, util_exports.sizeFromShape(newShape));
-  for (let i2 = 0; i2 < xSize; ++i2) {
-    const loc = util_exports.indexToLoc(i2, xRank, xStrides);
+  for (let i = 0; i < xSize; ++i) {
+    const loc = util_exports.indexToLoc(i, xRank, xStrides);
     const newLoc = new Array(loc.length);
-    for (let i3 = 0; i3 < newLoc.length; i3++) {
-      newLoc[i3] = loc[perm[i3]];
+    for (let i2 = 0; i2 < newLoc.length; i2++) {
+      newLoc[i2] = loc[perm[i2]];
     }
     const newIndex = util_exports.locToIndex(newLoc, xRank, newStrides);
-    result[newIndex] = xVals[i2];
+    result[newIndex] = xVals[i];
   }
   return result;
 }
@@ -41786,8 +41787,8 @@ function transpose2(args) {
   assertNotComplex(x, "transpose");
   const xRank = x.shape.length;
   const newShape = new Array(xRank);
-  for (let i2 = 0; i2 < newShape.length; i2++) {
-    newShape[i2] = x.shape[perm[i2]];
+  for (let i = 0; i < newShape.length; i++) {
+    newShape[i] = x.shape[perm[i]];
   }
   const values = backend2.data.get(x.dataId).values;
   const result = transposeImpl(values, x.shape, x.dtype, perm, newShape);
@@ -41804,13 +41805,13 @@ function prodImpl(xShape, xDtype, xVals, reductionAxes) {
   const outDtype = upcastType(xDtype, "int32");
   const outVals = util_exports.makeZerosTypedArray(util_exports.sizeFromShape(outShape), outDtype);
   const reduceSize = util_exports.sizeFromShape(reduceShape);
-  for (let i2 = 0; i2 < outVals.length; ++i2) {
-    const offset = i2 * reduceSize;
+  for (let i = 0; i < outVals.length; ++i) {
+    const offset = i * reduceSize;
     let prod6 = 1;
     for (let j = 0; j < reduceSize; ++j) {
       prod6 *= xVals[offset + j];
     }
-    outVals[i2] = prod6;
+    outVals[i] = prod6;
   }
   return { outVals, outShape, outDtype };
 }
@@ -41890,8 +41891,8 @@ var RaggedTensorToTensorOp = class {
       return 0;
     }
     let maxWidth = 0;
-    for (let i2 = 0; i2 < tensorLength - 1; ++i2) {
-      const currentWidth = rowSplit[i2 + 1] - rowSplit[i2];
+    for (let i = 0; i < tensorLength - 1; ++i) {
+      const currentWidth = rowSplit[i + 1] - rowSplit[i];
       if (currentWidth > maxWidth) {
         maxWidth = currentWidth;
       }
@@ -41906,12 +41907,12 @@ var RaggedTensorToTensorOp = class {
     let firstEqualIndex = 0;
     let firstEqualIndexValue = valueRowIds[0];
     let maxWidth = 0;
-    for (let i2 = 1; i2 < indexLength; ++i2) {
-      const value = valueRowIds[i2];
+    for (let i = 1; i < indexLength; ++i) {
+      const value = valueRowIds[i];
       if (value !== firstEqualIndexValue) {
         firstEqualIndexValue = value;
-        maxWidth = Math.max(i2 - firstEqualIndex, maxWidth);
-        firstEqualIndex = i2;
+        maxWidth = Math.max(i - firstEqualIndex, maxWidth);
+        firstEqualIndex = i;
       }
     }
     return Math.max(indexLength - firstEqualIndex, maxWidth);
@@ -41935,9 +41936,9 @@ var RaggedTensorToTensorOp = class {
     if (result[0] < 0) {
       result[0] = firstDim;
     }
-    for (let i2 = 1; i2 <= this.raggedRank; ++i2) {
-      if (result[i2] < 0) {
-        result[i2] = this.getMaxWidth(i2);
+    for (let i = 1; i <= this.raggedRank; ++i) {
+      if (result[i] < 0) {
+        result[i] = this.getMaxWidth(i);
       }
     }
     return result;
@@ -41946,10 +41947,10 @@ var RaggedTensorToTensorOp = class {
     const minDimension = Math.min(firstDimension, firstDimensionOutput);
     const result = [];
     let currentOutputIndex = 0;
-    for (let i2 = 0; i2 < minDimension; ++i2, currentOutputIndex += outputIndexMultiplier) {
+    for (let i = 0; i < minDimension; ++i, currentOutputIndex += outputIndexMultiplier) {
       result.push(currentOutputIndex);
     }
-    for (let i2 = minDimension; i2 < firstDimension; ++i2) {
+    for (let i = minDimension; i < firstDimension; ++i) {
       result.push(-1);
     }
     util_exports.assert(result.length === firstDimension, () => "Final length of result must be equal to firstDimension.");
@@ -41958,10 +41959,10 @@ var RaggedTensorToTensorOp = class {
   calculateOutputIndexRowSplit(rowSplit, parentOutputIndex, outputIndexMultiplier, outputSize2) {
     const rowSplitSize = rowSplit.length;
     const result = [];
-    for (let i2 = 0; i2 < rowSplitSize - 1; ++i2) {
-      const rowLength = rowSplit[i2 + 1] - rowSplit[i2];
+    for (let i = 0; i < rowSplitSize - 1; ++i) {
+      const rowLength = rowSplit[i + 1] - rowSplit[i];
       let realLength = Math.min(outputSize2, rowLength);
-      let parentOutputIndexCurrent = parentOutputIndex[i2];
+      let parentOutputIndexCurrent = parentOutputIndex[i];
       if (parentOutputIndexCurrent === -1) {
         realLength = 0;
       }
@@ -41991,8 +41992,8 @@ var RaggedTensorToTensorOp = class {
     }
     let currentOutputIndex = parentOutputIndex[currentValueRowId];
     result.push(currentOutputIndex);
-    for (let i2 = 1; i2 < indexSize; ++i2) {
-      const nextValueRowId = valueRowIds[i2];
+    for (let i = 1; i < indexSize; ++i) {
+      const nextValueRowId = valueRowIds[i];
       if (nextValueRowId === currentValueRowId) {
         if (currentOutputIndex >= 0) {
           ++currentOutputColumn;
@@ -42058,16 +42059,16 @@ var RaggedTensorToTensorOp = class {
     const outputSize2 = this.calculateOutputSize(firstDimension);
     const multiplier = new Array(this.raggedRank + 1);
     multiplier[multiplier.length - 1] = 1;
-    for (let i2 = multiplier.length - 2; i2 >= 0; --i2) {
-      multiplier[i2] = multiplier[i2 + 1] * outputSize2[i2 + 1];
+    for (let i = multiplier.length - 2; i >= 0; --i) {
+      multiplier[i] = multiplier[i + 1] * outputSize2[i + 1];
     }
     const outputShape = makeShape(outputSize2, false);
     const outputTensor = util_exports.getArrayFromDType(this.valuesDType, util_exports.sizeFromShape(outputShape));
     const fullSize = multiplier[0] * outputSize2[0];
     if (fullSize > 0) {
       let outputIndex = this.calculateFirstParentOutputIndex(firstDimension, multiplier[0], outputSize2[0]);
-      for (let i2 = 1; i2 <= this.raggedRank; ++i2) {
-        const newOutputIndex = this.calculateOutputIndex(i2 - 1, outputIndex, multiplier[i2], outputSize2[i2]);
+      for (let i = 1; i <= this.raggedRank; ++i) {
+        const newOutputIndex = this.calculateOutputIndex(i - 1, outputIndex, multiplier[i], outputSize2[i]);
         outputIndex = newOutputIndex;
       }
       this.setOutput(this.raggedRank, outputIndex, outputTensor, outputShape);
@@ -42136,8 +42137,8 @@ var RaggedTensorToTensorOp = class {
   }
 };
 function copyArray(dst, src, size2) {
-  for (let i2 = 0; i2 < size2; i2++) {
-    dst[i2] = src[i2];
+  for (let i = 0; i < size2; i++) {
+    dst[i] = src[i];
   }
 }
 function makeShape(shape, isPartial) {
@@ -42172,8 +42173,8 @@ function rangeImpl(start, stop, step5, dtype) {
     step5 = -1;
   }
   values[0] = start;
-  for (let i2 = 1; i2 < values.length; i2++) {
-    values[i2] = values[i2 - 1] + step5;
+  for (let i = 1; i < values.length; i++) {
+    values[i] = values[i - 1] + step5;
   }
   return values;
 }
@@ -42199,11 +42200,11 @@ function scatterImpl(indices, updates, shape, outputSize2, sliceSize, numUpdates
   } else if (typeof defaultValue === "boolean") {
     outBuf.values.fill(+defaultValue);
   }
-  for (let i2 = 0; i2 < numUpdates; i2++) {
+  for (let i = 0; i < numUpdates; i++) {
     const index2 = [];
     let flattenIndex = 0;
     for (let j = 0; j < sliceRank; j++) {
-      const dim = indicesData[i2 * sliceRank + j];
+      const dim = indicesData[i * sliceRank + j];
       index2.push(dim);
       flattenIndex += dim * strides2[j];
     }
@@ -42212,9 +42213,9 @@ function scatterImpl(indices, updates, shape, outputSize2, sliceSize, numUpdates
     }
     for (let k = 0; k < sliceSize; k++) {
       if (sumDupeIndices) {
-        outBuf.values[flattenIndex * sliceSize + k] += updatesData[i2 * sliceSize + k];
+        outBuf.values[flattenIndex * sliceSize + k] += updatesData[i * sliceSize + k];
       } else {
-        outBuf.values[flattenIndex * sliceSize + k] = updates.rank === 0 ? updatesData[0] : updatesData[i2 * sliceSize + k];
+        outBuf.values[flattenIndex * sliceSize + k] = updates.rank === 0 ? updatesData[0] : updatesData[i * sliceSize + k];
       }
     }
   }
@@ -42241,8 +42242,8 @@ function sliceImpl(vals, begin, size2, shape, dtype) {
   const decodedData = dtype === "string" ? backend_util_exports.fromUint8ToStringArray(vals) : vals;
   const inBuf = buffer(shape, dtype, decodedData);
   const outBuf = buffer(size2, dtype);
-  for (let i2 = 0; i2 < outBuf.size; ++i2) {
-    const outLoc = outBuf.indexToLoc(i2);
+  for (let i = 0; i < outBuf.size; ++i) {
+    const outLoc = outBuf.indexToLoc(i);
     const inLoc = outLoc.map((idx, j) => idx + begin[j]);
     outBuf.set(inBuf.get(...inLoc), ...outLoc);
   }
@@ -42290,13 +42291,13 @@ function sparseFillEmptyRowsImpl(indices, indicesShape, indicesDType, values, va
   let rowsAreOrdered = true;
   let lastIndicesRow = 0;
   const csrOffset = new Array(denseRows).fill(0);
-  for (let i2 = 0; i2 < indicesCount; ++i2) {
-    const row = indices[i2 * rank];
+  for (let i = 0; i < indicesCount; ++i) {
+    const row = indices[i * rank];
     if (row < 0) {
-      throw new Error(backend_util_exports.getSparseFillEmptyRowsNegativeIndexErrorMessage(i2, row));
+      throw new Error(backend_util_exports.getSparseFillEmptyRowsNegativeIndexErrorMessage(i, row));
     }
     if (row >= denseRows) {
-      throw new Error(backend_util_exports.getSparseFillEmptyRowsOutOfRangeIndexErrorMessage(i2, row, denseRows));
+      throw new Error(backend_util_exports.getSparseFillEmptyRowsOutOfRangeIndexErrorMessage(i, row, denseRows));
     }
     ++csrOffset[row];
     rowsAreOrdered = rowsAreOrdered && row >= lastIndicesRow;
@@ -42315,8 +42316,8 @@ function sparseFillEmptyRowsImpl(indices, indicesShape, indicesDType, values, va
   if (allRowsFull && rowsAreOrdered) {
     const outputIndices = indices;
     const outputValues = values;
-    for (let i2 = 0; i2 < indicesCount; ++i2) {
-      reverseIndexMap[i2] = i2;
+    for (let i = 0; i < indicesCount; ++i) {
+      reverseIndexMap[i] = i;
     }
     return [
       outputIndices,
@@ -42330,16 +42331,16 @@ function sparseFillEmptyRowsImpl(indices, indicesShape, indicesDType, values, va
     const outputIndices = util_exports.getArrayFromDType(indicesDType, fullIndicesCount * rank);
     const outputValues = util_exports.getArrayFromDType(valuesDType, fullIndicesCount);
     const filledCount = new Array(denseRows).fill(0);
-    for (let i2 = 0; i2 < indicesCount; ++i2) {
-      const row = indices[i2 * rank];
+    for (let i = 0; i < indicesCount; ++i) {
+      const row = indices[i * rank];
       const offset = filledCount[row];
       const outputI = (row === 0 ? 0 : csrOffset[row - 1]) + offset;
       filledCount[row]++;
       for (let j = 0; j < rank; ++j) {
-        outputIndices[outputI * rank + j] = indices[i2 * rank + j];
+        outputIndices[outputI * rank + j] = indices[i * rank + j];
       }
-      outputValues[outputI] = values[i2];
-      reverseIndexMap[i2] = outputI;
+      outputValues[outputI] = values[i];
+      reverseIndexMap[i] = outputI;
     }
     for (let row = 0; row < denseRows; ++row) {
       const rowCount = filledCount[row];
@@ -42414,13 +42415,13 @@ function sparseReshapeImpl(inputIndices, inputIndicesShape, inputDType, inputSha
     }
   }
   const newIndices = util_exports.getArrayFromDType(inputDType, nnz * outputRank);
-  for (let i2 = 0; i2 < nnz; ++i2) {
+  for (let i = 0; i < nnz; ++i) {
     let id = 0;
     for (let j = 0; j < inputRank; ++j) {
-      id += inputIndices[i2 * inputRank + j] * inputStrides[j];
+      id += inputIndices[i * inputRank + j] * inputStrides[j];
     }
     for (let j = 0; j < outputRank; ++j) {
-      newIndices[i2 * outputRank + j] = Math.trunc(id / outputStrides[j]);
+      newIndices[i * outputRank + j] = Math.trunc(id / outputStrides[j]);
       id %= outputStrides[j];
     }
   }
@@ -42469,10 +42470,10 @@ function sparseSegmentReductionImpl(input2, inputShape, inputDType, indices, seg
     if (outIndex > uninitializedIndex) {
       output.fill(defaultValue, uninitializedIndex * numCol, outIndex * numCol);
     }
-    for (let i2 = start; i2 < end; ++i2) {
-      const index2 = indices[i2];
+    for (let i = start; i < end; ++i) {
+      const index2 = indices[i];
       if (index2 < 0 || index2 >= inputFlat[0]) {
-        throw new Error(backend_util_exports.getSparseSegmentReductionIndicesOutOfRangeErrorMessage(i2, indices[i2], inputFlat[0]));
+        throw new Error(backend_util_exports.getSparseSegmentReductionIndicesOutOfRangeErrorMessage(i, indices[i], inputFlat[0]));
       }
       for (let j = 0; j < numCol; j++) {
         output[outIndex * numCol + j] += input2[index2 * numCol + j];
@@ -42515,8 +42516,8 @@ var squaredDifferenceConfig = {
 };
 function stridedSliceImpl(outShape, xBuf, strides2, begin) {
   const outBuf = buffer(outShape, xBuf.dtype);
-  for (let i2 = 0; i2 < outBuf.size; i2++) {
-    const loc = outBuf.indexToLoc(i2);
+  for (let i = 0; i < outBuf.size; i++) {
+    const loc = outBuf.indexToLoc(i);
     const newLoc = new Array(loc.length);
     for (let j = 0; j < newLoc.length; j++) {
       newLoc[j] = loc[j] * strides2[j] + begin[j];
@@ -42550,8 +42551,8 @@ var StringNGramsOp = class {
       const dataStartIndex = splitIndex + (leftPadding > 0 ? 0 : nGramIndex - padWidth);
       let nGramSize = 0;
       nGramSize += leftPadding * this.leftPad.length;
-      for (let n2 = 0; n2 < numTokens; ++n2) {
-        nGramSize += data[dataStartIndex + n2].length;
+      for (let n = 0; n < numTokens; ++n) {
+        nGramSize += data[dataStartIndex + n].length;
       }
       nGramSize += rightPadding * this.rightPad.length;
       const numSeparators = leftPadding + rightPadding + numTokens - 1;
@@ -42560,22 +42561,22 @@ var StringNGramsOp = class {
       const nGram = output[outputStartIndex + nGramIndex];
       let nextNGramIndex = 0;
       const appendToNGram = (str) => str.forEach((value) => nGram[nextNGramIndex++] = value);
-      for (let n2 = 0; n2 < leftPadding; ++n2) {
+      for (let n = 0; n < leftPadding; ++n) {
         appendToNGram(this.leftPad);
         appendToNGram(this.separator);
       }
-      for (let n2 = 0; n2 < numTokens - 1; ++n2) {
-        appendToNGram(data[dataStartIndex + n2]);
+      for (let n = 0; n < numTokens - 1; ++n) {
+        appendToNGram(data[dataStartIndex + n]);
         appendToNGram(this.separator);
       }
       if (numTokens > 0) {
         appendToNGram(data[dataStartIndex + numTokens - 1]);
-        for (let n2 = 0; n2 < rightPadding; ++n2) {
+        for (let n = 0; n < rightPadding; ++n) {
           appendToNGram(this.separator);
           appendToNGram(this.rightPad);
         }
       } else {
-        for (let n2 = 0; n2 < rightPadding - 1; ++n2) {
+        for (let n = 0; n < rightPadding - 1; ++n) {
           appendToNGram(this.rightPad);
           appendToNGram(this.separator);
         }
@@ -42591,13 +42592,13 @@ var StringNGramsOp = class {
       if (prevSplit !== 0) {
         throw new Error(`First split value must be 0, got ${prevSplit}`);
       }
-      for (let i2 = 1; i2 < splitsSize; ++i2) {
-        let validSplits = splits[i2] >= prevSplit;
-        validSplits = validSplits && splits[i2] <= inputDataSize;
+      for (let i = 1; i < splitsSize; ++i) {
+        let validSplits = splits[i] >= prevSplit;
+        validSplits = validSplits && splits[i] <= inputDataSize;
         if (!validSplits) {
-          throw new Error(`Invalid split value ${splits[i2]}, must be in [${prevSplit}, ${inputDataSize}]`);
+          throw new Error(`Invalid split value ${splits[i]}, must be in [${prevSplit}, ${inputDataSize}]`);
         }
-        prevSplit = splits[i2];
+        prevSplit = splits[i];
       }
       if (prevSplit !== inputDataSize) {
         throw new Error(`Last split value must be data size. Expected ${inputDataSize}, got ${prevSplit}`);
@@ -42607,14 +42608,14 @@ var StringNGramsOp = class {
     const nGramsSplits = util_exports.getArrayFromDType("int32", splitsSize);
     if (inputDataSize === 0 || splitsSize === 0) {
       const empty = new Array(inputDataSize);
-      for (let i2 = 0; i2 <= numBatchItems; ++i2) {
-        nGramsSplits[i2] = 0;
+      for (let i = 0; i <= numBatchItems; ++i) {
+        nGramsSplits[i] = 0;
       }
       return [empty, nGramsSplits];
     }
     nGramsSplits[0] = 0;
-    for (let i2 = 1; i2 <= numBatchItems; ++i2) {
-      const length = splits[i2] - splits[i2 - 1];
+    for (let i = 1; i <= numBatchItems; ++i) {
+      const length = splits[i] - splits[i - 1];
       let numNGrams = 0;
       this.nGramWidths.forEach((nGramWidth) => {
         numNGrams += this.getNumNGrams(length, nGramWidth);
@@ -42622,20 +42623,20 @@ var StringNGramsOp = class {
       if (this.preserveShort && length > 0 && numNGrams === 0) {
         numNGrams = 1;
       }
-      nGramsSplits[i2] = nGramsSplits[i2 - 1] + numNGrams;
+      nGramsSplits[i] = nGramsSplits[i - 1] + numNGrams;
     }
     const nGrams = new Array(nGramsSplits[numBatchItems]);
-    for (let i2 = 0; i2 < numBatchItems; ++i2) {
-      const splitIndex = splits[i2];
-      let outputStartIdx = nGramsSplits[i2];
+    for (let i = 0; i < numBatchItems; ++i) {
+      const splitIndex = splits[i];
+      let outputStartIdx = nGramsSplits[i];
       this.nGramWidths.forEach((nGramWidth) => {
-        const length = splits[i2 + 1] - splits[i2];
+        const length = splits[i + 1] - splits[i];
         const numNGrams = this.getNumNGrams(length, nGramWidth);
         this.createNGrams(data, splitIndex, nGrams, outputStartIdx, numNGrams, nGramWidth);
         outputStartIdx += numNGrams;
       });
-      if (this.preserveShort && outputStartIdx === nGramsSplits[i2]) {
-        const dataLength = splits[i2 + 1] - splits[i2];
+      if (this.preserveShort && outputStartIdx === nGramsSplits[i]) {
+        const dataLength = splits[i + 1] - splits[i];
         if (dataLength === 0) {
           continue;
         }
@@ -42655,8 +42656,8 @@ function split3(str, delimiters, skipEmpty, result) {
     return;
   }
   if (delimiters.length === 0) {
-    for (let i2 = 0; i2 < str.length; ++i2) {
-      result.push(str.subarray(i2, i2 + 1));
+    for (let i = 0; i < str.length; ++i) {
+      result.push(str.subarray(i, i + 1));
     }
     return;
   }
@@ -42677,13 +42678,13 @@ function split3(str, delimiters, skipEmpty, result) {
     return;
   }
   let tokenStart = 0;
-  for (let i2 = 0; i2 < str.length + 1; i2++) {
-    if (i2 === str.length || delimiters.indexOf(str[i2]) !== -1) {
-      const token = str.subarray(tokenStart, i2);
+  for (let i = 0; i < str.length + 1; i++) {
+    if (i === str.length || delimiters.indexOf(str[i]) !== -1) {
+      const token = str.subarray(tokenStart, i);
       if (!skipEmpty || token.length !== 0) {
         result.push(token);
       }
-      tokenStart = i2 + 1;
+      tokenStart = i + 1;
     }
   }
 }
@@ -42693,11 +42694,11 @@ function stringSplitImpl(input2, delimiter, skipEmpty) {
   let outputSize2 = 0;
   let maxNumEntries = 0;
   const numIndices = new Array(batchSize);
-  for (let i2 = 0; i2 < batchSize; ++i2) {
+  for (let i = 0; i < batchSize; ++i) {
     const prevTokensLength = tokens.length;
-    split3(input2[i2], delimiter, skipEmpty, tokens);
+    split3(input2[i], delimiter, skipEmpty, tokens);
     const nEntries = tokens.length - prevTokensLength;
-    numIndices[i2] = nEntries;
+    numIndices[i] = nEntries;
     outputSize2 += nEntries;
     maxNumEntries = Math.max(maxNumEntries, nEntries);
   }
@@ -42705,9 +42706,9 @@ function stringSplitImpl(input2, delimiter, skipEmpty) {
   const values = new Array(outputSize2);
   const shape = [batchSize, maxNumEntries];
   let c = 0;
-  for (let i2 = 0; i2 < batchSize; ++i2) {
-    for (let j = 0; j < numIndices[i2]; ++j) {
-      indices[c * 2] = i2;
+  for (let i = 0; i < batchSize; ++i) {
+    for (let j = 0; j < numIndices[i]; ++j) {
+      indices[c * 2] = i;
       indices[c * 2 + 1] = j;
       values[c] = tokens[c];
       ++c;
@@ -42717,8 +42718,8 @@ function stringSplitImpl(input2, delimiter, skipEmpty) {
 }
 function stringToHashBucketFastImpl(input2, numBuckets) {
   const output = util_exports.getArrayFromDType("int32", input2.length);
-  for (let i2 = 0; i2 < input2.length; ++i2) {
-    output[i2] = util_exports.fingerPrint64(input2[i2]).modulo(numBuckets).getLowBitsUnsigned();
+  for (let i = 0; i < input2.length; ++i) {
+    output[i] = util_exports.fingerPrint64(input2[i]).modulo(numBuckets).getLowBitsUnsigned();
   }
   return output;
 }
@@ -42734,18 +42735,18 @@ var subConfig = {
 };
 function tileImpl(xBuf, reps) {
   const newShape = new Array(xBuf.rank);
-  for (let i2 = 0; i2 < newShape.length; i2++) {
-    newShape[i2] = xBuf.shape[i2] * reps[i2];
+  for (let i = 0; i < newShape.length; i++) {
+    newShape[i] = xBuf.shape[i] * reps[i];
   }
   const result = buffer(newShape, xBuf.dtype);
-  for (let i2 = 0; i2 < result.values.length; ++i2) {
-    const newLoc = result.indexToLoc(i2);
+  for (let i = 0; i < result.values.length; ++i) {
+    const newLoc = result.indexToLoc(i);
     const originalLoc = new Array(xBuf.rank);
     for (let j = 0; j < originalLoc.length; j++) {
       originalLoc[j] = newLoc[j] % xBuf.shape[j];
     }
     const originalIndex = xBuf.locToIndex(originalLoc);
-    result.values[i2] = xBuf.values[originalIndex];
+    result.values[i] = xBuf.values[originalIndex];
   }
   return result;
 }
@@ -42756,28 +42757,28 @@ var comparePair = (a, b) => {
 function select(array2, k, left = 0, right = array2.length - 1) {
   while (right > left) {
     if (right - left > 600) {
-      const n2 = right - left + 1;
-      const i3 = k - left + 1;
-      const z = Math.log(n2);
-      const s2 = 0.5 * Math.exp(2 * z / 3);
-      const sd = 0.5 * Math.sqrt(z * s2 * (n2 - s2) / n2) * Math.sign(i3 - n2 / 2);
-      const newLeft = Math.max(left, Math.floor(k - i3 * s2 / n2 + sd));
-      const newRight = Math.min(right, Math.floor(k + (n2 - i3) * s2 / n2 + sd));
+      const n = right - left + 1;
+      const i2 = k - left + 1;
+      const z = Math.log(n);
+      const s = 0.5 * Math.exp(2 * z / 3);
+      const sd = 0.5 * Math.sqrt(z * s * (n - s) / n) * Math.sign(i2 - n / 2);
+      const newLeft = Math.max(left, Math.floor(k - i2 * s / n + sd));
+      const newRight = Math.min(right, Math.floor(k + (n - i2) * s / n + sd));
       select(array2, k, newLeft, newRight);
     }
     const t2 = array2[k];
-    let i2 = left;
+    let i = left;
     let j = right;
     util_exports.swap(array2, left, k);
     if (comparePair(array2[right], t2) > 0) {
       util_exports.swap(array2, left, right);
     }
-    while (i2 < j) {
-      util_exports.swap(array2, i2, j);
-      i2++;
+    while (i < j) {
+      util_exports.swap(array2, i, j);
+      i++;
       j--;
-      while (comparePair(array2[i2], t2) < 0) {
-        i2 = i2 + 1;
+      while (comparePair(array2[i], t2) < 0) {
+        i = i + 1;
       }
       while (comparePair(array2[j], t2) > 0) {
         j = j - 1;
@@ -42817,9 +42818,9 @@ function topKImpl(x, xShape, xDtype, k, sorted) {
     const outOffset = b * k;
     const topKVals = allTopKVals.subarray(outOffset, outOffset + k);
     const topKIndices = allTopKIndices.subarray(outOffset, outOffset + k);
-    for (let i2 = 0; i2 < k; i2++) {
-      topKVals[i2] = valAndInd[i2].value;
-      topKIndices[i2] = valAndInd[i2].index;
+    for (let i = 0; i < k; i++) {
+      topKVals[i] = valAndInd[i].value;
+      topKIndices[i] = valAndInd[i].index;
     }
   }
   const outputShape = xShape.slice();
@@ -42832,47 +42833,47 @@ function topKImpl(x, xShape, xDtype, k, sorted) {
 function uniqueImpl(values, axis, shape, dtype) {
   const $axis = util_exports.parseAxisParam(axis, shape)[0];
   const newShape = [1, shape[0], 1];
-  for (let i2 = 0; i2 < $axis; i2++) {
-    newShape[0] *= shape[i2];
+  for (let i = 0; i < $axis; i++) {
+    newShape[0] *= shape[i];
   }
   newShape[1] = shape[$axis];
-  for (let i2 = $axis + 1; i2 < shape.length; i2++) {
-    newShape[2] *= shape[i2];
+  for (let i = $axis + 1; i < shape.length; i++) {
+    newShape[2] *= shape[i];
   }
   const uniqueElements = {};
   const indices = new Int32Array(shape[$axis]);
   const inputBuffer = new TensorBuffer(newShape, dtype, values);
   const uniqueIndices = [];
   const is1DTensor = newShape[0] === 1 && newShape[2] === 1;
-  for (let i2 = 0; i2 < shape[$axis]; i2++) {
+  for (let i = 0; i < shape[$axis]; i++) {
     let element;
     if (is1DTensor) {
-      element = values[i2].toString();
+      element = values[i].toString();
     } else {
       const axisValues = [];
       for (let m = 0; m < newShape[0]; m++) {
-        for (let n2 = 0; n2 < newShape[2]; n2++) {
-          axisValues.push(inputBuffer.get(m, i2, n2));
+        for (let n = 0; n < newShape[2]; n++) {
+          axisValues.push(inputBuffer.get(m, i, n));
         }
       }
       element = axisValues.join(",");
     }
     if (uniqueElements[element] !== void 0) {
-      indices[i2] = uniqueElements[element];
+      indices[i] = uniqueElements[element];
     } else {
       const uniqueIndex = Object.keys(uniqueElements).length;
       uniqueElements[element] = uniqueIndex;
-      indices[i2] = uniqueIndex;
-      uniqueIndices.push(i2);
+      indices[i] = uniqueIndex;
+      uniqueIndices.push(i);
     }
   }
   const outputTmpShape = newShape.slice();
   outputTmpShape[1] = Object.keys(uniqueElements).length;
   const outputBuffer = new TensorBuffer(outputTmpShape, dtype);
-  uniqueIndices.forEach((uniqueElementIndex, i2) => {
+  uniqueIndices.forEach((uniqueElementIndex, i) => {
     for (let m = 0; m < newShape[0]; m++) {
-      for (let n2 = 0; n2 < newShape[2]; n2++) {
-        outputBuffer.set(inputBuffer.get(m, uniqueElementIndex, n2), m, i2, n2);
+      for (let n = 0; n < newShape[2]; n++) {
+        outputBuffer.set(inputBuffer.get(m, uniqueElementIndex, n), m, i, n);
       }
     }
   });
@@ -42899,8 +42900,8 @@ function leakyRelu2(args) {
   const xSize = util_exports.sizeFromShape(x.shape);
   const xVals = backend2.data.get(x.dataId).values;
   const outVals = util_exports.getTypedArrayFromDType("float32", xSize);
-  for (let i2 = 0; i2 < xVals.length; i2++) {
-    outVals[i2] = xVals[i2] < 0 ? alpha2 * xVals[i2] : xVals[i2];
+  for (let i = 0; i < xVals.length; i++) {
+    outVals[i] = xVals[i] < 0 ? alpha2 * xVals[i] : xVals[i];
   }
   return backend2.makeTensorInfo(x.shape, "float32", outVals);
 }
@@ -43020,17 +43021,17 @@ function batchMatMul(args) {
           const iBlock = Math.min(i0 + blockSize, leftDim);
           const jBlock = Math.min(j0 + blockSize, rightDim);
           const kBlock = Math.min(k02 + blockSize, sharedDim);
-          for (let i2 = i0; i2 < iBlock; i2++) {
+          for (let i = i0; i < iBlock; i++) {
             for (let j = j0; j < jBlock; j++) {
               let sum7 = 0;
               for (let k = k02; k < kBlock; k++) {
                 const batchOffsetA = Math.min(bi, batchDimA - 1) * aBatch;
                 const batchOffsetB = Math.min(bi, batchDimB - 1) * bBatch;
-                const aVal = a3dValues[batchOffsetA + i2 * aOuterStep + k * aInnerStep];
+                const aVal = a3dValues[batchOffsetA + i * aOuterStep + k * aInnerStep];
                 const bVal = b3dValues[k * bInnerStep + j * bOuterStep + batchOffsetB];
                 sum7 += aVal * bVal;
               }
-              resVals[bi * size2 + (i2 * rightDim + j)] += sum7;
+              resVals[bi * size2 + (i * rightDim + j)] += sum7;
             }
           }
         }
@@ -43066,8 +43067,8 @@ function _fusedMatMul(args) {
     intermediates.push(current);
     current = activationRes;
   }
-  for (const i2 of intermediates) {
-    backend2.disposeIntermediateTensorInfo(i2);
+  for (const i of intermediates) {
+    backend2.disposeIntermediateTensorInfo(i);
   }
   return current;
 }
@@ -43095,8 +43096,8 @@ function addN2(args) {
   const vals = tensors.map((t2) => backend2.data.get(t2.dataId).values);
   const outBuf = buffer(tensors[0].shape, tensors[0].dtype);
   const outVals = outBuf.values;
-  for (let i2 = 0; i2 < tensors.length; i2++) {
-    const currVals = vals[i2];
+  for (let i = 0; i < tensors.length; i++) {
+    const currVals = vals[i];
     for (let j = 0; j < outVals.length; j++) {
       outVals[j] += currVals[j];
     }
@@ -43126,14 +43127,14 @@ function all2(args) {
   const reduceSize = util_exports.sizeFromShape(reduceShape);
   const vals = util_exports.makeZerosTypedArray(util_exports.sizeFromShape(outShape), $x.dtype);
   const aVals = backend2.data.get($x.dataId).values;
-  for (let i2 = 0; i2 < vals.length; ++i2) {
-    const offset = i2 * reduceSize;
+  for (let i = 0; i < vals.length; ++i) {
+    const offset = i * reduceSize;
     let all52 = aVals[offset];
     for (let j = 0; j < reduceSize; ++j) {
       const value = aVals[offset + j];
       all52 = all52 && value;
     }
-    vals[i2] = all52;
+    vals[i] = all52;
   }
   if (permutedAxes != null) {
     backend2.disposeIntermediateTensorInfo($x);
@@ -43170,14 +43171,14 @@ function any2(args) {
   const reduceSize = util_exports.sizeFromShape(reduceShape);
   const vals = util_exports.makeZerosTypedArray(util_exports.sizeFromShape(outShape), $x.dtype);
   const aVals = backend2.data.get($x.dataId).values;
-  for (let i2 = 0; i2 < vals.length; ++i2) {
-    const offset = i2 * reduceSize;
+  for (let i = 0; i < vals.length; ++i) {
+    const offset = i * reduceSize;
     let anyVal = aVals[offset];
     for (let j = 0; j < reduceSize; ++j) {
       const value = aVals[offset + j];
       anyVal = anyVal || value;
     }
-    vals[i2] = anyVal;
+    vals[i] = anyVal;
   }
   if (permutedAxes != null) {
     backend2.disposeIntermediateTensorInfo($x);
@@ -43217,8 +43218,8 @@ function argMax2(args) {
   const vals = util_exports.makeZerosTypedArray(outSize, "int32");
   const reduceSize = util_exports.sizeFromShape(reduceShape);
   const aVals = backend2.data.get($x.dataId).values;
-  for (let i2 = 0; i2 < vals.length; ++i2) {
-    const offset = i2 * reduceSize;
+  for (let i = 0; i < vals.length; ++i) {
+    const offset = i * reduceSize;
     let max7 = aVals[offset];
     let maxIndex = 0;
     for (let j = 0; j < reduceSize; ++j) {
@@ -43228,7 +43229,7 @@ function argMax2(args) {
         maxIndex = j;
       }
     }
-    vals[i2] = maxIndex;
+    vals[i] = maxIndex;
   }
   intermediateTensorInfos.forEach((t2) => backend2.disposeIntermediateTensorInfo(t2));
   return backend2.makeTensorInfo(outShape, "int32", vals);
@@ -43259,8 +43260,8 @@ function argMin2(args) {
   const vals = util_exports.makeZerosTypedArray(outSize, "int32");
   const reduceSize = util_exports.sizeFromShape(reduceShape);
   const aVals = backend2.data.get($x.dataId).values;
-  for (let i2 = 0; i2 < vals.length; ++i2) {
-    const offset = i2 * reduceSize;
+  for (let i = 0; i < vals.length; ++i) {
+    const offset = i * reduceSize;
     let min7 = aVals[offset];
     let minIndex = 0;
     for (let j = 0; j < reduceSize; ++j) {
@@ -43270,7 +43271,7 @@ function argMin2(args) {
         minIndex = j;
       }
     }
-    vals[i2] = minIndex;
+    vals[i] = minIndex;
   }
   intermediateTensorInfos.forEach((t2) => backend2.disposeIntermediateTensorInfo(t2));
   return backend2.makeTensorInfo(outShape, "int32", vals);
@@ -43747,8 +43748,8 @@ function batchNorm2(args) {
   let mi = 0;
   let si = 0;
   let vi = 0;
-  for (let i2 = 0; i2 < xVals.length; ++i2) {
-    outVals[i2] = offVals[offi++] + (xVals[i2] - mVals[mi++]) * sVals[si++] / Math.sqrt(varVals[vi++] + varianceEpsilon);
+  for (let i = 0; i < xVals.length; ++i) {
+    outVals[i] = offVals[offi++] + (xVals[i] - mVals[mi++]) * sVals[si++] / Math.sqrt(varVals[vi++] + varianceEpsilon);
     if (offi >= offValsLength) {
       offi = 0;
     }
@@ -43846,10 +43847,10 @@ var complexAbs = (args) => {
   const imag5 = complexVals.complexTensorInfos.imag;
   const realVals = cpuBackend.data.get(real5.dataId).values;
   const imagVals = cpuBackend.data.get(imag5.dataId).values;
-  for (let i2 = 0; i2 < realVals.length; i2++) {
-    const real6 = realVals[i2];
-    const imag6 = imagVals[i2];
-    resultValues[i2] = Math.hypot(real6, imag6);
+  for (let i = 0; i < realVals.length; i++) {
+    const real6 = realVals[i];
+    const imag6 = imagVals[i];
+    resultValues[i] = Math.hypot(real6, imag6);
   }
   return cpuBackend.makeOutput(resultValues, x.shape, "float32");
 };
@@ -43890,8 +43891,8 @@ function concat2(args) {
     const realConcated = concat2({ inputs: reals, backend: backend2, attrs: { axis: $axis } });
     const imagConcated = concat2({ inputs: imags, backend: backend2, attrs: { axis: $axis } });
     const result = complex2({ inputs: { real: realConcated, imag: imagConcated }, backend: backend2 });
-    reals.forEach((r2) => backend2.disposeIntermediateTensorInfo(r2));
-    imags.forEach((i2) => backend2.disposeIntermediateTensorInfo(i2));
+    reals.forEach((r) => backend2.disposeIntermediateTensorInfo(r));
+    imags.forEach((i) => backend2.disposeIntermediateTensorInfo(i));
     backend2.disposeIntermediateTensorInfo(realConcated);
     backend2.disposeIntermediateTensorInfo(imagConcated);
     return result;
@@ -44434,14 +44435,14 @@ function cumprod2(args) {
   const vals = util_exports.makeOnesTypedArray(util_exports.sizeFromShape($x.shape), resultDtype);
   const aVals = backend2.data.get($x.dataId).values;
   const finalDim = $x.shape[$x.shape.length - 1];
-  const indexAdjuster = reverse5 ? (i2, j) => i2 + finalDim - j - 1 : (i2, j) => i2 + j;
-  for (let i2 = 0; i2 < aVals.length; i2 += finalDim) {
+  const indexAdjuster = reverse5 ? (i, j) => i + finalDim - j - 1 : (i, j) => i + j;
+  for (let i = 0; i < aVals.length; i += finalDim) {
     for (let j = 0; j < finalDim; j++) {
-      const idx = indexAdjuster(i2, j);
+      const idx = indexAdjuster(i, j);
       if (j === 0) {
         vals[idx] = exclusive ? 1 : aVals[idx];
       } else {
-        const prevIdx = indexAdjuster(i2, j - 1);
+        const prevIdx = indexAdjuster(i, j - 1);
         vals[idx] = exclusive ? aVals[prevIdx] * vals[prevIdx] : aVals[idx] * vals[prevIdx];
       }
     }
@@ -44479,14 +44480,14 @@ function cumsum2(args) {
   const vals = util_exports.makeZerosTypedArray(util_exports.sizeFromShape($x.shape), resultDtype);
   const aVals = backend2.data.get($x.dataId).values;
   const finalDim = $x.shape[$x.shape.length - 1];
-  const indexAdjuster = reverse5 ? (i2, j) => i2 + finalDim - j - 1 : (i2, j) => i2 + j;
-  for (let i2 = 0; i2 < aVals.length; i2 += finalDim) {
+  const indexAdjuster = reverse5 ? (i, j) => i + finalDim - j - 1 : (i, j) => i + j;
+  for (let i = 0; i < aVals.length; i += finalDim) {
     for (let j = 0; j < finalDim; j++) {
-      const idx = indexAdjuster(i2, j);
+      const idx = indexAdjuster(i, j);
       if (j === 0) {
         vals[idx] = exclusive ? 0 : aVals[idx];
       } else {
-        const prevIdx = indexAdjuster(i2, j - 1);
+        const prevIdx = indexAdjuster(i, j - 1);
         vals[idx] = exclusive ? aVals[prevIdx] + vals[prevIdx] : aVals[idx] + vals[prevIdx];
       }
     }
@@ -44740,8 +44741,8 @@ function diag2(args) {
   const xVals = backend2.data.get(x.dataId).values;
   const outBuf = buffer([xSize, xSize], x.dtype);
   const vals = outBuf.values;
-  for (let i2 = 0; i2 < xVals.length; i2++) {
-    vals[i2 * xSize + i2] = xVals[i2];
+  for (let i = 0; i < xVals.length; i++) {
+    vals[i * xSize + i] = xVals[i];
   }
   const outShape = [...x.shape, ...x.shape];
   return backend2.makeTensorInfo(outShape, outBuf.dtype, outBuf.values);
@@ -44920,13 +44921,13 @@ function sum3(args) {
   const reduceSize = util_exports.sizeFromShape(reduceShape);
   const vals = backend2.data.get(result.dataId).values;
   const aVals = backend2.data.get(permutedX.dataId).values;
-  for (let i2 = 0; i2 < vals.length; ++i2) {
-    const offset = i2 * reduceSize;
+  for (let i = 0; i < vals.length; ++i) {
+    const offset = i * reduceSize;
     let sum7 = 0;
     for (let j = 0; j < reduceSize; ++j) {
       sum7 += aVals[offset + j];
     }
-    vals[i2] = sum7;
+    vals[i] = sum7;
   }
   if (keepDims) {
     const newShape = backend_util_exports.expandShapeToKeepDim(result.shape, axes);
@@ -44956,8 +44957,8 @@ function einsum2(args) {
   let out = null;
   let numDimsRemaining = allDims.length;
   const tensorsToDispose = [];
-  for (let i2 = 0; i2 < nSteps; ++i2) {
-    for (const idTerm of steps[i2]) {
+  for (let i = 0; i < nSteps; ++i) {
+    for (const idTerm of steps[i]) {
       const { permutationIndices: perm, expandDims: dimsToExpand } = backend_util_exports.getEinsumPermutation(numDimsRemaining, idDims[idTerm]);
       let x;
       if (backend_util_exports.isIdentityPermutation(perm)) {
@@ -44981,13 +44982,13 @@ function einsum2(args) {
         tensorsToDispose.push(out);
       }
     }
-    if (i2 < nSteps - 1) {
-      if (path[i2] >= 0) {
+    if (i < nSteps - 1) {
+      if (path[i] >= 0) {
         out = sum3({
           inputs: { x: out },
           backend: backend2,
           attrs: {
-            axis: path[i2] - (allDims.length - numDimsRemaining),
+            axis: path[i] - (allDims.length - numDimsRemaining),
             keepDims: false
           }
         });
@@ -45016,12 +45017,12 @@ function eluGrad(args) {
   const resultValues = new Float32Array(util_exports.sizeFromShape(y.shape));
   const values = backend2.data.get(y.dataId).values;
   const dyValues = backend2.data.get(dy.dataId).values;
-  for (let i2 = 0; i2 < values.length; ++i2) {
-    const v = values[i2];
+  for (let i = 0; i < values.length; ++i) {
+    const v = values[i];
     if (v >= 1) {
-      resultValues[i2] = dyValues[i2];
+      resultValues[i] = dyValues[i];
     } else {
-      resultValues[i2] = dyValues[i2] * (v + 1);
+      resultValues[i] = dyValues[i] * (v + 1);
     }
   }
   return backend2.makeTensorInfo(y.shape, "float32", resultValues);
@@ -45086,17 +45087,17 @@ function fftBatch(input2, inverse, cpuBackend) {
   const resultReal = util_exports.getTypedArrayFromDType("float32", resultSize);
   const resultImag = util_exports.getTypedArrayFromDType("float32", resultSize);
   for (let b = 0; b < batch; b++) {
-    const r2 = slice2({
+    const r = slice2({
       inputs: { x: real2D },
       backend: cpuBackend,
       attrs: { begin: [b, 0], size: [1, innerDim] }
     });
-    const i2 = slice2({
+    const i = slice2({
       inputs: { x: imag2D },
       backend: cpuBackend,
       attrs: { begin: [b, 0], size: [1, innerDim] }
     });
-    const input3 = complex2({ inputs: { real: r2, imag: i2 }, backend: cpuBackend });
+    const input3 = complex2({ inputs: { real: r, imag: i }, backend: cpuBackend });
     const { real: real5, imag: imag5 } = fftImpl(input3, inverse, cpuBackend);
     const res = backend_util_exports.mergeRealAndImagArrays(real5, imag5);
     for (let d = 0; d < innerDim; d++) {
@@ -45104,8 +45105,8 @@ function fftBatch(input2, inverse, cpuBackend) {
       resultReal[b * innerDim + d] = c.real;
       resultImag[b * innerDim + d] = c.imag;
     }
-    cpuBackend.disposeIntermediateTensorInfo(r2);
-    cpuBackend.disposeIntermediateTensorInfo(i2);
+    cpuBackend.disposeIntermediateTensorInfo(r);
+    cpuBackend.disposeIntermediateTensorInfo(i);
     cpuBackend.disposeIntermediateTensorInfo(input3);
   }
   const $realInfo = cpuBackend.makeTensorInfo(resultShape, "float32", resultReal);
@@ -45187,10 +45188,10 @@ function fftRadix2(realVals, imagVals, size2, inverse, cpuBackend) {
   const $oddRealInfo = cpuBackend.makeTensorInfo($oddShape, "float32", $oddRealVals);
   const $oddImagInfo = cpuBackend.makeTensorInfo($oddShape, "float32", $oddImagVals);
   const $oddTensorInfo = complex2({ inputs: { real: $oddRealInfo, imag: $oddImagInfo }, backend: cpuBackend });
-  const e2 = backend_util_exports.exponents(size2, inverse);
-  const eShape = [e2.real.length];
-  const eRealInfo = cpuBackend.makeTensorInfo(eShape, "float32", e2.real);
-  const eImagInfo = cpuBackend.makeTensorInfo(eShape, "float32", e2.imag);
+  const e = backend_util_exports.exponents(size2, inverse);
+  const eShape = [e.real.length];
+  const eRealInfo = cpuBackend.makeTensorInfo(eShape, "float32", e.real);
+  const eImagInfo = cpuBackend.makeTensorInfo(eShape, "float32", e.imag);
   const complexInfo = complex2({ inputs: { real: eRealInfo, imag: eImagInfo }, backend: cpuBackend });
   const exponentInfo = multiply2({ inputs: { a: complexInfo, b: $oddTensorInfo }, backend: cpuBackend });
   const addPart = add4({
@@ -45245,20 +45246,20 @@ function fftRadix2(realVals, imagVals, size2, inverse, cpuBackend) {
 }
 function fourierTransformByMatmul(data, size2, inverse) {
   const ret = new Float32Array(size2 * 2);
-  for (let r2 = 0; r2 < size2; r2++) {
+  for (let r = 0; r < size2; r++) {
     let real5 = 0;
     let imag5 = 0;
     for (let c = 0; c < size2; c++) {
-      const e2 = backend_util_exports.exponent(r2 * c, size2, inverse);
+      const e = backend_util_exports.exponent(r * c, size2, inverse);
       const term = backend_util_exports.getComplexWithIndex(data, c);
-      real5 += term.real * e2.real - term.imag * e2.imag;
-      imag5 += term.real * e2.imag + term.imag * e2.real;
+      real5 += term.real * e.real - term.imag * e.imag;
+      imag5 += term.real * e.imag + term.imag * e.real;
     }
     if (inverse) {
       real5 /= size2;
       imag5 /= size2;
     }
-    backend_util_exports.assignToTypedArray(ret, real5, imag5, r2);
+    backend_util_exports.assignToTypedArray(ret, real5, imag5, r);
   }
   return ret;
 }
@@ -45440,8 +45441,8 @@ function gatherV2(args) {
   const parsedAxis = util_exports.parseAxisParam(axis, x.shape)[0];
   const indicesVals = backend2.data.get(indices.dataId).values;
   const axisDim = x.shape[parsedAxis];
-  for (let i2 = 0; i2 < indicesVals.length; ++i2) {
-    const index2 = indicesVals[i2];
+  for (let i = 0; i < indicesVals.length; ++i) {
+    const index2 = indicesVals[i];
     util_exports.assert(index2 <= axisDim - 1 && index2 >= 0, () => `GatherV2: the index value ${index2} is not in [0, ${axisDim - 1}]`);
   }
   let $batchDims = batchDims;
@@ -45645,8 +45646,8 @@ function max3(args) {
   let xVals = cpuBackend.data.get(x.dataId).values;
   if (permutedAxes != null) {
     const newShape = new Array(xRank);
-    for (let i2 = 0; i2 < newShape.length; i2++) {
-      newShape[i2] = xShape[permutedAxes[i2]];
+    for (let i = 0; i < newShape.length; i++) {
+      newShape[i] = xShape[permutedAxes[i]];
     }
     xVals = transposeImpl(xVals, xShape, x.dtype, permutedAxes, newShape);
     axes = backend_util_exports.getInnerMostAxes(axes.length, xRank);
@@ -45905,8 +45906,8 @@ function min3(args) {
   const reduceSize = util_exports.sizeFromShape(reduceShape);
   const vals = util_exports.makeZerosTypedArray(util_exports.sizeFromShape(outShape), $x.dtype);
   const aVals = backend2.data.get($x.dataId).values;
-  for (let i2 = 0; i2 < vals.length; ++i2) {
-    const offset = i2 * reduceSize;
+  for (let i = 0; i < vals.length; ++i) {
+    const offset = i * reduceSize;
     let min7 = aVals[offset];
     for (let j = 0; j < reduceSize; ++j) {
       const value = aVals[offset + j];
@@ -45914,7 +45915,7 @@ function min3(args) {
         min7 = value;
       }
     }
-    vals[i2] = min7;
+    vals[i] = min7;
   }
   if (permutedAxes != null) {
     backend2.disposeIntermediateTensorInfo($x);
@@ -45938,9 +45939,9 @@ function mirrorPad2(args) {
   const { x } = inputs;
   const { paddings, mode } = attrs;
   assertNotComplex(x, "mirrorPad");
-  const outShape = paddings.map((p2, i2) => p2[0] + x.shape[i2] + p2[1]);
+  const outShape = paddings.map((p2, i) => p2[0] + x.shape[i] + p2[1]);
   const start = paddings.map((p2) => p2[0]);
-  const end = paddings.map((p2, i2) => p2[0] + x.shape[i2]);
+  const end = paddings.map((p2, i) => p2[0] + x.shape[i]);
   const offset = mode === "reflect" ? 0 : 1;
   const xVals = backend2.data.get(x.dataId).values;
   const xRank = x.shape.length;
@@ -45949,18 +45950,18 @@ function mirrorPad2(args) {
   const resultRank = outShape.length;
   const resultStrides = util_exports.computeStrides(outShape);
   const resVals = util_exports.getTypedArrayFromDType(x.dtype, resultSize);
-  for (let i2 = 0; i2 < resultSize; i2++) {
-    let coords3 = util_exports.indexToLoc(i2, resultRank, resultStrides);
-    for (let i3 = 0; i3 < resultRank; i3++) {
-      if (coords3[i3] < start[i3]) {
-        coords3[i3] = start[i3] * 2 - coords3[i3] - offset;
-      } else if (coords3[i3] >= end[i3]) {
-        coords3[i3] = (end[i3] - 1) * 2 - coords3[i3] + offset;
+  for (let i = 0; i < resultSize; i++) {
+    let coords3 = util_exports.indexToLoc(i, resultRank, resultStrides);
+    for (let i2 = 0; i2 < resultRank; i2++) {
+      if (coords3[i2] < start[i2]) {
+        coords3[i2] = start[i2] * 2 - coords3[i2] - offset;
+      } else if (coords3[i2] >= end[i2]) {
+        coords3[i2] = (end[i2] - 1) * 2 - coords3[i2] + offset;
       }
     }
-    coords3 = coords3.map((c, i3) => c - start[i3]);
+    coords3 = coords3.map((c, i2) => c - start[i2]);
     const inIndex = util_exports.locToIndex(coords3, xRank, xStrides);
-    resVals[i2] = xVals[inIndex];
+    resVals[i] = xVals[inIndex];
   }
   const outId = backend2.write(resVals, outShape, x.dtype);
   return { dataId: outId, shape: outShape, dtype: x.dtype };
@@ -46044,10 +46045,10 @@ function multinomial2(args) {
     const random = seedrandom4.alea(seed.toString());
     const outOffset = b * numSamples;
     for (let sampleId = 0; sampleId < numSamples; ++sampleId) {
-      const r2 = random();
+      const r = random();
       resVals[outOffset + sampleId] = cdf.length;
       for (let event = 0; event < cdf.length; event++) {
-        if (r2 < cdf[event]) {
+        if (r < cdf[event]) {
           resVals[outOffset + sampleId] = event;
           break;
         }
@@ -46150,14 +46151,14 @@ function zerosLike2(args) {
     throw new Error("zerosLike is not supported for string tensors");
   } else if (x.dtype === "complex64") {
     const realPart = real2({ inputs: { input: x }, backend: backend2 });
-    const r2 = zerosLike2({ inputs: { x: realPart }, backend: backend2 });
+    const r = zerosLike2({ inputs: { x: realPart }, backend: backend2 });
     const imagPart = imag2({ inputs: { input: x }, backend: backend2 });
-    const i2 = zerosLike2({ inputs: { x: imagPart }, backend: backend2 });
-    const result = complex2({ inputs: { real: r2, imag: i2 }, backend: backend2 });
+    const i = zerosLike2({ inputs: { x: imagPart }, backend: backend2 });
+    const result = complex2({ inputs: { real: r, imag: i }, backend: backend2 });
     backend2.disposeIntermediateTensorInfo(realPart);
-    backend2.disposeIntermediateTensorInfo(r2);
+    backend2.disposeIntermediateTensorInfo(r);
     backend2.disposeIntermediateTensorInfo(imagPart);
-    backend2.disposeIntermediateTensorInfo(i2);
+    backend2.disposeIntermediateTensorInfo(i);
     return result;
   } else {
     return fill2({ backend: backend2, attrs: { shape: x.shape, value: 0, dtype: x.dtype } });
@@ -46175,14 +46176,14 @@ function onesLike2(args) {
     throw new Error("onesLike is not supported for string tensors");
   } else if (x.dtype === "complex64") {
     const realPart = real2({ inputs: { input: x }, backend: backend2 });
-    const r2 = onesLike2({ inputs: { x: realPart }, backend: backend2 });
+    const r = onesLike2({ inputs: { x: realPart }, backend: backend2 });
     const imagPart = imag2({ inputs: { input: x }, backend: backend2 });
-    const i2 = zerosLike2({ inputs: { x: imagPart }, backend: backend2 });
-    const result = complex2({ inputs: { real: r2, imag: i2 }, backend: backend2 });
+    const i = zerosLike2({ inputs: { x: imagPart }, backend: backend2 });
+    const result = complex2({ inputs: { real: r, imag: i }, backend: backend2 });
     backend2.disposeIntermediateTensorInfo(realPart);
-    backend2.disposeIntermediateTensorInfo(r2);
+    backend2.disposeIntermediateTensorInfo(r);
     backend2.disposeIntermediateTensorInfo(imagPart);
-    backend2.disposeIntermediateTensorInfo(i2);
+    backend2.disposeIntermediateTensorInfo(i);
     return result;
   } else {
     return fill2({ backend: backend2, attrs: { shape: x.shape, value: 1, dtype: x.dtype } });
@@ -46225,7 +46226,7 @@ function padV2(args) {
   const { x } = inputs;
   const { paddings, constantValue } = attrs;
   assertNotComplex(x, "pad");
-  const outShape = paddings.map((p2, i2) => p2[0] + x.shape[i2] + p2[1]);
+  const outShape = paddings.map((p2, i) => p2[0] + x.shape[i] + p2[1]);
   const start = paddings.map((p2) => p2[0]);
   const xVals = backend2.data.get(x.dataId).values;
   const xSize = util_exports.sizeFromShape(x.shape);
@@ -46238,11 +46239,11 @@ function padV2(args) {
   if (constantValue !== 0) {
     resVals.fill(constantValue);
   }
-  for (let i2 = 0; i2 < xSize; i2++) {
-    const coords3 = util_exports.indexToLoc(i2, xRank, xStrides);
-    const outCoords = coords3.map((c, i3) => c + start[i3]);
+  for (let i = 0; i < xSize; i++) {
+    const coords3 = util_exports.indexToLoc(i, xRank, xStrides);
+    const outCoords = coords3.map((c, i2) => c + start[i2]);
     const outIndex = util_exports.locToIndex(outCoords, resultRank, resultStrides);
-    resVals[outIndex] = xVals[i2];
+    resVals[outIndex] = xVals[i];
   }
   const outId = backend2.write(resVals, outShape, x.dtype);
   return { dataId: outId, shape: outShape, dtype: x.dtype };
@@ -46315,12 +46316,12 @@ function resizeBilinear2(args) {
   const effectiveRowSizeRatio = effectiveInputSize[0] / effectiveOutputSize[0];
   const effectiveColSizeRatio = effectiveInputSize[1] / effectiveOutputSize[1];
   for (let b = 0; b < batch; b++) {
-    for (let r2 = 0; r2 < newHeight; r2++) {
+    for (let r = 0; r < newHeight; r++) {
       let sourceFracRow;
       if (halfPixelCenters) {
-        sourceFracRow = effectiveRowSizeRatio * (r2 + 0.5) - 0.5;
+        sourceFracRow = effectiveRowSizeRatio * (r + 0.5) - 0.5;
       } else {
-        sourceFracRow = effectiveRowSizeRatio * r2;
+        sourceFracRow = effectiveRowSizeRatio * r;
       }
       const sourceRowFloor = Math.max(0, Math.floor(sourceFracRow));
       const rowFrac = sourceFracRow - sourceRowFloor;
@@ -46384,8 +46385,8 @@ function resizeBilinearGrad(args) {
   let offset = 0;
   for (let b = 0; b < batch; b++) {
     const bOffset = b * imagesStrides[0];
-    for (let r2 = 0; r2 < yHeight; r2++) {
-      const dxR = r2 * heightScale;
+    for (let r = 0; r < yHeight; r++) {
+      const dxR = r * heightScale;
       const topDxRIndex = Math.floor(dxR);
       const bottomDxRIndex = Math.min(Math.ceil(dxR), xHeight - 1);
       const topDxROffset = bOffset + topDxRIndex * imagesStrides[1];
@@ -46446,8 +46447,8 @@ function resizeNearestNeighbor2(args) {
   let outputOffset = 0;
   for (let b = 0; b < batch; b++) {
     const batchOffset = b * imagesStrides[0];
-    for (let r2 = 0; r2 < newHeight; r2++) {
-      const sourceFracRow = halfPixelCenters ? effectiveRowSizeRatio * (r2 + 0.5) : effectiveRowSizeRatio * r2;
+    for (let r = 0; r < newHeight; r++) {
+      const sourceFracRow = halfPixelCenters ? effectiveRowSizeRatio * (r + 0.5) : effectiveRowSizeRatio * r;
       let sourceNearestRow = Math.min(oldHeight - 1, alignCorners ? Math.round(sourceFracRow) : Math.floor(sourceFracRow));
       if (halfPixelCenters) {
         sourceNearestRow = Math.max(0, sourceNearestRow);
@@ -46501,9 +46502,9 @@ function resizeNearestNeighborGrad(args) {
   const winWidth = Math.ceil(invWidthScale) * 2 + 2;
   for (let b = 0; b < batch; b++) {
     const batchOffset = b * imagesStrides[0];
-    for (let r2 = 0; r2 < xHeight; r2++) {
-      const rowOffset = batchOffset + r2 * imagesStrides[1];
-      const startRLerp = Math.floor(r2 * invHeightScale);
+    for (let r = 0; r < xHeight; r++) {
+      const rowOffset = batchOffset + r * imagesStrides[1];
+      const startRLerp = Math.floor(r * invHeightScale);
       const startDyR = Math.floor(startRLerp - winHeight / 2);
       for (let c = 0; c < xWidth; c++) {
         const colOffset = rowOffset + c * imagesStrides[2];
@@ -46519,7 +46520,7 @@ function resizeNearestNeighborGrad(args) {
             const dyROffset = batchOffset + dyR * dyStrides[1];
             const sourceFracRow = dyR * heightScale;
             const sourceNearestRow = Math.min(xHeight - 1, alignCorners ? Math.round(sourceFracRow) : Math.floor(sourceFracRow));
-            if (r2 !== sourceNearestRow) {
+            if (r !== sourceNearestRow) {
               continue;
             }
             for (let dyCIndex = 0; dyCIndex < winWidth; dyCIndex++) {
@@ -46559,8 +46560,8 @@ function reverse2(args) {
   }
   const outBuf = new TensorBuffer(x.shape, x.dtype);
   const xBuf = backend2.bufferSync(x);
-  for (let i2 = 0; i2 < outBuf.size; i2++) {
-    const outLoc = outBuf.indexToLoc(i2);
+  for (let i = 0; i < outBuf.size; i++) {
+    const outLoc = outBuf.indexToLoc(i);
     const inLoc = outLoc.slice();
     $dims.forEach((d) => inLoc[d] = x.shape[d] - 1 - inLoc[d]);
     outBuf.set(xBuf.get(...inLoc), ...outLoc);
@@ -46692,8 +46693,8 @@ function searchSortedImpl(sortedInputs, values, batchSize, numInputs, numValues,
   for (let b = 0; b < batchSize; ++b) {
     const sortedInputsSlice = sortedInputs.slice(b * numInputs, (b + 1) * numInputs);
     const outputOffset = b * numValues;
-    for (let i2 = 0; i2 < numValues; ++i2) {
-      output[outputOffset + i2] = side === "left" ? lowerBound2(sortedInputsSlice, values[i2 + outputOffset]) : upperBound2(sortedInputsSlice, values[i2 + outputOffset]);
+    for (let i = 0; i < numValues; ++i) {
+      output[outputOffset + i] = side === "left" ? lowerBound2(sortedInputsSlice, values[i + outputOffset]) : upperBound2(sortedInputsSlice, values[i + outputOffset]);
     }
   }
   return output;
@@ -46714,22 +46715,22 @@ var searchSortedConfig = {
 };
 function select2(args) {
   const { inputs, backend: backend2 } = args;
-  const { condition, t: t2, e: e2 } = inputs;
-  assertNotComplex([condition, t2, e2], "select");
+  const { condition, t: t2, e } = inputs;
+  assertNotComplex([condition, t2, e], "select");
   const conditionRank = condition.shape.length;
   const values = backend2.data.get(condition.dataId).values;
   const tValues = backend2.data.get(t2.dataId).values;
-  const eValues = backend2.data.get(e2.dataId).values;
-  const resultDtype = upcastType(t2.dtype, e2.dtype);
+  const eValues = backend2.data.get(e.dataId).values;
+  const resultDtype = upcastType(t2.dtype, e.dtype);
   const newValues = util_exports.makeZerosTypedArray(util_exports.sizeFromShape(t2.shape), resultDtype);
   let index2 = 0;
   const offset = conditionRank === 0 || conditionRank > 1 || t2.shape.length === 1 ? 1 : util_exports.sizeFromShape(t2.shape.slice(1));
-  for (let i2 = 0; i2 < values.length; i2++) {
+  for (let i = 0; i < values.length; i++) {
     for (let j = 0; j < offset; j++) {
-      if (values[i2] === 1) {
-        newValues[index2++] = tValues[i2];
+      if (values[i] === 1) {
+        newValues[index2++] = tValues[i];
       } else {
-        newValues[index2++] = eValues[i2];
+        newValues[index2++] = eValues[i];
       }
     }
   }
@@ -46809,7 +46810,7 @@ function spaceToBatchND2(args) {
   const prod6 = util_exports.sizeFromShape(blockShape);
   const completePaddings = [[0, 0]];
   completePaddings.push(...paddings);
-  for (let i2 = 1 + blockShape.length; i2 < x.shape.length; ++i2) {
+  for (let i = 1 + blockShape.length; i < x.shape.length; ++i) {
     completePaddings.push([0, 0]);
   }
   const paddedX = padV2Config.kernelFunc({
@@ -47010,11 +47011,11 @@ function splitV(args) {
   const splitSizes = backend_util_exports.prepareSplitSize(x, numOrSizeSplits, $axis);
   const begin = new Array(x.shape.length).fill(0);
   const size2 = x.shape.slice();
-  return splitSizes.map((s2) => {
+  return splitSizes.map((s) => {
     const sliceSize = [...size2];
-    sliceSize[$axis] = s2;
+    sliceSize[$axis] = s;
     const sliceT = slice2({ inputs: { x }, backend: backend2, attrs: { begin, size: sliceSize } });
-    begin[$axis] += s2;
+    begin[$axis] += s;
     return sliceT;
   });
 }
@@ -47032,9 +47033,9 @@ var squareConfig = {
     assertNotComplex(x, "square");
     const values = cpuBackend.data.get(x.dataId).values;
     const newValues = new Float32Array(values.length);
-    for (let i2 = 0; i2 < values.length; ++i2) {
-      const value = values[i2];
-      newValues[i2] = value * value;
+    for (let i = 0; i < values.length; ++i) {
+      const value = values[i];
+      newValues[i] = value * value;
     }
     const dataId = cpuBackend.write(newValues, x.shape, x.dtype);
     return { dataId, shape: x.shape, dtype: x.dtype };
@@ -47357,19 +47358,19 @@ function unpack(args) {
   const num = value.shape[axis];
   const outShape = new Array(valueRank - 1);
   let outIndex = 0;
-  for (let i2 = 0; i2 < valueRank; i2++) {
-    if (i2 !== axis) {
-      outShape[outIndex++] = value.shape[i2];
+  for (let i = 0; i < valueRank; i++) {
+    if (i !== axis) {
+      outShape[outIndex++] = value.shape[i];
     }
   }
   const begin = new Array(valueRank).fill(0);
   const size2 = value.shape.slice();
   size2[axis] = 1;
   const res = new Array(num);
-  for (let i2 = 0; i2 < res.length; i2++) {
-    begin[axis] = i2;
+  for (let i = 0; i < res.length; i++) {
+    begin[axis] = i;
     const tempRes = slice2({ inputs: { x: value }, backend: backend2, attrs: { begin, size: size2 } });
-    res[i2] = reshape3({ inputs: { x: tempRes }, backend: backend2, attrs: { shape: outShape } });
+    res[i] = reshape3({ inputs: { x: tempRes }, backend: backend2, attrs: { shape: outShape } });
     backend2.disposeIntermediateTensorInfo(tempRes);
   }
   return res;
@@ -47390,13 +47391,13 @@ function unsortedSegmentSum2(args) {
   const intermediates = [];
   const numIters = xRank - segmentIdsRank;
   let $segmentIds = segmentIds;
-  for (let i2 = 0; i2 < numIters; ++i2) {
-    const expanded = expandDims3({ inputs: { input: $segmentIds }, backend: backend2, attrs: { dim: i2 + 1 } });
+  for (let i = 0; i < numIters; ++i) {
+    const expanded = expandDims3({ inputs: { input: $segmentIds }, backend: backend2, attrs: { dim: i + 1 } });
     $segmentIds = expanded;
     intermediates.push(expanded);
   }
-  for (let i2 = 0; i2 < numSegments; ++i2) {
-    const scalarValue = util_exports.createScalarValue(i2, "int32");
+  for (let i = 0; i < numSegments; ++i) {
+    const scalarValue = util_exports.createScalarValue(i, "int32");
     const segmentId = backend2.makeTensorInfo([], "int32", scalarValue);
     const mask2 = equal2({ inputs: { a: segmentId, b: $segmentIds }, backend: backend2 });
     const maskCasted = cast3({ inputs: { x: mask2 }, backend: backend2, attrs: { dtype: "float32" } });
@@ -47869,8 +47870,8 @@ function logShaderSourceAndInfoLog(shaderSource, shaderInfoLog) {
   const pad3 = shaderLines.length.toString().length + 2;
   const linesWithLineNumbers = shaderLines.map((line, lineNumber2) => util_exports.rightPad((lineNumber2 + 1).toString(), pad3) + line);
   let maxLineLength = 0;
-  for (let i2 = 0; i2 < linesWithLineNumbers.length; i2++) {
-    maxLineLength = Math.max(linesWithLineNumbers[i2].length, maxLineLength);
+  for (let i = 0; i < linesWithLineNumbers.length; i++) {
+    maxLineLength = Math.max(linesWithLineNumbers[i].length, maxLineLength);
   }
   const beforeErrorLines = linesWithLineNumbers.slice(0, lineNumber - 1);
   const errorLine = linesWithLineNumbers.slice(lineNumber - 1, lineNumber);
@@ -48038,7 +48039,7 @@ function getTextureShapeFromLogicalShape(logShape, isPacked = false) {
   let maxTexSize = env().getNumber("WEBGL_MAX_TEXTURE_SIZE");
   if (isPacked) {
     maxTexSize = maxTexSize * 2;
-    logShape = logShape.map((d, i2) => i2 >= logShape.length - 2 ? util_exports.nearestLargerEven(logShape[i2]) : logShape[i2]);
+    logShape = logShape.map((d, i) => i >= logShape.length - 2 ? util_exports.nearestLargerEven(logShape[i]) : logShape[i]);
     if (logShape.length === 1) {
       logShape = [2, logShape[0]];
     }
@@ -48073,8 +48074,8 @@ function getTextureShapeFromLogicalShape(logShape, isPacked = false) {
     return util_exports.sizeToSquarishShape(size2);
   }
 }
-function isEven(n2) {
-  return n2 % 2 === 0;
+function isEven(n) {
+  return n % 2 === 0;
 }
 function isReshapeFree(shape1, shape2) {
   shape1 = shape1.slice(-2);
@@ -48147,8 +48148,8 @@ function isWebGLVersionEnabled(webGLVersion) {
     if (gl != null) {
       return true;
     }
-  } catch (e2) {
-    console.log("Error when getting WebGL context: ", e2);
+  } catch (e) {
+    console.log("Error when getting WebGL context: ", e);
     return false;
   }
   return false;
@@ -48314,7 +48315,7 @@ ENV5.registerFlag("TOPK_K_CPU_HANDOFF_THRESHOLD", () => 128);
 ENV5.registerFlag("WEBGL_EXP_CONV", () => false);
 ENV5.registerFlag("SOFTWARE_WEBGL_ENABLED", () => ENV5.getBool("IS_TEST"));
 function getGlslDifferences() {
-  let version9;
+  let version10;
   let attribute;
   let varyingVs;
   let varyingFs;
@@ -48325,7 +48326,7 @@ function getGlslDifferences() {
   let defineSpecialInf;
   let defineRound;
   if (env().getNumber("WEBGL_VERSION") === 2) {
-    version9 = "#version 300 es";
+    version10 = "#version 300 es";
     attribute = "in";
     varyingVs = "out";
     varyingFs = "in";
@@ -48357,7 +48358,7 @@ function getGlslDifferences() {
       }
     `;
   } else {
-    version9 = "";
+    version10 = "";
     attribute = "attribute";
     varyingVs = "varying";
     varyingFs = "varying";
@@ -48394,7 +48395,7 @@ function getGlslDifferences() {
     `;
   }
   return {
-    version: version9,
+    version: version10,
     attribute,
     varyingVs,
     varyingFs,
@@ -48408,17 +48409,17 @@ function getGlslDifferences() {
 }
 function getLogicalCoordinatesFromFlatIndex(coords3, shape, index2 = "index") {
   const strides2 = util_exports.computeStrides(shape);
-  return strides2.map((stride, i2) => {
-    const line1 = `int ${coords3[i2]} = ${index2} / ${stride}`;
-    const line2 = i2 === strides2.length - 1 ? `int ${coords3[i2 + 1]} = ${index2} - ${coords3[i2]} * ${stride}` : `index -= ${coords3[i2]} * ${stride}`;
+  return strides2.map((stride, i) => {
+    const line1 = `int ${coords3[i]} = ${index2} / ${stride}`;
+    const line2 = i === strides2.length - 1 ? `int ${coords3[i + 1]} = ${index2} - ${coords3[i]} * ${stride}` : `index -= ${coords3[i]} * ${stride}`;
     return `${line1}; ${line2};`;
   }).join("");
 }
 function getOutputLogicalCoordinatesFromFlatIndexByUniform(coords3, shape, index2 = "index") {
   const strides2 = util_exports.computeStrides(shape);
-  return strides2.map((_, i2) => {
-    const line1 = `int ${coords3[i2]} = ${index2} / outShapeStrides[${i2}]`;
-    const line2 = i2 === strides2.length - 1 ? `int ${coords3[i2 + 1]} = ${index2} - ${coords3[i2]} * outShapeStrides[${i2}]` : `index -= ${coords3[i2]} * outShapeStrides[${i2}]`;
+  return strides2.map((_, i) => {
+    const line1 = `int ${coords3[i]} = ${index2} / outShapeStrides[${i}]`;
+    const line2 = i === strides2.length - 1 ? `int ${coords3[i + 1]} = ${index2} - ${coords3[i]} * outShapeStrides[${i}]` : `index -= ${coords3[i]} * outShapeStrides[${i}]`;
     return `${line1}; ${line2};`;
   }).join("");
 }
@@ -48427,17 +48428,17 @@ function symbolicallyComputeStrides(indicesArr, variableName) {
   const shape = indicesArr.map((d) => `${variableName}[${d}]`);
   const strides2 = new Array(numCoords - 1);
   strides2[numCoords - 2] = shape[numCoords - 1];
-  for (let i2 = numCoords - 3; i2 >= 0; --i2) {
-    strides2[i2] = `(${strides2[i2 + 1]} * ${shape[i2 + 1]})`;
+  for (let i = numCoords - 3; i >= 0; --i) {
+    strides2[i] = `(${strides2[i + 1]} * ${shape[i + 1]})`;
   }
   return strides2;
 }
 function getLogicalCoordinatesFromFlatIndexByUniform(coords3, variableName, index2 = "index") {
-  const indicesArray = coords3.map((_, i2) => i2);
+  const indicesArray = coords3.map((_, i) => i);
   const strides2 = symbolicallyComputeStrides(indicesArray, variableName);
-  return strides2.map((_, i2) => {
-    const line1 = `int ${coords3[i2]} = ${index2} / ${strides2[i2]}`;
-    const line2 = i2 === strides2.length - 1 ? `int ${coords3[i2 + 1]} = ${index2} - ${coords3[i2]} * ${strides2[i2]}` : `index -= ${coords3[i2]} * ${strides2[i2]}`;
+  return strides2.map((_, i) => {
+    const line1 = `int ${coords3[i]} = ${index2} / ${strides2[i]}`;
+    const line2 = i === strides2.length - 1 ? `int ${coords3[i + 1]} = ${index2} - ${coords3[i]} * ${strides2[i]}` : `index -= ${coords3[i]} * ${strides2[i]}`;
     return `${line1}; ${line2};`;
   }).join("");
 }
@@ -49966,7 +49967,7 @@ function getPackedSamplerAtOutputCoords(inputInfo, outShapeInfo) {
   if (outRank < 2 && inRank > 0) {
     unpackedCoordsSnippet = "coords";
   } else {
-    unpackedCoordsSnippet = inputInfo.shapeInfo.logicalShape.map((s2, i2) => `coords.${fields[i2 + rankDiff]}`).join(", ");
+    unpackedCoordsSnippet = inputInfo.shapeInfo.logicalShape.map((s, i) => `coords.${fields[i + rankDiff]}`).join(", ");
   }
   let output = `return outputValue;`;
   const inSize = util_exports.sizeFromShape(inputInfo.shapeInfo.logicalShape);
@@ -50038,7 +50039,7 @@ function getSamplerAtOutputCoords(inputInfo, outShapeInfo) {
   if (outRank < 2 && inRank > 0) {
     unpackedCoordsSnippet = "coords";
   } else {
-    unpackedCoordsSnippet = inputInfo.shapeInfo.logicalShape.map((s2, i2) => `coords.${fields[i2 + rankDiff]}`).join(", ");
+    unpackedCoordsSnippet = inputInfo.shapeInfo.logicalShape.map((s, i) => `coords.${fields[i + rankDiff]}`).join(", ");
   }
   return `
     float ${funcName}() {
@@ -50083,7 +50084,7 @@ function getSqueezedParams(params, keptDims) {
   return keptDims.map((d) => params[d]).join(", ");
 }
 function compileProgram(gpgpu, program, inputs, output) {
-  const inputInfos = inputs.map((input2, i2) => {
+  const inputInfos = inputs.map((input2, i) => {
     const shapeInfo = {
       logicalShape: input2.shape,
       texShape: input2.isUniform ? null : input2.texData.texShape,
@@ -50094,7 +50095,7 @@ function compileProgram(gpgpu, program, inputs, output) {
     if (input2.texData != null && input2.texData.slice != null && input2.texData.slice.flatOffset > 0) {
       shapeInfo.flatOffset = input2.texData.slice.flatOffset;
     }
-    return { name: program.variableNames[i2], shapeInfo };
+    return { name: program.variableNames[i], shapeInfo };
   });
   const inShapeInfos = inputInfos.map((x) => x.shapeInfo);
   const outShapeInfo = {
@@ -50151,8 +50152,8 @@ function getUniformLocations(gpgpu, program, webGLProgram) {
     infLoc = gpgpu.getUniformLocation(webGLProgram, "INFINITY", false);
   }
   const shouldThrow = false;
-  for (let i2 = 0; i2 < program.variableNames.length; i2++) {
-    const varName = program.variableNames[i2];
+  for (let i = 0; i < program.variableNames.length; i++) {
+    const varName = program.variableNames[i];
     uniformLocations[varName] = gpgpu.getUniformLocation(webGLProgram, varName, shouldThrow);
     uniformLocations[`offset${varName}`] = gpgpu.getUniformLocation(webGLProgram, `offset${varName}`, shouldThrow);
     if (program.enableShapeUniforms) {
@@ -50166,8 +50167,8 @@ function getUniformLocations(gpgpu, program, webGLProgram) {
     outTexShapeLocation = gpgpu.getUniformLocation(webGLProgram, "outTexShape", shouldThrow);
   }
   if (program.customUniforms) {
-    program.customUniforms.forEach((d, i2) => {
-      customUniformLocations[i2] = gpgpu.getUniformLocation(webGLProgram, d.name, shouldThrow);
+    program.customUniforms.forEach((d, i) => {
+      customUniformLocations[i] = gpgpu.getUniformLocation(webGLProgram, d.name, shouldThrow);
     });
   }
   return {
@@ -50186,17 +50187,17 @@ function validateBinaryAndProgram(shapeInfos, inputs) {
   if (shapeInfos.length !== inputs.length) {
     throw Error(`Binary was compiled with ${shapeInfos.length} inputs, but was executed with ${inputs.length} inputs`);
   }
-  shapeInfos.forEach((s2, i2) => {
-    const shapeA = s2.logicalShape;
-    const input2 = inputs[i2];
+  shapeInfos.forEach((s, i) => {
+    const shapeA = s.logicalShape;
+    const input2 = inputs[i];
     const shapeB = input2.shape;
     if (!util_exports.arraysEqual(shapeA, shapeB)) {
       throw Error(`Binary was compiled with different shapes than the current args. Shapes ${shapeA} and ${shapeB} must match`);
     }
-    if (s2.isUniform && input2.isUniform) {
+    if (s.isUniform && input2.isUniform) {
       return;
     }
-    const texShapeA = s2.texShape;
+    const texShapeA = s.texShape;
     const texShapeB = input2.isUniform ? null : input2.texData.texShape;
     if (!util_exports.arraysEqual(texShapeA, texShapeB)) {
       throw Error(`Binary was compiled with different texture shapes than the current args. Shape ${texShapeA} and ${texShapeB} must match`);
@@ -50224,8 +50225,8 @@ function runProgram(gpgpu, binary, inputs, output, customUniformValues) {
   if (binary.nanLoc !== null) {
     gpgpu.gl.uniform1f(binary.nanLoc, NaN);
   }
-  inputs.forEach((input2, i2) => {
-    const varName = binary.program.variableNames[i2];
+  inputs.forEach((input2, i) => {
+    const varName = binary.program.variableNames[i];
     const varLoc = binary.uniformLocations[varName];
     const varOffsetLoc = binary.uniformLocations[`offset${varName}`];
     const varShapeLoc = binary.inShapesLocations[`${varName}Shape`];
@@ -50270,7 +50271,7 @@ function runProgram(gpgpu, binary, inputs, output, customUniformValues) {
     if (input2.texData.slice != null && varOffsetLoc != null) {
       gpgpu.gl.uniform1i(varOffsetLoc, input2.texData.slice.flatOffset);
     }
-    gpgpu.setInputMatrixTexture(input2.texData.texture.texture, varLoc, i2);
+    gpgpu.setInputMatrixTexture(input2.texData.texture.texture, varLoc, i);
   });
   const outShapeLoc = binary.outShapeLocation;
   if (outShapeLoc) {
@@ -50311,9 +50312,9 @@ function runProgram(gpgpu, binary, inputs, output, customUniformValues) {
     gpgpu.gl.uniform2i(binary.outTexShapeLocation, output.texData.texShape[0], output.texData.texShape[1]);
   }
   if (binary.program.customUniforms && customUniformValues) {
-    binary.program.customUniforms.forEach((d, i2) => {
-      const customLoc = binary.customUniformLocations[i2];
-      const customValue = customUniformValues[i2];
+    binary.program.customUniforms.forEach((d, i) => {
+      const customLoc = binary.customUniformLocations[i];
+      const customValue = customUniformValues[i];
       if (d.type === "float") {
         gpgpu.gl.uniform1fv(customLoc, customValue);
       } else if (d.type === "vec2") {
@@ -51087,8 +51088,8 @@ var GPGPUContext = class {
   }
   pollItems() {
     const index2 = linearSearchLastTrue(this.itemsToPoll.map((x) => x.isDoneFn));
-    for (let i2 = 0; i2 <= index2; ++i2) {
-      const { resolveFn } = this.itemsToPoll[i2];
+    for (let i = 0; i <= index2; ++i) {
+      const { resolveFn } = this.itemsToPoll[i];
       resolveFn();
     }
     this.itemsToPoll = this.itemsToPoll.slice(index2 + 1);
@@ -51153,14 +51154,14 @@ var GPGPUContext = class {
   }
 };
 function linearSearchLastTrue(arr) {
-  let i2 = 0;
-  for (; i2 < arr.length; ++i2) {
-    const isDone = arr[i2]();
+  let i = 0;
+  for (; i < arr.length; ++i) {
+    const isDone = arr[i]();
     if (!isDone) {
       break;
     }
   }
-  return i2 - 1;
+  return i - 1;
 }
 var { addImpl: addImplCPU, bincountImpl: bincountImplCPU, bincountReduceImpl: bincountReduceImplCPU, castImpl: castImplCPU, ceilImpl: ceilImplCPU, concatImpl: concatImplCPU, equalImpl: equalImplCPU, expImpl: expImplCPU, expm1Impl: expm1ImplCPU, floorImpl: floorImplCPU, gatherNdImpl: gatherNdImplCPU, gatherV2Impl: gatherV2ImplCPU, greaterImpl: greaterImplCPU, greaterEqualImpl: greaterEqualImplCPU, lessImpl: lessImplCPU, lessEqualImpl: lessEqualImplCPU, linSpaceImpl: linSpaceImplCPU, logImpl: logImplCPU, maxImpl: maxImplCPU, maximumImpl: maximumImplCPU, minimumImpl: minimumImplCPU, multiplyImpl: multiplyImplCPU, negImpl: negImplCPU, notEqualImpl: notEqualImplCPU, prodImpl: prodImplCPU, raggedTensorToTensorImpl: raggedTensorToTensorImplCPU, rangeImpl: rangeImplCPU, rsqrtImpl: rsqrtImplCPU, scatterImpl: scatterImplCPU, sigmoidImpl: sigmoidImplCPU, simpleAbsImpl: simpleAbsImplCPU, sliceImpl: sliceImplCPU, sparseFillEmptyRowsImpl: sparseFillEmptyRowsImplCPU, sparseReshapeImpl: sparseReshapeImplCPU, sparseSegmentReductionImpl: sparseSegmentReductionImplCPU, sqrtImpl: sqrtImplCPU, stridedSliceImpl: stridedSliceImplCPU, stringNGramsImpl: stringNGramsImplCPU, stringSplitImpl: stringSplitImplCPU, stringToHashBucketFastImpl: stringToHashBucketFastImplCPU, subImpl: subImplCPU, tileImpl: tileImplCPU, topKImpl: topKImplCPU, transposeImpl: transposeImplCPU, uniqueImpl: uniqueImplCPU } = shared_exports;
 function getVecChannels(name, rank) {
@@ -51177,9 +51178,9 @@ function getSourceCoords(rank, dims) {
     return "rc";
   }
   let coords3 = "";
-  for (let i2 = 0; i2 < rank; i2++) {
-    coords3 += dims[i2];
-    if (i2 < rank - 1) {
+  for (let i = 0; i < rank; i++) {
+    coords3 += dims[i];
+    if (i < rank - 1) {
       coords3 += ",";
     }
   }
@@ -51238,9 +51239,9 @@ var PackProgram = class {
       return `rc > ${this.enableShapeUniforms ? "outShape" : this.outputShape[0]}`;
     }
     let cond = "";
-    for (let i2 = this.rank - 2; i2 < this.rank; i2++) {
-      cond += `${dims[i2]} >= ${this.enableShapeUniforms ? `outShape[${i2}]` : this.outputShape[i2]}`;
-      if (i2 < this.rank - 1) {
+    for (let i = this.rank - 2; i < this.rank; i++) {
+      cond += `${dims[i]} >= ${this.enableShapeUniforms ? `outShape[${i}]` : this.outputShape[i]}`;
+      if (i < this.rank - 1) {
         cond += "||";
       }
     }
@@ -51284,25 +51285,25 @@ var ReshapePackedProgram = class {
     this.outputShape = outputShape;
     this.enableShapeUniforms = useShapeUniforms(this.outputShape.length);
     let mainLoop = ``;
-    for (let i2 = 0; i2 < 4; i2++) {
+    for (let i = 0; i < 4; i++) {
       let thisRC = `thisRC = rc;`;
-      if (i2 % 2 === 1) {
+      if (i % 2 === 1) {
         thisRC += `thisRC.z += 1;`;
       }
-      if (i2 > 1) {
+      if (i > 1) {
         thisRC += `thisRC.y += 1;`;
       }
       mainLoop += `
         ${thisRC}
-        ${i2 > 0 ? `if(thisRC.y < rows && thisRC.z < cols){` : ""}
+        ${i > 0 ? `if(thisRC.y < rows && thisRC.z < cols){` : ""}
           int flatIndex = getFlatIndex(thisRC);
 
           ivec3 inputRC = inputCoordsFromReshapedOutCoords(flatIndex);
           vec2 inputRCInnerDims = vec2(float(inputRC.y),float(inputRC.z));
 
-          result[${i2}] =
+          result[${i}] =
             getChannel(getA(inputRC.x, inputRC.y, inputRC.z), inputRCInnerDims);
-        ${i2 > 0 ? "}" : ""}
+        ${i > 0 ? "}" : ""}
       `;
     }
     this.userCode = `
@@ -51895,8 +51896,8 @@ var MathBackendWebGL = class extends KernelBackend {
     if (values == null) {
       return;
     }
-    for (let i2 = 0; i2 < values.length; i2++) {
-      const num = values[i2];
+    for (let i = 0; i < values.length; i++) {
+      const num = values[i];
       if (!canBeRepresented(num)) {
         if (env().getBool("WEBGL_RENDER_FLOAT32_CAPABLE")) {
           throw Error(`The value ${num} cannot be represented with your current settings. Consider enabling float32 rendering: 'tf.env().set('WEBGL_RENDER_FLOAT32_ENABLED', true);'`);
@@ -51955,7 +51956,7 @@ var MathBackendWebGL = class extends KernelBackend {
       if (env().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE") > 0) {
         const kernelMs = await Promise.all(flattenedActiveTimerQueries);
         res["kernelMs"] = util_exports.sum(kernelMs);
-        res["getExtraProfileInfo"] = () => kernelMs.map((d, i2) => ({ name: flattenedActiveTimerNames[i2], ms: d })).map((d) => `${d.name}: ${d.ms}`).join(", ");
+        res["getExtraProfileInfo"] = () => kernelMs.map((d, i) => ({ name: flattenedActiveTimerNames[i], ms: d })).map((d) => `${d.name}: ${d.ms}`).join(", ");
       } else {
         res["kernelMs"] = {
           error: "WebGL query timers are not supported in this environment."
@@ -52435,8 +52436,8 @@ function float32ToTypedArray(a, dtype) {
     return a;
   } else if (dtype === "int32" || dtype === "bool") {
     const result = dtype === "int32" ? new Int32Array(a.length) : new Uint8Array(a.length);
-    for (let i2 = 0; i2 < result.length; ++i2) {
-      result[i2] = Math.round(a[i2]);
+    for (let i = 0; i < result.length; ++i) {
+      result[i] = Math.round(a[i]);
     }
     return result;
   } else {
@@ -53177,12 +53178,12 @@ function getReductionStages(inShape) {
 function reduce(x, dtype, reductionType, backend2) {
   const reductionStages = getReductionStages(x.shape);
   let result = x;
-  for (let i2 = 0; i2 < reductionStages.length; i2++) {
-    const { inSize, windowSize, outSize } = reductionStages[i2];
+  for (let i = 0; i < reductionStages.length; i++) {
+    const { inSize, windowSize, outSize } = reductionStages[i];
     let program;
     let previousResult;
     if (reductionType === "mean") {
-      program = i2 === 0 ? new MeanProgram({ windowSize, inSize, batchSize: x.shape[0], outSize }, inSize) : new MeanProgram({ windowSize, inSize, batchSize: x.shape[0], outSize });
+      program = i === 0 ? new MeanProgram({ windowSize, inSize, batchSize: x.shape[0], outSize }, inSize) : new MeanProgram({ windowSize, inSize, batchSize: x.shape[0], outSize });
     } else {
       program = new ReduceProgram({ windowSize, inSize, batchSize: x.shape[0], outSize }, reductionType);
     }
@@ -53198,8 +53199,8 @@ var TransposeProgram = class {
   constructor(aShape, newDim) {
     this.variableNames = ["A"];
     const outputShape = new Array(aShape.length);
-    for (let i2 = 0; i2 < outputShape.length; i2++) {
-      outputShape[i2] = aShape[newDim[i2]];
+    for (let i = 0; i < outputShape.length; i++) {
+      outputShape[i] = aShape[newDim[i]];
     }
     this.outputShape = outputShape;
     this.rank = outputShape.length;
@@ -53220,8 +53221,8 @@ function getSwitchedCoords(newDim) {
   }
   const originalOrder = ["resRC.x", "resRC.y", "resRC.z", "resRC.w", "resRC.u", "resRC.v"];
   const switchedCoords = new Array(rank);
-  for (let i2 = 0; i2 < newDim.length; i2++) {
-    switchedCoords[newDim[i2]] = originalOrder[i2];
+  for (let i = 0; i < newDim.length; i++) {
+    switchedCoords[newDim[i]] = originalOrder[i];
   }
   return switchedCoords.join();
 }
@@ -53231,8 +53232,8 @@ var TransposePackedProgram = class {
     this.packedInputs = true;
     this.packedOutput = true;
     const outputShape = new Array(aShape.length);
-    for (let i2 = 0; i2 < outputShape.length; i2++) {
-      outputShape[i2] = aShape[newDim[i2]];
+    for (let i = 0; i < outputShape.length; i++) {
+      outputShape[i] = aShape[newDim[i]];
     }
     this.outputShape = outputShape;
     this.rank = outputShape.length;
@@ -53242,8 +53243,8 @@ var TransposePackedProgram = class {
     const dtype = getCoordsDataType(this.rank);
     const outputOrder = getVecChannels("rc", this.rank);
     const switchedOrder = new Array(this.rank);
-    for (let i2 = 0; i2 < newDim.length; i2++) {
-      switchedOrder[newDim[i2]] = outputOrder[i2];
+    for (let i = 0; i < newDim.length; i++) {
+      switchedOrder[newDim[i]] = outputOrder[i];
     }
     const innerDims = `vec2(${switchedOrder.slice(-2).join()})`;
     const nextColumn = `++${outputOrder[this.rank - 1]} < ${outputShape[this.rank - 1]}`;
@@ -53322,8 +53323,8 @@ function transpose3(args) {
   const webglBackend = backend2;
   const xRank = x.shape.length;
   const newShape = new Array(xRank);
-  for (let i2 = 0; i2 < newShape.length; i2++) {
-    newShape[i2] = x.shape[perm[i2]];
+  for (let i = 0; i < newShape.length; i++) {
+    newShape[i] = x.shape[perm[i]];
   }
   let out;
   if (webglBackend.shouldExecuteOnCPU([x])) {
@@ -53425,8 +53426,8 @@ function batchMatMulImpl({ a, b, transposeA, transposeB, backend: backend2, bias
   }
   const outReshaped = reshape4({ inputs: { x: out }, backend: backend2, attrs: { shape: outShape } });
   intermediates.push(out);
-  for (const i2 of intermediates) {
-    backend2.disposeIntermediateTensorInfo(i2);
+  for (const i of intermediates) {
+    backend2.disposeIntermediateTensorInfo(i);
   }
   return outReshaped;
 }
@@ -53510,7 +53511,7 @@ var AddNProgram = class {
   constructor(outputShape, shapes) {
     this.outputShape = [];
     this.outputShape = outputShape;
-    this.variableNames = shapes.map((_, i2) => `T${i2}`);
+    this.variableNames = shapes.map((_, i) => `T${i}`);
     const snippets = [];
     this.variableNames.forEach((variable2) => {
       snippets.push(`float v${variable2} = get${variable2}AtOutCoords();`);
@@ -53534,7 +53535,7 @@ var AddNPackedProgram = class {
     this.packedInputs = true;
     this.packedOutput = true;
     this.outputShape = outputShape;
-    this.variableNames = shapes.map((_, i2) => `T${i2}`);
+    this.variableNames = shapes.map((_, i) => `T${i}`);
     const snippets = [];
     this.variableNames.forEach((variable2) => {
       snippets.push(`vec4 v${variable2} = get${variable2}AtOutCoords();`);
@@ -54648,8 +54649,8 @@ var SliceProgram = class {
     this.customUniforms = [{ name: "start", arrayIndex: this.rank, type: "int" }];
     const sourceCoords = getCoords(this.rank);
     let body4;
-    const coordSum = destSize.map((_, i2) => {
-      return `sourceLoc.${coords[i2]} = start[${i2}] + coords.${coords[i2]};`;
+    const coordSum = destSize.map((_, i) => {
+      return `sourceLoc.${coords[i]} = start[${i}] + coords.${coords[i]};`;
     });
     body4 = `
         ${dtype} sourceLoc;
@@ -54707,7 +54708,7 @@ var SlicePackedProgram = class {
       }
     `;
     const sourceLocSetup = this.rank <= 4 ? `sourceLoc = coords +
-            ${dtype}(${destSize.map((_, i2) => `start[${i2}]`).join()});` : destSize.map((_, i2) => `${sourceLoc[i2]} = ${coords3[i2]} + start[${i2}];`).join("\n");
+            ${dtype}(${destSize.map((_, i) => `start[${i}]`).join()});` : destSize.map((_, i) => `${sourceLoc[i]} = ${coords3[i]} + start[${i}];`).join("\n");
     this.userCode = `
       void main() {
         ${dtype} coords = getOutputCoords();
@@ -55021,16 +55022,16 @@ var ConcatProgram = class {
   constructor(shapes) {
     this.outputShape = [];
     this.outputShape = backend_util_exports.computeOutShape(shapes, 1);
-    this.variableNames = shapes.map((_, i2) => `T${i2}`);
+    this.variableNames = shapes.map((_, i) => `T${i}`);
     const offsets = new Array(shapes.length - 1);
     offsets[0] = shapes[0][1];
-    for (let i2 = 1; i2 < offsets.length; i2++) {
-      offsets[i2] = offsets[i2 - 1] + shapes[i2][1];
+    for (let i = 1; i < offsets.length; i++) {
+      offsets[i] = offsets[i - 1] + shapes[i][1];
     }
     const snippets = [`if (yC < ${offsets[0]}) setOutput(getT0(yR, yC));`];
-    for (let i2 = 1; i2 < offsets.length; i2++) {
-      const shift = offsets[i2 - 1];
-      snippets.push(`else if (yC < ${offsets[i2]}) setOutput(getT${i2}(yR, yC-${shift}));`);
+    for (let i = 1; i < offsets.length; i++) {
+      const shift = offsets[i - 1];
+      snippets.push(`else if (yC < ${offsets[i]}) setOutput(getT${i}(yR, yC-${shift}));`);
     }
     const lastIndex = offsets.length;
     const lastShift = offsets[offsets.length - 1];
@@ -55057,11 +55058,11 @@ var ConcatPackedProgram = class {
     const dtype = getCoordsDataType(rank);
     const coords3 = getChannels("coords", rank);
     const channels = ["x", "y", "z", "w", "u", "v"].slice(0, rank);
-    this.variableNames = shapes.map((_, i2) => `T${i2}`);
+    this.variableNames = shapes.map((_, i) => `T${i}`);
     const offsets = new Array(shapes.length - 1);
     offsets[0] = shapes[0][axis];
-    for (let i2 = 1; i2 < offsets.length; i2++) {
-      offsets[i2] = offsets[i2 - 1] + shapes[i2][axis];
+    for (let i = 1; i < offsets.length; i++) {
+      offsets[i] = offsets[i - 1] + shapes[i][axis];
     }
     const channel = channels[axis];
     const lastChannels = channels.slice(-2);
@@ -55070,12 +55071,12 @@ var ConcatPackedProgram = class {
         return getChannel(
             getT0(${allChannels}), vec2(${lastChannels.join()}));
         }`;
-    for (let i2 = 1; i2 < offsets.length; i2++) {
-      const shift2 = offsets[i2 - 1];
+    for (let i = 1; i < offsets.length; i++) {
+      const shift2 = offsets[i - 1];
       getValueSnippet += `
-        if (${channel} < ${offsets[i2]}  && ${channel} >= ${offsets[i2 - 1]}) {
+        if (${channel} < ${offsets[i]}  && ${channel} >= ${offsets[i - 1]}) {
           return getChannel(
-            getT${i2}(${shiftedChannels(channels, channel, shift2)}),
+            getT${i}(${shiftedChannels(channels, channel, shift2)}),
             vec2(${shiftedChannels(lastChannels, channel, shift2)}));
         }`;
     }
@@ -55144,8 +55145,8 @@ function concatImpl2(inputs, axis, backend2) {
     const realConcated = concatImpl2(reals, axis, backend2);
     const imagConcated = concatImpl2(imags, axis, backend2);
     const result2 = complex3({ inputs: { real: realConcated, imag: imagConcated }, backend: backend2 });
-    reals.forEach((r2) => backend2.disposeIntermediateTensorInfo(r2));
-    imags.forEach((i2) => backend2.disposeIntermediateTensorInfo(i2));
+    reals.forEach((r) => backend2.disposeIntermediateTensorInfo(r));
+    imags.forEach((i) => backend2.disposeIntermediateTensorInfo(i));
     backend2.disposeIntermediateTensorInfo(realConcated);
     backend2.disposeIntermediateTensorInfo(imagConcated);
     return result2;
@@ -55174,13 +55175,13 @@ function concatImpl2(inputs, axis, backend2) {
   const maxTexturesInShader = env().getNumber("WEBGL_MAX_TEXTURES_IN_SHADER");
   if (inputs.length > maxTexturesInShader) {
     const reducedInputs = [];
-    for (let i2 = 0; i2 < inputs.length; i2 += maxTexturesInShader) {
-      const subArray = inputs.slice(i2, i2 + maxTexturesInShader);
+    for (let i = 0; i < inputs.length; i += maxTexturesInShader) {
+      const subArray = inputs.slice(i, i + maxTexturesInShader);
       reducedInputs.push(concatImpl2(subArray, axis, backend2));
     }
     const result2 = concatImpl2(reducedInputs, axis, backend2);
-    for (const i2 of reducedInputs) {
-      backend2.disposeIntermediateTensorInfo(i2);
+    for (const i of reducedInputs) {
+      backend2.disposeIntermediateTensorInfo(i);
     }
     return result2;
   }
@@ -55191,7 +55192,7 @@ function concatImpl2(inputs, axis, backend2) {
   const { tensors2D, outShape } = computeTensors2D(inputs, axis, backend2);
   const program = new ConcatProgram(tensors2D.map((t2) => t2.shape));
   const result = backend2.runWebGLProgram(program, tensors2D, dtype);
-  tensors2D.forEach((r2) => backend2.disposeIntermediateTensorInfo(r2));
+  tensors2D.forEach((r) => backend2.disposeIntermediateTensorInfo(r));
   const reshapedResult = reshape4({ inputs: { x: result }, attrs: { shape: outShape }, backend: backend2 });
   backend2.disposeIntermediateTensorInfo(result);
   return reshapedResult;
@@ -56009,8 +56010,8 @@ function conv2dByMatMul({ x, filter, convInfo, backend: backend2, bias = null, p
     intermediates.push(filterReshaped);
     intermediates.push(result);
   }
-  for (const i2 of intermediates) {
-    backend2.disposeIntermediateTensorInfo(i2);
+  for (const i of intermediates) {
+    backend2.disposeIntermediateTensorInfo(i);
   }
   return out;
 }
@@ -56081,8 +56082,8 @@ function conv2dWithIm2Row({ x, filter, convInfo, backend: backend2, bias = null,
   const product = backend2.runWebGLProgram(matmulProgram, inputs, "float32");
   const out = reshape4({ inputs: { x: product }, backend: backend2, attrs: { shape: convInfo.outShape } });
   intermediates.push(product);
-  for (const i2 of intermediates) {
-    backend2.disposeIntermediateTensorInfo(i2);
+  for (const i of intermediates) {
+    backend2.disposeIntermediateTensorInfo(i);
   }
   return out;
 }
@@ -56640,9 +56641,9 @@ function cumImpl(op2, x, backend2, axis, exclusive, reverse5) {
   }
   const size2 = permutedX.shape[permutedAxis];
   let result = identity3({ inputs: { x: permutedX }, backend: backend2 });
-  for (let i2 = 0; i2 <= Math.ceil(Math.log2(size2)) - 1; i2++) {
+  for (let i = 0; i <= Math.ceil(Math.log2(size2)) - 1; i++) {
     const program = new CumProgram(op2, permutedX.shape, false, reverse5);
-    const customValues = [[i2]];
+    const customValues = [[i]];
     const prevResult = result;
     result = backend2.runWebGLProgram(program, [result], result.dtype, customValues);
     backend2.disposeIntermediateTensorInfo(prevResult);
@@ -57447,8 +57448,8 @@ function einsum3(args) {
   let out = null;
   let numDimsRemaining = allDims.length;
   const tensorsToDispose = [];
-  for (let i2 = 0; i2 < nSteps; ++i2) {
-    for (const idTerm of steps[i2]) {
+  for (let i = 0; i < nSteps; ++i) {
+    for (const idTerm of steps[i]) {
       const { permutationIndices: perm, expandDims: dimsToExpand } = backend_util_exports.getEinsumPermutation(numDimsRemaining, idDims[idTerm]);
       let x;
       if (backend_util_exports.isIdentityPermutation(perm)) {
@@ -57472,13 +57473,13 @@ function einsum3(args) {
         tensorsToDispose.push(out);
       }
     }
-    if (i2 < nSteps - 1) {
-      if (path[i2] >= 0) {
+    if (i < nSteps - 1) {
+      if (path[i] >= 0) {
         out = sum4({
           inputs: { x: out },
           backend: backend2,
           attrs: {
-            axis: path[i2] - (allDims.length - numDimsRemaining),
+            axis: path[i] - (allDims.length - numDimsRemaining),
             keepDims: false
           }
         });
@@ -58158,11 +58159,11 @@ var GatherProgram = class {
 function getSourceCoords2(aShape, axis) {
   const currentCoords = ["resRC.x", "resRC.y", "resRC.z", "resRC.w"];
   const sourceCoords = [];
-  for (let i2 = 0; i2 < aShape.length; i2++) {
-    if (i2 === 2) {
+  for (let i = 0; i < aShape.length; i++) {
+    if (i === 2) {
       sourceCoords.push("index");
     } else {
-      sourceCoords.push(`${currentCoords[i2]}`);
+      sourceCoords.push(`${currentCoords[i]}`);
     }
   }
   return sourceCoords.join();
@@ -58175,8 +58176,8 @@ function gatherV22(args) {
   if (env().get("DEBUG")) {
     const indicesVals = backend2.readSync(indices.dataId);
     const axisDim = x.shape[parsedAxis];
-    for (let i2 = 0; i2 < indicesVals.length; ++i2) {
-      const index2 = indicesVals[i2];
+    for (let i = 0; i < indicesVals.length; ++i) {
+      const index2 = indicesVals[i];
       util_exports.assert(index2 <= axisDim - 1 && index2 >= 0, () => `GatherV2: the index value ${index2} is not in [0, ${axisDim - 1}]`);
     }
   }
@@ -58632,8 +58633,8 @@ function max4(args) {
       const xTexData = backend2.texData.get(maxInput.dataId);
       const values = xTexData.values;
       const newShape = new Array(xRank);
-      for (let i2 = 0; i2 < newShape.length; i2++) {
-        newShape[i2] = x.shape[permutedAxes[i2]];
+      for (let i = 0; i < newShape.length; i++) {
+        newShape[i] = x.shape[permutedAxes[i]];
       }
       const maxInputValues = transposeImplCPU(values, x.shape, x.dtype, permutedAxes, newShape);
       maxInput = backend2.makeTensorInfo(newShape, x.dtype);
@@ -58960,8 +58961,8 @@ var meanConfig2 = {
         const xTexData = webglBackend.texData.get(meanInput.dataId);
         const values = xTexData.values;
         const newShape = new Array(xRank);
-        for (let i2 = 0; i2 < newShape.length; i2++) {
-          newShape[i2] = x.shape[permutedAxes[i2]];
+        for (let i = 0; i < newShape.length; i++) {
+          newShape[i] = x.shape[permutedAxes[i]];
         }
         const meanInputValues = transposeImplCPU(values, x.shape, x.dtype, permutedAxes, newShape);
         meanInput = webglBackend.makeTensorInfo(newShape, x.dtype);
@@ -58980,8 +58981,8 @@ var meanConfig2 = {
       outShape = backend_util_exports.expandShapeToKeepDim(meanOutShape, origAxes);
     }
     const out = meanImpl(meanInput, reduceShape, outShape, webglBackend);
-    for (const i2 of intermediates) {
-      webglBackend.disposeIntermediateTensorInfo(i2);
+    for (const i of intermediates) {
+      webglBackend.disposeIntermediateTensorInfo(i);
     }
     return out;
   }
@@ -59045,11 +59046,11 @@ var minimumConfig2 = {
 var MirrorPadProgram = class {
   constructor(xShape, paddings, mode) {
     this.variableNames = ["x"];
-    this.outputShape = paddings.map((p2, i2) => p2[0] + xShape[i2] + p2[1]);
+    this.outputShape = paddings.map((p2, i) => p2[0] + xShape[i] + p2[1]);
     const rank = xShape.length;
     const dtype = getCoordsDataType(rank);
     const start = paddings.map((p2) => p2[0]).join(",");
-    const end = paddings.map((p2, i2) => p2[0] + xShape[i2]).join(",");
+    const end = paddings.map((p2, i) => p2[0] + xShape[i]).join(",");
     const unpackedCoords = ["coords[0]", "coords[1]", "coords[2]", "coords[3]"].slice(0, rank);
     const offset = mode === "reflect" ? 0 : 1;
     if (rank === 1) {
@@ -59093,11 +59094,11 @@ var MirrorPadPackedProgram = class {
     this.variableNames = ["x"];
     this.packedInputs = true;
     this.packedOutput = true;
-    this.outputShape = paddings.map((p2, i2) => p2[0] + xShape[i2] + p2[1]);
+    this.outputShape = paddings.map((p2, i) => p2[0] + xShape[i] + p2[1]);
     const rank = xShape.length;
     const dtype = getCoordsDataType(rank);
     const start = paddings.map((p2) => p2[0]).join(",");
-    const end = paddings.map((p2, i2) => p2[0] + xShape[i2]).join(",");
+    const end = paddings.map((p2, i) => p2[0] + xShape[i]).join(",");
     const coords3 = getChannels("rc", rank);
     const source = getChannels("source", rank);
     const cLimit = `${coords3[rank - 1]} < ${this.outputShape[rank - 1]}`;
@@ -59450,14 +59451,14 @@ function zerosLike3(args) {
   const { x } = inputs;
   if (x.dtype === "complex64") {
     const realPart = real3({ inputs: { input: x }, backend: backend2 });
-    const r2 = zerosLike3({ inputs: { x: realPart }, backend: backend2 });
+    const r = zerosLike3({ inputs: { x: realPart }, backend: backend2 });
     const imagPart = imag3({ inputs: { input: x }, backend: backend2 });
-    const i2 = zerosLike3({ inputs: { x: imagPart }, backend: backend2 });
-    const result = complex3({ inputs: { real: r2, imag: i2 }, backend: backend2 });
+    const i = zerosLike3({ inputs: { x: imagPart }, backend: backend2 });
+    const result = complex3({ inputs: { real: r, imag: i }, backend: backend2 });
     backend2.disposeIntermediateTensorInfo(realPart);
-    backend2.disposeIntermediateTensorInfo(r2);
+    backend2.disposeIntermediateTensorInfo(r);
     backend2.disposeIntermediateTensorInfo(imagPart);
-    backend2.disposeIntermediateTensorInfo(i2);
+    backend2.disposeIntermediateTensorInfo(i);
     return result;
   } else {
     return fill3({
@@ -59482,14 +59483,14 @@ function onesLike3(args) {
     throw new Error("onesLike is not supported under string dtype");
   } else if (x.dtype === "complex64") {
     const realPart = real3({ inputs: { input: x }, backend: backend2 });
-    const r2 = onesLike3({ inputs: { x: realPart }, backend: backend2 });
+    const r = onesLike3({ inputs: { x: realPart }, backend: backend2 });
     const imagPart = imag3({ inputs: { input: x }, backend: backend2 });
-    const i2 = zerosLike3({ inputs: { x: imagPart }, backend: backend2 });
-    const result = complex3({ inputs: { real: r2, imag: i2 }, backend: backend2 });
+    const i = zerosLike3({ inputs: { x: imagPart }, backend: backend2 });
+    const result = complex3({ inputs: { real: r, imag: i }, backend: backend2 });
     backend2.disposeIntermediateTensorInfo(realPart);
-    backend2.disposeIntermediateTensorInfo(r2);
+    backend2.disposeIntermediateTensorInfo(r);
     backend2.disposeIntermediateTensorInfo(imagPart);
-    backend2.disposeIntermediateTensorInfo(i2);
+    backend2.disposeIntermediateTensorInfo(i);
     return result;
   } else {
     return fill3({ attrs: { shape: x.shape, dtype: x.dtype, value: 1 }, backend: backend2 });
@@ -59531,11 +59532,11 @@ var PadProgram = class {
   constructor(xShape, paddings, constantValue) {
     this.variableNames = ["x"];
     this.customUniforms = [{ name: "value", type: "float" }];
-    this.outputShape = paddings.map((p2, i2) => p2[0] + xShape[i2] + p2[1]);
+    this.outputShape = paddings.map((p2, i) => p2[0] + xShape[i] + p2[1]);
     const rank = xShape.length;
     const type = getCoordsDataType(rank);
     const start = paddings.map((p2) => p2[0]).join(",");
-    const end = paddings.map((p2, i2) => p2[0] + xShape[i2]).join(",");
+    const end = paddings.map((p2, i) => p2[0] + xShape[i]).join(",");
     const unpackedCoords = ["coords[0]", "coords[1]", "coords[2]", "coords[3]"].slice(0, rank);
     if (rank === 1) {
       this.userCode = `
@@ -59575,11 +59576,11 @@ var PadPackedProgram = class {
     this.packedInputs = true;
     this.packedOutput = true;
     this.customUniforms = [{ name: "value", type: "float" }];
-    this.outputShape = paddings.map((p2, i2) => p2[0] + xShape[i2] + p2[1]);
+    this.outputShape = paddings.map((p2, i) => p2[0] + xShape[i] + p2[1]);
     const rank = xShape.length;
     const dtype = getCoordsDataType(rank);
     const start = paddings.map((p2) => p2[0]).join(",");
-    const end = paddings.map((p2, i2) => p2[0] + xShape[i2]).join(",");
+    const end = paddings.map((p2, i) => p2[0] + xShape[i]).join(",");
     const coords3 = getChannels("rc", rank);
     const source = getChannels("source", rank);
     const cLimit = `${coords3[rank - 1]} < ${this.outputShape[rank - 1]}`;
@@ -59598,14 +59599,14 @@ var PadPackedProgram = class {
     ];
     const paddingArea = rank === 1 ? "rc < start || rc >= end" : "any(lessThan(rc, start)) || any(greaterThanEqual(rc, end))";
     let mainLoop = "";
-    for (let i2 = 0, j = rank === 1 ? 2 : 4; i2 < j; i2++) {
+    for (let i = 0, j = rank === 1 ? 2 : 4; i < j; i++) {
       mainLoop += `
-        ${componentSetup[i2]}
+        ${componentSetup[i]}
         if (${paddingArea}) {
-          result[${i2}] = float(value);
+          result[${i}] = float(value);
         } else {
           ${dtype} source = rc - start;
-          result[${i2}] = getChannel(getX(${source.join()}), ${innerDims});
+          result[${i}] = getChannel(getX(${source.join()}), ${innerDims});
         }
       `;
     }
@@ -59628,7 +59629,7 @@ var padV22 = (args) => {
   const { x } = inputs;
   const { paddings, constantValue } = attrs;
   if (util_exports.sizeFromShape(x.shape) === 0) {
-    const outputShape = paddings.map((p2, i2) => p2[0] + x.shape[i2] + p2[1]);
+    const outputShape = paddings.map((p2, i) => p2[0] + x.shape[i] + p2[1]);
     return fill3({
       backend: backend2,
       attrs: { shape: outputShape, value: constantValue, dtype: x.dtype }
@@ -60332,13 +60333,13 @@ var ReverseProgram = class {
       `;
       return;
     }
-    const getInCoord = (i2) => {
-      if (axis.indexOf(i2) !== -1 && xShape[i2] !== 1) {
-        return `${xShape[i2]} - coords[${i2}] - 1`;
+    const getInCoord = (i) => {
+      if (axis.indexOf(i) !== -1 && xShape[i] !== 1) {
+        return `${xShape[i]} - coords[${i}] - 1`;
       }
-      return `coords[${i2}]`;
+      return `coords[${i}]`;
     };
-    const inCoords = xShape.map((_, i2) => getInCoord(i2)).join(",");
+    const inCoords = xShape.map((_, i) => getInCoord(i)).join(",");
     const type = getCoordsDataType(rank);
     this.userCode = `
       void main() {
@@ -60412,16 +60413,16 @@ var ReversePackedProgram = class {
       return getChannel(channels2);
     }
     function getChannel(channels2) {
-      const inCoordsArray = xShape.map((_, i2) => getInCoord(i2, channels2));
+      const inCoordsArray = xShape.map((_, i) => getInCoord(i, channels2));
       const inCoords = inCoordsArray.join(",");
       const innerDims = inCoordsArray.slice(-2).join(",");
       return `getChannel(getX(${inCoords}), vec2(${innerDims}))`;
     }
-    function getInCoord(i2, channels1) {
-      if (axis.indexOf(i2) !== -1 && xShape[i2] !== 1) {
-        return `${xShape[i2]} - ${channels1[i2]} - 1`;
+    function getInCoord(i, channels1) {
+      if (axis.indexOf(i) !== -1 && xShape[i] !== 1) {
+        return `${xShape[i]} - ${channels1[i]} - 1`;
       } else {
-        return `${channels1[i2]}`;
+        return `${channels1[i]}`;
       }
     }
   }
@@ -60658,10 +60659,10 @@ var SelectProgram = class {
       const currentCoords = ["resRC.x", "resRC.y", "resRC.z", "resRC.w"];
       const cCoordVars = [];
       const abCoordVars = [];
-      for (let i2 = 0; i2 < shape.length; i2++) {
-        abCoordVars.push(`${currentCoords[i2]}`);
-        if (i2 < cRank) {
-          cCoordVars.push(`${currentCoords[i2]}`);
+      for (let i = 0; i < shape.length; i++) {
+        abCoordVars.push(`${currentCoords[i]}`);
+        if (i < cRank) {
+          cCoordVars.push(`${currentCoords[i]}`);
         }
       }
       cCoords = cCoordVars.join();
@@ -60683,9 +60684,9 @@ var SelectProgram = class {
 };
 function select3(args) {
   const { inputs, backend: backend2 } = args;
-  const { condition, t: t2, e: e2 } = inputs;
+  const { condition, t: t2, e } = inputs;
   const program = new SelectProgram(condition.shape.length, t2.shape, t2.shape.length);
-  return backend2.runWebGLProgram(program, [condition, t2, e2], upcastType(t2.dtype, e2.dtype));
+  return backend2.runWebGLProgram(program, [condition, t2, e], upcastType(t2.dtype, e.dtype));
 }
 var selectConfig2 = {
   kernelName: Select,
@@ -60793,7 +60794,7 @@ var spaceToBatchND3 = (args) => {
   const prod6 = blockShape.reduce((a, b) => a * b);
   const completePaddings = [[0, 0]];
   completePaddings.push(...paddings);
-  for (let i2 = 1 + blockShape.length; i2 < x.shape.length; ++i2) {
+  for (let i = 1 + blockShape.length; i < x.shape.length; ++i) {
     completePaddings.push([0, 0]);
   }
   const toDispose = [];
@@ -60968,11 +60969,11 @@ function splitV2(args) {
   const xRank = x.shape.length;
   const begin = new Array(xRank).fill(0);
   const size2 = x.shape.slice();
-  return splitSizes.map((s2) => {
+  return splitSizes.map((s) => {
     const sliceSize = [...size2];
-    sliceSize[$axis] = s2;
+    sliceSize[$axis] = s;
     const sliceT = slice3({ inputs: { x }, backend: backend2, attrs: { begin, size: sliceSize } });
-    begin[$axis] += s2;
+    begin[$axis] += s;
     return sliceT;
   });
 }
@@ -61027,9 +61028,9 @@ var StridedSliceProgram = class {
       newCoords = "coords * strides + begin";
     } else {
       let outputAxis = 0;
-      newCoords = size2.map((_, i2) => {
+      newCoords = size2.map((_, i) => {
         outputAxis++;
-        return size2.length === 1 ? `coords * strides[${i2}] + begin[${i2}]` : `coords[${outputAxis - 1}] * strides[${i2}] + begin[${i2}]`;
+        return size2.length === 1 ? `coords * strides[${i}] + begin[${i}]` : `coords[${outputAxis - 1}] * strides[${i}] + begin[${i}]`;
       }).join(",");
     }
     this.userCode = `
@@ -61163,8 +61164,8 @@ var TileProgram = class {
   constructor(aShape, reps) {
     this.variableNames = ["A"];
     const outputShape = new Array(aShape.length);
-    for (let i2 = 0; i2 < outputShape.length; i2++) {
-      outputShape[i2] = aShape[i2] * reps[i2];
+    for (let i = 0; i < outputShape.length; i++) {
+      outputShape[i] = aShape[i] * reps[i];
     }
     this.outputShape = outputShape;
     this.rank = outputShape.length;
@@ -61188,8 +61189,8 @@ function getSourceCoords3(aShape) {
   }
   const currentCoords = ["resRC.x", "resRC.y", "resRC.z", "resRC.w", "resRC.u"];
   const sourceCoords = [];
-  for (let i2 = 0; i2 < aShape.length; i2++) {
-    sourceCoords.push(`imod(${currentCoords[i2]}, ${aShape[i2]})`);
+  for (let i = 0; i < aShape.length; i++) {
+    sourceCoords.push(`imod(${currentCoords[i]}, ${aShape[i]})`);
   }
   return sourceCoords.join();
 }
@@ -61604,9 +61605,9 @@ function unpack2(args) {
   const num = value.shape[axis];
   const outShape = new Array(xRank - 1);
   let outIndex = 0;
-  for (let i2 = 0; i2 < xRank; i2++) {
-    if (i2 !== axis) {
-      outShape[outIndex++] = x.shape[i2];
+  for (let i = 0; i < xRank; i++) {
+    if (i !== axis) {
+      outShape[outIndex++] = x.shape[i];
     }
   }
   const toDispose = [];
@@ -61614,11 +61615,11 @@ function unpack2(args) {
   const size2 = x.shape.slice();
   size2[axis] = 1;
   const res = new Array(num);
-  for (let i2 = 0; i2 < res.length; i2++) {
-    begin[axis] = i2;
+  for (let i = 0; i < res.length; i++) {
+    begin[axis] = i;
     const sliced = slice3({ inputs: { x }, backend: backend2, attrs: { begin, size: size2 } });
     const reshaped = reshape4({ inputs: { x: sliced }, backend: backend2, attrs: { shape: outShape } });
-    res[i2] = reshaped;
+    res[i] = reshaped;
     toDispose.push(sliced);
   }
   toDispose.forEach((t2) => backend2.disposeIntermediateTensorInfo(t2));
@@ -62179,8 +62180,8 @@ function transpose4(args) {
   const { inputs, backend: backend2, attrs } = args;
   const [reducedShape, perm] = removeOneSizeDims(inputs.x.shape, attrs.perm);
   let permIsNoOp = true;
-  for (let i2 = 0; i2 < perm.length; i2++) {
-    if (perm[i2] !== i2) {
+  for (let i = 0; i < perm.length; i++) {
+    if (perm[i] !== i) {
       permIsNoOp = false;
     }
   }
@@ -62205,30 +62206,30 @@ function transpose4(args) {
 }
 function computeOutShape4(inShape, perm) {
   const outShape = new Array(inShape.length);
-  for (let i2 = 0; i2 < outShape.length; i2++) {
-    outShape[i2] = inShape[perm[i2]];
+  for (let i = 0; i < outShape.length; i++) {
+    outShape[i] = inShape[perm[i]];
   }
   return outShape;
 }
 function removeOneSizeDims(shape, perm) {
   const newShape = [];
   const newPerm = [];
-  for (let i2 = 0; i2 < shape.length; ++i2) {
-    if (shape[i2] !== 1) {
-      newShape.push(shape[i2]);
+  for (let i = 0; i < shape.length; ++i) {
+    if (shape[i] !== 1) {
+      newShape.push(shape[i]);
     }
-    if (shape[perm[i2]] !== 1) {
-      newPerm.push(perm[i2]);
+    if (shape[perm[i]] !== 1) {
+      newPerm.push(perm[i]);
     }
   }
-  for (let i2 = 0; i2 < newPerm.length; ++i2) {
+  for (let i = 0; i < newPerm.length; ++i) {
     let minValIdx = -1;
     for (let j = 0; j < newPerm.length; ++j) {
-      if (newPerm[j] >= i2 && (minValIdx === -1 || newPerm[minValIdx] > newPerm[j])) {
+      if (newPerm[j] >= i && (minValIdx === -1 || newPerm[minValIdx] > newPerm[j])) {
         minValIdx = j;
       }
     }
-    newPerm[minValIdx] = i2;
+    newPerm[minValIdx] = i;
   }
   return [newShape, newPerm];
 }
@@ -62248,8 +62249,8 @@ function permuteAxesAndTranspose(x, axis, backend2) {
   let inputWasTransposed = false;
   if (permutedAxes != null) {
     const newShape = new Array(xRank);
-    for (let i2 = 0; i2 < newShape.length; i2++) {
-      newShape[i2] = xShape[permutedAxes[i2]];
+    for (let i = 0; i < newShape.length; i++) {
+      newShape[i] = xShape[permutedAxes[i]];
     }
     axes = backend_util_exports.getInnerMostAxes(axes.length, xRank);
     xTransposed = transpose4({ inputs: { x }, attrs: { perm: permutedAxes }, backend: backend2 });
@@ -62552,8 +62553,8 @@ function slice2d2(xVals, xStride, outVals, begin, size2) {
   const beginI = begin[0];
   const beginJ = begin[1];
   const endI = beginI + size2[0];
-  for (let i2 = beginI; i2 < endI; i2++) {
-    const xOffset = i2 * xStride + beginJ;
+  for (let i = beginI; i < endI; i++) {
+    const xOffset = i * xStride + beginJ;
     outVals.set(xVals.subarray(xOffset, xOffset + size2[1]), outOffset);
     outOffset += size2[1];
   }
@@ -62565,9 +62566,9 @@ function slice3d2(xVals, xStride1, xStride2, outVals, begin, size2) {
   const beginK = begin[2];
   const endI = beginI + size2[0];
   const endJ = beginJ + size2[1];
-  for (let i2 = beginI; i2 < endI; i2++) {
+  for (let i = beginI; i < endI; i++) {
     for (let j = beginJ; j < endJ; j++) {
-      const xOffset = i2 * xStride1 + j * xStride2 + beginK;
+      const xOffset = i * xStride1 + j * xStride2 + beginK;
       outVals.set(xVals.subarray(xOffset, xOffset + size2[2]), outOffset);
       outOffset += size2[2];
     }
@@ -62582,10 +62583,10 @@ function slice4d2(xVals, xStride1, xStride2, xStride3, outVals, begin, size2) {
   const endJ = beginJ + size2[1];
   const endK = beginK + size2[2];
   const beginL = begin[3];
-  for (let i2 = beginI; i2 < endI; i2++) {
+  for (let i = beginI; i < endI; i++) {
     for (let j = beginJ; j < endJ; j++) {
       for (let k = beginK; k < endK; k++) {
-        const xOffset = i2 * xStride1 + j * xStride2 + k * xStride3 + beginL;
+        const xOffset = i * xStride1 + j * xStride2 + k * xStride3 + beginL;
         outVals.set(xVals.subarray(xOffset, xOffset + size2[3]), outOffset);
         outOffset += size2[3];
       }
@@ -62708,10 +62709,10 @@ function concat4(args) {
   const outVals = backend2.typedArrayFromHeap(out);
   for (let b = 0; b < batchDim; b++) {
     let outOffset = b * sumInnerDims;
-    for (let i2 = 0; i2 < inVals.length; i2++) {
-      const innerDim = innerDims[i2];
+    for (let i = 0; i < inVals.length; i++) {
+      const innerDim = innerDims[i];
       const inOffset = b * innerDim;
-      const vals = inVals[i2].subarray(inOffset, inOffset + innerDim);
+      const vals = inVals[i].subarray(inOffset, inOffset + innerDim);
       outVals.set(vals, outOffset);
       outOffset += innerDim;
     }
@@ -63404,8 +63405,8 @@ function gatherV23(args) {
   const parsedAxis = util_exports.parseAxisParam(axis, x.shape)[0];
   const indicesVals = backend2.readSync(indices.dataId);
   const axisDim = x.shape[parsedAxis];
-  for (let i2 = 0; i2 < indicesVals.length; ++i2) {
-    const index2 = indicesVals[i2];
+  for (let i = 0; i < indicesVals.length; ++i) {
+    const index2 = indicesVals[i];
     util_exports.assert(index2 <= axisDim - 1 && index2 >= 0, () => `GatherV2: the index value ${index2} is not in [0, ${axisDim - 1}]`);
   }
   const shapeInfo = backend_util_exports.segment_util.collectGatherOpShapeInfo(x, indices, parsedAxis, batchDims);
@@ -63723,7 +63724,7 @@ function setup26(backend2) {
 }
 function mirrorPad3(args) {
   const { inputs: { x }, backend: backend2, attrs: { paddings, mode } } = args;
-  const outShape = paddings.map((p2, i2) => p2[0] + x.shape[i2] + p2[1]);
+  const outShape = paddings.map((p2, i) => p2[0] + x.shape[i] + p2[1]);
   const xId = backend2.dataIdMap.get(x.dataId).id;
   const out = backend2.makeOutput(outShape, x.dtype);
   const outId = backend2.dataIdMap.get(out.dataId).id;
@@ -63937,7 +63938,7 @@ function setup31(backend2) {
 }
 function pad2(args) {
   const { inputs: { x }, backend: backend2, attrs: { paddings, constantValue } } = args;
-  const outShape = paddings.map((p2, i2) => p2[0] + x.shape[i2] + p2[1]);
+  const outShape = paddings.map((p2, i) => p2[0] + x.shape[i] + p2[1]);
   if (util_exports.sizeFromShape(x.shape) === 0) {
     return fill4({
       backend: backend2,
@@ -64286,10 +64287,10 @@ function setup39(backend2) {
 }
 function select4(args) {
   const { inputs, backend: backend2 } = args;
-  const { condition, t: t2, e: e2 } = inputs;
+  const { condition, t: t2, e } = inputs;
   const conditionId = backend2.dataIdMap.get(condition.dataId).id;
   const tId = backend2.dataIdMap.get(t2.dataId).id;
-  const eId = backend2.dataIdMap.get(e2.dataId).id;
+  const eId = backend2.dataIdMap.get(e.dataId).id;
   const out = backend2.makeOutput(t2.shape, t2.dtype);
   const outId = backend2.dataIdMap.get(out.dataId).id;
   const cRank = condition.shape.length;
@@ -64361,7 +64362,7 @@ function spaceToBatchND4(args) {
   const prod6 = util_exports.sizeFromShape(blockShape);
   const completePaddings = [[0, 0]];
   completePaddings.push(...paddings);
-  for (let i2 = 1 + blockShape.length; i2 < x.shape.length; ++i2) {
+  for (let i = 1 + blockShape.length; i < x.shape.length; ++i) {
     completePaddings.push([0, 0]);
   }
   const paddedX = padV2Config3.kernelFunc({
@@ -64644,11 +64645,11 @@ function splitV3(args) {
   const splitSizes = backend_util_exports.prepareSplitSize(x, numOrSizeSplits, $axis);
   const begin = new Array(x.shape.length).fill(0);
   const size2 = x.shape.slice();
-  return splitSizes.map((s2) => {
+  return splitSizes.map((s) => {
     const xSliceSize = [...size2];
-    xSliceSize[$axis] = s2;
+    xSliceSize[$axis] = s;
     const xSlice = slice4({ inputs: { x }, attrs: { begin, size: xSliceSize }, backend: backend2 });
-    begin[$axis] += s2;
+    begin[$axis] += s;
     return xSlice;
   });
 }
@@ -64867,8 +64868,8 @@ function tile5(args) {
   const xId = backend2.dataIdMap.get(x.dataId).id;
   const { reps } = attrs;
   const newShape = new Array(x.shape.length);
-  for (let i2 = 0; i2 < newShape.length; i2++) {
-    newShape[i2] = x.shape[i2] * reps[i2];
+  for (let i = 0; i < newShape.length; i++) {
+    newShape[i] = x.shape[i] * reps[i];
   }
   const xShapeBytes = new Uint8Array(new Int32Array(x.shape).buffer);
   const newShapeBytes = new Uint8Array(new Int32Array(newShape).buffer);
@@ -64997,18 +64998,18 @@ function unpack3(args) {
   const rank = value.shape.length;
   const outShape = new Array(rank - 1);
   let outIndex = 0;
-  for (let i2 = 0; i2 < rank; i2++) {
-    if (i2 !== axis) {
-      outShape[outIndex++] = value.shape[i2];
+  for (let i = 0; i < rank; i++) {
+    if (i !== axis) {
+      outShape[outIndex++] = value.shape[i];
     }
   }
   const outs = new Array(numOutputs);
   const begin = new Array(rank).fill(0);
   const size2 = value.shape.slice();
   size2[axis] = 1;
-  for (let i2 = 0; i2 < outs.length; i2++) {
-    begin[axis] = i2;
-    outs[i2] = slice4({ inputs: { x: value }, attrs: { begin, size: size2 }, backend: backend2 });
+  for (let i = 0; i < outs.length; i++) {
+    begin[axis] = i;
+    outs[i] = slice4({ inputs: { x: value }, attrs: { begin, size: size2 }, backend: backend2 });
   }
   return outs.map(({ dataId, dtype }) => ({ dataId, dtype, shape: outShape }));
 }
@@ -65222,7 +65223,7 @@ ENV6.registerFlag("WASM_HAS_MULTITHREAD_SUPPORT", async () => {
       26,
       11
     ]));
-  } catch (e2) {
+  } catch (e) {
     return false;
   }
 });
@@ -65709,8 +65710,8 @@ function symbolicallyComputeStrides2(indicesArr, variableName) {
   const shape = indicesArr.map((d) => `${variableName}[${d}]`);
   const strides2 = new Array(numCoords - 1);
   strides2[numCoords - 2] = shape[numCoords - 1];
-  for (let i2 = numCoords - 3; i2 >= 0; --i2) {
-    strides2[i2] = `(${strides2[i2 + 1]} * ${shape[i2 + 1]})`;
+  for (let i = numCoords - 3; i >= 0; --i) {
+    strides2[i] = `(${strides2[i + 1]} * ${shape[i + 1]})`;
   }
   return strides2;
 }
@@ -65846,8 +65847,8 @@ function makeShader2(inputInfo, outputData, program) {
     ].join("\n");
   }
   let uniformDeclaration = "struct Uniforms { NAN : f32, ";
-  program.variableNames.forEach((x, i2) => {
-    const perDataType = getCoordsDataType2(inputInfo[i2].shape.length);
+  program.variableNames.forEach((x, i) => {
+    const perDataType = getCoordsDataType2(inputInfo[i].shape.length);
     uniformDeclaration += `${x.charAt(0).toLowerCase() + x.slice(1)}Shape : ${perDataType}, `;
   });
   const outputDataType = getCoordsDataType2(outputData.shape.length);
@@ -65874,9 +65875,9 @@ function makeShader2(inputInfo, outputData, program) {
       @group(0) @binding(0) var<storage, read_write> result: array<${mapToWgslTypes(outputData.dtype, program.isVec4)}>;
     `);
   }
-  program.variableNames.forEach((x, i2) => {
+  program.variableNames.forEach((x, i) => {
     prefixSnippets.push(`
-      @group(0) @binding(${1 + i2}) var<storage, read> ${x}: array<${program.variableTypes ? program.variableTypes[i2] : mapToWgslTypes(inputInfo[i2].dtype, program.isVec4)}>;
+      @group(0) @binding(${1 + i}) var<storage, read> ${x}: array<${program.variableTypes ? program.variableTypes[i] : mapToWgslTypes(inputInfo[i].dtype, program.isVec4)}>;
         `);
   });
   if (uniformDeclaration !== "") {
@@ -65895,7 +65896,7 @@ function makeShader2(inputInfo, outputData, program) {
   if (!program.atomic) {
     sources.push(setOutputSnippet(outputData.shape, outputData.dtype, program.isVec4));
   }
-  const inputSnippet = inputInfo.map((x, i2) => getInputSnippet(x, outputData.shape, program.variableTypes ? program.variableTypes[i2] === "vec4<f32>" : program.isVec4, program.dispatchLayout.x.length === outputData.shape.length)).join("\n");
+  const inputSnippet = inputInfo.map((x, i) => getInputSnippet(x, outputData.shape, program.variableTypes ? program.variableTypes[i] === "vec4<f32>" : program.isVec4, program.dispatchLayout.x.length === outputData.shape.length)).join("\n");
   sources.push(inputSnippet);
   sources.push(program.getUserCode());
   const source = sources.join("\n");
@@ -65981,8 +65982,8 @@ function getCoordsFromIndexSnippet(shape) {
   const strides2 = util_exports.computeStrides(shape);
   const dtype = getCoordsDataType2(rank);
   const coords3 = [];
-  for (let i2 = 0; i2 < rank; i2++) {
-    coords3.push(`d${i2}`);
+  for (let i = 0; i < rank; i++) {
+    coords3.push(`d${i}`);
   }
   if (strides2.length === 1) {
     return `    fn getCoordsFromIndex(index : i32) -> vec2<i32> {
@@ -65991,9 +65992,9 @@ function getCoordsFromIndexSnippet(shape) {
     }`;
   }
   let snippet;
-  snippet = "var index2 = index;" + strides2.map((_, i2) => {
-    const line1 = `let ${coords3[i2]} = index2 / uniforms.outShapeStrides.${getCoordsXYZ(i2)}`;
-    const line2 = i2 === strides2.length - 1 ? `let ${coords3[i2 + 1]} = index2 - ${coords3[i2]} * uniforms.outShapeStrides.${getCoordsXYZ(i2)}` : `index2 = index2 - ${coords3[i2]} * uniforms.outShapeStrides.${getCoordsXYZ(i2)}`;
+  snippet = "var index2 = index;" + strides2.map((_, i) => {
+    const line1 = `let ${coords3[i]} = index2 / uniforms.outShapeStrides.${getCoordsXYZ(i)}`;
+    const line2 = i === strides2.length - 1 ? `let ${coords3[i + 1]} = index2 - ${coords3[i]} * uniforms.outShapeStrides.${getCoordsXYZ(i)}` : `index2 = index2 - ${coords3[i]} * uniforms.outShapeStrides.${getCoordsXYZ(i)}`;
     return `${line1}; ${line2};`;
   }).join("");
   return `
@@ -66111,7 +66112,7 @@ function getInputByOutputSnippet(inputInfo, outShape, isVec4, isFlatDispatchLayo
   } else {
     if (outRank > 1) {
       const coordsType = getCoordsDataType2(inRank);
-      const coordsValues = inputInfo.shape.map((s2, i2) => `coords.${getCoordsXYZ(i2 + rankDiff)}`).join(", ");
+      const coordsValues = inputInfo.shape.map((s, i) => `coords.${getCoordsXYZ(i + rankDiff)}`).join(", ");
       unpackedCoordsSnippet = `${coordsType}(${coordsValues})`;
     } else {
       unpackedCoordsSnippet = "coords";
@@ -66171,30 +66172,30 @@ function getOutputCoordsSnippet(outShape, dispatchLayout) {
   let gatherDimensionsStr = "";
   const dims = [x, y, z];
   let rank = 0;
-  for (let i2 = 0; i2 < dims.length; i2++) {
-    const arr = dims[i2];
+  for (let i = 0; i < dims.length; i++) {
+    const arr = dims[i];
     if (arr.length === 0) {
       continue;
     }
     rank += arr.length;
     if (arr.length === 1) {
-      gatherDimensionsStr += `let d${arr[0]} = i32(globalId[${i2}]);`;
+      gatherDimensionsStr += `let d${arr[0]} = i32(globalId[${i}]);`;
     } else {
       const strides2 = symbolicallyComputeStrides2(arr, "uniforms.outShape");
-      gatherDimensionsStr += `var index${i2} = i32(globalId[${i2}]);`;
+      gatherDimensionsStr += `var index${i} = i32(globalId[${i}]);`;
       for (let j = 0; j < strides2.length; j++) {
-        gatherDimensionsStr += `let d${arr[j]} = index${i2} / ${strides2[j]};`;
+        gatherDimensionsStr += `let d${arr[j]} = index${i} / ${strides2[j]};`;
         if (j === strides2.length - 1) {
-          gatherDimensionsStr += `let d${arr[j + 1]} = index${i2} - d${arr[j]} * ${strides2[j]};`;
+          gatherDimensionsStr += `let d${arr[j + 1]} = index${i} - d${arr[j]} * ${strides2[j]};`;
         } else {
-          gatherDimensionsStr += `index${i2} = index${i2} - d${arr[j]} * ${strides2[j]};`;
+          gatherDimensionsStr += `index${i} = index${i} - d${arr[j]} * ${strides2[j]};`;
         }
       }
     }
   }
   const dimensions = [];
-  for (let i2 = 0; i2 < rank; i2++) {
-    dimensions.push(`d${i2}`);
+  for (let i = 0; i < rank; i++) {
+    dimensions.push(`d${i}`);
   }
   const dtype = getCoordsDataType2(rank);
   let snippet = `fn getOutputCoords() -> ${dtype} {
@@ -66356,8 +66357,8 @@ __export2(webgpu_util_exports, {
 });
 var arrayProduct = (arr) => {
   let product = 1;
-  for (let i2 = 0; i2 < arr.length; i2++) {
-    product *= arr[i2];
+  for (let i = 0; i < arr.length; i++) {
+    product *= arr[i];
   }
   return product;
 };
@@ -66417,7 +66418,7 @@ function computeWorkPerThreadForConv2d(layout, outputShape, isVec4 = false) {
   return [2, 2, 1];
 }
 function flatDispatchLayout(shape) {
-  return { x: shape.map((d, i2) => i2) };
+  return { x: shape.map((d, i) => i) };
 }
 function GPUBytesPerElement(dtype) {
   if (dtype === "float32" || dtype === "int32" || dtype === "bool" || dtype === "string") {
@@ -66756,7 +66757,7 @@ var WebGPUBackend = class extends KernelBackend {
     };
     const kernelMs = await Promise.all(flattenedActiveTimerQueries);
     res["kernelMs"] = util_exports.sum(kernelMs);
-    res["getExtraProfileInfo"] = () => kernelMs.map((d, i2) => ({ name: flattenedActiveTimerNames[i2], ms: d })).map((d) => `${d.name}: ${d.ms}`).join(", ");
+    res["getExtraProfileInfo"] = () => kernelMs.map((d, i) => ({ name: flattenedActiveTimerNames[i], ms: d })).map((d) => `${d.name}: ${d.ms}`).join(", ");
     this.uploadWaitMs = 0;
     this.downloadWaitMs = 0;
     return res;
@@ -66859,8 +66860,8 @@ var WebGPUBackend = class extends KernelBackend {
       currentOffset += d.data.length * 4;
     });
     const arrayBuffer = new ArrayBuffer(currentOffset);
-    programUniform.forEach((d, i2) => {
-      const offset = offsets[i2];
+    programUniform.forEach((d, i) => {
+      const offset = offsets[i];
       if (d.type === "int32") {
         new Int32Array(arrayBuffer, offset, d.data.length).set(d.data);
       } else if (d.type === "uint32") {
@@ -66905,7 +66906,7 @@ var WebGPUBackend = class extends KernelBackend {
         programUniform.push({ type: uniformsType, data: [program.isVec4 ? size2 / 4 : size2] });
       }
     }
-    const inputsData = inputs.map((input2, i2) => {
+    const inputsData = inputs.map((input2, i) => {
       if (input2.dtype === "complex64") {
         throw new Error(`GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.`);
       }
@@ -66913,7 +66914,7 @@ var WebGPUBackend = class extends KernelBackend {
       return {
         dtype: this.tensorMap.get(input2.dataId).dtype,
         shape: input2.shape,
-        name: program.variableNames[i2]
+        name: program.variableNames[i]
       };
     });
     const key = makeShaderKey2(program, bufferShapes, inputsData, output);
@@ -66934,7 +66935,7 @@ var WebGPUBackend = class extends KernelBackend {
     ];
     const bindGroup = this.device.createBindGroup({
       layout: pipeline.getBindGroupLayout(0),
-      entries: bindings.map((b, i2) => ({ binding: i2, resource: b }))
+      entries: bindings.map((b, i) => ({ binding: i, resource: b }))
     });
     this.ensureCommandEncoderReady();
     const pass = this.getComputePass();
@@ -68221,8 +68222,8 @@ function batchMatMulImpl2({ a, b, transposeA, transposeB, backend: backend2, bia
         intermediates.push(out);
         const outReshaped2 = reshape6({ inputs: { x: outActivated }, backend: backend2, attrs: { shape: outShape } });
         intermediates.push(outActivated);
-        for (const i2 of intermediates) {
-          backend2.disposeData(i2.dataId);
+        for (const i of intermediates) {
+          backend2.disposeData(i.dataId);
         }
         return outReshaped2;
       }
@@ -68250,8 +68251,8 @@ function batchMatMulImpl2({ a, b, transposeA, transposeB, backend: backend2, bia
   out = backend2.runWebGPUProgram(program, inputs, a.dtype, dimensions, out);
   const outReshaped = reshape6({ inputs: { x: out }, backend: backend2, attrs: { shape: outShape } });
   intermediates.push(out);
-  for (const i2 of intermediates) {
-    backend2.disposeData(i2.dataId);
+  for (const i of intermediates) {
+    backend2.disposeData(i.dataId);
   }
   return outReshaped;
 }
@@ -68555,7 +68556,7 @@ var AddNPackedProgram2 = class {
     this.workGroupSize = [64, 1, 1];
     this.size = true;
     this.outputShape = shapes[0];
-    this.variableNames = shapes.map((_, i2) => `T${i2}`);
+    this.variableNames = shapes.map((_, i) => `T${i}`);
     this.dispatchLayout = flatDispatchLayout(this.outputShape);
     this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, this.workGroupSize, [this.workPerThread, 1, 1]);
     this.shaderKey = "addN";
@@ -68635,8 +68636,8 @@ var ArgMinMaxProgram2 = class {
           snippet += "outputCoords,";
         }
       } else {
-        for (let i2 = 0; i2 < this.outputShape.length; i2++) {
-          snippet += `outputCoords.${getCoordsXYZ(i2)},`;
+        for (let i = 0; i < this.outputShape.length; i++) {
+          snippet += `outputCoords.${getCoordsXYZ(i)},`;
         }
       }
       return snippet;
@@ -68722,8 +68723,8 @@ var TransposeSharedProgram = class {
     this.variableNames = ["A"];
     this.workGroupSize = [16, 16, 1];
     const outputShape = new Array(aShape.length);
-    for (let i2 = 0; i2 < outputShape.length; i2++) {
-      outputShape[i2] = aShape[newDim[i2]];
+    for (let i = 0; i < outputShape.length; i++) {
+      outputShape[i] = aShape[newDim[i]];
     }
     this.outputShape = outputShape;
     this.dispatchLayout = { x: [0], y: [1] };
@@ -68764,8 +68765,8 @@ var TransposeProgram2 = class {
     this.workGroupSize = [64, 1, 1];
     this.size = true;
     const outputShape = new Array(aShape.length);
-    for (let i2 = 0; i2 < outputShape.length; i2++) {
-      outputShape[i2] = aShape[newDim[i2]];
+    for (let i = 0; i < outputShape.length; i++) {
+      outputShape[i] = aShape[newDim[i]];
     }
     this.outputShape = outputShape;
     this.dispatchLayout = flatDispatchLayout(this.outputShape);
@@ -68797,8 +68798,8 @@ function getSwitchedCoords2(newDim) {
     throw Error(`Transpose for rank ${rank} is not yet supported`);
   }
   const switchedCoords = new Array(rank);
-  for (let i2 = 0; i2 < newDim.length; i2++) {
-    switchedCoords[newDim[i2]] = `resRC.${getCoordsXYZ(i2)}`;
+  for (let i = 0; i < newDim.length; i++) {
+    switchedCoords[newDim[i]] = `resRC.${getCoordsXYZ(i)}`;
   }
   return switchedCoords.join();
 }
@@ -68809,8 +68810,8 @@ function transpose5(args) {
   const webgpuBackend = backend2;
   const xRank = x.shape.length;
   const newShape = new Array(xRank);
-  for (let i2 = 0; i2 < newShape.length; i2++) {
-    newShape[i2] = x.shape[perm[i2]];
+  for (let i = 0; i < newShape.length; i++) {
+    newShape[i] = x.shape[perm[i]];
   }
   if (backend2.shouldExecuteOnCPU([x])) {
     const xData = webgpuBackend.tensorMap.get(x.dataId);
@@ -69223,12 +69224,12 @@ var SliceProgram2 = class {
     const sourceCoords = getCoords3(this.rank);
     let coordSum;
     if (this.start.length === 1) {
-      coordSum = this.outputShape.map((_, i2) => {
+      coordSum = this.outputShape.map((_, i) => {
         return `sourceLoc = uniforms.start + coords;`;
       });
     } else {
-      coordSum = this.outputShape.map((_, i2) => {
-        return `sourceLoc.${coords2[i2]} = uniforms.start.${getCoordsXYZ(i2)} + coords.${coords2[i2]};`;
+      coordSum = this.outputShape.map((_, i) => {
+        return `sourceLoc.${coords2[i]} = uniforms.start.${getCoordsXYZ(i)} + coords.${coords2[i]};`;
       });
     }
     const userCode = `
@@ -69480,12 +69481,12 @@ var ConcatProgram2 = class {
     this.workGroupSize = [64, 1, 1];
     this.size = true;
     this.outputShape = backend_util_exports.computeOutShape(shapes, 1);
-    this.variableNames = shapes.map((_, i2) => `T${i2}`);
+    this.variableNames = shapes.map((_, i) => `T${i}`);
     this.dispatchLayout = flatDispatchLayout(this.outputShape);
     this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, this.workGroupSize, [this.workPerThread, 1, 1]);
     this.offsetLength = shapes.length - 1;
-    for (let i2 = 0; i2 < this.offsetLength; i2++) {
-      this.uniforms += `offset${i2} : i32,`;
+    for (let i = 0; i < this.offsetLength; i++) {
+      this.uniforms += `offset${i} : i32,`;
     }
     this.shaderKey = "concat";
   }
@@ -69493,8 +69494,8 @@ var ConcatProgram2 = class {
     const snippets = [];
     if (this.offsetLength > 0) {
       snippets.push(`if (yC < uniforms.offset0){ setOutputAtCoords(coords.x, coords.y, getT0(yR, yC)); }`);
-      for (let i2 = 1; i2 < this.offsetLength; i2++) {
-        snippets.push(`else if (yC < uniforms.offset${[i2]}){ setOutputAtCoords(coords.x, coords.y, getT${i2}(yR, yC - uniforms.offset${i2 - 1})); }`);
+      for (let i = 1; i < this.offsetLength; i++) {
+        snippets.push(`else if (yC < uniforms.offset${[i]}){ setOutputAtCoords(coords.x, coords.y, getT${i}(yR, yC - uniforms.offset${i - 1})); }`);
       }
       const lastIndex = this.offsetLength;
       const lastShiftIndex = this.offsetLength - 1;
@@ -69538,8 +69539,8 @@ function concatImpl3(inputs, axis, backend2) {
     const realConcated = concatImpl3(reals, axis, backend2);
     const imagConcated = concatImpl3(imags, axis, backend2);
     const result = complex4({ inputs: { real: realConcated, imag: imagConcated }, backend: backend2 });
-    reals.forEach((r2) => backend2.disposeData(r2.dataId));
-    imags.forEach((i2) => backend2.disposeData(i2.dataId));
+    reals.forEach((r) => backend2.disposeData(r.dataId));
+    imags.forEach((i) => backend2.disposeData(i.dataId));
     backend2.disposeData(realConcated.dataId);
     backend2.disposeData(imagConcated.dataId);
     return result;
@@ -69568,13 +69569,13 @@ function concatImpl3(inputs, axis, backend2) {
   const maxInputNum = backend2.device.limits.maxStorageBuffersPerShaderStage - 1;
   if (inputs.length > maxInputNum) {
     const reducedInputs = [];
-    for (let i2 = 0; i2 < inputs.length; i2 += maxInputNum) {
-      const subArray = inputs.slice(i2, i2 + maxInputNum);
+    for (let i = 0; i < inputs.length; i += maxInputNum) {
+      const subArray = inputs.slice(i, i + maxInputNum);
       reducedInputs.push(concatImpl3(subArray, axis, backend2));
     }
     const result = concatImpl3(reducedInputs, axis, backend2);
-    for (const i2 of reducedInputs) {
-      backend2.disposeData(i2.dataId);
+    for (const i of reducedInputs) {
+      backend2.disposeData(i.dataId);
     }
     return result;
   }
@@ -69586,13 +69587,13 @@ function concatImpl3(inputs, axis, backend2) {
   if (offsets.length > 0) {
     offsets[0] = shapes[0][1];
     uniformData.push({ type: "int32", data: [offsets[0]] });
-    for (let i2 = 1; i2 < offsets.length; i2++) {
-      offsets[i2] = offsets[i2 - 1] + shapes[i2][1];
-      uniformData.push({ type: "int32", data: [offsets[i2]] });
+    for (let i = 1; i < offsets.length; i++) {
+      offsets[i] = offsets[i - 1] + shapes[i][1];
+      uniformData.push({ type: "int32", data: [offsets[i]] });
     }
   }
   const res = backend2.runWebGPUProgram(program, tensors2D, tensors2D[0].dtype, uniformData);
-  tensors2D.forEach((r2) => backend2.disposeData(r2.dataId));
+  tensors2D.forEach((r) => backend2.disposeData(r.dataId));
   const reshapedResult = reshape6({ inputs: { x: res }, backend: backend2, attrs: { shape: outShape } });
   backend2.disposeData(res.dataId);
   return reshapedResult;
@@ -69889,8 +69890,8 @@ function conv2dByMatMul2({ x, filter, convInfo, backend: backend2, bias = null,
   });
   const out = reshape6({ inputs: { x: result }, backend: backend2, attrs: { shape: convInfo.outShape } });
   intermediates.push(result);
-  for (const i2 of intermediates) {
-    backend2.disposeData(i2.dataId);
+  for (const i of intermediates) {
+    backend2.disposeData(i.dataId);
   }
   return out;
 }
@@ -69950,8 +69951,8 @@ function conv2DImpl({ x, filter, convInfo, backend: backend2, bias = null, prelu
     program.uniforms += " alpha : f32,";
   }
   const out = backend2.runWebGPUProgram(program, inputVar, x.dtype, dimensions);
-  for (const i2 of intermediates) {
-    backend2.disposeData(i2.dataId);
+  for (const i of intermediates) {
+    backend2.disposeData(i.dataId);
   }
   return out;
 }
@@ -70395,10 +70396,10 @@ function cumImpl2(op2, x, backend2, axis, exclusive, reverse5) {
   }
   const size2 = permutedX.shape[permutedAxis];
   let result = identity5({ inputs: { x: permutedX }, backend: backend2 });
-  for (let i2 = 0; i2 <= Math.ceil(Math.log2(size2)) - 1; i2++) {
+  for (let i = 0; i <= Math.ceil(Math.log2(size2)) - 1; i++) {
     const program = new CumProgram2(op2, permutedX.shape, false, reverse5);
     const prevResult = result;
-    const uniformData = [{ type: "float32", data: [i2] }];
+    const uniformData = [{ type: "float32", data: [i] }];
     result = backend2.runWebGPUProgram(program, [result], result.dtype, uniformData);
     backend2.disposeData(prevResult.dataId);
   }
@@ -70875,8 +70876,8 @@ function einsum4(args) {
   let out = null;
   let numDimsRemaining = allDims.length;
   const tensorsToDispose = [];
-  for (let i2 = 0; i2 < nSteps; ++i2) {
-    for (const idTerm of steps[i2]) {
+  for (let i = 0; i < nSteps; ++i) {
+    for (const idTerm of steps[i]) {
       const { permutationIndices: perm, expandDims: dimsToExpand } = backend_util_exports.getEinsumPermutation(numDimsRemaining, idDims[idTerm]);
       let x;
       if (backend_util_exports.isIdentityPermutation(perm)) {
@@ -70900,13 +70901,13 @@ function einsum4(args) {
         tensorsToDispose.push(out);
       }
     }
-    if (i2 < nSteps - 1) {
-      if (path[i2] >= 0) {
+    if (i < nSteps - 1) {
+      if (path[i] >= 0) {
         out = sum6({
           inputs: { x: out },
           backend: backend2,
           attrs: {
-            axis: path[i2] - (allDims.length - numDimsRemaining),
+            axis: path[i] - (allDims.length - numDimsRemaining),
             keepDims: false
           }
         });
@@ -71133,9 +71134,9 @@ function fromPixels3(args) {
     pixelArray = new Uint8Array(pixels.width * pixels.height * numChannels);
     const dataLength = imageData.length;
     let j = 0;
-    for (let i2 = 0; i2 < dataLength; i2++) {
-      if (i2 % 4 < numChannels) {
-        pixelArray[j++] = imageData[i2];
+    for (let i = 0; i < dataLength; i++) {
+      if (i % 4 < numChannels) {
+        pixelArray[j++] = imageData[i];
       }
     }
   }
@@ -71382,11 +71383,11 @@ var GatherProgram2 = class {
 function getSourceCoords4(aShape) {
   const currentCoords = ["resRC.x", "resRC.y", "resRC.z", "resRC.w"];
   const sourceCoords = [];
-  for (let i2 = 0; i2 < aShape.length; i2++) {
-    if (i2 === 2) {
+  for (let i = 0; i < aShape.length; i++) {
+    if (i === 2) {
       sourceCoords.push("indexZ");
     } else {
-      sourceCoords.push(`${currentCoords[i2]}`);
+      sourceCoords.push(`${currentCoords[i]}`);
     }
   }
   return sourceCoords.join();
@@ -71569,20 +71570,20 @@ var MirrorPadProgram2 = class {
     this.variableNames = ["x"];
     this.workGroupSize = [64, 1, 1];
     this.size = true;
-    this.outputShape = paddings.map((p2, i2) => p2[0] + xShape[i2] + p2[1]);
+    this.outputShape = paddings.map((p2, i) => p2[0] + xShape[i] + p2[1]);
     this.dispatchLayout = flatDispatchLayout(this.outputShape);
     this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, this.workGroupSize);
     this.xShape = xShape;
-    paddings.map((_, i2) => {
-      this.uniforms += ` pad${i2} : vec2<i32>,`;
+    paddings.map((_, i) => {
+      this.uniforms += ` pad${i} : vec2<i32>,`;
     });
     this.offset = mode === "reflect" ? 0 : 1;
     this.shaderKey = `mirrorPad_${mode}`;
   }
   getUserCode() {
     const rank = this.xShape.length;
-    const start = this.xShape.map((_, i2) => `uniforms.pad${i2}[0]`).join(",");
-    const end = this.xShape.map((_, i2) => `uniforms.pad${i2}[0] + uniforms.xShape${rank > 1 ? `[${i2}]` : ""}`).join(",");
+    const start = this.xShape.map((_, i) => `uniforms.pad${i}[0]`).join(",");
+    const end = this.xShape.map((_, i) => `uniforms.pad${i}[0] + uniforms.xShape${rank > 1 ? `[${i}]` : ""}`).join(",");
     const shaderStart = rank === 1 ? "start" : "start[i]";
     const shaderEnd = rank === 1 ? "end" : "end[i]";
     const shaderOutC = rank === 1 ? "outC" : "outC[i]";
@@ -71681,14 +71682,14 @@ function zerosLike5(args) {
   const { x } = inputs;
   if (x.dtype === "complex64") {
     const realPart = real4({ inputs: { input: x }, backend: backend2 });
-    const r2 = zerosLike5({ inputs: { x: realPart }, backend: backend2 });
+    const r = zerosLike5({ inputs: { x: realPart }, backend: backend2 });
     const imagPart = imag4({ inputs: { input: x }, backend: backend2 });
-    const i2 = zerosLike5({ inputs: { x: imagPart }, backend: backend2 });
-    const result = complex4({ inputs: { real: r2, imag: i2 }, backend: backend2 });
+    const i = zerosLike5({ inputs: { x: imagPart }, backend: backend2 });
+    const result = complex4({ inputs: { real: r, imag: i }, backend: backend2 });
     backend2.disposeData(realPart.dataId);
-    backend2.disposeData(r2.dataId);
+    backend2.disposeData(r.dataId);
     backend2.disposeData(imagPart.dataId);
-    backend2.disposeData(i2.dataId);
+    backend2.disposeData(i.dataId);
     return result;
   } else {
     return fill5({
@@ -71713,14 +71714,14 @@ function onesLike5(args) {
     throw new Error("onesLike is not supported under string dtype");
   } else if (x.dtype === "complex64") {
     const realPart = real4({ inputs: { input: x }, backend: backend2 });
-    const r2 = onesLike5({ inputs: { x: realPart }, backend: backend2 });
+    const r = onesLike5({ inputs: { x: realPart }, backend: backend2 });
     const imagPart = imag4({ inputs: { input: x }, backend: backend2 });
-    const i2 = zerosLike5({ inputs: { x: imagPart }, backend: backend2 });
-    const result = complex4({ inputs: { real: r2, imag: i2 }, backend: backend2 });
+    const i = zerosLike5({ inputs: { x: imagPart }, backend: backend2 });
+    const result = complex4({ inputs: { real: r, imag: i }, backend: backend2 });
     backend2.disposeData(realPart.dataId);
-    backend2.disposeData(r2.dataId);
+    backend2.disposeData(r.dataId);
     backend2.disposeData(imagPart.dataId);
-    backend2.disposeData(i2.dataId);
+    backend2.disposeData(i.dataId);
     return result;
   } else {
     return fill5({ attrs: { shape: x.shape, dtype: x.dtype, value: 1 }, backend: backend2 });
@@ -71764,11 +71765,11 @@ var PadProgram2 = class {
     this.uniforms = "constantValue : f32,";
     this.workGroupSize = [64, 1, 1];
     this.size = true;
-    this.outputShape = paddings.map((p2, i2) => p2[0] + xShape[i2] + p2[1]);
+    this.outputShape = paddings.map((p2, i) => p2[0] + xShape[i] + p2[1]);
     this.dispatchLayout = flatDispatchLayout(this.outputShape);
     this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, this.workGroupSize);
-    paddings.map((_, i2) => {
-      this.uniforms += ` pad${i2} : vec2<i32>,`;
+    paddings.map((_, i) => {
+      this.uniforms += ` pad${i} : vec2<i32>,`;
     });
     this.xShape = xShape;
     this.shaderKey = "pad";
@@ -71776,8 +71777,8 @@ var PadProgram2 = class {
   getUserCode() {
     const rank = this.xShape.length;
     const type = getCoordsDataType2(rank);
-    const start = this.xShape.map((_, i2) => `uniforms.pad${i2}[0]`).join(",");
-    const end = this.xShape.map((_, i2) => `uniforms.pad${i2}[0] + uniforms.xShape${rank > 1 ? `[${i2}]` : ""}`).join(",");
+    const start = this.xShape.map((_, i) => `uniforms.pad${i}[0]`).join(",");
+    const end = this.xShape.map((_, i) => `uniforms.pad${i}[0] + uniforms.xShape${rank > 1 ? `[${i}]` : ""}`).join(",");
     const startValue = rank > 1 ? `${type}(${start})` : `${start}`;
     const endValue = rank > 1 ? `${type}(${end})` : `${end}`;
     const leftPadCondition = rank > 1 ? `any(outC < start)` : `outC < start`;
@@ -71810,7 +71811,7 @@ var padV23 = (args) => {
     return identity5({ inputs: { x }, backend: backend2 });
   }
   if (util_exports.sizeFromShape(x.shape) === 0) {
-    const outputShape = paddings.map((p2, i2) => p2[0] + x.shape[i2] + p2[1]);
+    const outputShape = paddings.map((p2, i) => p2[0] + x.shape[i] + p2[1]);
     return fill5({
       backend: backend2,
       attrs: { shape: outputShape, value: constantValue, dtype: x.dtype }
@@ -72275,10 +72276,10 @@ var SelectProgram2 = class {
       const currentCoords = ["resRC.x", "resRC.y", "resRC.z", "resRC.w"];
       const cCoordVars = [];
       const abCoordVars = [];
-      for (let i2 = 0; i2 < this.outputShape.length; i2++) {
-        abCoordVars.push(`${currentCoords[i2]}`);
-        if (i2 < this.cRank) {
-          cCoordVars.push(`${currentCoords[i2]}`);
+      for (let i = 0; i < this.outputShape.length; i++) {
+        abCoordVars.push(`${currentCoords[i]}`);
+        if (i < this.cRank) {
+          cCoordVars.push(`${currentCoords[i]}`);
         }
       }
       cCoords = cCoordVars.join();
@@ -72302,9 +72303,9 @@ var SelectProgram2 = class {
 };
 function select5(args) {
   const { inputs, backend: backend2 } = args;
-  const { condition, t: t2, e: e2 } = inputs;
+  const { condition, t: t2, e } = inputs;
   const program = new SelectProgram2(condition.shape.length, t2.shape, t2.shape.length);
-  return backend2.runWebGPUProgram(program, [condition, t2, e2], upcastType(t2.dtype, e2.dtype));
+  return backend2.runWebGPUProgram(program, [condition, t2, e], upcastType(t2.dtype, e.dtype));
 }
 var selectConfig4 = {
   kernelName: Select,
@@ -72373,7 +72374,7 @@ var spaceToBatchND5 = (args) => {
   const prod6 = blockShape.reduce((a, b) => a * b);
   const completePaddings = [[0, 0]];
   completePaddings.push(...paddings);
-  for (let i2 = 1 + blockShape.length; i2 < x.shape.length; ++i2) {
+  for (let i = 1 + blockShape.length; i < x.shape.length; ++i) {
     completePaddings.push([0, 0]);
   }
   const toDispose = [];
@@ -72409,8 +72410,8 @@ var TileProgram2 = class {
     this.workGroupSize = [64, 1, 1];
     this.size = true;
     const outputShape = new Array(aShape.length);
-    for (let i2 = 0; i2 < outputShape.length; i2++) {
-      outputShape[i2] = aShape[i2] * reps[i2];
+    for (let i = 0; i < outputShape.length; i++) {
+      outputShape[i] = aShape[i] * reps[i];
     }
     this.outputShape = outputShape;
     this.dispatchLayout = flatDispatchLayout(this.outputShape);
@@ -72440,8 +72441,8 @@ function getSourceCoords5(rank, uniformPrefix = "") {
   }
   const currentCoords = ["resRC.x", "resRC.y", "resRC.z", "resRC.w"];
   const sourceCoords = [];
-  for (let i2 = 0; i2 < rank; i2++) {
-    sourceCoords.push(`(${currentCoords[i2]} % ${uniformPrefix}aShape[${i2}])`);
+  for (let i = 0; i < rank; i++) {
+    sourceCoords.push(`(${currentCoords[i]} % ${uniformPrefix}aShape[${i}])`);
   }
   return sourceCoords.join();
 }
@@ -72544,11 +72545,11 @@ function splitV4(args) {
   const xRank = x.shape.length;
   const begin = new Array(xRank).fill(0);
   const size2 = x.shape.slice();
-  return splitSizes.map((s2) => {
+  return splitSizes.map((s) => {
     const sliceSize = [...size2];
-    sliceSize[$axis] = s2;
+    sliceSize[$axis] = s;
     const sliceT = slice5({ inputs: { x }, backend: backend2, attrs: { begin, size: sliceSize } });
-    begin[$axis] += s2;
+    begin[$axis] += s;
     return sliceT;
   });
 }
@@ -72601,9 +72602,9 @@ var StridedSliceProgram2 = class {
       newCoords = "coords * uniforms.strides + uniforms.begin";
     } else {
       let outputAxis = 0;
-      newCoords = this.outputShape.map((_, i2) => {
+      newCoords = this.outputShape.map((_, i) => {
         outputAxis++;
-        return this.outputShape.length === 1 ? `coords * uniforms.strides[${i2}] + uniforms.begin[${i2}]` : `coords[${outputAxis - 1}] * uniforms.strides[${i2}] + uniforms.begin[${i2}]`;
+        return this.outputShape.length === 1 ? `coords * uniforms.strides[${i}] + uniforms.begin[${i}]` : `coords[${outputAxis - 1}] * uniforms.strides[${i}] + uniforms.begin[${i}]`;
       }).join(",");
     }
     const userCode = `
@@ -73128,9 +73129,9 @@ function unpack4(args) {
   const num = value.shape[axis];
   const outShape = new Array(xRank - 1);
   let outIndex = 0;
-  for (let i2 = 0; i2 < xRank; i2++) {
-    if (i2 !== axis) {
-      outShape[outIndex++] = x.shape[i2];
+  for (let i = 0; i < xRank; i++) {
+    if (i !== axis) {
+      outShape[outIndex++] = x.shape[i];
     }
   }
   const toDispose = [];
@@ -73138,11 +73139,11 @@ function unpack4(args) {
   const size2 = x.shape.slice();
   size2[axis] = 1;
   const res = new Array(num);
-  for (let i2 = 0; i2 < res.length; i2++) {
-    begin[axis] = i2;
+  for (let i = 0; i < res.length; i++) {
+    begin[axis] = i;
     const sliced = slice5({ inputs: { x }, backend: backend2, attrs: { begin, size: size2 } });
     const reshaped = reshape6({ inputs: { x: sliced }, backend: backend2, attrs: { shape: outShape } });
-    res[i2] = reshaped;
+    res[i] = reshaped;
     toDispose.push(sliced);
   }
   toDispose.forEach((t2) => backend2.disposeData(t2.dataId));
@@ -73262,14 +73263,22 @@ var kernelConfigs4 = [
 for (const kernelConfig of kernelConfigs4) {
   registerKernel(kernelConfig);
 }
-var e = "3.20.0";
-var s = "3.20.0";
-var t = "3.20.0";
-var i = "3.20.0";
-var n = "3.20.0";
-var r = "3.20.0";
-var l = "3.20.0";
-var V = { tfjs: e, "tfjs-core": s, "tfjs-data": t, "tfjs-layers": i, "tfjs-converter": n, "tfjs-backend-webgl": r, "tfjs-backend-wasm": l };
+var version9 = "3.20.0";
+var version22 = "3.20.0";
+var version32 = "3.20.0";
+var version42 = "3.20.0";
+var version52 = "3.20.0";
+var version62 = "3.20.0";
+var version72 = "3.20.0";
+var version82 = {
+  tfjs: version9,
+  "tfjs-core": version22,
+  "tfjs-data": version32,
+  "tfjs-layers": version42,
+  "tfjs-converter": version52,
+  "tfjs-backend-webgl": version62,
+  "tfjs-backend-wasm": version72
+};
 
 // src/image/imagefxshaders.ts
 var vertexIdentity = `
@@ -73373,8 +73382,8 @@ var convolution = `
 
 // src/image/imagefx.ts
 var collect = (source, prefix, collection) => {
-  const r2 = new RegExp("\\b" + prefix + " \\w+ (\\w+)", "ig");
-  source.replace(r2, (match3, name) => {
+  const r = new RegExp("\\b" + prefix + " \\w+ (\\w+)", "ig");
+  source.replace(r, (match3, name) => {
     collection[name] = 0;
     return match3;
   });
@@ -73907,17 +73916,17 @@ function GLImageFilter() {
       ]);
     },
     emboss: (size2) => {
-      const s2 = size2 || 1;
+      const s = size2 || 1;
       filter.convolution.call(this, [
-        -2 * s2,
-        -1 * s2,
+        -2 * s,
+        -1 * s,
         0,
-        -1 * s2,
+        -1 * s,
         1,
-        1 * s2,
+        1 * s,
         0,
-        1 * s2,
-        2 * s2
+        1 * s,
+        2 * s
       ]);
     },
     blur: (size2) => {
@@ -73963,9 +73972,9 @@ function GLImageFilter() {
     gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MIN_FILTER, gl.NEAREST);
     gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MAG_FILTER, gl.NEAREST);
     gl.texImage2D(gl.TEXTURE_2D, 0, gl.RGBA, gl.RGBA, gl.UNSIGNED_BYTE, image2);
-    for (let i2 = 0; i2 < filterChain.length; i2++) {
-      lastInChain = i2 === filterChain.length - 1;
-      const f = filterChain[i2];
+    for (let i = 0; i < filterChain.length; i++) {
+      lastInChain = i === filterChain.length - 1;
+      const f = filterChain[i];
       f.func.apply(this, f.args || []);
     }
     return fxcanvas;
@@ -74315,7 +74324,7 @@ var Env = class {
     __publicField(this, "ImageData");
     this.browser = typeof navigator !== "undefined";
     this.node = typeof process !== "undefined" && typeof process.versions !== "undefined" && typeof process.versions.node !== "undefined";
-    this.tfjs = { version: V["tfjs-core"] };
+    this.tfjs = { version: version82["tfjs-core"] };
     this.offscreen = typeof OffscreenCanvas !== "undefined";
     this.initial = true;
     this.worker = this.browser && this.offscreen ? typeof WorkerGlobalScope !== "undefined" : void 0;
@@ -74364,12 +74373,12 @@ var Env = class {
         const adapter = await navigator.gpu.requestAdapter();
         this.webgpu.adapter = adapter ? adapter.name : void 0;
       }
-    } catch (e2) {
+    } catch (e) {
       this.webgpu.supported = false;
     }
     try {
       this.kernels = getKernelsForBackend(getBackend()).map((kernel) => kernel.kernelName.toLowerCase());
-    } catch (e2) {
+    } catch (e) {
     }
   }
   updateCPU() {
@@ -74726,236 +74735,132 @@ var models_default = {
   "nanodet-t": nanodet_t
 };
 
+// src/tfjs/load.ts
+var options = {
+  cacheModels: true,
+  cacheSupported: true,
+  verbose: true,
+  debug: false,
+  modelBasePath: ""
+};
+var modelStats = {};
+async function httpHandler(url, init4) {
+  if (options.debug)
+    log("load model fetch:", url, init4);
+  return fetch(url, init4);
+}
+function setModelLoadOptions(config3) {
+  options.cacheModels = config3.cacheModels;
+  options.verbose = config3.debug;
+  options.modelBasePath = config3.modelBasePath;
+}
+async function loadModel(modelPath) {
+  var _a, _b, _c, _d;
+  let modelUrl = join(options.modelBasePath, modelPath || "");
+  if (!modelUrl.toLowerCase().endsWith(".json"))
+    modelUrl += ".json";
+  const modelPathSegments = modelUrl.includes("/") ? modelUrl.split("/") : modelUrl.split("\\");
+  const shortModelName = modelPathSegments[modelPathSegments.length - 1].replace(".json", "");
+  const cachedModelName = "indexeddb://" + shortModelName;
+  modelStats[shortModelName] = {
+    name: shortModelName,
+    sizeFromManifest: 0,
+    sizeLoadedWeights: 0,
+    sizeDesired: models_exports[shortModelName],
+    inCache: false
+  };
+  options.cacheSupported = typeof indexedDB !== "undefined";
+  let cachedModels = {};
+  try {
+    cachedModels = options.cacheSupported && options.cacheModels ? await io_exports.listModels() : {};
+  } catch (e) {
+    options.cacheSupported = false;
+  }
+  modelStats[shortModelName].inCache = options.cacheSupported && options.cacheModels && Object.keys(cachedModels).includes(cachedModelName);
+  const tfLoadOptions = typeof fetch === "undefined" ? {} : { fetchFunc: (url, init4) => httpHandler(url, init4) };
+  let model22 = new GraphModel(modelStats[shortModelName].inCache ? cachedModelName : modelUrl, tfLoadOptions);
+  let loaded = false;
+  try {
+    model22.findIOHandler();
+    if (options.debug)
+      log("model load handler:", model22["handler"]);
+  } catch (err) {
+    log("error finding model i/o handler:", modelUrl, err);
+  }
+  try {
+    const artifacts = await ((_a = model22.handler) == null ? void 0 : _a.load()) || null;
+    modelStats[shortModelName].sizeFromManifest = ((_b = artifacts == null ? void 0 : artifacts.weightData) == null ? void 0 : _b.byteLength) || 0;
+    if (artifacts)
+      model22.loadSync(artifacts);
+    else
+      model22 = await loadGraphModel(modelStats[shortModelName].inCache ? cachedModelName : modelUrl, tfLoadOptions);
+    modelStats[shortModelName].sizeLoadedWeights = ((_d = (_c = model22.artifacts) == null ? void 0 : _c.weightData) == null ? void 0 : _d.byteLength) || 0;
+    if (options.verbose)
+      log("load:", { model: shortModelName, url: model22["modelUrl"], bytes: modelStats[shortModelName].sizeLoadedWeights });
+    loaded = true;
+  } catch (err) {
+    log("error loading model:", modelUrl, err);
+  }
+  if (loaded && options.cacheModels && options.cacheSupported && !modelStats[shortModelName].inCache) {
+    try {
+      const saveResult = await model22.save(cachedModelName);
+      if (options.debug)
+        log("model saved:", cachedModelName, saveResult);
+    } catch (err) {
+      log("error saving model:", modelUrl, err);
+    }
+  }
+  return model22;
+}
+
+// package.json
+var version5 = "2.11.0";
+
 // src/models.ts
 var models_exports2 = {};
 __export(models_exports2, {
   Models: () => Models,
   getModelStats: () => getModelStats,
-  load: () => load20,
+  load: () => load22,
   reset: () => reset2,
   validate: () => validate2,
   validateModel: () => validateModel
 });
 
-// src/gear/gear.ts
+// src/face/antispoof.ts
 var model2;
-var last2 = [];
-var raceNames = ["white", "black", "asian", "indian", "other"];
-var ageWeights = [15, 23, 28, 35.5, 45.5, 55.5, 65];
+var cached = [];
+var skipped = Number.MAX_SAFE_INTEGER;
 var lastCount = 0;
 var lastTime = 0;
-var skipped = Number.MAX_SAFE_INTEGER;
 async function load(config3) {
   var _a;
   if (env2.initial)
     model2 = null;
   if (!model2)
-    model2 = await loadModel((_a = config3.face.gear) == null ? void 0 : _a.modelPath);
+    model2 = await loadModel((_a = config3.face.antispoof) == null ? void 0 : _a.modelPath);
   else if (config3.debug)
     log("cached model:", model2["modelUrl"]);
   return model2;
 }
 async function predict(image2, config3, idx, count3) {
   var _a, _b;
-  if (!model2)
-    return { age: 0, gender: "unknown", genderScore: 0, race: [] };
-  const skipFrame = skipped < (((_a = config3.face.gear) == null ? void 0 : _a.skipFrames) || 0);
-  const skipTime = (((_b = config3.face.gear) == null ? void 0 : _b.skipTime) || 0) > now() - lastTime;
-  if (config3.skipAllowed && skipTime && skipFrame && lastCount === count3 && last2[idx]) {
+  if (!model2 || !(model2 == null ? void 0 : model2["executor"]))
+    return 0;
+  const skipTime = (((_a = config3.face.antispoof) == null ? void 0 : _a.skipTime) || 0) > now() - lastTime;
+  const skipFrame = skipped < (((_b = config3.face.antispoof) == null ? void 0 : _b.skipFrames) || 0);
+  if (config3.skipAllowed && skipTime && skipFrame && lastCount === count3 && cached[idx]) {
     skipped++;
-    return last2[idx];
+    return cached[idx];
   }
   skipped = 0;
   return new Promise(async (resolve) => {
-    var _a2, _b2;
-    if (!(model2 == null ? void 0 : model2.inputs[0].shape))
-      return;
-    const t2 = {};
-    const box = [[0, 0.1, 0.9, 0.9]];
-    t2.resize = image.cropAndResize(image2, box, [0], [model2.inputs[0].shape[2], model2.inputs[0].shape[1]]);
-    const obj = { age: 0, gender: "unknown", genderScore: 0, race: [] };
-    if ((_a2 = config3.face.gear) == null ? void 0 : _a2.enabled)
-      [t2.age, t2.gender, t2.race] = model2.execute(t2.resize, ["age_output", "gender_output", "race_output"]);
-    const gender2 = await t2.gender.data();
-    obj.gender = gender2[0] > gender2[1] ? "male" : "female";
-    obj.genderScore = Math.round(100 * (gender2[0] > gender2[1] ? gender2[0] : gender2[1])) / 100;
-    const race = await t2.race.data();
-    for (let i2 = 0; i2 < race.length; i2++) {
-      if (race[i2] > (((_b2 = config3.face.gear) == null ? void 0 : _b2.minConfidence) || 0.2))
-        obj.race.push({ score: Math.round(100 * race[i2]) / 100, race: raceNames[i2] });
-    }
-    obj.race.sort((a, b) => b.score - a.score);
-    const ageDistribution = Array.from(await t2.age.data());
-    const ageSorted = ageDistribution.map((a, i2) => [ageWeights[i2], a]).sort((a, b) => b[1] - a[1]);
-    let age2 = ageSorted[0][0];
-    for (let i2 = 1; i2 < ageSorted.length; i2++)
-      age2 += ageSorted[i2][1] * (ageSorted[i2][0] - age2);
-    obj.age = Math.round(10 * age2) / 10;
-    Object.keys(t2).forEach((tensor2) => dispose(t2[tensor2]));
-    last2[idx] = obj;
-    lastCount = count3;
-    lastTime = now();
-    resolve(obj);
-  });
-}
-
-// src/tfjs/constants.ts
-var constants = {
-  tf255: 255,
-  tf1: 1,
-  tf2: 2,
-  tf05: 0.5,
-  tf127: 127.5,
-  rgb: [0.2989, 0.587, 0.114]
-};
-function init2() {
-  constants.tf255 = scalar(255, "float32");
-  constants.tf1 = scalar(1, "float32");
-  constants.tf2 = scalar(2, "float32");
-  constants.tf05 = scalar(0.5, "float32");
-  constants.tf127 = scalar(127.5, "float32");
-  constants.rgb = tensor1d([0.2989, 0.587, 0.114], "float32");
-}
-
-// src/gear/ssrnet-age.ts
-var model3;
-var last3 = [];
-var lastCount2 = 0;
-var lastTime2 = 0;
-var skipped2 = Number.MAX_SAFE_INTEGER;
-async function load2(config3) {
-  if (env2.initial)
-    model3 = null;
-  if (!model3)
-    model3 = await loadModel(config3.face["ssrnet"].modelPathAge);
-  else if (config3.debug)
-    log("cached model:", model3["modelUrl"]);
-  return model3;
-}
-async function predict2(image2, config3, idx, count3) {
-  var _a, _b, _c, _d;
-  if (!model3)
-    return { age: 0 };
-  const skipFrame = skipped2 < (((_a = config3.face["ssrnet"]) == null ? void 0 : _a.skipFrames) || 0);
-  const skipTime = (((_b = config3.face["ssrnet"]) == null ? void 0 : _b.skipTime) || 0) > now() - lastTime2;
-  if (config3.skipAllowed && skipFrame && skipTime && lastCount2 === count3 && ((_c = last3[idx]) == null ? void 0 : _c.age) && ((_d = last3[idx]) == null ? void 0 : _d.age) > 0) {
-    skipped2++;
-    return last3[idx];
-  }
-  skipped2 = 0;
-  return new Promise(async (resolve) => {
-    var _a2;
-    if (!(model3 == null ? void 0 : model3.inputs) || !model3.inputs[0] || !model3.inputs[0].shape)
-      return;
-    const t2 = {};
-    t2.resize = image.resizeBilinear(image2, [model3.inputs[0].shape[2], model3.inputs[0].shape[1]], false);
-    t2.enhance = mul(t2.resize, constants.tf255);
-    const obj = { age: 0 };
-    if ((_a2 = config3.face["ssrnet"]) == null ? void 0 : _a2.enabled)
-      t2.age = model3.execute(t2.enhance);
-    if (t2.age) {
-      const data = await t2.age.data();
-      obj.age = Math.trunc(10 * data[0]) / 10;
-    }
-    Object.keys(t2).forEach((tensor2) => dispose(t2[tensor2]));
-    last3[idx] = obj;
-    lastCount2 = count3;
-    lastTime2 = now();
-    resolve(obj);
-  });
-}
-
-// src/gear/ssrnet-gender.ts
-var model4;
-var last4 = [];
-var lastCount3 = 0;
-var lastTime3 = 0;
-var skipped3 = Number.MAX_SAFE_INTEGER;
-var rgb = [0.2989, 0.587, 0.114];
-async function load3(config3) {
-  var _a;
-  if (env2.initial)
-    model4 = null;
-  if (!model4)
-    model4 = await loadModel((_a = config3.face["ssrnet"]) == null ? void 0 : _a.modelPathGender);
-  else if (config3.debug)
-    log("cached model:", model4["modelUrl"]);
-  return model4;
-}
-async function predict3(image2, config3, idx, count3) {
-  var _a, _b, _c, _d;
-  if (!model4)
-    return { gender: "unknown", genderScore: 0 };
-  const skipFrame = skipped3 < (((_a = config3.face["ssrnet"]) == null ? void 0 : _a.skipFrames) || 0);
-  const skipTime = (((_b = config3.face["ssrnet"]) == null ? void 0 : _b.skipTime) || 0) > now() - lastTime3;
-  if (config3.skipAllowed && skipFrame && skipTime && lastCount3 === count3 && ((_c = last4[idx]) == null ? void 0 : _c.gender) && ((_d = last4[idx]) == null ? void 0 : _d.genderScore) > 0) {
-    skipped3++;
-    return last4[idx];
-  }
-  skipped3 = 0;
-  return new Promise(async (resolve) => {
-    var _a2;
-    if (!(model4 == null ? void 0 : model4.inputs[0].shape))
-      return;
-    const t2 = {};
-    t2.resize = image.resizeBilinear(image2, [model4.inputs[0].shape[2], model4.inputs[0].shape[1]], false);
-    t2.enhance = tidy(() => {
-      const [red, green, blue] = split(t2.resize, 3, 3);
-      const redNorm = mul(red, rgb[0]);
-      const greenNorm = mul(green, rgb[1]);
-      const blueNorm = mul(blue, rgb[2]);
-      const grayscale = addN([redNorm, greenNorm, blueNorm]);
-      const normalize = mul(sub(grayscale, constants.tf05), 2);
-      return normalize;
-    });
-    const obj = { gender: "unknown", genderScore: 0 };
-    if ((_a2 = config3.face["ssrnet"]) == null ? void 0 : _a2.enabled)
-      t2.gender = model4.execute(t2.enhance);
-    const data = await t2.gender.data();
-    obj.gender = data[0] > data[1] ? "female" : "male";
-    obj.genderScore = data[0] > data[1] ? Math.trunc(100 * data[0]) / 100 : Math.trunc(100 * data[1]) / 100;
-    Object.keys(t2).forEach((tensor2) => dispose(t2[tensor2]));
-    last4[idx] = obj;
-    lastCount3 = count3;
-    lastTime3 = now();
-    resolve(obj);
-  });
-}
-
-// src/face/antispoof.ts
-var model5;
-var cached = [];
-var skipped4 = Number.MAX_SAFE_INTEGER;
-var lastCount4 = 0;
-var lastTime4 = 0;
-async function load4(config3) {
-  var _a;
-  if (env2.initial)
-    model5 = null;
-  if (!model5)
-    model5 = await loadModel((_a = config3.face.antispoof) == null ? void 0 : _a.modelPath);
-  else if (config3.debug)
-    log("cached model:", model5["modelUrl"]);
-  return model5;
-}
-async function predict4(image2, config3, idx, count3) {
-  var _a, _b;
-  if (!model5 || !(model5 == null ? void 0 : model5["executor"]))
-    return 0;
-  const skipTime = (((_a = config3.face.antispoof) == null ? void 0 : _a.skipTime) || 0) > now() - lastTime4;
-  const skipFrame = skipped4 < (((_b = config3.face.antispoof) == null ? void 0 : _b.skipFrames) || 0);
-  if (config3.skipAllowed && skipTime && skipFrame && lastCount4 === count3 && cached[idx]) {
-    skipped4++;
-    return cached[idx];
-  }
-  skipped4 = 0;
-  return new Promise(async (resolve) => {
-    const resize = image.resizeBilinear(image2, [(model5 == null ? void 0 : model5.inputs[0].shape) ? model5.inputs[0].shape[2] : 0, (model5 == null ? void 0 : model5.inputs[0].shape) ? model5.inputs[0].shape[1] : 0], false);
-    const res = model5 == null ? void 0 : model5.execute(resize);
+    const resize = image.resizeBilinear(image2, [(model2 == null ? void 0 : model2.inputs[0].shape) ? model2.inputs[0].shape[2] : 0, (model2 == null ? void 0 : model2.inputs[0].shape) ? model2.inputs[0].shape[1] : 0], false);
+    const res = model2 == null ? void 0 : model2.execute(resize);
     const num = (await res.data())[0];
     cached[idx] = Math.round(100 * num) / 100;
-    lastCount4 = count3;
-    lastTime4 = now();
+    lastCount = count3;
+    lastTime = now();
     dispose([resize, res]);
     resolve(cached[idx]);
   });
@@ -78351,6 +78256,24 @@ var contourKeypoints = {
   faceOval: connectionsToIndices(pairsFaceContour)
 };
 
+// src/tfjs/constants.ts
+var constants = {
+  tf255: 255,
+  tf1: 1,
+  tf2: 2,
+  tf05: 0.5,
+  tf127: 127.5,
+  rgb: [0.2989, 0.587, 0.114]
+};
+function init2() {
+  constants.tf255 = scalar(255, "float32");
+  constants.tf1 = scalar(1, "float32");
+  constants.tf2 = scalar(2, "float32");
+  constants.tf05 = scalar(0.5, "float32");
+  constants.tf127 = scalar(127.5, "float32");
+  constants.rgb = tensor1d([0.2989, 0.587, 0.114], "float32");
+}
+
 // src/face/facemeshutil.ts
 var getBoxSize = (box) => [Math.abs(box.endPoint[0] - box.startPoint[0]), Math.abs(box.endPoint[1] - box.startPoint[1])];
 var getBoxCenter = (box) => [box.startPoint[0] + (box.endPoint[0] - box.startPoint[0]) / 2, box.startPoint[1] + (box.endPoint[1] - box.startPoint[1]) / 2, 1];
@@ -78403,14 +78326,14 @@ var computeRotation = (point1, point2) => normalizeRadians(Math.PI / 2 - Math.at
 var buildTranslationMatrix = (x, y) => [[1, 0, x], [0, 1, y], [0, 0, 1]];
 var dot4 = (v1, v2) => {
   let product = 0;
-  for (let i2 = 0; i2 < v1.length; i2++)
-    product += v1[i2] * v2[i2];
+  for (let i = 0; i < v1.length; i++)
+    product += v1[i] * v2[i];
   return product;
 };
 var getColumnFrom2DArr = (arr, columnIndex) => {
   const column = [];
-  for (let i2 = 0; i2 < arr.length; i2++)
-    column.push(arr[i2][columnIndex]);
+  for (let i = 0; i < arr.length; i++)
+    column.push(arr[i][columnIndex]);
   return column;
 };
 var multiplyTransformMatrices = (mat1, mat2) => {
@@ -78442,16 +78365,16 @@ var rotatePoint = (homogeneousCoordinate, rotationMatrix) => [dot4(homogeneousCo
 function generateAnchors(inputSize10) {
   const spec = inputSize10 === 192 ? { strides: [4], anchors: [1] } : { strides: [inputSize10 / 16, inputSize10 / 8], anchors: [2, 6] };
   const anchors3 = [];
-  for (let i2 = 0; i2 < spec.strides.length; i2++) {
-    const stride = spec.strides[i2];
+  for (let i = 0; i < spec.strides.length; i++) {
+    const stride = spec.strides[i];
     const gridRows = Math.floor((inputSize10 + stride - 1) / stride);
     const gridCols = Math.floor((inputSize10 + stride - 1) / stride);
-    const anchorsNum = spec.anchors[i2];
+    const anchorsNum = spec.anchors[i];
     for (let gridY = 0; gridY < gridRows; gridY++) {
       const anchorY = stride * (gridY + 0.5);
       for (let gridX = 0; gridX < gridCols; gridX++) {
         const anchorX = stride * (gridX + 0.5);
-        for (let n2 = 0; n2 < anchorsNum; n2++)
+        for (let n = 0; n < anchorsNum; n++)
           anchors3.push([anchorX, anchorY]);
       }
     }
@@ -78518,23 +78441,23 @@ var calculateFaceBox = (mesh, previousBox) => {
 // src/face/blazeface.ts
 var keypointsCount = 6;
 var faceBoxScaleFactor = 1.4;
-var model6;
+var model3;
 var anchors = null;
 var inputSize = 0;
 var inputSizeT = null;
 var size = () => inputSize;
-async function load5(config3) {
+async function load2(config3) {
   var _a;
   if (env2.initial)
-    model6 = null;
-  if (!model6)
-    model6 = await loadModel((_a = config3.face.detector) == null ? void 0 : _a.modelPath);
+    model3 = null;
+  if (!model3)
+    model3 = await loadModel((_a = config3.face.detector) == null ? void 0 : _a.modelPath);
   else if (config3.debug)
-    log("cached model:", model6["modelUrl"]);
-  inputSize = model6["executor"] && model6.inputs[0].shape ? model6.inputs[0].shape[2] : 256;
+    log("cached model:", model3["modelUrl"]);
+  inputSize = model3["executor"] && model3.inputs[0].shape ? model3.inputs[0].shape[2] : 256;
   inputSizeT = scalar(inputSize, "int32");
   anchors = tensor2d(generateAnchors(inputSize));
-  return model6;
+  return model3;
 }
 function decodeBoxes(boxOutputs) {
   const t2 = {};
@@ -78560,7 +78483,7 @@ async function getBoxes(inputImage, config3) {
   t2.resized = image.resizeBilinear(inputImage, [inputSize, inputSize]);
   t2.div = div(t2.resized, constants.tf127);
   t2.normalized = sub(t2.div, constants.tf05);
-  const res = model6 == null ? void 0 : model6.execute(t2.normalized);
+  const res = model3 == null ? void 0 : model3.execute(t2.normalized);
   if (Array.isArray(res) && res.length > 2) {
     const sorted = res.sort((a, b) => a.size - b.size);
     t2.concat384 = concat([sorted[0], sorted[2]], 2);
@@ -78581,12 +78504,12 @@ async function getBoxes(inputImage, config3) {
   const nms = await t2.nms.array();
   const boxes = [];
   const scores = await t2.scores.data();
-  for (let i2 = 0; i2 < nms.length; i2++) {
-    const confidence = scores[nms[i2]];
+  for (let i = 0; i < nms.length; i++) {
+    const confidence = scores[nms[i]];
     if (confidence > (((_d = config3.face.detector) == null ? void 0 : _d.minConfidence) || 0)) {
       const b = {};
-      b.bbox = slice(t2.boxes, [nms[i2], 0], [1, -1]);
-      b.slice = slice(t2.batch, [nms[i2], keypointsCount - 1], [1, -1]);
+      b.bbox = slice(t2.boxes, [nms[i], 0], [1, -1]);
+      b.slice = slice(t2.batch, [nms[i], keypointsCount - 1], [1, -1]);
       b.squeeze = squeeze(b.slice);
       b.landmarks = reshape(b.squeeze, [keypointsCount, -1]);
       const points = await b.bbox.data();
@@ -78746,7 +78669,7 @@ function scale2(box, scaleFact) {
 var env3 = { initial: true };
 var models2 = { detector: null, landmarks: null };
 var inputSize3 = { detector: [224, 224], landmarks: [256, 256] };
-var skipped5 = Number.MAX_SAFE_INTEGER;
+var skipped2 = Number.MAX_SAFE_INTEGER;
 var outputNodes = {
   landmarks: ["ld_3d", "activation_segmentation", "activation_heatmap", "world_3d", "output_poseflag"],
   detector: []
@@ -78754,7 +78677,7 @@ var outputNodes = {
 var cache = null;
 var cropBox;
 var padding = [[0, 0], [0, 0], [0, 0], [0, 0]];
-var lastTime5 = 0;
+var lastTime2 = 0;
 var sigmoid6 = (x) => 1 - 1 / (1 + Math.exp(x));
 async function loadDetect(config3) {
   var _a;
@@ -78866,14 +78789,14 @@ async function detectLandmarks(input2, config3, outputSize2) {
   Object.keys(t2).forEach((tensor2) => dispose(t2[tensor2]));
   const keypointsRelative = [];
   const depth = 5;
-  for (let i2 = 0; i2 < points.length / depth; i2++) {
-    const score = sigmoid6(points[depth * i2 + 3]);
-    const presence = sigmoid6(points[depth * i2 + 4]);
+  for (let i = 0; i < points.length / depth; i++) {
+    const score = sigmoid6(points[depth * i + 3]);
+    const presence = sigmoid6(points[depth * i + 4]);
     const adjScore = Math.trunc(100 * score * presence * poseScore) / 100;
-    const positionRaw = [points[depth * i2 + 0] / inputSize3.landmarks[0], points[depth * i2 + 1] / inputSize3.landmarks[1], points[depth * i2 + 2] + 0];
+    const positionRaw = [points[depth * i + 0] / inputSize3.landmarks[0], points[depth * i + 1] / inputSize3.landmarks[1], points[depth * i + 2] + 0];
     const position = [Math.trunc(outputSize2[0] * positionRaw[0]), Math.trunc(outputSize2[1] * positionRaw[1]), positionRaw[2]];
-    const distance2 = [distances[depth * i2 + 0], distances[depth * i2 + 1], distances[depth * i2 + 2] + 0];
-    keypointsRelative.push({ part: kpt[i2], positionRaw, position, distance: distance2, score: adjScore });
+    const distance2 = [distances[depth * i + 0], distances[depth * i + 1], distances[depth * i + 2] + 0];
+    keypointsRelative.push({ part: kpt[i], positionRaw, position, distance: distance2, score: adjScore });
   }
   if (poseScore < (config3.body.minConfidence || 0))
     return null;
@@ -78884,9 +78807,9 @@ async function detectLandmarks(input2, config3, outputSize2) {
   const annotations2 = {};
   for (const [name, indexes] of Object.entries(connected)) {
     const pt = [];
-    for (let i2 = 0; i2 < indexes.length - 1; i2++) {
-      const pt0 = keypoints.find((kpt4) => kpt4.part === indexes[i2]);
-      const pt1 = keypoints.find((kpt4) => kpt4.part === indexes[i2 + 1]);
+    for (let i = 0; i < indexes.length - 1; i++) {
+      const pt0 = keypoints.find((kpt4) => kpt4.part === indexes[i]);
+      const pt1 = keypoints.find((kpt4) => kpt4.part === indexes[i + 1]);
       if (pt0 && pt1)
         pt.push([pt0.position, pt1.position]);
     }
@@ -78895,19 +78818,19 @@ async function detectLandmarks(input2, config3, outputSize2) {
   const body4 = { id: 0, score: Math.trunc(100 * poseScore) / 100, box: boxes.box, boxRaw: boxes.boxRaw, keypoints, annotations: annotations2 };
   return body4;
 }
-async function predict5(input2, config3) {
+async function predict2(input2, config3) {
   const outputSize2 = [input2.shape[2] || 0, input2.shape[1] || 0];
-  const skipTime = (config3.body.skipTime || 0) > now() - lastTime5;
-  const skipFrame = skipped5 < (config3.body.skipFrames || 0);
+  const skipTime = (config3.body.skipTime || 0) > now() - lastTime2;
+  const skipFrame = skipped2 < (config3.body.skipFrames || 0);
   if (config3.skipAllowed && skipTime && skipFrame && cache !== null) {
-    skipped5++;
+    skipped2++;
   } else {
     const t2 = {};
     t2.landmarks = prepareImage(input2, 256);
     cache = await detectLandmarks(t2.landmarks, config3, outputSize2);
     Object.keys(t2).forEach((tensor2) => dispose(t2[tensor2]));
-    lastTime5 = now();
-    skipped5 = 0;
+    lastTime2 = now();
+    skipped2 = 0;
   }
   return cache ? [cache] : [];
 }
@@ -78997,21 +78920,21 @@ var labels = [
 ];
 
 // src/object/centernet.ts
-var model7;
+var model4;
 var inputSize4 = 0;
-var last5 = [];
-var lastTime6 = 0;
-var skipped6 = Number.MAX_SAFE_INTEGER;
-async function load6(config3) {
+var last2 = [];
+var lastTime3 = 0;
+var skipped3 = Number.MAX_SAFE_INTEGER;
+async function load3(config3) {
   if (env2.initial)
-    model7 = null;
-  if (!model7) {
-    model7 = await loadModel(config3.object.modelPath);
-    const inputs = (model7 == null ? void 0 : model7["executor"]) ? Object.values(model7.modelSignature["inputs"]) : void 0;
+    model4 = null;
+  if (!model4) {
+    model4 = await loadModel(config3.object.modelPath);
+    const inputs = (model4 == null ? void 0 : model4["executor"]) ? Object.values(model4.modelSignature["inputs"]) : void 0;
     inputSize4 = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 0;
   } else if (config3.debug)
-    log("cached model:", model7["modelUrl"]);
-  return model7;
+    log("cached model:", model4["modelUrl"]);
+  return model4;
 }
 async function process3(res, outputShape, config3) {
   if (!res)
@@ -79028,7 +78951,7 @@ async function process3(res, outputShape, config3) {
   dispose([res, ...arr]);
   t2.nms = await image.nonMaxSuppressionAsync(t2.boxes, t2.scores, config3.object.maxDetected, config3.object.iouThreshold, config3.object.minConfidence || 0);
   const nms = await t2.nms.data();
-  let i2 = 0;
+  let i = 0;
   for (const id of Array.from(nms)) {
     const score = Math.trunc(100 * detections[0][id][4]) / 100;
     const classVal = detections[0][id][5];
@@ -79051,29 +78974,29 @@ async function process3(res, outputShape, config3) {
       Math.trunc(boxRaw[2] * outputShape[0]),
       Math.trunc(boxRaw[3] * outputShape[1])
     ];
-    results.push({ id: i2++, score, class: classVal, label, box, boxRaw });
+    results.push({ id: i++, score, class: classVal, label, box, boxRaw });
   }
   Object.keys(t2).forEach((tensor2) => dispose(t2[tensor2]));
   return results;
 }
-async function predict6(input2, config3) {
-  if (!(model7 == null ? void 0 : model7["executor"]))
+async function predict3(input2, config3) {
+  if (!(model4 == null ? void 0 : model4["executor"]))
     return [];
-  const skipTime = (config3.object.skipTime || 0) > now() - lastTime6;
-  const skipFrame = skipped6 < (config3.object.skipFrames || 0);
-  if (config3.skipAllowed && skipTime && skipFrame && last5.length > 0) {
-    skipped6++;
-    return last5;
+  const skipTime = (config3.object.skipTime || 0) > now() - lastTime3;
+  const skipFrame = skipped3 < (config3.object.skipFrames || 0);
+  if (config3.skipAllowed && skipTime && skipFrame && last2.length > 0) {
+    skipped3++;
+    return last2;
   }
-  skipped6 = 0;
+  skipped3 = 0;
   return new Promise(async (resolve) => {
     const outputSize2 = [input2.shape[2] || 0, input2.shape[1] || 0];
     const resize = image.resizeBilinear(input2, [inputSize4, inputSize4]);
-    const objectT = config3.object.enabled ? model7 == null ? void 0 : model7.execute(resize, ["tower_0/detections"]) : null;
-    lastTime6 = now();
+    const objectT = config3.object.enabled ? model4 == null ? void 0 : model4.execute(resize, ["tower_0/detections"]) : null;
+    lastTime3 = now();
     dispose(resize);
     const obj = await process3(objectT, outputSize2, config3);
-    last5 = obj;
+    last2 = obj;
     resolve(obj);
   });
 }
@@ -79112,18 +79035,18 @@ var connected2 = {
 };
 
 // src/body/efficientpose.ts
-var model8;
-var lastTime7 = 0;
+var model5;
+var lastTime4 = 0;
 var cache2 = { id: 0, keypoints: [], box: [0, 0, 0, 0], boxRaw: [0, 0, 0, 0], score: 0, annotations: {} };
-var skipped7 = Number.MAX_SAFE_INTEGER;
-async function load7(config3) {
+var skipped4 = Number.MAX_SAFE_INTEGER;
+async function load4(config3) {
   if (env2.initial)
-    model8 = null;
-  if (!model8)
-    model8 = await loadModel(config3.body.modelPath);
+    model5 = null;
+  if (!model5)
+    model5 = await loadModel(config3.body.modelPath);
   else if (config3.debug)
-    log("cached model:", model8["modelUrl"]);
-  return model8;
+    log("cached model:", model5["modelUrl"]);
+  return model5;
 }
 async function max2d(inputs, minScore) {
   const [width, height] = inputs.shape;
@@ -79142,29 +79065,29 @@ async function max2d(inputs, minScore) {
   dispose([reshaped, max7]);
   return [0, 0, newScore];
 }
-async function predict7(image2, config3) {
-  if (!(model8 == null ? void 0 : model8["executor"]))
+async function predict4(image2, config3) {
+  if (!(model5 == null ? void 0 : model5["executor"]))
     return [];
-  const skipTime = (config3.body.skipTime || 0) > now() - lastTime7;
-  const skipFrame = skipped7 < (config3.body.skipFrames || 0);
+  const skipTime = (config3.body.skipTime || 0) > now() - lastTime4;
+  const skipFrame = skipped4 < (config3.body.skipFrames || 0);
   if (config3.skipAllowed && skipTime && skipFrame && Object.keys(cache2.keypoints).length > 0) {
-    skipped7++;
+    skipped4++;
     return [cache2];
   }
-  skipped7 = 0;
+  skipped4 = 0;
   return new Promise(async (resolve) => {
     const tensor2 = tidy(() => {
-      if (!(model8 == null ? void 0 : model8.inputs[0].shape))
+      if (!(model5 == null ? void 0 : model5.inputs[0].shape))
         return null;
-      const resize = image.resizeBilinear(image2, [model8.inputs[0].shape[2], model8.inputs[0].shape[1]], false);
+      const resize = image.resizeBilinear(image2, [model5.inputs[0].shape[2], model5.inputs[0].shape[1]], false);
       const enhance2 = mul(resize, constants.tf2);
       const norm2 = sub(enhance2, constants.tf1);
       return norm2;
     });
     let resT;
     if (config3.body.enabled)
-      resT = model8 == null ? void 0 : model8.execute(tensor2);
-    lastTime7 = now();
+      resT = model5 == null ? void 0 : model5.execute(tensor2);
+    lastTime4 = now();
     dispose(tensor2);
     if (resT) {
       cache2.keypoints.length = 0;
@@ -79179,17 +79102,17 @@ async function predict7(image2, config3) {
             score: Math.round(100 * partScore) / 100,
             part: kpt2[id],
             positionRaw: [
-              x2 / model8.inputs[0].shape[2],
-              y2 / model8.inputs[0].shape[1]
+              x2 / model5.inputs[0].shape[2],
+              y2 / model5.inputs[0].shape[1]
             ],
             position: [
-              Math.round(image2.shape[2] * x2 / model8.inputs[0].shape[2]),
-              Math.round(image2.shape[1] * y2 / model8.inputs[0].shape[1])
+              Math.round(image2.shape[2] * x2 / model5.inputs[0].shape[2]),
+              Math.round(image2.shape[1] * y2 / model5.inputs[0].shape[1])
             ]
           });
         }
       }
-      stack2.forEach((s2) => dispose(s2));
+      stack2.forEach((s) => dispose(s));
     }
     cache2.score = cache2.keypoints.reduce((prev, curr) => curr.score > prev ? curr.score : prev, 0);
     const x = cache2.keypoints.map((a) => a.position[0]);
@@ -79210,9 +79133,9 @@ async function predict7(image2, config3) {
     ];
     for (const [name, indexes] of Object.entries(connected2)) {
       const pt = [];
-      for (let i2 = 0; i2 < indexes.length - 1; i2++) {
-        const pt0 = cache2.keypoints.find((kpt4) => kpt4.part === indexes[i2]);
-        const pt1 = cache2.keypoints.find((kpt4) => kpt4.part === indexes[i2 + 1]);
+      for (let i = 0; i < indexes.length - 1; i++) {
+        const pt0 = cache2.keypoints.find((kpt4) => kpt4.part === indexes[i]);
+        const pt1 = cache2.keypoints.find((kpt4) => kpt4.part === indexes[i + 1]);
         if (pt0 && pt1 && pt0.score > (config3.body.minConfidence || 0) && pt1.score > (config3.body.minConfidence || 0))
           pt.push([pt0.position, pt1.position]);
       }
@@ -79224,148 +79147,61 @@ async function predict7(image2, config3) {
 
 // src/gear/emotion.ts
 var annotations = ["angry", "disgust", "fear", "happy", "sad", "surprise", "neutral"];
-var model9;
-var last6 = [];
-var lastCount5 = 0;
-var lastTime8 = 0;
-var skipped8 = Number.MAX_SAFE_INTEGER;
-async function load8(config3) {
+var model6;
+var last3 = [];
+var lastCount2 = 0;
+var lastTime5 = 0;
+var skipped5 = Number.MAX_SAFE_INTEGER;
+async function load5(config3) {
   var _a;
   if (env2.initial)
-    model9 = null;
-  if (!model9)
-    model9 = await loadModel((_a = config3.face.emotion) == null ? void 0 : _a.modelPath);
+    model6 = null;
+  if (!model6)
+    model6 = await loadModel((_a = config3.face.emotion) == null ? void 0 : _a.modelPath);
   else if (config3.debug)
-    log("cached model:", model9["modelUrl"]);
-  return model9;
+    log("cached model:", model6["modelUrl"]);
+  return model6;
 }
-async function predict8(image2, config3, idx, count3) {
+async function predict5(image2, config3, idx, count3) {
   var _a, _b;
-  if (!model9)
+  if (!model6)
     return [];
-  const skipFrame = skipped8 < (((_a = config3.face.emotion) == null ? void 0 : _a.skipFrames) || 0);
-  const skipTime = (((_b = config3.face.emotion) == null ? void 0 : _b.skipTime) || 0) > now() - lastTime8;
-  if (config3.skipAllowed && skipTime && skipFrame && lastCount5 === count3 && last6[idx] && last6[idx].length > 0) {
-    skipped8++;
-    return last6[idx];
+  const skipFrame = skipped5 < (((_a = config3.face.emotion) == null ? void 0 : _a.skipFrames) || 0);
+  const skipTime = (((_b = config3.face.emotion) == null ? void 0 : _b.skipTime) || 0) > now() - lastTime5;
+  if (config3.skipAllowed && skipTime && skipFrame && lastCount2 === count3 && last3[idx] && last3[idx].length > 0) {
+    skipped5++;
+    return last3[idx];
   }
-  skipped8 = 0;
+  skipped5 = 0;
   return new Promise(async (resolve) => {
     var _a2;
     const obj = [];
     if ((_a2 = config3.face.emotion) == null ? void 0 : _a2.enabled) {
       const t2 = {};
-      const inputSize10 = (model9 == null ? void 0 : model9.inputs[0].shape) ? model9.inputs[0].shape[2] : 0;
+      const inputSize10 = (model6 == null ? void 0 : model6.inputs[0].shape) ? model6.inputs[0].shape[2] : 0;
       t2.resize = image.resizeBilinear(image2, [inputSize10, inputSize10], false);
       t2.channels = mul(t2.resize, constants.rgb);
       t2.grayscale = sum2(t2.channels, 3, true);
       t2.grayscaleSub = sub(t2.grayscale, constants.tf05);
       t2.grayscaleMul = mul(t2.grayscaleSub, constants.tf2);
-      t2.emotion = model9 == null ? void 0 : model9.execute(t2.grayscaleMul);
-      lastTime8 = now();
+      t2.emotion = model6 == null ? void 0 : model6.execute(t2.grayscaleMul);
+      lastTime5 = now();
       const data = await t2.emotion.data();
-      for (let i2 = 0; i2 < data.length; i2++) {
-        if (data[i2] > (config3.face.emotion.minConfidence || 0))
-          obj.push({ score: Math.min(0.99, Math.trunc(100 * data[i2]) / 100), emotion: annotations[i2] });
+      for (let i = 0; i < data.length; i++) {
+        if (data[i] > (config3.face.emotion.minConfidence || 0))
+          obj.push({ score: Math.min(0.99, Math.trunc(100 * data[i]) / 100), emotion: annotations[i] });
       }
       obj.sort((a, b) => b.score - a.score);
       Object.keys(t2).forEach((tensor2) => dispose(t2[tensor2]));
     }
-    last6[idx] = obj;
-    lastCount5 = count3;
+    last3[idx] = obj;
+    lastCount2 = count3;
     resolve(obj);
   });
 }
 
-// src/face/mobilefacenet.ts
-var model10;
-var last7 = [];
-var lastCount6 = 0;
-var lastTime9 = 0;
-var skipped9 = Number.MAX_SAFE_INTEGER;
-async function load9(config3) {
-  var _a;
-  if (env2.initial)
-    model10 = null;
-  if (!model10)
-    model10 = await loadModel((_a = config3.face["mobilefacenet"]) == null ? void 0 : _a.modelPath);
-  else if (config3.debug)
-    log("cached model:", model10["modelUrl"]);
-  return model10;
-}
-async function predict9(input2, config3, idx, count3) {
-  var _a, _b;
-  if (!(model10 == null ? void 0 : model10["executor"]))
-    return [];
-  const skipFrame = skipped9 < (((_a = config3.face["mobilefacenet"]) == null ? void 0 : _a.skipFrames) || 0);
-  const skipTime = (((_b = config3.face["mobilefacenet"]) == null ? void 0 : _b.skipTime) || 0) > now() - lastTime9;
-  if (config3.skipAllowed && skipTime && skipFrame && lastCount6 === count3 && last7[idx]) {
-    skipped9++;
-    return last7[idx];
-  }
-  return new Promise(async (resolve) => {
-    var _a2;
-    let data = [];
-    if (((_a2 = config3.face["mobilefacenet"]) == null ? void 0 : _a2.enabled) && (model10 == null ? void 0 : model10.inputs[0].shape)) {
-      const t2 = {};
-      t2.crop = image.resizeBilinear(input2, [model10.inputs[0].shape[2], model10.inputs[0].shape[1]], false);
-      t2.data = model10.execute(t2.crop);
-      const output = await t2.data.data();
-      data = Array.from(output);
-      Object.keys(t2).forEach((tensor2) => dispose(t2[tensor2]));
-    }
-    last7[idx] = data;
-    lastCount6 = count3;
-    lastTime9 = now();
-    resolve(data);
-  });
-}
-
-// src/face/insightface.ts
-var model11;
-var last8 = [];
-var lastCount7 = 0;
-var lastTime10 = 0;
-var skipped10 = Number.MAX_SAFE_INTEGER;
-async function load10(config3) {
-  if (env2.initial)
-    model11 = null;
-  if (!model11)
-    model11 = await loadModel(config3.face["insightface"].modelPath);
-  else if (config3.debug)
-    log("cached model:", model11["modelUrl"]);
-  return model11;
-}
-async function predict10(input2, config3, idx, count3) {
-  var _a, _b;
-  if (!(model11 == null ? void 0 : model11["executor"]))
-    return [];
-  const skipFrame = skipped10 < (((_a = config3.face["insightface"]) == null ? void 0 : _a.skipFrames) || 0);
-  const skipTime = (((_b = config3.face["insightface"]) == null ? void 0 : _b.skipTime) || 0) > now() - lastTime10;
-  if (config3.skipAllowed && skipTime && skipFrame && lastCount7 === count3 && last8[idx]) {
-    skipped10++;
-    return last8[idx];
-  }
-  return new Promise(async (resolve) => {
-    var _a2;
-    let data = [];
-    if (((_a2 = config3.face["insightface"]) == null ? void 0 : _a2.enabled) && (model11 == null ? void 0 : model11.inputs[0].shape)) {
-      const t2 = {};
-      t2.crop = image.resizeBilinear(input2, [model11.inputs[0].shape[2], model11.inputs[0].shape[1]], false);
-      t2.data = model11.execute(t2.crop);
-      const output = await t2.data.data();
-      data = Array.from(output);
-      Object.keys(t2).forEach((tensor2) => dispose(t2[tensor2]));
-    }
-    last8[idx] = data;
-    lastCount7 = count3;
-    lastTime10 = now();
-    resolve(data);
-  });
-}
-
 // src/face/iris.ts
-var model12;
+var model7;
 var inputSize5 = 0;
 var irisEnlarge = 2.3;
 var leftOutline = meshAnnotations.leftEyeLower0;
@@ -79380,22 +79216,22 @@ var irisLandmarks = {
   index: 71,
   numCoordinates: 76
 };
-async function load11(config3) {
+async function load6(config3) {
   var _a, _b;
   if (env2.initial)
-    model12 = null;
-  if (!model12)
-    model12 = await loadModel((_a = config3.face.iris) == null ? void 0 : _a.modelPath);
+    model7 = null;
+  if (!model7)
+    model7 = await loadModel((_a = config3.face.iris) == null ? void 0 : _a.modelPath);
   else if (config3.debug)
-    log("cached model:", model12["modelUrl"]);
-  inputSize5 = (model12 == null ? void 0 : model12["executor"]) && ((_b = model12.inputs) == null ? void 0 : _b[0].shape) ? model12.inputs[0].shape[2] : 0;
+    log("cached model:", model7["modelUrl"]);
+  inputSize5 = (model7 == null ? void 0 : model7["executor"]) && ((_b = model7.inputs) == null ? void 0 : _b[0].shape) ? model7.inputs[0].shape[2] : 0;
   if (inputSize5 === -1)
     inputSize5 = 64;
-  return model12;
+  return model7;
 }
 function replaceIrisCoords(rawCoords, newCoords, prefix, keys) {
-  for (let i2 = 0; i2 < irisIndices.length; i2++) {
-    const { key, indices } = irisIndices[i2];
+  for (let i = 0; i < irisIndices.length; i++) {
+    const { key, indices } = irisIndices[i];
     const originalIndices = meshAnnotations[`${prefix}${key}`];
     if (!keys || keys.includes(key)) {
       for (let j = 0; j < indices.length; j++) {
@@ -79432,10 +79268,10 @@ var getEyeBox = (rawCoords, face4, eyeInnerCornerIndex, eyeOuterCornerIndex, mes
 };
 var getEyeCoords = (eyeData, eyeBox, eyeBoxSize, flip = false) => {
   const eyeRawCoords = [];
-  for (let i2 = 0; i2 < irisLandmarks.numCoordinates; i2++) {
-    const x = eyeData[i2 * 3];
-    const y = eyeData[i2 * 3 + 1];
-    const z = eyeData[i2 * 3 + 2];
+  for (let i = 0; i < irisLandmarks.numCoordinates; i++) {
+    const x = eyeData[i * 3];
+    const y = eyeData[i * 3 + 1];
+    const z = eyeData[i * 3 + 2];
     eyeRawCoords.push([
       (flip ? 1 - x / inputSize5 : x / inputSize5) * eyeBoxSize[0] + eyeBox.startPoint[0],
       y / inputSize5 * eyeBoxSize[1] + eyeBox.startPoint[1],
@@ -79448,25 +79284,25 @@ var getAdjustedIrisCoords = (rawCoords, irisCoords, direction) => {
   const upperCenterZ = rawCoords[meshAnnotations[`${direction}EyeUpper0`][irisLandmarks.upperCenter]][2];
   const lowerCenterZ = rawCoords[meshAnnotations[`${direction}EyeLower0`][irisLandmarks.lowerCenter]][2];
   const averageZ = (upperCenterZ + lowerCenterZ) / 2;
-  return irisCoords.map((coord, i2) => {
+  return irisCoords.map((coord, i) => {
     let z = averageZ;
-    if (i2 === 2) {
+    if (i === 2) {
       z = upperCenterZ;
-    } else if (i2 === 4) {
+    } else if (i === 4) {
       z = lowerCenterZ;
     }
     return [coord[0], coord[1], z];
   });
 };
 async function augmentIris(rawCoords, face4, meshSize) {
-  if (!(model12 == null ? void 0 : model12["executor"]))
+  if (!(model7 == null ? void 0 : model7["executor"]))
     return rawCoords;
   const { box: leftEyeBox, boxSize: leftEyeBoxSize, crop: leftEyeCrop } = getEyeBox(rawCoords, face4, eyeLandmarks.leftBounds[0], eyeLandmarks.leftBounds[1], meshSize, true);
   const { box: rightEyeBox, boxSize: rightEyeBoxSize, crop: rightEyeCrop } = getEyeBox(rawCoords, face4, eyeLandmarks.rightBounds[0], eyeLandmarks.rightBounds[1], meshSize, true);
   const combined = concat([leftEyeCrop, rightEyeCrop]);
   dispose(leftEyeCrop);
   dispose(rightEyeCrop);
-  const eyePredictions = model12.execute(combined);
+  const eyePredictions = model7.execute(combined);
   dispose(combined);
   const eyePredictionsData = await eyePredictions.data();
   dispose(eyePredictions);
@@ -79826,28 +79662,28 @@ var LANDMARKS_REFINEMENT_RIGHT_EYE_CONFIG = [
 async function augment(rawCoords, results) {
   var _a, _b, _c, _d, _e, _f, _g, _h, _i, _j;
   const t2 = {
-    lips: await ((_b = (_a = results.filter((r2) => r2.size === 160)) == null ? void 0 : _a[0]) == null ? void 0 : _b.data()),
-    irisL: await ((_d = (_c = results.filter((r2) => r2.size === 10)) == null ? void 0 : _c[0]) == null ? void 0 : _d.data()),
-    eyeL: await ((_f = (_e = results.filter((r2) => r2.size === 142)) == null ? void 0 : _e[0]) == null ? void 0 : _f.data()),
-    irisR: await ((_h = (_g = results.filter((r2) => r2.size === 10)) == null ? void 0 : _g[1]) == null ? void 0 : _h.data()),
-    eyeR: await ((_j = (_i = results.filter((r2) => r2.size === 142)) == null ? void 0 : _i[1]) == null ? void 0 : _j.data())
+    lips: await ((_b = (_a = results.filter((r) => r.size === 160)) == null ? void 0 : _a[0]) == null ? void 0 : _b.data()),
+    irisL: await ((_d = (_c = results.filter((r) => r.size === 10)) == null ? void 0 : _c[0]) == null ? void 0 : _d.data()),
+    eyeL: await ((_f = (_e = results.filter((r) => r.size === 142)) == null ? void 0 : _e[0]) == null ? void 0 : _f.data()),
+    irisR: await ((_h = (_g = results.filter((r) => r.size === 10)) == null ? void 0 : _g[1]) == null ? void 0 : _h.data()),
+    eyeR: await ((_j = (_i = results.filter((r) => r.size === 142)) == null ? void 0 : _i[1]) == null ? void 0 : _j.data())
   };
   for (const val of Object.values(t2)) {
     if (!val)
       return rawCoords;
   }
   const irisLDepth = LANDMARKS_REFINEMENT_LEFT_EYE_CONFIG.reduce((prev, curr) => prev += rawCoords[curr][2], 0) / LANDMARKS_REFINEMENT_LEFT_EYE_CONFIG.length;
-  for (let i2 = 0; i2 < t2.irisL.length / 2; i2++)
-    rawCoords.push([t2.irisL[2 * i2 + 0], t2.irisL[2 * i2 + 1], irisLDepth]);
+  for (let i = 0; i < t2.irisL.length / 2; i++)
+    rawCoords.push([t2.irisL[2 * i + 0], t2.irisL[2 * i + 1], irisLDepth]);
   const irisRDepth = LANDMARKS_REFINEMENT_RIGHT_EYE_CONFIG.reduce((prev, curr) => prev += rawCoords[curr][2], 0) / LANDMARKS_REFINEMENT_RIGHT_EYE_CONFIG.length;
-  for (let i2 = 0; i2 < t2.irisR.length / 2; i2++)
-    rawCoords.push([t2.irisR[2 * i2 + 0], t2.irisR[2 * i2 + 1], irisRDepth]);
-  for (let i2 = 0; i2 < t2.eyeL.length / 2; i2++)
-    rawCoords[LANDMARKS_REFINEMENT_LEFT_EYE_CONFIG[i2]] = [t2.eyeL[2 * i2 + 0], t2.eyeL[2 * i2 + 1], rawCoords[LANDMARKS_REFINEMENT_LEFT_EYE_CONFIG[i2]][2]];
-  for (let i2 = 0; i2 < t2.eyeR.length / 2; i2++)
-    rawCoords[LANDMARKS_REFINEMENT_RIGHT_EYE_CONFIG[i2]] = [t2.eyeR[2 * i2 + 0], t2.eyeR[2 * i2 + 1], rawCoords[LANDMARKS_REFINEMENT_RIGHT_EYE_CONFIG[i2]][2]];
-  for (let i2 = 0; i2 < t2.lips.length / 2; i2++)
-    rawCoords[LANDMARKS_REFINEMENT_LIPS_CONFIG[i2]] = [t2.lips[2 * i2 + 0], t2.lips[2 * i2 + 1], rawCoords[LANDMARKS_REFINEMENT_LIPS_CONFIG[i2]][2]];
+  for (let i = 0; i < t2.irisR.length / 2; i++)
+    rawCoords.push([t2.irisR[2 * i + 0], t2.irisR[2 * i + 1], irisRDepth]);
+  for (let i = 0; i < t2.eyeL.length / 2; i++)
+    rawCoords[LANDMARKS_REFINEMENT_LEFT_EYE_CONFIG[i]] = [t2.eyeL[2 * i + 0], t2.eyeL[2 * i + 1], rawCoords[LANDMARKS_REFINEMENT_LEFT_EYE_CONFIG[i]][2]];
+  for (let i = 0; i < t2.eyeR.length / 2; i++)
+    rawCoords[LANDMARKS_REFINEMENT_RIGHT_EYE_CONFIG[i]] = [t2.eyeR[2 * i + 0], t2.eyeR[2 * i + 1], rawCoords[LANDMARKS_REFINEMENT_RIGHT_EYE_CONFIG[i]][2]];
+  for (let i = 0; i < t2.lips.length / 2; i++)
+    rawCoords[LANDMARKS_REFINEMENT_LIPS_CONFIG[i]] = [t2.lips[2 * i + 0], t2.lips[2 * i + 1], rawCoords[LANDMARKS_REFINEMENT_LIPS_CONFIG[i]][2]];
   return rawCoords;
 }
 
@@ -79857,11 +79693,11 @@ var cache3 = {
   skipped: Number.MAX_SAFE_INTEGER,
   timestamp: 0
 };
-var model13 = null;
+var model8 = null;
 var inputSize6 = 0;
-async function predict11(input2, config3) {
+async function predict6(input2, config3) {
   var _a, _b, _c, _d, _e, _f, _g, _h, _i, _j;
-  if (!(model13 == null ? void 0 : model13["executor"]))
+  if (!(model8 == null ? void 0 : model8["executor"]))
     return [];
   const skipTime = (((_a = config3.face.detector) == null ? void 0 : _a.skipTime) || 0) > now() - cache3.timestamp;
   const skipFrame = cache3.skipped < (((_b = config3.face.detector) == null ? void 0 : _b.skipFrames) || 0);
@@ -79876,8 +79712,8 @@ async function predict11(input2, config3) {
   const newCache = [];
   let id = 0;
   const size2 = inputSize6;
-  for (let i2 = 0; i2 < cache3.boxes.length; i2++) {
-    const box = cache3.boxes[i2];
+  for (let i = 0; i < cache3.boxes.length; i++) {
+    const box = cache3.boxes[i];
     let angle = 0;
     let rotationMatrix;
     const face4 = {
@@ -79911,7 +79747,7 @@ async function predict11(input2, config3) {
       for (const key of Object.keys(blazeFaceLandmarks)) {
         face4.annotations[key] = [face4.mesh[blazeFaceLandmarks[key]]];
       }
-    } else if (!model13) {
+    } else if (!model8) {
       if (config3.debug)
         log("face mesh detection requested, but model is not loaded");
     } else {
@@ -79920,7 +79756,7 @@ async function predict11(input2, config3) {
         dispose(face4.tensor);
         return faces;
       }
-      const results = model13.execute(face4.tensor);
+      const results = model8.execute(face4.tensor);
       const confidenceT = results.find((t2) => t2.shape[t2.shape.length - 1] === 1);
       const faceConfidence = await confidenceT.data();
       face4.faceScore = Math.round(100 * faceConfidence[0]) / 100;
@@ -79969,54 +79805,54 @@ async function predict11(input2, config3) {
   cache3.boxes = newCache;
   return faces;
 }
-async function load12(config3) {
+async function load7(config3) {
   var _a, _b, _c, _d, _e, _f;
   if (env2.initial)
-    model13 = null;
-  if (((_a = config3.face.attention) == null ? void 0 : _a.enabled) && (model13 == null ? void 0 : model13["signature"])) {
-    if (Object.keys(((_b = model13 == null ? void 0 : model13["signature"]) == null ? void 0 : _b.outputs) || {}).length < 6)
-      model13 = null;
+    model8 = null;
+  if (((_a = config3.face.attention) == null ? void 0 : _a.enabled) && (model8 == null ? void 0 : model8["signature"])) {
+    if (Object.keys(((_b = model8 == null ? void 0 : model8["signature"]) == null ? void 0 : _b.outputs) || {}).length < 6)
+      model8 = null;
   }
-  if (!model13) {
+  if (!model8) {
     if ((_c = config3.face.attention) == null ? void 0 : _c.enabled)
-      model13 = await loadModel(config3.face.attention.modelPath);
+      model8 = await loadModel(config3.face.attention.modelPath);
     else
-      model13 = await loadModel((_d = config3.face.mesh) == null ? void 0 : _d.modelPath);
+      model8 = await loadModel((_d = config3.face.mesh) == null ? void 0 : _d.modelPath);
   } else if (config3.debug) {
-    log("cached model:", model13["modelUrl"]);
+    log("cached model:", model8["modelUrl"]);
   }
-  inputSize6 = model13["executor"] && ((_e = model13 == null ? void 0 : model13.inputs) == null ? void 0 : _e[0].shape) ? (_f = model13 == null ? void 0 : model13.inputs) == null ? void 0 : _f[0].shape[2] : 256;
-  return model13;
+  inputSize6 = model8["executor"] && ((_e = model8 == null ? void 0 : model8.inputs) == null ? void 0 : _e[0].shape) ? (_f = model8 == null ? void 0 : model8.inputs) == null ? void 0 : _f[0].shape[2] : 256;
+  return model8;
 }
 var triangulation = TRI468;
 var uvmap = UV468;
 
 // src/face/faceres.ts
-var model14;
-var last9 = [];
-var lastTime11 = 0;
-var lastCount8 = 0;
-var skipped11 = Number.MAX_SAFE_INTEGER;
-async function load13(config3) {
+var model9;
+var last4 = [];
+var lastTime6 = 0;
+var lastCount3 = 0;
+var skipped6 = Number.MAX_SAFE_INTEGER;
+async function load8(config3) {
   var _a;
   if (env2.initial)
-    model14 = null;
-  if (!model14)
-    model14 = await loadModel((_a = config3.face.description) == null ? void 0 : _a.modelPath);
+    model9 = null;
+  if (!model9)
+    model9 = await loadModel((_a = config3.face.description) == null ? void 0 : _a.modelPath);
   else if (config3.debug)
-    log("cached model:", model14["modelUrl"]);
-  return model14;
+    log("cached model:", model9["modelUrl"]);
+  return model9;
 }
 function enhance(input2) {
   const tensor2 = input2.image || input2.tensor || input2;
-  if (!(model14 == null ? void 0 : model14.inputs[0].shape))
+  if (!(model9 == null ? void 0 : model9.inputs[0].shape))
     return tensor2;
-  const crop = image.resizeBilinear(tensor2, [model14.inputs[0].shape[2], model14.inputs[0].shape[1]], false);
+  const crop = image.resizeBilinear(tensor2, [model9.inputs[0].shape[2], model9.inputs[0].shape[1]], false);
   const norm2 = mul(crop, constants.tf255);
   dispose(crop);
   return norm2;
 }
-async function predict12(image2, config3, idx, count3) {
+async function predict7(image2, config3, idx, count3) {
   var _a, _b, _c, _d;
   const obj = {
     age: 0,
@@ -80024,21 +79860,21 @@ async function predict12(image2, config3, idx, count3) {
     genderScore: 0,
     descriptor: []
   };
-  if (!(model14 == null ? void 0 : model14["executor"]))
+  if (!(model9 == null ? void 0 : model9["executor"]))
     return obj;
-  const skipFrame = skipped11 < (((_a = config3.face.description) == null ? void 0 : _a.skipFrames) || 0);
-  const skipTime = (((_b = config3.face.description) == null ? void 0 : _b.skipTime) || 0) > now() - lastTime11;
-  if (config3.skipAllowed && skipFrame && skipTime && lastCount8 === count3 && ((_c = last9 == null ? void 0 : last9[idx]) == null ? void 0 : _c.age) > 0 && ((_d = last9 == null ? void 0 : last9[idx]) == null ? void 0 : _d.genderScore) > 0) {
-    skipped11++;
-    return last9[idx];
+  const skipFrame = skipped6 < (((_a = config3.face.description) == null ? void 0 : _a.skipFrames) || 0);
+  const skipTime = (((_b = config3.face.description) == null ? void 0 : _b.skipTime) || 0) > now() - lastTime6;
+  if (config3.skipAllowed && skipFrame && skipTime && lastCount3 === count3 && ((_c = last4 == null ? void 0 : last4[idx]) == null ? void 0 : _c.age) > 0 && ((_d = last4 == null ? void 0 : last4[idx]) == null ? void 0 : _d.genderScore) > 0) {
+    skipped6++;
+    return last4[idx];
   }
-  skipped11 = 0;
+  skipped6 = 0;
   return new Promise(async (resolve) => {
     var _a2;
     if ((_a2 = config3.face.description) == null ? void 0 : _a2.enabled) {
       const enhanced = enhance(image2);
-      const resT = model14 == null ? void 0 : model14.execute(enhanced);
-      lastTime11 = now();
+      const resT = model9 == null ? void 0 : model9.execute(enhanced);
+      lastTime6 = now();
       dispose(enhanced);
       const genderT = resT.find((t2) => t2.shape[1] === 1);
       const gender2 = await genderT.data();
@@ -80054,14 +79890,76 @@ async function predict12(image2, config3, idx, count3) {
       const all6 = await ageT.data();
       obj.age = Math.round(all6[ageIdx - 1] > all6[ageIdx + 1] ? 10 * ageIdx - 100 * all6[ageIdx - 1] : 10 * ageIdx + 100 * all6[ageIdx + 1]) / 10;
       if (Number.isNaN(gender2[0]) || Number.isNaN(all6[0]))
-        log("faceres error:", { model: model14, result: resT });
+        log("faceres error:", { model: model9, result: resT });
       const desc = resT.find((t2) => t2.shape[1] === 1024);
       const descriptor = desc ? await desc.data() : [];
       obj.descriptor = Array.from(descriptor);
       resT.forEach((t2) => dispose(t2));
     }
-    last9[idx] = obj;
-    lastCount8 = count3;
+    last4[idx] = obj;
+    lastCount3 = count3;
+    resolve(obj);
+  });
+}
+
+// src/gear/gear.ts
+var model10;
+var last5 = [];
+var raceNames = ["white", "black", "asian", "indian", "other"];
+var ageWeights = [15, 23, 28, 35.5, 45.5, 55.5, 65];
+var lastCount4 = 0;
+var lastTime7 = 0;
+var skipped7 = Number.MAX_SAFE_INTEGER;
+async function load9(config3) {
+  var _a;
+  if (env2.initial)
+    model10 = null;
+  if (!model10)
+    model10 = await loadModel((_a = config3.face.gear) == null ? void 0 : _a.modelPath);
+  else if (config3.debug)
+    log("cached model:", model10["modelUrl"]);
+  return model10;
+}
+async function predict8(image2, config3, idx, count3) {
+  var _a, _b;
+  if (!model10)
+    return { age: 0, gender: "unknown", genderScore: 0, race: [] };
+  const skipFrame = skipped7 < (((_a = config3.face.gear) == null ? void 0 : _a.skipFrames) || 0);
+  const skipTime = (((_b = config3.face.gear) == null ? void 0 : _b.skipTime) || 0) > now() - lastTime7;
+  if (config3.skipAllowed && skipTime && skipFrame && lastCount4 === count3 && last5[idx]) {
+    skipped7++;
+    return last5[idx];
+  }
+  skipped7 = 0;
+  return new Promise(async (resolve) => {
+    var _a2, _b2;
+    if (!(model10 == null ? void 0 : model10.inputs[0].shape))
+      return;
+    const t2 = {};
+    const box = [[0, 0.1, 0.9, 0.9]];
+    t2.resize = image.cropAndResize(image2, box, [0], [model10.inputs[0].shape[2], model10.inputs[0].shape[1]]);
+    const obj = { age: 0, gender: "unknown", genderScore: 0, race: [] };
+    if ((_a2 = config3.face.gear) == null ? void 0 : _a2.enabled)
+      [t2.age, t2.gender, t2.race] = model10.execute(t2.resize, ["age_output", "gender_output", "race_output"]);
+    const gender2 = await t2.gender.data();
+    obj.gender = gender2[0] > gender2[1] ? "male" : "female";
+    obj.genderScore = Math.round(100 * (gender2[0] > gender2[1] ? gender2[0] : gender2[1])) / 100;
+    const race = await t2.race.data();
+    for (let i = 0; i < race.length; i++) {
+      if (race[i] > (((_b2 = config3.face.gear) == null ? void 0 : _b2.minConfidence) || 0.2))
+        obj.race.push({ score: Math.round(100 * race[i]) / 100, race: raceNames[i] });
+    }
+    obj.race.sort((a, b) => b.score - a.score);
+    const ageDistribution = Array.from(await t2.age.data());
+    const ageSorted = ageDistribution.map((a, i) => [ageWeights[i], a]).sort((a, b) => b[1] - a[1]);
+    let age2 = ageSorted[0][0];
+    for (let i = 1; i < ageSorted.length; i++)
+      age2 += ageSorted[i][1] * (ageSorted[i][0] - age2);
+    obj.age = Math.round(10 * age2) / 10;
+    Object.keys(t2).forEach((tensor2) => dispose(t2[tensor2]));
+    last5[idx] = obj;
+    lastCount4 = count3;
+    lastTime7 = now();
     resolve(obj);
   });
 }
@@ -80126,15 +80024,15 @@ function computeRotation2(point1, point2) {
 var buildTranslationMatrix2 = (x, y) => [[1, 0, x], [0, 1, y], [0, 0, 1]];
 function dot5(v1, v2) {
   let product = 0;
-  for (let i2 = 0; i2 < v1.length; i2++) {
-    product += v1[i2] * v2[i2];
+  for (let i = 0; i < v1.length; i++) {
+    product += v1[i] * v2[i];
   }
   return product;
 }
 function getColumnFrom2DArr2(arr, columnIndex) {
   const column = [];
-  for (let i2 = 0; i2 < arr.length; i2++) {
-    column.push(arr[i2][columnIndex]);
+  for (let i = 0; i < arr.length; i++) {
+    column.push(arr[i][columnIndex]);
   }
   return column;
 }
@@ -83128,7 +83026,7 @@ var anchors2 = [
 
 // src/hand/handposedetector.ts
 var HandDetector = class {
-  constructor(model20) {
+  constructor(model22) {
     __publicField(this, "model");
     __publicField(this, "anchors");
     __publicField(this, "anchorsTensor");
@@ -83136,7 +83034,7 @@ var HandDetector = class {
     __publicField(this, "inputSizeTensor");
     __publicField(this, "doubleInputSizeTensor");
     var _a, _b, _c, _d;
-    this.model = model20;
+    this.model = model22;
     this.anchors = anchors2.map((anchor) => [anchor.x, anchor.y]);
     this.anchorsTensor = tensor2d(this.anchors);
     this.inputSize = ((_d = (_c = (_b = (_a = this == null ? void 0 : this.model) == null ? void 0 : _a.inputs) == null ? void 0 : _b[0]) == null ? void 0 : _c.shape) == null ? void 0 : _d[2]) || 0;
@@ -83210,7 +83108,7 @@ var handBoxEnlargeFactor = 1.65;
 var palmLandmarkIds = [0, 5, 9, 13, 17, 1, 2];
 var palmLandmarksPalmBase = 0;
 var palmLandmarksMiddleFingerBase = 2;
-var lastTime12 = 0;
+var lastTime8 = 0;
 var HandPipeline = class {
   constructor(handDetector, handPoseModel2) {
     __publicField(this, "handDetector");
@@ -83243,8 +83141,8 @@ var HandPipeline = class {
     const boundingBox = this.calculateLandmarksBoundingBox(landmarks);
     const boxAroundHand = enlargeBox2(squarifyBox2(boundingBox), handBoxEnlargeFactor);
     boxAroundHand.palmLandmarks = [];
-    for (let i2 = 0; i2 < palmLandmarkIds.length; i2++) {
-      boxAroundHand.palmLandmarks.push(landmarks[palmLandmarkIds[i2]].slice(0, 2));
+    for (let i = 0; i < palmLandmarkIds.length; i++) {
+      boxAroundHand.palmLandmarks.push(landmarks[palmLandmarkIds[i]].slice(0, 2));
     }
     return boxAroundHand;
   }
@@ -83276,7 +83174,7 @@ var HandPipeline = class {
   async estimateHands(image2, config3) {
     let useFreshBox = false;
     let boxes;
-    const skipTime = (config3.hand.skipTime || 0) > now() - lastTime12;
+    const skipTime = (config3.hand.skipTime || 0) > now() - lastTime8;
     const skipFrame = this.skipped < (config3.hand.skipFrames || 0);
     if (config3.skipAllowed && skipTime && skipFrame) {
       boxes = await this.handDetector.predict(image2, config3);
@@ -83291,8 +83189,8 @@ var HandPipeline = class {
         useFreshBox = true;
     }
     const hands = [];
-    for (let i2 = 0; i2 < this.storedBoxes.length; i2++) {
-      const currentBox = this.storedBoxes[i2];
+    for (let i = 0; i < this.storedBoxes.length; i++) {
+      const currentBox = this.storedBoxes[i];
       if (!currentBox)
         continue;
       if (config3.hand.landmarks) {
@@ -83307,7 +83205,7 @@ var HandPipeline = class {
         dispose(croppedInput);
         dispose(rotatedImage);
         const [confidenceT, keypoints] = this.handPoseModel.execute(handImage);
-        lastTime12 = now();
+        lastTime8 = now();
         dispose(handImage);
         const confidence = (await confidenceT.data())[0];
         dispose(confidenceT);
@@ -83318,7 +83216,7 @@ var HandPipeline = class {
           dispose(keypointsReshaped);
           const coords3 = this.transformRawCoords(rawCoords, newBox, angle, rotationMatrix);
           const nextBoundingBox = this.getBoxForHandLandmarks(coords3);
-          this.storedBoxes[i2] = { ...nextBoundingBox, confidence };
+          this.storedBoxes[i] = { ...nextBoundingBox, confidence };
           const result = {
             landmarks: coords3,
             confidence,
@@ -83328,7 +83226,7 @@ var HandPipeline = class {
           };
           hands.push(result);
         } else {
-          this.storedBoxes[i2] = null;
+          this.storedBoxes[i] = null;
         }
         dispose(keypoints);
       } else {
@@ -83512,7 +83410,7 @@ var fingergesture_default = [ThumbsUp, Victory, Point, MiddleFinger, OpenPalm];
 
 // src/hand/fingerpose.ts
 var minConfidence = 0.7;
-var options = {
+var options2 = {
   HALF_CURL_START_LIMIT: 60,
   NO_CURL_START_LIMIT: 130,
   DISTANCE_VOTE_POWER: 1.1,
@@ -83570,9 +83468,9 @@ function estimateFingerCurl(startPoint, midPoint, endPoint) {
   let angleOfCurve = Math.acos(cos_in);
   angleOfCurve = 57.2958 * angleOfCurve % 180;
   let fingerCurl;
-  if (angleOfCurve > options.NO_CURL_START_LIMIT)
+  if (angleOfCurve > options2.NO_CURL_START_LIMIT)
     fingerCurl = FingerCurl.none;
-  else if (angleOfCurve > options.HALF_CURL_START_LIMIT)
+  else if (angleOfCurve > options2.HALF_CURL_START_LIMIT)
     fingerCurl = FingerCurl.half;
   else
     fingerCurl = FingerCurl.full;
@@ -83649,11 +83547,11 @@ function calculateFingerDirection(startPoint, midPoint, endPoint, fingerSlopes)
   let voteHorizontal = 0;
   const start_end_x_y_dist_ratio = max_dist_y / (max_dist_x + 1e-5);
   if (start_end_x_y_dist_ratio > 1.5)
-    voteVertical += options.DISTANCE_VOTE_POWER;
+    voteVertical += options2.DISTANCE_VOTE_POWER;
   else if (start_end_x_y_dist_ratio > 0.66)
-    voteDiagonal += options.DISTANCE_VOTE_POWER;
+    voteDiagonal += options2.DISTANCE_VOTE_POWER;
   else
-    voteHorizontal += options.DISTANCE_VOTE_POWER;
+    voteHorizontal += options2.DISTANCE_VOTE_POWER;
   const start_mid_dist = Math.sqrt(start_mid_x_dist * start_mid_x_dist + start_mid_y_dist * start_mid_y_dist);
   const start_end_dist = Math.sqrt(start_end_x_dist * start_end_x_dist + start_end_y_dist * start_end_y_dist);
   const mid_end_dist = Math.sqrt(mid_end_x_dist * mid_end_x_dist + mid_end_y_dist * mid_end_y_dist);
@@ -83672,12 +83570,12 @@ function calculateFingerDirection(startPoint, midPoint, endPoint, fingerSlopes)
   const calcStartPoint = [calc_start_point_x, calc_start_point_y];
   const calcEndPoint = [calc_end_point_x, calc_end_point_y];
   const totalAngle = getSlopes(calcStartPoint, calcEndPoint);
-  const votes = angleOrientationAt(totalAngle, options.TOTAL_ANGLE_VOTE_POWER);
+  const votes = angleOrientationAt(totalAngle, options2.TOTAL_ANGLE_VOTE_POWER);
   voteVertical += votes[0];
   voteDiagonal += votes[1];
   voteHorizontal += votes[2];
   for (const fingerSlope of fingerSlopes) {
-    const fingerVotes = angleOrientationAt(fingerSlope, options.SINGLE_ANGLE_VOTE_POWER);
+    const fingerVotes = angleOrientationAt(fingerSlope, options2.SINGLE_ANGLE_VOTE_POWER);
     voteVertical += fingerVotes[0];
     voteDiagonal += fingerVotes[1];
     voteHorizontal += fingerVotes[2];
@@ -83766,19 +83664,19 @@ var meshAnnotations2 = {
 var handDetectorModel;
 var handPoseModel;
 var handPipeline;
-async function predict13(input2, config3) {
+async function predict9(input2, config3) {
   const predictions = await handPipeline.estimateHands(input2, config3);
   if (!predictions)
     return [];
   const hands = [];
-  for (let i2 = 0; i2 < predictions.length; i2++) {
+  for (let i = 0; i < predictions.length; i++) {
     const annotations2 = {};
-    if (predictions[i2].landmarks) {
+    if (predictions[i].landmarks) {
       for (const key of Object.keys(meshAnnotations2)) {
-        annotations2[key] = meshAnnotations2[key].map((index2) => predictions[i2].landmarks[index2]);
+        annotations2[key] = meshAnnotations2[key].map((index2) => predictions[i].landmarks[index2]);
       }
     }
-    const keypoints = predictions[i2].landmarks;
+    const keypoints = predictions[i].landmarks;
     let box = [Number.MAX_SAFE_INTEGER, Number.MAX_SAFE_INTEGER, 0, 0];
     let boxRaw = [0, 0, 0, 0];
     if (keypoints && keypoints.length > 0) {
@@ -83796,25 +83694,25 @@ async function predict13(input2, config3) {
       box[3] -= box[1];
       boxRaw = [box[0] / (input2.shape[2] || 0), box[1] / (input2.shape[1] || 0), box[2] / (input2.shape[2] || 0), box[3] / (input2.shape[1] || 0)];
     } else {
-      box = predictions[i2].box ? [
-        Math.trunc(Math.max(0, predictions[i2].box.topLeft[0])),
-        Math.trunc(Math.max(0, predictions[i2].box.topLeft[1])),
-        Math.trunc(Math.min(input2.shape[2] || 0, predictions[i2].box.bottomRight[0]) - Math.max(0, predictions[i2].box.topLeft[0])),
-        Math.trunc(Math.min(input2.shape[1] || 0, predictions[i2].box.bottomRight[1]) - Math.max(0, predictions[i2].box.topLeft[1]))
+      box = predictions[i].box ? [
+        Math.trunc(Math.max(0, predictions[i].box.topLeft[0])),
+        Math.trunc(Math.max(0, predictions[i].box.topLeft[1])),
+        Math.trunc(Math.min(input2.shape[2] || 0, predictions[i].box.bottomRight[0]) - Math.max(0, predictions[i].box.topLeft[0])),
+        Math.trunc(Math.min(input2.shape[1] || 0, predictions[i].box.bottomRight[1]) - Math.max(0, predictions[i].box.topLeft[1]))
       ] : [0, 0, 0, 0];
       boxRaw = [
-        predictions[i2].box.topLeft[0] / (input2.shape[2] || 0),
-        predictions[i2].box.topLeft[1] / (input2.shape[1] || 0),
-        (predictions[i2].box.bottomRight[0] - predictions[i2].box.topLeft[0]) / (input2.shape[2] || 0),
-        (predictions[i2].box.bottomRight[1] - predictions[i2].box.topLeft[1]) / (input2.shape[1] || 0)
+        predictions[i].box.topLeft[0] / (input2.shape[2] || 0),
+        predictions[i].box.topLeft[1] / (input2.shape[1] || 0),
+        (predictions[i].box.bottomRight[0] - predictions[i].box.topLeft[0]) / (input2.shape[2] || 0),
+        (predictions[i].box.bottomRight[1] - predictions[i].box.topLeft[1]) / (input2.shape[1] || 0)
       ];
     }
     const landmarks = analyze(keypoints);
     hands.push({
-      id: i2,
-      score: Math.round(100 * predictions[i2].confidence) / 100,
-      boxScore: Math.round(100 * predictions[i2].boxConfidence) / 100,
-      fingerScore: Math.round(100 * predictions[i2].fingerConfidence) / 100,
+      id: i,
+      score: Math.round(100 * predictions[i].confidence) / 100,
+      boxScore: Math.round(100 * predictions[i].boxConfidence) / 100,
+      fingerScore: Math.round(100 * predictions[i].fingerConfidence) / 100,
       label: "hand",
       box,
       boxRaw,
@@ -83825,7 +83723,7 @@ async function predict13(input2, config3) {
   }
   return hands;
 }
-async function load14(config3) {
+async function load10(config3) {
   var _a, _b;
   if (env2.initial) {
     handDetectorModel = null;
@@ -83848,6 +83746,1496 @@ async function load14(config3) {
   return [handDetectorModel, handPoseModel];
 }
 
+// src/hand/handtrack.ts
+var models3 = [null, null];
+var modelOutputNodes = ["StatefulPartitionedCall/Postprocessor/Slice", "StatefulPartitionedCall/Postprocessor/ExpandDims_1"];
+var inputSize7 = [[0, 0], [0, 0]];
+var classes = ["hand", "fist", "pinch", "point", "face", "tip", "pinchtip"];
+var faceIndex = 4;
+var boxExpandFact = 1.6;
+var maxDetectorResolution = 512;
+var detectorExpandFact = 1.4;
+var skipped8 = Number.MAX_SAFE_INTEGER;
+var lastTime9 = 0;
+var outputSize = [0, 0];
+var cache4 = {
+  boxes: [],
+  hands: []
+};
+var fingerMap = {
+  thumb: [1, 2, 3, 4],
+  index: [5, 6, 7, 8],
+  middle: [9, 10, 11, 12],
+  ring: [13, 14, 15, 16],
+  pinky: [17, 18, 19, 20],
+  base: [0],
+  palm: [0, 17, 13, 9, 5, 1, 0]
+};
+async function loadDetect2(config3) {
+  var _a;
+  if (env2.initial)
+    models3[0] = null;
+  if (!models3[0]) {
+    fakeOps(["tensorlistreserve", "enter", "tensorlistfromtensor", "merge", "loopcond", "switch", "exit", "tensorliststack", "nextiteration", "tensorlistsetitem", "tensorlistgetitem", "reciprocal", "shape", "split", "where"], config3);
+    models3[0] = await loadModel((_a = config3.hand.detector) == null ? void 0 : _a.modelPath);
+    const inputs = models3[0]["executor"] ? Object.values(models3[0].modelSignature["inputs"]) : void 0;
+    inputSize7[0][0] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[1].size) : 0;
+    inputSize7[0][1] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 0;
+  } else if (config3.debug)
+    log("cached model:", models3[0]["modelUrl"]);
+  return models3[0];
+}
+async function loadSkeleton(config3) {
+  var _a;
+  if (env2.initial)
+    models3[1] = null;
+  if (!models3[1]) {
+    models3[1] = await loadModel((_a = config3.hand.skeleton) == null ? void 0 : _a.modelPath);
+    const inputs = models3[1]["executor"] ? Object.values(models3[1].modelSignature["inputs"]) : void 0;
+    inputSize7[1][0] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[1].size) : 0;
+    inputSize7[1][1] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 0;
+  } else if (config3.debug)
+    log("cached model:", models3[1]["modelUrl"]);
+  return models3[1];
+}
+async function detectHands(input2, config3) {
+  const hands = [];
+  if (!input2 || !models3[0])
+    return hands;
+  const t2 = {};
+  const ratio2 = (input2.shape[2] || 1) / (input2.shape[1] || 1);
+  const height = Math.min(Math.round((input2.shape[1] || 0) / 8) * 8, maxDetectorResolution);
+  const width = Math.round(height * ratio2 / 8) * 8;
+  t2.resize = image.resizeBilinear(input2, [height, width]);
+  t2.cast = cast(t2.resize, "int32");
+  [t2.rawScores, t2.rawBoxes] = await models3[0].executeAsync(t2.cast, modelOutputNodes);
+  t2.boxes = squeeze(t2.rawBoxes, [0, 2]);
+  t2.scores = squeeze(t2.rawScores, [0]);
+  const classScores = unstack(t2.scores, 1);
+  dispose(classScores[faceIndex]);
+  classScores.splice(faceIndex, 1);
+  t2.filtered = stack(classScores, 1);
+  dispose(classScores);
+  t2.max = max(t2.filtered, 1);
+  t2.argmax = argMax(t2.filtered, 1);
+  let id = 0;
+  t2.nms = await image.nonMaxSuppressionAsync(t2.boxes, t2.max, (config3.hand.maxDetected || 0) + 1, config3.hand.iouThreshold || 0, config3.hand.minConfidence || 1);
+  const nms = await t2.nms.data();
+  const scores = await t2.max.data();
+  const classNum = await t2.argmax.data();
+  for (const nmsIndex of Array.from(nms)) {
+    const boxSlice = slice(t2.boxes, nmsIndex, 1);
+    const boxYX = await boxSlice.data();
+    dispose(boxSlice);
+    const boxData = [boxYX[1], boxYX[0], boxYX[3] - boxYX[1], boxYX[2] - boxYX[0]];
+    const boxRaw = scale2(boxData, detectorExpandFact);
+    const boxFull = [Math.trunc(boxData[0] * outputSize[0]), Math.trunc(boxData[1] * outputSize[1]), Math.trunc(boxData[2] * outputSize[0]), Math.trunc(boxData[3] * outputSize[1])];
+    const score = scores[nmsIndex];
+    const label = classes[classNum[nmsIndex]];
+    const hand3 = { id: id++, score, box: boxFull, boxRaw, label };
+    hands.push(hand3);
+  }
+  Object.keys(t2).forEach((tensor2) => dispose(t2[tensor2]));
+  hands.sort((a, b) => b.score - a.score);
+  if (hands.length > (config3.hand.maxDetected || 1))
+    hands.length = config3.hand.maxDetected || 1;
+  return hands;
+}
+async function detectFingers(input2, h, config3) {
+  const hand3 = {
+    id: h.id,
+    score: Math.round(100 * h.score) / 100,
+    boxScore: Math.round(100 * h.score) / 100,
+    fingerScore: 0,
+    box: h.box,
+    boxRaw: h.boxRaw,
+    label: h.label,
+    keypoints: [],
+    landmarks: {},
+    annotations: {}
+  };
+  if (input2 && models3[1] && config3.hand.landmarks && h.score > (config3.hand.minConfidence || 0)) {
+    const t2 = {};
+    const boxCrop = [h.boxRaw[1], h.boxRaw[0], h.boxRaw[3] + h.boxRaw[1], h.boxRaw[2] + h.boxRaw[0]];
+    t2.crop = image.cropAndResize(input2, [boxCrop], [0], [inputSize7[1][0], inputSize7[1][1]], "bilinear");
+    t2.div = div(t2.crop, constants.tf255);
+    [t2.score, t2.keypoints] = models3[1].execute(t2.div, ["Identity_1", "Identity"]);
+    const rawScore = (await t2.score.data())[0];
+    const score = (100 - Math.trunc(100 / (1 + Math.exp(rawScore)))) / 100;
+    if (score >= (config3.hand.minConfidence || 0)) {
+      hand3.fingerScore = score;
+      t2.reshaped = reshape(t2.keypoints, [-1, 3]);
+      const coordsData = await t2.reshaped.array();
+      const coordsRaw = coordsData.map((kpt4) => [kpt4[0] / inputSize7[1][1], kpt4[1] / inputSize7[1][0], kpt4[2] || 0]);
+      const coordsNorm = coordsRaw.map((kpt4) => [kpt4[0] * h.boxRaw[2], kpt4[1] * h.boxRaw[3], kpt4[2] || 0]);
+      hand3.keypoints = coordsNorm.map((kpt4) => [outputSize[0] * (kpt4[0] + h.boxRaw[0]), outputSize[1] * (kpt4[1] + h.boxRaw[1]), kpt4[2] || 0]);
+      hand3.landmarks = analyze(hand3.keypoints);
+      for (const key of Object.keys(fingerMap)) {
+        hand3.annotations[key] = fingerMap[key].map((index2) => hand3.landmarks && hand3.keypoints[index2] ? hand3.keypoints[index2] : null);
+      }
+    }
+    Object.keys(t2).forEach((tensor2) => dispose(t2[tensor2]));
+  }
+  return hand3;
+}
+async function predict10(input2, config3) {
+  var _a, _b;
+  if (!((_a = models3[0]) == null ? void 0 : _a["executor"]) || !((_b = models3[1]) == null ? void 0 : _b["executor"]) || !models3[0].inputs[0].shape || !models3[1].inputs[0].shape)
+    return [];
+  outputSize = [input2.shape[2] || 0, input2.shape[1] || 0];
+  skipped8++;
+  const skipTime = (config3.hand.skipTime || 0) > now() - lastTime9;
+  const skipFrame = skipped8 < (config3.hand.skipFrames || 0);
+  if (config3.skipAllowed && skipTime && skipFrame) {
+    return cache4.hands;
+  }
+  return new Promise(async (resolve) => {
+    const skipTimeExtended = 3 * (config3.hand.skipTime || 0) > now() - lastTime9;
+    const skipFrameExtended = skipped8 < 3 * (config3.hand.skipFrames || 0);
+    if (config3.skipAllowed && cache4.hands.length === config3.hand.maxDetected) {
+      cache4.hands = await Promise.all(cache4.boxes.map((handBox) => detectFingers(input2, handBox, config3)));
+    } else if (config3.skipAllowed && skipTimeExtended && skipFrameExtended && cache4.hands.length > 0) {
+      cache4.hands = await Promise.all(cache4.boxes.map((handBox) => detectFingers(input2, handBox, config3)));
+    } else {
+      cache4.boxes = await detectHands(input2, config3);
+      lastTime9 = now();
+      cache4.hands = await Promise.all(cache4.boxes.map((handBox) => detectFingers(input2, handBox, config3)));
+      skipped8 = 0;
+    }
+    const oldCache = [...cache4.boxes];
+    cache4.boxes.length = 0;
+    if (config3.cacheSensitivity > 0) {
+      for (let i = 0; i < cache4.hands.length; i++) {
+        const boxKpt = square4(cache4.hands[i].keypoints, outputSize);
+        if (boxKpt.box[2] / (input2.shape[2] || 1) > 0.05 && boxKpt.box[3] / (input2.shape[1] || 1) > 0.05 && cache4.hands[i].fingerScore && cache4.hands[i].fingerScore > (config3.hand.minConfidence || 0)) {
+          const boxScale = scale2(boxKpt.box, boxExpandFact);
+          const boxScaleRaw = scale2(boxKpt.boxRaw, boxExpandFact);
+          cache4.boxes.push({ ...oldCache[i], box: boxScale, boxRaw: boxScaleRaw });
+        }
+      }
+    }
+    for (let i = 0; i < cache4.hands.length; i++) {
+      const bbox = calc(cache4.hands[i].keypoints, outputSize);
+      cache4.hands[i].box = bbox.box;
+      cache4.hands[i].boxRaw = bbox.boxRaw;
+    }
+    resolve(cache4.hands);
+  });
+}
+
+// src/face/insightface.ts
+var model11;
+var last6 = [];
+var lastCount5 = 0;
+var lastTime10 = 0;
+var skipped9 = Number.MAX_SAFE_INTEGER;
+async function load11(config3) {
+  if (env2.initial)
+    model11 = null;
+  if (!model11)
+    model11 = await loadModel(config3.face["insightface"].modelPath);
+  else if (config3.debug)
+    log("cached model:", model11["modelUrl"]);
+  return model11;
+}
+async function predict11(input2, config3, idx, count3) {
+  var _a, _b;
+  if (!(model11 == null ? void 0 : model11["executor"]))
+    return [];
+  const skipFrame = skipped9 < (((_a = config3.face["insightface"]) == null ? void 0 : _a.skipFrames) || 0);
+  const skipTime = (((_b = config3.face["insightface"]) == null ? void 0 : _b.skipTime) || 0) > now() - lastTime10;
+  if (config3.skipAllowed && skipTime && skipFrame && lastCount5 === count3 && last6[idx]) {
+    skipped9++;
+    return last6[idx];
+  }
+  return new Promise(async (resolve) => {
+    var _a2;
+    let data = [];
+    if (((_a2 = config3.face["insightface"]) == null ? void 0 : _a2.enabled) && (model11 == null ? void 0 : model11.inputs[0].shape)) {
+      const t2 = {};
+      t2.crop = image.resizeBilinear(input2, [model11.inputs[0].shape[2], model11.inputs[0].shape[1]], false);
+      t2.data = model11.execute(t2.crop);
+      const output = await t2.data.data();
+      data = Array.from(output);
+      Object.keys(t2).forEach((tensor2) => dispose(t2[tensor2]));
+    }
+    last6[idx] = data;
+    lastCount5 = count3;
+    lastTime10 = now();
+    resolve(data);
+  });
+}
+
+// src/face/liveness.ts
+var model12;
+var cached2 = [];
+var skipped10 = Number.MAX_SAFE_INTEGER;
+var lastCount6 = 0;
+var lastTime11 = 0;
+async function load12(config3) {
+  var _a;
+  if (env2.initial)
+    model12 = null;
+  if (!model12)
+    model12 = await loadModel((_a = config3.face.liveness) == null ? void 0 : _a.modelPath);
+  else if (config3.debug)
+    log("cached model:", model12["modelUrl"]);
+  return model12;
+}
+async function predict12(image2, config3, idx, count3) {
+  var _a, _b;
+  if (!(model12 == null ? void 0 : model12["executor"]))
+    return 0;
+  const skipTime = (((_a = config3.face.liveness) == null ? void 0 : _a.skipTime) || 0) > now() - lastTime11;
+  const skipFrame = skipped10 < (((_b = config3.face.liveness) == null ? void 0 : _b.skipFrames) || 0);
+  if (config3.skipAllowed && skipTime && skipFrame && lastCount6 === count3 && cached2[idx]) {
+    skipped10++;
+    return cached2[idx];
+  }
+  skipped10 = 0;
+  return new Promise(async (resolve) => {
+    const resize = image.resizeBilinear(image2, [(model12 == null ? void 0 : model12.inputs[0].shape) ? model12.inputs[0].shape[2] : 0, (model12 == null ? void 0 : model12.inputs[0].shape) ? model12.inputs[0].shape[1] : 0], false);
+    const res = model12 == null ? void 0 : model12.execute(resize);
+    const num = (await res.data())[0];
+    cached2[idx] = Math.round(100 * num) / 100;
+    lastCount6 = count3;
+    lastTime11 = now();
+    dispose([resize, res]);
+    resolve(cached2[idx]);
+  });
+}
+
+// src/segmentation/meet.ts
+var model13;
+async function load13(config3) {
+  if (!model13 || env2.initial)
+    model13 = await loadModel(config3.segmentation.modelPath);
+  else if (config3.debug)
+    log("cached model:", model13["modelUrl"]);
+  return model13;
+}
+async function predict13(input2, config3) {
+  var _a;
+  if (!model13)
+    model13 = await load13(config3);
+  if (!(model13 == null ? void 0 : model13["executor"]) || !((_a = model13 == null ? void 0 : model13.inputs) == null ? void 0 : _a[0].shape))
+    return null;
+  const t2 = {};
+  t2.resize = image.resizeBilinear(input2, [model13.inputs[0].shape ? model13.inputs[0].shape[1] : 0, model13.inputs[0].shape ? model13.inputs[0].shape[2] : 0], false);
+  t2.norm = div(t2.resize, constants.tf255);
+  t2.res = model13.execute(t2.norm);
+  t2.squeeze = squeeze(t2.res, 0);
+  [t2.bgRaw, t2.fgRaw] = unstack(t2.squeeze, 2);
+  t2.fg = softmax(t2.fgRaw);
+  t2.mul = mul(t2.fg, constants.tf255);
+  t2.expand = expandDims(t2.mul, 2);
+  t2.output = image.resizeBilinear(t2.expand, [input2.shape[1], input2.shape[2]]);
+  let rgba;
+  switch (config3.segmentation.mode || "default") {
+    case "default":
+      t2.input = squeeze(input2);
+      t2.concat = concat([t2.input, t2.output], -1);
+      rgba = cast(t2.concat, "int32");
+      break;
+    case "alpha":
+      rgba = cast(t2.output, "int32");
+      break;
+    default:
+      rgba = tensor(0);
+  }
+  Object.keys(t2).forEach((tensor2) => dispose(t2[tensor2]));
+  return rgba;
+}
+
+// src/face/mobilefacenet.ts
+var model14;
+var last7 = [];
+var lastCount7 = 0;
+var lastTime12 = 0;
+var skipped11 = Number.MAX_SAFE_INTEGER;
+async function load14(config3) {
+  var _a;
+  if (env2.initial)
+    model14 = null;
+  if (!model14)
+    model14 = await loadModel((_a = config3.face["mobilefacenet"]) == null ? void 0 : _a.modelPath);
+  else if (config3.debug)
+    log("cached model:", model14["modelUrl"]);
+  return model14;
+}
+async function predict14(input2, config3, idx, count3) {
+  var _a, _b;
+  if (!(model14 == null ? void 0 : model14["executor"]))
+    return [];
+  const skipFrame = skipped11 < (((_a = config3.face["mobilefacenet"]) == null ? void 0 : _a.skipFrames) || 0);
+  const skipTime = (((_b = config3.face["mobilefacenet"]) == null ? void 0 : _b.skipTime) || 0) > now() - lastTime12;
+  if (config3.skipAllowed && skipTime && skipFrame && lastCount7 === count3 && last7[idx]) {
+    skipped11++;
+    return last7[idx];
+  }
+  return new Promise(async (resolve) => {
+    var _a2;
+    let data = [];
+    if (((_a2 = config3.face["mobilefacenet"]) == null ? void 0 : _a2.enabled) && (model14 == null ? void 0 : model14.inputs[0].shape)) {
+      const t2 = {};
+      t2.crop = image.resizeBilinear(input2, [model14.inputs[0].shape[2], model14.inputs[0].shape[1]], false);
+      t2.data = model14.execute(t2.crop);
+      const output = await t2.data.data();
+      data = Array.from(output);
+      Object.keys(t2).forEach((tensor2) => dispose(t2[tensor2]));
+    }
+    last7[idx] = data;
+    lastCount7 = count3;
+    lastTime12 = now();
+    resolve(data);
+  });
+}
+
+// src/body/movenetcoords.ts
+var movenetcoords_exports = {};
+__export(movenetcoords_exports, {
+  connected: () => connected3,
+  horizontal: () => horizontal,
+  kpt: () => kpt3,
+  relative: () => relative,
+  vertical: () => vertical
+});
+var kpt3 = [
+  "nose",
+  "leftEye",
+  "rightEye",
+  "leftEar",
+  "rightEar",
+  "leftShoulder",
+  "rightShoulder",
+  "leftElbow",
+  "rightElbow",
+  "leftWrist",
+  "rightWrist",
+  "leftHip",
+  "rightHip",
+  "leftKnee",
+  "rightKnee",
+  "leftAnkle",
+  "rightAnkle"
+];
+var horizontal = [
+  ["leftEye", "rightEye"],
+  ["leftEar", "rightEar"],
+  ["leftShoulder", "rightShoulder"],
+  ["leftElbow", "rightElbow"],
+  ["leftWrist", "rightWrist"],
+  ["leftHip", "rightHip"],
+  ["leftKnee", "rightKnee"],
+  ["leftAnkle", "rightAnkle"]
+];
+var vertical = [
+  ["leftKnee", "leftShoulder"],
+  ["rightKnee", "rightShoulder"],
+  ["leftAnkle", "leftKnee"],
+  ["rightAnkle", "rightKnee"]
+];
+var relative = [
+  [["leftHip", "rightHip"], ["leftShoulder", "rightShoulder"]],
+  [["leftElbow", "rightElbow"], ["leftShoulder", "rightShoulder"]]
+];
+var connected3 = {
+  leftLeg: ["leftHip", "leftKnee", "leftAnkle"],
+  rightLeg: ["rightHip", "rightKnee", "rightAnkle"],
+  torso: ["leftShoulder", "rightShoulder", "rightHip", "leftHip", "leftShoulder"],
+  leftArm: ["leftShoulder", "leftElbow", "leftWrist"],
+  rightArm: ["rightShoulder", "rightElbow", "rightWrist"],
+  head: []
+};
+
+// src/body/movenetfix.ts
+var maxJitter = 5e-3;
+var cache5 = {
+  keypoints: [],
+  padding: [[0, 0], [0, 0], [0, 0], [0, 0]]
+};
+function bodyParts(body4) {
+  for (const pair of horizontal) {
+    const left = body4.keypoints.findIndex((kp) => kp.part === pair[0]);
+    const right = body4.keypoints.findIndex((kp) => kp.part === pair[1]);
+    if (body4.keypoints[left] && body4.keypoints[right]) {
+      if (body4.keypoints[left].position[0] < body4.keypoints[right].position[0]) {
+        const tmp = body4.keypoints[left];
+        body4.keypoints[left] = body4.keypoints[right];
+        body4.keypoints[right] = tmp;
+      }
+    }
+  }
+  for (const pair of vertical) {
+    const lower = body4.keypoints.findIndex((kp) => kp && kp.part === pair[0]);
+    const higher = body4.keypoints.findIndex((kp) => kp && kp.part === pair[1]);
+    if (body4.keypoints[lower] && body4.keypoints[higher]) {
+      if (body4.keypoints[lower].position[1] < body4.keypoints[higher].position[1]) {
+        body4.keypoints.splice(lower, 1);
+      }
+    }
+  }
+  for (const [pair, compare2] of relative) {
+    const left = body4.keypoints.findIndex((kp) => kp && kp.part === pair[0]);
+    const right = body4.keypoints.findIndex((kp) => kp && kp.part === pair[1]);
+    const leftTo = body4.keypoints.findIndex((kp) => kp && kp.part === compare2[0]);
+    const rightTo = body4.keypoints.findIndex((kp) => kp && kp.part === compare2[1]);
+    if (!body4.keypoints[leftTo] || !body4.keypoints[rightTo])
+      continue;
+    const distanceLeft = body4.keypoints[left] ? [
+      Math.abs(body4.keypoints[leftTo].position[0] - body4.keypoints[left].position[0]),
+      Math.abs(body4.keypoints[rightTo].position[0] - body4.keypoints[left].position[0])
+    ] : [0, 0];
+    const distanceRight = body4.keypoints[right] ? [
+      Math.abs(body4.keypoints[rightTo].position[0] - body4.keypoints[right].position[0]),
+      Math.abs(body4.keypoints[leftTo].position[0] - body4.keypoints[right].position[0])
+    ] : [0, 0];
+    if (distanceLeft[0] > distanceLeft[1] || distanceRight[0] > distanceRight[1]) {
+      const tmp = body4.keypoints[left];
+      body4.keypoints[left] = body4.keypoints[right];
+      body4.keypoints[right] = tmp;
+    }
+  }
+}
+function jitter(keypoints) {
+  for (let i = 0; i < keypoints.length; i++) {
+    if (keypoints[i] && cache5.keypoints[i]) {
+      const diff = [Math.abs(keypoints[i].positionRaw[0] - cache5.keypoints[i].positionRaw[0]), Math.abs(keypoints[i].positionRaw[1] - cache5.keypoints[i].positionRaw[1])];
+      if (diff[0] < maxJitter && diff[1] < maxJitter) {
+        keypoints[i] = cache5.keypoints[i];
+      } else {
+        cache5.keypoints[i] = keypoints[i];
+      }
+    } else {
+      cache5.keypoints[i] = keypoints[i];
+    }
+  }
+  return keypoints;
+}
+function padInput(input2, inputSize10) {
+  var _a, _b;
+  const t2 = {};
+  if (!((_a = input2 == null ? void 0 : input2.shape) == null ? void 0 : _a[1]) || !((_b = input2 == null ? void 0 : input2.shape) == null ? void 0 : _b[2]))
+    return input2;
+  cache5.padding = [
+    [0, 0],
+    [input2.shape[2] > input2.shape[1] ? Math.trunc((input2.shape[2] - input2.shape[1]) / 2) : 0, input2.shape[2] > input2.shape[1] ? Math.trunc((input2.shape[2] - input2.shape[1]) / 2) : 0],
+    [input2.shape[1] > input2.shape[2] ? Math.trunc((input2.shape[1] - input2.shape[2]) / 2) : 0, input2.shape[1] > input2.shape[2] ? Math.trunc((input2.shape[1] - input2.shape[2]) / 2) : 0],
+    [0, 0]
+  ];
+  t2.pad = pad(input2, cache5.padding);
+  t2.resize = image.resizeBilinear(t2.pad, [inputSize10, inputSize10]);
+  const final = cast(t2.resize, "int32");
+  Object.keys(t2).forEach((tensor2) => dispose(t2[tensor2]));
+  return final;
+}
+function rescaleBody(body4, outputSize2) {
+  body4.keypoints = body4.keypoints.filter((kpt4) => kpt4 == null ? void 0 : kpt4.position);
+  for (const kpt4 of body4.keypoints) {
+    kpt4.position = [
+      kpt4.position[0] * (outputSize2[0] + cache5.padding[2][0] + cache5.padding[2][1]) / outputSize2[0] - cache5.padding[2][0],
+      kpt4.position[1] * (outputSize2[1] + cache5.padding[1][0] + cache5.padding[1][1]) / outputSize2[1] - cache5.padding[1][0]
+    ];
+    kpt4.positionRaw = [
+      kpt4.position[0] / outputSize2[0],
+      kpt4.position[1] / outputSize2[1]
+    ];
+  }
+  const rescaledBoxes = calc(body4.keypoints.map((pt) => pt.position), outputSize2);
+  body4.box = rescaledBoxes.box;
+  body4.boxRaw = rescaledBoxes.boxRaw;
+  return body4;
+}
+
+// src/body/movenet.ts
+var model15;
+var inputSize8 = 0;
+var skipped12 = Number.MAX_SAFE_INTEGER;
+var cache6 = {
+  boxes: [],
+  bodies: [],
+  last: 0
+};
+async function load15(config3) {
+  var _a;
+  if (env2.initial)
+    model15 = null;
+  if (!model15) {
+    fakeOps(["size"], config3);
+    model15 = await loadModel(config3.body.modelPath);
+  } else if (config3.debug)
+    log("cached model:", model15["modelUrl"]);
+  inputSize8 = (model15 == null ? void 0 : model15["executor"]) && ((_a = model15 == null ? void 0 : model15.inputs) == null ? void 0 : _a[0].shape) ? model15.inputs[0].shape[2] : 0;
+  if (inputSize8 < 64)
+    inputSize8 = 256;
+  return model15;
+}
+function parseSinglePose(res, config3, image2) {
+  const kpt4 = res[0][0];
+  const keypoints = [];
+  let score = 0;
+  for (let id = 0; id < kpt4.length; id++) {
+    score = kpt4[id][2];
+    if (score > config3.body.minConfidence) {
+      const positionRaw = [kpt4[id][1], kpt4[id][0]];
+      keypoints.push({
+        score: Math.round(100 * score) / 100,
+        part: kpt3[id],
+        positionRaw,
+        position: [
+          Math.round((image2.shape[2] || 0) * positionRaw[0]),
+          Math.round((image2.shape[1] || 0) * positionRaw[1])
+        ]
+      });
+    }
+  }
+  score = keypoints.reduce((prev, curr) => curr.score > prev ? curr.score : prev, 0);
+  const bodies = [];
+  const newBox = calc(keypoints.map((pt) => pt.position), [image2.shape[2], image2.shape[1]]);
+  const annotations2 = {};
+  for (const [name, indexes] of Object.entries(connected3)) {
+    const pt = [];
+    for (let i = 0; i < indexes.length - 1; i++) {
+      const pt0 = keypoints.find((kp) => kp.part === indexes[i]);
+      const pt1 = keypoints.find((kp) => kp.part === indexes[i + 1]);
+      if (pt0 && pt1 && pt0.score > (config3.body.minConfidence || 0) && pt1.score > (config3.body.minConfidence || 0))
+        pt.push([pt0.position, pt1.position]);
+    }
+    annotations2[name] = pt;
+  }
+  const body4 = { id: 0, score, box: newBox.box, boxRaw: newBox.boxRaw, keypoints, annotations: annotations2 };
+  bodyParts(body4);
+  bodies.push(body4);
+  return bodies;
+}
+function parseMultiPose(res, config3, image2) {
+  const bodies = [];
+  for (let id = 0; id < res[0].length; id++) {
+    const kpt4 = res[0][id];
+    const totalScore = Math.round(100 * kpt4[51 + 4]) / 100;
+    if (totalScore > config3.body.minConfidence) {
+      const keypoints = [];
+      for (let i = 0; i < 17; i++) {
+        const score = kpt4[3 * i + 2];
+        if (score > config3.body.minConfidence) {
+          const positionRaw = [kpt4[3 * i + 1], kpt4[3 * i + 0]];
+          keypoints.push({
+            part: kpt3[i],
+            score: Math.round(100 * score) / 100,
+            positionRaw,
+            position: [Math.round((image2.shape[2] || 0) * positionRaw[0]), Math.round((image2.shape[1] || 0) * positionRaw[1])]
+          });
+        }
+      }
+      const newBox = calc(keypoints.map((pt) => pt.position), [image2.shape[2], image2.shape[1]]);
+      const annotations2 = {};
+      for (const [name, indexes] of Object.entries(connected3)) {
+        const pt = [];
+        for (let i = 0; i < indexes.length - 1; i++) {
+          const pt0 = keypoints.find((kp) => kp.part === indexes[i]);
+          const pt1 = keypoints.find((kp) => kp.part === indexes[i + 1]);
+          if (pt0 && pt1 && pt0.score > (config3.body.minConfidence || 0) && pt1.score > (config3.body.minConfidence || 0))
+            pt.push([pt0.position, pt1.position]);
+        }
+        annotations2[name] = pt;
+      }
+      const body4 = { id, score: totalScore, box: newBox.box, boxRaw: newBox.boxRaw, keypoints: [...keypoints], annotations: annotations2 };
+      bodyParts(body4);
+      bodies.push(body4);
+    }
+  }
+  bodies.sort((a, b) => b.score - a.score);
+  if (bodies.length > config3.body.maxDetected)
+    bodies.length = config3.body.maxDetected;
+  return bodies;
+}
+async function predict15(input2, config3) {
+  var _a;
+  if (!(model15 == null ? void 0 : model15["executor"]) || !((_a = model15 == null ? void 0 : model15.inputs) == null ? void 0 : _a[0].shape))
+    return [];
+  if (!config3.skipAllowed)
+    cache6.boxes.length = 0;
+  skipped12++;
+  const skipTime = (config3.body.skipTime || 0) > now() - cache6.last;
+  const skipFrame = skipped12 < (config3.body.skipFrames || 0);
+  if (config3.skipAllowed && skipTime && skipFrame) {
+    return cache6.bodies;
+  }
+  return new Promise(async (resolve) => {
+    const t2 = {};
+    skipped12 = 0;
+    t2.input = padInput(input2, inputSize8);
+    t2.res = model15 == null ? void 0 : model15.execute(t2.input);
+    cache6.last = now();
+    const res = await t2.res.array();
+    cache6.bodies = t2.res.shape[2] === 17 ? parseSinglePose(res, config3, input2) : parseMultiPose(res, config3, input2);
+    for (const body4 of cache6.bodies) {
+      rescaleBody(body4, [input2.shape[2] || 1, input2.shape[1] || 1]);
+      jitter(body4.keypoints);
+    }
+    Object.keys(t2).forEach((tensor2) => dispose(t2[tensor2]));
+    resolve(cache6.bodies);
+  });
+}
+
+// src/object/nanodet.ts
+var model16;
+var last8 = [];
+var lastTime13 = 0;
+var skipped13 = Number.MAX_SAFE_INTEGER;
+var inputSize9 = 0;
+var scaleBox = 2.5;
+async function load16(config3) {
+  if (!model16 || env2.initial) {
+    model16 = await loadModel(config3.object.modelPath);
+    const inputs = (model16 == null ? void 0 : model16["executor"]) ? Object.values(model16.modelSignature["inputs"]) : void 0;
+    inputSize9 = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 416;
+  } else if (config3.debug)
+    log("cached model:", model16["modelUrl"]);
+  return model16;
+}
+async function process4(res, outputShape, config3) {
+  let id = 0;
+  let results = [];
+  const size2 = inputSize9;
+  for (const strideSize of [1, 2, 4]) {
+    const baseSize = strideSize * 13;
+    const scoresT = squeeze(res.find((a) => a.shape[1] === baseSize ** 2 && (a.shape[2] || 0) === labels.length));
+    const scores = await scoresT.array();
+    const featuresT = squeeze(res.find((a) => a.shape[1] === baseSize ** 2 && (a.shape[2] || 0) < labels.length));
+    const boxesMaxT = featuresT.reshape([-1, 4, featuresT.shape[1] / 4]);
+    const boxIdxT = boxesMaxT.argMax(2);
+    const boxIdx = await boxIdxT.array();
+    for (let i = 0; i < scoresT.shape[0]; i++) {
+      for (let j = 0; j < scoresT.shape[1]; j++) {
+        const score = scores[i][j];
+        if (score > (config3.object.minConfidence || 0) && j !== 61) {
+          const cx = (0.5 + Math.trunc(i % baseSize)) / baseSize;
+          const cy = (0.5 + Math.trunc(i / baseSize)) / baseSize;
+          const boxOffset = boxIdx[i].map((a) => a * (baseSize / strideSize / size2));
+          const [x, y] = [
+            cx - scaleBox / strideSize * boxOffset[0],
+            cy - scaleBox / strideSize * boxOffset[1]
+          ];
+          const [w, h] = [
+            cx + scaleBox / strideSize * boxOffset[2] - x,
+            cy + scaleBox / strideSize * boxOffset[3] - y
+          ];
+          let boxRaw = [x, y, w, h];
+          boxRaw = boxRaw.map((a) => Math.max(0, Math.min(a, 1)));
+          const box = [
+            boxRaw[0] * outputShape[0],
+            boxRaw[1] * outputShape[1],
+            boxRaw[2] * outputShape[0],
+            boxRaw[3] * outputShape[1]
+          ];
+          const result = {
+            id: id++,
+            score: Math.round(100 * score) / 100,
+            class: j + 1,
+            label: labels[j].label,
+            box: box.map((a) => Math.trunc(a)),
+            boxRaw
+          };
+          results.push(result);
+        }
+      }
+    }
+    dispose([scoresT, featuresT, boxesMaxT, boxIdxT]);
+  }
+  const nmsBoxes = results.map((a) => [a.boxRaw[1], a.boxRaw[0], a.boxRaw[3], a.boxRaw[2]]);
+  const nmsScores = results.map((a) => a.score);
+  let nmsIdx = [];
+  if (nmsBoxes && nmsBoxes.length > 0) {
+    const nms = await image.nonMaxSuppressionAsync(nmsBoxes, nmsScores, config3.object.maxDetected, config3.object.iouThreshold, config3.object.minConfidence);
+    nmsIdx = await nms.data();
+    dispose(nms);
+  }
+  results = results.filter((_val, idx) => nmsIdx.includes(idx)).sort((a, b) => b.score - a.score);
+  return results;
+}
+async function predict16(image2, config3) {
+  if (!(model16 == null ? void 0 : model16["executor"]))
+    return [];
+  const skipTime = (config3.object.skipTime || 0) > now() - lastTime13;
+  const skipFrame = skipped13 < (config3.object.skipFrames || 0);
+  if (config3.skipAllowed && skipTime && skipFrame && last8.length > 0) {
+    skipped13++;
+    return last8;
+  }
+  skipped13 = 0;
+  if (!env2.kernels.includes("mod") || !env2.kernels.includes("sparsetodense"))
+    return last8;
+  return new Promise(async (resolve) => {
+    const outputSize2 = [image2.shape[2] || 0, image2.shape[1] || 0];
+    const resizeT = image.resizeBilinear(image2, [inputSize9, inputSize9], false);
+    const normT = div(resizeT, constants.tf255);
+    const transposeT = transpose(normT, [0, 3, 1, 2]);
+    let objectT;
+    if (config3.object.enabled)
+      objectT = model16.execute(transposeT);
+    lastTime13 = now();
+    const obj = await process4(objectT, outputSize2, config3);
+    last8 = obj;
+    dispose([resizeT, normT, transposeT, ...objectT]);
+    resolve(obj);
+  });
+}
+
+// src/body/posenetutils.ts
+var partNames = [
+  "nose",
+  "leftEye",
+  "rightEye",
+  "leftEar",
+  "rightEar",
+  "leftShoulder",
+  "rightShoulder",
+  "leftElbow",
+  "rightElbow",
+  "leftWrist",
+  "rightWrist",
+  "leftHip",
+  "rightHip",
+  "leftKnee",
+  "rightKnee",
+  "leftAnkle",
+  "rightAnkle"
+];
+var count2 = partNames.length;
+var partIds = partNames.reduce((result, jointName, i) => {
+  result[jointName] = i;
+  return result;
+}, {});
+var connectedPartNames = [
+  ["leftHip", "leftShoulder"],
+  ["leftElbow", "leftShoulder"],
+  ["leftElbow", "leftWrist"],
+  ["leftHip", "leftKnee"],
+  ["leftKnee", "leftAnkle"],
+  ["rightHip", "rightShoulder"],
+  ["rightElbow", "rightShoulder"],
+  ["rightElbow", "rightWrist"],
+  ["rightHip", "rightKnee"],
+  ["rightKnee", "rightAnkle"],
+  ["leftShoulder", "rightShoulder"],
+  ["leftHip", "rightHip"]
+];
+var connectedPartIndices = connectedPartNames.map(([jointNameA, jointNameB]) => [partIds[jointNameA], partIds[jointNameB]]);
+var poseChain = [
+  ["nose", "leftEye"],
+  ["leftEye", "leftEar"],
+  ["nose", "rightEye"],
+  ["rightEye", "rightEar"],
+  ["nose", "leftShoulder"],
+  ["leftShoulder", "leftElbow"],
+  ["leftElbow", "leftWrist"],
+  ["leftShoulder", "leftHip"],
+  ["leftHip", "leftKnee"],
+  ["leftKnee", "leftAnkle"],
+  ["nose", "rightShoulder"],
+  ["rightShoulder", "rightElbow"],
+  ["rightElbow", "rightWrist"],
+  ["rightShoulder", "rightHip"],
+  ["rightHip", "rightKnee"],
+  ["rightKnee", "rightAnkle"]
+];
+function getBoundingBox(keypoints) {
+  const coord = keypoints.reduce(({ maxX, maxY, minX, minY }, { position: { x, y } }) => ({
+    maxX: Math.max(maxX, x),
+    maxY: Math.max(maxY, y),
+    minX: Math.min(minX, x),
+    minY: Math.min(minY, y)
+  }), {
+    maxX: Number.NEGATIVE_INFINITY,
+    maxY: Number.NEGATIVE_INFINITY,
+    minX: Number.POSITIVE_INFINITY,
+    minY: Number.POSITIVE_INFINITY
+  });
+  return [coord.minX, coord.minY, coord.maxX - coord.minX, coord.maxY - coord.minY];
+}
+function scalePoses(poses, [height, width], [inputResolutionHeight, inputResolutionWidth]) {
+  const scaleY = height / inputResolutionHeight;
+  const scaleX = width / inputResolutionWidth;
+  const scalePose = (pose, i) => ({
+    id: i,
+    score: pose.score,
+    boxRaw: [pose.box[0] / inputResolutionWidth, pose.box[1] / inputResolutionHeight, pose.box[2] / inputResolutionWidth, pose.box[3] / inputResolutionHeight],
+    box: [Math.trunc(pose.box[0] * scaleX), Math.trunc(pose.box[1] * scaleY), Math.trunc(pose.box[2] * scaleX), Math.trunc(pose.box[3] * scaleY)],
+    keypoints: pose.keypoints.map(({ score, part, position }) => ({
+      score,
+      part,
+      position: [Math.trunc(position.x * scaleX), Math.trunc(position.y * scaleY)],
+      positionRaw: [position.x / inputResolutionHeight, position.y / inputResolutionHeight]
+    })),
+    annotations: {}
+  });
+  const scaledPoses = poses.map((pose, i) => scalePose(pose, i));
+  return scaledPoses;
+}
+var MaxHeap = class {
+  constructor(maxSize2, getElementValue) {
+    __publicField(this, "priorityQueue");
+    __publicField(this, "numberOfElements");
+    __publicField(this, "getElementValue");
+    this.priorityQueue = new Array(maxSize2);
+    this.numberOfElements = -1;
+    this.getElementValue = getElementValue;
+  }
+  enqueue(x) {
+    this.priorityQueue[++this.numberOfElements] = x;
+    this.swim(this.numberOfElements);
+  }
+  dequeue() {
+    const max7 = this.priorityQueue[0];
+    this.exchange(0, this.numberOfElements--);
+    this.sink(0);
+    this.priorityQueue[this.numberOfElements + 1] = null;
+    return max7;
+  }
+  empty() {
+    return this.numberOfElements === -1;
+  }
+  size() {
+    return this.numberOfElements + 1;
+  }
+  all() {
+    return this.priorityQueue.slice(0, this.numberOfElements + 1);
+  }
+  max() {
+    return this.priorityQueue[0];
+  }
+  swim(k) {
+    while (k > 0 && this.less(Math.floor(k / 2), k)) {
+      this.exchange(k, Math.floor(k / 2));
+      k = Math.floor(k / 2);
+    }
+  }
+  sink(k) {
+    while (2 * k <= this.numberOfElements) {
+      let j = 2 * k;
+      if (j < this.numberOfElements && this.less(j, j + 1))
+        j++;
+      if (!this.less(k, j))
+        break;
+      this.exchange(k, j);
+      k = j;
+    }
+  }
+  getValueAt(i) {
+    return this.getElementValue(this.priorityQueue[i]);
+  }
+  less(i, j) {
+    return this.getValueAt(i) < this.getValueAt(j);
+  }
+  exchange(i, j) {
+    const t2 = this.priorityQueue[i];
+    this.priorityQueue[i] = this.priorityQueue[j];
+    this.priorityQueue[j] = t2;
+  }
+};
+function getOffsetPoint(y, x, keypoint, offsets) {
+  return {
+    y: offsets.get(y, x, keypoint),
+    x: offsets.get(y, x, keypoint + count2)
+  };
+}
+function getImageCoords(part, outputStride2, offsets) {
+  const { heatmapY, heatmapX, id: keypoint } = part;
+  const { y, x } = getOffsetPoint(heatmapY, heatmapX, keypoint, offsets);
+  return {
+    x: part.heatmapX * outputStride2 + x,
+    y: part.heatmapY * outputStride2 + y
+  };
+}
+function clamp2(a, min7, max7) {
+  if (a < min7)
+    return min7;
+  if (a > max7)
+    return max7;
+  return a;
+}
+function squaredDistance(y1, x1, y2, x2) {
+  const dy = y2 - y1;
+  const dx = x2 - x1;
+  return dy * dy + dx * dx;
+}
+function addVectors(a, b) {
+  return { x: a.x + b.x, y: a.y + b.y };
+}
+
+// src/body/posenet.ts
+var model17;
+var poseNetOutputs = ["MobilenetV1/offset_2/BiasAdd", "MobilenetV1/heatmap_2/BiasAdd", "MobilenetV1/displacement_fwd_2/BiasAdd", "MobilenetV1/displacement_bwd_2/BiasAdd"];
+var localMaximumRadius = 1;
+var outputStride = 16;
+var squaredNmsRadius = 50 ** 2;
+function traverse(edgeId, sourceKeypoint, targetId, scores, offsets, displacements, offsetRefineStep = 2) {
+  const getDisplacement = (point2) => ({
+    y: displacements.get(point2.y, point2.x, edgeId),
+    x: displacements.get(point2.y, point2.x, displacements.shape[2] / 2 + edgeId)
+  });
+  const getStridedIndexNearPoint = (point2, height2, width2) => ({
+    y: clamp2(Math.round(point2.y / outputStride), 0, height2 - 1),
+    x: clamp2(Math.round(point2.x / outputStride), 0, width2 - 1)
+  });
+  const [height, width] = scores.shape;
+  const sourceKeypointIndices = getStridedIndexNearPoint(sourceKeypoint.position, height, width);
+  const displacement = getDisplacement(sourceKeypointIndices);
+  const displacedPoint = addVectors(sourceKeypoint.position, displacement);
+  let targetKeypoint = displacedPoint;
+  for (let i = 0; i < offsetRefineStep; i++) {
+    const targetKeypointIndices = getStridedIndexNearPoint(targetKeypoint, height, width);
+    const offsetPoint = getOffsetPoint(targetKeypointIndices.y, targetKeypointIndices.x, targetId, offsets);
+    targetKeypoint = addVectors(
+      { x: targetKeypointIndices.x * outputStride, y: targetKeypointIndices.y * outputStride },
+      { x: offsetPoint.x, y: offsetPoint.y }
+    );
+  }
+  const targetKeyPointIndices = getStridedIndexNearPoint(targetKeypoint, height, width);
+  const score = scores.get(targetKeyPointIndices.y, targetKeyPointIndices.x, targetId);
+  return { position: targetKeypoint, part: partNames[targetId], score };
+}
+function decodePose(root, scores, offsets, displacementsFwd, displacementsBwd) {
+  const tuples = poseChain.map(([parentJoinName, childJoinName]) => [partIds[parentJoinName], partIds[childJoinName]]);
+  const edgesFwd = tuples.map(([, childJointId]) => childJointId);
+  const edgesBwd = tuples.map(([parentJointId]) => parentJointId);
+  const numParts = scores.shape[2];
+  const numEdges = edgesFwd.length;
+  const keypoints = new Array(numParts);
+  const rootPoint = getImageCoords(root.part, outputStride, offsets);
+  keypoints[root.part.id] = {
+    score: root.score,
+    part: partNames[root.part.id],
+    position: rootPoint
+  };
+  for (let edge = numEdges - 1; edge >= 0; --edge) {
+    const sourceId = edgesFwd[edge];
+    const targetId = edgesBwd[edge];
+    if (keypoints[sourceId] && !keypoints[targetId]) {
+      keypoints[targetId] = traverse(edge, keypoints[sourceId], targetId, scores, offsets, displacementsBwd);
+    }
+  }
+  for (let edge = 0; edge < numEdges; ++edge) {
+    const sourceId = edgesBwd[edge];
+    const targetId = edgesFwd[edge];
+    if (keypoints[sourceId] && !keypoints[targetId]) {
+      keypoints[targetId] = traverse(edge, keypoints[sourceId], targetId, scores, offsets, displacementsFwd);
+    }
+  }
+  return keypoints;
+}
+function scoreIsMaximumInLocalWindow(keypointId, score, heatmapY, heatmapX, scores) {
+  const [height, width] = scores.shape;
+  let localMaximum = true;
+  const yStart = Math.max(heatmapY - localMaximumRadius, 0);
+  const yEnd = Math.min(heatmapY + localMaximumRadius + 1, height);
+  for (let yCurrent = yStart; yCurrent < yEnd; ++yCurrent) {
+    const xStart = Math.max(heatmapX - localMaximumRadius, 0);
+    const xEnd = Math.min(heatmapX + localMaximumRadius + 1, width);
+    for (let xCurrent = xStart; xCurrent < xEnd; ++xCurrent) {
+      if (scores.get(yCurrent, xCurrent, keypointId) > score) {
+        localMaximum = false;
+        break;
+      }
+    }
+    if (!localMaximum)
+      break;
+  }
+  return localMaximum;
+}
+function buildPartWithScoreQueue(minConfidence2, scores) {
+  const [height, width, numKeypoints] = scores.shape;
+  const queue = new MaxHeap(height * width * numKeypoints, ({ score }) => score);
+  for (let heatmapY = 0; heatmapY < height; ++heatmapY) {
+    for (let heatmapX = 0; heatmapX < width; ++heatmapX) {
+      for (let keypointId = 0; keypointId < numKeypoints; ++keypointId) {
+        const score = scores.get(heatmapY, heatmapX, keypointId);
+        if (score < minConfidence2)
+          continue;
+        if (scoreIsMaximumInLocalWindow(keypointId, score, heatmapY, heatmapX, scores))
+          queue.enqueue({ score, part: { heatmapY, heatmapX, id: keypointId } });
+      }
+    }
+  }
+  return queue;
+}
+function withinRadius(poses, { x, y }, keypointId) {
+  return poses.some(({ keypoints }) => {
+    var _a;
+    const correspondingKeypoint = (_a = keypoints[keypointId]) == null ? void 0 : _a.position;
+    if (!correspondingKeypoint)
+      return false;
+    return squaredDistance(y, x, correspondingKeypoint.y, correspondingKeypoint.x) <= squaredNmsRadius;
+  });
+}
+function getInstanceScore(existingPoses, keypoints) {
+  const notOverlappedKeypointScores = keypoints.reduce((result, { position, score }, keypointId) => {
+    if (!withinRadius(existingPoses, position, keypointId))
+      result += score;
+    return result;
+  }, 0);
+  return notOverlappedKeypointScores / keypoints.length;
+}
+function decode(offsets, scores, displacementsFwd, displacementsBwd, maxDetected, minConfidence2) {
+  const poses = [];
+  const queue = buildPartWithScoreQueue(minConfidence2, scores);
+  while (poses.length < maxDetected && !queue.empty()) {
+    const root = queue.dequeue();
+    const rootImageCoords = getImageCoords(root.part, outputStride, offsets);
+    if (withinRadius(poses, rootImageCoords, root.part.id))
+      continue;
+    let keypoints = decodePose(root, scores, offsets, displacementsFwd, displacementsBwd);
+    keypoints = keypoints.filter((a) => a.score > minConfidence2);
+    const score = getInstanceScore(poses, keypoints);
+    const box = getBoundingBox(keypoints);
+    if (score > minConfidence2)
+      poses.push({ keypoints, box, score: Math.round(100 * score) / 100 });
+  }
+  return poses;
+}
+async function predict17(input2, config3) {
+  if (!(model17 == null ? void 0 : model17["executor"]))
+    return [];
+  const res = tidy(() => {
+    if (!model17.inputs[0].shape)
+      return [];
+    const resized = image.resizeBilinear(input2, [model17.inputs[0].shape[2], model17.inputs[0].shape[1]]);
+    const normalized = sub(div(cast(resized, "float32"), 127.5), 1);
+    const results = model17.execute(normalized, poseNetOutputs);
+    const results3d = results.map((y) => squeeze(y, [0]));
+    results3d[1] = sigmoid(results3d[1]);
+    return results3d;
+  });
+  const buffers = await Promise.all(res.map((tensor2) => tensor2.buffer()));
+  for (const t2 of res)
+    dispose(t2);
+  const decoded = decode(buffers[0], buffers[1], buffers[2], buffers[3], config3.body.maxDetected, config3.body.minConfidence);
+  if (!model17.inputs[0].shape)
+    return [];
+  const scaled = scalePoses(decoded, [input2.shape[1], input2.shape[2]], [model17.inputs[0].shape[2], model17.inputs[0].shape[1]]);
+  return scaled;
+}
+async function load17(config3) {
+  if (!model17 || env2.initial)
+    model17 = await loadModel(config3.body.modelPath);
+  else if (config3.debug)
+    log("cached model:", model17["modelUrl"]);
+  return model17;
+}
+
+// src/segmentation/rvm.ts
+var model18;
+var outputNodes2 = ["fgr", "pha", "r1o", "r2o", "r3o", "r4o"];
+var t = {};
+var ratio = 0;
+function init3(config3) {
+  dispose([t.r1i, t.r2i, t.r3i, t.r4i, t.downsample_ratio]);
+  t.r1i = tensor(0);
+  t.r2i = tensor(0);
+  t.r3i = tensor(0);
+  t.r4i = tensor(0);
+  ratio = config3.segmentation.ratio || 0.5;
+  t.downsample_ratio = tensor(ratio);
+}
+async function load18(config3) {
+  if (!model18 || env2.initial)
+    model18 = await loadModel(config3.segmentation.modelPath);
+  else if (config3.debug)
+    log("cached model:", model18["modelUrl"]);
+  init3(config3);
+  return model18;
+}
+var normalize = (r) => tidy(() => {
+  const squeeze2 = squeeze(r, [0]);
+  const mul2 = mul(squeeze2, constants.tf255);
+  const cast7 = cast(mul2, "int32");
+  return cast7;
+});
+function getRGBA(fgr, pha) {
+  const rgb2 = fgr ? normalize(fgr) : fill([pha.shape[1] || 0, pha.shape[2] || 0, 3], 255, "int32");
+  const a = pha ? normalize(pha) : fill([fgr.shape[1] || 0, fgr.shape[2] || 0, 1], 255, "int32");
+  const rgba = concat([rgb2, a], -1);
+  dispose([rgb2, a]);
+  return rgba;
+}
+function getState(state) {
+  return tidy(() => {
+    const r = {};
+    r.unstack = unstack(state, -1);
+    r.concat = concat(r.unstack, 1);
+    r.split = split(r.concat, 4, 1);
+    r.stack = concat(r.split, 2);
+    r.squeeze = squeeze(r.stack, [0]);
+    r.expand = expandDims(r.squeeze, -1);
+    r.add = add2(r.expand, 1);
+    r.mul = mul(r.add, 127.5);
+    r.cast = cast(r.mul, "int32");
+    r.tile = tile(r.cast, [1, 1, 3]);
+    r.alpha = fill([r.tile.shape[0] || 0, r.tile.shape[1] || 0, 1], 255, "int32");
+    return concat([r.tile, r.alpha], -1);
+  });
+}
+async function predict18(input2, config3) {
+  if (!model18)
+    model18 = await load18(config3);
+  if (!(model18 == null ? void 0 : model18["executor"]))
+    return null;
+  t.src = div(input2, 255);
+  if (ratio !== config3.segmentation.ratio)
+    init3(config3);
+  const [fgr, pha, r1o, r2o, r3o, r4o] = await model18.executeAsync(t, outputNodes2);
+  let rgba;
+  switch (config3.segmentation.mode || "default") {
+    case "default":
+      rgba = getRGBA(fgr, pha);
+      break;
+    case "alpha":
+      rgba = getRGBA(null, pha);
+      break;
+    case "foreground":
+      rgba = getRGBA(fgr, null);
+      break;
+    case "state":
+      rgba = getState(r1o);
+      break;
+    default:
+      rgba = tensor(0);
+  }
+  dispose([t.src, fgr, pha, t.r1i, t.r2i, t.r3i, t.r4i]);
+  [t.r1i, t.r2i, t.r3i, t.r4i] = [r1o, r2o, r3o, r4o];
+  return rgba;
+}
+
+// src/segmentation/selfie.ts
+var model19;
+async function load19(config3) {
+  if (!model19 || env2.initial)
+    model19 = await loadModel(config3.segmentation.modelPath);
+  else if (config3.debug)
+    log("cached model:", model19["modelUrl"]);
+  return model19;
+}
+async function predict19(input2, config3) {
+  var _a;
+  if (!model19)
+    model19 = await load19(config3);
+  if (!(model19 == null ? void 0 : model19["executor"]) || !((_a = model19 == null ? void 0 : model19.inputs) == null ? void 0 : _a[0].shape))
+    return null;
+  const t2 = {};
+  t2.resize = image.resizeBilinear(input2, [model19.inputs[0].shape ? model19.inputs[0].shape[1] : 0, model19.inputs[0].shape ? model19.inputs[0].shape[2] : 0], false);
+  t2.norm = div(t2.resize, constants.tf255);
+  t2.res = model19.execute(t2.norm);
+  t2.squeeze = squeeze(t2.res, 0);
+  t2.alpha = image.resizeBilinear(t2.squeeze, [input2.shape[1], input2.shape[2]]);
+  t2.mul = mul(t2.alpha, constants.tf255);
+  let rgba;
+  switch (config3.segmentation.mode || "default") {
+    case "default":
+      t2.input = squeeze(input2);
+      t2.concat = concat([t2.input, t2.mul], -1);
+      rgba = cast(t2.concat, "int32");
+      break;
+    case "alpha":
+      rgba = cast(t2.mul, "int32");
+      break;
+    default:
+      rgba = tensor(0);
+  }
+  Object.keys(t2).forEach((tensor2) => dispose(t2[tensor2]));
+  return rgba;
+}
+
+// src/gear/ssrnet-age.ts
+var model20;
+var last9 = [];
+var lastCount8 = 0;
+var lastTime14 = 0;
+var skipped14 = Number.MAX_SAFE_INTEGER;
+async function load20(config3) {
+  if (env2.initial)
+    model20 = null;
+  if (!model20)
+    model20 = await loadModel(config3.face["ssrnet"].modelPathAge);
+  else if (config3.debug)
+    log("cached model:", model20["modelUrl"]);
+  return model20;
+}
+async function predict20(image2, config3, idx, count3) {
+  var _a, _b, _c, _d;
+  if (!model20)
+    return { age: 0 };
+  const skipFrame = skipped14 < (((_a = config3.face["ssrnet"]) == null ? void 0 : _a.skipFrames) || 0);
+  const skipTime = (((_b = config3.face["ssrnet"]) == null ? void 0 : _b.skipTime) || 0) > now() - lastTime14;
+  if (config3.skipAllowed && skipFrame && skipTime && lastCount8 === count3 && ((_c = last9[idx]) == null ? void 0 : _c.age) && ((_d = last9[idx]) == null ? void 0 : _d.age) > 0) {
+    skipped14++;
+    return last9[idx];
+  }
+  skipped14 = 0;
+  return new Promise(async (resolve) => {
+    var _a2;
+    if (!(model20 == null ? void 0 : model20.inputs) || !model20.inputs[0] || !model20.inputs[0].shape)
+      return;
+    const t2 = {};
+    t2.resize = image.resizeBilinear(image2, [model20.inputs[0].shape[2], model20.inputs[0].shape[1]], false);
+    t2.enhance = mul(t2.resize, constants.tf255);
+    const obj = { age: 0 };
+    if ((_a2 = config3.face["ssrnet"]) == null ? void 0 : _a2.enabled)
+      t2.age = model20.execute(t2.enhance);
+    if (t2.age) {
+      const data = await t2.age.data();
+      obj.age = Math.trunc(10 * data[0]) / 10;
+    }
+    Object.keys(t2).forEach((tensor2) => dispose(t2[tensor2]));
+    last9[idx] = obj;
+    lastCount8 = count3;
+    lastTime14 = now();
+    resolve(obj);
+  });
+}
+
+// src/gear/ssrnet-gender.ts
+var model21;
+var last10 = [];
+var lastCount9 = 0;
+var lastTime15 = 0;
+var skipped15 = Number.MAX_SAFE_INTEGER;
+var rgb = [0.2989, 0.587, 0.114];
+async function load21(config3) {
+  var _a;
+  if (env2.initial)
+    model21 = null;
+  if (!model21)
+    model21 = await loadModel((_a = config3.face["ssrnet"]) == null ? void 0 : _a.modelPathGender);
+  else if (config3.debug)
+    log("cached model:", model21["modelUrl"]);
+  return model21;
+}
+async function predict21(image2, config3, idx, count3) {
+  var _a, _b, _c, _d;
+  if (!model21)
+    return { gender: "unknown", genderScore: 0 };
+  const skipFrame = skipped15 < (((_a = config3.face["ssrnet"]) == null ? void 0 : _a.skipFrames) || 0);
+  const skipTime = (((_b = config3.face["ssrnet"]) == null ? void 0 : _b.skipTime) || 0) > now() - lastTime15;
+  if (config3.skipAllowed && skipFrame && skipTime && lastCount9 === count3 && ((_c = last10[idx]) == null ? void 0 : _c.gender) && ((_d = last10[idx]) == null ? void 0 : _d.genderScore) > 0) {
+    skipped15++;
+    return last10[idx];
+  }
+  skipped15 = 0;
+  return new Promise(async (resolve) => {
+    var _a2;
+    if (!(model21 == null ? void 0 : model21.inputs[0].shape))
+      return;
+    const t2 = {};
+    t2.resize = image.resizeBilinear(image2, [model21.inputs[0].shape[2], model21.inputs[0].shape[1]], false);
+    t2.enhance = tidy(() => {
+      const [red, green, blue] = split(t2.resize, 3, 3);
+      const redNorm = mul(red, rgb[0]);
+      const greenNorm = mul(green, rgb[1]);
+      const blueNorm = mul(blue, rgb[2]);
+      const grayscale = addN([redNorm, greenNorm, blueNorm]);
+      const normalize2 = mul(sub(grayscale, constants.tf05), 2);
+      return normalize2;
+    });
+    const obj = { gender: "unknown", genderScore: 0 };
+    if ((_a2 = config3.face["ssrnet"]) == null ? void 0 : _a2.enabled)
+      t2.gender = model21.execute(t2.enhance);
+    const data = await t2.gender.data();
+    obj.gender = data[0] > data[1] ? "female" : "male";
+    obj.genderScore = data[0] > data[1] ? Math.trunc(100 * data[0]) / 100 : Math.trunc(100 * data[1]) / 100;
+    Object.keys(t2).forEach((tensor2) => dispose(t2[tensor2]));
+    last10[idx] = obj;
+    lastCount9 = count3;
+    lastTime15 = now();
+    resolve(obj);
+  });
+}
+
+// src/models.ts
+var Models = class {
+  constructor() {
+    __publicField(this, "ssrnetage", null);
+    __publicField(this, "gear", null);
+    __publicField(this, "blazeposedetect", null);
+    __publicField(this, "blazepose", null);
+    __publicField(this, "centernet", null);
+    __publicField(this, "efficientpose", null);
+    __publicField(this, "mobilefacenet", null);
+    __publicField(this, "insightface", null);
+    __publicField(this, "emotion", null);
+    __publicField(this, "facedetect", null);
+    __publicField(this, "faceiris", null);
+    __publicField(this, "facemesh", null);
+    __publicField(this, "faceres", null);
+    __publicField(this, "ssrnetgender", null);
+    __publicField(this, "handpose", null);
+    __publicField(this, "handskeleton", null);
+    __publicField(this, "handtrack", null);
+    __publicField(this, "liveness", null);
+    __publicField(this, "meet", null);
+    __publicField(this, "movenet", null);
+    __publicField(this, "nanodet", null);
+    __publicField(this, "posenet", null);
+    __publicField(this, "selfie", null);
+    __publicField(this, "rvm", null);
+    __publicField(this, "antispoof", null);
+  }
+};
+var instance;
+var getModelStats = (currentInstance) => {
+  if (currentInstance)
+    instance = currentInstance;
+  if (!instance)
+    log("instance not registred");
+  let totalSizeFromManifest = 0;
+  let totalSizeWeights = 0;
+  let totalSizeLoading = 0;
+  for (const m of Object.values(modelStats)) {
+    totalSizeFromManifest += m.sizeFromManifest;
+    totalSizeWeights += m.sizeLoadedWeights;
+    totalSizeLoading += m.sizeDesired;
+  }
+  const percentageLoaded = totalSizeLoading > 0 ? totalSizeWeights / totalSizeLoading : 0;
+  return {
+    numLoadedModels: Object.values(modelStats).length,
+    numDefinedModels: Object.keys(instance.models).length,
+    percentageLoaded,
+    totalSizeFromManifest,
+    totalSizeWeights,
+    totalSizeLoading,
+    totalSizeEnabled: void 0,
+    modelStats: Object.values(modelStats)
+  };
+};
+function reset2(currentInstance) {
+  if (currentInstance)
+    instance = currentInstance;
+  for (const model22 of Object.keys(instance.models))
+    instance.models[model22] = null;
+}
+async function load22(currentInstance) {
+  var _a, _b, _c, _d, _e, _f, _g, _h, _i, _j, _k, _l, _m, _n, _o, _p, _q, _r, _s, _t, _u, _v, _w, _x, _y, _z, _A, _B, _C;
+  if (currentInstance)
+    instance = currentInstance;
+  if (!instance)
+    log("instance not registred");
+  if (env2.initial)
+    reset2(instance);
+  if (instance.config.hand.enabled) {
+    if (!instance.models.handpose && ((_b = (_a = instance.config.hand.detector) == null ? void 0 : _a.modelPath) == null ? void 0 : _b.includes("handdetect"))) {
+      [instance.models.handpose, instance.models.handskeleton] = await load10(instance.config);
+    }
+    if (!instance.models.handskeleton && instance.config.hand.landmarks && ((_d = (_c = instance.config.hand.detector) == null ? void 0 : _c.modelPath) == null ? void 0 : _d.includes("handdetect"))) {
+      [instance.models.handpose, instance.models.handskeleton] = await load10(instance.config);
+    }
+  }
+  if (instance.config.body.enabled && !instance.models.blazepose && ((_e = instance.config.body.modelPath) == null ? void 0 : _e.includes("blazepose")))
+    instance.models.blazepose = loadPose(instance.config);
+  if (instance.config.body.enabled && !instance.models.blazeposedetect && instance.config.body["detector"] && instance.config.body["detector"].modelPath)
+    instance.models.blazeposedetect = loadDetect(instance.config);
+  if (instance.config.body.enabled && !instance.models.efficientpose && ((_f = instance.config.body.modelPath) == null ? void 0 : _f.includes("efficientpose")))
+    instance.models.efficientpose = load4(instance.config);
+  if (instance.config.body.enabled && !instance.models.movenet && ((_g = instance.config.body.modelPath) == null ? void 0 : _g.includes("movenet")))
+    instance.models.movenet = load15(instance.config);
+  if (instance.config.body.enabled && !instance.models.posenet && ((_h = instance.config.body.modelPath) == null ? void 0 : _h.includes("posenet")))
+    instance.models.posenet = load17(instance.config);
+  if (instance.config.face.enabled && !instance.models.facedetect)
+    instance.models.facedetect = load2(instance.config);
+  if (instance.config.face.enabled && ((_i = instance.config.face.antispoof) == null ? void 0 : _i.enabled) && !instance.models.antispoof)
+    instance.models.antispoof = load(instance.config);
+  if (instance.config.face.enabled && ((_j = instance.config.face.liveness) == null ? void 0 : _j.enabled) && !instance.models.liveness)
+    instance.models.liveness = load12(instance.config);
+  if (instance.config.face.enabled && ((_k = instance.config.face.description) == null ? void 0 : _k.enabled) && !instance.models.faceres)
+    instance.models.faceres = load8(instance.config);
+  if (instance.config.face.enabled && ((_l = instance.config.face.emotion) == null ? void 0 : _l.enabled) && !instance.models.emotion)
+    instance.models.emotion = load5(instance.config);
+  if (instance.config.face.enabled && ((_m = instance.config.face.iris) == null ? void 0 : _m.enabled) && !((_n = instance.config.face.attention) == null ? void 0 : _n.enabled) && !instance.models.faceiris)
+    instance.models.faceiris = load6(instance.config);
+  if (instance.config.face.enabled && ((_o = instance.config.face.mesh) == null ? void 0 : _o.enabled) && !instance.models.facemesh)
+    instance.models.facemesh = load7(instance.config);
+  if (instance.config.face.enabled && ((_p = instance.config.face["gear"]) == null ? void 0 : _p.enabled) && !instance.models.gear)
+    instance.models.gear = load9(instance.config);
+  if (instance.config.face.enabled && ((_q = instance.config.face["ssrnet"]) == null ? void 0 : _q.enabled) && !instance.models.ssrnetage)
+    instance.models.ssrnetage = load20(instance.config);
+  if (instance.config.face.enabled && ((_r = instance.config.face["ssrnet"]) == null ? void 0 : _r.enabled) && !instance.models.ssrnetgender)
+    instance.models.ssrnetgender = load21(instance.config);
+  if (instance.config.face.enabled && ((_s = instance.config.face["mobilefacenet"]) == null ? void 0 : _s.enabled) && !instance.models.mobilefacenet)
+    instance.models.mobilefacenet = load14(instance.config);
+  if (instance.config.face.enabled && ((_t = instance.config.face["insightface"]) == null ? void 0 : _t.enabled) && !instance.models.insightface)
+    instance.models.insightface = load11(instance.config);
+  if (instance.config.hand.enabled && !instance.models.handtrack && ((_v = (_u = instance.config.hand.detector) == null ? void 0 : _u.modelPath) == null ? void 0 : _v.includes("handtrack")))
+    instance.models.handtrack = loadDetect2(instance.config);
+  if (instance.config.hand.enabled && instance.config.hand.landmarks && !instance.models.handskeleton && ((_x = (_w = instance.config.hand.detector) == null ? void 0 : _w.modelPath) == null ? void 0 : _x.includes("handtrack")))
+    instance.models.handskeleton = loadSkeleton(instance.config);
+  if (instance.config.object.enabled && !instance.models.centernet && ((_y = instance.config.object.modelPath) == null ? void 0 : _y.includes("centernet")))
+    instance.models.centernet = load3(instance.config);
+  if (instance.config.object.enabled && !instance.models.nanodet && ((_z = instance.config.object.modelPath) == null ? void 0 : _z.includes("nanodet")))
+    instance.models.nanodet = load16(instance.config);
+  if (instance.config.segmentation.enabled && !instance.models.selfie && ((_A = instance.config.segmentation.modelPath) == null ? void 0 : _A.includes("selfie")))
+    instance.models.selfie = load19(instance.config);
+  if (instance.config.segmentation.enabled && !instance.models.meet && ((_B = instance.config.segmentation.modelPath) == null ? void 0 : _B.includes("meet")))
+    instance.models.meet = load13(instance.config);
+  if (instance.config.segmentation.enabled && !instance.models.rvm && ((_C = instance.config.segmentation.modelPath) == null ? void 0 : _C.includes("rvm")))
+    instance.models.rvm = load18(instance.config);
+  for await (const model22 of Object.keys(instance.models)) {
+    if (instance.models[model22] && typeof instance.models[model22] !== "undefined") {
+      instance.models[model22] = await instance.models[model22];
+    }
+  }
+}
+function validateModel(currentInstance, model22, name) {
+  var _a, _b;
+  if (!model22)
+    return null;
+  if (currentInstance)
+    instance = currentInstance;
+  if (!instance)
+    log("instance not registred");
+  if (!((_a = instance == null ? void 0 : instance.config) == null ? void 0 : _a.validateModels))
+    return null;
+  const simpleOps = ["const", "placeholder", "noop", "pad", "squeeze", "add", "sub", "mul", "div"];
+  const ignoreOps = ["biasadd", "fusedbatchnormv3", "matmul", "switch", "shape", "merge", "split", "broadcastto"];
+  const ops = [];
+  const missing = [];
+  const url = model22["modelUrl"];
+  const executor = model22["executor"];
+  if ((_b = executor == null ? void 0 : executor.graph) == null ? void 0 : _b.nodes) {
+    for (const kernel of Object.values(executor.graph.nodes)) {
+      const op2 = kernel.op.toLowerCase();
+      if (!ops.includes(op2))
+        ops.push(op2);
+    }
+  } else {
+    if (!executor && instance.config.debug) {
+      log("model not loaded", name);
+    }
+  }
+  for (const op2 of ops) {
+    if (!simpleOps.includes(op2) && !ignoreOps.includes(op2) && !instance.env.kernels.includes(op2) && !instance.env.kernels.includes(op2.replace("_", "")) && !instance.env.kernels.includes(op2.replace("native", "")) && !instance.env.kernels.includes(op2.replace("v2", ""))) {
+      missing.push(op2);
+    }
+  }
+  if (instance.config.debug && missing.length > 0)
+    log("model validation failed:", name, missing);
+  return missing.length > 0 ? { name, missing, ops, url } : null;
+}
+function validate2(currentInstance) {
+  if (currentInstance)
+    instance = currentInstance;
+  if (!instance)
+    log("instance not registred");
+  const missing = [];
+  for (const defined of Object.keys(currentInstance.models)) {
+    const model22 = currentInstance.models[defined];
+    if (!model22)
+      continue;
+    const res = validateModel(currentInstance, model22, defined);
+    if (res)
+      missing.push(res);
+  }
+  return missing;
+}
+
 // src/tfjs/humangl.ts
 var config2 = {
   name: "humangl",
@@ -83900,17 +85288,17 @@ function register(instance2) {
         return;
       }
       if (config2.canvas) {
-        config2.canvas.addEventListener("webglcontextlost", (e2) => {
-          log("humangl error:", e2.type);
+        config2.canvas.addEventListener("webglcontextlost", (e) => {
+          log("humangl error:", e.type);
           log("possible browser memory leak using webgl or conflict with multiple backend registrations");
           instance2.emit("error");
           throw new Error("backend error: webgl context lost");
         });
-        config2.canvas.addEventListener("webglcontextrestored", (e2) => {
-          log("humangl error: context restored:", e2);
+        config2.canvas.addEventListener("webglcontextrestored", (e) => {
+          log("humangl error: context restored:", e);
         });
-        config2.canvas.addEventListener("webglcontextcreationerror", (e2) => {
-          log("humangl error: context create:", e2);
+        config2.canvas.addEventListener("webglcontextcreationerror", (e) => {
+          log("humangl error: context create:", e);
         });
       }
     } catch (err) {
@@ -84070,7 +85458,7 @@ async function check(instance2, force = false) {
             log(`wasm execution: ${simd ? "simd" : "no simd"} ${mt ? "multithreaded" : "singlethreaded"}`);
           if (instance2.config.debug && !simd)
             log("warning: wasm simd support is not enabled");
-        } catch (e2) {
+        } catch (e) {
           log("wasm detection failed");
         }
       }
@@ -84139,1297 +85527,6 @@ function fakeOps(kernelNames, config3) {
   env2.kernels = getKernelsForBackend(getBackend()).map((kernel) => kernel.kernelName.toLowerCase());
 }
 
-// src/hand/handtrack.ts
-var models3 = [null, null];
-var modelOutputNodes = ["StatefulPartitionedCall/Postprocessor/Slice", "StatefulPartitionedCall/Postprocessor/ExpandDims_1"];
-var inputSize7 = [[0, 0], [0, 0]];
-var classes = ["hand", "fist", "pinch", "point", "face", "tip", "pinchtip"];
-var faceIndex = 4;
-var boxExpandFact = 1.6;
-var maxDetectorResolution = 512;
-var detectorExpandFact = 1.4;
-var skipped12 = Number.MAX_SAFE_INTEGER;
-var lastTime13 = 0;
-var outputSize = [0, 0];
-var cache4 = {
-  boxes: [],
-  hands: []
-};
-var fingerMap = {
-  thumb: [1, 2, 3, 4],
-  index: [5, 6, 7, 8],
-  middle: [9, 10, 11, 12],
-  ring: [13, 14, 15, 16],
-  pinky: [17, 18, 19, 20],
-  base: [0],
-  palm: [0, 17, 13, 9, 5, 1, 0]
-};
-async function loadDetect2(config3) {
-  var _a;
-  if (env2.initial)
-    models3[0] = null;
-  if (!models3[0]) {
-    fakeOps(["tensorlistreserve", "enter", "tensorlistfromtensor", "merge", "loopcond", "switch", "exit", "tensorliststack", "nextiteration", "tensorlistsetitem", "tensorlistgetitem", "reciprocal", "shape", "split", "where"], config3);
-    models3[0] = await loadModel((_a = config3.hand.detector) == null ? void 0 : _a.modelPath);
-    const inputs = models3[0]["executor"] ? Object.values(models3[0].modelSignature["inputs"]) : void 0;
-    inputSize7[0][0] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[1].size) : 0;
-    inputSize7[0][1] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 0;
-  } else if (config3.debug)
-    log("cached model:", models3[0]["modelUrl"]);
-  return models3[0];
-}
-async function loadSkeleton(config3) {
-  var _a;
-  if (env2.initial)
-    models3[1] = null;
-  if (!models3[1]) {
-    models3[1] = await loadModel((_a = config3.hand.skeleton) == null ? void 0 : _a.modelPath);
-    const inputs = models3[1]["executor"] ? Object.values(models3[1].modelSignature["inputs"]) : void 0;
-    inputSize7[1][0] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[1].size) : 0;
-    inputSize7[1][1] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 0;
-  } else if (config3.debug)
-    log("cached model:", models3[1]["modelUrl"]);
-  return models3[1];
-}
-async function detectHands(input2, config3) {
-  const hands = [];
-  if (!input2 || !models3[0])
-    return hands;
-  const t2 = {};
-  const ratio = (input2.shape[2] || 1) / (input2.shape[1] || 1);
-  const height = Math.min(Math.round((input2.shape[1] || 0) / 8) * 8, maxDetectorResolution);
-  const width = Math.round(height * ratio / 8) * 8;
-  t2.resize = image.resizeBilinear(input2, [height, width]);
-  t2.cast = cast(t2.resize, "int32");
-  [t2.rawScores, t2.rawBoxes] = await models3[0].executeAsync(t2.cast, modelOutputNodes);
-  t2.boxes = squeeze(t2.rawBoxes, [0, 2]);
-  t2.scores = squeeze(t2.rawScores, [0]);
-  const classScores = unstack(t2.scores, 1);
-  dispose(classScores[faceIndex]);
-  classScores.splice(faceIndex, 1);
-  t2.filtered = stack(classScores, 1);
-  dispose(classScores);
-  t2.max = max(t2.filtered, 1);
-  t2.argmax = argMax(t2.filtered, 1);
-  let id = 0;
-  t2.nms = await image.nonMaxSuppressionAsync(t2.boxes, t2.max, (config3.hand.maxDetected || 0) + 1, config3.hand.iouThreshold || 0, config3.hand.minConfidence || 1);
-  const nms = await t2.nms.data();
-  const scores = await t2.max.data();
-  const classNum = await t2.argmax.data();
-  for (const nmsIndex of Array.from(nms)) {
-    const boxSlice = slice(t2.boxes, nmsIndex, 1);
-    const boxYX = await boxSlice.data();
-    dispose(boxSlice);
-    const boxData = [boxYX[1], boxYX[0], boxYX[3] - boxYX[1], boxYX[2] - boxYX[0]];
-    const boxRaw = scale2(boxData, detectorExpandFact);
-    const boxFull = [Math.trunc(boxData[0] * outputSize[0]), Math.trunc(boxData[1] * outputSize[1]), Math.trunc(boxData[2] * outputSize[0]), Math.trunc(boxData[3] * outputSize[1])];
-    const score = scores[nmsIndex];
-    const label = classes[classNum[nmsIndex]];
-    const hand3 = { id: id++, score, box: boxFull, boxRaw, label };
-    hands.push(hand3);
-  }
-  Object.keys(t2).forEach((tensor2) => dispose(t2[tensor2]));
-  hands.sort((a, b) => b.score - a.score);
-  if (hands.length > (config3.hand.maxDetected || 1))
-    hands.length = config3.hand.maxDetected || 1;
-  return hands;
-}
-async function detectFingers(input2, h, config3) {
-  const hand3 = {
-    id: h.id,
-    score: Math.round(100 * h.score) / 100,
-    boxScore: Math.round(100 * h.score) / 100,
-    fingerScore: 0,
-    box: h.box,
-    boxRaw: h.boxRaw,
-    label: h.label,
-    keypoints: [],
-    landmarks: {},
-    annotations: {}
-  };
-  if (input2 && models3[1] && config3.hand.landmarks && h.score > (config3.hand.minConfidence || 0)) {
-    const t2 = {};
-    const boxCrop = [h.boxRaw[1], h.boxRaw[0], h.boxRaw[3] + h.boxRaw[1], h.boxRaw[2] + h.boxRaw[0]];
-    t2.crop = image.cropAndResize(input2, [boxCrop], [0], [inputSize7[1][0], inputSize7[1][1]], "bilinear");
-    t2.div = div(t2.crop, constants.tf255);
-    [t2.score, t2.keypoints] = models3[1].execute(t2.div, ["Identity_1", "Identity"]);
-    const rawScore = (await t2.score.data())[0];
-    const score = (100 - Math.trunc(100 / (1 + Math.exp(rawScore)))) / 100;
-    if (score >= (config3.hand.minConfidence || 0)) {
-      hand3.fingerScore = score;
-      t2.reshaped = reshape(t2.keypoints, [-1, 3]);
-      const coordsData = await t2.reshaped.array();
-      const coordsRaw = coordsData.map((kpt4) => [kpt4[0] / inputSize7[1][1], kpt4[1] / inputSize7[1][0], kpt4[2] || 0]);
-      const coordsNorm = coordsRaw.map((kpt4) => [kpt4[0] * h.boxRaw[2], kpt4[1] * h.boxRaw[3], kpt4[2] || 0]);
-      hand3.keypoints = coordsNorm.map((kpt4) => [outputSize[0] * (kpt4[0] + h.boxRaw[0]), outputSize[1] * (kpt4[1] + h.boxRaw[1]), kpt4[2] || 0]);
-      hand3.landmarks = analyze(hand3.keypoints);
-      for (const key of Object.keys(fingerMap)) {
-        hand3.annotations[key] = fingerMap[key].map((index2) => hand3.landmarks && hand3.keypoints[index2] ? hand3.keypoints[index2] : null);
-      }
-    }
-    Object.keys(t2).forEach((tensor2) => dispose(t2[tensor2]));
-  }
-  return hand3;
-}
-async function predict14(input2, config3) {
-  var _a, _b;
-  if (!((_a = models3[0]) == null ? void 0 : _a["executor"]) || !((_b = models3[1]) == null ? void 0 : _b["executor"]) || !models3[0].inputs[0].shape || !models3[1].inputs[0].shape)
-    return [];
-  outputSize = [input2.shape[2] || 0, input2.shape[1] || 0];
-  skipped12++;
-  const skipTime = (config3.hand.skipTime || 0) > now() - lastTime13;
-  const skipFrame = skipped12 < (config3.hand.skipFrames || 0);
-  if (config3.skipAllowed && skipTime && skipFrame) {
-    return cache4.hands;
-  }
-  return new Promise(async (resolve) => {
-    const skipTimeExtended = 3 * (config3.hand.skipTime || 0) > now() - lastTime13;
-    const skipFrameExtended = skipped12 < 3 * (config3.hand.skipFrames || 0);
-    if (config3.skipAllowed && cache4.hands.length === config3.hand.maxDetected) {
-      cache4.hands = await Promise.all(cache4.boxes.map((handBox) => detectFingers(input2, handBox, config3)));
-    } else if (config3.skipAllowed && skipTimeExtended && skipFrameExtended && cache4.hands.length > 0) {
-      cache4.hands = await Promise.all(cache4.boxes.map((handBox) => detectFingers(input2, handBox, config3)));
-    } else {
-      cache4.boxes = await detectHands(input2, config3);
-      lastTime13 = now();
-      cache4.hands = await Promise.all(cache4.boxes.map((handBox) => detectFingers(input2, handBox, config3)));
-      skipped12 = 0;
-    }
-    const oldCache = [...cache4.boxes];
-    cache4.boxes.length = 0;
-    if (config3.cacheSensitivity > 0) {
-      for (let i2 = 0; i2 < cache4.hands.length; i2++) {
-        const boxKpt = square4(cache4.hands[i2].keypoints, outputSize);
-        if (boxKpt.box[2] / (input2.shape[2] || 1) > 0.05 && boxKpt.box[3] / (input2.shape[1] || 1) > 0.05 && cache4.hands[i2].fingerScore && cache4.hands[i2].fingerScore > (config3.hand.minConfidence || 0)) {
-          const boxScale = scale2(boxKpt.box, boxExpandFact);
-          const boxScaleRaw = scale2(boxKpt.boxRaw, boxExpandFact);
-          cache4.boxes.push({ ...oldCache[i2], box: boxScale, boxRaw: boxScaleRaw });
-        }
-      }
-    }
-    for (let i2 = 0; i2 < cache4.hands.length; i2++) {
-      const bbox = calc(cache4.hands[i2].keypoints, outputSize);
-      cache4.hands[i2].box = bbox.box;
-      cache4.hands[i2].boxRaw = bbox.boxRaw;
-    }
-    resolve(cache4.hands);
-  });
-}
-
-// src/face/liveness.ts
-var model15;
-var cached2 = [];
-var skipped13 = Number.MAX_SAFE_INTEGER;
-var lastCount9 = 0;
-var lastTime14 = 0;
-async function load15(config3) {
-  var _a;
-  if (env2.initial)
-    model15 = null;
-  if (!model15)
-    model15 = await loadModel((_a = config3.face.liveness) == null ? void 0 : _a.modelPath);
-  else if (config3.debug)
-    log("cached model:", model15["modelUrl"]);
-  return model15;
-}
-async function predict15(image2, config3, idx, count3) {
-  var _a, _b;
-  if (!(model15 == null ? void 0 : model15["executor"]))
-    return 0;
-  const skipTime = (((_a = config3.face.liveness) == null ? void 0 : _a.skipTime) || 0) > now() - lastTime14;
-  const skipFrame = skipped13 < (((_b = config3.face.liveness) == null ? void 0 : _b.skipFrames) || 0);
-  if (config3.skipAllowed && skipTime && skipFrame && lastCount9 === count3 && cached2[idx]) {
-    skipped13++;
-    return cached2[idx];
-  }
-  skipped13 = 0;
-  return new Promise(async (resolve) => {
-    const resize = image.resizeBilinear(image2, [(model15 == null ? void 0 : model15.inputs[0].shape) ? model15.inputs[0].shape[2] : 0, (model15 == null ? void 0 : model15.inputs[0].shape) ? model15.inputs[0].shape[1] : 0], false);
-    const res = model15 == null ? void 0 : model15.execute(resize);
-    const num = (await res.data())[0];
-    cached2[idx] = Math.round(100 * num) / 100;
-    lastCount9 = count3;
-    lastTime14 = now();
-    dispose([resize, res]);
-    resolve(cached2[idx]);
-  });
-}
-
-// src/body/movenetcoords.ts
-var movenetcoords_exports = {};
-__export(movenetcoords_exports, {
-  connected: () => connected3,
-  horizontal: () => horizontal,
-  kpt: () => kpt3,
-  relative: () => relative,
-  vertical: () => vertical
-});
-var kpt3 = [
-  "nose",
-  "leftEye",
-  "rightEye",
-  "leftEar",
-  "rightEar",
-  "leftShoulder",
-  "rightShoulder",
-  "leftElbow",
-  "rightElbow",
-  "leftWrist",
-  "rightWrist",
-  "leftHip",
-  "rightHip",
-  "leftKnee",
-  "rightKnee",
-  "leftAnkle",
-  "rightAnkle"
-];
-var horizontal = [
-  ["leftEye", "rightEye"],
-  ["leftEar", "rightEar"],
-  ["leftShoulder", "rightShoulder"],
-  ["leftElbow", "rightElbow"],
-  ["leftWrist", "rightWrist"],
-  ["leftHip", "rightHip"],
-  ["leftKnee", "rightKnee"],
-  ["leftAnkle", "rightAnkle"]
-];
-var vertical = [
-  ["leftKnee", "leftShoulder"],
-  ["rightKnee", "rightShoulder"],
-  ["leftAnkle", "leftKnee"],
-  ["rightAnkle", "rightKnee"]
-];
-var relative = [
-  [["leftHip", "rightHip"], ["leftShoulder", "rightShoulder"]],
-  [["leftElbow", "rightElbow"], ["leftShoulder", "rightShoulder"]]
-];
-var connected3 = {
-  leftLeg: ["leftHip", "leftKnee", "leftAnkle"],
-  rightLeg: ["rightHip", "rightKnee", "rightAnkle"],
-  torso: ["leftShoulder", "rightShoulder", "rightHip", "leftHip", "leftShoulder"],
-  leftArm: ["leftShoulder", "leftElbow", "leftWrist"],
-  rightArm: ["rightShoulder", "rightElbow", "rightWrist"],
-  head: []
-};
-
-// src/body/movenetfix.ts
-var maxJitter = 5e-3;
-var cache5 = {
-  keypoints: [],
-  padding: [[0, 0], [0, 0], [0, 0], [0, 0]]
-};
-function bodyParts(body4) {
-  for (const pair of horizontal) {
-    const left = body4.keypoints.findIndex((kp) => kp.part === pair[0]);
-    const right = body4.keypoints.findIndex((kp) => kp.part === pair[1]);
-    if (body4.keypoints[left] && body4.keypoints[right]) {
-      if (body4.keypoints[left].position[0] < body4.keypoints[right].position[0]) {
-        const tmp = body4.keypoints[left];
-        body4.keypoints[left] = body4.keypoints[right];
-        body4.keypoints[right] = tmp;
-      }
-    }
-  }
-  for (const pair of vertical) {
-    const lower = body4.keypoints.findIndex((kp) => kp && kp.part === pair[0]);
-    const higher = body4.keypoints.findIndex((kp) => kp && kp.part === pair[1]);
-    if (body4.keypoints[lower] && body4.keypoints[higher]) {
-      if (body4.keypoints[lower].position[1] < body4.keypoints[higher].position[1]) {
-        body4.keypoints.splice(lower, 1);
-      }
-    }
-  }
-  for (const [pair, compare2] of relative) {
-    const left = body4.keypoints.findIndex((kp) => kp && kp.part === pair[0]);
-    const right = body4.keypoints.findIndex((kp) => kp && kp.part === pair[1]);
-    const leftTo = body4.keypoints.findIndex((kp) => kp && kp.part === compare2[0]);
-    const rightTo = body4.keypoints.findIndex((kp) => kp && kp.part === compare2[1]);
-    if (!body4.keypoints[leftTo] || !body4.keypoints[rightTo])
-      continue;
-    const distanceLeft = body4.keypoints[left] ? [
-      Math.abs(body4.keypoints[leftTo].position[0] - body4.keypoints[left].position[0]),
-      Math.abs(body4.keypoints[rightTo].position[0] - body4.keypoints[left].position[0])
-    ] : [0, 0];
-    const distanceRight = body4.keypoints[right] ? [
-      Math.abs(body4.keypoints[rightTo].position[0] - body4.keypoints[right].position[0]),
-      Math.abs(body4.keypoints[leftTo].position[0] - body4.keypoints[right].position[0])
-    ] : [0, 0];
-    if (distanceLeft[0] > distanceLeft[1] || distanceRight[0] > distanceRight[1]) {
-      const tmp = body4.keypoints[left];
-      body4.keypoints[left] = body4.keypoints[right];
-      body4.keypoints[right] = tmp;
-    }
-  }
-}
-function jitter(keypoints) {
-  for (let i2 = 0; i2 < keypoints.length; i2++) {
-    if (keypoints[i2] && cache5.keypoints[i2]) {
-      const diff = [Math.abs(keypoints[i2].positionRaw[0] - cache5.keypoints[i2].positionRaw[0]), Math.abs(keypoints[i2].positionRaw[1] - cache5.keypoints[i2].positionRaw[1])];
-      if (diff[0] < maxJitter && diff[1] < maxJitter) {
-        keypoints[i2] = cache5.keypoints[i2];
-      } else {
-        cache5.keypoints[i2] = keypoints[i2];
-      }
-    } else {
-      cache5.keypoints[i2] = keypoints[i2];
-    }
-  }
-  return keypoints;
-}
-function padInput(input2, inputSize10) {
-  var _a, _b;
-  const t2 = {};
-  if (!((_a = input2 == null ? void 0 : input2.shape) == null ? void 0 : _a[1]) || !((_b = input2 == null ? void 0 : input2.shape) == null ? void 0 : _b[2]))
-    return input2;
-  cache5.padding = [
-    [0, 0],
-    [input2.shape[2] > input2.shape[1] ? Math.trunc((input2.shape[2] - input2.shape[1]) / 2) : 0, input2.shape[2] > input2.shape[1] ? Math.trunc((input2.shape[2] - input2.shape[1]) / 2) : 0],
-    [input2.shape[1] > input2.shape[2] ? Math.trunc((input2.shape[1] - input2.shape[2]) / 2) : 0, input2.shape[1] > input2.shape[2] ? Math.trunc((input2.shape[1] - input2.shape[2]) / 2) : 0],
-    [0, 0]
-  ];
-  t2.pad = pad(input2, cache5.padding);
-  t2.resize = image.resizeBilinear(t2.pad, [inputSize10, inputSize10]);
-  const final = cast(t2.resize, "int32");
-  Object.keys(t2).forEach((tensor2) => dispose(t2[tensor2]));
-  return final;
-}
-function rescaleBody(body4, outputSize2) {
-  body4.keypoints = body4.keypoints.filter((kpt4) => kpt4 == null ? void 0 : kpt4.position);
-  for (const kpt4 of body4.keypoints) {
-    kpt4.position = [
-      kpt4.position[0] * (outputSize2[0] + cache5.padding[2][0] + cache5.padding[2][1]) / outputSize2[0] - cache5.padding[2][0],
-      kpt4.position[1] * (outputSize2[1] + cache5.padding[1][0] + cache5.padding[1][1]) / outputSize2[1] - cache5.padding[1][0]
-    ];
-    kpt4.positionRaw = [
-      kpt4.position[0] / outputSize2[0],
-      kpt4.position[1] / outputSize2[1]
-    ];
-  }
-  const rescaledBoxes = calc(body4.keypoints.map((pt) => pt.position), outputSize2);
-  body4.box = rescaledBoxes.box;
-  body4.boxRaw = rescaledBoxes.boxRaw;
-  return body4;
-}
-
-// src/body/movenet.ts
-var model16;
-var inputSize8 = 0;
-var skipped14 = Number.MAX_SAFE_INTEGER;
-var cache6 = {
-  boxes: [],
-  bodies: [],
-  last: 0
-};
-async function load16(config3) {
-  var _a;
-  if (env2.initial)
-    model16 = null;
-  if (!model16) {
-    fakeOps(["size"], config3);
-    model16 = await loadModel(config3.body.modelPath);
-  } else if (config3.debug)
-    log("cached model:", model16["modelUrl"]);
-  inputSize8 = (model16 == null ? void 0 : model16["executor"]) && ((_a = model16 == null ? void 0 : model16.inputs) == null ? void 0 : _a[0].shape) ? model16.inputs[0].shape[2] : 0;
-  if (inputSize8 < 64)
-    inputSize8 = 256;
-  return model16;
-}
-function parseSinglePose(res, config3, image2) {
-  const kpt4 = res[0][0];
-  const keypoints = [];
-  let score = 0;
-  for (let id = 0; id < kpt4.length; id++) {
-    score = kpt4[id][2];
-    if (score > config3.body.minConfidence) {
-      const positionRaw = [kpt4[id][1], kpt4[id][0]];
-      keypoints.push({
-        score: Math.round(100 * score) / 100,
-        part: kpt3[id],
-        positionRaw,
-        position: [
-          Math.round((image2.shape[2] || 0) * positionRaw[0]),
-          Math.round((image2.shape[1] || 0) * positionRaw[1])
-        ]
-      });
-    }
-  }
-  score = keypoints.reduce((prev, curr) => curr.score > prev ? curr.score : prev, 0);
-  const bodies = [];
-  const newBox = calc(keypoints.map((pt) => pt.position), [image2.shape[2], image2.shape[1]]);
-  const annotations2 = {};
-  for (const [name, indexes] of Object.entries(connected3)) {
-    const pt = [];
-    for (let i2 = 0; i2 < indexes.length - 1; i2++) {
-      const pt0 = keypoints.find((kp) => kp.part === indexes[i2]);
-      const pt1 = keypoints.find((kp) => kp.part === indexes[i2 + 1]);
-      if (pt0 && pt1 && pt0.score > (config3.body.minConfidence || 0) && pt1.score > (config3.body.minConfidence || 0))
-        pt.push([pt0.position, pt1.position]);
-    }
-    annotations2[name] = pt;
-  }
-  const body4 = { id: 0, score, box: newBox.box, boxRaw: newBox.boxRaw, keypoints, annotations: annotations2 };
-  bodyParts(body4);
-  bodies.push(body4);
-  return bodies;
-}
-function parseMultiPose(res, config3, image2) {
-  const bodies = [];
-  for (let id = 0; id < res[0].length; id++) {
-    const kpt4 = res[0][id];
-    const totalScore = Math.round(100 * kpt4[51 + 4]) / 100;
-    if (totalScore > config3.body.minConfidence) {
-      const keypoints = [];
-      for (let i2 = 0; i2 < 17; i2++) {
-        const score = kpt4[3 * i2 + 2];
-        if (score > config3.body.minConfidence) {
-          const positionRaw = [kpt4[3 * i2 + 1], kpt4[3 * i2 + 0]];
-          keypoints.push({
-            part: kpt3[i2],
-            score: Math.round(100 * score) / 100,
-            positionRaw,
-            position: [Math.round((image2.shape[2] || 0) * positionRaw[0]), Math.round((image2.shape[1] || 0) * positionRaw[1])]
-          });
-        }
-      }
-      const newBox = calc(keypoints.map((pt) => pt.position), [image2.shape[2], image2.shape[1]]);
-      const annotations2 = {};
-      for (const [name, indexes] of Object.entries(connected3)) {
-        const pt = [];
-        for (let i2 = 0; i2 < indexes.length - 1; i2++) {
-          const pt0 = keypoints.find((kp) => kp.part === indexes[i2]);
-          const pt1 = keypoints.find((kp) => kp.part === indexes[i2 + 1]);
-          if (pt0 && pt1 && pt0.score > (config3.body.minConfidence || 0) && pt1.score > (config3.body.minConfidence || 0))
-            pt.push([pt0.position, pt1.position]);
-        }
-        annotations2[name] = pt;
-      }
-      const body4 = { id, score: totalScore, box: newBox.box, boxRaw: newBox.boxRaw, keypoints: [...keypoints], annotations: annotations2 };
-      bodyParts(body4);
-      bodies.push(body4);
-    }
-  }
-  bodies.sort((a, b) => b.score - a.score);
-  if (bodies.length > config3.body.maxDetected)
-    bodies.length = config3.body.maxDetected;
-  return bodies;
-}
-async function predict16(input2, config3) {
-  var _a;
-  if (!(model16 == null ? void 0 : model16["executor"]) || !((_a = model16 == null ? void 0 : model16.inputs) == null ? void 0 : _a[0].shape))
-    return [];
-  if (!config3.skipAllowed)
-    cache6.boxes.length = 0;
-  skipped14++;
-  const skipTime = (config3.body.skipTime || 0) > now() - cache6.last;
-  const skipFrame = skipped14 < (config3.body.skipFrames || 0);
-  if (config3.skipAllowed && skipTime && skipFrame) {
-    return cache6.bodies;
-  }
-  return new Promise(async (resolve) => {
-    const t2 = {};
-    skipped14 = 0;
-    t2.input = padInput(input2, inputSize8);
-    t2.res = model16 == null ? void 0 : model16.execute(t2.input);
-    cache6.last = now();
-    const res = await t2.res.array();
-    cache6.bodies = t2.res.shape[2] === 17 ? parseSinglePose(res, config3, input2) : parseMultiPose(res, config3, input2);
-    for (const body4 of cache6.bodies) {
-      rescaleBody(body4, [input2.shape[2] || 1, input2.shape[1] || 1]);
-      jitter(body4.keypoints);
-    }
-    Object.keys(t2).forEach((tensor2) => dispose(t2[tensor2]));
-    resolve(cache6.bodies);
-  });
-}
-
-// src/object/nanodet.ts
-var model17;
-var last10 = [];
-var lastTime15 = 0;
-var skipped15 = Number.MAX_SAFE_INTEGER;
-var inputSize9 = 0;
-var scaleBox = 2.5;
-async function load17(config3) {
-  if (!model17 || env2.initial) {
-    model17 = await loadModel(config3.object.modelPath);
-    const inputs = (model17 == null ? void 0 : model17["executor"]) ? Object.values(model17.modelSignature["inputs"]) : void 0;
-    inputSize9 = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 416;
-  } else if (config3.debug)
-    log("cached model:", model17["modelUrl"]);
-  return model17;
-}
-async function process4(res, outputShape, config3) {
-  let id = 0;
-  let results = [];
-  const size2 = inputSize9;
-  for (const strideSize of [1, 2, 4]) {
-    const baseSize = strideSize * 13;
-    const scoresT = squeeze(res.find((a) => a.shape[1] === baseSize ** 2 && (a.shape[2] || 0) === labels.length));
-    const scores = await scoresT.array();
-    const featuresT = squeeze(res.find((a) => a.shape[1] === baseSize ** 2 && (a.shape[2] || 0) < labels.length));
-    const boxesMaxT = featuresT.reshape([-1, 4, featuresT.shape[1] / 4]);
-    const boxIdxT = boxesMaxT.argMax(2);
-    const boxIdx = await boxIdxT.array();
-    for (let i2 = 0; i2 < scoresT.shape[0]; i2++) {
-      for (let j = 0; j < scoresT.shape[1]; j++) {
-        const score = scores[i2][j];
-        if (score > (config3.object.minConfidence || 0) && j !== 61) {
-          const cx = (0.5 + Math.trunc(i2 % baseSize)) / baseSize;
-          const cy = (0.5 + Math.trunc(i2 / baseSize)) / baseSize;
-          const boxOffset = boxIdx[i2].map((a) => a * (baseSize / strideSize / size2));
-          const [x, y] = [
-            cx - scaleBox / strideSize * boxOffset[0],
-            cy - scaleBox / strideSize * boxOffset[1]
-          ];
-          const [w, h] = [
-            cx + scaleBox / strideSize * boxOffset[2] - x,
-            cy + scaleBox / strideSize * boxOffset[3] - y
-          ];
-          let boxRaw = [x, y, w, h];
-          boxRaw = boxRaw.map((a) => Math.max(0, Math.min(a, 1)));
-          const box = [
-            boxRaw[0] * outputShape[0],
-            boxRaw[1] * outputShape[1],
-            boxRaw[2] * outputShape[0],
-            boxRaw[3] * outputShape[1]
-          ];
-          const result = {
-            id: id++,
-            score: Math.round(100 * score) / 100,
-            class: j + 1,
-            label: labels[j].label,
-            box: box.map((a) => Math.trunc(a)),
-            boxRaw
-          };
-          results.push(result);
-        }
-      }
-    }
-    dispose([scoresT, featuresT, boxesMaxT, boxIdxT]);
-  }
-  const nmsBoxes = results.map((a) => [a.boxRaw[1], a.boxRaw[0], a.boxRaw[3], a.boxRaw[2]]);
-  const nmsScores = results.map((a) => a.score);
-  let nmsIdx = [];
-  if (nmsBoxes && nmsBoxes.length > 0) {
-    const nms = await image.nonMaxSuppressionAsync(nmsBoxes, nmsScores, config3.object.maxDetected, config3.object.iouThreshold, config3.object.minConfidence);
-    nmsIdx = await nms.data();
-    dispose(nms);
-  }
-  results = results.filter((_val, idx) => nmsIdx.includes(idx)).sort((a, b) => b.score - a.score);
-  return results;
-}
-async function predict17(image2, config3) {
-  if (!(model17 == null ? void 0 : model17["executor"]))
-    return [];
-  const skipTime = (config3.object.skipTime || 0) > now() - lastTime15;
-  const skipFrame = skipped15 < (config3.object.skipFrames || 0);
-  if (config3.skipAllowed && skipTime && skipFrame && last10.length > 0) {
-    skipped15++;
-    return last10;
-  }
-  skipped15 = 0;
-  if (!env2.kernels.includes("mod") || !env2.kernels.includes("sparsetodense"))
-    return last10;
-  return new Promise(async (resolve) => {
-    const outputSize2 = [image2.shape[2] || 0, image2.shape[1] || 0];
-    const resizeT = image.resizeBilinear(image2, [inputSize9, inputSize9], false);
-    const normT = div(resizeT, constants.tf255);
-    const transposeT = transpose(normT, [0, 3, 1, 2]);
-    let objectT;
-    if (config3.object.enabled)
-      objectT = model17.execute(transposeT);
-    lastTime15 = now();
-    const obj = await process4(objectT, outputSize2, config3);
-    last10 = obj;
-    dispose([resizeT, normT, transposeT, ...objectT]);
-    resolve(obj);
-  });
-}
-
-// src/body/posenetutils.ts
-var partNames = [
-  "nose",
-  "leftEye",
-  "rightEye",
-  "leftEar",
-  "rightEar",
-  "leftShoulder",
-  "rightShoulder",
-  "leftElbow",
-  "rightElbow",
-  "leftWrist",
-  "rightWrist",
-  "leftHip",
-  "rightHip",
-  "leftKnee",
-  "rightKnee",
-  "leftAnkle",
-  "rightAnkle"
-];
-var count2 = partNames.length;
-var partIds = partNames.reduce((result, jointName, i2) => {
-  result[jointName] = i2;
-  return result;
-}, {});
-var connectedPartNames = [
-  ["leftHip", "leftShoulder"],
-  ["leftElbow", "leftShoulder"],
-  ["leftElbow", "leftWrist"],
-  ["leftHip", "leftKnee"],
-  ["leftKnee", "leftAnkle"],
-  ["rightHip", "rightShoulder"],
-  ["rightElbow", "rightShoulder"],
-  ["rightElbow", "rightWrist"],
-  ["rightHip", "rightKnee"],
-  ["rightKnee", "rightAnkle"],
-  ["leftShoulder", "rightShoulder"],
-  ["leftHip", "rightHip"]
-];
-var connectedPartIndices = connectedPartNames.map(([jointNameA, jointNameB]) => [partIds[jointNameA], partIds[jointNameB]]);
-var poseChain = [
-  ["nose", "leftEye"],
-  ["leftEye", "leftEar"],
-  ["nose", "rightEye"],
-  ["rightEye", "rightEar"],
-  ["nose", "leftShoulder"],
-  ["leftShoulder", "leftElbow"],
-  ["leftElbow", "leftWrist"],
-  ["leftShoulder", "leftHip"],
-  ["leftHip", "leftKnee"],
-  ["leftKnee", "leftAnkle"],
-  ["nose", "rightShoulder"],
-  ["rightShoulder", "rightElbow"],
-  ["rightElbow", "rightWrist"],
-  ["rightShoulder", "rightHip"],
-  ["rightHip", "rightKnee"],
-  ["rightKnee", "rightAnkle"]
-];
-function getBoundingBox(keypoints) {
-  const coord = keypoints.reduce(({ maxX, maxY, minX, minY }, { position: { x, y } }) => ({
-    maxX: Math.max(maxX, x),
-    maxY: Math.max(maxY, y),
-    minX: Math.min(minX, x),
-    minY: Math.min(minY, y)
-  }), {
-    maxX: Number.NEGATIVE_INFINITY,
-    maxY: Number.NEGATIVE_INFINITY,
-    minX: Number.POSITIVE_INFINITY,
-    minY: Number.POSITIVE_INFINITY
-  });
-  return [coord.minX, coord.minY, coord.maxX - coord.minX, coord.maxY - coord.minY];
-}
-function scalePoses(poses, [height, width], [inputResolutionHeight, inputResolutionWidth]) {
-  const scaleY = height / inputResolutionHeight;
-  const scaleX = width / inputResolutionWidth;
-  const scalePose = (pose, i2) => ({
-    id: i2,
-    score: pose.score,
-    boxRaw: [pose.box[0] / inputResolutionWidth, pose.box[1] / inputResolutionHeight, pose.box[2] / inputResolutionWidth, pose.box[3] / inputResolutionHeight],
-    box: [Math.trunc(pose.box[0] * scaleX), Math.trunc(pose.box[1] * scaleY), Math.trunc(pose.box[2] * scaleX), Math.trunc(pose.box[3] * scaleY)],
-    keypoints: pose.keypoints.map(({ score, part, position }) => ({
-      score,
-      part,
-      position: [Math.trunc(position.x * scaleX), Math.trunc(position.y * scaleY)],
-      positionRaw: [position.x / inputResolutionHeight, position.y / inputResolutionHeight]
-    })),
-    annotations: {}
-  });
-  const scaledPoses = poses.map((pose, i2) => scalePose(pose, i2));
-  return scaledPoses;
-}
-var MaxHeap = class {
-  constructor(maxSize2, getElementValue) {
-    __publicField(this, "priorityQueue");
-    __publicField(this, "numberOfElements");
-    __publicField(this, "getElementValue");
-    this.priorityQueue = new Array(maxSize2);
-    this.numberOfElements = -1;
-    this.getElementValue = getElementValue;
-  }
-  enqueue(x) {
-    this.priorityQueue[++this.numberOfElements] = x;
-    this.swim(this.numberOfElements);
-  }
-  dequeue() {
-    const max7 = this.priorityQueue[0];
-    this.exchange(0, this.numberOfElements--);
-    this.sink(0);
-    this.priorityQueue[this.numberOfElements + 1] = null;
-    return max7;
-  }
-  empty() {
-    return this.numberOfElements === -1;
-  }
-  size() {
-    return this.numberOfElements + 1;
-  }
-  all() {
-    return this.priorityQueue.slice(0, this.numberOfElements + 1);
-  }
-  max() {
-    return this.priorityQueue[0];
-  }
-  swim(k) {
-    while (k > 0 && this.less(Math.floor(k / 2), k)) {
-      this.exchange(k, Math.floor(k / 2));
-      k = Math.floor(k / 2);
-    }
-  }
-  sink(k) {
-    while (2 * k <= this.numberOfElements) {
-      let j = 2 * k;
-      if (j < this.numberOfElements && this.less(j, j + 1))
-        j++;
-      if (!this.less(k, j))
-        break;
-      this.exchange(k, j);
-      k = j;
-    }
-  }
-  getValueAt(i2) {
-    return this.getElementValue(this.priorityQueue[i2]);
-  }
-  less(i2, j) {
-    return this.getValueAt(i2) < this.getValueAt(j);
-  }
-  exchange(i2, j) {
-    const t2 = this.priorityQueue[i2];
-    this.priorityQueue[i2] = this.priorityQueue[j];
-    this.priorityQueue[j] = t2;
-  }
-};
-function getOffsetPoint(y, x, keypoint, offsets) {
-  return {
-    y: offsets.get(y, x, keypoint),
-    x: offsets.get(y, x, keypoint + count2)
-  };
-}
-function getImageCoords(part, outputStride2, offsets) {
-  const { heatmapY, heatmapX, id: keypoint } = part;
-  const { y, x } = getOffsetPoint(heatmapY, heatmapX, keypoint, offsets);
-  return {
-    x: part.heatmapX * outputStride2 + x,
-    y: part.heatmapY * outputStride2 + y
-  };
-}
-function clamp2(a, min7, max7) {
-  if (a < min7)
-    return min7;
-  if (a > max7)
-    return max7;
-  return a;
-}
-function squaredDistance(y1, x1, y2, x2) {
-  const dy = y2 - y1;
-  const dx = x2 - x1;
-  return dy * dy + dx * dx;
-}
-function addVectors(a, b) {
-  return { x: a.x + b.x, y: a.y + b.y };
-}
-
-// src/body/posenet.ts
-var model18;
-var poseNetOutputs = ["MobilenetV1/offset_2/BiasAdd", "MobilenetV1/heatmap_2/BiasAdd", "MobilenetV1/displacement_fwd_2/BiasAdd", "MobilenetV1/displacement_bwd_2/BiasAdd"];
-var localMaximumRadius = 1;
-var outputStride = 16;
-var squaredNmsRadius = 50 ** 2;
-function traverse(edgeId, sourceKeypoint, targetId, scores, offsets, displacements, offsetRefineStep = 2) {
-  const getDisplacement = (point2) => ({
-    y: displacements.get(point2.y, point2.x, edgeId),
-    x: displacements.get(point2.y, point2.x, displacements.shape[2] / 2 + edgeId)
-  });
-  const getStridedIndexNearPoint = (point2, height2, width2) => ({
-    y: clamp2(Math.round(point2.y / outputStride), 0, height2 - 1),
-    x: clamp2(Math.round(point2.x / outputStride), 0, width2 - 1)
-  });
-  const [height, width] = scores.shape;
-  const sourceKeypointIndices = getStridedIndexNearPoint(sourceKeypoint.position, height, width);
-  const displacement = getDisplacement(sourceKeypointIndices);
-  const displacedPoint = addVectors(sourceKeypoint.position, displacement);
-  let targetKeypoint = displacedPoint;
-  for (let i2 = 0; i2 < offsetRefineStep; i2++) {
-    const targetKeypointIndices = getStridedIndexNearPoint(targetKeypoint, height, width);
-    const offsetPoint = getOffsetPoint(targetKeypointIndices.y, targetKeypointIndices.x, targetId, offsets);
-    targetKeypoint = addVectors(
-      { x: targetKeypointIndices.x * outputStride, y: targetKeypointIndices.y * outputStride },
-      { x: offsetPoint.x, y: offsetPoint.y }
-    );
-  }
-  const targetKeyPointIndices = getStridedIndexNearPoint(targetKeypoint, height, width);
-  const score = scores.get(targetKeyPointIndices.y, targetKeyPointIndices.x, targetId);
-  return { position: targetKeypoint, part: partNames[targetId], score };
-}
-function decodePose(root, scores, offsets, displacementsFwd, displacementsBwd) {
-  const tuples = poseChain.map(([parentJoinName, childJoinName]) => [partIds[parentJoinName], partIds[childJoinName]]);
-  const edgesFwd = tuples.map(([, childJointId]) => childJointId);
-  const edgesBwd = tuples.map(([parentJointId]) => parentJointId);
-  const numParts = scores.shape[2];
-  const numEdges = edgesFwd.length;
-  const keypoints = new Array(numParts);
-  const rootPoint = getImageCoords(root.part, outputStride, offsets);
-  keypoints[root.part.id] = {
-    score: root.score,
-    part: partNames[root.part.id],
-    position: rootPoint
-  };
-  for (let edge = numEdges - 1; edge >= 0; --edge) {
-    const sourceId = edgesFwd[edge];
-    const targetId = edgesBwd[edge];
-    if (keypoints[sourceId] && !keypoints[targetId]) {
-      keypoints[targetId] = traverse(edge, keypoints[sourceId], targetId, scores, offsets, displacementsBwd);
-    }
-  }
-  for (let edge = 0; edge < numEdges; ++edge) {
-    const sourceId = edgesBwd[edge];
-    const targetId = edgesFwd[edge];
-    if (keypoints[sourceId] && !keypoints[targetId]) {
-      keypoints[targetId] = traverse(edge, keypoints[sourceId], targetId, scores, offsets, displacementsFwd);
-    }
-  }
-  return keypoints;
-}
-function scoreIsMaximumInLocalWindow(keypointId, score, heatmapY, heatmapX, scores) {
-  const [height, width] = scores.shape;
-  let localMaximum = true;
-  const yStart = Math.max(heatmapY - localMaximumRadius, 0);
-  const yEnd = Math.min(heatmapY + localMaximumRadius + 1, height);
-  for (let yCurrent = yStart; yCurrent < yEnd; ++yCurrent) {
-    const xStart = Math.max(heatmapX - localMaximumRadius, 0);
-    const xEnd = Math.min(heatmapX + localMaximumRadius + 1, width);
-    for (let xCurrent = xStart; xCurrent < xEnd; ++xCurrent) {
-      if (scores.get(yCurrent, xCurrent, keypointId) > score) {
-        localMaximum = false;
-        break;
-      }
-    }
-    if (!localMaximum)
-      break;
-  }
-  return localMaximum;
-}
-function buildPartWithScoreQueue(minConfidence2, scores) {
-  const [height, width, numKeypoints] = scores.shape;
-  const queue = new MaxHeap(height * width * numKeypoints, ({ score }) => score);
-  for (let heatmapY = 0; heatmapY < height; ++heatmapY) {
-    for (let heatmapX = 0; heatmapX < width; ++heatmapX) {
-      for (let keypointId = 0; keypointId < numKeypoints; ++keypointId) {
-        const score = scores.get(heatmapY, heatmapX, keypointId);
-        if (score < minConfidence2)
-          continue;
-        if (scoreIsMaximumInLocalWindow(keypointId, score, heatmapY, heatmapX, scores))
-          queue.enqueue({ score, part: { heatmapY, heatmapX, id: keypointId } });
-      }
-    }
-  }
-  return queue;
-}
-function withinRadius(poses, { x, y }, keypointId) {
-  return poses.some(({ keypoints }) => {
-    var _a;
-    const correspondingKeypoint = (_a = keypoints[keypointId]) == null ? void 0 : _a.position;
-    if (!correspondingKeypoint)
-      return false;
-    return squaredDistance(y, x, correspondingKeypoint.y, correspondingKeypoint.x) <= squaredNmsRadius;
-  });
-}
-function getInstanceScore(existingPoses, keypoints) {
-  const notOverlappedKeypointScores = keypoints.reduce((result, { position, score }, keypointId) => {
-    if (!withinRadius(existingPoses, position, keypointId))
-      result += score;
-    return result;
-  }, 0);
-  return notOverlappedKeypointScores / keypoints.length;
-}
-function decode(offsets, scores, displacementsFwd, displacementsBwd, maxDetected, minConfidence2) {
-  const poses = [];
-  const queue = buildPartWithScoreQueue(minConfidence2, scores);
-  while (poses.length < maxDetected && !queue.empty()) {
-    const root = queue.dequeue();
-    const rootImageCoords = getImageCoords(root.part, outputStride, offsets);
-    if (withinRadius(poses, rootImageCoords, root.part.id))
-      continue;
-    let keypoints = decodePose(root, scores, offsets, displacementsFwd, displacementsBwd);
-    keypoints = keypoints.filter((a) => a.score > minConfidence2);
-    const score = getInstanceScore(poses, keypoints);
-    const box = getBoundingBox(keypoints);
-    if (score > minConfidence2)
-      poses.push({ keypoints, box, score: Math.round(100 * score) / 100 });
-  }
-  return poses;
-}
-async function predict18(input2, config3) {
-  if (!(model18 == null ? void 0 : model18["executor"]))
-    return [];
-  const res = tidy(() => {
-    if (!model18.inputs[0].shape)
-      return [];
-    const resized = image.resizeBilinear(input2, [model18.inputs[0].shape[2], model18.inputs[0].shape[1]]);
-    const normalized = sub(div(cast(resized, "float32"), 127.5), 1);
-    const results = model18.execute(normalized, poseNetOutputs);
-    const results3d = results.map((y) => squeeze(y, [0]));
-    results3d[1] = sigmoid(results3d[1]);
-    return results3d;
-  });
-  const buffers = await Promise.all(res.map((tensor2) => tensor2.buffer()));
-  for (const t2 of res)
-    dispose(t2);
-  const decoded = decode(buffers[0], buffers[1], buffers[2], buffers[3], config3.body.maxDetected, config3.body.minConfidence);
-  if (!model18.inputs[0].shape)
-    return [];
-  const scaled = scalePoses(decoded, [input2.shape[1], input2.shape[2]], [model18.inputs[0].shape[2], model18.inputs[0].shape[1]]);
-  return scaled;
-}
-async function load18(config3) {
-  if (!model18 || env2.initial)
-    model18 = await loadModel(config3.body.modelPath);
-  else if (config3.debug)
-    log("cached model:", model18["modelUrl"]);
-  return model18;
-}
-
-// src/segmentation/segmentation.ts
-var model19;
-var busy = false;
-async function load19(config3) {
-  if (!model19 || env2.initial)
-    model19 = await loadModel(config3.segmentation.modelPath);
-  else if (config3.debug)
-    log("cached model:", model19["modelUrl"]);
-  return model19;
-}
-async function process5(input2, background, config3) {
-  var _a, _b;
-  if (busy)
-    return { data: [], canvas: null, alpha: null };
-  busy = true;
-  if (!model19)
-    await load19(config3);
-  const inputImage = await process2(input2, config3);
-  const width = ((_a = inputImage.tensor) == null ? void 0 : _a.shape[2]) || 0;
-  const height = ((_b = inputImage.tensor) == null ? void 0 : _b.shape[1]) || 0;
-  if (!inputImage.tensor)
-    return { data: [], canvas: null, alpha: null };
-  const t2 = {};
-  t2.resize = image.resizeBilinear(inputImage.tensor, [model19.inputs[0].shape ? model19.inputs[0].shape[1] : 0, model19.inputs[0].shape ? model19.inputs[0].shape[2] : 0], false);
-  dispose(inputImage.tensor);
-  t2.norm = div(t2.resize, constants.tf255);
-  t2.res = model19.execute(t2.norm);
-  t2.squeeze = squeeze(t2.res, 0);
-  if (t2.squeeze.shape[2] === 2) {
-    t2.softmax = softmax(t2.squeeze);
-    [t2.bg, t2.fg] = unstack(t2.softmax, 2);
-    t2.expand = expandDims(t2.fg, 2);
-    t2.pad = expandDims(t2.expand, 0);
-    t2.crop = image.cropAndResize(t2.pad, [[0, 0, 0.5, 0.5]], [0], [width, height]);
-    t2.data = squeeze(t2.crop, 0);
-  } else {
-    t2.data = image.resizeBilinear(t2.squeeze, [height, width]);
-  }
-  const data = Array.from(await t2.data.data());
-  if (env2.node && !env2.Canvas && typeof ImageData === "undefined") {
-    if (config3.debug)
-      log("canvas support missing");
-    Object.keys(t2).forEach((tensor2) => dispose(t2[tensor2]));
-    return { data, canvas: null, alpha: null };
-  }
-  const alphaCanvas = canvas(width, height);
-  if (browser_exports)
-    await browser_exports.toPixels(t2.data, alphaCanvas);
-  const alphaCtx = alphaCanvas.getContext("2d");
-  if (config3.segmentation.blur && config3.segmentation.blur > 0)
-    alphaCtx.filter = `blur(${config3.segmentation.blur}px)`;
-  const alphaData = alphaCtx.getImageData(0, 0, width, height);
-  const compositeCanvas = canvas(width, height);
-  const compositeCtx = compositeCanvas.getContext("2d");
-  if (inputImage.canvas)
-    compositeCtx.drawImage(inputImage.canvas, 0, 0);
-  compositeCtx.globalCompositeOperation = "darken";
-  if (config3.segmentation.blur && config3.segmentation.blur > 0)
-    compositeCtx.filter = `blur(${config3.segmentation.blur}px)`;
-  compositeCtx.drawImage(alphaCanvas, 0, 0);
-  compositeCtx.globalCompositeOperation = "source-over";
-  compositeCtx.filter = "none";
-  const compositeData = compositeCtx.getImageData(0, 0, width, height);
-  for (let i2 = 0; i2 < width * height; i2++)
-    compositeData.data[4 * i2 + 3] = alphaData.data[4 * i2 + 0];
-  compositeCtx.putImageData(compositeData, 0, 0);
-  let mergedCanvas = null;
-  if (background && compositeCanvas) {
-    mergedCanvas = canvas(width, height);
-    const bgImage = await process2(background, config3);
-    dispose(bgImage.tensor);
-    const ctxMerge = mergedCanvas.getContext("2d");
-    ctxMerge.drawImage(bgImage.canvas, 0, 0, mergedCanvas.width, mergedCanvas.height);
-    ctxMerge.drawImage(compositeCanvas, 0, 0);
-  }
-  Object.keys(t2).forEach((tensor2) => dispose(t2[tensor2]));
-  busy = false;
-  return { data, canvas: compositeCanvas, alpha: alphaCanvas };
-}
-
-// src/models.ts
-var Models = class {
-  constructor() {
-    __publicField(this, "ssrnetage", null);
-    __publicField(this, "gear", null);
-    __publicField(this, "blazeposedetect", null);
-    __publicField(this, "blazepose", null);
-    __publicField(this, "centernet", null);
-    __publicField(this, "efficientpose", null);
-    __publicField(this, "mobilefacenet", null);
-    __publicField(this, "insightface", null);
-    __publicField(this, "emotion", null);
-    __publicField(this, "facedetect", null);
-    __publicField(this, "faceiris", null);
-    __publicField(this, "facemesh", null);
-    __publicField(this, "faceres", null);
-    __publicField(this, "ssrnetgender", null);
-    __publicField(this, "handpose", null);
-    __publicField(this, "handskeleton", null);
-    __publicField(this, "handtrack", null);
-    __publicField(this, "liveness", null);
-    __publicField(this, "movenet", null);
-    __publicField(this, "nanodet", null);
-    __publicField(this, "posenet", null);
-    __publicField(this, "segmentation", null);
-    __publicField(this, "antispoof", null);
-  }
-};
-var instance;
-var getModelStats = (currentInstance) => {
-  if (currentInstance)
-    instance = currentInstance;
-  if (!instance)
-    log("instance not registred");
-  let totalSizeFromManifest = 0;
-  let totalSizeWeights = 0;
-  let totalSizeLoading = 0;
-  for (const m of Object.values(modelStats)) {
-    totalSizeFromManifest += m.sizeFromManifest;
-    totalSizeWeights += m.sizeLoadedWeights;
-    totalSizeLoading += m.sizeDesired;
-  }
-  const percentageLoaded = totalSizeLoading > 0 ? totalSizeWeights / totalSizeLoading : 0;
-  return {
-    numLoadedModels: Object.values(modelStats).length,
-    numEnabledModels: void 0,
-    numDefinedModels: Object.keys(instance.models).length,
-    percentageLoaded,
-    totalSizeFromManifest,
-    totalSizeWeights,
-    totalSizeLoading,
-    totalSizeEnabled: void 0,
-    modelStats: Object.values(modelStats)
-  };
-};
-function reset2(currentInstance) {
-  if (currentInstance)
-    instance = currentInstance;
-  for (const model20 of Object.keys(instance.models))
-    instance.models[model20] = null;
-}
-async function load20(currentInstance) {
-  var _a, _b, _c, _d, _e, _f, _g, _h, _i, _j, _k, _l, _m, _n, _o, _p, _q, _r, _s, _t, _u, _v, _w, _x, _y, _z;
-  if (currentInstance)
-    instance = currentInstance;
-  if (!instance)
-    log("instance not registred");
-  if (env2.initial)
-    reset2(instance);
-  if (instance.config.hand.enabled) {
-    if (!instance.models.handpose && ((_b = (_a = instance.config.hand.detector) == null ? void 0 : _a.modelPath) == null ? void 0 : _b.includes("handdetect"))) {
-      [instance.models.handpose, instance.models.handskeleton] = await load14(instance.config);
-    }
-    if (!instance.models.handskeleton && instance.config.hand.landmarks && ((_d = (_c = instance.config.hand.detector) == null ? void 0 : _c.modelPath) == null ? void 0 : _d.includes("handdetect"))) {
-      [instance.models.handpose, instance.models.handskeleton] = await load14(instance.config);
-    }
-  }
-  if (instance.config.body.enabled && !instance.models.blazepose && ((_e = instance.config.body.modelPath) == null ? void 0 : _e.includes("blazepose")))
-    instance.models.blazepose = loadPose(instance.config);
-  if (instance.config.body.enabled && !instance.models.blazeposedetect && instance.config.body["detector"] && instance.config.body["detector"].modelPath)
-    instance.models.blazeposedetect = loadDetect(instance.config);
-  if (instance.config.body.enabled && !instance.models.efficientpose && ((_f = instance.config.body.modelPath) == null ? void 0 : _f.includes("efficientpose")))
-    instance.models.efficientpose = load7(instance.config);
-  if (instance.config.body.enabled && !instance.models.movenet && ((_g = instance.config.body.modelPath) == null ? void 0 : _g.includes("movenet")))
-    instance.models.movenet = load16(instance.config);
-  if (instance.config.body.enabled && !instance.models.posenet && ((_h = instance.config.body.modelPath) == null ? void 0 : _h.includes("posenet")))
-    instance.models.posenet = load18(instance.config);
-  if (instance.config.face.enabled && !instance.models.facedetect)
-    instance.models.facedetect = load5(instance.config);
-  if (instance.config.face.enabled && ((_i = instance.config.face.antispoof) == null ? void 0 : _i.enabled) && !instance.models.antispoof)
-    instance.models.antispoof = load4(instance.config);
-  if (instance.config.face.enabled && ((_j = instance.config.face.liveness) == null ? void 0 : _j.enabled) && !instance.models.liveness)
-    instance.models.liveness = load15(instance.config);
-  if (instance.config.face.enabled && ((_k = instance.config.face.description) == null ? void 0 : _k.enabled) && !instance.models.faceres)
-    instance.models.faceres = load13(instance.config);
-  if (instance.config.face.enabled && ((_l = instance.config.face.emotion) == null ? void 0 : _l.enabled) && !instance.models.emotion)
-    instance.models.emotion = load8(instance.config);
-  if (instance.config.face.enabled && ((_m = instance.config.face.iris) == null ? void 0 : _m.enabled) && !((_n = instance.config.face.attention) == null ? void 0 : _n.enabled) && !instance.models.faceiris)
-    instance.models.faceiris = load11(instance.config);
-  if (instance.config.face.enabled && ((_o = instance.config.face.mesh) == null ? void 0 : _o.enabled) && !instance.models.facemesh)
-    instance.models.facemesh = load12(instance.config);
-  if (instance.config.face.enabled && ((_p = instance.config.face["gear"]) == null ? void 0 : _p.enabled) && !instance.models.gear)
-    instance.models.gear = load(instance.config);
-  if (instance.config.face.enabled && ((_q = instance.config.face["ssrnet"]) == null ? void 0 : _q.enabled) && !instance.models.ssrnetage)
-    instance.models.ssrnetage = load2(instance.config);
-  if (instance.config.face.enabled && ((_r = instance.config.face["ssrnet"]) == null ? void 0 : _r.enabled) && !instance.models.ssrnetgender)
-    instance.models.ssrnetgender = load3(instance.config);
-  if (instance.config.face.enabled && ((_s = instance.config.face["mobilefacenet"]) == null ? void 0 : _s.enabled) && !instance.models.mobilefacenet)
-    instance.models.mobilefacenet = load9(instance.config);
-  if (instance.config.face.enabled && ((_t = instance.config.face["insightface"]) == null ? void 0 : _t.enabled) && !instance.models.insightface)
-    instance.models.insightface = load10(instance.config);
-  if (instance.config.hand.enabled && !instance.models.handtrack && ((_v = (_u = instance.config.hand.detector) == null ? void 0 : _u.modelPath) == null ? void 0 : _v.includes("handtrack")))
-    instance.models.handtrack = loadDetect2(instance.config);
-  if (instance.config.hand.enabled && instance.config.hand.landmarks && !instance.models.handskeleton && ((_x = (_w = instance.config.hand.detector) == null ? void 0 : _w.modelPath) == null ? void 0 : _x.includes("handtrack")))
-    instance.models.handskeleton = loadSkeleton(instance.config);
-  if (instance.config.object.enabled && !instance.models.centernet && ((_y = instance.config.object.modelPath) == null ? void 0 : _y.includes("centernet")))
-    instance.models.centernet = load6(instance.config);
-  if (instance.config.object.enabled && !instance.models.nanodet && ((_z = instance.config.object.modelPath) == null ? void 0 : _z.includes("nanodet")))
-    instance.models.nanodet = load17(instance.config);
-  if (instance.config.segmentation.enabled && !instance.models.segmentation)
-    instance.models.segmentation = load19(instance.config);
-  for await (const model20 of Object.keys(instance.models)) {
-    if (instance.models[model20] && typeof instance.models[model20] !== "undefined") {
-      instance.models[model20] = await instance.models[model20];
-    }
-  }
-}
-function validateModel(currentInstance, model20, name) {
-  var _a, _b;
-  if (!model20)
-    return null;
-  if (currentInstance)
-    instance = currentInstance;
-  if (!instance)
-    log("instance not registred");
-  if (!((_a = instance == null ? void 0 : instance.config) == null ? void 0 : _a.validateModels))
-    return null;
-  const simpleOps = ["const", "placeholder", "noop", "pad", "squeeze", "add", "sub", "mul", "div"];
-  const ignoreOps = ["biasadd", "fusedbatchnormv3", "matmul"];
-  const ops = [];
-  const missing = [];
-  const url = model20["modelUrl"];
-  const executor = model20["executor"];
-  if ((_b = executor == null ? void 0 : executor.graph) == null ? void 0 : _b.nodes) {
-    for (const kernel of Object.values(executor.graph.nodes)) {
-      const op2 = kernel.op.toLowerCase();
-      if (!ops.includes(op2))
-        ops.push(op2);
-    }
-  } else {
-    if (!executor && instance.config.debug) {
-      log("model not loaded", name);
-    }
-  }
-  for (const op2 of ops) {
-    if (!simpleOps.includes(op2) && !ignoreOps.includes(op2) && !instance.env.kernels.includes(op2) && !instance.env.kernels.includes(op2.replace("_", "")) && !instance.env.kernels.includes(op2.replace("native", "")) && !instance.env.kernels.includes(op2.replace("v2", ""))) {
-      missing.push(op2);
-    }
-  }
-  if (instance.config.debug && missing.length > 0)
-    log("model validation failed:", name, missing);
-  return missing.length > 0 ? { name, missing, ops, url } : null;
-}
-function validate2(currentInstance) {
-  if (currentInstance)
-    instance = currentInstance;
-  if (!instance)
-    log("instance not registred");
-  const missing = [];
-  for (const defined of Object.keys(currentInstance.models)) {
-    const model20 = currentInstance.models[defined];
-    if (!model20)
-      continue;
-    const res = validateModel(currentInstance, model20, defined);
-    if (res)
-      missing.push(res);
-  }
-  return missing;
-}
-
-// src/tfjs/load.ts
-var options2 = {
-  cacheModels: true,
-  cacheSupported: true,
-  verbose: true,
-  debug: false,
-  modelBasePath: ""
-};
-var modelStats = {};
-async function httpHandler(url, init3) {
-  if (options2.debug)
-    log("load model fetch:", url, init3);
-  return fetch(url, init3);
-}
-function setModelLoadOptions(config3) {
-  options2.cacheModels = config3.cacheModels;
-  options2.verbose = config3.debug;
-  options2.modelBasePath = config3.modelBasePath;
-}
-async function loadModel(modelPath) {
-  var _a, _b, _c, _d;
-  let modelUrl = join(options2.modelBasePath, modelPath || "");
-  if (!modelUrl.toLowerCase().endsWith(".json"))
-    modelUrl += ".json";
-  const modelPathSegments = modelUrl.includes("/") ? modelUrl.split("/") : modelUrl.split("\\");
-  const shortModelName = modelPathSegments[modelPathSegments.length - 1].replace(".json", "");
-  const cachedModelName = "indexeddb://" + shortModelName;
-  modelStats[shortModelName] = {
-    name: shortModelName,
-    sizeFromManifest: 0,
-    sizeLoadedWeights: 0,
-    sizeDesired: models_exports[shortModelName],
-    inCache: false
-  };
-  options2.cacheSupported = typeof indexedDB !== "undefined";
-  let cachedModels = {};
-  try {
-    cachedModels = options2.cacheSupported && options2.cacheModels ? await io_exports.listModels() : {};
-  } catch (e2) {
-    options2.cacheSupported = false;
-  }
-  modelStats[shortModelName].inCache = options2.cacheSupported && options2.cacheModels && Object.keys(cachedModels).includes(cachedModelName);
-  const tfLoadOptions = typeof fetch === "undefined" ? {} : { fetchFunc: (url, init3) => httpHandler(url, init3) };
-  let model20 = new GraphModel(modelStats[shortModelName].inCache ? cachedModelName : modelUrl, tfLoadOptions);
-  let loaded = false;
-  try {
-    model20.findIOHandler();
-    if (options2.debug)
-      log("model load handler:", model20["handler"]);
-  } catch (err) {
-    log("error finding model i/o handler:", modelUrl, err);
-  }
-  try {
-    const artifacts = await ((_a = model20.handler) == null ? void 0 : _a.load()) || null;
-    modelStats[shortModelName].sizeFromManifest = ((_b = artifacts == null ? void 0 : artifacts.weightData) == null ? void 0 : _b.byteLength) || 0;
-    if (artifacts)
-      model20.loadSync(artifacts);
-    else
-      model20 = await loadGraphModel(modelStats[shortModelName].inCache ? cachedModelName : modelUrl, tfLoadOptions);
-    modelStats[shortModelName].sizeLoadedWeights = ((_d = (_c = model20.artifacts) == null ? void 0 : _c.weightData) == null ? void 0 : _d.byteLength) || 0;
-    if (options2.verbose)
-      log("load:", { model: shortModelName, url: model20["modelUrl"], bytes: modelStats[shortModelName].sizeLoadedWeights });
-    loaded = true;
-  } catch (err) {
-    log("error loading model:", modelUrl, err);
-  }
-  if (loaded && options2.cacheModels && options2.cacheSupported && !modelStats[shortModelName].inCache) {
-    try {
-      const saveResult = await model20.save(cachedModelName);
-      if (options2.debug)
-        log("model saved:", cachedModelName, saveResult);
-    } catch (err) {
-      log("error saving model:", modelUrl, err);
-    }
-  }
-  validateModel(null, model20, `${modelPath || ""}`);
-  return model20;
-}
-
-// package.json
-var version5 = "2.11.0";
-
 // src/draw/draw.ts
 var draw_exports = {};
 __export(draw_exports, {
@@ -85517,10 +85614,10 @@ function curves(ctx, points, localOptions) {
     return;
   }
   ctx.moveTo(points[0][0], points[0][1]);
-  for (let i2 = 0; i2 < points.length - 2; i2++) {
-    const xc = (points[i2][0] + points[i2 + 1][0]) / 2;
-    const yc = (points[i2][1] + points[i2 + 1][1]) / 2;
-    ctx.quadraticCurveTo(points[i2][0], points[i2][1], xc, yc);
+  for (let i = 0; i < points.length - 2; i++) {
+    const xc = (points[i][0] + points[i + 1][0]) / 2;
+    const yc = (points[i][1] + points[i + 1][1]) / 2;
+    ctx.quadraticCurveTo(points[i][0], points[i][1], xc, yc);
   }
   ctx.quadraticCurveTo(points[points.length - 2][0], points[points.length - 2][1], points[points.length - 1][0], points[points.length - 1][1]);
   ctx.stroke();
@@ -85608,15 +85705,15 @@ function drawLabels(f, ctx) {
     if (labels2.length === 0)
       labels2.push("face");
     ctx.fillStyle = opt.color;
-    for (let i2 = labels2.length - 1; i2 >= 0; i2--) {
+    for (let i = labels2.length - 1; i >= 0; i--) {
       const x = Math.max(f.box[0], 0);
-      const y = i2 * opt.lineHeight + f.box[1];
+      const y = i * opt.lineHeight + f.box[1];
       if (opt.shadowColor && opt.shadowColor !== "") {
         ctx.fillStyle = opt.shadowColor;
-        ctx.fillText(labels2[i2], x + 5, y + 16);
+        ctx.fillText(labels2[i], x + 5, y + 16);
       }
       ctx.fillStyle = opt.labelColor;
-      ctx.fillText(labels2[i2], x + 4, y + 15);
+      ctx.fillText(labels2[i], x + 4, y + 15);
     }
   }
 }
@@ -85691,8 +85788,8 @@ function drawGazeArrows(f, ctx) {
 function drawFacePolygons(f, ctx) {
   if (opt.drawPolygons && f.mesh.length >= 468) {
     ctx.lineWidth = 1;
-    for (let i2 = 0; i2 < TRI468.length / 3; i2++) {
-      const points = [TRI468[i2 * 3 + 0], TRI468[i2 * 3 + 1], TRI468[i2 * 3 + 2]].map((index2) => f.mesh[index2]);
+    for (let i = 0; i < TRI468.length / 3; i++) {
+      const points = [TRI468[i * 3 + 0], TRI468[i * 3 + 1], TRI468[i * 3 + 2]].map((index2) => f.mesh[index2]);
       lines(ctx, points, opt);
     }
     drawIrisElipse(f, ctx);
@@ -85700,15 +85797,15 @@ function drawFacePolygons(f, ctx) {
 }
 function drawFacePoints(f, ctx) {
   if (opt.drawPoints && f.mesh.length >= 468) {
-    for (let i2 = 0; i2 < f.mesh.length; i2++) {
-      point(ctx, f.mesh[i2][0], f.mesh[i2][1], f.mesh[i2][2], opt);
+    for (let i = 0; i < f.mesh.length; i++) {
+      point(ctx, f.mesh[i][0], f.mesh[i][1], f.mesh[i][2], opt);
       if (opt.drawAttention) {
-        if (LANDMARKS_REFINEMENT_LIPS_CONFIG.includes(i2))
-          point(ctx, f.mesh[i2][0], f.mesh[i2][1], f.mesh[i2][2] + 127, opt);
-        if (LANDMARKS_REFINEMENT_LEFT_EYE_CONFIG.includes(i2))
-          point(ctx, f.mesh[i2][0], f.mesh[i2][1], f.mesh[i2][2] - 127, opt);
-        if (LANDMARKS_REFINEMENT_RIGHT_EYE_CONFIG.includes(i2))
-          point(ctx, f.mesh[i2][0], f.mesh[i2][1], f.mesh[i2][2] - 127, opt);
+        if (LANDMARKS_REFINEMENT_LIPS_CONFIG.includes(i))
+          point(ctx, f.mesh[i][0], f.mesh[i][1], f.mesh[i][2] + 127, opt);
+        if (LANDMARKS_REFINEMENT_LEFT_EYE_CONFIG.includes(i))
+          point(ctx, f.mesh[i][0], f.mesh[i][1], f.mesh[i][2] - 127, opt);
+        if (LANDMARKS_REFINEMENT_RIGHT_EYE_CONFIG.includes(i))
+          point(ctx, f.mesh[i][0], f.mesh[i][1], f.mesh[i][2] - 127, opt);
       }
     }
   }
@@ -85749,41 +85846,41 @@ function body(inCanvas2, result, drawOptions) {
   if (!ctx)
     return;
   ctx.lineJoin = "round";
-  for (let i2 = 0; i2 < result.length; i2++) {
+  for (let i = 0; i < result.length; i++) {
     ctx.strokeStyle = localOptions.color;
     ctx.fillStyle = localOptions.color;
     ctx.lineWidth = localOptions.lineWidth;
     ctx.font = localOptions.font;
-    if (localOptions.drawBoxes && result[i2].box && result[i2].box.length === 4) {
-      rect(ctx, result[i2].box[0], result[i2].box[1], result[i2].box[2], result[i2].box[3], localOptions);
+    if (localOptions.drawBoxes && result[i].box && result[i].box.length === 4) {
+      rect(ctx, result[i].box[0], result[i].box[1], result[i].box[2], result[i].box[3], localOptions);
       if (localOptions.drawLabels) {
         if (localOptions.shadowColor && localOptions.shadowColor !== "") {
           ctx.fillStyle = localOptions.shadowColor;
-          ctx.fillText(`body ${100 * result[i2].score}%`, result[i2].box[0] + 3, 1 + result[i2].box[1] + localOptions.lineHeight, result[i2].box[2]);
+          ctx.fillText(`body ${100 * result[i].score}%`, result[i].box[0] + 3, 1 + result[i].box[1] + localOptions.lineHeight, result[i].box[2]);
         }
         ctx.fillStyle = localOptions.labelColor;
-        ctx.fillText(`body ${100 * result[i2].score}%`, result[i2].box[0] + 2, 0 + result[i2].box[1] + localOptions.lineHeight, result[i2].box[2]);
+        ctx.fillText(`body ${100 * result[i].score}%`, result[i].box[0] + 2, 0 + result[i].box[1] + localOptions.lineHeight, result[i].box[2]);
       }
     }
-    if (localOptions.drawPoints && result[i2].keypoints) {
-      for (let pt = 0; pt < result[i2].keypoints.length; pt++) {
-        if (!result[i2].keypoints[pt].score || result[i2].keypoints[pt].score === 0)
+    if (localOptions.drawPoints && result[i].keypoints) {
+      for (let pt = 0; pt < result[i].keypoints.length; pt++) {
+        if (!result[i].keypoints[pt].score || result[i].keypoints[pt].score === 0)
           continue;
-        ctx.fillStyle = colorDepth(result[i2].keypoints[pt].position[2], localOptions);
-        point(ctx, result[i2].keypoints[pt].position[0], result[i2].keypoints[pt].position[1], 0, localOptions);
+        ctx.fillStyle = colorDepth(result[i].keypoints[pt].position[2], localOptions);
+        point(ctx, result[i].keypoints[pt].position[0], result[i].keypoints[pt].position[1], 0, localOptions);
       }
     }
-    if (localOptions.drawLabels && result[i2].keypoints) {
+    if (localOptions.drawLabels && result[i].keypoints) {
       ctx.font = localOptions.font;
-      for (const pt of result[i2].keypoints) {
+      for (const pt of result[i].keypoints) {
         if (!pt.score || pt.score === 0)
           continue;
         ctx.fillStyle = colorDepth(pt.position[2], localOptions);
         ctx.fillText(`${pt.part} ${Math.trunc(100 * pt.score)}%`, pt.position[0] + 4, pt.position[1] + 4);
       }
     }
-    if (localOptions.drawPolygons && result[i2].keypoints && result[i2].annotations) {
-      for (const part of Object.values(result[i2].annotations)) {
+    if (localOptions.drawPolygons && result[i].keypoints && result[i].annotations) {
+      for (const part of Object.values(result[i].annotations)) {
         for (const connected4 of part)
           curves(ctx, connected4, localOptions);
       }
@@ -85844,12 +85941,12 @@ function hand(inCanvas2, result, drawOptions) {
       const addHandLine = (part) => {
         if (!part || part.length === 0 || !part[0])
           return;
-        for (let i2 = 0; i2 < part.length; i2++) {
+        for (let i = 0; i < part.length; i++) {
           ctx.beginPath();
-          const z = part[i2][2] || 0;
-          ctx.strokeStyle = colorDepth(i2 * z, localOptions);
-          ctx.moveTo(part[i2 > 0 ? i2 - 1 : 0][0], part[i2 > 0 ? i2 - 1 : 0][1]);
-          ctx.lineTo(part[i2][0], part[i2][1]);
+          const z = part[i][2] || 0;
+          ctx.strokeStyle = colorDepth(i * z, localOptions);
+          ctx.moveTo(part[i > 0 ? i - 1 : 0][0], part[i > 0 ? i - 1 : 0][1]);
+          ctx.lineTo(part[i][0], part[i][1]);
           ctx.stroke();
         }
       };
@@ -85903,7 +86000,7 @@ function gesture(inCanvas2, result, drawOptions) {
       return;
     ctx.font = localOptions.font;
     ctx.fillStyle = localOptions.color;
-    let i2 = 1;
+    let i = 1;
     for (let j = 0; j < result.length; j++) {
       let where2 = [];
       let what = [];
@@ -85913,11 +86010,11 @@ function gesture(inCanvas2, result, drawOptions) {
         const label = `${where2[0]} ${who}: ${what[1]}`;
         if (localOptions.shadowColor && localOptions.shadowColor !== "") {
           ctx.fillStyle = localOptions.shadowColor;
-          ctx.fillText(label, 8, 2 + i2 * localOptions.lineHeight);
+          ctx.fillText(label, 8, 2 + i * localOptions.lineHeight);
         }
         ctx.fillStyle = localOptions.labelColor;
-        ctx.fillText(label, 6, 0 + i2 * localOptions.lineHeight);
-        i2 += 1;
+        ctx.fillText(label, 6, 0 + i * localOptions.lineHeight);
+        i += 1;
       }
     }
   }
@@ -85934,19 +86031,19 @@ function person(inCanvas2, result, drawOptions) {
     return;
   ctx.lineJoin = "round";
   ctx.font = localOptions.font;
-  for (let i2 = 0; i2 < result.length; i2++) {
+  for (let i = 0; i < result.length; i++) {
     if (localOptions.drawBoxes) {
       ctx.strokeStyle = localOptions.color;
       ctx.fillStyle = localOptions.color;
-      rect(ctx, result[i2].box[0], result[i2].box[1], result[i2].box[2], result[i2].box[3], localOptions);
+      rect(ctx, result[i].box[0], result[i].box[1], result[i].box[2], result[i].box[3], localOptions);
       if (localOptions.drawLabels) {
-        const label = `person #${i2}`;
+        const label = `person #${i}`;
         if (localOptions.shadowColor && localOptions.shadowColor !== "") {
           ctx.fillStyle = localOptions.shadowColor;
-          ctx.fillText(label, result[i2].box[0] + 3, 1 + result[i2].box[1] + localOptions.lineHeight, result[i2].box[2]);
+          ctx.fillText(label, result[i].box[0] + 3, 1 + result[i].box[1] + localOptions.lineHeight, result[i].box[2]);
         }
         ctx.fillStyle = localOptions.labelColor;
-        ctx.fillText(label, result[i2].box[0] + 2, 0 + result[i2].box[1] + localOptions.lineHeight, result[i2].box[2]);
+        ctx.fillText(label, result[i].box[0] + 2, 0 + result[i].box[1] + localOptions.lineHeight, result[i].box[2]);
       }
       ctx.stroke();
     }
@@ -85983,8 +86080,8 @@ var alpha = 0.5;
 function insidePoly(x, y, polygon) {
   let inside = false;
   let j = polygon.length - 1;
-  for (let i2 = 0; i2 < polygon.length; j = i2++) {
-    if (polygon[i2].y > y !== polygon[j].y > y && x < (polygon[j].x - polygon[i2].x) * (y - polygon[i2].y) / (polygon[j].y - polygon[i2].y) + polygon[i2].x)
+  for (let i = 0; i < polygon.length; j = i++) {
+    if (polygon[i].y > y !== polygon[j].y > y && x < (polygon[j].x - polygon[i].x) * (y - polygon[i].y) / (polygon[j].y - polygon[i].y) + polygon[i].x)
       inside = !inside;
   }
   return inside;
@@ -86038,7 +86135,7 @@ var calculateGaze = (face4) => {
   return { bearing, strength };
 };
 var calculateFaceAngle = (face4, imageSize) => {
-  const normalize = (v) => {
+  const normalize2 = (v) => {
     const length = Math.sqrt(v[0] * v[0] + v[1] * v[1] + v[2] * v[2]);
     v[0] /= length;
     v[1] /= length;
@@ -86057,8 +86154,8 @@ var calculateFaceAngle = (face4, imageSize) => {
     const z = a[0] * b[1] - a[1] * b[0];
     return [x, y, z];
   };
-  const rotationMatrixToEulerAngle = (r2) => {
-    const [r00, _r01, _r02, r10, r11, r12, r20, r21, r22] = r2;
+  const rotationMatrixToEulerAngle = (r) => {
+    const [r00, _r01, _r02, r10, r11, r12, r20, r21, r22] = r;
     let thetaX;
     let thetaY;
     let thetaZ;
@@ -86090,9 +86187,9 @@ var calculateFaceAngle = (face4, imageSize) => {
     return { angle: { pitch: 0, yaw: 0, roll: 0 }, matrix: [1, 0, 0, 0, 1, 0, 0, 0, 1], gaze: { bearing: 0, strength: 0 } };
   const size2 = Math.max(face4.boxRaw[2] * imageSize[0], face4.boxRaw[3] * imageSize[1]) / 1.5;
   const pts = [mesh[10], mesh[152], mesh[234], mesh[454]].map((pt) => [pt[0] * imageSize[0] / size2, pt[1] * imageSize[1] / size2, pt[2]]);
-  const yAxis = normalize(subVectors(pts[1], pts[0]));
-  let xAxis = normalize(subVectors(pts[3], pts[2]));
-  const zAxis = normalize(crossVectors(xAxis, yAxis));
+  const yAxis = normalize2(subVectors(pts[1], pts[0]));
+  let xAxis = normalize2(subVectors(pts[3], pts[2]));
+  const zAxis = normalize2(crossVectors(xAxis, yAxis));
   xAxis = crossVectors(yAxis, zAxis);
   const matrix = [
     xAxis[0],
@@ -86125,104 +86222,104 @@ var detectFace = async (instance2, input2) => {
   let descRes;
   const faceRes = [];
   instance2.state = "run:face";
-  const faces = await predict11(input2, instance2.config);
+  const faces = await predict6(input2, instance2.config);
   instance2.performance.face = env2.perfadd ? (instance2.performance.face || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);
   if (!input2.shape || input2.shape.length !== 4)
     return [];
   if (!faces)
     return [];
-  for (let i2 = 0; i2 < faces.length; i2++) {
+  for (let i = 0; i < faces.length; i++) {
     instance2.analyze("Get Face");
-    if (!faces[i2].tensor || faces[i2].tensor.isDisposedInternal) {
-      log("Face object is disposed:", faces[i2].tensor);
+    if (!faces[i].tensor || faces[i].tensor.isDisposedInternal) {
+      log("Face object is disposed:", faces[i].tensor);
       continue;
     }
     if ((_a = instance2.config.face.detector) == null ? void 0 : _a.mask) {
-      const masked = await mask(faces[i2]);
-      dispose(faces[i2].tensor);
+      const masked = await mask(faces[i]);
+      dispose(faces[i].tensor);
       if (masked)
-        faces[i2].tensor = masked;
+        faces[i].tensor = masked;
     }
-    const rotation = faces[i2].mesh && faces[i2].mesh.length > 200 ? calculateFaceAngle(faces[i2], [input2.shape[2], input2.shape[1]]) : null;
+    const rotation = faces[i].mesh && faces[i].mesh.length > 200 ? calculateFaceAngle(faces[i], [input2.shape[2], input2.shape[1]]) : null;
     instance2.analyze("Start Emotion:");
     if (instance2.config.async) {
-      emotionRes = ((_b = instance2.config.face.emotion) == null ? void 0 : _b.enabled) ? predict8(faces[i2].tensor || tensor([]), instance2.config, i2, faces.length) : [];
+      emotionRes = ((_b = instance2.config.face.emotion) == null ? void 0 : _b.enabled) ? predict5(faces[i].tensor || tensor([]), instance2.config, i, faces.length) : [];
     } else {
       instance2.state = "run:emotion";
       timeStamp = now();
-      emotionRes = ((_c = instance2.config.face.emotion) == null ? void 0 : _c.enabled) ? await predict8(faces[i2].tensor || tensor([]), instance2.config, i2, faces.length) : [];
+      emotionRes = ((_c = instance2.config.face.emotion) == null ? void 0 : _c.enabled) ? await predict5(faces[i].tensor || tensor([]), instance2.config, i, faces.length) : [];
       instance2.performance.emotion = env2.perfadd ? (instance2.performance.emotion || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);
     }
     instance2.analyze("End Emotion:");
     instance2.analyze("Start AntiSpoof:");
     if (instance2.config.async) {
-      antispoofRes = ((_d = instance2.config.face.antispoof) == null ? void 0 : _d.enabled) ? predict4(faces[i2].tensor || tensor([]), instance2.config, i2, faces.length) : 0;
+      antispoofRes = ((_d = instance2.config.face.antispoof) == null ? void 0 : _d.enabled) ? predict(faces[i].tensor || tensor([]), instance2.config, i, faces.length) : 0;
     } else {
       instance2.state = "run:antispoof";
       timeStamp = now();
-      antispoofRes = ((_e = instance2.config.face.antispoof) == null ? void 0 : _e.enabled) ? await predict4(faces[i2].tensor || tensor([]), instance2.config, i2, faces.length) : 0;
+      antispoofRes = ((_e = instance2.config.face.antispoof) == null ? void 0 : _e.enabled) ? await predict(faces[i].tensor || tensor([]), instance2.config, i, faces.length) : 0;
       instance2.performance.antispoof = env2.perfadd ? (instance2.performance.antispoof || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);
     }
     instance2.analyze("End AntiSpoof:");
     instance2.analyze("Start Liveness:");
     if (instance2.config.async) {
-      livenessRes = ((_f = instance2.config.face.liveness) == null ? void 0 : _f.enabled) ? predict15(faces[i2].tensor || tensor([]), instance2.config, i2, faces.length) : 0;
+      livenessRes = ((_f = instance2.config.face.liveness) == null ? void 0 : _f.enabled) ? predict12(faces[i].tensor || tensor([]), instance2.config, i, faces.length) : 0;
     } else {
       instance2.state = "run:liveness";
       timeStamp = now();
-      livenessRes = ((_g = instance2.config.face.liveness) == null ? void 0 : _g.enabled) ? await predict15(faces[i2].tensor || tensor([]), instance2.config, i2, faces.length) : 0;
+      livenessRes = ((_g = instance2.config.face.liveness) == null ? void 0 : _g.enabled) ? await predict12(faces[i].tensor || tensor([]), instance2.config, i, faces.length) : 0;
       instance2.performance.liveness = env2.perfadd ? (instance2.performance.antispoof || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);
     }
     instance2.analyze("End Liveness:");
     instance2.analyze("Start GEAR:");
     if (instance2.config.async) {
-      gearRes = ((_h = instance2.config.face.gear) == null ? void 0 : _h.enabled) ? predict(faces[i2].tensor || tensor([]), instance2.config, i2, faces.length) : null;
+      gearRes = ((_h = instance2.config.face.gear) == null ? void 0 : _h.enabled) ? predict8(faces[i].tensor || tensor([]), instance2.config, i, faces.length) : null;
     } else {
       instance2.state = "run:gear";
       timeStamp = now();
-      gearRes = ((_i = instance2.config.face.gear) == null ? void 0 : _i.enabled) ? await predict(faces[i2].tensor || tensor([]), instance2.config, i2, faces.length) : null;
+      gearRes = ((_i = instance2.config.face.gear) == null ? void 0 : _i.enabled) ? await predict8(faces[i].tensor || tensor([]), instance2.config, i, faces.length) : null;
       instance2.performance.gear = Math.trunc(now() - timeStamp);
     }
     instance2.analyze("End GEAR:");
     instance2.analyze("Start SSRNet:");
     if (instance2.config.async) {
-      ageRes = ((_j = instance2.config.face["ssrnet"]) == null ? void 0 : _j.enabled) ? predict2(faces[i2].tensor || tensor([]), instance2.config, i2, faces.length) : null;
-      genderRes = ((_k = instance2.config.face["ssrnet"]) == null ? void 0 : _k.enabled) ? predict3(faces[i2].tensor || tensor([]), instance2.config, i2, faces.length) : null;
+      ageRes = ((_j = instance2.config.face["ssrnet"]) == null ? void 0 : _j.enabled) ? predict20(faces[i].tensor || tensor([]), instance2.config, i, faces.length) : null;
+      genderRes = ((_k = instance2.config.face["ssrnet"]) == null ? void 0 : _k.enabled) ? predict21(faces[i].tensor || tensor([]), instance2.config, i, faces.length) : null;
     } else {
       instance2.state = "run:ssrnet";
       timeStamp = now();
-      ageRes = ((_l = instance2.config.face["ssrnet"]) == null ? void 0 : _l.enabled) ? await predict2(faces[i2].tensor || tensor([]), instance2.config, i2, faces.length) : null;
-      genderRes = ((_m = instance2.config.face["ssrnet"]) == null ? void 0 : _m.enabled) ? await predict3(faces[i2].tensor || tensor([]), instance2.config, i2, faces.length) : null;
+      ageRes = ((_l = instance2.config.face["ssrnet"]) == null ? void 0 : _l.enabled) ? await predict20(faces[i].tensor || tensor([]), instance2.config, i, faces.length) : null;
+      genderRes = ((_m = instance2.config.face["ssrnet"]) == null ? void 0 : _m.enabled) ? await predict21(faces[i].tensor || tensor([]), instance2.config, i, faces.length) : null;
       instance2.performance.ssrnet = Math.trunc(now() - timeStamp);
     }
     instance2.analyze("End SSRNet:");
     instance2.analyze("Start MobileFaceNet:");
     if (instance2.config.async) {
-      mobilefacenetRes = ((_n = instance2.config.face["mobilefacenet"]) == null ? void 0 : _n.enabled) ? predict9(faces[i2].tensor || tensor([]), instance2.config, i2, faces.length) : null;
+      mobilefacenetRes = ((_n = instance2.config.face["mobilefacenet"]) == null ? void 0 : _n.enabled) ? predict14(faces[i].tensor || tensor([]), instance2.config, i, faces.length) : null;
     } else {
       instance2.state = "run:mobilefacenet";
       timeStamp = now();
-      mobilefacenetRes = ((_o = instance2.config.face["mobilefacenet"]) == null ? void 0 : _o.enabled) ? await predict9(faces[i2].tensor || tensor([]), instance2.config, i2, faces.length) : null;
+      mobilefacenetRes = ((_o = instance2.config.face["mobilefacenet"]) == null ? void 0 : _o.enabled) ? await predict14(faces[i].tensor || tensor([]), instance2.config, i, faces.length) : null;
       instance2.performance.mobilefacenet = Math.trunc(now() - timeStamp);
     }
     instance2.analyze("End MobileFaceNet:");
     instance2.analyze("Start InsightFace:");
     if (instance2.config.async) {
-      insightfaceRes = ((_p = instance2.config.face["insightface"]) == null ? void 0 : _p.enabled) ? predict10(faces[i2].tensor || tensor([]), instance2.config, i2, faces.length) : null;
+      insightfaceRes = ((_p = instance2.config.face["insightface"]) == null ? void 0 : _p.enabled) ? predict11(faces[i].tensor || tensor([]), instance2.config, i, faces.length) : null;
     } else {
       instance2.state = "run:mobilefacenet";
       timeStamp = now();
-      insightfaceRes = ((_q = instance2.config.face["insightface"]) == null ? void 0 : _q.enabled) ? await predict10(faces[i2].tensor || tensor([]), instance2.config, i2, faces.length) : null;
+      insightfaceRes = ((_q = instance2.config.face["insightface"]) == null ? void 0 : _q.enabled) ? await predict11(faces[i].tensor || tensor([]), instance2.config, i, faces.length) : null;
       instance2.performance.mobilefacenet = Math.trunc(now() - timeStamp);
     }
     instance2.analyze("End InsightFace:");
     instance2.analyze("Start Description:");
     if (instance2.config.async) {
-      descRes = predict12(faces[i2].tensor || tensor([]), instance2.config, i2, faces.length);
+      descRes = predict7(faces[i].tensor || tensor([]), instance2.config, i, faces.length);
     } else {
       instance2.state = "run:description";
       timeStamp = now();
-      descRes = await predict12(faces[i2].tensor || tensor([]), instance2.config, i2, faces.length);
+      descRes = await predict7(faces[i].tensor || tensor([]), instance2.config, i, faces.length);
       instance2.performance.description = env2.perfadd ? (instance2.performance.description || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);
     }
     instance2.analyze("End Description:");
@@ -86255,14 +86352,14 @@ var detectFace = async (instance2, input2) => {
     }
     if (!((_v = instance2.config.face.iris) == null ? void 0 : _v.enabled)) {
     }
-    const irisSize = ((_y = (_x = (_w = faces[i2]) == null ? void 0 : _w.annotations) == null ? void 0 : _x.leftEyeIris) == null ? void 0 : _y[0]) && ((_B = (_A = (_z = faces[i2]) == null ? void 0 : _z.annotations) == null ? void 0 : _A.rightEyeIris) == null ? void 0 : _B[0]) && faces[i2].annotations.leftEyeIris.length > 0 && faces[i2].annotations.rightEyeIris.length > 0 && faces[i2].annotations.leftEyeIris[0] !== null && faces[i2].annotations.rightEyeIris[0] !== null ? Math.max(Math.abs(faces[i2].annotations.leftEyeIris[3][0] - faces[i2].annotations.leftEyeIris[1][0]), Math.abs(faces[i2].annotations.rightEyeIris[4][1] - faces[i2].annotations.rightEyeIris[2][1])) / input2.shape[2] : 0;
-    const tensor2 = ((_C = instance2.config.face.detector) == null ? void 0 : _C.return) ? squeeze(faces[i2].tensor) : null;
-    dispose(faces[i2].tensor);
-    if (faces[i2].tensor)
-      delete faces[i2].tensor;
+    const irisSize = ((_y = (_x = (_w = faces[i]) == null ? void 0 : _w.annotations) == null ? void 0 : _x.leftEyeIris) == null ? void 0 : _y[0]) && ((_B = (_A = (_z = faces[i]) == null ? void 0 : _z.annotations) == null ? void 0 : _A.rightEyeIris) == null ? void 0 : _B[0]) && faces[i].annotations.leftEyeIris.length > 0 && faces[i].annotations.rightEyeIris.length > 0 && faces[i].annotations.leftEyeIris[0] !== null && faces[i].annotations.rightEyeIris[0] !== null ? Math.max(Math.abs(faces[i].annotations.leftEyeIris[3][0] - faces[i].annotations.leftEyeIris[1][0]), Math.abs(faces[i].annotations.rightEyeIris[4][1] - faces[i].annotations.rightEyeIris[2][1])) / input2.shape[2] : 0;
+    const tensor2 = ((_C = instance2.config.face.detector) == null ? void 0 : _C.return) ? squeeze(faces[i].tensor) : null;
+    dispose(faces[i].tensor);
+    if (faces[i].tensor)
+      delete faces[i].tensor;
     const res = {
-      ...faces[i2],
-      id: i2
+      ...faces[i],
+      id: i
     };
     if (descRes.age)
       res.age = descRes.age;
@@ -86308,20 +86405,20 @@ var body2 = (res) => {
   if (!res)
     return [];
   const gestures = [];
-  for (let i2 = 0; i2 < res.length; i2++) {
-    const leftWrist = res[i2].keypoints.find((a) => a.part === "leftWrist");
-    const rightWrist = res[i2].keypoints.find((a) => a.part === "rightWrist");
-    const nose = res[i2].keypoints.find((a) => a.part === "nose");
+  for (let i = 0; i < res.length; i++) {
+    const leftWrist = res[i].keypoints.find((a) => a.part === "leftWrist");
+    const rightWrist = res[i].keypoints.find((a) => a.part === "rightWrist");
+    const nose = res[i].keypoints.find((a) => a.part === "nose");
     if (nose && leftWrist && rightWrist && leftWrist.position[1] < nose.position[1] && rightWrist.position[1] < nose.position[1])
-      gestures.push({ body: i2, gesture: "i give up" });
+      gestures.push({ body: i, gesture: "i give up" });
     else if (nose && leftWrist && leftWrist.position[1] < nose.position[1])
-      gestures.push({ body: i2, gesture: "raise left hand" });
+      gestures.push({ body: i, gesture: "raise left hand" });
     else if (nose && rightWrist && rightWrist.position[1] < nose.position[1])
-      gestures.push({ body: i2, gesture: "raise right hand" });
-    const leftShoulder = res[i2].keypoints.find((a) => a.part === "leftShoulder");
-    const rightShoulder = res[i2].keypoints.find((a) => a.part === "rightShoulder");
+      gestures.push({ body: i, gesture: "raise right hand" });
+    const leftShoulder = res[i].keypoints.find((a) => a.part === "leftShoulder");
+    const rightShoulder = res[i].keypoints.find((a) => a.part === "rightShoulder");
     if (leftShoulder && rightShoulder && Math.abs(leftShoulder.positionRaw[1] - rightShoulder.positionRaw[1]) > 0.1) {
-      gestures.push({ body: i2, gesture: `leaning ${leftShoulder.position[1] > rightShoulder.position[1] ? "left" : "right"}` });
+      gestures.push({ body: i, gesture: `leaning ${leftShoulder.position[1] > rightShoulder.position[1] ? "left" : "right"}` });
     }
   }
   return gestures;
@@ -86330,26 +86427,26 @@ var face2 = (res) => {
   if (!res)
     return [];
   const gestures = [];
-  for (let i2 = 0; i2 < res.length; i2++) {
-    if (res[i2].mesh && res[i2].mesh.length > 450) {
-      const zDiff = (res[i2].mesh[33][2] || 0) - (res[i2].mesh[263][2] || 0);
-      const xDiff = res[i2].mesh[33][0] - res[i2].mesh[263][0];
+  for (let i = 0; i < res.length; i++) {
+    if (res[i].mesh && res[i].mesh.length > 450) {
+      const zDiff = (res[i].mesh[33][2] || 0) - (res[i].mesh[263][2] || 0);
+      const xDiff = res[i].mesh[33][0] - res[i].mesh[263][0];
       if (Math.abs(zDiff / xDiff) <= 0.15)
-        gestures.push({ face: i2, gesture: "facing center" });
+        gestures.push({ face: i, gesture: "facing center" });
       else
-        gestures.push({ face: i2, gesture: `facing ${zDiff < 0 ? "left" : "right"}` });
-      const openLeft = Math.abs(res[i2].mesh[374][1] - res[i2].mesh[386][1]) / Math.abs(res[i2].mesh[443][1] - res[i2].mesh[450][1]);
+        gestures.push({ face: i, gesture: `facing ${zDiff < 0 ? "left" : "right"}` });
+      const openLeft = Math.abs(res[i].mesh[374][1] - res[i].mesh[386][1]) / Math.abs(res[i].mesh[443][1] - res[i].mesh[450][1]);
       if (openLeft < 0.2)
-        gestures.push({ face: i2, gesture: "blink left eye" });
-      const openRight = Math.abs(res[i2].mesh[145][1] - res[i2].mesh[159][1]) / Math.abs(res[i2].mesh[223][1] - res[i2].mesh[230][1]);
+        gestures.push({ face: i, gesture: "blink left eye" });
+      const openRight = Math.abs(res[i].mesh[145][1] - res[i].mesh[159][1]) / Math.abs(res[i].mesh[223][1] - res[i].mesh[230][1]);
       if (openRight < 0.2)
-        gestures.push({ face: i2, gesture: "blink right eye" });
-      const mouthOpen = Math.min(100, 500 * Math.abs(res[i2].mesh[13][1] - res[i2].mesh[14][1]) / Math.abs(res[i2].mesh[10][1] - res[i2].mesh[152][1]));
+        gestures.push({ face: i, gesture: "blink right eye" });
+      const mouthOpen = Math.min(100, 500 * Math.abs(res[i].mesh[13][1] - res[i].mesh[14][1]) / Math.abs(res[i].mesh[10][1] - res[i].mesh[152][1]));
       if (mouthOpen > 10)
-        gestures.push({ face: i2, gesture: `mouth ${Math.trunc(mouthOpen)}% open` });
-      const chinDepth = res[i2].mesh[152][2] || 0;
+        gestures.push({ face: i, gesture: `mouth ${Math.trunc(mouthOpen)}% open` });
+      const chinDepth = res[i].mesh[152][2] || 0;
       if (Math.abs(chinDepth) > 10)
-        gestures.push({ face: i2, gesture: `head ${chinDepth < 0 ? "up" : "down"}` });
+        gestures.push({ face: i, gesture: `head ${chinDepth < 0 ? "up" : "down"}` });
     }
   }
   return gestures;
@@ -86359,42 +86456,42 @@ var iris2 = (res) => {
   if (!res)
     return [];
   const gestures = [];
-  for (let i2 = 0; i2 < res.length; i2++) {
-    if (!((_b = (_a = res[i2].annotations) == null ? void 0 : _a.leftEyeIris) == null ? void 0 : _b[0]) || !((_d = (_c = res[i2].annotations) == null ? void 0 : _c.rightEyeIris) == null ? void 0 : _d[0]))
+  for (let i = 0; i < res.length; i++) {
+    if (!((_b = (_a = res[i].annotations) == null ? void 0 : _a.leftEyeIris) == null ? void 0 : _b[0]) || !((_d = (_c = res[i].annotations) == null ? void 0 : _c.rightEyeIris) == null ? void 0 : _d[0]))
       continue;
-    const sizeXLeft = res[i2].annotations.leftEyeIris[3][0] - res[i2].annotations.leftEyeIris[1][0];
-    const sizeYLeft = res[i2].annotations.leftEyeIris[4][1] - res[i2].annotations.leftEyeIris[2][1];
+    const sizeXLeft = res[i].annotations.leftEyeIris[3][0] - res[i].annotations.leftEyeIris[1][0];
+    const sizeYLeft = res[i].annotations.leftEyeIris[4][1] - res[i].annotations.leftEyeIris[2][1];
     const areaLeft = Math.abs(sizeXLeft * sizeYLeft);
-    const sizeXRight = res[i2].annotations.rightEyeIris[3][0] - res[i2].annotations.rightEyeIris[1][0];
-    const sizeYRight = res[i2].annotations.rightEyeIris[4][1] - res[i2].annotations.rightEyeIris[2][1];
+    const sizeXRight = res[i].annotations.rightEyeIris[3][0] - res[i].annotations.rightEyeIris[1][0];
+    const sizeYRight = res[i].annotations.rightEyeIris[4][1] - res[i].annotations.rightEyeIris[2][1];
     const areaRight = Math.abs(sizeXRight * sizeYRight);
     let center = false;
     const difference = Math.abs(areaLeft - areaRight) / Math.max(areaLeft, areaRight);
     if (difference < 0.25) {
       center = true;
-      gestures.push({ iris: i2, gesture: "facing center" });
+      gestures.push({ iris: i, gesture: "facing center" });
     }
-    const leftIrisCenterX = Math.abs(res[i2].mesh[263][0] - res[i2].annotations.leftEyeIris[0][0]) / res[i2].box[2];
-    const rightIrisCenterX = Math.abs(res[i2].mesh[33][0] - res[i2].annotations.rightEyeIris[0][0]) / res[i2].box[2];
+    const leftIrisCenterX = Math.abs(res[i].mesh[263][0] - res[i].annotations.leftEyeIris[0][0]) / res[i].box[2];
+    const rightIrisCenterX = Math.abs(res[i].mesh[33][0] - res[i].annotations.rightEyeIris[0][0]) / res[i].box[2];
     if (leftIrisCenterX > 0.06 || rightIrisCenterX > 0.06)
       center = false;
     if (leftIrisCenterX > rightIrisCenterX) {
       if (leftIrisCenterX > 0.05)
-        gestures.push({ iris: i2, gesture: "looking right" });
+        gestures.push({ iris: i, gesture: "looking right" });
     } else {
       if (rightIrisCenterX > 0.05)
-        gestures.push({ iris: i2, gesture: "looking left" });
+        gestures.push({ iris: i, gesture: "looking left" });
     }
-    const rightIrisCenterY = Math.abs(res[i2].mesh[145][1] - res[i2].annotations.rightEyeIris[0][1]) / res[i2].box[3];
-    const leftIrisCenterY = Math.abs(res[i2].mesh[374][1] - res[i2].annotations.leftEyeIris[0][1]) / res[i2].box[3];
+    const rightIrisCenterY = Math.abs(res[i].mesh[145][1] - res[i].annotations.rightEyeIris[0][1]) / res[i].box[3];
+    const leftIrisCenterY = Math.abs(res[i].mesh[374][1] - res[i].annotations.leftEyeIris[0][1]) / res[i].box[3];
     if (leftIrisCenterY < 0.01 || rightIrisCenterY < 0.01 || leftIrisCenterY > 0.022 || rightIrisCenterY > 0.022)
       center = false;
     if (leftIrisCenterY < 0.01 || rightIrisCenterY < 0.01)
-      gestures.push({ iris: i2, gesture: "looking down" });
+      gestures.push({ iris: i, gesture: "looking down" });
     if (leftIrisCenterY > 0.022 || rightIrisCenterY > 0.022)
-      gestures.push({ iris: i2, gesture: "looking up" });
+      gestures.push({ iris: i, gesture: "looking up" });
     if (center)
-      gestures.push({ iris: i2, gesture: "looking center" });
+      gestures.push({ iris: i, gesture: "looking center" });
   }
   return gestures;
 };
@@ -86402,24 +86499,24 @@ var hand2 = (res) => {
   if (!res)
     return [];
   const gestures = [];
-  for (let i2 = 0; i2 < res.length; i2++) {
+  for (let i = 0; i < res.length; i++) {
     const fingers = [];
-    if (res[i2].annotations) {
-      for (const [finger, pos] of Object.entries(res[i2].annotations)) {
+    if (res[i].annotations) {
+      for (const [finger, pos] of Object.entries(res[i].annotations)) {
         if (finger !== "palmBase" && Array.isArray(pos) && pos[0])
           fingers.push({ name: finger.toLowerCase(), position: pos[0] });
       }
     }
     if (fingers && fingers.length > 0) {
       const closest = fingers.reduce((best, a) => (best.position[2] || 0) < (a.position[2] || 0) ? best : a);
-      gestures.push({ hand: i2, gesture: `${closest.name} forward` });
+      gestures.push({ hand: i, gesture: `${closest.name} forward` });
       const highest = fingers.reduce((best, a) => best.position[1] < a.position[1] ? best : a);
-      gestures.push({ hand: i2, gesture: `${highest.name} up` });
+      gestures.push({ hand: i, gesture: `${highest.name} up` });
     }
-    if (res[i2].keypoints) {
-      const poses = match(res[i2].keypoints);
+    if (res[i].keypoints) {
+      const poses = match(res[i].keypoints);
       for (const pose of poses)
-        gestures.push({ hand: i2, gesture: pose.name });
+        gestures.push({ hand: i, gesture: pose.name });
     }
   }
   return gestures;
@@ -86442,28 +86539,28 @@ function calc2(newResult, config3) {
   if (!bufferedResult.body || newResult.body.length !== bufferedResult.body.length) {
     bufferedResult.body = JSON.parse(JSON.stringify(newResult.body));
   } else {
-    for (let i2 = 0; i2 < newResult.body.length; i2++) {
-      const box = newResult.body[i2].box.map((newBoxCoord, j) => ((bufferedFactor - 1) * bufferedResult.body[i2].box[j] + newBoxCoord) / bufferedFactor);
-      const boxRaw = newResult.body[i2].boxRaw.map((newBoxCoord, j) => ((bufferedFactor - 1) * bufferedResult.body[i2].boxRaw[j] + newBoxCoord) / bufferedFactor);
-      const keypoints = newResult.body[i2].keypoints.map((newKpt, j) => {
+    for (let i = 0; i < newResult.body.length; i++) {
+      const box = newResult.body[i].box.map((newBoxCoord, j) => ((bufferedFactor - 1) * bufferedResult.body[i].box[j] + newBoxCoord) / bufferedFactor);
+      const boxRaw = newResult.body[i].boxRaw.map((newBoxCoord, j) => ((bufferedFactor - 1) * bufferedResult.body[i].boxRaw[j] + newBoxCoord) / bufferedFactor);
+      const keypoints = newResult.body[i].keypoints.map((newKpt, j) => {
         var _a2, _b2, _c2, _d2, _e2, _f2, _g2, _h2, _i2;
         return {
           score: newKpt.score,
           part: newKpt.part,
           position: [
-            bufferedResult.body[i2].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i2].keypoints[j].position[0] || 0) + (newKpt.position[0] || 0)) / bufferedFactor : newKpt.position[0],
-            bufferedResult.body[i2].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i2].keypoints[j].position[1] || 0) + (newKpt.position[1] || 0)) / bufferedFactor : newKpt.position[1],
-            bufferedResult.body[i2].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i2].keypoints[j].position[2] || 0) + (newKpt.position[2] || 0)) / bufferedFactor : newKpt.position[2]
+            bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].position[0] || 0) + (newKpt.position[0] || 0)) / bufferedFactor : newKpt.position[0],
+            bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].position[1] || 0) + (newKpt.position[1] || 0)) / bufferedFactor : newKpt.position[1],
+            bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].position[2] || 0) + (newKpt.position[2] || 0)) / bufferedFactor : newKpt.position[2]
           ],
           positionRaw: [
-            bufferedResult.body[i2].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i2].keypoints[j].positionRaw[0] || 0) + (newKpt.positionRaw[0] || 0)) / bufferedFactor : newKpt.positionRaw[0],
-            bufferedResult.body[i2].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i2].keypoints[j].positionRaw[1] || 0) + (newKpt.positionRaw[1] || 0)) / bufferedFactor : newKpt.positionRaw[1],
-            bufferedResult.body[i2].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i2].keypoints[j].positionRaw[2] || 0) + (newKpt.positionRaw[2] || 0)) / bufferedFactor : newKpt.positionRaw[2]
+            bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].positionRaw[0] || 0) + (newKpt.positionRaw[0] || 0)) / bufferedFactor : newKpt.positionRaw[0],
+            bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].positionRaw[1] || 0) + (newKpt.positionRaw[1] || 0)) / bufferedFactor : newKpt.positionRaw[1],
+            bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].positionRaw[2] || 0) + (newKpt.positionRaw[2] || 0)) / bufferedFactor : newKpt.positionRaw[2]
           ],
           distance: [
-            bufferedResult.body[i2].keypoints[j] ? ((bufferedFactor - 1) * (((_a2 = bufferedResult.body[i2].keypoints[j].distance) == null ? void 0 : _a2[0]) || 0) + (((_b2 = newKpt.distance) == null ? void 0 : _b2[0]) || 0)) / bufferedFactor : (_c2 = newKpt.distance) == null ? void 0 : _c2[0],
-            bufferedResult.body[i2].keypoints[j] ? ((bufferedFactor - 1) * (((_d2 = bufferedResult.body[i2].keypoints[j].distance) == null ? void 0 : _d2[1]) || 0) + (((_e2 = newKpt.distance) == null ? void 0 : _e2[1]) || 0)) / bufferedFactor : (_f2 = newKpt.distance) == null ? void 0 : _f2[1],
-            bufferedResult.body[i2].keypoints[j] ? ((bufferedFactor - 1) * (((_g2 = bufferedResult.body[i2].keypoints[j].distance) == null ? void 0 : _g2[2]) || 0) + (((_h2 = newKpt.distance) == null ? void 0 : _h2[2]) || 0)) / bufferedFactor : (_i2 = newKpt.distance) == null ? void 0 : _i2[2]
+            bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (((_a2 = bufferedResult.body[i].keypoints[j].distance) == null ? void 0 : _a2[0]) || 0) + (((_b2 = newKpt.distance) == null ? void 0 : _b2[0]) || 0)) / bufferedFactor : (_c2 = newKpt.distance) == null ? void 0 : _c2[0],
+            bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (((_d2 = bufferedResult.body[i].keypoints[j].distance) == null ? void 0 : _d2[1]) || 0) + (((_e2 = newKpt.distance) == null ? void 0 : _e2[1]) || 0)) / bufferedFactor : (_f2 = newKpt.distance) == null ? void 0 : _f2[1],
+            bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (((_g2 = bufferedResult.body[i].keypoints[j].distance) == null ? void 0 : _g2[2]) || 0) + (((_h2 = newKpt.distance) == null ? void 0 : _h2[2]) || 0)) / bufferedFactor : (_i2 = newKpt.distance) == null ? void 0 : _i2[2]
           ]
         };
       });
@@ -86485,61 +86582,61 @@ function calc2(newResult, config3) {
         }
         annotations2[name] = pt;
       }
-      bufferedResult.body[i2] = { ...newResult.body[i2], box, boxRaw, keypoints, annotations: annotations2 };
+      bufferedResult.body[i] = { ...newResult.body[i], box, boxRaw, keypoints, annotations: annotations2 };
     }
   }
   if (!bufferedResult.hand || newResult.hand.length !== bufferedResult.hand.length) {
     bufferedResult.hand = JSON.parse(JSON.stringify(newResult.hand));
   } else {
-    for (let i2 = 0; i2 < newResult.hand.length; i2++) {
-      const box = newResult.hand[i2].box.map((b, j) => ((bufferedFactor - 1) * bufferedResult.hand[i2].box[j] + b) / bufferedFactor);
-      const boxRaw = newResult.hand[i2].boxRaw.map((b, j) => ((bufferedFactor - 1) * bufferedResult.hand[i2].boxRaw[j] + b) / bufferedFactor);
-      if (bufferedResult.hand[i2].keypoints.length !== newResult.hand[i2].keypoints.length)
-        bufferedResult.hand[i2].keypoints = newResult.hand[i2].keypoints;
-      const keypoints = newResult.hand[i2].keypoints && newResult.hand[i2].keypoints.length > 0 ? newResult.hand[i2].keypoints.map((landmark, j) => landmark.map((coord, k) => ((bufferedFactor - 1) * (bufferedResult.hand[i2].keypoints[j][k] || 1) + (coord || 0)) / bufferedFactor)) : [];
+    for (let i = 0; i < newResult.hand.length; i++) {
+      const box = newResult.hand[i].box.map((b, j) => ((bufferedFactor - 1) * bufferedResult.hand[i].box[j] + b) / bufferedFactor);
+      const boxRaw = newResult.hand[i].boxRaw.map((b, j) => ((bufferedFactor - 1) * bufferedResult.hand[i].boxRaw[j] + b) / bufferedFactor);
+      if (bufferedResult.hand[i].keypoints.length !== newResult.hand[i].keypoints.length)
+        bufferedResult.hand[i].keypoints = newResult.hand[i].keypoints;
+      const keypoints = newResult.hand[i].keypoints && newResult.hand[i].keypoints.length > 0 ? newResult.hand[i].keypoints.map((landmark, j) => landmark.map((coord, k) => ((bufferedFactor - 1) * (bufferedResult.hand[i].keypoints[j][k] || 1) + (coord || 0)) / bufferedFactor)) : [];
       let annotations2 = {};
-      if (Object.keys(bufferedResult.hand[i2].annotations).length !== Object.keys(newResult.hand[i2].annotations).length) {
-        bufferedResult.hand[i2].annotations = newResult.hand[i2].annotations;
-        annotations2 = bufferedResult.hand[i2].annotations;
-      } else if (newResult.hand[i2].annotations) {
-        for (const key of Object.keys(newResult.hand[i2].annotations)) {
-          annotations2[key] = ((_f = (_e = (_d = newResult.hand[i2]) == null ? void 0 : _d.annotations) == null ? void 0 : _e[key]) == null ? void 0 : _f[0]) ? newResult.hand[i2].annotations[key].map((val, j) => val.map((coord, k) => ((bufferedFactor - 1) * bufferedResult.hand[i2].annotations[key][j][k] + coord) / bufferedFactor)) : null;
+      if (Object.keys(bufferedResult.hand[i].annotations).length !== Object.keys(newResult.hand[i].annotations).length) {
+        bufferedResult.hand[i].annotations = newResult.hand[i].annotations;
+        annotations2 = bufferedResult.hand[i].annotations;
+      } else if (newResult.hand[i].annotations) {
+        for (const key of Object.keys(newResult.hand[i].annotations)) {
+          annotations2[key] = ((_f = (_e = (_d = newResult.hand[i]) == null ? void 0 : _d.annotations) == null ? void 0 : _e[key]) == null ? void 0 : _f[0]) ? newResult.hand[i].annotations[key].map((val, j) => val.map((coord, k) => ((bufferedFactor - 1) * bufferedResult.hand[i].annotations[key][j][k] + coord) / bufferedFactor)) : null;
         }
       }
-      bufferedResult.hand[i2] = { ...newResult.hand[i2], box, boxRaw, keypoints, annotations: annotations2 };
+      bufferedResult.hand[i] = { ...newResult.hand[i], box, boxRaw, keypoints, annotations: annotations2 };
     }
   }
   if (!bufferedResult.face || newResult.face.length !== bufferedResult.face.length) {
     bufferedResult.face = JSON.parse(JSON.stringify(newResult.face));
   } else {
-    for (let i2 = 0; i2 < newResult.face.length; i2++) {
-      const box = newResult.face[i2].box.map((b, j) => ((bufferedFactor - 1) * bufferedResult.face[i2].box[j] + b) / bufferedFactor);
-      const boxRaw = newResult.face[i2].boxRaw.map((b, j) => ((bufferedFactor - 1) * bufferedResult.face[i2].boxRaw[j] + b) / bufferedFactor);
-      if (newResult.face[i2].rotation) {
+    for (let i = 0; i < newResult.face.length; i++) {
+      const box = newResult.face[i].box.map((b, j) => ((bufferedFactor - 1) * bufferedResult.face[i].box[j] + b) / bufferedFactor);
+      const boxRaw = newResult.face[i].boxRaw.map((b, j) => ((bufferedFactor - 1) * bufferedResult.face[i].boxRaw[j] + b) / bufferedFactor);
+      if (newResult.face[i].rotation) {
         const rotation = { matrix: [0, 0, 0, 0, 0, 0, 0, 0, 0], angle: { roll: 0, yaw: 0, pitch: 0 }, gaze: { bearing: 0, strength: 0 } };
-        rotation.matrix = (_g = newResult.face[i2].rotation) == null ? void 0 : _g.matrix;
+        rotation.matrix = (_g = newResult.face[i].rotation) == null ? void 0 : _g.matrix;
         rotation.angle = {
-          roll: ((bufferedFactor - 1) * (((_i = (_h = bufferedResult.face[i2].rotation) == null ? void 0 : _h.angle) == null ? void 0 : _i.roll) || 0) + (((_k = (_j = newResult.face[i2].rotation) == null ? void 0 : _j.angle) == null ? void 0 : _k.roll) || 0)) / bufferedFactor,
-          yaw: ((bufferedFactor - 1) * (((_m = (_l = bufferedResult.face[i2].rotation) == null ? void 0 : _l.angle) == null ? void 0 : _m.yaw) || 0) + (((_o = (_n = newResult.face[i2].rotation) == null ? void 0 : _n.angle) == null ? void 0 : _o.yaw) || 0)) / bufferedFactor,
-          pitch: ((bufferedFactor - 1) * (((_q = (_p = bufferedResult.face[i2].rotation) == null ? void 0 : _p.angle) == null ? void 0 : _q.pitch) || 0) + (((_s = (_r = newResult.face[i2].rotation) == null ? void 0 : _r.angle) == null ? void 0 : _s.pitch) || 0)) / bufferedFactor
+          roll: ((bufferedFactor - 1) * (((_i = (_h = bufferedResult.face[i].rotation) == null ? void 0 : _h.angle) == null ? void 0 : _i.roll) || 0) + (((_k = (_j = newResult.face[i].rotation) == null ? void 0 : _j.angle) == null ? void 0 : _k.roll) || 0)) / bufferedFactor,
+          yaw: ((bufferedFactor - 1) * (((_m = (_l = bufferedResult.face[i].rotation) == null ? void 0 : _l.angle) == null ? void 0 : _m.yaw) || 0) + (((_o = (_n = newResult.face[i].rotation) == null ? void 0 : _n.angle) == null ? void 0 : _o.yaw) || 0)) / bufferedFactor,
+          pitch: ((bufferedFactor - 1) * (((_q = (_p = bufferedResult.face[i].rotation) == null ? void 0 : _p.angle) == null ? void 0 : _q.pitch) || 0) + (((_s = (_r = newResult.face[i].rotation) == null ? void 0 : _r.angle) == null ? void 0 : _s.pitch) || 0)) / bufferedFactor
         };
         rotation.gaze = {
-          bearing: ((bufferedFactor - 1) * (((_t = bufferedResult.face[i2].rotation) == null ? void 0 : _t.gaze.bearing) || 0) + (((_u = newResult.face[i2].rotation) == null ? void 0 : _u.gaze.bearing) || 0)) / bufferedFactor,
-          strength: ((bufferedFactor - 1) * (((_v = bufferedResult.face[i2].rotation) == null ? void 0 : _v.gaze.strength) || 0) + (((_w = newResult.face[i2].rotation) == null ? void 0 : _w.gaze.strength) || 0)) / bufferedFactor
+          bearing: ((bufferedFactor - 1) * (((_t = bufferedResult.face[i].rotation) == null ? void 0 : _t.gaze.bearing) || 0) + (((_u = newResult.face[i].rotation) == null ? void 0 : _u.gaze.bearing) || 0)) / bufferedFactor,
+          strength: ((bufferedFactor - 1) * (((_v = bufferedResult.face[i].rotation) == null ? void 0 : _v.gaze.strength) || 0) + (((_w = newResult.face[i].rotation) == null ? void 0 : _w.gaze.strength) || 0)) / bufferedFactor
         };
-        bufferedResult.face[i2] = { ...newResult.face[i2], rotation, box, boxRaw };
+        bufferedResult.face[i] = { ...newResult.face[i], rotation, box, boxRaw };
       } else {
-        bufferedResult.face[i2] = { ...newResult.face[i2], box, boxRaw };
+        bufferedResult.face[i] = { ...newResult.face[i], box, boxRaw };
       }
     }
   }
   if (!bufferedResult.object || newResult.object.length !== bufferedResult.object.length) {
     bufferedResult.object = JSON.parse(JSON.stringify(newResult.object));
   } else {
-    for (let i2 = 0; i2 < newResult.object.length; i2++) {
-      const box = newResult.object[i2].box.map((b, j) => ((bufferedFactor - 1) * bufferedResult.object[i2].box[j] + b) / bufferedFactor);
-      const boxRaw = newResult.object[i2].boxRaw.map((b, j) => ((bufferedFactor - 1) * bufferedResult.object[i2].boxRaw[j] + b) / bufferedFactor);
-      bufferedResult.object[i2] = { ...newResult.object[i2], box, boxRaw };
+    for (let i = 0; i < newResult.object.length; i++) {
+      const box = newResult.object[i].box.map((b, j) => ((bufferedFactor - 1) * bufferedResult.object[i].box[j] + b) / bufferedFactor);
+      const boxRaw = newResult.object[i].boxRaw.map((b, j) => ((bufferedFactor - 1) * bufferedResult.object[i].boxRaw[j] + b) / bufferedFactor);
+      bufferedResult.object[i] = { ...newResult.object[i], box, boxRaw };
     }
   }
   if (newResult.persons) {
@@ -86547,8 +86644,8 @@ function calc2(newResult, config3) {
     if (!bufferedResult.persons || newPersons.length !== bufferedResult.persons.length) {
       bufferedResult.persons = JSON.parse(JSON.stringify(newPersons));
     } else {
-      for (let i2 = 0; i2 < newPersons.length; i2++) {
-        bufferedResult.persons[i2].box = newPersons[i2].box.map((box, j) => ((bufferedFactor - 1) * bufferedResult.persons[i2].box[j] + box) / bufferedFactor);
+      for (let i = 0; i < newPersons.length; i++) {
+        bufferedResult.persons[i].box = newPersons[i].box.map((box, j) => ((bufferedFactor - 1) * bufferedResult.persons[i].box[j] + box) / bufferedFactor);
       }
     }
   }
@@ -86572,8 +86669,8 @@ function distance(descriptor1, descriptor2, options4 = { order: 2, multiplier: 2
   if (!descriptor1 || !descriptor1)
     return Number.MAX_SAFE_INTEGER;
   let sum7 = 0;
-  for (let i2 = 0; i2 < descriptor1.length; i2++) {
-    const diff = !options4.order || options4.order === 2 ? descriptor1[i2] - descriptor2[i2] : Math.abs(descriptor1[i2] - descriptor2[i2]);
+  for (let i = 0; i < descriptor1.length; i++) {
+    const diff = !options4.order || options4.order === 2 ? descriptor1[i] - descriptor2[i] : Math.abs(descriptor1[i] - descriptor2[i]);
     sum7 += !options4.order || options4.order === 2 ? diff * diff : diff ** options4.order;
   }
   return (options4.multiplier || 20) * sum7;
@@ -86596,11 +86693,11 @@ function match2(descriptor, descriptors, options4 = { order: 2, multiplier: 25,
   }
   let lowestDistance = Number.MAX_SAFE_INTEGER;
   let index2 = -1;
-  for (let i2 = 0; i2 < descriptors.length; i2++) {
-    const res = descriptors[i2].length === descriptor.length ? distance(descriptor, descriptors[i2], options4) : Number.MAX_SAFE_INTEGER;
+  for (let i = 0; i < descriptors.length; i++) {
+    const res = descriptors[i].length === descriptor.length ? distance(descriptor, descriptors[i], options4) : Number.MAX_SAFE_INTEGER;
     if (res < lowestDistance) {
       lowestDistance = res;
-      index2 = i2;
+      index2 = i;
     }
     if (lowestDistance < (options4.threshold || 0))
       break;
@@ -87496,23 +87593,24 @@ async function runCompile(instance2) {
   env().set("ENGINE_COMPILE_ONLY", true);
   const numTensorsStart = engine().state.numTensors;
   const compiledModels = [];
-  for (const [modelName, model20] of Object.entries(instance2.models).filter(([key, val]) => key !== null && val !== null)) {
-    const shape = ((_b = (_a = model20.inputs) == null ? void 0 : _a[0]) == null ? void 0 : _b.shape) ? [...model20.inputs[0].shape] : [1, 64, 64, 3];
-    const dtype = ((_d = (_c = model20.inputs) == null ? void 0 : _c[0]) == null ? void 0 : _d.dtype) ? model20.inputs[0].dtype : "float32";
+  for (const [modelName, model22] of Object.entries(instance2.models).filter(([key, val]) => key !== null && val !== null)) {
+    const shape = ((_b = (_a = model22.inputs) == null ? void 0 : _a[0]) == null ? void 0 : _b.shape) ? [...model22.inputs[0].shape] : [1, 64, 64, 3];
+    const dtype = ((_d = (_c = model22.inputs) == null ? void 0 : _c[0]) == null ? void 0 : _d.dtype) ? model22.inputs[0].dtype : "float32";
     for (let dim = 0; dim < shape.length; dim++) {
       if (shape[dim] === -1)
         shape[dim] = dim === 0 ? 1 : 64;
     }
     const tensor2 = zeros(shape, dtype);
     try {
-      const res = model20.execute(tensor2);
+      const res = model22.execute(tensor2);
       compiledModels.push(modelName);
       if (Array.isArray(res))
         res.forEach((t2) => dispose(t2));
       else
         dispose(res);
-    } catch (e2) {
-      log("compile fail model:", modelName);
+    } catch (e) {
+      if (instance2.config.debug)
+        log("compile fail model:", modelName);
     }
     dispose(tensor2);
   }
@@ -87586,7 +87684,7 @@ var Human2 = class {
         return "input must be a tensor";
       try {
         this.tf.getBackend();
-      } catch (e2) {
+      } catch (e) {
         return "backend not loaded";
       }
       return null;
@@ -87602,7 +87700,7 @@ var Human2 = class {
     });
     __privateAdd(this, _loops, {});
     this.env = env2;
-    const tfVersion = (V.tfjs || version).replace(/-(.*)/, "");
+    const tfVersion = (version82.tfjs || version).replace(/-(.*)/, "");
     config.wasmPath = `https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-backend-wasm@${tfVersion}/dist/`;
     config.modelBasePath = env2.browser ? "../models/" : "file://models/";
     config.backend = env2.browser ? "webgl" : "tensorflow";
@@ -87673,8 +87771,24 @@ var Human2 = class {
   image(input2, getTensor2 = true) {
     return process2(input2, this.config, getTensor2);
   }
-  async segmentation(input2, background) {
-    return process5(input2, background, this.config);
+  async segmentation(input2, userConfig) {
+    var _a, _b, _c;
+    if (userConfig)
+      this.config = mergeDeep(this.config, userConfig);
+    if (!this.config.segmentation.enabled)
+      return null;
+    const processed = await process2(input2, this.config);
+    if (!processed.tensor)
+      return null;
+    let tensor2 = null;
+    if ((_a = this.config.segmentation.modelPath) == null ? void 0 : _a.includes("rvm"))
+      tensor2 = await predict18(processed.tensor, this.config);
+    if ((_b = this.config.segmentation.modelPath) == null ? void 0 : _b.includes("meet"))
+      tensor2 = await predict13(processed.tensor, this.config);
+    if ((_c = this.config.segmentation.modelPath) == null ? void 0 : _c.includes("selfie"))
+      tensor2 = await predict19(processed.tensor, this.config);
+    dispose(processed.tensor);
+    return tensor2;
   }
   enhance(input2) {
     return enhance(input2);
@@ -87690,7 +87804,7 @@ var Human2 = class {
   async load(userConfig) {
     this.state = "load";
     const timeStamp = now();
-    const count3 = Object.values(this.models).filter((model20) => model20).length;
+    const count3 = Object.values(this.models).filter((model22) => model22).length;
     if (userConfig)
       this.config = mergeDeep(this.config, userConfig);
     if (this.env.initial) {
@@ -87704,11 +87818,11 @@ var Human2 = class {
           log("tf flags:", this.tf.ENV.flags);
       }
     }
-    await load20(this);
+    await load22(this);
     if (this.env.initial && this.config.debug)
       log("tf engine state:", this.tf.engine().state.numBytes, "bytes", this.tf.engine().state.numTensors, "tensors");
     this.env.initial = false;
-    const loaded = Object.values(this.models).filter((model20) => model20).length;
+    const loaded = Object.values(this.models).filter((model22) => model22).length;
     if (loaded !== count3) {
       validate2(this);
       this.emit("load");
@@ -87813,25 +87927,25 @@ var Human2 = class {
       const bodyConfig = this.config.body.maxDetected === -1 ? mergeDeep(this.config, { body: { maxDetected: this.config.face.enabled ? 1 * faceRes.length : 1 } }) : this.config;
       if (this.config.async) {
         if ((_a = this.config.body.modelPath) == null ? void 0 : _a.includes("posenet"))
-          bodyRes = this.config.body.enabled ? predict18(img.tensor, bodyConfig) : [];
+          bodyRes = this.config.body.enabled ? predict17(img.tensor, bodyConfig) : [];
         else if ((_b = this.config.body.modelPath) == null ? void 0 : _b.includes("blazepose"))
-          bodyRes = this.config.body.enabled ? predict5(img.tensor, bodyConfig) : [];
+          bodyRes = this.config.body.enabled ? predict2(img.tensor, bodyConfig) : [];
         else if ((_c = this.config.body.modelPath) == null ? void 0 : _c.includes("efficientpose"))
-          bodyRes = this.config.body.enabled ? predict7(img.tensor, bodyConfig) : [];
+          bodyRes = this.config.body.enabled ? predict4(img.tensor, bodyConfig) : [];
         else if ((_d = this.config.body.modelPath) == null ? void 0 : _d.includes("movenet"))
-          bodyRes = this.config.body.enabled ? predict16(img.tensor, bodyConfig) : [];
+          bodyRes = this.config.body.enabled ? predict15(img.tensor, bodyConfig) : [];
         if (this.performance.body)
           delete this.performance.body;
       } else {
         timeStamp = now();
         if ((_e = this.config.body.modelPath) == null ? void 0 : _e.includes("posenet"))
-          bodyRes = this.config.body.enabled ? await predict18(img.tensor, bodyConfig) : [];
+          bodyRes = this.config.body.enabled ? await predict17(img.tensor, bodyConfig) : [];
         else if ((_f = this.config.body.modelPath) == null ? void 0 : _f.includes("blazepose"))
-          bodyRes = this.config.body.enabled ? await predict5(img.tensor, bodyConfig) : [];
+          bodyRes = this.config.body.enabled ? await predict2(img.tensor, bodyConfig) : [];
         else if ((_g = this.config.body.modelPath) == null ? void 0 : _g.includes("efficientpose"))
-          bodyRes = this.config.body.enabled ? await predict7(img.tensor, bodyConfig) : [];
+          bodyRes = this.config.body.enabled ? await predict4(img.tensor, bodyConfig) : [];
         else if ((_h = this.config.body.modelPath) == null ? void 0 : _h.includes("movenet"))
-          bodyRes = this.config.body.enabled ? await predict16(img.tensor, bodyConfig) : [];
+          bodyRes = this.config.body.enabled ? await predict15(img.tensor, bodyConfig) : [];
         this.performance.body = this.env.perfadd ? (this.performance.body || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);
       }
       this.analyze("End Body:");
@@ -87840,17 +87954,17 @@ var Human2 = class {
       const handConfig = this.config.hand.maxDetected === -1 ? mergeDeep(this.config, { hand: { maxDetected: this.config.face.enabled ? 2 * faceRes.length : 1 } }) : this.config;
       if (this.config.async) {
         if ((_j = (_i = this.config.hand.detector) == null ? void 0 : _i.modelPath) == null ? void 0 : _j.includes("handdetect"))
-          handRes = this.config.hand.enabled ? predict13(img.tensor, handConfig) : [];
+          handRes = this.config.hand.enabled ? predict9(img.tensor, handConfig) : [];
         else if ((_l = (_k = this.config.hand.detector) == null ? void 0 : _k.modelPath) == null ? void 0 : _l.includes("handtrack"))
-          handRes = this.config.hand.enabled ? predict14(img.tensor, handConfig) : [];
+          handRes = this.config.hand.enabled ? predict10(img.tensor, handConfig) : [];
         if (this.performance.hand)
           delete this.performance.hand;
       } else {
         timeStamp = now();
         if ((_n = (_m = this.config.hand.detector) == null ? void 0 : _m.modelPath) == null ? void 0 : _n.includes("handdetect"))
-          handRes = this.config.hand.enabled ? await predict13(img.tensor, handConfig) : [];
+          handRes = this.config.hand.enabled ? await predict9(img.tensor, handConfig) : [];
         else if ((_p = (_o = this.config.hand.detector) == null ? void 0 : _o.modelPath) == null ? void 0 : _p.includes("handtrack"))
-          handRes = this.config.hand.enabled ? await predict14(img.tensor, handConfig) : [];
+          handRes = this.config.hand.enabled ? await predict10(img.tensor, handConfig) : [];
         this.performance.hand = this.env.perfadd ? (this.performance.hand || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);
       }
       this.analyze("End Hand:");
@@ -87858,17 +87972,17 @@ var Human2 = class {
       this.state = "detect:object";
       if (this.config.async) {
         if ((_q = this.config.object.modelPath) == null ? void 0 : _q.includes("nanodet"))
-          objectRes = this.config.object.enabled ? predict17(img.tensor, this.config) : [];
+          objectRes = this.config.object.enabled ? predict16(img.tensor, this.config) : [];
         else if ((_r = this.config.object.modelPath) == null ? void 0 : _r.includes("centernet"))
-          objectRes = this.config.object.enabled ? predict6(img.tensor, this.config) : [];
+          objectRes = this.config.object.enabled ? predict3(img.tensor, this.config) : [];
         if (this.performance.object)
           delete this.performance.object;
       } else {
         timeStamp = now();
         if ((_s = this.config.object.modelPath) == null ? void 0 : _s.includes("nanodet"))
-          objectRes = this.config.object.enabled ? await predict17(img.tensor, this.config) : [];
+          objectRes = this.config.object.enabled ? await predict16(img.tensor, this.config) : [];
         else if ((_t = this.config.object.modelPath) == null ? void 0 : _t.includes("centernet"))
-          objectRes = this.config.object.enabled ? await predict6(img.tensor, this.config) : [];
+          objectRes = this.config.object.enabled ? await predict3(img.tensor, this.config) : [];
         this.performance.object = this.env.perfadd ? (this.performance.object || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);
       }
       this.analyze("End Object:");
diff --git a/dist/human.esm.js.map b/dist/human.esm.js.map
index b2668719..296f7fff 100644
--- a/dist/human.esm.js.map
+++ b/dist/human.esm.js.map
@@ -1,7 +1,7 @@
 {
   "version": 3,
-  "sources": ["../src/util/util.ts", "../src/config.ts", "tfjs.esm.js", "../src/image/imagefxshaders.ts", "../src/image/imagefx.ts", "../src/image/enhance.ts", "../src/image/image.ts", "../src/util/env.ts", "../src/util/webcam.ts", "../src/models.ts", "../src/gear/gear.ts", "../src/tfjs/constants.ts", "../src/gear/ssrnet-age.ts", "../src/gear/ssrnet-gender.ts", "../src/face/antispoof.ts", "../src/face/facemeshcoords.ts", "../src/face/facemeshutil.ts", "../src/face/blazeface.ts", "../src/body/blazeposecoords.ts", "../src/body/blazeposedetector.ts", "../src/util/box.ts", "../src/body/blazepose.ts", "../src/object/labels.ts", "../src/object/centernet.ts", "../src/body/efficientposecoords.ts", "../src/body/efficientpose.ts", "../src/gear/emotion.ts", "../src/face/mobilefacenet.ts", "../src/face/insightface.ts", "../src/face/iris.ts", "../src/face/constants.ts", "../src/face/attention.ts", "../src/face/facemesh.ts", "../src/face/faceres.ts", "../src/hand/handposeutil.ts", "../src/hand/handposeanchors.ts", "../src/hand/handposedetector.ts", "../src/hand/handposepipeline.ts", "../src/hand/fingerdef.ts", "../src/hand/fingergesture.ts", "../src/hand/fingerpose.ts", "../src/hand/handpose.ts", "../src/tfjs/humangl.ts", "../src/tfjs/backend.ts", "../src/hand/handtrack.ts", "../src/face/liveness.ts", "../src/body/movenetcoords.ts", "../src/body/movenetfix.ts", "../src/body/movenet.ts", "../src/object/nanodet.ts", "../src/body/posenetutils.ts", "../src/body/posenet.ts", "../src/segmentation/segmentation.ts", "../src/tfjs/load.ts", "../src/draw/draw.ts", "../src/draw/primitives.ts", "../src/draw/options.ts", "../src/draw/face.ts", "../src/draw/body.ts", "../src/draw/hand.ts", "../src/draw/object.ts", "../src/draw/gesture.ts", "../src/face/mask.ts", "../src/face/angles.ts", "../src/face/face.ts", "../src/gesture/gesture.ts", "../src/util/interpolate.ts", "../src/face/match.ts", "../src/util/persons.ts", "../src/sample.ts", "../src/warmup.ts", "../src/human.ts"],
-  "sourcesContent": ["import type { Config } from '../exports';\n\n/**\n * Simple helper functions used accross codebase\n */\n\n// helper function: wrapper around console output\nexport function log(...msg): void {\n  const dt = new Date();\n  const ts = `${dt.getHours().toString().padStart(2, '0')}:${dt.getMinutes().toString().padStart(2, '0')}:${dt.getSeconds().toString().padStart(2, '0')}.${dt.getMilliseconds().toString().padStart(3, '0')}`;\n  if (msg) console.log(ts, 'Human:', ...msg); // eslint-disable-line no-console\n}\n\n// helper function: join two paths\nexport function join(folder: string, file: string): string {\n  const separator = folder.endsWith('/') ? '' : '/';\n  const skipJoin = file.startsWith('.') || file.startsWith('/') || file.startsWith('http:') || file.startsWith('https:') || file.startsWith('file:');\n  const path = skipJoin ? `${file}` : `${folder}${separator}${file}`;\n  if (!path.toLocaleLowerCase().includes('.json')) throw new Error(`modelpath error: expecting json file: ${path}`);\n  return path;\n}\n\n// helper function: gets elapsed time on both browser and nodejs\nexport const now = () => {\n  if (typeof performance !== 'undefined') return performance.now();\n  return parseInt((Number(process.hrtime.bigint()) / 1000 / 1000).toString());\n};\n\n// helper function: checks current config validity\nexport function validate(defaults: Partial<Config>, config: Partial<Config>, parent = 'config', msgs: { reason: string, where: string, expected?: string }[] = []) {\n  for (const key of Object.keys(config)) {\n    if (typeof config[key] === 'object') {\n      validate(defaults[key], config[key], key, msgs);\n    } else {\n      const defined = defaults && (typeof defaults[key] !== 'undefined');\n      if (!defined) msgs.push({ reason: 'unknown property', where: `${parent}.${key} = ${config[key]}` });\n      const same = defaults && typeof defaults[key] === typeof config[key];\n      if (defined && !same) msgs.push({ reason: 'property type mismatch', where: `${parent}.${key} = ${config[key]}`, expected: typeof defaults[key] });\n    }\n    // ok = ok && defined && same;\n  }\n  if (config.debug && parent === 'config' && msgs.length > 0) log('invalid configuration', msgs);\n  return msgs;\n}\n\n// helper function: perform deep merge of multiple objects so it allows full inheritance with overrides\nexport function mergeDeep(...objects) {\n  const isObject = (obj) => obj && typeof obj === 'object';\n  return objects.reduce((prev, obj) => {\n    Object.keys(obj || {}).forEach((key) => {\n      const pVal = prev[key];\n      const oVal = obj[key];\n      if (Array.isArray(pVal) && Array.isArray(oVal)) prev[key] = pVal.concat(...oVal);\n      else if (isObject(pVal) && isObject(oVal)) prev[key] = mergeDeep(pVal, oVal);\n      else prev[key] = oVal;\n    });\n    return prev;\n  }, {});\n}\n\n// helper function: return min and max from input array\nexport const minmax = (data: number[]) => data.reduce((acc: number[], val) => {\n  acc[0] = (acc[0] === undefined || val < acc[0]) ? val : acc[0];\n  acc[1] = (acc[1] === undefined || val > acc[1]) ? val : acc[1];\n  return acc;\n}, []);\n\n// helper function: async wait\nexport async function wait(time: number) {\n  const waiting = new Promise((resolve) => { setTimeout(() => resolve(true), time); });\n  await waiting;\n}\n", "/* eslint-disable no-multi-spaces */\n\n/** Generic config type inherited by all module types */\nexport interface GenericConfig {\n  /** is module enabled? */\n  enabled: boolean,\n  /** path to model json file (relative to `modelBasePath` */\n  modelPath: string,\n  /** how many max frames to go without re-running model if cached results are acceptable\n   * for two-phase models such as face and hand caching applies to bounding boxes detection only */\n  skipFrames: number,\n  /** how many max milliseconds to go without re-running model if cached results are acceptable\n  * for two-phase models such as face and hand caching applies to bounding boxes detection only */\n  skipTime: number,\n}\n\n/** Detector part of face configuration */\nexport interface FaceDetectorConfig extends GenericConfig {\n  /** is face rotation correction performed after detecting face?\n   * used to correctly analyze faces under high angles\n  */\n  rotation: boolean,\n  /** maximum number of detected faces */\n  maxDetected: number,\n  /** minimum confidence for a detected face before results are discarded */\n  minConfidence: number,\n  /** minimum overlap between two detected faces before one is discarded */\n  iouThreshold: number,\n  /** should child models perform on masked image of a face */\n  mask: boolean,\n  /** should face detection return processed and cropped face tensor that can with an external model for addtional processing?\n   * if enabled it must be manually deallocated to avoid memory leak */\n  return: boolean,\n}\n\n/** Mesh part of face configuration */\nexport interface FaceMeshConfig extends GenericConfig {\n  /** Keep detected faces that cannot be verified using facemesh */\n  keepInvalid: boolean\n}\n\n/** Iris part of face configuration */\nexport interface FaceIrisConfig extends GenericConfig {}\n\n/** Attention part of face configuration */\nexport interface FaceAttentionConfig extends GenericConfig {}\n\n/** Description or face embedding part of face configuration\n * - also used by age and gender detection\n */\nexport interface FaceDescriptionConfig extends GenericConfig {\n  /** minimum confidence for a detected face before results are discarded */\n  minConfidence: number,\n}\n\n/** Emotion part of face configuration */\nexport interface FaceEmotionConfig extends GenericConfig {\n  /** minimum confidence for a detected face before results are discarded */\n  minConfidence: number,\n}\n\n/** Anti-spoofing part of face configuration */\nexport interface FaceAntiSpoofConfig extends GenericConfig {}\n\n/** Liveness part of face configuration */\nexport interface FaceLivenessConfig extends GenericConfig {}\n\n/** Gear part of face configuration */\nexport interface FaceGearConfig extends GenericConfig {\n  /** minimum confidence for a detected race before results are discarded */\n  minConfidence: number,\n}\n\n/** Configures all face-specific options: face detection, mesh analysis, age, gender, emotion detection and face description */\nexport interface FaceConfig extends GenericConfig {\n  detector: Partial<FaceDetectorConfig>,\n  mesh: Partial<FaceMeshConfig>,\n  attention: Partial<FaceAttentionConfig>,\n  iris: Partial<FaceIrisConfig>,\n  description: Partial<FaceDescriptionConfig>,\n  emotion: Partial<FaceEmotionConfig>,\n  antispoof: Partial<FaceAntiSpoofConfig>,\n  liveness: Partial<FaceLivenessConfig>,\n  gear: Partial<FaceGearConfig>,\n}\n\n/** Configures all body detection specific options */\nexport interface BodyConfig extends GenericConfig {\n  /** maximum number of detected bodies */\n  maxDetected: number,\n  /** minimum confidence for a detected body before results are discarded */\n  minConfidence: number,\n  /* experimental\n  /** experimental: detector used for body model before actual analysis\n  detector?: {\n    /** experimental: enable body detector before body landmarks\n    enabled: boolean,\n    /** experimental: path to optional body detector model json file\n    modelPath: string,\n    /** experimental: minimum confidence for a detected body before results are discarded\n    minConfidence: number,\n    /** experimental: minimum overlap between two detected bodies before one is discarded\n    iouThreshold: number\n  },\n  */\n}\n\n/** Configures all hand detection specific options */\nexport interface HandConfig extends GenericConfig {\n  /** should hand rotation correction be performed after hand detection? */\n  rotation: boolean,\n  /** minimum confidence for a detected hand before results are discarded */\n  minConfidence: number,\n  /** minimum overlap between two detected hands before one is discarded */\n  iouThreshold: number,\n  /** maximum number of detected hands */\n  maxDetected: number,\n  /** should hand landmarks be detected or just return detected hand box */\n  landmarks: boolean,\n  detector: {\n    /** path to hand detector model json */\n    modelPath?: string,\n  },\n  skeleton: {\n    /** path to hand skeleton model json */\n    modelPath?: string,\n  },\n}\n\n/** Configures all object detection specific options */\nexport interface ObjectConfig extends GenericConfig {\n  /** minimum confidence for a detected objects before results are discarded */\n  minConfidence: number,\n  /** minimum overlap between two detected objects before one is discarded */\n  iouThreshold: number,\n  /** maximum number of detected objects */\n  maxDetected: number,\n}\n\n/** Configures all body segmentation module\n * removes background from input containing person\n * if segmentation is enabled it will run as preprocessing task before any other model\n * alternatively leave it disabled and use it on-demand using human.segmentation method which can\n * remove background or replace it with user-provided background\n*/\nexport interface SegmentationConfig extends GenericConfig {\n  /** blur segmentation output by <number> pixels for more realistic image */\n  blur: number,\n}\n\n/** Run input through image filters before inference\n * - available only in Browser environments\n * - image filters run with near-zero latency as they are executed on the GPU using WebGL\n*/\nexport interface FilterConfig {\n  /** are image filters enabled? */\n  enabled: boolean,\n  /** perform image histogram equalization\n   * - equalization is performed on input as a whole and detected face before its passed for further analysis\n  */\n  equalization: boolean,\n  /** resize input width\n  * - if both width and height are set to 0, there is no resizing\n  * - if just one is set, second one is scaled automatically\n  * - if both are set, values are used as-is\n  */\n  width: number,\n  /** resize input height\n  * - if both width and height are set to 0, there is no resizing\n  * - if just one is set, second one is scaled automatically\n  * - if both are set, values are used as-is\n  */\n  height: number,\n  /** return processed canvas imagedata in result */\n  return: boolean,\n  /** flip input as mirror image */\n  flip: boolean,\n  /** range: -1 (darken) to 1 (lighten) */\n  brightness: number,\n  /** range: -1 (reduce contrast) to 1 (increase contrast) */\n  contrast: number,\n  /** range: 0 (no sharpening) to 1 (maximum sharpening) */\n  sharpness: number,\n  /** range: 0 (no blur) to N (blur radius in pixels) */\n  blur: number\n  /** range: -1 (reduce saturation) to 1 (increase saturation) */\n  saturation: number,\n  /** range: 0 (no change) to 360 (hue rotation in degrees) */\n  hue: number,\n  /** image negative */\n  negative: boolean,\n  /** image sepia colors */\n  sepia: boolean,\n  /** image vintage colors */\n  vintage: boolean,\n  /** image kodachrome colors */\n  kodachrome: boolean,\n  /** image technicolor colors */\n  technicolor: boolean,\n  /** image polaroid camera effect */\n  polaroid: boolean,\n  /** range: 0 (no pixelate) to N (number of pixels to pixelate) */\n  pixelate: number,\n}\n\n/** Controlls gesture detection */\nexport interface GestureConfig {\n  /** is gesture detection enabled? */\n  enabled: boolean,\n}\n/** Possible TensorFlow backends */\nexport type BackendEnum = '' | 'cpu' | 'wasm' | 'webgl' | 'humangl' | 'tensorflow' | 'webgpu';\n\n/** Possible values for `human.warmup` */\nexport type WarmupEnum = '' | 'none' | 'face' | 'full' | 'body';\n\n/**\n * Configuration interface definition for **Human** library\n * Contains all configurable parameters\n * Defaults: [config](https://github.com/vladmandic/human/blob/main/src/config.ts#L262)\n */\nexport interface Config {\n  /** Backend used for TFJS operations\n   * valid build-in backends are:\n   * - Browser: `cpu`, `wasm`, `webgl`, `humangl`, `webgpu`\n   * - NodeJS: `cpu`, `wasm`, `tensorflow`\n   * default: `webgl` for browser and `tensorflow` for nodejs\n  */\n  backend: BackendEnum,\n\n  /** Path to *.wasm files if backend is set to `wasm`\n   *\n   * default: auto-detects to link to CDN `jsdelivr` when running in browser\n  */\n  wasmPath: string,\n\n  /** Force WASM loader to use platform fetch\n   *\n   * default: false\n  */\n  wasmPlatformFetch: boolean,\n\n  /** Print debug statements to console\n   *\n   * default: `true`\n  */\n  debug: boolean,\n\n  /** Perform model loading and inference concurrently or sequentially\n   *\n   * default: `true`\n  */\n  async: boolean,\n\n  /** What to use for `human.warmup()`\n   * - warmup pre-initializes all models for faster inference but can take significant time on startup\n   * - used by `webgl`, `humangl` and `webgpu` backends\n   *\n   * default: `full`\n  */\n  warmup: WarmupEnum,\n\n  /** Base model path (typically starting with file://, http:// or https://) for all models\n   * - individual modelPath values are relative to this path\n   *\n   * default: `../models/` for browsers and `file://models/` for nodejs\n  */\n  modelBasePath: string,\n\n  /** Cache models in IndexDB on first sucessfull load\n   * default: true if indexdb is available (browsers), false if its not (nodejs)\n   */\n  cacheModels: boolean,\n\n  /** Validate kernel ops used in model during model load\n   * default: true\n   * any errors will be printed on console but will be treated as non-fatal\n   */\n  validateModels: boolean,\n\n  /** Cache sensitivity\n   * - values 0..1 where 0.01 means reset cache if input changed more than 1%\n   * - set to 0 to disable caching\n   *\n   * default: 0.7\n  */\n  cacheSensitivity: number;\n\n  /** Explicit flags passed to initialize TFJS */\n  flags: Record<string, unknown>,\n\n  /** Software Kernels\n   * Registers software kernel ops running on CPU when accelerated version of kernel is not found in the current backend\n  */\n  softwareKernels: boolean,\n\n  /** Perform immediate garbage collection on deallocated tensors instead of caching them */\n  deallocate: boolean;\n\n  /** Internal Variable */\n  skipAllowed: boolean;\n\n  /** Filter config {@link FilterConfig} */\n  filter: Partial<FilterConfig>,\n\n  /** Gesture config {@link GestureConfig} */\n  gesture: Partial<GestureConfig>;\n\n  /** Face config {@link FaceConfig} */\n  face: Partial<FaceConfig>,\n\n  /** Body config {@link BodyConfig} */\n  body: Partial<BodyConfig>,\n\n  /** Hand config {@link HandConfig} */\n  hand: Partial<HandConfig>,\n\n  /** Object config {@link ObjectConfig} */\n  object: Partial<ObjectConfig>,\n\n  /** Segmentation config {@link SegmentationConfig} */\n  segmentation: Partial<SegmentationConfig>,\n}\n\n/** - [See all default Config values...](https://github.com/vladmandic/human/blob/main/src/config.ts#L262) */\nconst config: Config = {\n  backend: '',\n  modelBasePath: '',\n  cacheModels: true,\n  validateModels: true,\n  wasmPath: '',\n  wasmPlatformFetch: false,\n  debug: false,\n  async: true,\n  warmup: 'full',\n  cacheSensitivity: 0.70,\n  skipAllowed: false,\n  deallocate: false,\n  flags: {},\n  softwareKernels: false,\n  filter: {\n    enabled: true,\n    equalization: false,\n    width: 0,\n    height: 0,\n    flip: false,\n    return: true,\n    brightness: 0,\n    contrast: 0,\n    sharpness: 0,\n    blur: 0,\n    saturation: 0,\n    hue: 0,\n    negative: false,\n    sepia: false,\n    vintage: false,\n    kodachrome: false,\n    technicolor: false,\n    polaroid: false,\n    pixelate: 0,\n  },\n  gesture: {\n    enabled: true,\n  },\n  face: {\n    enabled: true,\n    detector: {\n      modelPath: 'blazeface.json',\n      rotation: true,\n      maxDetected: 1,\n      skipFrames: 99,\n      skipTime: 2500,\n      minConfidence: 0.2,\n      iouThreshold: 0.1,\n      mask: false,\n      return: false,\n    },\n    mesh: {\n      enabled: true,\n      modelPath: 'facemesh.json',\n      keepInvalid: false,\n    },\n    attention: {\n      enabled: false,\n      modelPath: 'facemesh-attention.json',\n    },\n    iris: {\n      enabled: true,\n      modelPath: 'iris.json',\n    },\n    emotion: {\n      enabled: true,\n      minConfidence: 0.1,\n      skipFrames: 99,\n      skipTime: 1500,\n      modelPath: 'emotion.json',\n    },\n    description: {\n      enabled: true,\n      modelPath: 'faceres.json',\n      skipFrames: 99,\n      skipTime: 3000,\n      minConfidence: 0.1,\n    },\n    antispoof: {\n      enabled: false,\n      skipFrames: 99,\n      skipTime: 4000,\n      modelPath: 'antispoof.json',\n    },\n    liveness: {\n      enabled: false,\n      skipFrames: 99,\n      skipTime: 4000,\n      modelPath: 'liveness.json',\n    },\n  },\n  body: {\n    enabled: true,\n    modelPath: 'movenet-lightning.json',\n    maxDetected: -1,\n    minConfidence: 0.3,\n    skipFrames: 1,\n    skipTime: 200,\n  },\n  hand: {\n    enabled: true,\n    rotation: true,\n    skipFrames: 99,\n    skipTime: 1000,\n    minConfidence: 0.50,\n    iouThreshold: 0.2,\n    maxDetected: -1,\n    landmarks: true,\n    detector: {\n      modelPath: 'handtrack.json',\n    },\n    skeleton: {\n      modelPath: 'handlandmark-full.json',\n    },\n  },\n  object: {\n    enabled: false,\n    modelPath: 'mb3-centernet.json',\n    minConfidence: 0.2,\n    iouThreshold: 0.4,\n    maxDetected: 10,\n    skipFrames: 99,\n    skipTime: 2000,\n  },\n  segmentation: {\n    enabled: false,\n    modelPath: 'selfie.json',\n    blur: 8,\n  },\n};\n\nexport { config as defaults };\n", "/*\n  Human\n  homepage: <https://github.com/vladmandic/human>\n  author: <https://github.com/vladmandic>'\n*/\n\nvar __create = Object.create;\nvar __defProp = Object.defineProperty;\nvar __getOwnPropDesc = Object.getOwnPropertyDescriptor;\nvar __getOwnPropNames = Object.getOwnPropertyNames;\nvar __getProtoOf = Object.getPrototypeOf;\nvar __hasOwnProp = Object.prototype.hasOwnProperty;\nvar __commonJS = (cb, mod4) => function __require() {\n  return mod4 || (0, cb[__getOwnPropNames(cb)[0]])((mod4 = { exports: {} }).exports, mod4), mod4.exports;\n};\nvar __export = (target, all5) => {\n  for (var name in all5)\n    __defProp(target, name, { get: all5[name], enumerable: true });\n};\nvar __copyProps = (to, from, except, desc) => {\n  if (from && typeof from === \"object\" || typeof from === \"function\") {\n    for (let key of __getOwnPropNames(from))\n      if (!__hasOwnProp.call(to, key) && key !== except)\n        __defProp(to, key, { get: () => from[key], enumerable: !(desc = __getOwnPropDesc(from, key)) || desc.enumerable });\n  }\n  return to;\n};\nvar 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132,\n        32,\n        2,\n        173,\n        32,\n        3,\n        173,\n        66,\n        32,\n        134,\n        132,\n        130,\n        34,\n        4,\n        66,\n        32,\n        135,\n        167,\n        36,\n        0,\n        32,\n        4,\n        167,\n        11\n      ])), {}).exports;\n    } catch (e2) {\n    }\n    function Long2(low, high, unsigned) {\n      this.low = low | 0;\n      this.high = high | 0;\n      this.unsigned = !!unsigned;\n    }\n    Long2.prototype.__isLong__;\n    Object.defineProperty(Long2.prototype, \"__isLong__\", { value: true });\n    function isLong(obj) {\n      return (obj && obj[\"__isLong__\"]) === true;\n    }\n    Long2.isLong = isLong;\n    var INT_CACHE = {};\n    var UINT_CACHE = {};\n    function fromInt(value, unsigned) {\n      var obj, cachedObj, cache;\n      if (unsigned) {\n        value >>>= 0;\n        if (cache = 0 <= value && value < 256) {\n          cachedObj = UINT_CACHE[value];\n          if (cachedObj)\n            return cachedObj;\n        }\n        obj = fromBits(value, (value | 0) < 0 ? -1 : 0, true);\n        if (cache)\n          UINT_CACHE[value] = obj;\n        return obj;\n      } else {\n        value |= 0;\n        if (cache = -128 <= value && value < 128) {\n          cachedObj = INT_CACHE[value];\n          if (cachedObj)\n            return cachedObj;\n        }\n        obj = fromBits(value, value < 0 ? -1 : 0, false);\n        if (cache)\n          INT_CACHE[value] = obj;\n        return obj;\n      }\n    }\n    Long2.fromInt = fromInt;\n    function fromNumber(value, unsigned) {\n      if (isNaN(value))\n        return unsigned ? UZERO : ZERO;\n      if (unsigned) {\n        if (value < 0)\n          return UZERO;\n        if (value >= TWO_PWR_64_DBL)\n          return MAX_UNSIGNED_VALUE;\n      } else {\n        if (value <= -TWO_PWR_63_DBL)\n          return MIN_VALUE;\n        if (value + 1 >= TWO_PWR_63_DBL)\n          return MAX_VALUE;\n      }\n      if (value < 0)\n        return fromNumber(-value, unsigned).neg();\n      return fromBits(value % TWO_PWR_32_DBL | 0, value / TWO_PWR_32_DBL | 0, unsigned);\n    }\n    Long2.fromNumber = fromNumber;\n    function fromBits(lowBits, highBits, unsigned) {\n      return new Long2(lowBits, highBits, unsigned);\n    }\n    Long2.fromBits = fromBits;\n    var pow_dbl = Math.pow;\n    function fromString(str, unsigned, radix) {\n      if (str.length === 0)\n        throw Error(\"empty string\");\n      if (str === \"NaN\" || str === \"Infinity\" || str === \"+Infinity\" || str === \"-Infinity\")\n        return ZERO;\n      if (typeof unsigned === \"number\") {\n        radix = unsigned, unsigned = false;\n      } else {\n        unsigned = !!unsigned;\n      }\n      radix = radix || 10;\n      if (radix < 2 || 36 < radix)\n        throw RangeError(\"radix\");\n      var p2;\n      if ((p2 = str.indexOf(\"-\")) > 0)\n        throw Error(\"interior hyphen\");\n      else if (p2 === 0) {\n        return fromString(str.substring(1), unsigned, radix).neg();\n      }\n      var radixToPower = fromNumber(pow_dbl(radix, 8));\n      var result = ZERO;\n      for (var i2 = 0; i2 < str.length; i2 += 8) {\n        var size = Math.min(8, str.length - i2), value = parseInt(str.substring(i2, i2 + size), radix);\n        if (size < 8) {\n          var power = fromNumber(pow_dbl(radix, size));\n          result = result.mul(power).add(fromNumber(value));\n        } else {\n          result = result.mul(radixToPower);\n          result = result.add(fromNumber(value));\n        }\n      }\n      result.unsigned = unsigned;\n      return result;\n    }\n    Long2.fromString = fromString;\n    function fromValue(val, unsigned) {\n      if (typeof val === \"number\")\n        return fromNumber(val, unsigned);\n      if (typeof val === \"string\")\n        return fromString(val, unsigned);\n      return fromBits(val.low, val.high, typeof unsigned === \"boolean\" ? unsigned : val.unsigned);\n    }\n    Long2.fromValue = fromValue;\n    var TWO_PWR_16_DBL = 1 << 16;\n    var TWO_PWR_24_DBL = 1 << 24;\n    var TWO_PWR_32_DBL = TWO_PWR_16_DBL * TWO_PWR_16_DBL;\n    var TWO_PWR_64_DBL = TWO_PWR_32_DBL * TWO_PWR_32_DBL;\n    var TWO_PWR_63_DBL = TWO_PWR_64_DBL / 2;\n    var TWO_PWR_24 = fromInt(TWO_PWR_24_DBL);\n    var ZERO = fromInt(0);\n    Long2.ZERO = ZERO;\n    var UZERO = fromInt(0, true);\n    Long2.UZERO = UZERO;\n    var ONE = fromInt(1);\n    Long2.ONE = ONE;\n    var UONE = fromInt(1, true);\n    Long2.UONE = UONE;\n    var NEG_ONE = fromInt(-1);\n    Long2.NEG_ONE = NEG_ONE;\n    var MAX_VALUE = fromBits(4294967295 | 0, 2147483647 | 0, false);\n    Long2.MAX_VALUE = MAX_VALUE;\n    var MAX_UNSIGNED_VALUE = fromBits(4294967295 | 0, 4294967295 | 0, true);\n    Long2.MAX_UNSIGNED_VALUE = MAX_UNSIGNED_VALUE;\n    var MIN_VALUE = fromBits(0, 2147483648 | 0, false);\n    Long2.MIN_VALUE = MIN_VALUE;\n    var LongPrototype = Long2.prototype;\n    LongPrototype.toInt = function toInt() {\n      return this.unsigned ? this.low >>> 0 : this.low;\n    };\n    LongPrototype.toNumber = function toNumber() {\n      if (this.unsigned)\n        return (this.high >>> 0) * TWO_PWR_32_DBL + (this.low >>> 0);\n      return this.high * TWO_PWR_32_DBL + (this.low >>> 0);\n    };\n    LongPrototype.toString = function toString(radix) {\n      radix = radix || 10;\n      if (radix < 2 || 36 < radix)\n        throw RangeError(\"radix\");\n      if (this.isZero())\n        return \"0\";\n      if (this.isNegative()) {\n        if (this.eq(MIN_VALUE)) {\n          var radixLong = fromNumber(radix), div3 = this.div(radixLong), rem1 = div3.mul(radixLong).sub(this);\n          return div3.toString(radix) + rem1.toInt().toString(radix);\n        } else\n          return \"-\" + this.neg().toString(radix);\n      }\n      var radixToPower = fromNumber(pow_dbl(radix, 6), this.unsigned), rem = this;\n      var result = \"\";\n      while (true) {\n        var remDiv = rem.div(radixToPower), intval = rem.sub(remDiv.mul(radixToPower)).toInt() >>> 0, digits = intval.toString(radix);\n        rem = remDiv;\n        if (rem.isZero())\n          return digits + result;\n        else {\n          while (digits.length < 6)\n            digits = \"0\" + digits;\n          result = \"\" + digits + result;\n        }\n      }\n    };\n    LongPrototype.getHighBits = function getHighBits() {\n      return this.high;\n    };\n    LongPrototype.getHighBitsUnsigned = function getHighBitsUnsigned() {\n      return this.high >>> 0;\n    };\n    LongPrototype.getLowBits = function getLowBits() {\n      return this.low;\n    };\n    LongPrototype.getLowBitsUnsigned = function getLowBitsUnsigned() {\n      return this.low >>> 0;\n    };\n    LongPrototype.getNumBitsAbs = function getNumBitsAbs() {\n      if (this.isNegative())\n        return this.eq(MIN_VALUE) ? 64 : this.neg().getNumBitsAbs();\n      var val = this.high != 0 ? this.high : this.low;\n      for (var bit = 31; bit > 0; bit--)\n        if ((val & 1 << bit) != 0)\n          break;\n      return this.high != 0 ? bit + 33 : bit + 1;\n    };\n    LongPrototype.isZero = function isZero() {\n      return this.high === 0 && this.low === 0;\n    };\n    LongPrototype.eqz = LongPrototype.isZero;\n    LongPrototype.isNegative = function isNegative() {\n      return !this.unsigned && this.high < 0;\n    };\n    LongPrototype.isPositive = function isPositive() {\n      return this.unsigned || this.high >= 0;\n    };\n    LongPrototype.isOdd = function isOdd() {\n      return (this.low & 1) === 1;\n    };\n    LongPrototype.isEven = function isEven2() {\n      return (this.low & 1) === 0;\n    };\n    LongPrototype.equals = function equals(other) {\n      if (!isLong(other))\n        other = fromValue(other);\n      if (this.unsigned !== other.unsigned && this.high >>> 31 === 1 && other.high >>> 31 === 1)\n        return false;\n      return this.high === other.high && this.low === other.low;\n    };\n    LongPrototype.eq = LongPrototype.equals;\n    LongPrototype.notEquals = function notEquals(other) {\n      return !this.eq(other);\n    };\n    LongPrototype.neq = LongPrototype.notEquals;\n    LongPrototype.ne = LongPrototype.notEquals;\n    LongPrototype.lessThan = function lessThan(other) {\n      return this.comp(other) < 0;\n    };\n    LongPrototype.lt = LongPrototype.lessThan;\n    LongPrototype.lessThanOrEqual = function lessThanOrEqual(other) {\n      return this.comp(other) <= 0;\n    };\n    LongPrototype.lte = LongPrototype.lessThanOrEqual;\n    LongPrototype.le = LongPrototype.lessThanOrEqual;\n    LongPrototype.greaterThan = function greaterThan(other) {\n      return this.comp(other) > 0;\n    };\n    LongPrototype.gt = LongPrototype.greaterThan;\n    LongPrototype.greaterThanOrEqual = function greaterThanOrEqual(other) {\n      return this.comp(other) >= 0;\n    };\n    LongPrototype.gte = LongPrototype.greaterThanOrEqual;\n    LongPrototype.ge = LongPrototype.greaterThanOrEqual;\n    LongPrototype.compare = function compare(other) {\n      if (!isLong(other))\n        other = fromValue(other);\n      if (this.eq(other))\n        return 0;\n      var thisNeg = this.isNegative(), otherNeg = other.isNegative();\n      if (thisNeg && !otherNeg)\n        return -1;\n      if (!thisNeg && otherNeg)\n        return 1;\n      if (!this.unsigned)\n        return this.sub(other).isNegative() ? -1 : 1;\n      return other.high >>> 0 > this.high >>> 0 || other.high === this.high && other.low >>> 0 > this.low >>> 0 ? -1 : 1;\n    };\n    LongPrototype.comp = LongPrototype.compare;\n    LongPrototype.negate = function negate() {\n      if (!this.unsigned && this.eq(MIN_VALUE))\n        return MIN_VALUE;\n      return this.not().add(ONE);\n    };\n    LongPrototype.neg = LongPrototype.negate;\n    LongPrototype.add = function add5(addend) {\n      if (!isLong(addend))\n        addend = fromValue(addend);\n      var a48 = this.high >>> 16;\n      var a32 = this.high & 65535;\n      var a16 = this.low >>> 16;\n      var a00 = this.low & 65535;\n      var b48 = addend.high >>> 16;\n      var b32 = addend.high & 65535;\n      var b16 = addend.low >>> 16;\n      var b00 = addend.low & 65535;\n      var c48 = 0, c32 = 0, c16 = 0, c00 = 0;\n      c00 += a00 + b00;\n      c16 += c00 >>> 16;\n      c00 &= 65535;\n      c16 += a16 + b16;\n      c32 += c16 >>> 16;\n      c16 &= 65535;\n      c32 += a32 + b32;\n      c48 += c32 >>> 16;\n      c32 &= 65535;\n      c48 += a48 + b48;\n      c48 &= 65535;\n      return fromBits(c16 << 16 | c00, c48 << 16 | c32, this.unsigned);\n    };\n    LongPrototype.subtract = function subtract(subtrahend) {\n      if (!isLong(subtrahend))\n        subtrahend = fromValue(subtrahend);\n      return this.add(subtrahend.neg());\n    };\n    LongPrototype.sub = LongPrototype.subtract;\n    LongPrototype.multiply = function multiply4(multiplier) {\n      if (this.isZero())\n        return ZERO;\n      if (!isLong(multiplier))\n        multiplier = fromValue(multiplier);\n      if (wasm) {\n        var low = wasm.mul(\n          this.low,\n          this.high,\n          multiplier.low,\n          multiplier.high\n        );\n        return fromBits(low, wasm.get_high(), this.unsigned);\n      }\n      if (multiplier.isZero())\n        return ZERO;\n      if (this.eq(MIN_VALUE))\n        return multiplier.isOdd() ? MIN_VALUE : ZERO;\n      if (multiplier.eq(MIN_VALUE))\n        return this.isOdd() ? MIN_VALUE : ZERO;\n      if (this.isNegative()) {\n        if (multiplier.isNegative())\n          return this.neg().mul(multiplier.neg());\n        else\n          return this.neg().mul(multiplier).neg();\n      } else if (multiplier.isNegative())\n        return this.mul(multiplier.neg()).neg();\n      if (this.lt(TWO_PWR_24) && multiplier.lt(TWO_PWR_24))\n        return fromNumber(this.toNumber() * multiplier.toNumber(), this.unsigned);\n      var a48 = this.high >>> 16;\n      var a32 = this.high & 65535;\n      var a16 = this.low >>> 16;\n      var a00 = this.low & 65535;\n      var b48 = multiplier.high >>> 16;\n      var b32 = multiplier.high & 65535;\n      var b16 = multiplier.low >>> 16;\n      var b00 = multiplier.low & 65535;\n      var c48 = 0, c32 = 0, c16 = 0, c00 = 0;\n      c00 += a00 * b00;\n      c16 += c00 >>> 16;\n      c00 &= 65535;\n      c16 += a16 * b00;\n      c32 += c16 >>> 16;\n      c16 &= 65535;\n      c16 += a00 * b16;\n      c32 += c16 >>> 16;\n      c16 &= 65535;\n      c32 += a32 * b00;\n      c48 += c32 >>> 16;\n      c32 &= 65535;\n      c32 += a16 * b16;\n      c48 += c32 >>> 16;\n      c32 &= 65535;\n      c32 += a00 * b32;\n      c48 += c32 >>> 16;\n      c32 &= 65535;\n      c48 += a48 * b00 + a32 * b16 + a16 * b32 + a00 * b48;\n      c48 &= 65535;\n      return fromBits(c16 << 16 | c00, c48 << 16 | c32, this.unsigned);\n    };\n    LongPrototype.mul = LongPrototype.multiply;\n    LongPrototype.divide = function divide(divisor) {\n      if (!isLong(divisor))\n        divisor = fromValue(divisor);\n      if (divisor.isZero())\n        throw Error(\"division by zero\");\n      if (wasm) {\n        if (!this.unsigned && this.high === -2147483648 && divisor.low === -1 && divisor.high === -1) {\n          return this;\n        }\n        var low = (this.unsigned ? wasm.div_u : wasm.div_s)(\n          this.low,\n          this.high,\n          divisor.low,\n          divisor.high\n        );\n        return fromBits(low, wasm.get_high(), this.unsigned);\n      }\n      if (this.isZero())\n        return this.unsigned ? UZERO : ZERO;\n      var approx, rem, res;\n      if (!this.unsigned) {\n        if (this.eq(MIN_VALUE)) {\n          if (divisor.eq(ONE) || divisor.eq(NEG_ONE))\n            return MIN_VALUE;\n          else if (divisor.eq(MIN_VALUE))\n            return ONE;\n          else {\n            var halfThis = this.shr(1);\n            approx = halfThis.div(divisor).shl(1);\n            if (approx.eq(ZERO)) {\n              return divisor.isNegative() ? ONE : NEG_ONE;\n            } else {\n              rem = this.sub(divisor.mul(approx));\n              res = approx.add(rem.div(divisor));\n              return res;\n            }\n          }\n        } else if (divisor.eq(MIN_VALUE))\n          return this.unsigned ? UZERO : ZERO;\n        if (this.isNegative()) {\n          if (divisor.isNegative())\n            return this.neg().div(divisor.neg());\n          return this.neg().div(divisor).neg();\n        } else if (divisor.isNegative())\n          return this.div(divisor.neg()).neg();\n        res = ZERO;\n      } else {\n        if (!divisor.unsigned)\n          divisor = divisor.toUnsigned();\n        if (divisor.gt(this))\n          return UZERO;\n        if (divisor.gt(this.shru(1)))\n          return UONE;\n        res = UZERO;\n      }\n      rem = this;\n      while (rem.gte(divisor)) {\n        approx = Math.max(1, Math.floor(rem.toNumber() / divisor.toNumber()));\n        var log22 = Math.ceil(Math.log(approx) / Math.LN2), delta = log22 <= 48 ? 1 : pow_dbl(2, log22 - 48), approxRes = fromNumber(approx), approxRem = approxRes.mul(divisor);\n        while (approxRem.isNegative() || approxRem.gt(rem)) {\n          approx -= delta;\n          approxRes = fromNumber(approx, this.unsigned);\n          approxRem = approxRes.mul(divisor);\n        }\n        if (approxRes.isZero())\n          approxRes = ONE;\n        res = res.add(approxRes);\n        rem = rem.sub(approxRem);\n      }\n      return res;\n    };\n    LongPrototype.div = LongPrototype.divide;\n    LongPrototype.modulo = function modulo(divisor) {\n      if (!isLong(divisor))\n        divisor = fromValue(divisor);\n      if (wasm) {\n        var low = (this.unsigned ? wasm.rem_u : wasm.rem_s)(\n          this.low,\n          this.high,\n          divisor.low,\n          divisor.high\n        );\n        return fromBits(low, wasm.get_high(), this.unsigned);\n      }\n      return this.sub(this.div(divisor).mul(divisor));\n    };\n    LongPrototype.mod = LongPrototype.modulo;\n    LongPrototype.rem = LongPrototype.modulo;\n    LongPrototype.not = function not() {\n      return fromBits(~this.low, ~this.high, this.unsigned);\n    };\n    LongPrototype.and = function and(other) {\n      if (!isLong(other))\n        other = fromValue(other);\n      return fromBits(this.low & other.low, this.high & other.high, this.unsigned);\n    };\n    LongPrototype.or = function or(other) {\n      if (!isLong(other))\n        other = fromValue(other);\n      return fromBits(this.low | other.low, this.high | other.high, this.unsigned);\n    };\n    LongPrototype.xor = function xor(other) {\n      if (!isLong(other))\n        other = fromValue(other);\n      return fromBits(this.low ^ other.low, this.high ^ other.high, this.unsigned);\n    };\n    LongPrototype.shiftLeft = function shiftLeft(numBits) {\n      if (isLong(numBits))\n        numBits = numBits.toInt();\n      if ((numBits &= 63) === 0)\n        return this;\n      else if (numBits < 32)\n        return fromBits(this.low << numBits, this.high << numBits | this.low >>> 32 - numBits, this.unsigned);\n      else\n        return fromBits(0, this.low << numBits - 32, this.unsigned);\n    };\n    LongPrototype.shl = LongPrototype.shiftLeft;\n    LongPrototype.shiftRight = function shiftRight(numBits) {\n      if (isLong(numBits))\n        numBits = numBits.toInt();\n      if ((numBits &= 63) === 0)\n        return this;\n      else if (numBits < 32)\n        return fromBits(this.low >>> numBits | this.high << 32 - numBits, this.high >> numBits, this.unsigned);\n      else\n        return fromBits(this.high >> numBits - 32, this.high >= 0 ? 0 : -1, this.unsigned);\n    };\n    LongPrototype.shr = LongPrototype.shiftRight;\n    LongPrototype.shiftRightUnsigned = function shiftRightUnsigned(numBits) {\n      if (isLong(numBits))\n        numBits = numBits.toInt();\n      numBits &= 63;\n      if (numBits === 0)\n        return this;\n      else {\n        var high = this.high;\n        if (numBits < 32) {\n          var low = this.low;\n          return fromBits(low >>> numBits | high << 32 - numBits, high >>> numBits, this.unsigned);\n        } else if (numBits === 32)\n          return fromBits(high, 0, this.unsigned);\n        else\n          return fromBits(high >>> numBits - 32, 0, this.unsigned);\n      }\n    };\n    LongPrototype.shru = LongPrototype.shiftRightUnsigned;\n    LongPrototype.shr_u = LongPrototype.shiftRightUnsigned;\n    LongPrototype.toSigned = function toSigned() {\n      if (!this.unsigned)\n        return this;\n      return fromBits(this.low, this.high, false);\n    };\n    LongPrototype.toUnsigned = function toUnsigned() {\n      if (this.unsigned)\n        return this;\n      return fromBits(this.low, this.high, true);\n    };\n    LongPrototype.toBytes = function toBytes(le) {\n      return le ? this.toBytesLE() : this.toBytesBE();\n    };\n    LongPrototype.toBytesLE = function toBytesLE() {\n      var hi = this.high, lo = this.low;\n      return [\n        lo & 255,\n        lo >>> 8 & 255,\n        lo >>> 16 & 255,\n        lo >>> 24,\n        hi & 255,\n        hi >>> 8 & 255,\n        hi >>> 16 & 255,\n        hi >>> 24\n      ];\n    };\n    LongPrototype.toBytesBE = function toBytesBE() {\n      var hi = this.high, lo = this.low;\n      return [\n        hi >>> 24,\n        hi >>> 16 & 255,\n        hi >>> 8 & 255,\n        hi & 255,\n        lo >>> 24,\n        lo >>> 16 & 255,\n        lo >>> 8 & 255,\n        lo & 255\n      ];\n    };\n    Long2.fromBytes = function fromBytes(bytes, unsigned, le) {\n      return le ? Long2.fromBytesLE(bytes, unsigned) : Long2.fromBytesBE(bytes, unsigned);\n    };\n    Long2.fromBytesLE = function fromBytesLE(bytes, unsigned) {\n      return new Long2(\n        bytes[0] | bytes[1] << 8 | bytes[2] << 16 | bytes[3] << 24,\n        bytes[4] | bytes[5] << 8 | bytes[6] << 16 | bytes[7] << 24,\n        unsigned\n      );\n    };\n    Long2.fromBytesBE = function fromBytesBE(bytes, unsigned) {\n      return new Long2(\n        bytes[4] << 24 | bytes[5] << 16 | bytes[6] << 8 | bytes[7],\n        bytes[0] << 24 | bytes[1] << 16 | bytes[2] << 8 | bytes[3],\n        unsigned\n      );\n    };\n  }\n});\n\n// (disabled):node_modules/.pnpm/node-fetch@2.6.7/node_modules/node-fetch/browser.js\nvar require_browser = __commonJS({\n  \"(disabled):node_modules/.pnpm/node-fetch@2.6.7/node_modules/node-fetch/browser.js\"() {\n  }\n});\n\n// (disabled):util\nvar require_util = __commonJS({\n  \"(disabled):util\"() {\n  }\n});\n\n// node_modules/.pnpm/seedrandom@3.0.5/node_modules/seedrandom/lib/alea.js\nvar require_alea = __commonJS({\n  \"node_modules/.pnpm/seedrandom@3.0.5/node_modules/seedrandom/lib/alea.js\"(exports, module) {\n    (function(global2, module2, define2) {\n      function Alea(seed) {\n        var me = this, mash = Mash();\n        me.next = function() {\n          var t2 = 2091639 * me.s0 + me.c * 23283064365386963e-26;\n          me.s0 = me.s1;\n          me.s1 = me.s2;\n          return me.s2 = t2 - (me.c = t2 | 0);\n        };\n        me.c = 1;\n        me.s0 = mash(\" \");\n        me.s1 = mash(\" \");\n        me.s2 = mash(\" \");\n        me.s0 -= mash(seed);\n        if (me.s0 < 0) {\n          me.s0 += 1;\n        }\n        me.s1 -= mash(seed);\n        if (me.s1 < 0) {\n          me.s1 += 1;\n        }\n        me.s2 -= mash(seed);\n        if (me.s2 < 0) {\n          me.s2 += 1;\n        }\n        mash = null;\n      }\n      function copy(f, t2) {\n        t2.c = f.c;\n        t2.s0 = f.s0;\n        t2.s1 = f.s1;\n        t2.s2 = f.s2;\n        return t2;\n      }\n      function impl(seed, opts) {\n        var xg = new Alea(seed), state = opts && opts.state, prng = xg.next;\n        prng.int32 = function() {\n          return xg.next() * 4294967296 | 0;\n        };\n        prng.double = function() {\n          return prng() + (prng() * 2097152 | 0) * 11102230246251565e-32;\n        };\n        prng.quick = prng;\n        if (state) {\n          if (typeof state == \"object\")\n            copy(state, xg);\n          prng.state = function() {\n            return copy(xg, {});\n          };\n        }\n        return prng;\n      }\n      function Mash() {\n        var n2 = 4022871197;\n        var mash = function(data) {\n          data = String(data);\n          for (var i2 = 0; i2 < data.length; i2++) {\n            n2 += data.charCodeAt(i2);\n            var h = 0.02519603282416938 * n2;\n            n2 = h >>> 0;\n            h -= n2;\n            h *= n2;\n            n2 = h >>> 0;\n            h -= n2;\n            n2 += h * 4294967296;\n          }\n          return (n2 >>> 0) * 23283064365386963e-26;\n        };\n        return mash;\n      }\n      if (module2 && module2.exports) {\n        module2.exports = impl;\n      } else if (define2 && define2.amd) {\n        define2(function() {\n          return impl;\n        });\n      } else {\n        this.alea = impl;\n      }\n    })(\n      exports,\n      typeof module == \"object\" && module,\n      typeof define == \"function\" && define\n    );\n  }\n});\n\n// node_modules/.pnpm/seedrandom@3.0.5/node_modules/seedrandom/lib/xor128.js\nvar require_xor128 = __commonJS({\n  \"node_modules/.pnpm/seedrandom@3.0.5/node_modules/seedrandom/lib/xor128.js\"(exports, module) {\n    (function(global2, module2, define2) {\n      function XorGen(seed) {\n        var me = this, strseed = \"\";\n        me.x = 0;\n        me.y = 0;\n        me.z = 0;\n        me.w = 0;\n        me.next = function() {\n          var t2 = me.x ^ me.x << 11;\n          me.x = me.y;\n          me.y = me.z;\n          me.z = me.w;\n          return me.w ^= me.w >>> 19 ^ t2 ^ t2 >>> 8;\n        };\n        if (seed === (seed | 0)) {\n          me.x = seed;\n        } else {\n          strseed += seed;\n        }\n        for (var k = 0; k < strseed.length + 64; k++) {\n          me.x ^= strseed.charCodeAt(k) | 0;\n          me.next();\n        }\n      }\n      function copy(f, t2) {\n        t2.x = f.x;\n        t2.y = f.y;\n        t2.z = f.z;\n        t2.w = f.w;\n        return t2;\n      }\n      function impl(seed, opts) {\n        var xg = new XorGen(seed), state = opts && opts.state, prng = function() {\n          return (xg.next() >>> 0) / 4294967296;\n        };\n        prng.double = function() {\n          do {\n            var top = xg.next() >>> 11, bot = (xg.next() >>> 0) / 4294967296, result = (top + bot) / (1 << 21);\n          } while (result === 0);\n          return result;\n        };\n        prng.int32 = xg.next;\n        prng.quick = prng;\n        if (state) {\n          if (typeof state == \"object\")\n            copy(state, xg);\n          prng.state = function() {\n            return copy(xg, {});\n          };\n        }\n        return prng;\n      }\n      if (module2 && module2.exports) {\n        module2.exports = impl;\n      } else if (define2 && define2.amd) {\n        define2(function() {\n          return impl;\n        });\n      } else {\n        this.xor128 = impl;\n      }\n    })(\n      exports,\n      typeof module == \"object\" && module,\n      typeof define == \"function\" && define\n    );\n  }\n});\n\n// node_modules/.pnpm/seedrandom@3.0.5/node_modules/seedrandom/lib/xorwow.js\nvar require_xorwow = __commonJS({\n  \"node_modules/.pnpm/seedrandom@3.0.5/node_modules/seedrandom/lib/xorwow.js\"(exports, module) {\n    (function(global2, module2, define2) {\n      function XorGen(seed) {\n        var me = this, strseed = \"\";\n        me.next = function() {\n          var t2 = me.x ^ me.x >>> 2;\n          me.x = me.y;\n          me.y = me.z;\n          me.z = me.w;\n          me.w = me.v;\n          return (me.d = me.d + 362437 | 0) + (me.v = me.v ^ me.v << 4 ^ (t2 ^ t2 << 1)) | 0;\n        };\n        me.x = 0;\n        me.y = 0;\n        me.z = 0;\n        me.w = 0;\n        me.v = 0;\n        if (seed === (seed | 0)) {\n          me.x = seed;\n        } else {\n          strseed += seed;\n        }\n        for (var k = 0; k < strseed.length + 64; k++) {\n          me.x ^= strseed.charCodeAt(k) | 0;\n          if (k == strseed.length) {\n            me.d = me.x << 10 ^ me.x >>> 4;\n          }\n          me.next();\n        }\n      }\n      function copy(f, t2) {\n        t2.x = f.x;\n        t2.y = f.y;\n        t2.z = f.z;\n        t2.w = f.w;\n        t2.v = f.v;\n        t2.d = f.d;\n        return t2;\n      }\n      function impl(seed, opts) {\n        var xg = new XorGen(seed), state = opts && opts.state, prng = function() {\n          return (xg.next() >>> 0) / 4294967296;\n        };\n        prng.double = function() {\n          do {\n            var top = xg.next() >>> 11, bot = (xg.next() >>> 0) / 4294967296, result = (top + bot) / (1 << 21);\n          } while (result === 0);\n          return result;\n        };\n        prng.int32 = xg.next;\n        prng.quick = prng;\n        if (state) {\n          if (typeof state == \"object\")\n            copy(state, xg);\n          prng.state = function() {\n            return copy(xg, {});\n          };\n        }\n        return prng;\n      }\n      if (module2 && module2.exports) {\n        module2.exports = impl;\n      } else if (define2 && define2.amd) {\n        define2(function() {\n          return impl;\n        });\n      } else {\n        this.xorwow = impl;\n      }\n    })(\n      exports,\n      typeof module == \"object\" && module,\n      typeof define == \"function\" && define\n    );\n  }\n});\n\n// node_modules/.pnpm/seedrandom@3.0.5/node_modules/seedrandom/lib/xorshift7.js\nvar require_xorshift7 = __commonJS({\n  \"node_modules/.pnpm/seedrandom@3.0.5/node_modules/seedrandom/lib/xorshift7.js\"(exports, module) {\n    (function(global2, module2, define2) {\n      function XorGen(seed) {\n        var me = this;\n        me.next = function() {\n          var X = me.x, i2 = me.i, t2, v, w;\n          t2 = X[i2];\n          t2 ^= t2 >>> 7;\n          v = t2 ^ t2 << 24;\n          t2 = X[i2 + 1 & 7];\n          v ^= t2 ^ t2 >>> 10;\n          t2 = X[i2 + 3 & 7];\n          v ^= t2 ^ t2 >>> 3;\n          t2 = X[i2 + 4 & 7];\n          v ^= t2 ^ t2 << 7;\n          t2 = X[i2 + 7 & 7];\n          t2 = t2 ^ t2 << 13;\n          v ^= t2 ^ t2 << 9;\n          X[i2] = v;\n          me.i = i2 + 1 & 7;\n          return v;\n        };\n        function init2(me2, seed2) {\n          var j, w, X = [];\n          if (seed2 === (seed2 | 0)) {\n            w = X[0] = seed2;\n          } else {\n            seed2 = \"\" + seed2;\n            for (j = 0; j < seed2.length; ++j) {\n              X[j & 7] = X[j & 7] << 15 ^ seed2.charCodeAt(j) + X[j + 1 & 7] << 13;\n            }\n          }\n          while (X.length < 8)\n            X.push(0);\n          for (j = 0; j < 8 && X[j] === 0; ++j)\n            ;\n          if (j == 8)\n            w = X[7] = -1;\n          else\n            w = X[j];\n          me2.x = X;\n          me2.i = 0;\n          for (j = 256; j > 0; --j) {\n            me2.next();\n          }\n        }\n        init2(me, seed);\n      }\n      function copy(f, t2) {\n        t2.x = f.x.slice();\n        t2.i = f.i;\n        return t2;\n      }\n      function impl(seed, opts) {\n        if (seed == null)\n          seed = +new Date();\n        var xg = new XorGen(seed), state = opts && opts.state, prng = function() {\n          return (xg.next() >>> 0) / 4294967296;\n        };\n        prng.double = function() {\n          do {\n            var top = xg.next() >>> 11, bot = (xg.next() >>> 0) / 4294967296, result = (top + bot) / (1 << 21);\n          } while (result === 0);\n          return result;\n        };\n        prng.int32 = xg.next;\n        prng.quick = prng;\n        if (state) {\n          if (state.x)\n            copy(state, xg);\n          prng.state = function() {\n            return copy(xg, {});\n          };\n        }\n        return prng;\n      }\n      if (module2 && module2.exports) {\n        module2.exports = impl;\n      } else if (define2 && define2.amd) {\n        define2(function() {\n          return impl;\n        });\n      } else {\n        this.xorshift7 = impl;\n      }\n    })(\n      exports,\n      typeof module == \"object\" && module,\n      typeof define == \"function\" && define\n    );\n  }\n});\n\n// node_modules/.pnpm/seedrandom@3.0.5/node_modules/seedrandom/lib/xor4096.js\nvar require_xor4096 = __commonJS({\n  \"node_modules/.pnpm/seedrandom@3.0.5/node_modules/seedrandom/lib/xor4096.js\"(exports, module) {\n    (function(global2, module2, define2) {\n      function XorGen(seed) {\n        var me = this;\n        me.next = function() {\n          var w = me.w, X = me.X, i2 = me.i, t2, v;\n          me.w = w = w + 1640531527 | 0;\n          v = X[i2 + 34 & 127];\n          t2 = X[i2 = i2 + 1 & 127];\n          v ^= v << 13;\n          t2 ^= t2 << 17;\n          v ^= v >>> 15;\n          t2 ^= t2 >>> 12;\n          v = X[i2] = v ^ t2;\n          me.i = i2;\n          return v + (w ^ w >>> 16) | 0;\n        };\n        function init2(me2, seed2) {\n          var t2, v, i2, j, w, X = [], limit = 128;\n          if (seed2 === (seed2 | 0)) {\n            v = seed2;\n            seed2 = null;\n          } else {\n            seed2 = seed2 + \"\\0\";\n            v = 0;\n            limit = Math.max(limit, seed2.length);\n          }\n          for (i2 = 0, j = -32; j < limit; ++j) {\n            if (seed2)\n              v ^= seed2.charCodeAt((j + 32) % seed2.length);\n            if (j === 0)\n              w = v;\n            v ^= v << 10;\n            v ^= v >>> 15;\n            v ^= v << 4;\n            v ^= v >>> 13;\n            if (j >= 0) {\n              w = w + 1640531527 | 0;\n              t2 = X[j & 127] ^= v + w;\n              i2 = 0 == t2 ? i2 + 1 : 0;\n            }\n          }\n          if (i2 >= 128) {\n            X[(seed2 && seed2.length || 0) & 127] = -1;\n          }\n          i2 = 127;\n          for (j = 4 * 128; j > 0; --j) {\n            v = X[i2 + 34 & 127];\n            t2 = X[i2 = i2 + 1 & 127];\n            v ^= v << 13;\n            t2 ^= t2 << 17;\n            v ^= v >>> 15;\n            t2 ^= t2 >>> 12;\n            X[i2] = v ^ t2;\n          }\n          me2.w = w;\n          me2.X = X;\n          me2.i = i2;\n        }\n        init2(me, seed);\n      }\n      function copy(f, t2) {\n        t2.i = f.i;\n        t2.w = f.w;\n        t2.X = f.X.slice();\n        return t2;\n      }\n      ;\n      function impl(seed, opts) {\n        if (seed == null)\n          seed = +new Date();\n        var xg = new XorGen(seed), state = opts && opts.state, prng = function() {\n          return (xg.next() >>> 0) / 4294967296;\n        };\n        prng.double = function() {\n          do {\n            var top = xg.next() >>> 11, bot = (xg.next() >>> 0) / 4294967296, result = (top + bot) / (1 << 21);\n          } while (result === 0);\n          return result;\n        };\n        prng.int32 = xg.next;\n        prng.quick = prng;\n        if (state) {\n          if (state.X)\n            copy(state, xg);\n          prng.state = function() {\n            return copy(xg, {});\n          };\n        }\n        return prng;\n      }\n      if (module2 && module2.exports) {\n        module2.exports = impl;\n      } else if (define2 && define2.amd) {\n        define2(function() {\n          return impl;\n        });\n      } else {\n        this.xor4096 = impl;\n      }\n    })(\n      exports,\n      typeof module == \"object\" && module,\n      typeof define == \"function\" && define\n    );\n  }\n});\n\n// node_modules/.pnpm/seedrandom@3.0.5/node_modules/seedrandom/lib/tychei.js\nvar require_tychei = __commonJS({\n  \"node_modules/.pnpm/seedrandom@3.0.5/node_modules/seedrandom/lib/tychei.js\"(exports, module) {\n    (function(global2, module2, define2) {\n      function XorGen(seed) {\n        var me = this, strseed = \"\";\n        me.next = function() {\n          var b = me.b, c = me.c, d = me.d, a = me.a;\n          b = b << 25 ^ b >>> 7 ^ c;\n          c = c - d | 0;\n          d = d << 24 ^ d >>> 8 ^ a;\n          a = a - b | 0;\n          me.b = b = b << 20 ^ b >>> 12 ^ c;\n          me.c = c = c - d | 0;\n          me.d = d << 16 ^ c >>> 16 ^ a;\n          return me.a = a - b | 0;\n        };\n        me.a = 0;\n        me.b = 0;\n        me.c = 2654435769 | 0;\n        me.d = 1367130551;\n        if (seed === Math.floor(seed)) {\n          me.a = seed / 4294967296 | 0;\n          me.b = seed | 0;\n        } else {\n          strseed += seed;\n        }\n        for (var k = 0; k < strseed.length + 20; k++) {\n          me.b ^= strseed.charCodeAt(k) | 0;\n          me.next();\n        }\n      }\n      function copy(f, t2) {\n        t2.a = f.a;\n        t2.b = f.b;\n        t2.c = f.c;\n        t2.d = f.d;\n        return t2;\n      }\n      ;\n      function impl(seed, opts) {\n        var xg = new XorGen(seed), state = opts && opts.state, prng = function() {\n          return (xg.next() >>> 0) / 4294967296;\n        };\n        prng.double = function() {\n          do {\n            var top = xg.next() >>> 11, bot = (xg.next() >>> 0) / 4294967296, result = (top + bot) / (1 << 21);\n          } while (result === 0);\n          return result;\n        };\n        prng.int32 = xg.next;\n        prng.quick = prng;\n        if (state) {\n          if (typeof state == \"object\")\n            copy(state, xg);\n          prng.state = function() {\n            return copy(xg, {});\n          };\n        }\n        return prng;\n      }\n      if (module2 && module2.exports) {\n        module2.exports = impl;\n      } else if (define2 && define2.amd) {\n        define2(function() {\n          return impl;\n        });\n      } else {\n        this.tychei = impl;\n      }\n    })(\n      exports,\n      typeof module == \"object\" && module,\n      typeof define == \"function\" && define\n    );\n  }\n});\n\n// (disabled):crypto\nvar require_crypto = __commonJS({\n  \"(disabled):crypto\"() {\n  }\n});\n\n// node_modules/.pnpm/seedrandom@3.0.5/node_modules/seedrandom/seedrandom.js\nvar require_seedrandom = __commonJS({\n  \"node_modules/.pnpm/seedrandom@3.0.5/node_modules/seedrandom/seedrandom.js\"(exports, module) {\n    (function(global2, pool3, math) {\n      var width = 256, chunks = 6, digits = 52, rngname = \"random\", startdenom = math.pow(width, chunks), significance = math.pow(2, digits), overflow = significance * 2, mask = width - 1, nodecrypto;\n      function seedrandom5(seed, options, callback) {\n        var key = [];\n        options = options == true ? { entropy: true } : options || {};\n        var shortseed = mixkey(flatten4(\n          options.entropy ? [seed, tostring(pool3)] : seed == null ? autoseed() : seed,\n          3\n        ), key);\n        var arc4 = new ARC4(key);\n        var prng = function() {\n          var n2 = arc4.g(chunks), d = startdenom, x = 0;\n          while (n2 < significance) {\n            n2 = (n2 + x) * width;\n            d *= width;\n            x = arc4.g(1);\n          }\n          while (n2 >= overflow) {\n            n2 /= 2;\n            d /= 2;\n            x >>>= 1;\n          }\n          return (n2 + x) / d;\n        };\n        prng.int32 = function() {\n          return arc4.g(4) | 0;\n        };\n        prng.quick = function() {\n          return arc4.g(4) / 4294967296;\n        };\n        prng.double = prng;\n        mixkey(tostring(arc4.S), pool3);\n        return (options.pass || callback || function(prng2, seed2, is_math_call, state) {\n          if (state) {\n            if (state.S) {\n              copy(state, arc4);\n            }\n            prng2.state = function() {\n              return copy(arc4, {});\n            };\n          }\n          if (is_math_call) {\n            math[rngname] = prng2;\n            return seed2;\n          } else\n            return prng2;\n        })(\n          prng,\n          shortseed,\n          \"global\" in options ? options.global : this == math,\n          options.state\n        );\n      }\n      function ARC4(key) {\n        var t2, keylen = key.length, me = this, i2 = 0, j = me.i = me.j = 0, s2 = me.S = [];\n        if (!keylen) {\n          key = [keylen++];\n        }\n        while (i2 < width) {\n          s2[i2] = i2++;\n        }\n        for (i2 = 0; i2 < width; i2++) {\n          s2[i2] = s2[j = mask & j + key[i2 % keylen] + (t2 = s2[i2])];\n          s2[j] = t2;\n        }\n        (me.g = function(count2) {\n          var t3, r2 = 0, i3 = me.i, j2 = me.j, s3 = me.S;\n          while (count2--) {\n            t3 = s3[i3 = mask & i3 + 1];\n            r2 = r2 * width + s3[mask & (s3[i3] = s3[j2 = mask & j2 + t3]) + (s3[j2] = t3)];\n          }\n          me.i = i3;\n          me.j = j2;\n          return r2;\n        })(width);\n      }\n      function copy(f, t2) {\n        t2.i = f.i;\n        t2.j = f.j;\n        t2.S = f.S.slice();\n        return t2;\n      }\n      ;\n      function flatten4(obj, depth) {\n        var result = [], typ = typeof obj, prop;\n        if (depth && typ == \"object\") {\n          for (prop in obj) {\n            try {\n              result.push(flatten4(obj[prop], depth - 1));\n            } catch (e2) {\n            }\n          }\n        }\n        return result.length ? result : typ == \"string\" ? obj : obj + \"\\0\";\n      }\n      function mixkey(seed, key) {\n        var stringseed = seed + \"\", smear, j = 0;\n        while (j < stringseed.length) {\n          key[mask & j] = mask & (smear ^= key[mask & j] * 19) + stringseed.charCodeAt(j++);\n        }\n        return tostring(key);\n      }\n      function autoseed() {\n        try {\n          var out;\n          if (nodecrypto && (out = nodecrypto.randomBytes)) {\n            out = out(width);\n          } else {\n            out = new Uint8Array(width);\n            (global2.crypto || global2.msCrypto).getRandomValues(out);\n          }\n          return tostring(out);\n        } catch (e2) {\n          var browser = global2.navigator, plugins = browser && browser.plugins;\n          return [+new Date(), global2, plugins, global2.screen, tostring(pool3)];\n        }\n      }\n      function tostring(a) {\n        return String.fromCharCode.apply(0, a);\n      }\n      mixkey(math.random(), pool3);\n      if (typeof module == \"object\" && module.exports) {\n        module.exports = seedrandom5;\n        try {\n          nodecrypto = require_crypto();\n        } catch (ex) {\n        }\n      } else if (typeof define == \"function\" && define.amd) {\n        define(function() {\n          return seedrandom5;\n        });\n      } else {\n        math[\"seed\" + rngname] = seedrandom5;\n      }\n    })(\n      typeof self !== \"undefined\" ? self : exports,\n      [],\n      Math\n    );\n  }\n});\n\n// node_modules/.pnpm/seedrandom@3.0.5/node_modules/seedrandom/index.js\nvar require_seedrandom2 = __commonJS({\n  \"node_modules/.pnpm/seedrandom@3.0.5/node_modules/seedrandom/index.js\"(exports, module) {\n    var alea5 = require_alea();\n    var xor128 = require_xor128();\n    var xorwow = require_xorwow();\n    var xorshift7 = require_xorshift7();\n    var xor4096 = require_xor4096();\n    var tychei = require_tychei();\n    var sr = require_seedrandom();\n    sr.alea = alea5;\n    sr.xor128 = xor128;\n    sr.xorwow = xorwow;\n    sr.xorshift7 = xorshift7;\n    sr.xor4096 = xor4096;\n    sr.tychei = tychei;\n    module.exports = sr;\n  }\n});\n\n// (disabled):node_modules/.pnpm/string_decoder@1.3.0/node_modules/string_decoder/lib/string_decoder.js\nvar require_string_decoder = __commonJS({\n  \"(disabled):node_modules/.pnpm/string_decoder@1.3.0/node_modules/string_decoder/lib/string_decoder.js\"() {\n  }\n});\n\n// (disabled):fs\nvar require_fs = __commonJS({\n  \"(disabled):fs\"() {\n  }\n});\n\n// (disabled):path\nvar require_path = __commonJS({\n  \"(disabled):path\"() {\n  }\n});\n\n// (disabled):worker_threads\nvar require_worker_threads = __commonJS({\n  \"(disabled):worker_threads\"() {\n  }\n});\n\n// (disabled):perf_hooks\nvar require_perf_hooks = __commonJS({\n  \"(disabled):perf_hooks\"() {\n  }\n});\n\n// (disabled):os\nvar require_os = __commonJS({\n  \"(disabled):os\"() {\n  }\n});\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm-threaded-simd.js\nvar require_tfjs_backend_wasm_threaded_simd = __commonJS({\n  \"node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm-threaded-simd.js\"(exports, module) {\n    var WasmBackendModuleThreadedSimd2 = (() => {\n      var _scriptDir = typeof document !== \"undefined\" && document.currentScript ? document.currentScript.src : void 0;\n      if (typeof __filename !== \"undefined\")\n        _scriptDir = _scriptDir || __filename;\n      return function(WasmBackendModuleThreadedSimd3) {\n        WasmBackendModuleThreadedSimd3 = WasmBackendModuleThreadedSimd3 || {};\n        function GROWABLE_HEAP_I8() {\n          if (wasmMemory.buffer != buffer2) {\n            updateGlobalBufferAndViews(wasmMemory.buffer);\n          }\n          return HEAP8;\n        }\n        function GROWABLE_HEAP_U8() {\n          if (wasmMemory.buffer != buffer2) {\n            updateGlobalBufferAndViews(wasmMemory.buffer);\n          }\n          return HEAPU8;\n        }\n        function GROWABLE_HEAP_I16() {\n          if (wasmMemory.buffer != buffer2) {\n            updateGlobalBufferAndViews(wasmMemory.buffer);\n          }\n          return HEAP16;\n        }\n        function GROWABLE_HEAP_U16() {\n          if (wasmMemory.buffer != buffer2) {\n            updateGlobalBufferAndViews(wasmMemory.buffer);\n          }\n          return HEAPU16;\n        }\n        function GROWABLE_HEAP_I32() {\n          if (wasmMemory.buffer != buffer2) {\n            updateGlobalBufferAndViews(wasmMemory.buffer);\n          }\n          return HEAP32;\n        }\n        function GROWABLE_HEAP_F32() {\n          if (wasmMemory.buffer != buffer2) {\n            updateGlobalBufferAndViews(wasmMemory.buffer);\n          }\n          return HEAPF32;\n        }\n        function GROWABLE_HEAP_F64() {\n          if (wasmMemory.buffer != buffer2) {\n            updateGlobalBufferAndViews(wasmMemory.buffer);\n          }\n          return HEAPF64;\n        }\n        var Module = typeof WasmBackendModuleThreadedSimd3 !== \"undefined\" ? WasmBackendModuleThreadedSimd3 : {};\n        var readyPromiseResolve, readyPromiseReject;\n        Module[\"ready\"] = new Promise(function(resolve, reject) {\n          readyPromiseResolve = resolve;\n          readyPromiseReject = reject;\n        });\n        var beforeListeners;\n        if (typeof process !== \"undefined\" && process.listeners) {\n          beforeListeners = { uncaughtException: process.listeners(\"uncaughtException\"), unhandledRejection: process.listeners(\"unhandledRejection\") };\n        }\n        var moduleOverrides = Object.assign({}, Module);\n        var arguments_ = [];\n        var thisProgram = \"./this.program\";\n        var quit_ = (status, toThrow) => {\n          throw toThrow;\n        };\n        var ENVIRONMENT_IS_WEB = typeof window === \"object\";\n        var ENVIRONMENT_IS_WORKER = typeof importScripts === \"function\";\n        var ENVIRONMENT_IS_NODE = typeof process === \"object\" && typeof process.versions === \"object\" && typeof process.versions.node === \"string\";\n        var ENVIRONMENT_IS_PTHREAD = Module[\"ENVIRONMENT_IS_PTHREAD\"] || false;\n        var scriptDirectory = \"\";\n        function locateFile(path) {\n          if (Module[\"locateFile\"]) {\n            return Module[\"locateFile\"](path, scriptDirectory);\n          }\n          return scriptDirectory + path;\n        }\n        var read_, readAsync, readBinary, setWindowTitle;\n        function logExceptionOnExit(e2) {\n          if (e2 instanceof ExitStatus)\n            return;\n          let toLog = e2;\n          err(\"exiting due to exception: \" + toLog);\n        }\n        var fs;\n        var nodePath;\n        var requireNodeFS;\n        if (ENVIRONMENT_IS_NODE) {\n          if (ENVIRONMENT_IS_WORKER) {\n            scriptDirectory = require_path().dirname(scriptDirectory) + \"/\";\n          } else {\n            scriptDirectory = __dirname + \"/\";\n          }\n          requireNodeFS = () => {\n            if (!nodePath) {\n              fs = require_fs();\n              nodePath = require_path();\n            }\n          };\n          read_ = function shell_read(filename, binary) {\n            requireNodeFS();\n            filename = nodePath[\"normalize\"](filename);\n            return fs.readFileSync(filename, binary ? void 0 : \"utf8\");\n          };\n          readBinary = (filename) => {\n            var ret = read_(filename, true);\n            if (!ret.buffer) {\n              ret = new Uint8Array(ret);\n            }\n            return ret;\n          };\n          readAsync = (filename, onload, onerror) => {\n            requireNodeFS();\n            filename = nodePath[\"normalize\"](filename);\n            fs.readFile(filename, function(err2, data) {\n              if (err2)\n                onerror(err2);\n              else\n                onload(data.buffer);\n            });\n          };\n          if (process[\"argv\"].length > 1) {\n            thisProgram = process[\"argv\"][1].replace(/\\\\/g, \"/\");\n          }\n          arguments_ = process[\"argv\"].slice(2);\n          process[\"on\"](\"uncaughtException\", function(ex) {\n            if (!(ex instanceof ExitStatus)) {\n              throw ex;\n            }\n          });\n          process[\"on\"](\"unhandledRejection\", function(reason) {\n            throw reason;\n          });\n          quit_ = (status, toThrow) => {\n            if (keepRuntimeAlive()) {\n              process[\"exitCode\"] = status;\n              throw toThrow;\n            }\n            logExceptionOnExit(toThrow);\n            process[\"exit\"](status);\n          };\n          Module[\"inspect\"] = function() {\n            return \"[Emscripten Module object]\";\n          };\n          let nodeWorkerThreads;\n          try {\n            nodeWorkerThreads = require_worker_threads();\n          } catch (e2) {\n            console.error('The \"worker_threads\" module is not supported in this node.js build - perhaps a newer version is needed?');\n            throw e2;\n          }\n          global.Worker = nodeWorkerThreads.Worker;\n        } else if (ENVIRONMENT_IS_WEB || ENVIRONMENT_IS_WORKER) {\n          if (ENVIRONMENT_IS_WORKER) {\n            scriptDirectory = self.location.href;\n          } else if (typeof document !== \"undefined\" && document.currentScript) {\n            scriptDirectory = document.currentScript.src;\n          }\n          if (typeof _scriptDir !== \"undefined\" && _scriptDir) {\n            scriptDirectory = _scriptDir;\n          }\n          if (scriptDirectory.indexOf(\"blob:\") !== 0) {\n            scriptDirectory = scriptDirectory.substr(0, scriptDirectory.replace(/[?#].*/, \"\").lastIndexOf(\"/\") + 1);\n          } else {\n            scriptDirectory = \"\";\n          }\n          if (!ENVIRONMENT_IS_NODE) {\n            read_ = (url) => {\n              var xhr = new XMLHttpRequest();\n              xhr.open(\"GET\", url, false);\n              xhr.send(null);\n              return xhr.responseText;\n            };\n            if (ENVIRONMENT_IS_WORKER) {\n              readBinary = (url) => {\n                var xhr = new XMLHttpRequest();\n                xhr.open(\"GET\", url, false);\n                xhr.responseType = \"arraybuffer\";\n                xhr.send(null);\n                return new Uint8Array(xhr.response);\n              };\n            }\n            readAsync = (url, onload, onerror) => {\n              var xhr = new XMLHttpRequest();\n              xhr.open(\"GET\", url, true);\n              xhr.responseType = \"arraybuffer\";\n              xhr.onload = () => {\n                if (xhr.status == 200 || xhr.status == 0 && xhr.response) {\n                  onload(xhr.response);\n                  return;\n                }\n                onerror();\n              };\n              xhr.onerror = onerror;\n              xhr.send(null);\n            };\n          }\n          setWindowTitle = (title) => document.title = title;\n        } else {\n        }\n        if (ENVIRONMENT_IS_NODE) {\n          if (typeof performance === \"undefined\") {\n            global.performance = require_perf_hooks().performance;\n          }\n        }\n        var defaultPrint = console.log.bind(console);\n        var defaultPrintErr = console.warn.bind(console);\n        if (ENVIRONMENT_IS_NODE) {\n          requireNodeFS();\n          defaultPrint = (str) => fs.writeSync(1, str + \"\\n\");\n          defaultPrintErr = (str) => fs.writeSync(2, str + \"\\n\");\n        }\n        var out = Module[\"print\"] || defaultPrint;\n        var err = Module[\"printErr\"] || defaultPrintErr;\n        Object.assign(Module, moduleOverrides);\n        moduleOverrides = null;\n        if (Module[\"arguments\"])\n          arguments_ = Module[\"arguments\"];\n        if (Module[\"thisProgram\"])\n          thisProgram = Module[\"thisProgram\"];\n        if (Module[\"quit\"])\n          quit_ = Module[\"quit\"];\n        var POINTER_SIZE = 4;\n        function warnOnce(text) {\n          if (!warnOnce.shown)\n            warnOnce.shown = {};\n          if (!warnOnce.shown[text]) {\n            warnOnce.shown[text] = 1;\n            err(text);\n          }\n        }\n        function convertJsFunctionToWasm(func2, sig) {\n          if (typeof WebAssembly.Function === \"function\") {\n            var typeNames = { \"i\": \"i32\", \"j\": \"i64\", \"f\": \"f32\", \"d\": \"f64\" };\n            var type = { parameters: [], results: sig[0] == \"v\" ? [] : [typeNames[sig[0]]] };\n            for (var i2 = 1; i2 < sig.length; ++i2) {\n              type.parameters.push(typeNames[sig[i2]]);\n            }\n            return new WebAssembly.Function(type, func2);\n          }\n          var typeSection = [1, 0, 1, 96];\n          var sigRet = sig.slice(0, 1);\n          var sigParam = sig.slice(1);\n          var typeCodes = { \"i\": 127, \"j\": 126, \"f\": 125, \"d\": 124 };\n          typeSection.push(sigParam.length);\n          for (var i2 = 0; i2 < sigParam.length; ++i2) {\n            typeSection.push(typeCodes[sigParam[i2]]);\n          }\n          if (sigRet == \"v\") {\n            typeSection.push(0);\n          } else {\n            typeSection = typeSection.concat([1, typeCodes[sigRet]]);\n          }\n          typeSection[1] = typeSection.length - 2;\n          var bytes = new Uint8Array([0, 97, 115, 109, 1, 0, 0, 0].concat(typeSection, [2, 7, 1, 1, 101, 1, 102, 0, 0, 7, 5, 1, 1, 102, 0, 0]));\n          var module2 = new WebAssembly.Module(bytes);\n          var instance = new WebAssembly.Instance(module2, { \"e\": { \"f\": func2 } });\n          var wrappedFunc = instance.exports[\"f\"];\n          return wrappedFunc;\n        }\n        var freeTableIndexes = [];\n        var functionsInTableMap;\n        function getEmptyTableSlot() {\n          if (freeTableIndexes.length) {\n            return freeTableIndexes.pop();\n          }\n          try {\n            wasmTable.grow(1);\n          } catch (err2) {\n            if (!(err2 instanceof RangeError)) {\n              throw err2;\n            }\n            throw \"Unable to grow wasm table. Set ALLOW_TABLE_GROWTH.\";\n          }\n          return wasmTable.length - 1;\n        }\n        function updateTableMap(offset, count2) {\n          for (var i2 = offset; i2 < offset + count2; i2++) {\n            var item = getWasmTableEntry(i2);\n            if (item) {\n              functionsInTableMap.set(item, i2);\n            }\n          }\n        }\n        var tempRet0 = 0;\n        var setTempRet0 = (value) => {\n          tempRet0 = value;\n        };\n        var Atomics_load = Atomics.load;\n        var Atomics_store = Atomics.store;\n        var Atomics_compareExchange = Atomics.compareExchange;\n        var wasmBinary;\n        if (Module[\"wasmBinary\"])\n          wasmBinary = Module[\"wasmBinary\"];\n        var noExitRuntime = Module[\"noExitRuntime\"] || true;\n        if (typeof WebAssembly !== \"object\") {\n          abort(\"no native wasm support detected\");\n        }\n        var wasmMemory;\n        var wasmModule;\n        var ABORT = false;\n        var EXITSTATUS;\n        function assert3(condition, text) {\n          if (!condition) {\n            abort(text);\n          }\n        }\n        function getCFunc(ident) {\n          var func2 = Module[\"_\" + ident];\n          return func2;\n        }\n        function ccall(ident, returnType, argTypes, args, opts) {\n          var toC = { \"string\": function(str) {\n            var ret2 = 0;\n            if (str !== null && str !== void 0 && str !== 0) {\n              var len = (str.length << 2) + 1;\n              ret2 = stackAlloc(len);\n              stringToUTF8(str, ret2, len);\n            }\n            return ret2;\n          }, \"array\": function(arr) {\n            var ret2 = stackAlloc(arr.length);\n            writeArrayToMemory(arr, ret2);\n            return ret2;\n          } };\n          function convertReturnValue(ret2) {\n            if (returnType === \"string\")\n              return UTF8ToString(ret2);\n            if (returnType === \"boolean\")\n              return Boolean(ret2);\n            return ret2;\n          }\n          var func2 = getCFunc(ident);\n          var cArgs = [];\n          var stack2 = 0;\n          if (args) {\n            for (var i2 = 0; i2 < args.length; i2++) {\n              var converter = toC[argTypes[i2]];\n              if (converter) {\n                if (stack2 === 0)\n                  stack2 = stackSave();\n                cArgs[i2] = converter(args[i2]);\n              } else {\n                cArgs[i2] = args[i2];\n              }\n            }\n          }\n          var ret = func2.apply(null, cArgs);\n          function onDone(ret2) {\n            if (stack2 !== 0)\n              stackRestore(stack2);\n            return convertReturnValue(ret2);\n          }\n          ret = onDone(ret);\n          return ret;\n        }\n        function cwrap(ident, returnType, argTypes, opts) {\n          argTypes = argTypes || [];\n          var numericArgs = argTypes.every(function(type) {\n            return type === \"number\";\n          });\n          var numericRet = returnType !== \"string\";\n          if (numericRet && numericArgs && !opts) {\n            return getCFunc(ident);\n          }\n          return function() {\n            return ccall(ident, returnType, argTypes, arguments, opts);\n          };\n        }\n        var ALLOC_STACK = 1;\n        function TextDecoderWrapper(encoding) {\n          var textDecoder = new TextDecoder(encoding);\n          this.decode = (data) => {\n            if (data.buffer instanceof SharedArrayBuffer) {\n              data = new Uint8Array(data);\n            }\n            return textDecoder.decode.call(textDecoder, data);\n          };\n        }\n        var UTF8Decoder = typeof TextDecoder !== \"undefined\" ? new TextDecoderWrapper(\"utf8\") : void 0;\n        function UTF8ArrayToString(heap, idx, maxBytesToRead) {\n          var endIdx = idx + maxBytesToRead;\n          var endPtr = idx;\n          while (heap[endPtr] && !(endPtr >= endIdx))\n            ++endPtr;\n          if (endPtr - idx > 16 && heap.subarray && UTF8Decoder) {\n            return UTF8Decoder.decode(heap.subarray(idx, endPtr));\n          } else {\n            var str = \"\";\n            while (idx < endPtr) {\n              var u0 = heap[idx++];\n              if (!(u0 & 128)) {\n                str += String.fromCharCode(u0);\n                continue;\n              }\n              var u1 = heap[idx++] & 63;\n              if ((u0 & 224) == 192) {\n                str += String.fromCharCode((u0 & 31) << 6 | u1);\n                continue;\n              }\n              var u2 = heap[idx++] & 63;\n              if ((u0 & 240) == 224) {\n                u0 = (u0 & 15) << 12 | u1 << 6 | u2;\n              } else {\n                u0 = (u0 & 7) << 18 | u1 << 12 | u2 << 6 | heap[idx++] & 63;\n              }\n              if (u0 < 65536) {\n                str += String.fromCharCode(u0);\n              } else {\n                var ch = u0 - 65536;\n                str += String.fromCharCode(55296 | ch >> 10, 56320 | ch & 1023);\n              }\n            }\n          }\n          return str;\n        }\n        function UTF8ToString(ptr, maxBytesToRead) {\n          return ptr ? UTF8ArrayToString(GROWABLE_HEAP_U8(), ptr, maxBytesToRead) : \"\";\n        }\n        function stringToUTF8Array(str, heap, outIdx, maxBytesToWrite) {\n          if (!(maxBytesToWrite > 0))\n            return 0;\n          var startIdx = outIdx;\n          var endIdx = outIdx + maxBytesToWrite - 1;\n          for (var i2 = 0; i2 < str.length; ++i2) {\n            var u = str.charCodeAt(i2);\n            if (u >= 55296 && u <= 57343) {\n              var u1 = str.charCodeAt(++i2);\n              u = 65536 + ((u & 1023) << 10) | u1 & 1023;\n            }\n            if (u <= 127) {\n              if (outIdx >= endIdx)\n                break;\n              heap[outIdx++] = u;\n            } else if (u <= 2047) {\n              if (outIdx + 1 >= endIdx)\n                break;\n              heap[outIdx++] = 192 | u >> 6;\n              heap[outIdx++] = 128 | u & 63;\n            } else if (u <= 65535) {\n              if (outIdx + 2 >= endIdx)\n                break;\n              heap[outIdx++] = 224 | u >> 12;\n              heap[outIdx++] = 128 | u >> 6 & 63;\n              heap[outIdx++] = 128 | u & 63;\n            } else {\n              if (outIdx + 3 >= endIdx)\n                break;\n              heap[outIdx++] = 240 | u >> 18;\n              heap[outIdx++] = 128 | u >> 12 & 63;\n              heap[outIdx++] = 128 | u >> 6 & 63;\n              heap[outIdx++] = 128 | u & 63;\n            }\n          }\n          heap[outIdx] = 0;\n          return outIdx - startIdx;\n        }\n        function stringToUTF8(str, outPtr, maxBytesToWrite) {\n          return stringToUTF8Array(str, GROWABLE_HEAP_U8(), outPtr, maxBytesToWrite);\n        }\n        function lengthBytesUTF8(str) {\n          var len = 0;\n          for (var i2 = 0; i2 < str.length; ++i2) {\n            var u = str.charCodeAt(i2);\n            if (u >= 55296 && u <= 57343)\n              u = 65536 + ((u & 1023) << 10) | str.charCodeAt(++i2) & 1023;\n            if (u <= 127)\n              ++len;\n            else if (u <= 2047)\n              len += 2;\n            else if (u <= 65535)\n              len += 3;\n            else\n              len += 4;\n          }\n          return len;\n        }\n        var UTF16Decoder = typeof TextDecoder !== \"undefined\" ? new TextDecoderWrapper(\"utf-16le\") : void 0;\n        function writeArrayToMemory(array2, buffer3) {\n          GROWABLE_HEAP_I8().set(array2, buffer3);\n        }\n        function writeAsciiToMemory(str, buffer3, dontAddNull) {\n          for (var i2 = 0; i2 < str.length; ++i2) {\n            GROWABLE_HEAP_I8()[buffer3++ >> 0] = str.charCodeAt(i2);\n          }\n          if (!dontAddNull)\n            GROWABLE_HEAP_I8()[buffer3 >> 0] = 0;\n        }\n        function alignUp(x, multiple) {\n          if (x % multiple > 0) {\n            x += multiple - x % multiple;\n          }\n          return x;\n        }\n        var buffer2, HEAP8, HEAPU8, HEAP16, HEAPU16, HEAP32, HEAPU32, HEAPF32, HEAPF64;\n        if (ENVIRONMENT_IS_PTHREAD) {\n          buffer2 = Module[\"buffer\"];\n        }\n        function updateGlobalBufferAndViews(buf) {\n          buffer2 = buf;\n          Module[\"HEAP8\"] = HEAP8 = new Int8Array(buf);\n          Module[\"HEAP16\"] = HEAP16 = new Int16Array(buf);\n          Module[\"HEAP32\"] = HEAP32 = new Int32Array(buf);\n          Module[\"HEAPU8\"] = HEAPU8 = new Uint8Array(buf);\n          Module[\"HEAPU16\"] = HEAPU16 = new Uint16Array(buf);\n          Module[\"HEAPU32\"] = HEAPU32 = new Uint32Array(buf);\n          Module[\"HEAPF32\"] = HEAPF32 = new Float32Array(buf);\n          Module[\"HEAPF64\"] = HEAPF64 = new Float64Array(buf);\n        }\n        var INITIAL_MEMORY = Module[\"INITIAL_MEMORY\"] || 16777216;\n        if (ENVIRONMENT_IS_PTHREAD) {\n          wasmMemory = Module[\"wasmMemory\"];\n          buffer2 = Module[\"buffer\"];\n        } else {\n          if (Module[\"wasmMemory\"]) {\n            wasmMemory = Module[\"wasmMemory\"];\n          } else {\n            wasmMemory = new WebAssembly.Memory({ \"initial\": INITIAL_MEMORY / 65536, \"maximum\": 2147483648 / 65536, \"shared\": true });\n            if (!(wasmMemory.buffer instanceof SharedArrayBuffer)) {\n              err(\"requested a shared WebAssembly.Memory but the returned buffer is not a SharedArrayBuffer, indicating that while the browser has SharedArrayBuffer it does not have WebAssembly threads support - you may need to set a flag\");\n              if (ENVIRONMENT_IS_NODE) {\n                console.log(\"(on node you may need: --experimental-wasm-threads --experimental-wasm-bulk-memory and also use a recent version)\");\n              }\n              throw Error(\"bad memory\");\n            }\n          }\n        }\n        if (wasmMemory) {\n          buffer2 = wasmMemory.buffer;\n        }\n        INITIAL_MEMORY = buffer2.byteLength;\n        updateGlobalBufferAndViews(buffer2);\n        var wasmTable;\n        var __ATPRERUN__ = [];\n        var __ATINIT__ = [];\n        var __ATEXIT__ = [];\n        var __ATPOSTRUN__ = [];\n        var runtimeInitialized = false;\n        var runtimeExited = false;\n        var runtimeKeepaliveCounter = 0;\n        function keepRuntimeAlive() {\n          return noExitRuntime || runtimeKeepaliveCounter > 0;\n        }\n        function preRun() {\n          if (Module[\"preRun\"]) {\n            if (typeof Module[\"preRun\"] == \"function\")\n              Module[\"preRun\"] = [Module[\"preRun\"]];\n            while (Module[\"preRun\"].length) {\n              addOnPreRun(Module[\"preRun\"].shift());\n            }\n          }\n          callRuntimeCallbacks(__ATPRERUN__);\n        }\n        function initRuntime() {\n          runtimeInitialized = true;\n          if (ENVIRONMENT_IS_PTHREAD)\n            return;\n          callRuntimeCallbacks(__ATINIT__);\n        }\n        function exitRuntime() {\n          if (ENVIRONMENT_IS_PTHREAD)\n            return;\n          PThread.terminateAllThreads();\n          runtimeExited = true;\n        }\n        function postRun() {\n          if (ENVIRONMENT_IS_PTHREAD)\n            return;\n          if (Module[\"postRun\"]) {\n            if (typeof Module[\"postRun\"] == \"function\")\n              Module[\"postRun\"] = [Module[\"postRun\"]];\n            while (Module[\"postRun\"].length) {\n              addOnPostRun(Module[\"postRun\"].shift());\n            }\n          }\n          callRuntimeCallbacks(__ATPOSTRUN__);\n        }\n        function addOnPreRun(cb) {\n          __ATPRERUN__.unshift(cb);\n        }\n        function addOnInit(cb) {\n          __ATINIT__.unshift(cb);\n        }\n        function addOnPostRun(cb) {\n          __ATPOSTRUN__.unshift(cb);\n        }\n        var runDependencies = 0;\n        var runDependencyWatcher = null;\n        var dependenciesFulfilled = null;\n        function addRunDependency(id) {\n          runDependencies++;\n          if (Module[\"monitorRunDependencies\"]) {\n            Module[\"monitorRunDependencies\"](runDependencies);\n          }\n        }\n        function removeRunDependency(id) {\n          runDependencies--;\n          if (Module[\"monitorRunDependencies\"]) {\n            Module[\"monitorRunDependencies\"](runDependencies);\n          }\n          if (runDependencies == 0) {\n            if (runDependencyWatcher !== null) {\n              clearInterval(runDependencyWatcher);\n              runDependencyWatcher = null;\n            }\n            if (dependenciesFulfilled) {\n              var callback = dependenciesFulfilled;\n              dependenciesFulfilled = null;\n              callback();\n            }\n          }\n        }\n        Module[\"preloadedImages\"] = {};\n        Module[\"preloadedAudios\"] = {};\n        function abort(what) {\n          if (ENVIRONMENT_IS_PTHREAD) {\n            postMessage({ \"cmd\": \"onAbort\", \"arg\": what });\n          } else {\n            if (Module[\"onAbort\"]) {\n              Module[\"onAbort\"](what);\n            }\n          }\n          what = \"Aborted(\" + what + \")\";\n          err(what);\n          ABORT = true;\n          EXITSTATUS = 1;\n          what += \". Build with -s ASSERTIONS=1 for more info.\";\n          var e2 = new WebAssembly.RuntimeError(what);\n          readyPromiseReject(e2);\n          throw e2;\n        }\n        var dataURIPrefix = \"data:application/octet-stream;base64,\";\n        function isDataURI(filename) {\n          return filename.startsWith(dataURIPrefix);\n        }\n        function isFileURI(filename) {\n          return filename.startsWith(\"file://\");\n        }\n        var wasmBinaryFile;\n        wasmBinaryFile = \"tfjs-backend-wasm-threaded-simd.wasm\";\n        if (!isDataURI(wasmBinaryFile)) {\n          wasmBinaryFile = locateFile(wasmBinaryFile);\n        }\n        function getBinary(file) {\n          try {\n            if (file == wasmBinaryFile && wasmBinary) {\n              return new Uint8Array(wasmBinary);\n            }\n            if (readBinary) {\n              return readBinary(file);\n            } else {\n              throw \"both async and sync fetching of the wasm failed\";\n            }\n          } catch (err2) {\n            abort(err2);\n          }\n        }\n        function getBinaryPromise() {\n          if (!wasmBinary && (ENVIRONMENT_IS_WEB || ENVIRONMENT_IS_WORKER)) {\n            if (typeof fetch === \"function\" && !isFileURI(wasmBinaryFile)) {\n              return fetch(wasmBinaryFile, { credentials: \"same-origin\" }).then(function(response) {\n                if (!response[\"ok\"]) {\n                  throw \"failed to load wasm binary file at '\" + wasmBinaryFile + \"'\";\n                }\n                return response[\"arrayBuffer\"]();\n              }).catch(function() {\n                return getBinary(wasmBinaryFile);\n              });\n            } else {\n              if (readAsync) {\n                return new Promise(function(resolve, reject) {\n                  readAsync(wasmBinaryFile, function(response) {\n                    resolve(new Uint8Array(response));\n                  }, reject);\n                });\n              }\n            }\n          }\n          return Promise.resolve().then(function() {\n            return getBinary(wasmBinaryFile);\n          });\n        }\n        function createWasm() {\n          var info = { \"env\": asmLibraryArg, \"wasi_snapshot_preview1\": asmLibraryArg };\n          function receiveInstance(instance, module2) {\n            var exports3 = instance.exports;\n            Module[\"asm\"] = exports3;\n            registerTlsInit(Module[\"asm\"][\"emscripten_tls_init\"]);\n            wasmTable = Module[\"asm\"][\"__indirect_function_table\"];\n            addOnInit(Module[\"asm\"][\"__wasm_call_ctors\"]);\n            wasmModule = module2;\n            if (!ENVIRONMENT_IS_PTHREAD) {\n              var numWorkersToLoad = PThread.unusedWorkers.length;\n              PThread.unusedWorkers.forEach(function(w) {\n                PThread.loadWasmModuleToWorker(w, function() {\n                  if (!--numWorkersToLoad)\n                    removeRunDependency(\"wasm-instantiate\");\n                });\n              });\n            }\n          }\n          if (!ENVIRONMENT_IS_PTHREAD) {\n            addRunDependency(\"wasm-instantiate\");\n          }\n          function receiveInstantiationResult(result) {\n            receiveInstance(result[\"instance\"], result[\"module\"]);\n          }\n          function instantiateArrayBuffer(receiver) {\n            return getBinaryPromise().then(function(binary) {\n              return WebAssembly.instantiate(binary, info);\n            }).then(function(instance) {\n              return instance;\n            }).then(receiver, function(reason) {\n              err(\"failed to asynchronously prepare wasm: \" + reason);\n              abort(reason);\n            });\n          }\n          function instantiateAsync() {\n            if (!wasmBinary && typeof WebAssembly.instantiateStreaming === \"function\" && !isDataURI(wasmBinaryFile) && !isFileURI(wasmBinaryFile) && typeof fetch === \"function\") {\n              return fetch(wasmBinaryFile, { credentials: \"same-origin\" }).then(function(response) {\n                var result = WebAssembly.instantiateStreaming(response, info);\n                return result.then(receiveInstantiationResult, function(reason) {\n                  err(\"wasm streaming compile failed: \" + reason);\n                  err(\"falling back to ArrayBuffer instantiation\");\n                  return instantiateArrayBuffer(receiveInstantiationResult);\n                });\n              });\n            } else {\n              return instantiateArrayBuffer(receiveInstantiationResult);\n            }\n          }\n          if (Module[\"instantiateWasm\"]) {\n            try {\n              var exports2 = Module[\"instantiateWasm\"](info, receiveInstance);\n              return exports2;\n            } catch (e2) {\n              err(\"Module.instantiateWasm callback failed with error: \" + e2);\n              return false;\n            }\n          }\n          instantiateAsync().catch(readyPromiseReject);\n          return {};\n        }\n        var tempDouble;\n        var tempI64;\n        var ASM_CONSTS = {};\n        function callRuntimeCallbacks(callbacks2) {\n          while (callbacks2.length > 0) {\n            var callback = callbacks2.shift();\n            if (typeof callback == \"function\") {\n              callback(Module);\n              continue;\n            }\n            var func2 = callback.func;\n            if (typeof func2 === \"number\") {\n              if (callback.arg === void 0) {\n                getWasmTableEntry(func2)();\n              } else {\n                getWasmTableEntry(func2)(callback.arg);\n              }\n            } else {\n              func2(callback.arg === void 0 ? null : callback.arg);\n            }\n          }\n        }\n        function withStackSave(f) {\n          var stack2 = stackSave();\n          var ret = f();\n          stackRestore(stack2);\n          return ret;\n        }\n        function demangle(func2) {\n          return func2;\n        }\n        function demangleAll(text) {\n          var regex = /\\b_Z[\\w\\d_]+/g;\n          return text.replace(regex, function(x) {\n            var y = demangle(x);\n            return x === y ? x : y + \" [\" + x + \"]\";\n          });\n        }\n        function killThread(pthread_ptr) {\n          GROWABLE_HEAP_I32()[pthread_ptr >> 2] = 0;\n          var pthread = PThread.pthreads[pthread_ptr];\n          delete PThread.pthreads[pthread_ptr];\n          pthread.worker.terminate();\n          __emscripten_thread_free_data(pthread_ptr);\n          PThread.runningWorkers.splice(PThread.runningWorkers.indexOf(pthread.worker), 1);\n          pthread.worker.pthread = void 0;\n        }\n        function cancelThread(pthread_ptr) {\n          var pthread = PThread.pthreads[pthread_ptr];\n          pthread.worker.postMessage({ \"cmd\": \"cancel\" });\n        }\n        function cleanupThread(pthread_ptr) {\n          var pthread = PThread.pthreads[pthread_ptr];\n          if (pthread) {\n            GROWABLE_HEAP_I32()[pthread_ptr >> 2] = 0;\n            var worker = pthread.worker;\n            PThread.returnWorkerToPool(worker);\n          }\n        }\n        function _exit(status) {\n          exit(status);\n        }\n        function handleException(e2) {\n          if (e2 instanceof ExitStatus || e2 == \"unwind\") {\n            return EXITSTATUS;\n          }\n          quit_(1, e2);\n        }\n        var PThread = { unusedWorkers: [], runningWorkers: [], tlsInitFunctions: [], init: function() {\n          if (ENVIRONMENT_IS_PTHREAD) {\n            PThread.initWorker();\n          } else {\n            PThread.initMainThread();\n          }\n        }, initMainThread: function() {\n          var pthreadPoolSize = 8;\n          for (var i2 = 0; i2 < pthreadPoolSize; ++i2) {\n            PThread.allocateUnusedWorker();\n          }\n        }, initWorker: function() {\n          noExitRuntime = false;\n        }, pthreads: {}, setExitStatus: function(status) {\n          EXITSTATUS = status;\n        }, terminateAllThreads: function() {\n          for (var t2 in PThread.pthreads) {\n            var pthread = PThread.pthreads[t2];\n            if (pthread && pthread.worker) {\n              PThread.returnWorkerToPool(pthread.worker);\n            }\n          }\n          for (var i2 = 0; i2 < PThread.unusedWorkers.length; ++i2) {\n            var worker = PThread.unusedWorkers[i2];\n            worker.terminate();\n          }\n          PThread.unusedWorkers = [];\n        }, returnWorkerToPool: function(worker) {\n          PThread.runWithoutMainThreadQueuedCalls(function() {\n            delete PThread.pthreads[worker.pthread.threadInfoStruct];\n            PThread.unusedWorkers.push(worker);\n            PThread.runningWorkers.splice(PThread.runningWorkers.indexOf(worker), 1);\n            __emscripten_thread_free_data(worker.pthread.threadInfoStruct);\n            worker.pthread = void 0;\n          });\n        }, runWithoutMainThreadQueuedCalls: function(func2) {\n          GROWABLE_HEAP_I32()[__emscripten_allow_main_runtime_queued_calls >> 2] = 0;\n          try {\n            func2();\n          } finally {\n            GROWABLE_HEAP_I32()[__emscripten_allow_main_runtime_queued_calls >> 2] = 1;\n          }\n        }, receiveObjectTransfer: function(data) {\n        }, threadInit: function() {\n          for (var i2 in PThread.tlsInitFunctions) {\n            PThread.tlsInitFunctions[i2]();\n          }\n        }, loadWasmModuleToWorker: function(worker, onFinishedLoading) {\n          worker.onmessage = (e2) => {\n            var d = e2[\"data\"];\n            var cmd = d[\"cmd\"];\n            if (worker.pthread)\n              PThread.currentProxiedOperationCallerThread = worker.pthread.threadInfoStruct;\n            if (d[\"targetThread\"] && d[\"targetThread\"] != _pthread_self()) {\n              var thread = PThread.pthreads[d.targetThread];\n              if (thread) {\n                thread.worker.postMessage(d, d[\"transferList\"]);\n              } else {\n                err('Internal error! Worker sent a message \"' + cmd + '\" to target pthread ' + d[\"targetThread\"] + \", but that thread no longer exists!\");\n              }\n              PThread.currentProxiedOperationCallerThread = void 0;\n              return;\n            }\n            if (cmd === \"processQueuedMainThreadWork\") {\n              _emscripten_main_thread_process_queued_calls();\n            } else if (cmd === \"spawnThread\") {\n              spawnThread(d);\n            } else if (cmd === \"cleanupThread\") {\n              cleanupThread(d[\"thread\"]);\n            } else if (cmd === \"killThread\") {\n              killThread(d[\"thread\"]);\n            } else if (cmd === \"cancelThread\") {\n              cancelThread(d[\"thread\"]);\n            } else if (cmd === \"loaded\") {\n              worker.loaded = true;\n              if (onFinishedLoading)\n                onFinishedLoading(worker);\n              if (worker.runPthread) {\n                worker.runPthread();\n                delete worker.runPthread;\n              }\n            } else if (cmd === \"print\") {\n              out(\"Thread \" + d[\"threadId\"] + \": \" + d[\"text\"]);\n            } else if (cmd === \"printErr\") {\n              err(\"Thread \" + d[\"threadId\"] + \": \" + d[\"text\"]);\n            } else if (cmd === \"alert\") {\n              alert(\"Thread \" + d[\"threadId\"] + \": \" + d[\"text\"]);\n            } else if (d.target === \"setimmediate\") {\n              worker.postMessage(d);\n            } else if (cmd === \"onAbort\") {\n              if (Module[\"onAbort\"]) {\n                Module[\"onAbort\"](d[\"arg\"]);\n              }\n            } else {\n              err(\"worker sent an unknown command \" + cmd);\n            }\n            PThread.currentProxiedOperationCallerThread = void 0;\n          };\n          worker.onerror = (e2) => {\n            var message = \"worker sent an error!\";\n            err(message + \" \" + e2.filename + \":\" + e2.lineno + \": \" + e2.message);\n            throw e2;\n          };\n          if (ENVIRONMENT_IS_NODE) {\n            worker.on(\"message\", function(data) {\n              worker.onmessage({ data });\n            });\n            worker.on(\"error\", function(e2) {\n              worker.onerror(e2);\n            });\n            worker.on(\"detachedExit\", function() {\n            });\n          }\n          worker.postMessage({ \"cmd\": \"load\", \"urlOrBlob\": Module[\"mainScriptUrlOrBlob\"] || _scriptDir, \"wasmMemory\": wasmMemory, \"wasmModule\": wasmModule });\n        }, allocateUnusedWorker: function() {\n          var pthreadMainJs = locateFile(\"tfjs-backend-wasm-threaded-simd.worker.js\");\n          PThread.unusedWorkers.push(new Worker(pthreadMainJs));\n        }, getNewWorker: function() {\n          if (PThread.unusedWorkers.length == 0) {\n            PThread.allocateUnusedWorker();\n            PThread.loadWasmModuleToWorker(PThread.unusedWorkers[0]);\n          }\n          return PThread.unusedWorkers.pop();\n        } };\n        function establishStackSpace() {\n          var pthread_ptr = _pthread_self();\n          var stackTop = GROWABLE_HEAP_I32()[pthread_ptr + 44 >> 2];\n          var stackSize = GROWABLE_HEAP_I32()[pthread_ptr + 48 >> 2];\n          var stackMax = stackTop - stackSize;\n          _emscripten_stack_set_limits(stackTop, stackMax);\n          stackRestore(stackTop);\n        }\n        Module[\"establishStackSpace\"] = establishStackSpace;\n        function exitOnMainThread(returnCode) {\n          if (ENVIRONMENT_IS_PTHREAD)\n            return _emscripten_proxy_to_main_thread_js(1, 0, returnCode);\n          try {\n            _exit(returnCode);\n          } catch (e2) {\n            handleException(e2);\n          }\n        }\n        var wasmTableMirror = [];\n        function getWasmTableEntry(funcPtr) {\n          var func2 = wasmTableMirror[funcPtr];\n          if (!func2) {\n            if (funcPtr >= wasmTableMirror.length)\n              wasmTableMirror.length = funcPtr + 1;\n            wasmTableMirror[funcPtr] = func2 = wasmTable.get(funcPtr);\n          }\n          return func2;\n        }\n        function invokeEntryPoint(ptr, arg) {\n          return getWasmTableEntry(ptr)(arg);\n        }\n        Module[\"invokeEntryPoint\"] = invokeEntryPoint;\n        function jsStackTrace() {\n          var error = new Error();\n          if (!error.stack) {\n            try {\n              throw new Error();\n            } catch (e2) {\n              error = e2;\n            }\n            if (!error.stack) {\n              return \"(no stack trace available)\";\n            }\n          }\n          return error.stack.toString();\n        }\n        function registerTlsInit(tlsInitFunc, moduleExports, metadata) {\n          PThread.tlsInitFunctions.push(tlsInitFunc);\n        }\n        function setWasmTableEntry(idx, func2) {\n          wasmTable.set(idx, func2);\n          wasmTableMirror[idx] = func2;\n        }\n        var _emscripten_get_now;\n        if (ENVIRONMENT_IS_NODE) {\n          _emscripten_get_now = () => {\n            var t2 = process[\"hrtime\"]();\n            return t2[0] * 1e3 + t2[1] / 1e6;\n          };\n        } else if (ENVIRONMENT_IS_PTHREAD) {\n          _emscripten_get_now = () => performance.now() - Module[\"__performance_now_clock_drift\"];\n        } else\n          _emscripten_get_now = () => performance.now();\n        var _emscripten_get_now_is_monotonic = true;\n        function setErrNo(value) {\n          GROWABLE_HEAP_I32()[___errno_location() >> 2] = value;\n          return value;\n        }\n        function _clock_gettime(clk_id, tp) {\n          var now2;\n          if (clk_id === 0) {\n            now2 = Date.now();\n          } else if ((clk_id === 1 || clk_id === 4) && _emscripten_get_now_is_monotonic) {\n            now2 = _emscripten_get_now();\n          } else {\n            setErrNo(28);\n            return -1;\n          }\n          GROWABLE_HEAP_I32()[tp >> 2] = now2 / 1e3 | 0;\n          GROWABLE_HEAP_I32()[tp + 4 >> 2] = now2 % 1e3 * 1e3 * 1e3 | 0;\n          return 0;\n        }\n        function ___clock_gettime(a0, a12) {\n          return _clock_gettime(a0, a12);\n        }\n        function ___emscripten_init_main_thread_js(tb) {\n          __emscripten_thread_init(tb, !ENVIRONMENT_IS_WORKER, 1, !ENVIRONMENT_IS_WEB);\n          PThread.threadInit();\n        }\n        function ___emscripten_thread_cleanup(thread) {\n          if (!ENVIRONMENT_IS_PTHREAD)\n            cleanupThread(thread);\n          else\n            postMessage({ \"cmd\": \"cleanupThread\", \"thread\": thread });\n        }\n        function spawnThread(threadParams) {\n          var worker = PThread.getNewWorker();\n          if (!worker) {\n            return 6;\n          }\n          PThread.runningWorkers.push(worker);\n          var pthread = PThread.pthreads[threadParams.pthread_ptr] = { worker, threadInfoStruct: threadParams.pthread_ptr };\n          worker.pthread = pthread;\n          var msg = { \"cmd\": \"run\", \"start_routine\": threadParams.startRoutine, \"arg\": threadParams.arg, \"threadInfoStruct\": threadParams.pthread_ptr };\n          worker.runPthread = () => {\n            msg.time = performance.now();\n            worker.postMessage(msg, threadParams.transferList);\n          };\n          if (worker.loaded) {\n            worker.runPthread();\n            delete worker.runPthread;\n          }\n          return 0;\n        }\n        function ___pthread_create_js(pthread_ptr, attr, start_routine, arg) {\n          if (typeof SharedArrayBuffer === \"undefined\") {\n            err(\"Current environment does not support SharedArrayBuffer, pthreads are not available!\");\n            return 6;\n          }\n          var transferList = [];\n          var error = 0;\n          if (ENVIRONMENT_IS_PTHREAD && (transferList.length === 0 || error)) {\n            return _emscripten_sync_run_in_main_thread_4(687865856, pthread_ptr, attr, start_routine, arg);\n          }\n          if (error)\n            return error;\n          var threadParams = { startRoutine: start_routine, pthread_ptr, arg, transferList };\n          if (ENVIRONMENT_IS_PTHREAD) {\n            threadParams.cmd = \"spawnThread\";\n            postMessage(threadParams, transferList);\n            return 0;\n          }\n          return spawnThread(threadParams);\n        }\n        function __emscripten_default_pthread_stack_size() {\n          return 2097152;\n        }\n        function __emscripten_notify_thread_queue(targetThreadId, mainThreadId) {\n          if (targetThreadId == mainThreadId) {\n            postMessage({ \"cmd\": \"processQueuedMainThreadWork\" });\n          } else if (ENVIRONMENT_IS_PTHREAD) {\n            postMessage({ \"targetThread\": targetThreadId, \"cmd\": \"processThreadQueue\" });\n          } else {\n            var pthread = PThread.pthreads[targetThreadId];\n            var worker = pthread && pthread.worker;\n            if (!worker) {\n              return;\n            }\n            worker.postMessage({ \"cmd\": \"processThreadQueue\" });\n          }\n          return 1;\n        }\n        function _abort() {\n          abort(\"\");\n        }\n        function _emscripten_check_blocking_allowed() {\n          if (ENVIRONMENT_IS_NODE)\n            return;\n          if (ENVIRONMENT_IS_WORKER)\n            return;\n          warnOnce(\"Blocking on the main thread is very dangerous, see https://emscripten.org/docs/porting/pthreads.html#blocking-on-the-main-browser-thread\");\n        }\n        function _emscripten_get_heap_max() {\n          return 2147483648;\n        }\n        function _emscripten_memcpy_big(dest, src, num) {\n          GROWABLE_HEAP_U8().copyWithin(dest, src, src + num);\n        }\n        function _emscripten_num_logical_cores() {\n          if (ENVIRONMENT_IS_NODE)\n            return require_os().cpus().length;\n          return navigator[\"hardwareConcurrency\"];\n        }\n        function _emscripten_proxy_to_main_thread_js(index, sync) {\n          var numCallArgs = arguments.length - 2;\n          var outerArgs = arguments;\n          return withStackSave(function() {\n            var serializedNumCallArgs = numCallArgs;\n            var args = stackAlloc(serializedNumCallArgs * 8);\n            var b = args >> 3;\n            for (var i2 = 0; i2 < numCallArgs; i2++) {\n              var arg = outerArgs[2 + i2];\n              GROWABLE_HEAP_F64()[b + i2] = arg;\n            }\n            return _emscripten_run_in_main_runtime_thread_js(index, serializedNumCallArgs, args, sync);\n          });\n        }\n        var _emscripten_receive_on_main_thread_js_callArgs = [];\n        function _emscripten_receive_on_main_thread_js(index, numCallArgs, args) {\n          _emscripten_receive_on_main_thread_js_callArgs.length = numCallArgs;\n          var b = args >> 3;\n          for (var i2 = 0; i2 < numCallArgs; i2++) {\n            _emscripten_receive_on_main_thread_js_callArgs[i2] = GROWABLE_HEAP_F64()[b + i2];\n          }\n          var isEmAsmConst = index < 0;\n          var func2 = !isEmAsmConst ? proxiedFunctionTable[index] : ASM_CONSTS[-index - 1];\n          return func2.apply(null, _emscripten_receive_on_main_thread_js_callArgs);\n        }\n        function emscripten_realloc_buffer(size) {\n          try {\n            wasmMemory.grow(size - buffer2.byteLength + 65535 >>> 16);\n            updateGlobalBufferAndViews(wasmMemory.buffer);\n            return 1;\n          } catch (e2) {\n          }\n        }\n        function _emscripten_resize_heap(requestedSize) {\n          var oldSize = GROWABLE_HEAP_U8().length;\n          requestedSize = requestedSize >>> 0;\n          if (requestedSize <= oldSize) {\n            return false;\n          }\n          var maxHeapSize = _emscripten_get_heap_max();\n          if (requestedSize > maxHeapSize) {\n            return false;\n          }\n          for (var cutDown = 1; cutDown <= 4; cutDown *= 2) {\n            var overGrownHeapSize = oldSize * (1 + 0.2 / cutDown);\n            overGrownHeapSize = Math.min(overGrownHeapSize, requestedSize + 100663296);\n            var newSize = Math.min(maxHeapSize, alignUp(Math.max(requestedSize, overGrownHeapSize), 65536));\n            var replacement = emscripten_realloc_buffer(newSize);\n            if (replacement) {\n              return true;\n            }\n          }\n          return false;\n        }\n        var JSEvents = { inEventHandler: 0, removeAllEventListeners: function() {\n          for (var i2 = JSEvents.eventHandlers.length - 1; i2 >= 0; --i2) {\n            JSEvents._removeHandler(i2);\n          }\n          JSEvents.eventHandlers = [];\n          JSEvents.deferredCalls = [];\n        }, registerRemoveEventListeners: function() {\n          if (!JSEvents.removeEventListenersRegistered) {\n            __ATEXIT__.push(JSEvents.removeAllEventListeners);\n            JSEvents.removeEventListenersRegistered = true;\n          }\n        }, deferredCalls: [], deferCall: function(targetFunction, precedence, argsList) {\n          function arraysHaveEqualContent(arrA, arrB) {\n            if (arrA.length != arrB.length)\n              return false;\n            for (var i3 in arrA) {\n              if (arrA[i3] != arrB[i3])\n                return false;\n            }\n            return true;\n          }\n          for (var i2 in JSEvents.deferredCalls) {\n            var call = JSEvents.deferredCalls[i2];\n            if (call.targetFunction == targetFunction && arraysHaveEqualContent(call.argsList, argsList)) {\n              return;\n            }\n          }\n          JSEvents.deferredCalls.push({ targetFunction, precedence, argsList });\n          JSEvents.deferredCalls.sort(function(x, y) {\n            return x.precedence < y.precedence;\n          });\n        }, removeDeferredCalls: function(targetFunction) {\n          for (var i2 = 0; i2 < JSEvents.deferredCalls.length; ++i2) {\n            if (JSEvents.deferredCalls[i2].targetFunction == targetFunction) {\n              JSEvents.deferredCalls.splice(i2, 1);\n              --i2;\n            }\n          }\n        }, canPerformEventHandlerRequests: function() {\n          return JSEvents.inEventHandler && JSEvents.currentEventHandler.allowsDeferredCalls;\n        }, runDeferredCalls: function() {\n          if (!JSEvents.canPerformEventHandlerRequests()) {\n            return;\n          }\n          for (var i2 = 0; i2 < JSEvents.deferredCalls.length; ++i2) {\n            var call = JSEvents.deferredCalls[i2];\n            JSEvents.deferredCalls.splice(i2, 1);\n            --i2;\n            call.targetFunction.apply(null, call.argsList);\n          }\n        }, eventHandlers: [], removeAllHandlersOnTarget: function(target, eventTypeString) {\n          for (var i2 = 0; i2 < JSEvents.eventHandlers.length; ++i2) {\n            if (JSEvents.eventHandlers[i2].target == target && (!eventTypeString || eventTypeString == JSEvents.eventHandlers[i2].eventTypeString)) {\n              JSEvents._removeHandler(i2--);\n            }\n          }\n        }, _removeHandler: function(i2) {\n          var h = JSEvents.eventHandlers[i2];\n          h.target.removeEventListener(h.eventTypeString, h.eventListenerFunc, h.useCapture);\n          JSEvents.eventHandlers.splice(i2, 1);\n        }, registerOrRemoveHandler: function(eventHandler) {\n          var jsEventHandler = function jsEventHandler2(event) {\n            ++JSEvents.inEventHandler;\n            JSEvents.currentEventHandler = eventHandler;\n            JSEvents.runDeferredCalls();\n            eventHandler.handlerFunc(event);\n            JSEvents.runDeferredCalls();\n            --JSEvents.inEventHandler;\n          };\n          if (eventHandler.callbackfunc) {\n            eventHandler.eventListenerFunc = jsEventHandler;\n            eventHandler.target.addEventListener(eventHandler.eventTypeString, jsEventHandler, eventHandler.useCapture);\n            JSEvents.eventHandlers.push(eventHandler);\n            JSEvents.registerRemoveEventListeners();\n          } else {\n            for (var i2 = 0; i2 < JSEvents.eventHandlers.length; ++i2) {\n              if (JSEvents.eventHandlers[i2].target == eventHandler.target && JSEvents.eventHandlers[i2].eventTypeString == eventHandler.eventTypeString) {\n                JSEvents._removeHandler(i2--);\n              }\n            }\n          }\n        }, queueEventHandlerOnThread_iiii: function(targetThread, eventHandlerFunc, eventTypeId, eventData, userData) {\n          withStackSave(function() {\n            var varargs = stackAlloc(12);\n            GROWABLE_HEAP_I32()[varargs >> 2] = eventTypeId;\n            GROWABLE_HEAP_I32()[varargs + 4 >> 2] = eventData;\n            GROWABLE_HEAP_I32()[varargs + 8 >> 2] = userData;\n            _emscripten_dispatch_to_thread_(targetThread, 637534208, eventHandlerFunc, eventData, varargs);\n          });\n        }, getTargetThreadForEventCallback: function(targetThread) {\n          switch (targetThread) {\n            case 1:\n              return 0;\n            case 2:\n              return PThread.currentProxiedOperationCallerThread;\n            default:\n              return targetThread;\n          }\n        }, getNodeNameForTarget: function(target) {\n          if (!target)\n            return \"\";\n          if (target == window)\n            return \"#window\";\n          if (target == screen)\n            return \"#screen\";\n          return target && target.nodeName ? target.nodeName : \"\";\n        }, fullscreenEnabled: function() {\n          return document.fullscreenEnabled || document.webkitFullscreenEnabled;\n        } };\n        function stringToNewUTF8(jsString) {\n          var length = lengthBytesUTF8(jsString) + 1;\n          var cString = _malloc(length);\n          stringToUTF8(jsString, cString, length);\n          return cString;\n        }\n        function _emscripten_set_offscreencanvas_size_on_target_thread_js(targetThread, targetCanvas, width, height) {\n          withStackSave(function() {\n            var varargs = stackAlloc(12);\n            var targetCanvasPtr = 0;\n            if (targetCanvas) {\n              targetCanvasPtr = stringToNewUTF8(targetCanvas);\n            }\n            GROWABLE_HEAP_I32()[varargs >> 2] = targetCanvasPtr;\n            GROWABLE_HEAP_I32()[varargs + 4 >> 2] = width;\n            GROWABLE_HEAP_I32()[varargs + 8 >> 2] = height;\n            _emscripten_dispatch_to_thread_(targetThread, 657457152, 0, targetCanvasPtr, varargs);\n          });\n        }\n        function _emscripten_set_offscreencanvas_size_on_target_thread(targetThread, targetCanvas, width, height) {\n          targetCanvas = targetCanvas ? UTF8ToString(targetCanvas) : \"\";\n          _emscripten_set_offscreencanvas_size_on_target_thread_js(targetThread, targetCanvas, width, height);\n        }\n        function maybeCStringToJsString(cString) {\n          return cString > 2 ? UTF8ToString(cString) : cString;\n        }\n        var specialHTMLTargets = [0, typeof document !== \"undefined\" ? document : 0, typeof window !== \"undefined\" ? window : 0];\n        function findEventTarget(target) {\n          target = maybeCStringToJsString(target);\n          var domElement = specialHTMLTargets[target] || (typeof document !== \"undefined\" ? document.querySelector(target) : void 0);\n          return domElement;\n        }\n        function findCanvasEventTarget(target) {\n          return findEventTarget(target);\n        }\n        function _emscripten_set_canvas_element_size_calling_thread(target, width, height) {\n          var canvas = findCanvasEventTarget(target);\n          if (!canvas)\n            return -4;\n          if (canvas.canvasSharedPtr) {\n            GROWABLE_HEAP_I32()[canvas.canvasSharedPtr >> 2] = width;\n            GROWABLE_HEAP_I32()[canvas.canvasSharedPtr + 4 >> 2] = height;\n          }\n          if (canvas.offscreenCanvas || !canvas.controlTransferredOffscreen) {\n            if (canvas.offscreenCanvas)\n              canvas = canvas.offscreenCanvas;\n            var autoResizeViewport = false;\n            if (canvas.GLctxObject && canvas.GLctxObject.GLctx) {\n              var prevViewport = canvas.GLctxObject.GLctx.getParameter(2978);\n              autoResizeViewport = prevViewport[0] === 0 && prevViewport[1] === 0 && prevViewport[2] === canvas.width && prevViewport[3] === canvas.height;\n            }\n            canvas.width = width;\n            canvas.height = height;\n            if (autoResizeViewport) {\n              canvas.GLctxObject.GLctx.viewport(0, 0, width, height);\n            }\n          } else if (canvas.canvasSharedPtr) {\n            var targetThread = GROWABLE_HEAP_I32()[canvas.canvasSharedPtr + 8 >> 2];\n            _emscripten_set_offscreencanvas_size_on_target_thread(targetThread, target, width, height);\n            return 1;\n          } else {\n            return -4;\n          }\n          return 0;\n        }\n        function _emscripten_set_canvas_element_size_main_thread(target, width, height) {\n          if (ENVIRONMENT_IS_PTHREAD)\n            return _emscripten_proxy_to_main_thread_js(2, 1, target, width, height);\n          return _emscripten_set_canvas_element_size_calling_thread(target, width, height);\n        }\n        function _emscripten_set_canvas_element_size(target, width, height) {\n          var canvas = findCanvasEventTarget(target);\n          if (canvas) {\n            return _emscripten_set_canvas_element_size_calling_thread(target, width, height);\n          } else {\n            return _emscripten_set_canvas_element_size_main_thread(target, width, height);\n          }\n        }\n        function _emscripten_unwind_to_js_event_loop() {\n          throw \"unwind\";\n        }\n        function __webgl_enable_ANGLE_instanced_arrays(ctx) {\n          var ext = ctx.getExtension(\"ANGLE_instanced_arrays\");\n          if (ext) {\n            ctx[\"vertexAttribDivisor\"] = function(index, divisor) {\n              ext[\"vertexAttribDivisorANGLE\"](index, divisor);\n            };\n            ctx[\"drawArraysInstanced\"] = function(mode, first, count2, primcount) {\n              ext[\"drawArraysInstancedANGLE\"](mode, first, count2, primcount);\n            };\n            ctx[\"drawElementsInstanced\"] = function(mode, count2, type, indices, primcount) {\n              ext[\"drawElementsInstancedANGLE\"](mode, count2, type, indices, primcount);\n            };\n            return 1;\n          }\n        }\n        function __webgl_enable_OES_vertex_array_object(ctx) {\n          var ext = ctx.getExtension(\"OES_vertex_array_object\");\n          if (ext) {\n            ctx[\"createVertexArray\"] = function() {\n              return ext[\"createVertexArrayOES\"]();\n            };\n            ctx[\"deleteVertexArray\"] = function(vao) {\n              ext[\"deleteVertexArrayOES\"](vao);\n            };\n            ctx[\"bindVertexArray\"] = function(vao) {\n              ext[\"bindVertexArrayOES\"](vao);\n            };\n            ctx[\"isVertexArray\"] = function(vao) {\n              return ext[\"isVertexArrayOES\"](vao);\n            };\n            return 1;\n          }\n        }\n        function __webgl_enable_WEBGL_draw_buffers(ctx) {\n          var ext = ctx.getExtension(\"WEBGL_draw_buffers\");\n          if (ext) {\n            ctx[\"drawBuffers\"] = function(n2, bufs) {\n              ext[\"drawBuffersWEBGL\"](n2, bufs);\n            };\n            return 1;\n          }\n        }\n        function __webgl_enable_WEBGL_multi_draw(ctx) {\n          return !!(ctx.multiDrawWebgl = ctx.getExtension(\"WEBGL_multi_draw\"));\n        }\n        var GL = { counter: 1, buffers: [], programs: [], framebuffers: [], renderbuffers: [], textures: [], shaders: [], vaos: [], contexts: {}, offscreenCanvases: {}, queries: [], stringCache: {}, unpackAlignment: 4, recordError: function recordError(errorCode) {\n          if (!GL.lastError) {\n            GL.lastError = errorCode;\n          }\n        }, getNewId: function(table) {\n          var ret = GL.counter++;\n          for (var i2 = table.length; i2 < ret; i2++) {\n            table[i2] = null;\n          }\n          return ret;\n        }, getSource: function(shader, count2, string2, length) {\n          var source = \"\";\n          for (var i2 = 0; i2 < count2; ++i2) {\n            var len = length ? GROWABLE_HEAP_I32()[length + i2 * 4 >> 2] : -1;\n            source += UTF8ToString(GROWABLE_HEAP_I32()[string2 + i2 * 4 >> 2], len < 0 ? void 0 : len);\n          }\n          return source;\n        }, createContext: function(canvas, webGLContextAttributes) {\n          if (!canvas.getContextSafariWebGL2Fixed) {\n            canvas.getContextSafariWebGL2Fixed = canvas.getContext;\n            canvas.getContext = function(ver, attrs) {\n              var gl = canvas.getContextSafariWebGL2Fixed(ver, attrs);\n              return ver == \"webgl\" == gl instanceof WebGLRenderingContext ? gl : null;\n            };\n          }\n          var ctx = canvas.getContext(\"webgl\", webGLContextAttributes);\n          if (!ctx)\n            return 0;\n          var handle = GL.registerContext(ctx, webGLContextAttributes);\n          return handle;\n        }, registerContext: function(ctx, webGLContextAttributes) {\n          var handle = _malloc(8);\n          GROWABLE_HEAP_I32()[handle + 4 >> 2] = _pthread_self();\n          var context = { handle, attributes: webGLContextAttributes, version: webGLContextAttributes.majorVersion, GLctx: ctx };\n          if (ctx.canvas)\n            ctx.canvas.GLctxObject = context;\n          GL.contexts[handle] = context;\n          if (typeof webGLContextAttributes.enableExtensionsByDefault === \"undefined\" || webGLContextAttributes.enableExtensionsByDefault) {\n            GL.initExtensions(context);\n          }\n          return handle;\n        }, makeContextCurrent: function(contextHandle) {\n          GL.currentContext = GL.contexts[contextHandle];\n          Module.ctx = GLctx = GL.currentContext && GL.currentContext.GLctx;\n          return !(contextHandle && !GLctx);\n        }, getContext: function(contextHandle) {\n          return GL.contexts[contextHandle];\n        }, deleteContext: function(contextHandle) {\n          if (GL.currentContext === GL.contexts[contextHandle])\n            GL.currentContext = null;\n          if (typeof JSEvents === \"object\")\n            JSEvents.removeAllHandlersOnTarget(GL.contexts[contextHandle].GLctx.canvas);\n          if (GL.contexts[contextHandle] && GL.contexts[contextHandle].GLctx.canvas)\n            GL.contexts[contextHandle].GLctx.canvas.GLctxObject = void 0;\n          _free(GL.contexts[contextHandle].handle);\n          GL.contexts[contextHandle] = null;\n        }, initExtensions: function(context) {\n          if (!context)\n            context = GL.currentContext;\n          if (context.initExtensionsDone)\n            return;\n          context.initExtensionsDone = true;\n          var GLctx2 = context.GLctx;\n          __webgl_enable_ANGLE_instanced_arrays(GLctx2);\n          __webgl_enable_OES_vertex_array_object(GLctx2);\n          __webgl_enable_WEBGL_draw_buffers(GLctx2);\n          {\n            GLctx2.disjointTimerQueryExt = GLctx2.getExtension(\"EXT_disjoint_timer_query\");\n          }\n          __webgl_enable_WEBGL_multi_draw(GLctx2);\n          var exts = GLctx2.getSupportedExtensions() || [];\n          exts.forEach(function(ext) {\n            if (!ext.includes(\"lose_context\") && !ext.includes(\"debug\")) {\n              GLctx2.getExtension(ext);\n            }\n          });\n        } };\n        var __emscripten_webgl_power_preferences = [\"default\", \"low-power\", \"high-performance\"];\n        function _emscripten_webgl_do_create_context(target, attributes) {\n          var a = attributes >> 2;\n          var powerPreference = GROWABLE_HEAP_I32()[a + (24 >> 2)];\n          var contextAttributes = { \"alpha\": !!GROWABLE_HEAP_I32()[a + (0 >> 2)], \"depth\": !!GROWABLE_HEAP_I32()[a + (4 >> 2)], \"stencil\": !!GROWABLE_HEAP_I32()[a + (8 >> 2)], \"antialias\": !!GROWABLE_HEAP_I32()[a + (12 >> 2)], \"premultipliedAlpha\": !!GROWABLE_HEAP_I32()[a + (16 >> 2)], \"preserveDrawingBuffer\": !!GROWABLE_HEAP_I32()[a + (20 >> 2)], \"powerPreference\": __emscripten_webgl_power_preferences[powerPreference], \"failIfMajorPerformanceCaveat\": !!GROWABLE_HEAP_I32()[a + (28 >> 2)], majorVersion: GROWABLE_HEAP_I32()[a + (32 >> 2)], minorVersion: GROWABLE_HEAP_I32()[a + (36 >> 2)], enableExtensionsByDefault: GROWABLE_HEAP_I32()[a + (40 >> 2)], explicitSwapControl: GROWABLE_HEAP_I32()[a + (44 >> 2)], proxyContextToMainThread: GROWABLE_HEAP_I32()[a + (48 >> 2)], renderViaOffscreenBackBuffer: GROWABLE_HEAP_I32()[a + (52 >> 2)] };\n          var canvas = findCanvasEventTarget(target);\n          if (!canvas) {\n            return 0;\n          }\n          if (contextAttributes.explicitSwapControl) {\n            return 0;\n          }\n          var contextHandle = GL.createContext(canvas, contextAttributes);\n          return contextHandle;\n        }\n        function _emscripten_webgl_create_context(a0, a12) {\n          return _emscripten_webgl_do_create_context(a0, a12);\n        }\n        var SYSCALLS = { mappings: {}, buffers: [null, [], []], printChar: function(stream, curr) {\n          var buffer3 = SYSCALLS.buffers[stream];\n          if (curr === 0 || curr === 10) {\n            (stream === 1 ? out : err)(UTF8ArrayToString(buffer3, 0));\n            buffer3.length = 0;\n          } else {\n            buffer3.push(curr);\n          }\n        }, varargs: void 0, get: function() {\n          SYSCALLS.varargs += 4;\n          var ret = GROWABLE_HEAP_I32()[SYSCALLS.varargs - 4 >> 2];\n          return ret;\n        }, getStr: function(ptr) {\n          var ret = UTF8ToString(ptr);\n          return ret;\n        }, get64: function(low, high) {\n          return low;\n        } };\n        function _fd_close(fd) {\n          if (ENVIRONMENT_IS_PTHREAD)\n            return _emscripten_proxy_to_main_thread_js(3, 1, fd);\n          return 0;\n        }\n        function _fd_seek(fd, offset_low, offset_high, whence, newOffset) {\n          if (ENVIRONMENT_IS_PTHREAD)\n            return _emscripten_proxy_to_main_thread_js(4, 1, fd, offset_low, offset_high, whence, newOffset);\n        }\n        function _fd_write(fd, iov, iovcnt, pnum) {\n          if (ENVIRONMENT_IS_PTHREAD)\n            return _emscripten_proxy_to_main_thread_js(5, 1, fd, iov, iovcnt, pnum);\n          var num = 0;\n          for (var i2 = 0; i2 < iovcnt; i2++) {\n            var ptr = GROWABLE_HEAP_I32()[iov >> 2];\n            var len = GROWABLE_HEAP_I32()[iov + 4 >> 2];\n            iov += 8;\n            for (var j = 0; j < len; j++) {\n              SYSCALLS.printChar(fd, GROWABLE_HEAP_U8()[ptr + j]);\n            }\n            num += len;\n          }\n          GROWABLE_HEAP_I32()[pnum >> 2] = num;\n          return 0;\n        }\n        function _setTempRet0(val) {\n          setTempRet0(val);\n        }\n        PThread.init();\n        var GLctx;\n        var proxiedFunctionTable = [null, exitOnMainThread, _emscripten_set_canvas_element_size_main_thread, _fd_close, _fd_seek, _fd_write];\n        var ASSERTIONS = false;\n        var asmLibraryArg = { \"__clock_gettime\": ___clock_gettime, \"__emscripten_init_main_thread_js\": ___emscripten_init_main_thread_js, \"__emscripten_thread_cleanup\": ___emscripten_thread_cleanup, \"__pthread_create_js\": ___pthread_create_js, \"_emscripten_default_pthread_stack_size\": __emscripten_default_pthread_stack_size, \"_emscripten_notify_thread_queue\": __emscripten_notify_thread_queue, \"abort\": _abort, \"emscripten_check_blocking_allowed\": _emscripten_check_blocking_allowed, \"emscripten_get_heap_max\": _emscripten_get_heap_max, \"emscripten_get_now\": _emscripten_get_now, \"emscripten_memcpy_big\": _emscripten_memcpy_big, \"emscripten_num_logical_cores\": _emscripten_num_logical_cores, \"emscripten_receive_on_main_thread_js\": _emscripten_receive_on_main_thread_js, \"emscripten_resize_heap\": _emscripten_resize_heap, \"emscripten_set_canvas_element_size\": _emscripten_set_canvas_element_size, \"emscripten_unwind_to_js_event_loop\": _emscripten_unwind_to_js_event_loop, \"emscripten_webgl_create_context\": _emscripten_webgl_create_context, \"exit\": _exit, \"fd_close\": _fd_close, \"fd_seek\": _fd_seek, \"fd_write\": _fd_write, \"memory\": wasmMemory || Module[\"wasmMemory\"], \"setTempRet0\": _setTempRet0 };\n        var asm = createWasm();\n        var ___wasm_call_ctors = Module[\"___wasm_call_ctors\"] = function() {\n          return (___wasm_call_ctors = Module[\"___wasm_call_ctors\"] = Module[\"asm\"][\"__wasm_call_ctors\"]).apply(null, arguments);\n        };\n        var _init = Module[\"_init\"] = function() {\n          return (_init = Module[\"_init\"] = Module[\"asm\"][\"init\"]).apply(null, arguments);\n        };\n        var _init_with_threads_count = Module[\"_init_with_threads_count\"] = function() {\n          return (_init_with_threads_count = Module[\"_init_with_threads_count\"] = Module[\"asm\"][\"init_with_threads_count\"]).apply(null, arguments);\n        };\n        var _get_threads_count = Module[\"_get_threads_count\"] = function() {\n          return (_get_threads_count = Module[\"_get_threads_count\"] = Module[\"asm\"][\"get_threads_count\"]).apply(null, arguments);\n        };\n        var _register_tensor = Module[\"_register_tensor\"] = function() {\n          return (_register_tensor = Module[\"_register_tensor\"] = Module[\"asm\"][\"register_tensor\"]).apply(null, arguments);\n        };\n        var _dispose_data = Module[\"_dispose_data\"] = function() {\n          return (_dispose_data = Module[\"_dispose_data\"] = Module[\"asm\"][\"dispose_data\"]).apply(null, arguments);\n        };\n        var _dispose = Module[\"_dispose\"] = function() {\n          return (_dispose = Module[\"_dispose\"] = Module[\"asm\"][\"dispose\"]).apply(null, arguments);\n        };\n        var _Abs = Module[\"_Abs\"] = function() {\n          return (_Abs = Module[\"_Abs\"] = Module[\"asm\"][\"Abs\"]).apply(null, arguments);\n        };\n        var _Add = Module[\"_Add\"] = function() {\n          return (_Add = Module[\"_Add\"] = Module[\"asm\"][\"Add\"]).apply(null, arguments);\n        };\n        var _AddN = Module[\"_AddN\"] = function() {\n          return (_AddN = Module[\"_AddN\"] = Module[\"asm\"][\"AddN\"]).apply(null, arguments);\n        };\n        var _All = Module[\"_All\"] = function() {\n          return (_All = Module[\"_All\"] = Module[\"asm\"][\"All\"]).apply(null, arguments);\n        };\n        var _Any = Module[\"_Any\"] = function() {\n          return (_Any = Module[\"_Any\"] = Module[\"asm\"][\"Any\"]).apply(null, arguments);\n        };\n        var _ArgMax = Module[\"_ArgMax\"] = function() {\n          return (_ArgMax = Module[\"_ArgMax\"] = Module[\"asm\"][\"ArgMax\"]).apply(null, arguments);\n        };\n        var _AvgPool = Module[\"_AvgPool\"] = function() {\n          return (_AvgPool = Module[\"_AvgPool\"] = Module[\"asm\"][\"AvgPool\"]).apply(null, arguments);\n        };\n        var _BatchMatMul = Module[\"_BatchMatMul\"] = function() {\n          return (_BatchMatMul = Module[\"_BatchMatMul\"] = Module[\"asm\"][\"BatchMatMul\"]).apply(null, arguments);\n        };\n        var _Ceil = Module[\"_Ceil\"] = function() {\n          return (_Ceil = Module[\"_Ceil\"] = Module[\"asm\"][\"Ceil\"]).apply(null, arguments);\n        };\n        var _ClipByValue = Module[\"_ClipByValue\"] = function() {\n          return (_ClipByValue = Module[\"_ClipByValue\"] = Module[\"asm\"][\"ClipByValue\"]).apply(null, arguments);\n        };\n        var _Conv2D = Module[\"_Conv2D\"] = function() {\n          return (_Conv2D = Module[\"_Conv2D\"] = Module[\"asm\"][\"Conv2D\"]).apply(null, arguments);\n        };\n        var _Conv2DBackpropInput = Module[\"_Conv2DBackpropInput\"] = function() {\n          return (_Conv2DBackpropInput = Module[\"_Conv2DBackpropInput\"] = Module[\"asm\"][\"Conv2DBackpropInput\"]).apply(null, arguments);\n        };\n        var _Cos = Module[\"_Cos\"] = function() {\n          return (_Cos = Module[\"_Cos\"] = Module[\"asm\"][\"Cos\"]).apply(null, arguments);\n        };\n        var _Cosh = Module[\"_Cosh\"] = function() {\n          return (_Cosh = Module[\"_Cosh\"] = Module[\"asm\"][\"Cosh\"]).apply(null, arguments);\n        };\n        var _CropAndResize = Module[\"_CropAndResize\"] = function() {\n          return (_CropAndResize = Module[\"_CropAndResize\"] = Module[\"asm\"][\"CropAndResize\"]).apply(null, arguments);\n        };\n        var _Cumprod = Module[\"_Cumprod\"] = function() {\n          return (_Cumprod = Module[\"_Cumprod\"] = Module[\"asm\"][\"Cumprod\"]).apply(null, arguments);\n        };\n        var _Cumsum = Module[\"_Cumsum\"] = function() {\n          return (_Cumsum = Module[\"_Cumsum\"] = Module[\"asm\"][\"Cumsum\"]).apply(null, arguments);\n        };\n        var _DepthToSpace = Module[\"_DepthToSpace\"] = function() {\n          return (_DepthToSpace = Module[\"_DepthToSpace\"] = Module[\"asm\"][\"DepthToSpace\"]).apply(null, arguments);\n        };\n        var _DepthwiseConv2dNative = Module[\"_DepthwiseConv2dNative\"] = function() {\n          return (_DepthwiseConv2dNative = Module[\"_DepthwiseConv2dNative\"] = Module[\"asm\"][\"DepthwiseConv2dNative\"]).apply(null, arguments);\n        };\n        var _Elu = Module[\"_Elu\"] = function() {\n          return (_Elu = Module[\"_Elu\"] = Module[\"asm\"][\"Elu\"]).apply(null, arguments);\n        };\n        var _Equal = Module[\"_Equal\"] = function() {\n          return (_Equal = Module[\"_Equal\"] = Module[\"asm\"][\"Equal\"]).apply(null, arguments);\n        };\n        var _Exp = Module[\"_Exp\"] = function() {\n          return (_Exp = Module[\"_Exp\"] = Module[\"asm\"][\"Exp\"]).apply(null, arguments);\n        };\n        var _FlipLeftRight = Module[\"_FlipLeftRight\"] = function() {\n          return (_FlipLeftRight = Module[\"_FlipLeftRight\"] = Module[\"asm\"][\"FlipLeftRight\"]).apply(null, arguments);\n        };\n        var _Floor = Module[\"_Floor\"] = function() {\n        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arguments);\n        };\n        var _Minimum = Module[\"_Minimum\"] = function() {\n          return (_Minimum = Module[\"_Minimum\"] = Module[\"asm\"][\"Minimum\"]).apply(null, arguments);\n        };\n        var _MirrorPad = Module[\"_MirrorPad\"] = function() {\n          return (_MirrorPad = Module[\"_MirrorPad\"] = Module[\"asm\"][\"MirrorPad\"]).apply(null, arguments);\n        };\n        var _Multiply = Module[\"_Multiply\"] = function() {\n          return (_Multiply = Module[\"_Multiply\"] = Module[\"asm\"][\"Multiply\"]).apply(null, arguments);\n        };\n        var _Neg = Module[\"_Neg\"] = function() {\n          return (_Neg = Module[\"_Neg\"] = Module[\"asm\"][\"Neg\"]).apply(null, arguments);\n        };\n        var _NonMaxSuppressionV3 = Module[\"_NonMaxSuppressionV3\"] = function() {\n          return (_NonMaxSuppressionV3 = Module[\"_NonMaxSuppressionV3\"] = Module[\"asm\"][\"NonMaxSuppressionV3\"]).apply(null, arguments);\n        };\n        var 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Module[\"asm\"][\"dynCall_iijjiiii\"]).apply(null, arguments);\n        };\n        var dynCall_jiji = Module[\"dynCall_jiji\"] = function() {\n          return (dynCall_jiji = Module[\"dynCall_jiji\"] = Module[\"asm\"][\"dynCall_jiji\"]).apply(null, arguments);\n        };\n        var __emscripten_allow_main_runtime_queued_calls = Module[\"__emscripten_allow_main_runtime_queued_calls\"] = 21672;\n        Module[\"cwrap\"] = cwrap;\n        Module[\"keepRuntimeAlive\"] = keepRuntimeAlive;\n        Module[\"PThread\"] = PThread;\n        Module[\"PThread\"] = PThread;\n        Module[\"wasmMemory\"] = wasmMemory;\n        Module[\"ExitStatus\"] = ExitStatus;\n        var calledRun;\n        function ExitStatus(status) {\n          this.name = \"ExitStatus\";\n          this.message = \"Program terminated with exit(\" + status + \")\";\n          this.status = status;\n        }\n        dependenciesFulfilled = function runCaller() {\n          if (!calledRun)\n            run();\n      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        quit_(code, new ExitStatus(code));\n        }\n        if (Module[\"preInit\"]) {\n          if (typeof Module[\"preInit\"] == \"function\")\n            Module[\"preInit\"] = [Module[\"preInit\"]];\n          while (Module[\"preInit\"].length > 0) {\n            Module[\"preInit\"].pop()();\n          }\n        }\n        run();\n        var listenersAdded;\n        if (beforeListeners) {\n          listenersAdded = { uncaughtException: process.listeners(\"uncaughtException\").filter(function(listener) {\n            return !beforeListeners.uncaughtException.indexOf(listener) > -1;\n          }), unhandledRejection: process.listeners(\"unhandledRejection\").filter(function(listener) {\n            return !beforeListeners.unhandledRejection.indexOf(listener) > -1;\n          }) };\n        }\n        var actualModule;\n        if (typeof WasmBackendModule !== \"undefined\") {\n          actualModule = WasmBackendModule;\n        } else if (typeof WasmBackendModuleThreadedSimd3 !== \"undefined\") {\n          actualModule = WasmBackendModuleThreadedSimd3;\n        } else {\n          throw new Error(\"Could not find wasm module in post.js\");\n        }\n        if (listenersAdded) {\n          var tmpDispose = actualModule[\"_dispose\"];\n          actualModule[\"_dispose\"] = function() {\n            tmpDispose();\n            listenersAdded.uncaughtException.forEach(function(listener) {\n              process.removeListener(\"uncaughtException\", listener);\n            });\n            listenersAdded.unhandledRejection.forEach(function(listener) {\n              process.removeListener(\"unhandledRejection\", listener);\n            });\n          };\n        }\n        return WasmBackendModuleThreadedSimd3.ready;\n      };\n    })();\n    if (typeof exports === \"object\" && typeof module === \"object\")\n      module.exports = WasmBackendModuleThreadedSimd2;\n    else if (typeof define === \"function\" && define[\"amd\"])\n      define([], function() {\n        return WasmBackendModuleThreadedSimd2;\n      });\n    else if (typeof exports === \"object\")\n      exports[\"WasmBackendModuleThreadedSimd\"] = WasmBackendModuleThreadedSimd2;\n  }\n});\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm-threaded-simd.worker.js\nvar require_tfjs_backend_wasm_threaded_simd_worker = __commonJS({\n  \"node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm-threaded-simd.worker.js\"(exports, module) {\n    module.exports.wasmWorkerContents = `\"use strict\";var Module={};var ENVIRONMENT_IS_NODE=typeof process===\"object\"&&typeof process.versions===\"object\"&&typeof process.versions.node===\"string\";if(ENVIRONMENT_IS_NODE){var nodeWorkerThreads=require(\"worker_threads\");var parentPort=nodeWorkerThreads.parentPort;parentPort.on(\"message\",function(data){onmessage({data:data})});var fs=require(\"fs\");Object.assign(global,{self:global,require:require,Module:Module,location:{href:__filename},Worker:nodeWorkerThreads.Worker,importScripts:function(f){(0,eval)(fs.readFileSync(f,\"utf8\"))},postMessage:function(msg){parentPort.postMessage(msg)},performance:global.performance||{now:function(){return Date.now()}}})}function threadPrintErr(){var text=Array.prototype.slice.call(arguments).join(\" \");if(ENVIRONMENT_IS_NODE){fs.writeSync(2,text+\"\n\");return}console.error(text)}function threadAlert(){var text=Array.prototype.slice.call(arguments).join(\" \");postMessage({cmd:\"alert\",text:text,threadId:Module[\"_pthread_self\"]()})}var err=threadPrintErr;self.alert=threadAlert;Module[\"instantiateWasm\"]=((info,receiveInstance)=>{var instance=new WebAssembly.Instance(Module[\"wasmModule\"],info);receiveInstance(instance);Module[\"wasmModule\"]=null;return instance.exports});self.onmessage=(e=>{try{if(e.data.cmd===\"load\"){Module[\"wasmModule\"]=e.data.wasmModule;Module[\"wasmMemory\"]=e.data.wasmMemory;Module[\"buffer\"]=Module[\"wasmMemory\"].buffer;Module[\"ENVIRONMENT_IS_PTHREAD\"]=true;if(typeof e.data.urlOrBlob===\"string\"){importScripts(e.data.urlOrBlob)}else{var objectUrl=URL.createObjectURL(e.data.urlOrBlob);importScripts(objectUrl);URL.revokeObjectURL(objectUrl)}WasmBackendModuleThreadedSimd(Module).then(function(instance){Module=instance})}else if(e.data.cmd===\"run\"){Module[\"__performance_now_clock_drift\"]=performance.now()-e.data.time;Module[\"__emscripten_thread_init\"](e.data.threadInfoStruct,0,0,1);Module[\"establishStackSpace\"]();Module[\"PThread\"].receiveObjectTransfer(e.data);Module[\"PThread\"].threadInit();try{var result=Module[\"invokeEntryPoint\"](e.data.start_routine,e.data.arg);if(Module[\"keepRuntimeAlive\"]()){Module[\"PThread\"].setExitStatus(result)}else{Module[\"__emscripten_thread_exit\"](result)}}catch(ex){if(ex!=\"unwind\"){if(ex instanceof Module[\"ExitStatus\"]){if(Module[\"keepRuntimeAlive\"]()){}else{Module[\"__emscripten_thread_exit\"](ex.status)}}else{throw ex}}}}else if(e.data.cmd===\"cancel\"){if(Module[\"_pthread_self\"]()){Module[\"__emscripten_thread_exit\"](-1)}}else if(e.data.target===\"setimmediate\"){}else if(e.data.cmd===\"processThreadQueue\"){if(Module[\"_pthread_self\"]()){Module[\"_emscripten_current_thread_process_queued_calls\"]()}}else if(e.data.cmd===\"processProxyingQueue\"){if(Module[\"_pthread_self\"]()){Module[\"_emscripten_proxy_execute_queue\"](e.data.queue)}}else{err(\"worker.js received unknown command \"+e.data.cmd);err(e.data)}}catch(ex){err(\"worker.js onmessage() captured an uncaught exception: \"+ex);if(ex&&ex.stack)err(ex.stack);if(Module[\"__emscripten_thread_crashed\"]){Module[\"__emscripten_thread_crashed\"]()}throw ex}});`;\n  }\n});\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm.js\nvar require_tfjs_backend_wasm = __commonJS({\n  \"node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm.js\"(exports, module) {\n    var WasmBackendModule2 = (() => {\n      var _scriptDir = typeof document !== \"undefined\" && document.currentScript ? document.currentScript.src : void 0;\n      if (typeof __filename !== \"undefined\")\n        _scriptDir = _scriptDir || __filename;\n      return function(WasmBackendModule3) {\n        WasmBackendModule3 = WasmBackendModule3 || {};\n        var Module = typeof WasmBackendModule3 !== \"undefined\" ? WasmBackendModule3 : {};\n        var readyPromiseResolve, readyPromiseReject;\n        Module[\"ready\"] = new Promise(function(resolve, reject) {\n          readyPromiseResolve = resolve;\n          readyPromiseReject = reject;\n        });\n        var beforeListeners;\n        if (typeof process !== \"undefined\" && process.listeners) {\n          beforeListeners = { uncaughtException: process.listeners(\"uncaughtException\"), unhandledRejection: process.listeners(\"unhandledRejection\") };\n        }\n        var moduleOverrides = Object.assign({}, Module);\n        var arguments_ = [];\n        var thisProgram = \"./this.program\";\n        var quit_ = (status, toThrow) => {\n          throw toThrow;\n        };\n        var ENVIRONMENT_IS_WEB = typeof window === \"object\";\n        var ENVIRONMENT_IS_WORKER = typeof importScripts === \"function\";\n        var ENVIRONMENT_IS_NODE = typeof process === \"object\" && typeof process.versions === \"object\" && typeof process.versions.node === \"string\";\n        var scriptDirectory = \"\";\n        function locateFile(path) {\n          if (Module[\"locateFile\"]) {\n            return Module[\"locateFile\"](path, scriptDirectory);\n          }\n          return scriptDirectory + path;\n        }\n        var read_, readAsync, readBinary, setWindowTitle;\n        function logExceptionOnExit(e2) {\n          if (e2 instanceof ExitStatus)\n            return;\n          let toLog = e2;\n          err(\"exiting due to exception: \" + toLog);\n        }\n        var fs;\n        var nodePath;\n        var requireNodeFS;\n        if (ENVIRONMENT_IS_NODE) {\n          if (ENVIRONMENT_IS_WORKER) {\n            scriptDirectory = require_path().dirname(scriptDirectory) + \"/\";\n          } else {\n            scriptDirectory = __dirname + \"/\";\n          }\n          requireNodeFS = () => {\n            if (!nodePath) {\n              fs = require_fs();\n              nodePath = require_path();\n            }\n          };\n          read_ = function shell_read(filename, binary) {\n            requireNodeFS();\n            filename = nodePath[\"normalize\"](filename);\n            return fs.readFileSync(filename, binary ? void 0 : \"utf8\");\n          };\n          readBinary = (filename) => {\n            var ret = read_(filename, true);\n            if (!ret.buffer) {\n              ret = new Uint8Array(ret);\n            }\n            return ret;\n          };\n          readAsync = (filename, onload, onerror) => {\n            requireNodeFS();\n            filename = nodePath[\"normalize\"](filename);\n            fs.readFile(filename, function(err2, data) {\n              if (err2)\n                onerror(err2);\n              else\n                onload(data.buffer);\n            });\n          };\n          if (process[\"argv\"].length > 1) {\n            thisProgram = process[\"argv\"][1].replace(/\\\\/g, \"/\");\n          }\n          arguments_ = process[\"argv\"].slice(2);\n          process[\"on\"](\"uncaughtException\", function(ex) {\n            if (!(ex instanceof ExitStatus)) {\n              throw ex;\n            }\n          });\n          process[\"on\"](\"unhandledRejection\", function(reason) {\n            throw reason;\n          });\n          quit_ = (status, toThrow) => {\n            if (keepRuntimeAlive()) {\n              process[\"exitCode\"] = status;\n              throw toThrow;\n            }\n            logExceptionOnExit(toThrow);\n            process[\"exit\"](status);\n          };\n          Module[\"inspect\"] = function() {\n            return \"[Emscripten Module object]\";\n          };\n        } else if (ENVIRONMENT_IS_WEB || ENVIRONMENT_IS_WORKER) {\n          if (ENVIRONMENT_IS_WORKER) {\n            scriptDirectory = self.location.href;\n          } else if (typeof document !== \"undefined\" && document.currentScript) {\n            scriptDirectory = document.currentScript.src;\n          }\n          if (_scriptDir) {\n            scriptDirectory = _scriptDir;\n          }\n          if (scriptDirectory.indexOf(\"blob:\") !== 0) {\n            scriptDirectory = scriptDirectory.substr(0, scriptDirectory.replace(/[?#].*/, \"\").lastIndexOf(\"/\") + 1);\n          } else {\n            scriptDirectory = \"\";\n          }\n          {\n            read_ = (url) => {\n              var xhr = new XMLHttpRequest();\n              xhr.open(\"GET\", url, false);\n              xhr.send(null);\n              return xhr.responseText;\n            };\n            if (ENVIRONMENT_IS_WORKER) {\n              readBinary = (url) => {\n                var xhr = new XMLHttpRequest();\n                xhr.open(\"GET\", url, false);\n                xhr.responseType = \"arraybuffer\";\n                xhr.send(null);\n                return new Uint8Array(xhr.response);\n              };\n            }\n            readAsync = (url, onload, onerror) => {\n              var xhr = new XMLHttpRequest();\n              xhr.open(\"GET\", url, true);\n              xhr.responseType = \"arraybuffer\";\n              xhr.onload = () => {\n                if (xhr.status == 200 || xhr.status == 0 && xhr.response) {\n                  onload(xhr.response);\n                  return;\n                }\n                onerror();\n              };\n              xhr.onerror = onerror;\n              xhr.send(null);\n            };\n          }\n          setWindowTitle = (title) => document.title = title;\n        } else {\n        }\n        var out = Module[\"print\"] || console.log.bind(console);\n        var err = Module[\"printErr\"] || console.warn.bind(console);\n        Object.assign(Module, moduleOverrides);\n        moduleOverrides = null;\n        if (Module[\"arguments\"])\n          arguments_ = Module[\"arguments\"];\n        if (Module[\"thisProgram\"])\n          thisProgram = Module[\"thisProgram\"];\n        if (Module[\"quit\"])\n          quit_ = Module[\"quit\"];\n        var POINTER_SIZE = 4;\n        function warnOnce(text) {\n          if (!warnOnce.shown)\n            warnOnce.shown = {};\n          if (!warnOnce.shown[text]) {\n            warnOnce.shown[text] = 1;\n            err(text);\n          }\n        }\n        function convertJsFunctionToWasm(func2, sig) {\n          if (typeof WebAssembly.Function === \"function\") {\n            var typeNames = { \"i\": \"i32\", \"j\": \"i64\", \"f\": \"f32\", \"d\": \"f64\" };\n            var type = { parameters: [], results: sig[0] == \"v\" ? [] : [typeNames[sig[0]]] };\n            for (var i2 = 1; i2 < sig.length; ++i2) {\n              type.parameters.push(typeNames[sig[i2]]);\n            }\n            return new WebAssembly.Function(type, func2);\n          }\n          var typeSection = [1, 0, 1, 96];\n          var sigRet = sig.slice(0, 1);\n          var sigParam = sig.slice(1);\n          var typeCodes = { \"i\": 127, \"j\": 126, \"f\": 125, \"d\": 124 };\n          typeSection.push(sigParam.length);\n          for (var i2 = 0; i2 < sigParam.length; ++i2) {\n            typeSection.push(typeCodes[sigParam[i2]]);\n          }\n          if (sigRet == \"v\") {\n            typeSection.push(0);\n          } else {\n            typeSection = typeSection.concat([1, typeCodes[sigRet]]);\n          }\n          typeSection[1] = typeSection.length - 2;\n          var bytes = new Uint8Array([0, 97, 115, 109, 1, 0, 0, 0].concat(typeSection, [2, 7, 1, 1, 101, 1, 102, 0, 0, 7, 5, 1, 1, 102, 0, 0]));\n          var module2 = new WebAssembly.Module(bytes);\n          var instance = new WebAssembly.Instance(module2, { \"e\": { \"f\": func2 } });\n          var wrappedFunc = instance.exports[\"f\"];\n          return wrappedFunc;\n        }\n        var freeTableIndexes = [];\n        var functionsInTableMap;\n        function getEmptyTableSlot() {\n          if (freeTableIndexes.length) {\n            return freeTableIndexes.pop();\n          }\n          try {\n            wasmTable.grow(1);\n          } catch (err2) {\n            if (!(err2 instanceof RangeError)) {\n              throw err2;\n            }\n            throw \"Unable to grow wasm table. Set ALLOW_TABLE_GROWTH.\";\n          }\n          return wasmTable.length - 1;\n        }\n        function updateTableMap(offset, count2) {\n          for (var i2 = offset; i2 < offset + count2; i2++) {\n            var item = getWasmTableEntry(i2);\n            if (item) {\n              functionsInTableMap.set(item, i2);\n            }\n          }\n        }\n        var tempRet0 = 0;\n        var setTempRet0 = (value) => {\n          tempRet0 = value;\n        };\n        var wasmBinary;\n        if (Module[\"wasmBinary\"])\n          wasmBinary = Module[\"wasmBinary\"];\n        var noExitRuntime = Module[\"noExitRuntime\"] || true;\n        if (typeof WebAssembly !== \"object\") {\n          abort(\"no native wasm support detected\");\n        }\n        var wasmMemory;\n        var ABORT = false;\n        var EXITSTATUS;\n        function assert3(condition, text) {\n          if (!condition) {\n            abort(text);\n          }\n        }\n        function getCFunc(ident) {\n          var func2 = Module[\"_\" + ident];\n          return func2;\n        }\n        function ccall(ident, returnType, argTypes, args, opts) {\n          var toC = { \"string\": function(str) {\n            var ret2 = 0;\n            if (str !== null && str !== void 0 && str !== 0) {\n              var len = (str.length << 2) + 1;\n              ret2 = stackAlloc(len);\n              stringToUTF8(str, ret2, len);\n            }\n            return ret2;\n          }, \"array\": function(arr) {\n            var ret2 = stackAlloc(arr.length);\n            writeArrayToMemory(arr, ret2);\n            return ret2;\n          } };\n          function convertReturnValue(ret2) {\n            if (returnType === \"string\")\n              return UTF8ToString(ret2);\n            if (returnType === \"boolean\")\n              return Boolean(ret2);\n            return ret2;\n          }\n          var func2 = getCFunc(ident);\n          var cArgs = [];\n          var stack2 = 0;\n          if (args) {\n            for (var i2 = 0; i2 < args.length; i2++) {\n              var converter = toC[argTypes[i2]];\n              if (converter) {\n                if (stack2 === 0)\n                  stack2 = stackSave();\n                cArgs[i2] = converter(args[i2]);\n              } else {\n                cArgs[i2] = args[i2];\n              }\n            }\n          }\n          var ret = func2.apply(null, cArgs);\n          function onDone(ret2) {\n            if (stack2 !== 0)\n              stackRestore(stack2);\n            return convertReturnValue(ret2);\n          }\n          ret = onDone(ret);\n          return ret;\n        }\n        function cwrap(ident, returnType, argTypes, opts) {\n          argTypes = argTypes || [];\n          var numericArgs = argTypes.every(function(type) {\n            return type === \"number\";\n          });\n          var numericRet = returnType !== \"string\";\n          if (numericRet && numericArgs && !opts) {\n            return getCFunc(ident);\n          }\n          return function() {\n            return ccall(ident, returnType, argTypes, arguments, opts);\n          };\n        }\n        var ALLOC_STACK = 1;\n        var UTF8Decoder = typeof TextDecoder !== \"undefined\" ? new TextDecoder(\"utf8\") : void 0;\n        function UTF8ArrayToString(heap, idx, maxBytesToRead) {\n          var endIdx = idx + maxBytesToRead;\n          var endPtr = idx;\n          while (heap[endPtr] && !(endPtr >= endIdx))\n            ++endPtr;\n          if (endPtr - idx > 16 && heap.subarray && UTF8Decoder) {\n            return UTF8Decoder.decode(heap.subarray(idx, endPtr));\n          } else {\n            var str = \"\";\n            while (idx < endPtr) {\n              var u0 = heap[idx++];\n              if (!(u0 & 128)) {\n                str += String.fromCharCode(u0);\n                continue;\n              }\n              var u1 = heap[idx++] & 63;\n              if ((u0 & 224) == 192) {\n                str += String.fromCharCode((u0 & 31) << 6 | u1);\n                continue;\n              }\n              var u2 = heap[idx++] & 63;\n              if ((u0 & 240) == 224) {\n                u0 = (u0 & 15) << 12 | u1 << 6 | u2;\n              } else {\n                u0 = (u0 & 7) << 18 | u1 << 12 | u2 << 6 | heap[idx++] & 63;\n              }\n              if (u0 < 65536) {\n                str += String.fromCharCode(u0);\n              } else {\n                var ch = u0 - 65536;\n                str += String.fromCharCode(55296 | ch >> 10, 56320 | ch & 1023);\n              }\n            }\n          }\n          return str;\n        }\n        function UTF8ToString(ptr, maxBytesToRead) {\n          return ptr ? UTF8ArrayToString(HEAPU8, ptr, maxBytesToRead) : \"\";\n        }\n        function stringToUTF8Array(str, heap, outIdx, maxBytesToWrite) {\n          if (!(maxBytesToWrite > 0))\n            return 0;\n          var startIdx = outIdx;\n          var endIdx = outIdx + maxBytesToWrite - 1;\n          for (var i2 = 0; i2 < str.length; ++i2) {\n            var u = str.charCodeAt(i2);\n            if (u >= 55296 && u <= 57343) {\n              var u1 = str.charCodeAt(++i2);\n              u = 65536 + ((u & 1023) << 10) | u1 & 1023;\n            }\n            if (u <= 127) {\n              if (outIdx >= endIdx)\n                break;\n              heap[outIdx++] = u;\n            } else if (u <= 2047) {\n              if (outIdx + 1 >= endIdx)\n                break;\n              heap[outIdx++] = 192 | u >> 6;\n              heap[outIdx++] = 128 | u & 63;\n            } else if (u <= 65535) {\n              if (outIdx + 2 >= endIdx)\n                break;\n              heap[outIdx++] = 224 | u >> 12;\n              heap[outIdx++] = 128 | u >> 6 & 63;\n              heap[outIdx++] = 128 | u & 63;\n            } else {\n              if (outIdx + 3 >= endIdx)\n                break;\n              heap[outIdx++] = 240 | u >> 18;\n              heap[outIdx++] = 128 | u >> 12 & 63;\n              heap[outIdx++] = 128 | u >> 6 & 63;\n              heap[outIdx++] = 128 | u & 63;\n            }\n          }\n          heap[outIdx] = 0;\n          return outIdx - startIdx;\n        }\n        function stringToUTF8(str, outPtr, maxBytesToWrite) {\n          return stringToUTF8Array(str, HEAPU8, outPtr, maxBytesToWrite);\n        }\n        function lengthBytesUTF8(str) {\n          var len = 0;\n          for (var i2 = 0; i2 < str.length; ++i2) {\n            var u = str.charCodeAt(i2);\n            if (u >= 55296 && u <= 57343)\n              u = 65536 + ((u & 1023) << 10) | str.charCodeAt(++i2) & 1023;\n            if (u <= 127)\n              ++len;\n            else if (u <= 2047)\n              len += 2;\n            else if (u <= 65535)\n              len += 3;\n            else\n              len += 4;\n          }\n          return len;\n        }\n        var UTF16Decoder = typeof TextDecoder !== \"undefined\" ? new TextDecoder(\"utf-16le\") : void 0;\n        function writeArrayToMemory(array2, buffer3) {\n          HEAP8.set(array2, buffer3);\n        }\n        function writeAsciiToMemory(str, buffer3, dontAddNull) {\n          for (var i2 = 0; i2 < str.length; ++i2) {\n            HEAP8[buffer3++ >> 0] = str.charCodeAt(i2);\n          }\n          if (!dontAddNull)\n            HEAP8[buffer3 >> 0] = 0;\n        }\n        function alignUp(x, multiple) {\n          if (x % multiple > 0) {\n            x += multiple - x % multiple;\n          }\n          return x;\n        }\n        var buffer2, HEAP8, HEAPU8, HEAP16, HEAPU16, HEAP32, HEAPU32, HEAPF32, HEAPF64;\n        function updateGlobalBufferAndViews(buf) {\n          buffer2 = buf;\n          Module[\"HEAP8\"] = HEAP8 = new Int8Array(buf);\n          Module[\"HEAP16\"] = HEAP16 = new Int16Array(buf);\n          Module[\"HEAP32\"] = HEAP32 = new Int32Array(buf);\n          Module[\"HEAPU8\"] = HEAPU8 = new Uint8Array(buf);\n          Module[\"HEAPU16\"] = HEAPU16 = new Uint16Array(buf);\n          Module[\"HEAPU32\"] = HEAPU32 = new Uint32Array(buf);\n          Module[\"HEAPF32\"] = HEAPF32 = new Float32Array(buf);\n          Module[\"HEAPF64\"] = HEAPF64 = new Float64Array(buf);\n        }\n        var INITIAL_MEMORY = Module[\"INITIAL_MEMORY\"] || 16777216;\n        var wasmTable;\n        var __ATPRERUN__ = [];\n        var __ATINIT__ = [];\n        var __ATPOSTRUN__ = [];\n        var runtimeInitialized = false;\n        var runtimeExited = false;\n        var runtimeKeepaliveCounter = 0;\n        function keepRuntimeAlive() {\n          return noExitRuntime || runtimeKeepaliveCounter > 0;\n        }\n        function preRun() {\n          if (Module[\"preRun\"]) {\n            if (typeof Module[\"preRun\"] == \"function\")\n              Module[\"preRun\"] = [Module[\"preRun\"]];\n            while (Module[\"preRun\"].length) {\n              addOnPreRun(Module[\"preRun\"].shift());\n            }\n          }\n          callRuntimeCallbacks(__ATPRERUN__);\n        }\n        function initRuntime() {\n          runtimeInitialized = true;\n          callRuntimeCallbacks(__ATINIT__);\n        }\n        function exitRuntime() {\n          runtimeExited = true;\n        }\n        function postRun() {\n          if (Module[\"postRun\"]) {\n            if (typeof Module[\"postRun\"] == \"function\")\n              Module[\"postRun\"] = [Module[\"postRun\"]];\n            while (Module[\"postRun\"].length) {\n              addOnPostRun(Module[\"postRun\"].shift());\n            }\n          }\n          callRuntimeCallbacks(__ATPOSTRUN__);\n        }\n        function addOnPreRun(cb) {\n          __ATPRERUN__.unshift(cb);\n        }\n        function addOnInit(cb) {\n          __ATINIT__.unshift(cb);\n        }\n        function addOnPostRun(cb) {\n          __ATPOSTRUN__.unshift(cb);\n        }\n        var runDependencies = 0;\n        var runDependencyWatcher = null;\n        var dependenciesFulfilled = null;\n        function addRunDependency(id) {\n          runDependencies++;\n          if (Module[\"monitorRunDependencies\"]) {\n            Module[\"monitorRunDependencies\"](runDependencies);\n          }\n        }\n        function removeRunDependency(id) {\n          runDependencies--;\n          if (Module[\"monitorRunDependencies\"]) {\n            Module[\"monitorRunDependencies\"](runDependencies);\n          }\n          if (runDependencies == 0) {\n            if (runDependencyWatcher !== null) {\n              clearInterval(runDependencyWatcher);\n              runDependencyWatcher = null;\n            }\n            if (dependenciesFulfilled) {\n              var callback = dependenciesFulfilled;\n              dependenciesFulfilled = null;\n              callback();\n            }\n          }\n        }\n        Module[\"preloadedImages\"] = {};\n        Module[\"preloadedAudios\"] = {};\n        function abort(what) {\n          {\n            if (Module[\"onAbort\"]) {\n              Module[\"onAbort\"](what);\n            }\n          }\n          what = \"Aborted(\" + what + \")\";\n          err(what);\n          ABORT = true;\n          EXITSTATUS = 1;\n          what += \". Build with -s ASSERTIONS=1 for more info.\";\n          var e2 = new WebAssembly.RuntimeError(what);\n          readyPromiseReject(e2);\n          throw e2;\n        }\n        var dataURIPrefix = \"data:application/octet-stream;base64,\";\n        function isDataURI(filename) {\n          return filename.startsWith(dataURIPrefix);\n        }\n        function isFileURI(filename) {\n          return filename.startsWith(\"file://\");\n        }\n        var wasmBinaryFile;\n        wasmBinaryFile = \"tfjs-backend-wasm.wasm\";\n        if (!isDataURI(wasmBinaryFile)) {\n          wasmBinaryFile = locateFile(wasmBinaryFile);\n        }\n        function getBinary(file) {\n          try {\n            if (file == wasmBinaryFile && wasmBinary) {\n              return new Uint8Array(wasmBinary);\n            }\n            if (readBinary) {\n              return readBinary(file);\n            } else {\n              throw \"both async and sync fetching of the wasm failed\";\n            }\n          } catch (err2) {\n            abort(err2);\n          }\n        }\n        function getBinaryPromise() {\n          if (!wasmBinary && (ENVIRONMENT_IS_WEB || ENVIRONMENT_IS_WORKER)) {\n            if (typeof fetch === \"function\" && !isFileURI(wasmBinaryFile)) {\n              return fetch(wasmBinaryFile, { credentials: \"same-origin\" }).then(function(response) {\n                if (!response[\"ok\"]) {\n                  throw \"failed to load wasm binary file at '\" + wasmBinaryFile + \"'\";\n                }\n                return response[\"arrayBuffer\"]();\n              }).catch(function() {\n                return getBinary(wasmBinaryFile);\n              });\n            } else {\n              if (readAsync) {\n                return new Promise(function(resolve, reject) {\n                  readAsync(wasmBinaryFile, function(response) {\n                    resolve(new Uint8Array(response));\n                  }, reject);\n                });\n              }\n            }\n          }\n          return Promise.resolve().then(function() {\n            return getBinary(wasmBinaryFile);\n          });\n        }\n        function createWasm() {\n          var info = { \"env\": asmLibraryArg, \"wasi_snapshot_preview1\": asmLibraryArg };\n          function receiveInstance(instance, module2) {\n            var exports3 = instance.exports;\n            Module[\"asm\"] = exports3;\n            wasmMemory = Module[\"asm\"][\"memory\"];\n            updateGlobalBufferAndViews(wasmMemory.buffer);\n            wasmTable = Module[\"asm\"][\"__indirect_function_table\"];\n            addOnInit(Module[\"asm\"][\"__wasm_call_ctors\"]);\n            removeRunDependency(\"wasm-instantiate\");\n          }\n          addRunDependency(\"wasm-instantiate\");\n          function receiveInstantiationResult(result) {\n            receiveInstance(result[\"instance\"]);\n          }\n          function instantiateArrayBuffer(receiver) {\n            return getBinaryPromise().then(function(binary) {\n              return WebAssembly.instantiate(binary, info);\n            }).then(function(instance) {\n              return instance;\n            }).then(receiver, function(reason) {\n              err(\"failed to asynchronously prepare wasm: \" + reason);\n              abort(reason);\n            });\n          }\n          function instantiateAsync() {\n            if (!wasmBinary && typeof WebAssembly.instantiateStreaming === \"function\" && !isDataURI(wasmBinaryFile) && !isFileURI(wasmBinaryFile) && typeof fetch === \"function\") {\n              return fetch(wasmBinaryFile, { credentials: \"same-origin\" }).then(function(response) {\n                var result = WebAssembly.instantiateStreaming(response, info);\n                return result.then(receiveInstantiationResult, function(reason) {\n                  err(\"wasm streaming compile failed: \" + reason);\n                  err(\"falling back to ArrayBuffer instantiation\");\n                  return instantiateArrayBuffer(receiveInstantiationResult);\n                });\n              });\n            } else {\n              return instantiateArrayBuffer(receiveInstantiationResult);\n            }\n          }\n          if (Module[\"instantiateWasm\"]) {\n            try {\n              var exports2 = Module[\"instantiateWasm\"](info, receiveInstance);\n              return exports2;\n            } catch (e2) {\n              err(\"Module.instantiateWasm callback failed with error: \" + e2);\n              return false;\n            }\n          }\n          instantiateAsync().catch(readyPromiseReject);\n          return {};\n        }\n        var tempDouble;\n        var tempI64;\n        function callRuntimeCallbacks(callbacks2) {\n          while (callbacks2.length > 0) {\n            var callback = callbacks2.shift();\n            if (typeof callback == \"function\") {\n              callback(Module);\n              continue;\n            }\n            var func2 = callback.func;\n            if (typeof func2 === \"number\") {\n              if (callback.arg === void 0) {\n                getWasmTableEntry(func2)();\n              } else {\n                getWasmTableEntry(func2)(callback.arg);\n              }\n            } else {\n              func2(callback.arg === void 0 ? null : callback.arg);\n            }\n          }\n        }\n        function demangle(func2) {\n          return func2;\n        }\n        function demangleAll(text) {\n          var regex = /\\b_Z[\\w\\d_]+/g;\n          return text.replace(regex, function(x) {\n            var y = demangle(x);\n            return x === y ? x : y + \" [\" + x + \"]\";\n          });\n        }\n        var wasmTableMirror = [];\n        function getWasmTableEntry(funcPtr) {\n          var func2 = wasmTableMirror[funcPtr];\n          if (!func2) {\n            if (funcPtr >= wasmTableMirror.length)\n              wasmTableMirror.length = funcPtr + 1;\n            wasmTableMirror[funcPtr] = func2 = wasmTable.get(funcPtr);\n          }\n          return func2;\n        }\n        function jsStackTrace() {\n          var error = new Error();\n          if (!error.stack) {\n            try {\n              throw new Error();\n            } catch (e2) {\n              error = e2;\n            }\n            if (!error.stack) {\n              return \"(no stack trace available)\";\n            }\n          }\n          return error.stack.toString();\n        }\n        function setWasmTableEntry(idx, func2) {\n          wasmTable.set(idx, func2);\n          wasmTableMirror[idx] = func2;\n        }\n        function _abort() {\n          abort(\"\");\n        }\n        function _emscripten_get_heap_max() {\n          return 2147483648;\n        }\n        function _emscripten_memcpy_big(dest, src, num) {\n          HEAPU8.copyWithin(dest, src, src + num);\n        }\n        function emscripten_realloc_buffer(size) {\n          try {\n            wasmMemory.grow(size - buffer2.byteLength + 65535 >>> 16);\n            updateGlobalBufferAndViews(wasmMemory.buffer);\n            return 1;\n          } catch (e2) {\n          }\n        }\n        function _emscripten_resize_heap(requestedSize) {\n          var oldSize = HEAPU8.length;\n          requestedSize = requestedSize >>> 0;\n          var maxHeapSize = _emscripten_get_heap_max();\n          if (requestedSize > maxHeapSize) {\n            return false;\n          }\n          for (var cutDown = 1; cutDown <= 4; cutDown *= 2) {\n            var overGrownHeapSize = oldSize * (1 + 0.2 / cutDown);\n            overGrownHeapSize = Math.min(overGrownHeapSize, requestedSize + 100663296);\n            var newSize = Math.min(maxHeapSize, alignUp(Math.max(requestedSize, overGrownHeapSize), 65536));\n            var replacement = emscripten_realloc_buffer(newSize);\n            if (replacement) {\n              return true;\n            }\n          }\n          return false;\n        }\n        var SYSCALLS = { mappings: {}, buffers: [null, [], []], printChar: function(stream, curr) {\n          var buffer3 = SYSCALLS.buffers[stream];\n          if (curr === 0 || curr === 10) {\n            (stream === 1 ? out : err)(UTF8ArrayToString(buffer3, 0));\n            buffer3.length = 0;\n          } else {\n            buffer3.push(curr);\n          }\n        }, varargs: void 0, get: function() {\n          SYSCALLS.varargs += 4;\n          var ret = HEAP32[SYSCALLS.varargs - 4 >> 2];\n          return ret;\n        }, getStr: function(ptr) {\n          var ret = UTF8ToString(ptr);\n          return ret;\n        }, get64: function(low, high) {\n          return low;\n        } };\n        function _fd_close(fd) {\n          return 0;\n        }\n        function _fd_seek(fd, offset_low, offset_high, whence, newOffset) {\n        }\n        function _fd_write(fd, iov, iovcnt, pnum) {\n          var num = 0;\n          for (var i2 = 0; i2 < iovcnt; i2++) {\n            var ptr = HEAP32[iov >> 2];\n            var len = HEAP32[iov + 4 >> 2];\n            iov += 8;\n            for (var j = 0; j < len; j++) {\n              SYSCALLS.printChar(fd, HEAPU8[ptr + j]);\n            }\n            num += len;\n          }\n          HEAP32[pnum >> 2] = num;\n          return 0;\n        }\n        function _setTempRet0(val) {\n          setTempRet0(val);\n        }\n        var ASSERTIONS = false;\n        var asmLibraryArg = { \"abort\": _abort, \"emscripten_get_heap_max\": _emscripten_get_heap_max, \"emscripten_memcpy_big\": _emscripten_memcpy_big, \"emscripten_resize_heap\": _emscripten_resize_heap, \"fd_close\": _fd_close, \"fd_seek\": _fd_seek, \"fd_write\": _fd_write, \"setTempRet0\": _setTempRet0 };\n        var asm = createWasm();\n        var ___wasm_call_ctors = Module[\"___wasm_call_ctors\"] = function() {\n          return (___wasm_call_ctors = Module[\"___wasm_call_ctors\"] = Module[\"asm\"][\"__wasm_call_ctors\"]).apply(null, arguments);\n        };\n        var _init = Module[\"_init\"] = function() {\n          return (_init = Module[\"_init\"] = Module[\"asm\"][\"init\"]).apply(null, arguments);\n        };\n        var _init_with_threads_count = Module[\"_init_with_threads_count\"] = function() {\n          return (_init_with_threads_count = Module[\"_init_with_threads_count\"] = Module[\"asm\"][\"init_with_threads_count\"]).apply(null, arguments);\n        };\n        var _get_threads_count = Module[\"_get_threads_count\"] = function() {\n          return (_get_threads_count = Module[\"_get_threads_count\"] = Module[\"asm\"][\"get_threads_count\"]).apply(null, arguments);\n        };\n        var _register_tensor = Module[\"_register_tensor\"] = function() {\n          return (_register_tensor = Module[\"_register_tensor\"] = Module[\"asm\"][\"register_tensor\"]).apply(null, arguments);\n        };\n        var _dispose_data = Module[\"_dispose_data\"] = function() {\n          return (_dispose_data = Module[\"_dispose_data\"] = Module[\"asm\"][\"dispose_data\"]).apply(null, arguments);\n        };\n        var _dispose = Module[\"_dispose\"] = function() {\n          return (_dispose = Module[\"_dispose\"] = Module[\"asm\"][\"dispose\"]).apply(null, arguments);\n        };\n        var _Abs = Module[\"_Abs\"] = function() {\n          return (_Abs = Module[\"_Abs\"] = Module[\"asm\"][\"Abs\"]).apply(null, arguments);\n        };\n        var _Add = Module[\"_Add\"] = function() {\n          return (_Add = Module[\"_Add\"] = Module[\"asm\"][\"Add\"]).apply(null, arguments);\n        };\n        var _AddN = Module[\"_AddN\"] = function() {\n          return (_AddN = Module[\"_AddN\"] = Module[\"asm\"][\"AddN\"]).apply(null, arguments);\n        };\n        var _All = Module[\"_All\"] = function() {\n          return (_All = Module[\"_All\"] = Module[\"asm\"][\"All\"]).apply(null, arguments);\n        };\n        var _Any = Module[\"_Any\"] = function() {\n          return (_Any = Module[\"_Any\"] = Module[\"asm\"][\"Any\"]).apply(null, arguments);\n        };\n        var _ArgMax = Module[\"_ArgMax\"] = function() {\n          return (_ArgMax = Module[\"_ArgMax\"] = Module[\"asm\"][\"ArgMax\"]).apply(null, arguments);\n        };\n        var _AvgPool = Module[\"_AvgPool\"] = function() {\n          return (_AvgPool = Module[\"_AvgPool\"] = Module[\"asm\"][\"AvgPool\"]).apply(null, arguments);\n        };\n        var _BatchMatMul = Module[\"_BatchMatMul\"] = function() {\n          return (_BatchMatMul = Module[\"_BatchMatMul\"] = Module[\"asm\"][\"BatchMatMul\"]).apply(null, arguments);\n        };\n        var _Ceil = Module[\"_Ceil\"] = function() {\n          return (_Ceil = Module[\"_Ceil\"] = Module[\"asm\"][\"Ceil\"]).apply(null, arguments);\n        };\n        var _ClipByValue = Module[\"_ClipByValue\"] = function() {\n          return (_ClipByValue = Module[\"_ClipByValue\"] = Module[\"asm\"][\"ClipByValue\"]).apply(null, arguments);\n        };\n        var _Conv2D = Module[\"_Conv2D\"] = function() {\n          return (_Conv2D = Module[\"_Conv2D\"] = Module[\"asm\"][\"Conv2D\"]).apply(null, arguments);\n        };\n        var _Conv2DBackpropInput = Module[\"_Conv2DBackpropInput\"] = function() {\n          return (_Conv2DBackpropInput = Module[\"_Conv2DBackpropInput\"] = Module[\"asm\"][\"Conv2DBackpropInput\"]).apply(null, arguments);\n        };\n        var _Cos = Module[\"_Cos\"] = function() {\n          return (_Cos = Module[\"_Cos\"] = Module[\"asm\"][\"Cos\"]).apply(null, arguments);\n        };\n        var _Cosh = Module[\"_Cosh\"] = function() {\n          return (_Cosh = Module[\"_Cosh\"] = Module[\"asm\"][\"Cosh\"]).apply(null, arguments);\n        };\n        var _CropAndResize = Module[\"_CropAndResize\"] = function() {\n          return (_CropAndResize = Module[\"_CropAndResize\"] = Module[\"asm\"][\"CropAndResize\"]).apply(null, arguments);\n        };\n        var _Cumprod = Module[\"_Cumprod\"] = function() {\n          return (_Cumprod = Module[\"_Cumprod\"] = Module[\"asm\"][\"Cumprod\"]).apply(null, arguments);\n        };\n        var _Cumsum = Module[\"_Cumsum\"] = function() {\n          return (_Cumsum = Module[\"_Cumsum\"] = Module[\"asm\"][\"Cumsum\"]).apply(null, arguments);\n        };\n        var _DepthToSpace = Module[\"_DepthToSpace\"] = function() {\n          return (_DepthToSpace = Module[\"_DepthToSpace\"] = Module[\"asm\"][\"DepthToSpace\"]).apply(null, arguments);\n        };\n        var _DepthwiseConv2dNative = Module[\"_DepthwiseConv2dNative\"] = function() {\n          return (_DepthwiseConv2dNative = Module[\"_DepthwiseConv2dNative\"] = Module[\"asm\"][\"DepthwiseConv2dNative\"]).apply(null, arguments);\n        };\n        var _Elu = Module[\"_Elu\"] = function() {\n          return (_Elu = Module[\"_Elu\"] = Module[\"asm\"][\"Elu\"]).apply(null, arguments);\n        };\n        var _Equal = Module[\"_Equal\"] = function() {\n          return (_Equal = Module[\"_Equal\"] = Module[\"asm\"][\"Equal\"]).apply(null, arguments);\n        };\n        var _Exp = Module[\"_Exp\"] = function() {\n          return (_Exp = Module[\"_Exp\"] = Module[\"asm\"][\"Exp\"]).apply(null, arguments);\n        };\n        var _FlipLeftRight = Module[\"_FlipLeftRight\"] = function() {\n          return (_FlipLeftRight = Module[\"_FlipLeftRight\"] = Module[\"asm\"][\"FlipLeftRight\"]).apply(null, arguments);\n        };\n        var _Floor = Module[\"_Floor\"] = function() {\n          return (_Floor = Module[\"_Floor\"] = Module[\"asm\"][\"Floor\"]).apply(null, arguments);\n        };\n        var _FloorDiv = Module[\"_FloorDiv\"] = function() {\n          return (_FloorDiv = Module[\"_FloorDiv\"] = Module[\"asm\"][\"FloorDiv\"]).apply(null, arguments);\n        };\n        var _FusedBatchNorm = Module[\"_FusedBatchNorm\"] = function() {\n          return (_FusedBatchNorm = Module[\"_FusedBatchNorm\"] = Module[\"asm\"][\"FusedBatchNorm\"]).apply(null, arguments);\n        };\n        var _FusedConv2D = Module[\"_FusedConv2D\"] = function() {\n          return (_FusedConv2D = Module[\"_FusedConv2D\"] = Module[\"asm\"][\"FusedConv2D\"]).apply(null, arguments);\n        };\n        var _FusedDepthwiseConv2D = Module[\"_FusedDepthwiseConv2D\"] = function() {\n          return (_FusedDepthwiseConv2D = Module[\"_FusedDepthwiseConv2D\"] = Module[\"asm\"][\"FusedDepthwiseConv2D\"]).apply(null, arguments);\n        };\n        var _Gather = Module[\"_Gather\"] = function() {\n          return (_Gather = Module[\"_Gather\"] = Module[\"asm\"][\"Gather\"]).apply(null, arguments);\n        };\n        var _GatherNd = Module[\"_GatherNd\"] = function() {\n          return (_GatherNd = Module[\"_GatherNd\"] = Module[\"asm\"][\"GatherNd\"]).apply(null, arguments);\n        };\n        var _Greater = Module[\"_Greater\"] = function() {\n          return (_Greater = Module[\"_Greater\"] = Module[\"asm\"][\"Greater\"]).apply(null, arguments);\n        };\n        var _GreaterEqual = Module[\"_GreaterEqual\"] = function() {\n          return (_GreaterEqual = Module[\"_GreaterEqual\"] = Module[\"asm\"][\"GreaterEqual\"]).apply(null, arguments);\n        };\n        var _LeakyRelu = Module[\"_LeakyRelu\"] = function() {\n          return (_LeakyRelu = Module[\"_LeakyRelu\"] = Module[\"asm\"][\"LeakyRelu\"]).apply(null, arguments);\n        };\n        var _Less = Module[\"_Less\"] = function() {\n          return (_Less = Module[\"_Less\"] = Module[\"asm\"][\"Less\"]).apply(null, arguments);\n        };\n        var _LessEqual = Module[\"_LessEqual\"] = function() {\n          return (_LessEqual = Module[\"_LessEqual\"] = Module[\"asm\"][\"LessEqual\"]).apply(null, arguments);\n        };\n        var _Log = Module[\"_Log\"] = function() {\n          return (_Log = Module[\"_Log\"] = Module[\"asm\"][\"Log\"]).apply(null, arguments);\n        };\n        var _LogicalAnd = Module[\"_LogicalAnd\"] = function() {\n          return (_LogicalAnd = Module[\"_LogicalAnd\"] = Module[\"asm\"][\"LogicalAnd\"]).apply(null, arguments);\n        };\n        var _LogicalNot = Module[\"_LogicalNot\"] = function() {\n          return (_LogicalNot = Module[\"_LogicalNot\"] = Module[\"asm\"][\"LogicalNot\"]).apply(null, arguments);\n        };\n        var _LogicalOr = Module[\"_LogicalOr\"] = function() {\n          return (_LogicalOr = Module[\"_LogicalOr\"] = Module[\"asm\"][\"LogicalOr\"]).apply(null, arguments);\n        };\n        var _LogicalXor = Module[\"_LogicalXor\"] = function() {\n          return (_LogicalXor = Module[\"_LogicalXor\"] = Module[\"asm\"][\"LogicalXor\"]).apply(null, arguments);\n        };\n        var _Max = Module[\"_Max\"] = function() {\n          return (_Max = Module[\"_Max\"] = Module[\"asm\"][\"Max\"]).apply(null, arguments);\n        };\n        var _MaxPool = Module[\"_MaxPool\"] = function() {\n          return (_MaxPool = Module[\"_MaxPool\"] = Module[\"asm\"][\"MaxPool\"]).apply(null, arguments);\n        };\n        var _Maximum = Module[\"_Maximum\"] = function() {\n          return (_Maximum = Module[\"_Maximum\"] = Module[\"asm\"][\"Maximum\"]).apply(null, arguments);\n        };\n        var _Mean = Module[\"_Mean\"] = function() {\n          return (_Mean = Module[\"_Mean\"] = Module[\"asm\"][\"Mean\"]).apply(null, arguments);\n        };\n        var _Min = Module[\"_Min\"] = function() {\n          return (_Min = Module[\"_Min\"] = Module[\"asm\"][\"Min\"]).apply(null, arguments);\n        };\n        var _Minimum = Module[\"_Minimum\"] = function() {\n          return (_Minimum = Module[\"_Minimum\"] = Module[\"asm\"][\"Minimum\"]).apply(null, arguments);\n        };\n        var _MirrorPad = Module[\"_MirrorPad\"] = function() {\n          return (_MirrorPad = Module[\"_MirrorPad\"] = Module[\"asm\"][\"MirrorPad\"]).apply(null, arguments);\n        };\n        var _Multiply = Module[\"_Multiply\"] = function() {\n          return (_Multiply = Module[\"_Multiply\"] = Module[\"asm\"][\"Multiply\"]).apply(null, arguments);\n        };\n        var _Neg = Module[\"_Neg\"] = function() {\n          return (_Neg = Module[\"_Neg\"] = Module[\"asm\"][\"Neg\"]).apply(null, arguments);\n        };\n        var _NonMaxSuppressionV3 = Module[\"_NonMaxSuppressionV3\"] = function() {\n          return (_NonMaxSuppressionV3 = Module[\"_NonMaxSuppressionV3\"] = Module[\"asm\"][\"NonMaxSuppressionV3\"]).apply(null, arguments);\n        };\n        var _NonMaxSuppressionV4 = Module[\"_NonMaxSuppressionV4\"] = function() {\n          return (_NonMaxSuppressionV4 = Module[\"_NonMaxSuppressionV4\"] = Module[\"asm\"][\"NonMaxSuppressionV4\"]).apply(null, arguments);\n        };\n        var _NonMaxSuppressionV5 = Module[\"_NonMaxSuppressionV5\"] = function() {\n          return (_NonMaxSuppressionV5 = Module[\"_NonMaxSuppressionV5\"] = Module[\"asm\"][\"NonMaxSuppressionV5\"]).apply(null, arguments);\n        };\n        var _NotEqual = Module[\"_NotEqual\"] = function() {\n          return (_NotEqual = Module[\"_NotEqual\"] = Module[\"asm\"][\"NotEqual\"]).apply(null, arguments);\n        };\n        var _OneHot = Module[\"_OneHot\"] = function() {\n          return (_OneHot = Module[\"_OneHot\"] = Module[\"asm\"][\"OneHot\"]).apply(null, arguments);\n        };\n        var _PadV2 = Module[\"_PadV2\"] = function() {\n          return (_PadV2 = Module[\"_PadV2\"] = Module[\"asm\"][\"PadV2\"]).apply(null, arguments);\n        };\n        var _Pow = Module[\"_Pow\"] = function() {\n          return (_Pow = Module[\"_Pow\"] = Module[\"asm\"][\"Pow\"]).apply(null, arguments);\n        };\n        var _Prelu = Module[\"_Prelu\"] = function() {\n          return (_Prelu = Module[\"_Prelu\"] = Module[\"asm\"][\"Prelu\"]).apply(null, arguments);\n        };\n        var _Prod = Module[\"_Prod\"] = function() {\n          return (_Prod = Module[\"_Prod\"] = Module[\"asm\"][\"Prod\"]).apply(null, arguments);\n        };\n        var _RealDiv = Module[\"_RealDiv\"] = function() {\n          return (_RealDiv = Module[\"_RealDiv\"] = Module[\"asm\"][\"RealDiv\"]).apply(null, arguments);\n        };\n        var _Relu = Module[\"_Relu\"] = function() {\n          return (_Relu = Module[\"_Relu\"] = Module[\"asm\"][\"Relu\"]).apply(null, arguments);\n        };\n        var _Relu6 = Module[\"_Relu6\"] = function() {\n          return (_Relu6 = Module[\"_Relu6\"] = Module[\"asm\"][\"Relu6\"]).apply(null, arguments);\n        };\n        var _ResizeBilinear = Module[\"_ResizeBilinear\"] = function() {\n          return (_ResizeBilinear = Module[\"_ResizeBilinear\"] = Module[\"asm\"][\"ResizeBilinear\"]).apply(null, arguments);\n        };\n        var _ResizeNearestNeighbor = Module[\"_ResizeNearestNeighbor\"] = function() {\n          return (_ResizeNearestNeighbor = Module[\"_ResizeNearestNeighbor\"] = Module[\"asm\"][\"ResizeNearestNeighbor\"]).apply(null, arguments);\n        };\n        var _Reverse = Module[\"_Reverse\"] = function() {\n          return (_Reverse = Module[\"_Reverse\"] = Module[\"asm\"][\"Reverse\"]).apply(null, arguments);\n        };\n        var _RotateWithOffset = Module[\"_RotateWithOffset\"] = function() {\n          return (_RotateWithOffset = Module[\"_RotateWithOffset\"] = Module[\"asm\"][\"RotateWithOffset\"]).apply(null, arguments);\n        };\n        var _Round = Module[\"_Round\"] = function() {\n          return (_Round = Module[\"_Round\"] = Module[\"asm\"][\"Round\"]).apply(null, arguments);\n        };\n        var _Rsqrt = Module[\"_Rsqrt\"] = function() {\n          return (_Rsqrt = Module[\"_Rsqrt\"] = Module[\"asm\"][\"Rsqrt\"]).apply(null, arguments);\n        };\n        var _ScatterNd = Module[\"_ScatterNd\"] = function() {\n          return (_ScatterNd = Module[\"_ScatterNd\"] = Module[\"asm\"][\"ScatterNd\"]).apply(null, arguments);\n        };\n        var _SelectV2 = Module[\"_SelectV2\"] = function() {\n          return (_SelectV2 = Module[\"_SelectV2\"] = Module[\"asm\"][\"SelectV2\"]).apply(null, arguments);\n        };\n        var _Sigmoid = Module[\"_Sigmoid\"] = function() {\n          return (_Sigmoid = Module[\"_Sigmoid\"] = Module[\"asm\"][\"Sigmoid\"]).apply(null, arguments);\n        };\n        var _Sin = Module[\"_Sin\"] = function() {\n          return (_Sin = Module[\"_Sin\"] = Module[\"asm\"][\"Sin\"]).apply(null, arguments);\n        };\n        var _Softmax = Module[\"_Softmax\"] = function() {\n          return (_Softmax = Module[\"_Softmax\"] = Module[\"asm\"][\"Softmax\"]).apply(null, arguments);\n        };\n        var _SparseFillEmptyRows = Module[\"_SparseFillEmptyRows\"] = function() {\n          return (_SparseFillEmptyRows = Module[\"_SparseFillEmptyRows\"] = Module[\"asm\"][\"SparseFillEmptyRows\"]).apply(null, arguments);\n        };\n        var _SparseReshape = Module[\"_SparseReshape\"] = function() {\n          return (_SparseReshape = Module[\"_SparseReshape\"] = Module[\"asm\"][\"SparseReshape\"]).apply(null, arguments);\n        };\n        var _SparseSegmentReduction = Module[\"_SparseSegmentReduction\"] = function() {\n          return (_SparseSegmentReduction = Module[\"_SparseSegmentReduction\"] = Module[\"asm\"][\"SparseSegmentReduction\"]).apply(null, arguments);\n        };\n        var _Sqrt = Module[\"_Sqrt\"] = function() {\n          return (_Sqrt = Module[\"_Sqrt\"] = Module[\"asm\"][\"Sqrt\"]).apply(null, arguments);\n        };\n        var _Square = Module[\"_Square\"] = function() {\n          return (_Square = Module[\"_Square\"] = Module[\"asm\"][\"Square\"]).apply(null, arguments);\n        };\n        var _SquaredDifference = Module[\"_SquaredDifference\"] = function() {\n          return (_SquaredDifference = Module[\"_SquaredDifference\"] = Module[\"asm\"][\"SquaredDifference\"]).apply(null, arguments);\n        };\n        var _Step = Module[\"_Step\"] = function() {\n          return (_Step = Module[\"_Step\"] = Module[\"asm\"][\"Step\"]).apply(null, arguments);\n        };\n        var _StridedSlice = Module[\"_StridedSlice\"] = function() {\n          return (_StridedSlice = Module[\"_StridedSlice\"] = Module[\"asm\"][\"StridedSlice\"]).apply(null, arguments);\n        };\n        var _Sub = Module[\"_Sub\"] = function() {\n          return (_Sub = Module[\"_Sub\"] = Module[\"asm\"][\"Sub\"]).apply(null, arguments);\n        };\n        var _Sum = Module[\"_Sum\"] = function() {\n          return (_Sum = Module[\"_Sum\"] = Module[\"asm\"][\"Sum\"]).apply(null, arguments);\n        };\n        var _Tan = Module[\"_Tan\"] = function() {\n          return (_Tan = Module[\"_Tan\"] = Module[\"asm\"][\"Tan\"]).apply(null, arguments);\n        };\n        var _Tanh = Module[\"_Tanh\"] = function() {\n          return (_Tanh = Module[\"_Tanh\"] = Module[\"asm\"][\"Tanh\"]).apply(null, arguments);\n        };\n        var _Tile = Module[\"_Tile\"] = function() {\n          return (_Tile = Module[\"_Tile\"] = Module[\"asm\"][\"Tile\"]).apply(null, arguments);\n        };\n        var _TopK = Module[\"_TopK\"] = function() {\n          return (_TopK = Module[\"_TopK\"] = Module[\"asm\"][\"TopK\"]).apply(null, arguments);\n        };\n        var _Transform = Module[\"_Transform\"] = function() {\n          return (_Transform = Module[\"_Transform\"] = Module[\"asm\"][\"Transform\"]).apply(null, arguments);\n        };\n        var _Transpose = Module[\"_Transpose\"] = function() {\n          return (_Transpose = Module[\"_Transpose\"] = Module[\"asm\"][\"Transpose\"]).apply(null, arguments);\n        };\n        var __FusedMatMul = Module[\"__FusedMatMul\"] = function() {\n          return (__FusedMatMul = Module[\"__FusedMatMul\"] = Module[\"asm\"][\"_FusedMatMul\"]).apply(null, arguments);\n        };\n        var _malloc = Module[\"_malloc\"] = function() {\n          return (_malloc = Module[\"_malloc\"] = Module[\"asm\"][\"malloc\"]).apply(null, arguments);\n        };\n        var _free = Module[\"_free\"] = function() {\n          return (_free = Module[\"_free\"] = Module[\"asm\"][\"free\"]).apply(null, arguments);\n        };\n        var ___errno_location = Module[\"___errno_location\"] = function() {\n          return (___errno_location = Module[\"___errno_location\"] = Module[\"asm\"][\"__errno_location\"]).apply(null, arguments);\n        };\n        var _emscripten_main_thread_process_queued_calls = Module[\"_emscripten_main_thread_process_queued_calls\"] = function() {\n          return (_emscripten_main_thread_process_queued_calls = Module[\"_emscripten_main_thread_process_queued_calls\"] = Module[\"asm\"][\"emscripten_main_thread_process_queued_calls\"]).apply(null, arguments);\n        };\n        var stackSave = Module[\"stackSave\"] = function() {\n          return (stackSave = Module[\"stackSave\"] = Module[\"asm\"][\"stackSave\"]).apply(null, arguments);\n        };\n        var stackRestore = Module[\"stackRestore\"] = function() {\n          return (stackRestore = Module[\"stackRestore\"] = Module[\"asm\"][\"stackRestore\"]).apply(null, arguments);\n        };\n        var stackAlloc = Module[\"stackAlloc\"] = function() {\n          return (stackAlloc = Module[\"stackAlloc\"] = Module[\"asm\"][\"stackAlloc\"]).apply(null, arguments);\n        };\n        var dynCall_iijjiiii = Module[\"dynCall_iijjiiii\"] = function() {\n          return (dynCall_iijjiiii = Module[\"dynCall_iijjiiii\"] = Module[\"asm\"][\"dynCall_iijjiiii\"]).apply(null, arguments);\n        };\n        var dynCall_jiji = Module[\"dynCall_jiji\"] = function() {\n          return (dynCall_jiji = Module[\"dynCall_jiji\"] = Module[\"asm\"][\"dynCall_jiji\"]).apply(null, arguments);\n        };\n        Module[\"cwrap\"] = cwrap;\n        var calledRun;\n        function ExitStatus(status) {\n          this.name = \"ExitStatus\";\n          this.message = \"Program terminated with exit(\" + status + \")\";\n          this.status = status;\n        }\n        dependenciesFulfilled = function runCaller() {\n          if (!calledRun)\n            run();\n          if (!calledRun)\n            dependenciesFulfilled = runCaller;\n        };\n        function run(args) {\n          args = args || arguments_;\n          if (runDependencies > 0) {\n            return;\n          }\n          preRun();\n          if (runDependencies > 0) {\n            return;\n          }\n          function doRun() {\n            if (calledRun)\n              return;\n            calledRun = true;\n            Module[\"calledRun\"] = true;\n            if (ABORT)\n              return;\n            initRuntime();\n            readyPromiseResolve(Module);\n            if (Module[\"onRuntimeInitialized\"])\n              Module[\"onRuntimeInitialized\"]();\n            postRun();\n          }\n          if (Module[\"setStatus\"]) {\n            Module[\"setStatus\"](\"Running...\");\n            setTimeout(function() {\n              setTimeout(function() {\n                Module[\"setStatus\"](\"\");\n              }, 1);\n              doRun();\n            }, 1);\n          } else {\n            doRun();\n          }\n        }\n        Module[\"run\"] = run;\n        function procExit(code) {\n          EXITSTATUS = code;\n          if (!keepRuntimeAlive()) {\n            if (Module[\"onExit\"])\n              Module[\"onExit\"](code);\n            ABORT = true;\n          }\n          quit_(code, new ExitStatus(code));\n        }\n        if (Module[\"preInit\"]) {\n          if (typeof Module[\"preInit\"] == \"function\")\n            Module[\"preInit\"] = [Module[\"preInit\"]];\n          while (Module[\"preInit\"].length > 0) {\n            Module[\"preInit\"].pop()();\n          }\n        }\n        run();\n        var listenersAdded;\n        if (beforeListeners) {\n          listenersAdded = { uncaughtException: process.listeners(\"uncaughtException\").filter(function(listener) {\n            return !beforeListeners.uncaughtException.indexOf(listener) > -1;\n          }), unhandledRejection: process.listeners(\"unhandledRejection\").filter(function(listener) {\n            return !beforeListeners.unhandledRejection.indexOf(listener) > -1;\n          }) };\n        }\n        var actualModule;\n        if (typeof WasmBackendModule3 !== \"undefined\") {\n          actualModule = WasmBackendModule3;\n        } else if (typeof WasmBackendModuleThreadedSimd !== \"undefined\") {\n          actualModule = WasmBackendModuleThreadedSimd;\n        } else {\n          throw new Error(\"Could not find wasm module in post.js\");\n        }\n        if (listenersAdded) {\n          var tmpDispose = actualModule[\"_dispose\"];\n          actualModule[\"_dispose\"] = function() {\n            tmpDispose();\n            listenersAdded.uncaughtException.forEach(function(listener) {\n              process.removeListener(\"uncaughtException\", listener);\n            });\n            listenersAdded.unhandledRejection.forEach(function(listener) {\n              process.removeListener(\"unhandledRejection\", listener);\n            });\n          };\n        }\n        return WasmBackendModule3.ready;\n      };\n    })();\n    if (typeof exports === \"object\" && typeof module === \"object\")\n      module.exports = WasmBackendModule2;\n    else if (typeof define === \"function\" && define[\"amd\"])\n      define([], function() {\n        return WasmBackendModule2;\n      });\n    else if (typeof exports === \"object\")\n      exports[\"WasmBackendModule\"] = WasmBackendModule2;\n  }\n});\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/backends/backend.js\nvar EPSILON_FLOAT32 = 1e-7;\nvar EPSILON_FLOAT16 = 1e-4;\nvar DataStorage = class {\n  constructor(backend2, dataMover) {\n    this.backend = backend2;\n    this.dataMover = dataMover;\n    this.data = /* @__PURE__ */ new WeakMap();\n    this.dataIdsCount = 0;\n  }\n  get(dataId) {\n    if (!this.data.has(dataId)) {\n      this.dataMover.moveData(this.backend, dataId);\n    }\n    return this.data.get(dataId);\n  }\n  set(dataId, value) {\n    this.dataIdsCount++;\n    this.data.set(dataId, value);\n  }\n  has(dataId) {\n    return this.data.has(dataId);\n  }\n  delete(dataId) {\n    this.dataIdsCount--;\n    return this.data.delete(dataId);\n  }\n  numDataIds() {\n    return this.dataIdsCount;\n  }\n};\nvar KernelBackend = class {\n  refCount(dataId) {\n    return notYetImplemented(\"refCount\");\n  }\n  incRef(dataId) {\n    return notYetImplemented(\"incRef\");\n  }\n  timerAvailable() {\n    return true;\n  }\n  time(f) {\n    return notYetImplemented(\"time\");\n  }\n  read(dataId) {\n    return notYetImplemented(\"read\");\n  }\n  readSync(dataId) {\n    return notYetImplemented(\"readSync\");\n  }\n  readToGPU(dataId, options) {\n    return notYetImplemented(\"readToGPU\");\n  }\n  numDataIds() {\n    return notYetImplemented(\"numDataIds\");\n  }\n  disposeData(dataId, force) {\n    return notYetImplemented(\"disposeData\");\n  }\n  write(values, shape, dtype) {\n    return notYetImplemented(\"write\");\n  }\n  move(dataId, values, shape, dtype, refCount) {\n    return notYetImplemented(\"move\");\n  }\n  memory() {\n    return notYetImplemented(\"memory\");\n  }\n  floatPrecision() {\n    return notYetImplemented(\"floatPrecision\");\n  }\n  epsilon() {\n    return this.floatPrecision() === 32 ? EPSILON_FLOAT32 : EPSILON_FLOAT16;\n  }\n  dispose() {\n    return notYetImplemented(\"dispose\");\n  }\n};\nfunction notYetImplemented(kernelName) {\n  throw new Error(`'${kernelName}' not yet implemented or not found in the registry. This kernel may not be supported by the tfjs backend you have chosen`);\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/util_base.js\nfunction shuffle(array2) {\n  let counter = array2.length;\n  let index = 0;\n  while (counter > 0) {\n    index = Math.random() * counter | 0;\n    counter--;\n    swap(array2, counter, index);\n  }\n}\nfunction shuffleCombo(array2, array22) {\n  if (array2.length !== array22.length) {\n    throw new Error(`Array sizes must match to be shuffled together First array length was ${array2.length}Second array length was ${array22.length}`);\n  }\n  let counter = array2.length;\n  let index = 0;\n  while (counter > 0) {\n    index = Math.random() * counter | 0;\n    counter--;\n    swap(array2, counter, index);\n    swap(array22, counter, index);\n  }\n}\nfunction clamp(min7, x, max7) {\n  return Math.max(min7, Math.min(x, max7));\n}\nfunction nearestLargerEven(val) {\n  return val % 2 === 0 ? val : val + 1;\n}\nfunction swap(object, left, right) {\n  const temp = object[left];\n  object[left] = object[right];\n  object[right] = temp;\n}\nfunction sum(arr) {\n  let sum7 = 0;\n  for (let i2 = 0; i2 < arr.length; i2++) {\n    sum7 += arr[i2];\n  }\n  return sum7;\n}\nfunction randUniform(a, b) {\n  const r2 = Math.random();\n  return b * r2 + (1 - r2) * a;\n}\nfunction distSquared(a, b) {\n  let result = 0;\n  for (let i2 = 0; i2 < a.length; i2++) {\n    const diff = Number(a[i2]) - Number(b[i2]);\n    result += diff * diff;\n  }\n  return result;\n}\nfunction assert(expr, msg) {\n  if (!expr) {\n    throw new Error(typeof msg === \"string\" ? msg : msg());\n  }\n}\nfunction assertShapesMatch(shapeA, shapeB, errorMessagePrefix = \"\") {\n  assert(arraysEqual(shapeA, shapeB), () => errorMessagePrefix + ` Shapes ${shapeA} and ${shapeB} must match`);\n}\nfunction assertNonNull(a) {\n  assert(a != null, () => `The input to the tensor constructor must be a non-null value.`);\n}\nfunction flatten(arr, result = [], skipTypedArray = false) {\n  if (result == null) {\n    result = [];\n  }\n  if (Array.isArray(arr) || isTypedArray(arr) && !skipTypedArray) {\n    for (let i2 = 0; i2 < arr.length; ++i2) {\n      flatten(arr[i2], result, skipTypedArray);\n    }\n  } else {\n    result.push(arr);\n  }\n  return result;\n}\nfunction sizeFromShape(shape) {\n  if (shape.length === 0) {\n    return 1;\n  }\n  let size = shape[0];\n  for (let i2 = 1; i2 < shape.length; i2++) {\n    size *= shape[i2];\n  }\n  return size;\n}\nfunction isScalarShape(shape) {\n  return shape.length === 0;\n}\nfunction arraysEqual(n1, n2) {\n  if (n1 === n2) {\n    return true;\n  }\n  if (n1 == null || n2 == null) {\n    return false;\n  }\n  if (n1.length !== n2.length) {\n    return false;\n  }\n  for (let i2 = 0; i2 < n1.length; i2++) {\n    if (n1[i2] !== n2[i2]) {\n      return false;\n    }\n  }\n  return true;\n}\nfunction isInt(a) {\n  return a % 1 === 0;\n}\nfunction tanh(x) {\n  if (Math.tanh != null) {\n    return Math.tanh(x);\n  }\n  if (x === Infinity) {\n    return 1;\n  } else if (x === -Infinity) {\n    return -1;\n  } else {\n    const e2x = Math.exp(2 * x);\n    return (e2x - 1) / (e2x + 1);\n  }\n}\nfunction sizeToSquarishShape(size) {\n  const width = Math.ceil(Math.sqrt(size));\n  return [width, Math.ceil(size / width)];\n}\nfunction createShuffledIndices(n2) {\n  const shuffledIndices = new Uint32Array(n2);\n  for (let i2 = 0; i2 < n2; ++i2) {\n    shuffledIndices[i2] = i2;\n  }\n  shuffle(shuffledIndices);\n  return shuffledIndices;\n}\nfunction rightPad(a, size) {\n  if (size <= a.length) {\n    return a;\n  }\n  return a + \" \".repeat(size - a.length);\n}\nfunction repeatedTry(checkFn, delayFn = (counter) => 0, maxCounter) {\n  return new Promise((resolve, reject) => {\n    let tryCount = 0;\n    const tryFn = () => {\n      if (checkFn()) {\n        resolve();\n        return;\n      }\n      tryCount++;\n      const nextBackoff = delayFn(tryCount);\n      if (maxCounter != null && tryCount >= maxCounter) {\n        reject();\n        return;\n      }\n      setTimeout(tryFn, nextBackoff);\n    };\n    tryFn();\n  });\n}\nfunction inferFromImplicitShape(shape, size) {\n  let shapeProd = 1;\n  let implicitIdx = -1;\n  for (let i2 = 0; i2 < shape.length; ++i2) {\n    if (shape[i2] >= 0) {\n      shapeProd *= shape[i2];\n    } else if (shape[i2] === -1) {\n      if (implicitIdx !== -1) {\n        throw Error(`Shapes can only have 1 implicit size. Found -1 at dim ${implicitIdx} and dim ${i2}`);\n      }\n      implicitIdx = i2;\n    } else if (shape[i2] < 0) {\n      throw Error(`Shapes can not be < 0. Found ${shape[i2]} at dim ${i2}`);\n    }\n  }\n  if (implicitIdx === -1) {\n    if (size > 0 && size !== shapeProd) {\n      throw Error(`Size(${size}) must match the product of shape ${shape}`);\n    }\n    return shape;\n  }\n  if (shapeProd === 0) {\n    throw Error(`Cannot infer the missing size in [${shape}] when there are 0 elements`);\n  }\n  if (size % shapeProd !== 0) {\n    throw Error(`The implicit shape can't be a fractional number. Got ${size} / ${shapeProd}`);\n  }\n  const newShape = shape.slice();\n  newShape[implicitIdx] = size / shapeProd;\n  return newShape;\n}\nfunction parseAxisParam(axis, shape) {\n  const rank = shape.length;\n  axis = axis == null ? shape.map((s2, i2) => i2) : [].concat(axis);\n  assert(axis.every((ax) => ax >= -rank && ax < rank), () => `All values in axis param must be in range [-${rank}, ${rank}) but got axis ${axis}`);\n  assert(axis.every((ax) => isInt(ax)), () => `All values in axis param must be integers but got axis ${axis}`);\n  return axis.map((a) => a < 0 ? rank + a : a);\n}\nfunction squeezeShape(shape, axis) {\n  const newShape = [];\n  const keptDims = [];\n  const isEmptyArray = axis != null && Array.isArray(axis) && axis.length === 0;\n  const axes = axis == null || isEmptyArray ? null : parseAxisParam(axis, shape).sort();\n  let j = 0;\n  for (let i2 = 0; i2 < shape.length; ++i2) {\n    if (axes != null) {\n      if (axes[j] === i2 && shape[i2] !== 1) {\n        throw new Error(`Can't squeeze axis ${i2} since its dim '${shape[i2]}' is not 1`);\n      }\n      if ((axes[j] == null || axes[j] > i2) && shape[i2] === 1) {\n        newShape.push(shape[i2]);\n        keptDims.push(i2);\n      }\n      if (axes[j] <= i2) {\n        j++;\n      }\n    }\n    if (shape[i2] !== 1) {\n      newShape.push(shape[i2]);\n      keptDims.push(i2);\n    }\n  }\n  return { newShape, keptDims };\n}\nfunction getTypedArrayFromDType(dtype, size) {\n  let values = null;\n  if (dtype == null || dtype === \"float32\") {\n    values = new Float32Array(size);\n  } else if (dtype === \"int32\") {\n    values = new Int32Array(size);\n  } else if (dtype === \"bool\") {\n    values = new Uint8Array(size);\n  } else {\n    throw new Error(`Unknown data type ${dtype}`);\n  }\n  return values;\n}\nfunction getArrayFromDType(dtype, size) {\n  let values = null;\n  if (dtype == null || dtype === \"float32\") {\n    values = new Float32Array(size);\n  } else if (dtype === \"int32\") {\n    values = new Int32Array(size);\n  } else if (dtype === \"bool\") {\n    values = new Uint8Array(size);\n  } else if (dtype === \"string\") {\n    values = new Array(size);\n  } else {\n    throw new Error(`Unknown data type ${dtype}`);\n  }\n  return values;\n}\nfunction checkConversionForErrors(vals, dtype) {\n  for (let i2 = 0; i2 < vals.length; i2++) {\n    const num = vals[i2];\n    if (isNaN(num) || !isFinite(num)) {\n      throw Error(`A tensor of type ${dtype} being uploaded contains ${num}.`);\n    }\n  }\n}\nfunction isValidDtype(dtype) {\n  return dtype === \"bool\" || dtype === \"complex64\" || dtype === \"float32\" || dtype === \"int32\" || dtype === \"string\";\n}\nfunction hasEncodingLoss(oldType, newType) {\n  if (newType === \"complex64\") {\n    return false;\n  }\n  if (newType === \"float32\" && oldType !== \"complex64\") {\n    return false;\n  }\n  if (newType === \"int32\" && oldType !== \"float32\" && oldType !== \"complex64\") {\n    return false;\n  }\n  if (newType === \"bool\" && oldType === \"bool\") {\n    return false;\n  }\n  return true;\n}\nfunction isTypedArray(a) {\n  return a instanceof Float32Array || a instanceof Int32Array || a instanceof Uint8Array || a instanceof Uint8ClampedArray;\n}\nfunction bytesPerElement(dtype) {\n  if (dtype === \"float32\" || dtype === \"int32\") {\n    return 4;\n  } else if (dtype === \"complex64\") {\n    return 8;\n  } else if (dtype === \"bool\") {\n    return 1;\n  } else {\n    throw new Error(`Unknown dtype ${dtype}`);\n  }\n}\nfunction bytesFromStringArray(arr) {\n  if (arr == null) {\n    return 0;\n  }\n  let bytes = 0;\n  arr.forEach((x) => bytes += x.length);\n  return bytes;\n}\nfunction isString(value) {\n  return typeof value === \"string\" || value instanceof String;\n}\nfunction isBoolean(value) {\n  return typeof value === \"boolean\";\n}\nfunction isNumber(value) {\n  return typeof value === \"number\";\n}\nfunction inferDtype(values) {\n  if (Array.isArray(values)) {\n    return inferDtype(values[0]);\n  }\n  if (values instanceof Float32Array) {\n    return \"float32\";\n  } else if (values instanceof Int32Array || values instanceof Uint8Array || values instanceof Uint8ClampedArray) {\n    return \"int32\";\n  } else if (isNumber(values)) {\n    return \"float32\";\n  } else if (isString(values)) {\n    return \"string\";\n  } else if (isBoolean(values)) {\n    return \"bool\";\n  }\n  return \"float32\";\n}\nfunction isFunction(f) {\n  return !!(f && f.constructor && f.call && f.apply);\n}\nfunction nearestDivisor(size, start) {\n  for (let i2 = start; i2 < size; ++i2) {\n    if (size % i2 === 0) {\n      return i2;\n    }\n  }\n  return size;\n}\nfunction computeStrides(shape) {\n  const rank = shape.length;\n  if (rank < 2) {\n    return [];\n  }\n  const strides = new Array(rank - 1);\n  strides[rank - 2] = shape[rank - 1];\n  for (let i2 = rank - 3; i2 >= 0; --i2) {\n    strides[i2] = strides[i2 + 1] * shape[i2 + 1];\n  }\n  return strides;\n}\nfunction createNestedArray(offset, shape, a, isComplex = false) {\n  const ret = new Array();\n  if (shape.length === 1) {\n    const d = shape[0] * (isComplex ? 2 : 1);\n    for (let i2 = 0; i2 < d; i2++) {\n      ret[i2] = a[offset + i2];\n    }\n  } else {\n    const d = shape[0];\n    const rest = shape.slice(1);\n    const len = rest.reduce((acc, c) => acc * c) * (isComplex ? 2 : 1);\n    for (let i2 = 0; i2 < d; i2++) {\n      ret[i2] = createNestedArray(offset + i2 * len, rest, a, isComplex);\n    }\n  }\n  return ret;\n}\nfunction toNestedArray(shape, a, isComplex = false) {\n  if (shape.length === 0) {\n    return a[0];\n  }\n  const size = shape.reduce((acc, c) => acc * c) * (isComplex ? 2 : 1);\n  if (size === 0) {\n    return [];\n  }\n  if (size !== a.length) {\n    throw new Error(`[${shape}] does not match the input size ${a.length}${isComplex ? \" for a complex tensor\" : \"\"}.`);\n  }\n  return createNestedArray(0, shape, a, isComplex);\n}\nfunction makeOnesTypedArray(size, dtype) {\n  const array2 = makeZerosTypedArray(size, dtype);\n  for (let i2 = 0; i2 < array2.length; i2++) {\n    array2[i2] = 1;\n  }\n  return array2;\n}\nfunction makeZerosTypedArray(size, dtype) {\n  if (dtype == null || dtype === \"float32\" || dtype === \"complex64\") {\n    return new Float32Array(size);\n  } else if (dtype === \"int32\") {\n    return new Int32Array(size);\n  } else if (dtype === \"bool\") {\n    return new Uint8Array(size);\n  } else {\n    throw new Error(`Unknown data type ${dtype}`);\n  }\n}\nfunction makeZerosNestedTypedArray(shape, dtype) {\n  const size = shape.reduce((prev, curr) => prev * curr, 1);\n  if (dtype == null || dtype === \"float32\") {\n    return toNestedArray(shape, new Float32Array(size));\n  } else if (dtype === \"int32\") {\n    return toNestedArray(shape, new Int32Array(size));\n  } else if (dtype === \"bool\") {\n    return toNestedArray(shape, new Uint8Array(size));\n  } else {\n    throw new Error(`Unknown data type ${dtype}`);\n  }\n}\nfunction assertNonNegativeIntegerDimensions(shape) {\n  shape.forEach((dimSize) => {\n    assert(Number.isInteger(dimSize) && dimSize >= 0, () => `Tensor must have a shape comprised of positive integers but got shape [${shape}].`);\n  });\n}\nfunction locToIndex(locs, rank, strides) {\n  if (rank === 0) {\n    return 0;\n  } else if (rank === 1) {\n    return locs[0];\n  }\n  let index = locs[locs.length - 1];\n  for (let i2 = 0; i2 < locs.length - 1; ++i2) {\n    index += strides[i2] * locs[i2];\n  }\n  return index;\n}\nfunction indexToLoc(index, rank, strides) {\n  if (rank === 0) {\n    return [];\n  } else if (rank === 1) {\n    return [index];\n  }\n  const locs = new Array(rank);\n  for (let i2 = 0; i2 < locs.length - 1; ++i2) {\n    locs[i2] = Math.floor(index / strides[i2]);\n    index -= locs[i2] * strides[i2];\n  }\n  locs[locs.length - 1] = index;\n  return locs;\n}\nfunction isPromise(object) {\n  return object && object.then && typeof object.then === \"function\";\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/environment.js\nvar TENSORFLOWJS_FLAGS_PREFIX = \"tfjsflags\";\nvar Environment = class {\n  constructor(global2) {\n    this.global = global2;\n    this.flags = {};\n    this.flagRegistry = {};\n    this.urlFlags = {};\n    this.getQueryParams = getQueryParams;\n    this.populateURLFlags();\n  }\n  setPlatform(platformName, platform) {\n    if (this.platform != null) {\n      if (!(env().getBool(\"IS_TEST\") || env().getBool(\"PROD\"))) {\n        console.warn(`Platform ${this.platformName} has already been set. Overwriting the platform with ${platformName}.`);\n      }\n    }\n    this.platformName = platformName;\n    this.platform = platform;\n  }\n  registerFlag(flagName, evaluationFn, setHook) {\n    this.flagRegistry[flagName] = { evaluationFn, setHook };\n    if (this.urlFlags[flagName] != null) {\n      const flagValue = this.urlFlags[flagName];\n      if (!(env().getBool(\"IS_TEST\") || env().getBool(\"PROD\"))) {\n        console.warn(`Setting feature override from URL ${flagName}: ${flagValue}.`);\n      }\n      this.set(flagName, flagValue);\n    }\n  }\n  async getAsync(flagName) {\n    if (flagName in this.flags) {\n      return this.flags[flagName];\n    }\n    this.flags[flagName] = await this.evaluateFlag(flagName);\n    return this.flags[flagName];\n  }\n  get(flagName) {\n    if (flagName in this.flags) {\n      return this.flags[flagName];\n    }\n    const flagValue = this.evaluateFlag(flagName);\n    if (isPromise(flagValue)) {\n      throw new Error(`Flag ${flagName} cannot be synchronously evaluated. Please use getAsync() instead.`);\n    }\n    this.flags[flagName] = flagValue;\n    return this.flags[flagName];\n  }\n  getNumber(flagName) {\n    return this.get(flagName);\n  }\n  getBool(flagName) {\n    return this.get(flagName);\n  }\n  getFlags() {\n    return this.flags;\n  }\n  get features() {\n    return this.flags;\n  }\n  set(flagName, value) {\n    if (this.flagRegistry[flagName] == null) {\n      throw new Error(`Cannot set flag ${flagName} as it has not been registered.`);\n    }\n    this.flags[flagName] = value;\n    if (this.flagRegistry[flagName].setHook != null) {\n      this.flagRegistry[flagName].setHook(value);\n    }\n  }\n  evaluateFlag(flagName) {\n    if (this.flagRegistry[flagName] == null) {\n      throw new Error(`Cannot evaluate flag '${flagName}': no evaluation function found.`);\n    }\n    return this.flagRegistry[flagName].evaluationFn();\n  }\n  setFlags(flags) {\n    this.flags = Object.assign({}, flags);\n  }\n  reset() {\n    this.flags = {};\n    this.urlFlags = {};\n    this.populateURLFlags();\n  }\n  populateURLFlags() {\n    if (typeof this.global === \"undefined\" || typeof this.global.location === \"undefined\" || typeof this.global.location.search === \"undefined\") {\n      return;\n    }\n    const urlParams = this.getQueryParams(this.global.location.search);\n    if (TENSORFLOWJS_FLAGS_PREFIX in urlParams) {\n      const keyValues = urlParams[TENSORFLOWJS_FLAGS_PREFIX].split(\",\");\n      keyValues.forEach((keyValue) => {\n        const [key, value] = keyValue.split(\":\");\n        this.urlFlags[key] = parseValue(key, value);\n      });\n    }\n  }\n};\nfunction getQueryParams(queryString) {\n  const params = {};\n  queryString.replace(/[?&]([^=?&]+)(?:=([^&]*))?/g, (s2, ...t2) => {\n    decodeParam(params, t2[0], t2[1]);\n    return t2.join(\"=\");\n  });\n  return params;\n}\nfunction decodeParam(params, name, value) {\n  params[decodeURIComponent(name)] = decodeURIComponent(value || \"\");\n}\nfunction parseValue(flagName, value) {\n  value = value.toLowerCase();\n  if (value === \"true\" || value === \"false\") {\n    return value === \"true\";\n  } else if (`${+value}` === value) {\n    return +value;\n  }\n  throw new Error(`Could not parse value flag value ${value} for flag ${flagName}.`);\n}\nfunction env() {\n  return ENV;\n}\nvar ENV = null;\nfunction setEnvironmentGlobal(environment) {\n  ENV = environment;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/global_util.js\nvar globalNameSpace;\nfunction getGlobalNamespace() {\n  if (globalNameSpace == null) {\n    let ns;\n    if (typeof window !== \"undefined\") {\n      ns = window;\n    } else if (typeof global !== \"undefined\") {\n      ns = global;\n    } else if (typeof process !== \"undefined\") {\n      ns = process;\n    } else if (typeof self !== \"undefined\") {\n      ns = self;\n    } else {\n      throw new Error(\"Could not find a global object\");\n    }\n    globalNameSpace = ns;\n  }\n  return globalNameSpace;\n}\nfunction getGlobalMap() {\n  const ns = getGlobalNamespace();\n  if (ns._tfGlobals == null) {\n    ns._tfGlobals = /* @__PURE__ */ new Map();\n  }\n  return ns._tfGlobals;\n}\nfunction getGlobal(key, init2) {\n  const globalMap = getGlobalMap();\n  if (globalMap.has(key)) {\n    return globalMap.get(key);\n  } else {\n    const singleton = init2();\n    globalMap.set(key, singleton);\n    return globalMap.get(key);\n  }\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/kernel_names.js\nvar Abs = \"Abs\";\nvar Acos = \"Acos\";\nvar Acosh = \"Acosh\";\nvar Add = \"Add\";\nvar AddN = \"AddN\";\nvar All = \"All\";\nvar Any = \"Any\";\nvar ArgMax = \"ArgMax\";\nvar ArgMin = \"ArgMin\";\nvar Asin = \"Asin\";\nvar Asinh = \"Asinh\";\nvar Atan = \"Atan\";\nvar Atanh = \"Atanh\";\nvar Atan2 = \"Atan2\";\nvar AvgPool = \"AvgPool\";\nvar AvgPoolGrad = \"AvgPoolGrad\";\nvar AvgPool3D = \"AvgPool3D\";\nvar AvgPool3DGrad = \"AvgPool3DGrad\";\nvar BatchMatMul = \"BatchMatMul\";\nvar BatchToSpaceND = \"BatchToSpaceND\";\nvar Bincount = \"Bincount\";\nvar BroadcastTo = \"BroadcastTo\";\nvar BroadcastArgs = \"BroadcastArgs\";\nvar Cast = \"Cast\";\nvar Ceil = \"Ceil\";\nvar ClipByValue = \"ClipByValue\";\nvar Complex = \"Complex\";\nvar ComplexAbs = \"ComplexAbs\";\nvar Concat = \"Concat\";\nvar Conv2D = \"Conv2D\";\nvar Conv2DBackpropFilter = \"Conv2DBackpropFilter\";\nvar Conv2DBackpropInput = \"Conv2DBackpropInput\";\nvar Conv3D = \"Conv3D\";\nvar Conv3DBackpropFilterV2 = \"Conv3DBackpropFilterV2\";\nvar Conv3DBackpropInputV2 = \"Conv3DBackpropInputV2\";\nvar Cos = \"Cos\";\nvar Cosh = \"Cosh\";\nvar Cumprod = \"Cumprod\";\nvar Cumsum = \"Cumsum\";\nvar CropAndResize = \"CropAndResize\";\nvar DenseBincount = \"DenseBincount\";\nvar DepthToSpace = \"DepthToSpace\";\nvar DepthwiseConv2dNative = \"DepthwiseConv2dNative\";\nvar DepthwiseConv2dNativeBackpropFilter = \"DepthwiseConv2dNativeBackpropFilter\";\nvar DepthwiseConv2dNativeBackpropInput = \"DepthwiseConv2dNativeBackpropInput\";\nvar Diag = \"Diag\";\nvar Dilation2D = \"Dilation2D\";\nvar Dilation2DBackpropInput = \"Dilation2DBackpropInput\";\nvar Dilation2DBackpropFilter = \"Dilation2DBackpropFilter\";\nvar RealDiv = \"RealDiv\";\nvar Einsum = \"Einsum\";\nvar Elu = \"Elu\";\nvar EluGrad = \"EluGrad\";\nvar Erf = \"Erf\";\nvar Equal = \"Equal\";\nvar Exp = \"Exp\";\nvar ExpandDims = \"ExpandDims\";\nvar Expm1 = \"Expm1\";\nvar FFT = \"FFT\";\nvar Fill = \"Fill\";\nvar FlipLeftRight = \"FlipLeftRight\";\nvar Floor = \"Floor\";\nvar FloorDiv = \"FloorDiv\";\nvar FusedBatchNorm = \"FusedBatchNorm\";\nvar GatherV2 = \"GatherV2\";\nvar GatherNd = \"GatherNd\";\nvar Greater = \"Greater\";\nvar GreaterEqual = \"GreaterEqual\";\nvar Identity = \"Identity\";\nvar IFFT = \"IFFT\";\nvar Imag = \"Imag\";\nvar IsFinite = \"IsFinite\";\nvar IsInf = \"IsInf\";\nvar IsNan = \"IsNan\";\nvar LeakyRelu = \"LeakyRelu\";\nvar Less = \"Less\";\nvar LessEqual = \"LessEqual\";\nvar LinSpace = \"LinSpace\";\nvar Log = \"Log\";\nvar Log1p = \"Log1p\";\nvar LogicalAnd = \"LogicalAnd\";\nvar LogicalNot = \"LogicalNot\";\nvar LogicalOr = \"LogicalOr\";\nvar LogicalXor = \"LogicalXor\";\nvar LogSoftmax = \"LogSoftmax\";\nvar LowerBound = \"LowerBound\";\nvar LRN = \"LRN\";\nvar LRNGrad = \"LRNGrad\";\nvar Max = \"Max\";\nvar Maximum = \"Maximum\";\nvar MaxPool = \"MaxPool\";\nvar MaxPoolGrad = \"MaxPoolGrad\";\nvar MaxPool3D = \"MaxPool3D\";\nvar MaxPool3DGrad = \"MaxPool3DGrad\";\nvar MaxPoolWithArgmax = \"MaxPoolWithArgmax\";\nvar Mean = \"Mean\";\nvar Min = \"Min\";\nvar Minimum = \"Minimum\";\nvar MirrorPad = \"MirrorPad\";\nvar Mod = \"Mod\";\nvar Multinomial = \"Multinomial\";\nvar Multiply = \"Multiply\";\nvar Neg = \"Neg\";\nvar NotEqual = \"NotEqual\";\nvar NonMaxSuppressionV3 = \"NonMaxSuppressionV3\";\nvar NonMaxSuppressionV4 = \"NonMaxSuppressionV4\";\nvar NonMaxSuppressionV5 = \"NonMaxSuppressionV5\";\nvar OnesLike = \"OnesLike\";\nvar OneHot = \"OneHot\";\nvar Pack = \"Pack\";\nvar PadV2 = \"PadV2\";\nvar Pool = \"Pool\";\nvar Pow = \"Pow\";\nvar Prelu = \"Prelu\";\nvar Prod = \"Prod\";\nvar RaggedTensorToTensor = \"RaggedTensorToTensor\";\nvar Range = \"Range\";\nvar Real = \"Real\";\nvar Reciprocal = \"Reciprocal\";\nvar Relu = \"Relu\";\nvar Reshape = \"Reshape\";\nvar ResizeNearestNeighbor = \"ResizeNearestNeighbor\";\nvar ResizeNearestNeighborGrad = \"ResizeNearestNeighborGrad\";\nvar ResizeBilinear = \"ResizeBilinear\";\nvar ResizeBilinearGrad = \"ResizeBilinearGrad\";\nvar Relu6 = \"Relu6\";\nvar Reverse = \"Reverse\";\nvar Round = \"Round\";\nvar Rsqrt = \"Rsqrt\";\nvar ScatterNd = \"ScatterNd\";\nvar SearchSorted = \"SearchSorted\";\nvar Select = \"Select\";\nvar Selu = \"Selu\";\nvar Slice = \"Slice\";\nvar Sin = \"Sin\";\nvar Sinh = \"Sinh\";\nvar Sign = \"Sign\";\nvar Sigmoid = \"Sigmoid\";\nvar Softplus = \"Softplus\";\nvar Sqrt = \"Sqrt\";\nvar Sum = \"Sum\";\nvar SpaceToBatchND = \"SpaceToBatchND\";\nvar SplitV = \"SplitV\";\nvar Softmax = \"Softmax\";\nvar SparseFillEmptyRows = \"SparseFillEmptyRows\";\nvar SparseReshape = \"SparseReshape\";\nvar SparseSegmentMean = \"SparseSegmentMean\";\nvar SparseSegmentSum = \"SparseSegmentSum\";\nvar SparseToDense = \"SparseToDense\";\nvar SquaredDifference = \"SquaredDifference\";\nvar Square = \"Square\";\nvar StridedSlice = \"StridedSlice\";\nvar StringNGrams = \"StringNGrams\";\nvar StringSplit = \"StringSplit\";\nvar StringToHashBucketFast = \"StringToHashBucketFast\";\nvar Sub = \"Sub\";\nvar Tan = \"Tan\";\nvar Tanh = \"Tanh\";\nvar Tile = \"Tile\";\nvar TopK = \"TopK\";\nvar Transform = \"Transform\";\nvar Transpose = \"Transpose\";\nvar Unique = \"Unique\";\nvar Unpack = \"Unpack\";\nvar UnsortedSegmentSum = \"UnsortedSegmentSum\";\nvar UpperBound = \"UpperBound\";\nvar ZerosLike = \"ZerosLike\";\nvar Step = \"Step\";\nvar FromPixels = \"FromPixels\";\nvar RotateWithOffset = \"RotateWithOffset\";\nvar _FusedMatMul = \"_FusedMatMul\";\nvar FusedConv2D = \"FusedConv2D\";\nvar FusedDepthwiseConv2D = \"FusedDepthwiseConv2D\";\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/log.js\nfunction warn(...msg) {\n  if (!(env().getBool(\"IS_TEST\") || env().getBool(\"PROD\"))) {\n    console.warn(...msg);\n  }\n}\nfunction log(...msg) {\n  if (!(env().getBool(\"IS_TEST\") || env().getBool(\"PROD\"))) {\n    console.log(...msg);\n  }\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/kernel_registry.js\nvar kernelRegistry = getGlobal(\"kernelRegistry\", () => /* @__PURE__ */ new Map());\nvar gradRegistry = getGlobal(\"gradRegistry\", () => /* @__PURE__ */ new Map());\nfunction getKernel(kernelName, backendName) {\n  const key = makeKey(kernelName, backendName);\n  return kernelRegistry.get(key);\n}\nfunction getGradient(kernelName) {\n  return gradRegistry.get(kernelName);\n}\nfunction getKernelsForBackend(backendName) {\n  const it = kernelRegistry.entries();\n  const result = [];\n  while (true) {\n    const { done, value } = it.next();\n    if (done) {\n      break;\n    }\n    const [key, config] = value;\n    const [backend2] = key.split(\"_\");\n    if (backend2 === backendName) {\n      result.push(config);\n    }\n  }\n  return result;\n}\nfunction registerKernel(config) {\n  const { kernelName, backendName } = config;\n  const key = makeKey(kernelName, backendName);\n  if (kernelRegistry.has(key)) {\n    warn(`The kernel '${kernelName}' for backend '${backendName}' is already registered`);\n  }\n  kernelRegistry.set(key, config);\n}\nfunction registerGradient(config) {\n  const { kernelName } = config;\n  if (gradRegistry.has(kernelName)) {\n    if (env().getBool(\"DEBUG\")) {\n      warn(`Overriding the gradient for '${kernelName}'`);\n    }\n  }\n  gradRegistry.set(kernelName, config);\n}\nfunction unregisterKernel(kernelName, backendName) {\n  const key = makeKey(kernelName, backendName);\n  if (!kernelRegistry.has(key)) {\n    throw new Error(`The kernel '${kernelName}' for backend '${backendName}' is not registered`);\n  }\n  kernelRegistry.delete(key);\n}\nfunction unregisterGradient(kernelName) {\n  if (!gradRegistry.has(kernelName)) {\n    throw new Error(`The gradient '${kernelName}' for backend is not registered`);\n  }\n  gradRegistry.delete(kernelName);\n}\nfunction copyRegisteredKernels(registeredBackendName, newBackendName) {\n  const kernels = getKernelsForBackend(registeredBackendName);\n  kernels.forEach((kernelConfig) => {\n    const newKernelConfig = Object.assign({}, kernelConfig, { backendName: newBackendName });\n    registerKernel(newKernelConfig);\n  });\n}\nfunction makeKey(kernelName, backendName) {\n  return `${backendName}_${kernelName}`;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/util.js\nvar util_exports = {};\n__export(util_exports, {\n  arraysEqual: () => arraysEqual,\n  assert: () => assert,\n  assertNonNegativeIntegerDimensions: () => assertNonNegativeIntegerDimensions,\n  assertNonNull: () => assertNonNull,\n  assertShapesMatch: () => assertShapesMatch,\n  bytesFromStringArray: () => bytesFromStringArray,\n  bytesPerElement: () => bytesPerElement,\n  checkConversionForErrors: () => checkConversionForErrors,\n  clamp: () => clamp,\n  computeStrides: () => computeStrides,\n  createScalarValue: () => createScalarValue,\n  createShuffledIndices: () => createShuffledIndices,\n  decodeString: () => decodeString,\n  distSquared: () => distSquared,\n  encodeString: () => encodeString,\n  fetch: () => fetch3,\n  fingerPrint64: () => fingerPrint64,\n  flatten: () => flatten,\n  getArrayFromDType: () => getArrayFromDType,\n  getTypedArrayFromDType: () => getTypedArrayFromDType,\n  hasEncodingLoss: () => hasEncodingLoss,\n  hexToLong: () => hexToLong,\n  indexToLoc: () => indexToLoc,\n  inferDtype: () => inferDtype,\n  inferFromImplicitShape: () => inferFromImplicitShape,\n  isBoolean: () => isBoolean,\n  isFunction: () => isFunction,\n  isInt: () => isInt,\n  isNumber: () => isNumber,\n  isPromise: () => isPromise,\n  isScalarShape: () => isScalarShape,\n  isString: () => isString,\n  isTypedArray: () => isTypedArray,\n  isValidDtype: () => isValidDtype,\n  locToIndex: () => locToIndex,\n  makeOnesTypedArray: () => makeOnesTypedArray,\n  makeZerosNestedTypedArray: () => makeZerosNestedTypedArray,\n  makeZerosTypedArray: () => makeZerosTypedArray,\n  nearestDivisor: () => nearestDivisor,\n  nearestLargerEven: () => nearestLargerEven,\n  now: () => now,\n  parseAxisParam: () => parseAxisParam,\n  randUniform: () => randUniform,\n  repeatedTry: () => repeatedTry,\n  rightPad: () => rightPad,\n  shuffle: () => shuffle,\n  shuffleCombo: () => shuffleCombo,\n  sizeFromShape: () => sizeFromShape,\n  sizeToSquarishShape: () => sizeToSquarishShape,\n  squeezeShape: () => squeezeShape,\n  sum: () => sum,\n  swap: () => swap,\n  tanh: () => tanh,\n  toNestedArray: () => toNestedArray,\n  toTypedArray: () => toTypedArray\n});\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/hash_util.js\nvar LongExports = __toESM(require_long());\nvar Long = LongExports.default || LongExports;\nfunction hexToLong(hex) {\n  return Long.fromString(hex, true, 16);\n}\nvar k0 = hexToLong(\"c3a5c85c97cb3127\");\nvar k1 = hexToLong(\"b492b66fbe98f273\");\nvar k2 = hexToLong(\"9ae16a3b2f90404f\");\nfunction shiftMix(val) {\n  return val.xor(val.shru(47));\n}\nfunction fetch2(s2, offset, numBytes) {\n  const bytes = s2.slice(offset, offset + numBytes);\n  return Long.fromBytes(Array.from(bytes), true, true);\n}\nfunction fetch64(s2, offset) {\n  return fetch2(s2, offset, 8);\n}\nfunction fetch32(s2, offset) {\n  return fetch2(s2, offset, 4);\n}\nfunction rotate64(val, shift) {\n  return shift === 0 ? val : val.shru(shift).or(val.shl(64 - shift));\n}\nfunction hashLen16(u, v, mul2 = hexToLong(\"9ddfea08eb382d69\")) {\n  let a = u.xor(v).mul(mul2);\n  a = a.xor(a.shru(47));\n  let b = v.xor(a).mul(mul2);\n  b = b.xor(b.shru(47));\n  b = b.mul(mul2);\n  return b;\n}\nfunction weakHashLen32WithSeeds(w, x, y, z, a, b) {\n  a = a.add(w);\n  b = rotate64(b.add(a).add(z), 21);\n  const c = a;\n  a = a.add(x);\n  a = a.add(y);\n  b = b.add(rotate64(a, 44));\n  return [a.add(z), b.add(c)];\n}\nfunction weakHashLen32WithSeedsStr(s2, offset, a, b) {\n  return weakHashLen32WithSeeds(fetch64(s2, offset), fetch64(s2, offset + 8), fetch64(s2, offset + 16), fetch64(s2, offset + 24), a, b);\n}\nfunction hashLen0to16(s2, len = s2.length) {\n  if (len >= 8) {\n    const mul2 = k2.add(len * 2);\n    const a = fetch64(s2, 0).add(k2);\n    const b = fetch64(s2, len - 8);\n    const c = rotate64(b, 37).mul(mul2).add(a);\n    const d = rotate64(a, 25).add(b).mul(mul2);\n    return hashLen16(c, d, mul2);\n  }\n  if (len >= 4) {\n    const mul2 = k2.add(len * 2);\n    const a = fetch32(s2, 0);\n    return hashLen16(a.shl(3).add(len), fetch32(s2, len - 4), mul2);\n  }\n  if (len > 0) {\n    const a = s2[0];\n    const b = s2[len >> 1];\n    const c = s2[len - 1];\n    const y = a + (b << 8);\n    const z = len + (c << 2);\n    return shiftMix(k2.mul(y).xor(k0.mul(z))).mul(k2);\n  }\n  return k2;\n}\nfunction hashLen17to32(s2, len = s2.length) {\n  const mul2 = k2.add(len * 2);\n  const a = fetch64(s2, 0).mul(k1);\n  const b = fetch64(s2, 8);\n  const c = fetch64(s2, len - 8).mul(mul2);\n  const d = fetch64(s2, len - 16).mul(k2);\n  return hashLen16(rotate64(a.add(b), 43).add(rotate64(c, 30)).add(d), a.add(rotate64(b.add(k2), 18)).add(c), mul2);\n}\nfunction hashLen33to64(s2, len = s2.length) {\n  const mul2 = k2.add(len * 2);\n  const a = fetch64(s2, 0).mul(k2);\n  const b = fetch64(s2, 8);\n  const c = fetch64(s2, len - 8).mul(mul2);\n  const d = fetch64(s2, len - 16).mul(k2);\n  const y = rotate64(a.add(b), 43).add(rotate64(c, 30)).add(d);\n  const z = hashLen16(y, a.add(rotate64(b.add(k2), 18)).add(c), mul2);\n  const e2 = fetch64(s2, 16).mul(mul2);\n  const f = fetch64(s2, 24);\n  const g = y.add(fetch64(s2, len - 32)).mul(mul2);\n  const h = z.add(fetch64(s2, len - 24)).mul(mul2);\n  return hashLen16(rotate64(e2.add(f), 43).add(rotate64(g, 30)).add(h), e2.add(rotate64(f.add(a), 18)).add(g), mul2);\n}\nfunction fingerPrint64(s2, len = s2.length) {\n  const seed = Long.fromNumber(81, true);\n  if (len <= 32) {\n    if (len <= 16) {\n      return hashLen0to16(s2, len);\n    } else {\n      return hashLen17to32(s2, len);\n    }\n  } else if (len <= 64) {\n    return hashLen33to64(s2, len);\n  }\n  let x = seed;\n  let y = seed.mul(k1).add(113);\n  let z = shiftMix(y.mul(k2).add(113)).mul(k2);\n  let v = [Long.UZERO, Long.UZERO];\n  let w = [Long.UZERO, Long.UZERO];\n  x = x.mul(k2).add(fetch64(s2, 0));\n  let offset = 0;\n  const end = (len - 1 >> 6) * 64;\n  const last64 = end + (len - 1 & 63) - 63;\n  do {\n    x = rotate64(x.add(y).add(v[0]).add(fetch64(s2, offset + 8)), 37).mul(k1);\n    y = rotate64(y.add(v[1]).add(fetch64(s2, offset + 48)), 42).mul(k1);\n    x = x.xor(w[1]);\n    y = y.add(v[0]).add(fetch64(s2, offset + 40));\n    z = rotate64(z.add(w[0]), 33).mul(k1);\n    v = weakHashLen32WithSeedsStr(s2, offset, v[1].mul(k1), x.add(w[0]));\n    w = weakHashLen32WithSeedsStr(s2, offset + 32, z.add(w[1]), y.add(fetch64(s2, offset + 16)));\n    [z, x] = [x, z];\n    offset += 64;\n  } while (offset !== end);\n  const mul2 = k1.add(z.and(255).shl(1));\n  offset = last64;\n  w[0] = w[0].add(len - 1 & 63);\n  v[0] = v[0].add(w[0]);\n  w[0] = w[0].add(v[0]);\n  x = rotate64(x.add(y).add(v[0]).add(fetch64(s2, offset + 8)), 37).mul(mul2);\n  y = rotate64(y.add(v[1]).add(fetch64(s2, offset + 48)), 42).mul(mul2);\n  x = x.xor(w[1].mul(9));\n  y = y.add(v[0].mul(9).add(fetch64(s2, offset + 40)));\n  z = rotate64(z.add(w[0]), 33).mul(mul2);\n  v = weakHashLen32WithSeedsStr(s2, offset, v[1].mul(mul2), x.add(w[0]));\n  w = weakHashLen32WithSeedsStr(s2, offset + 32, z.add(w[1]), y.add(fetch64(s2, offset + 16)));\n  [z, x] = [x, z];\n  return hashLen16(hashLen16(v[0], w[0], mul2).add(shiftMix(y).mul(k0)).add(z), hashLen16(v[1], w[1], mul2).add(x), mul2);\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/util.js\nfunction createScalarValue(value, dtype) {\n  if (dtype === \"string\") {\n    return encodeString(value);\n  }\n  return toTypedArray([value], dtype);\n}\nfunction noConversionNeeded(a, dtype) {\n  return a instanceof Float32Array && dtype === \"float32\" || a instanceof Int32Array && dtype === \"int32\" || a instanceof Uint8Array && dtype === \"bool\";\n}\nfunction toTypedArray(a, dtype) {\n  if (dtype === \"string\") {\n    throw new Error(\"Cannot convert a string[] to a TypedArray\");\n  }\n  if (Array.isArray(a)) {\n    a = flatten(a);\n  }\n  if (env().getBool(\"DEBUG\")) {\n    checkConversionForErrors(a, dtype);\n  }\n  if (noConversionNeeded(a, dtype)) {\n    return a;\n  }\n  if (dtype == null || dtype === \"float32\" || dtype === \"complex64\") {\n    return new Float32Array(a);\n  } else if (dtype === \"int32\") {\n    return new Int32Array(a);\n  } else if (dtype === \"bool\") {\n    const bool = new Uint8Array(a.length);\n    for (let i2 = 0; i2 < bool.length; ++i2) {\n      if (Math.round(a[i2]) !== 0) {\n        bool[i2] = 1;\n      }\n    }\n    return bool;\n  } else {\n    throw new Error(`Unknown data type ${dtype}`);\n  }\n}\nfunction now() {\n  return env().platform.now();\n}\nfunction fetch3(path, requestInits) {\n  return env().platform.fetch(path, requestInits);\n}\nfunction encodeString(s2, encoding = \"utf-8\") {\n  encoding = encoding || \"utf-8\";\n  return env().platform.encode(s2, encoding);\n}\nfunction decodeString(bytes, encoding = \"utf-8\") {\n  encoding = encoding || \"utf-8\";\n  return env().platform.decode(bytes, encoding);\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/profiler.js\nvar Profiler = class {\n  constructor(backendTimer, logger) {\n    this.backendTimer = backendTimer;\n    this.logger = logger;\n    if (logger == null) {\n      this.logger = new Logger();\n    }\n  }\n  profileKernel(kernelName, inputs, f) {\n    let outputs;\n    const holdResultWrapperFn = () => {\n      outputs = f();\n    };\n    let timer;\n    const start = now();\n    if (this.backendTimer.timerAvailable()) {\n      timer = this.backendTimer.time(holdResultWrapperFn);\n    } else {\n      holdResultWrapperFn();\n      for (const output of outputs) {\n        output.dataSync();\n      }\n      timer = Promise.resolve({ kernelMs: now() - start });\n    }\n    if (env().getBool(\"CHECK_COMPUTATION_FOR_ERRORS\")) {\n      for (let i2 = 0; i2 < outputs.length; i2++) {\n        const output = outputs[i2];\n        output.data().then((tensorVals) => {\n          checkComputationForErrors(tensorVals, output.dtype, kernelName);\n        });\n      }\n    }\n    const kernelProfile = {\n      kernelName,\n      outputs,\n      inputs,\n      timeMs: timer.then((timing) => timing.kernelMs),\n      extraInfo: timer.then((timing) => timing.getExtraProfileInfo != null ? timing.getExtraProfileInfo() : \"\")\n    };\n    return kernelProfile;\n  }\n  logKernelProfile(kernelProfile) {\n    const { kernelName, outputs, timeMs, inputs, extraInfo } = kernelProfile;\n    outputs.forEach((result) => {\n      Promise.all([result.data(), timeMs, extraInfo]).then((valueContainer) => {\n        this.logger.logKernelProfile(kernelName, result, valueContainer[0], valueContainer[1], inputs, valueContainer[2]);\n      });\n    });\n  }\n};\nfunction checkComputationForErrors(vals, dtype, kernelName) {\n  if (dtype !== \"float32\") {\n    return false;\n  }\n  for (let i2 = 0; i2 < vals.length; i2++) {\n    const num = vals[i2];\n    if (isNaN(num) || !isFinite(num)) {\n      console.warn(`Found ${num} in the result of '${kernelName}'`);\n      return true;\n    }\n  }\n  return false;\n}\nvar Logger = class {\n  logKernelProfile(name, result, vals, timeMs, inputs, extraInfo) {\n    const time2 = typeof timeMs === \"number\" ? rightPad(`${timeMs}ms`, 9) : timeMs[\"error\"];\n    const paddedName = rightPad(name, 25);\n    const rank = result.rank;\n    const size = result.size;\n    const shape = rightPad(result.shape.toString(), 14);\n    let inputShapesDescription = \"\";\n    for (const name2 in inputs) {\n      const input2 = inputs[name2];\n      if (input2 != null) {\n        const inputShape = input2.shape || result.shape;\n        const inputRank = inputShape.length;\n        inputShapesDescription += `${name2}: ${inputRank}D ${inputRank > 0 ? inputShape : \"\"} `;\n      }\n    }\n    console.log(`%c${paddedName}\t%c${time2}\t%c${rank}D ${shape}\t%c${size}\t%c${inputShapesDescription}\t%c${extraInfo}`, \"font-weight:bold\", \"color:red\", \"color:blue\", \"color: orange\", \"color: green\", \"color: steelblue\");\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/tape.js\nfunction getFilteredNodesXToY(tape, xs, y) {\n  const tensorsFromX = {};\n  const nodesFromX = {};\n  for (let i2 = 0; i2 < xs.length; i2++) {\n    tensorsFromX[xs[i2].id] = true;\n  }\n  for (let i2 = 0; i2 < tape.length; i2++) {\n    const node = tape[i2];\n    const nodeInputs = node.inputs;\n    for (const inputName in nodeInputs) {\n      const input2 = nodeInputs[inputName];\n      let anyInputFromX = false;\n      for (let j = 0; j < xs.length; j++) {\n        if (tensorsFromX[input2.id]) {\n          node.outputs.forEach((output) => tensorsFromX[output.id] = true);\n          anyInputFromX = true;\n          nodesFromX[node.id] = true;\n          break;\n        }\n      }\n      if (anyInputFromX) {\n        break;\n      }\n    }\n  }\n  const tensorsLeadToY = {};\n  tensorsLeadToY[y.id] = true;\n  const nodesToY = {};\n  for (let i2 = tape.length - 1; i2 >= 0; i2--) {\n    const node = tape[i2];\n    const nodeInputs = node.inputs;\n    for (let j = 0; j < node.outputs.length; j++) {\n      if (tensorsLeadToY[node.outputs[j].id]) {\n        for (const inputName in nodeInputs) {\n          tensorsLeadToY[nodeInputs[inputName].id] = true;\n          nodesToY[node.id] = true;\n        }\n        break;\n      }\n    }\n  }\n  const filteredTape = [];\n  for (let i2 = 0; i2 < tape.length; i2++) {\n    const node = tape[i2];\n    if (nodesFromX[node.id] && nodesToY[node.id]) {\n      const prunedInputs = {};\n      for (const inputName in node.inputs) {\n        const nodeInput = node.inputs[inputName];\n        if (tensorsFromX[nodeInput.id]) {\n          prunedInputs[inputName] = nodeInput;\n        }\n      }\n      const prunedNode = Object.assign({}, node);\n      prunedNode.inputs = prunedInputs;\n      prunedNode.outputs = node.outputs;\n      filteredTape.push(prunedNode);\n    }\n  }\n  return filteredTape;\n}\nfunction backpropagateGradients(tensorAccumulatedGradientMap, filteredTape, tidy2, add5) {\n  for (let i2 = filteredTape.length - 1; i2 >= 0; i2--) {\n    const node = filteredTape[i2];\n    const dys = [];\n    node.outputs.forEach((o) => {\n      const gradTensor = tensorAccumulatedGradientMap[o.id];\n      if (gradTensor != null) {\n        dys.push(gradTensor);\n      } else {\n        dys.push(null);\n      }\n    });\n    if (node.gradient == null) {\n      throw new Error(`Cannot compute gradient: gradient function not found for ${node.kernelName}.`);\n    }\n    const inputGradients = node.gradient(dys);\n    for (const inputName in node.inputs) {\n      if (!(inputName in inputGradients)) {\n        throw new Error(`Cannot backprop through input ${inputName}. Available gradients found: ${Object.keys(inputGradients)}.`);\n      }\n      const dx = tidy2(() => inputGradients[inputName]());\n      if (dx.dtype !== \"float32\") {\n        throw new Error(`Error in gradient for op ${node.kernelName}. The gradient of input ${inputName} must have 'float32' dtype, but has '${dx.dtype}'`);\n      }\n      const x = node.inputs[inputName];\n      if (!arraysEqual(dx.shape, x.shape)) {\n        throw new Error(`Error in gradient for op ${node.kernelName}. The gradient of input '${inputName}' has shape '${dx.shape}', which does not match the shape of the input '${x.shape}'`);\n      }\n      if (tensorAccumulatedGradientMap[x.id] == null) {\n        tensorAccumulatedGradientMap[x.id] = dx;\n      } else {\n        const curGradient = tensorAccumulatedGradientMap[x.id];\n        tensorAccumulatedGradientMap[x.id] = add5(curGradient, dx);\n        curGradient.dispose();\n      }\n    }\n  }\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/tensor_format.js\nvar FORMAT_LIMIT_NUM_VALS = 20;\nvar FORMAT_NUM_FIRST_LAST_VALS = 3;\nvar FORMAT_NUM_SIG_DIGITS = 7;\nfunction tensorToString(vals, shape, dtype, verbose) {\n  const strides = computeStrides(shape);\n  const padPerCol = computeMaxSizePerColumn(vals, shape, dtype, strides);\n  const rank = shape.length;\n  const valsLines = subTensorToString(vals, shape, dtype, strides, padPerCol);\n  const lines = [\"Tensor\"];\n  if (verbose) {\n    lines.push(`  dtype: ${dtype}`);\n    lines.push(`  rank: ${rank}`);\n    lines.push(`  shape: [${shape}]`);\n    lines.push(`  values:`);\n  }\n  lines.push(valsLines.map((l3) => \"    \" + l3).join(\"\\n\"));\n  return lines.join(\"\\n\");\n}\nfunction computeMaxSizePerColumn(vals, shape, dtype, strides) {\n  const n2 = sizeFromShape(shape);\n  const numCols = strides[strides.length - 1];\n  const padPerCol = new Array(numCols).fill(0);\n  const rank = shape.length;\n  const valuesOrTuples = dtype === \"complex64\" ? createComplexTuples(vals) : vals;\n  if (rank > 1) {\n    for (let row = 0; row < n2 / numCols; row++) {\n      const offset = row * numCols;\n      for (let j = 0; j < numCols; j++) {\n        padPerCol[j] = Math.max(padPerCol[j], valToString(valuesOrTuples[offset + j], 0, dtype).length);\n      }\n    }\n  }\n  return padPerCol;\n}\nfunction valToString(val, pad3, dtype) {\n  let valStr;\n  if (Array.isArray(val)) {\n    valStr = `${parseFloat(val[0].toFixed(FORMAT_NUM_SIG_DIGITS))} + ${parseFloat(val[1].toFixed(FORMAT_NUM_SIG_DIGITS))}j`;\n  } else if (isString(val)) {\n    valStr = `'${val}'`;\n  } else if (dtype === \"bool\") {\n    valStr = boolNumToString(val);\n  } else {\n    valStr = parseFloat(val.toFixed(FORMAT_NUM_SIG_DIGITS)).toString();\n  }\n  return rightPad(valStr, pad3);\n}\nfunction boolNumToString(v) {\n  return v === 0 ? \"false\" : \"true\";\n}\nfunction subTensorToString(vals, shape, dtype, strides, padPerCol, isLast = true) {\n  const storagePerElement = dtype === \"complex64\" ? 2 : 1;\n  const size = shape[0];\n  const rank = shape.length;\n  if (rank === 0) {\n    if (dtype === \"complex64\") {\n      const complexTuple = createComplexTuples(vals);\n      return [valToString(complexTuple[0], 0, dtype)];\n    }\n    if (dtype === \"bool\") {\n      return [boolNumToString(vals[0])];\n    }\n    return [vals[0].toString()];\n  }\n  if (rank === 1) {\n    if (size > FORMAT_LIMIT_NUM_VALS) {\n      const firstValsSize = FORMAT_NUM_FIRST_LAST_VALS * storagePerElement;\n      let firstVals = Array.from(vals.slice(0, firstValsSize));\n      let lastVals = Array.from(vals.slice((size - FORMAT_NUM_FIRST_LAST_VALS) * storagePerElement, size * storagePerElement));\n      if (dtype === \"complex64\") {\n        firstVals = createComplexTuples(firstVals);\n        lastVals = createComplexTuples(lastVals);\n      }\n      return [\n        \"[\" + firstVals.map((x, i2) => valToString(x, padPerCol[i2], dtype)).join(\", \") + \", ..., \" + lastVals.map((x, i2) => valToString(x, padPerCol[size - FORMAT_NUM_FIRST_LAST_VALS + i2], dtype)).join(\", \") + \"]\"\n      ];\n    }\n    const displayVals = dtype === \"complex64\" ? createComplexTuples(vals) : Array.from(vals);\n    return [\n      \"[\" + displayVals.map((x, i2) => valToString(x, padPerCol[i2], dtype)).join(\", \") + \"]\"\n    ];\n  }\n  const subshape = shape.slice(1);\n  const substrides = strides.slice(1);\n  const stride = strides[0] * storagePerElement;\n  const lines = [];\n  if (size > FORMAT_LIMIT_NUM_VALS) {\n    for (let i2 = 0; i2 < FORMAT_NUM_FIRST_LAST_VALS; i2++) {\n      const start = i2 * stride;\n      const end = start + stride;\n      lines.push(...subTensorToString(vals.slice(start, end), subshape, dtype, substrides, padPerCol, false));\n    }\n    lines.push(\"...\");\n    for (let i2 = size - FORMAT_NUM_FIRST_LAST_VALS; i2 < size; i2++) {\n      const start = i2 * stride;\n      const end = start + stride;\n      lines.push(...subTensorToString(vals.slice(start, end), subshape, dtype, substrides, padPerCol, i2 === size - 1));\n    }\n  } else {\n    for (let i2 = 0; i2 < size; i2++) {\n      const start = i2 * stride;\n      const end = start + stride;\n      lines.push(...subTensorToString(vals.slice(start, end), subshape, dtype, substrides, padPerCol, i2 === size - 1));\n    }\n  }\n  const sep = rank === 2 ? \",\" : \"\";\n  lines[0] = \"[\" + lines[0] + sep;\n  for (let i2 = 1; i2 < lines.length - 1; i2++) {\n    lines[i2] = \" \" + lines[i2] + sep;\n  }\n  let newLineSep = \",\\n\";\n  for (let i2 = 2; i2 < rank; i2++) {\n    newLineSep += \"\\n\";\n  }\n  lines[lines.length - 1] = \" \" + lines[lines.length - 1] + \"]\" + (isLast ? \"\" : newLineSep);\n  return lines;\n}\nfunction createComplexTuples(vals) {\n  const complexTuples = [];\n  for (let i2 = 0; i2 < vals.length; i2 += 2) {\n    complexTuples.push([vals[i2], vals[i2 + 1]]);\n  }\n  return complexTuples;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/tensor.js\nvar TensorBuffer = class {\n  constructor(shape, dtype, values) {\n    this.dtype = dtype;\n    this.shape = shape.slice();\n    this.size = sizeFromShape(shape);\n    if (values != null) {\n      const n2 = values.length;\n      assert(n2 === this.size, () => `Length of values '${n2}' does not match the size inferred by the shape '${this.size}'.`);\n    }\n    if (dtype === \"complex64\") {\n      throw new Error(`complex64 dtype TensorBuffers are not supported. Please create a TensorBuffer for the real and imaginary parts separately and call tf.complex(real, imag).`);\n    }\n    this.values = values || getArrayFromDType(dtype, this.size);\n    this.strides = computeStrides(shape);\n  }\n  set(value, ...locs) {\n    if (locs.length === 0) {\n      locs = [0];\n    }\n    assert(locs.length === this.rank, () => `The number of provided coordinates (${locs.length}) must match the rank (${this.rank})`);\n    const index = this.locToIndex(locs);\n    this.values[index] = value;\n  }\n  get(...locs) {\n    if (locs.length === 0) {\n      locs = [0];\n    }\n    let i2 = 0;\n    for (const loc of locs) {\n      if (loc < 0 || loc >= this.shape[i2]) {\n        const msg = `Requested out of range element at ${locs}.   Buffer shape=${this.shape}`;\n        throw new Error(msg);\n      }\n      i2++;\n    }\n    let index = locs[locs.length - 1];\n    for (let i3 = 0; i3 < locs.length - 1; ++i3) {\n      index += this.strides[i3] * locs[i3];\n    }\n    return this.values[index];\n  }\n  locToIndex(locs) {\n    if (this.rank === 0) {\n      return 0;\n    } else if (this.rank === 1) {\n      return locs[0];\n    }\n    let index = locs[locs.length - 1];\n    for (let i2 = 0; i2 < locs.length - 1; ++i2) {\n      index += this.strides[i2] * locs[i2];\n    }\n    return index;\n  }\n  indexToLoc(index) {\n    if (this.rank === 0) {\n      return [];\n    } else if (this.rank === 1) {\n      return [index];\n    }\n    const locs = new Array(this.shape.length);\n    for (let i2 = 0; i2 < locs.length - 1; ++i2) {\n      locs[i2] = Math.floor(index / this.strides[i2]);\n      index -= locs[i2] * this.strides[i2];\n    }\n    locs[locs.length - 1] = index;\n    return locs;\n  }\n  get rank() {\n    return this.shape.length;\n  }\n  toTensor() {\n    return trackerFn().makeTensor(this.values, this.shape, this.dtype);\n  }\n};\nvar trackerFn = null;\nvar opHandler = null;\nvar deprecationWarningFn = null;\nfunction setTensorTracker(fn) {\n  trackerFn = fn;\n}\nfunction setOpHandler(handler) {\n  opHandler = handler;\n}\nfunction setDeprecationWarningFn(fn) {\n  deprecationWarningFn = fn;\n}\nvar Tensor = class {\n  constructor(shape, dtype, dataId, id) {\n    this.kept = false;\n    this.isDisposedInternal = false;\n    this.shape = shape.slice();\n    this.dtype = dtype || \"float32\";\n    this.size = sizeFromShape(shape);\n    this.strides = computeStrides(shape);\n    this.dataId = dataId;\n    this.id = id;\n    this.rankType = this.rank < 5 ? this.rank.toString() : \"higher\";\n  }\n  get rank() {\n    return this.shape.length;\n  }\n  async buffer() {\n    const vals = await this.data();\n    return opHandler.buffer(this.shape, this.dtype, vals);\n  }\n  bufferSync() {\n    return opHandler.buffer(this.shape, this.dtype, this.dataSync());\n  }\n  async array() {\n    const vals = await this.data();\n    return toNestedArray(this.shape, vals, this.dtype === \"complex64\");\n  }\n  arraySync() {\n    return toNestedArray(this.shape, this.dataSync(), this.dtype === \"complex64\");\n  }\n  async data() {\n    this.throwIfDisposed();\n    const data = trackerFn().read(this.dataId);\n    if (this.dtype === \"string\") {\n      const bytes = await data;\n      try {\n        return bytes.map((b) => decodeString(b));\n      } catch (_a) {\n        throw new Error(\"Failed to decode the string bytes into utf-8. To get the original bytes, call tensor.bytes().\");\n      }\n    }\n    return data;\n  }\n  dataToGPU(options) {\n    this.throwIfDisposed();\n    return trackerFn().readToGPU(this.dataId, options);\n  }\n  dataSync() {\n    this.throwIfDisposed();\n    const data = trackerFn().readSync(this.dataId);\n    if (this.dtype === \"string\") {\n      try {\n        return data.map((b) => decodeString(b));\n      } catch (_a) {\n        throw new Error(\"Failed to decode the string bytes into utf-8. To get the original bytes, call tensor.bytes().\");\n      }\n    }\n    return data;\n  }\n  async bytes() {\n    this.throwIfDisposed();\n    const data = await trackerFn().read(this.dataId);\n    if (this.dtype === \"string\") {\n      return data;\n    } else {\n      return new Uint8Array(data.buffer);\n    }\n  }\n  dispose() {\n    if (this.isDisposed) {\n      return;\n    }\n    trackerFn().disposeTensor(this);\n    this.isDisposedInternal = true;\n  }\n  get isDisposed() {\n    return this.isDisposedInternal;\n  }\n  throwIfDisposed() {\n    if (this.isDisposed) {\n      throw new Error(`Tensor is disposed.`);\n    }\n  }\n  print(verbose = false) {\n    return opHandler.print(this, verbose);\n  }\n  clone() {\n    this.throwIfDisposed();\n    return opHandler.clone(this);\n  }\n  toString(verbose = false) {\n    const vals = this.dataSync();\n    return tensorToString(vals, this.shape, this.dtype, verbose);\n  }\n  cast(dtype) {\n    this.throwIfDisposed();\n    return opHandler.cast(this, dtype);\n  }\n  variable(trainable = true, name, dtype) {\n    this.throwIfDisposed();\n    return trackerFn().makeVariable(this, trainable, name, dtype);\n  }\n};\nObject.defineProperty(Tensor, Symbol.hasInstance, {\n  value: (instance) => {\n    return !!instance && instance.data != null && instance.dataSync != null && instance.throwIfDisposed != null;\n  }\n});\nfunction getGlobalTensorClass() {\n  return getGlobal(\"Tensor\", () => {\n    return Tensor;\n  });\n}\ngetGlobalTensorClass();\nvar Variable = class extends Tensor {\n  constructor(initialValue, trainable, name, tensorId) {\n    super(initialValue.shape, initialValue.dtype, initialValue.dataId, tensorId);\n    this.trainable = trainable;\n    this.name = name;\n  }\n  assign(newValue) {\n    if (newValue.dtype !== this.dtype) {\n      throw new Error(`dtype of the new value (${newValue.dtype}) and previous value (${this.dtype}) must match`);\n    }\n    if (!arraysEqual(newValue.shape, this.shape)) {\n      throw new Error(`shape of the new value (${newValue.shape}) and previous value (${this.shape}) must match`);\n    }\n    trackerFn().disposeTensor(this);\n    this.dataId = newValue.dataId;\n    trackerFn().incRef(this, null);\n  }\n  dispose() {\n    trackerFn().disposeVariable(this);\n    this.isDisposedInternal = true;\n  }\n};\nObject.defineProperty(Variable, Symbol.hasInstance, {\n  value: (instance) => {\n    return instance instanceof Tensor && instance.assign != null && instance.assign instanceof Function;\n  }\n});\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/tensor_util.js\nvar tensor_util_exports = {};\n__export(tensor_util_exports, {\n  assertTypesMatch: () => assertTypesMatch,\n  getTensorsInContainer: () => getTensorsInContainer,\n  isTensorInList: () => isTensorInList,\n  makeTypesMatch: () => makeTypesMatch\n});\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/types.js\nvar Rank;\n(function(Rank2) {\n  Rank2[\"R0\"] = \"R0\";\n  Rank2[\"R1\"] = \"R1\";\n  Rank2[\"R2\"] = \"R2\";\n  Rank2[\"R3\"] = \"R3\";\n  Rank2[\"R4\"] = \"R4\";\n  Rank2[\"R5\"] = \"R5\";\n  Rank2[\"R6\"] = \"R6\";\n})(Rank || (Rank = {}));\nvar UpcastInt32AndMap;\n(function(UpcastInt32AndMap2) {\n  UpcastInt32AndMap2[\"float32\"] = \"float32\";\n  UpcastInt32AndMap2[\"int32\"] = \"int32\";\n  UpcastInt32AndMap2[\"bool\"] = \"int32\";\n  UpcastInt32AndMap2[\"complex64\"] = \"complex64\";\n})(UpcastInt32AndMap || (UpcastInt32AndMap = {}));\nvar UpcastBoolAndMap;\n(function(UpcastBoolAndMap2) {\n  UpcastBoolAndMap2[\"float32\"] = \"float32\";\n  UpcastBoolAndMap2[\"int32\"] = \"int32\";\n  UpcastBoolAndMap2[\"bool\"] = \"bool\";\n  UpcastBoolAndMap2[\"complex64\"] = \"complex64\";\n})(UpcastBoolAndMap || (UpcastBoolAndMap = {}));\nvar UpcastFloat32AndMap;\n(function(UpcastFloat32AndMap2) {\n  UpcastFloat32AndMap2[\"float32\"] = \"float32\";\n  UpcastFloat32AndMap2[\"int32\"] = \"float32\";\n  UpcastFloat32AndMap2[\"bool\"] = \"float32\";\n  UpcastFloat32AndMap2[\"complex64\"] = \"complex64\";\n})(UpcastFloat32AndMap || (UpcastFloat32AndMap = {}));\nvar UpcastComplex64AndMap;\n(function(UpcastComplex64AndMap2) {\n  UpcastComplex64AndMap2[\"float32\"] = \"complex64\";\n  UpcastComplex64AndMap2[\"int32\"] = \"complex64\";\n  UpcastComplex64AndMap2[\"bool\"] = \"complex64\";\n  UpcastComplex64AndMap2[\"complex64\"] = \"complex64\";\n})(UpcastComplex64AndMap || (UpcastComplex64AndMap = {}));\nvar upcastTypeMap = {\n  \"float32\": UpcastFloat32AndMap,\n  \"int32\": UpcastInt32AndMap,\n  \"bool\": UpcastBoolAndMap,\n  \"complex64\": UpcastComplex64AndMap\n};\nfunction upcastType(typeA, typeB) {\n  if (typeA === \"string\" || typeB === \"string\") {\n    if (typeA === \"string\" && typeB === \"string\") {\n      return \"string\";\n    }\n    throw new Error(`Can not upcast ${typeA} with ${typeB}`);\n  }\n  return upcastTypeMap[typeA][typeB];\n}\nfunction sumOutType(type) {\n  return upcastType(type, \"int32\");\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/tensor_util.js\nfunction makeTypesMatch(a, b) {\n  if (a.dtype === b.dtype) {\n    return [a, b];\n  }\n  const dtype = upcastType(a.dtype, b.dtype);\n  return [a.cast(dtype), b.cast(dtype)];\n}\nfunction assertTypesMatch(a, b) {\n  assert(a.dtype === b.dtype, () => `The dtypes of the first(${a.dtype}) and second(${b.dtype}) input must match`);\n}\nfunction isTensorInList(tensor2, tensorList) {\n  return tensorList.some((x) => x.id === tensor2.id);\n}\nfunction getTensorsInContainer(result) {\n  const list = [];\n  const seen = /* @__PURE__ */ new Set();\n  walkTensorContainer(result, list, seen);\n  return list;\n}\nfunction walkTensorContainer(container, list, seen) {\n  if (container == null) {\n    return;\n  }\n  if (container instanceof Tensor) {\n    list.push(container);\n    return;\n  }\n  if (!isIterable(container)) {\n    return;\n  }\n  const iterable = container;\n  for (const k in iterable) {\n    const val = iterable[k];\n    if (!seen.has(val)) {\n      seen.add(val);\n      walkTensorContainer(val, list, seen);\n    }\n  }\n}\nfunction isIterable(obj) {\n  return Array.isArray(obj) || typeof obj === \"object\";\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/engine.js\nfunction isRegisteredKernelInvocation(kernelInvocation) {\n  return kernelInvocation.kernelName != null;\n}\nvar EngineState = class {\n  constructor() {\n    this.registeredVariables = {};\n    this.nextTapeNodeId = 0;\n    this.numBytes = 0;\n    this.numTensors = 0;\n    this.numStringTensors = 0;\n    this.numDataBuffers = 0;\n    this.gradientDepth = 0;\n    this.kernelDepth = 0;\n    this.scopeStack = [];\n    this.numDataMovesStack = [];\n    this.nextScopeId = 0;\n    this.tensorInfo = /* @__PURE__ */ new WeakMap();\n    this.profiling = false;\n    this.activeProfile = {\n      newBytes: 0,\n      newTensors: 0,\n      peakBytes: 0,\n      kernels: [],\n      result: null,\n      get kernelNames() {\n        return Array.from(new Set(this.kernels.map((k) => k.name)));\n      }\n    };\n  }\n  dispose() {\n    for (const variableName in this.registeredVariables) {\n      this.registeredVariables[variableName].dispose();\n    }\n  }\n};\nvar Engine = class {\n  constructor(ENV8) {\n    this.ENV = ENV8;\n    this.registry = {};\n    this.registryFactory = {};\n    this.pendingBackendInitId = 0;\n    this.state = new EngineState();\n  }\n  async ready() {\n    if (this.pendingBackendInit != null) {\n      return this.pendingBackendInit.then(() => {\n      });\n    }\n    if (this.backendInstance != null) {\n      return;\n    }\n    const sortedBackends = this.getSortedBackends();\n    for (let i2 = 0; i2 < sortedBackends.length; i2++) {\n      const backendName = sortedBackends[i2];\n      const success = await this.initializeBackend(backendName).success;\n      if (success) {\n        await this.setBackend(backendName);\n        return;\n      }\n    }\n    throw new Error(`Could not initialize any backends, all backend initializations failed.`);\n  }\n  get backend() {\n    if (this.pendingBackendInit != null) {\n      throw new Error(`Backend '${this.backendName}' has not yet been initialized. Make sure to await tf.ready() or await tf.setBackend() before calling other methods`);\n    }\n    if (this.backendInstance == null) {\n      const { name, asyncInit } = this.initializeBackendsAndReturnBest();\n      if (asyncInit) {\n        throw new Error(`The highest priority backend '${name}' has not yet been initialized. Make sure to await tf.ready() or await tf.setBackend() before calling other methods`);\n      }\n      this.setBackend(name);\n    }\n    return this.backendInstance;\n  }\n  backendNames() {\n    return Object.keys(this.registryFactory);\n  }\n  findBackend(backendName) {\n    if (!(backendName in this.registry)) {\n      if (backendName in this.registryFactory) {\n        const { asyncInit } = this.initializeBackend(backendName);\n        if (asyncInit) {\n          return null;\n        }\n      } else {\n        return null;\n      }\n    }\n    return this.registry[backendName];\n  }\n  findBackendFactory(backendName) {\n    if (!(backendName in this.registryFactory)) {\n      return null;\n    }\n    return this.registryFactory[backendName].factory;\n  }\n  registerBackend(backendName, factory, priority = 1) {\n    if (backendName in this.registryFactory) {\n      warn(`${backendName} backend was already registered. Reusing existing backend factory.`);\n      return false;\n    }\n    this.registryFactory[backendName] = { factory, priority };\n    return true;\n  }\n  async setBackend(backendName) {\n    if (this.registryFactory[backendName] == null) {\n      throw new Error(`Backend name '${backendName}' not found in registry`);\n    }\n    this.backendName = backendName;\n    if (this.registry[backendName] == null) {\n      this.backendInstance = null;\n      const { success, asyncInit } = this.initializeBackend(backendName);\n      const result = asyncInit ? await success : success;\n      if (!result) {\n        return false;\n      }\n    }\n    this.backendInstance = this.registry[backendName];\n    this.setupRegisteredKernels();\n    this.profiler = new Profiler(this.backendInstance);\n    return true;\n  }\n  setupRegisteredKernels() {\n    const kernels = getKernelsForBackend(this.backendName);\n    kernels.forEach((kernel) => {\n      if (kernel.setupFunc != null) {\n        kernel.setupFunc(this.backendInstance);\n      }\n    });\n  }\n  disposeRegisteredKernels(backendName) {\n    const kernels = getKernelsForBackend(backendName);\n    kernels.forEach((kernel) => {\n      if (kernel.disposeFunc != null) {\n        kernel.disposeFunc(this.registry[backendName]);\n      }\n    });\n  }\n  initializeBackend(backendName) {\n    const registryFactoryEntry = this.registryFactory[backendName];\n    if (registryFactoryEntry == null) {\n      throw new Error(`Cannot initialize backend ${backendName}, no registration found.`);\n    }\n    try {\n      const backend2 = registryFactoryEntry.factory();\n      if (backend2 && !(backend2 instanceof KernelBackend) && typeof backend2.then === \"function\") {\n        const promiseId = ++this.pendingBackendInitId;\n        const success = backend2.then((backendInstance) => {\n          if (promiseId < this.pendingBackendInitId) {\n            return false;\n          }\n          this.registry[backendName] = backendInstance;\n          this.pendingBackendInit = null;\n          return true;\n        }).catch((err) => {\n          if (promiseId < this.pendingBackendInitId) {\n            return false;\n          }\n          this.pendingBackendInit = null;\n          warn(`Initialization of backend ${backendName} failed`);\n          warn(err.stack || err.message);\n          return false;\n        });\n        this.pendingBackendInit = success;\n        return { success, asyncInit: true };\n      } else {\n        this.registry[backendName] = backend2;\n        return { success: true, asyncInit: false };\n      }\n    } catch (err) {\n      warn(`Initialization of backend ${backendName} failed`);\n      warn(err.stack || err.message);\n      return { success: false, asyncInit: false };\n    }\n  }\n  removeBackend(backendName) {\n    if (!(backendName in this.registryFactory)) {\n      throw new Error(`${backendName} backend not found in registry`);\n    }\n    if (this.backendName === backendName && this.pendingBackendInit != null) {\n      this.pendingBackendInitId++;\n    }\n    if (backendName in this.registry) {\n      this.disposeRegisteredKernels(backendName);\n      this.registry[backendName].dispose();\n      delete this.registry[backendName];\n    }\n    delete this.registryFactory[backendName];\n    if (this.backendName === backendName) {\n      this.pendingBackendInit = null;\n      this.backendName = null;\n      this.backendInstance = null;\n    }\n  }\n  getSortedBackends() {\n    if (Object.keys(this.registryFactory).length === 0) {\n      throw new Error(\"No backend found in registry.\");\n    }\n    return Object.keys(this.registryFactory).sort((a, b) => {\n      return this.registryFactory[b].priority - this.registryFactory[a].priority;\n    });\n  }\n  initializeBackendsAndReturnBest() {\n    const sortedBackends = this.getSortedBackends();\n    for (let i2 = 0; i2 < sortedBackends.length; i2++) {\n      const backendName = sortedBackends[i2];\n      const { success, asyncInit } = this.initializeBackend(backendName);\n      if (asyncInit || success) {\n        return { name: backendName, asyncInit };\n      }\n    }\n    throw new Error(`Could not initialize any backends, all backend initializations failed.`);\n  }\n  moveData(backend2, dataId) {\n    const info = this.state.tensorInfo.get(dataId);\n    const srcBackend = info.backend;\n    const values = this.readSync(dataId);\n    const refCount = srcBackend.refCount(dataId);\n    srcBackend.disposeData(dataId, true);\n    info.backend = backend2;\n    backend2.move(dataId, values, info.shape, info.dtype, refCount);\n    if (this.shouldCheckForMemLeaks()) {\n      this.state.numDataMovesStack[this.state.numDataMovesStack.length - 1]++;\n    }\n  }\n  tidy(nameOrFn, fn) {\n    let name = null;\n    if (fn == null) {\n      if (typeof nameOrFn !== \"function\") {\n        throw new Error(\"Please provide a function to tidy()\");\n      }\n      fn = nameOrFn;\n    } else {\n      if (typeof nameOrFn !== \"string\" && !(nameOrFn instanceof String)) {\n        throw new Error(\"When calling with two arguments, the first argument to tidy() must be a string\");\n      }\n      if (typeof fn !== \"function\") {\n        throw new Error(\"When calling with two arguments, the 2nd argument to tidy() must be a function\");\n      }\n      name = nameOrFn;\n    }\n    let result;\n    return this.scopedRun(() => this.startScope(name), () => this.endScope(result), () => {\n      result = fn();\n      if (result instanceof Promise) {\n        console.error(\"Cannot return a Promise inside of tidy.\");\n      }\n      return result;\n    });\n  }\n  scopedRun(start, end, f) {\n    start();\n    try {\n      const res = f();\n      end();\n      return res;\n    } catch (ex) {\n      end();\n      throw ex;\n    }\n  }\n  nextTensorId() {\n    return Engine.nextTensorId++;\n  }\n  nextVariableId() {\n    return Engine.nextVariableId++;\n  }\n  clone(x) {\n    const y = ENGINE.runKernel(Identity, { x });\n    const inputs = { x };\n    const grad2 = (dy) => ({\n      x: () => {\n        const dtype = \"float32\";\n        const gradInputs = { x: dy };\n        const attrs = { dtype };\n        return ENGINE.runKernel(\n          Cast,\n          gradInputs,\n          attrs\n        );\n      }\n    });\n    const saved = [];\n    this.addTapeNode(this.state.activeScope.name, inputs, [y], grad2, saved, {});\n    return y;\n  }\n  runKernel(kernelName, inputs, attrs) {\n    if (this.backendName == null) {\n      this.backend;\n    }\n    const hasKernel = getKernel(kernelName, this.backendName) != null;\n    if (!hasKernel) {\n      throw new Error(`Kernel '${kernelName}' not registered for backend '${this.backendName}'`);\n    }\n    return this.runKernelFunc({ kernelName, inputs, attrs });\n  }\n  shouldCheckForMemLeaks() {\n    return this.ENV.getBool(\"IS_TEST\");\n  }\n  checkKernelForMemLeak(kernelName, numDataIdsBefore, outInfos) {\n    const numDataIdsAfter = this.backend.numDataIds();\n    let numOutputDataIds = 0;\n    outInfos.forEach((info) => {\n      numOutputDataIds += info.dtype === \"complex64\" ? 3 : 1;\n    });\n    const numMoves = this.state.numDataMovesStack[this.state.numDataMovesStack.length - 1];\n    const dataIdsLeaked = numDataIdsAfter - numDataIdsBefore - numOutputDataIds - numMoves;\n    if (dataIdsLeaked > 0) {\n      throw new Error(`Backend '${this.backendName}' has an internal memory leak (${dataIdsLeaked} data ids) after running '${kernelName}'`);\n    }\n  }\n  runKernelFunc(kernelParams) {\n    let outputs;\n    let saved = [];\n    const isTapeOn = this.isTapeOn();\n    const startingBytecount = this.state.numBytes;\n    const startingNumTensors = this.state.numTensors;\n    if (this.shouldCheckForMemLeaks()) {\n      this.state.numDataMovesStack.push(0);\n    }\n    let kernelFunc3;\n    if (this.backendName == null) {\n      this.backend;\n    }\n    let out;\n    const kernelOrScopeName = isRegisteredKernelInvocation(kernelParams) ? kernelParams.kernelName : this.state.activeScope != null ? this.state.activeScope.name : \"\";\n    if (isRegisteredKernelInvocation(kernelParams)) {\n      const { kernelName, inputs: inputs2, attrs: attrs2 } = kernelParams;\n      if (this.backendName == null) {\n        this.backend;\n      }\n      const kernel = getKernel(kernelName, this.backendName);\n      assert(kernel != null, () => `Cannot find registered kernel '${kernelName}' for backend '${this.backendName}'`);\n      kernelFunc3 = () => {\n        const numDataIdsBefore = this.backend.numDataIds();\n        out = kernel.kernelFunc({ inputs: inputs2, attrs: attrs2, backend: this.backend });\n        const outInfos = Array.isArray(out) ? out : [out];\n        if (this.shouldCheckForMemLeaks()) {\n          this.checkKernelForMemLeak(kernelName, numDataIdsBefore, outInfos);\n        }\n        const outTensors = outInfos.map((outInfo) => {\n          if (outInfo.rank != null) {\n            return outInfo;\n          }\n          return this.makeTensorFromTensorInfo(outInfo);\n        });\n        if (isTapeOn) {\n          const tensorsToSave = this.getTensorsForGradient(kernelName, inputs2, outTensors);\n          saved = this.saveTensorsForBackwardMode(tensorsToSave);\n        }\n        return outTensors;\n      };\n    } else {\n      const { forwardFunc } = kernelParams;\n      const saveFunc = (tensors) => {\n        if (!isTapeOn) {\n          return;\n        }\n        saved = tensors.map((tensor2) => this.keep(this.clone(tensor2)));\n      };\n      kernelFunc3 = () => {\n        const numDataIdsBefore = this.backend.numDataIds();\n        out = this.tidy(() => forwardFunc(this.backend, saveFunc));\n        const outs = Array.isArray(out) ? out : [out];\n        if (this.shouldCheckForMemLeaks()) {\n          this.checkKernelForMemLeak(kernelOrScopeName, numDataIdsBefore, outs);\n        }\n        return outs;\n      };\n    }\n    const { inputs, attrs } = kernelParams;\n    const backwardsFunc = isRegisteredKernelInvocation(kernelParams) ? null : kernelParams.backwardsFunc;\n    let kernelProfile;\n    this.scopedRun(\n      () => this.state.kernelDepth++,\n      () => this.state.kernelDepth--,\n      () => {\n        if (!this.ENV.getBool(\"DEBUG\") && !this.state.profiling) {\n          outputs = kernelFunc3();\n        } else {\n          kernelProfile = this.profiler.profileKernel(kernelOrScopeName, inputs, () => kernelFunc3());\n          if (this.ENV.getBool(\"DEBUG\")) {\n            this.profiler.logKernelProfile(kernelProfile);\n          }\n          outputs = kernelProfile.outputs;\n        }\n      }\n    );\n    if (isTapeOn) {\n      this.addTapeNode(kernelOrScopeName, inputs, outputs, backwardsFunc, saved, attrs);\n    }\n    if (this.state.profiling) {\n      this.state.activeProfile.kernels.push({\n        name: kernelOrScopeName,\n        bytesAdded: this.state.numBytes - startingBytecount,\n        totalBytesSnapshot: this.state.numBytes,\n        tensorsAdded: this.state.numTensors - startingNumTensors,\n        totalTensorsSnapshot: this.state.numTensors,\n        inputShapes: Object.keys(inputs).map((key) => inputs[key] != null ? inputs[key].shape : null),\n        outputShapes: outputs.map((item) => item.shape),\n        kernelTimeMs: kernelProfile.timeMs,\n        extraInfo: kernelProfile.extraInfo\n      });\n    }\n    return Array.isArray(out) ? outputs : outputs[0];\n  }\n  saveTensorsForBackwardMode(tensors) {\n    const saved = tensors.map((tensor2) => this.keep(this.clone(tensor2)));\n    return saved;\n  }\n  getTensorsForGradient(kernelName, inputs, outputs) {\n    const gradConfig = getGradient(kernelName);\n    if (gradConfig != null) {\n      const inputsToSave = gradConfig.inputsToSave || [];\n      const outputsToSave = gradConfig.outputsToSave || [];\n      let inputTensorsToSave;\n      if (gradConfig.saveAllInputs) {\n        assert(Array.isArray(inputs), () => \"saveAllInputs is true, expected inputs to be an array.\");\n        inputTensorsToSave = Object.keys(inputs).map((key) => inputs[key]);\n      } else {\n        inputTensorsToSave = inputsToSave.map((inputName) => inputs[inputName]);\n      }\n      const outputTensorsToSave = outputs.filter((_, i2) => outputsToSave[i2]);\n      return inputTensorsToSave.concat(outputTensorsToSave);\n    }\n    return [];\n  }\n  makeTensor(values, shape, dtype, backend2) {\n    if (values == null) {\n      throw new Error(\"Values passed to engine.makeTensor() are null\");\n    }\n    dtype = dtype || \"float32\";\n    backend2 = backend2 || this.backend;\n    let backendVals = values;\n    if (dtype === \"string\" && isString(values[0])) {\n      backendVals = values.map((d) => encodeString(d));\n    }\n    const dataId = backend2.write(backendVals, shape, dtype);\n    const t2 = new Tensor(shape, dtype, dataId, this.nextTensorId());\n    this.trackTensor(t2, backend2);\n    if (dtype === \"string\") {\n      const info = this.state.tensorInfo.get(dataId);\n      const newBytes = bytesFromStringArray(backendVals);\n      this.state.numBytes += newBytes - info.bytes;\n      info.bytes = newBytes;\n    }\n    return t2;\n  }\n  makeTensorFromDataId(dataId, shape, dtype, backend2) {\n    dtype = dtype || \"float32\";\n    const tensorInfo = { dataId, shape, dtype };\n    return this.makeTensorFromTensorInfo(tensorInfo, backend2);\n  }\n  makeTensorFromTensorInfo(tensorInfo, backend2) {\n    const { dataId, shape, dtype } = tensorInfo;\n    const t2 = new Tensor(shape, dtype, dataId, this.nextTensorId());\n    this.trackTensor(t2, backend2);\n    return t2;\n  }\n  makeVariable(initialValue, trainable = true, name, dtype) {\n    name = name || this.nextVariableId().toString();\n    if (dtype != null && dtype !== initialValue.dtype) {\n      initialValue = initialValue.cast(dtype);\n    }\n    const v = new Variable(initialValue, trainable, name, this.nextTensorId());\n    if (this.state.registeredVariables[v.name] != null) {\n      throw new Error(`Variable with name ${v.name} was already registered`);\n    }\n    this.state.registeredVariables[v.name] = v;\n    this.incRef(v, this.backend);\n    return v;\n  }\n  trackTensor(a, backend2) {\n    this.state.numTensors++;\n    if (a.dtype === \"string\") {\n      this.state.numStringTensors++;\n    }\n    let bytes = 0;\n    if (a.dtype !== \"complex64\" && a.dtype !== \"string\") {\n      bytes = a.size * bytesPerElement(a.dtype);\n    }\n    this.state.numBytes += bytes;\n    if (!this.state.tensorInfo.has(a.dataId)) {\n      this.state.numDataBuffers++;\n      this.state.tensorInfo.set(a.dataId, {\n        backend: backend2 || this.backend,\n        dtype: a.dtype,\n        shape: a.shape,\n        bytes\n      });\n    }\n    if (!(a instanceof Variable)) {\n      this.track(a);\n    }\n  }\n  incRef(a, backend2) {\n    this.trackTensor(a, backend2);\n    this.backend.incRef(a.dataId);\n  }\n  removeDataId(dataId, backend2) {\n    if (this.state.tensorInfo.has(dataId) && this.state.tensorInfo.get(dataId).backend === backend2) {\n      this.state.tensorInfo.delete(dataId);\n      this.state.numDataBuffers--;\n    }\n  }\n  disposeTensor(a) {\n    if (!this.state.tensorInfo.has(a.dataId)) {\n      return;\n    }\n    const info = this.state.tensorInfo.get(a.dataId);\n    this.state.numTensors--;\n    if (a.dtype === \"string\") {\n      this.state.numStringTensors--;\n      this.state.numBytes -= info.bytes;\n    }\n    if (a.dtype !== \"complex64\" && a.dtype !== \"string\") {\n      const bytes = a.size * bytesPerElement(a.dtype);\n      this.state.numBytes -= bytes;\n    }\n    if (info.backend.disposeData(a.dataId)) {\n      this.removeDataId(a.dataId, info.backend);\n    }\n  }\n  disposeVariables() {\n    for (const varName in this.state.registeredVariables) {\n      const v = this.state.registeredVariables[varName];\n      this.disposeVariable(v);\n    }\n  }\n  disposeVariable(v) {\n    this.disposeTensor(v);\n    if (this.state.registeredVariables[v.name] != null) {\n      delete this.state.registeredVariables[v.name];\n    }\n  }\n  memory() {\n    const info = this.backend.memory();\n    info.numTensors = this.state.numTensors;\n    info.numDataBuffers = this.state.numDataBuffers;\n    info.numBytes = this.state.numBytes;\n    if (this.state.numStringTensors > 0) {\n      info.unreliable = true;\n      if (info.reasons == null) {\n        info.reasons = [];\n      }\n      info.reasons.push(\"Memory usage by string tensors is approximate (2 bytes per character)\");\n    }\n    return info;\n  }\n  async profile(query) {\n    this.state.profiling = true;\n    const startBytes = this.state.numBytes;\n    const startNumTensors = this.state.numTensors;\n    this.state.activeProfile.kernels = [];\n    this.state.activeProfile.result = await query();\n    this.state.profiling = false;\n    this.state.activeProfile.peakBytes = Math.max(...this.state.activeProfile.kernels.map((d) => d.totalBytesSnapshot));\n    this.state.activeProfile.newBytes = this.state.numBytes - startBytes;\n    this.state.activeProfile.newTensors = this.state.numTensors - startNumTensors;\n    for (const kernel of this.state.activeProfile.kernels) {\n      kernel.kernelTimeMs = await kernel.kernelTimeMs;\n      kernel.extraInfo = await kernel.extraInfo;\n    }\n    return this.state.activeProfile;\n  }\n  isTapeOn() {\n    return this.state.gradientDepth > 0 && this.state.kernelDepth === 0;\n  }\n  addTapeNode(kernelName, inputs, outputs, gradientsFunc, saved, attrs) {\n    const tapeNode = { id: this.state.nextTapeNodeId++, kernelName, inputs, outputs, saved };\n    const gradConfig = getGradient(kernelName);\n    if (gradConfig != null) {\n      gradientsFunc = gradConfig.gradFunc;\n    }\n    if (gradientsFunc != null) {\n      tapeNode.gradient = (dys) => {\n        dys = dys.map((dy, i2) => {\n          if (dy == null) {\n            const output = outputs[i2];\n            const vals = makeZerosTypedArray(output.size, output.dtype);\n            return this.makeTensor(vals, output.shape, output.dtype);\n          }\n          return dy;\n        });\n        return gradientsFunc(dys.length > 1 ? dys : dys[0], saved, attrs);\n      };\n    }\n    this.state.activeTape.push(tapeNode);\n  }\n  keep(result) {\n    result.kept = true;\n    return result;\n  }\n  startTape() {\n    if (this.state.gradientDepth === 0) {\n      this.state.activeTape = [];\n    }\n    this.state.gradientDepth++;\n  }\n  endTape() {\n    this.state.gradientDepth--;\n  }\n  startScope(name) {\n    const scopeInfo = {\n      track: [],\n      name: \"unnamed scope\",\n      id: this.state.nextScopeId++\n    };\n    if (name) {\n      scopeInfo.name = name;\n    }\n    this.state.scopeStack.push(scopeInfo);\n    this.state.activeScope = scopeInfo;\n  }\n  endScope(result) {\n    const tensorsToTrackInParent = getTensorsInContainer(result);\n    const tensorsToTrackInParentSet = new Set(tensorsToTrackInParent.map((t2) => t2.id));\n    for (let i2 = 0; i2 < this.state.activeScope.track.length; i2++) {\n      const tensor2 = this.state.activeScope.track[i2];\n      if (!tensor2.kept && !tensorsToTrackInParentSet.has(tensor2.id)) {\n        tensor2.dispose();\n      }\n    }\n    const oldScope = this.state.scopeStack.pop();\n    this.state.activeScope = this.state.scopeStack.length === 0 ? null : this.state.scopeStack[this.state.scopeStack.length - 1];\n    tensorsToTrackInParent.forEach((tensor2) => {\n      if (!tensor2.kept && tensor2.scopeId === oldScope.id) {\n        this.track(tensor2);\n      }\n    });\n  }\n  gradients(f, xs, dy, allowNoGradients = false) {\n    assert(xs.length > 0, () => \"gradients() received an empty list of xs.\");\n    if (dy != null && dy.dtype !== \"float32\") {\n      throw new Error(`dy must have 'float32' dtype, but has '${dy.dtype}'`);\n    }\n    const y = this.scopedRun(() => this.startTape(), () => this.endTape(), () => this.tidy(\"forward\", f));\n    assert(y instanceof Tensor, () => \"The result y returned by f() must be a tensor.\");\n    const filteredTape = getFilteredNodesXToY(this.state.activeTape, xs, y);\n    if (!allowNoGradients && filteredTape.length === 0 && xs.length > 0) {\n      throw new Error(\"Cannot compute gradient of y=f(x) with respect to x. Make sure that the f you passed encloses all operations that lead from x to y.\");\n    }\n    return this.tidy(\"backward\", () => {\n      const accumulatedGradientMap = {};\n      accumulatedGradientMap[y.id] = dy == null ? ones(y.shape) : dy;\n      backpropagateGradients(\n        accumulatedGradientMap,\n        filteredTape,\n        (f2) => this.tidy(f2),\n        add\n      );\n      const grads2 = xs.map((x) => accumulatedGradientMap[x.id]);\n      if (this.state.gradientDepth === 0) {\n        this.state.activeTape.forEach((node) => {\n          for (const tensor2 of node.saved) {\n            tensor2.dispose();\n          }\n        });\n        this.state.activeTape = null;\n      }\n      return { value: y, grads: grads2 };\n    });\n  }\n  customGrad(f) {\n    assert(isFunction(f), () => \"The f passed in customGrad(f) must be a function.\");\n    return (...inputs) => {\n      assert(inputs.every((t2) => t2 instanceof Tensor), () => \"The args passed in customGrad(f)(x1, x2,...) must all be tensors\");\n      let res;\n      const inputMap = {};\n      inputs.forEach((input2, i2) => {\n        inputMap[i2] = input2;\n      });\n      const forwardFunc = (_, save) => {\n        res = f(...[...inputs, save]);\n        assert(res.value instanceof Tensor, () => \"The function f passed in customGrad(f) must return an object where `obj.value` is a tensor\");\n        assert(isFunction(res.gradFunc), () => \"The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function.\");\n        return res.value;\n      };\n      const backwardsFunc = (dy, saved) => {\n        const gradRes = res.gradFunc(dy, saved);\n        const grads2 = Array.isArray(gradRes) ? gradRes : [gradRes];\n        assert(grads2.length === inputs.length, () => \"The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function that returns the same number of tensors as inputs passed to f(...).\");\n        assert(grads2.every((t2) => t2 instanceof Tensor), () => \"The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function that returns a list of only tensors.\");\n        const gradMap = {};\n        grads2.forEach((grad2, i2) => {\n          gradMap[i2] = () => grad2;\n        });\n        return gradMap;\n      };\n      return this.runKernelFunc({\n        forwardFunc,\n        backwardsFunc,\n        inputs: inputMap\n      });\n    };\n  }\n  readSync(dataId) {\n    const info = this.state.tensorInfo.get(dataId);\n    return info.backend.readSync(dataId);\n  }\n  read(dataId) {\n    const info = this.state.tensorInfo.get(dataId);\n    return info.backend.read(dataId);\n  }\n  readToGPU(dataId, options) {\n    const info = this.state.tensorInfo.get(dataId);\n    return info.backend.readToGPU(dataId, options);\n  }\n  async time(query) {\n    const start = now();\n    const timingInfo = await this.backend.time(query);\n    timingInfo.wallMs = now() - start;\n    return timingInfo;\n  }\n  track(result) {\n    if (this.state.activeScope != null) {\n      result.scopeId = this.state.activeScope.id;\n      this.state.activeScope.track.push(result);\n    }\n    return result;\n  }\n  get registeredVariables() {\n    return this.state.registeredVariables;\n  }\n  reset() {\n    this.pendingBackendInitId++;\n    this.state.dispose();\n    this.ENV.reset();\n    this.state = new EngineState();\n    for (const backendName in this.registry) {\n      this.disposeRegisteredKernels(backendName);\n      this.registry[backendName].dispose();\n      delete this.registry[backendName];\n    }\n    this.backendName = null;\n    this.backendInstance = null;\n    this.pendingBackendInit = null;\n  }\n};\nEngine.nextTensorId = 0;\nEngine.nextVariableId = 0;\nfunction ones(shape) {\n  const values = makeOnesTypedArray(sizeFromShape(shape), \"float32\");\n  return ENGINE.makeTensor(values, shape, \"float32\");\n}\nfunction getOrMakeEngine() {\n  const ns = getGlobalNamespace();\n  if (ns._tfengine == null) {\n    const environment = new Environment(ns);\n    ns._tfengine = new Engine(environment);\n  }\n  setEnvironmentGlobal(ns._tfengine.ENV);\n  setTensorTracker(() => ns._tfengine);\n  return ns._tfengine;\n}\nvar ENGINE = getOrMakeEngine();\nfunction add(a, b) {\n  const inputs = { a, b };\n  return ENGINE.runKernel(Add, inputs);\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/device_util.js\nvar device_util_exports = {};\n__export(device_util_exports, {\n  isBrowser: () => isBrowser,\n  isMobile: () => isMobile,\n  mockIsMobile: () => mockIsMobile\n});\nfunction _isNavigatorDefined() {\n  return typeof navigator !== \"undefined\" && navigator != null;\n}\nvar isMobileMockValue;\nfunction mockIsMobile(value) {\n  isMobileMockValue = value;\n}\nfunction isMobile(nav) {\n  if (isMobileMockValue !== void 0) {\n    return isMobileMockValue;\n  }\n  if (nav || _isNavigatorDefined()) {\n    if (!nav) {\n      nav = navigator;\n    }\n    if (nav.product === \"ReactNative\") {\n      return true;\n    }\n    const a = nav.userAgent || nav.vendor || (typeof window !== \"undefined\" ? window.opera : \"\");\n    if (!a) {\n      const navAny = nav;\n      return navAny.userAgentData && navAny.userAgentData.mobile;\n    }\n    return /(android|bb\\d+|meego).+mobile|avantgo|bada\\/|blackberry|blazer|compal|elaine|fennec|hiptop|iemobile|ip(hone|od)|iris|kindle|lge |maemo|midp|mmp|mobile.+firefox|netfront|opera m(ob|in)i|palm( os)?|phone|p(ixi|re)\\/|plucker|pocket|psp|series(4|6)0|symbian|treo|up\\.(browser|link)|vodafone|wap|windows ce|xda|xiino/i.test(a) || /1207|6310|6590|3gso|4thp|50[1-6]i|770s|802s|a wa|abac|ac(er|oo|s\\-)|ai(ko|rn)|al(av|ca|co)|amoi|an(ex|ny|yw)|aptu|ar(ch|go)|as(te|us)|attw|au(di|\\-m|r |s )|avan|be(ck|ll|nq)|bi(lb|rd)|bl(ac|az)|br(e|v)w|bumb|bw\\-(n|u)|c55\\/|capi|ccwa|cdm\\-|cell|chtm|cldc|cmd\\-|co(mp|nd)|craw|da(it|ll|ng)|dbte|dc\\-s|devi|dica|dmob|do(c|p)o|ds(12|\\-d)|el(49|ai)|em(l2|ul)|er(ic|k0)|esl8|ez([4-7]0|os|wa|ze)|fetc|fly(\\-|_)|g1 u|g560|gene|gf\\-5|g\\-mo|go(\\.w|od)|gr(ad|un)|haie|hcit|hd\\-(m|p|t)|hei\\-|hi(pt|ta)|hp( i|ip)|hs\\-c|ht(c(\\-| |_|a|g|p|s|t)|tp)|hu(aw|tc)|i\\-(20|go|ma)|i230|iac( |\\-|\\/)|ibro|idea|ig01|ikom|im1k|inno|ipaq|iris|ja(t|v)a|jbro|jemu|jigs|kddi|keji|kgt( |\\/)|klon|kpt |kwc\\-|kyo(c|k)|le(no|xi)|lg( g|\\/(k|l|u)|50|54|\\-[a-w])|libw|lynx|m1\\-w|m3ga|m50\\/|ma(te|ui|xo)|mc(01|21|ca)|m\\-cr|me(rc|ri)|mi(o8|oa|ts)|mmef|mo(01|02|bi|de|do|t(\\-| |o|v)|zz)|mt(50|p1|v )|mwbp|mywa|n10[0-2]|n20[2-3]|n30(0|2)|n50(0|2|5)|n7(0(0|1)|10)|ne((c|m)\\-|on|tf|wf|wg|wt)|nok(6|i)|nzph|o2im|op(ti|wv)|oran|owg1|p800|pan(a|d|t)|pdxg|pg(13|\\-([1-8]|c))|phil|pire|pl(ay|uc)|pn\\-2|po(ck|rt|se)|prox|psio|pt\\-g|qa\\-a|qc(07|12|21|32|60|\\-[2-7]|i\\-)|qtek|r380|r600|raks|rim9|ro(ve|zo)|s55\\/|sa(ge|ma|mm|ms|ny|va)|sc(01|h\\-|oo|p\\-)|sdk\\/|se(c(\\-|0|1)|47|mc|nd|ri)|sgh\\-|shar|sie(\\-|m)|sk\\-0|sl(45|id)|sm(al|ar|b3|it|t5)|so(ft|ny)|sp(01|h\\-|v\\-|v )|sy(01|mb)|t2(18|50)|t6(00|10|18)|ta(gt|lk)|tcl\\-|tdg\\-|tel(i|m)|tim\\-|t\\-mo|to(pl|sh)|ts(70|m\\-|m3|m5)|tx\\-9|up(\\.b|g1|si)|utst|v400|v750|veri|vi(rg|te)|vk(40|5[0-3]|\\-v)|vm40|voda|vulc|vx(52|53|60|61|70|80|81|83|85|98)|w3c(\\-| )|webc|whit|wi(g |nc|nw)|wmlb|wonu|x700|yas\\-|your|zeto|zte\\-/i.test(a.substr(0, 4));\n  }\n  return false;\n}\nfunction isBrowser() {\n  return typeof window !== \"undefined\" && window.document != null || typeof WorkerGlobalScope !== \"undefined\";\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/flags.js\nvar ENV2 = env();\nENV2.registerFlag(\"DEBUG\", () => false, (debugValue) => {\n  if (debugValue) {\n    console.warn(\"Debugging mode is ON. The output of every math call will be downloaded to CPU and checked for NaNs. This significantly impacts performance.\");\n  }\n});\nENV2.registerFlag(\"IS_BROWSER\", () => isBrowser());\nENV2.registerFlag(\"IS_NODE\", () => typeof process !== \"undefined\" && typeof process.versions !== \"undefined\" && typeof process.versions.node !== \"undefined\");\nENV2.registerFlag(\"IS_CHROME\", () => typeof navigator !== \"undefined\" && navigator != null && navigator.userAgent != null && /Chrome/.test(navigator.userAgent) && /Google Inc/.test(navigator.vendor));\nENV2.registerFlag(\"PROD\", () => false);\nENV2.registerFlag(\"TENSORLIKE_CHECK_SHAPE_CONSISTENCY\", () => ENV2.getBool(\"DEBUG\"));\nENV2.registerFlag(\"DEPRECATION_WARNINGS_ENABLED\", () => true);\nENV2.registerFlag(\"IS_TEST\", () => false);\nENV2.registerFlag(\"CHECK_COMPUTATION_FOR_ERRORS\", () => true);\nENV2.registerFlag(\"WRAP_TO_IMAGEBITMAP\", () => false);\nENV2.registerFlag(\"ENGINE_COMPILE_ONLY\", () => false);\nENV2.registerFlag(\"CANVAS2D_WILL_READ_FREQUENTLY_FOR_GPU\", () => false);\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/tensor_util_env.js\nfunction inferShape(val, dtype) {\n  let firstElem = val;\n  if (isTypedArray(val)) {\n    return dtype === \"string\" ? [] : [val.length];\n  }\n  if (!Array.isArray(val)) {\n    return [];\n  }\n  const shape = [];\n  while (Array.isArray(firstElem) || isTypedArray(firstElem) && dtype !== \"string\") {\n    shape.push(firstElem.length);\n    firstElem = firstElem[0];\n  }\n  if (Array.isArray(val) && env().getBool(\"TENSORLIKE_CHECK_SHAPE_CONSISTENCY\")) {\n    deepAssertShapeConsistency(val, shape, []);\n  }\n  return shape;\n}\nfunction deepAssertShapeConsistency(val, shape, indices) {\n  indices = indices || [];\n  if (!Array.isArray(val) && !isTypedArray(val)) {\n    assert(shape.length === 0, () => `Element arr[${indices.join(\"][\")}] is a primitive, but should be an array/TypedArray of ${shape[0]} elements`);\n    return;\n  }\n  assert(shape.length > 0, () => `Element arr[${indices.join(\"][\")}] should be a primitive, but is an array of ${val.length} elements`);\n  assert(val.length === shape[0], () => `Element arr[${indices.join(\"][\")}] should have ${shape[0]} elements, but has ${val.length} elements`);\n  const subShape = shape.slice(1);\n  for (let i2 = 0; i2 < val.length; ++i2) {\n    deepAssertShapeConsistency(val[i2], subShape, indices.concat(i2));\n  }\n}\nfunction assertDtype(expectedDtype, actualDType, argName, functionName) {\n  if (expectedDtype === \"string_or_numeric\") {\n    return;\n  }\n  if (expectedDtype == null) {\n    throw new Error(`Expected dtype cannot be null.`);\n  }\n  if (expectedDtype !== \"numeric\" && expectedDtype !== actualDType || expectedDtype === \"numeric\" && actualDType === \"string\") {\n    throw new Error(`Argument '${argName}' passed to '${functionName}' must be ${expectedDtype} tensor, but got ${actualDType} tensor`);\n  }\n}\nfunction convertToTensor(x, argName, functionName, parseAsDtype = \"numeric\") {\n  if (x instanceof Tensor) {\n    assertDtype(parseAsDtype, x.dtype, argName, functionName);\n    return x;\n  }\n  let inferredDtype = inferDtype(x);\n  if (inferredDtype !== \"string\" && [\"bool\", \"int32\", \"float32\"].indexOf(parseAsDtype) >= 0) {\n    inferredDtype = parseAsDtype;\n  }\n  assertDtype(parseAsDtype, inferredDtype, argName, functionName);\n  if (x == null || !isTypedArray(x) && !Array.isArray(x) && typeof x !== \"number\" && typeof x !== \"boolean\" && typeof x !== \"string\") {\n    const type = x == null ? \"null\" : x.constructor.name;\n    throw new Error(`Argument '${argName}' passed to '${functionName}' must be a Tensor or TensorLike, but got '${type}'`);\n  }\n  const inferredShape = inferShape(x, inferredDtype);\n  if (!isTypedArray(x) && !Array.isArray(x)) {\n    x = [x];\n  }\n  const skipTypedArray = true;\n  const values = inferredDtype !== \"string\" ? toTypedArray(x, inferredDtype) : flatten(x, [], skipTypedArray);\n  return ENGINE.makeTensor(values, inferredShape, inferredDtype);\n}\nfunction convertToTensorArray(arg, argName, functionName, parseAsDtype = \"numeric\") {\n  if (!Array.isArray(arg)) {\n    throw new Error(`Argument ${argName} passed to ${functionName} must be a \\`Tensor[]\\` or \\`TensorLike[]\\``);\n  }\n  const tensors = arg;\n  return tensors.map((t2, i2) => convertToTensor(t2, `${argName}[${i2}]`, functionName, parseAsDtype));\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/operation.js\nvar OP_SCOPE_SUFFIX = \"__op\";\nfunction op(f) {\n  const keys = Object.keys(f);\n  if (keys.length !== 1) {\n    throw new Error(`Please provide an object with a single key (operation name) mapping to a function. Got an object with ${keys.length} keys.`);\n  }\n  let opName = keys[0];\n  const fn = f[opName];\n  if (opName.endsWith(\"_\")) {\n    opName = opName.substring(0, opName.length - 1);\n  }\n  opName = opName + OP_SCOPE_SUFFIX;\n  const f2 = (...args) => {\n    ENGINE.startScope(opName);\n    try {\n      const result = fn(...args);\n      if (isPromise(result)) {\n        console.error(\"Cannot return a Promise inside of tidy.\");\n      }\n      ENGINE.endScope(result);\n      return result;\n    } catch (ex) {\n      ENGINE.endScope(null);\n      throw ex;\n    }\n  };\n  Object.defineProperty(f2, \"name\", { value: opName, configurable: true });\n  return f2;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/complex.js\nfunction complex_(real5, imag5) {\n  const $real = convertToTensor(real5, \"real\", \"complex\");\n  const $imag = convertToTensor(imag5, \"imag\", \"complex\");\n  assertShapesMatch($real.shape, $imag.shape, `real and imag shapes, ${$real.shape} and ${$imag.shape}, must match in call to tf.complex().`);\n  const inputs = { real: $real, imag: $imag };\n  return ENGINE.runKernel(Complex, inputs);\n}\nvar complex = op({ complex_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/tensor_ops_util.js\nfunction makeTensor(values, shape, inferredShape, dtype) {\n  if (dtype == null) {\n    dtype = inferDtype(values);\n  }\n  if (dtype === \"complex64\") {\n    throw new Error(`Cannot construct a complex64 tensor directly. Please use tf.complex(real, imag).`);\n  }\n  if (!isTypedArray(values) && !Array.isArray(values) && typeof values !== \"number\" && typeof values !== \"boolean\" && typeof values !== \"string\") {\n    throw new Error(\"values passed to tensor(values) must be a number/boolean/string or an array of numbers/booleans/strings, or a TypedArray\");\n  }\n  if (shape != null) {\n    assertNonNegativeIntegerDimensions(shape);\n    const providedSize = sizeFromShape(shape);\n    const inferredSize = sizeFromShape(inferredShape);\n    assert(providedSize === inferredSize, () => `Based on the provided shape, [${shape}], the tensor should have ${providedSize} values but has ${inferredSize}`);\n    for (let i2 = 0; i2 < inferredShape.length; ++i2) {\n      const inferred = inferredShape[i2];\n      const flatDimsDontMatch = i2 === inferredShape.length - 1 ? inferred !== sizeFromShape(shape.slice(i2)) : true;\n      assert(inferredShape[i2] === shape[i2] || !flatDimsDontMatch, () => `Error creating a new Tensor. Inferred shape (${inferredShape}) does not match the provided shape (${shape}). `);\n    }\n  }\n  if (!isTypedArray(values) && !Array.isArray(values)) {\n    values = [values];\n  }\n  shape = shape || inferredShape;\n  values = dtype !== \"string\" ? toTypedArray(values, dtype) : flatten(values, [], true);\n  return ENGINE.makeTensor(values, shape, dtype);\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/tensor.js\nfunction tensor(values, shape, dtype) {\n  const inferredShape = inferShape(values, dtype);\n  return makeTensor(values, shape, inferredShape, dtype);\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/io/types.js\nvar DTYPE_VALUE_SIZE_MAP = {\n  \"float32\": 4,\n  \"float16\": 2,\n  \"int32\": 4,\n  \"uint16\": 2,\n  \"uint8\": 1,\n  \"bool\": 1,\n  \"complex64\": 8\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/io/io_utils.js\nvar NUM_BYTES_STRING_LENGTH = 4;\nasync function encodeWeights(tensors, group) {\n  const specs = [];\n  const dataPromises = [];\n  const names = Array.isArray(tensors) ? tensors.map((tensor2) => tensor2.name) : Object.keys(tensors);\n  for (let i2 = 0; i2 < names.length; ++i2) {\n    const name = names[i2];\n    const t2 = Array.isArray(tensors) ? tensors[i2].tensor : tensors[name];\n    if (t2.dtype !== \"float32\" && t2.dtype !== \"int32\" && t2.dtype !== \"bool\" && t2.dtype !== \"string\" && t2.dtype !== \"complex64\") {\n      throw new Error(`Unsupported dtype in weight '${name}': ${t2.dtype}`);\n    }\n    const spec = { name, shape: t2.shape, dtype: t2.dtype };\n    if (t2.dtype === \"string\") {\n      const utf8bytes = new Promise(async (resolve) => {\n        const vals = await t2.bytes();\n        const totalNumBytes = vals.reduce((p2, c) => p2 + c.length, 0) + NUM_BYTES_STRING_LENGTH * vals.length;\n        const bytes = new Uint8Array(totalNumBytes);\n        let offset = 0;\n        for (let i3 = 0; i3 < vals.length; i3++) {\n          const val = vals[i3];\n          const bytesOfLength = new Uint8Array(new Uint32Array([val.length]).buffer);\n          bytes.set(bytesOfLength, offset);\n          offset += NUM_BYTES_STRING_LENGTH;\n          bytes.set(val, offset);\n          offset += val.length;\n        }\n        resolve(bytes);\n      });\n      dataPromises.push(utf8bytes);\n    } else {\n      dataPromises.push(t2.data());\n    }\n    if (group != null) {\n      spec.group = group;\n    }\n    specs.push(spec);\n  }\n  const tensorValues = await Promise.all(dataPromises);\n  return { data: concatenateTypedArrays(tensorValues), specs };\n}\nfunction decodeWeights(buffer2, specs) {\n  const out = {};\n  let float16Decode;\n  let offset = 0;\n  for (const spec of specs) {\n    const name = spec.name;\n    const dtype = spec.dtype;\n    const shape = spec.shape;\n    const size = sizeFromShape(shape);\n    let values;\n    if (\"quantization\" in spec) {\n      const quantization = spec.quantization;\n      if (quantization.dtype === \"uint8\" || quantization.dtype === \"uint16\") {\n        if (!(\"min\" in quantization && \"scale\" in quantization)) {\n          throw new Error(`Weight ${spec.name} with quantization ${quantization.dtype} doesn't have corresponding metadata min and scale.`);\n        }\n      } else if (quantization.dtype === \"float16\") {\n        if (dtype !== \"float32\") {\n          throw new Error(`Weight ${spec.name} is quantized with ${quantization.dtype} which only supports weights of type float32 not ${dtype}.`);\n        }\n      } else {\n        throw new Error(`Weight ${spec.name} has unknown quantization dtype ${quantization.dtype}. Supported quantization dtypes are: 'uint8', 'uint16', and 'float16'.`);\n      }\n      const quantizationSizeFactor = DTYPE_VALUE_SIZE_MAP[quantization.dtype];\n      const byteBuffer = buffer2.slice(offset, offset + size * quantizationSizeFactor);\n      const quantizedArray = quantization.dtype === \"uint8\" ? new Uint8Array(byteBuffer) : new Uint16Array(byteBuffer);\n      if (dtype === \"float32\") {\n        if (quantization.dtype === \"uint8\" || quantization.dtype === \"uint16\") {\n          values = new Float32Array(quantizedArray.length);\n          for (let i2 = 0; i2 < quantizedArray.length; i2++) {\n            const v = quantizedArray[i2];\n            values[i2] = v * quantization.scale + quantization.min;\n          }\n        } else if (quantization.dtype === \"float16\") {\n          if (float16Decode === void 0) {\n            float16Decode = getFloat16Decoder();\n          }\n          values = float16Decode(quantizedArray);\n        } else {\n          throw new Error(`Unsupported quantization type ${quantization.dtype} for weight type float32.`);\n        }\n      } else if (dtype === \"int32\") {\n        if (quantization.dtype !== \"uint8\" && quantization.dtype !== \"uint16\") {\n          throw new Error(`Unsupported quantization type ${quantization.dtype} for weight type int32.`);\n        }\n        values = new Int32Array(quantizedArray.length);\n        for (let i2 = 0; i2 < quantizedArray.length; i2++) {\n          const v = quantizedArray[i2];\n          values[i2] = Math.round(v * quantization.scale + quantization.min);\n        }\n      } else {\n        throw new Error(`Unsupported dtype in weight '${name}': ${dtype}`);\n      }\n      offset += size * quantizationSizeFactor;\n    } else if (dtype === \"string\") {\n      const size2 = sizeFromShape(spec.shape);\n      values = [];\n      for (let i2 = 0; i2 < size2; i2++) {\n        const byteLength = new Uint32Array(buffer2.slice(offset, offset + NUM_BYTES_STRING_LENGTH))[0];\n        offset += NUM_BYTES_STRING_LENGTH;\n        const bytes = new Uint8Array(buffer2.slice(offset, offset + byteLength));\n        values.push(bytes);\n        offset += byteLength;\n      }\n    } else {\n      const dtypeFactor = DTYPE_VALUE_SIZE_MAP[dtype];\n      const byteBuffer = buffer2.slice(offset, offset + size * dtypeFactor);\n      if (dtype === \"float32\") {\n        values = new Float32Array(byteBuffer);\n      } else if (dtype === \"int32\") {\n        values = new Int32Array(byteBuffer);\n      } else if (dtype === \"bool\") {\n        values = new Uint8Array(byteBuffer);\n      } else if (dtype === \"complex64\") {\n        values = new Float32Array(byteBuffer);\n        const real5 = new Float32Array(values.length / 2);\n        const image2 = new Float32Array(values.length / 2);\n        for (let i2 = 0; i2 < real5.length; i2++) {\n          real5[i2] = values[i2 * 2];\n          image2[i2] = values[i2 * 2 + 1];\n        }\n        const realTensor = tensor(real5, shape, \"float32\");\n        const imageTensor = tensor(image2, shape, \"float32\");\n        out[name] = complex(realTensor, imageTensor);\n        realTensor.dispose();\n        imageTensor.dispose();\n      } else {\n        throw new Error(`Unsupported dtype in weight '${name}': ${dtype}`);\n      }\n      offset += size * dtypeFactor;\n    }\n    if (dtype !== \"complex64\") {\n      out[name] = tensor(values, shape, dtype);\n    }\n  }\n  return out;\n}\nfunction concatenateTypedArrays(xs) {\n  if (xs === null) {\n    throw new Error(`Invalid input value: ${JSON.stringify(xs)}`);\n  }\n  let totalByteLength = 0;\n  const normalizedXs = [];\n  xs.forEach((x) => {\n    totalByteLength += x.byteLength;\n    normalizedXs.push(x.byteLength === x.buffer.byteLength ? x : new x.constructor(x));\n    if (!(x instanceof Float32Array || x instanceof Int32Array || x instanceof Uint8Array)) {\n      throw new Error(`Unsupported TypedArray subtype: ${x.constructor.name}`);\n    }\n  });\n  const y = new Uint8Array(totalByteLength);\n  let offset = 0;\n  normalizedXs.forEach((x) => {\n    y.set(new Uint8Array(x.buffer), offset);\n    offset += x.byteLength;\n  });\n  return y.buffer;\n}\nvar useNodeBuffer = typeof Buffer !== \"undefined\" && (typeof Blob === \"undefined\" || typeof atob === \"undefined\" || typeof btoa === \"undefined\");\nfunction stringByteLength(str) {\n  if (useNodeBuffer) {\n    return Buffer.byteLength(str);\n  }\n  return new Blob([str]).size;\n}\nfunction arrayBufferToBase64String(buffer2) {\n  if (useNodeBuffer) {\n    return Buffer.from(buffer2).toString(\"base64\");\n  }\n  const buf = new Uint8Array(buffer2);\n  let s2 = \"\";\n  for (let i2 = 0, l3 = buf.length; i2 < l3; i2++) {\n    s2 += String.fromCharCode(buf[i2]);\n  }\n  return btoa(s2);\n}\nfunction base64StringToArrayBuffer(str) {\n  if (useNodeBuffer) {\n    const buf = Buffer.from(str, \"base64\");\n    return buf.buffer.slice(buf.byteOffset, buf.byteOffset + buf.byteLength);\n  }\n  const s2 = atob(str);\n  const buffer2 = new Uint8Array(s2.length);\n  for (let i2 = 0; i2 < s2.length; ++i2) {\n    buffer2.set([s2.charCodeAt(i2)], i2);\n  }\n  return buffer2.buffer;\n}\nfunction concatenateArrayBuffers(buffers) {\n  if (buffers.length === 1) {\n    return buffers[0];\n  }\n  let totalByteLength = 0;\n  buffers.forEach((buffer2) => {\n    totalByteLength += buffer2.byteLength;\n  });\n  const temp = new Uint8Array(totalByteLength);\n  let offset = 0;\n  buffers.forEach((buffer2) => {\n    temp.set(new Uint8Array(buffer2), offset);\n    offset += buffer2.byteLength;\n  });\n  return temp.buffer;\n}\nfunction basename(path) {\n  const SEPARATOR = \"/\";\n  path = path.trim();\n  while (path.endsWith(SEPARATOR)) {\n    path = path.slice(0, path.length - 1);\n  }\n  const items = path.split(SEPARATOR);\n  return items[items.length - 1];\n}\nfunction getModelJSONForModelArtifacts(artifacts, manifest) {\n  const result = {\n    modelTopology: artifacts.modelTopology,\n    format: artifacts.format,\n    generatedBy: artifacts.generatedBy,\n    convertedBy: artifacts.convertedBy,\n    weightsManifest: manifest\n  };\n  if (artifacts.signature != null) {\n    result.signature = artifacts.signature;\n  }\n  if (artifacts.userDefinedMetadata != null) {\n    result.userDefinedMetadata = artifacts.userDefinedMetadata;\n  }\n  if (artifacts.modelInitializer != null) {\n    result.modelInitializer = artifacts.modelInitializer;\n  }\n  if (artifacts.trainingConfig != null) {\n    result.trainingConfig = artifacts.trainingConfig;\n  }\n  return result;\n}\nasync function getModelArtifactsForJSON(modelJSON, loadWeights2) {\n  const modelArtifacts = {\n    modelTopology: modelJSON.modelTopology,\n    format: modelJSON.format,\n    generatedBy: modelJSON.generatedBy,\n    convertedBy: modelJSON.convertedBy\n  };\n  if (modelJSON.trainingConfig != null) {\n    modelArtifacts.trainingConfig = modelJSON.trainingConfig;\n  }\n  if (modelJSON.weightsManifest != null) {\n    const [weightSpecs, weightData] = await loadWeights2(modelJSON.weightsManifest);\n    modelArtifacts.weightSpecs = weightSpecs;\n    modelArtifacts.weightData = weightData;\n  }\n  if (modelJSON.signature != null) {\n    modelArtifacts.signature = modelJSON.signature;\n  }\n  if (modelJSON.userDefinedMetadata != null) {\n    modelArtifacts.userDefinedMetadata = modelJSON.userDefinedMetadata;\n  }\n  if (modelJSON.modelInitializer != null) {\n    modelArtifacts.modelInitializer = modelJSON.modelInitializer;\n  }\n  return modelArtifacts;\n}\nfunction getModelArtifactsInfoForJSON(modelArtifacts) {\n  if (modelArtifacts.modelTopology instanceof ArrayBuffer) {\n    throw new Error(\"Expected JSON model topology, received ArrayBuffer.\");\n  }\n  return {\n    dateSaved: new Date(),\n    modelTopologyType: \"JSON\",\n    modelTopologyBytes: modelArtifacts.modelTopology == null ? 0 : stringByteLength(JSON.stringify(modelArtifacts.modelTopology)),\n    weightSpecsBytes: modelArtifacts.weightSpecs == null ? 0 : stringByteLength(JSON.stringify(modelArtifacts.weightSpecs)),\n    weightDataBytes: modelArtifacts.weightData == null ? 0 : modelArtifacts.weightData.byteLength\n  };\n}\nfunction computeFloat16MantisaTable() {\n  const convertMantissa = (i2) => {\n    let m = i2 << 13;\n    let e2 = 0;\n    while ((m & 8388608) === 0) {\n      e2 -= 8388608;\n      m <<= 1;\n    }\n    m &= ~8388608;\n    e2 += 947912704;\n    return m | e2;\n  };\n  const mantisaTable = new Uint32Array(2048);\n  mantisaTable[0] = 0;\n  for (let i2 = 1; i2 < 1024; i2++) {\n    mantisaTable[i2] = convertMantissa(i2);\n  }\n  for (let i2 = 1024; i2 < 2048; i2++) {\n    mantisaTable[i2] = 939524096 + (i2 - 1024 << 13);\n  }\n  return mantisaTable;\n}\nfunction computeFloat16ExponentTable() {\n  const exponentTable = new Uint32Array(64);\n  exponentTable[0] = 0;\n  exponentTable[31] = 1199570944;\n  exponentTable[32] = 2147483648;\n  exponentTable[63] = 3347054592;\n  for (let i2 = 1; i2 < 31; i2++) {\n    exponentTable[i2] = i2 << 23;\n  }\n  for (let i2 = 33; i2 < 63; i2++) {\n    exponentTable[i2] = 2147483648 + (i2 - 32 << 23);\n  }\n  return exponentTable;\n}\nfunction computeFloat16OffsetTable() {\n  const offsetTable = new Uint32Array(64);\n  for (let i2 = 0; i2 < 64; i2++) {\n    offsetTable[i2] = 1024;\n  }\n  offsetTable[0] = offsetTable[32] = 0;\n  return offsetTable;\n}\nfunction getFloat16Decoder() {\n  const mantisaTable = computeFloat16MantisaTable();\n  const exponentTable = computeFloat16ExponentTable();\n  const offsetTable = computeFloat16OffsetTable();\n  return (quantizedArray) => {\n    const buffer2 = new ArrayBuffer(4 * quantizedArray.length);\n    const bufferUint32View = new Uint32Array(buffer2);\n    for (let index = 0; index < quantizedArray.length; index++) {\n      const float16Bits = quantizedArray[index];\n      const float32Bits = mantisaTable[offsetTable[float16Bits >> 10] + (float16Bits & 1023)] + exponentTable[float16Bits >> 10];\n      bufferUint32View[index] = float32Bits;\n    }\n    return new Float32Array(buffer2);\n  };\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/io/router_registry.js\nvar IORouterRegistry = class {\n  constructor() {\n    this.saveRouters = [];\n    this.loadRouters = [];\n  }\n  static getInstance() {\n    if (IORouterRegistry.instance == null) {\n      IORouterRegistry.instance = new IORouterRegistry();\n    }\n    return IORouterRegistry.instance;\n  }\n  static registerSaveRouter(saveRouter) {\n    IORouterRegistry.getInstance().saveRouters.push(saveRouter);\n  }\n  static registerLoadRouter(loadRouter) {\n    IORouterRegistry.getInstance().loadRouters.push(loadRouter);\n  }\n  static getSaveHandlers(url) {\n    return IORouterRegistry.getHandlers(url, \"save\");\n  }\n  static getLoadHandlers(url, loadOptions) {\n    return IORouterRegistry.getHandlers(url, \"load\", loadOptions);\n  }\n  static getHandlers(url, handlerType, loadOptions) {\n    const validHandlers = [];\n    const routers = handlerType === \"load\" ? IORouterRegistry.getInstance().loadRouters : IORouterRegistry.getInstance().saveRouters;\n    routers.forEach((router) => {\n      const handler = router(url, loadOptions);\n      if (handler !== null) {\n        validHandlers.push(handler);\n      }\n    });\n    return validHandlers;\n  }\n};\nvar registerSaveRouter = (loudRouter) => IORouterRegistry.registerSaveRouter(loudRouter);\nvar registerLoadRouter = (loudRouter) => IORouterRegistry.registerLoadRouter(loudRouter);\nvar getSaveHandlers = (url) => IORouterRegistry.getSaveHandlers(url);\nvar getLoadHandlers = (url, loadOptions) => IORouterRegistry.getLoadHandlers(url, loadOptions);\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/io/indexed_db.js\nvar DATABASE_NAME = \"tensorflowjs\";\nvar DATABASE_VERSION = 1;\nvar MODEL_STORE_NAME = \"models_store\";\nvar INFO_STORE_NAME = \"model_info_store\";\nfunction getIndexedDBFactory() {\n  if (!env().getBool(\"IS_BROWSER\")) {\n    throw new Error(\"Failed to obtain IndexedDB factory because the current environmentis not a web browser.\");\n  }\n  const theWindow = typeof window === \"undefined\" ? self : window;\n  const factory = theWindow.indexedDB || theWindow.mozIndexedDB || theWindow.webkitIndexedDB || theWindow.msIndexedDB || theWindow.shimIndexedDB;\n  if (factory == null) {\n    throw new Error(\"The current browser does not appear to support IndexedDB.\");\n  }\n  return factory;\n}\nfunction setUpDatabase(openRequest) {\n  const db = openRequest.result;\n  db.createObjectStore(MODEL_STORE_NAME, { keyPath: \"modelPath\" });\n  db.createObjectStore(INFO_STORE_NAME, { keyPath: \"modelPath\" });\n}\nvar BrowserIndexedDB = class {\n  constructor(modelPath) {\n    this.indexedDB = getIndexedDBFactory();\n    if (modelPath == null || !modelPath) {\n      throw new Error(\"For IndexedDB, modelPath must not be null, undefined or empty.\");\n    }\n    this.modelPath = modelPath;\n  }\n  async save(modelArtifacts) {\n    if (modelArtifacts.modelTopology instanceof ArrayBuffer) {\n      throw new Error(\"BrowserLocalStorage.save() does not support saving model topology in binary formats yet.\");\n    }\n    return this.databaseAction(this.modelPath, modelArtifacts);\n  }\n  async load() {\n    return this.databaseAction(this.modelPath);\n  }\n  databaseAction(modelPath, modelArtifacts) {\n    return new Promise((resolve, reject) => {\n      const openRequest = this.indexedDB.open(DATABASE_NAME, DATABASE_VERSION);\n      openRequest.onupgradeneeded = () => setUpDatabase(openRequest);\n      openRequest.onsuccess = () => {\n        const db = openRequest.result;\n        if (modelArtifacts == null) {\n          const modelTx = db.transaction(MODEL_STORE_NAME, \"readonly\");\n          const modelStore = modelTx.objectStore(MODEL_STORE_NAME);\n          const getRequest = modelStore.get(this.modelPath);\n          getRequest.onsuccess = () => {\n            if (getRequest.result == null) {\n              db.close();\n              return reject(new Error(`Cannot find model with path '${this.modelPath}' in IndexedDB.`));\n            } else {\n              resolve(getRequest.result.modelArtifacts);\n            }\n          };\n          getRequest.onerror = (error) => {\n            db.close();\n            return reject(getRequest.error);\n          };\n          modelTx.oncomplete = () => db.close();\n        } else {\n          const modelArtifactsInfo = getModelArtifactsInfoForJSON(modelArtifacts);\n          const infoTx = db.transaction(INFO_STORE_NAME, \"readwrite\");\n          let infoStore = infoTx.objectStore(INFO_STORE_NAME);\n          const putInfoRequest = infoStore.put({ modelPath: this.modelPath, modelArtifactsInfo });\n          let modelTx;\n          putInfoRequest.onsuccess = () => {\n            modelTx = db.transaction(MODEL_STORE_NAME, \"readwrite\");\n            const modelStore = modelTx.objectStore(MODEL_STORE_NAME);\n            const putModelRequest = modelStore.put({\n              modelPath: this.modelPath,\n              modelArtifacts,\n              modelArtifactsInfo\n            });\n            putModelRequest.onsuccess = () => resolve({ modelArtifactsInfo });\n            putModelRequest.onerror = (error) => {\n              infoStore = infoTx.objectStore(INFO_STORE_NAME);\n              const deleteInfoRequest = infoStore.delete(this.modelPath);\n              deleteInfoRequest.onsuccess = () => {\n                db.close();\n                return reject(putModelRequest.error);\n              };\n              deleteInfoRequest.onerror = (error2) => {\n                db.close();\n                return reject(putModelRequest.error);\n              };\n            };\n          };\n          putInfoRequest.onerror = (error) => {\n            db.close();\n            return reject(putInfoRequest.error);\n          };\n          infoTx.oncomplete = () => {\n            if (modelTx == null) {\n              db.close();\n            } else {\n              modelTx.oncomplete = () => db.close();\n            }\n          };\n        }\n      };\n      openRequest.onerror = (error) => reject(openRequest.error);\n    });\n  }\n};\nBrowserIndexedDB.URL_SCHEME = \"indexeddb://\";\nvar indexedDBRouter = (url) => {\n  if (!env().getBool(\"IS_BROWSER\")) {\n    return null;\n  } else {\n    if (!Array.isArray(url) && url.startsWith(BrowserIndexedDB.URL_SCHEME)) {\n      return browserIndexedDB(url.slice(BrowserIndexedDB.URL_SCHEME.length));\n    } else {\n      return null;\n    }\n  }\n};\nIORouterRegistry.registerSaveRouter(indexedDBRouter);\nIORouterRegistry.registerLoadRouter(indexedDBRouter);\nfunction browserIndexedDB(modelPath) {\n  return new BrowserIndexedDB(modelPath);\n}\nfunction maybeStripScheme(key) {\n  return key.startsWith(BrowserIndexedDB.URL_SCHEME) ? key.slice(BrowserIndexedDB.URL_SCHEME.length) : key;\n}\nvar BrowserIndexedDBManager = class {\n  constructor() {\n    this.indexedDB = getIndexedDBFactory();\n  }\n  async listModels() {\n    return new Promise((resolve, reject) => {\n      const openRequest = this.indexedDB.open(DATABASE_NAME, DATABASE_VERSION);\n      openRequest.onupgradeneeded = () => setUpDatabase(openRequest);\n      openRequest.onsuccess = () => {\n        const db = openRequest.result;\n        const tx = db.transaction(INFO_STORE_NAME, \"readonly\");\n        const store = tx.objectStore(INFO_STORE_NAME);\n        const getAllInfoRequest = store.getAll();\n        getAllInfoRequest.onsuccess = () => {\n          const out = {};\n          for (const item of getAllInfoRequest.result) {\n            out[item.modelPath] = item.modelArtifactsInfo;\n          }\n          resolve(out);\n        };\n        getAllInfoRequest.onerror = (error) => {\n          db.close();\n          return reject(getAllInfoRequest.error);\n        };\n        tx.oncomplete = () => db.close();\n      };\n      openRequest.onerror = (error) => reject(openRequest.error);\n    });\n  }\n  async removeModel(path) {\n    path = maybeStripScheme(path);\n    return new Promise((resolve, reject) => {\n      const openRequest = this.indexedDB.open(DATABASE_NAME, DATABASE_VERSION);\n      openRequest.onupgradeneeded = () => setUpDatabase(openRequest);\n      openRequest.onsuccess = () => {\n        const db = openRequest.result;\n        const infoTx = db.transaction(INFO_STORE_NAME, \"readwrite\");\n        const infoStore = infoTx.objectStore(INFO_STORE_NAME);\n        const getInfoRequest = infoStore.get(path);\n        let modelTx;\n        getInfoRequest.onsuccess = () => {\n          if (getInfoRequest.result == null) {\n            db.close();\n            return reject(new Error(`Cannot find model with path '${path}' in IndexedDB.`));\n          } else {\n            const deleteInfoRequest = infoStore.delete(path);\n            const deleteModelData = () => {\n              modelTx = db.transaction(MODEL_STORE_NAME, \"readwrite\");\n              const modelStore = modelTx.objectStore(MODEL_STORE_NAME);\n              const deleteModelRequest = modelStore.delete(path);\n              deleteModelRequest.onsuccess = () => resolve(getInfoRequest.result.modelArtifactsInfo);\n              deleteModelRequest.onerror = (error) => reject(getInfoRequest.error);\n            };\n            deleteInfoRequest.onsuccess = deleteModelData;\n            deleteInfoRequest.onerror = (error) => {\n              deleteModelData();\n              db.close();\n              return reject(getInfoRequest.error);\n            };\n          }\n        };\n        getInfoRequest.onerror = (error) => {\n          db.close();\n          return reject(getInfoRequest.error);\n        };\n        infoTx.oncomplete = () => {\n          if (modelTx == null) {\n            db.close();\n          } else {\n            modelTx.oncomplete = () => db.close();\n          }\n        };\n      };\n      openRequest.onerror = (error) => reject(openRequest.error);\n    });\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/io/local_storage.js\nvar PATH_SEPARATOR = \"/\";\nvar PATH_PREFIX = \"tensorflowjs_models\";\nvar INFO_SUFFIX = \"info\";\nvar MODEL_TOPOLOGY_SUFFIX = \"model_topology\";\nvar WEIGHT_SPECS_SUFFIX = \"weight_specs\";\nvar WEIGHT_DATA_SUFFIX = \"weight_data\";\nvar MODEL_METADATA_SUFFIX = \"model_metadata\";\nfunction getModelKeys(path) {\n  return {\n    info: [PATH_PREFIX, path, INFO_SUFFIX].join(PATH_SEPARATOR),\n    topology: [PATH_PREFIX, path, MODEL_TOPOLOGY_SUFFIX].join(PATH_SEPARATOR),\n    weightSpecs: [PATH_PREFIX, path, WEIGHT_SPECS_SUFFIX].join(PATH_SEPARATOR),\n    weightData: [PATH_PREFIX, path, WEIGHT_DATA_SUFFIX].join(PATH_SEPARATOR),\n    modelMetadata: [PATH_PREFIX, path, MODEL_METADATA_SUFFIX].join(PATH_SEPARATOR)\n  };\n}\nfunction removeItems(keys) {\n  for (const key of Object.values(keys)) {\n    window.localStorage.removeItem(key);\n  }\n}\nfunction getModelPathFromKey(key) {\n  const items = key.split(PATH_SEPARATOR);\n  if (items.length < 3) {\n    throw new Error(`Invalid key format: ${key}`);\n  }\n  return items.slice(1, items.length - 1).join(PATH_SEPARATOR);\n}\nfunction maybeStripScheme2(key) {\n  return key.startsWith(BrowserLocalStorage.URL_SCHEME) ? key.slice(BrowserLocalStorage.URL_SCHEME.length) : key;\n}\nvar BrowserLocalStorage = class {\n  constructor(modelPath) {\n    if (!env().getBool(\"IS_BROWSER\") || typeof window === \"undefined\" || typeof window.localStorage === \"undefined\") {\n      throw new Error(\"The current environment does not support local storage.\");\n    }\n    this.LS = window.localStorage;\n    if (modelPath == null || !modelPath) {\n      throw new Error(\"For local storage, modelPath must not be null, undefined or empty.\");\n    }\n    this.modelPath = modelPath;\n    this.keys = getModelKeys(this.modelPath);\n  }\n  async save(modelArtifacts) {\n    if (modelArtifacts.modelTopology instanceof ArrayBuffer) {\n      throw new Error(\"BrowserLocalStorage.save() does not support saving model topology in binary formats yet.\");\n    } else {\n      const topology = JSON.stringify(modelArtifacts.modelTopology);\n      const weightSpecs = JSON.stringify(modelArtifacts.weightSpecs);\n      const modelArtifactsInfo = getModelArtifactsInfoForJSON(modelArtifacts);\n      try {\n        this.LS.setItem(this.keys.info, JSON.stringify(modelArtifactsInfo));\n        this.LS.setItem(this.keys.topology, topology);\n        this.LS.setItem(this.keys.weightSpecs, weightSpecs);\n        this.LS.setItem(this.keys.weightData, arrayBufferToBase64String(modelArtifacts.weightData));\n        const metadata = {\n          format: modelArtifacts.format,\n          generatedBy: modelArtifacts.generatedBy,\n          convertedBy: modelArtifacts.convertedBy,\n          signature: modelArtifacts.signature != null ? modelArtifacts.signature : void 0,\n          userDefinedMetadata: modelArtifacts.userDefinedMetadata != null ? modelArtifacts.userDefinedMetadata : void 0,\n          modelInitializer: modelArtifacts.modelInitializer != null ? modelArtifacts.modelInitializer : void 0,\n          trainingConfig: modelArtifacts.trainingConfig != null ? modelArtifacts.trainingConfig : void 0\n        };\n        this.LS.setItem(this.keys.modelMetadata, JSON.stringify(metadata));\n        return { modelArtifactsInfo };\n      } catch (err) {\n        removeItems(this.keys);\n        throw new Error(`Failed to save model '${this.modelPath}' to local storage: size quota being exceeded is a possible cause of this failure: modelTopologyBytes=${modelArtifactsInfo.modelTopologyBytes}, weightSpecsBytes=${modelArtifactsInfo.weightSpecsBytes}, weightDataBytes=${modelArtifactsInfo.weightDataBytes}.`);\n      }\n    }\n  }\n  async load() {\n    const info = JSON.parse(this.LS.getItem(this.keys.info));\n    if (info == null) {\n      throw new Error(`In local storage, there is no model with name '${this.modelPath}'`);\n    }\n    if (info.modelTopologyType !== \"JSON\") {\n      throw new Error(\"BrowserLocalStorage does not support loading non-JSON model topology yet.\");\n    }\n    const out = {};\n    const topology = JSON.parse(this.LS.getItem(this.keys.topology));\n    if (topology == null) {\n      throw new Error(`In local storage, the topology of model '${this.modelPath}' is missing.`);\n    }\n    out.modelTopology = topology;\n    const weightSpecs = JSON.parse(this.LS.getItem(this.keys.weightSpecs));\n    if (weightSpecs == null) {\n      throw new Error(`In local storage, the weight specs of model '${this.modelPath}' are missing.`);\n    }\n    out.weightSpecs = weightSpecs;\n    const metadataString = this.LS.getItem(this.keys.modelMetadata);\n    if (metadataString != null) {\n      const metadata = JSON.parse(metadataString);\n      out.format = metadata.format;\n      out.generatedBy = metadata.generatedBy;\n      out.convertedBy = metadata.convertedBy;\n      if (metadata.signature != null) {\n        out.signature = metadata.signature;\n      }\n      if (metadata.userDefinedMetadata != null) {\n        out.userDefinedMetadata = metadata.userDefinedMetadata;\n      }\n      if (metadata.modelInitializer != null) {\n        out.modelInitializer = metadata.modelInitializer;\n      }\n      if (metadata.trainingConfig != null) {\n        out.trainingConfig = metadata.trainingConfig;\n      }\n    }\n    const weightDataBase64 = this.LS.getItem(this.keys.weightData);\n    if (weightDataBase64 == null) {\n      throw new Error(`In local storage, the binary weight values of model '${this.modelPath}' are missing.`);\n    }\n    out.weightData = base64StringToArrayBuffer(weightDataBase64);\n    return out;\n  }\n};\nBrowserLocalStorage.URL_SCHEME = \"localstorage://\";\nvar localStorageRouter = (url) => {\n  if (!env().getBool(\"IS_BROWSER\")) {\n    return null;\n  } else {\n    if (!Array.isArray(url) && url.startsWith(BrowserLocalStorage.URL_SCHEME)) {\n      return browserLocalStorage(url.slice(BrowserLocalStorage.URL_SCHEME.length));\n    } else {\n      return null;\n    }\n  }\n};\nIORouterRegistry.registerSaveRouter(localStorageRouter);\nIORouterRegistry.registerLoadRouter(localStorageRouter);\nfunction browserLocalStorage(modelPath) {\n  return new BrowserLocalStorage(modelPath);\n}\nvar BrowserLocalStorageManager = class {\n  constructor() {\n    assert(env().getBool(\"IS_BROWSER\"), () => \"Current environment is not a web browser\");\n    assert(typeof window === \"undefined\" || typeof window.localStorage !== \"undefined\", () => \"Current browser does not appear to support localStorage\");\n    this.LS = window.localStorage;\n  }\n  async listModels() {\n    const out = {};\n    const prefix = PATH_PREFIX + PATH_SEPARATOR;\n    const suffix = PATH_SEPARATOR + INFO_SUFFIX;\n    for (let i2 = 0; i2 < this.LS.length; ++i2) {\n      const key = this.LS.key(i2);\n      if (key.startsWith(prefix) && key.endsWith(suffix)) {\n        const modelPath = getModelPathFromKey(key);\n        out[modelPath] = JSON.parse(this.LS.getItem(key));\n      }\n    }\n    return out;\n  }\n  async removeModel(path) {\n    path = maybeStripScheme2(path);\n    const keys = getModelKeys(path);\n    if (this.LS.getItem(keys.info) == null) {\n      throw new Error(`Cannot find model at path '${path}'`);\n    }\n    const info = JSON.parse(this.LS.getItem(keys.info));\n    removeItems(keys);\n    return info;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/io/model_management.js\nvar URL_SCHEME_SUFFIX = \"://\";\nvar ModelStoreManagerRegistry = class {\n  constructor() {\n    this.managers = {};\n  }\n  static getInstance() {\n    if (ModelStoreManagerRegistry.instance == null) {\n      ModelStoreManagerRegistry.instance = new ModelStoreManagerRegistry();\n    }\n    return ModelStoreManagerRegistry.instance;\n  }\n  static registerManager(scheme, manager) {\n    assert(scheme != null, () => \"scheme must not be undefined or null.\");\n    if (scheme.endsWith(URL_SCHEME_SUFFIX)) {\n      scheme = scheme.slice(0, scheme.indexOf(URL_SCHEME_SUFFIX));\n    }\n    assert(scheme.length > 0, () => \"scheme must not be an empty string.\");\n    const registry = ModelStoreManagerRegistry.getInstance();\n    assert(registry.managers[scheme] == null, () => `A model store manager is already registered for scheme '${scheme}'.`);\n    registry.managers[scheme] = manager;\n  }\n  static getManager(scheme) {\n    const manager = ModelStoreManagerRegistry.getInstance().managers[scheme];\n    if (manager == null) {\n      throw new Error(`Cannot find model manager for scheme '${scheme}'`);\n    }\n    return manager;\n  }\n  static getSchemes() {\n    return Object.keys(ModelStoreManagerRegistry.getInstance().managers);\n  }\n};\nfunction parseURL(url) {\n  if (url.indexOf(URL_SCHEME_SUFFIX) === -1) {\n    throw new Error(`The url string provided does not contain a scheme. Supported schemes are: ${ModelStoreManagerRegistry.getSchemes().join(\",\")}`);\n  }\n  return {\n    scheme: url.split(URL_SCHEME_SUFFIX)[0],\n    path: url.split(URL_SCHEME_SUFFIX)[1]\n  };\n}\nasync function cloneModelInternal(sourceURL, destURL, deleteSource = false) {\n  assert(sourceURL !== destURL, () => `Old path and new path are the same: '${sourceURL}'`);\n  const loadHandlers = IORouterRegistry.getLoadHandlers(sourceURL);\n  assert(loadHandlers.length > 0, () => `Copying failed because no load handler is found for source URL ${sourceURL}.`);\n  assert(loadHandlers.length < 2, () => `Copying failed because more than one (${loadHandlers.length}) load handlers for source URL ${sourceURL}.`);\n  const loadHandler = loadHandlers[0];\n  const saveHandlers = IORouterRegistry.getSaveHandlers(destURL);\n  assert(saveHandlers.length > 0, () => `Copying failed because no save handler is found for destination URL ${destURL}.`);\n  assert(saveHandlers.length < 2, () => `Copying failed because more than one (${loadHandlers.length}) save handlers for destination URL ${destURL}.`);\n  const saveHandler = saveHandlers[0];\n  const sourceScheme = parseURL(sourceURL).scheme;\n  const sourcePath = parseURL(sourceURL).path;\n  const sameMedium = sourceScheme === parseURL(sourceURL).scheme;\n  const modelArtifacts = await loadHandler.load();\n  if (deleteSource && sameMedium) {\n    await ModelStoreManagerRegistry.getManager(sourceScheme).removeModel(sourcePath);\n  }\n  const saveResult = await saveHandler.save(modelArtifacts);\n  if (deleteSource && !sameMedium) {\n    await ModelStoreManagerRegistry.getManager(sourceScheme).removeModel(sourcePath);\n  }\n  return saveResult.modelArtifactsInfo;\n}\nasync function listModels() {\n  const schemes = ModelStoreManagerRegistry.getSchemes();\n  const out = {};\n  for (const scheme of schemes) {\n    const schemeOut = await ModelStoreManagerRegistry.getManager(scheme).listModels();\n    for (const path in schemeOut) {\n      const url = scheme + URL_SCHEME_SUFFIX + path;\n      out[url] = schemeOut[path];\n    }\n  }\n  return out;\n}\nasync function removeModel(url) {\n  const schemeAndPath = parseURL(url);\n  const manager = ModelStoreManagerRegistry.getManager(schemeAndPath.scheme);\n  return manager.removeModel(schemeAndPath.path);\n}\nasync function copyModel(sourceURL, destURL) {\n  const deleteSource = false;\n  return cloneModelInternal(sourceURL, destURL, deleteSource);\n}\nasync function moveModel(sourceURL, destURL) {\n  const deleteSource = true;\n  return cloneModelInternal(sourceURL, destURL, deleteSource);\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/platforms/platform_browser.js\nvar PlatformBrowser = class {\n  fetch(path, init2) {\n    return fetch(path, init2);\n  }\n  now() {\n    return performance.now();\n  }\n  encode(text, encoding) {\n    if (encoding !== \"utf-8\" && encoding !== \"utf8\") {\n      throw new Error(`Browser's encoder only supports utf-8, but got ${encoding}`);\n    }\n    if (this.textEncoder == null) {\n      this.textEncoder = new TextEncoder();\n    }\n    return this.textEncoder.encode(text);\n  }\n  decode(bytes, encoding) {\n    return new TextDecoder(encoding).decode(bytes);\n  }\n};\nif (env().get(\"IS_BROWSER\")) {\n  env().setPlatform(\"browser\", new PlatformBrowser());\n  try {\n    ModelStoreManagerRegistry.registerManager(BrowserLocalStorage.URL_SCHEME, new BrowserLocalStorageManager());\n  } catch (err) {\n  }\n  try {\n    ModelStoreManagerRegistry.registerManager(BrowserIndexedDB.URL_SCHEME, new BrowserIndexedDBManager());\n  } catch (err) {\n  }\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/platforms/platform_node.js\nvar getNodeFetch = {\n  importFetch: () => require_browser()\n};\nvar systemFetch;\nvar PlatformNode = class {\n  constructor() {\n    this.util = require_util();\n    this.textEncoder = new this.util.TextEncoder();\n  }\n  fetch(path, requestInits) {\n    if (env().global.fetch != null) {\n      return env().global.fetch(path, requestInits);\n    }\n    if (systemFetch == null) {\n      systemFetch = getNodeFetch.importFetch();\n    }\n    return systemFetch(path, requestInits);\n  }\n  now() {\n    const time2 = process.hrtime();\n    return time2[0] * 1e3 + time2[1] / 1e6;\n  }\n  encode(text, encoding) {\n    if (encoding !== \"utf-8\" && encoding !== \"utf8\") {\n      throw new Error(`Node built-in encoder only supports utf-8, but got ${encoding}`);\n    }\n    return this.textEncoder.encode(text);\n  }\n  decode(bytes, encoding) {\n    if (bytes.length === 0) {\n      return \"\";\n    }\n    return new this.util.TextDecoder(encoding).decode(bytes);\n  }\n};\nif (env().get(\"IS_NODE\") && !env().get(\"IS_BROWSER\")) {\n  env().setPlatform(\"node\", new PlatformNode());\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/buffer.js\nfunction buffer(shape, dtype = \"float32\", values) {\n  dtype = dtype || \"float32\";\n  assertNonNegativeIntegerDimensions(shape);\n  return new TensorBuffer(shape, dtype, values);\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/cast.js\nfunction cast_(x, dtype) {\n  const $x = convertToTensor(x, \"x\", \"cast\");\n  if (!isValidDtype(dtype)) {\n    throw new Error(`Failed to cast to unknown dtype ${dtype}`);\n  }\n  if (dtype === \"string\" && $x.dtype !== \"string\" || dtype !== \"string\" && $x.dtype === \"string\") {\n    throw new Error(\"Only strings can be casted to strings\");\n  }\n  const inputs = { x: $x };\n  const attrs = { dtype };\n  return ENGINE.runKernel(Cast, inputs, attrs);\n}\nvar cast = op({ cast_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/clone.js\nfunction clone_(x) {\n  const $x = convertToTensor(x, \"x\", \"clone\", \"string_or_numeric\");\n  const inputs = { x: $x };\n  return ENGINE.runKernel(Identity, inputs);\n}\nvar clone = op({ clone_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/print.js\nfunction print(x, verbose = false) {\n  console.log(x.toString(verbose));\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/base_side_effects.js\ngetOrMakeEngine();\nvar opHandler2 = {\n  buffer,\n  cast,\n  clone,\n  print\n};\nsetOpHandler(opHandler2);\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/io/io.js\nvar io_exports = {};\n__export(io_exports, {\n  browserFiles: () => browserFiles,\n  browserHTTPRequest: () => browserHTTPRequest,\n  concatenateArrayBuffers: () => concatenateArrayBuffers,\n  copyModel: () => copyModel,\n  decodeWeights: () => decodeWeights,\n  encodeWeights: () => encodeWeights,\n  fromMemory: () => fromMemory,\n  fromMemorySync: () => fromMemorySync,\n  getLoadHandlers: () => getLoadHandlers,\n  getModelArtifactsForJSON: () => getModelArtifactsForJSON,\n  getModelArtifactsInfoForJSON: () => getModelArtifactsInfoForJSON,\n  getSaveHandlers: () => getSaveHandlers,\n  http: () => http,\n  isHTTPScheme: () => isHTTPScheme,\n  listModels: () => listModels,\n  loadWeights: () => loadWeights,\n  moveModel: () => moveModel,\n  registerLoadRouter: () => registerLoadRouter,\n  registerSaveRouter: () => registerSaveRouter,\n  removeModel: () => removeModel,\n  weightsLoaderFactory: () => weightsLoaderFactory,\n  withSaveHandler: () => withSaveHandler,\n  withSaveHandlerSync: () => withSaveHandlerSync\n});\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/io/browser_files.js\nvar DEFAULT_FILE_NAME_PREFIX = \"model\";\nvar DEFAULT_JSON_EXTENSION_NAME = \".json\";\nvar DEFAULT_WEIGHT_DATA_EXTENSION_NAME = \".weights.bin\";\nfunction defer(f) {\n  return new Promise((resolve) => setTimeout(resolve)).then(f);\n}\nvar BrowserDownloads = class {\n  constructor(fileNamePrefix) {\n    if (!env().getBool(\"IS_BROWSER\")) {\n      throw new Error(\"browserDownloads() cannot proceed because the current environment is not a browser.\");\n    }\n    if (fileNamePrefix.startsWith(BrowserDownloads.URL_SCHEME)) {\n      fileNamePrefix = fileNamePrefix.slice(BrowserDownloads.URL_SCHEME.length);\n    }\n    if (fileNamePrefix == null || fileNamePrefix.length === 0) {\n      fileNamePrefix = DEFAULT_FILE_NAME_PREFIX;\n    }\n    this.modelJsonFileName = fileNamePrefix + DEFAULT_JSON_EXTENSION_NAME;\n    this.weightDataFileName = fileNamePrefix + DEFAULT_WEIGHT_DATA_EXTENSION_NAME;\n  }\n  async save(modelArtifacts) {\n    if (typeof document === \"undefined\") {\n      throw new Error(\"Browser downloads are not supported in this environment since `document` is not present\");\n    }\n    const weightsURL = window.URL.createObjectURL(new Blob([modelArtifacts.weightData], { type: \"application/octet-stream\" }));\n    if (modelArtifacts.modelTopology instanceof ArrayBuffer) {\n      throw new Error(\"BrowserDownloads.save() does not support saving model topology in binary formats yet.\");\n    } else {\n      const weightsManifest = [{\n        paths: [\"./\" + this.weightDataFileName],\n        weights: modelArtifacts.weightSpecs\n      }];\n      const modelJSON = getModelJSONForModelArtifacts(modelArtifacts, weightsManifest);\n      const modelJsonURL = window.URL.createObjectURL(new Blob([JSON.stringify(modelJSON)], { type: \"application/json\" }));\n      const jsonAnchor = this.modelJsonAnchor == null ? document.createElement(\"a\") : this.modelJsonAnchor;\n      jsonAnchor.download = this.modelJsonFileName;\n      jsonAnchor.href = modelJsonURL;\n      await defer(() => jsonAnchor.dispatchEvent(new MouseEvent(\"click\")));\n      if (modelArtifacts.weightData != null) {\n        const weightDataAnchor = this.weightDataAnchor == null ? document.createElement(\"a\") : this.weightDataAnchor;\n        weightDataAnchor.download = this.weightDataFileName;\n        weightDataAnchor.href = weightsURL;\n        await defer(() => weightDataAnchor.dispatchEvent(new MouseEvent(\"click\")));\n      }\n      return { modelArtifactsInfo: getModelArtifactsInfoForJSON(modelArtifacts) };\n    }\n  }\n};\nBrowserDownloads.URL_SCHEME = \"downloads://\";\nvar BrowserFiles = class {\n  constructor(files) {\n    if (files == null || files.length < 1) {\n      throw new Error(`When calling browserFiles, at least 1 file is required, but received ${files}`);\n    }\n    this.jsonFile = files[0];\n    this.weightsFiles = files.slice(1);\n  }\n  async load() {\n    return new Promise((resolve, reject) => {\n      const jsonReader = new FileReader();\n      jsonReader.onload = (event) => {\n        const modelJSON = JSON.parse(event.target.result);\n        const modelTopology = modelJSON.modelTopology;\n        if (modelTopology == null) {\n          reject(new Error(`modelTopology field is missing from file ${this.jsonFile.name}`));\n          return;\n        }\n        const weightsManifest = modelJSON.weightsManifest;\n        if (weightsManifest == null) {\n          reject(new Error(`weightManifest field is missing from file ${this.jsonFile.name}`));\n          return;\n        }\n        if (this.weightsFiles.length === 0) {\n          resolve({ modelTopology });\n          return;\n        }\n        const modelArtifactsPromise = getModelArtifactsForJSON(modelJSON, (weightsManifest2) => this.loadWeights(weightsManifest2));\n        resolve(modelArtifactsPromise);\n      };\n      jsonReader.onerror = (error) => reject(`Failed to read model topology and weights manifest JSON from file '${this.jsonFile.name}'. BrowserFiles supports loading Keras-style tf.Model artifacts only.`);\n      jsonReader.readAsText(this.jsonFile);\n    });\n  }\n  loadWeights(weightsManifest) {\n    const weightSpecs = [];\n    const paths = [];\n    for (const entry of weightsManifest) {\n      weightSpecs.push(...entry.weights);\n      paths.push(...entry.paths);\n    }\n    const pathToFile = this.checkManifestAndWeightFiles(weightsManifest);\n    const promises = paths.map((path) => this.loadWeightsFile(path, pathToFile[path]));\n    return Promise.all(promises).then((buffers) => [weightSpecs, concatenateArrayBuffers(buffers)]);\n  }\n  loadWeightsFile(path, file) {\n    return new Promise((resolve, reject) => {\n      const weightFileReader = new FileReader();\n      weightFileReader.onload = (event) => {\n        const weightData = event.target.result;\n        resolve(weightData);\n      };\n      weightFileReader.onerror = (error) => reject(`Failed to weights data from file of path '${path}'.`);\n      weightFileReader.readAsArrayBuffer(file);\n    });\n  }\n  checkManifestAndWeightFiles(manifest) {\n    const basenames = [];\n    const fileNames = this.weightsFiles.map((file) => basename(file.name));\n    const pathToFile = {};\n    for (const group of manifest) {\n      group.paths.forEach((path) => {\n        const pathBasename = basename(path);\n        if (basenames.indexOf(pathBasename) !== -1) {\n          throw new Error(`Duplicate file basename found in weights manifest: '${pathBasename}'`);\n        }\n        basenames.push(pathBasename);\n        if (fileNames.indexOf(pathBasename) === -1) {\n          throw new Error(`Weight file with basename '${pathBasename}' is not provided.`);\n        } else {\n          pathToFile[path] = this.weightsFiles[fileNames.indexOf(pathBasename)];\n        }\n      });\n    }\n    if (basenames.length !== this.weightsFiles.length) {\n      throw new Error(`Mismatch in the number of files in weights manifest (${basenames.length}) and the number of weight files provided (${this.weightsFiles.length}).`);\n    }\n    return pathToFile;\n  }\n};\nvar browserDownloadsRouter = (url) => {\n  if (!env().getBool(\"IS_BROWSER\")) {\n    return null;\n  } else {\n    if (!Array.isArray(url) && url.startsWith(BrowserDownloads.URL_SCHEME)) {\n      return browserDownloads(url.slice(BrowserDownloads.URL_SCHEME.length));\n    } else {\n      return null;\n    }\n  }\n};\nIORouterRegistry.registerSaveRouter(browserDownloadsRouter);\nfunction browserDownloads(fileNamePrefix = \"model\") {\n  return new BrowserDownloads(fileNamePrefix);\n}\nfunction browserFiles(files) {\n  return new BrowserFiles(files);\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/io/progress.js\nfunction monitorPromisesProgress(promises, onProgress, startFraction, endFraction) {\n  checkPromises(promises);\n  startFraction = startFraction == null ? 0 : startFraction;\n  endFraction = endFraction == null ? 1 : endFraction;\n  checkFraction(startFraction, endFraction);\n  let resolvedPromise = 0;\n  const registerMonitor = (promise) => {\n    promise.then((value) => {\n      const fraction = startFraction + ++resolvedPromise / promises.length * (endFraction - startFraction);\n      onProgress(fraction);\n      return value;\n    });\n    return promise;\n  };\n  function checkPromises(promises2) {\n    assert(promises2 != null && Array.isArray(promises2) && promises2.length > 0, () => \"promises must be a none empty array\");\n  }\n  function checkFraction(startFraction2, endFraction2) {\n    assert(startFraction2 >= 0 && startFraction2 <= 1, () => `Progress fraction must be in range [0, 1], but got startFraction ${startFraction2}`);\n    assert(endFraction2 >= 0 && endFraction2 <= 1, () => `Progress fraction must be in range [0, 1], but got endFraction ${endFraction2}`);\n    assert(endFraction2 >= startFraction2, () => `startFraction must be no more than endFraction, but got startFraction ${startFraction2} and endFraction ${endFraction2}`);\n  }\n  return Promise.all(promises.map(registerMonitor));\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/io/weights_loader.js\nasync function loadWeightsAsArrayBuffer(fetchURLs, loadOptions) {\n  if (loadOptions == null) {\n    loadOptions = {};\n  }\n  const fetchFunc = loadOptions.fetchFunc == null ? env().platform.fetch : loadOptions.fetchFunc;\n  const requests = fetchURLs.map((fetchURL) => fetchFunc(fetchURL, loadOptions.requestInit, { isBinary: true }));\n  const fetchStartFraction = 0;\n  const fetchEndFraction = 0.5;\n  const responses = loadOptions.onProgress == null ? await Promise.all(requests) : await monitorPromisesProgress(requests, loadOptions.onProgress, fetchStartFraction, fetchEndFraction);\n  const bufferPromises = responses.map((response) => response.arrayBuffer());\n  const bufferStartFraction = 0.5;\n  const bufferEndFraction = 1;\n  const buffers = loadOptions.onProgress == null ? await Promise.all(bufferPromises) : await monitorPromisesProgress(bufferPromises, loadOptions.onProgress, bufferStartFraction, bufferEndFraction);\n  return buffers;\n}\nasync function loadWeights(manifest, filePathPrefix = \"\", weightNames, requestInit) {\n  const fetchWeights = (fetchUrls) => loadWeightsAsArrayBuffer(fetchUrls, { requestInit });\n  const loadWeights2 = weightsLoaderFactory(fetchWeights);\n  return loadWeights2(manifest, filePathPrefix, weightNames);\n}\nfunction weightsLoaderFactory(fetchWeightsFunction) {\n  return async (manifest, filePathPrefix = \"\", weightNames) => {\n    const groupIndicesToFetchMap = manifest.map(() => false);\n    const groupWeightsToFetch = {};\n    const weightsFound = weightNames != null ? weightNames.map(() => false) : [];\n    const allManifestWeightNames = [];\n    manifest.forEach((manifestGroupConfig, groupIndex) => {\n      let groupOffset = 0;\n      manifestGroupConfig.weights.forEach((weightsEntry) => {\n        const rawDtype = \"quantization\" in weightsEntry ? weightsEntry.quantization.dtype : weightsEntry.dtype;\n        const weightsBytes = DTYPE_VALUE_SIZE_MAP[rawDtype] * sizeFromShape(weightsEntry.shape);\n        const enqueueWeightsForFetchingFn = () => {\n          groupIndicesToFetchMap[groupIndex] = true;\n          if (groupWeightsToFetch[groupIndex] == null) {\n            groupWeightsToFetch[groupIndex] = [];\n          }\n          groupWeightsToFetch[groupIndex].push({\n            manifestEntry: weightsEntry,\n            groupOffset,\n            sizeBytes: weightsBytes\n          });\n        };\n        if (weightNames != null) {\n          weightNames.forEach((weightName, weightIndex) => {\n            if (weightName === weightsEntry.name) {\n              enqueueWeightsForFetchingFn();\n              weightsFound[weightIndex] = true;\n            }\n          });\n        } else {\n          enqueueWeightsForFetchingFn();\n        }\n        allManifestWeightNames.push(weightsEntry.name);\n        groupOffset += weightsBytes;\n      });\n    });\n    if (!weightsFound.every((found) => found)) {\n      const weightsNotFound = weightNames.filter((_, i2) => !weightsFound[i2]);\n      throw new Error(`Could not find weights in manifest with names: ${weightsNotFound.join(\", \")}. \nManifest JSON has weights with names: ${allManifestWeightNames.join(\", \")}.`);\n    }\n    const groupIndicesToFetch = groupIndicesToFetchMap.reduce((accumulator, shouldFetch, i2) => {\n      if (shouldFetch) {\n        accumulator.push(i2);\n      }\n      return accumulator;\n    }, []);\n    const fetchUrls = [];\n    groupIndicesToFetch.forEach((i2) => {\n      manifest[i2].paths.forEach((filepath) => {\n        const fetchUrl = filePathPrefix + (!filePathPrefix.endsWith(\"/\") ? \"/\" : \"\") + filepath;\n        fetchUrls.push(fetchUrl);\n      });\n    });\n    const buffers = await fetchWeightsFunction(fetchUrls);\n    const weightsTensorMap = {};\n    let bufferIndexOffset = 0;\n    groupIndicesToFetch.forEach((i2) => {\n      const numBuffers = manifest[i2].paths.length;\n      let groupBytes = 0;\n      for (let i3 = 0; i3 < numBuffers; i3++) {\n        groupBytes += buffers[bufferIndexOffset + i3].byteLength;\n      }\n      const groupBuffer = new ArrayBuffer(groupBytes);\n      const groupByteBuffer = new Uint8Array(groupBuffer);\n      let groupBufferOffset = 0;\n      for (let i3 = 0; i3 < numBuffers; i3++) {\n        const buffer2 = new Uint8Array(buffers[bufferIndexOffset + i3]);\n        groupByteBuffer.set(buffer2, groupBufferOffset);\n        groupBufferOffset += buffer2.byteLength;\n      }\n      const weightsEntries = groupWeightsToFetch[i2];\n      weightsEntries.forEach((weightsEntry) => {\n        const byteBuffer = groupBuffer.slice(weightsEntry.groupOffset, weightsEntry.groupOffset + weightsEntry.sizeBytes);\n        const nameToTensorMap = decodeWeights(byteBuffer, [weightsEntry.manifestEntry]);\n        for (const name in nameToTensorMap) {\n          weightsTensorMap[name] = nameToTensorMap[name];\n        }\n      });\n      bufferIndexOffset += numBuffers;\n    });\n    return weightsTensorMap;\n  };\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/io/http.js\nvar OCTET_STREAM_MIME_TYPE = \"application/octet-stream\";\nvar JSON_TYPE = \"application/json\";\nvar HTTPRequest = class {\n  constructor(path, loadOptions) {\n    this.DEFAULT_METHOD = \"POST\";\n    if (loadOptions == null) {\n      loadOptions = {};\n    }\n    this.weightPathPrefix = loadOptions.weightPathPrefix;\n    this.onProgress = loadOptions.onProgress;\n    this.weightUrlConverter = loadOptions.weightUrlConverter;\n    if (loadOptions.fetchFunc != null) {\n      assert(typeof loadOptions.fetchFunc === \"function\", () => \"Must pass a function that matches the signature of `fetch` (see https://developer.mozilla.org/en-US/docs/Web/API/Fetch_API)\");\n      this.fetch = loadOptions.fetchFunc;\n    } else {\n      this.fetch = env().platform.fetch;\n    }\n    assert(path != null && path.length > 0, () => \"URL path for http must not be null, undefined or empty.\");\n    if (Array.isArray(path)) {\n      assert(path.length === 2, () => `URL paths for http must have a length of 2, (actual length is ${path.length}).`);\n    }\n    this.path = path;\n    if (loadOptions.requestInit != null && loadOptions.requestInit.body != null) {\n      throw new Error(\"requestInit is expected to have no pre-existing body, but has one.\");\n    }\n    this.requestInit = loadOptions.requestInit || {};\n  }\n  async save(modelArtifacts) {\n    if (modelArtifacts.modelTopology instanceof ArrayBuffer) {\n      throw new Error(\"BrowserHTTPRequest.save() does not support saving model topology in binary formats yet.\");\n    }\n    const init2 = Object.assign({ method: this.DEFAULT_METHOD }, this.requestInit);\n    init2.body = new FormData();\n    const weightsManifest = [{\n      paths: [\"./model.weights.bin\"],\n      weights: modelArtifacts.weightSpecs\n    }];\n    const modelTopologyAndWeightManifest = getModelJSONForModelArtifacts(modelArtifacts, weightsManifest);\n    init2.body.append(\"model.json\", new Blob([JSON.stringify(modelTopologyAndWeightManifest)], { type: JSON_TYPE }), \"model.json\");\n    if (modelArtifacts.weightData != null) {\n      init2.body.append(\"model.weights.bin\", new Blob([modelArtifacts.weightData], { type: OCTET_STREAM_MIME_TYPE }), \"model.weights.bin\");\n    }\n    const response = await this.fetch(this.path, init2);\n    if (response.ok) {\n      return {\n        modelArtifactsInfo: getModelArtifactsInfoForJSON(modelArtifacts),\n        responses: [response]\n      };\n    } else {\n      throw new Error(`BrowserHTTPRequest.save() failed due to HTTP response status ${response.status}.`);\n    }\n  }\n  async load() {\n    const modelConfigRequest = await this.fetch(this.path, this.requestInit);\n    if (!modelConfigRequest.ok) {\n      throw new Error(`Request to ${this.path} failed with status code ${modelConfigRequest.status}. Please verify this URL points to the model JSON of the model to load.`);\n    }\n    let modelJSON;\n    try {\n      modelJSON = await modelConfigRequest.json();\n    } catch (e2) {\n      let message = `Failed to parse model JSON of response from ${this.path}.`;\n      if (this.path.endsWith(\".pb\")) {\n        message += \" Your path contains a .pb file extension. Support for .pb models have been removed in TensorFlow.js 1.0 in favor of .json models. You can re-convert your Python TensorFlow model using the TensorFlow.js 1.0 conversion scripts or you can convert your.pb models with the 'pb2json'NPM script in the tensorflow/tfjs-converter repository.\";\n      } else {\n        message += \" Please make sure the server is serving valid JSON for this request.\";\n      }\n      throw new Error(message);\n    }\n    const modelTopology = modelJSON.modelTopology;\n    const weightsManifest = modelJSON.weightsManifest;\n    if (modelTopology == null && weightsManifest == null) {\n      throw new Error(`The JSON from HTTP path ${this.path} contains neither model topology or manifest for weights.`);\n    }\n    return getModelArtifactsForJSON(modelJSON, (weightsManifest2) => this.loadWeights(weightsManifest2));\n  }\n  async loadWeights(weightsManifest) {\n    const weightPath = Array.isArray(this.path) ? this.path[1] : this.path;\n    const [prefix, suffix] = parseUrl(weightPath);\n    const pathPrefix = this.weightPathPrefix || prefix;\n    const weightSpecs = [];\n    for (const entry of weightsManifest) {\n      weightSpecs.push(...entry.weights);\n    }\n    const fetchURLs = [];\n    const urlPromises = [];\n    for (const weightsGroup of weightsManifest) {\n      for (const path of weightsGroup.paths) {\n        if (this.weightUrlConverter != null) {\n          urlPromises.push(this.weightUrlConverter(path));\n        } else {\n          fetchURLs.push(pathPrefix + path + suffix);\n        }\n      }\n    }\n    if (this.weightUrlConverter) {\n      fetchURLs.push(...await Promise.all(urlPromises));\n    }\n    const buffers = await loadWeightsAsArrayBuffer(fetchURLs, {\n      requestInit: this.requestInit,\n      fetchFunc: this.fetch,\n      onProgress: this.onProgress\n    });\n    return [weightSpecs, concatenateArrayBuffers(buffers)];\n  }\n};\nHTTPRequest.URL_SCHEME_REGEX = /^https?:\\/\\//;\nfunction parseUrl(url) {\n  const lastSlash = url.lastIndexOf(\"/\");\n  const lastSearchParam = url.lastIndexOf(\"?\");\n  const prefix = url.substring(0, lastSlash);\n  const suffix = lastSearchParam > lastSlash ? url.substring(lastSearchParam) : \"\";\n  return [prefix + \"/\", suffix];\n}\nfunction isHTTPScheme(url) {\n  return url.match(HTTPRequest.URL_SCHEME_REGEX) != null;\n}\nvar httpRouter = (url, loadOptions) => {\n  if (typeof fetch === \"undefined\" && (loadOptions == null || loadOptions.fetchFunc == null)) {\n    return null;\n  } else {\n    let isHTTP = true;\n    if (Array.isArray(url)) {\n      isHTTP = url.every((urlItem) => isHTTPScheme(urlItem));\n    } else {\n      isHTTP = isHTTPScheme(url);\n    }\n    if (isHTTP) {\n      return http(url, loadOptions);\n    }\n  }\n  return null;\n};\nIORouterRegistry.registerSaveRouter(httpRouter);\nIORouterRegistry.registerLoadRouter(httpRouter);\nfunction http(path, loadOptions) {\n  return new HTTPRequest(path, loadOptions);\n}\nfunction browserHTTPRequest(path, loadOptions) {\n  return http(path, loadOptions);\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/io/passthrough.js\nvar PassthroughLoader = class {\n  constructor(modelArtifacts) {\n    this.modelArtifacts = modelArtifacts;\n  }\n  load() {\n    return this.modelArtifacts;\n  }\n};\nvar PassthroughSaver = class {\n  constructor(saveHandler) {\n    this.saveHandler = saveHandler;\n  }\n  save(modelArtifacts) {\n    return this.saveHandler(modelArtifacts);\n  }\n};\nvar PassthroughAsync = class {\n  constructor(handler) {\n    if (handler.load) {\n      this.load = () => Promise.resolve(handler.load());\n    }\n    if (handler.save) {\n      this.save = (modelArtifacts) => Promise.resolve(handler.save(modelArtifacts));\n    }\n  }\n};\nfunction fromMemory(modelArtifacts, weightSpecs, weightData, trainingConfig) {\n  const args = arguments;\n  return new PassthroughAsync(fromMemorySync(...args));\n}\nfunction fromMemorySync(modelArtifacts, weightSpecs, weightData, trainingConfig) {\n  if (arguments.length === 1) {\n    const isModelArtifacts = modelArtifacts.modelTopology != null || modelArtifacts.weightSpecs != null;\n    if (isModelArtifacts) {\n      return new PassthroughLoader(modelArtifacts);\n    } else {\n      console.warn(\"Please call tf.io.fromMemory() with only one argument. The argument should be of type ModelArtifacts. The multi-argument signature of tf.io.fromMemory() has been deprecated and will be removed in a future release.\");\n      return new PassthroughLoader({ modelTopology: modelArtifacts });\n    }\n  } else {\n    console.warn(\"Please call tf.io.fromMemory() with only one argument. The argument should be of type ModelArtifacts. The multi-argument signature of tf.io.fromMemory() has been deprecated and will be removed in a future release.\");\n    return new PassthroughLoader({\n      modelTopology: modelArtifacts,\n      weightSpecs,\n      weightData,\n      trainingConfig\n    });\n  }\n}\nfunction withSaveHandler(saveHandler) {\n  return new PassthroughSaver(saveHandler);\n}\nfunction withSaveHandlerSync(saveHandler) {\n  return new PassthroughSaver(saveHandler);\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/math.js\nvar math_exports = {};\n__export(math_exports, {\n  confusionMatrix: () => confusionMatrix\n});\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/mat_mul.js\nfunction matMul_(a, b, transposeA = false, transposeB = false) {\n  let $a = convertToTensor(a, \"a\", \"matMul\");\n  let $b = convertToTensor(b, \"b\", \"matMul\");\n  [$a, $b] = makeTypesMatch($a, $b);\n  const inputs = { a: $a, b: $b };\n  const attrs = { transposeA, transposeB };\n  return ENGINE.runKernel(BatchMatMul, inputs, attrs);\n}\nvar matMul = op({ matMul_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/one_hot.js\nfunction oneHot_(indices, depth, onValue = 1, offValue = 0, dtype = \"int32\") {\n  if (depth < 2) {\n    throw new Error(`Error in oneHot: depth must be >=2, but it is ${depth}`);\n  }\n  const $indices = convertToTensor(indices, \"indices\", \"oneHot\", \"int32\");\n  const inputs = { indices: $indices };\n  const attrs = { dtype, depth, onValue, offValue };\n  return ENGINE.runKernel(OneHot, inputs, attrs);\n}\nvar oneHot = op({ oneHot_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/globals.js\nfunction enableProdMode() {\n  env().set(\"PROD\", true);\n}\nfunction enableDebugMode() {\n  env().set(\"DEBUG\", true);\n}\nfunction disableDeprecationWarnings() {\n  env().set(\"DEPRECATION_WARNINGS_ENABLED\", false);\n  console.warn(`TensorFlow.js deprecation warnings have been disabled.`);\n}\nfunction deprecationWarn(msg) {\n  if (env().getBool(\"DEPRECATION_WARNINGS_ENABLED\")) {\n    console.warn(msg + \" You can disable deprecation warnings with tf.disableDeprecationWarnings().\");\n  }\n}\nsetDeprecationWarningFn(deprecationWarn);\nfunction disposeVariables() {\n  ENGINE.disposeVariables();\n}\nfunction engine() {\n  return ENGINE;\n}\nfunction memory() {\n  return ENGINE.memory();\n}\nfunction profile(f) {\n  return ENGINE.profile(f);\n}\nfunction tidy(nameOrFn, fn) {\n  return ENGINE.tidy(nameOrFn, fn);\n}\nfunction dispose(container) {\n  const tensors = getTensorsInContainer(container);\n  tensors.forEach((tensor2) => tensor2.dispose());\n}\nfunction keep(result) {\n  return ENGINE.keep(result);\n}\nfunction time(f) {\n  return ENGINE.time(f);\n}\nfunction setBackend(backendName) {\n  return ENGINE.setBackend(backendName);\n}\nfunction ready() {\n  return ENGINE.ready();\n}\nfunction getBackend() {\n  return ENGINE.backendName;\n}\nfunction removeBackend(name) {\n  ENGINE.removeBackend(name);\n}\nfunction findBackend(name) {\n  return ENGINE.findBackend(name);\n}\nfunction findBackendFactory(name) {\n  return ENGINE.findBackendFactory(name);\n}\nfunction registerBackend(name, factory, priority = 1) {\n  return ENGINE.registerBackend(name, factory, priority);\n}\nfunction backend() {\n  return ENGINE.backend;\n}\nfunction setPlatform(platformName, platform) {\n  env().setPlatform(platformName, platform);\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/imag.js\nfunction imag_(input2) {\n  const $input = convertToTensor(input2, \"input\", \"imag\");\n  const inputs = { input: $input };\n  return ENGINE.runKernel(Imag, inputs);\n}\nvar imag = op({ imag_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/neg.js\nfunction neg_(x) {\n  const $x = convertToTensor(x, \"x\", \"neg\");\n  const inputs = { x: $x };\n  return ENGINE.runKernel(Neg, inputs);\n}\nvar neg = op({ neg_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/real.js\nfunction real_(input2) {\n  const $input = convertToTensor(input2, \"input\", \"real\");\n  const inputs = { input: $input };\n  return ENGINE.runKernel(Real, inputs);\n}\nvar real = op({ real_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/transpose.js\nfunction transpose_(x, perm, conjugate) {\n  const $x = convertToTensor(x, \"x\", \"transpose\");\n  if (perm == null) {\n    perm = $x.shape.map((s2, i2) => i2).reverse();\n  }\n  assert($x.rank === perm.length, () => `Error in transpose: rank of input ${$x.rank} must match length of perm ${perm}.`);\n  perm.forEach((axis) => {\n    assert(axis >= 0 && axis < $x.rank, () => `All entries in 'perm' must be between 0 and ${$x.rank - 1} but got ${perm}`);\n  });\n  if ($x.rank <= 1) {\n    return $x.clone();\n  }\n  const inputs = { x: $x };\n  const attrs = { perm };\n  if ($x.dtype === \"complex64\") {\n    return tidy(() => {\n      let $real = real($x);\n      let $imag = imag($x);\n      $real = ENGINE.runKernel(Transpose, { x: $real }, attrs);\n      $imag = ENGINE.runKernel(Transpose, { x: $imag }, attrs);\n      if (conjugate) {\n        $imag = neg($imag);\n      }\n      return complex($real, $imag);\n    });\n  }\n  return ENGINE.runKernel(Transpose, inputs, attrs);\n}\nvar transpose = op({ transpose_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/confusion_matrix.js\nfunction confusionMatrix_(labels, predictions, numClasses) {\n  const $labels = convertToTensor(labels, \"labels\", \"confusionMatrix\");\n  const $predictions = convertToTensor(predictions, \"predictions\", \"confusionMatrix\");\n  assert(numClasses == null || numClasses > 0 && Number.isInteger(numClasses), () => `If provided, numClasses must be a positive integer, but got ${numClasses}`);\n  assert($labels.rank === 1, () => `Expected the rank of labels to be 1, but got ${$labels.rank}`);\n  assert($predictions.rank === 1, () => `Expected the rank of predictions to be 1, but got ${$predictions.rank}`);\n  assert($labels.shape[0] === $predictions.shape[0], () => `Mismatch in the number of examples: ${$labels.shape[0]} vs. ${$predictions.shape[0]}. Labels and predictions should have the same number of elements.`);\n  assert(numClasses > 0 && Number.isInteger(numClasses), () => `numClasses is required to be a positive integer, but got ${numClasses}`);\n  const oneHotLabels = oneHot(cast($labels, \"int32\"), numClasses);\n  const oneHotPredictions = oneHot(cast($predictions, \"int32\"), numClasses);\n  const oneHotLabelsT = transpose(oneHotLabels);\n  const product = matMul(oneHotLabelsT, oneHotPredictions);\n  return cast(product, \"int32\");\n}\nvar confusionMatrix = op({ confusionMatrix_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/broadcast_util.js\nvar broadcast_util_exports = {};\n__export(broadcast_util_exports, {\n  assertAndGetBroadcastShape: () => assertAndGetBroadcastShape,\n  getBroadcastDims: () => getBroadcastDims,\n  getReductionAxes: () => getReductionAxes\n});\nfunction getBroadcastDims(inShape, outShape) {\n  const inRank = inShape.length;\n  const dims = [];\n  for (let i2 = 0; i2 < inRank; i2++) {\n    const dim = inRank - 1 - i2;\n    const a = inShape[dim] || 1;\n    const b = outShape[outShape.length - 1 - i2] || 1;\n    if (b > 1 && a === 1) {\n      dims.unshift(dim);\n    }\n  }\n  return dims;\n}\nfunction getReductionAxes(inShape, outShape) {\n  const result = [];\n  for (let i2 = 0; i2 < outShape.length; i2++) {\n    const inDim = inShape[inShape.length - i2 - 1];\n    const outAxis = outShape.length - i2 - 1;\n    const outDim = outShape[outAxis];\n    if (inDim == null || inDim === 1 && outDim > 1) {\n      result.unshift(outAxis);\n    }\n  }\n  return result;\n}\nfunction assertAndGetBroadcastShape(shapeA, shapeB) {\n  const result = [];\n  const l3 = Math.max(shapeA.length, shapeB.length);\n  for (let i2 = 0; i2 < l3; i2++) {\n    let a = shapeA[shapeA.length - i2 - 1];\n    if (a == null) {\n      a = 1;\n    }\n    let b = shapeB[shapeB.length - i2 - 1];\n    if (b == null) {\n      b = 1;\n    }\n    if (a === 1) {\n      result.unshift(b);\n    } else if (b === 1) {\n      result.unshift(a);\n    } else if (a !== b) {\n      const errMsg = `Operands could not be broadcast together with shapes ${shapeA} and ${shapeB}.`;\n      throw Error(errMsg);\n    } else {\n      result.unshift(a);\n    }\n  }\n  return result;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/browser.js\nvar browser_exports = {};\n__export(browser_exports, {\n  fromPixels: () => fromPixels,\n  fromPixelsAsync: () => fromPixelsAsync,\n  toPixels: () => toPixels\n});\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/tensor3d.js\nfunction tensor3d(values, shape, dtype) {\n  assertNonNull(values);\n  if (shape != null && shape.length !== 3) {\n    throw new Error(\"tensor3d() requires shape to have three numbers\");\n  }\n  const inferredShape = inferShape(values, dtype);\n  if (inferredShape.length !== 3 && inferredShape.length !== 1) {\n    throw new Error(\"tensor3d() requires values to be number[][][] or flat/TypedArray\");\n  }\n  if (inferredShape.length === 1 && shape == null) {\n    throw new Error(\"tensor3d() requires shape to be provided when `values` are a flat array\");\n  }\n  return makeTensor(values, shape, inferredShape, dtype);\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/browser.js\nvar fromPixels2DContext;\nfunction fromPixels_(pixels, numChannels = 3) {\n  if (numChannels > 4) {\n    throw new Error(\"Cannot construct Tensor with more than 4 channels from pixels.\");\n  }\n  if (pixels == null) {\n    throw new Error(\"pixels passed to tf.browser.fromPixels() can not be null\");\n  }\n  let isPixelData2 = false;\n  let isImageData = false;\n  let isVideo = false;\n  let isImage = false;\n  let isCanvasLike = false;\n  let isImageBitmap = false;\n  if (pixels.data instanceof Uint8Array) {\n    isPixelData2 = true;\n  } else if (typeof ImageData !== \"undefined\" && pixels instanceof ImageData) {\n    isImageData = true;\n  } else if (typeof HTMLVideoElement !== \"undefined\" && pixels instanceof HTMLVideoElement) {\n    isVideo = true;\n  } else if (typeof HTMLImageElement !== \"undefined\" && pixels instanceof HTMLImageElement) {\n    isImage = true;\n  } else if (pixels.getContext != null) {\n    isCanvasLike = true;\n  } else if (typeof ImageBitmap !== \"undefined\" && pixels instanceof ImageBitmap) {\n    isImageBitmap = true;\n  } else {\n    throw new Error(`pixels passed to tf.browser.fromPixels() must be either an HTMLVideoElement, HTMLImageElement, HTMLCanvasElement, ImageData in browser, or OffscreenCanvas, ImageData in webworker or {data: Uint32Array, width: number, height: number}, but was ${pixels.constructor.name}`);\n  }\n  const kernel = getKernel(FromPixels, ENGINE.backendName);\n  if (kernel != null) {\n    const inputs = { pixels };\n    const attrs = { numChannels };\n    return ENGINE.runKernel(FromPixels, inputs, attrs);\n  }\n  const [width, height] = isVideo ? [\n    pixels.videoWidth,\n    pixels.videoHeight\n  ] : [pixels.width, pixels.height];\n  let vals;\n  if (isCanvasLike) {\n    vals = pixels.getContext(\"2d\").getImageData(0, 0, width, height).data;\n  } else if (isImageData || isPixelData2) {\n    vals = pixels.data;\n  } else if (isImage || isVideo || isImageBitmap) {\n    if (fromPixels2DContext == null) {\n      if (typeof document === \"undefined\") {\n        if (typeof OffscreenCanvas !== \"undefined\" && typeof OffscreenCanvasRenderingContext2D !== \"undefined\") {\n          fromPixels2DContext = new OffscreenCanvas(1, 1).getContext(\"2d\");\n        } else {\n          throw new Error(\"Cannot parse input in current context. Reason: OffscreenCanvas Context2D rendering is not supported.\");\n        }\n      } else {\n        fromPixels2DContext = document.createElement(\"canvas\").getContext(\"2d\", { willReadFrequently: true });\n      }\n    }\n    fromPixels2DContext.canvas.width = width;\n    fromPixels2DContext.canvas.height = height;\n    fromPixels2DContext.drawImage(pixels, 0, 0, width, height);\n    vals = fromPixels2DContext.getImageData(0, 0, width, height).data;\n  }\n  let values;\n  if (numChannels === 4) {\n    values = new Int32Array(vals);\n  } else {\n    const numPixels = width * height;\n    values = new Int32Array(numPixels * numChannels);\n    for (let i2 = 0; i2 < numPixels; i2++) {\n      for (let channel = 0; channel < numChannels; ++channel) {\n        values[i2 * numChannels + channel] = vals[i2 * 4 + channel];\n      }\n    }\n  }\n  const outShape = [height, width, numChannels];\n  return tensor3d(values, outShape, \"int32\");\n}\nfunction isPixelData(pixels) {\n  return pixels != null && pixels.data instanceof Uint8Array;\n}\nfunction isImageBitmapFullySupported() {\n  return typeof window !== \"undefined\" && typeof ImageBitmap !== \"undefined\" && window.hasOwnProperty(\"createImageBitmap\");\n}\nfunction isNonEmptyPixels(pixels) {\n  return pixels != null && pixels.width !== 0 && pixels.height !== 0;\n}\nfunction canWrapPixelsToImageBitmap(pixels) {\n  return isImageBitmapFullySupported() && !(pixels instanceof ImageBitmap) && isNonEmptyPixels(pixels) && !isPixelData(pixels);\n}\nasync function fromPixelsAsync(pixels, numChannels = 3) {\n  let inputs = null;\n  if (env().getBool(\"WRAP_TO_IMAGEBITMAP\") && canWrapPixelsToImageBitmap(pixels)) {\n    let imageBitmap;\n    try {\n      imageBitmap = await createImageBitmap(pixels, { premultiplyAlpha: \"none\" });\n    } catch (e2) {\n      imageBitmap = null;\n    }\n    if (imageBitmap != null && imageBitmap.width === pixels.width && imageBitmap.height === pixels.height) {\n      inputs = imageBitmap;\n    } else {\n      inputs = pixels;\n    }\n  } else {\n    inputs = pixels;\n  }\n  return fromPixels_(inputs, numChannels);\n}\nasync function toPixels(img, canvas) {\n  let $img = convertToTensor(img, \"img\", \"toPixels\");\n  if (!(img instanceof Tensor)) {\n    const originalImgTensor = $img;\n    $img = cast(originalImgTensor, \"int32\");\n    originalImgTensor.dispose();\n  }\n  if ($img.rank !== 2 && $img.rank !== 3) {\n    throw new Error(`toPixels only supports rank 2 or 3 tensors, got rank ${$img.rank}.`);\n  }\n  const [height, width] = $img.shape.slice(0, 2);\n  const depth = $img.rank === 2 ? 1 : $img.shape[2];\n  if (depth > 4 || depth === 2) {\n    throw new Error(`toPixels only supports depth of size 1, 3 or 4 but got ${depth}`);\n  }\n  if ($img.dtype !== \"float32\" && $img.dtype !== \"int32\") {\n    throw new Error(`Unsupported type for toPixels: ${$img.dtype}. Please use float32 or int32 tensors.`);\n  }\n  const data = await $img.data();\n  const multiplier = $img.dtype === \"float32\" ? 255 : 1;\n  const bytes = new Uint8ClampedArray(width * height * 4);\n  for (let i2 = 0; i2 < height * width; ++i2) {\n    const rgba = [0, 0, 0, 255];\n    for (let d = 0; d < depth; d++) {\n      const value = data[i2 * depth + d];\n      if ($img.dtype === \"float32\") {\n        if (value < 0 || value > 1) {\n          throw new Error(`Tensor values for a float32 Tensor must be in the range [0 - 1] but encountered ${value}.`);\n        }\n      } else if ($img.dtype === \"int32\") {\n        if (value < 0 || value > 255) {\n          throw new Error(`Tensor values for a int32 Tensor must be in the range [0 - 255] but encountered ${value}.`);\n        }\n      }\n      if (depth === 1) {\n        rgba[0] = value * multiplier;\n        rgba[1] = value * multiplier;\n        rgba[2] = value * multiplier;\n      } else {\n        rgba[d] = value * multiplier;\n      }\n    }\n    const j = i2 * 4;\n    bytes[j + 0] = Math.round(rgba[0]);\n    bytes[j + 1] = Math.round(rgba[1]);\n    bytes[j + 2] = Math.round(rgba[2]);\n    bytes[j + 3] = Math.round(rgba[3]);\n  }\n  if (canvas != null) {\n    canvas.width = width;\n    canvas.height = height;\n    const ctx = canvas.getContext(\"2d\");\n    const imageData = new ImageData(bytes, width, height);\n    ctx.putImageData(imageData, 0, 0);\n  }\n  if ($img !== img) {\n    $img.dispose();\n  }\n  return bytes;\n}\nvar fromPixels = op({ fromPixels_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/gather_nd_util.js\nvar gather_nd_util_exports = {};\n__export(gather_nd_util_exports, {\n  prepareAndValidate: () => prepareAndValidate\n});\nfunction prepareAndValidate(tensor2, indices) {\n  const tensorRank = tensor2.shape.length;\n  const indicesRank = indices.shape.length;\n  if (tensorRank < 1) {\n    throw new Error(`tf.gatherND() expects the input to be rank 1 or higher, but the rank was ${tensorRank}.`);\n  }\n  if (indicesRank < 1) {\n    throw new Error(`tf.gatherND() expects the indices to be rank 1 or higher, but the rank was ${indicesRank}.`);\n  }\n  if (indices.dtype !== \"int32\") {\n    throw new Error(`tf.gatherND() expects the indices to be int32 type, but the dtype was ${indices.dtype}.`);\n  }\n  if (indices.shape[indicesRank - 1] > tensorRank) {\n    throw new Error(`index innermost dimension length must be <= tensor rank; saw: ${indices.shape[indicesRank - 1]} vs. ${tensorRank}`);\n  }\n  if (sizeFromShape(tensor2.shape) === 0) {\n    throw new Error(`Requested more than 0 entries, but input is empty. Input shape: ${tensor2.shape}.`);\n  }\n  const indicesShape = indices.shape;\n  const sliceRank = indicesShape[indicesShape.length - 1];\n  let nResult = 1;\n  for (let i2 = 0; i2 < indicesShape.length - 1; ++i2) {\n    nResult *= indicesShape[i2];\n  }\n  const inputShape = tensor2.shape;\n  const resultShape = indicesShape.slice();\n  resultShape.pop();\n  let sliceSize = 1;\n  for (let i2 = sliceRank; i2 < tensorRank; ++i2) {\n    sliceSize *= inputShape[i2];\n    resultShape.push(inputShape[i2]);\n  }\n  const strides = [\n    ...computeStrides(tensor2.shape).map((stride) => stride / sliceSize),\n    1\n  ].slice(0, sliceRank);\n  return [resultShape, nResult, sliceSize, strides];\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/scatter_nd_util.js\nvar scatter_nd_util_exports = {};\n__export(scatter_nd_util_exports, {\n  calculateShapes: () => calculateShapes,\n  validateInput: () => validateInput,\n  validateUpdateShape: () => validateUpdateShape\n});\nfunction validateUpdateShape(shape, indices, updates) {\n  const sliceDim = indices.rank > 1 ? indices.shape[indices.rank - 1] : 1;\n  const batchDim = indices.rank > 1 ? indices.rank - 1 : 1;\n  const shapeError = `Must have updates.shape = indices.shape[:batchDim] + shape[sliceDim:], got updates.shape: ${updates.shape}, indices.shape: ${indices.shape}, shape: ${shape}, sliceDim: ${sliceDim}, and batchDim: ${batchDim}.`;\n  if (updates.rank < batchDim) {\n    throw new Error(shapeError + ` update.rank < ${batchDim}. `);\n  }\n  if (shape.length < sliceDim + (updates.rank - batchDim)) {\n    throw new Error(shapeError + ` Output shape length < ${sliceDim + (updates.rank - batchDim)}`);\n  }\n  if (updates.rank !== batchDim + shape.length - sliceDim) {\n    throw new Error(shapeError + ` update.rank != ${batchDim + shape.length - sliceDim}`);\n  }\n  for (let d = 0; d < batchDim; ++d) {\n    if (updates.shape[d] !== indices.shape[d]) {\n      throw new Error(shapeError + ` updates.shape[${d}] (${updates.shape[d]}) != indices.shape[${d}] (${indices.shape[d]}).`);\n    }\n  }\n  for (let d = 0; d < updates.rank - batchDim; ++d) {\n    if (updates.shape[d + batchDim] !== shape[d + sliceDim]) {\n      throw new Error(shapeError + ` updates.shape[${d + batchDim}] (${updates.shape[d + batchDim]}) != shape[${d + batchDim}] (${shape[d + batchDim]})`);\n    }\n  }\n}\nfunction validateInput(updates, indices, shape) {\n  if (indices.rank < 1) {\n    throw new Error(`tf.scatterND() expects the indices to be rank 1 or higher, but the rank was ${indices.rank}.`);\n  }\n  if (updates.rank < 1) {\n    throw new Error(`tf.scatterND() expects the updates to be rank 1 or higher, but the rank was ${updates.rank}.`);\n  }\n  if (indices.dtype !== \"int32\") {\n    throw new Error(`The dtype of 'indices' should be int32, but got dtype: ${indices.dtype}`);\n  }\n  if (shape.length < 1) {\n    throw new Error(`Output rank must be greater or equal to 1, but got shape: ${shape}`);\n  }\n  if (shape.length === 0) {\n    if (indices.size === 0) {\n      throw new Error(`Indices specified for empty output. indices shape: ${indices.shape}`);\n    }\n    if (updates.size === 0) {\n      throw new Error(`Updates specified for empty output. updates shape: ${updates.shape}`);\n    }\n  }\n  validateUpdateShape(shape, indices, updates);\n}\nfunction calculateShapes(updates, indices, shape) {\n  const indicesRank = indices.shape.length;\n  const sliceRank = indicesRank > 1 ? indices.shape[indicesRank - 1] : 1;\n  const totalNd = shape.length;\n  let sliceSize = 1;\n  for (let i2 = sliceRank; i2 < totalNd; ++i2) {\n    sliceSize *= shape[i2];\n  }\n  const safeSliceDim = sliceRank < 1 ? 1 : sliceRank;\n  const numUpdates = sizeFromShape(indices.shape) / safeSliceDim;\n  const strides = [...computeStrides(shape.slice(0, sliceRank)), 1];\n  const outputSize = sizeFromShape(shape);\n  return { sliceRank, numUpdates, sliceSize, strides, outputSize };\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/slice_util.js\nvar slice_util_exports = {};\n__export(slice_util_exports, {\n  assertParamsValid: () => assertParamsValid,\n  computeFlatOffset: () => computeFlatOffset,\n  computeOutShape: () => computeOutShape,\n  getNormalizedAxes: () => getNormalizedAxes,\n  isSliceContinous: () => isSliceContinous,\n  maskToAxes: () => maskToAxes,\n  parseSliceParams: () => parseSliceParams,\n  sliceInfo: () => sliceInfo,\n  startForAxis: () => startForAxis,\n  startIndicesWithElidedDims: () => startIndicesWithElidedDims,\n  stopForAxis: () => stopForAxis,\n  stopIndicesWithElidedDims: () => stopIndicesWithElidedDims,\n  stridesForAxis: () => stridesForAxis,\n  stridesWithElidedDims: () => stridesWithElidedDims\n});\nvar NEW_AXIS = -2;\nvar SHRINK_AXIS = -1;\nfunction assertParamsValid(input2, begin, size) {\n  const inputRank = input2.shape.length;\n  assert(inputRank === begin.length, () => `Error in slice${inputRank}D: Length of begin ${begin} must match the rank of the array (${inputRank}).`);\n  assert(inputRank === size.length, () => `Error in slice${inputRank}D: Length of size ${size} must match the rank of the array (${inputRank}).`);\n  for (let i2 = 0; i2 < inputRank; ++i2) {\n    assert(begin[i2] + size[i2] <= input2.shape[i2], () => `Error in slice${inputRank}D: begin[${i2}] + size[${i2}] (${begin[i2] + size[i2]}) would overflow input.shape[${i2}] (${input2.shape[i2]})`);\n  }\n}\nfunction maskToAxes(mask) {\n  const axes = [];\n  let axis = 0;\n  while (mask > 0) {\n    if (mask & 1) {\n      axes.push(axis);\n    }\n    mask /= 2;\n    axis++;\n  }\n  return axes;\n}\nfunction computeOutShape(begin, end, strides) {\n  const size = [];\n  for (let axis = 0; axis < begin.length; axis++) {\n    size[axis] = Math.ceil((end[axis] - begin[axis]) / strides[axis]);\n  }\n  return size;\n}\nfunction stridesWithElidedDims(strides, ellipsisInsertionIndex, numElidedAxes, inputShape) {\n  const newStrides = [...strides];\n  for (let i2 = newStrides.length; i2 < inputShape.length; i2++) {\n    newStrides.push(1);\n  }\n  for (let i2 = 0; i2 < numElidedAxes; i2++) {\n    if (i2 === 0) {\n      newStrides[ellipsisInsertionIndex] = 1;\n    } else {\n      newStrides.splice(ellipsisInsertionIndex, 0, 1);\n      newStrides.pop();\n    }\n  }\n  return newStrides;\n}\nfunction unnormalizeAxis(ellipsisInsertionIndex, numElidedAxes, normalizedAxis) {\n  if (normalizedAxis <= ellipsisInsertionIndex) {\n    return normalizedAxis;\n  }\n  return normalizedAxis - (numElidedAxes - 1);\n}\nfunction getElidedAxes(numElidedAxes, ellipsisInsertionIndex) {\n  const elidedAxes = [];\n  for (let i2 = 0; i2 < numElidedAxes; i2++) {\n    elidedAxes.push(ellipsisInsertionIndex + i2);\n  }\n  return elidedAxes;\n}\nfunction getNormalizedAxes(inputShape, ellipsisAxes, numInterpolatedAxes, begin, end, strides, beginMask, endMask, ellipsisMask) {\n  const inputRank = inputShape.length;\n  let normalizedBegin = new Array(inputRank), normalizedEnd = new Array(inputRank), normalizedStrides = new Array(inputRank);\n  if (ellipsisAxes.length && numInterpolatedAxes > 0) {\n    const fullIndex = ellipsisAxes[0];\n    const numElidedAxes = numInterpolatedAxes + 1;\n    normalizedBegin = startIndicesWithElidedDims(beginMask, fullIndex, numElidedAxes, begin, inputShape);\n    normalizedEnd = stopIndicesWithElidedDims(endMask, fullIndex, numElidedAxes, end, inputShape);\n    normalizedStrides = stridesWithElidedDims(strides, fullIndex, numElidedAxes, inputShape);\n  } else {\n    for (let axis = 0; axis < inputRank; axis++) {\n      normalizedBegin[axis] = startForAxis(beginMask, begin, strides, inputShape, axis, ellipsisMask);\n      normalizedEnd[axis] = stopForAxis(endMask, end, strides, inputShape, axis, ellipsisMask);\n      normalizedStrides[axis] = stridesForAxis(strides, axis, ellipsisMask);\n    }\n  }\n  return {\n    begin: normalizedBegin,\n    end: normalizedEnd,\n    strides: normalizedStrides\n  };\n}\nfunction startIndicesWithElidedDims(beginMask, ellipsisInsertionIndex, numElidedAxes, originalBegin, inputShape) {\n  const newIndices = [...inputShape];\n  const elidedAxes = getElidedAxes(numElidedAxes, ellipsisInsertionIndex);\n  for (let axis = 0; axis < newIndices.length; axis++) {\n    if (elidedAxes.indexOf(axis) > -1) {\n      newIndices[axis] = 0;\n    } else {\n      const originalAxis = unnormalizeAxis(ellipsisInsertionIndex, numElidedAxes, axis);\n      let originalValue = originalBegin[originalAxis];\n      if (beginMask & 1 << originalAxis) {\n        originalValue = 0;\n      }\n      newIndices[axis] = originalValue;\n    }\n  }\n  return newIndices;\n}\nfunction stopIndicesWithElidedDims(endMask, ellipsisInsertionIndex, numElidedAxes, originalEnd, inputShape) {\n  const newIndices = [...inputShape];\n  const elidedAxes = getElidedAxes(numElidedAxes, ellipsisInsertionIndex);\n  for (let axis = 0; axis < newIndices.length; axis++) {\n    if (elidedAxes.indexOf(axis) > -1) {\n      newIndices[axis] = Number.MAX_SAFE_INTEGER;\n    } else {\n      const originalAxis = unnormalizeAxis(ellipsisInsertionIndex, numElidedAxes, axis);\n      let originalValue = originalEnd[originalAxis];\n      if (endMask & 1 << originalAxis) {\n        originalValue = Number.MAX_SAFE_INTEGER;\n      }\n      newIndices[axis] = originalValue;\n    }\n  }\n  for (let i2 = 0; i2 < newIndices.length; i2++) {\n    const axisSize = inputShape[i2];\n    if (newIndices[i2] < 0) {\n      newIndices[i2] += axisSize;\n    }\n    newIndices[i2] = clamp(0, newIndices[i2], inputShape[i2]);\n  }\n  return newIndices;\n}\nfunction stridesForAxis(strides, axis, ellipsisMask) {\n  let stride = strides[axis];\n  if (ellipsisMask & 1 << axis || stride == null) {\n    stride = 1;\n  }\n  return stride;\n}\nfunction startForAxis(beginMask, startIndices, strides, inputShape, axis, ellipsisMask) {\n  let start = startIndices[axis];\n  const stride = strides[axis] || 1;\n  if (beginMask & 1 << axis || ellipsisMask & 1 << axis || start == null) {\n    if (stride > 0) {\n      start = Number.MIN_SAFE_INTEGER;\n    } else {\n      start = Number.MAX_SAFE_INTEGER;\n    }\n  }\n  const axisSize = inputShape[axis];\n  if (start < 0) {\n    start += axisSize;\n  }\n  start = clamp(0, start, axisSize - 1);\n  return start;\n}\nfunction stopForAxis(endMask, stopIndices, strides, inputShape, axis, ellipsisMask) {\n  let stop = stopIndices[axis];\n  const stride = strides[axis] || 1;\n  if (endMask & 1 << axis || ellipsisMask & 1 << axis || stop == null) {\n    if (stride > 0) {\n      stop = Number.MAX_SAFE_INTEGER;\n    } else {\n      stop = Number.MIN_SAFE_INTEGER;\n    }\n  }\n  const axisSize = inputShape[axis];\n  if (stop < 0) {\n    stop += axisSize;\n  }\n  if (stride > 0) {\n    stop = clamp(0, stop, axisSize);\n  } else {\n    stop = clamp(-1, stop, axisSize - 1);\n  }\n  return stop;\n}\nfunction isSliceContinous(shape, begin, size) {\n  let firstNonOneAxis = size.length;\n  for (let i2 = 0; i2 < size.length; i2++) {\n    if (size[i2] > 1) {\n      firstNonOneAxis = i2;\n      break;\n    }\n  }\n  for (let i2 = firstNonOneAxis + 1; i2 < size.length; i2++) {\n    if (begin[i2] > 0 || size[i2] !== shape[i2]) {\n      return false;\n    }\n  }\n  return true;\n}\nfunction computeFlatOffset(begin, strides) {\n  let flatOffset = begin.length > 0 ? begin[begin.length - 1] : 1;\n  for (let i2 = 0; i2 < begin.length - 1; i2++) {\n    flatOffset += begin[i2] * strides[i2];\n  }\n  return flatOffset;\n}\nfunction parseSliceParams(x, begin, size) {\n  let begin_;\n  const xRank = x.shape.length;\n  if (typeof begin === \"number\") {\n    begin_ = [begin, ...new Array(xRank - 1).fill(0)];\n  } else if (begin.length < xRank) {\n    begin_ = begin.concat(new Array(xRank - begin.length).fill(0));\n  } else {\n    begin_ = begin.slice();\n  }\n  begin_.forEach((d) => {\n    assert(d !== -1, () => \"slice() does not support negative begin indexing.\");\n  });\n  let size_;\n  if (size == null) {\n    size_ = new Array(xRank).fill(-1);\n  } else if (typeof size === \"number\") {\n    size_ = [size, ...new Array(xRank - 1).fill(-1)];\n  } else if (size.length < xRank) {\n    size_ = size.concat(new Array(xRank - size.length).fill(-1));\n  } else {\n    size_ = size;\n  }\n  size_ = size_.map((d, i2) => {\n    if (d >= 0) {\n      return d;\n    } else {\n      assert(d === -1, () => `Negative size values should be exactly -1 but got ${d} for the slice() size at index ${i2}.`);\n      return x.shape[i2] - begin_[i2];\n    }\n  });\n  return [begin_, size_];\n}\nfunction sliceInfo(xShape, begin, end, strides, beginMask, endMask, ellipsisMask, newAxisMask, shrinkAxisMask) {\n  let stridesNonNull;\n  if (strides == null) {\n    stridesNonNull = new Array(begin.length);\n    stridesNonNull.fill(1);\n  } else {\n    stridesNonNull = strides;\n  }\n  if (ellipsisMask != null && (ellipsisMask & ellipsisMask - 1) !== 0) {\n    throw new Error(\"Multiple ellipses in slice is not allowed.\");\n  }\n  let ellipsisSeen = false;\n  const sparseSpec = {\n    dims: stridesNonNull.length,\n    numAddAxisAfterEllipsis: 0,\n    begin: begin.slice(),\n    end: end.slice(),\n    strides: stridesNonNull.slice(),\n    beginMask,\n    endMask,\n    ellipsisMask,\n    newAxisMask,\n    shrinkAxisMask\n  };\n  for (let i2 = 0; i2 < sparseSpec.dims; i2++) {\n    if (ellipsisSeen && (1 << i2 & newAxisMask) !== 0) {\n      sparseSpec.numAddAxisAfterEllipsis++;\n    }\n    if (1 << i2 & ellipsisMask) {\n      ellipsisSeen = true;\n    }\n  }\n  if (!ellipsisSeen) {\n    sparseSpec.ellipsisMask |= 1 << sparseSpec.dims;\n    sparseSpec.dims++;\n  }\n  const denseSpec = {\n    dims: xShape.length,\n    beginMask: 0,\n    endMask: 0,\n    beginValid: false,\n    endValid: false\n  };\n  buildDenseSpec(sparseSpec, denseSpec);\n  let isIdentity = true;\n  let sliceDim0 = true;\n  let isSimpleSlice = true;\n  const processingShape = [];\n  const finalShape = [];\n  for (let i2 = 0; i2 < xShape.length; ++i2) {\n    if (denseSpec.strides[i2] === 0) {\n      throw Error(`strides[${i2}] must be non-zero`);\n    }\n    const shrinkI = !!(denseSpec.shrinkAxisMask & 1 << i2);\n    const dimI = xShape[i2];\n    if (dimI === -1) {\n      processingShape.push(shrinkI ? 1 : -1);\n      continue;\n    }\n    const masks = [denseSpec.beginMask & 1 << i2, denseSpec.endMask & 1 << i2];\n    const validRange = [\n      denseSpec.strides[i2] > 0 ? 0 : -1,\n      denseSpec.strides[i2] > 0 ? dimI : dimI - 1\n    ];\n    if (shrinkI && denseSpec.strides[i2] <= 0) {\n      throw Error(\"only stride 1 allowed on non-range indexing.\");\n    }\n    isSimpleSlice = isSimpleSlice && denseSpec.strides[i2] === 1;\n    const beginAndEndMasked = !!(denseSpec.beginMask & 1 << i2 && denseSpec.endMask & 1 << i2);\n    if (denseSpec.beginValid && denseSpec.endValid) {\n      if (shrinkI) {\n        const xFwd = denseSpec.begin[i2] < 0 ? dimI + denseSpec.begin[i2] : denseSpec.begin[i2];\n        denseSpec.begin[i2] = xFwd;\n        denseSpec.end[i2] = denseSpec.begin[i2] + 1;\n        if (xFwd < 0 || xFwd >= dimI) {\n          throw Error(`slice index ${denseSpec.begin[i2]} of dimension ${i2} out of bounds.`);\n        }\n      } else {\n        denseSpec.begin[i2] = canonical(denseSpec.begin[i2], 0, denseSpec.strides[i2], dimI, masks, validRange);\n        denseSpec.end[i2] = canonical(denseSpec.end[i2], 1, denseSpec.strides[i2], dimI, masks, validRange);\n      }\n      const takeAllInDimension = denseSpec.strides[i2] === 1 && denseSpec.begin[i2] === 0 && denseSpec.end[i2] === dimI;\n      isIdentity = isIdentity && takeAllInDimension;\n      sliceDim0 = sliceDim0 && (i2 === 0 && denseSpec.strides[i2] === 1 || takeAllInDimension);\n    } else {\n      isIdentity = isIdentity && (denseSpec.strides[i2] === 1 && beginAndEndMasked);\n      sliceDim0 = sliceDim0 && (i2 === 0 && denseSpec.strides[i2] === 1 || beginAndEndMasked);\n    }\n    let intervalLength;\n    let knownInterval = false;\n    if (denseSpec.beginValid && denseSpec.endValid) {\n      intervalLength = denseSpec.end[i2] - denseSpec.begin[i2];\n      knownInterval = true;\n    } else if (shrinkI) {\n      intervalLength = 1;\n      knownInterval = true;\n    } else if (beginAndEndMasked) {\n      if (dimI >= 0) {\n        if (denseSpec.strides[i2] < 0) {\n          intervalLength = -dimI;\n        } else {\n          intervalLength = dimI;\n        }\n        knownInterval = true;\n      }\n    }\n    if (knownInterval) {\n      let sizeI;\n      if (intervalLength === 0 || intervalLength < 0 !== denseSpec.strides[i2] < 0) {\n        sizeI = 0;\n      } else {\n        sizeI = Math.trunc(intervalLength / denseSpec.strides[i2]) + (intervalLength % denseSpec.strides[i2] !== 0 ? 1 : 0);\n      }\n      processingShape.push(sizeI);\n    } else {\n      processingShape.push(-1);\n    }\n  }\n  for (let denseDim = 0; denseDim < denseSpec.finalShapeGatherIndices.length; ++denseDim) {\n    const gatherIndex = denseSpec.finalShapeGatherIndices[denseDim];\n    if (gatherIndex >= 0) {\n      finalShape.push(processingShape[gatherIndex]);\n    } else if (gatherIndex === NEW_AXIS) {\n      finalShape.push(1);\n    }\n  }\n  const finalShapeSparse = finalShape.filter((dim, i2) => denseSpec.finalShapeGatherIndices[i2] !== NEW_AXIS);\n  return {\n    finalShapeSparse,\n    finalShape,\n    isIdentity,\n    sliceDim0,\n    isSimpleSlice,\n    begin: denseSpec.begin,\n    end: denseSpec.end,\n    strides: denseSpec.strides\n  };\n}\nfunction buildDenseSpec(sparse2, dense2) {\n  dense2.beginMask = 0;\n  dense2.endMask = 0;\n  dense2.shrinkAxisMask = 0;\n  let fullIndex = 0;\n  dense2.beginValid = sparse2.begin != null;\n  dense2.endValid = sparse2.end != null;\n  dense2.begin = new Array(dense2.dims);\n  dense2.end = new Array(dense2.dims);\n  dense2.strides = new Array(dense2.dims);\n  dense2.finalShapeGatherIndices = [];\n  dense2.finalShapeGatherIndicesSparse = [];\n  dense2.inputShapeGatherIndicesSparse = new Array(dense2.dims);\n  for (let i2 = 0; i2 < sparse2.dims; i2++) {\n    if (1 << i2 & sparse2.ellipsisMask) {\n      const nextIndex = Math.min(dense2.dims - (sparse2.dims - i2) + 1 + sparse2.numAddAxisAfterEllipsis, dense2.dims);\n      for (; fullIndex < nextIndex; fullIndex++) {\n        dense2.begin[fullIndex] = 0;\n        dense2.end[fullIndex] = 0;\n        dense2.strides[fullIndex] = 1;\n        dense2.beginMask |= 1 << fullIndex;\n        dense2.endMask |= 1 << fullIndex;\n        dense2.finalShapeGatherIndices.push(fullIndex);\n        dense2.finalShapeGatherIndicesSparse.push(-1);\n        dense2.inputShapeGatherIndicesSparse[fullIndex] = i2;\n      }\n    } else if (1 << i2 & sparse2.newAxisMask) {\n      dense2.finalShapeGatherIndices.push(NEW_AXIS);\n      dense2.finalShapeGatherIndicesSparse.push(-1);\n    } else {\n      if (fullIndex === dense2.begin.length) {\n        throw Error(`Index out of range using input dim ${fullIndex}; input has only ${dense2.dims} dims, ${dense2.begin.length}.`);\n      }\n      if (sparse2.begin != null) {\n        dense2.begin[fullIndex] = sparse2.begin[i2];\n      }\n      if (sparse2.end != null) {\n        dense2.end[fullIndex] = sparse2.end[i2];\n      }\n      dense2.strides[fullIndex] = sparse2.strides[i2];\n      if (sparse2.beginMask & 1 << i2) {\n        dense2.beginMask |= 1 << fullIndex;\n      }\n      if (sparse2.endMask & 1 << i2) {\n        dense2.endMask |= 1 << fullIndex;\n      }\n      if (sparse2.shrinkAxisMask & 1 << i2) {\n        dense2.finalShapeGatherIndices.push(SHRINK_AXIS);\n        dense2.finalShapeGatherIndicesSparse.push(-1);\n        dense2.shrinkAxisMask |= 1 << fullIndex;\n      } else {\n        dense2.finalShapeGatherIndices.push(fullIndex);\n        dense2.finalShapeGatherIndicesSparse.push(i2);\n      }\n      dense2.inputShapeGatherIndicesSparse[fullIndex] = i2;\n      fullIndex++;\n    }\n  }\n}\nfunction canonical(x, c, strideI, dimI, masks, validRange) {\n  if (masks[c]) {\n    return strideI > 0 ? validRange[c] : validRange[c + 1 & 1];\n  } else {\n    const xFwd = x < 0 ? dimI + x : x;\n    return xFwd < validRange[0] ? validRange[0] : xFwd > validRange[1] ? validRange[1] : xFwd;\n  }\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/serialization.js\nvar serialization_exports = {};\n__export(serialization_exports, {\n  Serializable: () => Serializable,\n  SerializationMap: () => SerializationMap,\n  registerClass: () => registerClass\n});\nvar Serializable = class {\n  getClassName() {\n    return this.constructor.className;\n  }\n  static fromConfig(cls, config) {\n    return new cls(config);\n  }\n};\nvar SerializationMap = class {\n  constructor() {\n    this.classNameMap = {};\n  }\n  static getMap() {\n    if (SerializationMap.instance == null) {\n      SerializationMap.instance = new SerializationMap();\n    }\n    return SerializationMap.instance;\n  }\n  static register(cls) {\n    SerializationMap.getMap().classNameMap[cls.className] = [cls, cls.fromConfig];\n  }\n};\nfunction registerClass(cls) {\n  assert(cls.className != null, () => `Class being registered does not have the static className property defined.`);\n  assert(typeof cls.className === \"string\", () => `className is required to be a string, but got type ` + typeof cls.className);\n  assert(cls.className.length > 0, () => `Class being registered has an empty-string as its className, which is disallowed.`);\n  SerializationMap.register(cls);\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/test_util.js\nvar test_util_exports = {};\n__export(test_util_exports, {\n  TEST_EPSILON_FLOAT16: () => TEST_EPSILON_FLOAT16,\n  createVideoElement: () => createVideoElement,\n  encodeStrings: () => encodeStrings,\n  expectArrayBuffersEqual: () => expectArrayBuffersEqual,\n  expectArraysClose: () => expectArraysClose,\n  expectArraysEqual: () => expectArraysEqual,\n  expectNumbersClose: () => expectNumbersClose,\n  expectPromiseToFail: () => expectPromiseToFail,\n  expectValuesInRange: () => expectValuesInRange,\n  play: () => play,\n  testEpsilon: () => testEpsilon\n});\nvar TEST_EPSILON_FLOAT32 = 1e-3;\nvar TEST_EPSILON_FLOAT16 = 0.1;\nfunction expectArraysClose(actual, expected, epsilon3) {\n  if (epsilon3 == null) {\n    epsilon3 = testEpsilon();\n  }\n  return expectArraysPredicate(actual, expected, (a, b) => areClose(a, b, epsilon3));\n}\nfunction testEpsilon() {\n  return ENGINE.backend.floatPrecision() === 32 ? TEST_EPSILON_FLOAT32 : TEST_EPSILON_FLOAT16;\n}\nfunction expectArraysPredicate(actual, expected, predicate) {\n  let checkClassType = true;\n  if (isTypedArray(actual) || isTypedArray(expected)) {\n    checkClassType = false;\n  }\n  if (isTypedArray(actual) && isTypedArray(expected)) {\n    checkClassType = true;\n  }\n  if (checkClassType) {\n    const aType = actual.constructor.name;\n    const bType = expected.constructor.name;\n    if (aType !== bType) {\n      throw new Error(`Arrays are of different type. Actual: ${aType}. Expected: ${bType}`);\n    }\n  }\n  if (Array.isArray(actual) && Array.isArray(expected)) {\n    const actualShape = inferShape(actual);\n    const expectedShape = inferShape(expected);\n    if (!arraysEqual(actualShape, expectedShape)) {\n      throw new Error(`Arrays have different shapes. Actual: [${actualShape}]. Expected: [${expectedShape}]`);\n    }\n  }\n  const actualFlat = isTypedArray(actual) ? actual : flatten(actual);\n  const expectedFlat = isTypedArray(expected) ? expected : flatten(expected);\n  if (actualFlat.length !== expectedFlat.length) {\n    throw new Error(`Arrays have different lengths actual: ${actualFlat.length} vs expected: ${expectedFlat.length}.\nActual:   ${actualFlat}.\nExpected: ${expectedFlat}.`);\n  }\n  for (let i2 = 0; i2 < expectedFlat.length; ++i2) {\n    const a = actualFlat[i2];\n    const e2 = expectedFlat[i2];\n    if (!predicate(a, e2)) {\n      throw new Error(`Arrays differ: actual[${i2}] = ${a}, expected[${i2}] = ${e2}.\nActual:   ${actualFlat}.\nExpected: ${expectedFlat}.`);\n    }\n  }\n  if (typeof expect !== \"undefined\") {\n    expect().nothing();\n  }\n}\nfunction expectPromiseToFail(fn, done) {\n  fn().then(() => done.fail(), () => done());\n  if (typeof expect !== \"undefined\") {\n    expect().nothing();\n  }\n}\nfunction expectArraysEqual(actual, expected) {\n  const exp5 = typeof expected === \"string\" || typeof expected === \"number\" || typeof expected === \"boolean\" ? [expected] : expected;\n  if (isString(actual) || isString(actual[0]) || isString(expected) || isString(expected[0])) {\n    return expectArraysPredicate(actual, exp5, (a, b) => a == b);\n  }\n  return expectArraysPredicate(actual, expected, (a, b) => areClose(a, b, 0));\n}\nfunction expectNumbersClose(a, e2, epsilon3) {\n  if (epsilon3 == null) {\n    epsilon3 = testEpsilon();\n  }\n  if (!areClose(a, e2, epsilon3)) {\n    throw new Error(`Numbers differ: actual === ${a}, expected === ${e2}`);\n  }\n  if (typeof expect !== \"undefined\") {\n    expect().nothing();\n  }\n}\nfunction areClose(a, e2, epsilon3) {\n  if (!isFinite(a) && !isFinite(e2)) {\n    return true;\n  }\n  if (isNaN(a) || isNaN(e2) || Math.abs(a - e2) > epsilon3) {\n    return false;\n  }\n  return true;\n}\nfunction expectValuesInRange(actual, low, high) {\n  for (let i2 = 0; i2 < actual.length; i2++) {\n    if (actual[i2] < low || actual[i2] > high) {\n      throw new Error(`Value out of range:${actual[i2]} low: ${low}, high: ${high}`);\n    }\n  }\n}\nfunction expectArrayBuffersEqual(actual, expected) {\n  const actualArray = new Float32Array(actual);\n  const expectedArray = new Float32Array(expected);\n  if (actualArray.length !== expectedArray.length) {\n    throw new Error(`Expected ArrayBuffer to be of length ${expectedArray.length}, but it was ${actualArray.length}`);\n  }\n  for (let i2 = 0; i2 < expectedArray.length; i2++) {\n    if (actualArray[i2] !== expectedArray[i2]) {\n      throw new Error(`Expected ArrayBuffer value at ${i2} to be ${expectedArray[i2]} but got ${actualArray[i2]} instead`);\n    }\n  }\n}\nfunction encodeStrings(a) {\n  for (let i2 = 0; i2 < a.length; i2++) {\n    const val = a[i2];\n    if (Array.isArray(val)) {\n      encodeStrings(val);\n    } else {\n      a[i2] = encodeString(val);\n    }\n  }\n  return a;\n}\nfunction createVideoElement(source) {\n  const video = document.createElement(\"video\");\n  if (\"playsInline\" in video) {\n    video.playsInline = true;\n  }\n  video.muted = true;\n  video.loop = true;\n  video.style.position = \"fixed\";\n  video.style.left = \"0px\";\n  video.style.top = \"0px\";\n  video.preload = \"auto\";\n  video.appendChild(source);\n  return new Promise((resolve) => {\n    video.addEventListener(\"loadeddata\", (_) => resolve(video));\n    video.load();\n  });\n}\nasync function play(video) {\n  await video.play();\n  if (\"requestVideoFrameCallback\" in video) {\n    await new Promise((resolve) => {\n      video.requestVideoFrameCallback(resolve);\n    });\n  }\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/version.js\nvar version = \"3.20.0\";\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/add.js\nfunction add_(a, b) {\n  let $a = convertToTensor(a, \"a\", \"add\");\n  let $b = convertToTensor(b, \"b\", \"add\");\n  [$a, $b] = makeTypesMatch($a, $b);\n  const inputs = { a: $a, b: $b };\n  return ENGINE.runKernel(Add, inputs);\n}\nvar add2 = op({ add_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/floorDiv.js\nfunction floorDiv_(a, b) {\n  let $a = convertToTensor(a, \"a\", \"floorDiv\");\n  let $b = convertToTensor(b, \"b\", \"floorDiv\");\n  [$a, $b] = makeTypesMatch($a, $b);\n  const inputs = { a: $a, b: $b };\n  return ENGINE.runKernel(FloorDiv, inputs);\n}\nvar floorDiv = op({ floorDiv_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/div.js\nfunction div_(a, b) {\n  let $a = convertToTensor(a, \"a\", \"div\");\n  let $b = convertToTensor(b, \"b\", \"div\");\n  [$a, $b] = makeTypesMatch($a, $b);\n  if ($a.dtype === \"int32\" && $b.dtype === \"int32\") {\n    return floorDiv($a, $b);\n  }\n  const inputs = { a: $a, b: $b };\n  const attrs = {};\n  return ENGINE.runKernel(RealDiv, inputs, attrs);\n}\nvar div = op({ div_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/mul.js\nfunction mul_(a, b) {\n  let $a = convertToTensor(a, \"a\", \"mul\");\n  let $b = convertToTensor(b, \"b\", \"mul\");\n  [$a, $b] = makeTypesMatch($a, $b);\n  const inputs = { a: $a, b: $b };\n  return ENGINE.runKernel(Multiply, inputs);\n}\nvar mul = op({ mul_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/abs.js\nfunction abs_(x) {\n  const $x = convertToTensor(x, \"x\", \"abs\");\n  if ($x.dtype === \"complex64\") {\n    const inputs = { x: $x };\n    return ENGINE.runKernel(ComplexAbs, inputs);\n  } else {\n    const inputs = { x: $x };\n    return ENGINE.runKernel(Abs, inputs);\n  }\n}\nvar abs = op({ abs_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/acos.js\nfunction acos_(x) {\n  const $x = convertToTensor(x, \"x\", \"acos\");\n  const inputs = { x: $x };\n  return ENGINE.runKernel(Acos, inputs);\n}\nvar acos = op({ acos_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/acosh.js\nfunction acosh_(x) {\n  const $x = convertToTensor(x, \"x\", \"acosh\");\n  const inputs = { x: $x };\n  return ENGINE.runKernel(Acosh, inputs);\n}\nvar acosh = op({ acosh_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/add_n.js\nfunction addN_(tensors) {\n  assert(Array.isArray(tensors), () => \"The argument passed to tf.addN() must be a list of tensors\");\n  assert(tensors.length >= 1, () => `Must pass at least one tensor to tf.addN(), but got ${tensors.length}`);\n  const $tensors = tensors.map((t2, i2) => convertToTensor(t2, `tensors${i2}`, \"addN\"));\n  const firstTensor = $tensors[0];\n  $tensors.forEach((t2) => {\n    if (t2.dtype !== firstTensor.dtype) {\n      throw new Error(\"All tensors passed to tf.addN() must have the same dtype\");\n    }\n  });\n  $tensors.forEach((t2) => {\n    if (!arraysEqual(t2.shape, firstTensor.shape)) {\n      throw new Error(\"All tensors passed to tf.addN() must have the same shape\");\n    }\n  });\n  const inputs = $tensors;\n  return ENGINE.runKernel(AddN, inputs);\n}\nvar addN = op({ addN_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/all.js\nfunction all_(x, axis = null, keepDims = false) {\n  const $x = convertToTensor(x, \"x\", \"all\", \"bool\");\n  const inputs = { x: $x };\n  const attrs = { axis, keepDims };\n  return ENGINE.runKernel(All, inputs, attrs);\n}\nvar all = op({ all_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/any.js\nfunction any_(x, axis = null, keepDims = false) {\n  const $x = convertToTensor(x, \"x\", \"any\", \"bool\");\n  const inputs = { x: $x };\n  const attrs = { axis, keepDims };\n  return ENGINE.runKernel(Any, inputs, attrs);\n}\nvar any = op({ any_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/arg_max.js\nfunction argMax_(x, axis = 0) {\n  const $x = convertToTensor(x, \"x\", \"argMax\");\n  const inputs = { x: $x };\n  const attrs = { axis };\n  return ENGINE.runKernel(ArgMax, inputs, attrs);\n}\nvar argMax = op({ argMax_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/arg_min.js\nfunction argMin_(x, axis = 0) {\n  const $x = convertToTensor(x, \"x\", \"argMin\");\n  const inputs = { x: $x };\n  const attrs = { axis };\n  return ENGINE.runKernel(ArgMin, inputs, attrs);\n}\nvar argMin = op({ argMin_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/asin.js\nfunction asin_(x) {\n  const $x = convertToTensor(x, \"x\", \"asin\");\n  const inputs = { x: $x };\n  return ENGINE.runKernel(Asin, inputs);\n}\nvar asin = op({ asin_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/asinh.js\nfunction asinh_(x) {\n  const $x = convertToTensor(x, \"x\", \"asinh\");\n  const inputs = { x: $x };\n  return ENGINE.runKernel(Asinh, inputs);\n}\nvar asinh = op({ asinh_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/atan.js\nfunction atan_(x) {\n  const $x = convertToTensor(x, \"x\", \"atan\");\n  const inputs = { x: $x };\n  return ENGINE.runKernel(Atan, inputs);\n}\nvar atan = op({ atan_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/atan2.js\nfunction atan2_(a, b) {\n  let $a = convertToTensor(a, \"a\", \"atan2\");\n  let $b = convertToTensor(b, \"b\", \"atan2\");\n  [$a, $b] = makeTypesMatch($a, $b);\n  const inputs = { a: $a, b: $b };\n  return ENGINE.runKernel(Atan2, inputs);\n}\nvar atan2 = op({ atan2_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/atanh.js\nfunction atanh_(x) {\n  const $x = convertToTensor(x, \"x\", \"atanh\");\n  const inputs = { x: $x };\n  return ENGINE.runKernel(Atanh, inputs);\n}\nvar atanh = op({ atanh_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/conv_util.js\nfunction computeDilation2DInfo(inputShape, filterShape, strides, pad3, dataFormat = \"NHWC\", dilations) {\n  const inputChannels = inputShape[3];\n  const $filterShape = [...filterShape, inputChannels];\n  const $dataFormat = convertConv2DDataFormat(dataFormat);\n  return computeConv2DInfo(inputShape, $filterShape, strides, dilations, pad3, null, null, $dataFormat);\n}\nfunction computePool2DInfo(inShape, filterSize, strides, dilations, pad3, roundingMode, dataFormat = \"channelsLast\") {\n  const [filterHeight, filterWidth] = parseTupleParam(filterSize);\n  let filterShape;\n  if (dataFormat === \"channelsLast\") {\n    filterShape = [filterHeight, filterWidth, inShape[3], inShape[3]];\n  } else if (dataFormat === \"channelsFirst\") {\n    filterShape = [filterHeight, filterWidth, inShape[1], inShape[1]];\n  } else {\n    throw new Error(`Unknown dataFormat ${dataFormat}`);\n  }\n  return computeConv2DInfo(inShape, filterShape, strides, dilations, pad3, roundingMode, false, dataFormat);\n}\nfunction computePool3DInfo(inShape, filterSize, strides, dilations, pad3, roundingMode, dataFormat = \"NDHWC\") {\n  const [filterDepth, filterHeight, filterWidth] = parse3TupleParam(filterSize);\n  let filterShape;\n  let $dataFormat;\n  if (dataFormat === \"NDHWC\") {\n    $dataFormat = \"channelsLast\";\n    filterShape = [filterDepth, filterHeight, filterWidth, inShape[4], inShape[4]];\n  } else if (dataFormat === \"NCDHW\") {\n    $dataFormat = \"channelsFirst\";\n    filterShape = [filterDepth, filterHeight, filterWidth, inShape[1], inShape[1]];\n  } else {\n    throw new Error(`Unknown dataFormat ${dataFormat}`);\n  }\n  return computeConv3DInfo(inShape, filterShape, strides, dilations, pad3, false, $dataFormat, roundingMode);\n}\nfunction computeConv2DInfo(inShape, filterShape, strides, dilations, pad3, roundingMode, depthwise = false, dataFormat = \"channelsLast\") {\n  let [batchSize, inHeight, inWidth, inChannels] = [-1, -1, -1, -1];\n  if (dataFormat === \"channelsLast\") {\n    [batchSize, inHeight, inWidth, inChannels] = inShape;\n  } else if (dataFormat === \"channelsFirst\") {\n    [batchSize, inChannels, inHeight, inWidth] = inShape;\n  } else {\n    throw new Error(`Unknown dataFormat ${dataFormat}`);\n  }\n  const [filterHeight, filterWidth, , filterChannels] = filterShape;\n  const [strideHeight, strideWidth] = parseTupleParam(strides);\n  const [dilationHeight, dilationWidth] = parseTupleParam(dilations);\n  const effectiveFilterHeight = getEffectiveFilterSize(filterHeight, dilationHeight);\n  const effectiveFilterWidth = getEffectiveFilterSize(filterWidth, dilationWidth);\n  const { padInfo, outHeight, outWidth } = getPadAndOutInfo(pad3, inHeight, inWidth, strideHeight, strideWidth, effectiveFilterHeight, effectiveFilterWidth, roundingMode, dataFormat);\n  const outChannels = depthwise ? filterChannels * inChannels : filterChannels;\n  let outShape;\n  if (dataFormat === \"channelsFirst\") {\n    outShape = [batchSize, outChannels, outHeight, outWidth];\n  } else if (dataFormat === \"channelsLast\") {\n    outShape = [batchSize, outHeight, outWidth, outChannels];\n  }\n  return {\n    batchSize,\n    dataFormat,\n    inHeight,\n    inWidth,\n    inChannels,\n    outHeight,\n    outWidth,\n    outChannels,\n    padInfo,\n    strideHeight,\n    strideWidth,\n    filterHeight,\n    filterWidth,\n    effectiveFilterHeight,\n    effectiveFilterWidth,\n    dilationHeight,\n    dilationWidth,\n    inShape,\n    outShape,\n    filterShape\n  };\n}\nfunction computeConv3DInfo(inShape, filterShape, strides, dilations, pad3, depthwise = false, dataFormat = \"channelsLast\", roundingMode) {\n  let [batchSize, inDepth, inHeight, inWidth, inChannels] = [-1, -1, -1, -1, -1];\n  if (dataFormat === \"channelsLast\") {\n    [batchSize, inDepth, inHeight, inWidth, inChannels] = inShape;\n  } else if (dataFormat === \"channelsFirst\") {\n    [batchSize, inChannels, inDepth, inHeight, inWidth] = inShape;\n  } else {\n    throw new Error(`Unknown dataFormat ${dataFormat}`);\n  }\n  const [filterDepth, filterHeight, filterWidth, , filterChannels] = filterShape;\n  const [strideDepth, strideHeight, strideWidth] = parse3TupleParam(strides);\n  const [dilationDepth, dilationHeight, dilationWidth] = parse3TupleParam(dilations);\n  const effectiveFilterDepth = getEffectiveFilterSize(filterDepth, dilationDepth);\n  const effectiveFilterHeight = getEffectiveFilterSize(filterHeight, dilationHeight);\n  const effectiveFilterWidth = getEffectiveFilterSize(filterWidth, dilationWidth);\n  const { padInfo, outDepth, outHeight, outWidth } = get3DPadAndOutInfo(pad3, inDepth, inHeight, inWidth, strideDepth, strideHeight, strideWidth, effectiveFilterDepth, effectiveFilterHeight, effectiveFilterWidth, roundingMode);\n  const outChannels = depthwise ? filterChannels * inChannels : filterChannels;\n  let outShape;\n  if (dataFormat === \"channelsFirst\") {\n    outShape = [batchSize, outChannels, outDepth, outHeight, outWidth];\n  } else if (dataFormat === \"channelsLast\") {\n    outShape = [batchSize, outDepth, outHeight, outWidth, outChannels];\n  }\n  return {\n    batchSize,\n    dataFormat,\n    inDepth,\n    inHeight,\n    inWidth,\n    inChannels,\n    outDepth,\n    outHeight,\n    outWidth,\n    outChannels,\n    padInfo,\n    strideDepth,\n    strideHeight,\n    strideWidth,\n    filterDepth,\n    filterHeight,\n    filterWidth,\n    effectiveFilterDepth,\n    effectiveFilterHeight,\n    effectiveFilterWidth,\n    dilationDepth,\n    dilationHeight,\n    dilationWidth,\n    inShape,\n    outShape,\n    filterShape\n  };\n}\nfunction computeOutputShape2D(inShape, fieldSize, stride, zeroPad, roundingMode) {\n  if (zeroPad == null) {\n    zeroPad = computeDefaultPad(inShape, fieldSize, stride);\n  }\n  const inputRows = inShape[0];\n  const inputCols = inShape[1];\n  const outputRows = round((inputRows - fieldSize + 2 * zeroPad) / stride + 1, roundingMode);\n  const outputCols = round((inputCols - fieldSize + 2 * zeroPad) / stride + 1, roundingMode);\n  return [outputRows, outputCols];\n}\nfunction computeOutputShape4D(inShape, fieldSize, outChannels, stride, zeroPad, roundingMode) {\n  if (zeroPad == null) {\n    zeroPad = computeDefaultPad(inShape, fieldSize, stride);\n  }\n  const inputDepth = inShape[0];\n  const inputRows = inShape[1];\n  const inputCols = inShape[2];\n  const outputDepths = round((inputDepth - fieldSize + 2 * zeroPad) / stride + 1, roundingMode);\n  const outputRows = round((inputRows - fieldSize + 2 * zeroPad) / stride + 1, roundingMode);\n  const outputCols = round((inputCols - fieldSize + 2 * zeroPad) / stride + 1, roundingMode);\n  return [outputDepths, outputRows, outputCols, outChannels];\n}\nfunction computeDefaultPad(inputShape, fieldSize, stride, dilation = 1) {\n  const effectiveFieldSize = getEffectiveFilterSize(fieldSize, dilation);\n  return Math.floor((inputShape[0] * (stride - 1) - stride + effectiveFieldSize) / 2);\n}\nfunction parseTupleParam(param) {\n  if (typeof param === \"number\") {\n    return [param, param, param];\n  }\n  if (param.length === 2) {\n    return [param[0], param[1], 1];\n  }\n  return param;\n}\nfunction parse3TupleParam(param) {\n  return typeof param === \"number\" ? [param, param, param] : param;\n}\nfunction getEffectiveFilterSize(filterSize, dilation) {\n  if (dilation <= 1) {\n    return filterSize;\n  }\n  return filterSize + (filterSize - 1) * (dilation - 1);\n}\nfunction getPadAndOutInfo(pad3, inHeight, inWidth, strideHeight, strideWidth, filterHeight, filterWidth, roundingMode, dataFormat) {\n  let padInfo;\n  let outHeight;\n  let outWidth;\n  if (typeof pad3 === \"number\") {\n    const padType = pad3 === 0 ? \"VALID\" : \"NUMBER\";\n    padInfo = { top: pad3, bottom: pad3, left: pad3, right: pad3, type: padType };\n    const outShape = computeOutputShape2D([inHeight, inWidth], filterHeight, strideHeight, pad3, roundingMode);\n    outHeight = outShape[0];\n    outWidth = outShape[1];\n  } else if (pad3 === \"same\") {\n    outHeight = Math.ceil(inHeight / strideHeight);\n    outWidth = Math.ceil(inWidth / strideWidth);\n    const padAlongHeight = Math.max(0, (outHeight - 1) * strideHeight + filterHeight - inHeight);\n    const padAlongWidth = Math.max(0, (outWidth - 1) * strideWidth + filterWidth - inWidth);\n    const top = Math.floor(padAlongHeight / 2);\n    const bottom = padAlongHeight - top;\n    const left = Math.floor(padAlongWidth / 2);\n    const right = padAlongWidth - left;\n    padInfo = { top, bottom, left, right, type: \"SAME\" };\n  } else if (pad3 === \"valid\") {\n    padInfo = { top: 0, bottom: 0, left: 0, right: 0, type: \"VALID\" };\n    outHeight = Math.ceil((inHeight - filterHeight + 1) / strideHeight);\n    outWidth = Math.ceil((inWidth - filterWidth + 1) / strideWidth);\n  } else if (typeof pad3 === \"object\") {\n    const top = dataFormat === \"channelsLast\" ? pad3[1][0] : pad3[2][0];\n    const bottom = dataFormat === \"channelsLast\" ? pad3[1][1] : pad3[2][1];\n    const left = dataFormat === \"channelsLast\" ? pad3[2][0] : pad3[3][0];\n    const right = dataFormat === \"channelsLast\" ? pad3[2][1] : pad3[3][1];\n    const padType = top === 0 && bottom === 0 && left === 0 && right === 0 ? \"VALID\" : \"EXPLICIT\";\n    padInfo = { top, bottom, left, right, type: padType };\n    outHeight = round((inHeight - filterHeight + top + bottom) / strideHeight + 1, roundingMode);\n    outWidth = round((inWidth - filterWidth + left + right) / strideWidth + 1, roundingMode);\n  } else {\n    throw Error(`Unknown padding parameter: ${pad3}`);\n  }\n  return { padInfo, outHeight, outWidth };\n}\nfunction get3DPadAndOutInfo(pad3, inDepth, inHeight, inWidth, strideDepth, strideHeight, strideWidth, filterDepth, filterHeight, filterWidth, roundingMode) {\n  let padInfo;\n  let outDepth;\n  let outHeight;\n  let outWidth;\n  if (typeof pad3 === \"number\") {\n    const padType = pad3 === 0 ? \"VALID\" : \"NUMBER\";\n    padInfo = {\n      top: pad3,\n      bottom: pad3,\n      left: pad3,\n      right: pad3,\n      front: pad3,\n      back: pad3,\n      type: padType\n    };\n    const outShape = computeOutputShape4D([inDepth, inHeight, inWidth, 1], filterDepth, 1, strideDepth, pad3, roundingMode);\n    outDepth = outShape[0];\n    outHeight = outShape[1];\n    outWidth = outShape[2];\n  } else if (pad3 === \"same\") {\n    outDepth = Math.ceil(inDepth / strideDepth);\n    outHeight = Math.ceil(inHeight / strideHeight);\n    outWidth = Math.ceil(inWidth / strideWidth);\n    const padAlongDepth = (outDepth - 1) * strideDepth + filterDepth - inDepth;\n    const padAlongHeight = (outHeight - 1) * strideHeight + filterHeight - inHeight;\n    const padAlongWidth = (outWidth - 1) * strideWidth + filterWidth - inWidth;\n    const front = Math.floor(padAlongDepth / 2);\n    const back = padAlongDepth - front;\n    const top = Math.floor(padAlongHeight / 2);\n    const bottom = padAlongHeight - top;\n    const left = Math.floor(padAlongWidth / 2);\n    const right = padAlongWidth - left;\n    padInfo = { top, bottom, left, right, front, back, type: \"SAME\" };\n  } else if (pad3 === \"valid\") {\n    padInfo = {\n      top: 0,\n      bottom: 0,\n      left: 0,\n      right: 0,\n      front: 0,\n      back: 0,\n      type: \"VALID\"\n    };\n    outDepth = Math.ceil((inDepth - filterDepth + 1) / strideDepth);\n    outHeight = Math.ceil((inHeight - filterHeight + 1) / strideHeight);\n    outWidth = Math.ceil((inWidth - filterWidth + 1) / strideWidth);\n  } else {\n    throw Error(`Unknown padding parameter: ${pad3}`);\n  }\n  return { padInfo, outDepth, outHeight, outWidth };\n}\nfunction round(value, roundingMode) {\n  if (!roundingMode) {\n    return Math.trunc(value);\n  }\n  switch (roundingMode) {\n    case \"round\":\n      return Math.round(value);\n    case \"ceil\":\n      return Math.ceil(value);\n    case \"floor\":\n      return Math.floor(value);\n    default:\n      throw new Error(`Unknown roundingMode ${roundingMode}`);\n  }\n}\nfunction tupleValuesAreOne(param) {\n  const [dimA, dimB, dimC] = parseTupleParam(param);\n  return dimA === 1 && dimB === 1 && dimC === 1;\n}\nfunction eitherStridesOrDilationsAreOne(strides, dilations) {\n  return tupleValuesAreOne(strides) || tupleValuesAreOne(dilations);\n}\nfunction convertConv2DDataFormat(dataFormat) {\n  if (dataFormat === \"NHWC\") {\n    return \"channelsLast\";\n  } else if (dataFormat === \"NCHW\") {\n    return \"channelsFirst\";\n  } else {\n    throw new Error(`Unknown dataFormat ${dataFormat}`);\n  }\n}\nfunction checkPadOnDimRoundingMode(opDesc, pad3, dimRoundingMode) {\n  if (dimRoundingMode != null) {\n    if (typeof pad3 === \"string\") {\n      throw Error(`Error in ${opDesc}: pad must be an integer when using dimRoundingMode ${dimRoundingMode} but got pad ${pad3}.`);\n    } else if (typeof pad3 === \"number\") {\n      assert(isInt(pad3), () => `Error in ${opDesc}: pad must be an integer when using dimRoundingMode ${dimRoundingMode} but got pad ${pad3}.`);\n    } else if (typeof pad3 === \"object\") {\n      pad3.forEach((p2) => {\n        p2.forEach((v) => {\n          assert(isInt(v), () => `Error in ${opDesc}: pad must be an integer when using dimRoundingMode ${dimRoundingMode} but got pad ${v}.`);\n        });\n      });\n    } else {\n      throw Error(`Error in ${opDesc}: Unknown padding parameter: ${pad3}`);\n    }\n  }\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/reshape.js\nfunction reshape_(x, shape) {\n  const $x = convertToTensor(x, \"x\", \"reshape\", \"string_or_numeric\");\n  const inputs = { x: $x };\n  const attrs = { shape };\n  return ENGINE.runKernel(Reshape, inputs, attrs);\n}\nvar reshape = op({ reshape_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/avg_pool.js\nfunction avgPool_(x, filterSize, strides, pad3, dimRoundingMode) {\n  const $x = convertToTensor(x, \"x\", \"avgPool\", \"float32\");\n  const dilations = 1;\n  assert(eitherStridesOrDilationsAreOne(strides, dilations), () => `Error in avgPool: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`);\n  let x4D = $x;\n  let reshapedTo4D = false;\n  if ($x.rank === 3) {\n    reshapedTo4D = true;\n    x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);\n  }\n  assert(x4D.rank === 4, () => `Error in avgPool: x must be rank 4 but got rank ${x4D.rank}.`);\n  checkPadOnDimRoundingMode(\"avgPool\", pad3, dimRoundingMode);\n  const inputs = { x: x4D };\n  const attrs = { filterSize, strides, pad: pad3, dimRoundingMode };\n  let res = ENGINE.runKernel(AvgPool, inputs, attrs);\n  res = cast(res, $x.dtype);\n  if (reshapedTo4D) {\n    return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);\n  }\n  return res;\n}\nvar avgPool = op({ avgPool_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/avg_pool_3d.js\nfunction avgPool3d_(x, filterSize, strides, pad3, dimRoundingMode, dataFormat = \"NDHWC\") {\n  const $x = convertToTensor(x, \"x\", \"avgPool3d\", \"float32\");\n  let x5D = $x;\n  let reshapedTo5D = false;\n  if ($x.rank === 4) {\n    reshapedTo5D = true;\n    x5D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2], $x.shape[3]]);\n  }\n  assert(x5D.rank === 5, () => `Error in avgPool3d: x must be rank 5 but got rank ${x5D.rank}.`);\n  assert(dataFormat === \"NDHWC\", () => `Error in avgPool3d: Only NDHWC is currently supported, but got dataFormat of ${dataFormat}`);\n  checkPadOnDimRoundingMode(\"avgPool3d\", pad3, dimRoundingMode);\n  const inputs = { x: x5D };\n  const attrs = { filterSize, strides, pad: pad3, dimRoundingMode, dataFormat };\n  let res = ENGINE.runKernel(AvgPool3D, inputs, attrs);\n  res = cast(res, x5D.dtype);\n  if (reshapedTo5D) {\n    return reshape(res, [res.shape[1], res.shape[2], res.shape[3], res.shape[4]]);\n  }\n  return res;\n}\nvar avgPool3d = op({ avgPool3d_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/concat.js\nfunction concat_(tensors, axis = 0) {\n  assert(tensors.length >= 1, () => \"Pass at least one tensor to concat\");\n  const $tensors = convertToTensorArray(tensors, \"tensors\", \"concat\", \"string_or_numeric\");\n  if ($tensors[0].dtype === \"complex64\") {\n    $tensors.forEach((tensor2) => {\n      if (tensor2.dtype !== \"complex64\") {\n        throw new Error(`Cannot concatenate complex64 tensors with a tensor\n          with dtype ${tensor2.dtype}. `);\n      }\n    });\n  }\n  if ($tensors.length === 1) {\n    return clone($tensors[0]);\n  }\n  const inputs = $tensors;\n  const attr = { axis };\n  return ENGINE.runKernel(Concat, inputs, attr);\n}\nvar concat = op({ concat_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/sigmoid.js\nfunction sigmoid_(x) {\n  const $x = convertToTensor(x, \"x\", \"sigmoid\", \"float32\");\n  const inputs = { x: $x };\n  return ENGINE.runKernel(Sigmoid, inputs);\n}\nvar sigmoid = op({ sigmoid_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/slice.js\nfunction slice_(x, begin, size) {\n  const $x = convertToTensor(x, \"x\", \"slice\", \"string_or_numeric\");\n  if ($x.rank === 0) {\n    throw new Error(\"Slicing scalar is not possible\");\n  }\n  const inputs = { x: $x };\n  const attrs = { begin, size };\n  return ENGINE.runKernel(Slice, inputs, attrs);\n}\nvar slice = op({ slice_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/tanh.js\nfunction tanh_(x) {\n  const $x = convertToTensor(x, \"x\", \"tanh\", \"float32\");\n  const inputs = { x: $x };\n  return ENGINE.runKernel(Tanh, inputs);\n}\nvar tanh2 = op({ tanh_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/basic_lstm_cell.js\nfunction basicLSTMCell_(forgetBias, lstmKernel, lstmBias, data, c, h) {\n  const $forgetBias = convertToTensor(forgetBias, \"forgetBias\", \"basicLSTMCell\");\n  const $lstmKernel = convertToTensor(lstmKernel, \"lstmKernel\", \"basicLSTMCell\");\n  const $lstmBias = convertToTensor(lstmBias, \"lstmBias\", \"basicLSTMCell\");\n  const $data = convertToTensor(data, \"data\", \"basicLSTMCell\");\n  const $c = convertToTensor(c, \"c\", \"basicLSTMCell\");\n  const $h = convertToTensor(h, \"h\", \"basicLSTMCell\");\n  const combined = concat([$data, $h], 1);\n  const weighted = matMul(combined, $lstmKernel);\n  const res = add2(weighted, $lstmBias);\n  const batchSize = res.shape[0];\n  const sliceCols = res.shape[1] / 4;\n  const sliceSize = [batchSize, sliceCols];\n  const i2 = slice(res, [0, 0], sliceSize);\n  const j = slice(res, [0, sliceCols], sliceSize);\n  const f = slice(res, [0, sliceCols * 2], sliceSize);\n  const o = slice(res, [0, sliceCols * 3], sliceSize);\n  const newC = add2(mul(sigmoid(i2), tanh2(j)), mul($c, sigmoid(add2($forgetBias, f))));\n  const newH = mul(tanh2(newC), sigmoid(o));\n  return [newC, newH];\n}\nvar basicLSTMCell = op({ basicLSTMCell_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/batch_to_space_nd.js\nfunction batchToSpaceND_(x, blockShape, crops) {\n  const $x = convertToTensor(x, \"x\", \"batchToSpaceND\");\n  const prod6 = blockShape.reduce((a, b) => a * b);\n  assert($x.rank >= 1 + blockShape.length, () => `input rank is ${$x.rank} but should be > than blockShape.length ${blockShape.length}`);\n  assert(crops.length === blockShape.length, () => `crops.length is ${crops.length} but should be equal to blockShape.length  ${blockShape.length}`);\n  assert($x.shape[0] % prod6 === 0, () => `input tensor batch is ${$x.shape[0]} but is not divisible by the product of the elements of blockShape ${blockShape.join(\" * \")} === ${prod6}`);\n  const inputs = { x: $x };\n  const attrs = { blockShape, crops };\n  return ENGINE.runKernel(BatchToSpaceND, inputs, attrs);\n}\nvar batchToSpaceND = op({ batchToSpaceND_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/batchnorm_util.js\nfunction xAs4D(x) {\n  let x4D;\n  if (x.rank === 0 || x.rank === 1) {\n    x4D = reshape(x, [1, 1, 1, x.size]);\n  } else if (x.rank === 2) {\n    x4D = reshape(x, [1, 1, x.shape[0], x.shape[1]]);\n  } else if (x.rank === 3) {\n    x4D = reshape(x, [1, x.shape[0], x.shape[1], x.shape[2]]);\n  } else {\n    x4D = x;\n  }\n  return x4D;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/batchnorm.js\nfunction batchNorm_(x, mean5, variance, offset, scale2, varianceEpsilon) {\n  if (varianceEpsilon == null) {\n    varianceEpsilon = 1e-3;\n  }\n  const $x = convertToTensor(x, \"x\", \"batchNorm\");\n  const $mean = convertToTensor(mean5, \"mean\", \"batchNorm\");\n  const $variance = convertToTensor(variance, \"variance\", \"batchNorm\");\n  let $scale;\n  if (scale2 != null) {\n    $scale = convertToTensor(scale2, \"scale\", \"batchNorm\");\n  }\n  let $offset;\n  if (offset != null) {\n    $offset = convertToTensor(offset, \"offset\", \"batchNorm\");\n  }\n  assert($mean.rank === $variance.rank, () => \"Batch normalization gradient requires mean and variance to have equal ranks.\");\n  assert($offset == null || $mean.rank === $offset.rank, () => \"Batch normalization gradient requires mean and offset to have equal ranks.\");\n  assert($scale == null || $mean.rank === $scale.rank, () => \"Batch normalization gradient requires mean and scale to have equal ranks.\");\n  const x4D = xAs4D($x);\n  const inputs = {\n    x: x4D,\n    scale: $scale,\n    offset: $offset,\n    mean: $mean,\n    variance: $variance\n  };\n  const attrs = { varianceEpsilon };\n  const res = ENGINE.runKernel(FusedBatchNorm, inputs, attrs);\n  return reshape(res, $x.shape);\n}\nvar batchNorm = op({ batchNorm_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/batchnorm2d.js\nfunction batchNorm2d_(x, mean5, variance, offset, scale2, varianceEpsilon) {\n  const $x = convertToTensor(x, \"x\", \"batchNorm\");\n  const $mean = convertToTensor(mean5, \"mean\", \"batchNorm\");\n  const $variance = convertToTensor(variance, \"variance\", \"batchNorm\");\n  let $scale;\n  if (scale2 != null) {\n    $scale = convertToTensor(scale2, \"scale\", \"batchNorm\");\n  }\n  let $offset;\n  if (offset != null) {\n    $offset = convertToTensor(offset, \"offset\", \"batchNorm\");\n  }\n  assert($x.rank === 2, () => `Error in batchNorm2D: x must be rank 2 but got rank ${$x.rank}.`);\n  assert($mean.rank === 2 || $mean.rank === 1, () => `Error in batchNorm2D: mean must be rank 2 or rank 1 but got rank ${$mean.rank}.`);\n  assert($variance.rank === 2 || $variance.rank === 1, () => `Error in batchNorm2D: variance must be rank 2 or rank 1 but got rank ${$variance.rank}.`);\n  if ($scale != null) {\n    assert($scale.rank === 2 || $scale.rank === 1, () => `Error in batchNorm2D: scale must be rank 2 or rank 1 but got rank ${$scale.rank}.`);\n  }\n  if ($offset != null) {\n    assert($offset.rank === 2 || $offset.rank === 1, () => `Error in batchNorm2D: offset must be rank 2 or rank 1 but got rank ${$offset.rank}.`);\n  }\n  return batchNorm($x, $mean, $variance, $offset, $scale, varianceEpsilon);\n}\nvar batchNorm2d = op({ batchNorm2d_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/batchnorm3d.js\nfunction batchNorm3d_(x, mean5, variance, offset, scale2, varianceEpsilon) {\n  const $x = convertToTensor(x, \"x\", \"batchNorm\");\n  const $mean = convertToTensor(mean5, \"mean\", \"batchNorm\");\n  const $variance = convertToTensor(variance, \"variance\", \"batchNorm\");\n  let $scale;\n  if (scale2 != null) {\n    $scale = convertToTensor(scale2, \"scale\", \"batchNorm\");\n  }\n  let $offset;\n  if (offset != null) {\n    $offset = convertToTensor(offset, \"offset\", \"batchNorm\");\n  }\n  assert($x.rank === 3, () => `Error in batchNorm3D: x must be rank 3 but got rank ${$x.rank}.`);\n  assert($mean.rank === 3 || $mean.rank === 1, () => `Error in batchNorm3D: mean must be rank 3 or rank 1 but got rank ${$mean.rank}.`);\n  assert($variance.rank === 3 || $variance.rank === 1, () => `Error in batchNorm3D: variance must be rank 3 or rank 1 but got rank ${$variance.rank}.`);\n  if ($scale != null) {\n    assert($scale.rank === 3 || $scale.rank === 1, () => `Error in batchNorm3D: scale must be rank 3 or rank 1 but got rank ${$scale.rank}.`);\n  }\n  if ($offset != null) {\n    assert($offset.rank === 3 || $offset.rank === 1, () => `Error in batchNorm3D: offset must be rank 3 or rank 1 but got rank ${$offset.rank}.`);\n  }\n  return batchNorm($x, $mean, $variance, $offset, $scale, varianceEpsilon);\n}\nvar batchNorm3d = op({ batchNorm3d_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/batchnorm4d.js\nfunction batchNorm4d_(x, mean5, variance, offset, scale2, varianceEpsilon) {\n  const $x = convertToTensor(x, \"x\", \"batchNorm\");\n  const $mean = convertToTensor(mean5, \"mean\", \"batchNorm\");\n  const $variance = convertToTensor(variance, \"variance\", \"batchNorm\");\n  let $scale;\n  if (scale2 != null) {\n    $scale = convertToTensor(scale2, \"scale\", \"batchNorm\");\n  }\n  let $offset;\n  if (offset != null) {\n    $offset = convertToTensor(offset, \"offset\", \"batchNorm\");\n  }\n  assert($x.rank === 4, () => `Error in batchNorm4D: x must be rank 4 but got rank ${$x.rank}.`);\n  assert($mean.rank === 4 || $mean.rank === 1, () => `Error in batchNorm4D: mean must be rank 4 or rank 1 but got rank ${$mean.rank}.`);\n  assert($variance.rank === 4 || $variance.rank === 1, () => `Error in batchNorm4D: variance must be rank 4 or rank 1 but got rank ${$variance.rank}.`);\n  if ($scale != null) {\n    assert($scale.rank === 4 || $scale.rank === 1, () => `Error in batchNorm4D: scale must be rank 4 or rank 1 but got rank ${$scale.rank}.`);\n  }\n  if ($offset != null) {\n    assert($offset.rank === 4 || $offset.rank === 1, () => `Error in batchNorm4D: offset must be rank 4 or rank 1 but got rank ${$offset.rank}.`);\n  }\n  return batchNorm($x, $mean, $variance, $offset, $scale, varianceEpsilon);\n}\nvar batchNorm4d = op({ batchNorm4d_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/bincount.js\nfunction bincount_(x, weights, size) {\n  const $x = convertToTensor(x, \"x\", \"bincount\");\n  const $weights = convertToTensor(weights, \"weights\", \"bincount\");\n  assert($x.dtype === \"int32\", () => `Error in bincount: input dtype must be int32, but got ${$x.dtype}`);\n  assert(size >= 0, () => `size must be non-negative, but got ${size}.`);\n  assert($weights.size === $x.size || $weights.size === 0, () => `Error in bincount: weights must have the same size as input or0-length, but got input shape: ${$x.shape}, weights shape: ${$weights.shape}.`);\n  const inputs = { x: $x, weights: $weights };\n  const attrs = { size };\n  return ENGINE.runKernel(Bincount, inputs, attrs);\n}\nvar bincount = op({ bincount_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/broadcast_args.js\nfunction broadcastArgs_(s0, s1) {\n  const shape1Input = convertToTensor(s0, \"s0\", \"broadcastArgs\", \"int32\");\n  const shape2Input = convertToTensor(s1, \"s1\", \"broadcastArgs\", \"int32\");\n  if (shape1Input.rank !== 1) {\n    throw new Error(`broadcastArgs(): first input must be a vector (rank=1). Has rank ${shape1Input.rank}`);\n  }\n  if (shape2Input.rank !== 1) {\n    throw new Error(`broadcastArgs(): second input must be a vector (rank=1). Has rank ${shape2Input.rank}`);\n  }\n  const inputs = { s0: shape1Input, s1: shape2Input };\n  return ENGINE.runKernel(BroadcastArgs, inputs);\n}\nvar broadcastArgs = op({ broadcastArgs_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/broadcast_to.js\nfunction broadcastTo_(x, shape) {\n  let input2 = convertToTensor(x, \"broadcastTo\", \"x\");\n  const xShape = input2.shape;\n  if (shape.some((d) => !(d > 0) || d % 1 !== 0)) {\n    throw new Error(`broadcastTo(): Invalid broadcast shape [${shape}].`);\n  }\n  if (shape.length < input2.rank) {\n    throw new Error(`broadcastTo(): shape.length=${shape.length} < input.rank=${input2.rank}.`);\n  }\n  if (shape.length > input2.rank) {\n    const newShape = input2.shape.slice();\n    while (newShape.length < shape.length) {\n      newShape.unshift(1);\n    }\n    input2 = reshape(input2, newShape);\n  }\n  const inputShape = input2.shape;\n  const reps = Array.from(shape);\n  for (let i2 = shape.length - 1; i2 >= 0; i2--) {\n    if (inputShape[i2] === shape[i2]) {\n      reps[i2] = 1;\n    } else if (input2.shape[i2] !== 1) {\n      throw new Error(`broadcastTo(): [${xShape}] cannot be broadcast to [${shape}].`);\n    }\n  }\n  const axes = reps.map((n2, i2) => n2 > 1 ? i2 : -1).filter((i2) => i2 >= 0);\n  if (axes.length === 0) {\n    return clone(input2);\n  }\n  const inputs = { x: input2 };\n  const attrs = { reps };\n  return ENGINE.runKernel(Tile, inputs, attrs);\n}\nvar broadcastTo = op({ broadcastTo_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/ceil.js\nfunction ceil_(x) {\n  const $x = convertToTensor(x, \"x\", \"ceil\", \"float32\");\n  const inputs = { x: $x };\n  return ENGINE.runKernel(Ceil, inputs);\n}\nvar ceil = op({ ceil_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/clip_by_value.js\nfunction clipByValue_(x, clipValueMin, clipValueMax) {\n  const $x = convertToTensor(x, \"x\", \"clipByValue\");\n  assert(clipValueMin <= clipValueMax, () => `Error in clip: min (${clipValueMin}) must be less than or equal to max (${clipValueMax}).`);\n  const inputs = { x: $x };\n  const attrs = { clipValueMin, clipValueMax };\n  return ENGINE.runKernel(ClipByValue, inputs, attrs);\n}\nvar clipByValue = op({ clipByValue_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/concat_1d.js\nfunction concat1d_(tensors) {\n  return concat(tensors, 0);\n}\nvar concat1d = op({ concat1d_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/concat_2d.js\nfunction concat2d_(tensors, axis) {\n  return concat(tensors, axis);\n}\nvar concat2d = op({ concat2d_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/concat_3d.js\nfunction concat3d_(tensors, axis) {\n  return concat(tensors, axis);\n}\nvar concat3d = op({ concat3d_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/concat_4d.js\nfunction concat4d_(tensors, axis) {\n  return concat(tensors, axis);\n}\nvar concat4d = op({ concat4d_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/conv2d.js\nfunction conv2d_(x, filter, strides, pad3, dataFormat = \"NHWC\", dilations = [1, 1], dimRoundingMode) {\n  const $x = convertToTensor(x, \"x\", \"conv2d\", \"float32\");\n  const $filter = convertToTensor(filter, \"filter\", \"conv2d\", \"float32\");\n  let x4D = $x;\n  let reshapedTo4D = false;\n  if ($x.rank === 3) {\n    reshapedTo4D = true;\n    x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);\n  }\n  assert(x4D.rank === 4, () => `Error in conv2d: input must be rank 4, but got rank ${x4D.rank}.`);\n  assert($filter.rank === 4, () => `Error in conv2d: filter must be rank 4, but got rank ${$filter.rank}.`);\n  checkPadOnDimRoundingMode(\"conv2d\", pad3, dimRoundingMode);\n  const inDepth = dataFormat === \"NHWC\" ? x4D.shape[3] : x4D.shape[1];\n  assert(inDepth === $filter.shape[2], () => `Error in conv2d: depth of input (${inDepth}) must match input depth for filter ${$filter.shape[2]}.`);\n  assert(eitherStridesOrDilationsAreOne(strides, dilations), () => `Error in conv2D: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`);\n  const inputs = { x: x4D, filter: $filter };\n  const attrs = { strides, pad: pad3, dataFormat, dilations, dimRoundingMode };\n  const res = ENGINE.runKernel(Conv2D, inputs, attrs);\n  if (reshapedTo4D) {\n    return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);\n  }\n  return res;\n}\nvar conv2d = op({ conv2d_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/conv1d.js\nfunction conv1d_(x, filter, stride, pad3, dataFormat = \"NWC\", dilation = 1, dimRoundingMode) {\n  const $x = convertToTensor(x, \"x\", \"conv1d\");\n  const $filter = convertToTensor(filter, \"filter\", \"conv1d\");\n  let x3D = $x;\n  let reshapedTo3D = false;\n  if ($x.rank === 2) {\n    reshapedTo3D = true;\n    x3D = reshape($x, [1, $x.shape[0], $x.shape[1]]);\n  }\n  assert(x3D.rank === 3, () => `Error in conv1d: input must be rank 3, but got rank ${x3D.rank}.`);\n  assert($filter.rank === 3, () => `Error in conv1d: filter must be rank 3, but got rank ${$filter.rank}.`);\n  checkPadOnDimRoundingMode(\"conv1d\", pad3, dimRoundingMode);\n  assert(x3D.shape[2] === $filter.shape[1], () => `Error in conv1d: depth of input (${x3D.shape[2]}) must match input depth for filter ${$filter.shape[1]}.`);\n  assert(eitherStridesOrDilationsAreOne(stride, dilation), () => `Error in conv1D: Either stride or dilation must be 1. Got stride ${stride} and dilation '${dilation}'`);\n  assert(dataFormat === \"NWC\", () => `Error in conv1d: got dataFormat of ${dataFormat} but only NWC is currently supported.`);\n  const filter4D = reshape($filter, [1, $filter.shape[0], $filter.shape[1], $filter.shape[2]]);\n  const input4D = reshape(x3D, [x3D.shape[0], 1, x3D.shape[1], x3D.shape[2]]);\n  const strides = [1, stride];\n  const dilations = [1, dilation];\n  const conv2dDataFormat = \"NHWC\";\n  const res = conv2d(input4D, filter4D, strides, pad3, conv2dDataFormat, dilations, dimRoundingMode);\n  if (reshapedTo3D) {\n    return reshape(res, [res.shape[2], res.shape[3]]);\n  }\n  return reshape(res, [res.shape[0], res.shape[2], res.shape[3]]);\n}\nvar conv1d = op({ conv1d_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/conv2d_backprop_input.js\nfunction conv2DBackpropInput_(xShape, dy, filter, strides, pad3, dataFormat = \"NHWC\", dimRoundingMode) {\n  assert(xShape.length === dy.rank, () => `Length of inShape (${xShape.length}) and rank of dy (${dy.rank}) must match`);\n  let xShape4D = xShape;\n  let dy4D = dy;\n  let reshapedTo4D = false;\n  if (dy.rank === 3) {\n    reshapedTo4D = true;\n    dy4D = reshape(dy, [1, dy.shape[0], dy.shape[1], dy.shape[2]]);\n    xShape4D = [1, xShape[0], xShape[1], xShape[2]];\n  }\n  assert(xShape4D.length === 4, () => `Error in conv2dDerInput: inShape must be length 4, but got length ${xShape4D.length}.`);\n  assert(dy4D.rank === 4, () => `Error in conv2dDerInput: dy must be rank 4, but got rank ${dy4D.rank}`);\n  assert(filter.rank === 4, () => `Error in conv2dDerInput: filter must be rank 4, but got rank ${filter.rank}`);\n  const inDepth = dataFormat === \"NHWC\" ? xShape4D[3] : xShape4D[1];\n  const outDepth = dataFormat === \"NHWC\" ? dy4D.shape[3] : dy4D.shape[1];\n  assert(inDepth === filter.shape[2], () => `Error in conv2dDerInput: depth of input (${inDepth}) must match input depth for filter ${filter.shape[2]}.`);\n  assert(outDepth === filter.shape[3], () => `Error in conv2dDerInput: depth of output (${outDepth}) must match output depth for filter ${filter.shape[3]}.`);\n  checkPadOnDimRoundingMode(\"conv2dDerInput\", pad3, dimRoundingMode);\n  const inputs = { dy: dy4D, filter };\n  const attrs = { strides, pad: pad3, dataFormat, dimRoundingMode, inputShape: xShape4D };\n  const res = ENGINE.runKernel(Conv2DBackpropInput, inputs, attrs);\n  if (reshapedTo4D) {\n    return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);\n  }\n  return res;\n}\nvar conv2DBackpropInput = op({ conv2DBackpropInput_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/conv2d_transpose.js\nfunction conv2dTranspose_(x, filter, outputShape, strides, pad3, dimRoundingMode) {\n  const $x = convertToTensor(x, \"x\", \"conv2dTranspose\");\n  const $filter = convertToTensor(filter, \"filter\", \"conv2dTranspose\");\n  return conv2DBackpropInput(outputShape, $x, $filter, strides, pad3, \"NHWC\", dimRoundingMode);\n}\nvar conv2dTranspose = op({ conv2dTranspose_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/conv3d.js\nfunction conv3d_(x, filter, strides, pad3, dataFormat = \"NDHWC\", dilations = [1, 1, 1]) {\n  const $x = convertToTensor(x, \"x\", \"conv3d\");\n  const $filter = convertToTensor(filter, \"filter\", \"conv3d\");\n  let x5D = $x;\n  let reshapedTo5D = false;\n  if ($x.rank === 4) {\n    reshapedTo5D = true;\n    x5D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2], $x.shape[3]]);\n  }\n  assert(x5D.rank === 5, () => `Error in conv3d: input must be rank 5, but got rank ${x5D.rank}.`);\n  assert($filter.rank === 5, () => `Error in conv3d: filter must be rank 5, but got rank ${$filter.rank}.`);\n  assert(x5D.shape[4] === $filter.shape[3], () => `Error in conv3d: depth of input (${x5D.shape[4]}) must match input depth for filter ${$filter.shape[3]}.`);\n  assert(eitherStridesOrDilationsAreOne(strides, dilations), () => `Error in conv3D: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`);\n  assert(dataFormat === \"NDHWC\", () => `Error in conv3d: got dataFormat of ${dataFormat} but only NDHWC is currently supported.`);\n  const inputs = { x: x5D, filter: $filter };\n  const attrs = { strides, pad: pad3, dataFormat, dilations };\n  const res = ENGINE.runKernel(Conv3D, inputs, attrs);\n  if (reshapedTo5D) {\n    return reshape(res, [res.shape[1], res.shape[2], res.shape[3], res.shape[4]]);\n  }\n  return res;\n}\nvar conv3d = op({ conv3d_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/conv3d_backprop_input.js\nfunction conv3DBackpropInput_(xShape, dy, filter, strides, pad3) {\n  assert(xShape.length === dy.rank, () => `Length of inShape (${xShape.length}) and rank of dy (${dy.rank}) must match`);\n  let xShape5D = xShape;\n  let dy5D = dy;\n  let reshapedTo5D = false;\n  if (dy.rank === 4) {\n    reshapedTo5D = true;\n    dy5D = reshape(dy, [1, dy.shape[0], dy.shape[1], dy.shape[2], dy.shape[3]]);\n    xShape5D = [1, xShape[0], xShape[1], xShape[2], xShape[3]];\n  }\n  const inDepth = xShape5D[4];\n  const outDepth = dy5D.shape[4];\n  assert(xShape5D.length === 5, () => `Error in conv3dDerInput: inShape must be length 5, but got length ${xShape5D.length}.`);\n  assert(dy5D.rank === 5, () => `Error in conv3dDerInput: dy must be rank 5, but got rank ${dy5D.rank}`);\n  assert(filter.rank === 5, () => `Error in conv3dDerInput: filter must be rank 5, but got rank ${filter.rank}`);\n  assert(inDepth === filter.shape[3], () => `Error in conv3dDerInput: depth of input (${inDepth}) must match input depth for filter ${filter.shape[3]}.`);\n  assert(outDepth === filter.shape[4], () => `Error in conv3dDerInput: depth of output (${outDepth}) must match output depth for filter ${filter.shape[4]}.`);\n  const inputs = { dy: dy5D, filter };\n  const attrs = { pad: pad3, strides, inputShape: xShape5D };\n  const res = ENGINE.runKernel(Conv3DBackpropInputV2, inputs, attrs);\n  if (reshapedTo5D) {\n    return reshape(res, [res.shape[1], res.shape[2], res.shape[3], res.shape[4]]);\n  }\n  return res;\n}\nvar conv3DBackpropInput = op({ conv3DBackpropInput_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/conv3d_transpose.js\nfunction conv3dTranspose_(x, filter, outputShape, strides, pad3) {\n  const $x = convertToTensor(x, \"x\", \"conv3dTranspose\");\n  const $filter = convertToTensor(filter, \"filter\", \"conv3dTranspose\");\n  return conv3DBackpropInput(outputShape, $x, $filter, strides, pad3);\n}\nvar conv3dTranspose = op({ conv3dTranspose_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/cos.js\nfunction cos_(x) {\n  const $x = convertToTensor(x, \"x\", \"cos\", \"float32\");\n  const inputs = { x: $x };\n  return ENGINE.runKernel(Cos, inputs);\n}\nvar cos = op({ cos_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/cosh.js\nfunction cosh_(x) {\n  const $x = convertToTensor(x, \"x\", \"cosh\", \"float32\");\n  const inputs = { x: $x };\n  return ENGINE.runKernel(Cosh, inputs);\n}\nvar cosh = op({ cosh_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/cumprod.js\nfunction cumprod_(x, axis = 0, exclusive = false, reverse5 = false) {\n  const $x = convertToTensor(x, \"x\", \"cumprod\");\n  const inputs = { x: $x };\n  const attrs = { axis, exclusive, reverse: reverse5 };\n  return ENGINE.runKernel(Cumprod, inputs, attrs);\n}\nvar cumprod = op({ cumprod_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/cumsum.js\nfunction cumsum_(x, axis = 0, exclusive = false, reverse5 = false) {\n  const $x = convertToTensor(x, \"x\", \"cumsum\");\n  const inputs = { x: $x };\n  const attrs = { axis, exclusive, reverse: reverse5 };\n  return ENGINE.runKernel(Cumsum, inputs, attrs);\n}\nvar cumsum = op({ cumsum_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/dense_bincount.js\nfunction denseBincount_(x, weights, size, binaryOutput = false) {\n  const $x = convertToTensor(x, \"x\", \"denseBincount\");\n  const $weights = convertToTensor(weights, \"weights\", \"denseBincount\");\n  assert($x.dtype === \"int32\", () => `Error in denseBincount: input dtype must be int32, but got ${$x.dtype}`);\n  assert($x.rank <= 2, () => `Error in denseBincount: input must be at most rank 2, but got rank ${$x.rank}.`);\n  assert(size >= 0, () => `size must be non-negative, but got ${size}.`);\n  assert($weights.size === $x.size || $weights.size === 0, () => `Error in denseBincount: weights must have the same shape as x or 0-length, but got x shape: ${$x.shape}, weights shape: ${$weights.shape}.`);\n  const inputs = { x: $x, weights: $weights };\n  const attrs = { size, binaryOutput };\n  return ENGINE.runKernel(DenseBincount, inputs, attrs);\n}\nvar denseBincount = op({ denseBincount_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/depth_to_space.js\nfunction depthToSpace_(x, blockSize, dataFormat = \"NHWC\") {\n  const $x = convertToTensor(x, \"x\", \"depthToSpace\", \"float32\");\n  const inputHeight = dataFormat === \"NHWC\" ? $x.shape[1] : $x.shape[2];\n  const inputWidth = dataFormat === \"NHWC\" ? $x.shape[2] : $x.shape[3];\n  const inputDepth = dataFormat === \"NHWC\" ? $x.shape[3] : $x.shape[1];\n  assert(blockSize > 1, () => `blockSize should be > 1 for depthToSpace, but was: ${blockSize}`);\n  assert(inputHeight * blockSize >= 0, () => `Negative dimension size caused by overflow when multiplying\n    ${inputHeight} and ${blockSize}  for depthToSpace with input shape\n    ${$x.shape}`);\n  assert(inputWidth * blockSize >= 0, () => `Negative dimension size caused by overflow when multiplying\n    ${inputWidth} and ${blockSize} for depthToSpace with input shape\n        ${$x.shape}`);\n  assert(inputDepth % (blockSize * blockSize) === 0, () => `Dimension size must be evenly divisible by ${blockSize * blockSize} but is ${inputDepth} for depthToSpace with input shape ${$x.shape}`);\n  const inputs = { x: $x };\n  const attrs = { blockSize, dataFormat };\n  return ENGINE.runKernel(DepthToSpace, inputs, attrs);\n}\nvar depthToSpace = op({ depthToSpace_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/depthwise_conv2d.js\nfunction depthwiseConv2d_(x, filter, strides, pad3, dataFormat = \"NHWC\", dilations = [1, 1], dimRoundingMode) {\n  const $x = convertToTensor(x, \"x\", \"depthwiseConv2d\", \"float32\");\n  const $filter = convertToTensor(filter, \"filter\", \"depthwiseConv2d\", \"float32\");\n  let x4D = $x;\n  let reshapedTo4D = false;\n  if ($x.rank === 3) {\n    reshapedTo4D = true;\n    x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);\n  }\n  assert(x4D.rank === 4, () => `Error in depthwiseConv2d: input must be rank 4, but got rank ${x4D.rank}.`);\n  assert($filter.rank === 4, () => `Error in depthwiseConv2d: filter must be rank 4, but got rank ${$filter.rank}.`);\n  const inChannels = dataFormat === \"NHWC\" ? x4D.shape[3] : x4D.shape[1];\n  assert(inChannels === $filter.shape[2], () => `Error in depthwiseConv2d: number of input channels (${inChannels}) must match the inChannels dimension in filter ${$filter.shape[2]}.`);\n  checkPadOnDimRoundingMode(\"depthwiseConv2d\", pad3, dimRoundingMode);\n  const inputs = { x: x4D, filter: $filter };\n  const attrs = { strides, pad: pad3, dataFormat, dilations, dimRoundingMode };\n  const res = ENGINE.runKernel(DepthwiseConv2dNative, inputs, attrs);\n  if (reshapedTo4D) {\n    return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);\n  }\n  return res;\n}\nvar depthwiseConv2d = op({ depthwiseConv2d_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/diag.js\nfunction diag_(x) {\n  const $x = convertToTensor(x, \"x\", \"diag\");\n  const inputs = { x: $x };\n  return ENGINE.runKernel(Diag, inputs);\n}\nvar diag = op({ diag_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/dilation2d.js\nfunction dilation2d_(x, filter, strides, pad3, dilations = [1, 1], dataFormat = \"NHWC\") {\n  const $x = convertToTensor(x, \"x\", \"dilation2d\");\n  const $filter = convertToTensor(filter, \"filter\", \"dilation2d\");\n  assert($x.rank === 3 || $x.rank === 4, () => `Error in dilation2d: input must be rank 3 or 4, but got rank ${$x.rank}.`);\n  assert($filter.rank === 3, () => `Error in dilation2d: filter must be rank 3, but got rank ${$filter.rank}.`);\n  assert(dataFormat === \"NHWC\", () => `Error in dilation2d: Only NHWC is currently supported, but got dataFormat of ${dataFormat}`);\n  let x4D = $x;\n  let reshapedTo4D = false;\n  if ($x.rank === 3) {\n    x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);\n    reshapedTo4D = true;\n  }\n  const inputs = { x: x4D, filter: $filter };\n  const attrs = { strides, pad: pad3, dilations };\n  const res = ENGINE.runKernel(Dilation2D, inputs, attrs);\n  if (reshapedTo4D) {\n    return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);\n  }\n  return res;\n}\nvar dilation2d = op({ dilation2d_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/equal.js\nfunction equal_(a, b) {\n  let $a = convertToTensor(a, \"a\", \"equal\", \"string_or_numeric\");\n  let $b = convertToTensor(b, \"b\", \"equal\", \"string_or_numeric\");\n  [$a, $b] = makeTypesMatch($a, $b);\n  assertAndGetBroadcastShape($a.shape, $b.shape);\n  const inputs = { a: $a, b: $b };\n  return ENGINE.runKernel(Equal, inputs);\n}\nvar equal = op({ equal_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/where.js\nfunction where_(condition, a, b) {\n  const $a = convertToTensor(a, \"a\", \"where\");\n  const $b = convertToTensor(b, \"b\", \"where\");\n  const $condition = convertToTensor(condition, \"condition\", \"where\", \"bool\");\n  const broadcastShape = assertAndGetBroadcastShape(assertAndGetBroadcastShape($condition.shape, $a.shape), $b.shape);\n  const $broadcastedCondition = broadcastTo($condition, broadcastShape);\n  const $broadcastedA = broadcastTo($a, broadcastShape);\n  const $broadcastedB = broadcastTo($b, broadcastShape);\n  const inputs = {\n    condition: $broadcastedCondition,\n    t: $broadcastedA,\n    e: $broadcastedB\n  };\n  return ENGINE.runKernel(Select, inputs);\n}\nvar where = op({ where_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/zeros_like.js\nfunction zerosLike_(x) {\n  const $x = convertToTensor(x, \"x\", \"zerosLike\");\n  const inputs = { x: $x };\n  return ENGINE.runKernel(ZerosLike, inputs);\n}\nvar zerosLike = op({ zerosLike_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/div_no_nan.js\nfunction divNoNan_(a, b) {\n  let $a = convertToTensor(a, \"a\", \"div\");\n  let $b = convertToTensor(b, \"b\", \"div\");\n  [$a, $b] = makeTypesMatch($a, $b);\n  const divResult = div($a, $b);\n  const zeros4 = zerosLike(divResult);\n  const bEqualsZero = equal($b, zeros4);\n  return where(bEqualsZero, zeros4, divResult);\n}\nvar divNoNan = op({ divNoNan_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/dot.js\nfunction dot_(t1, t2) {\n  const $t1 = convertToTensor(t1, \"t1\", \"dot\");\n  const $t2 = convertToTensor(t2, \"t2\", \"dot\");\n  assert(($t1.rank === 1 || $t1.rank === 2) && ($t2.rank === 1 || $t2.rank === 2), () => `Error in dot: inputs must all be rank 1 or 2, but got ranks ${$t1.rank} and ${$t2.rank}.`);\n  const t1Inner = $t1.rank === 1 ? $t1.size : $t1.shape[1];\n  const t2Inner = $t2.rank === 1 ? $t2.size : $t2.shape[0];\n  assert(t1Inner === t2Inner, () => `Error in dot: inner dimensions of inputs must match, but got ${t1Inner} and ${t2Inner}.`);\n  if ($t1.rank === 1 && $t2.rank === 1) {\n    const t12D = reshape($t1, [1, -1]);\n    const t22D = reshape($t2, [-1, 1]);\n    const t1t2 = matMul(t12D, t22D);\n    return reshape(t1t2, []);\n  } else if ($t1.rank === 1 && $t2.rank === 2) {\n    const t12D = reshape($t1, [1, -1]);\n    const t22D = reshape($t2, [$t2.shape[0], $t2.shape[1]]);\n    const t1t2 = matMul(t12D, t22D);\n    return reshape(t1t2, [t1t2.size]);\n  } else if ($t1.rank === 2 && $t2.rank === 1) {\n    const t22D = reshape($t2, [-1, 1]);\n    const t1t2 = matMul($t1, t22D);\n    return reshape(t1t2, [t1t2.size]);\n  } else {\n    const t22D = reshape($t2, [$t2.shape[0], $t2.shape[1]]);\n    const t1t2 = matMul($t1, t22D);\n    return t1t2;\n  }\n}\nvar dot = op({ dot_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/einsum.js\nfunction einsum_(equation, ...tensors) {\n  const $tensors = tensors.map((t2, i2) => convertToTensor(t2, `tensors${i2}`, \"einsum\"));\n  const attrs = { equation };\n  return ENGINE.runKernel(Einsum, $tensors, attrs);\n}\nvar einsum = op({ einsum_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/elu.js\nfunction elu_(x) {\n  const $x = convertToTensor(x, \"x\", \"elu\", \"float32\");\n  const inputs = { x: $x };\n  return ENGINE.runKernel(Elu, inputs);\n}\nvar elu = op({ elu_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/erf.js\nfunction erf_(x) {\n  let $x = convertToTensor(x, \"x\", \"erf\");\n  assert($x.dtype === \"int32\" || $x.dtype === \"float32\", () => \"Input dtype must be `int32` or `float32`.\");\n  if ($x.dtype === \"int32\") {\n    $x = cast($x, \"float32\");\n  }\n  const inputs = { x: $x };\n  return ENGINE.runKernel(Erf, inputs);\n}\nvar erf = op({ erf_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/axis_util.js\nfunction axesAreInnerMostDims(axes, rank) {\n  for (let i2 = 0; i2 < axes.length; ++i2) {\n    if (axes[axes.length - i2 - 1] !== rank - 1 - i2) {\n      return false;\n    }\n  }\n  return true;\n}\nfunction combineLocations(outputLoc, reduceLoc, axes) {\n  const rank = outputLoc.length + reduceLoc.length;\n  const loc = [];\n  let outIdx = 0;\n  let reduceIdx = 0;\n  for (let dim = 0; dim < rank; dim++) {\n    if (axes.indexOf(dim) === -1) {\n      loc.push(outputLoc[outIdx++]);\n    } else {\n      loc.push(reduceLoc[reduceIdx++]);\n    }\n  }\n  return loc;\n}\nfunction computeOutAndReduceShapes(aShape, axes) {\n  const outShape = [];\n  const rank = aShape.length;\n  for (let dim = 0; dim < rank; dim++) {\n    if (axes.indexOf(dim) === -1) {\n      outShape.push(aShape[dim]);\n    }\n  }\n  const reduceShape = axes.map((dim) => aShape[dim]);\n  return [outShape, reduceShape];\n}\nfunction expandShapeToKeepDim(shape, axes) {\n  const reduceSubShape = axes.map((x) => 1);\n  return combineLocations(shape, reduceSubShape, axes);\n}\nfunction assertAxesAreInnerMostDims(msg, axes, rank) {\n  assert(axesAreInnerMostDims(axes, rank), () => `${msg} supports only inner-most axes for now. Got axes ${axes} and rank-${rank} input.`);\n}\nfunction getAxesPermutation(axes, rank) {\n  if (axesAreInnerMostDims(axes, rank)) {\n    return null;\n  }\n  const result = [];\n  for (let i2 = 0; i2 < rank; ++i2) {\n    if (axes.indexOf(i2) === -1) {\n      result.push(i2);\n    }\n  }\n  axes.forEach((axis) => result.push(axis));\n  return result;\n}\nfunction getUndoAxesPermutation(axes) {\n  return axes.map((axis, i2) => [i2, axis]).sort((a, b) => a[1] - b[1]).map((x) => x[0]);\n}\nfunction getInnerMostAxes(numAxes, rank) {\n  const res = [];\n  for (let i2 = rank - numAxes; i2 < rank; ++i2) {\n    res.push(i2);\n  }\n  return res;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/max.js\nfunction max_(x, axis = null, keepDims = false) {\n  const $x = convertToTensor(x, \"x\", \"max\");\n  const inputs = { x: $x };\n  const attrs = { reductionIndices: axis, keepDims };\n  return ENGINE.runKernel(Max, inputs, attrs);\n}\nvar max = op({ max_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/min.js\nfunction min_(x, axis = null, keepDims = false) {\n  const $x = convertToTensor(x, \"x\", \"min\");\n  const inputs = { x: $x };\n  const attrs = { axis, keepDims };\n  return ENGINE.runKernel(Min, inputs, attrs);\n}\nvar min = op({ min_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/pow.js\nfunction pow_(base, exp5) {\n  let $base = convertToTensor(base, \"base\", \"pow\");\n  let $exp = convertToTensor(exp5, \"exp\", \"pow\");\n  [$base, $exp] = makeTypesMatch($base, $exp);\n  const inputs = { a: $base, b: $exp };\n  return ENGINE.runKernel(Pow, inputs);\n}\nvar pow = op({ pow_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/scalar.js\nfunction scalar(value, dtype) {\n  if ((isTypedArray(value) && dtype !== \"string\" || Array.isArray(value)) && dtype !== \"complex64\") {\n    throw new Error(\"Error creating a new Scalar: value must be a primitive (number|boolean|string)\");\n  }\n  if (dtype === \"string\" && isTypedArray(value) && !(value instanceof Uint8Array)) {\n    throw new Error(\"When making a scalar from encoded string, the value must be `Uint8Array`.\");\n  }\n  const shape = [];\n  const inferredShape = [];\n  return makeTensor(value, shape, inferredShape, dtype);\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/sqrt.js\nfunction sqrt_(x) {\n  const $x = convertToTensor(x, \"x\", \"sqrt\", \"float32\");\n  const inputs = { x: $x };\n  return ENGINE.runKernel(Sqrt, inputs);\n}\nvar sqrt = op({ sqrt_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/square.js\nfunction square_(x) {\n  const $x = convertToTensor(x, \"x\", \"square\");\n  const attrs = {};\n  return ENGINE.runKernel(\"Square\", { x: $x }, attrs);\n}\nvar square = op({ square_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/sum.js\nfunction sum_(x, axis = null, keepDims = false) {\n  let $x = convertToTensor(x, \"x\", \"sum\");\n  if ($x.dtype === \"bool\") {\n    $x = cast($x, \"int32\");\n  }\n  const inputs = { x: $x };\n  const attrs = { axis, keepDims };\n  return ENGINE.runKernel(Sum, inputs, attrs);\n}\nvar sum2 = op({ sum_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/norm.js\nfunction norm_(x, ord = \"euclidean\", axis = null, keepDims = false) {\n  x = convertToTensor(x, \"x\", \"norm\");\n  const norm2 = normImpl(x, ord, axis);\n  let keepDimsShape = norm2.shape;\n  if (keepDims) {\n    const axes = parseAxisParam(axis, x.shape);\n    keepDimsShape = expandShapeToKeepDim(norm2.shape, axes);\n  }\n  return reshape(norm2, keepDimsShape);\n}\nfunction normImpl(x, p2, axis = null) {\n  if (x.rank === 0) {\n    return abs(x);\n  }\n  if (x.rank !== 1 && axis === null) {\n    return normImpl(reshape(x, [-1]), p2, axis);\n  }\n  if (x.rank === 1 || typeof axis === \"number\" || Array.isArray(axis) && axis.length === 1) {\n    if (p2 === 1) {\n      return sum2(abs(x), axis);\n    }\n    if (p2 === Infinity) {\n      return max(abs(x), axis);\n    }\n    if (p2 === -Infinity) {\n      return min(abs(x), axis);\n    }\n    if (p2 === \"euclidean\" || p2 === 2) {\n      return sqrt(sum2(pow(abs(x), scalar(2, \"int32\")), axis));\n    }\n    throw new Error(`Error in norm: invalid ord value: ${p2}`);\n  }\n  if (Array.isArray(axis) && axis.length === 2) {\n    if (p2 === 1) {\n      return max(sum2(abs(x), axis[0]), axis[1] - 1);\n    }\n    if (p2 === Infinity) {\n      return max(sum2(abs(x), axis[1]), axis[0]);\n    }\n    if (p2 === -Infinity) {\n      return min(sum2(abs(x), axis[1]), axis[0]);\n    }\n    if (p2 === \"fro\" || p2 === \"euclidean\") {\n      return sqrt(sum2(square(x), axis));\n    }\n    throw new Error(`Error in norm: invalid ord value: ${p2}`);\n  }\n  throw new Error(`Error in norm: invalid axis: ${axis}`);\n}\nvar norm = op({ norm_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/euclidean_norm.js\nfunction euclideanNorm_(x, axis = null, keepDims = false) {\n  return norm(x, \"euclidean\", axis, keepDims);\n}\nvar euclideanNorm = op({ euclideanNorm_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/exp.js\nfunction exp_(x) {\n  const $x = convertToTensor(x, \"x\", \"exp\");\n  const inputs = { x: $x };\n  return ENGINE.runKernel(Exp, inputs);\n}\nvar exp = op({ exp_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/expand_dims.js\nfunction expandDims_(x, axis = 0) {\n  const $x = convertToTensor(x, \"x\", \"expandDims\", \"string_or_numeric\");\n  assert(axis <= $x.rank, () => \"Axis must be <= rank of the tensor\");\n  const inputs = { input: $x };\n  const attrs = { dim: axis };\n  return ENGINE.runKernel(ExpandDims, inputs, attrs);\n}\nvar expandDims = op({ expandDims_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/expm1.js\nfunction expm1_(x) {\n  const $x = convertToTensor(x, \"x\", \"expm1\");\n  const inputs = { x: $x };\n  return ENGINE.runKernel(Expm1, inputs);\n}\nvar expm1 = op({ expm1_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/tile.js\nfunction tile_(x, reps) {\n  const $x = convertToTensor(x, \"x\", \"tile\", \"string_or_numeric\");\n  assert($x.rank === reps.length, () => `Error in transpose: rank of input ${$x.rank} must match length of reps ${reps}.`);\n  const inputs = { x: $x };\n  const attrs = { reps };\n  return ENGINE.runKernel(Tile, inputs, attrs);\n}\nvar tile = op({ tile_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/eye.js\nfunction eye_(numRows, numColumns, batchShape, dtype = \"float32\") {\n  if (numColumns == null) {\n    numColumns = numRows;\n  }\n  const buff = buffer([numRows, numColumns], dtype);\n  const n2 = numRows <= numColumns ? numRows : numColumns;\n  for (let i2 = 0; i2 < n2; ++i2) {\n    buff.set(1, i2, i2);\n  }\n  const out = reshape(buff.toTensor(), [numRows, numColumns]);\n  if (batchShape == null) {\n    return out;\n  } else {\n    if (batchShape.length === 1) {\n      return tile(expandDims(out, 0), [batchShape[0], 1, 1]);\n    } else if (batchShape.length === 2) {\n      return tile(expandDims(expandDims(out, 0), 0), [batchShape[0], batchShape[1], 1, 1]);\n    } else if (batchShape.length === 3) {\n      return tile(expandDims(expandDims(expandDims(out, 0), 0), 0), [\n        batchShape[0],\n        batchShape[1],\n        batchShape[2],\n        1,\n        1\n      ]);\n    } else {\n      throw new Error(`eye() currently supports only 1D and 2D batchShapes, but received ${batchShape.length}D.`);\n    }\n  }\n}\nvar eye = op({ eye_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/fill.js\nfunction fill(shape, value, dtype) {\n  const attrs = { shape, value, dtype };\n  return ENGINE.runKernel(Fill, {}, attrs);\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/floor.js\nfunction floor_(x) {\n  const $x = convertToTensor(x, \"x\", \"floor\", \"float32\");\n  const inputs = { x: $x };\n  return ENGINE.runKernel(Floor, inputs);\n}\nvar floor = op({ floor_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/gather.js\nfunction gather_(x, indices, axis = 0, batchDims = 0) {\n  const $x = convertToTensor(x, \"x\", \"gather\");\n  const $indices = convertToTensor(indices, \"indices\", \"gather\", \"int32\");\n  const inputs = { x: $x, indices: $indices };\n  const attrs = { axis, batchDims };\n  return ENGINE.runKernel(GatherV2, inputs, attrs);\n}\nvar gather = op({ gather_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/greater.js\nfunction greater_(a, b) {\n  let $a = convertToTensor(a, \"a\", \"greater\", \"string_or_numeric\");\n  let $b = convertToTensor(b, \"b\", \"greater\", \"string_or_numeric\");\n  [$a, $b] = makeTypesMatch($a, $b);\n  assertAndGetBroadcastShape($a.shape, $b.shape);\n  const inputs = { a: $a, b: $b };\n  return ENGINE.runKernel(Greater, inputs);\n}\nvar greater = op({ greater_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/greater_equal.js\nfunction greaterEqual_(a, b) {\n  let $a = convertToTensor(a, \"a\", \"greaterEqual\", \"string_or_numeric\");\n  let $b = convertToTensor(b, \"b\", \"greaterEqual\", \"string_or_numeric\");\n  [$a, $b] = makeTypesMatch($a, $b);\n  assertAndGetBroadcastShape($a.shape, $b.shape);\n  const inputs = { a: $a, b: $b };\n  return ENGINE.runKernel(GreaterEqual, inputs);\n}\nvar greaterEqual = op({ greaterEqual_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/is_finite.js\nfunction isFinite_(x) {\n  const $x = convertToTensor(x, \"x\", \"isFinite\");\n  const inputs = { x: $x };\n  return ENGINE.runKernel(IsFinite, inputs);\n}\nvar isFinite2 = op({ isFinite_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/is_inf.js\nfunction isInf_(x) {\n  const $x = convertToTensor(x, \"x\", \"isInf\");\n  const inputs = { x: $x };\n  return ENGINE.runKernel(IsInf, inputs);\n}\nvar isInf = op({ isInf_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/is_nan.js\nfunction isNaN_(x) {\n  const $x = convertToTensor(x, \"x\", \"isNaN\");\n  const inputs = { x: $x };\n  return ENGINE.runKernel(IsNan, inputs);\n}\nvar isNaN2 = op({ isNaN_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/leaky_relu.js\nfunction leakyRelu_(x, alpha = 0.2) {\n  const $x = convertToTensor(x, \"x\", \"leakyRelu\");\n  const inputs = { x: $x };\n  const attrs = { alpha };\n  return ENGINE.runKernel(LeakyRelu, inputs, attrs);\n}\nvar leakyRelu = op({ leakyRelu_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/less.js\nfunction less_(a, b) {\n  let $a = convertToTensor(a, \"a\", \"less\", \"string_or_numeric\");\n  let $b = convertToTensor(b, \"b\", \"less\", \"string_or_numeric\");\n  [$a, $b] = makeTypesMatch($a, $b);\n  assertAndGetBroadcastShape($a.shape, $b.shape);\n  const inputs = { a: $a, b: $b };\n  return ENGINE.runKernel(Less, inputs);\n}\nvar less = op({ less_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/less_equal.js\nfunction lessEqual_(a, b) {\n  let $a = convertToTensor(a, \"a\", \"lessEqual\", \"string_or_numeric\");\n  let $b = convertToTensor(b, \"b\", \"lessEqual\", \"string_or_numeric\");\n  [$a, $b] = makeTypesMatch($a, $b);\n  assertAndGetBroadcastShape($a.shape, $b.shape);\n  const inputs = { a: $a, b: $b };\n  return ENGINE.runKernel(LessEqual, inputs);\n}\nvar lessEqual = op({ lessEqual_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/linspace.js\nfunction linspace(start, stop, num) {\n  if (num <= 0) {\n    throw new Error(\"The number of values should be positive.\");\n  }\n  const attrs = { start, stop, num };\n  return ENGINE.runKernel(LinSpace, {}, attrs);\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/local_response_normalization.js\nfunction localResponseNormalization_(x, depthRadius = 5, bias = 1, alpha = 1, beta = 0.5) {\n  const $x = convertToTensor(x, \"x\", \"localResponseNormalization\");\n  assert($x.rank === 4 || $x.rank === 3, () => `Error in localResponseNormalization: x must be rank 3 or 4 but got\n               rank ${$x.rank}.`);\n  assert(isInt(depthRadius), () => `Error in localResponseNormalization: depthRadius must be an integer but got depthRadius ${depthRadius}.`);\n  let x4D = $x;\n  let reshapedTo4D = false;\n  if ($x.rank === 3) {\n    reshapedTo4D = true;\n    x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);\n  }\n  const inputs = { x: x4D };\n  const attrs = { depthRadius, bias, alpha, beta };\n  const res = ENGINE.runKernel(LRN, inputs, attrs);\n  if (reshapedTo4D) {\n    return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);\n  } else {\n    return res;\n  }\n}\nvar localResponseNormalization = op({ localResponseNormalization_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/log.js\nfunction log_(x) {\n  const $x = convertToTensor(x, \"x\", \"log\", \"float32\");\n  const inputs = { x: $x };\n  return ENGINE.runKernel(Log, inputs);\n}\nvar log2 = op({ log_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/log1p.js\nfunction log1p_(x) {\n  const $x = convertToTensor(x, \"x\", \"log1p\");\n  const inputs = { x: $x };\n  return ENGINE.runKernel(Log1p, inputs);\n}\nvar log1p = op({ log1p_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/gradients.js\nfunction grad(f) {\n  assert(isFunction(f), () => \"The f passed in grad(f) must be a function\");\n  return (x, dy) => {\n    const $x = convertToTensor(x, \"x\", \"tf.grad\", \"string_or_numeric\");\n    const $dy = dy != null ? convertToTensor(dy, \"dy\", \"tf.grad\") : null;\n    return ENGINE.tidy(() => {\n      const { value, grads: grads2 } = ENGINE.gradients(() => f($x), [$x], $dy);\n      if ($dy != null) {\n        assertShapesMatch(value.shape, $dy.shape, \"The shape of dy passed in grad(f)(x, dy) must match the shape returned by f(x)\");\n      }\n      checkGrads(grads2);\n      return grads2[0];\n    });\n  };\n}\nfunction grads(f) {\n  assert(isFunction(f), () => \"The f passed in grads(f) must be a function\");\n  return (args, dy) => {\n    assert(Array.isArray(args), () => \"The args passed in grads(f)(args) must be an array of `Tensor`s or `TensorLike`s\");\n    const $args = convertToTensorArray(args, \"args\", \"tf.grads\", \"string_or_numeric\");\n    const $dy = dy != null ? convertToTensor(dy, \"dy\", \"tf.grads\") : null;\n    return ENGINE.tidy(() => {\n      const { value, grads: grads2 } = ENGINE.gradients(() => f(...$args), $args, $dy);\n      if ($dy != null) {\n        assertShapesMatch(value.shape, $dy.shape, \"The shape of dy passed in grads(f)([x1,...], dy) must match the shape returned by f([x1,...])\");\n      }\n      checkGrads(grads2);\n      return grads2;\n    });\n  };\n}\nfunction valueAndGrad(f) {\n  assert(isFunction(f), () => \"The f passed in valueAndGrad(f) must be a function\");\n  return (x, dy) => {\n    assert(x instanceof Tensor, () => \"The x passed in valueAndGrad(f)(x) must be a tensor\");\n    assert(dy == null || dy instanceof Tensor, () => \"The dy passed in valueAndGrad(f)(x, dy) must be a tensor\");\n    const { grads: grads2, value } = ENGINE.gradients(() => f(x), [x], dy);\n    checkGrads(grads2);\n    return { grad: grads2[0], value };\n  };\n}\nfunction valueAndGrads(f) {\n  assert(isFunction(f), () => \"The f passed in valueAndGrads(f) must be a function\");\n  return (args, dy) => {\n    assert(Array.isArray(args) && args.every((arg) => arg instanceof Tensor), () => \"The args passed in valueAndGrads(f)(args) must be array of tensors\");\n    assert(dy == null || dy instanceof Tensor, () => \"The dy passed in valueAndGrads(f)(args, dy) must be a tensor\");\n    const res = ENGINE.gradients(() => f(...args), args, dy);\n    if (dy != null) {\n      assertShapesMatch(res.value.shape, dy.shape, \"The shape of dy passed in valueAndGrads(f)([x1,...], dy) must match the shape returned by f([x1,...])\");\n    }\n    checkGrads(res.grads);\n    return res;\n  };\n}\nfunction variableGrads(f, varList) {\n  assert(isFunction(f), () => \"The f passed in variableGrads(f) must be a function\");\n  assert(varList == null || Array.isArray(varList) && varList.every((v) => v instanceof Variable), () => \"The varList passed in variableGrads(f, varList) must be an array of variables\");\n  const specifiedVarList = varList != null;\n  if (!specifiedVarList) {\n    varList = [];\n    for (const varName in ENGINE.registeredVariables) {\n      varList.push(ENGINE.registeredVariables[varName]);\n    }\n  }\n  const specifiedNonTrainable = specifiedVarList ? varList.filter((variable2) => !variable2.trainable) : null;\n  const originalVarCount = varList.length;\n  varList = varList.filter((variable2) => variable2.trainable);\n  assert(varList.length > 0, () => `variableGrads() expects at least one of the input variables to be trainable, but none of the ${originalVarCount} variables is trainable.`);\n  const allowNoGradients = true;\n  const { value, grads: grads2 } = ENGINE.gradients(f, varList, null, allowNoGradients);\n  assert(grads2.some((g) => g != null), () => \"Cannot find a connection between any variable and the result of the loss function y=f(x). Please make sure the operations that use variables are inside the function f passed to minimize().\");\n  assert(value.rank === 0, () => `The f passed in variableGrads(f) must return a scalar, but it returned a rank-${value.rank} tensor`);\n  const namedGrads = {};\n  varList.forEach((v, i2) => {\n    if (grads2[i2] != null) {\n      namedGrads[v.name] = grads2[i2];\n    }\n  });\n  if (specifiedNonTrainable != null) {\n    specifiedNonTrainable.forEach((v) => namedGrads[v.name] = null);\n  }\n  return { value, grads: namedGrads };\n}\nfunction customGrad(f) {\n  return ENGINE.customGrad(f);\n}\nfunction checkGrads(grads2) {\n  const numNullGradients = grads2.filter((g) => g == null).length;\n  if (numNullGradients > 0) {\n    throw new Error(`Cannot compute gradient of y=f(x) with respect to x. Make sure that\n    the f you passed encloses all operations that lead from x to y.`);\n  }\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/softplus.js\nfunction softplus_(x) {\n  const $x = convertToTensor(x, \"x\", \"softplus\");\n  const inputs = { x: $x };\n  return ENGINE.runKernel(Softplus, inputs);\n}\nvar softplus = op({ softplus_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/log_sigmoid.js\nfunction logSigmoid_(x) {\n  const $x = convertToTensor(x, \"x\", \"logSigmoid\");\n  const customOp = customGrad((x2) => {\n    const value = neg(softplus(neg(x2)));\n    const gradFunc = (dy) => {\n      const derX = mul(dy, sigmoid(neg(x2)));\n      return derX;\n    };\n    return { value, gradFunc };\n  });\n  return customOp($x);\n}\nvar logSigmoid = op({ logSigmoid_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/sub.js\nfunction sub_(a, b) {\n  let $a = convertToTensor(a, \"a\", \"sub\");\n  let $b = convertToTensor(b, \"b\", \"sub\");\n  [$a, $b] = makeTypesMatch($a, $b);\n  const inputs = { a: $a, b: $b };\n  return ENGINE.runKernel(Sub, inputs);\n}\nvar sub = op({ sub_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/log_softmax.js\nfunction logSoftmax_(logits, axis = -1) {\n  const $logits = convertToTensor(logits, \"logits\", \"logSoftmax\");\n  if (axis === -1) {\n    axis = $logits.rank - 1;\n  }\n  if (axis !== $logits.rank - 1) {\n    throw Error(`Log Softmax along a non-last dimension is not yet supported. Logits was rank ${$logits.rank} and axis was ${axis}`);\n  }\n  const customOp = customGrad((logits2, save) => {\n    const keepDims = true;\n    const xMax = max(logits2, axis, true);\n    const shifted = sub(logits2, xMax);\n    const value = sub(cast(shifted, \"float32\"), log2(sum2(exp(shifted), axis, keepDims)));\n    save([value]);\n    const gradFunc = (dy, saved) => {\n      const [value2] = saved;\n      const keepDims2 = true;\n      const softmax7 = exp(value2);\n      return sub(dy, mul(sum2(dy, axis, keepDims2), softmax7));\n    };\n    return { value, gradFunc };\n  });\n  return customOp($logits);\n}\nvar logSoftmax = op({ logSoftmax_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/log_sum_exp.js\nfunction logSumExp_(x, axis = null, keepDims = false) {\n  const $x = convertToTensor(x, \"x\", \"logSumExp\");\n  const axes = parseAxisParam(axis, $x.shape);\n  const xMax = max($x, axes, true);\n  const a = sub($x, xMax);\n  const b = exp(a);\n  const c = sum2(b, axes);\n  const d = log2(c);\n  const res = add2(reshape(xMax, d.shape), d);\n  if (keepDims) {\n    const newShape = expandShapeToKeepDim(res.shape, axes);\n    return reshape(res, newShape);\n  }\n  return res;\n}\nvar logSumExp = op({ logSumExp_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/logical_and.js\nfunction logicalAnd_(a, b) {\n  const $a = convertToTensor(a, \"a\", \"logicalAnd\", \"bool\");\n  const $b = convertToTensor(b, \"b\", \"logicalAnd\", \"bool\");\n  assertAndGetBroadcastShape($a.shape, $b.shape);\n  const inputs = { a: $a, b: $b };\n  return ENGINE.runKernel(LogicalAnd, inputs);\n}\nvar logicalAnd = op({ logicalAnd_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/logical_not.js\nfunction logicalNot_(x) {\n  const $x = convertToTensor(x, \"x\", \"logicalNot\", \"bool\");\n  const inputs = { x: $x };\n  return ENGINE.runKernel(LogicalNot, inputs);\n}\nvar logicalNot = op({ logicalNot_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/logical_or.js\nfunction logicalOr_(a, b) {\n  const $a = convertToTensor(a, \"a\", \"logicalOr\", \"bool\");\n  const $b = convertToTensor(b, \"b\", \"logicalOr\", \"bool\");\n  assertAndGetBroadcastShape($a.shape, $b.shape);\n  const inputs = { a: $a, b: $b };\n  return ENGINE.runKernel(LogicalOr, inputs);\n}\nvar logicalOr = op({ logicalOr_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/logical_xor.js\nfunction logicalXor_(a, b) {\n  const $a = convertToTensor(a, \"a\", \"logicalXor\", \"bool\");\n  const $b = convertToTensor(b, \"b\", \"logicalXor\", \"bool\");\n  assertAndGetBroadcastShape($a.shape, $b.shape);\n  return logicalAnd(logicalOr(a, b), logicalNot(logicalAnd(a, b)));\n}\nvar logicalXor = op({ logicalXor_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/search_sorted.js\nvar INT32_MAX = 2147483648;\nfunction searchSorted_(sortedSequence, values, side = \"left\") {\n  const $sortedSequence = convertToTensor(sortedSequence, \"sortedSequence\", \"searchSorted\");\n  const $values = convertToTensor(values, \"values\", \"searchSorted\");\n  const sequenceSize = $sortedSequence.shape[$sortedSequence.shape.length - 1];\n  const valuesSize = $values.shape[$values.shape.length - 1];\n  const $sortedSequence2D = reshape($sortedSequence, [-1, sequenceSize]);\n  const $values2D = reshape($values, [-1, valuesSize]);\n  if ($sortedSequence2D.rank < 2) {\n    throw new Error(`Sorted input argument must be at least 2-dimensional`);\n  }\n  if ($sortedSequence2D.shape[0] !== $values2D.shape[0]) {\n    throw new Error(`Leading dimension of 'sortedSequence' and 'values' must match.`);\n  }\n  if (sizeFromShape($values2D.shape) >= INT32_MAX) {\n    throw new Error(`values tensor size must less than ${INT32_MAX}`);\n  }\n  if ($sortedSequence2D.shape[1] >= INT32_MAX) {\n    throw new Error(`trailing dim_size must less than ${INT32_MAX} for int32 output type, was ${$sortedSequence2D.shape[1]}`);\n  }\n  const inputs = {\n    sortedSequence: $sortedSequence2D,\n    values: $values2D\n  };\n  const attrs = { side };\n  return ENGINE.runKernel(SearchSorted, inputs, attrs);\n}\nvar searchSorted = op({ searchSorted_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/lower_bound.js\nfunction lowerBound(sortedSequence, values) {\n  return searchSorted(sortedSequence, values, \"left\");\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/max_pool.js\nfunction maxPool_(x, filterSize, strides, pad3, dimRoundingMode) {\n  const $x = convertToTensor(x, \"x\", \"maxPool\");\n  const dilations = 1;\n  let x4D = $x;\n  let reshapedTo4D = false;\n  if ($x.rank === 3) {\n    reshapedTo4D = true;\n    x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);\n  }\n  assert(x4D.rank === 4, () => `Error in maxPool: input must be rank 4 but got rank ${x4D.rank}.`);\n  assert(eitherStridesOrDilationsAreOne(strides, dilations), () => `Error in maxPool: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`);\n  checkPadOnDimRoundingMode(\"maxPool\", pad3, dimRoundingMode);\n  const inputs = { x: x4D };\n  const attrs = { filterSize, strides, pad: pad3, dimRoundingMode };\n  const res = ENGINE.runKernel(MaxPool, inputs, attrs);\n  if (reshapedTo4D) {\n    return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);\n  }\n  return res;\n}\nvar maxPool = op({ maxPool_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/max_pool_3d.js\nfunction maxPool3d_(x, filterSize = [1, 1, 1], strides, pad3, dimRoundingMode, dataFormat = \"NDHWC\") {\n  const $x = convertToTensor(x, \"x\", \"maxPool3d\");\n  let x5D = $x;\n  let reshapedTo5D = false;\n  if ($x.rank === 4) {\n    reshapedTo5D = true;\n    x5D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2], $x.shape[3]]);\n  }\n  assert(x5D.rank === 5, () => `Error in maxPool3d: x must be rank 5 but got rank ${x5D.rank}.`);\n  assert(dataFormat === \"NDHWC\", () => `Error in maxPool3d: Only NDHWC is currently supported, but got dataFormat of ${dataFormat}`);\n  checkPadOnDimRoundingMode(\"maxPool3d\", pad3, dimRoundingMode);\n  const inputs = { x: x5D };\n  const attrs = { filterSize, strides, pad: pad3, dimRoundingMode, dataFormat };\n  const res = ENGINE.runKernel(MaxPool3D, inputs, attrs);\n  if (reshapedTo5D) {\n    return reshape(res, [res.shape[1], res.shape[2], res.shape[3], res.shape[4]]);\n  }\n  return res;\n}\nvar maxPool3d = op({ maxPool3d_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/max_pool_with_argmax.js\nfunction maxPoolWithArgmax_(x, filterSize, strides, pad3, includeBatchInIndex = false) {\n  const $x = convertToTensor(x, \"x\", \"maxPoolWithArgmax\");\n  const inputs = { x: $x };\n  const attrs = { filterSize, strides, pad: pad3, includeBatchInIndex };\n  const result = ENGINE.runKernel(MaxPoolWithArgmax, inputs, attrs);\n  return { result: result[0], indexes: result[1] };\n}\nvar maxPoolWithArgmax = op({ maxPoolWithArgmax_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/maximum.js\nfunction maximum_(a, b) {\n  let $a = convertToTensor(a, \"a\", \"maximum\");\n  let $b = convertToTensor(b, \"b\", \"maximum\");\n  [$a, $b] = makeTypesMatch($a, $b);\n  if ($a.dtype === \"bool\") {\n    $a = cast($a, \"int32\");\n    $b = cast($b, \"int32\");\n  }\n  assertAndGetBroadcastShape($a.shape, $b.shape);\n  const inputs = { a: $a, b: $b };\n  return ENGINE.runKernel(Maximum, inputs);\n}\nvar maximum = op({ maximum_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/mean.js\nfunction mean_(x, axis = null, keepDims = false) {\n  const $x = convertToTensor(x, \"x\", \"mean\");\n  const inputs = { x: $x };\n  const attrs = { axis, keepDims };\n  return ENGINE.runKernel(Mean, inputs, attrs);\n}\nvar mean = op({ mean_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/zeros.js\nfunction zeros(shape, dtype = \"float32\") {\n  if (dtype === \"complex64\") {\n    const real5 = zeros(shape, \"float32\");\n    const imag5 = zeros(shape, \"float32\");\n    return complex(real5, imag5);\n  }\n  const values = makeZerosTypedArray(sizeFromShape(shape), dtype);\n  return ENGINE.makeTensor(values, shape, dtype);\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/ones.js\nfunction ones2(shape, dtype = \"float32\") {\n  if (dtype === \"complex64\") {\n    const real5 = ones2(shape, \"float32\");\n    const imag5 = zeros(shape, \"float32\");\n    return complex(real5, imag5);\n  }\n  const values = makeOnesTypedArray(sizeFromShape(shape), dtype);\n  return ENGINE.makeTensor(values, shape, dtype);\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/meshgrid.js\nfunction meshgrid(x, y, { indexing = \"xy\" } = {}) {\n  if (indexing !== \"xy\" && indexing !== \"ij\") {\n    throw new TypeError(`${indexing} is not a valid third argument to meshgrid`);\n  }\n  if (x === void 0) {\n    return [];\n  }\n  let $x = convertToTensor(x, \"x\", \"meshgrid\", x instanceof Tensor ? x.dtype : \"float32\");\n  if (y === void 0) {\n    return [$x];\n  }\n  let $y = convertToTensor(y, \"y\", \"meshgrid\", y instanceof Tensor ? y.dtype : \"float32\");\n  const w = sizeFromShape($x.shape);\n  const h = sizeFromShape($y.shape);\n  if (indexing === \"xy\") {\n    $x = reshape($x, [1, -1]);\n    $y = reshape($y, [-1, 1]);\n    return [\n      matMul(ones2([h, 1], $x.dtype), $x),\n      matMul($y, ones2([1, w], $y.dtype))\n    ];\n  }\n  $x = reshape($x, [-1, 1]);\n  $y = reshape($y, [1, -1]);\n  return [\n    matMul($x, ones2([1, h], $x.dtype)),\n    matMul(ones2([w, 1], $y.dtype), $y)\n  ];\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/minimum.js\nfunction minimum_(a, b) {\n  let $a = convertToTensor(a, \"a\", \"minimum\");\n  let $b = convertToTensor(b, \"b\", \"minimum\");\n  [$a, $b] = makeTypesMatch($a, $b);\n  if ($a.dtype === \"bool\") {\n    $a = cast($a, \"int32\");\n    $b = cast($b, \"int32\");\n  }\n  assertAndGetBroadcastShape($a.shape, $b.shape);\n  const inputs = { a: $a, b: $b };\n  return ENGINE.runKernel(Minimum, inputs);\n}\nvar minimum = op({ minimum_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/mirror_pad.js\nfunction mirrorPad_(x, paddings, mode) {\n  assert(mode === \"reflect\" || mode === \"symmetric\", () => `Invalid mode. Mode must be either reflect or symmetric. Got ${mode}.`);\n  const $x = convertToTensor(x, \"x\", \"mirrorPad\");\n  if ($x.rank === 0) {\n    throw new Error(\"mirrorPad(scalar) is not defined. Pass non-scalar to mirrorPad\");\n  }\n  assert(paddings.length === $x.rank, () => `Padding doesn't match input. Must be ${$x.rank}. Got ${paddings.length}.`);\n  const shapeOffset = mode === \"reflect\" ? 1 : 0;\n  for (let i2 = 0; i2 < $x.rank; i2++) {\n    assert(paddings[i2].length === 2, () => `Invalid number of paddings. Must be length of 2 each.`);\n    assert(paddings[i2][0] >= 0 && paddings[i2][0] <= $x.shape[i2] - shapeOffset && paddings[i2][1] >= 0 && paddings[i2][1] <= $x.shape[i2] - shapeOffset, () => `Padding in dimension ${i2} cannot be greater than or equal to ${$x.shape[i2] - shapeOffset} or less than 0 for input of shape ${$x.shape}`);\n  }\n  const attrs = { paddings, mode };\n  const inputs = { x: $x };\n  return ENGINE.runKernel(MirrorPad, inputs, attrs);\n}\nvar mirrorPad = op({ mirrorPad_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/mod.js\nfunction mod_(a, b) {\n  let $a = convertToTensor(a, \"a\", \"mod\");\n  let $b = convertToTensor(b, \"b\", \"mod\");\n  [$a, $b] = makeTypesMatch($a, $b);\n  const inputs = { a: $a, b: $b };\n  return ENGINE.runKernel(Mod, inputs);\n}\nvar mod = op({ mod_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/moments.js\nfunction moments_(x, axis = null, keepDims = false) {\n  x = convertToTensor(x, \"x\", \"moments\");\n  const axes = parseAxisParam(axis, x.shape);\n  const xMean = mean(x, axes, keepDims);\n  let keepDimsShape = xMean.shape;\n  if (!keepDims) {\n    keepDimsShape = expandShapeToKeepDim(xMean.shape, axes);\n  }\n  const devSquared = square(sub(cast(x, \"float32\"), reshape(xMean, keepDimsShape)));\n  const variance = mean(devSquared, axes, keepDims);\n  return { mean: xMean, variance };\n}\nvar moments = op({ moments_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/multi_rnn_cell.js\nfunction multiRNNCell_(lstmCells, data, c, h) {\n  const $data = convertToTensor(data, \"data\", \"multiRNNCell\");\n  const $c = convertToTensorArray(c, \"c\", \"multiRNNCell\");\n  const $h = convertToTensorArray(h, \"h\", \"multiRNNCell\");\n  let input2 = $data;\n  const newStates = [];\n  for (let i2 = 0; i2 < lstmCells.length; i2++) {\n    const output = lstmCells[i2](input2, $c[i2], $h[i2]);\n    newStates.push(output[0]);\n    newStates.push(output[1]);\n    input2 = output[1];\n  }\n  const newC = [];\n  const newH = [];\n  for (let i2 = 0; i2 < newStates.length; i2 += 2) {\n    newC.push(newStates[i2]);\n    newH.push(newStates[i2 + 1]);\n  }\n  return [newC, newH];\n}\nvar multiRNNCell = op({ multiRNNCell_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/multinomial.js\nfunction multinomial_(logits, numSamples, seed, normalized = false) {\n  const $logits = convertToTensor(logits, \"logits\", \"multinomial\");\n  const numOutcomes = $logits.size;\n  const origRank = $logits.rank;\n  if (numOutcomes < 2) {\n    throw new Error(`Error in multinomial: you need at least 2 outcomes, but got ${numOutcomes}.`);\n  }\n  if (origRank > 2) {\n    throw new Error(`Rank of probabilities must be 1 or 2, but is ${origRank}`);\n  }\n  seed = seed || Math.random();\n  const logits2D = origRank === 1 ? reshape($logits, [1, -1]) : $logits;\n  const inputs = { logits: logits2D };\n  const attrs = { numSamples, seed, normalized };\n  const res = ENGINE.runKernel(Multinomial, inputs, attrs);\n  return origRank === 1 ? reshape(res, [res.size]) : res;\n}\nvar multinomial = op({ multinomial_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/not_equal.js\nfunction notEqual_(a, b) {\n  let $a = convertToTensor(a, \"a\", \"notEqual\", \"string_or_numeric\");\n  let $b = convertToTensor(b, \"b\", \"notEqual\", \"string_or_numeric\");\n  [$a, $b] = makeTypesMatch($a, $b);\n  assertAndGetBroadcastShape($a.shape, $b.shape);\n  const inputs = { a: $a, b: $b };\n  return ENGINE.runKernel(NotEqual, inputs);\n}\nvar notEqual = op({ notEqual_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/ones_like.js\nfunction onesLike_(x) {\n  const $x = convertToTensor(x, \"x\", \"onesLike\");\n  const inputs = { x: $x };\n  return ENGINE.runKernel(OnesLike, inputs);\n}\nvar onesLike = op({ onesLike_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/outer_product.js\nfunction outerProduct_(v1, v2) {\n  const $v1 = convertToTensor(v1, \"v1\", \"outerProduct\");\n  const $v2 = convertToTensor(v2, \"v2\", \"outerProduct\");\n  assert($v1.rank === 1 && $v2.rank === 1, () => `Error in outerProduct: inputs must be rank 1, but got ranks ${$v1.rank} and ${$v2.rank}.`);\n  const v12D = reshape($v1, [-1, 1]);\n  const v22D = reshape($v2, [1, -1]);\n  return matMul(v12D, v22D);\n}\nvar outerProduct = op({ outerProduct_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/pad.js\nfunction pad_(x, paddings, constantValue = 0) {\n  const $x = convertToTensor(x, \"x\", \"pad\");\n  if ($x.rank === 0) {\n    throw new Error(\"pad(scalar) is not defined. Pass non-scalar to pad\");\n  }\n  const attrs = { paddings, constantValue };\n  const inputs = { x: $x };\n  return ENGINE.runKernel(PadV2, inputs, attrs);\n}\nvar pad = op({ pad_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/pad1d.js\nfunction pad1d_(x, paddings, constantValue = 0) {\n  assert(paddings.length === 2, () => \"Invalid number of paddings. Must be length of 2.\");\n  return pad(x, [paddings], constantValue);\n}\nvar pad1d = op({ pad1d_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/pad2d.js\nfunction pad2d_(x, paddings, constantValue = 0) {\n  assert(paddings.length === 2 && paddings[0].length === 2 && paddings[1].length === 2, () => \"Invalid number of paddings. Must be length of 2 each.\");\n  return pad(x, paddings, constantValue);\n}\nvar pad2d = op({ pad2d_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/pad3d.js\nfunction pad3d_(x, paddings, constantValue = 0) {\n  assert(paddings.length === 3 && paddings[0].length === 2 && paddings[1].length === 2 && paddings[2].length === 2, () => \"Invalid number of paddings. Must be length of 2 each.\");\n  return pad(x, paddings, constantValue);\n}\nvar pad3d = op({ pad3d_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/pad4d.js\nfunction pad4d_(x, paddings, constantValue = 0) {\n  assert(paddings.length === 4 && paddings[0].length === 2 && paddings[1].length === 2 && paddings[2].length === 2 && paddings[3].length === 2, () => \"Invalid number of paddings. Must be length of 2 each.\");\n  return pad(x, paddings, constantValue);\n}\nvar pad4d = op({ pad4d_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/space_to_batch_nd.js\nfunction spaceToBatchND_(x, blockShape, paddings) {\n  const $x = convertToTensor(x, \"x\", \"spaceToBatchND\");\n  assert($x.rank >= 1 + blockShape.length, () => `input rank ${$x.rank} should be > than [blockShape] ${blockShape.length}`);\n  assert(paddings.length === blockShape.length, () => `paddings.shape[0] ${paddings.length} must be equal to [blockShape] ${blockShape.length}`);\n  assert($x.shape.reduce((a, b, i2) => {\n    if (i2 > 0 && i2 <= blockShape.length) {\n      return a && (b + paddings[i2 - 1][0] + paddings[i2 - 1][1]) % blockShape[i2 - 1] === 0;\n    }\n    return a;\n  }, true), () => `input spatial dimensions ${$x.shape.slice(1)} with paddings ${paddings.toString()} must be divisible by blockShapes ${blockShape.toString()}`);\n  const inputs = { x: $x };\n  const attrs = { blockShape, paddings };\n  return ENGINE.runKernel(SpaceToBatchND, inputs, attrs);\n}\nvar spaceToBatchND = op({ spaceToBatchND_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/pool.js\nfunction pool_(input2, windowShape, poolingType, pad3, dilations, strides, dimRoundingMode) {\n  if (dilations == null) {\n    dilations = [1, 1];\n  }\n  if (strides == null) {\n    strides = 1;\n  }\n  if (pad3 === 0) {\n    pad3 = \"valid\";\n  }\n  const $x = convertToTensor(input2, \"x\", \"maxPool\");\n  let x4D = $x;\n  let reshapedTo4D = false;\n  if ($x.rank === 3) {\n    reshapedTo4D = true;\n    x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);\n  }\n  assert(eitherStridesOrDilationsAreOne(strides, dilations), () => `Error in pool: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`);\n  const convInfo = computePool2DInfo(x4D.shape, windowShape, strides, dilations, pad3);\n  const dilation = [convInfo.dilationHeight, convInfo.dilationWidth];\n  let basePadding;\n  if (pad3 === \"same\") {\n    basePadding = withSpaceToBatchBasePaddings([convInfo.filterHeight, convInfo.filterWidth], dilation);\n  } else {\n    basePadding = [[0, 0], [0, 0]];\n  }\n  const isDilationOne = dilation[0] === 1 && dilation[1] === 1;\n  const [adjustedPadding, adjustedCrops] = requiredSpaceToBatchPaddings([convInfo.inHeight, convInfo.inWidth], dilation, basePadding);\n  const convertedPad = isDilationOne ? pad3 : \"valid\";\n  const convertedX = isDilationOne ? x4D : spaceToBatchND(x4D, dilation, adjustedPadding);\n  const forwardOp = poolingType === \"avg\" ? () => avgPool(convertedX, windowShape, strides, convertedPad, dimRoundingMode) : () => maxPool(convertedX, windowShape, strides, convertedPad, dimRoundingMode);\n  const y = forwardOp();\n  const res = isDilationOne ? y : batchToSpaceND(y, dilation, adjustedCrops);\n  if (reshapedTo4D) {\n    return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);\n  }\n  return res;\n}\nfunction requiredSpaceToBatchPaddings(inputShape, blockShape, basePadding) {\n  const padStart = basePadding.map((b) => b[0]);\n  const origPadEnd = basePadding.map((b) => b[1]);\n  const fullInputShape = inputShape.concat(padStart, origPadEnd);\n  const padEndExtra = blockShape.map((b, i2) => (b - fullInputShape[i2] % b) % b);\n  const padEnd = origPadEnd.map((s2, i2) => s2 + padEndExtra[i2]);\n  const paddings = blockShape.map((_, i2) => [padStart[i2], padEnd[i2]]);\n  const crops = blockShape.map((_, i2) => [0, padEndExtra[i2]]);\n  return [paddings, crops];\n}\nfunction withSpaceToBatchBasePaddings(filterShape, dilation) {\n  const dilatedFilterShape = filterShape.map((s2, i2) => {\n    return s2 + (s2 - 1) * (dilation[i2] - 1);\n  });\n  const padExtraShape = dilatedFilterShape.map((s2) => s2 - 1);\n  const padExtraStart = padExtraShape.map((s2) => Math.floor(s2 / 2));\n  const padExtraEnd = padExtraShape.map((s2, i2) => s2 - padExtraStart[i2]);\n  return padExtraShape.map((_, i2) => {\n    return [padExtraStart[i2], padExtraEnd[i2]];\n  });\n}\nvar pool = op({ pool_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/prelu.js\nfunction prelu_(x, alpha) {\n  const $x = convertToTensor(x, \"x\", \"prelu\");\n  const $alpha = convertToTensor(alpha, \"alpha\", \"prelu\");\n  const inputs = { x: $x, alpha: $alpha };\n  return ENGINE.runKernel(Prelu, inputs);\n}\nvar prelu = op({ prelu_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/prod.js\nfunction prod_(x, axis = null, keepDims = false) {\n  let $x = convertToTensor(x, \"x\", \"prod\");\n  if ($x.dtype === \"bool\") {\n    $x = cast($x, \"int32\");\n  }\n  const inputs = { x: $x };\n  const attrs = { axis, keepDims };\n  return ENGINE.runKernel(Prod, inputs, attrs);\n}\nvar prod = op({ prod_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/ragged_tensor_to_tensor.js\nfunction raggedTensorToTensor_(shape, values, defaultValue, rowPartitionTensors, rowPartitionTypes) {\n  const $shape = convertToTensor(shape, \"shape\", \"raggedTensorToTensor\", \"int32\");\n  const $values = convertToTensor(values, \"values\", \"raggedTensorToTensor\");\n  const $defaultValue = convertToTensor(defaultValue, \"defaultValue\", \"raggedTensorToTensor\", $values.dtype);\n  const $rowPartitionTensors = rowPartitionTensors.map((t2, i2) => convertToTensor(t2, `tensors${i2}`, \"raggedTensorToTensor\", \"int32\"));\n  const inputs = {\n    shape: $shape,\n    values: $values,\n    defaultValue: $defaultValue,\n    rowPartitionTensors: $rowPartitionTensors\n  };\n  const attrs = { rowPartitionTypes };\n  return ENGINE.runKernel(RaggedTensorToTensor, inputs, attrs);\n}\nvar raggedTensorToTensor = op({ raggedTensorToTensor_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/rand.js\nfunction rand_(shape, randFunction, dtype) {\n  const size = sizeFromShape(shape);\n  let values = null;\n  if (dtype == null || dtype === \"float32\") {\n    values = new Float32Array(size);\n  } else if (dtype === \"int32\") {\n    values = new Int32Array(size);\n  } else if (dtype === \"bool\") {\n    values = new Uint8Array(size);\n  } else {\n    throw new Error(`Unknown data type ${dtype}`);\n  }\n  for (let i2 = 0; i2 < size; i2++) {\n    values[i2] = randFunction();\n  }\n  return ENGINE.makeTensor(values, shape, dtype);\n}\nvar rand = op({ rand_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/rand_util.js\nvar seedrandom = __toESM(require_seedrandom2());\nvar MPRandGauss = class {\n  constructor(mean5, stdDeviation, dtype, truncated, seed) {\n    this.mean = mean5;\n    this.stdDev = stdDeviation;\n    this.dtype = dtype;\n    this.nextVal = NaN;\n    this.truncated = truncated;\n    if (this.truncated) {\n      this.upper = this.mean + this.stdDev * 2;\n      this.lower = this.mean - this.stdDev * 2;\n    }\n    const seedValue = seed ? seed : Math.random();\n    this.random = seedrandom.alea(seedValue.toString());\n  }\n  nextValue() {\n    if (!isNaN(this.nextVal)) {\n      const value = this.nextVal;\n      this.nextVal = NaN;\n      return value;\n    }\n    let resultX, resultY;\n    let isValid = false;\n    while (!isValid) {\n      let v1, v2, s2;\n      do {\n        v1 = 2 * this.random() - 1;\n        v2 = 2 * this.random() - 1;\n        s2 = v1 * v1 + v2 * v2;\n      } while (s2 >= 1 || s2 === 0);\n      const mul2 = Math.sqrt(-2 * Math.log(s2) / s2);\n      resultX = this.mean + this.stdDev * v1 * mul2;\n      resultY = this.mean + this.stdDev * v2 * mul2;\n      if (!this.truncated || this.isValidTruncated(resultX)) {\n        isValid = true;\n      }\n    }\n    if (!this.truncated || this.isValidTruncated(resultY)) {\n      this.nextVal = this.convertValue(resultY);\n    }\n    return this.convertValue(resultX);\n  }\n  convertValue(value) {\n    if (this.dtype == null || this.dtype === \"float32\") {\n      return value;\n    }\n    return Math.round(value);\n  }\n  isValidTruncated(value) {\n    return value <= this.upper && value >= this.lower;\n  }\n};\nvar RandGamma = class {\n  constructor(alpha, beta, dtype, seed) {\n    this.alpha = alpha;\n    this.beta = 1 / beta;\n    this.dtype = dtype;\n    const seedValue = seed ? seed : Math.random();\n    this.randu = seedrandom.alea(seedValue.toString());\n    this.randn = new MPRandGauss(0, 1, dtype, false, this.randu());\n    if (alpha < 1) {\n      this.d = alpha + 2 / 3;\n    } else {\n      this.d = alpha - 1 / 3;\n    }\n    this.c = 1 / Math.sqrt(9 * this.d);\n  }\n  nextValue() {\n    let x2, v0, v1, x, u, v;\n    while (true) {\n      do {\n        x = this.randn.nextValue();\n        v = 1 + this.c * x;\n      } while (v <= 0);\n      v *= v * v;\n      x2 = x * x;\n      v0 = 1 - 0.331 * x2 * x2;\n      v1 = 0.5 * x2 + this.d * (1 - v + Math.log(v));\n      u = this.randu();\n      if (u < v0 || Math.log(u) < v1) {\n        break;\n      }\n    }\n    v = 1 / this.beta * this.d * v;\n    if (this.alpha < 1) {\n      v *= Math.pow(this.randu(), 1 / this.alpha);\n    }\n    return this.convertValue(v);\n  }\n  convertValue(value) {\n    if (this.dtype === \"float32\") {\n      return value;\n    }\n    return Math.round(value);\n  }\n};\nvar UniformRandom = class {\n  constructor(min7 = 0, max7 = 1, dtype, seed) {\n    this.canReturnFloat = () => this.dtype == null || this.dtype === \"float32\";\n    this.min = min7;\n    this.range = max7 - min7;\n    this.dtype = dtype;\n    if (seed == null) {\n      seed = Math.random();\n    }\n    if (typeof seed === \"number\") {\n      seed = seed.toString();\n    }\n    if (!this.canReturnFloat() && this.range <= 1) {\n      throw new Error(`The difference between ${min7} - ${max7} <= 1 and dtype is not float`);\n    }\n    this.random = seedrandom.alea(seed);\n  }\n  convertValue(value) {\n    if (this.canReturnFloat()) {\n      return value;\n    }\n    return Math.round(value);\n  }\n  nextValue() {\n    return this.convertValue(this.min + this.range * this.random());\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/random_gamma.js\nfunction randomGamma_(shape, alpha, beta = 1, dtype = \"float32\", seed) {\n  if (beta == null) {\n    beta = 1;\n  }\n  if (dtype == null) {\n    dtype = \"float32\";\n  }\n  if (dtype !== \"float32\" && dtype !== \"int32\") {\n    throw new Error(`Unsupported data type ${dtype}`);\n  }\n  const rgamma = new RandGamma(alpha, beta, dtype, seed);\n  const res = buffer(shape, dtype);\n  for (let i2 = 0; i2 < res.values.length; i2++) {\n    res.values[i2] = rgamma.nextValue();\n  }\n  return res.toTensor();\n}\nvar randomGamma = op({ randomGamma_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/random_normal.js\nfunction randomNormal_(shape, mean5 = 0, stdDev = 1, dtype, seed) {\n  if (dtype != null && dtype === \"bool\") {\n    throw new Error(`Unsupported data type ${dtype}`);\n  }\n  const randGauss = new MPRandGauss(mean5, stdDev, dtype, false, seed);\n  const res = buffer(shape, dtype);\n  for (let i2 = 0; i2 < res.values.length; i2++) {\n    res.values[i2] = randGauss.nextValue();\n  }\n  return res.toTensor();\n}\nvar randomNormal = op({ randomNormal_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/random_standard_normal.js\nfunction randomStandardNormal_(shape, dtype, seed) {\n  if (dtype != null && dtype === \"bool\") {\n    throw new Error(`Unsupported data type ${dtype}`);\n  }\n  return randomNormal(shape, 0, 1, dtype, seed);\n}\nvar randomStandardNormal = op({ randomStandardNormal_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/random_uniform.js\nfunction randomUniform_(shape, minval = 0, maxval = 1, dtype = \"float32\", seed) {\n  const res = buffer(shape, dtype);\n  const random = new UniformRandom(minval, maxval, null, seed);\n  for (let i2 = 0; i2 < res.values.length; i2++) {\n    res.values[i2] = random.nextValue();\n  }\n  return res.toTensor();\n}\nvar randomUniform = op({ randomUniform_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/range.js\nfunction range(start, stop, step5 = 1, dtype = \"float32\") {\n  if (step5 === 0) {\n    throw new Error(\"Cannot have a step of zero\");\n  }\n  const attrs = { start, stop, step: step5, dtype };\n  return ENGINE.runKernel(Range, {}, attrs);\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/reciprocal.js\nfunction reciprocal_(x) {\n  const $x = convertToTensor(x, \"x\", \"reciprocal\");\n  const inputs = { x: $x };\n  return ENGINE.runKernel(Reciprocal, inputs);\n}\nvar reciprocal = op({ reciprocal_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/relu.js\nfunction relu_(x) {\n  const $x = convertToTensor(x, \"x\", \"relu\");\n  const inputs = { x: $x };\n  return ENGINE.runKernel(Relu, inputs);\n}\nvar relu = op({ relu_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/relu6.js\nfunction relu6_(x) {\n  const $x = convertToTensor(x, \"x\", \"relu6\");\n  const inputs = { x: $x };\n  return ENGINE.runKernel(Relu6, inputs);\n}\nvar relu6 = op({ relu6_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/reverse.js\nfunction reverse_(x, axis) {\n  const $x = convertToTensor(x, \"x\", \"reverse\");\n  const inputs = { x: $x };\n  const attrs = { dims: axis };\n  return ENGINE.runKernel(Reverse, inputs, attrs);\n}\nvar reverse = op({ reverse_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/reverse_1d.js\nfunction reverse1d_(x) {\n  const $x = convertToTensor(x, \"x\", \"reverse\");\n  assert($x.rank === 1, () => `Error in reverse1D: x must be rank 1 but got rank ${$x.rank}.`);\n  return reverse($x, 0);\n}\nvar reverse1d = op({ reverse1d_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/reverse_2d.js\nfunction reverse2d_(x, axis) {\n  const $x = convertToTensor(x, \"x\", \"reverse\");\n  assert($x.rank === 2, () => `Error in reverse2D: x must be rank 2 but got rank ${$x.rank}.`);\n  return reverse($x, axis);\n}\nvar reverse2d = op({ reverse2d_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/reverse_3d.js\nfunction reverse3d_(x, axis) {\n  const $x = convertToTensor(x, \"x\", \"reverse\");\n  assert($x.rank === 3, () => `Error in reverse3D: x must be rank 3 but got rank ${$x.rank}.`);\n  return reverse($x, axis);\n}\nvar reverse3d = op({ reverse3d_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/reverse_4d.js\nfunction reverse4d_(x, axis) {\n  const $x = convertToTensor(x, \"x\", \"reverse\");\n  assert($x.rank === 4, () => `Error in reverse4D: x must be rank 4 but got rank ${$x.rank}.`);\n  return reverse($x, axis);\n}\nvar reverse4d = op({ reverse4d_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/round.js\nfunction round_(x) {\n  const $x = convertToTensor(x, \"x\", \"round\");\n  const inputs = { x: $x };\n  return ENGINE.runKernel(Round, inputs);\n}\nvar round2 = op({ round_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/rsqrt.js\nfunction rsqrt_(x) {\n  const $x = convertToTensor(x, \"x\", \"rsqrt\", \"float32\");\n  const inputs = { x: $x };\n  return ENGINE.runKernel(Rsqrt, inputs);\n}\nvar rsqrt = op({ rsqrt_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/selu.js\nfunction selu_(x) {\n  const $x = convertToTensor(x, \"x\", \"selu\");\n  const inputs = { x: $x };\n  return ENGINE.runKernel(Selu, inputs);\n}\nvar selu = op({ selu_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/separable_conv2d.js\nfunction separableConv2d_(x, depthwiseFilter, pointwiseFilter, strides, pad3, dilation = [1, 1], dataFormat = \"NHWC\") {\n  const $x = convertToTensor(x, \"x\", \"separableConv2d\");\n  const $depthwiseFilter = convertToTensor(depthwiseFilter, \"depthwiseFilter\", \"separableConv2d\");\n  const $pointwiseFilter = convertToTensor(pointwiseFilter, \"pointwiseFilter\", \"separableConv2d\");\n  let x4D = $x;\n  let reshapedTo4D = false;\n  if ($x.rank === 3) {\n    reshapedTo4D = true;\n    x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);\n  }\n  if (dataFormat === \"NCHW\") {\n    throw new Error(\"separableConv2d currently does not support dataFormat NCHW; only NHWC is supported\");\n  }\n  assert(x4D.rank === 4, () => `Error in separableConv2d: input must be rank 4, but got rank ${x4D.rank}.`);\n  assert($depthwiseFilter.rank === 4, () => `Error in separableConv2d: depthwise filter must be rank 4, but got rank ${$depthwiseFilter.rank}.`);\n  assert($pointwiseFilter.rank === 4, () => `Error in separableConv2d: pointwise filter must be rank 4, but got rank ${$depthwiseFilter.rank}.`);\n  assert($pointwiseFilter.shape[0] === 1, () => `Error in separableConv2d: the first dimension of pointwise filter  must be 1, but got ${$pointwiseFilter.shape[0]}.`);\n  assert($pointwiseFilter.shape[1] === 1, () => `Error in separableConv2d: the second dimension of pointwise filter must be 1, but got ${$pointwiseFilter.shape[1]}.`);\n  const inChannels = $depthwiseFilter.shape[2];\n  const channelMultiplier = $depthwiseFilter.shape[3];\n  assert($pointwiseFilter.shape[2] === inChannels * channelMultiplier, () => `Error in separableConv2d: the third dimension of pointwise filter must be ${inChannels * channelMultiplier}, but got ${$pointwiseFilter.shape[2]}.`);\n  const depthwise = depthwiseConv2d(x4D, $depthwiseFilter, strides, pad3, dataFormat, dilation);\n  const pointwiseStride = 1;\n  const res = conv2d(depthwise, $pointwiseFilter, pointwiseStride, \"valid\", dataFormat);\n  if (reshapedTo4D) {\n    return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);\n  }\n  return res;\n}\nvar separableConv2d = op({ separableConv2d_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/setdiff1d_async.js\nasync function setdiff1dAsync_(x, y) {\n  const $x = convertToTensor(x, \"x\", \"setdiff1d\");\n  const $y = convertToTensor(y, \"y\", \"setdiff1d\");\n  assert($x.dtype === $y.dtype, () => `x and y should have the same dtype, but got x (${$x.dtype}) and y (${$y.dtype}).`);\n  assert($x.rank === 1, () => `x should be 1D tensor, but got x (${$x.shape}).`);\n  assert($y.rank === 1, () => `y should be 1D tensor, but got y (${$y.shape}).`);\n  const xVals = await $x.data();\n  const yVals = await $y.data();\n  const ySet = new Set(yVals);\n  let outputSize = 0;\n  for (let i2 = 0; i2 < xVals.length; i2++) {\n    if (!ySet.has(xVals[i2])) {\n      outputSize++;\n    }\n  }\n  const buffer2 = new TensorBuffer([outputSize], $x.dtype);\n  const indices = new TensorBuffer([outputSize], \"int32\");\n  for (let i2 = 0, p2 = 0; i2 < xVals.length; i2++) {\n    if (!ySet.has(xVals[i2])) {\n      buffer2.values[p2] = xVals[i2];\n      indices.values[p2] = i2;\n      p2++;\n    }\n  }\n  return [buffer2.toTensor(), indices.toTensor()];\n}\nvar setdiff1dAsync = setdiff1dAsync_;\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/sign.js\nfunction sign_(x) {\n  const $x = convertToTensor(x, \"x\", \"sign\");\n  const inputs = { x: $x };\n  return ENGINE.runKernel(Sign, inputs);\n}\nvar sign = op({ sign_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/sin.js\nfunction sin_(x) {\n  const $x = convertToTensor(x, \"x\", \"sin\", \"float32\");\n  const inputs = { x: $x };\n  return ENGINE.runKernel(Sin, inputs);\n}\nvar sin = op({ sin_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/sinh.js\nfunction sinh_(x) {\n  const $x = convertToTensor(x, \"x\", \"sinh\");\n  const inputs = { x: $x };\n  return ENGINE.runKernel(Sinh, inputs);\n}\nvar sinh = op({ sinh_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/slice1d.js\nfunction slice1d_(x, begin, size) {\n  const $x = convertToTensor(x, \"x\", \"slice1d\");\n  assert($x.rank === 1, () => `slice1d expects a rank-1 tensor, but got a rank-${$x.rank} tensor`);\n  return slice($x, [begin], [size]);\n}\nvar slice1d = op({ slice1d_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/slice2d.js\nfunction slice2d_(x, begin, size) {\n  const $x = convertToTensor(x, \"x\", \"slice2d\");\n  assert($x.rank === 2, () => `slice2d expects a rank-2 tensor, but got a rank-${$x.rank} tensor`);\n  return slice($x, begin, size);\n}\nvar slice2d = op({ slice2d_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/slice3d.js\nfunction slice3d_(x, begin, size) {\n  const $x = convertToTensor(x, \"x\", \"slice3d\");\n  assert($x.rank === 3, () => `slice3d expects a rank-3 tensor, but got a rank-${$x.rank} tensor`);\n  return slice($x, begin, size);\n}\nvar slice3d = op({ slice3d_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/slice4d.js\nfunction slice4d_(x, begin, size) {\n  const $x = convertToTensor(x, \"x\", \"slice4d\");\n  assert($x.rank === 4, () => `slice4d expects a rank-4 tensor, but got a rank-${$x.rank} tensor`);\n  return slice($x, begin, size);\n}\nvar slice4d = op({ slice4d_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/softmax.js\nfunction softmax_(logits, dim = -1) {\n  const $logits = convertToTensor(logits, \"logits\", \"softmax\", \"float32\");\n  if (dim === -1) {\n    dim = $logits.rank - 1;\n  }\n  if (dim !== $logits.rank - 1) {\n    throw Error(`Softmax along a non-last dimension is not yet supported. Logits was rank ${$logits.rank} and dim was ${dim}`);\n  }\n  const inputs = { logits: $logits };\n  const attrs = { dim };\n  return ENGINE.runKernel(Softmax, inputs, attrs);\n}\nvar softmax = op({ softmax_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/spectral/fft.js\nfunction fft_(input2) {\n  assert(input2.dtype === \"complex64\", () => `The dtype for tf.spectral.fft() must be complex64 but got ${input2.dtype}.`);\n  const inputs = { input: input2 };\n  return ENGINE.runKernel(FFT, inputs);\n}\nvar fft = op({ fft_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/spectral/ifft.js\nfunction ifft_(input2) {\n  assert(input2.dtype === \"complex64\", () => `The dtype for tf.spectral.ifft() must be complex64 but got ${input2.dtype}.`);\n  const inputs = { input: input2 };\n  return ENGINE.runKernel(IFFT, inputs);\n}\nvar ifft = op({ ifft_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/spectral/irfft.js\nfunction irfft_(input2) {\n  const innerDimensionSize = input2.shape[input2.shape.length - 1];\n  const batch = input2.size / innerDimensionSize;\n  let ret;\n  if (innerDimensionSize <= 2) {\n    const complexInput = reshape(input2, [batch, innerDimensionSize]);\n    ret = ifft(complexInput);\n  } else {\n    const outputShape = [batch, 2 * (innerDimensionSize - 1)];\n    const realInput = reshape(real(input2), [batch, innerDimensionSize]);\n    const imagInput = reshape(imag(input2), [batch, innerDimensionSize]);\n    const realConjugate = reverse(slice(realInput, [0, 1], [batch, innerDimensionSize - 2]), 1);\n    const imagConjugate = mul(reverse(slice(imagInput, [0, 1], [batch, innerDimensionSize - 2]), 1), scalar(-1));\n    const r2 = concat([realInput, realConjugate], 1);\n    const i2 = concat([imagInput, imagConjugate], 1);\n    const complexInput = reshape(complex(r2, i2), [outputShape[0], outputShape[1]]);\n    ret = ifft(complexInput);\n  }\n  ret = real(ret);\n  if (input2.rank === 3 && input2.shape[0] !== 0) {\n    const temp = ret;\n    const batch2 = input2.shape[0];\n    ret = reshape(ret, [batch2, ret.shape[0] / batch2, ret.shape[1]]);\n    temp.dispose();\n  }\n  return ret;\n}\nvar irfft = op({ irfft_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/split.js\nfunction split_(x, numOrSizeSplits, axis = 0) {\n  const $x = convertToTensor(x, \"x\", \"split\");\n  const inputs = { x: $x };\n  const attr = { numOrSizeSplits, axis };\n  return ENGINE.runKernel(SplitV, inputs, attr);\n}\nvar split = op({ split_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/spectral/rfft.js\nfunction rfft_(input2, fftLength) {\n  assert(input2.dtype === \"float32\", () => `The dtype for rfft() must be real value but got ${input2.dtype}`);\n  let innerDimensionSize = input2.shape[input2.shape.length - 1];\n  const batch = input2.size / innerDimensionSize;\n  let adjustedInput;\n  if (fftLength != null && fftLength < innerDimensionSize) {\n    const begin = input2.shape.map((v) => 0);\n    const size = input2.shape.map((v) => v);\n    size[input2.shape.length - 1] = fftLength;\n    adjustedInput = slice(input2, begin, size);\n    innerDimensionSize = fftLength;\n  } else if (fftLength != null && fftLength > innerDimensionSize) {\n    const zerosShape = input2.shape.map((v) => v);\n    zerosShape[input2.shape.length - 1] = fftLength - innerDimensionSize;\n    adjustedInput = concat([input2, zeros(zerosShape)], input2.shape.length - 1);\n    innerDimensionSize = fftLength;\n  } else {\n    adjustedInput = input2;\n  }\n  const zerosInput = zerosLike(adjustedInput);\n  const complexInput = reshape(complex(adjustedInput, zerosInput), [batch, innerDimensionSize]);\n  const ret = fft(complexInput);\n  const half = Math.floor(innerDimensionSize / 2) + 1;\n  const realValues = real(ret);\n  const imagValues = imag(ret);\n  const realComplexConjugate = split(realValues, [half, innerDimensionSize - half], realValues.shape.length - 1);\n  const imagComplexConjugate = split(imagValues, [half, innerDimensionSize - half], imagValues.shape.length - 1);\n  const outputShape = adjustedInput.shape.slice();\n  outputShape[adjustedInput.shape.length - 1] = half;\n  return reshape(complex(realComplexConjugate[0], imagComplexConjugate[0]), outputShape);\n}\nvar rfft = op({ rfft_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/squared_difference.js\nfunction squaredDifference_(a, b) {\n  let $a = convertToTensor(a, \"a\", \"squaredDifference\");\n  let $b = convertToTensor(b, \"b\", \"squaredDifference\");\n  [$a, $b] = makeTypesMatch($a, $b);\n  assertAndGetBroadcastShape($a.shape, $b.shape);\n  const inputs = { a: $a, b: $b };\n  const attrs = {};\n  return ENGINE.runKernel(SquaredDifference, inputs, attrs);\n}\nvar squaredDifference = op({ squaredDifference_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/squeeze.js\nfunction squeeze_(x, axis) {\n  const $x = convertToTensor(x, \"x\", \"squeeze\", \"string_or_numeric\");\n  return reshape($x, squeezeShape($x.shape, axis).newShape);\n}\nvar squeeze = op({ squeeze_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/stack.js\nfunction stack_(tensors, axis = 0) {\n  const $tensors = convertToTensorArray(tensors, \"tensors\", \"stack\", \"string_or_numeric\");\n  assert($tensors.length >= 1, () => \"Pass at least one tensor to tf.stack\");\n  if ($tensors.length > 0) {\n    assert(axis <= $tensors[0].rank, () => \"Axis must be <= rank of the tensor\");\n  }\n  const inputs = $tensors;\n  const attrs = { axis };\n  return ENGINE.runKernel(Pack, inputs, attrs);\n}\nvar stack = op({ stack_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/step.js\nfunction step_(x, alpha = 0) {\n  const $x = convertToTensor(x, \"x\", \"step\");\n  const inputs = { x: $x };\n  const attrs = { alpha };\n  return ENGINE.runKernel(Step, inputs, attrs);\n}\nvar step = op({ step_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/strided_slice.js\nfunction stridedSlice_(x, begin, end, strides, beginMask = 0, endMask = 0, ellipsisMask = 0, newAxisMask = 0, shrinkAxisMask = 0) {\n  const $x = convertToTensor(x, \"x\", \"stridedSlice\", \"string_or_numeric\");\n  const inputs = { x: $x };\n  const attrs = {\n    begin,\n    end,\n    strides,\n    beginMask,\n    endMask,\n    ellipsisMask,\n    newAxisMask,\n    shrinkAxisMask\n  };\n  return ENGINE.runKernel(StridedSlice, inputs, attrs);\n}\nvar stridedSlice = op({ stridedSlice_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/tan.js\nfunction tan_(x) {\n  const $x = convertToTensor(x, \"x\", \"tan\", \"float32\");\n  const inputs = { x: $x };\n  return ENGINE.runKernel(Tan, inputs);\n}\nvar tan = op({ tan_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/tensor1d.js\nfunction tensor1d(values, dtype) {\n  assertNonNull(values);\n  const inferredShape = inferShape(values, dtype);\n  if (inferredShape.length !== 1) {\n    throw new Error(\"tensor1d() requires values to be a flat/TypedArray\");\n  }\n  const shape = null;\n  return makeTensor(values, shape, inferredShape, dtype);\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/tensor2d.js\nfunction tensor2d(values, shape, dtype) {\n  assertNonNull(values);\n  if (shape != null && shape.length !== 2) {\n    throw new Error(\"tensor2d() requires shape to have two numbers\");\n  }\n  const inferredShape = inferShape(values, dtype);\n  if (inferredShape.length !== 2 && inferredShape.length !== 1) {\n    throw new Error(\"tensor2d() requires values to be number[][] or flat/TypedArray\");\n  }\n  if (inferredShape.length === 1 && shape == null) {\n    throw new Error(\"tensor2d() requires shape to be provided when `values` are a flat/TypedArray\");\n  }\n  return makeTensor(values, shape, inferredShape, dtype);\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/tensor4d.js\nfunction tensor4d(values, shape, dtype) {\n  assertNonNull(values);\n  if (shape != null && shape.length !== 4) {\n    throw new Error(\"tensor4d() requires shape to have four numbers\");\n  }\n  const inferredShape = inferShape(values, dtype);\n  if (inferredShape.length !== 4 && inferredShape.length !== 1) {\n    throw new Error(\"tensor4d() requires values to be number[][][][] or flat/TypedArray\");\n  }\n  if (inferredShape.length === 1 && shape == null) {\n    throw new Error(\"tensor4d() requires shape to be provided when `values` are a flat array\");\n  }\n  return makeTensor(values, shape, inferredShape, dtype);\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/tensor5d.js\nfunction tensor5d(values, shape, dtype) {\n  assertNonNull(values);\n  if (shape != null && shape.length !== 5) {\n    throw new Error(\"tensor5d() requires shape to have five numbers\");\n  }\n  const inferredShape = inferShape(values, dtype);\n  if (inferredShape.length !== 5 && inferredShape.length !== 1) {\n    throw new Error(\"tensor5d() requires values to be number[][][][][] or flat/TypedArray\");\n  }\n  if (inferredShape.length === 1 && shape == null) {\n    throw new Error(\"tensor5d() requires shape to be provided when `values` are a flat array\");\n  }\n  return makeTensor(values, shape, inferredShape, dtype);\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/tensor6d.js\nfunction tensor6d(values, shape, dtype) {\n  assertNonNull(values);\n  if (shape != null && shape.length !== 6) {\n    throw new Error(\"tensor6d() requires shape to have six numbers\");\n  }\n  const inferredShape = inferShape(values, dtype);\n  if (inferredShape.length !== 6 && inferredShape.length !== 1) {\n    throw new Error(\"tensor6d() requires values to be number[][][][][][] or flat/TypedArray\");\n  }\n  if (inferredShape.length === 1 && shape == null) {\n    throw new Error(\"tensor6d() requires shape to be provided when `values` are a flat array\");\n  }\n  shape = shape || inferredShape;\n  return makeTensor(values, shape, inferredShape, dtype);\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/topk.js\nfunction topk_(x, k = 1, sorted = true) {\n  const $x = convertToTensor(x, \"x\", \"topk\");\n  if ($x.rank === 0) {\n    throw new Error(\"topk() expects the input to be of rank 1 or higher\");\n  }\n  const lastDim = $x.shape[$x.shape.length - 1];\n  if (k < 0) {\n    throw new Error(`'k' passed to topk() must be >= 0 but got ${k}`);\n  }\n  if (k > lastDim) {\n    throw new Error(`'k' passed to topk() must be <= the last dimension (${lastDim}) but got ${k}`);\n  }\n  const inputs = { x: $x };\n  const attrs = { k, sorted };\n  const [values, indices] = ENGINE.runKernel(TopK, inputs, attrs);\n  return { values, indices };\n}\nvar topk = op({ topk_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/truncated_normal.js\nfunction truncatedNormal_(shape, mean5 = 0, stdDev = 1, dtype, seed) {\n  if (dtype != null && dtype === \"bool\") {\n    throw new Error(`Unsupported data type $ { dtype }`);\n  }\n  const randGauss = new MPRandGauss(mean5, stdDev, dtype, true, seed);\n  const res = buffer(shape, dtype);\n  for (let i2 = 0; i2 < res.values.length; i2++) {\n    res.values[i2] = randGauss.nextValue();\n  }\n  return res.toTensor();\n}\nvar truncatedNormal = op({ truncatedNormal_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/unique.js\nfunction unique_(x, axis = 0) {\n  const $x = convertToTensor(x, \"x\", \"unique\", \"string_or_numeric\");\n  assert($x.rank > 0, () => \"The input tensor must be at least 1D\");\n  const inputs = { x: $x };\n  const attrs = { axis };\n  const [values, indices] = ENGINE.runKernel(Unique, inputs, attrs);\n  return { values, indices };\n}\nvar unique = op({ unique_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/unsorted_segment_sum.js\nfunction unsortedSegmentSum_(x, segmentIds, numSegments) {\n  const $x = convertToTensor(x, \"x\", \"unsortedSegmentSum\");\n  const $segmentIds = convertToTensor(segmentIds, \"segmentIds\", \"unsortedSegmentSum\", \"int32\");\n  assert(isInt(numSegments), () => \"numSegments must be of dtype int\");\n  const inputs = { x: $x, segmentIds: $segmentIds };\n  const attrs = { numSegments };\n  return ENGINE.runKernel(UnsortedSegmentSum, inputs, attrs);\n}\nvar unsortedSegmentSum = op({ unsortedSegmentSum_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/unstack.js\nfunction unstack_(x, axis = 0) {\n  const $x = convertToTensor(x, \"x\", \"unstack\", \"string_or_numeric\");\n  assert(axis >= -$x.shape.length && axis < $x.shape.length, () => `Axis = ${axis} is not in [-${$x.shape.length}, ${$x.shape.length})`);\n  const inputs = { value: $x };\n  const attrs = { axis };\n  return ENGINE.runKernel(Unpack, inputs, attrs);\n}\nvar unstack = op({ unstack_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/upper_bound.js\nfunction upperBound(sortedSequence, values) {\n  return searchSorted(sortedSequence, values, \"right\");\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/variable.js\nfunction variable(initialValue, trainable = true, name, dtype) {\n  return ENGINE.makeVariable(initialValue, trainable, name, dtype);\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/backends/where_impl.js\nfunction whereImpl(condShape, condVals) {\n  const indices = [];\n  for (let i2 = 0; i2 < condVals.length; i2++) {\n    if (condVals[i2]) {\n      indices.push(i2);\n    }\n  }\n  const inBuffer = buffer(condShape, \"int32\");\n  const out = buffer([indices.length, condShape.length], \"int32\");\n  for (let i2 = 0; i2 < indices.length; i2++) {\n    const loc = inBuffer.indexToLoc(indices[i2]);\n    const offset = i2 * condShape.length;\n    out.values.set(loc, offset);\n  }\n  return out.toTensor();\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/where_async.js\nasync function whereAsync_(condition) {\n  const $condition = convertToTensor(condition, \"condition\", \"whereAsync\", \"bool\");\n  const vals = await $condition.data();\n  const res = whereImpl($condition.shape, vals);\n  if (condition !== $condition) {\n    $condition.dispose();\n  }\n  return res;\n}\nvar whereAsync = whereAsync_;\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/boolean_mask.js\nasync function booleanMaskAsync_(tensor2, mask, axis) {\n  const $tensor = convertToTensor(tensor2, \"tensor\", \"boolMask\");\n  const $mask = convertToTensor(mask, \"mask\", \"boolMask\", \"bool\");\n  const axisFrom = axis == null ? 0 : axis;\n  const maskDim = $mask.rank;\n  const tensorShape = $tensor.shape;\n  assert(maskDim > 0, () => \"mask cannot be scalar\");\n  assertShapesMatch(tensorShape.slice(axisFrom, axisFrom + maskDim), $mask.shape, `mask's shape must match the first K dimensions of tensor's shape,`);\n  let leadingSize = 1;\n  for (let i2 = axisFrom; i2 < axisFrom + maskDim; i2++) {\n    leadingSize *= tensorShape[i2];\n  }\n  const targetTensorShape = tensorShape.slice(0, axisFrom).concat([leadingSize], tensorShape.slice(axisFrom + maskDim));\n  const reshapedTensor = reshape($tensor, targetTensorShape);\n  const reshapedMask = reshape($mask, [-1]);\n  const positivePositions = await whereAsync(reshapedMask);\n  const indices = squeeze(positivePositions, [1]);\n  const res = gather(reshapedTensor, indices, axisFrom);\n  if (tensor2 !== $tensor) {\n    $tensor.dispose();\n  }\n  if (mask !== $mask) {\n    $mask.dispose();\n  }\n  indices.dispose();\n  reshapedTensor.dispose();\n  reshapedMask.dispose();\n  positivePositions.dispose();\n  return res;\n}\nvar booleanMaskAsync = booleanMaskAsync_;\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/moving_average.js\nfunction movingAverage_(v, x, decay, step5, zeroDebias = true) {\n  const $v = convertToTensor(v, \"v\", \"movingAverage\");\n  const $x = convertToTensor(x, \"x\", \"movingAverage\");\n  const $decay = convertToTensor(decay, \"decay\", \"movingAverage\");\n  assertTypesMatch($v, $x);\n  assert(arraysEqual($v.shape, $x.shape), () => \"Shape mismatch in v and x\");\n  const one = scalar(1);\n  const oneMinusDecay = sub(one, $decay);\n  let update = mul(sub($x, $v), oneMinusDecay);\n  if (zeroDebias) {\n    assert(step5 != null, () => \"When using zeroDebias: true, step is required.\");\n    const $step = convertToTensor(step5, \"step\", \"movingAverage\");\n    update = div(update, sub(one, pow($decay, $step)));\n  }\n  return add2($v, update);\n}\nvar movingAverage = op({ movingAverage_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/scatter_nd.js\nfunction scatterND_(indices, updates, shape) {\n  const $indices = convertToTensor(indices, \"indices\", \"scatterND\", \"int32\");\n  const $updates = convertToTensor(updates, \"updates\", \"scatterND\");\n  validateInput($updates, $indices, shape);\n  const inputs = { indices: $indices, updates: $updates };\n  const attrs = { shape };\n  return ENGINE.runKernel(ScatterNd, inputs, attrs);\n}\nvar scatterND = op({ scatterND_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/sparse_to_dense_util.js\nfunction validateInput2(sparseIndices, sparseValues, outputShape, defaultValues) {\n  if (sparseIndices.dtype !== \"int32\") {\n    throw new Error(`tf.sparseToDense() expects the indices to be int32 type, but the dtype was ${sparseIndices.dtype}.`);\n  }\n  if (sparseIndices.rank > 2) {\n    throw new Error(`sparseIndices should be a scalar, vector, or matrix, but got shape ${sparseIndices.shape}.`);\n  }\n  const numElems = sparseIndices.rank > 0 ? sparseIndices.shape[0] : 1;\n  const numDims = sparseIndices.rank > 1 ? sparseIndices.shape[1] : 1;\n  if (outputShape.length !== numDims) {\n    throw new Error(`outputShape has incorrect number of elements:, ${outputShape.length}, should be: ${numDims}.`);\n  }\n  const numValues = sparseValues.size;\n  if (!(sparseValues.rank === 0 || sparseValues.rank === 1 && numValues === numElems)) {\n    throw new Error(`sparseValues has incorrect shape ${sparseValues.shape}, should be [] or [${numElems}]`);\n  }\n  if (sparseValues.dtype !== defaultValues.dtype) {\n    throw new Error(\"sparseValues.dtype must match defaultValues.dtype\");\n  }\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/sparse_to_dense.js\nfunction sparseToDense_(sparseIndices, sparseValues, outputShape, defaultValue = 0) {\n  const $sparseIndices = convertToTensor(sparseIndices, \"sparseIndices\", \"sparseToDense\", \"int32\");\n  const $sparseValues = convertToTensor(sparseValues, \"sparseValues\", \"sparseToDense\", \"string_or_numeric\");\n  const $defaultValue = convertToTensor(defaultValue, \"defaultValue\", \"sparseToDense\", $sparseValues.dtype);\n  validateInput2($sparseIndices, $sparseValues, outputShape, $defaultValue);\n  const inputs = {\n    sparseIndices: $sparseIndices,\n    sparseValues: $sparseValues,\n    defaultValue: $defaultValue\n  };\n  const attrs = { outputShape };\n  return ENGINE.runKernel(SparseToDense, inputs, attrs);\n}\nvar sparseToDense = op({ sparseToDense_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/gather_nd.js\nfunction gatherND_(x, indices) {\n  const $indices = convertToTensor(indices, \"indices\", \"gatherND\", \"int32\");\n  const $x = convertToTensor(x, \"x\", \"gatherND\", \"string_or_numeric\");\n  const inputs = { params: $x, indices: $indices };\n  return ENGINE.runKernel(GatherNd, inputs);\n}\nvar gatherND = op({ gatherND_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/dropout_util.js\nfunction getNoiseShape(x, noiseShape) {\n  if (noiseShape == null) {\n    return x.shape.slice();\n  }\n  if (arraysEqual(x.shape, noiseShape)) {\n    return noiseShape;\n  }\n  if (x.shape.length === noiseShape.length) {\n    const newDimension = [];\n    for (let i2 = 0; i2 < x.shape.length; i2++) {\n      if (noiseShape[i2] == null && x.shape[i2] != null) {\n        newDimension.push(x.shape[i2]);\n      } else {\n        newDimension.push(noiseShape[i2]);\n      }\n    }\n    return newDimension;\n  }\n  return noiseShape;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/dropout.js\nfunction dropout_(x, rate, noiseShape, seed) {\n  const $x = convertToTensor(x, \"x\", \"dropout\");\n  assert($x.dtype === \"float32\", () => `x has to be a floating point tensor since it's going to be scaled, but got a ${$x.dtype} tensor instead.`);\n  assert(rate >= 0 && rate < 1, () => `rate must be a float in the range [0, 1), but got ${rate}.`);\n  if (rate === 0) {\n    return x instanceof Tensor ? $x.clone() : $x;\n  }\n  const $noiseShape = getNoiseShape($x, noiseShape);\n  const keepProb = 1 - rate;\n  const multiplier = div(floor(add2(randomUniform($noiseShape, 0, 1, \"float32\", seed), keepProb)), keepProb);\n  return mul($x, multiplier);\n}\nvar dropout = op({ dropout_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/signal_ops_util.js\nfunction enclosingPowerOfTwo(value) {\n  return Math.floor(Math.pow(2, Math.ceil(Math.log(value) / Math.log(2))));\n}\nfunction cosineWindow(windowLength, a, b) {\n  const even = 1 - windowLength % 2;\n  const newValues = new Float32Array(windowLength);\n  for (let i2 = 0; i2 < windowLength; ++i2) {\n    const cosArg = 2 * Math.PI * i2 / (windowLength + even - 1);\n    newValues[i2] = a - b * Math.cos(cosArg);\n  }\n  return tensor1d(newValues, \"float32\");\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/in_top_k.js\nasync function inTopKAsync_(predictions, targets, k = 1) {\n  const $predictions = convertToTensor(predictions, \"predictions\", \"inTopK\");\n  const $targets = convertToTensor(targets, \"targets\", \"inTopK\");\n  assert($predictions.rank > 1, () => `inTopK() expects the predictions to be of rank 2 or higher, but got ${$predictions.rank}`);\n  assert($predictions.rank - 1 === $targets.rank, () => `predictions rank should be 1 larger than targets rank, but got predictions rank ${$predictions.rank} and targets rank ${$targets.rank}`);\n  assertShapesMatch($predictions.shape.slice(0, $predictions.shape.length - 1), $targets.shape, `predictions's shape should be align with the targets' shape, except the last dimension.`);\n  const lastDim = $predictions.shape[$predictions.shape.length - 1];\n  assert(k > 0 && k <= lastDim, () => `'k' passed to inTopK() must be > 0 && <= the predictions last dimension (${lastDim}), but got ${k}`);\n  const predictionsVals = await $predictions.data();\n  const targetsVals = await $targets.data();\n  const [batch, size] = [predictionsVals.length / lastDim, lastDim];\n  const precision3 = getTypedArrayFromDType(\"bool\", batch);\n  for (let b = 0; b < batch; b++) {\n    const offset = b * size;\n    const vals = predictionsVals.subarray(offset, offset + size);\n    const valAndInd = [];\n    for (let i2 = 0; i2 < vals.length; i2++) {\n      valAndInd.push({ value: vals[i2], index: i2 });\n    }\n    valAndInd.sort((a, b2) => b2.value - a.value);\n    precision3[b] = 0;\n    for (let i2 = 0; i2 < k; i2++) {\n      if (valAndInd[i2].index === targetsVals[b]) {\n        precision3[b] = 1;\n        break;\n      }\n    }\n  }\n  if (predictions !== $predictions) {\n    $predictions.dispose();\n  }\n  if (targets !== $targets) {\n    $targets.dispose();\n  }\n  return tensor(precision3, $targets.shape, \"bool\");\n}\nvar inTopKAsync = inTopKAsync_;\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/fused_ops.js\nvar fused_ops_exports = {};\n__export(fused_ops_exports, {\n  conv2d: () => conv2d2,\n  depthwiseConv2d: () => depthwiseConv2d2,\n  matMul: () => matMul2\n});\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/conv2d_backprop_filter.js\nfunction conv2DBackpropFilter_(x, dy, filterShape, strides, pad3, dataFormat = \"NHWC\", dimRoundingMode) {\n  let x4D = x;\n  if (x.rank === 3) {\n    x4D = reshape(x, [1, x.shape[0], x.shape[1], x.shape[2]]);\n  }\n  let dy4D = dy;\n  if (dy4D.rank === 3) {\n    dy4D = reshape(dy, [1, dy.shape[0], dy.shape[1], dy.shape[2]]);\n  }\n  assert(x4D.rank === 4, () => `Error in conv2dDerFilter: input must be rank 4, but got shape ${x4D.shape}.`);\n  assert(dy4D.rank === 4, () => `Error in conv2dDerFilter: dy must be rank 4, but got shape ${dy4D.shape}.`);\n  assert(filterShape.length === 4, () => `Error in conv2dDerFilter: filterShape must be length 4, but got ${filterShape}.`);\n  const inDepth = dataFormat === \"NHWC\" ? x4D.shape[3] : x4D.shape[1];\n  const outDepth = dataFormat === \"NHWC\" ? dy4D.shape[3] : dy4D.shape[1];\n  assert(inDepth === filterShape[2], () => `Error in conv2dDerFilter: depth of input ${inDepth}) must match input depth in filter (${filterShape[2]}.`);\n  assert(outDepth === filterShape[3], () => `Error in conv2dDerFilter: depth of dy (${outDepth}) must match output depth for filter (${filterShape[3]}).`);\n  checkPadOnDimRoundingMode(\"conv2dDerFilter\", pad3, dimRoundingMode);\n  const inputs = { x: x4D, dy: dy4D };\n  const attrs = { strides, pad: pad3, dataFormat, dimRoundingMode, filterShape };\n  return ENGINE.runKernel(Conv2DBackpropFilter, inputs, attrs);\n}\nvar conv2DBackpropFilter = op({ conv2DBackpropFilter_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/fused_util.js\nfunction getFusedDyActivation(dy, y, activation2) {\n  if (activation2 == null || activation2 === \"linear\") {\n    return dy;\n  }\n  if (activation2 === \"relu\") {\n    return mul(dy, step(y));\n  }\n  throw new Error(`Cannot compute gradient for fused activation ${activation2}.`);\n}\nfunction getFusedBiasGradient(bias, dyActivation) {\n  let res = dyActivation;\n  const reduceAxes = getReductionAxes(bias.shape, dyActivation.shape);\n  if (reduceAxes.length > 0) {\n    res = sum2(res, reduceAxes);\n  }\n  return reshape(res, bias.shape);\n}\nfunction applyActivation(x, activation2, preluActivationWeights, leakyreluAlpha) {\n  if (activation2 === \"linear\") {\n    return x;\n  } else if (activation2 === \"relu\") {\n    return relu(x);\n  } else if (activation2 === \"elu\") {\n    return elu(x);\n  } else if (activation2 === \"relu6\") {\n    return relu6(x);\n  } else if (activation2 === \"prelu\") {\n    return prelu(x, preluActivationWeights);\n  } else if (activation2 === \"leakyrelu\") {\n    return leakyRelu(x, leakyreluAlpha);\n  } else if (activation2 === \"sigmoid\") {\n    return sigmoid(x);\n  }\n  throw new Error(`Unknown fused activation ${activation2}.`);\n}\nvar shouldFuse = (gradientDepth, activation2) => {\n  const gradientMode = gradientDepth > 0;\n  return !gradientMode || activation2 === \"linear\";\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/fused/conv2d.js\nfunction fusedConv2d_({ x, filter, strides, pad: pad3, dataFormat = \"NHWC\", dilations = [1, 1], dimRoundingMode, bias, activation: activation2 = \"linear\", preluActivationWeights, leakyreluAlpha }) {\n  activation2 = activation2 || \"linear\";\n  if (shouldFuse(ENGINE.state.gradientDepth, activation2) === false) {\n    assert(dataFormat === \"NHWC\", () => `Error in fused conv2d: got dataFormat of ${dataFormat} but only NHWC is currently supported for the case of gradient depth is 0 and the activation is not linear.`);\n    let result = conv2d(x, filter, strides, pad3, dataFormat, dilations, dimRoundingMode);\n    if (bias != null) {\n      result = add2(result, bias);\n    }\n    return applyActivation(result, activation2, preluActivationWeights, leakyreluAlpha);\n  }\n  const $x = convertToTensor(x, \"x\", \"conv2d\", \"float32\");\n  const $filter = convertToTensor(filter, \"filter\", \"conv2d\", \"float32\");\n  let x4D = $x;\n  let reshapedTo4D = false;\n  if ($x.rank === 3) {\n    reshapedTo4D = true;\n    x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);\n  }\n  assert(x4D.rank === 4, () => `Error in fused conv2d: input must be rank 4, but got rank ${x4D.rank}.`);\n  assert($filter.rank === 4, () => `Error in fused conv2d: filter must be rank 4, but got rank ${$filter.rank}.`);\n  checkPadOnDimRoundingMode(\"fused conv2d\", pad3, dimRoundingMode);\n  const inputChannels = dataFormat === \"NHWC\" ? x4D.shape[3] : x4D.shape[1];\n  assert($filter.shape[2] === inputChannels, () => `Error in conv2d: depth of input (${inputChannels}) must match input depth for filter ${$filter.shape[2]}.`);\n  assert(eitherStridesOrDilationsAreOne(strides, dilations), () => `Error in conv2D: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`);\n  const convInfo = computeConv2DInfo(x4D.shape, $filter.shape, strides, dilations, pad3, dimRoundingMode);\n  let $bias;\n  if (bias != null) {\n    $bias = convertToTensor(bias, \"bias\", \"fused conv2d\");\n    [$bias] = makeTypesMatch($bias, $x);\n    if (dataFormat === \"NHWC\") {\n      assertAndGetBroadcastShape(convInfo.outShape, $bias.shape);\n    } else {\n      assert($bias.shape.length <= 1, () => `Error in fused conv2d: only supports scalar or 1-D Tensor bias for NCHW format but got the bias of rank-${$bias.shape.length}.`);\n      assert($bias.shape.length === 0 || $bias.shape[0] === convInfo.outChannels || $bias.shape[0] === 1, () => `Error in fused conv2d: bias shape (${$bias.shape}) is not compatible with the number of output channels (${convInfo.outChannels})`);\n    }\n  }\n  let $preluActivationWeights;\n  if (preluActivationWeights != null) {\n    const alphaShape = preluActivationWeights.shape;\n    assert(alphaShape.length <= 1 || alphaShape.length === 3, () => `Error in fused conv2d: only supports scalar, 1-D Tensor or 3-D Tensor PReLU activation weights but got a tensor of rank-${alphaShape.length}.`);\n    if (alphaShape.length === 1) {\n      assert(alphaShape[0] === 1 || alphaShape[0] === convInfo.outChannels, () => `Error in fused conv2d: PReLU activation weights (${alphaShape}) is not compatible with the number of output channels (${convInfo.outChannels}).`);\n    } else if (alphaShape.length === 3) {\n      try {\n        assertAndGetBroadcastShape(alphaShape, convInfo.outShape);\n      } catch (e2) {\n        const errMsg = `Error in fused conv2d: PReLU activation weights (${alphaShape}) is not compatible with the output shape of the conv2d (${convInfo.outShape}).`;\n        throw Error(errMsg);\n      }\n    }\n    $preluActivationWeights = convertToTensor(preluActivationWeights, \"prelu weights\", \"fused conv2d\");\n  }\n  const grad2 = (dy, saved) => {\n    assert(dataFormat === \"NHWC\", () => `Error in gradient of fused conv2D: got dataFormat of ${dataFormat} but only NHWC is currently supported.`);\n    const [$filter2, x4D2, y, $bias2] = saved;\n    const dyActivation = getFusedDyActivation(dy, y, activation2);\n    assert(tupleValuesAreOne(dilations), () => `Error in gradient of fused conv2D: dilation rates greater than 1 are not yet supported in gradients. Got dilations '${dilations}'`);\n    const xDer = conv2DBackpropInput(x4D2.shape, dyActivation, $filter2, strides, pad3);\n    const filterDer = conv2DBackpropFilter(x4D2, dyActivation, $filter2.shape, strides, pad3);\n    const der = [xDer, filterDer];\n    if ($bias2 != null) {\n      const biasDer = getFusedBiasGradient($bias2, dyActivation);\n      der.push(biasDer);\n    }\n    return der;\n  };\n  const inputs = {\n    x: x4D,\n    filter: $filter,\n    bias: $bias,\n    preluActivationWeights: $preluActivationWeights\n  };\n  const attrs = {\n    strides,\n    pad: pad3,\n    dataFormat,\n    dilations,\n    dimRoundingMode,\n    activation: activation2,\n    leakyreluAlpha\n  };\n  if (bias == null) {\n    const customOp = customGrad((x4D2, filter2, save) => {\n      let res = ENGINE.runKernel(FusedConv2D, inputs, attrs);\n      save([filter2, x4D2, res]);\n      if (reshapedTo4D) {\n        res = reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);\n      }\n      return { value: res, gradFunc: grad2 };\n    });\n    return customOp(x4D, $filter);\n  } else {\n    const customOpWithBias = customGrad((x4D2, filter2, bias2, save) => {\n      let res = ENGINE.runKernel(FusedConv2D, inputs, attrs);\n      save([filter2, x4D2, res, bias2]);\n      if (reshapedTo4D) {\n        res = reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);\n      }\n      return { value: res, gradFunc: grad2 };\n    });\n    return customOpWithBias(x4D, $filter, $bias);\n  }\n}\nvar conv2d2 = op({ fusedConv2d_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/depthwise_conv2d_native_backprop_filter.js\nfunction depthwiseConv2dNativeBackpropFilter_(x, dy, filterShape, strides, pad3, dilations = [1, 1], dimRoundingMode) {\n  let x4D = x;\n  if (x.rank === 3) {\n    x4D = reshape(x, [1, x.shape[0], x.shape[1], x.shape[2]]);\n  }\n  let dy4D = dy;\n  if (dy4D.rank === 3) {\n    dy4D = reshape(dy, [1, dy.shape[0], dy.shape[1], dy.shape[2]]);\n  }\n  const inputs = { x: x4D, dy: dy4D };\n  const attrs = { strides, pad: pad3, dimRoundingMode, dilations, filterShape };\n  return ENGINE.runKernel(DepthwiseConv2dNativeBackpropFilter, inputs, attrs);\n}\nvar depthwiseConv2dNativeBackpropFilter = op({ depthwiseConv2dNativeBackpropFilter_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/depthwise_conv2d_native_backprop_input.js\nfunction depthwiseConv2dNativeBackpropInput_(xShape, dy, filter, strides, pad3, dilations = [1, 1], dimRoundingMode) {\n  let dy4D = dy;\n  let reshapedTo4D = false;\n  if (dy.rank === 3) {\n    reshapedTo4D = true;\n    dy4D = reshape(dy, [1, dy.shape[0], dy.shape[1], dy.shape[2]]);\n  }\n  const inputs = { dy: dy4D, filter };\n  const attrs = { strides, pad: pad3, dimRoundingMode, dilations, inputShape: xShape };\n  const res = ENGINE.runKernel(DepthwiseConv2dNativeBackpropInput, inputs, attrs);\n  if (reshapedTo4D) {\n    return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);\n  }\n  return res;\n}\nvar depthwiseConv2dNativeBackpropInput = op({ depthwiseConv2dNativeBackpropInput_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/fused/depthwise_conv2d.js\nfunction fusedDepthwiseConv2d_({ x, filter, strides, pad: pad3, dataFormat = \"NHWC\", dilations = [1, 1], dimRoundingMode, bias, activation: activation2 = \"linear\", preluActivationWeights, leakyreluAlpha }) {\n  if (shouldFuse(ENGINE.state.gradientDepth, activation2) === false) {\n    let result = depthwiseConv2d(x, filter, strides, pad3, dataFormat, dilations, dimRoundingMode);\n    if (bias != null) {\n      result = add2(result, bias);\n    }\n    return applyActivation(result, activation2, preluActivationWeights, leakyreluAlpha);\n  }\n  const $x = convertToTensor(x, \"x\", \"depthwiseConv2d\", \"float32\");\n  const $filter = convertToTensor(filter, \"filter\", \"depthwiseConv2d\", \"float32\");\n  let x4D = $x;\n  let reshapedTo4D = false;\n  if ($x.rank === 3) {\n    reshapedTo4D = true;\n    x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);\n  }\n  assert(x4D.rank === 4, () => `Error in fused depthwiseConv2d: input must be rank 4, but got rank ${x4D.rank}.`);\n  assert($filter.rank === 4, () => `Error in fused depthwiseConv2d: filter must be rank 4, but got rank ${$filter.rank}.`);\n  assert(x4D.shape[3] === $filter.shape[2], () => `Error in fused depthwiseConv2d: number of input channels (${x4D.shape[3]}) must match the inChannels dimension in filter ${$filter.shape[2]}.`);\n  if (dilations == null) {\n    dilations = [1, 1];\n  }\n  assert(eitherStridesOrDilationsAreOne(strides, dilations), () => `Error in fused depthwiseConv2d: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`);\n  checkPadOnDimRoundingMode(\"fused depthwiseConv2d\", pad3, dimRoundingMode);\n  const convInfo = computeConv2DInfo(x4D.shape, $filter.shape, strides, dilations, pad3, dimRoundingMode, true);\n  let $bias;\n  if (bias != null) {\n    $bias = convertToTensor(bias, \"bias\", \"fused conv2d\");\n    [$bias] = makeTypesMatch($bias, $x);\n    assertAndGetBroadcastShape(convInfo.outShape, $bias.shape);\n  }\n  let $preluActivationWeights;\n  if (preluActivationWeights != null) {\n    $preluActivationWeights = convertToTensor(preluActivationWeights, \"prelu weights\", \"fused depthwiseConv2d\");\n  }\n  const grad2 = (dy, saved) => {\n    assert(tupleValuesAreOne(dilations), () => `Error in gradient of fused depthwiseConv2d: dilation rates greater than 1 are not yet supported. Got dilations '${dilations}'`);\n    const [$filter2, x4D2, y, bias2] = saved;\n    const dyActivation = getFusedDyActivation(dy, y, activation2);\n    const xDer = depthwiseConv2dNativeBackpropInput(x4D2.shape, dyActivation, $filter2, strides, pad3, dilations, dimRoundingMode);\n    const filterDer = depthwiseConv2dNativeBackpropFilter(x4D2, dyActivation, $filter2.shape, strides, pad3, dilations, dimRoundingMode);\n    if (bias2 != null) {\n      const biasDer = getFusedBiasGradient($bias, dyActivation);\n      return [xDer, filterDer, biasDer];\n    }\n    return [xDer, filterDer];\n  };\n  const inputs = {\n    x: x4D,\n    filter: $filter,\n    bias: $bias,\n    preluActivationWeights: $preluActivationWeights\n  };\n  const attrs = {\n    strides,\n    pad: pad3,\n    dataFormat,\n    dilations,\n    dimRoundingMode,\n    activation: activation2,\n    leakyreluAlpha\n  };\n  if (bias == null) {\n    const customOp = customGrad((x4D2, filter2, save) => {\n      let res = ENGINE.runKernel(FusedDepthwiseConv2D, inputs, attrs);\n      save([filter2, x4D2, res]);\n      if (reshapedTo4D) {\n        res = reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);\n      }\n      return { value: res, gradFunc: grad2 };\n    });\n    return customOp(x4D, $filter);\n  } else {\n    const customOpWithBias = customGrad((x4D2, filter2, bias2, save) => {\n      let res = ENGINE.runKernel(FusedDepthwiseConv2D, inputs, attrs);\n      save([filter2, x4D2, res, bias2]);\n      if (reshapedTo4D) {\n        res = reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);\n      }\n      return { value: res, gradFunc: grad2 };\n    });\n    return customOpWithBias(x4D, $filter, $bias);\n  }\n}\nvar depthwiseConv2d2 = op({ fusedDepthwiseConv2d_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/fused/mat_mul.js\nfunction fusedMatMul_({ a, b, transposeA = false, transposeB = false, bias, activation: activation2 = \"linear\", preluActivationWeights, leakyreluAlpha = 0.2 }) {\n  if (shouldFuse(ENGINE.state.gradientDepth, activation2) === false) {\n    let result = matMul(a, b, transposeA, transposeB);\n    if (bias != null) {\n      result = add2(result, bias);\n    }\n    return applyActivation(result, activation2, preluActivationWeights, leakyreluAlpha);\n  }\n  let $a = convertToTensor(a, \"a\", \"fused matMul\");\n  let $b = convertToTensor(b, \"b\", \"fused matMul\");\n  [$a, $b] = makeTypesMatch($a, $b);\n  const innerShapeA = transposeA ? $a.shape[$a.rank - 2] : $a.shape[$a.rank - 1];\n  const innerShapeB = transposeB ? $b.shape[$b.rank - 1] : $b.shape[$b.rank - 2];\n  const outerShapeA = transposeA ? $a.shape[$a.rank - 1] : $a.shape[$a.rank - 2];\n  const outerShapeB = transposeB ? $b.shape[$b.rank - 2] : $b.shape[$b.rank - 1];\n  const outerDimsA = $a.shape.slice(0, -2);\n  const outerDimsB = $b.shape.slice(0, -2);\n  const batchDimA = sizeFromShape(outerDimsA);\n  const batchDimB = sizeFromShape(outerDimsB);\n  assert(innerShapeA === innerShapeB, () => `Error in fused matMul: inner shapes (${innerShapeA}) and (${innerShapeB}) of Tensors with shapes ${$a.shape} and ${$b.shape} and transposeA=${transposeA} and transposeB=${transposeB} must match.`);\n  const outShapeOuterDims = assertAndGetBroadcastShape($a.shape.slice(0, -2), $b.shape.slice(0, -2));\n  const outShape = outShapeOuterDims.concat([outerShapeA, outerShapeB]);\n  const a3D = transposeA ? reshape($a, [batchDimA, innerShapeA, outerShapeA]) : reshape($a, [batchDimA, outerShapeA, innerShapeA]);\n  const b3D = transposeB ? reshape($b, [batchDimB, outerShapeB, innerShapeB]) : reshape($b, [batchDimB, innerShapeB, outerShapeB]);\n  let $bias;\n  if (bias != null) {\n    $bias = convertToTensor(bias, \"bias\", \"fused matMul\");\n    [$bias] = makeTypesMatch($bias, $a);\n    assertAndGetBroadcastShape(outShape, $bias.shape);\n  }\n  let $preluActivationWeights;\n  if (preluActivationWeights != null) {\n    $preluActivationWeights = convertToTensor(preluActivationWeights, \"prelu weights\", \"fused matMul\");\n  }\n  const grad2 = (dy, saved) => {\n    const [a3D2, b3D2, y, $bias2] = saved;\n    const dyActivation = getFusedDyActivation(reshape(dy, y.shape), y, activation2);\n    let aDer;\n    let bDer;\n    if (!transposeA && !transposeB) {\n      aDer = matMul(dyActivation, b3D2, false, true);\n      bDer = matMul(a3D2, dyActivation, true, false);\n    } else if (!transposeA && transposeB) {\n      aDer = matMul(dyActivation, b3D2, false, false);\n      bDer = matMul(dyActivation, a3D2, true, false);\n    } else if (transposeA && !transposeB) {\n      aDer = matMul(b3D2, dyActivation, false, true);\n      bDer = matMul(a3D2, dyActivation, false, false);\n    } else {\n      aDer = matMul(b3D2, dyActivation, true, true);\n      bDer = matMul(dyActivation, a3D2, true, true);\n    }\n    if (bias != null) {\n      const biasDer = getFusedBiasGradient($bias2, dyActivation);\n      return [aDer, bDer, biasDer];\n    } else {\n      return [aDer, bDer];\n    }\n  };\n  const inputs = {\n    a: a3D,\n    b: b3D,\n    bias: $bias,\n    preluActivationWeights: $preluActivationWeights\n  };\n  const attrs = { transposeA, transposeB, activation: activation2, leakyreluAlpha };\n  if (bias == null) {\n    const customOp = customGrad((a3D2, b3D2, save) => {\n      const res = ENGINE.runKernel(_FusedMatMul, inputs, attrs);\n      save([a3D2, b3D2, res]);\n      return { value: reshape(res, outShape), gradFunc: grad2 };\n    });\n    return customOp(a3D, b3D);\n  } else {\n    const customOpWithBias = customGrad((a3D2, b3D2, $bias2, save) => {\n      const res = ENGINE.runKernel(_FusedMatMul, inputs, attrs);\n      save([a3D2, b3D2, res, $bias2]);\n      return { value: reshape(res, outShape), gradFunc: grad2 };\n    });\n    return customOpWithBias(a3D, b3D, $bias);\n  }\n}\nvar matMul2 = op({ fusedMatMul_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/signal/hamming_window.js\nfunction hammingWindow_(windowLength) {\n  return cosineWindow(windowLength, 0.54, 0.46);\n}\nvar hammingWindow = op({ hammingWindow_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/signal/hann_window.js\nfunction hannWindow_(windowLength) {\n  return cosineWindow(windowLength, 0.5, 0.5);\n}\nvar hannWindow = op({ hannWindow_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/signal/frame.js\nfunction frame_(signal2, frameLength, frameStep, padEnd = false, padValue = 0) {\n  let start = 0;\n  const output = [];\n  while (start + frameLength <= signal2.size) {\n    output.push(slice(signal2, start, frameLength));\n    start += frameStep;\n  }\n  if (padEnd) {\n    while (start < signal2.size) {\n      const padLen = start + frameLength - signal2.size;\n      const pad3 = concat([\n        slice(signal2, start, frameLength - padLen),\n        fill([padLen], padValue)\n      ]);\n      output.push(pad3);\n      start += frameStep;\n    }\n  }\n  if (output.length === 0) {\n    return tensor2d([], [0, frameLength]);\n  }\n  return reshape(concat(output), [output.length, frameLength]);\n}\nvar frame = op({ frame_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/signal/stft.js\nfunction stft_(signal2, frameLength, frameStep, fftLength, windowFn = hannWindow) {\n  if (fftLength == null) {\n    fftLength = enclosingPowerOfTwo(frameLength);\n  }\n  const framedSignal = frame(signal2, frameLength, frameStep);\n  const windowedSignal = mul(framedSignal, windowFn(frameLength));\n  return rfft(windowedSignal, fftLength);\n}\nvar stft = op({ stft_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/image/crop_and_resize.js\nfunction cropAndResize_(image2, boxes, boxInd, cropSize, method = \"bilinear\", extrapolationValue = 0) {\n  const $image = convertToTensor(image2, \"image\", \"cropAndResize\");\n  const $boxes = convertToTensor(boxes, \"boxes\", \"cropAndResize\", \"float32\");\n  const $boxInd = convertToTensor(boxInd, \"boxInd\", \"cropAndResize\", \"int32\");\n  const numBoxes = $boxes.shape[0];\n  assert($image.rank === 4, () => `Error in cropAndResize: image must be rank 4,but got rank ${$image.rank}.`);\n  assert($boxes.rank === 2 && $boxes.shape[1] === 4, () => `Error in cropAndResize: boxes must be have size [${numBoxes},4] but had shape ${$boxes.shape}.`);\n  assert($boxInd.rank === 1 && $boxInd.shape[0] === numBoxes, () => `Error in cropAndResize: boxInd must be have size [${numBoxes}] but had shape ${$boxes.shape}.`);\n  assert(cropSize.length === 2, () => `Error in cropAndResize: cropSize must be of length 2, but got length ${cropSize.length}.`);\n  assert(cropSize[0] >= 1 && cropSize[1] >= 1, () => `cropSize must be atleast [1,1], but was ${cropSize}`);\n  assert(method === \"bilinear\" || method === \"nearest\", () => `method must be bilinear or nearest, but was ${method}`);\n  const inputs = { image: $image, boxes: $boxes, boxInd: $boxInd };\n  const attrs = { method, extrapolationValue, cropSize };\n  const res = ENGINE.runKernel(CropAndResize, inputs, attrs);\n  return res;\n}\nvar cropAndResize = op({ cropAndResize_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/image/flip_left_right.js\nfunction flipLeftRight_(image2) {\n  const $image = convertToTensor(image2, \"image\", \"flipLeftRight\", \"float32\");\n  assert($image.rank === 4, () => `Error in flipLeftRight: image must be rank 4,but got rank ${$image.rank}.`);\n  const inputs = { image: $image };\n  const res = ENGINE.runKernel(FlipLeftRight, inputs, {});\n  return res;\n}\nvar flipLeftRight = op({ flipLeftRight_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/image/grayscale_to_rgb.js\nfunction grayscaleToRGB_(image2) {\n  const $image = convertToTensor(image2, \"image\", \"grayscaleToRGB\");\n  const lastDimsIdx = $image.rank - 1;\n  const lastDims = $image.shape[lastDimsIdx];\n  assert($image.rank >= 2, () => `Error in grayscaleToRGB: images must be at least rank 2, but got rank ${$image.rank}.`);\n  assert(lastDims === 1, () => `Error in grayscaleToRGB: last dimension of a grayscale image should be size 1, but got size ${lastDims}.`);\n  const reps = new Array($image.rank);\n  reps.fill(1, 0, lastDimsIdx);\n  reps[lastDimsIdx] = 3;\n  return tile($image, reps);\n}\nvar grayscaleToRGB = op({ grayscaleToRGB_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/image/rotate_with_offset.js\nfunction rotateWithOffset_(image2, radians, fillValue = 0, center = 0.5) {\n  const $image = convertToTensor(image2, \"image\", \"rotateWithOffset\", \"float32\");\n  assert($image.rank === 4, () => `Error in rotateWithOffset: image must be rank 4,but got rank ${$image.rank}.`);\n  const inputs = { image: $image };\n  const attrs = { radians, fillValue, center };\n  const res = ENGINE.runKernel(RotateWithOffset, inputs, attrs);\n  return res;\n}\nvar rotateWithOffset = op({ rotateWithOffset_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/nonmax_util.js\nfunction nonMaxSuppSanityCheck(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma) {\n  if (iouThreshold == null) {\n    iouThreshold = 0.5;\n  }\n  if (scoreThreshold == null) {\n    scoreThreshold = Number.NEGATIVE_INFINITY;\n  }\n  if (softNmsSigma == null) {\n    softNmsSigma = 0;\n  }\n  const numBoxes = boxes.shape[0];\n  maxOutputSize = Math.min(maxOutputSize, numBoxes);\n  assert(0 <= iouThreshold && iouThreshold <= 1, () => `iouThreshold must be in [0, 1], but was '${iouThreshold}'`);\n  assert(boxes.rank === 2, () => `boxes must be a 2D tensor, but was of rank '${boxes.rank}'`);\n  assert(boxes.shape[1] === 4, () => `boxes must have 4 columns, but 2nd dimension was ${boxes.shape[1]}`);\n  assert(scores.rank === 1, () => \"scores must be a 1D tensor\");\n  assert(scores.shape[0] === numBoxes, () => `scores has incompatible shape with boxes. Expected ${numBoxes}, but was ${scores.shape[0]}`);\n  assert(0 <= softNmsSigma && softNmsSigma <= 1, () => `softNmsSigma must be in [0, 1], but was '${softNmsSigma}'`);\n  return { maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma };\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/image/non_max_suppression.js\nfunction nonMaxSuppression_(boxes, scores, maxOutputSize, iouThreshold = 0.5, scoreThreshold = Number.NEGATIVE_INFINITY) {\n  const $boxes = convertToTensor(boxes, \"boxes\", \"nonMaxSuppression\", \"float32\");\n  const $scores = convertToTensor(scores, \"scores\", \"nonMaxSuppression\", \"float32\");\n  const inputs = nonMaxSuppSanityCheck($boxes, $scores, maxOutputSize, iouThreshold, scoreThreshold);\n  maxOutputSize = inputs.maxOutputSize;\n  iouThreshold = inputs.iouThreshold;\n  scoreThreshold = inputs.scoreThreshold;\n  const attrs = { maxOutputSize, iouThreshold, scoreThreshold };\n  return ENGINE.runKernel(NonMaxSuppressionV3, { boxes: $boxes, scores: $scores }, attrs);\n}\nvar nonMaxSuppression = op({ nonMaxSuppression_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/backends/non_max_suppression_util.js\nfunction binaryInsert(arr, element, comparator) {\n  const index = binarySearch(arr, element, comparator);\n  const insertionPoint = index < 0 ? -(index + 1) : index;\n  arr.splice(insertionPoint, 0, element);\n}\nfunction binarySearch(arr, target, comparator) {\n  return binarySearch_(arr, target, comparator || defaultComparator);\n}\nfunction defaultComparator(a, b) {\n  return a > b ? 1 : a < b ? -1 : 0;\n}\nfunction binarySearch_(arr, target, comparator) {\n  let left = 0;\n  let right = arr.length;\n  let middle = 0;\n  let found = false;\n  while (left < right) {\n    middle = left + (right - left >>> 1);\n    const compareResult = comparator(target, arr[middle]);\n    if (compareResult > 0) {\n      left = middle + 1;\n    } else {\n      right = middle;\n      found = !compareResult;\n    }\n  }\n  return found ? left : -left - 1;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/backends/non_max_suppression_impl.js\nfunction nonMaxSuppressionV3Impl(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold) {\n  return nonMaxSuppressionImpl_(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, 0);\n}\nfunction nonMaxSuppressionV4Impl(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, padToMaxOutputSize) {\n  return nonMaxSuppressionImpl_(\n    boxes,\n    scores,\n    maxOutputSize,\n    iouThreshold,\n    scoreThreshold,\n    0,\n    false,\n    padToMaxOutputSize,\n    true\n  );\n}\nfunction nonMaxSuppressionV5Impl(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma) {\n  return nonMaxSuppressionImpl_(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma, true);\n}\nfunction nonMaxSuppressionImpl_(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma, returnScoresTensor = false, padToMaxOutputSize = false, returnValidOutputs = false) {\n  const candidates = [];\n  for (let i2 = 0; i2 < scores.length; i2++) {\n    if (scores[i2] > scoreThreshold) {\n      candidates.push({ score: scores[i2], boxIndex: i2, suppressBeginIndex: 0 });\n    }\n  }\n  candidates.sort(ascendingComparator);\n  const scale2 = softNmsSigma > 0 ? -0.5 / softNmsSigma : 0;\n  const selectedIndices = [];\n  const selectedScores = [];\n  while (selectedIndices.length < maxOutputSize && candidates.length > 0) {\n    const candidate = candidates.pop();\n    const { score: originalScore, boxIndex, suppressBeginIndex } = candidate;\n    if (originalScore < scoreThreshold) {\n      break;\n    }\n    let ignoreCandidate = false;\n    for (let j = selectedIndices.length - 1; j >= suppressBeginIndex; --j) {\n      const iou = intersectionOverUnion(boxes, boxIndex, selectedIndices[j]);\n      if (iou >= iouThreshold) {\n        ignoreCandidate = true;\n        break;\n      }\n      candidate.score = candidate.score * suppressWeight(iouThreshold, scale2, iou);\n      if (candidate.score <= scoreThreshold) {\n        break;\n      }\n    }\n    candidate.suppressBeginIndex = selectedIndices.length;\n    if (!ignoreCandidate) {\n      if (candidate.score === originalScore) {\n        selectedIndices.push(boxIndex);\n        selectedScores.push(candidate.score);\n      } else if (candidate.score > scoreThreshold) {\n        binaryInsert(candidates, candidate, ascendingComparator);\n      }\n    }\n  }\n  const validOutputs = selectedIndices.length;\n  const elemsToPad = maxOutputSize - validOutputs;\n  if (padToMaxOutputSize && elemsToPad > 0) {\n    selectedIndices.push(...new Array(elemsToPad).fill(0));\n    selectedScores.push(...new Array(elemsToPad).fill(0));\n  }\n  const result = { selectedIndices };\n  if (returnScoresTensor) {\n    result[\"selectedScores\"] = selectedScores;\n  }\n  if (returnValidOutputs) {\n    result[\"validOutputs\"] = validOutputs;\n  }\n  return result;\n}\nfunction intersectionOverUnion(boxes, i2, j) {\n  const iCoord = boxes.subarray(i2 * 4, i2 * 4 + 4);\n  const jCoord = boxes.subarray(j * 4, j * 4 + 4);\n  const yminI = Math.min(iCoord[0], iCoord[2]);\n  const xminI = Math.min(iCoord[1], iCoord[3]);\n  const ymaxI = Math.max(iCoord[0], iCoord[2]);\n  const xmaxI = Math.max(iCoord[1], iCoord[3]);\n  const yminJ = Math.min(jCoord[0], jCoord[2]);\n  const xminJ = Math.min(jCoord[1], jCoord[3]);\n  const ymaxJ = Math.max(jCoord[0], jCoord[2]);\n  const xmaxJ = Math.max(jCoord[1], jCoord[3]);\n  const areaI = (ymaxI - yminI) * (xmaxI - xminI);\n  const areaJ = (ymaxJ - yminJ) * (xmaxJ - xminJ);\n  if (areaI <= 0 || areaJ <= 0) {\n    return 0;\n  }\n  const intersectionYmin = Math.max(yminI, yminJ);\n  const intersectionXmin = Math.max(xminI, xminJ);\n  const intersectionYmax = Math.min(ymaxI, ymaxJ);\n  const intersectionXmax = Math.min(xmaxI, xmaxJ);\n  const intersectionArea = Math.max(intersectionYmax - intersectionYmin, 0) * Math.max(intersectionXmax - intersectionXmin, 0);\n  return intersectionArea / (areaI + areaJ - intersectionArea);\n}\nfunction suppressWeight(iouThreshold, scale2, iou) {\n  const weight = Math.exp(scale2 * iou * iou);\n  return iou <= iouThreshold ? weight : 0;\n}\nfunction ascendingComparator(c1, c2) {\n  return c1.score - c2.score || c1.score === c2.score && c2.boxIndex - c1.boxIndex;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/image/non_max_suppression_async.js\nasync function nonMaxSuppressionAsync_(boxes, scores, maxOutputSize, iouThreshold = 0.5, scoreThreshold = Number.NEGATIVE_INFINITY) {\n  const $boxes = convertToTensor(boxes, \"boxes\", \"nonMaxSuppressionAsync\");\n  const $scores = convertToTensor(scores, \"scores\", \"nonMaxSuppressionAsync\");\n  const inputs = nonMaxSuppSanityCheck($boxes, $scores, maxOutputSize, iouThreshold, scoreThreshold);\n  maxOutputSize = inputs.maxOutputSize;\n  iouThreshold = inputs.iouThreshold;\n  scoreThreshold = inputs.scoreThreshold;\n  const boxesAndScores = await Promise.all([$boxes.data(), $scores.data()]);\n  const boxesVals = boxesAndScores[0];\n  const scoresVals = boxesAndScores[1];\n  const { selectedIndices } = nonMaxSuppressionV3Impl(boxesVals, scoresVals, maxOutputSize, iouThreshold, scoreThreshold);\n  if ($boxes !== boxes) {\n    $boxes.dispose();\n  }\n  if ($scores !== scores) {\n    $scores.dispose();\n  }\n  return tensor1d(selectedIndices, \"int32\");\n}\nvar nonMaxSuppressionAsync = nonMaxSuppressionAsync_;\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/image/non_max_suppression_with_score.js\nfunction nonMaxSuppressionWithScore_(boxes, scores, maxOutputSize, iouThreshold = 0.5, scoreThreshold = Number.NEGATIVE_INFINITY, softNmsSigma = 0) {\n  const $boxes = convertToTensor(boxes, \"boxes\", \"nonMaxSuppression\");\n  const $scores = convertToTensor(scores, \"scores\", \"nonMaxSuppression\");\n  const params = nonMaxSuppSanityCheck($boxes, $scores, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma);\n  maxOutputSize = params.maxOutputSize;\n  iouThreshold = params.iouThreshold;\n  scoreThreshold = params.scoreThreshold;\n  softNmsSigma = params.softNmsSigma;\n  const inputs = { boxes: $boxes, scores: $scores };\n  const attrs = { maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma };\n  const result = ENGINE.runKernel(NonMaxSuppressionV5, inputs, attrs);\n  return { selectedIndices: result[0], selectedScores: result[1] };\n}\nvar nonMaxSuppressionWithScore = op({ nonMaxSuppressionWithScore_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/image/non_max_suppression_with_score_async.js\nasync function nonMaxSuppressionWithScoreAsync_(boxes, scores, maxOutputSize, iouThreshold = 0.5, scoreThreshold = Number.NEGATIVE_INFINITY, softNmsSigma = 0) {\n  const $boxes = convertToTensor(boxes, \"boxes\", \"nonMaxSuppressionAsync\");\n  const $scores = convertToTensor(scores, \"scores\", \"nonMaxSuppressionAsync\");\n  const params = nonMaxSuppSanityCheck($boxes, $scores, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma);\n  maxOutputSize = params.maxOutputSize;\n  iouThreshold = params.iouThreshold;\n  scoreThreshold = params.scoreThreshold;\n  softNmsSigma = params.softNmsSigma;\n  const boxesAndScores = await Promise.all([$boxes.data(), $scores.data()]);\n  const boxesVals = boxesAndScores[0];\n  const scoresVals = boxesAndScores[1];\n  const { selectedIndices, selectedScores } = nonMaxSuppressionV5Impl(boxesVals, scoresVals, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma);\n  if ($boxes !== boxes) {\n    $boxes.dispose();\n  }\n  if ($scores !== scores) {\n    $scores.dispose();\n  }\n  return {\n    selectedIndices: tensor1d(selectedIndices, \"int32\"),\n    selectedScores: tensor1d(selectedScores)\n  };\n}\nvar nonMaxSuppressionWithScoreAsync = nonMaxSuppressionWithScoreAsync_;\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/image/non_max_suppression_padded.js\nfunction nonMaxSuppressionPadded_(boxes, scores, maxOutputSize, iouThreshold = 0.5, scoreThreshold = Number.NEGATIVE_INFINITY, padToMaxOutputSize = false) {\n  const $boxes = convertToTensor(boxes, \"boxes\", \"nonMaxSuppression\");\n  const $scores = convertToTensor(scores, \"scores\", \"nonMaxSuppression\");\n  const params = nonMaxSuppSanityCheck($boxes, $scores, maxOutputSize, iouThreshold, scoreThreshold, null);\n  const $maxOutputSize = params.maxOutputSize;\n  const $iouThreshold = params.iouThreshold;\n  const $scoreThreshold = params.scoreThreshold;\n  const inputs = { boxes: $boxes, scores: $scores };\n  const attrs = {\n    maxOutputSize: $maxOutputSize,\n    iouThreshold: $iouThreshold,\n    scoreThreshold: $scoreThreshold,\n    padToMaxOutputSize\n  };\n  const result = ENGINE.runKernel(NonMaxSuppressionV4, inputs, attrs);\n  return { selectedIndices: result[0], validOutputs: result[1] };\n}\nvar nonMaxSuppressionPadded = op({ nonMaxSuppressionPadded_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/image/non_max_suppression_padded_async.js\nasync function nonMaxSuppressionPaddedAsync_(boxes, scores, maxOutputSize, iouThreshold = 0.5, scoreThreshold = Number.NEGATIVE_INFINITY, padToMaxOutputSize = false) {\n  const $boxes = convertToTensor(boxes, \"boxes\", \"nonMaxSuppressionAsync\");\n  const $scores = convertToTensor(scores, \"scores\", \"nonMaxSuppressionAsync\");\n  const params = nonMaxSuppSanityCheck($boxes, $scores, maxOutputSize, iouThreshold, scoreThreshold, null);\n  const $maxOutputSize = params.maxOutputSize;\n  const $iouThreshold = params.iouThreshold;\n  const $scoreThreshold = params.scoreThreshold;\n  const [boxesVals, scoresVals] = await Promise.all([$boxes.data(), $scores.data()]);\n  const { selectedIndices, validOutputs } = nonMaxSuppressionV4Impl(boxesVals, scoresVals, $maxOutputSize, $iouThreshold, $scoreThreshold, padToMaxOutputSize);\n  if ($boxes !== boxes) {\n    $boxes.dispose();\n  }\n  if ($scores !== scores) {\n    $scores.dispose();\n  }\n  return {\n    selectedIndices: tensor1d(selectedIndices, \"int32\"),\n    validOutputs: scalar(validOutputs, \"int32\")\n  };\n}\nvar nonMaxSuppressionPaddedAsync = nonMaxSuppressionPaddedAsync_;\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/image/resize_bilinear.js\nfunction resizeBilinear_(images, size, alignCorners = false, halfPixelCenters = false) {\n  const $images = convertToTensor(images, \"images\", \"resizeBilinear\");\n  assert($images.rank === 3 || $images.rank === 4, () => `Error in resizeBilinear: x must be rank 3 or 4, but got rank ${$images.rank}.`);\n  assert(size.length === 2, () => `Error in resizeBilinear: new shape must 2D, but got shape ${size}.`);\n  assert(halfPixelCenters === false || alignCorners === false, () => `Error in resizeBilinear: If halfPixelCenters is true, alignCorners must be false.`);\n  let batchImages = $images;\n  let reshapedTo4D = false;\n  if ($images.rank === 3) {\n    reshapedTo4D = true;\n    batchImages = reshape($images, [1, $images.shape[0], $images.shape[1], $images.shape[2]]);\n  }\n  const [] = size;\n  const inputs = { images: batchImages };\n  const attrs = { alignCorners, halfPixelCenters, size };\n  const res = ENGINE.runKernel(ResizeBilinear, inputs, attrs);\n  if (reshapedTo4D) {\n    return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);\n  }\n  return res;\n}\nvar resizeBilinear = op({ resizeBilinear_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/image/resize_nearest_neighbor.js\nfunction resizeNearestNeighbor_(images, size, alignCorners = false, halfPixelCenters = false) {\n  const $images = convertToTensor(images, \"images\", \"resizeNearestNeighbor\");\n  assert($images.rank === 3 || $images.rank === 4, () => `Error in resizeNearestNeighbor: x must be rank 3 or 4, but got rank ${$images.rank}.`);\n  assert(size.length === 2, () => `Error in resizeNearestNeighbor: new shape must 2D, but got shape ${size}.`);\n  assert($images.dtype === \"float32\" || $images.dtype === \"int32\", () => \"`images` must have `int32` or `float32` as dtype\");\n  assert(halfPixelCenters === false || alignCorners === false, () => `Error in resizeNearestNeighbor: If halfPixelCenters is true, alignCorners must be false.`);\n  let batchImages = $images;\n  let reshapedTo4D = false;\n  if ($images.rank === 3) {\n    reshapedTo4D = true;\n    batchImages = reshape($images, [1, $images.shape[0], $images.shape[1], $images.shape[2]]);\n  }\n  const [] = size;\n  const inputs = { images: batchImages };\n  const attrs = { alignCorners, halfPixelCenters, size };\n  const res = ENGINE.runKernel(ResizeNearestNeighbor, inputs, attrs);\n  if (reshapedTo4D) {\n    return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);\n  }\n  return res;\n}\nvar resizeNearestNeighbor = op({ resizeNearestNeighbor_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/image/threshold.js\nfunction threshold_(image2, method = \"binary\", inverted = false, threshValue = 0.5) {\n  const $image = convertToTensor(image2, \"image\", \"threshold\");\n  const RED_INTENCITY_COEF = 0.2989;\n  const GREEN_INTENCITY_COEF = 0.587;\n  const BLUE_INTENCITY_COEF = 0.114;\n  const totalPixelsInImage = $image.shape[0] * $image.shape[1];\n  let $threshold = mul(tensor1d([threshValue]), 255);\n  let r2, g, b, grayscale;\n  assert($image.rank === 3, () => `Error in threshold: image must be rank 3,but got rank ${$image.rank}.`);\n  assert($image.shape[2] === 3 || $image.shape[2] === 1, () => `Error in threshold: image color channel must be equal to 3 or 1but got ${$image.shape[2]}.`);\n  assert($image.dtype === \"int32\" || $image.dtype === \"float32\", () => `Error in dtype: image dtype must be int32 or float32,but got dtype ${$image.dtype}.`);\n  assert(method === \"otsu\" || method === \"binary\", () => `Method must be binary or otsu, but was ${method}`);\n  if ($image.shape[2] === 3) {\n    [r2, g, b] = split($image, [1, 1, 1], -1);\n    const $r = mul(r2, RED_INTENCITY_COEF);\n    const $g = mul(g, GREEN_INTENCITY_COEF);\n    const $b = mul(b, BLUE_INTENCITY_COEF);\n    grayscale = add2(add2($r, $g), $b);\n  } else {\n    grayscale = image2;\n  }\n  if (method === \"otsu\") {\n    const $histogram = bincount(cast(round2(grayscale), \"int32\"), tensor([]), 256);\n    $threshold = otsu($histogram, totalPixelsInImage);\n  }\n  const invCondition = inverted ? lessEqual(grayscale, $threshold) : greater(grayscale, $threshold);\n  const result = cast(mul(invCondition, 255), \"int32\");\n  return result;\n}\nfunction otsu(histogram, total) {\n  let bestThresh = tensor1d([-1]);\n  let bestInBetVar = tensor1d([0]);\n  let cInBetVar = tensor1d([0]);\n  let classFirst, classSecond, meanFirst, meanSec, weightForeground, weightBack;\n  for (let index = 0; index < histogram.size - 1; index++) {\n    classFirst = slice(histogram, 0, index + 1);\n    classSecond = slice(histogram, index + 1);\n    weightForeground = div(sum2(classFirst), total);\n    weightBack = div(sum2(classSecond), total);\n    const meanFirstDivA = sum2(mul(classFirst, range(0, classFirst.size)));\n    meanFirst = div(meanFirstDivA, sum2(classFirst));\n    const meanSecFill = fill(classSecond.shape, classFirst.size);\n    const meanSecAdd = add2(range(0, classSecond.size), meanSecFill);\n    const meanSecMul = mul(classSecond, meanSecAdd);\n    meanSec = div(sum2(meanSecMul), sum2(classSecond));\n    const cInBetVarSubA = sub(meanFirst, meanSec);\n    const cInBetVarSubB = sub(meanFirst, meanSec);\n    const cInBetVarMul = mul(weightForeground, weightBack);\n    cInBetVar = mul(mul(cInBetVarMul, cInBetVarSubA), cInBetVarSubB);\n    const condition = greater(cInBetVar, bestInBetVar);\n    bestInBetVar = where(condition, cInBetVar, bestInBetVar);\n    bestThresh = where(condition, tensor1d([index]), bestThresh);\n  }\n  return bestThresh;\n}\nvar threshold = op({ threshold_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/image/transform.js\nfunction transform_(image2, transforms, interpolation = \"nearest\", fillMode = \"constant\", fillValue = 0, outputShape) {\n  const $image = convertToTensor(image2, \"image\", \"transform\", \"float32\");\n  const $transforms = convertToTensor(transforms, \"transforms\", \"transform\", \"float32\");\n  assert($image.rank === 4, () => `Error in transform: image must be rank 4,but got rank ${$image.rank}.`);\n  assert($transforms.rank === 2 && ($transforms.shape[0] === $image.shape[0] || $transforms.shape[0] === 1) && $transforms.shape[1] === 8, () => `Error in transform: Input transform should be batch x 8 or 1 x 8`);\n  assert(outputShape == null || outputShape.length === 2, () => `Error in transform: outputShape must be [height, width] or null, but got ${outputShape}.`);\n  const inputs = { image: $image, transforms: $transforms };\n  const attrs = { interpolation, fillMode, fillValue, outputShape };\n  return ENGINE.runKernel(Transform, inputs, attrs);\n}\nvar transform = op({ transform_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/linalg/band_part.js\nfunction bandPart_(a, numLower, numUpper) {\n  assert(numLower % 1 === 0, () => `bandPart(): numLower must be an integer, got ${numLower}.`);\n  assert(numUpper % 1 === 0, () => `bandPart(): numUpper must be an integer, got ${numUpper}.`);\n  const $a = convertToTensor(a, \"a\", \"bandPart\");\n  assert($a.rank >= 2, () => `bandPart(): Rank must be at least 2, got ${$a.rank}.`);\n  const shape = $a.shape;\n  const [M, N] = $a.shape.slice(-2);\n  if (!(numLower <= M)) {\n    throw new Error(`bandPart(): numLower (${numLower}) must not be greater than the number of rows (${M}).`);\n  }\n  if (!(numUpper <= N)) {\n    throw new Error(`bandPart(): numUpper (${numUpper}) must not be greater than the number of columns (${N}).`);\n  }\n  if (numLower < 0) {\n    numLower = M;\n  }\n  if (numUpper < 0) {\n    numUpper = N;\n  }\n  const i2 = reshape(range(0, M, 1, \"int32\"), [-1, 1]);\n  const j = range(0, N, 1, \"int32\");\n  const ij = sub(i2, j);\n  const inBand = logicalAnd(lessEqual(ij, scalar(+numLower, \"int32\")), greaterEqual(ij, scalar(-numUpper, \"int32\")));\n  const zero = zeros([M, N], $a.dtype);\n  return reshape(stack(unstack(reshape($a, [-1, M, N])).map((mat) => where(inBand, mat, zero))), shape);\n}\nvar bandPart = op({ bandPart_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/linalg/gram_schmidt.js\nfunction gramSchmidt_(xs) {\n  let inputIsTensor2D;\n  if (Array.isArray(xs)) {\n    inputIsTensor2D = false;\n    assert(xs != null && xs.length > 0, () => \"Gram-Schmidt process: input must not be null, undefined, or empty\");\n    const dim = xs[0].shape[0];\n    for (let i2 = 1; i2 < xs.length; ++i2) {\n      assert(xs[i2].shape[0] === dim, () => `Gram-Schmidt: Non-unique lengths found in the input vectors: (${xs[i2].shape[0]} vs. ${dim})`);\n    }\n  } else {\n    inputIsTensor2D = true;\n    xs = split(xs, xs.shape[0], 0).map((x) => squeeze(x, [0]));\n  }\n  assert(xs.length <= xs[0].shape[0], () => `Gram-Schmidt: Number of vectors (${xs.length}) exceeds number of dimensions (${xs[0].shape[0]}).`);\n  const ys = [];\n  const xs1d = xs;\n  for (let i2 = 0; i2 < xs.length; ++i2) {\n    ys.push(ENGINE.tidy(() => {\n      let x = xs1d[i2];\n      if (i2 > 0) {\n        for (let j = 0; j < i2; ++j) {\n          const proj = mul(sum2(mul(ys[j], x)), ys[j]);\n          x = sub(x, proj);\n        }\n      }\n      return div(x, norm(x, \"euclidean\"));\n    }));\n  }\n  if (inputIsTensor2D) {\n    return stack(ys, 0);\n  } else {\n    return ys;\n  }\n}\nvar gramSchmidt = op({ gramSchmidt_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/linalg/qr.js\nfunction qr_(x, fullMatrices = false) {\n  assert(x.rank >= 2, () => `qr() requires input tensor to have a rank >= 2, but got rank ${x.rank}`);\n  if (x.rank === 2) {\n    return qr2d(x, fullMatrices);\n  } else {\n    const outerDimsProd = x.shape.slice(0, x.shape.length - 2).reduce((value, prev) => value * prev);\n    const x2ds = unstack(reshape(x, [\n      outerDimsProd,\n      x.shape[x.shape.length - 2],\n      x.shape[x.shape.length - 1]\n    ]), 0);\n    const q2ds = [];\n    const r2ds = [];\n    x2ds.forEach((x2d) => {\n      const [q2d, r2d] = qr2d(x2d, fullMatrices);\n      q2ds.push(q2d);\n      r2ds.push(r2d);\n    });\n    const q = reshape(stack(q2ds, 0), x.shape);\n    const r2 = reshape(stack(r2ds, 0), x.shape);\n    return [q, r2];\n  }\n}\nfunction qr2d(x, fullMatrices = false) {\n  return ENGINE.tidy(() => {\n    assert(x.shape.length === 2, () => `qr2d() requires a 2D Tensor, but got a ${x.shape.length}D Tensor.`);\n    const m = x.shape[0];\n    const n2 = x.shape[1];\n    let q = eye(m);\n    let r2 = clone(x);\n    const one2D = tensor2d([[1]], [1, 1]);\n    let w = clone(one2D);\n    const iters = m >= n2 ? n2 : m;\n    for (let j = 0; j < iters; ++j) {\n      const rTemp = r2;\n      const wTemp = w;\n      const qTemp = q;\n      [w, r2, q] = ENGINE.tidy(() => {\n        const rjEnd1 = slice(r2, [j, j], [m - j, 1]);\n        const normX = norm(rjEnd1);\n        const rjj = slice(r2, [j, j], [1, 1]);\n        const s2 = where(greater(rjj, 0), tensor2d([[-1]]), tensor2d([[1]]));\n        const u1 = sub(rjj, mul(s2, normX));\n        const wPre = div(rjEnd1, u1);\n        if (wPre.shape[0] === 1) {\n          w = clone(one2D);\n        } else {\n          w = concat([\n            one2D,\n            slice(wPre, [1, 0], [wPre.shape[0] - 1, wPre.shape[1]])\n          ], 0);\n        }\n        const tau = neg(div(matMul(s2, u1), normX));\n        const rjEndAll = slice(r2, [j, 0], [m - j, n2]);\n        const tauTimesW = mul(tau, w);\n        const wT = transpose(w);\n        if (j === 0) {\n          r2 = sub(rjEndAll, matMul(tauTimesW, matMul(wT, rjEndAll)));\n        } else {\n          const rTimesTau = sub(rjEndAll, matMul(tauTimesW, matMul(wT, rjEndAll)));\n          r2 = concat([slice(r2, [0, 0], [j, n2]), rTimesTau], 0);\n        }\n        const tawTimesWT = transpose(tauTimesW);\n        const qAllJEnd = slice(q, [0, j], [m, q.shape[1] - j]);\n        if (j === 0) {\n          q = sub(qAllJEnd, matMul(matMul(qAllJEnd, w), tawTimesWT));\n        } else {\n          const qTimesTau = sub(qAllJEnd, matMul(matMul(qAllJEnd, w), tawTimesWT));\n          q = concat([slice(q, [0, 0], [m, j]), qTimesTau], 1);\n        }\n        return [w, r2, q];\n      });\n      dispose([rTemp, wTemp, qTemp]);\n    }\n    if (!fullMatrices && m > n2) {\n      q = slice(q, [0, 0], [m, n2]);\n      r2 = slice(r2, [0, 0], [n2, n2]);\n    }\n    return [q, r2];\n  });\n}\nvar qr = op({ qr_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/loss_ops_utils.js\nvar Reduction;\n(function(Reduction2) {\n  Reduction2[Reduction2[\"NONE\"] = 0] = \"NONE\";\n  Reduction2[Reduction2[\"MEAN\"] = 1] = \"MEAN\";\n  Reduction2[Reduction2[\"SUM\"] = 2] = \"SUM\";\n  Reduction2[Reduction2[\"SUM_BY_NONZERO_WEIGHTS\"] = 3] = \"SUM_BY_NONZERO_WEIGHTS\";\n})(Reduction || (Reduction = {}));\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/losses/compute_weighted_loss.js\nfunction computeWeightedLoss_(losses2, weights, reduction = Reduction.SUM_BY_NONZERO_WEIGHTS) {\n  const $losses = convertToTensor(losses2, \"losses\", \"computeWeightedLoss\");\n  let $weights = null;\n  if (weights != null) {\n    $weights = convertToTensor(weights, \"weights\", \"computeWeightedLoss\");\n  }\n  const weightedLoss = $weights == null ? $losses : mul($losses, $weights);\n  if (reduction === Reduction.NONE) {\n    return weightedLoss;\n  }\n  if (reduction === Reduction.SUM) {\n    return sum2(weightedLoss);\n  }\n  if (reduction === Reduction.MEAN) {\n    if ($weights == null) {\n      return mean(weightedLoss);\n    } else {\n      const broadcastFactor = $losses.size / $weights.size;\n      const result = div(sum2(weightedLoss), sum2($weights));\n      return broadcastFactor > 1 ? div(result, scalar(broadcastFactor)) : result;\n    }\n  }\n  if (reduction === Reduction.SUM_BY_NONZERO_WEIGHTS) {\n    if ($weights == null) {\n      return div(sum2(weightedLoss), scalar($losses.size));\n    } else {\n      const broadcastedWeights = mul($weights, ones2($losses.shape));\n      const numNonZeros = cast(sum2(notEqual(broadcastedWeights, scalar(0))), \"float32\");\n      return div(sum2(weightedLoss), numNonZeros);\n    }\n  }\n  throw Error(`Unknown reduction: ${reduction}`);\n}\nvar computeWeightedLoss = op({ computeWeightedLoss_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/losses/absolute_difference.js\nfunction absoluteDifference_(labels, predictions, weights, reduction = Reduction.SUM_BY_NONZERO_WEIGHTS) {\n  const $labels = convertToTensor(labels, \"labels\", \"absoluteDifference\");\n  const $predictions = convertToTensor(predictions, \"predictions\", \"absoluteDifference\");\n  let $weights = null;\n  if (weights != null) {\n    $weights = convertToTensor(weights, \"weights\", \"absoluteDifference\");\n  }\n  assertShapesMatch($labels.shape, $predictions.shape, \"Error in absoluteDifference: \");\n  const losses2 = abs(sub($labels, $predictions));\n  return computeWeightedLoss(losses2, $weights, reduction);\n}\nvar absoluteDifference = op({ absoluteDifference_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/losses/cosine_distance.js\nfunction cosineDistance_(labels, predictions, axis, weights, reduction = Reduction.SUM_BY_NONZERO_WEIGHTS) {\n  const $labels = convertToTensor(labels, \"labels\", \"cosineDistance\");\n  const $predictions = convertToTensor(predictions, \"predictions\", \"cosineDistance\");\n  let $weights = null;\n  if (weights != null) {\n    $weights = convertToTensor(weights, \"weights\", \"cosineDistance\");\n  }\n  assertShapesMatch($labels.shape, $predictions.shape, \"Error in cosineDistance: \");\n  const one = scalar(1);\n  const losses2 = sub(one, sum2(mul($labels, $predictions), axis, true));\n  return computeWeightedLoss(losses2, $weights, reduction);\n}\nvar cosineDistance = op({ cosineDistance_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/losses/hinge_loss.js\nfunction hingeLoss_(labels, predictions, weights, reduction = Reduction.SUM_BY_NONZERO_WEIGHTS) {\n  let $labels = convertToTensor(labels, \"labels\", \"hingeLoss\");\n  const $predictions = convertToTensor(predictions, \"predictions\", \"hingeLoss\");\n  let $weights = null;\n  if (weights != null) {\n    $weights = convertToTensor(weights, \"weights\", \"hingeLoss\");\n  }\n  assertShapesMatch($labels.shape, $predictions.shape, \"Error in hingeLoss: \");\n  const one = scalar(1);\n  $labels = sub(mul(scalar(2), $labels), one);\n  const losses2 = relu(sub(one, mul($labels, $predictions)));\n  return computeWeightedLoss(losses2, $weights, reduction);\n}\nvar hingeLoss = op({ hingeLoss_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/losses/huber_loss.js\nfunction huberLoss_(labels, predictions, weights, delta = 1, reduction = Reduction.SUM_BY_NONZERO_WEIGHTS) {\n  const $labels = convertToTensor(labels, \"labels\", \"huberLoss\");\n  const $predictions = convertToTensor(predictions, \"predictions\", \"huberLoss\");\n  let $weights = null;\n  if (weights != null) {\n    $weights = convertToTensor(weights, \"weights\", \"huberLoss\");\n  }\n  assertShapesMatch($labels.shape, $predictions.shape, \"Error in huberLoss: \");\n  const deltaScalar = scalar(delta);\n  const error = abs(sub($predictions, $labels));\n  const quadratic = minimum(error, deltaScalar);\n  const linear = sub(error, quadratic);\n  const losses2 = add2(mul(scalar(0.5), square(quadratic)), mul(deltaScalar, linear));\n  return computeWeightedLoss(losses2, $weights, reduction);\n}\nvar huberLoss = op({ huberLoss_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/losses/log_loss.js\nfunction logLoss_(labels, predictions, weights, epsilon3 = 1e-7, reduction = Reduction.SUM_BY_NONZERO_WEIGHTS) {\n  const $labels = convertToTensor(labels, \"labels\", \"logLoss\");\n  const $predictions = convertToTensor(predictions, \"predictions\", \"logLoss\");\n  let $weights = null;\n  if (weights != null) {\n    $weights = convertToTensor(weights, \"weights\", \"logLoss\");\n  }\n  assertShapesMatch($labels.shape, $predictions.shape, \"Error in logLoss: \");\n  const one = scalar(1);\n  const epsilonScalar = scalar(epsilon3);\n  const l13 = neg(mul($labels, log2(add2($predictions, epsilonScalar))));\n  const l23 = mul(sub(one, $labels), log2(add2(sub(one, $predictions), epsilonScalar)));\n  const losses2 = sub(l13, l23);\n  return computeWeightedLoss(losses2, $weights, reduction);\n}\nvar logLoss = op({ logLoss_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/losses/mean_squared_error.js\nfunction meanSquaredError_(labels, predictions, weights, reduction = Reduction.SUM_BY_NONZERO_WEIGHTS) {\n  const $labels = convertToTensor(labels, \"labels\", \"meanSquaredError\");\n  const $predictions = convertToTensor(predictions, \"predictions\", \"meanSquaredError\");\n  let $weights = null;\n  if (weights != null) {\n    $weights = convertToTensor(weights, \"weights\", \"meanSquaredError\");\n  }\n  assertShapesMatch($labels.shape, $predictions.shape, \"Error in meanSquaredError: \");\n  const losses2 = squaredDifference($labels, $predictions);\n  return computeWeightedLoss(losses2, $weights, reduction);\n}\nvar meanSquaredError = op({ meanSquaredError_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/losses/sigmoid_cross_entropy.js\nfunction sigmoidCrossEntropyWithLogits_(labels, logits) {\n  const $labels = convertToTensor(labels, \"labels\", \"sigmoidCrossEntropyWithLogits\");\n  const $logits = convertToTensor(logits, \"logits\", \"sigmoidCrossEntropyWithLogits\");\n  assertShapesMatch($labels.shape, $logits.shape, \"Error in sigmoidCrossEntropyWithLogits: \");\n  const maxOutput = relu($logits);\n  const outputXTarget = mul($logits, $labels);\n  const sigmoidOutput = log1p(exp(neg(abs($logits))));\n  return add2(sub(maxOutput, outputXTarget), sigmoidOutput);\n}\nfunction sigmoidCrossEntropy_(multiClassLabels, logits, weights, labelSmoothing = 0, reduction = Reduction.SUM_BY_NONZERO_WEIGHTS) {\n  let $multiClassLabels = convertToTensor(multiClassLabels, \"multiClassLabels\", \"sigmoidCrossEntropy\");\n  const $logits = convertToTensor(logits, \"logits\", \"sigmoidCrossEntropy\");\n  let $weights = null;\n  if (weights != null) {\n    $weights = convertToTensor(weights, \"weights\", \"sigmoidCrossEntropy\");\n  }\n  assertShapesMatch($multiClassLabels.shape, $logits.shape, \"Error in sigmoidCrossEntropy: \");\n  if (labelSmoothing > 0) {\n    const labelSmoothingScalar = scalar(labelSmoothing);\n    const one = scalar(1);\n    const half = scalar(0.5);\n    $multiClassLabels = add2(mul($multiClassLabels, sub(one, labelSmoothingScalar)), mul(half, labelSmoothingScalar));\n  }\n  const losses2 = sigmoidCrossEntropyWithLogits_($multiClassLabels, $logits);\n  return computeWeightedLoss(losses2, $weights, reduction);\n}\nvar sigmoidCrossEntropy = op({ sigmoidCrossEntropy_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/losses/softmax_cross_entropy.js\nfunction softmaxCrossEntropyWithLogits_(labels, logits, dim = -1) {\n  if (dim === -1) {\n    dim = logits.rank - 1;\n  }\n  if (dim !== logits.rank - 1) {\n    throw Error(`Softmax cross entropy along a non-last dimension is not yet supported. Labels / logits was rank ${logits.rank} and dim was ${dim}`);\n  }\n  const customOp = customGrad((labels2, logits2, save) => {\n    const keepDims = true;\n    const lse = logSumExp(logits2, [dim], keepDims);\n    const logResult = sub(cast(logits2, \"float32\"), lse);\n    save([labels2, logResult]);\n    const costVector = neg(mul(logResult, labels2));\n    const value = sum2(costVector, [dim]);\n    const gradFunc = (dy, saved) => {\n      const [labels3, logResult2] = saved;\n      const dyShape = expandShapeToKeepDim(dy.shape, [dim]);\n      return [\n        mul(reshape(dy, dyShape), sub(cast(labels3, \"float32\"), exp(logResult2))),\n        mul(reshape(dy, dyShape), sub(exp(logResult2), cast(labels3, \"float32\")))\n      ];\n    };\n    return { value, gradFunc };\n  });\n  return customOp(labels, logits);\n}\nfunction softmaxCrossEntropy_(onehotLabels, logits, weights, labelSmoothing = 0, reduction = Reduction.SUM_BY_NONZERO_WEIGHTS) {\n  let $onehotLabels = convertToTensor(onehotLabels, \"onehotLabels\", \"softmaxCrossEntropy\");\n  const $logits = convertToTensor(logits, \"logits\", \"softmaxCrossEntropy\");\n  let $weights = null;\n  if (weights != null) {\n    $weights = convertToTensor(weights, \"weights\", \"softmaxCrossEntropy\");\n  }\n  assertShapesMatch($onehotLabels.shape, $logits.shape, \"Error in softmaxCrossEntropy: \");\n  if (labelSmoothing > 0) {\n    const labelSmoothingScalar = scalar(labelSmoothing);\n    const one = scalar(1);\n    const numClasses = scalar($onehotLabels.shape[1]);\n    $onehotLabels = add2(mul($onehotLabels, sub(one, labelSmoothingScalar)), div(labelSmoothingScalar, numClasses));\n  }\n  const losses2 = softmaxCrossEntropyWithLogits_($onehotLabels, $logits);\n  return computeWeightedLoss(losses2, $weights, reduction);\n}\nvar softmaxCrossEntropy = op({ softmaxCrossEntropy_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/sparse/sparse_fill_empty_rows.js\nfunction sparseFillEmptyRows_(indices, values, denseShape, defaultValue) {\n  const $indices = convertToTensor(indices, \"indices\", \"sparseFillEmptyRows\", \"int32\");\n  const $values = convertToTensor(values, \"values\", \"sparseFillEmptyRows\");\n  const $denseShape = convertToTensor(denseShape, \"denseShape\", \"sparseFillEmptyRows\", \"int32\");\n  const $defaultValue = convertToTensor(defaultValue, \"defaultValue\", \"sparseFillEmptyRows\", $values.dtype);\n  if ($indices.rank !== 2) {\n    throw new Error(`Indices should be Tensor2D but received shape\n        ${$indices.shape}`);\n  }\n  if ($values.rank !== 1) {\n    throw new Error(`Values should be Tensor1D but received shape ${$values.shape}`);\n  }\n  if ($denseShape.rank !== 1) {\n    throw new Error(`Dense shape should be Tensor1D but received shape ${$denseShape.shape}`);\n  }\n  if ($defaultValue.rank !== 0) {\n    throw new Error(`Default value should be a scalar but received shape ${$defaultValue.shape}`);\n  }\n  const inputs = {\n    indices: $indices,\n    values: $values,\n    denseShape: $denseShape,\n    defaultValue: $defaultValue\n  };\n  const result = ENGINE.runKernel(SparseFillEmptyRows, inputs);\n  return {\n    outputIndices: result[0],\n    outputValues: result[1],\n    emptyRowIndicator: result[2],\n    reverseIndexMap: result[3]\n  };\n}\nvar sparseFillEmptyRows = op({ sparseFillEmptyRows_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/sparse/sparse_reshape.js\nfunction sparseReshape_(inputIndices, inputShape, newShape) {\n  const $inputIndices = convertToTensor(inputIndices, \"inputIndices\", \"sparseReshape\", \"int32\");\n  const $inputShape = convertToTensor(inputShape, \"inputShape\", \"sparseReshape\", \"int32\");\n  const $newShape = convertToTensor(newShape, \"newShape\", \"sparseReshape\", \"int32\");\n  if ($inputIndices.rank !== 2) {\n    throw new Error(`Input indices should be Tensor2D but received shape\n        ${$inputIndices.shape}`);\n  }\n  if ($inputShape.rank !== 1) {\n    throw new Error(`Input shape should be Tensor1D but received shape ${$inputShape.shape}`);\n  }\n  if ($newShape.rank !== 1) {\n    throw new Error(`New shape should be Tensor1D but received shape ${$newShape.shape}`);\n  }\n  const inputs = {\n    inputIndices: $inputIndices,\n    inputShape: $inputShape,\n    newShape: $newShape\n  };\n  const result = ENGINE.runKernel(SparseReshape, inputs);\n  return { outputIndices: result[0], outputShape: result[1] };\n}\nvar sparseReshape = op({ sparseReshape_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/sparse/sparse_segment_mean.js\nfunction sparseSegmentMean_(data, indices, segmentIds) {\n  const $data = convertToTensor(data, \"data\", \"sparseSegmentMean\");\n  const $indices = convertToTensor(indices, \"indices\", \"sparseSegmentMean\", \"int32\");\n  const $segmentIds = convertToTensor(segmentIds, \"segmentIds\", \"sparseSegmentMean\", \"int32\");\n  if ($data.rank < 1) {\n    throw new Error(`Data should be at least 1 dimensional but received scalar`);\n  }\n  if ($indices.rank !== 1) {\n    throw new Error(`Indices should be Tensor1D but received shape\n          ${$indices.shape}`);\n  }\n  if ($segmentIds.rank !== 1) {\n    throw new Error(`Segment ids should be Tensor1D but received shape\n          ${$segmentIds.shape}`);\n  }\n  const inputs = {\n    data: $data,\n    indices: $indices,\n    segmentIds: $segmentIds\n  };\n  return ENGINE.runKernel(SparseSegmentMean, inputs);\n}\nvar sparseSegmentMean = op({ sparseSegmentMean_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/sparse/sparse_segment_sum.js\nfunction sparseSegmentSum_(data, indices, segmentIds) {\n  const $data = convertToTensor(data, \"data\", \"sparseSegmentSum\");\n  const $indices = convertToTensor(indices, \"indices\", \"sparseSegmentSum\", \"int32\");\n  const $segmentIds = convertToTensor(segmentIds, \"segmentIds\", \"sparseSegmentSum\", \"int32\");\n  if ($data.rank < 1) {\n    throw new Error(`Data should be at least 1 dimensional but received scalar`);\n  }\n  if ($indices.rank !== 1) {\n    throw new Error(`Indices should be Tensor1D but received shape\n         ${$indices.shape}`);\n  }\n  if ($segmentIds.rank !== 1) {\n    throw new Error(`Segment ids should be Tensor1D but received shape\n         ${$segmentIds.shape}`);\n  }\n  const inputs = {\n    data: $data,\n    indices: $indices,\n    segmentIds: $segmentIds\n  };\n  return ENGINE.runKernel(SparseSegmentSum, inputs);\n}\nvar sparseSegmentSum = op({ sparseSegmentSum_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/string/string_n_grams.js\nfunction stringNGrams_(data, dataSplits, separator, nGramWidths, leftPad, rightPad2, padWidth, preserveShortSequences) {\n  const $data = convertToTensor(data, \"data\", \"stringNGrams\", \"string\");\n  if ($data.dtype !== \"string\") {\n    throw new Error(\"Data must be of datatype string\");\n  }\n  if ($data.shape.length !== 1) {\n    throw new Error(`Data must be a vector, saw: ${$data.shape}`);\n  }\n  const $dataSplits = convertToTensor(dataSplits, \"dataSplits\", \"stringNGrams\");\n  if ($dataSplits.dtype !== \"int32\") {\n    throw new Error(\"Data splits must be of datatype int32\");\n  }\n  const attrs = {\n    separator,\n    nGramWidths,\n    leftPad,\n    rightPad: rightPad2,\n    padWidth,\n    preserveShortSequences\n  };\n  const inputs = { data: $data, dataSplits: $dataSplits };\n  const result = ENGINE.runKernel(StringNGrams, inputs, attrs);\n  return { nGrams: result[0], nGramsSplits: result[1] };\n}\nvar stringNGrams = op({ stringNGrams_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/string/string_split.js\nfunction stringSplit_(input2, delimiter, skipEmpty = true) {\n  const $input = convertToTensor(input2, \"input\", \"stringSplit\", \"string\");\n  const $delimiter = convertToTensor(delimiter, \"delimiter\", \"stringSplit\", \"string\");\n  if ($input.rank !== 1) {\n    throw new Error(`Input should be Tensor1D but received shape ${$input.shape}`);\n  }\n  if ($delimiter.rank !== 0) {\n    throw new Error(`Delimiter should be a scalar but received shape ${$delimiter.shape}`);\n  }\n  const attrs = { skipEmpty };\n  const inputs = { input: $input, delimiter: $delimiter };\n  const result = ENGINE.runKernel(StringSplit, inputs, attrs);\n  return { indices: result[0], values: result[1], shape: result[2] };\n}\nvar stringSplit = op({ stringSplit_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/string/string_to_hash_bucket_fast.js\nfunction stringToHashBucketFast_(input2, numBuckets) {\n  const $input = convertToTensor(input2, \"input\", \"stringToHashBucketFast\", \"string\");\n  const attrs = { numBuckets };\n  if (numBuckets <= 0) {\n    throw new Error(`Number of buckets must be at least 1`);\n  }\n  const inputs = { input: $input };\n  return ENGINE.runKernel(StringToHashBucketFast, inputs, attrs);\n}\nvar stringToHashBucketFast = op({ stringToHashBucketFast_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/ops.js\nvar spectral = {\n  fft,\n  ifft,\n  rfft,\n  irfft\n};\nvar signal = {\n  hammingWindow,\n  hannWindow,\n  frame,\n  stft\n};\nvar image = {\n  flipLeftRight,\n  grayscaleToRGB,\n  resizeNearestNeighbor,\n  resizeBilinear,\n  rotateWithOffset,\n  cropAndResize,\n  nonMaxSuppression,\n  nonMaxSuppressionAsync,\n  nonMaxSuppressionWithScore,\n  nonMaxSuppressionWithScoreAsync,\n  nonMaxSuppressionPadded,\n  nonMaxSuppressionPaddedAsync,\n  threshold,\n  transform\n};\nvar linalg = {\n  bandPart,\n  gramSchmidt,\n  qr\n};\nvar losses = {\n  absoluteDifference,\n  computeWeightedLoss,\n  cosineDistance,\n  hingeLoss,\n  huberLoss,\n  logLoss,\n  meanSquaredError,\n  sigmoidCrossEntropy,\n  softmaxCrossEntropy\n};\nvar sparse = {\n  sparseFillEmptyRows,\n  sparseReshape,\n  sparseSegmentMean,\n  sparseSegmentSum\n};\nvar string = {\n  stringNGrams,\n  stringSplit,\n  stringToHashBucketFast\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/optimizers/optimizer.js\nvar Optimizer = class extends Serializable {\n  minimize(f, returnCost = false, varList) {\n    const { value, grads: grads2 } = this.computeGradients(f, varList);\n    if (varList != null) {\n      const gradArray = varList.map((v) => ({ name: v.name, tensor: grads2[v.name] }));\n      this.applyGradients(gradArray);\n    } else {\n      this.applyGradients(grads2);\n    }\n    dispose(grads2);\n    if (returnCost) {\n      return value;\n    } else {\n      value.dispose();\n      return null;\n    }\n  }\n  get iterations() {\n    if (this.iterations_ == null) {\n      this.iterations_ = 0;\n    }\n    return this.iterations_;\n  }\n  incrementIterations() {\n    this.iterations_ = this.iterations + 1;\n  }\n  computeGradients(f, varList) {\n    return variableGrads(f, varList);\n  }\n  dispose() {\n    if (this.iterations_ != null) {\n      dispose(this.iterations_);\n    }\n  }\n  async saveIterations() {\n    if (this.iterations_ == null) {\n      this.iterations_ = 0;\n    }\n    return {\n      name: \"iter\",\n      tensor: scalar(this.iterations_, \"int32\")\n    };\n  }\n  async getWeights() {\n    throw new Error(\"getWeights() is not implemented for this optimizer yet.\");\n  }\n  async setWeights(weightValues) {\n    throw new Error(`setWeights() is not implemented for this optimizer class ${this.getClassName()}`);\n  }\n  async extractIterations(weightValues) {\n    this.iterations_ = (await weightValues[0].tensor.data())[0];\n    return weightValues.slice(1);\n  }\n};\nObject.defineProperty(Optimizer, Symbol.hasInstance, {\n  value: (instance) => {\n    return instance.minimize != null && instance.computeGradients != null && instance.applyGradients != null;\n  }\n});\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/optimizers/adadelta_optimizer.js\nvar AdadeltaOptimizer = class extends Optimizer {\n  constructor(learningRate, rho, epsilon3 = null) {\n    super();\n    this.learningRate = learningRate;\n    this.rho = rho;\n    this.epsilon = epsilon3;\n    this.accumulatedGrads = [];\n    this.accumulatedUpdates = [];\n    if (epsilon3 == null) {\n      this.epsilon = ENGINE.backend.epsilon();\n    }\n  }\n  applyGradients(variableGradients) {\n    const variableNames = Array.isArray(variableGradients) ? variableGradients.map((item) => item.name) : Object.keys(variableGradients);\n    variableNames.forEach((name, i2) => {\n      const value = ENGINE.registeredVariables[name];\n      const trainable = false;\n      if (this.accumulatedGrads[i2] == null) {\n        this.accumulatedGrads[i2] = {\n          originalName: `${name}/accum_grad`,\n          variable: tidy(() => zerosLike(value).variable(trainable))\n        };\n      }\n      if (this.accumulatedUpdates[i2] == null) {\n        this.accumulatedUpdates[i2] = {\n          originalName: `${name}/accum_var`,\n          variable: tidy(() => zerosLike(value).variable(trainable))\n        };\n      }\n      const gradient = Array.isArray(variableGradients) ? variableGradients[i2].tensor : variableGradients[name];\n      if (gradient == null) {\n        return;\n      }\n      const accumulatedGrad = this.accumulatedGrads[i2].variable;\n      const accumulatedUpdate = this.accumulatedUpdates[i2].variable;\n      tidy(() => {\n        const newAccumulatedGrad = add2(mul(accumulatedGrad, this.rho), mul(square(gradient), 1 - this.rho));\n        const updates = mul(div(sqrt(add2(accumulatedUpdate, this.epsilon)), sqrt(add2(accumulatedGrad, this.epsilon))), gradient);\n        const newAccumulatedUpdate = add2(mul(accumulatedUpdate, this.rho), mul(square(updates), 1 - this.rho));\n        accumulatedGrad.assign(newAccumulatedGrad);\n        accumulatedUpdate.assign(newAccumulatedUpdate);\n        const newValue = add2(mul(updates, -this.learningRate), value);\n        value.assign(newValue);\n      });\n    });\n    this.incrementIterations();\n  }\n  dispose() {\n    if (this.accumulatedUpdates != null) {\n      dispose(this.accumulatedGrads.map((v) => v.variable));\n      dispose(this.accumulatedUpdates.map((v) => v.variable));\n    }\n  }\n  async getWeights() {\n    const variables = [...this.accumulatedGrads, ...this.accumulatedUpdates];\n    return [await this.saveIterations()].concat(variables.map((v) => ({ name: v.originalName, tensor: v.variable })));\n  }\n  async setWeights(weightValues) {\n    weightValues = await this.extractIterations(weightValues);\n    const variableCount = weightValues.length / 2;\n    const trainable = false;\n    this.accumulatedGrads = weightValues.slice(0, variableCount).map((v) => ({\n      originalName: v.name,\n      variable: v.tensor.variable(trainable)\n    }));\n    this.accumulatedUpdates = weightValues.slice(variableCount, variableCount * 2).map((v) => ({\n      originalName: v.name,\n      variable: v.tensor.variable(trainable)\n    }));\n  }\n  getConfig() {\n    return {\n      \"learningRate\": this.learningRate,\n      \"rho\": this.rho,\n      \"epsilon\": this.epsilon\n    };\n  }\n  static fromConfig(cls, config) {\n    return new cls(config[\"learningRate\"], config[\"rho\"], config[\"epsilon\"]);\n  }\n};\nAdadeltaOptimizer.className = \"Adadelta\";\nregisterClass(AdadeltaOptimizer);\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/optimizers/adagrad_optimizer.js\nvar AdagradOptimizer = class extends Optimizer {\n  constructor(learningRate, initialAccumulatorValue = 0.1) {\n    super();\n    this.learningRate = learningRate;\n    this.initialAccumulatorValue = initialAccumulatorValue;\n    this.accumulatedGrads = [];\n  }\n  applyGradients(variableGradients) {\n    const variableNames = Array.isArray(variableGradients) ? variableGradients.map((item) => item.name) : Object.keys(variableGradients);\n    variableNames.forEach((name, i2) => {\n      const value = ENGINE.registeredVariables[name];\n      if (this.accumulatedGrads[i2] == null) {\n        const trainable = false;\n        this.accumulatedGrads[i2] = {\n          originalName: `${name}/accumulator`,\n          variable: tidy(() => fill(value.shape, this.initialAccumulatorValue).variable(trainable))\n        };\n      }\n      const gradient = Array.isArray(variableGradients) ? variableGradients[i2].tensor : variableGradients[name];\n      if (gradient == null) {\n        return;\n      }\n      const accumulatedGrad = this.accumulatedGrads[i2].variable;\n      tidy(() => {\n        const newAccumulatedGrad = add2(accumulatedGrad, square(gradient));\n        accumulatedGrad.assign(newAccumulatedGrad);\n        const newValue = add2(mul(div(gradient, sqrt(add2(newAccumulatedGrad, ENGINE.backend.epsilon()))), -this.learningRate), value);\n        value.assign(newValue);\n      });\n    });\n    this.incrementIterations();\n  }\n  dispose() {\n    if (this.accumulatedGrads != null) {\n      dispose(this.accumulatedGrads.map((v) => v.variable));\n    }\n  }\n  async getWeights() {\n    return [await this.saveIterations()].concat(this.accumulatedGrads.map((v) => ({ name: v.originalName, tensor: v.variable })));\n  }\n  async setWeights(weightValues) {\n    weightValues = await this.extractIterations(weightValues);\n    const trainable = false;\n    this.accumulatedGrads = weightValues.map((v) => ({ originalName: v.name, variable: v.tensor.variable(trainable) }));\n  }\n  getConfig() {\n    return {\n      \"learningRate\": this.learningRate,\n      \"initialAccumulatorValue\": this.initialAccumulatorValue\n    };\n  }\n  static fromConfig(cls, config) {\n    return new cls(config[\"learningRate\"], config[\"initialAccumulatorValue\"]);\n  }\n};\nAdagradOptimizer.className = \"Adagrad\";\nregisterClass(AdagradOptimizer);\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/optimizers/adam_optimizer.js\nvar AdamOptimizer = class extends Optimizer {\n  constructor(learningRate, beta1, beta2, epsilon3 = null) {\n    super();\n    this.learningRate = learningRate;\n    this.beta1 = beta1;\n    this.beta2 = beta2;\n    this.epsilon = epsilon3;\n    this.accumulatedFirstMoment = [];\n    this.accumulatedSecondMoment = [];\n    tidy(() => {\n      this.accBeta1 = scalar(beta1).variable();\n      this.accBeta2 = scalar(beta2).variable();\n    });\n    if (epsilon3 == null) {\n      this.epsilon = ENGINE.backend.epsilon();\n    }\n  }\n  applyGradients(variableGradients) {\n    const varNames = Array.isArray(variableGradients) ? variableGradients.map((v) => v.name) : Object.keys(variableGradients);\n    tidy(() => {\n      const oneMinusAccBeta1 = sub(1, this.accBeta1);\n      const oneMinusAccBeta2 = sub(1, this.accBeta2);\n      varNames.forEach((name, i2) => {\n        const value = ENGINE.registeredVariables[name];\n        const trainable = false;\n        if (this.accumulatedFirstMoment[i2] == null) {\n          this.accumulatedFirstMoment[i2] = {\n            originalName: `${name}/m`,\n            variable: tidy(() => zerosLike(value).variable(trainable))\n          };\n        }\n        if (this.accumulatedSecondMoment[i2] == null) {\n          this.accumulatedSecondMoment[i2] = {\n            originalName: `${name}/v`,\n            variable: tidy(() => zerosLike(value).variable(trainable))\n          };\n        }\n        const gradient = Array.isArray(variableGradients) ? variableGradients[i2].tensor : variableGradients[name];\n        if (gradient == null) {\n          return;\n        }\n        const firstMoment = this.accumulatedFirstMoment[i2].variable;\n        const secondMoment = this.accumulatedSecondMoment[i2].variable;\n        const newFirstMoment = add2(mul(firstMoment, this.beta1), mul(gradient, 1 - this.beta1));\n        const newSecondMoment = add2(mul(secondMoment, this.beta2), mul(square(gradient), 1 - this.beta2));\n        const biasCorrectedFirstMoment = div(newFirstMoment, oneMinusAccBeta1);\n        const biasCorrectedSecondMoment = div(newSecondMoment, oneMinusAccBeta2);\n        firstMoment.assign(newFirstMoment);\n        secondMoment.assign(newSecondMoment);\n        const newValue = add2(mul(div(biasCorrectedFirstMoment, add2(sqrt(biasCorrectedSecondMoment), this.epsilon)), -this.learningRate), value);\n        value.assign(newValue);\n      });\n      this.accBeta1.assign(mul(this.accBeta1, this.beta1));\n      this.accBeta2.assign(mul(this.accBeta2, this.beta2));\n    });\n    this.incrementIterations();\n  }\n  dispose() {\n    this.accBeta1.dispose();\n    this.accBeta2.dispose();\n    if (this.accumulatedFirstMoment != null) {\n      dispose(this.accumulatedFirstMoment.map((v) => v.variable));\n    }\n    if (this.accumulatedSecondMoment != null) {\n      dispose(this.accumulatedSecondMoment.map((v) => v.variable));\n    }\n  }\n  async getWeights() {\n    const variables = [...this.accumulatedFirstMoment, ...this.accumulatedSecondMoment];\n    return [await this.saveIterations()].concat(variables.map((v) => ({ name: v.originalName, tensor: v.variable })));\n  }\n  async setWeights(weightValues) {\n    weightValues = await this.extractIterations(weightValues);\n    tidy(() => {\n      this.accBeta1.assign(pow(this.beta1, this.iterations_ + 1));\n      this.accBeta2.assign(pow(this.beta2, this.iterations_ + 1));\n    });\n    const variableCount = weightValues.length / 2;\n    const trainable = false;\n    this.accumulatedFirstMoment = weightValues.slice(0, variableCount).map((v) => ({\n      originalName: v.name,\n      variable: v.tensor.variable(trainable)\n    }));\n    this.accumulatedSecondMoment = weightValues.slice(variableCount, variableCount * 2).map((v) => ({\n      originalName: v.name,\n      variable: v.tensor.variable(trainable)\n    }));\n  }\n  getConfig() {\n    return {\n      \"learningRate\": this.learningRate,\n      \"beta1\": this.beta1,\n      \"beta2\": this.beta2,\n      \"epsilon\": this.epsilon\n    };\n  }\n  static fromConfig(cls, config) {\n    return new cls(config[\"learningRate\"], config[\"beta1\"], config[\"beta2\"], config[\"epsilon\"]);\n  }\n};\nAdamOptimizer.className = \"Adam\";\nregisterClass(AdamOptimizer);\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/optimizers/adamax_optimizer.js\nvar AdamaxOptimizer = class extends Optimizer {\n  constructor(learningRate, beta1, beta2, epsilon3 = null, decay = 0) {\n    super();\n    this.learningRate = learningRate;\n    this.beta1 = beta1;\n    this.beta2 = beta2;\n    this.epsilon = epsilon3;\n    this.decay = decay;\n    this.accumulatedFirstMoment = [];\n    this.accumulatedWeightedInfNorm = [];\n    tidy(() => {\n      this.iteration = scalar(0).variable();\n      this.accBeta1 = scalar(beta1).variable();\n    });\n    if (epsilon3 == null) {\n      this.epsilon = ENGINE.backend.epsilon();\n    }\n  }\n  applyGradients(variableGradients) {\n    const variableNames = Array.isArray(variableGradients) ? variableGradients.map((item) => item.name) : Object.keys(variableGradients);\n    tidy(() => {\n      const oneMinusAccBeta1 = sub(1, this.accBeta1);\n      const lr = div(-this.learningRate, add2(mul(this.iteration, this.decay), 1));\n      variableNames.forEach((name, i2) => {\n        const value = ENGINE.registeredVariables[name];\n        const trainable = false;\n        if (this.accumulatedFirstMoment[i2] == null) {\n          this.accumulatedFirstMoment[i2] = {\n            originalName: `${name}/m`,\n            variable: zerosLike(value).variable(trainable)\n          };\n        }\n        if (this.accumulatedWeightedInfNorm[i2] == null) {\n          this.accumulatedWeightedInfNorm[i2] = {\n            originalName: `${name}/v`,\n            variable: zerosLike(value).variable(trainable)\n          };\n        }\n        const gradient = Array.isArray(variableGradients) ? variableGradients[i2].tensor : variableGradients[name];\n        if (gradient == null) {\n          return;\n        }\n        const firstMoment = this.accumulatedFirstMoment[i2].variable;\n        const weightedInfNorm = this.accumulatedWeightedInfNorm[i2].variable;\n        const newFirstMoment = add2(mul(firstMoment, this.beta1), mul(gradient, 1 - this.beta1));\n        const ut0 = mul(weightedInfNorm, this.beta2);\n        const ut1 = abs(gradient);\n        const newWeightedInfNorm = maximum(ut0, ut1);\n        firstMoment.assign(newFirstMoment);\n        weightedInfNorm.assign(newWeightedInfNorm);\n        const newValue = add2(mul(div(lr, oneMinusAccBeta1), div(newFirstMoment, add2(newWeightedInfNorm, this.epsilon))), value);\n        value.assign(newValue);\n      });\n      this.iteration.assign(add2(this.iteration, 1));\n      this.accBeta1.assign(mul(this.accBeta1, this.beta1));\n    });\n    this.incrementIterations();\n  }\n  dispose() {\n    this.accBeta1.dispose();\n    this.iteration.dispose();\n    if (this.accumulatedFirstMoment != null) {\n      dispose(this.accumulatedFirstMoment.map((v) => v.variable));\n    }\n    if (this.accumulatedWeightedInfNorm != null) {\n      dispose(this.accumulatedWeightedInfNorm.map((v) => v.variable));\n    }\n  }\n  async getWeights() {\n    throw new Error(\"getWeights() is not implemented for Adamax yet.\");\n  }\n  async setWeights(weightValues) {\n    throw new Error(\"setWeights() is not implemented for Adamax yet.\");\n  }\n  getConfig() {\n    return {\n      \"learningRate\": this.learningRate,\n      \"beta1\": this.beta1,\n      \"beta2\": this.beta2,\n      \"epsilon\": this.epsilon,\n      \"decay\": this.decay\n    };\n  }\n  static fromConfig(cls, config) {\n    return new cls(config[\"learningRate\"], config[\"beta1\"], config[\"beta2\"], config[\"epsilon\"], config[\"decay\"]);\n  }\n};\nAdamaxOptimizer.className = \"Adamax\";\nregisterClass(AdamaxOptimizer);\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/optimizers/sgd_optimizer.js\nvar SGDOptimizer = class extends Optimizer {\n  constructor(learningRate) {\n    super();\n    this.learningRate = learningRate;\n    this.setLearningRate(learningRate);\n  }\n  applyGradients(variableGradients) {\n    const varNames = Array.isArray(variableGradients) ? variableGradients.map((v) => v.name) : Object.keys(variableGradients);\n    varNames.forEach((name, i2) => {\n      const gradient = Array.isArray(variableGradients) ? variableGradients[i2].tensor : variableGradients[name];\n      if (gradient == null) {\n        return;\n      }\n      const value = ENGINE.registeredVariables[name];\n      tidy(() => {\n        const newValue = add2(mul(this.c, gradient), value);\n        value.assign(newValue);\n      });\n    });\n    this.incrementIterations();\n  }\n  setLearningRate(learningRate) {\n    this.learningRate = learningRate;\n    if (this.c != null) {\n      this.c.dispose();\n    }\n    this.c = keep(scalar(-learningRate));\n  }\n  dispose() {\n    this.c.dispose();\n  }\n  async getWeights() {\n    return [await this.saveIterations()];\n  }\n  async setWeights(weightValues) {\n    weightValues = await this.extractIterations(weightValues);\n    if (weightValues.length !== 0) {\n      throw new Error(\"SGD optimizer does not have settable weights.\");\n    }\n  }\n  getConfig() {\n    return { \"learningRate\": this.learningRate };\n  }\n  static fromConfig(cls, config) {\n    return new cls(config[\"learningRate\"]);\n  }\n};\nSGDOptimizer.className = \"SGD\";\nregisterClass(SGDOptimizer);\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/optimizers/momentum_optimizer.js\nvar MomentumOptimizer = class extends SGDOptimizer {\n  constructor(learningRate, momentum, useNesterov = false) {\n    super(learningRate);\n    this.learningRate = learningRate;\n    this.momentum = momentum;\n    this.useNesterov = useNesterov;\n    this.accumulations = [];\n    this.m = scalar(this.momentum);\n  }\n  applyGradients(variableGradients) {\n    const variableNames = Array.isArray(variableGradients) ? variableGradients.map((item) => item.name) : Object.keys(variableGradients);\n    variableNames.forEach((name, i2) => {\n      const value = ENGINE.registeredVariables[name];\n      if (this.accumulations[i2] == null) {\n        const trainable = false;\n        this.accumulations[i2] = {\n          originalName: `${name}/momentum`,\n          variable: tidy(() => zerosLike(value).variable(trainable))\n        };\n      }\n      const accumulation = this.accumulations[i2].variable;\n      const gradient = Array.isArray(variableGradients) ? variableGradients[i2].tensor : variableGradients[name];\n      if (gradient == null) {\n        return;\n      }\n      tidy(() => {\n        let newValue;\n        const newAccumulation = add2(mul(this.m, accumulation), gradient);\n        if (this.useNesterov) {\n          newValue = add2(mul(this.c, add2(gradient, mul(newAccumulation, this.m))), value);\n        } else {\n          newValue = add2(mul(this.c, newAccumulation), value);\n        }\n        accumulation.assign(newAccumulation);\n        value.assign(newValue);\n      });\n    });\n    this.incrementIterations();\n  }\n  dispose() {\n    this.m.dispose();\n    if (this.accumulations != null) {\n      dispose(this.accumulations.map((v) => v.variable));\n    }\n  }\n  setMomentum(momentum) {\n    this.momentum = momentum;\n  }\n  async getWeights() {\n    return [await this.saveIterations()].concat(this.accumulations.map((v) => ({ name: v.originalName, tensor: v.variable })));\n  }\n  async setWeights(weightValues) {\n    weightValues = await this.extractIterations(weightValues);\n    const trainable = false;\n    this.accumulations = weightValues.map((v) => ({ originalName: v.name, variable: v.tensor.variable(trainable) }));\n  }\n  getConfig() {\n    return {\n      \"learningRate\": this.learningRate,\n      \"momentum\": this.momentum,\n      \"useNesterov\": this.useNesterov\n    };\n  }\n  static fromConfig(cls, config) {\n    return new cls(config[\"learningRate\"], config[\"momentum\"], config[\"useNesterov\"]);\n  }\n};\nMomentumOptimizer.className = \"Momentum\";\nregisterClass(MomentumOptimizer);\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/optimizers/rmsprop_optimizer.js\nvar RMSPropOptimizer = class extends Optimizer {\n  constructor(learningRate, decay = 0.9, momentum = 0, epsilon3 = null, centered = false) {\n    super();\n    this.learningRate = learningRate;\n    this.decay = decay;\n    this.momentum = momentum;\n    this.epsilon = epsilon3;\n    this.accumulatedMeanSquares = [];\n    this.accumulatedMoments = [];\n    this.accumulatedMeanGrads = [];\n    this.centered = centered;\n    if (epsilon3 == null) {\n      this.epsilon = ENGINE.backend.epsilon();\n    }\n    if (learningRate == null) {\n      throw new Error(`learningRate for RMSPropOptimizer must be defined.`);\n    }\n  }\n  applyGradients(variableGradients) {\n    const variableNames = Array.isArray(variableGradients) ? variableGradients.map((item) => item.name) : Object.keys(variableGradients);\n    variableNames.forEach((name, i2) => {\n      const value = ENGINE.registeredVariables[name];\n      const trainable = false;\n      if (this.accumulatedMeanSquares[i2] == null) {\n        this.accumulatedMeanSquares[i2] = {\n          originalName: `${name}/rms`,\n          variable: tidy(() => zerosLike(value).variable(trainable))\n        };\n      }\n      if (this.accumulatedMoments[i2] == null) {\n        this.accumulatedMoments[i2] = {\n          originalName: `${name}/momentum`,\n          variable: tidy(() => zerosLike(value).variable(trainable))\n        };\n      }\n      if (this.accumulatedMeanGrads[i2] == null && this.centered) {\n        this.accumulatedMeanGrads[i2] = {\n          originalName: `${name}/mg`,\n          variable: tidy(() => zerosLike(value).variable(trainable))\n        };\n      }\n      const gradient = Array.isArray(variableGradients) ? variableGradients[i2].tensor : variableGradients[name];\n      if (gradient == null) {\n        return;\n      }\n      const accumulatedMeanSquare = this.accumulatedMeanSquares[i2].variable;\n      const accumulatedMoments = this.accumulatedMoments[i2].variable;\n      tidy(() => {\n        const newAccumulatedMeanSquare = add2(mul(accumulatedMeanSquare, this.decay), mul(square(gradient), 1 - this.decay));\n        if (this.centered) {\n          const accumulatedMeanGrad = this.accumulatedMeanGrads[i2].variable;\n          const newAccumulatedMeanGrad = add2(mul(accumulatedMeanGrad, this.decay), mul(gradient, 1 - this.decay));\n          const gradContribution = div(mul(gradient, this.learningRate), sqrt(sub(newAccumulatedMeanSquare, add2(square(newAccumulatedMeanGrad), this.epsilon))));\n          const newAccumulatedMoments = add2(mul(accumulatedMoments, this.momentum), gradContribution);\n          accumulatedMeanSquare.assign(newAccumulatedMeanSquare);\n          accumulatedMeanGrad.assign(newAccumulatedMeanGrad);\n          accumulatedMoments.assign(newAccumulatedMoments);\n          const newValue = sub(value, newAccumulatedMoments);\n          value.assign(newValue);\n        } else {\n          const newAccumulatedMeanSquare2 = add2(mul(accumulatedMeanSquare, this.decay), mul(square(gradient), 1 - this.decay));\n          const newAccumulatedMoments = add2(mul(accumulatedMoments, this.momentum), div(mul(gradient, this.learningRate), sqrt(add2(newAccumulatedMeanSquare2, this.epsilon))));\n          accumulatedMeanSquare.assign(newAccumulatedMeanSquare2);\n          accumulatedMoments.assign(newAccumulatedMoments);\n          const newValue = sub(value, newAccumulatedMoments);\n          value.assign(newValue);\n        }\n      });\n    });\n    this.incrementIterations();\n  }\n  dispose() {\n    if (this.accumulatedMeanSquares != null) {\n      dispose(this.accumulatedMeanSquares.map((v) => v.variable));\n    }\n    if (this.accumulatedMeanGrads != null && this.centered) {\n      dispose(this.accumulatedMeanGrads.map((v) => v.variable));\n    }\n    if (this.accumulatedMoments != null) {\n      dispose(this.accumulatedMoments.map((v) => v.variable));\n    }\n  }\n  async getWeights() {\n    const variables = [...this.accumulatedMeanSquares, ...this.accumulatedMoments];\n    if (this.centered) {\n      variables.push(...this.accumulatedMeanGrads);\n    }\n    return [await this.saveIterations()].concat(variables.map((v) => ({ name: v.originalName, tensor: v.variable })));\n  }\n  async setWeights(weightValues) {\n    weightValues = await this.extractIterations(weightValues);\n    const variableCount = this.centered ? weightValues.length / 3 : weightValues.length / 2;\n    const trainable = false;\n    this.accumulatedMeanSquares = weightValues.slice(0, variableCount).map((v) => ({\n      originalName: v.name,\n      variable: v.tensor.variable(trainable)\n    }));\n    this.accumulatedMoments = weightValues.slice(variableCount, variableCount * 2).map((v) => ({\n      originalName: v.name,\n      variable: v.tensor.variable(trainable)\n    }));\n    if (this.centered) {\n      this.accumulatedMeanGrads = weightValues.slice(variableCount * 2, variableCount * 3).map((v) => ({\n        originalName: v.name,\n        variable: v.tensor.variable(trainable)\n      }));\n    }\n  }\n  getConfig() {\n    return {\n      \"learningRate\": this.learningRate,\n      \"decay\": this.decay,\n      \"momentum\": this.momentum,\n      \"epsilon\": this.epsilon,\n      \"centered\": this.centered\n    };\n  }\n  static fromConfig(cls, config) {\n    return new cls(config[\"learningRate\"], config[\"decay\"], config[\"momentum\"], config[\"epsilon\"], config[\"centered\"]);\n  }\n};\nRMSPropOptimizer.className = \"RMSProp\";\nregisterClass(RMSPropOptimizer);\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/optimizers/optimizer_constructors.js\nvar OptimizerConstructors = class {\n  static sgd(learningRate) {\n    return new SGDOptimizer(learningRate);\n  }\n  static momentum(learningRate, momentum, useNesterov = false) {\n    return new MomentumOptimizer(learningRate, momentum, useNesterov);\n  }\n  static rmsprop(learningRate, decay = 0.9, momentum = 0, epsilon3 = null, centered = false) {\n    return new RMSPropOptimizer(learningRate, decay, momentum, epsilon3, centered);\n  }\n  static adam(learningRate = 1e-3, beta1 = 0.9, beta2 = 0.999, epsilon3 = null) {\n    return new AdamOptimizer(learningRate, beta1, beta2, epsilon3);\n  }\n  static adadelta(learningRate = 1e-3, rho = 0.95, epsilon3 = null) {\n    return new AdadeltaOptimizer(learningRate, rho, epsilon3);\n  }\n  static adamax(learningRate = 2e-3, beta1 = 0.9, beta2 = 0.999, epsilon3 = null, decay = 0) {\n    return new AdamaxOptimizer(learningRate, beta1, beta2, epsilon3, decay);\n  }\n  static adagrad(learningRate, initialAccumulatorValue = 0.1) {\n    return new AdagradOptimizer(learningRate, initialAccumulatorValue);\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/train.js\nvar train = {\n  sgd: OptimizerConstructors.sgd,\n  momentum: OptimizerConstructors.momentum,\n  adadelta: OptimizerConstructors.adadelta,\n  adagrad: OptimizerConstructors.adagrad,\n  rmsprop: OptimizerConstructors.rmsprop,\n  adamax: OptimizerConstructors.adamax,\n  adam: OptimizerConstructors.adam\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/browser_util.js\nvar delayCallback = (() => {\n  if (typeof requestAnimationFrame !== \"undefined\") {\n    return requestAnimationFrame;\n  } else if (typeof setImmediate !== \"undefined\") {\n    return setImmediate;\n  }\n  return (f) => f();\n})();\nfunction nextFrame() {\n  return new Promise((resolve) => delayCallback(() => resolve()));\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/backends/backend_util.js\nvar backend_util_exports = {};\n__export(backend_util_exports, {\n  ERF_A1: () => ERF_A1,\n  ERF_A2: () => ERF_A2,\n  ERF_A3: () => ERF_A3,\n  ERF_A4: () => ERF_A4,\n  ERF_A5: () => ERF_A5,\n  ERF_P: () => ERF_P,\n  PARALLELIZE_THRESHOLD: () => PARALLELIZE_THRESHOLD,\n  RowPartitionType: () => RowPartitionType,\n  SELU_SCALE: () => SELU_SCALE,\n  SELU_SCALEALPHA: () => SELU_SCALEALPHA,\n  applyActivation: () => applyActivation,\n  assertAndGetBroadcastShape: () => assertAndGetBroadcastShape,\n  assertAxesAreInnerMostDims: () => assertAxesAreInnerMostDims,\n  assertParamsConsistent: () => assertParamsConsistent,\n  assignToTypedArray: () => assignToTypedArray,\n  axesAreInnerMostDims: () => axesAreInnerMostDims,\n  calculateShapes: () => calculateShapes,\n  checkEinsumDimSizes: () => checkEinsumDimSizes,\n  checkPadOnDimRoundingMode: () => checkPadOnDimRoundingMode,\n  combineLocations: () => combineLocations,\n  combineRaggedTensorToTensorShapes: () => combineRaggedTensorToTensorShapes,\n  complexWithEvenIndex: () => complexWithEvenIndex,\n  complexWithOddIndex: () => complexWithOddIndex,\n  computeConv2DInfo: () => computeConv2DInfo,\n  computeConv3DInfo: () => computeConv3DInfo,\n  computeDefaultPad: () => computeDefaultPad,\n  computeDilation2DInfo: () => computeDilation2DInfo,\n  computeOptimalWindowSize: () => computeOptimalWindowSize,\n  computeOutAndReduceShapes: () => computeOutAndReduceShapes,\n  computeOutShape: () => computeOutShape2,\n  computePool2DInfo: () => computePool2DInfo,\n  computePool3DInfo: () => computePool3DInfo,\n  convertConv2DDataFormat: () => convertConv2DDataFormat,\n  decodeEinsumEquation: () => decodeEinsumEquation,\n  eitherStridesOrDilationsAreOne: () => eitherStridesOrDilationsAreOne,\n  expandShapeToKeepDim: () => expandShapeToKeepDim,\n  exponent: () => exponent,\n  exponents: () => exponents,\n  fromStringArrayToUint8: () => fromStringArrayToUint8,\n  fromUint8ToStringArray: () => fromUint8ToStringArray,\n  getAxesPermutation: () => getAxesPermutation,\n  getBroadcastDims: () => getBroadcastDims,\n  getComplexWithIndex: () => getComplexWithIndex,\n  getEinsumComputePath: () => getEinsumComputePath,\n  getEinsumPermutation: () => getEinsumPermutation,\n  getFusedBiasGradient: () => getFusedBiasGradient,\n  getFusedDyActivation: () => getFusedDyActivation,\n  getImageCenter: () => getImageCenter,\n  getInnerMostAxes: () => getInnerMostAxes,\n  getPermuted: () => getPermuted,\n  getRaggedRank: () => getRaggedRank,\n  getReductionAxes: () => getReductionAxes,\n  getReshaped: () => getReshaped,\n  getReshapedPermuted: () => getReshapedPermuted,\n  getRowPartitionTypesHelper: () => getRowPartitionTypesHelper,\n  getSliceBeginCoords: () => getSliceBeginCoords,\n  getSliceSize: () => getSliceSize,\n  getSparseFillEmptyRowsIndicesDenseShapeMismatch: () => getSparseFillEmptyRowsIndicesDenseShapeMismatch,\n  getSparseFillEmptyRowsNegativeIndexErrorMessage: () => getSparseFillEmptyRowsNegativeIndexErrorMessage,\n  getSparseFillEmptyRowsOutOfRangeIndexErrorMessage: () => getSparseFillEmptyRowsOutOfRangeIndexErrorMessage,\n  getSparseReshapeEmptyTensorZeroOutputDimErrorMessage: () => getSparseReshapeEmptyTensorZeroOutputDimErrorMessage,\n  getSparseReshapeInputOutputMismatchErrorMessage: () => getSparseReshapeInputOutputMismatchErrorMessage,\n  getSparseReshapeInputOutputMultipleErrorMessage: () => getSparseReshapeInputOutputMultipleErrorMessage,\n  getSparseReshapeMultipleNegativeOneOutputDimErrorMessage: () => getSparseReshapeMultipleNegativeOneOutputDimErrorMessage,\n  getSparseReshapeNegativeOutputDimErrorMessage: () => getSparseReshapeNegativeOutputDimErrorMessage,\n  getSparseSegmentReductionIndicesOutOfRangeErrorMessage: () => getSparseSegmentReductionIndicesOutOfRangeErrorMessage,\n  getSparseSegmentReductionNegativeSegmentIdsErrorMessage: () => getSparseSegmentReductionNegativeSegmentIdsErrorMessage,\n  getSparseSegmentReductionNonIncreasingSegmentIdsErrorMessage: () => getSparseSegmentReductionNonIncreasingSegmentIdsErrorMessage,\n  getSparseSegmentReductionSegmentIdOutOfRangeErrorMessage: () => getSparseSegmentReductionSegmentIdOutOfRangeErrorMessage,\n  getUndoAxesPermutation: () => getUndoAxesPermutation,\n  isIdentityPermutation: () => isIdentityPermutation,\n  log: () => log,\n  mergeRealAndImagArrays: () => mergeRealAndImagArrays,\n  prepareAndValidate: () => prepareAndValidate,\n  prepareSplitSize: () => prepareSplitSize,\n  segment_util: () => segment_util_exports,\n  shouldFuse: () => shouldFuse,\n  slice_util: () => slice_util_exports,\n  splitRealAndImagArrays: () => splitRealAndImagArrays,\n  tupleValuesAreOne: () => tupleValuesAreOne,\n  upcastType: () => upcastType,\n  validateDefaultValueShape: () => validateDefaultValueShape,\n  validateInput: () => validateInput,\n  validateUpdateShape: () => validateUpdateShape,\n  warn: () => warn\n});\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/concat_util.js\nfunction assertParamsConsistent(shapes, axis) {\n  const rank = shapes[0].length;\n  shapes.forEach((shape, i2) => {\n    assert(shape.length === rank, () => `Error in concat${rank}D: rank of tensors[${i2}] must be the same as the rank of the rest (${rank})`);\n  });\n  assert(axis >= 0 && axis < rank, () => `Error in concat${rank}D: axis must be between 0 and ${rank - 1}.`);\n  const firstShape = shapes[0];\n  shapes.forEach((shape, i2) => {\n    for (let r2 = 0; r2 < rank; r2++) {\n      assert(r2 === axis || shape[r2] === firstShape[r2], () => `Error in concat${rank}D: Shape of tensors[${i2}] (${shape}) does not match the shape of the rest (${firstShape}) along the non-concatenated axis ${i2}.`);\n    }\n  });\n}\nfunction computeOutShape2(shapes, axis) {\n  const outputShape = shapes[0].slice();\n  for (let i2 = 1; i2 < shapes.length; i2++) {\n    outputShape[axis] += shapes[i2][axis];\n  }\n  return outputShape;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/ragged_to_dense_util.js\nvar RowPartitionType;\n(function(RowPartitionType3) {\n  RowPartitionType3[RowPartitionType3[\"FIRST_DIM_SIZE\"] = 0] = \"FIRST_DIM_SIZE\";\n  RowPartitionType3[RowPartitionType3[\"VALUE_ROWIDS\"] = 1] = \"VALUE_ROWIDS\";\n  RowPartitionType3[RowPartitionType3[\"ROW_LENGTHS\"] = 2] = \"ROW_LENGTHS\";\n  RowPartitionType3[RowPartitionType3[\"ROW_SPLITS\"] = 3] = \"ROW_SPLITS\";\n  RowPartitionType3[RowPartitionType3[\"ROW_LIMITS\"] = 4] = \"ROW_LIMITS\";\n  RowPartitionType3[RowPartitionType3[\"ROW_STARTS\"] = 5] = \"ROW_STARTS\";\n})(RowPartitionType || (RowPartitionType = {}));\nfunction combineRaggedTensorToTensorShapes(raggedRank, shape, valueShape) {\n  let outputShape = new Array();\n  if (valueShape == null && shape == null) {\n    return outputShape;\n  }\n  if (shape == null) {\n    while (outputShape.length < raggedRank + valueShape.length) {\n      outputShape.push(-1);\n    }\n  } else {\n    outputShape = shape.slice();\n  }\n  if (valueShape == null) {\n    return outputShape;\n  }\n  if (raggedRank + valueShape.length !== outputShape.length) {\n    throw new Error(`rt input.shape and shape=${shape} are incompatible: rt input.rank = ${raggedRank + valueShape.length}, but shape.rank = ${outputShape.length}`);\n  }\n  for (let i2 = 1; i2 < valueShape.length; ++i2) {\n    const valueDim = valueShape[i2];\n    const outputShapeDimIndex = outputShape[outputShape.length - valueShape.length + i2];\n    const outputShapeDim = outputShape[outputShapeDimIndex];\n    if (valueDim >= 0) {\n      if (outputShapeDim >= 0) {\n        if (outputShapeDim !== valueDim) {\n          throw new Error(`rt input.shape and shape=${shape} are incompatible: rt input.shape[${i2 + raggedRank}] = ${valueDim} but shape[${i2 + raggedRank}] = ${outputShapeDim}`);\n        }\n      } else {\n        outputShape[outputShapeDimIndex] = valueDim;\n      }\n    }\n  }\n  return outputShape;\n}\nfunction getRowPartitionTypesHelper(rowPartitionTypeStrings) {\n  const stringToType = {\n    \"FIRST_DIM_SIZE\": RowPartitionType.FIRST_DIM_SIZE,\n    \"VALUE_ROWIDS\": RowPartitionType.VALUE_ROWIDS,\n    \"ROW_LENGTHS\": RowPartitionType.ROW_LENGTHS,\n    \"ROW_SPLITS\": RowPartitionType.ROW_SPLITS,\n    \"ROW_LIMITS\": RowPartitionType.ROW_LIMITS,\n    \"ROW_STARTS\": RowPartitionType.ROW_STARTS\n  };\n  const result = [];\n  for (const typeStr of rowPartitionTypeStrings) {\n    if (typeStr in stringToType) {\n      result.push(stringToType[typeStr]);\n    } else {\n      break;\n    }\n  }\n  return result;\n}\nfunction getRaggedRank(rowPartitionTypes) {\n  if (rowPartitionTypes.length === 0) {\n    return 0;\n  }\n  if (rowPartitionTypes[0] === RowPartitionType.FIRST_DIM_SIZE) {\n    return rowPartitionTypes.length - 1;\n  }\n  return rowPartitionTypes.length;\n}\nfunction validateDefaultValueShape(defaultValueShape, valueShape) {\n  if (defaultValueShape == null || valueShape == null) {\n    return;\n  }\n  const defaultNDims = defaultValueShape.length;\n  const valuesNDims = valueShape.length;\n  if (defaultNDims >= valuesNDims) {\n    throw new Error(`defaultValue.shape=${defaultValueShape} and ragged tensor flatValues.shape=${valueShape}, are incompatible: defaultValue.rank = ${defaultNDims} must be less than ragged tensor input flatValues.rank = ${valuesNDims})`);\n  }\n  for (let i2 = 0; i2 < Math.min(defaultNDims, valuesNDims - 1); ++i2) {\n    const defaultDim = defaultValueShape[i2];\n    const valueDim = valueShape[i2 + 1];\n    if (defaultDim >= 0 && valueDim >= 0 && defaultDim !== 1 && defaultDim !== valueDim) {\n      throw new Error(`defaultValue.shape=${defaultValueShape}, and ragged tensor input flatValues.shape=${valueShape} are incompatible: defaultValue.shape[${i2 - defaultValueShape.length}] = ${defaultDim} but ragged tensor input.flatValues.shape[${i2 - defaultValueShape.length}] = ${valueDim}`);\n    }\n  }\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/reduce_util.js\nvar PARALLELIZE_THRESHOLD = 30;\nfunction computeOptimalWindowSize(inSize) {\n  if (inSize <= PARALLELIZE_THRESHOLD) {\n    return inSize;\n  }\n  return nearestDivisor(inSize, Math.floor(Math.sqrt(inSize)));\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/rotate_util.js\nfunction getImageCenter(center, imageHeight, imageWidth) {\n  const centerX = imageWidth * (typeof center === \"number\" ? center : center[0]);\n  const centerY = imageHeight * (typeof center === \"number\" ? center : center[1]);\n  return [centerX, centerY];\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/array_ops_util.js\nfunction getReshaped(inputShape, blockShape, prod6, batchToSpace = true) {\n  let reshaped = [];\n  if (batchToSpace) {\n    reshaped = reshaped.concat(blockShape.slice(0));\n    reshaped.push(inputShape[0] / prod6);\n    reshaped = reshaped.concat(inputShape.slice(1));\n  } else {\n    reshaped = reshaped.concat(inputShape[0]);\n    const spatialLength = blockShape.length;\n    for (let i2 = 0; i2 < spatialLength; ++i2) {\n      reshaped = reshaped.concat([inputShape[i2 + 1] / blockShape[i2], blockShape[i2]]);\n    }\n    reshaped = reshaped.concat(inputShape.slice(spatialLength + 1));\n  }\n  return reshaped;\n}\nfunction getPermuted(reshapedRank, blockShapeRank, batchToSpace = true) {\n  const permuted = [];\n  if (batchToSpace) {\n    permuted.push(blockShapeRank);\n    for (let i2 = blockShapeRank + 1; i2 < reshapedRank; ++i2) {\n      if (i2 <= 2 * blockShapeRank) {\n        permuted.push(i2);\n        permuted.push(i2 - (blockShapeRank + 1));\n      } else {\n        permuted.push(i2);\n      }\n    }\n  } else {\n    const permutedBeforeBatch = [];\n    const permutedAfterBatch = [];\n    for (let i2 = 1; i2 < reshapedRank; ++i2) {\n      if (i2 >= blockShapeRank * 2 + 1 || i2 % 2 === 1) {\n        permutedAfterBatch.push(i2);\n      } else {\n        permutedBeforeBatch.push(i2);\n      }\n    }\n    permuted.push(...permutedBeforeBatch);\n    permuted.push(0);\n    permuted.push(...permutedAfterBatch);\n  }\n  return permuted;\n}\nfunction getReshapedPermuted(inputShape, blockShape, prod6, batchToSpace = true) {\n  const reshapedPermuted = [];\n  if (batchToSpace) {\n    reshapedPermuted.push(inputShape[0] / prod6);\n  } else {\n    reshapedPermuted.push(inputShape[0] * prod6);\n  }\n  for (let i2 = 1; i2 < inputShape.length; ++i2) {\n    if (i2 <= blockShape.length) {\n      if (batchToSpace) {\n        reshapedPermuted.push(blockShape[i2 - 1] * inputShape[i2]);\n      } else {\n        reshapedPermuted.push(inputShape[i2] / blockShape[i2 - 1]);\n      }\n    } else {\n      reshapedPermuted.push(inputShape[i2]);\n    }\n  }\n  return reshapedPermuted;\n}\nfunction getSliceBeginCoords(crops, blockShape) {\n  const sliceBeginCoords = [0];\n  for (let i2 = 0; i2 < blockShape; ++i2) {\n    sliceBeginCoords.push(crops[i2][0]);\n  }\n  return sliceBeginCoords;\n}\nfunction getSliceSize(uncroppedShape, crops, blockShape) {\n  const sliceSize = uncroppedShape.slice(0, 1);\n  for (let i2 = 0; i2 < blockShape; ++i2) {\n    sliceSize.push(uncroppedShape[i2 + 1] - crops[i2][0] - crops[i2][1]);\n  }\n  return sliceSize;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/selu_util.js\nvar SELU_SCALEALPHA = 1.7580993408473768;\nvar SELU_SCALE = 1.0507009873554805;\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/erf_util.js\nvar ERF_P = 0.3275911;\nvar ERF_A1 = 0.254829592;\nvar ERF_A2 = -0.284496736;\nvar ERF_A3 = 1.421413741;\nvar ERF_A4 = -1.453152027;\nvar ERF_A5 = 1.061405429;\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/backends/complex_util.js\nfunction mergeRealAndImagArrays(real5, imag5) {\n  if (real5.length !== imag5.length) {\n    throw new Error(`Cannot merge real and imag arrays of different lengths. real:${real5.length}, imag: ${imag5.length}.`);\n  }\n  const result = new Float32Array(real5.length * 2);\n  for (let i2 = 0; i2 < result.length; i2 += 2) {\n    result[i2] = real5[i2 / 2];\n    result[i2 + 1] = imag5[i2 / 2];\n  }\n  return result;\n}\nfunction splitRealAndImagArrays(complex5) {\n  const real5 = new Float32Array(complex5.length / 2);\n  const imag5 = new Float32Array(complex5.length / 2);\n  for (let i2 = 0; i2 < complex5.length; i2 += 2) {\n    real5[i2 / 2] = complex5[i2];\n    imag5[i2 / 2] = complex5[i2 + 1];\n  }\n  return { real: real5, imag: imag5 };\n}\nfunction complexWithEvenIndex(complex5) {\n  const len = Math.ceil(complex5.length / 4);\n  const real5 = new Float32Array(len);\n  const imag5 = new Float32Array(len);\n  for (let i2 = 0; i2 < complex5.length; i2 += 4) {\n    real5[Math.floor(i2 / 4)] = complex5[i2];\n    imag5[Math.floor(i2 / 4)] = complex5[i2 + 1];\n  }\n  return { real: real5, imag: imag5 };\n}\nfunction complexWithOddIndex(complex5) {\n  const len = Math.floor(complex5.length / 4);\n  const real5 = new Float32Array(len);\n  const imag5 = new Float32Array(len);\n  for (let i2 = 2; i2 < complex5.length; i2 += 4) {\n    real5[Math.floor(i2 / 4)] = complex5[i2];\n    imag5[Math.floor(i2 / 4)] = complex5[i2 + 1];\n  }\n  return { real: real5, imag: imag5 };\n}\nfunction getComplexWithIndex(complex5, index) {\n  const real5 = complex5[index * 2];\n  const imag5 = complex5[index * 2 + 1];\n  return { real: real5, imag: imag5 };\n}\nfunction assignToTypedArray(data, real5, imag5, index) {\n  data[index * 2] = real5;\n  data[index * 2 + 1] = imag5;\n}\nfunction exponents(n2, inverse) {\n  const real5 = new Float32Array(n2 / 2);\n  const imag5 = new Float32Array(n2 / 2);\n  for (let i2 = 0; i2 < Math.ceil(n2 / 2); i2++) {\n    const x = (inverse ? 2 : -2) * Math.PI * (i2 / n2);\n    real5[i2] = Math.cos(x);\n    imag5[i2] = Math.sin(x);\n  }\n  return { real: real5, imag: imag5 };\n}\nfunction exponent(k, n2, inverse) {\n  const x = (inverse ? 2 : -2) * Math.PI * (k / n2);\n  const real5 = Math.cos(x);\n  const imag5 = Math.sin(x);\n  return { real: real5, imag: imag5 };\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/backends/einsum_util.js\nvar ARROW = \"->\";\nvar ARROW_REGEX = /->/g;\nvar COMMA = \",\";\nvar ELLIPSIS = \"...\";\nfunction decodeEinsumEquation(equation, numTensors) {\n  equation = equation.replace(/\\s/g, \"\");\n  const numArrows = (equation.length - equation.replace(ARROW_REGEX, \"\").length) / ARROW.length;\n  if (numArrows < 1) {\n    throw new Error(\"Equations without an arrow are not supported.\");\n  } else if (numArrows > 1) {\n    throw new Error(`Equation must contain exactly one arrow (\"${ARROW}\").`);\n  }\n  const [inputString, outputString] = equation.split(ARROW);\n  assert(inputString.indexOf(ELLIPSIS) === -1, () => `The ellipsis notation (\"${ELLIPSIS}\") is not supported yet.`);\n  const inputTerms = inputString.split(COMMA);\n  const numInputs = inputTerms.length;\n  if (numTensors !== numInputs) {\n    throw new Error(`Expected ${numInputs} input tensors, received ${numTensors}`);\n  }\n  if (numInputs > 2) {\n    throw new Error(\"Support for more than 2 input tensors is not implemented yet.\");\n  }\n  const allDims = [];\n  for (let i2 = 0; i2 < outputString.length; ++i2) {\n    const dimName = outputString[i2];\n    if (!inputTerms.some((inputTerm) => inputTerm.indexOf(dimName) !== -1)) {\n      throw new Error(`Output subscripts contain the label ${dimName} not present in the input subscripts.`);\n    }\n    if (allDims.indexOf(dimName) === -1) {\n      allDims.push(dimName);\n    }\n  }\n  for (let i2 = 0; i2 < inputString.length; ++i2) {\n    const dimName = inputString[i2];\n    if (allDims.indexOf(dimName) === -1 && dimName !== COMMA) {\n      allDims.push(dimName);\n    }\n  }\n  const idDims = new Array(inputTerms.length);\n  for (let i2 = 0; i2 < numInputs; ++i2) {\n    if (new Set(inputTerms[i2].split(\"\")).size !== inputTerms[i2].length) {\n      throw new Error(`Found duplicate axes in input component ${inputTerms[i2]}. Support for duplicate axes in input is not implemented yet.`);\n    }\n    idDims[i2] = [];\n    for (let j = 0; j < inputTerms[i2].length; ++j) {\n      idDims[i2].push(allDims.indexOf(inputTerms[i2][j]));\n    }\n  }\n  const numDims = allDims.length;\n  const numOutDims = outputString.length;\n  const summedDims = [];\n  for (let i2 = numOutDims; i2 < numDims; ++i2) {\n    summedDims.push(i2);\n  }\n  return { allDims, summedDims, idDims };\n}\nfunction getEinsumPermutation(nDims, idDims) {\n  let permutationIndices = new Array(nDims);\n  permutationIndices.fill(-1);\n  for (let i2 = 0; i2 < idDims.length; ++i2) {\n    permutationIndices[idDims[i2]] = i2;\n  }\n  const expandDims7 = [];\n  for (let i2 = 0; i2 < nDims; ++i2) {\n    if (permutationIndices[i2] === -1) {\n      expandDims7.push(i2);\n    }\n  }\n  permutationIndices = permutationIndices.filter((d) => d !== -1);\n  return { permutationIndices, expandDims: expandDims7 };\n}\nfunction checkEinsumDimSizes(nDims, idDims, tensors) {\n  const dimSizes = new Array(nDims);\n  for (let i2 = 0; i2 < tensors.length; ++i2) {\n    const shape = tensors[i2].shape;\n    for (let j = 0; j < idDims[i2].length; ++j) {\n      if (dimSizes[idDims[i2][j]] === void 0) {\n        dimSizes[idDims[i2][j]] = shape[j];\n      } else {\n        assert(dimSizes[idDims[i2][j]] === shape[j], () => `Expected dimension ${dimSizes[idDims[i2][j]]} at axis ${j} of input shaped ${JSON.stringify(shape)}, but got dimension ${shape[j]}`);\n      }\n    }\n  }\n}\nfunction getEinsumComputePath(summedDims, idDims) {\n  const path = summedDims;\n  const steps = [];\n  let nSteps = 0;\n  if (summedDims.length === 0) {\n    path.push(-1);\n  }\n  nSteps = summedDims.length + 1;\n  for (let i2 = 0; i2 < nSteps; ++i2) {\n    steps.push([]);\n  }\n  const computedTermIndices = [];\n  for (let i2 = 0; i2 < path.length; ++i2) {\n    const summedDim = path[i2];\n    const termIndices = findTermsWithDim(idDims, summedDim);\n    for (const termIndex of termIndices) {\n      if (computedTermIndices.indexOf(termIndex) === -1) {\n        steps[i2].push(termIndex);\n        computedTermIndices.push(termIndex);\n      }\n    }\n  }\n  return { path, steps };\n}\nfunction isIdentityPermutation(perm) {\n  return perm.every((dim, index) => dim === index);\n}\nfunction findTermsWithDim(idDims, dim) {\n  const termIndices = [];\n  for (let i2 = 0; i2 < idDims.length; ++i2) {\n    if (idDims[i2].length === 0 || idDims[i2].indexOf(dim) !== -1 || dim === -1) {\n      termIndices.push(i2);\n    }\n  }\n  return termIndices;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/split_util.js\nfunction prepareSplitSize(x, numOrSizeSplits, axis = 0) {\n  let splitSizes = [];\n  if (typeof numOrSizeSplits === \"number\") {\n    assert(x.shape[axis] % numOrSizeSplits === 0, () => \"Number of splits must evenly divide the axis.\");\n    splitSizes = new Array(numOrSizeSplits).fill(x.shape[axis] / numOrSizeSplits);\n  } else {\n    const numOfNegs = numOrSizeSplits.reduce((count2, value) => {\n      if (value === -1) {\n        count2 += 1;\n      }\n      return count2;\n    }, 0);\n    assert(numOfNegs <= 1, () => \"There should be only one negative value in split array.\");\n    const negIndex = numOrSizeSplits.indexOf(-1);\n    if (negIndex !== -1) {\n      const total = numOrSizeSplits.reduce((a, b) => b > 0 ? a + b : a);\n      numOrSizeSplits[negIndex] = x.shape[axis] - total;\n    }\n    assert(x.shape[axis] === numOrSizeSplits.reduce((a, b) => a + b), () => \"The sum of sizes must match the size of the axis dimension.\");\n    splitSizes = numOrSizeSplits;\n  }\n  return splitSizes;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/sparse/sparse_fill_empty_rows_util.js\nfunction getSparseFillEmptyRowsIndicesDenseShapeMismatch(indicesLength) {\n  return `Received SparseTensor with denseShape[0] = 0 but\n  indices.shape[0] = ${indicesLength}`;\n}\nfunction getSparseFillEmptyRowsNegativeIndexErrorMessage(index, value) {\n  return `indices(${index}, 0) is invalid: ${value} < 0`;\n}\nfunction getSparseFillEmptyRowsOutOfRangeIndexErrorMessage(index, value, limit) {\n  return `indices(${index}, 0) is invalid: ${value} >= ${limit}`;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/sparse/sparse_reshape_util.js\nfunction getSparseReshapeMultipleNegativeOneOutputDimErrorMessage(dim1, dim2) {\n  return `only one output dimension may be -1, not both ${dim1} and ${dim2}`;\n}\nfunction getSparseReshapeNegativeOutputDimErrorMessage(dim, value) {\n  return `size ${dim} must be non-negative, not ${value}`;\n}\nfunction getSparseReshapeEmptyTensorZeroOutputDimErrorMessage() {\n  return \"reshape cannot infer the missing input size for an empty tensor unless all specified input sizes are non-zero\";\n}\nfunction getSparseReshapeInputOutputMultipleErrorMessage(inputShape, outputShape) {\n  const inputSize = sizeFromShape(inputShape);\n  const outputSize = sizeFromShape(outputShape);\n  return `Input to reshape is a SparseTensor with ${inputSize}\n  dense values, but the requested shape requires a multiple of ${outputSize}. inputShape=${inputShape} outputShape= ${outputShape}`;\n}\nfunction getSparseReshapeInputOutputMismatchErrorMessage(inputShape, outputShape) {\n  const inputSize = sizeFromShape(inputShape);\n  const outputSize = sizeFromShape(outputShape);\n  return `Input to reshape is a tensor with ${inputSize} dense values, but the requested shape has ${outputSize}. inputShape=${inputShape} outputShape=${outputShape}`;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/sparse/sparse_segment_reduction_util.js\nfunction getSparseSegmentReductionNegativeSegmentIdsErrorMessage() {\n  return `segment ids must be >= 0`;\n}\nfunction getSparseSegmentReductionNonIncreasingSegmentIdsErrorMessage() {\n  return `segment ids are not increasing`;\n}\nfunction getSparseSegmentReductionSegmentIdOutOfRangeErrorMessage(segmentId, outputRows) {\n  return `Segment id ${segmentId} out of range [0, ${outputRows}), possibly because segmentIds input is not sorted.`;\n}\nfunction getSparseSegmentReductionIndicesOutOfRangeErrorMessage(index, indexValue, inputRows) {\n  return `Bad: indices[${index}] == ${indexValue} out of range [0, ${inputRows})`;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/segment_util.js\nvar segment_util_exports = {};\n__export(segment_util_exports, {\n  collectGatherOpShapeInfo: () => collectGatherOpShapeInfo,\n  computeOutShape: () => computeOutShape3,\n  segOpComputeOptimalWindowSize: () => segOpComputeOptimalWindowSize\n});\nfunction segOpComputeOptimalWindowSize(inSize, numSegments) {\n  let done = false;\n  let res;\n  if (inSize <= PARALLELIZE_THRESHOLD) {\n    res = inSize;\n    done = true;\n  } else {\n    res = nearestDivisor(inSize, Math.floor(Math.sqrt(inSize)));\n  }\n  while (!done) {\n    if (res > numSegments || res === inSize) {\n      done = true;\n    } else {\n      res = nearestDivisor(inSize, res + 1);\n    }\n  }\n  return res;\n}\nfunction computeOutShape3(aShape, axis, numSegments) {\n  const outShape = [];\n  const rank = aShape.length;\n  for (let dim = 0; dim < rank; dim++) {\n    if (dim !== axis) {\n      outShape.push(aShape[dim]);\n    } else {\n      outShape.push(numSegments);\n    }\n  }\n  return outShape;\n}\nfunction collectGatherOpShapeInfo(x, indices, axis, batchDims) {\n  const indicesRank = indices.shape.length;\n  const xRank = x.shape.length;\n  if (batchDims !== 0) {\n    if (batchDims < -indicesRank || batchDims > indicesRank) {\n      throw new Error(`Expect batchDims in the range of [-${indicesRank}, ${indicesRank}], but got ${batchDims}`);\n    }\n  }\n  if (batchDims < 0) {\n    batchDims += indicesRank;\n  }\n  if (batchDims > xRank) {\n    throw new Error(`batchDims (${batchDims}) must be less than rank(x) (\n    ${xRank}).`);\n  }\n  if (axis < batchDims) {\n    throw new Error(`batchDims (${batchDims}) must be less than or equal to axis (${axis}).`);\n  }\n  for (let i2 = 0; i2 < batchDims; ++i2) {\n    if (x.shape[i2] !== indices.shape[i2]) {\n      throw new Error(`x.shape[${i2}]: ${x.shape[i2]} should be equal to indices.shape[${i2}]: ${indices.shape[i2]}.`);\n    }\n  }\n  const dimSize = x.shape[axis];\n  const outputShape = [];\n  let batchSize = 1;\n  let outerSize = 1;\n  let sliceSize = 1;\n  for (let i2 = 0; i2 < batchDims; ++i2) {\n    outputShape.push(x.shape[i2]);\n    batchSize *= x.shape[i2];\n  }\n  for (let i2 = batchDims; i2 < axis; i2++) {\n    outputShape.push(x.shape[i2]);\n    outerSize *= x.shape[i2];\n  }\n  for (let i2 = batchDims; i2 < indicesRank; i2++) {\n    outputShape.push(indices.shape[i2]);\n  }\n  for (let i2 = axis + 1; i2 < xRank; i2++) {\n    outputShape.push(x.shape[i2]);\n    sliceSize *= x.shape[i2];\n  }\n  return { batchSize, sliceSize, outerSize, dimSize, outputShape };\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/backends/backend_util.js\nfunction fromUint8ToStringArray(vals) {\n  try {\n    return vals.map((val) => decodeString(val));\n  } catch (err) {\n    throw new Error(`Failed to decode encoded string bytes into utf-8, error: ${err}`);\n  }\n}\nfunction fromStringArrayToUint8(strings) {\n  return strings.map((s2) => encodeString(s2));\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/backends/kernel_impls.js\nvar kernel_impls_exports = {};\n__export(kernel_impls_exports, {\n  nonMaxSuppressionV3Impl: () => nonMaxSuppressionV3Impl,\n  nonMaxSuppressionV4Impl: () => nonMaxSuppressionV4Impl,\n  nonMaxSuppressionV5Impl: () => nonMaxSuppressionV5Impl,\n  whereImpl: () => whereImpl\n});\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Abs_grad.js\nvar absGradConfig = {\n  kernelName: Abs,\n  inputsToSave: [\"x\"],\n  gradFunc: (dy, saved) => {\n    const [x] = saved;\n    return { x: () => mul(dy, step(cast(x, \"float32\"), -1)) };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Acos_grad.js\nvar acosGradConfig = {\n  kernelName: Acos,\n  inputsToSave: [\"x\"],\n  gradFunc: (dy, saved) => {\n    const [x] = saved;\n    return {\n      x: () => {\n        const a = square(cast(x, \"float32\"));\n        const b = sqrt(sub(scalar(1), a));\n        return neg(div(dy, b));\n      }\n    };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Acosh_grad.js\nvar acoshGradConfig = {\n  kernelName: Acosh,\n  inputsToSave: [\"x\"],\n  gradFunc: (dy, saved) => {\n    const [x] = saved;\n    return {\n      x: () => {\n        const a = sqrt(sub(square(cast(x, \"float32\")), 1));\n        return div(dy, a);\n      }\n    };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Add_grad.js\nvar addGradConfig = {\n  kernelName: Add,\n  inputsToSave: [\"a\", \"b\"],\n  gradFunc: (dy, saved) => {\n    const [a, b] = saved;\n    const outShape = assertAndGetBroadcastShape(a.shape, b.shape);\n    const derA = () => {\n      let res = dy;\n      const reduceAxes = getReductionAxes(a.shape, outShape);\n      if (reduceAxes.length > 0) {\n        res = sum2(res, reduceAxes);\n      }\n      return reshape(res, a.shape);\n    };\n    const derB = () => {\n      let res = dy;\n      const reduceAxes = getReductionAxes(b.shape, outShape);\n      if (reduceAxes.length > 0) {\n        res = sum2(res, reduceAxes);\n      }\n      return reshape(res, b.shape);\n    };\n    return { a: derA, b: derB };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/gradients/AddN_grad.js\nvar addNGradConfig = {\n  kernelName: AddN,\n  saveAllInputs: true,\n  gradFunc: (dy, saved) => {\n    const ders = {};\n    saved.forEach((_, i2) => {\n      ders[i2] = () => dy.clone();\n    });\n    return ders;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/gradients/ArgMax_grad.js\nvar argMaxGradConfig = {\n  kernelName: ArgMax,\n  inputsToSave: [\"x\"],\n  gradFunc: (dy, saved) => {\n    const [x] = saved;\n    return { x: () => zerosLike(x) };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/gradients/ArgMin_grad.js\nvar argMinGradConfig = {\n  kernelName: ArgMin,\n  inputsToSave: [\"x\"],\n  gradFunc: (dy, saved) => {\n    const [x] = saved;\n    return { x: () => zerosLike(x) };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Asin_grad.js\nvar asinGradConfig = {\n  kernelName: Asin,\n  inputsToSave: [\"x\"],\n  gradFunc: (dy, saved) => {\n    const [x] = saved;\n    return { x: () => div(dy, sqrt(sub(scalar(1), square(cast(x, \"float32\"))))) };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Asinh_grad.js\nvar asinhGradConfig = {\n  kernelName: Asinh,\n  inputsToSave: [\"x\"],\n  gradFunc: (dy, saved) => {\n    const [x] = saved;\n    return {\n      x: () => {\n        const a = sqrt(add2(scalar(1), square(cast(x, \"float32\"))));\n        return div(dy, a);\n      }\n    };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Atan2_grad.js\nvar atan2GradConfig = {\n  kernelName: Atan2,\n  inputsToSave: [\"a\", \"b\"],\n  gradFunc: (dy, saved) => {\n    const [a, b] = saved;\n    const outShape = assertAndGetBroadcastShape(a.shape, b.shape);\n    const derA = () => {\n      const d = add2(square(a), square(b));\n      let res = mul(dy, div(b, d));\n      const reduceAxes = getReductionAxes(a.shape, outShape);\n      if (reduceAxes.length > 0) {\n        res = sum2(res, reduceAxes);\n      }\n      return reshape(res, a.shape);\n    };\n    const derB = () => {\n      const d = add2(square(a), square(b));\n      let res = neg(mul(dy, div(a, d)));\n      const reduceAxes = getReductionAxes(b.shape, outShape);\n      if (reduceAxes.length > 0) {\n        res = sum2(res, reduceAxes);\n      }\n      return reshape(res, b.shape);\n    };\n    return { a: derA, b: derB };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Atan_grad.js\nvar atanGradConfig = {\n  kernelName: Atan,\n  inputsToSave: [\"x\"],\n  gradFunc: (dy, saved) => {\n    const [x] = saved;\n    return { x: () => div(dy, add2(square(cast(x, \"float32\")), 1)) };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Atanh_grad.js\nvar atanhGradConfig = {\n  kernelName: Atanh,\n  inputsToSave: [\"x\"],\n  gradFunc: (dy, saved) => {\n    const [x] = saved;\n    return { x: () => div(dy, sub(scalar(1), square(cast(x, \"float32\")))) };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/avg_pool_3d_grad.js\nfunction avgPool3dGrad_(dy, input2, filterSize, strides, pad3, dimRoundingMode) {\n  const $dy = convertToTensor(dy, \"dy\", \"avgPool3dGrad\");\n  const $input = convertToTensor(input2, \"input\", \"avgPool3dGrad\");\n  let dy5D = $dy;\n  let input5D = $input;\n  let reshapedTo5D = false;\n  if ($input.rank === 4) {\n    reshapedTo5D = true;\n    dy5D = reshape($dy, [1, $dy.shape[0], $dy.shape[1], $dy.shape[2], $dy.shape[3]]);\n    input5D = reshape($input, [\n      1,\n      $input.shape[0],\n      $input.shape[1],\n      $input.shape[2],\n      $input.shape[3]\n    ]);\n  }\n  assert(dy5D.rank === 5, () => `Error in avgPool3dGrad: dy must be rank 5 but got rank ${dy5D.rank}.`);\n  assert(input5D.rank === 5, () => `Error in avgPool3dGrad: input must be rank 5 but got rank ${input5D.rank}.`);\n  checkPadOnDimRoundingMode(\"avgPool3dGrad\", pad3, dimRoundingMode);\n  const inputs = { dy: dy5D, input: input5D };\n  const attrs = { filterSize, strides, pad: pad3, dimRoundingMode };\n  const res = ENGINE.runKernel(AvgPool3DGrad, inputs, attrs);\n  if (reshapedTo5D) {\n    return reshape(res, [res.shape[1], res.shape[2], res.shape[3], res.shape[4]]);\n  }\n  return res;\n}\nvar avgPool3dGrad = op({ avgPool3dGrad_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/gradients/AvgPool3D_grad.js\nvar avgPool3DGradConfig = {\n  kernelName: AvgPool3D,\n  inputsToSave: [\"x\"],\n  gradFunc: (dy, saved, attrs) => {\n    const [x] = saved;\n    const { filterSize, strides, pad: pad3, dimRoundingMode } = attrs;\n    return {\n      x: () => avgPool3dGrad(dy, x, filterSize, strides, pad3, dimRoundingMode)\n    };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/avg_pool_grad.js\nfunction avgPoolGrad_(dy, input2, filterSize, strides, pad3) {\n  const $dy = convertToTensor(dy, \"dy\", \"avgPoolGrad\");\n  const $input = convertToTensor(input2, \"input\", \"avgPoolGrad\");\n  assert($input.rank === $dy.rank, () => `Rank of input (${$input.rank}) does not match rank of dy (${$dy.rank})`);\n  let input4D = $input;\n  let dy4D = $dy;\n  let reshapedTo4D = false;\n  if ($input.rank === 3) {\n    reshapedTo4D = true;\n    input4D = reshape($input, [1, $input.shape[0], $input.shape[1], $input.shape[2]]);\n    dy4D = reshape($dy, [1, $dy.shape[0], $dy.shape[1], $dy.shape[2]]);\n  }\n  assert(dy4D.rank === 4, () => `Error in avgPoolGrad: dy must be rank 4 but got rank ${dy4D.rank}.`);\n  assert(input4D.rank === 4, () => `Error in avgPoolGrad: input must be rank 4 but got rank ${input4D.rank}.`);\n  const inputs = { dy: dy4D, input: input4D };\n  const attrs = { filterSize, strides, pad: pad3 };\n  const res = ENGINE.runKernel(AvgPoolGrad, inputs, attrs);\n  if (reshapedTo4D) {\n    return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);\n  }\n  return res;\n}\nvar avgPoolGrad = op({ avgPoolGrad_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/gradients/AvgPool_grad.js\nvar avgPoolGradConfig = {\n  kernelName: AvgPool,\n  inputsToSave: [\"x\"],\n  gradFunc: (dy, saved, attrs) => {\n    const [x] = saved;\n    const { filterSize, strides, pad: pad3 } = attrs;\n    return { x: () => avgPoolGrad(dy, x, filterSize, strides, pad3) };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/gradients/BatchMatMul_grad.js\nvar batchMatMulGradConfig = {\n  kernelName: BatchMatMul,\n  inputsToSave: [\"a\", \"b\"],\n  gradFunc: (dy, saved, attrs) => {\n    const [a, b] = saved;\n    const { transposeA, transposeB } = attrs;\n    if (!transposeA && !transposeB) {\n      return {\n        a: () => matMul(dy, b, false, true),\n        b: () => matMul(a, dy, true, false)\n      };\n    } else if (!transposeA && transposeB) {\n      return {\n        a: () => matMul(dy, b, false, false),\n        b: () => matMul(dy, a, true, false)\n      };\n    } else if (transposeA && !transposeB) {\n      return {\n        a: () => matMul(b, dy, false, true),\n        b: () => matMul(a, dy, false, false)\n      };\n    } else {\n      return {\n        a: () => matMul(b, dy, true, true),\n        b: () => matMul(dy, a, true, true)\n      };\n    }\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/gradients/BatchToSpaceND_grad.js\nvar batchToSpaceNDGradConfig = {\n  kernelName: BatchToSpaceND,\n  gradFunc: (dy, saved, attrs) => {\n    const { blockShape, crops } = attrs;\n    return { x: () => spaceToBatchND(dy, blockShape, crops) };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/gradients/BroadcastTo_grad.js\nvar broadcastToGradConfig = {\n  kernelName: BroadcastTo,\n  gradFunc: (dy, saved, attrs) => {\n    const broadCastToAttrs = attrs;\n    const inputShape = broadCastToAttrs.inputShape;\n    const outputShape = broadCastToAttrs.shape;\n    const reps = Array.from(outputShape);\n    for (let i2 = inputShape.length - 1; i2 >= 0; i2--) {\n      if (inputShape[i2] === outputShape[i2]) {\n        reps[i2] = 1;\n      } else if (inputShape[i2] !== 1) {\n        throw new Error(`broadcastTo(): [${inputShape}] cannot be broadcast to [${outputShape}].`);\n      }\n    }\n    const axes = [];\n    for (let i2 = 0; i2 < reps.length; i2++) {\n      if (reps[i2] > 1) {\n        axes.push(i2);\n      }\n    }\n    return { x: () => sum2(dy, axes, true) };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Cast_grad.js\nvar castGradConfig = {\n  kernelName: Cast,\n  gradFunc: (dy) => {\n    return { x: () => dy.clone() };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Ceil_grad.js\nvar ceilGradConfig = {\n  kernelName: Ceil,\n  gradFunc: (dy) => {\n    return { x: () => zerosLike(dy) };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/gradients/ClipByValue_grad.js\nvar clipByValueGradConfig = {\n  kernelName: ClipByValue,\n  inputsToSave: [\"x\"],\n  gradFunc: (dy, saved, attrs) => {\n    const [x] = saved;\n    const { clipValueMin, clipValueMax } = attrs;\n    return {\n      x: () => where(logicalAnd(greaterEqual(x, clipValueMin), lessEqual(x, clipValueMax)), dy, zerosLike(dy))\n    };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/gradients/ComplexAbs_grad.js\nvar complexAbsGradConfig = {\n  kernelName: ComplexAbs,\n  inputsToSave: [\"x\"],\n  gradFunc: absGradConfig.gradFunc\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Concat_grad.js\nvar concatGradConfig = {\n  kernelName: Concat,\n  saveAllInputs: true,\n  gradFunc: (dy, saved, attrs) => {\n    const shapes = saved.map((t2) => t2.shape);\n    const { axis } = attrs;\n    const $axis = parseAxisParam(axis, saved[0].shape)[0];\n    const sizeSplits = shapes.map((s2) => s2[$axis]);\n    const derTensors = split(dy, sizeSplits, $axis);\n    return derTensors.map((t2) => () => t2);\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Conv2D_grad.js\nvar conv2DGradConfig = {\n  kernelName: Conv2D,\n  inputsToSave: [\"x\", \"filter\"],\n  gradFunc: (dy, saved, attrs) => {\n    const [x4D, $filter] = saved;\n    const { dilations, strides, pad: pad3, dataFormat } = attrs;\n    assert(tupleValuesAreOne(dilations), () => `Error in gradient of conv2D: dilation rates greater than 1 are not yet supported in gradients. Got dilations '${dilations}'`);\n    return {\n      x: () => conv2DBackpropInput(x4D.shape, dy, $filter, strides, pad3, dataFormat),\n      filter: () => conv2DBackpropFilter(x4D, dy, $filter.shape, strides, pad3, dataFormat)\n    };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Conv2DBackpropInput_grad.js\nvar conv2DBackpropInputGradConfig = {\n  kernelName: Conv2DBackpropInput,\n  inputsToSave: [\"dy\", \"filter\"],\n  gradFunc: (ddx, saved, attrs) => {\n    const [dy, filter] = saved;\n    const { strides, pad: pad3, dataFormat, dimRoundingMode } = attrs;\n    return {\n      dy: () => conv2d(ddx, filter, strides, pad3, dataFormat, 1, dimRoundingMode),\n      filter: () => conv2DBackpropFilter(ddx, dy, filter.shape, strides, pad3, dataFormat, dimRoundingMode)\n    };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/conv3d_backprop_filter.js\nfunction conv3DBackpropFilter_(x, dy, filterShape, strides, pad3) {\n  let x5D = x;\n  if (x.rank === 4) {\n    x5D = reshape(x, [1, x.shape[0], x.shape[1], x.shape[2], x.shape[3]]);\n  }\n  let dy5D = dy;\n  if (dy5D.rank === 4) {\n    dy5D = reshape(dy, [1, dy.shape[0], dy.shape[1], dy.shape[2], dy.shape[3]]);\n  }\n  assert(x5D.rank === 5, () => `Error in conv3dDerFilter: input must be rank 5, but got shape ${x5D.shape}.`);\n  assert(dy5D.rank === 5, () => `Error in conv3dDerFilter: dy must be rank 5, but got shape ${dy5D.shape}.`);\n  assert(filterShape.length === 5, () => `Error in conv3dDerFilter: filterShape must be length 5, but got ${filterShape}.`);\n  assert(x5D.shape[4] === filterShape[3], () => `Error in conv3dDerFilter: depth of input ${x5D.shape[4]}) must match input depth in filter (${filterShape[3]}.`);\n  assert(dy5D.shape[4] === filterShape[4], () => `Error in conv3dDerFilter: depth of dy (${dy5D.shape[4]}) must match output depth for filter (${filterShape[4]}).`);\n  const inputs = { x: x5D, dy: dy5D };\n  const attrs = { strides, pad: pad3, filterShape };\n  return ENGINE.runKernel(Conv3DBackpropFilterV2, inputs, attrs);\n}\nvar conv3DBackpropFilter = op({ conv3DBackpropFilter_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Conv3D_grad.js\nvar conv3DGradConfig = {\n  kernelName: Conv3D,\n  inputsToSave: [\"x\", \"filter\"],\n  gradFunc: (dy, saved, attrs) => {\n    const { dilations, strides, pad: pad3 } = attrs;\n    assert(tupleValuesAreOne(dilations), () => `Error in gradient of conv3D: dilation rates greater than 1 are not yet supported in gradients. Got dilations '${dilations}'`);\n    const [x5D, $filter] = saved;\n    return {\n      x: () => conv3DBackpropInput(x5D.shape, dy, $filter, strides, pad3),\n      filter: () => conv3DBackpropFilter(x5D, dy, $filter.shape, strides, pad3)\n    };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Cos_grad.js\nvar cosGradConfig = {\n  kernelName: Cos,\n  inputsToSave: [\"x\"],\n  gradFunc: (dy, saved) => {\n    const [x] = saved;\n    return { x: () => mul(neg(sin(cast(x, \"float32\"))), dy) };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Cosh_grad.js\nvar coshGradConfig = {\n  kernelName: Cosh,\n  inputsToSave: [\"x\"],\n  gradFunc: (dy, saved) => {\n    const [x] = saved;\n    return { x: () => mul(sinh(cast(x, \"float32\")), dy) };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Cumsum_grad.js\nvar cumsumGradConfig = {\n  kernelName: Cumsum,\n  inputsToSave: [\"x\"],\n  gradFunc: (dy, saved, attrs) => {\n    const [x] = saved;\n    const { axis, exclusive, reverse: reverse5 } = attrs;\n    return {\n      x: () => {\n        const permutation = getAxesPermutation([axis], x.rank);\n        let out = cumsum(dy, axis, exclusive, !reverse5);\n        if (permutation != null) {\n          out = transpose(out, permutation);\n        }\n        return out;\n      }\n    };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/gradients/DepthwiseConv2dNative_grad.js\nvar depthwiseConv2dNativeGradConfig = {\n  kernelName: DepthwiseConv2dNative,\n  inputsToSave: [\"x\", \"filter\"],\n  gradFunc: (dy, saved, attrs) => {\n    const { dilations, strides, pad: pad3, dimRoundingMode } = attrs;\n    const $dilations = dilations == null ? [1, 1] : dilations;\n    assert(tupleValuesAreOne($dilations), () => `Error in gradient of depthwiseConv2dNative: dilation rates greater than 1 are not yet supported. Got dilations '${$dilations}'`);\n    const [x, filter] = saved;\n    assert(x.rank === 4, () => `Error in gradient of depthwiseConv2dNative: input must be rank 4, but got rank ${x.rank}.`);\n    assert(filter.rank === 4, () => `Error in gradient of depthwiseConv2dNative: filter must be rank 4, but got rank ${filter.rank}.`);\n    assert(x.shape[3] === filter.shape[2], () => `Error in gradient of depthwiseConv2d: number of input channels (${x.shape[3]}) must match the inChannels dimension in filter ${filter.shape[2]}.`);\n    assert(eitherStridesOrDilationsAreOne(strides, $dilations), () => `Error in gradient of depthwiseConv2d: Either strides or dilations must be  1. Got strides ${strides} and dilations '${$dilations}'.`);\n    checkPadOnDimRoundingMode(\"depthwiseConv2d\", pad3, dimRoundingMode);\n    return {\n      x: () => depthwiseConv2dNativeBackpropInput(x.shape, dy, filter, strides, pad3, $dilations, dimRoundingMode),\n      filter: () => depthwiseConv2dNativeBackpropFilter(x, dy, filter.shape, strides, pad3, $dilations, dimRoundingMode)\n    };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Dilation2D_grad.js\nvar dilation2dGradConfig = {\n  kernelName: Dilation2D,\n  inputsToSave: [\"x\", \"filter\"],\n  gradFunc: (dy, saved, attrs) => {\n    const [x, filter] = saved;\n    const inputInputs = { x, filter, dy };\n    const filterInputs = { x, filter, dy };\n    return {\n      x: () => ENGINE.runKernel(Dilation2DBackpropInput, inputInputs, attrs),\n      filter: () => ENGINE.runKernel(Dilation2DBackpropFilter, filterInputs, attrs)\n    };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Elu_grad.js\nvar eluGradConfig = {\n  kernelName: Elu,\n  outputsToSave: [true],\n  gradFunc: (dy, saved) => {\n    const [y] = saved;\n    const inputs = { dy, y };\n    return { x: () => ENGINE.runKernel(EluGrad, inputs) };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Erf_grad.js\nvar erfGradConfig = {\n  kernelName: Erf,\n  inputsToSave: [\"x\"],\n  gradFunc: (dy, saved) => {\n    const [x] = saved;\n    const a = mul(exp(neg(square(x))), 2 / Math.sqrt(Math.PI));\n    return { x: () => mul(dy, a) };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Exp_grad.js\nvar expGradConfig = {\n  kernelName: Exp,\n  outputsToSave: [true],\n  gradFunc: (dy, saved) => {\n    const [y] = saved;\n    return { x: () => mul(dy, y) };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/gradients/ExpandDims_grad.js\nvar expandDimsGradConfig = {\n  kernelName: ExpandDims,\n  inputsToSave: [\"input\"],\n  gradFunc: (dy, saved) => {\n    const [input2] = saved;\n    return { input: () => reshape(dy, input2.shape) };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Expm1_grad.js\nvar expm1GradConfig = {\n  kernelName: Expm1,\n  inputsToSave: [\"x\"],\n  gradFunc: (dy, saved) => {\n    const [x] = saved;\n    return { x: () => mul(dy, exp(x)) };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Floor_grad.js\nvar floorGradConfig = {\n  kernelName: Floor,\n  gradFunc: (dy) => {\n    return { x: () => zerosLike(dy) };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/gradients/FloorDiv_grad.js\nvar floorDivGradConfig = {\n  kernelName: FloorDiv,\n  inputsToSave: [\"a\", \"b\"],\n  gradFunc: (dy, saved) => {\n    const [a, b] = saved;\n    const outShape = assertAndGetBroadcastShape(a.shape, b.shape);\n    const derA = () => {\n      const res = div(dy, cast(b, \"float32\"));\n      const reduceAxes = getReductionAxes(a.shape, outShape);\n      if (reduceAxes.length > 0) {\n        return reshape(sum2(res, reduceAxes), a.shape);\n      }\n      return res;\n    };\n    const derB = () => {\n      let res = mul(dy, cast(a, \"float32\"));\n      const reduceAxes = getReductionAxes(b.shape, outShape);\n      if (reduceAxes.length > 0) {\n        res = reshape(sum2(res, reduceAxes), b.shape);\n      }\n      const tmp = square(b);\n      return neg(div(res, cast(tmp, \"float32\")));\n    };\n    return { a: derA, b: derB };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/gradients/FusedBatchNorm_grad.js\nvar fusedBatchNormGradConfig = {\n  kernelName: FusedBatchNorm,\n  inputsToSave: [\"x\", \"mean\", \"variance\", \"scale\"],\n  gradFunc: (dy, saved, attrs) => {\n    const { varianceEpsilon } = attrs;\n    const [x, mean5, variance, scale2] = saved;\n    const scaleValue = scale2 == null ? scalar(1) : scale2;\n    const reductionAxes = getReductionAxes(mean5.shape, x.shape);\n    const tileShape = [];\n    if (mean5.rank === 1) {\n      for (let i2 = 0; i2 < x.shape.length - 1; ++i2) {\n        tileShape.push(x.shape[i2]);\n      }\n      tileShape.push(1);\n    }\n    const xMinusMean = sub(x, mean5);\n    const dyTimesScaleValue = mul(dy, scaleValue);\n    const oneOverSqrtVariance = rsqrt(add2(variance, scalar(varianceEpsilon)));\n    const minusHalfRCube = mul(mul(mul(oneOverSqrtVariance, oneOverSqrtVariance), oneOverSqrtVariance), scalar(-0.5));\n    const derX = () => {\n      if (mean5.rank === 1) {\n        return reshape(mul(mul(dy, tile(reshape(oneOverSqrtVariance, [1, 1, 1, mean5.shape[0]]), tileShape)), scaleValue), x.shape);\n      } else {\n        return reshape(mul(mul(dy, oneOverSqrtVariance), scaleValue), x.shape);\n      }\n    };\n    const derMean = () => {\n      let meanDer = mul(mul(oneOverSqrtVariance, scalar(-1)), dyTimesScaleValue);\n      if (mean5.rank === 1) {\n        meanDer = sum2(meanDer, reductionAxes);\n      }\n      return reshape(meanDer, mean5.shape);\n    };\n    const derVariance = () => {\n      let varianceDer = mul(mul(minusHalfRCube, xMinusMean), dyTimesScaleValue);\n      if (mean5.rank === 1) {\n        varianceDer = sum2(varianceDer, reductionAxes);\n      }\n      return reshape(varianceDer, mean5.shape);\n    };\n    const derScale = () => {\n      const xMinusMean2TimesRsqrt = mul(xMinusMean, oneOverSqrtVariance);\n      let scaleDer = mul(dy, xMinusMean2TimesRsqrt);\n      if (mean5.rank === 1) {\n        scaleDer = sum2(scaleDer, reductionAxes);\n      }\n      return reshape(scaleDer, mean5.shape);\n    };\n    const derOffset = () => {\n      let offsetDer = dy;\n      if (mean5.rank === 1) {\n        offsetDer = sum2(offsetDer, reductionAxes);\n      }\n      return reshape(offsetDer, mean5.shape);\n    };\n    return {\n      x: derX,\n      mean: derMean,\n      variance: derVariance,\n      scale: derScale,\n      offset: derOffset\n    };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/gradients/GatherV2_grad.js\nvar gatherGradConfig = {\n  kernelName: GatherV2,\n  inputsToSave: [\"x\", \"indices\"],\n  gradFunc: (dy, saved, attrs) => {\n    const [x, indices] = saved;\n    const { axis } = attrs;\n    const parsedAxis = parseAxisParam(axis, x.shape)[0];\n    const derX = () => {\n      const paramsShape = x.shape;\n      const indicesSize = indices.size;\n      const outerShape = paramsShape.slice(0, parsedAxis);\n      const outerDims = outerShape.length;\n      const innerShape = paramsShape.slice(axis, paramsShape.length).slice(1);\n      const innerDims = innerShape.length;\n      const outerAxesIndices = arrayRange(0, outerDims);\n      const innerAxesIndices = arrayRange(outerDims + 1, outerDims + 1 + innerDims);\n      const valuesShape = arrayConcat([outerShape, [indicesSize], innerShape]);\n      const values = reshape(dy, valuesShape);\n      const reshapedIndices = reshape(indices, [indicesSize]);\n      const transposeDims = arrayConcat([[outerDims], outerAxesIndices, innerAxesIndices]);\n      const valuesTranspose = transpose(values, transposeDims);\n      let paramsGrad = unsortedSegmentSum(valuesTranspose, reshapedIndices, x.shape[parsedAxis]);\n      const invertTransposeDims = getUndoAxesPermutation(transposeDims);\n      paramsGrad = transpose(paramsGrad, invertTransposeDims);\n      return paramsGrad;\n    };\n    return { x: derX, indices: () => indices };\n  }\n};\nfunction arrayRange(start, stop) {\n  const result = [];\n  for (let i2 = start; i2 < stop; ++i2) {\n    result.push(i2);\n  }\n  return result;\n}\nfunction arrayConcat(arrays) {\n  const result = [];\n  for (let i2 = 0; i2 < arrays.length; ++i2) {\n    for (let j = 0; j < arrays[i2].length; ++j) {\n      result.push(arrays[i2][j]);\n    }\n  }\n  return result;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/gradients/GreaterEqual_grad.js\nvar greaterEqualGradConfig = {\n  kernelName: GreaterEqual,\n  inputsToSave: [\"a\", \"b\"],\n  gradFunc: (dy, saved) => {\n    const [a, b] = saved;\n    return { a: () => zerosLike(a), b: () => zerosLike(b) };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Identity_grad.js\nvar identityGradConfig = {\n  kernelName: Identity,\n  gradFunc: (dy) => {\n    return { x: () => cast(dy, \"float32\") };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/gradients/IsFinite_grad.js\nvar isFiniteGradConfig = {\n  kernelName: IsFinite,\n  gradFunc: (dy) => {\n    return { x: () => zerosLike(dy) };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/gradients/IsInf_grad.js\nvar isInfGradConfig = {\n  kernelName: IsInf,\n  gradFunc: (dy) => {\n    return { x: () => zerosLike(dy) };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/gradients/IsNan_grad.js\nvar isNanGradConfig = {\n  kernelName: IsNan,\n  gradFunc: (dy) => {\n    return { x: () => zerosLike(dy) };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/gradients/LeakyRelu_grad.js\nvar leakyReluGradConfig = {\n  kernelName: LeakyRelu,\n  inputsToSave: [\"x\"],\n  gradFunc: (dy, saved, attrs) => {\n    const [x] = saved;\n    const { alpha } = attrs;\n    const mask = greater(x, 0);\n    return { x: () => where(mask, dy, mul(dy, alpha)) };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Log1p_grad.js\nvar log1pGradConfig = {\n  kernelName: Log1p,\n  inputsToSave: [\"x\"],\n  gradFunc: (dy, saved) => {\n    const [x] = saved;\n    return { x: () => div(dy, add2(x, 1)) };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Log_grad.js\nvar logGradConfig = {\n  kernelName: Log,\n  inputsToSave: [\"x\"],\n  gradFunc: (dy, saved) => {\n    const [x] = saved;\n    return { x: () => div(dy, cast(x, \"float32\")) };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/gradients/LogSoftmax_grad.js\nvar logSoftmaxGradConfig = {\n  kernelName: LogSoftmax,\n  inputsToSave: [],\n  outputsToSave: [true],\n  gradFunc: (dy, saved, attrs) => {\n    const [value] = saved;\n    const { axis } = attrs;\n    return {\n      logits: () => {\n        const keepDims = true;\n        const softmax7 = exp(value);\n        return sub(dy, mul(sum2(dy, axis, keepDims), softmax7));\n      }\n    };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/local_response_normalization_backprop.js\nfunction localResponseNormalizationBackprop_(x, y, dy, depthRadius = 5, bias = 1, alpha = 1, beta = 0.5) {\n  const inputs = { x, y, dy };\n  const attrs = { depthRadius, bias, alpha, beta };\n  return ENGINE.runKernel(LRNGrad, inputs, attrs);\n}\nvar localResponseNormalizationBackprop = op({ localResponseNormalizationBackprop_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/gradients/LRN_grad.js\nvar lrnGradConfig = {\n  kernelName: LRN,\n  inputsToSave: [\"x\"],\n  outputsToSave: [true],\n  gradFunc: (dy, saved, attrs) => {\n    const [x, y] = saved;\n    const { depthRadius, bias, alpha, beta } = attrs;\n    return {\n      x: () => localResponseNormalizationBackprop(x, y, dy, depthRadius, bias, alpha, beta)\n    };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/gradients/min_max_grad_util.js\nfunction gradForMinAndMax(dy, y, xOrig, origAxes) {\n  if (y.rank < xOrig.rank) {\n    y = reshape(y, expandShapeToKeepDim(y.shape, origAxes));\n  }\n  if (dy.rank < xOrig.rank) {\n    dy = reshape(dy, expandShapeToKeepDim(dy.shape, origAxes));\n  }\n  return {\n    x: () => {\n      const dx = mul(dy, cast(equal(xOrig, y), dy.dtype));\n      return dx;\n    }\n  };\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Max_grad.js\nvar maxGradConfig = {\n  kernelName: Max,\n  inputsToSave: [\"x\"],\n  outputsToSave: [true],\n  gradFunc: (dy, saved, attrs) => {\n    const maxAttrs = attrs;\n    const { reductionIndices } = maxAttrs;\n    const x = saved[0];\n    const y = saved[1];\n    const origAxes = parseAxisParam(reductionIndices, x.shape);\n    const maxGrad = gradForMinAndMax(dy, y, x, origAxes);\n    return {\n      x: () => {\n        return maxGrad[\"x\"]();\n      }\n    };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Maximum_grad.js\nvar maximumGradConfig = {\n  kernelName: Maximum,\n  inputsToSave: [\"a\", \"b\"],\n  gradFunc: (dy, saved) => {\n    const [a, b] = saved;\n    const derA = () => mul(dy, cast(greaterEqual(a, b), \"float32\"));\n    const derB = () => mul(dy, cast(less(a, b), \"float32\"));\n    return { a: derA, b: derB };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/max_pool_3d_grad.js\nfunction maxPool3dGrad_(dy, input2, output, filterSize, strides, pad3, dimRoundingMode) {\n  const $dy = convertToTensor(dy, \"dy\", \"maxPool3dGrad\");\n  const $input = convertToTensor(input2, \"input\", \"maxPool3dGrad\");\n  const $output = convertToTensor(output, \"output\", \"maxPool3dGrad\");\n  let dy5D = $dy;\n  let input5D = $input;\n  let output5D = $output;\n  let reshapedTo5D = false;\n  if ($input.rank === 4) {\n    reshapedTo5D = true;\n    dy5D = reshape($dy, [1, $dy.shape[0], $dy.shape[1], $dy.shape[2], $dy.shape[3]]);\n    input5D = reshape($input, [\n      1,\n      $input.shape[0],\n      $input.shape[1],\n      $input.shape[2],\n      $input.shape[3]\n    ]);\n    output5D = reshape($output, [\n      1,\n      $output.shape[0],\n      $output.shape[1],\n      $output.shape[2],\n      $output.shape[3]\n    ]);\n  }\n  assert(dy5D.rank === 5, () => `Error in maxPool3dGrad: dy must be rank 5 but got rank ${dy5D.rank}.`);\n  assert(input5D.rank === 5, () => `Error in maxPool3dGrad: input must be rank 5 but got rank ${input5D.rank}.`);\n  assert(output5D.rank === 5, () => `Error in maxPool3dGrad: output must be rank 5 but got rank ${output5D.rank}.`);\n  checkPadOnDimRoundingMode(\"maxPool3dGrad\", pad3, dimRoundingMode);\n  const inputs = { dy: dy5D, input: input5D, output: output5D };\n  const attrs = { filterSize, strides, pad: pad3, dimRoundingMode };\n  const res = ENGINE.runKernel(MaxPool3DGrad, inputs, attrs);\n  if (reshapedTo5D) {\n    return reshape(res, [res.shape[1], res.shape[2], res.shape[3], res.shape[4]]);\n  }\n  return res;\n}\nvar maxPool3dGrad = op({ maxPool3dGrad_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/gradients/MaxPool3D_grad.js\nvar maxPool3DGradConfig = {\n  kernelName: MaxPool3D,\n  inputsToSave: [\"x\"],\n  outputsToSave: [true],\n  gradFunc: (dy, saved, attrs) => {\n    const [x, y] = saved;\n    const { filterSize, strides, pad: pad3, dimRoundingMode } = attrs;\n    return {\n      x: () => maxPool3dGrad(dy, x, y, filterSize, strides, pad3, dimRoundingMode)\n    };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/max_pool_grad.js\nfunction maxPoolGrad_(dy, input2, output, filterSize, strides, pad3, dimRoundingMode) {\n  const $dy = convertToTensor(dy, \"dy\", \"maxPoolGrad\");\n  const $input = convertToTensor(input2, \"input\", \"maxPoolGrad\");\n  const $output = convertToTensor(output, \"output\", \"maxPoolGrad\");\n  assert($input.rank === $dy.rank, () => `Rank of input (${$input.rank}) does not match rank of dy (${$dy.rank})`);\n  assert($dy.rank === 4, () => `Error in maxPoolGrad: dy must be rank 4 but got rank ${$dy.rank}.`);\n  assert($input.rank === 4, () => `Error in maxPoolGrad: input must be rank 4 but got rank ${$input.rank}.`);\n  checkPadOnDimRoundingMode(\"maxPoolGrad\", pad3, dimRoundingMode);\n  const inputs = { dy: $dy, input: $input, output: $output };\n  const attrs = { filterSize, strides, pad: pad3, dimRoundingMode };\n  return ENGINE.runKernel(MaxPoolGrad, inputs, attrs);\n}\nvar maxPoolGrad = op({ maxPoolGrad_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/gradients/MaxPool_grad.js\nvar maxPoolGradConfig = {\n  kernelName: MaxPool,\n  inputsToSave: [\"x\"],\n  outputsToSave: [true],\n  gradFunc: (dy, saved, attrs) => {\n    const [x, y] = saved;\n    const { filterSize, strides, pad: pad3 } = attrs;\n    return {\n      x: () => maxPoolGrad(dy, x, y, filterSize, strides, pad3)\n    };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Mean_grad.js\nvar meanGradConfig = {\n  kernelName: Mean,\n  inputsToSave: [\"x\"],\n  gradFunc: (dy, saved, attrs) => {\n    const [x] = saved;\n    const { axis } = attrs;\n    const axes = parseAxisParam(axis, x.shape);\n    const shapes = computeOutAndReduceShapes(x.shape, axes);\n    const reduceShape = shapes[1];\n    const reduceSize = sizeFromShape(reduceShape);\n    const derX = () => {\n      const expandedDyShape = x.shape.slice();\n      axes.forEach((axis2) => {\n        expandedDyShape[axis2] = 1;\n      });\n      const expandedDy = reshape(dy, expandedDyShape);\n      const res = div(mul(expandedDy, ones2(x.shape, \"float32\")), reduceSize);\n      return res;\n    };\n    return { x: derX };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Min_grad.js\nvar minGradConfig = {\n  kernelName: Min,\n  inputsToSave: [\"x\"],\n  outputsToSave: [true],\n  gradFunc: (dy, saved, attrs) => {\n    const minAttrs = attrs;\n    const { axis } = minAttrs;\n    const [x, y] = saved;\n    const origAxes = parseAxisParam(axis, x.shape);\n    const minGrad = gradForMinAndMax(dy, y, x, origAxes);\n    return {\n      x: () => {\n        return minGrad[\"x\"]();\n      }\n    };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Minimum_grad.js\nvar minimumGradConfig = {\n  kernelName: Minimum,\n  inputsToSave: [\"a\", \"b\"],\n  gradFunc: (dy, saved) => {\n    const [a, b] = saved;\n    const derA = () => mul(dy, cast(lessEqual(a, b), \"float32\"));\n    const derB = () => mul(dy, cast(greater(a, b), \"float32\"));\n    return { a: derA, b: derB };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/gradients/MirrorPad_grad.js\nvar mirrorPadGradConfig = {\n  kernelName: MirrorPad,\n  inputsToSave: [\"x\"],\n  gradFunc: (dy, saved, attrs) => {\n    const x = saved[0];\n    const { paddings } = attrs;\n    const begin = paddings.map((p2) => p2[0]);\n    return { x: () => slice(dy, begin, x.shape) };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Mod_grad.js\nvar modGradConfig = {\n  kernelName: Mod,\n  inputsToSave: [\"a\", \"b\"],\n  gradFunc: (dy, saved) => {\n    const [a, b] = saved;\n    const outShape = assertAndGetBroadcastShape(a.shape, b.shape);\n    const derA = () => {\n      const reduceAxes = getReductionAxes(a.shape, outShape);\n      if (reduceAxes.length > 0) {\n        return reshape(sum2(dy, reduceAxes), a.shape);\n      }\n      return dy;\n    };\n    const derB = () => {\n      const res = mul(dy, neg(floor(div(a, b))));\n      const reduceAxes = getReductionAxes(b.shape, outShape);\n      if (reduceAxes.length > 0) {\n        return reshape(sum2(res, reduceAxes), b.shape);\n      }\n      return res;\n    };\n    return { a: derA, b: derB };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Multiply_grad.js\nvar multiplyGradConfig = {\n  kernelName: Multiply,\n  inputsToSave: [\"a\", \"b\"],\n  gradFunc: (dy, saved) => {\n    const [a, b] = saved;\n    const outShape = assertAndGetBroadcastShape(a.shape, b.shape);\n    const derA = () => {\n      const res = mul(dy, cast(b, \"float32\"));\n      const reduceAxes = getReductionAxes(a.shape, outShape);\n      if (reduceAxes.length > 0) {\n        return reshape(sum2(res, reduceAxes), a.shape);\n      }\n      return res;\n    };\n    const derB = () => {\n      const res = mul(dy, cast(a, \"float32\"));\n      const reduceAxes = getReductionAxes(b.shape, outShape);\n      if (reduceAxes.length > 0) {\n        return reshape(sum2(res, reduceAxes), b.shape);\n      }\n      return res;\n    };\n    return { a: derA, b: derB };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Neg_grad.js\nvar negGradConfig = {\n  kernelName: Neg,\n  gradFunc: (dy) => {\n    return { x: () => neg(dy) };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/gradients/OneHot_grad.js\nvar oneHotGradConfig = {\n  kernelName: OneHot,\n  inputsToSave: [\"indices\"],\n  gradFunc: (dy, saved) => {\n    const indices = saved[0];\n    return { indices: () => zeros(indices.shape, \"float32\") };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/gradients/OnesLike_grad.js\nvar onesLikeGradConfig = {\n  kernelName: OnesLike,\n  gradFunc: (dy) => {\n    return { x: () => zerosLike(dy) };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Pack_grad.js\nvar packGradConfig = {\n  kernelName: Pack,\n  saveAllInputs: true,\n  gradFunc: (dy, saved, attrs) => {\n    const { axis } = attrs;\n    const derTensors = unstack(dy, axis);\n    return derTensors.map((t2) => () => t2);\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/gradients/PadV2_grad.js\nvar padV2GradConfig = {\n  kernelName: PadV2,\n  inputsToSave: [\"x\"],\n  gradFunc: (dy, saved, attrs) => {\n    const x = saved[0];\n    const { paddings } = attrs;\n    const begin = paddings.map((p2) => p2[0]);\n    return { x: () => slice(dy, begin, x.shape) };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Pow_grad.js\nvar powGradConfig = {\n  kernelName: Pow,\n  inputsToSave: [\"a\", \"b\"],\n  outputsToSave: [true],\n  gradFunc: (dy, saved) => {\n    const [a, b, y] = saved;\n    const base = a;\n    const exp5 = b;\n    const outShape = assertAndGetBroadcastShape(base.shape, exp5.shape);\n    const derBase = () => {\n      const expFloat = cast(exp5, \"float32\");\n      let res = mul(dy, mul(expFloat, pow(base, sub(expFloat, scalar(1)))));\n      const reduceAxes = getReductionAxes(base.shape, outShape);\n      if (reduceAxes.length > 0) {\n        res = sum2(res, reduceAxes);\n      }\n      return reshape(res, base.shape);\n    };\n    const derExp = () => {\n      const condition = greater(base, 0);\n      const logBase = where(condition, log2(base), zerosLike(base));\n      let res = mul(dy, mul(y, logBase));\n      const reduceAxes = getReductionAxes(exp5.shape, outShape);\n      if (reduceAxes.length > 0) {\n        res = sum2(res, reduceAxes);\n      }\n      return reshape(res, exp5.shape);\n    };\n    return { a: derBase, b: derExp };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Prelu_grad.js\nvar preluGradConfig = {\n  kernelName: Prelu,\n  inputsToSave: [\"x\", \"alpha\"],\n  gradFunc: (dy, saved) => {\n    const [x, alpha] = saved;\n    const mask = greater(x, 0);\n    return {\n      x: () => where(mask, dy, mul(dy, alpha)),\n      alpha: () => {\n        let res = where(mask, zerosLike(dy), mul(dy, x));\n        const reduceAxes = getReductionAxes(alpha.shape, dy.shape);\n        if (reduceAxes.length > 0) {\n          res = sum2(res, reduceAxes);\n        }\n        return reshape(res, alpha.shape);\n      }\n    };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Prod_grad.js\nfunction prodGradFn_(x, dy, axis) {\n  const expandedYShape = x.shape.slice();\n  expandedYShape[axis] = 1;\n  const expandedDy = reshape(dy, expandedYShape);\n  const xCumProd = cumprod(x, axis, true, false);\n  const xCumRevProd = cumprod(x, axis, true, true);\n  const dx = mul(xCumProd, xCumRevProd);\n  return mul(expandedDy, dx);\n}\nfunction prodsGradFn_(x, dy, axis) {\n  const xRank = x.shape.length;\n  const finalProdAxis = xRank - axis.length;\n  const xPermutation = backend_util_exports.getAxesPermutation(axis, xRank);\n  let permutedX = x;\n  if (xPermutation != null) {\n    permutedX = transpose(x, xPermutation);\n  }\n  const newShape = permutedX.shape.slice();\n  const removedShape = newShape.splice(xRank - axis.length, axis.length);\n  const endPartShape = removedShape.reduce((p2, c) => p2 * c, 1);\n  newShape.push(endPartShape);\n  const reshapedPermutedX = permutedX.reshape(newShape);\n  let prodGrad = prodGradFn_(reshapedPermutedX, dy, finalProdAxis);\n  prodGrad = prodGrad.reshape(permutedX.shape);\n  if (xPermutation != null) {\n    const undoPermutation = backend_util_exports.getUndoAxesPermutation(xPermutation);\n    prodGrad = transpose(prodGrad, undoPermutation);\n  }\n  return prodGrad;\n}\nvar prodGradConfig = {\n  kernelName: Prod,\n  inputsToSave: [\"x\"],\n  gradFunc: (dy, saved, attrs) => {\n    const [x] = saved;\n    const { axis } = attrs;\n    let axisArr = [];\n    if (axis === void 0 || axis === null) {\n      axisArr = x.shape.map((_, i2) => i2);\n    } else if (typeof axis === \"number\") {\n      axisArr = [axis];\n    } else {\n      axisArr = axis;\n    }\n    return { x: () => prodsGradFn_(x, dy, axisArr) };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/gradients/RealDiv_grad.js\nvar divGradConfig = {\n  kernelName: RealDiv,\n  inputsToSave: [\"a\", \"b\"],\n  gradFunc: (dy, saved) => {\n    const [a, b] = saved;\n    const outShape = assertAndGetBroadcastShape(a.shape, b.shape);\n    const derA = () => {\n      const res = div(dy, cast(b, \"float32\"));\n      const reduceAxes = getReductionAxes(a.shape, outShape);\n      if (reduceAxes.length > 0) {\n        return reshape(sum2(res, reduceAxes), a.shape);\n      }\n      return res;\n    };\n    const derB = () => {\n      let res = mul(dy, cast(a, \"float32\"));\n      const reduceAxes = getReductionAxes(b.shape, outShape);\n      if (reduceAxes.length > 0) {\n        res = reshape(sum2(res, reduceAxes), b.shape);\n      }\n      const tmp = square(b);\n      return neg(div(res, cast(tmp, \"float32\")));\n    };\n    return { a: derA, b: derB };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Reciprocal_grad.js\nvar reciprocalGradConfig = {\n  kernelName: Reciprocal,\n  inputsToSave: [\"x\"],\n  gradFunc: (dy, saved) => {\n    const [x] = saved;\n    return { x: () => div(dy, neg(square(x))) };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Relu6_grad.js\nvar relu6GradConfig = {\n  kernelName: Relu6,\n  inputsToSave: [\"x\"],\n  gradFunc: (dy, saved) => {\n    const [x] = saved;\n    const mask = mul(lessEqual(x, 6), step(x));\n    return { x: () => mul(dy, cast(mask, \"float32\")) };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Relu_grad.js\nvar reluGradConfig = {\n  kernelName: Relu,\n  inputsToSave: [\"x\"],\n  gradFunc: (dy, saved) => {\n    const [x] = saved;\n    return { x: () => mul(dy, cast(step(x), \"float32\")) };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Reshape_grad.js\nvar reshapeGradConfig = {\n  kernelName: Reshape,\n  inputsToSave: [\"x\"],\n  gradFunc: (dy, saved) => {\n    const [x] = saved;\n    return { x: () => reshape(dy, x.shape) };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/gradients/ResizeBilinear_grad.js\nvar resizeBilinearGradConfig = {\n  kernelName: ResizeBilinear,\n  inputsToSave: [\"images\"],\n  gradFunc: (dy, saved, attrs) => {\n    const [images] = saved;\n    const inputs = { dy, images };\n    const imagesDer = () => ENGINE.runKernel(ResizeBilinearGrad, inputs, attrs);\n    return { images: imagesDer };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/gradients/ResizeNearestNeighbor_grad.js\nvar resizeNearestNeighborGradConfig = {\n  kernelName: ResizeNearestNeighbor,\n  inputsToSave: [\"images\"],\n  gradFunc: (dy, saved, attrs) => {\n    const [images] = saved;\n    const inputs = { dy, images };\n    const imagesDer = () => ENGINE.runKernel(ResizeNearestNeighborGrad, inputs, attrs);\n    return { images: imagesDer };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Reverse_grad.js\nvar reverseGradConfig = {\n  kernelName: Reverse,\n  gradFunc: (dy, saved, attrs) => {\n    const { dims } = attrs;\n    const axes = parseAxisParam(dims, dy.shape);\n    return { x: () => reverse(dy, axes) };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Round_grad.js\nvar roundGradConfig = {\n  kernelName: Round,\n  gradFunc: (dy) => {\n    return { x: () => zerosLike(dy) };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Rsqrt_grad.js\nvar rsqrtGradConfig = {\n  kernelName: Rsqrt,\n  inputsToSave: [\"x\"],\n  gradFunc: (dy, saved) => {\n    const [x] = saved;\n    return { x: () => neg(div(dy, mul(pow(x, 1.5), 2))) };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Select_grad.js\nvar selectGradConfig = {\n  kernelName: Select,\n  inputsToSave: [\"condition\"],\n  gradFunc: (dy, saved) => {\n    const [condition] = saved;\n    return {\n      condition: () => cast(zerosLike(condition), \"float32\"),\n      t: () => mul(dy, cast(condition, dy.dtype)),\n      e: () => mul(dy, cast(logicalNot(condition), dy.dtype))\n    };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Selu_grad.js\nvar seluGradConfig = {\n  kernelName: Selu,\n  inputsToSave: [\"x\"],\n  gradFunc: (dy, saved) => {\n    const [x] = saved;\n    return {\n      x: () => {\n        const mask = greater(x, scalar(0));\n        const scaleAlpha2 = scalar(SELU_SCALEALPHA);\n        const scale2 = scalar(SELU_SCALE);\n        const greaterThanZeroDer = mul(dy, scale2);\n        const lessEqualZeroDer = mul(mul(dy, scaleAlpha2), exp(cast(x, \"float32\")));\n        return where(mask, greaterThanZeroDer, lessEqualZeroDer);\n      }\n    };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Sigmoid_grad.js\nvar sigmoidGradConfig = {\n  kernelName: Sigmoid,\n  outputsToSave: [true],\n  gradFunc: (dy, saved) => {\n    const [y] = saved;\n    return { x: () => mul(dy, mul(y, sub(scalar(1), y))) };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Sign_grad.js\nvar signGradConfig = {\n  kernelName: Sign,\n  gradFunc: (dy) => {\n    return { x: () => zerosLike(dy) };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Sin_grad.js\nvar sinGradConfig = {\n  kernelName: Sin,\n  inputsToSave: [\"x\"],\n  gradFunc: (dy, saved) => {\n    const [x] = saved;\n    return { x: () => mul(cos(cast(x, \"float32\")), dy) };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Sinh_grad.js\nvar sinhGradConfig = {\n  kernelName: Sinh,\n  inputsToSave: [\"x\"],\n  gradFunc: (dy, saved) => {\n    const [x] = saved;\n    return { x: () => mul(cosh(cast(x, \"float32\")), dy) };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Slice_grad.js\nvar sliceGradConfig = {\n  kernelName: Slice,\n  inputsToSave: [\"x\"],\n  gradFunc: (dy, saved, attrs) => {\n    const [x] = saved;\n    const { begin, size } = attrs;\n    const inputShape = x.shape;\n    const [begin_, size_] = parseSliceParams(x, begin, size);\n    const paddings = [];\n    for (let i2 = 0; i2 < dy.rank; i2++) {\n      paddings.push([begin_[i2], inputShape[i2] - begin_[i2] - size_[i2]]);\n    }\n    return { x: () => pad(dy, paddings) };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Softmax_grad.js\nvar softmaxGradConfig = {\n  kernelName: Softmax,\n  outputsToSave: [true],\n  gradFunc: (dy, saved, attrs) => {\n    const [y] = saved;\n    const { dim } = attrs;\n    const keepDims = true;\n    const dyTimesY = mul(dy, y);\n    return {\n      logits: () => sub(dyTimesY, mul(sum2(dyTimesY, [dim], keepDims), y))\n    };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Softplus_grad.js\nvar softplusGradConfig = {\n  kernelName: Softplus,\n  inputsToSave: [\"x\"],\n  gradFunc: (dy, saved) => {\n    const [x] = saved;\n    return { x: () => mul(dy, sigmoid(x)) };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/gradients/SpaceToBatchND_grad.js\nvar spaceToBatchNDGradConfig = {\n  kernelName: SpaceToBatchND,\n  gradFunc: (dy, saved, attrs) => {\n    const { blockShape, paddings } = attrs;\n    return { x: () => batchToSpaceND(dy, blockShape, paddings) };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/gradients/SplitV_grad.js\nvar splitVGradConfig = {\n  kernelName: SplitV,\n  gradFunc: (dy, saved, attrs) => {\n    const { axis } = attrs;\n    return { x: () => concat(dy, axis) };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Sqrt_grad.js\nvar sqrtGradConfig = {\n  kernelName: Sqrt,\n  inputsToSave: [\"x\"],\n  gradFunc: (dy, saved) => {\n    const [x] = saved;\n    return { x: () => div(dy, mul(sqrt(cast(x, \"float32\")), 2)) };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Square_grad.js\nvar squareGradConfig = {\n  kernelName: Square,\n  inputsToSave: [\"x\"],\n  gradFunc: (dy, saved) => {\n    const [x] = saved;\n    return { x: () => mul(dy, mul(cast(x, \"float32\"), 2)) };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/gradients/SquaredDifference_grad.js\nvar squaredDifferenceGradConfig = {\n  kernelName: SquaredDifference,\n  inputsToSave: [\"a\", \"b\"],\n  gradFunc: (dy, saved) => {\n    const [a, b] = saved;\n    const two = scalar(2);\n    const derA = () => mul(dy, mul(two, sub(a, b)));\n    const derB = () => mul(dy, mul(two, sub(b, a)));\n    return { a: derA, b: derB };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Step_grad.js\nvar stepGradConfig = {\n  kernelName: Step,\n  gradFunc: (dy) => {\n    return { x: () => zerosLike(dy) };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Sub_grad.js\nvar subGradConfig = {\n  kernelName: Sub,\n  inputsToSave: [\"a\", \"b\"],\n  gradFunc: (dy, saved) => {\n    const [a, b] = saved;\n    const outShape = assertAndGetBroadcastShape(a.shape, b.shape);\n    const derA = () => {\n      let res = dy;\n      const reduceAxes = getReductionAxes(a.shape, outShape);\n      if (reduceAxes.length > 0) {\n        res = sum2(res, reduceAxes);\n      }\n      return reshape(res, a.shape);\n    };\n    const derB = () => {\n      let res = dy;\n      const reduceAxes = getReductionAxes(b.shape, outShape);\n      if (reduceAxes.length > 0) {\n        res = sum2(res, reduceAxes);\n      }\n      return reshape(neg(res), b.shape);\n    };\n    return { a: derA, b: derB };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Sum_grad.js\nvar sumGradConfig = {\n  kernelName: Sum,\n  inputsToSave: [\"x\"],\n  gradFunc: (dy, saved, attrs) => {\n    const [x] = saved;\n    const expandedDyShape = x.shape.slice();\n    const { axis } = attrs;\n    const axes = parseAxisParam(axis, x.shape);\n    axes.forEach((axis2) => {\n      expandedDyShape[axis2] = 1;\n    });\n    const expandedDy = reshape(dy, expandedDyShape);\n    const derX = mul(expandedDy, ones2(x.shape, \"float32\"));\n    return { x: () => derX };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Tan_grad.js\nvar tanGradConfig = {\n  kernelName: Tan,\n  inputsToSave: [\"x\"],\n  gradFunc: (dy, saved) => {\n    const [x] = saved;\n    return { x: () => div(dy, square(cos(x))) };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Tanh_grad.js\nvar tanhGradConfig = {\n  kernelName: Tanh,\n  outputsToSave: [true],\n  gradFunc: (dy, saved) => {\n    const [y] = saved;\n    return { x: () => mul(sub(scalar(1), square(y)), dy) };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Tile_grad.js\nvar tileGradConfig = {\n  kernelName: Tile,\n  inputsToSave: [\"x\"],\n  gradFunc: (dy, saved, attrs) => {\n    const [x] = saved;\n    const { reps } = attrs;\n    const derX = () => {\n      let xGrad = zerosLike(x);\n      if (x.rank === 1) {\n        for (let i2 = 0; i2 < reps[0]; ++i2) {\n          xGrad = add2(xGrad, slice(dy, [i2 * x.shape[0]], [x.shape[0]]));\n        }\n      } else if (x.rank === 2) {\n        for (let i2 = 0; i2 < reps[0]; ++i2) {\n          for (let j = 0; j < reps[1]; ++j) {\n            xGrad = add2(xGrad, slice(dy, [i2 * x.shape[0], j * x.shape[1]], [\n              x.shape[0],\n              x.shape[1]\n            ]));\n          }\n        }\n      } else if (x.rank === 3) {\n        for (let i2 = 0; i2 < reps[0]; ++i2) {\n          for (let j = 0; j < reps[1]; ++j) {\n            for (let k = 0; k < reps[2]; ++k) {\n              xGrad = add2(xGrad, slice(dy, [i2 * x.shape[0], j * x.shape[1], k * x.shape[2]], [x.shape[0], x.shape[1], x.shape[2]]));\n            }\n          }\n        }\n      } else if (x.rank === 4) {\n        for (let i2 = 0; i2 < reps[0]; ++i2) {\n          for (let j = 0; j < reps[1]; ++j) {\n            for (let k = 0; k < reps[2]; ++k) {\n              for (let l3 = 0; l3 < reps[3]; ++l3) {\n                xGrad = add2(xGrad, slice(dy, [\n                  i2 * x.shape[0],\n                  j * x.shape[1],\n                  k * x.shape[2],\n                  l3 * x.shape[3]\n                ], [x.shape[0], x.shape[1], x.shape[2], x.shape[3]]));\n              }\n            }\n          }\n        }\n      } else {\n        throw new Error(`Gradient for tile operation is not implemented for rank-${x.rank} tensors yet.`);\n      }\n      return xGrad;\n    };\n    return { x: derX };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Transpose_grad.js\nvar transposeGradConfig = {\n  kernelName: Transpose,\n  gradFunc: (dy, saved, attrs) => {\n    const transposeAttrs = attrs;\n    const { perm } = transposeAttrs;\n    const undoPerm = getUndoAxesPermutation(perm);\n    return { x: () => transpose(dy, undoPerm) };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Unpack_grad.js\nvar unpackGradConfig = {\n  kernelName: Unpack,\n  gradFunc: (dy, saved, attrs) => {\n    const unpackAttrs = attrs;\n    const { axis } = unpackAttrs;\n    return { value: () => stack(dy, axis) };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/gradients/UnsortedSegmentSum_grad.js\nvar unsortedSegmentSumGradConfig = {\n  kernelName: UnsortedSegmentSum,\n  inputsToSave: [\"segmentIds\"],\n  gradFunc: (dy, saved) => {\n    const [segmentIds] = saved;\n    const derX = () => {\n      return gatherDropNegatives(dy, segmentIds);\n    };\n    return { x: derX };\n  }\n};\nfunction gatherDropNegatives(x, indices) {\n  const zeroClippedIndices = maximum(indices, zerosLike(indices));\n  const gathered = gather(x, zeroClippedIndices);\n  let isPositive = greaterEqual(indices, scalar(0, \"int32\"));\n  const numIters = gathered.rank - isPositive.rank;\n  for (let i2 = 0; i2 < numIters; ++i2) {\n    isPositive = expandDims(isPositive, i2 + 1);\n  }\n  isPositive = logicalAnd(isPositive, ones2(gathered.shape, \"bool\"));\n  const zeroSlice = zerosLike(gathered);\n  return where(isPositive, gathered, zeroSlice);\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/gradients/ZerosLike_grad.js\nvar zerosLikeGradConfig = {\n  kernelName: ZerosLike,\n  gradFunc: (dy) => {\n    return { x: () => zerosLike(dy) };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/register_all_gradients.js\nvar gradConfigs = [\n  absGradConfig,\n  acosGradConfig,\n  acoshGradConfig,\n  addGradConfig,\n  addNGradConfig,\n  argMaxGradConfig,\n  argMinGradConfig,\n  asinGradConfig,\n  asinhGradConfig,\n  atan2GradConfig,\n  atanGradConfig,\n  atanhGradConfig,\n  avgPool3DGradConfig,\n  avgPoolGradConfig,\n  batchMatMulGradConfig,\n  batchToSpaceNDGradConfig,\n  broadcastToGradConfig,\n  castGradConfig,\n  ceilGradConfig,\n  clipByValueGradConfig,\n  complexAbsGradConfig,\n  concatGradConfig,\n  conv2DBackpropInputGradConfig,\n  conv2DGradConfig,\n  conv3DGradConfig,\n  cosGradConfig,\n  coshGradConfig,\n  cumsumGradConfig,\n  depthwiseConv2dNativeGradConfig,\n  dilation2dGradConfig,\n  divGradConfig,\n  eluGradConfig,\n  erfGradConfig,\n  expGradConfig,\n  expandDimsGradConfig,\n  expm1GradConfig,\n  floorDivGradConfig,\n  floorGradConfig,\n  fusedBatchNormGradConfig,\n  gatherGradConfig,\n  greaterEqualGradConfig,\n  identityGradConfig,\n  isFiniteGradConfig,\n  isInfGradConfig,\n  isNanGradConfig,\n  leakyReluGradConfig,\n  log1pGradConfig,\n  logGradConfig,\n  logSoftmaxGradConfig,\n  lrnGradConfig,\n  maxGradConfig,\n  maxGradConfig,\n  maximumGradConfig,\n  maxPool3DGradConfig,\n  maxPoolGradConfig,\n  meanGradConfig,\n  minGradConfig,\n  minimumGradConfig,\n  mirrorPadGradConfig,\n  modGradConfig,\n  multiplyGradConfig,\n  negGradConfig,\n  oneHotGradConfig,\n  onesLikeGradConfig,\n  packGradConfig,\n  padV2GradConfig,\n  padV2GradConfig,\n  powGradConfig,\n  preluGradConfig,\n  prodGradConfig,\n  reciprocalGradConfig,\n  relu6GradConfig,\n  reluGradConfig,\n  reshapeGradConfig,\n  resizeBilinearGradConfig,\n  resizeNearestNeighborGradConfig,\n  reverseGradConfig,\n  roundGradConfig,\n  rsqrtGradConfig,\n  selectGradConfig,\n  seluGradConfig,\n  sigmoidGradConfig,\n  signGradConfig,\n  sinGradConfig,\n  sinhGradConfig,\n  sliceGradConfig,\n  softmaxGradConfig,\n  softplusGradConfig,\n  spaceToBatchNDGradConfig,\n  spaceToBatchNDGradConfig,\n  splitVGradConfig,\n  splitVGradConfig,\n  sqrtGradConfig,\n  squaredDifferenceGradConfig,\n  squareGradConfig,\n  stepGradConfig,\n  subGradConfig,\n  sumGradConfig,\n  tanGradConfig,\n  tanhGradConfig,\n  tileGradConfig,\n  transposeGradConfig,\n  unpackGradConfig,\n  unsortedSegmentSumGradConfig,\n  zerosLikeGradConfig\n];\nfor (const gradientConfig of gradConfigs) {\n  registerGradient(gradientConfig);\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/abs.js\ngetGlobalTensorClass().prototype.abs = function() {\n  this.throwIfDisposed();\n  return abs(this);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/acos.js\ngetGlobalTensorClass().prototype.acos = function() {\n  this.throwIfDisposed();\n  return acos(this);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/acosh.js\ngetGlobalTensorClass().prototype.acosh = function() {\n  this.throwIfDisposed();\n  return acosh(this);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/add.js\ngetGlobalTensorClass().prototype.add = function(b) {\n  this.throwIfDisposed();\n  return add2(this, b);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/all.js\ngetGlobalTensorClass().prototype.all = function(axis, keepDims) {\n  this.throwIfDisposed();\n  return all(this, axis, keepDims);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/any.js\ngetGlobalTensorClass().prototype.any = function(axis, keepDims) {\n  this.throwIfDisposed();\n  return any(this, axis, keepDims);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/arg_max.js\ngetGlobalTensorClass().prototype.argMax = function(axis) {\n  this.throwIfDisposed();\n  return argMax(this, axis);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/arg_min.js\ngetGlobalTensorClass().prototype.argMin = function(axis) {\n  this.throwIfDisposed();\n  return argMin(this, axis);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/as_scalar.js\ngetGlobalTensorClass().prototype.asScalar = function() {\n  this.throwIfDisposed();\n  assert(this.size === 1, () => \"The array must have only 1 element.\");\n  return reshape(this, []);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/as_type.js\ngetGlobalTensorClass().prototype.asType = function(dtype) {\n  this.throwIfDisposed();\n  return cast(this, dtype);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/as1d.js\ngetGlobalTensorClass().prototype.as1D = function() {\n  this.throwIfDisposed();\n  return reshape(this, [this.size]);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/as2d.js\ngetGlobalTensorClass().prototype.as2D = function(rows, columns) {\n  this.throwIfDisposed();\n  return reshape(this, [rows, columns]);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/as3d.js\ngetGlobalTensorClass().prototype.as3D = function(rows, columns, depth) {\n  this.throwIfDisposed();\n  return reshape(this, [rows, columns, depth]);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/as4d.js\ngetGlobalTensorClass().prototype.as4D = function(rows, columns, depth, depth2) {\n  this.throwIfDisposed();\n  return reshape(this, [rows, columns, depth, depth2]);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/as5d.js\ngetGlobalTensorClass().prototype.as5D = function(rows, columns, depth, depth2, depth3) {\n  this.throwIfDisposed();\n  return reshape(this, [rows, columns, depth, depth2, depth3]);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/asin.js\ngetGlobalTensorClass().prototype.asin = function() {\n  this.throwIfDisposed();\n  return asin(this);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/asinh.js\ngetGlobalTensorClass().prototype.asinh = function() {\n  this.throwIfDisposed();\n  return asinh(this);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/atan.js\ngetGlobalTensorClass().prototype.atan = function() {\n  this.throwIfDisposed();\n  return atan(this);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/atan2.js\ngetGlobalTensorClass().prototype.atan2 = function(b) {\n  this.throwIfDisposed();\n  return atan2(this, b);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/atanh.js\ngetGlobalTensorClass().prototype.atanh = function() {\n  this.throwIfDisposed();\n  return atanh(this);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/avg_pool.js\ngetGlobalTensorClass().prototype.avgPool = function(filterSize, strides, pad3, dimRoundingMode) {\n  this.throwIfDisposed();\n  return avgPool(this, filterSize, strides, pad3, dimRoundingMode);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/batch_to_space_nd.js\ngetGlobalTensorClass().prototype.batchToSpaceND = function(blockShape, crops) {\n  this.throwIfDisposed();\n  return batchToSpaceND(this, blockShape, crops);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/batchnorm.js\ngetGlobalTensorClass().prototype.batchNorm = function(mean5, variance, offset, scale2, varianceEpsilon) {\n  this.throwIfDisposed();\n  return batchNorm(this, mean5, variance, offset, scale2, varianceEpsilon);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/broadcast_to.js\ngetGlobalTensorClass().prototype.broadcastTo = function(shape) {\n  this.throwIfDisposed();\n  return broadcastTo(this, shape);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/cast.js\ngetGlobalTensorClass().prototype.cast = function(dtype) {\n  this.throwIfDisposed();\n  return cast(this, dtype);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/ceil.js\ngetGlobalTensorClass().prototype.ceil = function() {\n  this.throwIfDisposed();\n  return ceil(this);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/clip_by_value.js\ngetGlobalTensorClass().prototype.clipByValue = function(min7, max7) {\n  this.throwIfDisposed();\n  return clipByValue(this, min7, max7);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/concat.js\ngetGlobalTensorClass().prototype.concat = function(x, axis) {\n  this.throwIfDisposed();\n  if (x instanceof Tensor) {\n    x = [x];\n  }\n  return concat([this, ...x], axis);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/conv1d.js\ngetGlobalTensorClass().prototype.conv1d = function(filter, stride, pad3, dataFormat, dilation, dimRoundingMode) {\n  this.throwIfDisposed();\n  return conv1d(this, filter, stride, pad3, dataFormat, dilation, dimRoundingMode);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/conv2d_transpose.js\ngetGlobalTensorClass().prototype.conv2dTranspose = function(filter, outputShape, strides, pad3, dimRoundingMode) {\n  this.throwIfDisposed();\n  return conv2dTranspose(this, filter, outputShape, strides, pad3, dimRoundingMode);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/conv2d.js\ngetGlobalTensorClass().prototype.conv2d = function(filter, strides, pad3, dataFormat, dilations, dimRoundingMode) {\n  this.throwIfDisposed();\n  return conv2d(this, filter, strides, pad3, dataFormat, dilations, dimRoundingMode);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/cos.js\ngetGlobalTensorClass().prototype.cos = function() {\n  this.throwIfDisposed();\n  return cos(this);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/cosh.js\ngetGlobalTensorClass().prototype.cosh = function() {\n  this.throwIfDisposed();\n  return cosh(this);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/cumprod.js\ngetGlobalTensorClass().prototype.cumprod = function(axis, exclusive, reverse5) {\n  this.throwIfDisposed();\n  return cumprod(this, axis, exclusive, reverse5);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/cumsum.js\ngetGlobalTensorClass().prototype.cumsum = function(axis, exclusive, reverse5) {\n  this.throwIfDisposed();\n  return cumsum(this, axis, exclusive, reverse5);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/depth_to_space.js\ngetGlobalTensorClass().prototype.depthToSpace = function(blockSize, dataFormat) {\n  this.throwIfDisposed();\n  return depthToSpace(this, blockSize, dataFormat);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/depthwise_conv2d.js\ngetGlobalTensorClass().prototype.depthwiseConv2d = function(filter, strides, pad3, dataFormat, dilations, dimRoundingMode) {\n  this.throwIfDisposed();\n  return depthwiseConv2d(this, filter, strides, pad3, dataFormat, dilations, dimRoundingMode);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/dilation2d.js\ngetGlobalTensorClass().prototype.dilation2d = function(filter, strides, pad3, dilations, dataFormat) {\n  this.throwIfDisposed();\n  return dilation2d(this, filter, strides, pad3, dilations, dataFormat);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/div_no_nan.js\ngetGlobalTensorClass().prototype.divNoNan = function(b) {\n  this.throwIfDisposed();\n  return divNoNan(this, b);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/div.js\ngetGlobalTensorClass().prototype.div = function(b) {\n  this.throwIfDisposed();\n  return div(this, b);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/dot.js\ngetGlobalTensorClass().prototype.dot = function(b) {\n  this.throwIfDisposed();\n  return dot(this, b);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/elu.js\ngetGlobalTensorClass().prototype.elu = function() {\n  this.throwIfDisposed();\n  return elu(this);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/equal.js\ngetGlobalTensorClass().prototype.equal = function(b) {\n  this.throwIfDisposed();\n  return equal(this, b);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/erf.js\ngetGlobalTensorClass().prototype.erf = function() {\n  this.throwIfDisposed();\n  return erf(this);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/euclidean_norm.js\ngetGlobalTensorClass().prototype.euclideanNorm = function(axis, keepDims) {\n  this.throwIfDisposed();\n  return euclideanNorm(this, axis, keepDims);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/exp.js\ngetGlobalTensorClass().prototype.exp = function() {\n  this.throwIfDisposed();\n  return exp(this);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/expand_dims.js\ngetGlobalTensorClass().prototype.expandDims = function(axis) {\n  this.throwIfDisposed();\n  return expandDims(this, axis);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/expm1.js\ngetGlobalTensorClass().prototype.expm1 = function() {\n  this.throwIfDisposed();\n  return expm1(this);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/fft.js\ngetGlobalTensorClass().prototype.fft = function() {\n  this.throwIfDisposed();\n  return fft(this);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/flatten.js\ngetGlobalTensorClass().prototype.flatten = function() {\n  this.throwIfDisposed();\n  return reshape(this, [this.size]);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/floor.js\ngetGlobalTensorClass().prototype.floor = function() {\n  this.throwIfDisposed();\n  return floor(this);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/floorDiv.js\ngetGlobalTensorClass().prototype.floorDiv = function(b) {\n  this.throwIfDisposed();\n  return floorDiv(this, b);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/gather.js\ngetGlobalTensorClass().prototype.gather = function(indices, axis) {\n  this.throwIfDisposed();\n  return gather(this, indices, axis);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/greater_equal.js\ngetGlobalTensorClass().prototype.greaterEqual = function(b) {\n  this.throwIfDisposed();\n  return greaterEqual(this, b);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/greater.js\ngetGlobalTensorClass().prototype.greater = function(b) {\n  this.throwIfDisposed();\n  return greater(this, b);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/ifft.js\ngetGlobalTensorClass().prototype.ifft = function() {\n  this.throwIfDisposed();\n  return ifft(this);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/irfft.js\ngetGlobalTensorClass().prototype.irfft = function() {\n  this.throwIfDisposed();\n  return irfft(this);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/is_finite.js\ngetGlobalTensorClass().prototype.isFinite = function() {\n  this.throwIfDisposed();\n  return isFinite2(this);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/is_inf.js\ngetGlobalTensorClass().prototype.isInf = function() {\n  this.throwIfDisposed();\n  return isInf(this);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/is_nan.js\ngetGlobalTensorClass().prototype.isNaN = function() {\n  this.throwIfDisposed();\n  return isNaN2(this);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/leaky_relu.js\ngetGlobalTensorClass().prototype.leakyRelu = function(alpha) {\n  this.throwIfDisposed();\n  return leakyRelu(this, alpha);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/less_equal.js\ngetGlobalTensorClass().prototype.lessEqual = function(b) {\n  this.throwIfDisposed();\n  return lessEqual(this, b);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/less.js\ngetGlobalTensorClass().prototype.less = function(b) {\n  this.throwIfDisposed();\n  return less(this, b);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/local_response_normalization.js\ngetGlobalTensorClass().prototype.localResponseNormalization = function(depthRadius, bias, alpha, beta) {\n  this.throwIfDisposed();\n  return localResponseNormalization(this, depthRadius, bias, alpha, beta);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/log_sigmoid.js\ngetGlobalTensorClass().prototype.logSigmoid = function() {\n  this.throwIfDisposed();\n  return logSigmoid(this);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/log_softmax.js\ngetGlobalTensorClass().prototype.logSoftmax = function(axis) {\n  this.throwIfDisposed();\n  return logSoftmax(this, axis);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/log_sum_exp.js\ngetGlobalTensorClass().prototype.logSumExp = function(axis, keepDims) {\n  this.throwIfDisposed();\n  return logSumExp(this, axis, keepDims);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/log.js\ngetGlobalTensorClass().prototype.log = function() {\n  this.throwIfDisposed();\n  return log2(this);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/log1p.js\ngetGlobalTensorClass().prototype.log1p = function() {\n  this.throwIfDisposed();\n  return log1p(this);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/logical_and.js\ngetGlobalTensorClass().prototype.logicalAnd = function(b) {\n  this.throwIfDisposed();\n  return logicalAnd(this, b);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/logical_not.js\ngetGlobalTensorClass().prototype.logicalNot = function() {\n  this.throwIfDisposed();\n  return logicalNot(this);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/logical_or.js\ngetGlobalTensorClass().prototype.logicalOr = function(b) {\n  this.throwIfDisposed();\n  return logicalOr(this, b);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/logical_xor.js\ngetGlobalTensorClass().prototype.logicalXor = function(b) {\n  this.throwIfDisposed();\n  return logicalXor(this, b);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/mat_mul.js\ngetGlobalTensorClass().prototype.matMul = function(b, transposeA, transposeB) {\n  this.throwIfDisposed();\n  return matMul(this, b, transposeA, transposeB);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/max_pool.js\ngetGlobalTensorClass().prototype.maxPool = function(filterSize, strides, pad3, dimRoundingMode) {\n  this.throwIfDisposed();\n  return maxPool(this, filterSize, strides, pad3, dimRoundingMode);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/max.js\ngetGlobalTensorClass().prototype.max = function(axis, keepDims) {\n  this.throwIfDisposed();\n  return max(this, axis, keepDims);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/maximum.js\ngetGlobalTensorClass().prototype.maximum = function(b) {\n  this.throwIfDisposed();\n  return maximum(this, b);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/mean.js\ngetGlobalTensorClass().prototype.mean = function(axis, keepDims) {\n  this.throwIfDisposed();\n  return mean(this, axis, keepDims);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/min.js\ngetGlobalTensorClass().prototype.min = function(axis, keepDims) {\n  this.throwIfDisposed();\n  return min(this, axis, keepDims);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/minimum.js\ngetGlobalTensorClass().prototype.minimum = function(b) {\n  this.throwIfDisposed();\n  return minimum(this, b);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/mirror_pad.js\ngetGlobalTensorClass().prototype.mirrorPad = function(paddings, mode) {\n  this.throwIfDisposed();\n  return mirrorPad(this, paddings, mode);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/mod.js\ngetGlobalTensorClass().prototype.mod = function(b) {\n  this.throwIfDisposed();\n  return mod(this, b);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/mul.js\ngetGlobalTensorClass().prototype.mul = function(b) {\n  this.throwIfDisposed();\n  return mul(this, b);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/neg.js\ngetGlobalTensorClass().prototype.neg = function() {\n  this.throwIfDisposed();\n  return neg(this);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/norm.js\ngetGlobalTensorClass().prototype.norm = function(ord, axis, keepDims) {\n  this.throwIfDisposed();\n  return norm(this, ord, axis, keepDims);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/not_equal.js\ngetGlobalTensorClass().prototype.notEqual = function(b) {\n  this.throwIfDisposed();\n  return notEqual(this, b);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/one_hot.js\ngetGlobalTensorClass().prototype.oneHot = function(depth, onValue = 1, offValue = 0) {\n  this.throwIfDisposed();\n  return oneHot(this, depth, onValue, offValue);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/ones_like.js\ngetGlobalTensorClass().prototype.onesLike = function() {\n  this.throwIfDisposed();\n  return onesLike(this);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/pad.js\ngetGlobalTensorClass().prototype.pad = function(paddings, constantValue) {\n  this.throwIfDisposed();\n  return pad(this, paddings, constantValue);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/pool.js\ngetGlobalTensorClass().prototype.pool = function(windowShape, poolingType, padding, dilationRate, strides, dimRoundingMode) {\n  this.throwIfDisposed();\n  return pool(this, windowShape, poolingType, padding, dilationRate, strides, dimRoundingMode);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/pow.js\ngetGlobalTensorClass().prototype.pow = function(exp5) {\n  this.throwIfDisposed();\n  return pow(this, exp5);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/prelu.js\ngetGlobalTensorClass().prototype.prelu = function(alpha) {\n  this.throwIfDisposed();\n  return prelu(this, alpha);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/prod.js\ngetGlobalTensorClass().prototype.prod = function(axis, keepDims) {\n  this.throwIfDisposed();\n  return prod(this, axis, keepDims);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/reciprocal.js\ngetGlobalTensorClass().prototype.reciprocal = function() {\n  this.throwIfDisposed();\n  return reciprocal(this);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/relu.js\ngetGlobalTensorClass().prototype.relu = function() {\n  this.throwIfDisposed();\n  return relu(this);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/relu6.js\ngetGlobalTensorClass().prototype.relu6 = function() {\n  this.throwIfDisposed();\n  return relu6(this);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/reshape_as.js\ngetGlobalTensorClass().prototype.reshapeAs = function(x) {\n  this.throwIfDisposed();\n  return reshape(this, x.shape);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/reshape.js\ngetGlobalTensorClass().prototype.reshape = function(shape) {\n  this.throwIfDisposed();\n  return reshape(this, shape);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/resize_bilinear.js\ngetGlobalTensorClass().prototype.resizeBilinear = function(newShape2D, alignCorners, halfPixelCenters) {\n  this.throwIfDisposed();\n  return resizeBilinear(this, newShape2D, alignCorners, halfPixelCenters);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/resize_nearest_neighbor.js\ngetGlobalTensorClass().prototype.resizeNearestNeighbor = function(newShape2D, alignCorners, halfFloatCenters) {\n  this.throwIfDisposed();\n  return resizeNearestNeighbor(this, newShape2D, alignCorners, halfFloatCenters);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/reverse.js\ngetGlobalTensorClass().prototype.reverse = function(axis) {\n  this.throwIfDisposed();\n  return reverse(this, axis);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/rfft.js\ngetGlobalTensorClass().prototype.rfft = function() {\n  this.throwIfDisposed();\n  return rfft(this);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/round.js\ngetGlobalTensorClass().prototype.round = function() {\n  this.throwIfDisposed();\n  return round2(this);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/rsqrt.js\ngetGlobalTensorClass().prototype.rsqrt = function() {\n  this.throwIfDisposed();\n  return rsqrt(this);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/selu.js\ngetGlobalTensorClass().prototype.selu = function() {\n  this.throwIfDisposed();\n  return selu(this);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/separable_conv2d.js\ngetGlobalTensorClass().prototype.separableConv2d = function(depthwiseFilter, pointwiseFilter, strides, pad3, dilation, dataFormat) {\n  this.throwIfDisposed();\n  return separableConv2d(this, depthwiseFilter, pointwiseFilter, strides, pad3, dilation, dataFormat);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/sigmoid.js\ngetGlobalTensorClass().prototype.sigmoid = function() {\n  this.throwIfDisposed();\n  return sigmoid(this);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/sign.js\ngetGlobalTensorClass().prototype.sign = function() {\n  this.throwIfDisposed();\n  return sign(this);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/sin.js\ngetGlobalTensorClass().prototype.sin = function() {\n  this.throwIfDisposed();\n  return sin(this);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/sinh.js\ngetGlobalTensorClass().prototype.sinh = function() {\n  this.throwIfDisposed();\n  return sinh(this);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/slice.js\ngetGlobalTensorClass().prototype.slice = function(begin, size) {\n  this.throwIfDisposed();\n  return slice(this, begin, size);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/softmax.js\ngetGlobalTensorClass().prototype.softmax = function(dim) {\n  this.throwIfDisposed();\n  return softmax(this, dim);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/softplus.js\ngetGlobalTensorClass().prototype.softplus = function() {\n  this.throwIfDisposed();\n  return softplus(this);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/space_to_batch_nd.js\ngetGlobalTensorClass().prototype.spaceToBatchND = function(blockShape, paddings) {\n  this.throwIfDisposed();\n  return spaceToBatchND(this, blockShape, paddings);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/split.js\ngetGlobalTensorClass().prototype.split = function(numOrSizeSplits, axis) {\n  this.throwIfDisposed();\n  return split(this, numOrSizeSplits, axis);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/sqrt.js\ngetGlobalTensorClass().prototype.sqrt = function() {\n  this.throwIfDisposed();\n  return sqrt(this);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/square.js\ngetGlobalTensorClass().prototype.square = function() {\n  this.throwIfDisposed();\n  return square(this);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/squared_difference.js\ngetGlobalTensorClass().prototype.squaredDifference = function(b) {\n  this.throwIfDisposed();\n  return squaredDifference(this, b);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/squeeze.js\ngetGlobalTensorClass().prototype.squeeze = function(axis) {\n  this.throwIfDisposed();\n  return squeeze(this, axis);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/stack.js\ngetGlobalTensorClass().prototype.stack = function(x, axis) {\n  this.throwIfDisposed();\n  const tensorsToBeStacked = x instanceof Tensor ? [this, x] : [this, ...x];\n  return stack(tensorsToBeStacked, axis);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/step.js\ngetGlobalTensorClass().prototype.step = function(alpha) {\n  this.throwIfDisposed();\n  return step(this, alpha);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/strided_slice.js\ngetGlobalTensorClass().prototype.stridedSlice = function(begin, end, strides, beginMask, endMask, ellipsisMask, newAxisMask, shrinkAxisMask) {\n  this.throwIfDisposed();\n  return stridedSlice(this, begin, end, strides, beginMask, endMask, ellipsisMask, newAxisMask, shrinkAxisMask);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/sub.js\ngetGlobalTensorClass().prototype.sub = function(b) {\n  this.throwIfDisposed();\n  return sub(this, b);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/sum.js\ngetGlobalTensorClass().prototype.sum = function(axis, keepDims) {\n  this.throwIfDisposed();\n  return sum2(this, axis, keepDims);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/tan.js\ngetGlobalTensorClass().prototype.tan = function() {\n  this.throwIfDisposed();\n  return tan(this);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/tanh.js\ngetGlobalTensorClass().prototype.tanh = function() {\n  this.throwIfDisposed();\n  return tanh2(this);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/tile.js\ngetGlobalTensorClass().prototype.tile = function(reps) {\n  this.throwIfDisposed();\n  return tile(this, reps);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/to_bool.js\ngetGlobalTensorClass().prototype.toBool = function() {\n  this.throwIfDisposed();\n  return cast(this, \"bool\");\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/to_float.js\ngetGlobalTensorClass().prototype.toFloat = function() {\n  this.throwIfDisposed();\n  return cast(this, \"float32\");\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/to_int.js\ngetGlobalTensorClass().prototype.toInt = function() {\n  this.throwIfDisposed();\n  return cast(this, \"int32\");\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/topk.js\ngetGlobalTensorClass().prototype.topk = function(k, sorted) {\n  this.throwIfDisposed();\n  return topk(this, k, sorted);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/transpose.js\ngetGlobalTensorClass().prototype.transpose = function(perm) {\n  this.throwIfDisposed();\n  return transpose(this, perm);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/unique.js\ngetGlobalTensorClass().prototype.unique = function(axis) {\n  this.throwIfDisposed();\n  return unique(this, axis);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/unsorted_segment_sum.js\ngetGlobalTensorClass().prototype.unsortedSegmentSum = function(segmentIds, numSegments) {\n  this.throwIfDisposed();\n  return unsortedSegmentSum(this, segmentIds, numSegments);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/unstack.js\ngetGlobalTensorClass().prototype.unstack = function(axis) {\n  this.throwIfDisposed();\n  return unstack(this, axis);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/where.js\ngetGlobalTensorClass().prototype.where = function(condition, x) {\n  this.throwIfDisposed();\n  return where(condition, this, x);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/zeros_like.js\ngetGlobalTensorClass().prototype.zerosLike = function() {\n  this.throwIfDisposed();\n  return zerosLike(this);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-layers@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-layers/dist/errors.js\nvar AttributeError = class extends Error {\n  constructor(message) {\n    super(message);\n    Object.setPrototypeOf(this, AttributeError.prototype);\n  }\n};\nvar RuntimeError = class extends Error {\n  constructor(message) {\n    super(message);\n    Object.setPrototypeOf(this, RuntimeError.prototype);\n  }\n};\nvar ValueError = class extends Error {\n  constructor(message) {\n    super(message);\n    Object.setPrototypeOf(this, ValueError.prototype);\n  }\n};\nvar NotImplementedError = class extends Error {\n  constructor(message) {\n    super(message);\n    Object.setPrototypeOf(this, NotImplementedError.prototype);\n  }\n};\nvar AssertionError = class extends Error {\n  constructor(message) {\n    super(message);\n    Object.setPrototypeOf(this, AssertionError.prototype);\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-layers@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-layers/dist/utils/executor_utils.js\nvar LruCache = class {\n  constructor(maxEntries) {\n    this.maxEntries = maxEntries || 100;\n    this.cache = /* @__PURE__ */ new Map();\n  }\n  get(key) {\n    let entry;\n    if (this.cache.has(key)) {\n      entry = this.cache.get(key);\n      this.cache.delete(key);\n      this.cache.set(key, entry);\n    }\n    return entry;\n  }\n  put(key, value) {\n    if (this.cache.has(key)) {\n      this.cache.delete(key);\n    } else if (this.cache.size >= this.maxEntries) {\n      const keyToDelete = this.cache.keys().next().value;\n      this.cache.delete(keyToDelete);\n    }\n    this.cache.set(key, value);\n  }\n  getMaxEntries() {\n    return this.maxEntries;\n  }\n  setMaxEntries(maxEntries) {\n    if (maxEntries < 0) {\n      throw new Error(`The maxEntries of LRU caches must be at least 0, but got ${maxEntries}.`);\n    }\n    if (this.maxEntries > maxEntries) {\n      for (let i2 = 0; i2 < this.maxEntries - maxEntries; i2++) {\n        const keyToDelete = this.cache.keys().next().value;\n        this.cache.delete(keyToDelete);\n      }\n    }\n    this.maxEntries = maxEntries;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-layers@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-layers/dist/utils/generic_utils.js\nfunction pyListRepeat(value, numValues) {\n  if (Array.isArray(value)) {\n    let newArray = [];\n    for (let i2 = 0; i2 < numValues; i2++) {\n      newArray = newArray.concat(value);\n    }\n    return newArray;\n  } else {\n    const newArray = new Array(numValues);\n    newArray.fill(value);\n    return newArray;\n  }\n}\nfunction assert2(val, message) {\n  if (!val) {\n    throw new AssertionError(message);\n  }\n}\nfunction count(array2, refernce) {\n  let counter = 0;\n  for (const item of array2) {\n    if (item === refernce) {\n      counter++;\n    }\n  }\n  return counter;\n}\nfunction singletonOrArray(xs) {\n  if (xs.length === 1) {\n    return xs[0];\n  }\n  return xs;\n}\nfunction toList(x) {\n  if (Array.isArray(x)) {\n    return x;\n  }\n  return [x];\n}\nfunction toSnakeCase(name) {\n  const intermediate = name.replace(/(.)([A-Z][a-z0-9]+)/g, \"$1_$2\");\n  const insecure = intermediate.replace(/([a-z])([A-Z])/g, \"$1_$2\").toLowerCase();\n  if (insecure[0] !== \"_\") {\n    return insecure;\n  }\n  return \"private\" + insecure;\n}\nfunction toCamelCase(identifier) {\n  if (identifier.length <= 1) {\n    return identifier;\n  }\n  if (identifier.indexOf(\"_\") === -1) {\n    return identifier;\n  }\n  return identifier.replace(/[_]+(\\w|$)/g, (m, p1) => p1.toUpperCase());\n}\nvar _GLOBAL_CUSTOM_OBJECTS = {};\nfunction serializeKerasObject(instance) {\n  if (instance === null || instance === void 0) {\n    return null;\n  }\n  const dict = {};\n  dict[\"className\"] = instance.getClassName();\n  dict[\"config\"] = instance.getConfig();\n  return dict;\n}\nfunction convertNDArrayScalarsInConfig(config) {\n  if (config == null || typeof config !== \"object\") {\n    return;\n  } else if (Array.isArray(config)) {\n    config.forEach((configItem) => convertNDArrayScalarsInConfig(configItem));\n  } else {\n    const fields = Object.keys(config);\n    for (const field of fields) {\n      const value = config[field];\n      if (value != null && typeof value === \"object\") {\n        if (!Array.isArray(value) && value[\"type\"] === \"ndarray\" && typeof value[\"value\"] === \"number\") {\n          config[field] = value[\"value\"];\n        } else {\n          convertNDArrayScalarsInConfig(value);\n        }\n      }\n    }\n  }\n}\nfunction deserializeKerasObject(identifier, moduleObjects = {}, customObjects = {}, printableModuleName = \"object\", fastWeightInit = false) {\n  if (typeof identifier === \"string\") {\n    const functionName = identifier;\n    let fn;\n    if (functionName in customObjects) {\n      fn = customObjects[functionName];\n    } else if (functionName in _GLOBAL_CUSTOM_OBJECTS) {\n      fn = _GLOBAL_CUSTOM_OBJECTS[functionName];\n    } else {\n      fn = moduleObjects[functionName];\n      if (fn == null) {\n        throw new ValueError(`Unknown ${printableModuleName}: ${identifier}. This may be due to one of the following reasons:\n1. The ${printableModuleName} is defined in Python, in which case it needs to be ported to TensorFlow.js or your JavaScript code.\n2. The custom ${printableModuleName} is defined in JavaScript, but is not registered properly with tf.serialization.registerClass().`);\n      }\n    }\n    return fn;\n  } else {\n    const config = identifier;\n    if (config[\"className\"] == null || config[\"config\"] == null) {\n      throw new ValueError(`${printableModuleName}: Improper config format: ${JSON.stringify(config)}.\n'className' and 'config' must set.`);\n    }\n    const className = config[\"className\"];\n    let cls, fromConfig;\n    if (className in customObjects) {\n      [cls, fromConfig] = customObjects[className];\n    } else if (className in _GLOBAL_CUSTOM_OBJECTS) {\n      [cls, fromConfig] = _GLOBAL_CUSTOM_OBJECTS[\"className\"];\n    } else if (className in moduleObjects) {\n      [cls, fromConfig] = moduleObjects[className];\n    }\n    if (cls == null) {\n      throw new ValueError(`Unknown ${printableModuleName}: ${className}. This may be due to one of the following reasons:\n1. The ${printableModuleName} is defined in Python, in which case it needs to be ported to TensorFlow.js or your JavaScript code.\n2. The custom ${printableModuleName} is defined in JavaScript, but is not registered properly with tf.serialization.registerClass().`);\n    }\n    if (fromConfig != null) {\n      const customObjectsCombined = {};\n      for (const key of Object.keys(_GLOBAL_CUSTOM_OBJECTS)) {\n        customObjectsCombined[key] = _GLOBAL_CUSTOM_OBJECTS[key];\n      }\n      for (const key of Object.keys(customObjects)) {\n        customObjectsCombined[key] = customObjects[key];\n      }\n      const nestedConfig = config[\"config\"];\n      nestedConfig[\"customObjects\"] = customObjectsCombined;\n      const backupCustomObjects = Object.assign({}, _GLOBAL_CUSTOM_OBJECTS);\n      for (const key of Object.keys(customObjects)) {\n        _GLOBAL_CUSTOM_OBJECTS[key] = customObjects[key];\n      }\n      convertNDArrayScalarsInConfig(config[\"config\"]);\n      const returnObj = fromConfig(cls, config[\"config\"], customObjects, fastWeightInit);\n      _GLOBAL_CUSTOM_OBJECTS = Object.assign({}, backupCustomObjects);\n      return returnObj;\n    } else {\n      const backupCustomObjects = Object.assign({}, _GLOBAL_CUSTOM_OBJECTS);\n      for (const key of Object.keys(customObjects)) {\n        _GLOBAL_CUSTOM_OBJECTS[key] = customObjects[key];\n      }\n      const returnObj = new cls(config[\"config\"]);\n      _GLOBAL_CUSTOM_OBJECTS = Object.assign({}, backupCustomObjects);\n      return returnObj;\n    }\n  }\n}\nfunction numberCompare(a, b) {\n  return a < b ? -1 : a > b ? 1 : 0;\n}\nfunction reverseNumberCompare(a, b) {\n  return -1 * numberCompare(a, b);\n}\nfunction unique2(xs) {\n  if (xs == null) {\n    return xs;\n  }\n  const out = [];\n  for (const x of xs) {\n    if (out.indexOf(x) === -1) {\n      out.push(x);\n    }\n  }\n  return out;\n}\nfunction isObjectEmpty(obj) {\n  if (obj == null) {\n    throw new ValueError(`Invalid value in obj: ${JSON.stringify(obj)}`);\n  }\n  for (const key in obj) {\n    if (obj.hasOwnProperty(key)) {\n      return false;\n    }\n  }\n  return true;\n}\nfunction checkStringTypeUnionValue(values, label, value) {\n  if (value == null) {\n    return;\n  }\n  if (values.indexOf(value) < 0) {\n    throw new ValueError(`${value} is not a valid ${label}.  Valid values are ${values} or null/undefined.`);\n  }\n}\nfunction checkArrayTypeAndLength(x, expectedType, minLength = 0, maxLength = Infinity) {\n  assert2(minLength >= 0);\n  assert2(maxLength >= minLength);\n  return Array.isArray(x) && x.length >= minLength && x.length <= maxLength && x.every((e2) => typeof e2 === expectedType);\n}\nfunction assertPositiveInteger(value, name) {\n  if (Array.isArray(value)) {\n    util_exports.assert(value.length > 0, () => `${name} is unexpectedly an empty array.`);\n    value.forEach((v, i2) => assertPositiveInteger(v, `element ${i2 + 1} of ${name}`));\n  } else {\n    util_exports.assert(Number.isInteger(value) && value > 0, () => `Expected ${name} to be a positive integer, but got ${formatAsFriendlyString(value)}.`);\n  }\n}\nfunction formatAsFriendlyString(value) {\n  if (value === null) {\n    return \"null\";\n  } else if (Array.isArray(value)) {\n    return \"[\" + value.map((v) => formatAsFriendlyString(v)).join(\",\") + \"]\";\n  } else if (typeof value === \"string\") {\n    return `\"${value}\"`;\n  } else {\n    return `${value}`;\n  }\n}\nfunction debounce(f, waitMs, nowFunc) {\n  let lastTime = nowFunc != null ? nowFunc() : util_exports.now();\n  let lastResult;\n  const f2 = (...args) => {\n    const now2 = nowFunc != null ? nowFunc() : util_exports.now();\n    if (now2 - lastTime < waitMs) {\n      return lastResult;\n    }\n    lastTime = now2;\n    lastResult = f(...args);\n    return lastResult;\n  };\n  return f2;\n}\nfunction mapActivationToFusedKernel(activationName) {\n  if (activationName === \"relu\") {\n    return \"relu\";\n  }\n  if (activationName === \"linear\") {\n    return \"linear\";\n  }\n  if (activationName === \"elu\") {\n    return \"elu\";\n  }\n  return null;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-layers@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-layers/dist/backend/state.js\nvar _nextUniqueTensorId = 0;\nfunction getNextUniqueTensorId() {\n  return _nextUniqueTensorId++;\n}\nvar _uidPrefixes = {};\nfunction getUid(prefix = \"\") {\n  if (!(prefix in _uidPrefixes)) {\n    _uidPrefixes[prefix] = 0;\n  }\n  _uidPrefixes[prefix] += 1;\n  return prefix + _uidPrefixes[prefix].toString();\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-layers@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-layers/dist/keras_format/common.js\nvar VALID_DATA_FORMAT_VALUES = [\"channelsFirst\", \"channelsLast\"];\nvar VALID_INTERPOLATION_FORMAT_VALUES = [\"nearest\", \"bilinear\"];\nvar VALID_PADDING_MODE_VALUES = [\"valid\", \"same\", \"causal\"];\nvar VALID_POOL_MODE_VALUES = [\"max\", \"avg\"];\nvar VALID_BIDIRECTIONAL_MERGE_MODES = [\"sum\", \"mul\", \"concat\", \"ave\"];\n\n// node_modules/.pnpm/@tensorflow+tfjs-layers@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-layers/dist/common.js\nvar nameMap = /* @__PURE__ */ new Map();\nfunction checkDataFormat(value) {\n  checkStringTypeUnionValue(VALID_DATA_FORMAT_VALUES, \"DataFormat\", value);\n}\nfunction checkInterpolationFormat(value) {\n  checkStringTypeUnionValue(VALID_INTERPOLATION_FORMAT_VALUES, \"InterpolationFormat\", value);\n}\nfunction checkPaddingMode(value) {\n  checkStringTypeUnionValue(VALID_PADDING_MODE_VALUES, \"PaddingMode\", value);\n}\nfunction checkPoolMode(value) {\n  checkStringTypeUnionValue(VALID_POOL_MODE_VALUES, \"PoolMode\", value);\n}\nvar _nameScopeStack = [];\nvar _nameScopeDivider = \"/\";\nfunction nameScope(name, fn) {\n  _nameScopeStack.push(name);\n  try {\n    const val = fn();\n    _nameScopeStack.pop();\n    return val;\n  } catch (e2) {\n    _nameScopeStack.pop();\n    throw e2;\n  }\n}\nfunction currentNameScopePrefix() {\n  if (_nameScopeStack.length === 0) {\n    return \"\";\n  } else {\n    return _nameScopeStack.join(_nameScopeDivider) + _nameScopeDivider;\n  }\n}\nfunction getScopedTensorName(tensorName) {\n  if (!isValidTensorName(tensorName)) {\n    throw new Error(\"Not a valid tensor name: '\" + tensorName + \"'\");\n  }\n  return currentNameScopePrefix() + tensorName;\n}\nfunction getUniqueTensorName(scopedName) {\n  if (!isValidTensorName(scopedName)) {\n    throw new Error(\"Not a valid tensor name: '\" + scopedName + \"'\");\n  }\n  if (!nameMap.has(scopedName)) {\n    nameMap.set(scopedName, 0);\n  }\n  const index = nameMap.get(scopedName);\n  nameMap.set(scopedName, nameMap.get(scopedName) + 1);\n  if (index > 0) {\n    const result = `${scopedName}_${index}`;\n    nameMap.set(result, 1);\n    return result;\n  } else {\n    return scopedName;\n  }\n}\nvar tensorNameRegex = new RegExp(/^[A-Za-z0-9][-A-Za-z0-9\\._\\/]*$/);\nfunction isValidTensorName(name) {\n  return !!name.match(tensorNameRegex);\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-layers@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-layers/dist/utils/math_utils.js\nfunction isInteger(x) {\n  return x === parseInt(x.toString(), 10);\n}\nfunction arrayProd(array2, begin, end) {\n  if (begin == null) {\n    begin = 0;\n  }\n  if (end == null) {\n    end = array2.length;\n  }\n  let prod6 = 1;\n  for (let i2 = begin; i2 < end; ++i2) {\n    prod6 *= array2[i2];\n  }\n  return prod6;\n}\nfunction min2(array2) {\n  if (array2.length === 0) {\n    return Number.NaN;\n  }\n  let min7 = Number.POSITIVE_INFINITY;\n  for (let i2 = 0; i2 < array2.length; i2++) {\n    const value = array2[i2];\n    if (value < min7) {\n      min7 = value;\n    }\n  }\n  return min7;\n}\nfunction max2(array2) {\n  if (array2.length === 0) {\n    return Number.NaN;\n  }\n  let max7 = Number.NEGATIVE_INFINITY;\n  for (let i2 = 0; i2 < array2.length; i2++) {\n    const value = array2[i2];\n    if (value > max7) {\n      max7 = value;\n    }\n  }\n  return max7;\n}\nfunction range2(begin, end) {\n  if (end < begin) {\n    throw new ValueError(`end (${end}) < begin (${begin}) is forbidden.`);\n  }\n  const out = [];\n  for (let i2 = begin; i2 < end; ++i2) {\n    out.push(i2);\n  }\n  return out;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-layers@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-layers/dist/backend/common.js\nvar _epsilon;\nfunction epsilon() {\n  if (_epsilon == null) {\n    _epsilon = backend().epsilon();\n  }\n  return _epsilon;\n}\nfunction imageDataFormat() {\n  return \"channelsLast\";\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-layers@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-layers/dist/backend/tfjs_backend.js\nfunction cast2(x, dtype) {\n  return cast(x, dtype);\n}\nfunction expandDims2(x, axis = -1) {\n  const outShape = x.shape.slice();\n  if (axis < 0) {\n    axis = outShape.length + axis + 1;\n  }\n  outShape.splice(axis, 0, 1);\n  return reshape(x, outShape);\n}\nfunction repeat(x, n2) {\n  return tidy(() => {\n    if (x.shape.length !== 2) {\n      throw new ValueError(`repeat() expects a rank-2 tensor, but received a rank-${x.shape.length} tensor.`);\n    }\n    const y = expandDims2(x, 1);\n    return tile2(y, [1, n2, 1]);\n  });\n}\nfunction flatten2(x) {\n  const newShape = [arrayProd(x.shape)];\n  return reshape(x, newShape);\n}\nfunction batchFlatten(x) {\n  if (x.rank <= 1) {\n    throw new ValueError(`batchFlatten requires a minimum rank of 2. Got rank: ${x.rank}.`);\n  }\n  const newShape = [x.shape[0], arrayProd(x.shape, 1)];\n  return reshape(x, newShape);\n}\nfunction sliceAlongFirstAxis(array2, start, size) {\n  return tidy(() => {\n    switch (array2.rank) {\n      case 1:\n        return slice1d(array2, start, size);\n      case 2:\n        return slice2d(array2, [start, 0], [size, array2.shape[1]]);\n      case 3:\n        return slice3d(array2, [start, 0, 0], [size, array2.shape[1], array2.shape[2]]);\n      case 4:\n        return slice4d(array2, [start, 0, 0, 0], [size, array2.shape[1], array2.shape[2], array2.shape[3]]);\n      case 5:\n        return slice(array2, [start, 0, 0, 0, 0], [\n          size,\n          array2.shape[1],\n          array2.shape[2],\n          array2.shape[3],\n          array2.shape[4]\n        ]);\n      case 6:\n        return slice(array2, [start, 0, 0, 0, 0, 0], [\n          size,\n          array2.shape[1],\n          array2.shape[2],\n          array2.shape[3],\n          array2.shape[4],\n          array2.shape[5]\n        ]);\n      default:\n        throw new ValueError(`sliceAlongFirstAxis() received an unsupported tensor rank: ${array2.rank}`);\n    }\n  });\n}\nfunction sliceAlongLastAxis(array2, start, size) {\n  return tidy(() => {\n    switch (array2.rank) {\n      case 1:\n        return slice1d(array2, start, size);\n      case 2:\n        return slice2d(array2, [0, start], [array2.shape[0], size]);\n      case 3:\n        return slice3d(array2, [0, 0, start], [array2.shape[0], array2.shape[1], size]);\n      case 4:\n        return slice4d(array2, [0, 0, 0, start], [array2.shape[0], array2.shape[1], array2.shape[2], size]);\n      default:\n        throw new ValueError(`sliceAlongLastAxis() received an unsupported tensor rank: ${array2.rank}`);\n    }\n  });\n}\nfunction sliceAlongAxis(array2, start, size, axis) {\n  return tidy(() => {\n    switch (array2.rank) {\n      case 1:\n        return slice1d(array2, start, size);\n      case 2:\n        switch (axis) {\n          case 1:\n            return sliceAlongFirstAxis(array2, start, size);\n          case 2:\n            return sliceAlongLastAxis(array2, start, size);\n          default:\n            throw new ValueError(`The axis is not within the rank of the tensor ${axis}`);\n        }\n      case 3:\n        switch (axis) {\n          case 1:\n            return sliceAlongFirstAxis(array2, start, size);\n          case 2:\n            return slice3d(array2, [0, start, 0], [array2.shape[0], size, array2.shape[2]]);\n          case 3:\n            return sliceAlongLastAxis(array2, start, size);\n          default:\n            throw new ValueError(`The axis is not within the rank of the tensor ${axis}`);\n        }\n      case 4:\n        switch (axis) {\n          case 1:\n            return sliceAlongFirstAxis(array2, start, size);\n          case 2:\n            return slice4d(array2, [0, start, 0, 0], [array2.shape[0], size, array2.shape[2], array2.shape[3]]);\n          case 3:\n            return slice4d(array2, [0, 0, start, 0], [array2.shape[0], array2.shape[1], size, array2.shape[3]]);\n          case 4:\n            return sliceAlongLastAxis(array2, start, size);\n          default:\n            throw new ValueError(`The axis is not within the rank of the tensor ${axis}`);\n        }\n      default:\n        throw new ValueError(`sliceAlongLastAxis() received an unsupported tensor rank: ${array2.rank}`);\n    }\n  });\n}\nfunction concatenate(tensors, axis = -1) {\n  let rank;\n  if (axis < 0) {\n    rank = tensors[0].rank;\n    if (rank !== 0) {\n      axis = rank;\n    } else {\n      axis = 0;\n    }\n  }\n  if (axis === tensors[0].rank) {\n    axis = -1;\n  }\n  return concat(tensors, axis);\n}\nfunction concatAlongFirstAxis(a, b) {\n  switch (a.rank) {\n    case 1:\n      return concat1d([a, b]);\n    case 2:\n      return concat2d([a, b], 0);\n    case 3:\n      return concat3d([a, b], 0);\n    case 4:\n      return concat4d([a, b], 0);\n    default:\n      throw new ValueError(`concatAlongFirstAxis() received an unsupported tensor rank: ${a.rank}`);\n  }\n}\nfunction tile2(x, n2) {\n  if (!Array.isArray(n2)) {\n    n2 = [n2];\n  }\n  if (x.rank !== n2.length) {\n    throw new ValueError(`The length of input n (${n2.length}) does not match the number of dimensions in input x (${x.rank})`);\n  }\n  return tile(x, n2);\n}\nfunction randomNormal2(shape, mean5 = 0, stddev = 1, dtype, seed) {\n  return randomNormal(shape, mean5, stddev, dtype, seed);\n}\nfunction dot2(a, b, activation2, bias) {\n  if (a.rank < 2 || b.rank < 2) {\n    throw new NotImplementedError(`dot requires both inputs to be rank >= 2 but got x shape = ${a.shape} and y shape = ${b.shape}`);\n  }\n  if (b.rank >= 3) {\n    const xLastDim = a.shape.slice(-1)[0];\n    const ySecondLastDim = b.shape.slice(-2)[0];\n    if (xLastDim !== ySecondLastDim) {\n      throw new NotImplementedError(`If rank y >= 3, then the second last dim of y must equal the last dim of x but got x shape = ${a.shape} and  y shape = ${b.shape}`);\n    }\n  }\n  if (a.rank === 2 && b.rank === 2) {\n    const transposeA = false;\n    const transposeB = false;\n    return fused_ops_exports.matMul({\n      a,\n      b,\n      transposeA,\n      transposeB,\n      bias: bias ? reshapeBias(a.rank, bias, imageDataFormat()) : null,\n      activation: activation2\n    });\n  } else {\n    const aFirstDims = a.shape.slice();\n    const aLastDim = aFirstDims.pop();\n    a = reshape(a, [-1, aLastDim]);\n    const bShape = b.shape.slice();\n    const bLastDim = bShape.pop();\n    const ySecondLastDim = bShape.pop();\n    const yOtherDims = [...bShape, bLastDim];\n    const perm = Array.from({ length: b.rank }, (_, i2) => {\n      if (i2 === 0) {\n        return b.rank - 2;\n      } else if (i2 <= b.rank - 2) {\n        return i2 - 1;\n      }\n      return i2;\n    });\n    b = reshape(transpose(b, perm), [ySecondLastDim, -1]);\n    const outputShape = [...aFirstDims, ...yOtherDims];\n    const transposeA = false;\n    const transposeB = false;\n    return reshape(fused_ops_exports.matMul({\n      a,\n      b,\n      transposeA,\n      transposeB,\n      bias: bias ? reshapeBias(a.rank, bias, imageDataFormat()) : null,\n      activation: activation2\n    }), outputShape);\n  }\n}\nfunction gather2(reference, indices, axis) {\n  return tidy(() => {\n    if (Array.isArray(indices)) {\n      indices = tensor1d(indices, \"int32\");\n    } else {\n      indices = cast(indices, \"int32\");\n    }\n    return gather(reference, indices, axis);\n  });\n}\nfunction square2(x) {\n  return mul(x, x);\n}\nfunction reshapeBias(xRank, bias, dataFormat) {\n  const biasShape = bias.shape;\n  if (bias.rank !== 1 && bias.rank !== xRank) {\n    throw new ValueError(`Unexpected bias dimensions: ${bias.rank}; expected it to be 1 or ${xRank}`);\n  }\n  if (xRank === 5) {\n    if (dataFormat === \"channelsFirst\") {\n      if (biasShape.length === 1) {\n        return reshape(bias, [1, biasShape[0], 1, 1, 1]);\n      } else {\n        return reshape(bias, [1, biasShape[3], biasShape[0], biasShape[1], biasShape[2]]);\n      }\n    } else if (dataFormat === \"channelsLast\") {\n      if (biasShape.length === 1) {\n        return reshape(bias, [1, 1, 1, 1, biasShape[0]]);\n      } else {\n        return reshape(bias, [1].concat(biasShape));\n      }\n    }\n  } else if (xRank === 4) {\n    if (dataFormat === \"channelsFirst\") {\n      if (biasShape.length === 1) {\n        return reshape(bias, [1, biasShape[0], 1, 1]);\n      } else {\n        return reshape(bias, [1, biasShape[2], biasShape[0], biasShape[1]]);\n      }\n    } else if (dataFormat === \"channelsLast\") {\n      if (biasShape.length === 1) {\n        return reshape(bias, [1, 1, 1, biasShape[0]]);\n      } else {\n        return reshape(bias, [1].concat(biasShape));\n      }\n    }\n  } else if (xRank === 3) {\n    if (dataFormat === \"channelsFirst\") {\n      if (biasShape.length === 1) {\n        return reshape(bias, [1, biasShape[0], 1]);\n      } else {\n        return reshape(bias, [1, biasShape[1], biasShape[0]]);\n      }\n    } else if (dataFormat === \"channelsLast\") {\n      if (biasShape.length === 1) {\n        return reshape(bias, [1, 1, biasShape[0]]);\n      } else {\n        return reshape(bias, [1].concat(biasShape));\n      }\n    }\n  } else if (xRank < 3) {\n    return bias;\n  }\n  throw new ValueError(`Unsupported input rank by biasAdd: ${bias.rank}`);\n}\nfunction biasAdd(x, bias, dataFormat) {\n  return tidy(() => {\n    if (dataFormat == null) {\n      dataFormat = imageDataFormat();\n    }\n    checkDataFormat(dataFormat);\n    return add2(x, reshapeBias(x.rank, bias, dataFormat));\n  });\n}\nfunction elu2(x, alpha = 1) {\n  if (alpha !== 1) {\n    throw new NotImplementedError(`Support for alpha values other than 1 (${alpha}) is not implemented yet.`);\n  }\n  return elu(x);\n}\nfunction softsign(x) {\n  return tidy(() => div(x, add2(abs(x), 1)));\n}\nfunction dropout2(x, level, noiseShape, seed) {\n  return tidy(() => dropout(x, level, noiseShape, seed));\n}\nfunction hardSigmoid(x) {\n  return tidy(() => {\n    const y = add2(0.5, mul(0.2, x));\n    return clipByValue(y, 0, 1);\n  });\n}\nfunction inTrainPhase(x, alt, training = false) {\n  return training ? x() : alt();\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-layers@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-layers/dist/keras_format/initializer_config.js\nvar VALID_FAN_MODE_VALUES = [\"fanIn\", \"fanOut\", \"fanAvg\"];\nvar VALID_DISTRIBUTION_VALUES = [\"normal\", \"uniform\", \"truncatedNormal\"];\n\n// node_modules/.pnpm/@tensorflow+tfjs-layers@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-layers/dist/initializers.js\nfunction checkFanMode(value) {\n  checkStringTypeUnionValue(VALID_FAN_MODE_VALUES, \"FanMode\", value);\n}\nfunction checkDistribution(value) {\n  checkStringTypeUnionValue(VALID_DISTRIBUTION_VALUES, \"Distribution\", value);\n}\nvar Initializer = class extends serialization_exports.Serializable {\n  fromConfigUsesCustomObjects() {\n    return false;\n  }\n  getConfig() {\n    return {};\n  }\n};\nvar Zeros = class extends Initializer {\n  apply(shape, dtype) {\n    return zeros(shape, dtype);\n  }\n};\nZeros.className = \"Zeros\";\nserialization_exports.registerClass(Zeros);\nvar Ones = class extends Initializer {\n  apply(shape, dtype) {\n    return ones2(shape, dtype);\n  }\n};\nOnes.className = \"Ones\";\nserialization_exports.registerClass(Ones);\nvar Constant = class extends Initializer {\n  constructor(args) {\n    super();\n    if (typeof args !== \"object\") {\n      throw new ValueError(`Expected argument of type ConstantConfig but got ${args}`);\n    }\n    if (args.value === void 0) {\n      throw new ValueError(`config must have value set but got ${args}`);\n    }\n    this.value = args.value;\n  }\n  apply(shape, dtype) {\n    return tidy(() => mul(scalar(this.value), ones2(shape, dtype)));\n  }\n  getConfig() {\n    return {\n      value: this.value\n    };\n  }\n};\nConstant.className = \"Constant\";\nserialization_exports.registerClass(Constant);\nvar RandomUniform = class extends Initializer {\n  constructor(args) {\n    super();\n    this.DEFAULT_MINVAL = -0.05;\n    this.DEFAULT_MAXVAL = 0.05;\n    this.minval = args.minval || this.DEFAULT_MINVAL;\n    this.maxval = args.maxval || this.DEFAULT_MAXVAL;\n    this.seed = args.seed;\n  }\n  apply(shape, dtype) {\n    return randomUniform(shape, this.minval, this.maxval, dtype);\n  }\n  getConfig() {\n    return { minval: this.minval, maxval: this.maxval, seed: this.seed };\n  }\n};\nRandomUniform.className = \"RandomUniform\";\nserialization_exports.registerClass(RandomUniform);\nvar RandomNormal = class extends Initializer {\n  constructor(args) {\n    super();\n    this.DEFAULT_MEAN = 0;\n    this.DEFAULT_STDDEV = 0.05;\n    this.mean = args.mean || this.DEFAULT_MEAN;\n    this.stddev = args.stddev || this.DEFAULT_STDDEV;\n    this.seed = args.seed;\n  }\n  apply(shape, dtype) {\n    dtype = dtype || \"float32\";\n    if (dtype !== \"float32\" && dtype !== \"int32\") {\n      throw new NotImplementedError(`randomNormal does not support dType ${dtype}.`);\n    }\n    return randomNormal2(shape, this.mean, this.stddev, dtype, this.seed);\n  }\n  getConfig() {\n    return { mean: this.mean, stddev: this.stddev, seed: this.seed };\n  }\n};\nRandomNormal.className = \"RandomNormal\";\nserialization_exports.registerClass(RandomNormal);\nvar TruncatedNormal = class extends Initializer {\n  constructor(args) {\n    super();\n    this.DEFAULT_MEAN = 0;\n    this.DEFAULT_STDDEV = 0.05;\n    this.mean = args.mean || this.DEFAULT_MEAN;\n    this.stddev = args.stddev || this.DEFAULT_STDDEV;\n    this.seed = args.seed;\n  }\n  apply(shape, dtype) {\n    dtype = dtype || \"float32\";\n    if (dtype !== \"float32\" && dtype !== \"int32\") {\n      throw new NotImplementedError(`truncatedNormal does not support dType ${dtype}.`);\n    }\n    return truncatedNormal(shape, this.mean, this.stddev, dtype, this.seed);\n  }\n  getConfig() {\n    return { mean: this.mean, stddev: this.stddev, seed: this.seed };\n  }\n};\nTruncatedNormal.className = \"TruncatedNormal\";\nserialization_exports.registerClass(TruncatedNormal);\nvar Identity2 = class extends Initializer {\n  constructor(args) {\n    super();\n    this.gain = args.gain != null ? args.gain : 1;\n  }\n  apply(shape, dtype) {\n    return tidy(() => {\n      if (shape.length !== 2 || shape[0] !== shape[1]) {\n        throw new ValueError(\"Identity matrix initializer can only be used for 2D square matrices.\");\n      } else {\n        return mul(this.gain, eye(shape[0]));\n      }\n    });\n  }\n  getConfig() {\n    return { gain: this.gain };\n  }\n};\nIdentity2.className = \"Identity\";\nserialization_exports.registerClass(Identity2);\nfunction computeFans(shape, dataFormat = \"channelsLast\") {\n  let fanIn;\n  let fanOut;\n  checkDataFormat(dataFormat);\n  if (shape.length === 2) {\n    fanIn = shape[0];\n    fanOut = shape[1];\n  } else if ([3, 4, 5].indexOf(shape.length) !== -1) {\n    if (dataFormat === \"channelsFirst\") {\n      const receptiveFieldSize = arrayProd(shape, 2);\n      fanIn = shape[1] * receptiveFieldSize;\n      fanOut = shape[0] * receptiveFieldSize;\n    } else if (dataFormat === \"channelsLast\") {\n      const receptiveFieldSize = arrayProd(shape, 0, shape.length - 2);\n      fanIn = shape[shape.length - 2] * receptiveFieldSize;\n      fanOut = shape[shape.length - 1] * receptiveFieldSize;\n    }\n  } else {\n    const shapeProd = arrayProd(shape);\n    fanIn = Math.sqrt(shapeProd);\n    fanOut = Math.sqrt(shapeProd);\n  }\n  return [fanIn, fanOut];\n}\nvar VarianceScaling = class extends Initializer {\n  constructor(args) {\n    super();\n    if (args.scale < 0) {\n      throw new ValueError(`scale must be a positive float. Got: ${args.scale}`);\n    }\n    this.scale = args.scale == null ? 1 : args.scale;\n    this.mode = args.mode == null ? \"fanIn\" : args.mode;\n    checkFanMode(this.mode);\n    this.distribution = args.distribution == null ? \"normal\" : args.distribution;\n    checkDistribution(this.distribution);\n    this.seed = args.seed;\n  }\n  apply(shape, dtype) {\n    const fans = computeFans(shape);\n    const fanIn = fans[0];\n    const fanOut = fans[1];\n    let scale2 = this.scale;\n    if (this.mode === \"fanIn\") {\n      scale2 /= Math.max(1, fanIn);\n    } else if (this.mode === \"fanOut\") {\n      scale2 /= Math.max(1, fanOut);\n    } else {\n      scale2 /= Math.max(1, (fanIn + fanOut) / 2);\n    }\n    if (this.distribution === \"normal\") {\n      const stddev = Math.sqrt(scale2);\n      dtype = dtype || \"float32\";\n      if (dtype !== \"float32\" && dtype !== \"int32\") {\n        throw new NotImplementedError(`${this.getClassName()} does not support dType ${dtype}.`);\n      }\n      return truncatedNormal(shape, 0, stddev, dtype, this.seed);\n    } else {\n      const limit = Math.sqrt(3 * scale2);\n      return randomUniform(shape, -limit, limit, dtype);\n    }\n  }\n  getConfig() {\n    return {\n      scale: this.scale,\n      mode: this.mode,\n      distribution: this.distribution,\n      seed: this.seed\n    };\n  }\n};\nVarianceScaling.className = \"VarianceScaling\";\nserialization_exports.registerClass(VarianceScaling);\nvar GlorotUniform = class extends VarianceScaling {\n  constructor(args) {\n    super({\n      scale: 1,\n      mode: \"fanAvg\",\n      distribution: \"uniform\",\n      seed: args == null ? null : args.seed\n    });\n  }\n  getClassName() {\n    return VarianceScaling.className;\n  }\n};\nGlorotUniform.className = \"GlorotUniform\";\nserialization_exports.registerClass(GlorotUniform);\nvar GlorotNormal = class extends VarianceScaling {\n  constructor(args) {\n    super({\n      scale: 1,\n      mode: \"fanAvg\",\n      distribution: \"normal\",\n      seed: args == null ? null : args.seed\n    });\n  }\n  getClassName() {\n    return VarianceScaling.className;\n  }\n};\nGlorotNormal.className = \"GlorotNormal\";\nserialization_exports.registerClass(GlorotNormal);\nvar HeNormal = class extends VarianceScaling {\n  constructor(args) {\n    super({\n      scale: 2,\n      mode: \"fanIn\",\n      distribution: \"normal\",\n      seed: args == null ? null : args.seed\n    });\n  }\n  getClassName() {\n    return VarianceScaling.className;\n  }\n};\nHeNormal.className = \"HeNormal\";\nserialization_exports.registerClass(HeNormal);\nvar HeUniform = class extends VarianceScaling {\n  constructor(args) {\n    super({\n      scale: 2,\n      mode: \"fanIn\",\n      distribution: \"uniform\",\n      seed: args == null ? null : args.seed\n    });\n  }\n  getClassName() {\n    return VarianceScaling.className;\n  }\n};\nHeUniform.className = \"HeUniform\";\nserialization_exports.registerClass(HeUniform);\nvar LeCunNormal = class extends VarianceScaling {\n  constructor(args) {\n    super({\n      scale: 1,\n      mode: \"fanIn\",\n      distribution: \"normal\",\n      seed: args == null ? null : args.seed\n    });\n  }\n  getClassName() {\n    return VarianceScaling.className;\n  }\n};\nLeCunNormal.className = \"LeCunNormal\";\nserialization_exports.registerClass(LeCunNormal);\nvar LeCunUniform = class extends VarianceScaling {\n  constructor(args) {\n    super({\n      scale: 1,\n      mode: \"fanIn\",\n      distribution: \"uniform\",\n      seed: args == null ? null : args.seed\n    });\n  }\n  getClassName() {\n    return VarianceScaling.className;\n  }\n};\nLeCunUniform.className = \"LeCunNormal\";\nserialization_exports.registerClass(LeCunUniform);\nvar Orthogonal = class extends Initializer {\n  constructor(args) {\n    super();\n    this.DEFAULT_GAIN = 1;\n    this.gain = args.gain == null ? this.DEFAULT_GAIN : args.gain;\n    this.seed = args.seed;\n    if (this.seed != null) {\n      throw new NotImplementedError(\"Random seed is not implemented for Orthogonal Initializer yet.\");\n    }\n  }\n  apply(shape, dtype) {\n    return tidy(() => {\n      if (shape.length < 2) {\n        throw new NotImplementedError(\"Shape must be at least 2D.\");\n      }\n      if (shape[0] * shape[1] > 2e3) {\n        console.warn(`Orthogonal initializer is being called on a matrix with more than 2000 (${shape[0] * shape[1]}) elements: Slowness may result.`);\n      }\n      const normalizedShape = shape[0] > shape[1] ? [shape[1], shape[0]] : shape;\n      const a = randomNormal2(normalizedShape, 0, 1, \"float32\");\n      let q = linalg.gramSchmidt(a);\n      if (shape[0] > shape[1]) {\n        q = transpose(q);\n      }\n      return mul(this.gain, q);\n    });\n  }\n  getConfig() {\n    return {\n      gain: this.gain,\n      seed: this.seed\n    };\n  }\n};\nOrthogonal.className = \"Orthogonal\";\nserialization_exports.registerClass(Orthogonal);\nvar INITIALIZER_IDENTIFIER_REGISTRY_SYMBOL_MAP = {\n  \"constant\": \"Constant\",\n  \"glorotNormal\": \"GlorotNormal\",\n  \"glorotUniform\": \"GlorotUniform\",\n  \"heNormal\": \"HeNormal\",\n  \"heUniform\": \"HeUniform\",\n  \"identity\": \"Identity\",\n  \"leCunNormal\": \"LeCunNormal\",\n  \"leCunUniform\": \"LeCunUniform\",\n  \"ones\": \"Ones\",\n  \"orthogonal\": \"Orthogonal\",\n  \"randomNormal\": \"RandomNormal\",\n  \"randomUniform\": \"RandomUniform\",\n  \"truncatedNormal\": \"TruncatedNormal\",\n  \"varianceScaling\": \"VarianceScaling\",\n  \"zeros\": \"Zeros\"\n};\nfunction deserializeInitializer(config, customObjects = {}) {\n  return deserializeKerasObject(config, serialization_exports.SerializationMap.getMap().classNameMap, customObjects, \"initializer\");\n}\nfunction serializeInitializer(initializer) {\n  return serializeKerasObject(initializer);\n}\nfunction getInitializer(identifier) {\n  if (typeof identifier === \"string\") {\n    const className = identifier in INITIALIZER_IDENTIFIER_REGISTRY_SYMBOL_MAP ? INITIALIZER_IDENTIFIER_REGISTRY_SYMBOL_MAP[identifier] : identifier;\n    if (className === \"GlorotNormal\") {\n      return new GlorotNormal();\n    } else if (className === \"GlorotUniform\") {\n      return new GlorotUniform();\n    } else if (className === \"HeNormal\") {\n      return new HeNormal();\n    } else if (className === \"HeUniform\") {\n      return new HeUniform();\n    } else if (className === \"LeCunNormal\") {\n      return new LeCunNormal();\n    } else if (className === \"LeCunUniform\") {\n      return new LeCunUniform();\n    } else {\n      const config = {};\n      config[\"className\"] = className;\n      config[\"config\"] = {};\n      return deserializeInitializer(config);\n    }\n  } else if (identifier instanceof Initializer) {\n    return identifier;\n  } else {\n    return deserializeInitializer(identifier);\n  }\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-layers@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-layers/dist/utils/types_utils.js\nfunction isArrayOfShapes(x) {\n  return Array.isArray(x) && Array.isArray(x[0]);\n}\nfunction normalizeShapeList(x) {\n  if (x.length === 0) {\n    return [];\n  }\n  if (!Array.isArray(x[0])) {\n    return [x];\n  }\n  return x;\n}\nfunction getExactlyOneTensor(xs) {\n  let x;\n  if (Array.isArray(xs)) {\n    if (xs.length !== 1) {\n      throw new ValueError(`Expected Tensor length to be 1; got ${xs.length}`);\n    }\n    x = xs[0];\n  } else {\n    x = xs;\n  }\n  return x;\n}\nfunction getExactlyOneShape(shapes) {\n  if (Array.isArray(shapes) && Array.isArray(shapes[0])) {\n    if (shapes.length === 1) {\n      shapes = shapes;\n      return shapes[0];\n    } else {\n      throw new ValueError(`Expected exactly 1 Shape; got ${shapes.length}`);\n    }\n  } else {\n    return shapes;\n  }\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-layers@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-layers/dist/utils/variable_utils.js\nfunction countParamsInWeights(weights) {\n  let count2 = 0;\n  for (const weight of weights) {\n    if (weight.shape.length === 0) {\n      count2 += 1;\n    } else {\n      count2 += weight.shape.reduce((a, b) => a * b);\n    }\n  }\n  return count2;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-layers@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-layers/dist/variables.js\nvar DEFAULT_VARIABLE_NAME_PREFIX = \"Variable\";\nvar LayerVariable = class {\n  constructor(val, dtype = \"float32\", name = DEFAULT_VARIABLE_NAME_PREFIX, trainable = true, constraint = null) {\n    this.dtype = dtype == null ? \"float32\" : dtype;\n    this.shape = val.shape;\n    this.id = getNextUniqueTensorId();\n    name = name == null ? DEFAULT_VARIABLE_NAME_PREFIX : name;\n    this.originalName = getScopedTensorName(name);\n    this.name = getUniqueTensorName(this.originalName);\n    this.trainable_ = trainable;\n    this.constraint = constraint;\n    this.val = variable(val, this.trainable_, this.name, this.dtype);\n  }\n  read() {\n    this.assertNotDisposed();\n    return this.val;\n  }\n  write(newVal) {\n    this.assertNotDisposed();\n    checkShapesMatch(this.val, newVal);\n    if (this.val.id !== newVal.id) {\n      this.val.assign(newVal);\n      if (this.constraint != null) {\n        this.val.assign(this.constraint.apply(this.val));\n      }\n    }\n    return this;\n  }\n  dispose() {\n    this.assertNotDisposed();\n    this.val.dispose();\n  }\n  assertNotDisposed() {\n    if (this.val.isDisposed) {\n      throw new Error(`LayersVariable ${this.name} is already disposed.`);\n    }\n  }\n  get trainable() {\n    return this.trainable_;\n  }\n  set trainable(trainable) {\n    this.trainable_ = trainable;\n    this.val.trainable = trainable;\n  }\n};\nfunction checkShapesMatch(x, y) {\n  if (x.shape.toString() !== y.shape.toString()) {\n    throw new Error(\"Shape mismatch: \" + JSON.stringify(x.shape) + \" vs. \" + JSON.stringify(y.shape));\n  }\n}\nfunction batchGetValue(xs) {\n  return xs.map((x) => x.read());\n}\nfunction batchSetValue(variablesAndValues) {\n  variablesAndValues.forEach((variableAndValue) => {\n    const variable2 = variableAndValue[0];\n    variable2.write(variableAndValue[1]);\n  });\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-layers@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-layers/dist/engine/topology.js\nvar InputSpec = class {\n  constructor(args) {\n    this.dtype = args.dtype;\n    this.shape = args.shape;\n    if (args.shape != null) {\n      this.ndim = args.shape.length;\n    } else {\n      this.ndim = args.ndim;\n    }\n    this.maxNDim = args.maxNDim;\n    this.minNDim = args.minNDim;\n    this.axes = args.axes || {};\n  }\n};\nvar SymbolicTensor = class {\n  constructor(dtype, shape, sourceLayer, inputs, callArgs, name, outputTensorIndex) {\n    this.dtype = dtype;\n    this.shape = shape;\n    this.sourceLayer = sourceLayer;\n    this.inputs = inputs;\n    this.callArgs = callArgs;\n    this.outputTensorIndex = outputTensorIndex;\n    this.id = getNextUniqueTensorId();\n    if (name != null) {\n      this.originalName = getScopedTensorName(name);\n      this.name = getUniqueTensorName(this.originalName);\n    }\n    this.rank = shape.length;\n  }\n};\nvar _nextNodeID = 0;\nvar Node = class {\n  constructor(args, callArgs) {\n    this.callArgs = callArgs;\n    this.id = _nextNodeID++;\n    this.outboundLayer = args.outboundLayer;\n    this.inboundLayers = args.inboundLayers;\n    this.nodeIndices = args.nodeIndices;\n    this.tensorIndices = args.tensorIndices;\n    this.inputTensors = args.inputTensors;\n    this.outputTensors = args.outputTensors;\n    this.inputMasks = args.inputMasks;\n    this.outputMasks = args.outputMasks;\n    this.inputShapes = args.inputShapes;\n    this.outputShapes = args.outputShapes;\n    for (const layer of args.inboundLayers) {\n      if (layer != null) {\n        layer.outboundNodes.push(this);\n      }\n    }\n    args.outboundLayer.inboundNodes.push(this);\n  }\n  getConfig() {\n    const inboundNames = [];\n    for (const layer of this.inboundLayers) {\n      if (layer != null) {\n        inboundNames.push(layer.name);\n      } else {\n        inboundNames.push(null);\n      }\n    }\n    return {\n      outboundLayer: this.outboundLayer ? this.outboundLayer.name : null,\n      inboundLayers: inboundNames,\n      nodeIndices: this.nodeIndices,\n      tensorIndices: this.tensorIndices\n    };\n  }\n};\nvar _nextLayerID = 0;\nvar Layer = class extends serialization_exports.Serializable {\n  constructor(args = {}) {\n    super();\n    this._callHook = null;\n    this._addedWeightNames = [];\n    this._stateful = false;\n    this.id = _nextLayerID++;\n    this.activityRegularizer = null;\n    this.inputSpec = null;\n    this.supportsMasking = false;\n    this._trainableWeights = [];\n    this._nonTrainableWeights = [];\n    this._losses = [];\n    this._updates = [];\n    this._built = false;\n    this.inboundNodes = [];\n    this.outboundNodes = [];\n    let name = args.name;\n    if (!name) {\n      const prefix = this.getClassName();\n      name = toSnakeCase(prefix) + \"_\" + getUid(prefix);\n    }\n    this.name = name;\n    this.trainable_ = args.trainable == null ? true : args.trainable;\n    if (args.inputShape != null || args.batchInputShape != null) {\n      let batchInputShape;\n      if (args.batchInputShape != null) {\n        batchInputShape = args.batchInputShape;\n      } else if (args.inputShape != null) {\n        let batchSize = null;\n        if (args.batchSize != null) {\n          batchSize = args.batchSize;\n        }\n        batchInputShape = [batchSize].concat(args.inputShape);\n      }\n      this.batchInputShape = batchInputShape;\n      let dtype = args.dtype;\n      if (dtype == null) {\n        dtype = args.inputDType;\n      }\n      if (dtype == null) {\n        dtype = \"float32\";\n      }\n      this.dtype = dtype;\n    }\n    if (args.weights != null) {\n      this.initialWeights = args.weights;\n    } else {\n      this.initialWeights = null;\n    }\n    this._refCount = null;\n    this.fastWeightInitDuringBuild = false;\n  }\n  static nodeKey(layer, nodeIndex) {\n    return layer.name + \"_ib-\" + nodeIndex.toString();\n  }\n  getNodeAtIndex(nodeIndex, attrName) {\n    if (this.inboundNodes.length === 0) {\n      throw new RuntimeError(`The layer has never been called and thus has no defined ${attrName}.`);\n    }\n    if (this.inboundNodes.length <= nodeIndex) {\n      throw new ValueError(`Asked to get ${attrName} at node ${nodeIndex}, but the layer has only ${this.inboundNodes.length} inbound nodes.`);\n    }\n    return this.inboundNodes[nodeIndex];\n  }\n  getInputAt(nodeIndex) {\n    return singletonOrArray(this.getNodeAtIndex(nodeIndex, \"input\").inputTensors);\n  }\n  getOutputAt(nodeIndex) {\n    return singletonOrArray(this.getNodeAtIndex(nodeIndex, \"output\").outputTensors);\n  }\n  get input() {\n    if (this.inboundNodes.length > 1) {\n      throw new AttributeError(`Layer ${this.name} has multiple inbound nodes, hence the notion of \"layer input\" is ill-defined. Use \\`getInputAt(nodeIndex)\\` instead.`);\n    } else if (this.inboundNodes.length === 0) {\n      throw new AttributeError(`Layer ${this.name} is not connected, no input to return.`);\n    }\n    return singletonOrArray(this.getNodeAtIndex(0, \"input\").inputTensors);\n  }\n  get output() {\n    if (this.inboundNodes.length === 0) {\n      throw new AttributeError(`Layer ${this.name} has no inbound nodes.`);\n    }\n    if (this.inboundNodes.length > 1) {\n      throw new AttributeError(`Layer ${this.name} has multiple inbound nodes, hence the notion of \"layer output\" is ill-defined. Use \\`getOutputAt(nodeIndex)\\` instead.`);\n    }\n    return singletonOrArray(this.getNodeAtIndex(0, \"output\").outputTensors);\n  }\n  get losses() {\n    return this._losses;\n  }\n  calculateLosses() {\n    return this.losses.map((lossFn) => lossFn());\n  }\n  get updates() {\n    return this._updates;\n  }\n  get built() {\n    return this._built;\n  }\n  set built(built) {\n    this._built = built;\n  }\n  get trainable() {\n    return this.trainable_;\n  }\n  set trainable(trainable) {\n    this._trainableWeights.forEach((w) => w.trainable = trainable);\n    this.trainable_ = trainable;\n  }\n  get trainableWeights() {\n    if (this.trainable_) {\n      return this._trainableWeights.filter((w) => w.trainable);\n    } else {\n      return [];\n    }\n  }\n  set trainableWeights(weights) {\n    this._trainableWeights = weights;\n  }\n  get nonTrainableWeights() {\n    if (this.trainable) {\n      return this._trainableWeights.filter((w) => !w.trainable).concat(this._nonTrainableWeights);\n    } else {\n      return this._trainableWeights.concat(this._nonTrainableWeights);\n    }\n  }\n  set nonTrainableWeights(weights) {\n    this._nonTrainableWeights = weights;\n  }\n  get weights() {\n    return this.trainableWeights.concat(this.nonTrainableWeights);\n  }\n  get stateful() {\n    return this._stateful;\n  }\n  resetStates() {\n    if (!this.stateful) {\n      throw new Error(\"Cannot call the resetStates() method of a non-stateful Layer object.\");\n    }\n  }\n  assertInputCompatibility(inputs) {\n    inputs = toList(inputs);\n    if (this.inputSpec == null || this.inputSpec.length === 0) {\n      return;\n    }\n    const inputSpec = toList(this.inputSpec);\n    if (inputs.length !== inputSpec.length) {\n      throw new ValueError(`Layer ${this.name} expects ${inputSpec.length} inputs, but it received ${inputs.length} input tensors. Input received: ${inputs}`);\n    }\n    for (let inputIndex = 0; inputIndex < inputs.length; inputIndex++) {\n      const x = inputs[inputIndex];\n      const spec = inputSpec[inputIndex];\n      if (spec == null) {\n        continue;\n      }\n      const ndim = x.rank;\n      if (spec.ndim != null) {\n        if (ndim !== spec.ndim) {\n          throw new ValueError(`Input ${inputIndex} is incompatible with layer ${this.name}: expected ndim=${spec.ndim}, found ndim=${ndim}`);\n        }\n      }\n      if (spec.maxNDim != null) {\n        if (ndim > spec.maxNDim) {\n          throw new ValueError(`Input ${inputIndex} is incompatible with layer ${this.name}: expected max_ndim=${spec.maxNDim}, found ndim=${ndim}`);\n        }\n      }\n      if (spec.minNDim != null) {\n        if (ndim < spec.minNDim) {\n          throw new ValueError(`Input ${inputIndex} is incompatible with layer ${this.name}: expected min_ndim=${spec.minNDim}, found ndim=${ndim}.`);\n        }\n      }\n      if (spec.dtype != null) {\n        if (x.dtype !== spec.dtype) {\n          throw new ValueError(`Input ${inputIndex} is incompatible with layer ${this.name} : expected dtype=${spec.dtype}, found dtype=${x.dtype}.`);\n        }\n      }\n      if (spec.axes) {\n        const xShape = x.shape;\n        for (const key in spec.axes) {\n          const axis = Number(key);\n          const value = spec.axes[key];\n          const xShapeAtAxis = axis >= 0 ? xShape[axis] : xShape[xShape.length + axis];\n          if (value != null && [value, null].indexOf(xShapeAtAxis) === -1) {\n            throw new ValueError(`Input ${inputIndex} is incompatible with layer ${this.name}: expected axis ${axis} of input shape to have value ${value} but got shape ${xShape}.`);\n          }\n        }\n      }\n      if (spec.shape != null) {\n        for (let i2 = 0; i2 < spec.shape.length; ++i2) {\n          const specDim = spec.shape[i2];\n          const dim = x.shape[i2];\n          if (specDim != null && dim != null) {\n            if (specDim !== dim) {\n              throw new ValueError(`Input ${inputIndex} is incompatible with layer ${this.name}: expected shape=${spec.shape}, found shape=${x.shape}.`);\n            }\n          }\n        }\n      }\n    }\n  }\n  call(inputs, kwargs) {\n    return inputs;\n  }\n  invokeCallHook(inputs, kwargs) {\n    if (this._callHook != null) {\n      this._callHook(inputs, kwargs);\n    }\n  }\n  setCallHook(callHook) {\n    this._callHook = callHook;\n  }\n  clearCallHook() {\n    this._callHook = null;\n  }\n  apply(inputs, kwargs) {\n    kwargs = kwargs || {};\n    this.assertNotDisposed();\n    const inputsList = toList(inputs);\n    let allAreSymbolic = true;\n    for (const input2 of inputsList) {\n      if (!(input2 instanceof SymbolicTensor)) {\n        allAreSymbolic = false;\n        break;\n      }\n    }\n    let noneAreSymbolic = true;\n    for (const input2 of inputsList) {\n      if (input2 instanceof SymbolicTensor) {\n        noneAreSymbolic = false;\n        break;\n      }\n    }\n    if (allAreSymbolic === noneAreSymbolic) {\n      throw new ValueError(\"Arguments to apply() must be all SymbolicTensors or all Tensors\");\n    }\n    return nameScope(this.name, () => {\n      if (!this.built) {\n        this.assertInputCompatibility(inputs);\n        const inputShapes = [];\n        for (const xElem of toList(inputs)) {\n          inputShapes.push(xElem.shape);\n        }\n        this.build(singletonOrArray(inputShapes));\n        this.built = true;\n        if (this.initialWeights) {\n          this.setWeights(this.initialWeights);\n        }\n        if (this._refCount === null && noneAreSymbolic) {\n          this._refCount = 1;\n        }\n      }\n      this.assertInputCompatibility(inputs);\n      if (noneAreSymbolic) {\n        let output = this.call(inputs, kwargs);\n        const outputList = toList(output);\n        const outputListCopy = [];\n        for (let x of outputList) {\n          if (inputsList.indexOf(x) !== -1) {\n            x = x.clone();\n          }\n          outputListCopy.push(x);\n        }\n        output = singletonOrArray(outputListCopy);\n        if (this.activityRegularizer != null) {\n          throw new NotImplementedError(\"Layer invocation in the presence of activity regularizer(s) is not supported yet.\");\n        }\n        return output;\n      } else {\n        const inputShape = collectInputShape(inputs);\n        const outputShape = this.computeOutputShape(inputShape);\n        let output;\n        const outputDType = guessOutputDType(inputs);\n        this.warnOnIncompatibleInputShape(Array.isArray(inputs) ? inputShape[0] : inputShape);\n        if (outputShape != null && outputShape.length > 0 && Array.isArray(outputShape[0])) {\n          output = outputShape.map((shape, index) => new SymbolicTensor(outputDType, shape, this, toList(inputs), kwargs, this.name, index));\n        } else {\n          output = new SymbolicTensor(outputDType, outputShape, this, toList(inputs), kwargs, this.name);\n        }\n        this.addInboundNode(inputs, output, null, null, inputShape, outputShape, kwargs);\n        this._refCount++;\n        if (this.activityRegularizer != null) {\n          throw new NotImplementedError(\"Layer invocation in the presence of activity regularizer(s) is not supported yet.\");\n        }\n        return output;\n      }\n    });\n  }\n  warnOnIncompatibleInputShape(inputShape) {\n    if (this.batchInputShape == null) {\n      return;\n    } else if (inputShape.length !== this.batchInputShape.length) {\n      console.warn(`The rank of the input tensor provided (shape: ${JSON.stringify(inputShape)}) does not match that of the batchInputShape (${JSON.stringify(this.batchInputShape)}) of the layer ${this.name}`);\n    } else {\n      let dimMismatch = false;\n      this.batchInputShape.forEach((dimension, i2) => {\n        if (dimension != null && inputShape[i2] != null && inputShape[i2] !== dimension) {\n          dimMismatch = true;\n        }\n      });\n      if (dimMismatch) {\n        console.warn(`The shape of the input tensor (${JSON.stringify(inputShape)}) does not match the expectation of layer ${this.name}: ${JSON.stringify(this.batchInputShape)}`);\n      }\n    }\n  }\n  get outputShape() {\n    if (this.inboundNodes == null || this.inboundNodes.length === 0) {\n      throw new AttributeError(`The layer ${this.name} has never been called and thus has no defined output shape.`);\n    }\n    const allOutputShapes = [];\n    for (const node of this.inboundNodes) {\n      const shapeString = JSON.stringify(node.outputShapes);\n      if (allOutputShapes.indexOf(shapeString) === -1) {\n        allOutputShapes.push(shapeString);\n      }\n    }\n    if (allOutputShapes.length === 1) {\n      const outputShapes = this.inboundNodes[0].outputShapes;\n      if (Array.isArray(outputShapes) && Array.isArray(outputShapes[0]) && outputShapes.length === 1) {\n        return outputShapes[0];\n      } else {\n        return outputShapes;\n      }\n    } else {\n      throw new AttributeError(`The layer ${this.name} has multiple inbound nodes with different output shapes. Hence the notion of \"output shape\" is ill-defined for the layer.`);\n    }\n  }\n  countParams() {\n    if (!this.built) {\n      throw new RuntimeError(`You tried to call countParams() on ${this.name}, but the layer is not built yet. Build it first by calling build(batchInputShape).`);\n    }\n    return countParamsInWeights(this.weights);\n  }\n  build(inputShape) {\n    this.built = true;\n  }\n  getWeights(trainableOnly = false) {\n    return batchGetValue(trainableOnly ? this.trainableWeights : this.weights);\n  }\n  setWeights(weights) {\n    tidy(() => {\n      const params = this.weights;\n      if (params.length !== weights.length) {\n        throw new ValueError(`You called setWeights(weights) on layer \"${this.name}\" with a weight list of length ${weights.length}, but the layer was expecting ${params.length} weights. Provided weights: ${weights}...`);\n      }\n      if (params.length === 0) {\n        return;\n      }\n      const weightValueTuples = [];\n      const paramValues = batchGetValue(params);\n      for (let i2 = 0; i2 < paramValues.length; ++i2) {\n        const pv = paramValues[i2];\n        const p2 = params[i2];\n        const w = weights[i2];\n        if (!util_exports.arraysEqual(pv.shape, w.shape)) {\n          throw new ValueError(`Layer weight shape ${pv.shape} not compatible with provided weight shape ${w.shape}`);\n        }\n        weightValueTuples.push([p2, w]);\n      }\n      batchSetValue(weightValueTuples);\n    });\n  }\n  addWeight(name, shape, dtype, initializer, regularizer, trainable, constraint, getInitializerFunc) {\n    if (this._addedWeightNames.indexOf(name) !== -1) {\n      throw new ValueError(`Duplicate weight name ${name} for layer ${this.name}`);\n    }\n    this._addedWeightNames.push(name);\n    if (dtype == null) {\n      dtype = \"float32\";\n    }\n    if (this.fastWeightInitDuringBuild) {\n      initializer = getInitializerFunc != null ? getInitializerFunc() : getInitializer(\"zeros\");\n    }\n    const initValue = initializer.apply(shape, dtype);\n    const weight = new LayerVariable(initValue, dtype, name, trainable, constraint);\n    initValue.dispose();\n    if (regularizer != null) {\n      this.addLoss(() => regularizer.apply(weight.read()));\n    }\n    if (trainable == null) {\n      trainable = true;\n    }\n    if (trainable) {\n      this._trainableWeights.push(weight);\n    } else {\n      this._nonTrainableWeights.push(weight);\n    }\n    return weight;\n  }\n  setFastWeightInitDuringBuild(value) {\n    this.fastWeightInitDuringBuild = value;\n  }\n  addLoss(losses2) {\n    if (losses2 == null || Array.isArray(losses2) && losses2.length === 0) {\n      return;\n    }\n    losses2 = toList(losses2);\n    if (this._losses !== void 0 && this._losses !== null) {\n      this.losses.push(...losses2);\n    }\n  }\n  computeOutputShape(inputShape) {\n    return inputShape;\n  }\n  computeMask(inputs, mask) {\n    if (!this.supportsMasking) {\n      if (mask != null) {\n        if (Array.isArray(mask)) {\n          mask.forEach((maskElement) => {\n            if (maskElement != null) {\n              throw new TypeError(`Layer ${this.name} does not support masking, but was passed an inputMask.`);\n            }\n          });\n        } else {\n          throw new TypeError(`Layer ${this.name} does not support masking, but was passed an inputMask.`);\n        }\n      }\n      return null;\n    }\n    return mask;\n  }\n  addInboundNode(inputTensors, outputTensors, inputMasks, outputMasks, inputShapes, outputShapes, kwargs = null) {\n    const inputTensorList = toList(inputTensors);\n    outputTensors = toList(outputTensors);\n    inputMasks = toList(inputMasks);\n    outputMasks = toList(outputMasks);\n    inputShapes = normalizeShapeList(inputShapes);\n    outputShapes = normalizeShapeList(outputShapes);\n    const inboundLayers = [];\n    const nodeIndices = [];\n    const tensorIndices = [];\n    for (const x of inputTensorList) {\n      inboundLayers.push(x.sourceLayer);\n      nodeIndices.push(x.nodeIndex);\n      tensorIndices.push(x.tensorIndex);\n    }\n    new Node({\n      outboundLayer: this,\n      inboundLayers,\n      nodeIndices,\n      tensorIndices,\n      inputTensors: inputTensorList,\n      outputTensors,\n      inputMasks,\n      outputMasks,\n      inputShapes,\n      outputShapes\n    }, kwargs);\n    for (let i2 = 0; i2 < outputTensors.length; i2++) {\n      outputTensors[i2].sourceLayer = this;\n      outputTensors[i2].nodeIndex = this.inboundNodes.length - 1;\n      outputTensors[i2].tensorIndex = i2;\n    }\n  }\n  getConfig() {\n    const config = { name: this.name, trainable: this.trainable };\n    if (this.batchInputShape != null) {\n      config[\"batchInputShape\"] = this.batchInputShape;\n    }\n    if (this.dtype != null) {\n      config[\"dtype\"] = this.dtype;\n    }\n    return config;\n  }\n  disposeWeights() {\n    this.weights.forEach((weight) => weight.dispose());\n    return this.weights.length;\n  }\n  assertNotDisposed() {\n    if (this._refCount === 0) {\n      throw new Error(`Layer '${this.name}' is already disposed.`);\n    }\n  }\n  dispose() {\n    if (!this.built) {\n      throw new Error(`Cannot dispose Layer ${this.name} because it has not been built yet.`);\n    }\n    if (this._refCount === null) {\n      throw new Error(`Cannot dispose Layer ${this.name} because it has not been used yet.`);\n    }\n    this.assertNotDisposed();\n    let numDisposedVariables = 0;\n    if (--this._refCount === 0) {\n      numDisposedVariables = this.disposeWeights();\n    }\n    return { refCountAfterDispose: this._refCount, numDisposedVariables };\n  }\n};\nfunction collectInputShape(inputTensors) {\n  inputTensors = toList(inputTensors);\n  const shapes = [];\n  for (const x of inputTensors) {\n    shapes.push(x.shape);\n  }\n  return singletonOrArray(shapes);\n}\nfunction guessOutputDType(inputTensors) {\n  return \"float32\";\n}\nfunction getSourceInputs(tensor2, layer, nodeIndex) {\n  if (layer == null || nodeIndex != null && nodeIndex > 0) {\n    layer = tensor2.sourceLayer;\n    nodeIndex = tensor2.nodeIndex;\n  }\n  if (layer.inboundNodes.length === 0) {\n    return [tensor2];\n  } else {\n    const node = layer.inboundNodes[nodeIndex];\n    if (node.inboundLayers.length === 0) {\n      return node.inputTensors;\n    } else {\n      const sourceTensors = [];\n      for (let i2 = 0; i2 < node.inboundLayers.length; i2++) {\n        const x = node.inputTensors[i2];\n        const layer2 = node.inboundLayers[i2];\n        const nodeIndex2 = node.nodeIndices[i2];\n        const previousSources = getSourceInputs(x, layer2, nodeIndex2);\n        for (const x2 of previousSources) {\n          if (sourceTensors.indexOf(x2) === -1) {\n            sourceTensors.push(x2);\n          }\n        }\n      }\n      return sourceTensors;\n    }\n  }\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-layers@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-layers/dist/engine/input_layer.js\nvar InputLayer = class extends Layer {\n  constructor(args) {\n    super({\n      dtype: args.dtype,\n      name: args.name != null ? args.name : getUid(\"input\").toString()\n    });\n    if (args.batchSize == null) {\n      args.batchSize = null;\n    }\n    if (args.sparse == null) {\n      args.sparse = false;\n    }\n    this.trainable = false;\n    this.built = true;\n    this.sparse = args.sparse;\n    if (args.inputShape != null && args.batchInputShape != null) {\n      throw new ValueError(\"Only provide the inputShape OR batchInputShape argument to inputLayer, not both at the same time.\");\n    }\n    let batchInputShape = args.batchInputShape;\n    if (batchInputShape == null) {\n      if (args.inputShape == null) {\n        throw new ValueError(\"An InputLayer should be passed either a `batchInputShape` or an `inputShape`.\");\n      } else {\n        batchInputShape = [args.batchSize].concat(args.inputShape);\n      }\n    } else {\n      if (args.batchSize != null) {\n        throw new ValueError(\"Cannot specify batchSize if batchInputShape is specified when creating an InputLayer.\");\n      }\n    }\n    const dtype = args.dtype || \"float32\";\n    this.batchInputShape = batchInputShape;\n    this.dtype = dtype;\n    this.inputSpec = [{ shape: batchInputShape }];\n    const inputTensor = new SymbolicTensor(this.dtype, this.batchInputShape, this, [], {}, this.name);\n    inputTensor.nodeIndex = 0;\n    inputTensor.tensorIndex = 0;\n    new Node({\n      outboundLayer: this,\n      inboundLayers: [],\n      nodeIndices: [],\n      tensorIndices: [],\n      inputTensors: [inputTensor],\n      outputTensors: [inputTensor],\n      inputMasks: [null],\n      outputMasks: [null],\n      inputShapes: [batchInputShape],\n      outputShapes: [batchInputShape]\n    });\n  }\n  apply(inputs, kwargs) {\n    throw new ValueError(`Cannot pass any input to an InputLayer's apply() method. InputLayer name: ${this.name}`);\n  }\n  dispose() {\n    return { refCountAfterDispose: this._refCount, numDisposedVariables: 0 };\n  }\n  getConfig() {\n    return {\n      batchInputShape: this.batchInputShape,\n      dtype: this.dtype,\n      sparse: this.sparse,\n      name: this.name\n    };\n  }\n};\nInputLayer.className = \"InputLayer\";\nserialization_exports.registerClass(InputLayer);\nfunction Input(config) {\n  if (config.batchShape == null && config.shape == null) {\n    throw new Error(\"Please provide to Input either a `shape` or a `batchShape` argument. Note that `shape` does not include the batch dimension.\");\n  }\n  if (config.batchShape != null && config.shape != null) {\n    throw new ValueError(\"Please provide either a `shape` or `batchShape` argument to Input, but not both.\");\n  }\n  let batchShape = config.batchShape;\n  if (config.shape != null && batchShape == null) {\n    batchShape = [null].concat(config.shape);\n  }\n  let dtype = config.dtype;\n  if (dtype == null) {\n    dtype = \"float32\";\n  }\n  const inputLayer2 = new InputLayer({\n    batchInputShape: batchShape,\n    name: config.name,\n    dtype,\n    sparse: config.sparse\n  });\n  const outputs = inputLayer2.inboundNodes[0].outputTensors;\n  return outputs[0];\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-layers@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-layers/dist/engine/executor.js\nfunction assertFeedCompatibility(key, val) {\n  if (key.dtype == null || key.dtype === val.dtype) {\n    return val;\n  }\n  try {\n    return cast(val, key.dtype);\n  } catch (err) {\n    throw new ValueError(`The dtype of the feed (${val.dtype}) can not be cast to the dtype of the key '${key.name}' (${key.dtype}).`);\n  }\n}\nvar FeedDict = class {\n  constructor(feeds) {\n    this.id2Value = {};\n    this.id2Mask = {};\n    this.name2Id = {};\n    if (feeds instanceof FeedDict) {\n      for (const id in feeds.id2Value) {\n        this.id2Value[id] = feeds.id2Value[id];\n        if (id in feeds.id2Mask) {\n          this.id2Mask[id] = feeds.id2Mask[id];\n        }\n      }\n    } else {\n      if (feeds == null) {\n        return;\n      }\n      for (const feed of feeds) {\n        this.add(feed.key, feed.value);\n      }\n    }\n  }\n  add(key, value, mask) {\n    if (this.id2Value[key.id] == null) {\n      this.id2Value[key.id] = assertFeedCompatibility(key, value);\n      this.name2Id[key.name] = key.id;\n      if (mask != null) {\n        this.id2Mask[key.id] = mask;\n      }\n    } else {\n      throw new ValueError(`Duplicate key: name=${key.name}, id=${key.id}`);\n    }\n    return this;\n  }\n  addFeed(feed) {\n    this.add(feed.key, feed.value);\n  }\n  hasKey(key) {\n    return this.id2Value[key.id] != null;\n  }\n  names() {\n    return Object.keys(this.name2Id);\n  }\n  getValue(key) {\n    if (key instanceof SymbolicTensor) {\n      if (this.id2Value[key.id] == null) {\n        throw new ValueError(`Nonexistent key: ${key.name}`);\n      } else {\n        return this.id2Value[key.id];\n      }\n    } else {\n      const id = this.name2Id[key];\n      if (id == null) {\n        throw new ValueError(`Feed dict has no SymbolicTensor name: ${key}`);\n      }\n      return this.id2Value[id];\n    }\n  }\n  getMask(key) {\n    if (key instanceof SymbolicTensor) {\n      if (this.id2Value[key.id] == null) {\n        throw new ValueError(`Nonexistent key: ${key.name}`);\n      } else {\n        return this.id2Mask[key.id];\n      }\n    } else {\n      const id = this.name2Id[key];\n      if (id == null) {\n        throw new ValueError(`Feed dict has no SymbolicTensor name: ${key}`);\n      }\n      return this.id2Mask[id];\n    }\n  }\n  disposeMasks() {\n    if (this.id2Mask != null) {\n      dispose(this.id2Mask);\n    }\n  }\n};\nvar cachedSorted = new LruCache();\nvar cachedRecipientCounts = new LruCache();\nfunction updateCacheMaxEntries(maxEntries) {\n  if (cachedSorted != null) {\n    cachedSorted.setMaxEntries(maxEntries);\n  }\n  if (cachedRecipientCounts != null) {\n    cachedRecipientCounts.setMaxEntries(maxEntries);\n  }\n}\nfunction execute(fetches, feedDict, kwargs, probe) {\n  const training = kwargs == null ? false : kwargs[\"training\"];\n  const arrayFetches = Array.isArray(fetches);\n  const fetchArray = arrayFetches ? fetches : [fetches];\n  const outputNames = fetchArray.map((t2) => t2.name);\n  const finalOutputs = [];\n  const feedNames = feedDict.names();\n  for (const outputName of outputNames) {\n    if (feedNames.indexOf(outputName) !== -1) {\n      finalOutputs.push(feedDict.getValue(outputName));\n    } else {\n      finalOutputs.push(null);\n    }\n  }\n  if (probe != null) {\n    probe.maxNumTensors = -Infinity;\n    probe.minNumTensors = Infinity;\n  }\n  const fetchAndFeedKey = outputNames.join(\",\") + \"|\" + feedDict.names().sort().join(\",\");\n  let sorted = cachedSorted.get(fetchAndFeedKey);\n  let recipientCounts;\n  if (sorted == null) {\n    const out = getTopologicalSortAndRecipientCounts(fetchArray, feedDict);\n    sorted = out.sorted;\n    recipientCounts = out.recipientCounts;\n    cachedSorted.put(fetchAndFeedKey, sorted);\n    cachedRecipientCounts.put(fetchAndFeedKey, recipientCounts);\n  }\n  recipientCounts = {};\n  if (!training) {\n    Object.assign(recipientCounts, cachedRecipientCounts.get(fetchAndFeedKey));\n  }\n  const internalFeedDict = new FeedDict(feedDict);\n  for (let i2 = 0; i2 < sorted.length; ++i2) {\n    if (probe != null) {\n      const numTensors = memory().numTensors;\n      if (numTensors > probe.maxNumTensors) {\n        probe.maxNumTensors = numTensors;\n      }\n      if (numTensors < probe.minNumTensors) {\n        probe.minNumTensors = numTensors;\n      }\n    }\n    const symbolic = sorted[i2];\n    const srcLayer = symbolic.sourceLayer;\n    if (srcLayer instanceof InputLayer) {\n      continue;\n    }\n    const inputValues = [];\n    const inputMasks = [];\n    const tensorsToDispose = [];\n    let maskExists = false;\n    for (const input2 of symbolic.inputs) {\n      const value = internalFeedDict.getValue(input2);\n      const mask = internalFeedDict.getMask(input2);\n      inputValues.push(value);\n      inputMasks.push(mask);\n      if (mask != null) {\n        maskExists = true;\n      }\n      if (!training) {\n        recipientCounts[input2.name]--;\n        if (recipientCounts[input2.name] === 0 && !feedDict.hasKey(input2) && outputNames.indexOf(input2.name) === -1 && !value.isDisposed && input2.sourceLayer.stateful !== true) {\n          tensorsToDispose.push(value);\n        }\n      }\n    }\n    if (maskExists) {\n      kwargs = kwargs || {};\n      kwargs[\"mask\"] = inputMasks[0];\n    }\n    const outputTensors = toList(srcLayer.apply(inputValues, kwargs));\n    let outputMask = null;\n    if (srcLayer.supportsMasking) {\n      outputMask = srcLayer.computeMask(inputValues, inputMasks);\n    }\n    const layerOutputs = getNodeOutputs(symbolic);\n    const outputSymbolicTensors = Array.isArray(layerOutputs) ? layerOutputs : [layerOutputs];\n    for (let i3 = 0; i3 < outputSymbolicTensors.length; ++i3) {\n      if (!internalFeedDict.hasKey(outputSymbolicTensors[i3])) {\n        internalFeedDict.add(outputSymbolicTensors[i3], outputTensors[i3], Array.isArray(outputMask) ? outputMask[0] : outputMask);\n      }\n      const index = outputNames.indexOf(outputSymbolicTensors[i3].name);\n      if (index !== -1) {\n        finalOutputs[index] = outputTensors[i3];\n      }\n    }\n    if (!training) {\n      dispose(tensorsToDispose);\n    }\n  }\n  internalFeedDict.disposeMasks();\n  return arrayFetches ? finalOutputs : finalOutputs[0];\n}\nfunction getTopologicalSortAndRecipientCounts(fetches, feedDict) {\n  util_exports.assert(fetches != null && fetches.length > 0, () => `Expected at least one fetch, got none`);\n  let finalSorted = [];\n  let finalRecipientMap = {};\n  if (fetches.length === 1) {\n    const out = getTopologicalSortAndRecipientCountsForOneFetch(fetches[0], feedDict);\n    finalSorted = out.sorted;\n    finalRecipientMap = out.recipientMap;\n  } else {\n    const visited = /* @__PURE__ */ new Set();\n    for (const fetch4 of fetches) {\n      const { sorted, recipientMap } = getTopologicalSortAndRecipientCountsForOneFetch(fetch4, feedDict);\n      for (const symbolicTensor of sorted) {\n        if (!visited.has(symbolicTensor.name)) {\n          finalSorted.push(symbolicTensor);\n          visited.add(symbolicTensor.name);\n        }\n      }\n      for (const name in recipientMap) {\n        if (finalRecipientMap[name] == null) {\n          finalRecipientMap[name] = /* @__PURE__ */ new Set();\n        }\n        recipientMap[name].forEach((recipient) => finalRecipientMap[name].add(recipient));\n      }\n    }\n  }\n  return {\n    sorted: finalSorted,\n    recipientCounts: recipientMap2Counts(finalRecipientMap)\n  };\n}\nfunction recipientMap2Counts(recipientMap) {\n  const recipientCounts = {};\n  for (const name in recipientMap) {\n    recipientCounts[name] = recipientMap[name].size;\n  }\n  return recipientCounts;\n}\nfunction getTopologicalSortAndRecipientCountsForOneFetch(fetch4, feedDict) {\n  const visited = /* @__PURE__ */ new Set();\n  const sorted = [];\n  const recipientMap = {};\n  for (const key of feedDict.names()) {\n    visited.add(key);\n  }\n  const stack2 = [];\n  const marks = [];\n  stack2.push(fetch4);\n  while (stack2.length > 0) {\n    const top = stack2[stack2.length - 1];\n    if (visited.has(top.name)) {\n      stack2.pop();\n      continue;\n    }\n    const topIsMarked = marks[marks.length - 1] === stack2.length - 1;\n    if (top.inputs.length === 0 || topIsMarked) {\n      stack2.pop();\n      sorted.push(top);\n      visited.add(top.name);\n      if (topIsMarked) {\n        marks.pop();\n      }\n    } else {\n      marks.push(stack2.length - 1);\n      for (const input2 of top.inputs) {\n        if (recipientMap[input2.name] == null) {\n          recipientMap[input2.name] = /* @__PURE__ */ new Set();\n        }\n        recipientMap[input2.name].add(top.name);\n        if (visited.has(input2.name)) {\n          continue;\n        }\n        stack2.push(input2);\n      }\n    }\n  }\n  return { sorted, recipientMap };\n}\nfunction getNodeOutputs(fetch4) {\n  let layerOutputs;\n  if (fetch4.sourceLayer.inboundNodes.length === 1) {\n    layerOutputs = fetch4.sourceLayer.output;\n  } else {\n    let nodeIndex = null;\n    for (let i2 = 0; i2 < fetch4.sourceLayer.inboundNodes.length; ++i2) {\n      for (const outputTensor of fetch4.sourceLayer.inboundNodes[i2].outputTensors) {\n        if (outputTensor.id === fetch4.id) {\n          nodeIndex = i2;\n          break;\n        }\n      }\n    }\n    layerOutputs = fetch4.sourceLayer.getOutputAt(nodeIndex);\n  }\n  return layerOutputs;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-layers@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-layers/dist/flags_layers.js\nvar ENV3 = env();\nENV3.registerFlag(\"TOPOLOGICAL_SORT_CACHE_MAX_ENTRIES\", () => 100, updateCacheMaxEntries);\n\n// node_modules/.pnpm/@tensorflow+tfjs-layers@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-layers/dist/exports_constraints.js\nvar exports_constraints_exports = {};\n__export(exports_constraints_exports, {\n  maxNorm: () => maxNorm,\n  minMaxNorm: () => minMaxNorm,\n  nonNeg: () => nonNeg,\n  unitNorm: () => unitNorm\n});\n\n// node_modules/.pnpm/@tensorflow+tfjs-layers@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-layers/dist/constraints.js\nfunction calcL2Norms(w, axis) {\n  return tidy(() => sqrt(sum2(mul(w, w), axis, true)));\n}\nvar Constraint = class extends serialization_exports.Serializable {\n  getConfig() {\n    return {};\n  }\n};\nvar MaxNorm = class extends Constraint {\n  constructor(args) {\n    super();\n    this.defaultMaxValue = 2;\n    this.defaultAxis = 0;\n    this.maxValue = args.maxValue != null ? args.maxValue : this.defaultMaxValue;\n    this.axis = args.axis != null ? args.axis : this.defaultAxis;\n  }\n  apply(w) {\n    return tidy(() => {\n      const norms = calcL2Norms(w, this.axis);\n      const desired = clipByValue(norms, 0, this.maxValue);\n      return mul(w, div(desired, add2(epsilon(), norms)));\n    });\n  }\n  getConfig() {\n    return { maxValue: this.maxValue, axis: this.axis };\n  }\n};\nMaxNorm.className = \"MaxNorm\";\nserialization_exports.registerClass(MaxNorm);\nvar UnitNorm = class extends Constraint {\n  constructor(args) {\n    super();\n    this.defaultAxis = 0;\n    this.axis = args.axis != null ? args.axis : this.defaultAxis;\n  }\n  apply(w) {\n    return tidy(() => div(w, add2(epsilon(), calcL2Norms(w, this.axis))));\n  }\n  getConfig() {\n    return { axis: this.axis };\n  }\n};\nUnitNorm.className = \"UnitNorm\";\nserialization_exports.registerClass(UnitNorm);\nvar NonNeg = class extends Constraint {\n  apply(w) {\n    return relu(w);\n  }\n};\nNonNeg.className = \"NonNeg\";\nserialization_exports.registerClass(NonNeg);\nvar MinMaxNorm = class extends Constraint {\n  constructor(args) {\n    super();\n    this.defaultMinValue = 0;\n    this.defaultMaxValue = 1;\n    this.defaultRate = 1;\n    this.defaultAxis = 0;\n    this.minValue = args.minValue != null ? args.minValue : this.defaultMinValue;\n    this.maxValue = args.maxValue != null ? args.maxValue : this.defaultMaxValue;\n    this.rate = args.rate != null ? args.rate : this.defaultRate;\n    this.axis = args.axis != null ? args.axis : this.defaultAxis;\n  }\n  apply(w) {\n    return tidy(() => {\n      const norms = calcL2Norms(w, this.axis);\n      const desired = add2(mul(this.rate, clipByValue(norms, this.minValue, this.maxValue)), mul(1 - this.rate, norms));\n      return mul(w, div(desired, add2(epsilon(), norms)));\n    });\n  }\n  getConfig() {\n    return {\n      minValue: this.minValue,\n      maxValue: this.maxValue,\n      rate: this.rate,\n      axis: this.axis\n    };\n  }\n};\nMinMaxNorm.className = \"MinMaxNorm\";\nserialization_exports.registerClass(MinMaxNorm);\nvar CONSTRAINT_IDENTIFIER_REGISTRY_SYMBOL_MAP = {\n  \"maxNorm\": \"MaxNorm\",\n  \"minMaxNorm\": \"MinMaxNorm\",\n  \"nonNeg\": \"NonNeg\",\n  \"unitNorm\": \"UnitNorm\"\n};\nfunction serializeConstraint(constraint) {\n  return serializeKerasObject(constraint);\n}\nfunction deserializeConstraint(config, customObjects = {}) {\n  return deserializeKerasObject(config, serialization_exports.SerializationMap.getMap().classNameMap, customObjects, \"constraint\");\n}\nfunction getConstraint(identifier) {\n  if (identifier == null) {\n    return null;\n  }\n  if (typeof identifier === \"string\") {\n    const className = identifier in CONSTRAINT_IDENTIFIER_REGISTRY_SYMBOL_MAP ? CONSTRAINT_IDENTIFIER_REGISTRY_SYMBOL_MAP[identifier] : identifier;\n    const config = { className, config: {} };\n    return deserializeConstraint(config);\n  } else if (identifier instanceof Constraint) {\n    return identifier;\n  } else {\n    return deserializeConstraint(identifier);\n  }\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-layers@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-layers/dist/exports_constraints.js\nfunction maxNorm(args) {\n  return new MaxNorm(args);\n}\nfunction unitNorm(args) {\n  return new UnitNorm(args);\n}\nfunction nonNeg() {\n  return new NonNeg();\n}\nfunction minMaxNorm(config) {\n  return new MinMaxNorm(config);\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-layers@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-layers/dist/exports_initializers.js\nvar exports_initializers_exports = {};\n__export(exports_initializers_exports, {\n  constant: () => constant,\n  glorotNormal: () => glorotNormal,\n  glorotUniform: () => glorotUniform,\n  heNormal: () => heNormal,\n  heUniform: () => heUniform,\n  identity: () => identity,\n  leCunNormal: () => leCunNormal,\n  leCunUniform: () => leCunUniform,\n  ones: () => ones3,\n  orthogonal: () => orthogonal,\n  randomNormal: () => randomNormal3,\n  randomUniform: () => randomUniform2,\n  truncatedNormal: () => truncatedNormal2,\n  varianceScaling: () => varianceScaling,\n  zeros: () => zeros2\n});\nfunction zeros2() {\n  return new Zeros();\n}\nfunction ones3() {\n  return new Ones();\n}\nfunction constant(args) {\n  return new Constant(args);\n}\nfunction randomUniform2(args) {\n  return new RandomUniform(args);\n}\nfunction randomNormal3(args) {\n  return new RandomNormal(args);\n}\nfunction truncatedNormal2(args) {\n  return new TruncatedNormal(args);\n}\nfunction identity(args) {\n  return new Identity2(args);\n}\nfunction varianceScaling(config) {\n  return new VarianceScaling(config);\n}\nfunction glorotUniform(args) {\n  return new GlorotUniform(args);\n}\nfunction glorotNormal(args) {\n  return new GlorotNormal(args);\n}\nfunction heNormal(args) {\n  return new HeNormal(args);\n}\nfunction heUniform(args) {\n  return new HeUniform(args);\n}\nfunction leCunNormal(args) {\n  return new LeCunNormal(args);\n}\nfunction leCunUniform(args) {\n  return new LeCunUniform(args);\n}\nfunction orthogonal(args) {\n  return new Orthogonal(args);\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-layers@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-layers/dist/exports_layers.js\nvar exports_layers_exports = {};\n__export(exports_layers_exports, {\n  Layer: () => Layer,\n  RNN: () => RNN,\n  RNNCell: () => RNNCell,\n  activation: () => activation,\n  add: () => add3,\n  alphaDropout: () => alphaDropout,\n  average: () => average,\n  averagePooling1d: () => averagePooling1d,\n  averagePooling2d: () => averagePooling2d,\n  averagePooling3d: () => averagePooling3d,\n  avgPool1d: () => avgPool1d,\n  avgPool2d: () => avgPool2d,\n  avgPool3d: () => avgPool3d2,\n  avgPooling1d: () => avgPooling1d,\n  avgPooling2d: () => avgPooling2d,\n  avgPooling3d: () => avgPooling3d,\n  batchNormalization: () => batchNormalization2,\n  bidirectional: () => bidirectional,\n  concatenate: () => concatenate2,\n  conv1d: () => conv1d2,\n  conv2d: () => conv2d3,\n  conv2dTranspose: () => conv2dTranspose2,\n  conv3d: () => conv3d2,\n  conv3dTranspose: () => conv3dTranspose2,\n  convLstm2d: () => convLstm2d,\n  convLstm2dCell: () => convLstm2dCell,\n  cropping2D: () => cropping2D,\n  dense: () => dense,\n  depthwiseConv2d: () => depthwiseConv2d4,\n  dot: () => dot3,\n  dropout: () => dropout3,\n  elu: () => elu3,\n  embedding: () => embedding,\n  flatten: () => flatten3,\n  gaussianDropout: () => gaussianDropout,\n  gaussianNoise: () => gaussianNoise,\n  globalAveragePooling1d: () => globalAveragePooling1d,\n  globalAveragePooling2d: () => globalAveragePooling2d,\n  globalMaxPool1d: () => globalMaxPool1d,\n  globalMaxPool2d: () => globalMaxPool2d,\n  globalMaxPooling1d: () => globalMaxPooling1d,\n  globalMaxPooling2d: () => globalMaxPooling2d,\n  gru: () => gru,\n  gruCell: () => gruCell,\n  input: () => input,\n  inputLayer: () => inputLayer,\n  layerNormalization: () => layerNormalization,\n  leakyReLU: () => leakyReLU,\n  lstm: () => lstm,\n  lstmCell: () => lstmCell,\n  masking: () => masking,\n  maxPool1d: () => maxPool1d,\n  maxPool2d: () => maxPool2d,\n  maxPooling1d: () => maxPooling1d,\n  maxPooling2d: () => maxPooling2d,\n  maxPooling3d: () => maxPooling3d,\n  maximum: () => maximum2,\n  minimum: () => minimum2,\n  multiply: () => multiply,\n  permute: () => permute,\n  prelu: () => prelu2,\n  reLU: () => reLU,\n  repeatVector: () => repeatVector,\n  reshape: () => reshape2,\n  rnn: () => rnn2,\n  separableConv2d: () => separableConv2d2,\n  simpleRNN: () => simpleRNN,\n  simpleRNNCell: () => simpleRNNCell,\n  softmax: () => softmax2,\n  spatialDropout1d: () => spatialDropout1d,\n  stackedRNNCells: () => stackedRNNCells,\n  thresholdedReLU: () => thresholdedReLU,\n  timeDistributed: () => timeDistributed,\n  upSampling2d: () => upSampling2d,\n  zeroPadding2d: () => zeroPadding2d\n});\n\n// node_modules/.pnpm/@tensorflow+tfjs-layers@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-layers/dist/logs.js\nasync function resolveScalarsInLogs(logs) {\n  if (logs == null) {\n    return;\n  }\n  const promises = [];\n  const keys = [];\n  const scalarsToDispose = [];\n  for (const key in logs) {\n    const value = logs[key];\n    if (typeof value !== \"number\") {\n      const valueScalar = value;\n      promises.push(valueScalar.data());\n      keys.push(key);\n      scalarsToDispose.push(valueScalar);\n    }\n  }\n  if (promises.length > 0) {\n    const values = await Promise.all(promises);\n    for (let i2 = 0; i2 < values.length; ++i2) {\n      logs[keys[i2]] = values[i2][0];\n    }\n    dispose(scalarsToDispose);\n  }\n}\nfunction disposeTensorsInLogs(logs) {\n  if (logs == null) {\n    return;\n  }\n  for (const key in logs) {\n    const value = logs[key];\n    if (typeof value !== \"number\") {\n      value.dispose();\n    }\n  }\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-layers@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-layers/dist/base_callbacks.js\nvar ModelLoggingVerbosity;\n(function(ModelLoggingVerbosity2) {\n  ModelLoggingVerbosity2[ModelLoggingVerbosity2[\"SILENT\"] = 0] = \"SILENT\";\n  ModelLoggingVerbosity2[ModelLoggingVerbosity2[\"VERBOSE\"] = 1] = \"VERBOSE\";\n})(ModelLoggingVerbosity || (ModelLoggingVerbosity = {}));\nvar DEFAULT_YIELD_EVERY_MS = 125;\nvar BaseCallback = class {\n  constructor() {\n    this.validationData = null;\n  }\n  setParams(params) {\n    this.params = params;\n  }\n  async onEpochBegin(epoch, logs) {\n  }\n  async onEpochEnd(epoch, logs) {\n  }\n  async onBatchBegin(batch, logs) {\n  }\n  async onBatchEnd(batch, logs) {\n  }\n  async onTrainBegin(logs) {\n  }\n  async onTrainEnd(logs) {\n  }\n  setModel(model2) {\n  }\n};\nvar CallbackList = class {\n  constructor(callbacks2, queueLength = 10) {\n    if (callbacks2 == null) {\n      callbacks2 = [];\n    }\n    this.callbacks = callbacks2;\n    this.queueLength = queueLength;\n  }\n  append(callback) {\n    this.callbacks.push(callback);\n  }\n  setParams(params) {\n    for (const callback of this.callbacks) {\n      callback.setParams(params);\n    }\n  }\n  setModel(model2) {\n    for (const callback of this.callbacks) {\n      callback.setModel(model2);\n    }\n  }\n  async onEpochBegin(epoch, logs) {\n    if (logs == null) {\n      logs = {};\n    }\n    for (const callback of this.callbacks) {\n      await callback.onEpochBegin(epoch, logs);\n    }\n  }\n  async onEpochEnd(epoch, logs) {\n    if (logs == null) {\n      logs = {};\n    }\n    for (const callback of this.callbacks) {\n      await callback.onEpochEnd(epoch, logs);\n    }\n  }\n  async onBatchBegin(batch, logs) {\n    if (logs == null) {\n      logs = {};\n    }\n    for (const callback of this.callbacks) {\n      await callback.onBatchBegin(batch, logs);\n    }\n  }\n  async onBatchEnd(batch, logs) {\n    if (logs == null) {\n      logs = {};\n    }\n    for (const callback of this.callbacks) {\n      await callback.onBatchEnd(batch, logs);\n    }\n  }\n  async onTrainBegin(logs) {\n    if (logs == null) {\n      logs = {};\n    }\n    for (const callback of this.callbacks) {\n      await callback.onTrainBegin(logs);\n    }\n  }\n  async onTrainEnd(logs) {\n    if (logs == null) {\n      logs = {};\n    }\n    for (const callback of this.callbacks) {\n      await callback.onTrainEnd(logs);\n    }\n  }\n};\nvar BaseLogger = class extends BaseCallback {\n  constructor() {\n    super();\n  }\n  async onEpochBegin(epoch) {\n    this.seen = 0;\n    this.totals = {};\n  }\n  async onBatchEnd(batch, logs) {\n    if (logs == null) {\n      logs = {};\n    }\n    const batchSize = logs[\"size\"] == null ? 0 : logs[\"size\"];\n    this.seen += batchSize;\n    for (const key in logs) {\n      const value = logs[key];\n      if (typeof value === \"number\") {\n        if (!this.totals.hasOwnProperty(key)) {\n          this.totals[key] = 0;\n        }\n        this.totals[key] = this.totals[key] + value * batchSize;\n      } else {\n        let oldTotalsToDispose;\n        if (key in this.totals) {\n          oldTotalsToDispose = this.totals[key];\n        } else {\n          this.totals[key] = 0;\n        }\n        const total = tidy(() => add2(this.totals[key], mul(value, batchSize)));\n        this.totals[key] = total;\n        if (oldTotalsToDispose != null) {\n          oldTotalsToDispose.dispose();\n        }\n      }\n    }\n  }\n  async onEpochEnd(epoch, logs) {\n    if (logs != null) {\n      for (const key of this.params[\"metrics\"]) {\n        if (this.totals[key] == null) {\n          continue;\n        }\n        if (typeof this.totals[key] === \"number\") {\n          logs[key] = this.totals[key] / this.seen;\n        } else {\n          tidy(() => {\n            const log6 = mul(div(1, this.seen), this.totals[key]);\n            logs[key] = log6;\n            this.totals[key].dispose();\n            keep(logs[key]);\n          });\n        }\n      }\n    }\n  }\n};\nvar History = class extends BaseCallback {\n  async onTrainBegin(logs) {\n    this.epoch = [];\n    this.history = {};\n  }\n  async onEpochEnd(epoch, logs) {\n    if (logs == null) {\n      logs = {};\n    }\n    this.epoch.push(epoch);\n    for (const key in logs) {\n      if (this.history[key] == null) {\n        this.history[key] = [];\n      }\n      this.history[key].push(logs[key]);\n    }\n  }\n  async syncData() {\n    const promises = [];\n    const keys = [];\n    const indices = [];\n    for (const key in this.history) {\n      const valueArray = this.history[key];\n      for (let i2 = 0; i2 < valueArray.length; ++i2) {\n        if (typeof valueArray[i2] !== \"number\") {\n          const valueScalar = valueArray[i2];\n          promises.push(valueScalar.data());\n          keys.push(key);\n          indices.push(i2);\n        }\n      }\n    }\n    const values = await Promise.all(promises);\n    for (let n2 = 0; n2 < values.length; ++n2) {\n      const tensorToDispose = this.history[keys[n2]][indices[n2]];\n      tensorToDispose.dispose();\n      this.history[keys[n2]][indices[n2]] = values[n2][0];\n    }\n  }\n};\nvar CustomCallback = class extends BaseCallback {\n  constructor(args, yieldEvery) {\n    super();\n    this.currentEpoch = 0;\n    this.nowFunc = args.nowFunc;\n    this.nextFrameFunc = args.nextFrameFunc || nextFrame;\n    this.yieldEvery = yieldEvery || \"auto\";\n    if (this.yieldEvery === \"auto\") {\n      this.yieldEvery = DEFAULT_YIELD_EVERY_MS;\n    }\n    if (this.yieldEvery === \"never\" && args.onYield != null) {\n      throw new Error(\"yieldEvery is `never` but you provided an `onYield` callback. Either change `yieldEvery` or remove the callback\");\n    }\n    if (util_exports.isNumber(this.yieldEvery)) {\n      this.maybeWait = debounce(this.maybeWait.bind(this), this.yieldEvery, this.nowFunc);\n    }\n    this.trainBegin = args.onTrainBegin;\n    this.trainEnd = args.onTrainEnd;\n    this.epochBegin = args.onEpochBegin;\n    this.epochEnd = args.onEpochEnd;\n    this.batchBegin = args.onBatchBegin;\n    this.batchEnd = args.onBatchEnd;\n    this.yield = args.onYield;\n  }\n  async maybeWait(epoch, batch, logs) {\n    const ps = [];\n    if (this.yield != null) {\n      await resolveScalarsInLogs(logs);\n      ps.push(this.yield(epoch, batch, logs));\n    }\n    ps.push(this.nextFrameFunc());\n    await Promise.all(ps);\n  }\n  async onEpochBegin(epoch, logs) {\n    this.currentEpoch = epoch;\n    if (this.epochBegin != null) {\n      await resolveScalarsInLogs(logs);\n      await this.epochBegin(epoch, logs);\n    }\n  }\n  async onEpochEnd(epoch, logs) {\n    const ps = [];\n    if (this.epochEnd != null) {\n      await resolveScalarsInLogs(logs);\n      ps.push(this.epochEnd(epoch, logs));\n    }\n    if (this.yieldEvery === \"epoch\") {\n      ps.push(this.nextFrameFunc());\n    }\n    await Promise.all(ps);\n  }\n  async onBatchBegin(batch, logs) {\n    if (this.batchBegin != null) {\n      await resolveScalarsInLogs(logs);\n      await this.batchBegin(batch, logs);\n    }\n  }\n  async onBatchEnd(batch, logs) {\n    const ps = [];\n    if (this.batchEnd != null) {\n      await resolveScalarsInLogs(logs);\n      ps.push(this.batchEnd(batch, logs));\n    }\n    if (this.yieldEvery === \"batch\") {\n      ps.push(this.nextFrameFunc());\n    } else if (util_exports.isNumber(this.yieldEvery)) {\n      ps.push(this.maybeWait(this.currentEpoch, batch, logs));\n    }\n    await Promise.all(ps);\n  }\n  async onTrainBegin(logs) {\n    if (this.trainBegin != null) {\n      await resolveScalarsInLogs(logs);\n      await this.trainBegin(logs);\n    }\n  }\n  async onTrainEnd(logs) {\n    if (this.trainEnd != null) {\n      await resolveScalarsInLogs(logs);\n      await this.trainEnd(logs);\n    }\n  }\n};\nfunction standardizeCallbacks(callbacks2, yieldEvery) {\n  if (callbacks2 == null) {\n    callbacks2 = {};\n  }\n  if (callbacks2 instanceof BaseCallback) {\n    return [callbacks2];\n  }\n  if (Array.isArray(callbacks2) && callbacks2[0] instanceof BaseCallback) {\n    return callbacks2;\n  }\n  const callbackConfigs = toList(callbacks2);\n  return callbackConfigs.map((callbackConfig) => new CustomCallback(callbackConfig, yieldEvery));\n}\nvar CallbackConstructorRegistry = class {\n  constructor() {\n  }\n  static registerCallbackConstructor(verbosityLevel, callbackConstructor) {\n    util_exports.assert(verbosityLevel >= 0 && Number.isInteger(verbosityLevel), () => `Verbosity level is expected to be an integer >= 0, but got ${verbosityLevel}`);\n    CallbackConstructorRegistry.checkForDuplicate(callbackConstructor);\n    if (CallbackConstructorRegistry.constructors[verbosityLevel] == null) {\n      CallbackConstructorRegistry.constructors[verbosityLevel] = [];\n    }\n    CallbackConstructorRegistry.constructors[verbosityLevel].push(callbackConstructor);\n  }\n  static checkForDuplicate(callbackConstructor) {\n    for (const levelName in CallbackConstructorRegistry.constructors) {\n      const constructors = CallbackConstructorRegistry.constructors[+levelName];\n      constructors.forEach((ctor) => {\n        if (ctor === callbackConstructor) {\n          throw new ValueError(\"Duplicate callback constructor.\");\n        }\n      });\n    }\n  }\n  static clear() {\n    CallbackConstructorRegistry.constructors = {};\n  }\n  static createCallbacks(verbosityLevel) {\n    const constructors = [];\n    for (const levelName in CallbackConstructorRegistry.constructors) {\n      const level = +levelName;\n      if (verbosityLevel >= level) {\n        constructors.push(...CallbackConstructorRegistry.constructors[level]);\n      }\n    }\n    return constructors.map((ctor) => new ctor());\n  }\n};\nCallbackConstructorRegistry.constructors = {};\nfunction configureCallbacks(callbacks2, verbose, epochs, initialEpoch, numTrainSamples, stepsPerEpoch, batchSize, doValidation, callbackMetrics) {\n  const history = new History();\n  const actualCallbacks = [\n    new BaseLogger(),\n    ...CallbackConstructorRegistry.createCallbacks(verbose)\n  ];\n  if (callbacks2 != null) {\n    actualCallbacks.push(...callbacks2);\n  }\n  actualCallbacks.push(history);\n  const callbackList = new CallbackList(actualCallbacks);\n  callbackList.setParams({\n    epochs,\n    initialEpoch,\n    samples: numTrainSamples,\n    steps: stepsPerEpoch,\n    batchSize,\n    verbose,\n    doValidation,\n    metrics: callbackMetrics\n  });\n  return { callbackList, history };\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-layers@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-layers/dist/layers/serialization.js\nfunction deserialize(config, customObjects = {}, fastWeightInit = false) {\n  return deserializeKerasObject(config, serialization_exports.SerializationMap.getMap().classNameMap, customObjects, \"layer\", fastWeightInit);\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-layers@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-layers/dist/losses.js\nfunction l2Normalize(x, axis) {\n  return tidy(() => {\n    if (x.dtype !== \"float32\") {\n      x = cast(x, \"float32\");\n    }\n    const squareSum = sum2(square2(x), axis, true);\n    const epsilonTensor = fill(squareSum.shape, epsilon());\n    const norm2 = sqrt(maximum(squareSum, epsilonTensor));\n    return div(x, norm2);\n  });\n}\nfunction meanSquaredError2(yTrue, yPred) {\n  return tidy(() => mean(square2(sub(yPred, yTrue)), -1));\n}\nfunction meanAbsoluteError(yTrue, yPred) {\n  return tidy(() => mean(abs(sub(yPred, yTrue)), -1));\n}\nfunction meanAbsolutePercentageError(yTrue, yPred) {\n  return tidy(() => {\n    const diff = sub(yTrue, yPred);\n    const clippedTrue = clipByValue(abs(yTrue), epsilon(), Number.MAX_VALUE);\n    const absResult = abs(div(diff, clippedTrue));\n    return mul(100, mean(absResult, -1));\n  });\n}\nfunction meanSquaredLogarithmicError(yTrue, yPred) {\n  return tidy(() => {\n    const clippedPred = clipByValue(yPred, epsilon(), Number.MAX_VALUE);\n    const firstLog = log2(add2(1, clippedPred));\n    const clippedTrue = clipByValue(yTrue, epsilon(), Number.MAX_VALUE);\n    const secondLog = log2(add2(1, clippedTrue));\n    return mean(square2(sub(firstLog, secondLog)), -1);\n  });\n}\nfunction squaredHinge(yTrue, yPred) {\n  return tidy(() => {\n    const maxResult = maximum(0, sub(1, mul(yTrue, yPred)));\n    return mean(square2(maxResult), -1);\n  });\n}\nfunction hinge(yTrue, yPred) {\n  return tidy(() => {\n    const maxResult = maximum(0, sub(1, mul(yTrue, yPred)));\n    return mean(maxResult, -1);\n  });\n}\nfunction categoricalHinge(yTrue, yPred) {\n  return tidy(() => {\n    const pos = sum2(mul(yTrue, yPred), -1);\n    const neg5 = max(mul(sub(1, yTrue), yPred), -1);\n    return maximum(0, add2(1, sub(neg5, pos)));\n  });\n}\nfunction logcosh(yTrue, yPred) {\n  return tidy(() => {\n    const log22 = Math.log(2);\n    const predictionDiff = sub(yPred, yTrue);\n    const logcoshResult = sub(add2(predictionDiff, softplus(mul(-2, predictionDiff))), log22);\n    return mean(logcoshResult, -1);\n  });\n}\nfunction categoricalCrossentropy(target, output, fromLogits = false) {\n  return tidy(() => {\n    if (fromLogits) {\n      output = softmax(output);\n    } else {\n      const outputSum = sum2(output, output.shape.length - 1, true);\n      output = div(output, outputSum);\n    }\n    output = clipByValue(output, epsilon(), 1 - epsilon());\n    return neg(sum2(mul(cast(target, \"float32\"), log2(output)), output.shape.length - 1));\n  });\n}\nfunction sparseCategoricalCrossentropy(target, output, fromLogits = false) {\n  return tidy(() => {\n    const flatTarget = cast(floor(flatten2(target)), \"int32\");\n    output = clipByValue(output, epsilon(), 1 - epsilon());\n    const outputShape = output.shape;\n    const oneHotTarget = reshape(oneHot(flatTarget, outputShape[outputShape.length - 1]), outputShape);\n    return categoricalCrossentropy(oneHotTarget, output, fromLogits);\n  });\n}\nfunction sigmoidCrossEntropyWithLogits(labels, logits) {\n  if (!util_exports.arraysEqual(labels.shape, logits.shape)) {\n    throw new ValueError(`logits and labels must have the same shape, but got shapes ${JSON.stringify(labels.shape)} and ${JSON.stringify(logits.shape)}`);\n  }\n  return tidy(() => {\n    const reluLogits = relu(logits);\n    const negAbsLogits = neg(abs(logits));\n    return add2(sub(reluLogits, mul(logits, labels)), log1p(exp(negAbsLogits)));\n  });\n}\nfunction binaryCrossentropy(yTrue, yPred) {\n  return tidy(() => {\n    let y;\n    y = clipByValue(yPred, epsilon(), 1 - epsilon());\n    y = log2(div(y, sub(1, y)));\n    return mean(sigmoidCrossEntropyWithLogits(yTrue, y), -1);\n  });\n}\nfunction kullbackLeiblerDivergence(yTrue, yPred) {\n  return tidy(() => {\n    const clippedTrue = clipByValue(yTrue, epsilon(), 1);\n    const clippedPred = clipByValue(yPred, epsilon(), 1);\n    return sum2(mul(yTrue, log2(div(clippedTrue, clippedPred))), -1);\n  });\n}\nfunction poisson(yTrue, yPred) {\n  return tidy(() => {\n    const logPred = log2(add2(epsilon(), yPred));\n    return mean(sub(yPred, mul(yTrue, logPred)), -1);\n  });\n}\nfunction cosineProximity(yTrue, yPred) {\n  return tidy(() => {\n    const trueNormalized = l2Normalize(yTrue, -1);\n    const predNormalized = l2Normalize(yPred, -1);\n    const trueXPred = mul(trueNormalized, predNormalized);\n    return neg(sum2(trueXPred, -1));\n  });\n}\nvar lossesMap = {\n  meanSquaredError: meanSquaredError2,\n  meanAbsoluteError,\n  meanAbsolutePercentageError,\n  meanSquaredLogarithmicError,\n  squaredHinge,\n  hinge,\n  categoricalHinge,\n  logcosh,\n  categoricalCrossentropy,\n  sparseCategoricalCrossentropy,\n  binaryCrossentropy,\n  kullbackLeiblerDivergence,\n  poisson,\n  cosineProximity\n};\nfunction get(identifierOrFn) {\n  if (typeof identifierOrFn === \"string\") {\n    if (identifierOrFn in lossesMap) {\n      return lossesMap[identifierOrFn];\n    }\n    let errMsg = `Unknown loss ${identifierOrFn}`;\n    if (identifierOrFn.toLowerCase().includes(\"softmaxcrossentropy\")) {\n      errMsg = `Unknown loss ${identifierOrFn}. Use \"categoricalCrossentropy\" as the string name for tf.losses.softmaxCrossEntropy`;\n    }\n    throw new ValueError(errMsg);\n  } else {\n    return identifierOrFn;\n  }\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-layers@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-layers/dist/metrics.js\nfunction binaryAccuracy(yTrue, yPred) {\n  return tidy(() => {\n    const threshold3 = mul(0.5, onesLike(yPred));\n    const yPredThresholded = cast2(greater(yPred, threshold3), yTrue.dtype);\n    return mean(equal(yTrue, yPredThresholded), -1);\n  });\n}\nfunction categoricalAccuracy(yTrue, yPred) {\n  return tidy(() => cast2(equal(argMax(yTrue, -1), argMax(yPred, -1)), \"float32\"));\n}\nfunction truePositives(yTrue, yPred) {\n  return tidy(() => {\n    return cast(sum2(logicalAnd(equal(yTrue, 1), equal(yPred, 1))), \"float32\");\n  });\n}\nfunction falseNegatives(yTrue, yPred) {\n  return tidy(() => {\n    return cast(sum2(logicalAnd(equal(yTrue, 1), equal(yPred, 0))), \"float32\");\n  });\n}\nfunction falsePositives(yTrue, yPred) {\n  return tidy(() => {\n    return cast(sum2(logicalAnd(equal(yTrue, 0), equal(yPred, 1))), \"float32\");\n  });\n}\nfunction precision(yTrue, yPred) {\n  return tidy(() => {\n    const tp = truePositives(yTrue, yPred);\n    const fp = falsePositives(yTrue, yPred);\n    const denominator = add2(tp, fp);\n    return cast(where(greater(denominator, 0), div(tp, denominator), 0), \"float32\");\n  });\n}\nfunction recall(yTrue, yPred) {\n  return tidy(() => {\n    const tp = truePositives(yTrue, yPred);\n    const fn = falseNegatives(yTrue, yPred);\n    const denominator = add2(tp, fn);\n    return cast(where(greater(denominator, 0), div(tp, denominator), 0), \"float32\");\n  });\n}\nfunction binaryCrossentropy2(yTrue, yPred) {\n  return binaryCrossentropy(yTrue, yPred);\n}\nfunction sparseCategoricalAccuracy(yTrue, yPred) {\n  if (yTrue.rank === yPred.rank) {\n    yTrue = squeeze(yTrue, [yTrue.rank - 1]);\n  }\n  yPred = argMax(yPred, -1);\n  if (yPred.dtype !== yTrue.dtype) {\n    yPred = cast(yPred, yTrue.dtype);\n  }\n  return cast(equal(yTrue, yPred), \"float32\");\n}\nvar mse = meanSquaredError2;\nvar MSE = meanSquaredError2;\nvar mae = meanAbsoluteError;\nvar MAE = meanAbsoluteError;\nvar mape = meanAbsolutePercentageError;\nvar MAPE = meanAbsolutePercentageError;\nvar categoricalCrossentropy2 = categoricalCrossentropy;\nvar cosine = cosineProximity;\nvar sparseCategoricalCrossentropy2 = sparseCategoricalCrossentropy;\nvar metricsMap = {\n  binaryAccuracy,\n  categoricalAccuracy,\n  precision,\n  categoricalCrossentropy: categoricalCrossentropy2,\n  sparseCategoricalCrossentropy: sparseCategoricalCrossentropy2,\n  mse,\n  MSE,\n  mae,\n  MAE,\n  mape,\n  MAPE,\n  cosine\n};\nfunction get2(identifier) {\n  if (typeof identifier === \"string\" && identifier in metricsMap) {\n    return metricsMap[identifier];\n  } else if (typeof identifier !== \"string\" && identifier != null) {\n    return identifier;\n  } else {\n    throw new ValueError(`Unknown metric ${identifier}`);\n  }\n}\nfunction getLossOrMetricName(fn) {\n  assert2(fn !== null, `Unknown LossOrMetricFn ${fn}`);\n  if (typeof fn === \"string\") {\n    return fn;\n  } else {\n    let fnName;\n    for (const key of Object.keys(lossesMap)) {\n      if (lossesMap[key] === fn) {\n        fnName = key;\n        break;\n      }\n    }\n    if (fnName !== void 0) {\n      return fnName;\n    }\n    for (const key of Object.keys(metricsMap)) {\n      if (metricsMap[key] === fn) {\n        fnName = key;\n        break;\n      }\n    }\n    if (fnName !== void 0) {\n      return fnName;\n    }\n    return fn.name;\n  }\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-layers@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-layers/dist/optimizers.js\nfunction getOptimizer(identifier) {\n  const optimizerMap = {\n    \"Adagrad\": () => train.adagrad(0.01),\n    \"Adadelta\": () => train.adadelta(1, 0.95, epsilon()),\n    \"Adam\": () => train.adam(1e-3, 0.9, 0.999, epsilon()),\n    \"Adamax\": () => train.adamax(2e-3, 0.9, 0.999, epsilon(), 0),\n    \"RMSProp\": () => train.rmsprop(1e-3, 0.9, 0, epsilon()),\n    \"SGD\": () => train.sgd(0.01)\n  };\n  optimizerMap[\"adagrad\"] = optimizerMap[\"Adagrad\"];\n  optimizerMap[\"adadelta\"] = optimizerMap[\"Adadelta\"];\n  optimizerMap[\"adam\"] = optimizerMap[\"Adam\"];\n  optimizerMap[\"adamax\"] = optimizerMap[\"Adamax\"];\n  optimizerMap[\"rmsprop\"] = optimizerMap[\"RMSProp\"];\n  optimizerMap[\"sgd\"] = optimizerMap[\"SGD\"];\n  if (identifier in optimizerMap) {\n    return optimizerMap[identifier]();\n  }\n  throw new ValueError(`Unknown Optimizer ${identifier}`);\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-layers@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-layers/dist/user_defined_metadata.js\nvar MAX_USER_DEFINED_METADATA_SERIALIZED_LENGTH = 1 * 1024 * 1024;\nfunction checkUserDefinedMetadata(userDefinedMetadata, modelName, checkSize = false) {\n  if (userDefinedMetadata == null || typeof userDefinedMetadata !== \"object\" || Object.getPrototypeOf(userDefinedMetadata) !== Object.prototype || !plainObjectCheck(userDefinedMetadata)) {\n    throw new Error(\"User-defined metadata is expected to be a JSON object, but is not.\");\n  }\n  if (checkSize) {\n    const out = JSON.stringify(userDefinedMetadata);\n    if (out.length > MAX_USER_DEFINED_METADATA_SERIALIZED_LENGTH) {\n      console.warn(`User-defined metadata of model \"${modelName}\" is too large in size (length=${out.length} when serialized). It is not recommended to store such large objects in user-defined metadata. Please make sure its serialized length is <= ${MAX_USER_DEFINED_METADATA_SERIALIZED_LENGTH}.`);\n    }\n  }\n}\nfunction plainObjectCheck(x) {\n  if (x === null) {\n    return true;\n  } else if (typeof x === \"object\") {\n    if (Object.getPrototypeOf(x) === Object.prototype) {\n      const keys = Object.keys(x);\n      for (const key of keys) {\n        if (typeof key !== \"string\") {\n          return false;\n        }\n        if (!plainObjectCheck(x[key])) {\n          return false;\n        }\n      }\n      return true;\n    } else {\n      if (Array.isArray(x)) {\n        for (const item of x) {\n          if (!plainObjectCheck(item)) {\n            return false;\n          }\n        }\n        return true;\n      } else {\n        return false;\n      }\n    }\n  } else {\n    const xType = typeof x;\n    return xType === \"string\" || xType === \"number\" || xType === \"boolean\";\n  }\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-layers@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-layers/dist/utils/layer_utils.js\nfunction printSummary(model2, lineLength, positions, printFn = console.log) {\n  const sequentialLike = isModelSequentialLike(model2);\n  const toDisplay = [\"Layer (type)\", \"Input Shape\", \"Output shape\", \"Param #\"];\n  if (sequentialLike) {\n    lineLength = lineLength || 90;\n    positions = positions || [0.32, 0.61, 0.89, 1];\n  } else {\n    lineLength = lineLength || 115;\n    positions = positions || [0.24, 0.48, 0.7, 0.8, 1];\n  }\n  if (positions[positions.length - 1] <= 1) {\n    positions = positions.map((p2) => Math.floor(lineLength * p2));\n  }\n  let relevantNodes;\n  if (!sequentialLike) {\n    toDisplay.push(\"Receives inputs\");\n    relevantNodes = [];\n    for (const depth in model2.nodesByDepth) {\n      relevantNodes.push(...model2.nodesByDepth[depth]);\n    }\n  }\n  printFn(\"_\".repeat(lineLength));\n  printRow(toDisplay, positions, printFn);\n  printFn(\"=\".repeat(lineLength));\n  const layers = model2.layers;\n  for (let i2 = 0; i2 < layers.length; ++i2) {\n    if (sequentialLike) {\n      printLayerSummary(layers[i2], positions, printFn);\n    } else {\n      printLayerSummaryWithConnections(layers[i2], positions, relevantNodes, printFn);\n    }\n    printFn((i2 === layers.length - 1 ? \"=\" : \"_\").repeat(lineLength));\n  }\n  model2.checkTrainableWeightsConsistency();\n  const trainableCount = countTrainableParams(model2);\n  const nonTrainableCount = countParamsInWeights(model2.nonTrainableWeights);\n  printFn(`Total params: ${trainableCount + nonTrainableCount}`);\n  printFn(`Trainable params: ${trainableCount}`);\n  printFn(`Non-trainable params: ${nonTrainableCount}`);\n  printFn(\"_\".repeat(lineLength));\n}\nfunction countTrainableParams(model2) {\n  let trainableCount;\n  if (model2.collectedTrainableWeights != null) {\n    trainableCount = countParamsInWeights(model2.collectedTrainableWeights);\n  } else {\n    trainableCount = countParamsInWeights(model2.trainableWeights);\n  }\n  return trainableCount;\n}\nfunction isModelSequentialLike(model2) {\n  let sequentialLike = true;\n  const nodesByDepth = [];\n  const nodes = [];\n  for (const depth in model2.nodesByDepth) {\n    nodesByDepth.push(model2.nodesByDepth[depth]);\n  }\n  for (const depthNodes of nodesByDepth) {\n    if (depthNodes.length > 1 || depthNodes.length === 1 && depthNodes[0].inboundLayers.length > 1) {\n      sequentialLike = false;\n      break;\n    }\n    nodes.push(...depthNodes);\n  }\n  if (sequentialLike) {\n    for (const layer of model2.layers) {\n      let flag = false;\n      for (const node of layer.inboundNodes) {\n        if (nodes.indexOf(node) !== -1) {\n          if (flag) {\n            sequentialLike = false;\n            break;\n          } else {\n            flag = true;\n          }\n        }\n      }\n      if (!sequentialLike) {\n        break;\n      }\n    }\n  }\n  return sequentialLike;\n}\nfunction printRow(fields, positions, printFn = console.log) {\n  let line = \"\";\n  for (let i2 = 0; i2 < fields.length; ++i2) {\n    if (i2 > 0) {\n      line = line.slice(0, line.length - 1) + \" \";\n    }\n    line += fields[i2];\n    line = line.slice(0, positions[i2]);\n    line += \" \".repeat(positions[i2] - line.length);\n  }\n  printFn(line);\n}\nfunction printLayerSummary(layer, positions, printFn) {\n  let outputShape;\n  let inputShape;\n  try {\n    inputShape = layer.inboundNodes.map((x) => JSON.stringify(x.inputShapes)).join(\",\");\n  } catch (err) {\n    inputShape = \"multiple\";\n  }\n  try {\n    outputShape = JSON.stringify(layer.outputShape);\n  } catch (err) {\n    outputShape = \"multiple\";\n  }\n  const name = layer.name;\n  const className = layer.getClassName();\n  const fields = [\n    `${name} (${className})`,\n    inputShape,\n    outputShape,\n    layer.countParams().toString()\n  ];\n  printRow(fields, positions, printFn);\n}\nfunction printLayerSummaryWithConnections(layer, positions, relevantNodes, printFn) {\n  let outputShape;\n  let inputShape;\n  try {\n    inputShape = layer.inboundNodes.map((x) => JSON.stringify(x.inputShapes)).join(\",\");\n  } catch (err) {\n    inputShape = \"multiple\";\n  }\n  try {\n    outputShape = JSON.stringify(layer.outputShape);\n  } catch (err) {\n    outputShape = \"multiple\";\n  }\n  const connections = [];\n  for (const node of layer.inboundNodes) {\n    if (relevantNodes != null && relevantNodes.length > 0 && relevantNodes.indexOf(node) === -1) {\n      continue;\n    }\n    for (let i2 = 0; i2 < node.inboundLayers.length; ++i2) {\n      const inboundLayer = node.inboundLayers[i2].name;\n      const inboundLayerIndex = node.nodeIndices[i2];\n      const inboundTensorIndex = node.tensorIndices[i2];\n      connections.push(`${inboundLayer}[${inboundLayerIndex}][${inboundTensorIndex}]`);\n    }\n  }\n  const name = layer.name;\n  const className = layer.getClassName();\n  const firstConnection = connections.length === 0 ? \"\" : connections[0];\n  const fields = [\n    `${name} (${className})`,\n    inputShape,\n    outputShape,\n    layer.countParams().toString(),\n    firstConnection\n  ];\n  printRow(fields, positions, printFn);\n  for (let i2 = 1; i2 < connections.length; ++i2) {\n    printRow([\"\", \"\", \"\", \"\", connections[i2]], positions, printFn);\n  }\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-layers@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-layers/dist/utils/serialization_utils.js\nfunction isArrayItemInputOrOutputName(key, index, value) {\n  return (key === \"inboundNodes\" || key === \"outputLayers\" || key === \"inputLayers\") && index === 0 && typeof value === \"string\";\n}\nfunction convertPythonicToTs(pythonicConfig, key) {\n  if (pythonicConfig === null) {\n    return null;\n  } else if (typeof pythonicConfig === \"string\") {\n    return toCamelCase(pythonicConfig);\n  } else if (typeof pythonicConfig === \"number\" || typeof pythonicConfig === \"boolean\") {\n    return pythonicConfig;\n  } else if (pythonicConfig instanceof Array) {\n    const tsArray = [];\n    const arrayLength = pythonicConfig.length;\n    for (let i2 = 0; i2 < arrayLength; ++i2) {\n      const item = pythonicConfig[i2];\n      if (isArrayItemInputOrOutputName(key, i2, item)) {\n        tsArray.push(item);\n      } else {\n        tsArray.push(convertPythonicToTs(item, key));\n      }\n    }\n    return tsArray;\n  } else {\n    const tsDict = {};\n    for (const pythonicKey of Object.keys(pythonicConfig)) {\n      const pythonicValue = pythonicConfig[pythonicKey];\n      if (pythonicKey === \"name\" && typeof pythonicValue === \"string\") {\n        tsDict[pythonicKey] = pythonicValue;\n      } else {\n        const tsKey = toCamelCase(pythonicKey);\n        tsDict[tsKey] = convertPythonicToTs(pythonicValue, tsKey);\n      }\n    }\n    return tsDict;\n  }\n}\nfunction convertTsToPythonic(tsConfig, key) {\n  if (tsConfig === null || tsConfig === void 0) {\n    return null;\n  } else if (typeof tsConfig === \"string\") {\n    return toSnakeCase(tsConfig);\n  } else if (typeof tsConfig === \"number\" || typeof tsConfig === \"boolean\") {\n    return tsConfig;\n  } else if (tsConfig instanceof Array) {\n    const pyArray = [];\n    const arrayLength = tsConfig.length;\n    for (let i2 = 0; i2 < arrayLength; ++i2) {\n      const item = tsConfig[i2];\n      if (isArrayItemInputOrOutputName(key, i2, item)) {\n        pyArray.push(item);\n      } else {\n        pyArray.push(convertTsToPythonic(item, key));\n      }\n    }\n    return pyArray;\n  } else {\n    const pyDict = {};\n    for (const tsKey of Object.keys(tsConfig)) {\n      const tsValue = tsConfig[tsKey];\n      const pyKey = toSnakeCase(tsKey);\n      if ((tsKey === \"name\" || tsKey === \"className\") && typeof tsValue === \"string\") {\n        pyDict[pyKey] = tsValue;\n      } else {\n        pyDict[pyKey] = convertTsToPythonic(tsValue, tsKey);\n      }\n    }\n    return pyDict;\n  }\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-layers@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-layers/dist/version.js\nvar version2 = \"3.20.0\";\n\n// node_modules/.pnpm/@tensorflow+tfjs-layers@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-layers/dist/engine/container.js\nvar Container = class extends Layer {\n  constructor(args) {\n    super({});\n    this.containerNodes = /* @__PURE__ */ new Set();\n    this.name = args.name;\n    if (this.name == null) {\n      const prefix = this.getClassName().toLowerCase();\n      this.name = getUid(prefix);\n    }\n    this.supportsMasking = false;\n    this.trainable_ = true;\n    if (Array.isArray(args.inputs)) {\n      this.inputs = args.inputs.slice();\n    } else {\n      this.inputs = [args.inputs];\n    }\n    if (Array.isArray(args.outputs)) {\n      this.outputs = args.outputs.slice();\n    } else {\n      this.outputs = [args.outputs];\n    }\n    if (unique2(this.inputs).length !== this.inputs.length) {\n      throw new ValueError(`The list of inputs passed to the model is redundant. All inputs should only appear once. Found: ${this.inputs.map((x) => x.name)}`);\n    }\n    if (unique2(this.outputs).length !== this.outputs.length) {\n      console.warn(`The list of outputs passed to the model is redundant. All outputs should only appear once. Found: ${this.outputs.map((x) => x.name)}`);\n    }\n    this.inputLayers = [];\n    this.inputLayersNodeIndices = [];\n    this.inputLayersTensorIndices = [];\n    this.outputLayers = [];\n    this.outputLayersNodeIndices = [];\n    this.outputLayersTensorIndices = [];\n    this.layers = [];\n    this.internalContainerRefs = [];\n    for (const x of this.outputs) {\n      const layer = x.sourceLayer;\n      const nodeIndex = x.nodeIndex;\n      const tensorIndex = x.tensorIndex;\n      this.outputLayers.push(layer);\n      this.outputLayersNodeIndices.push(nodeIndex);\n      this.outputLayersTensorIndices.push(tensorIndex);\n    }\n    for (const x of this.inputs) {\n      const layer = x.sourceLayer;\n      const nodeIndex = x.nodeIndex;\n      const tensorIndex = x.tensorIndex;\n      assert2(nodeIndex === 0, \"input layer has >1 nodes\");\n      assert2(tensorIndex === 0, \"input layer has >1 tensors\");\n      this.inputLayers.push(layer);\n      this.inputLayersNodeIndices.push(nodeIndex);\n      this.inputLayersTensorIndices.push(tensorIndex);\n    }\n    this.inputNames = [];\n    this.outputNames = [];\n    this.feedInputShapes = [];\n    this.feedInputNames = [];\n    this.feedOutputNames = [];\n    for (let i2 = 0; i2 < this.inputLayers.length; i2++) {\n      const layer = this.inputLayers[i2];\n      if (!(layer instanceof InputLayer)) {\n        throw new TypeError(`Input layers to a LayersModel must be InputLayer objects. Received inputs: ${args.inputs}. Input ${i2} (0-based) originates from layer type ${layer.getClassName()}.`);\n      }\n      this.inputNames.push(layer.name);\n      this.feedInputShapes.push(layer.batchInputShape);\n      this.feedInputNames.push(layer.name);\n    }\n    for (const layer of this.outputLayers) {\n      this.outputNames.push(layer.name);\n    }\n    this.internalInputShapes = this.inputs.map((x) => x.shape);\n    this.internalOutputShapes = this.outputs.map((x) => x.shape);\n    const nodesDepths = {};\n    const nodeIDToNode = {};\n    const layersDepths = {};\n    const layerIDToLayer = {};\n    const layerIndices = {};\n    const nodesInDecreasingDepth = [];\n    const buildMapOfGraph = (tensor2, finishedNodes2, nodesInProgress2, layer, nodeIndex, tensorIndex) => {\n      if (layer == null || nodeIndex == null || tensorIndex == null) {\n        layer = tensor2.sourceLayer;\n        nodeIndex = tensor2.nodeIndex;\n        tensorIndex = tensor2.tensorIndex;\n      }\n      const node = layer.inboundNodes[nodeIndex];\n      if (nodesInProgress2.indexOf(node) !== -1) {\n        throw new RuntimeError(`The tensor ${tensor2.name} at layer \"${layer.name}\" is part of a cycle.`);\n      }\n      if (finishedNodes2.indexOf(node) !== -1) {\n        return;\n      }\n      this.containerNodes.add(Container.nodeKey(layer, nodeIndex));\n      if (!(layer.id in layerIndices)) {\n        layerIndices[layer.id] = Object.keys(layerIndices).length;\n      }\n      if (nodesInProgress2.indexOf(node) === -1) {\n        nodesInProgress2.push(node);\n      }\n      const numInboundLayers = node.inboundLayers.length;\n      for (let i2 = 0; i2 < numInboundLayers; i2++) {\n        const x = node.inputTensors[i2];\n        const layer2 = node.inboundLayers[i2];\n        const nodeIndex2 = node.nodeIndices[i2];\n        const tensorIndex2 = node.tensorIndices[i2];\n        buildMapOfGraph(x, finishedNodes2, nodesInProgress2, layer2, nodeIndex2, tensorIndex2);\n      }\n      finishedNodes2.push(node);\n      while (nodesInProgress2.indexOf(node) >= 0) {\n        nodesInProgress2.splice(nodesInProgress2.indexOf(node), 1);\n      }\n      nodesInDecreasingDepth.push(node);\n    };\n    const finishedNodes = [];\n    const nodesInProgress = [];\n    for (const x of this.outputs) {\n      buildMapOfGraph(x, finishedNodes, nodesInProgress);\n    }\n    const reversedNodesInDecreasingDepth = nodesInDecreasingDepth.slice().reverse();\n    for (const node of reversedNodesInDecreasingDepth) {\n      nodeIDToNode[node.id] = node;\n      if (!(node.id in nodesDepths)) {\n        nodesDepths[node.id] = 0;\n      }\n      let depth = nodesDepths[node.id];\n      const previousDepth = layersDepths[node.outboundLayer.id] == null ? 0 : layersDepths[node.outboundLayer.id];\n      depth = Math.max(depth, previousDepth);\n      layersDepths[node.outboundLayer.id] = depth;\n      layerIDToLayer[node.outboundLayer.id] = node.outboundLayer;\n      nodesDepths[node.id] = depth;\n      for (let i2 = 0; i2 < node.inboundLayers.length; i2++) {\n        const inboundLayer = node.inboundLayers[i2];\n        const nodeIndex = node.nodeIndices[i2];\n        const inboundNode = inboundLayer.inboundNodes[nodeIndex];\n        const previousDepth2 = nodesDepths[inboundNode.id] == null ? 0 : nodesDepths[inboundNode.id];\n        nodesDepths[inboundNode.id] = Math.max(depth + 1, previousDepth2);\n        nodeIDToNode[inboundNode.id] = inboundNode;\n      }\n    }\n    const nodesByDepth = {};\n    for (const nodeID in nodesDepths) {\n      const depth = nodesDepths[nodeID];\n      if (!(depth in nodesByDepth)) {\n        nodesByDepth[depth] = [];\n      }\n      nodesByDepth[depth].push(nodeIDToNode[nodeID]);\n    }\n    const layersByDepth = {};\n    for (const layerID in layersDepths) {\n      const depth = layersDepths[layerID];\n      if (!(depth in layersByDepth)) {\n        layersByDepth[depth] = [];\n      }\n      layersByDepth[depth].push(layerIDToLayer[layerID]);\n    }\n    let depthKeys = Object.keys(layersByDepth).map((x) => parseInt(x, 10)).sort(reverseNumberCompare);\n    this.layers = [];\n    for (const depth of depthKeys) {\n      const layersForDepth = layersByDepth[depth];\n      layersForDepth.sort((a, b) => {\n        const aIndex = layerIndices[a.id];\n        const bIndex = layerIndices[b.id];\n        if (aIndex < bIndex) {\n          return -1;\n        }\n        if (aIndex > bIndex) {\n          return 1;\n        }\n        return 0;\n      });\n      for (const layer of layersForDepth) {\n        if (layer instanceof Container) {\n          this.internalContainerRefs.push(layer);\n        }\n        this.layers.push(layer);\n      }\n    }\n    this.layersByDepth = layersByDepth;\n    depthKeys = Object.keys(nodesByDepth).map((x) => parseInt(x, 10)).sort(reverseNumberCompare);\n    const computableTensors = this.inputs.slice();\n    const layersWithCompleteInput = [];\n    for (const depth of depthKeys) {\n      for (const node of nodesByDepth[depth]) {\n        const layer = node.outboundLayer;\n        if (layer != null) {\n          for (const x of node.inputTensors) {\n            if (computableTensors.indexOf(x) === -1) {\n              throw new RuntimeError(`Graph disconnected: cannot obtain value for tensor ${x} at layer \"${layer.name}\". The following previous layers were accessed without issue: ${layersWithCompleteInput}`);\n            }\n          }\n          for (const x of node.outputTensors) {\n            computableTensors.push(x);\n          }\n          layersWithCompleteInput.push(layer.name);\n        }\n      }\n    }\n    this.nodesByDepth = nodesByDepth;\n    const allNames = this.layers.map((x) => x.name);\n    for (const name of allNames) {\n      const numOccurrences = allNames.filter((x) => x === name).length;\n      if (numOccurrences !== 1) {\n        throw new RuntimeError(`The name \"${name}\" is used ${numOccurrences} times in the model. All layer names should be unique. Layer names: ` + JSON.stringify(allNames));\n      }\n    }\n    this.outboundNodes = [];\n    this.inboundNodes = [];\n    new Node({\n      outboundLayer: this,\n      inboundLayers: [],\n      nodeIndices: [],\n      tensorIndices: [],\n      inputTensors: this.inputs,\n      outputTensors: this.outputs,\n      inputMasks: this.inputs.map((x) => null),\n      outputMasks: this.outputs.map((x) => null),\n      inputShapes: this.inputs.map((x) => x.shape),\n      outputShapes: this.outputs.map((x) => x.shape)\n    });\n    this.built = true;\n    this._refCount = 1;\n  }\n  assertNotDisposed() {\n    if (this._refCount === 0) {\n      throw new Error(`Container '${this.name}' is already disposed.`);\n    }\n  }\n  dispose() {\n    this.assertNotDisposed();\n    const result = { refCountAfterDispose: null, numDisposedVariables: 0 };\n    if (--this._refCount === 0) {\n      for (const layer of this.layers) {\n        result.numDisposedVariables += layer.dispose().numDisposedVariables;\n      }\n      for (const container of this.internalContainerRefs) {\n        result.numDisposedVariables += container.dispose().numDisposedVariables;\n      }\n    }\n    result.refCountAfterDispose = this._refCount;\n    return result;\n  }\n  get trainable() {\n    return this.trainable_;\n  }\n  set trainable(trainable) {\n    this.layers.forEach((layer) => {\n      layer._trainableWeights.forEach((w) => w.trainable = trainable);\n    });\n    this.trainable_ = trainable;\n  }\n  get trainableWeights() {\n    if (this._trainableWeights.length > 0) {\n      throw new ValueError(\"Container instance unexpectedly contains _trainableWeights.The trainable weights of a Container are a union of the trainable weights of its consituent Layers. Its own _trainableWeights must remain an empty Array.\");\n    }\n    if (!this.trainable) {\n      return [];\n    }\n    let weights = [];\n    for (const layer of this.layers) {\n      weights = weights.concat(layer.trainableWeights);\n    }\n    return weights;\n  }\n  get nonTrainableWeights() {\n    const weights = [];\n    for (const layer of this.layers) {\n      weights.push(...layer.nonTrainableWeights);\n    }\n    if (!this.trainable) {\n      const trainableWeights = [];\n      for (const layer of this.layers) {\n        trainableWeights.push(...layer.trainableWeights);\n      }\n      return trainableWeights.concat(weights);\n    }\n    return weights;\n  }\n  get weights() {\n    return this.trainableWeights.concat(this.nonTrainableWeights);\n  }\n  loadWeights(weights, strict = true) {\n    const nameToWeight = {};\n    let totalWeightsCount = 0;\n    for (const layer of this.layers) {\n      for (const weight of layer.weights) {\n        if (nameToWeight[weight.originalName] != null) {\n          throw new ValueError(`Duplicate weight name: ${weight.originalName}`);\n        }\n        nameToWeight[weight.originalName] = weight;\n        totalWeightsCount++;\n      }\n    }\n    const weightValueTuples = [];\n    for (const name in weights) {\n      let validatedName = name;\n      if (nameToWeight[name] == null) {\n        const tokens = name.split(\"/\");\n        const shortenNameArray = tokens.slice(0, -2).concat([tokens[tokens.length - 1]]);\n        validatedName = shortenNameArray.join(\"/\");\n      }\n      if (nameToWeight[validatedName] != null) {\n        weightValueTuples.push([nameToWeight[validatedName], weights[name]]);\n      } else if (strict) {\n        throw new ValueError(`Provided weight data has no target variable: ${name}`);\n      }\n      delete nameToWeight[validatedName];\n    }\n    if (strict) {\n      const unsetNames = [];\n      for (const name in nameToWeight) {\n        unsetNames.push(name);\n      }\n      if (unsetNames.length > 0) {\n        throw new ValueError(`${unsetNames.length} of ${totalWeightsCount} weights are not set: ${unsetNames}`);\n      }\n    }\n    batchSetValue(weightValueTuples);\n  }\n  updatedConfig() {\n    const theConfig = this.getConfig();\n    const modelConfig = {};\n    modelConfig[\"className\"] = this.getClassName();\n    modelConfig[\"config\"] = theConfig;\n    modelConfig[\"kerasVersion\"] = `tfjs-layers ${version2}`;\n    modelConfig[\"backend\"] = \"TensorFlow.js\";\n    return modelConfig;\n  }\n  toJSON(unused, returnString = true) {\n    const modelConfig = convertTsToPythonic(this.updatedConfig());\n    return returnString ? JSON.stringify(modelConfig) : modelConfig;\n  }\n  call(inputs, kwargs) {\n    return tidy(() => {\n      inputs = toList(inputs);\n      const feedDict = new FeedDict();\n      for (let i2 = 0; i2 < this.inputs.length; ++i2) {\n        feedDict.add(this.inputs[i2], inputs[i2]);\n      }\n      return execute(this.outputs, feedDict, kwargs);\n    });\n  }\n  computeMask(inputs, mask) {\n    return tidy(() => {\n      inputs = toList(inputs);\n      let masks;\n      if (mask == null) {\n        masks = pyListRepeat(null, inputs.length);\n      } else {\n        masks = toList(mask);\n      }\n      return this.runInternalGraph(inputs, masks)[1];\n    });\n  }\n  computeOutputShape(inputShape) {\n    const inputShapes = normalizeShapeList(inputShape);\n    if (inputShapes.length !== this.inputLayers.length) {\n      throw new ValueError(`Invalid inputShape argument ${inputShape}: model has ${this.inputLayers.length} tensor inputs.`);\n    }\n    const layersToOutputShapes = {};\n    for (let i2 = 0; i2 < inputShapes.length; i2++) {\n      const layer = this.inputLayers[i2];\n      const inputShape2 = inputShapes[i2];\n      const shapeKey = layer.name + \"_0_0\";\n      layersToOutputShapes[shapeKey] = inputShape2;\n    }\n    const depthKeys = Object.keys(this.nodesByDepth).map((x) => parseInt(x, 10)).sort(reverseNumberCompare);\n    if (depthKeys.length > 1) {\n      for (const depth of depthKeys) {\n        const nodes = this.nodesByDepth[depth];\n        for (const node of nodes) {\n          const layer = node.outboundLayer;\n          if (this.inputLayers.map((x) => x.id).indexOf(layer.id) !== -1) {\n            continue;\n          }\n          const inputShapes2 = [];\n          for (let j = 0; j < node.inboundLayers.length; j++) {\n            const inboundLayer = node.inboundLayers[j];\n            const nodeIndex2 = node.nodeIndices[j];\n            const tensorIndex = node.tensorIndices[j];\n            const shapeKey = `${inboundLayer.name}_${nodeIndex2}_${tensorIndex}`;\n            const inputShape2 = layersToOutputShapes[shapeKey];\n            inputShapes2.push(inputShape2);\n          }\n          const outputShape = layer.computeOutputShape(singletonOrArray(inputShapes2));\n          const outputShapes2 = normalizeShapeList(outputShape);\n          const nodeIndex = layer.inboundNodes.indexOf(node);\n          for (let j = 0; j < outputShapes2.length; j++) {\n            const shapeKey = `${layer.name}_${nodeIndex}_${j}`;\n            layersToOutputShapes[shapeKey] = outputShapes2[j];\n          }\n        }\n      }\n    }\n    const outputShapes = [];\n    const outputShapeKeys = [];\n    for (let i2 = 0; i2 < this.outputLayers.length; i2++) {\n      const layer = this.outputLayers[i2];\n      const nodeIndex = this.outputLayersNodeIndices[i2];\n      const tensorIndex = this.outputLayersTensorIndices[i2];\n      const shapeKey = `${layer.name}_${nodeIndex}_${tensorIndex}`;\n      outputShapeKeys.push(shapeKey);\n    }\n    for (let i2 = 0; i2 < outputShapeKeys.length; i2++) {\n      const key = outputShapeKeys[i2];\n      assert2(key in layersToOutputShapes);\n      outputShapes.push(layersToOutputShapes[key]);\n    }\n    return singletonOrArray(outputShapes);\n  }\n  runInternalGraph(inputs, masks) {\n    if (masks == null) {\n      masks = pyListRepeat(null, inputs.length);\n    }\n    const tensorMap = {};\n    for (let i2 = 0; i2 < this.inputs.length; ++i2) {\n      const x = this.inputs[i2];\n      const y = inputs[i2];\n      const mask = masks[i2];\n      tensorMap[x.id] = [y, mask];\n    }\n    const depthKeys = Object.keys(this.nodesByDepth).map((x) => parseInt(x, 10)).sort(reverseNumberCompare);\n    for (const depth of depthKeys) {\n      const nodes = this.nodesByDepth[depth];\n      for (const node of nodes) {\n        const layer = node.outboundLayer;\n        const referenceInputTensors = node.inputTensors;\n        const referenceOutputTensors = node.outputTensors;\n        const computedData = new Array();\n        for (const x of referenceInputTensors) {\n          if (x.id in tensorMap) {\n            computedData.push(tensorMap[x.id]);\n          }\n        }\n        if (computedData.length === referenceInputTensors.length) {\n          let kwargs = {};\n          let computedTensors;\n          let computedMasks;\n          let outputTensors2;\n          let outputMasks2;\n          if (node.callArgs != null) {\n            kwargs = node.callArgs;\n          }\n          if (computedData.length === 1) {\n            const [computedTensor, computedMask] = computedData[0];\n            if (kwargs[\"mask\"] == null) {\n              kwargs[\"mask\"] = computedMask;\n            }\n            outputTensors2 = toList(layer.call(computedTensor, kwargs));\n            outputMasks2 = toList(layer.computeMask(computedTensor, computedMask));\n            computedTensors = [computedTensor];\n            computedMasks = [computedMask];\n          } else {\n            computedTensors = computedData.map((x) => x[0]);\n            computedMasks = computedData.map((x) => x[1]);\n            if (kwargs[\"mask\"] == null) {\n              kwargs[\"mask\"] = computedMasks;\n            }\n            outputTensors2 = toList(layer.call(computedTensors, kwargs));\n            outputMasks2 = toList(layer.computeMask(computedTensors, computedMasks));\n          }\n          if (layer.activityRegularizer) {\n            throw new NotImplementedError(\"LayersModel invocation with concrete Tensor value(s) in the presence of activity regularizer(s) is not supported yet.\");\n          }\n          for (let i2 = 0; i2 < referenceOutputTensors.length; ++i2) {\n            const x = referenceOutputTensors[i2];\n            const y = outputTensors2[i2];\n            const mask = outputMasks2[i2];\n            tensorMap[x.id] = [y, mask];\n          }\n        }\n      }\n    }\n    const outputTensors = [];\n    const outputMasks = [];\n    const outputShapes = [];\n    for (const x of this.outputs) {\n      assert2(x.id in tensorMap, `Could not compute output ${x.name} : ${x.id}`);\n      const [tensor2, mask] = tensorMap[x.id];\n      outputShapes.push(tensor2.shape);\n      outputTensors.push(tensor2);\n      outputMasks.push(mask);\n    }\n    return [outputTensors, outputMasks, outputShapes];\n  }\n  buildNodeConversionMap(layers) {\n    const nodeConversionMap = {};\n    let keptNodes;\n    for (const layer of this.layers) {\n      keptNodes = layer instanceof Container ? 1 : 0;\n      for (let originalNodeIndex = 0; originalNodeIndex < layer.inboundNodes.length; originalNodeIndex++) {\n        const nodeKey = Container.nodeKey(layer, originalNodeIndex);\n        if (this.containerNodes.has(nodeKey)) {\n          nodeConversionMap[nodeKey] = keptNodes;\n          keptNodes += 1;\n        }\n      }\n    }\n    return nodeConversionMap;\n  }\n  getLayer(name, index) {\n    if (index != null) {\n      if (this.layers.length <= index) {\n        throw new ValueError(`Was asked to retrieve layer at index ${index}, but model only has ${this.layers.length} layer(s).`);\n      } else {\n        return this.layers[index];\n      }\n    } else {\n      if (name == null) {\n        throw new ValueError(\"Provide either a layer name or layer index\");\n      }\n    }\n    for (const layer of this.layers) {\n      if (layer.name === name) {\n        return layer;\n      }\n    }\n    throw new ValueError(`No such layer: ${name}`);\n  }\n  calculateLosses() {\n    return tidy(() => {\n      const losses2 = [];\n      for (const layer of this.layers) {\n        for (let nodeIndex = 0; nodeIndex < layer.inboundNodes.length; ++nodeIndex) {\n          const nodeKey = Container.nodeKey(layer, nodeIndex);\n          if (this.containerNodes.has(nodeKey)) {\n            losses2.push(...layer.calculateLosses());\n          }\n        }\n      }\n      return losses2;\n    });\n  }\n  getConfig() {\n    const config = { name: this.name };\n    const nodeConversionMap = this.buildNodeConversionMap(this.layers);\n    const layerConfigs = [];\n    for (const layer of this.layers) {\n      const layerClassName = layer.getClassName();\n      const layerConfig = layer.getConfig();\n      const filteredInboundNodes = [];\n      for (let originalNodeIndex = 0; originalNodeIndex < layer.inboundNodes.length; originalNodeIndex++) {\n        const node = layer.inboundNodes[originalNodeIndex];\n        const nodeKey = Container.nodeKey(layer, originalNodeIndex);\n        let kwargs = {};\n        if (this.containerNodes.has(nodeKey)) {\n          if (node.callArgs) {\n            try {\n              JSON.stringify(node.callArgs);\n              kwargs = node.callArgs;\n            } catch (err) {\n              console.warn(`Layer ${layer.name} was passed non-serializable keyword arguments: ${node.callArgs}. They will not be included in the serialized model (and thus will be missing at deserialization time).`);\n              kwargs = {};\n            }\n          }\n          if (node.inboundLayers.length > 0) {\n            const nodeData = [];\n            for (let i2 = 0; i2 < node.inboundLayers.length; i2++) {\n              const inboundLayer = node.inboundLayers[i2];\n              const nodeIndex = node.nodeIndices[i2];\n              const tensorIndex = node.tensorIndices[i2];\n              const nodeKey2 = Container.nodeKey(inboundLayer, nodeIndex);\n              let newNodeIndex = nodeConversionMap[nodeKey2];\n              if (newNodeIndex == null) {\n                newNodeIndex = 0;\n              }\n              nodeData.push([inboundLayer.name, newNodeIndex, tensorIndex, kwargs]);\n            }\n            filteredInboundNodes.push(nodeData);\n          }\n        }\n      }\n      const dict = {};\n      dict[\"name\"] = layer.name;\n      dict[\"className\"] = layerClassName;\n      dict[\"config\"] = layerConfig;\n      dict[\"inboundNodes\"] = filteredInboundNodes;\n      layerConfigs.push(dict);\n    }\n    config[\"layers\"] = layerConfigs;\n    const modelInputs = [];\n    for (let i2 = 0; i2 < this.inputLayers.length; i2++) {\n      const layer = this.inputLayers[i2];\n      const nodeIndex = this.inputLayersNodeIndices[i2];\n      const nodeKey = Container.nodeKey(layer, nodeIndex);\n      if (!this.containerNodes.has(nodeKey)) {\n        continue;\n      }\n      let newNodeIndex = nodeConversionMap[nodeKey];\n      if (newNodeIndex === null || newNodeIndex === void 0) {\n        newNodeIndex = 0;\n      }\n      const tensorIndex = this.inputLayersTensorIndices[i2];\n      modelInputs.push([layer.name, newNodeIndex, tensorIndex]);\n    }\n    config[\"inputLayers\"] = modelInputs;\n    const modelOutputs = [];\n    for (let i2 = 0; i2 < this.outputLayers.length; i2++) {\n      const layer = this.outputLayers[i2];\n      const nodeIndex = this.outputLayersNodeIndices[i2];\n      const nodeKey = Container.nodeKey(layer, nodeIndex);\n      if (!this.containerNodes.has(nodeKey)) {\n        continue;\n      }\n      let newNodeIndex = nodeConversionMap[nodeKey];\n      if (newNodeIndex === null || newNodeIndex === void 0) {\n        newNodeIndex = 0;\n      }\n      const tensorIndex = this.outputLayersTensorIndices[i2];\n      modelOutputs.push([layer.name, newNodeIndex, tensorIndex]);\n    }\n    config[\"outputLayers\"] = modelOutputs;\n    return config;\n  }\n  static fromConfig(cls, config, customObjects = {}, fastWeightInit = false) {\n    const createdLayers = {};\n    const unprocessedNodes = {};\n    function addUnprocessedNode(layer, nodeData) {\n      if (!(layer.name in unprocessedNodes)) {\n        unprocessedNodes[layer.name] = [nodeData];\n      } else {\n        unprocessedNodes[layer.name].push(nodeData);\n      }\n    }\n    function processNode(layer, nodeData) {\n      const inputTensors2 = [];\n      let kwargs;\n      for (const inputData of nodeData) {\n        const inboundLayerName = inputData[0];\n        const inboundNodeIndex = inputData[1];\n        const inboundTensorIndex = inputData[2];\n        kwargs = inputData[3] == null ? {} : inputData[3];\n        if (!(inboundLayerName in createdLayers)) {\n          addUnprocessedNode(layer, nodeData);\n          return;\n        }\n        const inboundLayer = createdLayers[inboundLayerName];\n        if (inboundLayer.inboundNodes.length <= inboundNodeIndex) {\n          addUnprocessedNode(layer, nodeData);\n          return;\n        }\n        const inboundNode = inboundLayer.inboundNodes[inboundNodeIndex];\n        inputTensors2.push(inboundNode.outputTensors[inboundTensorIndex]);\n      }\n      if (inputTensors2.length > 0) {\n        layer.apply(singletonOrArray(inputTensors2), kwargs);\n      }\n    }\n    function processLayer(layerData) {\n      const layerName = layerData[\"name\"];\n      const layer = deserialize(layerData, config[\"customObjects\"] != null ? config[\"customObjects\"] : {});\n      layer.setFastWeightInitDuringBuild(fastWeightInit);\n      createdLayers[layerName] = layer;\n      const inboundNodesData = layerData[\"inboundNodes\"];\n      inboundNodesData.forEach((nodeData) => {\n        if (!(nodeData instanceof Array)) {\n          throw new ValueError(`Corrupted configuration, expected array for nodeData: ${nodeData}`);\n        }\n        addUnprocessedNode(layer, nodeData);\n      });\n    }\n    const name = config[\"name\"];\n    const layersFromConfig = config[\"layers\"];\n    for (const layerData of layersFromConfig) {\n      processLayer(layerData);\n    }\n    while (!isObjectEmpty(unprocessedNodes)) {\n      for (const layerData of layersFromConfig) {\n        const layer = createdLayers[layerData[\"name\"]];\n        if (layer.name in unprocessedNodes) {\n          const currentUnprocessedNodesForLayer = unprocessedNodes[layer.name];\n          delete unprocessedNodes[layer.name];\n          for (const nodeData of currentUnprocessedNodesForLayer) {\n            processNode(layer, nodeData);\n          }\n        }\n      }\n    }\n    const inputTensors = [];\n    const outputTensors = [];\n    const inputLayersFromConfig = config[\"inputLayers\"];\n    for (const layerData of inputLayersFromConfig) {\n      const layerName = layerData[0];\n      const nodeIndex = layerData[1];\n      const tensorIndex = layerData[2];\n      assert2(layerName in createdLayers);\n      const layer = createdLayers[layerName];\n      const layerOutputTensors = layer.inboundNodes[nodeIndex].outputTensors;\n      inputTensors.push(layerOutputTensors[tensorIndex]);\n    }\n    const outputLayersFromConfig = config[\"outputLayers\"];\n    for (const layerData of outputLayersFromConfig) {\n      const layerName = layerData[0];\n      const nodeIndex = layerData[1];\n      const tensorIndex = layerData[2];\n      assert2(layerName in createdLayers);\n      const layer = createdLayers[layerName];\n      const layerOutputTensors = layer.inboundNodes[nodeIndex].outputTensors;\n      outputTensors.push(layerOutputTensors[tensorIndex]);\n    }\n    return new cls({ inputs: inputTensors, outputs: outputTensors, name });\n  }\n  get stateful() {\n    if (this._stateful) {\n      throw new ValueError(\"Container instance unexpectedly has _stateful = true. The statefulness of a Container is determined by the Layers it contains. Its _stateful property must remain the default false.\");\n    }\n    for (const layer of this.layers) {\n      if (layer.stateful) {\n        return true;\n      }\n    }\n    return false;\n  }\n  resetStates() {\n    tidy(() => {\n      this.layers.forEach((layer) => {\n        if (layer.stateful) {\n          layer.resetStates();\n        }\n      });\n    });\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-layers@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-layers/dist/engine/training_utils.js\nfunction standardizeSampleOrClassWeights(xWeight, outputNames, weightType) {\n  const numOutputs = outputNames.length;\n  if (xWeight == null || Array.isArray(xWeight) && xWeight.length === 0) {\n    return outputNames.map((name) => null);\n  }\n  if (numOutputs === 1) {\n    if (Array.isArray(xWeight) && xWeight.length === 1) {\n      return xWeight;\n    } else if (typeof xWeight === \"object\" && outputNames[0] in xWeight) {\n      return [xWeight[outputNames[0]]];\n    } else {\n      return [xWeight];\n    }\n  }\n  if (Array.isArray(xWeight)) {\n    if (xWeight.length !== numOutputs) {\n      throw new Error(`Provided ${weightType} is an array of ${xWeight.length} element(s), but the model has ${numOutputs} outputs. Make sure a set of weights is provided for each model output.`);\n    }\n    return xWeight;\n  } else if (typeof xWeight === \"object\" && Object.keys(xWeight).length > 0 && typeof xWeight[Object.keys(xWeight)[0]] === \"object\") {\n    const output = [];\n    outputNames.forEach((outputName) => {\n      if (outputName in xWeight) {\n        output.push(xWeight[outputName]);\n      } else {\n        output.push(null);\n      }\n    });\n    return output;\n  } else {\n    throw new Error(`The model has multiple (${numOutputs}) outputs, so ${weightType} must be either an array with ${numOutputs} elements or an object with ${outputNames} keys. Provided ${weightType} not understood: ${JSON.stringify(xWeight)}`);\n  }\n}\nfunction standardizeClassWeights(classWeight, outputNames) {\n  return standardizeSampleOrClassWeights(classWeight, outputNames, \"classWeight\");\n}\nasync function standardizeWeights(y, sampleWeight, classWeight, sampleWeightMode) {\n  if (sampleWeight != null || sampleWeightMode != null) {\n    throw new Error(\"Support sampleWeight is not implemented yet\");\n  }\n  if (classWeight != null) {\n    const yClasses = tidy(() => {\n      if (y.shape.length === 1) {\n        return clone(y);\n      } else if (y.shape.length === 2) {\n        if (y.shape[1] > 1) {\n          const axis = 1;\n          return argMax(y, axis);\n        } else if (y.shape[1] === 1) {\n          return reshape(y, [y.shape[0]]);\n        } else {\n          throw new Error(`Encountered unexpected last-dimension size (${y.shape[1]}) during handling of class weights. The size is expected to be >= 1.`);\n        }\n      } else {\n        throw new Error(`Unexpected rank of target (y) tensor (${y.rank}) during handling of class weights. The rank is expected to be 1 or 2.`);\n      }\n    });\n    const yClassIndices = Array.from(await yClasses.data());\n    dispose(yClasses);\n    const classSampleWeight = [];\n    yClassIndices.forEach((classIndex) => {\n      if (classWeight[classIndex] == null) {\n        throw new Error(`classWeight must contain all classes in the training data. The class ${classIndex} exists in the data but not in classWeight`);\n      } else {\n        classSampleWeight.push(classWeight[classIndex]);\n      }\n    });\n    return tensor1d(classSampleWeight, \"float32\");\n  } else {\n    return null;\n  }\n}\nfunction computeWeightedLoss2(losses2, sampleWeights) {\n  return mul(losses2, sampleWeights);\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-layers@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-layers/dist/engine/training_dataset.js\nvar DEFAULT_VALIDATION_BATCH_SIZE = 32;\nfunction standardizeDataIteratorOutput(model2, iteratorOut) {\n  let xs;\n  let ys;\n  const iteratorOutObj = iteratorOut;\n  xs = iteratorOutObj[\"xs\"];\n  ys = iteratorOutObj[\"ys\"];\n  util_exports.assert(xs != null && ys != null, () => `A Dataset iterator for fitDataset() is expected to generate objects of the form \\`{xs: xVal, ys: yVal}\\`, where the two values may be \\`tf.Tensor\\`, an array of Tensors, or a map of string to Tensor.  The provided Dataset instead generates ${iteratorOut}`);\n  const flattenedXs = flattenTensorOrArrayOrMap(\"input\", model2.inputNames, xs);\n  const flattenedYs = flattenTensorOrArrayOrMap(\"output\", model2.outputNames, ys);\n  const batchSize = flattenedXs[0].shape[0];\n  util_exports.assert(flattenedXs.length === model2.inputs.length, () => `LayersModel has ${model2.inputs.length} inputs, but the dataset provides ${flattenedXs.length} inputs.  (Expected input keys: ${JSON.stringify(model2.inputNames)})`);\n  util_exports.assert(flattenedYs.length === model2.outputs.length, () => `LayersModel has ${model2.outputs.length} outputs, but the dataset provides ${flattenedYs.length} outputs.  (Expected output keys: ${JSON.stringify(model2.outputNames)})`);\n  for (let xIndex = 0; xIndex < flattenedXs.length; xIndex++) {\n    util_exports.assert(flattenedXs[xIndex].shape[0] === batchSize, () => `Batch size mismatch: input ${model2.inputNames[xIndex]} has ${flattenedXs[xIndex].shape[0]}; expected  ${batchSize} based on input ${model2.inputNames[0]}.`);\n  }\n  for (let yIndex = 0; yIndex < flattenedYs.length; yIndex++) {\n    util_exports.assert(flattenedYs[yIndex].shape[0] === batchSize, () => `Batch size mismatch: output ${model2.outputNames[yIndex]} has ${flattenedYs[yIndex].shape[0]}; expected  ${batchSize} based on input ${model2.inputNames[0]}.`);\n  }\n  return { xs: flattenedXs, ys: flattenedYs };\n}\nfunction flattenTensorOrArrayOrMap(inputOrOutput, names, values) {\n  if (values instanceof Tensor) {\n    return [values];\n  } else if (Array.isArray(values)) {\n    util_exports.assert(values.length === names.length, () => `Received an array of ${values.length} Tensors, but expected ${names.length} to match the ${inputOrOutput} keys ${names}.`);\n    return values;\n  } else {\n    const result = [];\n    for (const name of names) {\n      if (values[name] == null) {\n        throw new ValueError(`The feature data generated by the dataset lacks the required ${inputOrOutput} key '${name}'.`);\n      }\n      result.push(values[name]);\n    }\n    return result;\n  }\n}\nfunction standardizeTensorValidationData(data) {\n  if (data.length === 3) {\n    throw new NotImplementedError(\"Validation with sample weights is not implemented yet.\");\n  }\n  return { xs: data[0], ys: data[1] };\n}\nasync function fitDataset(model2, dataset, args) {\n  const hasBatchesPerEpoch = args.batchesPerEpoch != null;\n  util_exports.assert(model2.optimizer != null, () => \"You must compile a model before training/testing. Use LayersModel.compile(modelCompileConfig).\");\n  util_exports.assert(args != null, () => `For fitDataset(), the 2nd argument (config) is required, but it is not provided in this call.`);\n  util_exports.assert(args.epochs != null && args.epochs > 0 && Number.isInteger(args.epochs), () => `For fitDataset(), config.epochs is expected to be a positive integer, but got ${args.epochs}`);\n  util_exports.assert(!hasBatchesPerEpoch || args.batchesPerEpoch > 0 && Number.isInteger(args.batchesPerEpoch), () => `For fitDataset(), config.batchesPerEpoch is expected to be a positive integer if specified, but got ${args.batchesPerEpoch}`);\n  util_exports.assert(\n    args[\"validationSplit\"] == null,\n    () => \"`validationSplit` is not supported by `fitDataset()`. Use validationData instead.\"\n  );\n  if (model2.isTraining) {\n    throw new Error(\"Cannot start training because another fit() call is ongoing.\");\n  }\n  model2.isTraining = true;\n  try {\n    const doValidation = args.validationData != null;\n    let valXs;\n    let valYs;\n    if (doValidation) {\n      if (isDatasetObject(args.validationData)) {\n        util_exports.assert(args.validationBatches == null || args.validationBatches > 0 && Number.isInteger(args.validationBatches), () => `For fitDataset() with dataset-based validation, config.validationBatches is expected not to be provided, or to be a positive integer, but got ${args.validationBatches}`);\n      } else {\n        const validationData = standardizeTensorValidationData(args.validationData);\n        valXs = validationData.xs;\n        valYs = validationData.ys;\n      }\n    }\n    const trainFunction = model2.makeTrainFunction();\n    const outLabels = model2.getDedupedMetricsNames();\n    let callbackMetrics;\n    if (doValidation) {\n      callbackMetrics = outLabels.slice().concat(outLabels.map((n2) => \"val_\" + n2));\n    } else {\n      callbackMetrics = outLabels.slice();\n    }\n    const callbacks2 = standardizeCallbacks(args.callbacks, args.yieldEvery);\n    const verbose = args.verbose == null ? 1 : args.verbose;\n    const { callbackList, history } = configureCallbacks(\n      callbacks2,\n      verbose,\n      args.epochs,\n      null,\n      null,\n      getStepsPerEpoch(dataset, args),\n      null,\n      doValidation,\n      callbackMetrics\n    );\n    callbackList.setModel(model2);\n    model2.history = history;\n    await callbackList.onTrainBegin();\n    model2.stopTraining_ = false;\n    let epoch = args.initialEpoch == null ? 0 : args.initialEpoch;\n    let dataIterator = await dataset.iterator();\n    while (epoch < args.epochs) {\n      const epochLogs = {};\n      await callbackList.onEpochBegin(epoch);\n      let stepsDone = 0;\n      let batchIndex = 0;\n      if (!hasBatchesPerEpoch) {\n        dataIterator = await dataset.iterator();\n      }\n      while (hasBatchesPerEpoch ? stepsDone < args.batchesPerEpoch : true) {\n        const iteratorOut = await dataIterator.next();\n        if (hasBatchesPerEpoch && iteratorOut.done) {\n          console.warn(`You provided \\`batchesPerEpoch\\` as ${args.batchesPerEpoch}, but your dataset iterator ran out of data after ${stepsDone} batches; interrupting training. Make sure that your dataset can generate at least \\`batchesPerEpoch * epochs\\` batches (in this case, ${args.batchesPerEpoch * args.epochs} batches). You may need to use the repeat() function when building your dataset.`);\n          break;\n        }\n        if (iteratorOut.value != null) {\n          const { xs, ys } = standardizeDataIteratorOutput(model2, iteratorOut.value);\n          const batchLogs = {};\n          batchLogs[\"batch\"] = batchIndex;\n          batchLogs[\"size\"] = xs[0].shape[0];\n          await callbackList.onBatchBegin(batchIndex, batchLogs);\n          const sampleWeights = [];\n          if (args.classWeight != null) {\n            const standardClassWeights = standardizeClassWeights(args.classWeight, model2.outputNames);\n            for (let i2 = 0; i2 < standardClassWeights.length; ++i2) {\n              sampleWeights.push(await standardizeWeights(ys[i2], null, standardClassWeights[i2]));\n            }\n          }\n          const ins = xs.concat(ys).concat(sampleWeights);\n          const outs = trainFunction(ins);\n          dispose(ins);\n          for (let i2 = 0; i2 < outLabels.length; ++i2) {\n            const label = outLabels[i2];\n            const out = outs[i2];\n            batchLogs[label] = out;\n            keep(out);\n          }\n          await callbackList.onBatchEnd(batchIndex, batchLogs);\n          disposeTensorsInLogs(batchLogs);\n          batchIndex++;\n          stepsDone++;\n        }\n        if (hasBatchesPerEpoch ? stepsDone >= args.batchesPerEpoch : iteratorOut.done) {\n          if (doValidation) {\n            let valOuts;\n            if (isDatasetObject(args.validationData)) {\n              valOuts = toList(await model2.evaluateDataset(args.validationData, { batches: args.validationBatches }));\n            } else {\n              valOuts = toList(model2.evaluate(valXs, valYs, {\n                batchSize: args.validationBatchSize == null ? DEFAULT_VALIDATION_BATCH_SIZE : args.validationBatchSize,\n                verbose: 0\n              }));\n            }\n            for (let i2 = 0; i2 < model2.metricsNames.length; ++i2) {\n              epochLogs[`val_${model2.metricsNames[i2]}`] = valOuts[i2];\n            }\n          }\n          break;\n        }\n        if (model2.stopTraining_) {\n          break;\n        }\n      }\n      await callbackList.onEpochEnd(epoch, epochLogs);\n      epoch++;\n      if (model2.stopTraining_) {\n        break;\n      }\n    }\n    await callbackList.onTrainEnd();\n    await model2.history.syncData();\n    return model2.history;\n  } finally {\n    model2.isTraining = false;\n  }\n}\nfunction getStepsPerEpoch(dataset, args) {\n  let stepsPerEpoch = null;\n  if (args.batchesPerEpoch != null) {\n    stepsPerEpoch = args.batchesPerEpoch;\n  } else if (Number.isFinite(dataset.size)) {\n    stepsPerEpoch = dataset.size;\n  }\n  return stepsPerEpoch;\n}\nfunction isDatasetObject(dataset) {\n  return typeof dataset.iterator === \"function\";\n}\nfunction isLazyIteratorObject(iterator) {\n  return typeof iterator.next === \"function\";\n}\nasync function evaluateDataset(model2, dataset, args) {\n  args = args || {};\n  const hasBatches = args.batches != null;\n  const f = model2.testFunction;\n  let outs = [];\n  if (args.verbose > 0) {\n    throw new NotImplementedError(\"Verbose mode is not implemented yet.\");\n  }\n  util_exports.assert(!hasBatches || args.batches > 0 && Number.isInteger(args.batches), () => `Test loop expects \\`batches\\` to be a positive integer, but received ${JSON.stringify(args.batches)}`);\n  const dataIterator = isLazyIteratorObject(dataset) ? dataset : await dataset.iterator();\n  let numExamples = 0;\n  let batch = 0;\n  while (hasBatches ? batch < args.batches : true) {\n    const iteratorOut = await dataIterator.next();\n    outs = tidy(() => {\n      if (iteratorOut.value) {\n        const { xs, ys } = standardizeDataIteratorOutput(model2, iteratorOut.value);\n        const xsAndYs = xs.concat(ys);\n        const batchOuts = tidy(() => f(xsAndYs));\n        dispose(xsAndYs);\n        if (batch === 0) {\n          for (let i2 = 0; i2 < batchOuts.length; ++i2) {\n            outs.push(scalar(0));\n          }\n        }\n        const batchSize = xsAndYs[0].shape[0];\n        for (let i2 = 0; i2 < batchOuts.length; ++i2) {\n          const batchOut = batchOuts[i2];\n          const oldScalar = outs[i2];\n          outs[i2] = tidy(() => add2(outs[i2], mul(batchSize, batchOut)));\n          if (batch > 0) {\n            dispose(oldScalar);\n          }\n        }\n        dispose(batchOuts);\n        numExamples += batchSize;\n        ++batch;\n      }\n      return outs;\n    });\n    if (iteratorOut.done) {\n      if (hasBatches) {\n        console.warn(`Your dataset iterator ran out of data during evaluateDataset(). Interrupting evalution. Make sure that your dataset can generate at least \\`batches\\` batches (in this case, ${args.batches} batches). You may need to use the repeat() function when building your dataset.`);\n      }\n      break;\n    }\n  }\n  for (let i2 = 0; i2 < outs.length; ++i2) {\n    const oldScalar = outs[i2];\n    outs[i2] = div(outs[i2], numExamples);\n    dispose(oldScalar);\n  }\n  return singletonOrArray(outs);\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-layers@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-layers/dist/engine/training_tensors.js\nfunction checkBatchSize(batchSize) {\n  util_exports.assert(batchSize > 0 && Number.isInteger(batchSize), () => `batchSize is required to be a positive integer, but got ${batchSize}`);\n}\nfunction sliceArrays(arrays, start, stop) {\n  if (arrays == null) {\n    return [null];\n  } else if (Array.isArray(arrays)) {\n    return arrays.map((array2) => sliceAlongFirstAxis(array2, start, stop - start));\n  } else {\n    return sliceAlongFirstAxis(arrays, start, stop - start);\n  }\n}\nfunction sliceArraysByIndices(arrays, indices) {\n  return tidy(() => {\n    if (arrays == null) {\n      return null;\n    } else if (Array.isArray(arrays)) {\n      return arrays.map((array2) => sliceArraysByIndices(array2, indices));\n    } else {\n      return gather2(arrays, indices.dtype === \"int32\" ? indices : cast(indices, \"int32\"));\n    }\n  });\n}\nfunction makeBatches(size, batchSize) {\n  const output = [];\n  let batchStart = 0;\n  let batchEnd = null;\n  while (batchStart < size) {\n    batchEnd = batchStart + batchSize;\n    if (batchEnd >= size) {\n      batchEnd = size;\n    }\n    output.push([batchStart, batchEnd]);\n    batchStart = batchEnd;\n  }\n  return output;\n}\nasync function fitLoop(model2, f, ins, outLabels, batchSize, epochs, verbose, callbacks2, valF, valIns, shuffle2, callbackMetrics, initialEpoch, stepsPerEpoch, validationSteps) {\n  if (batchSize == null) {\n    batchSize = 32;\n  }\n  if (epochs == null) {\n    epochs = 1;\n  }\n  if (shuffle2 == null) {\n    shuffle2 = true;\n  }\n  if (initialEpoch == null) {\n    initialEpoch = 0;\n  }\n  let doValidation = false;\n  if (valF != null && valIns != null) {\n    doValidation = true;\n  }\n  if (validationSteps != null) {\n    doValidation = true;\n    if (stepsPerEpoch == null) {\n      throw new ValueError(\"Can only use `validationSteps` when doing step-wise training, i.e., `stepsPerEpoch` must be set.\");\n    }\n  }\n  const numTrainSamples = model2.checkNumSamples(ins, batchSize, stepsPerEpoch, \"steps_per_epoch\");\n  let indexArray;\n  if (numTrainSamples != null) {\n    indexArray = range2(0, numTrainSamples);\n  }\n  if (verbose == null) {\n    verbose = 1;\n  }\n  const { callbackList, history } = configureCallbacks(callbacks2, verbose, epochs, initialEpoch, numTrainSamples, stepsPerEpoch, batchSize, doValidation, callbackMetrics);\n  callbackList.setModel(model2);\n  model2.history = history;\n  await callbackList.onTrainBegin();\n  model2.stopTraining_ = false;\n  for (let epoch = initialEpoch; epoch < epochs; ++epoch) {\n    await callbackList.onEpochBegin(epoch);\n    const epochLogs = {};\n    if (stepsPerEpoch != null) {\n      throw new NotImplementedError(\"stepsPerEpoch mode is not implemented yet.\");\n    } else {\n      if (shuffle2 === \"batch\") {\n        throw new NotImplementedError(\"batch shuffling is not implemneted yet\");\n      } else if (shuffle2) {\n        util_exports.shuffle(indexArray);\n      }\n      const epochIndexArray1D = tensor1d(indexArray);\n      const batches = makeBatches(numTrainSamples, batchSize);\n      for (let batchIndex = 0; batchIndex < batches.length; ++batchIndex) {\n        const batchLogs = {};\n        await callbackList.onBatchBegin(batchIndex, batchLogs);\n        tidy(() => {\n          const batchStart = batches[batchIndex][0];\n          const batchEnd = batches[batchIndex][1];\n          const batchIds = sliceAlongFirstAxis(epochIndexArray1D, batchStart, batchEnd - batchStart);\n          batchLogs[\"batch\"] = batchIndex;\n          batchLogs[\"size\"] = batchEnd - batchStart;\n          const insBatch = sliceArraysByIndices(ins, batchIds);\n          const outs = f(insBatch);\n          for (let i2 = 0; i2 < outLabels.length; ++i2) {\n            const label = outLabels[i2];\n            const out = outs[i2];\n            batchLogs[label] = out;\n            keep(out);\n          }\n          if (batchIndex === batches.length - 1) {\n            if (doValidation) {\n              const valOuts = model2.testLoop(valF, valIns, batchSize);\n              for (let i2 = 0; i2 < outLabels.length; ++i2) {\n                const label = outLabels[i2];\n                const out = valOuts[i2];\n                keep(out);\n                epochLogs[\"val_\" + label] = out;\n              }\n            }\n          }\n        });\n        await callbackList.onBatchEnd(batchIndex, batchLogs);\n        disposeTensorsInLogs(batchLogs);\n        if (model2.stopTraining_) {\n          break;\n        }\n      }\n      epochIndexArray1D.dispose();\n    }\n    await callbackList.onEpochEnd(epoch, epochLogs);\n    if (model2.stopTraining_) {\n      break;\n    }\n  }\n  await callbackList.onTrainEnd();\n  await model2.history.syncData();\n  return model2.history;\n}\nasync function fitTensors(model2, x, y, args = {}) {\n  if (model2.isTraining) {\n    throw new Error(\"Cannot start training because another fit() call is ongoing.\");\n  }\n  model2.isTraining = true;\n  let inputs;\n  let targets;\n  let originalInputs;\n  let originalTargets;\n  let inputValX;\n  let inputValY;\n  let valX;\n  let valY;\n  let sampleWeights;\n  try {\n    const batchSize = args.batchSize == null ? 32 : args.batchSize;\n    checkBatchSize(batchSize);\n    const checkBatchAxis = false;\n    const standardizedOuts = await model2.standardizeUserData(x, y, args.sampleWeight, args.classWeight, checkBatchAxis, batchSize);\n    inputs = standardizedOuts[0];\n    targets = standardizedOuts[1];\n    sampleWeights = standardizedOuts[2];\n    let doValidation = false;\n    let valIns;\n    if (args.validationData != null && args.validationData.length > 0) {\n      doValidation = true;\n      if (args.validationData.length === 2) {\n        inputValX = args.validationData[0];\n        inputValY = args.validationData[1];\n      } else if (args.validationData.length === 3) {\n        throw new NotImplementedError(\"validationData including sample weights is not supported yet.\");\n      } else {\n        throw new ValueError(`When passing validation data, it must contain 2 (valX, valY) or 3 (valX, valY, valSampleWeight) items; ${args.validationData} is invalid.`);\n      }\n      const checkBatchAxis2 = true;\n      const valStandardized = await model2.standardizeUserData(inputValX, inputValY, null, null, checkBatchAxis2, batchSize);\n      valX = valStandardized[0];\n      valY = valStandardized[1];\n      valIns = valX.concat(valY);\n    } else if (args.validationSplit != null && args.validationSplit > 0 && args.validationSplit < 1) {\n      doValidation = true;\n      const splitAt = Math.floor(inputs[0].shape[0] * (1 - args.validationSplit));\n      const originalBatchSize = inputs[0].shape[0];\n      valX = sliceArrays(inputs, splitAt, originalBatchSize);\n      originalInputs = inputs;\n      inputs = sliceArrays(inputs, 0, splitAt);\n      valY = sliceArrays(targets, splitAt, originalBatchSize);\n      originalTargets = targets;\n      targets = sliceArrays(targets, 0, splitAt);\n      valIns = valX.concat(valY);\n    } else if (args.validationSteps != null) {\n      doValidation = true;\n    }\n    const ins = inputs.concat(targets).concat(sampleWeights);\n    model2.checkTrainableWeightsConsistency();\n    const trainFunction = model2.makeTrainFunction();\n    const outLabels = model2.getDedupedMetricsNames();\n    let valFunction;\n    let callbackMetrics;\n    if (doValidation) {\n      model2.makeTestFunction();\n      valFunction = model2.testFunction;\n      callbackMetrics = outLabels.slice().concat(outLabels.map((n2) => \"val_\" + n2));\n    } else {\n      valFunction = null;\n      valIns = [];\n      callbackMetrics = outLabels.slice();\n    }\n    const callbacks2 = standardizeCallbacks(args.callbacks, args.yieldEvery);\n    const out = await fitLoop(model2, trainFunction, ins, outLabels, batchSize, args.epochs, args.verbose, callbacks2, valFunction, valIns, args.shuffle, callbackMetrics, args.initialEpoch, null, null);\n    return out;\n  } finally {\n    model2.isTraining = false;\n    disposeNewTensors(inputs, x);\n    disposeNewTensors(targets, y);\n    disposeNewTensors(originalInputs, x);\n    disposeNewTensors(originalTargets, y);\n    disposeNewTensors(valX, inputValX);\n    disposeNewTensors(valY, inputValY);\n    if (sampleWeights != null) {\n      dispose(sampleWeights);\n    }\n  }\n}\nfunction ensureTensorsRank2OrHigher(tensors) {\n  const outs = [];\n  if (tensors instanceof Tensor) {\n    tensors = [tensors];\n  }\n  for (let i2 = 0; i2 < tensors.length; ++i2) {\n    const tensor2 = tensors[i2];\n    if (tensor2.rank === 1) {\n      outs.push(expandDims2(tensor2, 1));\n    } else if (tensor2.rank === 0) {\n      throw new Error(\"Expected tensor to be at least 1D, but received a 0D tensor (scalar).\");\n    } else {\n      outs.push(tensor2);\n    }\n  }\n  return outs;\n}\nfunction disposeNewTensors(tensors, refTensors) {\n  if (tensors == null) {\n    return;\n  }\n  const oldTensorIds = [];\n  if (refTensors instanceof Tensor) {\n    oldTensorIds.push(refTensors.id);\n  } else if (Array.isArray(refTensors)) {\n    refTensors.forEach((t2) => oldTensorIds.push(t2.id));\n  } else if (refTensors != null) {\n    for (const name in refTensors) {\n      const oldTensor = refTensors[name];\n      oldTensorIds.push(oldTensor.id);\n    }\n  }\n  const tensorsToDispose = [];\n  if (tensors instanceof Tensor) {\n    if (oldTensorIds.indexOf(tensors.id) === -1) {\n      tensorsToDispose.push(tensors);\n    }\n  } else if (Array.isArray(tensors)) {\n    tensors.forEach((t2) => {\n      if (oldTensorIds.indexOf(t2.id) === -1) {\n        tensorsToDispose.push(t2);\n      }\n    });\n  } else if (tensors != null) {\n    for (const name in tensors) {\n      const tensor2 = tensors[name];\n      if (oldTensorIds.indexOf(tensor2.id) === -1) {\n        tensorsToDispose.push(tensor2);\n      }\n    }\n  }\n  tensorsToDispose.forEach((t2) => {\n    if (!t2.isDisposed) {\n      t2.dispose();\n    }\n  });\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-layers@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-layers/dist/engine/training.js\nfunction isDataTensor(x) {\n  return x instanceof Tensor;\n}\nfunction isDataArray(x) {\n  return Array.isArray(x);\n}\nfunction isDataDict(x) {\n  return !isDataTensor(x) && !isDataArray(x);\n}\nfunction standardizeInputData(data, names, shapes, checkBatchAxis = true, exceptionPrefix = \"\") {\n  if (names == null || names.length === 0) {\n    if (data != null) {\n      let gotUnexpectedData = false;\n      if (isDataArray(data) && data.length > 0) {\n        gotUnexpectedData = true;\n      } else if (isDataDict(data)) {\n        for (const key in data) {\n          if (data.hasOwnProperty(key)) {\n            gotUnexpectedData = true;\n            break;\n          }\n        }\n      } else {\n        gotUnexpectedData = true;\n      }\n      if (gotUnexpectedData) {\n        throw new ValueError(`Error when checking model ${exceptionPrefix} expected no data, but got ${data}`);\n      }\n    }\n    return [];\n  }\n  if (data == null) {\n    return names.map((name) => null);\n  }\n  let arrays;\n  if (isDataDict(data)) {\n    data = data;\n    arrays = [];\n    for (const name of names) {\n      if (data[name] == null) {\n        throw new ValueError(`No data provided for \"${name}\". Need data for each key in: ${names}`);\n      }\n      arrays.push(data[name]);\n    }\n  } else if (isDataArray(data)) {\n    data = data;\n    if (data.length !== names.length) {\n      throw new ValueError(`Error when checking model ${exceptionPrefix}: the Array of Tensors that you are passing to your model is not the size the model expected. Expected to see ${names.length} Tensor(s), but instead got the following list of Tensor(s): ${data}`);\n    }\n    arrays = data;\n  } else {\n    data = data;\n    if (names.length > 1) {\n      throw new ValueError(`The model ${exceptionPrefix} expects ${names.length} Tensor(s), but only received one Tensor. Found: Tensor with shape ${data.shape}`);\n    }\n    arrays = [data];\n  }\n  arrays = ensureTensorsRank2OrHigher(arrays);\n  if (shapes != null) {\n    for (let i2 = 0; i2 < names.length; ++i2) {\n      if (shapes[i2] == null) {\n        continue;\n      }\n      const array2 = arrays[i2];\n      if (array2.shape.length !== shapes[i2].length) {\n        throw new ValueError(`Error when checking ${exceptionPrefix}: expected ${names[i2]} to have ${shapes[i2].length} dimension(s). but got array with shape ${array2.shape}`);\n      }\n      for (let j = 0; j < shapes[i2].length; ++j) {\n        if (j === 0 && !checkBatchAxis) {\n          continue;\n        }\n        const dim = array2.shape[j];\n        const refDim = shapes[i2][j];\n        if (refDim != null && refDim >= 0 && dim !== refDim) {\n          throw new ValueError(`${exceptionPrefix} expected a batch of elements where each example has shape [${shapes[i2].slice(1, shapes[i2].length)}] (i.e.,tensor shape [*,${shapes[i2].slice(1, shapes[i2].length)}]) but the ${exceptionPrefix} received an input with ${array2.shape[0]} examples, each with shape [${array2.shape.slice(1, array2.shape.length)}] (tensor shape [${array2.shape}])`);\n        }\n      }\n    }\n  }\n  return arrays;\n}\nfunction checkArrayLengths(inputs, targets, weights) {\n  const setX = unique2(inputs.map((input2) => input2.shape[0]));\n  setX.sort();\n  const setY = unique2(targets.map((target) => target.shape[0]));\n  setY.sort();\n  if (setX.length > 1) {\n    throw new ValueError(`All input Tensors (x) should have the same number of samples. Got array shapes: ${JSON.stringify(inputs.map((input2) => input2.shape))}`);\n  }\n  if (setY.length > 1) {\n    throw new ValueError(`All target Tensors (y) should have the same number of samples. Got array shapes: ${JSON.stringify(targets.map((target) => target.shape))}`);\n  }\n  if (setX.length > 0 && setY.length > 0 && !util_exports.arraysEqual(setX, setY)) {\n    throw new ValueError(`Input Tensors should have the same number of samples as target Tensors. Found ${setX[0]} input sample(s) and ${setY[0]} target sample(s).`);\n  }\n}\nfunction checkLossAndTargetCompatibility(targets, lossFns, outputShapes) {\n  const keyLosses = [\n    meanSquaredError2,\n    binaryCrossentropy,\n    categoricalCrossentropy\n  ];\n  for (let i2 = 0; i2 < targets.length; ++i2) {\n    const y = targets[i2];\n    const loss = lossFns[i2];\n    const shape = outputShapes[i2];\n    if (loss == null) {\n      continue;\n    }\n    if (loss === categoricalCrossentropy) {\n      if (y.shape[y.shape.length - 1] === 1) {\n        throw new ValueError(`You are passing a target array of shape ${y.shape} while using a loss 'categorical_crossentropy'. 'categorical_crossentropy'expects targets to be binary matrices (1s and 0s) of shape [samples, classes].`);\n      }\n    }\n    if (keyLosses.indexOf(loss) !== -1) {\n      const slicedYShape = y.shape.slice(1);\n      const slicedShape = shape.slice(1);\n      for (let j = 0; j < slicedYShape.length; ++j) {\n        const targetDim = slicedYShape[j];\n        const outDim = slicedShape[j];\n        if (outDim != null && targetDim !== outDim) {\n          throw new ValueError(`A target Tensor with shape ${y.shape} was passed for an output of shape ${shape}, while using a loss function that expects targets to have the same shape as the output.`);\n        }\n      }\n    }\n  }\n}\nfunction checkInputData(data, names, shapes, checkBatchAxis = true, exceptionPrefix = \"\") {\n  let arrays;\n  if (Array.isArray(data)) {\n    if (data.length !== names.length) {\n      throw new ValueError(`Error when checking model ${exceptionPrefix}: the Array of Tensors that you are passing to your model is not the size the the model expected. Expected to see ${names.length} Tensor(s), but instead got ${data.length} Tensors(s).`);\n    }\n    arrays = data;\n  } else {\n    if (names.length > 1) {\n      throw new ValueError(`The model expects ${names.length} ${exceptionPrefix} Tensors, but only received one Tensor. Found: array with shape ${JSON.stringify(data.shape)}.`);\n    }\n    arrays = [data];\n  }\n  if (shapes != null) {\n    for (let i2 = 0; i2 < names.length; ++i2) {\n      if (shapes[i2] == null) {\n        continue;\n      }\n      const array2 = arrays[i2];\n      if (array2.shape.length !== shapes[i2].length) {\n        throw new ValueError(`Error when checking ${exceptionPrefix}: expected ${names[i2]} to have ${shapes[i2].length} dimension(s), but got array with shape ${JSON.stringify(array2.shape)}`);\n      }\n      for (let j = 0; j < shapes[i2].length; ++j) {\n        if (j === 0 && !checkBatchAxis) {\n          continue;\n        }\n        const dim = array2.shape[j];\n        const refDim = shapes[i2][j];\n        if (refDim != null) {\n          if (refDim !== dim) {\n            throw new ValueError(`Error when checking ${exceptionPrefix}: expected ${names[i2]} to have shape ${JSON.stringify(shapes[i2])} but got array with shape ${JSON.stringify(array2.shape)}.`);\n          }\n        }\n      }\n    }\n  }\n}\nfunction collectMetrics(metrics, outputNames) {\n  if (metrics == null || Array.isArray(metrics) && metrics.length === 0) {\n    return outputNames.map((name) => []);\n  }\n  let wrappedMetrics;\n  if (typeof metrics === \"string\" || typeof metrics === \"function\") {\n    wrappedMetrics = [metrics];\n  } else if (Array.isArray(metrics) || typeof metrics === \"object\") {\n    wrappedMetrics = metrics;\n  } else {\n    throw new TypeError(`Type of metrics argument not understood. Expected an string,function, Array, or Object, found: ${metrics}`);\n  }\n  if (Array.isArray(wrappedMetrics)) {\n    return outputNames.map((name) => wrappedMetrics);\n  } else {\n    const nestedMetrics = [];\n    for (const name of outputNames) {\n      let outputMetrics = wrappedMetrics.hasOwnProperty(name) ? wrappedMetrics[name] : [];\n      if (!Array.isArray(outputMetrics)) {\n        outputMetrics = [outputMetrics];\n      }\n      nestedMetrics.push(outputMetrics);\n    }\n    return nestedMetrics;\n  }\n}\nvar LAYERS_MODEL_FORMAT_NAME = \"layers-model\";\nvar LayersModel = class extends Container {\n  constructor(args) {\n    super(args);\n    this.isTraining = false;\n  }\n  summary(lineLength, positions, printFn = console.log) {\n    if (!this.built) {\n      throw new ValueError(`This model has never been called, thus its weights have not been created yet. So no summary can be displayed. Build the model first (e.g., by calling it on some test data).`);\n    }\n    printSummary(this, lineLength, positions, printFn);\n  }\n  compile(args) {\n    if (args.loss == null) {\n      args.loss = [];\n    }\n    this.loss = args.loss;\n    if (typeof args.optimizer === \"string\") {\n      this.optimizer_ = getOptimizer(args.optimizer);\n      this.isOptimizerOwned = true;\n    } else {\n      if (!(args.optimizer instanceof Optimizer)) {\n        throw new ValueError(`User-defined optimizer must be an instance of tf.Optimizer.`);\n      }\n      this.optimizer_ = args.optimizer;\n      this.isOptimizerOwned = false;\n    }\n    let lossFunctions = [];\n    if (!Array.isArray(args.loss) && typeof args.loss !== \"string\" && typeof args.loss !== \"function\") {\n      args.loss = args.loss;\n      for (const name in args.loss) {\n        if (this.outputNames.indexOf(name) === -1) {\n          throw new ValueError(`Unknown entry in loss dictionary: \"${name}\". Only expected the following keys: ${this.outputNames}`);\n        }\n      }\n      for (const name of this.outputNames) {\n        if (args.loss[name] == null) {\n          console.warn(`Output \"${name}\" is missing from loss dictionary. We assume this was done on purpose, and we will not be expecting data to be passed to ${name} during training`);\n        }\n        lossFunctions.push(get(args.loss[name]));\n      }\n    } else if (Array.isArray(args.loss)) {\n      if (args.loss.length !== this.outputs.length) {\n        throw new ValueError(`When passing an Array as loss, it should have one entry per model output. The model has ${this.outputs.length} output(s), but you passed loss=${args.loss}.`);\n      }\n      const theLosses = args.loss;\n      lossFunctions = theLosses.map((l3) => get(l3));\n    } else {\n      const lossFunction = get(args.loss);\n      this.outputs.forEach((_) => {\n        lossFunctions.push(lossFunction);\n      });\n    }\n    this.lossFunctions = lossFunctions;\n    this.feedOutputNames = [];\n    this.feedOutputShapes = [];\n    this.feedLossFns = [];\n    for (let i2 = 0; i2 < this.outputs.length; ++i2) {\n      const shape = this.internalOutputShapes[i2];\n      const name = this.outputNames[i2];\n      this.feedOutputNames.push(name);\n      this.feedOutputShapes.push(shape);\n      this.feedLossFns.push(this.lossFunctions[i2]);\n    }\n    const skipTargetIndices = [];\n    this.metrics = args.metrics;\n    this.metricsNames = [\"loss\"];\n    this.metricsTensors = [];\n    nameScope(\"loss\", () => {\n      for (let i2 = 0; i2 < this.outputs.length; ++i2) {\n        if (skipTargetIndices.indexOf(i2) !== -1) {\n          continue;\n        }\n        const weightedLoss = this.lossFunctions[i2];\n        if (this.outputs.length > 1) {\n          this.metricsTensors.push([weightedLoss, i2]);\n          this.metricsNames.push(this.outputNames[i2] + \"_loss\");\n        }\n      }\n    });\n    const nestedMetrics = collectMetrics(args.metrics, this.outputNames);\n    const appendMetric = (outputIndex, metricName, metricTensor) => {\n      if (this.outputNames.length > 1) {\n        metricName = this.outputNames[outputIndex] + \"_\" + metricName;\n      }\n      this.metricsNames.push(metricName);\n      this.metricsTensors.push([metricTensor, outputIndex]);\n    };\n    nameScope(\"metric\", () => {\n      for (let i2 = 0; i2 < this.outputs.length; ++i2) {\n        if (skipTargetIndices.indexOf(i2) !== -1) {\n          continue;\n        }\n        const outputMetrics = nestedMetrics[i2];\n        const handleMetrics = (metrics) => {\n          const metricNamePrefix = \"\";\n          let metricName;\n          let accFn;\n          let weightedMetricFn;\n          for (const metric of metrics) {\n            if (typeof metric === \"string\" && [\"accuracy\", \"acc\", \"crossentropy\", \"ce\"].indexOf(metric) !== -1) {\n              const outputShape = this.internalOutputShapes[i2];\n              if (outputShape[outputShape.length - 1] === 1 || this.lossFunctions[i2] === binaryCrossentropy) {\n                if ([\"accuracy\", \"acc\"].indexOf(metric) !== -1) {\n                  accFn = binaryAccuracy;\n                } else if ([\"crossentropy\", \"ce\"].indexOf(metric) !== -1) {\n                  accFn = binaryCrossentropy2;\n                }\n              } else if (this.lossFunctions[i2] === sparseCategoricalCrossentropy) {\n                if ([\"accuracy\", \"acc\"].indexOf(metric) !== -1) {\n                  accFn = sparseCategoricalAccuracy;\n                } else if ([\"crossentropy\", \"ce\"].indexOf(metric) !== -1) {\n                  accFn = sparseCategoricalCrossentropy2;\n                }\n              } else {\n                if ([\"accuracy\", \"acc\"].indexOf(metric) !== -1) {\n                  accFn = categoricalAccuracy;\n                } else if ([\"crossentropy\", \"ce\"].indexOf(metric) !== -1) {\n                  accFn = categoricalCrossentropy2;\n                }\n              }\n              let suffix;\n              if ([\"accuracy\", \"acc\"].indexOf(metric) !== -1) {\n                suffix = \"acc\";\n              } else if ([\"crossentropy\", \"ce\"].indexOf(metric) !== -1) {\n                suffix = \"ce\";\n              }\n              weightedMetricFn = accFn;\n              metricName = metricNamePrefix + suffix;\n            } else {\n              const metricFn = get2(metric);\n              weightedMetricFn = metricFn;\n              metricName = metricNamePrefix + getLossOrMetricName(metric);\n            }\n            let metricResult;\n            nameScope(metricName, () => {\n              metricResult = weightedMetricFn;\n            });\n            appendMetric(i2, metricName, metricResult);\n          }\n        };\n        handleMetrics(outputMetrics);\n      }\n    });\n    this.collectedTrainableWeights = this.trainableWeights;\n  }\n  checkTrainableWeightsConsistency() {\n    if (this.collectedTrainableWeights == null) {\n      return;\n    }\n    if (this.trainableWeights.length !== this.collectedTrainableWeights.length) {\n      console.warn(\"Discrepancy between trainableweights and collected trainable weights. Did you set `model.trainable` without calling `model.compile()` afterwards?\");\n    }\n  }\n  evaluate(x, y, args = {}) {\n    const batchSize = args.batchSize == null ? 32 : args.batchSize;\n    checkBatchSize(batchSize);\n    const checkBatchAxis = true;\n    const standardizedOuts = this.standardizeUserDataXY(x, y, checkBatchAxis, batchSize);\n    try {\n      const ins = standardizedOuts[0].concat(standardizedOuts[1]);\n      this.makeTestFunction();\n      const f = this.testFunction;\n      const testOuts = this.testLoop(f, ins, batchSize, args.verbose, args.steps);\n      return singletonOrArray(testOuts);\n    } finally {\n      disposeNewTensors(standardizedOuts[0], x);\n      disposeNewTensors(standardizedOuts[1], y);\n    }\n  }\n  async evaluateDataset(dataset, args) {\n    this.makeTestFunction();\n    return evaluateDataset(this, dataset, args);\n  }\n  checkNumSamples(ins, batchSize, steps, stepsName = \"steps\") {\n    let numSamples;\n    if (steps != null) {\n      numSamples = null;\n      if (batchSize != null) {\n        throw new ValueError(`If ${stepsName} is set, batchSize must be null or undefined.Got batchSize = ${batchSize}`);\n      }\n    } else if (ins != null) {\n      if (Array.isArray(ins)) {\n        numSamples = ins[0].shape[0];\n      } else {\n        numSamples = ins.shape[0];\n      }\n    } else {\n      throw new ValueError(`Either the input data should have a defined shape, or ${stepsName} shoud be specified.`);\n    }\n    return numSamples;\n  }\n  execute(inputs, outputs) {\n    if (Array.isArray(outputs) && outputs.length === 0) {\n      throw new ValueError(\"`outputs` is an empty Array, which is not allowed.\");\n    }\n    const outputsIsArray = Array.isArray(outputs);\n    const outputNames = outputsIsArray ? outputs : [outputs];\n    const outputSymbolicTensors = this.retrieveSymbolicTensors(outputNames);\n    const feedDict = new FeedDict();\n    if (inputs instanceof Tensor) {\n      inputs = [inputs];\n    }\n    if (Array.isArray(inputs)) {\n      if (inputs.length !== this.inputs.length) {\n        throw new ValueError(`The number of inputs provided (${inputs.length}) does not match the number of inputs of this model (${this.inputs.length}).`);\n      }\n      for (let i2 = 0; i2 < this.inputs.length; ++i2) {\n        feedDict.add(this.inputs[i2], inputs[i2]);\n      }\n    } else {\n      for (const input2 of this.inputs) {\n        const tensorValue = inputs[input2.name];\n        if (tensorValue == null) {\n          throw new ValueError(`No value is provided for the model's input ${input2.name}`);\n        }\n        feedDict.add(input2, tensorValue);\n      }\n    }\n    const executeOutputs = execute(outputSymbolicTensors, feedDict);\n    return outputsIsArray ? executeOutputs : executeOutputs[0];\n  }\n  retrieveSymbolicTensors(symbolicTensorNames) {\n    const outputSymbolicTensors = pyListRepeat(null, symbolicTensorNames.length);\n    let outputsRemaining = symbolicTensorNames.length;\n    for (const layer of this.layers) {\n      const layerOutputs = Array.isArray(layer.output) ? layer.output : [layer.output];\n      const layerOutputNames = layerOutputs.map((output) => output.name);\n      for (let i2 = 0; i2 < symbolicTensorNames.length; ++i2) {\n        const index = layerOutputNames.indexOf(symbolicTensorNames[i2]);\n        if (index !== -1) {\n          outputSymbolicTensors[i2] = layerOutputs[index];\n          outputsRemaining--;\n        }\n        if (outputsRemaining === 0) {\n          break;\n        }\n      }\n      if (outputsRemaining === 0) {\n        break;\n      }\n    }\n    if (outputsRemaining > 0) {\n      const remainingNames = [];\n      outputSymbolicTensors.forEach((tensor2, i2) => {\n        if (tensor2 == null) {\n          remainingNames.push(symbolicTensorNames[i2]);\n        }\n      });\n      throw new ValueError(`Cannot find SymbolicTensors for output name(s): ${JSON.stringify(remainingNames)}`);\n    }\n    return outputSymbolicTensors;\n  }\n  predictLoop(ins, batchSize = 32, verbose = false) {\n    return tidy(() => {\n      const numSamples = this.checkNumSamples(ins);\n      if (verbose) {\n        throw new NotImplementedError(\"Verbose predictLoop() is not implemented yet.\");\n      }\n      const batches = makeBatches(numSamples, batchSize);\n      const outsBatches = this.outputs.map((output) => []);\n      for (let batchIndex = 0; batchIndex < batches.length; ++batchIndex) {\n        const batchOuts = tidy(() => {\n          const batchStart = batches[batchIndex][0];\n          const batchEnd = batches[batchIndex][1];\n          const insBatch = sliceArrays(ins, batchStart, batchEnd);\n          const feeds = [];\n          if (Array.isArray(insBatch)) {\n            for (let i2 = 0; i2 < insBatch.length; ++i2) {\n              feeds.push({ key: this.inputs[i2], value: insBatch[i2] });\n            }\n          } else {\n            feeds.push({ key: this.inputs[0], value: insBatch });\n          }\n          const feedDict = new FeedDict(feeds);\n          return execute(this.outputs, feedDict);\n        });\n        batchOuts.forEach((batchOut, i2) => outsBatches[i2].push(batchOut));\n      }\n      return singletonOrArray(outsBatches.map((batches2) => concat(batches2, 0)));\n    });\n  }\n  predict(x, args = {}) {\n    const xsRank2OrHigher = ensureTensorsRank2OrHigher(x);\n    checkInputData(xsRank2OrHigher, this.inputNames, this.feedInputShapes, false);\n    try {\n      const batchSize = args.batchSize == null ? 32 : args.batchSize;\n      checkBatchSize(batchSize);\n      return this.predictLoop(xsRank2OrHigher, batchSize);\n    } finally {\n      disposeNewTensors(xsRank2OrHigher, x);\n    }\n  }\n  predictOnBatch(x) {\n    checkInputData(x, this.inputNames, this.feedInputShapes, true);\n    const batchSize = (Array.isArray(x) ? x[0] : x).shape[0];\n    return this.predictLoop(x, batchSize);\n  }\n  standardizeUserDataXY(x, y, checkBatchAxis = true, batchSize) {\n    if (this.optimizer_ == null) {\n      throw new RuntimeError(\"You must compile a model before training/testing. Use LayersModel.compile(modelCompileArgs).\");\n    }\n    const outputShapes = [];\n    for (let i2 = 0; i2 < this.feedOutputShapes.length; ++i2) {\n      const outputShape = this.feedOutputShapes[i2];\n      const lossFn = this.feedLossFns[i2];\n      if (lossFn === sparseCategoricalCrossentropy) {\n        outputShapes.push(outputShape.slice(0, outputShape.length - 1).concat([1]));\n      } else {\n        outputShapes.push(outputShape);\n      }\n    }\n    x = standardizeInputData(x, this.feedInputNames, this.feedInputShapes, false, \"input\");\n    y = standardizeInputData(y, this.feedOutputNames, outputShapes, false, \"target\");\n    checkArrayLengths(x, y, null);\n    checkLossAndTargetCompatibility(y, this.feedLossFns, this.feedOutputShapes);\n    if (this.stateful && batchSize != null && batchSize > 0) {\n      if (x[0].shape[0] % batchSize !== 0) {\n        throw new ValueError(`In a stateful network, you should only pass inputs with a number of samples that is divisible by the batch size ${batchSize}. Found: ${x[0].shape[0]} sample(s).`);\n      }\n    }\n    return [x, y];\n  }\n  async standardizeUserData(x, y, sampleWeight, classWeight, checkBatchAxis = true, batchSize) {\n    const [standardXs, standardYs] = this.standardizeUserDataXY(x, y, checkBatchAxis, batchSize);\n    if (sampleWeight != null) {\n      throw new Error(\"sample weight is not supported yet.\");\n    }\n    let standardSampleWeights = null;\n    if (classWeight != null) {\n      const classWeights = standardizeClassWeights(classWeight, this.outputNames);\n      standardSampleWeights = [];\n      for (let i2 = 0; i2 < classWeights.length; ++i2) {\n        standardSampleWeights.push(await standardizeWeights(standardYs[i2], null, classWeights[i2]));\n      }\n    }\n    return [standardXs, standardYs, standardSampleWeights];\n  }\n  testLoop(f, ins, batchSize, verbose = 0, steps) {\n    return tidy(() => {\n      const numSamples = this.checkNumSamples(ins, batchSize, steps, \"steps\");\n      const outs = [];\n      if (verbose > 0) {\n        throw new NotImplementedError(\"Verbose mode is not implemented yet.\");\n      }\n      if (steps != null) {\n        throw new NotImplementedError(\"steps mode in testLoop() is not implemented yet\");\n      } else {\n        const batches = makeBatches(numSamples, batchSize);\n        const indexArray = tensor1d(range2(0, numSamples));\n        for (let batchIndex = 0; batchIndex < batches.length; ++batchIndex) {\n          const batchStart = batches[batchIndex][0];\n          const batchEnd = batches[batchIndex][1];\n          const batchIds = sliceAlongFirstAxis(indexArray, batchStart, batchEnd - batchStart);\n          const insBatch = sliceArraysByIndices(ins, batchIds);\n          const batchOuts = f(insBatch);\n          if (batchIndex === 0) {\n            for (let i2 = 0; i2 < batchOuts.length; ++i2) {\n              outs.push(scalar(0));\n            }\n          }\n          for (let i2 = 0; i2 < batchOuts.length; ++i2) {\n            const batchOut = batchOuts[i2];\n            outs[i2] = add2(outs[i2], mul(batchEnd - batchStart, batchOut));\n          }\n        }\n        for (let i2 = 0; i2 < outs.length; ++i2) {\n          outs[i2] = div(outs[i2], numSamples);\n        }\n      }\n      return outs;\n    });\n  }\n  getDedupedMetricsNames() {\n    const outLabels = this.metricsNames;\n    const dedupedOutLabels = [];\n    for (let i2 = 0; i2 < outLabels.length; ++i2) {\n      const label = outLabels[i2];\n      let newLabel = label;\n      if (count(outLabels, label) > 1) {\n        const dupIndex = count(outLabels.slice(0, i2), label);\n        newLabel += `_${dupIndex}`;\n      }\n      dedupedOutLabels.push(newLabel);\n    }\n    return dedupedOutLabels;\n  }\n  makeTrainFunction() {\n    return (data) => {\n      const lossValues = [];\n      const inputs = data.slice(0, this.inputs.length);\n      const targets = data.slice(this.inputs.length, this.inputs.length + this.outputs.length);\n      const sampleWeights = data.slice(this.inputs.length + this.outputs.length, this.inputs.length + this.outputs.length * 2);\n      const metricsValues = [];\n      const totalLossFunction = () => {\n        const feeds = [];\n        for (let i2 = 0; i2 < this.inputs.length; ++i2) {\n          feeds.push({ key: this.inputs[i2], value: inputs[i2] });\n        }\n        const feedDict = new FeedDict(feeds);\n        const outputs = execute(this.outputs, feedDict, { \"training\": true });\n        let totalLoss;\n        for (let i2 = 0; i2 < this.lossFunctions.length; ++i2) {\n          const lossFunction = this.lossFunctions[i2];\n          let loss = lossFunction(targets[i2], outputs[i2]);\n          if (sampleWeights[i2] != null) {\n            loss = computeWeightedLoss2(loss, sampleWeights[i2]);\n          }\n          const meanLoss = mean(loss);\n          lossValues.push(meanLoss);\n          if (i2 === 0) {\n            totalLoss = loss;\n          } else {\n            totalLoss = add2(totalLoss, loss);\n          }\n        }\n        for (let i2 = 0; i2 < this.metricsTensors.length; ++i2) {\n          let weightedMetric;\n          if (this.outputs.length > 1 && i2 < this.outputs.length) {\n            weightedMetric = lossValues[i2];\n          } else {\n            const metric = this.metricsTensors[i2][0];\n            const outputIndex = this.metricsTensors[i2][1];\n            weightedMetric = mean(metric(targets[outputIndex], outputs[outputIndex]));\n          }\n          keep(weightedMetric);\n          metricsValues.push(weightedMetric);\n        }\n        totalLoss = mean(totalLoss);\n        this.calculateLosses().forEach((regularizerLoss) => {\n          totalLoss = add2(totalLoss, regularizerLoss);\n        });\n        return totalLoss;\n      };\n      const variables = this.collectedTrainableWeights.map((param) => param.read());\n      const returnCost = true;\n      const totalLossValue = this.optimizer_.minimize(totalLossFunction, returnCost, variables);\n      return [totalLossValue].concat(metricsValues);\n    };\n  }\n  makeTestFunction() {\n    this.testFunction = (data) => {\n      return tidy(() => {\n        const valOutputs = [];\n        let totalLoss;\n        const inputs = data.slice(0, this.inputs.length);\n        const targets = data.slice(this.inputs.length, this.inputs.length + this.outputs.length);\n        const feeds = [];\n        for (let i2 = 0; i2 < this.inputs.length; ++i2) {\n          feeds.push({ key: this.inputs[i2], value: inputs[i2] });\n        }\n        const feedDict = new FeedDict(feeds);\n        const outputs = execute(this.outputs, feedDict);\n        for (let i2 = 0; i2 < this.lossFunctions.length; ++i2) {\n          const lossFunction = this.lossFunctions[i2];\n          const loss = mean(lossFunction(targets[i2], outputs[i2]));\n          if (i2 === 0) {\n            totalLoss = loss;\n          } else {\n            totalLoss = add2(totalLoss, loss);\n          }\n          valOutputs.push(totalLoss);\n        }\n        for (let i2 = 0; i2 < this.metricsTensors.length; ++i2) {\n          const metric = this.metricsTensors[i2][0];\n          const outputIndex = this.metricsTensors[i2][1];\n          const meanMetric = mean(metric(targets[outputIndex], outputs[outputIndex]));\n          valOutputs.push(meanMetric);\n        }\n        return valOutputs;\n      });\n    };\n  }\n  async fit(x, y, args = {}) {\n    return fitTensors(this, x, y, args);\n  }\n  async fitDataset(dataset, args) {\n    return fitDataset(this, dataset, args);\n  }\n  async trainOnBatch(x, y) {\n    const standardizeOut = await this.standardizeUserData(x, y);\n    const inputs = standardizeOut[0];\n    const targets = standardizeOut[1];\n    const trainFunction = this.makeTrainFunction();\n    const losses2 = trainFunction(inputs.concat(targets));\n    const lossValues = [];\n    for (const loss of losses2) {\n      const v = await loss.data();\n      lossValues.push(v[0]);\n    }\n    dispose(losses2);\n    disposeNewTensors(standardizeOut[0], x);\n    disposeNewTensors(standardizeOut[1], y);\n    return singletonOrArray(lossValues);\n  }\n  getNamedWeights(config) {\n    const namedWeights = [];\n    const trainableOnly = config != null && config.trainableOnly;\n    const weights = trainableOnly ? this.trainableWeights : this.weights;\n    const weightValues = this.getWeights(trainableOnly);\n    for (let i2 = 0; i2 < weights.length; ++i2) {\n      if (trainableOnly && !weights[i2].trainable) {\n        continue;\n      }\n      namedWeights.push({ name: weights[i2].originalName, tensor: weightValues[i2] });\n    }\n    return namedWeights;\n  }\n  set stopTraining(stop) {\n    this.stopTraining_ = stop;\n  }\n  get stopTraining() {\n    return this.stopTraining_;\n  }\n  get optimizer() {\n    return this.optimizer_;\n  }\n  set optimizer(optimizer) {\n    if (this.optimizer_ !== optimizer) {\n      this.optimizer_ = optimizer;\n      this.isOptimizerOwned = false;\n    }\n  }\n  dispose() {\n    const result = super.dispose();\n    if (result.refCountAfterDispose === 0 && this.optimizer != null && this.isOptimizerOwned) {\n      const numTensorsBeforeOptmizerDisposal = memory().numTensors;\n      this.optimizer_.dispose();\n      result.numDisposedVariables += numTensorsBeforeOptmizerDisposal - memory().numTensors;\n    }\n    return result;\n  }\n  getLossIdentifiers() {\n    let lossNames;\n    if (typeof this.loss === \"string\") {\n      lossNames = toSnakeCase(this.loss);\n    } else if (Array.isArray(this.loss)) {\n      for (const loss of this.loss) {\n        if (typeof loss !== \"string\") {\n          throw new Error(\"Serialization of non-string loss is not supported.\");\n        }\n      }\n      lossNames = this.loss.map((name) => toSnakeCase(name));\n    } else {\n      const outputNames = Object.keys(this.loss);\n      lossNames = {};\n      const losses2 = this.loss;\n      for (const outputName of outputNames) {\n        if (typeof losses2[outputName] === \"string\") {\n          lossNames[outputName] = toSnakeCase(losses2[outputName]);\n        } else {\n          throw new Error(\"Serialization of non-string loss is not supported.\");\n        }\n      }\n    }\n    return lossNames;\n  }\n  getMetricIdentifiers() {\n    if (typeof this.metrics === \"string\" || typeof this.metrics === \"function\") {\n      return [toSnakeCase(getLossOrMetricName(this.metrics))];\n    } else if (Array.isArray(this.metrics)) {\n      return this.metrics.map((metric) => toSnakeCase(getLossOrMetricName(metric)));\n    } else {\n      const metricsIdentifiers = {};\n      for (const key in this.metrics) {\n        metricsIdentifiers[key] = toSnakeCase(getLossOrMetricName(this.metrics[key]));\n      }\n      return metricsIdentifiers;\n    }\n  }\n  getTrainingConfig() {\n    return {\n      loss: this.getLossIdentifiers(),\n      metrics: this.getMetricIdentifiers(),\n      optimizer_config: {\n        class_name: this.optimizer.getClassName(),\n        config: this.optimizer.getConfig()\n      }\n    };\n  }\n  loadTrainingConfig(trainingConfig) {\n    if (trainingConfig.weighted_metrics != null) {\n      throw new Error(\"Loading weight_metrics is not supported yet.\");\n    }\n    if (trainingConfig.loss_weights != null) {\n      throw new Error(\"Loading loss_weights is not supported yet.\");\n    }\n    if (trainingConfig.sample_weight_mode != null) {\n      throw new Error(\"Loading sample_weight_mode is not supported yet.\");\n    }\n    const tsConfig = convertPythonicToTs(trainingConfig.optimizer_config);\n    const optimizer = deserialize(tsConfig);\n    let loss;\n    if (typeof trainingConfig.loss === \"string\") {\n      loss = toCamelCase(trainingConfig.loss);\n    } else if (Array.isArray(trainingConfig.loss)) {\n      loss = trainingConfig.loss.map((lossEntry) => toCamelCase(lossEntry));\n    } else if (trainingConfig.loss != null) {\n      loss = {};\n      for (const key in trainingConfig.loss) {\n        loss[key] = toCamelCase(trainingConfig.loss[key]);\n      }\n    }\n    let metrics;\n    if (Array.isArray(trainingConfig.metrics)) {\n      metrics = trainingConfig.metrics.map((metric) => toCamelCase(metric));\n    } else if (trainingConfig.metrics != null) {\n      metrics = {};\n      for (const key in trainingConfig.metrics) {\n        metrics[key] = toCamelCase(trainingConfig.metrics[key]);\n      }\n    }\n    this.compile({ loss, metrics, optimizer });\n  }\n  async save(handlerOrURL, config) {\n    if (typeof handlerOrURL === \"string\") {\n      const handlers = io_exports.getSaveHandlers(handlerOrURL);\n      if (handlers.length === 0) {\n        throw new ValueError(`Cannot find any save handlers for URL '${handlerOrURL}'`);\n      } else if (handlers.length > 1) {\n        throw new ValueError(`Found more than one (${handlers.length}) save handlers for URL '${handlerOrURL}'`);\n      }\n      handlerOrURL = handlers[0];\n    }\n    if (handlerOrURL.save == null) {\n      throw new ValueError(\"LayersModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.\");\n    }\n    const weightDataAndSpecs = await io_exports.encodeWeights(this.getNamedWeights(config));\n    const returnString = false;\n    const unusedArg = null;\n    const modelConfig = this.toJSON(unusedArg, returnString);\n    const modelArtifacts = {\n      modelTopology: modelConfig,\n      format: LAYERS_MODEL_FORMAT_NAME,\n      generatedBy: `TensorFlow.js tfjs-layers v${version2}`,\n      convertedBy: null\n    };\n    const includeOptimizer = config == null ? false : config.includeOptimizer;\n    if (includeOptimizer && this.optimizer != null) {\n      modelArtifacts.trainingConfig = this.getTrainingConfig();\n      const weightType = \"optimizer\";\n      const { data: optimizerWeightData, specs: optimizerWeightSpecs } = await io_exports.encodeWeights(await this.optimizer.getWeights(), weightType);\n      weightDataAndSpecs.specs.push(...optimizerWeightSpecs);\n      weightDataAndSpecs.data = io_exports.concatenateArrayBuffers([weightDataAndSpecs.data, optimizerWeightData]);\n    }\n    if (this.userDefinedMetadata != null) {\n      const checkSize = true;\n      checkUserDefinedMetadata(this.userDefinedMetadata, this.name, checkSize);\n      modelArtifacts.userDefinedMetadata = this.userDefinedMetadata;\n    }\n    modelArtifacts.weightData = weightDataAndSpecs.data;\n    modelArtifacts.weightSpecs = weightDataAndSpecs.specs;\n    return handlerOrURL.save(modelArtifacts);\n  }\n  setUserDefinedMetadata(userDefinedMetadata) {\n    checkUserDefinedMetadata(userDefinedMetadata, this.name);\n    this.userDefinedMetadata = userDefinedMetadata;\n  }\n  getUserDefinedMetadata() {\n    return this.userDefinedMetadata;\n  }\n};\nLayersModel.className = \"Model\";\nserialization_exports.registerClass(LayersModel);\nvar Functional = class extends LayersModel {\n};\nFunctional.className = \"Functional\";\nserialization_exports.registerClass(Functional);\n\n// node_modules/.pnpm/@tensorflow+tfjs-layers@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-layers/dist/models.js\nasync function modelFromJSON(modelAndWeightsConfig, customObjects) {\n  if (!(\"modelTopology\" in modelAndWeightsConfig)) {\n    modelAndWeightsConfig = { modelTopology: modelAndWeightsConfig };\n  }\n  modelAndWeightsConfig = modelAndWeightsConfig;\n  let modelTopology = modelAndWeightsConfig.modelTopology;\n  if (modelTopology[\"model_config\"] != null) {\n    modelTopology = modelTopology[\"model_config\"];\n  }\n  const tsConfig = convertPythonicToTs(modelTopology);\n  const model2 = deserialize(tsConfig, customObjects);\n  if (modelAndWeightsConfig.weightsManifest != null) {\n    const weightValues = await io_exports.loadWeights(modelAndWeightsConfig.weightsManifest, modelAndWeightsConfig.pathPrefix, model2.weights.map((weight) => weight.originalName));\n    const uniqueWeightValues = {};\n    for (const weight of model2.weights) {\n      uniqueWeightValues[weight.originalName] = weightValues[weight.originalName];\n    }\n    model2.loadWeights(uniqueWeightValues);\n    dispose(weightValues);\n  }\n  return model2;\n}\nasync function loadLayersModelInternal(pathOrIOHandler, options) {\n  if (options == null) {\n    options = {};\n  }\n  if (typeof pathOrIOHandler === \"string\") {\n    const handlers = io_exports.getLoadHandlers(pathOrIOHandler, options);\n    if (handlers.length === 0) {\n      handlers.push(io_exports.browserHTTPRequest(pathOrIOHandler, options));\n    } else if (handlers.length > 1) {\n      throw new ValueError(`Found more than one (${handlers.length}) load handlers for URL '${pathOrIOHandler}'`);\n    }\n    pathOrIOHandler = handlers[0];\n  }\n  return loadLayersModelFromIOHandler(pathOrIOHandler, void 0, options);\n}\nasync function loadLayersModelFromIOHandler(handler, customObjects, options) {\n  if (options == null) {\n    options = {};\n  }\n  if (handler.load == null) {\n    throw new ValueError(\"Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.\");\n  }\n  const artifacts = await handler.load();\n  let modelTopology = artifacts.modelTopology;\n  if (modelTopology[\"model_config\"] != null) {\n    modelTopology = modelTopology[\"model_config\"];\n  }\n  const strict = options.strict == null ? true : options.strict;\n  const fastWeightInit = artifacts.weightData != null && artifacts.weightSpecs != null && strict;\n  const model2 = deserialize(convertPythonicToTs(modelTopology), customObjects, fastWeightInit);\n  const trainingConfig = artifacts.trainingConfig;\n  if (trainingConfig != null) {\n    model2.loadTrainingConfig(trainingConfig);\n  }\n  if (artifacts.userDefinedMetadata != null) {\n    model2.setUserDefinedMetadata(artifacts.userDefinedMetadata);\n  }\n  if (artifacts.weightData != null) {\n    if (artifacts.weightSpecs == null) {\n      throw new ValueError(\"LayersModel artifacts contains weight data, but not weight specs. Therefore loading of weights cannot proceed.\");\n    }\n    const { modelWeights, optimizerWeights } = decodeModelAndOptimizerWeights(artifacts.weightData, artifacts.weightSpecs);\n    model2.loadWeights(modelWeights, strict);\n    if (model2.optimizer != null && optimizerWeights.length > 0) {\n      await model2.optimizer.setWeights(optimizerWeights);\n    }\n    dispose(modelWeights);\n    dispose(optimizerWeights.map((w) => w.tensor));\n  }\n  return model2;\n}\nfunction decodeModelAndOptimizerWeights(buffer2, specs) {\n  const name2Tensor = io_exports.decodeWeights(buffer2, specs);\n  const modelWeights = {};\n  const optimizerWeights = [];\n  specs.forEach((spec) => {\n    if (spec.group === \"optimizer\") {\n      optimizerWeights.push({ name: spec.name, tensor: name2Tensor[spec.name] });\n    } else {\n      modelWeights[spec.name] = name2Tensor[spec.name];\n    }\n  });\n  return { modelWeights, optimizerWeights };\n}\nvar Sequential = class extends LayersModel {\n  constructor(args) {\n    super({ inputs: [], outputs: [] });\n    args = args || {};\n    this.trainable = true;\n    this.built = false;\n    this.name = args.name != null ? args.name : getUid(\"sequential_\");\n    if (args.layers != null) {\n      for (const layer of args.layers) {\n        this.add(layer);\n      }\n    }\n  }\n  checkShape(layer) {\n    const shape = layer.inboundNodes[0].outputTensors[0].shape;\n    if (shape.some((x) => x < 0)) {\n      throw new ValueError(`Negative dimension size caused by adding layer ${layer.name} with input shape [${layer.inboundNodes[0].inputTensors[0].shape}]`);\n    }\n  }\n  add(layer) {\n    const isLayerModelInstance = layer instanceof Sequential || layer instanceof LayersModel;\n    let modelLayer;\n    if (isLayerModelInstance) {\n      modelLayer = layer;\n      if (modelLayer.outputs.length !== 1) {\n        throw new ValueError(\"All layers in a Sequential model should have a single output tensor. For multi-output layers, use the functional API.\");\n      }\n      if (modelLayer.inputs.length !== 1) {\n        throw new ValueError(\"All layers in a Sequential model should have a single input tensor. For multi-input layers, use the functional API.\");\n      }\n    }\n    if (this.outputs.length === 0) {\n      if (layer.inboundNodes.length === 0) {\n        if (layer.batchInputShape == null) {\n          throw new ValueError(\"The first layer in a Sequential model must get an `inputShape` or `batchInputShape` argument.\");\n        }\n        const x = Input({\n          batchShape: layer.batchInputShape,\n          dtype: layer.dtype,\n          name: layer.name + \"_input\"\n        });\n        layer.apply(x);\n      }\n      if (isLayerModelInstance) {\n        this.outputs = modelLayer.outputs;\n        this.inputs = modelLayer.inputs;\n      } else {\n        if (layer.inboundNodes.length !== 1) {\n          throw new ValueError(`A layer added to a Sequential model must not already be connected somewhere else. LayersModel received layer ${layer.name} which has ${layer.inboundNodes.length} pre-existing inbound connections.`);\n        }\n        if (layer.inboundNodes[0].outputTensors.length !== 1) {\n          throw new ValueError(\"All layers in a Sequential model should have a single output tensor. For multi-output layers, use the functional API.\");\n        }\n        this.checkShape(layer);\n        this.outputs = [layer.inboundNodes[0].outputTensors[0]];\n        this.inputs = getSourceInputs(this.outputs[0]);\n      }\n      this.inboundNodes = [];\n      new Node({\n        outboundLayer: this,\n        inboundLayers: [],\n        nodeIndices: [],\n        tensorIndices: [],\n        inputTensors: this.inputs,\n        outputTensors: this.outputs,\n        inputMasks: pyListRepeat(null, this.inputs.length),\n        outputMasks: [null],\n        inputShapes: this.inputs.map((x) => x.shape),\n        outputShapes: this.outputs[0].shape\n      });\n    } else {\n      const outputTensor = layer.apply(this.outputs[0]);\n      if (Array.isArray(outputTensor)) {\n        throw new TypeError(\"All layers in a Sequential model should have a single output tensor. For multi-output layers, use the functional API.\");\n      }\n      this.checkShape(layer);\n      this.outputs = [outputTensor];\n      this.inboundNodes[0].outputTensors = this.outputs;\n      this.inboundNodes[0].outputShapes = [this.outputs[0].shape];\n    }\n    this.layers.push(layer);\n    this.built = false;\n  }\n  pop() {\n    if (this.layers.length === 0) {\n      throw new TypeError(\"There are no layers in the model.\");\n    }\n    this.layers.pop();\n    if (this.layers.length === 0) {\n      this.outputs = [];\n      this.inboundNodes = [];\n      this.outboundNodes = [];\n    } else {\n      const lastLayerIndex = this.layers.length - 1;\n      this.layers[lastLayerIndex].outboundNodes = [];\n      this.outputs = [this.layers[lastLayerIndex].output];\n      this.inboundNodes[0].outputTensors = this.outputs;\n      this.inboundNodes[0].outputShapes = [this.outputs[0].shape];\n    }\n  }\n  call(inputs, kwargs) {\n    if (this.model == null) {\n      this.build();\n    }\n    return this.model.call(inputs, kwargs);\n  }\n  build(inputShape) {\n    getExactlyOneShape(inputShape);\n    if (this.inputs.length === 0 || this.outputs.length === 0) {\n      throw new TypeError(\"Sequential model cannot be built: model is empty. Add some layers first.\");\n    }\n    this.model = new LayersModel({\n      inputs: this.inputs,\n      outputs: this.outputs[0],\n      name: this.name + \"_model\"\n    });\n    this.model.trainable = this.trainable;\n    this.supportsMasking = this.model.supportsMasking;\n    this.inputLayers = this.model.inputLayers;\n    this.inputLayersNodeIndices = this.model.inputLayersNodeIndices;\n    this.inputLayersTensorIndices = this.model.inputLayersTensorIndices;\n    this.outputLayers = this.model.outputLayers;\n    this.outputLayersNodeIndices = this.model.outputLayersNodeIndices;\n    this.outputLayersTensorIndices = this.model.outputLayersTensorIndices;\n    this.nodesByDepth = this.model.nodesByDepth;\n    this.containerNodes = this.model.containerNodes;\n    this.outputNames = this.model.outputNames;\n    this.inputNames = this.model.inputNames;\n    this.built = true;\n  }\n  countParams() {\n    if (!this.built) {\n      this.build();\n    }\n    return super.countParams();\n  }\n  summary(lineLength, positions, printFn = console.log) {\n    if (!this.built) {\n      this.build();\n    }\n    super.summary(lineLength, positions, printFn);\n  }\n  setWeights(weights) {\n    if (this.model == null) {\n      this.build();\n    }\n    this.model.setWeights(weights);\n  }\n  evaluate(x, y, args = {}) {\n    if (!this.built) {\n      throw new RuntimeError(\"The model needs to be compiled before being used.\");\n    }\n    return this.model.evaluate(x, y, args);\n  }\n  async evaluateDataset(dataset, args) {\n    if (!this.built) {\n      throw new RuntimeError(\"The model needs to be compiled before being used.\");\n    }\n    return this.model.evaluateDataset(dataset, args);\n  }\n  predict(x, args = {}) {\n    if (this.model == null) {\n      this.build();\n    }\n    return this.model.predict(x, args);\n  }\n  predictOnBatch(x) {\n    if (this.model == null) {\n      this.build();\n    }\n    return this.model.predictOnBatch(x);\n  }\n  compile(args) {\n    this.build();\n    this.model.compile(args);\n    this.optimizer_ = this.model.optimizer;\n    this.isOptimizerOwned = this.model.isOptimizerOwned;\n    this.loss = this.model.loss;\n    this.metrics = this.model.metrics;\n    this.metricsTensors = this.model.metricsTensors;\n    this.metricsNames = this.model.metricsNames;\n  }\n  get optimizer() {\n    return this.model == null ? void 0 : this.model.optimizer;\n  }\n  set optimizer(optimizer) {\n    this.model.optimizer = optimizer;\n  }\n  async fit(x, y, args = {}) {\n    if (!this.built) {\n      throw new RuntimeError(\"The model needs to be compiled before being used.\");\n    }\n    return this.model.fit(x, y, args);\n  }\n  async fitDataset(dataset, args) {\n    if (!this.built) {\n      throw new RuntimeError(\"The model needs to be compiled before being used.\");\n    }\n    return this.model.fitDataset(dataset, args);\n  }\n  async trainOnBatch(x, y) {\n    return this.model.trainOnBatch(x, y);\n  }\n  static fromConfig(cls, config, customObjects = {}, fastWeightInit = false) {\n    let configArray;\n    let extraModelConfig = {};\n    if (config instanceof Array) {\n      if (!(config[0].className != null) || config[0][\"className\"] === \"Merge\") {\n        throw new ValueError(\"Legacy serialization format not supported yet.\");\n      }\n      configArray = config;\n    } else {\n      util_exports.assert(config[\"layers\"] != null, () => `When the config data for a Sequential model is not an Array, it must be an Object that contains the 'layers' field.`);\n      configArray = config[\"layers\"];\n      delete config[\"layers\"];\n      extraModelConfig = config;\n    }\n    const model2 = new cls(extraModelConfig);\n    if (!(model2 instanceof Sequential)) {\n      throw new NotImplementedError(`Sequential.fromConfig called on non-Sequential input: ${model2}`);\n    }\n    for (const conf of configArray) {\n      const customObjects2 = void 0;\n      const layer = deserialize(conf, customObjects2, fastWeightInit);\n      if (fastWeightInit) {\n        layer.setFastWeightInitDuringBuild(true);\n      }\n      model2.add(layer);\n    }\n    return model2;\n  }\n  set stopTraining(stop) {\n    if (this.model == null) {\n      throw new ValueError(\"Cannot set the stopTraining property of a sequential model before it is compiled.\");\n    }\n    this.model.stopTraining = stop;\n  }\n  get stopTraining() {\n    if (this.model == null) {\n      throw new ValueError(\"Cannot get the stopTraining property of a sequential model before it is compiled.\");\n    }\n    return this.model.stopTraining;\n  }\n  getConfig() {\n    const layers = [];\n    for (const layer of this.layers) {\n      const dict = {};\n      dict[\"className\"] = layer.getClassName();\n      dict[\"config\"] = layer.getConfig();\n      layers.push(dict);\n    }\n    return { name: this.name, layers };\n  }\n};\nSequential.className = \"Sequential\";\nserialization_exports.registerClass(Sequential);\n\n// node_modules/.pnpm/@tensorflow+tfjs-layers@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-layers/dist/exports.js\nfunction model(args) {\n  return new LayersModel(args);\n}\nfunction sequential(config) {\n  return new Sequential(config);\n}\nfunction loadLayersModel(pathOrIOHandler, options) {\n  if (options == null) {\n    options = {};\n  }\n  return loadLayersModelInternal(pathOrIOHandler, options);\n}\nfunction input(config) {\n  return Input(config);\n}\nfunction registerCallbackConstructor(verbosityLevel, callbackConstructor) {\n  CallbackConstructorRegistry.registerCallbackConstructor(verbosityLevel, callbackConstructor);\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-layers@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-layers/dist/activations.js\nvar Activation = class extends serialization_exports.Serializable {\n  getConfig() {\n    return {};\n  }\n};\nvar Elu2 = class extends Activation {\n  apply(x, alpha = 1) {\n    return elu2(x, alpha);\n  }\n};\nElu2.className = \"elu\";\nserialization_exports.registerClass(Elu2);\nvar Selu2 = class extends Activation {\n  apply(x) {\n    return selu(x);\n  }\n};\nSelu2.className = \"selu\";\nserialization_exports.registerClass(Selu2);\nvar Relu2 = class extends Activation {\n  apply(x) {\n    return relu(x);\n  }\n};\nRelu2.className = \"relu\";\nserialization_exports.registerClass(Relu2);\nvar Relu62 = class extends Activation {\n  apply(x) {\n    return tidy(() => minimum(6, relu(x)));\n  }\n};\nRelu62.className = \"relu6\";\nserialization_exports.registerClass(Relu62);\nvar Linear = class extends Activation {\n  apply(x) {\n    return x;\n  }\n};\nLinear.className = \"linear\";\nserialization_exports.registerClass(Linear);\nvar Sigmoid2 = class extends Activation {\n  apply(x) {\n    return sigmoid(x);\n  }\n};\nSigmoid2.className = \"sigmoid\";\nserialization_exports.registerClass(Sigmoid2);\nvar HardSigmoid = class extends Activation {\n  apply(x) {\n    return hardSigmoid(x);\n  }\n};\nHardSigmoid.className = \"hardSigmoid\";\nserialization_exports.registerClass(HardSigmoid);\nvar Softplus2 = class extends Activation {\n  apply(x) {\n    return softplus(x);\n  }\n};\nSoftplus2.className = \"softplus\";\nserialization_exports.registerClass(Softplus2);\nvar Softsign = class extends Activation {\n  apply(x) {\n    return softsign(x);\n  }\n};\nSoftsign.className = \"softsign\";\nserialization_exports.registerClass(Softsign);\nvar Tanh2 = class extends Activation {\n  apply(x) {\n    return tanh2(x);\n  }\n};\nTanh2.className = \"tanh\";\nserialization_exports.registerClass(Tanh2);\nvar Softmax2 = class extends Activation {\n  apply(x, axis = -1) {\n    return softmax(x, axis);\n  }\n};\nSoftmax2.className = \"softmax\";\nserialization_exports.registerClass(Softmax2);\nvar LogSoftmax2 = class extends Activation {\n  apply(x, axis = -1) {\n    return logSoftmax(x, axis);\n  }\n};\nLogSoftmax2.className = \"logSoftmax\";\nserialization_exports.registerClass(LogSoftmax2);\nvar Swish = class extends Activation {\n  apply(x, alpha = 1) {\n    return tidy(() => mul(sigmoid(mul(x, alpha)), x));\n  }\n};\nSwish.className = \"swish\";\nserialization_exports.registerClass(Swish);\nvar Mish = class extends Activation {\n  apply(x) {\n    return tidy(() => mul(x, tanh2(softplus(x))));\n  }\n};\nMish.className = \"mish\";\nserialization_exports.registerClass(Mish);\nfunction serializeActivation(activation2) {\n  return activation2.getClassName();\n}\nfunction deserializeActivation(config, customObjects = {}) {\n  return deserializeKerasObject(config, serialization_exports.SerializationMap.getMap().classNameMap, customObjects, \"activation\");\n}\nfunction getActivation(identifier) {\n  if (identifier == null) {\n    const config = {};\n    config[\"className\"] = \"linear\";\n    config[\"config\"] = {};\n    return deserializeActivation(config);\n  }\n  if (typeof identifier === \"string\") {\n    const config = {};\n    config[\"className\"] = identifier;\n    config[\"config\"] = {};\n    return deserializeActivation(config);\n  } else if (identifier instanceof Activation) {\n    return identifier;\n  } else {\n    return deserializeActivation(identifier);\n  }\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-layers@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-layers/dist/regularizers.js\nfunction assertObjectArgs(args) {\n  if (args != null && typeof args !== \"object\") {\n    throw new Error(`Argument to L1L2 regularizer's constructor is expected to be an object, but received: ${args}`);\n  }\n}\nvar Regularizer = class extends serialization_exports.Serializable {\n};\nvar L1L2 = class extends Regularizer {\n  constructor(args) {\n    super();\n    assertObjectArgs(args);\n    this.l1 = args == null || args.l1 == null ? 0.01 : args.l1;\n    this.l2 = args == null || args.l2 == null ? 0.01 : args.l2;\n    this.hasL1 = this.l1 !== 0;\n    this.hasL2 = this.l2 !== 0;\n  }\n  apply(x) {\n    return tidy(() => {\n      let regularization = zeros([1]);\n      if (this.hasL1) {\n        regularization = add2(regularization, sum2(mul(this.l1, abs(x))));\n      }\n      if (this.hasL2) {\n        regularization = add2(regularization, sum2(mul(this.l2, square2(x))));\n      }\n      return reshape(regularization, []);\n    });\n  }\n  getConfig() {\n    return { \"l1\": this.l1, \"l2\": this.l2 };\n  }\n  static fromConfig(cls, config) {\n    return new cls({ l1: config[\"l1\"], l2: config[\"l2\"] });\n  }\n};\nL1L2.className = \"L1L2\";\nserialization_exports.registerClass(L1L2);\nfunction l1(args) {\n  assertObjectArgs(args);\n  return new L1L2({ l1: args != null ? args.l1 : null, l2: 0 });\n}\nfunction l2(args) {\n  assertObjectArgs(args);\n  return new L1L2({ l2: args != null ? args.l2 : null, l1: 0 });\n}\nvar REGULARIZER_IDENTIFIER_REGISTRY_SYMBOL_MAP = {\n  \"l1l2\": \"L1L2\"\n};\nfunction serializeRegularizer(constraint) {\n  return serializeKerasObject(constraint);\n}\nfunction deserializeRegularizer(config, customObjects = {}) {\n  return deserializeKerasObject(config, serialization_exports.SerializationMap.getMap().classNameMap, customObjects, \"regularizer\");\n}\nfunction getRegularizer(identifier) {\n  if (identifier == null) {\n    return null;\n  }\n  if (typeof identifier === \"string\") {\n    const className = identifier in REGULARIZER_IDENTIFIER_REGISTRY_SYMBOL_MAP ? REGULARIZER_IDENTIFIER_REGISTRY_SYMBOL_MAP[identifier] : identifier;\n    const config = { className, config: {} };\n    return deserializeRegularizer(config);\n  } else if (identifier instanceof Regularizer) {\n    return identifier;\n  } else {\n    return deserializeRegularizer(identifier);\n  }\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-layers@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-layers/dist/layers/advanced_activations.js\nvar ReLU = class extends Layer {\n  constructor(args) {\n    super(args == null ? {} : args);\n    this.supportsMasking = true;\n    if (args != null) {\n      this.maxValue = args.maxValue;\n    }\n  }\n  call(inputs, kwargs) {\n    inputs = getExactlyOneTensor(inputs);\n    let output = relu(inputs);\n    if (this.maxValue != null) {\n      output = clipByValue(output, 0, this.maxValue);\n    }\n    return output;\n  }\n  computeOutputShape(inputShape) {\n    return inputShape;\n  }\n  getConfig() {\n    const config = { maxValue: this.maxValue };\n    const baseConfig = super.getConfig();\n    Object.assign(config, baseConfig);\n    return config;\n  }\n};\nReLU.className = \"ReLU\";\nserialization_exports.registerClass(ReLU);\nvar LeakyReLU = class extends Layer {\n  constructor(args) {\n    super(args == null ? {} : args);\n    this.DEFAULT_ALPHA = 0.3;\n    if (args == null) {\n      args = {};\n    }\n    this.alpha = args.alpha == null ? this.DEFAULT_ALPHA : args.alpha;\n  }\n  call(inputs, kwargs) {\n    const x = getExactlyOneTensor(inputs);\n    return leakyRelu(x, this.alpha);\n  }\n  computeOutputShape(inputShape) {\n    return inputShape;\n  }\n  getConfig() {\n    const config = { alpha: this.alpha };\n    const baseConfig = super.getConfig();\n    Object.assign(config, baseConfig);\n    return config;\n  }\n};\nLeakyReLU.className = \"LeakyReLU\";\nserialization_exports.registerClass(LeakyReLU);\nvar PReLU = class extends Layer {\n  constructor(args) {\n    super(args == null ? {} : args);\n    this.DEFAULT_ALPHA_INITIALIZER = \"zeros\";\n    if (args == null) {\n      args = {};\n    }\n    this.supportsMasking = true;\n    this.alphaInitializer = getInitializer(args.alphaInitializer || this.DEFAULT_ALPHA_INITIALIZER);\n    this.alphaRegularizer = getRegularizer(args.alphaRegularizer);\n    this.alphaConstraint = getConstraint(args.alphaConstraint);\n    if (args.sharedAxes == null) {\n      this.sharedAxes = null;\n    } else if (Array.isArray(args.sharedAxes)) {\n      this.sharedAxes = args.sharedAxes;\n    } else if (typeof args.sharedAxes === \"number\") {\n      this.sharedAxes = [args.sharedAxes];\n    } else {\n      throw new ValueError(`Expected sharedAxes to be a number or an array of numbers, but got ${args.sharedAxes}`);\n    }\n  }\n  build(inputShape) {\n    inputShape = getExactlyOneShape(inputShape);\n    const paramShape = inputShape.slice(1);\n    if (this.sharedAxes != null) {\n      for (const i2 of this.sharedAxes) {\n        paramShape[i2 - 1] = 1;\n      }\n    }\n    this.alpha = this.addWeight(\"alpha\", paramShape, \"float32\", this.alphaInitializer, this.alphaRegularizer, true, this.alphaConstraint);\n    const axes = {};\n    if (this.sharedAxes != null) {\n      for (let i2 = 1; i2 < inputShape.length; ++i2) {\n        axes[i2] = inputShape[i2];\n      }\n    }\n    this.inputSpec = [new InputSpec({\n      ndim: inputShape.length,\n      axes\n    })];\n    this.built = true;\n  }\n  call(inputs, kwargs) {\n    inputs = getExactlyOneTensor(inputs);\n    return prelu(inputs, this.alpha.read());\n  }\n  getConfig() {\n    const config = {\n      alphaInitializer: serializeInitializer(this.alphaInitializer),\n      alphaRegularizer: serializeRegularizer(this.alphaRegularizer),\n      alphaConstraint: serializeConstraint(this.alphaConstraint),\n      sharedAxes: this.sharedAxes\n    };\n    const baseConfig = super.getConfig();\n    Object.assign(config, baseConfig);\n    return config;\n  }\n};\nPReLU.className = \"PReLU\";\nserialization_exports.registerClass(PReLU);\nvar ELU = class extends Layer {\n  constructor(args) {\n    super(args == null ? {} : args);\n    this.DEFAULT_ALPHA = 1;\n    if (args == null) {\n      args = {};\n    }\n    if (args.alpha != null && args.alpha !== this.DEFAULT_ALPHA) {\n      throw new NotImplementedError(`Non-default alpha value (${args.alpha}) is not supported by the ELU layer yet.`);\n    }\n    this.alpha = args.alpha == null ? this.DEFAULT_ALPHA : args.alpha;\n  }\n  call(inputs, kwargs) {\n    const x = getExactlyOneTensor(inputs);\n    return elu(x);\n  }\n  computeOutputShape(inputShape) {\n    return inputShape;\n  }\n  getConfig() {\n    const config = { alpha: this.alpha };\n    const baseConfig = super.getConfig();\n    Object.assign(config, baseConfig);\n    return config;\n  }\n};\nELU.className = \"ELU\";\nserialization_exports.registerClass(ELU);\nvar ThresholdedReLU = class extends Layer {\n  constructor(args) {\n    super(args == null ? {} : args);\n    this.DEFAULT_THETA = 1;\n    if (args == null) {\n      args = {};\n    }\n    this.theta = args.theta == null ? this.DEFAULT_THETA : args.theta;\n  }\n  call(inputs, kwargs) {\n    const x = getExactlyOneTensor(inputs);\n    return mul(x, cast(greater(x, this.theta), \"float32\"));\n  }\n  computeOutputShape(inputShape) {\n    return inputShape;\n  }\n  getConfig() {\n    const config = { theta: this.theta };\n    const baseConfig = super.getConfig();\n    Object.assign(config, baseConfig);\n    return config;\n  }\n};\nThresholdedReLU.className = \"ThresholdedReLU\";\nserialization_exports.registerClass(ThresholdedReLU);\nvar Softmax3 = class extends Layer {\n  constructor(args) {\n    super(args == null ? {} : args);\n    this.DEFAULT_AXIS = 1;\n    if (args == null) {\n      args = {};\n    }\n    this.softmax = new Softmax2().apply;\n    this.axis = args.axis == null ? this.DEFAULT_AXIS : args.axis;\n  }\n  call(inputs, kwargs) {\n    const x = getExactlyOneTensor(inputs);\n    return this.softmax(x, this.axis);\n  }\n  computeOutputShape(inputShape) {\n    return inputShape;\n  }\n  getConfig() {\n    const config = { axis: this.axis };\n    const baseConfig = super.getConfig();\n    Object.assign(config, baseConfig);\n    return config;\n  }\n};\nSoftmax3.className = \"Softmax\";\nserialization_exports.registerClass(Softmax3);\n\n// node_modules/.pnpm/@tensorflow+tfjs-layers@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-layers/dist/utils/conv_utils.js\nfunction normalizeArray(value, n2, name) {\n  if (typeof value === \"number\") {\n    return pyListRepeat(value, n2);\n  } else {\n    if (value.length !== n2) {\n      throw new ValueError(`The ${name} argument must be an integer or tuple of ${n2} integers. Received: ${value.length} elements.`);\n    }\n    for (let i2 = 0; i2 < n2; ++i2) {\n      const singleValue = value[i2];\n      if (!isInteger(singleValue)) {\n        throw new ValueError(`The ${name} argument must be an integer or tuple of ${n2} integers. Received: ${JSON.stringify(value)} including a non-integer number ${singleValue}`);\n      }\n    }\n    return value;\n  }\n}\nfunction convOutputLength(inputLength, filterSize, padding, stride, dilation = 1) {\n  if (inputLength == null) {\n    return inputLength;\n  }\n  const dilatedFilterSize = filterSize + (filterSize - 1) * (dilation - 1);\n  let outputLength;\n  if (padding === \"same\") {\n    outputLength = inputLength;\n  } else {\n    outputLength = inputLength - dilatedFilterSize + 1;\n  }\n  return Math.floor((outputLength + stride - 1) / stride);\n}\nfunction deconvLength(dimSize, strideSize, kernelSize, padding) {\n  if (dimSize == null) {\n    return null;\n  }\n  if (padding === \"valid\") {\n    dimSize = dimSize * strideSize + max2([kernelSize - strideSize, 0]);\n  } else if (padding === \"same\") {\n    dimSize = dimSize * strideSize;\n  } else {\n    throw new ValueError(`Unsupport padding mode: ${padding}.`);\n  }\n  return dimSize;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-layers@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-layers/dist/layers/convolutional.js\nfunction preprocessConv2DInput(x, dataFormat) {\n  return tidy(() => {\n    checkDataFormat(dataFormat);\n    if (dataFormat === \"channelsFirst\") {\n      return transpose(x, [0, 2, 3, 1]);\n    } else {\n      return x;\n    }\n  });\n}\nfunction preprocessConv3DInput(x, dataFormat) {\n  return tidy(() => {\n    checkDataFormat(dataFormat);\n    if (dataFormat === \"channelsFirst\") {\n      return transpose(x, [0, 2, 3, 4, 1]);\n    } else {\n      return x;\n    }\n  });\n}\nfunction conv1dWithBias(x, kernel, bias, strides = 1, padding = \"valid\", dataFormat, dilationRate = 1) {\n  return tidy(() => {\n    if (dataFormat == null) {\n      dataFormat = imageDataFormat();\n    }\n    checkDataFormat(dataFormat);\n    if (x.shape.length !== 3) {\n      throw new ValueError(`The input of a conv1dWithBias operation should be 3, but is ${x.shape.length} instead.`);\n    }\n    if (kernel.shape.length !== 3) {\n      throw new ValueError(`The kernel for a conv1dWithBias operation should be 3, but is ${kernel.shape.length} instead`);\n    }\n    if (bias != null && bias.shape.length !== 1) {\n      throw new ValueError(`The bias for a conv1dWithBias operation should be 1, but is ${kernel.shape.length} instead`);\n    }\n    if (dataFormat === \"channelsFirst\") {\n      x = transpose(x, [0, 2, 1]);\n    }\n    if (padding === \"causal\") {\n      throw new NotImplementedError(\"The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.\");\n    }\n    let y = conv1d(x, kernel, strides, padding === \"same\" ? \"same\" : \"valid\", \"NWC\", dilationRate);\n    if (bias != null) {\n      y = biasAdd(y, bias);\n    }\n    return y;\n  });\n}\nfunction conv2dWithBiasActivation(x, kernel, bias, strides = [1, 1], padding = \"valid\", dataFormat, dilationRate, activation2 = null) {\n  return tidy(() => {\n    if (dataFormat == null) {\n      dataFormat = imageDataFormat();\n    }\n    checkDataFormat(dataFormat);\n    if (x.rank !== 3 && x.rank !== 4) {\n      throw new ValueError(`conv2dWithBiasActivation expects input to be of rank 3 or 4, but received ${x.rank}.`);\n    }\n    if (kernel.rank !== 3 && kernel.rank !== 4) {\n      throw new ValueError(`conv2dWithBiasActivation expects kernel to be of rank 3 or 4, but received ${x.rank}.`);\n    }\n    let y = preprocessConv2DInput(x, dataFormat);\n    if (padding === \"causal\") {\n      throw new NotImplementedError(\"The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.\");\n    }\n    y = fused_ops_exports.conv2d({\n      x: y,\n      filter: kernel,\n      strides,\n      pad: padding === \"same\" ? \"same\" : \"valid\",\n      dilations: dilationRate,\n      dataFormat: \"NHWC\",\n      bias,\n      activation: activation2\n    });\n    if (dataFormat === \"channelsFirst\") {\n      y = transpose(y, [0, 3, 1, 2]);\n    }\n    return y;\n  });\n}\nfunction conv3dWithBias(x, kernel, bias, strides = [1, 1, 1], padding = \"valid\", dataFormat, dilationRate) {\n  return tidy(() => {\n    if (dataFormat == null) {\n      dataFormat = imageDataFormat();\n    }\n    checkDataFormat(dataFormat);\n    if (x.rank !== 4 && x.rank !== 5) {\n      throw new ValueError(`conv3dWithBias expects input to be of rank 4 or 5, but received ${x.rank}.`);\n    }\n    if (kernel.rank !== 4 && kernel.rank !== 5) {\n      throw new ValueError(`conv3dWithBias expects kernel to be of rank 4 or 5, but received ${x.rank}.`);\n    }\n    let y = preprocessConv3DInput(x, dataFormat);\n    if (padding === \"causal\") {\n      throw new NotImplementedError(\"The support for CAUSAL padding mode in conv3dWithBias is not implemented yet.\");\n    }\n    y = conv3d(y, kernel, strides, padding === \"same\" ? \"same\" : \"valid\", \"NDHWC\", dilationRate);\n    if (bias != null) {\n      y = biasAdd(y, bias);\n    }\n    if (dataFormat === \"channelsFirst\") {\n      y = transpose(y, [0, 4, 1, 2, 3]);\n    }\n    return y;\n  });\n}\nvar BaseConv = class extends Layer {\n  constructor(rank, args) {\n    super(args);\n    this.bias = null;\n    this.DEFAULT_KERNEL_INITIALIZER = \"glorotNormal\";\n    this.DEFAULT_BIAS_INITIALIZER = \"zeros\";\n    BaseConv.verifyArgs(args);\n    this.rank = rank;\n    assertPositiveInteger(this.rank, \"rank\");\n    if (this.rank !== 1 && this.rank !== 2 && this.rank !== 3) {\n      throw new NotImplementedError(`Convolution layer for rank other than 1, 2, or 3 (${this.rank}) is not implemented yet.`);\n    }\n    this.kernelSize = normalizeArray(args.kernelSize, rank, \"kernelSize\");\n    this.strides = normalizeArray(args.strides == null ? 1 : args.strides, rank, \"strides\");\n    this.padding = args.padding == null ? \"valid\" : args.padding;\n    checkPaddingMode(this.padding);\n    this.dataFormat = args.dataFormat == null ? \"channelsLast\" : args.dataFormat;\n    checkDataFormat(this.dataFormat);\n    this.activation = getActivation(args.activation);\n    this.useBias = args.useBias == null ? true : args.useBias;\n    this.biasInitializer = getInitializer(args.biasInitializer || this.DEFAULT_BIAS_INITIALIZER);\n    this.biasConstraint = getConstraint(args.biasConstraint);\n    this.biasRegularizer = getRegularizer(args.biasRegularizer);\n    this.activityRegularizer = getRegularizer(args.activityRegularizer);\n    this.dilationRate = normalizeArray(args.dilationRate == null ? 1 : args.dilationRate, rank, \"dilationRate\");\n    if (this.rank === 1 && (Array.isArray(this.dilationRate) && this.dilationRate.length !== 1)) {\n      throw new ValueError(`dilationRate must be a number or an array of a single number for 1D convolution, but received ${JSON.stringify(this.dilationRate)}`);\n    } else if (this.rank === 2) {\n      if (typeof this.dilationRate === \"number\") {\n        this.dilationRate = [this.dilationRate, this.dilationRate];\n      } else if (this.dilationRate.length !== 2) {\n        throw new ValueError(`dilationRate must be a number or array of two numbers for 2D convolution, but received ${JSON.stringify(this.dilationRate)}`);\n      }\n    } else if (this.rank === 3) {\n      if (typeof this.dilationRate === \"number\") {\n        this.dilationRate = [this.dilationRate, this.dilationRate, this.dilationRate];\n      } else if (this.dilationRate.length !== 3) {\n        throw new ValueError(`dilationRate must be a number or array of three numbers for 3D convolution, but received ${JSON.stringify(this.dilationRate)}`);\n      }\n    }\n  }\n  static verifyArgs(args) {\n    assert2(\"kernelSize\" in args, `required key 'kernelSize' not in config`);\n    if (typeof args.kernelSize !== \"number\" && !checkArrayTypeAndLength(args.kernelSize, \"number\", 1, 3)) {\n      throw new ValueError(`BaseConv expects config.kernelSize to be number or number[] with length 1, 2, or 3, but received ${JSON.stringify(args.kernelSize)}.`);\n    }\n  }\n  getConfig() {\n    const config = {\n      kernelSize: this.kernelSize,\n      strides: this.strides,\n      padding: this.padding,\n      dataFormat: this.dataFormat,\n      dilationRate: this.dilationRate,\n      activation: serializeActivation(this.activation),\n      useBias: this.useBias,\n      biasInitializer: serializeInitializer(this.biasInitializer),\n      biasRegularizer: serializeRegularizer(this.biasRegularizer),\n      activityRegularizer: serializeRegularizer(this.activityRegularizer),\n      biasConstraint: serializeConstraint(this.biasConstraint)\n    };\n    const baseConfig = super.getConfig();\n    Object.assign(config, baseConfig);\n    return config;\n  }\n};\nvar Conv = class extends BaseConv {\n  constructor(rank, args) {\n    super(rank, args);\n    this.kernel = null;\n    Conv.verifyArgs(args);\n    this.filters = args.filters;\n    assertPositiveInteger(this.filters, \"filters\");\n    this.kernelInitializer = getInitializer(args.kernelInitializer || this.DEFAULT_KERNEL_INITIALIZER);\n    this.kernelConstraint = getConstraint(args.kernelConstraint);\n    this.kernelRegularizer = getRegularizer(args.kernelRegularizer);\n  }\n  build(inputShape) {\n    inputShape = getExactlyOneShape(inputShape);\n    const channelAxis = this.dataFormat === \"channelsFirst\" ? 1 : inputShape.length - 1;\n    if (inputShape[channelAxis] == null) {\n      throw new ValueError(`The channel dimension of the input should be defined. Found ${inputShape[channelAxis]}`);\n    }\n    const inputDim = inputShape[channelAxis];\n    const kernelShape = this.kernelSize.concat([inputDim, this.filters]);\n    this.kernel = this.addWeight(\"kernel\", kernelShape, null, this.kernelInitializer, this.kernelRegularizer, true, this.kernelConstraint);\n    if (this.useBias) {\n      this.bias = this.addWeight(\"bias\", [this.filters], null, this.biasInitializer, this.biasRegularizer, true, this.biasConstraint);\n    }\n    this.inputSpec = [{ ndim: this.rank + 2, axes: { [channelAxis]: inputDim } }];\n    this.built = true;\n  }\n  call(inputs, kwargs) {\n    return tidy(() => {\n      inputs = getExactlyOneTensor(inputs);\n      let outputs;\n      const biasValue = this.bias == null ? null : this.bias.read();\n      const fusedActivationName = mapActivationToFusedKernel(this.activation.getClassName());\n      if (fusedActivationName != null && this.rank === 2) {\n        outputs = conv2dWithBiasActivation(inputs, this.kernel.read(), biasValue, this.strides, this.padding, this.dataFormat, this.dilationRate, fusedActivationName);\n      } else {\n        if (this.rank === 1) {\n          outputs = conv1dWithBias(inputs, this.kernel.read(), biasValue, this.strides[0], this.padding, this.dataFormat, this.dilationRate[0]);\n        } else if (this.rank === 2) {\n          outputs = conv2dWithBiasActivation(inputs, this.kernel.read(), biasValue, this.strides, this.padding, this.dataFormat, this.dilationRate);\n        } else if (this.rank === 3) {\n          outputs = conv3dWithBias(inputs, this.kernel.read(), biasValue, this.strides, this.padding, this.dataFormat, this.dilationRate);\n        } else {\n          throw new NotImplementedError(\"convolutions greater than 3D are not implemented yet.\");\n        }\n        if (this.activation != null) {\n          outputs = this.activation.apply(outputs);\n        }\n      }\n      return outputs;\n    });\n  }\n  computeOutputShape(inputShape) {\n    inputShape = getExactlyOneShape(inputShape);\n    const newSpace = [];\n    const space = this.dataFormat === \"channelsLast\" ? inputShape.slice(1, inputShape.length - 1) : inputShape.slice(2);\n    for (let i2 = 0; i2 < space.length; ++i2) {\n      const newDim = convOutputLength(space[i2], this.kernelSize[i2], this.padding, this.strides[i2], typeof this.dilationRate === \"number\" ? this.dilationRate : this.dilationRate[i2]);\n      newSpace.push(newDim);\n    }\n    let outputShape = [inputShape[0]];\n    if (this.dataFormat === \"channelsLast\") {\n      outputShape = outputShape.concat(newSpace);\n      outputShape.push(this.filters);\n    } else {\n      outputShape.push(this.filters);\n      outputShape = outputShape.concat(newSpace);\n    }\n    return outputShape;\n  }\n  getConfig() {\n    const config = {\n      filters: this.filters,\n      kernelInitializer: serializeInitializer(this.kernelInitializer),\n      kernelRegularizer: serializeRegularizer(this.kernelRegularizer),\n      kernelConstraint: serializeConstraint(this.kernelConstraint)\n    };\n    const baseConfig = super.getConfig();\n    Object.assign(config, baseConfig);\n    return config;\n  }\n  static verifyArgs(args) {\n    if (!(\"filters\" in args) || typeof args.filters !== \"number\" || args.filters < 1) {\n      throw new ValueError(`Convolution layer expected config.filters to be a 'number' > 0 but got ${JSON.stringify(args.filters)}`);\n    }\n  }\n};\nvar Conv2D2 = class extends Conv {\n  constructor(args) {\n    super(2, args);\n    Conv2D2.verifyArgs(args);\n  }\n  getConfig() {\n    const config = super.getConfig();\n    delete config[\"rank\"];\n    return config;\n  }\n  static verifyArgs(args) {\n    if (typeof args.kernelSize !== \"number\" && !checkArrayTypeAndLength(args.kernelSize, \"number\", 1, 2)) {\n      throw new ValueError(`Conv2D expects config.kernelSize to be number or number[] with length 1 or 2, but received ${JSON.stringify(args.kernelSize)}.`);\n    }\n  }\n};\nConv2D2.className = \"Conv2D\";\nserialization_exports.registerClass(Conv2D2);\nvar Conv3D2 = class extends Conv {\n  constructor(args) {\n    super(3, args);\n    Conv3D2.verifyArgs(args);\n  }\n  getConfig() {\n    const config = super.getConfig();\n    delete config[\"rank\"];\n    return config;\n  }\n  static verifyArgs(args) {\n    if (typeof args.kernelSize !== \"number\") {\n      if (!(Array.isArray(args.kernelSize) && (args.kernelSize.length === 1 || args.kernelSize.length === 3))) {\n        throw new ValueError(`Conv3D expects config.kernelSize to be number or [number, number, number], but received ${JSON.stringify(args.kernelSize)}.`);\n      }\n    }\n  }\n};\nConv3D2.className = \"Conv3D\";\nserialization_exports.registerClass(Conv3D2);\nvar Conv2DTranspose = class extends Conv2D2 {\n  constructor(args) {\n    super(args);\n    this.inputSpec = [new InputSpec({ ndim: 4 })];\n    if (this.padding !== \"same\" && this.padding !== \"valid\") {\n      throw new ValueError(`Conv2DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`);\n    }\n  }\n  build(inputShape) {\n    inputShape = getExactlyOneShape(inputShape);\n    if (inputShape.length !== 4) {\n      throw new ValueError(\"Input should have rank 4; Received input shape: \" + JSON.stringify(inputShape));\n    }\n    const channelAxis = this.dataFormat === \"channelsFirst\" ? 1 : inputShape.length - 1;\n    if (inputShape[channelAxis] == null) {\n      throw new ValueError(\"The channel dimension of the inputs should be defined. Found `None`.\");\n    }\n    const inputDim = inputShape[channelAxis];\n    const kernelShape = this.kernelSize.concat([this.filters, inputDim]);\n    this.kernel = this.addWeight(\"kernel\", kernelShape, \"float32\", this.kernelInitializer, this.kernelRegularizer, true, this.kernelConstraint);\n    if (this.useBias) {\n      this.bias = this.addWeight(\"bias\", [this.filters], \"float32\", this.biasInitializer, this.biasRegularizer, true, this.biasConstraint);\n    }\n    this.inputSpec = [new InputSpec({ ndim: 4, axes: { [channelAxis]: inputDim } })];\n    this.built = true;\n  }\n  call(inputs, kwargs) {\n    return tidy(() => {\n      let input2 = getExactlyOneTensor(inputs);\n      if (input2.shape.length !== 4) {\n        throw new ValueError(`Conv2DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${input2.shape.length}`);\n      }\n      const inputShape = input2.shape;\n      const batchSize = inputShape[0];\n      let hAxis;\n      let wAxis;\n      if (this.dataFormat === \"channelsFirst\") {\n        hAxis = 2;\n        wAxis = 3;\n      } else {\n        hAxis = 1;\n        wAxis = 2;\n      }\n      const height = inputShape[hAxis];\n      const width = inputShape[wAxis];\n      const kernelH = this.kernelSize[0];\n      const kernelW = this.kernelSize[1];\n      const strideH = this.strides[0];\n      const strideW = this.strides[1];\n      const outHeight = deconvLength(height, strideH, kernelH, this.padding);\n      const outWidth = deconvLength(width, strideW, kernelW, this.padding);\n      const outputShape = [batchSize, outHeight, outWidth, this.filters];\n      if (this.dataFormat !== \"channelsLast\") {\n        input2 = transpose(input2, [0, 2, 3, 1]);\n      }\n      let outputs = conv2dTranspose(input2, this.kernel.read(), outputShape, this.strides, this.padding);\n      if (this.dataFormat !== \"channelsLast\") {\n        outputs = transpose(outputs, [0, 3, 1, 2]);\n      }\n      if (this.bias != null) {\n        outputs = biasAdd(outputs, this.bias.read(), this.dataFormat);\n      }\n      if (this.activation != null) {\n        outputs = this.activation.apply(outputs);\n      }\n      return outputs;\n    });\n  }\n  computeOutputShape(inputShape) {\n    inputShape = getExactlyOneShape(inputShape);\n    const outputShape = inputShape.slice();\n    let channelAxis;\n    let heightAxis;\n    let widthAxis;\n    if (this.dataFormat === \"channelsFirst\") {\n      channelAxis = 1;\n      heightAxis = 2;\n      widthAxis = 3;\n    } else {\n      channelAxis = 3;\n      heightAxis = 1;\n      widthAxis = 2;\n    }\n    const kernelH = this.kernelSize[0];\n    const kernelW = this.kernelSize[1];\n    const strideH = this.strides[0];\n    const strideW = this.strides[1];\n    outputShape[channelAxis] = this.filters;\n    outputShape[heightAxis] = deconvLength(outputShape[heightAxis], strideH, kernelH, this.padding);\n    outputShape[widthAxis] = deconvLength(outputShape[widthAxis], strideW, kernelW, this.padding);\n    return outputShape;\n  }\n  getConfig() {\n    const config = super.getConfig();\n    delete config[\"dilationRate\"];\n    return config;\n  }\n};\nConv2DTranspose.className = \"Conv2DTranspose\";\nserialization_exports.registerClass(Conv2DTranspose);\nvar Conv3DTranspose = class extends Conv3D2 {\n  constructor(args) {\n    super(args);\n    this.inputSpec = [new InputSpec({ ndim: 5 })];\n    if (this.padding !== \"same\" && this.padding !== \"valid\") {\n      throw new ValueError(`Conv3DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`);\n    }\n  }\n  build(inputShape) {\n    inputShape = getExactlyOneShape(inputShape);\n    if (inputShape.length !== 5) {\n      throw new ValueError(\"Input should have rank 5; Received input shape: \" + JSON.stringify(inputShape));\n    }\n    const channelAxis = this.dataFormat === \"channelsFirst\" ? 1 : inputShape.length - 1;\n    if (inputShape[channelAxis] == null) {\n      throw new ValueError(\"The channel dimension of the inputs should be defined. Found `None`.\");\n    }\n    const inputDim = inputShape[channelAxis];\n    const kernelShape = this.kernelSize.concat([this.filters, inputDim]);\n    this.kernel = this.addWeight(\"kernel\", kernelShape, \"float32\", this.kernelInitializer, this.kernelRegularizer, true, this.kernelConstraint);\n    if (this.useBias) {\n      this.bias = this.addWeight(\"bias\", [this.filters], \"float32\", this.biasInitializer, this.biasRegularizer, true, this.biasConstraint);\n    }\n    this.inputSpec = [new InputSpec({ ndim: 5, axes: { [channelAxis]: inputDim } })];\n    this.built = true;\n  }\n  call(inputs, kwargs) {\n    return tidy(() => {\n      let input2 = getExactlyOneTensor(inputs);\n      if (input2.shape.length !== 5) {\n        throw new ValueError(`Conv3DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${input2.shape.length}`);\n      }\n      const inputShape = input2.shape;\n      const batchSize = inputShape[0];\n      let hAxis;\n      let wAxis;\n      let dAxis;\n      if (this.dataFormat === \"channelsFirst\") {\n        dAxis = 2;\n        hAxis = 3;\n        wAxis = 4;\n      } else {\n        dAxis = 1;\n        hAxis = 2;\n        wAxis = 3;\n      }\n      const depth = inputShape[dAxis];\n      const height = inputShape[hAxis];\n      const width = inputShape[wAxis];\n      const kernelD = this.kernelSize[0];\n      const kernelH = this.kernelSize[1];\n      const kernelW = this.kernelSize[2];\n      const strideD = this.strides[0];\n      const strideH = this.strides[1];\n      const strideW = this.strides[2];\n      const outDepth = deconvLength(depth, strideD, kernelD, this.padding);\n      const outHeight = deconvLength(height, strideH, kernelH, this.padding);\n      const outWidth = deconvLength(width, strideW, kernelW, this.padding);\n      const outputShape = [batchSize, outDepth, outHeight, outWidth, this.filters];\n      if (this.dataFormat !== \"channelsLast\") {\n        input2 = transpose(input2, [0, 2, 3, 4, 1]);\n      }\n      let outputs = conv3dTranspose(input2, this.kernel.read(), outputShape, this.strides, this.padding);\n      if (this.dataFormat !== \"channelsLast\") {\n        outputs = transpose(outputs, [0, 4, 1, 2, 3]);\n      }\n      if (this.bias !== null) {\n        outputs = biasAdd(outputs, this.bias.read(), this.dataFormat);\n      }\n      if (this.activation !== null) {\n        outputs = this.activation.apply(outputs);\n      }\n      return outputs;\n    });\n  }\n  computeOutputShape(inputShape) {\n    inputShape = getExactlyOneShape(inputShape);\n    const outputShape = inputShape.slice();\n    let channelAxis;\n    let depthAxis;\n    let heightAxis;\n    let widthAxis;\n    if (this.dataFormat === \"channelsFirst\") {\n      channelAxis = 1;\n      depthAxis = 2;\n      heightAxis = 3;\n      widthAxis = 4;\n    } else {\n      channelAxis = 4;\n      depthAxis = 1;\n      heightAxis = 2;\n      widthAxis = 3;\n    }\n    const kernelD = this.kernelSize[0];\n    const kernelH = this.kernelSize[1];\n    const kernelW = this.kernelSize[2];\n    const strideD = this.strides[0];\n    const strideH = this.strides[1];\n    const strideW = this.strides[2];\n    outputShape[channelAxis] = this.filters;\n    outputShape[depthAxis] = deconvLength(outputShape[depthAxis], strideD, kernelD, this.padding);\n    outputShape[heightAxis] = deconvLength(outputShape[heightAxis], strideH, kernelH, this.padding);\n    outputShape[widthAxis] = deconvLength(outputShape[widthAxis], strideW, kernelW, this.padding);\n    return outputShape;\n  }\n  getConfig() {\n    const config = super.getConfig();\n    delete config[\"dilationRate\"];\n    return config;\n  }\n};\nConv3DTranspose.className = \"Conv3DTranspose\";\nserialization_exports.registerClass(Conv3DTranspose);\nvar SeparableConv = class extends Conv {\n  constructor(rank, config) {\n    super(rank, config);\n    this.DEFAULT_DEPTHWISE_INITIALIZER = \"glorotUniform\";\n    this.DEFAULT_POINTWISE_INITIALIZER = \"glorotUniform\";\n    this.depthwiseKernel = null;\n    this.pointwiseKernel = null;\n    if (config.filters == null) {\n      throw new ValueError(\"The `filters` configuration field is required by SeparableConv, but is unspecified.\");\n    }\n    if (config.kernelInitializer != null || config.kernelRegularizer != null || config.kernelConstraint != null) {\n      throw new ValueError(\"Fields kernelInitializer, kernelRegularizer and kernelConstraint are invalid for SeparableConv2D. Use depthwiseInitializer, depthwiseRegularizer, depthwiseConstraint, pointwiseInitializer, pointwiseRegularizer and pointwiseConstraint instead.\");\n    }\n    if (config.padding != null && config.padding !== \"same\" && config.padding !== \"valid\") {\n      throw new ValueError(`SeparableConv${this.rank}D supports only padding modes: 'same' and 'valid', but received ${JSON.stringify(config.padding)}`);\n    }\n    this.depthMultiplier = config.depthMultiplier == null ? 1 : config.depthMultiplier;\n    this.depthwiseInitializer = getInitializer(config.depthwiseInitializer || this.DEFAULT_DEPTHWISE_INITIALIZER);\n    this.depthwiseRegularizer = getRegularizer(config.depthwiseRegularizer);\n    this.depthwiseConstraint = getConstraint(config.depthwiseConstraint);\n    this.pointwiseInitializer = getInitializer(config.depthwiseInitializer || this.DEFAULT_POINTWISE_INITIALIZER);\n    this.pointwiseRegularizer = getRegularizer(config.pointwiseRegularizer);\n    this.pointwiseConstraint = getConstraint(config.pointwiseConstraint);\n  }\n  build(inputShape) {\n    inputShape = getExactlyOneShape(inputShape);\n    if (inputShape.length < this.rank + 2) {\n      throw new ValueError(`Inputs to SeparableConv${this.rank}D should have rank ${this.rank + 2}, but received input shape: ${JSON.stringify(inputShape)}`);\n    }\n    const channelAxis = this.dataFormat === \"channelsFirst\" ? 1 : inputShape.length - 1;\n    if (inputShape[channelAxis] == null || inputShape[channelAxis] < 0) {\n      throw new ValueError(`The channel dimension of the inputs should be defined, but found ${JSON.stringify(inputShape[channelAxis])}`);\n    }\n    const inputDim = inputShape[channelAxis];\n    const depthwiseKernelShape = this.kernelSize.concat([inputDim, this.depthMultiplier]);\n    const pointwiseKernelShape = [];\n    for (let i2 = 0; i2 < this.rank; ++i2) {\n      pointwiseKernelShape.push(1);\n    }\n    pointwiseKernelShape.push(inputDim * this.depthMultiplier, this.filters);\n    const trainable = true;\n    this.depthwiseKernel = this.addWeight(\"depthwise_kernel\", depthwiseKernelShape, \"float32\", this.depthwiseInitializer, this.depthwiseRegularizer, trainable, this.depthwiseConstraint);\n    this.pointwiseKernel = this.addWeight(\"pointwise_kernel\", pointwiseKernelShape, \"float32\", this.pointwiseInitializer, this.pointwiseRegularizer, trainable, this.pointwiseConstraint);\n    if (this.useBias) {\n      this.bias = this.addWeight(\"bias\", [this.filters], \"float32\", this.biasInitializer, this.biasRegularizer, trainable, this.biasConstraint);\n    } else {\n      this.bias = null;\n    }\n    this.inputSpec = [new InputSpec({ ndim: this.rank + 2, axes: { [channelAxis]: inputDim } })];\n    this.built = true;\n  }\n  call(inputs, kwargs) {\n    return tidy(() => {\n      inputs = getExactlyOneTensor(inputs);\n      let output;\n      if (this.rank === 1) {\n        throw new NotImplementedError(\"1D separable convolution is not implemented yet.\");\n      } else if (this.rank === 2) {\n        if (this.dataFormat === \"channelsFirst\") {\n          inputs = transpose(inputs, [0, 2, 3, 1]);\n        }\n        output = separableConv2d(inputs, this.depthwiseKernel.read(), this.pointwiseKernel.read(), this.strides, this.padding, this.dilationRate, \"NHWC\");\n      }\n      if (this.useBias) {\n        output = biasAdd(output, this.bias.read(), this.dataFormat);\n      }\n      if (this.activation != null) {\n        output = this.activation.apply(output);\n      }\n      if (this.dataFormat === \"channelsFirst\") {\n        output = transpose(output, [0, 3, 1, 2]);\n      }\n      return output;\n    });\n  }\n  getConfig() {\n    const config = super.getConfig();\n    delete config[\"rank\"];\n    delete config[\"kernelInitializer\"];\n    delete config[\"kernelRegularizer\"];\n    delete config[\"kernelConstraint\"];\n    config[\"depthwiseInitializer\"] = serializeInitializer(this.depthwiseInitializer);\n    config[\"pointwiseInitializer\"] = serializeInitializer(this.pointwiseInitializer);\n    config[\"depthwiseRegularizer\"] = serializeRegularizer(this.depthwiseRegularizer);\n    config[\"pointwiseRegularizer\"] = serializeRegularizer(this.pointwiseRegularizer);\n    config[\"depthwiseConstraint\"] = serializeConstraint(this.depthwiseConstraint);\n    config[\"pointwiseConstraint\"] = serializeConstraint(this.pointwiseConstraint);\n    return config;\n  }\n};\nSeparableConv.className = \"SeparableConv\";\nvar SeparableConv2D = class extends SeparableConv {\n  constructor(args) {\n    super(2, args);\n  }\n};\nSeparableConv2D.className = \"SeparableConv2D\";\nserialization_exports.registerClass(SeparableConv2D);\nvar Conv1D = class extends Conv {\n  constructor(args) {\n    super(1, args);\n    Conv1D.verifyArgs(args);\n    this.inputSpec = [{ ndim: 3 }];\n  }\n  getConfig() {\n    const config = super.getConfig();\n    delete config[\"rank\"];\n    delete config[\"dataFormat\"];\n    return config;\n  }\n  static verifyArgs(args) {\n    if (typeof args.kernelSize !== \"number\" && !checkArrayTypeAndLength(args.kernelSize, \"number\", 1, 1)) {\n      throw new ValueError(`Conv1D expects config.kernelSize to be number or number[] with length 1, but received ${JSON.stringify(args.kernelSize)}.`);\n    }\n  }\n};\nConv1D.className = \"Conv1D\";\nserialization_exports.registerClass(Conv1D);\nvar Cropping2D = class extends Layer {\n  constructor(args) {\n    super(args);\n    if (typeof args.cropping === \"number\") {\n      this.cropping = [[args.cropping, args.cropping], [args.cropping, args.cropping]];\n    } else if (typeof args.cropping[0] === \"number\") {\n      this.cropping = [\n        [args.cropping[0], args.cropping[0]],\n        [args.cropping[1], args.cropping[1]]\n      ];\n    } else {\n      this.cropping = args.cropping;\n    }\n    this.dataFormat = args.dataFormat === void 0 ? \"channelsLast\" : args.dataFormat;\n    this.inputSpec = [{ ndim: 4 }];\n  }\n  computeOutputShape(inputShape) {\n    if (this.dataFormat === \"channelsFirst\") {\n      return [\n        inputShape[0],\n        inputShape[1],\n        inputShape[2] - this.cropping[0][0] - this.cropping[0][1],\n        inputShape[3] - this.cropping[1][0] - this.cropping[1][1]\n      ];\n    } else {\n      return [\n        inputShape[0],\n        inputShape[1] - this.cropping[0][0] - this.cropping[0][1],\n        inputShape[2] - this.cropping[1][0] - this.cropping[1][1],\n        inputShape[3]\n      ];\n    }\n  }\n  call(inputs, kwargs) {\n    return tidy(() => {\n      inputs = getExactlyOneTensor(inputs);\n      if (this.dataFormat === \"channelsLast\") {\n        const hSliced = sliceAlongAxis(inputs, this.cropping[0][0], inputs.shape[1] - this.cropping[0][0] - this.cropping[0][1], 2);\n        return sliceAlongAxis(hSliced, this.cropping[1][0], inputs.shape[2] - this.cropping[1][1] - this.cropping[1][0], 3);\n      } else {\n        const hSliced = sliceAlongAxis(inputs, this.cropping[0][0], inputs.shape[2] - this.cropping[0][0] - this.cropping[0][1], 3);\n        return sliceAlongAxis(hSliced, this.cropping[1][0], inputs.shape[3] - this.cropping[1][1] - this.cropping[1][0], 4);\n      }\n    });\n  }\n  getConfig() {\n    const config = { cropping: this.cropping, dataFormat: this.dataFormat };\n    const baseConfig = super.getConfig();\n    Object.assign(config, baseConfig);\n    return config;\n  }\n};\nCropping2D.className = \"Cropping2D\";\nserialization_exports.registerClass(Cropping2D);\nvar UpSampling2D = class extends Layer {\n  constructor(args) {\n    super(args);\n    this.DEFAULT_SIZE = [2, 2];\n    this.inputSpec = [{ ndim: 4 }];\n    this.size = args.size == null ? this.DEFAULT_SIZE : args.size;\n    this.dataFormat = args.dataFormat == null ? \"channelsLast\" : args.dataFormat;\n    checkDataFormat(this.dataFormat);\n    this.interpolation = args.interpolation == null ? \"nearest\" : args.interpolation;\n    checkInterpolationFormat(this.interpolation);\n  }\n  computeOutputShape(inputShape) {\n    if (this.dataFormat === \"channelsFirst\") {\n      const height = inputShape[2] == null ? null : this.size[0] * inputShape[2];\n      const width = inputShape[3] == null ? null : this.size[1] * inputShape[3];\n      return [inputShape[0], inputShape[1], height, width];\n    } else {\n      const height = inputShape[1] == null ? null : this.size[0] * inputShape[1];\n      const width = inputShape[2] == null ? null : this.size[1] * inputShape[2];\n      return [inputShape[0], height, width, inputShape[3]];\n    }\n  }\n  call(inputs, kwargs) {\n    return tidy(() => {\n      let input2 = getExactlyOneTensor(inputs);\n      const inputShape = input2.shape;\n      if (this.dataFormat === \"channelsFirst\") {\n        input2 = transpose(input2, [0, 2, 3, 1]);\n        const height = this.size[0] * inputShape[2];\n        const width = this.size[1] * inputShape[3];\n        const resized = this.interpolation === \"nearest\" ? image.resizeNearestNeighbor(input2, [height, width]) : image.resizeBilinear(input2, [height, width]);\n        return transpose(resized, [0, 3, 1, 2]);\n      } else {\n        const height = this.size[0] * inputShape[1];\n        const width = this.size[1] * inputShape[2];\n        return this.interpolation === \"nearest\" ? image.resizeNearestNeighbor(input2, [height, width]) : image.resizeBilinear(input2, [height, width]);\n      }\n    });\n  }\n  getConfig() {\n    const config = {\n      size: this.size,\n      dataFormat: this.dataFormat,\n      interpolation: this.interpolation\n    };\n    const baseConfig = super.getConfig();\n    Object.assign(config, baseConfig);\n    return config;\n  }\n};\nUpSampling2D.className = \"UpSampling2D\";\nserialization_exports.registerClass(UpSampling2D);\n\n// node_modules/.pnpm/@tensorflow+tfjs-layers@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-layers/dist/layers/convolutional_depthwise.js\nfunction depthwiseConv2d3(x, depthwiseKernel, strides = [1, 1], padding = \"valid\", dataFormat, dilationRate) {\n  return tidy(() => {\n    if (dataFormat == null) {\n      dataFormat = imageDataFormat();\n    }\n    checkDataFormat(dataFormat);\n    let y = preprocessConv2DInput(x, dataFormat);\n    if (x.rank !== 4) {\n      throw new ValueError(`Input for depthwiseConv2d is required to be 4-D, but is instead ${x.rank}-D`);\n    }\n    if (depthwiseKernel.rank !== 4) {\n      throw new ValueError(`depthwiseKernel is required to be 4-D, but is instead ${depthwiseKernel.rank}-D`);\n    }\n    y = depthwiseConv2d(y, depthwiseKernel, strides, padding === \"same\" ? \"same\" : \"valid\", \"NHWC\", dilationRate);\n    if (dataFormat === \"channelsFirst\") {\n      y = transpose(y, [0, 3, 1, 2]);\n    }\n    return y;\n  });\n}\nvar DepthwiseConv2D = class extends BaseConv {\n  constructor(args) {\n    super(2, args);\n    this.depthwiseKernel = null;\n    this.depthMultiplier = args.depthMultiplier == null ? 1 : args.depthMultiplier;\n    this.depthwiseInitializer = getInitializer(args.depthwiseInitializer || this.DEFAULT_KERNEL_INITIALIZER);\n    this.depthwiseConstraint = getConstraint(args.depthwiseConstraint);\n    this.depthwiseRegularizer = getRegularizer(args.depthwiseRegularizer);\n  }\n  build(inputShape) {\n    inputShape = getExactlyOneShape(inputShape);\n    if (inputShape.length < 4) {\n      throw new ValueError(`Inputs to DepthwiseConv2D should have rank 4. Received input shape: ${JSON.stringify(inputShape)}.`);\n    }\n    const channelAxis = this.dataFormat === \"channelsFirst\" ? 1 : 3;\n    if (inputShape[channelAxis] == null || inputShape[channelAxis] < 0) {\n      throw new ValueError(`The channel dimension of the inputs to DepthwiseConv2D should be defined, but is not (${inputShape[channelAxis]}).`);\n    }\n    const inputDim = inputShape[channelAxis];\n    const depthwiseKernelShape = [\n      this.kernelSize[0],\n      this.kernelSize[1],\n      inputDim,\n      this.depthMultiplier\n    ];\n    this.depthwiseKernel = this.addWeight(\"depthwise_kernel\", depthwiseKernelShape, null, this.depthwiseInitializer, this.depthwiseRegularizer, true, this.depthwiseConstraint);\n    if (this.useBias) {\n      this.bias = this.addWeight(\"bias\", [inputDim * this.depthMultiplier], null, this.biasInitializer, this.biasRegularizer, true, this.biasConstraint);\n    } else {\n      this.bias = null;\n    }\n    this.built = true;\n  }\n  call(inputs, kwargs) {\n    return tidy(() => {\n      inputs = getExactlyOneTensor(inputs);\n      let outputs = depthwiseConv2d3(inputs, this.depthwiseKernel.read(), this.strides, this.padding, this.dataFormat, null);\n      if (this.useBias) {\n        outputs = biasAdd(outputs, this.bias.read(), this.dataFormat);\n      }\n      if (this.activation != null) {\n        outputs = this.activation.apply(outputs);\n      }\n      return outputs;\n    });\n  }\n  computeOutputShape(inputShape) {\n    inputShape = getExactlyOneShape(inputShape);\n    const rows = this.dataFormat === \"channelsFirst\" ? inputShape[2] : inputShape[1];\n    const cols = this.dataFormat === \"channelsFirst\" ? inputShape[3] : inputShape[2];\n    const outFilters = this.dataFormat === \"channelsFirst\" ? inputShape[1] * this.depthMultiplier : inputShape[3] * this.depthMultiplier;\n    const outRows = convOutputLength(rows, this.kernelSize[0], this.padding, this.strides[0]);\n    const outCols = convOutputLength(cols, this.kernelSize[1], this.padding, this.strides[1]);\n    if (this.dataFormat === \"channelsFirst\") {\n      return [inputShape[0], outFilters, outRows, outCols];\n    } else {\n      return [inputShape[0], outRows, outCols, outFilters];\n    }\n  }\n  getConfig() {\n    const config = super.getConfig();\n    config[\"depthMultiplier\"] = this.depthMultiplier;\n    config[\"depthwiseInitializer\"] = serializeInitializer(this.depthwiseInitializer);\n    config[\"depthwiseRegularizer\"] = serializeRegularizer(this.depthwiseRegularizer);\n    config[\"depthwiseConstraint\"] = serializeConstraint(this.depthwiseRegularizer);\n    return config;\n  }\n};\nDepthwiseConv2D.className = \"DepthwiseConv2D\";\nserialization_exports.registerClass(DepthwiseConv2D);\n\n// node_modules/.pnpm/@tensorflow+tfjs-layers@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-layers/dist/layers/recurrent.js\nfunction standardizeArgs(inputs, initialState, constants, numConstants) {\n  if (Array.isArray(inputs)) {\n    if (initialState != null || constants != null) {\n      throw new ValueError(\"When inputs is an array, neither initialState or constants should be provided\");\n    }\n    if (numConstants != null) {\n      constants = inputs.slice(inputs.length - numConstants, inputs.length);\n      inputs = inputs.slice(0, inputs.length - numConstants);\n    }\n    if (inputs.length > 1) {\n      initialState = inputs.slice(1, inputs.length);\n    }\n    inputs = inputs[0];\n  }\n  function toListOrNull(x) {\n    if (x == null || Array.isArray(x)) {\n      return x;\n    } else {\n      return [x];\n    }\n  }\n  initialState = toListOrNull(initialState);\n  constants = toListOrNull(constants);\n  return { inputs, initialState, constants };\n}\nfunction rnn(stepFunction, inputs, initialStates, goBackwards = false, mask, constants, unroll = false, needPerStepOutputs = false) {\n  return tidy(() => {\n    const ndim = inputs.shape.length;\n    if (ndim < 3) {\n      throw new ValueError(`Input should be at least 3D, but is ${ndim}D.`);\n    }\n    const axes = [1, 0].concat(range2(2, ndim));\n    inputs = transpose(inputs, axes);\n    if (constants != null) {\n      throw new NotImplementedError(\"The rnn() functoin of the deeplearn.js backend does not support constants yet.\");\n    }\n    if (unroll) {\n      console.warn(\"Backend rnn(): the unroll = true option is not applicable to the imperative deeplearn.js backend.\");\n    }\n    if (mask != null) {\n      mask = cast(cast(mask, \"bool\"), \"float32\");\n      if (mask.rank === ndim - 1) {\n        mask = expandDims(mask, -1);\n      }\n      mask = transpose(mask, axes);\n    }\n    if (goBackwards) {\n      inputs = reverse(inputs, 0);\n      if (mask != null) {\n        mask = reverse(mask, 0);\n      }\n    }\n    const perStepOutputs = [];\n    let lastOutput;\n    let states = initialStates;\n    const timeSteps = inputs.shape[0];\n    const perStepInputs = unstack(inputs);\n    let perStepMasks;\n    if (mask != null) {\n      perStepMasks = unstack(mask);\n    }\n    for (let t2 = 0; t2 < timeSteps; ++t2) {\n      const currentInput = perStepInputs[t2];\n      const stepOutputs = tidy(() => stepFunction(currentInput, states));\n      if (mask == null) {\n        lastOutput = stepOutputs[0];\n        states = stepOutputs[1];\n      } else {\n        const maskedOutputs = tidy(() => {\n          const stepMask = perStepMasks[t2];\n          const negStepMask = sub(onesLike(stepMask), stepMask);\n          const output = add2(mul(stepOutputs[0], stepMask), mul(states[0], negStepMask));\n          const newStates = states.map((state, i2) => {\n            return add2(mul(stepOutputs[1][i2], stepMask), mul(state, negStepMask));\n          });\n          return { output, newStates };\n        });\n        lastOutput = maskedOutputs.output;\n        states = maskedOutputs.newStates;\n      }\n      if (needPerStepOutputs) {\n        perStepOutputs.push(lastOutput);\n      }\n    }\n    let outputs;\n    if (needPerStepOutputs) {\n      const axis = 1;\n      outputs = stack(perStepOutputs, axis);\n    }\n    return [lastOutput, outputs, states];\n  });\n}\nvar RNN = class extends Layer {\n  constructor(args) {\n    super(args);\n    let cell;\n    if (args.cell == null) {\n      throw new ValueError(\"cell property is missing for the constructor of RNN.\");\n    } else if (Array.isArray(args.cell)) {\n      cell = new StackedRNNCells({ cells: args.cell });\n    } else {\n      cell = args.cell;\n    }\n    if (cell.stateSize == null) {\n      throw new ValueError(\"The RNN cell should have an attribute `stateSize` (tuple of integers, one integer per RNN state).\");\n    }\n    this.cell = cell;\n    this.returnSequences = args.returnSequences == null ? false : args.returnSequences;\n    this.returnState = args.returnState == null ? false : args.returnState;\n    this.goBackwards = args.goBackwards == null ? false : args.goBackwards;\n    this._stateful = args.stateful == null ? false : args.stateful;\n    this.unroll = args.unroll == null ? false : args.unroll;\n    this.supportsMasking = true;\n    this.inputSpec = [new InputSpec({ ndim: 3 })];\n    this.stateSpec = null;\n    this.states_ = null;\n    this.numConstants = null;\n    this.keptStates = [];\n  }\n  getStates() {\n    if (this.states_ == null) {\n      const numStates = Array.isArray(this.cell.stateSize) ? this.cell.stateSize.length : 1;\n      return range2(0, numStates).map((x) => null);\n    } else {\n      return this.states_;\n    }\n  }\n  setStates(states) {\n    this.states_ = states;\n  }\n  computeOutputShape(inputShape) {\n    if (isArrayOfShapes(inputShape)) {\n      inputShape = inputShape[0];\n    }\n    inputShape = inputShape;\n    let stateSize = this.cell.stateSize;\n    if (!Array.isArray(stateSize)) {\n      stateSize = [stateSize];\n    }\n    const outputDim = stateSize[0];\n    let outputShape;\n    if (this.returnSequences) {\n      outputShape = [inputShape[0], inputShape[1], outputDim];\n    } else {\n      outputShape = [inputShape[0], outputDim];\n    }\n    if (this.returnState) {\n      const stateShape = [];\n      for (const dim of stateSize) {\n        stateShape.push([inputShape[0], dim]);\n      }\n      return [outputShape].concat(stateShape);\n    } else {\n      return outputShape;\n    }\n  }\n  computeMask(inputs, mask) {\n    return tidy(() => {\n      if (Array.isArray(mask)) {\n        mask = mask[0];\n      }\n      const outputMask = this.returnSequences ? mask : null;\n      if (this.returnState) {\n        const stateMask = this.states.map((s2) => null);\n        return [outputMask].concat(stateMask);\n      } else {\n        return outputMask;\n      }\n    });\n  }\n  get states() {\n    if (this.states_ == null) {\n      const numStates = Array.isArray(this.cell.stateSize) ? this.cell.stateSize.length : 1;\n      const output = [];\n      for (let i2 = 0; i2 < numStates; ++i2) {\n        output.push(null);\n      }\n      return output;\n    } else {\n      return this.states_;\n    }\n  }\n  set states(s2) {\n    this.states_ = s2;\n  }\n  build(inputShape) {\n    const constantShape = null;\n    if (this.numConstants != null) {\n      throw new NotImplementedError(\"Constants support is not implemented in RNN yet.\");\n    }\n    if (isArrayOfShapes(inputShape)) {\n      inputShape = inputShape[0];\n    }\n    inputShape = inputShape;\n    const batchSize = this.stateful ? inputShape[0] : null;\n    const inputDim = inputShape.slice(2);\n    this.inputSpec[0] = new InputSpec({ shape: [batchSize, null, ...inputDim] });\n    const stepInputShape = [inputShape[0]].concat(inputShape.slice(2));\n    if (constantShape != null) {\n      throw new NotImplementedError(\"Constants support is not implemented in RNN yet.\");\n    } else {\n      this.cell.build(stepInputShape);\n    }\n    let stateSize;\n    if (Array.isArray(this.cell.stateSize)) {\n      stateSize = this.cell.stateSize;\n    } else {\n      stateSize = [this.cell.stateSize];\n    }\n    if (this.stateSpec != null) {\n      if (!util_exports.arraysEqual(this.stateSpec.map((spec) => spec.shape[spec.shape.length - 1]), stateSize)) {\n        throw new ValueError(`An initialState was passed that is not compatible with cell.stateSize. Received stateSpec=${this.stateSpec}; However cell.stateSize is ${this.cell.stateSize}`);\n      }\n    } else {\n      this.stateSpec = stateSize.map((dim) => new InputSpec({ shape: [null, dim] }));\n    }\n    if (this.stateful) {\n      this.resetStates();\n    }\n  }\n  resetStates(states, training = false) {\n    tidy(() => {\n      if (!this.stateful) {\n        throw new AttributeError(\"Cannot call resetStates() on an RNN Layer that is not stateful.\");\n      }\n      const batchSize = this.inputSpec[0].shape[0];\n      if (batchSize == null) {\n        throw new ValueError(\"If an RNN is stateful, it needs to know its batch size. Specify the batch size of your input tensors: \\n- If using a Sequential model, specify the batch size by passing a `batchInputShape` option to your first layer.\\n- If using the functional API, specify the batch size by passing a `batchShape` option to your Input layer.\");\n      }\n      if (this.states_ == null) {\n        if (Array.isArray(this.cell.stateSize)) {\n          this.states_ = this.cell.stateSize.map((dim) => zeros([batchSize, dim]));\n        } else {\n          this.states_ = [zeros([batchSize, this.cell.stateSize])];\n        }\n      } else if (states == null) {\n        dispose(this.states_);\n        if (this.keptStates != null) {\n          dispose(this.keptStates);\n          this.keptStates = [];\n        }\n        if (Array.isArray(this.cell.stateSize)) {\n          this.states_ = this.cell.stateSize.map((dim) => zeros([batchSize, dim]));\n        } else {\n          this.states_[0] = zeros([batchSize, this.cell.stateSize]);\n        }\n      } else {\n        if (!Array.isArray(states)) {\n          states = [states];\n        }\n        if (states.length !== this.states_.length) {\n          throw new ValueError(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${states.length} state value(s). Input received: ${states}`);\n        }\n        if (training === true) {\n          this.keptStates.push(this.states_.slice());\n        } else {\n          dispose(this.states_);\n        }\n        for (let index = 0; index < this.states_.length; ++index) {\n          const value = states[index];\n          const dim = Array.isArray(this.cell.stateSize) ? this.cell.stateSize[index] : this.cell.stateSize;\n          const expectedShape = [batchSize, dim];\n          if (!util_exports.arraysEqual(value.shape, expectedShape)) {\n            throw new ValueError(`State ${index} is incompatible with layer ${this.name}: expected shape=${expectedShape}, received shape=${value.shape}`);\n          }\n          this.states_[index] = value;\n        }\n      }\n      this.states_ = this.states_.map((state) => keep(state.clone()));\n    });\n  }\n  apply(inputs, kwargs) {\n    let initialState = kwargs == null ? null : kwargs[\"initialState\"];\n    let constants = kwargs == null ? null : kwargs[\"constants\"];\n    if (kwargs == null) {\n      kwargs = {};\n    }\n    const standardized = standardizeArgs(inputs, initialState, constants, this.numConstants);\n    inputs = standardized.inputs;\n    initialState = standardized.initialState;\n    constants = standardized.constants;\n    let additionalInputs = [];\n    let additionalSpecs = [];\n    if (initialState != null) {\n      kwargs[\"initialState\"] = initialState;\n      additionalInputs = additionalInputs.concat(initialState);\n      this.stateSpec = [];\n      for (const state of initialState) {\n        this.stateSpec.push(new InputSpec({ shape: state.shape }));\n      }\n      additionalSpecs = additionalSpecs.concat(this.stateSpec);\n    }\n    if (constants != null) {\n      kwargs[\"constants\"] = constants;\n      additionalInputs = additionalInputs.concat(constants);\n      this.numConstants = constants.length;\n    }\n    const isTensor = additionalInputs[0] instanceof SymbolicTensor;\n    if (isTensor) {\n      const fullInput = [inputs].concat(additionalInputs);\n      const fullInputSpec = this.inputSpec.concat(additionalSpecs);\n      const originalInputSpec = this.inputSpec;\n      this.inputSpec = fullInputSpec;\n      const output = super.apply(fullInput, kwargs);\n      this.inputSpec = originalInputSpec;\n      return output;\n    } else {\n      return super.apply(inputs, kwargs);\n    }\n  }\n  call(inputs, kwargs) {\n    return tidy(() => {\n      const mask = kwargs == null ? null : kwargs[\"mask\"];\n      const training = kwargs == null ? null : kwargs[\"training\"];\n      let initialState = kwargs == null ? null : kwargs[\"initialState\"];\n      inputs = getExactlyOneTensor(inputs);\n      if (initialState == null) {\n        if (this.stateful) {\n          initialState = this.states_;\n        } else {\n          initialState = this.getInitialState(inputs);\n        }\n      }\n      const numStates = Array.isArray(this.cell.stateSize) ? this.cell.stateSize.length : 1;\n      if (initialState.length !== numStates) {\n        throw new ValueError(`RNN Layer has ${numStates} state(s) but was passed ${initialState.length} initial state(s).`);\n      }\n      if (this.unroll) {\n        console.warn(\"Ignoring unroll = true for RNN layer, due to imperative backend.\");\n      }\n      const cellCallKwargs = { training };\n      const step5 = (inputs2, states2) => {\n        const outputs2 = this.cell.call([inputs2].concat(states2), cellCallKwargs);\n        return [outputs2[0], outputs2.slice(1)];\n      };\n      const rnnOutputs = rnn(step5, inputs, initialState, this.goBackwards, mask, null, this.unroll, this.returnSequences);\n      const lastOutput = rnnOutputs[0];\n      const outputs = rnnOutputs[1];\n      const states = rnnOutputs[2];\n      if (this.stateful) {\n        this.resetStates(states, training);\n      }\n      const output = this.returnSequences ? outputs : lastOutput;\n      if (this.returnState) {\n        return [output].concat(states);\n      } else {\n        return output;\n      }\n    });\n  }\n  getInitialState(inputs) {\n    return tidy(() => {\n      let initialState = zeros(inputs.shape);\n      initialState = sum2(initialState, [1, 2]);\n      initialState = expandDims2(initialState);\n      if (Array.isArray(this.cell.stateSize)) {\n        return this.cell.stateSize.map((dim) => dim > 1 ? tile2(initialState, [1, dim]) : initialState);\n      } else {\n        return this.cell.stateSize > 1 ? [tile2(initialState, [1, this.cell.stateSize])] : [initialState];\n      }\n    });\n  }\n  get trainableWeights() {\n    if (!this.trainable) {\n      return [];\n    }\n    return this.cell.trainableWeights;\n  }\n  get nonTrainableWeights() {\n    if (!this.trainable) {\n      return this.cell.weights;\n    }\n    return this.cell.nonTrainableWeights;\n  }\n  setFastWeightInitDuringBuild(value) {\n    super.setFastWeightInitDuringBuild(value);\n    if (this.cell != null) {\n      this.cell.setFastWeightInitDuringBuild(value);\n    }\n  }\n  getConfig() {\n    const baseConfig = super.getConfig();\n    const config = {\n      returnSequences: this.returnSequences,\n      returnState: this.returnState,\n      goBackwards: this.goBackwards,\n      stateful: this.stateful,\n      unroll: this.unroll\n    };\n    if (this.numConstants != null) {\n      config[\"numConstants\"] = this.numConstants;\n    }\n    const cellConfig = this.cell.getConfig();\n    if (this.getClassName() === RNN.className) {\n      config[\"cell\"] = {\n        \"className\": this.cell.getClassName(),\n        \"config\": cellConfig\n      };\n    }\n    return Object.assign({}, cellConfig, baseConfig, config);\n  }\n  static fromConfig(cls, config, customObjects = {}) {\n    const cellConfig = config[\"cell\"];\n    const cell = deserialize(cellConfig, customObjects);\n    return new cls(Object.assign(config, { cell }));\n  }\n};\nRNN.className = \"RNN\";\nserialization_exports.registerClass(RNN);\nvar RNNCell = class extends Layer {\n};\nvar SimpleRNNCell = class extends RNNCell {\n  constructor(args) {\n    super(args);\n    this.DEFAULT_ACTIVATION = \"tanh\";\n    this.DEFAULT_KERNEL_INITIALIZER = \"glorotNormal\";\n    this.DEFAULT_RECURRENT_INITIALIZER = \"orthogonal\";\n    this.DEFAULT_BIAS_INITIALIZER = \"zeros\";\n    this.units = args.units;\n    assertPositiveInteger(this.units, `units`);\n    this.activation = getActivation(args.activation == null ? this.DEFAULT_ACTIVATION : args.activation);\n    this.useBias = args.useBias == null ? true : args.useBias;\n    this.kernelInitializer = getInitializer(args.kernelInitializer || this.DEFAULT_KERNEL_INITIALIZER);\n    this.recurrentInitializer = getInitializer(args.recurrentInitializer || this.DEFAULT_RECURRENT_INITIALIZER);\n    this.biasInitializer = getInitializer(args.biasInitializer || this.DEFAULT_BIAS_INITIALIZER);\n    this.kernelRegularizer = getRegularizer(args.kernelRegularizer);\n    this.recurrentRegularizer = getRegularizer(args.recurrentRegularizer);\n    this.biasRegularizer = getRegularizer(args.biasRegularizer);\n    this.kernelConstraint = getConstraint(args.kernelConstraint);\n    this.recurrentConstraint = getConstraint(args.recurrentConstraint);\n    this.biasConstraint = getConstraint(args.biasConstraint);\n    this.dropout = min2([1, max2([0, args.dropout == null ? 0 : args.dropout])]);\n    this.recurrentDropout = min2([\n      1,\n      max2([0, args.recurrentDropout == null ? 0 : args.recurrentDropout])\n    ]);\n    this.dropoutFunc = args.dropoutFunc;\n    this.stateSize = this.units;\n    this.dropoutMask = null;\n    this.recurrentDropoutMask = null;\n  }\n  build(inputShape) {\n    inputShape = getExactlyOneShape(inputShape);\n    this.kernel = this.addWeight(\"kernel\", [inputShape[inputShape.length - 1], this.units], null, this.kernelInitializer, this.kernelRegularizer, true, this.kernelConstraint);\n    this.recurrentKernel = this.addWeight(\"recurrent_kernel\", [this.units, this.units], null, this.recurrentInitializer, this.recurrentRegularizer, true, this.recurrentConstraint);\n    if (this.useBias) {\n      this.bias = this.addWeight(\"bias\", [this.units], null, this.biasInitializer, this.biasRegularizer, true, this.biasConstraint);\n    } else {\n      this.bias = null;\n    }\n    this.built = true;\n  }\n  call(inputs, kwargs) {\n    return tidy(() => {\n      inputs = inputs;\n      if (inputs.length !== 2) {\n        throw new ValueError(`SimpleRNNCell expects 2 input Tensors, got ${inputs.length}.`);\n      }\n      let prevOutput = inputs[1];\n      inputs = inputs[0];\n      const training = kwargs[\"training\"] == null ? false : kwargs[\"training\"];\n      if (0 < this.dropout && this.dropout < 1 && this.dropoutMask == null) {\n        this.dropoutMask = generateDropoutMask({\n          ones: () => onesLike(inputs),\n          rate: this.dropout,\n          training,\n          dropoutFunc: this.dropoutFunc\n        });\n      }\n      if (0 < this.recurrentDropout && this.recurrentDropout < 1 && this.recurrentDropoutMask == null) {\n        this.recurrentDropoutMask = generateDropoutMask({\n          ones: () => onesLike(prevOutput),\n          rate: this.recurrentDropout,\n          training,\n          dropoutFunc: this.dropoutFunc\n        });\n      }\n      let h;\n      const dpMask = this.dropoutMask;\n      const recDpMask = this.recurrentDropoutMask;\n      if (dpMask != null) {\n        h = dot2(mul(inputs, dpMask), this.kernel.read());\n      } else {\n        h = dot2(inputs, this.kernel.read());\n      }\n      if (this.bias != null) {\n        h = biasAdd(h, this.bias.read());\n      }\n      if (recDpMask != null) {\n        prevOutput = mul(prevOutput, recDpMask);\n      }\n      let output = add2(h, dot2(prevOutput, this.recurrentKernel.read()));\n      if (this.activation != null) {\n        output = this.activation.apply(output);\n      }\n      return [output, output];\n    });\n  }\n  getConfig() {\n    const baseConfig = super.getConfig();\n    const config = {\n      units: this.units,\n      activation: serializeActivation(this.activation),\n      useBias: this.useBias,\n      kernelInitializer: serializeInitializer(this.kernelInitializer),\n      recurrentInitializer: serializeInitializer(this.recurrentInitializer),\n      biasInitializer: serializeInitializer(this.biasInitializer),\n      kernelRegularizer: serializeRegularizer(this.kernelRegularizer),\n      recurrentRegularizer: serializeRegularizer(this.recurrentRegularizer),\n      biasRegularizer: serializeRegularizer(this.biasRegularizer),\n      activityRegularizer: serializeRegularizer(this.activityRegularizer),\n      kernelConstraint: serializeConstraint(this.kernelConstraint),\n      recurrentConstraint: serializeConstraint(this.recurrentConstraint),\n      biasConstraint: serializeConstraint(this.biasConstraint),\n      dropout: this.dropout,\n      recurrentDropout: this.recurrentDropout\n    };\n    return Object.assign({}, baseConfig, config);\n  }\n};\nSimpleRNNCell.className = \"SimpleRNNCell\";\nserialization_exports.registerClass(SimpleRNNCell);\nvar SimpleRNN = class extends RNN {\n  constructor(args) {\n    args.cell = new SimpleRNNCell(args);\n    super(args);\n  }\n  call(inputs, kwargs) {\n    return tidy(() => {\n      if (this.cell.dropoutMask != null) {\n        dispose(this.cell.dropoutMask);\n        this.cell.dropoutMask = null;\n      }\n      if (this.cell.recurrentDropoutMask != null) {\n        dispose(this.cell.recurrentDropoutMask);\n        this.cell.recurrentDropoutMask = null;\n      }\n      const mask = kwargs == null ? null : kwargs[\"mask\"];\n      const training = kwargs == null ? null : kwargs[\"training\"];\n      const initialState = kwargs == null ? null : kwargs[\"initialState\"];\n      return super.call(inputs, { mask, training, initialState });\n    });\n  }\n  static fromConfig(cls, config) {\n    return new cls(config);\n  }\n};\nSimpleRNN.className = \"SimpleRNN\";\nserialization_exports.registerClass(SimpleRNN);\nvar GRUCell = class extends RNNCell {\n  constructor(args) {\n    super(args);\n    this.DEFAULT_ACTIVATION = \"tanh\";\n    this.DEFAULT_RECURRENT_ACTIVATION = \"hardSigmoid\";\n    this.DEFAULT_KERNEL_INITIALIZER = \"glorotNormal\";\n    this.DEFAULT_RECURRENT_INITIALIZER = \"orthogonal\";\n    this.DEFAULT_BIAS_INITIALIZER = \"zeros\";\n    if (args.resetAfter) {\n      throw new ValueError(`GRUCell does not support reset_after parameter set to true.`);\n    }\n    this.units = args.units;\n    assertPositiveInteger(this.units, \"units\");\n    this.activation = getActivation(args.activation === void 0 ? this.DEFAULT_ACTIVATION : args.activation);\n    this.recurrentActivation = getActivation(args.recurrentActivation === void 0 ? this.DEFAULT_RECURRENT_ACTIVATION : args.recurrentActivation);\n    this.useBias = args.useBias == null ? true : args.useBias;\n    this.kernelInitializer = getInitializer(args.kernelInitializer || this.DEFAULT_KERNEL_INITIALIZER);\n    this.recurrentInitializer = getInitializer(args.recurrentInitializer || this.DEFAULT_RECURRENT_INITIALIZER);\n    this.biasInitializer = getInitializer(args.biasInitializer || this.DEFAULT_BIAS_INITIALIZER);\n    this.kernelRegularizer = getRegularizer(args.kernelRegularizer);\n    this.recurrentRegularizer = getRegularizer(args.recurrentRegularizer);\n    this.biasRegularizer = getRegularizer(args.biasRegularizer);\n    this.kernelConstraint = getConstraint(args.kernelConstraint);\n    this.recurrentConstraint = getConstraint(args.recurrentConstraint);\n    this.biasConstraint = getConstraint(args.biasConstraint);\n    this.dropout = min2([1, max2([0, args.dropout == null ? 0 : args.dropout])]);\n    this.recurrentDropout = min2([\n      1,\n      max2([0, args.recurrentDropout == null ? 0 : args.recurrentDropout])\n    ]);\n    this.dropoutFunc = args.dropoutFunc;\n    this.implementation = args.implementation;\n    this.stateSize = this.units;\n    this.dropoutMask = null;\n    this.recurrentDropoutMask = null;\n  }\n  build(inputShape) {\n    inputShape = getExactlyOneShape(inputShape);\n    const inputDim = inputShape[inputShape.length - 1];\n    this.kernel = this.addWeight(\"kernel\", [inputDim, this.units * 3], null, this.kernelInitializer, this.kernelRegularizer, true, this.kernelConstraint);\n    this.recurrentKernel = this.addWeight(\"recurrent_kernel\", [this.units, this.units * 3], null, this.recurrentInitializer, this.recurrentRegularizer, true, this.recurrentConstraint);\n    if (this.useBias) {\n      this.bias = this.addWeight(\"bias\", [this.units * 3], null, this.biasInitializer, this.biasRegularizer, true, this.biasConstraint);\n    } else {\n      this.bias = null;\n    }\n    this.built = true;\n  }\n  call(inputs, kwargs) {\n    return tidy(() => {\n      inputs = inputs;\n      if (inputs.length !== 2) {\n        throw new ValueError(`GRUCell expects 2 input Tensors (inputs, h, c), got ${inputs.length}.`);\n      }\n      const training = kwargs[\"training\"] == null ? false : kwargs[\"training\"];\n      let hTMinus1 = inputs[1];\n      inputs = inputs[0];\n      if (0 < this.dropout && this.dropout < 1 && this.dropoutMask == null) {\n        this.dropoutMask = generateDropoutMask({\n          ones: () => onesLike(inputs),\n          rate: this.dropout,\n          training,\n          count: 3,\n          dropoutFunc: this.dropoutFunc\n        });\n      }\n      if (0 < this.recurrentDropout && this.recurrentDropout < 1 && this.recurrentDropoutMask == null) {\n        this.recurrentDropoutMask = generateDropoutMask({\n          ones: () => onesLike(hTMinus1),\n          rate: this.recurrentDropout,\n          training,\n          count: 3,\n          dropoutFunc: this.dropoutFunc\n        });\n      }\n      const dpMask = this.dropoutMask;\n      const recDpMask = this.recurrentDropoutMask;\n      let z;\n      let r2;\n      let hh;\n      if (0 < this.dropout && this.dropout < 1) {\n        inputs = mul(inputs, dpMask[0]);\n      }\n      let matrixX = dot2(inputs, this.kernel.read());\n      if (this.useBias) {\n        matrixX = biasAdd(matrixX, this.bias.read());\n      }\n      if (0 < this.recurrentDropout && this.recurrentDropout < 1) {\n        hTMinus1 = mul(hTMinus1, recDpMask[0]);\n      }\n      const recurrentKernelValue = this.recurrentKernel.read();\n      const [rk1, rk2] = split(recurrentKernelValue, [2 * this.units, this.units], recurrentKernelValue.rank - 1);\n      const matrixInner = dot2(hTMinus1, rk1);\n      const [xZ, xR, xH] = split(matrixX, 3, matrixX.rank - 1);\n      const [recurrentZ, recurrentR] = split(matrixInner, 2, matrixInner.rank - 1);\n      z = this.recurrentActivation.apply(add2(xZ, recurrentZ));\n      r2 = this.recurrentActivation.apply(add2(xR, recurrentR));\n      const recurrentH = dot2(mul(r2, hTMinus1), rk2);\n      hh = this.activation.apply(add2(xH, recurrentH));\n      const h = add2(mul(z, hTMinus1), mul(add2(1, neg(z)), hh));\n      return [h, h];\n    });\n  }\n  getConfig() {\n    const baseConfig = super.getConfig();\n    const config = {\n      units: this.units,\n      activation: serializeActivation(this.activation),\n      recurrentActivation: serializeActivation(this.recurrentActivation),\n      useBias: this.useBias,\n      kernelInitializer: serializeInitializer(this.kernelInitializer),\n      recurrentInitializer: serializeInitializer(this.recurrentInitializer),\n      biasInitializer: serializeInitializer(this.biasInitializer),\n      kernelRegularizer: serializeRegularizer(this.kernelRegularizer),\n      recurrentRegularizer: serializeRegularizer(this.recurrentRegularizer),\n      biasRegularizer: serializeRegularizer(this.biasRegularizer),\n      activityRegularizer: serializeRegularizer(this.activityRegularizer),\n      kernelConstraint: serializeConstraint(this.kernelConstraint),\n      recurrentConstraint: serializeConstraint(this.recurrentConstraint),\n      biasConstraint: serializeConstraint(this.biasConstraint),\n      dropout: this.dropout,\n      recurrentDropout: this.recurrentDropout,\n      implementation: this.implementation,\n      resetAfter: false\n    };\n    return Object.assign({}, baseConfig, config);\n  }\n};\nGRUCell.className = \"GRUCell\";\nserialization_exports.registerClass(GRUCell);\nvar GRU = class extends RNN {\n  constructor(args) {\n    if (args.implementation === 0) {\n      console.warn(\"`implementation=0` has been deprecated, and now defaults to `implementation=1`. Please update your layer call.\");\n    }\n    args.cell = new GRUCell(args);\n    super(args);\n  }\n  call(inputs, kwargs) {\n    return tidy(() => {\n      if (this.cell.dropoutMask != null) {\n        dispose(this.cell.dropoutMask);\n        this.cell.dropoutMask = null;\n      }\n      if (this.cell.recurrentDropoutMask != null) {\n        dispose(this.cell.recurrentDropoutMask);\n        this.cell.recurrentDropoutMask = null;\n      }\n      const mask = kwargs == null ? null : kwargs[\"mask\"];\n      const training = kwargs == null ? null : kwargs[\"training\"];\n      const initialState = kwargs == null ? null : kwargs[\"initialState\"];\n      return super.call(inputs, { mask, training, initialState });\n    });\n  }\n  static fromConfig(cls, config) {\n    if (config[\"implmentation\"] === 0) {\n      config[\"implementation\"] = 1;\n    }\n    return new cls(config);\n  }\n};\nGRU.className = \"GRU\";\nserialization_exports.registerClass(GRU);\nvar LSTMCell = class extends RNNCell {\n  constructor(args) {\n    super(args);\n    this.DEFAULT_ACTIVATION = \"tanh\";\n    this.DEFAULT_RECURRENT_ACTIVATION = \"hardSigmoid\";\n    this.DEFAULT_KERNEL_INITIALIZER = \"glorotNormal\";\n    this.DEFAULT_RECURRENT_INITIALIZER = \"orthogonal\";\n    this.DEFAULT_BIAS_INITIALIZER = \"zeros\";\n    this.units = args.units;\n    assertPositiveInteger(this.units, \"units\");\n    this.activation = getActivation(args.activation === void 0 ? this.DEFAULT_ACTIVATION : args.activation);\n    this.recurrentActivation = getActivation(args.recurrentActivation === void 0 ? this.DEFAULT_RECURRENT_ACTIVATION : args.recurrentActivation);\n    this.useBias = args.useBias == null ? true : args.useBias;\n    this.kernelInitializer = getInitializer(args.kernelInitializer || this.DEFAULT_KERNEL_INITIALIZER);\n    this.recurrentInitializer = getInitializer(args.recurrentInitializer || this.DEFAULT_RECURRENT_INITIALIZER);\n    this.biasInitializer = getInitializer(args.biasInitializer || this.DEFAULT_BIAS_INITIALIZER);\n    this.unitForgetBias = args.unitForgetBias;\n    this.kernelRegularizer = getRegularizer(args.kernelRegularizer);\n    this.recurrentRegularizer = getRegularizer(args.recurrentRegularizer);\n    this.biasRegularizer = getRegularizer(args.biasRegularizer);\n    this.kernelConstraint = getConstraint(args.kernelConstraint);\n    this.recurrentConstraint = getConstraint(args.recurrentConstraint);\n    this.biasConstraint = getConstraint(args.biasConstraint);\n    this.dropout = min2([1, max2([0, args.dropout == null ? 0 : args.dropout])]);\n    this.recurrentDropout = min2([\n      1,\n      max2([0, args.recurrentDropout == null ? 0 : args.recurrentDropout])\n    ]);\n    this.dropoutFunc = args.dropoutFunc;\n    this.implementation = args.implementation;\n    this.stateSize = [this.units, this.units];\n    this.dropoutMask = null;\n    this.recurrentDropoutMask = null;\n  }\n  build(inputShape) {\n    var _a;\n    inputShape = getExactlyOneShape(inputShape);\n    const inputDim = inputShape[inputShape.length - 1];\n    this.kernel = this.addWeight(\"kernel\", [inputDim, this.units * 4], null, this.kernelInitializer, this.kernelRegularizer, true, this.kernelConstraint);\n    this.recurrentKernel = this.addWeight(\"recurrent_kernel\", [this.units, this.units * 4], null, this.recurrentInitializer, this.recurrentRegularizer, true, this.recurrentConstraint);\n    let biasInitializer;\n    if (this.useBias) {\n      if (this.unitForgetBias) {\n        const capturedBiasInit = this.biasInitializer;\n        const capturedUnits = this.units;\n        biasInitializer = new (_a = class CustomInit extends Initializer {\n          apply(shape, dtype) {\n            const bI = capturedBiasInit.apply([capturedUnits]);\n            const bF = new Ones().apply([capturedUnits]);\n            const bCAndH = capturedBiasInit.apply([capturedUnits * 2]);\n            return concatAlongFirstAxis(concatAlongFirstAxis(bI, bF), bCAndH);\n          }\n        }, _a.className = \"CustomInit\", _a)();\n      } else {\n        biasInitializer = this.biasInitializer;\n      }\n      this.bias = this.addWeight(\"bias\", [this.units * 4], null, biasInitializer, this.biasRegularizer, true, this.biasConstraint);\n    } else {\n      this.bias = null;\n    }\n    this.built = true;\n  }\n  call(inputs, kwargs) {\n    return tidy(() => {\n      const training = kwargs[\"training\"] == null ? false : kwargs[\"training\"];\n      inputs = inputs;\n      if (inputs.length !== 3) {\n        throw new ValueError(`LSTMCell expects 3 input Tensors (inputs, h, c), got ${inputs.length}.`);\n      }\n      let hTMinus1 = inputs[1];\n      const cTMinus1 = inputs[2];\n      inputs = inputs[0];\n      if (0 < this.dropout && this.dropout < 1 && this.dropoutMask == null) {\n        this.dropoutMask = generateDropoutMask({\n          ones: () => onesLike(inputs),\n          rate: this.dropout,\n          training,\n          count: 4,\n          dropoutFunc: this.dropoutFunc\n        });\n      }\n      if (0 < this.recurrentDropout && this.recurrentDropout < 1 && this.recurrentDropoutMask == null) {\n        this.recurrentDropoutMask = generateDropoutMask({\n          ones: () => onesLike(hTMinus1),\n          rate: this.recurrentDropout,\n          training,\n          count: 4,\n          dropoutFunc: this.dropoutFunc\n        });\n      }\n      const dpMask = this.dropoutMask;\n      const recDpMask = this.recurrentDropoutMask;\n      let i2;\n      let f;\n      let c;\n      let o;\n      if (0 < this.dropout && this.dropout < 1) {\n        inputs = mul(inputs, dpMask[0]);\n      }\n      let z = dot2(inputs, this.kernel.read());\n      if (0 < this.recurrentDropout && this.recurrentDropout < 1) {\n        hTMinus1 = mul(hTMinus1, recDpMask[0]);\n      }\n      z = add2(z, dot2(hTMinus1, this.recurrentKernel.read()));\n      if (this.useBias) {\n        z = biasAdd(z, this.bias.read());\n      }\n      const [z0, z1, z2, z3] = split(z, 4, z.rank - 1);\n      i2 = this.recurrentActivation.apply(z0);\n      f = this.recurrentActivation.apply(z1);\n      c = add2(mul(f, cTMinus1), mul(i2, this.activation.apply(z2)));\n      o = this.recurrentActivation.apply(z3);\n      const h = mul(o, this.activation.apply(c));\n      return [h, h, c];\n    });\n  }\n  getConfig() {\n    const baseConfig = super.getConfig();\n    const config = {\n      units: this.units,\n      activation: serializeActivation(this.activation),\n      recurrentActivation: serializeActivation(this.recurrentActivation),\n      useBias: this.useBias,\n      kernelInitializer: serializeInitializer(this.kernelInitializer),\n      recurrentInitializer: serializeInitializer(this.recurrentInitializer),\n      biasInitializer: serializeInitializer(this.biasInitializer),\n      unitForgetBias: this.unitForgetBias,\n      kernelRegularizer: serializeRegularizer(this.kernelRegularizer),\n      recurrentRegularizer: serializeRegularizer(this.recurrentRegularizer),\n      biasRegularizer: serializeRegularizer(this.biasRegularizer),\n      activityRegularizer: serializeRegularizer(this.activityRegularizer),\n      kernelConstraint: serializeConstraint(this.kernelConstraint),\n      recurrentConstraint: serializeConstraint(this.recurrentConstraint),\n      biasConstraint: serializeConstraint(this.biasConstraint),\n      dropout: this.dropout,\n      recurrentDropout: this.recurrentDropout,\n      implementation: this.implementation\n    };\n    return Object.assign({}, baseConfig, config);\n  }\n};\nLSTMCell.className = \"LSTMCell\";\nserialization_exports.registerClass(LSTMCell);\nvar LSTM = class extends RNN {\n  constructor(args) {\n    if (args.implementation === 0) {\n      console.warn(\"`implementation=0` has been deprecated, and now defaults to `implementation=1`. Please update your layer call.\");\n    }\n    args.cell = new LSTMCell(args);\n    super(args);\n  }\n  call(inputs, kwargs) {\n    return tidy(() => {\n      if (this.cell.dropoutMask != null) {\n        dispose(this.cell.dropoutMask);\n        this.cell.dropoutMask = null;\n      }\n      if (this.cell.recurrentDropoutMask != null) {\n        dispose(this.cell.recurrentDropoutMask);\n        this.cell.recurrentDropoutMask = null;\n      }\n      const mask = kwargs == null ? null : kwargs[\"mask\"];\n      const training = kwargs == null ? null : kwargs[\"training\"];\n      const initialState = kwargs == null ? null : kwargs[\"initialState\"];\n      return super.call(inputs, { mask, training, initialState });\n    });\n  }\n  static fromConfig(cls, config) {\n    if (config[\"implmentation\"] === 0) {\n      config[\"implementation\"] = 1;\n    }\n    return new cls(config);\n  }\n};\nLSTM.className = \"LSTM\";\nserialization_exports.registerClass(LSTM);\nvar StackedRNNCells = class extends RNNCell {\n  constructor(args) {\n    super(args);\n    this.cells = args.cells;\n  }\n  get stateSize() {\n    const stateSize = [];\n    for (const cell of this.cells.slice().reverse()) {\n      if (Array.isArray(cell.stateSize)) {\n        stateSize.push(...cell.stateSize);\n      } else {\n        stateSize.push(cell.stateSize);\n      }\n    }\n    return stateSize;\n  }\n  call(inputs, kwargs) {\n    return tidy(() => {\n      inputs = inputs;\n      let states = inputs.slice(1);\n      const nestedStates = [];\n      for (const cell of this.cells.slice().reverse()) {\n        if (Array.isArray(cell.stateSize)) {\n          nestedStates.push(states.splice(0, cell.stateSize.length));\n        } else {\n          nestedStates.push(states.splice(0, 1));\n        }\n      }\n      nestedStates.reverse();\n      const newNestedStates = [];\n      let callInputs;\n      for (let i2 = 0; i2 < this.cells.length; ++i2) {\n        const cell = this.cells[i2];\n        states = nestedStates[i2];\n        if (i2 === 0) {\n          callInputs = [inputs[0]].concat(states);\n        } else {\n          callInputs = [callInputs[0]].concat(states);\n        }\n        callInputs = cell.call(callInputs, kwargs);\n        newNestedStates.push(callInputs.slice(1));\n      }\n      states = [];\n      for (const cellStates of newNestedStates.slice().reverse()) {\n        states.push(...cellStates);\n      }\n      return [callInputs[0]].concat(states);\n    });\n  }\n  build(inputShape) {\n    if (isArrayOfShapes(inputShape)) {\n      inputShape = inputShape[0];\n    }\n    inputShape = inputShape;\n    let outputDim;\n    this.cells.forEach((cell, i2) => {\n      nameScope(`RNNCell_${i2}`, () => {\n        cell.build(inputShape);\n        if (Array.isArray(cell.stateSize)) {\n          outputDim = cell.stateSize[0];\n        } else {\n          outputDim = cell.stateSize;\n        }\n        inputShape = [inputShape[0], outputDim];\n      });\n    });\n    this.built = true;\n  }\n  getConfig() {\n    const baseConfig = super.getConfig();\n    const getCellConfig = (cell) => {\n      return {\n        \"className\": cell.getClassName(),\n        \"config\": cell.getConfig()\n      };\n    };\n    const cellConfigs = this.cells.map(getCellConfig);\n    const config = { \"cells\": cellConfigs };\n    return Object.assign({}, baseConfig, config);\n  }\n  static fromConfig(cls, config, customObjects = {}) {\n    const cells = [];\n    for (const cellConfig of config[\"cells\"]) {\n      cells.push(deserialize(cellConfig, customObjects));\n    }\n    return new cls({ cells });\n  }\n  get trainableWeights() {\n    if (!this.trainable) {\n      return [];\n    }\n    const weights = [];\n    for (const cell of this.cells) {\n      weights.push(...cell.trainableWeights);\n    }\n    return weights;\n  }\n  get nonTrainableWeights() {\n    const weights = [];\n    for (const cell of this.cells) {\n      weights.push(...cell.nonTrainableWeights);\n    }\n    if (!this.trainable) {\n      const trainableWeights = [];\n      for (const cell of this.cells) {\n        trainableWeights.push(...cell.trainableWeights);\n      }\n      return trainableWeights.concat(weights);\n    }\n    return weights;\n  }\n  getWeights() {\n    const weights = [];\n    for (const cell of this.cells) {\n      weights.push(...cell.weights);\n    }\n    return batchGetValue(weights);\n  }\n  setWeights(weights) {\n    const tuples = [];\n    for (const cell of this.cells) {\n      const numParams = cell.weights.length;\n      const inputWeights = weights.splice(numParams);\n      for (let i2 = 0; i2 < cell.weights.length; ++i2) {\n        tuples.push([cell.weights[i2], inputWeights[i2]]);\n      }\n    }\n    batchSetValue(tuples);\n  }\n};\nStackedRNNCells.className = \"StackedRNNCells\";\nserialization_exports.registerClass(StackedRNNCells);\nfunction generateDropoutMask(args) {\n  const { ones: ones4, rate, training = false, count: count2 = 1, dropoutFunc } = args;\n  const droppedInputs = () => dropoutFunc != null ? dropoutFunc(ones4(), rate) : dropout2(ones4(), rate);\n  const createMask = () => inTrainPhase(droppedInputs, ones4, training);\n  if (!count2 || count2 <= 1) {\n    return keep(createMask().clone());\n  }\n  const masks = Array(count2).fill(void 0).map(createMask);\n  return masks.map((m) => keep(m.clone()));\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-layers@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-layers/dist/layers/convolutional_recurrent.js\nvar __rest = function(s2, e2) {\n  var t2 = {};\n  for (var p2 in s2)\n    if (Object.prototype.hasOwnProperty.call(s2, p2) && e2.indexOf(p2) < 0)\n      t2[p2] = s2[p2];\n  if (s2 != null && typeof Object.getOwnPropertySymbols === \"function\")\n    for (var i2 = 0, p2 = Object.getOwnPropertySymbols(s2); i2 < p2.length; i2++) {\n      if (e2.indexOf(p2[i2]) < 0 && Object.prototype.propertyIsEnumerable.call(s2, p2[i2]))\n        t2[p2[i2]] = s2[p2[i2]];\n    }\n  return t2;\n};\nvar ConvRNN2D = class extends RNN {\n  constructor(args) {\n    if (args.unroll) {\n      throw new NotImplementedError(\"Unrolling is not possible with convolutional RNNs.\");\n    }\n    if (Array.isArray(args.cell)) {\n      throw new NotImplementedError(\"It is not possible at the moment to stack convolutional cells.\");\n    }\n    super(args);\n    this.inputSpec = [new InputSpec({ ndim: 5 })];\n  }\n  call(inputs, kwargs) {\n    return tidy(() => {\n      if (this.cell.dropoutMask != null) {\n        dispose(this.cell.dropoutMask);\n        this.cell.dropoutMask = null;\n      }\n      if (this.cell.recurrentDropoutMask != null) {\n        dispose(this.cell.recurrentDropoutMask);\n        this.cell.recurrentDropoutMask = null;\n      }\n      if (kwargs && kwargs[\"constants\"]) {\n        throw new ValueError(\"ConvRNN2D cell does not support constants\");\n      }\n      const mask = kwargs == null ? null : kwargs[\"mask\"];\n      const training = kwargs == null ? null : kwargs[\"training\"];\n      const initialState = kwargs == null ? null : kwargs[\"initialState\"];\n      return super.call(inputs, { mask, training, initialState });\n    });\n  }\n  computeOutputShape(inputShape) {\n    let outShape = this.computeSingleOutputShape(inputShape);\n    if (!this.returnSequences) {\n      outShape = [outShape[0], ...outShape.slice(2)];\n    }\n    if (this.returnState) {\n      outShape = [outShape, ...Array(2).fill([inputShape[0], ...outShape.slice(-3)])];\n    }\n    return outShape;\n  }\n  getInitialState(inputs) {\n    return tidy(() => {\n      const { stateSize } = this.cell;\n      const inputShape = inputs.shape;\n      const outputShape = this.computeSingleOutputShape(inputShape);\n      const stateShape = [outputShape[0], ...outputShape.slice(2)];\n      const initialState = zeros(stateShape);\n      if (Array.isArray(stateSize)) {\n        return Array(stateSize.length).fill(initialState);\n      }\n      return [initialState];\n    });\n  }\n  resetStates(states, training = false) {\n    tidy(() => {\n      if (!this.stateful) {\n        throw new AttributeError(\"Cannot call resetStates() on an RNN Layer that is not stateful.\");\n      }\n      const inputShape = this.inputSpec[0].shape;\n      const outputShape = this.computeSingleOutputShape(inputShape);\n      const stateShape = [outputShape[0], ...outputShape.slice(2)];\n      const batchSize = inputShape[0];\n      if (batchSize == null) {\n        throw new ValueError(\"If an RNN is stateful, it needs to know its batch size. Specify the batch size of your input tensors: \\n- If using a Sequential model, specify the batch size by passing a `batchInputShape` option to your first layer.\\n- If using the functional API, specify the batch size by passing a `batchShape` option to your Input layer.\");\n      }\n      if (this.getStates() == null) {\n        if (Array.isArray(this.cell.stateSize)) {\n          this.states_ = this.cell.stateSize.map(() => zeros(stateShape));\n        } else {\n          this.states_ = [zeros(stateShape)];\n        }\n      } else if (states == null) {\n        dispose(this.states_);\n        if (this.keptStates != null) {\n          dispose(this.keptStates);\n          this.keptStates = [];\n        }\n        if (Array.isArray(this.cell.stateSize)) {\n          this.states_ = this.cell.stateSize.map(() => zeros(stateShape));\n        } else {\n          this.states_[0] = zeros(stateShape);\n        }\n      } else {\n        if (!Array.isArray(states)) {\n          states = [states];\n        }\n        if (states.length !== this.states_.length) {\n          throw new ValueError(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${states.length} state value(s). Input received: ${states}`);\n        }\n        if (training) {\n          this.keptStates.push(this.states_.slice());\n        } else {\n          dispose(this.states_);\n        }\n        for (let index = 0; index < this.states_.length; ++index) {\n          const value = states[index];\n          const expectedShape = stateShape;\n          if (!util_exports.arraysEqual(value.shape, expectedShape)) {\n            throw new ValueError(`State ${index} is incompatible with layer ${this.name}: expected shape=${expectedShape}, received shape=${value.shape}`);\n          }\n          this.states_[index] = value;\n        }\n      }\n      this.states_ = this.states_.map((state) => keep(state.clone()));\n    });\n  }\n  computeSingleOutputShape(inputShape) {\n    const { dataFormat, filters, kernelSize, padding, strides, dilationRate } = this.cell;\n    const isChannelsFirst = dataFormat === \"channelsFirst\";\n    const h = inputShape[isChannelsFirst ? 3 : 2];\n    const w = inputShape[isChannelsFirst ? 4 : 3];\n    const hOut = convOutputLength(h, kernelSize[0], padding, strides[0], dilationRate[0]);\n    const wOut = convOutputLength(w, kernelSize[1], padding, strides[1], dilationRate[1]);\n    const outShape = [\n      ...inputShape.slice(0, 2),\n      ...isChannelsFirst ? [filters, hOut, wOut] : [hOut, wOut, filters]\n    ];\n    return outShape;\n  }\n};\nConvRNN2D.className = \"ConvRNN2D\";\nvar ConvLSTM2DCell = class extends LSTMCell {\n  constructor(args) {\n    const { filters, kernelSize, strides, padding, dataFormat, dilationRate } = args;\n    super(Object.assign({}, args, { units: filters }));\n    this.filters = filters;\n    assertPositiveInteger(this.filters, \"filters\");\n    this.kernelSize = normalizeArray(kernelSize, 2, \"kernelSize\");\n    this.kernelSize.forEach((size) => assertPositiveInteger(size, \"kernelSize\"));\n    this.strides = normalizeArray(strides || 1, 2, \"strides\");\n    this.strides.forEach((stride) => assertPositiveInteger(stride, \"strides\"));\n    this.padding = padding || \"valid\";\n    checkPaddingMode(this.padding);\n    this.dataFormat = dataFormat || \"channelsLast\";\n    checkDataFormat(this.dataFormat);\n    this.dilationRate = normalizeArray(dilationRate || 1, 2, \"dilationRate\");\n    this.dilationRate.forEach((rate) => assertPositiveInteger(rate, \"dilationRate\"));\n  }\n  build(inputShape) {\n    var _a;\n    inputShape = getExactlyOneShape(inputShape);\n    const channelAxis = this.dataFormat === \"channelsFirst\" ? 1 : inputShape.length - 1;\n    if (inputShape[channelAxis] == null) {\n      throw new ValueError(`The channel dimension of the input should be defined. Found ${inputShape[channelAxis]}`);\n    }\n    const inputDim = inputShape[channelAxis];\n    const numOfKernels = 4;\n    const kernelShape = this.kernelSize.concat([inputDim, this.filters * numOfKernels]);\n    this.kernel = this.addWeight(\"kernel\", kernelShape, null, this.kernelInitializer, this.kernelRegularizer, true, this.kernelConstraint);\n    const recurrentKernelShape = this.kernelSize.concat([this.filters, this.filters * numOfKernels]);\n    this.recurrentKernel = this.addWeight(\"recurrent_kernel\", recurrentKernelShape, null, this.recurrentInitializer, this.recurrentRegularizer, true, this.recurrentConstraint);\n    if (this.useBias) {\n      let biasInitializer;\n      if (this.unitForgetBias) {\n        const init2 = this.biasInitializer;\n        const filters = this.filters;\n        biasInitializer = new (_a = class CustomInit extends Initializer {\n          apply(shape, dtype) {\n            const biasI = init2.apply([filters]);\n            const biasF = ones2([filters]);\n            const biasCAndO = init2.apply([filters * 2]);\n            return concatenate([biasI, biasF, biasCAndO]);\n          }\n        }, _a.className = \"CustomInit\", _a)();\n      } else {\n        biasInitializer = this.biasInitializer;\n      }\n      this.bias = this.addWeight(\"bias\", [this.filters * numOfKernels], null, biasInitializer, this.biasRegularizer, true, this.biasConstraint);\n    }\n    this.built = true;\n  }\n  call(inputs, kwargs) {\n    return tidy(() => {\n      if (inputs.length !== 3) {\n        throw new ValueError(`ConvLSTM2DCell expects 3 input Tensors (inputs, h, c), got ${inputs.length}.`);\n      }\n      const training = kwargs[\"training\"] || false;\n      const x = inputs[0];\n      const hTMinus1 = inputs[1];\n      const cTMinus1 = inputs[2];\n      const numOfKernels = 4;\n      if (0 < this.dropout && this.dropout < 1 && this.dropoutMask == null) {\n        this.dropoutMask = generateDropoutMask({\n          ones: () => onesLike(x),\n          rate: this.dropout,\n          training,\n          count: numOfKernels,\n          dropoutFunc: this.dropoutFunc\n        });\n      }\n      const dropoutMask = this.dropoutMask;\n      const applyDropout = (x2, mask, index) => {\n        if (!mask || !mask[index]) {\n          return x2;\n        }\n        return mul(mask[index], x2);\n      };\n      let xI = applyDropout(x, dropoutMask, 0);\n      let xF = applyDropout(x, dropoutMask, 1);\n      let xC = applyDropout(x, dropoutMask, 2);\n      let xO = applyDropout(x, dropoutMask, 3);\n      if (0 < this.recurrentDropout && this.recurrentDropout < 1 && this.recurrentDropoutMask == null) {\n        this.recurrentDropoutMask = generateDropoutMask({\n          ones: () => onesLike(hTMinus1),\n          rate: this.recurrentDropout,\n          training,\n          count: numOfKernels,\n          dropoutFunc: this.dropoutFunc\n        });\n      }\n      const recDropoutMask = this.recurrentDropoutMask;\n      let hI = applyDropout(hTMinus1, recDropoutMask, 0);\n      let hF = applyDropout(hTMinus1, recDropoutMask, 1);\n      let hC = applyDropout(hTMinus1, recDropoutMask, 2);\n      let hO = applyDropout(hTMinus1, recDropoutMask, 3);\n      const kernelChannelAxis = 3;\n      const [kernelI, kernelF, kernelC, kernelO] = split(this.kernel.read(), numOfKernels, kernelChannelAxis);\n      const [biasI, biasF, biasC, biasO] = this.useBias ? split(this.bias.read(), numOfKernels) : [null, null, null, null];\n      xI = this.inputConv(xI, kernelI, biasI, this.padding);\n      xF = this.inputConv(xF, kernelF, biasF, this.padding);\n      xC = this.inputConv(xC, kernelC, biasC, this.padding);\n      xO = this.inputConv(xO, kernelO, biasO, this.padding);\n      const [recKernelI, recKernelF, recKernelC, recKernelO] = split(this.recurrentKernel.read(), numOfKernels, kernelChannelAxis);\n      hI = this.recurrentConv(hI, recKernelI);\n      hF = this.recurrentConv(hF, recKernelF);\n      hC = this.recurrentConv(hC, recKernelC);\n      hO = this.recurrentConv(hO, recKernelO);\n      const i2 = this.recurrentActivation.apply(add2(xI, hI));\n      const f = this.recurrentActivation.apply(add2(xF, hF));\n      const c = add2(mul(f, cTMinus1), mul(i2, this.activation.apply(add2(xC, hC))));\n      const h = mul(this.recurrentActivation.apply(add2(xO, hO)), this.activation.apply(c));\n      return [h, h, c];\n    });\n  }\n  getConfig() {\n    const _a = super.getConfig(), { \"units\": _ } = _a, baseConfig = __rest(_a, [\"units\"]);\n    const config = {\n      filters: this.filters,\n      kernelSize: this.kernelSize,\n      padding: this.padding,\n      dataFormat: this.dataFormat,\n      dilationRate: this.dilationRate,\n      strides: this.strides\n    };\n    return Object.assign({}, baseConfig, config);\n  }\n  inputConv(x, w, b, padding) {\n    const out = conv2d(x, w, this.strides, padding || \"valid\", this.dataFormat === \"channelsFirst\" ? \"NCHW\" : \"NHWC\", this.dilationRate);\n    if (b) {\n      return biasAdd(out, b, this.dataFormat);\n    }\n    return out;\n  }\n  recurrentConv(x, w) {\n    const strides = 1;\n    return conv2d(x, w, strides, \"same\", this.dataFormat === \"channelsFirst\" ? \"NCHW\" : \"NHWC\");\n  }\n};\nConvLSTM2DCell.className = \"ConvLSTM2DCell\";\nserialization_exports.registerClass(ConvLSTM2DCell);\nvar ConvLSTM2D = class extends ConvRNN2D {\n  constructor(args) {\n    const cell = new ConvLSTM2DCell(args);\n    super(Object.assign({}, args, { cell }));\n  }\n  static fromConfig(cls, config) {\n    return new cls(config);\n  }\n};\nConvLSTM2D.className = \"ConvLSTM2D\";\nserialization_exports.registerClass(ConvLSTM2D);\n\n// node_modules/.pnpm/@tensorflow+tfjs-layers@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-layers/dist/layers/core.js\nvar Dropout = class extends Layer {\n  constructor(args) {\n    super(args);\n    this.rate = Math.max(Math.min(args.rate, 1), 0);\n    this.noiseShape = args.noiseShape;\n    this.seed = args.seed;\n    this.supportsMasking = true;\n  }\n  getNoiseShape(input2) {\n    if (this.noiseShape == null) {\n      return this.noiseShape;\n    }\n    const inputShape = input2.shape;\n    const noiseShape = [];\n    for (let i2 = 0; i2 < this.noiseShape.length; ++i2) {\n      noiseShape.push(this.noiseShape[i2] == null ? inputShape[i2] : this.noiseShape[i2]);\n    }\n    return noiseShape;\n  }\n  call(inputs, kwargs) {\n    return tidy(() => {\n      this.invokeCallHook(inputs, kwargs);\n      const input2 = getExactlyOneTensor(inputs);\n      if (0 < this.rate && this.rate < 1) {\n        const training = kwargs[\"training\"] == null ? false : kwargs[\"training\"];\n        const noiseShape = this.getNoiseShape(input2);\n        const output = inTrainPhase(() => dropout2(input2, this.rate, noiseShape, this.seed), () => input2, training);\n        return output;\n      }\n      return inputs;\n    });\n  }\n  getConfig() {\n    const config = {\n      rate: this.rate,\n      noiseShape: this.noiseShape,\n      seed: this.seed\n    };\n    const baseConfig = super.getConfig();\n    Object.assign(config, baseConfig);\n    return config;\n  }\n  dispose() {\n    return super.dispose();\n  }\n};\nDropout.className = \"Dropout\";\nserialization_exports.registerClass(Dropout);\nvar SpatialDropout1D = class extends Dropout {\n  constructor(args) {\n    super(args);\n    this.inputSpec = [{ ndim: 3 }];\n  }\n  getNoiseShape(input2) {\n    const inputShape = input2.shape;\n    return [inputShape[0], 1, inputShape[2]];\n  }\n};\nSpatialDropout1D.className = \"SpatialDropout1D\";\nserialization_exports.registerClass(SpatialDropout1D);\nvar Dense = class extends Layer {\n  constructor(args) {\n    super(args);\n    this.activation = null;\n    this.useBias = true;\n    this.kernel = null;\n    this.bias = null;\n    this.DEFAULT_KERNEL_INITIALIZER = \"glorotNormal\";\n    this.DEFAULT_BIAS_INITIALIZER = \"zeros\";\n    if (args.batchInputShape == null && args.inputShape == null && args.inputDim != null) {\n      let batchSize = null;\n      if (args.batchSize != null) {\n        batchSize = args.batchSize;\n      }\n      this.batchInputShape = [batchSize, args.inputDim];\n    }\n    this.units = args.units;\n    assertPositiveInteger(this.units, \"units\");\n    this.activation = getActivation(args.activation);\n    if (args.useBias != null) {\n      this.useBias = args.useBias;\n    }\n    this.kernelInitializer = getInitializer(args.kernelInitializer || this.DEFAULT_KERNEL_INITIALIZER);\n    this.biasInitializer = getInitializer(args.biasInitializer || this.DEFAULT_BIAS_INITIALIZER);\n    this.kernelConstraint = getConstraint(args.kernelConstraint);\n    this.biasConstraint = getConstraint(args.biasConstraint);\n    this.kernelRegularizer = getRegularizer(args.kernelRegularizer);\n    this.biasRegularizer = getRegularizer(args.biasRegularizer);\n    this.activityRegularizer = getRegularizer(args.activityRegularizer);\n    this.supportsMasking = true;\n    this.inputSpec = [{ minNDim: 2 }];\n  }\n  build(inputShape) {\n    inputShape = getExactlyOneShape(inputShape);\n    const inputLastDim = inputShape[inputShape.length - 1];\n    if (this.kernel == null) {\n      this.kernel = this.addWeight(\"kernel\", [inputLastDim, this.units], null, this.kernelInitializer, this.kernelRegularizer, true, this.kernelConstraint);\n      if (this.useBias) {\n        this.bias = this.addWeight(\"bias\", [this.units], null, this.biasInitializer, this.biasRegularizer, true, this.biasConstraint);\n      }\n    }\n    this.inputSpec = [{ minNDim: 2, axes: { [-1]: inputLastDim } }];\n    this.built = true;\n  }\n  computeOutputShape(inputShape) {\n    inputShape = getExactlyOneShape(inputShape);\n    const outputShape = inputShape.slice();\n    outputShape[outputShape.length - 1] = this.units;\n    return outputShape;\n  }\n  call(inputs, kwargs) {\n    return tidy(() => {\n      this.invokeCallHook(inputs, kwargs);\n      const input2 = getExactlyOneTensor(inputs);\n      const fusedActivationName = mapActivationToFusedKernel(this.activation.getClassName());\n      let output;\n      if (fusedActivationName != null) {\n        output = dot2(input2, this.kernel.read(), fusedActivationName, this.bias ? this.bias.read() : null);\n      } else {\n        output = dot2(input2, this.kernel.read());\n        if (this.bias != null) {\n          output = biasAdd(output, this.bias.read());\n        }\n        if (this.activation != null) {\n          output = this.activation.apply(output);\n        }\n      }\n      return output;\n    });\n  }\n  getConfig() {\n    const config = {\n      units: this.units,\n      activation: serializeActivation(this.activation),\n      useBias: this.useBias,\n      kernelInitializer: serializeInitializer(this.kernelInitializer),\n      biasInitializer: serializeInitializer(this.biasInitializer),\n      kernelRegularizer: serializeRegularizer(this.kernelRegularizer),\n      biasRegularizer: serializeRegularizer(this.biasRegularizer),\n      activityRegularizer: serializeRegularizer(this.activityRegularizer),\n      kernelConstraint: serializeConstraint(this.kernelConstraint),\n      biasConstraint: serializeConstraint(this.biasConstraint)\n    };\n    const baseConfig = super.getConfig();\n    Object.assign(config, baseConfig);\n    return config;\n  }\n};\nDense.className = \"Dense\";\nserialization_exports.registerClass(Dense);\nvar Flatten = class extends Layer {\n  constructor(args) {\n    args = args || {};\n    super(args);\n    this.inputSpec = [{ minNDim: 3 }];\n    this.dataFormat = args.dataFormat;\n  }\n  computeOutputShape(inputShape) {\n    inputShape = getExactlyOneShape(inputShape);\n    for (const dim of inputShape.slice(1)) {\n      if (dim == null) {\n        throw new ValueError(`The shape of the input to \"Flatten\" is not fully defined (got ${inputShape.slice(1)}). Make sure to pass a complete \"input_shape\" or \"batch_input_shape\" argument to the first layer in your model.`);\n      }\n    }\n    return [inputShape[0], arrayProd(inputShape, 1)];\n  }\n  call(inputs, kwargs) {\n    return tidy(() => {\n      this.invokeCallHook(inputs, kwargs);\n      let input2 = getExactlyOneTensor(inputs);\n      if (this.dataFormat === \"channelsFirst\" && input2.rank > 1) {\n        const permutation = [0];\n        for (let i2 = 2; i2 < input2.rank; ++i2) {\n          permutation.push(i2);\n        }\n        permutation.push(1);\n        input2 = transpose(input2, permutation);\n      }\n      return batchFlatten(input2);\n    });\n  }\n  getConfig() {\n    const config = {};\n    if (this.dataFormat != null) {\n      config[\"dataFormat\"] = this.dataFormat;\n    }\n    const baseConfig = super.getConfig();\n    Object.assign(config, baseConfig);\n    return config;\n  }\n};\nFlatten.className = \"Flatten\";\nserialization_exports.registerClass(Flatten);\nvar Activation2 = class extends Layer {\n  constructor(args) {\n    super(args);\n    this.supportsMasking = true;\n    this.activation = getActivation(args.activation);\n  }\n  call(inputs, kwargs) {\n    return tidy(() => {\n      this.invokeCallHook(inputs, kwargs);\n      const input2 = getExactlyOneTensor(inputs);\n      return this.activation.apply(input2);\n    });\n  }\n  getConfig() {\n    const config = { activation: serializeActivation(this.activation) };\n    const baseConfig = super.getConfig();\n    Object.assign(config, baseConfig);\n    return config;\n  }\n};\nActivation2.className = \"Activation\";\nserialization_exports.registerClass(Activation2);\nvar RepeatVector = class extends Layer {\n  constructor(args) {\n    super(args);\n    this.n = args.n;\n    this.inputSpec = [{ ndim: 2 }];\n  }\n  computeOutputShape(inputShape) {\n    return [inputShape[0], this.n, inputShape[1]];\n  }\n  call(inputs, kwargs) {\n    return tidy(() => {\n      inputs = getExactlyOneTensor(inputs);\n      return repeat(inputs, this.n);\n    });\n  }\n  getConfig() {\n    const config = {\n      n: this.n\n    };\n    const baseConfig = super.getConfig();\n    Object.assign(config, baseConfig);\n    return config;\n  }\n};\nRepeatVector.className = \"RepeatVector\";\nserialization_exports.registerClass(RepeatVector);\nvar Reshape2 = class extends Layer {\n  constructor(args) {\n    super(args);\n    this.targetShape = args.targetShape;\n    for (let i2 = 0; i2 < this.targetShape.length; ++i2) {\n      if (this.isUnknown(this.targetShape[i2])) {\n        this.targetShape[i2] = null;\n      }\n    }\n  }\n  isUnknown(dim) {\n    return dim < 0 || dim == null;\n  }\n  fixUnknownDimension(inputShape, outputShape) {\n    const errorMsg = \"Total size of new array must be unchanged.\";\n    const finalShape = outputShape.slice();\n    let known = 1;\n    let unknown = null;\n    for (let i2 = 0; i2 < finalShape.length; ++i2) {\n      const dim = finalShape[i2];\n      if (this.isUnknown(dim)) {\n        if (unknown === null) {\n          unknown = i2;\n        } else {\n          throw new ValueError(\"Can only specifiy one unknown dimension.\");\n        }\n      } else {\n        known *= dim;\n      }\n    }\n    const originalSize = arrayProd(inputShape);\n    if (unknown !== null) {\n      if (known === 0 || originalSize % known !== 0) {\n        throw new ValueError(errorMsg);\n      }\n      finalShape[unknown] = originalSize / known;\n    } else if (originalSize !== known) {\n      throw new ValueError(errorMsg);\n    }\n    return finalShape;\n  }\n  computeOutputShape(inputShape) {\n    let anyUnknownDims = false;\n    for (let i2 = 0; i2 < inputShape.length; ++i2) {\n      if (this.isUnknown(inputShape[i2])) {\n        anyUnknownDims = true;\n        break;\n      }\n    }\n    if (anyUnknownDims) {\n      return inputShape.slice(0, 1).concat(this.targetShape);\n    } else {\n      return inputShape.slice(0, 1).concat(this.fixUnknownDimension(inputShape.slice(1), this.targetShape));\n    }\n  }\n  call(inputs, kwargs) {\n    return tidy(() => {\n      this.invokeCallHook(inputs, kwargs);\n      const input2 = getExactlyOneTensor(inputs);\n      const inputShape = input2.shape;\n      const outputShape = inputShape.slice(0, 1).concat(this.fixUnknownDimension(inputShape.slice(1), this.targetShape));\n      return reshape(input2, outputShape);\n    });\n  }\n  getConfig() {\n    const config = {\n      targetShape: this.targetShape\n    };\n    const baseConfig = super.getConfig();\n    Object.assign(config, baseConfig);\n    return config;\n  }\n};\nReshape2.className = \"Reshape\";\nserialization_exports.registerClass(Reshape2);\nvar Permute = class extends Layer {\n  constructor(args) {\n    super(args);\n    if (args.dims == null) {\n      throw new Error(\"Required configuration field `dims` is missing during Permute constructor call.\");\n    }\n    if (!Array.isArray(args.dims)) {\n      throw new Error(`Permute constructor requires \\`dims\\` to be an Array, but received ${args.dims} instead.`);\n    }\n    const expectedSortedIndices = range2(1, args.dims.length + 1);\n    if (!util_exports.arraysEqual(args.dims.slice().sort(), expectedSortedIndices)) {\n      throw new Error(\"Invalid permutation `dims`: \" + JSON.stringify(args.dims) + \" `dims` must contain consecutive integers starting from 1.\");\n    }\n    this.dims = args.dims;\n    this.dimsIncludingBatch = [0].concat(this.dims);\n    this.inputSpec = [new InputSpec({ ndim: this.dims.length + 1 })];\n  }\n  computeOutputShape(inputShape) {\n    inputShape = getExactlyOneShape(inputShape);\n    const outputShape = inputShape.slice();\n    this.dims.forEach((dim, i2) => {\n      outputShape[i2 + 1] = inputShape[dim];\n    });\n    return outputShape;\n  }\n  call(inputs, kwargs) {\n    return transpose(getExactlyOneTensor(inputs), this.dimsIncludingBatch);\n  }\n  getConfig() {\n    const config = {\n      dims: this.dims\n    };\n    const baseConfig = super.getConfig();\n    Object.assign(config, baseConfig);\n    return config;\n  }\n};\nPermute.className = \"Permute\";\nserialization_exports.registerClass(Permute);\nvar Masking = class extends Layer {\n  constructor(args) {\n    super(args == null ? {} : args);\n    this.supportsMasking = true;\n    if (args != null) {\n      this.maskValue = args.maskValue == null ? 0 : args.maskValue;\n    } else {\n      this.maskValue = 0;\n    }\n  }\n  computeOutputShape(inputShape) {\n    return inputShape;\n  }\n  getConfig() {\n    const baseConfig = super.getConfig();\n    const config = { maskValue: this.maskValue };\n    Object.assign(config, baseConfig);\n    return config;\n  }\n  computeMask(inputs, mask) {\n    const input2 = getExactlyOneTensor(inputs);\n    const axis = -1;\n    return any(notEqual(input2, this.maskValue), axis);\n  }\n  call(inputs, kwargs) {\n    return tidy(() => {\n      this.invokeCallHook(inputs, kwargs);\n      const input2 = getExactlyOneTensor(inputs);\n      const axis = -1;\n      const keepDims = true;\n      const booleanMask = any(notEqual(input2, this.maskValue), axis, keepDims);\n      const output = mul(input2, cast(booleanMask, input2.dtype));\n      return output;\n    });\n  }\n};\nMasking.className = \"Masking\";\nserialization_exports.registerClass(Masking);\n\n// node_modules/.pnpm/@tensorflow+tfjs-layers@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-layers/dist/layers/embeddings.js\nvar Embedding = class extends Layer {\n  constructor(args) {\n    super(args);\n    this.embeddings = null;\n    this.DEFAULT_EMBEDDINGS_INITIALIZER = \"randomUniform\";\n    if (args.batchInputShape == null && args.inputShape == null) {\n      let batchSize = null;\n      if (args.batchSize != null) {\n        batchSize = args.batchSize;\n      }\n      if (args.inputLength == null) {\n        this.batchInputShape = [batchSize, null];\n      } else {\n        this.batchInputShape = [batchSize].concat(toList(args.inputLength));\n      }\n    }\n    this.inputDim = args.inputDim;\n    assertPositiveInteger(this.inputDim, \"inputDim\");\n    this.outputDim = args.outputDim;\n    assertPositiveInteger(this.outputDim, \"outputDim\");\n    this.embeddingsInitializer = getInitializer(args.embeddingsInitializer || this.DEFAULT_EMBEDDINGS_INITIALIZER);\n    this.embeddingsRegularizer = getRegularizer(args.embeddingsRegularizer);\n    this.activityRegularizer = getRegularizer(args.activityRegularizer);\n    this.embeddingsConstraint = getConstraint(args.embeddingsConstraint);\n    this.maskZero = args.maskZero;\n    this.supportsMasking = args.maskZero;\n    this.inputLength = args.inputLength;\n  }\n  build(inputShape) {\n    this.embeddings = this.addWeight(\"embeddings\", [this.inputDim, this.outputDim], this.dtype, this.embeddingsInitializer, this.embeddingsRegularizer, true, this.embeddingsConstraint);\n    this.built = true;\n  }\n  warnOnIncompatibleInputShape(inputShape) {\n  }\n  computeMask(inputs, mask) {\n    return tidy(() => {\n      if (!this.maskZero) {\n        return null;\n      } else {\n        inputs = getExactlyOneTensor(inputs);\n        return notEqual(inputs, zerosLike(inputs));\n      }\n    });\n  }\n  computeOutputShape(inputShape) {\n    inputShape = getExactlyOneShape(inputShape);\n    if (this.inputLength == null) {\n      return [...inputShape, this.outputDim];\n    }\n    const inLens = toList(this.inputLength);\n    if (inLens.length !== inputShape.length - 1) {\n      throw new ValueError(`\"inputLength\" is ${this.inputLength}, but received input shape has shape ${inputShape}`);\n    } else {\n      let i2 = 0;\n      for (let k = 0; k < inLens.length; ++k) {\n        const s1 = inLens[k];\n        const s2 = inputShape[k + 1];\n        if (s1 != null && s2 != null && s1 !== s2) {\n          throw new ValueError(`\"inputLength\" is ${this.inputLength}, but received input shape has shape ${inputShape}`);\n        } else if (s1 == null) {\n          inLens[i2] = s2;\n        }\n        i2++;\n      }\n    }\n    return [inputShape[0], ...inLens, this.outputDim];\n  }\n  call(inputs, kwargs) {\n    return tidy(() => {\n      this.invokeCallHook(inputs, kwargs);\n      let input2 = getExactlyOneTensor(inputs);\n      if (input2.dtype !== \"int32\") {\n        input2 = cast2(input2, \"int32\");\n      }\n      const output = gather2(this.embeddings.read(), reshape(input2, [input2.size]));\n      return reshape(output, getExactlyOneShape(this.computeOutputShape(input2.shape)));\n    });\n  }\n  getConfig() {\n    const config = {\n      inputDim: this.inputDim,\n      outputDim: this.outputDim,\n      embeddingsInitializer: serializeInitializer(this.embeddingsInitializer),\n      embeddingsRegularizer: serializeRegularizer(this.embeddingsRegularizer),\n      activityRegularizer: serializeRegularizer(this.activityRegularizer),\n      embeddingsConstraint: serializeConstraint(this.embeddingsConstraint),\n      maskZero: this.maskZero,\n      inputLength: this.inputLength\n    };\n    const baseConfig = super.getConfig();\n    Object.assign(config, baseConfig);\n    return config;\n  }\n};\nEmbedding.className = \"Embedding\";\nserialization_exports.registerClass(Embedding);\n\n// node_modules/.pnpm/@tensorflow+tfjs-layers@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-layers/dist/layers/merge.js\nvar Merge = class extends Layer {\n  constructor(args) {\n    super(args || {});\n    this.supportsMasking = true;\n  }\n  mergeFunction(inputs) {\n    throw new NotImplementedError();\n  }\n  computeElementwiseOpOutputShape(shape1, shape2) {\n    if (shape1 == null || shape2 == null) {\n      return null;\n    } else if (shape1.length < shape2.length) {\n      return this.computeElementwiseOpOutputShape(shape2, shape1);\n    } else if (shape2.length === 0) {\n      return shape1;\n    }\n    const outputShape = shape1.slice(0, shape1.length - shape2.length);\n    for (let k = 0; k < shape2.length; ++k) {\n      const i2 = shape1[shape1.length - shape2.length + k];\n      const j = shape2[k];\n      if (i2 == null || j == null || i2 < 0 || j < 0) {\n        outputShape.push(null);\n      } else if (i2 === 1) {\n        outputShape.push(j);\n      } else if (j === 1) {\n        outputShape.push(i2);\n      } else {\n        if (i2 !== j) {\n          throw new ValueError(\"Operands could not be broadcast together with shapes \" + JSON.stringify(shape1) + \" \" + JSON.stringify(shape2));\n        }\n        outputShape.push(i2);\n      }\n    }\n    return outputShape;\n  }\n  build(inputShape) {\n    if (Array.isArray(inputShape) && !Array.isArray(inputShape[0])) {\n      inputShape = [getExactlyOneShape(inputShape)];\n    }\n    inputShape = inputShape;\n    if (inputShape.length < 2) {\n      throw new ValueError(`A merge layer should be called on an Array of at least 2 inputs. Got ${inputShape.length} input(s).`);\n    }\n    let batchSizes = [];\n    for (const shape of inputShape) {\n      if (shape != null && shape[0] !== null) {\n        batchSizes.push(shape[0]);\n      }\n    }\n    batchSizes = unique2(batchSizes);\n    if (batchSizes.length > 1) {\n      throw new ValueError(`Can not merge tensors with different batch sizes. Got tensors with shapes: ${JSON.stringify(inputShape)}.`);\n    }\n    let outputShape = inputShape[0] == null ? null : inputShape[0].slice(1);\n    for (let i2 = 1; i2 < inputShape.length; ++i2) {\n      const shape = inputShape[i2] == null ? null : inputShape[i2].slice(1);\n      outputShape = this.computeElementwiseOpOutputShape(outputShape, shape);\n    }\n    const allRanks = inputShape.map((shape) => shape.length);\n    if (inputShape.indexOf(null) === -1 && unique2(allRanks).length === 1) {\n      this.reshapeRequired = false;\n    } else {\n      this.reshapeRequired = true;\n    }\n  }\n  call(inputs, kwargs) {\n    return tidy(() => {\n      inputs = inputs;\n      if (this.reshapeRequired) {\n        const reshapedInputs = [];\n        const inputDims = inputs.map((input2) => input2.rank);\n        if (inputDims.indexOf(null) === -1) {\n          const maxNDim = max2(inputDims);\n          for (let x of inputs) {\n            const xNDim = x.rank;\n            for (let k = 0; k < maxNDim - xNDim; ++k) {\n              x = expandDims2(x, 1);\n            }\n            reshapedInputs.push(x);\n          }\n          return this.mergeFunction(reshapedInputs);\n        } else {\n          let transposed = false;\n          for (const x of inputs) {\n            const xNDim = x.rank;\n            if (xNDim == null) {\n              const xShape = x.shape;\n              const batchSize = xShape[0];\n              const newShape = xShape.slice(1).concat([batchSize]);\n              let xTransposed = reshape(x, [batchSize].concat(arrayProd(xShape.slice(1))));\n              xTransposed = transpose(xTransposed, [1, 0]);\n              xTransposed = reshape(xTransposed, newShape);\n              reshapedInputs.push(xTransposed);\n              transposed = true;\n            } else if (xNDim > 1) {\n              const dims = range2(1, xNDim).concat([0]);\n              reshapedInputs.push(transpose(x, dims));\n              transposed = true;\n            } else {\n              reshapedInputs.push(x);\n            }\n          }\n          let y = this.mergeFunction(reshapedInputs);\n          const yNDim = y.rank;\n          if (transposed) {\n            if (yNDim == null) {\n              const yShape = y.shape;\n              const yNDim2 = yShape.length;\n              const batchSize = yShape[yNDim2 - 1];\n              const newShape = [batchSize].concat(yShape.slice(0, yShape.length - 1));\n              y = reshape(transpose(reshape(y, [-1, batchSize]), [1, 0]), newShape);\n            } else if (yNDim > 1) {\n              const dims = [yNDim - 1].concat(range2(0, yNDim - 1));\n              y = transpose(y, dims);\n            }\n          }\n          return y;\n        }\n      } else {\n        return this.mergeFunction(inputs);\n      }\n    });\n  }\n  computeOutputShape(inputShape) {\n    inputShape = inputShape;\n    let outputShape;\n    if (inputShape[0] == null) {\n      outputShape = null;\n    } else {\n      outputShape = inputShape[0].slice(1);\n    }\n    for (let i2 = 1; i2 < inputShape.length; ++i2) {\n      const shape = inputShape[i2] == null ? null : inputShape[i2].slice(1);\n      outputShape = this.computeElementwiseOpOutputShape(outputShape, shape);\n    }\n    let batchSizes = [];\n    for (const shape of inputShape) {\n      if (shape != null && shape[0] !== null) {\n        batchSizes.push(shape[0]);\n      }\n    }\n    batchSizes = unique2(batchSizes);\n    if (batchSizes.length === 1) {\n      outputShape = batchSizes.concat(outputShape);\n    } else {\n      outputShape = [null].concat(outputShape);\n    }\n    return outputShape;\n  }\n  computeMask(inputs, mask) {\n    return tidy(() => {\n      if (mask == null) {\n        return null;\n      }\n      if (!Array.isArray(mask)) {\n        throw new ValueError(\"`mask` should be an Array\");\n      }\n      if (!Array.isArray(inputs)) {\n        throw new ValueError(\"`inputs` should be an Array\");\n      }\n      if (mask.length !== inputs.length) {\n        throw new ValueError(`The Array 'inputs' and 'mask' are expected to have the same length, but have different lengths (${inputs.length} vs ${mask.length})`);\n      }\n      if (mask.every((m) => m == null)) {\n        return null;\n      }\n      mask = mask.map((m) => m == null ? m : expandDims(m, 0));\n      let output = mask[0];\n      for (let i2 = 1; i2 < mask.length - 1; ++i2) {\n        output = logicalAnd(output, mask[i2]);\n      }\n      return output;\n    });\n  }\n};\nvar Add2 = class extends Merge {\n  constructor(args) {\n    super(args);\n  }\n  mergeFunction(inputs) {\n    return tidy(() => {\n      let output = inputs[0].clone();\n      for (let i2 = 1; i2 < inputs.length; ++i2) {\n        output = add2(output, inputs[i2]);\n      }\n      return output;\n    });\n  }\n};\nAdd2.className = \"Add\";\nserialization_exports.registerClass(Add2);\nvar Multiply2 = class extends Merge {\n  constructor(args) {\n    super(args);\n  }\n  mergeFunction(inputs) {\n    return tidy(() => {\n      let output = inputs[0].clone();\n      for (let i2 = 1; i2 < inputs.length; ++i2) {\n        output = mul(output, inputs[i2]);\n      }\n      return output;\n    });\n  }\n};\nMultiply2.className = \"Multiply\";\nserialization_exports.registerClass(Multiply2);\nvar Average = class extends Merge {\n  constructor(args) {\n    super(args);\n  }\n  mergeFunction(inputs) {\n    return tidy(() => {\n      let output = inputs[0].clone();\n      for (let i2 = 1; i2 < inputs.length; ++i2) {\n        output = add2(output, inputs[i2]);\n      }\n      return mul(1 / inputs.length, output);\n    });\n  }\n};\nAverage.className = \"Average\";\nserialization_exports.registerClass(Average);\nvar Maximum2 = class extends Merge {\n  constructor(args) {\n    super(args);\n  }\n  mergeFunction(inputs) {\n    return tidy(() => {\n      let output = inputs[0];\n      for (let i2 = 1; i2 < inputs.length; ++i2) {\n        output = maximum(output, inputs[i2]);\n      }\n      return output;\n    });\n  }\n};\nMaximum2.className = \"Maximum\";\nserialization_exports.registerClass(Maximum2);\nvar Minimum2 = class extends Merge {\n  constructor(args) {\n    super(args);\n  }\n  mergeFunction(inputs) {\n    return tidy(() => {\n      let output = inputs[0];\n      for (let i2 = 1; i2 < inputs.length; ++i2) {\n        output = minimum(output, inputs[i2]);\n      }\n      return output;\n    });\n  }\n};\nMinimum2.className = \"Minimum\";\nserialization_exports.registerClass(Minimum2);\nvar Concatenate = class extends Merge {\n  constructor(args) {\n    super(args);\n    this.DEFAULT_AXIS = -1;\n    if (args == null) {\n      args = {};\n    }\n    this.axis = args.axis == null ? this.DEFAULT_AXIS : args.axis;\n    this.supportsMasking = true;\n    this.reshapeRequired = false;\n  }\n  build(inputShape) {\n    if (!(Array.isArray(inputShape) && Array.isArray(inputShape[0])) || inputShape.length === 1) {\n      throw new ValueError(\"A `Concatenate` layer should be called on a list of at least 2 inputs\");\n    }\n    inputShape = inputShape;\n    let allNoneShape = true;\n    for (const shape of inputShape) {\n      if (shape != null) {\n        allNoneShape = false;\n        break;\n      }\n    }\n    if (allNoneShape) {\n      return;\n    }\n    const shapeSet = [];\n    for (let i2 = 0; i2 < inputShape.length; ++i2) {\n      const shapeWithoutConcatAxis = inputShape[i2].slice();\n      shapeWithoutConcatAxis.splice(this.axis, 1);\n      let exists = false;\n      for (const shape of shapeSet) {\n        if (util_exports.arraysEqual(shape, shapeWithoutConcatAxis)) {\n          exists = true;\n          break;\n        }\n      }\n      if (!exists) {\n        shapeSet.push(shapeWithoutConcatAxis);\n      }\n    }\n    if (shapeSet.length > 1) {\n      throw new ValueError(\"A `Concatenate` layer requires inputs with matching shapes except for the concat axis. Got input shapes: \" + JSON.stringify(inputShape));\n    }\n  }\n  mergeFunction(inputs) {\n    return tidy(() => {\n      return concatenate(inputs, this.axis);\n    });\n  }\n  computeOutputShape(inputShape) {\n    if (!(Array.isArray(inputShape) && Array.isArray(inputShape[0]))) {\n      throw new ValueError(\"A `Concatenate` layer should be called on a list of inputs.\");\n    }\n    const inputShapes = inputShape;\n    const outputShape = inputShapes[0].slice();\n    const axis = this.axis < 0 ? outputShape.length + this.axis : this.axis;\n    for (const shape of inputShapes.slice(1)) {\n      if (outputShape[axis] == null || shape[axis] == null) {\n        outputShape[axis] = null;\n        break;\n      }\n      outputShape[axis] += shape[axis];\n    }\n    return outputShape;\n  }\n  computeMask(inputs, mask) {\n    if (mask == null) {\n      return null;\n    }\n    if (!Array.isArray(mask)) {\n      throw new ValueError(\"`mask` should be an array for Concatenate\");\n    }\n    if (!Array.isArray(inputs)) {\n      throw new ValueError(\"`inputs` should be an array for Concatenate\");\n    }\n    if (mask.length !== inputs.length) {\n      throw new ValueError(`Mismatch in the length of mask (${mask.length}) and the legnth of inputs (${inputs.length})`);\n    }\n    return tidy(() => {\n      let allNullMasks = true;\n      mask.forEach((m) => {\n        if (m != null) {\n          allNullMasks = false;\n          return;\n        }\n      });\n      if (allNullMasks) {\n        return null;\n      }\n      const outputMasks = [];\n      for (let i2 = 0; i2 < inputs.length; ++i2) {\n        if (mask[i2] == null) {\n          outputMasks.push(cast(onesLike(inputs[i2]), \"bool\"));\n        } else if (mask[i2].rank < inputs[i2].rank) {\n          outputMasks.push(expandDims(mask[i2], -1));\n        } else {\n          outputMasks.push(mask[i2]);\n        }\n      }\n      const concatenatedMasks = concat(outputMasks, this.axis);\n      return all(concatenatedMasks, -1, false);\n    });\n  }\n  getConfig() {\n    const config = {\n      \"axis\": this.axis\n    };\n    const baseConfig = super.getConfig();\n    Object.assign(config, baseConfig);\n    return config;\n  }\n};\nConcatenate.className = \"Concatenate\";\nserialization_exports.registerClass(Concatenate);\nfunction interpretAxis(axis, dim) {\n  while (axis < 0) {\n    axis += dim;\n  }\n  return axis;\n}\nfunction batchDot(x, y, axes) {\n  if (x.shape.length > 3 || y.shape.length > 3) {\n    throw new NotImplementedError(\"batchDot is not implemented for tensors of 4D or higher rank yet\");\n  }\n  util_exports.assert(x.shape.length >= 2, () => `batchDot requires the rank of x to be >= 2, but got ${x.shape.length}`);\n  util_exports.assert(x.shape.length >= 2, () => `batchDot requires the rank of y to be >= 2, but got ${y.shape.length}`);\n  if (typeof axes === \"number\") {\n    axes = [axes, axes];\n  }\n  if (x.dtype === \"complex64\" || y.dtype === \"complex64\") {\n    throw new NotImplementedError(\"batchDot is not implemented for complex64-type Tensors yet.\");\n  }\n  const xNDim = x.shape.length;\n  const yNDim = y.shape.length;\n  if (axes == null) {\n    axes = [xNDim - 1, yNDim - 2];\n  }\n  const axesArray = axes;\n  return tidy(() => {\n    let diff;\n    if (xNDim > yNDim) {\n      diff = xNDim - yNDim;\n      const diffShape = [];\n      for (let i2 = 0; i2 < diff; ++i2) {\n        diffShape.push(1);\n      }\n      y = reshape(y, y.shape.concat(diffShape));\n    } else if (yNDim > xNDim) {\n      diff = yNDim - xNDim;\n      const diffShape = [];\n      for (let i2 = 0; i2 < diff; ++i2) {\n        diffShape.push(1);\n      }\n      x = reshape(x, x.shape.concat(diffShape));\n    } else {\n      diff = 0;\n    }\n    let out;\n    if (x.shape.length === 2 && y.shape.length === 2) {\n      if (axesArray[0] === axesArray[1]) {\n        out = sum2(mul(x, y), axesArray[0]);\n      } else {\n        out = sum2(mul(transpose(x, [1, 0]), y), axesArray[1]);\n      }\n    } else {\n      const adjX = axesArray[0] !== x.shape.length - 1;\n      const adjY = axesArray[1] === y.shape.length - 1;\n      out = matMul(x, y, adjX, adjY);\n    }\n    if (diff > 0) {\n      let idx;\n      if (xNDim > yNDim) {\n        idx = xNDim + yNDim - 3;\n      } else {\n        idx = xNDim - 1;\n      }\n      const squeezeAxes = [];\n      for (let i2 = idx; i2 < idx + diff; ++i2) {\n        squeezeAxes.push(i2);\n      }\n      out = squeeze(out, squeezeAxes);\n    }\n    if (out.shape.length === 1) {\n      out = expandDims(out, 1);\n    }\n    return out;\n  });\n}\nvar Dot = class extends Merge {\n  constructor(args) {\n    super(args);\n    this.axes = args.axes;\n    this.normalize = args.normalize == null ? false : args.normalize;\n    this.supportsMasking = true;\n    this.reshapeRequired = false;\n  }\n  build(inputShape) {\n    util_exports.assert(Array.isArray(inputShape) && inputShape.length === 2 && Array.isArray(inputShape[0]) && Array.isArray(inputShape[1]), () => \"A `Dot` layer should be called on a list of exactly 2 inputs.\");\n    const shape1 = inputShape[0];\n    const shape2 = inputShape[1];\n    if (shape1.length > 3 || shape2.length > 3) {\n      throw new NotImplementedError(\"Dot layer does not support tensors of 4D or higher rank yet.\");\n    }\n    const axes = this.interpretAxes(shape1, shape2);\n    if (shape1[axes[0]] !== shape2[axes[1]]) {\n      throw new ValueError(`Dimension incompatibility: ${shape1[axes[0]]} !== ${shape2[axes[1]]}`);\n    }\n  }\n  mergeFunction(inputs) {\n    if (inputs.length !== 2) {\n      throw new ValueError(`A \\`Dot\\` layer must be called on exactly 2 inputs, but received ${inputs.length} input(s).`);\n    }\n    let x1 = inputs[0];\n    let x2 = inputs[1];\n    let axes;\n    if (!Array.isArray(this.axes)) {\n      axes = [\n        interpretAxis(this.axes, x1.shape.length),\n        interpretAxis(this.axes, x2.shape.length)\n      ];\n    } else {\n      axes = this.axes.map((axis, i2) => interpretAxis(axis, inputs[i2].shape.length));\n    }\n    if (this.normalize) {\n      x1 = l2Normalize(x1, axes[0]);\n      x2 = l2Normalize(x2, axes[1]);\n    }\n    return batchDot(x1, x2, axes);\n  }\n  interpretAxes(shape1, shape2) {\n    let axes;\n    if (!Array.isArray(this.axes)) {\n      axes = [\n        interpretAxis(this.axes, shape1.length),\n        interpretAxis(this.axes, shape2.length)\n      ];\n    } else {\n      axes = this.axes;\n    }\n    return axes;\n  }\n  computeOutputShape(inputShape) {\n    util_exports.assert(Array.isArray(inputShape) && inputShape.length === 2 && Array.isArray(inputShape[0]) && Array.isArray(inputShape[1]), () => \"A `Dot` layer should be called on a list of exactly 2 inputs.\");\n    const shape1 = inputShape[0].slice();\n    const shape2 = inputShape[1].slice();\n    if (shape1.length > 3 || shape2.length > 3) {\n      throw new NotImplementedError(\"Dot layer does not support tensors of 4D or higher rank yet.\");\n    }\n    const axes = this.interpretAxes(shape1, shape2);\n    shape1.splice(axes[0], 1);\n    shape2.splice(axes[1], 1);\n    shape2.splice(0, 1);\n    const outputShape = shape1.concat(shape2);\n    if (outputShape.length === 1) {\n      outputShape.push(1);\n    }\n    return outputShape;\n  }\n  computeMask(inputs, mask) {\n    return null;\n  }\n  getConfig() {\n    const config = {\n      \"axes\": this.axes,\n      \"normalize\": this.normalize\n    };\n    const baseConfig = super.getConfig();\n    Object.assign(config, baseConfig);\n    return config;\n  }\n};\nDot.className = \"Dot\";\nserialization_exports.registerClass(Dot);\n\n// node_modules/.pnpm/@tensorflow+tfjs-layers@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-layers/dist/layers/noise.js\nvar GaussianNoise = class extends Layer {\n  constructor(args) {\n    super(args);\n    this.supportsMasking = true;\n    this.stddev = args.stddev;\n  }\n  computeOutputShape(inputShape) {\n    return inputShape;\n  }\n  getConfig() {\n    const baseConfig = super.getConfig();\n    const config = { stddev: this.stddev };\n    Object.assign(config, baseConfig);\n    return config;\n  }\n  call(inputs, kwargs) {\n    return tidy(() => {\n      this.invokeCallHook(inputs, kwargs);\n      const input2 = getExactlyOneTensor(inputs);\n      const noised = () => add2(randomNormal2(input2.shape, 0, this.stddev), input2);\n      const output = inTrainPhase(noised, () => input2, kwargs[\"training\"] || false);\n      return output;\n    });\n  }\n};\nGaussianNoise.className = \"GaussianNoise\";\nserialization_exports.registerClass(GaussianNoise);\nvar GaussianDropout = class extends Layer {\n  constructor(args) {\n    super(args);\n    this.supportsMasking = true;\n    this.rate = args.rate;\n  }\n  computeOutputShape(inputShape) {\n    return inputShape;\n  }\n  getConfig() {\n    const baseConfig = super.getConfig();\n    const config = { rate: this.rate };\n    Object.assign(config, baseConfig);\n    return config;\n  }\n  call(inputs, kwargs) {\n    return tidy(() => {\n      this.invokeCallHook(inputs, kwargs);\n      const input2 = getExactlyOneTensor(inputs);\n      if (this.rate > 0 && this.rate < 1) {\n        const noised = () => {\n          const stddev = Math.sqrt(this.rate / (1 - this.rate));\n          return mul(input2, randomNormal2(input2.shape, 1, stddev));\n        };\n        return inTrainPhase(noised, () => input2, kwargs[\"training\"] || false);\n      }\n      return input2;\n    });\n  }\n};\nGaussianDropout.className = \"GaussianDropout\";\nserialization_exports.registerClass(GaussianDropout);\nvar AlphaDropout = class extends Layer {\n  constructor(args) {\n    super(args);\n    this.supportsMasking = true;\n    this.rate = args.rate;\n    this.noiseShape = args.noiseShape;\n  }\n  _getNoiseShape(inputs) {\n    return this.noiseShape || getExactlyOneTensor(inputs).shape;\n  }\n  computeOutputShape(inputShape) {\n    return inputShape;\n  }\n  getConfig() {\n    const baseConfig = super.getConfig();\n    const config = { rate: this.rate };\n    Object.assign(config, baseConfig);\n    return config;\n  }\n  call(inputs, kwargs) {\n    return tidy(() => {\n      if (this.rate < 1 && this.rate > 0) {\n        const noiseShape = this._getNoiseShape(inputs);\n        const droppedInputs = () => {\n          const input2 = getExactlyOneTensor(inputs);\n          const alpha = 1.6732632423543772;\n          const scale2 = 1.0507009873554805;\n          const alphaP = -alpha * scale2;\n          let keptIdx = greaterEqual(randomUniform(noiseShape), this.rate);\n          keptIdx = cast2(keptIdx, \"float32\");\n          const a = ((1 - this.rate) * (1 + this.rate * alphaP ** 2)) ** -0.5;\n          const b = -a * alphaP * this.rate;\n          const x = add2(mul(input2, keptIdx), mul(add2(keptIdx, -1), alphaP));\n          return add2(mul(x, a), b);\n        };\n        return inTrainPhase(droppedInputs, () => getExactlyOneTensor(inputs), kwargs[\"training\"] || false);\n      }\n      return inputs;\n    });\n  }\n};\nAlphaDropout.className = \"AlphaDropout\";\nserialization_exports.registerClass(AlphaDropout);\n\n// node_modules/.pnpm/@tensorflow+tfjs-layers@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-layers/dist/layers/normalization.js\nfunction batchNormalization(x, mean5, variance, beta, gamma, epsilon3 = 1e-3) {\n  let out;\n  if (x.rank === 2) {\n    out = batchNorm2d(x, mean5, variance, beta, gamma, epsilon3);\n  } else if (x.rank === 3) {\n    out = batchNorm3d(x, mean5, variance, beta, gamma, epsilon3);\n  } else if (x.rank === 4) {\n    out = batchNorm4d(x, mean5, variance, beta, gamma, epsilon3);\n  } else {\n    throw new NotImplementedError(`batchNormalization is not implemented for array of rank ${x.rank} yet`);\n  }\n  return out;\n}\nfunction regularNormalizeBatchInTraining(x, gamma, beta, reductionAxes, epsilon3 = 1e-3) {\n  return tidy(() => {\n    const meanAndVariance = moments(x, reductionAxes);\n    const mean5 = meanAndVariance.mean;\n    const variance = meanAndVariance.variance;\n    const normed = batchNormalization(x, mean5, variance, beta, gamma, epsilon3);\n    return [normed, mean5, variance];\n  });\n}\nfunction broadcastNormalizeBatchInTraining(x, gamma, beta, reductionAxes, epsilon3 = 1e-3) {\n  return tidy(() => {\n    const meanAndVariance = moments(x, reductionAxes);\n    const mean5 = meanAndVariance.mean;\n    const variance = meanAndVariance.variance;\n    const targetShape = [];\n    for (const axis of range2(0, x.rank)) {\n      if (reductionAxes.indexOf(axis) !== -1) {\n        targetShape.push(1);\n      } else {\n        targetShape.push(x.shape[axis]);\n      }\n    }\n    const broadcastMean = reshape(mean5, targetShape);\n    const broadcastVariance = reshape(variance, targetShape);\n    const broadcastGamma = gamma == null ? null : reshape(gamma, targetShape);\n    const broadcastBeta = beta == null ? null : reshape(beta, targetShape);\n    const normed = batchNormalization(x, broadcastMean, broadcastVariance, broadcastBeta, broadcastGamma, epsilon3);\n    return [normed, mean5, variance];\n  });\n}\nfunction normalizeBatchInTraining(x, gamma, beta, reductionAxes, epsilon3 = 1e-3) {\n  if (util_exports.arraysEqual(reductionAxes.slice().sort(), range2(0, x.rank - 1))) {\n    return regularNormalizeBatchInTraining(x, gamma, beta, reductionAxes, epsilon3);\n  } else {\n    return broadcastNormalizeBatchInTraining(x, gamma, beta, reductionAxes, epsilon3);\n  }\n}\nvar BatchNormalization = class extends Layer {\n  constructor(args) {\n    if (args == null) {\n      args = {};\n    }\n    super(args);\n    this.supportsMasking = true;\n    this.axis = args.axis == null ? -1 : args.axis;\n    this.momentum = args.momentum == null ? 0.99 : args.momentum;\n    this.epsilon = args.epsilon == null ? 1e-3 : args.epsilon;\n    this.center = args.center == null ? true : args.center;\n    this.scale = args.scale == null ? true : args.scale;\n    this.betaInitializer = getInitializer(args.betaInitializer || \"zeros\");\n    this.gammaInitializer = getInitializer(args.gammaInitializer || \"ones\");\n    this.movingMeanInitializer = getInitializer(args.movingMeanInitializer || \"zeros\");\n    this.movingVarianceInitializer = getInitializer(args.movingVarianceInitializer || \"ones\");\n    this.betaConstraint = getConstraint(args.betaConstraint);\n    this.gammaConstraint = getConstraint(args.gammaConstraint);\n    this.betaRegularizer = getRegularizer(args.betaRegularizer);\n    this.gammaRegularizer = getRegularizer(args.gammaRegularizer);\n  }\n  build(inputShape) {\n    inputShape = getExactlyOneShape(inputShape);\n    const axis = this.axis >= 0 ? this.axis : this.axis + inputShape.length;\n    const dim = inputShape[axis];\n    if (dim == null) {\n      throw new ValueError(`Axis ${axis} of input tensor should have a defined dimension but the layer received an input with shape ${JSON.stringify(inputShape)}.`);\n    }\n    this.inputSpec = [new InputSpec({ ndim: inputShape.length, axes: { [axis]: dim } })];\n    const shape = [dim];\n    if (this.scale) {\n      this.gamma = this.addWeight(\"gamma\", shape, null, this.gammaInitializer, this.gammaRegularizer, true, this.gammaConstraint);\n    }\n    if (this.center) {\n      this.beta = this.addWeight(\"beta\", shape, null, this.betaInitializer, this.betaRegularizer, true, this.betaConstraint);\n    }\n    this.movingMean = this.addWeight(\"moving_mean\", shape, null, this.movingMeanInitializer, null, false);\n    this.movingVariance = this.addWeight(\"moving_variance\", shape, null, this.movingVarianceInitializer, null, false);\n    this.built = true;\n  }\n  call(inputs, kwargs) {\n    return tidy(() => {\n      const training = kwargs[\"training\"] == null ? false : kwargs[\"training\"];\n      const input2 = getExactlyOneTensor(inputs);\n      const inputShape = input2.shape;\n      const ndim = inputShape.length;\n      const reductionAxes = range2(0, ndim);\n      const axis = this.axis >= 0 ? this.axis : this.axis + ndim;\n      reductionAxes.splice(axis, 1);\n      const broadcastShape = pyListRepeat(1, ndim);\n      broadcastShape[axis] = inputShape[axis];\n      const sortedReductionAxes = reductionAxes.slice();\n      sortedReductionAxes.sort();\n      const needsBroadcasting = !util_exports.arraysEqual(sortedReductionAxes, range2(0, ndim).slice(0, ndim - 1));\n      const normalizeInference = () => {\n        if (needsBroadcasting) {\n          const broadcastMovingMean = reshape(this.movingMean.read(), broadcastShape);\n          const broadcastMovingVariance = reshape(this.movingVariance.read(), broadcastShape);\n          const broadcastBeta = this.center ? reshape(this.beta.read(), broadcastShape) : null;\n          const broadcastGamma = this.scale ? reshape(this.gamma.read(), broadcastShape) : null;\n          return batchNormalization(input2, broadcastMovingMean, broadcastMovingVariance, broadcastBeta, broadcastGamma, this.epsilon);\n        } else {\n          return batchNormalization(input2, this.movingMean.read(), this.movingVariance.read(), this.beta == null ? null : this.beta.read(), this.gamma == null ? null : this.gamma.read(), this.epsilon);\n        }\n      };\n      if (!training) {\n        return normalizeInference();\n      }\n      const [normedTraining, mean5, variance] = normalizeBatchInTraining(input2, this.gamma.read(), this.beta.read(), reductionAxes, this.epsilon);\n      const doMovingAverage = (variable2, value, momentum) => {\n        tidy(() => {\n          const decay = 1 - momentum;\n          const origValue = variable2.read();\n          const updateDelta = mul(sub(origValue, value), decay);\n          variable2.write(sub(origValue, updateDelta));\n        });\n      };\n      const updateMovingMeanAndVariance = () => {\n        doMovingAverage(this.movingMean, mean5, this.momentum);\n        doMovingAverage(this.movingVariance, variance, this.momentum);\n      };\n      updateMovingMeanAndVariance();\n      return normedTraining;\n    });\n  }\n  getConfig() {\n    const config = {\n      axis: this.axis,\n      momentum: this.momentum,\n      epsilon: this.epsilon,\n      center: this.center,\n      scale: this.scale,\n      betaInitializer: serializeInitializer(this.betaInitializer),\n      gammaInitializer: serializeInitializer(this.gammaInitializer),\n      movingMeanInitializer: serializeInitializer(this.movingMeanInitializer),\n      movingVarianceInitializer: serializeInitializer(this.movingVarianceInitializer),\n      betaRegularizer: serializeRegularizer(this.betaRegularizer),\n      gammaRegularizer: serializeRegularizer(this.gammaRegularizer),\n      betaConstraint: serializeConstraint(this.betaConstraint),\n      gammaConstraint: serializeConstraint(this.gammaConstraint)\n    };\n    const baseConfig = super.getConfig();\n    Object.assign(config, baseConfig);\n    return config;\n  }\n};\nBatchNormalization.className = \"BatchNormalization\";\nserialization_exports.registerClass(BatchNormalization);\nvar LayerNormalization = class extends Layer {\n  constructor(args) {\n    if (args == null) {\n      args = {};\n    }\n    super(args);\n    this.axis = args.axis == null ? -1 : args.axis;\n    if (typeof this.axis === \"number\") {\n      if (!Number.isInteger(this.axis)) {\n        throw new Error(`Expected axis to be an integer, but received ${this.axis}`);\n      }\n    } else if (Array.isArray(this.axis)) {\n      for (const axis of this.axis) {\n        if (!Number.isInteger(axis)) {\n          throw new Error(`Expected axis to be an array of integers, but received ${JSON.stringify(this.axis)}`);\n        }\n      }\n    } else {\n      throw new Error(`Expected axis to be an integer or an array of integers, but received ${JSON.stringify(this.axis)}`);\n    }\n    this.epsilon = args.epsilon == null ? 1e-3 : args.epsilon;\n    this.center = args.center == null ? true : args.center;\n    this.scale = args.scale == null ? true : args.scale;\n    this.betaInitializer = getInitializer(args.betaInitializer || \"zeros\");\n    this.gammaInitializer = getInitializer(args.gammaInitializer || \"ones\");\n    this.betaRegularizer = getRegularizer(args.betaRegularizer);\n    this.gammaRegularizer = getRegularizer(args.gammaRegularizer);\n    this.supportsMasking = true;\n  }\n  build(inputShape) {\n    inputShape = getExactlyOneShape(inputShape);\n    const nDims = inputShape.length;\n    if (typeof this.axis === \"number\") {\n      this.axis = [this.axis];\n    }\n    for (let i2 = 0; i2 < this.axis.length; ++i2) {\n      if (this.axis[i2] < 0) {\n        this.axis[i2] += nDims;\n      }\n    }\n    for (const axis of this.axis) {\n      if (axis < 0 || axis >= nDims) {\n        throw new Error(`Invalid axis: ${axis}`);\n      }\n    }\n    if (this.axis.length !== unique2(this.axis).length) {\n      throw new Error(`Found duplicate axes in: ${this.axis}`);\n    }\n    const paramShape = this.axis.map((axis) => inputShape[axis]);\n    const trainable = true;\n    if (this.scale) {\n      this.gamma = this.addWeight(\"gamma\", paramShape, \"float32\", this.gammaInitializer, this.gammaRegularizer, trainable);\n    } else {\n      this.gamma = null;\n    }\n    if (this.center) {\n      this.beta = this.addWeight(\"beta\", paramShape, \"float32\", this.betaInitializer, this.betaRegularizer, trainable);\n    } else {\n      this.beta = null;\n    }\n    this.built = true;\n  }\n  call(inputs, kwargs) {\n    const input2 = getExactlyOneTensor(inputs);\n    const inputShape = input2.shape;\n    const nDims = inputShape.length;\n    return tidy(() => {\n      const keepDims = true;\n      let { mean: mean5, variance } = moments(input2, this.axis, keepDims);\n      const broadcastShape = pyListRepeat(1, nDims);\n      for (const dim of this.axis) {\n        broadcastShape[dim] = inputShape[dim];\n      }\n      const broadcast = (v) => {\n        if (v != null && v.shape.length !== nDims) {\n          return reshape(v, broadcastShape);\n        } else {\n          return v;\n        }\n      };\n      let scale2 = this.scale ? broadcast(this.gamma.read()) : null;\n      let offset = this.center ? broadcast(this.beta.read()) : null;\n      const momentsTiling = [];\n      const scaleOffsetTiling = [];\n      for (let i2 = 0; i2 < nDims; ++i2) {\n        if (this.axis.indexOf(i2) !== -1) {\n          momentsTiling.push(inputShape[i2]);\n          scaleOffsetTiling.push(1);\n        } else {\n          momentsTiling.push(1);\n          scaleOffsetTiling.push(inputShape[i2]);\n        }\n      }\n      mean5 = tile(mean5, momentsTiling);\n      variance = tile(variance, momentsTiling);\n      if (scale2 != null) {\n        scale2 = tile(scale2, scaleOffsetTiling);\n      }\n      if (offset != null) {\n        offset = tile(offset, scaleOffsetTiling);\n      }\n      return batchNormalization(input2, mean5, variance, offset, scale2, this.epsilon);\n    });\n  }\n  getConfig() {\n    const config = {\n      axis: this.axis,\n      epsilon: this.epsilon,\n      center: this.center,\n      scale: this.scale,\n      betaInitializer: serializeInitializer(this.betaInitializer),\n      gammaInitializer: serializeInitializer(this.gammaInitializer),\n      betaRegularizer: serializeRegularizer(this.betaRegularizer),\n      gammaRegularizer: serializeRegularizer(this.gammaRegularizer)\n    };\n    const baseConfig = super.getConfig();\n    Object.assign(config, baseConfig);\n    return config;\n  }\n};\nLayerNormalization.className = \"LayerNormalization\";\nserialization_exports.registerClass(LayerNormalization);\n\n// node_modules/.pnpm/@tensorflow+tfjs-layers@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-layers/dist/layers/padding.js\nfunction spatial2dPadding(x, padding, dataFormat) {\n  return tidy(() => {\n    if (x.rank !== 4) {\n      throw new ValueError(`temporalPadding expects input tensor to be 4-D, but received a ${x.rank}-D tensor.`);\n    }\n    if (padding == null) {\n      padding = [[1, 1], [1, 1]];\n    }\n    if (padding.length !== 2 || padding[0].length !== 2 || padding[1].length !== 2) {\n      throw new ValueError(\"spatial2dPadding expects `padding` to be an Array of two Arrays, each of which is an Array of two integers.\");\n    }\n    if (dataFormat == null) {\n      dataFormat = imageDataFormat();\n    }\n    if (dataFormat !== \"channelsLast\" && dataFormat !== \"channelsFirst\") {\n      throw new ValueError(`Unknown data format: ${dataFormat}. Supported data formats are 'channelsLast' and 'channelsFirst.`);\n    }\n    let pattern;\n    if (dataFormat === \"channelsFirst\") {\n      pattern = [[0, 0], [0, 0], padding[0], padding[1]];\n    } else {\n      pattern = [[0, 0], padding[0], padding[1], [0, 0]];\n    }\n    return pad(x, pattern);\n  });\n}\nvar ZeroPadding2D = class extends Layer {\n  constructor(args) {\n    if (args == null) {\n      args = {};\n    }\n    super(args);\n    this.dataFormat = args.dataFormat == null ? imageDataFormat() : args.dataFormat;\n    if (args.padding == null) {\n      this.padding = [[1, 1], [1, 1]];\n    } else if (typeof args.padding === \"number\") {\n      this.padding = [[args.padding, args.padding], [args.padding, args.padding]];\n    } else {\n      args.padding = args.padding;\n      if (args.padding.length !== 2) {\n        throw new ValueError(`ZeroPadding2D expects padding to be a length-2 array, but received a length-${args.padding.length} array.`);\n      }\n      let heightPadding;\n      let widthPadding;\n      if (typeof args.padding[0] === \"number\") {\n        heightPadding = [args.padding[0], args.padding[0]];\n        widthPadding = [args.padding[1], args.padding[1]];\n      } else {\n        args.padding = args.padding;\n        if (args.padding[0].length !== 2) {\n          throw new ValueError(`ZeroPadding2D expects height padding to be a length-2 array, but received a length-${args.padding[0].length} array.`);\n        }\n        heightPadding = args.padding[0];\n        if (args.padding[1].length !== 2) {\n          throw new ValueError(`ZeroPadding2D expects width padding to be a length-2 array, but received a length-${args.padding[1].length} array.`);\n        }\n        widthPadding = args.padding[1];\n      }\n      this.padding = [heightPadding, widthPadding];\n    }\n    this.inputSpec = [new InputSpec({ ndim: 4 })];\n  }\n  computeOutputShape(inputShape) {\n    inputShape = getExactlyOneShape(inputShape);\n    let rows;\n    let cols;\n    if (this.dataFormat === \"channelsFirst\") {\n      if (inputShape[2] != null && inputShape[2] >= 0) {\n        rows = inputShape[2] + this.padding[0][0] + this.padding[0][1];\n      } else {\n        rows = null;\n      }\n      if (inputShape[3] != null && inputShape[3] >= 0) {\n        cols = inputShape[3] + this.padding[1][0] + this.padding[1][1];\n      } else {\n        cols = null;\n      }\n      return [inputShape[0], inputShape[1], rows, cols];\n    } else {\n      if (inputShape[1] != null && inputShape[1] >= 0) {\n        rows = inputShape[1] + this.padding[0][0] + this.padding[0][1];\n      } else {\n        rows = null;\n      }\n      if (inputShape[2] != null && inputShape[2] >= 0) {\n        cols = inputShape[2] + this.padding[1][0] + this.padding[1][1];\n      } else {\n        cols = null;\n      }\n      return [inputShape[0], rows, cols, inputShape[3]];\n    }\n  }\n  call(inputs, kwargs) {\n    return tidy(() => spatial2dPadding(getExactlyOneTensor(inputs), this.padding, this.dataFormat));\n  }\n  getConfig() {\n    const config = {\n      padding: this.padding,\n      dataFormat: this.dataFormat\n    };\n    const baseConfig = super.getConfig();\n    Object.assign(config, baseConfig);\n    return config;\n  }\n};\nZeroPadding2D.className = \"ZeroPadding2D\";\nserialization_exports.registerClass(ZeroPadding2D);\n\n// node_modules/.pnpm/@tensorflow+tfjs-layers@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-layers/dist/layers/pooling.js\nfunction pool2d(x, poolSize, strides, padding, dataFormat, poolMode) {\n  return tidy(() => {\n    checkDataFormat(dataFormat);\n    checkPoolMode(poolMode);\n    checkPaddingMode(padding);\n    if (strides == null) {\n      strides = [1, 1];\n    }\n    if (padding == null) {\n      padding = \"valid\";\n    }\n    if (dataFormat == null) {\n      dataFormat = imageDataFormat();\n    }\n    if (poolMode == null) {\n      poolMode = \"max\";\n    }\n    x = preprocessConv2DInput(x, dataFormat);\n    let y;\n    const paddingString = padding === \"same\" ? \"same\" : \"valid\";\n    if (poolMode === \"max\") {\n      y = maxPool(x, poolSize, strides, paddingString);\n    } else {\n      y = avgPool(\n        x,\n        poolSize,\n        strides,\n        paddingString\n      );\n    }\n    if (dataFormat === \"channelsFirst\") {\n      y = transpose(y, [0, 3, 1, 2]);\n    }\n    return y;\n  });\n}\nfunction pool3d(x, poolSize, strides, padding, dataFormat, poolMode) {\n  return tidy(() => {\n    checkDataFormat(dataFormat);\n    checkPoolMode(poolMode);\n    checkPaddingMode(padding);\n    if (strides == null) {\n      strides = [1, 1, 1];\n    }\n    if (padding == null) {\n      padding = \"valid\";\n    }\n    if (dataFormat == null) {\n      dataFormat = imageDataFormat();\n    }\n    if (poolMode == null) {\n      poolMode = \"max\";\n    }\n    x = preprocessConv3DInput(x, dataFormat);\n    let y;\n    const paddingString = padding === \"same\" ? \"same\" : \"valid\";\n    if (poolMode === \"max\") {\n      y = maxPool3d(x, poolSize, strides, paddingString);\n    } else {\n      y = avgPool3d(x, poolSize, strides, paddingString);\n    }\n    if (dataFormat === \"channelsFirst\") {\n      y = transpose(y, [0, 4, 1, 2, 3]);\n    }\n    return y;\n  });\n}\nvar Pooling1D = class extends Layer {\n  constructor(args) {\n    if (args.poolSize == null) {\n      args.poolSize = 2;\n    }\n    super(args);\n    if (typeof args.poolSize === \"number\") {\n      this.poolSize = [args.poolSize];\n    } else if (Array.isArray(args.poolSize) && args.poolSize.length === 1 && typeof args.poolSize[0] === \"number\") {\n      this.poolSize = args.poolSize;\n    } else {\n      throw new ValueError(`poolSize for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(args.poolSize)}`);\n    }\n    assertPositiveInteger(this.poolSize, \"poolSize\");\n    if (args.strides == null) {\n      this.strides = this.poolSize;\n    } else {\n      if (typeof args.strides === \"number\") {\n        this.strides = [args.strides];\n      } else if (Array.isArray(args.strides) && args.strides.length === 1 && typeof args.strides[0] === \"number\") {\n        this.strides = args.strides;\n      } else {\n        throw new ValueError(`strides for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(args.strides)}`);\n      }\n    }\n    assertPositiveInteger(this.strides, \"strides\");\n    this.padding = args.padding == null ? \"valid\" : args.padding;\n    checkPaddingMode(this.padding);\n    this.inputSpec = [new InputSpec({ ndim: 3 })];\n  }\n  computeOutputShape(inputShape) {\n    inputShape = getExactlyOneShape(inputShape);\n    const length = convOutputLength(inputShape[1], this.poolSize[0], this.padding, this.strides[0]);\n    return [inputShape[0], length, inputShape[2]];\n  }\n  call(inputs, kwargs) {\n    return tidy(() => {\n      this.invokeCallHook(inputs, kwargs);\n      inputs = expandDims2(getExactlyOneTensor(inputs), 2);\n      const output = this.poolingFunction(getExactlyOneTensor(inputs), [this.poolSize[0], 1], [this.strides[0], 1], this.padding, \"channelsLast\");\n      return squeeze(output, [2]);\n    });\n  }\n  getConfig() {\n    const config = {\n      poolSize: this.poolSize,\n      padding: this.padding,\n      strides: this.strides\n    };\n    const baseConfig = super.getConfig();\n    Object.assign(config, baseConfig);\n    return config;\n  }\n};\nvar MaxPooling1D = class extends Pooling1D {\n  constructor(args) {\n    super(args);\n  }\n  poolingFunction(inputs, poolSize, strides, padding, dataFormat) {\n    checkDataFormat(dataFormat);\n    checkPaddingMode(padding);\n    return pool2d(inputs, poolSize, strides, padding, dataFormat, \"max\");\n  }\n};\nMaxPooling1D.className = \"MaxPooling1D\";\nserialization_exports.registerClass(MaxPooling1D);\nvar AveragePooling1D = class extends Pooling1D {\n  constructor(args) {\n    super(args);\n  }\n  poolingFunction(inputs, poolSize, strides, padding, dataFormat) {\n    checkDataFormat(dataFormat);\n    checkPaddingMode(padding);\n    return pool2d(inputs, poolSize, strides, padding, dataFormat, \"avg\");\n  }\n};\nAveragePooling1D.className = \"AveragePooling1D\";\nserialization_exports.registerClass(AveragePooling1D);\nvar Pooling2D = class extends Layer {\n  constructor(args) {\n    if (args.poolSize == null) {\n      args.poolSize = [2, 2];\n    }\n    super(args);\n    this.poolSize = Array.isArray(args.poolSize) ? args.poolSize : [args.poolSize, args.poolSize];\n    if (args.strides == null) {\n      this.strides = this.poolSize;\n    } else if (Array.isArray(args.strides)) {\n      if (args.strides.length !== 2) {\n        throw new ValueError(`If the strides property of a 2D pooling layer is an Array, it is expected to have a length of 2, but received length ${args.strides.length}.`);\n      }\n      this.strides = args.strides;\n    } else {\n      this.strides = [args.strides, args.strides];\n    }\n    assertPositiveInteger(this.poolSize, \"poolSize\");\n    assertPositiveInteger(this.strides, \"strides\");\n    this.padding = args.padding == null ? \"valid\" : args.padding;\n    this.dataFormat = args.dataFormat == null ? \"channelsLast\" : args.dataFormat;\n    checkDataFormat(this.dataFormat);\n    checkPaddingMode(this.padding);\n    this.inputSpec = [new InputSpec({ ndim: 4 })];\n  }\n  computeOutputShape(inputShape) {\n    inputShape = getExactlyOneShape(inputShape);\n    let rows = this.dataFormat === \"channelsFirst\" ? inputShape[2] : inputShape[1];\n    let cols = this.dataFormat === \"channelsFirst\" ? inputShape[3] : inputShape[2];\n    rows = convOutputLength(rows, this.poolSize[0], this.padding, this.strides[0]);\n    cols = convOutputLength(cols, this.poolSize[1], this.padding, this.strides[1]);\n    if (this.dataFormat === \"channelsFirst\") {\n      return [inputShape[0], inputShape[1], rows, cols];\n    } else {\n      return [inputShape[0], rows, cols, inputShape[3]];\n    }\n  }\n  call(inputs, kwargs) {\n    return tidy(() => {\n      this.invokeCallHook(inputs, kwargs);\n      return this.poolingFunction(getExactlyOneTensor(inputs), this.poolSize, this.strides, this.padding, this.dataFormat);\n    });\n  }\n  getConfig() {\n    const config = {\n      poolSize: this.poolSize,\n      padding: this.padding,\n      strides: this.strides,\n      dataFormat: this.dataFormat\n    };\n    const baseConfig = super.getConfig();\n    Object.assign(config, baseConfig);\n    return config;\n  }\n};\nvar MaxPooling2D = class extends Pooling2D {\n  constructor(args) {\n    super(args);\n  }\n  poolingFunction(inputs, poolSize, strides, padding, dataFormat) {\n    checkDataFormat(dataFormat);\n    checkPaddingMode(padding);\n    return pool2d(inputs, poolSize, strides, padding, dataFormat, \"max\");\n  }\n};\nMaxPooling2D.className = \"MaxPooling2D\";\nserialization_exports.registerClass(MaxPooling2D);\nvar AveragePooling2D = class extends Pooling2D {\n  constructor(args) {\n    super(args);\n  }\n  poolingFunction(inputs, poolSize, strides, padding, dataFormat) {\n    checkDataFormat(dataFormat);\n    checkPaddingMode(padding);\n    return pool2d(inputs, poolSize, strides, padding, dataFormat, \"avg\");\n  }\n};\nAveragePooling2D.className = \"AveragePooling2D\";\nserialization_exports.registerClass(AveragePooling2D);\nvar Pooling3D = class extends Layer {\n  constructor(args) {\n    if (args.poolSize == null) {\n      args.poolSize = [2, 2, 2];\n    }\n    super(args);\n    this.poolSize = Array.isArray(args.poolSize) ? args.poolSize : [args.poolSize, args.poolSize, args.poolSize];\n    if (args.strides == null) {\n      this.strides = this.poolSize;\n    } else if (Array.isArray(args.strides)) {\n      if (args.strides.length !== 3) {\n        throw new ValueError(`If the strides property of a 3D pooling layer is an Array, it is expected to have a length of 3, but received length ${args.strides.length}.`);\n      }\n      this.strides = args.strides;\n    } else {\n      this.strides = [args.strides, args.strides, args.strides];\n    }\n    assertPositiveInteger(this.poolSize, \"poolSize\");\n    assertPositiveInteger(this.strides, \"strides\");\n    this.padding = args.padding == null ? \"valid\" : args.padding;\n    this.dataFormat = args.dataFormat == null ? \"channelsLast\" : args.dataFormat;\n    checkDataFormat(this.dataFormat);\n    checkPaddingMode(this.padding);\n    this.inputSpec = [new InputSpec({ ndim: 5 })];\n  }\n  computeOutputShape(inputShape) {\n    inputShape = getExactlyOneShape(inputShape);\n    let depths = this.dataFormat === \"channelsFirst\" ? inputShape[2] : inputShape[1];\n    let rows = this.dataFormat === \"channelsFirst\" ? inputShape[3] : inputShape[2];\n    let cols = this.dataFormat === \"channelsFirst\" ? inputShape[4] : inputShape[3];\n    depths = convOutputLength(depths, this.poolSize[0], this.padding, this.strides[0]);\n    rows = convOutputLength(rows, this.poolSize[1], this.padding, this.strides[1]);\n    cols = convOutputLength(cols, this.poolSize[2], this.padding, this.strides[2]);\n    if (this.dataFormat === \"channelsFirst\") {\n      return [inputShape[0], inputShape[1], depths, rows, cols];\n    } else {\n      return [inputShape[0], depths, rows, cols, inputShape[4]];\n    }\n  }\n  call(inputs, kwargs) {\n    return tidy(() => {\n      this.invokeCallHook(inputs, kwargs);\n      return this.poolingFunction(getExactlyOneTensor(inputs), this.poolSize, this.strides, this.padding, this.dataFormat);\n    });\n  }\n  getConfig() {\n    const config = {\n      poolSize: this.poolSize,\n      padding: this.padding,\n      strides: this.strides,\n      dataFormat: this.dataFormat\n    };\n    const baseConfig = super.getConfig();\n    Object.assign(config, baseConfig);\n    return config;\n  }\n};\nvar MaxPooling3D = class extends Pooling3D {\n  constructor(args) {\n    super(args);\n  }\n  poolingFunction(inputs, poolSize, strides, padding, dataFormat) {\n    checkDataFormat(dataFormat);\n    checkPaddingMode(padding);\n    return pool3d(inputs, poolSize, strides, padding, dataFormat, \"max\");\n  }\n};\nMaxPooling3D.className = \"MaxPooling3D\";\nserialization_exports.registerClass(MaxPooling3D);\nvar AveragePooling3D = class extends Pooling3D {\n  constructor(args) {\n    super(args);\n  }\n  poolingFunction(inputs, poolSize, strides, padding, dataFormat) {\n    checkDataFormat(dataFormat);\n    checkPaddingMode(padding);\n    return pool3d(inputs, poolSize, strides, padding, dataFormat, \"avg\");\n  }\n};\nAveragePooling3D.className = \"AveragePooling3D\";\nserialization_exports.registerClass(AveragePooling3D);\nvar GlobalPooling1D = class extends Layer {\n  constructor(args) {\n    super(args);\n    this.inputSpec = [new InputSpec({ ndim: 3 })];\n  }\n  computeOutputShape(inputShape) {\n    return [inputShape[0], inputShape[2]];\n  }\n  call(inputs, kwargs) {\n    throw new NotImplementedError();\n  }\n};\nvar GlobalAveragePooling1D = class extends GlobalPooling1D {\n  constructor(args) {\n    super(args || {});\n  }\n  call(inputs, kwargs) {\n    return tidy(() => {\n      const input2 = getExactlyOneTensor(inputs);\n      return mean(input2, 1);\n    });\n  }\n};\nGlobalAveragePooling1D.className = \"GlobalAveragePooling1D\";\nserialization_exports.registerClass(GlobalAveragePooling1D);\nvar GlobalMaxPooling1D = class extends GlobalPooling1D {\n  constructor(args) {\n    super(args || {});\n  }\n  call(inputs, kwargs) {\n    return tidy(() => {\n      const input2 = getExactlyOneTensor(inputs);\n      return max(input2, 1);\n    });\n  }\n};\nGlobalMaxPooling1D.className = \"GlobalMaxPooling1D\";\nserialization_exports.registerClass(GlobalMaxPooling1D);\nvar GlobalPooling2D = class extends Layer {\n  constructor(args) {\n    super(args);\n    this.dataFormat = args.dataFormat == null ? \"channelsLast\" : args.dataFormat;\n    checkDataFormat(this.dataFormat);\n    this.inputSpec = [new InputSpec({ ndim: 4 })];\n  }\n  computeOutputShape(inputShape) {\n    inputShape = inputShape;\n    if (this.dataFormat === \"channelsLast\") {\n      return [inputShape[0], inputShape[3]];\n    } else {\n      return [inputShape[0], inputShape[1]];\n    }\n  }\n  call(inputs, kwargs) {\n    throw new NotImplementedError();\n  }\n  getConfig() {\n    const config = { dataFormat: this.dataFormat };\n    const baseConfig = super.getConfig();\n    Object.assign(config, baseConfig);\n    return config;\n  }\n};\nvar GlobalAveragePooling2D = class extends GlobalPooling2D {\n  call(inputs, kwargs) {\n    return tidy(() => {\n      const input2 = getExactlyOneTensor(inputs);\n      if (this.dataFormat === \"channelsLast\") {\n        return mean(input2, [1, 2]);\n      } else {\n        return mean(input2, [2, 3]);\n      }\n    });\n  }\n};\nGlobalAveragePooling2D.className = \"GlobalAveragePooling2D\";\nserialization_exports.registerClass(GlobalAveragePooling2D);\nvar GlobalMaxPooling2D = class extends GlobalPooling2D {\n  call(inputs, kwargs) {\n    return tidy(() => {\n      const input2 = getExactlyOneTensor(inputs);\n      if (this.dataFormat === \"channelsLast\") {\n        return max(input2, [1, 2]);\n      } else {\n        return max(input2, [2, 3]);\n      }\n    });\n  }\n};\nGlobalMaxPooling2D.className = \"GlobalMaxPooling2D\";\nserialization_exports.registerClass(GlobalMaxPooling2D);\n\n// node_modules/.pnpm/@tensorflow+tfjs-layers@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-layers/dist/layers/wrappers.js\nvar Wrapper = class extends Layer {\n  constructor(args) {\n    super(args);\n    this.layer = args.layer;\n  }\n  build(inputShape) {\n    this.built = true;\n  }\n  get trainable() {\n    if (this.layer != null) {\n      return this.layer.trainable;\n    } else {\n      return false;\n    }\n  }\n  set trainable(value) {\n    if (this.layer != null) {\n      this.layer.trainable = value;\n    }\n  }\n  get trainableWeights() {\n    return this.layer.trainableWeights;\n  }\n  get nonTrainableWeights() {\n    return this.layer.nonTrainableWeights;\n  }\n  get updates() {\n    return this.layer._updates;\n  }\n  get losses() {\n    return this.layer.losses;\n  }\n  getWeights() {\n    return this.layer.getWeights();\n  }\n  setWeights(weights) {\n    this.layer.setWeights(weights);\n  }\n  getConfig() {\n    const config = {\n      \"layer\": {\n        \"className\": this.layer.getClassName(),\n        \"config\": this.layer.getConfig()\n      }\n    };\n    const baseConfig = super.getConfig();\n    Object.assign(config, baseConfig);\n    return config;\n  }\n  setFastWeightInitDuringBuild(value) {\n    super.setFastWeightInitDuringBuild(value);\n    if (this.layer != null) {\n      this.layer.setFastWeightInitDuringBuild(value);\n    }\n  }\n  static fromConfig(cls, config, customObjects = {}) {\n    const layerConfig = config[\"layer\"];\n    const layer = deserialize(layerConfig, customObjects);\n    delete config[\"layer\"];\n    const newConfig = { layer };\n    Object.assign(newConfig, config);\n    return new cls(newConfig);\n  }\n};\nvar TimeDistributed = class extends Wrapper {\n  constructor(args) {\n    super(args);\n    this.supportsMasking = true;\n  }\n  build(inputShape) {\n    inputShape = getExactlyOneShape(inputShape);\n    if (inputShape.length < 3) {\n      throw new ValueError(`TimeDistributed layer expects an input shape >= 3D, but received input shape ${JSON.stringify(inputShape)}`);\n    }\n    this.inputSpec = [{ shape: inputShape }];\n    const childInputShape = [inputShape[0]].concat(inputShape.slice(2));\n    if (!this.layer.built) {\n      this.layer.build(childInputShape);\n      this.layer.built = true;\n    }\n    super.build(inputShape);\n  }\n  computeOutputShape(inputShape) {\n    inputShape = getExactlyOneShape(inputShape);\n    const childInputShape = [inputShape[0]].concat(inputShape.slice(2));\n    const childOutputShape = this.layer.computeOutputShape(childInputShape);\n    const timesteps = inputShape[1];\n    return [childOutputShape[0], timesteps].concat(childOutputShape.slice(1));\n  }\n  call(inputs, kwargs) {\n    return tidy(() => {\n      inputs = getExactlyOneTensor(inputs);\n      const step5 = (inputs2, states) => {\n        const output = getExactlyOneTensor(this.layer.call(inputs2, kwargs));\n        return [output, []];\n      };\n      const rnnOutputs = rnn(step5, inputs, [], false, null, null, false, true);\n      const y = rnnOutputs[1];\n      return y;\n    });\n  }\n};\nTimeDistributed.className = \"TimeDistributed\";\nserialization_exports.registerClass(TimeDistributed);\nfunction checkBidirectionalMergeMode(value) {\n  checkStringTypeUnionValue(VALID_BIDIRECTIONAL_MERGE_MODES, \"BidirectionalMergeMode\", value);\n}\nvar DEFAULT_BIDIRECTIONAL_MERGE_MODE = \"concat\";\nvar Bidirectional = class extends Wrapper {\n  constructor(args) {\n    super(args);\n    const layerConfig = args.layer.getConfig();\n    const forwDict = {};\n    forwDict[\"className\"] = args.layer.getClassName();\n    forwDict[\"config\"] = layerConfig;\n    this.forwardLayer = deserialize(forwDict);\n    layerConfig[\"goBackwards\"] = layerConfig[\"goBackwards\"] === true ? false : true;\n    const backDict = {};\n    backDict[\"className\"] = args.layer.getClassName();\n    backDict[\"config\"] = layerConfig;\n    this.backwardLayer = deserialize(backDict);\n    this.forwardLayer.name = \"forward_\" + this.forwardLayer.name;\n    this.backwardLayer.name = \"backward_\" + this.backwardLayer.name;\n    this.mergeMode = args.mergeMode === void 0 ? DEFAULT_BIDIRECTIONAL_MERGE_MODE : args.mergeMode;\n    checkBidirectionalMergeMode(this.mergeMode);\n    if (args.weights) {\n      throw new NotImplementedError(\"weights support is not implemented for Bidirectional layer yet.\");\n    }\n    this._stateful = args.layer.stateful;\n    this.returnSequences = args.layer.returnSequences;\n    this.returnState = args.layer.returnState;\n    this.supportsMasking = true;\n    this._trainable = true;\n    this.inputSpec = args.layer.inputSpec;\n    this.numConstants = null;\n  }\n  get trainable() {\n    return this._trainable;\n  }\n  set trainable(value) {\n    this._trainable = value;\n    if (this.forwardLayer != null) {\n      this.forwardLayer.trainable = value;\n    }\n    if (this.backwardLayer != null) {\n      this.backwardLayer.trainable = value;\n    }\n  }\n  getWeights() {\n    return this.forwardLayer.getWeights().concat(this.backwardLayer.getWeights());\n  }\n  setWeights(weights) {\n    const numWeights = weights.length;\n    const numeightsOver2 = Math.floor(numWeights / 2);\n    this.forwardLayer.setWeights(weights.slice(0, numeightsOver2));\n    this.backwardLayer.setWeights(weights.slice(numeightsOver2));\n  }\n  computeOutputShape(inputShape) {\n    let layerShapes = this.forwardLayer.computeOutputShape(inputShape);\n    if (!(Array.isArray(layerShapes) && Array.isArray(layerShapes[0]))) {\n      layerShapes = [layerShapes];\n    }\n    layerShapes = layerShapes;\n    let outputShape;\n    let outputShapes;\n    let stateShape;\n    if (this.returnState) {\n      stateShape = layerShapes.slice(1);\n      outputShape = layerShapes[0];\n    } else {\n      outputShape = layerShapes[0];\n    }\n    outputShape = outputShape;\n    if (this.mergeMode === \"concat\") {\n      outputShape[outputShape.length - 1] *= 2;\n      outputShapes = [outputShape];\n    } else if (this.mergeMode == null) {\n      outputShapes = [outputShape, outputShape.slice()];\n    } else {\n      outputShapes = [outputShape];\n    }\n    if (this.returnState) {\n      if (this.mergeMode == null) {\n        return outputShapes.concat(stateShape).concat(stateShape.slice());\n      }\n      return [outputShape].concat(stateShape).concat(stateShape.slice());\n    }\n    return singletonOrArray(outputShapes);\n  }\n  apply(inputs, kwargs) {\n    let initialState = kwargs == null ? null : kwargs[\"initialState\"];\n    let constants = kwargs == null ? null : kwargs[\"constants\"];\n    if (kwargs == null) {\n      kwargs = {};\n    }\n    const standardized = standardizeArgs(inputs, initialState, constants, this.numConstants);\n    inputs = standardized.inputs;\n    initialState = standardized.initialState;\n    constants = standardized.constants;\n    if (Array.isArray(inputs)) {\n      initialState = inputs.slice(1);\n      inputs = inputs[0];\n    }\n    if ((initialState == null || initialState.length === 0) && constants == null) {\n      return super.apply(inputs, kwargs);\n    }\n    const additionalInputs = [];\n    const additionalSpecs = [];\n    if (initialState != null) {\n      const numStates = initialState.length;\n      if (numStates % 2 > 0) {\n        throw new ValueError(\"When passing `initialState` to a Bidrectional RNN, the state should be an Array containing the states of the underlying RNNs.\");\n      }\n      kwargs[\"initialState\"] = initialState;\n      additionalInputs.push(...initialState);\n      const stateSpecs = initialState.map((state) => new InputSpec({ shape: state.shape }));\n      this.forwardLayer.stateSpec = stateSpecs.slice(0, numStates / 2);\n      this.backwardLayer.stateSpec = stateSpecs.slice(numStates / 2);\n      additionalSpecs.push(...stateSpecs);\n    }\n    if (constants != null) {\n      throw new NotImplementedError(\"Support for constants in Bidirectional layers is not implemented yet.\");\n    }\n    const isSymbolicTensor = additionalInputs[0] instanceof SymbolicTensor;\n    for (const tensor2 of additionalInputs) {\n      if (tensor2 instanceof SymbolicTensor !== isSymbolicTensor) {\n        throw new ValueError(\"The initial state of a Bidirectional layer cannot be specified as a mix of symbolic and non-symbolic tensors\");\n      }\n    }\n    if (isSymbolicTensor) {\n      const fullInput = [inputs].concat(additionalInputs);\n      const fullInputSpec = this.inputSpec.concat(additionalSpecs);\n      const originalInputSpec = this.inputSpec;\n      this.inputSpec = fullInputSpec;\n      const output = super.apply(fullInput, kwargs);\n      this.inputSpec = originalInputSpec;\n      return output;\n    } else {\n      return super.apply(inputs, kwargs);\n    }\n  }\n  call(inputs, kwargs) {\n    return tidy(() => {\n      const initialState = kwargs[\"initialState\"];\n      let y;\n      let yRev;\n      if (initialState == null) {\n        y = this.forwardLayer.call(inputs, kwargs);\n        yRev = this.backwardLayer.call(inputs, kwargs);\n      } else {\n        const forwardState = initialState.slice(0, initialState.length / 2);\n        const backwardState = initialState.slice(initialState.length / 2);\n        y = this.forwardLayer.call(inputs, Object.assign(kwargs, { initialState: forwardState }));\n        yRev = this.backwardLayer.call(inputs, Object.assign(kwargs, { initialState: backwardState }));\n      }\n      let states;\n      if (this.returnState) {\n        if (Array.isArray(y)) {\n          states = y.slice(1).concat(yRev.slice(1));\n        } else {\n        }\n        y = y[0];\n        yRev = yRev[0];\n      }\n      if (this.returnSequences) {\n        yRev = reverse(yRev, 1);\n      }\n      let output;\n      if (this.mergeMode === \"concat\") {\n        output = concatenate([y, yRev]);\n      } else if (this.mergeMode === \"sum\") {\n        output = add2(y, yRev);\n      } else if (this.mergeMode === \"ave\") {\n        output = mul(0.5, add2(y, yRev));\n      } else if (this.mergeMode === \"mul\") {\n        output = mul(y, yRev);\n      } else if (this.mergeMode == null) {\n        output = [y, yRev];\n      }\n      if (this.returnState) {\n        if (this.mergeMode == null) {\n          return output.concat(states);\n        }\n        return [output].concat(states);\n      }\n      return output;\n    });\n  }\n  resetStates(states) {\n    this.forwardLayer.resetStates();\n    this.backwardLayer.resetStates();\n  }\n  build(inputShape) {\n    nameScope(this.forwardLayer.name, () => {\n      this.forwardLayer.build(inputShape);\n    });\n    nameScope(this.backwardLayer.name, () => {\n      this.backwardLayer.build(inputShape);\n    });\n    this.built = true;\n  }\n  computeMask(inputs, mask) {\n    if (Array.isArray(mask)) {\n      mask = mask[0];\n    }\n    let outputMask;\n    if (this.returnSequences) {\n      if (this.mergeMode == null) {\n        outputMask = [mask, mask];\n      } else {\n        outputMask = mask;\n      }\n    } else {\n      if (this.mergeMode == null) {\n        outputMask = [null, null];\n      } else {\n        outputMask = null;\n      }\n    }\n    if (this.returnState) {\n      const states = this.forwardLayer.states;\n      const stateMask = states.map((state) => null);\n      if (Array.isArray(outputMask)) {\n        return outputMask.concat(stateMask).concat(stateMask);\n      } else {\n        return [outputMask].concat(stateMask).concat(stateMask);\n      }\n    } else {\n      return outputMask;\n    }\n  }\n  get trainableWeights() {\n    return this.forwardLayer.trainableWeights.concat(this.backwardLayer.trainableWeights);\n  }\n  get nonTrainableWeights() {\n    return this.forwardLayer.nonTrainableWeights.concat(this.backwardLayer.nonTrainableWeights);\n  }\n  setFastWeightInitDuringBuild(value) {\n    super.setFastWeightInitDuringBuild(value);\n    if (this.forwardLayer != null) {\n      this.forwardLayer.setFastWeightInitDuringBuild(value);\n    }\n    if (this.backwardLayer != null) {\n      this.backwardLayer.setFastWeightInitDuringBuild(value);\n    }\n  }\n  getConfig() {\n    const config = {\n      \"mergeMode\": this.mergeMode\n    };\n    const baseConfig = super.getConfig();\n    Object.assign(config, baseConfig);\n    return config;\n  }\n  static fromConfig(cls, config) {\n    const rnnLayer = deserialize(config[\"layer\"]);\n    delete config[\"layer\"];\n    if (config[\"numConstants\"] != null) {\n      throw new NotImplementedError(`Deserialization of a Bidirectional layer with numConstants present is not supported yet.`);\n    }\n    const newConfig = config;\n    newConfig[\"layer\"] = rnnLayer;\n    return new cls(newConfig);\n  }\n};\nBidirectional.className = \"Bidirectional\";\nserialization_exports.registerClass(Bidirectional);\n\n// node_modules/.pnpm/@tensorflow+tfjs-layers@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-layers/dist/exports_layers.js\nfunction inputLayer(args) {\n  return new InputLayer(args);\n}\nfunction elu3(args) {\n  return new ELU(args);\n}\nfunction reLU(args) {\n  return new ReLU(args);\n}\nfunction leakyReLU(args) {\n  return new LeakyReLU(args);\n}\nfunction prelu2(args) {\n  return new PReLU(args);\n}\nfunction softmax2(args) {\n  return new Softmax3(args);\n}\nfunction thresholdedReLU(args) {\n  return new ThresholdedReLU(args);\n}\nfunction conv1d2(args) {\n  return new Conv1D(args);\n}\nfunction conv2d3(args) {\n  return new Conv2D2(args);\n}\nfunction conv2dTranspose2(args) {\n  return new Conv2DTranspose(args);\n}\nfunction conv3d2(args) {\n  return new Conv3D2(args);\n}\nfunction conv3dTranspose2(args) {\n  return new Conv3DTranspose(args);\n}\nfunction separableConv2d2(args) {\n  return new SeparableConv2D(args);\n}\nfunction cropping2D(args) {\n  return new Cropping2D(args);\n}\nfunction upSampling2d(args) {\n  return new UpSampling2D(args);\n}\nfunction depthwiseConv2d4(args) {\n  return new DepthwiseConv2D(args);\n}\nfunction activation(args) {\n  return new Activation2(args);\n}\nfunction dense(args) {\n  return new Dense(args);\n}\nfunction dropout3(args) {\n  return new Dropout(args);\n}\nfunction spatialDropout1d(args) {\n  return new SpatialDropout1D(args);\n}\nfunction flatten3(args) {\n  return new Flatten(args);\n}\nfunction repeatVector(args) {\n  return new RepeatVector(args);\n}\nfunction reshape2(args) {\n  return new Reshape2(args);\n}\nfunction permute(args) {\n  return new Permute(args);\n}\nfunction embedding(args) {\n  return new Embedding(args);\n}\nfunction add3(args) {\n  return new Add2(args);\n}\nfunction average(args) {\n  return new Average(args);\n}\nfunction concatenate2(args) {\n  return new Concatenate(args);\n}\nfunction maximum2(args) {\n  return new Maximum2(args);\n}\nfunction minimum2(args) {\n  return new Minimum2(args);\n}\nfunction multiply(args) {\n  return new Multiply2(args);\n}\nfunction dot3(args) {\n  return new Dot(args);\n}\nfunction batchNormalization2(args) {\n  return new BatchNormalization(args);\n}\nfunction layerNormalization(args) {\n  return new LayerNormalization(args);\n}\nfunction zeroPadding2d(args) {\n  return new ZeroPadding2D(args);\n}\nfunction averagePooling1d(args) {\n  return new AveragePooling1D(args);\n}\nfunction avgPool1d(args) {\n  return averagePooling1d(args);\n}\nfunction avgPooling1d(args) {\n  return averagePooling1d(args);\n}\nfunction averagePooling2d(args) {\n  return new AveragePooling2D(args);\n}\nfunction avgPool2d(args) {\n  return averagePooling2d(args);\n}\nfunction avgPooling2d(args) {\n  return averagePooling2d(args);\n}\nfunction averagePooling3d(args) {\n  return new AveragePooling3D(args);\n}\nfunction avgPool3d2(args) {\n  return averagePooling3d(args);\n}\nfunction avgPooling3d(args) {\n  return averagePooling3d(args);\n}\nfunction globalAveragePooling1d(args) {\n  return new GlobalAveragePooling1D(args);\n}\nfunction globalAveragePooling2d(args) {\n  return new GlobalAveragePooling2D(args);\n}\nfunction globalMaxPooling1d(args) {\n  return new GlobalMaxPooling1D(args);\n}\nfunction globalMaxPooling2d(args) {\n  return new GlobalMaxPooling2D(args);\n}\nfunction maxPooling1d(args) {\n  return new MaxPooling1D(args);\n}\nfunction maxPooling2d(args) {\n  return new MaxPooling2D(args);\n}\nfunction maxPooling3d(args) {\n  return new MaxPooling3D(args);\n}\nfunction gru(args) {\n  return new GRU(args);\n}\nfunction gruCell(args) {\n  return new GRUCell(args);\n}\nfunction lstm(args) {\n  return new LSTM(args);\n}\nfunction lstmCell(args) {\n  return new LSTMCell(args);\n}\nfunction simpleRNN(args) {\n  return new SimpleRNN(args);\n}\nfunction simpleRNNCell(args) {\n  return new SimpleRNNCell(args);\n}\nfunction convLstm2d(args) {\n  return new ConvLSTM2D(args);\n}\nfunction convLstm2dCell(args) {\n  return new ConvLSTM2DCell(args);\n}\nfunction rnn2(args) {\n  return new RNN(args);\n}\nfunction stackedRNNCells(args) {\n  return new StackedRNNCells(args);\n}\nfunction bidirectional(args) {\n  return new Bidirectional(args);\n}\nfunction timeDistributed(args) {\n  return new TimeDistributed(args);\n}\nvar globalMaxPool1d = globalMaxPooling1d;\nvar globalMaxPool2d = globalMaxPooling2d;\nvar maxPool1d = maxPooling1d;\nvar maxPool2d = maxPooling2d;\nfunction gaussianNoise(args) {\n  return new GaussianNoise(args);\n}\nfunction gaussianDropout(args) {\n  return new GaussianDropout(args);\n}\nfunction alphaDropout(args) {\n  return new AlphaDropout(args);\n}\nfunction masking(args) {\n  return new Masking(args);\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-layers@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-layers/dist/exports_metrics.js\nvar exports_metrics_exports = {};\n__export(exports_metrics_exports, {\n  MAPE: () => MAPE2,\n  MSE: () => MSE2,\n  binaryAccuracy: () => binaryAccuracy2,\n  binaryCrossentropy: () => binaryCrossentropy3,\n  categoricalAccuracy: () => categoricalAccuracy2,\n  categoricalCrossentropy: () => categoricalCrossentropy3,\n  cosineProximity: () => cosineProximity2,\n  mape: () => mape2,\n  meanAbsoluteError: () => meanAbsoluteError2,\n  meanAbsolutePercentageError: () => meanAbsolutePercentageError2,\n  meanSquaredError: () => meanSquaredError3,\n  mse: () => mse2,\n  precision: () => precision2,\n  recall: () => recall2,\n  sparseCategoricalAccuracy: () => sparseCategoricalAccuracy2\n});\nfunction binaryAccuracy2(yTrue, yPred) {\n  return binaryAccuracy(yTrue, yPred);\n}\nfunction binaryCrossentropy3(yTrue, yPred) {\n  return binaryCrossentropy2(yTrue, yPred);\n}\nfunction sparseCategoricalAccuracy2(yTrue, yPred) {\n  return sparseCategoricalAccuracy(yTrue, yPred);\n}\nfunction categoricalAccuracy2(yTrue, yPred) {\n  return categoricalAccuracy(yTrue, yPred);\n}\nfunction categoricalCrossentropy3(yTrue, yPred) {\n  return categoricalCrossentropy2(yTrue, yPred);\n}\nfunction precision2(yTrue, yPred) {\n  return precision(yTrue, yPred);\n}\nfunction recall2(yTrue, yPred) {\n  return recall(yTrue, yPred);\n}\nfunction cosineProximity2(yTrue, yPred) {\n  return cosineProximity(yTrue, yPred);\n}\nfunction meanAbsoluteError2(yTrue, yPred) {\n  return meanAbsoluteError(yTrue, yPred);\n}\nfunction meanAbsolutePercentageError2(yTrue, yPred) {\n  return meanAbsolutePercentageError(yTrue, yPred);\n}\nfunction MAPE2(yTrue, yPred) {\n  return meanAbsolutePercentageError(yTrue, yPred);\n}\nfunction mape2(yTrue, yPred) {\n  return meanAbsolutePercentageError(yTrue, yPred);\n}\nfunction meanSquaredError3(yTrue, yPred) {\n  return meanSquaredError2(yTrue, yPred);\n}\nfunction MSE2(yTrue, yPred) {\n  return meanSquaredError2(yTrue, yPred);\n}\nfunction mse2(yTrue, yPred) {\n  return meanSquaredError2(yTrue, yPred);\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-layers@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-layers/dist/exports_models.js\nvar exports_models_exports = {};\n__export(exports_models_exports, {\n  modelFromJSON: () => modelFromJSON\n});\n\n// node_modules/.pnpm/@tensorflow+tfjs-layers@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-layers/dist/exports_regularizers.js\nvar exports_regularizers_exports = {};\n__export(exports_regularizers_exports, {\n  l1: () => l12,\n  l1l2: () => l1l2,\n  l2: () => l22\n});\nfunction l1l2(config) {\n  return new L1L2(config);\n}\nfunction l12(config) {\n  return l1(config);\n}\nfunction l22(config) {\n  return l2(config);\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-layers@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-layers/dist/callbacks.js\nvar Callback = class extends BaseCallback {\n  constructor() {\n    super(...arguments);\n    this.model = null;\n  }\n  setModel(model2) {\n    if (!(model2 instanceof LayersModel)) {\n      throw new Error(\"model must be a LayersModel, not some other Container\");\n    }\n    this.model = model2;\n  }\n};\nfunction less2(currVal, prevVal) {\n  return currVal < prevVal;\n}\nfunction greater2(currVal, prevVal) {\n  return currVal > prevVal;\n}\nvar EarlyStopping = class extends Callback {\n  constructor(args) {\n    super();\n    if (args == null) {\n      args = {};\n    }\n    if (args.restoreBestWeights) {\n      throw new NotImplementedError(\"restoreBestWeights = True is not implemented in EarlyStopping yet.\");\n    }\n    this.monitor = args.monitor || \"val_loss\";\n    this.minDelta = Math.abs(args.minDelta || 0);\n    this.patience = args.patience || 0;\n    this.verbose = args.verbose || 0;\n    this.mode = args.mode || \"auto\";\n    this.baseline = args.baseline;\n    if ([\"auto\", \"min\", \"max\"].indexOf(this.mode) === -1) {\n      console.warn(`EarlyStopping mode '${this.mode}' is invalid. Falling back to mode 'auto'.`);\n      this.mode = \"auto\";\n    }\n    if (this.mode === \"min\") {\n      this.monitorFunc = less2;\n    } else if (this.mode === \"max\") {\n      this.monitorFunc = greater2;\n    } else {\n      if (this.monitor.indexOf(\"acc\") !== -1) {\n        this.monitorFunc = greater2;\n      } else {\n        this.monitorFunc = less2;\n      }\n    }\n    if (this.monitorFunc === less2) {\n      this.minDelta *= -1;\n    }\n  }\n  async onTrainBegin(logs) {\n    this.wait = 0;\n    this.stoppedEpoch = 0;\n    if (this.baseline != null) {\n      this.best = this.baseline;\n    } else {\n      this.best = this.monitorFunc === less2 ? Infinity : -Infinity;\n    }\n  }\n  async onEpochEnd(epoch, logs) {\n    await resolveScalarsInLogs(logs);\n    const current = this.getMonitorValue(logs);\n    if (current == null) {\n      return;\n    }\n    if (this.monitorFunc(current - this.minDelta, this.best)) {\n      this.best = current;\n      this.wait = 0;\n    } else {\n      this.wait++;\n      if (this.wait >= this.patience) {\n        this.stoppedEpoch = epoch;\n        this.model.stopTraining = true;\n      }\n    }\n  }\n  async onTrainEnd(logs) {\n    if (this.stoppedEpoch > 0 && this.verbose) {\n      console.log(`Epoch ${this.stoppedEpoch}: early stopping.`);\n    }\n  }\n  getMonitorValue(logs) {\n    if (logs == null) {\n      logs = {};\n    }\n    const monitorValue = logs[this.monitor];\n    if (monitorValue == null) {\n      console.warn(`Metric for EarlyStopping ${this.monitor} is not available. Available metrics are: ${Object.keys(logs)}`);\n    }\n    return monitorValue;\n  }\n};\nfunction earlyStopping(args) {\n  return new EarlyStopping(args);\n}\nvar callbacks = { earlyStopping };\n\n// node_modules/.pnpm/@tensorflow+tfjs-converter@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-converter/dist/flags.js\nvar ENV4 = env();\nENV4.registerFlag(\"KEEP_INTERMEDIATE_TENSORS\", () => false, (debugValue) => {\n  if (debugValue) {\n    console.warn(\"Keep intermediate tensors is ON. This will print the values of all intermediate tensors during model inference. Not all models support this mode. For details, check e2e/benchmarks/ model_config.js. This significantly impacts performance.\");\n  }\n});\n\n// node_modules/.pnpm/@tensorflow+tfjs-converter@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-converter/dist/data/compiled_api.js\nvar DataType;\n(function(DataType2) {\n  DataType2[DataType2[\"DT_INVALID\"] = 0] = \"DT_INVALID\";\n  DataType2[DataType2[\"DT_FLOAT\"] = 1] = \"DT_FLOAT\";\n  DataType2[DataType2[\"DT_DOUBLE\"] = 2] = \"DT_DOUBLE\";\n  DataType2[DataType2[\"DT_INT32\"] = 3] = \"DT_INT32\";\n  DataType2[DataType2[\"DT_UINT8\"] = 4] = \"DT_UINT8\";\n  DataType2[DataType2[\"DT_INT16\"] = 5] = \"DT_INT16\";\n  DataType2[DataType2[\"DT_INT8\"] = 6] = \"DT_INT8\";\n  DataType2[DataType2[\"DT_STRING\"] = 7] = \"DT_STRING\";\n  DataType2[DataType2[\"DT_COMPLEX64\"] = 8] = \"DT_COMPLEX64\";\n  DataType2[DataType2[\"DT_INT64\"] = 9] = \"DT_INT64\";\n  DataType2[DataType2[\"DT_BOOL\"] = 10] = \"DT_BOOL\";\n  DataType2[DataType2[\"DT_QINT8\"] = 11] = \"DT_QINT8\";\n  DataType2[DataType2[\"DT_QUINT8\"] = 12] = \"DT_QUINT8\";\n  DataType2[DataType2[\"DT_QINT32\"] = 13] = \"DT_QINT32\";\n  DataType2[DataType2[\"DT_BFLOAT16\"] = 14] = \"DT_BFLOAT16\";\n  DataType2[DataType2[\"DT_QINT16\"] = 15] = \"DT_QINT16\";\n  DataType2[DataType2[\"DT_QUINT16\"] = 16] = \"DT_QUINT16\";\n  DataType2[DataType2[\"DT_UINT16\"] = 17] = \"DT_UINT16\";\n  DataType2[DataType2[\"DT_COMPLEX128\"] = 18] = \"DT_COMPLEX128\";\n  DataType2[DataType2[\"DT_HALF\"] = 19] = \"DT_HALF\";\n  DataType2[DataType2[\"DT_RESOURCE\"] = 20] = \"DT_RESOURCE\";\n  DataType2[DataType2[\"DT_VARIANT\"] = 21] = \"DT_VARIANT\";\n  DataType2[DataType2[\"DT_UINT32\"] = 22] = \"DT_UINT32\";\n  DataType2[DataType2[\"DT_UINT64\"] = 23] = \"DT_UINT64\";\n  DataType2[DataType2[\"DT_FLOAT_REF\"] = 101] = \"DT_FLOAT_REF\";\n  DataType2[DataType2[\"DT_DOUBLE_REF\"] = 102] = \"DT_DOUBLE_REF\";\n  DataType2[DataType2[\"DT_INT32_REF\"] = 103] = \"DT_INT32_REF\";\n  DataType2[DataType2[\"DT_UINT8_REF\"] = 104] = \"DT_UINT8_REF\";\n  DataType2[DataType2[\"DT_INT16_REF\"] = 105] = \"DT_INT16_REF\";\n  DataType2[DataType2[\"DT_INT8_REF\"] = 106] = \"DT_INT8_REF\";\n  DataType2[DataType2[\"DT_STRING_REF\"] = 107] = \"DT_STRING_REF\";\n  DataType2[DataType2[\"DT_COMPLEX64_REF\"] = 108] = \"DT_COMPLEX64_REF\";\n  DataType2[DataType2[\"DT_INT64_REF\"] = 109] = \"DT_INT64_REF\";\n  DataType2[DataType2[\"DT_BOOL_REF\"] = 110] = \"DT_BOOL_REF\";\n  DataType2[DataType2[\"DT_QINT8_REF\"] = 111] = \"DT_QINT8_REF\";\n  DataType2[DataType2[\"DT_QUINT8_REF\"] = 112] = \"DT_QUINT8_REF\";\n  DataType2[DataType2[\"DT_QINT32_REF\"] = 113] = \"DT_QINT32_REF\";\n  DataType2[DataType2[\"DT_BFLOAT16_REF\"] = 114] = \"DT_BFLOAT16_REF\";\n  DataType2[DataType2[\"DT_QINT16_REF\"] = 115] = \"DT_QINT16_REF\";\n  DataType2[DataType2[\"DT_QUINT16_REF\"] = 116] = \"DT_QUINT16_REF\";\n  DataType2[DataType2[\"DT_UINT16_REF\"] = 117] = \"DT_UINT16_REF\";\n  DataType2[DataType2[\"DT_COMPLEX128_REF\"] = 118] = \"DT_COMPLEX128_REF\";\n  DataType2[DataType2[\"DT_HALF_REF\"] = 119] = \"DT_HALF_REF\";\n  DataType2[DataType2[\"DT_RESOURCE_REF\"] = 120] = \"DT_RESOURCE_REF\";\n  DataType2[DataType2[\"DT_VARIANT_REF\"] = 121] = \"DT_VARIANT_REF\";\n  DataType2[DataType2[\"DT_UINT32_REF\"] = 122] = \"DT_UINT32_REF\";\n  DataType2[DataType2[\"DT_UINT64_REF\"] = 123] = \"DT_UINT64_REF\";\n})(DataType || (DataType = {}));\nvar SaverDef;\n(function(SaverDef2) {\n  let CheckpointFormatVersion;\n  (function(CheckpointFormatVersion2) {\n    CheckpointFormatVersion2[CheckpointFormatVersion2[\"LEGACY\"] = 0] = \"LEGACY\";\n    CheckpointFormatVersion2[CheckpointFormatVersion2[\"V1\"] = 1] = \"V1\";\n    CheckpointFormatVersion2[CheckpointFormatVersion2[\"V2\"] = 2] = \"V2\";\n  })(CheckpointFormatVersion = SaverDef2.CheckpointFormatVersion || (SaverDef2.CheckpointFormatVersion = {}));\n})(SaverDef || (SaverDef = {}));\n\n// node_modules/.pnpm/@tensorflow+tfjs-converter@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-converter/dist/operations/custom_op/register.js\nvar CUSTOM_OPS = {};\nfunction registerOp(name, opFunc) {\n  const opMapper = {\n    tfOpName: name,\n    category: \"custom\",\n    inputs: [],\n    attrs: [],\n    customExecutor: opFunc\n  };\n  CUSTOM_OPS[name] = opMapper;\n}\nfunction getRegisteredOp(name) {\n  return CUSTOM_OPS[name];\n}\nfunction deregisterOp(name) {\n  delete CUSTOM_OPS[name];\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-converter@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-converter/dist/operations/executors/utils.js\nfunction getParamValue(paramName, node, tensorMap, context, resourceManager) {\n  const inputParam = node.inputParams[paramName];\n  if (inputParam && inputParam.inputIndexStart !== void 0) {\n    const start = inputParam.inputIndexStart;\n    const end = inputParam.inputIndexEnd === 0 ? void 0 : inputParam.inputIndexEnd === void 0 ? start + 1 : inputParam.inputIndexEnd;\n    if (inputParam.type === \"tensor\") {\n      return getTensor(node.inputNames[inputParam.inputIndexStart], tensorMap, context, resourceManager);\n    }\n    if (inputParam.type === \"tensors\") {\n      const inputs = node.inputNames.slice(start, end);\n      return inputs.map((name) => getTensor(name, tensorMap, context, resourceManager));\n    }\n    const tensor2 = getTensor(node.inputNames.slice(start)[0], tensorMap, context, resourceManager);\n    const data = tensor2.dataSync();\n    return inputParam.type === \"number\" ? data[0] : util_exports.toNestedArray(tensor2.shape, data);\n  }\n  const attrParam = node.attrParams[paramName];\n  return attrParam && attrParam.value;\n}\nfunction getTensor(name, tensorsMap, context, resourceManager) {\n  const [nodeName, index] = parseNodeName(name);\n  if (resourceManager != null) {\n    const tensor2 = resourceManager.getHashTableHandleByName(nodeName);\n    if (tensor2 != null) {\n      return tensor2;\n    }\n  }\n  const contextId = context.currentContextIds.find((contextId2) => {\n    return !!tensorsMap[getNodeNameWithContextId(nodeName, contextId2)];\n  });\n  return contextId !== void 0 ? tensorsMap[getNodeNameWithContextId(nodeName, contextId)][index] : void 0;\n}\nfunction getTensorsForCurrentContenxt(name, tensorsMap, context) {\n  return tensorsMap[getNodeNameWithContextId(name, context.currentContextId)];\n}\nfunction getNodeNameAndIndex(inputName, context) {\n  const [nodeName, index, outputName] = parseNodeName(inputName);\n  return [\n    getNodeNameWithContextId(nodeName, context && context.currentContextId),\n    index,\n    outputName\n  ];\n}\nfunction getNodeNameWithContextId(name, contextId) {\n  return !!contextId ? `${name}-${contextId}` : name;\n}\nfunction parseNodeName(name) {\n  const parts = name.split(\":\");\n  if (parts.length === 1) {\n    return [name, 0, void 0];\n  }\n  const nodeName = parts[0];\n  const outputName = parts.length === 3 ? parts[1] : void 0;\n  const index = Number(parts[parts.length - 1]);\n  return [nodeName, index, outputName];\n}\nfunction getPadding(node, tensorMap, context) {\n  let pad3 = getParamValue(\"pad\", node, tensorMap, context);\n  if (pad3 === \"explicit\") {\n    pad3 = getParamValue(\"explicitPaddings\", node, tensorMap, context);\n    const explicitPadding = [[0, 0], [0, 0], [0, 0], [0, 0]];\n    for (let i2 = 0; i2 < 4; i2++) {\n      explicitPadding[i2][0] = pad3[i2 * 2];\n      explicitPadding[i2][1] = pad3[i2 * 2 + 1];\n    }\n    return explicitPadding;\n  }\n  return pad3;\n}\nfunction cloneTensor(tensor2) {\n  return tensor2.kept ? tensor2 : clone(tensor2);\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-converter@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-converter/dist/operations/op_list/arithmetic.js\nvar arithmetic_exports = {};\n__export(arithmetic_exports, {\n  json: () => json\n});\nvar json = [\n  {\n    \"tfOpName\": \"Add\",\n    \"category\": \"arithmetic\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"a\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"b\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"T\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"AddV2\",\n    \"category\": \"arithmetic\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"a\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"b\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"T\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"AddN\",\n    \"category\": \"arithmetic\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"end\": 0,\n        \"name\": \"tensors\",\n        \"type\": \"tensors\"\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"BiasAdd\",\n    \"category\": \"arithmetic\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"a\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"b\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"T\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      },\n      {\n        \"tfName\": \"data_format\",\n        \"name\": \"dataFormat\",\n        \"type\": \"string\",\n        \"notSupported\": true\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"Sub\",\n    \"category\": \"arithmetic\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"a\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"b\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"T\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"RealDiv\",\n    \"category\": \"arithmetic\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"a\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"b\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"T\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"Div\",\n    \"category\": \"arithmetic\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"a\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"b\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"T\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"DivNoNan\",\n    \"category\": \"arithmetic\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"a\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"b\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"T\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"FloorDiv\",\n    \"category\": \"arithmetic\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"a\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"b\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"T\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"Mul\",\n    \"category\": \"arithmetic\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"a\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"b\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"T\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"Maximum\",\n    \"category\": \"arithmetic\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"a\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"b\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"T\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"Minimum\",\n    \"category\": \"arithmetic\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"a\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"b\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"T\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"Pow\",\n    \"category\": \"arithmetic\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"a\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"b\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"T\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"SquaredDifference\",\n    \"category\": \"arithmetic\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"a\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"b\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"T\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"Mod\",\n    \"category\": \"arithmetic\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"a\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"b\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"T\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"FloorMod\",\n    \"category\": \"arithmetic\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"a\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"b\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"T\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      }\n    ]\n  }\n];\n\n// node_modules/.pnpm/@tensorflow+tfjs-converter@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-converter/dist/operations/op_list/basic_math.js\nvar basic_math_exports = {};\n__export(basic_math_exports, {\n  json: () => json2\n});\nvar json2 = [\n  {\n    \"tfOpName\": \"Abs\",\n    \"category\": \"basic_math\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"T\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"Acos\",\n    \"category\": \"basic_math\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"T\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"Asin\",\n    \"category\": \"basic_math\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"T\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"Atan\",\n    \"category\": \"basic_math\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"T\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"Atan2\",\n    \"category\": \"basic_math\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"y\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"T\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"Ceil\",\n    \"category\": \"basic_math\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"T\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"ClipByValue\",\n    \"category\": \"basic_math\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"clipValueMin\",\n        \"type\": \"number\"\n      },\n      {\n        \"start\": 2,\n        \"name\": \"clipValueMax\",\n        \"type\": \"number\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"T\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"Complex\",\n    \"category\": \"basic_math\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"real\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"imag\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"T\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"ComplexAbs\",\n    \"category\": \"basic_math\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"T\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"Cos\",\n    \"category\": \"basic_math\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"T\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"Cosh\",\n    \"category\": \"basic_math\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"T\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"Elu\",\n    \"category\": \"basic_math\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"T\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"Exp\",\n    \"category\": \"basic_math\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"T\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"Floor\",\n    \"category\": \"basic_math\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"T\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"Log\",\n    \"category\": \"basic_math\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"T\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"Imag\",\n    \"category\": \"basic_math\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"T\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      },\n      {\n        \"tfName\": \"Tout\",\n        \"name\": \"outputType\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"Neg\",\n    \"category\": \"basic_math\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"T\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"Real\",\n    \"category\": \"basic_math\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"T\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      },\n      {\n        \"tfName\": \"Tout\",\n        \"name\": \"outputType\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"Prelu\",\n    \"category\": \"basic_math\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"alpha\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"T\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"Relu\",\n    \"category\": \"basic_math\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"T\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"Relu6\",\n    \"category\": \"basic_math\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"T\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"Selu\",\n    \"category\": \"basic_math\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"T\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"Sigmoid\",\n    \"category\": \"basic_math\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"T\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"Sin\",\n    \"category\": \"basic_math\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"T\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"Sinh\",\n    \"category\": \"basic_math\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"T\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"Sqrt\",\n    \"category\": \"basic_math\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"T\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"Rsqrt\",\n    \"category\": \"basic_math\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"T\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"Square\",\n    \"category\": \"basic_math\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"T\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"Tan\",\n    \"category\": \"basic_math\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"T\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"Tanh\",\n    \"category\": \"basic_math\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"T\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"Sign\",\n    \"category\": \"basic_math\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"T\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"Round\",\n    \"category\": \"basic_math\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"T\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"Expm1\",\n    \"category\": \"basic_math\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"T\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"Log1p\",\n    \"category\": \"basic_math\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"T\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"Reciprocal\",\n    \"category\": \"basic_math\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"T\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"Softplus\",\n    \"category\": \"basic_math\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"T\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"Asinh\",\n    \"category\": \"basic_math\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"T\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"Acosh\",\n    \"category\": \"basic_math\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"T\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"Atanh\",\n    \"category\": \"basic_math\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"T\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"Erf\",\n    \"category\": \"basic_math\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"T\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"Prod\",\n    \"category\": \"basic_math\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"axes\",\n        \"type\": \"number[]\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"keep_dims\",\n        \"name\": \"keepDims\",\n        \"type\": \"bool\",\n        \"notSupported\": true\n      },\n      {\n        \"tfName\": \"T\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"LeakyRelu\",\n    \"category\": \"basic_math\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"alpha\",\n        \"name\": \"alpha\",\n        \"type\": \"number\",\n        \"defaultValue\": 0.2\n      },\n      {\n        \"tfName\": \"T\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"IsNan\",\n    \"category\": \"basic_math\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"T\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      }\n    ]\n  }\n];\n\n// node_modules/.pnpm/@tensorflow+tfjs-converter@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-converter/dist/operations/op_list/control.js\nvar control_exports = {};\n__export(control_exports, {\n  json: () => json3\n});\nvar json3 = [\n  {\n    \"tfOpName\": \"EmptyTensorList\",\n    \"category\": \"control\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"elementShape\",\n        \"type\": \"shape\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"maxNumElements\",\n        \"type\": \"number\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"element_dtype\",\n        \"name\": \"elementDType\",\n        \"type\": \"dtype\"\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"LoopCond\",\n    \"category\": \"control\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"pred\",\n        \"type\": \"tensor\"\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"Switch\",\n    \"category\": \"control\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"data\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"pred\",\n        \"type\": \"tensor\"\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"Merge\",\n    \"category\": \"control\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"end\": 0,\n        \"name\": \"tensors\",\n        \"type\": \"tensors\"\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"Enter\",\n    \"category\": \"control\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"tensor\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"T\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      },\n      {\n        \"tfName\": \"frame_name\",\n        \"name\": \"frameName\",\n        \"type\": \"string\"\n      },\n      {\n        \"tfName\": \"is_constant\",\n        \"name\": \"isConstant\",\n        \"type\": \"bool\"\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"Exit\",\n    \"category\": \"control\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"tensor\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"T\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"NextIteration\",\n    \"category\": \"control\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"tensor\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"T\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"TensorArrayV3\",\n    \"category\": \"control\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"size\",\n        \"type\": \"number\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"dtype\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\"\n      },\n      {\n        \"tfName\": \"element_shape\",\n        \"name\": \"elementShape\",\n        \"type\": \"shape\"\n      },\n      {\n        \"tfName\": \"dynamic_size\",\n        \"name\": \"dynamicSize\",\n        \"type\": \"bool\"\n      },\n      {\n        \"tfName\": \"clear_after_read\",\n        \"name\": \"clearAfterRead\",\n        \"type\": \"bool\"\n      },\n      {\n        \"tfName\": \"identical_element_shapes\",\n        \"name\": \"identicalElementShapes\",\n        \"type\": \"bool\"\n      },\n      {\n        \"tfName\": \"tensor_array_name\",\n        \"name\": \"name\",\n        \"type\": \"string\"\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"TensorArrayWriteV3\",\n    \"category\": \"control\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"tensorArrayId\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"index\",\n        \"type\": \"number\"\n      },\n      {\n        \"start\": 2,\n        \"name\": \"tensor\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 3,\n        \"name\": \"flowIn\",\n        \"type\": \"number\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"T\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"TensorArrayReadV3\",\n    \"category\": \"control\",\n    \"inputs\": 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\"category\": \"control\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"tensorListId\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"element_shape\",\n        \"name\": \"elementShape\",\n        \"type\": \"shape\"\n      },\n      {\n        \"tfName\": \"element_dtype\",\n        \"name\": \"elementDType\",\n        \"type\": \"dtype\"\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"TensorListConcatV2\",\n    \"category\": \"control\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"tensorListId\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"element_shape\",\n        \"name\": \"elementShape\",\n        \"type\": \"shape\"\n      },\n      {\n        \"tfName\": \"element_dtype\",\n        \"name\": \"elementDType\",\n        \"type\": \"dtype\"\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"TensorListPopBack\",\n    \"category\": \"control\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"tensorListId\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"elementShape\",\n        \"type\": \"shape\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"element_dtype\",\n        \"name\": \"elementDType\",\n        \"type\": \"dtype\"\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"TensorListPushBack\",\n    \"category\": \"control\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"tensorListId\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"tensor\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"element_dtype\",\n        \"name\": \"elementDType\",\n        \"type\": \"dtype\"\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"TensorListLength\",\n    \"category\": \"control\",\n    \"inputs\": [\n     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\"string\"\n      },\n      {\n        \"tfName\": \"data_format\",\n        \"name\": \"dataFormat\",\n        \"type\": \"string\",\n        \"notSupported\": true\n      },\n      {\n        \"tfName\": \"ksize\",\n        \"name\": \"kernelSize\",\n        \"type\": \"number[]\"\n      },\n      {\n        \"tfName\": \"explicit_paddings\",\n        \"name\": \"explicitPaddings\",\n        \"type\": \"number[]\",\n        \"defaultValue\": [],\n        \"notSupported\": true\n      },\n      {\n        \"tfName\": \"T\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"MaxPoolWithArgmax\",\n    \"category\": \"convolution\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"strides\",\n        \"name\": \"strides\",\n        \"type\": \"number[]\"\n      },\n      {\n        \"tfName\": \"padding\",\n        \"name\": \"pad\",\n        \"type\": \"string\"\n      },\n      {\n        \"tfName\": \"ksize\",\n        \"name\": \"kernelSize\",\n        \"type\": \"number[]\"\n      },\n      {\n        \"tfName\": \"include_batch_in_index\",\n        \"name\": \"includeBatchInIndex\",\n        \"type\": \"bool\"\n      },\n      {\n        \"tfName\": \"T\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"AvgPool3D\",\n    \"category\": \"convolution\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"strides\",\n        \"name\": \"strides\",\n        \"type\": \"number[]\"\n      },\n      {\n        \"tfName\": \"padding\",\n        \"name\": \"pad\",\n        \"type\": \"string\"\n      },\n      {\n        \"tfName\": \"data_format\",\n        \"name\": \"dataFormat\",\n        \"type\": \"string\",\n        \"notSupported\": true\n      },\n      {\n        \"tfName\": \"ksize\",\n        \"name\": \"kernelSize\",\n        \"type\": \"number[]\"\n      },\n      {\n        \"tfName\": \"T\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"MaxPool3D\",\n    \"category\": \"convolution\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"strides\",\n        \"name\": \"strides\",\n        \"type\": \"number[]\"\n      },\n      {\n        \"tfName\": \"padding\",\n        \"name\": \"pad\",\n        \"type\": \"string\"\n      },\n      {\n        \"tfName\": \"data_format\",\n        \"name\": \"dataFormat\",\n        \"type\": \"string\",\n        \"notSupported\": true\n      },\n      {\n        \"tfName\": \"ksize\",\n        \"name\": \"kernelSize\",\n        \"type\": \"number[]\"\n      },\n      {\n        \"tfName\": \"T\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"Conv1D\",\n    \"category\": \"convolution\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"filter\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"stride\",\n        \"name\": \"stride\",\n        \"type\": \"number\"\n      },\n      {\n        \"tfName\": \"padding\",\n        \"name\": \"pad\",\n        \"type\": \"string\"\n      },\n      {\n        \"tfName\": \"data_format\",\n        \"name\": \"dataFormat\",\n        \"type\": \"string\",\n        \"defaultValue\": \"NWC\"\n      },\n      {\n        \"tfName\": \"T\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      },\n      {\n        \"tfName\": \"dilation\",\n        \"name\": \"dilation\",\n        \"type\": \"number\",\n        \"defaultValue\": 1\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"Conv2D\",\n    \"category\": \"convolution\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"filter\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"T\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      },\n      {\n        \"tfName\": \"strides\",\n        \"name\": \"strides\",\n        \"type\": \"number[]\"\n      },\n      {\n        \"tfName\": \"padding\",\n        \"name\": \"pad\",\n        \"type\": \"string\"\n      },\n      {\n        \"tfName\": \"useCudnnOnGpu\",\n        \"name\": \"useCudnnOnGpu\",\n        \"type\": \"bool\"\n      },\n      {\n        \"tfName\": \"data_format\",\n        \"name\": \"dataFormat\",\n        \"type\": \"string\",\n        \"defaultValue\": \"NHWC\"\n      },\n      {\n        \"tfName\": \"explicit_paddings\",\n        \"name\": \"explicitPaddings\",\n        \"type\": \"number[]\",\n        \"defaultValue\": []\n      },\n      {\n        \"tfName\": \"dilations\",\n        \"name\": \"dilations\",\n        \"type\": \"number[]\"\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"_FusedConv2D\",\n    \"category\": \"convolution\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"filter\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 2,\n        \"end\": 0,\n        \"name\": \"args\",\n        \"type\": \"tensors\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"num_args\",\n        \"name\": \"numArgs\",\n        \"type\": \"number\"\n      },\n      {\n        \"tfName\": \"T\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      },\n      {\n        \"tfName\": \"strides\",\n        \"name\": \"strides\",\n        \"type\": \"number[]\"\n      },\n      {\n        \"tfName\": \"padding\",\n        \"name\": \"pad\",\n        \"type\": \"string\"\n      },\n      {\n        \"tfName\": \"explicit_paddings\",\n        \"name\": \"explicitPaddings\",\n        \"type\": \"number[]\",\n        \"defaultValue\": []\n      },\n      {\n        \"tfName\": \"use_cudnn_on_gpu\",\n        \"name\": \"useCudnnOnGpu\",\n        \"type\": \"bool\",\n        \"defaultValue\": true\n      },\n      {\n        \"tfName\": \"data_format\",\n        \"name\": \"dataFormat\",\n        \"type\": \"string\",\n        \"defaultValue\": \"NHWC\"\n      },\n      {\n        \"tfName\": \"dilations\",\n        \"name\": \"dilations\",\n        \"type\": \"number[]\",\n        \"defaultValue\": [\n          1,\n          1,\n          1,\n          1\n        ]\n      },\n      {\n        \"tfName\": \"fused_ops\",\n        \"name\": \"fusedOps\",\n        \"type\": \"string[]\",\n        \"defaultValue\": []\n      },\n      {\n        \"tfName\": \"epsilon\",\n        \"name\": \"epsilon\",\n        \"type\": \"number\",\n        \"defaultValue\": 1e-4\n      },\n      {\n        \"tfName\": \"leakyrelu_alpha\",\n        \"name\": \"leakyreluAlpha\",\n        \"type\": \"number\",\n        \"defaultValue\": 0.2\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"Conv2DBackpropInput\",\n    \"category\": \"convolution\",\n    \"inputs\": [\n      {\n        \"start\": 2,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"filter\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 0,\n        \"name\": \"outputShape\",\n        \"type\": \"number[]\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"strides\",\n        \"name\": \"strides\",\n        \"type\": \"number[]\"\n      },\n      {\n        \"tfName\": \"padding\",\n        \"name\": \"pad\",\n        \"type\": \"string\"\n      },\n      {\n        \"tfName\": \"data_format\",\n        \"name\": \"dataFormat\",\n        \"type\": \"string\",\n        \"notSupported\": true\n      },\n      {\n        \"tfName\": \"explicit_paddings\",\n        \"name\": \"explicitPaddings\",\n        \"type\": \"number[]\",\n        \"defaultValue\": []\n      },\n      {\n        \"tfName\": \"dilations\",\n        \"name\": \"dilations\",\n        \"type\": \"number[]\",\n        \"notSupported\": true\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"DepthwiseConv2d\",\n    \"category\": \"convolution\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"input\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"filter\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"strides\",\n        \"name\": \"strides\",\n        \"type\": \"number[]\"\n      },\n      {\n        \"tfName\": \"padding\",\n        \"name\": \"pad\",\n        \"type\": \"string\"\n      },\n      {\n        \"tfName\": \"data_format\",\n        \"name\": \"dataFormat\",\n        \"type\": \"string\",\n        \"defaultValue\": \"NHWC\"\n      },\n      {\n        \"tfName\": \"explicit_paddings\",\n        \"name\": \"explicitPaddings\",\n        \"type\": \"number[]\",\n        \"defaultValue\": []\n      },\n      {\n        \"tfName\": \"dilations\",\n        \"name\": \"dilations\",\n        \"type\": \"number[]\"\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"DepthwiseConv2dNative\",\n    \"category\": \"convolution\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"input\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"filter\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"strides\",\n        \"name\": \"strides\",\n        \"type\": \"number[]\"\n      },\n      {\n        \"tfName\": \"padding\",\n        \"name\": \"pad\",\n        \"type\": \"string\"\n      },\n      {\n        \"tfName\": \"data_format\",\n        \"name\": \"dataFormat\",\n        \"type\": \"string\",\n        \"defaultValue\": \"NHWC\"\n      },\n      {\n        \"tfName\": \"explicit_paddings\",\n        \"name\": \"explicitPaddings\",\n        \"type\": \"number[]\",\n        \"defaultValue\": []\n      },\n      {\n        \"tfName\": \"dilations\",\n        \"name\": \"dilations\",\n        \"type\": \"number[]\"\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"FusedDepthwiseConv2dNative\",\n    \"category\": \"convolution\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"filter\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 2,\n        \"end\": 0,\n        \"name\": \"args\",\n        \"type\": \"tensors\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"num_args\",\n        \"name\": \"numArgs\",\n        \"type\": \"number\"\n      },\n      {\n        \"tfName\": \"T\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      },\n      {\n        \"tfName\": \"strides\",\n        \"name\": \"strides\",\n        \"type\": \"number[]\"\n      },\n      {\n        \"tfName\": \"padding\",\n        \"name\": \"pad\",\n        \"type\": \"string\"\n      },\n      {\n        \"tfName\": \"data_format\",\n        \"name\": \"dataFormat\",\n        \"type\": \"string\",\n        \"defaultValue\": \"NHWC\"\n      },\n      {\n        \"tfName\": \"dilations\",\n        \"name\": \"dilations\",\n        \"type\": \"number[]\",\n        \"defaultValue\": [\n          1,\n          1,\n          1,\n          1\n        ]\n      },\n      {\n        \"tfName\": \"fused_ops\",\n        \"name\": \"fusedOps\",\n        \"type\": \"string[]\",\n        \"defaultValue\": []\n      },\n      {\n        \"tfName\": \"explicit_paddings\",\n        \"name\": \"explicitPaddings\",\n        \"type\": \"number[]\",\n        \"defaultValue\": []\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"Conv3D\",\n    \"category\": \"convolution\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"filter\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"strides\",\n        \"name\": \"strides\",\n        \"type\": \"number[]\"\n      },\n      {\n        \"tfName\": \"padding\",\n        \"name\": \"pad\",\n        \"type\": \"string\"\n      },\n      {\n        \"tfName\": \"data_format\",\n        \"name\": \"dataFormat\",\n        \"type\": \"string\",\n        \"defaultValue\": \"NHWC\"\n      },\n      {\n        \"tfName\": \"dilations\",\n        \"name\": \"dilations\",\n        \"type\": \"number[]\"\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"Dilation2D\",\n    \"category\": \"convolution\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"filter\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"strides\",\n        \"name\": \"strides\",\n        \"type\": \"number[]\"\n      },\n      {\n        \"tfName\": \"rates\",\n        \"name\": \"dilations\",\n        \"type\": \"number[]\"\n      },\n      {\n        \"tfName\": \"padding\",\n        \"name\": \"pad\",\n        \"type\": \"string\"\n      }\n    ]\n  }\n];\n\n// node_modules/.pnpm/@tensorflow+tfjs-converter@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-converter/dist/operations/op_list/creation.js\nvar creation_exports = {};\n__export(creation_exports, {\n  json: () => json5\n});\nvar json5 = [\n  {\n    \"tfOpName\": \"Fill\",\n    \"category\": \"creation\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"shape\",\n        \"type\": \"number[]\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"value\",\n        \"type\": \"number\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"T\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\"\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"LinSpace\",\n    \"category\": \"creation\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"start\",\n        \"type\": \"number\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"stop\",\n        \"type\": \"number\"\n      },\n      {\n        \"start\": 2,\n        \"name\": \"num\",\n        \"type\": \"number\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"T\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"OneHot\",\n    \"category\": \"creation\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"indices\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"depth\",\n        \"type\": \"number\"\n      },\n      {\n        \"start\": 2,\n        \"name\": \"onValue\",\n        \"type\": \"number\",\n        \"defaultValue\": 1\n      },\n      {\n        \"start\": 3,\n        \"name\": \"offValue\",\n        \"type\": \"number\",\n        \"defaultValue\": 0\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"axis\",\n        \"name\": \"axis\",\n        \"type\": \"number\",\n        \"notSupported\": true\n      },\n      {\n        \"tfName\": \"T\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\"\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"Ones\",\n    \"category\": \"creation\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"shape\",\n        \"type\": \"number[]\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"T\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\"\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"OnesLike\",\n    \"category\": \"creation\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"dtype\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\"\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"RandomStandardNormal\",\n    \"category\": \"creation\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"shape\",\n        \"type\": \"number[]\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"seed\",\n        \"name\": \"seed\",\n        \"type\": \"number\",\n        \"defaultValue\": 0\n      },\n      {\n        \"tfName\": \"seed2\",\n        \"name\": \"seed2\",\n        \"type\": \"number\",\n        \"defaultValue\": 0,\n        \"notSupported\": true\n      },\n      {\n        \"tfName\": \"dtype\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\"\n      },\n      {\n        \"tfName\": \"T\",\n        \"name\": \"T\",\n        \"type\": \"number\",\n        \"notSupported\": true\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"RandomUniform\",\n    \"category\": \"creation\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"shape\",\n        \"type\": \"number[]\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"minval\",\n        \"name\": \"minval\",\n        \"type\": \"number\",\n        \"defaultValue\": 0\n      },\n      {\n        \"tfName\": \"maxval\",\n        \"name\": \"maxval\",\n        \"type\": \"number\",\n        \"defaultValue\": 1\n      },\n      {\n        \"tfName\": \"dtype\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\"\n      },\n      {\n        \"tfName\": \"seed\",\n        \"name\": \"seed\",\n        \"type\": \"number\",\n        \"defaultValue\": 0\n      },\n      {\n        \"tfName\": \"seed2\",\n        \"name\": \"seed2\",\n        \"type\": \"number\",\n        \"defaultValue\": 0,\n        \"notSupported\": true\n      },\n      {\n        \"tfName\": \"T\",\n        \"name\": \"T\",\n        \"type\": \"number\",\n        \"notSupported\": true\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"Range\",\n    \"category\": \"creation\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"start\",\n        \"type\": \"number\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"stop\",\n        \"type\": \"number\"\n      },\n      {\n        \"start\": 2,\n        \"name\": \"step\",\n        \"type\": \"number\",\n        \"defaultValue\": 0\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"Tidx\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\"\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"TruncatedNormal\",\n    \"category\": \"creation\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"shape\",\n        \"type\": \"number[]\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"means\",\n        \"name\": \"mean\",\n        \"type\": \"number\",\n        \"defaultValue\": 0\n      },\n      {\n        \"tfName\": \"stddev\",\n        \"name\": \"stdDev\",\n        \"type\": \"number\",\n        \"defaultValue\": 1\n      },\n      {\n        \"tfName\": \"seed\",\n        \"name\": \"seed\",\n        \"type\": \"number\"\n      },\n      {\n        \"tfName\": \"seed2\",\n        \"name\": \"seed2\",\n        \"type\": \"number\",\n        \"defaultValue\": 0,\n        \"notSupported\": true\n      },\n      {\n        \"tfName\": \"dtype\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\"\n      },\n      {\n        \"tfName\": \"T\",\n        \"name\": \"T\",\n        \"type\": \"number\",\n        \"notSupported\": true\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"Zeros\",\n    \"category\": \"creation\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"shape\",\n        \"type\": \"number[]\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"T\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\"\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"ZerosLike\",\n    \"category\": \"creation\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"T\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\"\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"Multinomial\",\n    \"category\": \"creation\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"logits\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"numSamples\",\n        \"type\": \"number\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"seed\",\n        \"name\": \"seed\",\n        \"type\": \"number\"\n      },\n      {\n        \"tfName\": \"seed2\",\n        \"name\": \"seed2\",\n        \"type\": \"number\"\n      },\n      {\n        \"tfName\": \"T\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\"\n      },\n      {\n        \"tfName\": \"output_dtype\",\n        \"name\": \"output_dtype\",\n        \"type\": \"dtype\"\n      }\n    ]\n  }\n];\n\n// node_modules/.pnpm/@tensorflow+tfjs-converter@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-converter/dist/operations/op_list/dynamic.js\nvar dynamic_exports = {};\n__export(dynamic_exports, {\n  json: () => json6\n});\nvar json6 = [\n  {\n    \"tfOpName\": \"NonMaxSuppressionV2\",\n    \"category\": \"dynamic\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"boxes\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"scores\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 2,\n        \"name\": \"maxOutputSize\",\n        \"type\": \"number\"\n      },\n      {\n        \"start\": 3,\n        \"name\": \"iouThreshold\",\n        \"type\": \"number\"\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"NonMaxSuppressionV3\",\n    \"category\": \"dynamic\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"boxes\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"scores\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 2,\n        \"name\": \"maxOutputSize\",\n        \"type\": \"number\"\n      },\n      {\n        \"start\": 3,\n        \"name\": \"iouThreshold\",\n        \"type\": \"number\"\n      },\n      {\n        \"start\": 4,\n        \"name\": \"scoreThreshold\",\n        \"type\": \"number\"\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"NonMaxSuppressionV4\",\n    \"category\": \"dynamic\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"boxes\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"scores\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 2,\n        \"name\": \"maxOutputSize\",\n        \"type\": \"number\"\n      },\n      {\n        \"start\": 3,\n        \"name\": \"iouThreshold\",\n        \"type\": \"number\"\n      },\n      {\n        \"start\": 4,\n        \"name\": \"scoreThreshold\",\n        \"type\": \"number\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"T\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      },\n      {\n        \"tfName\": \"T_threshold\",\n        \"name\": \"threshold\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      },\n      {\n        \"tfName\": \"pad_to_max_output_size\",\n        \"name\": \"padToMaxOutputSize\",\n        \"type\": \"bool\"\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"NonMaxSuppressionV5\",\n    \"category\": \"dynamic\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"boxes\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"scores\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 2,\n        \"name\": \"maxOutputSize\",\n        \"type\": \"number\"\n      },\n      {\n        \"start\": 3,\n        \"name\": \"iouThreshold\",\n        \"type\": \"number\"\n      },\n      {\n        \"start\": 4,\n        \"name\": \"scoreThreshold\",\n        \"type\": \"number\"\n      },\n      {\n        \"start\": 5,\n        \"name\": \"softNmsSigma\",\n        \"type\": \"number\"\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"Where\",\n    \"category\": \"dynamic\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"condition\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"T\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"ListDiff\",\n    \"category\": \"dynamic\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"y\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"T\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      }\n    ]\n  }\n];\n\n// node_modules/.pnpm/@tensorflow+tfjs-converter@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-converter/dist/operations/op_list/evaluation.js\nvar evaluation_exports = {};\n__export(evaluation_exports, {\n  json: () => json7\n});\nvar json7 = [\n  {\n    \"tfOpName\": \"LowerBound\",\n    \"category\": \"evaluation\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"sortedSequence\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"values\",\n        \"type\": \"tensor\"\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"TopKV2\",\n    \"category\": \"evaluation\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"k\",\n        \"type\": \"number\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"sorted\",\n        \"name\": \"sorted\",\n        \"type\": \"bool\"\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"UpperBound\",\n    \"category\": \"evaluation\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"sortedSequence\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"values\",\n        \"type\": \"tensor\"\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"Unique\",\n    \"category\": \"evaluation\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"UniqueV2\",\n    \"category\": \"evaluation\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"axis\",\n        \"type\": \"number\"\n      }\n    ]\n  }\n];\n\n// node_modules/.pnpm/@tensorflow+tfjs-converter@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-converter/dist/operations/op_list/graph.js\nvar graph_exports = {};\n__export(graph_exports, {\n  json: () => json8\n});\nvar json8 = [\n  {\n    \"tfOpName\": \"PlaceholderWithDefault\",\n    \"category\": \"graph\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"default\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"shape\",\n        \"name\": \"shape\",\n        \"type\": \"shape\"\n      },\n      {\n        \"tfName\": \"dtype\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\"\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"Placeholder\",\n    \"category\": \"graph\",\n    \"attrs\": [\n      {\n        \"tfName\": \"shape\",\n        \"name\": \"shape\",\n        \"type\": \"shape\"\n      },\n      {\n        \"tfName\": \"dtype\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\"\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"Const\",\n    \"category\": \"graph\"\n  },\n  {\n    \"tfOpName\": \"Identity\",\n    \"category\": \"graph\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"IdentityN\",\n    \"category\": \"graph\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"end\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensors\"\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"Snapshot\",\n    \"category\": \"graph\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"Rank\",\n    \"category\": \"graph\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"Size\",\n    \"category\": \"graph\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"Shape\",\n    \"category\": \"graph\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"ShapeN\",\n    \"category\": \"graph\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"end\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensors\"\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"Print\",\n    \"category\": \"graph\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"data\",\n        \"type\": \"tensors\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"message\",\n        \"name\": \"message\",\n        \"type\": \"string\"\n      },\n      {\n        \"tfName\": \"first_n\",\n        \"name\": \"firstN\",\n        \"type\": \"number\",\n        \"notSupported\": true\n      },\n      {\n        \"tfName\": \"summarize\",\n        \"name\": \"summarize\",\n        \"type\": \"number\",\n        \"defaultValue\": 3\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"NoOp\",\n    \"category\": \"graph\",\n    \"inputs\": []\n  },\n  {\n    \"tfOpName\": \"StopGradient\",\n    \"category\": \"graph\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"FakeQuantWithMinMaxVars\",\n    \"category\": \"graph\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"min\",\n        \"name\": \"min\",\n        \"type\": \"number\"\n      },\n      {\n        \"tfName\": \"max\",\n        \"name\": \"max\",\n        \"type\": \"number\"\n      }\n    ]\n  }\n];\n\n// node_modules/.pnpm/@tensorflow+tfjs-converter@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-converter/dist/operations/op_list/hash_table.js\nvar hash_table_exports = {};\n__export(hash_table_exports, {\n  json: () => json9\n});\nvar json9 = [\n  {\n    \"tfOpName\": \"HashTable\",\n    \"category\": \"hash_table\",\n    \"inputs\": [],\n    \"attrs\": [\n      {\n        \"tfName\": \"shared_name\",\n        \"name\": \"sharedName\",\n        \"type\": \"string\"\n      },\n      {\n        \"tfName\": \"use_node_name_sharing\",\n        \"name\": \"useNodeNameSharing\",\n        \"type\": \"bool\"\n      },\n      {\n        \"tfName\": \"key_dtype\",\n        \"name\": \"keyDType\",\n        \"type\": \"dtype\"\n      },\n      {\n        \"tfName\": \"value_dtype\",\n        \"name\": \"valueDType\",\n        \"type\": \"dtype\"\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"HashTableV2\",\n    \"category\": \"hash_table\",\n    \"inputs\": [],\n    \"attrs\": [\n      {\n        \"tfName\": \"shared_name\",\n        \"name\": \"sharedName\",\n        \"type\": \"string\"\n      },\n      {\n        \"tfName\": \"use_node_name_sharing\",\n        \"name\": \"useNodeNameSharing\",\n        \"type\": \"bool\"\n      },\n      {\n        \"tfName\": \"key_dtype\",\n        \"name\": \"keyDType\",\n        \"type\": \"dtype\"\n      },\n      {\n        \"tfName\": \"value_dtype\",\n        \"name\": \"valueDType\",\n        \"type\": \"dtype\"\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"LookupTableImport\",\n    \"category\": \"hash_table\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"tableHandle\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"keys\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 2,\n        \"name\": \"values\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"Tin\",\n        \"name\": \"tIn\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      },\n      {\n        \"tfName\": \"Tout\",\n        \"name\": \"tOut\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"LookupTableImportV2\",\n    \"category\": \"hash_table\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"tableHandle\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"keys\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 2,\n        \"name\": \"values\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"Tin\",\n        \"name\": \"tIn\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      },\n      {\n        \"tfName\": \"Tout\",\n        \"name\": \"tOut\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"LookupTableFind\",\n    \"category\": \"hash_table\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"tableHandle\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"keys\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 2,\n        \"name\": \"defaultValue\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"Tin\",\n        \"name\": \"tIn\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      },\n      {\n        \"tfName\": \"Tout\",\n        \"name\": \"tOut\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"LookupTableFindV2\",\n    \"category\": \"hash_table\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"tableHandle\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"keys\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 2,\n        \"name\": \"defaultValue\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"Tin\",\n        \"name\": \"tIn\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      },\n      {\n        \"tfName\": \"Tout\",\n        \"name\": \"tOut\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"LookupTableSize\",\n    \"category\": \"hash_table\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"tableHandle\",\n        \"type\": \"tensor\"\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"LookupTableSizeV2\",\n    \"category\": \"hash_table\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"tableHandle\",\n        \"type\": \"tensor\"\n      }\n    ]\n  }\n];\n\n// node_modules/.pnpm/@tensorflow+tfjs-converter@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-converter/dist/operations/op_list/image.js\nvar image_exports = {};\n__export(image_exports, {\n  json: () => json10\n});\nvar json10 = [\n  {\n    \"tfOpName\": \"ResizeBilinear\",\n    \"category\": \"image\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"images\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"size\",\n        \"type\": \"number[]\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"align_corners\",\n        \"name\": \"alignCorners\",\n        \"type\": \"bool\"\n      },\n      {\n        \"tfName\": \"half_pixel_centers\",\n        \"name\": \"halfPixelCenters\",\n        \"type\": \"bool\"\n      },\n      {\n        \"tfName\": \"T\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"ResizeNearestNeighbor\",\n    \"category\": \"image\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"images\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"size\",\n        \"type\": \"number[]\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"align_corners\",\n        \"name\": \"alignCorners\",\n        \"type\": \"bool\"\n      },\n      {\n        \"tfName\": \"half_pixel_centers\",\n        \"name\": \"halfPixelCenters\",\n        \"type\": \"bool\"\n      },\n      {\n        \"tfName\": \"T\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"CropAndResize\",\n    \"category\": \"image\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"image\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"boxes\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 2,\n        \"name\": \"boxInd\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 3,\n        \"name\": \"cropSize\",\n        \"type\": \"number[]\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"method\",\n        \"name\": \"method\",\n        \"type\": \"string\"\n      },\n      {\n        \"tfName\": \"extrapolation_value\",\n        \"name\": \"extrapolationValue\",\n        \"type\": \"number\"\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"ImageProjectiveTransformV3\",\n    \"category\": \"image\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"images\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"transforms\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 2,\n        \"name\": \"outputShape\",\n        \"type\": \"number[]\"\n      },\n      {\n        \"start\": 3,\n        \"name\": \"fillValue\",\n        \"type\": \"number\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"interpolation\",\n        \"name\": \"interpolation\",\n        \"type\": \"string\"\n      },\n      {\n        \"tfName\": \"fill_mode\",\n        \"name\": \"fillMode\",\n        \"type\": \"string\"\n      }\n    ]\n  }\n];\n\n// node_modules/.pnpm/@tensorflow+tfjs-converter@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-converter/dist/operations/op_list/logical.js\nvar logical_exports = {};\n__export(logical_exports, {\n  json: () => json11\n});\nvar json11 = [\n  {\n    \"tfOpName\": \"Equal\",\n    \"category\": \"logical\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"a\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"b\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"T\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"NotEqual\",\n    \"category\": \"logical\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"a\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"b\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"T\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"Greater\",\n    \"category\": \"logical\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"a\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"b\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"T\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"GreaterEqual\",\n    \"category\": \"logical\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"a\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"b\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"T\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"Less\",\n    \"category\": \"logical\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"a\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"b\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"T\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"LessEqual\",\n    \"category\": \"logical\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"a\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"b\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"T\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"LogicalAnd\",\n    \"category\": \"logical\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"a\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"b\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"T\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"LogicalNot\",\n    \"category\": \"logical\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"a\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"T\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"LogicalOr\",\n    \"category\": \"logical\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"a\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"b\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"T\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"Select\",\n    \"category\": \"logical\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"condition\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"a\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 2,\n        \"name\": \"b\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"T\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"SelectV2\",\n    \"category\": \"logical\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"condition\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"a\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 2,\n        \"name\": \"b\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"T\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      }\n    ]\n  }\n];\n\n// node_modules/.pnpm/@tensorflow+tfjs-converter@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-converter/dist/operations/op_list/matrices.js\nvar matrices_exports = {};\n__export(matrices_exports, {\n  json: () => json12\n});\nvar json12 = [\n  {\n    \"tfOpName\": \"_FusedMatMul\",\n    \"category\": \"matrices\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"a\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"b\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 2,\n        \"end\": 0,\n        \"name\": \"args\",\n        \"type\": \"tensors\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"num_args\",\n        \"name\": \"numArgs\",\n        \"type\": \"number\"\n      },\n      {\n        \"tfName\": \"fused_ops\",\n        \"name\": \"fusedOps\",\n        \"type\": \"string[]\",\n        \"defaultValue\": []\n      },\n      {\n        \"tfName\": \"epsilon\",\n        \"name\": \"epsilon\",\n        \"type\": \"number\",\n        \"defaultValue\": 1e-4\n      },\n      {\n        \"tfName\": \"transpose_a\",\n        \"name\": \"transposeA\",\n        \"type\": \"bool\",\n        \"defaultValue\": false\n      },\n      {\n        \"tfName\": \"transpose_b\",\n        \"name\": \"transposeB\",\n        \"type\": \"bool\",\n        \"defaultValue\": false\n      },\n      {\n        \"tfName\": \"leakyrelu_alpha\",\n        \"name\": \"leakyreluAlpha\",\n        \"type\": \"number\",\n        \"defaultValue\": 0.2\n      },\n      {\n        \"tfName\": \"T\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"MatMul\",\n    \"category\": \"matrices\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"a\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"b\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"transpose_a\",\n        \"name\": \"transposeA\",\n        \"type\": \"bool\",\n        \"defaultValue\": false\n      },\n      {\n        \"tfName\": \"transpose_b\",\n        \"name\": \"transposeB\",\n        \"type\": \"bool\",\n        \"defaultValue\": false\n      },\n      {\n        \"tfName\": \"T\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"BatchMatMul\",\n    \"category\": \"matrices\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"a\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"b\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"adj_x\",\n        \"name\": \"transposeA\",\n        \"type\": \"bool\",\n        \"defaultValue\": false\n      },\n      {\n        \"tfName\": \"adj_y\",\n        \"name\": \"transposeB\",\n        \"type\": \"bool\",\n        \"defaultValue\": false\n      },\n      {\n        \"tfName\": \"T\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"BatchMatMulV2\",\n    \"category\": \"matrices\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"a\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"b\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"adj_x\",\n        \"name\": \"transposeA\",\n        \"type\": \"bool\",\n        \"defaultValue\": false\n      },\n      {\n        \"tfName\": \"adj_y\",\n        \"name\": \"transposeB\",\n        \"type\": \"bool\",\n        \"defaultValue\": false\n      },\n      {\n        \"tfName\": \"T\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"Transpose\",\n    \"category\": \"matrices\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"perm\",\n        \"type\": \"number[]\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"T\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"Einsum\",\n    \"category\": \"matrices\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"end\": 0,\n        \"name\": \"tensors\",\n        \"type\": \"tensors\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"equation\",\n        \"name\": \"equation\",\n        \"type\": \"string\"\n      },\n      {\n        \"tfName\": \"N\",\n        \"name\": \"n\",\n        \"type\": \"number\",\n        \"defaultValue\": 2\n      },\n      {\n        \"tfName\": \"T\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\"\n      }\n    ]\n  }\n];\n\n// node_modules/.pnpm/@tensorflow+tfjs-converter@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-converter/dist/operations/op_list/normalization.js\nvar normalization_exports = {};\n__export(normalization_exports, {\n  json: () => json13\n});\nvar json13 = [\n  {\n    \"tfOpName\": \"EuclideanNorm\",\n    \"category\": \"normalization\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"axis\",\n        \"type\": \"number[]\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"keep_dims\",\n        \"name\": \"keepDims\",\n        \"type\": \"bool\",\n        \"defaultValue\": false\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"FusedBatchNorm\",\n    \"category\": \"normalization\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"scale\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 2,\n        \"name\": \"offset\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 3,\n        \"name\": \"mean\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 4,\n        \"name\": \"variance\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"epsilon\",\n        \"name\": \"epsilon\",\n        \"type\": \"number\",\n        \"defaultValue\": 1e-3\n      },\n      {\n        \"tfName\": \"data_format\",\n        \"name\": \"dataFormat\",\n        \"type\": \"string\",\n        \"notSupported\": true\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"FusedBatchNormV2\",\n    \"category\": \"normalization\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"scale\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 2,\n        \"name\": \"offset\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 3,\n        \"name\": \"mean\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 4,\n        \"name\": \"variance\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"epsilon\",\n        \"name\": \"epsilon\",\n        \"type\": \"number\",\n        \"defaultValue\": 1e-3\n      },\n      {\n        \"tfName\": \"data_format\",\n        \"name\": \"dataFormat\",\n        \"type\": \"string\",\n        \"notSupported\": true\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"FusedBatchNormV3\",\n    \"category\": \"normalization\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"scale\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 2,\n        \"name\": \"offset\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 3,\n        \"name\": \"mean\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 4,\n        \"name\": \"variance\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"epsilon\",\n        \"name\": \"epsilon\",\n        \"type\": \"number\",\n        \"defaultValue\": 1e-3\n      },\n      {\n        \"tfName\": \"data_format\",\n        \"name\": \"dataFormat\",\n        \"type\": \"string\",\n        \"notSupported\": true\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"LRN\",\n    \"category\": \"normalization\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"depth_radius\",\n        \"name\": \"radius\",\n        \"type\": \"number\",\n        \"defaultValue\": 5\n      },\n      {\n        \"tfName\": \"bias\",\n        \"name\": \"bias\",\n        \"type\": \"number\",\n        \"defaultValue\": 1\n      },\n      {\n        \"tfName\": \"alpha\",\n        \"name\": \"alpha\",\n        \"type\": \"number\",\n        \"defaultValue\": 1\n      },\n      {\n        \"tfName\": \"beta\",\n        \"name\": \"beta\",\n        \"type\": \"number\",\n        \"defaultValue\": 0.5\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"Softmax\",\n    \"category\": \"normalization\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"LogSoftmax\",\n    \"category\": \"normalization\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"SparseToDense\",\n    \"category\": \"normalization\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"sparseIndices\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"outputShape\",\n        \"type\": \"number[]\"\n      },\n      {\n        \"start\": 2,\n        \"name\": \"sparseValues\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 3,\n        \"name\": \"defaultValue\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"validate_indices\",\n        \"name\": \"validateIndices\",\n        \"type\": \"bool\",\n        \"defaultValue\": true,\n        \"notSupported\": true\n      }\n    ]\n  }\n];\n\n// node_modules/.pnpm/@tensorflow+tfjs-converter@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-converter/dist/operations/op_list/reduction.js\nvar reduction_exports = {};\n__export(reduction_exports, {\n  json: () => json14\n});\nvar json14 = [\n  {\n    \"tfOpName\": \"Bincount\",\n    \"category\": \"reduction\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"size\",\n        \"type\": \"number\"\n      },\n      {\n        \"start\": 2,\n        \"name\": \"weights\",\n        \"type\": \"tensor\"\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"DenseBincount\",\n    \"category\": \"reduction\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"size\",\n        \"type\": \"number\"\n      },\n      {\n        \"start\": 2,\n        \"name\": \"weights\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"binary_output\",\n        \"name\": \"binaryOutput\",\n        \"type\": \"bool\"\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"Max\",\n    \"category\": \"reduction\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"axis\",\n        \"type\": \"number[]\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"keep_dims\",\n        \"name\": \"keepDims\",\n        \"type\": \"bool\"\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"Mean\",\n    \"category\": \"reduction\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"axis\",\n        \"type\": \"number[]\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"keep_dims\",\n        \"name\": \"keepDims\",\n        \"type\": \"bool\"\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"Min\",\n    \"category\": \"reduction\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"axis\",\n        \"type\": \"number[]\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"keep_dims\",\n        \"name\": \"keepDims\",\n        \"type\": \"bool\"\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"Sum\",\n    \"category\": \"reduction\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"axis\",\n        \"type\": \"number[]\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"keep_dims\",\n        \"name\": \"keepDims\",\n        \"type\": \"bool\"\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"All\",\n    \"category\": \"reduction\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"axis\",\n        \"type\": \"number[]\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"keep_dims\",\n        \"name\": \"keepDims\",\n        \"type\": \"bool\"\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"Any\",\n    \"category\": \"reduction\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"axis\",\n        \"type\": \"number[]\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"keep_dims\",\n        \"name\": \"keepDims\",\n        \"type\": \"bool\"\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"ArgMax\",\n    \"category\": \"reduction\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"axis\",\n        \"type\": \"number\"\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"ArgMin\",\n    \"category\": \"reduction\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"axis\",\n        \"type\": \"number\"\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"Prod\",\n    \"category\": \"reduction\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"axis\",\n        \"type\": \"number[]\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"keep_dims\",\n        \"name\": \"keepDims\",\n        \"type\": \"bool\"\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"Cumprod\",\n    \"category\": \"reduction\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"axis\",\n        \"type\": \"number\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"exclusive\",\n        \"name\": \"exclusive\",\n        \"type\": \"bool\"\n      },\n      {\n        \"tfName\": \"reverse\",\n        \"name\": \"reverse\",\n        \"type\": \"bool\"\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"Cumsum\",\n    \"category\": \"reduction\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"axis\",\n        \"type\": \"number\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"exclusive\",\n        \"name\": \"exclusive\",\n        \"type\": \"bool\"\n      },\n      {\n        \"tfName\": \"reverse\",\n        \"name\": \"reverse\",\n        \"type\": \"bool\"\n      }\n    ]\n  }\n];\n\n// node_modules/.pnpm/@tensorflow+tfjs-converter@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-converter/dist/operations/op_list/slice_join.js\nvar slice_join_exports = {};\n__export(slice_join_exports, {\n  json: () => json15\n});\nvar json15 = [\n  {\n    \"tfOpName\": \"ConcatV2\",\n    \"category\": \"slice_join\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"end\": -1,\n        \"name\": \"tensors\",\n        \"type\": \"tensors\"\n      },\n      {\n        \"start\": -1,\n        \"name\": \"axis\",\n        \"type\": \"number\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"N\",\n        \"name\": \"n\",\n        \"type\": \"number\",\n        \"defaultValue\": 2\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"Concat\",\n    \"category\": \"slice_join\",\n    \"inputs\": [\n      {\n        \"start\": 1,\n        \"end\": 0,\n        \"name\": \"tensors\",\n        \"type\": \"tensors\"\n      },\n      {\n        \"start\": 0,\n        \"name\": \"axis\",\n        \"type\": \"number\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"N\",\n        \"name\": \"n\",\n        \"type\": \"number\",\n        \"defaultValue\": 2\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"GatherV2\",\n    \"category\": \"slice_join\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"indices\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 2,\n        \"name\": \"axis\",\n        \"type\": \"number\",\n        \"defaultValue\": 0\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"batch_dims\",\n        \"name\": \"batchDims\",\n        \"type\": \"number\",\n        \"defaultValue\": 0\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"Gather\",\n    \"category\": \"slice_join\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"indices\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"validate_indices\",\n        \"name\": \"validateIndices\",\n        \"type\": \"bool\",\n        \"notSupported\": true\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"Reverse\",\n    \"category\": \"slice_join\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"dims\",\n        \"type\": \"bool[]\"\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"ReverseV2\",\n    \"category\": \"slice_join\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"axis\",\n        \"type\": \"number[]\"\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"Slice\",\n    \"category\": \"slice_join\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"begin\",\n        \"type\": \"number[]\"\n      },\n      {\n        \"start\": 2,\n        \"name\": \"size\",\n        \"type\": \"number[]\"\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"StridedSlice\",\n    \"category\": \"slice_join\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"begin\",\n        \"type\": \"number[]\"\n      },\n      {\n        \"start\": 2,\n        \"name\": \"end\",\n        \"type\": \"number[]\"\n      },\n      {\n        \"start\": 3,\n        \"name\": \"strides\",\n        \"type\": \"number[]\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"begin_mask\",\n        \"name\": \"beginMask\",\n        \"type\": \"number\",\n        \"defaultValue\": 0\n      },\n      {\n        \"tfName\": \"end_mask\",\n        \"name\": \"endMask\",\n        \"type\": \"number\",\n        \"defaultValue\": 0\n      },\n      {\n        \"tfName\": \"new_axis_mask\",\n        \"name\": \"newAxisMask\",\n        \"type\": \"number\",\n        \"defaultValue\": 0\n      },\n      {\n        \"tfName\": \"ellipsis_mask\",\n        \"name\": \"ellipsisMask\",\n        \"type\": \"number\",\n        \"defaultValue\": 0\n      },\n      {\n        \"tfName\": \"shrink_axis_mask\",\n        \"name\": \"shrinkAxisMask\",\n        \"type\": \"number\",\n        \"defaultValue\": 0\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"Pack\",\n    \"category\": \"slice_join\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"end\": 0,\n        \"name\": \"tensors\",\n        \"type\": \"tensors\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"axis\",\n        \"name\": \"axis\",\n        \"type\": \"number\",\n        \"defaultValue\": 0\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"Unpack\",\n    \"category\": \"slice_join\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"tensor\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"axis\",\n        \"name\": \"axis\",\n        \"type\": \"number\",\n        \"defaultValue\": 0\n      },\n      {\n        \"tfName\": \"num\",\n        \"name\": \"num\",\n        \"type\": \"number\",\n        \"defaultValue\": 0,\n        \"notSupported\": true\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"Tile\",\n    \"category\": \"slice_join\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"reps\",\n        \"type\": \"number[]\"\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"Split\",\n    \"category\": \"slice_join\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"axis\",\n        \"type\": \"number\",\n        \"defaultValue\": 0\n      },\n      {\n        \"start\": 1,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"num_split\",\n        \"name\": \"numOrSizeSplits\",\n        \"type\": \"number\",\n        \"defaultValue\": 1\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"SplitV\",\n    \"category\": \"slice_join\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"numOrSizeSplits\",\n        \"type\": \"number[]\"\n      },\n      {\n        \"start\": 2,\n        \"name\": \"axis\",\n        \"type\": \"number\",\n        \"defaultValue\": 0\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"ScatterNd\",\n    \"category\": \"slice_join\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"indices\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"values\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 2,\n        \"name\": \"shape\",\n        \"type\": \"number[]\"\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"GatherNd\",\n    \"category\": \"slice_join\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"indices\",\n        \"type\": \"tensor\"\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"SparseToDense\",\n    \"category\": \"slice_join\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"sparseIndices\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"outputShape\",\n        \"type\": \"number[]\"\n      },\n      {\n        \"start\": 2,\n        \"name\": \"sparseValues\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 3,\n        \"name\": \"defaultValue\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"validate_indices\",\n        \"name\": \"validateIndices\",\n        \"type\": \"bool\",\n        \"defaultValue\": false,\n        \"notSupported\": true\n      }\n    ]\n  }\n];\n\n// node_modules/.pnpm/@tensorflow+tfjs-converter@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-converter/dist/operations/op_list/sparse.js\nvar sparse_exports = {};\n__export(sparse_exports, {\n  json: () => json16\n});\nvar json16 = [\n  {\n    \"tfOpName\": \"SparseFillEmptyRows\",\n    \"category\": \"sparse\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"indices\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"values\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 2,\n        \"name\": \"denseShape\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 3,\n        \"name\": \"defaultValue\",\n        \"type\": \"tensor\"\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"SparseReshape\",\n    \"category\": \"sparse\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"inputIndices\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"inputShape\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 2,\n        \"name\": \"newShape\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"T\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"SparseSegmentMean\",\n    \"category\": \"sparse\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"data\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"indices\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 2,\n        \"name\": \"segmentIds\",\n        \"type\": \"tensor\"\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"SparseSegmentSum\",\n    \"category\": \"sparse\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"data\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"indices\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 2,\n        \"name\": \"segmentIds\",\n        \"type\": \"tensor\"\n      }\n    ]\n  }\n];\n\n// node_modules/.pnpm/@tensorflow+tfjs-converter@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-converter/dist/operations/op_list/spectral.js\nvar spectral_exports = {};\n__export(spectral_exports, {\n  json: () => json17\n});\nvar json17 = [\n  {\n    \"tfOpName\": \"FFT\",\n    \"category\": \"spectral\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"IFFT\",\n    \"category\": \"spectral\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"RFFT\",\n    \"category\": \"spectral\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"fft_length\",\n        \"type\": \"number\",\n        \"notSupported\": true\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"IRFFT\",\n    \"category\": \"spectral\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"fft_length\",\n        \"type\": \"number\",\n        \"notSupported\": true\n      }\n    ]\n  }\n];\n\n// node_modules/.pnpm/@tensorflow+tfjs-converter@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-converter/dist/operations/op_list/string.js\nvar string_exports = {};\n__export(string_exports, {\n  json: () => json18\n});\nvar json18 = [\n  {\n    \"tfOpName\": \"StringNGrams\",\n    \"category\": \"string\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"data\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"dataSplits\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"separator\",\n        \"name\": \"separator\",\n        \"type\": \"string\"\n      },\n      {\n        \"tfName\": \"ngram_widths\",\n        \"name\": \"nGramWidths\",\n        \"type\": \"number[]\"\n      },\n      {\n        \"tfName\": \"left_pad\",\n        \"name\": \"leftPad\",\n        \"type\": \"string\"\n      },\n      {\n        \"tfName\": \"right_pad\",\n        \"name\": \"rightPad\",\n        \"type\": \"string\"\n      },\n      {\n        \"tfName\": \"pad_width\",\n        \"name\": \"padWidth\",\n        \"type\": \"number\"\n      },\n      {\n        \"tfName\": \"preserve_short_sequences\",\n        \"name\": \"preserveShortSequences\",\n        \"type\": \"bool\"\n      }\n    ],\n    \"outputs\": [\n      \"ngrams\",\n      \"ngrams_splits\"\n    ]\n  },\n  {\n    \"tfOpName\": \"StringSplit\",\n    \"category\": \"string\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"input\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"delimiter\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"skip_empty\",\n        \"name\": \"skipEmpty\",\n        \"type\": \"bool\"\n      }\n    ],\n    \"outputs\": [\n      \"indices\",\n      \"values\",\n      \"shape\"\n    ]\n  },\n  {\n    \"tfOpName\": \"StringToHashBucketFast\",\n    \"category\": \"string\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"input\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"num_buckets\",\n        \"name\": \"numBuckets\",\n        \"type\": \"number\"\n      }\n    ]\n  }\n];\n\n// node_modules/.pnpm/@tensorflow+tfjs-converter@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-converter/dist/operations/op_list/transformation.js\nvar transformation_exports = {};\n__export(transformation_exports, {\n  json: () => json19\n});\nvar json19 = [\n  {\n    \"tfOpName\": \"Cast\",\n    \"category\": \"transformation\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"SrcT\",\n        \"name\": \"sdtype\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      },\n      {\n        \"tfName\": \"DstT\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\"\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"ExpandDims\",\n    \"category\": \"transformation\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"axis\",\n        \"type\": \"number\"\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"MirrorPad\",\n    \"category\": \"transformation\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"padding\",\n        \"type\": \"number[]\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"mode\",\n        \"name\": \"mode\",\n        \"type\": \"string\"\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"Pad\",\n    \"category\": \"transformation\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"padding\",\n        \"type\": \"number[]\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"constant_value\",\n        \"name\": \"constantValue\",\n        \"type\": \"number\",\n        \"defaultValue\": 0\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"PadV2\",\n    \"category\": \"transformation\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"padding\",\n        \"type\": \"number[]\"\n      },\n      {\n        \"start\": 2,\n        \"name\": \"constantValue\",\n        \"type\": \"number\",\n        \"defaultValue\": 0\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"Reshape\",\n    \"category\": \"transformation\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"shape\",\n        \"type\": \"number[]\"\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"Squeeze\",\n    \"category\": \"transformation\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"axis\",\n        \"tfDeprecatedName\": \"squeeze_dims\",\n        \"name\": \"axis\",\n        \"type\": \"number[]\"\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"SpaceToBatchND\",\n    \"category\": \"transformation\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"blockShape\",\n        \"type\": \"number[]\"\n      },\n      {\n        \"start\": 2,\n        \"name\": \"paddings\",\n        \"type\": \"number[]\"\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"BatchToSpaceND\",\n    \"category\": \"transformation\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"blockShape\",\n        \"type\": \"number[]\"\n      },\n      {\n        \"start\": 2,\n        \"name\": \"crops\",\n        \"type\": \"number[]\"\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"DepthToSpace\",\n    \"category\": \"transformation\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"block_size\",\n        \"name\": \"blockSize\",\n        \"type\": \"number\"\n      },\n      {\n        \"tfName\": \"data_format\",\n        \"name\": \"dataFormat\",\n        \"type\": \"string\"\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"BroadcastTo\",\n    \"category\": \"transformation\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"shape\",\n        \"type\": \"number[]\"\n      }\n    ],\n    \"attrs\": []\n  },\n  {\n    \"tfOpName\": \"BroadcastArgs\",\n    \"category\": \"transformation\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"s0\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"s1\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": []\n  }\n];\n\n// node_modules/.pnpm/@tensorflow+tfjs-converter@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-converter/dist/operations/operation_mapper.js\nvar OperationMapper = class {\n  static get Instance() {\n    return this._instance || (this._instance = new this());\n  }\n  constructor() {\n    const ops = [\n      arithmetic_exports,\n      basic_math_exports,\n      control_exports,\n      convolution_exports,\n      creation_exports,\n      dynamic_exports,\n      evaluation_exports,\n      graph_exports,\n      hash_table_exports,\n      image_exports,\n      logical_exports,\n      matrices_exports,\n      normalization_exports,\n      reduction_exports,\n      slice_join_exports,\n      sparse_exports,\n      spectral_exports,\n      string_exports,\n      transformation_exports\n    ];\n    const mappersJson = [].concat(...ops.map((op2) => op2.json));\n    this.opMappers = mappersJson.reduce((map, mapper) => {\n      map[mapper.tfOpName] = mapper;\n      return map;\n    }, {});\n  }\n  transformGraph(graph, signature = {}) {\n    const tfNodes = graph.node;\n    const placeholders = [];\n    const weights = [];\n    const initNodes = [];\n    const nodes = tfNodes.reduce((map, node) => {\n      map[node.name] = this.mapNode(node);\n      if (node.op.startsWith(\"Placeholder\")) {\n        placeholders.push(map[node.name]);\n      } else if (node.op === \"Const\") {\n        weights.push(map[node.name]);\n      } else if (node.input == null || node.input.length === 0) {\n        initNodes.push(map[node.name]);\n      }\n      return map;\n    }, {});\n    let inputs = [];\n    const outputs = [];\n    let inputNodeNameToKey = {};\n    let outputNodeNameToKey = {};\n    if (signature != null) {\n      inputNodeNameToKey = this.mapSignatureEntries(signature.inputs);\n      outputNodeNameToKey = this.mapSignatureEntries(signature.outputs);\n    }\n    const allNodes = Object.keys(nodes);\n    allNodes.forEach((key) => {\n      const node = nodes[key];\n      node.inputNames.forEach((name, index) => {\n        const [nodeName, , outputName] = getNodeNameAndIndex(name);\n        const inputNode = nodes[nodeName];\n        if (inputNode.outputs != null) {\n          const outputIndex = inputNode.outputs.indexOf(outputName);\n          if (outputIndex !== -1) {\n            const inputName = `${nodeName}:${outputIndex}`;\n            node.inputNames[index] = inputName;\n          }\n        }\n        node.inputs.push(inputNode);\n        inputNode.children.push(node);\n      });\n    });\n    if (Object.keys(outputNodeNameToKey).length === 0) {\n      allNodes.forEach((key) => {\n        const node = nodes[key];\n        if (node.children.length === 0) {\n          outputs.push(node);\n        }\n      });\n    } else {\n      Object.keys(outputNodeNameToKey).forEach((name) => {\n        const [nodeName] = getNodeNameAndIndex(name);\n        const node = nodes[nodeName];\n        if (node != null) {\n          node.signatureKey = outputNodeNameToKey[name];\n          outputs.push(node);\n        }\n      });\n    }\n    if (Object.keys(inputNodeNameToKey).length > 0) {\n      Object.keys(inputNodeNameToKey).forEach((name) => {\n        const [nodeName] = getNodeNameAndIndex(name);\n        const node = nodes[nodeName];\n        if (node) {\n          node.signatureKey = inputNodeNameToKey[name];\n          inputs.push(node);\n        }\n      });\n    } else {\n      inputs = placeholders;\n    }\n    let functions = {};\n    if (graph.library != null && graph.library.function != null) {\n      functions = graph.library.function.reduce((functions2, func2) => {\n        functions2[func2.signature.name] = this.mapFunction(func2);\n        return functions2;\n      }, {});\n    }\n    const result = { nodes, inputs, outputs, weights, placeholders, signature, functions };\n    if (initNodes.length > 0) {\n      result.initNodes = initNodes;\n    }\n    return result;\n  }\n  mapSignatureEntries(entries) {\n    return Object.keys(entries || {}).reduce((prev, curr) => {\n      prev[entries[curr].name] = curr;\n      return prev;\n    }, {});\n  }\n  mapNode(node) {\n    const mapper = getRegisteredOp(node.op) || this.opMappers[node.op] || {};\n    if (node.attr == null) {\n      node.attr = {};\n    }\n    const newNode = {\n      name: node.name,\n      op: node.op,\n      category: mapper.category,\n      inputNames: (node.input || []).map((input2) => input2.startsWith(\"^\") ? input2.slice(1) : input2),\n      inputs: [],\n      children: [],\n      inputParams: {},\n      attrParams: {},\n      rawAttrs: node.attr,\n      outputs: mapper.outputs\n    };\n    if (mapper.inputs != null) {\n      newNode.inputParams = mapper.inputs.reduce((map, param) => {\n        map[param.name] = {\n          type: param.type,\n          inputIndexStart: param.start,\n          inputIndexEnd: param.end\n        };\n        return map;\n      }, {});\n    }\n    if (mapper.attrs != null) {\n      newNode.attrParams = mapper.attrs.reduce((map, param) => {\n        const type = param.type;\n        let value = void 0;\n        switch (param.type) {\n          case \"string\":\n            value = getStringParam(node.attr, param.tfName, param.defaultValue);\n            if (value === void 0 && !!param.tfDeprecatedName) {\n              value = getStringParam(node.attr, param.tfDeprecatedName, param.defaultValue);\n            }\n            break;\n          case \"string[]\":\n            value = getStringArrayParam(node.attr, param.tfName, param.defaultValue);\n            if (value === void 0 && !!param.tfDeprecatedName) {\n              value = getStringArrayParam(node.attr, param.tfDeprecatedName, param.defaultValue);\n            }\n            break;\n          case \"number\":\n            value = getNumberParam(node.attr, param.tfName, param.defaultValue || 0);\n            if (value === void 0 && !!param.tfDeprecatedName) {\n              value = getNumberParam(node.attr, param.tfDeprecatedName, param.defaultValue);\n            }\n            break;\n          case \"number[]\":\n            value = getNumericArrayParam(node.attr, param.tfName, param.defaultValue);\n            if (value === void 0 && !!param.tfDeprecatedName) {\n              value = getNumericArrayParam(node.attr, param.tfDeprecatedName, param.defaultValue);\n            }\n            break;\n          case \"bool\":\n            value = getBoolParam(node.attr, param.tfName, param.defaultValue);\n            if (value === void 0 && !!param.tfDeprecatedName) {\n              value = getBoolParam(node.attr, param.tfDeprecatedName, param.defaultValue);\n            }\n            break;\n          case \"bool[]\":\n            value = getBoolArrayParam(node.attr, param.tfName, param.defaultValue);\n            if (value === void 0 && !!param.tfDeprecatedName) {\n              value = getBoolArrayParam(node.attr, param.tfDeprecatedName, param.defaultValue);\n            }\n            break;\n          case \"shape\":\n            value = getTensorShapeParam(node.attr, param.tfName, param.defaultValue);\n            if (value === void 0 && !!param.tfDeprecatedName) {\n              value = getTensorShapeParam(node.attr, param.tfDeprecatedName, param.defaultValue);\n            }\n            break;\n          case \"shape[]\":\n            value = getTensorShapeArrayParam(node.attr, param.tfName, param.defaultValue);\n            if (value === void 0 && !!param.tfDeprecatedName) {\n              value = getTensorShapeArrayParam(node.attr, param.tfDeprecatedName, param.defaultValue);\n            }\n            break;\n          case \"dtype\":\n            value = getDtypeParam(node.attr, param.tfName, param.defaultValue);\n            if (value === void 0 && !!param.tfDeprecatedName) {\n              value = getDtypeParam(node.attr, param.tfDeprecatedName, param.defaultValue);\n            }\n            break;\n          case \"dtype[]\":\n            value = getDtypeArrayParam(node.attr, param.tfName, param.defaultValue);\n            if (value === void 0 && !!param.tfDeprecatedName) {\n              value = getDtypeArrayParam(node.attr, param.tfDeprecatedName, param.defaultValue);\n            }\n            break;\n          case \"func\":\n            value = getFuncParam(node.attr, param.tfName, param.defaultValue);\n            if (value === void 0 && !!param.tfDeprecatedName) {\n              value = getFuncParam(node.attr, param.tfDeprecatedName, param.defaultValue);\n            }\n            break;\n          case \"tensor\":\n          case \"tensors\":\n            break;\n          default:\n            throw new Error(`Unsupported param type: ${param.type} for op: ${node.op}`);\n        }\n        map[param.name] = { value, type };\n        return map;\n      }, {});\n    }\n    return newNode;\n  }\n  mapFunction(functionDef) {\n    const tfNodes = functionDef.nodeDef;\n    const placeholders = [];\n    const weights = [];\n    let nodes = {};\n    if (tfNodes != null) {\n      nodes = tfNodes.reduce((map, node) => {\n        map[node.name] = this.mapNode(node);\n        if (node.op === \"Const\") {\n          weights.push(map[node.name]);\n        }\n        return map;\n      }, {});\n    }\n    const inputs = [];\n    const outputs = [];\n    functionDef.signature.inputArg.forEach((arg) => {\n      const [nodeName] = getNodeNameAndIndex(arg.name);\n      const node = {\n        name: nodeName,\n        op: \"Placeholder\",\n        inputs: [],\n        inputNames: [],\n        category: \"graph\",\n        inputParams: {},\n        attrParams: { dtype: { value: parseDtypeParam(arg.type), type: \"dtype\" } },\n        children: []\n      };\n      node.signatureKey = arg.name;\n      inputs.push(node);\n      nodes[nodeName] = node;\n    });\n    const allNodes = Object.keys(nodes);\n    allNodes.forEach((key) => {\n      const node = nodes[key];\n      node.inputNames.forEach((name, index) => {\n        const [nodeName, , outputName] = getNodeNameAndIndex(name);\n        const inputNode = nodes[nodeName];\n        if (inputNode.outputs != null) {\n          const outputIndex = inputNode.outputs.indexOf(outputName);\n          if (outputIndex !== -1) {\n            const inputName = `${nodeName}:${outputIndex}`;\n            node.inputNames[index] = inputName;\n          }\n        }\n        node.inputs.push(inputNode);\n        inputNode.children.push(node);\n      });\n    });\n    const returnNodeMap = functionDef.ret;\n    functionDef.signature.outputArg.forEach((output) => {\n      const [nodeName, index] = getNodeNameAndIndex(returnNodeMap[output.name]);\n      const node = nodes[nodeName];\n      if (node != null) {\n        node.defaultOutput = index;\n        outputs.push(node);\n      }\n    });\n    const signature = this.mapArgsToSignature(functionDef);\n    return { nodes, inputs, outputs, weights, placeholders, signature };\n  }\n  mapArgsToSignature(functionDef) {\n    return {\n      methodName: functionDef.signature.name,\n      inputs: functionDef.signature.inputArg.reduce((map, arg) => {\n        map[arg.name] = this.mapArgToTensorInfo(arg);\n        return map;\n      }, {}),\n      outputs: functionDef.signature.outputArg.reduce((map, arg) => {\n        map[arg.name] = this.mapArgToTensorInfo(arg, functionDef.ret);\n        return map;\n      }, {})\n    };\n  }\n  mapArgToTensorInfo(arg, nameMap2) {\n    let name = arg.name;\n    if (nameMap2 != null) {\n      name = nameMap2[name];\n    }\n    return { name, dtype: arg.type };\n  }\n};\nfunction decodeBase64(text) {\n  const global2 = env().global;\n  if (typeof global2.atob !== \"undefined\") {\n    return global2.atob(text);\n  } else if (typeof Buffer !== \"undefined\") {\n    return new Buffer(text, \"base64\").toString();\n  } else {\n    throw new Error(\"Unable to decode base64 in this environment. Missing built-in atob() or Buffer()\");\n  }\n}\nfunction parseStringParam(s2, keepCase) {\n  const value = Array.isArray(s2) ? String.fromCharCode.apply(null, s2) : decodeBase64(s2);\n  return keepCase ? value : value.toLowerCase();\n}\nfunction getStringParam(attrs, name, def, keepCase = false) {\n  const param = attrs[name];\n  if (param != null) {\n    return parseStringParam(param.s, keepCase);\n  }\n  return def;\n}\nfunction getBoolParam(attrs, name, def) {\n  const param = attrs[name];\n  return param ? param.b : def;\n}\nfunction getNumberParam(attrs, name, def) {\n  const param = attrs[name] || {};\n  const value = param[\"i\"] != null ? param[\"i\"] : param[\"f\"] != null ? param[\"f\"] : def;\n  return typeof value === \"number\" ? value : parseInt(value, 10);\n}\nfunction parseDtypeParam(value) {\n  if (typeof value === \"string\") {\n    value = DataType[value];\n  }\n  switch (value) {\n    case DataType.DT_FLOAT:\n    case DataType.DT_HALF:\n      return \"float32\";\n    case DataType.DT_INT32:\n    case DataType.DT_INT64:\n    case DataType.DT_INT8:\n    case DataType.DT_UINT8:\n      return \"int32\";\n    case DataType.DT_BOOL:\n      return \"bool\";\n    case DataType.DT_DOUBLE:\n      return \"float32\";\n    case DataType.DT_STRING:\n      return \"string\";\n    default:\n      return null;\n  }\n}\nfunction getFuncParam(attrs, name, def) {\n  const param = attrs[name];\n  if (param && param.func) {\n    return param.func.name;\n  }\n  return def;\n}\nfunction getDtypeParam(attrs, name, def) {\n  const param = attrs[name];\n  if (param && param.type) {\n    return parseDtypeParam(param.type);\n  }\n  return def;\n}\nfunction getDtypeArrayParam(attrs, name, def) {\n  const param = attrs[name];\n  if (param && param.list && param.list.type) {\n    return param.list.type.map((v) => parseDtypeParam(v));\n  }\n  return def;\n}\nfunction parseTensorShapeParam(shape) {\n  if (shape.unknownRank) {\n    return void 0;\n  }\n  if (shape.dim != null) {\n    return shape.dim.map((dim) => typeof dim.size === \"number\" ? dim.size : parseInt(dim.size, 10));\n  }\n  return [];\n}\nfunction getTensorShapeParam(attrs, name, def) {\n  const param = attrs[name];\n  if (param && param.shape) {\n    return parseTensorShapeParam(param.shape);\n  }\n  return def;\n}\nfunction getNumericArrayParam(attrs, name, def) {\n  const param = attrs[name];\n  if (param) {\n    return ((param.list.f && param.list.f.length ? param.list.f : param.list.i) || []).map((v) => typeof v === \"number\" ? v : parseInt(v, 10));\n  }\n  return def;\n}\nfunction getStringArrayParam(attrs, name, def, keepCase = false) {\n  const param = attrs[name];\n  if (param && param.list && param.list.s) {\n    return param.list.s.map((v) => {\n      return parseStringParam(v, keepCase);\n    });\n  }\n  return def;\n}\nfunction getTensorShapeArrayParam(attrs, name, def) {\n  const param = attrs[name];\n  if (param && param.list && param.list.shape) {\n    return param.list.shape.map((v) => {\n      return parseTensorShapeParam(v);\n    });\n  }\n  return def;\n}\nfunction getBoolArrayParam(attrs, name, def) {\n  const param = attrs[name];\n  if (param && param.list && param.list.b) {\n    return param.list.b;\n  }\n  return def;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-converter@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-converter/dist/operations/custom_op/node_value_impl.js\nvar NodeValueImpl = class {\n  constructor(node, tensorMap, context) {\n    this.node = node;\n    this.tensorMap = tensorMap;\n    this.context = context;\n    this.inputs = [];\n    this.attrs = {};\n    this.inputs = node.inputNames.map((name) => this.getInput(name));\n    if (node.rawAttrs != null) {\n      this.attrs = Object.keys(node.rawAttrs).reduce((attrs, key) => {\n        attrs[key] = this.getAttr(key);\n        return attrs;\n      }, {});\n    }\n  }\n  getInput(name) {\n    return getTensor(name, this.tensorMap, this.context);\n  }\n  getAttr(name, defaultValue) {\n    const value = this.node.rawAttrs[name];\n    if (value.tensor != null) {\n      return getTensor(name, this.tensorMap, this.context);\n    }\n    if (value.i != null || value.f != null) {\n      return getNumberParam(this.node.rawAttrs, name, defaultValue);\n    }\n    if (value.s != null) {\n      return getStringParam(this.node.rawAttrs, name, defaultValue);\n    }\n    if (value.b != null) {\n      return getBoolParam(this.node.rawAttrs, name, defaultValue);\n    }\n    if (value.shape != null) {\n      return getTensorShapeParam(this.node.rawAttrs, name, defaultValue);\n    }\n    if (value.type != null) {\n      return getDtypeParam(this.node.rawAttrs, name, defaultValue);\n    }\n    if (value.list != null) {\n      if (value.list.i != null || value.list.f != null) {\n        return getNumericArrayParam(this.node.rawAttrs, name, defaultValue);\n      }\n      if (value.list.s != null) {\n        return getStringArrayParam(this.node.rawAttrs, name, defaultValue);\n      }\n      if (value.list.shape != null) {\n        return getTensorShapeArrayParam(this.node.rawAttrs, name, defaultValue);\n      }\n      if (value.list.b != null) {\n        return getBoolArrayParam(this.node.rawAttrs, name, defaultValue);\n      }\n      if (value.list.type != null) {\n        return getDtypeArrayParam(this.node.rawAttrs, name, defaultValue);\n      }\n    }\n    return defaultValue;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/ops_for_converter.js\nvar ops_for_converter_exports = {};\n__export(ops_for_converter_exports, {\n  OP_SCOPE_SUFFIX: () => OP_SCOPE_SUFFIX,\n  abs: () => abs,\n  acos: () => acos,\n  acosh: () => acosh,\n  add: () => add2,\n  addN: () => addN,\n  all: () => all,\n  any: () => any,\n  argMax: () => argMax,\n  argMin: () => argMin,\n  asin: () => asin,\n  asinh: () => asinh,\n  atan: () => atan,\n  atan2: () => atan2,\n  atanh: () => atanh,\n  avgPool: () => avgPool,\n  avgPool3d: () => avgPool3d,\n  basicLSTMCell: () => basicLSTMCell,\n  batchNorm: () => batchNorm,\n  batchNorm2d: () => batchNorm2d,\n  batchNorm3d: () => batchNorm3d,\n  batchNorm4d: () => batchNorm4d,\n  batchToSpaceND: () => batchToSpaceND,\n  bincount: () => bincount,\n  booleanMaskAsync: () => booleanMaskAsync,\n  broadcastArgs: () => broadcastArgs,\n  broadcastTo: () => broadcastTo,\n  buffer: () => buffer,\n  cast: () => cast,\n  ceil: () => ceil,\n  clipByValue: () => clipByValue,\n  clone: () => clone,\n  complex: () => complex,\n  concat: () => concat,\n  concat1d: () => concat1d,\n  concat2d: () => concat2d,\n  concat3d: () => concat3d,\n  concat4d: () => concat4d,\n  conv1d: () => conv1d,\n  conv2d: () => conv2d,\n  conv2dTranspose: () => conv2dTranspose,\n  conv3d: () => conv3d,\n  conv3dTranspose: () => conv3dTranspose,\n  cos: () => cos,\n  cosh: () => cosh,\n  cosineWindow: () => cosineWindow,\n  cumprod: () => cumprod,\n  cumsum: () => cumsum,\n  denseBincount: () => denseBincount,\n  depthToSpace: () => depthToSpace,\n  depthwiseConv2d: () => depthwiseConv2d,\n  diag: () => diag,\n  dilation2d: () => dilation2d,\n  div: () => div,\n  divNoNan: () => divNoNan,\n  dot: () => dot,\n  dropout: () => dropout,\n  einsum: () => einsum,\n  elu: () => elu,\n  enclosingPowerOfTwo: () => enclosingPowerOfTwo,\n  equal: () => equal,\n  erf: () => erf,\n  euclideanNorm: () => euclideanNorm,\n  exp: () => exp,\n  expandDims: () => expandDims,\n  expm1: () => expm1,\n  eye: () => eye,\n  fft: () => fft,\n  fill: () => fill,\n  floor: () => floor,\n  floorDiv: () => floorDiv,\n  fused: () => fused_ops_exports,\n  gather: () => gather,\n  gatherND: () => gatherND,\n  greater: () => greater,\n  greaterEqual: () => greaterEqual,\n  ifft: () => ifft,\n  imag: () => imag,\n  image: () => image,\n  inTopKAsync: () => inTopKAsync,\n  irfft: () => irfft,\n  isFinite: () => isFinite2,\n  isInf: () => isInf,\n  isNaN: () => isNaN2,\n  leakyRelu: () => leakyRelu,\n  less: () => less,\n  lessEqual: () => lessEqual,\n  linalg: () => linalg,\n  linspace: () => linspace,\n  localResponseNormalization: () => localResponseNormalization,\n  log: () => log2,\n  log1p: () => log1p,\n  logSigmoid: () => logSigmoid,\n  logSoftmax: () => logSoftmax,\n  logSumExp: () => logSumExp,\n  logicalAnd: () => logicalAnd,\n  logicalNot: () => logicalNot,\n  logicalOr: () => logicalOr,\n  logicalXor: () => logicalXor,\n  losses: () => losses,\n  lowerBound: () => lowerBound,\n  matMul: () => matMul,\n  max: () => max,\n  maxPool: () => maxPool,\n  maxPool3d: () => maxPool3d,\n  maxPoolWithArgmax: () => maxPoolWithArgmax,\n  maximum: () => maximum,\n  mean: () => mean,\n  meshgrid: () => meshgrid,\n  min: () => min,\n  minimum: () => minimum,\n  mirrorPad: () => mirrorPad,\n  mod: () => mod,\n  moments: () => moments,\n  movingAverage: () => movingAverage,\n  mul: () => mul,\n  multiRNNCell: () => multiRNNCell,\n  multinomial: () => multinomial,\n  neg: () => neg,\n  norm: () => norm,\n  notEqual: () => notEqual,\n  oneHot: () => oneHot,\n  ones: () => ones2,\n  onesLike: () => onesLike,\n  op: () => op,\n  outerProduct: () => outerProduct,\n  pad: () => pad,\n  pad1d: () => pad1d,\n  pad2d: () => pad2d,\n  pad3d: () => pad3d,\n  pad4d: () => pad4d,\n  pool: () => pool,\n  pow: () => pow,\n  prelu: () => prelu,\n  print: () => print,\n  prod: () => prod,\n  raggedTensorToTensor: () => raggedTensorToTensor,\n  rand: () => rand,\n  randomGamma: () => randomGamma,\n  randomNormal: () => randomNormal,\n  randomStandardNormal: () => randomStandardNormal,\n  randomUniform: () => randomUniform,\n  range: () => range,\n  real: () => real,\n  reciprocal: () => reciprocal,\n  relu: () => relu,\n  relu6: () => relu6,\n  reshape: () => reshape,\n  reverse: () => reverse,\n  reverse1d: () => reverse1d,\n  reverse2d: () => reverse2d,\n  reverse3d: () => reverse3d,\n  reverse4d: () => reverse4d,\n  rfft: () => rfft,\n  round: () => round2,\n  rsqrt: () => rsqrt,\n  scalar: () => scalar,\n  scatterND: () => scatterND,\n  searchSorted: () => searchSorted,\n  selu: () => selu,\n  separableConv2d: () => separableConv2d,\n  setdiff1dAsync: () => setdiff1dAsync,\n  sigmoid: () => sigmoid,\n  sign: () => sign,\n  signal: () => signal,\n  sin: () => sin,\n  sinh: () => sinh,\n  slice: () => slice,\n  slice1d: () => slice1d,\n  slice2d: () => slice2d,\n  slice3d: () => slice3d,\n  slice4d: () => slice4d,\n  softmax: () => softmax,\n  softplus: () => softplus,\n  spaceToBatchND: () => spaceToBatchND,\n  sparse: () => sparse,\n  sparseToDense: () => sparseToDense,\n  spectral: () => spectral,\n  split: () => split,\n  sqrt: () => sqrt,\n  square: () => square,\n  squaredDifference: () => squaredDifference,\n  squeeze: () => squeeze,\n  stack: () => stack,\n  step: () => step,\n  stridedSlice: () => stridedSlice,\n  string: () => string,\n  sub: () => sub,\n  sum: () => sum2,\n  tan: () => tan,\n  tanh: () => tanh2,\n  tensor: () => tensor,\n  tensor1d: () => tensor1d,\n  tensor2d: () => tensor2d,\n  tensor3d: () => tensor3d,\n  tensor4d: () => tensor4d,\n  tensor5d: () => tensor5d,\n  tensor6d: () => tensor6d,\n  tile: () => tile,\n  topk: () => topk,\n  transpose: () => transpose,\n  truncatedNormal: () => truncatedNormal,\n  unique: () => unique,\n  unsortedSegmentSum: () => unsortedSegmentSum,\n  unstack: () => unstack,\n  upperBound: () => upperBound,\n  variable: () => variable,\n  where: () => where,\n  whereAsync: () => whereAsync,\n  zeros: () => zeros,\n  zerosLike: () => zerosLike\n});\n\n// node_modules/.pnpm/@tensorflow+tfjs-converter@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-converter/dist/operations/executors/arithmetic_executor.js\nvar executeOp = (node, tensorMap, context, ops = ops_for_converter_exports) => {\n  switch (node.op) {\n    case \"BiasAdd\":\n    case \"AddV2\":\n    case \"Add\": {\n      return [ops.add(getParamValue(\"a\", node, tensorMap, context), getParamValue(\"b\", node, tensorMap, context))];\n    }\n    case \"AddN\": {\n      return [ops.addN(getParamValue(\"tensors\", node, tensorMap, context))];\n    }\n    case \"FloorMod\":\n    case \"Mod\":\n      return [ops.mod(getParamValue(\"a\", node, tensorMap, context), getParamValue(\"b\", node, tensorMap, context))];\n    case \"Mul\":\n      return [ops.mul(getParamValue(\"a\", node, tensorMap, context), getParamValue(\"b\", node, tensorMap, context))];\n    case \"RealDiv\":\n    case \"Div\": {\n      return [ops.div(getParamValue(\"a\", node, tensorMap, context), getParamValue(\"b\", node, tensorMap, context))];\n    }\n    case \"DivNoNan\": {\n      return [ops.divNoNan(getParamValue(\"a\", node, tensorMap, context), getParamValue(\"b\", node, tensorMap, context))];\n    }\n    case \"FloorDiv\": {\n      return [ops.floorDiv(getParamValue(\"a\", node, tensorMap, context), getParamValue(\"b\", node, tensorMap, context))];\n    }\n    case \"Sub\": {\n      return [ops.sub(getParamValue(\"a\", node, tensorMap, context), getParamValue(\"b\", node, tensorMap, context))];\n    }\n    case \"Minimum\": {\n      return [ops.minimum(getParamValue(\"a\", node, tensorMap, context), getParamValue(\"b\", node, tensorMap, context))];\n    }\n    case \"Maximum\": {\n      return [ops.maximum(getParamValue(\"a\", node, tensorMap, context), getParamValue(\"b\", node, tensorMap, context))];\n    }\n    case \"Pow\": {\n      return [ops.pow(getParamValue(\"a\", node, tensorMap, context), getParamValue(\"b\", node, tensorMap, context))];\n    }\n    case \"SquaredDifference\": {\n      return [ops.squaredDifference(getParamValue(\"a\", node, tensorMap, context), getParamValue(\"b\", node, tensorMap, context))];\n    }\n    default:\n      throw TypeError(`Node type ${node.op} is not implemented`);\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-converter@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-converter/dist/operations/executors/basic_math_executor.js\nvar executeOp2 = (node, tensorMap, context, ops = ops_for_converter_exports) => {\n  switch (node.op) {\n    case \"Abs\":\n    case \"ComplexAbs\":\n      return [ops.abs(getParamValue(\"x\", node, tensorMap, context))];\n    case \"Acos\":\n      return [ops.acos(getParamValue(\"x\", node, tensorMap, context))];\n    case \"Acosh\":\n      return [ops.acosh(getParamValue(\"x\", node, tensorMap, context))];\n    case \"Asin\":\n      return [ops.asin(getParamValue(\"x\", node, tensorMap, context))];\n    case \"Asinh\":\n      return [ops.asinh(getParamValue(\"x\", node, tensorMap, context))];\n    case \"Atan\":\n      return [ops.atan(getParamValue(\"x\", node, tensorMap, context))];\n    case \"Atan2\":\n      return [ops.atan2(getParamValue(\"x\", node, tensorMap, context), getParamValue(\"y\", node, tensorMap, context))];\n    case \"Atanh\":\n      return [ops.atanh(getParamValue(\"x\", node, tensorMap, context))];\n    case \"Ceil\":\n      return [ops.ceil(getParamValue(\"x\", node, tensorMap, context))];\n    case \"Complex\":\n      return [ops.complex(getParamValue(\"real\", node, tensorMap, context), getParamValue(\"imag\", node, tensorMap, context))];\n    case \"Cos\":\n      return [ops.cos(getParamValue(\"x\", node, tensorMap, context))];\n    case \"Cosh\":\n      return [ops.cosh(getParamValue(\"x\", node, tensorMap, context))];\n    case \"Elu\":\n      return [ops.elu(getParamValue(\"x\", node, tensorMap, context))];\n    case \"Erf\":\n      return [ops.erf(getParamValue(\"x\", node, tensorMap, context))];\n    case \"Exp\":\n      return [ops.exp(getParamValue(\"x\", node, tensorMap, context))];\n    case \"Expm1\": {\n      return [ops.expm1(getParamValue(\"x\", node, tensorMap, context))];\n    }\n    case \"Floor\":\n      return [ops.floor(getParamValue(\"x\", node, tensorMap, context))];\n    case \"Log\":\n      return [ops.log(getParamValue(\"x\", node, tensorMap, context))];\n    case \"Log1p\": {\n      return [ops.log1p(getParamValue(\"x\", node, tensorMap, context))];\n    }\n    case \"Imag\":\n      return [ops.imag(getParamValue(\"x\", node, tensorMap, context))];\n    case \"Neg\":\n      return [ops.neg(getParamValue(\"x\", node, tensorMap, context))];\n    case \"Reciprocal\": {\n      return [ops.reciprocal(getParamValue(\"x\", node, tensorMap, context))];\n    }\n    case \"Real\":\n      return [ops.real(getParamValue(\"x\", node, tensorMap, context))];\n    case \"Relu\":\n      return [ops.relu(getParamValue(\"x\", node, tensorMap, context))];\n    case \"Round\": {\n      return [ops.round(getParamValue(\"x\", node, tensorMap, context))];\n    }\n    case \"Selu\":\n      return [ops.selu(getParamValue(\"x\", node, tensorMap, context))];\n    case \"Sigmoid\":\n      return [ops.sigmoid(getParamValue(\"x\", node, tensorMap, context))];\n    case \"Sin\":\n      return [ops.sin(getParamValue(\"x\", node, tensorMap, context))];\n    case \"Sign\": {\n      return [ops.sign(getParamValue(\"x\", node, tensorMap, context))];\n    }\n    case \"Sinh\": {\n      return [ops.sinh(getParamValue(\"x\", node, tensorMap, context))];\n    }\n    case \"Softplus\": {\n      return [ops.softplus(getParamValue(\"x\", node, tensorMap, context))];\n    }\n    case \"Sqrt\": {\n      return [ops.sqrt(getParamValue(\"x\", node, tensorMap, context))];\n    }\n    case \"Square\": {\n      return [ops.square(getParamValue(\"x\", node, tensorMap, context))];\n    }\n    case \"Tanh\": {\n      return [ops.tanh(getParamValue(\"x\", node, tensorMap, context))];\n    }\n    case \"Tan\":\n      return [ops.tan(getParamValue(\"x\", node, tensorMap, context))];\n    case \"ClipByValue\":\n      return [ops.clipByValue(getParamValue(\"x\", node, tensorMap, context), getParamValue(\"clipValueMin\", node, tensorMap, context), getParamValue(\"clipValueMax\", node, tensorMap, context))];\n    case \"Relu6\":\n      return [ops.relu6(getParamValue(\"x\", node, tensorMap, context))];\n    case \"Rsqrt\":\n      return [ops.rsqrt(getTensor(node.inputNames[0], tensorMap, context))];\n    case \"Prod\":\n      return [ops.prod(getParamValue(\"x\", node, tensorMap, context), getParamValue(\"axes\", node, tensorMap, context))];\n    case \"LeakyRelu\":\n      return [ops.leakyRelu(getParamValue(\"x\", node, tensorMap, context), getParamValue(\"alpha\", node, tensorMap, context))];\n    case \"Prelu\":\n      return [ops.prelu(getParamValue(\"x\", node, tensorMap, context), getParamValue(\"alpha\", node, tensorMap, context))];\n    case \"IsNan\":\n      return [ops.isNaN(getTensor(node.inputNames[0], tensorMap, context))];\n    default:\n      throw TypeError(`Node type ${node.op} is not implemented`);\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-converter@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-converter/dist/executor/tensor_utils.js\nfunction assertShapesMatchAllowUndefinedSize(shapeA, shapeB, errorMessagePrefix = \"\") {\n  if (typeof shapeA === \"number\" || typeof shapeB === \"number\") {\n    return;\n  }\n  util_exports.assert(shapeA.length === shapeB.length, () => errorMessagePrefix + ` Shapes ${shapeA} and ${shapeB} must match`);\n  for (let i2 = 0; i2 < shapeA.length; i2++) {\n    const dim0 = shapeA[i2];\n    const dim1 = shapeB[i2];\n    util_exports.assert(dim0 < 0 || dim1 < 0 || dim0 === dim1, () => errorMessagePrefix + ` Shapes ${shapeA} and ${shapeB} must match`);\n  }\n}\nfunction fullDefinedShape(elementShape) {\n  if (typeof elementShape === \"number\" || elementShape.some((dim) => dim < 0)) {\n    return false;\n  }\n  return true;\n}\nfunction inferElementShape(listElementShape, tensors, elementShape) {\n  let partialShape = mergeElementShape(listElementShape, elementShape);\n  const notfullDefinedShape = !fullDefinedShape(partialShape);\n  if (notfullDefinedShape && tensors.length === 0) {\n    throw new Error(`Tried to calculate elements of an empty list with non-fully-defined elementShape: ${partialShape}`);\n  }\n  if (notfullDefinedShape) {\n    tensors.forEach((tensor2) => {\n      partialShape = mergeElementShape(tensor2.shape, partialShape);\n    });\n  }\n  if (!fullDefinedShape(partialShape)) {\n    throw new Error(`Non-fully-defined elementShape: ${partialShape}`);\n  }\n  return partialShape;\n}\nfunction mergeElementShape(elementShapeA, elementShapeB) {\n  if (typeof elementShapeA === \"number\") {\n    return elementShapeB;\n  }\n  if (typeof elementShapeB === \"number\") {\n    return elementShapeA;\n  }\n  if (elementShapeA.length !== elementShapeB.length) {\n    throw new Error(`Incompatible ranks during merge: ${elementShapeA} vs. ${elementShapeB}`);\n  }\n  const result = [];\n  for (let i2 = 0; i2 < elementShapeA.length; ++i2) {\n    const dim0 = elementShapeA[i2];\n    const dim1 = elementShapeB[i2];\n    if (dim0 >= 0 && dim1 >= 0 && dim0 !== dim1) {\n      throw new Error(`Incompatible shape during merge: ${elementShapeA} vs. ${elementShapeB}`);\n    }\n    result[i2] = dim0 >= 0 ? dim0 : dim1;\n  }\n  return result;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-converter@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-converter/dist/executor/tensor_array.js\nvar TensorArray = class {\n  constructor(name, dtype, maxSize, elementShape, identicalElementShapes, dynamicSize, clearAfterRead) {\n    this.name = name;\n    this.dtype = dtype;\n    this.maxSize = maxSize;\n    this.elementShape = elementShape;\n    this.identicalElementShapes = identicalElementShapes;\n    this.dynamicSize = dynamicSize;\n    this.clearAfterRead = clearAfterRead;\n    this.tensors = [];\n    this.closed_ = false;\n    this.idTensor = scalar(0);\n    keep(this.idTensor);\n  }\n  get id() {\n    return this.idTensor.id;\n  }\n  get closed() {\n    return this.closed_;\n  }\n  clearAndClose(keepIds) {\n    this.tensors.forEach((tensor2) => {\n      if (keepIds == null || !keepIds.has(tensor2.tensor.id)) {\n        tensor2.tensor.dispose();\n      }\n    });\n    this.tensors = [];\n    this.closed_ = true;\n    this.idTensor.dispose();\n  }\n  size() {\n    return this.tensors.length;\n  }\n  read(index) {\n    if (this.closed_) {\n      throw new Error(`TensorArray ${this.name} has already been closed.`);\n    }\n    if (index < 0 || index >= this.size()) {\n      throw new Error(`Tried to read from index ${index}, but array size is: ${this.size()}`);\n    }\n    const tensorWithState = this.tensors[index];\n    if (tensorWithState.cleared) {\n      throw new Error(`TensorArray ${this.name}: Could not read index ${index} twice because it was cleared after a previous read (perhaps try setting clear_after_read = false?).`);\n    }\n    if (this.clearAfterRead) {\n      tensorWithState.cleared = true;\n    }\n    tensorWithState.read = true;\n    return tensorWithState.tensor;\n  }\n  readMany(indices) {\n    return indices.map((index) => this.read(index));\n  }\n  write(index, tensor2) {\n    if (this.closed_) {\n      throw new Error(`TensorArray ${this.name} has already been closed.`);\n    }\n    if (index < 0 || !this.dynamicSize && index >= this.maxSize) {\n      throw new Error(`Tried to write to index ${index}, but array is not resizeable and size is: ${this.maxSize}`);\n    }\n    const t2 = this.tensors[index] || {};\n    if (tensor2.dtype !== this.dtype) {\n      throw new Error(`TensorArray ${this.name}: Could not write to TensorArray index ${index},\n          because the value dtype is ${tensor2.dtype}, but TensorArray dtype is ${this.dtype}.`);\n    }\n    if (this.size() === 0 && (this.elementShape == null || this.elementShape.length === 0)) {\n      this.elementShape = tensor2.shape;\n    }\n    assertShapesMatchAllowUndefinedSize(this.elementShape, tensor2.shape, `TensorArray ${this.name}: Could not write to TensorArray index ${index}.`);\n    if (t2.read) {\n      throw new Error(`TensorArray ${this.name}: Could not write to TensorArray index ${index}, because it has already been read.`);\n    }\n    if (t2.written) {\n      throw new Error(`TensorArray ${this.name}: Could not write to TensorArray index ${index}, because it has already been written.`);\n    }\n    t2.tensor = tensor2;\n    keep(tensor2);\n    t2.written = true;\n    this.tensors[index] = t2;\n  }\n  writeMany(indices, tensors) {\n    if (indices.length !== tensors.length) {\n      throw new Error(`TensorArray ${this.name}: could not write multiple tensors,because the index size: ${indices.length} is not the same as tensors size: ${tensors.length}.`);\n    }\n    indices.forEach((i2, index) => this.write(i2, tensors[index]));\n  }\n  gather(indices, dtype) {\n    if (!!dtype && dtype !== this.dtype) {\n      throw new Error(`TensorArray dtype is ${this.dtype} but gather requested dtype ${dtype}`);\n    }\n    if (!indices) {\n      indices = [];\n      for (let i2 = 0; i2 < this.size(); i2++) {\n        indices.push(i2);\n      }\n    } else {\n      indices = indices.slice(0, this.size());\n    }\n    if (indices.length === 0) {\n      return tensor([], [0].concat(this.elementShape));\n    }\n    const tensors = this.readMany(indices);\n    assertShapesMatchAllowUndefinedSize(this.elementShape, tensors[0].shape, \"TensorArray shape mismatch: \");\n    return stack(tensors, 0);\n  }\n  concat(dtype) {\n    if (!!dtype && dtype !== this.dtype) {\n      throw new Error(`TensorArray dtype is ${this.dtype} but concat requested dtype ${dtype}`);\n    }\n    if (this.size() === 0) {\n      return tensor([], [0].concat(this.elementShape));\n    }\n    const indices = [];\n    for (let i2 = 0; i2 < this.size(); i2++) {\n      indices.push(i2);\n    }\n    const tensors = this.readMany(indices);\n    assertShapesMatchAllowUndefinedSize(this.elementShape, tensors[0].shape, `TensorArray shape mismatch: tensor array shape (${this.elementShape}) vs first tensor shape (${tensors[0].shape})`);\n    return concat(tensors, 0);\n  }\n  scatter(indices, tensor2) {\n    if (tensor2.dtype !== this.dtype) {\n      throw new Error(`TensorArray dtype is ${this.dtype} but tensor has dtype ${tensor2.dtype}`);\n    }\n    if (indices.length !== tensor2.shape[0]) {\n      throw new Error(`Expected len(indices) == tensor.shape[0], but saw: ${indices.length} vs. ${tensor2.shape[0]}`);\n    }\n    const maxIndex = Math.max(...indices);\n    if (!this.dynamicSize && maxIndex >= this.maxSize) {\n      throw new Error(`Max index must be < array size (${maxIndex}  vs. ${this.maxSize})`);\n    }\n    this.writeMany(indices, unstack(tensor2, 0));\n  }\n  split(length, tensor2) {\n    if (tensor2.dtype !== this.dtype) {\n      throw new Error(`TensorArray dtype is ${this.dtype} but tensor has dtype ${tensor2.dtype}`);\n    }\n    let totalLength = 0;\n    const cumulativeLengths = length.map((len) => {\n      totalLength += len;\n      return totalLength;\n    });\n    if (totalLength !== tensor2.shape[0]) {\n      throw new Error(`Expected sum of lengths to be equal to\n          tensor.shape[0], but sum of lengths is\n        ${totalLength}, and tensor's shape is: ${tensor2.shape}`);\n    }\n    if (!this.dynamicSize && length.length !== this.maxSize) {\n      throw new Error(`TensorArray's size is not equal to the size of lengths (${this.maxSize} vs. ${length.length}), and the TensorArray is not marked as dynamically resizeable`);\n    }\n    const elementPerRow = totalLength === 0 ? 0 : tensor2.size / totalLength;\n    const tensors = [];\n    tidy(() => {\n      tensor2 = reshape(tensor2, [1, totalLength, elementPerRow]);\n      for (let i2 = 0; i2 < length.length; ++i2) {\n        const previousLength = i2 === 0 ? 0 : cumulativeLengths[i2 - 1];\n        const indices2 = [0, previousLength, 0];\n        const sizes = [1, length[i2], elementPerRow];\n        tensors[i2] = reshape(slice(tensor2, indices2, sizes), this.elementShape);\n      }\n      return tensors;\n    });\n    const indices = [];\n    for (let i2 = 0; i2 < length.length; i2++) {\n      indices[i2] = i2;\n    }\n    this.writeMany(indices, tensors);\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-converter@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-converter/dist/executor/tensor_list.js\nvar TensorList = class {\n  constructor(tensors, elementShape, elementDtype, maxNumElements = -1) {\n    this.tensors = tensors;\n    this.elementShape = elementShape;\n    this.elementDtype = elementDtype;\n    if (tensors != null) {\n      tensors.forEach((tensor2) => {\n        if (elementDtype !== tensor2.dtype) {\n          throw new Error(`Invalid data types; op elements ${elementDtype}, but list elements ${tensor2.dtype}`);\n        }\n        assertShapesMatchAllowUndefinedSize(elementShape, tensor2.shape, \"TensorList shape mismatch: \");\n        keep(tensor2);\n      });\n    }\n    this.idTensor = scalar(0);\n    this.maxNumElements = maxNumElements;\n    keep(this.idTensor);\n  }\n  get id() {\n    return this.idTensor.id;\n  }\n  copy() {\n    return new TensorList([...this.tensors], this.elementShape, this.elementDtype);\n  }\n  clearAndClose(keepIds) {\n    this.tensors.forEach((tensor2) => {\n      if (keepIds == null || !keepIds.has(tensor2.id)) {\n        tensor2.dispose();\n      }\n    });\n    this.tensors.length = 0;\n    this.idTensor.dispose();\n  }\n  size() {\n    return this.tensors.length;\n  }\n  stack(elementShape, elementDtype, numElements = -1) {\n    if (elementDtype !== this.elementDtype) {\n      throw new Error(`Invalid data types; op elements ${elementDtype}, but list elements ${this.elementDtype}`);\n    }\n    if (numElements !== -1 && this.tensors.length !== numElements) {\n      throw new Error(`Operation expected a list with ${numElements} elements but got a list with ${this.tensors.length} elements.`);\n    }\n    assertShapesMatchAllowUndefinedSize(elementShape, this.elementShape, \"TensorList shape mismatch: \");\n    const outputElementShape = inferElementShape(this.elementShape, this.tensors, elementShape);\n    return tidy(() => {\n      const reshapedTensors = this.tensors.map((tensor2) => reshape(tensor2, outputElementShape));\n      return stack(reshapedTensors, 0);\n    });\n  }\n  popBack(elementShape, elementDtype) {\n    if (elementDtype !== this.elementDtype) {\n      throw new Error(`Invalid data types; op elements ${elementDtype}, but list elements ${this.elementDtype}`);\n    }\n    if (this.size() === 0) {\n      throw new Error(\"Trying to pop from an empty list.\");\n    }\n    const outputElementShape = inferElementShape(this.elementShape, this.tensors, elementShape);\n    const tensor2 = this.tensors.pop();\n    tensor2.kept = false;\n    assertShapesMatchAllowUndefinedSize(tensor2.shape, elementShape, \"TensorList shape mismatch: \");\n    return reshape(tensor2, outputElementShape);\n  }\n  pushBack(tensor2) {\n    if (tensor2.dtype !== this.elementDtype) {\n      throw new Error(`Invalid data types; op elements ${tensor2.dtype}, but list elements ${this.elementDtype}`);\n    }\n    assertShapesMatchAllowUndefinedSize(tensor2.shape, this.elementShape, \"TensorList shape mismatch: \");\n    if (this.maxNumElements === this.size()) {\n      throw new Error(`Trying to push element into a full list.`);\n    }\n    keep(tensor2);\n    this.tensors.push(tensor2);\n  }\n  resize(size) {\n    if (size < 0) {\n      throw new Error(`TensorListResize expects size to be non-negative. Got: ${size}`);\n    }\n    if (this.maxNumElements !== -1 && size > this.maxNumElements) {\n      throw new Error(`TensorListResize input size ${size} is greater maxNumElement ${this.maxNumElements}.`);\n    }\n    const destTensorList = new TensorList([], this.elementShape, this.elementDtype, this.maxNumElements);\n    destTensorList.tensors.length = size;\n    for (let i2 = 0; i2 < Math.min(this.tensors.length, size); ++i2) {\n      destTensorList.tensors[i2] = this.tensors[i2];\n    }\n    return destTensorList;\n  }\n  getItem(elementIndex, elementShape, elementDtype) {\n    if (elementDtype !== this.elementDtype) {\n      throw new Error(`Invalid data types; op elements ${elementDtype}, but list elements ${this.elementDtype}`);\n    }\n    if (elementIndex < 0 || elementIndex > this.tensors.length) {\n      throw new Error(`Trying to access element ${elementIndex} in a list with ${this.tensors.length} elements.`);\n    }\n    if (this.tensors[elementIndex] == null) {\n      throw new Error(`element at index ${elementIndex} is null.`);\n    }\n    assertShapesMatchAllowUndefinedSize(this.tensors[elementIndex].shape, elementShape, \"TensorList shape mismatch: \");\n    const outputElementShape = inferElementShape(this.elementShape, this.tensors, elementShape);\n    return reshape(this.tensors[elementIndex], outputElementShape);\n  }\n  setItem(elementIndex, tensor2) {\n    if (tensor2.dtype !== this.elementDtype) {\n      throw new Error(`Invalid data types; op elements ${tensor2.dtype}, but list elements ${this.elementDtype}`);\n    }\n    if (elementIndex < 0 || this.maxNumElements !== -1 && elementIndex >= this.maxNumElements) {\n      throw new Error(`Trying to set element ${elementIndex} in a list with max ${this.maxNumElements} elements.`);\n    }\n    assertShapesMatchAllowUndefinedSize(this.elementShape, tensor2.shape, \"TensorList shape mismatch: \");\n    keep(tensor2);\n    if (this.tensors[elementIndex] != null) {\n      this.tensors[elementIndex].kept = false;\n    }\n    this.tensors[elementIndex] = tensor2;\n  }\n  gather(indices, elementDtype, elementShape) {\n    if (elementDtype !== this.elementDtype) {\n      throw new Error(`Invalid data types; op elements ${elementDtype}, but list elements ${this.elementDtype}`);\n    }\n    assertShapesMatchAllowUndefinedSize(this.elementShape, elementShape, \"TensorList shape mismatch: \");\n    indices = indices.slice(0, this.size());\n    const outputElementShape = inferElementShape(this.elementShape, this.tensors, elementShape);\n    if (indices.length === 0) {\n      return tensor([], [0].concat(outputElementShape));\n    }\n    return tidy(() => {\n      const tensors = indices.map((i2) => reshape(this.tensors[i2], outputElementShape));\n      return stack(tensors, 0);\n    });\n  }\n  concat(elementDtype, elementShape) {\n    if (!!elementDtype && elementDtype !== this.elementDtype) {\n      throw new Error(`TensorList dtype is ${this.elementDtype} but concat requested dtype ${elementDtype}`);\n    }\n    assertShapesMatchAllowUndefinedSize(this.elementShape, elementShape, \"TensorList shape mismatch: \");\n    const outputElementShape = inferElementShape(this.elementShape, this.tensors, elementShape);\n    if (this.size() === 0) {\n      return tensor([], [0].concat(outputElementShape));\n    }\n    return tidy(() => {\n      const tensors = this.tensors.map((t2) => reshape(t2, outputElementShape));\n      return concat(tensors, 0);\n    });\n  }\n};\nfunction fromTensor(tensor2, elementShape, elementDtype) {\n  const dtype = tensor2.dtype;\n  if (tensor2.shape.length < 1) {\n    throw new Error(`Tensor must be at least a vector, but saw shape: ${tensor2.shape}`);\n  }\n  if (tensor2.dtype !== elementDtype) {\n    throw new Error(`Invalid data types; op elements ${tensor2.dtype}, but list elements ${elementDtype}`);\n  }\n  const tensorElementShape = tensor2.shape.slice(1);\n  assertShapesMatchAllowUndefinedSize(tensorElementShape, elementShape, \"TensorList shape mismatch: \");\n  const tensorList = unstack(tensor2);\n  return new TensorList(tensorList, elementShape, dtype);\n}\nfunction reserve(elementShape, elementDtype, numElements, maxNumElements) {\n  return new TensorList([], elementShape, elementDtype, maxNumElements);\n}\nfunction scatter(tensor2, indices, elementShape, numElements) {\n  if (indices.length !== tensor2.shape[0]) {\n    throw new Error(`Expected len(indices) == tensor.shape[0], but saw: ${indices.length} vs. ${tensor2.shape[0]}`);\n  }\n  const maxIndex = Math.max(...indices);\n  if (numElements != null && numElements !== -1 && maxIndex >= numElements) {\n    throw new Error(`Max index must be < array size (${maxIndex}  vs. ${numElements})`);\n  }\n  const list = new TensorList([], elementShape, tensor2.dtype, numElements);\n  const tensors = unstack(tensor2, 0);\n  indices.forEach((value, index) => {\n    list.setItem(value, tensors[index]);\n  });\n  return list;\n}\nfunction split2(tensor2, length, elementShape) {\n  let totalLength = 0;\n  const cumulativeLengths = length.map((len) => {\n    totalLength += len;\n    return totalLength;\n  });\n  if (totalLength !== tensor2.shape[0]) {\n    throw new Error(`Expected sum of lengths to be equal to\n          tensor.shape[0], but sum of lengths is\n        ${totalLength}, and tensor's shape is: ${tensor2.shape}`);\n  }\n  const shapeWithoutFirstDim = tensor2.shape.slice(1);\n  const outputElementShape = mergeElementShape(shapeWithoutFirstDim, elementShape);\n  const elementPerRow = totalLength === 0 ? 0 : tensor2.size / totalLength;\n  const tensors = tidy(() => {\n    const tensors2 = [];\n    tensor2 = reshape(tensor2, [1, totalLength, elementPerRow]);\n    for (let i2 = 0; i2 < length.length; ++i2) {\n      const previousLength = i2 === 0 ? 0 : cumulativeLengths[i2 - 1];\n      const indices = [0, previousLength, 0];\n      const sizes = [1, length[i2], elementPerRow];\n      tensors2[i2] = reshape(slice(tensor2, indices, sizes), outputElementShape);\n    }\n    tensor2.dispose();\n    return tensors2;\n  });\n  const list = new TensorList([], elementShape, tensor2.dtype, length.length);\n  for (let i2 = 0; i2 < tensors.length; i2++) {\n    list.setItem(i2, tensors[i2]);\n  }\n  return list;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-converter@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-converter/dist/operations/executors/control_executor.js\nvar executeOp3 = async (node, tensorMap, context) => {\n  switch (node.op) {\n    case \"If\":\n    case \"StatelessIf\": {\n      const thenFunc = getParamValue(\"thenBranch\", node, tensorMap, context);\n      const elseFunc = getParamValue(\"elseBranch\", node, tensorMap, context);\n      const cond = getParamValue(\"cond\", node, tensorMap, context);\n      const args = getParamValue(\"args\", node, tensorMap, context);\n      const condValue = await cond.data();\n      if (condValue[0]) {\n        return context.functionMap[thenFunc].executeFunctionAsync(args, context.tensorArrayMap, context.tensorListMap);\n      } else {\n        return context.functionMap[elseFunc].executeFunctionAsync(args, context.tensorArrayMap, context.tensorListMap);\n      }\n    }\n    case \"While\":\n    case \"StatelessWhile\": {\n      const bodyFunc = getParamValue(\"body\", node, tensorMap, context);\n      const condFunc = getParamValue(\"cond\", node, tensorMap, context);\n      const args = getParamValue(\"args\", node, tensorMap, context);\n      const condResult = await context.functionMap[condFunc].executeFunctionAsync(args, context.tensorArrayMap, context.tensorListMap);\n      const argIds = args.map((tensor2) => tensor2.id);\n      let condValue = await condResult[0].data();\n      condResult.forEach((tensor2) => {\n        if (!tensor2.kept && argIds.indexOf(tensor2.id) === -1) {\n          tensor2.dispose();\n        }\n      });\n      let result = args;\n      while (condValue[0]) {\n        const origResult = result;\n        result = await context.functionMap[bodyFunc].executeFunctionAsync(result, context.tensorArrayMap, context.tensorListMap);\n        const resultIds = result.map((tensor2) => tensor2.id);\n        origResult.forEach((tensor2) => {\n          if (!tensor2.kept && argIds.indexOf(tensor2.id) === -1 && resultIds.indexOf(tensor2.id) === -1) {\n            tensor2.dispose();\n          }\n        });\n        const condResult2 = await context.functionMap[condFunc].executeFunctionAsync(result, context.tensorArrayMap, context.tensorListMap);\n        condValue = await condResult2[0].data();\n        condResult2.forEach((tensor2) => {\n          if (!tensor2.kept && argIds.indexOf(tensor2.id) === -1 && resultIds.indexOf(tensor2.id) === -1) {\n            tensor2.dispose();\n          }\n        });\n      }\n      return result;\n    }\n    case \"LoopCond\": {\n      const pred = getParamValue(\"pred\", node, tensorMap, context);\n      return [cloneTensor(pred)];\n    }\n    case \"Switch\": {\n      const pred = getParamValue(\"pred\", node, tensorMap, context);\n      let data = getParamValue(\"data\", node, tensorMap, context);\n      if (!data.kept) {\n        data = cloneTensor(data);\n      }\n      return (await pred.data())[0] ? [void 0, data] : [data, void 0];\n    }\n    case \"Merge\": {\n      const inputName = node.inputNames.find((name) => getTensor(name, tensorMap, context) !== void 0);\n      if (inputName) {\n        const data = getTensor(inputName, tensorMap, context);\n        return [cloneTensor(data)];\n      }\n      return void 0;\n    }\n    case \"Enter\": {\n      const frameId = getParamValue(\"frameName\", node, tensorMap, context);\n      const data = getParamValue(\"tensor\", node, tensorMap, context);\n      context.enterFrame(frameId);\n      return [cloneTensor(data)];\n    }\n    case \"Exit\": {\n      const data = getParamValue(\"tensor\", node, tensorMap, context);\n      context.exitFrame();\n      return [cloneTensor(data)];\n    }\n    case \"NextIteration\": {\n      const data = getParamValue(\"tensor\", node, tensorMap, context);\n      context.nextIteration();\n      return [cloneTensor(data)];\n    }\n    case \"TensorArrayV3\": {\n      const size = getParamValue(\"size\", node, tensorMap, context);\n      const dtype = getParamValue(\"dtype\", node, tensorMap, context);\n      const elementShape = getParamValue(\"elementShape\", node, tensorMap, context);\n      const dynamicSize = getParamValue(\"dynamicSize\", node, tensorMap, context);\n      const clearAfterRead = getParamValue(\"clearAfterRead\", node, tensorMap, context);\n      const identicalElementShapes = getParamValue(\"identicalElementShapes\", node, tensorMap, context);\n      const name = getParamValue(\"name\", node, tensorMap, context);\n      const tensorArray = new TensorArray(name, dtype, size, elementShape, identicalElementShapes, dynamicSize, clearAfterRead);\n      context.addTensorArray(tensorArray);\n      return [tensorArray.idTensor, scalar(1)];\n    }\n    case \"TensorArrayWriteV3\": {\n      const id = getParamValue(\"tensorArrayId\", node, tensorMap, context);\n      const index = getParamValue(\"index\", node, tensorMap, context);\n      const writeTensor = getParamValue(\"tensor\", node, tensorMap, context);\n      const writeTensorArray = context.getTensorArray(id.id);\n      writeTensorArray.write(index, writeTensor);\n      return [writeTensorArray.idTensor];\n    }\n    case \"TensorArrayReadV3\": {\n      const readId = getParamValue(\"tensorArrayId\", node, tensorMap, context);\n      const readIndex = getParamValue(\"index\", node, tensorMap, context);\n      const readTensorArray = context.getTensorArray(readId.id);\n      return [readTensorArray.read(readIndex)];\n    }\n    case \"TensorArrayGatherV3\": {\n      const gatherId = getParamValue(\"tensorArrayId\", node, tensorMap, context);\n      const gatherIndices = getParamValue(\"indices\", node, tensorMap, context);\n      const gatherDtype = getParamValue(\"dtype\", node, tensorMap, context);\n      const gatherTensorArray = context.getTensorArray(gatherId.id);\n      return [gatherTensorArray.gather(gatherIndices, gatherDtype)];\n    }\n    case \"TensorArrayScatterV3\": {\n      const scatterId = getParamValue(\"tensorArrayId\", node, tensorMap, context);\n      const scatterIndices = getParamValue(\"indices\", node, tensorMap, context);\n      const scatterTensor = getParamValue(\"tensor\", node, tensorMap, context);\n      const scatterTensorArray = context.getTensorArray(scatterId.id);\n      scatterTensorArray.scatter(scatterIndices, scatterTensor);\n      return [scatterTensorArray.idTensor];\n    }\n    case \"TensorArrayConcatV3\": {\n      const concatId = getParamValue(\"tensorArrayId\", node, tensorMap, context);\n      const concatTensorArray = context.getTensorArray(concatId.id);\n      const concatDtype = getParamValue(\"dtype\", node, tensorMap, context);\n      return [concatTensorArray.concat(concatDtype)];\n    }\n    case \"TensorArraySplitV3\": {\n      const splitId = getParamValue(\"tensorArrayId\", node, tensorMap, context);\n      const splitTensor = getParamValue(\"tensor\", node, tensorMap, context);\n      const lengths = getParamValue(\"lengths\", node, tensorMap, context);\n      const splitTensorArray = context.getTensorArray(splitId.id);\n      splitTensorArray.split(lengths, splitTensor);\n      return [splitTensorArray.idTensor];\n    }\n    case \"TensorArraySizeV3\": {\n      const sizeId = getParamValue(\"tensorArrayId\", node, tensorMap, context);\n      const sizeTensorArray = context.getTensorArray(sizeId.id);\n      return [scalar(sizeTensorArray.size(), \"int32\")];\n    }\n    case \"TensorArrayCloseV3\": {\n      const closeId = getParamValue(\"tensorArrayId\", node, tensorMap, context);\n      const closeTensorArray = context.getTensorArray(closeId.id);\n      closeTensorArray.clearAndClose();\n      return [closeTensorArray.idTensor];\n    }\n    case \"TensorListSetItem\": {\n      const idTensor = getParamValue(\"tensorListId\", node, tensorMap, context);\n      const index = getParamValue(\"index\", node, tensorMap, context);\n      const writeTensor = getParamValue(\"tensor\", node, tensorMap, context);\n      const tensorList = context.getTensorList(idTensor.id);\n      tensorList.setItem(index, writeTensor);\n      return [tensorList.idTensor];\n    }\n    case \"TensorListGetItem\": {\n      const idTensor = getParamValue(\"tensorListId\", node, tensorMap, context);\n      const readIndex = getParamValue(\"index\", node, tensorMap, context);\n      const elementShape = getParamValue(\"elementShape\", node, tensorMap, context);\n      const elementDType = getParamValue(\"elementDType\", node, tensorMap, context);\n      const tensorList = context.getTensorList(idTensor.id);\n      return [tensorList.getItem(readIndex, elementShape, elementDType)];\n    }\n    case \"TensorListScatterV2\":\n    case \"TensorListScatter\": {\n      const scatterIndices = getParamValue(\"indices\", node, tensorMap, context);\n      const scatterTensor = getParamValue(\"tensor\", node, tensorMap, context);\n      const elementShape = getParamValue(\"elementShape\", node, tensorMap, context);\n      const numElements = getParamValue(\"numElements\", node, tensorMap, context);\n      const tensorList = scatter(scatterTensor, scatterIndices, elementShape, numElements);\n      context.addTensorList(tensorList);\n      return [tensorList.idTensor];\n    }\n    case \"TensorListReserve\":\n    case \"EmptyTensorList\": {\n      const elementShape = getParamValue(\"elementShape\", node, tensorMap, context);\n      const elementDtype = getParamValue(\"elementDType\", node, tensorMap, context);\n      let numElementsParam;\n      if (node.op === \"TensorListReserve\") {\n        numElementsParam = \"numElements\";\n      } else {\n        numElementsParam = \"maxNumElements\";\n      }\n      const numElements = getParamValue(numElementsParam, node, tensorMap, context);\n      const maxNumElements = node.op === \"TensorListReserve\" ? -1 : numElements;\n      const tensorList = reserve(elementShape, elementDtype, numElements, maxNumElements);\n      context.addTensorList(tensorList);\n      return [tensorList.idTensor];\n    }\n    case \"TensorListGather\": {\n      const gatherId = getParamValue(\"tensorListId\", node, tensorMap, context);\n      const gatherIndices = getParamValue(\"indices\", node, tensorMap, context);\n      const elementShape = getParamValue(\"elementShape\", node, tensorMap, context);\n      const elementDtype = getParamValue(\"elementDType\", node, tensorMap, context);\n      const tensorList = context.getTensorList(gatherId.id);\n      return [tensorList.gather(gatherIndices, elementDtype, elementShape)];\n    }\n    case \"TensorListStack\": {\n      const idTensor = getParamValue(\"tensorListId\", node, tensorMap, context);\n      const elementShape = getParamValue(\"elementShape\", node, tensorMap, context);\n      const elementDtype = getParamValue(\"elementDType\", node, tensorMap, context);\n      const numElements = getParamValue(\"numElements\", node, tensorMap, context);\n      const tensorList = context.getTensorList(idTensor.id);\n      return [tensorList.stack(elementShape, elementDtype, numElements)];\n    }\n    case \"TensorListFromTensor\": {\n      const tensor2 = getParamValue(\"tensor\", node, tensorMap, context);\n      const elementShape = getParamValue(\"elementShape\", node, tensorMap, context);\n      const elementDtype = getParamValue(\"elementDType\", node, tensorMap, context);\n      const tensorList = fromTensor(tensor2, elementShape, elementDtype);\n      context.addTensorList(tensorList);\n      return [tensorList.idTensor];\n    }\n    case \"TensorListConcat\":\n    case \"TensorListConcatV2\": {\n      const concatId = getParamValue(\"tensorListId\", node, tensorMap, context);\n      const tensorList = context.getTensorList(concatId.id);\n      const concatDtype = getParamValue(\"dtype\", node, tensorMap, context);\n      const elementShape = getParamValue(\"elementShape\", node, tensorMap, context);\n      return [tensorList.concat(concatDtype, elementShape)];\n    }\n    case \"TensorListPushBack\": {\n      const idTensor = getParamValue(\"tensorListId\", node, tensorMap, context);\n      const writeTensor = getParamValue(\"tensor\", node, tensorMap, context);\n      const tensorList = context.getTensorList(idTensor.id);\n      tensorList.pushBack(writeTensor);\n      return [tensorList.idTensor];\n    }\n    case \"TensorListPopBack\": {\n      const idTensor = getParamValue(\"tensorListId\", node, tensorMap, context);\n      const elementShape = getParamValue(\"elementShape\", node, tensorMap, context);\n      const elementDType = getParamValue(\"elementDType\", node, tensorMap, context);\n      const tensorList = context.getTensorList(idTensor.id);\n      return [tensorList.popBack(elementShape, elementDType)];\n    }\n    case \"TensorListSplit\": {\n      const splitTensor = getParamValue(\"tensor\", node, tensorMap, context);\n      const elementShape = getParamValue(\"elementShape\", node, tensorMap, context);\n      const lengths = getParamValue(\"lengths\", node, tensorMap, context);\n      const tensorList = split2(splitTensor, lengths, elementShape);\n      context.addTensorList(tensorList);\n      return [tensorList.idTensor];\n    }\n    case \"TensorListLength\": {\n      const idTensor = getParamValue(\"tensorListId\", node, tensorMap, context);\n      const tensorList = context.getTensorList(idTensor.id);\n      return [scalar(tensorList.size(), \"int32\")];\n    }\n    case \"TensorListResize\": {\n      const idTensor = getParamValue(\"tensorListId\", node, tensorMap, context);\n      const size = getParamValue(\"size\", node, tensorMap, context);\n      const srcTensorList = context.getTensorList(idTensor.id);\n      const destTensorList = srcTensorList.resize(size);\n      context.addTensorList(destTensorList);\n      return [destTensorList.idTensor];\n    }\n    default:\n      throw TypeError(`Node type ${node.op} is not implemented`);\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-converter@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-converter/dist/operations/executors/convolution_executor.js\nfunction fusedConvAndDepthWiseParams(node, tensorMap, context) {\n  const [extraOp, activationFunc] = getParamValue(\"fusedOps\", node, tensorMap, context);\n  const isBiasAdd = extraOp === \"biasadd\";\n  const noBiasAdd = !isBiasAdd;\n  const isPrelu = activationFunc === \"prelu\";\n  const isBatchNorm = extraOp === \"fusedbatchnorm\";\n  const numArgs = getParamValue(\"numArgs\", node, tensorMap, context);\n  if (isBiasAdd) {\n    if (isPrelu && numArgs !== 2) {\n      throw new Error(\"FusedConv2d and DepthwiseConv2d with BiasAdd and Prelu must have two extra arguments: bias and alpha.\");\n    }\n    if (!isPrelu && isBiasAdd && numArgs !== 1) {\n      throw new Error(\"FusedConv2d and DepthwiseConv2d with BiasAdd must have one extra argument: bias.\");\n    }\n  }\n  if (isBatchNorm) {\n    throw new Error(\"FusedConv2d and DepthwiseConv2d with FusedBatchNorm is not supported\");\n  }\n  const stride = getParamValue(\"strides\", node, tensorMap, context);\n  const pad3 = getPadding(node, tensorMap, context);\n  const dataFormat = getParamValue(\"dataFormat\", node, tensorMap, context).toUpperCase();\n  const dilations = getParamValue(\"dilations\", node, tensorMap, context);\n  let [biasArg, preluArg] = getParamValue(\"args\", node, tensorMap, context);\n  if (noBiasAdd) {\n    preluArg = biasArg;\n    biasArg = void 0;\n  }\n  const leakyreluAlpha = getParamValue(\"leakyreluAlpha\", node, tensorMap, context);\n  return {\n    stride,\n    pad: pad3,\n    dataFormat,\n    dilations,\n    biasArg,\n    preluArg,\n    activationFunc,\n    leakyreluAlpha\n  };\n}\nvar executeOp4 = (node, tensorMap, context, ops = ops_for_converter_exports) => {\n  switch (node.op) {\n    case \"Conv1D\": {\n      const stride = getParamValue(\"stride\", node, tensorMap, context);\n      const pad3 = getParamValue(\"pad\", node, tensorMap, context);\n      const dataFormat = getParamValue(\"dataFormat\", node, tensorMap, context).toUpperCase();\n      const dilation = getParamValue(\"dilation\", node, tensorMap, context);\n      return [ops.conv1d(getParamValue(\"x\", node, tensorMap, context), getParamValue(\"filter\", node, tensorMap, context), stride, pad3, dataFormat, dilation)];\n    }\n    case \"Conv2D\": {\n      const stride = getParamValue(\"strides\", node, tensorMap, context);\n      const pad3 = getPadding(node, tensorMap, context);\n      const dataFormat = getParamValue(\"dataFormat\", node, tensorMap, context).toUpperCase();\n      const dilations = getParamValue(\"dilations\", node, tensorMap, context);\n      return [ops.conv2d(getParamValue(\"x\", node, tensorMap, context), getParamValue(\"filter\", node, tensorMap, context), [stride[1], stride[2]], pad3, dataFormat, [dilations[1], dilations[2]])];\n    }\n    case \"_FusedConv2D\": {\n      const { stride, pad: pad3, dataFormat, dilations, biasArg, preluArg, activationFunc, leakyreluAlpha } = fusedConvAndDepthWiseParams(node, tensorMap, context);\n      return [ops.fused.conv2d({\n        x: getParamValue(\"x\", node, tensorMap, context),\n        filter: getParamValue(\"filter\", node, tensorMap, context),\n        strides: [stride[1], stride[2]],\n        pad: pad3,\n        dataFormat,\n        dilations: [dilations[1], dilations[2]],\n        bias: biasArg,\n        activation: activationFunc,\n        preluActivationWeights: preluArg,\n        leakyreluAlpha\n      })];\n    }\n    case \"FusedDepthwiseConv2dNative\": {\n      const { stride, pad: pad3, dataFormat, dilations, biasArg, preluArg, activationFunc, leakyreluAlpha } = fusedConvAndDepthWiseParams(node, tensorMap, context);\n      return [ops.fused.depthwiseConv2d({\n        x: getParamValue(\"x\", node, tensorMap, context),\n        filter: getParamValue(\"filter\", node, tensorMap, context),\n        strides: [stride[1], stride[2]],\n        pad: pad3,\n        dataFormat,\n        dilations: [dilations[1], dilations[2]],\n        bias: biasArg,\n        activation: activationFunc,\n        preluActivationWeights: preluArg,\n        leakyreluAlpha\n      })];\n    }\n    case \"Conv2DBackpropInput\":\n    case \"Conv2dTranspose\": {\n      const shape = getParamValue(\"outputShape\", node, tensorMap, context);\n      const stride = getParamValue(\"strides\", node, tensorMap, context);\n      const pad3 = getPadding(node, tensorMap, context);\n      return [ops.conv2dTranspose(getParamValue(\"x\", node, tensorMap, context), getParamValue(\"filter\", node, tensorMap, context), shape, [stride[1], stride[2]], pad3)];\n    }\n    case \"DepthwiseConv2dNative\":\n    case \"DepthwiseConv2d\": {\n      const stride = getParamValue(\"strides\", node, tensorMap, context);\n      const pad3 = getPadding(node, tensorMap, context);\n      const dilations = getParamValue(\"dilations\", node, tensorMap, context);\n      const dataFormat = getParamValue(\"dataFormat\", node, tensorMap, context).toUpperCase();\n      return [ops.depthwiseConv2d(getParamValue(\"input\", node, tensorMap, context), getParamValue(\"filter\", node, tensorMap, context), [stride[1], stride[2]], pad3, dataFormat, [dilations[1], dilations[2]])];\n    }\n    case \"Conv3D\": {\n      const stride = getParamValue(\"strides\", node, tensorMap, context);\n      const pad3 = getParamValue(\"pad\", node, tensorMap, context);\n      const dataFormat = getParamValue(\"dataFormat\", node, tensorMap, context).toUpperCase();\n      const dilations = getParamValue(\"dilations\", node, tensorMap, context);\n      return [ops.conv3d(getParamValue(\"x\", node, tensorMap, context), getParamValue(\"filter\", node, tensorMap, context), [stride[1], stride[2], stride[3]], pad3, dataFormat, [dilations[1], dilations[2], dilations[3]])];\n    }\n    case \"AvgPool\": {\n      const stride = getParamValue(\"strides\", node, tensorMap, context);\n      const pad3 = getParamValue(\"pad\", node, tensorMap, context);\n      const kernelSize = getParamValue(\"kernelSize\", node, tensorMap, context);\n      return [ops.avgPool(getParamValue(\"x\", node, tensorMap, context), [kernelSize[1], kernelSize[2]], [stride[1], stride[2]], pad3)];\n    }\n    case \"MaxPool\": {\n      const stride = getParamValue(\"strides\", node, tensorMap, context);\n      const pad3 = getParamValue(\"pad\", node, tensorMap, context);\n      const kernelSize = getParamValue(\"kernelSize\", node, tensorMap, context);\n      return [ops.maxPool(getParamValue(\"x\", node, tensorMap, context), [kernelSize[1], kernelSize[2]], [stride[1], stride[2]], pad3)];\n    }\n    case \"MaxPoolWithArgmax\": {\n      const stride = getParamValue(\"strides\", node, tensorMap, context);\n      const pad3 = getParamValue(\"pad\", node, tensorMap, context);\n      const kernelSize = getParamValue(\"kernelSize\", node, tensorMap, context);\n      const includeBatchInIndex = getParamValue(\"includeBatchInIndex\", node, tensorMap, context);\n      const { result, indexes } = ops.maxPoolWithArgmax(getParamValue(\"x\", node, tensorMap, context), [kernelSize[1], kernelSize[2]], [stride[1], stride[2]], pad3, includeBatchInIndex);\n      return [result, indexes];\n    }\n    case \"AvgPool3D\": {\n      const stride = getParamValue(\"strides\", node, tensorMap, context);\n      const pad3 = getParamValue(\"pad\", node, tensorMap, context);\n      const kernelSize = getParamValue(\"kernelSize\", node, tensorMap, context);\n      return [ops.avgPool3d(getParamValue(\"x\", node, tensorMap, context), [kernelSize[1], kernelSize[2], kernelSize[3]], [stride[1], stride[2], stride[3]], pad3)];\n    }\n    case \"MaxPool3D\": {\n      const stride = getParamValue(\"strides\", node, tensorMap, context);\n      const pad3 = getParamValue(\"pad\", node, tensorMap, context);\n      const kernelSize = getParamValue(\"kernelSize\", node, tensorMap, context);\n      return [ops.maxPool3d(getParamValue(\"x\", node, tensorMap, context), [kernelSize[1], kernelSize[2], kernelSize[3]], [stride[1], stride[2], stride[3]], pad3)];\n    }\n    case \"Dilation2D\": {\n      const strides = getParamValue(\"strides\", node, tensorMap, context);\n      const pad3 = getParamValue(\"pad\", node, tensorMap, context);\n      const dilations = getParamValue(\"dilations\", node, tensorMap, context);\n      const strideHeight = strides[1];\n      const strideWidth = strides[2];\n      const dilationHeight = dilations[1];\n      const dilationWidth = dilations[2];\n      return [ops.dilation2d(getParamValue(\"x\", node, tensorMap, context), getParamValue(\"filter\", node, tensorMap, context), [strideHeight, strideWidth], pad3, [dilationHeight, dilationWidth], \"NHWC\")];\n    }\n    default:\n      throw TypeError(`Node type ${node.op} is not implemented`);\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-converter@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-converter/dist/operations/executors/creation_executor.js\nvar executeOp5 = (node, tensorMap, context, ops = ops_for_converter_exports) => {\n  switch (node.op) {\n    case \"Fill\": {\n      const shape = getParamValue(\"shape\", node, tensorMap, context);\n      const dtype = getParamValue(\"dtype\", node, tensorMap, context);\n      const value = getParamValue(\"value\", node, tensorMap, context);\n      return [ops.fill(shape, value, dtype)];\n    }\n    case \"LinSpace\": {\n      const start = getParamValue(\"start\", node, tensorMap, context);\n      const stop = getParamValue(\"stop\", node, tensorMap, context);\n      const num = getParamValue(\"num\", node, tensorMap, context);\n      return [ops.linspace(start, stop, num)];\n    }\n    case \"Multinomial\": {\n      const logits = getParamValue(\"logits\", node, tensorMap, context);\n      const numSamples = getParamValue(\"numSamples\", node, tensorMap, context);\n      const seed = getParamValue(\"seed\", node, tensorMap, context);\n      return [ops.multinomial(logits, numSamples, seed)];\n    }\n    case \"OneHot\": {\n      const indices = getParamValue(\"indices\", node, tensorMap, context);\n      const depth = getParamValue(\"depth\", node, tensorMap, context);\n      const onValue = getParamValue(\"onValue\", node, tensorMap, context);\n      const offValue = getParamValue(\"offValue\", node, tensorMap, context);\n      const dtype = getParamValue(\"dtype\", node, tensorMap, context);\n      return [ops.oneHot(indices, depth, onValue, offValue, dtype)];\n    }\n    case \"Ones\": {\n      return [ops.ones(getParamValue(\"shape\", node, tensorMap, context), getParamValue(\"dtype\", node, tensorMap, context))];\n    }\n    case \"OnesLike\": {\n      return [ops.onesLike(getParamValue(\"x\", node, tensorMap, context))];\n    }\n    case \"RandomStandardNormal\": {\n      return [ops.randomStandardNormal(getParamValue(\"shape\", node, tensorMap, context), getParamValue(\"dtype\", node, tensorMap, context), getParamValue(\"seed\", node, tensorMap, context))];\n    }\n    case \"RandomUniform\": {\n      return [ops.randomUniform(\n        getParamValue(\"shape\", node, tensorMap, context),\n        getParamValue(\"minval\", node, tensorMap, context),\n        getParamValue(\"maxval\", node, tensorMap, context),\n        getParamValue(\"dtype\", node, tensorMap, context)\n      )];\n    }\n    case \"Range\": {\n      const start = getParamValue(\"start\", node, tensorMap, context);\n      const stop = getParamValue(\"stop\", node, tensorMap, context);\n      const step5 = getParamValue(\"step\", node, tensorMap, context);\n      return [ops.range(start, stop, step5, getParamValue(\"dtype\", node, tensorMap, context))];\n    }\n    case \"TruncatedNormal\": {\n      const shape = getParamValue(\"shape\", node, tensorMap, context);\n      const mean5 = getParamValue(\"mean\", node, tensorMap, context);\n      const stdDev = getParamValue(\"stdDev\", node, tensorMap, context);\n      const seed = getParamValue(\"seed\", node, tensorMap, context);\n      return [ops.truncatedNormal(shape, mean5, stdDev, getParamValue(\"dtype\", node, tensorMap, context), seed)];\n    }\n    case \"Zeros\": {\n      return [ops.zeros(getParamValue(\"shape\", node, tensorMap, context), getParamValue(\"dtype\", node, tensorMap, context))];\n    }\n    case \"ZerosLike\": {\n      return [ops.zerosLike(getParamValue(\"x\", node, tensorMap, context))];\n    }\n    default:\n      throw TypeError(`Node type ${node.op} is not implemented`);\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-converter@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-converter/dist/operations/executors/dynamic_executor.js\nfunction nmsParams(node, tensorMap, context) {\n  const boxes = getParamValue(\"boxes\", node, tensorMap, context);\n  const scores = getParamValue(\"scores\", node, tensorMap, context);\n  const maxOutputSize = getParamValue(\"maxOutputSize\", node, tensorMap, context);\n  const iouThreshold = getParamValue(\"iouThreshold\", node, tensorMap, context);\n  const scoreThreshold = getParamValue(\"scoreThreshold\", node, tensorMap, context);\n  const softNmsSigma = getParamValue(\"softNmsSigma\", node, tensorMap, context);\n  return {\n    boxes,\n    scores,\n    maxOutputSize,\n    iouThreshold,\n    scoreThreshold,\n    softNmsSigma\n  };\n}\nvar executeOp6 = async (node, tensorMap, context, resourceManager, ops = ops_for_converter_exports) => {\n  switch (node.op) {\n    case \"NonMaxSuppressionV5\": {\n      const { boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma } = nmsParams(node, tensorMap, context);\n      const result = await ops.image.nonMaxSuppressionWithScoreAsync(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma);\n      return [result.selectedIndices, result.selectedScores];\n    }\n    case \"NonMaxSuppressionV4\": {\n      const { boxes, scores, maxOutputSize, iouThreshold, scoreThreshold } = nmsParams(node, tensorMap, context);\n      const padToMaxOutputSize = getParamValue(\"padToMaxOutputSize\", node, tensorMap, context);\n      const result = await ops.image.nonMaxSuppressionPaddedAsync(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, padToMaxOutputSize);\n      return [result.selectedIndices, result.validOutputs];\n    }\n    case \"NonMaxSuppressionV3\":\n    case \"NonMaxSuppressionV2\": {\n      const { boxes, scores, maxOutputSize, iouThreshold, scoreThreshold } = nmsParams(node, tensorMap, context);\n      return [await ops.image.nonMaxSuppressionAsync(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold)];\n    }\n    case \"Where\": {\n      const condition = ops.cast(getParamValue(\"condition\", node, tensorMap, context), \"bool\");\n      const result = [await ops.whereAsync(condition)];\n      condition.dispose();\n      return result;\n    }\n    case \"ListDiff\": {\n      return ops.setdiff1dAsync(getParamValue(\"x\", node, tensorMap, context), getParamValue(\"y\", node, tensorMap, context));\n    }\n    default:\n      throw TypeError(`Node type ${node.op} is not implemented`);\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-converter@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-converter/dist/operations/executors/evaluation_executor.js\nvar executeOp7 = (node, tensorMap, context, ops = ops_for_converter_exports) => {\n  switch (node.op) {\n    case \"LowerBound\": {\n      const sortedSequence = getParamValue(\"sortedSequence\", node, tensorMap, context);\n      const values = getParamValue(\"values\", node, tensorMap, context);\n      return [ops.lowerBound(sortedSequence, values)];\n    }\n    case \"TopKV2\": {\n      const x = getParamValue(\"x\", node, tensorMap, context);\n      const k = getParamValue(\"k\", node, tensorMap, context);\n      const sorted = getParamValue(\"sorted\", node, tensorMap, context);\n      const result = ops.topk(x, k, sorted);\n      return [result.values, result.indices];\n    }\n    case \"UpperBound\": {\n      const sortedSequence = getParamValue(\"sortedSequence\", node, tensorMap, context);\n      const values = getParamValue(\"values\", node, tensorMap, context);\n      return [ops.upperBound(sortedSequence, values)];\n    }\n    case \"Unique\": {\n      const x = getParamValue(\"x\", node, tensorMap, context);\n      const result = ops.unique(x);\n      return [result.values, result.indices];\n    }\n    case \"UniqueV2\": {\n      const x = getParamValue(\"x\", node, tensorMap, context);\n      const axis = getParamValue(\"axis\", node, tensorMap, context);\n      const result = ops.unique(x, axis);\n      return [result.values, result.indices];\n    }\n    default:\n      throw TypeError(`Node type ${node.op} is not implemented`);\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-converter@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-converter/dist/operations/executors/graph_executor.js\nvar executeOp8 = (node, tensorMap, context, ops = ops_for_converter_exports) => {\n  switch (node.op) {\n    case \"Const\": {\n      return tensorMap[node.name];\n    }\n    case \"PlaceholderWithDefault\":\n      const def = getParamValue(\"default\", node, tensorMap, context);\n      return [getTensor(node.name, tensorMap, context) || def];\n    case \"Placeholder\":\n      return [getTensor(node.name, tensorMap, context)];\n    case \"Identity\":\n    case \"StopGradient\":\n    case \"FakeQuantWithMinMaxVars\": {\n      const data2 = getParamValue(\"x\", node, tensorMap, context);\n      return [cloneTensor(data2)];\n    }\n    case \"IdentityN\":\n      return getParamValue(\"x\", node, tensorMap, context).map((t2) => cloneTensor(t2));\n    case \"Snapshot\":\n      const snapshot = getParamValue(\"x\", node, tensorMap, context);\n      return [cloneTensor(snapshot)];\n    case \"Shape\":\n      return [ops.tensor1d(getParamValue(\"x\", node, tensorMap, context).shape, \"int32\")];\n    case \"ShapeN\":\n      return getParamValue(\"x\", node, tensorMap, context).map((t2) => ops.tensor1d(t2.shape));\n    case \"Size\":\n      return [ops.scalar(getParamValue(\"x\", node, tensorMap, context).size, \"int32\")];\n    case \"Rank\":\n      return [ops.scalar(getParamValue(\"x\", node, tensorMap, context).rank, \"int32\")];\n    case \"NoOp\":\n      return [ops.scalar(1)];\n    case \"Print\":\n      const input2 = getParamValue(\"x\", node, tensorMap, context);\n      const data = getParamValue(\"data\", node, tensorMap, context);\n      const message = getParamValue(\"message\", node, tensorMap, context);\n      const summarize = getParamValue(\"summarize\", node, tensorMap, context);\n      console.warn(\"The graph has a tf.print() operation,usually used for debugging, which slows down performance.\");\n      console.log(message);\n      for (let i2 = 0; i2 < data.length; i2++) {\n        console.log(Array.prototype.slice.call(data[i2].dataSync()).slice(0, summarize));\n      }\n      return [input2];\n    default:\n      throw TypeError(`Node type ${node.op} is not implemented`);\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-converter@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-converter/dist/executor/hash_table.js\nvar HashTable = class {\n  constructor(keyDType, valueDType) {\n    this.keyDType = keyDType;\n    this.valueDType = valueDType;\n    this.handle = scalar(0);\n    this.tensorMap = /* @__PURE__ */ new Map();\n    keep(this.handle);\n  }\n  get id() {\n    return this.handle.id;\n  }\n  clearAndClose() {\n    this.tensorMap.forEach((value) => value.dispose());\n    this.tensorMap.clear();\n    this.handle.dispose();\n  }\n  size() {\n    return this.tensorMap.size;\n  }\n  tensorSize() {\n    return scalar(this.size(), \"int32\");\n  }\n  async import(keys, values) {\n    this.checkKeyAndValueTensor(keys, values);\n    const $keys = await keys.data();\n    this.tensorMap.forEach((value) => value.dispose());\n    this.tensorMap.clear();\n    return tidy(() => {\n      const $values = unstack(values);\n      const keysLength = $keys.length;\n      const valuesLength = $values.length;\n      util_exports.assert(keysLength === valuesLength, () => `The number of elements doesn't match, keys has ${keysLength} elements, the values has ${valuesLength} elements.`);\n      for (let i2 = 0; i2 < keysLength; i2++) {\n        const key = $keys[i2];\n        const value = $values[i2];\n        keep(value);\n        this.tensorMap.set(key, value);\n      }\n      return this.handle;\n    });\n  }\n  async find(keys, defaultValue) {\n    this.checkKeyAndValueTensor(keys, defaultValue);\n    const $keys = await keys.data();\n    return tidy(() => {\n      const result = [];\n      for (let i2 = 0; i2 < $keys.length; i2++) {\n        const key = $keys[i2];\n        const value = this.findWithDefault(key, defaultValue);\n        result.push(value);\n      }\n      return stack(result);\n    });\n  }\n  findWithDefault(key, defaultValue) {\n    const result = this.tensorMap.get(key);\n    return result != null ? result : defaultValue;\n  }\n  checkKeyAndValueTensor(key, value) {\n    if (key.dtype !== this.keyDType) {\n      throw new Error(`Expect key dtype ${this.keyDType}, but got ${key.dtype}`);\n    }\n    if (value.dtype !== this.valueDType) {\n      throw new Error(`Expect value dtype ${this.valueDType}, but got ${value.dtype}`);\n    }\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-converter@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-converter/dist/operations/executors/hash_table_executor.js\nvar executeOp9 = async (node, tensorMap, context, resourceManager) => {\n  switch (node.op) {\n    case \"HashTable\":\n    case \"HashTableV2\": {\n      const keyDType = getParamValue(\"keyDType\", node, tensorMap, context);\n      const valueDType = getParamValue(\"valueDType\", node, tensorMap, context);\n      const hashTable = new HashTable(keyDType, valueDType);\n      resourceManager.addHashTable(node.name, hashTable);\n      return [hashTable.handle];\n    }\n    case \"LookupTableImport\":\n    case \"LookupTableImportV2\": {\n      const handle = getParamValue(\"tableHandle\", node, tensorMap, context, resourceManager);\n      const keys = getParamValue(\"keys\", node, tensorMap, context);\n      const values = getParamValue(\"values\", node, tensorMap, context);\n      const hashTable = resourceManager.getHashTableById(handle.id);\n      return [await hashTable.import(keys, values)];\n    }\n    case \"LookupTableFind\":\n    case \"LookupTableFindV2\": {\n      const handle = getParamValue(\"tableHandle\", node, tensorMap, context, resourceManager);\n      const keys = getParamValue(\"keys\", node, tensorMap, context);\n      const defaultValue = getParamValue(\"defaultValue\", node, tensorMap, context);\n      const hashTable = resourceManager.getHashTableById(handle.id);\n      return [await hashTable.find(keys, defaultValue)];\n    }\n    case \"LookupTableSize\":\n    case \"LookupTableSizeV2\": {\n      const handle = getParamValue(\"tableHandle\", node, tensorMap, context, resourceManager);\n      const hashTable = resourceManager.getHashTableById(handle.id);\n      return [hashTable.tensorSize()];\n    }\n    default:\n      throw TypeError(`Node type ${node.op} is not implemented`);\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-converter@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-converter/dist/operations/executors/image_executor.js\nvar executeOp10 = (node, tensorMap, context, ops = ops_for_converter_exports) => {\n  switch (node.op) {\n    case \"ResizeBilinear\": {\n      const images = getParamValue(\"images\", node, tensorMap, context);\n      const size = getParamValue(\"size\", node, tensorMap, context);\n      const alignCorners = getParamValue(\"alignCorners\", node, tensorMap, context);\n      const halfPixelCenters = getParamValue(\"halfPixelCenters\", node, tensorMap, context);\n      return [ops.image.resizeBilinear(images, [size[0], size[1]], alignCorners, halfPixelCenters)];\n    }\n    case \"ResizeNearestNeighbor\": {\n      const images = getParamValue(\"images\", node, tensorMap, context);\n      const size = getParamValue(\"size\", node, tensorMap, context);\n      const alignCorners = getParamValue(\"alignCorners\", node, tensorMap, context);\n      const halfPixelCenters = getParamValue(\"halfPixelCenters\", node, tensorMap, context);\n      return [ops.image.resizeNearestNeighbor(images, [size[0], size[1]], alignCorners, halfPixelCenters)];\n    }\n    case \"CropAndResize\": {\n      const image2 = getParamValue(\"image\", node, tensorMap, context);\n      const boxes = getParamValue(\"boxes\", node, tensorMap, context);\n      const boxInd = getParamValue(\"boxInd\", node, tensorMap, context);\n      const cropSize = getParamValue(\"cropSize\", node, tensorMap, context);\n      const method = getParamValue(\"method\", node, tensorMap, context);\n      const extrapolationValue = getParamValue(\"extrapolationValue\", node, tensorMap, context);\n      return [ops.image.cropAndResize(image2, boxes, boxInd, cropSize, method, extrapolationValue)];\n    }\n    case \"ImageProjectiveTransformV3\": {\n      const images = getParamValue(\"images\", node, tensorMap, context);\n      const transforms = getParamValue(\"transforms\", node, tensorMap, context);\n      const outputShape = getParamValue(\"outputShape\", node, tensorMap, context);\n      const fillValue = getParamValue(\"fillValue\", node, tensorMap, context);\n      const interpolation = getParamValue(\"interpolation\", node, tensorMap, context);\n      const fillMode = getParamValue(\"fillMode\", node, tensorMap, context);\n      return [ops.image.transform(images, transforms, interpolation.toLowerCase(), fillMode.toLowerCase(), fillValue, outputShape)];\n    }\n    default:\n      throw TypeError(`Node type ${node.op} is not implemented`);\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-converter@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-converter/dist/operations/executors/logical_executor.js\nvar executeOp11 = (node, tensorMap, context, ops = ops_for_converter_exports) => {\n  switch (node.op) {\n    case \"Equal\": {\n      return [ops.equal(getParamValue(\"a\", node, tensorMap, context), getParamValue(\"b\", node, tensorMap, context))];\n    }\n    case \"NotEqual\": {\n      return [ops.notEqual(getParamValue(\"a\", node, tensorMap, context), getParamValue(\"b\", node, tensorMap, context))];\n    }\n    case \"Greater\": {\n      return [ops.greater(getParamValue(\"a\", node, tensorMap, context), getParamValue(\"b\", node, tensorMap, context))];\n    }\n    case \"GreaterEqual\": {\n      return [ops.greaterEqual(getParamValue(\"a\", node, tensorMap, context), getParamValue(\"b\", node, tensorMap, context))];\n    }\n    case \"Less\": {\n      return [ops.less(getParamValue(\"a\", node, tensorMap, context), getParamValue(\"b\", node, tensorMap, context))];\n    }\n    case \"LessEqual\": {\n      return [ops.lessEqual(getParamValue(\"a\", node, tensorMap, context), getParamValue(\"b\", node, tensorMap, context))];\n    }\n    case \"LogicalAnd\": {\n      return [ops.logicalAnd(getParamValue(\"a\", node, tensorMap, context), getParamValue(\"b\", node, tensorMap, context))];\n    }\n    case \"LogicalNot\": {\n      return [ops.logicalNot(getParamValue(\"a\", node, tensorMap, context))];\n    }\n    case \"LogicalOr\": {\n      return [ops.logicalOr(getParamValue(\"a\", node, tensorMap, context), getParamValue(\"b\", node, tensorMap, context))];\n    }\n    case \"Select\":\n    case \"SelectV2\": {\n      return [ops.where(getParamValue(\"condition\", node, tensorMap, context), getParamValue(\"a\", node, tensorMap, context), getParamValue(\"b\", node, tensorMap, context))];\n    }\n    default:\n      throw TypeError(`Node type ${node.op} is not implemented`);\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-converter@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-converter/dist/operations/executors/matrices_executor.js\nvar executeOp12 = (node, tensorMap, context, ops = ops_for_converter_exports) => {\n  switch (node.op) {\n    case \"BatchMatMul\":\n    case \"BatchMatMulV2\":\n    case \"MatMul\":\n      return [ops.matMul(getParamValue(\"a\", node, tensorMap, context), getParamValue(\"b\", node, tensorMap, context), getParamValue(\"transposeA\", node, tensorMap, context), getParamValue(\"transposeB\", node, tensorMap, context))];\n    case \"Einsum\":\n      return [ops.einsum(getParamValue(\"equation\", node, tensorMap, context), ...getParamValue(\"tensors\", node, tensorMap, context))];\n    case \"Transpose\":\n      return [ops.transpose(getParamValue(\"x\", node, tensorMap, context), getParamValue(\"perm\", node, tensorMap, context))];\n    case \"_FusedMatMul\":\n      const [extraOp, activationFunc] = getParamValue(\"fusedOps\", node, tensorMap, context);\n      const isBiasAdd = extraOp === \"biasadd\";\n      const isPrelu = activationFunc === \"prelu\";\n      const numArgs = getParamValue(\"numArgs\", node, tensorMap, context);\n      const leakyreluAlpha = getParamValue(\"leakyreluAlpha\", node, tensorMap, context);\n      if (isBiasAdd) {\n        if (isPrelu && numArgs !== 2) {\n          throw new Error(\"Fused MatMul with BiasAdd and Prelu must have two extra arguments: bias and alpha.\");\n        }\n        if (!isPrelu && numArgs !== 1) {\n          throw new Error(\"Fused MatMul with BiasAdd must have one extra argument: bias.\");\n        }\n      }\n      const [biasArg, preluArg] = getParamValue(\"args\", node, tensorMap, context);\n      return [ops.fused.matMul({\n        a: getParamValue(\"a\", node, tensorMap, context),\n        b: getParamValue(\"b\", node, tensorMap, context),\n        transposeA: getParamValue(\"transposeA\", node, tensorMap, context),\n        transposeB: getParamValue(\"transposeB\", node, tensorMap, context),\n        bias: biasArg,\n        activation: activationFunc,\n        preluActivationWeights: preluArg,\n        leakyreluAlpha\n      })];\n    default:\n      throw TypeError(`Node type ${node.op} is not implemented`);\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-converter@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-converter/dist/operations/executors/normalization_executor.js\nvar executeOp13 = (node, tensorMap, context, ops = ops_for_converter_exports) => {\n  switch (node.op) {\n    case \"EuclideanNorm\":\n      return [ops.euclideanNorm(getParamValue(\"x\", node, tensorMap, context), getParamValue(\"axis\", node, tensorMap, context), getParamValue(\"keepDims\", node, tensorMap, context))];\n    case \"FusedBatchNorm\":\n    case \"FusedBatchNormV2\": {\n      return [ops.batchNorm(getParamValue(\"x\", node, tensorMap, context), getParamValue(\"mean\", node, tensorMap, context), getParamValue(\"variance\", node, tensorMap, context), getParamValue(\"offset\", node, tensorMap, context), getParamValue(\"scale\", node, tensorMap, context), getParamValue(\"epsilon\", node, tensorMap, context))];\n    }\n    case \"FusedBatchNormV3\": {\n      return [ops.batchNorm(getParamValue(\"x\", node, tensorMap, context), getParamValue(\"mean\", node, tensorMap, context), getParamValue(\"variance\", node, tensorMap, context), getParamValue(\"offset\", node, tensorMap, context), getParamValue(\"scale\", node, tensorMap, context), getParamValue(\"epsilon\", node, tensorMap, context))];\n    }\n    case \"LRN\": {\n      return [ops.localResponseNormalization(getParamValue(\"x\", node, tensorMap, context), getParamValue(\"radius\", node, tensorMap, context), getParamValue(\"bias\", node, tensorMap, context), getParamValue(\"alpha\", node, tensorMap, context), getParamValue(\"beta\", node, tensorMap, context))];\n    }\n    case \"Softmax\": {\n      return [ops.softmax(getParamValue(\"x\", node, tensorMap, context))];\n    }\n    case \"LogSoftmax\": {\n      return [ops.logSoftmax(getParamValue(\"x\", node, tensorMap, context))];\n    }\n    case \"SparseToDense\": {\n      return [ops.sparseToDense(getParamValue(\"sparseIndices\", node, tensorMap, context), getParamValue(\"outputShape\", node, tensorMap, context), getParamValue(\"sparseValues\", node, tensorMap, context), getParamValue(\"defaultValue\", node, tensorMap, context))];\n    }\n    default:\n      throw TypeError(`Node type ${node.op} is not implemented`);\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-converter@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-converter/dist/operations/executors/reduction_executor.js\nvar executeOp14 = (node, tensorMap, context, ops = ops_for_converter_exports) => {\n  switch (node.op) {\n    case \"Max\": {\n      const axis = getParamValue(\"axis\", node, tensorMap, context);\n      const keepDims = getParamValue(\"keepDims\", node, tensorMap, context);\n      return [ops.max(getParamValue(\"x\", node, tensorMap, context), axis, keepDims)];\n    }\n    case \"Mean\": {\n      const axis = getParamValue(\"axis\", node, tensorMap, context);\n      const keepDims = getParamValue(\"keepDims\", node, tensorMap, context);\n      return [ops.mean(getParamValue(\"x\", node, tensorMap, context), axis, keepDims)];\n    }\n    case \"Min\": {\n      const axis = getParamValue(\"axis\", node, tensorMap, context);\n      const keepDims = getParamValue(\"keepDims\", node, tensorMap, context);\n      return [ops.min(getParamValue(\"x\", node, tensorMap, context), axis, keepDims)];\n    }\n    case \"Sum\": {\n      const axis = getParamValue(\"axis\", node, tensorMap, context);\n      const keepDims = getParamValue(\"keepDims\", node, tensorMap, context);\n      return [ops.sum(getParamValue(\"x\", node, tensorMap, context), axis, keepDims)];\n    }\n    case \"All\": {\n      const axis = getParamValue(\"axis\", node, tensorMap, context);\n      const keepDims = getParamValue(\"keepDims\", node, tensorMap, context);\n      return [ops.all(getParamValue(\"x\", node, tensorMap, context), axis, keepDims)];\n    }\n    case \"Any\": {\n      const axis = getParamValue(\"axis\", node, tensorMap, context);\n      const keepDims = getParamValue(\"keepDims\", node, tensorMap, context);\n      return [ops.any(getParamValue(\"x\", node, tensorMap, context), axis, keepDims)];\n    }\n    case \"ArgMax\": {\n      const axis = getParamValue(\"axis\", node, tensorMap, context);\n      return [ops.argMax(getParamValue(\"x\", node, tensorMap, context), axis)];\n    }\n    case \"ArgMin\": {\n      const axis = getParamValue(\"axis\", node, tensorMap, context);\n      return [ops.argMin(getParamValue(\"x\", node, tensorMap, context), axis)];\n    }\n    case \"Prod\": {\n      const axis = getParamValue(\"axis\", node, tensorMap, context);\n      const keepDims = getParamValue(\"keepDims\", node, tensorMap, context);\n      return [ops.prod(getParamValue(\"x\", node, tensorMap, context), axis, keepDims)];\n    }\n    case \"Cumprod\": {\n      const axis = getParamValue(\"axis\", node, tensorMap, context);\n      const exclusive = getParamValue(\"exclusive\", node, tensorMap, context);\n      const reverse5 = getParamValue(\"reverse\", node, tensorMap, context);\n      return [ops.cumprod(getParamValue(\"x\", node, tensorMap, context), axis, exclusive, reverse5)];\n    }\n    case \"Cumsum\": {\n      const axis = getParamValue(\"axis\", node, tensorMap, context);\n      const exclusive = getParamValue(\"exclusive\", node, tensorMap, context);\n      const reverse5 = getParamValue(\"reverse\", node, tensorMap, context);\n      return [ops.cumsum(getParamValue(\"x\", node, tensorMap, context), axis, exclusive, reverse5)];\n    }\n    case \"Bincount\":\n      const x = getParamValue(\"x\", node, tensorMap, context);\n      const weights = getParamValue(\"weights\", node, tensorMap, context);\n      const size = getParamValue(\"size\", node, tensorMap, context);\n      return [ops.bincount(x, weights, size)];\n    case \"DenseBincount\": {\n      const x2 = getParamValue(\"x\", node, tensorMap, context);\n      const weights2 = getParamValue(\"weights\", node, tensorMap, context);\n      const size2 = getParamValue(\"size\", node, tensorMap, context);\n      const binaryOutput = getParamValue(\"binaryOutput\", node, tensorMap, context);\n      return [ops.denseBincount(x2, weights2, size2, binaryOutput)];\n    }\n    default:\n      throw TypeError(`Node type ${node.op} is not implemented`);\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-converter@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-converter/dist/operations/executors/slice_join_executor.js\nvar executeOp15 = (node, tensorMap, context, ops = ops_for_converter_exports) => {\n  switch (node.op) {\n    case \"ConcatV2\":\n    case \"Concat\": {\n      const n2 = getParamValue(\"n\", node, tensorMap, context);\n      const axis = getParamValue(\"axis\", node, tensorMap, context);\n      let inputs = getParamValue(\"tensors\", node, tensorMap, context);\n      inputs = inputs.slice(0, n2);\n      return [ops.concat(inputs, axis)];\n    }\n    case \"Gather\": {\n      const input2 = getParamValue(\"x\", node, tensorMap, context);\n      const indices = getParamValue(\"indices\", node, tensorMap, context);\n      return [ops.gather(input2, ops.cast(indices, \"int32\"), 0)];\n    }\n    case \"GatherV2\": {\n      const axis = getParamValue(\"axis\", node, tensorMap, context);\n      const batchDims = getParamValue(\"batchDims\", node, tensorMap, context);\n      const input2 = getParamValue(\"x\", node, tensorMap, context);\n      const indices = getParamValue(\"indices\", node, tensorMap, context);\n      return [ops.gather(input2, ops.cast(indices, \"int32\"), axis, batchDims)];\n    }\n    case \"Reverse\": {\n      const dims = getParamValue(\"dims\", node, tensorMap, context);\n      const axis = [];\n      for (let i2 = 0; i2 < dims.length; i2++) {\n        if (dims[i2]) {\n          axis.push(i2);\n        }\n      }\n      const input2 = getParamValue(\"x\", node, tensorMap, context);\n      return [ops.reverse(input2, axis)];\n    }\n    case \"ReverseV2\": {\n      const axis = getParamValue(\"axis\", node, tensorMap, context);\n      const input2 = getParamValue(\"x\", node, tensorMap, context);\n      return [ops.reverse(input2, axis)];\n    }\n    case \"Slice\": {\n      const begin = getParamValue(\"begin\", node, tensorMap, context);\n      const size = getParamValue(\"size\", node, tensorMap, context);\n      return [ops.slice(getParamValue(\"x\", node, tensorMap, context), begin, size)];\n    }\n    case \"StridedSlice\": {\n      const begin = getParamValue(\"begin\", node, tensorMap, context);\n      const end = getParamValue(\"end\", node, tensorMap, context);\n      const strides = getParamValue(\"strides\", node, tensorMap, context);\n      const beginMask = getParamValue(\"beginMask\", node, tensorMap, context);\n      const endMask = getParamValue(\"endMask\", node, tensorMap, context);\n      const ellipsisMask = getParamValue(\"ellipsisMask\", node, tensorMap, context);\n      const newAxisMask = getParamValue(\"newAxisMask\", node, tensorMap, context);\n      const shrinkAxisMask = getParamValue(\"shrinkAxisMask\", node, tensorMap, context);\n      const tensor2 = getParamValue(\"x\", node, tensorMap, context);\n      return [ops.stridedSlice(tensor2, begin, end, strides, beginMask, endMask, ellipsisMask, newAxisMask, shrinkAxisMask)];\n    }\n    case \"Pack\": {\n      return tidy(() => {\n        const axis = getParamValue(\"axis\", node, tensorMap, context);\n        const tensors = getParamValue(\"tensors\", node, tensorMap, context);\n        const shape = tensors[0].shape;\n        const squeezedShape = ops.squeeze(tensors[0]).shape;\n        const mapped = tensors.map((tensor2) => {\n          const sameShape = util_exports.arraysEqual(tensor2.shape, shape);\n          if (!sameShape && !util_exports.arraysEqual(ops.squeeze(tensor2).shape, squeezedShape)) {\n            throw new Error(\"the input tensors shape does not match\");\n          }\n          return sameShape ? tensor2 : ops.reshape(tensor2, shape);\n        });\n        return [ops.stack(mapped, axis)];\n      });\n    }\n    case \"Unpack\": {\n      const axis = getParamValue(\"axis\", node, tensorMap, context);\n      const tensor2 = getParamValue(\"tensor\", node, tensorMap, context);\n      return ops.unstack(tensor2, axis);\n    }\n    case \"Tile\": {\n      const reps = getParamValue(\"reps\", node, tensorMap, context);\n      return [ops.tile(getParamValue(\"x\", node, tensorMap, context), reps)];\n    }\n    case \"Split\":\n    case \"SplitV\": {\n      const axis = getParamValue(\"axis\", node, tensorMap, context);\n      const numOrSizeSplits = getParamValue(\"numOrSizeSplits\", node, tensorMap, context);\n      const tensor2 = getParamValue(\"x\", node, tensorMap, context);\n      return ops.split(tensor2, numOrSizeSplits, axis);\n    }\n    case \"ScatterNd\": {\n      const indices = getParamValue(\"indices\", node, tensorMap, context);\n      const values = getParamValue(\"values\", node, tensorMap, context);\n      const shape = getParamValue(\"shape\", node, tensorMap, context);\n      return [ops.scatterND(indices, values, shape)];\n    }\n    case \"GatherNd\": {\n      const x = getParamValue(\"x\", node, tensorMap, context);\n      const indices = getParamValue(\"indices\", node, tensorMap, context);\n      return [ops.gatherND(x, indices)];\n    }\n    case \"SparseToDense\": {\n      const indices = getParamValue(\"sparseIndices\", node, tensorMap, context);\n      const shape = getParamValue(\"outputShape\", node, tensorMap, context);\n      const sparseValues = getParamValue(\"sparseValues\", node, tensorMap, context);\n      const defaultValue = getParamValue(\"defaultValue\", node, tensorMap, context);\n      return [ops.sparseToDense(indices, sparseValues, shape, sparseValues.dtype === defaultValue.dtype ? defaultValue : ops.cast(defaultValue, sparseValues.dtype))];\n    }\n    default:\n      throw TypeError(`Node type ${node.op} is not implemented`);\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-converter@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-converter/dist/operations/executors/sparse_executor.js\nvar executeOp16 = (node, tensorMap, context, ops = ops_for_converter_exports) => {\n  switch (node.op) {\n    case \"SparseFillEmptyRows\": {\n      const { outputIndices, outputValues, emptyRowIndicator, reverseIndexMap } = ops.sparse.sparseFillEmptyRows(getParamValue(\"indices\", node, tensorMap, context), getParamValue(\"values\", node, tensorMap, context), getParamValue(\"denseShape\", node, tensorMap, context), getParamValue(\"defaultValue\", node, tensorMap, context));\n      return [\n        outputIndices,\n        outputValues,\n        emptyRowIndicator,\n        reverseIndexMap\n      ];\n    }\n    case \"SparseReshape\": {\n      const { outputIndices, outputShape } = ops.sparse.sparseReshape(getParamValue(\"inputIndices\", node, tensorMap, context), getParamValue(\"inputShape\", node, tensorMap, context), getParamValue(\"newShape\", node, tensorMap, context));\n      return [outputIndices, outputShape];\n    }\n    case \"SparseSegmentMean\": {\n      const outputData = ops.sparse.sparseSegmentMean(getParamValue(\"data\", node, tensorMap, context), getParamValue(\"indices\", node, tensorMap, context), getParamValue(\"segmentIds\", node, tensorMap, context));\n      return [outputData];\n    }\n    case \"SparseSegmentSum\": {\n      const outputData = ops.sparse.sparseSegmentSum(getParamValue(\"data\", node, tensorMap, context), getParamValue(\"indices\", node, tensorMap, context), getParamValue(\"segmentIds\", node, tensorMap, context));\n      return [outputData];\n    }\n    default:\n      throw TypeError(`Node type ${node.op} is not implemented`);\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-converter@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-converter/dist/operations/executors/spectral_executor.js\nvar executeOp17 = (node, tensorMap, context, ops = ops_for_converter_exports) => {\n  switch (node.op) {\n    case \"FFT\": {\n      return [ops.fft(getParamValue(\"x\", node, tensorMap, context))];\n    }\n    case \"IFFT\": {\n      return [ops.ifft(getParamValue(\"x\", node, tensorMap, context))];\n    }\n    case \"RFFT\": {\n      return [ops.rfft(getParamValue(\"x\", node, tensorMap, context))];\n    }\n    case \"IRFFT\": {\n      return [ops.irfft(getParamValue(\"x\", node, tensorMap, context))];\n    }\n    default:\n      throw TypeError(`Node type ${node.op} is not implemented`);\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-converter@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-converter/dist/operations/executors/string_executor.js\nvar executeOp18 = (node, tensorMap, context, ops = ops_for_converter_exports) => {\n  switch (node.op) {\n    case \"StringNGrams\": {\n      const { nGrams, nGramsSplits } = ops.string.stringNGrams(getParamValue(\"data\", node, tensorMap, context), getParamValue(\"dataSplits\", node, tensorMap, context), getParamValue(\"separator\", node, tensorMap, context), getParamValue(\"nGramWidths\", node, tensorMap, context), getParamValue(\"leftPad\", node, tensorMap, context), getParamValue(\"rightPad\", node, tensorMap, context), getParamValue(\"padWidth\", node, tensorMap, context), getParamValue(\"preserveShortSequences\", node, tensorMap, context));\n      return [nGrams, nGramsSplits];\n    }\n    case \"StringSplit\": {\n      const { indices, values, shape } = ops.string.stringSplit(getParamValue(\"input\", node, tensorMap, context), getParamValue(\"delimiter\", node, tensorMap, context), getParamValue(\"skipEmpty\", node, tensorMap, context));\n      return [indices, values, shape];\n    }\n    case \"StringToHashBucketFast\": {\n      const output = ops.string.stringToHashBucketFast(getParamValue(\"input\", node, tensorMap, context), getParamValue(\"numBuckets\", node, tensorMap, context));\n      return [output];\n    }\n    default:\n      throw TypeError(`Node type ${node.op} is not implemented`);\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-converter@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-converter/dist/operations/executors/transformation_executor.js\nvar executeOp19 = (node, tensorMap, context, ops = ops_for_converter_exports) => {\n  switch (node.op) {\n    case \"Cast\": {\n      return [ops.cast(getParamValue(\"x\", node, tensorMap, context), getParamValue(\"dtype\", node, tensorMap, context))];\n    }\n    case \"ExpandDims\": {\n      const axis = getParamValue(\"axis\", node, tensorMap, context);\n      return [ops.expandDims(getParamValue(\"x\", node, tensorMap, context), axis)];\n    }\n    case \"Squeeze\": {\n      const axis = getParamValue(\"axis\", node, tensorMap, context);\n      return [ops.squeeze(getParamValue(\"x\", node, tensorMap, context), axis)];\n    }\n    case \"Reshape\": {\n      return [ops.reshape(getParamValue(\"x\", node, tensorMap, context), getParamValue(\"shape\", node, tensorMap, context))];\n    }\n    case \"MirrorPad\": {\n      return [ops.mirrorPad(getParamValue(\"x\", node, tensorMap, context), getParamValue(\"padding\", node, tensorMap, context), getParamValue(\"mode\", node, tensorMap, context))];\n    }\n    case \"PadV2\":\n    case \"Pad\": {\n      return [ops.pad(getParamValue(\"x\", node, tensorMap, context), getParamValue(\"padding\", node, tensorMap, context), getParamValue(\"constantValue\", node, tensorMap, context))];\n    }\n    case \"SpaceToBatchND\": {\n      const blockShape = getParamValue(\"blockShape\", node, tensorMap, context);\n      const paddings = getParamValue(\"paddings\", node, tensorMap, context);\n      return [ops.spaceToBatchND(getParamValue(\"x\", node, tensorMap, context), blockShape, paddings)];\n    }\n    case \"BatchToSpaceND\": {\n      const blockShape = getParamValue(\"blockShape\", node, tensorMap, context);\n      const crops = getParamValue(\"crops\", node, tensorMap, context);\n      return [ops.batchToSpaceND(getParamValue(\"x\", node, tensorMap, context), blockShape, crops)];\n    }\n    case \"DepthToSpace\": {\n      const blockSize = getParamValue(\"blockSize\", node, tensorMap, context);\n      const dataFormat = getParamValue(\"dataFormat\", node, tensorMap, context).toUpperCase();\n      return [ops.depthToSpace(getParamValue(\"x\", node, tensorMap, context), blockSize, dataFormat)];\n    }\n    case \"BroadcastTo\": {\n      return [ops.broadcastTo(getParamValue(\"x\", node, tensorMap, context), getParamValue(\"shape\", node, tensorMap, context))];\n    }\n    case \"BroadcastArgs\": {\n      return [ops.broadcastArgs(getParamValue(\"s0\", node, tensorMap, context), getParamValue(\"s1\", node, tensorMap, context))];\n    }\n    default:\n      throw TypeError(`Node type ${node.op} is not implemented`);\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-converter@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-converter/dist/operations/operation_executor.js\nfunction executeOp20(node, tensorMap, context, resourceManager, tidy2 = tidy) {\n  const value = ((node2, tensorMap2, context2) => {\n    switch (node2.category) {\n      case \"arithmetic\":\n        return tidy2(() => executeOp(node2, tensorMap2, context2));\n      case \"basic_math\":\n        return tidy2(() => executeOp2(node2, tensorMap2, context2));\n      case \"control\":\n        return executeOp3(node2, tensorMap2, context2);\n      case \"convolution\":\n        return tidy2(() => executeOp4(node2, tensorMap2, context2));\n      case \"creation\":\n        return tidy2(() => executeOp5(node2, tensorMap2, context2));\n      case \"dynamic\":\n        return executeOp6(node2, tensorMap2, context2);\n      case \"evaluation\":\n        return tidy2(() => executeOp7(node2, tensorMap2, context2));\n      case \"image\":\n        return tidy2(() => executeOp10(node2, tensorMap2, context2));\n      case \"graph\":\n        return tidy2(() => executeOp8(node2, tensorMap2, context2));\n      case \"logical\":\n        return tidy2(() => executeOp11(node2, tensorMap2, context2));\n      case \"matrices\":\n        return tidy2(() => executeOp12(node2, tensorMap2, context2));\n      case \"normalization\":\n        return tidy2(() => executeOp13(node2, tensorMap2, context2));\n      case \"reduction\":\n        return tidy2(() => executeOp14(node2, tensorMap2, context2));\n      case \"slice_join\":\n        return tidy2(() => executeOp15(node2, tensorMap2, context2));\n      case \"sparse\":\n        return tidy2(() => executeOp16(node2, tensorMap2, context2));\n      case \"spectral\":\n        return tidy2(() => executeOp17(node2, tensorMap2, context2));\n      case \"string\":\n        return tidy2(() => executeOp18(node2, tensorMap2, context2));\n      case \"transformation\":\n        return tidy2(() => executeOp19(node2, tensorMap2, context2));\n      case \"hash_table\":\n        return executeOp9(node2, tensorMap2, context2, resourceManager);\n      case \"custom\":\n        const opMapper = getRegisteredOp(node2.op);\n        if (opMapper && opMapper.customExecutor) {\n          return opMapper.customExecutor(new NodeValueImpl(node2, tensorMap2, context2));\n        } else {\n          throw TypeError(`Custom op ${node2.op} is not registered.`);\n        }\n      default:\n        throw TypeError(`Unknown op '${node2.op}'. File an issue at https://github.com/tensorflow/tfjs/issues so we can add it, or register a custom execution with tf.registerOp()`);\n    }\n  })(node, tensorMap, context);\n  if (util_exports.isPromise(value)) {\n    return value.then((data) => [].concat(data));\n  }\n  return [].concat(value);\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-converter@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-converter/dist/executor/execution_context.js\nvar ExecutionContext = class {\n  constructor(weightMap = {}, tensorArrayMap = {}, tensorListMap = {}, functionMap = {}) {\n    this.weightMap = weightMap;\n    this.tensorArrayMap = tensorArrayMap;\n    this.tensorListMap = tensorListMap;\n    this.functionMap = functionMap;\n    this.rootContext = { id: 0, frameName: \"\", iterationId: 0 };\n    this.contexts = [this.rootContext];\n    this.lastId = 0;\n    this.generateCurrentContextIds();\n  }\n  newFrame(id, frameName) {\n    return { id, frameName, iterationId: 0 };\n  }\n  set currentContext(contexts2) {\n    if (this.contexts !== contexts2) {\n      this.contexts = contexts2;\n      this.generateCurrentContextIds();\n    }\n  }\n  get currentContext() {\n    return this.contexts;\n  }\n  get currentContextId() {\n    return this._currentContextIds[0];\n  }\n  get currentContextIds() {\n    return this._currentContextIds;\n  }\n  generateCurrentContextIds() {\n    const names = [];\n    for (let i2 = 0; i2 < this.contexts.length - 1; i2++) {\n      const contexts2 = this.contexts.slice(0, this.contexts.length - i2);\n      names.push(this.contextIdforContexts(contexts2));\n    }\n    names.push(\"\");\n    this._currentContextIds = names;\n  }\n  contextIdforContexts(contexts2) {\n    return contexts2 ? contexts2.map((context) => context.id === 0 && context.iterationId === 0 ? \"\" : `${context.frameName}-${context.iterationId}`).join(\"/\") : \"\";\n  }\n  enterFrame(frameId) {\n    if (this.contexts) {\n      this.lastId++;\n      this.contexts = this.contexts.slice();\n      this.contexts.push(this.newFrame(this.lastId, frameId));\n      this._currentContextIds.unshift(this.contextIdforContexts(this.contexts));\n    }\n  }\n  exitFrame() {\n    if (this.contexts && this.contexts.length > 1) {\n      this.contexts = this.contexts.slice();\n      this.contexts.splice(-1);\n      this.currentContextIds.shift();\n    } else {\n      throw new Error(\"Cannot exit frame, the context is empty\");\n    }\n  }\n  nextIteration() {\n    if (this.contexts && this.contexts.length > 0) {\n      this.contexts = this.contexts.slice();\n      this.lastId++;\n      const context = Object.assign({}, this.contexts[this.contexts.length - 1]);\n      context.iterationId += 1;\n      context.id = this.lastId;\n      this.contexts.splice(-1, 1, context);\n      this._currentContextIds.splice(0, 1, this.contextIdforContexts(this.contexts));\n    } else {\n      throw new Error(\"Cannot increase frame iteration, the context is empty\");\n    }\n  }\n  getWeight(name) {\n    return this.weightMap[name];\n  }\n  addTensorArray(tensorArray) {\n    this.tensorArrayMap[tensorArray.id] = tensorArray;\n  }\n  getTensorArray(id) {\n    return this.tensorArrayMap[id];\n  }\n  addTensorList(tensorList) {\n    this.tensorListMap[tensorList.id] = tensorList;\n  }\n  getTensorList(id) {\n    return this.tensorListMap[id];\n  }\n  dispose(keepIds) {\n    for (const key in this.tensorArrayMap) {\n      this.tensorArrayMap[key].clearAndClose(keepIds);\n    }\n    for (const key in this.tensorListMap) {\n      this.tensorListMap[key].clearAndClose(keepIds);\n    }\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-converter@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-converter/dist/executor/model_analysis.js\nfunction getExecutionSubgraph(inputs, outputs, weightMap, initNodes) {\n  const usedNodes = /* @__PURE__ */ new Set();\n  const missingInputs = [];\n  let dynamicNode = null;\n  let syncInputs = null;\n  const seen = /* @__PURE__ */ new Set();\n  const inputNodeNames = Object.keys(inputs).map((name) => parseNodeName(name)[0]);\n  let initNodeNames = [];\n  if (initNodes != null) {\n    initNodeNames = initNodes.map((node) => parseNodeName(node.name)[0]);\n  }\n  const frontier = [...outputs];\n  while (frontier.length > 0) {\n    const node = frontier.pop();\n    if (isControlFlow(node) || isDynamicShape(node) || isHashTable(node)) {\n      if (dynamicNode == null) {\n        dynamicNode = node;\n        syncInputs = dynamicNode.children.map((child) => child.name).filter((name) => usedNodes.has(name));\n      }\n    }\n    usedNodes.add(node.name);\n    if (weightMap[node.name] != null) {\n      continue;\n    }\n    if (inputNodeNames.indexOf(node.name) !== -1) {\n      continue;\n    }\n    if (initNodeNames.indexOf(node.name) !== -1) {\n      continue;\n    }\n    if (node.inputs.length === 0) {\n      missingInputs.push(node.name);\n      continue;\n    }\n    node.inputs.forEach((input2) => {\n      if (seen.has(input2.name)) {\n        return;\n      }\n      seen.add(input2.name);\n      frontier.push(input2);\n    });\n  }\n  return { inputs, outputs, usedNodes, missingInputs, dynamicNode, syncInputs };\n}\nfunction getNodesInTopologicalOrder(graph, weightMap, executionInfo) {\n  const { usedNodes, inputs } = executionInfo;\n  const frontier = [];\n  const inputNodes = Object.keys(inputs).map((name) => parseNodeName(name)[0]).map((name) => graph.nodes[name]);\n  const initNodes = graph.initNodes;\n  inputNodes.forEach((input2) => {\n    if (usedNodes.has(input2.name)) {\n      frontier.push(input2);\n    }\n  });\n  graph.weights.forEach((weight) => {\n    if (usedNodes.has(weight.name)) {\n      frontier.push(weight);\n    }\n  });\n  if (initNodes != null) {\n    initNodes.forEach((node) => {\n      if (usedNodes.has(node.name)) {\n        frontier.push(node);\n      }\n    });\n  }\n  const seen = /* @__PURE__ */ new Set();\n  const orderedNodes = [];\n  while (frontier.length > 0) {\n    const node = frontier.pop();\n    seen.add(node.name);\n    if (!weightMap[node.name]) {\n      orderedNodes.push(node);\n    }\n    node.children.forEach((child) => {\n      if (!seen.has(child.name) && usedNodes.has(child.name) && child.inputs.every((input2) => seen.has(input2.name))) {\n        frontier.push(child);\n      }\n    });\n  }\n  return orderedNodes;\n}\nvar CONTROL_FLOW_OPS = [\n  \"Switch\",\n  \"Merge\",\n  \"Enter\",\n  \"Exit\",\n  \"NextIteration\",\n  \"StatelessIf\",\n  \"StatelessWhile\",\n  \"if\",\n  \"While\"\n];\nvar DYNAMIC_SHAPE_OPS = [\n  \"NonMaxSuppressionV2\",\n  \"NonMaxSuppressionV3\",\n  \"NonMaxSuppressionV5\",\n  \"Where\"\n];\nvar HASH_TABLE_OPS = [\n  \"HashTable\",\n  \"HashTableV2\",\n  \"LookupTableImport\",\n  \"LookupTableImportV2\",\n  \"LookupTableFind\",\n  \"LookupTableFindV2\",\n  \"LookupTableSize\",\n  \"LookupTableSizeV2\"\n];\nfunction isControlFlow(node) {\n  return CONTROL_FLOW_OPS.indexOf(node.op) >= 0;\n}\nfunction isDynamicShape(node) {\n  return DYNAMIC_SHAPE_OPS.indexOf(node.op) >= 0;\n}\nfunction isHashTable(node) {\n  return HASH_TABLE_OPS.indexOf(node.op) >= 0;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-converter@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-converter/dist/executor/graph_executor.js\nvar GraphExecutor = class {\n  constructor(graph, parent) {\n    this.graph = graph;\n    this.parent = parent;\n    this.compiledMap = /* @__PURE__ */ new Map();\n    this._weightMap = {};\n    this.SEPERATOR = \",\";\n    this._functions = {};\n    this._functionExecutorMap = {};\n    this.intermediateTensors = {};\n    this.keepTensorForDebug = false;\n    this._outputs = graph.outputs;\n    this._inputs = graph.inputs;\n    this._initNodes = graph.initNodes;\n    this._signature = graph.signature;\n    this._functions = graph.functions;\n    if (graph.functions != null) {\n      Object.keys(graph.functions).forEach((name) => {\n        this._functionExecutorMap[name] = new GraphExecutor(graph.functions[name], this);\n      });\n    }\n  }\n  get weightIds() {\n    return this.parent ? this.parent.weightIds : this._weightIds;\n  }\n  get functionExecutorMap() {\n    return this.parent ? this.parent.functionExecutorMap : this._functionExecutorMap;\n  }\n  get weightMap() {\n    return this.parent ? this.parent.weightMap : this._weightMap;\n  }\n  set weightMap(weightMap) {\n    const weightIds = Object.keys(weightMap).map((key) => weightMap[key].map((tensor2) => tensor2.id));\n    this._weightIds = [].concat(...weightIds);\n    this._weightMap = weightMap;\n  }\n  set resourceManager(resourceManager) {\n    this._resourceManager = resourceManager;\n  }\n  get inputs() {\n    return this._inputs.map((node) => {\n      return {\n        name: node.name,\n        shape: node.attrParams[\"shape\"] ? node.attrParams[\"shape\"].value : void 0,\n        dtype: node.attrParams[\"dtype\"] ? node.attrParams[\"dtype\"].value : void 0\n      };\n    });\n  }\n  get outputs() {\n    return this._outputs.map((node) => {\n      return {\n        name: node.name,\n        shape: node.attrParams[\"shape\"] ? node.attrParams[\"shape\"].value : void 0,\n        dtype: node.attrParams[\"dtype\"] ? node.attrParams[\"dtype\"].value : void 0\n      };\n    });\n  }\n  get inputNodes() {\n    return this._inputs.map((node) => node.signatureKey || node.name);\n  }\n  get outputNodes() {\n    return this._outputs.map((node) => {\n      const name = node.signatureKey || node.name;\n      return node.defaultOutput ? `${name}:${node.defaultOutput}` : name;\n    });\n  }\n  get functions() {\n    return Object.keys(this._functions).reduce((map, key) => {\n      map[key] = this._functions[key].signature;\n      return map;\n    }, {});\n  }\n  getCompilationKey(inputs, outputs) {\n    const sortedInputs = inputs.map((node) => node.name).sort();\n    const sortedOutputs = outputs.map((node) => node.name).sort();\n    return sortedInputs.join(this.SEPERATOR) + \"--\" + sortedOutputs.join(this.SEPERATOR);\n  }\n  compile(inputs, outputs) {\n    const executionInfo = getExecutionSubgraph(inputs, outputs, this.weightMap, this._initNodes);\n    const { missingInputs, dynamicNode, syncInputs } = executionInfo;\n    if (dynamicNode != null) {\n      throw new Error(`This execution contains the node '${dynamicNode.name}', which has the dynamic op '${dynamicNode.op}'. Please use model.executeAsync() instead. Alternatively, to avoid the dynamic ops, specify the inputs [${syncInputs}]`);\n    }\n    if (missingInputs.length > 0) {\n      const outNames = outputs.map((n2) => n2.name);\n      const inNames = Object.keys(inputs);\n      throw new Error(`Cannot compute the outputs [${outNames}] from the provided inputs [${inNames}]. Missing the following inputs: [${missingInputs}]`);\n    }\n    return getNodesInTopologicalOrder(this.graph, this.weightMap, executionInfo);\n  }\n  execute(inputs, outputs) {\n    inputs = this.mapInputs(inputs);\n    const names = Object.keys(inputs).sort();\n    this.checkInputs(inputs);\n    this.checkInputShapeAndType(inputs);\n    outputs = this.mapOutputs(outputs);\n    this.checkOutputs(outputs);\n    const inputNodes = names.map((name) => this.graph.nodes[parseNodeName(name)[0]]);\n    const outputNodeNames = outputs.map((name) => parseNodeName(name)[0]);\n    let outputNodes = outputNodeNames.map((name) => this.graph.nodes[name]);\n    this.resetIntermediateTensors();\n    if (outputNodes.length === 0) {\n      outputNodes = this._outputs;\n    }\n    const compilationKey = this.getCompilationKey(inputNodes, outputNodes);\n    let orderedNodes = this.compiledMap.get(compilationKey);\n    if (orderedNodes == null) {\n      orderedNodes = this.compile(inputs, outputNodes);\n      this.compiledMap.set(compilationKey, orderedNodes);\n    }\n    const tensorArrayMap = {};\n    const tensorListMap = {};\n    return tidy(() => {\n      const context = new ExecutionContext(this.weightMap, tensorArrayMap, tensorListMap, this.functionExecutorMap);\n      const tensorsMap = Object.assign({}, this.weightMap);\n      Object.keys(inputs).forEach((name) => {\n        const [nodeName, index] = parseNodeName(name);\n        const tensors = [];\n        tensors[index] = inputs[name];\n        tensorsMap[nodeName] = tensors;\n      });\n      const tensorsToKeep = this.getFrozenTensorIds(tensorsMap);\n      const intermediateTensorConsumerCount = {};\n      for (let i2 = 0; i2 < orderedNodes.length; i2++) {\n        const node = orderedNodes[i2];\n        if (!tensorsMap[node.name]) {\n          const tensors = executeOp20(node, tensorsMap, context, this._resourceManager);\n          if (util_exports.isPromise(tensors)) {\n            throw new Error(`The execution of the op '${node.op}' returned a promise. Please use model.executeAsync() instead.`);\n          }\n          tensorsMap[node.name] = tensors;\n          this.checkTensorForDisposal(node.name, node, tensorsMap, context, tensorsToKeep, outputNodeNames, intermediateTensorConsumerCount);\n        }\n      }\n      if (this.parent == null) {\n        context.dispose(tensorsToKeep);\n      }\n      return outputs.map((name) => getTensor(name, tensorsMap, context));\n    });\n  }\n  getFrozenTensorIds(tensorMap) {\n    const ids = [].concat.apply([], Object.keys(tensorMap).map((key) => tensorMap[key]).map((tensors) => tensors.map((tensor2) => tensor2.id)));\n    return new Set(ids);\n  }\n  checkTensorForDisposal(nodeName, node, tensorMap, context, tensorsToKeep, outputNames, intermediateTensorConsumerCount) {\n    if (node.category === \"control\" || outputNames.indexOf(nodeName) !== -1) {\n      return;\n    }\n    tensorMap[nodeName].forEach((tensor2) => {\n      if (tensor2 != null) {\n        intermediateTensorConsumerCount[tensor2.id] = (intermediateTensorConsumerCount[tensor2.id] || 0) + node.children.length;\n      }\n    });\n    node.inputs.forEach((input2) => {\n      if (input2.category !== \"control\") {\n        const tensors = getTensorsForCurrentContenxt(input2.name, tensorMap, context);\n        if (tensors != null) {\n          tensors.forEach((tensor2) => {\n            if (tensor2 && !tensor2.kept && !tensorsToKeep.has(tensor2.id)) {\n              const count2 = intermediateTensorConsumerCount[tensor2.id];\n              if (count2 === 1) {\n                if (!this.keepTensorForDebug) {\n                  tensor2.dispose();\n                } else {\n                  const [nodeName2, index] = getNodeNameAndIndex(node.name, context);\n                  if (this.intermediateTensors[nodeName2]) {\n                    this.intermediateTensors[nodeName2][index] = tensor2;\n                  } else {\n                    this.intermediateTensors[nodeName2] = [];\n                    this.intermediateTensors[nodeName2][index] = tensor2;\n                  }\n                }\n                delete intermediateTensorConsumerCount[tensor2.id];\n              } else if (count2 != null) {\n                intermediateTensorConsumerCount[tensor2.id]--;\n              }\n            }\n          });\n        }\n      }\n    });\n  }\n  async executeAsync(inputs, outputs) {\n    return this._executeAsync(inputs, outputs);\n  }\n  disposeIntermediateTensors() {\n    if (!this.intermediateTensors) {\n      return;\n    }\n    Object.keys(this.intermediateTensors).forEach((key) => this.intermediateTensors[key].forEach((tensor2) => tensor2.dispose()));\n    this.disposeTensorsMap();\n  }\n  disposeTensorsMap() {\n    if (!this.tensorsMap) {\n      return;\n    }\n    Object.keys(this.tensorsMap).forEach((key) => {\n      const tensorArray = this.tensorsMap[key];\n      tensorArray.forEach((tensor2) => {\n        if (tensor2 && !tensor2.kept && !tensor2.isDisposed && !this.keepIds.has(tensor2.id)) {\n          tensor2.dispose();\n        }\n      });\n    });\n  }\n  getIntermediateTensors() {\n    return this.tensorsMap;\n  }\n  resetIntermediateTensors() {\n    for (const key in this.intermediateTensors) {\n      this.intermediateTensors[key].forEach((tensor2) => tensor2.dispose());\n      delete this.intermediateTensors[key];\n    }\n  }\n  async _executeAsync(inputs, outputs, isFunctionExecution = false, tensorArrayMap = {}, tensorListMap = {}) {\n    if (!isFunctionExecution) {\n      inputs = this.mapInputs(inputs);\n      this.checkInputs(inputs);\n      this.checkInputShapeAndType(inputs);\n      outputs = this.mapOutputs(outputs);\n      this.checkOutputs(outputs);\n    }\n    try {\n      this.keepTensorForDebug = env().getBool(\"KEEP_INTERMEDIATE_TENSORS\");\n    } catch (e2) {\n      console.warn(e2.message);\n    }\n    this.resetIntermediateTensors();\n    const context = new ExecutionContext(this.weightMap, tensorArrayMap, tensorListMap, this.functionExecutorMap);\n    this.tensorsMap = await this.executeWithControlFlow(inputs, context, outputs, isFunctionExecution);\n    const results = outputs.map((name) => getTensor(name, this.tensorsMap, context));\n    const outputIds = results.map((t2) => t2.id);\n    const inputIds = Object.keys(inputs).map((name) => inputs[name].id);\n    this.keepIds = /* @__PURE__ */ new Set([...outputIds, ...inputIds, ...this.weightIds]);\n    if (!this.keepTensorForDebug) {\n      this.disposeTensorsMap();\n    }\n    if (this.parent == null) {\n      context.dispose(this.keepIds);\n    }\n    return results;\n  }\n  async executeFunctionAsync(inputs, tensorArrayMap, tensorListMap) {\n    const mappedInputs = inputs.reduce((map, tensor2, index) => {\n      map[this.inputs[index].name] = tensor2;\n      return map;\n    }, {});\n    return this._executeAsync(mappedInputs, this.outputNodes, true, tensorArrayMap, tensorListMap);\n  }\n  async executeWithControlFlow(inputs, context, outputNames, isFunctionExecution) {\n    const names = Object.keys(inputs);\n    const inputNodes = names.map((name) => this.graph.nodes[parseNodeName(name)[0]]);\n    const outputNodeNames = outputNames.map((name) => parseNodeName(name)[0]);\n    let outputNodes = outputNodeNames.map((name) => this.graph.nodes[name]);\n    if (outputNodes.length === 0) {\n      outputNodes = this._outputs;\n    }\n    const { usedNodes, missingInputs, dynamicNode, syncInputs } = getExecutionSubgraph(inputs, outputNodes, this.weightMap, this._initNodes);\n    const stack2 = [\n      ...inputNodes,\n      ...this.graph.weights,\n      ...this._initNodes || []\n    ].map((node) => {\n      return { node, contexts: context.currentContext };\n    });\n    const tensorsMap = Object.assign({}, this.weightMap);\n    Object.keys(inputs).forEach((name) => {\n      const [nodeName, index] = parseNodeName(name);\n      const tensors = [];\n      tensors[index] = inputs[name];\n      tensorsMap[nodeName] = tensors;\n    });\n    const intermediateTensorConsumerCount = {};\n    const tensorsToKeep = this.getFrozenTensorIds(tensorsMap);\n    const added = {};\n    while (stack2.length > 0) {\n      const promises = this.processStack(inputNodes, stack2, context, tensorsMap, added, tensorsToKeep, outputNodeNames, intermediateTensorConsumerCount, usedNodes);\n      await Promise.all(promises);\n    }\n    if (dynamicNode == null && !isFunctionExecution) {\n      console.warn(`This model execution did not contain any nodes with control flow or dynamic output shapes. You can use model.execute() instead.`);\n    }\n    const missingOutputs = outputNodes.filter((node) => !isControlFlow(node) && !getTensor(node.name, tensorsMap, context)).map((node) => node.name);\n    if (missingOutputs.length > 0) {\n      let alternativeMsg = \"\";\n      if (dynamicNode != null) {\n        alternativeMsg = `Alternatively, to avoid the dynamic ops, use model.execute() and specify the inputs [${syncInputs}]`;\n      }\n      throw new Error(`Cannot compute the outputs [${missingOutputs}] from the provided inputs [${names}]. Consider providing the following inputs: [${missingInputs}]. ${alternativeMsg}`);\n    }\n    return tensorsMap;\n  }\n  processStack(inputNodes, stack2, context, tensorMap, added, tensorsToKeep, outputNames, intermediateTensorConsumerCount, usedNodes) {\n    const promises = [];\n    while (stack2.length > 0) {\n      const item = stack2.pop();\n      context.currentContext = item.contexts;\n      let nodeName = \"\";\n      if (item.node.op === \"Enter\" && getParamValue(\"isConstant\", item.node, tensorMap, context)) {\n        [nodeName] = getNodeNameAndIndex(item.node.name, context);\n      }\n      if (tensorMap[item.node.name] == null) {\n        const tensors = executeOp20(item.node, tensorMap, context, this._resourceManager);\n        if (!nodeName) {\n          [nodeName] = getNodeNameAndIndex(item.node.name, context);\n        }\n        const currentContext = context.currentContext;\n        if (util_exports.isPromise(tensors)) {\n          promises.push(tensors.then((t2) => {\n            tensorMap[nodeName] = t2;\n            context.currentContext = currentContext;\n            this.checkTensorForDisposal(nodeName, item.node, tensorMap, context, tensorsToKeep, outputNames, intermediateTensorConsumerCount);\n            this.processChildNodes(item.node, stack2, context, tensorMap, added, usedNodes);\n            return t2;\n          }));\n        } else {\n          tensorMap[nodeName] = tensors;\n          this.checkTensorForDisposal(nodeName, item.node, tensorMap, context, tensorsToKeep, outputNames, intermediateTensorConsumerCount);\n          this.processChildNodes(item.node, stack2, context, tensorMap, added, usedNodes);\n        }\n      } else {\n        this.processChildNodes(item.node, stack2, context, tensorMap, added, usedNodes);\n      }\n    }\n    return promises;\n  }\n  processChildNodes(node, stack2, context, tensorMap, added, usedNodes) {\n    node.children.forEach((childNode) => {\n      const [nodeName] = getNodeNameAndIndex(childNode.name, context);\n      if (added[nodeName] || !usedNodes.has(childNode.name)) {\n        return;\n      }\n      if (childNode.op === \"Merge\") {\n        if (childNode.inputNames.some((name) => {\n          return !!getTensor(name, tensorMap, context);\n        })) {\n          added[nodeName] = true;\n          stack2.push({ contexts: context.currentContext, node: childNode });\n        }\n      } else if (childNode.inputNames.every((name) => {\n        return !!getTensor(name, tensorMap, context);\n      })) {\n        added[nodeName] = true;\n        stack2.push({ contexts: context.currentContext, node: childNode });\n      }\n    });\n  }\n  dispose() {\n    Object.keys(this.weightMap).forEach((key) => this.weightMap[key].forEach((tensor2) => tensor2.dispose()));\n  }\n  checkInputShapeAndType(inputs) {\n    Object.keys(inputs).forEach((name) => {\n      const input2 = inputs[name];\n      const [nodeName] = parseNodeName(name);\n      const node = this.graph.nodes[nodeName];\n      if (node.attrParams[\"shape\"] && node.attrParams[\"shape\"].value) {\n        const shape = node.attrParams[\"shape\"].value;\n        const match = shape.length === input2.shape.length && input2.shape.every((dim, index) => shape[index] === -1 || shape[index] === dim);\n        util_exports.assert(match, () => `The shape of dict['${node.name}'] provided in model.execute(dict) must be [${shape}], but was [${input2.shape}]`);\n      }\n      if (node.attrParams[\"dtype\"] && node.attrParams[\"dtype\"].value) {\n        util_exports.assert(input2.dtype === node.attrParams[\"dtype\"].value, () => `The dtype of dict['${node.name}'] provided in model.execute(dict) must be ${node.attrParams[\"dtype\"].value}, but was ${input2.dtype}`);\n      }\n    });\n  }\n  mapInputs(inputs) {\n    const result = {};\n    for (const inputName in inputs) {\n      if (this._signature != null && this._signature.inputs != null && this._signature.inputs[inputName] != null) {\n        const tensor2 = this._signature.inputs[inputName];\n        result[tensor2.name] = inputs[inputName];\n      } else {\n        result[inputName] = inputs[inputName];\n      }\n    }\n    return result;\n  }\n  checkInputs(inputs) {\n    const notInGraph = Object.keys(inputs).filter((name) => {\n      const [nodeName] = parseNodeName(name);\n      return this.graph.nodes[nodeName] == null;\n    });\n    if (notInGraph.length > 0) {\n      throw new Error(`The dict provided in model.execute(dict) has keys: [${notInGraph}] that are not part of graph`);\n    }\n  }\n  mapOutputs(outputs) {\n    return outputs.map((name) => {\n      if (this._signature != null && this._signature.outputs != null && this._signature.outputs[name] != null) {\n        const tensor2 = this._signature.outputs[name];\n        return tensor2.name;\n      }\n      return name;\n    }, {});\n  }\n  checkOutputs(outputs) {\n    outputs.forEach((name) => {\n      const [normalizedName] = parseNodeName(name);\n      if (!this.graph.nodes[normalizedName]) {\n        throw new Error(`The output '${name}' is not found in the graph`);\n      }\n    });\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-converter@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-converter/dist/executor/resource_manager.js\nvar ResourceManager = class {\n  constructor(hashTableNameToHandle = {}, hashTableMap = {}) {\n    this.hashTableNameToHandle = hashTableNameToHandle;\n    this.hashTableMap = hashTableMap;\n  }\n  addHashTable(name, hashTable) {\n    this.hashTableNameToHandle[name] = hashTable.handle;\n    this.hashTableMap[hashTable.id] = hashTable;\n  }\n  getHashTableHandleByName(name) {\n    return this.hashTableNameToHandle[name];\n  }\n  getHashTableById(id) {\n    return this.hashTableMap[id];\n  }\n  dispose() {\n    for (const key in this.hashTableMap) {\n      this.hashTableMap[key].clearAndClose();\n      delete this.hashTableMap[key];\n    }\n    for (const name in this.hashTableNameToHandle) {\n      this.hashTableNameToHandle[name].dispose();\n      delete this.hashTableNameToHandle[name];\n    }\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-converter@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-converter/dist/executor/graph_model.js\nvar TFHUB_SEARCH_PARAM = \"?tfjs-format=file\";\nvar DEFAULT_MODEL_NAME = \"model.json\";\nvar GraphModel = class {\n  constructor(modelUrl, loadOptions = {}, tfio = io_exports) {\n    this.modelUrl = modelUrl;\n    this.loadOptions = loadOptions;\n    this.version = \"n/a\";\n    this.io = tfio;\n    if (loadOptions == null) {\n      this.loadOptions = {};\n    }\n    this.resourceManager = new ResourceManager();\n  }\n  get modelVersion() {\n    return this.version;\n  }\n  get inputNodes() {\n    return this.executor.inputNodes;\n  }\n  get outputNodes() {\n    return this.executor.outputNodes;\n  }\n  get inputs() {\n    return this.executor.inputs;\n  }\n  get outputs() {\n    return this.executor.outputs;\n  }\n  get weights() {\n    return this.executor.weightMap;\n  }\n  get metadata() {\n    return this.artifacts.userDefinedMetadata;\n  }\n  get modelSignature() {\n    return this.signature;\n  }\n  get modelStructuredOutputKeys() {\n    return this.structuredOutputKeys;\n  }\n  findIOHandler() {\n    const path = this.modelUrl;\n    if (path.load != null) {\n      this.handler = path;\n    } else if (this.loadOptions.requestInit != null) {\n      this.handler = this.io.browserHTTPRequest(path, this.loadOptions);\n    } else {\n      const handlers = this.io.getLoadHandlers(path, this.loadOptions);\n      if (handlers.length === 0) {\n        handlers.push(this.io.browserHTTPRequest(path, this.loadOptions));\n      } else if (handlers.length > 1) {\n        throw new Error(`Found more than one (${handlers.length}) load handlers for URL '${[path]}'`);\n      }\n      this.handler = handlers[0];\n    }\n  }\n  load() {\n    this.findIOHandler();\n    if (this.handler.load == null) {\n      throw new Error(\"Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.\");\n    }\n    const loadResult = this.handler.load();\n    if (util_exports.isPromise(loadResult)) {\n      return loadResult.then((artifacts) => this.loadSync(artifacts));\n    }\n    return this.loadSync(loadResult);\n  }\n  loadSync(artifacts) {\n    this.artifacts = artifacts;\n    const graph = this.artifacts.modelTopology;\n    let signature = this.artifacts.signature;\n    if (this.artifacts.userDefinedMetadata != null) {\n      const metadata = this.artifacts.userDefinedMetadata;\n      if (metadata.signature != null) {\n        signature = metadata.signature;\n      }\n      if (metadata.structuredOutputKeys != null) {\n        this.structuredOutputKeys = metadata.structuredOutputKeys;\n      }\n    }\n    this.signature = signature;\n    this.version = `${graph.versions.producer}.${graph.versions.minConsumer}`;\n    const weightMap = this.io.decodeWeights(this.artifacts.weightData, this.artifacts.weightSpecs);\n    this.executor = new GraphExecutor(OperationMapper.Instance.transformGraph(graph, this.signature));\n    this.executor.weightMap = this.convertTensorMapToTensorsMap(weightMap);\n    this.executor.resourceManager = this.resourceManager;\n    if (artifacts.modelInitializer != null && artifacts.modelInitializer.node != null) {\n      const initializer = OperationMapper.Instance.transformGraph(artifacts.modelInitializer);\n      this.initializer = new GraphExecutor(initializer);\n      this.initializer.weightMap = this.executor.weightMap;\n      this.initializer.resourceManager = this.resourceManager;\n      this.initializer.executeAsync({}, []);\n    }\n    return true;\n  }\n  async save(handlerOrURL, config) {\n    if (typeof handlerOrURL === \"string\") {\n      const handlers = this.io.getSaveHandlers(handlerOrURL);\n      if (handlers.length === 0) {\n        throw new Error(`Cannot find any save handlers for URL '${handlerOrURL}'`);\n      } else if (handlers.length > 1) {\n        throw new Error(`Found more than one (${handlers.length}) save handlers for URL '${handlerOrURL}'`);\n      }\n      handlerOrURL = handlers[0];\n    }\n    if (handlerOrURL.save == null) {\n      throw new Error(\"GraphModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.\");\n    }\n    return handlerOrURL.save(this.artifacts);\n  }\n  predict(inputs, config) {\n    const outputTensors = this.execute(inputs, this.outputNodes);\n    if (this.structuredOutputKeys) {\n      const outputTensorsArray = outputTensors instanceof Tensor ? [outputTensors] : outputTensors;\n      const outputTensorMap = {};\n      outputTensorsArray.forEach((outputTensor, i2) => outputTensorMap[this.structuredOutputKeys[i2]] = outputTensor);\n      return outputTensorMap;\n    }\n    return outputTensors;\n  }\n  normalizeInputs(inputs) {\n    if (!(inputs instanceof Tensor) && !Array.isArray(inputs)) {\n      return inputs;\n    }\n    inputs = Array.isArray(inputs) ? inputs : [inputs];\n    if (inputs.length !== this.inputNodes.length) {\n      throw new Error(`Input tensor count mismatch,the graph model has ${this.inputNodes.length} placeholders, while there are ${inputs.length} input tensors.`);\n    }\n    return this.inputNodes.reduce((map, inputName, i2) => {\n      map[inputName] = inputs[i2];\n      return map;\n    }, {});\n  }\n  normalizeOutputs(outputs) {\n    outputs = outputs || this.outputNodes;\n    return !Array.isArray(outputs) ? [outputs] : outputs;\n  }\n  execute(inputs, outputs) {\n    inputs = this.normalizeInputs(inputs);\n    outputs = this.normalizeOutputs(outputs);\n    const result = this.executor.execute(inputs, outputs);\n    return result.length > 1 ? result : result[0];\n  }\n  async executeAsync(inputs, outputs) {\n    inputs = this.normalizeInputs(inputs);\n    outputs = this.normalizeOutputs(outputs);\n    const result = await this.executor.executeAsync(inputs, outputs);\n    return result.length > 1 ? result : result[0];\n  }\n  getIntermediateTensors() {\n    return this.executor.getIntermediateTensors();\n  }\n  disposeIntermediateTensors() {\n    this.executor.disposeIntermediateTensors();\n  }\n  convertTensorMapToTensorsMap(map) {\n    return Object.keys(map).reduce((newMap, key) => {\n      newMap[key] = [map[key]];\n      return newMap;\n    }, {});\n  }\n  dispose() {\n    this.executor.dispose();\n    if (this.initializer) {\n      this.initializer.dispose();\n    }\n    this.resourceManager.dispose();\n  }\n};\nasync function loadGraphModel(modelUrl, options = {}, tfio = io_exports) {\n  if (modelUrl == null) {\n    throw new Error(\"modelUrl in loadGraphModel() cannot be null. Please provide a url or an IOHandler that loads the model\");\n  }\n  if (options == null) {\n    options = {};\n  }\n  if (options.fromTFHub && typeof modelUrl === \"string\") {\n    modelUrl = getTFHubUrl(modelUrl);\n  }\n  const model2 = new GraphModel(modelUrl, options, tfio);\n  await model2.load();\n  return model2;\n}\nfunction loadGraphModelSync(modelSource) {\n  if (modelSource == null) {\n    throw new Error(\"modelUrl in loadGraphModelSync() cannot be null. Please provide a url or an IOHandler that loads the model\");\n  }\n  if (!modelSource.load) {\n    throw new Error(`modelUrl IO Handler ${modelSource} has no load function`);\n  }\n  const model2 = new GraphModel(modelSource);\n  model2.load();\n  return model2;\n}\nfunction getTFHubUrl(modelUrl) {\n  if (!modelUrl.endsWith(\"/\")) {\n    modelUrl = modelUrl + \"/\";\n  }\n  return `${modelUrl}${DEFAULT_MODEL_NAME}${TFHUB_SEARCH_PARAM}`;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-converter@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-converter/dist/version.js\nvar version3 = \"3.20.0\";\n\n// node_modules/.pnpm/@tensorflow+tfjs-data@3.20.0_k7dauiu3y265wd6lcplf62oi7i/node_modules/@tensorflow/tfjs-data/dist/index.js\nvar dist_exports2 = {};\n__export(dist_exports2, {\n  CSVDataset: () => CSVDataset,\n  Dataset: () => Dataset,\n  FileDataSource: () => FileDataSource,\n  TextLineDataset: () => TextLineDataset,\n  URLDataSource: () => URLDataSource,\n  array: () => array,\n  csv: () => csv,\n  func: () => func,\n  generator: () => generator,\n  microphone: () => microphone,\n  version_data: () => version4,\n  webcam: () => webcam,\n  zip: () => zip\n});\n\n// node_modules/.pnpm/@tensorflow+tfjs-data@3.20.0_k7dauiu3y265wd6lcplf62oi7i/node_modules/@tensorflow/tfjs-data/dist/dataset.js\nvar seedrandom3 = __toESM(require_seedrandom2());\n\n// node_modules/.pnpm/@tensorflow+tfjs-data@3.20.0_k7dauiu3y265wd6lcplf62oi7i/node_modules/@tensorflow/tfjs-data/dist/iterators/lazy_iterator.js\nvar seedrandom2 = __toESM(require_seedrandom2());\n\n// node_modules/.pnpm/@tensorflow+tfjs-data@3.20.0_k7dauiu3y265wd6lcplf62oi7i/node_modules/@tensorflow/tfjs-data/dist/util/deep_map.js\nfunction deepMap(input2, mapFn) {\n  return deepMapInternal(input2, mapFn);\n}\nfunction deepMapInternal(input2, mapFn, seen = /* @__PURE__ */ new Map(), containedIn = /* @__PURE__ */ new Set()) {\n  if (input2 == null) {\n    return null;\n  }\n  if (typeof Blob === \"function\" && input2 instanceof Blob) {\n    return input2.slice();\n  }\n  if (containedIn.has(input2)) {\n    throw new Error(\"Circular references are not supported.\");\n  }\n  if (seen.has(input2)) {\n    return seen.get(input2);\n  }\n  const result = mapFn(input2);\n  if (result.recurse && result.value !== null) {\n    throw new Error(\"A deep map function may not return both a value and recurse=true.\");\n  }\n  if (!result.recurse) {\n    seen.set(input2, result.value);\n    return result.value;\n  } else if (isIterable2(input2)) {\n    const mappedIterable = Array.isArray(input2) ? [] : {};\n    containedIn.add(input2);\n    for (const k in input2) {\n      const child = input2[k];\n      const childResult = deepMapInternal(child, mapFn, seen, containedIn);\n      mappedIterable[k] = childResult;\n    }\n    containedIn.delete(input2);\n    if (input2.__proto__) {\n      mappedIterable.__proto__ = input2.__proto__;\n    }\n    return mappedIterable;\n  } else {\n    throw new Error(`Can't recurse into non-iterable type: ${input2}`);\n  }\n}\nfunction deepZip(inputs, zipFn = zipToList) {\n  return deepZipInternal(inputs, zipFn);\n}\nfunction deepZipInternal(inputs, zipFn, containedIn = /* @__PURE__ */ new Set()) {\n  const input2 = inputs[0];\n  if (containedIn.has(input2)) {\n    throw new Error(\"Circular references are not supported.\");\n  }\n  const result = zipFn(inputs);\n  if (result.recurse && result.value !== null) {\n    throw new Error(\"A deep zip function may not return both a value and recurse=true.\");\n  }\n  if (!result.recurse) {\n    return result.value;\n  } else if (isIterable2(input2)) {\n    const mappedIterable = Array.isArray(input2) ? [] : {};\n    containedIn.add(input2);\n    for (const k in input2) {\n      const children = inputs.map((x) => x[k]);\n      const childResult = deepZipInternal(children, zipFn, containedIn);\n      mappedIterable[k] = childResult;\n    }\n    containedIn.delete(input2);\n    return mappedIterable;\n  } else {\n    throw new Error(`Can't recurse into non-iterable type: ${input2}`);\n  }\n}\nfunction zipToList(x) {\n  if (x === null) {\n    return null;\n  }\n  if (isIterable2(x[0])) {\n    return { value: null, recurse: true };\n  } else {\n    return { value: x, recurse: false };\n  }\n}\nasync function deepMapAndAwaitAll(input2, mapFn) {\n  const seen = /* @__PURE__ */ new Map();\n  deepMapInternal(input2, mapFn, seen);\n  for (const key of Array.from(seen.keys())) {\n    const value = seen.get(key);\n    if (util_exports.isPromise(value)) {\n      const mappedValue = await value;\n      seen.set(key, mappedValue);\n    }\n  }\n  const result = deepMapInternal(input2, mapFn, seen);\n  return result;\n}\nfunction isIterable2(obj) {\n  let isTextDecoder = false;\n  if (env().get(\"IS_BROWSER\")) {\n    isTextDecoder = obj instanceof TextDecoder;\n  } else {\n    const { StringDecoder } = require_string_decoder();\n    isTextDecoder = obj instanceof StringDecoder;\n  }\n  return obj != null && !ArrayBuffer.isView(obj) && (Array.isArray(obj) || typeof obj === \"object\" && !(obj instanceof Tensor) && !(obj instanceof Promise) && !isTextDecoder);\n}\nfunction canTensorify(obj) {\n  return obj == null || isPrimitive(obj) || Array.isArray(obj) || typeof obj === \"object\" && obj instanceof Tensor || util_exports.isTypedArray(obj);\n}\nfunction isPrimitive(value) {\n  return value === null || typeof value !== \"object\" && typeof value !== \"function\";\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-data@3.20.0_k7dauiu3y265wd6lcplf62oi7i/node_modules/@tensorflow/tfjs-data/dist/util/deep_clone.js\nfunction deepClone(container) {\n  return deepMap(container, cloneIfTensor);\n}\nfunction cloneIfTensor(item) {\n  if (item instanceof Tensor) {\n    return { value: item.clone(), recurse: false };\n  } else if (isIterable2(item)) {\n    return { value: null, recurse: true };\n  } else {\n    return { value: item, recurse: false };\n  }\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-data@3.20.0_k7dauiu3y265wd6lcplf62oi7i/node_modules/@tensorflow/tfjs-data/dist/util/ring_buffer.js\nvar RingBuffer = class {\n  constructor(capacity) {\n    this.capacity = capacity;\n    this.begin = 0;\n    this.end = 0;\n    if (capacity == null) {\n      throw new RangeError(\"Can't create a ring buffer of unknown capacity.\");\n    }\n    if (capacity < 1) {\n      throw new RangeError(\"Can't create ring buffer of capacity < 1.\");\n    }\n    this.data = new Array(capacity);\n    this.doubledCapacity = 2 * capacity;\n  }\n  wrap(index) {\n    while (index < 0) {\n      index += this.doubledCapacity;\n    }\n    return index % this.doubledCapacity;\n  }\n  get(index) {\n    if (index < 0) {\n      throw new RangeError(\"Can't get item at a negative index.\");\n    }\n    return this.data[index % this.capacity];\n  }\n  set(index, value) {\n    if (index < 0) {\n      throw new RangeError(\"Can't set item at a negative index.\");\n    }\n    this.data[index % this.capacity] = value;\n  }\n  length() {\n    let length = this.end - this.begin;\n    if (length < 0) {\n      length = this.doubledCapacity + length;\n    }\n    return length;\n  }\n  isFull() {\n    return this.length() === this.capacity;\n  }\n  isEmpty() {\n    return this.length() === 0;\n  }\n  push(value) {\n    if (this.isFull()) {\n      throw new RangeError(\"Ring buffer is full.\");\n    }\n    this.set(this.end, value);\n    this.end = this.wrap(this.end + 1);\n  }\n  pushAll(values) {\n    for (const value of values) {\n      this.push(value);\n    }\n  }\n  pop() {\n    if (this.isEmpty()) {\n      throw new RangeError(\"Ring buffer is empty.\");\n    }\n    this.end = this.wrap(this.end - 1);\n    const result = this.get(this.end);\n    this.set(this.end, void 0);\n    return result;\n  }\n  unshift(value) {\n    if (this.isFull()) {\n      throw new RangeError(\"Ring buffer is full.\");\n    }\n    this.begin = this.wrap(this.begin - 1);\n    this.set(this.begin, value);\n  }\n  shift() {\n    if (this.isEmpty()) {\n      throw new RangeError(\"Ring buffer is empty.\");\n    }\n    const result = this.get(this.begin);\n    this.set(this.begin, void 0);\n    this.begin = this.wrap(this.begin + 1);\n    return result;\n  }\n  shuffleExcise(relativeIndex) {\n    if (this.isEmpty()) {\n      throw new RangeError(\"Ring buffer is empty.\");\n    }\n    const index = this.wrap(this.begin + relativeIndex);\n    const result = this.get(index);\n    this.set(index, this.pop());\n    return result;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-data@3.20.0_k7dauiu3y265wd6lcplf62oi7i/node_modules/@tensorflow/tfjs-data/dist/util/growing_ring_buffer.js\nvar GrowingRingBuffer = class extends RingBuffer {\n  constructor() {\n    super(GrowingRingBuffer.INITIAL_CAPACITY);\n  }\n  isFull() {\n    return false;\n  }\n  push(value) {\n    if (super.isFull()) {\n      this.expand();\n    }\n    super.push(value);\n  }\n  unshift(value) {\n    if (super.isFull()) {\n      this.expand();\n    }\n    super.unshift(value);\n  }\n  expand() {\n    const newCapacity = this.capacity * 2;\n    const newData = new Array(newCapacity);\n    const len = this.length();\n    for (let i2 = 0; i2 < len; i2++) {\n      newData[i2] = this.get(this.wrap(this.begin + i2));\n    }\n    this.data = newData;\n    this.capacity = newCapacity;\n    this.doubledCapacity = 2 * this.capacity;\n    this.begin = 0;\n    this.end = len;\n  }\n};\nGrowingRingBuffer.INITIAL_CAPACITY = 32;\n\n// node_modules/.pnpm/@tensorflow+tfjs-data@3.20.0_k7dauiu3y265wd6lcplf62oi7i/node_modules/@tensorflow/tfjs-data/dist/iterators/lazy_iterator.js\nfunction iteratorFromItems(items) {\n  return new ArrayIterator(items);\n}\nfunction iteratorFromFunction(func2) {\n  return new FunctionCallIterator(func2);\n}\nfunction iteratorFromConcatenated(baseIterators, baseErrorHandler) {\n  return new ChainedIterator(baseIterators, baseErrorHandler);\n}\nfunction iteratorFromZipped(iterators, mismatchMode = ZipMismatchMode.FAIL) {\n  return new ZipIterator(iterators, mismatchMode);\n}\nvar LazyIterator = class {\n  async toArray() {\n    const result = [];\n    let x = await this.next();\n    while (!x.done) {\n      result.push(x.value);\n      x = await this.next();\n    }\n    return result;\n  }\n  async toArrayForTest() {\n    const stream = this.prefetch(100);\n    const result = [];\n    let x = await stream.next();\n    while (!x.done) {\n      result.push(x.value);\n      x = await stream.next();\n    }\n    return result;\n  }\n  async resolveFully() {\n    let x = await this.next();\n    while (!x.done) {\n      x = await this.next();\n    }\n  }\n  async resolveWhile(predicate) {\n    let x = await this.next();\n    let shouldContinue = predicate(x.value);\n    while (!x.done && shouldContinue) {\n      x = await this.next();\n      shouldContinue = predicate(x.value);\n    }\n  }\n  handleErrors(handler) {\n    return new ErrorHandlingLazyIterator(this, handler);\n  }\n  filter(predicate) {\n    return new FilterIterator(this, predicate);\n  }\n  map(transform6) {\n    return new MapIterator(this, transform6);\n  }\n  mapAsync(transform6) {\n    return new AsyncMapIterator(this, transform6);\n  }\n  serialMapAsync(transform6) {\n    return new AsyncMapIterator(this, transform6).serial();\n  }\n  flatmap(transform6) {\n    return new FlatmapIterator(this, transform6);\n  }\n  async forEachAsync(f) {\n    return this.map(f).resolveFully();\n  }\n  async serialForEach(f) {\n    return this.serialMapAsync(f).resolveWhile((x) => x === true);\n  }\n  rowMajorBatch(batchSize, smallLastBatch = true) {\n    return new RowMajorBatchIterator(this, batchSize, smallLastBatch);\n  }\n  columnMajorBatch(batchSize, smallLastBatch = true, zipFn = zipToList) {\n    const rowBatches = this.rowMajorBatch(batchSize, smallLastBatch);\n    return rowBatches.map((x) => deepZip(x, zipFn));\n  }\n  concatenate(iterator, baseErrorHandler) {\n    return new ChainedIterator(iteratorFromItems([this, iterator]), baseErrorHandler);\n  }\n  take(count2) {\n    if (count2 < 0 || count2 == null) {\n      return this;\n    }\n    return new TakeIterator(this, count2);\n  }\n  skip(count2) {\n    if (count2 < 0 || count2 == null) {\n      return this;\n    }\n    return new SkipIterator(this, count2);\n  }\n  prefetch(bufferSize) {\n    return new PrefetchIterator(this, bufferSize);\n  }\n  shuffle(windowSize, seed) {\n    return new ShuffleIterator(this, windowSize, seed);\n  }\n  serial() {\n    return new SerialIterator(this);\n  }\n};\nvar ArrayIterator = class extends LazyIterator {\n  constructor(items) {\n    super();\n    this.items = items;\n    this.trav = 0;\n  }\n  summary() {\n    return `Array of ${this.items.length} items`;\n  }\n  async next() {\n    if (this.trav >= this.items.length) {\n      return { value: null, done: true };\n    }\n    const item = this.items[this.trav];\n    this.trav++;\n    return { value: deepClone(item), done: false };\n  }\n};\nvar FunctionCallIterator = class extends LazyIterator {\n  constructor(nextFn) {\n    super();\n    this.nextFn = nextFn;\n  }\n  summary() {\n    return `Function call`;\n  }\n  async next() {\n    try {\n      return this.nextFn();\n    } catch (e2) {\n      e2.message = `Error thrown while iterating through a dataset: ${e2.message}`;\n      throw e2;\n    }\n  }\n};\nvar SerialIterator = class extends LazyIterator {\n  constructor(upstream) {\n    super();\n    this.upstream = upstream;\n    this.lastRead = Promise.resolve({ value: null, done: false });\n  }\n  summary() {\n    return `${this.upstream.summary()} -> Serial`;\n  }\n  async next() {\n    this.lastRead = this.lastRead.then(() => this.serialNext());\n    return this.lastRead;\n  }\n  async serialNext() {\n    return this.upstream.next();\n  }\n};\nvar SkipIterator = class extends LazyIterator {\n  constructor(upstream, maxCount) {\n    super();\n    this.upstream = upstream;\n    this.maxCount = maxCount;\n    this.count = 0;\n    this.lastRead = Promise.resolve({ value: null, done: false });\n  }\n  summary() {\n    return `${this.upstream.summary()} -> Skip`;\n  }\n  async next() {\n    this.lastRead = this.lastRead.then(() => this.serialNext());\n    return this.lastRead;\n  }\n  async serialNext() {\n    while (this.count++ < this.maxCount) {\n      const skipped = await this.upstream.next();\n      if (skipped.done) {\n        return skipped;\n      }\n      dispose(skipped.value);\n    }\n    return this.upstream.next();\n  }\n};\nvar TakeIterator = class extends LazyIterator {\n  constructor(upstream, maxCount) {\n    super();\n    this.upstream = upstream;\n    this.maxCount = maxCount;\n    this.count = 0;\n  }\n  summary() {\n    return `${this.upstream.summary()} -> Take`;\n  }\n  async next() {\n    if (this.count++ >= this.maxCount) {\n      return { value: null, done: true };\n    }\n    return this.upstream.next();\n  }\n};\nvar RowMajorBatchIterator = class extends LazyIterator {\n  constructor(upstream, batchSize, enableSmallLastBatch = true) {\n    super();\n    this.upstream = upstream;\n    this.batchSize = batchSize;\n    this.enableSmallLastBatch = enableSmallLastBatch;\n    this.lastRead = Promise.resolve({ value: null, done: false });\n  }\n  summary() {\n    return `${this.upstream.summary()} -> RowMajorBatch`;\n  }\n  async next() {\n    this.lastRead = this.lastRead.then(() => this.serialNext());\n    return this.lastRead;\n  }\n  async serialNext() {\n    const batch = [];\n    while (batch.length < this.batchSize) {\n      const item = await this.upstream.next();\n      if (item.done) {\n        if (this.enableSmallLastBatch && batch.length > 0) {\n          return { value: batch, done: false };\n        }\n        return { value: null, done: true };\n      }\n      batch.push(item.value);\n    }\n    return { value: batch, done: false };\n  }\n};\nvar FilterIterator = class extends LazyIterator {\n  constructor(upstream, predicate) {\n    super();\n    this.upstream = upstream;\n    this.predicate = predicate;\n    this.lastRead = Promise.resolve({ value: null, done: false });\n  }\n  summary() {\n    return `${this.upstream.summary()} -> Filter`;\n  }\n  async next() {\n    this.lastRead = this.lastRead.then(() => this.serialNext());\n    return this.lastRead;\n  }\n  async serialNext() {\n    while (true) {\n      const item = await this.upstream.next();\n      if (item.done || this.predicate(item.value)) {\n        return item;\n      }\n      dispose(item.value);\n    }\n  }\n};\nvar MapIterator = class extends LazyIterator {\n  constructor(upstream, transform6) {\n    super();\n    this.upstream = upstream;\n    this.transform = transform6;\n  }\n  summary() {\n    return `${this.upstream.summary()} -> Map`;\n  }\n  async next() {\n    const item = await this.upstream.next();\n    if (item.done) {\n      return { value: null, done: true };\n    }\n    const inputTensors = tensor_util_exports.getTensorsInContainer(item.value);\n    const mapped = this.transform(item.value);\n    const outputTensors = tensor_util_exports.getTensorsInContainer(mapped);\n    for (const t2 of inputTensors) {\n      if (!tensor_util_exports.isTensorInList(t2, outputTensors)) {\n        t2.dispose();\n      }\n    }\n    return { value: mapped, done: false };\n  }\n};\nvar ErrorHandlingLazyIterator = class extends LazyIterator {\n  constructor(upstream, handler) {\n    super();\n    this.upstream = upstream;\n    this.handler = handler;\n    this.count = 0;\n    this.lastRead = Promise.resolve({ value: null, done: false });\n  }\n  summary() {\n    return `${this.upstream.summary()} -> handleErrors`;\n  }\n  async next() {\n    this.lastRead = this.lastRead.then(() => this.serialNext());\n    return this.lastRead;\n  }\n  async serialNext() {\n    while (true) {\n      try {\n        return await this.upstream.next();\n      } catch (e2) {\n        if (!this.handler(e2)) {\n          return { value: null, done: true };\n        }\n      }\n    }\n  }\n};\nvar AsyncMapIterator = class extends LazyIterator {\n  constructor(upstream, transform6) {\n    super();\n    this.upstream = upstream;\n    this.transform = transform6;\n  }\n  summary() {\n    return `${this.upstream.summary()} -> AsyncMap`;\n  }\n  async next() {\n    const item = await this.upstream.next();\n    if (item.done) {\n      return { value: null, done: true };\n    }\n    const inputTensors = tensor_util_exports.getTensorsInContainer(item.value);\n    const mapped = await this.transform(item.value);\n    const outputTensors = tensor_util_exports.getTensorsInContainer(mapped);\n    for (const t2 of inputTensors) {\n      if (!tensor_util_exports.isTensorInList(t2, outputTensors)) {\n        t2.dispose();\n      }\n    }\n    return { value: mapped, done: false };\n  }\n};\nvar OneToManyIterator = class extends LazyIterator {\n  constructor() {\n    super();\n    this.outputQueue = new GrowingRingBuffer();\n    this.lastRead = Promise.resolve({ value: null, done: false });\n  }\n  async next() {\n    this.lastRead = this.lastRead.then(() => this.serialNext());\n    return this.lastRead;\n  }\n  async serialNext() {\n    while (this.outputQueue.length() === 0) {\n      if (!await this.pump()) {\n        return { value: null, done: true };\n      }\n    }\n    return { value: this.outputQueue.shift(), done: false };\n  }\n};\nvar FlatmapIterator = class extends OneToManyIterator {\n  constructor(upstream, transform6) {\n    super();\n    this.upstream = upstream;\n    this.transform = transform6;\n  }\n  summary() {\n    return `${this.upstream.summary()} -> Flatmap`;\n  }\n  async pump() {\n    const item = await this.upstream.next();\n    if (item.done) {\n      return false;\n    }\n    const inputTensors = tensor_util_exports.getTensorsInContainer(item.value);\n    const mappedArray = this.transform(item.value);\n    const outputTensors = tensor_util_exports.getTensorsInContainer(mappedArray);\n    this.outputQueue.pushAll(mappedArray);\n    for (const t2 of inputTensors) {\n      if (!tensor_util_exports.isTensorInList(t2, outputTensors)) {\n        t2.dispose();\n      }\n    }\n    return true;\n  }\n};\nvar ChainedIterator = class extends LazyIterator {\n  constructor(iterators, baseErrorHandler) {\n    super();\n    this.baseErrorHandler = baseErrorHandler;\n    this.lastRead = null;\n    this.iterator = null;\n    this.moreIterators = iterators;\n  }\n  summary() {\n    const upstreamSummaries = \"TODO: fill in upstream of chained summaries\";\n    return `${upstreamSummaries} -> Chained`;\n  }\n  async next() {\n    this.lastRead = this.readFromChain(this.lastRead);\n    return this.lastRead;\n  }\n  async readFromChain(lastRead) {\n    await lastRead;\n    if (this.iterator == null) {\n      const iteratorResult = await this.moreIterators.next();\n      if (iteratorResult.done) {\n        return { value: null, done: true };\n      }\n      this.iterator = iteratorResult.value;\n      if (this.baseErrorHandler != null) {\n        this.iterator = this.iterator.handleErrors(this.baseErrorHandler);\n      }\n    }\n    const itemResult = await this.iterator.next();\n    if (itemResult.done) {\n      this.iterator = null;\n      return this.readFromChain(lastRead);\n    }\n    return itemResult;\n  }\n};\nvar ZipMismatchMode;\n(function(ZipMismatchMode2) {\n  ZipMismatchMode2[ZipMismatchMode2[\"FAIL\"] = 0] = \"FAIL\";\n  ZipMismatchMode2[ZipMismatchMode2[\"SHORTEST\"] = 1] = \"SHORTEST\";\n  ZipMismatchMode2[ZipMismatchMode2[\"LONGEST\"] = 2] = \"LONGEST\";\n})(ZipMismatchMode || (ZipMismatchMode = {}));\nvar ZipIterator = class extends LazyIterator {\n  constructor(iterators, mismatchMode = ZipMismatchMode.FAIL) {\n    super();\n    this.iterators = iterators;\n    this.mismatchMode = mismatchMode;\n    this.count = 0;\n    this.currentPromise = null;\n  }\n  summary() {\n    const upstreamSummaries = \"TODO: fill in upstream of zip summaries\";\n    return `{${upstreamSummaries}} -> Zip`;\n  }\n  async nextState(afterState) {\n    await afterState;\n    let numIterators = 0;\n    let iteratorsDone = 0;\n    function getNext(container) {\n      if (container instanceof LazyIterator) {\n        const result = container.next();\n        return {\n          value: result.then((x) => {\n            numIterators++;\n            if (x.done) {\n              iteratorsDone++;\n            }\n            return x.value;\n          }),\n          recurse: false\n        };\n      } else {\n        return { value: null, recurse: true };\n      }\n    }\n    const mapped = await deepMapAndAwaitAll(this.iterators, getNext);\n    if (numIterators === iteratorsDone) {\n      return { value: null, done: true };\n    }\n    if (iteratorsDone > 0) {\n      switch (this.mismatchMode) {\n        case ZipMismatchMode.FAIL:\n          throw new Error(`Zipped streams should have the same length. Mismatched at element ${this.count}.`);\n        case ZipMismatchMode.SHORTEST:\n          return { value: null, done: true };\n        case ZipMismatchMode.LONGEST:\n        default:\n      }\n    }\n    this.count++;\n    return { value: mapped, done: false };\n  }\n  async next() {\n    this.currentPromise = this.nextState(this.currentPromise);\n    return this.currentPromise;\n  }\n};\nvar PrefetchIterator = class extends LazyIterator {\n  constructor(upstream, bufferSize) {\n    super();\n    this.upstream = upstream;\n    this.bufferSize = bufferSize;\n    this.buffer = new RingBuffer(bufferSize);\n  }\n  summary() {\n    return `${this.upstream.summary()} -> Prefetch`;\n  }\n  refill() {\n    while (!this.buffer.isFull()) {\n      const v = this.upstream.next();\n      this.buffer.push(v);\n    }\n  }\n  next() {\n    this.refill();\n    return this.buffer.shift();\n  }\n};\nvar ShuffleIterator = class extends PrefetchIterator {\n  constructor(upstream, windowSize, seed) {\n    super(upstream, windowSize);\n    this.upstream = upstream;\n    this.windowSize = windowSize;\n    this.upstreamExhausted = false;\n    this.random = seedrandom2.alea(seed || util_exports.now().toString());\n    this.lastRead = Promise.resolve({ value: null, done: false });\n  }\n  async next() {\n    this.lastRead = this.lastRead.then(() => this.serialNext());\n    return this.lastRead;\n  }\n  randomInt(max7) {\n    return Math.floor(this.random() * max7);\n  }\n  chooseIndex() {\n    return this.randomInt(this.buffer.length());\n  }\n  async serialNext() {\n    if (!this.upstreamExhausted) {\n      this.refill();\n    }\n    while (!this.buffer.isEmpty()) {\n      const chosenIndex = this.chooseIndex();\n      const result = await this.buffer.shuffleExcise(chosenIndex);\n      if (result.done) {\n        this.upstreamExhausted = true;\n      } else {\n        this.refill();\n        return result;\n      }\n    }\n    return { value: null, done: true };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-data@3.20.0_k7dauiu3y265wd6lcplf62oi7i/node_modules/@tensorflow/tfjs-data/dist/dataset.js\nvar Dataset = class {\n  constructor() {\n    this.size = null;\n  }\n  batch(batchSize, smallLastBatch = true) {\n    const base = this;\n    util_exports.assert(batchSize > 0, () => `batchSize needs to be positive, but it is\n      ${batchSize}`);\n    let size;\n    if (this.size === Infinity || this.size == null) {\n      size = this.size;\n    } else if (smallLastBatch) {\n      size = Math.ceil(this.size / batchSize);\n    } else {\n      size = Math.floor(this.size / batchSize);\n    }\n    return datasetFromIteratorFn(async () => {\n      return (await base.iterator()).columnMajorBatch(batchSize, smallLastBatch, deepBatchConcat);\n    }, size);\n  }\n  concatenate(dataset) {\n    const base = this;\n    let size;\n    if (this.size === Infinity || dataset.size === Infinity) {\n      size = Infinity;\n    } else if (this.size != null && dataset.size != null) {\n      size = this.size + dataset.size;\n    } else {\n      size = null;\n    }\n    return datasetFromIteratorFn(async () => (await base.iterator()).concatenate(await dataset.iterator()), size);\n  }\n  filter(predicate) {\n    const base = this;\n    let size;\n    if (this.size === Infinity) {\n      size = Infinity;\n    } else {\n      size = null;\n    }\n    return datasetFromIteratorFn(async () => {\n      return (await base.iterator()).filter((x) => tidy(() => predicate(x)));\n    }, size);\n  }\n  async forEachAsync(f) {\n    return (await this.iterator()).forEachAsync(f);\n  }\n  map(transform6) {\n    const base = this;\n    return datasetFromIteratorFn(async () => {\n      return (await base.iterator()).map((x) => tidy(() => transform6(x)));\n    }, this.size);\n  }\n  mapAsync(transform6) {\n    const base = this;\n    return datasetFromIteratorFn(async () => {\n      return (await base.iterator()).mapAsync(transform6);\n    }, this.size);\n  }\n  prefetch(bufferSize) {\n    if (bufferSize == null) {\n      throw new RangeError(\"`Dataset.prefetch()` requires bufferSize to be specified.\");\n    }\n    const base = this;\n    return datasetFromIteratorFn(async () => (await base.iterator()).prefetch(bufferSize), this.size);\n  }\n  repeat(count2) {\n    const base = this;\n    let size;\n    if (this.size != null && count2 > 0) {\n      size = this.size * count2;\n    } else if (count2 === 0) {\n      size = 0;\n    } else if (this.size != null && (count2 === void 0 || count2 < 0)) {\n      size = Infinity;\n    } else {\n      size = null;\n    }\n    return datasetFromIteratorFn(async () => {\n      const iteratorIterator = iteratorFromFunction(async () => ({ value: await base.iterator(), done: false }));\n      return iteratorFromConcatenated(iteratorIterator.take(count2));\n    }, size);\n  }\n  skip(count2) {\n    const base = this;\n    let size;\n    if (this.size != null && count2 >= 0 && this.size >= count2) {\n      size = this.size - count2;\n    } else if (this.size != null && (this.size < count2 || count2 === void 0 || count2 < 0)) {\n      size = 0;\n    } else {\n      size = null;\n    }\n    return datasetFromIteratorFn(async () => (await base.iterator()).skip(count2), size);\n  }\n  shuffle(bufferSize, seed, reshuffleEachIteration = true) {\n    if (bufferSize == null || bufferSize < 0) {\n      if (this.size == null) {\n        throw new RangeError(\"`Dataset.shuffle()` requires bufferSize to be specified.\");\n      } else {\n        throw new RangeError(`\\`Dataset.shuffle()\\` requires bufferSize to be specified.  If your data fits in main memory (for regular JS objects), and/or GPU memory (for \\`tf.Tensor\\`s), consider setting bufferSize to the dataset size (${this.size} elements)`);\n      }\n    }\n    const base = this;\n    const random = seedrandom3.alea(seed || util_exports.now().toString());\n    return datasetFromIteratorFn(async () => {\n      let seed2 = random.int32();\n      if (reshuffleEachIteration) {\n        seed2 += random.int32();\n      }\n      return (await base.iterator()).shuffle(bufferSize, seed2.toString());\n    }, this.size);\n  }\n  take(count2) {\n    const base = this;\n    let size;\n    if (this.size != null && this.size > count2) {\n      size = count2;\n    } else if (this.size != null && this.size <= count2) {\n      size = this.size;\n    } else {\n      size = null;\n    }\n    return datasetFromIteratorFn(async () => (await base.iterator()).take(count2), size);\n  }\n  async toArray() {\n    if (this.size === Infinity) {\n      throw new Error(\"Can not convert infinite data stream to array.\");\n    }\n    return (await this.iterator()).toArray();\n  }\n  async toArrayForTest() {\n    if (this.size === Infinity) {\n      throw new Error(\"Can not convert infinite data stream to array.\");\n    }\n    return (await this.iterator()).toArrayForTest();\n  }\n};\nDataset.MAX_BUFFER_SIZE = 1e4;\nfunction datasetFromIteratorFn(iteratorFn, size = null) {\n  return new class extends Dataset {\n    constructor() {\n      super(...arguments);\n      this.size = size;\n    }\n    async iterator() {\n      return iteratorFn();\n    }\n  }();\n}\nfunction array(items) {\n  return datasetFromIteratorFn(async () => iteratorFromItems(items), items.length);\n}\nfunction zip(datasets) {\n  if (!isIterable2(datasets)) {\n    throw new Error(\"The argument to zip() must be an object or array.\");\n  }\n  let size;\n  if (Array.isArray(datasets)) {\n    for (let i2 = 0; i2 < datasets.length; i2++) {\n      size = size == null ? datasets[i2].size : Math.min(size, datasets[i2].size);\n    }\n  } else if (datasets instanceof Object) {\n    for (const ds in datasets) {\n      size = size == null ? datasets[ds].size : Math.min(size, datasets[ds].size);\n    }\n  }\n  return datasetFromIteratorFn(async () => {\n    const streams = await deepMapAndAwaitAll(datasets, (d) => {\n      if (d instanceof Dataset) {\n        return { value: d.iterator(), recurse: false };\n      } else if (isIterable2(d)) {\n        return { value: null, recurse: true };\n      } else {\n        throw new Error(\"Leaves of the structure passed to zip() must be Datasets, not primitives.\");\n      }\n    });\n    return iteratorFromZipped(streams, ZipMismatchMode.SHORTEST);\n  }, size);\n}\nfunction deepBatchConcat(rows) {\n  if (rows === null) {\n    return null;\n  }\n  const exampleRow = rows[0];\n  if (canTensorify(exampleRow)) {\n    const value = batchConcat(rows);\n    return { value, recurse: false };\n  }\n  return { value: null, recurse: true };\n}\nfunction batchConcat(arrays) {\n  if (arrays.length === 0) {\n    throw new Error(\"Can't make a batch of zero elements.\");\n  }\n  if (arrays[0] instanceof Tensor) {\n    return stack(arrays);\n  } else {\n    return tensor(arrays);\n  }\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-data@3.20.0_k7dauiu3y265wd6lcplf62oi7i/node_modules/@tensorflow/tfjs-data/dist/datasets/text_line_dataset.js\nvar TextLineDataset = class extends Dataset {\n  constructor(input2) {\n    super();\n    this.input = input2;\n  }\n  async iterator() {\n    const inputIterator = await this.input.iterator();\n    const utf8Iterator = inputIterator.decodeUTF8();\n    const lineIterator = utf8Iterator.split(\"\\n\").map((line) => {\n      if (line.endsWith(\"\\r\")) {\n        line = line.slice(0, -1);\n      }\n      return line;\n    });\n    return lineIterator;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-data@3.20.0_k7dauiu3y265wd6lcplf62oi7i/node_modules/@tensorflow/tfjs-data/dist/datasets/csv_dataset.js\nvar CODE_QUOTE = '\"';\nvar STATE_OUT = Symbol(\"out\");\nvar STATE_FIELD = Symbol(\"field\");\nvar STATE_QUOTE = Symbol(\"quote\");\nvar STATE_QUOTE_AFTER_QUOTE = Symbol(\"quoteafterquote\");\nvar STATE_WITHIN_QUOTE_IN_QUOTE = Symbol(\"quoteinquote\");\nvar CSVDataset = class extends Dataset {\n  constructor(input2, csvConfig) {\n    super();\n    this.input = input2;\n    this.hasHeader = true;\n    this.fullColumnNames = null;\n    this.columnNamesValidated = false;\n    this.columnConfigs = null;\n    this.configuredColumnsOnly = false;\n    this.delimiter = \",\";\n    this.delimWhitespace = false;\n    this.base = new TextLineDataset(input2);\n    if (!csvConfig) {\n      csvConfig = {};\n    }\n    this.hasHeader = csvConfig.hasHeader === false ? false : true;\n    this.fullColumnNames = csvConfig.columnNames;\n    this.columnConfigs = csvConfig.columnConfigs;\n    this.configuredColumnsOnly = csvConfig.configuredColumnsOnly;\n    if (csvConfig.delimWhitespace) {\n      util_exports.assert(csvConfig.delimiter == null, () => \"Delimiter should not be provided when delimWhitespace is true.\");\n      this.delimWhitespace = true;\n      this.delimiter = \" \";\n    } else {\n      this.delimiter = csvConfig.delimiter ? csvConfig.delimiter : \",\";\n    }\n  }\n  async columnNames() {\n    if (!this.columnNamesValidated) {\n      await this.setColumnNames();\n    }\n    return this.configuredColumnsOnly ? Object.keys(this.columnConfigs) : this.fullColumnNames;\n  }\n  async setColumnNames() {\n    const columnNamesFromFile = await this.maybeReadHeaderLine();\n    if (!this.fullColumnNames && !columnNamesFromFile) {\n      throw new Error(\"Column names must be provided if there is no header line.\");\n    } else if (this.fullColumnNames && columnNamesFromFile) {\n      util_exports.assert(columnNamesFromFile.length === this.fullColumnNames.length, () => \"The length of provided columnNames (\" + this.fullColumnNames.length.toString() + \") does not match the length of the header line read from file (\" + columnNamesFromFile.length.toString() + \").\");\n    }\n    if (!this.fullColumnNames) {\n      this.fullColumnNames = columnNamesFromFile;\n    }\n    const counts = this.fullColumnNames.reduce((countAcc, name) => {\n      countAcc[name] = countAcc[name] + 1 || 1;\n      return countAcc;\n    }, {});\n    const duplicateNames = Object.keys(counts).filter((name) => counts[name] > 1);\n    util_exports.assert(duplicateNames.length === 0, () => \"Duplicate column names found: \" + duplicateNames.toString());\n    if (this.columnConfigs) {\n      for (const key of Object.keys(this.columnConfigs)) {\n        const index = this.fullColumnNames.indexOf(key);\n        if (index === -1) {\n          throw new Error('The key \"' + key + '\" provided in columnConfigs does not match any of the column names (' + this.fullColumnNames.toString() + \").\");\n        }\n      }\n    }\n    this.columnNamesValidated = true;\n  }\n  async maybeReadHeaderLine() {\n    if (this.hasHeader) {\n      const iter = await this.base.iterator();\n      const firstElement = await iter.next();\n      if (firstElement.done) {\n        throw new Error(\"No data was found for CSV parsing.\");\n      }\n      const firstLine = firstElement.value;\n      const headers = this.parseRow(firstLine, false);\n      return headers;\n    } else {\n      return null;\n    }\n  }\n  async iterator() {\n    if (!this.columnNamesValidated) {\n      await this.setColumnNames();\n    }\n    let lines = await this.base.iterator();\n    if (this.hasHeader) {\n      lines = lines.skip(1);\n    }\n    return lines.map((x) => this.makeDataElement(x));\n  }\n  makeDataElement(line) {\n    const values = this.parseRow(line);\n    const features = {};\n    const labels = {};\n    for (let i2 = 0; i2 < this.fullColumnNames.length; i2++) {\n      const key = this.fullColumnNames[i2];\n      const config = this.columnConfigs ? this.columnConfigs[key] : null;\n      if (this.configuredColumnsOnly && !config) {\n        continue;\n      } else {\n        const value = values[i2];\n        let parsedValue = null;\n        if (value === \"\") {\n          if (config && config.default !== void 0) {\n            parsedValue = config.default;\n          } else if (config && (config.required || config.isLabel)) {\n            throw new Error(`Required column ${key} is empty in this line: ${line}`);\n          } else {\n            parsedValue = void 0;\n          }\n        } else {\n          const valueAsNum = Number(value);\n          if (isNaN(valueAsNum)) {\n            if (config && config.dtype === \"bool\") {\n              parsedValue = this.getBoolean(value);\n            } else {\n              parsedValue = value;\n            }\n          } else if (!config || !config.dtype) {\n            parsedValue = valueAsNum;\n          } else {\n            switch (config.dtype) {\n              case \"float32\":\n                parsedValue = valueAsNum;\n                break;\n              case \"int32\":\n                parsedValue = Math.floor(valueAsNum);\n                break;\n              case \"bool\":\n                parsedValue = this.getBoolean(value);\n                break;\n              default:\n                parsedValue = valueAsNum;\n            }\n          }\n        }\n        config && config.isLabel ? labels[key] = parsedValue : features[key] = parsedValue;\n      }\n    }\n    if (Object.keys(labels).length === 0) {\n      return features;\n    } else {\n      return { xs: features, ys: labels };\n    }\n  }\n  getBoolean(value) {\n    if (value === \"1\" || value.toLowerCase() === \"true\") {\n      return 1;\n    } else {\n      return 0;\n    }\n  }\n  parseRow(line, validateElementCount = true) {\n    const result = [];\n    let readOffset = 0;\n    const readLength = line.length;\n    let currentState = STATE_OUT;\n    for (let i2 = 0; i2 < readLength; i2++) {\n      switch (currentState) {\n        case STATE_OUT:\n          switch (line.charAt(i2)) {\n            case CODE_QUOTE:\n              readOffset = i2 + 1;\n              currentState = STATE_QUOTE;\n              break;\n            case this.delimiter:\n              readOffset = i2 + 1;\n              if (this.delimiter === \" \" && this.delimWhitespace) {\n                break;\n              }\n              result.push(\"\");\n              currentState = STATE_OUT;\n              break;\n            default:\n              currentState = STATE_FIELD;\n              readOffset = i2;\n              break;\n          }\n          break;\n        case STATE_FIELD:\n          switch (line.charAt(i2)) {\n            case this.delimiter:\n              result.push(line.substring(readOffset, i2));\n              currentState = STATE_OUT;\n              readOffset = i2 + 1;\n              break;\n            default:\n          }\n          break;\n        case STATE_QUOTE:\n          switch (line.charAt(i2)) {\n            case CODE_QUOTE:\n              currentState = STATE_QUOTE_AFTER_QUOTE;\n              break;\n            default:\n          }\n          break;\n        case STATE_QUOTE_AFTER_QUOTE:\n          switch (line.charAt(i2)) {\n            case this.delimiter:\n              result.push(line.substring(readOffset, i2 - 1));\n              currentState = STATE_OUT;\n              readOffset = i2 + 1;\n              break;\n            case CODE_QUOTE:\n              currentState = STATE_QUOTE;\n              break;\n            default:\n              currentState = STATE_WITHIN_QUOTE_IN_QUOTE;\n              break;\n          }\n          break;\n        case STATE_WITHIN_QUOTE_IN_QUOTE:\n          switch (line.charAt(i2)) {\n            case CODE_QUOTE:\n              currentState = STATE_QUOTE;\n              break;\n            default:\n          }\n          break;\n        default:\n      }\n    }\n    if (currentState === STATE_QUOTE_AFTER_QUOTE) {\n      result.push(line.substring(readOffset, readLength - 1));\n    } else {\n      result.push(line.substring(readOffset));\n    }\n    if (validateElementCount && result.length !== this.fullColumnNames.length) {\n      throw new Error(`Invalid row in csv file. Should have ${this.fullColumnNames.length} elements in a row, but got ${result}`);\n    }\n    return result;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-data@3.20.0_k7dauiu3y265wd6lcplf62oi7i/node_modules/@tensorflow/tfjs-data/dist/iterators/microphone_iterator.js\nvar MicrophoneIterator = class extends LazyIterator {\n  constructor(microphoneConfig) {\n    super();\n    this.microphoneConfig = microphoneConfig;\n    this.isClosed = false;\n    this.fftSize = microphoneConfig.fftSize || 1024;\n    const fftSizeLog2 = Math.log2(this.fftSize);\n    if (this.fftSize < 0 || fftSizeLog2 < 4 || fftSizeLog2 > 14 || !Number.isInteger(fftSizeLog2)) {\n      throw new Error(`Invalid fftSize: it must be a power of 2 between 2 to 4 and 2 to 14, but got ${this.fftSize}`);\n    }\n    this.numFrames = microphoneConfig.numFramesPerSpectrogram || 43;\n    this.sampleRateHz = microphoneConfig.sampleRateHz;\n    this.columnTruncateLength = microphoneConfig.columnTruncateLength || this.fftSize;\n    this.audioTrackConstraints = microphoneConfig.audioTrackConstraints;\n    this.smoothingTimeConstant = microphoneConfig.smoothingTimeConstant || 0;\n    this.includeSpectrogram = microphoneConfig.includeSpectrogram === false ? false : true;\n    this.includeWaveform = microphoneConfig.includeWaveform === true ? true : false;\n    if (!this.includeSpectrogram && !this.includeWaveform) {\n      throw new Error(\"Both includeSpectrogram and includeWaveform are false. At least one type of data should be returned.\");\n    }\n  }\n  summary() {\n    return `microphone`;\n  }\n  static async create(microphoneConfig = {}) {\n    if (!env().get(\"IS_BROWSER\")) {\n      throw new Error(\"microphone API is only supported in browser environment.\");\n    }\n    const microphoneIterator = new MicrophoneIterator(microphoneConfig);\n    await microphoneIterator.start();\n    return microphoneIterator;\n  }\n  async start() {\n    try {\n      this.stream = await navigator.mediaDevices.getUserMedia({\n        audio: this.audioTrackConstraints == null ? true : this.audioTrackConstraints,\n        video: false\n      });\n    } catch (e2) {\n      throw new Error(`Error thrown while initializing video stream: ${e2.message}`);\n    }\n    if (!this.stream) {\n      throw new Error(\"Could not obtain audio from microphone.\");\n    }\n    const ctxConstructor = window.AudioContext || window.webkitAudioContext;\n    this.audioContext = new ctxConstructor();\n    if (!this.sampleRateHz) {\n      this.sampleRateHz = this.audioContext.sampleRate;\n    } else if (this.audioContext.sampleRate !== this.sampleRateHz) {\n      throw new Error(`Mismatch in sampling rate: Expected: ${this.sampleRateHz}; Actual: ${this.audioContext.sampleRate}`);\n    }\n    const streamSource = this.audioContext.createMediaStreamSource(this.stream);\n    this.analyser = this.audioContext.createAnalyser();\n    this.analyser.fftSize = this.fftSize * 2;\n    this.analyser.smoothingTimeConstant = this.smoothingTimeConstant;\n    streamSource.connect(this.analyser);\n    this.freqData = new Float32Array(this.fftSize);\n    this.timeData = new Float32Array(this.fftSize);\n    return;\n  }\n  async next() {\n    if (this.isClosed) {\n      return { value: null, done: true };\n    }\n    let spectrogramTensor;\n    let waveformTensor;\n    const audioDataQueue = await this.getAudioData();\n    if (this.includeSpectrogram) {\n      const freqData = this.flattenQueue(audioDataQueue.freqDataQueue);\n      spectrogramTensor = this.getTensorFromAudioDataArray(freqData, [this.numFrames, this.columnTruncateLength, 1]);\n    }\n    if (this.includeWaveform) {\n      const timeData = this.flattenQueue(audioDataQueue.timeDataQueue);\n      waveformTensor = this.getTensorFromAudioDataArray(timeData, [this.numFrames * this.fftSize, 1]);\n    }\n    return {\n      value: { \"spectrogram\": spectrogramTensor, \"waveform\": waveformTensor },\n      done: false\n    };\n  }\n  async capture() {\n    return (await this.next()).value;\n  }\n  async getAudioData() {\n    const freqDataQueue = [];\n    const timeDataQueue = [];\n    let currentFrames = 0;\n    return new Promise((resolve) => {\n      const intervalID = setInterval(() => {\n        if (this.includeSpectrogram) {\n          this.analyser.getFloatFrequencyData(this.freqData);\n          if (this.freqData[0] === -Infinity) {\n            resolve({ freqDataQueue, timeDataQueue });\n          }\n          freqDataQueue.push(this.freqData.slice(0, this.columnTruncateLength));\n        }\n        if (this.includeWaveform) {\n          this.analyser.getFloatTimeDomainData(this.timeData);\n          timeDataQueue.push(this.timeData.slice());\n        }\n        if (++currentFrames === this.numFrames) {\n          clearInterval(intervalID);\n          resolve({ freqDataQueue, timeDataQueue });\n        }\n      }, this.fftSize / this.sampleRateHz * 1e3);\n    });\n  }\n  stop() {\n    if (!this.isClosed) {\n      this.isClosed = true;\n      this.analyser.disconnect();\n      this.audioContext.close();\n      if (this.stream != null && this.stream.getTracks().length > 0) {\n        this.stream.getTracks()[0].stop();\n      }\n    }\n  }\n  toArray() {\n    throw new Error(\"Can not convert infinite audio stream to array.\");\n  }\n  getSampleRate() {\n    return this.sampleRateHz;\n  }\n  flattenQueue(queue) {\n    const frameSize = queue[0].length;\n    const freqData = new Float32Array(queue.length * frameSize);\n    queue.forEach((data, i2) => freqData.set(data, i2 * frameSize));\n    return freqData;\n  }\n  getTensorFromAudioDataArray(freqData, shape) {\n    const vals = new Float32Array(util_exports.sizeFromShape(shape));\n    vals.set(freqData, vals.length - freqData.length);\n    return tensor(vals, shape);\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-data@3.20.0_k7dauiu3y265wd6lcplf62oi7i/node_modules/@tensorflow/tfjs-data/dist/iterators/webcam_iterator.js\nvar WebcamIterator = class extends LazyIterator {\n  constructor(webcamVideoElement, webcamConfig) {\n    super();\n    this.webcamVideoElement = webcamVideoElement;\n    this.webcamConfig = webcamConfig;\n    this.isClosed = true;\n    this.resize = false;\n    if (this.needToResize()) {\n      this.resize = true;\n      this.cropSize = [this.webcamConfig.resizeHeight, this.webcamConfig.resizeWidth];\n      this.cropBoxInd = tensor1d([0], \"int32\");\n      if (this.webcamConfig.centerCrop) {\n        const widthCroppingRatio = this.webcamConfig.resizeWidth * 1 / this.webcamVideoElement.width;\n        const heightCroppingRatio = this.webcamConfig.resizeHeight * 1 / this.webcamVideoElement.height;\n        const widthCropStart = (1 - widthCroppingRatio) / 2;\n        const heightCropStart = (1 - heightCroppingRatio) / 2;\n        const widthCropEnd = widthCropStart + widthCroppingRatio;\n        const heightCropEnd = heightCroppingRatio + heightCropStart;\n        this.cropBox = tensor2d([heightCropStart, widthCropStart, heightCropEnd, widthCropEnd], [1, 4]);\n      } else {\n        this.cropBox = tensor2d([0, 0, 1, 1], [1, 4]);\n      }\n    }\n  }\n  summary() {\n    return `webcam`;\n  }\n  static async create(webcamVideoElement, webcamConfig = {}) {\n    if (!env().get(\"IS_BROWSER\")) {\n      throw new Error(\"tf.data.webcam is only supported in browser environment.\");\n    }\n    if (!webcamVideoElement) {\n      webcamVideoElement = document.createElement(\"video\");\n      if (!webcamConfig.resizeWidth || !webcamConfig.resizeHeight) {\n        throw new Error(\"Please provide webcam video element, or resizeWidth and resizeHeight to create a hidden video element.\");\n      }\n      webcamVideoElement.width = webcamConfig.resizeWidth;\n      webcamVideoElement.height = webcamConfig.resizeHeight;\n    }\n    const webcamIterator = new WebcamIterator(webcamVideoElement, webcamConfig);\n    await webcamIterator.start();\n    return webcamIterator;\n  }\n  async start() {\n    if (this.webcamConfig.facingMode) {\n      util_exports.assert(this.webcamConfig.facingMode === \"user\" || this.webcamConfig.facingMode === \"environment\", () => `Invalid webcam facing mode: ${this.webcamConfig.facingMode}. Please provide 'user' or 'environment'`);\n    }\n    try {\n      this.stream = await navigator.mediaDevices.getUserMedia({\n        video: {\n          deviceId: this.webcamConfig.deviceId,\n          facingMode: this.webcamConfig.facingMode ? this.webcamConfig.facingMode : \"user\",\n          width: this.webcamVideoElement.width,\n          height: this.webcamVideoElement.height\n        }\n      });\n    } catch (e2) {\n      e2.message = `Error thrown while initializing video stream: ${e2.message}`;\n      throw e2;\n    }\n    if (!this.stream) {\n      throw new Error(\"Could not obtain video from webcam.\");\n    }\n    try {\n      this.webcamVideoElement.srcObject = this.stream;\n    } catch (error) {\n      console.log(error);\n      this.webcamVideoElement.src = window.URL.createObjectURL(this.stream);\n    }\n    this.webcamVideoElement.play();\n    this.isClosed = false;\n    return new Promise((resolve) => {\n      this.webcamVideoElement.onloadedmetadata = () => {\n        resolve();\n      };\n    });\n  }\n  async next() {\n    if (this.isClosed) {\n      return { value: null, done: true };\n    }\n    let img;\n    try {\n      img = browser_exports.fromPixels(this.webcamVideoElement);\n    } catch (e2) {\n      throw new Error(`Error thrown converting video to pixels: ${JSON.stringify(e2)}`);\n    }\n    if (this.resize) {\n      try {\n        return { value: this.cropAndResizeFrame(img), done: false };\n      } catch (e2) {\n        throw new Error(`Error thrown cropping the video: ${e2.message}`);\n      } finally {\n        img.dispose();\n      }\n    } else {\n      return { value: img, done: false };\n    }\n  }\n  needToResize() {\n    if (this.webcamConfig.resizeWidth && this.webcamConfig.resizeHeight && (this.webcamVideoElement.width !== this.webcamConfig.resizeWidth || this.webcamVideoElement.height !== this.webcamConfig.resizeHeight)) {\n      return true;\n    }\n    return false;\n  }\n  cropAndResizeFrame(img) {\n    return tidy(() => {\n      const expandedImage = expandDims(cast(img, \"float32\"), 0);\n      let resizedImage;\n      resizedImage = image.cropAndResize(expandedImage, this.cropBox, this.cropBoxInd, this.cropSize, \"bilinear\");\n      const shape = resizedImage.shape;\n      return reshape(resizedImage, shape.slice(1));\n    });\n  }\n  async capture() {\n    return (await this.next()).value;\n  }\n  stop() {\n    const tracks = this.stream.getTracks();\n    tracks.forEach((track) => track.stop());\n    try {\n      this.webcamVideoElement.srcObject = null;\n    } catch (error) {\n      console.log(error);\n      this.webcamVideoElement.src = null;\n    }\n    this.isClosed = true;\n  }\n  toArray() {\n    throw new Error(\"Can not convert infinite video stream to array.\");\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-data@3.20.0_k7dauiu3y265wd6lcplf62oi7i/node_modules/@tensorflow/tfjs-data/dist/datasource.js\nvar DataSource = class {\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-data@3.20.0_k7dauiu3y265wd6lcplf62oi7i/node_modules/@tensorflow/tfjs-data/dist/iterators/string_iterator.js\nvar StringIterator = class extends LazyIterator {\n  split(separator) {\n    return new SplitIterator(this, separator);\n  }\n};\nvar SplitIterator = class extends StringIterator {\n  constructor(upstream, separator) {\n    super();\n    this.upstream = upstream;\n    this.impl = new SplitIteratorImpl(upstream, separator);\n  }\n  summary() {\n    return this.impl.summary();\n  }\n  async next() {\n    return this.impl.next();\n  }\n};\nvar SplitIteratorImpl = class extends OneToManyIterator {\n  constructor(upstream, separator) {\n    super();\n    this.upstream = upstream;\n    this.separator = separator;\n    this.carryover = \"\";\n  }\n  summary() {\n    return `${this.upstream.summary()} -> Split('${this.separator}')`;\n  }\n  async pump() {\n    const chunkResult = await this.upstream.next();\n    if (chunkResult.done) {\n      if (this.carryover === \"\") {\n        return false;\n      }\n      this.outputQueue.push(this.carryover);\n      this.carryover = \"\";\n      return true;\n    }\n    const lines = chunkResult.value.split(this.separator);\n    lines[0] = this.carryover + lines[0];\n    for (const line of lines.slice(0, -1)) {\n      this.outputQueue.push(line);\n    }\n    this.carryover = lines[lines.length - 1];\n    return true;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-data@3.20.0_k7dauiu3y265wd6lcplf62oi7i/node_modules/@tensorflow/tfjs-data/dist/iterators/byte_chunk_iterator.js\nvar ByteChunkIterator = class extends LazyIterator {\n  decodeUTF8() {\n    return new Utf8Iterator(this);\n  }\n};\nvar Utf8Iterator = class extends StringIterator {\n  constructor(upstream) {\n    super();\n    this.upstream = upstream;\n    this.impl = new Utf8IteratorImpl(upstream);\n  }\n  summary() {\n    return this.impl.summary();\n  }\n  async next() {\n    return this.impl.next();\n  }\n};\nvar Utf8IteratorImpl = class extends OneToManyIterator {\n  constructor(upstream) {\n    super();\n    this.upstream = upstream;\n    if (env().get(\"IS_BROWSER\")) {\n      this.decoder = new TextDecoder(\"utf-8\");\n    } else {\n      const { StringDecoder } = require_string_decoder();\n      this.decoder = new StringDecoder(\"utf8\");\n    }\n  }\n  summary() {\n    return `${this.upstream.summary()} -> Utf8`;\n  }\n  async pump() {\n    const chunkResult = await this.upstream.next();\n    let chunk;\n    if (chunkResult.done) {\n      return false;\n    } else {\n      chunk = chunkResult.value;\n    }\n    let text;\n    if (env().get(\"IS_BROWSER\")) {\n      text = this.decoder.decode(chunk, { stream: true });\n    } else {\n      text = this.decoder.write(Buffer.from(chunk.buffer));\n    }\n    this.outputQueue.push(text);\n    return true;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-data@3.20.0_k7dauiu3y265wd6lcplf62oi7i/node_modules/@tensorflow/tfjs-data/dist/iterators/file_chunk_iterator.js\nvar FileChunkIterator = class extends ByteChunkIterator {\n  constructor(file, options = {}) {\n    super();\n    this.file = file;\n    this.options = options;\n    util_exports.assert(file instanceof Uint8Array || (env().get(\"IS_BROWSER\") ? file instanceof File || file instanceof Blob : false), () => \"FileChunkIterator only supports File, Blob and Uint8Array right now.\");\n    this.offset = options.offset || 0;\n    this.chunkSize = options.chunkSize || 1024 * 1024;\n  }\n  summary() {\n    return `FileChunks ${this.file}`;\n  }\n  async next() {\n    if (this.offset >= (this.file instanceof Uint8Array ? this.file.byteLength : this.file.size)) {\n      return { value: null, done: true };\n    }\n    const chunk = new Promise((resolve, reject) => {\n      const end = this.offset + this.chunkSize;\n      if (this.file instanceof Uint8Array) {\n        resolve(new Uint8Array(this.file.slice(this.offset, end)));\n      } else {\n        const fileReader = new FileReader();\n        fileReader.onload = (event) => {\n          let data = fileReader.result;\n          if (data instanceof ArrayBuffer) {\n            data = new Uint8Array(data);\n          }\n          if (!(data instanceof Uint8Array)) {\n            return reject(new TypeError(\"FileReader returned unknown type.\"));\n          }\n          resolve(data);\n        };\n        fileReader.onabort = (event) => {\n          return reject(new Error(\"Aborted\"));\n        };\n        fileReader.onerror = (event) => {\n          return reject(new Error(event.type));\n        };\n        const slice6 = this.file.slice(this.offset, end);\n        fileReader.readAsArrayBuffer(slice6);\n      }\n      this.offset = end;\n    });\n    return { value: await chunk, done: false };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-data@3.20.0_k7dauiu3y265wd6lcplf62oi7i/node_modules/@tensorflow/tfjs-data/dist/iterators/url_chunk_iterator.js\nasync function urlChunkIterator(url, options = {}, fetchFunc) {\n  let urlString;\n  let requestInit;\n  if (typeof url === \"string\") {\n    urlString = url;\n  } else {\n    urlString = url.url;\n    requestInit = getRequestInitFromRequest(url);\n  }\n  const response = await (fetchFunc || util_exports.fetch)(urlString, requestInit);\n  if (response.ok) {\n    const uint8Array = new Uint8Array(await response.arrayBuffer());\n    return new FileChunkIterator(uint8Array, options);\n  } else {\n    throw new Error(response.statusText);\n  }\n}\nvar getRequestInitFromRequest = (request) => {\n  const init2 = {\n    method: request.method,\n    headers: request.headers,\n    body: request.body,\n    mode: request.mode,\n    credentials: request.credentials,\n    cache: request.cache,\n    redirect: request.redirect,\n    referrer: request.referrer,\n    integrity: request.integrity\n  };\n  return init2;\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-data@3.20.0_k7dauiu3y265wd6lcplf62oi7i/node_modules/@tensorflow/tfjs-data/dist/util/source_util.js\nfunction isLocalPath(source) {\n  return typeof source === \"string\" && source.slice(0, 7) === \"file://\";\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-data@3.20.0_k7dauiu3y265wd6lcplf62oi7i/node_modules/@tensorflow/tfjs-data/dist/sources/file_data_source.js\nvar FileDataSource = class extends DataSource {\n  constructor(input2, options = {}) {\n    super();\n    this.input = input2;\n    this.options = options;\n  }\n  async iterator() {\n    if (isLocalPath(this.input) && env().get(\"IS_NODE\")) {\n      const fs = require_fs();\n      this.input = fs.readFileSync(this.input.slice(7));\n    }\n    return new FileChunkIterator(this.input, this.options);\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-data@3.20.0_k7dauiu3y265wd6lcplf62oi7i/node_modules/@tensorflow/tfjs-data/dist/sources/url_data_source.js\nvar URLDataSource = class extends DataSource {\n  constructor(url, fileOptions = {}) {\n    super();\n    this.url = url;\n    this.fileOptions = fileOptions;\n  }\n  async iterator() {\n    if (isLocalPath(this.url)) {\n      return new FileDataSource(this.url, this.fileOptions).iterator();\n    } else {\n      return urlChunkIterator(this.url, this.fileOptions);\n    }\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-data@3.20.0_k7dauiu3y265wd6lcplf62oi7i/node_modules/@tensorflow/tfjs-data/dist/readers.js\nfunction csv(source, csvConfig = {}) {\n  return new CSVDataset(new URLDataSource(source), csvConfig);\n}\nfunction func(f) {\n  const iter = iteratorFromFunction(f);\n  return datasetFromIteratorFn(async () => iter);\n}\nfunction generator(generator2) {\n  return datasetFromIteratorFn(async () => {\n    const gen = await generator2();\n    return iteratorFromFunction(() => gen.next());\n  });\n}\nasync function webcam(webcamVideoElement, webcamConfig) {\n  return WebcamIterator.create(webcamVideoElement, webcamConfig);\n}\nasync function microphone(microphoneConfig) {\n  return MicrophoneIterator.create(microphoneConfig);\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-data@3.20.0_k7dauiu3y265wd6lcplf62oi7i/node_modules/@tensorflow/tfjs-data/dist/version.js\nvar version4 = \"3.20.0\";\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/cpu_util.js\nfunction assertNotComplex(tensor2, opName) {\n  if (!Array.isArray(tensor2)) {\n    tensor2 = [tensor2];\n  }\n  tensor2.forEach((t2) => {\n    if (t2 != null) {\n      util_exports.assert(t2.dtype !== \"complex64\", () => `${opName} does not support complex64 tensors in the CPU backend.`);\n    }\n  });\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/backend_cpu.js\nvar whereImpl2 = kernel_impls_exports.whereImpl;\nvar MathBackendCPU = class extends KernelBackend {\n  constructor() {\n    super();\n    this.blockSize = 48;\n    this.firstUse = true;\n    this.data = new DataStorage(this, engine());\n  }\n  nextDataId() {\n    return MathBackendCPU.nextDataId++;\n  }\n  write(values, shape, dtype) {\n    if (this.firstUse) {\n      this.firstUse = false;\n      if (env().get(\"IS_NODE\")) {\n        backend_util_exports.warn(\"\\n============================\\nHi, looks like you are running TensorFlow.js in Node.js. To speed things up dramatically, install our node backend, visit https://github.com/tensorflow/tfjs-node for more details. \\n============================\");\n      }\n    }\n    const dataId = { id: this.nextDataId() };\n    this.data.set(dataId, { values, dtype, refCount: 1 });\n    return dataId;\n  }\n  makeTensorInfo(shape, dtype, values) {\n    let outId;\n    if (dtype === \"string\" && values != null && values.length > 0 && util_exports.isString(values[0])) {\n      const encodedValues = values.map((d) => util_exports.encodeString(d));\n      outId = this.write(encodedValues, shape, dtype);\n    } else {\n      outId = this.write(values, shape, dtype);\n    }\n    return { dataId: outId, shape, dtype };\n  }\n  refCount(dataId) {\n    if (this.data.has(dataId)) {\n      const tensorData = this.data.get(dataId);\n      return tensorData.refCount;\n    }\n    return 0;\n  }\n  incRef(dataId) {\n    const tensorData = this.data.get(dataId);\n    tensorData.refCount++;\n  }\n  decRef(dataId) {\n    if (this.data.has(dataId)) {\n      const tensorData = this.data.get(dataId);\n      tensorData.refCount--;\n    }\n  }\n  move(dataId, values, shape, dtype, refCount) {\n    this.data.set(dataId, { values, dtype, refCount });\n  }\n  numDataIds() {\n    return this.data.numDataIds();\n  }\n  async read(dataId) {\n    return this.readSync(dataId);\n  }\n  readSync(dataId) {\n    const { dtype, complexTensorInfos } = this.data.get(dataId);\n    if (dtype === \"complex64\") {\n      const realValues = this.readSync(complexTensorInfos.real.dataId);\n      const imagValues = this.readSync(complexTensorInfos.imag.dataId);\n      return backend_util_exports.mergeRealAndImagArrays(realValues, imagValues);\n    }\n    return this.data.get(dataId).values;\n  }\n  bufferSync(t2) {\n    const data = this.readSync(t2.dataId);\n    if (t2.dtype === \"string\") {\n      try {\n        const strings = data.map((d) => util_exports.decodeString(d));\n        return buffer(t2.shape, t2.dtype, strings);\n      } catch (_a) {\n        throw new Error(\"Failed to decode encoded string bytes into utf-8\");\n      }\n    }\n    return buffer(t2.shape, t2.dtype, data);\n  }\n  makeOutput(values, shape, dtype) {\n    return engine().makeTensorFromTensorInfo(this.makeTensorInfo(shape, dtype, values), this);\n  }\n  disposeData(dataId, force = false) {\n    if (this.data.has(dataId)) {\n      this.data.get(dataId).refCount--;\n      if (!force && this.data.get(dataId).refCount > 0) {\n        return false;\n      }\n      const { complexTensorInfos } = this.data.get(dataId);\n      if (complexTensorInfos != null) {\n        this.disposeData(complexTensorInfos.real.dataId, true);\n        this.disposeData(complexTensorInfos.imag.dataId, true);\n      }\n      this.data.delete(dataId);\n    }\n    return true;\n  }\n  disposeIntermediateTensorInfo(tensorInfo) {\n    this.disposeData(tensorInfo.dataId);\n  }\n  async time(f) {\n    const start = util_exports.now();\n    f();\n    const kernelMs = util_exports.now() - start;\n    return { kernelMs };\n  }\n  memory() {\n    return {\n      unreliable: true,\n      reasons: [\"The reported memory is an upper bound. Due to automatic garbage collection, the true allocated memory may be less.\"]\n    };\n  }\n  where(condition) {\n    assertNotComplex([condition], \"where\");\n    const condVals = this.readSync(condition.dataId);\n    return whereImpl2(condition.shape, condVals);\n  }\n  dispose() {\n  }\n  floatPrecision() {\n    return 32;\n  }\n  epsilon() {\n    return super.epsilon();\n  }\n};\nMathBackendCPU.nextDataId = 0;\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/shared.js\nvar shared_exports = {};\n__export(shared_exports, {\n  addImpl: () => addImpl,\n  bincountImpl: () => bincountImpl,\n  bincountReduceImpl: () => bincountReduceImpl,\n  castImpl: () => castImpl,\n  ceilImpl: () => ceilImpl,\n  concatImpl: () => concatImpl,\n  equalImpl: () => equalImpl,\n  expImpl: () => expImpl,\n  expm1Impl: () => expm1Impl,\n  floorImpl: () => floorImpl,\n  gatherNdImpl: () => gatherNdImpl,\n  gatherV2Impl: () => gatherV2Impl,\n  greaterEqualImpl: () => greaterEqualImpl,\n  greaterImpl: () => greaterImpl,\n  lessEqualImpl: () => lessEqualImpl,\n  lessImpl: () => lessImpl,\n  linSpaceImpl: () => linSpaceImpl,\n  logImpl: () => logImpl,\n  maxImpl: () => maxImpl,\n  maximumImpl: () => maximumImpl,\n  minimumImpl: () => minimumImpl,\n  multiplyImpl: () => multiplyImpl,\n  negImpl: () => negImpl,\n  notEqualImpl: () => notEqualImpl,\n  prodImpl: () => prodImpl,\n  raggedTensorToTensorImpl: () => raggedTensorToTensorImpl,\n  rangeImpl: () => rangeImpl,\n  rsqrtImpl: () => rsqrtImpl,\n  scatterImpl: () => scatterImpl,\n  sigmoidImpl: () => sigmoidImpl,\n  simpleAbsImpl: () => simpleAbsImpl,\n  sliceImpl: () => sliceImpl,\n  sparseFillEmptyRowsImpl: () => sparseFillEmptyRowsImpl,\n  sparseReshapeImpl: () => sparseReshapeImpl,\n  sparseSegmentReductionImpl: () => sparseSegmentReductionImpl,\n  sqrtImpl: () => sqrtImpl,\n  squaredDifferenceImpl: () => squaredDifferenceImpl,\n  stridedSliceImpl: () => stridedSliceImpl,\n  stringNGramsImpl: () => stringNGramsImpl,\n  stringSplitImpl: () => stringSplitImpl,\n  stringToHashBucketFastImpl: () => stringToHashBucketFastImpl,\n  subImpl: () => subImpl,\n  tileImpl: () => tileImpl,\n  topKImpl: () => topKImpl,\n  transposeImpl: () => transposeImpl,\n  uniqueImpl: () => uniqueImpl\n});\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Abs.js\nfunction simpleAbsImpl(vals) {\n  const resultValues = new Float32Array(vals.length);\n  for (let i2 = 0; i2 < vals.length; ++i2) {\n    resultValues[i2] = Math.abs(vals[i2]);\n  }\n  return resultValues;\n}\nvar abs2 = (args) => {\n  const { x } = args.inputs;\n  const cpuBackend = args.backend;\n  assertNotComplex(x, \"abs\");\n  let resultValues = new Float32Array(util_exports.sizeFromShape(x.shape));\n  const values = cpuBackend.data.get(x.dataId).values;\n  resultValues = simpleAbsImpl(values);\n  return cpuBackend.makeOutput(resultValues, x.shape, x.dtype);\n};\nvar absConfig = {\n  kernelName: Abs,\n  backendName: \"cpu\",\n  kernelFunc: abs2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/utils/binary_impl.js\nfunction createSimpleBinaryKernelImpl(op2) {\n  return (aShape, bShape, aVals, bVals, dtype) => {\n    const newShape = backend_util_exports.assertAndGetBroadcastShape(aShape, bShape);\n    const resultRank = newShape.length;\n    const resultStrides = util_exports.computeStrides(newShape);\n    const resultSize = util_exports.sizeFromShape(newShape);\n    const result = util_exports.getTypedArrayFromDType(dtype, resultSize);\n    const aRank = aShape.length;\n    const bRank = bShape.length;\n    const aStrides = util_exports.computeStrides(aShape);\n    const bStrides = util_exports.computeStrides(bShape);\n    const aBroadcastDims = backend_util_exports.getBroadcastDims(aShape, newShape);\n    const bBroadcastDims = backend_util_exports.getBroadcastDims(bShape, newShape);\n    if (aBroadcastDims.length + bBroadcastDims.length === 0) {\n      for (let i2 = 0; i2 < result.length; ++i2) {\n        result[i2] = op2(aVals[i2 % aVals.length], bVals[i2 % bVals.length]);\n      }\n    } else {\n      for (let i2 = 0; i2 < result.length; ++i2) {\n        const loc = util_exports.indexToLoc(i2, resultRank, resultStrides);\n        const aLoc = loc.slice(-aRank);\n        aBroadcastDims.forEach((d) => aLoc[d] = 0);\n        const aIndex = util_exports.locToIndex(aLoc, aRank, aStrides);\n        const bLoc = loc.slice(-bRank);\n        bBroadcastDims.forEach((d) => bLoc[d] = 0);\n        const bIndex = util_exports.locToIndex(bLoc, bRank, bStrides);\n        result[i2] = op2(aVals[aIndex], bVals[bIndex]);\n      }\n    }\n    return [result, newShape];\n  };\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Complex.js\nfunction complex2(args) {\n  const { inputs, backend: backend2 } = args;\n  const { real: real5, imag: imag5 } = inputs;\n  const realVals = backend2.data.get(real5.dataId).values;\n  const imagVals = backend2.data.get(imag5.dataId).values;\n  const complexInfo = backend2.makeTensorInfo(real5.shape, \"complex64\");\n  const complex5 = backend2.data.get(complexInfo.dataId);\n  complex5.complexTensorInfos = {\n    real: backend2.makeTensorInfo(real5.shape, \"float32\", realVals),\n    imag: backend2.makeTensorInfo(imag5.shape, \"float32\", imagVals)\n  };\n  return complexInfo;\n}\nvar complexConfig = {\n  kernelName: Complex,\n  backendName: \"cpu\",\n  kernelFunc: complex2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/utils/zeros_impl.js\nfunction zeros3(backend2, shape, dtype = \"float32\") {\n  if (dtype === \"complex64\") {\n    const real5 = zeros3(backend2, shape, \"float32\");\n    const imag5 = zeros3(backend2, shape, \"float32\");\n    return complex2({ inputs: { real: real5, imag: imag5 }, backend: backend2 });\n  }\n  const values = util_exports.makeZerosTypedArray(util_exports.sizeFromShape(shape), dtype);\n  return backend2.makeTensorInfo(shape, dtype, values);\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Identity.js\nfunction identity2(args) {\n  const { inputs, backend: backend2 } = args;\n  const { x } = inputs;\n  backend2.incRef(x.dataId);\n  return { dataId: x.dataId, shape: x.shape, dtype: x.dtype };\n}\nvar identityConfig = {\n  kernelName: Identity,\n  backendName: \"cpu\",\n  kernelFunc: identity2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Real.js\nfunction real2(args) {\n  const { inputs, backend: backend2 } = args;\n  const { input: input2 } = inputs;\n  const real5 = backend2.data.get(input2.dataId).complexTensorInfos.real;\n  const realVal = backend2.data.get(real5.dataId).values;\n  return backend2.makeTensorInfo(real5.shape, real5.dtype, realVal);\n}\nvar realConfig = {\n  kernelName: Real,\n  backendName: \"cpu\",\n  kernelFunc: real2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Cast.js\nfunction castImpl(values, shape, inputType, dtype) {\n  if (dtype === \"int32\") {\n    const resultValues = Int32Array.from(values);\n    return [shape, \"int32\", resultValues];\n  }\n  if (dtype === \"bool\") {\n    const zero = util_exports.toTypedArray([0], inputType);\n    const [resultData, resultShape] = createSimpleBinaryKernelImpl((a, b) => a !== b ? 1 : 0)(shape, [], values, zero, \"bool\");\n    return [resultShape, \"bool\", resultData];\n  }\n  throw new Error(`Error in Cast: failed to cast ${inputType} to ${dtype}`);\n}\nfunction cast3(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x } = inputs;\n  const { dtype } = attrs;\n  if (dtype === \"complex64\") {\n    if (x.dtype === \"complex64\") {\n      return identity2({ inputs: { x }, backend: backend2 });\n    }\n    const zerosTensorInfo = zeros3(backend2, x.shape, x.dtype);\n    const floatX = cast3({ inputs: { x }, backend: backend2, attrs: { dtype: \"float32\" } });\n    const result = complex2({ inputs: { real: floatX, imag: zerosTensorInfo }, backend: backend2 });\n    backend2.disposeIntermediateTensorInfo(zerosTensorInfo);\n    backend2.disposeIntermediateTensorInfo(floatX);\n    return result;\n  }\n  if (x.dtype === \"complex64\") {\n    const realPart = real2({ inputs: { input: x }, backend: backend2 });\n    const result = cast3({ inputs: { x: realPart }, backend: backend2, attrs: { dtype } });\n    backend2.disposeIntermediateTensorInfo(realPart);\n    return result;\n  }\n  if (!util_exports.hasEncodingLoss(x.dtype, dtype)) {\n    const result = identity2({ inputs: { x }, backend: backend2 });\n    return { dataId: result.dataId, shape: result.shape, dtype };\n  }\n  const values = backend2.data.get(x.dataId).values;\n  const [resultShape, resultType, resultData] = castImpl(values, x.shape, x.dtype, dtype);\n  return backend2.makeTensorInfo(resultShape, resultType, resultData);\n}\nvar castConfig = {\n  kernelName: Cast,\n  backendName: \"cpu\",\n  kernelFunc: cast3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/utils/binary_utils.js\nfunction binaryKernelFunc(name, simpleImpl, complexImpl, dtype) {\n  if (complexImpl == null) {\n    return ({ inputs, backend: backend2 }) => {\n      const { a, b } = inputs;\n      const cpuBackend = backend2;\n      assertNotComplex([a, b], name);\n      const aVals = cpuBackend.data.get(a.dataId).values;\n      const bVals = cpuBackend.data.get(b.dataId).values;\n      const decodedAVals = a.dtype === \"string\" ? backend_util_exports.fromUint8ToStringArray(aVals) : aVals;\n      const decodedBVals = a.dtype === \"string\" ? backend_util_exports.fromUint8ToStringArray(bVals) : bVals;\n      const $dtype = dtype || a.dtype;\n      const [resultData, resultShape] = simpleImpl(a.shape, b.shape, decodedAVals, decodedBVals, $dtype);\n      return cpuBackend.makeTensorInfo(resultShape, $dtype, resultData);\n    };\n  }\n  return ({ inputs, backend: backend2 }) => {\n    const { a, b } = inputs;\n    const cpuBackend = backend2;\n    if (a.dtype === \"complex64\" || b.dtype === \"complex64\") {\n      const $aComplex = cast3({ inputs: { x: a }, backend: cpuBackend, attrs: { dtype: \"complex64\" } });\n      const $aComplexVals = cpuBackend.data.get($aComplex.dataId);\n      const aReal = $aComplexVals.complexTensorInfos.real;\n      const aImag = $aComplexVals.complexTensorInfos.imag;\n      const aRealVals = cpuBackend.data.get(aReal.dataId).values;\n      const aImagVals = cpuBackend.data.get(aImag.dataId).values;\n      const $bComplex = cast3({ inputs: { x: b }, backend: cpuBackend, attrs: { dtype: \"complex64\" } });\n      const $bComplexVals = cpuBackend.data.get($bComplex.dataId);\n      const bReal = $bComplexVals.complexTensorInfos.real;\n      const bImag = $bComplexVals.complexTensorInfos.imag;\n      const bRealVals = cpuBackend.data.get(bReal.dataId).values;\n      const bImagVals = cpuBackend.data.get(bImag.dataId).values;\n      const [resultRealData, resultImagData, resultShape] = complexImpl(a.shape, b.shape, aRealVals, aImagVals, bRealVals, bImagVals);\n      const resultReal = cpuBackend.makeTensorInfo(resultShape, \"float32\", resultRealData);\n      const resultImag = cpuBackend.makeTensorInfo(resultShape, \"float32\", resultImagData);\n      const result = complex2({ inputs: { real: resultReal, imag: resultImag }, backend: cpuBackend });\n      cpuBackend.disposeIntermediateTensorInfo($aComplex);\n      cpuBackend.disposeIntermediateTensorInfo($bComplex);\n      cpuBackend.disposeIntermediateTensorInfo(resultReal);\n      cpuBackend.disposeIntermediateTensorInfo(resultImag);\n      return result;\n    } else {\n      const aVals = cpuBackend.data.get(a.dataId).values;\n      const bVals = cpuBackend.data.get(b.dataId).values;\n      const $dtype = dtype || a.dtype;\n      const [resultData, resultShape] = simpleImpl(a.shape, b.shape, aVals, bVals, $dtype);\n      return cpuBackend.makeTensorInfo(resultShape, $dtype, resultData);\n    }\n  };\n}\nfunction createComplexBinaryKernelImpl(op2) {\n  return (aShape, bShape, aRealVals, aImagVals, bRealVals, bImagVals) => {\n    const resultShape = backend_util_exports.assertAndGetBroadcastShape(aShape, bShape);\n    const resultSize = util_exports.sizeFromShape(resultShape);\n    const resultRank = resultShape.length;\n    const resultStrides = util_exports.computeStrides(resultShape);\n    const resultRealVals = util_exports.getTypedArrayFromDType(\"float32\", resultSize);\n    const resultImagVals = util_exports.getTypedArrayFromDType(\"float32\", resultSize);\n    const aBroadcastDims = backend_util_exports.getBroadcastDims(aShape, resultShape);\n    const bBroadcastDims = backend_util_exports.getBroadcastDims(bShape, resultShape);\n    const aVals = backend_util_exports.mergeRealAndImagArrays(aRealVals, aImagVals);\n    const bVals = backend_util_exports.mergeRealAndImagArrays(bRealVals, bImagVals);\n    const aRank = aShape.length;\n    const aStrides = util_exports.computeStrides(aShape);\n    const bRank = bShape.length;\n    const bStrides = util_exports.computeStrides(bShape);\n    if (aBroadcastDims.length + bBroadcastDims.length === 0) {\n      for (let i2 = 0; i2 < resultRealVals.length; i2++) {\n        const aIdx = i2 % aVals.length;\n        const bIdx = i2 % bVals.length;\n        const result = op2(aVals[aIdx * 2], aVals[aIdx * 2 + 1], bVals[bIdx * 2], bVals[bIdx * 2 + 1]);\n        resultRealVals[i2] = result.real;\n        resultImagVals[i2] = result.imag;\n      }\n    } else {\n      for (let i2 = 0; i2 < resultRealVals.length; i2++) {\n        const loc = util_exports.indexToLoc(i2, resultRank, resultStrides);\n        const aLoc = loc.slice(-aRank);\n        aBroadcastDims.forEach((d) => aLoc[d] = 0);\n        const aIndex = util_exports.locToIndex(aLoc, aRank, aStrides);\n        const bLoc = loc.slice(-bRank);\n        bBroadcastDims.forEach((d) => bLoc[d] = 0);\n        const bIndex = util_exports.locToIndex(bLoc, bRank, bStrides);\n        const opResult = op2(aVals[aIndex * 2], aVals[aIndex * 2 + 1], bVals[bIndex * 2], bVals[bIndex * 2 + 1]);\n        resultRealVals[i2] = opResult.real;\n        resultImagVals[i2] = opResult.imag;\n      }\n    }\n    return [resultRealVals, resultImagVals, resultShape];\n  };\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Add.js\nvar addImpl = createSimpleBinaryKernelImpl((a, b) => a + b);\nvar addComplexImpl = createComplexBinaryKernelImpl((aReal, aImag, bReal, bImag) => {\n  return { real: aReal + bReal, imag: aImag + bImag };\n});\nvar add4 = binaryKernelFunc(Add, addImpl, addComplexImpl);\nvar addConfig = {\n  kernelName: Add,\n  backendName: \"cpu\",\n  kernelFunc: add4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Bincount_impl.js\nfunction bincountImpl(xVals, weightsVals, weightsDtype, weightsShape, size) {\n  const weightsSize = util_exports.sizeFromShape(weightsShape);\n  const outVals = util_exports.makeZerosTypedArray(size, weightsDtype);\n  for (let i2 = 0; i2 < xVals.length; i2++) {\n    const value = xVals[i2];\n    if (value < 0) {\n      throw new Error(\"Input x must be non-negative!\");\n    }\n    if (value >= size) {\n      continue;\n    }\n    if (weightsSize > 0) {\n      outVals[value] += weightsVals[i2];\n    } else {\n      outVals[value] += 1;\n    }\n  }\n  return outVals;\n}\nfunction bincountReduceImpl(xBuf, weightsBuf, size, binaryOutput = false) {\n  const numRows = xBuf.shape[0];\n  const numCols = xBuf.shape[1];\n  const outBuf = buffer([numRows, size], weightsBuf.dtype);\n  for (let i2 = 0; i2 < numRows; i2++) {\n    for (let j = 0; j < numCols; j++) {\n      const value = xBuf.get(i2, j);\n      if (value < 0) {\n        throw new Error(\"Input x must be non-negative!\");\n      }\n      if (value >= size) {\n        continue;\n      }\n      if (binaryOutput) {\n        outBuf.set(1, i2, value);\n      } else {\n        if (weightsBuf.size > 0) {\n          outBuf.set(outBuf.get(i2, value) + weightsBuf.get(i2, j), i2, value);\n        } else {\n          outBuf.set(outBuf.get(i2, value) + 1, i2, value);\n        }\n      }\n    }\n  }\n  return outBuf;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/utils/unary_impl.js\nfunction createSimpleUnaryImpl(op2) {\n  return (values, dtype, attrs) => {\n    const newValues = util_exports.getTypedArrayFromDType(dtype, values.length);\n    for (let i2 = 0; i2 < values.length; ++i2) {\n      newValues[i2] = op2(values[i2], attrs);\n    }\n    return newValues;\n  };\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/utils/unary_utils.js\nfunction unaryKernelFunc(name, op2, dtype) {\n  return ({ inputs, attrs, backend: backend2 }) => {\n    const { x } = inputs;\n    assertNotComplex(x, name);\n    if (x.dtype === \"string\" || dtype === \"string\") {\n      throw new Error(\"unaryKernelFunc does not support string input/output\");\n    }\n    const cpuBackend = backend2;\n    const values = cpuBackend.data.get(x.dataId).values;\n    const xSize = util_exports.sizeFromShape(x.shape);\n    const $dtype = dtype || x.dtype;\n    const newValues = util_exports.getArrayFromDType($dtype, xSize);\n    for (let i2 = 0; i2 < xSize; ++i2) {\n      newValues[i2] = op2(values[i2], attrs);\n    }\n    return cpuBackend.makeTensorInfo(x.shape, $dtype, newValues);\n  };\n}\nfunction unaryKernelFuncFromImpl(name, unaryImpl, dtype) {\n  return ({ inputs, attrs, backend: backend2 }) => {\n    const { x } = inputs;\n    assertNotComplex(x, name);\n    if (x.dtype === \"string\" || dtype === \"string\") {\n      throw new Error(\"unaryKernelFunc does not support string input/output\");\n    }\n    const cpuBackend = backend2;\n    const values = cpuBackend.data.get(x.dataId).values;\n    const $dtype = dtype || x.dtype;\n    const newValues = unaryImpl(values, $dtype, attrs);\n    return cpuBackend.makeTensorInfo(x.shape, $dtype, newValues);\n  };\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Ceil.js\nvar ceilImpl = createSimpleUnaryImpl((xi) => Math.ceil(xi));\nvar ceil2 = unaryKernelFuncFromImpl(Ceil, ceilImpl);\nvar ceilConfig = {\n  kernelName: Ceil,\n  backendName: \"cpu\",\n  kernelFunc: ceil2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Concat_impl.js\nfunction concatImpl(inputs, outShape, dtype, simplyConcat) {\n  const outVals = util_exports.getArrayFromDType(dtype, util_exports.sizeFromShape(outShape));\n  if (simplyConcat && dtype !== \"string\") {\n    let offset = 0;\n    inputs.forEach((input2) => {\n      const size = util_exports.sizeFromShape(input2.shape);\n      outVals.set(input2.vals, offset);\n      offset += size;\n    });\n  } else {\n    let colOffset = 0;\n    inputs.forEach((input2) => {\n      const decodedData = dtype === \"string\" ? backend_util_exports.fromUint8ToStringArray(input2.vals) : input2.vals;\n      let tIdx = 0;\n      for (let row = 0; row < input2.shape[0]; ++row) {\n        const resIdx = row * outShape[1] + colOffset;\n        for (let col = 0; col < input2.shape[1]; ++col) {\n          outVals[resIdx + col] = decodedData[tIdx++];\n        }\n      }\n      colOffset += input2.shape[1];\n    });\n  }\n  return outVals;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Equal.js\nvar equalImpl = createSimpleBinaryKernelImpl((a, b) => a === b ? 1 : 0);\nvar equal2 = binaryKernelFunc(Equal, equalImpl, null, \"bool\");\nvar equalConfig = {\n  kernelName: Equal,\n  backendName: \"cpu\",\n  kernelFunc: equal2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Exp.js\nvar expImpl = createSimpleUnaryImpl((xi) => Math.exp(xi));\nvar exp2 = unaryKernelFuncFromImpl(Exp, expImpl, \"float32\");\nvar expConfig = {\n  kernelName: Exp,\n  backendName: \"cpu\",\n  kernelFunc: exp2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Expm1.js\nvar expm1Impl = createSimpleUnaryImpl((xi) => Math.expm1(xi));\nvar expm12 = unaryKernelFuncFromImpl(Expm1, expm1Impl);\nvar expm1Config = {\n  kernelName: Expm1,\n  backendName: \"cpu\",\n  kernelFunc: expm12\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Floor.js\nvar floorImpl = createSimpleUnaryImpl((xi) => Math.floor(xi));\nvar floor2 = unaryKernelFuncFromImpl(Floor, floorImpl);\nvar floorConfig = {\n  kernelName: Floor,\n  backendName: \"cpu\",\n  kernelFunc: floor2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/GatherNd_Impl.js\nfunction gatherNdImpl(indicesData, paramsBuf, dtype, numSlices, sliceRank, sliceSize, strides, paramsShape, paramsSize) {\n  const outBuf = buffer([numSlices, sliceSize], dtype);\n  for (let i2 = 0; i2 < numSlices; i2++) {\n    const index = [];\n    let flattenIndex = 0;\n    for (let j = 0; j < sliceRank; j++) {\n      const dim = indicesData[i2 * sliceRank + j];\n      flattenIndex += dim * strides[j];\n      index.push(dim);\n    }\n    if (flattenIndex < 0 || flattenIndex >= paramsSize / sliceSize) {\n      throw new Error(`Invalid indices: ${index} does not index into ${paramsShape}`);\n    }\n    for (let k = 0; k < sliceSize; k++) {\n      outBuf.values[i2 * sliceSize + k] = paramsBuf.get(...paramsBuf.indexToLoc(flattenIndex * sliceSize + k));\n    }\n  }\n  return outBuf;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/GatherV2_impl.js\nfunction gatherV2Impl(xBuf, indicesBuf, flattenOutputShape) {\n  const outBuf = buffer(flattenOutputShape, xBuf.dtype);\n  for (let i2 = 0; i2 < outBuf.size; ++i2) {\n    const newLoc = outBuf.indexToLoc(i2);\n    const originalLoc = newLoc.slice();\n    const batchIdx = originalLoc[0];\n    const indicesIdx = originalLoc[2];\n    const indicesIndex = indicesBuf.locToIndex([batchIdx, indicesIdx]);\n    originalLoc[2] = indicesBuf.values[indicesIndex];\n    const originalIndex = xBuf.locToIndex(originalLoc);\n    if (0 <= originalIndex && originalIndex < xBuf.values.length) {\n      outBuf.values[i2] = xBuf.values[originalIndex];\n    }\n  }\n  return outBuf;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Greater.js\nvar greaterImpl = createSimpleBinaryKernelImpl((a, b) => a > b ? 1 : 0);\nvar greater3 = binaryKernelFunc(Greater, greaterImpl, null, \"bool\");\nvar greaterConfig = {\n  kernelName: Greater,\n  backendName: \"cpu\",\n  kernelFunc: greater3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/GreaterEqual.js\nvar greaterEqualImpl = createSimpleBinaryKernelImpl((a, b) => a >= b ? 1 : 0);\nvar greaterEqual2 = binaryKernelFunc(GreaterEqual, greaterEqualImpl, null, \"bool\");\nvar greaterEqualConfig = {\n  kernelName: GreaterEqual,\n  backendName: \"cpu\",\n  kernelFunc: greaterEqual2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Less.js\nvar lessImpl = createSimpleBinaryKernelImpl((a, b) => a < b ? 1 : 0);\nvar less3 = binaryKernelFunc(Less, lessImpl, null, \"bool\");\nvar lessConfig = {\n  kernelName: Less,\n  backendName: \"cpu\",\n  kernelFunc: less3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/LessEqual.js\nvar lessEqualImpl = createSimpleBinaryKernelImpl((a, b) => a <= b ? 1 : 0);\nvar lessEqual2 = binaryKernelFunc(LessEqual, lessEqualImpl, null, \"bool\");\nvar lessEqualConfig = {\n  kernelName: LessEqual,\n  backendName: \"cpu\",\n  kernelFunc: lessEqual2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/LinSpace_impl.js\nfunction linSpaceImpl(start, stop, num) {\n  const step5 = (stop - start) / (num - 1);\n  const values = util_exports.makeZerosTypedArray(num, \"float32\");\n  values[0] = start;\n  for (let i2 = 1; i2 < values.length; i2++) {\n    values[i2] = values[i2 - 1] + step5;\n  }\n  return values;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Log.js\nvar logImpl = createSimpleUnaryImpl((xi) => Math.log(xi));\nvar log3 = unaryKernelFuncFromImpl(Log, logImpl);\nvar logConfig = {\n  kernelName: Log,\n  backendName: \"cpu\",\n  kernelFunc: log3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Max_impl.js\nfunction maxImpl(aVals, reduceSize, outShape, dtype) {\n  const vals = util_exports.getTypedArrayFromDType(dtype, util_exports.sizeFromShape(outShape));\n  for (let i2 = 0; i2 < vals.length; ++i2) {\n    const offset = i2 * reduceSize;\n    let max7 = aVals[offset];\n    for (let j = 0; j < reduceSize; ++j) {\n      const value = aVals[offset + j];\n      if (Number.isNaN(value) || value > max7) {\n        max7 = value;\n      }\n    }\n    vals[i2] = max7;\n  }\n  return vals;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Maximum.js\nvar maximumImpl = createSimpleBinaryKernelImpl((aValue, bValue) => Math.max(aValue, bValue));\nvar maximum3 = binaryKernelFunc(Maximum, maximumImpl);\nvar maximumConfig = {\n  kernelName: Maximum,\n  backendName: \"cpu\",\n  kernelFunc: maximum3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Minimum.js\nvar minimumImpl = createSimpleBinaryKernelImpl((aValue, bValue) => Math.min(aValue, bValue));\nvar minimum3 = binaryKernelFunc(Minimum, minimumImpl);\nvar minimumConfig = {\n  kernelName: Minimum,\n  backendName: \"cpu\",\n  kernelFunc: minimum3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Multiply.js\nvar multiplyImpl = createSimpleBinaryKernelImpl((aValue, bValue) => aValue * bValue);\nvar multiplyComplexImpl = createComplexBinaryKernelImpl((aReal, aImag, bReal, bImag) => {\n  return {\n    real: aReal * bReal - aImag * bImag,\n    imag: aReal * bImag + aImag * bReal\n  };\n});\nvar multiply2 = binaryKernelFunc(Multiply, multiplyImpl, multiplyComplexImpl);\nvar multiplyConfig = {\n  kernelName: Multiply,\n  backendName: \"cpu\",\n  kernelFunc: multiply2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Neg.js\nfunction negImpl(xVals, xShape, xDtype) {\n  const minusOne = util_exports.createScalarValue(-1, xDtype);\n  return multiplyImpl([], xShape, minusOne, xVals, xDtype);\n}\nfunction neg2(args) {\n  const { inputs, backend: backend2 } = args;\n  const { x } = inputs;\n  assertNotComplex(x, \"neg\");\n  const xVals = backend2.data.get(x.dataId).values;\n  const [res, newShape] = negImpl(xVals, x.shape, x.dtype);\n  return backend2.makeTensorInfo(newShape, x.dtype, res);\n}\nvar negConfig = {\n  kernelName: Neg,\n  backendName: \"cpu\",\n  kernelFunc: neg2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/NotEqual.js\nvar notEqualImpl = createSimpleBinaryKernelImpl((a, b) => a !== b ? 1 : 0);\nvar notEqual2 = binaryKernelFunc(NotEqual, notEqualImpl, null, \"bool\");\nvar notEqualConfig = {\n  kernelName: NotEqual,\n  backendName: \"cpu\",\n  kernelFunc: notEqual2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Transpose_impl.js\nfunction transposeImpl(xVals, xShape, dtype, perm, newShape) {\n  const xRank = xShape.length;\n  const xSize = util_exports.sizeFromShape(xShape);\n  const xStrides = util_exports.computeStrides(xShape);\n  const newStrides = util_exports.computeStrides(newShape);\n  const result = util_exports.getTypedArrayFromDType(dtype, util_exports.sizeFromShape(newShape));\n  for (let i2 = 0; i2 < xSize; ++i2) {\n    const loc = util_exports.indexToLoc(i2, xRank, xStrides);\n    const newLoc = new Array(loc.length);\n    for (let i3 = 0; i3 < newLoc.length; i3++) {\n      newLoc[i3] = loc[perm[i3]];\n    }\n    const newIndex = util_exports.locToIndex(newLoc, xRank, newStrides);\n    result[newIndex] = xVals[i2];\n  }\n  return result;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Transpose.js\nfunction transpose2(args) {\n  const { inputs, attrs, backend: backend2 } = args;\n  const { x } = inputs;\n  const { perm } = attrs;\n  assertNotComplex(x, \"transpose\");\n  const xRank = x.shape.length;\n  const newShape = new Array(xRank);\n  for (let i2 = 0; i2 < newShape.length; i2++) {\n    newShape[i2] = x.shape[perm[i2]];\n  }\n  const values = backend2.data.get(x.dataId).values;\n  const result = transposeImpl(values, x.shape, x.dtype, perm, newShape);\n  const dataId = backend2.write(result, newShape, x.dtype);\n  return { dataId, shape: newShape, dtype: x.dtype };\n}\nvar transposeConfig = {\n  kernelName: Transpose,\n  backendName: \"cpu\",\n  kernelFunc: transpose2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Prod.js\nfunction prodImpl(xShape, xDtype, xVals, reductionAxes) {\n  const [outShape, reduceShape] = backend_util_exports.computeOutAndReduceShapes(xShape, reductionAxes);\n  const outDtype = upcastType(xDtype, \"int32\");\n  const outVals = util_exports.makeZerosTypedArray(util_exports.sizeFromShape(outShape), outDtype);\n  const reduceSize = util_exports.sizeFromShape(reduceShape);\n  for (let i2 = 0; i2 < outVals.length; ++i2) {\n    const offset = i2 * reduceSize;\n    let prod6 = 1;\n    for (let j = 0; j < reduceSize; ++j) {\n      prod6 *= xVals[offset + j];\n    }\n    outVals[i2] = prod6;\n  }\n  return { outVals, outShape, outDtype };\n}\nfunction prod2(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x } = inputs;\n  const { axis, keepDims } = attrs;\n  assertNotComplex(x, \"prod\");\n  const xRank = x.shape.length;\n  const axes = util_exports.parseAxisParam(axis, x.shape);\n  const permutation = backend_util_exports.getAxesPermutation(axes, xRank);\n  let reductionAxes = axes;\n  let permutedX = x;\n  const intermediateTensorInfos = [];\n  if (permutation != null) {\n    permutedX = transpose2({ inputs: { x }, backend: backend2, attrs: { perm: permutation } });\n    intermediateTensorInfos.push(permutedX);\n    reductionAxes = backend_util_exports.getInnerMostAxes(reductionAxes.length, xRank);\n  }\n  const xVals = backend2.data.get(permutedX.dataId).values;\n  const { outVals, outShape, outDtype } = prodImpl(permutedX.shape, permutedX.dtype, xVals, reductionAxes);\n  let resultShape = outShape;\n  if (keepDims) {\n    resultShape = backend_util_exports.expandShapeToKeepDim(outShape, axes);\n  }\n  intermediateTensorInfos.forEach((t2) => backend2.disposeIntermediateTensorInfo(t2));\n  return backend2.makeTensorInfo(resultShape, outDtype, outVals);\n}\nvar prodConfig = {\n  kernelName: Prod,\n  backendName: \"cpu\",\n  kernelFunc: prod2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/RaggedTensorToTensor_impl.js\nvar RowPartitionType2 = backend_util_exports.RowPartitionType;\nvar RaggedTensorToTensorOp = class {\n  constructor(shape, shapeShape, values, valuesShape, valuesDType, defaultValue, defaultValueShape, rowPartitionValues, rowPartitionValuesShapes, rowPartitionTypeStrings) {\n    this.shape = shape;\n    this.shapeShape = shapeShape;\n    this.values = values;\n    this.valuesShape = valuesShape;\n    this.valuesDType = valuesDType;\n    this.defaultValue = defaultValue;\n    this.defaultValueShape = defaultValueShape;\n    this.rowPartitionValues = rowPartitionValues;\n    this.rowPartitionValuesShapes = rowPartitionValuesShapes;\n    this.rowPartitionTypes = backend_util_exports.getRowPartitionTypesHelper(rowPartitionTypeStrings);\n    this.raggedRank = backend_util_exports.getRaggedRank(this.rowPartitionTypes);\n  }\n  getRowPartitionTypeByDimension(dimension) {\n    if (this.rowPartitionTypes[0] === RowPartitionType2.FIRST_DIM_SIZE) {\n      return this.rowPartitionTypes[dimension + 1];\n    } else {\n      return this.rowPartitionTypes[dimension];\n    }\n  }\n  getRowPartitionTensor(dimension) {\n    if (this.rowPartitionTypes[0] === RowPartitionType2.FIRST_DIM_SIZE) {\n      return this.rowPartitionValues[dimension + 1];\n    } else {\n      return this.rowPartitionValues[dimension];\n    }\n  }\n  getMaxWidth(dimension) {\n    const rowPartitionTensor = this.getRowPartitionTensor(dimension - 1);\n    switch (this.getRowPartitionTypeByDimension(dimension - 1)) {\n      case RowPartitionType2.VALUE_ROWIDS:\n        return RaggedTensorToTensorOp.getMaxWidthValueRowID(rowPartitionTensor);\n      case RowPartitionType2.ROW_SPLITS:\n        return RaggedTensorToTensorOp.getMaxWidthRowSplit(rowPartitionTensor);\n      default:\n        throw new Error(`Cannot handle partition type ${RowPartitionType2[this.getRowPartitionTypeByDimension(dimension - 1)]}`);\n    }\n  }\n  static getMaxWidthRowSplit(rowSplit) {\n    const tensorLength = rowSplit.length;\n    if (tensorLength === 0 || tensorLength === 1) {\n      return 0;\n    }\n    let maxWidth = 0;\n    for (let i2 = 0; i2 < tensorLength - 1; ++i2) {\n      const currentWidth = rowSplit[i2 + 1] - rowSplit[i2];\n      if (currentWidth > maxWidth) {\n        maxWidth = currentWidth;\n      }\n    }\n    return maxWidth;\n  }\n  static getMaxWidthValueRowID(valueRowIds) {\n    const indexLength = valueRowIds.length;\n    if (indexLength === 0) {\n      return 0;\n    }\n    let firstEqualIndex = 0;\n    let firstEqualIndexValue = valueRowIds[0];\n    let maxWidth = 0;\n    for (let i2 = 1; i2 < indexLength; ++i2) {\n      const value = valueRowIds[i2];\n      if (value !== firstEqualIndexValue) {\n        firstEqualIndexValue = value;\n        maxWidth = Math.max(i2 - firstEqualIndex, maxWidth);\n        firstEqualIndex = i2;\n      }\n    }\n    return Math.max(indexLength - firstEqualIndex, maxWidth);\n  }\n  tensorShapeFromTensor(t2, tShape, isPartial = true) {\n    if (tShape.length === 0) {\n      if (t2[0] === -1) {\n        return [];\n      }\n      throw new Error(`The only valid scalar shape tensor is the fully unknown shape specified as -1.`);\n    }\n    return makeShape(t2, isPartial);\n  }\n  calculateOutputSize(firstDim) {\n    const valueShape = this.valuesShape;\n    const defaultValueShape = this.defaultValueShape;\n    backend_util_exports.validateDefaultValueShape(defaultValueShape, valueShape);\n    const shape = this.tensorShapeFromTensor(this.shape, this.shapeShape);\n    const outputShape = backend_util_exports.combineRaggedTensorToTensorShapes(this.raggedRank, shape, valueShape);\n    const result = outputShape;\n    if (result[0] < 0) {\n      result[0] = firstDim;\n    }\n    for (let i2 = 1; i2 <= this.raggedRank; ++i2) {\n      if (result[i2] < 0) {\n        result[i2] = this.getMaxWidth(i2);\n      }\n    }\n    return result;\n  }\n  calculateFirstParentOutputIndex(firstDimension, outputIndexMultiplier, firstDimensionOutput) {\n    const minDimension = Math.min(firstDimension, firstDimensionOutput);\n    const result = [];\n    let currentOutputIndex = 0;\n    for (let i2 = 0; i2 < minDimension; ++i2, currentOutputIndex += outputIndexMultiplier) {\n      result.push(currentOutputIndex);\n    }\n    for (let i2 = minDimension; i2 < firstDimension; ++i2) {\n      result.push(-1);\n    }\n    util_exports.assert(result.length === firstDimension, () => \"Final length of result must be equal to firstDimension.\");\n    return result;\n  }\n  calculateOutputIndexRowSplit(rowSplit, parentOutputIndex, outputIndexMultiplier, outputSize) {\n    const rowSplitSize = rowSplit.length;\n    const result = [];\n    for (let i2 = 0; i2 < rowSplitSize - 1; ++i2) {\n      const rowLength = rowSplit[i2 + 1] - rowSplit[i2];\n      let realLength = Math.min(outputSize, rowLength);\n      let parentOutputIndexCurrent = parentOutputIndex[i2];\n      if (parentOutputIndexCurrent === -1) {\n        realLength = 0;\n      }\n      for (let j = 0; j < realLength; ++j) {\n        result.push(parentOutputIndexCurrent);\n        parentOutputIndexCurrent += outputIndexMultiplier;\n      }\n      for (let j = 0; j < rowLength - realLength; ++j) {\n        result.push(-1);\n      }\n    }\n    if (rowSplitSize > 0 && result.length !== rowSplit[rowSplitSize - 1]) {\n      throw new Error(\"Invalid row split size.\");\n    }\n    return result;\n  }\n  calculateOutputIndexValueRowID(valueRowIds, parentOutputIndex, outputIndexMultiplier, outputSize) {\n    const indexSize = valueRowIds.length;\n    const result = [];\n    if (indexSize === 0) {\n      return [];\n    }\n    let currentOutputColumn = 0;\n    let currentValueRowId = valueRowIds[0];\n    if (currentValueRowId >= parentOutputIndex.length) {\n      throw new Error(`Got currentValueRowId=${currentValueRowId}, which is not less than ${parentOutputIndex.length}`);\n    }\n    let currentOutputIndex = parentOutputIndex[currentValueRowId];\n    result.push(currentOutputIndex);\n    for (let i2 = 1; i2 < indexSize; ++i2) {\n      const nextValueRowId = valueRowIds[i2];\n      if (nextValueRowId === currentValueRowId) {\n        if (currentOutputIndex >= 0) {\n          ++currentOutputColumn;\n          if (currentOutputColumn < outputSize) {\n            currentOutputIndex += outputIndexMultiplier;\n          } else {\n            currentOutputIndex = -1;\n          }\n        }\n      } else {\n        currentOutputColumn = 0;\n        currentValueRowId = nextValueRowId;\n        if (nextValueRowId >= parentOutputIndex.length) {\n          throw new Error(`Got nextValueRowId=${nextValueRowId} which is not less than ${parentOutputIndex.length}`);\n        }\n        currentOutputIndex = parentOutputIndex[nextValueRowId];\n      }\n      result.push(currentOutputIndex);\n    }\n    if (result.length !== valueRowIds.length) {\n      throw new Error(\"Invalid row ids.\");\n    }\n    return result;\n  }\n  calculateOutputIndex(dimension, parentOutputIndex, outputIndexMultiplier, outputSize) {\n    const rowPartitionTensor = this.getRowPartitionTensor(dimension);\n    const partitionType = this.getRowPartitionTypeByDimension(dimension);\n    switch (partitionType) {\n      case RowPartitionType2.VALUE_ROWIDS:\n        return this.calculateOutputIndexValueRowID(rowPartitionTensor, parentOutputIndex, outputIndexMultiplier, outputSize);\n      case RowPartitionType2.ROW_SPLITS:\n        if (rowPartitionTensor.length - 1 > parentOutputIndex.length) {\n          throw new Error(`Row partition size is greater than output size: ${rowPartitionTensor.length - 1} > ${parentOutputIndex.length}`);\n        }\n        return this.calculateOutputIndexRowSplit(rowPartitionTensor, parentOutputIndex, outputIndexMultiplier, outputSize);\n      default:\n        throw new Error(`Unsupported partition type: ${RowPartitionType2[partitionType]}`);\n    }\n  }\n  getFirstDimensionSize() {\n    const firstPartitionTensor = this.rowPartitionValues[0];\n    if (this.rowPartitionTypes.length === 0) {\n      throw new Error(\"No row_partition_types given.\");\n    }\n    const firstPartitionType = this.rowPartitionTypes[0];\n    switch (firstPartitionType) {\n      case RowPartitionType2.FIRST_DIM_SIZE:\n        return firstPartitionTensor[0];\n      case RowPartitionType2.VALUE_ROWIDS:\n        throw new Error(\"Cannot handle VALUE_ROWIDS in first dimension.\");\n      case RowPartitionType2.ROW_SPLITS:\n        return this.rowPartitionValuesShapes[0][0] - 1;\n      default:\n        throw new Error(`Cannot handle type ${RowPartitionType2[firstPartitionType]}`);\n    }\n  }\n  compute() {\n    const firstPartitionTensor = this.rowPartitionValues[0];\n    if (firstPartitionTensor.length <= 0) {\n      throw new Error(\"Invalid first partition input. Tensor requires at least one element.\");\n    }\n    const firstDimension = this.getFirstDimensionSize();\n    const outputSize = this.calculateOutputSize(firstDimension);\n    const multiplier = new Array(this.raggedRank + 1);\n    multiplier[multiplier.length - 1] = 1;\n    for (let i2 = multiplier.length - 2; i2 >= 0; --i2) {\n      multiplier[i2] = multiplier[i2 + 1] * outputSize[i2 + 1];\n    }\n    const outputShape = makeShape(outputSize, false);\n    const outputTensor = util_exports.getArrayFromDType(this.valuesDType, util_exports.sizeFromShape(outputShape));\n    const fullSize = multiplier[0] * outputSize[0];\n    if (fullSize > 0) {\n      let outputIndex = this.calculateFirstParentOutputIndex(firstDimension, multiplier[0], outputSize[0]);\n      for (let i2 = 1; i2 <= this.raggedRank; ++i2) {\n        const newOutputIndex = this.calculateOutputIndex(i2 - 1, outputIndex, multiplier[i2], outputSize[i2]);\n        outputIndex = newOutputIndex;\n      }\n      this.setOutput(this.raggedRank, outputIndex, outputTensor, outputShape);\n    }\n    return [outputShape, outputTensor];\n  }\n  setOutput(raggedRank, outputIndex, outputTensor, outputShape) {\n    if (outputTensor.length === 0) {\n      return;\n    }\n    const valuesBase = this.values;\n    const outputBase = outputTensor;\n    let elementShape = outputShape.slice();\n    elementShape = elementShape.slice(raggedRank + 1);\n    const valueElementSize = util_exports.sizeFromShape(elementShape);\n    const outputIndexSize = outputIndex.length;\n    let defaultValue = this.defaultValue;\n    if (defaultValue.length !== valueElementSize && defaultValue.length !== 1) {\n      const srcShape = this.defaultValueShape;\n      tidy(() => {\n        const defaultValueTensor = reshape(defaultValue, srcShape);\n        const bCastDefault = broadcastTo(defaultValueTensor, elementShape);\n        defaultValue = bCastDefault.dataSync();\n      });\n    }\n    let srcStart = 0;\n    let dstStart = 0;\n    let dstEnd = 0;\n    for (let srcI = 0; srcI <= outputIndexSize; ++srcI) {\n      let dstI = srcI < outputIndexSize ? outputIndex[srcI] : -1;\n      if (dstI === dstEnd) {\n        ++dstEnd;\n        continue;\n      }\n      if (dstStart < dstEnd) {\n        const src = valuesBase.subarray(srcStart * valueElementSize);\n        const dst = outputBase.subarray(dstStart * valueElementSize);\n        const nVals = (dstEnd - dstStart) * valueElementSize;\n        copyArray(dst, src, nVals);\n      }\n      if (srcI >= outputIndexSize) {\n        const outputSize = outputTensor.length;\n        dstI = Math.floor(outputSize / valueElementSize);\n      }\n      if (dstI > dstEnd) {\n        if (this.defaultValue.length === 1) {\n          outputBase.subarray(dstEnd * valueElementSize, dstI * valueElementSize).fill(this.defaultValue[0]);\n          dstEnd = dstI;\n        } else {\n          while (dstI > dstEnd) {\n            const dst = outputBase.slice(dstEnd * valueElementSize);\n            copyArray(dst, defaultValue, valueElementSize);\n            ++dstEnd;\n          }\n        }\n      }\n      if (dstI < 0) {\n        srcStart = srcI + 1;\n        dstStart = dstEnd;\n      } else {\n        srcStart = srcI;\n        dstStart = dstEnd;\n        dstEnd = dstStart + 1;\n      }\n    }\n  }\n};\nfunction copyArray(dst, src, size) {\n  for (let i2 = 0; i2 < size; i2++) {\n    dst[i2] = src[i2];\n  }\n}\nfunction makeShape(shape, isPartial) {\n  const out = [];\n  for (let dim of shape) {\n    if (dim < 0) {\n      if (!isPartial) {\n        throw new Error(`Dimension ${dim} must be >= 0`);\n      }\n      if (dim < -1) {\n        throw new Error(`Dimension ${dim} must be >= -1`);\n      }\n      dim = -1;\n    }\n    out.push(dim);\n  }\n  return out;\n}\nfunction raggedTensorToTensorImpl(shape, shapesShape, values, valuesShape, valuesDType, defaultValue, defaultValueShape, rowPartitionValues, rowPartitionValuesShapes, rowPartitionTypes) {\n  return new RaggedTensorToTensorOp(shape, shapesShape, values, valuesShape, valuesDType, defaultValue, defaultValueShape, rowPartitionValues, rowPartitionValuesShapes, rowPartitionTypes).compute();\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Range_impl.js\nfunction rangeImpl(start, stop, step5, dtype) {\n  const sameStartStop = start === stop;\n  const increasingRangeNegativeStep = start < stop && step5 < 0;\n  const decreasingRangePositiveStep = stop < start && step5 > 1;\n  if (sameStartStop || increasingRangeNegativeStep || decreasingRangePositiveStep) {\n    return util_exports.makeZerosTypedArray(0, dtype);\n  }\n  const numElements = Math.abs(Math.ceil((stop - start) / step5));\n  const values = util_exports.makeZerosTypedArray(numElements, dtype);\n  if (stop < start && step5 === 1) {\n    step5 = -1;\n  }\n  values[0] = start;\n  for (let i2 = 1; i2 < values.length; i2++) {\n    values[i2] = values[i2 - 1] + step5;\n  }\n  return values;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Rsqrt.js\nvar rsqrtImpl = createSimpleUnaryImpl((xi) => 1 / Math.sqrt(xi));\nvar rsqrt2 = unaryKernelFuncFromImpl(Rsqrt, rsqrtImpl);\nvar rsqrtConfig = {\n  kernelName: Rsqrt,\n  backendName: \"cpu\",\n  kernelFunc: rsqrt2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Scatter_impl.js\nfunction scatterImpl(indices, updates, shape, outputSize, sliceSize, numUpdates, sliceRank, strides, defaultValue, sumDupeIndices) {\n  const flattenShape = [outputSize / sliceSize, sliceSize];\n  const indicesData = indices.values;\n  const updatesData = updates.values;\n  if (outputSize === 0) {\n    return buffer(shape, updates.dtype);\n  }\n  const outBuf = buffer(flattenShape, updates.dtype);\n  if (typeof defaultValue === \"string\") {\n    outBuf.values.fill(defaultValue);\n  } else if (typeof defaultValue === \"number\") {\n    outBuf.values.fill(defaultValue);\n  } else if (typeof defaultValue === \"boolean\") {\n    outBuf.values.fill(+defaultValue);\n  }\n  for (let i2 = 0; i2 < numUpdates; i2++) {\n    const index = [];\n    let flattenIndex = 0;\n    for (let j = 0; j < sliceRank; j++) {\n      const dim = indicesData[i2 * sliceRank + j];\n      index.push(dim);\n      flattenIndex += dim * strides[j];\n    }\n    if (flattenIndex < 0 || flattenIndex >= outputSize / sliceSize) {\n      throw new Error(`Invalid indices: ${index} does not index into ${shape}`);\n    }\n    for (let k = 0; k < sliceSize; k++) {\n      if (sumDupeIndices) {\n        outBuf.values[flattenIndex * sliceSize + k] += updatesData[i2 * sliceSize + k];\n      } else {\n        outBuf.values[flattenIndex * sliceSize + k] = updates.rank === 0 ? updatesData[0] : updatesData[i2 * sliceSize + k];\n      }\n    }\n  }\n  return outBuf;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Sigmoid.js\nvar sigmoidImpl = createSimpleUnaryImpl((xi) => 1 / (1 + Math.exp(-xi)));\nvar sigmoid2 = unaryKernelFunc(Sigmoid, (xi) => 1 / (1 + Math.exp(-xi)));\nvar sigmoidConfig = {\n  kernelName: Sigmoid,\n  backendName: \"cpu\",\n  kernelFunc: sigmoid2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Slice.js\nfunction sliceImpl(vals, begin, size, shape, dtype) {\n  const isContinous = slice_util_exports.isSliceContinous(shape, begin, size);\n  const length = util_exports.sizeFromShape(size);\n  const xStrides = util_exports.computeStrides(shape);\n  if (isContinous) {\n    const flatOffset = slice_util_exports.computeFlatOffset(begin, xStrides);\n    if (dtype === \"string\") {\n      return vals.slice(flatOffset, flatOffset + length);\n    }\n    return vals.subarray(flatOffset, flatOffset + length);\n  }\n  const decodedData = dtype === \"string\" ? backend_util_exports.fromUint8ToStringArray(vals) : vals;\n  const inBuf = buffer(shape, dtype, decodedData);\n  const outBuf = buffer(size, dtype);\n  for (let i2 = 0; i2 < outBuf.size; ++i2) {\n    const outLoc = outBuf.indexToLoc(i2);\n    const inLoc = outLoc.map((idx, j) => idx + begin[j]);\n    outBuf.set(inBuf.get(...inLoc), ...outLoc);\n  }\n  if (dtype === \"string\") {\n    return backend_util_exports.fromStringArrayToUint8(outBuf.values);\n  }\n  return outBuf.values;\n}\nfunction slice2(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x } = inputs;\n  const { begin, size } = attrs;\n  assertNotComplex(x, \"slice\");\n  const [$begin, $size] = slice_util_exports.parseSliceParams(x, begin, size);\n  slice_util_exports.assertParamsValid(x, $begin, $size);\n  const vals = backend2.data.get(x.dataId).values;\n  const outVals = sliceImpl(vals, $begin, $size, x.shape, x.dtype);\n  return backend2.makeTensorInfo($size, x.dtype, outVals);\n}\nvar sliceConfig = {\n  kernelName: Slice,\n  backendName: \"cpu\",\n  kernelFunc: slice2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/SparseFillEmptyRows_impl.js\nfunction sparseFillEmptyRowsImpl(indices, indicesShape, indicesDType, values, valuesDType, denseShape, defaultValue) {\n  const indicesCount = indicesShape[0];\n  const denseRows = denseShape[0];\n  const emptyRowIndicator = new Array(denseRows);\n  const reverseIndexMap = new Array(indicesCount);\n  const rank = indicesShape[1];\n  if (denseRows === 0) {\n    if (indicesCount !== 0) {\n      throw new Error(backend_util_exports.getSparseFillEmptyRowsIndicesDenseShapeMismatch(indicesCount));\n    }\n    const outputIndices = util_exports.getArrayFromDType(indicesDType, 0);\n    const outputValues = util_exports.getArrayFromDType(valuesDType, 0);\n    return [\n      outputIndices,\n      [0, rank],\n      outputValues,\n      emptyRowIndicator,\n      reverseIndexMap\n    ];\n  }\n  let rowsAreOrdered = true;\n  let lastIndicesRow = 0;\n  const csrOffset = new Array(denseRows).fill(0);\n  for (let i2 = 0; i2 < indicesCount; ++i2) {\n    const row = indices[i2 * rank];\n    if (row < 0) {\n      throw new Error(backend_util_exports.getSparseFillEmptyRowsNegativeIndexErrorMessage(i2, row));\n    }\n    if (row >= denseRows) {\n      throw new Error(backend_util_exports.getSparseFillEmptyRowsOutOfRangeIndexErrorMessage(i2, row, denseRows));\n    }\n    ++csrOffset[row];\n    rowsAreOrdered = rowsAreOrdered && row >= lastIndicesRow;\n    lastIndicesRow = row;\n  }\n  let allRowsFull = true;\n  for (let row = 0; row < denseRows; ++row) {\n    const rowEmpty = csrOffset[row] === 0;\n    emptyRowIndicator[row] = rowEmpty;\n    allRowsFull = allRowsFull && !rowEmpty;\n    csrOffset[row] = Math.max(csrOffset[row], 1);\n    if (row > 0) {\n      csrOffset[row] += csrOffset[row - 1];\n    }\n  }\n  if (allRowsFull && rowsAreOrdered) {\n    const outputIndices = indices;\n    const outputValues = values;\n    for (let i2 = 0; i2 < indicesCount; ++i2) {\n      reverseIndexMap[i2] = i2;\n    }\n    return [\n      outputIndices,\n      [indicesCount, rank],\n      outputValues,\n      emptyRowIndicator,\n      reverseIndexMap\n    ];\n  } else {\n    const fullIndicesCount = csrOffset[denseRows - 1];\n    const outputIndices = util_exports.getArrayFromDType(indicesDType, fullIndicesCount * rank);\n    const outputValues = util_exports.getArrayFromDType(valuesDType, fullIndicesCount);\n    const filledCount = new Array(denseRows).fill(0);\n    for (let i2 = 0; i2 < indicesCount; ++i2) {\n      const row = indices[i2 * rank];\n      const offset = filledCount[row];\n      const outputI = (row === 0 ? 0 : csrOffset[row - 1]) + offset;\n      filledCount[row]++;\n      for (let j = 0; j < rank; ++j) {\n        outputIndices[outputI * rank + j] = indices[i2 * rank + j];\n      }\n      outputValues[outputI] = values[i2];\n      reverseIndexMap[i2] = outputI;\n    }\n    for (let row = 0; row < denseRows; ++row) {\n      const rowCount = filledCount[row];\n      if (rowCount === 0) {\n        const startingIndex = row === 0 ? 0 : csrOffset[row - 1];\n        outputIndices[startingIndex * rank + 0] = row;\n        for (let col = 1; col < rank; ++col) {\n          outputIndices[startingIndex * rank + col] = 0;\n        }\n        outputValues[startingIndex] = defaultValue;\n      }\n    }\n    return [\n      outputIndices,\n      [fullIndicesCount, rank],\n      outputValues,\n      emptyRowIndicator,\n      reverseIndexMap\n    ];\n  }\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/SparseReshape_impl.js\nfunction sparseReshapeImpl(inputIndices, inputIndicesShape, inputDType, inputShape, targetShape) {\n  const denseSize = util_exports.sizeFromShape(inputShape);\n  const nnz = inputIndicesShape[0];\n  const outputRank = targetShape.length;\n  const outputShape = [];\n  let product = 1;\n  let unknownIndex = -1;\n  for (let d = 0; d < outputRank; ++d) {\n    const size = targetShape[d];\n    if (size === -1) {\n      if (unknownIndex !== -1) {\n        throw new Error(backend_util_exports.getSparseReshapeMultipleNegativeOneOutputDimErrorMessage(unknownIndex, d));\n      }\n      unknownIndex = d;\n      outputShape.push(1);\n    } else {\n      if (size < 0) {\n        throw new Error(backend_util_exports.getSparseReshapeNegativeOutputDimErrorMessage(d, size));\n      }\n      product *= size;\n      outputShape.push(size);\n    }\n  }\n  if (unknownIndex !== -1) {\n    if (product <= 0) {\n      throw new Error(backend_util_exports.getSparseReshapeEmptyTensorZeroOutputDimErrorMessage());\n    }\n    const missing = Math.trunc(denseSize / product);\n    if (product * missing !== denseSize) {\n      throw new Error(backend_util_exports.getSparseReshapeInputOutputMultipleErrorMessage(inputShape, outputShape));\n    }\n    outputShape[unknownIndex] = missing;\n  }\n  const outputSize = util_exports.sizeFromShape(outputShape);\n  if (outputSize !== denseSize) {\n    throw new Error(backend_util_exports.getSparseReshapeInputOutputMismatchErrorMessage(inputShape, outputShape));\n  }\n  const inputRank = inputShape.length;\n  const inputStrides = [];\n  if (inputRank > 0) {\n    inputStrides[inputRank - 1] = 1;\n    for (let d = inputRank - 2; d >= 0; --d) {\n      inputStrides[d] = inputStrides[d + 1] * inputShape[d + 1];\n    }\n  }\n  const outputStrides = [];\n  if (outputRank > 0) {\n    outputStrides[outputRank - 1] = 1;\n    for (let d = outputRank - 2; d >= 0; --d) {\n      outputStrides[d] = outputStrides[d + 1] * outputShape[d + 1];\n    }\n  }\n  const newIndices = util_exports.getArrayFromDType(inputDType, nnz * outputRank);\n  for (let i2 = 0; i2 < nnz; ++i2) {\n    let id = 0;\n    for (let j = 0; j < inputRank; ++j) {\n      id += inputIndices[i2 * inputRank + j] * inputStrides[j];\n    }\n    for (let j = 0; j < outputRank; ++j) {\n      newIndices[i2 * outputRank + j] = Math.trunc(id / outputStrides[j]);\n      id %= outputStrides[j];\n    }\n  }\n  return [newIndices, [nnz, outputRank], outputShape];\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/SparseSegmentReduction_impl.js\nfunction sparseSegmentReductionImpl(input2, inputShape, inputDType, indices, segmentIds, isMean = false, defaultValue = 0) {\n  const numIndices = indices.length;\n  const inputFlat = [inputShape[0], input2.length / inputShape[0]];\n  const numCol = inputFlat[1];\n  const lastSegmentIdPlusOne = numIndices > 0 ? segmentIds[numIndices - 1] + 1 : 0;\n  const outputRows = lastSegmentIdPlusOne;\n  if (outputRows < 0) {\n    throw new Error(backend_util_exports.getSparseSegmentReductionNegativeSegmentIdsErrorMessage());\n  }\n  const outputShape = inputShape.slice();\n  outputShape[0] = outputRows;\n  const outputLength = outputShape.reduce((product, value) => product * value, 1);\n  const output = util_exports.getArrayFromDType(inputDType, outputLength);\n  if (numIndices === 0) {\n    if (outputRows > 0) {\n      output.fill(defaultValue);\n    }\n    return [output, outputShape];\n  }\n  if (outputRows <= 0) {\n    throw new Error(backend_util_exports.getSparseSegmentReductionNegativeSegmentIdsErrorMessage());\n  }\n  let start = 0, end = 1;\n  let uninitializedIndex = 0;\n  let outIndex = segmentIds[start];\n  while (true) {\n    let nextIndex = 0;\n    if (end < numIndices) {\n      nextIndex = segmentIds[end];\n      if (outIndex === nextIndex) {\n        ++end;\n        continue;\n      }\n      if (outIndex >= nextIndex) {\n        throw new Error(backend_util_exports.getSparseSegmentReductionNonIncreasingSegmentIdsErrorMessage());\n      }\n    }\n    if (outIndex < 0 || outIndex >= outputRows) {\n      throw new Error(backend_util_exports.getSparseSegmentReductionSegmentIdOutOfRangeErrorMessage(outIndex, outputRows));\n    }\n    if (outIndex > uninitializedIndex) {\n      output.fill(defaultValue, uninitializedIndex * numCol, outIndex * numCol);\n    }\n    for (let i2 = start; i2 < end; ++i2) {\n      const index = indices[i2];\n      if (index < 0 || index >= inputFlat[0]) {\n        throw new Error(backend_util_exports.getSparseSegmentReductionIndicesOutOfRangeErrorMessage(i2, indices[i2], inputFlat[0]));\n      }\n      for (let j = 0; j < numCol; j++) {\n        output[outIndex * numCol + j] += input2[index * numCol + j];\n      }\n    }\n    if (isMean) {\n      for (let j = 0; j < numCol; j++) {\n        output[outIndex * numCol + j] /= end - start;\n      }\n    }\n    start = end;\n    ++end;\n    uninitializedIndex = outIndex + 1;\n    outIndex = nextIndex;\n    if (end > numIndices) {\n      break;\n    }\n  }\n  if (uninitializedIndex < outputRows) {\n    output.fill(defaultValue, uninitializedIndex * numCol, outputRows * numCol);\n  }\n  return [output, outputShape];\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Sqrt.js\nvar sqrtImpl = createSimpleUnaryImpl((xi) => Math.sqrt(xi));\nvar sqrt2 = unaryKernelFunc(Sqrt, (xi) => Math.sqrt(xi));\nvar sqrtConfig = {\n  kernelName: Sqrt,\n  backendName: \"cpu\",\n  kernelFunc: sqrt2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/SquaredDifference.js\nvar squaredDifferenceImpl = createSimpleBinaryKernelImpl((a, b) => {\n  const diff = a - b;\n  return diff * diff;\n});\nvar squaredDifference2 = binaryKernelFunc(SquaredDifference, squaredDifferenceImpl);\nvar squaredDifferenceConfig = {\n  kernelName: SquaredDifference,\n  backendName: \"cpu\",\n  kernelFunc: squaredDifference2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/StridedSlice_impl.js\nfunction stridedSliceImpl(outShape, xBuf, strides, begin) {\n  const outBuf = buffer(outShape, xBuf.dtype);\n  for (let i2 = 0; i2 < outBuf.size; i2++) {\n    const loc = outBuf.indexToLoc(i2);\n    const newLoc = new Array(loc.length);\n    for (let j = 0; j < newLoc.length; j++) {\n      newLoc[j] = loc[j] * strides[j] + begin[j];\n    }\n    outBuf.set(xBuf.get(...newLoc), ...loc);\n  }\n  return outBuf;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/StringNGrams_impl.js\nvar StringNGramsOp = class {\n  constructor(separator, nGramWidths, leftPad, rightPad2, padWidth, preserveShortSequences) {\n    this.separator = util_exports.encodeString(separator);\n    this.nGramWidths = nGramWidths;\n    this.leftPad = util_exports.encodeString(leftPad);\n    this.rightPad = util_exports.encodeString(rightPad2);\n    this.padWidth = padWidth;\n    this.preserveShort = preserveShortSequences;\n  }\n  getPadWidth(nGramWidth) {\n    return Math.min(this.padWidth < 0 ? nGramWidth - 1 : this.padWidth, nGramWidth - 1);\n  }\n  getNumNGrams(length, nGramWidth) {\n    const padWidth = this.getPadWidth(nGramWidth);\n    return Math.max(0, length + 2 * padWidth - nGramWidth + 1);\n  }\n  createNGrams(data, splitIndex, output, outputStartIndex, numNGrams, nGramWidth) {\n    for (let nGramIndex = 0; nGramIndex < numNGrams; ++nGramIndex) {\n      const padWidth = this.getPadWidth(nGramWidth);\n      const leftPadding = Math.max(0, padWidth - nGramIndex);\n      const rightPadding = Math.max(0, padWidth - (numNGrams - (nGramIndex + 1)));\n      const numTokens = nGramWidth - (leftPadding + rightPadding);\n      const dataStartIndex = splitIndex + (leftPadding > 0 ? 0 : nGramIndex - padWidth);\n      let nGramSize = 0;\n      nGramSize += leftPadding * this.leftPad.length;\n      for (let n2 = 0; n2 < numTokens; ++n2) {\n        nGramSize += data[dataStartIndex + n2].length;\n      }\n      nGramSize += rightPadding * this.rightPad.length;\n      const numSeparators = leftPadding + rightPadding + numTokens - 1;\n      nGramSize += numSeparators * this.separator.length;\n      output[outputStartIndex + nGramIndex] = new Uint8Array(nGramSize);\n      const nGram = output[outputStartIndex + nGramIndex];\n      let nextNGramIndex = 0;\n      const appendToNGram = (str) => str.forEach((value) => nGram[nextNGramIndex++] = value);\n      for (let n2 = 0; n2 < leftPadding; ++n2) {\n        appendToNGram(this.leftPad);\n        appendToNGram(this.separator);\n      }\n      for (let n2 = 0; n2 < numTokens - 1; ++n2) {\n        appendToNGram(data[dataStartIndex + n2]);\n        appendToNGram(this.separator);\n      }\n      if (numTokens > 0) {\n        appendToNGram(data[dataStartIndex + numTokens - 1]);\n        for (let n2 = 0; n2 < rightPadding; ++n2) {\n          appendToNGram(this.separator);\n          appendToNGram(this.rightPad);\n        }\n      } else {\n        for (let n2 = 0; n2 < rightPadding - 1; ++n2) {\n          appendToNGram(this.rightPad);\n          appendToNGram(this.separator);\n        }\n        appendToNGram(this.rightPad);\n      }\n    }\n  }\n  compute(data, splits) {\n    const inputDataSize = data.length;\n    const splitsSize = splits.length;\n    if (splitsSize > 0) {\n      let prevSplit = splits[0];\n      if (prevSplit !== 0) {\n        throw new Error(`First split value must be 0, got ${prevSplit}`);\n      }\n      for (let i2 = 1; i2 < splitsSize; ++i2) {\n        let validSplits = splits[i2] >= prevSplit;\n        validSplits = validSplits && splits[i2] <= inputDataSize;\n        if (!validSplits) {\n          throw new Error(`Invalid split value ${splits[i2]}, must be in [${prevSplit}, ${inputDataSize}]`);\n        }\n        prevSplit = splits[i2];\n      }\n      if (prevSplit !== inputDataSize) {\n        throw new Error(`Last split value must be data size. Expected ${inputDataSize}, got ${prevSplit}`);\n      }\n    }\n    const numBatchItems = splitsSize - 1;\n    const nGramsSplits = util_exports.getArrayFromDType(\"int32\", splitsSize);\n    if (inputDataSize === 0 || splitsSize === 0) {\n      const empty = new Array(inputDataSize);\n      for (let i2 = 0; i2 <= numBatchItems; ++i2) {\n        nGramsSplits[i2] = 0;\n      }\n      return [empty, nGramsSplits];\n    }\n    nGramsSplits[0] = 0;\n    for (let i2 = 1; i2 <= numBatchItems; ++i2) {\n      const length = splits[i2] - splits[i2 - 1];\n      let numNGrams = 0;\n      this.nGramWidths.forEach((nGramWidth) => {\n        numNGrams += this.getNumNGrams(length, nGramWidth);\n      });\n      if (this.preserveShort && length > 0 && numNGrams === 0) {\n        numNGrams = 1;\n      }\n      nGramsSplits[i2] = nGramsSplits[i2 - 1] + numNGrams;\n    }\n    const nGrams = new Array(nGramsSplits[numBatchItems]);\n    for (let i2 = 0; i2 < numBatchItems; ++i2) {\n      const splitIndex = splits[i2];\n      let outputStartIdx = nGramsSplits[i2];\n      this.nGramWidths.forEach((nGramWidth) => {\n        const length = splits[i2 + 1] - splits[i2];\n        const numNGrams = this.getNumNGrams(length, nGramWidth);\n        this.createNGrams(data, splitIndex, nGrams, outputStartIdx, numNGrams, nGramWidth);\n        outputStartIdx += numNGrams;\n      });\n      if (this.preserveShort && outputStartIdx === nGramsSplits[i2]) {\n        const dataLength = splits[i2 + 1] - splits[i2];\n        if (dataLength === 0) {\n          continue;\n        }\n        const nGramWidth = dataLength + 2 * this.padWidth;\n        const numNGrams = 1;\n        this.createNGrams(data, splitIndex, nGrams, outputStartIdx, numNGrams, nGramWidth);\n      }\n    }\n    return [nGrams, nGramsSplits];\n  }\n};\nfunction stringNGramsImpl(data, dataSplits, separator, nGramWidths, leftPad, rightPad2, padWidth, preserveShortSequences) {\n  return new StringNGramsOp(separator, nGramWidths, leftPad, rightPad2, padWidth, preserveShortSequences).compute(data, dataSplits);\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/StringSplit_impl.js\nfunction split3(str, delimiters, skipEmpty, result) {\n  if (!str.length) {\n    return;\n  }\n  if (delimiters.length === 0) {\n    for (let i2 = 0; i2 < str.length; ++i2) {\n      result.push(str.subarray(i2, i2 + 1));\n    }\n    return;\n  }\n  if (delimiters.length === 1) {\n    const delimiter = delimiters[0];\n    let f = str.indexOf(delimiter);\n    while (f !== -1) {\n      const token = str.subarray(0, f);\n      if (!skipEmpty || token.length !== 0) {\n        result.push(token);\n      }\n      str = str.subarray(f + 1);\n      f = str.indexOf(delimiter);\n    }\n    if (!skipEmpty || str.length !== 0) {\n      result.push(str);\n    }\n    return;\n  }\n  let tokenStart = 0;\n  for (let i2 = 0; i2 < str.length + 1; i2++) {\n    if (i2 === str.length || delimiters.indexOf(str[i2]) !== -1) {\n      const token = str.subarray(tokenStart, i2);\n      if (!skipEmpty || token.length !== 0) {\n        result.push(token);\n      }\n      tokenStart = i2 + 1;\n    }\n  }\n}\nfunction stringSplitImpl(input2, delimiter, skipEmpty) {\n  const batchSize = input2.length;\n  const tokens = [];\n  let outputSize = 0;\n  let maxNumEntries = 0;\n  const numIndices = new Array(batchSize);\n  for (let i2 = 0; i2 < batchSize; ++i2) {\n    const prevTokensLength = tokens.length;\n    split3(input2[i2], delimiter, skipEmpty, tokens);\n    const nEntries = tokens.length - prevTokensLength;\n    numIndices[i2] = nEntries;\n    outputSize += nEntries;\n    maxNumEntries = Math.max(maxNumEntries, nEntries);\n  }\n  const indices = util_exports.getArrayFromDType(\"int32\", outputSize * 2);\n  const values = new Array(outputSize);\n  const shape = [batchSize, maxNumEntries];\n  let c = 0;\n  for (let i2 = 0; i2 < batchSize; ++i2) {\n    for (let j = 0; j < numIndices[i2]; ++j) {\n      indices[c * 2] = i2;\n      indices[c * 2 + 1] = j;\n      values[c] = tokens[c];\n      ++c;\n    }\n  }\n  return [indices, values, shape];\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/StringToHashBucketFast_impl.js\nfunction stringToHashBucketFastImpl(input2, numBuckets) {\n  const output = util_exports.getArrayFromDType(\"int32\", input2.length);\n  for (let i2 = 0; i2 < input2.length; ++i2) {\n    output[i2] = util_exports.fingerPrint64(input2[i2]).modulo(numBuckets).getLowBitsUnsigned();\n  }\n  return output;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Sub.js\nvar subImpl = createSimpleBinaryKernelImpl((aValue, bValue) => aValue - bValue);\nvar subComplexImpl = createComplexBinaryKernelImpl((aReal, aImag, bReal, bImag) => {\n  return { real: aReal - bReal, imag: aImag - bImag };\n});\nvar sub2 = binaryKernelFunc(Sub, subImpl, subComplexImpl);\nvar subConfig = {\n  kernelName: Sub,\n  backendName: \"cpu\",\n  kernelFunc: sub2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Tile_impl.js\nfunction tileImpl(xBuf, reps) {\n  const newShape = new Array(xBuf.rank);\n  for (let i2 = 0; i2 < newShape.length; i2++) {\n    newShape[i2] = xBuf.shape[i2] * reps[i2];\n  }\n  const result = buffer(newShape, xBuf.dtype);\n  for (let i2 = 0; i2 < result.values.length; ++i2) {\n    const newLoc = result.indexToLoc(i2);\n    const originalLoc = new Array(xBuf.rank);\n    for (let j = 0; j < originalLoc.length; j++) {\n      originalLoc[j] = newLoc[j] % xBuf.shape[j];\n    }\n    const originalIndex = xBuf.locToIndex(originalLoc);\n    result.values[i2] = xBuf.values[originalIndex];\n  }\n  return result;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/TopK_impl.js\nvar comparePair = (a, b) => {\n  const valueDiff = b.value - a.value;\n  return valueDiff === 0 ? a.index - b.index : valueDiff;\n};\nfunction select(array2, k, left = 0, right = array2.length - 1) {\n  while (right > left) {\n    if (right - left > 600) {\n      const n2 = right - left + 1;\n      const i3 = k - left + 1;\n      const z = Math.log(n2);\n      const s2 = 0.5 * Math.exp(2 * z / 3);\n      const sd = 0.5 * Math.sqrt(z * s2 * (n2 - s2) / n2) * Math.sign(i3 - n2 / 2);\n      const newLeft = Math.max(left, Math.floor(k - i3 * s2 / n2 + sd));\n      const newRight = Math.min(right, Math.floor(k + (n2 - i3) * s2 / n2 + sd));\n      select(array2, k, newLeft, newRight);\n    }\n    const t2 = array2[k];\n    let i2 = left;\n    let j = right;\n    util_exports.swap(array2, left, k);\n    if (comparePair(array2[right], t2) > 0) {\n      util_exports.swap(array2, left, right);\n    }\n    while (i2 < j) {\n      util_exports.swap(array2, i2, j);\n      i2++;\n      j--;\n      while (comparePair(array2[i2], t2) < 0) {\n        i2 = i2 + 1;\n      }\n      while (comparePair(array2[j], t2) > 0) {\n        j = j - 1;\n      }\n    }\n    if (comparePair(array2[left], t2) === 0) {\n      util_exports.swap(array2, left, j);\n    } else {\n      j = j + 1;\n      util_exports.swap(array2, j, right);\n    }\n    if (j <= k) {\n      left = j + 1;\n    }\n    if (k <= j) {\n      right = j - 1;\n    }\n  }\n}\nfunction topKImpl(x, xShape, xDtype, k, sorted) {\n  const lastDim = xShape[xShape.length - 1];\n  const [batch, size] = [x.length / lastDim, lastDim];\n  const allTopKVals = util_exports.getTypedArrayFromDType(xDtype, batch * k);\n  const allTopKIndices = util_exports.getTypedArrayFromDType(\"int32\", batch * k);\n  for (let b = 0; b < batch; b++) {\n    const offset = b * size;\n    const vals = x.subarray(offset, offset + size);\n    let valAndInd = new Array(vals.length);\n    vals.forEach((value, index) => valAndInd[index] = { value, index });\n    if (k < valAndInd.length) {\n      select(valAndInd, k);\n      valAndInd = valAndInd.slice(0, k);\n    }\n    if (sorted) {\n      valAndInd.sort(comparePair);\n    }\n    const outOffset = b * k;\n    const topKVals = allTopKVals.subarray(outOffset, outOffset + k);\n    const topKIndices = allTopKIndices.subarray(outOffset, outOffset + k);\n    for (let i2 = 0; i2 < k; i2++) {\n      topKVals[i2] = valAndInd[i2].value;\n      topKIndices[i2] = valAndInd[i2].index;\n    }\n  }\n  const outputShape = xShape.slice();\n  outputShape[outputShape.length - 1] = k;\n  return [\n    buffer(outputShape, xDtype, allTopKVals),\n    buffer(outputShape, \"int32\", allTopKIndices)\n  ];\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Unique_impl.js\nfunction uniqueImpl(values, axis, shape, dtype) {\n  const $axis = util_exports.parseAxisParam(axis, shape)[0];\n  const newShape = [1, shape[0], 1];\n  for (let i2 = 0; i2 < $axis; i2++) {\n    newShape[0] *= shape[i2];\n  }\n  newShape[1] = shape[$axis];\n  for (let i2 = $axis + 1; i2 < shape.length; i2++) {\n    newShape[2] *= shape[i2];\n  }\n  const uniqueElements = {};\n  const indices = new Int32Array(shape[$axis]);\n  const inputBuffer = new TensorBuffer(newShape, dtype, values);\n  const uniqueIndices = [];\n  const is1DTensor = newShape[0] === 1 && newShape[2] === 1;\n  for (let i2 = 0; i2 < shape[$axis]; i2++) {\n    let element;\n    if (is1DTensor) {\n      element = values[i2].toString();\n    } else {\n      const axisValues = [];\n      for (let m = 0; m < newShape[0]; m++) {\n        for (let n2 = 0; n2 < newShape[2]; n2++) {\n          axisValues.push(inputBuffer.get(m, i2, n2));\n        }\n      }\n      element = axisValues.join(\",\");\n    }\n    if (uniqueElements[element] !== void 0) {\n      indices[i2] = uniqueElements[element];\n    } else {\n      const uniqueIndex = Object.keys(uniqueElements).length;\n      uniqueElements[element] = uniqueIndex;\n      indices[i2] = uniqueIndex;\n      uniqueIndices.push(i2);\n    }\n  }\n  const outputTmpShape = newShape.slice();\n  outputTmpShape[1] = Object.keys(uniqueElements).length;\n  const outputBuffer = new TensorBuffer(outputTmpShape, dtype);\n  uniqueIndices.forEach((uniqueElementIndex, i2) => {\n    for (let m = 0; m < newShape[0]; m++) {\n      for (let n2 = 0; n2 < newShape[2]; n2++) {\n        outputBuffer.set(inputBuffer.get(m, uniqueElementIndex, n2), m, i2, n2);\n      }\n    }\n  });\n  const outputShape = shape.slice();\n  outputShape[$axis] = outputTmpShape[1];\n  return {\n    outputValues: outputBuffer.values,\n    outputShape,\n    indices\n  };\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/base.js\nregisterBackend(\"cpu\", () => new MathBackendCPU(), 1);\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Elu.js\nvar elu4 = unaryKernelFunc(Elu, (xi) => xi >= 0 ? xi : Math.exp(xi) - 1);\nvar eluConfig = {\n  kernelName: Elu,\n  backendName: \"cpu\",\n  kernelFunc: elu4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/LeakyRelu.js\nfunction leakyRelu2(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x } = inputs;\n  const { alpha } = attrs;\n  assertNotComplex([x], \"leakyRelu\");\n  const xSize = util_exports.sizeFromShape(x.shape);\n  const xVals = backend2.data.get(x.dataId).values;\n  const outVals = util_exports.getTypedArrayFromDType(\"float32\", xSize);\n  for (let i2 = 0; i2 < xVals.length; i2++) {\n    outVals[i2] = xVals[i2] < 0 ? alpha * xVals[i2] : xVals[i2];\n  }\n  return backend2.makeTensorInfo(x.shape, \"float32\", outVals);\n}\nvar leakyReluConfig = {\n  kernelName: LeakyRelu,\n  backendName: \"cpu\",\n  kernelFunc: leakyRelu2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Prelu.js\nvar preluImpl = createSimpleBinaryKernelImpl((xValue, aValue) => xValue < 0 ? aValue * xValue : xValue);\nfunction prelu3(args) {\n  const { inputs, backend: backend2 } = args;\n  const { x, alpha } = inputs;\n  assertNotComplex([x, alpha], \"prelu\");\n  const aVals = backend2.data.get(x.dataId).values;\n  const bVals = backend2.data.get(alpha.dataId).values;\n  const [resultData, resultShape] = preluImpl(x.shape, alpha.shape, aVals, bVals, \"float32\");\n  return backend2.makeTensorInfo(resultShape, \"float32\", resultData);\n}\nvar preluConfig = {\n  kernelName: Prelu,\n  backendName: \"cpu\",\n  kernelFunc: prelu3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Relu.js\nvar relu2 = unaryKernelFunc(Relu, (xi) => Math.max(0, xi));\nvar reluConfig = {\n  kernelName: Relu,\n  backendName: \"cpu\",\n  kernelFunc: relu2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Relu6.js\nvar relu62 = unaryKernelFunc(Relu6, (xi) => Math.min(Math.max(0, xi), 6));\nvar relu6Config = {\n  kernelName: Relu6,\n  backendName: \"cpu\",\n  kernelFunc: relu62\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/utils/fused_utils.js\nfunction applyActivation2(backend2, x, activation2, preluActivationWeights, leakyreluAlpha) {\n  if (activation2 === \"linear\") {\n    return identity2({ inputs: { x }, backend: backend2 });\n  } else if (activation2 === \"relu\") {\n    return relu2({ inputs: { x }, backend: backend2 });\n  } else if (activation2 === \"elu\") {\n    return elu4({ inputs: { x }, backend: backend2 });\n  } else if (activation2 === \"relu6\") {\n    return relu62({ inputs: { x }, backend: backend2 });\n  } else if (activation2 === \"prelu\") {\n    return prelu3({ inputs: { x, alpha: preluActivationWeights }, backend: backend2 });\n  } else if (activation2 === \"leakyrelu\") {\n    return leakyRelu2({ inputs: { x }, backend: backend2, attrs: { alpha: leakyreluAlpha } });\n  } else if (activation2 === \"sigmoid\") {\n    return sigmoid2({ inputs: { x }, backend: backend2 });\n  }\n  throw new Error(`Activation ${activation2} has not been implemented for the CPU backend.`);\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Reshape.js\nfunction reshape3(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x } = inputs;\n  const { shape } = attrs;\n  const xSize = util_exports.sizeFromShape(x.shape);\n  const $shape = util_exports.inferFromImplicitShape(shape, xSize);\n  const $xSize = util_exports.sizeFromShape($shape);\n  util_exports.assert(xSize === $xSize, () => `The new shape (${$shape}) has ${$xSize} elements and the old shape (${x.shape}) has ${xSize} elements. The new shape and old shape must have the same number of elements.`);\n  backend2.incRef(x.dataId);\n  const xData = backend2.data.get(x.dataId);\n  if (xData.complexTensorInfos != null) {\n    const real5 = xData.complexTensorInfos.real;\n    const imag5 = xData.complexTensorInfos.imag;\n    real5.shape = $shape;\n    imag5.shape = $shape;\n  }\n  return { dataId: x.dataId, shape: $shape, dtype: x.dtype };\n}\nvar reshapeConfig = {\n  kernelName: Reshape,\n  backendName: \"cpu\",\n  kernelFunc: reshape3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/BatchMatMul.js\nfunction batchMatMul(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { a, b } = inputs;\n  const { transposeA, transposeB } = attrs;\n  assertNotComplex([a, b], \"matMul\");\n  const aRank = a.shape.length;\n  const bRank = b.shape.length;\n  const innerShapeA = transposeA ? a.shape[aRank - 2] : a.shape[aRank - 1];\n  const innerShapeB = transposeB ? b.shape[bRank - 1] : b.shape[bRank - 2];\n  const outerShapeA = transposeA ? a.shape[aRank - 1] : a.shape[aRank - 2];\n  const outerShapeB = transposeB ? b.shape[bRank - 2] : b.shape[bRank - 1];\n  const outerDimsA = a.shape.slice(0, -2);\n  const outerDimsB = b.shape.slice(0, -2);\n  const batchDimA = util_exports.sizeFromShape(outerDimsA);\n  const batchDimB = util_exports.sizeFromShape(outerDimsB);\n  const outShapeOuterDims = broadcast_util_exports.assertAndGetBroadcastShape(a.shape.slice(0, -2), b.shape.slice(0, -2));\n  const outShape = outShapeOuterDims.concat([outerShapeA, outerShapeB]);\n  util_exports.assert(innerShapeA === innerShapeB, () => `Error in matMul: inner shapes (${innerShapeA}) and (${innerShapeB}) of Tensors with shapes ${a.shape} and ${b.shape} and transposeA=${transposeA} and transposeB=${transposeB} must match.`);\n  const a3dShape = transposeA ? [batchDimA, innerShapeA, outerShapeA] : [batchDimA, outerShapeA, innerShapeA];\n  const b3dShape = transposeB ? [batchDimB, outerShapeB, innerShapeB] : [batchDimB, innerShapeB, outerShapeB];\n  const a3d = reshape3({ inputs: { x: a }, backend: backend2, attrs: { shape: a3dShape } });\n  const b3d = reshape3({ inputs: { x: b }, backend: backend2, attrs: { shape: b3dShape } });\n  const sharedDim = transposeA ? a3d.shape[1] : a3d.shape[2];\n  const leftDim = transposeA ? a3d.shape[2] : a3d.shape[1];\n  const rightDim = transposeB ? b3d.shape[1] : b3d.shape[2];\n  const batchDim = Math.max(batchDimA, batchDimB);\n  const a3dValues = backend2.data.get(a3d.dataId).values;\n  const b3dValues = backend2.data.get(b3d.dataId).values;\n  const a3dStrides = util_exports.computeStrides(a3d.shape);\n  const b3dStrides = util_exports.computeStrides(b3d.shape);\n  const [aBatch, aOuterStep, aInnerStep] = transposeA ? [a3dStrides[0], 1, a3dStrides[1]] : [a3dStrides[0], a3dStrides[1], 1];\n  const [bInnerStep, bOuterStep, bBatch] = transposeB ? [1, b3dStrides[1], b3dStrides[0]] : [b3dStrides[1], 1, b3dStrides[0]];\n  const size = leftDim * rightDim;\n  const result = buffer([batchDim, leftDim, rightDim], a3d.dtype);\n  const resVals = result.values;\n  const blockSize = backend2.blockSize;\n  for (let bi = 0; bi < batchDim; bi++) {\n    for (let i0 = 0; i0 < leftDim; i0 += blockSize) {\n      for (let j0 = 0; j0 < rightDim; j0 += blockSize) {\n        for (let k02 = 0; k02 < sharedDim; k02 += blockSize) {\n          const iBlock = Math.min(i0 + blockSize, leftDim);\n          const jBlock = Math.min(j0 + blockSize, rightDim);\n          const kBlock = Math.min(k02 + blockSize, sharedDim);\n          for (let i2 = i0; i2 < iBlock; i2++) {\n            for (let j = j0; j < jBlock; j++) {\n              let sum7 = 0;\n              for (let k = k02; k < kBlock; k++) {\n                const batchOffsetA = Math.min(bi, batchDimA - 1) * aBatch;\n                const batchOffsetB = Math.min(bi, batchDimB - 1) * bBatch;\n                const aVal = a3dValues[batchOffsetA + i2 * aOuterStep + k * aInnerStep];\n                const bVal = b3dValues[k * bInnerStep + j * bOuterStep + batchOffsetB];\n                sum7 += aVal * bVal;\n              }\n              resVals[bi * size + (i2 * rightDim + j)] += sum7;\n            }\n          }\n        }\n      }\n    }\n  }\n  backend2.disposeIntermediateTensorInfo(a3d);\n  backend2.disposeIntermediateTensorInfo(b3d);\n  return backend2.makeTensorInfo(outShape, result.dtype, result.values);\n}\nvar batchMatMulConfig = {\n  kernelName: BatchMatMul,\n  backendName: \"cpu\",\n  kernelFunc: batchMatMul\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/_FusedMatMul.js\nfunction _fusedMatMul(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { a, b, bias, preluActivationWeights } = inputs;\n  const { transposeA, transposeB, activation: activation2, leakyreluAlpha } = attrs;\n  let current;\n  let addRes;\n  let activationRes;\n  const intermediates = [];\n  const matMulRes = batchMatMul({ inputs: { a, b }, attrs: { transposeA, transposeB }, backend: backend2 });\n  current = matMulRes;\n  if (bias) {\n    addRes = add4({ inputs: { a: current, b: bias }, backend: backend2 });\n    intermediates.push(current);\n    current = addRes;\n  }\n  if (activation2) {\n    activationRes = applyActivation2(backend2, current, activation2, preluActivationWeights, leakyreluAlpha);\n    intermediates.push(current);\n    current = activationRes;\n  }\n  for (const i2 of intermediates) {\n    backend2.disposeIntermediateTensorInfo(i2);\n  }\n  return current;\n}\nvar _fusedMatMulConfig = {\n  kernelName: _FusedMatMul,\n  backendName: \"cpu\",\n  kernelFunc: _fusedMatMul\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Acos.js\nvar acos2 = unaryKernelFunc(Acos, (xi) => Math.acos(xi));\nvar acosConfig = {\n  kernelName: Acos,\n  backendName: \"cpu\",\n  kernelFunc: acos2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Acosh.js\nvar acosh2 = unaryKernelFunc(Acosh, (xi) => Math.acosh(xi));\nvar acoshConfig = {\n  kernelName: Acosh,\n  backendName: \"cpu\",\n  kernelFunc: acosh2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/AddN.js\nfunction addN2(args) {\n  const { inputs, backend: backend2 } = args;\n  const tensors = inputs;\n  assertNotComplex(inputs, \"addN\");\n  const vals = tensors.map((t2) => backend2.data.get(t2.dataId).values);\n  const outBuf = buffer(tensors[0].shape, tensors[0].dtype);\n  const outVals = outBuf.values;\n  for (let i2 = 0; i2 < tensors.length; i2++) {\n    const currVals = vals[i2];\n    for (let j = 0; j < outVals.length; j++) {\n      outVals[j] += currVals[j];\n    }\n  }\n  return backend2.makeTensorInfo(outBuf.shape, outBuf.dtype, outBuf.values);\n}\nvar addNConfig = {\n  kernelName: AddN,\n  backendName: \"cpu\",\n  kernelFunc: addN2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/All.js\nfunction all2(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x } = inputs;\n  const { axis, keepDims } = attrs;\n  assertNotComplex(x, \"all\");\n  const origAxes = util_exports.parseAxisParam(axis, x.shape);\n  let axes = origAxes;\n  const permutedAxes = backend_util_exports.getAxesPermutation(axes, x.shape.length);\n  let $x = x;\n  if (permutedAxes != null) {\n    $x = transpose2({ inputs: { x }, backend: backend2, attrs: { perm: permutedAxes } });\n    axes = backend_util_exports.getInnerMostAxes(axes.length, x.shape.length);\n  }\n  backend_util_exports.assertAxesAreInnerMostDims(\"all\", axes, $x.shape.length);\n  const [outShape, reduceShape] = backend_util_exports.computeOutAndReduceShapes($x.shape, axes);\n  const reduceSize = util_exports.sizeFromShape(reduceShape);\n  const vals = util_exports.makeZerosTypedArray(util_exports.sizeFromShape(outShape), $x.dtype);\n  const aVals = backend2.data.get($x.dataId).values;\n  for (let i2 = 0; i2 < vals.length; ++i2) {\n    const offset = i2 * reduceSize;\n    let all5 = aVals[offset];\n    for (let j = 0; j < reduceSize; ++j) {\n      const value = aVals[offset + j];\n      all5 = all5 && value;\n    }\n    vals[i2] = all5;\n  }\n  if (permutedAxes != null) {\n    backend2.disposeIntermediateTensorInfo($x);\n  }\n  const result = backend2.makeTensorInfo(outShape, $x.dtype, vals);\n  if (keepDims) {\n    const expandedShape = backend_util_exports.expandShapeToKeepDim(outShape, origAxes);\n    const reshapedResult = reshape3({ inputs: { x: result }, backend: backend2, attrs: { shape: expandedShape } });\n    backend2.disposeIntermediateTensorInfo(result);\n    return reshapedResult;\n  }\n  return result;\n}\nvar allConfig = {\n  kernelName: All,\n  backendName: \"cpu\",\n  kernelFunc: all2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Any.js\nfunction any2(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x } = inputs;\n  const { axis, keepDims } = attrs;\n  assertNotComplex(x, \"any\");\n  const origAxes = util_exports.parseAxisParam(axis, x.shape);\n  let axes = origAxes;\n  const permutedAxes = backend_util_exports.getAxesPermutation(axes, x.shape.length);\n  let $x = x;\n  if (permutedAxes != null) {\n    $x = transpose2({ inputs: { x }, backend: backend2, attrs: { perm: permutedAxes } });\n    axes = backend_util_exports.getInnerMostAxes(axes.length, x.shape.length);\n  }\n  backend_util_exports.assertAxesAreInnerMostDims(\"any\", axes, $x.shape.length);\n  const [outShape, reduceShape] = backend_util_exports.computeOutAndReduceShapes($x.shape, axes);\n  const reduceSize = util_exports.sizeFromShape(reduceShape);\n  const vals = util_exports.makeZerosTypedArray(util_exports.sizeFromShape(outShape), $x.dtype);\n  const aVals = backend2.data.get($x.dataId).values;\n  for (let i2 = 0; i2 < vals.length; ++i2) {\n    const offset = i2 * reduceSize;\n    let anyVal = aVals[offset];\n    for (let j = 0; j < reduceSize; ++j) {\n      const value = aVals[offset + j];\n      anyVal = anyVal || value;\n    }\n    vals[i2] = anyVal;\n  }\n  if (permutedAxes != null) {\n    backend2.disposeIntermediateTensorInfo($x);\n  }\n  const result = backend2.makeTensorInfo(outShape, $x.dtype, vals);\n  if (keepDims) {\n    const expandedShape = backend_util_exports.expandShapeToKeepDim(outShape, origAxes);\n    const reshapedResult = reshape3({ inputs: { x: result }, backend: backend2, attrs: { shape: expandedShape } });\n    backend2.disposeIntermediateTensorInfo(result);\n    return reshapedResult;\n  }\n  return result;\n}\nvar anyConfig = {\n  kernelName: Any,\n  backendName: \"cpu\",\n  kernelFunc: any2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/ArgMax.js\nfunction argMax2(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x } = inputs;\n  const { axis } = attrs;\n  assertNotComplex(x, \"argMax\");\n  let axes = util_exports.parseAxisParam(axis, x.shape);\n  const permutedAxes = backend_util_exports.getAxesPermutation(axes, x.shape.length);\n  let $x = x;\n  const intermediateTensorInfos = [];\n  if (permutedAxes != null) {\n    $x = transpose2({ inputs: { x }, backend: backend2, attrs: { perm: permutedAxes } });\n    intermediateTensorInfos.push($x);\n    axes = backend_util_exports.getInnerMostAxes(axes.length, $x.shape.length);\n  }\n  axes = [axes[0]];\n  backend_util_exports.assertAxesAreInnerMostDims(\"argMax\", axes, $x.shape.length);\n  const [outShape, reduceShape] = backend_util_exports.computeOutAndReduceShapes($x.shape, axes);\n  const outSize = util_exports.sizeFromShape(outShape);\n  const vals = util_exports.makeZerosTypedArray(outSize, \"int32\");\n  const reduceSize = util_exports.sizeFromShape(reduceShape);\n  const aVals = backend2.data.get($x.dataId).values;\n  for (let i2 = 0; i2 < vals.length; ++i2) {\n    const offset = i2 * reduceSize;\n    let max7 = aVals[offset];\n    let maxIndex = 0;\n    for (let j = 0; j < reduceSize; ++j) {\n      const value = aVals[offset + j];\n      if (value > max7) {\n        max7 = value;\n        maxIndex = j;\n      }\n    }\n    vals[i2] = maxIndex;\n  }\n  intermediateTensorInfos.forEach((t2) => backend2.disposeIntermediateTensorInfo(t2));\n  return backend2.makeTensorInfo(outShape, \"int32\", vals);\n}\nvar argMaxConfig = {\n  kernelName: ArgMax,\n  backendName: \"cpu\",\n  kernelFunc: argMax2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/ArgMin.js\nfunction argMin2(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x } = inputs;\n  const { axis } = attrs;\n  assertNotComplex(x, \"argMin\");\n  let axes = util_exports.parseAxisParam(axis, x.shape);\n  const permutedAxes = backend_util_exports.getAxesPermutation(axes, x.shape.length);\n  let $x = x;\n  const intermediateTensorInfos = [];\n  if (permutedAxes != null) {\n    $x = transpose2({ inputs: { x }, backend: backend2, attrs: { perm: permutedAxes } });\n    intermediateTensorInfos.push($x);\n    axes = backend_util_exports.getInnerMostAxes(axes.length, $x.shape.length);\n  }\n  axes = [axes[0]];\n  backend_util_exports.assertAxesAreInnerMostDims(\"argMin\", axes, $x.shape.length);\n  const [outShape, reduceShape] = backend_util_exports.computeOutAndReduceShapes($x.shape, axes);\n  const outSize = util_exports.sizeFromShape(outShape);\n  const vals = util_exports.makeZerosTypedArray(outSize, \"int32\");\n  const reduceSize = util_exports.sizeFromShape(reduceShape);\n  const aVals = backend2.data.get($x.dataId).values;\n  for (let i2 = 0; i2 < vals.length; ++i2) {\n    const offset = i2 * reduceSize;\n    let min7 = aVals[offset];\n    let minIndex = 0;\n    for (let j = 0; j < reduceSize; ++j) {\n      const value = aVals[offset + j];\n      if (value < min7) {\n        min7 = value;\n        minIndex = j;\n      }\n    }\n    vals[i2] = minIndex;\n  }\n  intermediateTensorInfos.forEach((t2) => backend2.disposeIntermediateTensorInfo(t2));\n  return backend2.makeTensorInfo(outShape, \"int32\", vals);\n}\nvar argMinConfig = {\n  kernelName: ArgMin,\n  backendName: \"cpu\",\n  kernelFunc: argMin2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Asin.js\nvar asin2 = unaryKernelFunc(Asin, (xi) => Math.asin(xi));\nvar asinConfig = {\n  kernelName: Asin,\n  backendName: \"cpu\",\n  kernelFunc: asin2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Asinh.js\nvar asinh2 = unaryKernelFunc(Asinh, (xi) => Math.asinh(xi));\nvar asinhConfig = {\n  kernelName: Asinh,\n  backendName: \"cpu\",\n  kernelFunc: asinh2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Atan.js\nvar atan3 = unaryKernelFunc(Atan, (xi) => Math.atan(xi));\nvar atanConfig = {\n  kernelName: Atan,\n  backendName: \"cpu\",\n  kernelFunc: atan3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Atan2.js\nvar atan2Impl = createSimpleBinaryKernelImpl((aValue, bValue) => Math.atan2(aValue, bValue));\nvar atan22 = binaryKernelFunc(Atan2, atan2Impl);\nvar atan2Config = {\n  kernelName: Atan2,\n  backendName: \"cpu\",\n  kernelFunc: atan22\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Atanh.js\nvar atanh2 = unaryKernelFunc(Atanh, (xi) => Math.atanh(xi));\nvar atanhConfig = {\n  kernelName: Atanh,\n  backendName: \"cpu\",\n  kernelFunc: atanh2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/utils/pool_utils.js\nfunction pool2(xValues, xShape, dtype, strides, convInfo, poolType) {\n  const strideHeight = convInfo.strideHeight;\n  const strideWidth = convInfo.strideWidth;\n  const dilationHeight = convInfo.dilationHeight;\n  const dilationWidth = convInfo.dilationWidth;\n  const effectiveFilterHeight = convInfo.effectiveFilterHeight;\n  const effectiveFilterWidth = convInfo.effectiveFilterWidth;\n  const padTop = convInfo.padInfo.top;\n  const padLeft = convInfo.padInfo.left;\n  const initialValue = poolType === \"max\" ? Number.NEGATIVE_INFINITY : Number.POSITIVE_INFINITY;\n  const output = buffer(convInfo.outShape, dtype);\n  const outputVals = output.values;\n  const outputBatchStrides = convInfo.outShape[1] * convInfo.outShape[2] * convInfo.outShape[3];\n  const outputRowStrides = convInfo.outShape[2] * convInfo.outShape[3];\n  const outputColStrides = convInfo.outShape[3];\n  for (let b = 0; b < convInfo.batchSize; ++b) {\n    const outputBatchOffset = b * outputBatchStrides;\n    const inputBatchOffset = b * strides[0];\n    for (let d = 0; d < convInfo.inChannels; ++d) {\n      for (let yR = 0; yR < convInfo.outHeight; ++yR) {\n        const xRCorner = yR * strideHeight - padTop;\n        const xRMin = Math.max(0, xRCorner);\n        const xRMax = Math.min(convInfo.inHeight, effectiveFilterHeight + xRCorner);\n        const outputRowOffset = outputBatchOffset + yR * outputRowStrides;\n        for (let yC = 0; yC < convInfo.outWidth; ++yC) {\n          const xCCorner = yC * strideWidth - padLeft;\n          const xCMin = Math.max(0, xCCorner);\n          const xCMax = Math.min(convInfo.inWidth, effectiveFilterWidth + xCCorner);\n          let minMaxValue = initialValue;\n          let avgValue = 0;\n          let count2 = 0;\n          for (let xR = xRMin; xR < xRMax; xR += dilationHeight) {\n            const xROffset = inputBatchOffset + xR * strides[1];\n            for (let xC = xCMin; xC < xCMax; xC += dilationWidth) {\n              const xCOffset = xROffset + xC * strides[2];\n              const pixel = xValues[xCOffset + d];\n              if (poolType === \"max\" && pixel > minMaxValue) {\n                minMaxValue = pixel;\n              } else if (poolType === \"avg\") {\n                avgValue += pixel;\n                count2++;\n              }\n            }\n            if (isNaN(minMaxValue)) {\n              break;\n            }\n          }\n          const outputOffset = outputRowOffset + yC * outputColStrides + d;\n          outputVals[outputOffset] = poolType === \"avg\" ? avgValue / count2 : minMaxValue;\n        }\n      }\n    }\n  }\n  return output;\n}\nfunction maxPoolPositions(xValues, xShape, dtype, convInfo, flattenPositions = false, includeBatchInIndex = false) {\n  const maxPositions = buffer(convInfo.outShape, \"int32\");\n  const strideHeight = convInfo.strideHeight;\n  const strideWidth = convInfo.strideWidth;\n  const dilationHeight = convInfo.dilationHeight;\n  const dilationWidth = convInfo.dilationWidth;\n  const effectiveFilterHeight = convInfo.effectiveFilterHeight;\n  const effectiveFilterWidth = convInfo.effectiveFilterWidth;\n  const padTop = convInfo.padInfo.top;\n  const padLeft = convInfo.padInfo.left;\n  const xBuf = buffer(xShape, dtype, xValues);\n  for (let b = 0; b < convInfo.batchSize; ++b) {\n    for (let d = 0; d < convInfo.inChannels; ++d) {\n      for (let yR = 0; yR < convInfo.outHeight; ++yR) {\n        const xRCorner = yR * strideHeight - padTop;\n        let xRMin = xRCorner;\n        while (xRMin < 0) {\n          xRMin += dilationHeight;\n        }\n        const xRMax = Math.min(convInfo.inHeight, effectiveFilterHeight + xRCorner);\n        for (let yC = 0; yC < convInfo.outWidth; ++yC) {\n          const xCCorner = yC * strideWidth - padLeft;\n          let xCMin = xCCorner;\n          while (xCMin < 0) {\n            xCMin += dilationWidth;\n          }\n          const xCMax = Math.min(convInfo.inWidth, effectiveFilterWidth + xCCorner);\n          let maxValue = Number.NEGATIVE_INFINITY;\n          let maxPosition = -1;\n          for (let xR = xRMin; xR < xRMax; xR += dilationHeight) {\n            const wR = xR - xRCorner;\n            for (let xC = xCMin; xC < xCMax; xC += dilationWidth) {\n              const wC = xC - xCCorner;\n              const pixel = xBuf.get(b, xR, xC, d);\n              if (pixel > maxValue) {\n                maxValue = pixel;\n                if (flattenPositions) {\n                  maxPosition = includeBatchInIndex ? ((b * convInfo.inHeight + xR) * convInfo.inWidth + xC) * convInfo.inChannels + d : (xR * convInfo.inWidth + xC) * convInfo.inChannels + d;\n                } else {\n                  maxPosition = wR * effectiveFilterWidth + wC;\n                }\n              }\n            }\n          }\n          maxPositions.set(maxPosition, b, yR, yC, d);\n        }\n      }\n    }\n  }\n  return maxPositions;\n}\nfunction pool3d2(xValues, xShape, dtype, strides, convInfo, poolType) {\n  const strideDepth = convInfo.strideDepth;\n  const strideHeight = convInfo.strideHeight;\n  const strideWidth = convInfo.strideWidth;\n  const dilationDepth = convInfo.dilationDepth;\n  const dilationHeight = convInfo.dilationHeight;\n  const dilationWidth = convInfo.dilationWidth;\n  const effectiveFilterDepth = convInfo.effectiveFilterDepth;\n  const effectiveFilterHeight = convInfo.effectiveFilterHeight;\n  const effectiveFilterWidth = convInfo.effectiveFilterWidth;\n  const padFront = convInfo.padInfo.front;\n  const padTop = convInfo.padInfo.top;\n  const padLeft = convInfo.padInfo.left;\n  const initialValue = poolType === \"max\" ? Number.NEGATIVE_INFINITY : Number.POSITIVE_INFINITY;\n  const output = buffer(convInfo.outShape, dtype);\n  const outputVals = output.values;\n  const outputBatchStrides = convInfo.outShape[1] * convInfo.outShape[2] * convInfo.outShape[3] * convInfo.outShape[4];\n  const outputDepthStrides = convInfo.outShape[2] * convInfo.outShape[3] * convInfo.outShape[4];\n  const outputRowStrides = convInfo.outShape[3] * convInfo.outShape[4];\n  const outputColStrides = convInfo.outShape[4];\n  for (let batch = 0; batch < convInfo.batchSize; ++batch) {\n    const outputBatchOffset = batch * outputBatchStrides;\n    const inputBatchOffset = batch * strides[0];\n    for (let channel = 0; channel < convInfo.inChannels; ++channel) {\n      for (let yDepth = 0; yDepth < convInfo.outDepth; ++yDepth) {\n        const xDepthCorner = yDepth * strideDepth - padFront;\n        let xDepthMin = xDepthCorner;\n        while (xDepthMin < 0) {\n          xDepthMin += dilationDepth;\n        }\n        const xDepthMax = Math.min(convInfo.inDepth, effectiveFilterDepth + xDepthCorner);\n        const outputDepthOffset = outputBatchOffset + yDepth * outputDepthStrides;\n        for (let yRow = 0; yRow < convInfo.outHeight; ++yRow) {\n          const xRowCorner = yRow * strideHeight - padTop;\n          let xRowMin = xRowCorner;\n          while (xRowMin < 0) {\n            xRowMin += dilationHeight;\n          }\n          const xRowMax = Math.min(convInfo.inHeight, effectiveFilterHeight + xRowCorner);\n          const outputRowOffset = outputDepthOffset + yRow * outputRowStrides;\n          for (let yCol = 0; yCol < convInfo.outWidth; ++yCol) {\n            const xColCorner = yCol * strideWidth - padLeft;\n            let xColMin = xColCorner;\n            while (xColMin < 0) {\n              xColMin += dilationWidth;\n            }\n            const xColMax = Math.min(convInfo.inWidth, effectiveFilterWidth + xColCorner);\n            const outputColOffset = outputRowOffset + yCol * outputColStrides;\n            let minMaxValue = initialValue;\n            let avgValue = 0;\n            let count2 = 0;\n            for (let xDepth = xDepthMin; xDepth < xDepthMax; xDepth += dilationDepth) {\n              const xDepthOffset = inputBatchOffset + xDepth * strides[1];\n              for (let xRow = xRowMin; xRow < xRowMax; xRow += dilationHeight) {\n                const xRowOffset = xDepthOffset + xRow * strides[2];\n                for (let xCol = xColMin; xCol < xColMax; xCol += dilationWidth) {\n                  const xColOffset = xRowOffset + xCol * strides[3];\n                  const pixel = xValues[xColOffset + channel];\n                  if (poolType === \"max\" && pixel > minMaxValue) {\n                    minMaxValue = pixel;\n                  } else if (poolType === \"avg\") {\n                    avgValue += pixel;\n                    count2++;\n                  }\n                  if (isNaN(minMaxValue)) {\n                    break;\n                  }\n                }\n                if (isNaN(minMaxValue)) {\n                  break;\n                }\n              }\n              if (isNaN(minMaxValue)) {\n                break;\n              }\n            }\n            const outputOffset = outputColOffset + channel;\n            outputVals[outputOffset] = poolType === \"avg\" ? avgValue / count2 : minMaxValue;\n          }\n        }\n      }\n    }\n  }\n  return output;\n}\nfunction maxPool3dPositions(xBuf, convInfo) {\n  const maxPositions = buffer(convInfo.outShape, \"int32\");\n  const strideDepth = convInfo.strideDepth;\n  const strideHeight = convInfo.strideHeight;\n  const strideWidth = convInfo.strideWidth;\n  const dilationDepth = convInfo.dilationDepth;\n  const dilationHeight = convInfo.dilationHeight;\n  const dilationWidth = convInfo.dilationWidth;\n  const effectiveFilterDepth = convInfo.effectiveFilterDepth;\n  const effectiveFilterHeight = convInfo.effectiveFilterHeight;\n  const effectiveFilterWidth = convInfo.effectiveFilterWidth;\n  const padFront = convInfo.padInfo.front;\n  const padTop = convInfo.padInfo.top;\n  const padLeft = convInfo.padInfo.left;\n  for (let batch = 0; batch < convInfo.batchSize; ++batch) {\n    for (let channel = 0; channel < convInfo.inChannels; ++channel) {\n      for (let yDepth = 0; yDepth < convInfo.outDepth; ++yDepth) {\n        const xDepthCorner = yDepth * strideDepth - padFront;\n        let xDepthMin = xDepthCorner;\n        while (xDepthMin < 0) {\n          xDepthMin += dilationDepth;\n        }\n        const xDepthMax = Math.min(convInfo.inDepth, effectiveFilterDepth + xDepthCorner);\n        for (let yRow = 0; yRow < convInfo.outHeight; ++yRow) {\n          const xRowCorner = yRow * strideHeight - padTop;\n          let xRowMin = xRowCorner;\n          while (xRowMin < 0) {\n            xRowMin += dilationHeight;\n          }\n          const xRowMax = Math.min(convInfo.inHeight, effectiveFilterHeight + xRowCorner);\n          for (let yCol = 0; yCol < convInfo.outWidth; ++yCol) {\n            const xColCorner = yCol * strideWidth - padLeft;\n            let xColMin = xColCorner;\n            while (xColMin < 0) {\n              xColMin += dilationWidth;\n            }\n            const xColMax = Math.min(convInfo.inWidth, effectiveFilterWidth + xColCorner);\n            let maxValue = Number.NEGATIVE_INFINITY;\n            let maxPosition = -1;\n            for (let xDepth = xDepthMin; xDepth < xDepthMax; xDepth += dilationDepth) {\n              const wDepth = xDepth - xDepthCorner;\n              for (let xRow = xRowMin; xRow < xRowMax; xRow += dilationHeight) {\n                const wRow = xRow - xRowCorner;\n                for (let xCol = xColMin; xCol < xColMax; xCol += dilationWidth) {\n                  const wCol = xCol - xColCorner;\n                  const pixel = xBuf.get(batch, xDepth, xRow, xCol, channel);\n                  if (pixel >= maxValue) {\n                    maxValue = pixel;\n                    maxPosition = wDepth * effectiveFilterHeight * effectiveFilterWidth + wRow * effectiveFilterHeight + wCol;\n                  }\n                }\n              }\n            }\n            maxPositions.set(maxPosition, batch, yDepth, yRow, yCol, channel);\n          }\n        }\n      }\n    }\n  }\n  return maxPositions;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/AvgPool.js\nfunction avgPool2(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x } = inputs;\n  assertNotComplex(x, \"avgPool\");\n  const { filterSize, strides, pad: pad3, dimRoundingMode } = attrs;\n  const dilations = 1;\n  util_exports.assert(backend_util_exports.eitherStridesOrDilationsAreOne(strides, dilations), () => `Error in avgPool: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`);\n  const convInfo = backend_util_exports.computePool2DInfo(x.shape, filterSize, strides, dilations, pad3, dimRoundingMode);\n  let res;\n  if (convInfo.filterWidth === 1 && convInfo.filterHeight === 1 && util_exports.arraysEqual(convInfo.inShape, convInfo.outShape)) {\n    res = identity2({ inputs: { x }, backend: backend2 });\n  } else {\n    const xValues = backend2.data.get(x.dataId).values;\n    const strides2 = util_exports.computeStrides(x.shape);\n    const buffer2 = pool2(xValues, x.shape, x.dtype, strides2, convInfo, \"avg\");\n    res = backend2.makeTensorInfo(convInfo.outShape, x.dtype, buffer2.values);\n  }\n  return res;\n}\nvar avgPoolConfig = {\n  kernelName: AvgPool,\n  backendName: \"cpu\",\n  kernelFunc: avgPool2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/AvgPool3D.js\nfunction avgPool3D(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x } = inputs;\n  const { filterSize, strides, pad: pad3, dimRoundingMode, dataFormat } = attrs;\n  assertNotComplex(x, \"avgPool3d\");\n  const convInfo = backend_util_exports.computePool3DInfo(x.shape, filterSize, strides, 1, pad3, dimRoundingMode, dataFormat);\n  const xValues = backend2.data.get(x.dataId).values;\n  const outBuf = pool3d2(xValues, x.shape, x.dtype, util_exports.computeStrides(x.shape), convInfo, \"avg\");\n  return backend2.makeTensorInfo(outBuf.shape, \"float32\", outBuf.values);\n}\nvar avgPool3DConfig = {\n  kernelName: AvgPool3D,\n  backendName: \"cpu\",\n  kernelFunc: avgPool3D\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/AvgPool3DGrad.js\nfunction avgPool3DGrad(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { dy, input: input2 } = inputs;\n  const { filterSize, strides, pad: pad3, dimRoundingMode } = attrs;\n  assertNotComplex([dy, input2], \"avgPool3DGrad\");\n  const convInfo = backend_util_exports.computePool3DInfo(input2.shape, filterSize, strides, 1, pad3, dimRoundingMode);\n  const strideDepth = convInfo.strideDepth;\n  const strideHeight = convInfo.strideHeight;\n  const strideWidth = convInfo.strideWidth;\n  const filterDepth = convInfo.filterDepth;\n  const filterHeight = convInfo.filterHeight;\n  const filterWidth = convInfo.filterWidth;\n  const dilationDepth = convInfo.dilationDepth;\n  const dilationHeight = convInfo.dilationHeight;\n  const dilationWidth = convInfo.dilationWidth;\n  const effectiveFilterDepth = convInfo.effectiveFilterDepth;\n  const effectiveFilterHeight = convInfo.effectiveFilterHeight;\n  const effectiveFilterWidth = convInfo.effectiveFilterWidth;\n  const padFront = effectiveFilterDepth - 1 - convInfo.padInfo.front;\n  const padLeft = effectiveFilterWidth - 1 - convInfo.padInfo.left;\n  const padTop = effectiveFilterHeight - 1 - convInfo.padInfo.top;\n  const dx = buffer(input2.shape, \"float32\");\n  const avgMultiplier = 1 / (filterDepth * filterHeight * filterWidth);\n  const dyBuf = backend2.bufferSync(dy);\n  for (let batch = 0; batch < convInfo.batchSize; ++batch) {\n    for (let channel = 0; channel < convInfo.inChannels; ++channel) {\n      for (let dxDepth = 0; dxDepth < convInfo.inDepth; ++dxDepth) {\n        for (let dxRow = 0; dxRow < convInfo.inHeight; ++dxRow) {\n          for (let dxCol = 0; dxCol < convInfo.inWidth; ++dxCol) {\n            const dyDepthCorner = dxDepth - padFront;\n            const dyRowCorner = dxRow - padTop;\n            const dyColCorner = dxCol - padLeft;\n            let dotProd = 0;\n            for (let wDepth = 0; wDepth < effectiveFilterDepth; wDepth += dilationDepth) {\n              const dyDepth = (dyDepthCorner + wDepth) / strideDepth;\n              if (dyDepth < 0 || dyDepth >= convInfo.outDepth || Math.floor(dyDepth) !== dyDepth) {\n                continue;\n              }\n              for (let wRow = 0; wRow < effectiveFilterHeight; wRow += dilationHeight) {\n                const dyRow = (dyRowCorner + wRow) / strideHeight;\n                if (dyRow < 0 || dyRow >= convInfo.outHeight || Math.floor(dyRow) !== dyRow) {\n                  continue;\n                }\n                for (let wCol = 0; wCol < effectiveFilterWidth; wCol += dilationWidth) {\n                  const dyCol = (dyColCorner + wCol) / strideWidth;\n                  if (dyCol < 0 || dyCol >= convInfo.outWidth || Math.floor(dyCol) !== dyCol) {\n                    continue;\n                  }\n                  const pixel = dyBuf.get(batch, dyDepth, dyRow, dyCol, channel);\n                  dotProd += pixel;\n                }\n              }\n            }\n            dx.set(dotProd * avgMultiplier, batch, dxDepth, dxRow, dxCol, channel);\n          }\n        }\n      }\n    }\n  }\n  return backend2.makeTensorInfo(dx.shape, dx.dtype, dx.values);\n}\nvar avgPool3DGradConfig2 = {\n  kernelName: AvgPool3DGrad,\n  backendName: \"cpu\",\n  kernelFunc: avgPool3DGrad\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/AvgPoolGrad.js\nfunction avgPoolGrad2(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { dy, input: input2 } = inputs;\n  const x = input2;\n  assertNotComplex([dy, input2], \"avgPoolGrad\");\n  const { filterSize, strides, pad: pad3 } = attrs;\n  const convInfo = backend_util_exports.computePool2DInfo(x.shape, filterSize, strides, 1, pad3);\n  const strideHeight = convInfo.strideHeight;\n  const strideWidth = convInfo.strideWidth;\n  const filterHeight = convInfo.filterHeight;\n  const filterWidth = convInfo.filterWidth;\n  const dilationHeight = convInfo.dilationHeight;\n  const dilationWidth = convInfo.dilationWidth;\n  const effectiveFilterHeight = convInfo.effectiveFilterHeight;\n  const effectiveFilterWidth = convInfo.effectiveFilterWidth;\n  const padLeft = effectiveFilterWidth - 1 - convInfo.padInfo.left;\n  const padTop = effectiveFilterHeight - 1 - convInfo.padInfo.top;\n  const dx = buffer(x.shape, \"float32\");\n  const avgMultiplier = 1 / (filterHeight * filterWidth);\n  const dyData = backend2.data.get(dy.dataId).values;\n  const dyBuf = buffer(dy.shape, \"float32\", dyData);\n  for (let b = 0; b < convInfo.batchSize; ++b) {\n    for (let d = 0; d < convInfo.inChannels; ++d) {\n      for (let dxR = 0; dxR < convInfo.inHeight; ++dxR) {\n        for (let dxC = 0; dxC < convInfo.inWidth; ++dxC) {\n          const dyRCorner = dxR - padTop;\n          const dyCCorner = dxC - padLeft;\n          let dotProd = 0;\n          for (let wR = 0; wR < effectiveFilterHeight; wR += dilationHeight) {\n            const dyR = (dyRCorner + wR) / strideHeight;\n            if (dyR < 0 || dyR >= convInfo.outHeight || Math.floor(dyR) !== dyR) {\n              continue;\n            }\n            for (let wC = 0; wC < effectiveFilterWidth; wC += dilationWidth) {\n              const dyC = (dyCCorner + wC) / strideWidth;\n              if (dyC < 0 || dyC >= convInfo.outWidth || Math.floor(dyC) !== dyC) {\n                continue;\n              }\n              const pixel = dyBuf.get(b, dyR, dyC, d);\n              dotProd += pixel;\n            }\n          }\n          dx.set(dotProd * avgMultiplier, b, dxR, dxC, d);\n        }\n      }\n    }\n  }\n  return backend2.makeTensorInfo(dx.shape, dx.dtype, dx.values);\n}\nvar avgPoolGradConfig2 = {\n  kernelName: AvgPoolGrad,\n  backendName: \"cpu\",\n  kernelFunc: avgPoolGrad2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/BatchNorm.js\nfunction batchNorm2(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x, scale: scale2, offset, mean: mean5, variance } = inputs;\n  util_exports.assert(mean5.shape.length === variance.shape.length, () => \"Batch normalization gradient requires mean and variance to have equal ranks.\");\n  util_exports.assert(offset == null || mean5.shape.length === offset.shape.length, () => \"Batch normalization gradient requires mean and offset to have equal ranks.\");\n  util_exports.assert(scale2 == null || mean5.shape.length === scale2.shape.length, () => \"Batch normalization gradient requires mean and scale to have equal ranks.\");\n  assertNotComplex([x, mean5, variance, scale2, offset], \"batchNorm\");\n  let { varianceEpsilon } = attrs;\n  if (varianceEpsilon == null) {\n    varianceEpsilon = 1e-3;\n  }\n  const xVals = backend2.data.get(x.dataId).values;\n  const mVals = backend2.data.get(mean5.dataId).values;\n  const varVals = backend2.data.get(variance.dataId).values;\n  const sVals = scale2 ? backend2.data.get(scale2.dataId).values : new Float32Array([1]);\n  const offVals = offset ? backend2.data.get(offset.dataId).values : new Float32Array([0]);\n  const outVals = new Float32Array(xVals.length);\n  const offValsLength = offVals.length;\n  const sValsLength = sVals.length;\n  const varValsLength = varVals.length;\n  const mValsLength = mVals.length;\n  let offi = 0;\n  let mi = 0;\n  let si = 0;\n  let vi = 0;\n  for (let i2 = 0; i2 < xVals.length; ++i2) {\n    outVals[i2] = offVals[offi++] + (xVals[i2] - mVals[mi++]) * sVals[si++] / Math.sqrt(varVals[vi++] + varianceEpsilon);\n    if (offi >= offValsLength) {\n      offi = 0;\n    }\n    if (mi >= mValsLength) {\n      mi = 0;\n    }\n    if (si >= sValsLength) {\n      si = 0;\n    }\n    if (vi >= varValsLength) {\n      vi = 0;\n    }\n  }\n  return backend2.makeTensorInfo(x.shape, x.dtype, outVals);\n}\nvar batchNormConfig = {\n  kernelName: FusedBatchNorm,\n  backendName: \"cpu\",\n  kernelFunc: batchNorm2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/BatchToSpaceND.js\nfunction batchToSpaceND2(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x } = inputs;\n  const { blockShape, crops } = attrs;\n  assertNotComplex([x], \"batchToSpaceND\");\n  const prod6 = blockShape.reduce((a, b) => a * b);\n  const reshaped = backend_util_exports.getReshaped(x.shape, blockShape, prod6);\n  const permuted = backend_util_exports.getPermuted(reshaped.length, blockShape.length);\n  const reshapedPermuted = backend_util_exports.getReshapedPermuted(x.shape, blockShape, prod6);\n  const sliceBeginCoords = backend_util_exports.getSliceBeginCoords(crops, blockShape.length);\n  const sliceSize = backend_util_exports.getSliceSize(reshapedPermuted, crops, blockShape.length);\n  const xReshaped = reshape3({ inputs: { x }, backend: backend2, attrs: { shape: reshaped } });\n  const xTransposed = transpose2({ inputs: { x: xReshaped }, backend: backend2, attrs: { perm: permuted } });\n  const xTransposedReshaped = reshape3({ inputs: { x: xTransposed }, backend: backend2, attrs: { shape: reshapedPermuted } });\n  const result = slice2({\n    inputs: { x: xTransposedReshaped },\n    backend: backend2,\n    attrs: { begin: sliceBeginCoords, size: sliceSize }\n  });\n  backend2.disposeIntermediateTensorInfo(xReshaped);\n  backend2.disposeIntermediateTensorInfo(xTransposed);\n  backend2.disposeIntermediateTensorInfo(xTransposedReshaped);\n  return result;\n}\nvar batchToSpaceNDConfig = {\n  kernelName: BatchToSpaceND,\n  backendName: \"cpu\",\n  kernelFunc: batchToSpaceND2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Bincount.js\nfunction bincount2(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x, weights } = inputs;\n  const { size } = attrs;\n  const xVals = backend2.data.get(x.dataId).values;\n  const weightsVals = backend2.data.get(weights.dataId).values;\n  const outVals = bincountImpl(xVals, weightsVals, weights.dtype, weights.shape, size);\n  return backend2.makeTensorInfo([size], weights.dtype, outVals);\n}\nvar bincountConfig = {\n  kernelName: Bincount,\n  backendName: \"cpu\",\n  kernelFunc: bincount2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/BroadcastArgs.js\nfunction broadcastArgs2(args) {\n  const { inputs, backend: backend2 } = args;\n  const { s0, s1 } = inputs;\n  const s0Vals = backend2.data.get(s0.dataId).values;\n  const s1Vals = backend2.data.get(s1.dataId).values;\n  const broadcastShape = backend_util_exports.assertAndGetBroadcastShape(Array.from(s0Vals), Array.from(s1Vals));\n  return backend2.makeTensorInfo([broadcastShape.length], \"int32\", Int32Array.from(broadcastShape));\n}\nvar broadcastArgsConfig = {\n  kernelName: BroadcastArgs,\n  backendName: \"cpu\",\n  kernelFunc: broadcastArgs2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/ClipByValue.js\nvar clipByValue2 = unaryKernelFunc(ClipByValue, (xi, attrs) => {\n  const clipAttrs = attrs;\n  if (xi > clipAttrs.clipValueMax) {\n    return clipAttrs.clipValueMax;\n  }\n  return xi < clipAttrs.clipValueMin ? clipAttrs.clipValueMin : xi;\n});\nvar clipByValueConfig = {\n  kernelName: ClipByValue,\n  backendName: \"cpu\",\n  kernelFunc: clipByValue2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/ComplexAbs.js\nvar complexAbs = (args) => {\n  const { x } = args.inputs;\n  const cpuBackend = args.backend;\n  const resultValues = new Float32Array(util_exports.sizeFromShape(x.shape));\n  const complexVals = cpuBackend.data.get(x.dataId);\n  const real5 = complexVals.complexTensorInfos.real;\n  const imag5 = complexVals.complexTensorInfos.imag;\n  const realVals = cpuBackend.data.get(real5.dataId).values;\n  const imagVals = cpuBackend.data.get(imag5.dataId).values;\n  for (let i2 = 0; i2 < realVals.length; i2++) {\n    const real6 = realVals[i2];\n    const imag6 = imagVals[i2];\n    resultValues[i2] = Math.hypot(real6, imag6);\n  }\n  return cpuBackend.makeOutput(resultValues, x.shape, \"float32\");\n};\nvar complexAbsConfig = {\n  kernelName: ComplexAbs,\n  backendName: \"cpu\",\n  kernelFunc: complexAbs\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Imag.js\nfunction imag2(args) {\n  const { inputs, backend: backend2 } = args;\n  const { input: input2 } = inputs;\n  const imag5 = backend2.data.get(input2.dataId).complexTensorInfos.imag;\n  const imagVal = backend2.data.get(imag5.dataId).values;\n  return backend2.makeTensorInfo(imag5.shape, imag5.dtype, imagVal);\n}\nvar imagConfig = {\n  kernelName: Imag,\n  backendName: \"cpu\",\n  kernelFunc: imag2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Concat.js\nfunction concat2(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { axis } = attrs;\n  const $axis = util_exports.parseAxisParam(axis, inputs[0].shape)[0];\n  let outShape = backend_util_exports.computeOutShape(inputs.map((t2) => t2.shape), $axis);\n  if (util_exports.sizeFromShape(outShape) === 0) {\n    return backend2.makeTensorInfo(outShape, inputs[0].dtype, []);\n  }\n  const $inputs = inputs.filter((t2) => util_exports.sizeFromShape(t2.shape) > 0);\n  if ($inputs.length === 1) {\n    return identity2({ inputs: { x: $inputs[0] }, backend: backend2 });\n  }\n  const shapes = $inputs.map((t2) => t2.shape);\n  backend_util_exports.assertParamsConsistent(shapes, $axis);\n  if ($inputs[0].dtype === \"complex64\") {\n    const reals = $inputs.map((t2) => real2({ inputs: { input: t2 }, backend: backend2 }));\n    const imags = $inputs.map((t2) => imag2({ inputs: { input: t2 }, backend: backend2 }));\n    const realConcated = concat2({ inputs: reals, backend: backend2, attrs: { axis: $axis } });\n    const imagConcated = concat2({ inputs: imags, backend: backend2, attrs: { axis: $axis } });\n    const result = complex2({ inputs: { real: realConcated, imag: imagConcated }, backend: backend2 });\n    reals.forEach((r2) => backend2.disposeIntermediateTensorInfo(r2));\n    imags.forEach((i2) => backend2.disposeIntermediateTensorInfo(i2));\n    backend2.disposeIntermediateTensorInfo(realConcated);\n    backend2.disposeIntermediateTensorInfo(imagConcated);\n    return result;\n  }\n  const inputs2D = $inputs.map((t2) => {\n    const innerSize = util_exports.sizeFromShape(t2.shape.slice($axis));\n    const shape = [-1, innerSize];\n    return reshape3({ inputs: { x: t2 }, backend: backend2, attrs: { shape } });\n  });\n  const inputsValShapes = inputs2D.map((t2) => {\n    return { vals: backend2.data.get(t2.dataId).values, shape: t2.shape };\n  });\n  outShape = backend_util_exports.computeOutShape(inputs2D.map((t2) => t2.shape), 1);\n  const simplyConcat = inputs2D[0].shape[0] === 1;\n  const outVals = concatImpl(inputsValShapes, outShape, inputs[0].dtype, simplyConcat);\n  const finalOutShape = backend_util_exports.computeOutShape($inputs.map((t2) => t2.shape), $axis);\n  const outInfo = backend2.makeTensorInfo(finalOutShape, inputs[0].dtype, outVals);\n  inputs2D.forEach((t2) => backend2.disposeIntermediateTensorInfo(t2));\n  return outInfo;\n}\nvar concatConfig = {\n  kernelName: Concat,\n  backendName: \"cpu\",\n  kernelFunc: concat2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Conv2D.js\nfunction conv2D(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x, filter } = inputs;\n  const { strides, pad: pad3, dataFormat, dilations, dimRoundingMode } = attrs;\n  assertNotComplex([x, filter], \"conv2d\");\n  const $dataFormat = backend_util_exports.convertConv2DDataFormat(dataFormat);\n  const convInfo = backend_util_exports.computeConv2DInfo(x.shape, filter.shape, strides, dilations, pad3, dimRoundingMode, false, $dataFormat);\n  const filterHeight = convInfo.filterHeight;\n  const filterWidth = convInfo.filterWidth;\n  const dilationHeight = convInfo.dilationHeight;\n  const dilationWidth = convInfo.dilationWidth;\n  const padLeft = convInfo.padInfo.left;\n  const padTop = convInfo.padInfo.top;\n  const isChannelsLast = convInfo.dataFormat === \"channelsLast\";\n  const y = new TensorBuffer(convInfo.outShape, x.dtype);\n  const xStrides = util_exports.computeStrides(x.shape);\n  const filterStrides = util_exports.computeStrides(filter.shape);\n  const xBatchStride = xStrides[0];\n  const xRowStride = isChannelsLast ? xStrides[1] : xStrides[2];\n  const xColStride = isChannelsLast ? xStrides[2] : 1;\n  const xChannelStride = isChannelsLast ? 1 : xStrides[1];\n  const yBatchStride = y.strides[0];\n  const yRowStride = isChannelsLast ? y.strides[1] : y.strides[2];\n  const yColStride = isChannelsLast ? y.strides[2] : 1;\n  const yChannelStride = isChannelsLast ? 1 : y.strides[1];\n  const xVals = backend2.data.get(x.dataId).values;\n  const wVals = backend2.data.get(filter.dataId).values;\n  const yVals = y.values;\n  for (let b = 0; b < convInfo.batchSize; ++b) {\n    const xOffset1 = b * xBatchStride;\n    const yOffset1 = b * yBatchStride;\n    for (let yR = 0; yR < convInfo.outHeight; ++yR) {\n      const yOffset2 = yOffset1 + yR * yRowStride;\n      const xRCorner = yR * convInfo.strideHeight - padTop;\n      for (let wR = 0; wR < filterHeight; ++wR) {\n        const xR = xRCorner + wR * dilationHeight;\n        if (xR < 0 || xR >= convInfo.inHeight) {\n          continue;\n        }\n        const wOffset1 = wR * filterStrides[0];\n        const xOffset2 = xOffset1 + xR * xRowStride;\n        for (let yC = 0; yC < convInfo.outWidth; ++yC) {\n          const yOffset3 = yOffset2 + yC * yColStride;\n          const xCCorner = yC * convInfo.strideWidth - padLeft;\n          for (let wC = 0; wC < filterWidth; ++wC) {\n            const xC = xCCorner + wC * dilationWidth;\n            if (xC < 0 || xC >= convInfo.inWidth) {\n              continue;\n            }\n            const wOffset2 = wOffset1 + wC * filterStrides[1];\n            const xOffset3 = xOffset2 + xC * xColStride;\n            let wOffset3 = wOffset2;\n            for (let d1 = 0; d1 < convInfo.inChannels; ++d1) {\n              const xVal = xVals[xOffset3 + d1 * xChannelStride];\n              for (let d2 = 0; d2 < convInfo.outChannels; ++d2) {\n                yVals[yOffset3 + d2 * yChannelStride] += xVal * wVals[wOffset3 + d2];\n              }\n              wOffset3 += convInfo.outChannels;\n            }\n          }\n        }\n      }\n    }\n  }\n  return backend2.makeTensorInfo(y.shape, y.dtype, yVals);\n}\nvar conv2DConfig = {\n  kernelName: Conv2D,\n  backendName: \"cpu\",\n  kernelFunc: conv2D\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Conv2DBackpropFilter.js\nfunction conv2DBackpropFilter2(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x, dy } = inputs;\n  const { strides, pad: pad3, dataFormat, dimRoundingMode, filterShape } = attrs;\n  assertNotComplex([x, dy], \"conv2dBackpropFilter\");\n  const $dataFormat = backend_util_exports.convertConv2DDataFormat(dataFormat);\n  const convInfo = backend_util_exports.computeConv2DInfo(x.shape, filterShape, strides, 1, pad3, dimRoundingMode, false, $dataFormat);\n  const { strideHeight, strideWidth, filterHeight, filterWidth } = convInfo;\n  const isChannelsLast = convInfo.dataFormat === \"channelsLast\";\n  const dW = new TensorBuffer(convInfo.filterShape, \"float32\");\n  const leftPad = convInfo.padInfo.left;\n  const topPad = convInfo.padInfo.top;\n  const xVals = backend2.data.get(x.dataId).values;\n  const dyVals = backend2.data.get(dy.dataId).values;\n  const xBuf = new TensorBuffer(x.shape, x.dtype, xVals);\n  const dyBuf = new TensorBuffer(dy.shape, dy.dtype, dyVals);\n  for (let wR = 0; wR < filterHeight; ++wR) {\n    const yRMin = Math.max(0, Math.ceil((topPad - wR) / strideHeight));\n    const yRMax = Math.min(convInfo.outHeight, (convInfo.inHeight + topPad - wR) / strideHeight);\n    for (let wC = 0; wC < filterWidth; ++wC) {\n      const yCMin = Math.max(0, Math.ceil((leftPad - wC) / strideWidth));\n      const yCMax = Math.min(convInfo.outWidth, (convInfo.inWidth + leftPad - wC) / strideWidth);\n      for (let d1 = 0; d1 < convInfo.inChannels; ++d1) {\n        for (let d2 = 0; d2 < convInfo.outChannels; ++d2) {\n          let dotProd = 0;\n          for (let b = 0; b < convInfo.batchSize; ++b) {\n            for (let yR = yRMin; yR < yRMax; ++yR) {\n              const xR = wR + yR * strideHeight - topPad;\n              for (let yC = yCMin; yC < yCMax; ++yC) {\n                const xC = wC + yC * strideWidth - leftPad;\n                if (isChannelsLast) {\n                  dotProd += xBuf.get(b, xR, xC, d1) * dyBuf.get(b, yR, yC, d2);\n                } else {\n                  dotProd += xBuf.get(b, d1, xR, xC) * dyBuf.get(b, d2, yR, yC);\n                }\n              }\n            }\n          }\n          dW.set(dotProd, wR, wC, d1, d2);\n        }\n      }\n    }\n  }\n  return backend2.makeTensorInfo(dW.shape, dW.dtype, dW.values);\n}\nvar conv2DBackpropFilterConfig = {\n  kernelName: Conv2DBackpropFilter,\n  backendName: \"cpu\",\n  kernelFunc: conv2DBackpropFilter2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Conv2DBackpropInput.js\nfunction conv2DBackpropInput2(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { dy, filter } = inputs;\n  const { inputShape, strides, pad: pad3, dataFormat, dimRoundingMode } = attrs;\n  assertNotComplex([dy, filter], \"conv2dBackpropInput\");\n  const filterStrides = util_exports.computeStrides(filter.shape);\n  const dyStrides = util_exports.computeStrides(dy.shape);\n  let $dataFormat = backend_util_exports.convertConv2DDataFormat(dataFormat);\n  const convInfo = backend_util_exports.computeConv2DInfo(inputShape, filter.shape, strides, 1, pad3, dimRoundingMode, false, $dataFormat);\n  const dx = new TensorBuffer(convInfo.inShape, \"float32\");\n  const dxValues = dx.values;\n  const dyValues = backend2.data.get(dy.dataId).values;\n  const fltValues = backend2.data.get(filter.dataId).values;\n  const [fltS0, fltS1, fltS2] = filterStrides;\n  const { batchSize, filterHeight, filterWidth, inChannels, inHeight, inWidth, outChannels, outHeight, outWidth, strideHeight, strideWidth } = convInfo;\n  $dataFormat = convInfo.dataFormat;\n  const topPad = filterHeight - 1 - convInfo.padInfo.top;\n  const leftPad = filterWidth - 1 - convInfo.padInfo.left;\n  const isChannelsLast = $dataFormat === \"channelsLast\";\n  const xBatchStride = dx.strides[0];\n  const xRowStride = isChannelsLast ? dx.strides[1] : dx.strides[2];\n  const xColStride = isChannelsLast ? dx.strides[2] : 1;\n  const xChannelStride = isChannelsLast ? 1 : dx.strides[1];\n  const yBatchStride = dyStrides[0];\n  const yRowStride = isChannelsLast ? dyStrides[1] : dyStrides[2];\n  const yColStride = isChannelsLast ? dyStrides[2] : 1;\n  const yChannelStride = isChannelsLast ? 1 : dyStrides[1];\n  for (let b = 0; b < batchSize; ++b) {\n    for (let d1 = 0; d1 < inChannels; ++d1) {\n      for (let xR = 0; xR < inHeight; ++xR) {\n        const xRCorner = xR - topPad;\n        const xRMin = Math.max(0, Math.ceil(xRCorner / strideHeight));\n        const yRMax = Math.min(outHeight, (filterHeight + xRCorner) / strideHeight);\n        for (let xC = 0; xC < inWidth; ++xC) {\n          const xCCorner = xC - leftPad;\n          const xCMin = Math.max(0, Math.ceil(xCCorner / strideWidth));\n          const yCMax = Math.min(outWidth, (filterWidth + xCCorner) / strideWidth);\n          let dotProd = 0;\n          for (let yR = xRMin; yR < yRMax; ++yR) {\n            const wR = yR * strideHeight - xRCorner;\n            for (let yC = xCMin; yC < yCMax; ++yC) {\n              const wC = yC * strideWidth - xCCorner;\n              const dyOffset = yBatchStride * b + yRowStride * yR + yColStride * yC;\n              const fltOffset = fltS0 * (filterHeight - 1 - wR) + fltS1 * (filterWidth - 1 - wC) + fltS2 * d1;\n              for (let d2 = 0; d2 < outChannels; ++d2) {\n                const pixel = dyValues[dyOffset + yChannelStride * d2];\n                const weight = fltValues[fltOffset + d2];\n                dotProd += pixel * weight;\n              }\n            }\n          }\n          const dxOffset = xBatchStride * b + xRowStride * xR + xColStride * xC + xChannelStride * d1;\n          dxValues[dxOffset] = dotProd;\n        }\n      }\n    }\n  }\n  return backend2.makeTensorInfo(dx.shape, dx.dtype, dx.values);\n}\nvar conv2DBackpropInputConfig = {\n  kernelName: Conv2DBackpropInput,\n  backendName: \"cpu\",\n  kernelFunc: conv2DBackpropInput2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Conv3D.js\nfunction conv3D(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x, filter } = inputs;\n  const { strides, pad: pad3, dilations } = attrs;\n  assertNotComplex([x, filter], \"conv3d\");\n  const convInfo = backend_util_exports.computeConv3DInfo(x.shape, filter.shape, strides, dilations, pad3);\n  const { filterDepth, filterHeight, filterWidth, dilationDepth, dilationHeight, dilationWidth, padInfo } = convInfo;\n  const padFront = padInfo.front;\n  const padLeft = padInfo.left;\n  const padTop = padInfo.top;\n  const y = new TensorBuffer(convInfo.outShape, x.dtype);\n  const xVals = backend2.data.get(x.dataId).values;\n  const wVals = backend2.data.get(filter.dataId).values;\n  const yVals = y.values;\n  const xStrides = util_exports.computeStrides(x.shape);\n  const filterStrides = util_exports.computeStrides(filter.shape);\n  for (let b = 0; b < convInfo.batchSize; ++b) {\n    const xOffset1 = b * xStrides[0];\n    const yOffset1 = b * y.strides[0];\n    for (let yF = 0; yF < convInfo.outDepth; ++yF) {\n      const yOffset2 = yOffset1 + yF * y.strides[1];\n      const xFCorner = yF * convInfo.strideDepth - padFront;\n      for (let wF = 0; wF < filterDepth; ++wF) {\n        const xF = xFCorner + wF * dilationDepth;\n        if (xF < 0 || xF >= convInfo.inDepth) {\n          continue;\n        }\n        const wOffset1 = wF * filterStrides[0];\n        const xOffset2 = xOffset1 + xF * xStrides[1];\n        for (let yR = 0; yR < convInfo.outHeight; ++yR) {\n          const yOffset3 = yOffset2 + yR * y.strides[2];\n          const xRCorner = yR * convInfo.strideHeight - padTop;\n          for (let wR = 0; wR < filterHeight; ++wR) {\n            const xR = xRCorner + wR * dilationHeight;\n            if (xR < 0 || xR >= convInfo.inHeight) {\n              continue;\n            }\n            const wOffset2 = wOffset1 + wR * filterStrides[1];\n            const xOffset3 = xOffset2 + xR * xStrides[2];\n            for (let yC = 0; yC < convInfo.outWidth; ++yC) {\n              const yOffset4 = yOffset3 + yC * convInfo.outChannels;\n              const xCCorner = yC * convInfo.strideWidth - padLeft;\n              for (let wC = 0; wC < filterWidth; ++wC) {\n                const xC = xCCorner + wC * dilationWidth;\n                if (xC < 0 || xC >= convInfo.inWidth) {\n                  continue;\n                }\n                const wOffset3 = wOffset2 + wC * filterStrides[2];\n                const xOffset4 = xOffset3 + xC * convInfo.inChannels;\n                let wOffset4 = wOffset3;\n                for (let d1 = 0; d1 < convInfo.inChannels; ++d1) {\n                  const xVal = xVals[xOffset4 + d1];\n                  for (let d2 = 0; d2 < convInfo.outChannels; ++d2) {\n                    yVals[yOffset4 + d2] += xVal * wVals[wOffset4 + d2];\n                  }\n                  wOffset4 += convInfo.outChannels;\n                }\n              }\n            }\n          }\n        }\n      }\n    }\n  }\n  return backend2.makeTensorInfo(y.shape, y.dtype, y.values);\n}\nvar conv3DConfig = {\n  kernelName: Conv3D,\n  backendName: \"cpu\",\n  kernelFunc: conv3D\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Conv3DBackpropFilterV2.js\nfunction conv3DBackpropFilterV2(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x, dy } = inputs;\n  const { strides, pad: pad3, filterShape } = attrs;\n  assertNotComplex([x, dy], \"conv3dBackpropFilterV2\");\n  const xStrides = util_exports.computeStrides(x.shape);\n  const dyStrides = util_exports.computeStrides(dy.shape);\n  const convInfo = backend_util_exports.computeConv3DInfo(x.shape, filterShape, strides, 1, pad3);\n  const strideDepth = convInfo.strideDepth;\n  const strideHeight = convInfo.strideHeight;\n  const strideWidth = convInfo.strideWidth;\n  const filterDepth = convInfo.filterDepth;\n  const filterHeight = convInfo.filterHeight;\n  const filterWidth = convInfo.filterWidth;\n  const dw = new TensorBuffer(convInfo.filterShape, \"float32\");\n  const dwValues = dw.values;\n  const [dwS0, dwS1, dwS2, dwS3] = dw.strides;\n  const dyValues = backend2.data.get(dy.dataId).values;\n  const [dyS0, dyS1, dyS2, dyS3] = dyStrides;\n  const xValues = backend2.data.get(x.dataId).values;\n  const [xS0, xS1, xS2, xS3] = xStrides;\n  const frontPad = convInfo.padInfo.front;\n  const leftPad = convInfo.padInfo.left;\n  const topPad = convInfo.padInfo.top;\n  for (let wF = 0; wF < filterDepth; ++wF) {\n    const yFMin = Math.max(0, Math.ceil((frontPad - wF) / strideDepth));\n    const yFMax = Math.min(convInfo.outDepth, (convInfo.inDepth + frontPad - wF) / strideDepth);\n    const wOffset1 = wF * dwS0;\n    for (let wR = 0; wR < filterHeight; ++wR) {\n      const yRMin = Math.max(0, Math.ceil((topPad - wR) / strideHeight));\n      const yRMax = Math.min(convInfo.outHeight, (convInfo.inHeight + topPad - wR) / strideHeight);\n      const wOffset2 = wR * dwS1 + wOffset1;\n      for (let wC = 0; wC < filterWidth; ++wC) {\n        const yCMin = Math.max(0, Math.ceil((leftPad - wC) / strideWidth));\n        const yCMax = Math.min(convInfo.outWidth, (convInfo.inWidth + leftPad - wC) / strideWidth);\n        const wOffset3 = wC * dwS2 + wOffset2;\n        for (let d1 = 0; d1 < convInfo.inChannels; ++d1) {\n          const wOffset4 = d1 * dwS3 + wOffset3;\n          for (let d2 = 0; d2 < convInfo.outChannels; ++d2) {\n            let dotProd = 0;\n            for (let b = 0; b < convInfo.batchSize; ++b) {\n              const xOffset1 = b * xS0;\n              const yOffset1 = b * dyS0;\n              for (let yF = yFMin; yF < yFMax; ++yF) {\n                const xF = wF + yF * strideDepth - frontPad;\n                const xOffset2 = xF * xS1 + xOffset1;\n                const yOffset2 = yF * dyS1 + yOffset1;\n                for (let yR = yRMin; yR < yRMax; ++yR) {\n                  const xR = wR + yR * strideHeight - topPad;\n                  const xOffset3 = xR * xS2 + xOffset2;\n                  const yOffset3 = yR * dyS2 + yOffset2;\n                  for (let yC = yCMin; yC < yCMax; ++yC) {\n                    const xC = wC + yC * strideWidth - leftPad;\n                    const xOffset4 = xC * xS3 + xOffset3;\n                    const yOffset4 = yC * dyS3 + yOffset3;\n                    dotProd += xValues[xOffset4 + d1] * dyValues[yOffset4 + d2];\n                  }\n                }\n              }\n            }\n            dwValues[wOffset4 + d2] = dotProd;\n          }\n        }\n      }\n    }\n  }\n  return backend2.makeTensorInfo(dw.shape, dw.dtype, dw.values);\n}\nvar conv3DBackpropFilterV2Config = {\n  kernelName: Conv3DBackpropFilterV2,\n  backendName: \"cpu\",\n  kernelFunc: conv3DBackpropFilterV2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Conv3DBackpropInputV2.js\nfunction conv3DBackpropInputV2(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { dy, filter } = inputs;\n  const { pad: pad3, strides, inputShape } = attrs;\n  assertNotComplex([dy], \"conv3dBackpropInputV2\");\n  const dyStrides = util_exports.computeStrides(dy.shape);\n  const filterStrides = util_exports.computeStrides(filter.shape);\n  const convInfo = backend_util_exports.computeConv3DInfo(inputShape, filter.shape, strides, 1, pad3);\n  const dx = new TensorBuffer(convInfo.inShape, \"float32\");\n  const dxValues = dx.values;\n  const [dxS0, dxS1, dxS2, dxS3] = dx.strides;\n  const dyValues = backend2.data.get(dy.dataId).values;\n  const [dyS0, dyS1, dyS2, dyS3] = dyStrides;\n  const fltValues = backend2.data.get(filter.dataId).values;\n  const [fltS0, fltS1, fltS2, fltS3] = filterStrides;\n  const { batchSize, filterDepth, filterHeight, filterWidth, inChannels, inDepth, inHeight, inWidth, outChannels, outDepth, outHeight, outWidth, strideDepth, strideHeight, strideWidth } = convInfo;\n  const frontPad = filterDepth - 1 - convInfo.padInfo.front;\n  const topPad = filterHeight - 1 - convInfo.padInfo.top;\n  const leftPad = filterWidth - 1 - convInfo.padInfo.left;\n  for (let b = 0; b < batchSize; ++b) {\n    for (let d1 = 0; d1 < inChannels; ++d1) {\n      for (let xF = 0; xF < inDepth; ++xF) {\n        const xFCorner = xF - frontPad;\n        const xFMin = Math.max(0, Math.ceil(xFCorner / strideDepth));\n        const yFMax = Math.min(outDepth, (filterDepth + xFCorner) / strideDepth);\n        for (let xR = 0; xR < inHeight; ++xR) {\n          const xRCorner = xR - topPad;\n          const xRMin = Math.max(0, Math.ceil(xRCorner / strideHeight));\n          const yRMax = Math.min(outHeight, (filterHeight + xRCorner) / strideHeight);\n          for (let xC = 0; xC < inWidth; ++xC) {\n            const xCCorner = xC - leftPad;\n            const xCMin = Math.max(0, Math.ceil(xCCorner / strideWidth));\n            const yCMax = Math.min(outWidth, (filterWidth + xCCorner) / strideWidth);\n            let dotProd = 0;\n            for (let yF = xFMin; yF < yFMax; ++yF) {\n              const wF = yF * strideDepth - xFCorner;\n              for (let yR = xRMin; yR < yRMax; ++yR) {\n                const wR = yR * strideHeight - xRCorner;\n                for (let yC = xCMin; yC < yCMax; ++yC) {\n                  const wC = yC * strideWidth - xCCorner;\n                  const dyOffset = dyS0 * b + dyS1 * yF + dyS2 * yR + dyS3 * yC;\n                  const fltOffset = fltS0 * (filterDepth - 1 - wF) + fltS1 * (filterHeight - 1 - wR) + fltS2 * (filterWidth - 1 - wC) + fltS3 * d1;\n                  for (let d2 = 0; d2 < outChannels; ++d2) {\n                    const pixel = dyValues[dyOffset + d2];\n                    const weight = fltValues[fltOffset + d2];\n                    dotProd += pixel * weight;\n                  }\n                }\n              }\n            }\n            dxValues[dxS0 * b + dxS1 * xF + dxS2 * xR + dxS3 * xC + d1] = dotProd;\n          }\n        }\n      }\n    }\n  }\n  return backend2.makeTensorInfo(dx.shape, dx.dtype, dx.values);\n}\nvar conv3DBackpropInputV2Config = {\n  kernelName: Conv3DBackpropInputV2,\n  backendName: \"cpu\",\n  kernelFunc: conv3DBackpropInputV2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Cos.js\nvar cos2 = unaryKernelFunc(Cos, (xi) => Math.cos(xi));\nvar cosConfig = {\n  kernelName: Cos,\n  backendName: \"cpu\",\n  kernelFunc: cos2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Cosh.js\nvar cosh2 = unaryKernelFunc(Cosh, (xi) => Math.cosh(xi));\nvar coshConfig = {\n  kernelName: Cosh,\n  backendName: \"cpu\",\n  kernelFunc: cosh2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/CropAndResize.js\nfunction cropAndResize2(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { image: image2, boxes, boxInd } = inputs;\n  const { cropSize, method, extrapolationValue } = attrs;\n  const [batch, imageHeight, imageWidth, numChannels] = image2.shape;\n  const numBoxes = boxes.shape[0];\n  const [cropHeight, cropWidth] = cropSize;\n  const output = buffer([numBoxes, cropHeight, cropWidth, numChannels], \"float32\");\n  const boxVals = backend2.data.get(boxes.dataId).values;\n  const boxIndVals = backend2.data.get(boxInd.dataId).values;\n  const imageVals = backend2.data.get(image2.dataId).values;\n  const inStride = util_exports.computeStrides(image2.shape);\n  const outStride = util_exports.computeStrides(output.shape);\n  for (let b = 0; b < numBoxes; b++) {\n    const startInd = b * 4;\n    const y1 = boxVals[startInd];\n    const x1 = boxVals[startInd + 1];\n    const y2 = boxVals[startInd + 2];\n    const x2 = boxVals[startInd + 3];\n    const bInd = boxIndVals[b];\n    if (bInd >= batch) {\n      continue;\n    }\n    const heightScale = cropHeight > 1 ? (y2 - y1) * (imageHeight - 1) / (cropHeight - 1) : 0;\n    const widthScale = cropWidth > 1 ? (x2 - x1) * (imageWidth - 1) / (cropWidth - 1) : 0;\n    for (let y = 0; y < cropHeight; y++) {\n      const yInd = cropHeight > 1 ? y1 * (imageHeight - 1) + y * heightScale : 0.5 * (y1 + y2) * (imageHeight - 1);\n      if (yInd < 0 || yInd > imageHeight - 1) {\n        for (let x = 0; x < cropWidth; x++) {\n          for (let c = 0; c < numChannels; c++) {\n            const ind = c + x * outStride[2] + y * outStride[1] + b * outStride[0];\n            output.values[ind] = extrapolationValue;\n          }\n        }\n        continue;\n      }\n      if (method === \"bilinear\") {\n        const topInd = Math.floor(yInd);\n        const bottomInd = Math.ceil(yInd);\n        const yLerp = yInd - topInd;\n        for (let x = 0; x < cropWidth; x++) {\n          const xInd = cropWidth > 1 ? x1 * (imageWidth - 1) + x * widthScale : 0.5 * (x1 + x2) * (imageWidth - 1);\n          if (xInd < 0 || xInd > imageWidth - 1) {\n            for (let c = 0; c < numChannels; c++) {\n              const ind = c + x * outStride[2] + y * outStride[1] + b * outStride[0];\n              output.values[ind] = extrapolationValue;\n            }\n            continue;\n          }\n          const leftInd = Math.floor(xInd);\n          const rightInd = Math.ceil(xInd);\n          const xLerp = xInd - leftInd;\n          for (let c = 0; c < numChannels; c++) {\n            let ind = c + leftInd * inStride[2] + topInd * inStride[1] + bInd * inStride[0];\n            const topLeft = imageVals[ind];\n            ind = c + rightInd * inStride[2] + topInd * inStride[1] + bInd * inStride[0];\n            const topRight = imageVals[ind];\n            ind = c + leftInd * inStride[2] + bottomInd * inStride[1] + bInd * inStride[0];\n            const bottomLeft = imageVals[ind];\n            ind = c + rightInd * inStride[2] + bottomInd * inStride[1] + bInd * inStride[0];\n            const bottomRight = imageVals[ind];\n            const top = topLeft + (topRight - topLeft) * xLerp;\n            const bottom = bottomLeft + (bottomRight - bottomLeft) * xLerp;\n            ind = c + x * outStride[2] + y * outStride[1] + b * outStride[0];\n            output.values[ind] = top + (bottom - top) * yLerp;\n          }\n        }\n      } else {\n        for (let x = 0; x < cropWidth; ++x) {\n          const xInd = cropWidth > 1 ? x1 * (imageWidth - 1) + x * widthScale : 0.5 * (x1 + x2) * (imageWidth - 1);\n          if (xInd < 0 || xInd > imageWidth - 1) {\n            for (let c = 0; c < numChannels; c++) {\n              const ind = c + x * outStride[2] + y * outStride[1] + b * outStride[0];\n              output.values[ind] = extrapolationValue;\n            }\n            continue;\n          }\n          const closestX = Math.round(xInd);\n          const closestY = Math.round(yInd);\n          for (let c = 0; c < numChannels; c++) {\n            const inInd = c + closestX * inStride[2] + closestY * inStride[1] + bInd * inStride[0];\n            const outInd = c + x * outStride[2] + y * outStride[1] + b * outStride[0];\n            output.values[outInd] = imageVals[inInd];\n          }\n        }\n      }\n    }\n  }\n  return backend2.makeTensorInfo(output.shape, output.dtype, output.values);\n}\nvar cropAndResizeConfig = {\n  kernelName: CropAndResize,\n  backendName: \"cpu\",\n  kernelFunc: cropAndResize2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Cumprod.js\nfunction cumprod2(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x } = inputs;\n  const { axis, exclusive, reverse: reverse5 } = attrs;\n  assertNotComplex(x, \"cumprod\");\n  const permutation = backend_util_exports.getAxesPermutation([axis], x.shape.length);\n  let $x = x;\n  if (permutation != null) {\n    $x = transpose2({ inputs: { x }, backend: backend2, attrs: { perm: permutation } });\n  }\n  const permutedAxis = backend_util_exports.getInnerMostAxes(1, x.shape.length)[0];\n  if (permutedAxis !== $x.shape.length - 1) {\n    throw new Error(`backend.cumprod in CPU expects an inner-most axis=${$x.shape.length - 1} but got axis=${permutedAxis}`);\n  }\n  const resultDtype = upcastType($x.dtype, \"int32\");\n  const vals = util_exports.makeOnesTypedArray(util_exports.sizeFromShape($x.shape), resultDtype);\n  const aVals = backend2.data.get($x.dataId).values;\n  const finalDim = $x.shape[$x.shape.length - 1];\n  const indexAdjuster = reverse5 ? (i2, j) => i2 + finalDim - j - 1 : (i2, j) => i2 + j;\n  for (let i2 = 0; i2 < aVals.length; i2 += finalDim) {\n    for (let j = 0; j < finalDim; j++) {\n      const idx = indexAdjuster(i2, j);\n      if (j === 0) {\n        vals[idx] = exclusive ? 1 : aVals[idx];\n      } else {\n        const prevIdx = indexAdjuster(i2, j - 1);\n        vals[idx] = exclusive ? aVals[prevIdx] * vals[prevIdx] : aVals[idx] * vals[prevIdx];\n      }\n    }\n  }\n  const result = backend2.makeTensorInfo($x.shape, resultDtype, vals);\n  if (permutation != null) {\n    const reversePermutation = backend_util_exports.getUndoAxesPermutation(permutation);\n    const reverseTransposedResult = transpose2({ inputs: { x: result }, backend: backend2, attrs: { perm: reversePermutation } });\n    backend2.disposeIntermediateTensorInfo(result);\n    backend2.disposeIntermediateTensorInfo($x);\n    return reverseTransposedResult;\n  }\n  return result;\n}\nvar cumprodConfig = {\n  kernelName: Cumprod,\n  backendName: \"cpu\",\n  kernelFunc: cumprod2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Cumsum.js\nfunction cumsum2(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x } = inputs;\n  const { axis, exclusive, reverse: reverse5 } = attrs;\n  assertNotComplex(x, \"cumsum\");\n  const permutation = backend_util_exports.getAxesPermutation([axis], x.shape.length);\n  let $x = x;\n  if (permutation != null) {\n    $x = transpose2({ inputs: { x }, backend: backend2, attrs: { perm: permutation } });\n  }\n  const permutedAxis = backend_util_exports.getInnerMostAxes(1, x.shape.length)[0];\n  if (permutedAxis !== $x.shape.length - 1) {\n    throw new Error(`backend.cumsum in CPU expects an inner-most axis=${$x.shape.length - 1} but got axis=${permutedAxis}`);\n  }\n  const resultDtype = upcastType($x.dtype, \"int32\");\n  const vals = util_exports.makeZerosTypedArray(util_exports.sizeFromShape($x.shape), resultDtype);\n  const aVals = backend2.data.get($x.dataId).values;\n  const finalDim = $x.shape[$x.shape.length - 1];\n  const indexAdjuster = reverse5 ? (i2, j) => i2 + finalDim - j - 1 : (i2, j) => i2 + j;\n  for (let i2 = 0; i2 < aVals.length; i2 += finalDim) {\n    for (let j = 0; j < finalDim; j++) {\n      const idx = indexAdjuster(i2, j);\n      if (j === 0) {\n        vals[idx] = exclusive ? 0 : aVals[idx];\n      } else {\n        const prevIdx = indexAdjuster(i2, j - 1);\n        vals[idx] = exclusive ? aVals[prevIdx] + vals[prevIdx] : aVals[idx] + vals[prevIdx];\n      }\n    }\n  }\n  const result = backend2.makeTensorInfo($x.shape, resultDtype, vals);\n  if (permutation != null) {\n    const reversePermutation = backend_util_exports.getUndoAxesPermutation(permutation);\n    const reverseTransposedResult = transpose2({ inputs: { x: result }, backend: backend2, attrs: { perm: reversePermutation } });\n    backend2.disposeIntermediateTensorInfo(result);\n    backend2.disposeIntermediateTensorInfo($x);\n    return reverseTransposedResult;\n  }\n  return result;\n}\nvar cumsumConfig = {\n  kernelName: Cumsum,\n  backendName: \"cpu\",\n  kernelFunc: cumsum2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/DenseBincount.js\nfunction denseBincount2(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x, weights } = inputs;\n  const { size, binaryOutput } = attrs;\n  if (x.shape.length === 1) {\n    const xVals = backend2.data.get(x.dataId).values;\n    const weightsVals = backend2.data.get(weights.dataId).values;\n    const outVals = bincountImpl(xVals, weightsVals, weights.dtype, weights.shape, size);\n    return backend2.makeTensorInfo([size], weights.dtype, outVals);\n  } else if (x.shape.length === 2) {\n    const xBuf = backend2.bufferSync(x);\n    const weightsBuf = backend2.bufferSync(weights);\n    const outBuf = bincountReduceImpl(xBuf, weightsBuf, size, binaryOutput);\n    return backend2.makeTensorInfo(outBuf.shape, weights.dtype, outBuf.values);\n  }\n  throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${x.shape.length}.`);\n}\nvar denseBincountConfig = {\n  kernelName: DenseBincount,\n  backendName: \"cpu\",\n  kernelFunc: denseBincount2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/DepthToSpace.js\nfunction depthToSpace2(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x } = inputs;\n  const { blockSize, dataFormat } = attrs;\n  util_exports.assert(dataFormat === \"NHWC\", () => `Only NHWC dataFormat supported on CPU for depthToSpace. Got ${dataFormat}`);\n  const batchSize = x.shape[0];\n  const inputHeight = x.shape[1];\n  const inputWidth = x.shape[2];\n  const inputDepth = x.shape[3];\n  const outputHeight = inputHeight * blockSize;\n  const outputWidth = inputWidth * blockSize;\n  const outputDepth = inputDepth / (blockSize * blockSize);\n  const xValues = backend2.data.get(x.dataId).values;\n  const result = new Float32Array(batchSize * outputHeight * outputWidth * outputDepth);\n  let outputIdx = 0;\n  for (let b = 0; b < batchSize; ++b) {\n    for (let h = 0; h < outputHeight; ++h) {\n      const inH = Math.floor(h / blockSize);\n      const offsetH = h % blockSize;\n      for (let w = 0; w < outputWidth; ++w) {\n        const inW = Math.floor(w / blockSize);\n        const offsetW = w % blockSize;\n        const offsetD = (offsetH * blockSize + offsetW) * outputDepth;\n        for (let d = 0; d < outputDepth; ++d) {\n          const inD = d + offsetD;\n          const inputIdx = inD + inputDepth * (inW + inputWidth * (inH + inputHeight * b));\n          result[outputIdx++] = xValues[inputIdx];\n        }\n      }\n    }\n  }\n  return backend2.makeTensorInfo([batchSize, outputHeight, outputWidth, outputDepth], x.dtype, result);\n}\nvar depthToSpaceConfig = {\n  kernelName: DepthToSpace,\n  backendName: \"cpu\",\n  kernelFunc: depthToSpace2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/DepthwiseConv2dNative.js\nfunction depthwiseConv2dNative(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x, filter } = inputs;\n  const { strides, pad: pad3, dilations, dimRoundingMode } = attrs;\n  assertNotComplex([x, filter], \"depthwiseConv2DNative\");\n  const xStrides = util_exports.computeStrides(x.shape);\n  const filterStrides = util_exports.computeStrides(filter.shape);\n  let $dilations = dilations;\n  if ($dilations == null) {\n    $dilations = [1, 1];\n  }\n  util_exports.assert(backend_util_exports.eitherStridesOrDilationsAreOne(strides, $dilations), () => `Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${strides} and dilations '${$dilations}'`);\n  const convInfo = backend_util_exports.computeConv2DInfo(x.shape, filter.shape, strides, $dilations, pad3, dimRoundingMode, true);\n  const { filterHeight, filterWidth, dilationHeight, dilationWidth, padInfo } = convInfo;\n  const padLeft = padInfo.left;\n  const padTop = padInfo.top;\n  const chMul = convInfo.outChannels / convInfo.inChannels;\n  const y = new TensorBuffer(convInfo.outShape, x.dtype);\n  const xVals = backend2.data.get(x.dataId).values;\n  const wVals = backend2.data.get(filter.dataId).values;\n  const yVals = y.values;\n  for (let b = 0; b < convInfo.batchSize; ++b) {\n    const xOffset1 = b * xStrides[0];\n    const yOffset1 = b * y.strides[0];\n    for (let yR = 0; yR < convInfo.outHeight; ++yR) {\n      const yOffset2 = yOffset1 + yR * y.strides[1];\n      const xRCorner = yR * convInfo.strideHeight - padTop;\n      for (let wR = 0; wR < filterHeight; ++wR) {\n        const xR = xRCorner + wR * dilationHeight;\n        if (xR < 0 || xR >= convInfo.inHeight) {\n          continue;\n        }\n        const wOffset1 = wR * filterStrides[0];\n        const xOffset2 = xOffset1 + xR * xStrides[1];\n        for (let yC = 0; yC < convInfo.outWidth; ++yC) {\n          const yOffset3 = yOffset2 + yC * y.strides[2];\n          const xCCorner = yC * convInfo.strideWidth - padLeft;\n          for (let wC = 0; wC < filterWidth; ++wC) {\n            const xC = xCCorner + wC * dilationWidth;\n            if (xC < 0 || xC >= convInfo.inWidth) {\n              continue;\n            }\n            const wOffset2 = wOffset1 + wC * filterStrides[1];\n            const xOffset3 = xOffset2 + xC * convInfo.inChannels;\n            let yOffset4 = yOffset3;\n            let wOffset3 = wOffset2;\n            for (let d1 = 0; d1 < convInfo.inChannels; ++d1) {\n              const xVal = xVals[xOffset3 + d1];\n              for (let q = 0; q < chMul; ++q) {\n                yVals[yOffset4 + q] += xVal * wVals[wOffset3 + q];\n              }\n              yOffset4 += chMul;\n              wOffset3 += chMul;\n            }\n          }\n        }\n      }\n    }\n  }\n  return backend2.makeTensorInfo(y.shape, y.dtype, y.values);\n}\nvar depthwiseConv2dNativeConfig = {\n  kernelName: DepthwiseConv2dNative,\n  backendName: \"cpu\",\n  kernelFunc: depthwiseConv2dNative\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/DepthwiseConv2dNativeBackpropFilter.js\nfunction depthwiseConv2dNativeBackpropFilter2(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x, dy } = inputs;\n  const { strides, dilations, pad: pad3, dimRoundingMode, filterShape } = attrs;\n  assertNotComplex([x, dy], \"depthwiseConv2dNativeBackpropFilter\");\n  const convInfo = backend_util_exports.computeConv2DInfo(x.shape, filterShape, strides, dilations, pad3, dimRoundingMode, true);\n  const { strideHeight, strideWidth, filterHeight, filterWidth } = convInfo;\n  const dW = new TensorBuffer(convInfo.filterShape, \"float32\");\n  const leftPad = convInfo.padInfo.left;\n  const topPad = convInfo.padInfo.top;\n  const chMul = convInfo.outChannels / convInfo.inChannels;\n  const xVals = backend2.data.get(x.dataId).values;\n  const xBuf = new TensorBuffer(x.shape, x.dtype, xVals);\n  const dyVals = backend2.data.get(dy.dataId).values;\n  const dyBuf = new TensorBuffer(dy.shape, dy.dtype, dyVals);\n  for (let wR = 0; wR < filterHeight; ++wR) {\n    const yRMin = Math.max(0, Math.ceil((topPad - wR) / strideHeight));\n    const yRMax = Math.min(convInfo.outHeight, (convInfo.inHeight + topPad - wR) / strideHeight);\n    for (let wC = 0; wC < filterWidth; ++wC) {\n      const yCMin = Math.max(0, Math.ceil((leftPad - wC) / strideWidth));\n      const yCMax = Math.min(convInfo.outWidth, (convInfo.inWidth + leftPad - wC) / strideWidth);\n      for (let d2 = 0; d2 < convInfo.outChannels; ++d2) {\n        const d1 = Math.trunc(d2 / chMul);\n        const dm = d2 % chMul;\n        let dotProd = 0;\n        for (let b = 0; b < convInfo.batchSize; ++b) {\n          for (let yR = yRMin; yR < yRMax; ++yR) {\n            const xR = wR + yR * strideHeight - topPad;\n            for (let yC = yCMin; yC < yCMax; ++yC) {\n              const xC = wC + yC * strideWidth - leftPad;\n              dotProd += xBuf.get(b, xR, xC, d1) * dyBuf.get(b, yR, yC, d2);\n            }\n          }\n        }\n        dW.set(dotProd, wR, wC, d1, dm);\n      }\n    }\n  }\n  return backend2.makeTensorInfo(dW.shape, dW.dtype, dW.values);\n}\nvar depthwiseConv2dNativeBackpropFilterConfig = {\n  kernelName: DepthwiseConv2dNativeBackpropFilter,\n  backendName: \"cpu\",\n  kernelFunc: depthwiseConv2dNativeBackpropFilter2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/DepthwiseConv2dNativeBackpropInput.js\nfunction depthwiseConv2dNativeBackpropInput2(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { dy, filter } = inputs;\n  const { strides, dilations, pad: pad3, dimRoundingMode, inputShape } = attrs;\n  assertNotComplex([dy, filter], \"depthwiseConv2DNativeBackpropInput\");\n  const dyStrides = util_exports.computeStrides(dy.shape);\n  const filterStrides = util_exports.computeStrides(filter.shape);\n  const convInfo = backend_util_exports.computeConv2DInfo(inputShape, filter.shape, strides, dilations, pad3, dimRoundingMode, true);\n  const dx = new TensorBuffer(convInfo.inShape, \"float32\");\n  const dxValues = dx.values;\n  const [dxS0, dxS1, dxS2] = dx.strides;\n  const dyValues = backend2.data.get(dy.dataId).values;\n  const [dyS0, dyS1, dyS2] = dyStrides;\n  const fltValues = backend2.data.get(filter.dataId).values;\n  const [fltS0, fltS1, fltS2] = filterStrides;\n  const { batchSize, filterHeight, filterWidth, inChannels, inHeight, inWidth, outChannels, outHeight, outWidth, strideHeight, strideWidth } = convInfo;\n  const topPad = filterHeight - 1 - convInfo.padInfo.top;\n  const leftPad = filterWidth - 1 - convInfo.padInfo.left;\n  const chMul = outChannels / inChannels;\n  for (let b = 0; b < batchSize; ++b) {\n    for (let d1 = 0; d1 < inChannels; ++d1) {\n      for (let xR = 0; xR < inHeight; ++xR) {\n        const xRCorner = xR - topPad;\n        const xRMin = Math.max(0, Math.ceil(xRCorner / strideHeight));\n        const yRMax = Math.min(outHeight, (filterHeight + xRCorner) / strideHeight);\n        for (let xC = 0; xC < inWidth; ++xC) {\n          const xCCorner = xC - leftPad;\n          const xCMin = Math.max(0, Math.ceil(xCCorner / strideWidth));\n          const yCMax = Math.min(outWidth, (filterWidth + xCCorner) / strideWidth);\n          let dotProd = 0;\n          for (let yR = xRMin; yR < yRMax; ++yR) {\n            const wR = yR * strideHeight - xRCorner;\n            for (let yC = xCMin; yC < yCMax; ++yC) {\n              const wC = yC * strideWidth - xCCorner;\n              const dyOffset = dyS0 * b + dyS1 * yR + dyS2 * yC;\n              const fltOffset = fltS0 * (filterHeight - 1 - wR) + fltS1 * (filterWidth - 1 - wC) + fltS2 * d1;\n              for (let dm = 0; dm < chMul; ++dm) {\n                const d2 = d1 * chMul + dm;\n                const pixel = dyValues[dyOffset + d2];\n                const weight = fltValues[fltOffset + dm];\n                dotProd += pixel * weight;\n              }\n            }\n          }\n          dxValues[dxS0 * b + dxS1 * xR + dxS2 * xC + d1] = dotProd;\n        }\n      }\n    }\n  }\n  return backend2.makeTensorInfo(dx.shape, dx.dtype, dx.values);\n}\nvar depthwiseConv2dNativeBackpropInputConfig = {\n  kernelName: DepthwiseConv2dNativeBackpropInput,\n  backendName: \"cpu\",\n  kernelFunc: depthwiseConv2dNativeBackpropInput2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Diag.js\nfunction diag2(args) {\n  const { inputs, backend: backend2 } = args;\n  const { x } = inputs;\n  const xSize = util_exports.sizeFromShape(x.shape);\n  const xVals = backend2.data.get(x.dataId).values;\n  const outBuf = buffer([xSize, xSize], x.dtype);\n  const vals = outBuf.values;\n  for (let i2 = 0; i2 < xVals.length; i2++) {\n    vals[i2 * xSize + i2] = xVals[i2];\n  }\n  const outShape = [...x.shape, ...x.shape];\n  return backend2.makeTensorInfo(outShape, outBuf.dtype, outBuf.values);\n}\nvar diagConfig = {\n  kernelName: Diag,\n  backendName: \"cpu\",\n  kernelFunc: diag2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Dilation2D.js\nvar dilation2DConfig = {\n  kernelName: Dilation2D,\n  backendName: \"cpu\",\n  kernelFunc: ({ inputs, backend: backend2, attrs }) => {\n    const { x, filter } = inputs;\n    const { strides, pad: pad3, dilations } = attrs;\n    const cpuBackend = backend2;\n    const xVals = cpuBackend.data.get(x.dataId).values;\n    const xRank = x.shape.length;\n    const filterVals = cpuBackend.data.get(filter.dataId).values;\n    const filterRank = filter.shape.length;\n    const { batchSize, inHeight, inWidth, inChannels, outHeight, outWidth, padInfo, strideHeight, strideWidth, filterHeight, filterWidth, dilationHeight, dilationWidth, outShape } = backend_util_exports.computeDilation2DInfo(x.shape, filter.shape, strides, pad3, \"NHWC\", dilations);\n    const outSize = util_exports.sizeFromShape(outShape);\n    const outRank = outShape.length;\n    const outputVals = util_exports.getArrayFromDType(x.dtype, outSize);\n    for (let b = 0; b < batchSize; ++b) {\n      for (let hOut = 0; hOut < outHeight; ++hOut) {\n        const hBeg = hOut * strideHeight - padInfo.top;\n        for (let wOut = 0; wOut < outWidth; ++wOut) {\n          const wBeg = wOut * strideWidth - padInfo.left;\n          for (let d = 0; d < inChannels; ++d) {\n            let curVal = Number.MIN_SAFE_INTEGER;\n            for (let h = 0; h < filterHeight; ++h) {\n              const hIn = hBeg + h * dilationHeight;\n              if (hIn >= 0 && hIn < inHeight) {\n                for (let w = 0; w < filterWidth; ++w) {\n                  const wIn = wBeg + w * dilationWidth;\n                  if (wIn >= 0 && wIn < inWidth) {\n                    const xIndex = util_exports.locToIndex([b, hIn, wIn, d], xRank, util_exports.computeStrides(x.shape));\n                    const filterIndex = util_exports.locToIndex([h, w, d], filterRank, util_exports.computeStrides(filter.shape));\n                    const val = xVals[xIndex] + filterVals[filterIndex];\n                    if (val > curVal) {\n                      curVal = val;\n                    }\n                  }\n                }\n              }\n            }\n            const outputIndex = util_exports.locToIndex([b, hOut, wOut, d], outRank, util_exports.computeStrides(outShape));\n            outputVals[outputIndex] = curVal;\n          }\n        }\n      }\n    }\n    const dataId = cpuBackend.write(util_exports.toTypedArray(outputVals, x.dtype), outShape, x.dtype);\n    return { dataId, shape: outShape, dtype: x.dtype };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Dilation2DBackpropFilter.js\nvar dilation2DBackpropFilterConfig = {\n  kernelName: Dilation2DBackpropFilter,\n  backendName: \"cpu\",\n  kernelFunc: ({ inputs, backend: backend2, attrs }) => {\n    const { x, filter, dy } = inputs;\n    const { strides, pad: pad3, dilations } = attrs;\n    const cpuBackend = backend2;\n    const $x = util_exports.toNestedArray(x.shape, cpuBackend.data.get(x.dataId).values);\n    const $filter = util_exports.toNestedArray(filter.shape, cpuBackend.data.get(filter.dataId).values);\n    const { batchSize, inHeight, inWidth, inChannels, outHeight, outWidth, padInfo, strideHeight, strideWidth, filterHeight, filterWidth, dilationHeight, dilationWidth, outShape } = backend_util_exports.computeDilation2DInfo(x.shape, filter.shape, strides, pad3, \"NHWC\", dilations);\n    util_exports.assert(dy.rank === outShape.length, () => `Error in ${Dilation2DBackpropFilter}, dy must have the same rank as output ${outShape.length}, but got ${dy.rank}`);\n    const $dy = util_exports.toNestedArray(outShape, cpuBackend.data.get(dy.dataId).values);\n    const gradients = util_exports.makeZerosNestedTypedArray(filter.shape, filter.dtype);\n    for (let b = 0; b < batchSize; ++b) {\n      for (let hOut = 0; hOut < outHeight; ++hOut) {\n        const hBeg = hOut * strideHeight - padInfo.top;\n        for (let wOut = 0; wOut < outWidth; ++wOut) {\n          const wBeg = wOut * strideWidth - padInfo.left;\n          for (let d = 0; d < inChannels; ++d) {\n            let curVal = Number.MIN_SAFE_INTEGER;\n            let hMax = 0;\n            let wMax = 0;\n            for (let h = 0; h < filterHeight; ++h) {\n              const hIn = hBeg + h * dilationHeight;\n              if (hIn >= 0 && hIn < inHeight) {\n                for (let w = 0; w < filterWidth; ++w) {\n                  const wIn = wBeg + w * dilationWidth;\n                  if (wIn >= 0 && wIn < inWidth) {\n                    const val = $x[b][hIn][wIn][d] + $filter[h][w][d];\n                    if (val > curVal) {\n                      curVal = val;\n                      hMax = h;\n                      wMax = w;\n                    }\n                  }\n                }\n              }\n            }\n            gradients[hMax][wMax][d] += $dy[b][hOut][wOut][d];\n          }\n        }\n      }\n    }\n    const dataId = cpuBackend.write(util_exports.toTypedArray(gradients, x.dtype), filter.shape, filter.dtype);\n    return { dataId, shape: filter.shape, dtype: filter.dtype };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Dilation2DBackpropInput.js\nvar dilation2DBackpropInputConfig = {\n  kernelName: Dilation2DBackpropInput,\n  backendName: \"cpu\",\n  kernelFunc: ({ inputs, backend: backend2, attrs }) => {\n    const { x, filter, dy } = inputs;\n    const { strides, pad: pad3, dilations } = attrs;\n    const cpuBackend = backend2;\n    const $x = util_exports.toNestedArray(x.shape, cpuBackend.data.get(x.dataId).values);\n    const $filter = util_exports.toNestedArray(filter.shape, cpuBackend.data.get(filter.dataId).values);\n    const { batchSize, inHeight, inWidth, inChannels, outHeight, outWidth, padInfo, strideHeight, strideWidth, filterHeight, filterWidth, dilationHeight, dilationWidth, outShape } = backend_util_exports.computeDilation2DInfo(x.shape, filter.shape, strides, pad3, \"NHWC\", dilations);\n    util_exports.assert(dy.rank === outShape.length, () => `Error in ${Dilation2DBackpropInput}, dy must have the same rank as output ${outShape.length}, but got ${dy.rank}`);\n    const $dy = util_exports.toNestedArray(outShape, cpuBackend.data.get(dy.dataId).values);\n    const gradients = util_exports.makeZerosNestedTypedArray(x.shape, x.dtype);\n    for (let b = 0; b < batchSize; ++b) {\n      for (let hOut = 0; hOut < outHeight; ++hOut) {\n        const hBeg = hOut * strideHeight - padInfo.top;\n        for (let wOut = 0; wOut < outWidth; ++wOut) {\n          const wBeg = wOut * strideWidth - padInfo.left;\n          for (let d = 0; d < inChannels; ++d) {\n            let curVal = Number.MIN_SAFE_INTEGER;\n            let hInMax = hBeg < 0 ? 0 : hBeg;\n            let wInMax = wBeg < 0 ? 0 : wBeg;\n            for (let h = 0; h < filterHeight; ++h) {\n              const hIn = hBeg + h * dilationHeight;\n              if (hIn >= 0 && hIn < inHeight) {\n                for (let w = 0; w < filterWidth; ++w) {\n                  const wIn = wBeg + w * dilationWidth;\n                  if (wIn >= 0 && wIn < inWidth) {\n                    const val = $x[b][hIn][wIn][d] + $filter[h][w][d];\n                    if (val > curVal) {\n                      curVal = val;\n                      hInMax = hIn;\n                      wInMax = wIn;\n                    }\n                  }\n                }\n              }\n            }\n            gradients[b][hInMax][wInMax][d] += $dy[b][hOut][wOut][d];\n          }\n        }\n      }\n    }\n    const dataId = cpuBackend.write(util_exports.toTypedArray(gradients, x.dtype), x.shape, x.dtype);\n    return { dataId, shape: x.shape, dtype: x.dtype };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Sum.js\nfunction sum3(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x } = inputs;\n  const { axis, keepDims } = attrs;\n  assertNotComplex(x, \"sum\");\n  let $x;\n  if (x.dtype === \"bool\") {\n    $x = cast3({ inputs: { x }, backend: backend2, attrs: { dtype: \"int32\" } });\n  } else {\n    $x = identity2({ inputs: { x }, backend: backend2 });\n  }\n  const xRank = $x.shape.length;\n  const axes = util_exports.parseAxisParam(axis, $x.shape);\n  const permutation = backend_util_exports.getAxesPermutation(axes, xRank);\n  let reductionAxes = axes;\n  let permutedX = $x;\n  if (permutation != null) {\n    permutedX = transpose2({ inputs: { x: $x }, backend: backend2, attrs: { perm: permutation } });\n    reductionAxes = backend_util_exports.getInnerMostAxes(reductionAxes.length, xRank);\n  }\n  backend_util_exports.assertAxesAreInnerMostDims(\"sum\", reductionAxes, permutedX.shape.length);\n  const [outShape, reduceShape] = backend_util_exports.computeOutAndReduceShapes(permutedX.shape, reductionAxes);\n  const resultDtype = backend_util_exports.upcastType(permutedX.dtype, \"int32\");\n  let result = zeros3(backend2, outShape, resultDtype);\n  const reduceSize = util_exports.sizeFromShape(reduceShape);\n  const vals = backend2.data.get(result.dataId).values;\n  const aVals = backend2.data.get(permutedX.dataId).values;\n  for (let i2 = 0; i2 < vals.length; ++i2) {\n    const offset = i2 * reduceSize;\n    let sum7 = 0;\n    for (let j = 0; j < reduceSize; ++j) {\n      sum7 += aVals[offset + j];\n    }\n    vals[i2] = sum7;\n  }\n  if (keepDims) {\n    const newShape = backend_util_exports.expandShapeToKeepDim(result.shape, axes);\n    const oldResult = result;\n    result = reshape3({ inputs: { x: result }, backend: backend2, attrs: { shape: newShape } });\n    backend2.disposeIntermediateTensorInfo(oldResult);\n  }\n  backend2.disposeIntermediateTensorInfo($x);\n  if (permutation != null) {\n    backend2.disposeIntermediateTensorInfo(permutedX);\n  }\n  return result;\n}\nvar sumConfig = {\n  kernelName: Sum,\n  backendName: \"cpu\",\n  kernelFunc: sum3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Einsum.js\nfunction einsum2(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { equation } = attrs;\n  const tensors = inputs;\n  const { allDims, summedDims, idDims } = backend_util_exports.decodeEinsumEquation(equation, tensors.length);\n  backend_util_exports.checkEinsumDimSizes(allDims.length, idDims, tensors);\n  const { path, steps } = backend_util_exports.getEinsumComputePath(summedDims, idDims);\n  const nSteps = steps.length;\n  let out = null;\n  let numDimsRemaining = allDims.length;\n  const tensorsToDispose = [];\n  for (let i2 = 0; i2 < nSteps; ++i2) {\n    for (const idTerm of steps[i2]) {\n      const { permutationIndices: perm, expandDims: dimsToExpand } = backend_util_exports.getEinsumPermutation(numDimsRemaining, idDims[idTerm]);\n      let x;\n      if (backend_util_exports.isIdentityPermutation(perm)) {\n        x = tensors[idTerm];\n      } else {\n        x = transpose2({ inputs: { x: tensors[idTerm] }, backend: backend2, attrs: { perm } });\n        tensorsToDispose.push(x);\n      }\n      const targetShape = x.shape.slice();\n      for (let k = 0; k < dimsToExpand.length; ++k) {\n        targetShape.splice(dimsToExpand[k], 0, 1);\n      }\n      if (!util_exports.arraysEqual(x.shape, targetShape)) {\n        x = reshape3({ inputs: { x }, backend: backend2, attrs: { shape: targetShape } });\n        tensorsToDispose.push(x);\n      }\n      if (out === null) {\n        out = x;\n      } else {\n        out = multiply2({ inputs: { a: x, b: out }, backend: backend2 });\n        tensorsToDispose.push(out);\n      }\n    }\n    if (i2 < nSteps - 1) {\n      if (path[i2] >= 0) {\n        out = sum3({\n          inputs: { x: out },\n          backend: backend2,\n          attrs: {\n            axis: path[i2] - (allDims.length - numDimsRemaining),\n            keepDims: false\n          }\n        });\n        tensorsToDispose.push(out);\n      }\n      numDimsRemaining--;\n    }\n  }\n  for (const tensorInfo of tensorsToDispose) {\n    if (tensorInfo === out) {\n      continue;\n    }\n    backend2.disposeIntermediateTensorInfo(tensorInfo);\n  }\n  return out;\n}\nvar einsumConfig = {\n  kernelName: Einsum,\n  backendName: \"cpu\",\n  kernelFunc: einsum2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/EluGrad.js\nfunction eluGrad(args) {\n  const { inputs, backend: backend2 } = args;\n  const { dy, y } = inputs;\n  assertNotComplex([dy, y], \"eluGrad\");\n  const resultValues = new Float32Array(util_exports.sizeFromShape(y.shape));\n  const values = backend2.data.get(y.dataId).values;\n  const dyValues = backend2.data.get(dy.dataId).values;\n  for (let i2 = 0; i2 < values.length; ++i2) {\n    const v = values[i2];\n    if (v >= 1) {\n      resultValues[i2] = dyValues[i2];\n    } else {\n      resultValues[i2] = dyValues[i2] * (v + 1);\n    }\n  }\n  return backend2.makeTensorInfo(y.shape, \"float32\", resultValues);\n}\nvar eluGradConfig2 = {\n  kernelName: EluGrad,\n  backendName: \"cpu\",\n  kernelFunc: eluGrad\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Erf.js\nvar p = backend_util_exports.ERF_P;\nvar a1 = backend_util_exports.ERF_A1;\nvar a2 = backend_util_exports.ERF_A2;\nvar a3 = backend_util_exports.ERF_A3;\nvar a4 = backend_util_exports.ERF_A4;\nvar a5 = backend_util_exports.ERF_A5;\nvar erf2 = unaryKernelFunc(Erf, (xi) => {\n  const sign4 = Math.sign(xi);\n  const v = Math.abs(xi);\n  const t2 = 1 / (1 + p * v);\n  return sign4 * (1 - ((((a5 * t2 + a4) * t2 + a3) * t2 + a2) * t2 + a1) * t2 * Math.exp(-v * v));\n});\nvar erfConfig = {\n  kernelName: Erf,\n  backendName: \"cpu\",\n  kernelFunc: erf2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/ExpandDims.js\nfunction expandDims3(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { input: input2 } = inputs;\n  const { dim } = attrs;\n  const inputRank = input2.shape.length;\n  const newShape = input2.shape.slice();\n  let $dim = dim;\n  if (dim < 0) {\n    util_exports.assert(-(inputRank + 1) <= dim, () => `Axis must be in the interval [${-(inputRank + 1)}, ${inputRank}]`);\n    $dim = inputRank + dim + 1;\n  }\n  newShape.splice($dim, 0, 1);\n  return reshape3({ inputs: { x: input2 }, backend: backend2, attrs: { shape: newShape } });\n}\nvar expandDimsConfig = {\n  kernelName: ExpandDims,\n  backendName: \"cpu\",\n  kernelFunc: expandDims3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/RealDiv.js\nvar realDivImpl = createSimpleBinaryKernelImpl((a, b) => a / b);\nvar div2 = binaryKernelFunc(RealDiv, realDivImpl);\nvar realDivConfig = {\n  kernelName: RealDiv,\n  backendName: \"cpu\",\n  kernelFunc: div2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/utils/fft_utils.js\nfunction fftBatch(input2, inverse, cpuBackend) {\n  const inputShape = input2.shape;\n  const batch = inputShape[0];\n  const innerDim = inputShape[1];\n  const inputVals = cpuBackend.data.get(input2.dataId);\n  const real2D = inputVals.complexTensorInfos.real;\n  const imag2D = inputVals.complexTensorInfos.imag;\n  const resultShape = [batch, innerDim];\n  const resultSize = util_exports.sizeFromShape(resultShape);\n  const resultReal = util_exports.getTypedArrayFromDType(\"float32\", resultSize);\n  const resultImag = util_exports.getTypedArrayFromDType(\"float32\", resultSize);\n  for (let b = 0; b < batch; b++) {\n    const r2 = slice2({\n      inputs: { x: real2D },\n      backend: cpuBackend,\n      attrs: { begin: [b, 0], size: [1, innerDim] }\n    });\n    const i2 = slice2({\n      inputs: { x: imag2D },\n      backend: cpuBackend,\n      attrs: { begin: [b, 0], size: [1, innerDim] }\n    });\n    const input3 = complex2({ inputs: { real: r2, imag: i2 }, backend: cpuBackend });\n    const { real: real5, imag: imag5 } = fftImpl(input3, inverse, cpuBackend);\n    const res = backend_util_exports.mergeRealAndImagArrays(real5, imag5);\n    for (let d = 0; d < innerDim; d++) {\n      const c = backend_util_exports.getComplexWithIndex(res, d);\n      resultReal[b * innerDim + d] = c.real;\n      resultImag[b * innerDim + d] = c.imag;\n    }\n    cpuBackend.disposeIntermediateTensorInfo(r2);\n    cpuBackend.disposeIntermediateTensorInfo(i2);\n    cpuBackend.disposeIntermediateTensorInfo(input3);\n  }\n  const $realInfo = cpuBackend.makeTensorInfo(resultShape, \"float32\", resultReal);\n  const $imagInfo = cpuBackend.makeTensorInfo(resultShape, \"float32\", resultImag);\n  const result = complex2({ inputs: { real: $realInfo, imag: $imagInfo }, backend: cpuBackend });\n  cpuBackend.disposeIntermediateTensorInfo($realInfo);\n  cpuBackend.disposeIntermediateTensorInfo($imagInfo);\n  return result;\n}\nfunction fftImpl(input2, inverse, cpuBackend) {\n  const inputSize = util_exports.sizeFromShape(input2.shape);\n  const inputVals = cpuBackend.data.get(input2.dataId);\n  const realVals = cpuBackend.data.get(inputVals.complexTensorInfos.real.dataId).values;\n  const imagVals = cpuBackend.data.get(inputVals.complexTensorInfos.imag.dataId).values;\n  if (isExponentOf2(inputSize)) {\n    const result = fftRadix2(realVals, imagVals, inputSize, inverse, cpuBackend);\n    const resultShape = [input2.shape[0], input2.shape[1]];\n    if (inverse) {\n      const realInfo = cpuBackend.makeTensorInfo(resultShape, \"float32\", result.real);\n      const imagInfo = cpuBackend.makeTensorInfo(resultShape, \"float32\", result.imag);\n      const sizeInfo = cpuBackend.makeTensorInfo([], \"float32\", util_exports.createScalarValue(inputSize, \"float32\"));\n      const sizeInfoCopy = identity2({ inputs: { x: sizeInfo }, backend: cpuBackend });\n      const divRealInfo = realDivConfig.kernelFunc({ inputs: { a: realInfo, b: sizeInfo }, backend: cpuBackend });\n      const divImagInfo = realDivConfig.kernelFunc({ inputs: { a: imagInfo, b: sizeInfoCopy }, backend: cpuBackend });\n      const divRealVals = cpuBackend.data.get(divRealInfo.dataId).values;\n      const divImagVals = cpuBackend.data.get(divImagInfo.dataId).values;\n      cpuBackend.disposeIntermediateTensorInfo(realInfo);\n      cpuBackend.disposeIntermediateTensorInfo(imagInfo);\n      cpuBackend.disposeIntermediateTensorInfo(sizeInfo);\n      cpuBackend.disposeIntermediateTensorInfo(sizeInfoCopy);\n      cpuBackend.disposeIntermediateTensorInfo(divRealInfo);\n      cpuBackend.disposeIntermediateTensorInfo(divImagInfo);\n      return { real: divRealVals, imag: divImagVals };\n    }\n    return result;\n  } else {\n    const data = backend_util_exports.mergeRealAndImagArrays(realVals, imagVals);\n    const rawOutput = fourierTransformByMatmul(data, inputSize, inverse);\n    return backend_util_exports.splitRealAndImagArrays(rawOutput);\n  }\n}\nfunction isExponentOf2(size) {\n  return (size & size - 1) === 0;\n}\nfunction fftRadix2(realVals, imagVals, size, inverse, cpuBackend) {\n  if (size === 1) {\n    return { real: realVals, imag: imagVals };\n  }\n  const data = backend_util_exports.mergeRealAndImagArrays(realVals, imagVals);\n  const half = size / 2;\n  const evenComplex = backend_util_exports.complexWithEvenIndex(data);\n  const evenRealVals = evenComplex.real;\n  const evenImagVals = evenComplex.imag;\n  const evenShape = [evenRealVals.length];\n  const evenRealInfo = cpuBackend.makeTensorInfo(evenShape, \"float32\", evenRealVals);\n  const evenImagInfo = cpuBackend.makeTensorInfo(evenShape, \"float32\", evenImagVals);\n  const evenTensorInfo = complex2({ inputs: { real: evenRealInfo, imag: evenImagInfo }, backend: cpuBackend });\n  const oddComplex = backend_util_exports.complexWithOddIndex(data);\n  const oddRealVals = oddComplex.real;\n  const oddImagVals = oddComplex.imag;\n  const oddShape = [oddRealVals.length];\n  const oddRealInfo = cpuBackend.makeTensorInfo(oddShape, \"float32\", oddRealVals);\n  const oddImagInfo = cpuBackend.makeTensorInfo(oddShape, \"float32\", oddImagVals);\n  const oddTensorInfo = complex2({ inputs: { real: oddRealInfo, imag: oddImagInfo }, backend: cpuBackend });\n  const $evenComplex = fftRadix2(evenRealVals, evenImagVals, half, inverse, cpuBackend);\n  const $evenRealVals = $evenComplex.real;\n  const $evenImagVals = $evenComplex.imag;\n  const $evenShape = [$evenRealVals.length];\n  const $evenRealInfo = cpuBackend.makeTensorInfo($evenShape, \"float32\", $evenRealVals);\n  const $evenImagInfo = cpuBackend.makeTensorInfo($evenShape, \"float32\", $evenImagVals);\n  const $evenTensorInfo = complex2({\n    inputs: { real: $evenRealInfo, imag: $evenImagInfo },\n    backend: cpuBackend\n  });\n  const $oddComplex = fftRadix2(oddRealVals, oddImagVals, half, inverse, cpuBackend);\n  const $oddRealVals = $oddComplex.real;\n  const $oddImagVals = $oddComplex.imag;\n  const $oddShape = [$oddRealVals.length];\n  const $oddRealInfo = cpuBackend.makeTensorInfo($oddShape, \"float32\", $oddRealVals);\n  const $oddImagInfo = cpuBackend.makeTensorInfo($oddShape, \"float32\", $oddImagVals);\n  const $oddTensorInfo = complex2({ inputs: { real: $oddRealInfo, imag: $oddImagInfo }, backend: cpuBackend });\n  const e2 = backend_util_exports.exponents(size, inverse);\n  const eShape = [e2.real.length];\n  const eRealInfo = cpuBackend.makeTensorInfo(eShape, \"float32\", e2.real);\n  const eImagInfo = cpuBackend.makeTensorInfo(eShape, \"float32\", e2.imag);\n  const complexInfo = complex2({ inputs: { real: eRealInfo, imag: eImagInfo }, backend: cpuBackend });\n  const exponentInfo = multiply2({ inputs: { a: complexInfo, b: $oddTensorInfo }, backend: cpuBackend });\n  const addPart = add4({\n    inputs: { a: $evenTensorInfo, b: exponentInfo },\n    backend: cpuBackend\n  });\n  const subPart = sub2({\n    inputs: { a: $evenTensorInfo, b: exponentInfo },\n    backend: cpuBackend\n  });\n  const addPartReal = real2({ inputs: { input: addPart }, backend: cpuBackend });\n  const subPartReal = real2({ inputs: { input: subPart }, backend: cpuBackend });\n  const addPartImag = imag2({ inputs: { input: addPart }, backend: cpuBackend });\n  const subPartImag = imag2({ inputs: { input: subPart }, backend: cpuBackend });\n  const $real = concat2({\n    inputs: [addPartReal, subPartReal],\n    backend: cpuBackend,\n    attrs: { axis: 0 }\n  });\n  const $imag = concat2({\n    inputs: [addPartImag, subPartImag],\n    backend: cpuBackend,\n    attrs: { axis: 0 }\n  });\n  const $realVals = cpuBackend.data.get($real.dataId).values;\n  const $imagVals = cpuBackend.data.get($imag.dataId).values;\n  cpuBackend.disposeIntermediateTensorInfo(evenRealInfo);\n  cpuBackend.disposeIntermediateTensorInfo(evenImagInfo);\n  cpuBackend.disposeIntermediateTensorInfo(evenTensorInfo);\n  cpuBackend.disposeIntermediateTensorInfo(oddRealInfo);\n  cpuBackend.disposeIntermediateTensorInfo(oddImagInfo);\n  cpuBackend.disposeIntermediateTensorInfo(oddTensorInfo);\n  cpuBackend.disposeIntermediateTensorInfo($evenRealInfo);\n  cpuBackend.disposeIntermediateTensorInfo($evenImagInfo);\n  cpuBackend.disposeIntermediateTensorInfo($evenTensorInfo);\n  cpuBackend.disposeIntermediateTensorInfo($oddRealInfo);\n  cpuBackend.disposeIntermediateTensorInfo($oddImagInfo);\n  cpuBackend.disposeIntermediateTensorInfo($oddTensorInfo);\n  cpuBackend.disposeIntermediateTensorInfo(eRealInfo);\n  cpuBackend.disposeIntermediateTensorInfo(eImagInfo);\n  cpuBackend.disposeIntermediateTensorInfo(complexInfo);\n  cpuBackend.disposeIntermediateTensorInfo(exponentInfo);\n  cpuBackend.disposeIntermediateTensorInfo(addPart);\n  cpuBackend.disposeIntermediateTensorInfo(subPart);\n  cpuBackend.disposeIntermediateTensorInfo(addPartReal);\n  cpuBackend.disposeIntermediateTensorInfo(addPartImag);\n  cpuBackend.disposeIntermediateTensorInfo(subPartReal);\n  cpuBackend.disposeIntermediateTensorInfo(subPartImag);\n  cpuBackend.disposeIntermediateTensorInfo($real);\n  cpuBackend.disposeIntermediateTensorInfo($imag);\n  return { real: $realVals, imag: $imagVals };\n}\nfunction fourierTransformByMatmul(data, size, inverse) {\n  const ret = new Float32Array(size * 2);\n  for (let r2 = 0; r2 < size; r2++) {\n    let real5 = 0;\n    let imag5 = 0;\n    for (let c = 0; c < size; c++) {\n      const e2 = backend_util_exports.exponent(r2 * c, size, inverse);\n      const term = backend_util_exports.getComplexWithIndex(data, c);\n      real5 += term.real * e2.real - term.imag * e2.imag;\n      imag5 += term.real * e2.imag + term.imag * e2.real;\n    }\n    if (inverse) {\n      real5 /= size;\n      imag5 /= size;\n    }\n    backend_util_exports.assignToTypedArray(ret, real5, imag5, r2);\n  }\n  return ret;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/FFT.js\nfunction fft2(args) {\n  const { inputs, backend: backend2 } = args;\n  const { input: input2 } = inputs;\n  const inputSize = util_exports.sizeFromShape(input2.shape);\n  const innerDimensionSize = input2.shape[input2.shape.length - 1];\n  const batch = inputSize / innerDimensionSize;\n  const input2D = reshape3({\n    inputs: { x: input2 },\n    backend: backend2,\n    attrs: { shape: [batch, innerDimensionSize] }\n  });\n  const result = fftBatch(input2D, false, backend2);\n  const resultReshaped = reshape3({ inputs: { x: result }, backend: backend2, attrs: { shape: input2.shape } });\n  backend2.disposeIntermediateTensorInfo(input2D);\n  backend2.disposeIntermediateTensorInfo(result);\n  return resultReshaped;\n}\nvar fftConfig = {\n  kernelName: FFT,\n  backendName: \"cpu\",\n  kernelFunc: fft2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Fill.js\nfunction fill2(args) {\n  const { backend: backend2, attrs } = args;\n  const { shape, value, dtype } = attrs;\n  const $dtype = dtype || util_exports.inferDtype(value);\n  const values = util_exports.getArrayFromDType($dtype, util_exports.sizeFromShape(shape));\n  fillValues(values, value, $dtype);\n  return backend2.makeTensorInfo(shape, $dtype, values);\n}\nvar fillConfig = {\n  kernelName: Fill,\n  backendName: \"cpu\",\n  kernelFunc: fill2\n};\nfunction fillValues(values, value, dtype) {\n  if (dtype === \"string\") {\n    values.fill(value);\n  } else {\n    values.fill(value);\n  }\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/FlipLeftRight.js\nvar flipLeftRightConfig = {\n  kernelName: FlipLeftRight,\n  backendName: \"cpu\",\n  kernelFunc: ({ inputs, attrs, backend: backend2 }) => {\n    const { image: image2 } = inputs;\n    const cpuBackend = backend2;\n    const output = util_exports.getTypedArrayFromDType(image2.dtype, util_exports.sizeFromShape(image2.shape));\n    const [batch, imageHeight, imageWidth, numChannels] = image2.shape;\n    const imageVals = cpuBackend.data.get(image2.dataId).values;\n    for (let batchIdx = 0; batchIdx < batch; batchIdx++) {\n      const batchOffset = batchIdx * imageWidth * imageHeight * numChannels;\n      for (let row = 0; row < imageHeight; row++) {\n        const rowOffset = row * (imageWidth * numChannels);\n        for (let col = 0; col < imageWidth; col++) {\n          const colOffset = col * numChannels;\n          for (let channel = 0; channel < numChannels; channel++) {\n            const coordX = Math.round(imageWidth - col - 1);\n            const outIdx = batchOffset + rowOffset + colOffset + channel;\n            let outputValue = imageVals[outIdx];\n            if (coordX >= 0 && coordX < imageWidth) {\n              const rotatedColOffset = coordX * numChannels;\n              const imageIdx = batchOffset + rowOffset + rotatedColOffset + channel;\n              outputValue = imageVals[imageIdx];\n            }\n            output[outIdx] = outputValue;\n          }\n        }\n      }\n    }\n    const dataId = cpuBackend.write(output, image2.shape, image2.dtype);\n    return { dataId, shape: image2.shape, dtype: image2.dtype };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/FloorDiv.js\nvar floorDivImpl = createSimpleBinaryKernelImpl((a, b) => Math.floor(a / b));\nvar floorDiv2 = binaryKernelFunc(FloorDiv, floorDivImpl, null, \"int32\");\nvar floorDivConfig = {\n  kernelName: FloorDiv,\n  backendName: \"cpu\",\n  kernelFunc: floorDiv2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/FusedConv2D.js\nfunction fusedConv2D(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x, filter, bias, preluActivationWeights } = inputs;\n  const { strides, pad: pad3, dataFormat, dilations, dimRoundingMode, activation: activation2, leakyreluAlpha } = attrs;\n  let result = conv2D({\n    inputs: { x, filter },\n    backend: backend2,\n    attrs: { strides, pad: pad3, dataFormat, dilations, dimRoundingMode }\n  });\n  if (bias) {\n    const resultOld = result;\n    if (dataFormat === \"NCHW\" && bias.shape.length === 1 && bias.shape[0] !== 1) {\n      const reshapedBias = reshape3({ inputs: { x: bias }, backend: backend2, attrs: { shape: [bias.shape[0], 1, 1] } });\n      result = add4({ inputs: { a: result, b: reshapedBias }, backend: backend2 });\n      backend2.disposeIntermediateTensorInfo(reshapedBias);\n    } else {\n      result = add4({ inputs: { a: result, b: bias }, backend: backend2 });\n    }\n    backend2.disposeIntermediateTensorInfo(resultOld);\n  }\n  if (activation2) {\n    const resultOld = result;\n    if (dataFormat === \"NCHW\" && activation2 === \"prelu\" && preluActivationWeights.shape.length === 1 && preluActivationWeights.shape[0] !== 1) {\n      const reshapedAlpha = reshape3({\n        inputs: { x: preluActivationWeights },\n        backend: backend2,\n        attrs: { shape: [preluActivationWeights.shape[0], 1, 1] }\n      });\n      result = applyActivation2(backend2, result, activation2, reshapedAlpha, leakyreluAlpha);\n      backend2.disposeIntermediateTensorInfo(reshapedAlpha);\n    } else {\n      result = applyActivation2(backend2, result, activation2, preluActivationWeights, leakyreluAlpha);\n    }\n    backend2.disposeIntermediateTensorInfo(resultOld);\n  }\n  return result;\n}\nvar fusedConv2DConfig = {\n  kernelName: FusedConv2D,\n  backendName: \"cpu\",\n  kernelFunc: fusedConv2D\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/FusedDepthwiseConv2D.js\nfunction fusedDepthwiseConv2D(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x, filter, bias, preluActivationWeights } = inputs;\n  const { strides, pad: pad3, dataFormat, dilations, dimRoundingMode, activation: activation2, leakyreluAlpha } = attrs;\n  let result = depthwiseConv2dNative({\n    inputs: { x, filter },\n    backend: backend2,\n    attrs: { strides, pad: pad3, dataFormat, dilations, dimRoundingMode }\n  });\n  if (bias) {\n    const oldResult = result;\n    result = add4({ inputs: { a: result, b: bias }, backend: backend2 });\n    backend2.disposeIntermediateTensorInfo(oldResult);\n  }\n  if (activation2) {\n    const oldResult = result;\n    result = applyActivation2(backend2, result, activation2, preluActivationWeights, leakyreluAlpha);\n    backend2.disposeIntermediateTensorInfo(oldResult);\n  }\n  return result;\n}\nvar fusedDepthwiseConv2DConfig = {\n  kernelName: FusedDepthwiseConv2D,\n  backendName: \"cpu\",\n  kernelFunc: fusedDepthwiseConv2D\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/GatherNd.js\nfunction gatherNd(args) {\n  const { inputs, backend: backend2 } = args;\n  const { params, indices } = inputs;\n  const paramsSize = util_exports.sizeFromShape(params.shape);\n  const indicesShape = indices.shape;\n  const sliceRank = indicesShape[indicesShape.length - 1];\n  const [resultShape, numSlices, sliceSize, strides] = backend_util_exports.prepareAndValidate(params, indices);\n  if (numSlices === 0) {\n    return backend2.makeTensorInfo(resultShape, params.dtype, []);\n  }\n  const indicesData = backend2.data.get(indices.dataId).values;\n  const paramsBuf = backend2.bufferSync(params);\n  const outBuf = gatherNdImpl(indicesData, paramsBuf, params.dtype, numSlices, sliceRank, sliceSize, strides, params.shape, paramsSize);\n  return backend2.makeTensorInfo(resultShape, params.dtype, outBuf.values);\n}\nvar gatherNdConfig = {\n  kernelName: GatherNd,\n  backendName: \"cpu\",\n  kernelFunc: gatherNd\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/GatherV2.js\nfunction gatherV2(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x, indices } = inputs;\n  const { axis, batchDims } = attrs;\n  assertNotComplex([x, indices], \"gatherV2\");\n  const parsedAxis = util_exports.parseAxisParam(axis, x.shape)[0];\n  const indicesVals = backend2.data.get(indices.dataId).values;\n  const axisDim = x.shape[parsedAxis];\n  for (let i2 = 0; i2 < indicesVals.length; ++i2) {\n    const index = indicesVals[i2];\n    util_exports.assert(index <= axisDim - 1 && index >= 0, () => `GatherV2: the index value ${index} is not in [0, ${axisDim - 1}]`);\n  }\n  let $batchDims = batchDims;\n  if (batchDims == null) {\n    $batchDims = 0;\n  }\n  const indicesSize = util_exports.sizeFromShape(indices.shape);\n  const shapeInfo = backend_util_exports.segment_util.collectGatherOpShapeInfo(x, indices, parsedAxis, $batchDims);\n  const flattenX = reshape3({\n    inputs: { x },\n    backend: backend2,\n    attrs: {\n      shape: [\n        shapeInfo.batchSize,\n        shapeInfo.outerSize,\n        shapeInfo.dimSize,\n        shapeInfo.sliceSize\n      ]\n    }\n  });\n  const flattenIndex = reshape3({\n    inputs: { x: indices },\n    backend: backend2,\n    attrs: { shape: [shapeInfo.batchSize, indicesSize / shapeInfo.batchSize] }\n  });\n  const flattenOutputShape = [\n    shapeInfo.batchSize,\n    shapeInfo.outerSize,\n    indicesSize / shapeInfo.batchSize,\n    shapeInfo.sliceSize\n  ];\n  const indicesBuf = backend2.bufferSync(flattenIndex);\n  const xBuf = backend2.bufferSync(flattenX);\n  const outBuf = gatherV2Impl(xBuf, indicesBuf, flattenOutputShape);\n  backend2.disposeIntermediateTensorInfo(flattenX);\n  backend2.disposeIntermediateTensorInfo(flattenIndex);\n  return backend2.makeTensorInfo(shapeInfo.outputShape, outBuf.dtype, outBuf.values);\n}\nvar gatherV2Config = {\n  kernelName: GatherV2,\n  backendName: \"cpu\",\n  kernelFunc: gatherV2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/IFFT.js\nfunction ifft2(args) {\n  const { inputs, backend: backend2 } = args;\n  const { input: input2 } = inputs;\n  const inputSize = util_exports.sizeFromShape(input2.shape);\n  const innerDimensionSize = input2.shape[input2.shape.length - 1];\n  const batch = inputSize / innerDimensionSize;\n  const input2D = reshape3({\n    inputs: { x: input2 },\n    backend: backend2,\n    attrs: { shape: [batch, innerDimensionSize] }\n  });\n  const result = fftBatch(input2D, true, backend2);\n  const resultReshaped = reshape3({ inputs: { x: result }, backend: backend2, attrs: { shape: input2.shape } });\n  backend2.disposeIntermediateTensorInfo(input2D);\n  backend2.disposeIntermediateTensorInfo(result);\n  return resultReshaped;\n}\nvar ifftConfig = {\n  kernelName: IFFT,\n  backendName: \"cpu\",\n  kernelFunc: ifft2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/IsFinite.js\nvar isFinite3 = unaryKernelFunc(IsFinite, (xi) => Number.isFinite(xi) ? 1 : 0, \"bool\");\nvar isFiniteConfig = {\n  kernelName: IsFinite,\n  backendName: \"cpu\",\n  kernelFunc: isFinite3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/IsInf.js\nvar isInf2 = unaryKernelFunc(IsInf, (xi) => Math.abs(xi) === Infinity ? 1 : 0, \"bool\");\nvar isInfConfig = {\n  kernelName: IsInf,\n  backendName: \"cpu\",\n  kernelFunc: isInf2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/IsNaN.js\nvar isNaN3 = unaryKernelFunc(IsNan, (xi) => Number.isNaN(xi) ? 1 : 0, \"bool\");\nvar isNaNConfig = {\n  kernelName: IsNan,\n  backendName: \"cpu\",\n  kernelFunc: isNaN3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/LinSpace.js\nfunction linSpace(args) {\n  const { backend: backend2, attrs } = args;\n  const { start, stop, num } = attrs;\n  const outVals = linSpaceImpl(start, stop, num);\n  return backend2.makeTensorInfo([outVals.length], \"float32\", outVals);\n}\nvar linSpaceConfig = {\n  kernelName: LinSpace,\n  backendName: \"cpu\",\n  kernelFunc: linSpace\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Log1p.js\nvar log1p2 = unaryKernelFunc(Log1p, (xi) => Math.log1p(xi));\nvar log1pConfig = {\n  kernelName: Log1p,\n  backendName: \"cpu\",\n  kernelFunc: log1p2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/LogicalAnd.js\nvar logicalAndImpl = createSimpleBinaryKernelImpl((a, b) => a && b);\nvar logicalAnd2 = binaryKernelFunc(LogicalAnd, logicalAndImpl, null, \"bool\");\nvar logicalAndConfig = {\n  kernelName: LogicalAnd,\n  backendName: \"cpu\",\n  kernelFunc: logicalAnd2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/LogicalNot.js\nvar logicalNot2 = unaryKernelFunc(LogicalNot, (xi) => xi ? 0 : 1, \"bool\");\nvar logicalNotConfig = {\n  kernelName: LogicalNot,\n  backendName: \"cpu\",\n  kernelFunc: logicalNot2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/LogicalOr.js\nvar logicalOrImpl = createSimpleBinaryKernelImpl((a, b) => a || b);\nvar logicalOr2 = binaryKernelFunc(LogicalOr, logicalOrImpl, null, \"bool\");\nvar logicalOrConfig = {\n  kernelName: LogicalOr,\n  backendName: \"cpu\",\n  kernelFunc: logicalOr2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/LRN.js\nfunction lRN(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x } = inputs;\n  const { depthRadius, bias, alpha, beta } = attrs;\n  assertNotComplex(x, \"LRN\");\n  const channels = x.shape[3];\n  const maxD = channels - 1;\n  const xValues = backend2.data.get(x.dataId).values;\n  const size = util_exports.sizeFromShape(x.shape);\n  const result = new Float32Array(size);\n  function sumAcrossChannels(offset) {\n    const currentChannel = offset % channels;\n    let beginSumOffset = offset - currentChannel + Math.max(0, currentChannel - depthRadius);\n    const endSumOffset = offset - currentChannel + Math.min(currentChannel + depthRadius, maxD);\n    let sum7 = 0;\n    for (; beginSumOffset <= endSumOffset; beginSumOffset++) {\n      const z = xValues[beginSumOffset];\n      sum7 += z * z;\n    }\n    return sum7;\n  }\n  for (let offset = 0; offset < size; offset++) {\n    const sum7 = sumAcrossChannels(offset);\n    const val = xValues[offset] * Math.pow(bias + alpha * sum7, -beta);\n    result[offset] = val;\n  }\n  return backend2.makeTensorInfo(x.shape, x.dtype, result);\n}\nvar LRNConfig = {\n  kernelName: LRN,\n  backendName: \"cpu\",\n  kernelFunc: lRN\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/LRNGrad.js\nfunction lRNGrad(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x, y, dy } = inputs;\n  const { depthRadius, bias, alpha, beta } = attrs;\n  assertNotComplex(dy, \"LRNGrad\");\n  const dySize = util_exports.sizeFromShape(dy.shape);\n  const channels = dy.shape[3];\n  const dyValues = backend2.data.get(dy.dataId).values;\n  const xValues = backend2.data.get(x.dataId).values;\n  const yValues = backend2.data.get(y.dataId).values;\n  const result = new Float32Array(dySize);\n  const size = dySize;\n  for (let offset = 0; offset < size; offset++) {\n    const currentChannel = offset % channels;\n    const depthBegin = offset - currentChannel + Math.max(0, currentChannel - depthRadius);\n    const depthEnd = offset - currentChannel + Math.min(channels, currentChannel + depthRadius + 1);\n    let norm2 = 0;\n    for (let k = depthBegin; k < depthEnd; k++) {\n      norm2 += Math.pow(xValues[k], 2);\n    }\n    norm2 = alpha * norm2 + bias;\n    for (let k = depthBegin; k < depthEnd; k++) {\n      let dyi = -2 * alpha * beta * xValues[k] * yValues[offset] / norm2;\n      if (offset === k) {\n        dyi += Math.pow(norm2, -beta);\n      }\n      dyi *= dyValues[offset];\n      result[k] += dyi;\n    }\n  }\n  return backend2.makeTensorInfo(dy.shape, x.dtype, result);\n}\nvar LRNGradConfig = {\n  kernelName: LRNGrad,\n  backendName: \"cpu\",\n  kernelFunc: lRNGrad\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Max.js\nfunction max3(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x } = inputs;\n  const { reductionIndices, keepDims } = attrs;\n  const cpuBackend = backend2;\n  let xShape = x.shape;\n  const xRank = xShape.length;\n  const origAxes = util_exports.parseAxisParam(reductionIndices, xShape);\n  let axes = origAxes;\n  const permutedAxes = backend_util_exports.getAxesPermutation(axes, xRank);\n  let xVals = cpuBackend.data.get(x.dataId).values;\n  if (permutedAxes != null) {\n    const newShape = new Array(xRank);\n    for (let i2 = 0; i2 < newShape.length; i2++) {\n      newShape[i2] = xShape[permutedAxes[i2]];\n    }\n    xVals = transposeImpl(xVals, xShape, x.dtype, permutedAxes, newShape);\n    axes = backend_util_exports.getInnerMostAxes(axes.length, xRank);\n    xShape = newShape;\n  }\n  assertNotComplex(x, \"max\");\n  backend_util_exports.assertAxesAreInnerMostDims(\"max\", axes, xRank);\n  const [maxOutShape, reduceShape] = backend_util_exports.computeOutAndReduceShapes(xShape, axes);\n  const reduceSize = util_exports.sizeFromShape(reduceShape);\n  const result = maxImpl(xVals, reduceSize, maxOutShape, x.dtype);\n  const dataId = cpuBackend.write(result, maxOutShape, x.dtype);\n  let outShape = maxOutShape;\n  if (keepDims) {\n    const newShape = backend_util_exports.expandShapeToKeepDim(maxOutShape, origAxes);\n    outShape = newShape;\n  }\n  return { dataId, shape: outShape, dtype: x.dtype };\n}\nvar maxConfig = {\n  kernelName: Max,\n  backendName: \"cpu\",\n  kernelFunc: max3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/MaxPool.js\nfunction maxPool2(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x } = inputs;\n  assertNotComplex(x, \"maxPool\");\n  const { filterSize, strides, pad: pad3, dimRoundingMode } = attrs;\n  const dilations = 1;\n  util_exports.assert(backend_util_exports.eitherStridesOrDilationsAreOne(strides, dilations), () => `Error in maxPool: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`);\n  const convInfo = backend_util_exports.computePool2DInfo(x.shape, filterSize, strides, dilations, pad3, dimRoundingMode);\n  let res;\n  if (convInfo.filterWidth === 1 && convInfo.filterHeight === 1 && util_exports.arraysEqual(convInfo.inShape, convInfo.outShape)) {\n    res = identity2({ inputs: { x }, backend: backend2 });\n  } else {\n    const xValues = backend2.data.get(x.dataId).values;\n    const strides2 = util_exports.computeStrides(x.shape);\n    const buffer2 = pool2(xValues, x.shape, x.dtype, strides2, convInfo, \"max\");\n    res = backend2.makeTensorInfo(convInfo.outShape, x.dtype, buffer2.values);\n  }\n  return res;\n}\nvar maxPoolConfig = {\n  kernelName: MaxPool,\n  backendName: \"cpu\",\n  kernelFunc: maxPool2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/MaxPool3D.js\nfunction maxPool3D(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x } = inputs;\n  const { filterSize, strides, pad: pad3, dimRoundingMode, dataFormat } = attrs;\n  assertNotComplex(x, \"maxPool3d\");\n  const convInfo = backend_util_exports.computePool3DInfo(x.shape, filterSize, strides, 1, pad3, dimRoundingMode, dataFormat);\n  const xValues = backend2.data.get(x.dataId).values;\n  const outBuf = pool3d2(xValues, x.shape, x.dtype, util_exports.computeStrides(x.shape), convInfo, \"max\");\n  return backend2.makeTensorInfo(outBuf.shape, \"float32\", outBuf.values);\n}\nvar maxPool3DConfig = {\n  kernelName: MaxPool3D,\n  backendName: \"cpu\",\n  kernelFunc: maxPool3D\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/MaxPool3DGrad.js\nfunction maxPool3DGrad(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { dy, input: input2 } = inputs;\n  const { filterSize, strides, pad: pad3, dimRoundingMode } = attrs;\n  assertNotComplex([dy, input2], \"maxPool3DGrad\");\n  const convInfo = backend_util_exports.computePool3DInfo(input2.shape, filterSize, strides, 1, pad3, dimRoundingMode);\n  const inputBuf = backend2.bufferSync(input2);\n  const maxPosBuf = maxPool3dPositions(inputBuf, convInfo);\n  const strideDepth = convInfo.strideDepth;\n  const strideHeight = convInfo.strideHeight;\n  const strideWidth = convInfo.strideWidth;\n  const dilationDepth = convInfo.dilationDepth;\n  const dilationHeight = convInfo.dilationHeight;\n  const dilationWidth = convInfo.dilationWidth;\n  const effectiveFilterDepth = convInfo.effectiveFilterDepth;\n  const effectiveFilterHeight = convInfo.effectiveFilterHeight;\n  const effectiveFilterWidth = convInfo.effectiveFilterWidth;\n  const padFront = effectiveFilterDepth - 1 - convInfo.padInfo.front;\n  const padLeft = effectiveFilterWidth - 1 - convInfo.padInfo.left;\n  const padTop = effectiveFilterHeight - 1 - convInfo.padInfo.top;\n  const dx = buffer(input2.shape, \"float32\");\n  const dyBuf = backend2.bufferSync(dy);\n  for (let batch = 0; batch < convInfo.batchSize; ++batch) {\n    for (let channel = 0; channel < convInfo.inChannels; ++channel) {\n      for (let dxDepth = 0; dxDepth < convInfo.inDepth; ++dxDepth) {\n        for (let dxRow = 0; dxRow < convInfo.inHeight; ++dxRow) {\n          for (let dxCol = 0; dxCol < convInfo.inWidth; ++dxCol) {\n            const dyDepthCorner = dxDepth - padFront;\n            const dyRowCorner = dxRow - padTop;\n            const dyColCorner = dxCol - padLeft;\n            let dotProd = 0;\n            for (let wDepth = 0; wDepth < effectiveFilterDepth; wDepth += dilationDepth) {\n              const dyDepth = (dyDepthCorner + wDepth) / strideDepth;\n              if (dyDepth < 0 || dyDepth >= convInfo.outDepth || Math.floor(dyDepth) !== dyDepth) {\n                continue;\n              }\n              for (let wRow = 0; wRow < effectiveFilterHeight; wRow += dilationHeight) {\n                const dyRow = (dyRowCorner + wRow) / strideHeight;\n                if (dyRow < 0 || dyRow >= convInfo.outHeight || Math.floor(dyRow) !== dyRow) {\n                  continue;\n                }\n                for (let wCol = 0; wCol < effectiveFilterWidth; wCol += dilationWidth) {\n                  const dyCol = (dyColCorner + wCol) / strideWidth;\n                  if (dyCol < 0 || dyCol >= convInfo.outWidth || Math.floor(dyCol) !== dyCol) {\n                    continue;\n                  }\n                  const maxPos = effectiveFilterDepth * effectiveFilterHeight * effectiveFilterWidth - 1 - maxPosBuf.get(batch, dyDepth, dyRow, dyCol, channel);\n                  const curPos = wDepth * effectiveFilterHeight * effectiveFilterWidth + wRow * effectiveFilterWidth + wCol;\n                  const mask = maxPos === curPos ? 1 : 0;\n                  if (mask === 0) {\n                    continue;\n                  }\n                  const pixel = dyBuf.get(batch, dyDepth, dyRow, dyCol, channel);\n                  dotProd += pixel * mask;\n                }\n              }\n            }\n            dx.set(dotProd, batch, dxDepth, dxRow, dxCol, channel);\n          }\n        }\n      }\n    }\n  }\n  return backend2.makeTensorInfo(dx.shape, dx.dtype, dx.values);\n}\nvar maxPool3DGradConfig2 = {\n  kernelName: MaxPool3DGrad,\n  backendName: \"cpu\",\n  kernelFunc: maxPool3DGrad\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/MaxPoolGrad.js\nfunction maxPoolGrad2(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { dy, input: input2, output } = inputs;\n  const x = input2;\n  assertNotComplex([input2, output], \"maxPoolGrad\");\n  const { filterSize, strides, pad: pad3, dimRoundingMode } = attrs;\n  const convInfo = backend_util_exports.computePool2DInfo(x.shape, filterSize, strides, 1, pad3, dimRoundingMode);\n  const xValues = backend2.data.get(x.dataId).values;\n  const maxPosBuf = buffer(convInfo.outShape, x.dtype, maxPoolPositions(xValues, x.shape, x.dtype, convInfo).values);\n  const strideHeight = convInfo.strideHeight;\n  const strideWidth = convInfo.strideWidth;\n  const dilationHeight = convInfo.dilationHeight;\n  const dilationWidth = convInfo.dilationWidth;\n  const effectiveFilterHeight = convInfo.effectiveFilterHeight;\n  const effectiveFilterWidth = convInfo.effectiveFilterWidth;\n  const padLeft = effectiveFilterWidth - 1 - convInfo.padInfo.left;\n  const padTop = effectiveFilterHeight - 1 - convInfo.padInfo.top;\n  const dx = buffer(x.shape, \"float32\");\n  const dyData = backend2.data.get(dy.dataId).values;\n  const dyBuf = buffer(dy.shape, \"float32\", dyData);\n  for (let b = 0; b < convInfo.batchSize; ++b) {\n    for (let d = 0; d < convInfo.inChannels; ++d) {\n      for (let dxR = 0; dxR < convInfo.inHeight; ++dxR) {\n        for (let dxC = 0; dxC < convInfo.inWidth; ++dxC) {\n          const dyRCorner = dxR - padTop;\n          const dyCCorner = dxC - padLeft;\n          let dotProd = 0;\n          for (let wR = 0; wR < effectiveFilterHeight; wR += dilationHeight) {\n            const dyR = (dyRCorner + wR) / strideHeight;\n            if (dyR < 0 || dyR >= convInfo.outHeight || Math.floor(dyR) !== dyR) {\n              continue;\n            }\n            for (let wC = 0; wC < effectiveFilterWidth; wC += dilationWidth) {\n              const dyC = (dyCCorner + wC) / strideWidth;\n              if (dyC < 0 || dyC >= convInfo.outWidth || Math.floor(dyC) !== dyC) {\n                continue;\n              }\n              const maxPos = effectiveFilterHeight * effectiveFilterWidth - 1 - maxPosBuf.get(b, dyR, dyC, d);\n              const curPos = wR * effectiveFilterWidth + wC;\n              const mask = maxPos === curPos ? 1 : 0;\n              if (mask === 0) {\n                continue;\n              }\n              const pixel = dyBuf.get(b, dyR, dyC, d);\n              dotProd += pixel * mask;\n            }\n          }\n          dx.set(dotProd, b, dxR, dxC, d);\n        }\n      }\n    }\n  }\n  return backend2.makeTensorInfo(dx.shape, dx.dtype, dx.values);\n}\nvar maxPoolGradConfig2 = {\n  kernelName: MaxPoolGrad,\n  backendName: \"cpu\",\n  kernelFunc: maxPoolGrad2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/MaxPoolWithArgmax_impl.js\nfunction maxPoolWithArgmaxImpl(xValues, xShape, dtype, includeBatchInIndex, convInfo) {\n  const strides = util_exports.computeStrides(xShape);\n  const maxPools = pool2(xValues, xShape, dtype, strides, convInfo, \"max\");\n  const maxPositions = maxPoolPositions(xValues, xShape, dtype, convInfo, true, includeBatchInIndex);\n  return [maxPools.values, maxPositions.values];\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/MaxPoolWithArgmax.js\nvar maxPoolWithArgmaxConfig = {\n  kernelName: MaxPoolWithArgmax,\n  backendName: \"cpu\",\n  kernelFunc: ({ inputs, attrs, backend: backend2 }) => {\n    const { x } = inputs;\n    const { filterSize, strides, pad: pad3, includeBatchInIndex } = attrs;\n    const cpuBackend = backend2;\n    assertNotComplex(x, \"MaxPoolWithArgmax\");\n    const values = cpuBackend.data.get(x.dataId).values;\n    const convInfo = backend_util_exports.computePool2DInfo(x.shape, filterSize, strides, [1, 1], pad3);\n    const [pooled, indexes] = maxPoolWithArgmaxImpl(values, x.shape, x.dtype, includeBatchInIndex, convInfo);\n    const pooledDataId = cpuBackend.write(pooled, convInfo.outShape, x.dtype);\n    const indexesDataId = cpuBackend.write(indexes, convInfo.outShape, x.dtype);\n    return [\n      { dataId: pooledDataId, shape: convInfo.outShape, dtype: x.dtype },\n      { dataId: indexesDataId, shape: convInfo.outShape, dtype: \"int32\" }\n    ];\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Mean.js\nfunction mean2(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x } = inputs;\n  const { axis, keepDims } = attrs;\n  const axes = util_exports.parseAxisParam(axis, x.shape);\n  const shapes = backend_util_exports.computeOutAndReduceShapes(x.shape, axes);\n  const reduceShape = shapes[1];\n  const reduceSize = util_exports.sizeFromShape(reduceShape);\n  const toDispose = [];\n  const reduceSizeScalar = backend2.makeTensorInfo([], \"float32\", new Float32Array([reduceSize]));\n  toDispose.push(reduceSizeScalar);\n  const $x = cast3({ inputs: { x }, backend: backend2, attrs: { dtype: \"float32\" } });\n  toDispose.push($x);\n  const res = div2({ inputs: { a: $x, b: reduceSizeScalar }, backend: backend2 });\n  toDispose.push(res);\n  const result = sum3({ inputs: { x: res }, backend: backend2, attrs: { axis, keepDims } });\n  toDispose.forEach((t2) => backend2.disposeIntermediateTensorInfo(t2));\n  return result;\n}\nvar meanConfig = {\n  kernelName: Mean,\n  backendName: \"cpu\",\n  kernelFunc: mean2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Min.js\nfunction min3(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x } = inputs;\n  const { axis, keepDims } = attrs;\n  assertNotComplex(x, \"min\");\n  const origAxes = util_exports.parseAxisParam(axis, x.shape);\n  let axes = origAxes;\n  const permutedAxes = backend_util_exports.getAxesPermutation(axes, x.shape.length);\n  let $x = x;\n  if (permutedAxes != null) {\n    $x = transpose2({ inputs: { x }, backend: backend2, attrs: { perm: permutedAxes } });\n    axes = backend_util_exports.getInnerMostAxes(axes.length, x.shape.length);\n  }\n  backend_util_exports.assertAxesAreInnerMostDims(\"min\", axes, $x.shape.length);\n  const [outShape, reduceShape] = backend_util_exports.computeOutAndReduceShapes($x.shape, axes);\n  const reduceSize = util_exports.sizeFromShape(reduceShape);\n  const vals = util_exports.makeZerosTypedArray(util_exports.sizeFromShape(outShape), $x.dtype);\n  const aVals = backend2.data.get($x.dataId).values;\n  for (let i2 = 0; i2 < vals.length; ++i2) {\n    const offset = i2 * reduceSize;\n    let min7 = aVals[offset];\n    for (let j = 0; j < reduceSize; ++j) {\n      const value = aVals[offset + j];\n      if (Number.isNaN(value) || value < min7) {\n        min7 = value;\n      }\n    }\n    vals[i2] = min7;\n  }\n  if (permutedAxes != null) {\n    backend2.disposeIntermediateTensorInfo($x);\n  }\n  const result = backend2.makeTensorInfo(outShape, $x.dtype, vals);\n  if (keepDims) {\n    const expandedShape = backend_util_exports.expandShapeToKeepDim(outShape, origAxes);\n    const reshapedResult = reshape3({ inputs: { x: result }, backend: backend2, attrs: { shape: expandedShape } });\n    backend2.disposeIntermediateTensorInfo(result);\n    return reshapedResult;\n  }\n  return result;\n}\nvar minConfig = {\n  kernelName: Min,\n  backendName: \"cpu\",\n  kernelFunc: min3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/MirrorPad.js\nfunction mirrorPad2(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x } = inputs;\n  const { paddings, mode } = attrs;\n  assertNotComplex(x, \"mirrorPad\");\n  const outShape = paddings.map((p2, i2) => p2[0] + x.shape[i2] + p2[1]);\n  const start = paddings.map((p2) => p2[0]);\n  const end = paddings.map((p2, i2) => p2[0] + x.shape[i2]);\n  const offset = mode === \"reflect\" ? 0 : 1;\n  const xVals = backend2.data.get(x.dataId).values;\n  const xRank = x.shape.length;\n  const xStrides = util_exports.computeStrides(x.shape);\n  const resultSize = util_exports.sizeFromShape(outShape);\n  const resultRank = outShape.length;\n  const resultStrides = util_exports.computeStrides(outShape);\n  const resVals = util_exports.getTypedArrayFromDType(x.dtype, resultSize);\n  for (let i2 = 0; i2 < resultSize; i2++) {\n    let coords3 = util_exports.indexToLoc(i2, resultRank, resultStrides);\n    for (let i3 = 0; i3 < resultRank; i3++) {\n      if (coords3[i3] < start[i3]) {\n        coords3[i3] = start[i3] * 2 - coords3[i3] - offset;\n      } else if (coords3[i3] >= end[i3]) {\n        coords3[i3] = (end[i3] - 1) * 2 - coords3[i3] + offset;\n      }\n    }\n    coords3 = coords3.map((c, i3) => c - start[i3]);\n    const inIndex = util_exports.locToIndex(coords3, xRank, xStrides);\n    resVals[i2] = xVals[inIndex];\n  }\n  const outId = backend2.write(resVals, outShape, x.dtype);\n  return { dataId: outId, shape: outShape, dtype: x.dtype };\n}\nvar mirrorPadConfig = {\n  kernelName: MirrorPad,\n  backendName: \"cpu\",\n  kernelFunc: mirrorPad2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Mod.js\nvar modImpl = createSimpleBinaryKernelImpl((aValue, bValue) => {\n  const rem = aValue % bValue;\n  if (aValue < 0 && bValue < 0 || aValue >= 0 && bValue >= 0) {\n    return rem;\n  } else {\n    return (rem + bValue) % bValue;\n  }\n});\nvar mod2 = binaryKernelFunc(Mod, modImpl);\nvar modConfig = {\n  kernelName: Mod,\n  backendName: \"cpu\",\n  kernelFunc: mod2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Multinomial.js\nvar seedrandom4 = __toESM(require_seedrandom2());\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Softmax.js\nfunction softmax3(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { logits } = inputs;\n  const { dim } = attrs;\n  const logitsRank = logits.shape.length;\n  let $dim = dim;\n  if ($dim === -1) {\n    $dim = logitsRank - 1;\n  }\n  if ($dim !== logitsRank - 1) {\n    throw Error(`Softmax along a non-last dimension is not yet supported. Logits was rank ${logitsRank} and dim was ${$dim}`);\n  }\n  const axes = util_exports.parseAxisParam([$dim], logits.shape);\n  const maxLogit = max3({\n    inputs: { x: logits },\n    backend: backend2,\n    attrs: { reductionIndices: axes, keepDims: false }\n  });\n  const expandedShape = backend_util_exports.expandShapeToKeepDim(maxLogit.shape, axes);\n  const maxLogitReshaped = reshape3({ inputs: { x: maxLogit }, backend: backend2, attrs: { shape: expandedShape } });\n  const a = sub2({ inputs: { a: logits, b: maxLogitReshaped }, backend: backend2 });\n  const b = exp2({ inputs: { x: a }, backend: backend2 });\n  const sumExp = sum3({ inputs: { x: b }, backend: backend2, attrs: { axis: axes, keepDims: false } });\n  const sumReshaped = reshape3({ inputs: { x: sumExp }, backend: backend2, attrs: { shape: expandedShape } });\n  const result = div2({ inputs: { a: b, b: sumReshaped }, backend: backend2 });\n  backend2.disposeIntermediateTensorInfo(maxLogit);\n  backend2.disposeIntermediateTensorInfo(maxLogitReshaped);\n  backend2.disposeIntermediateTensorInfo(a);\n  backend2.disposeIntermediateTensorInfo(b);\n  backend2.disposeIntermediateTensorInfo(sumExp);\n  backend2.disposeIntermediateTensorInfo(sumReshaped);\n  return result;\n}\nvar softmaxConfig = {\n  kernelName: Softmax,\n  backendName: \"cpu\",\n  kernelFunc: softmax3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Multinomial.js\nfunction multinomial2(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { logits } = inputs;\n  const { numSamples, seed, normalized } = attrs;\n  assertNotComplex(logits, \"multinomial\");\n  const probabilities = normalized ? logits : softmax3({ inputs: { logits }, backend: backend2, attrs: { dim: -1 } });\n  const batchSize = probabilities.shape[0];\n  const numEvents = probabilities.shape[1];\n  const probVals = backend2.data.get(probabilities.dataId).values;\n  const resShape = [batchSize, numSamples];\n  const resVals = util_exports.makeZerosTypedArray(util_exports.sizeFromShape(resShape), \"int32\");\n  for (let b = 0; b < batchSize; ++b) {\n    const offset = b * numEvents;\n    const cdf = new Float32Array(numEvents - 1);\n    cdf[0] = probVals[offset];\n    for (let event = 1; event < cdf.length; ++event) {\n      cdf[event] = cdf[event - 1] + probVals[offset + event];\n    }\n    const random = seedrandom4.alea(seed.toString());\n    const outOffset = b * numSamples;\n    for (let sampleId = 0; sampleId < numSamples; ++sampleId) {\n      const r2 = random();\n      resVals[outOffset + sampleId] = cdf.length;\n      for (let event = 0; event < cdf.length; event++) {\n        if (r2 < cdf[event]) {\n          resVals[outOffset + sampleId] = event;\n          break;\n        }\n      }\n    }\n  }\n  if (!normalized) {\n    backend2.disposeIntermediateTensorInfo(probabilities);\n  }\n  return backend2.makeTensorInfo(resShape, \"int32\", resVals);\n}\nvar multinomialConfig = {\n  kernelName: Multinomial,\n  backendName: \"cpu\",\n  kernelFunc: multinomial2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/NonMaxSuppressionV3.js\nvar nonMaxSuppressionV3Impl2 = kernel_impls_exports.nonMaxSuppressionV3Impl;\nfunction nonMaxSuppressionV3(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { boxes, scores } = inputs;\n  const { maxOutputSize, iouThreshold, scoreThreshold } = attrs;\n  assertNotComplex(boxes, \"NonMaxSuppression\");\n  const boxesVals = backend2.data.get(boxes.dataId).values;\n  const scoresVals = backend2.data.get(scores.dataId).values;\n  const { selectedIndices } = nonMaxSuppressionV3Impl2(boxesVals, scoresVals, maxOutputSize, iouThreshold, scoreThreshold);\n  return backend2.makeTensorInfo([selectedIndices.length], \"int32\", new Int32Array(selectedIndices));\n}\nvar nonMaxSuppressionV3Config = {\n  kernelName: NonMaxSuppressionV3,\n  backendName: \"cpu\",\n  kernelFunc: nonMaxSuppressionV3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/NonMaxSuppressionV4.js\nvar nonMaxSuppressionV4Impl2 = kernel_impls_exports.nonMaxSuppressionV4Impl;\nfunction nonMaxSuppressionV4(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { boxes, scores } = inputs;\n  const { maxOutputSize, iouThreshold, scoreThreshold, padToMaxOutputSize } = attrs;\n  assertNotComplex(boxes, \"NonMaxSuppressionPadded\");\n  const boxesVals = backend2.data.get(boxes.dataId).values;\n  const scoresVals = backend2.data.get(scores.dataId).values;\n  const { selectedIndices, validOutputs } = nonMaxSuppressionV4Impl2(boxesVals, scoresVals, maxOutputSize, iouThreshold, scoreThreshold, padToMaxOutputSize);\n  return [\n    backend2.makeTensorInfo([selectedIndices.length], \"int32\", new Int32Array(selectedIndices)),\n    backend2.makeTensorInfo([], \"int32\", new Int32Array([validOutputs]))\n  ];\n}\nvar nonMaxSuppressionV4Config = {\n  kernelName: NonMaxSuppressionV4,\n  backendName: \"cpu\",\n  kernelFunc: nonMaxSuppressionV4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/NonMaxSuppressionV5.js\nvar nonMaxSuppressionV5Impl2 = kernel_impls_exports.nonMaxSuppressionV5Impl;\nfunction nonMaxSuppressionV5(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { boxes, scores } = inputs;\n  const { maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma } = attrs;\n  assertNotComplex(boxes, \"NonMaxSuppressionWithScore\");\n  const boxesVals = backend2.data.get(boxes.dataId).values;\n  const scoresVals = backend2.data.get(scores.dataId).values;\n  const maxOutputSizeVal = maxOutputSize;\n  const iouThresholdVal = iouThreshold;\n  const scoreThresholdVal = scoreThreshold;\n  const softNmsSigmaVal = softNmsSigma;\n  const { selectedIndices, selectedScores } = nonMaxSuppressionV5Impl2(boxesVals, scoresVals, maxOutputSizeVal, iouThresholdVal, scoreThresholdVal, softNmsSigmaVal);\n  return [\n    backend2.makeTensorInfo([selectedIndices.length], \"int32\", new Int32Array(selectedIndices)),\n    backend2.makeTensorInfo([selectedScores.length], \"float32\", new Float32Array(selectedScores))\n  ];\n}\nvar nonMaxSuppressionV5Config = {\n  kernelName: NonMaxSuppressionV5,\n  backendName: \"cpu\",\n  kernelFunc: nonMaxSuppressionV5\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/OneHot.js\nfunction oneHot2(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { indices } = inputs;\n  const { dtype, depth, onValue, offValue } = attrs;\n  assertNotComplex(indices, \"oneHot\");\n  const indicesSize = util_exports.sizeFromShape(indices.shape);\n  const res = new Float32Array(indicesSize * depth);\n  res.fill(offValue);\n  const indicesVal = backend2.data.get(indices.dataId).values;\n  for (let event = 0; event < indicesSize; ++event) {\n    if (indicesVal[event] >= 0 && indicesVal[event] < depth) {\n      res[event * depth + indicesVal[event]] = onValue;\n    }\n  }\n  return backend2.makeTensorInfo([...indices.shape, depth], dtype, res);\n}\nvar oneHotConfig = {\n  kernelName: OneHot,\n  backendName: \"cpu\",\n  kernelFunc: oneHot2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/ZerosLike.js\nfunction zerosLike2(args) {\n  const { inputs, backend: backend2 } = args;\n  const { x } = inputs;\n  if (x.dtype === \"string\") {\n    throw new Error(\"zerosLike is not supported for string tensors\");\n  } else if (x.dtype === \"complex64\") {\n    const realPart = real2({ inputs: { input: x }, backend: backend2 });\n    const r2 = zerosLike2({ inputs: { x: realPart }, backend: backend2 });\n    const imagPart = imag2({ inputs: { input: x }, backend: backend2 });\n    const i2 = zerosLike2({ inputs: { x: imagPart }, backend: backend2 });\n    const result = complex2({ inputs: { real: r2, imag: i2 }, backend: backend2 });\n    backend2.disposeIntermediateTensorInfo(realPart);\n    backend2.disposeIntermediateTensorInfo(r2);\n    backend2.disposeIntermediateTensorInfo(imagPart);\n    backend2.disposeIntermediateTensorInfo(i2);\n    return result;\n  } else {\n    return fill2({ backend: backend2, attrs: { shape: x.shape, value: 0, dtype: x.dtype } });\n  }\n}\nvar zerosLikeConfig = {\n  kernelName: ZerosLike,\n  backendName: \"cpu\",\n  kernelFunc: zerosLike2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/OnesLike.js\nfunction onesLike2(args) {\n  const { inputs, backend: backend2 } = args;\n  const { x } = inputs;\n  if (x.dtype === \"string\") {\n    throw new Error(\"onesLike is not supported for string tensors\");\n  } else if (x.dtype === \"complex64\") {\n    const realPart = real2({ inputs: { input: x }, backend: backend2 });\n    const r2 = onesLike2({ inputs: { x: realPart }, backend: backend2 });\n    const imagPart = imag2({ inputs: { input: x }, backend: backend2 });\n    const i2 = zerosLike2({ inputs: { x: imagPart }, backend: backend2 });\n    const result = complex2({ inputs: { real: r2, imag: i2 }, backend: backend2 });\n    backend2.disposeIntermediateTensorInfo(realPart);\n    backend2.disposeIntermediateTensorInfo(r2);\n    backend2.disposeIntermediateTensorInfo(imagPart);\n    backend2.disposeIntermediateTensorInfo(i2);\n    return result;\n  } else {\n    return fill2({ backend: backend2, attrs: { shape: x.shape, value: 1, dtype: x.dtype } });\n  }\n}\nvar onesLikeConfig = {\n  kernelName: OnesLike,\n  backendName: \"cpu\",\n  kernelFunc: onesLike2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Pack.js\nfunction pack(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { axis } = attrs;\n  if (inputs.length === 1) {\n    return expandDims3({ inputs: { input: inputs[0] }, backend: backend2, attrs: { dim: axis } });\n  }\n  const shape = inputs[0].shape;\n  const dtype = inputs[0].dtype;\n  inputs.forEach((t2) => {\n    util_exports.assertShapesMatch(shape, t2.shape, \"All tensors passed to stack must have matching shapes\");\n    util_exports.assert(dtype === t2.dtype, () => \"All tensors passed to stack must have matching dtypes\");\n  });\n  const intermediateTensorInfos = [];\n  const expandedTensors = inputs.map((t2) => {\n    const expandedT = expandDims3({ inputs: { input: t2 }, backend: backend2, attrs: { dim: axis } });\n    intermediateTensorInfos.push(expandedT);\n    return expandedT;\n  });\n  const result = concat2({ inputs: expandedTensors, backend: backend2, attrs: { axis } });\n  intermediateTensorInfos.forEach((t2) => backend2.disposeIntermediateTensorInfo(t2));\n  return result;\n}\nvar packConfig = {\n  kernelName: Pack,\n  backendName: \"cpu\",\n  kernelFunc: pack\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/PadV2.js\nfunction padV2(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x } = inputs;\n  const { paddings, constantValue } = attrs;\n  assertNotComplex(x, \"pad\");\n  const outShape = paddings.map((p2, i2) => p2[0] + x.shape[i2] + p2[1]);\n  const start = paddings.map((p2) => p2[0]);\n  const xVals = backend2.data.get(x.dataId).values;\n  const xSize = util_exports.sizeFromShape(x.shape);\n  const xRank = x.shape.length;\n  const xStrides = util_exports.computeStrides(x.shape);\n  const resultSize = util_exports.sizeFromShape(outShape);\n  const resultRank = outShape.length;\n  const resultStrides = util_exports.computeStrides(outShape);\n  const resVals = util_exports.getTypedArrayFromDType(x.dtype, resultSize);\n  if (constantValue !== 0) {\n    resVals.fill(constantValue);\n  }\n  for (let i2 = 0; i2 < xSize; i2++) {\n    const coords3 = util_exports.indexToLoc(i2, xRank, xStrides);\n    const outCoords = coords3.map((c, i3) => c + start[i3]);\n    const outIndex = util_exports.locToIndex(outCoords, resultRank, resultStrides);\n    resVals[outIndex] = xVals[i2];\n  }\n  const outId = backend2.write(resVals, outShape, x.dtype);\n  return { dataId: outId, shape: outShape, dtype: x.dtype };\n}\nvar padV2Config = {\n  kernelName: PadV2,\n  backendName: \"cpu\",\n  kernelFunc: padV2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Pow.js\nvar powImpl = createSimpleBinaryKernelImpl((a, b) => Math.pow(a, b));\nvar pow2 = binaryKernelFunc(Pow, powImpl);\nvar powConfig = {\n  kernelName: Pow,\n  backendName: \"cpu\",\n  kernelFunc: pow2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/RaggedTensorToTensor.js\nfunction raggedTensorToTensor2(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { shape, values, defaultValue, rowPartitionTensors } = inputs;\n  const { rowPartitionTypes } = attrs;\n  const $shape = backend2.data.get(shape.dataId).values;\n  const $values = backend2.data.get(values.dataId).values;\n  const $defaultValue = backend2.data.get(defaultValue.dataId).values;\n  const $rowPartitionValues = rowPartitionTensors.map((t2) => backend2.data.get(t2.dataId).values);\n  const rowPartitionValuesShapes = rowPartitionTensors.map((t2) => t2.shape);\n  const [outputShape, output] = raggedTensorToTensorImpl($shape, shape.shape, $values, values.shape, values.dtype, $defaultValue, defaultValue.shape, $rowPartitionValues, rowPartitionValuesShapes, rowPartitionTypes);\n  return backend2.makeTensorInfo(outputShape, values.dtype, output);\n}\nvar raggedTensorToTensorConfig = {\n  kernelName: RaggedTensorToTensor,\n  backendName: \"cpu\",\n  kernelFunc: raggedTensorToTensor2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Range.js\nfunction range3(args) {\n  const { backend: backend2, attrs } = args;\n  const { start, stop, dtype, step: step5 } = attrs;\n  const values = rangeImpl(start, stop, step5, dtype);\n  return backend2.makeTensorInfo([values.length], dtype, values);\n}\nvar rangeConfig = {\n  kernelName: Range,\n  backendName: \"cpu\",\n  kernelFunc: range3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Reciprocal.js\nvar reciprocal2 = unaryKernelFunc(Reciprocal, (xi) => 1 / xi);\nvar reciprocalConfig = {\n  kernelName: Reciprocal,\n  backendName: \"cpu\",\n  kernelFunc: reciprocal2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/ResizeBilinear.js\nfunction resizeBilinear2(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { images } = inputs;\n  const { alignCorners, halfPixelCenters, size } = attrs;\n  assertNotComplex(images, \"resizeBilinear\");\n  const imagesStrides = util_exports.computeStrides(images.shape);\n  const [newHeight, newWidth] = size;\n  const [batch, oldHeight, oldWidth, numChannels] = images.shape;\n  const xValues = backend2.data.get(images.dataId).values;\n  const result = new Float32Array(util_exports.sizeFromShape([batch, newHeight, newWidth, numChannels]));\n  const effectiveInputSize = [\n    alignCorners && newHeight > 1 ? oldHeight - 1 : oldHeight,\n    alignCorners && newWidth > 1 ? oldWidth - 1 : oldWidth\n  ];\n  const effectiveOutputSize = [\n    alignCorners && newHeight > 1 ? newHeight - 1 : newHeight,\n    alignCorners && newWidth > 1 ? newWidth - 1 : newWidth\n  ];\n  let outputIdx = 0;\n  const effectiveRowSizeRatio = effectiveInputSize[0] / effectiveOutputSize[0];\n  const effectiveColSizeRatio = effectiveInputSize[1] / effectiveOutputSize[1];\n  for (let b = 0; b < batch; b++) {\n    for (let r2 = 0; r2 < newHeight; r2++) {\n      let sourceFracRow;\n      if (halfPixelCenters) {\n        sourceFracRow = effectiveRowSizeRatio * (r2 + 0.5) - 0.5;\n      } else {\n        sourceFracRow = effectiveRowSizeRatio * r2;\n      }\n      const sourceRowFloor = Math.max(0, Math.floor(sourceFracRow));\n      const rowFrac = sourceFracRow - sourceRowFloor;\n      const sourceRowCeil = Math.min(oldHeight - 1, Math.ceil(sourceFracRow));\n      const topRowOffset = b * imagesStrides[0] + sourceRowFloor * imagesStrides[1];\n      const botRowOffset = b * imagesStrides[0] + sourceRowCeil * imagesStrides[1];\n      for (let c = 0; c < newWidth; c++) {\n        let sourceFracCol;\n        if (halfPixelCenters) {\n          sourceFracCol = effectiveColSizeRatio * (c + 0.5) - 0.5;\n        } else {\n          sourceFracCol = effectiveColSizeRatio * c;\n        }\n        const sourceColFloor = Math.max(0, Math.floor(sourceFracCol));\n        const colFrac = sourceFracCol - sourceColFloor;\n        const sourceColCeil = Math.min(oldWidth - 1, Math.ceil(sourceFracCol));\n        const topLeftOffest = topRowOffset + sourceColFloor * imagesStrides[2];\n        const botLeftOffset = botRowOffset + sourceColFloor * imagesStrides[2];\n        const topRightOffset = topRowOffset + sourceColCeil * imagesStrides[2];\n        const botRightOffest = botRowOffset + sourceColCeil * imagesStrides[2];\n        for (let d = 0; d < numChannels; d++) {\n          const topLeft = xValues[topLeftOffest + d];\n          const bottomLeft = xValues[botLeftOffset + d];\n          const topRight = xValues[topRightOffset + d];\n          const bottomRight = xValues[botRightOffest + d];\n          const top = topLeft + (topRight - topLeft) * colFrac;\n          const bottom = bottomLeft + (bottomRight - bottomLeft) * colFrac;\n          const newValue = top + (bottom - top) * rowFrac;\n          result[outputIdx++] = newValue;\n        }\n      }\n    }\n  }\n  return backend2.makeTensorInfo([batch, newHeight, newWidth, numChannels], \"float32\", result);\n}\nvar resizeBilinearConfig = {\n  kernelName: ResizeBilinear,\n  backendName: \"cpu\",\n  kernelFunc: resizeBilinear2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/ResizeBilinearGrad.js\nfunction resizeBilinearGrad(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { images, dy } = inputs;\n  const { alignCorners } = attrs;\n  assertNotComplex([dy, images], \"resizeBilinearGrad\");\n  const imagesStrides = util_exports.computeStrides(images.shape);\n  const [batch, xHeight, xWidth, depth] = images.shape;\n  const [, yHeight, yWidth] = dy.shape;\n  const output = new Float32Array(batch * xHeight * xWidth * depth);\n  const effectiveXSize = [\n    alignCorners && yHeight > 1 ? xHeight - 1 : xHeight,\n    alignCorners && yWidth > 1 ? xWidth - 1 : xWidth\n  ];\n  const effectiveYSize = [\n    alignCorners && yHeight > 1 ? yHeight - 1 : yHeight,\n    alignCorners && yWidth > 1 ? yWidth - 1 : yWidth\n  ];\n  const heightScale = effectiveXSize[0] / effectiveYSize[0];\n  const widthScale = effectiveXSize[1] / effectiveYSize[1];\n  const dyValues = backend2.data.get(dy.dataId).values;\n  let offset = 0;\n  for (let b = 0; b < batch; b++) {\n    const bOffset = b * imagesStrides[0];\n    for (let r2 = 0; r2 < yHeight; r2++) {\n      const dxR = r2 * heightScale;\n      const topDxRIndex = Math.floor(dxR);\n      const bottomDxRIndex = Math.min(Math.ceil(dxR), xHeight - 1);\n      const topDxROffset = bOffset + topDxRIndex * imagesStrides[1];\n      const bottomDxROffset = bOffset + bottomDxRIndex * imagesStrides[1];\n      const dxRLerp = dxR - topDxRIndex;\n      const inverseDxRLerp = 1 - dxRLerp;\n      for (let c = 0; c < yWidth; c++) {\n        const dxC = c * widthScale;\n        const leftDxCIndex = Math.floor(dxC);\n        const rightDxCIndex = Math.min(Math.ceil(dxC), xWidth - 1);\n        const dxCLerp = dxC - leftDxCIndex;\n        const inverseDxCLerp = 1 - dxCLerp;\n        const topLeftRCOffset = topDxROffset + leftDxCIndex * imagesStrides[2];\n        const topRightRCOffset = topDxROffset + rightDxCIndex * imagesStrides[2];\n        const bottomLeftRCOffset = bottomDxROffset + leftDxCIndex * imagesStrides[2];\n        const bottomRightRCOffset = bottomDxROffset + rightDxCIndex * imagesStrides[2];\n        const inverseDxRLerpTimesInverseDxCLerp = inverseDxRLerp * inverseDxCLerp;\n        const inverseDxRLerpTimesDxCLerp = inverseDxRLerp * dxCLerp;\n        const dxRLerpTimesInverseDxCLerp = dxRLerp * inverseDxCLerp;\n        const dxRLerpTimesDxCLerp = dxRLerp * dxCLerp;\n        for (let d = 0; d < depth; d++) {\n          const dyVal = dyValues[offset++];\n          output[topLeftRCOffset + d] += dyVal * inverseDxRLerpTimesInverseDxCLerp;\n          output[topRightRCOffset + d] += dyVal * inverseDxRLerpTimesDxCLerp;\n          output[bottomLeftRCOffset + d] += dyVal * dxRLerpTimesInverseDxCLerp;\n          output[bottomRightRCOffset + d] += dyVal * dxRLerpTimesDxCLerp;\n        }\n      }\n    }\n  }\n  return backend2.makeTensorInfo([batch, xWidth, xHeight, depth], \"float32\", output);\n}\nvar resizeBilinearGradConfig2 = {\n  kernelName: ResizeBilinearGrad,\n  backendName: \"cpu\",\n  kernelFunc: resizeBilinearGrad\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/ResizeNearestNeighbor.js\nfunction resizeNearestNeighbor2(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { images } = inputs;\n  const { alignCorners, halfPixelCenters, size } = attrs;\n  assertNotComplex(images, \"resizeNearestNeighbor\");\n  const imagesStrides = util_exports.computeStrides(images.shape);\n  const [newHeight, newWidth] = size;\n  const [batch, oldHeight, oldWidth, numChannels] = images.shape;\n  const xValues = backend2.data.get(images.dataId).values;\n  const output = new Float32Array(batch * newHeight * newWidth * numChannels);\n  const effectiveInputSize = [\n    alignCorners && newHeight > 1 ? oldHeight - 1 : oldHeight,\n    alignCorners && newWidth > 1 ? oldWidth - 1 : oldWidth\n  ];\n  const effectiveOutputSize = [\n    alignCorners && newHeight > 1 ? newHeight - 1 : newHeight,\n    alignCorners && newWidth > 1 ? newWidth - 1 : newWidth\n  ];\n  const effectiveRowSizeRatio = effectiveInputSize[0] / effectiveOutputSize[0];\n  const effectiveColSizeRatio = effectiveInputSize[1] / effectiveOutputSize[1];\n  let outputOffset = 0;\n  for (let b = 0; b < batch; b++) {\n    const batchOffset = b * imagesStrides[0];\n    for (let r2 = 0; r2 < newHeight; r2++) {\n      const sourceFracRow = halfPixelCenters ? effectiveRowSizeRatio * (r2 + 0.5) : effectiveRowSizeRatio * r2;\n      let sourceNearestRow = Math.min(oldHeight - 1, alignCorners ? Math.round(sourceFracRow) : Math.floor(sourceFracRow));\n      if (halfPixelCenters) {\n        sourceNearestRow = Math.max(0, sourceNearestRow);\n      }\n      const rowOffset = batchOffset + sourceNearestRow * imagesStrides[1];\n      for (let c = 0; c < newWidth; c++) {\n        const sourceFracCol = halfPixelCenters ? effectiveColSizeRatio * (c + 0.5) : effectiveColSizeRatio * c;\n        let sourceNearestCol = Math.min(oldWidth - 1, alignCorners ? Math.round(sourceFracCol) : Math.floor(sourceFracCol));\n        if (halfPixelCenters) {\n          sourceNearestCol = Math.max(0, sourceNearestCol);\n        }\n        const colOffset = rowOffset + sourceNearestCol * imagesStrides[2];\n        for (let d = 0; d < numChannels; d++) {\n          const newVal = xValues[colOffset + d];\n          output[outputOffset++] = newVal;\n        }\n      }\n    }\n  }\n  return backend2.makeTensorInfo([batch, newHeight, newWidth, numChannels], images.dtype, output);\n}\nvar resizeNearestNeighborConfig = {\n  kernelName: ResizeNearestNeighbor,\n  backendName: \"cpu\",\n  kernelFunc: resizeNearestNeighbor2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/ResizeNearestNeighborGrad.js\nfunction resizeNearestNeighborGrad(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { images, dy } = inputs;\n  const { alignCorners } = attrs;\n  assertNotComplex([dy, images], \"resizeNearestNeighborGrad\");\n  const imagesStrides = util_exports.computeStrides(images.shape);\n  const dyStrides = util_exports.computeStrides(dy.shape);\n  const [batch, xHeight, xWidth, depth] = images.shape;\n  const [, yHeight, yWidth] = dy.shape;\n  const output = new Float32Array(batch * xHeight * xWidth * depth);\n  const dyValues = backend2.data.get(dy.dataId).values;\n  const effectiveXSize = [\n    alignCorners && yHeight > 1 ? xHeight - 1 : xHeight,\n    alignCorners && yWidth > 1 ? xWidth - 1 : xWidth\n  ];\n  const effectiveYSize = [\n    alignCorners && yHeight > 1 ? yHeight - 1 : yHeight,\n    alignCorners && yWidth > 1 ? yWidth - 1 : yWidth\n  ];\n  const heightScale = effectiveXSize[0] / effectiveYSize[0];\n  const widthScale = effectiveXSize[1] / effectiveYSize[1];\n  const invHeightScale = 1 / heightScale;\n  const invWidthScale = 1 / widthScale;\n  const winHeight = Math.ceil(invHeightScale) * 2 + 2;\n  const winWidth = Math.ceil(invWidthScale) * 2 + 2;\n  for (let b = 0; b < batch; b++) {\n    const batchOffset = b * imagesStrides[0];\n    for (let r2 = 0; r2 < xHeight; r2++) {\n      const rowOffset = batchOffset + r2 * imagesStrides[1];\n      const startRLerp = Math.floor(r2 * invHeightScale);\n      const startDyR = Math.floor(startRLerp - winHeight / 2);\n      for (let c = 0; c < xWidth; c++) {\n        const colOffset = rowOffset + c * imagesStrides[2];\n        const startCLerp = Math.floor(c * invWidthScale);\n        const startDyC = Math.floor(startCLerp - winWidth / 2);\n        for (let d = 0; d < depth; d++) {\n          let accum = 0;\n          for (let dyRIndex = 0; dyRIndex < winHeight; dyRIndex++) {\n            const dyR = dyRIndex + startDyR;\n            if (dyR < 0 || dyR >= yHeight) {\n              continue;\n            }\n            const dyROffset = batchOffset + dyR * dyStrides[1];\n            const sourceFracRow = dyR * heightScale;\n            const sourceNearestRow = Math.min(xHeight - 1, alignCorners ? Math.round(sourceFracRow) : Math.floor(sourceFracRow));\n            if (r2 !== sourceNearestRow) {\n              continue;\n            }\n            for (let dyCIndex = 0; dyCIndex < winWidth; dyCIndex++) {\n              const dyC = dyCIndex + startDyC;\n              if (dyC < 0 || dyC >= yWidth) {\n                continue;\n              }\n              const dyCOffset = dyROffset + dyC * dyStrides[2];\n              const sourceFracCol = dyC * widthScale;\n              const sourceNearestCol = Math.min(xWidth - 1, alignCorners ? Math.round(sourceFracCol) : Math.floor(sourceFracCol));\n              if (c === sourceNearestCol) {\n                accum += dyValues[dyCOffset + d];\n              }\n            }\n          }\n          output[colOffset + d] = accum;\n        }\n      }\n    }\n  }\n  return backend2.makeTensorInfo(images.shape, images.dtype, output);\n}\nvar resizeNearestNeighborGradConfig2 = {\n  kernelName: ResizeNearestNeighborGrad,\n  backendName: \"cpu\",\n  kernelFunc: resizeNearestNeighborGrad\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Reverse.js\nfunction reverse2(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x } = inputs;\n  const { dims } = attrs;\n  assertNotComplex(x, \"reverse\");\n  const xRank = x.shape.length;\n  const $dims = util_exports.parseAxisParam(dims, x.shape);\n  if (xRank === 0) {\n    return identity2({ inputs: { x }, backend: backend2 });\n  }\n  const outBuf = new TensorBuffer(x.shape, x.dtype);\n  const xBuf = backend2.bufferSync(x);\n  for (let i2 = 0; i2 < outBuf.size; i2++) {\n    const outLoc = outBuf.indexToLoc(i2);\n    const inLoc = outLoc.slice();\n    $dims.forEach((d) => inLoc[d] = x.shape[d] - 1 - inLoc[d]);\n    outBuf.set(xBuf.get(...inLoc), ...outLoc);\n  }\n  return backend2.makeTensorInfo(outBuf.shape, outBuf.dtype, outBuf.values);\n}\nvar reverseConfig = {\n  kernelName: Reverse,\n  backendName: \"cpu\",\n  kernelFunc: reverse2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/RotateWithOffset.js\nvar rotateWithOffsetConfig = {\n  kernelName: RotateWithOffset,\n  backendName: \"cpu\",\n  kernelFunc: ({ inputs, attrs, backend: backend2 }) => {\n    const { image: image2 } = inputs;\n    const { radians, fillValue, center } = attrs;\n    const cpuBackend = backend2;\n    const output = util_exports.getTypedArrayFromDType(image2.dtype, util_exports.sizeFromShape(image2.shape));\n    const [batch, imageHeight, imageWidth, numChannels] = image2.shape;\n    const [centerX, centerY] = backend_util_exports.getImageCenter(center, imageHeight, imageWidth);\n    const fullOpacityValue = 255;\n    const sinFactor = Math.sin(radians);\n    const cosFactor = Math.cos(radians);\n    const imageVals = cpuBackend.data.get(image2.dataId).values;\n    for (let batchIdx = 0; batchIdx < batch; batchIdx++) {\n      const batchOffset = batchIdx * imageWidth * imageHeight * numChannels;\n      for (let row = 0; row < imageHeight; row++) {\n        const rowOffset = row * (imageWidth * numChannels);\n        for (let col = 0; col < imageWidth; col++) {\n          const colOffset = col * numChannels;\n          for (let channel = 0; channel < numChannels; channel++) {\n            const coords3 = [batch, row, col, channel];\n            const x = coords3[2];\n            const y = coords3[1];\n            let coordX = (x - centerX) * cosFactor - (y - centerY) * sinFactor;\n            let coordY = (x - centerX) * sinFactor + (y - centerY) * cosFactor;\n            coordX = Math.round(coordX + centerX);\n            coordY = Math.round(coordY + centerY);\n            let outputValue = fillValue;\n            if (typeof fillValue !== \"number\") {\n              if (channel === 3) {\n                outputValue = fullOpacityValue;\n              } else {\n                outputValue = fillValue[channel];\n              }\n            }\n            if (coordX >= 0 && coordX < imageWidth && coordY >= 0 && coordY < imageHeight) {\n              const rotatedRowOffset = coordY * (imageWidth * numChannels);\n              const rotatedColOffset = coordX * numChannels;\n              const imageIdx = batchOffset + rotatedRowOffset + rotatedColOffset + channel;\n              outputValue = imageVals[imageIdx];\n            }\n            const outIdx = batchOffset + rowOffset + colOffset + channel;\n            output[outIdx] = outputValue;\n          }\n        }\n      }\n    }\n    const dataId = cpuBackend.write(output, image2.shape, image2.dtype);\n    return { dataId, shape: image2.shape, dtype: image2.dtype };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Round.js\nvar round3 = unaryKernelFunc(Round, (xi) => {\n  const base = Math.floor(xi);\n  if (xi - base < 0.5) {\n    return Math.floor(xi);\n  } else if (xi - base > 0.5) {\n    return Math.ceil(xi);\n  } else {\n    if (base % 2 === 0) {\n      return base;\n    } else {\n      return base + 1;\n    }\n  }\n});\nvar roundConfig = {\n  kernelName: Round,\n  backendName: \"cpu\",\n  kernelFunc: round3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/ScatterNd.js\nfunction scatterNd(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { indices, updates } = inputs;\n  const { shape } = attrs;\n  const { sliceRank, numUpdates, sliceSize, strides, outputSize } = backend_util_exports.calculateShapes(updates, indices, shape);\n  const sumDupeIndices = true;\n  const indicesBuf = backend2.bufferSync(indices);\n  const updatesBuf = backend2.bufferSync(updates);\n  const outBuf = scatterImpl(indicesBuf, updatesBuf, shape, outputSize, sliceSize, numUpdates, sliceRank, strides, 0, sumDupeIndices);\n  return backend2.makeTensorInfo(shape, outBuf.dtype, outBuf.values);\n}\nvar scatterNdConfig = {\n  kernelName: ScatterNd,\n  backendName: \"cpu\",\n  kernelFunc: scatterNd\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/SearchSorted_impl.js\nfunction lowerBound2(array2, value) {\n  let left = 0;\n  let right = array2.length;\n  let mid = 0;\n  while (left < right) {\n    mid = Math.floor((left + right) / 2);\n    if (array2[mid] < value) {\n      left = mid + 1;\n    } else {\n      right = mid;\n    }\n  }\n  return right;\n}\nfunction upperBound2(array2, value) {\n  let left = 0;\n  let right = array2.length;\n  let mid = 0;\n  while (left < right) {\n    mid = Math.floor((left + right) / 2);\n    if (array2[mid] <= value) {\n      left = mid + 1;\n    } else {\n      right = mid;\n    }\n  }\n  return right;\n}\nfunction searchSortedImpl(sortedInputs, values, batchSize, numInputs, numValues, side) {\n  const output = util_exports.getArrayFromDType(\"int32\", batchSize * numValues);\n  for (let b = 0; b < batchSize; ++b) {\n    const sortedInputsSlice = sortedInputs.slice(b * numInputs, (b + 1) * numInputs);\n    const outputOffset = b * numValues;\n    for (let i2 = 0; i2 < numValues; ++i2) {\n      output[outputOffset + i2] = side === \"left\" ? lowerBound2(sortedInputsSlice, values[i2 + outputOffset]) : upperBound2(sortedInputsSlice, values[i2 + outputOffset]);\n    }\n  }\n  return output;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/SearchSorted.js\nfunction searchSorted2(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { sortedSequence, values } = inputs;\n  const { side } = attrs;\n  const $sortedSequence = backend2.data.get(sortedSequence.dataId).values;\n  const $values = backend2.data.get(values.dataId).values;\n  const output = searchSortedImpl($sortedSequence, $values, sortedSequence.shape[0], sortedSequence.shape[1], values.shape[1], side);\n  return backend2.makeTensorInfo(values.shape, \"int32\", output);\n}\nvar searchSortedConfig = {\n  kernelName: SearchSorted,\n  backendName: \"cpu\",\n  kernelFunc: searchSorted2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Select.js\nfunction select2(args) {\n  const { inputs, backend: backend2 } = args;\n  const { condition, t: t2, e: e2 } = inputs;\n  assertNotComplex([condition, t2, e2], \"select\");\n  const conditionRank = condition.shape.length;\n  const values = backend2.data.get(condition.dataId).values;\n  const tValues = backend2.data.get(t2.dataId).values;\n  const eValues = backend2.data.get(e2.dataId).values;\n  const resultDtype = upcastType(t2.dtype, e2.dtype);\n  const newValues = util_exports.makeZerosTypedArray(util_exports.sizeFromShape(t2.shape), resultDtype);\n  let index = 0;\n  const offset = conditionRank === 0 || conditionRank > 1 || t2.shape.length === 1 ? 1 : util_exports.sizeFromShape(t2.shape.slice(1));\n  for (let i2 = 0; i2 < values.length; i2++) {\n    for (let j = 0; j < offset; j++) {\n      if (values[i2] === 1) {\n        newValues[index++] = tValues[i2];\n      } else {\n        newValues[index++] = eValues[i2];\n      }\n    }\n  }\n  return backend2.makeTensorInfo(t2.shape, resultDtype, newValues);\n}\nvar selectConfig = {\n  kernelName: Select,\n  backendName: \"cpu\",\n  kernelFunc: select2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Selu.js\nvar scaleAlpha = backend_util_exports.SELU_SCALEALPHA;\nvar scale = backend_util_exports.SELU_SCALE;\nvar selu2 = unaryKernelFunc(Selu, (xi) => {\n  if (xi >= 0) {\n    return scale * xi;\n  } else {\n    return scaleAlpha * (Math.exp(xi) - 1);\n  }\n});\nvar seluConfig = {\n  kernelName: Selu,\n  backendName: \"cpu\",\n  kernelFunc: selu2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Sign.js\nvar sign2 = unaryKernelFunc(Sign, (xi) => {\n  if (xi < 0) {\n    return -1;\n  } else if (xi > 0) {\n    return 1;\n  } else {\n    return 0;\n  }\n});\nvar signConfig = {\n  kernelName: Sign,\n  backendName: \"cpu\",\n  kernelFunc: sign2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Sin.js\nvar sin2 = unaryKernelFunc(Sin, (xi) => Math.sin(xi));\nvar sinConfig = {\n  kernelName: Sin,\n  backendName: \"cpu\",\n  kernelFunc: sin2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Sinh.js\nvar sinh2 = unaryKernelFunc(Sinh, (xi) => Math.sinh(xi));\nvar sinhConfig = {\n  kernelName: Sinh,\n  backendName: \"cpu\",\n  kernelFunc: sinh2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Softplus.js\nvar epsilon2 = 11920928955078125e-23;\nvar threshold2 = Math.log(epsilon2) + 2;\nvar softplus2 = unaryKernelFunc(Softplus, (xi) => {\n  const tooLarge = xi > -threshold2;\n  const tooSmall = xi < threshold2;\n  const expX = Math.exp(xi);\n  let result;\n  if (tooSmall) {\n    result = expX;\n  } else if (tooLarge) {\n    result = xi;\n  } else {\n    result = Math.log(1 + expX);\n  }\n  return result;\n});\nvar softplusConfig = {\n  kernelName: Softplus,\n  backendName: \"cpu\",\n  kernelFunc: softplus2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/SpaceToBatchND.js\nfunction spaceToBatchND2(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x } = inputs;\n  const { blockShape, paddings } = attrs;\n  assertNotComplex([x], \"spaceToBatchND\");\n  const prod6 = util_exports.sizeFromShape(blockShape);\n  const completePaddings = [[0, 0]];\n  completePaddings.push(...paddings);\n  for (let i2 = 1 + blockShape.length; i2 < x.shape.length; ++i2) {\n    completePaddings.push([0, 0]);\n  }\n  const paddedX = padV2Config.kernelFunc({\n    inputs: { x },\n    backend: backend2,\n    attrs: { paddings: completePaddings, constantValue: 0 }\n  });\n  const reshapedPaddedShape = backend_util_exports.getReshaped(paddedX.shape, blockShape, prod6, false);\n  const permutedReshapedPaddedPermutation = backend_util_exports.getPermuted(reshapedPaddedShape.length, blockShape.length, false);\n  const flattenShape = backend_util_exports.getReshapedPermuted(paddedX.shape, blockShape, prod6, false);\n  const reshapeInputs = { x: paddedX };\n  const reshapeAttrs = { shape: reshapedPaddedShape };\n  const paddedXReshaped = reshape3({ inputs: reshapeInputs, backend: backend2, attrs: reshapeAttrs });\n  const transposeInputs = { x: paddedXReshaped };\n  const transposeAttrs = { perm: permutedReshapedPaddedPermutation };\n  const paddedXT = transpose2({ inputs: transposeInputs, backend: backend2, attrs: transposeAttrs });\n  const resultReshapeInputs = { x: paddedXT };\n  const resultReshapeAttrs = { shape: flattenShape };\n  const result = reshape3({ inputs: resultReshapeInputs, backend: backend2, attrs: resultReshapeAttrs });\n  backend2.disposeIntermediateTensorInfo(paddedX);\n  backend2.disposeIntermediateTensorInfo(paddedXReshaped);\n  backend2.disposeIntermediateTensorInfo(paddedXT);\n  return result;\n}\nvar spaceToBatchNDConfig = {\n  kernelName: SpaceToBatchND,\n  backendName: \"cpu\",\n  kernelFunc: spaceToBatchND2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/SparseFillEmptyRows.js\nfunction sparseFillEmptyRows2(args) {\n  const { inputs, backend: backend2 } = args;\n  const { indices, values, denseShape, defaultValue } = inputs;\n  if (denseShape.shape.length !== 1) {\n    throw new Error(`Dense shape must be a vector, saw:\n        ${denseShape.shape}`);\n  }\n  if (indices.shape.length !== 2) {\n    throw new Error(`Indices must be a matrix, saw:\n        ${indices.shape}`);\n  }\n  if (values.shape.length !== 1) {\n    throw new Error(`Values must be a vector, saw:\n        ${values.shape}`);\n  }\n  if (defaultValue.shape.length !== 0) {\n    throw new Error(`Default value must be a scalar, saw:\n        ${defaultValue.shape}`);\n  }\n  const $indices = backend2.data.get(indices.dataId).values;\n  const $values = backend2.data.get(values.dataId).values;\n  const $denseShape = backend2.data.get(denseShape.dataId).values;\n  const $defaultValue = backend2.data.get(defaultValue.dataId).values[0];\n  const [outputIndices, outputIndicesShape, outputValues, emptyRowIndicator, reverseIndexMap] = sparseFillEmptyRowsImpl($indices, indices.shape, indices.dtype, $values, values.dtype, $denseShape, $defaultValue);\n  return [\n    backend2.makeTensorInfo(outputIndicesShape, indices.dtype, outputIndices),\n    backend2.makeTensorInfo([outputIndicesShape[0]], values.dtype, outputValues),\n    backend2.makeTensorInfo([emptyRowIndicator.length], \"bool\", new Uint8Array(emptyRowIndicator.map((value) => Number(value)))),\n    backend2.makeTensorInfo([reverseIndexMap.length], indices.dtype, new Int32Array(reverseIndexMap))\n  ];\n}\nvar sparseFillEmptyRowsConfig = {\n  kernelName: SparseFillEmptyRows,\n  backendName: \"cpu\",\n  kernelFunc: sparseFillEmptyRows2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/SparseReshape.js\nfunction sparseReshape2(args) {\n  const { inputs, backend: backend2 } = args;\n  const { inputIndices, inputShape, newShape } = inputs;\n  if (inputIndices.shape.length !== 2) {\n    throw new Error(`Input indices should be a matrix but received shape\n        ${inputIndices.shape}`);\n  }\n  if (inputShape.shape.length !== 1) {\n    throw new Error(`Input shape should be a vector but received shape\n        ${inputShape.shape}`);\n  }\n  if (newShape.shape.length !== 1) {\n    throw new Error(`Target shape should be a vector but received shape ${newShape.shape}`);\n  }\n  const $inputShape = Array.from(backend2.data.get(inputShape.dataId).values);\n  const $inputIndices = backend2.data.get(inputIndices.dataId).values;\n  const targetShape = Array.from(backend2.data.get(newShape.dataId).values);\n  const [newIndices, indicesShape, outputShape] = sparseReshapeImpl($inputIndices, inputIndices.shape, inputIndices.dtype, $inputShape, targetShape);\n  return [\n    backend2.makeTensorInfo(indicesShape, inputIndices.dtype, newIndices),\n    backend2.makeTensorInfo([outputShape.length], newShape.dtype, new Int32Array(outputShape))\n  ];\n}\nvar sparseReshapeConfig = {\n  kernelName: SparseReshape,\n  backendName: \"cpu\",\n  kernelFunc: sparseReshape2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/SparseSegmentMean.js\nfunction sparseSegmentMean2(args) {\n  const { inputs, backend: backend2 } = args;\n  const { data, indices, segmentIds } = inputs;\n  if (data.shape.length < 1) {\n    throw new Error(`Data should be at least 1 dimensional but received scalar`);\n  }\n  if (indices.shape.length !== 1) {\n    throw new Error(`Indices should be a vector but received shape\n          ${indices.shape}`);\n  }\n  if (segmentIds.shape.length !== 1) {\n    throw new Error(`Segment ids should be a vector but received shape\n          ${segmentIds.shape}`);\n  }\n  if (indices.shape[0] !== segmentIds.shape[0]) {\n    throw new Error(`segmentIds and indices should have same size.`);\n  }\n  const $data = backend2.data.get(data.dataId).values;\n  const $indices = backend2.data.get(indices.dataId).values;\n  const $segmentIds = backend2.data.get(segmentIds.dataId).values;\n  const [outputData, outputDataShape] = sparseSegmentReductionImpl($data, data.shape, data.dtype, $indices, $segmentIds, true);\n  return backend2.makeTensorInfo(outputDataShape, data.dtype, outputData);\n}\nvar sparseSegmentMeanConfig = {\n  kernelName: SparseSegmentMean,\n  backendName: \"cpu\",\n  kernelFunc: sparseSegmentMean2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/SparseSegmentSum.js\nfunction sparseSegmentSum2(args) {\n  const { inputs, backend: backend2 } = args;\n  const { data, indices, segmentIds } = inputs;\n  if (data.shape.length < 1) {\n    throw new Error(`Data should be at least 1 dimensional but received scalar`);\n  }\n  if (indices.shape.length !== 1) {\n    throw new Error(`Indices should be a vector but received shape\n         ${indices.shape}`);\n  }\n  if (segmentIds.shape.length !== 1) {\n    throw new Error(`Segment ids should be a vector but received shape\n         ${segmentIds.shape}`);\n  }\n  if (indices.shape[0] !== segmentIds.shape[0]) {\n    throw new Error(`segmentIds and indices should have same size.`);\n  }\n  const $data = backend2.data.get(data.dataId).values;\n  const $indices = backend2.data.get(indices.dataId).values;\n  const $segmentIds = backend2.data.get(segmentIds.dataId).values;\n  const [outputData, outputDataShape] = sparseSegmentReductionImpl($data, data.shape, data.dtype, $indices, $segmentIds);\n  return backend2.makeTensorInfo(outputDataShape, data.dtype, outputData);\n}\nvar sparseSegmentSumConfig = {\n  kernelName: SparseSegmentSum,\n  backendName: \"cpu\",\n  kernelFunc: sparseSegmentSum2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/SparseToDense.js\nfunction sparseToDense2(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { sparseIndices, sparseValues, defaultValue } = inputs;\n  const { outputShape } = attrs;\n  const { sliceRank, numUpdates, sliceSize, strides, outputSize } = backend_util_exports.calculateShapes(sparseValues, sparseIndices, outputShape);\n  const sumDupeIndices = false;\n  const indicesBuf = backend2.bufferSync(sparseIndices);\n  let outBuf;\n  switch (sparseValues.dtype) {\n    case \"bool\": {\n      const updatesBuf = backend2.bufferSync(sparseValues);\n      const $defaultValue = Boolean(backend2.data.get(defaultValue.dataId).values[0]);\n      outBuf = scatterImpl(indicesBuf, updatesBuf, outputShape, outputSize, sliceSize, numUpdates, sliceRank, strides, $defaultValue, sumDupeIndices);\n      break;\n    }\n    case \"float32\": {\n      const updatesBuf = backend2.bufferSync(sparseValues);\n      const $defaultValue = backend2.data.get(defaultValue.dataId).values[0];\n      outBuf = scatterImpl(indicesBuf, updatesBuf, outputShape, outputSize, sliceSize, numUpdates, sliceRank, strides, $defaultValue, sumDupeIndices);\n      break;\n    }\n    case \"int32\": {\n      const updatesBuf = backend2.bufferSync(sparseValues);\n      const $defaultValue = backend2.data.get(defaultValue.dataId).values[0];\n      outBuf = scatterImpl(indicesBuf, updatesBuf, outputShape, outputSize, sliceSize, numUpdates, sliceRank, strides, $defaultValue, sumDupeIndices);\n      break;\n    }\n    case \"string\": {\n      const updatesBuf = backend2.bufferSync(sparseValues);\n      const $defaultValue = util_exports.decodeString(backend2.data.get(defaultValue.dataId).values[0]);\n      outBuf = scatterImpl(indicesBuf, updatesBuf, outputShape, outputSize, sliceSize, numUpdates, sliceRank, strides, $defaultValue, sumDupeIndices);\n      break;\n    }\n    default:\n      throw new Error(`Unsupported type ${sparseValues.dtype}`);\n  }\n  return backend2.makeTensorInfo(outputShape, outBuf.dtype, outBuf.values);\n}\nvar sparseToDenseConfig = {\n  kernelName: SparseToDense,\n  backendName: \"cpu\",\n  kernelFunc: sparseToDense2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/SplitV.js\nfunction splitV(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x } = inputs;\n  const { numOrSizeSplits, axis } = attrs;\n  const $axis = util_exports.parseAxisParam(axis, x.shape)[0];\n  const splitSizes = backend_util_exports.prepareSplitSize(x, numOrSizeSplits, $axis);\n  const begin = new Array(x.shape.length).fill(0);\n  const size = x.shape.slice();\n  return splitSizes.map((s2) => {\n    const sliceSize = [...size];\n    sliceSize[$axis] = s2;\n    const sliceT = slice2({ inputs: { x }, backend: backend2, attrs: { begin, size: sliceSize } });\n    begin[$axis] += s2;\n    return sliceT;\n  });\n}\nvar splitVConfig = {\n  kernelName: SplitV,\n  backendName: \"cpu\",\n  kernelFunc: splitV\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Square.js\nvar squareConfig = {\n  kernelName: Square,\n  backendName: \"cpu\",\n  kernelFunc: ({ inputs, backend: backend2 }) => {\n    const { x } = inputs;\n    const cpuBackend = backend2;\n    assertNotComplex(x, \"square\");\n    const values = cpuBackend.data.get(x.dataId).values;\n    const newValues = new Float32Array(values.length);\n    for (let i2 = 0; i2 < values.length; ++i2) {\n      const value = values[i2];\n      newValues[i2] = value * value;\n    }\n    const dataId = cpuBackend.write(newValues, x.shape, x.dtype);\n    return { dataId, shape: x.shape, dtype: x.dtype };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Step.js\nvar step2 = unaryKernelFunc(Step, (xi, attrs) => {\n  const stepAttrs = attrs;\n  if (isNaN(xi)) {\n    return NaN;\n  } else {\n    return xi > 0 ? 1 : stepAttrs.alpha;\n  }\n});\nvar stepConfig = {\n  kernelName: Step,\n  backendName: \"cpu\",\n  kernelFunc: step2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/StridedSlice.js\nfunction stridedSlice2(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x } = inputs;\n  const { begin, end, strides, beginMask, endMask, ellipsisMask, newAxisMask, shrinkAxisMask } = attrs;\n  assertNotComplex(x, \"stridedSlice\");\n  const { finalShapeSparse, finalShape, isIdentity, sliceDim0, isSimpleSlice, begin: $begin, end: $end, strides: $strides } = slice_util_exports.sliceInfo(x.shape, begin, end, strides, beginMask, endMask, ellipsisMask, newAxisMask, shrinkAxisMask);\n  let result;\n  if (isIdentity) {\n    result = reshape3({ inputs: { x }, backend: backend2, attrs: { shape: finalShape } });\n  } else if (sliceDim0 || isSimpleSlice) {\n    util_exports.assert(x.shape.length >= 1, () => `Input must have rank at least 1, got: ${x.shape.length}`);\n    const size = slice_util_exports.computeOutShape($begin, $end, $strides);\n    const sliced = slice2({ inputs: { x }, backend: backend2, attrs: { begin: $begin, size } });\n    result = reshape3({ inputs: { x: sliced }, backend: backend2, attrs: { shape: finalShape } });\n    backend2.disposeIntermediateTensorInfo(sliced);\n  } else {\n    const xBuf = backend2.bufferSync(x);\n    const outBuf = stridedSliceImpl(finalShapeSparse, xBuf, $strides, $begin);\n    result = backend2.makeTensorInfo(finalShape, outBuf.dtype, outBuf.values);\n  }\n  return result;\n}\nvar stridedSliceConfig = {\n  kernelName: StridedSlice,\n  backendName: \"cpu\",\n  kernelFunc: stridedSlice2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/StringNGrams.js\nfunction stringNGrams2(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { separator, nGramWidths, leftPad, rightPad: rightPad2, padWidth, preserveShortSequences } = attrs;\n  const { data, dataSplits } = inputs;\n  const $data = backend2.data.get(data.dataId).values;\n  const $dataSplits = backend2.data.get(dataSplits.dataId).values;\n  const [nGrams, nGramsSplits] = stringNGramsImpl($data, $dataSplits, separator, nGramWidths, leftPad, rightPad2, padWidth, preserveShortSequences);\n  return [\n    backend2.makeTensorInfo([nGrams.length], \"string\", nGrams),\n    backend2.makeTensorInfo(dataSplits.shape, \"int32\", nGramsSplits)\n  ];\n}\nvar stringNGramsConfig = {\n  kernelName: StringNGrams,\n  backendName: \"cpu\",\n  kernelFunc: stringNGrams2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/StringSplit.js\nfunction stringSplit2(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { skipEmpty } = attrs;\n  const { input: input2, delimiter } = inputs;\n  if (input2.dtype !== \"string\") {\n    throw new Error(\"Input must be of datatype string\");\n  }\n  if (input2.shape.length !== 1) {\n    throw new Error(`Input must be a vector, got shape: ${input2.shape}`);\n  }\n  if (delimiter.shape.length !== 0) {\n    throw new Error(`Delimiter must be a scalar, got shape: ${delimiter.shape}`);\n  }\n  const $input = backend2.data.get(input2.dataId).values;\n  const $delimiter = backend2.data.get(delimiter.dataId).values[0];\n  const [indices, values, shape] = stringSplitImpl($input, $delimiter, skipEmpty);\n  const outputSize = values.length;\n  return [\n    backend2.makeTensorInfo([outputSize, 2], \"int32\", indices),\n    backend2.makeTensorInfo([outputSize], \"string\", values),\n    backend2.makeTensorInfo([2], \"int32\", new Int32Array(shape))\n  ];\n}\nvar stringSplitConfig = {\n  kernelName: StringSplit,\n  backendName: \"cpu\",\n  kernelFunc: stringSplit2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/StringToHashBucketFast.js\nfunction stringToHashBucketFast2(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { numBuckets } = attrs;\n  const { input: input2 } = inputs;\n  if (input2.dtype !== \"string\") {\n    throw new Error(\"Input must be of datatype string\");\n  }\n  if (numBuckets <= 0) {\n    throw new Error(`Number of buckets must be at least 1`);\n  }\n  const $input = backend2.data.get(input2.dataId).values;\n  const output = stringToHashBucketFastImpl($input, numBuckets);\n  return backend2.makeTensorInfo(input2.shape, \"int32\", output);\n}\nvar stringToHashBucketFastConfig = {\n  kernelName: StringToHashBucketFast,\n  backendName: \"cpu\",\n  kernelFunc: stringToHashBucketFast2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Tan.js\nvar tan2 = unaryKernelFunc(Tan, (xi) => Math.tan(xi));\nvar tanConfig = {\n  kernelName: Tan,\n  backendName: \"cpu\",\n  kernelFunc: tan2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Tanh.js\nvar tanh3 = unaryKernelFunc(Tanh, (xi) => Math.tanh(xi));\nvar tanhConfig = {\n  kernelName: Tanh,\n  backendName: \"cpu\",\n  kernelFunc: tanh3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Tile.js\nfunction tile3(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x } = inputs;\n  const { reps } = attrs;\n  assertNotComplex(x, \"tile\");\n  const outBuf = tileImpl(backend2.bufferSync(x), reps);\n  return backend2.makeTensorInfo(outBuf.shape, outBuf.dtype, outBuf.values);\n}\nvar tileConfig = {\n  kernelName: Tile,\n  backendName: \"cpu\",\n  kernelFunc: tile3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/TopK.js\nfunction topK(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x } = inputs;\n  const { k, sorted } = attrs;\n  assertNotComplex(x, \"topk\");\n  const xVals = backend2.data.get(x.dataId).values;\n  const [allTopKVals, allTopKIndices] = topKImpl(xVals, x.shape, x.dtype, k, sorted);\n  return [\n    backend2.makeTensorInfo(allTopKVals.shape, allTopKVals.dtype, allTopKVals.values),\n    backend2.makeTensorInfo(allTopKIndices.shape, allTopKIndices.dtype, allTopKIndices.values)\n  ];\n}\nvar topKConfig = {\n  kernelName: TopK,\n  backendName: \"cpu\",\n  kernelFunc: topK\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Transform.js\nfunction transform2(args) {\n  const { inputs, attrs, backend: backend2 } = args;\n  const { image: image2, transforms } = inputs;\n  const { interpolation, fillMode, fillValue, outputShape } = attrs;\n  const [batch, imageHeight, imageWidth, numChannels] = image2.shape;\n  const [outHeight, outWidth] = outputShape != null ? outputShape : [imageHeight, imageWidth];\n  const outShape = [batch, outHeight, outWidth, numChannels];\n  const inStrides = util_exports.computeStrides(image2.shape);\n  const batchInStride = inStrides[0];\n  const rowInStride = inStrides[1];\n  const colInStride = inStrides[2];\n  const outStrides = util_exports.computeStrides(outShape);\n  const batchOutStride = outStrides[0];\n  const rowOutStride = outStrides[1];\n  const colOutStride = outStrides[2];\n  const outVals = util_exports.getTypedArrayFromDType(image2.dtype, util_exports.sizeFromShape(outShape));\n  outVals.fill(fillValue);\n  const imageVals = backend2.data.get(image2.dataId).values;\n  const transformVals = backend2.data.get(transforms.dataId).values;\n  for (let b = 0; b < batch; ++b) {\n    const transform6 = transforms.shape[0] === 1 ? transformVals : transformVals.subarray(b * 8, b * 8 + 8);\n    for (let outY = 0; outY < outHeight; ++outY) {\n      for (let outX = 0; outX < outWidth; ++outX) {\n        for (let channel = 0; channel < numChannels; ++channel) {\n          let val;\n          const projection = transform6[6] * outX + transform6[7] * outY + 1;\n          if (projection === 0) {\n            continue;\n          }\n          const inX = (transform6[0] * outX + transform6[1] * outY + transform6[2]) / projection;\n          const inY = (transform6[3] * outX + transform6[4] * outY + transform6[5]) / projection;\n          const x = mapCoord(inX, imageWidth, fillMode);\n          const y = mapCoord(inY, imageHeight, fillMode);\n          switch (interpolation) {\n            case \"nearest\":\n              val = nearestInterpolation(imageVals, imageHeight, imageWidth, batchInStride, rowInStride, colInStride, b, y, x, channel, fillValue);\n              break;\n            case \"bilinear\":\n              val = bilinearInterpolation(imageVals, imageHeight, imageWidth, batchInStride, rowInStride, colInStride, b, y, x, channel, fillValue);\n              break;\n            default:\n              throw new Error(`Error in Transform: Expect 'nearest' or 'bilinear', but got ${interpolation}`);\n          }\n          const ind = b * batchOutStride + outY * rowOutStride + outX * colOutStride + channel;\n          outVals[ind] = val;\n        }\n      }\n    }\n    return backend2.makeTensorInfo(outShape, image2.dtype, outVals);\n  }\n  const dataId = backend2.write(outVals, outShape, image2.dtype);\n  return { dataId, shape: image2.shape, dtype: image2.dtype };\n}\nvar transformConfig = {\n  kernelName: Transform,\n  backendName: \"cpu\",\n  kernelFunc: transform2\n};\nfunction mapCoord(outCoord, len, mode) {\n  switch (mode) {\n    case \"reflect\":\n      return mapCoordReflect(outCoord, len);\n    case \"wrap\":\n      return mapCoordWrap(outCoord, len);\n    case \"nearest\":\n      return mapCoordNearest(outCoord, len);\n    case \"constant\":\n    default:\n      return mapCoordConstant(outCoord, len);\n  }\n}\nfunction mapCoordReflect(outCoord, len) {\n  let inCoord = outCoord;\n  if (inCoord < 0) {\n    if (len <= 1) {\n      inCoord = 0;\n    } else {\n      const sz2 = 2 * len;\n      if (inCoord < sz2) {\n        inCoord = sz2 * Math.trunc(-inCoord / sz2) + inCoord;\n      }\n      inCoord = inCoord < -len ? inCoord + sz2 : -inCoord - 1;\n    }\n  } else if (inCoord > len - 1) {\n    if (len <= 1) {\n      inCoord = 0;\n    } else {\n      const sz2 = 2 * len;\n      inCoord -= sz2 * Math.trunc(inCoord / sz2);\n      if (inCoord >= len) {\n        inCoord = sz2 - inCoord - 1;\n      }\n    }\n  }\n  return util_exports.clamp(0, inCoord, len - 1);\n}\nfunction mapCoordWrap(outCoord, len) {\n  let inCoord = outCoord;\n  if (inCoord < 0) {\n    if (len <= 1) {\n      inCoord = 0;\n    } else {\n      const sz = len - 1;\n      inCoord += len * (Math.trunc(-inCoord / sz) + 1);\n    }\n  } else if (inCoord > len - 1) {\n    if (len <= 1) {\n      inCoord = 0;\n    } else {\n      const sz = len - 1;\n      inCoord -= len * Math.trunc(inCoord / sz);\n    }\n  }\n  return util_exports.clamp(0, inCoord, len - 1);\n}\nfunction mapCoordConstant(outCoord, len) {\n  return outCoord;\n}\nfunction mapCoordNearest(outCoord, len) {\n  return util_exports.clamp(0, outCoord, len - 1);\n}\nfunction readWithFillValue(imageVals, imageHeight, imageWidth, batchStride, rowStride, colStride, batch, y, x, channel, fillValue) {\n  const ind = batch * batchStride + y * rowStride + x * colStride + channel;\n  if (0 <= y && y < imageHeight && 0 <= x && x < imageWidth) {\n    return imageVals[ind];\n  } else {\n    return fillValue;\n  }\n}\nfunction nearestInterpolation(imageVals, imageHeight, imageWidth, batchStride, rowStride, colStride, batch, y, x, channel, fillValue) {\n  const $y = Math.round(y);\n  const $x = Math.round(x);\n  return readWithFillValue(imageVals, imageHeight, imageWidth, batchStride, rowStride, colStride, batch, $y, $x, channel, fillValue);\n}\nfunction bilinearInterpolation(imageVals, imageHeight, imageWidth, batchStride, rowStride, colStride, batch, y, x, channel, fillValue) {\n  const yFloor = Math.floor(y);\n  const xFloor = Math.floor(x);\n  const yCeil = yFloor + 1;\n  const xCeil = xFloor + 1;\n  const valueYFloor = (xCeil - x) * readWithFillValue(imageVals, imageHeight, imageWidth, batchStride, rowStride, colStride, batch, yFloor, xFloor, channel, fillValue) + (x - xFloor) * readWithFillValue(imageVals, imageHeight, imageWidth, batchStride, rowStride, colStride, batch, yFloor, xCeil, channel, fillValue);\n  const valueYCeil = (xCeil - x) * readWithFillValue(imageVals, imageHeight, imageWidth, batchStride, rowStride, colStride, batch, yCeil, xFloor, channel, fillValue) + (x - xFloor) * readWithFillValue(imageVals, imageHeight, imageWidth, batchStride, rowStride, colStride, batch, yCeil, xCeil, channel, fillValue);\n  return (yCeil - y) * valueYFloor + (y - yFloor) * valueYCeil;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Unique.js\nfunction unique3(args) {\n  const { inputs, attrs, backend: backend2 } = args;\n  const { axis } = attrs;\n  const { x } = inputs;\n  assertNotComplex(x, \"unique\");\n  const values = backend2.data.get(x.dataId).values;\n  const { outputValues, outputShape, indices } = uniqueImpl(values, axis, x.shape, x.dtype);\n  return [\n    backend2.makeTensorInfo(outputShape, x.dtype, outputValues),\n    backend2.makeTensorInfo([indices.length], \"int32\", indices)\n  ];\n}\nvar uniqueConfig = {\n  kernelName: Unique,\n  backendName: \"cpu\",\n  kernelFunc: unique3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Unpack.js\nfunction unpack(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { value } = inputs;\n  let { axis } = attrs;\n  if (axis < 0) {\n    axis += value.shape.length;\n  }\n  const valueRank = value.shape.length;\n  const num = value.shape[axis];\n  const outShape = new Array(valueRank - 1);\n  let outIndex = 0;\n  for (let i2 = 0; i2 < valueRank; i2++) {\n    if (i2 !== axis) {\n      outShape[outIndex++] = value.shape[i2];\n    }\n  }\n  const begin = new Array(valueRank).fill(0);\n  const size = value.shape.slice();\n  size[axis] = 1;\n  const res = new Array(num);\n  for (let i2 = 0; i2 < res.length; i2++) {\n    begin[axis] = i2;\n    const tempRes = slice2({ inputs: { x: value }, backend: backend2, attrs: { begin, size } });\n    res[i2] = reshape3({ inputs: { x: tempRes }, backend: backend2, attrs: { shape: outShape } });\n    backend2.disposeIntermediateTensorInfo(tempRes);\n  }\n  return res;\n}\nvar unpackConfig = {\n  kernelName: Unpack,\n  backendName: \"cpu\",\n  kernelFunc: unpack\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/UnsortedSegmentSum.js\nfunction unsortedSegmentSum2(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x, segmentIds } = inputs;\n  const { numSegments } = attrs;\n  assertNotComplex(x, \"unsortedSegmentSum\");\n  const xRank = x.shape.length;\n  const segmentIdsRank = segmentIds.shape.length;\n  const res = [];\n  const intermediates = [];\n  const numIters = xRank - segmentIdsRank;\n  let $segmentIds = segmentIds;\n  for (let i2 = 0; i2 < numIters; ++i2) {\n    const expanded = expandDims3({ inputs: { input: $segmentIds }, backend: backend2, attrs: { dim: i2 + 1 } });\n    $segmentIds = expanded;\n    intermediates.push(expanded);\n  }\n  for (let i2 = 0; i2 < numSegments; ++i2) {\n    const scalarValue = util_exports.createScalarValue(i2, \"int32\");\n    const segmentId = backend2.makeTensorInfo([], \"int32\", scalarValue);\n    const mask = equal2({ inputs: { a: segmentId, b: $segmentIds }, backend: backend2 });\n    const maskCasted = cast3({ inputs: { x: mask }, backend: backend2, attrs: { dtype: \"float32\" } });\n    const mul2 = multiply2({ inputs: { a: maskCasted, b: x }, backend: backend2 });\n    const sumTensorInfo = sum3({ inputs: { x: mul2 }, backend: backend2, attrs: { axis: 0, keepDims: false } });\n    res.push(sumTensorInfo);\n    intermediates.push(segmentId);\n    intermediates.push(mask);\n    intermediates.push(maskCasted);\n    intermediates.push(mul2);\n    intermediates.push(sumTensorInfo);\n  }\n  const result = pack({ inputs: res, backend: backend2, attrs: { axis: 0 } });\n  intermediates.forEach((t2) => backend2.disposeIntermediateTensorInfo(t2));\n  return result;\n}\nvar unsortedSegmentSumConfig = {\n  kernelName: UnsortedSegmentSum,\n  backendName: \"cpu\",\n  kernelFunc: unsortedSegmentSum2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/register_all_kernels.js\nvar kernelConfigs = [\n  _fusedMatMulConfig,\n  absConfig,\n  acosConfig,\n  acoshConfig,\n  addConfig,\n  addNConfig,\n  allConfig,\n  anyConfig,\n  argMaxConfig,\n  argMinConfig,\n  asinConfig,\n  asinhConfig,\n  atanConfig,\n  atan2Config,\n  atanhConfig,\n  avgPoolConfig,\n  avgPool3DConfig,\n  avgPool3DGradConfig2,\n  avgPoolGradConfig2,\n  batchMatMulConfig,\n  batchNormConfig,\n  batchToSpaceNDConfig,\n  bincountConfig,\n  broadcastArgsConfig,\n  castConfig,\n  ceilConfig,\n  clipByValueConfig,\n  complexConfig,\n  complexAbsConfig,\n  concatConfig,\n  conv2DConfig,\n  conv2DBackpropFilterConfig,\n  conv2DBackpropInputConfig,\n  conv3DConfig,\n  conv3DBackpropFilterV2Config,\n  conv3DBackpropInputV2Config,\n  cosConfig,\n  coshConfig,\n  cropAndResizeConfig,\n  cumprodConfig,\n  cumsumConfig,\n  denseBincountConfig,\n  depthToSpaceConfig,\n  depthwiseConv2dNativeConfig,\n  depthwiseConv2dNativeBackpropFilterConfig,\n  depthwiseConv2dNativeBackpropInputConfig,\n  diagConfig,\n  dilation2DConfig,\n  dilation2DBackpropFilterConfig,\n  dilation2DBackpropInputConfig,\n  einsumConfig,\n  eluConfig,\n  eluGradConfig2,\n  equalConfig,\n  erfConfig,\n  expConfig,\n  expandDimsConfig,\n  expm1Config,\n  fftConfig,\n  fillConfig,\n  flipLeftRightConfig,\n  floorConfig,\n  floorDivConfig,\n  fusedConv2DConfig,\n  fusedDepthwiseConv2DConfig,\n  gatherNdConfig,\n  gatherV2Config,\n  greaterConfig,\n  greaterEqualConfig,\n  identityConfig,\n  ifftConfig,\n  imagConfig,\n  isFiniteConfig,\n  isInfConfig,\n  isNaNConfig,\n  leakyReluConfig,\n  lessConfig,\n  lessEqualConfig,\n  linSpaceConfig,\n  logConfig,\n  log1pConfig,\n  logicalAndConfig,\n  logicalNotConfig,\n  logicalOrConfig,\n  LRNConfig,\n  LRNGradConfig,\n  maxConfig,\n  maximumConfig,\n  maxPoolConfig,\n  maxPool3DConfig,\n  maxPool3DGradConfig2,\n  maxPoolGradConfig2,\n  maxPoolWithArgmaxConfig,\n  meanConfig,\n  minConfig,\n  minimumConfig,\n  mirrorPadConfig,\n  modConfig,\n  multinomialConfig,\n  multiplyConfig,\n  negConfig,\n  nonMaxSuppressionV3Config,\n  nonMaxSuppressionV4Config,\n  nonMaxSuppressionV5Config,\n  notEqualConfig,\n  oneHotConfig,\n  onesLikeConfig,\n  packConfig,\n  padV2Config,\n  powConfig,\n  preluConfig,\n  prodConfig,\n  raggedTensorToTensorConfig,\n  rangeConfig,\n  realConfig,\n  realDivConfig,\n  reciprocalConfig,\n  reluConfig,\n  relu6Config,\n  reshapeConfig,\n  resizeBilinearConfig,\n  resizeBilinearGradConfig2,\n  resizeNearestNeighborConfig,\n  resizeNearestNeighborGradConfig2,\n  reverseConfig,\n  rotateWithOffsetConfig,\n  roundConfig,\n  rsqrtConfig,\n  scatterNdConfig,\n  searchSortedConfig,\n  selectConfig,\n  seluConfig,\n  sigmoidConfig,\n  signConfig,\n  sinConfig,\n  sinhConfig,\n  sliceConfig,\n  softmaxConfig,\n  softplusConfig,\n  spaceToBatchNDConfig,\n  sparseFillEmptyRowsConfig,\n  sparseReshapeConfig,\n  sparseSegmentMeanConfig,\n  sparseSegmentSumConfig,\n  sparseToDenseConfig,\n  splitVConfig,\n  sqrtConfig,\n  squareConfig,\n  squaredDifferenceConfig,\n  stepConfig,\n  stridedSliceConfig,\n  stringNGramsConfig,\n  stringSplitConfig,\n  stringToHashBucketFastConfig,\n  subConfig,\n  sumConfig,\n  tanConfig,\n  tanhConfig,\n  tileConfig,\n  topKConfig,\n  transformConfig,\n  transposeConfig,\n  uniqueConfig,\n  unpackConfig,\n  unsortedSegmentSumConfig,\n  zerosLikeConfig\n];\nfor (const kernelConfig of kernelConfigs) {\n  registerKernel(kernelConfig);\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/webgl_util.js\nvar webgl_util_exports = {};\n__export(webgl_util_exports, {\n  assertNotComplex: () => assertNotComplex2,\n  bindCanvasToFramebuffer: () => bindCanvasToFramebuffer,\n  bindColorTextureToFramebuffer: () => bindColorTextureToFramebuffer,\n  bindTextureToProgramUniformSampler: () => bindTextureToProgramUniformSampler,\n  bindTextureUnit: () => bindTextureUnit,\n  bindVertexBufferToProgramAttribute: () => bindVertexBufferToProgramAttribute,\n  callAndCheck: () => callAndCheck,\n  canBeRepresented: () => canBeRepresented,\n  createFragmentShader: () => createFragmentShader,\n  createFramebuffer: () => createFramebuffer,\n  createProgram: () => createProgram,\n  createStaticIndexBuffer: () => createStaticIndexBuffer,\n  createStaticVertexBuffer: () => createStaticVertexBuffer,\n  createTexture: () => createTexture,\n  createVertexShader: () => createVertexShader,\n  getBatchDim: () => getBatchDim,\n  getExtensionOrThrow: () => getExtensionOrThrow,\n  getFramebufferErrorMessage: () => getFramebufferErrorMessage,\n  getMaxTexturesInShader: () => getMaxTexturesInShader,\n  getNumChannels: () => getNumChannels,\n  getProgramUniformLocation: () => getProgramUniformLocation,\n  getProgramUniformLocationOrThrow: () => getProgramUniformLocationOrThrow,\n  getRowsCols: () => getRowsCols,\n  getShapeAs3D: () => getShapeAs3D,\n  getTextureShapeFromLogicalShape: () => getTextureShapeFromLogicalShape,\n  getWebGLDisjointQueryTimerVersion: () => getWebGLDisjointQueryTimerVersion,\n  getWebGLErrorMessage: () => getWebGLErrorMessage,\n  getWebGLMaxTextureSize: () => getWebGLMaxTextureSize,\n  hasExtension: () => hasExtension,\n  isCapableOfRenderingToFloatTexture: () => isCapableOfRenderingToFloatTexture,\n  isDownloadFloatTextureEnabled: () => isDownloadFloatTextureEnabled,\n  isReshapeFree: () => isReshapeFree,\n  isWebGLFenceEnabled: () => isWebGLFenceEnabled,\n  isWebGLVersionEnabled: () => isWebGLVersionEnabled,\n  linkProgram: () => linkProgram,\n  logShaderSourceAndInfoLog: () => logShaderSourceAndInfoLog,\n  resetMaxTextureSize: () => resetMaxTextureSize,\n  resetMaxTexturesInShader: () => resetMaxTexturesInShader,\n  unbindColorTextureFromFramebuffer: () => unbindColorTextureFromFramebuffer,\n  unbindTextureUnit: () => unbindTextureUnit,\n  validateFramebuffer: () => validateFramebuffer,\n  validateProgram: () => validateProgram,\n  validateTextureSize: () => validateTextureSize\n});\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/canvas_util.js\nvar contexts = {};\nvar WEBGL_ATTRIBUTES = {\n  alpha: false,\n  antialias: false,\n  premultipliedAlpha: false,\n  preserveDrawingBuffer: false,\n  depth: false,\n  stencil: false,\n  failIfMajorPerformanceCaveat: true\n};\nfunction setWebGLContext(webGLVersion, gl) {\n  contexts[webGLVersion] = gl;\n}\nfunction getWebGLContext(webGLVersion, customCanvas) {\n  if (!(webGLVersion in contexts) || customCanvas != null) {\n    const newCtx = getWebGLRenderingContext(webGLVersion, customCanvas);\n    if (newCtx !== null) {\n      contexts[webGLVersion] = newCtx;\n    } else {\n      console.log(\"Could not get context for WebGL version\", webGLVersion);\n      return null;\n    }\n  }\n  const gl = contexts[webGLVersion];\n  if (gl == null || gl.isContextLost()) {\n    delete contexts[webGLVersion];\n    return getWebGLContext(webGLVersion);\n  }\n  gl.disable(gl.DEPTH_TEST);\n  gl.disable(gl.STENCIL_TEST);\n  gl.disable(gl.BLEND);\n  gl.disable(gl.DITHER);\n  gl.disable(gl.POLYGON_OFFSET_FILL);\n  gl.disable(gl.SAMPLE_COVERAGE);\n  gl.enable(gl.SCISSOR_TEST);\n  gl.enable(gl.CULL_FACE);\n  gl.cullFace(gl.BACK);\n  return contexts[webGLVersion];\n}\nfunction createCanvas(webGLVersion) {\n  if (typeof OffscreenCanvas !== \"undefined\" && webGLVersion === 2) {\n    return new OffscreenCanvas(300, 150);\n  } else if (typeof document !== \"undefined\") {\n    return document.createElement(\"canvas\");\n  } else {\n    throw new Error(\"Cannot create a canvas in this context\");\n  }\n}\nfunction getWebGLRenderingContext(webGLVersion, customCanvas) {\n  if (webGLVersion !== 1 && webGLVersion !== 2) {\n    throw new Error(\"Cannot get WebGL rendering context, WebGL is disabled.\");\n  }\n  const canvas = customCanvas == null ? createCanvas(webGLVersion) : customCanvas;\n  canvas.addEventListener(\"webglcontextlost\", (ev) => {\n    ev.preventDefault();\n    delete contexts[webGLVersion];\n  }, false);\n  if (env().getBool(\"SOFTWARE_WEBGL_ENABLED\")) {\n    WEBGL_ATTRIBUTES.failIfMajorPerformanceCaveat = false;\n  }\n  if (webGLVersion === 1) {\n    return canvas.getContext(\"webgl\", WEBGL_ATTRIBUTES) || canvas.getContext(\"experimental-webgl\", WEBGL_ATTRIBUTES);\n  }\n  return canvas.getContext(\"webgl2\", WEBGL_ATTRIBUTES);\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/tex_util.js\nvar PackingScheme;\n(function(PackingScheme2) {\n  PackingScheme2[PackingScheme2[\"DENSE\"] = 0] = \"DENSE\";\n  PackingScheme2[PackingScheme2[\"SHARED_BATCH\"] = 1] = \"SHARED_BATCH\";\n})(PackingScheme || (PackingScheme = {}));\nvar TextureUsage;\n(function(TextureUsage2) {\n  TextureUsage2[TextureUsage2[\"RENDER\"] = 0] = \"RENDER\";\n  TextureUsage2[TextureUsage2[\"UPLOAD\"] = 1] = \"UPLOAD\";\n  TextureUsage2[TextureUsage2[\"PIXELS\"] = 2] = \"PIXELS\";\n  TextureUsage2[TextureUsage2[\"DOWNLOAD\"] = 3] = \"DOWNLOAD\";\n})(TextureUsage || (TextureUsage = {}));\nvar PhysicalTextureType;\n(function(PhysicalTextureType2) {\n  PhysicalTextureType2[PhysicalTextureType2[\"UNPACKED_FLOAT16\"] = 0] = \"UNPACKED_FLOAT16\";\n  PhysicalTextureType2[PhysicalTextureType2[\"UNPACKED_FLOAT32\"] = 1] = \"UNPACKED_FLOAT32\";\n  PhysicalTextureType2[PhysicalTextureType2[\"PACKED_4X1_UNSIGNED_BYTE\"] = 2] = \"PACKED_4X1_UNSIGNED_BYTE\";\n  PhysicalTextureType2[PhysicalTextureType2[\"PACKED_2X2_FLOAT32\"] = 3] = \"PACKED_2X2_FLOAT32\";\n  PhysicalTextureType2[PhysicalTextureType2[\"PACKED_2X2_FLOAT16\"] = 4] = \"PACKED_2X2_FLOAT16\";\n})(PhysicalTextureType || (PhysicalTextureType = {}));\nfunction getUnpackedMatrixTextureShapeWidthHeight(rows, columns) {\n  return [columns, rows];\n}\nfunction getUnpackedArraySizeFromMatrixSize(matrixSize, channelsPerTexture) {\n  return matrixSize * channelsPerTexture;\n}\nfunction getDenseTexShape(shape) {\n  const size = util_exports.sizeFromShape(shape);\n  const texelsNeeded = Math.ceil(size / 4);\n  return util_exports.sizeToSquarishShape(texelsNeeded);\n}\nfunction getPackedMatrixTextureShapeWidthHeight(rows, columns) {\n  return [\n    Math.max(1, Math.ceil(columns / 2)),\n    Math.max(1, Math.ceil(rows / 2))\n  ];\n}\nfunction getPackedRGBAArraySizeFromMatrixShape(rows, columns) {\n  const [w, h] = getPackedMatrixTextureShapeWidthHeight(rows, columns);\n  return w * h * 4;\n}\nfunction getTextureConfig(gl, textureHalfFloatExtension) {\n  const glany = gl;\n  let internalFormatFloat;\n  let internalFormatHalfFloat;\n  let internalFormatPackedHalfFloat;\n  let internalFormatPackedFloat;\n  let textureFormatFloat;\n  let downloadTextureFormat;\n  let downloadUnpackNumChannels;\n  let defaultNumChannels;\n  let textureTypeHalfFloat;\n  let textureTypeFloat;\n  if (env().getNumber(\"WEBGL_VERSION\") === 2) {\n    internalFormatFloat = glany.R32F;\n    internalFormatHalfFloat = glany.R16F;\n    internalFormatPackedHalfFloat = glany.RGBA16F;\n    internalFormatPackedFloat = glany.RGBA32F;\n    textureFormatFloat = glany.RED;\n    downloadUnpackNumChannels = 4;\n    defaultNumChannels = 1;\n    textureTypeHalfFloat = glany.HALF_FLOAT;\n    textureTypeFloat = glany.FLOAT;\n    downloadTextureFormat = glany.RGBA8;\n  } else {\n    internalFormatFloat = gl.RGBA;\n    internalFormatHalfFloat = gl.RGBA;\n    internalFormatPackedHalfFloat = gl.RGBA;\n    internalFormatPackedFloat = glany.RGBA;\n    textureFormatFloat = gl.RGBA;\n    downloadUnpackNumChannels = 4;\n    defaultNumChannels = 4;\n    textureTypeHalfFloat = textureHalfFloatExtension != null ? textureHalfFloatExtension.HALF_FLOAT_OES : null;\n    textureTypeFloat = gl.FLOAT;\n    downloadTextureFormat = gl.RGBA;\n  }\n  return {\n    internalFormatFloat,\n    internalFormatHalfFloat,\n    internalFormatPackedHalfFloat,\n    internalFormatPackedFloat,\n    textureFormatFloat,\n    downloadTextureFormat,\n    downloadUnpackNumChannels,\n    defaultNumChannels,\n    textureTypeHalfFloat,\n    textureTypeFloat\n  };\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/webgl_util.js\nfunction callAndCheck(gl, func2) {\n  const returnValue = func2();\n  if (env().getBool(\"DEBUG\")) {\n    checkWebGLError(gl);\n  }\n  return returnValue;\n}\nfunction checkWebGLError(gl) {\n  const error = gl.getError();\n  if (error !== gl.NO_ERROR) {\n    throw new Error(\"WebGL Error: \" + getWebGLErrorMessage(gl, error));\n  }\n}\nvar MIN_FLOAT16 = 596e-10;\nvar MAX_FLOAT16 = 65504;\nfunction canBeRepresented(num) {\n  if (env().getBool(\"WEBGL_RENDER_FLOAT32_ENABLED\") || num === 0 || MIN_FLOAT16 < Math.abs(num) && Math.abs(num) < MAX_FLOAT16) {\n    return true;\n  }\n  return false;\n}\nfunction getWebGLErrorMessage(gl, status) {\n  switch (status) {\n    case gl.NO_ERROR:\n      return \"NO_ERROR\";\n    case gl.INVALID_ENUM:\n      return \"INVALID_ENUM\";\n    case gl.INVALID_VALUE:\n      return \"INVALID_VALUE\";\n    case gl.INVALID_OPERATION:\n      return \"INVALID_OPERATION\";\n    case gl.INVALID_FRAMEBUFFER_OPERATION:\n      return \"INVALID_FRAMEBUFFER_OPERATION\";\n    case gl.OUT_OF_MEMORY:\n      return \"OUT_OF_MEMORY\";\n    case gl.CONTEXT_LOST_WEBGL:\n      return \"CONTEXT_LOST_WEBGL\";\n    default:\n      return `Unknown error code ${status}`;\n  }\n}\nfunction getExtensionOrThrow(gl, extensionName) {\n  return throwIfNull(gl, () => gl.getExtension(extensionName), 'Extension \"' + extensionName + '\" not supported on this browser.');\n}\nfunction createVertexShader(gl, vertexShaderSource) {\n  const vertexShader = throwIfNull(gl, () => gl.createShader(gl.VERTEX_SHADER), \"Unable to create vertex WebGLShader.\");\n  callAndCheck(gl, () => gl.shaderSource(vertexShader, vertexShaderSource));\n  callAndCheck(gl, () => gl.compileShader(vertexShader));\n  if (gl.getShaderParameter(vertexShader, gl.COMPILE_STATUS) === false) {\n    console.log(gl.getShaderInfoLog(vertexShader));\n    throw new Error(\"Failed to compile vertex shader.\");\n  }\n  return vertexShader;\n}\nfunction createFragmentShader(gl, fragmentShaderSource) {\n  const fragmentShader = throwIfNull(gl, () => gl.createShader(gl.FRAGMENT_SHADER), \"Unable to create fragment WebGLShader.\");\n  callAndCheck(gl, () => gl.shaderSource(fragmentShader, fragmentShaderSource));\n  callAndCheck(gl, () => gl.compileShader(fragmentShader));\n  if (env().get(\"ENGINE_COMPILE_ONLY\")) {\n    return fragmentShader;\n  }\n  if (gl.getShaderParameter(fragmentShader, gl.COMPILE_STATUS) === false) {\n    logShaderSourceAndInfoLog(fragmentShaderSource, gl.getShaderInfoLog(fragmentShader));\n    throw new Error(\"Failed to compile fragment shader.\");\n  }\n  return fragmentShader;\n}\nvar lineNumberRegex = /ERROR: [0-9]+:([0-9]+):/g;\nfunction logShaderSourceAndInfoLog(shaderSource, shaderInfoLog) {\n  const lineNumberRegexResult = lineNumberRegex.exec(shaderInfoLog);\n  if (lineNumberRegexResult == null) {\n    console.log(`Couldn't parse line number in error: ${shaderInfoLog}`);\n    console.log(shaderSource);\n    return;\n  }\n  const lineNumber = +lineNumberRegexResult[1];\n  const shaderLines = shaderSource.split(\"\\n\");\n  const pad3 = shaderLines.length.toString().length + 2;\n  const linesWithLineNumbers = shaderLines.map((line, lineNumber2) => util_exports.rightPad((lineNumber2 + 1).toString(), pad3) + line);\n  let maxLineLength = 0;\n  for (let i2 = 0; i2 < linesWithLineNumbers.length; i2++) {\n    maxLineLength = Math.max(linesWithLineNumbers[i2].length, maxLineLength);\n  }\n  const beforeErrorLines = linesWithLineNumbers.slice(0, lineNumber - 1);\n  const errorLine = linesWithLineNumbers.slice(lineNumber - 1, lineNumber);\n  const afterErrorLines = linesWithLineNumbers.slice(lineNumber);\n  console.log(beforeErrorLines.join(\"\\n\"));\n  console.log(shaderInfoLog.split(\"\\n\")[0]);\n  console.log(`%c ${util_exports.rightPad(errorLine[0], maxLineLength)}`, \"border:1px solid red; background-color:#e3d2d2; color:#a61717\");\n  console.log(afterErrorLines.join(\"\\n\"));\n}\nfunction createProgram(gl) {\n  return throwIfNull(gl, () => gl.createProgram(), \"Unable to create WebGLProgram.\");\n}\nfunction linkProgram(gl, program) {\n  callAndCheck(gl, () => gl.linkProgram(program));\n  if (env().get(\"ENGINE_COMPILE_ONLY\")) {\n    return;\n  }\n  if (gl.getProgramParameter(program, gl.LINK_STATUS) === false) {\n    console.log(gl.getProgramInfoLog(program));\n    throw new Error(\"Failed to link vertex and fragment shaders.\");\n  }\n}\nfunction validateProgram(gl, program) {\n  callAndCheck(gl, () => gl.validateProgram(program));\n  if (gl.getProgramParameter(program, gl.VALIDATE_STATUS) === false) {\n    console.log(gl.getProgramInfoLog(program));\n    throw new Error(\"Shader program validation failed.\");\n  }\n}\nfunction createStaticVertexBuffer(gl, data) {\n  const buffer2 = throwIfNull(gl, () => gl.createBuffer(), \"Unable to create WebGLBuffer\");\n  callAndCheck(gl, () => gl.bindBuffer(gl.ARRAY_BUFFER, buffer2));\n  callAndCheck(gl, () => gl.bufferData(gl.ARRAY_BUFFER, data, gl.STATIC_DRAW));\n  return buffer2;\n}\nfunction createStaticIndexBuffer(gl, data) {\n  const buffer2 = throwIfNull(gl, () => gl.createBuffer(), \"Unable to create WebGLBuffer\");\n  callAndCheck(gl, () => gl.bindBuffer(gl.ELEMENT_ARRAY_BUFFER, buffer2));\n  callAndCheck(gl, () => gl.bufferData(gl.ELEMENT_ARRAY_BUFFER, data, gl.STATIC_DRAW));\n  return buffer2;\n}\nfunction getNumChannels() {\n  if (env().getNumber(\"WEBGL_VERSION\") === 2) {\n    return 1;\n  }\n  return 4;\n}\nfunction createTexture(gl) {\n  return throwIfNull(gl, () => gl.createTexture(), \"Unable to create WebGLTexture.\");\n}\nfunction validateTextureSize(width, height) {\n  const maxTextureSize = env().getNumber(\"WEBGL_MAX_TEXTURE_SIZE\");\n  if (width <= 0 || height <= 0) {\n    const requested = `[${width}x${height}]`;\n    throw new Error(\"Requested texture size \" + requested + \" is invalid.\");\n  }\n  if (width > maxTextureSize || height > maxTextureSize) {\n    const requested = `[${width}x${height}]`;\n    const max7 = `[${maxTextureSize}x${maxTextureSize}]`;\n    throw new Error(\"Requested texture size \" + requested + \" greater than WebGL maximum on this browser / GPU \" + max7 + \".\");\n  }\n}\nfunction createFramebuffer(gl) {\n  return throwIfNull(gl, () => gl.createFramebuffer(), \"Unable to create WebGLFramebuffer.\");\n}\nfunction bindVertexBufferToProgramAttribute(gl, program, attribute, buffer2, arrayEntriesPerItem, itemStrideInBytes, itemOffsetInBytes) {\n  const loc = gl.getAttribLocation(program, attribute);\n  if (loc === -1) {\n    return false;\n  }\n  callAndCheck(gl, () => gl.bindBuffer(gl.ARRAY_BUFFER, buffer2));\n  callAndCheck(gl, () => gl.vertexAttribPointer(loc, arrayEntriesPerItem, gl.FLOAT, false, itemStrideInBytes, itemOffsetInBytes));\n  callAndCheck(gl, () => gl.enableVertexAttribArray(loc));\n  return true;\n}\nfunction bindTextureUnit(gl, texture, textureUnit) {\n  validateTextureUnit(gl, textureUnit);\n  callAndCheck(gl, () => gl.activeTexture(gl.TEXTURE0 + textureUnit));\n  callAndCheck(gl, () => gl.bindTexture(gl.TEXTURE_2D, texture));\n}\nfunction unbindTextureUnit(gl, textureUnit) {\n  validateTextureUnit(gl, textureUnit);\n  callAndCheck(gl, () => gl.activeTexture(gl.TEXTURE0 + textureUnit));\n  callAndCheck(gl, () => gl.bindTexture(gl.TEXTURE_2D, null));\n}\nfunction getProgramUniformLocationOrThrow(gl, program, uniformName) {\n  return throwIfNull(gl, () => gl.getUniformLocation(program, uniformName), 'uniform \"' + uniformName + '\" not present in program.');\n}\nfunction getProgramUniformLocation(gl, program, uniformName) {\n  return gl.getUniformLocation(program, uniformName);\n}\nfunction bindTextureToProgramUniformSampler(gl, texture, uniformSamplerLocation, textureUnit) {\n  callAndCheck(gl, () => bindTextureUnit(gl, texture, textureUnit));\n  callAndCheck(gl, () => gl.uniform1i(uniformSamplerLocation, textureUnit));\n}\nfunction bindCanvasToFramebuffer(gl) {\n  callAndCheck(gl, () => gl.bindFramebuffer(gl.FRAMEBUFFER, null));\n  callAndCheck(gl, () => gl.viewport(0, 0, gl.canvas.width, gl.canvas.height));\n  callAndCheck(gl, () => gl.scissor(0, 0, gl.canvas.width, gl.canvas.height));\n}\nfunction bindColorTextureToFramebuffer(gl, texture, framebuffer) {\n  callAndCheck(gl, () => gl.bindFramebuffer(gl.FRAMEBUFFER, framebuffer));\n  callAndCheck(gl, () => gl.framebufferTexture2D(gl.FRAMEBUFFER, gl.COLOR_ATTACHMENT0, gl.TEXTURE_2D, texture, 0));\n}\nfunction unbindColorTextureFromFramebuffer(gl, framebuffer) {\n  callAndCheck(gl, () => gl.bindFramebuffer(gl.FRAMEBUFFER, framebuffer));\n  callAndCheck(gl, () => gl.framebufferTexture2D(gl.FRAMEBUFFER, gl.COLOR_ATTACHMENT0, gl.TEXTURE_2D, null, 0));\n}\nfunction validateFramebuffer(gl) {\n  const status = gl.checkFramebufferStatus(gl.FRAMEBUFFER);\n  if (status !== gl.FRAMEBUFFER_COMPLETE) {\n    throw new Error(\"Error binding framebuffer: \" + getFramebufferErrorMessage(gl, status));\n  }\n}\nfunction getFramebufferErrorMessage(gl, status) {\n  switch (status) {\n    case gl.FRAMEBUFFER_INCOMPLETE_ATTACHMENT:\n      return \"FRAMEBUFFER_INCOMPLETE_ATTACHMENT\";\n    case gl.FRAMEBUFFER_INCOMPLETE_MISSING_ATTACHMENT:\n      return \"FRAMEBUFFER_INCOMPLETE_MISSING_ATTACHMENT\";\n    case gl.FRAMEBUFFER_INCOMPLETE_DIMENSIONS:\n      return \"FRAMEBUFFER_INCOMPLETE_DIMENSIONS\";\n    case gl.FRAMEBUFFER_UNSUPPORTED:\n      return \"FRAMEBUFFER_UNSUPPORTED\";\n    default:\n      return `unknown error ${status}`;\n  }\n}\nfunction throwIfNull(gl, returnTOrNull, failureMessage) {\n  const tOrNull = callAndCheck(gl, () => returnTOrNull());\n  if (tOrNull == null) {\n    throw new Error(failureMessage);\n  }\n  return tOrNull;\n}\nfunction validateTextureUnit(gl, textureUnit) {\n  const maxTextureUnit = gl.MAX_COMBINED_TEXTURE_IMAGE_UNITS - 1;\n  const glTextureUnit = textureUnit + gl.TEXTURE0;\n  if (glTextureUnit < gl.TEXTURE0 || glTextureUnit > maxTextureUnit) {\n    const textureUnitRange = `[gl.TEXTURE0, gl.TEXTURE${maxTextureUnit}]`;\n    throw new Error(`textureUnit must be in ${textureUnitRange}.`);\n  }\n}\nfunction getBatchDim(shape, dimsToSkip = 2) {\n  return util_exports.sizeFromShape(shape.slice(0, shape.length - dimsToSkip));\n}\nfunction getRowsCols(shape) {\n  if (shape.length === 0) {\n    throw Error(\"Cannot get rows and columns of an empty shape array.\");\n  }\n  return [\n    shape.length > 1 ? shape[shape.length - 2] : 1,\n    shape[shape.length - 1]\n  ];\n}\nfunction getShapeAs3D(shape) {\n  let shapeAs3D = [1, 1, 1];\n  const isScalar = shape.length === 0 || shape.length === 1 && shape[0] === 1;\n  if (!isScalar) {\n    shapeAs3D = [getBatchDim(shape), ...getRowsCols(shape)];\n  }\n  return shapeAs3D;\n}\nfunction getTextureShapeFromLogicalShape(logShape, isPacked = false) {\n  let maxTexSize = env().getNumber(\"WEBGL_MAX_TEXTURE_SIZE\");\n  if (isPacked) {\n    maxTexSize = maxTexSize * 2;\n    logShape = logShape.map((d, i2) => i2 >= logShape.length - 2 ? util_exports.nearestLargerEven(logShape[i2]) : logShape[i2]);\n    if (logShape.length === 1) {\n      logShape = [2, logShape[0]];\n    }\n  }\n  if (logShape.length !== 2) {\n    const squeezeResult = util_exports.squeezeShape(logShape);\n    logShape = squeezeResult.newShape;\n  }\n  let size = util_exports.sizeFromShape(logShape);\n  if (logShape.length <= 1 && size <= maxTexSize) {\n    return [1, size];\n  } else if (logShape.length === 2 && logShape[0] <= maxTexSize && logShape[1] <= maxTexSize) {\n    return logShape;\n  } else if (logShape.length === 3 && logShape[0] * logShape[1] <= maxTexSize && logShape[2] <= maxTexSize) {\n    return [logShape[0] * logShape[1], logShape[2]];\n  } else if (logShape.length === 3 && logShape[0] <= maxTexSize && logShape[1] * logShape[2] <= maxTexSize) {\n    return [logShape[0], logShape[1] * logShape[2]];\n  } else if (logShape.length === 4 && logShape[0] * logShape[1] * logShape[2] <= maxTexSize && logShape[3] <= maxTexSize) {\n    return [logShape[0] * logShape[1] * logShape[2], logShape[3]];\n  } else if (logShape.length === 4 && logShape[0] <= maxTexSize && logShape[1] * logShape[2] * logShape[3] <= maxTexSize) {\n    return [logShape[0], logShape[1] * logShape[2] * logShape[3]];\n  } else {\n    if (isPacked) {\n      const batchDim = getBatchDim(logShape);\n      let rows = 2, cols = 2;\n      if (logShape.length) {\n        [rows, cols] = getRowsCols(logShape);\n      }\n      size = batchDim * (rows / 2) * (cols / 2);\n      return util_exports.sizeToSquarishShape(size).map((d) => d * 2);\n    }\n    return util_exports.sizeToSquarishShape(size);\n  }\n}\nfunction isEven(n2) {\n  return n2 % 2 === 0;\n}\nfunction isReshapeFree(shape1, shape2) {\n  shape1 = shape1.slice(-2);\n  shape2 = shape2.slice(-2);\n  if (util_exports.arraysEqual(shape1, shape2)) {\n    return true;\n  }\n  if (!shape1.length || !shape2.length) {\n    return true;\n  }\n  if (shape1[0] === 0 || shape1[1] === 0 || shape2[0] === 0 || shape2[1] === 0) {\n    return true;\n  }\n  if (shape1.length !== shape2.length) {\n    const shape1Cols = shape1.slice(-1)[0];\n    const shape2Cols = shape2.slice(-1)[0];\n    if (shape1Cols === shape2Cols) {\n      return true;\n    }\n    if (isEven(shape1Cols) && isEven(shape2Cols) && (shape1[0] === 1 || shape2[0] === 1)) {\n      return true;\n    }\n  }\n  return shape1[1] === shape2[1] && isEven(shape1[0]) && isEven(shape2[0]);\n}\nvar MAX_TEXTURE_SIZE;\nvar MAX_TEXTURES_IN_SHADER;\nfunction getWebGLMaxTextureSize(webGLVersion) {\n  if (MAX_TEXTURE_SIZE == null) {\n    const gl = getWebGLContext(webGLVersion);\n    MAX_TEXTURE_SIZE = gl.getParameter(gl.MAX_TEXTURE_SIZE);\n  }\n  return MAX_TEXTURE_SIZE;\n}\nfunction resetMaxTextureSize() {\n  MAX_TEXTURE_SIZE = null;\n}\nfunction resetMaxTexturesInShader() {\n  MAX_TEXTURES_IN_SHADER = null;\n}\nfunction getMaxTexturesInShader(webGLVersion) {\n  if (MAX_TEXTURES_IN_SHADER == null) {\n    const gl = getWebGLContext(webGLVersion);\n    MAX_TEXTURES_IN_SHADER = gl.getParameter(gl.MAX_TEXTURE_IMAGE_UNITS);\n  }\n  return Math.min(16, MAX_TEXTURES_IN_SHADER);\n}\nfunction getWebGLDisjointQueryTimerVersion(webGLVersion) {\n  if (webGLVersion === 0) {\n    return 0;\n  }\n  let queryTimerVersion;\n  const gl = getWebGLContext(webGLVersion);\n  if (hasExtension(gl, \"EXT_disjoint_timer_query_webgl2\") && webGLVersion === 2) {\n    queryTimerVersion = 2;\n  } else if (hasExtension(gl, \"EXT_disjoint_timer_query\")) {\n    queryTimerVersion = 1;\n  } else {\n    queryTimerVersion = 0;\n  }\n  return queryTimerVersion;\n}\nfunction hasExtension(gl, extensionName) {\n  const ext = gl.getExtension(extensionName);\n  return ext != null;\n}\nfunction isWebGLVersionEnabled(webGLVersion) {\n  try {\n    const gl = getWebGLContext(webGLVersion);\n    if (gl != null) {\n      return true;\n    }\n  } catch (e2) {\n    console.log(\"Error when getting WebGL context: \", e2);\n    return false;\n  }\n  return false;\n}\nfunction isCapableOfRenderingToFloatTexture(webGLVersion) {\n  if (webGLVersion === 0) {\n    return false;\n  }\n  const gl = getWebGLContext(webGLVersion);\n  if (webGLVersion === 1) {\n    if (!hasExtension(gl, \"OES_texture_float\")) {\n      return false;\n    }\n  } else {\n    if (!hasExtension(gl, \"EXT_color_buffer_float\")) {\n      return false;\n    }\n  }\n  const isFrameBufferComplete = createFloatTextureAndBindToFramebuffer(gl);\n  return isFrameBufferComplete;\n}\nfunction isDownloadFloatTextureEnabled(webGLVersion) {\n  if (webGLVersion === 0) {\n    return false;\n  }\n  const gl = getWebGLContext(webGLVersion);\n  if (webGLVersion === 1) {\n    if (!hasExtension(gl, \"OES_texture_float\")) {\n      return false;\n    }\n    if (!hasExtension(gl, \"WEBGL_color_buffer_float\")) {\n      return false;\n    }\n  } else {\n    if (hasExtension(gl, \"EXT_color_buffer_float\")) {\n      return createFloatTextureAndBindToFramebuffer(gl);\n    }\n    const COLOR_BUFFER_HALF_FLOAT = \"EXT_color_buffer_half_float\";\n    if (hasExtension(gl, COLOR_BUFFER_HALF_FLOAT)) {\n      const textureHalfFloatExtension = gl.getExtension(COLOR_BUFFER_HALF_FLOAT);\n      return createHalfFloatTextureAndBindToFramebuffer(gl, textureHalfFloatExtension);\n    }\n    return false;\n  }\n  const isFrameBufferComplete = createFloatTextureAndBindToFramebuffer(gl);\n  return isFrameBufferComplete;\n}\nfunction createFloatTextureAndBindToFramebuffer(gl) {\n  const texConfig = getTextureConfig(gl);\n  const texture = gl.createTexture();\n  gl.bindTexture(gl.TEXTURE_2D, texture);\n  const width = 1;\n  const height = 1;\n  gl.texImage2D(gl.TEXTURE_2D, 0, texConfig.internalFormatFloat, width, height, 0, texConfig.textureFormatFloat, texConfig.textureTypeFloat, null);\n  const frameBuffer = gl.createFramebuffer();\n  gl.bindFramebuffer(gl.FRAMEBUFFER, frameBuffer);\n  gl.framebufferTexture2D(gl.FRAMEBUFFER, gl.COLOR_ATTACHMENT0, gl.TEXTURE_2D, texture, 0);\n  const isFrameBufferComplete = gl.checkFramebufferStatus(gl.FRAMEBUFFER) === gl.FRAMEBUFFER_COMPLETE;\n  gl.bindTexture(gl.TEXTURE_2D, null);\n  gl.bindFramebuffer(gl.FRAMEBUFFER, null);\n  gl.deleteTexture(texture);\n  gl.deleteFramebuffer(frameBuffer);\n  return isFrameBufferComplete;\n}\nfunction createHalfFloatTextureAndBindToFramebuffer(gl, textureHalfFloatExtension) {\n  const texConfig = getTextureConfig(gl, textureHalfFloatExtension);\n  const texture = gl.createTexture();\n  gl.bindTexture(gl.TEXTURE_2D, texture);\n  const width = 1;\n  const height = 1;\n  gl.texImage2D(gl.TEXTURE_2D, 0, texConfig.internalFormatHalfFloat, width, height, 0, texConfig.textureFormatFloat, texConfig.textureTypeHalfFloat, null);\n  const frameBuffer = gl.createFramebuffer();\n  gl.bindFramebuffer(gl.FRAMEBUFFER, frameBuffer);\n  gl.framebufferTexture2D(gl.FRAMEBUFFER, gl.COLOR_ATTACHMENT0, gl.TEXTURE_2D, texture, 0);\n  const isFrameBufferComplete = gl.checkFramebufferStatus(gl.FRAMEBUFFER) === gl.FRAMEBUFFER_COMPLETE;\n  gl.bindTexture(gl.TEXTURE_2D, null);\n  gl.bindFramebuffer(gl.FRAMEBUFFER, null);\n  gl.deleteTexture(texture);\n  gl.deleteFramebuffer(frameBuffer);\n  return isFrameBufferComplete;\n}\nfunction isWebGLFenceEnabled(webGLVersion) {\n  if (webGLVersion !== 2) {\n    return false;\n  }\n  const gl = getWebGLContext(webGLVersion);\n  const isEnabled = gl.fenceSync != null;\n  return isEnabled;\n}\nfunction assertNotComplex2(tensor2, opName) {\n  if (!Array.isArray(tensor2)) {\n    tensor2 = [tensor2];\n  }\n  tensor2.forEach((t2) => {\n    if (t2 != null) {\n      util_exports.assert(t2.dtype !== \"complex64\", () => `${opName} does not support complex64 tensors in the WebGL backend.`);\n    }\n  });\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/flags_webgl.js\nvar ENV5 = env();\nENV5.registerFlag(\"HAS_WEBGL\", () => ENV5.getNumber(\"WEBGL_VERSION\") > 0);\nENV5.registerFlag(\"WEBGL_VERSION\", () => {\n  if (isWebGLVersionEnabled(2)) {\n    return 2;\n  } else if (isWebGLVersionEnabled(1)) {\n    return 1;\n  }\n  return 0;\n});\nENV5.registerFlag(\"WEBGL_CHECK_NUMERICAL_PROBLEMS\", () => false);\nENV5.registerFlag(\"WEBGL_BUFFER_SUPPORTED\", () => ENV5.get(\"WEBGL_VERSION\") === 2);\nENV5.registerFlag(\"WEBGL_CPU_FORWARD\", () => true);\nENV5.registerFlag(\"WEBGL_FORCE_F16_TEXTURES\", () => false);\nENV5.registerFlag(\"WEBGL_PACK\", () => ENV5.getBool(\"HAS_WEBGL\"));\nENV5.registerFlag(\"WEBGL_PACK_NORMALIZATION\", () => ENV5.getBool(\"WEBGL_PACK\"));\nENV5.registerFlag(\"WEBGL_PACK_CLIP\", () => ENV5.getBool(\"WEBGL_PACK\"));\nENV5.registerFlag(\"WEBGL_PACK_DEPTHWISECONV\", () => ENV5.getBool(\"WEBGL_PACK\"));\nENV5.registerFlag(\"WEBGL_PACK_BINARY_OPERATIONS\", () => ENV5.getBool(\"WEBGL_PACK\"));\nENV5.registerFlag(\"WEBGL_PACK_UNARY_OPERATIONS\", () => ENV5.getBool(\"WEBGL_PACK\"));\nENV5.registerFlag(\"WEBGL_PACK_ARRAY_OPERATIONS\", () => ENV5.getBool(\"WEBGL_PACK\"));\nENV5.registerFlag(\"WEBGL_PACK_IMAGE_OPERATIONS\", () => ENV5.getBool(\"WEBGL_PACK\"));\nENV5.registerFlag(\"WEBGL_PACK_REDUCE\", () => ENV5.getBool(\"WEBGL_PACK\"));\nENV5.registerFlag(\"WEBGL_LAZILY_UNPACK\", () => ENV5.getBool(\"WEBGL_PACK\"));\nENV5.registerFlag(\"WEBGL_CONV_IM2COL\", () => ENV5.getBool(\"WEBGL_PACK\"));\nENV5.registerFlag(\"WEBGL_MAX_TEXTURE_SIZE\", () => getWebGLMaxTextureSize(ENV5.getNumber(\"WEBGL_VERSION\")));\nENV5.registerFlag(\"WEBGL_MAX_TEXTURES_IN_SHADER\", () => getMaxTexturesInShader(ENV5.getNumber(\"WEBGL_VERSION\")));\nENV5.registerFlag(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION\", () => {\n  const webGLVersion = ENV5.getNumber(\"WEBGL_VERSION\");\n  if (webGLVersion === 0) {\n    return 0;\n  }\n  return getWebGLDisjointQueryTimerVersion(webGLVersion);\n});\nENV5.registerFlag(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE\", () => ENV5.getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION\") > 0 && !device_util_exports.isMobile());\nENV5.registerFlag(\"WEBGL_RENDER_FLOAT32_CAPABLE\", () => isCapableOfRenderingToFloatTexture(ENV5.getNumber(\"WEBGL_VERSION\")));\nENV5.registerFlag(\"WEBGL_RENDER_FLOAT32_ENABLED\", () => {\n  return ENV5.getBool(\"WEBGL_FORCE_F16_TEXTURES\") ? false : ENV5.getBool(\"WEBGL_RENDER_FLOAT32_CAPABLE\");\n});\nENV5.registerFlag(\"WEBGL_DOWNLOAD_FLOAT_ENABLED\", () => isDownloadFloatTextureEnabled(ENV5.getNumber(\"WEBGL_VERSION\")));\nENV5.registerFlag(\"WEBGL_FENCE_API_ENABLED\", () => isWebGLFenceEnabled(ENV5.getNumber(\"WEBGL_VERSION\")));\nENV5.registerFlag(\"WEBGL_SIZE_UPLOAD_UNIFORM\", () => {\n  const useUniforms = ENV5.getBool(\"WEBGL_RENDER_FLOAT32_ENABLED\");\n  return useUniforms ? 4 : 0;\n});\nENV5.registerFlag(\"WEBGL_DELETE_TEXTURE_THRESHOLD\", () => {\n  return -1;\n}, (threshold3) => {\n  if (threshold3 < 0 && threshold3 !== -1) {\n    throw new Error(`WEBGL_DELETE_TEXTURE_THRESHOLD must be -1 (indicating never delete) or at least 0, but got ${threshold3}.`);\n  }\n});\nENV5.registerFlag(\"WEBGL_FLUSH_THRESHOLD\", () => {\n  return device_util_exports.isMobile() ? 1 : -1;\n}, (threshold3) => {\n  if (threshold3 < 0 && threshold3 !== -1) {\n    throw new Error(`WEBGL_FLUSH_THRESHOLD must be -1 (indicating never manual flush) or at least 0, but got ${threshold3}.`);\n  }\n});\nENV5.registerFlag(\"CPU_HANDOFF_SIZE_THRESHOLD\", () => 128);\nENV5.registerFlag(\"WEBGL_USE_SHAPES_UNIFORMS\", () => false);\nENV5.registerFlag(\"TOPK_LAST_DIM_CPU_HANDOFF_SIZE_THRESHOLD\", () => 1e5);\nENV5.registerFlag(\"TOPK_K_CPU_HANDOFF_THRESHOLD\", () => 128);\nENV5.registerFlag(\"WEBGL_EXP_CONV\", () => false);\nENV5.registerFlag(\"SOFTWARE_WEBGL_ENABLED\", () => ENV5.getBool(\"IS_TEST\"));\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/glsl_version.js\nfunction getGlslDifferences() {\n  let version9;\n  let attribute;\n  let varyingVs;\n  let varyingFs;\n  let texture2D;\n  let output;\n  let defineOutput;\n  let defineSpecialNaN;\n  let defineSpecialInf;\n  let defineRound;\n  if (env().getNumber(\"WEBGL_VERSION\") === 2) {\n    version9 = \"#version 300 es\";\n    attribute = \"in\";\n    varyingVs = \"out\";\n    varyingFs = \"in\";\n    texture2D = \"texture\";\n    output = \"outputColor\";\n    defineOutput = \"out vec4 outputColor;\";\n    defineSpecialNaN = `\n      bool isnan_custom(float val) {\n        uint floatToUint = floatBitsToUint(val);\n        return (floatToUint & 0x7fffffffu) > 0x7f800000u;\n      }\n\n      bvec4 isnan_custom(vec4 val) {\n        return bvec4(isnan_custom(val.x),\n          isnan_custom(val.y), isnan_custom(val.z), isnan_custom(val.w));\n      }\n\n      #define isnan(value) isnan_custom(value)\n    `;\n    defineSpecialInf = ``;\n    defineRound = `\n      #define round(value) newRound(value)\n      int newRound(float value) {\n        return int(floor(value + 0.5));\n      }\n\n      ivec4 newRound(vec4 value) {\n        return ivec4(floor(value + vec4(0.5)));\n      }\n    `;\n  } else {\n    version9 = \"\";\n    attribute = \"attribute\";\n    varyingVs = \"varying\";\n    varyingFs = \"varying\";\n    texture2D = \"texture2D\";\n    output = \"gl_FragColor\";\n    defineOutput = \"\";\n    defineSpecialNaN = `\n      #define isnan(value) isnan_custom(value)\n      bool isnan_custom(float val) {\n        return (val > 0. || val < 1. || val == 0.) ? false : true;\n      }\n      bvec4 isnan_custom(vec4 val) {\n        return bvec4(isnan(val.x), isnan(val.y), isnan(val.z), isnan(val.w));\n      }\n    `;\n    defineSpecialInf = `\n      uniform float INFINITY;\n\n      bool isinf(float val) {\n        return abs(val) == INFINITY;\n      }\n      bvec4 isinf(vec4 val) {\n        return equal(abs(val), vec4(INFINITY));\n      }\n    `;\n    defineRound = `\n      int round(float value) {\n        return int(floor(value + 0.5));\n      }\n\n      ivec4 round(vec4 value) {\n        return ivec4(floor(value + vec4(0.5)));\n      }\n    `;\n  }\n  return {\n    version: version9,\n    attribute,\n    varyingVs,\n    varyingFs,\n    texture2D,\n    output,\n    defineOutput,\n    defineSpecialNaN,\n    defineSpecialInf,\n    defineRound\n  };\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/shader_compiler_util.js\nfunction getLogicalCoordinatesFromFlatIndex(coords3, shape, index = \"index\") {\n  const strides = util_exports.computeStrides(shape);\n  return strides.map((stride, i2) => {\n    const line1 = `int ${coords3[i2]} = ${index} / ${stride}`;\n    const line2 = i2 === strides.length - 1 ? `int ${coords3[i2 + 1]} = ${index} - ${coords3[i2]} * ${stride}` : `index -= ${coords3[i2]} * ${stride}`;\n    return `${line1}; ${line2};`;\n  }).join(\"\");\n}\nfunction getOutputLogicalCoordinatesFromFlatIndexByUniform(coords3, shape, index = \"index\") {\n  const strides = util_exports.computeStrides(shape);\n  return strides.map((_, i2) => {\n    const line1 = `int ${coords3[i2]} = ${index} / outShapeStrides[${i2}]`;\n    const line2 = i2 === strides.length - 1 ? `int ${coords3[i2 + 1]} = ${index} - ${coords3[i2]} * outShapeStrides[${i2}]` : `index -= ${coords3[i2]} * outShapeStrides[${i2}]`;\n    return `${line1}; ${line2};`;\n  }).join(\"\");\n}\nfunction symbolicallyComputeStrides(indicesArr, variableName) {\n  const numCoords = indicesArr.length;\n  const shape = indicesArr.map((d) => `${variableName}[${d}]`);\n  const strides = new Array(numCoords - 1);\n  strides[numCoords - 2] = shape[numCoords - 1];\n  for (let i2 = numCoords - 3; i2 >= 0; --i2) {\n    strides[i2] = `(${strides[i2 + 1]} * ${shape[i2 + 1]})`;\n  }\n  return strides;\n}\nfunction getLogicalCoordinatesFromFlatIndexByUniform(coords3, variableName, index = \"index\") {\n  const indicesArray = coords3.map((_, i2) => i2);\n  const strides = symbolicallyComputeStrides(indicesArray, variableName);\n  return strides.map((_, i2) => {\n    const line1 = `int ${coords3[i2]} = ${index} / ${strides[i2]}`;\n    const line2 = i2 === strides.length - 1 ? `int ${coords3[i2 + 1]} = ${index} - ${coords3[i2]} * ${strides[i2]}` : `index -= ${coords3[i2]} * ${strides[i2]}`;\n    return `${line1}; ${line2};`;\n  }).join(\"\");\n}\nfunction getFlatIndexFrom3D(shape) {\n  const strides = util_exports.computeStrides(shape).map((d) => d.toString());\n  return `\n  int getFlatIndex(ivec3 coords) {\n    return coords.x * ${strides[0]} + coords.y * ${strides[1]} + coords.z;\n  }\n`;\n}\nfunction getFlatIndexFrom3DOutput() {\n  return `\n  int getFlatIndex(ivec3 coords) {\n    return coords.x * outShapeStrides[0] + coords.y * outShapeStrides[1] + coords.z;\n  }\n`;\n}\nvar ENCODE_FLOAT_SNIPPET = `\n  const float FLOAT_MAX = 1.70141184e38;\n  const float FLOAT_MIN = 1.17549435e-38;\n\n  lowp vec4 encode_float(highp float v) {\n    if (isnan(v)) {\n      return vec4(255, 255, 255, 255);\n    }\n\n    highp float av = abs(v);\n\n    if(av < FLOAT_MIN) {\n      return vec4(0.0, 0.0, 0.0, 0.0);\n    } else if(v > FLOAT_MAX) {\n      return vec4(0.0, 0.0, 128.0, 127.0) / 255.0;\n    } else if(v < -FLOAT_MAX) {\n      return vec4(0.0, 0.0,  128.0, 255.0) / 255.0;\n    }\n\n    highp vec4 c = vec4(0,0,0,0);\n\n    highp float e = floor(log2(av));\n    highp float m = exp2(fract(log2(av))) - 1.0;\n\n    c[2] = floor(128.0 * m);\n    m -= c[2] / 128.0;\n    c[1] = floor(32768.0 * m);\n    m -= c[1] / 32768.0;\n    c[0] = floor(8388608.0 * m);\n\n    highp float ebias = e + 127.0;\n    c[3] = floor(ebias / 2.0);\n    ebias -= c[3] * 2.0;\n    c[2] += floor(ebias) * 128.0;\n\n    c[3] += 128.0 * step(0.0, -v);\n\n    return c / 255.0;\n  }\n`;\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/shader_compiler.js\nvar { getBroadcastDims: getBroadcastDims2 } = backend_util_exports;\nfunction makeShader(inputsInfo, outputShape, program) {\n  const prefixSnippets = [];\n  inputsInfo.forEach((x) => {\n    const size = util_exports.sizeFromShape(x.shapeInfo.logicalShape);\n    if (x.shapeInfo.isUniform) {\n      prefixSnippets.push(`uniform float ${x.name}${size > 1 ? `[${size}]` : \"\"};`);\n    } else {\n      prefixSnippets.push(`uniform sampler2D ${x.name};`);\n      prefixSnippets.push(`uniform int offset${x.name};`);\n    }\n    if (program.enableShapeUniforms) {\n      const { uniformShape } = getUniformInfoFromShape(program.packedInputs, x.shapeInfo.logicalShape, x.shapeInfo.texShape);\n      switch (uniformShape.length) {\n        case 1:\n          prefixSnippets.push(`uniform int ${x.name}Shape;`);\n          break;\n        case 2:\n          prefixSnippets.push(`uniform ivec2 ${x.name}Shape;`);\n          break;\n        case 3:\n          prefixSnippets.push(`uniform ivec3 ${x.name}Shape;`);\n          break;\n        case 4:\n          prefixSnippets.push(`uniform ivec4 ${x.name}Shape;`);\n          break;\n        default:\n          break;\n      }\n      prefixSnippets.push(`uniform ivec2 ${x.name}TexShape;`);\n    }\n  });\n  if (program.enableShapeUniforms) {\n    switch (outputShape.logicalShape.length) {\n      case 1:\n        prefixSnippets.push(`uniform int outShape;`);\n        break;\n      case 2:\n        prefixSnippets.push(`uniform ivec2 outShape;`);\n        prefixSnippets.push(`uniform int outShapeStrides;`);\n        break;\n      case 3:\n        prefixSnippets.push(`uniform ivec3 outShape;`);\n        prefixSnippets.push(`uniform ivec2 outShapeStrides;`);\n        break;\n      case 4:\n        prefixSnippets.push(`uniform ivec4 outShape;`);\n        prefixSnippets.push(`uniform ivec3 outShapeStrides;`);\n        break;\n      default:\n        break;\n    }\n    prefixSnippets.push(`uniform ivec2 outTexShape;`);\n  }\n  if (program.customUniforms) {\n    program.customUniforms.forEach((d) => {\n      prefixSnippets.push(`uniform ${d.type} ${d.name}${d.arrayIndex ? `[${d.arrayIndex}]` : \"\"};`);\n    });\n  }\n  const inputPrefixSnippet = prefixSnippets.join(\"\\n\");\n  const inputSamplingSnippet = inputsInfo.map((x) => getInputSamplingSnippet(x, outputShape, program.packedInputs, program.enableShapeUniforms)).join(\"\\n\");\n  const outTexShape = outputShape.texShape;\n  const glsl = getGlslDifferences();\n  const floatTextureSampleSnippet = getFloatTextureSampleSnippet(glsl);\n  let outputSamplingSnippet;\n  let floatTextureSetOutputSnippet;\n  let shaderPrefix = getShaderPrefix(glsl);\n  if (outputShape.isPacked) {\n    outputSamplingSnippet = getPackedOutputSamplingSnippet(outputShape.logicalShape, outTexShape, program.enableShapeUniforms);\n    floatTextureSetOutputSnippet = getFloatTextureSetRGBASnippet(glsl);\n  } else {\n    outputSamplingSnippet = getOutputSamplingSnippet(outputShape.logicalShape, outTexShape, program.enableShapeUniforms);\n    floatTextureSetOutputSnippet = getFloatTextureSetRSnippet(glsl);\n  }\n  if (program.packedInputs) {\n    shaderPrefix += SHADER_PACKED_PREFIX;\n  }\n  const source = [\n    shaderPrefix,\n    floatTextureSampleSnippet,\n    floatTextureSetOutputSnippet,\n    inputPrefixSnippet,\n    outputSamplingSnippet,\n    inputSamplingSnippet,\n    program.userCode\n  ].join(\"\\n\");\n  return source;\n}\nfunction getSamplerFromInInfo(inInfo, enableShapeUniforms = false) {\n  const shape = inInfo.shapeInfo.logicalShape;\n  switch (shape.length) {\n    case 0:\n      return getSamplerScalar(inInfo, enableShapeUniforms);\n    case 1:\n      return getSampler1D(inInfo, enableShapeUniforms);\n    case 2:\n      return getSampler2D(inInfo, enableShapeUniforms);\n    case 3:\n      return getSampler3D(inInfo, enableShapeUniforms);\n    case 4:\n      return getSampler4D(inInfo, enableShapeUniforms);\n    case 5:\n      return getSampler5D(inInfo);\n    case 6:\n      return getSampler6D(inInfo);\n    default:\n      throw new Error(`${shape.length}-D input sampling is not yet supported`);\n  }\n}\nfunction getPackedSamplerFromInInfo(inInfo, enableShapeUniforms) {\n  const shape = inInfo.shapeInfo.logicalShape;\n  switch (shape.length) {\n    case 0:\n      return getPackedSamplerScalar(inInfo);\n    case 1:\n      return getPackedSampler1D(inInfo, enableShapeUniforms);\n    case 2:\n      return getPackedSampler2D(inInfo, enableShapeUniforms);\n    case 3:\n      return getPackedSampler3D(inInfo, enableShapeUniforms);\n    default:\n      return getPackedSamplerND(inInfo, enableShapeUniforms);\n  }\n}\nfunction getInputSamplingSnippet(inInfo, outShapeInfo, usesPackedTextures = false, enableShapeUniforms) {\n  let res = \"\";\n  if (usesPackedTextures) {\n    res += getPackedSamplerFromInInfo(inInfo, enableShapeUniforms);\n  } else {\n    res += getSamplerFromInInfo(inInfo, enableShapeUniforms);\n  }\n  const inShape = inInfo.shapeInfo.logicalShape;\n  const outShape = outShapeInfo.logicalShape;\n  if (inShape.length <= outShape.length) {\n    if (usesPackedTextures) {\n      res += getPackedSamplerAtOutputCoords(inInfo, outShapeInfo);\n    } else {\n      res += getSamplerAtOutputCoords(inInfo, outShapeInfo);\n    }\n  }\n  return res;\n}\nfunction getPackedOutputSamplingSnippet(outShape, outTexShape, enableShapeUniforms) {\n  switch (outShape.length) {\n    case 0:\n      return getOutputScalarCoords();\n    case 1:\n      return getOutputPacked1DCoords(outShape, outTexShape, enableShapeUniforms);\n    case 2:\n      return getOutputPacked2DCoords(outShape, outTexShape, enableShapeUniforms);\n    case 3:\n      return getOutputPacked3DCoords(outShape, outTexShape, enableShapeUniforms);\n    default:\n      return getOutputPackedNDCoords(outShape, outTexShape, enableShapeUniforms);\n  }\n}\nfunction getOutputSamplingSnippet(outShape, outTexShape, enableShapeUniforms) {\n  switch (outShape.length) {\n    case 0:\n      return getOutputScalarCoords();\n    case 1:\n      return getOutput1DCoords(outShape, outTexShape, enableShapeUniforms);\n    case 2:\n      return getOutput2DCoords(outShape, outTexShape, enableShapeUniforms);\n    case 3:\n      return getOutput3DCoords(outShape, outTexShape, enableShapeUniforms);\n    case 4:\n      return getOutput4DCoords(outShape, outTexShape, enableShapeUniforms);\n    case 5:\n      return getOutput5DCoords(outShape, outTexShape);\n    case 6:\n      return getOutput6DCoords(outShape, outTexShape);\n    default:\n      throw new Error(`${outShape.length}-D output sampling is not yet supported`);\n  }\n}\nfunction getFloatTextureSampleSnippet(glsl) {\n  return `\n    float sampleTexture(sampler2D textureSampler, vec2 uv) {\n      return ${glsl.texture2D}(textureSampler, uv).r;\n    }\n  `;\n}\nfunction getFloatTextureSetRSnippet(glsl) {\n  return `\n    void setOutput(float val) {\n      ${glsl.output} = vec4(val, 0, 0, 0);\n    }\n  `;\n}\nfunction getFloatTextureSetRGBASnippet(glsl) {\n  return `\n    void setOutput(vec4 val) {\n      ${glsl.output} = val;\n    }\n  `;\n}\nfunction getShaderPrefix(glsl) {\n  const SHADER_PREFIX = `${glsl.version}\n    precision highp float;\n    precision highp int;\n    precision highp sampler2D;\n    ${glsl.varyingFs} vec2 resultUV;\n    ${glsl.defineOutput}\n    const vec2 halfCR = vec2(0.5, 0.5);\n\n    struct ivec5\n    {\n      int x;\n      int y;\n      int z;\n      int w;\n      int u;\n    };\n\n    struct ivec6\n    {\n      int x;\n      int y;\n      int z;\n      int w;\n      int u;\n      int v;\n    };\n\n    uniform float NAN;\n    ${glsl.defineSpecialNaN}\n    ${glsl.defineSpecialInf}\n    ${glsl.defineRound}\n\n    int imod(int x, int y) {\n      return x - y * (x / y);\n    }\n\n    int idiv(int a, int b, float sign) {\n      int res = a / b;\n      int mod = imod(a, b);\n      if (sign < 0. && mod != 0) {\n        res -= 1;\n      }\n      return res;\n    }\n\n    //Based on the work of Dave Hoskins\n    //https://www.shadertoy.com/view/4djSRW\n    #define HASHSCALE1 443.8975\n    float random(float seed){\n      vec2 p = resultUV * seed;\n      vec3 p3  = fract(vec3(p.xyx) * HASHSCALE1);\n      p3 += dot(p3, p3.yzx + 19.19);\n      return fract((p3.x + p3.y) * p3.z);\n    }\n\n    ${SAMPLE_1D_SNIPPET}\n    ${SAMPLE_2D_SNIPPET}\n    ${SAMPLE_3D_SNIPPET}\n  `;\n  return SHADER_PREFIX;\n}\nvar SAMPLE_1D_SNIPPET = `\nvec2 uvFromFlat(int texNumR, int texNumC, int index) {\n  int texR = index / texNumC;\n  int texC = index - texR * texNumC;\n  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n}\nvec2 packedUVfrom1D(int texNumR, int texNumC, int index) {\n  int texelIndex = index / 2;\n  int texR = texelIndex / texNumC;\n  int texC = texelIndex - texR * texNumC;\n  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n}\n`;\nvar SAMPLE_2D_SNIPPET = `\nvec2 packedUVfrom2D(int texelsInLogicalRow, int texNumR,\n  int texNumC, int row, int col) {\n  int texelIndex = (row / 2) * texelsInLogicalRow + (col / 2);\n  int texR = texelIndex / texNumC;\n  int texC = texelIndex - texR * texNumC;\n  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n}\n`;\nvar SAMPLE_3D_SNIPPET = `\nvec2 packedUVfrom3D(int texNumR, int texNumC,\n    int texelsInBatch, int texelsInLogicalRow, int b,\n    int row, int col) {\n  int index = b * texelsInBatch + (row / 2) * texelsInLogicalRow + (col / 2);\n  int texR = index / texNumC;\n  int texC = index - texR * texNumC;\n  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n}\n`;\nvar SHADER_PACKED_PREFIX = `\n  float getChannel(vec4 frag, vec2 innerDims) {\n    vec2 modCoord = mod(innerDims, 2.);\n    return modCoord.x == 0. ?\n      (modCoord.y == 0. ? frag.r : frag.g) :\n      (modCoord.y == 0. ? frag.b : frag.a);\n  }\n  float getChannel(vec4 frag, int dim) {\n    float modCoord = mod(float(dim), 2.);\n    return modCoord == 0. ? frag.r : frag.g;\n  }\n`;\nfunction getOutputScalarCoords() {\n  return `\n    int getOutputCoords() {\n      return 0;\n    }\n  `;\n}\nfunction getOutputPacked1DCoords(shape, texShape, enableShapeUniforms) {\n  const packedTexShape = [Math.ceil(texShape[0] / 2), Math.ceil(texShape[1] / 2)];\n  if (packedTexShape[0] === 1) {\n    if (enableShapeUniforms) {\n      return `\n      int getOutputCoords() {\n        return 2 * int(resultUV.x * ceil(float(outTexShape[1]) / 2.0));\n      }\n    `;\n    }\n    return `\n      int getOutputCoords() {\n        return 2 * int(resultUV.x * ${packedTexShape[1]}.0);\n      }\n    `;\n  }\n  if (packedTexShape[1] === 1) {\n    if (enableShapeUniforms) {\n      return `\n      int getOutputCoords() {\n        return 2 * int(resultUV.y * ceil(float(outTexShape[0]) / 2.0));\n      }\n    `;\n    }\n    return `\n      int getOutputCoords() {\n        return 2 * int(resultUV.y * ${packedTexShape[0]}.0);\n      }\n    `;\n  }\n  if (enableShapeUniforms) {\n    return `\n    int getOutputCoords() {\n      ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(packedTexShape[0], packedTexShape[1]));\n      return 2 * (resTexRC.x * packedTexShape[1] + resTexRC.y);\n    }\n  `;\n  }\n  return `\n    int getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(${packedTexShape[0]}, ${packedTexShape[1]}));\n      return 2 * (resTexRC.x * ${packedTexShape[1]} + resTexRC.y);\n    }\n  `;\n}\nfunction getOutput1DCoords(shape, texShape, enableShapeUniforms) {\n  if (texShape[0] === 1) {\n    if (enableShapeUniforms) {\n      return `\n      int getOutputCoords() {\n        return int(resultUV.x * float(outTexShape[1]));\n      }\n    `;\n    }\n    return `\n      int getOutputCoords() {\n        return int(resultUV.x * ${texShape[1]}.0);\n      }\n    `;\n  }\n  if (texShape[1] === 1) {\n    if (enableShapeUniforms) {\n      return `\n      int getOutputCoords() {\n        return int(resultUV.y * float(outTexShape[0]));\n      }\n    `;\n    }\n    return `\n      int getOutputCoords() {\n        return int(resultUV.y * ${texShape[0]}.0);\n      }\n    `;\n  }\n  if (enableShapeUniforms) {\n    return `\n    int getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(outTexShape[0], outTexShape[1]));\n      return resTexRC.x * outTexShape[1] + resTexRC.y;\n    }\n  `;\n  }\n  return `\n    int getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(${texShape[0]}, ${texShape[1]}));\n      return resTexRC.x * ${texShape[1]} + resTexRC.y;\n    }\n  `;\n}\nfunction getOutputPacked3DCoords(shape, texShape, enableShapeUniforms) {\n  if (enableShapeUniforms) {\n    return `\n    ivec3 getOutputCoords() {\n      ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));\n      int texelsInLogicalRow = int(ceil(float(outShape[2]) / 2.0));\n      int texelsInBatch = texelsInLogicalRow * int(ceil(float(outShape[1]) / 2.0));\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(packedTexShape[0], packedTexShape[1]));\n      int index = resTexRC.x * packedTexShape[1] + resTexRC.y;\n\n      int b = index / texelsInBatch;\n      index -= b * texelsInBatch;\n\n      int r = 2 * (index / texelsInLogicalRow);\n      int c = imod(index, texelsInLogicalRow) * 2;\n\n      return ivec3(b, r, c);\n    }\n  `;\n  }\n  const packedTexShape = [Math.ceil(texShape[0] / 2), Math.ceil(texShape[1] / 2)];\n  const texelsInLogicalRow = Math.ceil(shape[2] / 2);\n  const texelsInBatch = texelsInLogicalRow * Math.ceil(shape[1] / 2);\n  return `\n    ivec3 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(${packedTexShape[0]}, ${packedTexShape[1]}));\n      int index = resTexRC.x * ${packedTexShape[1]} + resTexRC.y;\n\n      int b = index / ${texelsInBatch};\n      index -= b * ${texelsInBatch};\n\n      int r = 2 * (index / ${texelsInLogicalRow});\n      int c = imod(index, ${texelsInLogicalRow}) * 2;\n\n      return ivec3(b, r, c);\n    }\n  `;\n}\nfunction getOutput3DCoords(shape, texShape, enableShapeUniforms) {\n  if (enableShapeUniforms) {\n    const coordsFromIndexSnippet2 = getOutputLogicalCoordinatesFromFlatIndexByUniform([\"r\", \"c\", \"d\"], shape);\n    return `\n  ivec3 getOutputCoords() {\n    ivec2 resTexRC = ivec2(resultUV.yx *\n                           vec2(outTexShape[0], outTexShape[1]));\n    int index = resTexRC.x * outTexShape[1] + resTexRC.y;\n    ${coordsFromIndexSnippet2}\n    return ivec3(r, c, d);\n  }\n`;\n  }\n  const coordsFromIndexSnippet = getLogicalCoordinatesFromFlatIndex([\"r\", \"c\", \"d\"], shape);\n  return `\n    ivec3 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(${texShape[0]}, ${texShape[1]}));\n      int index = resTexRC.x * ${texShape[1]} + resTexRC.y;\n      ${coordsFromIndexSnippet}\n      return ivec3(r, c, d);\n    }\n  `;\n}\nfunction getOutputPackedNDCoords(shape, texShape, enableShapeUniforms) {\n  if (enableShapeUniforms) {\n    return `\n    ivec4 getOutputCoords() {\n      ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(packedTexShape[0], packedTexShape[1]));\n      int index = resTexRC.x * packedTexShape[1] + resTexRC.y;\n\n      int texelsInLogicalRow = int(ceil(float(outShape[3]) / 2.0));\n      int texelsInBatch = texelsInLogicalRow * int(ceil(float(outShape[2]) / 2.0));\n      int texelsInBatchN = texelsInBatch * outShape[1];\n\n      int b2 = index / texelsInBatchN;\n      index -= b2 * texelsInBatchN;\n\n      int b = index / texelsInBatch;\n      index -= b * texelsInBatch;\n\n      int r = 2 * (index / texelsInLogicalRow);\n      int c = imod(index, texelsInLogicalRow) * 2;\n\n      return ivec4(b2, b, r, c);\n    }\n  `;\n  }\n  const packedTexShape = [Math.ceil(texShape[0] / 2), Math.ceil(texShape[1] / 2)];\n  const texelsInLogicalRow = Math.ceil(shape[shape.length - 1] / 2);\n  const texelsInBatch = texelsInLogicalRow * Math.ceil(shape[shape.length - 2] / 2);\n  let texelsInBatchN = texelsInBatch;\n  let batches = ``;\n  let coords3 = \"b, r, c\";\n  for (let b = 2; b < shape.length - 1; b++) {\n    texelsInBatchN *= shape[shape.length - b - 1];\n    batches = `\n      int b${b} = index / ${texelsInBatchN};\n      index -= b${b} * ${texelsInBatchN};\n    ` + batches;\n    coords3 = `b${b}, ` + coords3;\n  }\n  return `\n    ivec${shape.length} getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(${packedTexShape[0]}, ${packedTexShape[1]}));\n      int index = resTexRC.x * ${packedTexShape[1]} + resTexRC.y;\n\n      ${batches}\n\n      int b = index / ${texelsInBatch};\n      index -= b * ${texelsInBatch};\n\n      int r = 2 * (index / ${texelsInLogicalRow});\n      int c = imod(index, ${texelsInLogicalRow}) * 2;\n\n      return ivec${shape.length}(${coords3});\n    }\n  `;\n}\nfunction getOutput4DCoords(shape, texShape, enableShapeUniforms) {\n  if (enableShapeUniforms) {\n    const coordsFromIndexSnippet2 = getOutputLogicalCoordinatesFromFlatIndexByUniform([\"r\", \"c\", \"d\", \"d2\"], shape);\n    return `\n    ivec4 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n        vec2(outTexShape[0], outTexShape[1]));\n      int index = resTexRC.x * outTexShape[1] + resTexRC.y;\n      ${coordsFromIndexSnippet2}\n      return ivec4(r, c, d, d2);\n    }\n  `;\n  }\n  const coordsFromIndexSnippet = getLogicalCoordinatesFromFlatIndex([\"r\", \"c\", \"d\", \"d2\"], shape);\n  return `\n    ivec4 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n        vec2(${texShape[0]}, ${texShape[1]}));\n      int index = resTexRC.x * ${texShape[1]} + resTexRC.y;\n      ${coordsFromIndexSnippet}\n      return ivec4(r, c, d, d2);\n    }\n  `;\n}\nfunction getOutput5DCoords(shape, texShape) {\n  const coordsFromIndexSnippet = getLogicalCoordinatesFromFlatIndex([\"r\", \"c\", \"d\", \"d2\", \"d3\"], shape);\n  return `\n    ivec5 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx * vec2(${texShape[0]},\n                             ${texShape[1]}));\n\n      int index = resTexRC.x * ${texShape[1]} + resTexRC.y;\n\n      ${coordsFromIndexSnippet}\n\n      ivec5 outShape = ivec5(r, c, d, d2, d3);\n      return outShape;\n    }\n  `;\n}\nfunction getOutput6DCoords(shape, texShape) {\n  const coordsFromIndexSnippet = getLogicalCoordinatesFromFlatIndex([\"r\", \"c\", \"d\", \"d2\", \"d3\", \"d4\"], shape);\n  return `\n    ivec6 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n        vec2(${texShape[0]}, ${texShape[1]}));\n      int index = resTexRC.x * ${texShape[1]} + resTexRC.y;\n\n      ${coordsFromIndexSnippet}\n\n      ivec6 result = ivec6(r, c, d, d2, d3, d4);\n      return result;\n    }\n  `;\n}\nfunction getOutputPacked2DCoords(shape, texShape, enableShapeUniforms) {\n  const packedTexShape = [Math.ceil(texShape[0] / 2), Math.ceil(texShape[1] / 2)];\n  if (util_exports.arraysEqual(shape, texShape)) {\n    if (enableShapeUniforms) {\n      return `\n      ivec2 getOutputCoords() {\n        ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));\n        return 2 * ivec2(resultUV.yx * vec2(packedTexShape[0], packedTexShape[1]));\n      }\n    `;\n    }\n    return `\n      ivec2 getOutputCoords() {\n        return 2 * ivec2(resultUV.yx * vec2(${packedTexShape[0]}, ${packedTexShape[1]}));\n      }\n    `;\n  }\n  const texelsInLogicalRow = Math.ceil(shape[1] / 2);\n  if (enableShapeUniforms) {\n    return `\n    ivec2 getOutputCoords() {\n      ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));\n      int texelsInLogicalRow = int(ceil(float(outShape[1]) / 2.0));\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(packedTexShape[0], packedTexShape[1]));\n\n      int index = resTexRC.x * packedTexShape[1] + resTexRC.y;\n      int r = 2 * (index / texelsInLogicalRow);\n      int c = imod(index, texelsInLogicalRow) * 2;\n\n      return ivec2(r, c);\n    }\n  `;\n  }\n  return `\n    ivec2 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(${packedTexShape[0]}, ${packedTexShape[1]}));\n\n      int index = resTexRC.x * ${packedTexShape[1]} + resTexRC.y;\n      int r = 2 * (index / ${texelsInLogicalRow});\n      int c = imod(index, ${texelsInLogicalRow}) * 2;\n\n      return ivec2(r, c);\n    }\n  `;\n}\nfunction getOutput2DCoords(shape, texShape, enableShapeUniforms) {\n  if (util_exports.arraysEqual(shape, texShape)) {\n    if (enableShapeUniforms) {\n      return `\n      ivec2 getOutputCoords() {\n        return ivec2(resultUV.yx * vec2(outTexShape[0], outTexShape[1]));\n      }\n    `;\n    }\n    return `\n      ivec2 getOutputCoords() {\n        return ivec2(resultUV.yx * vec2(${texShape[0]}, ${texShape[1]}));\n      }\n    `;\n  }\n  if (shape[1] === 1) {\n    if (enableShapeUniforms) {\n      return `\n      ivec2 getOutputCoords() {\n        ivec2 resTexRC = ivec2(resultUV.yx *\n                               vec2(outTexShape[0], outTexShape[1]));\n        int index = resTexRC.x * outTexShape[1] + resTexRC.y;\n        return ivec2(index, 0);\n      }\n    `;\n    }\n    return `\n      ivec2 getOutputCoords() {\n        ivec2 resTexRC = ivec2(resultUV.yx *\n                               vec2(${texShape[0]}, ${texShape[1]}));\n        int index = resTexRC.x * ${texShape[1]} + resTexRC.y;\n        return ivec2(index, 0);\n      }\n    `;\n  }\n  if (shape[0] === 1) {\n    if (enableShapeUniforms) {\n      return `\n      ivec2 getOutputCoords() {\n        ivec2 resTexRC = ivec2(resultUV.yx *\n                               vec2(outTexShape[0], outTexShape[1]));\n        int index = resTexRC.x * outTexShape[1] + resTexRC.y;\n        return ivec2(0, index);\n      }\n    `;\n    }\n    return `\n      ivec2 getOutputCoords() {\n        ivec2 resTexRC = ivec2(resultUV.yx *\n                               vec2(${texShape[0]}, ${texShape[1]}));\n        int index = resTexRC.x * ${texShape[1]} + resTexRC.y;\n        return ivec2(0, index);\n      }\n    `;\n  }\n  if (enableShapeUniforms) {\n    return `\n    ivec2 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(outTexShape[0], outTexShape[1]));\n      int index = resTexRC.x * outTexShape[1] + resTexRC.y;\n      int r = index / outShape[1];\n      int c = index - r * outShape[1];\n      return ivec2(r, c);\n    }\n  `;\n  }\n  return `\n    ivec2 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(${texShape[0]}, ${texShape[1]}));\n      int index = resTexRC.x * ${texShape[1]} + resTexRC.y;\n      int r = index / ${shape[1]};\n      int c = index - r * ${shape[1]};\n      return ivec2(r, c);\n    }\n  `;\n}\nfunction getFlatOffsetUniformName(texName) {\n  return `offset${texName}`;\n}\nfunction getPackedSamplerScalar(inputInfo) {\n  const texName = inputInfo.name;\n  const funcName = \"get\" + texName.charAt(0).toUpperCase() + texName.slice(1);\n  const glsl = getGlslDifferences();\n  return `\n    vec4 ${funcName}() {\n      return ${glsl.texture2D}(${texName}, halfCR);\n    }\n  `;\n}\nfunction getSamplerScalar(inputInfo, enableShapeUniforms) {\n  const texName = inputInfo.name;\n  const funcName = \"get\" + texName.charAt(0).toUpperCase() + texName.slice(1);\n  if (inputInfo.shapeInfo.isUniform) {\n    return `float ${funcName}() {return ${texName};}`;\n  }\n  const [texNumR, texNumC] = inputInfo.shapeInfo.texShape;\n  if (texNumR === 1 && texNumC === 1) {\n    return `\n      float ${funcName}() {\n        return sampleTexture(${texName}, halfCR);\n      }\n    `;\n  }\n  const offset = getFlatOffsetUniformName(texName);\n  if (enableShapeUniforms) {\n    return `\n    float ${funcName}() {\n      vec2 uv = uvFromFlat(${texName}TexShape[0], ${texName}TexShape[1], ${offset});\n      return sampleTexture(${texName}, uv);\n    }\n  `;\n  }\n  const [tNumR, tNumC] = inputInfo.shapeInfo.texShape;\n  return `\n    float ${funcName}() {\n      vec2 uv = uvFromFlat(${tNumR}, ${tNumC}, ${offset});\n      return sampleTexture(${texName}, uv);\n    }\n  `;\n}\nfunction getPackedSampler1D(inputInfo, enableShapeUniforms) {\n  const texName = inputInfo.name;\n  const funcName = \"get\" + texName.charAt(0).toUpperCase() + texName.slice(1);\n  const texShape = inputInfo.shapeInfo.texShape;\n  const glsl = getGlslDifferences();\n  if (enableShapeUniforms) {\n    return `\n    vec4 ${funcName}(int index) {\n      ivec2 packedTexShape = ivec2(ceil(float(${texName}TexShape[0]) / 2.0), ceil(float(${texName}TexShape[1]) / 2.0));\n      vec2 uv = packedUVfrom1D(\n        packedTexShape[0], packedTexShape[1], index);\n      return ${glsl.texture2D}(${texName}, uv);\n    }\n  `;\n  }\n  const packedTexShape = [Math.ceil(texShape[0] / 2), Math.ceil(texShape[1] / 2)];\n  return `\n    vec4 ${funcName}(int index) {\n      vec2 uv = packedUVfrom1D(\n        ${packedTexShape[0]}, ${packedTexShape[1]}, index);\n      return ${glsl.texture2D}(${texName}, uv);\n    }\n  `;\n}\nfunction getSampler1D(inputInfo, enableShapeUniforms) {\n  const texName = inputInfo.name;\n  const funcName = \"get\" + texName.charAt(0).toUpperCase() + texName.slice(1);\n  if (inputInfo.shapeInfo.isUniform) {\n    return `\n      float ${funcName}(int index) {\n        ${getUniformSampler(inputInfo)}\n      }\n    `;\n  }\n  const texShape = inputInfo.shapeInfo.texShape;\n  const tNumR = texShape[0];\n  const tNumC = texShape[1];\n  if (tNumC === 1 && tNumR === 1) {\n    return `\n      float ${funcName}(int index) {\n        return sampleTexture(${texName}, halfCR);\n      }\n    `;\n  }\n  const offset = getFlatOffsetUniformName(texName);\n  if (tNumC === 1) {\n    if (enableShapeUniforms) {\n      return `\n      float ${funcName}(int index) {\n        vec2 uv = vec2(0.5, (float(index + ${offset}) + 0.5) / float(${texName}TexShape[0]));\n        return sampleTexture(${texName}, uv);\n      }\n    `;\n    }\n    return `\n      float ${funcName}(int index) {\n        vec2 uv = vec2(0.5, (float(index + ${offset}) + 0.5) / ${tNumR}.0);\n        return sampleTexture(${texName}, uv);\n      }\n    `;\n  }\n  if (tNumR === 1) {\n    if (enableShapeUniforms) {\n      return `\n      float ${funcName}(int index) {\n        vec2 uv = vec2((float(index + ${offset}) + 0.5) / float(${texName}TexShape[1]), 0.5);\n        return sampleTexture(${texName}, uv);\n      }\n    `;\n    }\n    return `\n      float ${funcName}(int index) {\n        vec2 uv = vec2((float(index + ${offset}) + 0.5) / ${tNumC}.0, 0.5);\n        return sampleTexture(${texName}, uv);\n      }\n    `;\n  }\n  if (enableShapeUniforms) {\n    return `\n    float ${funcName}(int index) {\n      vec2 uv = uvFromFlat(${texName}TexShape[0], ${texName}TexShape[1], index + ${offset});\n      return sampleTexture(${texName}, uv);\n    }\n  `;\n  }\n  return `\n    float ${funcName}(int index) {\n      vec2 uv = uvFromFlat(${tNumR}, ${tNumC}, index + ${offset});\n      return sampleTexture(${texName}, uv);\n    }\n  `;\n}\nfunction getPackedSampler2D(inputInfo, enableShapeUniforms) {\n  const shape = inputInfo.shapeInfo.logicalShape;\n  const texName = inputInfo.name;\n  const funcName = \"get\" + texName.charAt(0).toUpperCase() + texName.slice(1);\n  const texShape = inputInfo.shapeInfo.texShape;\n  const texNumR = texShape[0];\n  const texNumC = texShape[1];\n  const glsl = getGlslDifferences();\n  if (texShape != null && util_exports.arraysEqual(shape, texShape)) {\n    if (enableShapeUniforms) {\n      return `\n      vec4 ${funcName}(int row, int col) {\n        vec2 uv = (vec2(col, row) + halfCR) / vec2(${texName}TexShape[1], ${texName}TexShape[0]);\n\n        return ${glsl.texture2D}(${texName}, uv);\n      }\n    `;\n    }\n    return `\n      vec4 ${funcName}(int row, int col) {\n        vec2 uv = (vec2(col, row) + halfCR) / vec2(${texNumC}.0, ${texNumR}.0);\n\n        return ${glsl.texture2D}(${texName}, uv);\n      }\n    `;\n  }\n  if (enableShapeUniforms) {\n    return `\n    vec4 ${funcName}(int row, int col) {\n      ivec2 packedTexShape = ivec2(ceil(float(${texName}TexShape[0]) / 2.0), ceil(float(${texName}TexShape[1]) / 2.0));\n      int valuesPerRow = int(ceil(float(${texName}Shape[1]) / 2.0));\n      vec2 uv = packedUVfrom2D(valuesPerRow, packedTexShape[0], packedTexShape[1], row, col);\n      return ${glsl.texture2D}(${texName}, uv);\n    }\n  `;\n  }\n  const packedTexShape = [Math.ceil(texShape[0] / 2), Math.ceil(texShape[1] / 2)];\n  const valuesPerRow = Math.ceil(shape[1] / 2);\n  return `\n    vec4 ${funcName}(int row, int col) {\n      vec2 uv = packedUVfrom2D(${valuesPerRow}, ${packedTexShape[0]}, ${packedTexShape[1]}, row, col);\n      return ${glsl.texture2D}(${texName}, uv);\n    }\n  `;\n}\nfunction getSampler2D(inputInfo, enableShapeUniforms) {\n  const shape = inputInfo.shapeInfo.logicalShape;\n  const texName = inputInfo.name;\n  const funcName = \"get\" + texName.charAt(0).toUpperCase() + texName.slice(1);\n  const texShape = inputInfo.shapeInfo.texShape;\n  if (texShape != null && util_exports.arraysEqual(shape, texShape)) {\n    if (enableShapeUniforms) {\n      return `\n      float ${funcName}(int row, int col) {\n        vec2 uv = (vec2(col, row) + halfCR) / vec2(${texName}TexShape[1], ${texName}TexShape[0]);\n        return sampleTexture(${texName}, uv);\n      }\n    `;\n    }\n    const texNumR2 = texShape[0];\n    const texNumC2 = texShape[1];\n    return `\n    float ${funcName}(int row, int col) {\n      vec2 uv = (vec2(col, row) + halfCR) / vec2(${texNumC2}.0, ${texNumR2}.0);\n      return sampleTexture(${texName}, uv);\n    }\n  `;\n  }\n  const { newShape, keptDims } = util_exports.squeezeShape(shape);\n  const squeezedShape = newShape;\n  if (squeezedShape.length < shape.length) {\n    const newInputInfo = squeezeInputInfo(inputInfo, squeezedShape);\n    const params = [\"row\", \"col\"];\n    return `\n      ${getSamplerFromInInfo(newInputInfo, enableShapeUniforms)}\n      float ${funcName}(int row, int col) {\n        return ${funcName}(${getSqueezedParams(params, keptDims)});\n      }\n    `;\n  }\n  if (inputInfo.shapeInfo.isUniform) {\n    return `\n      float ${funcName}(int row, int col) {\n        int index = round(dot(vec2(row, col), vec2(${shape[1]}, 1)));\n        ${getUniformSampler(inputInfo)}\n      }\n    `;\n  }\n  const texNumR = texShape[0];\n  const texNumC = texShape[1];\n  const offset = getFlatOffsetUniformName(texName);\n  if (texNumC === 1) {\n    if (enableShapeUniforms) {\n      return `\n      float ${funcName}(int row, int col) {\n        float index = dot(vec3(row, col, ${offset}), vec3(${texName}Shape[1], 1, 1));\n        vec2 uv = vec2(0.5, (index + 0.5) / float(${texName}TexShape[0]));\n        return sampleTexture(${texName}, uv);\n      }\n    `;\n    }\n    return `\n    float ${funcName}(int row, int col) {\n      float index = dot(vec3(row, col, ${offset}), vec3(${shape[1]}, 1, 1));\n      vec2 uv = vec2(0.5, (index + 0.5) / ${texNumR}.0);\n      return sampleTexture(${texName}, uv);\n    }\n  `;\n  }\n  if (texNumR === 1) {\n    if (enableShapeUniforms) {\n      return `\n      float ${funcName}(int row, int col) {\n        float index = dot(vec3(row, col, ${offset}), vec3(${texName}Shape[1], 1, 1));\n        vec2 uv = vec2((index + 0.5) / float(${texName}TexShape[1]), 0.5);\n        return sampleTexture(${texName}, uv);\n      }\n    `;\n    }\n    return `\n    float ${funcName}(int row, int col) {\n      float index = dot(vec3(row, col, ${offset}), vec3(${shape[1]}, 1, 1));\n      vec2 uv = vec2((index + 0.5) / ${texNumC}.0, 0.5);\n      return sampleTexture(${texName}, uv);\n    }\n  `;\n  }\n  if (enableShapeUniforms) {\n    return `\n      float ${funcName}(int row, int col) {\n        // Explicitly use integer operations as dot() only works on floats.\n        int index = row * ${texName}Shape[1] + col + ${offset};\n        vec2 uv = uvFromFlat(${texName}TexShape[0], ${texName}TexShape[1], index);\n        return sampleTexture(${texName}, uv);\n      }\n    `;\n  }\n  return `\n  float ${funcName}(int row, int col) {\n    // Explicitly use integer operations as dot() only works on floats.\n    int index = row * ${shape[1]} + col + ${offset};\n    vec2 uv = uvFromFlat(${texNumR}, ${texNumC}, index);\n    return sampleTexture(${texName}, uv);\n  }\n`;\n}\nfunction getPackedSampler3D(inputInfo, enableShapeUniforms) {\n  const shape = inputInfo.shapeInfo.logicalShape;\n  const texName = inputInfo.name;\n  const funcName = \"get\" + texName.charAt(0).toUpperCase() + texName.slice(1);\n  const texShape = inputInfo.shapeInfo.texShape;\n  const packedTexShape = [Math.ceil(texShape[0] / 2), Math.ceil(texShape[1] / 2)];\n  if (shape[0] === 1) {\n    const squeezedShape = shape.slice(1);\n    const keptDims = [1, 2];\n    const newInputInfo = squeezeInputInfo(inputInfo, squeezedShape);\n    const params = [\"b\", \"row\", \"col\"];\n    return `\n        ${getPackedSamplerFromInInfo(newInputInfo, enableShapeUniforms)}\n        vec4 ${funcName}(int b, int row, int col) {\n          return ${funcName}(${getSqueezedParams(params, keptDims)});\n        }\n      `;\n  }\n  const glsl = getGlslDifferences();\n  if (enableShapeUniforms) {\n    return `\n    vec4 ${funcName}(int b, int row, int col) {\n      ivec2 packedTexShape = ivec2(ceil(float(${texName}TexShape[0]) / 2.0), ceil(float(${texName}TexShape[1]) / 2.0));\n      int valuesPerRow = int(ceil(float(${texName}Shape[2]) / 2.0));\n      int texelsInBatch = valuesPerRow * int(ceil(float(${texName}Shape[1]) / 2.0));\n      vec2 uv = packedUVfrom3D(\n        packedTexShape[0], packedTexShape[1], texelsInBatch, valuesPerRow, b, row, col);\n      return ${glsl.texture2D}(${texName}, uv);\n    }\n  `;\n  }\n  const texNumR = packedTexShape[0];\n  const texNumC = packedTexShape[1];\n  const valuesPerRow = Math.ceil(shape[2] / 2);\n  const texelsInBatch = valuesPerRow * Math.ceil(shape[1] / 2);\n  return `\n    vec4 ${funcName}(int b, int row, int col) {\n      vec2 uv = packedUVfrom3D(\n        ${texNumR}, ${texNumC}, ${texelsInBatch}, ${valuesPerRow}, b, row, col);\n      return ${glsl.texture2D}(${texName}, uv);\n    }\n  `;\n}\nfunction getSampler3D(inputInfo, enableShapeUniforms) {\n  const shape = inputInfo.shapeInfo.logicalShape;\n  const texName = inputInfo.name;\n  const funcName = \"get\" + texName.charAt(0).toUpperCase() + texName.slice(1);\n  const stride0 = shape[1] * shape[2];\n  const stride1 = shape[2];\n  const { newShape, keptDims } = util_exports.squeezeShape(shape);\n  const squeezedShape = newShape;\n  if (squeezedShape.length < shape.length) {\n    const newInputInfo = squeezeInputInfo(inputInfo, squeezedShape);\n    const params = [\"row\", \"col\", \"depth\"];\n    return `\n        ${getSamplerFromInInfo(newInputInfo, enableShapeUniforms)}\n        float ${funcName}(int row, int col, int depth) {\n          return ${funcName}(${getSqueezedParams(params, keptDims)});\n        }\n      `;\n  }\n  if (inputInfo.shapeInfo.isUniform) {\n    return `\n      float ${funcName}(int row, int col, int depth) {\n        int index = round(dot(vec3(row, col, depth),\n                          vec3(${stride0}, ${stride1}, 1)));\n        ${getUniformSampler(inputInfo)}\n      }\n    `;\n  }\n  const texShape = inputInfo.shapeInfo.texShape;\n  const texNumR = texShape[0];\n  const texNumC = texShape[1];\n  const flatOffset = inputInfo.shapeInfo.flatOffset;\n  if (texNumC === stride0 && flatOffset == null) {\n    if (enableShapeUniforms) {\n      return `\n      float ${funcName}(int row, int col, int depth) {\n        int stride1 = ${texName}Shape[2];\n        float texR = float(row);\n        float texC = dot(vec2(col, depth), vec2(stride1, 1));\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                   vec2(${texName}TexShape[1], ${texName}TexShape[0]);\n        return sampleTexture(${texName}, uv);\n      }\n    `;\n    }\n    return `\n        float ${funcName}(int row, int col, int depth) {\n          float texR = float(row);\n          float texC = dot(vec2(col, depth), vec2(${stride1}, 1));\n          vec2 uv = (vec2(texC, texR) + halfCR) /\n                     vec2(${texNumC}.0, ${texNumR}.0);\n          return sampleTexture(${texName}, uv);\n        }\n      `;\n  }\n  if (texNumC === stride1 && flatOffset == null) {\n    if (enableShapeUniforms) {\n      return `\n      float ${funcName}(int row, int col, int depth) {\n        float texR = dot(vec2(row, col), vec2(${texName}Shape[1], 1));\n        float texC = float(depth);\n        vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${texName}TexShape[1], ${texName}TexShape[0]);\n        return sampleTexture(${texName}, uv);\n      }\n    `;\n    }\n    return `\n    float ${funcName}(int row, int col, int depth) {\n      float texR = dot(vec2(row, col), vec2(${shape[1]}, 1));\n      float texC = float(depth);\n      vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${texNumC}.0, ${texNumR}.0);\n      return sampleTexture(${texName}, uv);\n    }\n  `;\n  }\n  const offset = getFlatOffsetUniformName(texName);\n  if (enableShapeUniforms) {\n    return `\n    float ${funcName}(int row, int col, int depth) {\n      // Explicitly use integer operations as dot() only works on floats.\n      int stride0 = ${texName}Shape[1] * ${texName}Shape[2];\n      int stride1 = ${texName}Shape[2];\n      int index = row * ${stride0} + col * ${stride1} + depth + ${offset};\n      vec2 uv = uvFromFlat(${texName}TexShape[0], ${texName}TexShape[1], index);\n      return sampleTexture(${texName}, uv);\n    }\n    `;\n  }\n  return `\n      float ${funcName}(int row, int col, int depth) {\n        // Explicitly use integer operations as dot() only works on floats.\n        int index = row * ${stride0} + col * ${stride1} + depth + ${offset};\n        vec2 uv = uvFromFlat(${texNumR}, ${texNumC}, index);\n        return sampleTexture(${texName}, uv);\n      }\n  `;\n}\nfunction getPackedSamplerND(inputInfo, enableShapeUniforms) {\n  const texName = inputInfo.name;\n  const funcName = \"get\" + texName.charAt(0).toUpperCase() + texName.slice(1);\n  const glsl = getGlslDifferences();\n  if (enableShapeUniforms) {\n    return `\n    vec4 ${funcName}(int b2, int b, int row, int col) {\n      int valuesPerRow = int(ceil(float(${texName}Shape[3]) / 2.0));\n      int texelsInBatch = valuesPerRow * int(ceil(float(${texName}Shape[2]) / 2.0));\n      int index = b * texelsInBatch + (row / 2) * valuesPerRow + (col / 2);\n      texelsInBatch *= ${texName}Shape[1];\n      index = b2 * texelsInBatch + index;\n      ivec2 packedTexShape = ivec2(ceil(float(${texName}TexShape[0]) / 2.0), ceil(float(${texName}TexShape[1]) / 2.0));\n      int texR = index / packedTexShape[1];\n      int texC = index - texR * packedTexShape[1];\n      vec2 uv = (vec2(texC, texR) + halfCR) / vec2(packedTexShape[1], packedTexShape[0]); return ${glsl.texture2D}(${texName}, uv);\n    }\n  `;\n  }\n  const shape = inputInfo.shapeInfo.logicalShape;\n  const rank = shape.length;\n  const texShape = inputInfo.shapeInfo.texShape;\n  const packedTexShape = [Math.ceil(texShape[0] / 2), Math.ceil(texShape[1] / 2)];\n  const texNumR = packedTexShape[0];\n  const texNumC = packedTexShape[1];\n  const valuesPerRow = Math.ceil(shape[rank - 1] / 2);\n  let texelsInBatch = valuesPerRow * Math.ceil(shape[rank - 2] / 2);\n  let params = `int b, int row, int col`;\n  let index = `b * ${texelsInBatch} + (row / 2) * ${valuesPerRow} + (col / 2)`;\n  for (let b = 2; b < rank - 1; b++) {\n    params = `int b${b}, ` + params;\n    texelsInBatch *= shape[rank - b - 1];\n    index = `b${b} * ${texelsInBatch} + ` + index;\n  }\n  return `\n    vec4 ${funcName}(${params}) {\n      int index = ${index};\n      int texR = index / ${texNumC};\n      int texC = index - texR * ${texNumC};\n      vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${texNumC}, ${texNumR});\n      return ${glsl.texture2D}(${texName}, uv);\n    }\n  `;\n}\nfunction getSampler4D(inputInfo, enableShapeUniforms) {\n  const shape = inputInfo.shapeInfo.logicalShape;\n  const texName = inputInfo.name;\n  const funcName = \"get\" + texName.charAt(0).toUpperCase() + texName.slice(1);\n  const stride2 = shape[3];\n  const stride1 = shape[2] * stride2;\n  const stride0 = shape[1] * stride1;\n  const { newShape, keptDims } = util_exports.squeezeShape(shape);\n  if (newShape.length < shape.length) {\n    const newInputInfo = squeezeInputInfo(inputInfo, newShape);\n    const params = [\"row\", \"col\", \"depth\", \"depth2\"];\n    return `\n      ${getSamplerFromInInfo(newInputInfo, enableShapeUniforms)}\n      float ${funcName}(int row, int col, int depth, int depth2) {\n        return ${funcName}(${getSqueezedParams(params, keptDims)});\n      }\n    `;\n  }\n  if (inputInfo.shapeInfo.isUniform) {\n    return `\n      float ${funcName}(int row, int col, int depth, int depth2) {\n        int index = round(dot(vec4(row, col, depth, depth2),\n                          vec4(${stride0}, ${stride1}, ${stride2}, 1)));\n        ${getUniformSampler(inputInfo)}\n      }\n    `;\n  }\n  const flatOffset = inputInfo.shapeInfo.flatOffset;\n  const texShape = inputInfo.shapeInfo.texShape;\n  const texNumR = texShape[0];\n  const texNumC = texShape[1];\n  const stride2Str = `int stride2 = ${texName}Shape[3];`;\n  const stride1Str = `int stride1 = ${texName}Shape[2] * stride2;`;\n  const stride0Str = `int stride0 = ${texName}Shape[1] * stride1;`;\n  if (texNumC === stride0 && flatOffset == null) {\n    if (enableShapeUniforms) {\n      return `\n      float ${funcName}(int row, int col, int depth, int depth2) {\n        ${stride2Str}\n        ${stride1Str}\n        float texR = float(row);\n        float texC =\n            dot(vec3(col, depth, depth2),\n                vec3(stride1, stride2, 1));\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                   vec2(${texName}TexShape[1], ${texName}TexShape[0]);\n        return sampleTexture(${texName}, uv);\n      }\n    `;\n    }\n    return `\n      float ${funcName}(int row, int col, int depth, int depth2) {\n        float texR = float(row);\n        float texC =\n            dot(vec3(col, depth, depth2),\n                vec3(${stride1}, ${stride2}, 1));\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                   vec2(${texNumC}.0, ${texNumR}.0);\n        return sampleTexture(${texName}, uv);\n      }\n    `;\n  }\n  if (texNumC === stride2 && flatOffset == null) {\n    if (enableShapeUniforms) {\n      return `\n      float ${funcName}(int row, int col, int depth, int depth2) {\n        float texR = dot(vec3(row, col, depth),\n                         vec3(${texName}Shape[1] * ${texName}Shape[2], ${texName}Shape[2], 1));\n        float texC = float(depth2);\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                  vec2(${texName}TexShape[1], ${texName}TexShape[0]);\n        return sampleTexture(${texName}, uv);\n      }\n    `;\n    }\n    return `\n      float ${funcName}(int row, int col, int depth, int depth2) {\n        float texR = dot(vec3(row, col, depth),\n                         vec3(${shape[1] * shape[2]}, ${shape[2]}, 1));\n        float texC = float(depth2);\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                  vec2(${texNumC}.0, ${texNumR}.0);\n        return sampleTexture(${texName}, uv);\n      }\n    `;\n  }\n  const offset = getFlatOffsetUniformName(texName);\n  if (enableShapeUniforms) {\n    return `\n    float ${funcName}(int row, int col, int depth, int depth2) {\n      // Explicitly use integer operations as dot() only works on floats.\n      ${stride2Str}\n      ${stride1Str}\n      ${stride0Str}\n      int index = row * stride0 + col * stride1 +\n          depth * stride2 + depth2;\n      vec2 uv = uvFromFlat(${texName}TexShape[0], ${texName}TexShape[1], index + ${offset});\n      return sampleTexture(${texName}, uv);\n    }\n  `;\n  }\n  return `\n    float ${funcName}(int row, int col, int depth, int depth2) {\n      // Explicitly use integer operations as dot() only works on floats.\n      int index = row * ${stride0} + col * ${stride1} +\n          depth * ${stride2} + depth2;\n      vec2 uv = uvFromFlat(${texNumR}, ${texNumC}, index + ${offset});\n      return sampleTexture(${texName}, uv);\n    }\n  `;\n}\nfunction getSampler5D(inputInfo) {\n  const shape = inputInfo.shapeInfo.logicalShape;\n  const texName = inputInfo.name;\n  const funcName = \"get\" + texName.charAt(0).toUpperCase() + texName.slice(1);\n  const stride3 = shape[4];\n  const stride2 = shape[3] * stride3;\n  const stride1 = shape[2] * stride2;\n  const stride0 = shape[1] * stride1;\n  const { newShape, keptDims } = util_exports.squeezeShape(shape);\n  if (newShape.length < shape.length) {\n    const newInputInfo = squeezeInputInfo(inputInfo, newShape);\n    const params = [\"row\", \"col\", \"depth\", \"depth2\", \"depth3\"];\n    return `\n      ${getSamplerFromInInfo(newInputInfo)}\n      float ${funcName}(int row, int col, int depth, int depth2, int depth3) {\n        return ${funcName}(${getSqueezedParams(params, keptDims)});\n      }\n    `;\n  }\n  if (inputInfo.shapeInfo.isUniform) {\n    return `\n      float ${funcName}(int row, int col, int depth, int depth2, int depth3) {\n        float index = dot(\n          vec4(row, col, depth, depth2),\n          vec4(${stride0}, ${stride1}, ${stride2}, ${stride3})) +\n          depth3;\n        ${getUniformSampler(inputInfo)}\n      }\n    `;\n  }\n  const flatOffset = inputInfo.shapeInfo.flatOffset;\n  const texShape = inputInfo.shapeInfo.texShape;\n  const texNumR = texShape[0];\n  const texNumC = texShape[1];\n  if (texNumC === stride0 && flatOffset == null) {\n    return `\n      float ${funcName}(int row, int col, int depth, int depth2, int depth3) {\n        int texR = row;\n        float texC = dot(vec4(col, depth, depth2, depth3),\n                         vec4(${stride1}, ${stride2}, ${stride3}, 1));\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                   vec2(${texNumC}.0, ${texNumR}.0);\n        return sampleTexture(${texName}, uv);\n      }\n    `;\n  }\n  if (texNumC === stride3 && flatOffset == null) {\n    return `\n      float ${funcName}(int row, int col, int depth, int depth2, int depth3) {\n        float texR = dot(\n          vec4(row, col, depth, depth2),\n          vec4(${shape[1] * shape[2] * shape[3]},\n               ${shape[2] * shape[3]}, ${shape[3]}, 1));\n        int texC = depth3;\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                  vec2(${texNumC}.0, ${texNumR}.0);\n        return sampleTexture(${texName}, uv);\n      }\n    `;\n  }\n  const offset = getFlatOffsetUniformName(texName);\n  return `\n    float ${funcName}(int row, int col, int depth, int depth2, int depth3) {\n      // Explicitly use integer operations as dot() only works on floats.\n      int index = row * ${stride0} + col * ${stride1} + depth * ${stride2} +\n          depth2 * ${stride3} + depth3 + ${offset};\n      vec2 uv = uvFromFlat(${texNumR}, ${texNumC}, index);\n      return sampleTexture(${texName}, uv);\n    }\n  `;\n}\nfunction getSampler6D(inputInfo) {\n  const shape = inputInfo.shapeInfo.logicalShape;\n  const texName = inputInfo.name;\n  const funcName = \"get\" + texName.charAt(0).toUpperCase() + texName.slice(1);\n  const { newShape, keptDims } = util_exports.squeezeShape(shape);\n  if (newShape.length < shape.length) {\n    const newInputInfo = squeezeInputInfo(inputInfo, newShape);\n    const params = [\"row\", \"col\", \"depth\", \"depth2\", \"depth3\", \"depth4\"];\n    return `\n      ${getSamplerFromInInfo(newInputInfo)}\n      float ${funcName}(int row, int col, int depth,\n                    int depth2, int depth3, int depth4) {\n        return ${funcName}(${getSqueezedParams(params, keptDims)});\n      }\n    `;\n  }\n  const stride4 = shape[5];\n  const stride3 = shape[4] * stride4;\n  const stride2 = shape[3] * stride3;\n  const stride1 = shape[2] * stride2;\n  const stride0 = shape[1] * stride1;\n  if (inputInfo.shapeInfo.isUniform) {\n    return `\n      float ${funcName}(int row, int col, int depth,\n                  int depth2, int depth3, int depth4) {\n        int index = round(dot(\n          vec4(row, col, depth, depth2),\n          vec4(${stride0}, ${stride1}, ${stride2}, ${stride3})) +\n          dot(\n            vec2(depth3, depth4),\n            vec2(${stride4}, 1)));\n        ${getUniformSampler(inputInfo)}\n      }\n    `;\n  }\n  const flatOffset = inputInfo.shapeInfo.flatOffset;\n  const texShape = inputInfo.shapeInfo.texShape;\n  const texNumR = texShape[0];\n  const texNumC = texShape[1];\n  if (texNumC === stride0 && flatOffset == null) {\n    return `\n      float ${funcName}(int row, int col, int depth,\n                    int depth2, int depth3, int depth4) {\n        int texR = row;\n        float texC = dot(vec4(col, depth, depth2, depth3),\n          vec4(${stride1}, ${stride2}, ${stride3}, ${stride4})) +\n               float(depth4);\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                   vec2(${texNumC}.0, ${texNumR}.0);\n        return sampleTexture(${texName}, uv);\n      }\n    `;\n  }\n  if (texNumC === stride4 && flatOffset == null) {\n    return `\n      float ${funcName}(int row, int col, int depth,\n                    int depth2, int depth3, int depth4) {\n        float texR = dot(vec4(row, col, depth, depth2),\n          vec4(${shape[1] * shape[2] * shape[3] * shape[4]},\n               ${shape[2] * shape[3] * shape[4]},\n               ${shape[3] * shape[4]},\n               ${shape[4]})) + float(depth3);\n        int texC = depth4;\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                  vec2(${texNumC}.0, ${texNumR}.0);\n        return sampleTexture(${texName}, uv);\n      }\n    `;\n  }\n  const offset = getFlatOffsetUniformName(texName);\n  return `\n    float ${funcName}(int row, int col, int depth,\n                  int depth2, int depth3, int depth4) {\n      // Explicitly use integer operations as dot() only works on floats.\n      int index = row * ${stride0} + col * ${stride1} + depth * ${stride2} +\n          depth2 * ${stride3} + depth3 * ${stride4} + depth4 + ${offset};\n      vec2 uv = uvFromFlat(${texNumR}, ${texNumC}, index);\n      return sampleTexture(${texName}, uv);\n    }\n  `;\n}\nfunction getUniformSampler(inputInfo) {\n  const texName = inputInfo.name;\n  const inSize = util_exports.sizeFromShape(inputInfo.shapeInfo.logicalShape);\n  if (inSize < 2) {\n    return `return ${texName};`;\n  }\n  return `\n    for (int i = 0; i < ${inSize}; i++) {\n      if (i == index) {\n        return ${texName}[i];\n      }\n    }\n  `;\n}\nfunction getPackedSamplerAtOutputCoords(inputInfo, outShapeInfo) {\n  const texName = inputInfo.name;\n  const texFuncSnippet = texName.charAt(0).toUpperCase() + texName.slice(1);\n  const funcName = \"get\" + texFuncSnippet + \"AtOutCoords\";\n  const inRank = inputInfo.shapeInfo.logicalShape.length;\n  const outRank = outShapeInfo.logicalShape.length;\n  const broadcastDims = getBroadcastDims2(inputInfo.shapeInfo.logicalShape, outShapeInfo.logicalShape);\n  const type = getCoordsDataType(outRank);\n  const rankDiff = outRank - inRank;\n  let coordsSnippet;\n  const fields = [\"x\", \"y\", \"z\", \"w\", \"u\", \"v\"];\n  if (inRank === 0) {\n    coordsSnippet = \"\";\n  } else if (outRank < 2 && broadcastDims.length >= 1) {\n    coordsSnippet = \"coords = 0;\";\n  } else {\n    coordsSnippet = broadcastDims.map((d) => `coords.${fields[d + rankDiff]} = 0;`).join(\"\\n\");\n  }\n  let unpackedCoordsSnippet = \"\";\n  if (outRank < 2 && inRank > 0) {\n    unpackedCoordsSnippet = \"coords\";\n  } else {\n    unpackedCoordsSnippet = inputInfo.shapeInfo.logicalShape.map((s2, i2) => `coords.${fields[i2 + rankDiff]}`).join(\", \");\n  }\n  let output = `return outputValue;`;\n  const inSize = util_exports.sizeFromShape(inputInfo.shapeInfo.logicalShape);\n  const isInputScalar = inSize === 1;\n  const outSize = util_exports.sizeFromShape(outShapeInfo.logicalShape);\n  const isOutputScalar = outSize === 1;\n  if (inRank === 1 && !isInputScalar && !isOutputScalar) {\n    output = `\n      return vec4(outputValue.xy, outputValue.xy);\n    `;\n  } else if (isInputScalar && !isOutputScalar) {\n    if (outRank === 1) {\n      output = `\n        return vec4(outputValue.x, outputValue.x, 0., 0.);\n      `;\n    } else {\n      output = `\n        return vec4(outputValue.x);\n      `;\n    }\n  } else if (broadcastDims.length) {\n    const rows = inRank - 2;\n    const cols = inRank - 1;\n    if (broadcastDims.indexOf(rows) > -1 && broadcastDims.indexOf(cols) > -1) {\n      output = `return vec4(outputValue.x);`;\n    } else if (broadcastDims.indexOf(rows) > -1) {\n      output = `return vec4(outputValue.x, outputValue.y, outputValue.x, outputValue.y);`;\n    } else if (broadcastDims.indexOf(cols) > -1) {\n      output = `return vec4(outputValue.xx, outputValue.zz);`;\n    }\n  }\n  return `\n    vec4 ${funcName}() {\n      ${type} coords = getOutputCoords();\n      ${coordsSnippet}\n      vec4 outputValue = get${texFuncSnippet}(${unpackedCoordsSnippet});\n      ${output}\n    }\n  `;\n}\nfunction getSamplerAtOutputCoords(inputInfo, outShapeInfo) {\n  const texName = inputInfo.name;\n  const texFuncSnippet = texName.charAt(0).toUpperCase() + texName.slice(1);\n  const funcName = \"get\" + texFuncSnippet + \"AtOutCoords\";\n  const outTexShape = outShapeInfo.texShape;\n  const inTexShape = inputInfo.shapeInfo.texShape;\n  const inRank = inputInfo.shapeInfo.logicalShape.length;\n  const outRank = outShapeInfo.logicalShape.length;\n  if (!inputInfo.shapeInfo.isUniform && inRank === outRank && inputInfo.shapeInfo.flatOffset == null && util_exports.arraysEqual(inTexShape, outTexShape)) {\n    return `\n      float ${funcName}() {\n        return sampleTexture(${texName}, resultUV);\n      }\n    `;\n  }\n  const type = getCoordsDataType(outRank);\n  const broadcastDims = getBroadcastDims2(inputInfo.shapeInfo.logicalShape, outShapeInfo.logicalShape);\n  const rankDiff = outRank - inRank;\n  let coordsSnippet;\n  const fields = [\"x\", \"y\", \"z\", \"w\", \"u\", \"v\"];\n  if (inRank === 0) {\n    coordsSnippet = \"\";\n  } else if (outRank < 2 && broadcastDims.length >= 1) {\n    coordsSnippet = \"coords = 0;\";\n  } else {\n    coordsSnippet = broadcastDims.map((d) => `coords.${fields[d + rankDiff]} = 0;`).join(\"\\n\");\n  }\n  let unpackedCoordsSnippet = \"\";\n  if (outRank < 2 && inRank > 0) {\n    unpackedCoordsSnippet = \"coords\";\n  } else {\n    unpackedCoordsSnippet = inputInfo.shapeInfo.logicalShape.map((s2, i2) => `coords.${fields[i2 + rankDiff]}`).join(\", \");\n  }\n  return `\n    float ${funcName}() {\n      ${type} coords = getOutputCoords();\n      ${coordsSnippet}\n      return get${texFuncSnippet}(${unpackedCoordsSnippet});\n    }\n  `;\n}\nfunction getCoordsDataType(rank) {\n  if (rank <= 1) {\n    return \"int\";\n  } else if (rank === 2) {\n    return \"ivec2\";\n  } else if (rank === 3) {\n    return \"ivec3\";\n  } else if (rank === 4) {\n    return \"ivec4\";\n  } else if (rank === 5) {\n    return \"ivec5\";\n  } else if (rank === 6) {\n    return \"ivec6\";\n  } else {\n    throw Error(`GPU for rank ${rank} is not yet supported`);\n  }\n}\nfunction getUniformInfoFromShape(isPacked, shape, texShape) {\n  const { newShape, keptDims } = util_exports.squeezeShape(shape);\n  const rank = shape.length;\n  const useSqueezePackedShape = isPacked && rank === 3 && shape[0] === 1;\n  const squeezeShape2 = useSqueezePackedShape ? shape.slice(1) : newShape;\n  const useSqueezeShape = !isPacked && rank > 1 && !util_exports.arraysEqual(shape, texShape) && newShape.length < rank || useSqueezePackedShape;\n  const uniformShape = useSqueezeShape ? squeezeShape2 : shape;\n  return { useSqueezeShape, uniformShape, keptDims };\n}\nfunction squeezeInputInfo(inInfo, squeezedShape) {\n  const newInputInfo = JSON.parse(JSON.stringify(inInfo));\n  newInputInfo.shapeInfo.logicalShape = squeezedShape;\n  return newInputInfo;\n}\nfunction getSqueezedParams(params, keptDims) {\n  return keptDims.map((d) => params[d]).join(\", \");\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/gpgpu_math.js\nfunction compileProgram(gpgpu, program, inputs, output) {\n  const inputInfos = inputs.map((input2, i2) => {\n    const shapeInfo = {\n      logicalShape: input2.shape,\n      texShape: input2.isUniform ? null : input2.texData.texShape,\n      isUniform: input2.isUniform,\n      isPacked: input2.isUniform ? false : input2.texData.isPacked,\n      flatOffset: null\n    };\n    if (input2.texData != null && input2.texData.slice != null && input2.texData.slice.flatOffset > 0) {\n      shapeInfo.flatOffset = input2.texData.slice.flatOffset;\n    }\n    return { name: program.variableNames[i2], shapeInfo };\n  });\n  const inShapeInfos = inputInfos.map((x) => x.shapeInfo);\n  const outShapeInfo = {\n    logicalShape: output.shape,\n    texShape: output.texData.texShape,\n    isUniform: false,\n    isPacked: output.texData.isPacked,\n    flatOffset: null\n  };\n  const source = makeShader(inputInfos, outShapeInfo, program);\n  const fragmentShader = createFragmentShader(gpgpu.gl, source);\n  const webGLProgram = gpgpu.createProgram(fragmentShader);\n  if (!env().get(\"ENGINE_COMPILE_ONLY\")) {\n    return Object.assign({\n      program,\n      fragmentShader,\n      source,\n      webGLProgram,\n      inShapeInfos,\n      outShapeInfo\n    }, getUniformLocations(gpgpu, program, webGLProgram));\n  } else {\n    return {\n      program,\n      fragmentShader,\n      source,\n      webGLProgram,\n      inShapeInfos,\n      outShapeInfo,\n      uniformLocations: null,\n      customUniformLocations: null,\n      infLoc: null,\n      nanLoc: null,\n      inShapesLocations: null,\n      inTexShapesLocations: null,\n      outShapeLocation: null,\n      outShapeStridesLocation: null,\n      outTexShapeLocation: null\n    };\n  }\n}\nfunction getUniformLocations(gpgpu, program, webGLProgram) {\n  const uniformLocations = {};\n  const inShapesLocations = {};\n  const inTexShapesLocations = {};\n  const customUniformLocations = [];\n  let outShapeLocation;\n  let outTexShapeLocation;\n  let outShapeStridesLocation;\n  let infLoc = null;\n  let nanLoc = null;\n  nanLoc = gpgpu.getUniformLocation(webGLProgram, \"NAN\", false);\n  if (env().getNumber(\"WEBGL_VERSION\") === 1) {\n    infLoc = gpgpu.getUniformLocation(webGLProgram, \"INFINITY\", false);\n  }\n  const shouldThrow = false;\n  for (let i2 = 0; i2 < program.variableNames.length; i2++) {\n    const varName = program.variableNames[i2];\n    uniformLocations[varName] = gpgpu.getUniformLocation(webGLProgram, varName, shouldThrow);\n    uniformLocations[`offset${varName}`] = gpgpu.getUniformLocation(webGLProgram, `offset${varName}`, shouldThrow);\n    if (program.enableShapeUniforms) {\n      inShapesLocations[`${varName}Shape`] = gpgpu.getUniformLocation(webGLProgram, `${varName}Shape`, shouldThrow);\n      inTexShapesLocations[`${varName}TexShape`] = gpgpu.getUniformLocation(webGLProgram, `${varName}TexShape`, shouldThrow);\n    }\n  }\n  if (program.enableShapeUniforms) {\n    outShapeLocation = gpgpu.getUniformLocation(webGLProgram, \"outShape\", shouldThrow);\n    outShapeStridesLocation = gpgpu.getUniformLocation(webGLProgram, \"outShapeStrides\", shouldThrow);\n    outTexShapeLocation = gpgpu.getUniformLocation(webGLProgram, \"outTexShape\", shouldThrow);\n  }\n  if (program.customUniforms) {\n    program.customUniforms.forEach((d, i2) => {\n      customUniformLocations[i2] = gpgpu.getUniformLocation(webGLProgram, d.name, shouldThrow);\n    });\n  }\n  return {\n    uniformLocations,\n    customUniformLocations,\n    infLoc,\n    nanLoc,\n    inShapesLocations,\n    inTexShapesLocations,\n    outShapeLocation,\n    outShapeStridesLocation,\n    outTexShapeLocation\n  };\n}\nfunction validateBinaryAndProgram(shapeInfos, inputs) {\n  if (shapeInfos.length !== inputs.length) {\n    throw Error(`Binary was compiled with ${shapeInfos.length} inputs, but was executed with ${inputs.length} inputs`);\n  }\n  shapeInfos.forEach((s2, i2) => {\n    const shapeA = s2.logicalShape;\n    const input2 = inputs[i2];\n    const shapeB = input2.shape;\n    if (!util_exports.arraysEqual(shapeA, shapeB)) {\n      throw Error(`Binary was compiled with different shapes than the current args. Shapes ${shapeA} and ${shapeB} must match`);\n    }\n    if (s2.isUniform && input2.isUniform) {\n      return;\n    }\n    const texShapeA = s2.texShape;\n    const texShapeB = input2.isUniform ? null : input2.texData.texShape;\n    if (!util_exports.arraysEqual(texShapeA, texShapeB)) {\n      throw Error(`Binary was compiled with different texture shapes than the current args. Shape ${texShapeA} and ${texShapeB} must match`);\n    }\n  });\n}\nfunction runProgram(gpgpu, binary, inputs, output, customUniformValues) {\n  if (!binary.program.enableShapeUniforms) {\n    validateBinaryAndProgram(binary.inShapeInfos, inputs);\n    validateBinaryAndProgram([binary.outShapeInfo], [output]);\n  }\n  const outTex = output.texData.texture;\n  const outTexShape = output.texData.texShape;\n  if (output.texData.isPacked) {\n    gpgpu.setOutputPackedMatrixTexture(outTex.texture, outTexShape[0], outTexShape[1]);\n  } else {\n    gpgpu.setOutputMatrixTexture(outTex.texture, outTexShape[0], outTexShape[1]);\n  }\n  gpgpu.setProgram(binary.webGLProgram);\n  if (env().getNumber(\"WEBGL_VERSION\") === 1) {\n    if (binary.infLoc !== null) {\n      gpgpu.gl.uniform1f(binary.infLoc, Infinity);\n    }\n  }\n  if (binary.nanLoc !== null) {\n    gpgpu.gl.uniform1f(binary.nanLoc, NaN);\n  }\n  inputs.forEach((input2, i2) => {\n    const varName = binary.program.variableNames[i2];\n    const varLoc = binary.uniformLocations[varName];\n    const varOffsetLoc = binary.uniformLocations[`offset${varName}`];\n    const varShapeLoc = binary.inShapesLocations[`${varName}Shape`];\n    const varTexShapeLoc = binary.inTexShapesLocations[`${varName}TexShape`];\n    if (varShapeLoc) {\n      const { uniformShape } = getUniformInfoFromShape(binary.program.packedInputs, input2.shape, input2.texData.texShape);\n      switch (uniformShape.length) {\n        case 1:\n          gpgpu.gl.uniform1iv(varShapeLoc, new Int32Array(uniformShape));\n          break;\n        case 2:\n          gpgpu.gl.uniform2iv(varShapeLoc, new Int32Array(uniformShape));\n          break;\n        case 3:\n          gpgpu.gl.uniform3iv(varShapeLoc, new Int32Array(uniformShape));\n          break;\n        case 4:\n          gpgpu.gl.uniform4iv(varShapeLoc, new Int32Array(uniformShape));\n          break;\n        default:\n          break;\n      }\n    }\n    if (varTexShapeLoc) {\n      gpgpu.gl.uniform2i(varTexShapeLoc, input2.texData.texShape[0], input2.texData.texShape[1]);\n    }\n    if (varLoc == null) {\n      return;\n    }\n    if (input2.isUniform) {\n      if (util_exports.sizeFromShape(input2.shape) < 2) {\n        gpgpu.gl.uniform1f(varLoc, input2.uniformValues[0]);\n      } else {\n        let vals = input2.uniformValues;\n        if (!(vals instanceof Float32Array)) {\n          vals = new Float32Array(vals);\n        }\n        gpgpu.gl.uniform1fv(varLoc, vals);\n      }\n      return;\n    }\n    if (input2.texData.slice != null && varOffsetLoc != null) {\n      gpgpu.gl.uniform1i(varOffsetLoc, input2.texData.slice.flatOffset);\n    }\n    gpgpu.setInputMatrixTexture(input2.texData.texture.texture, varLoc, i2);\n  });\n  const outShapeLoc = binary.outShapeLocation;\n  if (outShapeLoc) {\n    switch (output.shape.length) {\n      case 1:\n        gpgpu.gl.uniform1iv(outShapeLoc, new Int32Array(output.shape));\n        break;\n      case 2:\n        gpgpu.gl.uniform2iv(outShapeLoc, new Int32Array(output.shape));\n        break;\n      case 3:\n        gpgpu.gl.uniform3iv(outShapeLoc, new Int32Array(output.shape));\n        break;\n      case 4:\n        gpgpu.gl.uniform4iv(outShapeLoc, new Int32Array(output.shape));\n        break;\n      default:\n        break;\n    }\n  }\n  if (binary.outShapeStridesLocation) {\n    const strides = util_exports.computeStrides(output.shape);\n    switch (output.shape.length) {\n      case 2:\n        gpgpu.gl.uniform1iv(binary.outShapeStridesLocation, new Int32Array(strides));\n        break;\n      case 3:\n        gpgpu.gl.uniform2iv(binary.outShapeStridesLocation, new Int32Array(strides));\n        break;\n      case 4:\n        gpgpu.gl.uniform3iv(binary.outShapeStridesLocation, new Int32Array(strides));\n        break;\n      default:\n        break;\n    }\n  }\n  if (binary.outTexShapeLocation) {\n    gpgpu.gl.uniform2i(binary.outTexShapeLocation, output.texData.texShape[0], output.texData.texShape[1]);\n  }\n  if (binary.program.customUniforms && customUniformValues) {\n    binary.program.customUniforms.forEach((d, i2) => {\n      const customLoc = binary.customUniformLocations[i2];\n      const customValue = customUniformValues[i2];\n      if (d.type === \"float\") {\n        gpgpu.gl.uniform1fv(customLoc, customValue);\n      } else if (d.type === \"vec2\") {\n        gpgpu.gl.uniform2fv(customLoc, customValue);\n      } else if (d.type === \"vec3\") {\n        gpgpu.gl.uniform3fv(customLoc, customValue);\n      } else if (d.type === \"vec4\") {\n        gpgpu.gl.uniform4fv(customLoc, customValue);\n      } else if (d.type === \"int\") {\n        gpgpu.gl.uniform1iv(customLoc, customValue);\n      } else if (d.type === \"ivec2\") {\n        gpgpu.gl.uniform2iv(customLoc, customValue);\n      } else if (d.type === \"ivec3\") {\n        gpgpu.gl.uniform3iv(customLoc, customValue);\n      } else if (d.type === \"ivec4\") {\n        gpgpu.gl.uniform4iv(customLoc, customValue);\n      } else {\n        throw Error(`uniform type ${d.type} is not supported yet.`);\n      }\n    });\n  }\n  gpgpu.executeProgram();\n}\nfunction makeShaderKey(program, inputs, output) {\n  let keyInputs = \"\";\n  inputs.concat(output).forEach((x) => {\n    const hasOffset = x.texData != null && x.texData.slice != null && x.texData.slice.flatOffset > 0;\n    if (program.enableShapeUniforms && !x.isUniform) {\n      const xTexShape = x.texData.texShape;\n      const { useSqueezeShape, uniformShape, keptDims } = getUniformInfoFromShape(program.packedInputs, x.shape, xTexShape);\n      let rank1 = \"\", rank2 = \"\", rank34 = \"\";\n      if (uniformShape.length === 1 && program.packedInputs) {\n        const packedTexShape = [Math.ceil(xTexShape[0] / 2), Math.ceil(xTexShape[1] / 2)];\n        rank1 = `${packedTexShape[0] > 1}_${packedTexShape[1] > 1}`;\n      } else if (uniformShape.length === 2 && !program.packedInputs) {\n        rank2 = `${uniformShape[0] > 1}_${uniformShape[1] > 1}`;\n      } else if (uniformShape.length > 2 && !program.packedInputs) {\n        const strides = util_exports.computeStrides(uniformShape);\n        rank34 = `${strides[0] === xTexShape[1]}_${strides[strides.length - 1] === xTexShape[1]}`;\n      }\n      const xRank = x.shape.length;\n      const isLogicalShapTexShapeEqual = uniformShape.length === 2 && util_exports.arraysEqual(x.shape, xTexShape);\n      const isScalar = util_exports.sizeFromShape(x.shape) === 1;\n      const broadcastDims = backend_util_exports.getBroadcastDims(x.shape, output.shape);\n      const isInOutTexShapeEqual = !program.packedInputs && xRank === output.shape.length && util_exports.arraysEqual(xTexShape, output.texData.texShape);\n      const isTexShapeGreaterThanOne = program.packedInputs || uniformShape.length > 2 ? \"\" : `${xTexShape[0] > 1}_${xTexShape[1] > 1}`;\n      keyInputs += `${xRank}_${isInOutTexShapeEqual}_${useSqueezeShape ? keptDims : \"\"}_${uniformShape.length}_${isScalar}_${broadcastDims}_${isLogicalShapTexShapeEqual}_${rank1}_${rank2}_${rank34}_${isTexShapeGreaterThanOne}_${hasOffset}`;\n    } else {\n      const texShape = x.isUniform ? \"uniform\" : x.texData.texShape;\n      keyInputs += `${x.shape}_${texShape}_${hasOffset}`;\n    }\n  });\n  const keyUserCode = program.userCode;\n  let key = program.constructor.name;\n  key += \"_\" + keyInputs + \"_\" + keyUserCode + `${env().getNumber(\"WEBGL_VERSION\")}`;\n  return key;\n}\nfunction useShapeUniforms(rank) {\n  return env().getBool(\"WEBGL_USE_SHAPES_UNIFORMS\") && rank <= 4;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/decode_matrix_gpu.js\nvar DecodeMatrixProgram = class {\n  constructor(outputShape) {\n    this.variableNames = [\"A\"];\n    this.packedInputs = false;\n    this.packedOutput = true;\n    this.outPackingScheme = PackingScheme.DENSE;\n    this.customUniforms = [{ name: \"texShape\", type: \"ivec2\" }];\n    const glsl = getGlslDifferences();\n    this.outputShape = outputShape;\n    this.enableShapeUniforms = useShapeUniforms(this.outputShape.length);\n    this.userCode = `\n      ivec3 outCoordsFromFlatIndex(int index) {\n        ${this.enableShapeUniforms ? getOutputLogicalCoordinatesFromFlatIndexByUniform([\"r\", \"c\", \"d\"], outputShape) : getLogicalCoordinatesFromFlatIndex([\"r\", \"c\", \"d\"], outputShape)}\n        return ivec3(r, c, d);\n      }\n\n      void main() {\n        ivec2 resTexRC = ivec2(resultUV.yx * vec2(texShape[0], texShape[1]));\n        int index = 4 * (resTexRC.x * texShape[1] + resTexRC.y);\n\n        vec4 result = vec4(0.);\n\n        for (int i=0; i<4; i++) {\n          int flatIndex = index + i;\n          ivec3 rc = outCoordsFromFlatIndex(flatIndex);\n          result[i] = getA(rc.x, rc.y, rc.z);\n        }\n\n        ${glsl.output} = result;\n      }\n    `;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/decode_matrix_packed_gpu.js\nvar DecodeMatrixPackedProgram = class {\n  constructor(outputShape) {\n    this.variableNames = [\"A\"];\n    this.packedInputs = true;\n    this.packedOutput = true;\n    this.outPackingScheme = PackingScheme.DENSE;\n    this.customUniforms = [{ name: \"texShape\", type: \"ivec2\" }];\n    const glsl = getGlslDifferences();\n    this.outputShape = outputShape;\n    this.enableShapeUniforms = useShapeUniforms(this.outputShape.length);\n    this.userCode = `\n      ivec3 outCoordsFromFlatIndex(int index) {\n        ${this.enableShapeUniforms ? getOutputLogicalCoordinatesFromFlatIndexByUniform([\"r\", \"c\", \"d\"], outputShape) : getLogicalCoordinatesFromFlatIndex([\"r\", \"c\", \"d\"], outputShape)}\n        return ivec3(r, c, d);\n      }\n\n      void main() {\n        ivec2 resTexRC = ivec2(resultUV.yx * vec2(texShape[0], texShape[1]));\n        int index = 4 * (resTexRC.x * texShape[1] + resTexRC.y);\n\n        vec4 result = vec4(0.);\n\n        for (int i=0; i<4; i++) {\n          int flatIndex = index + i;\n          ivec3 rc = outCoordsFromFlatIndex(flatIndex);\n          result[i] = getChannel(getA(rc.x, rc.y, rc.z), vec2(rc.y, rc.z));\n        }\n\n        ${glsl.output} = result;\n      }\n    `;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/encode_float_gpu.js\nvar EncodeFloatProgram = class {\n  constructor(outputShape) {\n    this.variableNames = [\"A\"];\n    this.outTexUsage = TextureUsage.DOWNLOAD;\n    const glsl = getGlslDifferences();\n    this.outputShape = outputShape;\n    this.userCode = `\n      ${ENCODE_FLOAT_SNIPPET}\n\n      void main() {\n        float x = getAAtOutCoords();\n        ${glsl.output} = encode_float(x);\n      }\n    `;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/encode_float_packed_gpu.js\nvar EncodeFloatPackedProgram = class {\n  constructor(outputShape) {\n    this.variableNames = [\"A\"];\n    this.packedInputs = true;\n    this.packedOutput = false;\n    this.outTexUsage = TextureUsage.DOWNLOAD;\n    const glsl = getGlslDifferences();\n    this.outputShape = outputShape;\n    this.userCode = `\n      ${ENCODE_FLOAT_SNIPPET}\n\n      void main() {\n        ivec3 coords = getOutputCoords();\n        float x = getChannel(getAAtOutCoords(), vec2(coords.y, coords.z));\n        ${glsl.output} = encode_float(x);\n      }\n    `;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/encode_matrix_gpu.js\nvar EncodeMatrixProgram = class {\n  constructor(outputShape, inputIsUnsignedByte = false) {\n    this.variableNames = [\"A\"];\n    this.customUniforms = [{ name: \"texShape\", type: \"ivec2\" }];\n    const glsl = getGlslDifferences();\n    this.outputShape = outputShape;\n    this.enableShapeUniforms = useShapeUniforms(this.outputShape.length);\n    let output = `result`;\n    if (inputIsUnsignedByte) {\n      output = `floor(result * 255. + 0.5)`;\n    }\n    this.userCode = `\n      ${this.enableShapeUniforms ? getFlatIndexFrom3DOutput() : getFlatIndexFrom3D(outputShape)}\n\n      void main() {\n        ivec3 coords = getOutputCoords();\n\n        int flatIndex = getFlatIndex(coords);\n        int offset = imod(flatIndex, 4);\n\n        flatIndex = idiv(flatIndex, 4, 1.);\n\n        int r = flatIndex / texShape[1];\n        int c = imod(flatIndex, texShape[1]);\n        vec2 uv = (vec2(c, r) + halfCR) / vec2(texShape[1], texShape[0]);\n        vec4 values = ${glsl.texture2D}(A, uv);\n\n        float result;\n\n        if(offset == 0) {\n          result = values[0];\n        } else if(offset == 1) {\n          result = values[1];\n        } else if(offset == 2) {\n          result = values[2];\n        } else {\n          result = values[3];\n        }\n\n        ${glsl.output} = vec4(${output}, 0., 0., 0.);\n      }\n    `;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/encode_matrix_packed_gpu.js\nvar EncodeMatrixPackedProgram = class {\n  constructor(outputShape, inputIsUnsignedByte = false) {\n    this.variableNames = [\"A\"];\n    this.packedInputs = false;\n    this.packedOutput = true;\n    this.customUniforms = [{ name: \"texShape\", type: \"ivec2\" }];\n    const glsl = getGlslDifferences();\n    this.outputShape = outputShape;\n    this.enableShapeUniforms = useShapeUniforms(this.outputShape.length);\n    let mainLoop = \"\";\n    let output = \"result\";\n    if (inputIsUnsignedByte) {\n      output = \"floor(result * 255. + 0.5)\";\n    }\n    for (let row = 0; row <= 1; row++) {\n      for (let col = 0; col <= 1; col++) {\n        const channel = row * 2 + col;\n        mainLoop += `\n          localCoords = coords;\n          if(localCoords[2] + ${col} < ${this.enableShapeUniforms ? \"outShape[2]\" : `${outputShape[2]}`}) {\n          localCoords[2] += ${col};\n          if (localCoords[1] + ${row} < ${this.enableShapeUniforms ? \"outShape[1]\" : `${outputShape[1]}`}) {\n            localCoords[1] += ${row};\n\n            flatIndex = getFlatIndex(localCoords);\n            offset = imod(flatIndex, 4);\n\n            flatIndex = idiv(flatIndex, 4, 1.);\n\n            int r = flatIndex / texShape[1];\n            int c = imod(flatIndex, texShape[1]);\n            vec2 uv = (vec2(c, r) + halfCR) / vec2(texShape[1], texShape[0]);\n            values = ${glsl.texture2D}(A, uv);\n\n            if (offset == 0) {\n              result[${channel}] = values[0];\n            } else if (offset == 1) {\n              result[${channel}] = values[1];\n            } else if (offset == 2) {\n              result[${channel}] = values[2];\n            } else {\n              result[${channel}] = values[3];\n            }\n          }\n        }\n        `;\n      }\n    }\n    this.userCode = `\n        ${this.enableShapeUniforms ? getFlatIndexFrom3DOutput() : getFlatIndexFrom3D(outputShape)}\n\n        void main() {\n          ivec3 coords = getOutputCoords();\n\n          vec4 result = vec4(0.);\n          int flatIndex, r, c, offset;\n          ivec3 localCoords;\n          vec2 uv;\n          vec4 values;\n\n          ${mainLoop}\n\n          ${glsl.output} = ${output};\n        }\n    `;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/gpgpu_util.js\nvar gpgpu_util_exports = {};\n__export(gpgpu_util_exports, {\n  bindVertexProgramAttributeStreams: () => bindVertexProgramAttributeStreams,\n  createBufferFromOutputTexture: () => createBufferFromOutputTexture,\n  createFloat16MatrixTexture: () => createFloat16MatrixTexture,\n  createFloat16PackedMatrixTexture: () => createFloat16PackedMatrixTexture,\n  createFloat32MatrixTexture: () => createFloat32MatrixTexture,\n  createIndexBuffer: () => createIndexBuffer,\n  createPackedMatrixTexture: () => createPackedMatrixTexture,\n  createUnsignedBytesMatrixTexture: () => createUnsignedBytesMatrixTexture,\n  createVertexBuffer: () => createVertexBuffer,\n  createVertexShader: () => createVertexShader2,\n  downloadByteEncodedFloatMatrixFromOutputTexture: () => downloadByteEncodedFloatMatrixFromOutputTexture,\n  downloadFloat32MatrixFromBuffer: () => downloadFloat32MatrixFromBuffer,\n  downloadMatrixFromPackedOutputTexture: () => downloadMatrixFromPackedOutputTexture,\n  downloadPackedMatrixFromBuffer: () => downloadPackedMatrixFromBuffer,\n  getInternalFormatForFloat16MatrixTexture: () => getInternalFormatForFloat16MatrixTexture,\n  getInternalFormatForFloat16PackedMatrixTexture: () => getInternalFormatForFloat16PackedMatrixTexture,\n  getInternalFormatForFloat32MatrixTexture: () => getInternalFormatForFloat32MatrixTexture,\n  getInternalFormatForPackedMatrixTexture: () => getInternalFormatForPackedMatrixTexture,\n  getInternalFormatForUnsignedBytesMatrixTexture: () => getInternalFormatForUnsignedBytesMatrixTexture,\n  uploadDenseMatrixToTexture: () => uploadDenseMatrixToTexture,\n  uploadPixelDataToTexture: () => uploadPixelDataToTexture\n});\nfunction createVertexShader2(gl) {\n  const glsl = getGlslDifferences();\n  const vertexShaderSource = `${glsl.version}\n    precision highp float;\n    ${glsl.attribute} vec3 clipSpacePos;\n    ${glsl.attribute} vec2 uv;\n    ${glsl.varyingVs} vec2 resultUV;\n\n    void main() {\n      gl_Position = vec4(clipSpacePos, 1);\n      resultUV = uv;\n    }`;\n  return createVertexShader(gl, vertexShaderSource);\n}\nfunction createVertexBuffer(gl) {\n  const vertexArray = new Float32Array([-1, 1, 0, 0, 1, -1, -1, 0, 0, 0, 1, 1, 0, 1, 1, 1, -1, 0, 1, 0]);\n  return createStaticVertexBuffer(gl, vertexArray);\n}\nfunction createIndexBuffer(gl) {\n  const triangleVertexIndices = new Uint16Array([0, 1, 2, 2, 1, 3]);\n  return createStaticIndexBuffer(gl, triangleVertexIndices);\n}\nfunction createAndConfigureTexture(gl, width, height, internalFormat, textureFormat, textureType) {\n  validateTextureSize(width, height);\n  const texture = createTexture(gl);\n  const tex2d = gl.TEXTURE_2D;\n  callAndCheck(gl, () => gl.bindTexture(tex2d, texture));\n  callAndCheck(gl, () => gl.texParameteri(tex2d, gl.TEXTURE_WRAP_S, gl.CLAMP_TO_EDGE));\n  callAndCheck(gl, () => gl.texParameteri(tex2d, gl.TEXTURE_WRAP_T, gl.CLAMP_TO_EDGE));\n  callAndCheck(gl, () => gl.texParameteri(tex2d, gl.TEXTURE_MIN_FILTER, gl.NEAREST));\n  callAndCheck(gl, () => gl.texParameteri(tex2d, gl.TEXTURE_MAG_FILTER, gl.NEAREST));\n  if (env().getNumber(\"WEBGL_VERSION\") === 1) {\n    callAndCheck(gl, () => gl.texImage2D(tex2d, 0, internalFormat, width, height, 0, textureFormat, textureType, null));\n  } else {\n    callAndCheck(gl, () => gl.texStorage2D(tex2d, 1, internalFormat, width, height));\n  }\n  callAndCheck(gl, () => gl.bindTexture(gl.TEXTURE_2D, null));\n  return { texture, texShape: [height, width] };\n}\nfunction getInternalFormatForFloat32MatrixTexture(textureConfig) {\n  return textureConfig.internalFormatFloat;\n}\nfunction createFloat32MatrixTexture(gl, rows, columns, textureConfig) {\n  const [width, height] = getUnpackedMatrixTextureShapeWidthHeight(rows, columns);\n  return createAndConfigureTexture(gl, width, height, getInternalFormatForFloat32MatrixTexture(textureConfig), textureConfig.textureFormatFloat, gl.FLOAT);\n}\nfunction getInternalFormatForFloat16MatrixTexture(textureConfig) {\n  return textureConfig.internalFormatHalfFloat;\n}\nfunction createFloat16MatrixTexture(gl, rows, columns, textureConfig) {\n  const [width, height] = getUnpackedMatrixTextureShapeWidthHeight(rows, columns);\n  return createAndConfigureTexture(gl, width, height, getInternalFormatForFloat16MatrixTexture(textureConfig), textureConfig.textureFormatFloat, textureConfig.textureTypeHalfFloat);\n}\nfunction getInternalFormatForUnsignedBytesMatrixTexture(textureConfig) {\n  return textureConfig.downloadTextureFormat;\n}\nfunction createUnsignedBytesMatrixTexture(gl, rows, columns, textureConfig) {\n  const [width, height] = getUnpackedMatrixTextureShapeWidthHeight(rows, columns);\n  return createAndConfigureTexture(gl, width, height, getInternalFormatForUnsignedBytesMatrixTexture(textureConfig), gl.RGBA, gl.UNSIGNED_BYTE);\n}\nfunction getInternalFormatForPackedMatrixTexture(textureConfig) {\n  return textureConfig.internalFormatPackedFloat;\n}\nfunction createPackedMatrixTexture(gl, rows, columns, textureConfig) {\n  const [width, height] = getPackedMatrixTextureShapeWidthHeight(rows, columns);\n  return createAndConfigureTexture(gl, width, height, getInternalFormatForPackedMatrixTexture(textureConfig), gl.RGBA, gl.FLOAT);\n}\nfunction getInternalFormatForFloat16PackedMatrixTexture(textureConfig) {\n  return textureConfig.internalFormatPackedHalfFloat;\n}\nfunction createFloat16PackedMatrixTexture(gl, rows, columns, textureConfig) {\n  const [width, height] = getPackedMatrixTextureShapeWidthHeight(rows, columns);\n  return createAndConfigureTexture(gl, width, height, getInternalFormatForFloat16PackedMatrixTexture(textureConfig), gl.RGBA, textureConfig.textureTypeHalfFloat);\n}\nfunction bindVertexProgramAttributeStreams(gl, program, vertexBuffer) {\n  const posOffset = 0;\n  const uvOffset = 3 * 4;\n  const stride = 3 * 4 + 2 * 4;\n  callAndCheck(gl, () => gl.bindBuffer(gl.ARRAY_BUFFER, vertexBuffer));\n  const success = bindVertexBufferToProgramAttribute(gl, program, \"clipSpacePos\", vertexBuffer, 3, stride, posOffset);\n  return success && bindVertexBufferToProgramAttribute(gl, program, \"uv\", vertexBuffer, 2, stride, uvOffset);\n}\nfunction uploadDenseMatrixToTexture(gl, texture, width, height, data, textureConfig) {\n  callAndCheck(gl, () => gl.bindTexture(gl.TEXTURE_2D, texture));\n  let dataForUpload, texelDataType, internalFormat;\n  if (data instanceof Uint8Array) {\n    dataForUpload = new Uint8Array(width * height * 4);\n    texelDataType = gl.UNSIGNED_BYTE;\n    internalFormat = gl.RGBA;\n  } else {\n    dataForUpload = new Float32Array(width * height * 4);\n    texelDataType = gl.FLOAT;\n    internalFormat = textureConfig.internalFormatPackedFloat;\n  }\n  dataForUpload.set(data);\n  if (env().getNumber(\"WEBGL_VERSION\") === 2) {\n    callAndCheck(gl, () => gl.texSubImage2D(gl.TEXTURE_2D, 0, 0, 0, width, height, gl.RGBA, texelDataType, dataForUpload));\n  } else {\n    callAndCheck(gl, () => gl.texImage2D(gl.TEXTURE_2D, 0, internalFormat, width, height, 0, gl.RGBA, texelDataType, dataForUpload));\n  }\n  callAndCheck(gl, () => gl.bindTexture(gl.TEXTURE_2D, null));\n}\nfunction uploadPixelDataToTexture(gl, texture, pixels) {\n  callAndCheck(gl, () => gl.bindTexture(gl.TEXTURE_2D, texture));\n  if (pixels.data instanceof Uint8Array) {\n    if (env().getNumber(\"WEBGL_VERSION\") === 2) {\n      callAndCheck(gl, () => gl.texSubImage2D(gl.TEXTURE_2D, 0, 0, 0, pixels.width, pixels.height, gl.RGBA, gl.UNSIGNED_BYTE, pixels.data));\n    } else {\n      callAndCheck(gl, () => gl.texImage2D(gl.TEXTURE_2D, 0, gl.RGBA, pixels.width, pixels.height, 0, gl.RGBA, gl.UNSIGNED_BYTE, pixels.data));\n    }\n  } else {\n    if (env().getNumber(\"WEBGL_VERSION\") === 2) {\n      callAndCheck(gl, () => gl.texSubImage2D(gl.TEXTURE_2D, 0, 0, 0, gl.RGBA, gl.UNSIGNED_BYTE, pixels));\n    } else {\n      callAndCheck(gl, () => gl.texImage2D(gl.TEXTURE_2D, 0, gl.RGBA, gl.RGBA, gl.UNSIGNED_BYTE, pixels));\n    }\n  }\n  callAndCheck(gl, () => gl.bindTexture(gl.TEXTURE_2D, null));\n}\nfunction createBufferFromOutputTexture(gl2, rows, columns, textureConfig) {\n  const buffer2 = gl2.createBuffer();\n  callAndCheck(gl2, () => gl2.bindBuffer(gl2.PIXEL_PACK_BUFFER, buffer2));\n  const bytesPerFloat = 4;\n  const valuesPerTexel = 4;\n  const bufferSizeBytes = bytesPerFloat * valuesPerTexel * rows * columns;\n  callAndCheck(gl2, () => gl2.bufferData(gl2.PIXEL_PACK_BUFFER, bufferSizeBytes, gl2.STREAM_READ));\n  callAndCheck(gl2, () => gl2.readPixels(0, 0, columns, rows, gl2.RGBA, gl2.FLOAT, 0));\n  callAndCheck(gl2, () => gl2.bindBuffer(gl2.PIXEL_PACK_BUFFER, null));\n  return buffer2;\n}\nfunction downloadFloat32MatrixFromBuffer(gl, buffer2, size) {\n  const gl2 = gl;\n  const downloadTarget = new Float32Array(size);\n  gl2.bindBuffer(gl2.PIXEL_PACK_BUFFER, buffer2);\n  gl2.getBufferSubData(gl2.PIXEL_PACK_BUFFER, 0, downloadTarget);\n  gl2.bindBuffer(gl2.PIXEL_PACK_BUFFER, null);\n  return downloadTarget;\n}\nfunction downloadByteEncodedFloatMatrixFromOutputTexture(gl, rows, columns, textureConfig) {\n  const [w, h] = getUnpackedMatrixTextureShapeWidthHeight(rows, columns);\n  const numChannels = 4;\n  const downloadTarget = new Uint8Array(getUnpackedArraySizeFromMatrixSize(rows * columns, numChannels));\n  callAndCheck(gl, () => gl.readPixels(0, 0, w, h, textureConfig.downloadTextureFormat, gl.UNSIGNED_BYTE, downloadTarget));\n  return new Float32Array(downloadTarget.buffer);\n}\nfunction downloadPackedMatrixFromBuffer(gl, buffer2, batch, rows, cols, physicalRows, physicalCols, textureConfig) {\n  const gl2 = gl;\n  const downloadTarget = new Float32Array(getPackedRGBAArraySizeFromMatrixShape(physicalRows, physicalCols));\n  gl2.bindBuffer(gl2.PIXEL_PACK_BUFFER, buffer2);\n  gl2.getBufferSubData(gl2.PIXEL_PACK_BUFFER, 0, downloadTarget);\n  gl2.bindBuffer(gl2.PIXEL_PACK_BUFFER, null);\n  return downloadTarget;\n}\nfunction downloadMatrixFromPackedOutputTexture(gl, physicalRows, physicalCols) {\n  const packedRGBA = new Float32Array(physicalRows * physicalCols * 4);\n  callAndCheck(gl, () => gl.readPixels(0, 0, physicalCols, physicalRows, gl.RGBA, gl.FLOAT, packedRGBA));\n  return packedRGBA;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/gpgpu_context.js\nvar GPGPUContext = class {\n  constructor(gl) {\n    this.outputTexture = null;\n    this.program = null;\n    this.disposed = false;\n    this.vertexAttrsAreBound = false;\n    this.itemsToPoll = [];\n    const glVersion = env().getNumber(\"WEBGL_VERSION\");\n    if (gl != null) {\n      this.gl = gl;\n      setWebGLContext(glVersion, gl);\n    } else {\n      this.gl = getWebGLContext(glVersion);\n    }\n    let COLOR_BUFFER_FLOAT = \"WEBGL_color_buffer_float\";\n    const COLOR_BUFFER_HALF_FLOAT = \"EXT_color_buffer_half_float\";\n    this.parallelCompilationExtension = this.gl.getExtension(\"KHR_parallel_shader_compile\");\n    if (env().getNumber(\"WEBGL_VERSION\") === 1) {\n      const TEXTURE_FLOAT = \"OES_texture_float\";\n      const TEXTURE_HALF_FLOAT = \"OES_texture_half_float\";\n      this.textureFloatExtension = getExtensionOrThrow(this.gl, TEXTURE_FLOAT);\n      if (hasExtension(this.gl, TEXTURE_HALF_FLOAT)) {\n        this.textureHalfFloatExtension = getExtensionOrThrow(this.gl, TEXTURE_HALF_FLOAT);\n      } else if (env().get(\"WEBGL_FORCE_F16_TEXTURES\")) {\n        throw new Error(\"GL context does not support half float textures, yet the environment flag WEBGL_FORCE_F16_TEXTURES is set to true.\");\n      }\n      this.colorBufferFloatExtension = this.gl.getExtension(COLOR_BUFFER_FLOAT);\n      if (hasExtension(this.gl, COLOR_BUFFER_HALF_FLOAT)) {\n        this.colorBufferHalfFloatExtension = getExtensionOrThrow(this.gl, COLOR_BUFFER_HALF_FLOAT);\n      } else if (env().get(\"WEBGL_FORCE_F16_TEXTURES\")) {\n        throw new Error(\"GL context does not support color renderable half floats, yet the environment flag WEBGL_FORCE_F16_TEXTURES is set to true.\");\n      }\n    } else {\n      COLOR_BUFFER_FLOAT = \"EXT_color_buffer_float\";\n      if (hasExtension(this.gl, COLOR_BUFFER_FLOAT)) {\n        this.colorBufferFloatExtension = this.gl.getExtension(COLOR_BUFFER_FLOAT);\n      } else if (hasExtension(this.gl, COLOR_BUFFER_HALF_FLOAT)) {\n        this.colorBufferHalfFloatExtension = this.gl.getExtension(COLOR_BUFFER_HALF_FLOAT);\n      } else {\n        throw new Error(\"GL context does not support color renderable floats\");\n      }\n    }\n    this.vertexBuffer = createVertexBuffer(this.gl);\n    this.indexBuffer = createIndexBuffer(this.gl);\n    this.framebuffer = createFramebuffer(this.gl);\n    this.textureConfig = getTextureConfig(this.gl, this.textureHalfFloatExtension);\n  }\n  get debug() {\n    return env().getBool(\"DEBUG\");\n  }\n  dispose() {\n    if (this.disposed) {\n      return;\n    }\n    if (this.program != null) {\n      console.warn(\"Disposing a GPGPUContext that still has a bound WebGLProgram. This is probably a resource leak, delete the program with GPGPUContext.deleteProgram before disposing.\");\n    }\n    if (this.outputTexture != null) {\n      console.warn(\"Disposing a GPGPUContext that still has a bound output matrix texture.  This is probably a resource leak, delete the output matrix texture with GPGPUContext.deleteMatrixTexture before disposing.\");\n    }\n    const gl = this.gl;\n    callAndCheck(gl, () => gl.finish());\n    callAndCheck(gl, () => gl.bindFramebuffer(gl.FRAMEBUFFER, null));\n    callAndCheck(gl, () => gl.deleteFramebuffer(this.framebuffer));\n    callAndCheck(gl, () => gl.bindBuffer(gl.ARRAY_BUFFER, null));\n    callAndCheck(gl, () => gl.bindBuffer(gl.ELEMENT_ARRAY_BUFFER, null));\n    callAndCheck(gl, () => gl.deleteBuffer(this.indexBuffer));\n    this.disposed = true;\n  }\n  createFloat32MatrixTexture(rows, columns) {\n    this.throwIfDisposed();\n    return createFloat32MatrixTexture(this.gl, rows, columns, this.textureConfig);\n  }\n  createFloat16MatrixTexture(rows, columns) {\n    this.throwIfDisposed();\n    return createFloat16MatrixTexture(this.gl, rows, columns, this.textureConfig);\n  }\n  createUnsignedBytesMatrixTexture(rows, columns) {\n    this.throwIfDisposed();\n    return createUnsignedBytesMatrixTexture(this.gl, rows, columns, this.textureConfig);\n  }\n  uploadPixelDataToTexture(texture, pixels) {\n    this.throwIfDisposed();\n    uploadPixelDataToTexture(this.gl, texture, pixels);\n  }\n  uploadDenseMatrixToTexture(texture, width, height, data) {\n    this.throwIfDisposed();\n    uploadDenseMatrixToTexture(this.gl, texture, width, height, data, this.textureConfig);\n  }\n  createFloat16PackedMatrixTexture(rows, columns) {\n    this.throwIfDisposed();\n    return createFloat16PackedMatrixTexture(this.gl, rows, columns, this.textureConfig);\n  }\n  createPackedMatrixTexture(rows, columns) {\n    this.throwIfDisposed();\n    return createPackedMatrixTexture(this.gl, rows, columns, this.textureConfig);\n  }\n  deleteMatrixTexture(texture) {\n    this.throwIfDisposed();\n    if (this.outputTexture === texture) {\n      unbindColorTextureFromFramebuffer(this.gl, this.framebuffer);\n      this.outputTexture = null;\n    }\n    callAndCheck(this.gl, () => this.gl.deleteTexture(texture));\n  }\n  downloadByteEncodedFloatMatrixFromOutputTexture(texture, rows, columns) {\n    return this.downloadMatrixDriver(texture, () => downloadByteEncodedFloatMatrixFromOutputTexture(this.gl, rows, columns, this.textureConfig));\n  }\n  downloadPackedMatrixFromBuffer(buffer2, batch, rows, columns, physicalRows, physicalCols) {\n    return downloadPackedMatrixFromBuffer(this.gl, buffer2, batch, rows, columns, physicalRows, physicalCols, this.textureConfig);\n  }\n  downloadFloat32MatrixFromBuffer(buffer2, size) {\n    return downloadFloat32MatrixFromBuffer(this.gl, buffer2, size);\n  }\n  createBufferFromTexture(texture, rows, columns) {\n    this.bindTextureToFrameBuffer(texture);\n    const result = createBufferFromOutputTexture(this.gl, rows, columns, this.textureConfig);\n    this.unbindTextureToFrameBuffer();\n    return result;\n  }\n  createAndWaitForFence() {\n    const fenceContext = this.createFence(this.gl);\n    return this.pollFence(fenceContext);\n  }\n  createFence(gl) {\n    let query;\n    let isFencePassed;\n    if (env().getBool(\"WEBGL_FENCE_API_ENABLED\")) {\n      const gl2 = gl;\n      const sync = gl2.fenceSync(gl2.SYNC_GPU_COMMANDS_COMPLETE, 0);\n      gl.flush();\n      isFencePassed = () => {\n        const status = gl2.clientWaitSync(sync, 0, 0);\n        return status === gl2.ALREADY_SIGNALED || status === gl2.CONDITION_SATISFIED;\n      };\n      query = sync;\n    } else if (env().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION\") > 0) {\n      query = this.beginQuery();\n      this.endQuery();\n      isFencePassed = () => this.isQueryAvailable(query, env().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION\"));\n    } else {\n      isFencePassed = () => true;\n    }\n    return { query, isFencePassed };\n  }\n  downloadMatrixFromPackedTexture(texture, physicalRows, physicalCols) {\n    return this.downloadMatrixDriver(texture, () => downloadMatrixFromPackedOutputTexture(this.gl, physicalRows, physicalCols));\n  }\n  createProgram(fragmentShader) {\n    this.throwIfDisposed();\n    const gl = this.gl;\n    if (this.vertexShader == null) {\n      this.vertexShader = createVertexShader2(gl);\n    }\n    const program = createProgram(gl);\n    callAndCheck(gl, () => gl.attachShader(program, this.vertexShader));\n    callAndCheck(gl, () => gl.attachShader(program, fragmentShader));\n    linkProgram(gl, program);\n    if (this.debug) {\n      validateProgram(gl, program);\n    }\n    if (!this.vertexAttrsAreBound) {\n      this.setProgram(program);\n      this.vertexAttrsAreBound = bindVertexProgramAttributeStreams(gl, this.program, this.vertexBuffer);\n    }\n    return program;\n  }\n  deleteProgram(program) {\n    this.throwIfDisposed();\n    if (program === this.program) {\n      this.program = null;\n    }\n    if (program != null) {\n      callAndCheck(this.gl, () => this.gl.deleteProgram(program));\n    }\n  }\n  setProgram(program) {\n    this.throwIfDisposed();\n    this.program = program;\n    if (this.program != null && this.debug) {\n      validateProgram(this.gl, this.program);\n    }\n    callAndCheck(this.gl, () => this.gl.useProgram(program));\n  }\n  getUniformLocation(program, uniformName, shouldThrow = true) {\n    this.throwIfDisposed();\n    if (shouldThrow) {\n      return getProgramUniformLocationOrThrow(this.gl, program, uniformName);\n    } else {\n      return getProgramUniformLocation(this.gl, program, uniformName);\n    }\n  }\n  getAttributeLocation(program, attribute) {\n    this.throwIfDisposed();\n    return callAndCheck(this.gl, () => this.gl.getAttribLocation(program, attribute));\n  }\n  getUniformLocationNoThrow(program, uniformName) {\n    this.throwIfDisposed();\n    return this.gl.getUniformLocation(program, uniformName);\n  }\n  setInputMatrixTexture(inputMatrixTexture, uniformLocation, textureUnit) {\n    this.throwIfDisposed();\n    this.throwIfNoProgram();\n    bindTextureToProgramUniformSampler(this.gl, inputMatrixTexture, uniformLocation, textureUnit);\n  }\n  setOutputMatrixTexture(outputMatrixTexture, rows, columns) {\n    this.setOutputMatrixTextureDriver(outputMatrixTexture, columns, rows);\n  }\n  setOutputPackedMatrixTexture(outputPackedMatrixTexture, rows, columns) {\n    this.throwIfDisposed();\n    const [width, height] = getPackedMatrixTextureShapeWidthHeight(rows, columns);\n    this.setOutputMatrixTextureDriver(outputPackedMatrixTexture, width, height);\n  }\n  setOutputMatrixWriteRegion(startRow, numRows, startColumn, numColumns) {\n    this.setOutputMatrixWriteRegionDriver(startColumn, startRow, numColumns, numRows);\n  }\n  setOutputPackedMatrixWriteRegion(startRow, numRows, startColumn, numColumns) {\n    throw new Error(\"setOutputPackedMatrixWriteRegion not implemented.\");\n  }\n  debugValidate() {\n    if (this.program != null) {\n      validateProgram(this.gl, this.program);\n    }\n    validateFramebuffer(this.gl);\n  }\n  executeProgram() {\n    this.throwIfDisposed();\n    this.throwIfNoProgram();\n    const gl = this.gl;\n    if (this.debug) {\n      this.debugValidate();\n    }\n    callAndCheck(gl, () => gl.drawElements(gl.TRIANGLES, 6, gl.UNSIGNED_SHORT, 0));\n  }\n  blockUntilAllProgramsCompleted() {\n    this.throwIfDisposed();\n    callAndCheck(this.gl, () => this.gl.finish());\n  }\n  getQueryTimerExtension() {\n    if (this.disjointQueryTimerExtension == null) {\n      this.disjointQueryTimerExtension = getExtensionOrThrow(this.gl, env().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION\") === 2 ? \"EXT_disjoint_timer_query_webgl2\" : \"EXT_disjoint_timer_query\");\n    }\n    return this.disjointQueryTimerExtension;\n  }\n  getQueryTimerExtensionWebGL2() {\n    return this.getQueryTimerExtension();\n  }\n  getQueryTimerExtensionWebGL1() {\n    return this.getQueryTimerExtension();\n  }\n  beginQuery() {\n    if (env().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION\") === 2) {\n      const gl2 = this.gl;\n      const ext2 = this.getQueryTimerExtensionWebGL2();\n      const query2 = gl2.createQuery();\n      gl2.beginQuery(ext2.TIME_ELAPSED_EXT, query2);\n      return query2;\n    }\n    const ext = this.getQueryTimerExtensionWebGL1();\n    const query = ext.createQueryEXT();\n    ext.beginQueryEXT(ext.TIME_ELAPSED_EXT, query);\n    return query;\n  }\n  endQuery() {\n    if (env().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION\") === 2) {\n      const gl2 = this.gl;\n      const ext2 = this.getQueryTimerExtensionWebGL2();\n      gl2.endQuery(ext2.TIME_ELAPSED_EXT);\n      return;\n    }\n    const ext = this.getQueryTimerExtensionWebGL1();\n    ext.endQueryEXT(ext.TIME_ELAPSED_EXT);\n  }\n  async waitForQueryAndGetTime(query) {\n    await util_exports.repeatedTry(() => this.disposed || this.isQueryAvailable(query, env().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION\")));\n    return this.getQueryTime(query, env().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION\"));\n  }\n  getQueryTime(query, queryTimerVersion) {\n    if (queryTimerVersion === 0) {\n      return null;\n    }\n    if (queryTimerVersion === 2) {\n      const gl2 = this.gl;\n      const timeElapsedNanos = gl2.getQueryParameter(query, gl2.QUERY_RESULT);\n      return timeElapsedNanos / 1e6;\n    } else {\n      const ext = this.getQueryTimerExtensionWebGL1();\n      const timeElapsedNanos = ext.getQueryObjectEXT(query, ext.QUERY_RESULT_EXT);\n      return timeElapsedNanos / 1e6;\n    }\n  }\n  isQueryAvailable(query, queryTimerVersion) {\n    if (queryTimerVersion === 0) {\n      return true;\n    }\n    if (queryTimerVersion === 2) {\n      const gl2 = this.gl;\n      const ext = this.getQueryTimerExtensionWebGL2();\n      const available = gl2.getQueryParameter(query, gl2.QUERY_RESULT_AVAILABLE);\n      if (this.disjoint == null) {\n        this.disjoint = this.gl.getParameter(ext.GPU_DISJOINT_EXT);\n      }\n      return available && !this.disjoint;\n    } else {\n      const ext = this.getQueryTimerExtensionWebGL1();\n      const available = ext.getQueryObjectEXT(query, ext.QUERY_RESULT_AVAILABLE_EXT);\n      if (this.disjoint == null) {\n        this.disjoint = this.gl.getParameter(ext.GPU_DISJOINT_EXT);\n      }\n      return available && !this.disjoint;\n    }\n  }\n  pollFence(fenceContext) {\n    return new Promise((resolve) => {\n      this.addItemToPoll(() => fenceContext.isFencePassed(), () => resolve());\n    });\n  }\n  pollItems() {\n    const index = linearSearchLastTrue(this.itemsToPoll.map((x) => x.isDoneFn));\n    for (let i2 = 0; i2 <= index; ++i2) {\n      const { resolveFn } = this.itemsToPoll[i2];\n      resolveFn();\n    }\n    this.itemsToPoll = this.itemsToPoll.slice(index + 1);\n  }\n  addItemToPoll(isDoneFn, resolveFn) {\n    this.itemsToPoll.push({ isDoneFn, resolveFn });\n    if (this.itemsToPoll.length > 1) {\n      return;\n    }\n    util_exports.repeatedTry(() => {\n      this.pollItems();\n      return this.itemsToPoll.length === 0;\n    });\n  }\n  bindTextureToFrameBuffer(texture) {\n    this.throwIfDisposed();\n    bindColorTextureToFramebuffer(this.gl, texture, this.framebuffer);\n    if (this.debug) {\n      validateFramebuffer(this.gl);\n    }\n  }\n  unbindTextureToFrameBuffer() {\n    if (this.outputTexture != null) {\n      bindColorTextureToFramebuffer(this.gl, this.outputTexture, this.framebuffer);\n      if (this.debug) {\n        validateFramebuffer(this.gl);\n      }\n    } else {\n      unbindColorTextureFromFramebuffer(this.gl, this.framebuffer);\n    }\n  }\n  downloadMatrixDriver(texture, downloadAndDecode) {\n    this.bindTextureToFrameBuffer(texture);\n    const result = downloadAndDecode();\n    this.unbindTextureToFrameBuffer();\n    return result;\n  }\n  setOutputMatrixTextureDriver(outputMatrixTextureMaybePacked, width, height) {\n    this.throwIfDisposed();\n    const gl = this.gl;\n    bindColorTextureToFramebuffer(gl, outputMatrixTextureMaybePacked, this.framebuffer);\n    if (this.debug) {\n      validateFramebuffer(gl);\n    }\n    this.outputTexture = outputMatrixTextureMaybePacked;\n    callAndCheck(gl, () => gl.viewport(0, 0, width, height));\n    callAndCheck(gl, () => gl.scissor(0, 0, width, height));\n  }\n  setOutputMatrixWriteRegionDriver(x, y, width, height) {\n    this.throwIfDisposed();\n    callAndCheck(this.gl, () => this.gl.scissor(x, y, width, height));\n  }\n  throwIfDisposed() {\n    if (this.disposed) {\n      throw new Error(\"Attempted to use disposed GPGPUContext.\");\n    }\n  }\n  throwIfNoProgram() {\n    if (this.program == null) {\n      throw new Error(\"No GPU program is currently set.\");\n    }\n  }\n};\nfunction linearSearchLastTrue(arr) {\n  let i2 = 0;\n  for (; i2 < arr.length; ++i2) {\n    const isDone = arr[i2]();\n    if (!isDone) {\n      break;\n    }\n  }\n  return i2 - 1;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernel_utils/shared.js\nvar { addImpl: addImplCPU, bincountImpl: bincountImplCPU, bincountReduceImpl: bincountReduceImplCPU, castImpl: castImplCPU, ceilImpl: ceilImplCPU, concatImpl: concatImplCPU, equalImpl: equalImplCPU, expImpl: expImplCPU, expm1Impl: expm1ImplCPU, floorImpl: floorImplCPU, gatherNdImpl: gatherNdImplCPU, gatherV2Impl: gatherV2ImplCPU, greaterImpl: greaterImplCPU, greaterEqualImpl: greaterEqualImplCPU, lessImpl: lessImplCPU, lessEqualImpl: lessEqualImplCPU, linSpaceImpl: linSpaceImplCPU, logImpl: logImplCPU, maxImpl: maxImplCPU, maximumImpl: maximumImplCPU, minimumImpl: minimumImplCPU, multiplyImpl: multiplyImplCPU, negImpl: negImplCPU, notEqualImpl: notEqualImplCPU, prodImpl: prodImplCPU, raggedTensorToTensorImpl: raggedTensorToTensorImplCPU, rangeImpl: rangeImplCPU, rsqrtImpl: rsqrtImplCPU, scatterImpl: scatterImplCPU, sigmoidImpl: sigmoidImplCPU, simpleAbsImpl: simpleAbsImplCPU, sliceImpl: sliceImplCPU, sparseFillEmptyRowsImpl: sparseFillEmptyRowsImplCPU, sparseReshapeImpl: sparseReshapeImplCPU, sparseSegmentReductionImpl: sparseSegmentReductionImplCPU, sqrtImpl: sqrtImplCPU, stridedSliceImpl: stridedSliceImplCPU, stringNGramsImpl: stringNGramsImplCPU, stringSplitImpl: stringSplitImplCPU, stringToHashBucketFastImpl: stringToHashBucketFastImplCPU, subImpl: subImplCPU, tileImpl: tileImplCPU, topKImpl: topKImplCPU, transposeImpl: transposeImplCPU, uniqueImpl: uniqueImplCPU } = shared_exports;\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/packing_util.js\nfunction getVecChannels(name, rank) {\n  return [\"x\", \"y\", \"z\", \"w\", \"u\", \"v\"].slice(0, rank).map((d) => `${name}.${d}`);\n}\nfunction getChannels(name, rank) {\n  if (rank === 1) {\n    return [name];\n  }\n  return getVecChannels(name, rank);\n}\nfunction getSourceCoords(rank, dims) {\n  if (rank === 1) {\n    return \"rc\";\n  }\n  let coords3 = \"\";\n  for (let i2 = 0; i2 < rank; i2++) {\n    coords3 += dims[i2];\n    if (i2 < rank - 1) {\n      coords3 += \",\";\n    }\n  }\n  return coords3;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/pack_gpu.js\nvar PackProgram = class {\n  constructor(outputShape) {\n    this.variableNames = [\"A\"];\n    this.packedInputs = false;\n    this.packedOutput = true;\n    this.outputShape = outputShape;\n    this.rank = outputShape.length;\n    this.enableShapeUniforms = useShapeUniforms(this.outputShape.length);\n    if (this.rank === 0) {\n      this.userCode = `\n        void main() {\n          setOutput(vec4(getA(), 0., 0., 0.));\n        }\n      `;\n    } else {\n      const channels = getChannels(\"rc\", this.rank);\n      const dtype = getCoordsDataType(this.rank);\n      const outOfBoundsCondition = this.getOutOfBoundsCondition(channels);\n      const setup51 = this.getSetup(channels);\n      const output = this.getOutput(channels);\n      this.userCode = `\n        void main() {\n          ${dtype} rc = getOutputCoords();\n\n          if(${outOfBoundsCondition}) {\n            setOutput(vec4(0));\n          } else {\n            ${setup51}\n\n            setOutput(vec4(${output}));\n          }\n        }\n      `;\n    }\n  }\n  getSourceCoordsArr(dims) {\n    const coords3 = [];\n    for (let row = 0; row <= 1; row++) {\n      for (let col = 0; col <= 1; col++) {\n        let coord = `${row === 0 ? \"r\" : \"rp1\"}, ${col === 0 ? \"c\" : \"cp1\"}`;\n        for (let d = 2; d < this.rank; d++) {\n          coord = `${dims[dims.length - 1 - d]},` + coord;\n        }\n        coords3.push(coord);\n      }\n    }\n    return coords3;\n  }\n  getOutOfBoundsCondition(dims) {\n    if (this.rank === 1) {\n      return `rc > ${this.enableShapeUniforms ? \"outShape\" : this.outputShape[0]}`;\n    }\n    let cond = \"\";\n    for (let i2 = this.rank - 2; i2 < this.rank; i2++) {\n      cond += `${dims[i2]} >= ${this.enableShapeUniforms ? `outShape[${i2}]` : this.outputShape[i2]}`;\n      if (i2 < this.rank - 1) {\n        cond += \"||\";\n      }\n    }\n    return cond;\n  }\n  getSetup(dims) {\n    if (this.rank === 1) {\n      return \"\";\n    }\n    const innerDims = dims.slice(-2);\n    const col = this.enableShapeUniforms ? `outShape[${this.rank} - 1]` : this.outputShape[this.rank - 1];\n    const row = this.enableShapeUniforms ? `outShape[${this.rank} - 2]` : this.outputShape[this.rank - 2];\n    return `\n      int r = ${innerDims[0]};\n      int c = ${innerDims[1]};\n      int rp1 = r + 1;\n      int cp1 = c + 1;\n\n      bool cEdge = cp1 >= ${col};\n      bool rEdge = rp1 >= ${row};\n    `;\n  }\n  getOutput(dims) {\n    const sourceCoords = this.getSourceCoordsArr(dims);\n    if (this.rank === 1) {\n      const outShape = this.enableShapeUniforms ? \"outShape\" : this.outputShape[0];\n      return `getA(rc), (rc + 1 >= ${outShape} ? 0. : getA(rc + 1)), 0, 0`;\n    }\n    return `getA(${sourceCoords[0]}),\n            cEdge ? 0. : getA(${sourceCoords[1]}),\n            rEdge ? 0. : getA(${sourceCoords[2]}),\n            rEdge || cEdge ? 0. : getA(${sourceCoords[3]})`;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/reshape_packed_gpu.js\nvar ReshapePackedProgram = class {\n  constructor(outputShape, inputShape) {\n    this.variableNames = [\"A\"];\n    this.packedInputs = true;\n    this.packedOutput = true;\n    this.customUniforms = [{ name: \"inputShape\", type: \"ivec3\" }];\n    this.outputShape = outputShape;\n    this.enableShapeUniforms = useShapeUniforms(this.outputShape.length);\n    let mainLoop = ``;\n    for (let i2 = 0; i2 < 4; i2++) {\n      let thisRC = `thisRC = rc;`;\n      if (i2 % 2 === 1) {\n        thisRC += `thisRC.z += 1;`;\n      }\n      if (i2 > 1) {\n        thisRC += `thisRC.y += 1;`;\n      }\n      mainLoop += `\n        ${thisRC}\n        ${i2 > 0 ? `if(thisRC.y < rows && thisRC.z < cols){` : \"\"}\n          int flatIndex = getFlatIndex(thisRC);\n\n          ivec3 inputRC = inputCoordsFromReshapedOutCoords(flatIndex);\n          vec2 inputRCInnerDims = vec2(float(inputRC.y),float(inputRC.z));\n\n          result[${i2}] =\n            getChannel(getA(inputRC.x, inputRC.y, inputRC.z), inputRCInnerDims);\n        ${i2 > 0 ? \"}\" : \"\"}\n      `;\n    }\n    this.userCode = `\n      ${getReshapedInputCoords(inputShape, this.enableShapeUniforms)}\n      ${this.enableShapeUniforms ? getFlatIndexFrom3DOutput() : getFlatIndexFrom3D(outputShape)}\n\n      void main() {\n        ivec3 rc = getOutputCoords();\n\n        vec4 result = vec4(0.);\n\n        ivec3 thisRC;\n        int rows = ${this.enableShapeUniforms ? \"outShape[1]\" : outputShape[1]};\n        int cols = ${this.enableShapeUniforms ? \"outShape[2]\" : outputShape[2]};\n\n        ${mainLoop}\n\n        setOutput(result);\n      }\n    `;\n  }\n};\nfunction getReshapedInputCoords(shape, enableShapeUniforms) {\n  const coordsFromIndexSnippet = enableShapeUniforms ? getLogicalCoordinatesFromFlatIndexByUniform([\"r\", \"c\", \"d\"], \"inputShape\") : getLogicalCoordinatesFromFlatIndex([\"r\", \"c\", \"d\"], shape);\n  return `\n    ivec3 inputCoordsFromReshapedOutCoords(int index) {\n      ${coordsFromIndexSnippet}\n      return ivec3(r, c, d);\n    }\n  `;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/texture_manager.js\nvar TextureManager = class {\n  constructor(gpgpu) {\n    this.gpgpu = gpgpu;\n    this.numUsedTextures = 0;\n    this.numFreeTextures = 0;\n    this._numBytesAllocated = 0;\n    this._numBytesFree = 0;\n    this.freeTextures = {};\n    this.logEnabled = false;\n    this.usedTextures = {};\n  }\n  acquireTexture(shapeRC, usage, isPacked) {\n    const physicalTexType = getPhysicalFromLogicalTextureType(usage, isPacked);\n    const shapeKey = getKeyFromTextureShape(shapeRC, physicalTexType, isPacked);\n    if (!(shapeKey in this.freeTextures)) {\n      this.freeTextures[shapeKey] = [];\n    }\n    if (!(shapeKey in this.usedTextures)) {\n      this.usedTextures[shapeKey] = [];\n    }\n    const texBytes = computeBytes(shapeRC, physicalTexType, this.gpgpu.gl, this.gpgpu.textureConfig, isPacked);\n    if (this.freeTextures[shapeKey].length > 0) {\n      this.numFreeTextures--;\n      this.numUsedTextures++;\n      this._numBytesFree -= texBytes;\n      this.log();\n      const newTexture2 = this.freeTextures[shapeKey].shift();\n      this.usedTextures[shapeKey].push(newTexture2);\n      return newTexture2;\n    }\n    let newTexture;\n    if (physicalTexType === PhysicalTextureType.PACKED_2X2_FLOAT32) {\n      newTexture = this.gpgpu.createPackedMatrixTexture(shapeRC[0], shapeRC[1]);\n    } else if (physicalTexType === PhysicalTextureType.PACKED_2X2_FLOAT16) {\n      newTexture = this.gpgpu.createFloat16PackedMatrixTexture(shapeRC[0], shapeRC[1]);\n    } else if (physicalTexType === PhysicalTextureType.UNPACKED_FLOAT32) {\n      newTexture = this.gpgpu.createFloat32MatrixTexture(shapeRC[0], shapeRC[1]);\n    } else if (physicalTexType === PhysicalTextureType.UNPACKED_FLOAT16) {\n      newTexture = this.gpgpu.createFloat16MatrixTexture(shapeRC[0], shapeRC[1]);\n    } else if (physicalTexType === PhysicalTextureType.PACKED_4X1_UNSIGNED_BYTE) {\n      newTexture = this.gpgpu.createUnsignedBytesMatrixTexture(shapeRC[0], shapeRC[1]);\n    }\n    this.usedTextures[shapeKey].push(newTexture);\n    this.numUsedTextures++;\n    this._numBytesAllocated += texBytes;\n    this.log();\n    return newTexture;\n  }\n  releaseTexture(texture, shape, logicalTexType, isPacked) {\n    if (this.freeTextures == null) {\n      return;\n    }\n    const physicalTexType = getPhysicalFromLogicalTextureType(logicalTexType, isPacked);\n    const shapeKey = getKeyFromTextureShape(shape, physicalTexType, isPacked);\n    if (!(shapeKey in this.freeTextures)) {\n      this.freeTextures[shapeKey] = [];\n    }\n    const texBytes = computeBytes(shape, physicalTexType, this.gpgpu.gl, this.gpgpu.textureConfig, isPacked);\n    const deleteTexThreshold = env().get(\"WEBGL_DELETE_TEXTURE_THRESHOLD\");\n    if (deleteTexThreshold !== -1 && this._numBytesAllocated > deleteTexThreshold) {\n      this.gpgpu.deleteMatrixTexture(texture.texture);\n      this._numBytesAllocated -= texBytes;\n    } else {\n      this.freeTextures[shapeKey].push(texture);\n      this.numFreeTextures++;\n      this._numBytesFree += texBytes;\n    }\n    this.numUsedTextures--;\n    const texList = this.usedTextures[shapeKey];\n    const texIndex = texList.indexOf(texture);\n    if (texIndex < 0) {\n      throw new Error(\"Cannot release a texture that was never provided by this texture manager\");\n    }\n    texList.splice(texIndex, 1);\n    this.log();\n  }\n  log() {\n    if (!this.logEnabled) {\n      return;\n    }\n    const total = this.numFreeTextures + this.numUsedTextures;\n    console.log(\"Free/Used\", `${this.numFreeTextures} / ${this.numUsedTextures}`, `(${total})`);\n    const freeRatio = this._numBytesFree / this._numBytesAllocated;\n    console.log(`Bytes allocated: ${this._numBytesAllocated}`);\n    console.log(`Bytes unused: ${this._numBytesFree} (${Math.round(100 * freeRatio)}%)`);\n  }\n  get numBytesAllocated() {\n    return this._numBytesAllocated;\n  }\n  get numBytesFree() {\n    return this._numBytesFree;\n  }\n  getNumUsedTextures() {\n    return this.numUsedTextures;\n  }\n  getNumFreeTextures() {\n    return this.numFreeTextures;\n  }\n  dispose() {\n    if (this.freeTextures == null) {\n      return;\n    }\n    for (const texShape in this.freeTextures) {\n      this.freeTextures[texShape].forEach((tex) => {\n        this.gpgpu.deleteMatrixTexture(tex.texture);\n      });\n    }\n    for (const texShape in this.usedTextures) {\n      this.usedTextures[texShape].forEach((tex) => {\n        this.gpgpu.deleteMatrixTexture(tex.texture);\n      });\n    }\n    this.freeTextures = null;\n    this.usedTextures = null;\n    this.numUsedTextures = 0;\n    this.numFreeTextures = 0;\n    this._numBytesAllocated = 0;\n    this._numBytesFree = 0;\n  }\n};\nfunction numBytesForInternalFormat(gl, internalFormat) {\n  const glany = gl;\n  if (internalFormat === glany.R32F) {\n    return 4;\n  } else if (internalFormat === glany.R16F) {\n    return 2;\n  } else if (internalFormat === glany.RGBA32F) {\n    return 16;\n  } else if (internalFormat === gl.RGBA) {\n    return 16;\n  } else if (internalFormat === glany.RGBA16F) {\n    return 8;\n  } else if (internalFormat === glany.RGBA8) {\n    return 4;\n  }\n  throw new Error(`Unknown internal format ${internalFormat}`);\n}\nfunction computeBytes(shape, physicalTexType, gl, textureConfig, isPacked) {\n  const internalFormat = internalFormatForPhysicalTexType(physicalTexType, textureConfig);\n  let numElements;\n  if (isPacked) {\n    const [packedWidth, packedHeight] = getPackedMatrixTextureShapeWidthHeight(shape[0], shape[1]);\n    numElements = packedWidth * packedHeight;\n  } else {\n    const [width, height] = getUnpackedMatrixTextureShapeWidthHeight(shape[0], shape[1]);\n    numElements = width * height;\n  }\n  const bytesPerElement2 = numBytesForInternalFormat(gl, internalFormat);\n  return numElements * bytesPerElement2;\n}\nfunction internalFormatForPhysicalTexType(physicalTexType, textureConfig) {\n  switch (physicalTexType) {\n    case PhysicalTextureType.PACKED_2X2_FLOAT32:\n      return getInternalFormatForPackedMatrixTexture(textureConfig);\n    case PhysicalTextureType.PACKED_2X2_FLOAT16:\n      return getInternalFormatForFloat16PackedMatrixTexture(textureConfig);\n    case PhysicalTextureType.UNPACKED_FLOAT32:\n      return getInternalFormatForFloat32MatrixTexture(textureConfig);\n    case PhysicalTextureType.UNPACKED_FLOAT16:\n      return getInternalFormatForFloat16MatrixTexture(textureConfig);\n    case PhysicalTextureType.PACKED_4X1_UNSIGNED_BYTE:\n      return getInternalFormatForUnsignedBytesMatrixTexture(textureConfig);\n    default:\n      throw new Error(`Unknown physical texture type ${physicalTexType}`);\n  }\n}\nfunction getPhysicalTextureForRendering(isPacked) {\n  if (env().getBool(\"WEBGL_RENDER_FLOAT32_ENABLED\")) {\n    if (isPacked) {\n      return PhysicalTextureType.PACKED_2X2_FLOAT32;\n    }\n    return PhysicalTextureType.UNPACKED_FLOAT32;\n  }\n  if (isPacked) {\n    return PhysicalTextureType.PACKED_2X2_FLOAT16;\n  }\n  return PhysicalTextureType.UNPACKED_FLOAT16;\n}\nfunction getPhysicalFromLogicalTextureType(logicalTexType, isPacked) {\n  if (logicalTexType === TextureUsage.UPLOAD) {\n    return PhysicalTextureType.PACKED_2X2_FLOAT32;\n  } else if (logicalTexType === TextureUsage.RENDER || logicalTexType == null) {\n    return getPhysicalTextureForRendering(isPacked);\n  } else if (logicalTexType === TextureUsage.DOWNLOAD || logicalTexType === TextureUsage.PIXELS) {\n    return PhysicalTextureType.PACKED_4X1_UNSIGNED_BYTE;\n  }\n  throw new Error(`Unknown logical texture type ${logicalTexType}`);\n}\nfunction getKeyFromTextureShape(shapeRowsCol, physicalTexType, isPacked) {\n  return `${shapeRowsCol[0]}_${shapeRowsCol[1]}_${physicalTexType}_${isPacked}`;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/unaryop_gpu.js\nvar UnaryOpProgram = class {\n  constructor(aShape, opSnippet) {\n    this.variableNames = [\"A\"];\n    this.outputShape = aShape;\n    this.enableShapeUniforms = useShapeUniforms(this.outputShape.length);\n    this.userCode = `\n      float unaryOperation(float x) {\n        ${opSnippet}\n      }\n\n      void main() {\n        float x = getAAtOutCoords();\n        float y = unaryOperation(x);\n\n        setOutput(y);\n      }\n    `;\n  }\n};\nvar CHECK_NAN_SNIPPET = `if (isnan(x)) return x;`;\nvar LINEAR = `return x;`;\nvar ABS = `return abs(x);`;\nvar ELU2 = `return (x >= 0.0) ? x : (exp(x) - 1.0);`;\nvar RELU = CHECK_NAN_SNIPPET + `\n  return (x < 0.0) ? 0.0 : x;\n`;\nvar RELU6 = CHECK_NAN_SNIPPET + `\n  return (x < 0.0) ? 0.0 : min(6.0, x);\n`;\nvar CLONE = \"return x;\";\nvar SIGMOID = `return 1.0 / (1.0 + exp(-1.0 * x));`;\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/unaryop_packed_gpu.js\nvar LINEAR2 = `return x;`;\nvar ELU3 = `\n  vec4 result;\n\n  result.r = (x.r >= 0.0) ? x.r : (exp(x.r) - 1.0);\n  result.g = (x.g >= 0.0) ? x.g : (exp(x.g) - 1.0);\n  result.b = (x.b >= 0.0) ? x.b : (exp(x.b) - 1.0);\n  result.a = (x.a >= 0.0) ? x.a : (exp(x.a) - 1.0);\n\n  return result;\n`;\nvar RELU2 = `\n  vec4 result = x * vec4(greaterThanEqual(x, vec4(0.0)));\n  bvec4 isNaN = isnan(x);\n\n  result.r = isNaN.r ? x.r : result.r;\n  result.g = isNaN.g ? x.g : result.g;\n  result.b = isNaN.b ? x.b : result.b;\n  result.a = isNaN.a ? x.a : result.a;\n\n  return result;\n`;\nvar RELU62 = `\n  vec4 result = min(x, vec4(6.)) * vec4(greaterThanEqual(x, vec4(0.0)));\n  bvec4 isNaN = isnan(x);\n\n  result.r = isNaN.r ? x.r : result.r;\n  result.g = isNaN.g ? x.g : result.g;\n  result.b = isNaN.b ? x.b : result.b;\n  result.a = isNaN.a ? x.a : result.a;\n\n  return result;\n`;\nvar SIGMOID2 = `return 1.0 / (1.0 + exp(-1.0 * x));`;\nvar UnaryOpPackedProgram = class {\n  constructor(aShape, opSnippet) {\n    this.variableNames = [\"A\"];\n    this.packedInputs = true;\n    this.packedOutput = true;\n    this.outputShape = aShape;\n    this.enableShapeUniforms = useShapeUniforms(this.outputShape.length);\n    this.userCode = `\n      vec4 unaryOperation(vec4 x) {\n        ${opSnippet}\n      }\n\n      void main() {\n        vec4 x = getAAtOutCoords();\n        vec4 y = unaryOperation(x);\n\n        setOutput(y);\n      }\n    `;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/unpack_gpu.js\nvar UnpackProgram = class {\n  constructor(outputShape) {\n    this.variableNames = [\"A\"];\n    this.packedInputs = true;\n    this.packedOutput = false;\n    this.outputShape = outputShape;\n    this.enableShapeUniforms = useShapeUniforms(this.outputShape.length);\n    const rank = outputShape.length;\n    const channels = getChannels(\"rc\", rank);\n    const dtype = getCoordsDataType(rank);\n    const sourceCoords = getSourceCoords(rank, channels);\n    const innerDims = channels.slice(-2);\n    const coords3 = rank <= 1 ? \"rc\" : `vec2(${innerDims.join(\",\")})`;\n    this.userCode = `\n      void main() {\n        ${dtype} rc = getOutputCoords();\n        vec4 packedInput = getA(${sourceCoords});\n\n        setOutput(getChannel(packedInput, ${coords3}));\n      }\n    `;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/backend_webgl.js\nvar whereImpl3 = kernel_impls_exports.whereImpl;\nvar EPSILON_FLOAT322 = 1e-7;\nvar EPSILON_FLOAT162 = 1e-4;\nvar binaryCaches = {};\nfunction getBinaryCache(webGLVersion) {\n  if (webGLVersion in binaryCaches) {\n    return binaryCaches[webGLVersion];\n  }\n  binaryCaches[webGLVersion] = {};\n  return binaryCaches[webGLVersion];\n}\nvar CPU_HANDOFF_SIZE_THRESHOLD = env().getNumber(\"CPU_HANDOFF_SIZE_THRESHOLD\");\nvar BEFORE_PAGING_CONSTANT = 600;\nfunction numMBBeforeWarning() {\n  if (env().global.screen == null) {\n    return 1024;\n  }\n  return env().global.screen.height * env().global.screen.width * window.devicePixelRatio * BEFORE_PAGING_CONSTANT / 1024 / 1024;\n}\nvar MathBackendWebGL = class extends KernelBackend {\n  constructor(gpuResource) {\n    super();\n    this.pendingRead = /* @__PURE__ */ new WeakMap();\n    this.pendingDisposal = /* @__PURE__ */ new WeakSet();\n    this.dataRefCount = /* @__PURE__ */ new WeakMap();\n    this.numBytesInGPU = 0;\n    this.uploadWaitMs = 0;\n    this.downloadWaitMs = 0;\n    this.lastGlFlushTime = 0;\n    this.warnedAboutMemory = false;\n    this.pendingDeletes = 0;\n    this.disposed = false;\n    if (!env().getBool(\"HAS_WEBGL\")) {\n      throw new Error(\"WebGL is not supported on this device\");\n    }\n    let newGPGPU;\n    if (gpuResource != null) {\n      if (gpuResource instanceof GPGPUContext) {\n        newGPGPU = gpuResource;\n      } else {\n        const gl = getWebGLContext(env().getNumber(\"WEBGL_VERSION\"), gpuResource);\n        newGPGPU = new GPGPUContext(gl);\n      }\n      this.binaryCache = {};\n      this.gpgpuCreatedLocally = false;\n    } else {\n      const gl = getWebGLContext(env().getNumber(\"WEBGL_VERSION\"));\n      newGPGPU = new GPGPUContext(gl);\n      this.binaryCache = getBinaryCache(env().getNumber(\"WEBGL_VERSION\"));\n      this.gpgpuCreatedLocally = true;\n    }\n    this.gpgpu = newGPGPU;\n    this.canvas = this.gpgpu.gl.canvas;\n    this.textureManager = new TextureManager(this.gpgpu);\n    this.numMBBeforeWarning = numMBBeforeWarning();\n    this.texData = new DataStorage(this, engine());\n  }\n  nextDataId() {\n    return MathBackendWebGL.nextDataId++;\n  }\n  numDataIds() {\n    return this.texData.numDataIds() - this.pendingDeletes;\n  }\n  write(values, shape, dtype) {\n    if (env().getBool(\"WEBGL_CHECK_NUMERICAL_PROBLEMS\") || env().getBool(\"DEBUG\")) {\n      this.checkNumericalProblems(values);\n    }\n    if (dtype === \"complex64\" && values != null) {\n      throw new Error(`Cannot write to a complex64 dtype. Please use tf.complex(real, imag).`);\n    }\n    const dataId = { id: this.nextDataId() };\n    this.texData.set(dataId, { shape, dtype, values, usage: TextureUsage.UPLOAD, refCount: 1 });\n    return dataId;\n  }\n  refCount(dataId) {\n    if (this.texData.has(dataId)) {\n      const tensorData = this.texData.get(dataId);\n      return tensorData.refCount;\n    }\n    return 0;\n  }\n  incRef(dataId) {\n    const texData = this.texData.get(dataId);\n    texData.refCount++;\n  }\n  decRef(dataId) {\n    if (this.texData.has(dataId)) {\n      const texData = this.texData.get(dataId);\n      texData.refCount--;\n    }\n  }\n  move(dataId, values, shape, dtype, refCount) {\n    if (env().getBool(\"DEBUG\")) {\n      this.checkNumericalProblems(values);\n    }\n    if (dtype === \"complex64\") {\n      throw new Error(`Cannot write to a complex64 dtype. Please use tf.complex(real, imag).`);\n    }\n    this.texData.set(dataId, { shape, dtype, values, usage: TextureUsage.UPLOAD, refCount });\n  }\n  disposeIntermediateTensorInfo(tensorInfo) {\n    this.disposeData(tensorInfo.dataId);\n  }\n  readSync(dataId) {\n    const texData = this.texData.get(dataId);\n    const { values, dtype, complexTensorInfos, slice: slice6, shape, isPacked } = texData;\n    if (slice6 != null) {\n      let program;\n      if (isPacked) {\n        program = new UnaryOpPackedProgram(shape, CLONE);\n      } else {\n        program = new UnaryOpProgram(shape, CLONE);\n      }\n      const res = this.runWebGLProgram(program, [{ dataId, shape, dtype }], dtype);\n      const data = this.readSync(res.dataId);\n      this.disposeIntermediateTensorInfo(res);\n      return data;\n    }\n    if (values != null) {\n      return this.convertAndCacheOnCPU(dataId);\n    }\n    if (dtype === \"string\") {\n      return values;\n    }\n    const shouldTimeProgram = this.activeTimers != null;\n    let start;\n    if (shouldTimeProgram) {\n      start = util_exports.now();\n    }\n    let result;\n    if (dtype === \"complex64\") {\n      const realValues = this.readSync(complexTensorInfos.real.dataId);\n      const imagValues = this.readSync(complexTensorInfos.imag.dataId);\n      result = backend_util_exports.mergeRealAndImagArrays(realValues, imagValues);\n    } else {\n      result = this.getValuesFromTexture(dataId);\n    }\n    if (shouldTimeProgram) {\n      this.downloadWaitMs += util_exports.now() - start;\n    }\n    return this.convertAndCacheOnCPU(dataId, result);\n  }\n  async read(dataId) {\n    if (this.pendingRead.has(dataId)) {\n      const subscribers2 = this.pendingRead.get(dataId);\n      return new Promise((resolve) => subscribers2.push(resolve));\n    }\n    const texData = this.texData.get(dataId);\n    const { values, shape, slice: slice6, dtype, complexTensorInfos, isPacked } = texData;\n    if (slice6 != null) {\n      let program;\n      if (isPacked) {\n        program = new UnaryOpPackedProgram(shape, CLONE);\n      } else {\n        program = new UnaryOpProgram(shape, CLONE);\n      }\n      const res = this.runWebGLProgram(program, [{ dataId, shape, dtype }], dtype);\n      const data = this.read(res.dataId);\n      this.disposeIntermediateTensorInfo(res);\n      return data;\n    }\n    if (values != null) {\n      return this.convertAndCacheOnCPU(dataId);\n    }\n    if (env().getBool(\"DEBUG\")) {\n      if (!env().getBool(\"WEBGL_DOWNLOAD_FLOAT_ENABLED\") && env().getNumber(\"WEBGL_VERSION\") === 2) {\n        throw new Error(`tensor.data() with WEBGL_DOWNLOAD_FLOAT_ENABLED=false and WEBGL_VERSION=2 not yet supported.`);\n      }\n    }\n    let buffer2 = null;\n    let tmpDownloadTarget;\n    if (dtype !== \"complex64\" && env().get(\"WEBGL_BUFFER_SUPPORTED\")) {\n      tmpDownloadTarget = this.decode(dataId);\n      const tmpData = this.texData.get(tmpDownloadTarget.dataId);\n      buffer2 = this.gpgpu.createBufferFromTexture(tmpData.texture.texture, ...getDenseTexShape(shape));\n    }\n    this.pendingRead.set(dataId, []);\n    if (dtype !== \"complex64\") {\n      await this.gpgpu.createAndWaitForFence();\n    }\n    let vals;\n    if (dtype === \"complex64\") {\n      const ps = await Promise.all([\n        this.read(complexTensorInfos.real.dataId),\n        this.read(complexTensorInfos.imag.dataId)\n      ]);\n      const realValues = ps[0];\n      const imagValues = ps[1];\n      vals = backend_util_exports.mergeRealAndImagArrays(realValues, imagValues);\n    } else if (buffer2 == null) {\n      vals = this.getValuesFromTexture(dataId);\n    } else {\n      const size = util_exports.sizeFromShape(shape);\n      vals = this.gpgpu.downloadFloat32MatrixFromBuffer(buffer2, size);\n    }\n    if (tmpDownloadTarget != null) {\n      this.disposeIntermediateTensorInfo(tmpDownloadTarget);\n    }\n    if (buffer2 != null) {\n      const gl = this.gpgpu.gl;\n      callAndCheck(gl, () => gl.deleteBuffer(buffer2));\n    }\n    const dTypeVals = this.convertAndCacheOnCPU(dataId, vals);\n    const subscribers = this.pendingRead.get(dataId);\n    this.pendingRead.delete(dataId);\n    subscribers.forEach((resolve) => resolve(dTypeVals));\n    if (this.pendingDisposal.has(dataId)) {\n      this.pendingDisposal.delete(dataId);\n      if (this.disposeData(dataId)) {\n        engine().removeDataId(dataId, this);\n      }\n      this.pendingDeletes--;\n    }\n    return dTypeVals;\n  }\n  readToGPU(dataId, options = {}) {\n    const texData = this.texData.get(dataId);\n    const { values, shape, slice: slice6, dtype, isPacked, texture } = texData;\n    if (dtype === \"complex64\") {\n      throw new Error(\"Does not support reading texture for complex64 dtype.\");\n    }\n    if (slice6 != null) {\n      let program;\n      if (isPacked) {\n        program = new UnaryOpPackedProgram(shape, CLONE);\n      } else {\n        program = new UnaryOpProgram(shape, CLONE);\n      }\n      const res = this.runWebGLProgram(program, [{ dataId, shape, dtype }], dtype);\n      const gpuResouorce = this.readToGPU(res, options);\n      this.disposeIntermediateTensorInfo(res);\n      return gpuResouorce;\n    }\n    if (texture == null) {\n      if (values != null) {\n        throw new Error(\"Data is not on GPU but on CPU.\");\n      } else {\n        throw new Error(\"There is no data on GPU or CPU.\");\n      }\n    }\n    const tmpTarget = this.decode(dataId, options.customTexShape);\n    const tensorRef = engine().makeTensorFromTensorInfo(tmpTarget);\n    const tmpData = this.texData.get(tmpTarget.dataId);\n    return Object.assign({ tensorRef }, tmpData.texture);\n  }\n  bufferSync(t2) {\n    const data = this.readSync(t2.dataId);\n    if (t2.dtype === \"string\") {\n      try {\n        const strings = data.map((d) => util_exports.decodeString(d));\n        return buffer(t2.shape, t2.dtype, strings);\n      } catch (_a) {\n        throw new Error(\"Failed to decode encoded string bytes into utf-8\");\n      }\n    }\n    return buffer(t2.shape, t2.dtype, data);\n  }\n  checkNumericalProblems(values) {\n    if (values == null) {\n      return;\n    }\n    for (let i2 = 0; i2 < values.length; i2++) {\n      const num = values[i2];\n      if (!canBeRepresented(num)) {\n        if (env().getBool(\"WEBGL_RENDER_FLOAT32_CAPABLE\")) {\n          throw Error(`The value ${num} cannot be represented with your current settings. Consider enabling float32 rendering: 'tf.env().set('WEBGL_RENDER_FLOAT32_ENABLED', true);'`);\n        }\n        throw Error(`The value ${num} cannot be represented on this device.`);\n      }\n    }\n  }\n  getValuesFromTexture(dataId) {\n    const { shape, dtype, isPacked } = this.texData.get(dataId);\n    const size = util_exports.sizeFromShape(shape);\n    if (env().getBool(\"WEBGL_DOWNLOAD_FLOAT_ENABLED\")) {\n      const tmpTarget = this.decode(dataId);\n      const tmpData2 = this.texData.get(tmpTarget.dataId);\n      const vals2 = this.gpgpu.downloadMatrixFromPackedTexture(tmpData2.texture.texture, ...getDenseTexShape(shape)).subarray(0, size);\n      this.disposeIntermediateTensorInfo(tmpTarget);\n      return vals2;\n    }\n    const shouldUsePackedProgram = env().getBool(\"WEBGL_PACK\") && isPacked === true;\n    const outputShape = shouldUsePackedProgram ? getShapeAs3D(shape) : shape;\n    const program = shouldUsePackedProgram ? new EncodeFloatPackedProgram(outputShape) : new EncodeFloatProgram(outputShape);\n    const output = this.runWebGLProgram(program, [{ shape: outputShape, dtype, dataId }], \"float32\");\n    const tmpData = this.texData.get(output.dataId);\n    const vals = this.gpgpu.downloadByteEncodedFloatMatrixFromOutputTexture(tmpData.texture.texture, tmpData.texShape[0], tmpData.texShape[1]).subarray(0, size);\n    this.disposeIntermediateTensorInfo(output);\n    return vals;\n  }\n  timerAvailable() {\n    return env().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE\") > 0;\n  }\n  time(f) {\n    const oldActiveTimers = this.activeTimers;\n    const newActiveTimers = [];\n    let outerMostTime = false;\n    if (this.programTimersStack == null) {\n      this.programTimersStack = newActiveTimers;\n      outerMostTime = true;\n    } else {\n      this.activeTimers.push(newActiveTimers);\n    }\n    this.activeTimers = newActiveTimers;\n    f();\n    const flattenedActiveTimerQueries = util_exports.flatten(this.activeTimers.map((d) => d.query)).filter((d) => d != null);\n    const flattenedActiveTimerNames = util_exports.flatten(this.activeTimers.map((d) => d.name)).filter((d) => d != null);\n    this.activeTimers = oldActiveTimers;\n    if (outerMostTime) {\n      this.programTimersStack = null;\n    }\n    const res = {\n      uploadWaitMs: this.uploadWaitMs,\n      downloadWaitMs: this.downloadWaitMs,\n      kernelMs: null,\n      wallMs: null\n    };\n    return (async () => {\n      if (env().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE\") > 0) {\n        const kernelMs = await Promise.all(flattenedActiveTimerQueries);\n        res[\"kernelMs\"] = util_exports.sum(kernelMs);\n        res[\"getExtraProfileInfo\"] = () => kernelMs.map((d, i2) => ({ name: flattenedActiveTimerNames[i2], ms: d })).map((d) => `${d.name}: ${d.ms}`).join(\", \");\n      } else {\n        res[\"kernelMs\"] = {\n          error: \"WebGL query timers are not supported in this environment.\"\n        };\n      }\n      this.uploadWaitMs = 0;\n      this.downloadWaitMs = 0;\n      return res;\n    })();\n  }\n  memory() {\n    return {\n      unreliable: false,\n      numBytesInGPU: this.numBytesInGPU,\n      numBytesInGPUAllocated: this.textureManager.numBytesAllocated,\n      numBytesInGPUFree: this.textureManager.numBytesFree\n    };\n  }\n  startTimer() {\n    if (env().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE\") > 0) {\n      return this.gpgpu.beginQuery();\n    }\n    return { startMs: util_exports.now(), endMs: null };\n  }\n  endTimer(query) {\n    if (env().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE\") > 0) {\n      this.gpgpu.endQuery();\n      return query;\n    }\n    query.endMs = util_exports.now();\n    return query;\n  }\n  async getQueryTime(query) {\n    if (env().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE\") > 0) {\n      return this.gpgpu.waitForQueryAndGetTime(query);\n    }\n    const timerQuery = query;\n    return timerQuery.endMs - timerQuery.startMs;\n  }\n  disposeData(dataId, force = false) {\n    if (this.pendingDisposal.has(dataId)) {\n      return false;\n    }\n    if (!this.texData.has(dataId)) {\n      return true;\n    }\n    if (force) {\n      this.texData.get(dataId).refCount = 0;\n    } else {\n      this.texData.get(dataId).refCount--;\n    }\n    if (!force && this.texData.get(dataId).refCount > 0) {\n      return false;\n    }\n    if (this.pendingRead.has(dataId)) {\n      this.pendingDisposal.add(dataId);\n      this.pendingDeletes++;\n      return false;\n    }\n    this.releaseGPUData(dataId);\n    const { complexTensorInfos } = this.texData.get(dataId);\n    if (complexTensorInfos != null) {\n      this.disposeData(complexTensorInfos.real.dataId, force);\n      this.disposeData(complexTensorInfos.imag.dataId, force);\n    }\n    this.texData.delete(dataId);\n    return true;\n  }\n  releaseGPUData(dataId) {\n    const { texture, dtype, texShape, usage, isPacked, slice: slice6 } = this.texData.get(dataId);\n    const key = slice6 && slice6.origDataId || dataId;\n    const refCount = this.dataRefCount.get(key);\n    if (refCount > 1) {\n      this.dataRefCount.set(key, refCount - 1);\n    } else {\n      this.dataRefCount.delete(key);\n      if (texture != null) {\n        this.numBytesInGPU -= this.computeBytes(texShape, dtype);\n        this.textureManager.releaseTexture(texture, texShape, usage, isPacked);\n      }\n    }\n    const texData = this.texData.get(dataId);\n    texData.texture = null;\n    texData.texShape = null;\n    texData.isPacked = false;\n    texData.slice = null;\n  }\n  getTexture(dataId) {\n    this.uploadToGPU(dataId);\n    return this.texData.get(dataId).texture.texture;\n  }\n  getDataInfo(dataId) {\n    return this.texData.get(dataId);\n  }\n  shouldExecuteOnCPU(inputs, sizeThreshold = CPU_HANDOFF_SIZE_THRESHOLD) {\n    return env().getBool(\"WEBGL_CPU_FORWARD\") && inputs.every((input2) => this.texData.get(input2.dataId).texture == null && util_exports.sizeFromShape(input2.shape) < sizeThreshold);\n  }\n  getGPGPUContext() {\n    return this.gpgpu;\n  }\n  where(condition) {\n    backend_util_exports.warn(\"tf.where() in webgl locks the UI thread. Call tf.whereAsync() instead\");\n    const condVals = condition.dataSync();\n    return whereImpl3(condition.shape, condVals);\n  }\n  packedUnaryOp(x, op2, dtype) {\n    const program = new UnaryOpPackedProgram(x.shape, op2);\n    const outInfo = this.compileAndRun(program, [x], dtype);\n    return engine().makeTensorFromTensorInfo(outInfo);\n  }\n  abs(x) {\n    if (this.shouldExecuteOnCPU([x]) && x.dtype !== \"complex64\") {\n      const outValues = simpleAbsImplCPU(this.texData.get(x.dataId).values);\n      return this.makeOutput(x.shape, x.dtype, outValues);\n    }\n    if (env().getBool(\"WEBGL_PACK_UNARY_OPERATIONS\")) {\n      return this.packedUnaryOp(x, ABS, x.dtype);\n    }\n    const program = new UnaryOpProgram(x.shape, ABS);\n    const outInfo = this.compileAndRun(program, [x]);\n    return engine().makeTensorFromTensorInfo(outInfo);\n  }\n  makeTensorInfo(shape, dtype, values) {\n    let dataId;\n    if (dtype === \"string\" && values != null && values.length > 0 && util_exports.isString(values[0])) {\n      const encodedValues = values.map((d) => util_exports.encodeString(d));\n      dataId = this.write(encodedValues, shape, dtype);\n    } else {\n      dataId = this.write(values, shape, dtype);\n    }\n    this.texData.get(dataId).usage = null;\n    return { dataId, shape, dtype };\n  }\n  makeOutput(shape, dtype, values) {\n    return engine().makeTensorFromTensorInfo(this.makeTensorInfo(shape, dtype, values), this);\n  }\n  unpackTensor(input2) {\n    const program = new UnpackProgram(input2.shape);\n    return this.runWebGLProgram(program, [input2], input2.dtype);\n  }\n  packTensor(input2) {\n    const program = new PackProgram(input2.shape);\n    const preventEagerUnpackingOutput = true;\n    return this.runWebGLProgram(program, [input2], input2.dtype, null, preventEagerUnpackingOutput);\n  }\n  packedReshape(input2, afterShape) {\n    const input3DShape = [\n      getBatchDim(input2.shape),\n      ...getRowsCols(input2.shape)\n    ];\n    const input3D = {\n      dtype: input2.dtype,\n      shape: input3DShape,\n      dataId: input2.dataId\n    };\n    const afterShapeAs3D = [\n      getBatchDim(afterShape),\n      ...getRowsCols(afterShape)\n    ];\n    const program = new ReshapePackedProgram(afterShapeAs3D, input3DShape);\n    const preventEagerUnpackingOfOutput = true;\n    const customValues = [input3DShape];\n    const output = this.runWebGLProgram(program, [input3D], input2.dtype, customValues, preventEagerUnpackingOfOutput);\n    return { dataId: output.dataId, shape: afterShape, dtype: output.dtype };\n  }\n  decode(dataId, customTexShape) {\n    const texData = this.texData.get(dataId);\n    const { isPacked, shape, dtype } = texData;\n    if (customTexShape != null) {\n      const size = util_exports.sizeFromShape(shape);\n      const texSize = customTexShape[0] * customTexShape[1] * 4;\n      util_exports.assert(size <= texSize, () => \"customTexShape is too small. Row * Column * 4 should be equal or larger than the size of the tensor data.\");\n    }\n    const shapeAs3D = getShapeAs3D(shape);\n    let program;\n    if (isPacked) {\n      program = new DecodeMatrixPackedProgram(shapeAs3D);\n    } else {\n      program = new DecodeMatrixProgram(shapeAs3D);\n    }\n    const preventEagerUnpackingOfOutput = true;\n    const customValues = [customTexShape != null ? customTexShape : getDenseTexShape(shapeAs3D)];\n    const out = this.runWebGLProgram(program, [{ shape: shapeAs3D, dtype, dataId }], dtype, customValues, preventEagerUnpackingOfOutput, customTexShape);\n    return { dtype, shape, dataId: out.dataId };\n  }\n  runWebGLProgram(program, inputs, outputDtype, customUniformValues, preventEagerUnpackingOfOutput = false, customTexShape) {\n    const output = this.makeTensorInfo(program.outputShape, outputDtype);\n    const outData = this.texData.get(output.dataId);\n    if (program.packedOutput) {\n      outData.isPacked = true;\n    }\n    if (program.outPackingScheme === PackingScheme.DENSE) {\n      const texelShape = customTexShape != null ? customTexShape : getDenseTexShape(program.outputShape);\n      outData.texShape = texelShape.map((d) => d * 2);\n    }\n    if (program.outTexUsage != null) {\n      outData.usage = program.outTexUsage;\n    }\n    if (util_exports.sizeFromShape(output.shape) === 0) {\n      outData.values = util_exports.getTypedArrayFromDType(output.dtype, 0);\n      return output;\n    }\n    const dataToDispose = [];\n    const inputsData = inputs.map((input2) => {\n      if (input2.dtype === \"complex64\") {\n        throw new Error(`GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.`);\n      }\n      let texData = this.texData.get(input2.dataId);\n      if (texData.texture == null) {\n        if (!program.packedInputs && util_exports.sizeFromShape(input2.shape) <= env().getNumber(\"WEBGL_SIZE_UPLOAD_UNIFORM\")) {\n          return {\n            shape: input2.shape,\n            texData: null,\n            isUniform: true,\n            uniformValues: texData.values\n          };\n        }\n        if (program.packedInputs) {\n          texData.isPacked = true;\n          texData.shape = input2.shape;\n        }\n      }\n      this.uploadToGPU(input2.dataId);\n      if (!!texData.isPacked !== !!program.packedInputs) {\n        input2 = texData.isPacked ? this.unpackTensor(input2) : this.packTensor(input2);\n        dataToDispose.push(input2);\n        texData = this.texData.get(input2.dataId);\n      } else if (texData.isPacked && !isReshapeFree(texData.shape, input2.shape)) {\n        const savedInput = input2;\n        const targetShape = input2.shape;\n        input2.shape = texData.shape;\n        input2 = this.packedReshape(input2, targetShape);\n        dataToDispose.push(input2);\n        texData = this.texData.get(input2.dataId);\n        savedInput.shape = targetShape;\n      }\n      return { shape: input2.shape, texData, isUniform: false };\n    });\n    this.uploadToGPU(output.dataId);\n    const outputData = { shape: output.shape, texData: outData, isUniform: false };\n    const key = makeShaderKey(program, inputsData, outputData);\n    const binary = this.getAndSaveBinary(key, () => {\n      return compileProgram(this.gpgpu, program, inputsData, outputData);\n    });\n    const shouldTimeProgram = this.activeTimers != null;\n    let query;\n    if (shouldTimeProgram) {\n      query = this.startTimer();\n    }\n    if (!env().get(\"ENGINE_COMPILE_ONLY\")) {\n      runProgram(this.gpgpu, binary, inputsData, outputData, customUniformValues);\n    }\n    dataToDispose.forEach((info) => this.disposeIntermediateTensorInfo(info));\n    if (shouldTimeProgram) {\n      query = this.endTimer(query);\n      this.activeTimers.push({ name: program.constructor.name, query: this.getQueryTime(query) });\n    }\n    const glFlushThreshold = env().get(\"WEBGL_FLUSH_THRESHOLD\");\n    if (glFlushThreshold > 0) {\n      const time2 = util_exports.now();\n      if (time2 - this.lastGlFlushTime > glFlushThreshold) {\n        this.gpgpu.gl.flush();\n        this.lastGlFlushTime = time2;\n      }\n    }\n    if (!env().getBool(\"WEBGL_LAZILY_UNPACK\") && outData.isPacked && preventEagerUnpackingOfOutput === false) {\n      const unpacked = this.unpackTensor(output);\n      this.disposeIntermediateTensorInfo(output);\n      return unpacked;\n    }\n    return output;\n  }\n  compileAndRun(program, inputs, outputDtype, customUniformValues, preventEagerUnpackingOfOutput = false) {\n    outputDtype = outputDtype || inputs[0].dtype;\n    const outInfo = this.runWebGLProgram(program, inputs, outputDtype, customUniformValues, preventEagerUnpackingOfOutput);\n    return outInfo;\n  }\n  getAndSaveBinary(key, getBinary) {\n    if (!(key in this.binaryCache)) {\n      this.binaryCache[key] = getBinary();\n    }\n    return this.binaryCache[key];\n  }\n  getTextureManager() {\n    return this.textureManager;\n  }\n  dispose() {\n    if (this.disposed) {\n      return;\n    }\n    if (!env().getBool(\"IS_TEST\")) {\n      const allKeys = Object.keys(this.binaryCache);\n      allKeys.forEach((key) => {\n        this.gpgpu.deleteProgram(this.binaryCache[key].webGLProgram);\n        delete this.binaryCache[key];\n      });\n    }\n    this.textureManager.dispose();\n    if (this.canvas != null && (typeof HTMLCanvasElement !== \"undefined\" && this.canvas instanceof HTMLCanvasElement)) {\n      this.canvas.remove();\n    } else {\n      this.canvas = null;\n    }\n    if (this.gpgpuCreatedLocally) {\n      this.gpgpu.program = null;\n      this.gpgpu.dispose();\n    }\n    this.disposed = true;\n  }\n  floatPrecision() {\n    if (this.floatPrecisionValue == null) {\n      this.floatPrecisionValue = tidy(() => {\n        if (!env().get(\"WEBGL_RENDER_FLOAT32_ENABLED\")) {\n          const debugFlag = env().getBool(\"DEBUG\");\n          env().set(\"DEBUG\", false);\n          const underflowCheckValue = this.abs(scalar(1e-8)).dataSync()[0];\n          env().set(\"DEBUG\", debugFlag);\n          if (underflowCheckValue > 0) {\n            return 32;\n          }\n        }\n        return 16;\n      });\n    }\n    return this.floatPrecisionValue;\n  }\n  epsilon() {\n    return this.floatPrecision() === 32 ? EPSILON_FLOAT322 : EPSILON_FLOAT162;\n  }\n  uploadToGPU(dataId) {\n    const texData = this.texData.get(dataId);\n    const { shape, dtype, values, texture, usage, isPacked } = texData;\n    if (texture != null) {\n      return;\n    }\n    const shouldTimeProgram = this.activeTimers != null;\n    let start;\n    if (shouldTimeProgram) {\n      start = util_exports.now();\n    }\n    let texShape = texData.texShape;\n    if (texShape == null) {\n      texShape = getTextureShapeFromLogicalShape(shape, isPacked);\n      texData.texShape = texShape;\n    }\n    if (values != null) {\n      const shapeAs3D = getShapeAs3D(shape);\n      let program;\n      let width = texShape[1], height = texShape[0];\n      const isByteArray = values instanceof Uint8Array || values instanceof Uint8ClampedArray;\n      if (isPacked || !isByteArray) {\n        [width, height] = getPackedMatrixTextureShapeWidthHeight(texShape[0], texShape[1]);\n      }\n      if (isPacked) {\n        program = new EncodeMatrixPackedProgram(shapeAs3D, isByteArray);\n      } else {\n        program = new EncodeMatrixProgram(shapeAs3D, isByteArray);\n      }\n      const tempDenseInputTexShape = isByteArray ? [height, width] : texShape;\n      const tempDenseInputHandle = this.makeTensorInfo(tempDenseInputTexShape, dtype);\n      const tempDenseInputTexData = this.texData.get(tempDenseInputHandle.dataId);\n      if (isByteArray) {\n        tempDenseInputTexData.usage = TextureUsage.PIXELS;\n      } else {\n        tempDenseInputTexData.usage = TextureUsage.UPLOAD;\n      }\n      tempDenseInputTexData.texShape = tempDenseInputTexShape;\n      this.gpgpu.uploadDenseMatrixToTexture(this.getTexture(tempDenseInputHandle.dataId), width, height, values);\n      const customValues = [[height, width]];\n      const preventEagerUnpacking = true;\n      const encodedOutputTarget = this.runWebGLProgram(program, [tempDenseInputHandle], dtype, customValues, preventEagerUnpacking);\n      const outputTexData = this.texData.get(encodedOutputTarget.dataId);\n      texData.texShape = outputTexData.texShape;\n      texData.isPacked = outputTexData.isPacked;\n      texData.usage = outputTexData.usage;\n      if (!env().get(\"ENGINE_COMPILE_ONLY\")) {\n        texData.texture = outputTexData.texture;\n        texData.values = null;\n        this.texData.delete(encodedOutputTarget.dataId);\n      } else {\n        this.disposeData(encodedOutputTarget.dataId);\n      }\n      this.disposeIntermediateTensorInfo(tempDenseInputHandle);\n      if (shouldTimeProgram) {\n        this.uploadWaitMs += util_exports.now() - start;\n      }\n    } else {\n      const newTexture = this.acquireTexture(texShape, usage, dtype, isPacked);\n      texData.texture = newTexture;\n    }\n  }\n  convertAndCacheOnCPU(dataId, float32Values) {\n    const texData = this.texData.get(dataId);\n    const { dtype } = texData;\n    this.releaseGPUData(dataId);\n    if (float32Values != null) {\n      texData.values = float32ToTypedArray(float32Values, dtype);\n    }\n    return texData.values;\n  }\n  acquireTexture(texShape, texType, dtype, isPacked) {\n    this.numBytesInGPU += this.computeBytes(texShape, dtype);\n    if (!this.warnedAboutMemory && this.numBytesInGPU > this.numMBBeforeWarning * 1024 * 1024) {\n      const mb = (this.numBytesInGPU / 1024 / 1024).toFixed(2);\n      this.warnedAboutMemory = true;\n      console.warn(`High memory usage in GPU: ${mb} MB, most likely due to a memory leak`);\n    }\n    return this.textureManager.acquireTexture(texShape, texType, isPacked);\n  }\n  computeBytes(shape, dtype) {\n    return shape[0] * shape[1] * util_exports.bytesPerElement(dtype);\n  }\n  checkCompileCompletion() {\n    for (const [, binary] of Object.entries(this.binaryCache)) {\n      this.checkCompletion_(binary);\n    }\n  }\n  async checkCompileCompletionAsync() {\n    const ps = [];\n    if (this.gpgpu.parallelCompilationExtension) {\n      for (const [, binary] of Object.entries(this.binaryCache)) {\n        ps.push(this.checkCompletionAsync_(binary));\n      }\n      return Promise.all(ps);\n    } else {\n      for (const [, binary] of Object.entries(this.binaryCache)) {\n        const p2 = new Promise((resolve) => {\n          try {\n            this.checkCompletion_(binary);\n            resolve(true);\n          } catch (error) {\n            throw error;\n          }\n        });\n        ps.push(p2);\n      }\n      return Promise.all(ps);\n    }\n  }\n  async checkCompletionAsync_(binary) {\n    if (this.gpgpu.gl.getProgramParameter(binary.webGLProgram, this.gpgpu.parallelCompilationExtension.COMPLETION_STATUS_KHR)) {\n      return this.checkCompletion_(binary);\n    } else {\n      await nextFrame();\n      return this.checkCompletionAsync_(binary);\n    }\n  }\n  checkCompletion_(binary) {\n    if (this.gpgpu.gl.getProgramParameter(binary.webGLProgram, this.gpgpu.gl.LINK_STATUS) === false) {\n      console.log(this.gpgpu.gl.getProgramInfoLog(binary.webGLProgram));\n      if (this.gpgpu.gl.getShaderParameter(binary.fragmentShader, this.gpgpu.gl.COMPILE_STATUS) === false) {\n        logShaderSourceAndInfoLog(binary.source, this.gpgpu.gl.getShaderInfoLog(binary.fragmentShader));\n        throw new Error(\"Failed to compile fragment shader.\");\n      }\n      throw new Error(\"Failed to link vertex and fragment shaders.\");\n    }\n    return true;\n  }\n  getUniformLocations() {\n    for (const [, binary] of Object.entries(this.binaryCache)) {\n      const { uniformLocations, customUniformLocations, infLoc, nanLoc, inShapesLocations, inTexShapesLocations, outShapeLocation, outShapeStridesLocation, outTexShapeLocation } = getUniformLocations(this.gpgpu, binary.program, binary.webGLProgram);\n      binary.uniformLocations = uniformLocations;\n      binary.customUniformLocations = customUniformLocations;\n      binary.infLoc = infLoc;\n      binary.nanLoc = nanLoc;\n      binary.inShapesLocations = inShapesLocations;\n      binary.inTexShapesLocations = inTexShapesLocations;\n      binary.outShapeLocation = outShapeLocation;\n      binary.outShapeStridesLocation = outShapeStridesLocation;\n      binary.outTexShapeLocation = outTexShapeLocation;\n    }\n  }\n};\nMathBackendWebGL.nextDataId = 0;\nfunction float32ToTypedArray(a, dtype) {\n  if (dtype === \"float32\" || dtype === \"complex64\") {\n    return a;\n  } else if (dtype === \"int32\" || dtype === \"bool\") {\n    const result = dtype === \"int32\" ? new Int32Array(a.length) : new Uint8Array(a.length);\n    for (let i2 = 0; i2 < result.length; ++i2) {\n      result[i2] = Math.round(a[i2]);\n    }\n    return result;\n  } else {\n    throw new Error(`Unknown dtype ${dtype}`);\n  }\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/version.js\nvar version6 = \"3.20.0\";\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/webgl.js\nfunction forceHalfFloat() {\n  env().set(\"WEBGL_FORCE_F16_TEXTURES\", true);\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/base.js\nif (device_util_exports.isBrowser()) {\n  registerBackend(\"webgl\", () => new MathBackendWebGL(), 2);\n}\nvar webgl = { forceHalfFloat };\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/binaryop_gpu.js\nvar CHECK_NAN_SNIPPET2 = `\n  if (isnan(a)) return a;\n  if (isnan(b)) return b;\n`;\nvar BinaryOpProgram = class {\n  constructor(op2, aShape, bShape) {\n    this.variableNames = [\"A\", \"B\"];\n    this.outputShape = backend_util_exports.assertAndGetBroadcastShape(aShape, bShape);\n    this.enableShapeUniforms = useShapeUniforms(this.outputShape.length);\n    this.userCode = `\n      float binaryOperation(float a, float b) {\n        ${op2}\n      }\n\n      void main() {\n        float a = getAAtOutCoords();\n        float b = getBAtOutCoords();\n        setOutput(binaryOperation(a, b));\n      }\n    `;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/binaryop_packed_gpu.js\nvar CHECK_NAN_SNIPPET3 = `\n  result.r = isNaN.r > 0. ? NAN : result.r;\n  result.g = isNaN.g > 0. ? NAN : result.g;\n  result.b = isNaN.b > 0. ? NAN : result.b;\n  result.a = isNaN.a > 0. ? NAN : result.a;\n`;\nvar BinaryOpPackedProgram = class {\n  constructor(op2, aShape, bShape, checkOutOfBounds = false) {\n    this.variableNames = [\"A\", \"B\"];\n    this.supportsBroadcasting = true;\n    this.packedInputs = true;\n    this.packedOutput = true;\n    this.outputShape = backend_util_exports.assertAndGetBroadcastShape(aShape, bShape);\n    const rank = this.outputShape.length;\n    this.enableShapeUniforms = useShapeUniforms(rank);\n    let checkOutOfBoundsString = \"\";\n    if (checkOutOfBounds) {\n      if (rank === 0 || util_exports.sizeFromShape(this.outputShape) === 1) {\n        checkOutOfBoundsString = `\n          result.y = 0.;\n          result.z = 0.;\n          result.w = 0.;\n        `;\n      } else {\n        const dtype = getCoordsDataType(rank);\n        checkOutOfBoundsString = `\n          ${dtype} coords = getOutputCoords();\n        `;\n        if (rank === 1) {\n          if (this.enableShapeUniforms) {\n            checkOutOfBoundsString += `\n            result.y = (coords + 1) >= outShape ? 0. : result.y;\n            result.z = 0.;\n            result.w = 0.;\n          `;\n          } else {\n            checkOutOfBoundsString += `\n            result.y = (coords + 1) >= ${this.outputShape[0]} ? 0. : result.y;\n            result.z = 0.;\n            result.w = 0.;\n          `;\n          }\n        } else {\n          const channels = getChannels(\"coords\", rank);\n          if (this.enableShapeUniforms) {\n            checkOutOfBoundsString += `\n            bool nextRowOutOfBounds =\n              (${channels[rank - 2]} + 1) >= outShape[${rank} - 2];\n            bool nextColOutOfBounds =\n              (${channels[rank - 1]} + 1) >= outShape[${rank} - 1];\n            result.y = nextColOutOfBounds ? 0. : result.y;\n            result.z = nextRowOutOfBounds ? 0. : result.z;\n            result.w = nextColOutOfBounds || nextRowOutOfBounds ? 0. : result.w;\n          `;\n          } else {\n            checkOutOfBoundsString += `\n            bool nextRowOutOfBounds =\n              (${channels[rank - 2]} + 1) >= ${this.outputShape[rank - 2]};\n            bool nextColOutOfBounds =\n              (${channels[rank - 1]} + 1) >= ${this.outputShape[rank - 1]};\n            result.y = nextColOutOfBounds ? 0. : result.y;\n            result.z = nextRowOutOfBounds ? 0. : result.z;\n            result.w = nextColOutOfBounds || nextRowOutOfBounds ? 0. : result.w;\n          `;\n          }\n        }\n      }\n    }\n    this.userCode = `\n      vec4 binaryOperation(vec4 a, vec4 b) {\n        ${op2}\n      }\n\n      void main() {\n        vec4 a = getAAtOutCoords();\n        vec4 b = getBAtOutCoords();\n\n        vec4 result = binaryOperation(a, b);\n        ${checkOutOfBoundsString}\n\n        setOutput(result);\n      }\n    `;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Identity.js\nfunction identity3(args) {\n  const { inputs, backend: backend2 } = args;\n  const { x } = inputs;\n  backend2.incRef(x.dataId);\n  return { dataId: x.dataId, shape: x.shape, dtype: x.dtype };\n}\nvar identityConfig2 = {\n  kernelName: Identity,\n  backendName: \"webgl\",\n  kernelFunc: identity3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Complex.js\nfunction complex3(args) {\n  const { inputs, backend: backend2 } = args;\n  const { real: real5, imag: imag5 } = inputs;\n  const complexInfo = backend2.makeTensorInfo(real5.shape, \"complex64\");\n  const complex5 = backend2.texData.get(complexInfo.dataId);\n  const realTensorInfo = identity3({ inputs: { x: real5 }, backend: backend2 });\n  const imagTensorInfo = identity3({ inputs: { x: imag5 }, backend: backend2 });\n  complex5.complexTensorInfos = { real: realTensorInfo, imag: imagTensorInfo };\n  return complexInfo;\n}\nvar complexConfig2 = {\n  kernelName: Complex,\n  backendName: \"webgl\",\n  kernelFunc: complex3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/LeakyRelu.js\nvar LEAKYRELU = `return (a < 0.) ? b * a : a;`;\nvar LEAKYRELU_PACKED = `\n  vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));\n  return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);\n`;\nfunction leakyRelu3(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x } = inputs;\n  const { alpha } = attrs;\n  const $alpha = backend2.makeTensorInfo([], \"float32\", util_exports.createScalarValue(alpha, \"float32\"));\n  const program = env().getBool(\"WEBGL_PACK_BINARY_OPERATIONS\") ? new BinaryOpPackedProgram(LEAKYRELU_PACKED, x.shape, $alpha.shape) : new BinaryOpProgram(LEAKYRELU, x.shape, $alpha.shape);\n  const result = backend2.runWebGLProgram(program, [x, $alpha], \"float32\");\n  backend2.disposeIntermediateTensorInfo($alpha);\n  return result;\n}\nvar leakyReluConfig2 = {\n  kernelName: LeakyRelu,\n  backendName: \"webgl\",\n  kernelFunc: leakyRelu3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Prelu.js\nvar PRELU = `return (a < 0.) ? b * a : a;`;\nvar PRELU_PACKED = `\n  vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));\n  return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);\n`;\nfunction prelu4(args) {\n  const { inputs, backend: backend2 } = args;\n  const { x, alpha } = inputs;\n  const program = env().getBool(\"WEBGL_PACK_BINARY_OPERATIONS\") ? new BinaryOpPackedProgram(PRELU_PACKED, x.shape, alpha.shape) : new BinaryOpProgram(PRELU, x.shape, alpha.shape);\n  return backend2.runWebGLProgram(program, [x, alpha], \"float32\");\n}\nvar preluConfig2 = {\n  kernelName: Prelu,\n  backendName: \"webgl\",\n  kernelFunc: prelu4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernel_utils/kernel_funcs_utils.js\nvar CHECK_NAN_SNIPPET_UNARY = `if (isnan(x)) return x;`;\nvar CHECK_NAN_SNIPPET_BINARY = `\n  if (isnan(a)) return a;\n  if (isnan(b)) return b;\n`;\nvar CHECK_NAN_SNIPPET_BINARY_PACKED = `\n  result.r = isNaN.r > 0. ? NAN : result.r;\n  result.g = isNaN.g > 0. ? NAN : result.g;\n  result.b = isNaN.b > 0. ? NAN : result.b;\n  result.a = isNaN.a > 0. ? NAN : result.a;\n`;\nfunction unaryKernelFunc2({ opSnippet, packedOpSnippet, cpuKernelImpl, dtype }) {\n  return ({ inputs, backend: backend2 }) => {\n    const { x } = inputs;\n    const webglBackend = backend2;\n    const $dtype = dtype || x.dtype;\n    if (webglBackend.shouldExecuteOnCPU([x]) && cpuKernelImpl != null) {\n      const xData = webglBackend.texData.get(x.dataId);\n      const outValues = cpuKernelImpl(xData.values, $dtype);\n      return webglBackend.makeTensorInfo(x.shape, $dtype, outValues);\n    }\n    const shouldUsePackedProgram = env().getBool(\"WEBGL_PACK_UNARY_OPERATIONS\") && packedOpSnippet != null;\n    let program;\n    if (shouldUsePackedProgram) {\n      program = new UnaryOpPackedProgram(x.shape, packedOpSnippet);\n    } else {\n      program = new UnaryOpProgram(x.shape, opSnippet);\n    }\n    return webglBackend.runWebGLProgram(program, [x], $dtype);\n  };\n}\nfunction binaryKernelFunc2({ opSnippet, packedOpSnippet, checkOutOfBounds = false, supportsComplex = false, cpuKernelImpl, dtype }) {\n  return ({ inputs, backend: backend2 }) => {\n    const { a, b } = inputs;\n    const webglBackend = backend2;\n    if (supportsComplex && a.dtype === \"complex64\") {\n      const aData = webglBackend.texData.get(a.dataId);\n      const bData = webglBackend.texData.get(b.dataId);\n      const [real5, imag5] = [\n        [aData.complexTensorInfos.real, bData.complexTensorInfos.real],\n        [aData.complexTensorInfos.imag, bData.complexTensorInfos.imag]\n      ].map((complexParts) => {\n        const [aPart, bPart] = complexParts;\n        const aHandle = {\n          dataId: aPart.dataId,\n          dtype: aPart.dtype,\n          shape: a.shape\n        };\n        const bHandle = {\n          dataId: bPart.dataId,\n          dtype: bPart.dtype,\n          shape: b.shape\n        };\n        const program2 = new BinaryOpProgram(opSnippet, a.shape, b.shape);\n        return webglBackend.runWebGLProgram(program2, [aHandle, bHandle], upcastType(aPart.dtype, bPart.dtype));\n      });\n      const complexOutput = complex3({ inputs: { real: real5, imag: imag5 }, backend: webglBackend });\n      webglBackend.disposeIntermediateTensorInfo(real5);\n      webglBackend.disposeIntermediateTensorInfo(imag5);\n      return complexOutput;\n    }\n    const $dtype = dtype || upcastType(a.dtype, b.dtype);\n    if ((a.dtype === \"string\" || b.dtype === \"string\" || webglBackend.shouldExecuteOnCPU([a, b])) && cpuKernelImpl != null) {\n      const aVals = webglBackend.texData.get(a.dataId).values;\n      const bVals = webglBackend.texData.get(b.dataId).values;\n      const decodedAVals = a.dtype === \"string\" ? backend_util_exports.fromUint8ToStringArray(aVals) : aVals;\n      const decodedBVals = a.dtype === \"string\" ? backend_util_exports.fromUint8ToStringArray(bVals) : bVals;\n      const [outValues, outShape] = cpuKernelImpl(a.shape, b.shape, decodedAVals, decodedBVals, $dtype);\n      const out = webglBackend.makeTensorInfo(outShape, $dtype);\n      const outData = webglBackend.texData.get(out.dataId);\n      outData.values = outValues;\n      return out;\n    }\n    const shouldUsePackedProgram = env().getBool(\"WEBGL_PACK_BINARY_OPERATIONS\") && packedOpSnippet != null;\n    let program;\n    if (shouldUsePackedProgram) {\n      program = new BinaryOpPackedProgram(packedOpSnippet, a.shape, b.shape, checkOutOfBounds);\n    } else {\n      program = new BinaryOpProgram(opSnippet, a.shape, b.shape);\n    }\n    return webglBackend.runWebGLProgram(program, [a, b], $dtype);\n  };\n}\nfunction mapActivationToShaderProgram(activation2, packed = false) {\n  if (activation2 === \"linear\") {\n    if (packed) {\n      return LINEAR2;\n    }\n    return LINEAR;\n  } else if (activation2 === \"relu\") {\n    if (packed) {\n      return RELU2;\n    }\n    return RELU;\n  } else if (activation2 === \"elu\") {\n    if (packed) {\n      return ELU3;\n    }\n    return ELU2;\n  } else if (activation2 === \"relu6\") {\n    if (packed) {\n      return RELU62;\n    }\n    return RELU6;\n  } else if (activation2 === \"prelu\") {\n    if (packed) {\n      return PRELU_PACKED;\n    }\n    return PRELU;\n  } else if (activation2 === \"leakyrelu\") {\n    if (packed) {\n      return LEAKYRELU_PACKED;\n    }\n    return LEAKYRELU;\n  } else if (activation2 === \"sigmoid\") {\n    if (packed) {\n      return SIGMOID2;\n    }\n    return SIGMOID;\n  }\n  throw new Error(`Activation ${activation2} has not been implemented for the WebGL backend.`);\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/mulmat_packed_gpu.js\nvar MatMulPackedProgram = class {\n  constructor(aShape, bShape, outputShape, transposeA = false, transposeB = false, addBias = false, activation2 = null, hasPreluActivation = false, hasLeakyreluActivation = false) {\n    this.variableNames = [\"matrixA\", \"matrixB\"];\n    this.packedInputs = true;\n    this.packedOutput = true;\n    this.outputShape = outputShape;\n    this.enableShapeUniforms = useShapeUniforms(this.outputShape.length);\n    const sharedDim = transposeA ? aShape[1] : aShape[2];\n    const sharedDimensionPacked = Math.ceil(sharedDim / 2);\n    const aSample = transposeA ? \"i * 2, rc.y\" : \"rc.y, i * 2\";\n    const bSample = transposeB ? \"rc.z, i * 2\" : \"i * 2, rc.z\";\n    const aSwizzle = transposeA ? [\"a.xxyy\", \"a.zzww\"] : [\"a.xxzz\", \"a.yyww\"];\n    const bSwizzle = transposeB ? [\"b.xzxz\", \"b.ywyw\"] : [\"b.xyxy\", \"b.zwzw\"];\n    let activationSnippet = \"\", applyActivationSnippet = \"\";\n    if (activation2) {\n      if (hasPreluActivation) {\n        activationSnippet = `vec4 activation(vec4 a) {\n          vec4 b = getPreluActivationWeightsAtOutCoords();\n          ${activation2}\n        }`;\n      } else if (hasLeakyreluActivation) {\n        activationSnippet = `vec4 activation(vec4 a) {\n          vec4 b = getLeakyreluAlphaAtOutCoords();\n          ${activation2}\n        }`;\n      } else {\n        activationSnippet = `vec4 activation(vec4 x) {\n          ${activation2}\n        }`;\n      }\n      applyActivationSnippet = `result = activation(result);`;\n    }\n    const addBiasSnippet = addBias ? \"result += getBiasAtOutCoords();\" : \"\";\n    if (addBias) {\n      this.variableNames.push(\"bias\");\n    }\n    if (hasPreluActivation) {\n      this.variableNames.push(\"preluActivationWeights\");\n    }\n    if (hasLeakyreluActivation) {\n      this.variableNames.push(\"leakyreluAlpha\");\n    }\n    let batchASnippet = \"rc.x\";\n    let batchBSnippet = \"rc.x\";\n    if (aShape[0] < bShape[0]) {\n      batchASnippet = `int(min(float(rc.x), ${aShape[0] - 1}.))`;\n    } else if (bShape[0] < aShape[0]) {\n      batchBSnippet = `int(min(float(rc.x), ${bShape[0] - 1}.))`;\n    }\n    this.userCode = `\n      ${activationSnippet}\n      // Don't use uniform for sharedDimensionPacked for performance.\n      const float sharedDimension = ${sharedDimensionPacked}.0;\n\n      vec4 dot2x2ARowBCol(ivec3 rc) {\n        vec4 result = vec4(0);\n        for (int i = 0; i < ${sharedDimensionPacked}; i++) {\n          int batchA = ${batchASnippet};\n          int batchB = ${batchBSnippet};\n          vec4 a = getMatrixA(batchA, ${aSample});\n          vec4 b = getMatrixB(batchB, ${bSample});\n\n          // These swizzled products need to be separately added.\n          // See: https://github.com/tensorflow/tfjs/issues/1735\n          result += (${aSwizzle[0]} * ${bSwizzle[0]});\n          result += (${aSwizzle[1]} * ${bSwizzle[1]});\n        }\n        return result;\n      }\n\n      void main() {\n        ivec3 rc = getOutputCoords();\n        vec4 result = dot2x2ARowBCol(rc);\n\n        ${addBiasSnippet}\n\n        ${applyActivationSnippet}\n\n        setOutput(result);\n      }\n    `;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/binaryop_complex_gpu.js\nvar COMPLEX_MULTIPLY = {\n  REAL: \"return areal * breal - aimag * bimag;\",\n  IMAG: \"return areal * bimag + aimag * breal;\"\n};\nvar BinaryOpComplexProgram = class {\n  constructor(op2, aShape, bShape) {\n    this.variableNames = [\"AReal\", \"AImag\", \"BReal\", \"BImag\"];\n    this.outputShape = backend_util_exports.assertAndGetBroadcastShape(aShape, bShape);\n    this.userCode = `\n      float binaryOpComplex(\n          float areal, float aimag, float breal, float bimag) {\n        ${op2}\n      }\n\n      void main() {\n        float areal = getARealAtOutCoords();\n        float aimag = getAImagAtOutCoords();\n        float breal = getBRealAtOutCoords();\n        float bimag = getBImagAtOutCoords();\n        setOutput(binaryOpComplex(areal, aimag, breal, bimag));\n      }\n    `;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Multiply.js\nvar MUL = \"return a * b;\";\nfunction multiply3(args) {\n  const { inputs, backend: backend2 } = args;\n  const { a, b } = inputs;\n  const dtype = backend_util_exports.upcastType(a.dtype, b.dtype);\n  if (a.dtype === \"complex64\") {\n    const aData = backend2.texData.get(a.dataId);\n    const bData = backend2.texData.get(b.dataId);\n    const realProgram = new BinaryOpComplexProgram(COMPLEX_MULTIPLY.REAL, a.shape, b.shape);\n    const imagProgram = new BinaryOpComplexProgram(COMPLEX_MULTIPLY.IMAG, a.shape, b.shape);\n    const inputs2 = [\n      {\n        dataId: aData.complexTensorInfos.real.dataId,\n        dtype: aData.complexTensorInfos.real.dtype,\n        shape: a.shape\n      },\n      {\n        dataId: aData.complexTensorInfos.imag.dataId,\n        dtype: aData.complexTensorInfos.imag.dtype,\n        shape: a.shape\n      },\n      {\n        dataId: bData.complexTensorInfos.real.dataId,\n        dtype: bData.complexTensorInfos.real.dtype,\n        shape: b.shape\n      },\n      {\n        dataId: bData.complexTensorInfos.imag.dataId,\n        dtype: bData.complexTensorInfos.imag.dtype,\n        shape: b.shape\n      }\n    ];\n    const realPart = backend2.runWebGLProgram(realProgram, inputs2, \"float32\");\n    const imagPart = backend2.runWebGLProgram(imagProgram, inputs2, \"float32\");\n    const complexOutput = complex3({ inputs: { real: realPart, imag: imagPart }, backend: backend2 });\n    backend2.disposeIntermediateTensorInfo(realPart);\n    backend2.disposeIntermediateTensorInfo(imagPart);\n    return complexOutput;\n  }\n  if (backend2.shouldExecuteOnCPU([a, b])) {\n    const aData = backend2.texData.get(a.dataId);\n    const bData = backend2.texData.get(b.dataId);\n    const [outValues, outShape] = multiplyImplCPU(a.shape, b.shape, aData.values, bData.values, dtype);\n    const out = backend2.makeTensorInfo(outShape, dtype);\n    const outData = backend2.texData.get(out.dataId);\n    outData.values = outValues;\n    return out;\n  }\n  let program;\n  if (env().getBool(\"WEBGL_PACK_BINARY_OPERATIONS\")) {\n    program = new BinaryOpPackedProgram(MUL, a.shape, b.shape);\n  } else {\n    program = new BinaryOpProgram(MUL, a.shape, b.shape);\n  }\n  return backend2.runWebGLProgram(program, [a, b], dtype);\n}\nvar multiplyConfig2 = {\n  kernelName: Multiply,\n  backendName: \"webgl\",\n  kernelFunc: multiply3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernel_utils/reshape.js\nfunction packedReshape(input2, afterShape, backend2) {\n  const input3DShape = [\n    getBatchDim(input2.shape),\n    ...getRowsCols(input2.shape)\n  ];\n  const input3D = {\n    dtype: input2.dtype,\n    shape: input3DShape,\n    dataId: input2.dataId\n  };\n  const afterShapeAs3D = [\n    getBatchDim(afterShape),\n    ...getRowsCols(afterShape)\n  ];\n  const program = new ReshapePackedProgram(afterShapeAs3D, input3DShape);\n  const preventEagerUnpackingOfOutput = true;\n  const customValues = [input3DShape];\n  const output = backend2.runWebGLProgram(program, [input3D], input2.dtype, customValues, preventEagerUnpackingOfOutput);\n  return { dataId: output.dataId, shape: afterShape, dtype: output.dtype };\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Reshape.js\nfunction reshape4(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x } = inputs;\n  const { shape } = attrs;\n  const webglBackend = backend2;\n  const xSize = util_exports.sizeFromShape(x.shape);\n  const $shape = util_exports.inferFromImplicitShape(shape, xSize);\n  const $xSize = util_exports.sizeFromShape($shape);\n  util_exports.assert(xSize === $xSize, () => `The new shape (${$shape}) has ${$xSize} elements and the old shape (${x.shape}) has ${xSize} elements. The new shape and old shape must have the same number of elements.`);\n  const xTexData = webglBackend.texData.get(x.dataId);\n  if (xTexData.isPacked && !isReshapeFree(x.shape, $shape) && !(xTexData.texture !== null && isReshapeFree(xTexData.shape, $shape))) {\n    return packedReshape(x, $shape, webglBackend);\n  }\n  webglBackend.incRef(x.dataId);\n  return { dataId: x.dataId, shape: $shape, dtype: x.dtype };\n}\nvar reshapeConfig2 = {\n  kernelName: Reshape,\n  backendName: \"webgl\",\n  kernelFunc: reshape4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/mean_gpu.js\nvar MeanProgram = class {\n  constructor(reduceInfo, divisor) {\n    this.variableNames = [\"x\"];\n    const { windowSize, batchSize, inSize, outSize } = reduceInfo;\n    this.outputShape = [batchSize, outSize];\n    const windowSizeNearestVec4 = Math.floor(windowSize / 4) * 4;\n    const windowSizeVec4Remainder = windowSize % 4;\n    let updateSnippet = `sumValue += dot(values, ones);`;\n    if (divisor != null) {\n      const denominator = 1 / divisor;\n      updateSnippet = `sumValue += dot(values * ${util_exports.isInt(denominator) ? denominator.toPrecision(2) : denominator}, ones);`;\n    }\n    let checkOutOfBounds = \"\";\n    if (inSize % windowSize > 0) {\n      checkOutOfBounds = `\n        if (inIdx < 0 || inIdx >= ${inSize}) {\n          return 0.0;\n        }\n      `;\n    }\n    this.userCode = `\n      const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);\n\n      float getValue(int batch, int inIdx) {\n        ${checkOutOfBounds}\n        return getX(batch, inIdx);\n      }\n\n      void main() {\n        ivec2 coords = getOutputCoords();\n        int batch = coords[0];\n        int outIdx = coords[1];\n        int inOffset = outIdx * ${windowSize};\n\n        float sumValue = 0.0;\n\n        for (int i = 0; i < ${windowSizeNearestVec4}; i += 4) {\n          int inIdx = inOffset + i;\n          vec4 values = vec4(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            getValue(batch, inIdx + 2),\n            getValue(batch, inIdx + 3)\n          );\n\n          ${updateSnippet}\n        }\n\n        int inIdx = inOffset + ${windowSizeNearestVec4};\n        if (${windowSizeVec4Remainder === 1}) {\n          vec4 values = vec4(getValue(batch, inIdx), 0.0, 0.0, 0.0);\n\n          ${updateSnippet}\n        } else if (${windowSizeVec4Remainder === 2}) {\n          vec4 values = vec4(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1), 0.0, 0.0);\n\n          ${updateSnippet}\n        } else if (${windowSizeVec4Remainder === 3}) {\n          vec4 values = vec4(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            getValue(batch, inIdx + 2), 0.0);\n\n          ${updateSnippet}\n        }\n        setOutput(sumValue);\n      }\n    `;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/reduce_gpu.js\nvar ReduceProgram = class {\n  constructor(reduceInfo, reduceType) {\n    this.variableNames = [\"x\"];\n    const { windowSize, batchSize, inSize, outSize } = reduceInfo;\n    this.outputShape = [batchSize, outSize];\n    let initializationValue = \"0.0\";\n    let compareOp = ``;\n    if (reduceType === \"prod\") {\n      initializationValue = \"1.0\";\n    } else if (reduceType === \"min\") {\n      initializationValue = \"1.0 / 1e-20\";\n      compareOp = `min`;\n    } else if (reduceType === \"max\") {\n      initializationValue = \"-1.0 / 1e-20\";\n      compareOp = `max`;\n    }\n    let returnValue = `${reduceType}(${reduceType}(${reduceType}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;\n    if (reduceType === \"sum\") {\n      returnValue = `sumValue`;\n    } else if (reduceType === \"prod\") {\n      returnValue = `prodValue`;\n    } else if (reduceType === \"all\") {\n      returnValue = `allValue`;\n    } else if (reduceType === \"any\") {\n      returnValue = `anyValue`;\n    }\n    const windowSizeNearestVec4 = Math.floor(windowSize / 4) * 4;\n    const windowSizeVec4Remainder = windowSize % 4;\n    let updateSnippet = `\n      if (${reduceType === \"sum\"}) {\n        sumValue += dot(values, ones);\n      } else if (${reduceType === \"prod\"}) {\n        vec2 tmp = vec2(values[0], values[1]) * vec2(values[2], values[3]);\n        prodValue *= tmp[0] * tmp[1];\n      } else {\n        minMaxValue = ${compareOp}(values, minMaxValue);\n        if (${reduceType === \"min\"} || ${reduceType === \"max\"}) {\n          minMaxValue = ${compareOp}(values, minMaxValue);\n          bvec4 isNaN = isnan(values);\n          if (isNaN.r || isNaN.g || isNaN.b || isNaN.a) {\n            minMaxValue = vec4(NAN);\n          }\n        }\n      }\n    `;\n    let vecType = `vec4`;\n    if (reduceType === \"all\") {\n      initializationValue = \"1.0\";\n      updateSnippet = `\n        bool reducedAllValue = all(values);\n        float floatedReducedAllValue = float(reducedAllValue);\n        allValue = float(allValue >= 1.0 && floatedReducedAllValue >= 1.0);\n      `;\n      vecType = `bvec4`;\n    } else if (reduceType === \"any\") {\n      initializationValue = \"0.0\";\n      updateSnippet = `\n        bool reducedAnyValue = any(values);\n        float floatedReducedAnyValue = float(reducedAnyValue);\n        anyValue = float(anyValue >= 1.0 || floatedReducedAnyValue >= 1.0);\n      `;\n      vecType = `bvec4`;\n    }\n    let checkOutOfBounds = \"\";\n    if (inSize % windowSize > 0) {\n      checkOutOfBounds = `\n        if (inIdx < 0 || inIdx >= ${inSize}) {\n          return initializationValue;\n        }\n      `;\n    }\n    this.userCode = `\n      const float initializationValue = ${initializationValue};\n      const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);\n\n      float getValue(int batch, int inIdx) {\n        ${checkOutOfBounds}\n        return getX(batch, inIdx);\n      }\n\n      void main() {\n        ivec2 coords = getOutputCoords();\n        int batch = coords[0];\n        int outIdx = coords[1];\n        int inOffset = outIdx * ${windowSize};\n\n        vec4 minMaxValue = vec4(${initializationValue});\n        float prodValue = 1.0;\n        float sumValue = 0.0;\n        float allValue = 1.0;\n        float anyValue = 0.0;\n\n        for (int i = 0; i < ${windowSizeNearestVec4}; i += 4) {\n          int inIdx = inOffset + i;\n          ${vecType} values = ${vecType}(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            getValue(batch, inIdx + 2),\n            getValue(batch, inIdx + 3)\n          );\n\n          ${updateSnippet}\n        }\n\n        int inIdx = inOffset + ${windowSizeNearestVec4};\n        if (${windowSizeVec4Remainder === 1}) {\n          ${vecType} values = ${vecType}(\n            getValue(batch, inIdx),\n            initializationValue,\n            initializationValue,\n            initializationValue\n          );\n\n          ${updateSnippet}\n        } else if (${windowSizeVec4Remainder === 2}) {\n          ${vecType} values = ${vecType}(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            initializationValue,\n            initializationValue\n          );\n\n          ${updateSnippet}\n        } else if (${windowSizeVec4Remainder === 3}) {\n          ${vecType} values = ${vecType}(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            getValue(batch, inIdx + 2),\n            initializationValue\n          );\n\n          ${updateSnippet}\n        }\n        setOutput(${returnValue});\n      }\n    `;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernel_utils/reduce.js\nfunction getReductionStages(inShape) {\n  const stages = [];\n  while (stages.length === 0 || stages[stages.length - 1].outSize !== 1) {\n    const outSize = stages.length ? stages[stages.length - 1].outSize : inShape[1];\n    const windowSize = backend_util_exports.computeOptimalWindowSize(outSize);\n    stages.push({\n      inSize: outSize,\n      windowSize,\n      outSize: Math.ceil(outSize / windowSize)\n    });\n  }\n  return stages;\n}\nfunction reduce(x, dtype, reductionType, backend2) {\n  const reductionStages = getReductionStages(x.shape);\n  let result = x;\n  for (let i2 = 0; i2 < reductionStages.length; i2++) {\n    const { inSize, windowSize, outSize } = reductionStages[i2];\n    let program;\n    let previousResult;\n    if (reductionType === \"mean\") {\n      program = i2 === 0 ? new MeanProgram({ windowSize, inSize, batchSize: x.shape[0], outSize }, inSize) : new MeanProgram({ windowSize, inSize, batchSize: x.shape[0], outSize });\n    } else {\n      program = new ReduceProgram({ windowSize, inSize, batchSize: x.shape[0], outSize }, reductionType);\n    }\n    previousResult = result;\n    result = backend2.runWebGLProgram(program, [result], dtype);\n    if (previousResult.dataId !== x.dataId) {\n      backend2.disposeIntermediateTensorInfo(previousResult);\n    }\n  }\n  return result;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/transpose_gpu.js\nvar TransposeProgram = class {\n  constructor(aShape, newDim) {\n    this.variableNames = [\"A\"];\n    const outputShape = new Array(aShape.length);\n    for (let i2 = 0; i2 < outputShape.length; i2++) {\n      outputShape[i2] = aShape[newDim[i2]];\n    }\n    this.outputShape = outputShape;\n    this.rank = outputShape.length;\n    const dtype = getCoordsDataType(this.rank);\n    const switched = getSwitchedCoords(newDim);\n    this.userCode = `\n    void main() {\n      ${dtype} resRC = getOutputCoords();\n      setOutput(getA(${switched}));\n    }\n    `;\n  }\n};\nfunction getSwitchedCoords(newDim) {\n  const rank = newDim.length;\n  if (rank > 6) {\n    throw Error(`Transpose for rank ${rank} is not yet supported`);\n  }\n  const originalOrder = [\"resRC.x\", \"resRC.y\", \"resRC.z\", \"resRC.w\", \"resRC.u\", \"resRC.v\"];\n  const switchedCoords = new Array(rank);\n  for (let i2 = 0; i2 < newDim.length; i2++) {\n    switchedCoords[newDim[i2]] = originalOrder[i2];\n  }\n  return switchedCoords.join();\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/transpose_packed_gpu.js\nvar TransposePackedProgram = class {\n  constructor(aShape, newDim) {\n    this.variableNames = [\"A\"];\n    this.packedInputs = true;\n    this.packedOutput = true;\n    const outputShape = new Array(aShape.length);\n    for (let i2 = 0; i2 < outputShape.length; i2++) {\n      outputShape[i2] = aShape[newDim[i2]];\n    }\n    this.outputShape = outputShape;\n    this.rank = outputShape.length;\n    if (this.rank > 6) {\n      throw Error(`Packed transpose for rank ${this.rank} is not yet supported.`);\n    }\n    const dtype = getCoordsDataType(this.rank);\n    const outputOrder = getVecChannels(\"rc\", this.rank);\n    const switchedOrder = new Array(this.rank);\n    for (let i2 = 0; i2 < newDim.length; i2++) {\n      switchedOrder[newDim[i2]] = outputOrder[i2];\n    }\n    const innerDims = `vec2(${switchedOrder.slice(-2).join()})`;\n    const nextColumn = `++${outputOrder[this.rank - 1]} < ${outputShape[this.rank - 1]}`;\n    const getc = `getChannel(getA(${switchedOrder.join()}), ${innerDims})`;\n    this.userCode = `\n    void main() {\n      ${dtype} rc = getOutputCoords();\n      vec4 result = vec4(0.);\n      result[0] = ${getc};\n      if(${nextColumn}) {\n        result[1] = ${getc};\n      }\n      --${outputOrder[this.rank - 1]};\n      if(++${outputOrder[this.rank - 2]} < ${outputShape[this.rank - 2]}) {\n        result[2] = ${getc};\n        if(${nextColumn}) {\n          result[3] = ${getc};\n        }\n      }\n      setOutput(result);\n    }\n    `;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Transpose_impl.js\nfunction transposeImpl2(x, perm, backend2) {\n  const program = env().getBool(\"WEBGL_PACK_ARRAY_OPERATIONS\") ? new TransposePackedProgram(x.shape, perm) : new TransposeProgram(x.shape, perm);\n  return backend2.runWebGLProgram(program, [x], x.dtype);\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Sum_impl.js\nfunction sumImpl(x, axis, keepDims, backend2) {\n  const reductionIndices = axis;\n  const xRank = x.shape.length;\n  const origAxes = util_exports.parseAxisParam(reductionIndices, x.shape);\n  let axes = origAxes;\n  const permutedAxes = backend_util_exports.getAxesPermutation(axes, xRank);\n  const sumInputIsTransposed = permutedAxes != null;\n  let sumInput = x;\n  if (sumInputIsTransposed) {\n    sumInput = transposeImpl2(x, permutedAxes, backend2);\n    axes = backend_util_exports.getInnerMostAxes(axes.length, xRank);\n  }\n  backend_util_exports.assertAxesAreInnerMostDims(\"sum\", axes, xRank);\n  const [sumOutShape, reduceShape] = backend_util_exports.computeOutAndReduceShapes(sumInput.shape, axes);\n  let outShape = sumOutShape;\n  if (keepDims) {\n    outShape = backend_util_exports.expandShapeToKeepDim(sumOutShape, origAxes);\n  }\n  const inSize = util_exports.sizeFromShape(reduceShape);\n  const xSize = util_exports.sizeFromShape(x.shape);\n  const batchSize = xSize / inSize;\n  const reshapedInput = reshape4({ inputs: { x: sumInput }, attrs: { shape: [batchSize, inSize] }, backend: backend2 });\n  const outType = sumOutType(x.dtype);\n  const reduced = reduce(reshapedInput, outType, \"sum\", backend2);\n  const out = reshape4({ inputs: { x: reduced }, attrs: { shape: outShape }, backend: backend2 });\n  backend2.disposeIntermediateTensorInfo(reshapedInput);\n  backend2.disposeIntermediateTensorInfo(reduced);\n  if (sumInputIsTransposed) {\n    backend2.disposeIntermediateTensorInfo(sumInput);\n  }\n  return out;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Sum.js\nfunction sum4(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x } = inputs;\n  const { axis, keepDims } = attrs;\n  return sumImpl(x, axis, keepDims, backend2);\n}\nvar sumConfig2 = {\n  kernelName: Sum,\n  backendName: \"webgl\",\n  kernelFunc: sum4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Transpose.js\nfunction transpose3(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x } = inputs;\n  const { perm } = attrs;\n  const webglBackend = backend2;\n  const xRank = x.shape.length;\n  const newShape = new Array(xRank);\n  for (let i2 = 0; i2 < newShape.length; i2++) {\n    newShape[i2] = x.shape[perm[i2]];\n  }\n  let out;\n  if (webglBackend.shouldExecuteOnCPU([x])) {\n    const xTexData = webglBackend.texData.get(x.dataId);\n    const values = xTexData.values;\n    const outValues = transposeImplCPU(values, x.shape, x.dtype, perm, newShape);\n    out = webglBackend.makeTensorInfo(newShape, x.dtype);\n    const outData = webglBackend.texData.get(out.dataId);\n    outData.values = outValues;\n  } else {\n    out = transposeImpl2(x, perm, webglBackend);\n  }\n  return out;\n}\nvar transposeConfig2 = {\n  kernelName: Transpose,\n  backendName: \"webgl\",\n  kernelFunc: transpose3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/BatchMatMul_impl.js\nvar MATMUL_SHARED_DIM_THRESHOLD = 1e3;\nfunction batchMatMulImpl({ a, b, transposeA, transposeB, backend: backend2, bias = null, preluActivationWeights = null, leakyreluAlpha = 0, activation: activation2 = null }) {\n  const aRank = a.shape.length;\n  const bRank = b.shape.length;\n  const innerShapeA = transposeA ? a.shape[aRank - 2] : a.shape[aRank - 1];\n  const innerShapeB = transposeB ? b.shape[bRank - 1] : b.shape[bRank - 2];\n  const outerShapeA = transposeA ? a.shape[aRank - 1] : a.shape[aRank - 2];\n  const outerShapeB = transposeB ? b.shape[bRank - 2] : b.shape[bRank - 1];\n  const outerDimsA = a.shape.slice(0, -2);\n  const outerDimsB = b.shape.slice(0, -2);\n  const batchDimA = util_exports.sizeFromShape(outerDimsA);\n  const batchDimB = util_exports.sizeFromShape(outerDimsB);\n  const outShapeOuterDims = broadcast_util_exports.assertAndGetBroadcastShape(a.shape.slice(0, -2), b.shape.slice(0, -2));\n  const outShape = outShapeOuterDims.concat([outerShapeA, outerShapeB]);\n  util_exports.assert(innerShapeA === innerShapeB, () => `Error in matMul: inner shapes (${innerShapeA}) and (${innerShapeB}) of Tensors with shapes ${a.shape} and ${b.shape} and transposeA=${transposeA} and transposeB=${transposeB} must match.`);\n  const a3dShape = transposeA ? [batchDimA, innerShapeA, outerShapeA] : [batchDimA, outerShapeA, innerShapeA];\n  const b3dShape = transposeB ? [batchDimB, outerShapeB, innerShapeB] : [batchDimB, innerShapeB, outerShapeB];\n  const a3d = reshape4({ inputs: { x: a }, backend: backend2, attrs: { shape: a3dShape } });\n  const b3d = reshape4({ inputs: { x: b }, backend: backend2, attrs: { shape: b3dShape } });\n  const intermediates = [a3d, b3d];\n  const batchDim = Math.max(batchDimA, batchDimB);\n  const sharedDim = transposeA ? a3d.shape[1] : a3d.shape[2];\n  const hasBias = bias != null;\n  const hasPreluActivationWeights = preluActivationWeights != null;\n  const hasLeakyreluAlpha = activation2 === \"leakyrelu\";\n  const fusedActivation = activation2 != null ? mapActivationToShaderProgram(activation2, true) : null;\n  const containsFusedOps = hasBias || hasPreluActivationWeights || hasLeakyreluAlpha || fusedActivation != null;\n  let out;\n  if ((outerShapeA === 1 || outerShapeB === 1) && sharedDim > MATMUL_SHARED_DIM_THRESHOLD && containsFusedOps === false) {\n    let aVec = a3d;\n    let bVec = b3d;\n    if (transposeA) {\n      aVec = transpose3({ inputs: { x: a3d }, backend: backend2, attrs: { perm: [0, 2, 1] } });\n      intermediates.push(aVec);\n    }\n    if (transposeB) {\n      bVec = transpose3({ inputs: { x: b3d }, backend: backend2, attrs: { perm: [0, 2, 1] } });\n      intermediates.push(bVec);\n    }\n    const shouldReshapeA = outerShapeB !== 1;\n    const shouldReshapeB = outerShapeB === 1;\n    let aVec3d = aVec;\n    if (shouldReshapeA) {\n      aVec3d = reshape4({\n        inputs: { x: aVec },\n        backend: backend2,\n        attrs: { shape: [batchDim, sharedDim, 1] }\n      });\n      intermediates.push(aVec3d);\n    }\n    const axis = outerShapeB === 1 ? 2 : 1;\n    let bVec3d = bVec;\n    if (shouldReshapeB) {\n      bVec3d = reshape4({\n        inputs: { x: bVec },\n        backend: backend2,\n        attrs: { shape: [batchDim, 1, sharedDim] }\n      });\n      intermediates.push(bVec3d);\n    }\n    const product = multiply3({ inputs: { a: aVec3d, b: bVec3d }, backend: backend2 });\n    out = sum4({ inputs: { x: product }, backend: backend2, attrs: { axis, keepDims: true } });\n    intermediates.push(product);\n  } else {\n    const dtype = upcastType(a.dtype, b.dtype);\n    const program = new MatMulPackedProgram(a3dShape, b3dShape, [batchDim, outerShapeA, outerShapeB], transposeA, transposeB, hasBias, fusedActivation, hasPreluActivationWeights, hasLeakyreluAlpha);\n    const inputs = [a3d, b3d];\n    if (bias != null) {\n      inputs.push(bias);\n    }\n    if (hasPreluActivationWeights) {\n      inputs.push(preluActivationWeights);\n    }\n    if (hasLeakyreluAlpha) {\n      const $leakyreluAlpha = backend2.makeTensorInfo([], \"float32\", util_exports.createScalarValue(leakyreluAlpha, \"float32\"));\n      inputs.push($leakyreluAlpha);\n      intermediates.push($leakyreluAlpha);\n    }\n    out = backend2.runWebGLProgram(program, inputs, dtype);\n  }\n  const outReshaped = reshape4({ inputs: { x: out }, backend: backend2, attrs: { shape: outShape } });\n  intermediates.push(out);\n  for (const i2 of intermediates) {\n    backend2.disposeIntermediateTensorInfo(i2);\n  }\n  return outReshaped;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/_FusedMatMul.js\nfunction _fusedMatMul2(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { a, b, bias, preluActivationWeights } = inputs;\n  const { transposeA, transposeB, activation: activation2, leakyreluAlpha } = attrs;\n  return batchMatMulImpl({\n    a,\n    b,\n    transposeA,\n    transposeB,\n    backend: backend2,\n    bias,\n    preluActivationWeights,\n    leakyreluAlpha,\n    activation: activation2\n  });\n}\nvar _fusedMatMulConfig2 = {\n  kernelName: _FusedMatMul,\n  backendName: \"webgl\",\n  kernelFunc: _fusedMatMul2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Abs.js\nvar ABS2 = `return abs(x);`;\nfunction abs3(args) {\n  const { inputs, backend: backend2 } = args;\n  const { x } = inputs;\n  if (backend2.shouldExecuteOnCPU([x]) && x.dtype !== \"complex64\") {\n    const xData = backend2.texData.get(x.dataId);\n    const outValues = simpleAbsImplCPU(xData.values);\n    return backend2.makeTensorInfo(x.shape, x.dtype, outValues);\n  }\n  let program;\n  if (env().getBool(\"WEBGL_PACK_UNARY_OPERATIONS\")) {\n    program = new UnaryOpPackedProgram(x.shape, ABS2);\n  } else {\n    program = new UnaryOpProgram(x.shape, ABS2);\n  }\n  return backend2.runWebGLProgram(program, [x], x.dtype);\n}\nvar absConfig2 = {\n  kernelName: Abs,\n  backendName: \"webgl\",\n  kernelFunc: abs3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Acos.js\nvar ACOS = CHECK_NAN_SNIPPET + `\n  if (abs(x) > 1.) {\n    return NAN;\n  }\n  return acos(x);\n`;\nvar acos3 = unaryKernelFunc2({ opSnippet: ACOS });\nvar acosConfig2 = {\n  kernelName: Acos,\n  backendName: \"webgl\",\n  kernelFunc: acos3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Acosh.js\nvar ACOSH = CHECK_NAN_SNIPPET + `\n  if (x < 1.0) return NAN;\nreturn log(x + sqrt(x * x - 1.0));`;\nvar acosh3 = unaryKernelFunc2({ opSnippet: ACOSH });\nvar acoshConfig2 = {\n  kernelName: Acosh,\n  backendName: \"webgl\",\n  kernelFunc: acosh3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Add.js\nvar ADD = \"return a + b;\";\nvar addKernelFunc = binaryKernelFunc2({\n  opSnippet: ADD,\n  packedOpSnippet: ADD,\n  supportsComplex: true,\n  cpuKernelImpl: addImplCPU\n});\nvar addConfig2 = {\n  kernelName: Add,\n  backendName: \"webgl\",\n  kernelFunc: addKernelFunc\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/addn_gpu.js\nvar AddNProgram = class {\n  constructor(outputShape, shapes) {\n    this.outputShape = [];\n    this.outputShape = outputShape;\n    this.variableNames = shapes.map((_, i2) => `T${i2}`);\n    const snippets = [];\n    this.variableNames.forEach((variable2) => {\n      snippets.push(`float v${variable2} = get${variable2}AtOutCoords();`);\n    });\n    const operation = this.variableNames.map((variable2) => {\n      return `v${variable2}`;\n    }).join(\" + \");\n    this.userCode = `\n      void main() {\n        ${snippets.join(\"\\n        \")}\n\n        float result = ${operation};\n        setOutput(result);\n      }\n    `;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/addn_packed_gpu.js\nvar AddNPackedProgram = class {\n  constructor(outputShape, shapes) {\n    this.outputShape = [];\n    this.packedInputs = true;\n    this.packedOutput = true;\n    this.outputShape = outputShape;\n    this.variableNames = shapes.map((_, i2) => `T${i2}`);\n    const snippets = [];\n    this.variableNames.forEach((variable2) => {\n      snippets.push(`vec4 v${variable2} = get${variable2}AtOutCoords();`);\n    });\n    const operation = this.variableNames.map((variable2) => {\n      return `v${variable2}`;\n    }).join(\" + \");\n    this.userCode = `\n      void main() {\n        ${snippets.join(\"\\n        \")}\n\n        vec4 result = ${operation};\n        setOutput(result);\n      }\n    `;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/AddN.js\nfunction addN3(args) {\n  const { inputs, backend: backend2 } = args;\n  const tensors = inputs;\n  if (tensors.length === 1) {\n    return identity3({ inputs: { x: tensors[0] }, backend: backend2 });\n  }\n  if (tensors.length > env().get(\"WEBGL_MAX_TEXTURES_IN_SHADER\")) {\n    const midIndex = Math.floor(tensors.length / 2);\n    const leftSide = addN3({ inputs: tensors.slice(0, midIndex), backend: backend2 });\n    const rightSide = addN3({ inputs: tensors.slice(midIndex), backend: backend2 });\n    return addN3({ inputs: [leftSide, rightSide], backend: backend2 });\n  }\n  const dtype = tensors.map((t2) => t2.dtype).reduce((d1, d2) => upcastType(d1, d2));\n  const shapes = tensors.map((t2) => t2.shape);\n  const usePackedOp = env().getBool(\"WEBGL_PACK\");\n  const program = usePackedOp ? new AddNPackedProgram(tensors[0].shape, shapes) : new AddNProgram(tensors[0].shape, shapes);\n  return backend2.runWebGLProgram(program, tensors, dtype);\n}\nvar addNConfig2 = {\n  kernelName: AddN,\n  backendName: \"webgl\",\n  kernelFunc: addN3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/All.js\nfunction all3(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x } = inputs;\n  const { axis, keepDims } = attrs;\n  const xRank = x.shape.length;\n  const origAxes = util_exports.parseAxisParam(axis, x.shape);\n  let axes = origAxes;\n  const permutedAxes = backend_util_exports.getAxesPermutation(axes, xRank);\n  let permutedX = x;\n  if (permutedAxes != null) {\n    permutedX = transpose3({ inputs: { x }, backend: backend2, attrs: { perm: permutedAxes } });\n    axes = backend_util_exports.getInnerMostAxes(axes.length, xRank);\n  }\n  backend_util_exports.assertAxesAreInnerMostDims(\"all\", axes, xRank);\n  const [outShape, reduceShape] = backend_util_exports.computeOutAndReduceShapes(permutedX.shape, axes);\n  const inSize = util_exports.sizeFromShape(reduceShape);\n  const a2D = reshape4({ inputs: { x: permutedX }, backend: backend2, attrs: { shape: [-1, inSize] } });\n  const reduced = reduce(a2D, a2D.dtype, \"all\", backend2);\n  let res;\n  if (keepDims) {\n    const newShape = backend_util_exports.expandShapeToKeepDim(outShape, origAxes);\n    res = reshape4({ inputs: { x: reduced }, backend: backend2, attrs: { shape: newShape } });\n  } else {\n    res = reshape4({ inputs: { x: reduced }, backend: backend2, attrs: { shape: outShape } });\n  }\n  backend2.disposeIntermediateTensorInfo(a2D);\n  backend2.disposeIntermediateTensorInfo(reduced);\n  if (permutedAxes != null) {\n    backend2.disposeIntermediateTensorInfo(permutedX);\n  }\n  return res;\n}\nvar allConfig2 = {\n  kernelName: All,\n  backendName: \"webgl\",\n  kernelFunc: all3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Any.js\nfunction any3(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x } = inputs;\n  const { axis, keepDims } = attrs;\n  const xRank = x.shape.length;\n  const origAxes = util_exports.parseAxisParam(axis, x.shape);\n  let axes = origAxes;\n  const permutedAxes = backend_util_exports.getAxesPermutation(axes, xRank);\n  let permutedX = x;\n  if (permutedAxes != null) {\n    permutedX = transpose3({ inputs: { x }, backend: backend2, attrs: { perm: permutedAxes } });\n    axes = backend_util_exports.getInnerMostAxes(axes.length, xRank);\n  }\n  backend_util_exports.assertAxesAreInnerMostDims(\"any\", axes, xRank);\n  const [outShape, reduceShape] = backend_util_exports.computeOutAndReduceShapes(permutedX.shape, axes);\n  const inSize = util_exports.sizeFromShape(reduceShape);\n  const a2D = reshape4({ inputs: { x: permutedX }, backend: backend2, attrs: { shape: [-1, inSize] } });\n  const reduced = reduce(a2D, a2D.dtype, \"any\", backend2);\n  let res;\n  if (keepDims) {\n    const newShape = backend_util_exports.expandShapeToKeepDim(outShape, origAxes);\n    res = reshape4({ inputs: { x: reduced }, backend: backend2, attrs: { shape: newShape } });\n  } else {\n    res = reshape4({ inputs: { x: reduced }, backend: backend2, attrs: { shape: outShape } });\n  }\n  backend2.disposeIntermediateTensorInfo(a2D);\n  backend2.disposeIntermediateTensorInfo(reduced);\n  if (permutedAxes != null) {\n    backend2.disposeIntermediateTensorInfo(permutedX);\n  }\n  return res;\n}\nvar anyConfig2 = {\n  kernelName: Any,\n  backendName: \"webgl\",\n  kernelFunc: any3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/argminmax_gpu.js\nvar ArgMinMaxProgram = class {\n  constructor(reduceInfo, op2, firstPass) {\n    this.variableNames = [\"A\"];\n    const { windowSize, batchSize, outSize } = reduceInfo;\n    if (!firstPass) {\n      this.variableNames.push(\"bestIndicesA\");\n    }\n    this.outputShape = [batchSize, outSize];\n    const compOp = op2 === \"max\" ? \">\" : \"<\";\n    const indexSnippet = firstPass ? \"inOffset + i;\" : \"round(getBestIndicesA(batch, inOffset + i));\";\n    this.userCode = `\n      void main() {\n        ivec2 coords = getOutputCoords();\n        int batch = coords[0];\n        int outIdx = coords[1];\n        int inOffset = outIdx * ${windowSize};\n\n        int bestIndex = inOffset;\n        float bestValue = getA(batch, bestIndex);\n\n        for (int i = 0; i < ${windowSize}; i++) {\n          int inIdx = ${indexSnippet};\n          float candidate = getA(batch, inIdx);\n          if (candidate ${compOp} bestValue) {\n            bestValue = candidate;\n            bestIndex = inIdx;\n          }\n        }\n        setOutput(float(bestIndex));\n      }\n    `;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/argminmax_packed_gpu.js\nvar ArgMinMaxPackedProgram = class {\n  constructor(shape, windowSize, op2, firstPass) {\n    this.variableNames = [\"A\"];\n    this.packedInputs = true;\n    this.packedOutput = true;\n    util_exports.assert(shape.length > 2, () => `Packed arg${op2.charAt(0).toUpperCase() + op2.slice(1)} supports only inputs with rank above 2.`);\n    const inSize = shape[shape.length - 1];\n    const outSize = Math.ceil(inSize / windowSize);\n    this.outputShape = shape.slice(0, -1);\n    if (outSize > 1) {\n      this.outputShape.push(outSize);\n    }\n    if (!firstPass) {\n      this.variableNames.push(\"bestIndicesA\");\n    }\n    const outShape = this.outputShape;\n    const rank = outShape.length;\n    const dtype = getCoordsDataType(rank);\n    const coords3 = getChannels(\"coords\", rank);\n    let sourceLocSetup;\n    let sourceRank;\n    if (outSize === 1) {\n      sourceRank = rank + 1;\n      const sourceLocDType = getCoordsDataType(sourceRank);\n      sourceLocSetup = `\n        ${sourceLocDType} sourceLocR = ${sourceLocDType}(${coords3.join()}, 0);\n        ++${coords3[rank - 1]};\n        ${sourceLocDType} sourceLocG = ${sourceLocDType}(${coords3.join()}, 0);\n        ++${coords3[rank - 2]};\n        ${sourceLocDType} sourceLocA = ${sourceLocDType}(${coords3.join()}, 0);\n        --${coords3[rank - 1]};\n        ${sourceLocDType} sourceLocB = ${sourceLocDType}(${coords3.join()}, 0);\n        --${coords3[rank - 2]};`;\n    } else {\n      sourceRank = rank;\n      sourceLocSetup = `\n        ${dtype} sourceLocR = coords;\n        ++${coords3[rank - 1]};\n        ${dtype} sourceLocG = coords;\n        ++${coords3[rank - 2]};\n        ${dtype} sourceLocA = coords;\n        --${coords3[rank - 1]};\n        ${dtype} sourceLocB = coords;\n        --${coords3[rank - 2]};`;\n    }\n    const channels = [\"x\", \"y\", \"z\", \"w\", \"u\", \"v\"].slice(0, sourceRank);\n    const inChannel = \".\" + channels[sourceRank - 1];\n    const intChannels = channels.map((x) => \"int \" + x);\n    const srcRCoords = getChannels(\"sourceLocR\", sourceRank - 1).concat(\"inIdx.r\");\n    const srcGCoords = getChannels(\"sourceLocG\", sourceRank - 1).concat(\"inIdx.g\");\n    const srcBCoords = getChannels(\"sourceLocB\", sourceRank - 1).concat(\"inIdx.b\");\n    const srcACoords = getChannels(\"sourceLocA\", sourceRank - 1).concat(\"inIdx.a\");\n    const compOp = op2 === \"max\" ? \"greaterThan\" : \"lessThan\";\n    const fetchCandidateIdx = firstPass ? \"\" : `\n          inIdx = round(vec4(getBestIndicesAChannel(${srcRCoords.join()}),\n                             getBestIndicesAChannel(${srcGCoords.join()}),\n                             getBestIndicesAChannel(${srcBCoords.join()}),\n                             getBestIndicesAChannel(${srcACoords.join()})));`;\n    const fetchValue = `vec4(\n            getAChannel(${srcRCoords.join()}),\n            hasNextCol ? getAChannel(${srcGCoords.join()}) : 0.,\n            hasNextRow ? getAChannel(${srcBCoords.join()}) : 0.,\n            hasNextRow && hasNextCol ? getAChannel(${srcACoords.join()}) : 0.)`;\n    const getBestIndicesAChannelSnippet = firstPass ? \"\" : `\n      float getBestIndicesAChannel(${intChannels.join()}) {\n        return getChannel(getBestIndicesA(${channels.join()}),\n                                          vec2(${channels.slice(-2).join()}));\n      }`;\n    this.userCode = `\n      float getAChannel(${intChannels.join()}) {\n        return getChannel(getA(${channels.join()}),\n                               vec2(${channels.slice(-2).join()}));\n      }\n      ${getBestIndicesAChannelSnippet}\n      void main() {\n        ${dtype} coords = getOutputCoords();\n        bool hasNextCol = ${coords3[rank - 1]} < ${outShape[rank - 1] - 1};\n        bool hasNextRow = ${coords3[rank - 2]} < ${outShape[rank - 2] - 1};\n        ${sourceLocSetup}\n        ivec4 srcIdx = ivec4(sourceLocR${inChannel}, sourceLocG${inChannel},\n          sourceLocB${inChannel}, sourceLocA${inChannel}) * ${windowSize};\n        ivec4 inIdx = srcIdx;\n        vec4 bestIndex = vec4(inIdx);\n        vec4 bestValue = ${fetchValue};\n\n        for (int i = 0; i < ${windowSize}; i++) {\n          inIdx = srcIdx;\n          ${fetchCandidateIdx}\n          vec4 candidate = ${fetchValue};\n          bvec4 nan = isnan(candidate);\n          bvec4 replace = bvec4(\n            vec4(${compOp}(candidate, bestValue)) * (vec4(1.0) - vec4(nan)));\n\n          bestValue = vec4(replace.x  ? candidate.x : bestValue.x,\n                           replace.y  ? candidate.y : bestValue.y,\n                           replace.z  ? candidate.z : bestValue.z,\n                           replace.w  ? candidate.w : bestValue.w);\n          bestIndex = mix(bestIndex, vec4(inIdx), vec4(replace));\n          srcIdx++;\n        }\n        setOutput(bestIndex);\n      }\n    `;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernel_utils/arg_min_max.js\nfunction argReduce(backend2, x, reduceType, bestIndicesA = null) {\n  let batchSize = x.shape[0];\n  let inSize = x.shape[1];\n  if (bestIndicesA != null) {\n    batchSize = bestIndicesA.shape[0];\n    inSize = bestIndicesA.shape[1];\n  }\n  const windowSize = backend_util_exports.computeOptimalWindowSize(inSize);\n  const reduceInfo = { windowSize, inSize, batchSize, outSize: Math.ceil(inSize / windowSize) };\n  const program = new ArgMinMaxProgram(reduceInfo, reduceType, bestIndicesA == null);\n  const inputs = [x];\n  if (bestIndicesA != null) {\n    inputs.push(bestIndicesA);\n  }\n  const output = backend2.runWebGLProgram(program, inputs, \"int32\");\n  if (output.shape[1] === 1) {\n    return output;\n  }\n  const result = argReduce(backend2, x, reduceType, output);\n  backend2.disposeIntermediateTensorInfo(output);\n  return result;\n}\nfunction argReducePacked(backend2, x, reduceType, bestIndicesA = null) {\n  const inShape = bestIndicesA != null ? bestIndicesA.shape : x.shape;\n  const inSize = inShape[inShape.length - 1];\n  const windowSize = backend_util_exports.computeOptimalWindowSize(inSize);\n  const program = new ArgMinMaxPackedProgram(inShape, windowSize, reduceType, bestIndicesA == null);\n  const inputs = bestIndicesA == null ? [x] : [x, bestIndicesA];\n  const output = backend2.runWebGLProgram(program, inputs, \"int32\");\n  if (output.shape.length === x.shape.length) {\n    const result = argReducePacked(backend2, x, reduceType, output);\n    backend2.disposeIntermediateTensorInfo(output);\n    return result;\n  }\n  return output;\n}\nfunction argMinMaxReduce(backend2, x, axis, reduceType) {\n  const axes = [axis];\n  backend_util_exports.assertAxesAreInnerMostDims(\"arg\" + reduceType.charAt(0).toUpperCase() + reduceType.slice(1), axes, x.shape.length);\n  if (!env().getBool(\"WEBGL_PACK_REDUCE\") || x.shape.length <= 2) {\n    const intermediateTensorInfos = [];\n    const xtexData = backend2.texData.get(x.dataId);\n    const xIsPacked = xtexData !== null && xtexData.isPacked;\n    let xUnPacked = x;\n    if (xIsPacked) {\n      xUnPacked = backend2.unpackTensor(x);\n      intermediateTensorInfos.push(xUnPacked);\n    }\n    const [outShape, reduceShape] = backend_util_exports.computeOutAndReduceShapes(xUnPacked.shape, axes);\n    const inSize = util_exports.sizeFromShape(reduceShape);\n    const a2D = reshape4({ inputs: { x: xUnPacked }, backend: backend2, attrs: { shape: [-1, inSize] } });\n    intermediateTensorInfos.push(a2D);\n    const reduced = argReduce(backend2, a2D, reduceType);\n    intermediateTensorInfos.push(reduced);\n    const reshaped = reshape4({ inputs: { x: reduced }, backend: backend2, attrs: { shape: outShape } });\n    intermediateTensorInfos.forEach((t2) => backend2.disposeIntermediateTensorInfo(t2));\n    return reshaped;\n  }\n  return argReducePacked(backend2, x, reduceType);\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/ArgMax.js\nfunction argMax3(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x } = inputs;\n  const { axis } = attrs;\n  let axes = util_exports.parseAxisParam(axis, x.shape);\n  const permutedAxes = backend_util_exports.getAxesPermutation(axes, x.shape.length);\n  let $x = x;\n  const intermediateTensorInfos = [];\n  if (permutedAxes != null) {\n    $x = transpose3({ inputs: { x }, backend: backend2, attrs: { perm: permutedAxes } });\n    intermediateTensorInfos.push($x);\n    axes = backend_util_exports.getInnerMostAxes(axes.length, $x.shape.length);\n  }\n  backend_util_exports.assertAxesAreInnerMostDims(\"argMax\", [axes[0]], $x.shape.length);\n  const out = argMinMaxReduce(backend2, $x, axes[0], \"max\");\n  intermediateTensorInfos.forEach((t2) => backend2.disposeIntermediateTensorInfo(t2));\n  return out;\n}\nvar argMaxConfig2 = {\n  kernelName: ArgMax,\n  backendName: \"webgl\",\n  kernelFunc: argMax3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/ArgMin.js\nfunction argMin3(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x } = inputs;\n  const { axis } = attrs;\n  let axes = util_exports.parseAxisParam(axis, x.shape);\n  const permutedAxes = backend_util_exports.getAxesPermutation(axes, x.shape.length);\n  let $x = x;\n  const intermediateTensorInfos = [];\n  if (permutedAxes != null) {\n    $x = transpose3({ inputs: { x }, backend: backend2, attrs: { perm: permutedAxes } });\n    intermediateTensorInfos.push($x);\n    axes = backend_util_exports.getInnerMostAxes(axes.length, $x.shape.length);\n  }\n  backend_util_exports.assertAxesAreInnerMostDims(\"argMin\", [axes[0]], $x.shape.length);\n  const out = argMinMaxReduce(backend2, $x, axes[0], \"min\");\n  intermediateTensorInfos.forEach((t2) => backend2.disposeIntermediateTensorInfo(t2));\n  return out;\n}\nvar argMinConfig2 = {\n  kernelName: ArgMin,\n  backendName: \"webgl\",\n  kernelFunc: argMin3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Asin.js\nvar ASIN = CHECK_NAN_SNIPPET + `\n  if (abs(x) > 1.) {\n    return NAN;\n  }\n  return asin(x);\n`;\nvar asin3 = unaryKernelFunc2({ opSnippet: ASIN });\nvar asinConfig2 = {\n  kernelName: Asin,\n  backendName: \"webgl\",\n  kernelFunc: asin3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Asinh.js\nvar ASINH = CHECK_NAN_SNIPPET + `return log(x + sqrt(x * x + 1.0));`;\nvar asinh3 = unaryKernelFunc2({ opSnippet: ASINH });\nvar asinhConfig2 = {\n  kernelName: Asinh,\n  backendName: \"webgl\",\n  kernelFunc: asinh3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Atan.js\nvar ATAN = CHECK_NAN_SNIPPET + `\n  return atan(x);\n`;\nvar atan4 = unaryKernelFunc2({ opSnippet: ATAN });\nvar atanConfig2 = {\n  kernelName: Atan,\n  backendName: \"webgl\",\n  kernelFunc: atan4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Atan2.js\nvar ATAN2 = CHECK_NAN_SNIPPET_BINARY + `\n  return atan(a, b);\n`;\nvar ATAN2_PACKED = `\n  vec4 result = atan(a, b);\n  vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));\n  ` + CHECK_NAN_SNIPPET_BINARY_PACKED + `\n  return result;\n`;\nvar atan23 = binaryKernelFunc2({ opSnippet: ATAN2, packedOpSnippet: ATAN2_PACKED });\nvar atan2Config2 = {\n  kernelName: Atan2,\n  backendName: \"webgl\",\n  kernelFunc: atan23\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Atanh.js\nvar ATANH = CHECK_NAN_SNIPPET + `\n  if ((x < -1.0) || (x > 1.0)) return NAN;\nreturn (log(1.0 + x) - log(1.0 - x)) / 2.0;`;\nvar atanh3 = unaryKernelFunc2({ opSnippet: ATANH });\nvar atanhConfig2 = {\n  kernelName: Atanh,\n  backendName: \"webgl\",\n  kernelFunc: atanh3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/pool_gpu.js\nvar Pool2DProgram = class {\n  constructor(convInfo, poolType, computePositions, flattenPositions = false, includeBatchInIndex = false) {\n    this.variableNames = [\"x\"];\n    if (poolType === \"avg\" && computePositions) {\n      throw new Error(\"Cannot compute positions for average pool.\");\n    }\n    const filterWidth = convInfo.filterWidth;\n    const strideHeight = convInfo.strideHeight;\n    const strideWidth = convInfo.strideWidth;\n    const dilationHeight = convInfo.dilationHeight;\n    const dilationWidth = convInfo.dilationWidth;\n    const effectiveFilterHeight = convInfo.effectiveFilterHeight;\n    const effectiveFilterWidth = convInfo.effectiveFilterWidth;\n    const padTop = convInfo.padInfo.top;\n    const padLeft = convInfo.padInfo.left;\n    this.outputShape = convInfo.outShape;\n    const isAvgPool = poolType === \"avg\";\n    const batchFlattenPositionStr = `((batch  * ${convInfo.inHeight} + xR) * ${convInfo.inWidth} + xC) * ${convInfo.inChannels} + d`;\n    const flattenPositionStr = `(xR * ${convInfo.inWidth} + xC) * ${convInfo.inChannels} + d`;\n    let initializationValue = \"0.0\";\n    if (!isAvgPool) {\n      initializationValue = \"-1.0 / 1e-20\";\n    }\n    if (computePositions) {\n      const compareOp2 = \">=\";\n      this.userCode = `\n        const ivec2 strides = ivec2(${strideHeight}, ${strideWidth});\n        const ivec2 pads = ivec2(${padTop}, ${padLeft});\n\n        void main() {\n          ivec4 coords = getOutputCoords();\n          int batch = coords[0];\n          int d = coords[3];\n\n          ivec2 xRCCorner = coords.yz * strides - pads;\n          int xRCorner = xRCCorner.x;\n          int xCCorner = xRCCorner.y;\n\n          // max/min x(?, ?, d) to get y(yR, yC, d).\n          // ? = to be determined\n          float minMaxValue = 0.0;\n          float minMaxValueFound = 0.0;\n          int minMaxPosition = 0;\n          float avgValue = 0.0;\n\n          for (int wR = 0; wR < ${effectiveFilterHeight};\n              wR += ${dilationHeight}) {\n            int xR = xRCorner + wR;\n\n            if (xR < 0 || xR >= ${convInfo.inHeight}) {\n              continue;\n            }\n\n            for (int wC = 0; wC < ${effectiveFilterWidth};\n                wC += ${dilationWidth}) {\n              int xC = xCCorner + wC;\n\n              if (xC < 0 || xC >= ${convInfo.inWidth}) {\n                continue;\n              }\n\n              float value = getX(batch, xR, xC, d);\n\n              // If a min / max value has already been found, use it. If not,\n              // use the current value.\n              float currMinMaxValue = mix(\n                  value, minMaxValue, minMaxValueFound);\n              if (value ${compareOp2} currMinMaxValue) {\n                minMaxValue = value;\n                minMaxValueFound = 1.0;\n                minMaxPosition = ${flattenPositions ? includeBatchInIndex ? batchFlattenPositionStr : flattenPositionStr : `wR * ${effectiveFilterWidth} + wC`};\n              }\n            }\n          }\n          setOutput(float(minMaxPosition));\n        }\n      `;\n      return;\n    }\n    const compareOp = \"max\";\n    let returnValue = `${poolType}(${poolType}(${poolType}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;\n    if (poolType === \"avg\") {\n      returnValue = `avgValue / count`;\n    }\n    const filterWidthNearestVec4 = Math.floor(filterWidth / 4) * 4;\n    const filterWidthVec4Remainder = filterWidth % 4;\n    const updateSnippet = `\n      if (${isAvgPool}) {\n        avgValue += dot(values, ones);\n      } else {\n        minMaxValue = ${compareOp}(values, minMaxValue);\n      }\n    `;\n    this.userCode = `\n      const ivec2 strides = ivec2(${strideHeight}, ${strideWidth});\n      const ivec2 pads = ivec2(${padTop}, ${padLeft});\n      const float initializationValue = ${initializationValue};\n      const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);\n\n      float count = 0.0;\n\n      float getValue(int batch, int xR, int xC, int d) {\n        if (xC < 0 || xC >= ${convInfo.inWidth}) {\n          return initializationValue;\n        }\n        count += 1.0;\n        return getX(batch, xR, xC, d);\n      }\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int batch = coords[0];\n        int d = coords[3];\n\n        ivec2 xRCCorner = coords.yz * strides - pads;\n        int xRCorner = xRCCorner.x;\n        int xCCorner = xRCCorner.y;\n\n        // max/min x(?, ?, d) to get y(yR, yC, d).\n        // ? = to be determined\n        vec4 minMaxValue = vec4(${initializationValue});\n        float avgValue = 0.0;\n        count = 0.0;\n\n        for (int wR = 0; wR < ${effectiveFilterHeight};\n            wR += ${dilationHeight}) {\n          int xR = xRCorner + wR;\n\n          if (xR < 0 || xR >= ${convInfo.inHeight}) {\n            continue;\n          }\n\n          for (int wC = 0; wC < ${filterWidthNearestVec4}; wC += 4) {\n            int xC = xCCorner + wC * ${dilationWidth};\n\n            vec4 values = vec4(\n              getValue(batch, xR, xC, d),\n              getValue(batch, xR, xC + ${dilationWidth}, d),\n              getValue(batch, xR, xC + 2 * ${dilationWidth}, d),\n              getValue(batch, xR, xC + 3 * ${dilationWidth}, d)\n            );\n\n            ${updateSnippet}\n          }\n\n          int xC = xCCorner + ${filterWidthNearestVec4};\n          if (${filterWidthVec4Remainder === 1}) {\n            vec4 values = vec4(\n              getValue(batch, xR, xC, d),\n              initializationValue,\n              initializationValue,\n              initializationValue\n            );\n\n            ${updateSnippet}\n          } else if (${filterWidthVec4Remainder === 2}) {\n            vec4 values = vec4(\n              getValue(batch, xR, xC, d),\n              getValue(batch, xR, xC + ${dilationWidth}, d),\n              initializationValue,\n              initializationValue\n            );\n\n            ${updateSnippet}\n          } else if (${filterWidthVec4Remainder === 3}) {\n            vec4 values = vec4(\n              getValue(batch, xR, xC, d),\n              getValue(batch, xR, xC + ${dilationWidth}, d),\n              getValue(batch, xR, xC + 2 * ${dilationWidth}, d),\n              initializationValue\n            );\n\n            ${updateSnippet}\n          }\n        }\n        setOutput(${returnValue});\n      }\n    `;\n  }\n};\nvar Pool3DProgram = class {\n  constructor(convInfo, poolType, computePositions, flattenPositions = false, includeBatchInIndex = false) {\n    this.variableNames = [\"x\"];\n    if (poolType === \"avg\" && computePositions) {\n      throw new Error(\"Cannot compute positions for average pool.\");\n    }\n    const filterWidth = convInfo.filterWidth;\n    const strideDepth = convInfo.strideDepth;\n    const strideHeight = convInfo.strideHeight;\n    const strideWidth = convInfo.strideWidth;\n    const dilationDepth = convInfo.dilationDepth;\n    const dilationHeight = convInfo.dilationHeight;\n    const dilationWidth = convInfo.dilationWidth;\n    const effectiveFilterDepth = convInfo.effectiveFilterDepth;\n    const effectiveFilterHeight = convInfo.effectiveFilterHeight;\n    const effectiveFilterWidth = convInfo.effectiveFilterWidth;\n    const padFront = convInfo.padInfo.front;\n    const padTop = convInfo.padInfo.top;\n    const padLeft = convInfo.padInfo.left;\n    this.outputShape = convInfo.outShape;\n    const isAvgPool = poolType === \"avg\";\n    let initializationValue = \"0.0\";\n    if (!isAvgPool) {\n      initializationValue = \"-1.0 / 1e-20\";\n    }\n    if (computePositions) {\n      const compareOp2 = \">=\";\n      this.userCode = `\n        const ivec3 strides =\n            ivec3(${strideDepth}, ${strideHeight}, ${strideWidth});\n        const ivec3 pads = ivec3(${padFront}, ${padTop}, ${padLeft});\n\n        void main() {\n          ivec5 coords = getOutputCoords();\n          int batch = coords.x;\n          int ch = coords.u;\n\n          ivec3 xCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;\n          int xDCorner = xCorner.x;\n          int xRCorner = xCorner.y;\n          int xCCorner = xCorner.z;\n\n          // max/min x(?, ?, ?, ch) to get y(yD, yR, yC, ch).\n          // ? = to be determined\n          float minMaxValue = 0.0;\n          float minMaxValueFound = 0.0;\n          int minMaxPosition = 0;\n\n          for (int wD = 0; wD < ${effectiveFilterDepth};\n              wD += ${dilationDepth}) {\n            int xD = xDCorner + wD;\n\n            if (xD < 0 || xD >= ${convInfo.inDepth}) {\n              continue;\n            }\n\n            for (int wR = 0; wR < ${effectiveFilterHeight};\n                wR += ${dilationHeight}) {\n              int xR = xRCorner + wR;\n\n              if (xR < 0 || xR >= ${convInfo.inHeight}) {\n                continue;\n              }\n\n              for (int wC = 0; wC < ${effectiveFilterWidth};\n                  wC += ${dilationWidth}) {\n                int xC = xCCorner + wC;\n\n                if (xC < 0 || xC >= ${convInfo.inWidth}) {\n                  continue;\n                }\n\n                float value = getX(batch, xD, xR, xC, ch);\n\n                // If a min / max value has already been found, use it. If not,\n                // use the current value.\n                float currMinMaxValue = mix(\n                    value, minMaxValue, minMaxValueFound);\n                if (value ${compareOp2} currMinMaxValue) {\n                  minMaxValue = value;\n                  minMaxValueFound = 1.0;\n                  minMaxPosition = ${flattenPositions ? includeBatchInIndex ? `(((batch * ${convInfo.inDepth} + xD) * ${convInfo.inHeight} + xR) * ${convInfo.inWidth} + xC) * ${convInfo.inChannels} + ch` : `((xD * ${convInfo.inHeight} + xR) * ${convInfo.inWidth} + xC) * ${convInfo.inChannels} + ch` : `wD * ${effectiveFilterHeight} * ${effectiveFilterWidth} +\n                      wR * ${effectiveFilterWidth} + wC`};\n                }\n              }\n            }\n          }\n          setOutput(float(minMaxPosition));\n        }\n      `;\n      return;\n    }\n    const compareOp = \"max\";\n    let returnValue = `${poolType}(${poolType}(${poolType}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;\n    if (poolType === \"avg\") {\n      returnValue = `avgValue / count`;\n    }\n    const filterWidthNearestVec4 = Math.floor(filterWidth / 4) * 4;\n    const filterWidthVec4Remainder = filterWidth % 4;\n    const updateSnippet = `\n      if (${isAvgPool}) {\n        avgValue += dot(values, ones);\n      } else {\n        minMaxValue = ${compareOp}(values, minMaxValue);\n      }\n    `;\n    this.userCode = `\n      const ivec3 strides =\n        ivec3(${strideDepth}, ${strideHeight}, ${strideWidth});\n      const ivec3 pads = ivec3(${padFront}, ${padTop}, ${padLeft});\n      const float initializationValue = ${initializationValue};\n      const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);\n\n      float count = 0.0;\n\n      float getValue(int batch, int xD, int xR, int xC, int ch) {\n        if (xC < 0 || xC >= ${convInfo.inWidth}) {\n          return initializationValue;\n        }\n        count += 1.0;\n        return getX(batch, xD, xR, xC, ch);\n      }\n\n      void main() {\n        ivec5 coords = getOutputCoords();\n        int batch = coords.x;\n        int ch = coords.u;\n\n        ivec3 xCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;\n        int xDCorner = xCorner.x;\n        int xRCorner = xCorner.y;\n        int xCCorner = xCorner.z;\n\n        // max/min x(?, ?, ?, d) to get y(yD, yR, yC, ch).\n        // ? = to be determined\n        vec4 minMaxValue = vec4(${initializationValue});\n        float avgValue = 0.0;\n        count = 0.0;\n\n        for (int wD = 0; wD < ${effectiveFilterDepth};\n            wD += ${dilationDepth}) {\n          int xD = xDCorner + wD;\n\n          if (xD < 0 || xD >= ${convInfo.inDepth}) {\n            continue;\n          }\n\n          for (int wR = 0; wR < ${effectiveFilterHeight};\n            wR += ${dilationHeight}) {\n            int xR = xRCorner + wR;\n\n            if (xR < 0 || xR >= ${convInfo.inHeight}) {\n              continue;\n            }\n\n            for (int wC = 0; wC < ${filterWidthNearestVec4}; wC += 4) {\n              int xC = xCCorner + wC * ${dilationWidth};\n\n              vec4 values = vec4(\n                getValue(batch, xD, xR, xC, ch),\n                getValue(batch, xD, xR, xC + ${dilationWidth}, ch),\n                getValue(batch, xD, xR, xC + 2 * ${dilationWidth}, ch),\n                getValue(batch, xD, xR, xC + 3 * ${dilationWidth}, ch)\n              );\n\n              ${updateSnippet}\n            }\n\n            int xC = xCCorner + ${filterWidthNearestVec4};\n            if (${filterWidthVec4Remainder === 1}) {\n              vec4 values = vec4(\n                getValue(batch, xD, xR, xC, ch),\n                initializationValue,\n                initializationValue,\n                initializationValue\n              );\n\n              ${updateSnippet}\n            } else if (${filterWidthVec4Remainder === 2}) {\n              vec4 values = vec4(\n                getValue(batch, xD, xR, xC, ch),\n                getValue(batch, xD, xR, xC + ${dilationWidth}, ch),\n                initializationValue,\n                initializationValue\n              );\n\n              ${updateSnippet}\n            } else if (${filterWidthVec4Remainder === 3}) {\n              vec4 values = vec4(\n                getValue(batch, xD, xR, xC, ch),\n                getValue(batch, xD, xR, xC + ${dilationWidth}, ch),\n                getValue(batch, xD, xR, xC + 2 * ${dilationWidth}, ch),\n                initializationValue\n              );\n\n              ${updateSnippet}\n            }\n          }\n          setOutput(${returnValue});\n        }\n      }\n    `;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/AvgPool.js\nfunction avgPool3(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x } = inputs;\n  assertNotComplex2(x, \"avgPool\");\n  const { filterSize, strides, pad: pad3, dimRoundingMode } = attrs;\n  const dilations = 1;\n  util_exports.assert(backend_util_exports.eitherStridesOrDilationsAreOne(strides, dilations), () => `Error in avgPool: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`);\n  const convInfo = backend_util_exports.computePool2DInfo(x.shape, filterSize, strides, dilations, pad3, dimRoundingMode);\n  if (convInfo.filterWidth === 1 && convInfo.filterHeight === 1 && util_exports.arraysEqual(convInfo.inShape, convInfo.outShape)) {\n    return identity3({ inputs: { x }, backend: backend2 });\n  }\n  const avgPoolProgram = new Pool2DProgram(convInfo, \"avg\", false);\n  return backend2.runWebGLProgram(avgPoolProgram, [x], \"float32\");\n}\nvar avgPoolConfig2 = {\n  kernelName: AvgPool,\n  backendName: \"webgl\",\n  kernelFunc: avgPool3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/AvgPool3D.js\nfunction avgPool3D2(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x } = inputs;\n  const { filterSize, strides, pad: pad3, dimRoundingMode, dataFormat } = attrs;\n  const dilations = [1, 1, 1];\n  const convInfo = backend_util_exports.computePool3DInfo(x.shape, filterSize, strides, dilations, pad3, dimRoundingMode, dataFormat);\n  const avgPoolProgram = new Pool3DProgram(convInfo, \"avg\", false);\n  return backend2.runWebGLProgram(avgPoolProgram, [x], \"float32\");\n}\nvar avgPool3DConfig2 = {\n  kernelName: AvgPool3D,\n  backendName: \"webgl\",\n  kernelFunc: avgPool3D2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/avg_pool_backprop_gpu.js\nvar AvgPool2DBackpropProgram = class {\n  constructor(convInfo) {\n    this.variableNames = [\"dy\"];\n    this.outputShape = convInfo.inShape;\n    const filterHeight = convInfo.filterHeight;\n    const filterWidth = convInfo.filterWidth;\n    const strideHeight = convInfo.strideHeight;\n    const strideWidth = convInfo.strideWidth;\n    const dilationHeight = convInfo.dilationHeight;\n    const dilationWidth = convInfo.dilationWidth;\n    const effectiveFilterHeight = convInfo.effectiveFilterHeight;\n    const effectiveFilterWidth = convInfo.effectiveFilterWidth;\n    const padTop = effectiveFilterHeight - 1 - convInfo.padInfo.top;\n    const padLeft = effectiveFilterWidth - 1 - convInfo.padInfo.left;\n    const avgMultiplier = 1 / (filterHeight * filterWidth);\n    this.userCode = `\n      const ivec2 pads = ivec2(${padTop}, ${padLeft});\n      const float avgMultiplier = float(${avgMultiplier});\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n\n        ivec2 dyRCCorner = coords.yz - pads;\n        int dyRCorner = dyRCCorner.x;\n        int dyCCorner = dyRCCorner.y;\n\n        // Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n        for (int wR = 0; wR < ${effectiveFilterHeight};\n            wR += ${dilationHeight}) {\n          float dyR = float(dyRCorner + wR) / ${strideHeight}.0;\n\n          if (dyR < 0.0 || dyR >= ${convInfo.outHeight}.0 || fract(dyR) > 0.0) {\n            continue;\n          }\n          int idyR = int(dyR);\n\n          for (int wC = 0; wC < ${effectiveFilterWidth};\n            wC+= ${dilationWidth}) {\n            float dyC = float(dyCCorner + wC) / ${strideWidth}.0;\n\n            if (dyC < 0.0 || dyC >= ${convInfo.outWidth}.0 ||\n                fract(dyC) > 0.0) {\n              continue;\n            }\n            int idyC = int(dyC);\n\n            float dyValue = getDy(b, idyR, idyC, d);\n\n            dotProd += dyValue * avgMultiplier;\n          }\n        }\n        setOutput(dotProd);\n      }\n    `;\n  }\n};\nvar AvgPool3DBackpropProgram = class {\n  constructor(convInfo) {\n    this.variableNames = [\"dy\"];\n    this.outputShape = convInfo.inShape;\n    const filterDepth = convInfo.filterDepth;\n    const filterHeight = convInfo.filterHeight;\n    const filterWidth = convInfo.filterWidth;\n    const strideDepth = convInfo.strideDepth;\n    const strideHeight = convInfo.strideHeight;\n    const strideWidth = convInfo.strideWidth;\n    const dilationDepth = convInfo.dilationDepth;\n    const dilationHeight = convInfo.dilationHeight;\n    const dilationWidth = convInfo.dilationWidth;\n    const effectiveFilterDepth = convInfo.effectiveFilterDepth;\n    const effectiveFilterHeight = convInfo.effectiveFilterHeight;\n    const effectiveFilterWidth = convInfo.effectiveFilterWidth;\n    const padFront = effectiveFilterDepth - 1 - convInfo.padInfo.front;\n    const padTop = effectiveFilterHeight - 1 - convInfo.padInfo.top;\n    const padLeft = effectiveFilterWidth - 1 - convInfo.padInfo.left;\n    const avgMultiplier = 1 / (filterDepth * filterHeight * filterWidth);\n    this.userCode = `\n      const ivec3 pads = ivec3(${padFront}, ${padTop}, ${padLeft});\n      const float avgMultiplier = float(${avgMultiplier});\n\n      void main() {\n        ivec5 coords = getOutputCoords();\n        int batch = coords.x;\n        int ch = coords.u;\n\n        ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;\n        int dyDCorner = dyCorner.x;\n        int dyRCorner = dyCorner.y;\n        int dyCCorner = dyCorner.z;\n\n        // Convolve dy(?, ?, ?, d) with pos mask(:, :, :, ch) to get\n        // dx(xD, xR, xC, ch).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n\n        for (int wD = 0; wD < ${effectiveFilterDepth};\n            wD += ${dilationDepth}) {\n          float dyD = float(dyDCorner + wD) / ${strideDepth}.0;\n\n          if (dyD < 0.0 || dyD >= ${convInfo.outDepth}.0 || fract(dyD) > 0.0) {\n            continue;\n          }\n          int idyD = int(dyD);\n\n          for (int wR = 0; wR < ${effectiveFilterHeight};\n              wR += ${dilationHeight}) {\n            float dyR = float(dyRCorner + wR) / ${strideHeight}.0;\n\n            if (dyR < 0.0 || dyR >= ${convInfo.outHeight}.0 ||\n                fract(dyR) > 0.0) {\n              continue;\n            }\n            int idyR = int(dyR);\n\n            for (int wC = 0; wC < ${effectiveFilterWidth};\n                wC += ${dilationWidth}) {\n              float dyC = float(dyCCorner + wC) / ${strideWidth}.0;\n\n              if (dyC < 0.0 || dyC >= ${convInfo.outWidth}.0 ||\n                  fract(dyC) > 0.0) {\n                continue;\n              }\n              int idyC = int(dyC);\n\n              float dyValue = getDy(batch, idyD, idyR, idyC, ch);\n\n              dotProd += dyValue * avgMultiplier;\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    `;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/AvgPool3DGrad.js\nfunction avgPool3DGrad2(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { dy, input: input2 } = inputs;\n  const x = input2;\n  const { filterSize, strides, pad: pad3, dimRoundingMode } = attrs;\n  const dilations = [1, 1, 1];\n  const convInfo = backend_util_exports.computePool3DInfo(x.shape, filterSize, strides, dilations, pad3, dimRoundingMode);\n  const avgPoolBackpropProgram = new AvgPool3DBackpropProgram(convInfo);\n  return backend2.runWebGLProgram(avgPoolBackpropProgram, [dy], x.dtype);\n}\nvar avgPool3DGradConfig3 = {\n  kernelName: AvgPool3DGrad,\n  backendName: \"webgl\",\n  kernelFunc: avgPool3DGrad2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/AvgPoolGrad.js\nfunction avgPoolGrad3(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { dy, input: input2 } = inputs;\n  const x = input2;\n  assertNotComplex2([dy, input2], \"avgPoolGrad\");\n  const { filterSize, strides, pad: pad3 } = attrs;\n  const convInfo = backend_util_exports.computePool2DInfo(x.shape, filterSize, strides, 1, pad3);\n  const avgPoolBackpropProgram = new AvgPool2DBackpropProgram(convInfo);\n  return backend2.runWebGLProgram(avgPoolBackpropProgram, [dy], x.dtype);\n}\nvar avgPoolGradConfig3 = {\n  kernelName: AvgPoolGrad,\n  backendName: \"webgl\",\n  kernelFunc: avgPoolGrad3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/BatchMatMul.js\nfunction batchMatMul2(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { a, b } = inputs;\n  const { transposeA, transposeB } = attrs;\n  return batchMatMulImpl({ a, b, transposeA, transposeB, backend: backend2 });\n}\nvar batchMatMulConfig2 = {\n  kernelName: BatchMatMul,\n  backendName: \"webgl\",\n  kernelFunc: batchMatMul2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/batchnorm_gpu.js\nvar BatchNormProgram = class {\n  constructor(xShape, meanShape, varianceShape, offsetShape, scaleShape, varianceEpsilon) {\n    this.outputShape = [];\n    this.variableNames = [\"x\", \"mean\", \"variance\"];\n    backend_util_exports.assertAndGetBroadcastShape(xShape, meanShape);\n    backend_util_exports.assertAndGetBroadcastShape(xShape, varianceShape);\n    let offsetSnippet = \"0.0\";\n    if (offsetShape != null) {\n      backend_util_exports.assertAndGetBroadcastShape(xShape, offsetShape);\n      this.variableNames.push(\"offset\");\n      offsetSnippet = \"getOffsetAtOutCoords()\";\n    }\n    let scaleSnippet = \"1.0\";\n    if (scaleShape != null) {\n      backend_util_exports.assertAndGetBroadcastShape(xShape, scaleShape);\n      this.variableNames.push(\"scale\");\n      scaleSnippet = \"getScaleAtOutCoords()\";\n    }\n    this.outputShape = xShape;\n    this.userCode = `\n      void main() {\n        float x = getXAtOutCoords();\n        float mean = getMeanAtOutCoords();\n        float variance = getVarianceAtOutCoords();\n        float offset = ${offsetSnippet};\n        float scale = ${scaleSnippet};\n        float inv = scale * inversesqrt(variance + float(${varianceEpsilon}));\n        setOutput(dot(vec3(x, -mean, offset), vec3(inv, inv, 1)));\n      }\n    `;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/batchnorm_packed_gpu.js\nvar BatchNormPackedProgram = class {\n  constructor(xShape, meanShape, varianceShape, offsetShape, scaleShape, varianceEpsilon) {\n    this.packedInputs = true;\n    this.packedOutput = true;\n    this.variableNames = [\"x\", \"mean\", \"variance\"];\n    backend_util_exports.assertAndGetBroadcastShape(xShape, meanShape);\n    backend_util_exports.assertAndGetBroadcastShape(xShape, varianceShape);\n    let offsetSnippet = \"vec4(0.0)\";\n    if (offsetShape != null) {\n      backend_util_exports.assertAndGetBroadcastShape(xShape, offsetShape);\n      this.variableNames.push(\"offset\");\n      offsetSnippet = \"getOffsetAtOutCoords()\";\n    }\n    let scaleSnippet = \"vec4(1.0)\";\n    if (scaleShape != null) {\n      backend_util_exports.assertAndGetBroadcastShape(xShape, scaleShape);\n      this.variableNames.push(\"scale\");\n      scaleSnippet = \"getScaleAtOutCoords()\";\n    }\n    this.outputShape = xShape;\n    this.userCode = `\n      void main() {\n        vec4 offset = ${offsetSnippet};\n        vec4 scale = ${scaleSnippet};\n\n        vec4 x = getXAtOutCoords();\n        vec4 mean = getMeanAtOutCoords();\n        vec4 variance = getVarianceAtOutCoords();\n\n        vec4 inv = scale * inversesqrt(variance + vec4(${varianceEpsilon}));\n\n        setOutput((x - mean) * inv + offset);\n      }\n    `;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/BatchNorm.js\nvar batchNorm3 = ({ inputs, backend: backend2, attrs }) => {\n  const { x, mean: mean5, variance, offset, scale: scale2 } = inputs;\n  util_exports.assert(mean5.shape.length === variance.shape.length, () => \"Batch normalization gradient requires mean and variance to have equal ranks.\");\n  util_exports.assert(offset == null || mean5.shape.length === offset.shape.length, () => \"Batch normalization gradient requires mean and offset to have equal ranks.\");\n  util_exports.assert(scale2 == null || mean5.shape.length === scale2.shape.length, () => \"Batch normalization gradient requires mean and scale to have equal ranks.\");\n  let { varianceEpsilon } = attrs;\n  if (varianceEpsilon == null) {\n    varianceEpsilon = 1e-3;\n  }\n  const finalInputs = [x, mean5, variance];\n  let offsetShape = null;\n  if (offset != null) {\n    offsetShape = offset.shape;\n    finalInputs.push(offset);\n  }\n  let scaleShape = null;\n  if (scale2 != null) {\n    scaleShape = scale2.shape;\n    finalInputs.push(scale2);\n  }\n  const program = env().getBool(\"WEBGL_PACK_NORMALIZATION\") ? new BatchNormPackedProgram(x.shape, mean5.shape, variance.shape, offsetShape, scaleShape, varianceEpsilon) : new BatchNormProgram(x.shape, mean5.shape, variance.shape, offsetShape, scaleShape, varianceEpsilon);\n  const output = backend2.runWebGLProgram(program, finalInputs, finalInputs[0].dtype);\n  return output;\n};\nvar batchNormConfig2 = {\n  kernelName: FusedBatchNorm,\n  backendName: \"webgl\",\n  kernelFunc: batchNorm3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/slice_gpu.js\nvar SliceProgram = class {\n  constructor(destSize) {\n    this.variableNames = [\"source\"];\n    this.outputShape = destSize;\n    this.rank = destSize.length;\n    const dtype = getCoordsDataType(this.rank);\n    this.customUniforms = [{ name: \"start\", arrayIndex: this.rank, type: \"int\" }];\n    const sourceCoords = getCoords(this.rank);\n    let body;\n    const coordSum = destSize.map((_, i2) => {\n      return `sourceLoc.${coords[i2]} = start[${i2}] + coords.${coords[i2]};`;\n    });\n    body = `\n        ${dtype} sourceLoc;\n        ${dtype} coords = getOutputCoords();\n        ${coordSum.join(\"\\n\")}\n      `;\n    this.userCode = `\n      void main() {\n        ${body}\n        setOutput(getSource(${sourceCoords}));\n      }\n    `;\n  }\n};\nvar coords = [\"x\", \"y\", \"z\", \"w\", \"u\", \"v\"];\nfunction getCoords(rank) {\n  if (rank === 1) {\n    return \"sourceLoc\";\n  } else if (rank <= 6) {\n    return coords.slice(0, rank).map((x) => \"sourceLoc.\" + x).join(\",\");\n  } else {\n    throw Error(`Slicing for rank ${rank} is not yet supported`);\n  }\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/slice_packed_gpu.js\nvar SlicePackedProgram = class {\n  constructor(destSize) {\n    this.variableNames = [\"source\"];\n    this.packedInputs = true;\n    this.packedOutput = true;\n    this.outputShape = destSize;\n    this.rank = destSize.length;\n    this.customUniforms = [{ name: \"start\", arrayIndex: this.rank, type: \"int\" }];\n    const dtype = getCoordsDataType(this.rank);\n    const coords3 = getChannels(\"coords\", this.rank);\n    const sourceLoc = getChannels(\"sourceLoc\", this.rank);\n    const innerDims = this.rank === 1 ? \"sourceLoc\" : `vec2(${sourceLoc.slice(-2).join()})`;\n    const getChannel = `getChannel(getSource(${sourceLoc.join()}), ${innerDims})`;\n    const upperRow = `\n      result.x = ${getChannel};\n      if (++${coords3[this.rank - 1]} < ${destSize[this.rank - 1]}) {\n        ++${sourceLoc[this.rank - 1]};\n        result.y = ${getChannel};\n        --${sourceLoc[this.rank - 1]};\n      }\n    `;\n    const lowerRow = this.rank === 1 ? \"\" : `\n      --${coords3[this.rank - 1]};\n      if (++${coords3[this.rank - 2]} < ${destSize[this.rank - 2]}) {\n        ++${sourceLoc[this.rank - 2]};\n        result.z = ${getChannel};\n        if (++${coords3[this.rank - 1]} < ${destSize[this.rank - 1]}) {\n          ++${sourceLoc[this.rank - 1]};\n          result.w = ${getChannel};\n        }\n      }\n    `;\n    const sourceLocSetup = this.rank <= 4 ? `sourceLoc = coords +\n            ${dtype}(${destSize.map((_, i2) => `start[${i2}]`).join()});` : destSize.map((_, i2) => `${sourceLoc[i2]} = ${coords3[i2]} + start[${i2}];`).join(\"\\n\");\n    this.userCode = `\n      void main() {\n        ${dtype} coords = getOutputCoords();\n        ${dtype} sourceLoc;\n        ${sourceLocSetup}\n        vec4 result = vec4(0.);\n        ${upperRow}\n        ${lowerRow}\n        setOutput(result);\n      }\n    `;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Slice.js\nfunction shallowSlice(x, begin, size, backend2) {\n  const xTexData = backend2.texData.get(x.dataId);\n  const t2 = backend2.makeTensorInfo(size, x.dtype);\n  const newTexData = backend2.texData.get(t2.dataId);\n  Object.assign(newTexData, xTexData);\n  newTexData.refCount = 1;\n  newTexData.shape = size;\n  newTexData.dtype = x.dtype;\n  let flatOffset = slice_util_exports.computeFlatOffset(begin, util_exports.computeStrides(x.shape));\n  if (xTexData.slice) {\n    flatOffset += xTexData.slice.flatOffset;\n  }\n  newTexData.slice = {\n    flatOffset,\n    origDataId: xTexData.slice && xTexData.slice.origDataId || x.dataId\n  };\n  const refCount = backend2.dataRefCount.get(newTexData.slice.origDataId) || 1;\n  backend2.dataRefCount.set(newTexData.slice.origDataId, refCount + 1);\n  return t2;\n}\nfunction slice3(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x } = inputs;\n  const { begin, size } = attrs;\n  const [$begin, $size] = slice_util_exports.parseSliceParams(x, begin, size);\n  slice_util_exports.assertParamsValid(x, $begin, $size);\n  if (util_exports.sizeFromShape($size) === 0) {\n    return backend2.makeTensorInfo($size, x.dtype, []);\n  }\n  if (backend2.shouldExecuteOnCPU([x]) || x.dtype === \"string\") {\n    const xTexData = backend2.texData.get(x.dataId);\n    const outValues = sliceImplCPU(xTexData.values, $begin, $size, x.shape, x.dtype);\n    return backend2.makeTensorInfo($size, x.dtype, outValues);\n  }\n  const { isPacked } = backend2.texData.get(x.dataId);\n  const isContinous = slice_util_exports.isSliceContinous(x.shape, $begin, $size);\n  if (isPacked || !isContinous) {\n    const program = env().getBool(\"WEBGL_PACK_ARRAY_OPERATIONS\") ? new SlicePackedProgram($size) : new SliceProgram($size);\n    const customValues = [$begin];\n    return backend2.runWebGLProgram(program, [x], x.dtype, customValues);\n  }\n  backend2.uploadToGPU(x.dataId);\n  return shallowSlice(x, $begin, $size, backend2);\n}\nvar sliceConfig2 = {\n  kernelName: Slice,\n  backendName: \"webgl\",\n  kernelFunc: slice3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/BatchToSpaceND.js\nvar batchToSpaceND3 = (args) => {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x } = inputs;\n  const { blockShape, crops } = attrs;\n  util_exports.assert(x.shape.length <= 4, () => \"batchToSpaceND for rank > 4 with a WebGL backend not implemented yet\");\n  const prod6 = blockShape.reduce((a, b) => a * b);\n  const reshaped = backend_util_exports.getReshaped(x.shape, blockShape, prod6);\n  const permuted = backend_util_exports.getPermuted(reshaped.length, blockShape.length);\n  const reshapedPermuted = backend_util_exports.getReshapedPermuted(x.shape, blockShape, prod6);\n  const sliceBeginCoords = backend_util_exports.getSliceBeginCoords(crops, blockShape.length);\n  const sliceSize = backend_util_exports.getSliceSize(reshapedPermuted, crops, blockShape.length);\n  const toDispose = [];\n  const reshapedIntermediate = reshape4({ inputs: { x }, backend: backend2, attrs: { shape: reshaped } });\n  const transposedIntermediate = transpose3({ inputs: { x: reshapedIntermediate }, backend: backend2, attrs: { perm: permuted } });\n  const reshapedIntermediate2 = reshape4({\n    inputs: { x: transposedIntermediate },\n    backend: backend2,\n    attrs: { shape: reshapedPermuted }\n  });\n  const sliced = slice3({\n    inputs: { x: reshapedIntermediate2 },\n    backend: backend2,\n    attrs: { begin: sliceBeginCoords, size: sliceSize }\n  });\n  toDispose.push(reshapedIntermediate);\n  toDispose.push(transposedIntermediate);\n  toDispose.push(reshapedIntermediate2);\n  toDispose.forEach((t2) => backend2.disposeIntermediateTensorInfo(t2));\n  return sliced;\n};\nvar batchToSpaceNDConfig2 = {\n  kernelName: BatchToSpaceND,\n  backendName: \"webgl\",\n  kernelFunc: batchToSpaceND3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Bincount.js\nfunction bincount3(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x, weights } = inputs;\n  const { size } = attrs;\n  const xVals = backend2.readSync(x.dataId);\n  const weightsVals = backend2.readSync(weights.dataId);\n  const outVals = bincountImplCPU(xVals, weightsVals, weights.dtype, weights.shape, size);\n  return backend2.makeTensorInfo([size], weights.dtype, outVals);\n}\nvar bincountConfig2 = {\n  kernelName: Bincount,\n  backendName: \"webgl\",\n  kernelFunc: bincount3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/BroadcastArgs.js\nfunction broadcastArgs3(args) {\n  const { inputs, backend: backend2 } = args;\n  const { s0, s1 } = inputs;\n  const s0Vals = backend2.readSync(s0.dataId);\n  const s1Vals = backend2.readSync(s1.dataId);\n  const broadcastShape = backend_util_exports.assertAndGetBroadcastShape(Array.from(s0Vals), Array.from(s1Vals));\n  return backend2.makeTensorInfo([broadcastShape.length], \"int32\", Int32Array.from(broadcastShape));\n}\nvar broadcastArgsConfig2 = {\n  kernelName: BroadcastArgs,\n  backendName: \"webgl\",\n  kernelFunc: broadcastArgs3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/NotEqual.js\nvar NOT_EQUAL = `return float(a != b);`;\nvar notEqual3 = binaryKernelFunc2({ opSnippet: NOT_EQUAL, cpuKernelImpl: notEqualImplCPU, dtype: \"bool\" });\nvar notEqualConfig2 = {\n  kernelName: NotEqual,\n  backendName: \"webgl\",\n  kernelFunc: notEqual3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Real.js\nfunction real3(args) {\n  const { inputs, backend: backend2 } = args;\n  const { input: input2 } = inputs;\n  const inputData = backend2.texData.get(input2.dataId);\n  return identity3({ inputs: { x: inputData.complexTensorInfos.real }, backend: backend2 });\n}\nvar realConfig2 = {\n  kernelName: Real,\n  backendName: \"webgl\",\n  kernelFunc: real3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernel_utils/int.js\nvar TO_INT = `return float(int(x));`;\nfunction int(input2, backend2) {\n  const program = new UnaryOpProgram(input2.shape, TO_INT);\n  const output = backend2.runWebGLProgram(program, [input2], \"int32\");\n  return { dataId: output.dataId, shape: output.shape, dtype: output.dtype };\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Cast.js\nfunction cast4(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x } = inputs;\n  const { dtype } = attrs;\n  if (dtype === \"complex64\") {\n    if (x.dtype === \"complex64\") {\n      return identity3({ inputs: { x }, backend: backend2 });\n    }\n    const zerosTensor = zeros(x.shape);\n    const floatX = cast4({ inputs: { x }, backend: backend2, attrs: { dtype: \"float32\" } });\n    const result = complex3({ inputs: { real: floatX, imag: zerosTensor }, backend: backend2 });\n    zerosTensor.dispose();\n    backend2.disposeIntermediateTensorInfo(floatX);\n    return result;\n  }\n  if (x.dtype === \"complex64\") {\n    const realPart = real3({ inputs: { input: x }, backend: backend2 });\n    const result = cast4({ inputs: { x: realPart }, backend: backend2, attrs: { dtype } });\n    backend2.disposeIntermediateTensorInfo(realPart);\n    return result;\n  }\n  if (!util_exports.hasEncodingLoss(x.dtype, dtype)) {\n    const result = identity3({ inputs: { x }, backend: backend2 });\n    return { dataId: result.dataId, shape: result.shape, dtype };\n  }\n  if (backend2.shouldExecuteOnCPU([x])) {\n    const values = backend2.texData.get(x.dataId).values;\n    const [resultShape, resultType, resultData] = castImplCPU(values, x.shape, x.dtype, dtype);\n    return backend2.makeTensorInfo(resultShape, resultType, resultData);\n  }\n  if (dtype === \"int32\") {\n    return int(x, backend2);\n  }\n  if (dtype === \"bool\") {\n    const zerosTensorInfo = backend2.makeTensorInfo([], \"bool\", util_exports.getTypedArrayFromDType(\"bool\", 1));\n    const binaryInputs = { a: x, b: zerosTensorInfo };\n    const result = notEqual3({ inputs: binaryInputs, backend: backend2 });\n    backend2.disposeIntermediateTensorInfo(zerosTensorInfo);\n    return result;\n  }\n  throw new Error(`Error in Cast: failed to cast ${x.dtype} to ${dtype}`);\n}\nvar castConfig2 = {\n  kernelName: Cast,\n  backendName: \"webgl\",\n  kernelFunc: cast4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Ceil.js\nvar CEIL = `return ceil(x);`;\nvar ceil3 = unaryKernelFunc2({ opSnippet: CEIL, packedOpSnippet: CEIL, cpuKernelImpl: ceilImplCPU });\nvar ceilConfig2 = {\n  kernelName: Ceil,\n  backendName: \"webgl\",\n  kernelFunc: ceil3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/clip_gpu.js\nvar ClipProgram = class {\n  constructor(aShape) {\n    this.variableNames = [\"A\"];\n    this.customUniforms = [\n      { name: \"minVal\", type: \"float\" },\n      { name: \"maxVal\", type: \"float\" }\n    ];\n    this.outputShape = aShape;\n    this.userCode = `\n\n      void main() {\n        float value = getAAtOutCoords();\n        if (isnan(value)) {\n          setOutput(value);\n          return;\n        }\n\n        setOutput(clamp(value, minVal, maxVal));\n      }\n    `;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/clip_packed_gpu.js\nvar ClipPackedProgram = class {\n  constructor(aShape) {\n    this.variableNames = [\"A\"];\n    this.packedInputs = true;\n    this.packedOutput = true;\n    this.customUniforms = [\n      { name: \"minVal\", type: \"float\" },\n      { name: \"maxVal\", type: \"float\" }\n    ];\n    this.outputShape = aShape;\n    this.userCode = `\n      void main() {\n        vec4 value = getAAtOutCoords();\n\n        if (any(isnan(value))) {\n          setOutput(value);\n          return;\n        }\n\n        setOutput(clamp(value, vec4(minVal), vec4(maxVal)));\n      }\n    `;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/ClipByValue.js\nfunction clipByValue3(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x } = inputs;\n  const { clipValueMin, clipValueMax } = attrs;\n  let program;\n  if (env().getBool(\"WEBGL_PACK_CLIP\")) {\n    program = new ClipPackedProgram(x.shape);\n  } else {\n    program = new ClipProgram(x.shape);\n  }\n  const customValues = [[clipValueMin], [clipValueMax]];\n  return backend2.runWebGLProgram(program, [x], x.dtype, customValues);\n}\nvar clipByValueConfig2 = {\n  kernelName: ClipByValue,\n  backendName: \"webgl\",\n  kernelFunc: clipByValue3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/complex_abs_gpu.js\nvar ComplexAbsProgram = class {\n  constructor(shape) {\n    this.variableNames = [\"real\", \"imag\"];\n    this.outputShape = shape;\n    this.userCode = `\n      void main() {\n        float re = abs(getRealAtOutCoords());\n        float im = abs(getImagAtOutCoords());\n        float mx = max(re, im);\n\n        // sadly the length function in glsl is not underflow-safe\n        // (at least not on Intel GPUs). So the safe solution is\n        // to ensure underflow-safety in all cases.\n        setOutput(\n          mx == 0.0 ? 0.0 : mx * length(vec2(1, min(re, im)/mx))\n        );\n      }\n    `;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/ComplexAbs.js\nfunction makeComplexComponentTensorInfo(complexTensor, complexPart) {\n  return {\n    dataId: complexPart.dataId,\n    dtype: complexPart.dtype,\n    shape: complexTensor.shape\n  };\n}\nfunction complexAbs2(args) {\n  const { inputs, backend: backend2 } = args;\n  const { x } = inputs;\n  const xData = backend2.texData.get(x.dataId);\n  const program = new ComplexAbsProgram(x.shape);\n  const programInputs = [\n    makeComplexComponentTensorInfo(x, xData.complexTensorInfos.real),\n    makeComplexComponentTensorInfo(x, xData.complexTensorInfos.imag)\n  ];\n  return backend2.runWebGLProgram(program, programInputs, programInputs[0].dtype);\n}\nvar complexAbsConfig2 = {\n  kernelName: ComplexAbs,\n  backendName: \"webgl\",\n  kernelFunc: complexAbs2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/concat_gpu.js\nvar ConcatProgram = class {\n  constructor(shapes) {\n    this.outputShape = [];\n    this.outputShape = backend_util_exports.computeOutShape(shapes, 1);\n    this.variableNames = shapes.map((_, i2) => `T${i2}`);\n    const offsets = new Array(shapes.length - 1);\n    offsets[0] = shapes[0][1];\n    for (let i2 = 1; i2 < offsets.length; i2++) {\n      offsets[i2] = offsets[i2 - 1] + shapes[i2][1];\n    }\n    const snippets = [`if (yC < ${offsets[0]}) setOutput(getT0(yR, yC));`];\n    for (let i2 = 1; i2 < offsets.length; i2++) {\n      const shift = offsets[i2 - 1];\n      snippets.push(`else if (yC < ${offsets[i2]}) setOutput(getT${i2}(yR, yC-${shift}));`);\n    }\n    const lastIndex = offsets.length;\n    const lastShift = offsets[offsets.length - 1];\n    snippets.push(`else setOutput(getT${lastIndex}(yR, yC-${lastShift}));`);\n    this.userCode = `\n      void main() {\n        ivec2 coords = getOutputCoords();\n        int yR = coords.x;\n        int yC = coords.y;\n\n        ${snippets.join(\"\\n        \")}\n      }\n    `;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/concat_packed_gpu.js\nvar ConcatPackedProgram = class {\n  constructor(shapes, axis) {\n    this.packedInputs = true;\n    this.packedOutput = true;\n    this.outputShape = [];\n    this.outputShape = backend_util_exports.computeOutShape(shapes, axis);\n    const shape = this.outputShape;\n    const rank = shape.length;\n    const dtype = getCoordsDataType(rank);\n    const coords3 = getChannels(\"coords\", rank);\n    const channels = [\"x\", \"y\", \"z\", \"w\", \"u\", \"v\"].slice(0, rank);\n    this.variableNames = shapes.map((_, i2) => `T${i2}`);\n    const offsets = new Array(shapes.length - 1);\n    offsets[0] = shapes[0][axis];\n    for (let i2 = 1; i2 < offsets.length; i2++) {\n      offsets[i2] = offsets[i2 - 1] + shapes[i2][axis];\n    }\n    const channel = channels[axis];\n    const lastChannels = channels.slice(-2);\n    const allChannels = channels.join();\n    let getValueSnippet = `if (${channel} < ${offsets[0]}) {\n        return getChannel(\n            getT0(${allChannels}), vec2(${lastChannels.join()}));\n        }`;\n    for (let i2 = 1; i2 < offsets.length; i2++) {\n      const shift2 = offsets[i2 - 1];\n      getValueSnippet += `\n        if (${channel} < ${offsets[i2]}  && ${channel} >= ${offsets[i2 - 1]}) {\n          return getChannel(\n            getT${i2}(${shiftedChannels(channels, channel, shift2)}),\n            vec2(${shiftedChannels(lastChannels, channel, shift2)}));\n        }`;\n    }\n    const lastIndex = offsets.length;\n    const shift = offsets[offsets.length - 1];\n    getValueSnippet += `\n        return getChannel(\n          getT${lastIndex}(${shiftedChannels(channels, channel, shift)}),\n          vec2(${shiftedChannels(lastChannels, channel, shift)}));`;\n    this.userCode = `\n      float getValue(${channels.map((x) => \"int \" + x)}) {\n        ${getValueSnippet}\n      }\n\n      void main() {\n        ${dtype} coords = getOutputCoords();\n        vec4 result = vec4(getValue(${coords3}), 0., 0., 0.);\n\n        ${coords3[rank - 1]} = ${coords3[rank - 1]} + 1;\n        if (${coords3[rank - 1]} < ${shape[rank - 1]}) {\n          result.g = getValue(${coords3});\n        }\n\n        ${coords3[rank - 2]} = ${coords3[rank - 2]} + 1;\n        if (${coords3[rank - 2]} < ${shape[rank - 2]}) {\n          result.a = getValue(${coords3});\n        }\n\n        ${coords3[rank - 1]} = ${coords3[rank - 1]} - 1;\n        if (${coords3[rank - 2]} < ${shape[rank - 2]} &&\n            ${coords3[rank - 1]} < ${shape[rank - 1]}) {\n          result.b = getValue(${coords3});\n        }\n        setOutput(result);\n      }\n    `;\n  }\n};\nfunction shiftedChannels(channels, channel, shift) {\n  const channelIdx = channels.indexOf(channel);\n  const res = channels.map((c, idx) => {\n    if (idx === channelIdx) {\n      return `${c} - ${shift}`;\n    } else {\n      return c;\n    }\n  });\n  return res.join();\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Imag.js\nfunction imag3(args) {\n  const { inputs, backend: backend2 } = args;\n  const { input: input2 } = inputs;\n  const inputData = backend2.texData.get(input2.dataId);\n  return identity3({ inputs: { x: inputData.complexTensorInfos.imag }, backend: backend2 });\n}\nvar imagConfig2 = {\n  kernelName: Imag,\n  backendName: \"webgl\",\n  kernelFunc: imag3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Concat_impl.js\nfunction concatImpl2(inputs, axis, backend2) {\n  const dtype = inputs[0].dtype;\n  if (dtype === \"complex64\") {\n    const reals = inputs.map((t2) => real3({ inputs: { input: t2 }, backend: backend2 }));\n    const imags = inputs.map((t2) => imag3({ inputs: { input: t2 }, backend: backend2 }));\n    const realConcated = concatImpl2(reals, axis, backend2);\n    const imagConcated = concatImpl2(imags, axis, backend2);\n    const result2 = complex3({ inputs: { real: realConcated, imag: imagConcated }, backend: backend2 });\n    reals.forEach((r2) => backend2.disposeIntermediateTensorInfo(r2));\n    imags.forEach((i2) => backend2.disposeIntermediateTensorInfo(i2));\n    backend2.disposeIntermediateTensorInfo(realConcated);\n    backend2.disposeIntermediateTensorInfo(imagConcated);\n    return result2;\n  }\n  let runOnCpu = backend2.shouldExecuteOnCPU(inputs);\n  if (dtype === \"string\") {\n    runOnCpu = true;\n  }\n  if (runOnCpu) {\n    const tensors2D2 = inputs.map((t2) => {\n      const innerSize = util_exports.sizeFromShape(t2.shape.slice(axis));\n      const shape = [-1, innerSize];\n      return reshape4({ inputs: { x: t2 }, backend: backend2, attrs: { shape } });\n    });\n    const inputsValShapes = tensors2D2.map((t2) => {\n      return { vals: backend2.readSync(t2.dataId), shape: t2.shape };\n    });\n    const outShape2 = backend_util_exports.computeOutShape(tensors2D2.map((t2) => t2.shape), 1);\n    const simplyConcat = tensors2D2[0].shape[0] === 1;\n    const outVals = concatImplCPU(inputsValShapes, outShape2, dtype, simplyConcat);\n    const finalOutShape = backend_util_exports.computeOutShape(inputs.map((t2) => t2.shape), axis);\n    const outInfo = backend2.makeTensorInfo(finalOutShape, dtype, outVals);\n    tensors2D2.forEach((t2) => backend2.disposeIntermediateTensorInfo(t2));\n    return outInfo;\n  }\n  const maxTexturesInShader = env().getNumber(\"WEBGL_MAX_TEXTURES_IN_SHADER\");\n  if (inputs.length > maxTexturesInShader) {\n    const reducedInputs = [];\n    for (let i2 = 0; i2 < inputs.length; i2 += maxTexturesInShader) {\n      const subArray = inputs.slice(i2, i2 + maxTexturesInShader);\n      reducedInputs.push(concatImpl2(subArray, axis, backend2));\n    }\n    const result2 = concatImpl2(reducedInputs, axis, backend2);\n    for (const i2 of reducedInputs) {\n      backend2.disposeIntermediateTensorInfo(i2);\n    }\n    return result2;\n  }\n  if (env().getBool(\"WEBGL_PACK_ARRAY_OPERATIONS\") && inputs[0].shape.length > 1) {\n    const program2 = new ConcatPackedProgram(inputs.map((t2) => t2.shape), axis);\n    return backend2.runWebGLProgram(program2, inputs, dtype);\n  }\n  const { tensors2D, outShape } = computeTensors2D(inputs, axis, backend2);\n  const program = new ConcatProgram(tensors2D.map((t2) => t2.shape));\n  const result = backend2.runWebGLProgram(program, tensors2D, dtype);\n  tensors2D.forEach((r2) => backend2.disposeIntermediateTensorInfo(r2));\n  const reshapedResult = reshape4({ inputs: { x: result }, attrs: { shape: outShape }, backend: backend2 });\n  backend2.disposeIntermediateTensorInfo(result);\n  return reshapedResult;\n}\nfunction computeTensors2D(inputs, axis, backend2) {\n  const outShape = backend_util_exports.computeOutShape(inputs.map((t2) => t2.shape), axis);\n  const tensors2D = inputs.map((x) => reshape4({\n    inputs: { x },\n    attrs: { shape: [-1, util_exports.sizeFromShape(x.shape.slice(axis))] },\n    backend: backend2\n  }));\n  return { tensors2D, outShape };\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Concat.js\nfunction concat3(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { axis } = attrs;\n  const $axis = util_exports.parseAxisParam(axis, inputs[0].shape)[0];\n  const outShape = backend_util_exports.computeOutShape(inputs.map((t2) => t2.shape), $axis);\n  if (util_exports.sizeFromShape(outShape) === 0) {\n    return backend2.makeTensorInfo(outShape, inputs[0].dtype, []);\n  }\n  const $inputs = inputs.filter((t2) => util_exports.sizeFromShape(t2.shape) > 0);\n  if ($inputs.length === 1) {\n    return identity3({ inputs: { x: $inputs[0] }, backend: backend2 });\n  }\n  const shapes = $inputs.map((t2) => t2.shape);\n  backend_util_exports.assertParamsConsistent(shapes, $axis);\n  return concatImpl2($inputs, $axis, backend2);\n}\nvar concatConfig2 = {\n  kernelName: Concat,\n  backendName: \"webgl\",\n  kernelFunc: concat3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/conv_gpu.js\nvar Conv2DProgram = class {\n  constructor(convInfo, addBias = false, activation2 = null, hasPreluActivationWeights = false, hasLeakyreluAlpha = false) {\n    this.variableNames = [\"x\", \"W\"];\n    this.outputShape = convInfo.outShape;\n    const padTop = convInfo.padInfo.top;\n    const padLeft = convInfo.padInfo.left;\n    const strideHeight = convInfo.strideHeight;\n    const strideWidth = convInfo.strideWidth;\n    const dilationHeight = convInfo.dilationHeight;\n    const dilationWidth = convInfo.dilationWidth;\n    const filterHeight = convInfo.filterHeight;\n    const filterWidth = convInfo.filterWidth;\n    const inputDepthNearestVec4 = Math.floor(convInfo.inChannels / 4) * 4;\n    const inputDepthVec4Remainder = convInfo.inChannels % 4;\n    const isChannelsLast = convInfo.dataFormat === \"channelsLast\";\n    const rowDim = isChannelsLast ? 1 : 2;\n    const colDim = isChannelsLast ? 2 : 3;\n    const channelDim = isChannelsLast ? 3 : 1;\n    let activationSnippet = \"\", applyActivationSnippet = \"\";\n    if (activation2) {\n      if (hasPreluActivationWeights) {\n        activationSnippet = `float activation(float a) {\n          float b = getPreluActivationWeightsAtOutCoords();\n          ${activation2}\n        }`;\n      } else if (hasLeakyreluAlpha) {\n        activationSnippet = `float activation(float a) {\n          float b = getLeakyreluAlphaAtOutCoords();\n          ${activation2}\n        }`;\n      } else {\n        activationSnippet = `\n          float activation(float x) {\n            ${activation2}\n          }\n        `;\n      }\n      applyActivationSnippet = `result = activation(result);`;\n    }\n    const addBiasSnippet = addBias ? \"result += getBiasAtOutCoords();\" : \"\";\n    if (addBias) {\n      this.variableNames.push(\"bias\");\n    }\n    if (hasPreluActivationWeights) {\n      this.variableNames.push(\"preluActivationWeights\");\n    }\n    if (hasLeakyreluAlpha) {\n      this.variableNames.push(\"leakyreluAlpha\");\n    }\n    this.userCode = `\n      ${activationSnippet}\n\n      const ivec2 strides = ivec2(${strideHeight}, ${strideWidth});\n      const ivec2 pads = ivec2(${padTop}, ${padLeft});\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int batch = coords[0];\n        int d2 = coords[${channelDim}];\n\n        ivec2 xRCCorner =\n            ivec2(coords[${rowDim}], coords[${colDim}]) * strides - pads;\n        int xRCorner = xRCCorner.x;\n        int xCCorner = xRCCorner.y;\n\n        // Convolve x(?, ?, d1) with w(:, :, d1, d2) to get y(yR, yC, d2).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n        for (int wR = 0; wR < ${filterHeight}; wR++) {\n          int xR = xRCorner + wR * ${dilationHeight};\n\n          if (xR < 0 || xR >= ${convInfo.inHeight}) {\n            continue;\n          }\n\n          for (int wC = 0; wC < ${filterWidth}; wC++) {\n            int xC = xCCorner + wC * ${dilationWidth};\n\n            if (xC < 0 || xC >= ${convInfo.inWidth}) {\n              continue;\n            }\n\n            for (int d1 = 0; d1 < ${inputDepthNearestVec4}; d1 += 4) {\n              vec4 wValues = vec4(\n                getW(wR, wC, d1, d2),\n                getW(wR, wC, d1 + 1, d2),\n                getW(wR, wC, d1 + 2, d2),\n                getW(wR, wC, d1 + 3, d2)\n              );\n\n              if (${isChannelsLast}) {\n                vec4 xValues = vec4(\n                  getX(batch, xR, xC, d1),\n                  getX(batch, xR, xC, d1 + 1),\n                  getX(batch, xR, xC, d1 + 2),\n                  getX(batch, xR, xC, d1 + 3)\n                );\n                dotProd += dot(xValues, wValues);\n              } else {\n                vec4 xValues = vec4(\n                  getX(batch, d1, xR, xC),\n                  getX(batch, d1 + 1, xR, xC),\n                  getX(batch, d1 + 2, xR, xC),\n                  getX(batch, d1 + 3, xR, xC)\n                );\n                dotProd += dot(xValues, wValues);\n              }\n            }\n\n            if (${inputDepthVec4Remainder === 1}) {\n\n              if (${isChannelsLast}) {\n                dotProd +=\n                    getX(batch, xR, xC, ${inputDepthNearestVec4}) *\n                    getW(wR, wC, ${inputDepthNearestVec4}, d2);\n              } else {\n                dotProd +=\n                    getX(batch, ${inputDepthNearestVec4}, xR, xC) *\n                    getW(wR, wC, ${inputDepthNearestVec4}, d2);\n              }\n\n            } else if (${inputDepthVec4Remainder === 2}) {\n              vec2 wValues = vec2(\n                getW(wR, wC, ${inputDepthNearestVec4}, d2),\n                getW(wR, wC, ${inputDepthNearestVec4} + 1, d2)\n              );\n\n              if (${isChannelsLast}) {\n                vec2 xValues = vec2(\n                  getX(batch, xR, xC, ${inputDepthNearestVec4}),\n                  getX(batch, xR, xC, ${inputDepthNearestVec4} + 1)\n                );\n                dotProd += dot(xValues, wValues);\n              } else {\n                vec2 xValues = vec2(\n                  getX(batch, ${inputDepthNearestVec4}, xR, xC),\n                  getX(batch, ${inputDepthNearestVec4} + 1, xR, xC)\n                );\n                dotProd += dot(xValues, wValues);\n              }\n\n            } else if (${inputDepthVec4Remainder === 3}) {\n              vec3 wValues = vec3(\n                getW(wR, wC, ${inputDepthNearestVec4}, d2),\n                getW(wR, wC, ${inputDepthNearestVec4} + 1, d2),\n                getW(wR, wC, ${inputDepthNearestVec4} + 2, d2)\n              );\n\n              if (${isChannelsLast}) {\n                vec3 xValues = vec3(\n                  getX(batch, xR, xC, ${inputDepthNearestVec4}),\n                  getX(batch, xR, xC, ${inputDepthNearestVec4} + 1),\n                  getX(batch, xR, xC, ${inputDepthNearestVec4} + 2)\n                );\n                dotProd += dot(xValues, wValues);\n              } else {\n                vec3 xValues = vec3(\n                  getX(batch, ${inputDepthNearestVec4}, xR, xC),\n                  getX(batch, ${inputDepthNearestVec4} + 1, xR, xC),\n                  getX(batch, ${inputDepthNearestVec4} + 2, xR, xC)\n                );\n                dotProd += dot(xValues, wValues);\n              }\n\n            }\n          }\n        }\n\n        float result = dotProd;\n        ${addBiasSnippet}\n        ${applyActivationSnippet}\n        setOutput(result);\n      }\n    `;\n  }\n};\nvar Conv3DProgram = class {\n  constructor(convInfo) {\n    this.variableNames = [\"x\", \"W\"];\n    this.outputShape = convInfo.outShape;\n    const padFront = convInfo.padInfo.front;\n    const padTop = convInfo.padInfo.top;\n    const padLeft = convInfo.padInfo.left;\n    const strideDepth = convInfo.strideDepth;\n    const strideHeight = convInfo.strideHeight;\n    const strideWidth = convInfo.strideWidth;\n    const dilationDepth = convInfo.dilationDepth;\n    const dilationHeight = convInfo.dilationHeight;\n    const dilationWidth = convInfo.dilationWidth;\n    const filterDepth = convInfo.filterDepth;\n    const filterHeight = convInfo.filterHeight;\n    const filterWidth = convInfo.filterWidth;\n    const inputDepthNearestVec4 = Math.floor(convInfo.inChannels / 4) * 4;\n    const inputDepthVec4Remainder = convInfo.inChannels % 4;\n    this.userCode = `\n      const ivec3 strides = ivec3(${strideDepth}, ${strideHeight}, ${strideWidth});\n      const ivec3 pads = ivec3(${padFront}, ${padTop}, ${padLeft});\n\n      void main() {\n        ivec5 coords = getOutputCoords();\n        int batch = coords.x;\n        int d2 = coords.u;\n\n        ivec3 xFRCCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;\n        int xFCorner = xFRCCorner.x;\n        int xRCorner = xFRCCorner.y;\n        int xCCorner = xFRCCorner.z;\n\n        // Convolve x(?, ?, ?, d1) with w(:, :, :, d1, d2) to get\n        // y(yF, yR, yC, d2). ? = to be determined. : = across all\n        // values in that axis.\n        float dotProd = 0.0;\n        for (int wF = 0; wF < ${filterDepth}; wF++) {\n          int xF = xFCorner + wF * ${dilationDepth};\n\n          if (xF < 0 || xF >= ${convInfo.inDepth}) {\n            continue;\n          }\n\n          for (int wR = 0; wR < ${filterHeight}; wR++) {\n            int xR = xRCorner + wR * ${dilationHeight};\n\n            if (xR < 0 || xR >= ${convInfo.inHeight}) {\n              continue;\n            }\n\n            for (int wC = 0; wC < ${filterWidth}; wC++) {\n              int xC = xCCorner + wC * ${dilationWidth};\n\n              if (xC < 0 || xC >= ${convInfo.inWidth}) {\n                continue;\n              }\n\n              for (int d1 = 0; d1 < ${inputDepthNearestVec4}; d1 += 4) {\n                vec4 xValues = vec4(\n                  getX(batch, xF, xR, xC, d1),\n                  getX(batch, xF, xR, xC, d1 + 1),\n                  getX(batch, xF, xR, xC, d1 + 2),\n                  getX(batch, xF, xR, xC, d1 + 3)\n                );\n                vec4 wValues = vec4(\n                  getW(wF, wR, wC, d1, d2),\n                  getW(wF, wR, wC, d1 + 1, d2),\n                  getW(wF, wR, wC, d1 + 2, d2),\n                  getW(wF, wR, wC, d1 + 3, d2)\n                );\n\n                dotProd += dot(xValues, wValues);\n              }\n\n              if (${inputDepthVec4Remainder === 1}) {\n                dotProd +=\n                  getX(batch, xF, xR, xC, ${inputDepthNearestVec4}) *\n                  getW(wF, wR, wC, ${inputDepthNearestVec4}, d2);\n              } else if (${inputDepthVec4Remainder === 2}) {\n                vec2 xValues = vec2(\n                  getX(batch, xF, xR, xC, ${inputDepthNearestVec4}),\n                  getX(batch, xF, xR, xC, ${inputDepthNearestVec4} + 1)\n                );\n                vec2 wValues = vec2(\n                  getW(wF, wR, wC, ${inputDepthNearestVec4}, d2),\n                  getW(wF, wR, wC, ${inputDepthNearestVec4} + 1, d2)\n                );\n                dotProd += dot(xValues, wValues);\n              } else if (${inputDepthVec4Remainder === 3}) {\n                vec3 xValues = vec3(\n                  getX(batch, xF, xR, xC, ${inputDepthNearestVec4}),\n                  getX(batch, xF, xR, xC, ${inputDepthNearestVec4} + 1),\n                  getX(batch, xF, xR, xC, ${inputDepthNearestVec4} + 2)\n                );\n                vec3 wValues = vec3(\n                  getW(wF, wR, wC, ${inputDepthNearestVec4}, d2),\n                  getW(wF, wR, wC, ${inputDepthNearestVec4} + 1, d2),\n                  getW(wF, wR, wC, ${inputDepthNearestVec4} + 2, d2)\n                );\n                dotProd += dot(xValues, wValues);\n              }\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    `;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/conv_packed_gpu.js\nvar Conv2DPackedProgram = class {\n  constructor(convInfo, addBias = false, activation2 = null, hasPreluActivation = false, hasLeakyReluAlpha = false) {\n    this.variableNames = [\"x\", \"W\"];\n    this.packedInputs = true;\n    this.packedOutput = true;\n    this.customUniforms = [\n      { name: \"pads\", type: \"ivec2\" },\n      { name: \"strides\", type: \"ivec2\" },\n      { name: \"dilations\", type: \"ivec2\" },\n      { name: \"inDims\", type: \"ivec2\" }\n    ];\n    this.outputShape = convInfo.outShape;\n    this.enableShapeUniforms = useShapeUniforms(this.outputShape.length);\n    const padLeft = convInfo.padInfo.left;\n    const strideWidth = convInfo.strideWidth;\n    const dilationWidth = convInfo.dilationWidth;\n    const filterHeight = convInfo.filterHeight;\n    const filterWidth = convInfo.filterWidth;\n    const texelsAcross = filterWidth;\n    let mainLoop = `\n       int xR; int xC; int xCOffset;\n       vec4 wTexel; vec4 previous; vec4 final;`;\n    for (let c = 0; c < filterWidth; c++) {\n      mainLoop += `\n           vec4 xTexelC${c * 2};\n           int xTexelC${c * 2}Ready;\n           vec4 xTexelC${c * 2 + 1};\n           int xTexelC${c * 2 + 1}Ready;\n           vec4 xC${c};`;\n    }\n    mainLoop += `\n     for (int r = 0; r < ${filterHeight}; r++) {\n      for (int d1 = 0; d1 < ${convInfo.inChannels}; d1 += 2) {\n       `;\n    for (let c = 0; c < filterWidth; c++) {\n      mainLoop += `\n           xTexelC${c * 2} = vec4(0.0);\n           xTexelC${c * 2}Ready = 0;\n           xTexelC${c * 2 + 1} = vec4(0.0);\n           xTexelC${c * 2 + 1}Ready = 0;\n           xC${c} = vec4(0.0);`;\n    }\n    mainLoop += `\n         xR = xRCorner + r * dilations[0];\n         if (xR >=0 && xR < inDims[0]) {\n       `;\n    for (let texelC = 0; texelC < (texelsAcross + 1) / 2; texelC++) {\n      const colIndex = texelC * 2;\n      mainLoop += `\n           xC = xCCorner + ${colIndex * dilationWidth};\n           `;\n      if (strideWidth === 1) {\n        if (colIndex < filterWidth) {\n          if (padLeft % 2 === 1) {\n            mainLoop += `\n                 xCOffset = xC + 1;\n                 if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${colIndex}Ready == 0) {\n                   xTexelC${colIndex} = getX(batch, xR, xCOffset, d1);\n\n                   // Need to manually clear unused channels in case\n                   // we're reading from recycled texture.\n                   if (xCOffset + 1 >= inDims[1]) {\n                     xTexelC${colIndex}.zw = vec2(0.0);\n                   }\n                   xTexelC${colIndex}Ready = 1;\n                 }\n               `;\n            if (dilationWidth === 1 && colIndex > 0) {\n              mainLoop += `\n                 xC${colIndex} = vec4(xTexelC${colIndex - 2}.zw, xTexelC${colIndex}.xy);\n                 `;\n            } else {\n              mainLoop += `\n                   xCOffset = xC + 1 - 2;\n\n                   if (xCOffset >= 0 && xCOffset < inDims[1]) {\n                     previous = getX(batch, xR, xCOffset, d1);\n\n                     // Need to manually clear unused channels in case\n                     // we're reading from recycled texture.\n                     if (xCOffset + 1 >= inDims[1]) {\n                       previous.zw = vec2(0.0);\n                     }\n\n                     xC${colIndex} = vec4(previous.zw, xTexelC${colIndex}.xy);\n                   } else {\n                     xC${colIndex} = vec4(0.0, 0.0, xTexelC${colIndex}.xy);\n                   }\n                   `;\n            }\n          } else {\n            mainLoop += `\n                 if (xC >= 0 && xC < inDims[1] && xTexelC${colIndex}Ready == 0) {\n                   xTexelC${colIndex} = getX(batch, xR, xC, d1);\n                   if (xC + 1 >= inDims[1]) {\n                     xTexelC${colIndex}.zw = vec2(0.0);\n                   }\n                   xTexelC${colIndex}Ready = 1;\n                 }\n\n                 xC${colIndex} = xTexelC${colIndex};\n                 `;\n          }\n          if (colIndex + 1 < filterWidth) {\n            const nextTexelOffset = padLeft % 2 === 0 ? util_exports.nearestLargerEven(dilationWidth) : dilationWidth;\n            if (dilationWidth % 2 === 0 && padLeft % 2 === 1 || dilationWidth % 2 !== 0 && padLeft % 2 !== 1) {\n              mainLoop += `\n                   xCOffset = xC + imod(pads[1], 2) + ${nextTexelOffset};\n\n                   if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${colIndex + 1}Ready == 0) {\n                     xTexelC${colIndex + 1} = getX(batch, xR, xCOffset, d1);\n\n                     // Need to manually clear unused channels in case\n                     // we're reading from recycled texture.\n                     if (xCOffset + 1 >= inDims[1]) {\n                       xTexelC${colIndex + 1}.zw = vec2(0.0);\n                     }\n                     xTexelC${colIndex + 1}Ready = 1;\n                   }\n                   `;\n              if (dilationWidth > 1) {\n                mainLoop += `\n                     xCOffset -= 2;\n                     if (xCOffset >= 0 && xCOffset < inDims[1]) {\n                      previous = getX(batch, xR, xCOffset, d1);\n                      xC${colIndex + 1} = vec4(previous.zw, xTexelC${colIndex + 1}.xy);\n                     } else {\n                      xC${colIndex + 1} = vec4(0.0, 0.0, xTexelC${colIndex + 1}.xy);\n                     }\n                     `;\n              } else {\n                mainLoop += `\n                     xC${colIndex + 1} = vec4(xTexelC${colIndex}.zw, xTexelC${colIndex + 1}.xy);\n                     `;\n              }\n            } else {\n              if (nextTexelOffset === 1) {\n                mainLoop += `\n                     xC${colIndex + 1} = xTexelC${colIndex};\n                     `;\n              } else {\n                mainLoop += `\n                     xCOffset = xC + ${nextTexelOffset};\n\n                     if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${colIndex + 1}Ready == 0) {\n                       xTexelC${colIndex + 1} = getX(batch, xR, xCOffset, d1);\n                       if (xCOffset + 1 >= inDims[1]) {\n                         xTexelC${colIndex + 1}.zw = vec2(0.0);\n                       }\n                       xTexelC${colIndex + 1}Ready = 1;\n                     }\n\n                     xC${colIndex + 1} = xTexelC${colIndex + 1};\n                     `;\n              }\n            }\n          }\n        }\n      } else {\n        if (colIndex < filterWidth) {\n          if (padLeft % 2 === 1) {\n            mainLoop += `\n                 xCOffset = xC + 1 - strides[1];\n                 if(xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${colIndex}Ready == 0) {\n                   xTexelC${colIndex} = getX(batch, xR, xCOffset, d1);\n                   // Need to manually clear unused channels in case\n                   // we're reading from recycled texture.\n                   if (xCOffset + 1 >= inDims[1]) {\n                     xTexelC${colIndex}.zw = vec2(0.0);\n                   }\n                   xTexelC${colIndex}Ready = 1;\n                 }\n\n                 if(xC + 1 >= 0 && xC + 1 < inDims[1] && xTexelC${colIndex + 1}Ready == 0) {\n                   xTexelC${colIndex + 1} = getX(batch, xR, xC + 1, d1);\n                   // Need to manually clear unused channels in case\n                   // we're reading from recycled texture.\n                   if (xC + 2 >= inDims[1]) {\n                     xTexelC${colIndex + 1}.zw = vec2(0.0);\n                   }\n                   xTexelC${colIndex + 1}Ready = 1;\n                 }\n\n                 xC${colIndex} = vec4(xTexelC${colIndex}.zw, xTexelC${colIndex + 1}.zw);\n               `;\n            if (colIndex + 1 < filterWidth) {\n              mainLoop += `\n                   final = vec4(0.0);\n                   xCOffset = xC + 1 + strides[1];\n                   if(xCOffset >= 0 && xCOffset < inDims[1]) {\n                     final = getX(batch, xR, xCOffset, d1);\n                   }\n                   xC${colIndex + 1} = vec4(xTexelC${colIndex + 1}.xy, final.xy);\n                 `;\n            }\n          } else {\n            mainLoop += `\n                 if(xC >= 0 && xC < inDims[1] && xTexelC${colIndex}Ready == 0) {\n                   xTexelC${colIndex} = getX(batch, xR, xC, d1);\n                   if (xC + 1 >= inDims[1]) {\n                     xTexelC${colIndex}.zw = vec2(0.0);\n                   }\n                   xTexelC${colIndex}Ready = 1;\n                 }\n\n                 xCOffset = xC + strides[1];\n                 if(xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${colIndex + 1}Ready == 0) {\n                   xTexelC${colIndex + 1} = getX(batch, xR, xCOffset, d1);\n                   if (xCOffset + 1 >= inDims[1]) {\n                     xTexelC${colIndex + 1}.zw = vec2(0.);\n                   }\n                   xTexelC${colIndex + 1}Ready = 1;\n                 }\n\n                 xC${colIndex} = vec4(\n                   xTexelC${colIndex}.xy, xTexelC${colIndex + 1}.xy);\n               `;\n            if (colIndex + 1 < filterWidth) {\n              mainLoop += `\n                   xC${colIndex + 1} = vec4(xTexelC${colIndex}.zw, xTexelC${colIndex + 1}.zw);\n                 `;\n            }\n          }\n        }\n      }\n      if (colIndex < filterWidth) {\n        mainLoop += `\n             wTexel = getW(r, ${colIndex}, d1, d2);\n             dotProd += xC${colIndex}.xxzz * vec4(wTexel.xy, wTexel.xy);\n             if(d1 + 1 < ${convInfo.inChannels}) {\n               dotProd += xC${colIndex}.yyww * vec4(wTexel.zw, wTexel.zw);\n             }\n           `;\n        if (colIndex + 1 < filterWidth) {\n          mainLoop += `\n               wTexel = getW(r, ${colIndex + 1}, d1, d2);\n               dotProd += xC${colIndex + 1}.xxzz * vec4(wTexel.xy, wTexel.xy);\n               if(d1 + 1 < ${convInfo.inChannels}) {\n                 dotProd += xC${colIndex + 1}.yyww * vec4(wTexel.zw, wTexel.zw);\n               }\n             `;\n        }\n      }\n    }\n    mainLoop += `\n     }\n   `;\n    mainLoop += `\n     }\n   `;\n    mainLoop += `\n     }\n   `;\n    let activationSnippet = \"\", applyActivationSnippet = \"\";\n    if (activation2) {\n      if (hasPreluActivation) {\n        activationSnippet = `vec4 activation(vec4 a) {\n           vec4 b = getPreluActivationWeightsAtOutCoords();\n           ${activation2}\n         }`;\n      } else if (hasLeakyReluAlpha) {\n        activationSnippet = `vec4 activation(vec4 a) {\n           vec4 b = getLeakyreluAlphaAtOutCoords();\n           ${activation2}\n         }`;\n      } else {\n        activationSnippet = `vec4 activation(vec4 x) {\n           ${activation2}\n         }`;\n      }\n      applyActivationSnippet = `result = activation(result);`;\n    }\n    const addBiasSnippet = addBias ? \"result += getBiasAtOutCoords();\" : \"\";\n    if (addBias) {\n      this.variableNames.push(\"bias\");\n    }\n    if (hasPreluActivation) {\n      this.variableNames.push(\"preluActivationWeights\");\n    }\n    if (hasLeakyReluAlpha) {\n      this.variableNames.push(\"leakyreluAlpha\");\n    }\n    this.userCode = `\n       ${activationSnippet}\n\n       void main() {\n         ivec4 coords = getOutputCoords();\n         int batch = coords.x;\n         ivec2 xRCCorner = coords.yz * strides - pads;\n         int d2 = coords.w;\n         int xRCorner = xRCCorner.x;\n         int xCCorner = xRCCorner.y;\n\n         //intialize dotProd with a small epsilon seems to reduce GPU accuracy loss.\n         vec4 dotProd = vec4(0.000000000000001);\n\n         ${mainLoop}\n\n         vec4 result = dotProd - vec4(0.000000000000001);\n         ${addBiasSnippet}\n         ${applyActivationSnippet}\n         setOutput(result);\n       }\n     `;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/im2col_packed_gpu.js\nvar Im2ColPackedProgram = class {\n  constructor(outputShape, convInfo) {\n    this.variableNames = [\"A\"];\n    this.packedInputs = true;\n    this.packedOutput = true;\n    this.customUniforms = [\n      { name: \"inputShape\", type: \"ivec4\" },\n      { name: \"pad\", type: \"ivec2\" },\n      { name: \"stride\", type: \"ivec2\" },\n      { name: \"dilation\", type: \"ivec2\" },\n      { name: \"inChannels\", type: \"int\" },\n      { name: \"itemsPerBlockRow\", type: \"int\" },\n      { name: \"outWidth\", type: \"int\" }\n    ];\n    this.outputShape = outputShape;\n    this.enableShapeUniforms = useShapeUniforms(this.outputShape.length);\n    const { dataFormat } = convInfo;\n    const glsl = getGlslDifferences();\n    const isChannelsLast = dataFormat === \"channelsLast\";\n    const rowDim = isChannelsLast ? 1 : 2;\n    const colDim = isChannelsLast ? 2 : 3;\n    const boundsCheckingSnippet = this.enableShapeUniforms ? \"if(blockIndex < outShape[2] && pos < outShape[1]) {\" : `if(blockIndex < ${outputShape[2]} && pos < ${outputShape[1]}) {`;\n    let unrolled = ``;\n    for (let row = 0; row <= 1; row++) {\n      for (let col = 0; col <= 1; col++) {\n        unrolled += `\n          blockIndex = rc.z + ${col};\n          pos = rc.y + ${row};\n\n          ${boundsCheckingSnippet}\n            offsetY = int(blockIndex / outWidth) * stride[0] - pad[0];\n            d0 = offsetY + dilation[0] * (pos / itemsPerBlockRow);\n\n            if(d0 < inputShape[${rowDim}] && d0 >= 0) {\n              // Use custom imod instead mod. On Intel GPU, mod may generate\n              // unexpected value.\n              // https://github.com/tensorflow/tfjs/issues/5447\n              offsetX = imod(blockIndex, outWidth) * stride[1] - pad[1];\n              d1 = offsetX + dilation[1] * (imod(pos, itemsPerBlockRow) /\n                  inChannels);\n\n              if(d1 < inputShape[${colDim}] && d1 >= 0) {\n\n                ch = imod(pos, inChannels);\n\n                if (${isChannelsLast}) {\n                  innerDims = vec2(d1, ch);\n                  result[${row * 2 + col}] = getChannel(\n                    getA(rc.x, d0, int(innerDims.x),\n                    int(innerDims.y)), innerDims);\n                } else {\n                  innerDims = vec2(d0, d1);\n                  result[${row * 2 + col}] = getChannel(\n                    getA(rc.x, ch, int(innerDims.x),\n                    int(innerDims.y)), innerDims);\n                }\n              }\n            }\n          }\n        `;\n      }\n    }\n    this.userCode = `\n      void main() {\n        ivec3 rc = getOutputCoords();\n\n        vec4 result = vec4(0);\n\n        int blockIndex, pos, offsetY, d0, offsetX, d1, ch;\n        vec2 innerDims;\n\n        ${unrolled}\n\n        ${glsl.output} = result;\n      }\n    `;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Conv2D_impl.js\nfunction getShapeForBatchMatMul(shape, isChannelsLast) {\n  const length = shape.length;\n  if (length >= 3) {\n    return isChannelsLast ? [\n      ...shape.slice(0, -3),\n      shape[length - 3] * shape[length - 2],\n      shape[length - 1]\n    ] : [\n      ...shape.slice(0, -3),\n      shape[length - 3],\n      shape[length - 2] * shape[length - 1]\n    ];\n  } else if (!isChannelsLast && length === 1 && shape[0] > 1) {\n    return [shape[0], 1];\n  } else {\n    return null;\n  }\n}\nfunction conv2dByMatMul({ x, filter, convInfo, backend: backend2, bias = null, preluActivationWeights = null, leakyreluAlpha = 0, activation: activation2 = null }) {\n  const xShape = x.shape;\n  const xTexData = backend2.texData.get(x.dataId);\n  const sharedMatMulDim = convInfo.inChannels;\n  const outerShapeX = xShape[0] * xShape[1] * xShape[2];\n  const outerShapeFilter = convInfo.outChannels;\n  const isChannelsLast = convInfo.dataFormat === \"channelsLast\";\n  const transposeA = false;\n  const transposeB = false;\n  let out;\n  const intermediates = [];\n  if (preluActivationWeights != null) {\n    const targetShape = getShapeForBatchMatMul(preluActivationWeights.shape, isChannelsLast);\n    if (targetShape != null) {\n      preluActivationWeights = reshape4({\n        inputs: { x: preluActivationWeights },\n        backend: backend2,\n        attrs: { shape: targetShape }\n      });\n      intermediates.push(preluActivationWeights);\n    }\n  }\n  if (bias != null) {\n    const targetShape = getShapeForBatchMatMul(bias.shape, isChannelsLast);\n    if (targetShape != null) {\n      bias = reshape4({ inputs: { x: bias }, backend: backend2, attrs: { shape: targetShape } });\n      intermediates.push(bias);\n    }\n  }\n  const batchMatMulWillBeUnpacked = (outerShapeX === 1 || outerShapeFilter === 1) && sharedMatMulDim > MATMUL_SHARED_DIM_THRESHOLD;\n  const canOptimize = !batchMatMulWillBeUnpacked && xTexData.isPacked && isChannelsLast && xTexData.texture != null && xShape[2] % 2 !== 0 && util_exports.arraysEqual(xTexData.shape.slice(-3), xShape.slice(-3));\n  if (canOptimize) {\n    const targetShape = xShape[0] * xShape[1] * (xShape[2] + 1);\n    const xReshaped = {\n      dataId: x.dataId,\n      shape: [1, targetShape, convInfo.inChannels],\n      dtype: x.dtype\n    };\n    const originalXTexDataShape = xTexData.shape;\n    xTexData.shape = xTexData.shape.slice();\n    xTexData.shape[xTexData.shape.length - 2]++;\n    util_exports.assert(isReshapeFree(xTexData.shape, xReshaped.shape), () => `packed reshape ${xTexData.shape} to ${xReshaped.shape} isn't free`);\n    const filterReshaped = reshape4({\n      inputs: { x: filter },\n      backend: backend2,\n      attrs: { shape: [1, convInfo.inChannels, convInfo.outChannels] }\n    });\n    intermediates.push(filterReshaped);\n    const pointwiseConv = batchMatMulImpl({\n      a: xReshaped,\n      b: filterReshaped,\n      backend: backend2,\n      transposeA,\n      transposeB,\n      bias,\n      activation: activation2,\n      preluActivationWeights,\n      leakyreluAlpha\n    });\n    const pointwiseConvTexData = backend2.texData.get(pointwiseConv.dataId);\n    util_exports.assert(pointwiseConvTexData.isPacked, () => \"batchMatMul result is expected to be packed\");\n    xTexData.shape = originalXTexDataShape;\n    pointwiseConvTexData.shape = convInfo.outShape;\n    out = identity3({ inputs: { x: pointwiseConv }, backend: backend2 });\n    out.shape = convInfo.outShape;\n    intermediates.push(pointwiseConv);\n  } else {\n    const numCols = convInfo.outHeight * convInfo.outWidth;\n    const xReshaped = reshape4({\n      inputs: { x },\n      backend: backend2,\n      attrs: {\n        shape: isChannelsLast ? [convInfo.batchSize, numCols, convInfo.inChannels] : [convInfo.batchSize, convInfo.inChannels, numCols]\n      }\n    });\n    const filterReshaped = reshape4({\n      inputs: { x: filter },\n      backend: backend2,\n      attrs: { shape: [1, convInfo.inChannels, convInfo.outChannels] }\n    });\n    const result = batchMatMulImpl({\n      a: isChannelsLast ? xReshaped : filterReshaped,\n      b: isChannelsLast ? filterReshaped : xReshaped,\n      transposeA: !isChannelsLast,\n      transposeB,\n      backend: backend2,\n      bias,\n      activation: activation2,\n      preluActivationWeights,\n      leakyreluAlpha\n    });\n    out = reshape4({ inputs: { x: result }, backend: backend2, attrs: { shape: convInfo.outShape } });\n    intermediates.push(xReshaped);\n    intermediates.push(filterReshaped);\n    intermediates.push(result);\n  }\n  for (const i2 of intermediates) {\n    backend2.disposeIntermediateTensorInfo(i2);\n  }\n  return out;\n}\nfunction conv2dWithIm2Row({ x, filter, convInfo, backend: backend2, bias = null, preluActivationWeights = null, leakyreluAlpha = 0, activation: activation2 = null }) {\n  const { filterWidth, filterHeight, inChannels, outWidth, outHeight, dataFormat } = convInfo;\n  const isChannelsLast = dataFormat === \"channelsLast\";\n  const sharedDim = filterWidth * filterHeight * inChannels;\n  const numCols = outHeight * outWidth;\n  const x2ColShape = [convInfo.batchSize, sharedDim, numCols];\n  const transposeA = true;\n  const transposeB = false;\n  const intermediates = [];\n  if (preluActivationWeights != null) {\n    const targetShape = getShapeForBatchMatMul(preluActivationWeights.shape, isChannelsLast);\n    if (targetShape != null) {\n      preluActivationWeights = reshape4({\n        inputs: { x: preluActivationWeights },\n        backend: backend2,\n        attrs: { shape: targetShape }\n      });\n      intermediates.push(preluActivationWeights);\n    }\n  }\n  if (bias != null) {\n    const targetShape = getShapeForBatchMatMul(bias.shape, isChannelsLast);\n    if (targetShape != null) {\n      bias = reshape4({ inputs: { x: bias }, backend: backend2, attrs: { shape: targetShape } });\n      intermediates.push(bias);\n    }\n  }\n  const w2Row = reshape4({\n    inputs: { x: filter },\n    backend: backend2,\n    attrs: { shape: [1, sharedDim, util_exports.sizeFromShape(filter.shape) / sharedDim] }\n  });\n  intermediates.push(w2Row);\n  const im2ColProgram = new Im2ColPackedProgram(x2ColShape, convInfo);\n  const customValues = [\n    x.shape,\n    [convInfo.padInfo.top, convInfo.padInfo.left],\n    [convInfo.strideHeight, convInfo.strideWidth],\n    [convInfo.dilationHeight, convInfo.dilationWidth],\n    [convInfo.inChannels],\n    [convInfo.filterWidth * convInfo.inChannels],\n    [convInfo.outWidth]\n  ];\n  const im2Col = backend2.runWebGLProgram(im2ColProgram, [x], \"float32\", customValues);\n  const im2ColReshaped = reshape4({ inputs: { x: im2Col }, backend: backend2, attrs: { shape: x2ColShape } });\n  intermediates.push(im2Col);\n  intermediates.push(im2ColReshaped);\n  const hasBias = bias != null;\n  const hasPreluActivationWeights = preluActivationWeights != null;\n  const hasLeakyreluAlpha = activation2 === \"leakyrelu\";\n  const fusedActivation = activation2 ? mapActivationToShaderProgram(activation2, true) : null;\n  const matmulProgram = new MatMulPackedProgram(isChannelsLast ? im2ColReshaped.shape : w2Row.shape, isChannelsLast ? w2Row.shape : im2ColReshaped.shape, isChannelsLast ? [convInfo.batchSize, numCols, convInfo.outChannels] : [convInfo.batchSize, convInfo.outChannels, numCols], transposeA, transposeB, hasBias, fusedActivation, hasPreluActivationWeights, hasLeakyreluAlpha);\n  const inputs = isChannelsLast ? [im2ColReshaped, w2Row] : [w2Row, im2ColReshaped];\n  if (bias) {\n    inputs.push(bias);\n  }\n  if (hasPreluActivationWeights) {\n    inputs.push(preluActivationWeights);\n  }\n  if (hasLeakyreluAlpha) {\n    const $leakyreluAlpha = backend2.makeTensorInfo([], \"float32\", util_exports.createScalarValue(leakyreluAlpha, \"float32\"));\n    inputs.push($leakyreluAlpha);\n    intermediates.push($leakyreluAlpha);\n  }\n  const product = backend2.runWebGLProgram(matmulProgram, inputs, \"float32\");\n  const out = reshape4({ inputs: { x: product }, backend: backend2, attrs: { shape: convInfo.outShape } });\n  intermediates.push(product);\n  for (const i2 of intermediates) {\n    backend2.disposeIntermediateTensorInfo(i2);\n  }\n  return out;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Conv2D.js\nfunction conv2d4(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x, filter } = inputs;\n  const { strides, pad: pad3, dataFormat, dilations, dimRoundingMode } = attrs;\n  const $dataFormat = backend_util_exports.convertConv2DDataFormat(dataFormat);\n  const convInfo = backend_util_exports.computeConv2DInfo(x.shape, filter.shape, strides, dilations, pad3, dimRoundingMode, false, $dataFormat);\n  let out;\n  if (convInfo.filterHeight === 1 && convInfo.filterWidth === 1 && convInfo.dilationHeight === 1 && convInfo.dilationWidth === 1 && convInfo.strideHeight === 1 && convInfo.strideWidth === 1 && (convInfo.padInfo.type === \"SAME\" || convInfo.padInfo.type === \"VALID\")) {\n    out = conv2dByMatMul({ x, filter, convInfo, backend: backend2 });\n  } else if (convInfo.strideWidth <= 2 && $dataFormat === \"channelsLast\" && env().getBool(\"WEBGL_EXP_CONV\")) {\n    const program = new Conv2DPackedProgram(convInfo);\n    const customValues = [\n      [convInfo.padInfo.top, convInfo.padInfo.left],\n      [convInfo.strideHeight, convInfo.strideWidth],\n      [convInfo.dilationHeight, convInfo.dilationWidth],\n      [convInfo.inHeight, convInfo.inWidth]\n    ];\n    out = backend2.runWebGLProgram(program, [x, filter], \"float32\", customValues);\n  } else if (env().getBool(\"WEBGL_CONV_IM2COL\")) {\n    out = conv2dWithIm2Row({ x, filter, convInfo, backend: backend2 });\n  } else {\n    const program = new Conv2DProgram(convInfo);\n    out = backend2.runWebGLProgram(program, [x, filter], \"float32\");\n  }\n  const outReshaped = reshape4({ inputs: { x: out }, backend: backend2, attrs: { shape: convInfo.outShape } });\n  backend2.disposeIntermediateTensorInfo(out);\n  return outReshaped;\n}\nvar conv2DConfig2 = {\n  kernelName: Conv2D,\n  backendName: \"webgl\",\n  kernelFunc: conv2d4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/conv_backprop_gpu.js\nvar Conv2DDerFilterProgram = class {\n  constructor(convInfo) {\n    this.variableNames = [\"x\", \"dy\"];\n    this.outputShape = convInfo.filterShape;\n    const strideHeight = convInfo.strideHeight;\n    const strideWidth = convInfo.strideWidth;\n    const padTop = convInfo.padInfo.top;\n    const padLeft = convInfo.padInfo.left;\n    const isChannelsLast = convInfo.dataFormat === \"channelsLast\";\n    this.userCode = `\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int wR = coords.x;\n        int wC = coords.y;\n        int d1 = coords.z;\n        int d2 = coords.w;\n\n        // Convolve x(?, ?, d1) with dy(:, :, d2) to get dw(wR, wC, d1, d2).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n\n        for (int b = 0; b < ${convInfo.batchSize}; b++) {\n          for (int yR = 0; yR < ${convInfo.outHeight}; yR++) {\n            int xR = wR + yR * ${strideHeight} - ${padTop};\n\n            if (xR < 0 || xR >= ${convInfo.inHeight}) {\n              continue;\n            }\n\n            for (int yC = 0; yC < ${convInfo.outWidth}; yC++) {\n              int xC = wC + yC * ${strideWidth} - ${padLeft};\n\n              if (xC < 0 || xC >= ${convInfo.inWidth}) {\n                continue;\n              }\n\n              if (${isChannelsLast}) {\n                float dyValue = getDy(b, yR, yC, d2);\n                float xValue = getX(b, xR, xC, d1);\n                dotProd += (xValue * dyValue);\n              } else {\n                float dyValue = getDy(b, d2, yR, yC);\n                float xValue = getX(b, d1, xR, xC);\n                dotProd += (xValue * dyValue);\n              }\n\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    `;\n  }\n};\nvar Conv2DDerInputProgram = class {\n  constructor(convInfo) {\n    this.variableNames = [\"dy\", \"W\"];\n    this.outputShape = convInfo.inShape;\n    const filterHeight = convInfo.filterHeight;\n    const filterWidth = convInfo.filterWidth;\n    const strideHeight = convInfo.strideHeight;\n    const strideWidth = convInfo.strideWidth;\n    const isChannelsLast = convInfo.dataFormat === \"channelsLast\";\n    const padTop = filterHeight - 1 - convInfo.padInfo.top;\n    const padLeft = filterWidth - 1 - convInfo.padInfo.left;\n    const rowDim = isChannelsLast ? 1 : 2;\n    const colDim = isChannelsLast ? 2 : 3;\n    const channelDim = isChannelsLast ? 3 : 1;\n    this.userCode = `\n      const ivec2 pads = ivec2(${padTop}, ${padLeft});\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int batch = coords[0];\n        int d1 = coords[${channelDim}];\n\n        ivec2 dyCorner = ivec2(coords[${rowDim}], coords[${colDim}]) - pads;\n        int dyRCorner = dyCorner.x;\n        int dyCCorner = dyCorner.y;\n\n        // Convolve dy(?, ?, d2) with w(:, :, d1, d2) to compute dx(xR, xC, d1).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n        for (int wR = 0; wR < ${filterHeight}; wR++) {\n          float dyR = float(dyRCorner + wR) / ${strideHeight}.0;\n\n          if (dyR < 0.0 || dyR >= ${convInfo.outHeight}.0 || fract(dyR) > 0.0) {\n            continue;\n          }\n          int idyR = int(dyR);\n\n          int wRPerm = ${filterHeight} - 1 - wR;\n\n          for (int wC = 0; wC < ${filterWidth}; wC++) {\n            float dyC = float(dyCCorner + wC) / ${strideWidth}.0;\n\n            if (dyC < 0.0 || dyC >= ${convInfo.outWidth}.0 ||\n                fract(dyC) > 0.0) {\n              continue;\n            }\n            int idyC = int(dyC);\n\n            int wCPerm = ${filterWidth} - 1 - wC;\n\n            for (int d2 = 0; d2 < ${convInfo.outChannels}; d2++) {\n\n              if (${isChannelsLast}) {\n                float xValue = getDy(batch, idyR, idyC, d2);\n                float wValue = getW(wRPerm, wCPerm, d1, d2);\n                dotProd += xValue * wValue;\n              } else {\n                float xValue = getDy(batch, d2, idyR, idyC);\n                float wValue = getW(wRPerm, wCPerm, d1, d2);\n                dotProd += xValue * wValue;\n              }\n\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    `;\n  }\n};\nvar Conv3DDerFilterProgram = class {\n  constructor(convInfo) {\n    this.variableNames = [\"x\", \"dy\"];\n    this.outputShape = convInfo.filterShape;\n    const strideDepth = convInfo.strideDepth;\n    const strideHeight = convInfo.strideHeight;\n    const strideWidth = convInfo.strideWidth;\n    const padFront = convInfo.padInfo.front;\n    const padTop = convInfo.padInfo.top;\n    const padLeft = convInfo.padInfo.left;\n    this.userCode = `\n      void main() {\n        ivec5 coords = getOutputCoords();\n        int wF = coords.x;\n        int wR = coords.y;\n        int wC = coords.z;\n        int d1 = coords.w;\n        int d2 = coords.u;\n\n        float dotProd = 0.0;\n\n        for (int b = 0; b < ${convInfo.batchSize}; b++) {\n          for (int yF = 0; yF < ${convInfo.outDepth}; yF++) {\n            int xF = wF + yF * ${strideDepth} - ${padFront};\n\n            if (xF < 0 || xF >= ${convInfo.inDepth}) {\n              continue;\n            }\n\n            for (int yR = 0; yR < ${convInfo.outHeight}; yR++) {\n              int xR = wR + yR * ${strideHeight} - ${padTop};\n\n              if (xR < 0 || xR >= ${convInfo.inHeight}) {\n                continue;\n              }\n\n              for (int yC = 0; yC < ${convInfo.outWidth}; yC++) {\n                int xC = wC + yC * ${strideWidth} - ${padLeft};\n\n                if (xC < 0 || xC >= ${convInfo.inWidth}) {\n                  continue;\n                }\n\n                float dyValue = getDy(b, yF, yR, yC, d2);\n                float xValue = getX(b, xF, xR, xC, d1);\n                dotProd += (xValue * dyValue);\n              }\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    `;\n  }\n};\nvar Conv3DDerInputProgram = class {\n  constructor(convInfo) {\n    this.variableNames = [\"dy\", \"W\"];\n    this.outputShape = convInfo.inShape;\n    const filterDepth = convInfo.filterDepth;\n    const filterHeight = convInfo.filterHeight;\n    const filterWidth = convInfo.filterWidth;\n    const strideDepth = convInfo.strideDepth;\n    const strideHeight = convInfo.strideHeight;\n    const strideWidth = convInfo.strideWidth;\n    const padFront = filterDepth - 1 - convInfo.padInfo.front;\n    const padTop = filterHeight - 1 - convInfo.padInfo.top;\n    const padLeft = filterWidth - 1 - convInfo.padInfo.left;\n    this.userCode = `\n      const ivec3 pads = ivec3(${padFront}, ${padTop}, ${padLeft});\n\n      void main() {\n        ivec5 coords = getOutputCoords();\n        int batch = coords.x;\n        int d1 = coords.u;\n\n\n        ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;\n        int dyFCorner = dyCorner.x;\n        int dyRCorner = dyCorner.y;\n        int dyCCorner = dyCorner.z;\n\n        float dotProd = 0.0;\n        for (int wF = 0; wF < ${filterDepth}; wF++) {\n          float dyF = float(dyFCorner + wF) / ${strideDepth}.0;\n\n          if (dyF < 0.0 || dyF >= ${convInfo.outDepth}.0 || fract(dyF) > 0.0) {\n            continue;\n          }\n          int idyF = int(dyF);\n\n          int wFPerm = ${filterDepth} - 1 - wF;\n\n          for (int wR = 0; wR < ${filterHeight}; wR++) {\n            float dyR = float(dyRCorner + wR) / ${strideHeight}.0;\n\n            if (dyR < 0.0 || dyR >= ${convInfo.outHeight}.0 ||\n              fract(dyR) > 0.0) {\n              continue;\n            }\n            int idyR = int(dyR);\n\n            int wRPerm = ${filterHeight} - 1 - wR;\n\n            for (int wC = 0; wC < ${filterWidth}; wC++) {\n              float dyC = float(dyCCorner + wC) / ${strideWidth}.0;\n\n              if (dyC < 0.0 || dyC >= ${convInfo.outWidth}.0 ||\n                  fract(dyC) > 0.0) {\n                continue;\n              }\n              int idyC = int(dyC);\n\n              int wCPerm = ${filterWidth} - 1 - wC;\n\n              for (int d2 = 0; d2 < ${convInfo.outChannels}; d2++) {\n                float xValue = getDy(batch, idyF, idyR, idyC, d2);\n                float wValue = getW(wFPerm, wRPerm, wCPerm, d1, d2);\n                dotProd += xValue * wValue;\n              }\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    `;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Conv2DBackpropFilter.js\nfunction conv2DBackpropFilter3(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x, dy } = inputs;\n  const { strides, pad: pad3, dataFormat, dimRoundingMode, filterShape } = attrs;\n  const $dataFormat = backend_util_exports.convertConv2DDataFormat(dataFormat);\n  const convInfo = backend_util_exports.computeConv2DInfo(x.shape, filterShape, strides, 1, pad3, dimRoundingMode, false, $dataFormat);\n  const program = new Conv2DDerFilterProgram(convInfo);\n  return backend2.runWebGLProgram(program, [x, dy], \"float32\");\n}\nvar conv2DBackpropFilterConfig2 = {\n  kernelName: Conv2DBackpropFilter,\n  backendName: \"webgl\",\n  kernelFunc: conv2DBackpropFilter3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Conv2DBackpropInput.js\nfunction conv2DBackpropInput3(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { dy, filter } = inputs;\n  const { inputShape, strides, pad: pad3, dataFormat, dimRoundingMode } = attrs;\n  const $dataFormat = backend_util_exports.convertConv2DDataFormat(dataFormat);\n  const convInfo = backend_util_exports.computeConv2DInfo(inputShape, filter.shape, strides, 1, pad3, dimRoundingMode, false, $dataFormat);\n  const program = new Conv2DDerInputProgram(convInfo);\n  return backend2.runWebGLProgram(program, [dy, filter], \"float32\");\n}\nvar conv2DBackpropInputConfig2 = {\n  kernelName: Conv2DBackpropInput,\n  backendName: \"webgl\",\n  kernelFunc: conv2DBackpropInput3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Conv3D.js\nfunction conv3D2(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x, filter } = inputs;\n  const { strides, pad: pad3, dilations } = attrs;\n  const convInfo = backend_util_exports.computeConv3DInfo(x.shape, filter.shape, strides, dilations, pad3);\n  const program = new Conv3DProgram(convInfo);\n  return backend2.runWebGLProgram(program, [x, filter], \"float32\");\n}\nvar conv3DConfig2 = {\n  kernelName: Conv3D,\n  backendName: \"webgl\",\n  kernelFunc: conv3D2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Conv3DBackpropFilterV2.js\nfunction conv3DBackpropFilterV22(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x, dy } = inputs;\n  const { strides, pad: pad3, filterShape } = attrs;\n  const convInfo = backend_util_exports.computeConv3DInfo(x.shape, filterShape, strides, 1, pad3);\n  const program = new Conv3DDerFilterProgram(convInfo);\n  return backend2.runWebGLProgram(program, [x, dy], \"float32\");\n}\nvar conv3DBackpropFilterV2Config2 = {\n  kernelName: Conv3DBackpropFilterV2,\n  backendName: \"webgl\",\n  kernelFunc: conv3DBackpropFilterV22\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Conv3DBackpropInputV2.js\nfunction conv3DBackpropInput2(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { dy, filter } = inputs;\n  const { pad: pad3, strides, inputShape } = attrs;\n  const convInfo = backend_util_exports.computeConv3DInfo(inputShape, filter.shape, strides, 1, pad3);\n  const program = new Conv3DDerInputProgram(convInfo);\n  return backend2.runWebGLProgram(program, [dy, filter], \"float32\");\n}\nvar conv3DBackpropInputConfig = {\n  kernelName: Conv3DBackpropInputV2,\n  backendName: \"webgl\",\n  kernelFunc: conv3DBackpropInput2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Cos.js\nvar COS = CHECK_NAN_SNIPPET_UNARY + `\n  return cos(x);\n`;\nvar cos3 = unaryKernelFunc2({ opSnippet: COS });\nvar cosConfig2 = {\n  kernelName: Cos,\n  backendName: \"webgl\",\n  kernelFunc: cos3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Cosh.js\nvar COSH = `\n  float e2x = exp(-x);\n  return (e2x + 1.0 / e2x) / 2.0;\n`;\nvar cosh3 = unaryKernelFunc2({ opSnippet: COSH });\nvar coshConfig2 = {\n  kernelName: Cosh,\n  backendName: \"webgl\",\n  kernelFunc: cosh3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/crop_and_resize_gpu.js\nvar CropAndResizeProgram = class {\n  constructor(imageShape, boxShape, cropSize, method, extrapolationValue) {\n    this.variableNames = [\"Image\", \"Boxes\", \"BoxInd\"];\n    this.outputShape = [];\n    const [batch, imageHeight, imageWidth, depth] = imageShape;\n    const [numBoxes] = boxShape;\n    const [cropHeight, cropWidth] = cropSize;\n    this.outputShape = [numBoxes, cropHeight, cropWidth, depth];\n    const methodId = method === \"bilinear\" ? 1 : 0;\n    const [inputHeightFloat, inputWidthFloat] = [`${imageHeight - 1}.0`, `${imageWidth - 1}.0`];\n    const [heightRatio, heightScale, inY] = cropHeight > 1 ? [\n      `${(imageHeight - 1) / (cropHeight - 1)}`,\n      \"(y2-y1) * height_ratio\",\n      `y1*${inputHeightFloat} + float(y)*(height_scale)`\n    ] : [\n      \"0.0\",\n      \"0.0\",\n      `0.5 * (y1+y2) * ${inputHeightFloat}`\n    ];\n    const [widthRatio, widthScale, inX] = cropWidth > 1 ? [\n      `${(imageWidth - 1) / (cropWidth - 1)}`,\n      \"(x2-x1) * width_ratio\",\n      `x1*${inputWidthFloat} + float(x)*(width_scale)`\n    ] : [\n      \"0.0\",\n      \"0.0\",\n      `0.5 * (x1+x2) * ${inputWidthFloat}`\n    ];\n    this.userCode = `\n      const float height_ratio = float(${heightRatio});\n      const float width_ratio = float(${widthRatio});\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int y = coords[1];\n        int x = coords[2];\n        int d = coords[3];\n\n        // get box vals\n        float y1 = getBoxes(b,0);\n        float x1 = getBoxes(b,1);\n        float y2 = getBoxes(b,2);\n        float x2 = getBoxes(b,3);\n\n        // get image in batch index\n        int bInd = round(getBoxInd(b));\n        if(bInd < 0 || bInd >= ${batch}) {\n          return;\n        }\n\n        float height_scale = ${heightScale};\n        float width_scale = ${widthScale};\n\n        float in_y = ${inY};\n        if( in_y < 0.0 || in_y > ${inputHeightFloat} ) {\n          setOutput(float(${extrapolationValue}));\n          return;\n        }\n        float in_x = ${inX};\n        if( in_x < 0.0 || in_x > ${inputWidthFloat} ) {\n          setOutput(float(${extrapolationValue}));\n          return;\n        }\n\n        vec2 sourceFracIndexCR = vec2(in_x,in_y);\n        if(${methodId} == 1) {\n          // Compute the four integer indices.\n          ivec2 sourceFloorCR = ivec2(sourceFracIndexCR);\n          ivec2 sourceCeilCR = ivec2(ceil(sourceFracIndexCR));\n\n          float topLeft = getImage(b, sourceFloorCR.y, sourceFloorCR.x, d);\n          float bottomLeft = getImage(b, sourceCeilCR.y, sourceFloorCR.x, d);\n          float topRight = getImage(b, sourceFloorCR.y, sourceCeilCR.x, d);\n          float bottomRight = getImage(b, sourceCeilCR.y, sourceCeilCR.x, d);\n\n          vec2 fracCR = sourceFracIndexCR - vec2(sourceFloorCR);\n\n          float top = topLeft + (topRight - topLeft) * fracCR.x;\n          float bottom = bottomLeft + (bottomRight - bottomLeft) * fracCR.x;\n          float newValue = top + (bottom - top) * fracCR.y;\n          setOutput(newValue);\n        } else {\n          // Compute the coordinators of nearest neighbor point.\n          ivec2 sourceNearestCR = ivec2(floor(\n            sourceFracIndexCR + vec2(0.5,0.5)));\n          float newValue = getImage(b, sourceNearestCR.y, sourceNearestCR.x, d);\n          setOutput(newValue);\n        }\n      }\n    `;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/CropAndResize.js\nvar cropAndResize3 = (args) => {\n  const { inputs, backend: backend2, attrs } = args;\n  const { image: image2, boxes, boxInd } = inputs;\n  const { cropSize, method, extrapolationValue } = attrs;\n  const program = new CropAndResizeProgram(image2.shape, boxes.shape, cropSize, method, extrapolationValue);\n  return backend2.runWebGLProgram(program, [image2, boxes, boxInd], \"float32\");\n};\nvar cropAndResizeConfig2 = {\n  kernelName: CropAndResize,\n  backendName: \"webgl\",\n  kernelFunc: cropAndResize3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/cum_gpu.js\nvar CumOpType;\n(function(CumOpType3) {\n  CumOpType3[\"Prod\"] = \"*\";\n  CumOpType3[\"Sum\"] = \"+\";\n})(CumOpType || (CumOpType = {}));\nvar CumProgram = class {\n  constructor(op2, outputShape, exclusive, reverse5) {\n    this.op = op2;\n    this.outputShape = outputShape;\n    this.variableNames = [\"x\"];\n    this.customUniforms = [{ name: \"index\", type: \"float\" }];\n    const rank = this.outputShape.length;\n    const initVal = this.op === CumOpType.Prod ? \"1.0\" : \"0.0\";\n    const val = exclusive ? initVal : `getX(${getCoords2(rank, \"coords\", this.op)})`;\n    const length = this.outputShape[this.outputShape.length - 1];\n    let condition = \"\";\n    let idxString = \"\";\n    if (exclusive) {\n      condition = reverse5 ? `end != ${length - 1}` : \"end != 0\";\n      idxString = reverse5 ? \"end + 1\" : \"end - 1\";\n    } else {\n      condition = reverse5 ? `end + pow2 < ${length}` : \"end >= pow2\";\n      idxString = reverse5 ? \"end + pow2\" : \"end - pow2\";\n    }\n    this.userCode = `\n      void main() {\n        ${getCoordsDataType(rank)} coords = getOutputCoords();\n        int end = ${getFinalCoord(rank, \"coords\", this.op)};\n        float val = ${val};\n        int pow2 = int(pow(2.0, index));\n        if (${condition}) {\n          int idx = ${idxString};\n          ${getFinalCoord(rank, \"coords\", this.op)} = idx;\n          val ${this.op}= getX(${getCoords2(rank, \"coords\", this.op)});\n        }\n        setOutput(val);\n      }\n    `;\n  }\n};\nfunction getCoords2(rank, name, op2) {\n  if (rank === 1) {\n    return `${name}`;\n  } else if (rank === 2) {\n    return `${name}.x, ${name}.y`;\n  } else if (rank === 3) {\n    return `${name}.x, ${name}.y, ${name}.z`;\n  } else if (rank === 4) {\n    return `${name}.x, ${name}.y, ${name}.z, ${name}.w`;\n  } else {\n    throw new Error(`Cumulative ${op2} for rank ${rank} is not yet supported`);\n  }\n}\nfunction getFinalCoord(rank, name, op2) {\n  if (rank === 1) {\n    return `${name}`;\n  } else if (rank === 2) {\n    return `${name}.y`;\n  } else if (rank === 3) {\n    return `${name}.z`;\n  } else if (rank === 4) {\n    return `${name}.w`;\n  } else {\n    throw new Error(`Cumulative ${op2} for rank ${rank} is not yet supported`);\n  }\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Cum_impl.js\nfunction cumImpl(op2, x, backend2, axis, exclusive, reverse5) {\n  const xRank = x.shape.length;\n  const permutation = backend_util_exports.getAxesPermutation([axis], xRank);\n  let permutedX = x;\n  if (permutation != null) {\n    permutedX = transpose3({ inputs: { x }, backend: backend2, attrs: { perm: permutation } });\n  }\n  const permutedAxis = backend_util_exports.getInnerMostAxes(1, xRank)[0];\n  if (permutedAxis !== xRank - 1) {\n    throw new Error(`WebGL cumprod shader expects an inner-most axis=${x.shape.length - 1} but got axis=${axis}`);\n  }\n  const size = permutedX.shape[permutedAxis];\n  let result = identity3({ inputs: { x: permutedX }, backend: backend2 });\n  for (let i2 = 0; i2 <= Math.ceil(Math.log2(size)) - 1; i2++) {\n    const program = new CumProgram(op2, permutedX.shape, false, reverse5);\n    const customValues = [[i2]];\n    const prevResult = result;\n    result = backend2.runWebGLProgram(program, [result], result.dtype, customValues);\n    backend2.disposeIntermediateTensorInfo(prevResult);\n  }\n  if (exclusive) {\n    const program = new CumProgram(op2, permutedX.shape, exclusive, reverse5);\n    const prevResult = result;\n    result = backend2.runWebGLProgram(program, [result], result.dtype);\n    backend2.disposeIntermediateTensorInfo(prevResult);\n  }\n  if (permutation != null) {\n    const reversePermutation = backend_util_exports.getUndoAxesPermutation(permutation);\n    const reverseTransposedResult = transpose3({ inputs: { x: result }, backend: backend2, attrs: { perm: reversePermutation } });\n    backend2.disposeIntermediateTensorInfo(result);\n    backend2.disposeIntermediateTensorInfo(permutedX);\n    return reverseTransposedResult;\n  }\n  return result;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Cumprod.js\nfunction cumprod3(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x } = inputs;\n  const { axis, exclusive, reverse: reverse5 } = attrs;\n  return cumImpl(CumOpType.Prod, x, backend2, axis, exclusive, reverse5);\n}\nvar cumprodConfig2 = {\n  kernelName: Cumprod,\n  backendName: \"webgl\",\n  kernelFunc: cumprod3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Cumsum.js\nfunction cumsum3(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x } = inputs;\n  const { axis, exclusive, reverse: reverse5 } = attrs;\n  return cumImpl(CumOpType.Sum, x, backend2, axis, exclusive, reverse5);\n}\nvar cumsumConfig2 = {\n  kernelName: Cumsum,\n  backendName: \"webgl\",\n  kernelFunc: cumsum3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/DenseBincount.js\nfunction denseBincount3(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x, weights } = inputs;\n  const { size, binaryOutput } = attrs;\n  if (x.shape.length === 1) {\n    const xVals = backend2.readSync(x.dataId);\n    const weightsVals = backend2.readSync(weights.dataId);\n    const outVals = bincountImplCPU(xVals, weightsVals, weights.dtype, weights.shape, size);\n    return backend2.makeTensorInfo([size], weights.dtype, outVals);\n  } else if (x.shape.length === 2) {\n    const xBuf = backend2.bufferSync(x);\n    const weightsBuf = backend2.bufferSync(weights);\n    const outBuf = bincountReduceImplCPU(xBuf, weightsBuf, size, binaryOutput);\n    return backend2.makeTensorInfo(outBuf.shape, weights.dtype, outBuf.values);\n  }\n  throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${x.shape.length}.`);\n}\nvar denseBincountConfig2 = {\n  kernelName: DenseBincount,\n  backendName: \"webgl\",\n  kernelFunc: denseBincount3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/depth_to_space_gpu.js\nvar DepthToSpaceProgram = class {\n  constructor(outputShape, blockSize, dataFormat) {\n    this.variableNames = [\"x\"];\n    this.outputShape = [];\n    this.outputShape = outputShape;\n    this.blockSize = blockSize;\n    this.dataFormat = dataFormat;\n    this.userCode = `\n    void main() {\n      ivec4 coords = getOutputCoords();\n      int b = coords[0];\n      int h = ${this.getHeightCoordString()};\n      int w = ${this.getWidthCoordString()};\n      int d = ${this.getDepthCoordString()};\n\n      int in_h = h / ${blockSize};\n      int offset_h = imod(h, ${blockSize});\n      int in_w = w / ${blockSize};\n      int offset_w = imod(w, ${blockSize});\n      int offset_d = (offset_h * ${blockSize} + offset_w) *\n        ${this.getOutputDepthSize()};\n      int in_d = d + offset_d;\n\n      float result = ${this.getInputSamplingString()};\n      setOutput(result);\n    }\n  `;\n  }\n  getHeightCoordString() {\n    if (this.dataFormat === \"NHWC\") {\n      return `coords[1]`;\n    } else {\n      return `coords[2]`;\n    }\n  }\n  getWidthCoordString() {\n    if (this.dataFormat === \"NHWC\") {\n      return `coords[2]`;\n    } else {\n      return `coords[3]`;\n    }\n  }\n  getDepthCoordString() {\n    if (this.dataFormat === \"NHWC\") {\n      return `coords[3]`;\n    } else {\n      return `coords[1]`;\n    }\n  }\n  getOutputDepthSize() {\n    if (this.dataFormat === \"NHWC\") {\n      return this.outputShape[3];\n    } else {\n      return this.outputShape[1];\n    }\n  }\n  getInputSamplingString() {\n    if (this.dataFormat === \"NHWC\") {\n      return `getX(b, in_h, in_w, in_d)`;\n    } else {\n      return `getX(b, in_d, in_h, in_w)`;\n    }\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/DepthToSpace.js\nfunction depthToSpace3(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x } = inputs;\n  const { blockSize, dataFormat } = attrs;\n  const batchSize = x.shape[0];\n  const inputHeight = dataFormat === \"NHWC\" ? x.shape[1] : x.shape[2];\n  const inputWidth = dataFormat === \"NHWC\" ? x.shape[2] : x.shape[3];\n  const inputDepth = dataFormat === \"NHWC\" ? x.shape[3] : x.shape[1];\n  const outputHeight = inputHeight * blockSize;\n  const outputWidth = inputWidth * blockSize;\n  const outputDepth = inputDepth / (blockSize * blockSize);\n  const outputShape = dataFormat === \"NHWC\" ? [batchSize, outputHeight, outputWidth, outputDepth] : [batchSize, outputDepth, outputHeight, outputWidth];\n  const program = new DepthToSpaceProgram(outputShape, blockSize, dataFormat);\n  return backend2.runWebGLProgram(program, [x], x.dtype);\n}\nvar depthToSpaceConfig2 = {\n  kernelName: DepthToSpace,\n  backendName: \"webgl\",\n  kernelFunc: depthToSpace3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/conv_gpu_depthwise.js\nvar DepthwiseConv2DProgram = class {\n  constructor(convInfo, addBias = false, activation2 = null, hasPreluActivation = false, hasLeakyReluAlpha = false) {\n    this.variableNames = [\"x\", \"W\"];\n    this.customUniforms = [\n      { name: \"pads\", type: \"ivec2\" },\n      { name: \"strides\", type: \"ivec2\" },\n      { name: \"dilations\", type: \"ivec2\" },\n      { name: \"inDims\", type: \"ivec2\" }\n    ];\n    this.outputShape = convInfo.outShape;\n    this.enableShapeUniforms = useShapeUniforms(this.outputShape.length);\n    const filterHeight = convInfo.filterHeight;\n    const filterWidth = convInfo.filterWidth;\n    const channelMul = convInfo.outChannels / convInfo.inChannels;\n    let activationSnippet = \"\", applyActivationSnippet = \"\";\n    if (activation2) {\n      if (hasPreluActivation) {\n        activationSnippet = `float activation(float a) {\n          float b = getPreluActivationWeightsAtOutCoords();\n          ${activation2}\n        }`;\n      } else if (hasLeakyReluAlpha) {\n        activationSnippet = `float activation(float a) {\n          float b = getLeakyreluAlphaAtOutCoords();\n          ${activation2}\n        }`;\n      } else {\n        activationSnippet = `\n          float activation(float x) {\n            ${activation2}\n          }\n        `;\n      }\n      applyActivationSnippet = `result = activation(result);`;\n    }\n    const addBiasSnippet = addBias ? \"result += getBiasAtOutCoords();\" : \"\";\n    if (addBias) {\n      this.variableNames.push(\"bias\");\n    }\n    if (hasPreluActivation) {\n      this.variableNames.push(\"preluActivationWeights\");\n    }\n    if (hasLeakyReluAlpha) {\n      this.variableNames.push(\"leakyreluAlpha\");\n    }\n    this.userCode = `\n      ${activationSnippet}\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int batch = coords.x;\n        ivec2 xRCCorner = coords.yz * strides - pads;\n        int d2 = coords.w;\n        int d1 = d2 / ${channelMul};\n        int q = d2 - d1 * ${channelMul};\n\n        int xRCorner = xRCCorner.x;\n        int xCCorner = xRCCorner.y;\n\n        // Convolve x(?, ?, d1) with w(:, :, d1, q) to get y(yR, yC, d2).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n        // TO DO(dsmilkov): Flatten the two for loops and vec4 the operations.\n        for (int wR = 0; wR < ${filterHeight}; wR++) {\n          int xR = xRCorner + wR * dilations[0];\n\n          if (xR < 0 || xR >= inDims[0]) {\n            continue;\n          }\n\n          for (int wC = 0; wC < ${filterWidth}; wC++) {\n            int xC = xCCorner + wC * dilations[1];\n\n            if (xC < 0 || xC >= inDims[1]) {\n              continue;\n            }\n\n            float xVal = getX(batch, xR, xC, d1);\n            float wVal = getW(wR, wC, d1, q);\n            dotProd += xVal * wVal;\n          }\n        }\n\n        float result = dotProd;\n        ${addBiasSnippet}\n        ${applyActivationSnippet}\n        setOutput(result);\n      }\n    `;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/conv_packed_gpu_depthwise.js\nvar DepthwiseConvPacked2DProgram = class {\n  constructor(convInfo, addBias = false, activation2 = null, hasPreluActivation = false, hasLeakyReluAlpha = false) {\n    this.variableNames = [\"x\", \"W\"];\n    this.packedInputs = true;\n    this.packedOutput = true;\n    this.customUniforms = [\n      { name: \"pads\", type: \"ivec2\" },\n      { name: \"strides\", type: \"ivec2\" },\n      { name: \"dilations\", type: \"ivec2\" },\n      { name: \"inDims\", type: \"ivec2\" }\n    ];\n    this.outputShape = convInfo.outShape;\n    this.enableShapeUniforms = useShapeUniforms(this.outputShape.length);\n    const channelMul = convInfo.outChannels / convInfo.inChannels;\n    const padLeft = convInfo.padInfo.left;\n    const strideWidth = convInfo.strideWidth;\n    const dilationWidth = convInfo.dilationWidth;\n    const filterHeight = convInfo.filterHeight;\n    const filterWidth = convInfo.filterWidth;\n    const texelsAcross = filterWidth;\n    let mainLoop = `\n      int xR; int xC; int xCOffset;\n      vec4 wTexel; vec4 previous; vec4 final;`;\n    for (let c = 0; c < filterWidth; c++) {\n      mainLoop += `\n          vec4 xTexelC${c * 2};\n          int xTexelC${c * 2}Ready;\n          vec4 xTexelC${c * 2 + 1};\n          int xTexelC${c * 2 + 1}Ready;\n          vec4 xC${c};`;\n    }\n    mainLoop += `\n    for (int r = 0; r < ${filterHeight}; r++) {\n      `;\n    for (let c = 0; c < filterWidth; c++) {\n      mainLoop += `\n          xTexelC${c * 2} = vec4(0.0);\n          xTexelC${c * 2}Ready = 0;\n          xTexelC${c * 2 + 1} = vec4(0.0);\n          xTexelC${c * 2 + 1}Ready = 0;\n          xC${c} = vec4(0.0);`;\n    }\n    mainLoop += `\n        xR = xRCorner + r * dilations[0];\n        if (xR >=0 && xR < inDims[0]) {\n      `;\n    for (let texelC = 0; texelC < (texelsAcross + 1) / 2; texelC++) {\n      const colIndex = texelC * 2;\n      mainLoop += `\n          xC = xCCorner + ${colIndex * dilationWidth};\n          `;\n      if (strideWidth === 1) {\n        if (colIndex < filterWidth) {\n          if (padLeft % 2 === 1) {\n            mainLoop += `\n                xCOffset = xC + 1;\n                if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${colIndex}Ready == 0) {\n                  xTexelC${colIndex} = getX(batch, xR, xCOffset, d1);\n\n                  // Need to manually clear unused channels in case\n                  // we're reading from recycled texture.\n                  if (xCOffset + 1 >= inDims[1]) {\n                    xTexelC${colIndex}.zw = vec2(0.0);\n                  }\n                  xTexelC${colIndex}Ready = 1;\n                }\n              `;\n            if (dilationWidth === 1 && colIndex > 0) {\n              mainLoop += `\n                xC${colIndex} = vec4(xTexelC${colIndex - 2}.zw, xTexelC${colIndex}.xy);\n                `;\n            } else {\n              mainLoop += `\n                  xCOffset = xC + 1 - 2;\n\n                  if (xCOffset >= 0 && xCOffset < inDims[1]) {\n                    previous = getX(batch, xR, xCOffset, d1);\n\n                    // Need to manually clear unused channels in case\n                    // we're reading from recycled texture.\n                    if (xCOffset + 1 >= inDims[1]) {\n                      previous.zw = vec2(0.0);\n                    }\n\n                    xC${colIndex} = vec4(previous.zw, xTexelC${colIndex}.xy);\n                  } else {\n                    xC${colIndex} = vec4(0.0, 0.0, xTexelC${colIndex}.xy);\n                  }\n                  `;\n            }\n          } else {\n            mainLoop += `\n                if (xC >= 0 && xC < inDims[1] && xTexelC${colIndex}Ready == 0) {\n                  xTexelC${colIndex} = getX(batch, xR, xC, d1);\n                  if (xC + 1 >= inDims[1]) {\n                    xTexelC${colIndex}.zw = vec2(0.0);\n                  }\n                  xTexelC${colIndex}Ready = 1;\n                }\n\n                xC${colIndex} = xTexelC${colIndex};\n                `;\n          }\n          if (colIndex + 1 < filterWidth) {\n            const nextTexelOffset = padLeft % 2 === 0 ? util_exports.nearestLargerEven(dilationWidth) : dilationWidth;\n            if (dilationWidth % 2 === 0 && padLeft % 2 === 1 || dilationWidth % 2 !== 0 && padLeft % 2 !== 1) {\n              mainLoop += `\n                  xCOffset = xC + imod(pads[1], 2) + ${nextTexelOffset};\n\n                  if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${colIndex + 1}Ready == 0) {\n                    xTexelC${colIndex + 1} = getX(batch, xR, xCOffset, d1);\n\n                    // Need to manually clear unused channels in case\n                    // we're reading from recycled texture.\n                    if (xCOffset + 1 >= inDims[1]) {\n                      xTexelC${colIndex + 1}.zw = vec2(0.0);\n                    }\n                    xTexelC${colIndex + 1}Ready = 1;\n                  }\n                  `;\n              if (dilationWidth > 1) {\n                mainLoop += `\n                    xCOffset -= 2;\n                    if (xCOffset >= 0 && xCOffset < inDims[1]) {\n                     previous = getX(batch, xR, xCOffset, d1);\n                     xC${colIndex + 1} = vec4(previous.zw, xTexelC${colIndex + 1}.xy);\n                    } else {\n                     xC${colIndex + 1} = vec4(0.0, 0.0, xTexelC${colIndex + 1}.xy);\n                    }\n                    `;\n              } else {\n                mainLoop += `\n                    xC${colIndex + 1} = vec4(xTexelC${colIndex}.zw, xTexelC${colIndex + 1}.xy);\n                    `;\n              }\n            } else {\n              if (nextTexelOffset === 1) {\n                mainLoop += `\n                    xC${colIndex + 1} = xTexelC${colIndex};\n                    `;\n              } else {\n                mainLoop += `\n                    xCOffset = xC + ${nextTexelOffset};\n\n                    if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${colIndex + 1}Ready == 0) {\n                      xTexelC${colIndex + 1} = getX(batch, xR, xCOffset, d1);\n                      if (xCOffset + 1 >= inDims[1]) {\n                        xTexelC${colIndex + 1}.zw = vec2(0.0);\n                      }\n                      xTexelC${colIndex + 1}Ready = 1;\n                    }\n\n                    xC${colIndex + 1} = xTexelC${colIndex + 1};\n                    `;\n              }\n            }\n          }\n        }\n      } else {\n        if (colIndex < filterWidth) {\n          if (padLeft % 2 === 1) {\n            mainLoop += `\n                xCOffset = xC + 1 - strides[1];\n                if(xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${colIndex}Ready == 0) {\n                  xTexelC${colIndex} = getX(batch, xR, xCOffset, d1);\n                  // Need to manually clear unused channels in case\n                  // we're reading from recycled texture.\n                  if (xCOffset + 1 >= inDims[1]) {\n                    xTexelC${colIndex}.zw = vec2(0.0);\n                  }\n                  xTexelC${colIndex}Ready = 1;\n                }\n\n                if(xC + 1 >= 0 && xC + 1 < inDims[1] && xTexelC${colIndex + 1}Ready == 0) {\n                  xTexelC${colIndex + 1} = getX(batch, xR, xC + 1, d1);\n                  // Need to manually clear unused channels in case\n                  // we're reading from recycled texture.\n                  if (xC + 2 >= inDims[1]) {\n                    xTexelC${colIndex + 1}.zw = vec2(0.0);\n                  }\n                  xTexelC${colIndex + 1}Ready = 1;\n                }\n\n                xC${colIndex} = vec4(xTexelC${colIndex}.zw, xTexelC${colIndex + 1}.zw);\n              `;\n            if (colIndex + 1 < filterWidth) {\n              mainLoop += `\n                  final = vec4(0.0);\n                  xCOffset = xC + 1 + strides[1];\n                  if(xCOffset >= 0 && xCOffset < inDims[1]) {\n                    final = getX(batch, xR, xCOffset, d1);\n                  }\n                  xC${colIndex + 1} = vec4(xTexelC${colIndex + 1}.xy, final.xy);\n                `;\n            }\n          } else {\n            mainLoop += `\n                if(xC >= 0 && xC < inDims[1] && xTexelC${colIndex}Ready == 0) {\n                  xTexelC${colIndex} = getX(batch, xR, xC, d1);\n                  if (xC + 1 >= inDims[1]) {\n                    xTexelC${colIndex}.zw = vec2(0.0);\n                  }\n                  xTexelC${colIndex}Ready = 1;\n                }\n\n                xCOffset = xC + strides[1];\n                if(xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${colIndex + 1}Ready == 0) {\n                  xTexelC${colIndex + 1} = getX(batch, xR, xCOffset, d1);\n                  if (xCOffset + 1 >= inDims[1]) {\n                    xTexelC${colIndex + 1}.zw = vec2(0.);\n                  }\n                  xTexelC${colIndex + 1}Ready = 1;\n                }\n\n                xC${colIndex} = vec4(\n                  xTexelC${colIndex}.xy, xTexelC${colIndex + 1}.xy);\n              `;\n            if (colIndex + 1 < filterWidth) {\n              mainLoop += `\n                  xC${colIndex + 1} = vec4(xTexelC${colIndex}.zw, xTexelC${colIndex + 1}.zw);\n                `;\n            }\n          }\n        }\n      }\n      if (colIndex < filterWidth) {\n        mainLoop += `\n            wTexel = getW(r, ${colIndex}, d1, q);\n            dotProd += xC${colIndex} * vec4(wTexel.xz, wTexel.xz);\n          `;\n        if (colIndex + 1 < filterWidth) {\n          mainLoop += `\n              wTexel = getW(r, ${colIndex + 1}, d1, q);\n              dotProd += xC${colIndex + 1} * vec4(wTexel.xz, wTexel.xz);\n            `;\n        }\n      }\n    }\n    mainLoop += `\n    }\n  `;\n    mainLoop += `\n      }\n    `;\n    let activationSnippet = \"\", applyActivationSnippet = \"\";\n    if (activation2) {\n      if (hasPreluActivation) {\n        activationSnippet = `vec4 activation(vec4 a) {\n          vec4 b = getPreluActivationWeightsAtOutCoords();\n          ${activation2}\n        }`;\n      } else if (hasLeakyReluAlpha) {\n        activationSnippet = `vec4 activation(vec4 a) {\n          vec4 b = getLeakyreluAlphaAtOutCoords();\n          ${activation2}\n        }`;\n      } else {\n        activationSnippet = `vec4 activation(vec4 x) {\n          ${activation2}\n        }`;\n      }\n      applyActivationSnippet = `result = activation(result);`;\n    }\n    const addBiasSnippet = addBias ? \"result += getBiasAtOutCoords();\" : \"\";\n    if (addBias) {\n      this.variableNames.push(\"bias\");\n    }\n    if (hasPreluActivation) {\n      this.variableNames.push(\"preluActivationWeights\");\n    }\n    if (hasLeakyReluAlpha) {\n      this.variableNames.push(\"leakyreluAlpha\");\n    }\n    this.userCode = `\n      ${activationSnippet}\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int batch = coords.x;\n        ivec2 xRCCorner = coords.yz * strides - pads;\n        int d2 = coords.w;\n        int d1 = d2 / ${channelMul};\n        int q = d2 - d1 * ${channelMul};\n        int xRCorner = xRCCorner.x;\n        int xCCorner = xRCCorner.y;\n\n        //intialize dotProd with a small epsilon seems to reduce GPU accuracy loss.\n        vec4 dotProd = vec4(0.000000000000001);\n\n        ${mainLoop}\n\n        vec4 result = dotProd - vec4(0.000000000000001);\n        ${addBiasSnippet}\n        ${applyActivationSnippet}\n        setOutput(result);\n      }\n    `;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/DepthwiseConv2dNative.js\nfunction depthwiseConv2dNative2(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x, filter } = inputs;\n  const { strides, pad: pad3, dilations, dimRoundingMode } = attrs;\n  let $dilations = dilations;\n  if ($dilations == null) {\n    $dilations = [1, 1];\n  }\n  util_exports.assert(backend_util_exports.eitherStridesOrDilationsAreOne(strides, $dilations), () => `Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${strides} and dilations '${$dilations}'`);\n  const convInfo = backend_util_exports.computeConv2DInfo(x.shape, filter.shape, strides, $dilations, pad3, dimRoundingMode, true);\n  let program;\n  if (env().getBool(\"WEBGL_PACK_DEPTHWISECONV\") && convInfo.strideWidth <= 2 && convInfo.outChannels / convInfo.inChannels === 1) {\n    program = new DepthwiseConvPacked2DProgram(convInfo);\n  } else {\n    program = new DepthwiseConv2DProgram(convInfo);\n  }\n  const customValues = [\n    [convInfo.padInfo.top, convInfo.padInfo.left],\n    [convInfo.strideHeight, convInfo.strideWidth],\n    [convInfo.dilationHeight, convInfo.dilationWidth],\n    [convInfo.inHeight, convInfo.inWidth]\n  ];\n  return backend2.runWebGLProgram(program, [x, filter], \"float32\", customValues);\n}\nvar depthwiseConv2dNativeConfig2 = {\n  kernelName: DepthwiseConv2dNative,\n  backendName: \"webgl\",\n  kernelFunc: depthwiseConv2dNative2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/conv_backprop_gpu_depthwise.js\nvar DepthwiseConv2DDerFilterProgram = class {\n  constructor(convInfo) {\n    this.variableNames = [\"x\", \"dy\"];\n    this.outputShape = convInfo.filterShape;\n    const strideHeight = convInfo.strideHeight;\n    const strideWidth = convInfo.strideWidth;\n    const padTop = convInfo.padInfo.top;\n    const padLeft = convInfo.padInfo.left;\n    const channelMul = convInfo.outChannels / convInfo.inChannels;\n    this.userCode = `\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int wR = coords.x;\n        int wC = coords.y;\n        int d1 = coords.z;\n        int dm = coords.w;\n        int d2 = d1 * ${channelMul} + dm;\n\n        float dotProd = 0.0;\n\n        // TO DO: Vec4 over the batch size\n        for (int b = 0; b < ${convInfo.batchSize}; b++) {\n          for (int yR = 0; yR < ${convInfo.outHeight}; yR++) {\n            int xR = wR + yR * ${strideHeight} - ${padTop};\n\n            if (xR < 0 || xR >= ${convInfo.inHeight}) {\n              continue;\n            }\n\n            for (int yC = 0; yC < ${convInfo.outWidth}; yC++) {\n              int xC = wC + yC * ${strideWidth} - ${padLeft};\n\n              if (xC < 0 || xC >= ${convInfo.inWidth}) {\n                continue;\n              }\n\n              float dyValue = getDy(b, yR, yC, d2);\n              float xValue = getX(b, xR, xC, d1);\n              dotProd += (xValue * dyValue);\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    `;\n  }\n};\nvar DepthwiseConv2DDerInputProgram = class {\n  constructor(convInfo) {\n    this.variableNames = [\"dy\", \"W\"];\n    this.outputShape = convInfo.inShape;\n    const filterHeight = convInfo.filterHeight;\n    const filterWidth = convInfo.filterWidth;\n    const strideHeight = convInfo.strideHeight;\n    const strideWidth = convInfo.strideWidth;\n    const padTop = filterHeight - 1 - convInfo.padInfo.top;\n    const padLeft = filterWidth - 1 - convInfo.padInfo.left;\n    const channelMul = convInfo.outChannels / convInfo.inChannels;\n    this.userCode = `\n      const ivec2 pads = ivec2(${padTop}, ${padLeft});\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int batch = coords[0];\n        int d1 = coords[3];\n        ivec2 dyCorner = coords.yz - pads;\n        int dyRCorner = dyCorner.x;\n        int dyCCorner = dyCorner.y;\n\n        float dotProd = 0.0;\n\n        for (int wR = 0; wR < ${filterHeight}; wR++) {\n          float dyR = float(dyRCorner + wR) / ${strideHeight}.0;\n\n          if (dyR < 0.0 || dyR >= ${convInfo.outHeight}.0 || fract(dyR) > 0.0) {\n            continue;\n          }\n          int idyR = int(dyR);\n\n          int wRPerm = ${filterHeight} - 1 - wR;\n\n          for (int wC = 0; wC < ${filterWidth}; wC++) {\n            float dyC = float(dyCCorner + wC) / ${strideWidth}.0;\n\n            if (dyC < 0.0 || dyC >= ${convInfo.outWidth}.0 ||\n                fract(dyC) > 0.0) {\n              continue;\n            }\n            int idyC = int(dyC);\n\n            int wCPerm = ${filterWidth} - 1 - wC;\n\n            // TO DO: Vec4 over the channelMul\n            for (int dm = 0; dm < ${channelMul}; dm++) {\n              int d2 = d1 * ${channelMul} + dm;\n              float xValue = getDy(batch, idyR, idyC, d2);\n              float wValue = getW(wRPerm, wCPerm, d1, dm);\n              dotProd += xValue * wValue;\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    `;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/DepthwiseConv2dNativeBackpropFilter.js\nfunction depthwiseConv2dNativeBackpropFilter3(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x, dy } = inputs;\n  const { strides, dilations, pad: pad3, dimRoundingMode, filterShape } = attrs;\n  const convInfo = backend_util_exports.computeConv2DInfo(x.shape, filterShape, strides, dilations, pad3, dimRoundingMode, true);\n  const program = new DepthwiseConv2DDerFilterProgram(convInfo);\n  return backend2.runWebGLProgram(program, [x, dy], \"float32\");\n}\nvar depthwiseConv2dNativeBackpropFilterConfig2 = {\n  kernelName: DepthwiseConv2dNativeBackpropFilter,\n  backendName: \"webgl\",\n  kernelFunc: depthwiseConv2dNativeBackpropFilter3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/DepthwiseConv2dNativeBackpropInput.js\nfunction depthwiseConv2dNativeBackpropInput3(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { dy, filter } = inputs;\n  const { strides, dilations, pad: pad3, dimRoundingMode, inputShape } = attrs;\n  const convInfo = backend_util_exports.computeConv2DInfo(inputShape, filter.shape, strides, dilations, pad3, dimRoundingMode, true);\n  const program = new DepthwiseConv2DDerInputProgram(convInfo);\n  return backend2.runWebGLProgram(program, [dy, filter], \"float32\");\n}\nvar depthwiseConv2dNativeBackpropInputConfig2 = {\n  kernelName: DepthwiseConv2dNativeBackpropInput,\n  backendName: \"webgl\",\n  kernelFunc: depthwiseConv2dNativeBackpropInput3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/diag_gpu.js\nvar DiagProgram = class {\n  constructor(size) {\n    this.variableNames = [\"X\"];\n    this.outputShape = [size, size];\n    this.userCode = `\n      void main() {\n          ivec2 coords = getOutputCoords();\n          float val = coords[0] == coords[1] ? getX(coords[0]) : 0.0;\n          setOutput(val);\n      }\n    `;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Diag.js\nfunction diag3(args) {\n  const { inputs, backend: backend2 } = args;\n  const { x } = inputs;\n  const outShape = [...x.shape, ...x.shape];\n  const xSize = util_exports.sizeFromShape(x.shape);\n  const flat = reshape4({ inputs: { x }, backend: backend2, attrs: { shape: [xSize] } });\n  const program = new DiagProgram(xSize);\n  const res = backend2.runWebGLProgram(program, [flat], flat.dtype);\n  const out = reshape4({ inputs: { x: res }, backend: backend2, attrs: { shape: outShape } });\n  backend2.disposeIntermediateTensorInfo(flat);\n  backend2.disposeIntermediateTensorInfo(res);\n  return out;\n}\nvar diagConfig2 = {\n  kernelName: Diag,\n  backendName: \"webgl\",\n  kernelFunc: diag3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/dilation_gpu.js\nvar Dilation2DProgram = class {\n  constructor(convInfo) {\n    this.variableNames = [\"x\", \"W\"];\n    this.outputShape = convInfo.outShape;\n    const { inHeight, inWidth, padInfo, strideHeight, strideWidth, filterHeight, filterWidth, dilationHeight, dilationWidth } = convInfo;\n    const { top: padTop, left: padLeft } = padInfo;\n    this.userCode = `\n      const ivec2 strides = ivec2(${strideHeight}, ${strideWidth});\n      const ivec2 pads = ivec2(${padTop}, ${padLeft});\n      const float neg_infinity = -3.4e38;\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int batch = coords.x;\n        int d1 = coords.w;\n        ivec2 outTopLeftCorner =\n            coords.yz * strides - pads;\n        int hBeg = outTopLeftCorner.x;\n        int wBeg = outTopLeftCorner.y;\n\n        float curVal = neg_infinity;\n        for (int h = 0; h < ${filterHeight}; h++) {\n          int hIn = hBeg + h * ${dilationHeight};\n\n          if (hIn >= 0 && hIn < ${inHeight}) {\n            for (int w = 0; w < ${filterWidth}; w++) {\n              int wIn = wBeg + w * ${dilationWidth};\n\n              if (wIn >= 0 && wIn < ${inWidth}) {\n                float xVal = getX(batch, hIn, wIn, d1);\n                float wVal = getW(h, w, d1);\n\n                float val = xVal + wVal;\n                if (val > curVal) {\n                  curVal = val;\n                }\n              }\n            }\n          }\n        }\n\n        float result = curVal;\n        setOutput(result);\n      }\n    `;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Dilation2D.js\nfunction dilation2D(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x, filter } = inputs;\n  const { strides, pad: pad3, dilations } = attrs;\n  const convInfo = backend_util_exports.computeDilation2DInfo(x.shape, filter.shape, strides, pad3, \"NHWC\", dilations);\n  let out;\n  const program = new Dilation2DProgram(convInfo);\n  out = backend2.runWebGLProgram(program, [x, filter], \"float32\");\n  const outReshaped = reshape4({ inputs: { x: out }, backend: backend2, attrs: { shape: convInfo.outShape } });\n  backend2.disposeIntermediateTensorInfo(out);\n  return outReshaped;\n}\nvar dilation2DConfig2 = {\n  kernelName: Dilation2D,\n  backendName: \"webgl\",\n  kernelFunc: dilation2D\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Einsum.js\nfunction einsum3(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { equation } = attrs;\n  const tensors = inputs;\n  const { allDims, summedDims, idDims } = backend_util_exports.decodeEinsumEquation(equation, tensors.length);\n  backend_util_exports.checkEinsumDimSizes(allDims.length, idDims, tensors);\n  const { path, steps } = backend_util_exports.getEinsumComputePath(summedDims, idDims);\n  const nSteps = steps.length;\n  let out = null;\n  let numDimsRemaining = allDims.length;\n  const tensorsToDispose = [];\n  for (let i2 = 0; i2 < nSteps; ++i2) {\n    for (const idTerm of steps[i2]) {\n      const { permutationIndices: perm, expandDims: dimsToExpand } = backend_util_exports.getEinsumPermutation(numDimsRemaining, idDims[idTerm]);\n      let x;\n      if (backend_util_exports.isIdentityPermutation(perm)) {\n        x = tensors[idTerm];\n      } else {\n        x = transpose3({ inputs: { x: tensors[idTerm] }, backend: backend2, attrs: { perm } });\n        tensorsToDispose.push(x);\n      }\n      const targetShape = x.shape.slice();\n      for (let k = 0; k < dimsToExpand.length; ++k) {\n        targetShape.splice(dimsToExpand[k], 0, 1);\n      }\n      if (!util_exports.arraysEqual(x.shape, targetShape)) {\n        x = reshape4({ inputs: { x }, backend: backend2, attrs: { shape: targetShape } });\n        tensorsToDispose.push(x);\n      }\n      if (out === null) {\n        out = x;\n      } else {\n        out = multiply3({ inputs: { a: x, b: out }, backend: backend2 });\n        tensorsToDispose.push(out);\n      }\n    }\n    if (i2 < nSteps - 1) {\n      if (path[i2] >= 0) {\n        out = sum4({\n          inputs: { x: out },\n          backend: backend2,\n          attrs: {\n            axis: path[i2] - (allDims.length - numDimsRemaining),\n            keepDims: false\n          }\n        });\n        tensorsToDispose.push(out);\n      }\n      numDimsRemaining--;\n    }\n  }\n  for (const tensorInfo of tensorsToDispose) {\n    if (tensorInfo === out) {\n      continue;\n    }\n    backend2.disposeIntermediateTensorInfo(tensorInfo);\n  }\n  return out;\n}\nvar einsumConfig2 = {\n  kernelName: Einsum,\n  backendName: \"webgl\",\n  kernelFunc: einsum3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Elu.js\nvar ELU4 = `return (x >= 0.0) ? x : (exp(x) - 1.0);`;\nvar ELU_PACKED = `\n  vec4 result;\n\n  result.r = (x.r >= 0.0) ? x.r : (exp(x.r) - 1.0);\n  result.g = (x.g >= 0.0) ? x.g : (exp(x.g) - 1.0);\n  result.b = (x.b >= 0.0) ? x.b : (exp(x.b) - 1.0);\n  result.a = (x.a >= 0.0) ? x.a : (exp(x.a) - 1.0);\n\n  return result;\n`;\nvar elu5 = unaryKernelFunc2({ opSnippet: ELU4, packedOpSnippet: ELU_PACKED });\nvar eluConfig2 = {\n  kernelName: Elu,\n  backendName: \"webgl\",\n  kernelFunc: elu5\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/EluGrad.js\nvar ELU_DER = `return (b >= 1.0) ? a : a * (b + 1.0);`;\nvar ELU_DER_PACKED = `\n  vec4 bGTEZero = vec4(greaterThanEqual(b, vec4(0.)));\n  return (bGTEZero * a) + ((vec4(1.0) - bGTEZero) * (a * (b + vec4(1.0))));\n`;\nvar eluGrad2 = (args) => {\n  const { inputs, backend: backend2 } = args;\n  const { dy, y } = inputs;\n  const program = env().getBool(\"WEBGL_PACK_BINARY_OPERATIONS\") ? new BinaryOpPackedProgram(ELU_DER_PACKED, dy.shape, y.shape) : new BinaryOpProgram(ELU_DER, dy.shape, y.shape);\n  return backend2.runWebGLProgram(program, [dy, y], dy.dtype);\n};\nvar eluGradConfig3 = {\n  kernelName: EluGrad,\n  backendName: \"webgl\",\n  kernelFunc: eluGrad2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Equal.js\nvar PACKED_EQUAL = `\n  return vec4(equal(a, b));\n`;\nvar EQUAL = `return float(a == b);`;\nvar equal3 = binaryKernelFunc2({\n  opSnippet: EQUAL,\n  packedOpSnippet: PACKED_EQUAL,\n  dtype: \"bool\",\n  cpuKernelImpl: equalImplCPU\n});\nvar equalConfig2 = {\n  kernelName: Equal,\n  backendName: \"webgl\",\n  kernelFunc: equal3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Erf.js\nvar ERF = `\n  // Error function is calculated approximately with elementary function.\n  // See \"Handbook of Mathematical Functions with Formulas,\n  // Graphs, and Mathematical Tables\", Abramowitz and Stegun.\n  float p = ${backend_util_exports.ERF_P};\n  float a1 = ${backend_util_exports.ERF_A1};\n  float a2 = ${backend_util_exports.ERF_A2};\n  float a3 = ${backend_util_exports.ERF_A3};\n  float a4 = ${backend_util_exports.ERF_A4};\n  float a5 = ${backend_util_exports.ERF_A5};\n\n  float sign = sign(x);\n  x = abs(x);\n  float t = 1.0 / (1.0 + p * x);\n  return sign * (1.0 - (((((a5*t + a4)*t) + a3)*t + a2)*t + a1)*t*exp(-x*x));\n`;\nvar erf3 = unaryKernelFunc2({ opSnippet: ERF });\nvar erfConfig2 = {\n  kernelName: Erf,\n  backendName: \"webgl\",\n  kernelFunc: erf3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Exp.js\nvar EXP = CHECK_NAN_SNIPPET_UNARY + `\n  return exp(x);\n`;\nvar EXP_PACKED = `\n  vec4 result = exp(x);\n  bvec4 isNaN = isnan(x);\n  result.r = isNaN.r ? x.r : result.r;\n  result.g = isNaN.g ? x.g : result.g;\n  result.b = isNaN.b ? x.b : result.b;\n  result.a = isNaN.a ? x.a : result.a;\n\n  return result;\n`;\nvar exp3 = unaryKernelFunc2({\n  opSnippet: EXP,\n  packedOpSnippet: EXP_PACKED,\n  cpuKernelImpl: expImplCPU,\n  dtype: \"float32\"\n});\nvar expConfig2 = {\n  kernelName: Exp,\n  backendName: \"webgl\",\n  kernelFunc: exp3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/ExpandDims.js\nfunction expandDims4(args) {\n  const { inputs, attrs, backend: backend2 } = args;\n  const { dim } = attrs;\n  const { input: input2 } = inputs;\n  const inputRank = input2.shape.length;\n  const newShape = input2.shape.slice();\n  let $dim = dim;\n  if (dim < 0) {\n    util_exports.assert(-(inputRank + 1) <= dim, () => `Axis must be in the interval [${-(inputRank + 1)}, ${inputRank}]`);\n    $dim = inputRank + dim + 1;\n  }\n  newShape.splice($dim, 0, 1);\n  return reshape4({ inputs: { x: input2 }, backend: backend2, attrs: { shape: newShape } });\n}\nvar expandDimsConfig2 = {\n  kernelName: ExpandDims,\n  backendName: \"webgl\",\n  kernelFunc: expandDims4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Expm1.js\nvar EXPM1 = `return exp(x) - 1.0;`;\nvar expm13 = unaryKernelFunc2({ opSnippet: EXPM1, packedOpSnippet: EXPM1, cpuKernelImpl: expm1ImplCPU });\nvar expm1Config2 = {\n  kernelName: Expm1,\n  backendName: \"webgl\",\n  kernelFunc: expm13\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/fft_gpu.js\nvar FFTProgram = class {\n  constructor(component, inputShape, inverse) {\n    this.variableNames = [\"real\", \"imag\"];\n    const innerDim = inputShape[1];\n    this.outputShape = inputShape;\n    const exponentMultiplierSnippet = inverse ? `2.0 * ${Math.PI}` : `-2.0 * ${Math.PI}`;\n    const resultDenominator = inverse ? `${innerDim}.0` : \"1.0\";\n    let opString;\n    if (component === \"real\") {\n      opString = \"return real * expR - imag * expI;\";\n    } else if (component === \"imag\") {\n      opString = \"return real * expI + imag * expR;\";\n    } else {\n      throw new Error(`FFT component must be either \"real\" or \"imag\", got ${component}.`);\n    }\n    this.userCode = `\n      const float exponentMultiplier = ${exponentMultiplierSnippet};\n\n      float unaryOpComplex(float real, float expR, float imag, float expI) {\n        ${opString}\n      }\n\n      float mulMatDFT(int batch, int index) {\n        float indexRatio = float(index) / float(${innerDim});\n        float exponentMultiplierTimesIndexRatio =\n            exponentMultiplier * indexRatio;\n\n        float result = 0.0;\n\n        for (int i = 0; i < ${innerDim}; i++) {\n          // x = (-2|2 * PI / N) * index * i;\n          float x = exponentMultiplierTimesIndexRatio * float(i);\n          float expR = cos(x);\n          float expI = sin(x);\n          float real = getReal(batch, i);\n          float imag = getImag(batch, i);\n\n          result +=\n              unaryOpComplex(real, expR, imag, expI) / ${resultDenominator};\n        }\n\n        return result;\n      }\n\n      void main() {\n        ivec2 coords = getOutputCoords();\n        setOutput(mulMatDFT(coords[0], coords[1]));\n      }\n    `;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/FFT_impl.js\nfunction fftImpl2(x, inverse, backend2) {\n  const xData = backend2.texData.get(x.dataId);\n  const inputSize = util_exports.sizeFromShape(x.shape);\n  const innerDimensionSize = x.shape[x.shape.length - 1];\n  const batch = inputSize / innerDimensionSize;\n  const input2D = reshape4({ inputs: { x }, backend: backend2, attrs: { shape: [batch, innerDimensionSize] } });\n  const xShape = input2D.shape;\n  const realProgram = new FFTProgram(\"real\", xShape, inverse);\n  const imagProgram = new FFTProgram(\"imag\", xShape, inverse);\n  const inputs = [\n    {\n      dataId: xData.complexTensorInfos.real.dataId,\n      dtype: xData.complexTensorInfos.real.dtype,\n      shape: xShape\n    },\n    {\n      dataId: xData.complexTensorInfos.imag.dataId,\n      dtype: xData.complexTensorInfos.imag.dtype,\n      shape: xShape\n    }\n  ];\n  const realPart = backend2.runWebGLProgram(realProgram, inputs, \"float32\");\n  const imagPart = backend2.runWebGLProgram(imagProgram, inputs, \"float32\");\n  const complexOutput = complex3({ inputs: { real: realPart, imag: imagPart }, backend: backend2 });\n  backend2.disposeIntermediateTensorInfo(realPart);\n  backend2.disposeIntermediateTensorInfo(imagPart);\n  const complexOutputReshaped = reshape4({ inputs: { x: complexOutput }, backend: backend2, attrs: { shape: x.shape } });\n  backend2.disposeIntermediateTensorInfo(input2D);\n  backend2.disposeIntermediateTensorInfo(complexOutput);\n  return complexOutputReshaped;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/FFT.js\nfunction fft3(args) {\n  const { inputs, backend: backend2 } = args;\n  const { input: input2 } = inputs;\n  return fftImpl2(input2, false, backend2);\n}\nvar fftConfig2 = {\n  kernelName: FFT,\n  backendName: \"webgl\",\n  kernelFunc: fft3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/fill_gpu.js\nvar FillProgram = class {\n  constructor(shape, value) {\n    this.outputShape = [];\n    this.customUniforms = [{ name: \"value\", type: \"float\" }];\n    this.variableNames = [\"x\"];\n    this.outputShape = shape;\n    this.userCode = `\n      void main() {\n        // Input can be obtained from uniform value.\n        setOutput(value);\n      }\n    `;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Fill.js\nfunction fill3(args) {\n  const { backend: backend2, attrs } = args;\n  const { shape, value } = attrs;\n  let { dtype } = attrs;\n  dtype = dtype || util_exports.inferDtype(value);\n  if (dtype === \"string\") {\n    const values = util_exports.getArrayFromDType(dtype, util_exports.sizeFromShape(shape));\n    values.fill(value);\n    return backend2.makeTensorInfo(shape, dtype, values);\n  } else {\n    const program = new FillProgram(shape, value);\n    const customValues = [[value]];\n    return backend2.runWebGLProgram(program, [], dtype, customValues);\n  }\n}\nvar fillConfig2 = {\n  kernelName: Fill,\n  backendName: \"webgl\",\n  kernelFunc: fill3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/flip_left_right_gpu.js\nvar FlipLeftRightProgram = class {\n  constructor(imageShape) {\n    this.variableNames = [\"Image\"];\n    this.outputShape = [];\n    const imageWidth = imageShape[2];\n    this.outputShape = imageShape;\n    this.userCode = `\n        void main() {\n          ivec4 coords = getOutputCoords();\n          int x = coords[2];\n\n          int coordX = ${imageWidth} - x - 1;\n          float outputValue;\n          if(coordX >= 0 && coordX < ${imageWidth}) {\n            outputValue = getImage(coords[0], coords[1], coordX, coords[3]);\n          } else {\n            outputValue = getImage(coords[0], coords[1], coords[2], coords[3]);\n          }\n          setOutput(outputValue);\n        }\n    `;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/FlipLeftRight.js\nvar flipLeftRightConfig2 = {\n  kernelName: FlipLeftRight,\n  backendName: \"webgl\",\n  kernelFunc: ({ inputs, backend: backend2 }) => {\n    const { image: image2 } = inputs;\n    const webglBackend = backend2;\n    const program = new FlipLeftRightProgram(image2.shape);\n    const output = webglBackend.runWebGLProgram(program, [image2], image2.dtype);\n    return output;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Floor.js\nvar FLOOR = `return floor(x);`;\nvar floor3 = unaryKernelFunc2({ opSnippet: FLOOR, packedOpSnippet: FLOOR, cpuKernelImpl: floorImplCPU });\nvar floorConfig2 = {\n  kernelName: Floor,\n  backendName: \"webgl\",\n  kernelFunc: floor3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/FloorDiv.js\nvar INT_DIV = `\n  float s = sign(a) * sign(b);\n  int ia = round(a);\n  int ib = round(b);\n  if (ib != 0) {\n    // Windows (D3D) wants guaranteed non-zero int division at compile-time.\n    return float(idiv(ia, ib, s));\n  } else {\n    return NAN;\n  }\n`;\nvar INT_DIV_PACKED = `\n  ivec4 ia = round(a);\n  ivec4 ib = round(b);\n  bvec4 cond = notEqual(ib, ivec4(0));\n  ivec4 result = ivec4(0);\n  vec4 s = sign(a) * sign(b);\n\n  // Windows (D3D) wants guaranteed non-zero int division at compile-time.\n  if (cond[0]) {\n    result[0] = idiv(ia[0], ib[0], s[0]);\n  }\n  if (cond[1]) {\n    result[1] = idiv(ia[1], ib[1], s[1]);\n  }\n  if (cond[2]) {\n    result[2] = idiv(ia[2], ib[2], s[2]);\n  }\n  if (cond[3]) {\n    result[3] = idiv(ia[3], ib[3], s[3]);\n  }\n  return vec4(result);\n`;\nvar floorDiv3 = binaryKernelFunc2({ opSnippet: INT_DIV, packedOpSnippet: INT_DIV_PACKED, dtype: \"int32\" });\nvar floorDivConfig2 = {\n  kernelName: FloorDiv,\n  backendName: \"webgl\",\n  kernelFunc: floorDiv3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/FromPixels_utils/from_pixels_gpu.js\nvar FromPixelsProgram = class {\n  constructor(outputShape) {\n    this.variableNames = [\"A\"];\n    const glsl = getGlslDifferences();\n    const [height, width] = outputShape;\n    this.outputShape = outputShape;\n    this.userCode = `\n      void main() {\n        ivec3 coords = getOutputCoords();\n        int texR = coords[0];\n        int texC = coords[1];\n        int depth = coords[2];\n        vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${width}.0, ${height}.0);\n\n        vec4 values = ${glsl.texture2D}(A, uv);\n        float value;\n        if (depth == 0) {\n          value = values.r;\n        } else if (depth == 1) {\n          value = values.g;\n        } else if (depth == 2) {\n          value = values.b;\n        } else if (depth == 3) {\n          value = values.a;\n        }\n\n        setOutput(floor(value * 255.0 + 0.5));\n      }\n    `;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/FromPixels_utils/from_pixels_packed_gpu.js\nvar FromPixelsPackedProgram = class {\n  constructor(outputShape) {\n    this.variableNames = [\"A\"];\n    this.packedInputs = false;\n    this.packedOutput = true;\n    const glsl = getGlslDifferences();\n    const [height, width] = outputShape;\n    this.outputShape = outputShape;\n    this.userCode = `\n      void main() {\n        ivec3 coords = getOutputCoords();\n        int texR = coords[0];\n        int texC = coords[1];\n        int depth = coords[2];\n\n        vec4 result = vec4(0.);\n\n        for(int row=0; row<=1; row++) {\n          for(int col=0; col<=1; col++) {\n            texC = coords[1] + row;\n            depth = coords[2] + col;\n\n            vec2 uv = (vec2(texC, texR) + halfCR) /\n                       vec2(${width}.0, ${height}.0);\n            vec4 values = ${glsl.texture2D}(A, uv);\n            float value;\n            if (depth == 0) {\n              value = values.r;\n            } else if (depth == 1) {\n              value = values.g;\n            } else if (depth == 2) {\n              value = values.b;\n            } else if (depth == 3) {\n              value = values.a;\n            }\n\n            result[row * 2 + col] = floor(value * 255.0 + 0.5);\n          }\n        }\n\n        ${glsl.output} = result;\n      }\n    `;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/FromPixels.js\nvar fromPixelsConfig = {\n  kernelName: FromPixels,\n  backendName: \"webgl\",\n  kernelFunc: fromPixels2\n};\nvar fromPixels2DContext2;\nvar willReadFrequently = env().getBool(\"CANVAS2D_WILL_READ_FREQUENTLY_FOR_GPU\");\nfunction fromPixels2(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  let { pixels } = inputs;\n  const { numChannels } = attrs;\n  const isVideo = typeof HTMLVideoElement !== \"undefined\" && pixels instanceof HTMLVideoElement;\n  const isImage = typeof HTMLImageElement !== \"undefined\" && pixels instanceof HTMLImageElement;\n  const [width, height] = isVideo ? [\n    pixels.videoWidth,\n    pixels.videoHeight\n  ] : [pixels.width, pixels.height];\n  const texShape = [height, width];\n  const outShape = [height, width, numChannels];\n  if (isImage || isVideo) {\n    const newWillReadFrequently = env().getBool(\"CANVAS2D_WILL_READ_FREQUENTLY_FOR_GPU\");\n    if (fromPixels2DContext2 == null || newWillReadFrequently !== willReadFrequently) {\n      willReadFrequently = newWillReadFrequently;\n      fromPixels2DContext2 = document.createElement(\"canvas\").getContext(\"2d\", { willReadFrequently });\n    }\n    fromPixels2DContext2.canvas.width = width;\n    fromPixels2DContext2.canvas.height = height;\n    fromPixels2DContext2.drawImage(pixels, 0, 0, width, height);\n    pixels = fromPixels2DContext2.canvas;\n  }\n  const tempPixelHandle = backend2.makeTensorInfo(texShape, \"int32\");\n  backend2.texData.get(tempPixelHandle.dataId).usage = TextureUsage.PIXELS;\n  backend2.gpgpu.uploadPixelDataToTexture(backend2.getTexture(tempPixelHandle.dataId), pixels);\n  const program = env().getBool(\"WEBGL_PACK\") ? new FromPixelsPackedProgram(outShape) : new FromPixelsProgram(outShape);\n  const res = backend2.runWebGLProgram(program, [tempPixelHandle], \"int32\");\n  backend2.disposeData(tempPixelHandle.dataId);\n  return res;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/FusedConv2D.js\nfunction fusedConv2d(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x, filter, bias, preluActivationWeights } = inputs;\n  const { strides, pad: pad3, dataFormat, dilations, dimRoundingMode, activation: activation2, leakyreluAlpha } = attrs;\n  const $dataFormat = backend_util_exports.convertConv2DDataFormat(dataFormat);\n  const convInfo = backend_util_exports.computeConv2DInfo(x.shape, filter.shape, strides, dilations, pad3, dimRoundingMode, false, $dataFormat);\n  let out;\n  const intermediates = [];\n  const hasBias = bias != null;\n  const hasPreluActivationWeights = preluActivationWeights != null;\n  const hasLeakyreluAlpha = activation2 === \"leakyrelu\";\n  const prepareInputs = () => {\n    const inputs2 = [x, filter];\n    const alignInputWithDataFormat = (input2, dataFormat2) => {\n      if (dataFormat2 === \"NCHW\" && input2.shape.length === 1 && input2.shape[0] !== 1) {\n        const alignedInput = reshape4({\n          inputs: { x: input2 },\n          backend: backend2,\n          attrs: { shape: [input2.shape[0], 1, 1] }\n        });\n        intermediates.push(alignedInput);\n        return alignedInput;\n      }\n      return input2;\n    };\n    if (hasBias) {\n      inputs2.push(alignInputWithDataFormat(bias, dataFormat));\n    }\n    if (hasPreluActivationWeights) {\n      inputs2.push(alignInputWithDataFormat(preluActivationWeights, dataFormat));\n    }\n    if (hasLeakyreluAlpha) {\n      const $leakyreluAlpha = backend2.makeTensorInfo([], \"float32\", util_exports.createScalarValue(leakyreluAlpha, \"float32\"));\n      inputs2.push($leakyreluAlpha);\n      intermediates.push($leakyreluAlpha);\n    }\n    return inputs2;\n  };\n  if (convInfo.filterHeight === 1 && convInfo.filterWidth === 1 && convInfo.dilationHeight === 1 && convInfo.dilationWidth === 1 && convInfo.strideHeight === 1 && convInfo.strideWidth === 1 && (convInfo.padInfo.type === \"SAME\" || convInfo.padInfo.type === \"VALID\")) {\n    out = conv2dByMatMul({\n      x,\n      filter,\n      convInfo,\n      backend: backend2,\n      bias,\n      activation: activation2,\n      preluActivationWeights,\n      leakyreluAlpha\n    });\n  } else if (convInfo.strideWidth <= 2 && $dataFormat === \"channelsLast\" && env().getBool(\"WEBGL_EXP_CONV\")) {\n    const fusedActivation = activation2 ? mapActivationToShaderProgram(activation2, true) : null;\n    const program = new Conv2DPackedProgram(convInfo, hasBias, fusedActivation, hasPreluActivationWeights, hasLeakyreluAlpha);\n    const customValues = [\n      [convInfo.padInfo.top, convInfo.padInfo.left],\n      [convInfo.strideHeight, convInfo.strideWidth],\n      [convInfo.dilationHeight, convInfo.dilationWidth],\n      [convInfo.inHeight, convInfo.inWidth]\n    ];\n    const inputs2 = prepareInputs();\n    out = backend2.runWebGLProgram(program, inputs2, \"float32\", customValues);\n  } else if (env().getBool(\"WEBGL_CONV_IM2COL\")) {\n    out = conv2dWithIm2Row({\n      x,\n      filter,\n      convInfo,\n      backend: backend2,\n      bias,\n      activation: activation2,\n      preluActivationWeights,\n      leakyreluAlpha\n    });\n  } else {\n    const fusedActivation = activation2 ? mapActivationToShaderProgram(activation2, false) : null;\n    const program = new Conv2DProgram(convInfo, hasBias, fusedActivation, hasPreluActivationWeights, hasLeakyreluAlpha);\n    const inputs2 = prepareInputs();\n    out = backend2.runWebGLProgram(program, inputs2, \"float32\");\n  }\n  const outReshaped = reshape4({ inputs: { x: out }, backend: backend2, attrs: { shape: convInfo.outShape } });\n  intermediates.push(out);\n  intermediates.forEach((t2) => backend2.disposeIntermediateTensorInfo(t2));\n  return outReshaped;\n}\nvar fusedConv2DConfig2 = {\n  kernelName: FusedConv2D,\n  backendName: \"webgl\",\n  kernelFunc: fusedConv2d\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/FusedDepthwiseConv2D.js\nfunction fusedDepthwiseConv2D2(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x, filter, bias, preluActivationWeights } = inputs;\n  const { strides, pad: pad3, dilations, dimRoundingMode, activation: activation2, leakyreluAlpha } = attrs;\n  const intermediates = [];\n  let $dilations = dilations;\n  if ($dilations == null) {\n    $dilations = [1, 1];\n  }\n  util_exports.assert(backend_util_exports.eitherStridesOrDilationsAreOne(strides, $dilations), () => `Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${strides} and dilations '${$dilations}'`);\n  const convInfo = backend_util_exports.computeConv2DInfo(x.shape, filter.shape, strides, $dilations, pad3, dimRoundingMode, true);\n  const shouldPackDepthwiseConv = env().getBool(\"WEBGL_PACK_DEPTHWISECONV\") && convInfo.strideWidth <= 2 && convInfo.outChannels / convInfo.inChannels === 1;\n  const fusedActivation = activation2 ? mapActivationToShaderProgram(activation2, shouldPackDepthwiseConv) : null;\n  const programInputs = [x, filter];\n  const hasBias = bias != null;\n  const hasPreluActivationWeights = preluActivationWeights != null;\n  const hasLeakyreluAlpha = activation2 === \"leakyrelu\";\n  if (hasBias) {\n    programInputs.push(bias);\n  }\n  if (hasPreluActivationWeights) {\n    programInputs.push(preluActivationWeights);\n  }\n  if (hasLeakyreluAlpha) {\n    const $leakyreluAlpha = backend2.makeTensorInfo([], \"float32\", util_exports.createScalarValue(leakyreluAlpha, \"float32\"));\n    programInputs.push($leakyreluAlpha);\n    intermediates.push($leakyreluAlpha);\n  }\n  let program;\n  if (shouldPackDepthwiseConv) {\n    program = new DepthwiseConvPacked2DProgram(convInfo, hasBias, fusedActivation, hasPreluActivationWeights, hasLeakyreluAlpha);\n  } else {\n    program = new DepthwiseConv2DProgram(convInfo, hasBias, fusedActivation, hasPreluActivationWeights, hasLeakyreluAlpha);\n  }\n  const customValues = [\n    [convInfo.padInfo.top, convInfo.padInfo.left],\n    [convInfo.strideHeight, convInfo.strideWidth],\n    [convInfo.dilationHeight, convInfo.dilationWidth],\n    [convInfo.inHeight, convInfo.inWidth]\n  ];\n  const result = backend2.runWebGLProgram(program, programInputs, \"float32\", customValues);\n  intermediates.forEach((t2) => backend2.disposeIntermediateTensorInfo(t2));\n  return result;\n}\nvar fusedDepthwiseConv2DConfig2 = {\n  kernelName: FusedDepthwiseConv2D,\n  backendName: \"webgl\",\n  kernelFunc: fusedDepthwiseConv2D2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/gather_nd_gpu.js\nvar GatherNDProgram = class {\n  constructor(sliceDim, strides, shape, paramsShape) {\n    this.sliceDim = sliceDim;\n    this.strides = strides;\n    this.paramsShape = paramsShape;\n    this.variableNames = [\"x\", \"indices\"];\n    this.outputShape = shape;\n    const stridesType = getCoordsDataType(strides.length);\n    const dtype = getCoordsDataType(shape.length);\n    const strideString = this.sliceDim > 1 ? \"strides[j]\" : \"strides\";\n    const paramsShapeType = getCoordsDataType(paramsShape.length);\n    const paramsShapeString = paramsShape.length > 1 ? \"paramsShape[j]\" : \"paramsShape\";\n    this.userCode = `\n        ${stridesType} strides = ${stridesType}(${this.strides});\n        ${paramsShapeType} paramsShape = ${paramsShapeType}(${this.paramsShape});\n         void main() {\n          ${dtype} coords = getOutputCoords();\n          int flattenIndex = 0;\n          bool out_of_bounds = false;\n          for (int j = 0; j < ${this.sliceDim}; j++) {\n            int index = round(getIndices(coords[0], j));\n            out_of_bounds = out_of_bounds || index < 0;\n            out_of_bounds = out_of_bounds || index >= ${paramsShapeString};\n            flattenIndex += index * ${strideString};\n          }\n          setOutput(out_of_bounds ? 0.0 : getX(flattenIndex, coords[1]));\n        }\n      `;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/GatherNd.js\nfunction gatherNd2(args) {\n  const { inputs, backend: backend2 } = args;\n  const { params, indices } = inputs;\n  const indicesShape = indices.shape;\n  const sliceRank = indicesShape[indicesShape.length - 1];\n  const paramsSize = util_exports.sizeFromShape(params.shape);\n  const [resultShape, numSlices, sliceSize, strides] = backend_util_exports.prepareAndValidate(params, indices);\n  const flattenIndices = reshape4({ inputs: { x: indices }, backend: backend2, attrs: { shape: [numSlices, sliceRank] } });\n  const flattenX = reshape4({\n    inputs: { x: params },\n    backend: backend2,\n    attrs: { shape: [util_exports.sizeFromShape(params.shape) / sliceSize, sliceSize] }\n  });\n  if (backend2.shouldExecuteOnCPU([params, indices]) || params.dtype === \"string\") {\n    const indicesData = backend2.readSync(indices.dataId);\n    const paramsBuf = backend2.bufferSync(params);\n    const outValue = gatherNdImplCPU(indicesData, paramsBuf, params.dtype, numSlices, sliceRank, sliceSize, strides, params.shape, paramsSize);\n    return backend2.makeTensorInfo(resultShape, params.dtype, outValue.values);\n  }\n  const program = new GatherNDProgram(sliceRank, strides, [numSlices, sliceSize], params.shape);\n  const res = backend2.runWebGLProgram(program, [flattenX, flattenIndices], flattenX.dtype);\n  const reshaped = reshape4({ inputs: { x: res }, backend: backend2, attrs: { shape: resultShape } });\n  backend2.disposeIntermediateTensorInfo(flattenIndices);\n  backend2.disposeIntermediateTensorInfo(flattenX);\n  backend2.disposeIntermediateTensorInfo(res);\n  return reshaped;\n}\nvar gatherNdConfig2 = {\n  kernelName: GatherNd,\n  backendName: \"webgl\",\n  kernelFunc: gatherNd2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/gather_gpu.js\nvar GatherProgram = class {\n  constructor(aShape, outputShape) {\n    this.variableNames = [\"A\", \"indices\"];\n    this.outputShape = outputShape;\n    this.rank = outputShape.length;\n    const dtype = getCoordsDataType(this.rank);\n    const sourceCoords = getSourceCoords2(aShape, 2);\n    this.userCode = `\n      void main() {\n        ${dtype} resRC = getOutputCoords();\n        int index = int(getIndices(resRC.x, resRC.z));\n        float inBounds = (index >= 0) && (index < ${aShape[2]}) ? 1.0 : 0.0;\n        setOutput(inBounds * getA(${sourceCoords}));\n      }\n    `;\n  }\n};\nfunction getSourceCoords2(aShape, axis) {\n  const currentCoords = [\"resRC.x\", \"resRC.y\", \"resRC.z\", \"resRC.w\"];\n  const sourceCoords = [];\n  for (let i2 = 0; i2 < aShape.length; i2++) {\n    if (i2 === 2) {\n      sourceCoords.push(\"index\");\n    } else {\n      sourceCoords.push(`${currentCoords[i2]}`);\n    }\n  }\n  return sourceCoords.join();\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/GatherV2.js\nfunction gatherV22(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x, indices } = inputs;\n  const { axis, batchDims } = attrs;\n  const parsedAxis = util_exports.parseAxisParam(axis, x.shape)[0];\n  if (env().get(\"DEBUG\")) {\n    const indicesVals = backend2.readSync(indices.dataId);\n    const axisDim = x.shape[parsedAxis];\n    for (let i2 = 0; i2 < indicesVals.length; ++i2) {\n      const index = indicesVals[i2];\n      util_exports.assert(index <= axisDim - 1 && index >= 0, () => `GatherV2: the index value ${index} is not in [0, ${axisDim - 1}]`);\n    }\n  }\n  const shapeInfo = backend_util_exports.segment_util.collectGatherOpShapeInfo(x, indices, parsedAxis, batchDims);\n  const indicesSize = util_exports.sizeFromShape(indices.shape);\n  const toDispose = [];\n  const flattenX = reshape4({\n    inputs: { x },\n    backend: backend2,\n    attrs: {\n      shape: [\n        shapeInfo.batchSize,\n        shapeInfo.outerSize,\n        shapeInfo.dimSize,\n        shapeInfo.sliceSize\n      ]\n    }\n  });\n  const flattenIndex = reshape4({\n    inputs: { x: indices },\n    backend: backend2,\n    attrs: { shape: [shapeInfo.batchSize, indicesSize / shapeInfo.batchSize] }\n  });\n  toDispose.push(flattenX);\n  toDispose.push(flattenIndex);\n  const flattenOutputShape = [\n    shapeInfo.batchSize,\n    shapeInfo.outerSize,\n    indicesSize / shapeInfo.batchSize,\n    shapeInfo.sliceSize\n  ];\n  if (backend2.shouldExecuteOnCPU([x, indices]) || x.dtype === \"string\") {\n    const indicesBuf = backend2.bufferSync(flattenIndex);\n    const xBuf = backend2.bufferSync(flattenX);\n    const outBuf = gatherV2ImplCPU(xBuf, indicesBuf, flattenOutputShape);\n    toDispose.forEach((t2) => backend2.disposeIntermediateTensorInfo(t2));\n    return backend2.makeTensorInfo(shapeInfo.outputShape, outBuf.dtype, outBuf.values);\n  }\n  const program = new GatherProgram(flattenX.shape, flattenOutputShape);\n  const res = backend2.runWebGLProgram(program, [flattenX, flattenIndex], flattenX.dtype);\n  toDispose.push(res);\n  const reshaped = reshape4({ inputs: { x: res }, backend: backend2, attrs: { shape: shapeInfo.outputShape } });\n  toDispose.forEach((t2) => backend2.disposeIntermediateTensorInfo(t2));\n  return reshaped;\n}\nvar gatherV2Config2 = {\n  kernelName: GatherV2,\n  backendName: \"webgl\",\n  kernelFunc: gatherV22\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Greater.js\nvar GREATER = `return float(a > b);`;\nvar GREATER_PACKED = `\n  return vec4(greaterThan(a, b));\n`;\nvar greater4 = binaryKernelFunc2({\n  opSnippet: GREATER,\n  packedOpSnippet: GREATER_PACKED,\n  cpuKernelImpl: greaterImplCPU,\n  dtype: \"bool\"\n});\nvar greaterConfig2 = {\n  kernelName: Greater,\n  backendName: \"webgl\",\n  kernelFunc: greater4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/GreaterEqual.js\nvar GREATER_EQUAL = `return float(a >= b);`;\nvar GREATER_EQUAL_PACKED = `\n  return vec4(greaterThanEqual(a, b));\n`;\nvar greaterEqual3 = binaryKernelFunc2({\n  opSnippet: GREATER_EQUAL,\n  packedOpSnippet: GREATER_EQUAL_PACKED,\n  dtype: \"bool\",\n  cpuKernelImpl: greaterEqualImplCPU\n});\nvar greaterEqualConfig2 = {\n  kernelName: GreaterEqual,\n  backendName: \"webgl\",\n  kernelFunc: greaterEqual3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/IFFT.js\nfunction ifft3(args) {\n  const { inputs, backend: backend2 } = args;\n  const { input: input2 } = inputs;\n  return fftImpl2(input2, true, backend2);\n}\nvar ifftConfig2 = {\n  kernelName: IFFT,\n  backendName: \"webgl\",\n  kernelFunc: ifft3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/IsFinite.js\nvar IS_FINITE = `return float(!isnan(x) && !isinf(x));`;\nvar isFinite4 = unaryKernelFunc2({ opSnippet: IS_FINITE, dtype: \"bool\" });\nvar isFiniteConfig2 = {\n  kernelName: IsFinite,\n  backendName: \"webgl\",\n  kernelFunc: isFinite4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/IsInf.js\nvar IS_INF = `return float(isinf(x));`;\nvar isInf3 = unaryKernelFunc2({ opSnippet: IS_INF, dtype: \"bool\" });\nvar isInfConfig2 = {\n  kernelName: IsInf,\n  backendName: \"webgl\",\n  kernelFunc: isInf3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/IsNaN.js\nvar IS_NAN = `return float(isnan(x));`;\nvar isNaN4 = unaryKernelFunc2({ opSnippet: IS_NAN, dtype: \"bool\" });\nvar isNaNConfig2 = {\n  kernelName: IsNan,\n  backendName: \"webgl\",\n  kernelFunc: isNaN4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Less.js\nvar LESS = `return float(a < b);`;\nvar LESS_PACKED = `\n  return vec4(lessThan(a, b));\n`;\nvar less4 = binaryKernelFunc2({\n  opSnippet: LESS,\n  packedOpSnippet: LESS_PACKED,\n  cpuKernelImpl: lessImplCPU,\n  dtype: \"bool\"\n});\nvar lessConfig2 = {\n  kernelName: Less,\n  backendName: \"webgl\",\n  kernelFunc: less4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/LessEqual.js\nvar LESS_EQUAL = `return float(a <= b);`;\nvar LESS_EQUAL_PACKED = `\n  return vec4(lessThanEqual(a, b));\n`;\nvar lessEqual3 = binaryKernelFunc2({\n  opSnippet: LESS_EQUAL,\n  packedOpSnippet: LESS_EQUAL_PACKED,\n  cpuKernelImpl: lessEqualImplCPU,\n  dtype: \"bool\"\n});\nvar lessEqualConfig2 = {\n  kernelName: LessEqual,\n  backendName: \"webgl\",\n  kernelFunc: lessEqual3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/LinSpace.js\nfunction linSpace2(args) {\n  const { backend: backend2, attrs } = args;\n  const { start, stop, num } = attrs;\n  const outVals = linSpaceImplCPU(start, stop, num);\n  return backend2.makeTensorInfo([outVals.length], \"float32\", outVals);\n}\nvar linSpaceConfig2 = {\n  kernelName: LinSpace,\n  backendName: \"webgl\",\n  kernelFunc: linSpace2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Log.js\nvar LOG = CHECK_NAN_SNIPPET_UNARY + `\n  return x < 0.0 ? 0./0. : log(x);\n`;\nvar LOG_PACKED = `\n  vec4 result = log(x);\n  bvec4 isNaN = isnan(x);\n  result.r = isNaN.r ? x.r : (x.r < 0.0 ? 0./0. : result.r);\n  result.g = isNaN.g ? x.g : (x.g < 0.0 ? 0./0. : result.g);\n  result.b = isNaN.b ? x.b : (x.b < 0.0 ? 0./0. : result.b);\n  result.a = isNaN.a ? x.a : (x.a < 0.0 ? 0./0. : result.a);\n  return result;\n`;\nvar log4 = unaryKernelFunc2({ opSnippet: LOG, packedOpSnippet: LOG_PACKED, cpuKernelImpl: logImplCPU });\nvar logConfig2 = {\n  kernelName: Log,\n  backendName: \"webgl\",\n  kernelFunc: log4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Log1p.js\nvar LOG1P = CHECK_NAN_SNIPPET_UNARY + `\n  return log(1.0 + x);\n`;\nvar log1p3 = unaryKernelFunc2({ opSnippet: LOG1P });\nvar log1pConfig2 = {\n  kernelName: Log1p,\n  backendName: \"webgl\",\n  kernelFunc: log1p3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/LogicalAnd.js\nvar LOGICAL_AND = `return float(a >= 1.0 && b >= 1.0);`;\nvar LOGICAL_AND_PACKED = `\n  return vec4(\n    vec4(greaterThanEqual(a, vec4(1.0))) *\n    vec4(greaterThanEqual(b, vec4(1.0))));\n`;\nvar logicalAnd3 = binaryKernelFunc2({\n  opSnippet: LOGICAL_AND,\n  packedOpSnippet: LOGICAL_AND_PACKED,\n  dtype: \"bool\"\n});\nvar logicalAndConfig2 = {\n  kernelName: LogicalAnd,\n  backendName: \"webgl\",\n  kernelFunc: logicalAnd3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/LogicalNot.js\nvar LOGICAL_NOT = `return float(!(x >= 1.0));`;\nvar logicalNot3 = unaryKernelFunc2({ opSnippet: LOGICAL_NOT });\nvar logicalNotConfig2 = {\n  kernelName: LogicalNot,\n  backendName: \"webgl\",\n  kernelFunc: logicalNot3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/LogicalOr.js\nvar LOGICAL_OR = `return float(a >= 1.0 || b >= 1.0);`;\nvar LOGICAL_OR_PACKED = `\n  return min(\n    vec4(greaterThanEqual(a, vec4(1.0))) +\n    vec4(greaterThanEqual(b, vec4(1.0))),\n    vec4(1.0));\n`;\nvar logicalOr3 = binaryKernelFunc2({ opSnippet: LOGICAL_OR, packedOpSnippet: LOGICAL_OR_PACKED, dtype: \"bool\" });\nvar logicalOrConfig2 = {\n  kernelName: LogicalOr,\n  backendName: \"webgl\",\n  kernelFunc: logicalOr3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/lrn_gpu.js\nvar LRNProgram = class {\n  constructor(xShape, radius, bias, alpha, beta) {\n    this.variableNames = [\"x\"];\n    this.outputShape = [];\n    const rad = radius;\n    const maxD = xShape[3] - 1;\n    this.outputShape = xShape;\n    let powOperator;\n    const basis = `float(${bias}) + float(${alpha}) * sum`;\n    if (beta === 0.5) {\n      powOperator = `inversesqrt(${basis})`;\n    } else if (beta === 1) {\n      powOperator = `1.0/(${basis})`;\n    } else {\n      powOperator = `exp(log(${basis}) * float(-${beta}));`;\n    }\n    this.userCode = `\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int r = coords[1];\n        int c = coords[2];\n        int d = coords[3];\n        float x = getX(b, r, c, d);\n        float sum = 0.0;\n        for (int j = -${rad}; j <= ${rad}; j++) {\n          int idx = d + j;\n          if (idx >= 0 && idx <=  ${maxD}) {\n            float z = getX(b, r, c, idx);\n            sum += z * z;\n          }\n        }\n        float val = x * ${powOperator};\n        setOutput(val);\n      }\n    `;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/lrn_packed_gpu.js\nvar LRNPackedProgram = class {\n  constructor(xShape, radius, bias, alpha, beta) {\n    this.variableNames = [\"x\"];\n    this.outputShape = [];\n    this.packedInputs = true;\n    this.packedOutput = true;\n    const rad = radius;\n    const maxD = xShape[3] - 1;\n    this.outputShape = xShape;\n    let powOperator;\n    const basis = `float(${bias}) + float(${alpha}) * sum`;\n    if (beta === 0.5) {\n      powOperator = `inversesqrt(${basis})`;\n    } else if (beta === 1) {\n      powOperator = `1.0/(${basis})`;\n    } else {\n      powOperator = `exp(log(${basis}) * float(-${beta}));`;\n    }\n    this.userCode = `\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords.x;\n        int r = coords.y;\n        int c = coords.z;\n        int d = coords.w;\n\n        bool hasNextCol = d < ${this.outputShape[3]};\n        bool hasNextRow = c < ${this.outputShape[2]};\n\n        vec4 sum = vec4(0.);\n        vec4 xFragAtOutputCoords = getX(b, r, c, d);\n\n        vec4 xAtOutputCoords = vec4(\n          getChannel(xFragAtOutputCoords, vec2(c, d)),\n          hasNextCol ?\n            getChannel(xFragAtOutputCoords, vec2(c, d + 1)) : 0.0,\n          hasNextRow ?\n            getChannel(xFragAtOutputCoords , vec2(c + 1, d)) : 0.0,\n          (hasNextRow && hasNextCol) ?\n            getChannel(xFragAtOutputCoords, vec2(c + 1, d + 1)) : 0.0\n        );\n\n        int firstChannel = d - ${rad};\n        vec2 cache = vec2(0.);\n        if(firstChannel >= 0){\n          vec4 firstChannelFrag = getX(b, r, c, firstChannel);\n          cache.x = getChannel(firstChannelFrag, vec2(c, firstChannel));\n            if(hasNextRow){\n              cache.y = getChannel(firstChannelFrag, vec2(c + 1, firstChannel));\n            }\n        }\n\n        ivec2 depth = ivec2(d, d + 1);\n        for (int j = - ${rad}; j <= ${rad}; j++) {\n          ivec2 idx = depth + j;\n          bvec2 aboveLowerBound = greaterThanEqual(idx, ivec2(0));\n          bvec2 belowUpperBound = lessThanEqual(idx, ivec2(${maxD}));\n\n          bool depthInRange = aboveLowerBound.x && belowUpperBound.x;\n          bool depthPlusOneInRange = aboveLowerBound.y && belowUpperBound.y;\n\n          if(depthInRange || depthPlusOneInRange){\n            vec4 z = vec4(0.);\n            vec4 xFragAtCurrentDepth;\n            z.xz = cache.xy;\n            if(depthPlusOneInRange && hasNextCol){\n              xFragAtCurrentDepth = idx.y != d ?\n                getX(b, r, c, idx.y) : xFragAtOutputCoords;\n              z.y = getChannel(xFragAtCurrentDepth, vec2(c, idx.y));\n              if(hasNextRow){\n                z.w = getChannel(xFragAtCurrentDepth, vec2(c + 1, idx.y));\n              }\n            }\n            cache.xy = z.yw;\n            sum += z * z;\n          }\n        }\n        vec4 result = xAtOutputCoords * ${powOperator};\n        setOutput(result);\n      }\n    `;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/LRN.js\nvar lrn = (args) => {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x } = inputs;\n  const { depthRadius, bias, alpha, beta } = attrs;\n  const program = env().getBool(\"WEBGL_PACK_NORMALIZATION\") ? new LRNPackedProgram(x.shape, depthRadius, bias, alpha, beta) : new LRNProgram(x.shape, depthRadius, bias, alpha, beta);\n  return backend2.runWebGLProgram(program, [x], x.dtype);\n};\nvar LRNConfig2 = {\n  kernelName: LRN,\n  backendName: \"webgl\",\n  kernelFunc: lrn\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/lrn_grad_gpu.js\nvar LRNGradProgram = class {\n  constructor(inputShape, depthRadius, bias, alpha, beta) {\n    this.variableNames = [\"inputImage\", \"outputImage\", \"dy\"];\n    this.outputShape = [];\n    this.outputShape = inputShape;\n    this.depth = inputShape[3];\n    this.depthRadius = depthRadius;\n    this.bias = bias;\n    this.alpha = alpha;\n    this.beta = beta;\n    this.userCode = `\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int r = coords[1];\n        int c = coords[2];\n\n        float result = 0.0;\n        for (int d = 0; d < ${this.depth}; ++d) {\n          int depthBegin = int(max(0.0, float(d - ${depthRadius})));\n          int depthEnd = int(min(float(${this.depth}),\n              float(d + ${depthRadius} + 1)));\n\n          const int MIN_DEPTH_BEGIN = 0;\n          const int MAX_DEPTH_END = ${this.depth};\n\n          float norm = 0.0;\n          for (int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k) {\n            if (k < depthBegin){\n              continue;\n            }\n            else if (k >= depthBegin && k < depthEnd) {\n              norm += getInputImage(b, r, c, k) * getInputImage(b, r, c, k);\n            }\n            else {\n              break;\n            }\n          }\n\n          norm = float(${alpha}) * norm + float(${bias});\n\n          for(int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k){\n            if (k < depthBegin){\n              continue;\n            }\n            else if (k >= depthBegin && k < depthEnd){\n              float dyi = -2.0 * float(${alpha})\n                * float(${beta})\n                * getInputImage(b ,r ,c, k) * getOutputImage(b, r, c, d)\n                / norm;\n              if (k == d) {\n                dyi += pow(norm, -1.0 * ${beta});\n              }\n              if (k == coords[3]) {\n                dyi *= getDy(b, r, c, d);\n                result += dyi;\n              }\n            }\n            else {\n              break;\n            }\n          }\n      }\n      setOutput(result);\n      }\n    `;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/LRNGrad.js\nvar lrnGrad = (args) => {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x, y, dy } = inputs;\n  const { depthRadius, bias, alpha, beta } = attrs;\n  const program = new LRNGradProgram(x.shape, depthRadius, bias, alpha, beta);\n  return backend2.runWebGLProgram(program, [x, y, dy], x.dtype);\n};\nvar LRNGradConfig2 = {\n  kernelName: LRNGrad,\n  backendName: \"webgl\",\n  kernelFunc: lrnGrad\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Max_impl.js\nfunction maxImpl2(x, reduceShape, outShape, backend2) {\n  const inSize = util_exports.sizeFromShape(reduceShape);\n  const xSize = util_exports.sizeFromShape(x.shape);\n  const batchSize = xSize / inSize;\n  const reshapedInput = reshape4({ inputs: { x }, attrs: { shape: [batchSize, inSize] }, backend: backend2 });\n  const reduced = reduce(reshapedInput, x.dtype, \"max\", backend2);\n  const reshapedOutput = reshape4({ inputs: { x: reduced }, attrs: { shape: outShape }, backend: backend2 });\n  backend2.disposeIntermediateTensorInfo(reshapedInput);\n  backend2.disposeIntermediateTensorInfo(reduced);\n  return reshapedOutput;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Max.js\nfunction max4(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x } = inputs;\n  const { reductionIndices, keepDims } = attrs;\n  const xRank = x.shape.length;\n  const origAxes = util_exports.parseAxisParam(reductionIndices, x.shape);\n  let axes = origAxes;\n  const permutedAxes = backend_util_exports.getAxesPermutation(axes, xRank);\n  const maxInputIsTransposed = permutedAxes != null;\n  const shouldExecuteOnCPU = backend2.shouldExecuteOnCPU([x]);\n  let maxInput = x;\n  if (maxInputIsTransposed) {\n    if (shouldExecuteOnCPU) {\n      const xTexData = backend2.texData.get(maxInput.dataId);\n      const values = xTexData.values;\n      const newShape = new Array(xRank);\n      for (let i2 = 0; i2 < newShape.length; i2++) {\n        newShape[i2] = x.shape[permutedAxes[i2]];\n      }\n      const maxInputValues = transposeImplCPU(values, x.shape, x.dtype, permutedAxes, newShape);\n      maxInput = backend2.makeTensorInfo(newShape, x.dtype);\n      const maxInputData = backend2.texData.get(maxInput.dataId);\n      maxInputData.values = maxInputValues;\n    } else {\n      maxInput = transposeImpl2(x, permutedAxes, backend2);\n    }\n    axes = backend_util_exports.getInnerMostAxes(axes.length, xRank);\n  }\n  backend_util_exports.assertAxesAreInnerMostDims(\"max\", axes, xRank);\n  const [maxOutShape, reduceShape] = backend_util_exports.computeOutAndReduceShapes(maxInput.shape, axes);\n  let outShape = maxOutShape;\n  if (keepDims) {\n    outShape = backend_util_exports.expandShapeToKeepDim(maxOutShape, origAxes);\n  }\n  let out;\n  if (shouldExecuteOnCPU) {\n    const xTexData = backend2.texData.get(maxInput.dataId);\n    const values = xTexData.values;\n    const outValues = maxImplCPU(values, util_exports.sizeFromShape(reduceShape), outShape, x.dtype);\n    out = backend2.makeTensorInfo(outShape, x.dtype);\n    const outData = backend2.texData.get(out.dataId);\n    outData.values = outValues;\n  } else {\n    out = maxImpl2(maxInput, reduceShape, outShape, backend2);\n  }\n  if (maxInputIsTransposed) {\n    backend2.disposeIntermediateTensorInfo(maxInput);\n  }\n  return out;\n}\nvar maxConfig2 = {\n  kernelName: Max,\n  backendName: \"webgl\",\n  kernelFunc: max4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Maximum.js\nvar MAXIMUM = CHECK_NAN_SNIPPET2 + `\n  return max(a, b);\n`;\nvar MAXIMUM_PACKED = `\n  vec4 result = vec4(max(a, b));\n  vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));\n  ` + CHECK_NAN_SNIPPET3 + `\n  return result;\n`;\nvar maximum4 = binaryKernelFunc2({\n  opSnippet: MAXIMUM,\n  packedOpSnippet: MAXIMUM_PACKED,\n  cpuKernelImpl: maximumImplCPU\n});\nvar maximumConfig2 = {\n  kernelName: Maximum,\n  backendName: \"webgl\",\n  kernelFunc: maximum4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/MaxPool.js\nfunction maxPool3(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x } = inputs;\n  assertNotComplex2(x, \"maxPool\");\n  const { filterSize, strides, pad: pad3, dimRoundingMode } = attrs;\n  const dilations = 1;\n  util_exports.assert(backend_util_exports.eitherStridesOrDilationsAreOne(strides, dilations), () => `Error in maxPool: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`);\n  const convInfo = backend_util_exports.computePool2DInfo(x.shape, filterSize, strides, dilations, pad3, dimRoundingMode);\n  if (convInfo.filterWidth === 1 && convInfo.filterHeight === 1 && util_exports.arraysEqual(convInfo.inShape, convInfo.outShape)) {\n    return identity3({ inputs: { x }, backend: backend2 });\n  }\n  const maxPoolProgram = new Pool2DProgram(convInfo, \"max\", false);\n  return backend2.runWebGLProgram(maxPoolProgram, [x], x.dtype);\n}\nvar maxPoolConfig2 = {\n  kernelName: MaxPool,\n  backendName: \"webgl\",\n  kernelFunc: maxPool3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/MaxPool3D.js\nfunction maxPool3d2(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x } = inputs;\n  const { filterSize, strides, pad: pad3, dataFormat, dimRoundingMode } = attrs;\n  const dilations = [1, 1, 1];\n  const convInfo = backend_util_exports.computePool3DInfo(x.shape, filterSize, strides, dilations, pad3, dimRoundingMode, dataFormat);\n  const maxPoolProgram = new Pool3DProgram(convInfo, \"max\", false);\n  return backend2.runWebGLProgram(maxPoolProgram, [x], x.dtype);\n}\nvar maxPool3DConfig2 = {\n  kernelName: MaxPool3D,\n  backendName: \"webgl\",\n  kernelFunc: maxPool3d2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/max_pool_backprop_gpu.js\nvar MaxPool2DBackpropProgram = class {\n  constructor(convInfo) {\n    this.variableNames = [\"dy\", \"maxPos\"];\n    this.outputShape = convInfo.inShape;\n    const strideHeight = convInfo.strideHeight;\n    const strideWidth = convInfo.strideWidth;\n    const dilationHeight = convInfo.dilationHeight;\n    const effectiveFilterHeight = convInfo.effectiveFilterHeight;\n    const effectiveFilterWidth = convInfo.effectiveFilterWidth;\n    const padTop = effectiveFilterHeight - 1 - convInfo.padInfo.top;\n    const padLeft = effectiveFilterWidth - 1 - convInfo.padInfo.left;\n    const lastIndex = effectiveFilterHeight * effectiveFilterWidth - 1;\n    this.userCode = `\n      const ivec2 pads = ivec2(${padTop}, ${padLeft});\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n\n        ivec2 dyRCCorner = coords.yz - pads;\n        int dyRCorner = dyRCCorner.x;\n        int dyCCorner = dyRCCorner.y;\n\n        // Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n        for (int wR = 0; wR < ${effectiveFilterHeight};\n          wR += ${dilationHeight}) {\n          float dyR = float(dyRCorner + wR) / ${strideHeight}.0;\n\n          if (dyR < 0.0 || dyR >= ${convInfo.outHeight}.0 || fract(dyR) > 0.0) {\n            continue;\n          }\n          int idyR = int(dyR);\n\n          for (int wC = 0; wC < ${effectiveFilterWidth}; wC++) {\n            float dyC = float(dyCCorner + wC) / ${strideWidth}.0;\n\n            if (dyC < 0.0 || dyC >= ${convInfo.outWidth}.0 ||\n                fract(dyC) > 0.0) {\n              continue;\n            }\n            int idyC = int(dyC);\n\n            float dyValue = getDy(b, idyR, idyC, d);\n            int maxPosValue = ${lastIndex} - int(getMaxPos(b, idyR, idyC, d));\n\n            // Get the current value, check it against the value from the\n            // position matrix.\n            int curPosValue = wR * ${effectiveFilterWidth} + wC;\n            float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);\n\n            dotProd += dyValue * mask;\n          }\n        }\n        setOutput(dotProd);\n      }\n    `;\n  }\n};\nvar MaxPool3DBackpropProgram = class {\n  constructor(convInfo) {\n    this.variableNames = [\"dy\", \"maxPos\"];\n    this.outputShape = convInfo.inShape;\n    const strideDepth = convInfo.strideDepth;\n    const strideHeight = convInfo.strideHeight;\n    const strideWidth = convInfo.strideWidth;\n    const dilationDepth = convInfo.dilationDepth;\n    const dilationHeight = convInfo.dilationHeight;\n    const dilationWidth = convInfo.dilationWidth;\n    const effectiveFilterDepth = convInfo.effectiveFilterDepth;\n    const effectiveFilterHeight = convInfo.effectiveFilterHeight;\n    const effectiveFilterWidth = convInfo.effectiveFilterWidth;\n    const padFront = effectiveFilterDepth - 1 - convInfo.padInfo.front;\n    const padTop = effectiveFilterHeight - 1 - convInfo.padInfo.top;\n    const padLeft = effectiveFilterWidth - 1 - convInfo.padInfo.left;\n    const lastIndex = effectiveFilterDepth * effectiveFilterHeight * effectiveFilterWidth - 1;\n    this.userCode = `\n      const ivec3 pads = ivec3(${padFront}, ${padTop}, ${padLeft});\n\n      void main() {\n        ivec5 coords = getOutputCoords();\n        int batch = coords.x;\n        int ch = coords.u;\n\n        ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;\n        int dyDCorner = dyCorner.x;\n        int dyRCorner = dyCorner.y;\n        int dyCCorner = dyCorner.z;\n\n        // Convolve dy(?, ?, ?, ch) with pos mask(:, :, :, d) to get\n        // dx(xD, xR, xC, ch).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n\n        for (int wD = 0; wD < ${effectiveFilterDepth};\n           wD += ${dilationDepth}) {\n          float dyD = float(dyDCorner + wD) / ${strideDepth}.0;\n\n          if (dyD < 0.0 || dyD >= ${convInfo.outDepth}.0 || fract(dyD) > 0.0) {\n            continue;\n          }\n          int idyD = int(dyD);\n\n          for (int wR = 0; wR < ${effectiveFilterHeight};\n              wR += ${dilationHeight}) {\n            float dyR = float(dyRCorner + wR) / ${strideHeight}.0;\n\n            if (dyR < 0.0 || dyR >= ${convInfo.outHeight}.0 ||\n                fract(dyR) > 0.0) {\n              continue;\n            }\n            int idyR = int(dyR);\n\n            for (int wC = 0; wC < ${effectiveFilterWidth};\n                wC += ${dilationWidth}) {\n              float dyC = float(dyCCorner + wC) / ${strideWidth}.0;\n\n              if (dyC < 0.0 || dyC >= ${convInfo.outWidth}.0 ||\n                  fract(dyC) > 0.0) {\n                continue;\n              }\n              int idyC = int(dyC);\n\n              float dyValue = getDy(batch, idyD, idyR, idyC, ch);\n              int maxPosValue = ${lastIndex} -\n                  int(getMaxPos(batch, idyD, idyR, idyC, ch));\n\n              // Get the current value, check it against the value from the\n              // position matrix.\n              int curPosValue =\n                  wD * ${effectiveFilterHeight} * ${effectiveFilterWidth} +\n                  wR * ${effectiveFilterWidth} + wC;\n              float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);\n\n              dotProd += dyValue * mask;\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    `;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/MaxPool3DGrad.js\nfunction maxPool3DGrad2(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { dy, input: input2 } = inputs;\n  const x = input2;\n  const { filterSize, strides, pad: pad3, dimRoundingMode } = attrs;\n  const dilations = [1, 1, 1];\n  const convInfo = backend_util_exports.computePool3DInfo(x.shape, filterSize, strides, dilations, pad3, dimRoundingMode);\n  const maxPool3dPositionsProgram = new Pool3DProgram(convInfo, \"max\", true);\n  const maxPool3dPositions2 = backend2.runWebGLProgram(maxPool3dPositionsProgram, [x], x.dtype);\n  const maxPoolBackpropProgram = new MaxPool3DBackpropProgram(convInfo);\n  const result = backend2.runWebGLProgram(maxPoolBackpropProgram, [dy, maxPool3dPositions2], x.dtype);\n  backend2.disposeIntermediateTensorInfo(maxPool3dPositions2);\n  return result;\n}\nvar maxPool3DGradConfig3 = {\n  kernelName: MaxPool3DGrad,\n  backendName: \"webgl\",\n  kernelFunc: maxPool3DGrad2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/MaxPoolGrad.js\nfunction maxPoolGrad3(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { dy, input: input2, output } = inputs;\n  const x = input2;\n  assertNotComplex2([input2, output], \"maxPoolGrad\");\n  const { filterSize, strides, pad: pad3, dimRoundingMode } = attrs;\n  const convInfo = backend_util_exports.computePool2DInfo(x.shape, filterSize, strides, 1, pad3, dimRoundingMode);\n  const getPositions = true;\n  const maxPoolPositionsProgram = new Pool2DProgram(convInfo, \"max\", getPositions);\n  const maxPoolPositions2 = backend2.runWebGLProgram(maxPoolPositionsProgram, [x], x.dtype);\n  const maxPoolBackPropProgram = new MaxPool2DBackpropProgram(convInfo);\n  const result = backend2.runWebGLProgram(maxPoolBackPropProgram, [dy, maxPoolPositions2], x.dtype);\n  backend2.disposeIntermediateTensorInfo(maxPoolPositions2);\n  return result;\n}\nvar maxPoolGradConfig3 = {\n  kernelName: MaxPoolGrad,\n  backendName: \"webgl\",\n  kernelFunc: maxPoolGrad3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/MaxPoolWithArgmax_impl.js\nfunction maxPoolWithArgmaxImpl2(x, includeBatchInIndex, convInfo, backend2) {\n  let program = new Pool2DProgram(convInfo, \"max\", false);\n  const poolOutput = backend2.runWebGLProgram(program, [x], \"float32\");\n  program = new Pool2DProgram(convInfo, \"max\", true, true, includeBatchInIndex);\n  const indexOutput = backend2.runWebGLProgram(program, [x], \"float32\");\n  return [poolOutput, indexOutput];\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/MaxPoolWithArgmax.js\nvar maxPoolWithArgmaxConfig2 = {\n  kernelName: MaxPoolWithArgmax,\n  backendName: \"webgl\",\n  kernelFunc: ({ inputs, attrs, backend: backend2 }) => {\n    const { x } = inputs;\n    const { filterSize, strides, pad: pad3, includeBatchInIndex } = attrs;\n    const webglBackend = backend2;\n    util_exports.assert(x.shape.length === 4, () => `Error in maxPool: input must be rank 4 but got rank ${x.shape.length}.`);\n    const dilations = [1, 1];\n    util_exports.assert(backend_util_exports.eitherStridesOrDilationsAreOne(strides, dilations), () => `Error in maxPool: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`);\n    const convInfo = backend_util_exports.computePool2DInfo(x.shape, filterSize, strides, dilations, pad3);\n    const [result, indexes] = maxPoolWithArgmaxImpl2(x, includeBatchInIndex, convInfo, webglBackend);\n    return [result, indexes];\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Mean_impl.js\nfunction meanImpl(x, reduceShape, outShape, backend2) {\n  const inSize = util_exports.sizeFromShape(reduceShape);\n  const xSize = util_exports.sizeFromShape(x.shape);\n  const batchSize = xSize / inSize;\n  const reshapedInput = reshape4({ inputs: { x }, attrs: { shape: [batchSize, inSize] }, backend: backend2 });\n  const reduced = reduce(reshapedInput, \"float32\", \"mean\", backend2);\n  const reshapedOutput = reshape4({ inputs: { x: reduced }, attrs: { shape: outShape }, backend: backend2 });\n  backend2.disposeIntermediateTensorInfo(reshapedInput);\n  backend2.disposeIntermediateTensorInfo(reduced);\n  return reshapedOutput;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Mean.js\nvar meanConfig2 = {\n  kernelName: Mean,\n  backendName: \"webgl\",\n  kernelFunc: ({ inputs, attrs, backend: backend2 }) => {\n    const { x } = inputs;\n    const { keepDims, axis } = attrs;\n    const webglBackend = backend2;\n    const xRank = x.shape.length;\n    const origAxes = util_exports.parseAxisParam(axis, x.shape);\n    let axes = origAxes;\n    const permutedAxes = backend_util_exports.getAxesPermutation(axes, xRank);\n    const meanInputIsTransposed = permutedAxes != null;\n    const shouldExecuteOnCPU = webglBackend.shouldExecuteOnCPU([x]);\n    const intermediates = [];\n    let meanInput = x;\n    if (meanInputIsTransposed) {\n      if (shouldExecuteOnCPU) {\n        const xTexData = webglBackend.texData.get(meanInput.dataId);\n        const values = xTexData.values;\n        const newShape = new Array(xRank);\n        for (let i2 = 0; i2 < newShape.length; i2++) {\n          newShape[i2] = x.shape[permutedAxes[i2]];\n        }\n        const meanInputValues = transposeImplCPU(values, x.shape, x.dtype, permutedAxes, newShape);\n        meanInput = webglBackend.makeTensorInfo(newShape, x.dtype);\n        const meanInputData = webglBackend.texData.get(meanInput.dataId);\n        meanInputData.values = meanInputValues;\n      } else {\n        meanInput = transposeImpl2(x, permutedAxes, webglBackend);\n      }\n      intermediates.push(meanInput);\n      axes = backend_util_exports.getInnerMostAxes(axes.length, xRank);\n    }\n    backend_util_exports.assertAxesAreInnerMostDims(\"sum\", axes, xRank);\n    const [meanOutShape, reduceShape] = backend_util_exports.computeOutAndReduceShapes(meanInput.shape, axes);\n    let outShape = meanOutShape;\n    if (keepDims) {\n      outShape = backend_util_exports.expandShapeToKeepDim(meanOutShape, origAxes);\n    }\n    const out = meanImpl(meanInput, reduceShape, outShape, webglBackend);\n    for (const i2 of intermediates) {\n      webglBackend.disposeIntermediateTensorInfo(i2);\n    }\n    return out;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Min.js\nfunction min4(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x } = inputs;\n  const { axis, keepDims } = attrs;\n  const xRank = x.shape.length;\n  const origAxes = util_exports.parseAxisParam(axis, x.shape);\n  let axes = origAxes;\n  const permutedAxes = backend_util_exports.getAxesPermutation(axes, xRank);\n  let permutedX = x;\n  if (permutedAxes != null) {\n    permutedX = transpose3({ inputs: { x }, backend: backend2, attrs: { perm: permutedAxes } });\n    axes = backend_util_exports.getInnerMostAxes(axes.length, x.shape.length);\n  }\n  backend_util_exports.assertAxesAreInnerMostDims(\"min\", axes, xRank);\n  const [outShape, reduceShape] = backend_util_exports.computeOutAndReduceShapes(permutedX.shape, axes);\n  const inSize = util_exports.sizeFromShape(reduceShape);\n  const a2D = reshape4({ inputs: { x: permutedX }, backend: backend2, attrs: { shape: [-1, inSize] } });\n  const reduced = reduce(a2D, a2D.dtype, \"min\", backend2);\n  let res;\n  if (keepDims) {\n    const newShape = backend_util_exports.expandShapeToKeepDim(outShape, origAxes);\n    res = reshape4({ inputs: { x: reduced }, backend: backend2, attrs: { shape: newShape } });\n  } else {\n    res = reshape4({ inputs: { x: reduced }, backend: backend2, attrs: { shape: outShape } });\n  }\n  backend2.disposeIntermediateTensorInfo(a2D);\n  backend2.disposeIntermediateTensorInfo(reduced);\n  if (permutedAxes != null) {\n    backend2.disposeIntermediateTensorInfo(permutedX);\n  }\n  return res;\n}\nvar minConfig2 = {\n  kernelName: Min,\n  backendName: \"webgl\",\n  kernelFunc: min4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Minimum.js\nvar MINIMUM = CHECK_NAN_SNIPPET2 + `\n  return min(a, b);\n`;\nvar MINIMUM_PACKED = `\n  vec4 result = vec4(min(a, b));\n  vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));\n  ` + CHECK_NAN_SNIPPET3 + `\n  return result;\n`;\nvar minimum4 = binaryKernelFunc2({\n  opSnippet: MINIMUM,\n  packedOpSnippet: MINIMUM_PACKED,\n  cpuKernelImpl: minimumImplCPU\n});\nvar minimumConfig2 = {\n  kernelName: Minimum,\n  backendName: \"webgl\",\n  kernelFunc: minimum4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/mirror_pad_gpu.js\nvar MirrorPadProgram = class {\n  constructor(xShape, paddings, mode) {\n    this.variableNames = [\"x\"];\n    this.outputShape = paddings.map((p2, i2) => p2[0] + xShape[i2] + p2[1]);\n    const rank = xShape.length;\n    const dtype = getCoordsDataType(rank);\n    const start = paddings.map((p2) => p2[0]).join(\",\");\n    const end = paddings.map((p2, i2) => p2[0] + xShape[i2]).join(\",\");\n    const unpackedCoords = [\"coords[0]\", \"coords[1]\", \"coords[2]\", \"coords[3]\"].slice(0, rank);\n    const offset = mode === \"reflect\" ? 0 : 1;\n    if (rank === 1) {\n      this.userCode = `\n        int start = ${start};\n        int end = ${end};\n\n        void main() {\n          int outC = getOutputCoords();\n          if (outC < start) {\n            outC = start * 2 - outC - ${offset};\n          } else if(outC >= end) {\n            outC = (end - 1) * 2 - outC + ${offset};\n          }\n          setOutput(getX(outC - start));\n        }\n      `;\n      return;\n    }\n    this.userCode = `\n      ${dtype} start = ${dtype}(${start});\n      ${dtype} end = ${dtype}(${end});\n\n      void main() {\n        ${dtype} outC = getOutputCoords();\n        for (int i = 0; i < ${rank}; i++) {\n          if (outC[i] < start[i]) {\n            outC[i] = start[i] * 2 - outC[i] - ${offset};\n          } else if(outC[i] >= end[i]) {\n            outC[i] = (end[i] - 1) * 2 - outC[i] + ${offset};\n          }\n        }\n        ${dtype} coords = outC - start;\n        setOutput(getX(${unpackedCoords}));\n      }\n    `;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/mirror_pad_packed_gpu.js\nvar MirrorPadPackedProgram = class {\n  constructor(xShape, paddings, mode) {\n    this.variableNames = [\"x\"];\n    this.packedInputs = true;\n    this.packedOutput = true;\n    this.outputShape = paddings.map((p2, i2) => p2[0] + xShape[i2] + p2[1]);\n    const rank = xShape.length;\n    const dtype = getCoordsDataType(rank);\n    const start = paddings.map((p2) => p2[0]).join(\",\");\n    const end = paddings.map((p2, i2) => p2[0] + xShape[i2]).join(\",\");\n    const coords3 = getChannels(\"rc\", rank);\n    const source = getChannels(\"source\", rank);\n    const cLimit = `${coords3[rank - 1]} < ${this.outputShape[rank - 1]}`;\n    const innerDims = rank === 1 ? \"source\" : `vec2(${source.slice(-2).join()})`;\n    const offset = mode === \"reflect\" ? 0 : 1;\n    let mainLoop = \"\";\n    if (rank === 1) {\n      const padSetup = `\n        ${dtype} source = rc;\n        if (source < start) {\n          source = start * 2 - source - ${offset};\n        } else if (source >= end) {\n          source = (end - 1) * 2 - source + ${offset};\n        }\n        source -= start;\n      `;\n      mainLoop = `\n        ${dtype} rc = outputLoc;\n        ${padSetup}\n        result[0] = getChannel(getX(${source.join()}), ${innerDims});\n        ${coords3[rank - 1]} += 1;\n        if(${cLimit}) {\n          ${padSetup}\n          result[1] = getChannel(getX(${source.join()}), ${innerDims});\n        }\n      `;\n    } else {\n      const padSetup = `\n        ${dtype} source = rc;\n        ${dtype} lt = ${dtype}(lessThan(source, start));\n        ${dtype} gte = ${dtype}(greaterThanEqual(source, end));\n        ${dtype} orig = 1 - (lt + gte);\n        source = orig * source +\n                lt * (start * 2 - source - ${offset}) +\n                gte * ((end - 1) * 2 - source + ${offset});\n        source -= start;\n      `;\n      mainLoop = `\n        ${dtype} rc = outputLoc;\n        ${padSetup}\n        result[0] = getChannel(getX(${source.join()}), ${innerDims});\n        ${coords3[rank - 1]} += 1;\n        if(${cLimit}) {\n          ${padSetup}\n          result[1] = getChannel(getX(${source.join()}), ${innerDims});\n        }\n        rc = outputLoc;\n        ${coords3[rank - 2]} += 1;\n        if(${coords3[rank - 2]} < ${this.outputShape[rank - 2]}) {\n          ${padSetup}\n          result[2] = getChannel(getX(${source.join()}), ${innerDims});\n          ${coords3[rank - 1]} += 1;\n          if(${cLimit}) {\n            ${padSetup}\n            result[3] = getChannel(getX(${source.join()}), ${innerDims});\n          }\n        }\n      `;\n    }\n    this.userCode = `\n      const ${dtype} start = ${dtype}(${start});\n      const ${dtype} end = ${dtype}(${end});\n\n      void main() {\n        ${dtype} outputLoc = getOutputCoords();\n        vec4 result = vec4(0.);\n        ${mainLoop}\n        setOutput(result);\n      }\n    `;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/MirrorPad.js\nvar mirrorPadKernelFunc = ({ inputs, backend: backend2, attrs }) => {\n  const { x } = inputs;\n  const { paddings, mode } = attrs;\n  const program = env().getBool(\"WEBGL_PACK_ARRAY_OPERATIONS\") ? new MirrorPadPackedProgram(x.shape, paddings, mode) : new MirrorPadProgram(x.shape, paddings, mode);\n  const output = backend2.runWebGLProgram(program, [x], x.dtype);\n  return output;\n};\nvar mirrorPadConfig2 = {\n  kernelName: MirrorPad,\n  backendName: \"webgl\",\n  kernelFunc: mirrorPadKernelFunc\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Mod.js\nvar MOD = `if (b == 0.0) return NAN;\n  return mod(a, b);`;\nvar MOD_PACKED = `\n  vec4 result = mod(a, b);\n  vec4 isNaN = vec4(equal(b, vec4(0.0)));\n  ` + CHECK_NAN_SNIPPET3 + `\n  return result;\n`;\nvar mod3 = binaryKernelFunc2({\n  opSnippet: MOD,\n  packedOpSnippet: MOD_PACKED\n});\nvar modConfig2 = {\n  kernelName: Mod,\n  backendName: \"webgl\",\n  kernelFunc: mod3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/multinomial_gpu.js\nvar MultinomialProgram = class {\n  constructor(batchSize, numOutcomes, numSamples) {\n    this.variableNames = [\"probs\"];\n    this.customUniforms = [{ name: \"seed\", type: \"float\" }];\n    this.outputShape = [batchSize, numSamples];\n    this.userCode = `\n      void main() {\n        ivec2 coords = getOutputCoords();\n        int batch = coords[0];\n\n        float r = random(seed);\n        float cdf = 0.0;\n\n        for (int i = 0; i < ${numOutcomes - 1}; i++) {\n          cdf += getProbs(batch, i);\n\n          if (r < cdf) {\n            setOutput(float(i));\n            return;\n          }\n        }\n\n        // If no other event happened, last event happened.\n        setOutput(float(${numOutcomes - 1}));\n      }\n    `;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/RealDiv.js\nvar DIV = `\nif (a == b) {\n  return 1.0;\n};\nreturn a / b;`;\nvar DIV_PACKED = `\n  // vec4 one = vec4(equal(a, b));\n  // return one + (vec4(1.0) - one) * a / b;\n  vec4 result = a / b;\n  if(a.x == b.x) {\n    result.x = 1.;\n  }\n  if(a.y == b.y) {\n    result.y = 1.;\n  }\n  if(a.z == b.z) {\n    result.z = 1.;\n  }\n  if(a.w == b.w) {\n    result.w = 1.;\n  }\n\n  return result;\n`;\nvar realDiv = binaryKernelFunc2({ opSnippet: DIV, packedOpSnippet: DIV_PACKED, checkOutOfBounds: true });\nvar realDivConfig2 = {\n  kernelName: RealDiv,\n  backendName: \"webgl\",\n  kernelFunc: realDiv\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Sub.js\nvar SUB = \"return a - b;\";\nvar sub3 = binaryKernelFunc2({\n  opSnippet: SUB,\n  packedOpSnippet: SUB,\n  supportsComplex: true,\n  cpuKernelImpl: subImplCPU\n});\nvar subConfig2 = {\n  kernelName: Sub,\n  backendName: \"webgl\",\n  kernelFunc: sub3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Softmax.js\nfunction softmax4(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { logits } = inputs;\n  const { dim } = attrs;\n  const axes = util_exports.parseAxisParam([dim], logits.shape);\n  const maxLogit = max4({\n    inputs: { x: logits },\n    backend: backend2,\n    attrs: { reductionIndices: axes, keepDims: false }\n  });\n  const expandedShape = backend_util_exports.expandShapeToKeepDim(maxLogit.shape, axes);\n  const maxLogitsReshaped = reshape4({ inputs: { x: maxLogit }, backend: backend2, attrs: { shape: expandedShape } });\n  const a = sub3({ inputs: { a: logits, b: maxLogitsReshaped }, backend: backend2 });\n  const b = exp3({ inputs: { x: a }, backend: backend2 });\n  const sumExp = sum4({ inputs: { x: b }, backend: backend2, attrs: { axis: axes, keepDims: false } });\n  const sumExpReshaped = reshape4({ inputs: { x: sumExp }, backend: backend2, attrs: { shape: expandedShape } });\n  const res = realDiv({ inputs: { a: b, b: sumExpReshaped }, backend: backend2 });\n  backend2.disposeIntermediateTensorInfo(maxLogit);\n  backend2.disposeIntermediateTensorInfo(maxLogitsReshaped);\n  backend2.disposeIntermediateTensorInfo(a);\n  backend2.disposeIntermediateTensorInfo(b);\n  backend2.disposeIntermediateTensorInfo(sumExp);\n  backend2.disposeIntermediateTensorInfo(sumExpReshaped);\n  return res;\n}\nvar softmaxConfig2 = {\n  kernelName: Softmax,\n  backendName: \"webgl\",\n  kernelFunc: softmax4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Multinomial.js\nfunction multinomial3(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { logits } = inputs;\n  const { numSamples, seed, normalized } = attrs;\n  const probs = normalized ? logits : softmax4({ inputs: { logits }, backend: backend2, attrs: { dim: logits.shape.length - 1 } });\n  const batchSize = probs.shape[0];\n  const numOutcomes = probs.shape[1];\n  const program = new MultinomialProgram(batchSize, numOutcomes, numSamples);\n  const customValues = [[seed]];\n  const res = backend2.runWebGLProgram(program, [probs], \"int32\", customValues);\n  if (!normalized) {\n    backend2.disposeIntermediateTensorInfo(probs);\n  }\n  return res;\n}\nvar multinomialConfig2 = {\n  kernelName: Multinomial,\n  backendName: \"webgl\",\n  kernelFunc: multinomial3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Neg.js\nvar NEG = CHECK_NAN_SNIPPET + `\n  return -x;\n`;\nvar NEG_PACKED = `\n  vec4 result = -x;\n  bvec4 isNaN = isnan(x);\n\n  result.r = isNaN.r ? x.r : result.r;\n  result.g = isNaN.g ? x.g : result.g;\n  result.b = isNaN.b ? x.b : result.b;\n  result.a = isNaN.a ? x.a : result.a;\n\n  return result;\n`;\nfunction neg3(args) {\n  const { inputs, backend: backend2 } = args;\n  const { x } = inputs;\n  if (backend2.shouldExecuteOnCPU([x])) {\n    const xData = backend2.texData.get(x.dataId);\n    const [outValues, newShape] = negImplCPU(xData.values, x.shape, x.dtype);\n    return backend2.makeTensorInfo(newShape, x.dtype, outValues);\n  }\n  let program;\n  if (env().getBool(\"WEBGL_PACK_UNARY_OPERATIONS\")) {\n    program = new UnaryOpPackedProgram(x.shape, NEG_PACKED);\n  } else {\n    program = new UnaryOpProgram(x.shape, NEG);\n  }\n  return backend2.runWebGLProgram(program, [x], x.dtype);\n}\nvar negConfig2 = {\n  kernelName: Neg,\n  backendName: \"webgl\",\n  kernelFunc: neg3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/NonMaxSuppressionV3.js\nvar nonMaxSuppressionV3Impl3 = kernel_impls_exports.nonMaxSuppressionV3Impl;\nfunction nonMaxSuppressionV32(args) {\n  backend_util_exports.warn(\"tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead\");\n  const { inputs, backend: backend2, attrs } = args;\n  const { boxes, scores } = inputs;\n  const { maxOutputSize, iouThreshold, scoreThreshold } = attrs;\n  const boxesVals = backend2.readSync(boxes.dataId);\n  const scoresVals = backend2.readSync(scores.dataId);\n  const { selectedIndices } = nonMaxSuppressionV3Impl3(boxesVals, scoresVals, maxOutputSize, iouThreshold, scoreThreshold);\n  return backend2.makeTensorInfo([selectedIndices.length], \"int32\", new Int32Array(selectedIndices));\n}\nvar nonMaxSuppressionV3Config2 = {\n  kernelName: NonMaxSuppressionV3,\n  backendName: \"webgl\",\n  kernelFunc: nonMaxSuppressionV32\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/NonMaxSuppressionV4.js\nvar nonMaxSuppressionV4Impl3 = kernel_impls_exports.nonMaxSuppressionV4Impl;\nfunction nonMaxSuppressionV42(args) {\n  backend_util_exports.warn(\"tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead\");\n  const { inputs, backend: backend2, attrs } = args;\n  const { boxes, scores } = inputs;\n  const { maxOutputSize, iouThreshold, scoreThreshold, padToMaxOutputSize } = attrs;\n  const boxesVals = backend2.readSync(boxes.dataId);\n  const scoresVals = backend2.readSync(scores.dataId);\n  const { selectedIndices, validOutputs } = nonMaxSuppressionV4Impl3(boxesVals, scoresVals, maxOutputSize, iouThreshold, scoreThreshold, padToMaxOutputSize);\n  return [\n    backend2.makeTensorInfo([selectedIndices.length], \"int32\", new Int32Array(selectedIndices)),\n    backend2.makeTensorInfo([], \"int32\", new Int32Array([validOutputs]))\n  ];\n}\nvar nonMaxSuppressionV4Config2 = {\n  kernelName: NonMaxSuppressionV4,\n  backendName: \"webgl\",\n  kernelFunc: nonMaxSuppressionV42\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/NonMaxSuppressionV5.js\nvar nonMaxSuppressionV5Impl3 = kernel_impls_exports.nonMaxSuppressionV5Impl;\nfunction nonMaxSuppressionV52(args) {\n  backend_util_exports.warn(\"tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead\");\n  const { inputs, backend: backend2, attrs } = args;\n  const { boxes, scores } = inputs;\n  const { maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma } = attrs;\n  const boxesVals = backend2.readSync(boxes.dataId);\n  const scoresVals = backend2.readSync(scores.dataId);\n  const maxOutputSizeVal = maxOutputSize;\n  const iouThresholdVal = iouThreshold;\n  const scoreThresholdVal = scoreThreshold;\n  const softNmsSigmaVal = softNmsSigma;\n  const { selectedIndices, selectedScores } = nonMaxSuppressionV5Impl3(boxesVals, scoresVals, maxOutputSizeVal, iouThresholdVal, scoreThresholdVal, softNmsSigmaVal);\n  return [\n    backend2.makeTensorInfo([selectedIndices.length], \"int32\", new Int32Array(selectedIndices)),\n    backend2.makeTensorInfo([selectedScores.length], \"float32\", new Float32Array(selectedScores))\n  ];\n}\nvar nonMaxSuppressionV5Config2 = {\n  kernelName: NonMaxSuppressionV5,\n  backendName: \"webgl\",\n  kernelFunc: nonMaxSuppressionV52\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/onehot_gpu.js\nvar OneHotProgram = class {\n  constructor(numIndices, depth, onValue, offValue) {\n    this.variableNames = [\"indices\"];\n    this.outputShape = [numIndices, depth];\n    this.userCode = `\n      void main() {\n        ivec2 coords = getOutputCoords();\n        int index = round(getIndices(coords.x));\n        setOutput(mix(float(${offValue}), float(${onValue}),\n                      float(index == coords.y)));\n      }\n    `;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/OneHot.js\nvar oneHot3 = (args) => {\n  const { inputs, backend: backend2, attrs } = args;\n  const { indices } = inputs;\n  const { dtype, depth, onValue, offValue } = attrs;\n  const indicesSize = util_exports.sizeFromShape(indices.shape);\n  const program = new OneHotProgram(indicesSize, depth, onValue, offValue);\n  const reshaped = reshape4({ inputs: { x: indices }, backend: backend2, attrs: { shape: [indicesSize] } });\n  const result = backend2.runWebGLProgram(program, [reshaped], dtype);\n  backend2.disposeIntermediateTensorInfo(reshaped);\n  const outShape = [...indices.shape, depth];\n  const out = reshape4({ inputs: { x: result }, backend: backend2, attrs: { shape: outShape } });\n  backend2.disposeIntermediateTensorInfo(result);\n  return out;\n};\nvar oneHotConfig2 = {\n  kernelName: OneHot,\n  backendName: \"webgl\",\n  kernelFunc: oneHot3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/ZerosLike.js\nfunction zerosLike3(args) {\n  const { inputs, backend: backend2 } = args;\n  const { x } = inputs;\n  if (x.dtype === \"complex64\") {\n    const realPart = real3({ inputs: { input: x }, backend: backend2 });\n    const r2 = zerosLike3({ inputs: { x: realPart }, backend: backend2 });\n    const imagPart = imag3({ inputs: { input: x }, backend: backend2 });\n    const i2 = zerosLike3({ inputs: { x: imagPart }, backend: backend2 });\n    const result = complex3({ inputs: { real: r2, imag: i2 }, backend: backend2 });\n    backend2.disposeIntermediateTensorInfo(realPart);\n    backend2.disposeIntermediateTensorInfo(r2);\n    backend2.disposeIntermediateTensorInfo(imagPart);\n    backend2.disposeIntermediateTensorInfo(i2);\n    return result;\n  } else {\n    return fill3({\n      attrs: {\n        shape: x.shape,\n        dtype: x.dtype,\n        value: x.dtype === \"string\" ? \"\" : 0\n      },\n      backend: backend2\n    });\n  }\n}\nvar zerosLikeConfig2 = {\n  kernelName: ZerosLike,\n  backendName: \"webgl\",\n  kernelFunc: zerosLike3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/OnesLike.js\nfunction onesLike3(args) {\n  const { inputs, backend: backend2 } = args;\n  const { x } = inputs;\n  if (x.dtype === \"string\") {\n    throw new Error(\"onesLike is not supported under string dtype\");\n  } else if (x.dtype === \"complex64\") {\n    const realPart = real3({ inputs: { input: x }, backend: backend2 });\n    const r2 = onesLike3({ inputs: { x: realPart }, backend: backend2 });\n    const imagPart = imag3({ inputs: { input: x }, backend: backend2 });\n    const i2 = zerosLike3({ inputs: { x: imagPart }, backend: backend2 });\n    const result = complex3({ inputs: { real: r2, imag: i2 }, backend: backend2 });\n    backend2.disposeIntermediateTensorInfo(realPart);\n    backend2.disposeIntermediateTensorInfo(r2);\n    backend2.disposeIntermediateTensorInfo(imagPart);\n    backend2.disposeIntermediateTensorInfo(i2);\n    return result;\n  } else {\n    return fill3({ attrs: { shape: x.shape, dtype: x.dtype, value: 1 }, backend: backend2 });\n  }\n}\nvar onesLikeConfig2 = {\n  kernelName: OnesLike,\n  backendName: \"webgl\",\n  kernelFunc: onesLike3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Pack.js\nfunction pack2(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { axis } = attrs;\n  if (inputs.length === 1) {\n    return expandDims4({ inputs: { input: inputs[0] }, backend: backend2, attrs: { dim: axis } });\n  }\n  const shape = inputs[0].shape;\n  const dtype = inputs[0].dtype;\n  inputs.forEach((t2) => {\n    util_exports.assertShapesMatch(shape, t2.shape, \"All tensors passed to stack must have matching shapes\");\n    util_exports.assert(dtype === t2.dtype, () => \"All tensors passed to stack must have matching dtypes\");\n  });\n  const intermediateTensorInfos = [];\n  const expandedTensors = inputs.map((t2) => {\n    const expandedT = expandDims4({ inputs: { input: t2 }, backend: backend2, attrs: { dim: axis } });\n    intermediateTensorInfos.push(expandedT);\n    return expandedT;\n  });\n  const result = concat3({ inputs: expandedTensors, backend: backend2, attrs: { axis } });\n  intermediateTensorInfos.forEach((t2) => backend2.disposeIntermediateTensorInfo(t2));\n  return result;\n}\nvar packConfig2 = {\n  kernelName: Pack,\n  backendName: \"webgl\",\n  kernelFunc: pack2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/pad_gpu.js\nvar PadProgram = class {\n  constructor(xShape, paddings, constantValue) {\n    this.variableNames = [\"x\"];\n    this.customUniforms = [{ name: \"value\", type: \"float\" }];\n    this.outputShape = paddings.map((p2, i2) => p2[0] + xShape[i2] + p2[1]);\n    const rank = xShape.length;\n    const type = getCoordsDataType(rank);\n    const start = paddings.map((p2) => p2[0]).join(\",\");\n    const end = paddings.map((p2, i2) => p2[0] + xShape[i2]).join(\",\");\n    const unpackedCoords = [\"coords[0]\", \"coords[1]\", \"coords[2]\", \"coords[3]\"].slice(0, rank);\n    if (rank === 1) {\n      this.userCode = `\n        int start = ${start};\n        int end = ${end};\n\n        void main() {\n          int outC = getOutputCoords();\n          if (outC < start || outC >= end) {\n            setOutput(value);\n          } else {\n            setOutput(getX(outC - start));\n          }\n        }\n      `;\n      return;\n    }\n    this.userCode = `\n      ${type} start = ${type}(${start});\n      ${type} end = ${type}(${end});\n\n      void main() {\n        ${type} outC = getOutputCoords();\n        if (any(lessThan(outC, start)) || any(greaterThanEqual(outC, end))) {\n          setOutput(value);\n        } else {\n          ${type} coords = outC - start;\n          setOutput(getX(${unpackedCoords}));\n        }\n      }\n    `;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/pad_packed_gpu.js\nvar PadPackedProgram = class {\n  constructor(xShape, paddings, constantValue) {\n    this.variableNames = [\"x\"];\n    this.packedInputs = true;\n    this.packedOutput = true;\n    this.customUniforms = [{ name: \"value\", type: \"float\" }];\n    this.outputShape = paddings.map((p2, i2) => p2[0] + xShape[i2] + p2[1]);\n    const rank = xShape.length;\n    const dtype = getCoordsDataType(rank);\n    const start = paddings.map((p2) => p2[0]).join(\",\");\n    const end = paddings.map((p2, i2) => p2[0] + xShape[i2]).join(\",\");\n    const coords3 = getChannels(\"rc\", rank);\n    const source = getChannels(\"source\", rank);\n    const cLimit = `${coords3[rank - 1]} < ${this.outputShape[rank - 1]}`;\n    const innerDims = rank === 1 ? \"source\" : `vec2(${source.slice(-2).join()})`;\n    const componentSetup = [\n      `${dtype} rc = outputLoc;`,\n      `${coords3[rank - 1]} += 1;\n       if(${cLimit}) {\n      `,\n      rank === 1 ? \"\" : `}\n       rc = outputLoc;\n       ${coords3[rank - 2]} += 1;\n       if(${coords3[rank - 2]} < ${this.outputShape[rank - 2]}) {`,\n      rank === 1 ? \"\" : `  ${coords3[rank - 1]} += 1;\n         if(${cLimit}) {`\n    ];\n    const paddingArea = rank === 1 ? \"rc < start || rc >= end\" : \"any(lessThan(rc, start)) || any(greaterThanEqual(rc, end))\";\n    let mainLoop = \"\";\n    for (let i2 = 0, j = rank === 1 ? 2 : 4; i2 < j; i2++) {\n      mainLoop += `\n        ${componentSetup[i2]}\n        if (${paddingArea}) {\n          result[${i2}] = float(value);\n        } else {\n          ${dtype} source = rc - start;\n          result[${i2}] = getChannel(getX(${source.join()}), ${innerDims});\n        }\n      `;\n    }\n    mainLoop += rank === 1 ? `} ` : `}}`;\n    this.userCode = `\n      const ${dtype} start = ${dtype}(${start});\n      const ${dtype} end = ${dtype}(${end});\n\n      void main() {\n        ${dtype} outputLoc = getOutputCoords();\n        vec4 result = vec4(0.);\n        ${mainLoop}\n        setOutput(result);\n      }\n    `;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/PadV2.js\nvar padV22 = (args) => {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x } = inputs;\n  const { paddings, constantValue } = attrs;\n  if (util_exports.sizeFromShape(x.shape) === 0) {\n    const outputShape = paddings.map((p2, i2) => p2[0] + x.shape[i2] + p2[1]);\n    return fill3({\n      backend: backend2,\n      attrs: { shape: outputShape, value: constantValue, dtype: x.dtype }\n    });\n  }\n  const program = env().getBool(\"WEBGL_PACK_ARRAY_OPERATIONS\") ? new PadPackedProgram(x.shape, paddings, constantValue) : new PadProgram(x.shape, paddings, constantValue);\n  const customValues = [[constantValue]];\n  return backend2.runWebGLProgram(program, [x], x.dtype, customValues);\n};\nvar padV2Config2 = {\n  kernelName: PadV2,\n  backendName: \"webgl\",\n  kernelFunc: padV22\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Pow.js\nvar POW = `\n  if(a < 0.0 && floor(b) < b){\n    return NAN;\n  }\n  if (b == 0.0) {\n    return 1.0;\n  }\n  return (round(mod(b, 2.0)) != 1) ?\n      pow(abs(a), b) : sign(a) * pow(abs(a), b);\n`;\nvar POW_PACKED = `\n  // isModRound1 has 1 for components with round(mod(b, 2.0)) == 1, 0 otherwise.\n  vec4 isModRound1 = vec4(equal(round(mod(b, 2.0)), ivec4(1)));\n  vec4 multiplier = sign(a) * isModRound1 + (vec4(1.0) - isModRound1);\n  vec4 result = multiplier * pow(abs(a), b);\n\n  // Ensure that a^0 = 1, including 0^0 = 1 as this correspond to TF and JS\n  bvec4 isExpZero = equal(b, vec4(0.0));\n  result.r = isExpZero.r ? 1.0 : result.r;\n  result.g = isExpZero.g ? 1.0 : result.g;\n  result.b = isExpZero.b ? 1.0 : result.b;\n  result.a = isExpZero.a ? 1.0 : result.a;\n\n  vec4 isNaN = vec4(lessThan(a, vec4(0.0))) * vec4(lessThan(floor(b), b));\n  ` + CHECK_NAN_SNIPPET3 + `\n  return result;\n`;\nvar pow3 = binaryKernelFunc2({ opSnippet: POW, packedOpSnippet: POW_PACKED });\nvar powConfig2 = {\n  kernelName: Pow,\n  backendName: \"webgl\",\n  kernelFunc: pow3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Prod.js\nfunction prod3(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x } = inputs;\n  const { axis, keepDims } = attrs;\n  const xRank = x.shape.length;\n  const toDispose = [];\n  const origAxes = util_exports.parseAxisParam(axis, x.shape);\n  let axes = origAxes;\n  const permutedAxes = backend_util_exports.getAxesPermutation(axes, xRank);\n  let permutedX = x;\n  if (permutedAxes != null) {\n    permutedX = transpose3({ inputs: { x }, backend: backend2, attrs: { perm: permutedAxes } });\n    axes = backend_util_exports.getInnerMostAxes(axes.length, xRank);\n    toDispose.push(permutedX);\n  }\n  backend_util_exports.assertAxesAreInnerMostDims(\"prod\", axes, xRank);\n  let res;\n  if (backend2.shouldExecuteOnCPU([permutedX])) {\n    const xVals = backend2.texData.get(permutedX.dataId).values;\n    const { outVals, outShape, outDtype } = prodImplCPU(permutedX.shape, permutedX.dtype, xVals, axes);\n    res = backend2.makeTensorInfo(outShape, outDtype, outVals);\n  } else {\n    const [outShape, reduceShape] = backend_util_exports.computeOutAndReduceShapes(permutedX.shape, axes);\n    const inSize = util_exports.sizeFromShape(reduceShape);\n    const a2D = reshape4({ inputs: { x: permutedX }, backend: backend2, attrs: { shape: [-1, inSize] } });\n    const outputDType = sumOutType(x.dtype);\n    const reduced = reduce(a2D, outputDType, \"prod\", backend2);\n    res = reshape4({ inputs: { x: reduced }, backend: backend2, attrs: { shape: outShape } });\n    toDispose.push(a2D);\n    toDispose.push(reduced);\n  }\n  if (keepDims) {\n    toDispose.push(res);\n    const newShape = backend_util_exports.expandShapeToKeepDim(res.shape, origAxes);\n    res = reshape4({ inputs: { x: res }, backend: backend2, attrs: { shape: newShape } });\n  }\n  toDispose.forEach((t2) => backend2.disposeIntermediateTensorInfo(t2));\n  return res;\n}\nvar prodConfig2 = {\n  kernelName: Prod,\n  backendName: \"webgl\",\n  kernelFunc: prod3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/RaggedTensorToTensor.js\nfunction raggedTensorToTensor3(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { shape, values, defaultValue, rowPartitionTensors } = inputs;\n  const { rowPartitionTypes } = attrs;\n  const $shape = backend2.readSync(shape.dataId);\n  const $values = backend2.readSync(values.dataId);\n  const $defaultValue = backend2.readSync(defaultValue.dataId);\n  const $rowPartitionValues = rowPartitionTensors.map((t2) => backend2.readSync(t2.dataId));\n  const rowPartitionValuesShapes = rowPartitionTensors.map((t2) => t2.shape);\n  const [outputShape, output] = raggedTensorToTensorImplCPU($shape, shape.shape, $values, values.shape, values.dtype, $defaultValue, defaultValue.shape, $rowPartitionValues, rowPartitionValuesShapes, rowPartitionTypes);\n  return backend2.makeTensorInfo(outputShape, values.dtype, output);\n}\nvar raggedTensorToTensorConfig2 = {\n  kernelName: RaggedTensorToTensor,\n  backendName: \"webgl\",\n  kernelFunc: raggedTensorToTensor3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Range.js\nvar range4 = (args) => {\n  const { backend: backend2, attrs } = args;\n  const { start, stop, step: step5, dtype } = attrs;\n  const values = rangeImplCPU(start, stop, step5, dtype);\n  return backend2.makeTensorInfo([values.length], dtype, values);\n};\nvar rangeConfig2 = {\n  kernelName: Range,\n  backendName: \"webgl\",\n  kernelFunc: range4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Reciprocal.js\nvar RECIPROCAL = `return 1.0 / x;`;\nvar reciprocal3 = unaryKernelFunc2({ opSnippet: RECIPROCAL });\nvar reciprocalConfig2 = {\n  kernelName: Reciprocal,\n  backendName: \"webgl\",\n  kernelFunc: reciprocal3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Relu.js\nvar RELU3 = CHECK_NAN_SNIPPET + `\n  return (x < 0.0) ? 0.0 : x;\n`;\nvar RELU_PACKED = `\n  vec4 result = x * vec4(greaterThanEqual(x, vec4(0.0)));\n  bvec4 isNaN = isnan(x);\n\n  result.r = isNaN.r ? x.r : result.r;\n  result.g = isNaN.g ? x.g : result.g;\n  result.b = isNaN.b ? x.b : result.b;\n  result.a = isNaN.a ? x.a : result.a;\n\n  return result;\n`;\nvar relu3 = unaryKernelFunc2({ opSnippet: RELU3, packedOpSnippet: RELU_PACKED });\nvar reluConfig2 = {\n  kernelName: Relu,\n  backendName: \"webgl\",\n  kernelFunc: relu3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Relu6.js\nvar RELU63 = CHECK_NAN_SNIPPET + `\n  return (x < 0.0) ? 0.0 : min(6.0, x);\n`;\nvar RELU6_PACKED = `\n  vec4 result = min(x, vec4(6.)) * vec4(greaterThanEqual(x, vec4(0.0)));\n  bvec4 isNaN = isnan(x);\n\n  result.r = isNaN.r ? x.r : result.r;\n  result.g = isNaN.g ? x.g : result.g;\n  result.b = isNaN.b ? x.b : result.b;\n  result.a = isNaN.a ? x.a : result.a;\n\n  return result;\n`;\nvar relu63 = unaryKernelFunc2({ opSnippet: RELU63, packedOpSnippet: RELU6_PACKED });\nvar relu6Config2 = {\n  kernelName: Relu6,\n  backendName: \"webgl\",\n  kernelFunc: relu63\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/resize_bilinear_gpu.js\nvar ResizeBilinearProgram = class {\n  constructor(inputShape, newHeight, newWidth, alignCorners, halfPixelCenters) {\n    this.variableNames = [\"A\"];\n    this.outputShape = [];\n    const [batch, oldHeight, oldWidth, depth] = inputShape;\n    this.outputShape = [batch, newHeight, newWidth, depth];\n    const effectiveInSize = [\n      alignCorners && newHeight > 1 ? oldHeight - 1 : oldHeight,\n      alignCorners && newWidth > 1 ? oldWidth - 1 : oldWidth\n    ];\n    const effectiveOutSize = [\n      alignCorners && newHeight > 1 ? newHeight - 1 : newHeight,\n      alignCorners && newWidth > 1 ? newWidth - 1 : newWidth\n    ];\n    let sourceFracIndexRC;\n    if (halfPixelCenters) {\n      sourceFracIndexRC = `(vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC - vec2(0.5)`;\n    } else {\n      sourceFracIndexRC = `vec2(yRC) * effectiveInputOverOutputRatioRC`;\n    }\n    this.userCode = `\n      const vec2 effectiveInputOverOutputRatioRC = vec2(\n          ${effectiveInSize[0] / effectiveOutSize[0]},\n          ${effectiveInSize[1] / effectiveOutSize[1]});\n      const vec2 inputShapeRC = vec2(${oldHeight}.0, ${oldWidth}.0);\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n        ivec2 yRC = coords.yz;\n\n        // Fractional source index.\n        vec2 sourceFracIndexRC = ${sourceFracIndexRC};\n\n        // Compute the four integer indices.\n        ivec2 sourceFloorRC = ivec2(max(sourceFracIndexRC, vec2(0.0)));\n        ivec2 sourceCeilRC = ivec2(\n          min(inputShapeRC - 1.0, ceil(sourceFracIndexRC)));\n\n        float topLeft = getA(b, sourceFloorRC.x, sourceFloorRC.y, d);\n        float bottomLeft = getA(b, sourceCeilRC.x, sourceFloorRC.y, d);\n        float topRight = getA(b, sourceFloorRC.x, sourceCeilRC.y, d);\n        float bottomRight = getA(b, sourceCeilRC.x, sourceCeilRC.y, d);\n\n        vec2 fracRC = sourceFracIndexRC - vec2(sourceFloorRC);\n\n        float top = topLeft + (topRight - topLeft) * fracRC.y;\n        float bottom = bottomLeft + (bottomRight - bottomLeft) * fracRC.y;\n        float newValue = top + (bottom - top) * fracRC.x;\n\n        setOutput(newValue);\n      }\n    `;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/resize_bilinear_packed_gpu.js\nvar ResizeBilinearPackedProgram = class {\n  constructor(inputShape, newHeight, newWidth, alignCorners, halfPixelCenters) {\n    this.variableNames = [\"A\"];\n    this.packedInputs = true;\n    this.packedOutput = true;\n    this.outputShape = [];\n    const [batch, oldHeight, oldWidth, depth] = inputShape;\n    this.outputShape = [batch, newHeight, newWidth, depth];\n    const effectiveInSize = [\n      alignCorners && newHeight > 1 ? oldHeight - 1 : oldHeight,\n      alignCorners && newWidth > 1 ? oldWidth - 1 : oldWidth\n    ];\n    const effectiveOutSize = [\n      alignCorners && newHeight > 1 ? newHeight - 1 : newHeight,\n      alignCorners && newWidth > 1 ? newWidth - 1 : newWidth\n    ];\n    let sourceFracIndexRC;\n    if (halfPixelCenters) {\n      sourceFracIndexRC = `(vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC - vec3(0.5)`;\n    } else {\n      sourceFracIndexRC = `vec3(yRC) * effectiveInputOverOutputRatioRC`;\n    }\n    this.userCode = `\n      const vec3 effectiveInputOverOutputRatioRC = vec3(\n          ${effectiveInSize[0] / effectiveOutSize[0]},\n          ${effectiveInSize[1] / effectiveOutSize[1]},\n          ${effectiveInSize[1] / effectiveOutSize[1]});\n      const vec3 inputShapeRC = vec3(${oldHeight}.0, ${oldWidth}.0,\n                                     ${oldWidth}.0);\n\n      float getAValue(int b, int r, int c, int d) {\n        return getChannel(getA(b, r, c, d), vec2(c, d));\n      }\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n        // Calculate values for next column in yRC.z.\n        ivec3 yRC = coords.yzz + ivec3(0, 0, 1);\n\n        // Fractional source index.\n        vec3 sourceFracIndexRC = ${sourceFracIndexRC};\n\n        // Compute the four integer indices.\n        ivec3 sourceFloorRC = ivec3(max(sourceFracIndexRC, vec3(0.0)));\n        ivec3 sourceCeilRC = ivec3(\n          min(inputShapeRC - 1.0, ceil(sourceFracIndexRC)));\n\n        // Should we calculate next column and row elements in 2x2 packed cell.\n        bool hasNextCol = d < ${depth - 1};\n        bool hasNextRow = coords.z < ${newWidth - 1};\n\n        // In parallel, construct four corners for all four components in\n        // packed 2x2 cell.\n        vec4 topLeft = vec4(\n          getAValue(b, sourceFloorRC.x, sourceFloorRC.y, d),\n          hasNextCol ? getAValue(b, sourceFloorRC.x, sourceFloorRC.y, d + 1)\n                     : 0.0,\n          hasNextRow ? getAValue(b, sourceFloorRC.x, sourceFloorRC.z, d)\n                     : 0.0,\n          (hasNextRow && hasNextCol) ?\n            getAValue(b, sourceFloorRC.x, sourceFloorRC.z, d + 1) : 0.0);\n\n        vec4 bottomLeft = vec4(\n          getAValue(b, sourceCeilRC.x, sourceFloorRC.y, d),\n          hasNextCol ? getAValue(b, sourceCeilRC.x, sourceFloorRC.y, d + 1)\n                     : 0.0,\n          hasNextRow ? getAValue(b, sourceCeilRC.x, sourceFloorRC.z, d)\n                     : 0.0,\n          (hasNextRow && hasNextCol) ?\n            getAValue(b, sourceCeilRC.x, sourceFloorRC.z, d + 1) : 0.0);\n\n        vec4 topRight = vec4(\n          getAValue(b, sourceFloorRC.x, sourceCeilRC.y, d),\n          hasNextCol ? getAValue(b, sourceFloorRC.x, sourceCeilRC.y, d + 1)\n                     : 0.0,\n          hasNextRow ? getAValue(b, sourceFloorRC.x, sourceCeilRC.z, d)\n                     : 0.0,\n          (hasNextRow && hasNextCol) ?\n            getAValue(b, sourceFloorRC.x, sourceCeilRC.z, d + 1) : 0.0);\n\n        vec4 bottomRight = vec4(\n          getAValue(b, sourceCeilRC.x, sourceCeilRC.y, d),\n          hasNextCol ? getAValue(b, sourceCeilRC.x, sourceCeilRC.y, d + 1)\n                     : 0.0,\n          hasNextRow ? getAValue(b, sourceCeilRC.x, sourceCeilRC.z, d)\n                     : 0.0,\n          (hasNextRow && hasNextCol) ?\n            getAValue(b, sourceCeilRC.x, sourceCeilRC.z, d + 1) : 0.0);\n\n        vec3 fracRC = sourceFracIndexRC - vec3(sourceFloorRC);\n\n        vec4 top = mix(topLeft, topRight, fracRC.yyzz);\n        vec4 bottom = mix(bottomLeft, bottomRight, fracRC.yyzz);\n        vec4 newValue = mix(top, bottom, fracRC.x);\n\n        setOutput(newValue);\n      }\n    `;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/ResizeBilinear.js\nfunction resizeBilinear3(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { images } = inputs;\n  const { alignCorners, halfPixelCenters, size } = attrs;\n  const [newHeight, newWidth] = size;\n  const program = env().getBool(\"WEBGL_PACK_IMAGE_OPERATIONS\") ? new ResizeBilinearPackedProgram(images.shape, newHeight, newWidth, alignCorners, halfPixelCenters) : new ResizeBilinearProgram(images.shape, newHeight, newWidth, alignCorners, halfPixelCenters);\n  return backend2.runWebGLProgram(program, [images], \"float32\");\n}\nvar resizeBilinearConfig2 = {\n  kernelName: ResizeBilinear,\n  backendName: \"webgl\",\n  kernelFunc: resizeBilinear3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/resize_bilinear_backprop_gpu.js\nvar ResizeBilinearBackpropProgram = class {\n  constructor(dyShape, inputShape, alignCorners) {\n    this.variableNames = [\"dy\"];\n    this.outputShape = [];\n    this.outputShape = inputShape;\n    const [, xHeight, xWidth] = inputShape;\n    const [, yHeight, yWidth] = dyShape;\n    const effectiveXSize = [\n      alignCorners && yHeight > 1 ? xHeight - 1 : xHeight,\n      alignCorners && yWidth > 1 ? xWidth - 1 : xWidth\n    ];\n    const effectiveYSize = [\n      alignCorners && yHeight > 1 ? yHeight - 1 : yHeight,\n      alignCorners && yWidth > 1 ? yWidth - 1 : yWidth\n    ];\n    const heightScale = effectiveXSize[0] / effectiveYSize[0];\n    const widthScale = effectiveXSize[1] / effectiveYSize[1];\n    const invHeightScale = 1 / heightScale;\n    const invWidthScale = 1 / widthScale;\n    const winHeight = Math.ceil(invHeightScale) * 2 + 2;\n    const winWidth = Math.ceil(invWidthScale) * 2 + 2;\n    this.userCode = `\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n        int r = coords[1];\n        int c = coords[2];\n\n        float accumulator = 0.0;\n\n        const float heightScale = float(${heightScale});\n        const float widthScale = float(${widthScale});\n\n        const float invHeightScale = float(${invHeightScale});\n        const float invWidthScale = float(${invWidthScale});\n\n        const int winHeight = int(${winHeight});\n        const int winWidth = int(${winWidth});\n\n        // Compute bounds for where in dy we will look\n        float startRLerp = floor(float(r) * invHeightScale);\n        int startDyR = int(startRLerp - float(winHeight / 2));\n\n        float startCLerp = floor(float(c) * invWidthScale);\n        int startDyC = int(startCLerp - float(winWidth / 2));\n\n        // Loop over dy\n        for (int dyROffset = 0; dyROffset < winHeight; dyROffset++) {\n          int dyR = dyROffset + startDyR;\n\n          // Guard against the window exceeding the bounds of dy\n          if (dyR < 0 || dyR >= ${yHeight}) {\n            continue;\n          }\n\n          for (int dyCOffset = 0; dyCOffset < winWidth; dyCOffset++) {\n            int dyC = dyCOffset + startDyC;\n\n            // Guard against the window exceeding the bounds of dy\n            if (dyC < 0 || dyC >= ${yWidth}) {\n              continue;\n            }\n\n            float dxR = float(dyR) * heightScale;\n            int topDxRIndex = int(floor(dxR));\n            int bottomDxRIndex = int(min(ceil(dxR), ${xHeight - 1}.0));\n            float dxRLerp = dxR - float(topDxRIndex);\n            float inverseDxRLerp = 1.0 - dxRLerp;\n\n            float dxC = float(dyC) * widthScale;\n            int leftDxCIndex = int(floor(dxC));\n            int rightDxCIndex = int(min(ceil(dxC), ${xWidth - 1}.0));\n            float dxCLerp = dxC - float(leftDxCIndex);\n            float inverseDxCLerp = 1.0 - dxCLerp;\n\n            if (r == topDxRIndex && c == leftDxCIndex) {\n              // topLeft\n              accumulator +=\n                getDy(b, dyR, dyC, d) * inverseDxRLerp * inverseDxCLerp;\n            }\n\n            if (r == topDxRIndex && c == rightDxCIndex) {\n              // topRight\n              accumulator += getDy(b, dyR, dyC, d) * inverseDxRLerp * dxCLerp;\n            }\n\n            if (r == bottomDxRIndex && c == leftDxCIndex) {\n              // bottomLeft\n              accumulator += getDy(b, dyR, dyC, d) * dxRLerp * inverseDxCLerp;\n            }\n\n            if (r == bottomDxRIndex && c == rightDxCIndex) {\n              // bottomRight\n              accumulator += getDy(b, dyR, dyC, d) * dxRLerp * dxCLerp;\n            }\n          }\n        }\n        // End loop over dy\n\n        setOutput(accumulator);\n      }\n    `;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/ResizeBilinearGrad.js\nfunction resizeBilinearGrad2(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { images, dy } = inputs;\n  const { alignCorners } = attrs;\n  const program = new ResizeBilinearBackpropProgram(dy.shape, images.shape, alignCorners);\n  return backend2.runWebGLProgram(program, [dy], dy.dtype);\n}\nvar resizeBilinearGradConfig3 = {\n  kernelName: ResizeBilinearGrad,\n  backendName: \"webgl\",\n  kernelFunc: resizeBilinearGrad2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/resize_nearest_neighbor_gpu.js\nvar ResizeNearestNeighborProgram = class {\n  constructor(inputShape, newHeight, newWidth, alignCorners, halfPixelCenters) {\n    this.variableNames = [\"A\"];\n    this.outputShape = [];\n    const [batch, oldHeight, oldWidth, depth] = inputShape;\n    this.outputShape = [batch, newHeight, newWidth, depth];\n    const effectiveInSize = [\n      alignCorners && newHeight > 1 ? oldHeight - 1 : oldHeight,\n      alignCorners && newWidth > 1 ? oldWidth - 1 : oldWidth\n    ];\n    const effectiveOutSize = [\n      alignCorners && newHeight > 1 ? newHeight - 1 : newHeight,\n      alignCorners && newWidth > 1 ? newWidth - 1 : newWidth\n    ];\n    const roundBase = alignCorners ? \"0.5\" : \"0.0\";\n    let sourceFracIndexRC;\n    if (halfPixelCenters) {\n      sourceFracIndexRC = `max((vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC, vec2(0.0))`;\n    } else {\n      sourceFracIndexRC = `vec2(yRC) * effectiveInputOverOutputRatioRC`;\n    }\n    this.userCode = `\n      const vec2 effectiveInputOverOutputRatioRC = vec2(\n          ${effectiveInSize[0] / effectiveOutSize[0]},\n          ${effectiveInSize[1] / effectiveOutSize[1]});\n      const vec2 inputShapeRC = vec2(${oldHeight}.0, ${oldWidth}.0);\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n        ivec2 yRC = coords.yz;\n\n        // Fractional source index.\n        vec2 sourceFracIndexRC = ${sourceFracIndexRC};\n\n        // Compute the coordinators of nearest neighbor point.\n        ivec2 sourceNearestRC = ivec2(\n          min(inputShapeRC - 1.0, floor(sourceFracIndexRC + ${roundBase})));\n        float newValue = getA(b, sourceNearestRC.x, sourceNearestRC.y, d);\n\n        setOutput(newValue);\n      }\n    `;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/resize_nearest_neighbor_packed_gpu.js\nvar ResizeNearestNeighborPackedProgram = class {\n  constructor(inputShape, newHeight, newWidth, alignCorners, halfPixelCenters) {\n    this.variableNames = [\"A\"];\n    this.packedInputs = true;\n    this.packedOutput = true;\n    this.outputShape = [];\n    const [batch, oldHeight, oldWidth, depth] = inputShape;\n    this.outputShape = [batch, newHeight, newWidth, depth];\n    const effectiveInSize = [\n      alignCorners && newHeight > 1 ? oldHeight - 1 : oldHeight,\n      alignCorners && newWidth > 1 ? oldWidth - 1 : oldWidth\n    ];\n    const effectiveOutSize = [\n      alignCorners && newHeight > 1 ? newHeight - 1 : newHeight,\n      alignCorners && newWidth > 1 ? newWidth - 1 : newWidth\n    ];\n    const roundBase = alignCorners ? \"0.5\" : \"0.0\";\n    let sourceFracIndexRC;\n    if (halfPixelCenters) {\n      sourceFracIndexRC = `max((vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC, vec3(0.0))`;\n    } else {\n      sourceFracIndexRC = `vec3(yRC) * effectiveInputOverOutputRatioRC`;\n    }\n    this.userCode = `\n      const vec3 effectiveInputOverOutputRatioRC = vec3(\n          ${effectiveInSize[0] / effectiveOutSize[0]},\n          ${effectiveInSize[1] / effectiveOutSize[1]},\n          ${effectiveInSize[1] / effectiveOutSize[1]});\n      const vec3 inputShapeRC = vec3(${oldHeight}.0, ${oldWidth}.0,\n                                     ${oldWidth}.0);\n\n      float getAValue(int b, int r, int c, int d) {\n        return getChannel(getA(b, r, c, d), vec2(c, d));\n      }\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n        // Calculate values for next column in yRC.z.\n        ivec3 yRC = coords.yzz + ivec3(0, 0, 1);\n\n        // Fractional source index.\n        vec3 sourceFracIndexRC = ${sourceFracIndexRC};\n\n        // Compute the coordinators of nearest neighbor point.\n        ivec3 sourceNearestRC = ivec3(\n          min(inputShapeRC - 1.0, floor(sourceFracIndexRC + ${roundBase})));\n\n        // Should we calculate next column and row elements in 2x2 packed cell.\n        bool hasNextCol = d < ${depth - 1};\n        bool hasNextRow = coords.z < ${newWidth - 1};\n\n        vec4 newValue = vec4(\n          getAValue(b, sourceNearestRC.x, sourceNearestRC.y, d),\n          hasNextCol ? getAValue(b, sourceNearestRC.x, sourceNearestRC.y, d + 1)\n                     : 0.0,\n          hasNextRow ? getAValue(b, sourceNearestRC.x, sourceNearestRC.z, d)\n                     : 0.0,\n          (hasNextRow && hasNextCol) ?\n            getAValue(b, sourceNearestRC.x, sourceNearestRC.z, d + 1) : 0.0);\n\n        setOutput(newValue);\n      }\n    `;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/ResizeNearestNeighbor.js\nfunction resizeNearestNeighbor3(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { images } = inputs;\n  const { alignCorners, halfPixelCenters, size } = attrs;\n  const [newHeight, newWidth] = size;\n  const program = env().getBool(\"WEBGL_PACK_IMAGE_OPERATIONS\") ? new ResizeNearestNeighborPackedProgram(images.shape, newHeight, newWidth, alignCorners, halfPixelCenters) : new ResizeNearestNeighborProgram(images.shape, newHeight, newWidth, alignCorners, halfPixelCenters);\n  return backend2.runWebGLProgram(program, [images], images.dtype);\n}\nvar resizeNearestNeighborConfig2 = {\n  kernelName: ResizeNearestNeighbor,\n  backendName: \"webgl\",\n  kernelFunc: resizeNearestNeighbor3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/resize_nearest_neighbor_backprop_gpu.js\nvar ResizeNearestNeigborBackpropProgram = class {\n  constructor(dyShape, inputShape, alignCorners) {\n    this.variableNames = [\"dy\"];\n    this.outputShape = [];\n    this.outputShape = inputShape;\n    const [, xHeight, xWidth] = inputShape;\n    const [, yHeight, yWidth] = dyShape;\n    const effectiveXSize = [\n      alignCorners && yHeight > 1 ? xHeight - 1 : xHeight,\n      alignCorners && yWidth > 1 ? xWidth - 1 : xWidth\n    ];\n    const effectiveYSize = [\n      alignCorners && yHeight > 1 ? yHeight - 1 : yHeight,\n      alignCorners && yWidth > 1 ? yWidth - 1 : yWidth\n    ];\n    const heightScale = effectiveXSize[0] / effectiveYSize[0];\n    const widthScale = effectiveXSize[1] / effectiveYSize[1];\n    const invHeightScale = 1 / heightScale;\n    const invWidthScale = 1 / widthScale;\n    const winHeight = Math.ceil(invHeightScale) * 2 + 2;\n    const winWidth = Math.ceil(invWidthScale) * 2 + 2;\n    this.userCode = `\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n        int r = coords[1];\n        int c = coords[2];\n\n        float accumulator = 0.0;\n\n        const float heightScale = float(${heightScale});\n        const float widthScale = float(${widthScale});\n\n        const float invHeightScale = float(${invHeightScale});\n        const float invWidthScale = float(${invWidthScale});\n\n        const int winHeight = int(${winHeight});\n        const int winWidth = int(${winWidth});\n\n        // Compute bounds for where in dy we will look\n        float startRLerp = floor(float(r) * invHeightScale);\n        int startDyR = int(floor(startRLerp - float(winHeight / 2)));\n\n        float startCLerp = floor(float(c) * invWidthScale);\n        int startDyC = int(floor(startCLerp - float(winWidth / 2)));\n\n        // Loop over dy\n        for (int dyROffset = 0; dyROffset < winHeight; dyROffset++) {\n          int dyR = dyROffset + startDyR;\n\n          // Guard against the window exceeding the bounds of dy\n          if (dyR < 0 || dyR >= ${yHeight}) {\n            continue;\n          }\n\n          for (int dyCOffset = 0; dyCOffset < winWidth; dyCOffset++) {\n            int dyC = dyCOffset + startDyC;\n\n            // Guard against the window exceeding the bounds of dy\n            if (dyC < 0 || dyC >= ${yWidth}) {\n              continue;\n            }\n\n            float sourceFracRow =\n              float(${effectiveXSize[0]}) *\n                (float(dyR) / float(${effectiveYSize[0]}));\n\n            float sourceFracCol =\n                float(${effectiveXSize[1]}) *\n                  (float(dyC) / float(${effectiveYSize[1]}));\n\n            int sourceNearestRow = int(min(\n                float(int(${xHeight}) - 1),\n                ${alignCorners} ? float(round(sourceFracRow)) :\n                                  float(floor(sourceFracRow))));\n\n            int sourceNearestCol = int(min(\n                float(int(${xWidth}) - 1),\n                ${alignCorners} ? float(round(sourceFracCol)) :\n                                  float(floor(sourceFracCol))));\n\n            if (r == sourceNearestRow && c == sourceNearestCol) {\n              accumulator += getDy(b, dyR, dyC, d);\n            }\n          }\n        }\n        // End loop over dy\n\n        setOutput(accumulator);\n      }\n    `;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/ResizeNearestNeighborGrad.js\nfunction resizeNearestNeighborGrad2(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { images, dy } = inputs;\n  const { alignCorners } = attrs;\n  const program = new ResizeNearestNeigborBackpropProgram(dy.shape, images.shape, alignCorners);\n  return backend2.runWebGLProgram(program, [dy], dy.dtype);\n}\nvar resizeNearestNeighborGradConfig3 = {\n  kernelName: ResizeNearestNeighborGrad,\n  backendName: \"webgl\",\n  kernelFunc: resizeNearestNeighborGrad2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/reverse_gpu.js\nvar ReverseProgram = class {\n  constructor(xShape, axis) {\n    this.variableNames = [\"x\"];\n    const rank = xShape.length;\n    if (rank > 4) {\n      throw new Error(`WebGL backend: Reverse of rank-${rank} tensor is not yet supported`);\n    }\n    this.outputShape = xShape;\n    if (rank === 1) {\n      this.userCode = `\n        void main() {\n          int coord = getOutputCoords();\n          setOutput(getX(${xShape[0]} - coord - 1));\n        }\n      `;\n      return;\n    }\n    const getInCoord = (i2) => {\n      if (axis.indexOf(i2) !== -1 && xShape[i2] !== 1) {\n        return `${xShape[i2]} - coords[${i2}] - 1`;\n      }\n      return `coords[${i2}]`;\n    };\n    const inCoords = xShape.map((_, i2) => getInCoord(i2)).join(\",\");\n    const type = getCoordsDataType(rank);\n    this.userCode = `\n      void main() {\n        ${type} coords = getOutputCoords();\n        setOutput(getX(${inCoords}));\n      }\n    `;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/reverse_packed_gpu.js\nvar ReversePackedProgram = class {\n  constructor(xShape, axis) {\n    this.variableNames = [\"x\"];\n    this.packedInputs = true;\n    this.packedOutput = true;\n    const rank = xShape.length;\n    if (rank > 4) {\n      throw new Error(`WebGL backend: Reverse of rank-${rank} tensor is not yet supported`);\n    }\n    this.outputShape = xShape;\n    const channels = getChannels(\"rc\", rank);\n    const nextColumn = `${channels[rank - 1]} + 1 < ${this.outputShape[rank - 1]}`;\n    const nextRow = `${channels[rank - 2]} + 1 < ${this.outputShape[rank - 2]}`;\n    const type = getCoordsDataType(rank);\n    if (rank === 1) {\n      this.userCode = `\n        void main(){\n          int rc = getOutputCoords();\n          vec4 result = vec4(0.);\n          result.r = getChannel(getX(${xShape[0]} - rc - 1),\n            ${xShape[0]} - rc - 1);\n          if(${nextColumn}){\n              result.g = getChannel(getX(${xShape[0]} - (rc  + 1) - 1),\n                ${xShape[0]} - (rc  + 1) - 1);\n          }\n          setOutput(result);\n        }\n      `;\n    } else {\n      this.userCode = `\n        void main() {\n          ${type} rc = getOutputCoords();\n          vec4 result = vec4(0.);\n          result.r = ${getR(channels.slice())};\n          if(${nextColumn}){\n            result.g = ${getG(channels.slice())};\n          }\n          if(${nextRow}) {\n            result.b = ${getB(channels.slice())};\n            if(${nextColumn}) {\n              result.a = ${getA(channels.slice())};\n            }\n          }\n          setOutput(result);\n        }\n    `;\n    }\n    function getR(channels2) {\n      return getChannel(channels2);\n    }\n    function getG(channels2) {\n      channels2[rank - 1] = \"(\" + channels2[rank - 1] + ` + 1)`;\n      return getChannel(channels2);\n    }\n    function getB(channels2) {\n      channels2[rank - 2] = \"(\" + channels2[rank - 2] + ` + 1)`;\n      return getChannel(channels2);\n    }\n    function getA(channels2) {\n      channels2[rank - 1] = \"(\" + channels2[rank - 1] + ` + 1)`;\n      channels2[rank - 2] = \"(\" + channels2[rank - 2] + ` + 1)`;\n      return getChannel(channels2);\n    }\n    function getChannel(channels2) {\n      const inCoordsArray = xShape.map((_, i2) => getInCoord(i2, channels2));\n      const inCoords = inCoordsArray.join(\",\");\n      const innerDims = inCoordsArray.slice(-2).join(\",\");\n      return `getChannel(getX(${inCoords}), vec2(${innerDims}))`;\n    }\n    function getInCoord(i2, channels1) {\n      if (axis.indexOf(i2) !== -1 && xShape[i2] !== 1) {\n        return `${xShape[i2]} - ${channels1[i2]} - 1`;\n      } else {\n        return `${channels1[i2]}`;\n      }\n    }\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Reverse.js\nfunction reverse3(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x } = inputs;\n  const { dims } = attrs;\n  const xRank = x.shape.length;\n  const $dims = util_exports.parseAxisParam(dims, x.shape);\n  if (xRank === 0) {\n    return identity3({ inputs: { x }, backend: backend2 });\n  }\n  const program = env().getBool(\"WEBGL_PACK_ARRAY_OPERATIONS\") ? new ReversePackedProgram(x.shape, $dims) : new ReverseProgram(x.shape, $dims);\n  return backend2.runWebGLProgram(program, [x], x.dtype);\n}\nvar reverseConfig2 = {\n  kernelName: Reverse,\n  backendName: \"webgl\",\n  kernelFunc: reverse3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/rotate_gpu.js\nvar RotateProgram = class {\n  constructor(imageShape, fillValue) {\n    this.variableNames = [\"Image\"];\n    this.outputShape = [];\n    this.customUniforms = [{ name: \"params\", type: \"vec4\" }];\n    const imageHeight = imageShape[1];\n    const imageWidth = imageShape[2];\n    this.outputShape = imageShape;\n    let fillSnippet = \"\";\n    if (typeof fillValue === \"number\") {\n      fillSnippet = `float outputValue = ${fillValue.toFixed(2)};`;\n    } else {\n      fillSnippet = `\n        vec3 fill = vec3(${fillValue.join(\",\")});\n        float outputValue = fill[coords[3]];`;\n    }\n    this.userCode = `\n        void main() {\n          ivec4 coords = getOutputCoords();\n          int x = coords[2];\n          int y = coords[1];\n          float coordXFloat = (float(x) - params[0]) * params[3] -\n            (float(y) - params[1]) * params[2];\n          float coordYFloat = (float(x) - params[0]) * params[2] +\n            (float(y) - params[1]) * params[3];\n          int coordX = int(round(coordXFloat + params[0]));\n          int coordY = int(round(coordYFloat + params[1]));\n          ${fillSnippet}\n          if(coordX >= 0 && coordX < ${imageWidth} && coordY >= 0 && coordY < ${imageHeight}) {\n            outputValue = getImage(coords[0], coordY, coordX, coords[3]);\n          }\n          setOutput(outputValue);\n        }\n    `;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/RotateWithOffset.js\nvar rotateWithOffsetConfig2 = {\n  kernelName: RotateWithOffset,\n  backendName: \"webgl\",\n  kernelFunc: ({ inputs, attrs, backend: backend2 }) => {\n    const { image: image2 } = inputs;\n    const { radians, fillValue, center } = attrs;\n    const webglBackend = backend2;\n    const program = new RotateProgram(image2.shape, fillValue);\n    const [centerX, centerY] = backend_util_exports.getImageCenter(center, image2.shape[1], image2.shape[2]);\n    const customValues = [[centerX, centerY, Math.sin(radians), Math.cos(radians)]];\n    const output = webglBackend.runWebGLProgram(program, [image2], image2.dtype, customValues);\n    return output;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Round.js\nvar ROUND = `\n  // OpenGL ES does not support round function.\n  // The algorithm is based on banker's rounding.\n  float base = floor(x);\n  if ((x - base) < 0.5) {\n    return floor(x);\n  } else if ((x - base) > 0.5) {\n    return ceil(x);\n  } else {\n    if (mod(base, 2.0) == 0.0) {\n      return base;\n    } else {\n      return base + 1.0;\n    }\n  }\n`;\nvar round4 = unaryKernelFunc2({ opSnippet: ROUND });\nvar roundConfig2 = {\n  kernelName: Round,\n  backendName: \"webgl\",\n  kernelFunc: round4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Rsqrt.js\nvar RSQRT = `return inversesqrt(x);`;\nvar rsqrt3 = unaryKernelFunc2({ opSnippet: RSQRT, cpuKernelImpl: rsqrtImplCPU });\nvar rsqrtConfig2 = {\n  kernelName: Rsqrt,\n  backendName: \"webgl\",\n  kernelFunc: rsqrt3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/scatter_gpu.js\nvar ScatterProgram = class {\n  constructor(updateSize, sliceDim, indicesRank, updatesRank, strides, shape, summingDupeIndex = true) {\n    this.variableNames = [\"updates\", \"indices\", \"defaultValue\"];\n    this.outputShape = shape;\n    const stridesType = getCoordsDataType(strides.length);\n    const dtype = getCoordsDataType(shape.length);\n    let indicesString = \"\";\n    if (indicesRank === 1) {\n      indicesString = \"i\";\n    } else if (indicesRank === 2) {\n      indicesString = \"i, j\";\n    }\n    const indicesSnippet = `getIndices(${indicesString})`;\n    let updatesString = \"\";\n    if (updatesRank === 1) {\n      updatesString = \"i\";\n    } else if (updatesRank === 2) {\n      updatesString = \"i, coords[1]\";\n    }\n    const updatesSnippet = `getUpdates(${updatesString})`;\n    const strideString = sliceDim > 1 ? \"strides[j]\" : \"strides\";\n    this.userCode = `\n        ${stridesType} strides = ${stridesType}(${strides});\n\n        void main() {\n          ${dtype} coords = getOutputCoords();\n          float sum = 0.0;\n          bool found = false;\n          for (int i = 0; i < ${updateSize}; i++) {\n            int flattenedIndex = 0;\n            for (int j = 0; j < ${sliceDim}; j++) {\n              int index = round(${indicesSnippet});\n              flattenedIndex += index * ${strideString};\n            }\n            if (flattenedIndex == coords[0]) {\n              sum += ${updatesSnippet};\n              found = true;\n            }\n          }\n          setOutput(mix(getDefaultValue(), sum, float(found)));\n        }\n      `;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/ScatterNd.js\nfunction scatterNd2(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { indices, updates } = inputs;\n  const { shape } = attrs;\n  const { sliceRank, numUpdates, sliceSize, strides, outputSize } = backend_util_exports.calculateShapes(updates, indices, shape);\n  const flattenShape = [outputSize / sliceSize, sliceSize];\n  if (outputSize === 0) {\n    return backend2.makeTensorInfo(shape, indices.dtype);\n  }\n  const flattenIndices = reshape4({ inputs: { x: indices }, backend: backend2, attrs: { shape: [numUpdates, sliceRank] } });\n  const flattenX = reshape4({ inputs: { x: updates }, backend: backend2, attrs: { shape: [numUpdates, sliceSize] } });\n  const defaultValue = backend2.makeTensorInfo([], \"float32\", new Float32Array([0]));\n  const program = new ScatterProgram(numUpdates, sliceRank, flattenIndices.shape.length, flattenX.shape.length, strides, flattenShape);\n  const res = backend2.runWebGLProgram(program, [flattenX, flattenIndices, defaultValue], flattenX.dtype);\n  const reshaped = reshape4({ inputs: { x: res }, backend: backend2, attrs: { shape } });\n  backend2.disposeIntermediateTensorInfo(flattenIndices);\n  backend2.disposeIntermediateTensorInfo(flattenX);\n  backend2.disposeIntermediateTensorInfo(res);\n  backend2.disposeIntermediateTensorInfo(defaultValue);\n  return reshaped;\n}\nvar scatterNdConfig2 = {\n  kernelName: ScatterNd,\n  backendName: \"webgl\",\n  kernelFunc: scatterNd2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/search_sorted_gpu.js\nvar SearchSortedProgram = class {\n  constructor(batchSize, numInputs, numValues, side) {\n    this.variableNames = [\"sortedSequence\", \"values\"];\n    this.customUniforms = [{ name: \"numInputs\", type: \"int\" }];\n    this.outputShape = [batchSize, numValues];\n    const webGL2LoopHead = \"while (left < right) {\";\n    const webGL1LoopHead = `for (int i = 0; i < ${Math.ceil(Math.log2(numInputs + 1))}; ++i) { if (left >= right) break;`;\n    const loopHead = env().getNumber(\"WEBGL_VERSION\") === 2 ? webGL2LoopHead : webGL1LoopHead;\n    const boundComparator = side === \"left\" ? \"<\" : \"<=\";\n    this.userCode = `\n       int findBound(int batch, float value) {\n         int left = 0;\n         int right = numInputs;\n         int mid;\n         ${loopHead}\n           mid = (left + right) / 2;\n           if (getSortedSequence(batch, mid) ${boundComparator} value) {\n             left = mid + 1;\n           } else {\n             right = mid;\n           }\n         }\n         return right;\n       }\n\n       void main() {\n         ivec2 coords = getOutputCoords();\n         int batch = coords[0];\n         int valueIndex = coords[1];\n\n         float value = getValues(batch, valueIndex);\n\n         setOutput(float(findBound(batch, value)));\n       }\n     `;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/SearchSorted.js\nfunction searchSorted3(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { sortedSequence, values } = inputs;\n  const { side } = attrs;\n  const program = new SearchSortedProgram(sortedSequence.shape[0], sortedSequence.shape[1], values.shape[1], side);\n  const customValues = [[sortedSequence.shape[1]]];\n  return backend2.runWebGLProgram(program, [sortedSequence, values], \"int32\", customValues);\n}\nvar searchSortedConfig2 = {\n  kernelName: SearchSorted,\n  backendName: \"webgl\",\n  kernelFunc: searchSorted3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/select_gpu.js\nvar SelectProgram = class {\n  constructor(cRank, shape, rank) {\n    this.variableNames = [\"c\", \"a\", \"b\"];\n    this.outputShape = shape;\n    let cCoords;\n    let abCoords;\n    if (rank > 4) {\n      throw Error(`Where for rank ${rank} is not yet supported`);\n    }\n    if (rank === 1) {\n      abCoords = `resRC`;\n      cCoords = `resRC`;\n    } else {\n      const currentCoords = [\"resRC.x\", \"resRC.y\", \"resRC.z\", \"resRC.w\"];\n      const cCoordVars = [];\n      const abCoordVars = [];\n      for (let i2 = 0; i2 < shape.length; i2++) {\n        abCoordVars.push(`${currentCoords[i2]}`);\n        if (i2 < cRank) {\n          cCoordVars.push(`${currentCoords[i2]}`);\n        }\n      }\n      cCoords = cCoordVars.join();\n      abCoords = abCoordVars.join();\n    }\n    const dtype = getCoordsDataType(rank);\n    this.userCode = `\n      void main() {\n        ${dtype} resRC = getOutputCoords();\n        float cVal = getC(${cCoords});\n        if (cVal >= 1.0) {\n          setOutput(getA(${abCoords}));\n        } else {\n          setOutput(getB(${abCoords}));\n        }\n      }\n    `;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Select.js\nfunction select3(args) {\n  const { inputs, backend: backend2 } = args;\n  const { condition, t: t2, e: e2 } = inputs;\n  const program = new SelectProgram(condition.shape.length, t2.shape, t2.shape.length);\n  return backend2.runWebGLProgram(program, [condition, t2, e2], upcastType(t2.dtype, e2.dtype));\n}\nvar selectConfig2 = {\n  kernelName: Select,\n  backendName: \"webgl\",\n  kernelFunc: select3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Selu.js\nvar SELU = `\n  // Stable and Attracting Fixed Point (0, 1) for Normalized Weights.\n  // see: https://arxiv.org/abs/1706.02515\n  float scaleAlpha = ${backend_util_exports.SELU_SCALEALPHA};\n  float scale = ${backend_util_exports.SELU_SCALE};\n  return (x >= 0.0) ? scale * x : scaleAlpha * (exp(x) - 1.0);\n`;\nvar selu3 = unaryKernelFunc2({ opSnippet: SELU });\nvar seluConfig2 = {\n  kernelName: Selu,\n  backendName: \"webgl\",\n  kernelFunc: selu3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Sigmoid.js\nvar SIGMOID3 = CHECK_NAN_SNIPPET_UNARY + `\n  return 1.0 / (1.0 + exp(-1.0 * x));\n`;\nvar SIGMOID_PACKED = `\n  vec4 result = 1.0 / (1.0 + exp(-1.0 * x));\n  bvec4 isNaN = isnan(x);\n\n  result.r = isNaN.r ? x.r : result.r;\n  result.g = isNaN.g ? x.g : result.g;\n  result.b = isNaN.b ? x.b : result.b;\n  result.a = isNaN.a ? x.a : result.a;\n\n  return result;\n`;\nvar sigmoid3 = unaryKernelFunc2({\n  opSnippet: SIGMOID3,\n  packedOpSnippet: SIGMOID_PACKED,\n  cpuKernelImpl: sigmoidImplCPU\n});\nvar sigmoidConfig2 = {\n  kernelName: Sigmoid,\n  backendName: \"webgl\",\n  kernelFunc: sigmoid3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Sign.js\nvar SIGN = `\n  if (isnan(x)) { return 0.0; }\n  return sign(x);\n`;\nvar sign3 = unaryKernelFunc2({ opSnippet: SIGN });\nvar signConfig2 = {\n  kernelName: Sign,\n  backendName: \"webgl\",\n  kernelFunc: sign3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Sin.js\nvar SIN = CHECK_NAN_SNIPPET_UNARY + `\n  return sin(x);\n`;\nvar sin3 = unaryKernelFunc2({ opSnippet: SIN });\nvar sinConfig2 = {\n  kernelName: Sin,\n  backendName: \"webgl\",\n  kernelFunc: sin3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Sinh.js\nvar SINH = `\n  float e2x = exp(x);\n  return (e2x - 1.0 / e2x) / 2.0;\n`;\nvar sinh3 = unaryKernelFunc2({ opSnippet: SINH });\nvar sinhConfig2 = {\n  kernelName: Sinh,\n  backendName: \"webgl\",\n  kernelFunc: sinh3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Softplus.js\nvar SOFTPLUS = `\n  float epsilon = 1.1920928955078125e-7;\n  float threshold = log(epsilon) + 2.0;\n\n  bool too_large = x > -threshold;\n  bool too_small = x < threshold;\n\n  float result;\n  float exp_x = exp(x);\n\n  if (too_large){\n    result = x;\n  }\n  else if (too_small){\n    result = exp_x;\n  }\n  else{\n    result = log(exp_x + 1.0);\n  }\n  return result;\n`;\nvar softplus3 = unaryKernelFunc2({ opSnippet: SOFTPLUS });\nvar softplusConfig2 = {\n  kernelName: Softplus,\n  backendName: \"webgl\",\n  kernelFunc: softplus3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/SpaceToBatchND.js\nvar spaceToBatchND3 = (args) => {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x } = inputs;\n  const { blockShape, paddings } = attrs;\n  util_exports.assert(x.shape.length <= 4, () => \"spaceToBatchND for rank > 4 with a WebGL backend not implemented yet\");\n  const prod6 = blockShape.reduce((a, b) => a * b);\n  const completePaddings = [[0, 0]];\n  completePaddings.push(...paddings);\n  for (let i2 = 1 + blockShape.length; i2 < x.shape.length; ++i2) {\n    completePaddings.push([0, 0]);\n  }\n  const toDispose = [];\n  const paddedX = padV22({\n    inputs: { x },\n    backend: backend2,\n    attrs: { paddings: completePaddings, constantValue: 0 }\n  });\n  const reshapedPaddedShape = backend_util_exports.getReshaped(paddedX.shape, blockShape, prod6, false);\n  const permutedReshapedPaddedPermutation = backend_util_exports.getPermuted(reshapedPaddedShape.length, blockShape.length, false);\n  const flattenShape = backend_util_exports.getReshapedPermuted(paddedX.shape, blockShape, prod6, false);\n  const reshapedPaddedX = reshape4({ inputs: { x: paddedX }, backend: backend2, attrs: { shape: reshapedPaddedShape } });\n  const paddedXT = transpose3({\n    inputs: { x: reshapedPaddedX },\n    backend: backend2,\n    attrs: { perm: permutedReshapedPaddedPermutation }\n  });\n  const result = reshape4({ inputs: { x: paddedXT }, backend: backend2, attrs: { shape: flattenShape } });\n  toDispose.push(paddedX);\n  toDispose.push(reshapedPaddedX);\n  toDispose.push(paddedXT);\n  toDispose.forEach((t2) => backend2.disposeIntermediateTensorInfo(t2));\n  return result;\n};\nvar spaceToBatchNDConfig2 = {\n  kernelName: SpaceToBatchND,\n  backendName: \"webgl\",\n  kernelFunc: spaceToBatchND3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/SparseFillEmptyRows.js\nfunction sparseFillEmptyRows3(args) {\n  const { inputs, backend: backend2 } = args;\n  const { indices, values, denseShape, defaultValue } = inputs;\n  if (denseShape.shape.length !== 1) {\n    throw new Error(`Dense shape must be a vector, saw:\n         ${denseShape.shape}`);\n  }\n  if (indices.shape.length !== 2) {\n    throw new Error(`Indices must be a matrix, saw:\n         ${indices.shape}`);\n  }\n  if (values.shape.length !== 1) {\n    throw new Error(`Values must be a vector, saw:\n         ${values.shape}`);\n  }\n  if (defaultValue.shape.length !== 0) {\n    throw new Error(`Default value must be a scalar, saw:\n        ${defaultValue.shape}`);\n  }\n  const $indices = backend2.readSync(indices.dataId);\n  const $values = backend2.readSync(values.dataId);\n  const $denseShape = backend2.readSync(denseShape.dataId);\n  const $defaultValue = backend2.readSync(defaultValue.dataId)[0];\n  const [outputIndices, outputIndicesShape, outputValues, emptyRowIndicator, reverseIndexMap] = sparseFillEmptyRowsImplCPU($indices, indices.shape, indices.dtype, $values, values.dtype, $denseShape, $defaultValue);\n  return [\n    backend2.makeTensorInfo(outputIndicesShape, indices.dtype, outputIndices),\n    backend2.makeTensorInfo([outputIndicesShape[0]], values.dtype, outputValues),\n    backend2.makeTensorInfo([emptyRowIndicator.length], \"bool\", new Uint8Array(emptyRowIndicator.map((value) => Number(value)))),\n    backend2.makeTensorInfo([reverseIndexMap.length], indices.dtype, new Int32Array(reverseIndexMap))\n  ];\n}\nvar sparseFillEmptyRowsConfig2 = {\n  kernelName: SparseFillEmptyRows,\n  backendName: \"webgl\",\n  kernelFunc: sparseFillEmptyRows3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/SparseReshape.js\nfunction sparseReshape3(args) {\n  const { inputs, backend: backend2 } = args;\n  const { inputIndices, inputShape, newShape } = inputs;\n  if (inputIndices.shape.length !== 2) {\n    throw new Error(`Input indices should be a matrix but received shape ${inputIndices.shape}`);\n  }\n  if (inputShape.shape.length !== 1) {\n    throw new Error(`Input shape should be a vector but received shape ${inputShape.shape}`);\n  }\n  if (newShape.shape.length !== 1) {\n    throw new Error(`Target shape should be a vector but received shape ${newShape.shape}`);\n  }\n  const $inputShape = Array.from(backend2.readSync(inputShape.dataId));\n  const $inputIndices = backend2.readSync(inputIndices.dataId);\n  const targetShape = Array.from(backend2.readSync(newShape.dataId));\n  const [newIndices, indicesShape, outputShape] = sparseReshapeImplCPU($inputIndices, inputIndices.shape, inputIndices.dtype, $inputShape, targetShape);\n  return [\n    backend2.makeTensorInfo(indicesShape, inputIndices.dtype, newIndices),\n    backend2.makeTensorInfo([outputShape.length], newShape.dtype, new Int32Array(outputShape))\n  ];\n}\nvar sparseReshapeConfig2 = {\n  kernelName: SparseReshape,\n  backendName: \"webgl\",\n  kernelFunc: sparseReshape3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/SparseSegmentMean.js\nfunction sparseSegmentMean3(args) {\n  const { inputs, backend: backend2 } = args;\n  const { data, indices, segmentIds } = inputs;\n  if (data.shape.length < 1) {\n    throw new Error(`Data should be at least 1 dimensional but received scalar`);\n  }\n  if (indices.shape.length !== 1) {\n    throw new Error(`Indices should be a vector but received shape\n              ${indices.shape}`);\n  }\n  if (segmentIds.shape.length !== 1) {\n    throw new Error(`Segment ids should be a vector but received shape\n              ${segmentIds.shape}`);\n  }\n  const $data = backend2.readSync(data.dataId);\n  const $indices = backend2.readSync(indices.dataId);\n  const $segmentIds = backend2.readSync(segmentIds.dataId);\n  const [outputData, outputDataShape] = sparseSegmentReductionImplCPU($data, data.shape, data.dtype, $indices, $segmentIds, true);\n  return backend2.makeTensorInfo(outputDataShape, data.dtype, outputData);\n}\nvar sparseSegmentMeanConfig2 = {\n  kernelName: SparseSegmentMean,\n  backendName: \"webgl\",\n  kernelFunc: sparseSegmentMean3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/SparseSegmentSum.js\nfunction sparseSegmentSum3(args) {\n  const { inputs, backend: backend2 } = args;\n  const { data, indices, segmentIds } = inputs;\n  if (data.shape.length < 1) {\n    throw new Error(`Data should be at least 1 dimensional but received scalar`);\n  }\n  if (indices.shape.length !== 1) {\n    throw new Error(`Indices should be a vector but received shape\n             ${indices.shape}`);\n  }\n  if (segmentIds.shape.length !== 1) {\n    throw new Error(`Segment ids should be a vector but received shape\n             ${segmentIds.shape}`);\n  }\n  const $data = backend2.readSync(data.dataId);\n  const $indices = backend2.readSync(indices.dataId);\n  const $segmentIds = backend2.readSync(segmentIds.dataId);\n  const [outputData, outputDataShape] = sparseSegmentReductionImplCPU($data, data.shape, data.dtype, $indices, $segmentIds);\n  return backend2.makeTensorInfo(outputDataShape, data.dtype, outputData);\n}\nvar sparseSegmentSumConfig2 = {\n  kernelName: SparseSegmentSum,\n  backendName: \"webgl\",\n  kernelFunc: sparseSegmentSum3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/SparseToDense.js\nfunction sparseToDense3(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { sparseIndices, sparseValues, defaultValue } = inputs;\n  const { outputShape } = attrs;\n  const { sliceRank, numUpdates, sliceSize, strides, outputSize } = backend_util_exports.calculateShapes(sparseValues, sparseIndices, outputShape);\n  const sumDupeIndices = false;\n  if (sparseValues.dtype === \"string\") {\n    const indicesBuf = backend2.bufferSync(sparseIndices);\n    const updatesBuf = backend2.bufferSync(sparseValues);\n    const $defaultValue = util_exports.decodeString(backend2.readSync(defaultValue.dataId)[0]);\n    const outBuf = scatterImplCPU(indicesBuf, updatesBuf, outputShape, outputSize, sliceSize, numUpdates, sliceRank, strides, $defaultValue, sumDupeIndices);\n    return backend2.makeTensorInfo(outputShape, outBuf.dtype, outBuf.values);\n  }\n  const program = new ScatterProgram(numUpdates, sliceRank, sparseIndices.shape.length, sparseValues.shape.length, strides, [outputSize, 1], sumDupeIndices);\n  const res = backend2.runWebGLProgram(program, [sparseValues, sparseIndices, defaultValue], sparseValues.dtype);\n  const reshaped = reshape4({ inputs: { x: res }, backend: backend2, attrs: { shape: outputShape } });\n  backend2.disposeIntermediateTensorInfo(res);\n  return reshaped;\n}\nvar sparseToDenseConfig2 = {\n  kernelName: SparseToDense,\n  backendName: \"webgl\",\n  kernelFunc: sparseToDense3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/SplitV.js\nfunction splitV2(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x } = inputs;\n  const { numOrSizeSplits, axis } = attrs;\n  const $axis = util_exports.parseAxisParam(axis, x.shape)[0];\n  const splitSizes = backend_util_exports.prepareSplitSize(x, numOrSizeSplits, $axis);\n  const xRank = x.shape.length;\n  const begin = new Array(xRank).fill(0);\n  const size = x.shape.slice();\n  return splitSizes.map((s2) => {\n    const sliceSize = [...size];\n    sliceSize[$axis] = s2;\n    const sliceT = slice3({ inputs: { x }, backend: backend2, attrs: { begin, size: sliceSize } });\n    begin[$axis] += s2;\n    return sliceT;\n  });\n}\nvar splitVConfig2 = {\n  kernelName: SplitV,\n  backendName: \"webgl\",\n  kernelFunc: splitV2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Sqrt.js\nvar SQRT = `return sqrt(x);`;\nvar sqrt3 = unaryKernelFunc2({ opSnippet: SQRT, packedOpSnippet: SQRT, cpuKernelImpl: sqrtImplCPU });\nvar sqrtConfig2 = {\n  kernelName: Sqrt,\n  backendName: \"webgl\",\n  kernelFunc: sqrt3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Square.js\nvar SQUARE = `return x * x;`;\nvar square3 = unaryKernelFunc2({ opSnippet: SQUARE });\nvar squareConfig2 = {\n  kernelName: Square,\n  backendName: \"webgl\",\n  kernelFunc: square3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/SquaredDifference.js\nvar SQUARED_DIFFERENCE = \"return (a - b) * (a - b);\";\nvar squaredDifference3 = binaryKernelFunc2({ opSnippet: SQUARED_DIFFERENCE, packedOpSnippet: SQUARED_DIFFERENCE });\nvar squaredDifferenceConfig2 = {\n  kernelName: SquaredDifference,\n  backendName: \"webgl\",\n  kernelFunc: squaredDifference3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Step.js\nfunction step3({ inputs, attrs, backend: backend2 }) {\n  const { x } = inputs;\n  const opSnippet = CHECK_NAN_SNIPPET + `\n    return x > 0.0 ? 1.0 : float(${attrs.alpha});\n  `;\n  const program = new UnaryOpProgram(x.shape, opSnippet);\n  return backend2.runWebGLProgram(program, [x], x.dtype);\n}\nvar stepConfig2 = {\n  kernelName: Step,\n  backendName: \"webgl\",\n  kernelFunc: step3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/strided_slice_gpu.js\nvar StridedSliceProgram = class {\n  constructor(begin, strides, size) {\n    this.variableNames = [\"x\"];\n    this.outputShape = size;\n    const rank = size.length;\n    const inputDtype = getCoordsDataType(size.length);\n    const dtype = getCoordsDataType(size.length);\n    let newCoords = \"\";\n    if (rank === 1) {\n      newCoords = \"coords * strides + begin\";\n    } else {\n      let outputAxis = 0;\n      newCoords = size.map((_, i2) => {\n        outputAxis++;\n        return size.length === 1 ? `coords * strides[${i2}] + begin[${i2}]` : `coords[${outputAxis - 1}] * strides[${i2}] + begin[${i2}]`;\n      }).join(\",\");\n    }\n    this.userCode = `\n      ${inputDtype} begin = ${inputDtype}(${begin});\n      ${inputDtype} strides = ${inputDtype}(${strides});\n\n      void main() {\n        ${dtype} coords = getOutputCoords();\n        setOutput(getX(${newCoords}));\n      }\n    `;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/StridedSlice.js\nfunction stridedSlice3(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x } = inputs;\n  const { begin, end, strides, beginMask, endMask, ellipsisMask, newAxisMask, shrinkAxisMask } = attrs;\n  const { finalShapeSparse, finalShape, isIdentity, sliceDim0, isSimpleSlice, begin: $begin, end: $end, strides: $strides } = slice_util_exports.sliceInfo(x.shape, begin, end, strides, beginMask, endMask, ellipsisMask, newAxisMask, shrinkAxisMask);\n  let result;\n  if (isIdentity) {\n    result = reshape4({ inputs: { x }, backend: backend2, attrs: { shape: finalShape } });\n  } else if (sliceDim0 || isSimpleSlice) {\n    util_exports.assert(x.shape.length >= 1, () => `Input must have rank at least 1, got: ${x.shape.length}`);\n    const size = slice_util_exports.computeOutShape($begin, $end, $strides);\n    const sliced = slice3({ inputs: { x }, backend: backend2, attrs: { begin: $begin, size } });\n    result = reshape4({ inputs: { x: sliced }, backend: backend2, attrs: { shape: finalShape } });\n    backend2.disposeIntermediateTensorInfo(sliced);\n  } else {\n    const shouldExecuteOnCPU = backend2.shouldExecuteOnCPU([x]);\n    if (shouldExecuteOnCPU) {\n      const values = backend2.readSync(x.dataId);\n      const xBuf = buffer(x.shape, x.dtype, values);\n      const resultValues = stridedSliceImplCPU(finalShapeSparse, xBuf, $strides, $begin);\n      result = backend2.makeTensorInfo(finalShape, x.dtype, resultValues.values);\n    } else {\n      const program = new StridedSliceProgram($begin, $strides, finalShapeSparse);\n      result = backend2.runWebGLProgram(program, [x], x.dtype);\n    }\n  }\n  const resultReshaped = reshape4({ inputs: { x: result }, backend: backend2, attrs: { shape: finalShape } });\n  backend2.disposeIntermediateTensorInfo(result);\n  return resultReshaped;\n}\nvar stridedSliceConfig2 = {\n  kernelName: StridedSlice,\n  backendName: \"webgl\",\n  kernelFunc: stridedSlice3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/StringNGrams.js\nfunction stringNGrams3(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { separator, nGramWidths, leftPad, rightPad: rightPad2, padWidth, preserveShortSequences } = attrs;\n  const { data, dataSplits } = inputs;\n  const $data = backend2.readSync(data.dataId);\n  const $dataSplits = backend2.readSync(dataSplits.dataId);\n  const [nGrams, nGramsSplits] = stringNGramsImplCPU($data, $dataSplits, separator, nGramWidths, leftPad, rightPad2, padWidth, preserveShortSequences);\n  return [\n    backend2.makeTensorInfo([nGrams.length], \"string\", nGrams),\n    backend2.makeTensorInfo(dataSplits.shape, \"int32\", nGramsSplits)\n  ];\n}\nvar stringNGramsConfig2 = {\n  kernelName: StringNGrams,\n  backendName: \"webgl\",\n  kernelFunc: stringNGrams3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/StringSplit.js\nfunction stringSplit3(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { skipEmpty } = attrs;\n  const { input: input2, delimiter } = inputs;\n  if (input2.dtype !== \"string\") {\n    throw new Error(\"Input must be of datatype string\");\n  }\n  if (input2.shape.length !== 1) {\n    throw new Error(`Input must be a vector, got shape: ${input2.shape}`);\n  }\n  if (delimiter.shape.length !== 0) {\n    throw new Error(`Delimiter must be a scalar, got shape: ${delimiter.shape}`);\n  }\n  const $input = backend2.readSync(input2.dataId);\n  const $delimiter = backend2.readSync(delimiter.dataId)[0];\n  const [indices, values, shape] = stringSplitImplCPU($input, $delimiter, skipEmpty);\n  const outputSize = values.length;\n  return [\n    backend2.makeTensorInfo([outputSize, 2], \"int32\", indices),\n    backend2.makeTensorInfo([outputSize], \"string\", values),\n    backend2.makeTensorInfo([2], \"int32\", new Int32Array(shape))\n  ];\n}\nvar stringSplitConfig2 = {\n  kernelName: StringSplit,\n  backendName: \"webgl\",\n  kernelFunc: stringSplit3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/StringToHashBucketFast.js\nfunction stringToHashBucketFast3(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { numBuckets } = attrs;\n  const { input: input2 } = inputs;\n  if (input2.dtype !== \"string\") {\n    throw new Error(\"Input must be of datatype string\");\n  }\n  if (numBuckets <= 0) {\n    throw new Error(`Number of buckets must be at least 1`);\n  }\n  const $input = backend2.readSync(input2.dataId);\n  const output = stringToHashBucketFastImplCPU($input, numBuckets);\n  return backend2.makeTensorInfo(input2.shape, \"int32\", output);\n}\nvar stringToHashBucketFastConfig2 = {\n  kernelName: StringToHashBucketFast,\n  backendName: \"webgl\",\n  kernelFunc: stringToHashBucketFast3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Tan.js\nvar TAN = `return tan(x);`;\nvar tan3 = unaryKernelFunc2({ opSnippet: TAN });\nvar tanConfig2 = {\n  kernelName: Tan,\n  backendName: \"webgl\",\n  kernelFunc: tan3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Tanh.js\nvar TANH = `\n  float e2x = exp(-2.0 * abs(x));\n  return sign(x) * (1.0 - e2x) / (1.0 + e2x);\n`;\nvar tanh4 = unaryKernelFunc2({ opSnippet: TANH });\nvar tanhConfig2 = {\n  kernelName: Tanh,\n  backendName: \"webgl\",\n  kernelFunc: tanh4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/tile_gpu.js\nvar TileProgram = class {\n  constructor(aShape, reps) {\n    this.variableNames = [\"A\"];\n    const outputShape = new Array(aShape.length);\n    for (let i2 = 0; i2 < outputShape.length; i2++) {\n      outputShape[i2] = aShape[i2] * reps[i2];\n    }\n    this.outputShape = outputShape;\n    this.rank = outputShape.length;\n    const dtype = getCoordsDataType(this.rank);\n    const sourceCoords = getSourceCoords3(aShape);\n    this.userCode = `\n      void main() {\n        ${dtype} resRC = getOutputCoords();\n        setOutput(getA(${sourceCoords}));\n      }\n    `;\n  }\n};\nfunction getSourceCoords3(aShape) {\n  const rank = aShape.length;\n  if (rank > 5) {\n    throw Error(`Tile for rank ${rank} is not yet supported`);\n  }\n  if (rank === 1) {\n    return `imod(resRC, ${aShape[0]})`;\n  }\n  const currentCoords = [\"resRC.x\", \"resRC.y\", \"resRC.z\", \"resRC.w\", \"resRC.u\"];\n  const sourceCoords = [];\n  for (let i2 = 0; i2 < aShape.length; i2++) {\n    sourceCoords.push(`imod(${currentCoords[i2]}, ${aShape[i2]})`);\n  }\n  return sourceCoords.join();\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Tile.js\nfunction tile4(params) {\n  const { inputs, backend: backend2, attrs } = params;\n  const { x } = inputs;\n  const { reps } = attrs;\n  if (x.dtype === \"string\" || x.shape.length > 5) {\n    const data = backend2.readSync(x.dataId);\n    const value = x.dtype === \"string\" ? data.map((d) => util_exports.decodeString(d)) : data;\n    const buf = buffer(x.shape, x.dtype, value);\n    const outBuf = tileImplCPU(buf, reps);\n    return backend2.makeTensorInfo(outBuf.shape, outBuf.dtype, outBuf.values);\n  }\n  const program = new TileProgram(x.shape, reps);\n  const output = backend2.runWebGLProgram(program, [x], x.dtype);\n  return output;\n}\nvar tileConfig2 = {\n  kernelName: Tile,\n  backendName: \"webgl\",\n  kernelFunc: tile4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/top_k_gpu.js\nvar SwapProgram = class {\n  constructor(shape) {\n    this.variableNames = [\"x\", \"indices\"];\n    this.customUniforms = [\n      { name: \"n\", type: \"int\" },\n      { name: \"firstPass\", type: \"int\" },\n      { name: \"negativeInf\", type: \"float\" },\n      { name: \"dir\", type: \"int\" },\n      { name: \"inc\", type: \"int\" }\n    ];\n    this.outputShape = shape;\n    this.userCode = `\n       void main() {\n         ivec2 coords = getOutputCoords();\n         int batch = coords[0];\n         int elemIdx = coords[1];\n\n         // We compare elements pair-wise within a group of size 2 * inc.\n         // The comparing rule for each group alternates between ascending\n         // and descending. Within each group, we compare each pair at\n         // positions i and i+inc. To decide whether an element at position i\n         // is x0 or x1, we mod it by 2 * inc, if the result is smaller than\n         // inc, it is in the first half of the group, we denote it as x0,\n         // otherwise we denote it as x1.\n         // For example, as shown in the Bitonic top K paper referenced above,\n         // Figure5(a) shows that element[1] is in the\n         // second half of the group when group size is 2, but it is in the\n         // first half of the group when group size is 4.\n\n         bool isFirstInPair = imod(elemIdx, 2 * inc) < inc;\n         int i = isFirstInPair ? elemIdx : elemIdx - inc;\n\n         int i0 = firstPass == 1 ? i : int(getIndices(batch, i));\n         int i1 = firstPass == 1 ? i + inc : int(getIndices(batch, i + inc));\n         float x0 = i0 < n ? getX(batch, i0) : negativeInf;\n         float x1 = i1 < n ? getX(batch, i1) : negativeInf;\n\n         // Denotes which direction indices are in (ascending or descending).\n         bool reverse = imod(elemIdx, 2 * dir) >= dir;\n         bool isGreater = x0 > x1 || (x0 == x1 && i1 > i0);\n         if (reverse == isGreater) { // Elements in opposite order of direction\n           int iTemp = i0;\n           i0 = i1;\n           i1 = iTemp;\n         }\n         if (isFirstInPair) {\n            setOutput(float(i0));\n         } else {\n            setOutput(float(i1));\n         }\n       }\n     `;\n  }\n};\nvar MergeProgram = class {\n  constructor(shape) {\n    this.variableNames = [\"x\", \"indices\"];\n    this.customUniforms = [\n      { name: \"n\", type: \"int\" },\n      { name: \"firstPass\", type: \"int\" },\n      { name: \"k\", type: \"int\" }\n    ];\n    this.outputShape = shape;\n    this.userCode = `\n    void main() {\n         // Takes max of indices (0, k), (1, k + 1), (2, k + 2) ...\n         ivec2 coords = getOutputCoords();\n         int batch = coords[0];\n         int elemIdx = coords[1];\n\n         // The output size is half of the previous size.\n         // If the previous sequence is | | | | _ _ _ _  | | | |  _ _ _ _ (k=4),\n         // we only need to output the indices at positions |, the indices at\n         // positions _ can be thrown away, see Figure5(b) After Phase 2\n         // (Merge phase) in the Bitonic Top K paper referenced above.\n         // For example, the paper shows we only need to output the orange bars.\n         // The output sequence should look like this | | | | | | | |.\n         // Because the sequence is halved, to map the output index back\n         // to the previous sequence to find the corresponding value,\n         // we need to double the index. When we double the index,\n         // we basically interpolate a position, so 2i looks like\n         // | _ | _ | _ | _ | _ | _ | _. We move the | to the first k position\n         // of each 2k positions by - elemIdx % k. E.g. for output at\n         // index 4,5,6,7, we want to get the corresponding element at\n         // original index 8,9,10,11, for output at index 8,9,10,11,\n         // we want to get the corresponding element at original index\n         // 16,17,18,19, so on and so forth.\n\n         int i = elemIdx < k ? elemIdx : (elemIdx * 2 - imod(elemIdx, k));\n         int i0 = firstPass == 1 ? i : int(getIndices(batch, i));\n         int i1 = firstPass == 1 ? i + k : int(getIndices(batch, i + k));\n\n         float x0 = getX(batch, i0);\n         float x1 = i1 < n ? getX(batch, i1) : x0;\n\n         setOutput(x0 >= x1 ? float(i0) : float(i1));\n       }\n     `;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/TopK.js\nfunction disposeIntermediateTensorInfoOrNull(backend2, tensorInfo) {\n  if (tensorInfo !== null) {\n    backend2.disposeIntermediateTensorInfo(tensorInfo);\n  }\n}\nfunction roundUpToPow2(num) {\n  let pow22 = 1;\n  while (pow22 < num) {\n    pow22 *= 2;\n  }\n  return pow22;\n}\nfunction topK2(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x } = inputs;\n  const { k, sorted } = attrs;\n  const TOPK_LAST_DIM_CPU_HANDOFF_SIZE_THRESHOLD = env().getNumber(\"TOPK_LAST_DIM_CPU_HANDOFF_SIZE_THRESHOLD\");\n  const TOPK_K_CPU_HANDOFF_THRESHOLD = env().getNumber(\"TOPK_K_CPU_HANDOFF_THRESHOLD\");\n  const xShape = x.shape;\n  const lastDim = xShape[xShape.length - 1];\n  if (backend2.shouldExecuteOnCPU([x]) || lastDim < TOPK_LAST_DIM_CPU_HANDOFF_SIZE_THRESHOLD || k > TOPK_K_CPU_HANDOFF_THRESHOLD) {\n    const xVals = backend2.readSync(x.dataId);\n    const [allTopKVals, allTopKIndices] = topKImplCPU(xVals, xShape, x.dtype, k, sorted);\n    return [\n      backend2.makeTensorInfo(allTopKVals.shape, allTopKVals.dtype, allTopKVals.values),\n      backend2.makeTensorInfo(allTopKIndices.shape, allTopKIndices.dtype, allTopKIndices.values)\n    ];\n  }\n  if (k === 0) {\n    xShape[xShape.length - 1] = 0;\n    return [\n      backend2.makeTensorInfo(xShape, x.dtype, []),\n      backend2.makeTensorInfo(xShape, \"int32\", [])\n    ];\n  }\n  if (lastDim === 1) {\n    return [\n      x,\n      fill3({ attrs: { shape: xShape, dtype: \"int32\", value: 0 }, backend: backend2 })\n    ];\n  }\n  const xtexData = backend2.texData.get(x.dataId);\n  const xIsPacked = xtexData !== null && xtexData.isPacked;\n  const xUnPacked = xIsPacked ? backend2.unpackTensor(x) : x;\n  const xSize = util_exports.sizeFromShape(xShape);\n  const batch = xSize / lastDim;\n  const x2D = reshape4({ inputs: { x: xUnPacked }, attrs: { shape: [batch, lastDim] }, backend: backend2 });\n  if (xIsPacked) {\n    disposeIntermediateTensorInfoOrNull(backend2, xUnPacked);\n  }\n  const kPow2 = roundUpToPow2(k);\n  const lastDimPow2 = roundUpToPow2(lastDim);\n  let indices = null;\n  const getInputs = () => indices === null ? [x2D, x2D] : [x2D, indices];\n  const runSwap = (dir, inc, shape) => {\n    const inputs2 = getInputs();\n    const program = new SwapProgram(shape);\n    const fistPass = indices === null ? 1 : 0;\n    const customValues = [[lastDim], [fistPass], [Number.NEGATIVE_INFINITY], [dir], [inc]];\n    const prevIndices2 = indices;\n    indices = backend2.runWebGLProgram(program, inputs2, \"int32\", customValues);\n    disposeIntermediateTensorInfoOrNull(backend2, prevIndices2);\n  };\n  for (let len = 1; len < kPow2; len *= 2) {\n    const dir = len * 2;\n    for (let inc = len; inc >= 1; inc /= 2) {\n      runSwap(dir, inc, [batch, lastDimPow2]);\n    }\n  }\n  for (let indicesSize = lastDimPow2; indicesSize > kPow2; indicesSize /= 2) {\n    const inputs2 = getInputs();\n    const mergeProgram = new MergeProgram([batch, indicesSize / 2]);\n    const firstPass = indices === null ? 1 : 0;\n    const customValues = [[lastDim], [firstPass], [kPow2]];\n    const prevIndices2 = indices;\n    indices = backend2.runWebGLProgram(mergeProgram, inputs2, \"int32\", customValues);\n    disposeIntermediateTensorInfoOrNull(backend2, prevIndices2);\n    const len = kPow2 / 2;\n    const dir = len * 2;\n    for (let inc = len; inc >= 1; inc /= 2) {\n      runSwap(dir, inc, indices.shape);\n    }\n  }\n  let prevIndices = indices;\n  indices = slice3({ inputs: { x: indices }, backend: backend2, attrs: { begin: 0, size: [batch, k] } });\n  disposeIntermediateTensorInfoOrNull(backend2, prevIndices);\n  let values = gatherV22({ inputs: { x: x2D, indices }, backend: backend2, attrs: { axis: 1, batchDims: 1 } });\n  disposeIntermediateTensorInfoOrNull(backend2, x2D);\n  const newShape = xShape.slice(0, -1);\n  newShape.push(k);\n  prevIndices = indices;\n  indices = reshape4({ inputs: { x: indices }, attrs: { shape: newShape }, backend: backend2 });\n  disposeIntermediateTensorInfoOrNull(backend2, prevIndices);\n  const prevValues = values;\n  values = reshape4({ inputs: { x: values }, attrs: { shape: newShape }, backend: backend2 });\n  disposeIntermediateTensorInfoOrNull(backend2, prevValues);\n  return [values, indices];\n}\nvar topKConfig2 = {\n  kernelName: TopK,\n  backendName: \"webgl\",\n  kernelFunc: topK2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/transform_gpu.js\nvar TransformProgram = class {\n  constructor(imageHeight, imageWidth, interpolation, fillMode, fillValue, outShape) {\n    this.variableNames = [\"Image\", \"Transforms\"];\n    this.outputShape = outShape;\n    const interpolationModeId = interpolation === \"nearest\" ? 1 : 2;\n    let fillModeId;\n    switch (fillMode) {\n      case \"constant\":\n        fillModeId = 1;\n        break;\n      case \"reflect\":\n        fillModeId = 2;\n        break;\n      case \"wrap\":\n        fillModeId = 3;\n        break;\n      case \"nearest\":\n        fillModeId = 4;\n        break;\n      default:\n        fillModeId = 1;\n        break;\n    }\n    this.userCode = `\n            float mapCoord(float outCoord, float len) {\n              float inCoord = outCoord;\n              if(${fillModeId} == 2) {\n                if (inCoord < 0.0) {\n                  if (len <= 1.0) {\n                    inCoord = 0.0;\n                  } else {\n                    float sz2 = 2.0 * len;\n                    if (inCoord < sz2) {\n                      inCoord = sz2 * float(int(float(-inCoord / sz2))) +\n                      inCoord;\n                    }\n                    inCoord = inCoord < -len ? inCoord + sz2 : -inCoord - 1.0;\n                  }\n                } else if (inCoord > len - 1.0) {\n                  if (len <= 1.0) {\n                    inCoord = 0.0;\n                  } else {\n                    float sz2 = 2.0 * len;\n                    inCoord -= sz2 * float(int(float(inCoord / sz2)));\n                    if (inCoord >= len) {\n                      inCoord = sz2 - inCoord - 1.0;\n                    }\n                  }\n                }\n                return clamp(inCoord, 0.0, len - 1.0);\n              } else if (${fillModeId} == 3) {\n                if (inCoord < 0.0) {\n                  if (len <= 1.0) {\n                    inCoord = 0.0;\n                  } else {\n                    float sz = len - 1.0;\n                    inCoord += len * (float(int(float(-inCoord / sz))) + 1.0);\n                  }\n                } else if (inCoord > len - 1.0) {\n                  if (len <= 1.0) {\n                    inCoord = 0.0;\n                  } else {\n                    float sz = len - 1.0;\n                    inCoord -= len * float(int(float(inCoord / sz)));\n                  }\n                }\n                return clamp(inCoord, 0.0, len - 1.0);\n              } else if (${fillModeId} == 4) {\n                return clamp(outCoord, 0.0, len - 1.0);\n              } else {\n                return outCoord;\n              }\n            }\n\n            float readWithFillValue(int batch, int coordY, int coordX,\n              int channel) {\n              float outputValue;\n              if (0 <= coordY && coordY < ${imageHeight} && 0 <= coordX && coordX < ${imageWidth}) {\n                  outputValue = getImage(batch, coordY, coordX, channel);\n              } else {\n                outputValue = float(${fillValue});\n              }\n              return outputValue;\n            }\n\n            void main() {\n              ivec4 coords = getOutputCoords();\n              float outputValue;\n              int batch = coords[0];\n              int x = coords[2];\n              int y = coords[1];\n              int channel = coords[3];\n              float xf = float(x);\n              float yf = float(y);\n              float a1 = getTransforms(batch, 0);\n              float a2 = getTransforms(batch, 1);\n              float a3 = getTransforms(batch, 2);\n              float b1 = getTransforms(batch, 3);\n              float b2 = getTransforms(batch, 4);\n              float b3 = getTransforms(batch, 5);\n              float c1 = getTransforms(batch, 6);\n              float c2 = getTransforms(batch, 7);\n              float projection = c1 * xf + c2 * yf + 1.0;\n              if (projection == 0.0) {\n                outputValue = float(${fillValue});\n              } else {\n                float inX = (a1 * xf + a2 * yf + a3) / projection;\n                float inY = (b1 * xf + b2 * yf + b3) / projection;\n                float mapX = mapCoord(inX, float(${imageWidth}));\n                float mapY = mapCoord(inY, float(${imageHeight}));\n\n                if (${interpolationModeId} == 1) {\n                  int coordY = int(round(mapY));\n                  int coordX = int(round(mapX));\n                  outputValue = readWithFillValue(batch, coordY, coordX,\n                    channel);\n                } else {\n                  float yFloor = floor(mapY);\n                  float xFloor = floor(mapX);\n                  float yCeil = yFloor + 1.0;\n                  float xCeil = xFloor + 1.0;\n                  float valueYFloor = (xCeil - mapX) *\n                  readWithFillValue(batch, int(yFloor), int(xFloor), channel) +\n                  (mapX - xFloor) *\n                  readWithFillValue(batch, int(yFloor), int(xCeil), channel);\n                  float valueYCeil = (xCeil - mapX) *\n                  readWithFillValue(batch, int(yCeil), int(xFloor), channel) +\n                  (mapX - xFloor) *\n                  readWithFillValue(batch, int(yCeil), int(xCeil), channel);\n                  outputValue = (yCeil - mapY) * valueYFloor +\n                  (mapY - yFloor) * valueYCeil;\n                }\n              }\n              setOutput(outputValue);\n            }\n        `;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Transform.js\nfunction transform3(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { image: image2, transforms } = inputs;\n  const { interpolation, fillMode, fillValue, outputShape } = attrs;\n  const [batch, imageHeight, imageWidth, numChannels] = image2.shape;\n  const [outHeight, outWidth] = outputShape != null ? outputShape : [imageHeight, imageWidth];\n  const outShape = [\n    batch,\n    outHeight,\n    outWidth,\n    numChannels\n  ];\n  const program = new TransformProgram(imageHeight, imageWidth, interpolation, fillMode, fillValue, outShape);\n  return backend2.runWebGLProgram(program, [image2, transforms], \"float32\");\n}\nvar transformConfig2 = {\n  kernelName: Transform,\n  backendName: \"webgl\",\n  kernelFunc: transform3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Unique.js\nfunction unique4(args) {\n  const { inputs, attrs, backend: backend2 } = args;\n  const { axis } = attrs;\n  const { x } = inputs;\n  assertNotComplex2(x, \"unique\");\n  console.warn(\"WARNING: \", \"UI might be locked temporarily as data is being downloaded\");\n  const values = backend2.readSync(x.dataId);\n  const { outputValues, outputShape, indices } = uniqueImplCPU(values, axis, x.shape, x.dtype);\n  return [\n    backend2.makeTensorInfo(outputShape, x.dtype, outputValues),\n    backend2.makeTensorInfo([indices.length], \"int32\", indices)\n  ];\n}\nvar uniqueConfig2 = {\n  kernelName: Unique,\n  backendName: \"webgl\",\n  kernelFunc: unique4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Unpack.js\nfunction unpack2(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { value } = inputs;\n  let { axis } = attrs;\n  if (axis < 0) {\n    axis += value.shape.length;\n  }\n  const x = value;\n  const xRank = x.shape.length;\n  const num = value.shape[axis];\n  const outShape = new Array(xRank - 1);\n  let outIndex = 0;\n  for (let i2 = 0; i2 < xRank; i2++) {\n    if (i2 !== axis) {\n      outShape[outIndex++] = x.shape[i2];\n    }\n  }\n  const toDispose = [];\n  const begin = new Array(xRank).fill(0);\n  const size = x.shape.slice();\n  size[axis] = 1;\n  const res = new Array(num);\n  for (let i2 = 0; i2 < res.length; i2++) {\n    begin[axis] = i2;\n    const sliced = slice3({ inputs: { x }, backend: backend2, attrs: { begin, size } });\n    const reshaped = reshape4({ inputs: { x: sliced }, backend: backend2, attrs: { shape: outShape } });\n    res[i2] = reshaped;\n    toDispose.push(sliced);\n  }\n  toDispose.forEach((t2) => backend2.disposeIntermediateTensorInfo(t2));\n  return res;\n}\nvar unpackConfig2 = {\n  kernelName: Unpack,\n  backendName: \"webgl\",\n  kernelFunc: unpack2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/segment_gpu.js\nvar SegmentOpProgram = class {\n  constructor(segOpInfo, segOpType) {\n    this.variableNames = [\"x\", \"segmentIds\"];\n    const windowSize = segOpInfo.windowSize;\n    const batchSize = segOpInfo.batchSize;\n    const inSize = segOpInfo.inSize;\n    const numSegments = segOpInfo.numSegments;\n    const outSize = numSegments * Math.ceil(inSize / windowSize);\n    this.outputShape = [batchSize, outSize];\n    const initializationValue = \"0.0\";\n    const returnValue = `sumValue`;\n    const windowSizeNearestVec4 = Math.floor(windowSize / 4) * 4;\n    const windowSizeVec4Remainder = windowSize % 4;\n    const updateSnippet = `\n        sumValue += dot(values, segFilter);\n    `;\n    let checkValueOutOfBounds = \"\";\n    if (inSize % windowSize > 0) {\n      checkValueOutOfBounds = `\n        if (inIdx < 0 || inIdx >= ${inSize}) {\n          return initializationValue;\n        }\n      `;\n    }\n    let checkSegmentIdOutOfBounds = \"\";\n    if (inSize % windowSize > 0) {\n      checkSegmentIdOutOfBounds = `\n        if (inIdx < 0 || inIdx >= ${inSize}) {\n          return -1.0;\n        }\n      `;\n    }\n    this.userCode = `\n      const float initializationValue = ${initializationValue};\n\n      float getValue(int batch, int inIdx) {\n        ${checkValueOutOfBounds}\n        return getX(batch, inIdx);\n      }\n\n      float getSegmentIdAtIndex(int inIdx) {\n        ${checkSegmentIdOutOfBounds}\n        return getSegmentIds(inIdx);\n      }\n\n      void main() {\n        ivec2 coords = getOutputCoords();\n        int batch = coords[0];\n        int outIdx = coords[1];\n        int inOffset = int(floor(float(outIdx) / float(\n          ${numSegments})) * float(${windowSize}));\n        int currentSeg = int(mod(float(outIdx), float(${numSegments})));\n\n        float sumValue = 0.0;\n\n        for (int i = 0; i < ${windowSizeNearestVec4}; i += 4) {\n          int inIdx = inOffset + i;\n          vec4 values = vec4(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            getValue(batch, inIdx + 2),\n            getValue(batch, inIdx + 3)\n          );\n\n          vec4 segFilter = vec4(\n            int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,\n            int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,\n            int(getSegmentIdAtIndex(inIdx + 2)) == currentSeg ? 1 : 0,\n            int(getSegmentIdAtIndex(inIdx + 3)) == currentSeg ? 1 : 0\n          );\n\n          ${updateSnippet}\n        }\n\n        int inIdx = inOffset + ${windowSizeNearestVec4};\n        if (${windowSizeVec4Remainder === 1}) {\n          vec4 values = vec4(\n            getValue(batch, inIdx),\n            initializationValue,\n            initializationValue,\n            initializationValue\n          );\n\n          int inIdxSeg = int(getSegmentIdAtIndex(inIdx));\n\n          vec4 segFilter = vec4(\n            int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,\n            0,\n            0,\n            0\n          );\n\n          ${updateSnippet}\n        } else if (${windowSizeVec4Remainder === 2}) {\n          vec4 values = vec4(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            initializationValue,\n            initializationValue\n          );\n\n          vec4 segFilter = vec4(\n            int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,\n            int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,\n              0,\n              0\n          );\n\n          ${updateSnippet}\n        } else if (${windowSizeVec4Remainder === 3}) {\n          vec4 values = vec4(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            getValue(batch, inIdx + 2),\n            initializationValue\n          );\n\n          vec4 segFilter = vec4(\n            int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,\n            int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,\n            int(getSegmentIdAtIndex(inIdx + 2)) == currentSeg ? 1 : 0,\n            0\n          );\n\n          ${updateSnippet}\n        }\n        setOutput(${returnValue});\n      }\n    `;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/UnsortedSegmentSum.js\nfunction unsortedSegmentSum3(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x, segmentIds } = inputs;\n  const { numSegments } = attrs;\n  const xRank = x.shape.length;\n  const toDispose = [];\n  let axis = 0;\n  const permutation = backend_util_exports.getAxesPermutation([axis], xRank);\n  let permutedX = x;\n  if (permutation != null) {\n    permutedX = transpose3({ inputs: { x }, backend: backend2, attrs: { perm: permutation } });\n    toDispose.push(permutedX);\n    axis = backend_util_exports.getInnerMostAxes(1, xRank)[0];\n  }\n  const outShape = backend_util_exports.segment_util.computeOutShape(permutedX.shape, axis, numSegments);\n  const inSize = util_exports.sizeFromShape([permutedX.shape[axis]]);\n  const a2D = reshape4({ inputs: { x: permutedX }, backend: backend2, attrs: { shape: [-1, inSize] } });\n  toDispose.push(a2D);\n  const outputDType = sumOutType(x.dtype);\n  const segOpCompute = (x2, segOpType, segmentIds2, dtype, numSegments2) => {\n    const batchSize = x2.shape[0];\n    const inSize2 = x2.shape[1];\n    const windowSize = backend_util_exports.segment_util.segOpComputeOptimalWindowSize(inSize2, numSegments2);\n    const segOpInfo = { windowSize, inSize: inSize2, batchSize, numSegments: numSegments2 };\n    const program = new SegmentOpProgram(segOpInfo, segOpType);\n    const output = backend2.compileAndRun(program, [x2, segmentIds2], dtype);\n    toDispose.push(output);\n    if (output.shape[1] === numSegments2) {\n      return output;\n    }\n    const rangeInfo = range4({\n      backend: backend2,\n      attrs: { start: 0, stop: numSegments2, step: 1, dtype: \"float32\" }\n    });\n    const tileInfo = tile4({\n      inputs: { x: rangeInfo },\n      backend: backend2,\n      attrs: { reps: [inSize2 / windowSize] }\n    });\n    toDispose.push(rangeInfo);\n    toDispose.push(tileInfo);\n    const result2 = segOpCompute(output, segOpType, tileInfo, dtype, numSegments2);\n    return result2;\n  };\n  const segOpResult = segOpCompute(a2D, \"unsortedSegmentSum\", segmentIds, outputDType, numSegments);\n  const reshaped = reshape4({ inputs: { x: segOpResult }, backend: backend2, attrs: { shape: outShape } });\n  let result = reshaped;\n  if (permutation != null) {\n    toDispose.push(reshaped);\n    const perm = backend_util_exports.getUndoAxesPermutation(permutation);\n    result = transpose3({ inputs: { x: result }, backend: backend2, attrs: { perm } });\n  }\n  toDispose.forEach((t2) => backend2.disposeIntermediateTensorInfo(t2));\n  return result;\n}\nvar unsortedSegmentSumConfig2 = {\n  kernelName: UnsortedSegmentSum,\n  backendName: \"webgl\",\n  kernelFunc: unsortedSegmentSum3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/register_all_kernels.js\nvar kernelConfigs2 = [\n  _fusedMatMulConfig2,\n  absConfig2,\n  acosConfig2,\n  acoshConfig2,\n  addConfig2,\n  addNConfig2,\n  allConfig2,\n  anyConfig2,\n  argMaxConfig2,\n  argMinConfig2,\n  asinConfig2,\n  asinhConfig2,\n  atanConfig2,\n  atan2Config2,\n  atanhConfig2,\n  avgPoolConfig2,\n  avgPool3DConfig2,\n  avgPool3DGradConfig3,\n  avgPoolGradConfig3,\n  batchMatMulConfig2,\n  batchNormConfig2,\n  batchToSpaceNDConfig2,\n  bincountConfig2,\n  broadcastArgsConfig2,\n  castConfig2,\n  ceilConfig2,\n  clipByValueConfig2,\n  complexConfig2,\n  complexAbsConfig2,\n  concatConfig2,\n  conv2DConfig2,\n  conv2DBackpropFilterConfig2,\n  conv2DBackpropInputConfig2,\n  conv3DConfig2,\n  conv3DBackpropFilterV2Config2,\n  conv3DBackpropInputConfig,\n  cosConfig2,\n  coshConfig2,\n  cropAndResizeConfig2,\n  cumprodConfig2,\n  cumsumConfig2,\n  denseBincountConfig2,\n  depthToSpaceConfig2,\n  depthwiseConv2dNativeConfig2,\n  depthwiseConv2dNativeBackpropFilterConfig2,\n  depthwiseConv2dNativeBackpropInputConfig2,\n  diagConfig2,\n  dilation2DConfig2,\n  einsumConfig2,\n  eluConfig2,\n  eluGradConfig3,\n  equalConfig2,\n  erfConfig2,\n  expConfig2,\n  expandDimsConfig2,\n  expm1Config2,\n  fftConfig2,\n  fillConfig2,\n  flipLeftRightConfig2,\n  floorConfig2,\n  floorDivConfig2,\n  fromPixelsConfig,\n  fusedConv2DConfig2,\n  fusedDepthwiseConv2DConfig2,\n  gatherNdConfig2,\n  gatherV2Config2,\n  greaterConfig2,\n  greaterEqualConfig2,\n  identityConfig2,\n  ifftConfig2,\n  imagConfig2,\n  isFiniteConfig2,\n  isInfConfig2,\n  isNaNConfig2,\n  leakyReluConfig2,\n  lessConfig2,\n  lessEqualConfig2,\n  linSpaceConfig2,\n  logConfig2,\n  log1pConfig2,\n  logicalAndConfig2,\n  logicalNotConfig2,\n  logicalOrConfig2,\n  LRNConfig2,\n  LRNGradConfig2,\n  maxConfig2,\n  maximumConfig2,\n  maxPoolConfig2,\n  maxPool3DConfig2,\n  maxPool3DGradConfig3,\n  maxPoolGradConfig3,\n  maxPoolWithArgmaxConfig2,\n  meanConfig2,\n  minConfig2,\n  minimumConfig2,\n  mirrorPadConfig2,\n  modConfig2,\n  multinomialConfig2,\n  multiplyConfig2,\n  negConfig2,\n  nonMaxSuppressionV3Config2,\n  nonMaxSuppressionV4Config2,\n  nonMaxSuppressionV5Config2,\n  notEqualConfig2,\n  oneHotConfig2,\n  onesLikeConfig2,\n  packConfig2,\n  padV2Config2,\n  powConfig2,\n  preluConfig2,\n  prodConfig2,\n  raggedTensorToTensorConfig2,\n  rangeConfig2,\n  realConfig2,\n  realDivConfig2,\n  reciprocalConfig2,\n  reluConfig2,\n  relu6Config2,\n  reshapeConfig2,\n  resizeBilinearConfig2,\n  resizeBilinearGradConfig3,\n  resizeNearestNeighborConfig2,\n  resizeNearestNeighborGradConfig3,\n  reverseConfig2,\n  rotateWithOffsetConfig2,\n  roundConfig2,\n  rsqrtConfig2,\n  scatterNdConfig2,\n  searchSortedConfig2,\n  selectConfig2,\n  seluConfig2,\n  sigmoidConfig2,\n  signConfig2,\n  sinConfig2,\n  sinhConfig2,\n  sliceConfig2,\n  softmaxConfig2,\n  softplusConfig2,\n  spaceToBatchNDConfig2,\n  sparseFillEmptyRowsConfig2,\n  sparseReshapeConfig2,\n  sparseSegmentMeanConfig2,\n  sparseSegmentSumConfig2,\n  sparseToDenseConfig2,\n  splitVConfig2,\n  sqrtConfig2,\n  squareConfig2,\n  squaredDifferenceConfig2,\n  stepConfig2,\n  stridedSliceConfig2,\n  stringNGramsConfig2,\n  stringSplitConfig2,\n  stringToHashBucketFastConfig2,\n  subConfig2,\n  sumConfig2,\n  tanConfig2,\n  tanhConfig2,\n  tileConfig2,\n  topKConfig2,\n  transformConfig2,\n  transposeConfig2,\n  uniqueConfig2,\n  unpackConfig2,\n  unsortedSegmentSumConfig2,\n  zerosLikeConfig2\n];\nfor (const kernelConfig of kernelConfigs2) {\n  registerKernel(kernelConfig);\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/types.js\nvar CppDType;\n(function(CppDType2) {\n  CppDType2[CppDType2[\"float32\"] = 0] = \"float32\";\n  CppDType2[CppDType2[\"int32\"] = 1] = \"int32\";\n  CppDType2[CppDType2[\"bool\"] = 2] = \"bool\";\n  CppDType2[CppDType2[\"string\"] = 3] = \"string\";\n  CppDType2[CppDType2[\"complex64\"] = 4] = \"complex64\";\n})(CppDType || (CppDType = {}));\nvar FusableActivation;\n(function(FusableActivation2) {\n  FusableActivation2[FusableActivation2[\"linear\"] = 0] = \"linear\";\n  FusableActivation2[FusableActivation2[\"relu\"] = 1] = \"relu\";\n  FusableActivation2[FusableActivation2[\"relu6\"] = 2] = \"relu6\";\n  FusableActivation2[FusableActivation2[\"prelu\"] = 3] = \"prelu\";\n  FusableActivation2[FusableActivation2[\"leakyrelu\"] = 4] = \"leakyrelu\";\n  FusableActivation2[FusableActivation2[\"sigmoid\"] = 5] = \"sigmoid\";\n  FusableActivation2[FusableActivation2[\"elu\"] = 6] = \"elu\";\n})(FusableActivation || (FusableActivation = {}));\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/_FusedMatMul.js\nvar wasmFusedMatMul;\nfunction setup(backend2) {\n  wasmFusedMatMul = backend2.wasm.cwrap(_FusedMatMul, null, [\n    \"number\",\n    \"array\",\n    \"number\",\n    \"number\",\n    \"array\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\"\n  ]);\n}\nfunction fusedBatchMatMul(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { a, b, bias, preluActivationWeights } = inputs;\n  if (a.dtype !== \"float32\" || b.dtype !== \"float32\") {\n    throw new Error(`_FusedMatMul for non non-float32 tensors not yet supported.`);\n  }\n  const { transposeA, transposeB, activation: activation2, leakyreluAlpha } = attrs;\n  const aId = backend2.dataIdMap.get(a.dataId).id;\n  const bId = backend2.dataIdMap.get(b.dataId).id;\n  let biasId = 0;\n  if (bias != null) {\n    const biasData = backend2.dataIdMap.get(bias.dataId);\n    if (biasData.shape.length !== 1) {\n      throw new Error(`_FusedMatMul only supports rank-1 bias but got rank ${biasData.shape.length}.`);\n    }\n    biasId = biasData.id;\n  }\n  const preluActivationWeightsId = preluActivationWeights == null ? 0 : backend2.dataIdMap.get(preluActivationWeights.dataId).id;\n  const fusedActivation = FusableActivation[activation2];\n  if (fusedActivation == null) {\n    throw new Error(`${activation2} activation not yet supported for FusedConv2D in the wasm backend.`);\n  }\n  const leftDim = transposeA ? a.shape[2] : a.shape[1];\n  const rightDim = transposeB ? b.shape[1] : b.shape[2];\n  const batchDims = broadcast_util_exports.assertAndGetBroadcastShape(a.shape.slice(0, -2), b.shape.slice(0, -2));\n  const out = backend2.makeOutput([...batchDims, leftDim, rightDim], a.dtype);\n  const outId = backend2.dataIdMap.get(out.dataId).id;\n  const aShapeBytes = new Uint8Array(new Int32Array(a.shape).buffer);\n  const bShapeBytes = new Uint8Array(new Int32Array(b.shape).buffer);\n  wasmFusedMatMul(aId, aShapeBytes, a.shape.length, bId, bShapeBytes, b.shape.length, transposeA, transposeB, fusedActivation, biasId, preluActivationWeightsId, leakyreluAlpha || 0, outId);\n  return out;\n}\nvar _fusedMatMulConfig3 = {\n  kernelName: _FusedMatMul,\n  backendName: \"wasm\",\n  setupFunc: setup,\n  kernelFunc: fusedBatchMatMul\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/unary_kernel.js\nfunction createUnaryKernelConfig(kernelName, outType) {\n  let wasmFunc9;\n  function setupFunc3(backend2) {\n    wasmFunc9 = backend2.wasm.cwrap(kernelName, null, [\n      \"number\",\n      \"number\",\n      \"number\"\n    ]);\n  }\n  function kernelFunc3(args) {\n    const { backend: backend2, inputs: { x } } = args;\n    const xId = backend2.dataIdMap.get(x.dataId).id;\n    const out = backend2.makeOutput(x.shape, outType || x.dtype);\n    const outId = backend2.dataIdMap.get(out.dataId).id;\n    if (util_exports.sizeFromShape(out.shape) === 0) {\n      return out;\n    }\n    wasmFunc9(xId, CppDType[x.dtype], outId);\n    return out;\n  }\n  return { kernelName, backendName: \"wasm\", setupFunc: setupFunc3, kernelFunc: kernelFunc3 };\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Abs.js\nvar absConfig3 = createUnaryKernelConfig(Abs);\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/binary_kernel.js\nfunction createBinaryKernelConfig(kernelName, supportsFullBroadcast19, dtype) {\n  let wasmFunc9;\n  function setupFunc3(backend2) {\n    wasmFunc9 = backend2.wasm.cwrap(kernelName, null, [\n      \"number\",\n      \"array\",\n      \"number\",\n      \"number\",\n      \"array\",\n      \"number\",\n      \"number\",\n      \"number\"\n    ]);\n  }\n  function kernelFunc3(args) {\n    const { backend: backend2, inputs } = args;\n    const { a, b } = inputs;\n    const aId = backend2.dataIdMap.get(a.dataId).id;\n    const bId = backend2.dataIdMap.get(b.dataId).id;\n    const outputType = dtype != null ? dtype : a.dtype;\n    const newShape = backend_util_exports.assertAndGetBroadcastShape(a.shape, b.shape);\n    const out = backend2.makeOutput(newShape, outputType);\n    if (util_exports.sizeFromShape(newShape) === 0) {\n      return out;\n    }\n    const aShapeBytes = new Uint8Array(new Int32Array(a.shape).buffer);\n    const bShapeBytes = new Uint8Array(new Int32Array(b.shape).buffer);\n    const outId = backend2.dataIdMap.get(out.dataId).id;\n    const kernelFunc4 = () => wasmFunc9(aId, aShapeBytes, a.shape.length, bId, bShapeBytes, b.shape.length, CppDType[a.dtype], outId);\n    kernelFunc4();\n    return out;\n  }\n  return { kernelName, backendName: \"wasm\", setupFunc: setupFunc3, kernelFunc: kernelFunc3 };\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Add.js\nvar supportsFullBroadcast = true;\nvar addConfig3 = createBinaryKernelConfig(Add, supportsFullBroadcast);\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/AddN.js\nvar wasmFunc;\nfunction setupFunc(backend2) {\n  wasmFunc = backend2.wasm.cwrap(AddN, null, [\n    \"array\",\n    \"number\",\n    \"number\",\n    \"number\"\n  ]);\n}\nfunction addn(args) {\n  const { inputs, backend: backend2 } = args;\n  const out = backend2.makeOutput(inputs[0].shape, inputs[0].dtype);\n  if (util_exports.sizeFromShape(out.shape) === 0) {\n    return out;\n  }\n  const inputIds = inputs.map((x) => backend2.dataIdMap.get(x.dataId).id);\n  const inputIdsBytes = new Uint8Array(new Int32Array(inputIds).buffer);\n  const outId = backend2.dataIdMap.get(out.dataId).id;\n  wasmFunc(inputIdsBytes, inputIds.length, CppDType[out.dtype], outId);\n  return out;\n}\nvar addNConfig3 = {\n  kernelName: AddN,\n  backendName: \"wasm\",\n  setupFunc,\n  kernelFunc: addn\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Identity.js\nfunction identity4(args) {\n  const { inputs: { x }, backend: backend2 } = args;\n  const out = backend2.makeOutput(x.shape, x.dtype);\n  const inVals = backend2.typedArrayFromHeap(x);\n  const outVals = backend2.typedArrayFromHeap(out);\n  outVals.set(inVals);\n  return out;\n}\nvar identityConfig3 = {\n  kernelName: Identity,\n  backendName: \"wasm\",\n  kernelFunc: identity4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Transpose.js\nvar wasmTranspose;\nfunction setup2(backend2) {\n  wasmTranspose = backend2.wasm.cwrap(Transpose, null, [\n    \"number\",\n    \"array\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"array\",\n    \"number\"\n  ]);\n}\nfunction transpose4(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const [reducedShape, perm] = removeOneSizeDims(inputs.x.shape, attrs.perm);\n  let permIsNoOp = true;\n  for (let i2 = 0; i2 < perm.length; i2++) {\n    if (perm[i2] !== i2) {\n      permIsNoOp = false;\n    }\n  }\n  const outShape = computeOutShape4(inputs.x.shape, attrs.perm);\n  const x = {\n    dataId: inputs.x.dataId,\n    shape: reducedShape,\n    dtype: inputs.x.dtype\n  };\n  if (permIsNoOp) {\n    const cloned = identity4({ inputs, backend: backend2 });\n    cloned.shape = outShape;\n    return cloned;\n  }\n  const out = backend2.makeOutput(outShape, x.dtype);\n  const xId = backend2.dataIdMap.get(x.dataId).id;\n  const outId = backend2.dataIdMap.get(out.dataId).id;\n  const permBytes = new Uint8Array(new Int32Array(perm).buffer);\n  const xShapeBytes = new Uint8Array(new Int32Array(x.shape).buffer);\n  wasmTranspose(xId, xShapeBytes, x.shape.length, CppDType[x.dtype], outId, permBytes, perm.length);\n  return out;\n}\nfunction computeOutShape4(inShape, perm) {\n  const outShape = new Array(inShape.length);\n  for (let i2 = 0; i2 < outShape.length; i2++) {\n    outShape[i2] = inShape[perm[i2]];\n  }\n  return outShape;\n}\nfunction removeOneSizeDims(shape, perm) {\n  const newShape = [];\n  const newPerm = [];\n  for (let i2 = 0; i2 < shape.length; ++i2) {\n    if (shape[i2] !== 1) {\n      newShape.push(shape[i2]);\n    }\n    if (shape[perm[i2]] !== 1) {\n      newPerm.push(perm[i2]);\n    }\n  }\n  for (let i2 = 0; i2 < newPerm.length; ++i2) {\n    let minValIdx = -1;\n    for (let j = 0; j < newPerm.length; ++j) {\n      if (newPerm[j] >= i2 && (minValIdx === -1 || newPerm[minValIdx] > newPerm[j])) {\n        minValIdx = j;\n      }\n    }\n    newPerm[minValIdx] = i2;\n  }\n  return [newShape, newPerm];\n}\nvar transposeConfig3 = {\n  kernelName: Transpose,\n  backendName: \"wasm\",\n  kernelFunc: transpose4,\n  setupFunc: setup2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/kernel_utils.js\nfunction permuteAxesAndTranspose(x, axis, backend2) {\n  const xShape = x.shape;\n  const xRank = x.shape.length;\n  const originalAxes = util_exports.parseAxisParam(axis, xShape);\n  let axes = originalAxes;\n  const permutedAxes = backend_util_exports.getAxesPermutation(axes, xRank);\n  let xTransposed = null;\n  let inputWasTransposed = false;\n  if (permutedAxes != null) {\n    const newShape = new Array(xRank);\n    for (let i2 = 0; i2 < newShape.length; i2++) {\n      newShape[i2] = xShape[permutedAxes[i2]];\n    }\n    axes = backend_util_exports.getInnerMostAxes(axes.length, xRank);\n    xTransposed = transpose4({ inputs: { x }, attrs: { perm: permutedAxes }, backend: backend2 });\n    const xId = backend2.dataIdMap.get(x.dataId).id;\n    const transposedId = backend2.dataIdMap.get(xTransposed.dataId).id;\n    if (transposedId !== xId) {\n      inputWasTransposed = true;\n    }\n  }\n  return { transposed: xTransposed, originalAxes, axes, inputWasTransposed };\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/All.js\nvar wasmAll;\nfunction setup3(backend2) {\n  wasmAll = backend2.wasm.cwrap(All, null, [\"number, number, number\"]);\n}\nfunction all4(args) {\n  const { backend: backend2, inputs, attrs } = args;\n  const { axis, keepDims } = attrs;\n  const { x } = inputs;\n  const xId = backend2.dataIdMap.get(x.dataId).id;\n  let inputId = xId;\n  let input2 = x;\n  const { transposed, axes, originalAxes, inputWasTransposed } = permuteAxesAndTranspose(x, axis, backend2);\n  if (inputWasTransposed) {\n    const transposedId = backend2.dataIdMap.get(transposed.dataId).id;\n    input2 = transposed;\n    inputId = transposedId;\n  }\n  const inputRank = input2.shape.length;\n  backend_util_exports.assertAxesAreInnerMostDims(\"all\", axes, inputRank);\n  const [outShape, reduceShape] = backend_util_exports.computeOutAndReduceShapes(input2.shape, axes);\n  const reduceSize = util_exports.sizeFromShape(reduceShape);\n  const out = backend2.makeOutput(outShape, x.dtype);\n  if (util_exports.sizeFromShape(input2.shape) !== 0) {\n    const outId = backend2.dataIdMap.get(out.dataId).id;\n    wasmAll(inputId, reduceSize, outId);\n  }\n  if (inputWasTransposed) {\n    backend2.disposeData(transposed.dataId);\n  }\n  if (keepDims) {\n    const newShape = backend_util_exports.expandShapeToKeepDim(out.shape, originalAxes);\n    out.shape = newShape;\n  }\n  return out;\n}\nvar allConfig3 = {\n  kernelName: All,\n  backendName: \"wasm\",\n  setupFunc: setup3,\n  kernelFunc: all4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Any.js\nvar wasmAny;\nfunction setup4(backend2) {\n  wasmAny = backend2.wasm.cwrap(Any, null, [\"number, number, number\"]);\n}\nfunction any4(args) {\n  const { backend: backend2, inputs, attrs } = args;\n  const { axis, keepDims } = attrs;\n  const { x } = inputs;\n  const xId = backend2.dataIdMap.get(x.dataId).id;\n  let inputId = xId;\n  let input2 = x;\n  const { transposed, axes, originalAxes, inputWasTransposed } = permuteAxesAndTranspose(x, axis, backend2);\n  if (inputWasTransposed) {\n    const transposedId = backend2.dataIdMap.get(transposed.dataId).id;\n    input2 = transposed;\n    inputId = transposedId;\n  }\n  const inputRank = input2.shape.length;\n  backend_util_exports.assertAxesAreInnerMostDims(\"any\", axes, inputRank);\n  const [outShape, reduceShape] = backend_util_exports.computeOutAndReduceShapes(input2.shape, axes);\n  const reduceSize = util_exports.sizeFromShape(reduceShape);\n  const out = backend2.makeOutput(outShape, x.dtype);\n  if (util_exports.sizeFromShape(input2.shape) !== 0) {\n    const outId = backend2.dataIdMap.get(out.dataId).id;\n    wasmAny(inputId, reduceSize, outId);\n  }\n  if (inputWasTransposed) {\n    backend2.disposeData(transposed.dataId);\n  }\n  if (keepDims) {\n    const newShape = backend_util_exports.expandShapeToKeepDim(out.shape, originalAxes);\n    out.shape = newShape;\n  }\n  return out;\n}\nvar anyConfig3 = {\n  kernelName: Any,\n  backendName: \"wasm\",\n  setupFunc: setup4,\n  kernelFunc: any4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/ArgMax.js\nvar wasmFunc2;\nfunction setup5(backend2) {\n  wasmFunc2 = backend2.wasm.cwrap(ArgMax, null, [\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\"\n  ]);\n}\nfunction argmax(args) {\n  const { backend: backend2, inputs, attrs } = args;\n  const { axis } = attrs;\n  const { x } = inputs;\n  const xId = backend2.dataIdMap.get(x.dataId).id;\n  let inputId = xId;\n  let input2 = x;\n  const { transposed, axes, inputWasTransposed } = permuteAxesAndTranspose(x, axis, backend2);\n  if (inputWasTransposed) {\n    const transposedId = backend2.dataIdMap.get(transposed.dataId).id;\n    if (transposedId !== xId) {\n      input2 = transposed;\n      inputId = transposedId;\n    }\n  }\n  const outShape = input2.shape.slice(0, -1);\n  const out = backend2.makeOutput(outShape, \"int32\");\n  const outId = backend2.dataIdMap.get(out.dataId).id;\n  const outerSize = util_exports.sizeFromShape(out.shape);\n  const innerSize = input2.shape[axes[0]];\n  wasmFunc2(inputId, CppDType[input2.dtype], outerSize, innerSize, outId);\n  if (inputWasTransposed) {\n    backend2.disposeData(transposed.dataId);\n  }\n  return out;\n}\nvar argMaxConfig3 = {\n  kernelName: ArgMax,\n  backendName: \"wasm\",\n  kernelFunc: argmax,\n  setupFunc: setup5\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/AvgPool.js\nvar wasmAvgPool;\nfunction setup6(backend2) {\n  wasmAvgPool = backend2.wasm.cwrap(AvgPool, null, [\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\"\n  ]);\n}\nfunction avgPool4(args) {\n  const { inputs, attrs, backend: backend2 } = args;\n  const x = inputs.x;\n  const xId = backend2.dataIdMap.get(x.dataId).id;\n  const { filterSize, strides, pad: pad3, dimRoundingMode } = attrs;\n  const convInfo = backend_util_exports.computePool2DInfo(x.shape, filterSize, strides, 1, pad3, dimRoundingMode);\n  const filterHeight = convInfo.filterHeight;\n  const filterWidth = convInfo.filterWidth;\n  const padTop = convInfo.padInfo.top;\n  const padRight = convInfo.padInfo.right;\n  const padBottom = convInfo.padInfo.bottom;\n  const padLeft = convInfo.padInfo.left;\n  const strideHeight = convInfo.strideHeight;\n  const strideWidth = convInfo.strideWidth;\n  const channels = convInfo.inChannels;\n  if (convInfo.dataFormat !== \"channelsLast\") {\n    throw new Error(`wasm backend does not support dataFormat:'${convInfo.dataFormat}'. Please use 'channelsLast'.`);\n  }\n  if (convInfo.dilationWidth !== 1 || convInfo.dilationHeight !== 1) {\n    throw new Error(`was backend only supports average pooling with dilation = [1, 1], got [${convInfo.dilationHeight}, ${convInfo.dilationWidth}].`);\n  }\n  const out = backend2.makeOutput(convInfo.outShape, \"float32\");\n  const outId = backend2.dataIdMap.get(out.dataId).id;\n  wasmAvgPool(xId, x.shape[0], x.shape[1], x.shape[2], filterHeight, filterWidth, padTop, padRight, padBottom, padLeft, strideHeight, strideWidth, channels, outId);\n  return out;\n}\nvar avgPoolConfig3 = {\n  kernelName: AvgPool,\n  backendName: \"wasm\",\n  setupFunc: setup6,\n  kernelFunc: avgPool4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Reshape.js\nfunction reshape5(args) {\n  const { inputs, attrs } = args;\n  const { x } = inputs;\n  const { shape } = attrs;\n  const xSize = util_exports.sizeFromShape(x.shape);\n  const $shape = util_exports.inferFromImplicitShape(shape, xSize);\n  util_exports.assert(xSize === util_exports.sizeFromShape($shape), () => `new shape: ${$shape}, old shape: ${x.shape}. New shape and old shape must have the same number of elements.`);\n  args.backend.incRef(x.dataId);\n  return { dataId: x.dataId, shape: $shape, dtype: x.dtype };\n}\nvar reshapeConfig3 = {\n  kernelName: Reshape,\n  backendName: \"wasm\",\n  kernelFunc: reshape5\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/BatchMatMul.js\nvar wasmBatchMatMul;\nfunction setup7(backend2) {\n  wasmBatchMatMul = backend2.wasm.cwrap(BatchMatMul, null, [\n    \"number\",\n    \"array\",\n    \"number\",\n    \"number\",\n    \"array\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\"\n  ]);\n}\nfunction batchMatMul3(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { a, b } = inputs;\n  const { transposeA, transposeB } = attrs;\n  if (a.dtype !== \"float32\" || b.dtype !== \"float32\") {\n    throw new Error(`BatchMatMul for non non-float32 tensors not yet supported.`);\n  }\n  const aRank = a.shape.length;\n  const bRank = b.shape.length;\n  const innerShapeA = transposeA ? a.shape[aRank - 2] : a.shape[aRank - 1];\n  const innerShapeB = transposeB ? b.shape[bRank - 1] : b.shape[bRank - 2];\n  const outerShapeA = transposeA ? a.shape[aRank - 1] : a.shape[aRank - 2];\n  const outerShapeB = transposeB ? b.shape[bRank - 2] : b.shape[bRank - 1];\n  const outerDimsA = a.shape.slice(0, -2);\n  const outerDimsB = b.shape.slice(0, -2);\n  const batchDimA = util_exports.sizeFromShape(outerDimsA);\n  const batchDimB = util_exports.sizeFromShape(outerDimsB);\n  const outShapeOuterDims = broadcast_util_exports.assertAndGetBroadcastShape(a.shape.slice(0, -2), b.shape.slice(0, -2));\n  const outShape = outShapeOuterDims.concat([outerShapeA, outerShapeB]);\n  util_exports.assert(innerShapeA === innerShapeB, () => `Error in matMul: inner shapes (${innerShapeA}) and (${innerShapeB}) of Tensors with shapes ${a.shape} and ${b.shape} and transposeA=${transposeA} and transposeB=${transposeB} must match.`);\n  const a3dShape = transposeA ? [batchDimA, innerShapeA, outerShapeA] : [batchDimA, outerShapeA, innerShapeA];\n  const b3dShape = transposeB ? [batchDimB, outerShapeB, innerShapeB] : [batchDimB, innerShapeB, outerShapeB];\n  const a3d = reshape5({ inputs: { x: a }, backend: backend2, attrs: { shape: a3dShape } });\n  const b3d = reshape5({ inputs: { x: b }, backend: backend2, attrs: { shape: b3dShape } });\n  const a3dId = backend2.dataIdMap.get(a3d.dataId).id;\n  const b3dId = backend2.dataIdMap.get(b3d.dataId).id;\n  const leftDim = transposeA ? a3d.shape[2] : a3d.shape[1];\n  const rightDim = transposeB ? b3d.shape[1] : b3d.shape[2];\n  const batchDim = Math.max(batchDimA, batchDimB);\n  const out = backend2.makeOutput([batchDim, leftDim, rightDim], a3d.dtype);\n  const outId = backend2.dataIdMap.get(out.dataId).id;\n  const aShapeBytes = new Uint8Array(new Int32Array(a3d.shape).buffer);\n  const bShapeBytes = new Uint8Array(new Int32Array(b3d.shape).buffer);\n  wasmBatchMatMul(a3dId, aShapeBytes, a3d.shape.length, b3dId, bShapeBytes, b3d.shape.length, transposeA, transposeB, outId);\n  backend2.disposeData(a3d.dataId);\n  backend2.disposeData(b3d.dataId);\n  out.shape = outShape;\n  return out;\n}\nvar batchMatMulConfig3 = {\n  kernelName: BatchMatMul,\n  backendName: \"wasm\",\n  setupFunc: setup7,\n  kernelFunc: batchMatMul3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Slice.js\nfunction slice4(args) {\n  const { inputs: { x }, attrs: { begin, size }, backend: backend2 } = args;\n  const [begin_, size_] = slice_util_exports.parseSliceParams(x, begin, size);\n  const isContinous = slice_util_exports.isSliceContinous(x.shape, begin_, size_);\n  const xVals = backend2.readSync(x.dataId);\n  const out = backend2.makeOutput(size_, x.dtype);\n  const xStrides = util_exports.computeStrides(x.shape);\n  const outData = backend2.dataIdMap.get(out.dataId);\n  if (isContinous) {\n    const flatOffset = slice_util_exports.computeFlatOffset(begin_, xStrides);\n    if (x.dtype === \"string\") {\n      outData.stringBytes = xVals.slice(flatOffset, flatOffset + util_exports.sizeFromShape(size_));\n    } else {\n      const outVals2 = backend2.typedArrayFromHeap(out);\n      outVals2.set(xVals.subarray(flatOffset, flatOffset + util_exports.sizeFromShape(size_)));\n    }\n    return out;\n  }\n  if (x.dtype === \"string\") {\n    const res = sliceImpl(xVals, begin_, size_, x.shape, x.dtype);\n    outData.stringBytes = res;\n    return out;\n  }\n  const outVals = backend2.typedArrayFromHeap(out);\n  const rank = x.shape.length;\n  if (rank === 2) {\n    slice2d2(xVals, xStrides[0], outVals, begin_, size_);\n  } else if (rank === 3) {\n    slice3d2(xVals, xStrides[0], xStrides[1], outVals, begin_, size_);\n  } else if (rank === 4) {\n    slice4d2(xVals, xStrides[0], xStrides[1], xStrides[2], outVals, begin_, size_);\n  } else {\n    const res = sliceImpl(xVals, begin_, size_, x.shape, x.dtype);\n    outVals.set(res);\n  }\n  return out;\n}\nfunction slice2d2(xVals, xStride, outVals, begin, size) {\n  let outOffset = 0;\n  const beginI = begin[0];\n  const beginJ = begin[1];\n  const endI = beginI + size[0];\n  for (let i2 = beginI; i2 < endI; i2++) {\n    const xOffset = i2 * xStride + beginJ;\n    outVals.set(xVals.subarray(xOffset, xOffset + size[1]), outOffset);\n    outOffset += size[1];\n  }\n}\nfunction slice3d2(xVals, xStride1, xStride2, outVals, begin, size) {\n  let outOffset = 0;\n  const beginI = begin[0];\n  const beginJ = begin[1];\n  const beginK = begin[2];\n  const endI = beginI + size[0];\n  const endJ = beginJ + size[1];\n  for (let i2 = beginI; i2 < endI; i2++) {\n    for (let j = beginJ; j < endJ; j++) {\n      const xOffset = i2 * xStride1 + j * xStride2 + beginK;\n      outVals.set(xVals.subarray(xOffset, xOffset + size[2]), outOffset);\n      outOffset += size[2];\n    }\n  }\n}\nfunction slice4d2(xVals, xStride1, xStride2, xStride3, outVals, begin, size) {\n  let outOffset = 0;\n  const beginI = begin[0];\n  const beginJ = begin[1];\n  const beginK = begin[2];\n  const endI = beginI + size[0];\n  const endJ = beginJ + size[1];\n  const endK = beginK + size[2];\n  const beginL = begin[3];\n  for (let i2 = beginI; i2 < endI; i2++) {\n    for (let j = beginJ; j < endJ; j++) {\n      for (let k = beginK; k < endK; k++) {\n        const xOffset = i2 * xStride1 + j * xStride2 + k * xStride3 + beginL;\n        outVals.set(xVals.subarray(xOffset, xOffset + size[3]), outOffset);\n        outOffset += size[3];\n      }\n    }\n  }\n}\nvar sliceConfig3 = {\n  kernelName: Slice,\n  backendName: \"wasm\",\n  kernelFunc: slice4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/BatchToSpaceND.js\nfunction batchToSpaceND4(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x } = inputs;\n  const { blockShape, crops } = attrs;\n  const prod6 = blockShape.reduce((a, b) => a * b);\n  const reshaped = backend_util_exports.getReshaped(x.shape, blockShape, prod6);\n  const permuted = backend_util_exports.getPermuted(reshaped.length, blockShape.length);\n  const reshapedPermuted = backend_util_exports.getReshapedPermuted(x.shape, blockShape, prod6);\n  const sliceBeginCoords = backend_util_exports.getSliceBeginCoords(crops, blockShape.length);\n  const sliceSize = backend_util_exports.getSliceSize(reshapedPermuted, crops, blockShape.length);\n  const xReshaped = reshape5({ inputs: { x }, backend: backend2, attrs: { shape: reshaped } });\n  const xTransposed = transpose4({ inputs: { x: xReshaped }, backend: backend2, attrs: { perm: permuted } });\n  const xTransposedReshaped = reshape5({ inputs: { x: xTransposed }, backend: backend2, attrs: { shape: reshapedPermuted } });\n  const result = slice4({\n    inputs: { x: xTransposedReshaped },\n    backend: backend2,\n    attrs: { begin: sliceBeginCoords, size: sliceSize }\n  });\n  backend2.disposeData(xReshaped.dataId);\n  backend2.disposeData(xTransposed.dataId);\n  backend2.disposeData(xReshaped.dataId);\n  return result;\n}\nvar batchToSpaceNDConfig3 = {\n  kernelName: BatchToSpaceND,\n  backendName: \"wasm\",\n  kernelFunc: batchToSpaceND4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Cast.js\nfunction cast5(args) {\n  const { inputs: { x }, attrs: { dtype }, backend: backend2 } = args;\n  const out = backend2.makeOutput(x.shape, dtype);\n  const inVals = backend2.typedArrayFromHeap(x);\n  const outVals = backend2.typedArrayFromHeap(out);\n  outVals.set(inVals);\n  return out;\n}\nvar castConfig3 = {\n  kernelName: Cast,\n  backendName: \"wasm\",\n  kernelFunc: cast5\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Ceil.js\nvar ceilConfig3 = createUnaryKernelConfig(Ceil);\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/ClipByValue.js\nvar wasmClip;\nfunction setup8(backend2) {\n  wasmClip = backend2.wasm.cwrap(ClipByValue, null, [\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\"\n  ]);\n}\nfunction clip(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x } = inputs;\n  const { clipValueMin, clipValueMax } = attrs;\n  const xId = backend2.dataIdMap.get(x.dataId).id;\n  const out = backend2.makeOutput(x.shape, x.dtype);\n  const outId = backend2.dataIdMap.get(out.dataId).id;\n  wasmClip(xId, clipValueMin, clipValueMax, outId);\n  return out;\n}\nvar clipByValueConfig3 = {\n  kernelName: ClipByValue,\n  backendName: \"wasm\",\n  setupFunc: setup8,\n  kernelFunc: clip\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Concat.js\nfunction concat4(args) {\n  const { inputs, backend: backend2 } = args;\n  const axis = util_exports.parseAxisParam(args.attrs.axis, inputs[0].shape)[0];\n  let outShape = backend_util_exports.computeOutShape(inputs.map((t2) => t2.shape), axis);\n  const $inputs = inputs.filter((t2) => util_exports.sizeFromShape(t2.shape) > 0);\n  if ($inputs.length === 1) {\n    return identity4({ inputs: { x: $inputs[0] }, backend: backend2 });\n  }\n  const out = backend2.makeOutput(outShape, inputs[0].dtype);\n  if (util_exports.sizeFromShape(outShape) === 0) {\n    return out;\n  }\n  const shapes = $inputs.map((t2) => t2.shape);\n  backend_util_exports.assertParamsConsistent(shapes, axis);\n  if ($inputs[0].dtype === \"string\") {\n    const inputs2D = $inputs.map((t2) => {\n      const innerSize = util_exports.sizeFromShape(t2.shape.slice(axis));\n      const shape = [-1, innerSize];\n      return reshape5({ inputs: { x: t2 }, backend: backend2, attrs: { shape } });\n    });\n    const inputsValShapes = inputs2D.map((t2) => {\n      return { vals: backend2.readSync(t2.dataId), shape: t2.shape };\n    });\n    outShape = backend_util_exports.computeOutShape(inputs2D.map((t2) => t2.shape), 1);\n    const simplyConcat = inputs2D[0].shape[0] === 1;\n    const outVals2 = concatImpl(inputsValShapes, outShape, inputs[0].dtype, simplyConcat);\n    const finalOutShape = backend_util_exports.computeOutShape($inputs.map((t2) => t2.shape), axis);\n    out.shape = finalOutShape;\n    const outData = backend2.dataIdMap.get(out.dataId);\n    outData.stringBytes = backend_util_exports.fromStringArrayToUint8(outVals2);\n    inputs2D.forEach((t2) => backend2.disposeData(t2.dataId));\n    return out;\n  }\n  const batchDim = util_exports.sizeFromShape($inputs[0].shape.slice(0, axis));\n  let sumInnerDims = 0;\n  const innerDims = $inputs.map((input2) => {\n    const innerDim = util_exports.sizeFromShape(input2.shape.slice(axis));\n    sumInnerDims += innerDim;\n    return innerDim;\n  });\n  const inVals = $inputs.map((input2) => backend2.typedArrayFromHeap(input2));\n  const outVals = backend2.typedArrayFromHeap(out);\n  for (let b = 0; b < batchDim; b++) {\n    let outOffset = b * sumInnerDims;\n    for (let i2 = 0; i2 < inVals.length; i2++) {\n      const innerDim = innerDims[i2];\n      const inOffset = b * innerDim;\n      const vals = inVals[i2].subarray(inOffset, inOffset + innerDim);\n      outVals.set(vals, outOffset);\n      outOffset += innerDim;\n    }\n  }\n  return out;\n}\nvar concatConfig3 = {\n  kernelName: Concat,\n  backendName: \"wasm\",\n  kernelFunc: concat4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Conv2D.js\nvar wasmConv2d;\nfunction setup9(backend2) {\n  wasmConv2d = backend2.wasm.cwrap(Conv2D, null, [\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\"\n  ]);\n}\nfunction conv2d5(args) {\n  const { inputs, attrs, backend: backend2 } = args;\n  const { x, filter } = inputs;\n  const xId = backend2.dataIdMap.get(x.dataId).id;\n  const filterId = backend2.dataIdMap.get(filter.dataId).id;\n  const { strides, dilations, pad: pad3, dimRoundingMode, dataFormat } = attrs;\n  const $dataFormat = backend_util_exports.convertConv2DDataFormat(dataFormat);\n  const convInfo = backend_util_exports.computeConv2DInfo(x.shape, filter.shape, strides, dilations, pad3, dimRoundingMode, false, $dataFormat);\n  const filterHeight = convInfo.filterHeight;\n  const filterWidth = convInfo.filterWidth;\n  const padTop = convInfo.padInfo.top;\n  const padRight = convInfo.padInfo.right;\n  const padBottom = convInfo.padInfo.bottom;\n  const padLeft = convInfo.padInfo.left;\n  const dilationHeight = convInfo.dilationHeight;\n  const dilationWidth = convInfo.dilationWidth;\n  const strideHeight = convInfo.strideHeight;\n  const strideWidth = convInfo.strideWidth;\n  const inputChannels = convInfo.inChannels;\n  const outputChannels = convInfo.outChannels;\n  const isSamePad = convInfo.padInfo.type === \"SAME\" ? 1 : 0;\n  if (convInfo.dataFormat !== \"channelsLast\") {\n    throw new Error(`wasm backend Conv2D does not support dataFormat:'${convInfo.dataFormat}'. Please use 'channelsLast'.`);\n  }\n  const out = backend2.makeOutput(convInfo.outShape, \"float32\");\n  const outId = backend2.dataIdMap.get(out.dataId).id;\n  wasmConv2d(xId, x.shape[0], x.shape[1], x.shape[2], filterId, filterHeight, filterWidth, padTop, padRight, padBottom, padLeft, isSamePad, dilationHeight, dilationWidth, strideHeight, strideWidth, inputChannels, outputChannels, outId);\n  return out;\n}\nvar conv2DConfig3 = {\n  kernelName: Conv2D,\n  backendName: \"wasm\",\n  setupFunc: setup9,\n  kernelFunc: conv2d5\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Conv2DBackpropInput.js\nvar wasmConv2DBackpropInput;\nfunction setup10(backend2) {\n  wasmConv2DBackpropInput = backend2.wasm.cwrap(Conv2DBackpropInput, null, [\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\"\n  ]);\n}\nfunction conv2DBackpropInput4(args) {\n  const { backend: backend2, inputs, attrs } = args;\n  const { dy, filter } = inputs;\n  const { strides, pad: pad3, dataFormat, dimRoundingMode, inputShape } = attrs;\n  const dilations = 1;\n  const $dataFormat = backend_util_exports.convertConv2DDataFormat(dataFormat);\n  const convInfo = backend_util_exports.computeConv2DInfo(inputShape, filter.shape, strides, dilations, pad3, dimRoundingMode, false, $dataFormat);\n  const { batchSize, filterHeight, filterWidth, inChannels, inHeight, inWidth, outChannels, outHeight, outWidth, strideHeight, strideWidth } = convInfo;\n  const topPad = filterHeight - 1 - convInfo.padInfo.top;\n  const leftPad = filterWidth - 1 - convInfo.padInfo.left;\n  const isChannelsLast = convInfo.dataFormat === \"channelsLast\";\n  const dxStrides = util_exports.computeStrides(convInfo.inShape);\n  const dyStrides = util_exports.computeStrides(dy.shape);\n  const [fltS0, fltS1, fltS2] = util_exports.computeStrides(filter.shape);\n  const xBatchStride = dxStrides[0];\n  const xRowStride = isChannelsLast ? dxStrides[1] : dxStrides[2];\n  const xColStride = isChannelsLast ? dxStrides[2] : 1;\n  const xChannelStride = isChannelsLast ? 1 : dxStrides[1];\n  const yBatchStride = dyStrides[0];\n  const yRowStride = isChannelsLast ? dyStrides[1] : dyStrides[2];\n  const yColStride = isChannelsLast ? dyStrides[2] : 1;\n  const yChannelStride = isChannelsLast ? 1 : dyStrides[1];\n  const out = backend2.makeOutput(convInfo.inShape, \"float32\");\n  const outId = backend2.dataIdMap.get(out.dataId).id;\n  const dyId = backend2.dataIdMap.get(dy.dataId).id;\n  const filterId = backend2.dataIdMap.get(filter.dataId).id;\n  wasmConv2DBackpropInput(dyId, filterId, batchSize, filterHeight, filterWidth, inHeight, inWidth, inChannels, outHeight, outWidth, outChannels, strideHeight, strideWidth, topPad, leftPad, fltS0, fltS1, fltS2, xBatchStride, xRowStride, xColStride, xChannelStride, yBatchStride, yRowStride, yColStride, yChannelStride, outId);\n  return out;\n}\nvar conv2DBackpropInputConfig3 = {\n  kernelName: Conv2DBackpropInput,\n  backendName: \"wasm\",\n  setupFunc: setup10,\n  kernelFunc: conv2DBackpropInput4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Cos.js\nvar cosConfig3 = createUnaryKernelConfig(Cos);\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Cosh.js\nvar coshConfig3 = createUnaryKernelConfig(Cosh);\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/CropAndResize.js\nvar InterpolationMethod;\n(function(InterpolationMethod2) {\n  InterpolationMethod2[InterpolationMethod2[\"bilinear\"] = 0] = \"bilinear\";\n  InterpolationMethod2[InterpolationMethod2[\"nearest\"] = 1] = \"nearest\";\n})(InterpolationMethod || (InterpolationMethod = {}));\nvar wasmCropAndResize;\nfunction setup11(backend2) {\n  wasmCropAndResize = backend2.wasm.cwrap(CropAndResize, null, [\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"array\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\"\n  ]);\n}\nfunction cropAndResize4(args) {\n  const { backend: backend2, inputs, attrs } = args;\n  const { method, extrapolationValue, cropSize } = attrs;\n  const { image: image2, boxes, boxInd } = inputs;\n  const numBoxes = boxes.shape[0];\n  const [cropHeight, cropWidth] = cropSize;\n  const outShape = [numBoxes, cropHeight, cropWidth, image2.shape[3]];\n  let imagesData = backend2.dataIdMap.get(image2.dataId);\n  let castedData;\n  if (image2.dtype !== \"float32\") {\n    castedData = cast5({ backend: backend2, inputs: { x: image2 }, attrs: { dtype: \"float32\" } });\n    imagesData = backend2.dataIdMap.get(castedData.dataId);\n  }\n  const imagesId = imagesData.id;\n  const boxesId = backend2.dataIdMap.get(boxes.dataId).id;\n  const boxIndId = backend2.dataIdMap.get(boxInd.dataId).id;\n  const out = backend2.makeOutput(outShape, \"float32\");\n  const outId = backend2.dataIdMap.get(out.dataId).id;\n  const imagesShapeBytes = new Uint8Array(new Int32Array(image2.shape).buffer);\n  wasmCropAndResize(imagesId, boxesId, boxIndId, numBoxes, imagesShapeBytes, cropHeight, cropWidth, InterpolationMethod[method], extrapolationValue, outId);\n  if (castedData != null) {\n    backend2.disposeData(castedData.dataId);\n  }\n  return out;\n}\nvar cropAndResizeConfig3 = {\n  kernelName: CropAndResize,\n  backendName: \"wasm\",\n  setupFunc: setup11,\n  kernelFunc: cropAndResize4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Cumprod.js\nvar wasmCumprod;\nfunction setup12(backend2) {\n  wasmCumprod = backend2.wasm.cwrap(Cumprod, null, [\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\"\n  ]);\n}\nfunction cumprod4(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x } = inputs;\n  const { axis, exclusive, reverse: reverse5 } = attrs;\n  const xRank = x.shape.length;\n  util_exports.assert(x.dtype === \"float32\" || x.dtype === \"int32\", () => `cumprod does not support ${x.dtype} tensors in the WASM backend`);\n  const permutation = backend_util_exports.getAxesPermutation([axis], xRank);\n  let permutedX = x;\n  if (permutation !== null) {\n    permutedX = transpose4({ inputs: { x }, attrs: { perm: permutation }, backend: backend2 });\n  }\n  const permutedAxis = backend_util_exports.getInnerMostAxes(1, xRank)[0];\n  backend_util_exports.assertAxesAreInnerMostDims(\"cumprod\", [permutedAxis], xRank);\n  const permutedOut = backend2.makeOutput(permutedX.shape, permutedX.dtype);\n  const finalDim = permutedX.shape[permutedAxis];\n  const permutedXId = backend2.dataIdMap.get(permutedX.dataId).id;\n  const permutedOutId = backend2.dataIdMap.get(permutedOut.dataId).id;\n  wasmCumprod(permutedXId, exclusive ? 1 : 0, reverse5 ? 1 : 0, finalDim, permutedOutId, CppDType[x.dtype]);\n  let out = permutedOut;\n  if (permutation !== null) {\n    const undoPermutation = backend_util_exports.getUndoAxesPermutation(permutation);\n    out = transpose4({ inputs: { x: permutedOut }, attrs: { perm: undoPermutation }, backend: backend2 });\n    backend2.disposeData(permutedX.dataId);\n    backend2.disposeData(permutedOut.dataId);\n  }\n  return out;\n}\nvar cumprodConfig3 = {\n  kernelName: Cumprod,\n  backendName: \"wasm\",\n  setupFunc: setup12,\n  kernelFunc: cumprod4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Cumsum.js\nvar wasmCumsum;\nfunction setup13(backend2) {\n  wasmCumsum = backend2.wasm.cwrap(Cumsum, null, [\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\"\n  ]);\n}\nfunction cumsum4(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x } = inputs;\n  const { axis, exclusive, reverse: reverse5 } = attrs;\n  const xRank = x.shape.length;\n  util_exports.assert(x.dtype === \"float32\" || x.dtype === \"int32\", () => `cumsum does not support ${x.dtype} tensors in the WASM backend`);\n  const permutation = backend_util_exports.getAxesPermutation([axis], xRank);\n  let permutedX = x;\n  if (permutation !== null) {\n    permutedX = transpose4({ inputs: { x }, attrs: { perm: permutation }, backend: backend2 });\n  }\n  const permutedAxis = backend_util_exports.getInnerMostAxes(1, xRank)[0];\n  backend_util_exports.assertAxesAreInnerMostDims(\"cumsum\", [permutedAxis], xRank);\n  const permutedOut = backend2.makeOutput(permutedX.shape, permutedX.dtype);\n  const finalDim = permutedX.shape[permutedAxis];\n  const permutedXId = backend2.dataIdMap.get(permutedX.dataId).id;\n  const permutedOutId = backend2.dataIdMap.get(permutedOut.dataId).id;\n  wasmCumsum(permutedXId, exclusive ? 1 : 0, reverse5 ? 1 : 0, finalDim, permutedOutId, CppDType[x.dtype]);\n  let out = permutedOut;\n  if (permutation !== null) {\n    const undoPermutation = backend_util_exports.getUndoAxesPermutation(permutation);\n    out = transpose4({ inputs: { x: permutedOut }, attrs: { perm: undoPermutation }, backend: backend2 });\n    backend2.disposeData(permutedX.dataId);\n    backend2.disposeData(permutedOut.dataId);\n  }\n  return out;\n}\nvar cumsumConfig3 = {\n  kernelName: Cumsum,\n  backendName: \"wasm\",\n  setupFunc: setup13,\n  kernelFunc: cumsum4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/DepthToSpace.js\nvar wasmDepthToSpace;\nfunction setup14(backend2) {\n  wasmDepthToSpace = backend2.wasm.cwrap(DepthToSpace, null, [\n    \"number\",\n    \"number\",\n    \"number\",\n    \"array\",\n    \"number\",\n    \"array\",\n    \"array\",\n    \"number\",\n    \"number\"\n  ]);\n}\nfunction depthToSpace4(args) {\n  const { backend: backend2, inputs, attrs } = args;\n  const { x } = inputs;\n  const { blockSize, dataFormat } = attrs;\n  const batchSize = x.shape[0];\n  const inputHeight = dataFormat === \"NHWC\" ? x.shape[1] : x.shape[2];\n  const inputWidth = dataFormat === \"NHWC\" ? x.shape[2] : x.shape[3];\n  const inputDepth = dataFormat === \"NHWC\" ? x.shape[3] : x.shape[1];\n  const outputHeight = inputHeight * blockSize;\n  const outputWidth = inputWidth * blockSize;\n  const outputDepth = inputDepth / (blockSize * blockSize);\n  const outputShape = dataFormat === \"NHWC\" ? [batchSize, outputHeight, outputWidth, outputDepth] : [batchSize, outputDepth, outputHeight, outputWidth];\n  const out = backend2.makeOutput(outputShape, \"float32\");\n  const xData = backend2.dataIdMap.get(x.dataId);\n  const xId = xData.id;\n  const xStridesBytes = new Uint8Array(new Int32Array(util_exports.computeStrides(x.shape)).buffer);\n  const outputShapeBytes = new Uint8Array(new Int32Array(outputShape).buffer);\n  const outStridesBytes = new Uint8Array(new Int32Array(util_exports.computeStrides(outputShape)).buffer);\n  const outId = backend2.dataIdMap.get(out.dataId).id;\n  const channelsLast = dataFormat === \"NHWC\" ? 1 : 0;\n  wasmDepthToSpace(xId, blockSize, channelsLast, xStridesBytes, x.shape.length - 1, outputShapeBytes, outStridesBytes, outputShape.length, outId);\n  return out;\n}\nvar depthToSpaceConfig3 = {\n  kernelName: DepthToSpace,\n  backendName: \"wasm\",\n  setupFunc: setup14,\n  kernelFunc: depthToSpace4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/DepthwiseConv2dNative.js\nvar wasmDepthwiseConv2d;\nfunction setup15(backend2) {\n  wasmDepthwiseConv2d = backend2.wasm.cwrap(DepthwiseConv2dNative, null, [\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\"\n  ]);\n}\nfunction depthwiseConv2d5(args) {\n  const { inputs, attrs, backend: backend2 } = args;\n  const { x, filter } = inputs;\n  const xId = backend2.dataIdMap.get(x.dataId).id;\n  const filterId = backend2.dataIdMap.get(filter.dataId).id;\n  const { strides, dilations, pad: pad3, dimRoundingMode } = attrs;\n  const $dilations = dilations == null ? [1, 1] : dilations;\n  const convInfo = backend_util_exports.computeConv2DInfo(x.shape, filter.shape, strides, $dilations, pad3, dimRoundingMode, true);\n  const filterHeight = convInfo.filterHeight;\n  const filterWidth = convInfo.filterWidth;\n  const padTop = convInfo.padInfo.top;\n  const padRight = convInfo.padInfo.right;\n  const padBottom = convInfo.padInfo.bottom;\n  const padLeft = convInfo.padInfo.left;\n  const dilationHeight = convInfo.dilationHeight;\n  const dilationWidth = convInfo.dilationWidth;\n  const strideHeight = convInfo.strideHeight;\n  const strideWidth = convInfo.strideWidth;\n  const inputChannels = convInfo.inChannels;\n  const outputChannels = convInfo.outChannels;\n  const isSamePad = convInfo.padInfo.type === \"SAME\" ? 1 : 0;\n  if (convInfo.dataFormat !== \"channelsLast\") {\n    throw new Error(`wasm backend DepthwiseConv2dNative does not support dataFormat:'${convInfo.dataFormat}'. Please use 'channelsLast'.`);\n  }\n  const out = backend2.makeOutput(convInfo.outShape, \"float32\");\n  const outId = backend2.dataIdMap.get(out.dataId).id;\n  wasmDepthwiseConv2d(xId, x.shape[0], x.shape[1], x.shape[2], filterId, filterHeight, filterWidth, padTop, padRight, padBottom, padLeft, isSamePad, dilationHeight, dilationWidth, strideHeight, strideWidth, inputChannels, outputChannels, outId);\n  return out;\n}\nvar depthwiseConv2dNativeConfig3 = {\n  kernelName: DepthwiseConv2dNative,\n  backendName: \"wasm\",\n  setupFunc: setup15,\n  kernelFunc: depthwiseConv2d5\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Elu.js\nvar eluConfig3 = createUnaryKernelConfig(Elu);\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Equal.js\nvar supportsFullBroadcast2 = false;\nvar equalConfig3 = createBinaryKernelConfig(Equal, supportsFullBroadcast2, \"bool\");\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Exp.js\nvar expConfig3 = createUnaryKernelConfig(Exp, \"float32\");\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/ExpandDims.js\nfunction expandDims5(args) {\n  const { inputs, attrs, backend: backend2 } = args;\n  const { input: input2 } = inputs;\n  const { dim } = attrs;\n  const inputRank = input2.shape.length;\n  const newShape = input2.shape.slice();\n  let $dim = dim;\n  if (dim < 0) {\n    util_exports.assert(-(inputRank + 1) <= dim, () => `Axis must be in the interval [${-(inputRank + 1)}, ${inputRank}]`);\n    $dim = inputRank + dim + 1;\n  }\n  newShape.splice($dim, 0, 1);\n  return reshape5({ inputs: { x: input2 }, backend: backend2, attrs: { shape: newShape } });\n}\nvar expandDimsConfig3 = {\n  kernelName: ExpandDims,\n  backendName: \"wasm\",\n  kernelFunc: expandDims5\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Fill.js\nfunction fill4(args) {\n  const { attrs: { shape, value, dtype }, backend: backend2 } = args;\n  const out = backend2.makeOutput(shape, dtype);\n  const outVals = backend2.typedArrayFromHeap(out);\n  outVals.fill(value);\n  return out;\n}\nvar fillConfig3 = {\n  kernelName: Fill,\n  backendName: \"wasm\",\n  kernelFunc: fill4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/FlipLeftRight.js\nvar wasmFlipLeftRight;\nfunction setup16(backend2) {\n  wasmFlipLeftRight = backend2.wasm.cwrap(FlipLeftRight, null, [\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\"\n  ]);\n}\nfunction flipLeftRight2(args) {\n  const { inputs, backend: backend2 } = args;\n  const { image: image2 } = inputs;\n  const out = backend2.makeOutput(image2.shape, image2.dtype);\n  const imageId = backend2.dataIdMap.get(image2.dataId).id;\n  const outId = backend2.dataIdMap.get(out.dataId).id;\n  const [batch, imageHeight, imageWidth, numChannels] = image2.shape;\n  wasmFlipLeftRight(imageId, batch, imageHeight, imageWidth, numChannels, outId);\n  return out;\n}\nvar flipLeftRightConfig3 = {\n  kernelName: FlipLeftRight,\n  backendName: \"wasm\",\n  kernelFunc: flipLeftRight2,\n  setupFunc: setup16\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Floor.js\nvar floorConfig3 = createUnaryKernelConfig(Floor);\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/FloorDiv.js\nvar supportsFullBroadcast3 = false;\nvar floorDivConfig3 = createBinaryKernelConfig(FloorDiv, supportsFullBroadcast3);\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/FusedBatchNorm.js\nvar wasmBatchNorm;\nfunction setup17(backend2) {\n  wasmBatchNorm = backend2.wasm.cwrap(FusedBatchNorm, null, [\"number\", \"number\", \"number\", \"number\", \"number\", \"number\", \"number\"]);\n}\nfunction fusedBatchNorm(args) {\n  const { backend: backend2, inputs, attrs } = args;\n  const { varianceEpsilon } = attrs;\n  const { x, mean: mean5, variance, offset, scale: scale2 } = inputs;\n  const xId = backend2.dataIdMap.get(x.dataId).id;\n  const meanId = backend2.dataIdMap.get(mean5.dataId).id;\n  const varianceId = backend2.dataIdMap.get(variance.dataId).id;\n  const offsetId = offset != null ? backend2.dataIdMap.get(offset.dataId).id : 0;\n  const scaleId = scale2 != null ? backend2.dataIdMap.get(scale2.dataId).id : 0;\n  const out = backend2.makeOutput(x.shape, x.dtype);\n  if (util_exports.sizeFromShape(x.shape) === 0) {\n    return out;\n  }\n  const outId = backend2.dataIdMap.get(out.dataId).id;\n  wasmBatchNorm(xId, meanId, varianceId, offsetId, scaleId, varianceEpsilon, outId);\n  return out;\n}\nvar fusedBatchNormConfig = {\n  kernelName: FusedBatchNorm,\n  backendName: \"wasm\",\n  setupFunc: setup17,\n  kernelFunc: fusedBatchNorm\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/FusedConv2D.js\nvar wasmFusedConv2d;\nfunction setup18(backend2) {\n  wasmFusedConv2d = backend2.wasm.cwrap(FusedConv2D, null, [\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\"\n  ]);\n}\nfunction fusedConv2d2(args) {\n  const { inputs, attrs, backend: backend2 } = args;\n  const { x, filter, bias, preluActivationWeights } = inputs;\n  const { strides, pad: pad3, dilations, dataFormat, dimRoundingMode, activation: activation2, leakyreluAlpha } = attrs;\n  const convInfo = backend_util_exports.computeConv2DInfo(x.shape, filter.shape, strides, dilations, pad3, dimRoundingMode);\n  const fusedActivation = FusableActivation[activation2];\n  if (fusedActivation == null) {\n    throw new Error(`${activation2} activation not yet supported for FusedConv2D in the wasm backend.`);\n  }\n  const xId = backend2.dataIdMap.get(x.dataId).id;\n  const filterId = backend2.dataIdMap.get(filter.dataId).id;\n  const outputChannels = convInfo.outChannels;\n  let biasId = 0;\n  if (bias != null) {\n    const biasData = backend2.dataIdMap.get(bias.dataId);\n    if (biasData.shape.length !== 1) {\n      throw new Error(`FusedConv2D only supports rank-1 bias but got rank ${biasData.shape.length}.`);\n    }\n    if (biasData.shape[0] !== outputChannels) {\n      throw new Error(`FusedConv2D bias shape (${biasData.shape}) does not match the number of output channels (${outputChannels})`);\n    }\n    biasId = biasData.id;\n  }\n  const filterHeight = convInfo.filterHeight;\n  const filterWidth = convInfo.filterWidth;\n  const padTop = convInfo.padInfo.top;\n  const padRight = convInfo.padInfo.right;\n  const padBottom = convInfo.padInfo.bottom;\n  const padLeft = convInfo.padInfo.left;\n  const dilationHeight = convInfo.dilationHeight;\n  const dilationWidth = convInfo.dilationWidth;\n  const strideHeight = convInfo.strideHeight;\n  const strideWidth = convInfo.strideWidth;\n  const inputChannels = convInfo.inChannels;\n  const isSamePad = convInfo.padInfo.type === \"SAME\" ? 1 : 0;\n  const batchSize = convInfo.batchSize;\n  const inHeight = convInfo.inHeight;\n  const inWidth = convInfo.inWidth;\n  if (dataFormat !== \"NHWC\") {\n    throw new Error(`wasm backend FusedConv2D does not support dataFormat:'${dataFormat}'. Please use 'NHWC'.`);\n  }\n  const out = backend2.makeOutput(convInfo.outShape, \"float32\");\n  const outId = backend2.dataIdMap.get(out.dataId).id;\n  const preluActivationWeightsId = preluActivationWeights == null ? 0 : backend2.dataIdMap.get(preluActivationWeights.dataId).id;\n  wasmFusedConv2d(xId, batchSize, inHeight, inWidth, filterId, filterHeight, filterWidth, biasId, padTop, padRight, padBottom, padLeft, isSamePad, dilationHeight, dilationWidth, strideHeight, strideWidth, inputChannels, outputChannels, fusedActivation, preluActivationWeightsId, leakyreluAlpha || 0, outId);\n  return out;\n}\nvar fusedConv2DConfig3 = {\n  kernelName: FusedConv2D,\n  backendName: \"wasm\",\n  setupFunc: setup18,\n  kernelFunc: fusedConv2d2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/FusedDepthwiseConv2D.js\nvar wasmFusedDepthwiseConv2d;\nfunction setup19(backend2) {\n  wasmFusedDepthwiseConv2d = backend2.wasm.cwrap(FusedDepthwiseConv2D, null, [\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\"\n  ]);\n}\nfunction fusedDepthwiseConv2d(args) {\n  const { inputs, attrs, backend: backend2 } = args;\n  const { x, filter, bias, preluActivationWeights } = inputs;\n  const { strides, pad: pad3, dilations, dataFormat, dimRoundingMode, activation: activation2, leakyreluAlpha } = attrs;\n  const convInfo = backend_util_exports.computeConv2DInfo(x.shape, filter.shape, strides, dilations, pad3, dimRoundingMode, true);\n  const fusedActivation = FusableActivation[activation2];\n  if (fusedActivation == null) {\n    throw new Error(`${activation2} activation not yet supported for FusedDepthwiseConv2D in the wasm backend.`);\n  }\n  const xId = backend2.dataIdMap.get(x.dataId).id;\n  const filterId = backend2.dataIdMap.get(filter.dataId).id;\n  const outputChannels = convInfo.outChannels;\n  let biasId = 0;\n  if (bias != null) {\n    const biasData = backend2.dataIdMap.get(bias.dataId);\n    if (biasData.shape.length !== 1) {\n      throw new Error(`FusedDepthwiseConv2D only supports rank-1 bias but got rank ${biasData.shape.length}.`);\n    }\n    if (biasData.shape[0] !== outputChannels) {\n      throw new Error(`FusedDepthwiseConv2D bias shape (${biasData.shape}) does not match the number of output channels (${outputChannels})`);\n    }\n    biasId = biasData.id;\n  }\n  const filterHeight = convInfo.filterHeight;\n  const filterWidth = convInfo.filterWidth;\n  const padTop = convInfo.padInfo.top;\n  const padRight = convInfo.padInfo.right;\n  const padBottom = convInfo.padInfo.bottom;\n  const padLeft = convInfo.padInfo.left;\n  const dilationHeight = convInfo.dilationHeight;\n  const dilationWidth = convInfo.dilationWidth;\n  const strideHeight = convInfo.strideHeight;\n  const strideWidth = convInfo.strideWidth;\n  const inputChannels = convInfo.inChannels;\n  const isSamePad = convInfo.padInfo.type === \"SAME\" ? 1 : 0;\n  const batchSize = convInfo.batchSize;\n  const inHeight = convInfo.inHeight;\n  const inWidth = convInfo.inWidth;\n  if (dataFormat !== \"NHWC\") {\n    throw new Error(`wasm backend FusedDepthwiseConv2D does not support dataFormat:'${dataFormat}'. Please use 'NHWC'.`);\n  }\n  const out = backend2.makeOutput(convInfo.outShape, \"float32\");\n  const outId = backend2.dataIdMap.get(out.dataId).id;\n  const preluActivationWeightsId = preluActivationWeights == null ? 0 : backend2.dataIdMap.get(preluActivationWeights.dataId).id;\n  wasmFusedDepthwiseConv2d(xId, batchSize, inHeight, inWidth, filterId, filterHeight, filterWidth, biasId, padTop, padRight, padBottom, padLeft, isSamePad, dilationHeight, dilationWidth, strideHeight, strideWidth, inputChannels, outputChannels, fusedActivation, preluActivationWeightsId, leakyreluAlpha || 0, outId);\n  return out;\n}\nvar fusedDepthwiseConv2DConfig3 = {\n  kernelName: FusedDepthwiseConv2D,\n  backendName: \"wasm\",\n  setupFunc: setup19,\n  kernelFunc: fusedDepthwiseConv2d\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/GatherNd.js\nvar wasmGatherNd;\nfunction setup20(backend2) {\n  wasmGatherNd = backend2.wasm.cwrap(GatherNd, null, [\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"array\",\n    \"number\"\n  ]);\n}\nfunction gatherNd3(args) {\n  const { backend: backend2, inputs } = args;\n  const { params, indices } = inputs;\n  const [resultShape, numSlices, sliceSize, strides] = gather_nd_util_exports.prepareAndValidate(params, indices);\n  const out = backend2.makeOutput(resultShape, params.dtype);\n  if (numSlices === 0) {\n    return out;\n  }\n  const indicesShape = indices.shape;\n  const sliceRank = indicesShape[indicesShape.length - 1];\n  const xData = backend2.dataIdMap.get(params.dataId);\n  const xId = xData.id;\n  const indicesData = backend2.dataIdMap.get(indices.dataId);\n  const indicesId = indicesData.id;\n  const stridesBytes = new Uint8Array(new Int32Array(strides).buffer);\n  const outId = backend2.dataIdMap.get(out.dataId).id;\n  wasmGatherNd(xId, CppDType[params.dtype], indicesId, numSlices, sliceRank, sliceSize, stridesBytes, outId);\n  return out;\n}\nvar gatherNdConfig3 = {\n  kernelName: GatherNd,\n  backendName: \"wasm\",\n  setupFunc: setup20,\n  kernelFunc: gatherNd3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/GatherV2.js\nvar wasmGather;\nfunction setup21(backend2) {\n  wasmGather = backend2.wasm.cwrap(\"Gather\", null, [\n    \"number\",\n    \"number\",\n    \"array\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"array\",\n    \"number\"\n  ]);\n}\nfunction gatherV23(args) {\n  const { backend: backend2, inputs, attrs } = args;\n  const { x, indices } = inputs;\n  const { axis, batchDims } = attrs;\n  const parsedAxis = util_exports.parseAxisParam(axis, x.shape)[0];\n  const indicesVals = backend2.readSync(indices.dataId);\n  const axisDim = x.shape[parsedAxis];\n  for (let i2 = 0; i2 < indicesVals.length; ++i2) {\n    const index = indicesVals[i2];\n    util_exports.assert(index <= axisDim - 1 && index >= 0, () => `GatherV2: the index value ${index} is not in [0, ${axisDim - 1}]`);\n  }\n  const shapeInfo = backend_util_exports.segment_util.collectGatherOpShapeInfo(x, indices, parsedAxis, batchDims);\n  const flattenX = reshape5({\n    inputs: { x },\n    attrs: {\n      shape: [\n        shapeInfo.batchSize,\n        shapeInfo.outerSize,\n        shapeInfo.dimSize,\n        shapeInfo.sliceSize\n      ]\n    },\n    backend: backend2\n  });\n  const indicesSize = util_exports.sizeFromShape(indices.shape);\n  const flattenIndex = reshape5({\n    inputs: { x: indices },\n    attrs: { shape: [shapeInfo.batchSize, indicesSize / shapeInfo.batchSize] },\n    backend: backend2\n  });\n  const flattenOutputShape = [\n    shapeInfo.batchSize,\n    shapeInfo.outerSize,\n    indicesSize / shapeInfo.batchSize,\n    shapeInfo.sliceSize\n  ];\n  const out = backend2.makeOutput(flattenOutputShape, x.dtype);\n  if (util_exports.sizeFromShape(x.shape) === 0) {\n    return out;\n  }\n  const stridesSize = flattenX.shape.length - 1;\n  const xData = backend2.dataIdMap.get(flattenX.dataId);\n  const xId = xData.id;\n  const indicesData = backend2.dataIdMap.get(flattenIndex.dataId);\n  const indicesId = indicesData.id;\n  const outId = backend2.dataIdMap.get(out.dataId).id;\n  const xStridesBytes = new Uint8Array(new Int32Array(util_exports.computeStrides(flattenX.shape)).buffer);\n  const outStridesBytes = new Uint8Array(new Int32Array(util_exports.computeStrides(flattenOutputShape)).buffer);\n  wasmGather(xId, CppDType[x.dtype], xStridesBytes, stridesSize, indicesId, shapeInfo.batchSize, outStridesBytes, outId);\n  backend2.disposeData(flattenX.dataId);\n  backend2.disposeData(flattenIndex.dataId);\n  out.shape = shapeInfo.outputShape;\n  return out;\n}\nvar gatherV2Config3 = {\n  kernelName: GatherV2,\n  backendName: \"wasm\",\n  setupFunc: setup21,\n  kernelFunc: gatherV23\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Greater.js\nvar supportsFullBroadcast4 = false;\nvar greaterConfig3 = createBinaryKernelConfig(Greater, supportsFullBroadcast4, \"bool\");\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/GreaterEqual.js\nvar supportsFullBroadcast5 = false;\nvar greaterEqualConfig3 = createBinaryKernelConfig(GreaterEqual, supportsFullBroadcast5, \"bool\");\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/LeakyRelu.js\nvar wasmFunc3;\nfunction setupFunc2(backend2) {\n  wasmFunc3 = backend2.wasm.cwrap(LeakyRelu, null, [\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\"\n  ]);\n}\nfunction leakyRelu4(args) {\n  const { inputs: { x }, attrs: { alpha }, backend: backend2 } = args;\n  const xId = backend2.dataIdMap.get(x.dataId).id;\n  const out = backend2.makeOutput(x.shape, \"float32\");\n  if (util_exports.sizeFromShape(x.shape) !== 0) {\n    const outId = backend2.dataIdMap.get(out.dataId).id;\n    wasmFunc3(xId, CppDType[x.dtype], alpha, outId);\n  }\n  return out;\n}\nvar leakyReluConfig3 = {\n  kernelName: LeakyRelu,\n  backendName: \"wasm\",\n  setupFunc: setupFunc2,\n  kernelFunc: leakyRelu4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Less.js\nvar supportsFullBroadcast6 = false;\nvar lessConfig3 = createBinaryKernelConfig(Less, supportsFullBroadcast6, \"bool\");\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/LessEqual.js\nvar supportsFullBroadcast7 = false;\nvar lessEqualConfig3 = createBinaryKernelConfig(LessEqual, supportsFullBroadcast7, \"bool\");\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Log.js\nvar logConfig3 = createUnaryKernelConfig(Log);\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/LogicalAnd.js\nvar supportsFullBroadcast8 = false;\nvar logicalAndConfig3 = createBinaryKernelConfig(LogicalAnd, supportsFullBroadcast8, \"bool\");\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/LogicalNot.js\nvar logicalNotConfig3 = createUnaryKernelConfig(LogicalNot);\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/LogicalOr.js\nvar supportsFullBroadcast9 = false;\nvar logicalOrConfig3 = createBinaryKernelConfig(LogicalOr, supportsFullBroadcast9, \"bool\");\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/LogicalXor.js\nvar supportsFullBroadcast10 = false;\nvar logicalXorConfig = createBinaryKernelConfig(LogicalXor, supportsFullBroadcast10, \"bool\");\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Max.js\nvar wasmMax;\nfunction setup22(backend2) {\n  wasmMax = backend2.wasm.cwrap(Max, null, [\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\"\n  ]);\n}\nfunction max5(args) {\n  const { backend: backend2, inputs, attrs } = args;\n  const { reductionIndices: axis, keepDims } = attrs;\n  const { x } = inputs;\n  const xId = backend2.dataIdMap.get(x.dataId).id;\n  let inputId = xId;\n  let input2 = x;\n  const { transposed, axes, originalAxes, inputWasTransposed } = permuteAxesAndTranspose(x, axis, backend2);\n  if (inputWasTransposed) {\n    const transposedId = backend2.dataIdMap.get(transposed.dataId).id;\n    input2 = transposed;\n    inputId = transposedId;\n  }\n  const inputRank = input2.shape.length;\n  backend_util_exports.assertAxesAreInnerMostDims(\"max\", axes, inputRank);\n  const [outShape, reduceShape] = backend_util_exports.computeOutAndReduceShapes(input2.shape, axes);\n  const reduceSize = util_exports.sizeFromShape(reduceShape);\n  const out = backend2.makeOutput(outShape, x.dtype);\n  if (util_exports.sizeFromShape(input2.shape) !== 0) {\n    const outId = backend2.dataIdMap.get(out.dataId).id;\n    wasmMax(inputId, CppDType[x.dtype], reduceSize, outId);\n  }\n  if (inputWasTransposed) {\n    backend2.disposeData(transposed.dataId);\n  }\n  if (keepDims) {\n    const newShape = backend_util_exports.expandShapeToKeepDim(out.shape, originalAxes);\n    out.shape = newShape;\n  }\n  return out;\n}\nvar maxConfig3 = {\n  kernelName: Max,\n  backendName: \"wasm\",\n  setupFunc: setup22,\n  kernelFunc: max5\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Maximum.js\nvar supportsFullBroadcast11 = false;\nvar maximumConfig3 = createBinaryKernelConfig(Maximum, supportsFullBroadcast11);\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/MaxPool.js\nvar wasmMaxPool;\nfunction setup23(backend2) {\n  wasmMaxPool = backend2.wasm.cwrap(MaxPool, null, [\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\"\n  ]);\n}\nfunction maxPool4(args) {\n  const { inputs, attrs, backend: backend2 } = args;\n  const x = inputs.x;\n  const xId = backend2.dataIdMap.get(x.dataId).id;\n  util_exports.assert(x.dtype === \"float32\", () => `Error in MaxPool: only float32 input is supported. Got ${x.dtype}.`);\n  const { filterSize, strides, pad: pad3, dimRoundingMode } = attrs;\n  const convInfo = backend_util_exports.computePool2DInfo(x.shape, filterSize, strides, 1, pad3, dimRoundingMode);\n  const filterHeight = convInfo.filterHeight;\n  const filterWidth = convInfo.filterWidth;\n  const padTop = convInfo.padInfo.top;\n  const padRight = convInfo.padInfo.right;\n  const padBottom = convInfo.padInfo.bottom;\n  const padLeft = convInfo.padInfo.left;\n  const dilationHeight = convInfo.dilationHeight;\n  const dilationWidth = convInfo.dilationWidth;\n  const strideHeight = convInfo.strideHeight;\n  const strideWidth = convInfo.strideWidth;\n  const inputChannels = convInfo.inChannels;\n  const outputChannels = convInfo.outChannels;\n  if (convInfo.dataFormat !== \"channelsLast\") {\n    throw new Error(`wasm backend does not support dataFormat:'${convInfo.dataFormat}'. Please use 'channelsLast'.`);\n  }\n  const out = backend2.makeOutput(convInfo.outShape, \"float32\");\n  const outId = backend2.dataIdMap.get(out.dataId).id;\n  wasmMaxPool(xId, x.shape[0], x.shape[1], x.shape[2], filterHeight, filterWidth, padTop, padRight, padBottom, padLeft, dilationHeight, dilationWidth, strideHeight, strideWidth, inputChannels, outputChannels, outId);\n  return out;\n}\nvar maxPoolConfig3 = {\n  kernelName: MaxPool,\n  backendName: \"wasm\",\n  setupFunc: setup23,\n  kernelFunc: maxPool4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Mean.js\nvar wasmMean;\nfunction setup24(backend2) {\n  wasmMean = backend2.wasm.cwrap(Mean, null, [\"number, number, number\"]);\n}\nfunction mean3(args) {\n  const { backend: backend2, inputs, attrs } = args;\n  const { axis, keepDims } = attrs;\n  const { x } = inputs;\n  const xId = backend2.dataIdMap.get(x.dataId).id;\n  let inputId = xId;\n  let input2 = x;\n  const { transposed, axes, originalAxes, inputWasTransposed } = permuteAxesAndTranspose(x, axis, backend2);\n  let reductionAxes = axes;\n  if (inputWasTransposed) {\n    const transposedId = backend2.dataIdMap.get(transposed.dataId).id;\n    if (transposedId !== xId) {\n      input2 = transposed;\n      inputId = transposedId;\n      reductionAxes = backend_util_exports.getInnerMostAxes(reductionAxes.length, input2.shape.length);\n    }\n  }\n  backend_util_exports.assertAxesAreInnerMostDims(\"mean\", reductionAxes, input2.shape.length);\n  const [outShape, reduceShape] = backend_util_exports.computeOutAndReduceShapes(input2.shape, reductionAxes);\n  const reduceSize = util_exports.sizeFromShape(reduceShape);\n  let castedInput = input2;\n  if (input2.dtype !== \"float32\") {\n    castedInput = cast5({ backend: backend2, inputs: { x: input2 }, attrs: { dtype: \"float32\" } });\n    inputId = backend2.dataIdMap.get(castedInput.dataId).id;\n  }\n  const out = backend2.makeOutput(outShape, \"float32\");\n  if (util_exports.sizeFromShape(input2.shape) !== 0) {\n    const outId = backend2.dataIdMap.get(out.dataId).id;\n    wasmMean(inputId, reduceSize, outId);\n  }\n  if (inputWasTransposed) {\n    backend2.disposeData(transposed.dataId);\n  }\n  if (keepDims) {\n    const newShape = backend_util_exports.expandShapeToKeepDim(out.shape, originalAxes);\n    out.shape = newShape;\n  }\n  if (input2.dtype !== \"float32\") {\n    backend2.disposeData(castedInput.dataId);\n  }\n  return out;\n}\nvar meanConfig3 = {\n  kernelName: Mean,\n  backendName: \"wasm\",\n  setupFunc: setup24,\n  kernelFunc: mean3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Min.js\nvar wasmMin;\nfunction setup25(backend2) {\n  wasmMin = backend2.wasm.cwrap(Min, null, [\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\"\n  ]);\n}\nfunction min5(args) {\n  const { backend: backend2, inputs, attrs } = args;\n  const { axis, keepDims } = attrs;\n  const { x } = inputs;\n  const xId = backend2.dataIdMap.get(x.dataId).id;\n  let inputId = xId;\n  let input2 = x;\n  const { transposed, axes, originalAxes, inputWasTransposed } = permuteAxesAndTranspose(x, axis, backend2);\n  if (inputWasTransposed) {\n    const transposedId = backend2.dataIdMap.get(transposed.dataId).id;\n    if (transposedId !== xId) {\n      input2 = transposed;\n      inputId = transposedId;\n    }\n  }\n  const inputRank = input2.shape.length;\n  backend_util_exports.assertAxesAreInnerMostDims(\"min\", axes, inputRank);\n  const [outShape, reduceShape] = backend_util_exports.computeOutAndReduceShapes(input2.shape, axes);\n  const reduceSize = util_exports.sizeFromShape(reduceShape);\n  const out = backend2.makeOutput(outShape, input2.dtype);\n  if (util_exports.sizeFromShape(input2.shape) !== 0) {\n    const outId = backend2.dataIdMap.get(out.dataId).id;\n    wasmMin(inputId, CppDType[x.dtype], reduceSize, outId);\n  }\n  if (inputWasTransposed) {\n    backend2.disposeData(transposed.dataId);\n  }\n  if (keepDims) {\n    const newShape = backend_util_exports.expandShapeToKeepDim(out.shape, originalAxes);\n    out.shape = newShape;\n  }\n  return out;\n}\nvar minConfig3 = {\n  kernelName: Min,\n  backendName: \"wasm\",\n  setupFunc: setup25,\n  kernelFunc: min5\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Minimum.js\nvar supportsFullBroadcast12 = false;\nvar minimumConfig3 = createBinaryKernelConfig(Minimum, supportsFullBroadcast12);\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/MirrorPad.js\nvar MirrorPaddingMode;\n(function(MirrorPaddingMode2) {\n  MirrorPaddingMode2[MirrorPaddingMode2[\"reflect\"] = 0] = \"reflect\";\n  MirrorPaddingMode2[MirrorPaddingMode2[\"symmetric\"] = 1] = \"symmetric\";\n})(MirrorPaddingMode || (MirrorPaddingMode = {}));\nvar wasmMirrorPad;\nfunction setup26(backend2) {\n  wasmMirrorPad = backend2.wasm.cwrap(MirrorPad, null, [\n    \"number\",\n    \"array\",\n    \"number\",\n    \"number\",\n    \"array\",\n    \"array\",\n    \"number\",\n    \"number\"\n  ]);\n}\nfunction mirrorPad3(args) {\n  const { inputs: { x }, backend: backend2, attrs: { paddings, mode } } = args;\n  const outShape = paddings.map((p2, i2) => p2[0] + x.shape[i2] + p2[1]);\n  const xId = backend2.dataIdMap.get(x.dataId).id;\n  const out = backend2.makeOutput(outShape, x.dtype);\n  const outId = backend2.dataIdMap.get(out.dataId).id;\n  const xShapeBytes = new Uint8Array(new Int32Array(x.shape).buffer);\n  const prePaddingsFlat = paddings.map((padTuple) => padTuple[0]);\n  const postPaddingsFlat = paddings.map((padTuple) => padTuple[1]);\n  const prePaddingsBytes = new Uint8Array(new Int32Array(prePaddingsFlat).buffer);\n  const postPaddingsBytes = new Uint8Array(new Int32Array(postPaddingsFlat).buffer);\n  wasmMirrorPad(xId, xShapeBytes, x.shape.length, CppDType[x.dtype], prePaddingsBytes, postPaddingsBytes, MirrorPaddingMode[mode], outId);\n  return out;\n}\nvar mirrorPadConfig3 = {\n  kernelName: MirrorPad,\n  backendName: \"wasm\",\n  kernelFunc: mirrorPad3,\n  setupFunc: setup26\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Multiply.js\nvar supportsFullBroadcast13 = true;\nvar multiplyConfig3 = createBinaryKernelConfig(Multiply, supportsFullBroadcast13);\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Neg.js\nvar negConfig3 = createUnaryKernelConfig(Neg);\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/NonMaxSuppression_util.js\nfunction parseResultStruct(backend2, resOffset) {\n  const result = new Int32Array(backend2.wasm.HEAPU8.buffer, resOffset, 4);\n  const pSelectedIndices = result[0];\n  const selectedSize = result[1];\n  const pSelectedScores = result[2];\n  const pValidOutputs = result[3];\n  backend2.wasm._free(resOffset);\n  return { pSelectedIndices, selectedSize, pSelectedScores, pValidOutputs };\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/NonMaxSuppressionV3.js\nvar wasmFunc4;\nfunction setup27(backend2) {\n  wasmFunc4 = backend2.wasm.cwrap(\n    NonMaxSuppressionV3,\n    \"number\",\n    [\n      \"number\",\n      \"number\",\n      \"number\",\n      \"number\",\n      \"number\"\n    ]\n  );\n}\nfunction kernelFunc(args) {\n  const { backend: backend2, inputs, attrs } = args;\n  const { iouThreshold, maxOutputSize, scoreThreshold } = attrs;\n  const { boxes, scores } = inputs;\n  const boxesId = backend2.dataIdMap.get(boxes.dataId).id;\n  const scoresId = backend2.dataIdMap.get(scores.dataId).id;\n  const resOffset = wasmFunc4(boxesId, scoresId, maxOutputSize, iouThreshold, scoreThreshold);\n  const { pSelectedIndices, selectedSize, pSelectedScores, pValidOutputs } = parseResultStruct(backend2, resOffset);\n  backend2.wasm._free(pSelectedScores);\n  backend2.wasm._free(pValidOutputs);\n  const selectedIndicesTensor = backend2.makeOutput([selectedSize], \"int32\", pSelectedIndices);\n  return selectedIndicesTensor;\n}\nvar nonMaxSuppressionV3Config3 = {\n  kernelName: NonMaxSuppressionV3,\n  backendName: \"wasm\",\n  setupFunc: setup27,\n  kernelFunc\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/NonMaxSuppressionV4.js\nvar wasmFunc5;\nfunction setup28(backend2) {\n  wasmFunc5 = backend2.wasm.cwrap(\n    NonMaxSuppressionV4,\n    \"number\",\n    [\n      \"number\",\n      \"number\",\n      \"number\",\n      \"number\",\n      \"number\",\n      \"bool\"\n    ]\n  );\n}\nfunction nonMaxSuppressionV43(args) {\n  const { backend: backend2, inputs, attrs } = args;\n  const { iouThreshold, maxOutputSize, scoreThreshold, padToMaxOutputSize } = attrs;\n  const { boxes, scores } = inputs;\n  const boxesId = backend2.dataIdMap.get(boxes.dataId).id;\n  const scoresId = backend2.dataIdMap.get(scores.dataId).id;\n  const resOffset = wasmFunc5(boxesId, scoresId, maxOutputSize, iouThreshold, scoreThreshold, padToMaxOutputSize);\n  const { pSelectedIndices, selectedSize, pSelectedScores, pValidOutputs } = parseResultStruct(backend2, resOffset);\n  backend2.wasm._free(pSelectedScores);\n  const selectedIndicesTensor = backend2.makeOutput([selectedSize], \"int32\", pSelectedIndices);\n  const validOutputsTensor = backend2.makeOutput([], \"int32\", pValidOutputs);\n  return [selectedIndicesTensor, validOutputsTensor];\n}\nvar nonMaxSuppressionV4Config3 = {\n  kernelName: NonMaxSuppressionV4,\n  backendName: \"wasm\",\n  setupFunc: setup28,\n  kernelFunc: nonMaxSuppressionV43\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/NonMaxSuppressionV5.js\nvar wasmFunc6;\nfunction setup29(backend2) {\n  wasmFunc6 = backend2.wasm.cwrap(\n    NonMaxSuppressionV5,\n    \"number\",\n    [\n      \"number\",\n      \"number\",\n      \"number\",\n      \"number\",\n      \"number\",\n      \"number\"\n    ]\n  );\n}\nfunction kernelFunc2(args) {\n  const { backend: backend2, inputs, attrs } = args;\n  const { iouThreshold, maxOutputSize, scoreThreshold, softNmsSigma } = attrs;\n  const { boxes, scores } = inputs;\n  const boxesId = backend2.dataIdMap.get(boxes.dataId).id;\n  const scoresId = backend2.dataIdMap.get(scores.dataId).id;\n  const resOffset = wasmFunc6(boxesId, scoresId, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma);\n  const { pSelectedIndices, selectedSize, pSelectedScores, pValidOutputs } = parseResultStruct(backend2, resOffset);\n  backend2.wasm._free(pValidOutputs);\n  const selectedIndicesTensor = backend2.makeOutput([selectedSize], \"int32\", pSelectedIndices);\n  const selectedScoresTensor = backend2.makeOutput([selectedSize], \"float32\", pSelectedScores);\n  return [selectedIndicesTensor, selectedScoresTensor];\n}\nvar nonMaxSuppressionV5Config3 = {\n  kernelName: NonMaxSuppressionV5,\n  backendName: \"wasm\",\n  setupFunc: setup29,\n  kernelFunc: kernelFunc2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/NotEqual.js\nvar supportsFullBroadcast14 = false;\nvar notEqualConfig3 = createBinaryKernelConfig(NotEqual, supportsFullBroadcast14, \"bool\");\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/OneHot.js\nvar wasmOneHot;\nfunction setup30(backend2) {\n  wasmOneHot = backend2.wasm.cwrap(OneHot, null, [\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\"\n  ]);\n}\nfunction oneHot4(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { indices } = inputs;\n  const { dtype, depth, onValue, offValue } = attrs;\n  const out = backend2.makeOutput([...indices.shape, depth], dtype);\n  const outId = backend2.dataIdMap.get(out.dataId).id;\n  const indicesData = backend2.dataIdMap.get(indices.dataId);\n  const indicesId = indicesData.id;\n  wasmOneHot(indicesId, depth, onValue, offValue, outId);\n  return out;\n}\nvar oneHotConfig3 = {\n  kernelName: OneHot,\n  backendName: \"wasm\",\n  setupFunc: setup30,\n  kernelFunc: oneHot4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/OnesLike.js\nfunction onesLike4(args) {\n  const { inputs: { x }, backend: backend2 } = args;\n  const out = backend2.makeOutput(x.shape, x.dtype);\n  const outVals = backend2.typedArrayFromHeap(out);\n  outVals.fill(1);\n  return out;\n}\nvar onesLikeConfig3 = {\n  kernelName: OnesLike,\n  backendName: \"wasm\",\n  kernelFunc: onesLike4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Pack.js\nfunction pack3(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { axis } = attrs;\n  if (inputs.length === 1) {\n    return expandDims5({ inputs: { input: inputs[0] }, backend: backend2, attrs: { dim: axis } });\n  }\n  const shape = inputs[0].shape;\n  const dtype = inputs[0].dtype;\n  inputs.forEach((t2) => {\n    util_exports.assertShapesMatch(shape, t2.shape, \"All tensors passed to stack must have matching shapes\");\n    util_exports.assert(dtype === t2.dtype, () => \"All tensors passed to stack must have matching dtypes\");\n  });\n  const intermediateTensorInfos = [];\n  const expandedTensors = inputs.map((t2) => {\n    const expandedT = expandDims5({ inputs: { input: t2 }, backend: backend2, attrs: { dim: axis } });\n    intermediateTensorInfos.push(expandedT);\n    return expandedT;\n  });\n  const result = concat4({ inputs: expandedTensors, backend: backend2, attrs: { axis } });\n  intermediateTensorInfos.forEach((t2) => backend2.disposeData(t2.dataId));\n  return result;\n}\nvar packConfig3 = {\n  kernelName: Pack,\n  backendName: \"wasm\",\n  kernelFunc: pack3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/PadV2.js\nvar wasmPadV2;\nfunction setup31(backend2) {\n  wasmPadV2 = backend2.wasm.cwrap(PadV2, null, [\n    \"number\",\n    \"array\",\n    \"number\",\n    \"number\",\n    \"array\",\n    \"array\",\n    \"number\",\n    \"number\"\n  ]);\n}\nfunction pad2(args) {\n  const { inputs: { x }, backend: backend2, attrs: { paddings, constantValue } } = args;\n  const outShape = paddings.map((p2, i2) => p2[0] + x.shape[i2] + p2[1]);\n  if (util_exports.sizeFromShape(x.shape) === 0) {\n    return fill4({\n      backend: backend2,\n      attrs: { shape: outShape, value: constantValue, dtype: x.dtype }\n    });\n  }\n  const xId = backend2.dataIdMap.get(x.dataId).id;\n  const out = backend2.makeOutput(outShape, x.dtype);\n  const outTensorData = backend2.dataIdMap.get(out.dataId);\n  const outId = outTensorData.id;\n  const xShapeBytes = new Uint8Array(new Int32Array(x.shape).buffer);\n  const prePaddingsFlat = paddings.map((padTuple) => padTuple[0]);\n  const postPaddingsFlat = paddings.map((padTuple) => padTuple[1]);\n  const prePaddingsBytes = new Uint8Array(new Int32Array(prePaddingsFlat).buffer);\n  const postPaddingsBytes = new Uint8Array(new Int32Array(postPaddingsFlat).buffer);\n  wasmPadV2(xId, xShapeBytes, x.shape.length, CppDType[x.dtype], prePaddingsBytes, postPaddingsBytes, constantValue, outId);\n  return out;\n}\nvar padV2Config3 = {\n  kernelName: PadV2,\n  backendName: \"wasm\",\n  kernelFunc: pad2,\n  setupFunc: setup31\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Pow.js\nvar supportsFullBroadcast15 = false;\nvar powConfig3 = createBinaryKernelConfig(Pow, supportsFullBroadcast15);\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Prelu.js\nvar wasmPrelu;\nfunction setup32(backend2) {\n  wasmPrelu = backend2.wasm.cwrap(Prelu, null, [\n    \"number\",\n    \"number\",\n    \"number\"\n  ]);\n}\nfunction prelu5(args) {\n  const { inputs, backend: backend2 } = args;\n  const { x, alpha } = inputs;\n  const xId = backend2.dataIdMap.get(x.dataId).id;\n  const weightsId = backend2.dataIdMap.get(alpha.dataId).id;\n  let inputId = xId;\n  const input2 = x;\n  let castedInput = input2;\n  if (input2.dtype !== \"float32\") {\n    castedInput = cast5({ backend: backend2, inputs: { x }, attrs: { dtype: \"float32\" } });\n    inputId = backend2.dataIdMap.get(castedInput.dataId).id;\n  }\n  const out = backend2.makeOutput(x.shape, \"float32\");\n  const outId = backend2.dataIdMap.get(out.dataId).id;\n  wasmPrelu(inputId, weightsId, outId);\n  if (input2.dtype !== \"float32\") {\n    backend2.disposeData(castedInput.dataId);\n  }\n  return out;\n}\nvar preluConfig3 = {\n  kernelName: Prelu,\n  backendName: \"wasm\",\n  setupFunc: setup32,\n  kernelFunc: prelu5\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Prod.js\nvar wasmProd;\nfunction setup33(backend2) {\n  wasmProd = backend2.wasm.cwrap(Prod, null, [\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\"\n  ]);\n}\nfunction prod4(args) {\n  const { backend: backend2, inputs, attrs } = args;\n  const { axis, keepDims } = attrs;\n  const { x } = inputs;\n  const xId = backend2.dataIdMap.get(x.dataId).id;\n  let inputId = xId;\n  let input2 = x;\n  const { transposed, axes, originalAxes, inputWasTransposed } = permuteAxesAndTranspose(x, axis, backend2);\n  let reductionAxes = axes;\n  if (inputWasTransposed) {\n    const transposedId = backend2.dataIdMap.get(transposed.dataId).id;\n    if (transposedId !== xId) {\n      input2 = transposed;\n      inputId = transposedId;\n      reductionAxes = backend_util_exports.getInnerMostAxes(reductionAxes.length, input2.shape.length);\n    }\n  }\n  backend_util_exports.assertAxesAreInnerMostDims(\"prod\", reductionAxes, input2.shape.length);\n  const [outShape, reduceShape] = backend_util_exports.computeOutAndReduceShapes(input2.shape, reductionAxes);\n  const reduceSize = util_exports.sizeFromShape(reduceShape);\n  const out = backend2.makeOutput(outShape, input2.dtype);\n  if (util_exports.sizeFromShape(input2.shape) !== 0) {\n    const outId = backend2.dataIdMap.get(out.dataId).id;\n    wasmProd(inputId, reduceSize, CppDType[out.dtype], outId);\n  }\n  if (inputWasTransposed) {\n    backend2.disposeData(transposed.dataId);\n  }\n  if (keepDims) {\n    const newShape = backend_util_exports.expandShapeToKeepDim(out.shape, originalAxes);\n    out.shape = newShape;\n  }\n  return out;\n}\nvar prodConfig3 = {\n  kernelName: Prod,\n  backendName: \"wasm\",\n  setupFunc: setup33,\n  kernelFunc: prod4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Range.js\nvar range5 = (args) => {\n  const { backend: backend2, attrs } = args;\n  const { start, stop, step: step5, dtype } = attrs;\n  const values = rangeImpl(start, stop, step5, dtype);\n  const out = backend2.makeOutput([values.length], dtype);\n  const outVals = backend2.typedArrayFromHeap(out);\n  outVals.set(values);\n  return out;\n};\nvar rangeConfig3 = {\n  kernelName: Range,\n  backendName: \"wasm\",\n  kernelFunc: range5\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/RealDiv.js\nvar supportsFullBroadcast16 = true;\nvar realDivConfig3 = createBinaryKernelConfig(RealDiv, supportsFullBroadcast16);\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Relu.js\nvar reluConfig3 = createUnaryKernelConfig(Relu);\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Relu6.js\nvar relu6Config3 = createUnaryKernelConfig(Relu6);\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/ResizeBilinear.js\nvar wasmResizeBilinear;\nfunction setup34(backend2) {\n  wasmResizeBilinear = backend2.wasm.cwrap(ResizeBilinear, null, [\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\"\n  ]);\n}\nfunction resizeBilinear4(args) {\n  const { backend: backend2, inputs, attrs } = args;\n  const { images } = inputs;\n  const { alignCorners, halfPixelCenters, size } = attrs;\n  const [newHeight, newWidth] = size;\n  const [batch, oldHeight, oldWidth, numChannels] = images.shape;\n  const outShape = [batch, newHeight, newWidth, numChannels];\n  let xData = backend2.dataIdMap.get(images.dataId);\n  let castedData;\n  if (xData.dtype !== \"float32\") {\n    castedData = cast5({ backend: backend2, inputs: { x: images }, attrs: { dtype: \"float32\" } });\n    xData = backend2.dataIdMap.get(castedData.dataId);\n  }\n  const xId = xData.id;\n  const out = backend2.makeOutput(outShape, \"float32\");\n  if (util_exports.sizeFromShape(images.shape) === 0) {\n    return out;\n  }\n  const outId = backend2.dataIdMap.get(out.dataId).id;\n  wasmResizeBilinear(xId, batch, oldHeight, oldWidth, numChannels, newHeight, newWidth, alignCorners ? 1 : 0, halfPixelCenters ? 1 : 0, outId);\n  if (castedData != null) {\n    backend2.disposeData(castedData.dataId);\n  }\n  return out;\n}\nvar resizeBilinearConfig3 = {\n  kernelName: ResizeBilinear,\n  backendName: \"wasm\",\n  setupFunc: setup34,\n  kernelFunc: resizeBilinear4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/ResizeNearestNeighbor.js\nvar wasmResizeNearestNeighbor;\nfunction setup35(backend2) {\n  wasmResizeNearestNeighbor = backend2.wasm.cwrap(ResizeNearestNeighbor, null, [\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\"\n  ]);\n}\nfunction resizeNearestNeighbor4(args) {\n  const { backend: backend2, inputs, attrs } = args;\n  const { images } = inputs;\n  const { alignCorners, halfPixelCenters, size } = attrs;\n  const [newHeight, newWidth] = size;\n  const [batch, oldHeight, oldWidth, numChannels] = images.shape;\n  const outShape = [batch, newHeight, newWidth, numChannels];\n  const out = backend2.makeOutput(outShape, \"float32\");\n  if (util_exports.sizeFromShape(images.shape) === 0) {\n    return out;\n  }\n  let xData = backend2.dataIdMap.get(images.dataId);\n  let castedData;\n  if (xData.dtype !== \"float32\") {\n    castedData = cast5({\n      backend: backend2,\n      inputs: { x: images },\n      attrs: { dtype: \"float32\" }\n    });\n    xData = backend2.dataIdMap.get(castedData.dataId);\n  }\n  const xId = xData.id;\n  const outId = backend2.dataIdMap.get(out.dataId).id;\n  wasmResizeNearestNeighbor(xId, batch, oldHeight, oldWidth, numChannels, newHeight, newWidth, alignCorners ? 1 : 0, halfPixelCenters ? 1 : 0, outId);\n  if (castedData != null) {\n    backend2.disposeData(castedData.dataId);\n  }\n  return out;\n}\nvar resizeNearestNeighborConfig3 = {\n  kernelName: ResizeNearestNeighbor,\n  backendName: \"wasm\",\n  setupFunc: setup35,\n  kernelFunc: resizeNearestNeighbor4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Reverse.js\nvar wasmReverse;\nfunction setup36(backend2) {\n  wasmReverse = backend2.wasm.cwrap(Reverse, null, [\n    \"number\",\n    \"array\",\n    \"number\",\n    \"array\",\n    \"number\",\n    \"number\"\n  ]);\n}\nfunction reverse4(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x } = inputs;\n  const { dims } = attrs;\n  const axes = util_exports.parseAxisParam(dims, x.shape);\n  if (x.shape.length === 0) {\n    return identity4({ inputs: { x }, backend: backend2 });\n  }\n  const out = backend2.makeOutput(x.shape, x.dtype);\n  const xId = backend2.dataIdMap.get(x.dataId).id;\n  const outId = backend2.dataIdMap.get(out.dataId).id;\n  const axesBytes = new Uint8Array(new Int32Array(axes).buffer);\n  const outShapeBytes = new Uint8Array(new Int32Array(x.shape).buffer);\n  wasmReverse(xId, axesBytes, axes.length, outShapeBytes, x.shape.length, outId);\n  const reshaped = reshape5({ inputs: { x: out }, attrs: { shape: x.shape }, backend: backend2 });\n  backend2.disposeData(out.dataId);\n  return reshaped;\n}\nvar reverseConfig3 = {\n  kernelName: Reverse,\n  backendName: \"wasm\",\n  kernelFunc: reverse4,\n  setupFunc: setup36\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/RotateWithOffset.js\nvar wasmRotate;\nfunction setup37(backend2) {\n  wasmRotate = backend2.wasm.cwrap(RotateWithOffset, null, [\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"array\",\n    \"number\",\n    \"number\"\n  ]);\n}\nfunction rotateWithOffset2(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { image: image2 } = inputs;\n  const { radians, fillValue, center } = attrs;\n  const out = backend2.makeOutput(image2.shape, image2.dtype);\n  const imageId = backend2.dataIdMap.get(image2.dataId).id;\n  const outId = backend2.dataIdMap.get(out.dataId).id;\n  const [batch, imageHeight, imageWidth, numChannels] = image2.shape;\n  const [centerX, centerY] = backend_util_exports.getImageCenter(center, imageHeight, imageWidth);\n  const fillIsBlack = fillValue === 0;\n  const fullOpacityValue = 255;\n  const fillValues2 = typeof fillValue === \"number\" ? [fillValue, fillValue, fillValue, fillIsBlack ? 0 : fullOpacityValue] : [...fillValue, fullOpacityValue];\n  const fillBytes = new Uint8Array(new Int32Array(fillValues2).buffer);\n  wasmRotate(imageId, batch, imageHeight, imageWidth, numChannels, radians, centerX, centerY, fillBytes, fillValues2.length, outId);\n  return out;\n}\nvar rotateWithOffsetConfig3 = {\n  kernelName: RotateWithOffset,\n  backendName: \"wasm\",\n  kernelFunc: rotateWithOffset2,\n  setupFunc: setup37\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Round.js\nvar roundConfig3 = createUnaryKernelConfig(Round);\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Rsqrt.js\nvar rsqrtConfig3 = createUnaryKernelConfig(Rsqrt);\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/ScatterNd.js\nvar wasmScatterNd;\nfunction setup38(backend2) {\n  wasmScatterNd = backend2.wasm.cwrap(ScatterNd, null, [\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"array\",\n    \"number\",\n    \"number\"\n  ]);\n}\nfunction scatterNd3(args) {\n  const { backend: backend2, inputs, attrs } = args;\n  const { indices, updates } = inputs;\n  const { shape } = attrs;\n  const out = backend2.makeOutput(shape, updates.dtype);\n  if (util_exports.sizeFromShape(shape) === 0) {\n    return out;\n  }\n  const { sliceRank, numUpdates, sliceSize, strides, outputSize } = scatter_nd_util_exports.calculateShapes(updates, indices, shape);\n  const indicesData = backend2.dataIdMap.get(indices.dataId);\n  const indicesId = indicesData.id;\n  const updatesData = backend2.dataIdMap.get(updates.dataId);\n  const updatesId = updatesData.id;\n  const stridesBytes = new Uint8Array(new Int32Array(strides).buffer);\n  const outId = backend2.dataIdMap.get(out.dataId).id;\n  wasmScatterNd(indicesId, updatesId, CppDType[updates.dtype], sliceRank, numUpdates, sliceSize, stridesBytes, outputSize, outId);\n  return out;\n}\nvar scatterNdConfig3 = {\n  kernelName: ScatterNd,\n  backendName: \"wasm\",\n  setupFunc: setup38,\n  kernelFunc: scatterNd3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Select.js\nvar wasmSelect;\nfunction setup39(backend2) {\n  wasmSelect = backend2.wasm.cwrap(\"SelectV2\", null, [\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\"\n  ]);\n}\nfunction select4(args) {\n  const { inputs, backend: backend2 } = args;\n  const { condition, t: t2, e: e2 } = inputs;\n  const conditionId = backend2.dataIdMap.get(condition.dataId).id;\n  const tId = backend2.dataIdMap.get(t2.dataId).id;\n  const eId = backend2.dataIdMap.get(e2.dataId).id;\n  const out = backend2.makeOutput(t2.shape, t2.dtype);\n  const outId = backend2.dataIdMap.get(out.dataId).id;\n  const cRank = condition.shape.length;\n  const tRank = t2.shape.length;\n  const offset = cRank === 0 || cRank > 1 || tRank === 1 ? 1 : util_exports.sizeFromShape(t2.shape.slice(1));\n  wasmSelect(conditionId, tId, eId, offset, outId);\n  return out;\n}\nvar selectConfig3 = {\n  kernelName: Select,\n  backendName: \"wasm\",\n  kernelFunc: select4,\n  setupFunc: setup39\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Sigmoid.js\nvar wasmFunc7;\nfunction setup40(backend2) {\n  wasmFunc7 = backend2.wasm.cwrap(Sigmoid, null, [\"number\", \"number\"]);\n}\nfunction sigmoid4(args) {\n  const { backend: backend2, inputs: { x } } = args;\n  const xId = backend2.dataIdMap.get(x.dataId).id;\n  const out = backend2.makeOutput(x.shape, x.dtype);\n  const outId = backend2.dataIdMap.get(out.dataId).id;\n  if (util_exports.sizeFromShape(out.shape) === 0) {\n    return out;\n  }\n  wasmFunc7(xId, outId);\n  return out;\n}\nvar sigmoidConfig3 = {\n  kernelName: \"Sigmoid\",\n  backendName: \"wasm\",\n  setupFunc: setup40,\n  kernelFunc: sigmoid4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Sin.js\nvar sinConfig3 = createUnaryKernelConfig(Sin);\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Softmax.js\nvar wasmFunc8;\nfunction setup41(backend2) {\n  wasmFunc8 = backend2.wasm.cwrap(Softmax, null, [\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\"\n  ]);\n}\nfunction softmax5(args) {\n  const { backend: backend2, inputs: { logits }, attrs: { dim } } = args;\n  const xId = backend2.dataIdMap.get(logits.dataId).id;\n  const out = backend2.makeOutput(logits.shape, logits.dtype);\n  const outId = backend2.dataIdMap.get(out.dataId).id;\n  const channels = logits.shape[dim];\n  const batch = util_exports.sizeFromShape(logits.shape) / channels;\n  if (util_exports.sizeFromShape(out.shape) === 0) {\n    return out;\n  }\n  wasmFunc8(xId, outId, channels, batch);\n  return out;\n}\nvar softmaxConfig3 = {\n  kernelName: Softmax,\n  backendName: \"wasm\",\n  setupFunc: setup41,\n  kernelFunc: softmax5\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/SpaceToBatchND.js\nfunction spaceToBatchND4(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x } = inputs;\n  const { blockShape, paddings } = attrs;\n  const prod6 = util_exports.sizeFromShape(blockShape);\n  const completePaddings = [[0, 0]];\n  completePaddings.push(...paddings);\n  for (let i2 = 1 + blockShape.length; i2 < x.shape.length; ++i2) {\n    completePaddings.push([0, 0]);\n  }\n  const paddedX = padV2Config3.kernelFunc({\n    inputs: { x },\n    backend: backend2,\n    attrs: { paddings: completePaddings, constantValue: 0 }\n  });\n  const reshapedPaddedShape = backend_util_exports.getReshaped(paddedX.shape, blockShape, prod6, false);\n  const permutedReshapedPaddedPermutation = backend_util_exports.getPermuted(reshapedPaddedShape.length, blockShape.length, false);\n  const flattenShape = backend_util_exports.getReshapedPermuted(paddedX.shape, blockShape, prod6, false);\n  const reshapeInputs = { x: paddedX };\n  const reshapeAttrs = { shape: reshapedPaddedShape };\n  const paddedXReshaped = reshape5({ inputs: reshapeInputs, backend: backend2, attrs: reshapeAttrs });\n  const transposeInputs = { x: paddedXReshaped };\n  const transposeAttrs = { perm: permutedReshapedPaddedPermutation };\n  const paddedXT = transpose4({ inputs: transposeInputs, backend: backend2, attrs: transposeAttrs });\n  const resultReshapeInputs = { x: paddedXT };\n  const resultReshapeAttrs = { shape: flattenShape };\n  const result = reshape5({ inputs: resultReshapeInputs, backend: backend2, attrs: resultReshapeAttrs });\n  backend2.disposeData(paddedX.dataId);\n  backend2.disposeData(paddedXReshaped.dataId);\n  backend2.disposeData(paddedXT.dataId);\n  return result;\n}\nvar spaceToBatchNDConfig3 = {\n  kernelName: SpaceToBatchND,\n  backendName: \"wasm\",\n  kernelFunc: spaceToBatchND4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/SparseFillEmptyRows.js\nvar wasmSparseFillEmptyRows;\nfunction setup42(backend2) {\n  wasmSparseFillEmptyRows = backend2.wasm.cwrap(\"SparseFillEmptyRows\", \"number\", [\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\"\n  ]);\n}\nfunction sparseFillEmptyRows4(args) {\n  const { backend: backend2, inputs } = args;\n  const { indices, values, denseShape, defaultValue } = inputs;\n  const indicesCount = indices.shape[0];\n  const rank = indices.shape[1];\n  const denseRows = backend2.readSync(denseShape.dataId)[0];\n  const maxOutputIndicesShape = [indicesCount + denseRows, rank];\n  const indicesId = backend2.dataIdMap.get(indices.dataId).id;\n  const valuesId = backend2.dataIdMap.get(values.dataId).id;\n  const defaultValueId = backend2.dataIdMap.get(defaultValue.dataId).id;\n  const outputIndices = backend2.makeOutput(maxOutputIndicesShape, indices.dtype);\n  const outputIndicesId = backend2.dataIdMap.get(outputIndices.dataId).id;\n  const outputValues = backend2.makeOutput(maxOutputIndicesShape.slice(0, 1), values.dtype);\n  const outputValuesId = backend2.dataIdMap.get(outputValues.dataId).id;\n  const emptyRowIndicator = backend2.makeOutput([denseRows], \"bool\");\n  const emptyRowIndicatorId = backend2.dataIdMap.get(emptyRowIndicator.dataId).id;\n  const reverseIndexMap = backend2.makeOutput([indicesCount], indices.dtype);\n  const reverseIndexMapId = backend2.dataIdMap.get(reverseIndexMap.dataId).id;\n  const exceptionValues = backend2.makeOutput([4], \"int32\");\n  const exceptionValuesId = backend2.dataIdMap.get(exceptionValues.dataId).id;\n  const outputRows = wasmSparseFillEmptyRows(indicesId, valuesId, CppDType[values.dtype], indicesCount, denseRows, rank, defaultValueId, outputIndicesId, outputValuesId, emptyRowIndicatorId, reverseIndexMapId, exceptionValuesId);\n  const exceptionValuesArray = backend2.readSync(exceptionValues.dataId);\n  let exceptionMessage;\n  switch (exceptionValuesArray[0]) {\n    case 1: {\n      exceptionMessage = backend_util_exports.getSparseFillEmptyRowsIndicesDenseShapeMismatch(exceptionValuesArray[1]);\n      break;\n    }\n    case 2: {\n      exceptionMessage = backend_util_exports.getSparseFillEmptyRowsNegativeIndexErrorMessage(exceptionValuesArray[1], exceptionValuesArray[2]);\n      break;\n    }\n    case 3:\n      exceptionMessage = backend_util_exports.getSparseFillEmptyRowsOutOfRangeIndexErrorMessage(exceptionValuesArray[1], exceptionValuesArray[2], exceptionValuesArray[3]);\n      break;\n    default:\n      exceptionMessage = \"\";\n  }\n  backend2.disposeData(exceptionValues.dataId);\n  if (exceptionMessage) {\n    backend2.disposeData(outputIndices.dataId);\n    backend2.disposeData(outputValues.dataId);\n    backend2.disposeData(emptyRowIndicator.dataId);\n    backend2.disposeData(reverseIndexMap.dataId);\n    throw new Error(exceptionMessage);\n  }\n  let resizedIndices = outputIndices;\n  let resizedValues = outputValues;\n  if (outputRows !== maxOutputIndicesShape[0]) {\n    resizedIndices = slice4({\n      inputs: { x: outputIndices },\n      attrs: { begin: 0, size: [outputRows, rank] },\n      backend: backend2\n    });\n    resizedValues = slice4({\n      inputs: { x: outputValues },\n      attrs: { begin: 0, size: outputRows },\n      backend: backend2\n    });\n    backend2.disposeData(outputIndices.dataId);\n    backend2.disposeData(outputValues.dataId);\n  }\n  return [resizedIndices, resizedValues, emptyRowIndicator, reverseIndexMap];\n}\nvar sparseFillEmptyRowsConfig3 = {\n  kernelName: SparseFillEmptyRows,\n  backendName: \"wasm\",\n  setupFunc: setup42,\n  kernelFunc: sparseFillEmptyRows4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/SparseReshape.js\nvar wasmSparseReshape;\nfunction setup43(backend2) {\n  wasmSparseReshape = backend2.wasm.cwrap(SparseReshape, null, [\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\"\n  ]);\n}\nfunction sparseReshape4(args) {\n  const { backend: backend2, inputs } = args;\n  const { inputIndices, inputShape, newShape } = inputs;\n  if (inputIndices.shape.length !== 2) {\n    throw new Error(`Input indices should be a matrix but received shape\n        ${inputIndices.shape}`);\n  }\n  if (inputShape.shape.length !== 1) {\n    throw new Error(`Input shape should be a vector but received shape\n        ${inputShape.shape}`);\n  }\n  if (newShape.shape.length !== 1) {\n    throw new Error(`Target shape should be a vector but received shape ${newShape.shape}`);\n  }\n  const inputIndicesId = backend2.dataIdMap.get(inputIndices.dataId).id;\n  const inputShapeId = backend2.dataIdMap.get(inputShape.dataId).id;\n  const newShapeId = backend2.dataIdMap.get(newShape.dataId).id;\n  const nnz = inputIndices.shape[0];\n  const outputRank = util_exports.sizeFromShape(newShape.shape);\n  const newIndices = backend2.makeOutput([nnz, outputRank], inputIndices.dtype);\n  const newIndicesId = backend2.dataIdMap.get(newIndices.dataId).id;\n  const outputShape = backend2.makeOutput([outputRank], newShape.dtype);\n  const outputShapeId = backend2.dataIdMap.get(outputShape.dataId).id;\n  const exceptionValues = backend2.makeOutput([3], \"int32\");\n  const exceptionValuesId = backend2.dataIdMap.get(exceptionValues.dataId).id;\n  wasmSparseReshape(inputIndicesId, inputShapeId, newShapeId, nnz, newIndicesId, outputShapeId, exceptionValuesId);\n  const exceptionValuesArray = backend2.readSync(exceptionValues.dataId);\n  let exceptionMessage;\n  switch (exceptionValuesArray[0]) {\n    case 0: {\n      exceptionMessage = backend_util_exports.getSparseReshapeMultipleNegativeOneOutputDimErrorMessage(exceptionValuesArray[1], exceptionValuesArray[2]);\n      break;\n    }\n    case 1: {\n      exceptionMessage = backend_util_exports.getSparseReshapeNegativeOutputDimErrorMessage(exceptionValuesArray[1], exceptionValuesArray[2]);\n      break;\n    }\n    case 2:\n      exceptionMessage = backend_util_exports.getSparseReshapeEmptyTensorZeroOutputDimErrorMessage();\n      break;\n    case 3: {\n      const inputShapeValues = Array.from(backend2.readSync(inputShape.dataId)), outputShapeValues = Array.from(backend2.readSync(outputShape.dataId));\n      exceptionMessage = backend_util_exports.getSparseReshapeInputOutputMultipleErrorMessage(inputShapeValues, outputShapeValues);\n      break;\n    }\n    case 4: {\n      const inputShapeValues = Array.from(backend2.readSync(inputShape.dataId)), outputShapeValues = Array.from(backend2.readSync(outputShape.dataId));\n      exceptionMessage = backend_util_exports.getSparseReshapeInputOutputMismatchErrorMessage(inputShapeValues, outputShapeValues);\n      break;\n    }\n    default:\n      exceptionMessage = \"\";\n  }\n  backend2.disposeData(exceptionValues.dataId);\n  if (exceptionMessage) {\n    backend2.disposeData(newIndices.dataId);\n    backend2.disposeData(outputShape.dataId);\n    throw new Error(exceptionMessage);\n  }\n  return [newIndices, outputShape];\n}\nvar sparseReshapeConfig3 = {\n  kernelName: SparseReshape,\n  backendName: \"wasm\",\n  setupFunc: setup43,\n  kernelFunc: sparseReshape4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/SparseSegmentReduction.js\nvar wasmSparseSegmentReduction;\nfunction setup44(backend2) {\n  wasmSparseSegmentReduction = backend2.wasm.cwrap(\"SparseSegmentReduction\", null, [\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\"\n  ]);\n}\nfunction sparseSegmentReduction(args, isMean) {\n  const { backend: backend2, inputs } = args;\n  const { data, indices, segmentIds } = inputs;\n  const numIndices = indices.shape[0];\n  const segmentIdsBack = backend2.readSync(segmentIds.dataId, numIndices - 1, numIndices)[0];\n  const lastSegmentIdPlusOne = numIndices > 0 ? segmentIdsBack + 1 : 0;\n  const outputRows = lastSegmentIdPlusOne;\n  if (outputRows < 0) {\n    throw new Error(backend_util_exports.getSparseSegmentReductionNegativeSegmentIdsErrorMessage());\n  }\n  const outputShape = data.shape.slice();\n  outputShape[0] = outputRows;\n  const dataId = backend2.dataIdMap.get(data.dataId).id;\n  const indicesId = backend2.dataIdMap.get(indices.dataId).id;\n  const segmentIdsId = backend2.dataIdMap.get(segmentIds.dataId).id;\n  const output = backend2.makeOutput(outputShape, data.dtype);\n  const outputId = backend2.dataIdMap.get(output.dataId).id;\n  const exceptionValues = backend2.makeOutput([4], \"int32\");\n  const exceptionValuesId = backend2.dataIdMap.get(exceptionValues.dataId).id;\n  wasmSparseSegmentReduction(dataId, CppDType[data.dtype], data.shape[0], indicesId, segmentIdsId, outputId, exceptionValuesId, isMean, 0);\n  const exceptionValuesArray = backend2.readSync(exceptionValues.dataId);\n  let exceptionMessage;\n  switch (exceptionValuesArray[0]) {\n    case 0: {\n      exceptionMessage = backend_util_exports.getSparseSegmentReductionNegativeSegmentIdsErrorMessage();\n      break;\n    }\n    case 1: {\n      exceptionMessage = backend_util_exports.getSparseSegmentReductionNonIncreasingSegmentIdsErrorMessage();\n      break;\n    }\n    case 2:\n      exceptionMessage = backend_util_exports.getSparseSegmentReductionSegmentIdOutOfRangeErrorMessage(exceptionValuesArray[1], exceptionValuesArray[2]);\n      break;\n    case 3:\n      exceptionMessage = backend_util_exports.getSparseSegmentReductionIndicesOutOfRangeErrorMessage(exceptionValuesArray[1], exceptionValuesArray[2], exceptionValuesArray[3]);\n      break;\n    default:\n      exceptionMessage = \"\";\n  }\n  backend2.disposeData(exceptionValues.dataId);\n  if (exceptionMessage) {\n    backend2.disposeData(output.dataId);\n    throw new Error(exceptionMessage);\n  }\n  return output;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/SparseSegmentMean.js\nfunction sparseSegmentMean4(args) {\n  return sparseSegmentReduction(args, true);\n}\nvar sparseSegmentMeanConfig3 = {\n  kernelName: SparseSegmentMean,\n  backendName: \"wasm\",\n  setupFunc: setup44,\n  kernelFunc: sparseSegmentMean4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/SparseSegmentSum.js\nfunction sparseSegmentSum4(args) {\n  return sparseSegmentReduction(args, false);\n}\nvar sparseSegmentSumConfig3 = {\n  kernelName: SparseSegmentSum,\n  backendName: \"wasm\",\n  setupFunc: setup44,\n  kernelFunc: sparseSegmentSum4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/SplitV.js\nfunction splitV3(args) {\n  const { inputs, attrs, backend: backend2 } = args;\n  const { x } = inputs;\n  const { numOrSizeSplits, axis } = attrs;\n  const $axis = util_exports.parseAxisParam(axis, x.shape)[0];\n  const splitSizes = backend_util_exports.prepareSplitSize(x, numOrSizeSplits, $axis);\n  const begin = new Array(x.shape.length).fill(0);\n  const size = x.shape.slice();\n  return splitSizes.map((s2) => {\n    const xSliceSize = [...size];\n    xSliceSize[$axis] = s2;\n    const xSlice = slice4({ inputs: { x }, attrs: { begin, size: xSliceSize }, backend: backend2 });\n    begin[$axis] += s2;\n    return xSlice;\n  });\n}\nvar splitVConfig3 = {\n  kernelName: SplitV,\n  backendName: \"wasm\",\n  kernelFunc: splitV3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Sqrt.js\nvar sqrtConfig3 = createUnaryKernelConfig(Sqrt);\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Square.js\nvar squareConfig3 = createUnaryKernelConfig(Square);\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/SquaredDifference.js\nvar supportsFullBroadcast17 = true;\nvar squaredDifferenceConfig3 = createBinaryKernelConfig(SquaredDifference, supportsFullBroadcast17);\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Step.js\nvar wasmStep;\nfunction setup45(backend2) {\n  wasmStep = backend2.wasm.cwrap(Step, null, [\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\"\n  ]);\n}\nfunction step4(args) {\n  const { backend: backend2, inputs, attrs } = args;\n  const { alpha } = attrs;\n  const { x } = inputs;\n  const xId = backend2.dataIdMap.get(x.dataId).id;\n  const out = backend2.makeOutput(x.shape, x.dtype);\n  const outId = backend2.dataIdMap.get(out.dataId).id;\n  wasmStep(xId, alpha, CppDType[x.dtype], outId);\n  return out;\n}\nvar stepConfig3 = {\n  kernelName: Step,\n  backendName: \"wasm\",\n  setupFunc: setup45,\n  kernelFunc: step4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/StridedSlice.js\nvar wasmStridedSlice;\nfunction setup46(backend2) {\n  wasmStridedSlice = backend2.wasm.cwrap(StridedSlice, null, [\n    \"number\",\n    \"array\",\n    \"number\",\n    \"array\",\n    \"array\",\n    \"array\",\n    \"array\",\n    \"array\",\n    \"number\",\n    \"number\"\n  ]);\n}\nfunction stridedSlice4(args) {\n  const { backend: backend2, inputs, attrs } = args;\n  const { x } = inputs;\n  const { begin, end, strides, beginMask, endMask, ellipsisMask, newAxisMask, shrinkAxisMask } = attrs;\n  const { finalShapeSparse, finalShape, isIdentity, sliceDim0, isSimpleSlice, begin: $begin, end: $end, strides: $strides } = slice_util_exports.sliceInfo(x.shape, begin, end, strides, beginMask, endMask, ellipsisMask, newAxisMask, shrinkAxisMask);\n  let result;\n  if (isIdentity) {\n    result = reshape5({ inputs: { x }, backend: backend2, attrs: { shape: finalShape } });\n  } else if (sliceDim0 || isSimpleSlice) {\n    util_exports.assert(x.shape.length >= 1, () => `Input must have rank at least 1, got: ${x.shape.length}`);\n    const size = slice_util_exports.computeOutShape($begin, $end, $strides);\n    const sliced = slice4({ inputs: { x }, backend: backend2, attrs: { begin: $begin, size } });\n    result = reshape5({ inputs: { x: sliced }, backend: backend2, attrs: { shape: finalShape } });\n    backend2.disposeData(sliced.dataId);\n  } else {\n    const out = backend2.makeOutput(finalShapeSparse, \"float32\");\n    const xId = backend2.dataIdMap.get(x.dataId).id;\n    const xStridesBytes = new Uint8Array(new Int32Array(util_exports.computeStrides(x.shape)).buffer);\n    const beginBytes = new Uint8Array(new Int32Array($begin).buffer);\n    const endBytes = new Uint8Array(new Int32Array($end).buffer);\n    const stridesBytes = new Uint8Array(new Int32Array($strides).buffer);\n    const outputShapeBytes = new Uint8Array(new Int32Array(finalShapeSparse).buffer);\n    const outStridesBytes = new Uint8Array(new Int32Array(util_exports.computeStrides(finalShapeSparse)).buffer);\n    const outId = backend2.dataIdMap.get(out.dataId).id;\n    wasmStridedSlice(xId, xStridesBytes, x.shape.length, beginBytes, endBytes, stridesBytes, outputShapeBytes, outStridesBytes, finalShapeSparse.length, outId);\n    result = reshape5({ inputs: { x: out }, backend: backend2, attrs: { shape: finalShape } });\n    backend2.disposeData(out.dataId);\n  }\n  return result;\n}\nvar stridedSliceConfig3 = {\n  kernelName: StridedSlice,\n  backendName: \"wasm\",\n  setupFunc: setup46,\n  kernelFunc: stridedSlice4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/StringNGrams.js\nfunction stringNGrams4(args) {\n  const { backend: backend2, inputs, attrs } = args;\n  const { data, dataSplits } = inputs;\n  const { separator, nGramWidths, leftPad, rightPad: rightPad2, padWidth, preserveShortSequences } = attrs;\n  const $data = backend2.readSync(data.dataId);\n  const $dataSplits = backend2.readSync(dataSplits.dataId);\n  const [nGrams, nGramsSplits] = stringNGramsImpl($data, $dataSplits, separator, nGramWidths, leftPad, rightPad2, padWidth, preserveShortSequences);\n  const nGramsOut = backend2.makeOutput([nGrams.length], \"string\");\n  const nGramsOutData = backend2.dataIdMap.get(nGramsOut.dataId);\n  nGramsOutData.stringBytes = nGrams;\n  const nGramsSplitsOut = backend2.makeOutput(dataSplits.shape, \"int32\");\n  const nGramsSplitsOutVals = backend2.typedArrayFromHeap(nGramsSplitsOut);\n  nGramsSplitsOutVals.set(nGramsSplits);\n  return [nGramsOut, nGramsSplitsOut];\n}\nvar stringNGramsConfig3 = {\n  kernelName: StringNGrams,\n  backendName: \"wasm\",\n  kernelFunc: stringNGrams4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/StringSplit.js\nfunction stringSplit4(args) {\n  const { backend: backend2, inputs, attrs } = args;\n  const { input: input2, delimiter } = inputs;\n  const { skipEmpty } = attrs;\n  const inputVals = backend2.readSync(input2.dataId);\n  const delimiterVals = backend2.readSync(delimiter.dataId);\n  const [indices, values, shape] = stringSplitImpl(inputVals, delimiterVals[0], skipEmpty);\n  const outputSize = values.length;\n  const indicesOut = backend2.makeOutput([outputSize, 2], \"int32\");\n  const indicesOutVals = backend2.typedArrayFromHeap(indicesOut);\n  indicesOutVals.set(indices);\n  const valuesOut = backend2.makeOutput([outputSize], \"string\");\n  const valuesOutData = backend2.dataIdMap.get(valuesOut.dataId);\n  valuesOutData.stringBytes = values;\n  const shapeOut = backend2.makeOutput([2], \"int32\");\n  const shapeOutVals = backend2.typedArrayFromHeap(shapeOut);\n  shapeOutVals.set(shape);\n  return [indicesOut, valuesOut, shapeOut];\n}\nvar stringSplitConfig3 = {\n  kernelName: StringSplit,\n  backendName: \"wasm\",\n  kernelFunc: stringSplit4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/StringToHashBucketFast.js\nfunction stringToHashBucketFast4(args) {\n  const { backend: backend2, inputs, attrs } = args;\n  const { input: input2 } = inputs;\n  const { numBuckets } = attrs;\n  const inputVals = backend2.readSync(input2.dataId);\n  const values = stringToHashBucketFastImpl(inputVals, numBuckets);\n  const out = backend2.makeOutput(input2.shape, \"int32\");\n  const outVals = backend2.typedArrayFromHeap(out);\n  outVals.set(values);\n  return out;\n}\nvar stringToHashBucketFastConfig3 = {\n  kernelName: StringToHashBucketFast,\n  backendName: \"wasm\",\n  kernelFunc: stringToHashBucketFast4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Sub.js\nvar supportsFullBroadcast18 = true;\nvar subConfig3 = createBinaryKernelConfig(Sub, supportsFullBroadcast18);\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Sum.js\nvar wasmSum;\nfunction setup47(backend2) {\n  wasmSum = backend2.wasm.cwrap(Sum, null, [\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\"\n  ]);\n}\nfunction sum5(args) {\n  const { backend: backend2, inputs, attrs } = args;\n  const { axis, keepDims } = attrs;\n  const { x } = inputs;\n  const xId = backend2.dataIdMap.get(x.dataId).id;\n  let inputId = xId;\n  let input2 = x;\n  const { transposed, axes, originalAxes, inputWasTransposed } = permuteAxesAndTranspose(x, axis, backend2);\n  let reductionAxes = axes;\n  if (inputWasTransposed) {\n    const transposedId = backend2.dataIdMap.get(transposed.dataId).id;\n    if (transposedId !== xId) {\n      input2 = transposed;\n      inputId = transposedId;\n      reductionAxes = backend_util_exports.getInnerMostAxes(reductionAxes.length, input2.shape.length);\n    }\n  }\n  backend_util_exports.assertAxesAreInnerMostDims(\"sum\", reductionAxes, input2.shape.length);\n  const [outShape, reduceShape] = backend_util_exports.computeOutAndReduceShapes(input2.shape, reductionAxes);\n  const reduceSize = util_exports.sizeFromShape(reduceShape);\n  const out = backend2.makeOutput(outShape, input2.dtype);\n  if (util_exports.sizeFromShape(input2.shape) !== 0) {\n    const outId = backend2.dataIdMap.get(out.dataId).id;\n    wasmSum(inputId, reduceSize, CppDType[out.dtype], outId);\n  }\n  if (inputWasTransposed) {\n    backend2.disposeData(transposed.dataId);\n  }\n  if (keepDims) {\n    const newShape = backend_util_exports.expandShapeToKeepDim(out.shape, originalAxes);\n    out.shape = newShape;\n  }\n  return out;\n}\nvar sumConfig3 = {\n  kernelName: Sum,\n  backendName: \"wasm\",\n  setupFunc: setup47,\n  kernelFunc: sum5\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Tan.js\nvar tanConfig3 = createUnaryKernelConfig(Tan);\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Tanh.js\nvar tanhConfig3 = createUnaryKernelConfig(Tanh);\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Tile.js\nvar wasmTile;\nfunction setup48(backend2) {\n  wasmTile = backend2.wasm.cwrap(Tile, null, [\n    \"number\",\n    \"array\",\n    \"number\",\n    \"array\",\n    \"number\",\n    \"number\"\n  ]);\n}\nfunction tile5(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x } = inputs;\n  const xId = backend2.dataIdMap.get(x.dataId).id;\n  const { reps } = attrs;\n  const newShape = new Array(x.shape.length);\n  for (let i2 = 0; i2 < newShape.length; i2++) {\n    newShape[i2] = x.shape[i2] * reps[i2];\n  }\n  const xShapeBytes = new Uint8Array(new Int32Array(x.shape).buffer);\n  const newShapeBytes = new Uint8Array(new Int32Array(newShape).buffer);\n  const out = backend2.makeOutput(newShape, x.dtype);\n  const outId = backend2.dataIdMap.get(out.dataId).id;\n  wasmTile(xId, xShapeBytes, x.shape.length, newShapeBytes, newShape.length, CppDType[out.dtype], outId);\n  return out;\n}\nvar tileConfig3 = {\n  kernelName: Tile,\n  backendName: \"wasm\",\n  setupFunc: setup48,\n  kernelFunc: tile5\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/TopK.js\nvar wasmTopK;\nfunction setup49(backend2) {\n  wasmTopK = backend2.wasm.cwrap(TopK, null, [\n    \"number\",\n    \"array\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"bool\",\n    \"number\",\n    \"number\"\n  ]);\n}\nvar topk2 = ({ inputs, backend: backend2, attrs }) => {\n  const { x } = inputs;\n  const { k, sorted } = attrs;\n  const xId = backend2.dataIdMap.get(x.dataId).id;\n  const xShapeBytes = new Uint8Array(new Int32Array(x.shape).buffer);\n  const outputShape = x.shape.slice();\n  outputShape[outputShape.length - 1] = k;\n  const outValues = backend2.makeOutput(outputShape, x.dtype);\n  const outValuesId = backend2.dataIdMap.get(outValues.dataId).id;\n  const outIndices = backend2.makeOutput(outputShape, \"int32\");\n  const outIndicesId = backend2.dataIdMap.get(outIndices.dataId).id;\n  wasmTopK(xId, xShapeBytes, x.shape.length, CppDType[x.dtype], k, sorted, outValuesId, outIndicesId);\n  return [outValues, outIndices];\n};\nvar topKConfig3 = {\n  kernelName: TopK,\n  backendName: \"wasm\",\n  setupFunc: setup49,\n  kernelFunc: topk2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Transform.js\nvar wasmTransform;\nfunction setup50(backend2) {\n  wasmTransform = backend2.wasm.cwrap(Transform, null, [\n    \"number\",\n    \"number\",\n    \"bool\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"array\",\n    \"number\",\n    \"array\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\"\n  ]);\n}\nfunction transform4(args) {\n  const { backend: backend2, inputs, attrs } = args;\n  const { image: image2, transforms } = inputs;\n  const { interpolation, fillMode, fillValue, outputShape } = attrs;\n  const [batch, imageHeight, imageWidth, numChannels] = image2.shape;\n  const [outHeight, outWidth] = outputShape != null ? outputShape : [imageHeight, imageWidth];\n  const outShape = [\n    batch,\n    outHeight,\n    outWidth,\n    numChannels\n  ];\n  const inputStrides = new Uint8Array(new Int32Array(util_exports.computeStrides(image2.shape)).buffer);\n  const outputStrides = new Uint8Array(new Int32Array(util_exports.computeStrides(outShape)).buffer);\n  const out = backend2.makeOutput(outShape, image2.dtype);\n  const outId = backend2.dataIdMap.get(out.dataId).id;\n  const imageData = backend2.dataIdMap.get(image2.dataId);\n  const imageId = imageData.id;\n  const transformsData = backend2.dataIdMap.get(transforms.dataId);\n  const transformsId = transformsData.id;\n  const interpolationModeId = interpolation === \"nearest\" ? 1 : 2;\n  let fillModeId;\n  switch (fillMode) {\n    case \"constant\":\n      fillModeId = 1;\n      break;\n    case \"reflect\":\n      fillModeId = 2;\n      break;\n    case \"wrap\":\n      fillModeId = 3;\n      break;\n    case \"nearest\":\n      fillModeId = 4;\n      break;\n    default:\n      fillModeId = 1;\n      break;\n  }\n  wasmTransform(imageId, transformsId, transforms.shape[0] > 1, batch, outHeight, outWidth, numChannels, imageWidth, imageHeight, inputStrides, image2.shape.length - 1, outputStrides, outShape.length - 1, interpolationModeId, fillModeId, fillValue, outId);\n  return out;\n}\nvar transformConfig3 = {\n  kernelName: Transform,\n  backendName: \"wasm\",\n  setupFunc: setup50,\n  kernelFunc: transform4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Unpack.js\nfunction unpack3(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { value } = inputs;\n  let { axis } = attrs;\n  if (axis < 0) {\n    axis += value.shape.length;\n  }\n  const numOutputs = value.shape[axis];\n  const rank = value.shape.length;\n  const outShape = new Array(rank - 1);\n  let outIndex = 0;\n  for (let i2 = 0; i2 < rank; i2++) {\n    if (i2 !== axis) {\n      outShape[outIndex++] = value.shape[i2];\n    }\n  }\n  const outs = new Array(numOutputs);\n  const begin = new Array(rank).fill(0);\n  const size = value.shape.slice();\n  size[axis] = 1;\n  for (let i2 = 0; i2 < outs.length; i2++) {\n    begin[axis] = i2;\n    outs[i2] = slice4({ inputs: { x: value }, attrs: { begin, size }, backend: backend2 });\n  }\n  return outs.map(({ dataId, dtype }) => ({ dataId, dtype, shape: outShape }));\n}\nvar unpackConfig3 = {\n  kernelName: Unpack,\n  backendName: \"wasm\",\n  kernelFunc: unpack3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/ZerosLike.js\nfunction zerosLike4(args) {\n  const { inputs: { x }, backend: backend2 } = args;\n  const out = backend2.makeOutput(x.shape, x.dtype);\n  const outVals = backend2.typedArrayFromHeap(out);\n  outVals.fill(0);\n  return out;\n}\nvar zerosLikeConfig3 = {\n  kernelName: ZerosLike,\n  backendName: \"wasm\",\n  kernelFunc: zerosLike4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-wasm/dist/register_all_kernels.js\nvar kernelConfigs3 = [\n  _fusedMatMulConfig3,\n  absConfig3,\n  addConfig3,\n  addNConfig3,\n  allConfig3,\n  anyConfig3,\n  argMaxConfig3,\n  avgPoolConfig3,\n  batchMatMulConfig3,\n  batchToSpaceNDConfig3,\n  castConfig3,\n  ceilConfig3,\n  clipByValueConfig3,\n  concatConfig3,\n  conv2DConfig3,\n  conv2DBackpropInputConfig3,\n  cosConfig3,\n  coshConfig3,\n  cropAndResizeConfig3,\n  cumprodConfig3,\n  cumsumConfig3,\n  depthToSpaceConfig3,\n  depthwiseConv2dNativeConfig3,\n  eluConfig3,\n  equalConfig3,\n  expConfig3,\n  expandDimsConfig3,\n  fillConfig3,\n  flipLeftRightConfig3,\n  floorConfig3,\n  floorDivConfig3,\n  fusedBatchNormConfig,\n  fusedConv2DConfig3,\n  fusedDepthwiseConv2DConfig3,\n  gatherNdConfig3,\n  gatherV2Config3,\n  greaterConfig3,\n  greaterEqualConfig3,\n  identityConfig3,\n  leakyReluConfig3,\n  lessConfig3,\n  lessEqualConfig3,\n  logConfig3,\n  logicalAndConfig3,\n  logicalNotConfig3,\n  logicalOrConfig3,\n  logicalXorConfig,\n  maxConfig3,\n  maximumConfig3,\n  maxPoolConfig3,\n  meanConfig3,\n  minConfig3,\n  minimumConfig3,\n  mirrorPadConfig3,\n  multiplyConfig3,\n  negConfig3,\n  nonMaxSuppressionV3Config3,\n  nonMaxSuppressionV4Config3,\n  nonMaxSuppressionV5Config3,\n  notEqualConfig3,\n  oneHotConfig3,\n  onesLikeConfig3,\n  packConfig3,\n  padV2Config3,\n  powConfig3,\n  preluConfig3,\n  prodConfig3,\n  rangeConfig3,\n  realDivConfig3,\n  reluConfig3,\n  relu6Config3,\n  reshapeConfig3,\n  resizeBilinearConfig3,\n  resizeNearestNeighborConfig3,\n  reverseConfig3,\n  rotateWithOffsetConfig3,\n  roundConfig3,\n  rsqrtConfig3,\n  scatterNdConfig3,\n  selectConfig3,\n  sigmoidConfig3,\n  sinConfig3,\n  sliceConfig3,\n  softmaxConfig3,\n  spaceToBatchNDConfig3,\n  sparseFillEmptyRowsConfig3,\n  sparseReshapeConfig3,\n  sparseSegmentMeanConfig3,\n  sparseSegmentSumConfig3,\n  splitVConfig3,\n  sqrtConfig3,\n  squareConfig3,\n  squaredDifferenceConfig3,\n  stepConfig3,\n  stridedSliceConfig3,\n  stringNGramsConfig3,\n  stringSplitConfig3,\n  stringToHashBucketFastConfig3,\n  subConfig3,\n  sumConfig3,\n  tanConfig3,\n  tanhConfig3,\n  tileConfig3,\n  topKConfig3,\n  transformConfig3,\n  transposeConfig3,\n  unpackConfig3,\n  zerosLikeConfig3\n];\nfor (const kernelConfig of kernelConfigs3) {\n  registerKernel(kernelConfig);\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-wasm/dist/flags_wasm.js\nvar ENV6 = env();\nENV6.registerFlag(\n  \"WASM_HAS_SIMD_SUPPORT\",\n  async () => WebAssembly.validate(new Uint8Array([\n    0,\n    97,\n    115,\n    109,\n    1,\n    0,\n    0,\n    0,\n    1,\n    4,\n    1,\n    96,\n    0,\n    0,\n    3,\n    2,\n    1,\n    0,\n    10,\n    9,\n    1,\n    7,\n    0,\n    65,\n    0,\n    253,\n    15,\n    26,\n    11\n  ]))\n);\nENV6.registerFlag(\"WASM_HAS_MULTITHREAD_SUPPORT\", async () => {\n  if (ENV6.get(\"IS_NODE\")) {\n    return false;\n  }\n  try {\n    new MessageChannel().port1.postMessage(new SharedArrayBuffer(1));\n    return WebAssembly.validate(new Uint8Array([\n      0,\n      97,\n      115,\n      109,\n      1,\n      0,\n      0,\n      0,\n      1,\n      4,\n      1,\n      96,\n      0,\n      0,\n      3,\n      2,\n      1,\n      0,\n      5,\n      4,\n      1,\n      3,\n      1,\n      1,\n      10,\n      11,\n      1,\n      9,\n      0,\n      65,\n      0,\n      254,\n      16,\n      2,\n      0,\n      26,\n      11\n    ]));\n  } catch (e2) {\n    return false;\n  }\n});\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-wasm/dist/backend_wasm.js\nvar wasmFactoryThreadedSimd_import = __toESM(require_tfjs_backend_wasm_threaded_simd());\nvar import_tfjs_backend_wasm_threaded_simd_worker = __toESM(require_tfjs_backend_wasm_threaded_simd_worker());\nvar wasmFactory_import = __toESM(require_tfjs_backend_wasm());\nvar wasmFactoryThreadedSimd = wasmFactoryThreadedSimd_import.default || wasmFactoryThreadedSimd_import;\nvar wasmFactory = wasmFactory_import.default || wasmFactory_import;\nvar BackendWasm = class extends KernelBackend {\n  constructor(wasm) {\n    super();\n    this.wasm = wasm;\n    this.dataIdNextNumber = 1;\n    this.wasm.tfjs.initWithThreadsCount(threadsCount);\n    actualThreadsCount = this.wasm.tfjs.getThreadsCount();\n    this.dataIdMap = new DataStorage(this, engine());\n  }\n  write(values, shape, dtype) {\n    const dataId = { id: this.dataIdNextNumber++ };\n    this.move(dataId, values, shape, dtype, 1);\n    return dataId;\n  }\n  numDataIds() {\n    return this.dataIdMap.numDataIds();\n  }\n  async time(f) {\n    const start = util_exports.now();\n    f();\n    const kernelMs = util_exports.now() - start;\n    return { kernelMs };\n  }\n  move(dataId, values, shape, dtype, refCount) {\n    const id = this.dataIdNextNumber++;\n    if (dtype === \"string\") {\n      const stringBytes = values;\n      this.dataIdMap.set(dataId, { id, stringBytes, shape, dtype, memoryOffset: null, refCount });\n      return;\n    }\n    const size = util_exports.sizeFromShape(shape);\n    const numBytes = size * util_exports.bytesPerElement(dtype);\n    const memoryOffset = this.wasm._malloc(numBytes);\n    this.dataIdMap.set(dataId, { id, memoryOffset, shape, dtype, refCount });\n    this.wasm.tfjs.registerTensor(id, size, memoryOffset);\n    if (values != null) {\n      this.wasm.HEAPU8.set(new Uint8Array(values.buffer, values.byteOffset, numBytes), memoryOffset);\n    }\n  }\n  async read(dataId) {\n    return this.readSync(dataId);\n  }\n  readSync(dataId, start, end) {\n    const { memoryOffset, dtype, shape, stringBytes } = this.dataIdMap.get(dataId);\n    if (dtype === \"string\") {\n      if ((start == null || start === 0) && (end == null || end >= stringBytes.length)) {\n        return stringBytes;\n      }\n      return stringBytes.slice(start, end);\n    }\n    start = start || 0;\n    end = end || util_exports.sizeFromShape(shape);\n    const bytesPerElement2 = util_exports.bytesPerElement(dtype);\n    const bytes = this.wasm.HEAPU8.slice(memoryOffset + start * bytesPerElement2, memoryOffset + end * bytesPerElement2);\n    return typedArrayFromBuffer(bytes.buffer, dtype);\n  }\n  disposeData(dataId, force = false) {\n    if (this.dataIdMap.has(dataId)) {\n      const data = this.dataIdMap.get(dataId);\n      data.refCount--;\n      if (!force && data.refCount > 0) {\n        return false;\n      }\n      this.wasm._free(data.memoryOffset);\n      this.wasm.tfjs.disposeData(data.id);\n      this.dataIdMap.delete(dataId);\n    }\n    return true;\n  }\n  refCount(dataId) {\n    if (this.dataIdMap.has(dataId)) {\n      const tensorData = this.dataIdMap.get(dataId);\n      return tensorData.refCount;\n    }\n    return 0;\n  }\n  incRef(dataId) {\n    const data = this.dataIdMap.get(dataId);\n    if (data != null) {\n      data.refCount++;\n    }\n  }\n  floatPrecision() {\n    return 32;\n  }\n  getMemoryOffset(dataId) {\n    return this.dataIdMap.get(dataId).memoryOffset;\n  }\n  dispose() {\n    this.wasm.tfjs.dispose();\n    if (\"PThread\" in this.wasm) {\n      this.wasm.PThread.terminateAllThreads();\n    }\n    this.wasm = null;\n  }\n  memory() {\n    return { unreliable: false };\n  }\n  makeOutput(shape, dtype, memoryOffset) {\n    let dataId;\n    if (memoryOffset == null) {\n      dataId = this.write(null, shape, dtype);\n    } else {\n      const id = this.dataIdNextNumber++;\n      dataId = { id };\n      this.dataIdMap.set(dataId, { id, memoryOffset, shape, dtype, refCount: 1 });\n      const size = util_exports.sizeFromShape(shape);\n      this.wasm.tfjs.registerTensor(id, size, memoryOffset);\n    }\n    return { dataId, shape, dtype };\n  }\n  typedArrayFromHeap({ shape, dtype, dataId }) {\n    const buffer2 = this.wasm.HEAPU8.buffer;\n    const { memoryOffset } = this.dataIdMap.get(dataId);\n    const size = util_exports.sizeFromShape(shape);\n    switch (dtype) {\n      case \"float32\":\n        return new Float32Array(buffer2, memoryOffset, size);\n      case \"int32\":\n        return new Int32Array(buffer2, memoryOffset, size);\n      case \"bool\":\n        return new Uint8Array(buffer2, memoryOffset, size);\n      default:\n        throw new Error(`Unknown dtype ${dtype}`);\n    }\n  }\n};\nfunction createInstantiateWasmFunc(path) {\n  return (imports, callback) => {\n    util_exports.fetch(path, { credentials: \"same-origin\" }).then((response) => {\n      if (!response[\"ok\"]) {\n        imports.env.a(`failed to load wasm binary file at '${path}'`);\n      }\n      response.arrayBuffer().then((binary) => {\n        WebAssembly.instantiate(binary, imports).then((output) => {\n          callback(output.instance, output.module);\n        });\n      });\n    });\n    return {};\n  };\n}\nfunction getPathToWasmBinary(simdSupported, threadsSupported, wasmModuleFolder) {\n  if (wasmPath != null) {\n    return wasmPath;\n  }\n  let path = \"tfjs-backend-wasm.wasm\";\n  if (simdSupported && threadsSupported) {\n    path = \"tfjs-backend-wasm-threaded-simd.wasm\";\n  } else if (simdSupported) {\n    path = \"tfjs-backend-wasm-simd.wasm\";\n  }\n  if (wasmFileMap != null) {\n    if (wasmFileMap[path] != null) {\n      return wasmFileMap[path];\n    }\n  }\n  return wasmModuleFolder + path;\n}\nasync function init() {\n  const [simdSupported, threadsSupported] = await Promise.all([\n    env().getAsync(\"WASM_HAS_SIMD_SUPPORT\"),\n    env().getAsync(\"WASM_HAS_MULTITHREAD_SUPPORT\")\n  ]);\n  return new Promise((resolve, reject) => {\n    const factoryConfig = {};\n    factoryConfig.locateFile = (path, prefix) => {\n      if (path.endsWith(\".worker.js\")) {\n        const response = import_tfjs_backend_wasm_threaded_simd_worker.wasmWorkerContents.replace(/\\n/g, \"\\\\n\");\n        const blob = new Blob([response], { type: \"application/javascript\" });\n        return URL.createObjectURL(blob);\n      }\n      if (path.endsWith(\".wasm\")) {\n        return getPathToWasmBinary(simdSupported, threadsSupported, wasmPathPrefix != null ? wasmPathPrefix : prefix);\n      }\n      return prefix + path;\n    };\n    if (customFetch) {\n      factoryConfig.instantiateWasm = createInstantiateWasmFunc(getPathToWasmBinary(simdSupported, threadsSupported, wasmPathPrefix != null ? wasmPathPrefix : \"\"));\n    }\n    let initialized = false;\n    factoryConfig.onAbort = () => {\n      if (initialized) {\n        return;\n      }\n      if (initAborted) {\n        return;\n      }\n      initAborted = true;\n      const rejectMsg = \"Make sure the server can serve the `.wasm` file relative to the bundled js file. For more details see https://github.com/tensorflow/tfjs/blob/master/tfjs-backend-wasm/README.md#using-bundlers\";\n      reject({ message: rejectMsg });\n    };\n    let wasm;\n    if (threadsSupported && simdSupported && wasmPath == null) {\n      factoryConfig.mainScriptUrlOrBlob = new Blob([`var WasmBackendModuleThreadedSimd = ` + wasmFactoryThreadedSimd.toString()], { type: \"text/javascript\" });\n      wasm = wasmFactoryThreadedSimd(factoryConfig);\n    } else {\n      wasm = wasmFactory(factoryConfig);\n    }\n    wasm.then((module) => {\n      initialized = true;\n      initAborted = false;\n      const voidReturnType = null;\n      module.tfjs = {\n        init: module.cwrap(\"init\", null, []),\n        initWithThreadsCount: module.cwrap(\"init_with_threads_count\", null, [\"number\"]),\n        getThreadsCount: module.cwrap(\"get_threads_count\", \"number\", []),\n        registerTensor: module.cwrap(\"register_tensor\", null, [\n          \"number\",\n          \"number\",\n          \"number\"\n        ]),\n        disposeData: module.cwrap(\"dispose_data\", voidReturnType, [\"number\"]),\n        dispose: module.cwrap(\"dispose\", voidReturnType, [])\n      };\n      resolve({ wasm: module });\n    }).catch(reject);\n  });\n}\nfunction typedArrayFromBuffer(buffer2, dtype) {\n  switch (dtype) {\n    case \"float32\":\n      return new Float32Array(buffer2);\n    case \"int32\":\n      return new Int32Array(buffer2);\n    case \"bool\":\n      return new Uint8Array(buffer2);\n    default:\n      throw new Error(`Unknown dtype ${dtype}`);\n  }\n}\nvar wasmBinaryNames = [\n  \"tfjs-backend-wasm.wasm\",\n  \"tfjs-backend-wasm-simd.wasm\",\n  \"tfjs-backend-wasm-threaded-simd.wasm\"\n];\nvar wasmPath = null;\nvar wasmPathPrefix = null;\nvar wasmFileMap = {};\nvar initAborted = false;\nvar customFetch = false;\nfunction setWasmPath(path, usePlatformFetch = false) {\n  deprecationWarn(\"setWasmPath has been deprecated in favor of setWasmPaths and will be removed in a future release.\");\n  if (initAborted) {\n    throw new Error(\"The WASM backend was already initialized. Make sure you call `setWasmPath()` before you call `tf.setBackend()` or `tf.ready()`\");\n  }\n  wasmPath = path;\n  customFetch = usePlatformFetch;\n}\nfunction setWasmPaths(prefixOrFileMap, usePlatformFetch = false) {\n  if (initAborted) {\n    throw new Error(\"The WASM backend was already initialized. Make sure you call `setWasmPaths()` before you call `tf.setBackend()` or `tf.ready()`\");\n  }\n  if (typeof prefixOrFileMap === \"string\") {\n    wasmPathPrefix = prefixOrFileMap;\n  } else {\n    wasmFileMap = prefixOrFileMap;\n    const missingPaths = wasmBinaryNames.filter((name) => wasmFileMap[name] == null);\n    if (missingPaths.length > 0) {\n      throw new Error(`There were no entries found for the following binaries: ${missingPaths.join(\",\")}. Please either call setWasmPaths with a map providing a path for each binary, or with a string indicating the directory where all the binaries can be found.`);\n    }\n  }\n  customFetch = usePlatformFetch;\n}\nvar threadsCount = -1;\nvar actualThreadsCount = -1;\nfunction setThreadsCount(numThreads) {\n  threadsCount = numThreads;\n}\nfunction getThreadsCount() {\n  if (actualThreadsCount === -1) {\n    throw new Error(`WASM backend not initialized.`);\n  }\n  return actualThreadsCount;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-wasm/dist/version.js\nvar version8 = \"3.20.0\";\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-wasm/dist/base.js\nvar WASM_PRIORITY = 2;\nregisterBackend(\"wasm\", async () => {\n  const { wasm } = await init();\n  return new BackendWasm(wasm);\n}, WASM_PRIORITY);\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/flags_webgpu.js\nvar ENV7 = env();\nENV7.registerFlag(\"WEBGPU_DEFERRED_SUBMIT_BATCH_SIZE\", () => 15);\nENV7.registerFlag(\"WEBGPU_CPU_FORWARD\", () => true);\nENV7.registerFlag(\"WEBGPU_MATMUL_PROGRAM_TYPE\", () => -1);\nENV7.registerFlag(\"WEBGPU_USE_NAIVE_CONV2D_TRANSPOSE\", () => false);\nENV7.registerFlag(\"WEBGPU_USE_LOW_POWER_GPU\", () => false);\nENV7.registerFlag(\"WEBGPU_CPU_HANDOFF_SIZE_THRESHOLD\", () => 1e3);\nENV7.registerFlag(\"WEBGPU_USE_PROFILE_TOOL\", () => false);\nENV7.registerFlag(\"WEBGPU_IMPORT_EXTERNAL_TEXTURE\", () => true);\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/buffer_manager.js\nvar BufferManager = class {\n  constructor(device) {\n    this.device = device;\n    this.numUsedBuffers = 0;\n    this.numFreeBuffers = 0;\n    this.freeBuffers = /* @__PURE__ */ new Map();\n    this.usedBuffers = /* @__PURE__ */ new Map();\n    this.numBytesUsed = 0;\n    this.numBytesAllocated = 0;\n  }\n  acquireUploadBuffer(size, usage) {\n    return this.acquireBuffer(size, usage, true);\n  }\n  acquireBuffer(size, usage, mappedAtCreation = false) {\n    const key = getBufferKey(size, usage);\n    if (!this.freeBuffers.has(key)) {\n      this.freeBuffers.set(key, []);\n    }\n    if (!this.usedBuffers.has(key)) {\n      this.usedBuffers.set(key, []);\n    }\n    this.numBytesUsed += size;\n    this.numUsedBuffers++;\n    if (this.freeBuffers.get(key).length > 0) {\n      this.numFreeBuffers--;\n      const newBuffer2 = this.freeBuffers.get(key).shift();\n      this.usedBuffers.get(key).push(newBuffer2);\n      return newBuffer2;\n    }\n    this.numBytesAllocated += size;\n    const newBuffer = this.device.createBuffer({ size, usage, mappedAtCreation });\n    this.usedBuffers.get(key).push(newBuffer);\n    return newBuffer;\n  }\n  releaseBuffer(buffer2, size, usage) {\n    if (this.freeBuffers.size === 0) {\n      return;\n    }\n    const key = getBufferKey(size, usage);\n    if (!this.freeBuffers.has(key)) {\n      this.freeBuffers.set(key, []);\n    }\n    this.freeBuffers.get(key).push(buffer2);\n    this.numFreeBuffers++;\n    this.numUsedBuffers--;\n    const bufferList = this.usedBuffers.get(key);\n    const bufferIndex = bufferList.indexOf(buffer2);\n    if (bufferIndex < 0) {\n      throw new Error(\"Cannot release a buffer that was never provided by this buffer manager\");\n    }\n    bufferList.splice(bufferIndex, 1);\n    this.numBytesUsed -= size;\n  }\n  releaseUploadBuffer(buffer2, size, usage) {\n    buffer2.mapAsync(GPUMapMode.WRITE).then(() => {\n      this.releaseBuffer(buffer2, size, usage);\n    }, (err) => {\n    });\n  }\n  getNumUsedBuffers() {\n    return this.numUsedBuffers;\n  }\n  getNumFreeBuffers() {\n    return this.numFreeBuffers;\n  }\n  dispose() {\n    this.freeBuffers.forEach((buffers, key) => {\n      buffers.forEach((buffer2) => {\n        buffer2.destroy();\n      });\n    });\n    this.usedBuffers.forEach((buffers, key) => {\n      buffers.forEach((buffer2) => {\n        buffer2.destroy();\n      });\n    });\n    this.freeBuffers = /* @__PURE__ */ new Map();\n    this.usedBuffers = /* @__PURE__ */ new Map();\n    this.numUsedBuffers = 0;\n    this.numFreeBuffers = 0;\n    this.numBytesUsed = 0;\n    this.numBytesAllocated = 0;\n  }\n};\nfunction getBufferKey(size, usage) {\n  return `${size}_${usage}`;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/texture_manager.js\nvar TextureManager2 = class {\n  constructor(device) {\n    this.device = device;\n    this.numUsedTextures = 0;\n    this.numFreeTextures = 0;\n    this.freeTextures = /* @__PURE__ */ new Map();\n    this.usedTextures = /* @__PURE__ */ new Map();\n    this.numBytesUsed = 0;\n    this.numBytesAllocated = 0;\n  }\n  acquireTexture(width, height, format, usage) {\n    const bytesPerElement2 = getBytesPerElement(format);\n    const byteSize = width * height * bytesPerElement2;\n    const key = getTextureKey(width, height, format, usage);\n    if (!this.freeTextures.has(key)) {\n      this.freeTextures.set(key, []);\n    }\n    if (!this.usedTextures.has(key)) {\n      this.usedTextures.set(key, []);\n    }\n    this.numBytesUsed += byteSize;\n    this.numUsedTextures++;\n    if (this.freeTextures.get(key).length > 0) {\n      this.numFreeTextures--;\n      const newTexture2 = this.freeTextures.get(key).shift();\n      this.usedTextures.get(key).push(newTexture2);\n      return newTexture2;\n    }\n    this.numBytesAllocated += byteSize;\n    const newTexture = this.device.createTexture({\n      size: [width, height],\n      format,\n      usage\n    });\n    this.usedTextures.get(key).push(newTexture);\n    return newTexture;\n  }\n  releaseTexture(texture, width, height, format, usage) {\n    if (this.freeTextures.size === 0) {\n      return;\n    }\n    const key = getTextureKey(width, height, format, usage);\n    if (!this.freeTextures.has(key)) {\n      this.freeTextures.set(key, []);\n    }\n    this.freeTextures.get(key).push(texture);\n    this.numFreeTextures++;\n    this.numUsedTextures--;\n    const textureList = this.usedTextures.get(key);\n    const textureIndex = textureList.indexOf(texture);\n    if (textureIndex < 0) {\n      throw new Error(\"Cannot release a texture that was never provided by this texture manager\");\n    }\n    textureList.splice(textureIndex, 1);\n    const bytesPerElement2 = getBytesPerElement(format);\n    const byteSize = width * height * bytesPerElement2;\n    this.numBytesUsed -= byteSize;\n  }\n  getNumUsedTextures() {\n    return this.numUsedTextures;\n  }\n  getNumFreeTextures() {\n    return this.numFreeTextures;\n  }\n  dispose() {\n    this.freeTextures.forEach((textures, key) => {\n      textures.forEach((texture) => {\n        texture.destroy();\n      });\n    });\n    this.usedTextures.forEach((textures, key) => {\n      textures.forEach((texture) => {\n        texture.destroy();\n      });\n    });\n    this.freeTextures = /* @__PURE__ */ new Map();\n    this.usedTextures = /* @__PURE__ */ new Map();\n    this.numUsedTextures = 0;\n    this.numFreeTextures = 0;\n    this.numBytesUsed = 0;\n    this.numBytesAllocated = 0;\n  }\n};\nfunction getTextureKey(width, height, format, usage) {\n  return `${width}_${height}_${format}_${usage}`;\n}\nfunction getBytesPerElement(format) {\n  if (format === \"rgba8unorm\") {\n    return 16;\n  } else {\n    throw new Error(`${format} is not supported!`);\n  }\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/shader_util.js\nfunction symbolicallyComputeStrides2(indicesArr, variableName) {\n  if (Math.max(...indicesArr) > 3) {\n    throw new Error(\"Cannot symbolically compute strides for rank > 4 tensor.\");\n  }\n  const numCoords = indicesArr.length;\n  const shape = indicesArr.map((d) => `${variableName}[${d}]`);\n  const strides = new Array(numCoords - 1);\n  strides[numCoords - 2] = shape[numCoords - 1];\n  for (let i2 = numCoords - 3; i2 >= 0; --i2) {\n    strides[i2] = `(${strides[i2 + 1]} * ${shape[i2 + 1]})`;\n  }\n  return strides;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/webgpu_program.js\nvar compileProgram2 = (device, program, inputsData, output) => {\n  const outputData = { dtype: output.dtype, shape: output.shape };\n  const source = makeShader2(inputsData, outputData, program);\n  const module = device.createShaderModule({ code: source, label: program.constructor.name });\n  const pipeline = device.createComputePipeline({\n    compute: { module, entryPoint: \"_start\" },\n    label: program.constructor.name,\n    layout: \"auto\"\n  });\n  return pipeline;\n};\nfunction getCoordsDataType2(rank) {\n  if (rank <= 1) {\n    return \"i32\";\n  } else if (rank === 2) {\n    return `vec2<i32>`;\n  } else if (rank === 3) {\n    return `vec3<i32>`;\n  } else if (rank === 4) {\n    return `vec4<i32>`;\n  } else if (rank === 5) {\n    return `vec5`;\n  } else if (rank === 6) {\n    return `vec6`;\n  } else {\n    throw Error(`GPU for rank ${rank} is not yet supported`);\n  }\n}\nfunction getCoordsXYZ(index) {\n  if (index === 0) {\n    return \"x\";\n  } else if (index === 1) {\n    return \"y\";\n  } else if (index === 2) {\n    return \"z\";\n  } else if (index === 3) {\n    return \"w\";\n  } else if (index === 4) {\n    return \"u\";\n  } else if (index === 5) {\n    return \"v\";\n  } else {\n    throw Error(`Index ${index} is not yet supported`);\n  }\n}\nfunction getMainHeaderString(...params) {\n  let snippet;\n  switch (params.length) {\n    case 0:\n      snippet = `\n        ${getWorkGroupSizeString()}\n        fn _start(@builtin(local_invocation_id) LocalId : vec3<u32>,\n                  @builtin(global_invocation_id) GlobalId : vec3<u32>,\n                  @builtin(num_workgroups) NumWorkgroups : vec3<u32>) {\n          localId = LocalId;\n          globalId = GlobalId;\n          numWorkgroups = NumWorkgroups;\n          main();\n        }\n\n        fn main()\n      `;\n      break;\n    case 1:\n      snippet = `\n        ${getWorkGroupSizeString()}\n        fn _start(@builtin(local_invocation_id) LocalId : vec3<u32>,\n                  @builtin(global_invocation_id) GlobalId : vec3<u32>,\n                  @builtin(num_workgroups) NumWorkgroups : vec3<u32>) {\n          localId = LocalId;\n          globalId = GlobalId;\n          numWorkgroups = NumWorkgroups;\n          main(getGlobalIndex());\n        }\n\n        fn main(${params[0]} : i32)\n      `;\n      break;\n    default:\n      throw Error(\"Unreachable\");\n  }\n  return snippet;\n}\nfunction getWorkGroupSizeString() {\n  return `\n  @compute @workgroup_size(workGroupSizeX, workGroupSizeY, workGroupSizeZ)\n`;\n}\nfunction makeShader2(inputInfo, outputData, program) {\n  const prefixSnippets = [];\n  prefixSnippets.push(`\n      const workGroupSizeX = ${program.workGroupSize[0]}u;\n      const workGroupSizeY = ${program.workGroupSize[1]}u;\n      const workGroupSizeZ = ${program.workGroupSize[2]}u;\n\n      var<private> localId: vec3<u32>;\n      var<private> globalId: vec3<u32>;\n      var<private> numWorkgroups: vec3<u32>;\n\n      // Only used when the y/z dimension of workgroup size is 1.\n      fn getGlobalIndex() -> i32 {\n        ${isFlatDispatch(program) ? `  return i32(globalId.x);` : `  let localInvocationIndex = localId.z * workGroupSizeX * workGroupSizeY +\n                   localId.y * workGroupSizeX + localId.x;\n               let workGroupID = (globalId - localId)/vec3<u32>(\n                   workGroupSizeX, workGroupSizeY, workGroupSizeZ);\n\n               return i32((workGroupID.z * numWorkgroups.x * numWorkgroups.y +\n                   workGroupID.y * numWorkgroups.x + workGroupID.x) *\n                   (workGroupSizeX * workGroupSizeY * workGroupSizeZ) +\n                   localInvocationIndex);\n        `}\n      }\n    `);\n  if (program.isFromPixels) {\n    prefixSnippets.push(`\n        struct Uniform {\n          size            : i32,\n          numChannels     : i32,\n          outShapeStrides : vec2<i32>,\n        };\n\n        @group(0) @binding(0) var<storage, read_write> result: array<${mapToWgslTypes(outputData.dtype, program.isVec4)}>;\n        @group(0) @binding(2) var<uniform> uniforms: Uniform;\n      `);\n    return [\n      commonSnippet,\n      prefixSnippets.join(\"\\n\"),\n      getCoordsFromIndexSnippet(outputData.shape),\n      program.getUserCode()\n    ].join(\"\\n\");\n  }\n  let uniformDeclaration = \"struct Uniforms { NAN : f32, \";\n  program.variableNames.forEach((x, i2) => {\n    const perDataType = getCoordsDataType2(inputInfo[i2].shape.length);\n    uniformDeclaration += `${x.charAt(0).toLowerCase() + x.slice(1)}Shape : ${perDataType}, `;\n  });\n  const outputDataType = getCoordsDataType2(outputData.shape.length);\n  uniformDeclaration += `outShape : ${outputDataType}, `;\n  const stridesLength = outputData.shape.length - 1;\n  const stridesDataType = getCoordsDataType2(stridesLength);\n  uniformDeclaration += `\n         outShapeStrides: ${stridesDataType}, `;\n  if (program.size) {\n    uniformDeclaration += \"size : i32, \";\n  }\n  if (program.uniforms) {\n    uniformDeclaration += program.uniforms;\n  }\n  uniformDeclaration += \"};\";\n  uniformDeclaration = insertAlignment(uniformDeclaration);\n  prefixSnippets.push(uniformDeclaration);\n  if (program.atomic) {\n    prefixSnippets.push(`\n      @group(0) @binding(0) var<storage, read_write> result: array<atomic<i32>>;\n    `);\n  } else {\n    prefixSnippets.push(`\n      @group(0) @binding(0) var<storage, read_write> result: array<${mapToWgslTypes(outputData.dtype, program.isVec4)}>;\n    `);\n  }\n  program.variableNames.forEach((x, i2) => {\n    prefixSnippets.push(`\n      @group(0) @binding(${1 + i2}) var<storage, read> ${x}: array<${program.variableTypes ? program.variableTypes[i2] : mapToWgslTypes(inputInfo[i2].dtype, program.isVec4)}>;\n        `);\n  });\n  if (uniformDeclaration !== \"\") {\n    prefixSnippets.push(`\n      @group(0) @binding(${1 + program.variableNames.length}) var<uniform> uniforms: Uniforms;\n      `);\n  }\n  const coordsSnippet = getOutputCoordsSnippet(outputData.shape, program.dispatchLayout);\n  const sources = [\n    commonSnippet,\n    prefixSnippets.join(\"\\n\"),\n    getCoordsFromIndexSnippet(outputData.shape),\n    coordsSnippet,\n    getOutputIndexFromCoordsSnippet(outputData.shape.length)\n  ];\n  if (!program.atomic) {\n    sources.push(setOutputSnippet(outputData.shape, outputData.dtype, program.isVec4));\n  }\n  const inputSnippet = inputInfo.map((x, i2) => getInputSnippet(x, outputData.shape, program.variableTypes ? program.variableTypes[i2] === \"vec4<f32>\" : program.isVec4, program.dispatchLayout.x.length === outputData.shape.length)).join(\"\\n\");\n  sources.push(inputSnippet);\n  sources.push(program.getUserCode());\n  const source = sources.join(\"\\n\");\n  return source;\n}\nfunction makeShaderKey2(program, shapes, inputsData, output) {\n  let key = program.shaderKey;\n  if (program.isFromPixels) {\n    return key;\n  }\n  const types = inputsData.map((d) => d.dtype).concat(output.dtype);\n  const broadcastDims = inputsData.map((d) => backend_util_exports.getBroadcastDims(d.shape, output.shape));\n  const inputShapesEqualsOutShape = inputsData.map((d) => util_exports.arraysEqual(d.shape, output.shape)).join(\"_\");\n  const broadcastDimsKey = broadcastDims.map((d) => d.join(\"_\")).join(\";\");\n  const flatDispatchString = isFlatDispatch(program) ? \"flatDispatch\" : \"\";\n  key += \"_\" + (program.workGroupSize ? program.workGroupSize.join(\",\") : \"\") + shapes.map((shape) => shape.length).join(\",\") + types.join(\",\") + program.variableNames.join(\",\") + broadcastDimsKey + inputShapesEqualsOutShape + flatDispatchString;\n  return key;\n}\nvar commonSnippet = `\n  struct vec5 {x: i32, y: i32, z: i32, w: i32, u: i32};\n  struct vec6 {x: i32, y: i32, z: i32, w: i32, u: i32, v: i32};\n\n  // Checks whether coordinates lie within the bounds of the shape.\n  fn coordsInBounds2D(coord : vec2<i32>, shape : vec2<i32>) -> bool {\n    return all(coord >= vec2<i32>(0)) && all(coord < shape);\n  }\n  fn coordsInBounds3D(coord : vec3<i32>, shape : vec3<i32>) -> bool {\n    return all(coord >= vec3<i32>(0)) && all(coord < shape);\n  }\n  fn coordsInBounds4D(coord : vec4<i32>, shape : vec4<i32>) -> bool {\n    return all(coord >= vec4<i32>(0)) && all(coord < shape);\n  }\n\n  fn getIndexFromCoords1D(coord : i32, shape : i32) -> i32 {\n    return coord;\n  }\n  fn getIndexFromCoords2D(coords : vec2<i32>, shape : vec2<i32>) -> i32 {\n    return dot(coords, vec2<i32>(shape.y, 1));\n  }\n  fn getIndexFromCoords3D(coords : vec3<i32>, shape : vec3<i32>) -> i32 {\n    return dot(coords, vec3<i32>(shape.y * shape.z, shape.z, 1));\n  }\n  fn getIndexFromCoords4D(coords : vec4<i32>, shape : vec4<i32>) -> i32 {\n    return dot(coords, vec4<i32>(\n        shape.y * shape.z * shape.w, shape.z * shape.w, shape.w, 1));\n  }\n  fn getIndexFromCoords5D(coords : vec5, shape : vec5) -> i32 {\n    let shapeStrides: vec5 = vec5(shape.y * shape.z * shape.w * shape.u, shape.z * shape.w * shape.u, shape.w * shape.u, shape.u, 1);\n    return coords.x*shapeStrides.x + coords.y*shapeStrides.y + coords.z*shapeStrides.z + coords.w*shapeStrides.w + coords.u*shapeStrides.u;\n  }\n  fn getIndexFromCoords6D(coords : vec6, shape : vec6) -> i32 {\n    let shapeStrides: vec6 = vec6(shape.y * shape.z * shape.w * shape.u * shape.v, shape.z * shape.w * shape.u * shape.v, shape.w * shape.u * shape.v, shape.u * shape.v, shape.v, 1);\n    return coords.x*shapeStrides.x + coords.y*shapeStrides.y + coords.z*shapeStrides.z + coords.w*shapeStrides.w + coords.u*shapeStrides.u + coords.v*shapeStrides.v;\n  }\n\n  fn idiv(a: i32, b: i32, sign: f32) -> i32 {\n    var res: i32 = a / b;\n    let modulo: i32 = a % b;\n    if (sign < 0. && modulo != 0) {\n      res = res - 1;\n    }\n    return res;\n  }\n\n  // NaN defination in IEEE 754-1985 is :\n  //   - sign = either 0 or 1.\n  //   - biased exponent = all 1 bits.\n  //   - fraction = anything except all 0 bits (since all 0 bits represents infinity).\n  // https://en.wikipedia.org/wiki/IEEE_754-1985#Representation_of_non-numbers\n  fn isnan(val: f32) -> bool {\n    let floatToUint: u32 = bitcast<u32>(val);\n    return (floatToUint & 0x7fffffffu) > 0x7f800000u;\n  }\n  fn isnanVec4(val : vec4<f32>) -> vec4<bool> {\n    return vec4<bool>(isnan(val[0]), isnan(val[1]), isnan(val[2]), isnan(val[3]));\n  }\n`;\nfunction getCoordsFromIndexSnippet(shape) {\n  const rank = shape.length;\n  if (rank <= 1) {\n    return `fn getCoordsFromIndex(index : i32) -> i32 { return index; }`;\n  }\n  const strides = util_exports.computeStrides(shape);\n  const dtype = getCoordsDataType2(rank);\n  const coords3 = [];\n  for (let i2 = 0; i2 < rank; i2++) {\n    coords3.push(`d${i2}`);\n  }\n  if (strides.length === 1) {\n    return `    fn getCoordsFromIndex(index : i32) -> vec2<i32> {\n      let d0 = index / uniforms.outShapeStrides; let d1 = index - d0 * uniforms.outShapeStrides;\n      return vec2<i32>(d0, d1);\n    }`;\n  }\n  let snippet;\n  snippet = \"var index2 = index;\" + strides.map((_, i2) => {\n    const line1 = `let ${coords3[i2]} = index2 / uniforms.outShapeStrides.${getCoordsXYZ(i2)}`;\n    const line2 = i2 === strides.length - 1 ? `let ${coords3[i2 + 1]} = index2 - ${coords3[i2]} * uniforms.outShapeStrides.${getCoordsXYZ(i2)}` : `index2 = index2 - ${coords3[i2]} * uniforms.outShapeStrides.${getCoordsXYZ(i2)}`;\n    return `${line1}; ${line2};`;\n  }).join(\"\");\n  return `\n    fn getCoordsFromIndex(index : i32) -> ${dtype} {\n      ${snippet}\n      return ${dtype}(${coords3.join(\",\")});\n    }\n  `;\n}\nfunction getInputAtCoordsSnippet(inputInfo, isVec4) {\n  const texName = inputInfo.name;\n  const rank = inputInfo.shape.length;\n  const type = getCoordsDataType2(rank);\n  const funcName = \"get\" + texName.charAt(0).toUpperCase() + texName.slice(1);\n  const dims = [\"d0\", \"d1\", \"d2\", \"d3\", \"d4\", \"d5\"].slice(0, rank);\n  const inputs = dims.map((d) => `${d} : i32`).join(\", \");\n  if (rank < 1) {\n    if (isVec4) {\n      return `\n        fn ${funcName}() -> vec4<f32> {\n          return vec4<f32>(${texName}[0]);\n        }\n      `;\n    }\n    return `\n      fn ${funcName}() ->f32 {\n        return f32(${texName}[0]);\n      }\n    `;\n  }\n  const shapeStr = `uniforms.${texName.charAt(0).toLowerCase() + texName.slice(1)}Shape`;\n  let rankStr = `${rank}D`;\n  if (rank === 0) {\n    rankStr = \"1D\";\n  }\n  if (isVec4) {\n    return `\n      fn ${funcName}(${inputs}) -> vec4<f32> {\n        return vec4<f32>(${texName}[getIndexFromCoords${rankStr}(${type}(${dims.join(\",\")}),\n          ${shapeStr}) / 4]);\n      }\n      `;\n  }\n  return `\n    fn ${funcName}(${inputs}) -> f32 {\n      return f32(${texName}[getIndexFromCoords${rankStr}(${type}(${dims.join(\",\")}),\n        ${shapeStr})]);\n    }\n   `;\n}\nfunction getInputByOutputSnippet(inputInfo, outShape, isVec4, isFlatDispatchLayout) {\n  const texName = inputInfo.name;\n  const texFuncSnippet = texName.charAt(0).toUpperCase() + texName.slice(1);\n  const funcName = \"get\" + texFuncSnippet + \"ByOutput\";\n  const inRank = inputInfo.shape.length;\n  const outRank = outShape.length;\n  const type = getCoordsDataType2(outRank);\n  if (util_exports.arraysEqual(inputInfo.shape, outShape) && isFlatDispatchLayout) {\n    if (isVec4) {\n      return `\n      fn ${funcName}Index(globalIndex : i32) -> vec4<f32> {\n        return vec4<f32>(${texName}[globalIndex]);\n      }\n\n      fn ${funcName}Coords(coords : ${type}) -> vec4<f32> {\n        return vec4<f32>(${texName}[${outRank > 1 ? \"getOutputIndexFromCoords(coords)\" : \"coords\"} / 4]);\n      }\n      `;\n    } else {\n      return `\n    fn ${funcName}Index(globalIndex : i32) -> f32 {\n      return f32(${texName}[globalIndex]);\n    }\n\n    fn ${funcName}Coords(coords : ${type}) -> f32 {\n      return f32(${texName}[${outRank > 1 ? \"getOutputIndexFromCoords(coords)\" : \"coords\"}]);\n    }\n    `;\n    }\n  }\n  const broadcastDims = backend_util_exports.getBroadcastDims(inputInfo.shape, outShape);\n  const rankDiff = outRank - inRank;\n  let coordsSnippet = \"\";\n  if (inRank === 0) {\n    if (isVec4) {\n      return `\n    fn ${funcName}Index(globalIndex : i32) -> vec4<f32> {\n      return get${texFuncSnippet}();\n    }\n\n    fn ${funcName}Coords(coords : ${type}) -> vec4<f32> {\n      return get${texFuncSnippet}();\n    }\n  `;\n    }\n    return `\n    fn ${funcName}Index(globalIndex : i32) -> f32{\n      return get${texFuncSnippet}();\n    }\n\n    fn ${funcName}Coords(coords : ${type}) -> f32{\n      return get${texFuncSnippet}();\n    }\n  `;\n  } else {\n    if (outRank < 2 && broadcastDims.length >= 1) {\n      coordsSnippet = \"coords = 0;\";\n    } else {\n      coordsSnippet = broadcastDims.map((d) => `coords.${getCoordsXYZ(d + rankDiff)} = 0;`).join(\"\\n\");\n    }\n  }\n  let unpackedCoordsSnippet = \"\";\n  if (outRank < 2 && inRank > 0) {\n    unpackedCoordsSnippet = \"coords\";\n  } else {\n    if (outRank > 1) {\n      const coordsType = getCoordsDataType2(inRank);\n      const coordsValues = inputInfo.shape.map((s2, i2) => `coords.${getCoordsXYZ(i2 + rankDiff)}`).join(\", \");\n      unpackedCoordsSnippet = `${coordsType}(${coordsValues})`;\n    } else {\n      unpackedCoordsSnippet = \"coords\";\n    }\n  }\n  const shapeStr = `uniforms.${texName.charAt(0).toLowerCase() + texName.slice(1)}Shape`;\n  const rankStr = `${inRank}D`;\n  if (isVec4) {\n    return `\n    fn ${funcName}Index(globalIndex : i32) -> vec4<f32> {\n      var coords = getCoordsFromIndex(globalIndex);\n      ${coordsSnippet}\n      return ${texName}[getIndexFromCoords${rankStr}(${unpackedCoordsSnippet}, ${shapeStr}) / 4];\n    }\n\n    fn ${funcName}Coords(coordsIn : ${type}) -> vec4<f32> {\n      var coords = coordsIn;\n      ${coordsSnippet}\n      return ${texName}[getIndexFromCoords${rankStr}(${unpackedCoordsSnippet}, ${shapeStr}) / 4];\n    }\n  `;\n  }\n  return `\n  fn ${funcName}Index(globalIndex : i32) -> f32 {\n    var coords = getCoordsFromIndex(globalIndex);\n    ${coordsSnippet}\n    return f32(${texName}[getIndexFromCoords${rankStr}(${unpackedCoordsSnippet}, ${shapeStr})]);\n  }\n\n  fn ${funcName}Coords(coordsIn : ${type}) -> f32 {\n    var coords = coordsIn;\n    ${coordsSnippet}\n    return f32(${texName}[getIndexFromCoords${rankStr}(${unpackedCoordsSnippet}, ${shapeStr})]);\n  }\n`;\n}\nfunction getInputSnippet(inputInfo, outShape, isVec4, isFlatDispatchLayout) {\n  let res = getInputAtCoordsSnippet(inputInfo, isVec4);\n  const inShape = inputInfo.shape;\n  if (inShape.length <= outShape.length) {\n    res += getInputByOutputSnippet(inputInfo, outShape, isVec4, isFlatDispatchLayout);\n  }\n  return res;\n}\nfunction getOutputCoordsSnippet(outShape, dispatchLayout) {\n  const { x, y = [], z = [] } = dispatchLayout;\n  const outRank = outShape.length;\n  if (x.length === outRank) {\n    const dtype2 = getCoordsDataType2(outRank);\n    const snippet2 = `fn getOutputCoords() -> ${dtype2}{\n    let globalIndex = getGlobalIndex();\n    return getCoordsFromIndex(globalIndex);\n  }\n  `;\n    return snippet2;\n  }\n  let gatherDimensionsStr = \"\";\n  const dims = [x, y, z];\n  let rank = 0;\n  for (let i2 = 0; i2 < dims.length; i2++) {\n    const arr = dims[i2];\n    if (arr.length === 0) {\n      continue;\n    }\n    rank += arr.length;\n    if (arr.length === 1) {\n      gatherDimensionsStr += `let d${arr[0]} = i32(globalId[${i2}]);`;\n    } else {\n      const strides = symbolicallyComputeStrides2(arr, \"uniforms.outShape\");\n      gatherDimensionsStr += `var index${i2} = i32(globalId[${i2}]);`;\n      for (let j = 0; j < strides.length; j++) {\n        gatherDimensionsStr += `let d${arr[j]} = index${i2} / ${strides[j]};`;\n        if (j === strides.length - 1) {\n          gatherDimensionsStr += `let d${arr[j + 1]} = index${i2} - d${arr[j]} * ${strides[j]};`;\n        } else {\n          gatherDimensionsStr += `index${i2} = index${i2} - d${arr[j]} * ${strides[j]};`;\n        }\n      }\n    }\n  }\n  const dimensions = [];\n  for (let i2 = 0; i2 < rank; i2++) {\n    dimensions.push(`d${i2}`);\n  }\n  const dtype = getCoordsDataType2(rank);\n  let snippet = `fn getOutputCoords() -> ${dtype} {\n  ${gatherDimensionsStr}\n`;\n  if (dimensions.length === 0) {\n    snippet += `return ${dtype}(0); }`;\n  } else {\n    snippet += `return ${dtype}(${dimensions.join(\",\")}); }`;\n  }\n  return snippet;\n}\nfunction getOutputIndexFromCoordsSnippet(outRank) {\n  let snippet = \"\";\n  switch (outRank) {\n    case 0:\n    case 1:\n      snippet += `\n        fn getOutputIndexFromCoords(coords : i32) -> i32 {\n          return coords;\n        }\n        `;\n      break;\n    case 2:\n      snippet += `\n        fn getOutputIndexFromCoords(coords : vec2<i32>) -> i32 {\n          return dot(coords, vec2<i32>(uniforms.outShapeStrides, 1));\n        }\n        `;\n      break;\n    case 3:\n      snippet += `\n        fn getOutputIndexFromCoords(coords : vec3<i32>) -> i32 {\n          return dot(coords, vec3<i32>(uniforms.outShapeStrides.x, uniforms.outShapeStrides.y, 1));\n        }\n        `;\n      break;\n    case 4:\n      snippet += `\n        fn getOutputIndexFromCoords(coords : vec4<i32>) -> i32 {\n          return dot(coords, vec4<i32>(\n            uniforms.outShapeStrides.x, uniforms.outShapeStrides.y, uniforms.outShapeStrides.z, 1));\n        }\n        `;\n      break;\n    case 5:\n      snippet += `\n        fn getOutputIndexFromCoords(coords : vec5) -> i32 {\n          return coords.x * uniforms.outShapeStrides.x +\n              coords.y * uniforms.outShapeStrides.y +\n              coords.z * uniforms.outShapeStrides.z +\n              coords.w * uniforms.outShapeStrides.w +\n              coords.u;\n        }\n        `;\n      break;\n    case 6:\n      snippet += `\n        fn getOutputIndexFromCoords(coords : vec6) -> i32 {\n          return coords.x * uniforms.outShapeStrides.x +\n              coords.y * uniforms.outShapeStrides.y +\n              coords.z * uniforms.outShapeStrides.z +\n              coords.w * uniforms.outShapeStrides.w +\n              coords.u * uniforms.outShapeStrides.u +\n              coords.v;\n        }\n        `;\n      break;\n    default:\n      util_exports.assert(false, () => `Unsupported ${outRank}D shape`);\n      break;\n  }\n  return snippet;\n}\nfunction isFlatDispatch(program) {\n  return program.dispatch[1] === 1 && program.dispatch[2] === 1;\n}\nfunction mapToWgslTypes(type, isVec4) {\n  if (type === \"float32\") {\n    return isVec4 ? \"vec4<f32>\" : \"f32\";\n  } else if (type === \"int32\") {\n    return isVec4 ? \"vec4<i32>\" : \"i32\";\n  } else if (type === \"bool\") {\n    return isVec4 ? \"vec4<i32>\" : \"i32\";\n  }\n  return type;\n}\nfunction setOutputSnippet(outShape, outBufferType, isVec4) {\n  const outRank = outShape.length;\n  const wgslType = mapToWgslTypes(outBufferType, isVec4);\n  let snippet;\n  if (isVec4) {\n    snippet = `fn setOutputAtIndex(flatIndex : i32, value : vec4<f32>) {\n      result[flatIndex] = ${wgslType}(value);\n    }\n    fn setOutputAtIndexI32(flatIndex : i32, value : vec4<i32>) {\n      result[flatIndex] = ${wgslType}(value);\n    }`;\n  } else {\n    snippet = `fn setOutputAtIndex(flatIndex : i32, value : f32) {\n      result[flatIndex] = ${wgslType}(value);\n    }\n    fn setOutputAtIndexI32(flatIndex : i32, value : i32) {\n      result[flatIndex] = ${wgslType}(value);\n    }`;\n  }\n  if (outRank >= 2) {\n    const dims = [\"d0\", \"d1\", \"d2\", \"d3\", \"d4\", \"d5\"].slice(0, outRank);\n    const type = getCoordsDataType2(outRank);\n    if (isVec4) {\n      snippet += `\n      fn setOutputAtCoords(${dims.map((d) => `${d} : i32`).join(\", \")}, value : vec4<f32>) {\n        let flatIndex = getOutputIndexFromCoords(${type}(${dims.join(\", \")}));\n        setOutputAtIndex(flatIndex / 4, value);\n      }\n      fn setOutputAtCoordsI32(${dims.map((d) => `${d} : i32`).join(\", \")}, value : vec4<i32>) {\n        let flatIndex = getOutputIndexFromCoords(${type}(${dims.join(\", \")}));\n        setOutputAtIndexI32(flatIndex / 4, value);\n      }\n    `;\n    } else {\n      snippet += `\n      fn setOutputAtCoords(${dims.map((d) => `${d} : i32`).join(\", \")}, value : f32) {\n        let flatIndex = getOutputIndexFromCoords(${type}(${dims.join(\", \")}));\n        setOutputAtIndex(flatIndex, value);\n      }\n      fn setOutputAtCoordsI32(${dims.map((d) => `${d} : i32`).join(\", \")}, value : i32) {\n        let flatIndex = getOutputIndexFromCoords(${type}(${dims.join(\", \")}));\n        setOutputAtIndexI32(flatIndex, value);\n      }\n    `;\n    }\n  }\n  return snippet;\n}\nfunction insertAlignment(uniformShader) {\n  const curInsertRe = /(\\w+)\\s*:\\s*vec(5|6)/g;\n  uniformShader = uniformShader.replace(curInsertRe, (match) => {\n    return \"@align(16) \" + match;\n  });\n  const preInsertRe = /vec(5|6)\\s*,\\s*(\\w+)/g;\n  uniformShader = uniformShader.replace(preInsertRe, (_, p1, p2) => {\n    return `vec${p1}, @align(16) ${p2}`;\n  });\n  return uniformShader;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/webgpu_util.js\nvar webgpu_util_exports = {};\n__export(webgpu_util_exports, {\n  ArrayBufferToTypedArray: () => ArrayBufferToTypedArray,\n  GPUBytesPerElement: () => GPUBytesPerElement,\n  MatMulProgramType: () => MatMulProgramType,\n  computeDispatch: () => computeDispatch,\n  computeWorkGroupInfoForMatMul: () => computeWorkGroupInfoForMatMul,\n  computeWorkGroupSizeForConv2d: () => computeWorkGroupSizeForConv2d,\n  computeWorkPerThreadForConv2d: () => computeWorkPerThreadForConv2d,\n  flatDispatchLayout: () => flatDispatchLayout,\n  isWebGPUSupported: () => isWebGPUSupported,\n  tilesFitEvenlyIntoShape: () => tilesFitEvenlyIntoShape\n});\nvar arrayProduct = (arr) => {\n  let product = 1;\n  for (let i2 = 0; i2 < arr.length; i2++) {\n    product *= arr[i2];\n  }\n  return product;\n};\nfunction tilesFitEvenlyIntoShape(tileSize, shape) {\n  if (tileSize.length !== shape.length) {\n    throw new Error(`Cannot compute whether rank ${tileSize.length} tiles fit evenly into rank ${shape.length} shape - ranks must match.`);\n  }\n  return shape.every((dim, dimIdx) => dim % tileSize[dimIdx] === 0);\n}\nfunction computeDispatch(layout, outputShape, workGroupSize = [1, 1, 1], elementsPerThread = [1, 1, 1]) {\n  const [dispatchX, dispatchY, dispatchZ] = [\n    Math.ceil(arrayProduct(layout.x.map((d) => outputShape[d])) / (workGroupSize[0] * elementsPerThread[0])),\n    layout.y ? Math.ceil(arrayProduct(layout.y.map((d) => outputShape[d])) / (workGroupSize[1] * elementsPerThread[1])) : 1,\n    layout.z ? Math.ceil(arrayProduct(layout.z.map((d) => outputShape[d])) / (workGroupSize[2] * elementsPerThread[2])) : 1\n  ];\n  return [dispatchX, dispatchY, dispatchZ];\n}\nfunction computeWorkGroupInfoForMatMul(dimAOuter, dimInner, dimBOuter, transposeA = false) {\n  const workGroupSize = [8, 8, 1];\n  const elementsPerThread = [4, 4, 1];\n  if (!transposeA) {\n    if (dimAOuter <= 8) {\n      elementsPerThread[1] = 1;\n    }\n    if (dimInner <= 16 && dimBOuter <= 16) {\n      workGroupSize[0] = 4;\n    }\n  }\n  return { workGroupSize, elementsPerThread };\n}\nfunction computeWorkGroupSizeForConv2d(layout, outputShape, isVec4 = false) {\n  if (isVec4) {\n    return [8, 8, 1];\n  }\n  const dim0 = arrayProduct(layout.x.map((d) => outputShape[d]));\n  const dim1 = arrayProduct(layout.y.map((d) => outputShape[d]));\n  if (dim0 <= 4) {\n    return [4, 16, 1];\n  }\n  if (dim1 <= 4) {\n    return [16, 4, 1];\n  }\n  return [16, 16, 1];\n}\nfunction computeWorkPerThreadForConv2d(layout, outputShape, isVec4 = false) {\n  if (isVec4) {\n    return [4, 4, 1];\n  }\n  const dim0 = arrayProduct(layout.x.map((d) => outputShape[d]));\n  const dim1 = arrayProduct(layout.y.map((d) => outputShape[d]));\n  if (dim0 <= 4) {\n    return [1, 2, 1];\n  }\n  if (dim1 <= 4) {\n    return [2, 1, 1];\n  }\n  return [2, 2, 1];\n}\nfunction flatDispatchLayout(shape) {\n  return { x: shape.map((d, i2) => i2) };\n}\nfunction GPUBytesPerElement(dtype) {\n  if (dtype === \"float32\" || dtype === \"int32\" || dtype === \"bool\" || dtype === \"string\") {\n    return 4;\n  } else if (dtype === \"complex64\") {\n    return 8;\n  } else {\n    throw new Error(`Unknown dtype ${dtype}`);\n  }\n}\nfunction ArrayBufferToTypedArray(data, dtype) {\n  if (dtype === \"float32\") {\n    return new Float32Array(data);\n  } else if (dtype === \"int32\") {\n    return new Int32Array(data);\n  } else if (dtype === \"bool\" || dtype === \"string\") {\n    return Uint8Array.from(new Int32Array(data));\n  } else {\n    throw new Error(`Unknown dtype ${dtype}`);\n  }\n}\nfunction isWebGPUSupported() {\n  return (typeof window !== \"undefined\" || typeof WorkerGlobalScope !== \"undefined\") && !!navigator.gpu;\n}\nvar MatMulProgramType;\n(function(MatMulProgramType2) {\n  MatMulProgramType2[MatMulProgramType2[\"MatMulReduceProgram\"] = 0] = \"MatMulReduceProgram\";\n  MatMulProgramType2[MatMulProgramType2[\"MatMulSplitKProgram\"] = 1] = \"MatMulSplitKProgram\";\n  MatMulProgramType2[MatMulProgramType2[\"MatMulSmallOutputSizeProgram\"] = 2] = \"MatMulSmallOutputSizeProgram\";\n  MatMulProgramType2[MatMulProgramType2[\"MatMulPackedProgram\"] = 3] = \"MatMulPackedProgram\";\n  MatMulProgramType2[MatMulProgramType2[\"MatMulMax\"] = 4] = \"MatMulMax\";\n})(MatMulProgramType || (MatMulProgramType = {}));\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/backend_webgpu.js\nvar CPU_HANDOFF_SIZE_THRESHOLD2 = env().getNumber(\"WEBGPU_CPU_HANDOFF_SIZE_THRESHOLD\");\nvar reshapeDispatch = (device, program) => {\n  const MAX_COMPUTE_PER_DIMENSION_DISPATCH_SIZE = device.limits.maxComputeWorkgroupsPerDimension;\n  const layout = program[\"dispatchLayout\"];\n  const dispatch = program[\"dispatch\"];\n  if (dispatch.every((d) => d <= MAX_COMPUTE_PER_DIMENSION_DISPATCH_SIZE)) {\n    return dispatch;\n  }\n  util_exports.assert(dispatch[0] > MAX_COMPUTE_PER_DIMENSION_DISPATCH_SIZE && layout.y === void 0 && layout.z === void 0, () => \"Dispatch size exceeds WebGPU limits in Y or Z dimension.\");\n  let dispatchAverage = Math.ceil(Math.sqrt(dispatch[0]));\n  if (dispatchAverage > MAX_COMPUTE_PER_DIMENSION_DISPATCH_SIZE) {\n    dispatchAverage = Math.ceil(Math.cbrt(dispatch[0]));\n    util_exports.assert(dispatchAverage <= MAX_COMPUTE_PER_DIMENSION_DISPATCH_SIZE, () => \"Total dispatch size exceeds WebGPU maximum.\");\n    return [dispatchAverage, dispatchAverage, dispatchAverage];\n  } else {\n    return [dispatchAverage, dispatchAverage, 1];\n  }\n};\nvar WebGPUBackend = class extends KernelBackend {\n  constructor(device) {\n    super();\n    this.commandQueueOwnedIds = /* @__PURE__ */ new WeakSet();\n    this.dispatchNumberInEncoder = 0;\n    this.disposed = false;\n    this.downloadWaitMs = 0;\n    this.tensorDataPendingDisposal = [];\n    this.stagingPendingDisposal = [];\n    this.uniformPendingDisposal = [];\n    this.uploadWaitMs = 0;\n    if (!isWebGPUSupported()) {\n      throw new Error(\"WebGPU is not supported on this device\");\n    }\n    this.pipelineCache = {};\n    this.device = device;\n    this.queue = device.queue;\n    this.currentCommandEncoder = null;\n    this.currentComputePass = null;\n    this.supportTimeQuery = device.features.has(\"timestamp-query\");\n    this.bufferManager = new BufferManager(this.device);\n    this.textureManager = new TextureManager2(this.device);\n    this.tensorMap = new DataStorage(this, engine());\n    if (this.supportTimeQuery) {\n      this.querySet = this.device.createQuerySet({\n        type: \"timestamp\",\n        count: 2\n      });\n    }\n    if (env().getBool(\"WEBGPU_USE_PROFILE_TOOL\")) {\n      this.dummyCanvas = document.createElement(\"canvas\");\n      this.dummyCanvas.width = 1;\n      this.dummyCanvas.height = 1;\n      this.dummyContext = this.dummyCanvas.getContext(\"webgpu\");\n      this.dummyContext.configure({\n        device,\n        format: \"bgra8unorm\"\n      });\n      document.body.appendChild(this.dummyCanvas);\n    }\n  }\n  nextDataId() {\n    return WebGPUBackend.nextDataId++;\n  }\n  floatPrecision() {\n    return 32;\n  }\n  defaultGpuBufferUsage() {\n    return GPUBufferUsage.STORAGE | GPUBufferUsage.COPY_SRC | GPUBufferUsage.COPY_DST;\n  }\n  disposeData(dataId, force = false) {\n    if (this.tensorDataPendingDisposal.indexOf(dataId) >= 0) {\n      return false;\n    }\n    if (!this.tensorMap.has(dataId)) {\n      return true;\n    }\n    const tensorData = this.tensorMap.get(dataId);\n    this.decRef(dataId);\n    if (!force && tensorData.refCount > 0) {\n      return false;\n    }\n    if (this.commandQueueOwnedIds.has(dataId)) {\n      this.tensorDataPendingDisposal.push(dataId);\n      return false;\n    }\n    const { complexTensorInfos } = this.tensorMap.get(dataId);\n    if (complexTensorInfos != null) {\n      this.disposeData(complexTensorInfos.real.dataId, force);\n      this.disposeData(complexTensorInfos.imag.dataId, force);\n    }\n    this.releaseResource(dataId);\n    this.tensorMap.delete(dataId);\n    return true;\n  }\n  memory() {\n    return {\n      numBytesInGPU: this.bufferManager.numBytesUsed,\n      numBytesAllocatedInGPU: this.bufferManager.numBytesAllocated,\n      unreliable: false\n    };\n  }\n  releaseResource(dataId) {\n    const tensorData = this.tensorMap.get(dataId);\n    if (!tensorData || !tensorData.resourceInfo) {\n      return;\n    }\n    if (\"texture\" in tensorData.resourceInfo) {\n      const textureInfo = tensorData.resourceInfo;\n      if (textureInfo.texture instanceof GPUTexture) {\n        this.textureManager.releaseTexture(textureInfo.texture, textureInfo.width, textureInfo.height, textureInfo.format, textureInfo.usage);\n      }\n      textureInfo.texture = null;\n    } else {\n      const bufferInfo = tensorData.resourceInfo;\n      this.bufferManager.releaseBuffer(bufferInfo.buffer, bufferInfo.size, bufferInfo.usage);\n      bufferInfo.buffer = null;\n    }\n    tensorData.resourceInfo = null;\n  }\n  refCount(dataId) {\n    if (this.tensorMap.has(dataId)) {\n      const tensorData = this.tensorMap.get(dataId);\n      return tensorData.refCount;\n    }\n    return 0;\n  }\n  incRef(dataId) {\n    const tensorData = this.tensorMap.get(dataId);\n    tensorData.refCount++;\n  }\n  decRef(dataId) {\n    if (this.tensorMap.has(dataId)) {\n      const tensorData = this.tensorMap.get(dataId);\n      tensorData.refCount--;\n    }\n  }\n  write(values, shape, dtype) {\n    if (dtype === \"complex64\" && values != null) {\n      throw new Error(`Cannot write to a complex64 dtype. Please use tf.complex(real, imag).`);\n    }\n    const dataId = { id: this.nextDataId() };\n    this.tensorMap.set(dataId, { dtype, shape, values, refCount: 1 });\n    return dataId;\n  }\n  move(dataId, values, shape, dtype, refCount) {\n    if (dtype === \"complex64\") {\n      throw new Error(`Cannot write to a complex64 dtype. Please use tf.complex(real, imag).`);\n    }\n    this.tensorMap.set(dataId, { dtype, shape, values, refCount });\n  }\n  submitQueue() {\n    this.ensureComputePassEnded();\n    this.queue.submit([this.currentCommandEncoder.finish()]);\n    this.currentCommandEncoder = null;\n    this.dispatchNumberInEncoder = 0;\n    this.commandQueueOwnedIds = /* @__PURE__ */ new WeakSet();\n    this.tensorDataPendingDisposal.forEach((d) => {\n      this.releaseResource(d);\n      this.tensorMap.delete(d);\n    });\n    this.uniformPendingDisposal.forEach((d) => this.bufferManager.releaseBuffer(d.buffer, d.size, d.usage));\n    this.stagingPendingDisposal.forEach((d) => this.bufferManager.releaseUploadBuffer(d.buffer, d.size, d.usage));\n    this.tensorDataPendingDisposal = [];\n    this.uniformPendingDisposal = [];\n    this.stagingPendingDisposal = [];\n  }\n  ensureCommandEncoderReady() {\n    if (!this.currentCommandEncoder) {\n      this.currentCommandEncoder = this.device.createCommandEncoder();\n    }\n  }\n  ensureComputePassEnded() {\n    if (this.currentComputePass) {\n      this.currentComputePass.end();\n      this.currentComputePass = null;\n    }\n  }\n  getComputePass() {\n    if (!this.currentComputePass) {\n      this.currentComputePass = this.currentCommandEncoder.beginComputePass();\n    }\n    return this.currentComputePass;\n  }\n  async getBufferData(buffer2, size) {\n    const staging = this.bufferManager.acquireBuffer(size, GPUBufferUsage.COPY_DST | GPUBufferUsage.MAP_READ);\n    this.ensureCommandEncoderReady();\n    this.ensureComputePassEnded();\n    this.currentCommandEncoder.copyBufferToBuffer(buffer2, 0, staging, 0, size);\n    this.submitQueue();\n    await staging.mapAsync(GPUMapMode.READ);\n    const values = staging.getMappedRange().slice(0);\n    staging.unmap();\n    if (staging != null) {\n      this.bufferManager.releaseBuffer(staging, size, GPUBufferUsage.COPY_DST | GPUBufferUsage.MAP_READ);\n    }\n    if (env().getBool(\"WEBGPU_USE_PROFILE_TOOL\")) {\n      util_exports.assert(this.dummyContext !== void 0, () => `Fail to get context for profiling tool`);\n      this.dummyContext.getCurrentTexture();\n    }\n    return values;\n  }\n  convertAndCacheOnCPU(dataId, data) {\n    const tensorData = this.tensorMap.get(dataId);\n    this.releaseResource(dataId);\n    tensorData.values = data;\n    return tensorData.values;\n  }\n  readSync(dataId) {\n    const tensorData = this.tensorMap.get(dataId);\n    const { values } = tensorData;\n    if (values == null) {\n      throw new Error(\"WebGPU readSync is only available for CPU-resident tensors.\");\n    }\n    return values;\n  }\n  async read(dataId) {\n    if (!this.tensorMap.has(dataId)) {\n      throw new Error(`Tensor ${dataId} was not registered!`);\n    }\n    const tensorData = this.tensorMap.get(dataId);\n    const { values } = tensorData;\n    if (values != null) {\n      return this.convertAndCacheOnCPU(dataId, values);\n    }\n    let vals;\n    if (tensorData.dtype === \"complex64\") {\n      const ps = await Promise.all([\n        this.read(tensorData.complexTensorInfos.real.dataId),\n        this.read(tensorData.complexTensorInfos.imag.dataId)\n      ]);\n      const realValues = ps[0];\n      const imagValues = ps[1];\n      vals = backend_util_exports.mergeRealAndImagArrays(realValues, imagValues);\n    } else {\n      const bufferInfo = tensorData.resourceInfo;\n      const data = await this.getBufferData(bufferInfo.buffer, bufferInfo.size);\n      vals = ArrayBufferToTypedArray(data, tensorData.dtype);\n    }\n    this.convertAndCacheOnCPU(dataId, vals);\n    return vals;\n  }\n  readToGPU(dataId) {\n    const srcTensorData = this.tensorMap.get(dataId);\n    const { values, dtype, shape, resourceInfo } = srcTensorData;\n    if (dtype === \"complex64\") {\n      throw new Error(\"Does not support reading buffer for complex64 dtype.\");\n    }\n    if (resourceInfo == null) {\n      if (values != null) {\n        throw new Error(\"Data is not on GPU but on CPU.\");\n      } else {\n        throw new Error(\"There is no data on GPU or CPU.\");\n      }\n    }\n    const size = resourceInfo.size;\n    const buffer2 = this.bufferManager.acquireBuffer(size, resourceInfo.usage);\n    this.ensureCommandEncoderReady();\n    this.ensureComputePassEnded();\n    this.currentCommandEncoder.copyBufferToBuffer(resourceInfo.buffer, 0, buffer2, 0, size);\n    this.submitQueue();\n    const tensorInfo = this.makeTensorInfo(shape, dtype);\n    const tensorRef = engine().makeTensorFromTensorInfo(tensorInfo);\n    const tensorData = this.tensorMap.get(tensorInfo.dataId);\n    tensorData.resourceInfo = { size, usage: this.defaultGpuBufferUsage(), buffer: buffer2 };\n    return { tensorRef, buffer: buffer2, bufSize: size };\n  }\n  bufferSync(t2) {\n    const data = this.readSync(t2.dataId);\n    if (t2.dtype === \"string\") {\n      try {\n        const strings = data.map((d) => util_exports.decodeString(d));\n        return buffer(t2.shape, t2.dtype, strings);\n      } catch (_a) {\n        throw new Error(\"Failed to decode encoded string bytes into utf-8\");\n      }\n    }\n    return buffer(t2.shape, t2.dtype, data);\n  }\n  async time(f) {\n    if (!this.supportTimeQuery) {\n      console.warn(`This device doesn't support timestamp-query extension. Start Chrome browser with flag --disable-dawn-features=disallow_unsafe_apis then try again. Otherwise, zero will be shown for the kernel time when profiling mode is enabled. Using performance.now is not workable for webgpu since it doesn't support synchronous data read from GPU.`);\n    }\n    const oldActiveTimers = this.activeTimers;\n    const newActiveTimers = [];\n    let outerMostTime = false;\n    if (this.programTimersStack == null) {\n      this.programTimersStack = newActiveTimers;\n      outerMostTime = true;\n    } else {\n      this.activeTimers.push(newActiveTimers);\n    }\n    this.activeTimers = newActiveTimers;\n    f();\n    const flattenedActiveTimerQueries = util_exports.flatten(this.activeTimers.map((d) => d.query)).filter((d) => d != null);\n    const flattenedActiveTimerNames = util_exports.flatten(this.activeTimers.map((d) => d.name)).filter((d) => d != null);\n    this.activeTimers = oldActiveTimers;\n    if (outerMostTime) {\n      this.programTimersStack = null;\n    }\n    const res = {\n      uploadWaitMs: this.uploadWaitMs,\n      downloadWaitMs: this.downloadWaitMs,\n      kernelMs: null,\n      wallMs: null\n    };\n    const kernelMs = await Promise.all(flattenedActiveTimerQueries);\n    res[\"kernelMs\"] = util_exports.sum(kernelMs);\n    res[\"getExtraProfileInfo\"] = () => kernelMs.map((d, i2) => ({ name: flattenedActiveTimerNames[i2], ms: d })).map((d) => `${d.name}: ${d.ms}`).join(\", \");\n    this.uploadWaitMs = 0;\n    this.downloadWaitMs = 0;\n    return res;\n  }\n  makeTensorInfo(shape, dtype, values) {\n    if (dtype === \"string\" && values != null && values.length > 0 && util_exports.isString(values[0])) {\n      values = values.map((d) => util_exports.encodeString(d));\n    }\n    const dataId = this.write(values, shape, dtype);\n    return { dataId, shape, dtype };\n  }\n  tensorToBinding(tensor2) {\n    if (!tensor2) {\n      return null;\n    }\n    const tensorData = this.tensorMap.get(tensor2.dataId);\n    if (\"texture\" in tensorData.resourceInfo) {\n      const info = tensorData.resourceInfo;\n      if (info.texture instanceof GPUExternalTexture) {\n        return info.texture;\n      } else {\n        return info.texture.createView();\n      }\n    }\n    const bufferInfo = tensorData.resourceInfo;\n    return { offset: 0, size: bufferInfo.size, buffer: bufferInfo.buffer };\n  }\n  async getQueryTime(query) {\n    if (this.supportTimeQuery) {\n      return this.getTimeFromQuerySet(query);\n    } else {\n      return 0;\n    }\n  }\n  uploadToGPU(dataId) {\n    const tensorData = this.tensorMap.get(dataId);\n    if (tensorData.resourceInfo) {\n      return;\n    }\n    const size = GPUBytesPerElement(tensorData.dtype) * util_exports.sizeFromShape(tensorData.shape);\n    const buffer2 = this.bufferManager.acquireBuffer(size, this.defaultGpuBufferUsage());\n    tensorData.resourceInfo = { size, usage: this.defaultGpuBufferUsage(), buffer: buffer2 };\n    if (tensorData.values) {\n      const stagingBuffer = this.bufferManager.acquireUploadBuffer(size, GPUBufferUsage.MAP_WRITE | GPUBufferUsage.COPY_SRC);\n      const arrayBuffer = stagingBuffer.getMappedRange();\n      if (tensorData.dtype === \"int32\" || tensorData.dtype === \"bool\") {\n        new Int32Array(arrayBuffer).set(tensorData.values);\n      } else {\n        new Float32Array(arrayBuffer).set(tensorData.values);\n      }\n      stagingBuffer.unmap();\n      this.ensureCommandEncoderReady();\n      this.ensureComputePassEnded();\n      this.currentCommandEncoder.copyBufferToBuffer(stagingBuffer, 0, buffer2, 0, size);\n      const stagingInfo = {\n        size,\n        usage: GPUBufferUsage.MAP_WRITE | GPUBufferUsage.COPY_SRC,\n        buffer: stagingBuffer\n      };\n      this.stagingPendingDisposal.push(stagingInfo);\n    }\n  }\n  makeUniforms(programUniform) {\n    let currentOffset = 0;\n    let preLength = 0;\n    const offsets = [];\n    programUniform.forEach((d) => {\n      if (d.data.length === 0) {\n        d.data = [1];\n      }\n      let baseAlignment;\n      switch (d.data.length) {\n        case 1:\n          baseAlignment = 4;\n          break;\n        case 2:\n          baseAlignment = 8;\n          break;\n        case 3:\n          baseAlignment = 16;\n          break;\n        case 4:\n          baseAlignment = 16;\n          break;\n        case 5:\n          baseAlignment = 16;\n          break;\n        case 6:\n          baseAlignment = 16;\n          break;\n        default:\n          util_exports.assert(false, () => `Unsupported ${d.data.length}D shape`);\n      }\n      if (preLength === 5 || preLength === 6) {\n        baseAlignment = 16;\n      }\n      currentOffset = Math.ceil(currentOffset / baseAlignment) * baseAlignment;\n      preLength = d.data.length;\n      offsets.push(currentOffset);\n      currentOffset += d.data.length * 4;\n    });\n    const arrayBuffer = new ArrayBuffer(currentOffset);\n    programUniform.forEach((d, i2) => {\n      const offset = offsets[i2];\n      if (d.type === \"int32\") {\n        new Int32Array(arrayBuffer, offset, d.data.length).set(d.data);\n      } else if (d.type === \"uint32\") {\n        new Uint32Array(arrayBuffer, offset, d.data.length).set(d.data);\n      } else {\n        new Float32Array(arrayBuffer, offset, d.data.length).set(d.data);\n      }\n    });\n    const uniformBuffer = this.bufferManager.acquireBuffer(currentOffset, GPUBufferUsage.COPY_DST | GPUBufferUsage.UNIFORM);\n    this.queue.writeBuffer(uniformBuffer, 0, arrayBuffer, 0, currentOffset);\n    const uniformInfo = {\n      size: currentOffset,\n      usage: GPUBufferUsage.COPY_DST | GPUBufferUsage.UNIFORM,\n      buffer: uniformBuffer\n    };\n    this.uniformPendingDisposal.push(uniformInfo);\n    return { offset: 0, size: currentOffset, buffer: uniformBuffer };\n  }\n  runWebGPUProgram(program, inputs, outputDtype, programDefinedUniform, output) {\n    if (!output) {\n      output = this.makeTensorInfo(program.outputShape, outputDtype);\n    }\n    if (util_exports.sizeFromShape(output.shape) === 0) {\n      this.tensorMap.get(output.dataId).values = util_exports.getTypedArrayFromDType(output.dtype, 0);\n      return output;\n    }\n    this.uploadToGPU(output.dataId);\n    program.dispatch = reshapeDispatch(this.device, program);\n    let programUniform = [];\n    let bufferShapes = [];\n    if (!program.isFromPixels) {\n      programUniform.push({ type: \"float32\", data: [NaN] });\n      bufferShapes = inputs.concat(output).map((d) => d.shape);\n      const uniformsType = \"int32\";\n      bufferShapes.map((d) => {\n        programUniform.push({ type: uniformsType, data: d });\n      });\n      const strides = util_exports.computeStrides(output.shape);\n      programUniform.push({ type: uniformsType, data: strides });\n      if (program.size) {\n        const size = util_exports.sizeFromShape(program.outputShape);\n        programUniform.push({ type: uniformsType, data: [program.isVec4 ? size / 4 : size] });\n      }\n    }\n    const inputsData = inputs.map((input2, i2) => {\n      if (input2.dtype === \"complex64\") {\n        throw new Error(`GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.`);\n      }\n      this.uploadToGPU(input2.dataId);\n      return {\n        dtype: this.tensorMap.get(input2.dataId).dtype,\n        shape: input2.shape,\n        name: program.variableNames[i2]\n      };\n    });\n    const key = makeShaderKey2(program, bufferShapes, inputsData, output);\n    let pipeline;\n    if (key in this.pipelineCache) {\n      pipeline = this.pipelineCache[key];\n    } else {\n      pipeline = compileProgram2(this.device, program, inputsData, output);\n      this.pipelineCache[key] = pipeline;\n    }\n    if (programDefinedUniform) {\n      programUniform = [...programUniform, ...programDefinedUniform];\n    }\n    const bindings = [\n      this.tensorToBinding(output),\n      ...inputs.map((t2) => this.tensorToBinding(t2)),\n      this.makeUniforms(programUniform)\n    ];\n    const bindGroup = this.device.createBindGroup({\n      layout: pipeline.getBindGroupLayout(0),\n      entries: bindings.map((b, i2) => ({ binding: i2, resource: b }))\n    });\n    this.ensureCommandEncoderReady();\n    const pass = this.getComputePass();\n    const shouldTimeProgram = this.activeTimers != null;\n    if (shouldTimeProgram) {\n      if (this.supportTimeQuery) {\n        pass.writeTimestamp(this.querySet, 0);\n      }\n    }\n    pass.setPipeline(pipeline);\n    pass.setBindGroup(0, bindGroup);\n    pass.dispatchWorkgroups(program.dispatch[0], program.dispatch[1], program.dispatch[2]);\n    if (shouldTimeProgram) {\n      if (this.supportTimeQuery) {\n        pass.writeTimestamp(this.querySet, 1);\n      }\n    }\n    this.dispatchNumberInEncoder++;\n    inputs.forEach((input2) => {\n      this.commandQueueOwnedIds.add(input2.dataId);\n    });\n    this.commandQueueOwnedIds.add(output.dataId);\n    if (env().get(\"WEBGPU_DEFERRED_SUBMIT_BATCH_SIZE\") <= this.dispatchNumberInEncoder) {\n      this.submitQueue();\n    }\n    if (shouldTimeProgram) {\n      this.activeTimers.push({\n        name: program.constructor.name,\n        query: this.getQueryTime(this.querySet)\n      });\n    }\n    return output;\n  }\n  async getTimeFromQuerySet(querySet) {\n    const queryBuffer = this.bufferManager.acquireBuffer(16, GPUBufferUsage.COPY_SRC | GPUBufferUsage.QUERY_RESOLVE);\n    const dst = this.bufferManager.acquireBuffer(16, GPUBufferUsage.MAP_READ | GPUBufferUsage.COPY_DST);\n    this.ensureCommandEncoderReady();\n    this.ensureComputePassEnded();\n    this.currentCommandEncoder.resolveQuerySet(querySet, 0, 2, queryBuffer, 0);\n    this.currentCommandEncoder.copyBufferToBuffer(queryBuffer, 0, dst, 0, 16);\n    this.submitQueue();\n    await dst.mapAsync(GPUMapMode.READ);\n    const arrayBuf = new BigUint64Array(dst.getMappedRange());\n    const timeElapsedNanos = Number(arrayBuf[1] - arrayBuf[0]);\n    dst.unmap();\n    this.bufferManager.releaseBuffer(dst, 16, GPUBufferUsage.MAP_READ | GPUBufferUsage.COPY_DST);\n    this.bufferManager.releaseBuffer(queryBuffer, 16, GPUBufferUsage.COPY_SRC | GPUBufferUsage.QUERY_RESOLVE);\n    return timeElapsedNanos / 1e6;\n  }\n  shouldExecuteOnCPU(inputs, sizeThreshold = CPU_HANDOFF_SIZE_THRESHOLD2) {\n    return env().getBool(\"WEBGPU_CPU_FORWARD\") && inputs.every((input2) => this.tensorMap.get(input2.dataId).resourceInfo == null && util_exports.sizeFromShape(input2.shape) < sizeThreshold);\n  }\n  numDataIds() {\n    return this.tensorMap.numDataIds() - this.tensorDataPendingDisposal.length;\n  }\n  dispose() {\n    if (this.disposed) {\n      return;\n    }\n    this.bufferManager.dispose();\n    this.textureManager.dispose();\n    this.disposed = true;\n  }\n};\nWebGPUBackend.nextDataId = 0;\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/base.js\nif (isWebGPUSupported()) {\n  registerBackend(\"webgpu\", async () => {\n    env().set(\"CHECK_COMPUTATION_FOR_ERRORS\", false);\n    const gpuDescriptor = {\n      powerPreference: env().get(\"WEBGPU_USE_LOW_POWER_GPU\") ? \"low-power\" : \"high-performance\"\n    };\n    const adapter = await navigator.gpu.requestAdapter(gpuDescriptor);\n    const adapterLimits = adapter.limits;\n    const deviceDescriptor = {};\n    const supportTimeQuery = adapter.features.has(\"timestamp-query\");\n    deviceDescriptor.requiredLimits = {\n      \"maxComputeWorkgroupStorageSize\": adapterLimits.maxComputeWorkgroupStorageSize,\n      \"maxComputeWorkgroupsPerDimension\": adapterLimits.maxComputeWorkgroupsPerDimension,\n      \"maxStorageBufferBindingSize\": adapterLimits.maxStorageBufferBindingSize\n    };\n    if (supportTimeQuery) {\n      deviceDescriptor.requiredFeatures = [\"timestamp-query\"];\n    }\n    const device = await adapter.requestDevice(deviceDescriptor);\n    return new WebGPUBackend(device);\n  }, 3);\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/binary_op_util.js\nvar BinaryOpType;\n(function(BinaryOpType2) {\n  BinaryOpType2[BinaryOpType2[\"MUL\"] = 0] = \"MUL\";\n  BinaryOpType2[BinaryOpType2[\"ADD\"] = 1] = \"ADD\";\n  BinaryOpType2[BinaryOpType2[\"ATAN2\"] = 2] = \"ATAN2\";\n  BinaryOpType2[BinaryOpType2[\"SUB\"] = 3] = \"SUB\";\n  BinaryOpType2[BinaryOpType2[\"DIV\"] = 4] = \"DIV\";\n  BinaryOpType2[BinaryOpType2[\"EQUAL\"] = 5] = \"EQUAL\";\n  BinaryOpType2[BinaryOpType2[\"GREATER\"] = 6] = \"GREATER\";\n  BinaryOpType2[BinaryOpType2[\"GREATER_EQUAL\"] = 7] = \"GREATER_EQUAL\";\n  BinaryOpType2[BinaryOpType2[\"LESS\"] = 8] = \"LESS\";\n  BinaryOpType2[BinaryOpType2[\"LESS_EQUAL\"] = 9] = \"LESS_EQUAL\";\n  BinaryOpType2[BinaryOpType2[\"LOGICAL_AND\"] = 10] = \"LOGICAL_AND\";\n  BinaryOpType2[BinaryOpType2[\"NOT_EQUAL\"] = 11] = \"NOT_EQUAL\";\n  BinaryOpType2[BinaryOpType2[\"SQUARED_DIFFERENCE\"] = 12] = \"SQUARED_DIFFERENCE\";\n  BinaryOpType2[BinaryOpType2[\"INT_DIV\"] = 13] = \"INT_DIV\";\n  BinaryOpType2[BinaryOpType2[\"POW\"] = 14] = \"POW\";\n  BinaryOpType2[BinaryOpType2[\"PRELU\"] = 15] = \"PRELU\";\n  BinaryOpType2[BinaryOpType2[\"MAX\"] = 16] = \"MAX\";\n  BinaryOpType2[BinaryOpType2[\"MIN\"] = 17] = \"MIN\";\n  BinaryOpType2[BinaryOpType2[\"COMPLEX_MULTIPLY_REAL\"] = 18] = \"COMPLEX_MULTIPLY_REAL\";\n  BinaryOpType2[BinaryOpType2[\"COMPLEX_MULTIPLY_IMAG\"] = 19] = \"COMPLEX_MULTIPLY_IMAG\";\n})(BinaryOpType || (BinaryOpType = {}));\nvar CHECK_NAN_SNIPPET4 = `\n  if (isnan(a)) { return a; }\n  if (isnan(b)) { return b; }\n  `;\nvar CHECK_NAN_SNIPPET_VEC4_INNER = `\n  if (isNaN.r) {\n    resultTemp.r = valueForNaN;\n  }\n  if (isNaN.g) {\n    resultTemp.g = valueForNaN;\n  }\n  if (isNaN.b) {\n    resultTemp.b = valueForNaN;\n  }\n  if (isNaN.a) {\n    resultTemp.a = valueForNaN;\n  }\n  `;\nvar CHECK_NAN_SNIPPET_VEC4 = `\n  let isNaN = isnanVec4(a) | isnanVec4(b);\n  ${CHECK_NAN_SNIPPET_VEC4_INNER}\n  `;\nvar ADD2 = \"return a + b;\";\nvar COMPLEX_MULTIPLY_REAL = \"return areal * breal - aimag * bimag;\";\nvar COMPLEX_MULTIPLY_IMAG = \"return areal * bimag + aimag * breal;\";\nvar DIV2 = \"return a / b;\";\nvar MUL2 = \"return a * b;\";\nvar SQUARED_DIFFERENCE2 = \"return (a - b) * (a - b);\";\nvar SUB2 = \"return a - b;\";\nvar EQUAL2 = \"return f32(a == b);\";\nvar EQUAL_VEC4 = \"return vec4<f32>(a == b);\";\nvar GREATER2 = \"return f32(a > b);\";\nvar GREATER_VEC4 = \"return vec4<f32>(a > b);\";\nvar GREATER_EQUAL2 = \"return f32(a >= b);\";\nvar GREATER_EQUAL_VEC4 = \"return vec4<f32>(a >= b);\";\nvar LESS2 = \"return f32(a < b);\";\nvar LESS_VEC4 = \"return vec4<f32>(a < b);\";\nvar LESS_EQUAL2 = \"return f32(a <= b);\";\nvar LESS_EQUAL_VEC4 = \"return vec4<f32>(a <= b);\";\nvar LOGICAL_AND2 = \"return f32(f32(a) >= 1.0 && f32(b) >= 1.0);\";\nvar LOGICAL_AND_VEC4 = `return (vec4<f32>(a >= vec4<f32>(1.0)) *\n  vec4<f32>(b >= vec4<f32>(1.0)));`;\nvar INT_DIV2 = `\n  let s = sign(a) * sign(b);\n  let ia = i32(round(a));\n  let ib = i32(round(b));\n  return f32(idiv(ia, ib, s));\n  `;\nvar INT_DIV_VEC4 = `\n  let ia = vec4<i32>(round(a));\n  let ib = vec4<i32>(round(b));\n  let cond = ib != vec4<i32>(0);\n  var resultTemp = vec4<i32>(0);\n  let s = sign(a) * sign(b);\n\n  // Windows (D3D) wants guaranteed non-zero int division at compile-time.\n  if (cond[0]) {\n    resultTemp[0] = idiv(ia[0], ib[0], s[0]);\n  }\n  if (cond[1]) {\n    resultTemp[1] = idiv(ia[1], ib[1], s[1]);\n  }\n  if (cond[2]) {\n    resultTemp[2] = idiv(ia[2], ib[2], s[2]);\n  }\n  if (cond[3]) {\n    resultTemp[3] = idiv(ia[3], ib[3], s[3]);\n  }\n  return vec4<f32>(resultTemp);\n  `;\nvar NOT_EQUAL2 = `\n  if (isnan(a) || isnan(b)) {\n    return 1.0;\n  }\n  return f32(a != b);\n`;\nvar NOT_EQUAL_VEC4 = `\n  var resultTemp = vec4<f32>(a != b);\n  let valueForNaN = 1.0;\n  ${CHECK_NAN_SNIPPET_VEC4}\n\n  return resultTemp;\n`;\nvar POW2 = `\n  if(a < 0.0 && floor(b) < b) {\n    return uniforms.NAN;\n  }\n  if (b == 0.0) {\n    return 1.0;\n  }\n  if (round(abs(b) % 2.0) != 1.0) {\n    return pow(abs(a), b);\n  }\n  return sign(a) * pow(abs(a), b);\n  `;\nvar POW_VEC4 = `\n  let isModRound1Bool = vec4<i32>(round(abs(b) % vec4<f32>(2.0))) == vec4<i32>(1);\n  let isModRound1 = vec4<f32>(isModRound1Bool);\n  let multiplier = sign(a) * isModRound1 + (vec4<f32>(1.0) - isModRound1);\n  var resultTemp = multiplier * pow(abs(a), b);\n\n  // Ensure that a^0 = 1, including 0^0 = 1 as this correspond to TF and JS\n  let isExpZero = b == vec4<f32>(0.0);\n  if (isExpZero.r) {\n    resultTemp.r = 1.0;\n  }\n  if (isExpZero.g) {\n    resultTemp.g = 1.0;\n  }\n  if (isExpZero.b) {\n    resultTemp.b = 1.0;\n  }\n  if (isExpZero.a) {\n    resultTemp.a = 1.0;\n  }\n  let isNaN = a < vec4<f32>(0.0) & floor(b) < b;\n  let valueForNaN = uniforms.NAN;\n  ${CHECK_NAN_SNIPPET_VEC4_INNER}\n  return resultTemp;\n  `;\nvar PRELU2 = `if (a < 0.0) { return b * a; }  return a;`;\nvar PRELU_VEC4 = `\n  let aLessThanZero = vec4<f32>(a < vec4<f32>(0.0));\n  return (aLessThanZero * (b * a)) + ((vec4<f32>(1.0) - aLessThanZero) * a);\n  `;\nfunction getBinaryWithNanString(op2, useVec4, valueForNaN = \"uniforms.NAN\") {\n  const checkNanSnippet = useVec4 ? CHECK_NAN_SNIPPET_VEC4 : CHECK_NAN_SNIPPET4;\n  return useVec4 ? `\n    let valueForNaN = ${valueForNaN};\n    var resultTemp = vec4<f32>(${op2}(a, b));\n    ` + checkNanSnippet + `\n    return resultTemp;\n  ` : checkNanSnippet + `\n    return ${op2}(a, b);\n  `;\n}\nfunction getBinaryOpString(type, useVec4) {\n  switch (type) {\n    case BinaryOpType.MUL:\n      return MUL2;\n    case BinaryOpType.ADD:\n      return ADD2;\n    case BinaryOpType.ATAN2:\n      return getBinaryWithNanString(\"atan2\", useVec4);\n    case BinaryOpType.SUB:\n      return SUB2;\n    case BinaryOpType.DIV:\n      return DIV2;\n    case BinaryOpType.EQUAL:\n      return useVec4 ? EQUAL_VEC4 : EQUAL2;\n    case BinaryOpType.GREATER:\n      return useVec4 ? GREATER_VEC4 : GREATER2;\n    case BinaryOpType.GREATER_EQUAL:\n      return useVec4 ? GREATER_EQUAL_VEC4 : GREATER_EQUAL2;\n    case BinaryOpType.LESS:\n      return useVec4 ? LESS_VEC4 : LESS2;\n    case BinaryOpType.LESS_EQUAL:\n      return useVec4 ? LESS_EQUAL_VEC4 : LESS_EQUAL2;\n    case BinaryOpType.LOGICAL_AND:\n      return useVec4 ? LOGICAL_AND_VEC4 : LOGICAL_AND2;\n    case BinaryOpType.NOT_EQUAL:\n      return useVec4 ? NOT_EQUAL_VEC4 : NOT_EQUAL2;\n    case BinaryOpType.SQUARED_DIFFERENCE:\n      return SQUARED_DIFFERENCE2;\n    case BinaryOpType.INT_DIV:\n      return useVec4 ? INT_DIV_VEC4 : INT_DIV2;\n    case BinaryOpType.PRELU:\n      return useVec4 ? PRELU_VEC4 : PRELU2;\n    case BinaryOpType.MAX:\n      return getBinaryWithNanString(\"max\", useVec4);\n    case BinaryOpType.MIN:\n      return getBinaryWithNanString(\"min\", useVec4);\n    case BinaryOpType.POW:\n      return useVec4 ? POW_VEC4 : POW2;\n    case BinaryOpType.COMPLEX_MULTIPLY_REAL:\n      return COMPLEX_MULTIPLY_REAL;\n    case BinaryOpType.COMPLEX_MULTIPLY_IMAG:\n      return COMPLEX_MULTIPLY_IMAG;\n    default:\n      throw new Error(`BinaryType ${type} is not implemented!`);\n  }\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/unary_op_util.js\nvar UnaryOpType;\n(function(UnaryOpType2) {\n  UnaryOpType2[UnaryOpType2[\"ABS\"] = 0] = \"ABS\";\n  UnaryOpType2[UnaryOpType2[\"CEIL\"] = 1] = \"CEIL\";\n  UnaryOpType2[UnaryOpType2[\"COS\"] = 2] = \"COS\";\n  UnaryOpType2[UnaryOpType2[\"COSH\"] = 3] = \"COSH\";\n  UnaryOpType2[UnaryOpType2[\"ELU\"] = 4] = \"ELU\";\n  UnaryOpType2[UnaryOpType2[\"EXP\"] = 5] = \"EXP\";\n  UnaryOpType2[UnaryOpType2[\"EXPM1\"] = 6] = \"EXPM1\";\n  UnaryOpType2[UnaryOpType2[\"FLOOR\"] = 7] = \"FLOOR\";\n  UnaryOpType2[UnaryOpType2[\"IS_NAN\"] = 8] = \"IS_NAN\";\n  UnaryOpType2[UnaryOpType2[\"LINEAR\"] = 9] = \"LINEAR\";\n  UnaryOpType2[UnaryOpType2[\"LOG\"] = 10] = \"LOG\";\n  UnaryOpType2[UnaryOpType2[\"LOGICAL_NOT\"] = 11] = \"LOGICAL_NOT\";\n  UnaryOpType2[UnaryOpType2[\"NEG\"] = 12] = \"NEG\";\n  UnaryOpType2[UnaryOpType2[\"RELU\"] = 13] = \"RELU\";\n  UnaryOpType2[UnaryOpType2[\"RELU6\"] = 14] = \"RELU6\";\n  UnaryOpType2[UnaryOpType2[\"LEAKYRELU\"] = 15] = \"LEAKYRELU\";\n  UnaryOpType2[UnaryOpType2[\"RECIPROCAL\"] = 16] = \"RECIPROCAL\";\n  UnaryOpType2[UnaryOpType2[\"RSQRT\"] = 17] = \"RSQRT\";\n  UnaryOpType2[UnaryOpType2[\"SIN\"] = 18] = \"SIN\";\n  UnaryOpType2[UnaryOpType2[\"SINH\"] = 19] = \"SINH\";\n  UnaryOpType2[UnaryOpType2[\"SIGMOID\"] = 20] = \"SIGMOID\";\n  UnaryOpType2[UnaryOpType2[\"SQRT\"] = 21] = \"SQRT\";\n  UnaryOpType2[UnaryOpType2[\"SQUARE\"] = 22] = \"SQUARE\";\n  UnaryOpType2[UnaryOpType2[\"TANH\"] = 23] = \"TANH\";\n  UnaryOpType2[UnaryOpType2[\"TO_INT\"] = 24] = \"TO_INT\";\n})(UnaryOpType || (UnaryOpType = {}));\nvar ABS3 = `return abs(a);`;\nvar CEIL2 = `return ceil(a);`;\nvar COS2 = `return cos(a);`;\nvar COSH2 = `\n  let e2x = exp(-a);\n  return (e2x + 1.0 / e2x) / 2.0;\n`;\nvar EXPM12 = `return exp(a) - 1.0;`;\nvar ELU5 = `if (a >= 0.0) { return a; }  return (exp(a) - 1.0);`;\nvar ELU_VEC4 = `\n  var resFloat = exp(a) - vec4<f32>(1.0);\n  if (a.r >= 0.0) {\n    resFloat.r = a.r;\n  }\n  if (a.g >= 0.0) {\n    resFloat.g = a.g;\n  }\n  if (a.b >= 0.0) {\n    resFloat.b = a.b;\n  }\n  if (a.a >= 0.0) {\n    resFloat.a = a.a;\n  }\n  return resFloat;\n`;\nvar EXP2 = `return exp(a);`;\nvar FLOOR2 = `return floor(a);`;\nvar IS_NAN2 = `return f32(isnan(a));`;\nvar LINEAR3 = `return a;`;\nvar LOG2 = `if (a < 0.0) { return 1.0/0.0; }\n  return log(a);`;\nvar LOGICAL_NOT2 = `return f32(!(a >= 1.0));`;\nvar NEG2 = `return -a;`;\nvar LEAKYRELU2 = `if (a < 0.0) { return uniforms.alpha * a; } return a;`;\nvar LEAKYRELU_VEC4 = `\n  let aLessThanZero = vec4<f32>(a < vec4<f32>(0.0));\n  return (aLessThanZero * (uniforms.alpha * a)) + ((vec4<f32>(1.0) - aLessThanZero) * a);\n`;\nvar RECIPROCAL2 = `return 1.0 / a;`;\nvar RELU4 = `return select(a, 0.0, a < 0.0);`;\nvar RELU64 = \"return clamp(a, 0.0, 6.0);\";\nvar RELU6_VEC4 = \"return clamp(a, vec4<f32>(0.0, 0.0, 0.0, 0.0), vec4<f32>(6.0, 6.0, 6.0, 6.0));\";\nvar RELU_VEC4 = `\n  return select(a, vec4<f32>(0.0), a < vec4<f32>(0.0));\n`;\nvar RSQRT2 = `return 1.0/sqrt(a);`;\nvar SIGMOID4 = `return 1.0 / (1.0 + exp(-1.0 * a));`;\nvar SIN2 = `return sin(a);`;\nvar SINH2 = `\n  let e2x = exp(a);\n  return (e2x - 1.0 / e2x) / 2.0;\n`;\nvar SQRT2 = `return sqrt(a);`;\nvar SQUARE2 = `return a * a;`;\nvar TANH2 = `\n  let e2x = exp(-2.0 * abs(a));\n  return sign(a) * (1.0 - e2x) / (1.0 + e2x);\n`;\nvar TO_INT2 = `return f32(i32((a)));`;\nfunction getUnaryOpString(type, useVec4) {\n  switch (type) {\n    case UnaryOpType.ABS:\n      return ABS3;\n    case UnaryOpType.COS:\n      return COS2;\n    case UnaryOpType.COSH:\n      return COSH2;\n    case UnaryOpType.CEIL:\n      return CEIL2;\n    case UnaryOpType.ELU:\n      return useVec4 ? ELU_VEC4 : ELU5;\n    case UnaryOpType.EXP:\n      return EXP2;\n    case UnaryOpType.EXPM1:\n      return EXPM12;\n    case UnaryOpType.FLOOR:\n      return FLOOR2;\n    case UnaryOpType.IS_NAN:\n      return IS_NAN2;\n    case UnaryOpType.LINEAR:\n      return LINEAR3;\n    case UnaryOpType.LOG:\n      return LOG2;\n    case UnaryOpType.LOGICAL_NOT:\n      return LOGICAL_NOT2;\n    case UnaryOpType.NEG:\n      return NEG2;\n    case UnaryOpType.LEAKYRELU:\n      return useVec4 ? LEAKYRELU_VEC4 : LEAKYRELU2;\n    case UnaryOpType.RECIPROCAL:\n      return RECIPROCAL2;\n    case UnaryOpType.RELU:\n      return useVec4 ? RELU_VEC4 : RELU4;\n    case UnaryOpType.RELU6:\n      return useVec4 ? RELU6_VEC4 : RELU64;\n    case UnaryOpType.RSQRT:\n      return RSQRT2;\n    case UnaryOpType.SIGMOID:\n      return SIGMOID4;\n    case UnaryOpType.SIN:\n      return SIN2;\n    case UnaryOpType.SINH:\n      return SINH2;\n    case UnaryOpType.SQRT:\n      return SQRT2;\n    case UnaryOpType.SQUARE:\n      return SQUARE2;\n    case UnaryOpType.TANH:\n      return TANH2;\n    case UnaryOpType.TO_INT:\n      return TO_INT2;\n    default:\n      throw new Error(`BinaryType ${type} is not implemented!`);\n  }\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/activation_util.js\nvar typeSnippet = (component) => {\n  switch (component) {\n    case 1:\n      return \"f32\";\n    case 2:\n      return \"vec2<f32>\";\n    case 3:\n      return \"vec3<f32>\";\n    case 4:\n      return \"vec4<f32>\";\n    default:\n      throw new Error(`${component}-component is not supported.`);\n  }\n};\nfunction activationFnSnippet(activation2, hasPreluActivationWeights = false, packed = false, coordsLength = 3) {\n  if (activation2 === null) {\n    return \"\";\n  }\n  let activationOpSnippet = \"\";\n  if (activation2 === \"linear\") {\n    activationOpSnippet = getUnaryOpString(UnaryOpType.LINEAR);\n  } else if (activation2 === \"relu\") {\n    activationOpSnippet = getUnaryOpString(UnaryOpType.RELU, packed);\n  } else if (activation2 === \"elu\") {\n    activationOpSnippet = getUnaryOpString(UnaryOpType.ELU, packed);\n  } else if (activation2 === \"relu6\") {\n    activationOpSnippet = getUnaryOpString(UnaryOpType.RELU6, packed);\n  } else if (activation2 === \"prelu\") {\n    activationOpSnippet = getBinaryOpString(BinaryOpType.PRELU, packed);\n  } else if (activation2 === \"sigmoid\") {\n    activationOpSnippet = getUnaryOpString(UnaryOpType.SIGMOID, packed);\n  } else if (activation2 === \"leakyrelu\") {\n    activationOpSnippet = getUnaryOpString(UnaryOpType.LEAKYRELU, packed);\n  } else {\n    throw new Error(`Activation ${activation2} has not been implemented for the WebGPU backend.`);\n  }\n  const elementSize = packed ? 4 : 1;\n  const dataType = typeSnippet(elementSize);\n  let activationFnSnippet2 = \"\";\n  if (hasPreluActivationWeights) {\n    activationFnSnippet2 = `\n      fn activation(a : ${dataType}, coords : vec${coordsLength}<i32>) -> ${dataType} {\n        let b = getPreluActivationWeightsByOutputCoords(coords);\n        ${activationOpSnippet}\n      }`;\n  } else {\n    activationFnSnippet2 = `\n      fn activation(a : ${dataType}, coords : vec${coordsLength}<i32>) -> ${dataType} {\n        ${activationOpSnippet}\n      }`;\n  }\n  return activationFnSnippet2;\n}\nfunction biasActivationSnippet(hasBias, activation2) {\n  return `\n      ${hasBias ? \"value = value + getBiasByOutputCoords(coords);\" : \"\"}\n      ${activation2 ? \"value = activation(value, coords);\" : \"\"}\n      `;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/matmul_packed_webgpu.js\nfunction matMulReadFnSource(batchAEqualOne, batchBEqualOne, transposeA, transposeB, fitAOuter = false, fitBOuter = false, fitInner = false, component = 1) {\n  util_exports.assert(transposeA && component === 1 || !transposeA, () => `transposeA ${transposeA} is not compatible with component size ${component}`);\n  const sampleA = `\n      let batch = ${batchAEqualOne ? \"0\" : \"batchIn\"};\n      let batchASize = uniforms.aShape[1] * uniforms.aShape[2];\n      ${transposeA ? `value = A[(batch * batchASize + col * uniforms.aShape[2] + row) / ${component}];` : `value = A[(batch * batchASize + row * uniforms.aShape[2] + col) / ${component}];`}\n\n    `;\n  let sampleB;\n  if (transposeB === false) {\n    sampleB = `value = B[(batch * batchBSize + row * uniforms.bShape[2] + col) / ${component}];`;\n  } else {\n    sampleB = `value = B[(batch * batchBSize + col * uniforms.bShape[2] + row) / ${component}];`;\n  }\n  return `\n  fn mm_readA(batchIn: i32, row: i32, colIn: i32) -> ${typeSnippet(component)} {\n    var value = ${typeSnippet(component)}(0.0);\n    let col = colIn * ${component};\n    ${fitAOuter && fitInner ? sampleA : `\n    ${transposeA ? `if(row < uniforms.dimAOuter && col < uniforms.dimInner)` : `if(row < uniforms.aShape[1] && col < uniforms.aShape[2])`}\n    {\n      ${sampleA}\n    }\n    `}\n    return value;\n  }\n\n  fn mm_readB(batchIn: i32, row: i32, colIn: i32) -> ${typeSnippet(component)} {\n    let col = colIn * ${component};\n    let batch = ${batchBEqualOne ? \"0\" : \"batchIn\"};\n    let batchBSize = uniforms.bShape[1] * uniforms.bShape[2];\n    var value = ${typeSnippet(component)}(0.0);\n    ${sampleB}\n    return value;\n  }\n  `;\n}\nfunction matMulReadWriteFnSource(hasBias, activation2, batchAEqualOne, batchBEqualOne, transposeA, transposeB, fitAOuter = false, fitBOuter = false, fitInner = false, component = 1) {\n  return `\n  ${matMulReadFnSource(batchAEqualOne, batchBEqualOne, transposeA, transposeB, fitAOuter, fitBOuter, fitInner, component)}\n  fn mm_write(batch: i32, row: i32, colIn: i32, valueIn: ${typeSnippet(component)}) {\n    let col = colIn * ${component};\n    ${fitAOuter && fitBOuter ? \"\" : \"if (row < uniforms.dimAOuter && col < uniforms.dimBOuter)\"}\n    {\n      var value = valueIn;\n      let coords = vec3<i32>(batch, row, col);\n      ${biasActivationSnippet(hasBias, activation2)}\n      setOutputAtCoords(coords[0], coords[1], coords[2], value);\n    }\n  }\n  `;\n}\nvar writeDataToSubAVec4Snippet = (transpose6) => {\n  if (transpose6) {\n    return `\n        mm_Asub[inputRow][inputCol] = mm_readA(batch,\n          kStart + inputRow,\n          globalRowStart / InnerElementSize + inputCol);\n        `;\n  } else {\n    return `\n        mm_Asub[inputRow][inputCol] = mm_readA(batch,\n          globalRow + innerRow,\n          kStart / InnerElementSize + inputCol);\n        `;\n  }\n};\nvar calculateResultSnippet = (transposeA, innerElementSize) => {\n  if (transposeA) {\n    return `\n        let ACached0 = mm_Asub[k * InnerElementSize][localRow];\n        let ACached1 = mm_Asub[k * InnerElementSize + 1][localRow];\n        let ACached2 = mm_Asub[k * InnerElementSize + 2][localRow];\n        ${innerElementSize === 3 ? \"\" : \"let ACached3 = mm_Asub[k * InnerElementSize + 3][localRow];\"}\n        for (var i = 0; i < RowPerThread; i = i + 1) {\n          acc[i] = BCached0 * ACached0[i] + acc[i];\n          acc[i] = BCached1 * ACached1[i] + acc[i];\n          acc[i] = BCached2 * ACached2[i] + acc[i];\n          ${innerElementSize === 3 ? \"\" : \"acc[i] = BCached3 * ACached3[i] + acc[i];\"}\n        }`;\n  } else {\n    return `\n        for (var i = 0; i < RowPerThread; i = i + 1) {\n          let ACached = mm_Asub[tileRow + i][k];\n          acc[i] = BCached0 * ACached.x + acc[i];\n          acc[i] = BCached1 * ACached.y + acc[i];\n          acc[i] = BCached2 * ACached.z + acc[i];\n          ${innerElementSize === 3 ? \"\" : \"acc[i] = BCached3 * ACached.w + acc[i];\"}\n        }`;\n  }\n};\nfunction makeMatMulPackedVec4Source(workPerThread, workGroupSize, transposeA = false, tileInner = 32, splitK = false, splitedDimInner = 32, isVectorA = false) {\n  const tileAOuter = workGroupSize[1] * workPerThread[1];\n  const tileBOuter = workGroupSize[0] * workPerThread[0];\n  const tileAWidth = transposeA ? tileAOuter : tileInner;\n  const tileAHight = transposeA ? tileInner : tileAOuter;\n  const innerElementSize = tileAWidth / workGroupSize[0];\n  const rowPerThreadB = tileInner / workGroupSize[1];\n  util_exports.assert((transposeA && innerElementSize === 4 && workPerThread[1] === 4 || !transposeA && (innerElementSize === 3 || innerElementSize === 4)) && tileAWidth % workGroupSize[0] === 0 && tileInner % workGroupSize[1] === 0 && workPerThread[0] === 4, () => `If transposeA ${transposeA} is true, innerElementSize ${innerElementSize} and workPerThread[1] ${workPerThread[1]} must be 4.\n          Otherwise, innerElementSize ${innerElementSize} must be 3 or 4.\n      tileAWidth ${tileAWidth} must be divisible by workGroupSize[0]${workGroupSize[0]}. tileInner ${tileInner} must be divisible by workGroupSize[1] ${workGroupSize[1]}. ColPerThread ${workPerThread[0]} must be 4.`);\n  return `\n  var<workgroup> mm_Asub : array<array<vec${innerElementSize}<f32>, ${tileAWidth / innerElementSize}>, ${tileAHight}>;\n  var<workgroup> mm_Bsub : array<array<vec4<f32>, ${tileBOuter / workPerThread[0]}>, ${tileInner}>;\n\n  const RowPerThread = ${workPerThread[1]};\n  const ColPerThread = ${workPerThread[0]};\n  const InnerElementSize = ${innerElementSize};\n  const TileInner = ${tileInner};\n\n  @compute @workgroup_size(workGroupSizeX, workGroupSizeY, workGroupSizeZ)\n  fn _start(@builtin(local_invocation_id) LocalId : vec3<u32>,\n            @builtin(global_invocation_id) GlobalId : vec3<u32>,\n            @builtin(num_workgroups) NumWorkgroups: vec3<u32>,\n            @builtin(workgroup_id) workgroupId: vec3<u32>) {\n    localId = LocalId;\n    globalId = GlobalId;\n    numWorkgroups = NumWorkgroups;\n\n    let localRow = i32(localId.y);\n    let tileRow = ${isVectorA ? \"0\" : \"localRow * RowPerThread\"};\n    let tileCol = i32(localId.x);\n\n    let globalRow = ${isVectorA ? \"0\" : \"i32(globalId.y) * RowPerThread\"};\n    let globalCol = i32(globalId.x);\n    let batch = ${splitK ? \"0\" : \"i32(globalId.z)\"};\n    let globalRowStart = i32(workgroupId.y) * ${tileAOuter};\n\n    let numTiles = ${splitK ? `${Math.ceil(splitedDimInner / tileInner)}` : \"(uniforms.dimInner - 1) / TileInner + 1\"};\n    var kStart = ${splitK ? `i32(globalId.z) * ${splitedDimInner}` : \"0\"};\n\n    var acc: array<vec4<f32>, RowPerThread>;\n\n    // Loop over shared dimension.\n    let tileRowB = localRow * ${rowPerThreadB};\n    for (var t = 0; t < numTiles; t = t + 1) {\n        // Load one tile of A into local memory.\n        for (var innerRow = 0; innerRow < RowPerThread; innerRow = innerRow + 1) {\n            let inputRow = tileRow + innerRow;\n            let inputCol = tileCol;\n            ${writeDataToSubAVec4Snippet(transposeA)}\n        }\n\n        // Load one tile of B into local memory.\n        for (var innerRow = 0; innerRow < ${rowPerThreadB}; innerRow = innerRow + 1) {\n            let inputRow = tileRowB + innerRow;\n            let inputCol = tileCol;\n            mm_Bsub[inputRow][inputCol] = mm_readB(batch, kStart + inputRow, globalCol);\n        }\n        kStart = kStart + TileInner;\n        workgroupBarrier();\n\n        // Compute acc values for a single thread.\n        for (var k = 0; k < TileInner / InnerElementSize; k = k + 1) {\n            let BCached0 = mm_Bsub[k * InnerElementSize][tileCol];\n            let BCached1 = mm_Bsub[k * InnerElementSize + 1][tileCol];\n            let BCached2 = mm_Bsub[k * InnerElementSize + 2][tileCol];\n            ${innerElementSize === 3 ? \"\" : \"let BCached3 = mm_Bsub[k * InnerElementSize + 3][tileCol];\"}\n\n            ${calculateResultSnippet(transposeA, innerElementSize)}\n        }\n\n        workgroupBarrier();\n    }\n\n    for (var innerRow = 0; innerRow < RowPerThread; innerRow = innerRow + 1) {\n        mm_write(batch, globalRow + innerRow, globalCol, acc[innerRow]);\n    }\n  }`;\n}\nvar writeDataToSubASnippet = (transpose6) => {\n  if (transpose6) {\n    return `\n        mm_Asub[inputRow][inputCol] = mm_readA(batch,\n          kStart + inputRow,\n          globalRowStart + inputCol);\n        `;\n  } else {\n    return `\n        mm_Asub[inputRow][inputCol] = mm_readA(batch,\n          globalRowStart + inputRow,\n          kStart + inputCol);\n        `;\n  }\n};\nvar readDataFromSubASnippet = (transposeA) => {\n  return transposeA ? \"let ACached = mm_Asub[k][tileRow + innerRow];\" : \"let ACached = mm_Asub[tileRow + innerRow][k];\";\n};\nfunction makeMatMulPackedSource(workPerThread, workGroupSize, transposeA = false, tileInner = 32, splitK = false, splitedDimInner = 32) {\n  const tileAOuter = workPerThread[1] * workGroupSize[1];\n  const tileBOuter = workPerThread[0] * workGroupSize[0];\n  const tileAWidth = transposeA ? tileAOuter : tileInner;\n  const tileAHight = transposeA ? tileInner : tileAOuter;\n  util_exports.assert(tileAHight % workGroupSize[1] === 0 && tileAWidth % workGroupSize[0] === 0 && tileInner % workGroupSize[1] === 0, () => `tileAHight ${tileAHight} must be divisible by workGroupSize[1]${workGroupSize[1]}, tileAWidth ${tileAWidth} must be divisible by workGroupSize[0]${workGroupSize[0]}, tileInner ${tileInner} must be divisible by workGroupSize[1]${workGroupSize[1]}`);\n  const rowPerThreadA = tileAHight / workGroupSize[1];\n  const colPerThreadA = tileAWidth / workGroupSize[0];\n  const rowPerThreadB = tileInner / workGroupSize[1];\n  return `\n    var<workgroup> mm_Asub : array<array<f32, ${tileAWidth}>, ${tileAHight}>;\n    var<workgroup> mm_Bsub : array<array<f32, ${tileBOuter}>, ${tileInner}>;\n    const RowPerThread = ${workPerThread[1]};\n    const ColPerThread = ${workPerThread[0]};\n    const TileInner = ${tileInner};\n\n    @compute @workgroup_size(workGroupSizeX, workGroupSizeY, workGroupSizeZ)\n    fn _start(@builtin(local_invocation_id) LocalId : vec3<u32>,\n              @builtin(global_invocation_id) GlobalId : vec3<u32>,\n              @builtin(num_workgroups) NumWorkgroups: vec3<u32>,\n              @builtin(workgroup_id) workgroupId: vec3<u32>) {\n      localId = LocalId;\n      globalId = GlobalId;\n      numWorkgroups = NumWorkgroups;\n\n      let tileRow = i32(localId.y) * RowPerThread;\n      let tileCol = i32(localId.x) * ColPerThread;\n\n      let globalRow = i32(globalId.y) * RowPerThread;\n      let globalCol = i32(globalId.x) * ColPerThread;\n      let batch = ${splitK ? \"0\" : \"i32(globalId.z)\"};\n      let globalRowStart = i32(workgroupId.y) * ${tileAOuter};\n\n      let numTiles = ${splitK ? `${Math.ceil(splitedDimInner / tileInner)}` : \"(uniforms.dimInner - 1) / TileInner + 1\"};\n      var kStart = ${splitK ? `i32(globalId.z) * ${splitedDimInner}` : \"0\"};\n\n      var acc : array<array<f32, ColPerThread>, RowPerThread>;\n\n      // Without this initialization strange values show up in acc.\n      for (var innerRow = 0; innerRow < RowPerThread; innerRow = innerRow + 1) {\n        for (var innerCol = 0; innerCol < ColPerThread; innerCol = innerCol + 1) {\n          acc[innerRow][innerCol] = 0.0;\n        }\n      }\n\n      let tileRowA = i32(localId.y) * ${rowPerThreadA};\n      let tileColA = i32(localId.x) * ${colPerThreadA};\n      let tileRowB = i32(localId.y) * ${rowPerThreadB};\n      // Loop over shared dimension.\n      for (var t = 0; t < numTiles; t = t + 1) {\n        // Load one tile of A into local memory.\n        for (var innerRow = 0; innerRow < ${rowPerThreadA}; innerRow = innerRow + 1) {\n          for (var innerCol = 0; innerCol < ${colPerThreadA}; innerCol = innerCol + 1) {\n            let inputRow = tileRowA + innerRow;\n            let inputCol = tileColA + innerCol;\n            ${writeDataToSubASnippet(transposeA)}\n          }\n        }\n\n        // Load one tile of B into local memory.\n        for (var innerRow = 0; innerRow < ${rowPerThreadB}; innerRow = innerRow + 1) {\n          for (var innerCol = 0; innerCol < ColPerThread; innerCol = innerCol + 1) {\n            let inputRow = tileRowB + innerRow;\n            let inputCol = tileCol + innerCol;\n            mm_Bsub[inputRow][inputCol] = mm_readB(batch,\n              kStart + inputRow,\n              globalCol + innerCol);\n          }\n        }\n        kStart = kStart + TileInner;\n        workgroupBarrier();\n\n        // Compute acc values for a single thread.\n        var BCached : array<f32, ColPerThread>;\n        for (var k = 0; k < TileInner; k = k + 1) {\n          for (var inner = 0; inner < ColPerThread; inner = inner + 1) {\n            BCached[inner] = mm_Bsub[k][tileCol + inner];\n          }\n\n          for (var innerRow = 0; innerRow < RowPerThread; innerRow = innerRow + 1) {\n            ${readDataFromSubASnippet(transposeA)}\n            for (var innerCol = 0; innerCol < ColPerThread; innerCol = innerCol + 1) {\n              acc[innerRow][innerCol] = acc[innerRow][innerCol] + ACached * BCached[innerCol];\n            }\n          }\n        }\n\n        workgroupBarrier();\n      }\n\n      for (var innerRow = 0; innerRow < RowPerThread; innerRow = innerRow + 1) {\n        for (var innerCol = 0; innerCol < ColPerThread; innerCol = innerCol + 1) {\n          mm_write(batch, globalRow + innerRow, globalCol + innerCol,\n              acc[innerRow][innerCol]);\n        }\n      }\n    }\n  `;\n}\nvar readVectorASnippet = (transpose6) => {\n  return transpose6 ? `\n      mm_readA(batch, colA, globalRow),\n      mm_readA(batch, colA + 1, globalRow),\n      mm_readA(batch, colA + 2, globalRow),\n      mm_readA(batch, colA + 3, globalRow)\n  ` : `\n      mm_readA(batch, globalRow, colA),\n      mm_readA(batch, globalRow, colA + 1),\n      mm_readA(batch, globalRow, colA + 2),\n      mm_readA(batch, globalRow, colA + 3)\n  `;\n};\nfunction makeVectorMatrixProductSource(workGroupSize, transposeA = false) {\n  util_exports.assert(workGroupSize[1] === 1 && workGroupSize[2] === 1, () => `A linear work group size is required. But got ${workGroupSize}.`);\n  return `\n    const TileSize = ${workGroupSize[0] * 4};\n    var<workgroup> mm_Asub : array<vec4<f32>, ${workGroupSize[0]}>;\n\n    ${getMainHeaderString()} {\n      let tileCol = i32(localId.x);\n      let globalCol = i32(globalId.x);\n      let globalRow = i32(globalId.y);\n\n      let numTiles = (uniforms.dimInner - 1) / TileSize + 1;\n      let batch = i32(globalId.z);\n      // Without this initialization strange values show up in acc.\n      var acc = 0.0;\n\n      // Loop over shared dimension.\n      for (var t = 0; t < numTiles; t = t + 1) {\n        // Load one tile of A into local memory.\n        let colA = t * TileSize + tileCol * 4;\n        mm_Asub[tileCol] = vec4<f32>(${readVectorASnippet(transposeA)});\n        workgroupBarrier();\n\n        // Compute acc values for a single thread.\n        for (var k = 0; k < TileSize / 4; k = k + 1) {\n          let rowB = t * TileSize + k * 4;\n          let BCached = vec4<f32>(mm_readB(batch, rowB, globalCol),\n                              mm_readB(batch, rowB + 1, globalCol),\n                              mm_readB(batch, rowB + 2, globalCol),\n                              mm_readB(batch, rowB + 3, globalCol));\n\n          let ACached = mm_Asub[k];\n          acc = acc + dot(ACached, BCached);\n        }\n\n        workgroupBarrier();\n      }\n\n      mm_write(batch, globalRow, globalCol, acc);\n    }\n  `;\n}\nvar MatMulPackedProgram2 = class {\n  constructor(aShape, outputShape, batchAEqualOne, batchBEqualOne, transposeA = false, transposeB = false, bias = null, activation2 = null, preluActivationWeights = null) {\n    this.variableNames = [\"A\", \"B\"];\n    this.uniforms = `dimAOuter : i32, dimBOuter : i32, dimInner : i32,`;\n    this.outputShape = outputShape;\n    this.dispatchLayout = { x: [2], y: [1], z: [0] };\n    const dimInner = transposeA ? aShape[1] : aShape[2];\n    this.isVec4 = (dimInner % 4 === 0 && !transposeA || outputShape[1] % 4 === 0 && transposeA) && outputShape[2] % 4 === 0 && !transposeB;\n    this.isVectorA = outputShape[1] === 1 && !transposeA;\n    if (!this.isVec4 && this.isVectorA) {\n      this.elementsPerThread = [1, 1, 1];\n      this.workGroupSize = [32, 1, 1];\n    } else {\n      const workGroupInfo = computeWorkGroupInfoForMatMul(outputShape[1], dimInner, outputShape[2], transposeA);\n      this.workGroupSize = workGroupInfo.workGroupSize;\n      this.elementsPerThread = workGroupInfo.elementsPerThread;\n    }\n    this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, this.workGroupSize, this.elementsPerThread);\n    const addBias = bias != null;\n    const hasPreluActivationWeights = preluActivationWeights != null;\n    if (addBias) {\n      this.variableNames.push(\"bias\");\n    }\n    if (hasPreluActivationWeights) {\n      this.variableNames.push(\"preluActivationWeights\");\n    }\n    this.transposeA = transposeA;\n    this.transposeB = transposeB;\n    this.addBias = addBias;\n    this.activation = activation2;\n    this.hasPreluActivationWeights = hasPreluActivationWeights;\n    this.batchAEqualOne = batchAEqualOne;\n    this.batchBEqualOne = batchBEqualOne;\n    [this.fitAOuter, this.fitBOuter, this.fitInner] = this.getShapeFit(outputShape[1], outputShape[2], dimInner);\n    this.shaderKey = `matMulPacked_${this.elementsPerThread}_${transposeA}_${transposeB}_${this.activation}_${this.fitAOuter}_${this.fitBOuter}_${this.fitInner}_${this.isVec4}_${this.isVectorA}_${this.batchAEqualOne}_${this.batchBEqualOne}`;\n  }\n  getShapeFit(dimAOuter, dimBOuter, dimInner) {\n    const tileAOuter = this.workGroupSize[1] * this.elementsPerThread[1];\n    const tileBOuter = this.workGroupSize[0] * this.elementsPerThread[0];\n    if (!this.isVec4 && this.isVectorA) {\n      this.tileInner = this.workGroupSize[0] * 4;\n    } else {\n      this.tileInner = tileBOuter;\n    }\n    const fitAOuter = dimAOuter % tileAOuter === 0;\n    const fitBOuter = dimBOuter % tileBOuter === 0;\n    const fitInner = dimInner % this.tileInner === 0;\n    return [fitAOuter, fitBOuter, fitInner];\n  }\n  getUserCode() {\n    const userCode = `\n      ${activationFnSnippet(this.activation, this.hasPreluActivationWeights, this.isVec4)}\n      ${matMulReadWriteFnSource(this.addBias, this.activation, this.batchAEqualOne, this.batchBEqualOne, false, this.transposeB, this.fitAOuter, this.fitBOuter, this.fitInner, this.isVec4 ? 4 : 1)}\n      ${this.isVec4 ? makeMatMulPackedVec4Source(this.elementsPerThread, this.workGroupSize, this.transposeA, this.tileInner, false, null, this.isVectorA) : this.isVectorA ? makeVectorMatrixProductSource(this.workGroupSize, this.transposeA) : makeMatMulPackedSource(this.elementsPerThread, this.workGroupSize, this.transposeA, this.tileInner)}\n    `;\n    return userCode;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/matmul_reduce_webgpu.js\nfunction makeMatMulReduceSource() {\n  return `\n    var<workgroup> sumValues : array<f32, workGroupSizeX>;\n    ${getMainHeaderString()} {\n      let coords = getOutputCoords();\n      let batch = coords[0];\n      let row = coords[1];\n      let col = coords[2];\n      var sum = 0.0;\n      let Length = uniforms.dimInner;\n      for (var k = i32(localId.x); k < Length; k = k + i32(workGroupSizeX)) {\n        let dataA = mm_readA(batch, row, k);\n        let dataB = mm_readB(batch, k, col);\n        sum = sum + dataA * dataB;\n      }\n      sumValues[localId.x] = sum;\n      workgroupBarrier();\n\n      for(var currentSize = workGroupSizeX / 2u; currentSize > 1u;\n          currentSize = currentSize / 2u) {\n        if (localId.x < currentSize)\n        {\n          sumValues[localId.x] = sumValues[localId.x] + sumValues[localId.x + currentSize];\n        }\n        workgroupBarrier();\n      }\n\n      if (localId.x == 0u) {\n        sum = sumValues[0] + sumValues[1];\n        mm_write(batch, row, col, sum);\n      }\n    }\n  `;\n}\nvar MatMulReduceProgram = class {\n  constructor(outputShape, batchAEqualOne, batchBEqualOne, transposeA = false, transposeB = false, bias = null, activation2 = null, preluActivationWeights = null) {\n    this.variableNames = [\"A\", \"B\"];\n    this.uniforms = `dimAOuter : i32, dimBOuter : i32, dimInner : i32,`;\n    this.workGroupSize = [256, 1, 1];\n    this.outputShape = outputShape;\n    this.dispatchLayout = { x: [], y: [1, 2], z: [0] };\n    this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, this.workGroupSize);\n    const addBias = bias != null;\n    const hasPreluActivationWeights = preluActivationWeights != null;\n    if (addBias) {\n      this.variableNames.push(\"bias\");\n    }\n    if (hasPreluActivationWeights) {\n      this.variableNames.push(\"preluActivationWeights\");\n    }\n    this.transposeA = transposeA;\n    this.transposeB = transposeB;\n    this.addBias = addBias;\n    this.activation = activation2;\n    this.hasPreluActivationWeights = hasPreluActivationWeights;\n    this.batchAEqualOne = batchAEqualOne;\n    this.batchBEqualOne = batchBEqualOne;\n    this.shaderKey = `matMulReduce_${this.activation}_${transposeA}_${transposeB}_${this.batchAEqualOne}_${this.batchBEqualOne}`;\n  }\n  getUserCode() {\n    const userCode = `\n      ${activationFnSnippet(this.activation, this.hasPreluActivationWeights)}\n      ${matMulReadWriteFnSource(this.addBias, this.activation, this.batchAEqualOne, this.batchBEqualOne, this.transposeA, this.transposeB)}\n      ${makeMatMulReduceSource()}\n    `;\n    return userCode;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/matmul_small_output_size_webgpu.js\nfunction makeMatMulSmallOutputSizeSource(workGroupSize) {\n  const tileAOuter = workGroupSize[1];\n  const tileBOuter = workGroupSize[0];\n  const tileInner = tileAOuter > tileBOuter ? tileAOuter : tileBOuter;\n  return `\n  var<workgroup> mm_Asub : array<array<f32, ${tileInner}>, ${tileAOuter}>;\n  var<workgroup> mm_Bsub : array<array<f32, ${tileBOuter}>, ${tileInner}>;\n\n  // If the output size is small for matrix multiplication, avoid to use vec4\n  // and handle some elements per thread to optimally utilize the ALU.\n  // Read data from global memory to registers firstly, then store them into\n  // shared memory, so it is instruction-Level parallelism for arithmetic\n  // operations and others handle IO operations between barrier api, makes ALU\n  // and load/store units work simultaneously, could improves the performance.\n  ${getMainHeaderString()} {\n    let tileRow = i32(localId.y);\n    let tileCol = i32(localId.x);\n    let globalRow = i32(globalId.y);\n    let globalCol = i32(globalId.x);\n    let batch = i32(globalId.z);\n\n    // uniforms.dimInner should be greater than 0.\n    let numTiles = (uniforms.dimInner - 1) / ${tileInner} + 1;\n    var acc = 0.0;\n\n    var globalColA = tileCol;\n    var globalRowB = 0;\n    var regA = mm_readA(batch, globalRow, globalColA);\n    var regB0 = mm_readB(batch, globalRowB + 2 * tileRow, globalCol);\n    var regB1 = mm_readB(batch, globalRowB + 2 * tileRow + 1, globalCol);\n    globalColA = globalColA + ${tileInner};\n    globalRowB = globalRowB + ${tileInner};\n\n    for (var t = 0; t < numTiles; t = t + 1) {\n      mm_Asub[tileRow][tileCol] = regA;\n      mm_Bsub[2 * tileRow][tileCol] = regB0;\n      mm_Bsub[2 * tileRow + 1][tileCol] = regB1;\n\n      workgroupBarrier();\n\n      regA = mm_readA(batch, globalRow, globalColA);\n      regB0 = mm_readB(batch, globalRowB + 2 * tileRow, globalCol);\n      regB1 = mm_readB(batch, globalRowB + 2 * tileRow + 1, globalCol);\n      globalColA = globalColA + ${tileInner};\n      globalRowB = globalRowB + ${tileInner};\n\n      for (var k = 0; k < ${tileInner}; k = k + 1) {\n        acc = acc + mm_Asub[tileRow][k] * mm_Bsub[k][tileCol];\n      }\n      workgroupBarrier();\n    }\n\n    mm_write(batch, globalRow, globalCol, acc);\n  }\n  `;\n}\nvar MatMulSmallOutputSizeProgram = class {\n  constructor(aShape, bShape, outputShape, transposeA = false, transposeB = false, bias = null, activation2 = null, preluActivationWeights = null) {\n    this.variableNames = [\"A\", \"B\"];\n    this.uniforms = `dimAOuter : i32, dimBOuter : i32, dimInner : i32,`;\n    this.workGroupSize = [16, 8, 1];\n    this.outputShape = outputShape;\n    this.dispatchLayout = { x: [2], y: [1], z: [0] };\n    this.dispatch = [\n      Math.ceil(outputShape[2] / this.workGroupSize[0]),\n      Math.ceil(outputShape[1] / this.workGroupSize[1]),\n      outputShape[0]\n    ];\n    const addBias = bias != null;\n    if (addBias) {\n      this.variableNames.push(\"bias\");\n    }\n    const hasPreluActivationWeights = preluActivationWeights != null;\n    if (hasPreluActivationWeights) {\n      this.variableNames.push(\"preluActivationWeights\");\n    }\n    this.transposeA = transposeA;\n    this.transposeB = transposeB;\n    this.addBias = addBias;\n    this.activation = activation2;\n    this.hasPreluActivationWeights = hasPreluActivationWeights;\n    this.batchAEqualOne = aShape[0] === 1;\n    this.batchBEqualOne = bShape[0] === 1;\n    this.shaderKey = `matMulSmallOutputSize_${this.activation}_${transposeA}_${transposeB}_${this.batchAEqualOne}_${this.batchBEqualOne}`;\n  }\n  getUserCode() {\n    const userCode = `\n      ${activationFnSnippet(this.activation, this.hasPreluActivationWeights)}\n      ${matMulReadWriteFnSource(this.addBias, this.activation, this.batchAEqualOne, this.batchBEqualOne, this.transposeA, this.transposeB)}\n      ${makeMatMulSmallOutputSizeSource(this.workGroupSize)}\n    `;\n    return userCode;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/matmul_splitK_webgpu.js\nvar MatMulSplitKProgram = class {\n  constructor(outputShape, dimInner, batchAEqualOne, batchBEqualOne, transposeA = false, transposeB = false) {\n    this.variableNames = [\"A\", \"B\"];\n    this.uniforms = `dimAOuter : i32, dimBOuter : i32, dimInner : i32,`;\n    this.workGroupSize = [8, 8, 1];\n    this.atomic = true;\n    this.isVec4 = false;\n    this.splitedDimInner = 128;\n    util_exports.assert(outputShape[0] === 1, () => \"MatMulSplitKProgram only supports batch = 1.\");\n    this.outputShape = outputShape;\n    this.dispatchLayout = { x: [2], y: [1], z: [0, 3] };\n    this.isVec4 = (transposeA && this.outputShape[1] % 4 === 0 || !transposeA && dimInner % 4 === 0) && this.outputShape[2] % 4 === 0;\n    this.elementsPerThread = [4, 4, this.splitedDimInner];\n    if (!this.isVec4) {\n      if (this.outputShape[1] < 16) {\n        this.elementsPerThread[1] = 1;\n      }\n      if (this.outputShape[2] < 16) {\n        this.elementsPerThread[0] = 1;\n      }\n    }\n    this.dispatch = computeDispatch(this.dispatchLayout, [\n      this.outputShape[0],\n      this.outputShape[1],\n      this.outputShape[2],\n      dimInner\n    ], this.workGroupSize, this.elementsPerThread);\n    this.transposeA = transposeA;\n    this.transposeB = transposeB;\n    this.batchAEqualOne = batchAEqualOne;\n    this.batchBEqualOne = batchBEqualOne;\n    this.shaderKey = `matMulSplitK_${transposeA}_${transposeB}_${batchAEqualOne}_${batchBEqualOne}_${this.elementsPerThread}_${this.isVec4}`;\n  }\n  getUserCode() {\n    const atomicAddSnippet = (component2) => {\n      return `\n      for (var i = 0; i < ${component2}; i = i + 1)\n      {\n        var oldValue = atomicLoad(&(result[flatIndex + i]));\n        var exchanged = false;\n        for (; !exchanged;) {\n          let newValueF32 = bitcast<f32>(oldValue) + ${component2 > 1 ? \"value[i]\" : \"value\"};\n          let newValue = bitcast<i32>(newValueF32);\n          let res = atomicCompareExchangeWeak(&(result[flatIndex + i]), oldValue, newValue);\n          oldValue = res.old_value;\n          exchanged = res.exchanged;\n        }\n      }\n      `;\n    };\n    const component = this.isVec4 ? 4 : 1;\n    const userCode = `\n      ${matMulReadFnSource(this.batchAEqualOne, this.batchBEqualOne, false, this.transposeB, false, false, false, component)}\n      fn mm_write(batch: i32, row : i32, colIn : i32, value : ${typeSnippet(component)}) {\n        let col = colIn * ${component};\n        if (row < uniforms.dimAOuter && col < uniforms.dimBOuter) {\n          let coords = vec3<i32>(batch, row, col);\n          let flatIndex = getOutputIndexFromCoords(coords);\n          // The problem is that we should initialize output to zero before using.\n          // Otherwise, the original value will be added to the result.\n          ${atomicAddSnippet(component)}\n        }\n      }\n      ${this.isVec4 ? makeMatMulPackedVec4Source(this.elementsPerThread, this.workGroupSize, this.transposeA, 32, true, this.splitedDimInner) : makeMatMulPackedSource(this.elementsPerThread, this.workGroupSize, this.transposeA, 32, true, this.splitedDimInner)}\n    `;\n    return userCode;\n  }\n};\nvar BiasActivationProgram = class {\n  constructor(outputShape, bias = null, activation2 = null, preluActivationWeights = null) {\n    this.uniforms = \"\";\n    this.variableNames = [\"x\"];\n    this.workGroupSize = [64, 1, 1];\n    this.size = true;\n    this.outputShape = outputShape;\n    this.dispatchLayout = flatDispatchLayout(this.outputShape);\n    this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, this.workGroupSize);\n    this.addBias = bias != null;\n    this.hasPreluActivationWeights = preluActivationWeights != null;\n    this.activation = activation2;\n    if (this.addBias) {\n      this.variableNames.push(\"bias\");\n    }\n    if (this.hasPreluActivationWeights) {\n      this.variableNames.push(\"preluActivationWeights\");\n    }\n    this.shaderKey = `biasActivation_${activation2}`;\n  }\n  getUserCode() {\n    return `\n    ${activationFnSnippet(this.activation, this.hasPreluActivationWeights)}\n    ${getMainHeaderString(\"index\")} {\n      if (index < uniforms.size) {\n        let coords = getCoordsFromIndex(index);\n        var value = getXByOutputIndex(index);\n        ${biasActivationSnippet(this.addBias, this.activation)}\n        setOutputAtIndex(index, value);\n      }\n    }\n    `;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/fill_webgpu.js\nvar FillProgram2 = class {\n  constructor(shape) {\n    this.variableNames = [];\n    this.outputShape = [];\n    this.uniforms = \"value : f32,\";\n    this.workGroupSize = [64, 1, 1];\n    this.size = true;\n    this.outputShape = shape;\n    this.dispatchLayout = flatDispatchLayout(this.outputShape);\n    this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, this.workGroupSize);\n    this.shaderKey = \"fill\";\n  }\n  getUserCode() {\n    const userCode = `\n    ${getMainHeaderString(\"index\")} {\n      if (index < uniforms.size) {\n        setOutputAtIndex(index, uniforms.value);\n      }\n    }\n  `;\n    return userCode;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/Fill.js\nfunction fill5(args) {\n  const { backend: backend2, attrs } = args;\n  const { shape, value } = attrs;\n  let { dtype } = attrs;\n  dtype = dtype || util_exports.inferDtype(value);\n  if (dtype === \"string\") {\n    const values = util_exports.getArrayFromDType(dtype, util_exports.sizeFromShape(shape));\n    values.fill(value);\n    return backend2.makeTensorInfo(shape, dtype, values);\n  } else {\n    const program = new FillProgram2(shape);\n    const uniformData = [{ type: \"float32\", data: [value] }];\n    return backend2.runWebGPUProgram(program, [], dtype, uniformData);\n  }\n}\nvar fillConfig4 = {\n  kernelName: Fill,\n  backendName: \"webgpu\",\n  kernelFunc: fill5\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/Reshape.js\nfunction reshape6(args) {\n  const { inputs, attrs } = args;\n  const { x } = inputs;\n  const { shape } = attrs;\n  const xSize = util_exports.sizeFromShape(x.shape);\n  const $shape = util_exports.inferFromImplicitShape(shape, xSize);\n  const $xSize = util_exports.sizeFromShape($shape);\n  util_exports.assert(xSize === $xSize, () => `The new shape (${$shape}) has ${$xSize} elements and the old shape (${x.shape}) has ${xSize} elements. The new shape and old shape must have the same number of elements.`);\n  args.backend.incRef(x.dataId);\n  return { dataId: x.dataId, shape: $shape, dtype: x.dtype };\n}\nvar reshapeConfig4 = {\n  kernelName: Reshape,\n  backendName: \"webgpu\",\n  kernelFunc: reshape6\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/BatchMatMul_impl.js\nfunction batchMatMulImpl2({ a, b, transposeA, transposeB, backend: backend2, bias = null, preluActivationWeights = null, leakyreluAlpha = 0, activation: activation2 = null }) {\n  const aRank = a.shape.length;\n  const bRank = b.shape.length;\n  const innerShapeA = transposeA ? a.shape[aRank - 2] : a.shape[aRank - 1];\n  const innerShapeB = transposeB ? b.shape[bRank - 1] : b.shape[bRank - 2];\n  const outerShapeA = transposeA ? a.shape[aRank - 1] : a.shape[aRank - 2];\n  const outerShapeB = transposeB ? b.shape[bRank - 2] : b.shape[bRank - 1];\n  const outerDimsA = a.shape.slice(0, -2);\n  const outerDimsB = b.shape.slice(0, -2);\n  const batchDimA = util_exports.sizeFromShape(outerDimsA);\n  const batchDimB = util_exports.sizeFromShape(outerDimsB);\n  const outShapeOuterDims = broadcast_util_exports.assertAndGetBroadcastShape(a.shape.slice(0, -2), b.shape.slice(0, -2));\n  const outShape = outShapeOuterDims.concat([outerShapeA, outerShapeB]);\n  util_exports.assert(innerShapeA === innerShapeB, () => `Error in matMul: inner shapes (${innerShapeA}) and (${innerShapeB}) of Tensors with shapes ${a.shape} and ${b.shape} and transposeA=${transposeA} and transposeB=${transposeB} must match.`);\n  const a3dShape = transposeA ? [batchDimA, innerShapeA, outerShapeA] : [batchDimA, outerShapeA, innerShapeA];\n  const b3dShape = transposeB ? [batchDimB, outerShapeB, innerShapeB] : [batchDimB, innerShapeB, outerShapeB];\n  const a3d = reshape6({ inputs: { x: a }, backend: backend2, attrs: { shape: a3dShape } });\n  const b3d = reshape6({ inputs: { x: b }, backend: backend2, attrs: { shape: b3dShape } });\n  const intermediates = [a3d, b3d];\n  const batchDim = Math.max(batchDimA, batchDimB);\n  const batchAEqualOne = batchDimA === 1;\n  const batchBEqualOne = batchDimB === 1;\n  const inputs = [a3d, b3d];\n  const dimensions = [\n    { type: \"int32\", data: [outerShapeA] },\n    { type: \"int32\", data: [outerShapeB] },\n    { type: \"int32\", data: [innerShapeA] }\n  ];\n  let program;\n  let out;\n  const outputShape = [batchDim, outerShapeA, outerShapeB];\n  let matmulProgramType = env().get(\"WEBGPU_MATMUL_PROGRAM_TYPE\");\n  if (matmulProgramType < 0) {\n    if (outerShapeA * outerShapeB <= 128) {\n      matmulProgramType = MatMulProgramType.MatMulReduceProgram;\n    } else if (batchDim === 1 && outerShapeA <= 128 && outerShapeB <= 48 && innerShapeB >= 2e3) {\n      matmulProgramType = MatMulProgramType.MatMulSplitKProgram;\n    } else if (outerShapeA <= 16 && (outerShapeB <= 512 || innerShapeB >= 2 * outerShapeB) || outerShapeB <= 16 && (outerShapeA <= 512 || innerShapeA >= 2 * outerShapeA)) {\n      matmulProgramType = MatMulProgramType.MatMulSmallOutputSizeProgram;\n    } else {\n      matmulProgramType = MatMulProgramType.MatMulPackedProgram;\n    }\n  }\n  switch (matmulProgramType) {\n    case MatMulProgramType.MatMulReduceProgram:\n      program = new MatMulReduceProgram(outputShape, batchAEqualOne, batchBEqualOne, transposeA, transposeB, bias, activation2, preluActivationWeights);\n      break;\n    case MatMulProgramType.MatMulSplitKProgram: {\n      out = fill5({ backend: backend2, attrs: { shape: outputShape, value: 0, dtype: a.dtype } });\n      program = new MatMulSplitKProgram(outputShape, innerShapeB, batchAEqualOne, batchBEqualOne, transposeA, transposeB);\n      if (bias || activation2) {\n        out = backend2.runWebGPUProgram(program, inputs, a.dtype, dimensions, out);\n        const biasActivationProgram = new BiasActivationProgram(out.shape, bias, activation2, preluActivationWeights);\n        let uniformData = null;\n        const activationInputs = [out];\n        if (bias) {\n          activationInputs.push(bias);\n        }\n        if (preluActivationWeights) {\n          activationInputs.push(preluActivationWeights);\n        }\n        if (activation2 === \"leakyrelu\") {\n          uniformData = [{ type: \"float32\", data: [leakyreluAlpha] }];\n          biasActivationProgram.uniforms += \" alpha : f32,\";\n        }\n        const outActivated = backend2.runWebGPUProgram(biasActivationProgram, activationInputs, out.dtype, uniformData);\n        intermediates.push(out);\n        const outReshaped2 = reshape6({ inputs: { x: outActivated }, backend: backend2, attrs: { shape: outShape } });\n        intermediates.push(outActivated);\n        for (const i2 of intermediates) {\n          backend2.disposeData(i2.dataId);\n        }\n        return outReshaped2;\n      }\n      break;\n    }\n    case MatMulProgramType.MatMulSmallOutputSizeProgram:\n      program = new MatMulSmallOutputSizeProgram(a3dShape, b3dShape, outputShape, transposeA, transposeB, bias, activation2, preluActivationWeights);\n      break;\n    case MatMulProgramType.MatMulPackedProgram:\n      program = new MatMulPackedProgram2(a3dShape, outputShape, batchAEqualOne, batchBEqualOne, transposeA, transposeB, bias, activation2, preluActivationWeights);\n      break;\n    default:\n      throw new Error(`Unsupported MatMulProgramType ${matmulProgramType}.`);\n  }\n  if (bias) {\n    inputs.push(bias);\n  }\n  if (preluActivationWeights) {\n    inputs.push(preluActivationWeights);\n  }\n  if (activation2 === \"leakyrelu\") {\n    dimensions.push({ type: \"float32\", data: [leakyreluAlpha] });\n    program.uniforms += \" alpha : f32,\";\n  }\n  out = backend2.runWebGPUProgram(program, inputs, a.dtype, dimensions, out);\n  const outReshaped = reshape6({ inputs: { x: out }, backend: backend2, attrs: { shape: outShape } });\n  intermediates.push(out);\n  for (const i2 of intermediates) {\n    backend2.disposeData(i2.dataId);\n  }\n  return outReshaped;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/_FusedMatMul.js\nfunction _fusedMatMul3(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { a, b, bias, preluActivationWeights } = inputs;\n  const { transposeA, transposeB, activation: activation2, leakyreluAlpha } = attrs;\n  return batchMatMulImpl2({\n    a,\n    b,\n    transposeA,\n    transposeB,\n    backend: backend2,\n    bias,\n    preluActivationWeights,\n    leakyreluAlpha,\n    activation: activation2\n  });\n}\nvar _fusedMatMulConfig4 = {\n  kernelName: _FusedMatMul,\n  backendName: \"webgpu\",\n  kernelFunc: _fusedMatMul3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/binary_op_complex_webgpu.js\nvar BinaryOpComplexProgram2 = class {\n  constructor(op2, aShape, bShape) {\n    this.variableNames = [\"AReal\", \"AImag\", \"BReal\", \"BImag\"];\n    this.workGroupSize = [128, 1, 1];\n    this.size = true;\n    this.outputShape = backend_util_exports.assertAndGetBroadcastShape(aShape, bShape);\n    this.dispatchLayout = flatDispatchLayout(this.outputShape);\n    this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, this.workGroupSize);\n    this.shaderKey = `binaryOpComplex_${op2}`;\n    this.op = op2;\n  }\n  getUserCode() {\n    const opStr = getBinaryOpString(this.op, false);\n    const userCode = `\n      fn binaryOpComplex(\n          areal : f32, aimag : f32, breal : f32, bimag : f32) -> f32 {\n        ${opStr}\n      }\n\n      ${getMainHeaderString(\"index\")} {\n        if(index < uniforms.size) {\n          let areal = getARealByOutputIndex(index);\n          let aimag = getAImagByOutputIndex(index);\n          let breal = getBRealByOutputIndex(index);\n          let bimag = getBImagByOutputIndex(index);\n          setOutputAtIndex(index, binaryOpComplex(areal, aimag, breal, bimag));\n        }\n      }\n    `;\n    return userCode;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/binary_op_webgpu.js\nvar BinaryOpProgram2 = class {\n  constructor(op2, aShape, bShape) {\n    this.size = true;\n    this.variableNames = [\"A\", \"B\"];\n    this.outputShape = backend_util_exports.assertAndGetBroadcastShape(aShape, bShape);\n    this.dispatchLayout = flatDispatchLayout(this.outputShape);\n    this.op = op2;\n    this.useSharedMemoryWithA = aShape.length === 1 && bShape.length > 1 && aShape[0] < 1024;\n    this.useSharedMemoryWithB = bShape.length === 1 && aShape.length > 1 && bShape[0] < 1024;\n    if (this.useSharedMemoryWithA || this.useSharedMemoryWithB) {\n      this.isVec4 = false;\n      this.lastDimensionSize = this.useSharedMemoryWithB ? bShape[0] : aShape[0];\n      this.shaderKey = `binary_${this.type}_${op2}_${this.lastDimensionSize}_${this.useSharedMemoryWithB}`;\n      this.type = \"shared\";\n      this.workGroupSize = [256, 1, 1];\n      if (this.lastDimensionSize < 256) {\n        this.workPerThread = 1;\n      } else if (this.lastDimensionSize < 512) {\n        this.workPerThread = 2;\n      } else {\n        this.workPerThread = 4;\n      }\n    } else {\n      if (util_exports.arraysEqual(aShape, bShape) && util_exports.sizeFromShape(aShape) % 4 === 0) {\n        this.isVec4 = true;\n        this.type = \"vec4\";\n        this.workPerThread = 4;\n      } else {\n        this.isVec4 = false;\n        this.type = \"plain\";\n        this.workPerThread = 1;\n      }\n      this.shaderKey = `binary_${this.type}_${op2}`;\n      this.workGroupSize = [128, 1, 1];\n    }\n    this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, this.workGroupSize, [this.workPerThread, 1, 1]);\n  }\n  getUserCode() {\n    let userCode;\n    if (this.type === \"shared\") {\n      const sharedIndexSnippet = this.lastDimensionSize > 1 ? `coords[${this.outputShape.length - 1}]` : \"0\";\n      const accessDataSnippet = this.useSharedMemoryWithB ? `let a = getAByOutputCoords(coords);\n          let b = sharedBuf[${sharedIndexSnippet}];` : `let a = sharedBuf[${sharedIndexSnippet}];\n          let b = getBByOutputCoords(coords);`;\n      const opStr = getBinaryOpString(this.op, this.isVec4);\n      userCode = `\n        fn binaryOperation(a : f32, b : f32) -> f32 {\n          ${opStr}\n        }\n        var<workgroup> sharedBuf : array<f32, ${this.lastDimensionSize}>;\n        ${getMainHeaderString(\"index\")} {\n          // Fill in the shared memory buffer. Here we need a loop to make sure\n          // that all data in A|B are uploaded when |sharedMemorySize| is larger\n          // than work group size.\n          for(var localIndex = i32(localId.x); localIndex < ${this.lastDimensionSize}; localIndex = localIndex + ${this.workGroupSize[0]}) {\n            sharedBuf[localIndex] = f32(${this.useSharedMemoryWithB ? \"B\" : \"A\"}[localIndex]);\n          }\n          workgroupBarrier();\n\n          for(var i = 0; i < ${this.workPerThread}; i = i + 1) {\n            let flatIndex = index * ${this.workPerThread} + i;\n            if(flatIndex < uniforms.size) {\n              let coords = getCoordsFromIndex(flatIndex);\n\n              ${accessDataSnippet}\n              setOutputAtIndex(flatIndex, binaryOperation(a, b));\n            }\n          }\n        }\n        `;\n    } else {\n      const dType = this.type === \"vec4\" ? \"vec4<f32>\" : \"f32\";\n      const opStr = getBinaryOpString(this.op, this.isVec4);\n      userCode = `\n       fn binaryOperation(a : ${dType}, b : ${dType}) -> ${dType} {\n         ${opStr}\n       }\n       ${getMainHeaderString(\"index\")} {\n         if (index < uniforms.size) {\n           let a = getAByOutputIndex(index);\n           let b = getBByOutputIndex(index);\n           setOutputAtIndex(index, binaryOperation(a, b));\n         }\n       }\n       `;\n    }\n    return userCode;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/Identity.js\nfunction identity5(args) {\n  const { inputs } = args;\n  const { x } = inputs;\n  args.backend.incRef(x.dataId);\n  return { dataId: x.dataId, shape: x.shape, dtype: x.dtype };\n}\nvar identityConfig4 = {\n  kernelName: Identity,\n  backendName: \"webgpu\",\n  kernelFunc: identity5\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/Complex.js\nfunction complex4(args) {\n  const { inputs, backend: backend2 } = args;\n  const { real: real5, imag: imag5 } = inputs;\n  const complexInfo = backend2.makeTensorInfo(real5.shape, \"complex64\");\n  const complex5 = backend2.tensorMap.get(complexInfo.dataId);\n  const realTensorInfo = identity5({ inputs: { x: real5 }, backend: backend2 });\n  const imagTensorInfo = identity5({ inputs: { x: imag5 }, backend: backend2 });\n  complex5.complexTensorInfos = { real: realTensorInfo, imag: imagTensorInfo };\n  return complexInfo;\n}\nvar complexConfig3 = {\n  kernelName: Complex,\n  backendName: \"webgpu\",\n  kernelFunc: complex4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/unary_op_webgpu.js\nvar UnaryOpProgram2 = class {\n  constructor(outputShape, op2) {\n    this.variableNames = [\"A\"];\n    this.size = true;\n    const workGroupSizeX = 128;\n    this.workGroupSize = [workGroupSizeX, 1, 1];\n    this.outputShape = outputShape;\n    this.dispatchLayout = flatDispatchLayout(this.outputShape);\n    this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, this.workGroupSize);\n    this.op = op2;\n    this.shaderKey = `unary_${op2}`;\n  }\n  getUserCode() {\n    return `\n      fn unaryOperation(a : f32) -> f32 {\n        ${getUnaryOpString(this.op, false)}\n      }\n      ${getMainHeaderString(\"index\")} {\n        if (index < uniforms.size) {\n          let a = getAByOutputIndex(index);\n          setOutputAtIndex(index, unaryOperation(a));\n        }\n      }\n      `;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernel_utils/kernel_funcs_utils.js\nfunction unaryKernelFunc3({ opType, cpuKernelImpl, dtype }) {\n  return ({ inputs, backend: backend2 }) => {\n    const { x } = inputs;\n    const webgpuBackend = backend2;\n    const $dtype = dtype || x.dtype;\n    if (webgpuBackend.shouldExecuteOnCPU([x]) && cpuKernelImpl != null) {\n      const xData = webgpuBackend.tensorMap.get(x.dataId);\n      const outValues = cpuKernelImpl(xData.values, $dtype);\n      return webgpuBackend.makeTensorInfo(x.shape, $dtype, outValues);\n    }\n    const program = new UnaryOpProgram2(x.shape, opType);\n    return webgpuBackend.runWebGPUProgram(program, [x], $dtype);\n  };\n}\nfunction binaryKernelFunc3({ opType, cpuKernelImpl, supportsComplex = false, dtype }) {\n  return ({ inputs, backend: backend2 }) => {\n    const { a, b } = inputs;\n    const webgpuBackend = backend2;\n    if (supportsComplex && a.dtype === \"complex64\") {\n      const aData = webgpuBackend.tensorMap.get(a.dataId);\n      const bData = webgpuBackend.tensorMap.get(b.dataId);\n      let real5, imag5;\n      if (opType !== BinaryOpType.MUL) {\n        [real5, imag5] = [\n          [aData.complexTensorInfos.real, bData.complexTensorInfos.real],\n          [aData.complexTensorInfos.imag, bData.complexTensorInfos.imag]\n        ].map((complexParts) => {\n          const [aPart, bPart] = complexParts;\n          const aHandle = {\n            dataId: aPart.dataId,\n            dtype: aPart.dtype,\n            shape: a.shape\n          };\n          const bHandle = {\n            dataId: bPart.dataId,\n            dtype: bPart.dtype,\n            shape: b.shape\n          };\n          const program2 = new BinaryOpProgram2(opType, a.shape, b.shape);\n          return webgpuBackend.runWebGPUProgram(program2, [aHandle, bHandle], upcastType(aPart.dtype, bPart.dtype));\n        });\n      } else {\n        const realProgram = new BinaryOpComplexProgram2(BinaryOpType.COMPLEX_MULTIPLY_REAL, a.shape, b.shape);\n        const imagProgram = new BinaryOpComplexProgram2(BinaryOpType.COMPLEX_MULTIPLY_IMAG, a.shape, b.shape);\n        const inputs2 = [\n          {\n            dataId: aData.complexTensorInfos.real.dataId,\n            dtype: aData.complexTensorInfos.real.dtype,\n            shape: a.shape\n          },\n          {\n            dataId: aData.complexTensorInfos.imag.dataId,\n            dtype: aData.complexTensorInfos.imag.dtype,\n            shape: a.shape\n          },\n          {\n            dataId: bData.complexTensorInfos.real.dataId,\n            dtype: bData.complexTensorInfos.real.dtype,\n            shape: b.shape\n          },\n          {\n            dataId: bData.complexTensorInfos.imag.dataId,\n            dtype: bData.complexTensorInfos.imag.dtype,\n            shape: b.shape\n          }\n        ];\n        real5 = webgpuBackend.runWebGPUProgram(realProgram, inputs2, \"float32\");\n        imag5 = webgpuBackend.runWebGPUProgram(imagProgram, inputs2, \"float32\");\n      }\n      const complexOutput = complex4({ inputs: { real: real5, imag: imag5 }, backend: webgpuBackend });\n      webgpuBackend.disposeData(real5.dataId);\n      webgpuBackend.disposeData(imag5.dataId);\n      return complexOutput;\n    }\n    const $dtype = dtype || upcastType(a.dtype, b.dtype);\n    if ((a.dtype === \"string\" || b.dtype === \"string\" || webgpuBackend.shouldExecuteOnCPU([a, b])) && cpuKernelImpl != null) {\n      const aData = webgpuBackend.tensorMap.get(a.dataId).values;\n      const bData = webgpuBackend.tensorMap.get(b.dataId).values;\n      const decodedAVals = a.dtype === \"string\" ? backend_util_exports.fromUint8ToStringArray(aData) : aData;\n      const decodedBVals = a.dtype === \"string\" ? backend_util_exports.fromUint8ToStringArray(bData) : bData;\n      const [outValues, outShape] = cpuKernelImpl(a.shape, b.shape, decodedAVals, decodedBVals, $dtype);\n      return webgpuBackend.makeTensorInfo(outShape, $dtype, outValues);\n    }\n    const program = new BinaryOpProgram2(opType, a.shape, b.shape);\n    return webgpuBackend.runWebGPUProgram(program, [a, b], $dtype);\n  };\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernel_utils/shared.js\nvar { addImpl: addImplCPU2, castImpl: castImplCPU2, ceilImpl: ceilImplCPU2, concatImpl: concatImplCPU2, equalImpl: equalImplCPU2, expImpl: expImplCPU2, expm1Impl: expm1ImplCPU2, floorImpl: floorImplCPU2, gatherNdImpl: gatherNdImplCPU2, gatherV2Impl: gatherV2ImplCPU2, greaterEqualImpl: greaterEqualImplCPU2, greaterImpl: greaterImplCPU2, lessEqualImpl: lessEqualImplCPU2, lessImpl: lessImplCPU2, logImpl: logImplCPU2, maxImpl: maxImplCPU2, maximumImpl: maximumImplCPU2, minimumImpl: minimumImplCPU2, multiplyImpl: multiplyImplCPU2, negImpl: negImplCPU2, notEqualImpl: notEqualImplCPU2, prodImpl: prodImplCPU2, rangeImpl: rangeImplCPU2, rsqrtImpl: rsqrtImplCPU2, scatterImpl: scatterImplCPU2, simpleAbsImpl: simpleAbsImplCPU2, sliceImpl: sliceImplCPU2, stridedSliceImpl: stridedSliceImplCPU2, stringNGramsImpl: stringNGramsImplCPU2, subImpl: subImplCPU2, tileImpl: tileImplCPU2, topKImpl: topKImplCPU2, transposeImpl: transposeImplCPU2, uniqueImpl: uniqueImplCPU2 } = shared_exports;\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/Abs.js\nvar abs4 = unaryKernelFunc3({ opType: UnaryOpType.ABS, cpuKernelImpl: simpleAbsImplCPU2 });\nvar absConfig4 = {\n  kernelName: Abs,\n  backendName: \"webgpu\",\n  kernelFunc: abs4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/Add.js\nvar addKernelFunc2 = binaryKernelFunc3({ opType: BinaryOpType.ADD, cpuKernelImpl: addImplCPU2, supportsComplex: true });\nvar addConfig4 = {\n  kernelName: Add,\n  backendName: \"webgpu\",\n  kernelFunc: addKernelFunc2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/addn_packed_webgpu.js\nvar AddNPackedProgram2 = class {\n  constructor(shapes) {\n    this.workPerThread = 4;\n    this.workGroupSize = [64, 1, 1];\n    this.size = true;\n    this.outputShape = shapes[0];\n    this.variableNames = shapes.map((_, i2) => `T${i2}`);\n    this.dispatchLayout = flatDispatchLayout(this.outputShape);\n    this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, this.workGroupSize, [this.workPerThread, 1, 1]);\n    this.shaderKey = \"addN\";\n  }\n  getUserCode() {\n    const snippets = [];\n    this.variableNames.forEach((variable2) => {\n      snippets.push(`let v${variable2} = get${variable2}ByOutputCoords(coords);`);\n    });\n    const operation = this.variableNames.map((variable2) => {\n      return `v${variable2}`;\n    }).join(\" + \");\n    const userCode = `\n      ${getMainHeaderString(\"index\")} {\n        for (var i = 0; i < ${this.workPerThread}; i = i + 1) {\n          let flatIndex = index * ${this.workPerThread} + i;\n          if (flatIndex < uniforms.size) {\n            let coords = getCoordsFromIndex(flatIndex);\n            ${snippets.join(\"\\n        \")}\n            setOutputAtIndex(flatIndex, ${operation});\n          }\n        }\n      }\n    `;\n    return userCode;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/AddN.js\nfunction addN4(args) {\n  const { inputs, backend: backend2 } = args;\n  const tensors = inputs;\n  if (tensors.length === 1) {\n    return identity5({ inputs: { x: tensors[0] }, backend: backend2 });\n  }\n  const dtype = tensors.map((t2) => t2.dtype).reduce((d1, d2) => upcastType(d1, d2));\n  const shapes = tensors.map((t2) => t2.shape);\n  const program = new AddNPackedProgram2(shapes);\n  return backend2.runWebGPUProgram(program, tensors, dtype);\n}\nvar addNConfig4 = {\n  kernelName: AddN,\n  backendName: \"webgpu\",\n  kernelFunc: addN4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/argminmax_webgpu.js\nvar ArgMinMaxProgram2 = class {\n  constructor(inputShape, axis, reduceType) {\n    this.workGroupSize = [64, 1, 1];\n    this.variableNames = [\"x\"];\n    this.uniforms = \"infinityValue : f32,\";\n    this.size = true;\n    const axes = [axis];\n    this.op = reduceType === \"min\" ? \"<\" : \">\";\n    const [outputShape, reduceShape] = backend_util_exports.computeOutAndReduceShapes(inputShape, axes);\n    this.outputShape = outputShape.length === 0 ? [1] : outputShape;\n    this.dispatchLayout = flatDispatchLayout(this.outputShape);\n    if (util_exports.sizeFromShape(reduceShape) < 32 || util_exports.sizeFromShape(outputShape) > 1e3) {\n      this.type = \"plain\";\n      this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, this.workGroupSize);\n    } else {\n      this.type = \"shared\";\n      this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, [1, 1, 1]);\n    }\n    this.inputShape = inputShape;\n    this.shaderKey = `argMinMax_${this.op}_${this.type}`;\n  }\n  getUserCode() {\n    const getInputShapeLastDim = () => {\n      if (this.inputShape.length === 1) {\n        return \"uniforms.xShape\";\n      } else {\n        return `uniforms.xShape.${getCoordsXYZ(this.inputShape.length - 1)}`;\n      }\n    };\n    const splitOutputCoords = () => {\n      let snippet = \"\";\n      if (this.outputShape.length === 1) {\n        if (this.inputShape.length !== 1) {\n          snippet += \"outputCoords,\";\n        }\n      } else {\n        for (let i2 = 0; i2 < this.outputShape.length; i2++) {\n          snippet += `outputCoords.${getCoordsXYZ(i2)},`;\n        }\n      }\n      return snippet;\n    };\n    if (this.type === \"shared\") {\n      const sharedMemorySnippet = `\n      var<workgroup> xBestIndices : array<i32, ${this.workGroupSize[0]}>;\n      var<workgroup> xBestValues : array<f32, ${this.workGroupSize[0]}>;\n    `;\n      const userCode = `\n      fn DIV_CEIL(a : u32, b : u32) -> u32 {\n        return ((a - 1u) / b + 1u);\n      }\n\n      ${sharedMemorySnippet}\n\n      ${getMainHeaderString(\"index\")} {\n        let outputIndex = index / i32(workGroupSizeX);\n        let reduceLength = ${getInputShapeLastDim()};\n\n        var bestIndex = i32(localId.x);\n        var bestValue = uniforms.infinityValue;\n        let outputCoords = getCoordsFromIndex(outputIndex);\n        for (var k = i32(localId.x); k < reduceLength && outputIndex < uniforms.size;\n            k = k + i32(workGroupSizeX)) {\n          let candidate = getX(${splitOutputCoords()} k);\n          if (!isnan(candidate) && candidate ${this.op} bestValue) {\n            bestValue = candidate;\n            bestIndex = k;\n          }\n        }\n        xBestValues[localId.x] = bestValue;\n        xBestIndices[localId.x] = bestIndex;\n        workgroupBarrier();\n\n        var reduceSize = min(u32(reduceLength), workGroupSizeX);\n        for (var currentSize = reduceSize / 2u; reduceSize > 1u;\n            currentSize = reduceSize / 2u) {\n          let interval = DIV_CEIL(reduceSize, 2u);\n          if (localId.x < currentSize) {\n            let candidate = xBestValues[localId.x + interval];\n            if (candidate ${this.op} bestValue) {\n              bestValue = candidate;\n              xBestValues[localId.x] = bestValue;\n              xBestIndices[localId.x] = xBestIndices[localId.x + interval];\n            }\n          }\n          reduceSize = interval;\n          workgroupBarrier();\n        }\n\n        if (localId.x == 0u && outputIndex < uniforms.size) {\n          setOutputAtIndexI32(outputIndex, xBestIndices[localId.x]);\n        }\n      }\n    `;\n      return userCode;\n    } else {\n      const userCode = `\n      ${getMainHeaderString(\"index\")} {\n        if (index < uniforms.size) {\n          let outputCoords = getCoordsFromIndex(index);\n          var bestIndex = 0;\n          var bestValue = getX(${splitOutputCoords()} 0);\n          let reduceLength = ${getInputShapeLastDim()};\n          for (var i = 1; i < reduceLength; i++) {\n            let candidate = getX(${splitOutputCoords()} i);\n            if (candidate ${this.op} bestValue) {\n              bestValue = candidate;\n              bestIndex = i;\n            }\n          }\n          setOutputAtIndexI32(index, bestIndex);\n        }\n      }\n      `;\n      return userCode;\n    }\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/transpose_shared_webgpu.js\nvar TransposeSharedProgram = class {\n  constructor(aShape, newDim) {\n    this.variableNames = [\"A\"];\n    this.workGroupSize = [16, 16, 1];\n    const outputShape = new Array(aShape.length);\n    for (let i2 = 0; i2 < outputShape.length; i2++) {\n      outputShape[i2] = aShape[newDim[i2]];\n    }\n    this.outputShape = outputShape;\n    this.dispatchLayout = { x: [0], y: [1] };\n    this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, this.workGroupSize, [1, 1, 1]);\n    this.shaderKey = \"transposeShared\";\n  }\n  getUserCode() {\n    const userCode = `\n      const TILE_DIM = ${this.workGroupSize[0]};\n      var<workgroup> tile : array<array<f32, ${this.workGroupSize[0] + 1}>, ${this.workGroupSize[0]}>;\n      ${getWorkGroupSizeString()}\n      fn _start(@builtin(local_invocation_id) localId : vec3<u32>,\n                @builtin(workgroup_id) workgroupId : vec3<u32>) {\n        var x = i32(workgroupId.x) * TILE_DIM + i32(localId.x);\n        var y = i32(workgroupId.y) * TILE_DIM + i32(localId.y);\n        let width = uniforms.outShape[0];\n        let height = uniforms.outShape[1];\n        if (x < width && y < height) {\n          tile[localId.y][localId.x] = A[y * width + x];\n        }\n        workgroupBarrier();\n\n        x = i32(workgroupId.y) * TILE_DIM + i32(localId.x);\n        y = i32(workgroupId.x) * TILE_DIM + i32(localId.y);\n        if (x < height && y < width) {\n          setOutputAtIndex((y * height + x), tile[localId.x]\n            [localId.y]);\n        }\n      }\n    `;\n    return userCode;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/transpose_webgpu.js\nvar TransposeProgram2 = class {\n  constructor(aShape, newDim) {\n    this.variableNames = [\"A\"];\n    this.workPerThread = 4;\n    this.workGroupSize = [64, 1, 1];\n    this.size = true;\n    const outputShape = new Array(aShape.length);\n    for (let i2 = 0; i2 < outputShape.length; i2++) {\n      outputShape[i2] = aShape[newDim[i2]];\n    }\n    this.outputShape = outputShape;\n    this.dispatchLayout = flatDispatchLayout(this.outputShape);\n    this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, this.workGroupSize, [this.workPerThread, 1, 1]);\n    this.newDim = newDim;\n    this.shaderKey = `transpose_${newDim}`;\n  }\n  getUserCode() {\n    const dtype = getCoordsDataType2(this.outputShape.length);\n    const switched = getSwitchedCoords2(this.newDim);\n    const userCode = `\n      ${getMainHeaderString(\"index\")} {\n        for(var i = 0; i < ${this.workPerThread}; i = i + 1) {\n          let flatIndex = index * ${this.workPerThread} + i;\n          if(flatIndex < uniforms.size) {\n            let resRC = getCoordsFromIndex(flatIndex);\n            setOutputAtIndex(flatIndex, A[getIndexFromCoords${this.outputShape.length}D(\n              ${dtype}(${switched}), uniforms.aShape)]);\n          }\n        }\n      }\n    `;\n    return userCode;\n  }\n};\nfunction getSwitchedCoords2(newDim) {\n  const rank = newDim.length;\n  if (rank > 6) {\n    throw Error(`Transpose for rank ${rank} is not yet supported`);\n  }\n  const switchedCoords = new Array(rank);\n  for (let i2 = 0; i2 < newDim.length; i2++) {\n    switchedCoords[newDim[i2]] = `resRC.${getCoordsXYZ(i2)}`;\n  }\n  return switchedCoords.join();\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/Transpose.js\nfunction transpose5(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x } = inputs;\n  const { perm } = attrs;\n  const webgpuBackend = backend2;\n  const xRank = x.shape.length;\n  const newShape = new Array(xRank);\n  for (let i2 = 0; i2 < newShape.length; i2++) {\n    newShape[i2] = x.shape[perm[i2]];\n  }\n  if (backend2.shouldExecuteOnCPU([x])) {\n    const xData = webgpuBackend.tensorMap.get(x.dataId);\n    const values = xData.values;\n    const outValues = transposeImplCPU2(values, x.shape, x.dtype, perm, newShape);\n    return backend2.makeTensorInfo(newShape, x.dtype, outValues);\n  }\n  if (x.shape.length === 2 && util_exports.arraysEqual(perm, [1, 0])) {\n    const program2 = new TransposeSharedProgram(x.shape, perm);\n    return webgpuBackend.runWebGPUProgram(program2, [x], x.dtype);\n  }\n  const program = new TransposeProgram2(x.shape, perm);\n  return webgpuBackend.runWebGPUProgram(program, [x], x.dtype);\n}\nvar transposeConfig4 = {\n  kernelName: Transpose,\n  backendName: \"webgpu\",\n  kernelFunc: transpose5\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/ArgMax.js\nfunction argMax4(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x } = inputs;\n  const { axis } = attrs;\n  let axes = util_exports.parseAxisParam(axis, x.shape);\n  const permutedAxes = backend_util_exports.getAxesPermutation(axes, x.shape.length);\n  let $x = x;\n  const intermediateTensorInfos = [];\n  if (permutedAxes != null) {\n    $x = transpose5({ inputs: { x }, backend: backend2, attrs: { perm: permutedAxes } });\n    intermediateTensorInfos.push($x);\n    axes = backend_util_exports.getInnerMostAxes(axes.length, $x.shape.length);\n  }\n  backend_util_exports.assertAxesAreInnerMostDims(\"argMax\", [axes[0]], $x.shape.length);\n  const program = new ArgMinMaxProgram2($x.shape, axes[0], \"max\");\n  const uniformData = [{ type: \"float32\", data: [Number.NEGATIVE_INFINITY] }];\n  const out = backend2.runWebGPUProgram(program, [$x], \"int32\", uniformData);\n  intermediateTensorInfos.forEach((t2) => backend2.disposeData(t2.dataId));\n  return out;\n}\nvar argMaxConfig4 = {\n  kernelName: ArgMax,\n  backendName: \"webgpu\",\n  kernelFunc: argMax4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/ArgMin.js\nfunction argMin4(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x } = inputs;\n  const { axis } = attrs;\n  let axes = util_exports.parseAxisParam(axis, x.shape);\n  const permutedAxes = backend_util_exports.getAxesPermutation(axes, x.shape.length);\n  let $x = x;\n  const intermediateTensorInfos = [];\n  if (permutedAxes != null) {\n    $x = transpose5({ inputs: { x }, backend: backend2, attrs: { perm: permutedAxes } });\n    intermediateTensorInfos.push($x);\n    axes = backend_util_exports.getInnerMostAxes(axes.length, $x.shape.length);\n  }\n  backend_util_exports.assertAxesAreInnerMostDims(\"argMin\", [axes[0]], $x.shape.length);\n  const program = new ArgMinMaxProgram2($x.shape, axes[0], \"min\");\n  const uniformData = [{ type: \"float32\", data: [Number.POSITIVE_INFINITY] }];\n  const out = backend2.runWebGPUProgram(program, [$x], \"int32\", uniformData);\n  intermediateTensorInfos.forEach((t2) => backend2.disposeData(t2.dataId));\n  return out;\n}\nvar argMinConfig3 = {\n  kernelName: ArgMin,\n  backendName: \"webgpu\",\n  kernelFunc: argMin4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/Atan2.js\nvar atan24 = binaryKernelFunc3({ opType: BinaryOpType.ATAN2 });\nvar atan2Config3 = {\n  kernelName: Atan2,\n  backendName: \"webgpu\",\n  kernelFunc: atan24\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/pool2d_webgpu.js\nvar Pool2DProgram2 = class {\n  constructor(convInfo, poolType) {\n    this.variableNames = [\"x\"];\n    this.uniforms = `stride : vec2<i32>, pad : vec2<i32>, dilation : vec2<i32>, convDims : vec2<i32>, filterDims : vec2<i32>,`;\n    this.workGroupSize = [128, 1, 1];\n    this.size = true;\n    this.outputShape = convInfo.outShape;\n    this.dispatchLayout = flatDispatchLayout(this.outputShape);\n    this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, this.workGroupSize);\n    this.shaderKey = `pool2D_${poolType}`;\n    this.poolType = poolType;\n  }\n  getUserCode() {\n    let updateSnippet = `resultValue = max(value, resultValue);`;\n    if (this.poolType === \"avg\") {\n      updateSnippet = `resultValue = resultValue + value; count = count + 1.0;`;\n    }\n    let returnValue = `resultValue`;\n    if (this.poolType === \"avg\") {\n      returnValue = `resultValue / count`;\n    }\n    const userCode = `\n      ${getMainHeaderString(\"index\")} {\n      if (index < uniforms.size) {\n        let coords = getCoordsFromIndex(index);\n          let batch = coords[0];\n          let xRCCorner = vec2<i32>(coords.yz) * uniforms.stride - uniforms.pad;\n          let xRCorner = xRCCorner.x;\n          let xCCorner = xRCCorner.y;\n\n          var resultValue = ${this.poolType === \"avg\" ? \"0.0\" : \"-1.0 / pow(10.0, -20.0)\"};\n          var count = 0.0;\n\n          for (var wR = 0; wR < uniforms.filterDims.x; wR = wR + uniforms.dilation.x) {\n            let xR = xRCorner + wR;\n\n            if (xR < 0 || xR >= uniforms.convDims.x) {\n              continue;\n            }\n\n            for (var wC = 0; wC < uniforms.filterDims.y; wC = wC + uniforms.dilation.y) {\n              let xC = xCCorner + wC;\n              if (xC < 0 || xC >= uniforms.convDims.y) {\n                continue;\n              }\n\n              let value = getX(batch, xR, xC, coords[3]);\n              ${updateSnippet}\n            }\n          }\n\n          setOutputAtIndex(index, ${returnValue});\n        }\n      }\n    `;\n    return userCode;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/pool_filtersizeone_webgpu.js\nvar PoolWithFilterSizeEqualsOneProgram = class {\n  constructor(convInfo) {\n    this.variableNames = [\"x\"];\n    this.uniforms = `stride : vec2<i32>,`;\n    this.workGroupSize = [256, 1, 1];\n    this.size = true;\n    this.outputShape = convInfo.outShape;\n    this.dispatchLayout = flatDispatchLayout(this.outputShape);\n    this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, this.workGroupSize);\n    this.shaderKey = \"poolWithFilterSizeEqualsOne\";\n  }\n  getUserCode() {\n    const userCode = `\n      ${getMainHeaderString(\"index\")} {\n        if (index < uniforms.size) {\n          let coords = getCoordsFromIndex(index);\n          let batch = coords[0];\n          let d = coords[3];\n\n          let xRCCorner = coords.yz * uniforms.stride;\n          let xRCorner = xRCCorner.x;\n          let xCCorner = xRCCorner.y;\n\n          let value = getX(batch, xRCorner, xCCorner, d);\n          setOutputAtIndex(index, value);\n        }\n      }\n    `;\n    return userCode;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/reduce_webgpu.js\nvar ReduceProgram2 = class {\n  constructor(reduceInfo, reduceType) {\n    this.workGroupSize = [64, 1, 1];\n    this.variableNames = [\"x\"];\n    this.uniforms = \"reduceSize : i32,\";\n    this.size = true;\n    this.inputShape = [reduceInfo.batchSize, reduceInfo.inSize];\n    const [outputShape] = backend_util_exports.computeOutAndReduceShapes(this.inputShape, [1]);\n    this.outputShape = outputShape.length === 0 ? [1] : outputShape;\n    this.dispatchLayout = flatDispatchLayout(this.outputShape);\n    this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, [1, 1, 1]);\n    this.reduceType = reduceType;\n    this.shaderKey = `reduce_${reduceType}`;\n  }\n  getUserCode() {\n    let reduceOp = ``;\n    let initValue = \"0.0\";\n    if (this.reduceType === \"min\" || this.reduceType === \"max\") {\n      reduceOp = `\n         if (isnan(candidate)) {\n          bestValue = uniforms.NAN;\n         } else if (!isnan(bestValue) && candidate ${this.reduceType === \"min\" ? \"<\" : \">\"} bestValue)\n           {  bestValue = candidate; }`;\n      initValue = \"f32(x[offset])\";\n    } else if (this.reduceType === \"sum\" || this.reduceType === \"mean\") {\n      reduceOp = \" bestValue = bestValue + candidate; \";\n    } else if (this.reduceType === \"prod\") {\n      reduceOp = \" bestValue = bestValue * candidate; \";\n      initValue = \"1.0\";\n    }\n    const outputSnippet = this.reduceType === \"mean\" ? `setOutputAtIndex(outputIndex, bestValue / f32(uniforms.reduceSize));` : `setOutputAtIndex(outputIndex, bestValue);`;\n    const sharedMemorySnippet = `\n         var<workgroup> xBestValues : array<f32, ${this.workGroupSize[0]}>;\n       `;\n    const userCode = `\n       fn DIV_CEIL(a : u32, b : u32) -> u32 {\n        return ((a - 1u) / b + 1u);\n       }\n\n       ${sharedMemorySnippet}\n       fn getOffset(outputIndex : i32) -> i32 {\n         let outputCoords = getCoordsFromIndex(outputIndex);\n         let offset = ${this.outputShape.length === 1 ? \"outputCoords\" : \"outputCoords[0]\"} * uniforms.reduceSize;\n          return offset;\n       }\n       ${getMainHeaderString(\"index\")} {\n         let outputIndex = index / i32(workGroupSizeX);\n         let offset = getOffset(outputIndex);\n         var bestValue = ${initValue};\n         let Length = uniforms.reduceSize;\n         let WorkPerThread = DIV_CEIL(u32(Length), workGroupSizeX);\n         for (var k = i32(localId.x); k < Length && outputIndex < uniforms.size;\n             k = k + i32(workGroupSizeX)) {\n           let candidate = f32(x[offset + k]);\n           ${reduceOp}\n         }\n         xBestValues[localId.x] = bestValue;\n         workgroupBarrier();\n\n         var reduceSize = min(u32(Length), workGroupSizeX);\n         for (var currentSize = reduceSize / 2u; reduceSize > 1u;\n             currentSize = reduceSize / 2u) {\n           let interval = DIV_CEIL(reduceSize, 2u);\n           if (localId.x < currentSize) {\n            let candidate = xBestValues[localId.x + interval];\n            ${reduceOp}\n            xBestValues[localId.x] = bestValue;\n           }\n           reduceSize = interval;\n           workgroupBarrier();\n         }\n\n         if (localId.x == 0u && outputIndex < uniforms.size) {\n          ${outputSnippet}\n        }\n       }\n     `;\n    return userCode;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernel_utils/reduce.js\nfunction reduce2(x, axis, keepDims, reduceType, backend2) {\n  const xRank = x.shape.length;\n  const toDispose = [];\n  const origAxes = util_exports.parseAxisParam(axis, x.shape);\n  let axes = origAxes;\n  const permutedAxes = backend_util_exports.getAxesPermutation(axes, xRank);\n  let input2 = x;\n  if (permutedAxes != null) {\n    input2 = transpose5({ inputs: { x }, attrs: { perm: permutedAxes }, backend: backend2 });\n    axes = backend_util_exports.getInnerMostAxes(axes.length, xRank);\n    toDispose.push(input2);\n  }\n  backend_util_exports.assertAxesAreInnerMostDims(reduceType, axes, xRank);\n  const [reduceOutShape, reduceShape] = backend_util_exports.computeOutAndReduceShapes(input2.shape, axes);\n  let resOutShape = reduceOutShape;\n  if (keepDims) {\n    resOutShape = backend_util_exports.expandShapeToKeepDim(reduceOutShape, origAxes);\n  }\n  let res;\n  if ((reduceType === \"max\" || reduceType === \"prod\") && backend2.shouldExecuteOnCPU([input2])) {\n    const xVals = backend2.tensorMap.get(input2.dataId).values;\n    switch (reduceType) {\n      case \"max\":\n        const outValues = maxImplCPU2(xVals, util_exports.sizeFromShape(reduceShape), resOutShape, x.dtype);\n        res = backend2.makeTensorInfo(resOutShape, x.dtype, outValues);\n        break;\n      case \"prod\":\n        const { outVals, outShape, outDtype } = prodImplCPU2(input2.shape, input2.dtype, xVals, axes);\n        res = backend2.makeTensorInfo(outShape, outDtype, outVals);\n        break;\n      default:\n        throw new Error(`${reduceType} CPU implementation is not yet supported.`);\n    }\n  } else {\n    const inSize = util_exports.sizeFromShape(reduceShape);\n    const xSize = util_exports.sizeFromShape(input2.shape);\n    const batchSize = xSize / inSize;\n    const reduceInfo = { windowSize: inSize, inSize, batchSize, outSize: 1 };\n    const dtype = reduceType === \"mean\" ? \"float32\" : sumOutType(x.dtype);\n    const uniformData = [\n      { type: \"int32\", data: [inSize] }\n    ];\n    const program = new ReduceProgram2(reduceInfo, reduceType);\n    const reduced = backend2.runWebGPUProgram(program, [input2], dtype, uniformData);\n    toDispose.push(reduced);\n    res = reshape6({ inputs: { x: reduced }, attrs: { shape: resOutShape }, backend: backend2 });\n  }\n  toDispose.forEach((t2) => backend2.disposeData(t2.dataId));\n  return res;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/Max.js\nfunction max6(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x } = inputs;\n  const { reductionIndices, keepDims } = attrs;\n  return reduce2(x, reductionIndices, keepDims, \"max\", backend2);\n}\nvar maxConfig4 = {\n  kernelName: Max,\n  backendName: \"webgpu\",\n  kernelFunc: max6\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/Mean.js\nfunction mean4(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x } = inputs;\n  const { keepDims, axis } = attrs;\n  return reduce2(x, axis, keepDims, \"mean\", backend2);\n}\nvar meanConfig4 = {\n  kernelName: Mean,\n  backendName: \"webgpu\",\n  kernelFunc: mean4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/Pool_impl.js\nfunction poolImpl(x, convInfo, poolType, backend2) {\n  if (convInfo.filterWidth === 1 && convInfo.filterHeight === 1 && util_exports.arraysEqual(convInfo.inShape, convInfo.outShape)) {\n    return identity5({ inputs: { x }, backend: backend2 });\n  }\n  if (convInfo.filterWidth === convInfo.inWidth && convInfo.filterHeight === convInfo.inHeight && convInfo.batchSize === 1 && convInfo.padInfo.type === \"VALID\") {\n    const length = x.shape.length;\n    const reshapeX = reshape6({\n      inputs: { x },\n      backend: backend2,\n      attrs: {\n        shape: [\n          x.shape[length - 3] * x.shape[length - 2],\n          x.shape[length - 1]\n        ]\n      }\n    });\n    let reduceX;\n    if (poolType === \"avg\") {\n      reduceX = mean4({ inputs: { x: reshapeX }, backend: backend2, attrs: { axis: 0, keepDims: false } });\n    } else {\n      util_exports.assert(poolType === \"max\", () => `Invalid pool type ${poolType}`);\n      reduceX = max6({\n        inputs: { x: reshapeX },\n        backend: backend2,\n        attrs: { reductionIndices: 0, keepDims: false }\n      });\n    }\n    const result = reshape6({ inputs: { x: reduceX }, backend: backend2, attrs: { shape: convInfo.outShape } });\n    backend2.disposeData(reshapeX.dataId);\n    backend2.disposeData(reduceX.dataId);\n    return result;\n  }\n  let program;\n  const dimensions = [{ type: \"int32\", data: [convInfo.strideHeight, convInfo.strideWidth] }];\n  if (convInfo.filterHeight === 1 && convInfo.filterWidth === 1) {\n    program = new PoolWithFilterSizeEqualsOneProgram(convInfo);\n  } else {\n    if (poolType === \"avg\") {\n      program = new Pool2DProgram2(convInfo, \"avg\");\n    } else {\n      util_exports.assert(poolType === \"max\", () => `Invalid pool type ${poolType}`);\n      program = new Pool2DProgram2(convInfo, \"max\");\n    }\n    dimensions.push({ type: \"int32\", data: [convInfo.padInfo.top, convInfo.padInfo.left] }, {\n      type: \"int32\",\n      data: [convInfo.dilationHeight, convInfo.dilationWidth]\n    }, { type: \"int32\", data: [convInfo.inHeight, convInfo.inWidth] }, {\n      type: \"int32\",\n      data: [convInfo.effectiveFilterHeight, convInfo.effectiveFilterWidth]\n    });\n  }\n  return backend2.runWebGPUProgram(program, [x], x.dtype, dimensions);\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/AvgPool.js\nfunction avgPool5(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x } = inputs;\n  const { filterSize, strides, pad: pad3, dimRoundingMode } = attrs;\n  const dilations = 1;\n  const convInfo = backend_util_exports.computePool2DInfo(x.shape, filterSize, strides, dilations, pad3, dimRoundingMode);\n  return poolImpl(x, convInfo, \"avg\", backend2);\n}\nvar avgPoolConfig4 = {\n  kernelName: AvgPool,\n  backendName: \"webgpu\",\n  kernelFunc: avgPool5\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/BatchMatMul.js\nfunction batchMatMul4(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { a, b } = inputs;\n  const { transposeA, transposeB } = attrs;\n  return batchMatMulImpl2({ a, b, transposeA, transposeB, backend: backend2 });\n}\nvar batchMatMulConfig4 = {\n  kernelName: BatchMatMul,\n  backendName: \"webgpu\",\n  kernelFunc: batchMatMul4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/slice_webgpu.js\nvar SliceProgram2 = class {\n  constructor(start, destSize) {\n    this.variableNames = [\"source\"];\n    this.workPerThread = 1;\n    this.workGroupSize = [64, 1, 1];\n    this.size = true;\n    this.outputShape = destSize;\n    this.rank = destSize.length;\n    this.dispatchLayout = flatDispatchLayout(this.outputShape);\n    this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, this.workGroupSize, [this.workPerThread, 1, 1]);\n    this.start = start;\n    this.uniforms = `start : ${getCoordsDataType2(start.length)}, `;\n    this.shaderKey = \"slice\";\n  }\n  getUserCode() {\n    const dtype = getCoordsDataType2(this.rank);\n    const sourceCoords = getCoords3(this.rank);\n    let coordSum;\n    if (this.start.length === 1) {\n      coordSum = this.outputShape.map((_, i2) => {\n        return `sourceLoc = uniforms.start + coords;`;\n      });\n    } else {\n      coordSum = this.outputShape.map((_, i2) => {\n        return `sourceLoc.${coords2[i2]} = uniforms.start.${getCoordsXYZ(i2)} + coords.${coords2[i2]};`;\n      });\n    }\n    const userCode = `\n      ${getMainHeaderString(\"index\")} {\n        if (index < uniforms.size) {\n          var sourceLoc : ${dtype};\n          let coords = getCoordsFromIndex(index);\n          ${coordSum.join(\"\\n\")}\n          setOutputAtIndex(index, getSource(${sourceCoords}));\n        }\n      }\n    `;\n    return userCode;\n  }\n};\nvar coords2 = [\"x\", \"y\", \"z\", \"w\", \"u\", \"v\"];\nfunction getCoords3(rank) {\n  if (rank === 1) {\n    return \"sourceLoc\";\n  } else if (rank <= 6) {\n    return coords2.slice(0, rank).map((coord) => `sourceLoc.${coord}`).join(\",\");\n  } else {\n    throw Error(`Slicing for rank ${rank} is not yet supported`);\n  }\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/Slice.js\nfunction slice5(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x } = inputs;\n  const { begin, size } = attrs;\n  const [$begin, $size] = slice_util_exports.parseSliceParams(x, begin, size);\n  slice_util_exports.assertParamsValid(x, $begin, $size);\n  if (backend2.shouldExecuteOnCPU([x]) || x.dtype === \"string\") {\n    const xBufferInfo = backend2.tensorMap.get(x.dataId);\n    const outValues = sliceImplCPU2(xBufferInfo.values, $begin, $size, x.shape, x.dtype);\n    return backend2.makeTensorInfo($size, x.dtype, outValues);\n  }\n  if (util_exports.sizeFromShape($size) === 0) {\n    return backend2.makeTensorInfo($size, x.dtype, []);\n  }\n  const program = new SliceProgram2($begin, $size);\n  const uniformData = [{ type: \"int32\", data: $begin }];\n  return backend2.runWebGPUProgram(program, [x], x.dtype, uniformData);\n}\nvar sliceConfig4 = {\n  kernelName: Slice,\n  backendName: \"webgpu\",\n  kernelFunc: slice5\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/BatchToSpaceND.js\nvar batchToSpaceND5 = (args) => {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x } = inputs;\n  const { blockShape, crops } = attrs;\n  util_exports.assert(x.shape.length <= 4, () => \"batchToSpaceND for rank > 4 with a WebGPU backend not implemented yet\");\n  const prod6 = blockShape.reduce((a, b) => a * b);\n  const reshaped = backend_util_exports.getReshaped(x.shape, blockShape, prod6);\n  const permuted = backend_util_exports.getPermuted(reshaped.length, blockShape.length);\n  const reshapedPermuted = backend_util_exports.getReshapedPermuted(x.shape, blockShape, prod6);\n  const sliceBeginCoords = backend_util_exports.getSliceBeginCoords(crops, blockShape.length);\n  const sliceSize = backend_util_exports.getSliceSize(reshapedPermuted, crops, blockShape.length);\n  const toDispose = [];\n  const reshapedIntermediate = reshape6({ inputs: { x }, backend: backend2, attrs: { shape: reshaped } });\n  const transposedIntermediate = transpose5({ inputs: { x: reshapedIntermediate }, backend: backend2, attrs: { perm: permuted } });\n  const reshapedIntermediate2 = reshape6({\n    inputs: { x: transposedIntermediate },\n    backend: backend2,\n    attrs: { shape: reshapedPermuted }\n  });\n  const sliced = slice5({\n    inputs: { x: reshapedIntermediate2 },\n    backend: backend2,\n    attrs: { begin: sliceBeginCoords, size: sliceSize }\n  });\n  toDispose.push(reshapedIntermediate);\n  toDispose.push(transposedIntermediate);\n  toDispose.push(reshapedIntermediate2);\n  toDispose.forEach((t2) => backend2.disposeData(t2.dataId));\n  return sliced;\n};\nvar batchToSpaceNDConfig4 = {\n  kernelName: BatchToSpaceND,\n  backendName: \"webgpu\",\n  kernelFunc: batchToSpaceND5\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/NotEqual.js\nvar notEqual4 = binaryKernelFunc3({\n  opType: BinaryOpType.NOT_EQUAL,\n  dtype: \"bool\",\n  cpuKernelImpl: notEqualImplCPU2\n});\nvar notEqualConfig4 = {\n  kernelName: NotEqual,\n  backendName: \"webgpu\",\n  kernelFunc: notEqual4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/Real.js\nfunction real4(args) {\n  const { inputs, backend: backend2 } = args;\n  const { input: input2 } = inputs;\n  const inputData = backend2.tensorMap.get(input2.dataId);\n  return identity5({ inputs: { x: inputData.complexTensorInfos.real }, backend: backend2 });\n}\nvar realConfig3 = {\n  kernelName: Real,\n  backendName: \"webgpu\",\n  kernelFunc: real4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernel_utils/int.js\nfunction int2(input2, backend2) {\n  const program = new UnaryOpProgram2(input2.shape, UnaryOpType.TO_INT);\n  const output = backend2.runWebGPUProgram(program, [input2], \"int32\");\n  return { dataId: output.dataId, shape: output.shape, dtype: output.dtype };\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/Cast.js\nfunction cast6(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x } = inputs;\n  const { dtype } = attrs;\n  if (dtype === \"complex64\") {\n    if (x.dtype === \"complex64\") {\n      return identity5({ inputs: { x }, backend: backend2 });\n    }\n    const zerosTensor = zeros(x.shape);\n    const floatX = cast6({ inputs: { x }, backend: backend2, attrs: { dtype: \"float32\" } });\n    const result = complex4({ inputs: { real: floatX, imag: zerosTensor }, backend: backend2 });\n    zerosTensor.dispose();\n    backend2.disposeData(floatX.dataId);\n    return result;\n  }\n  if (x.dtype === \"complex64\") {\n    const realPart = real4({ inputs: { input: x }, backend: backend2 });\n    const result = cast6({ inputs: { x: realPart }, backend: backend2, attrs: { dtype } });\n    backend2.disposeData(realPart.dataId);\n    return result;\n  }\n  if (!util_exports.hasEncodingLoss(x.dtype, dtype)) {\n    const result = identity5({ inputs: { x }, backend: backend2 });\n    return { dataId: result.dataId, shape: result.shape, dtype };\n  }\n  if (backend2.shouldExecuteOnCPU([x])) {\n    const values = backend2.tensorMap.get(x.dataId).values;\n    const [resultShape, resultType, resultData] = castImplCPU2(values, x.shape, x.dtype, dtype);\n    return backend2.makeTensorInfo(resultShape, resultType, resultData);\n  }\n  if (dtype === \"int32\") {\n    return int2(x, backend2);\n  }\n  if (dtype === \"bool\") {\n    const zerosTensorInfo = backend2.makeTensorInfo([], \"bool\", util_exports.getTypedArrayFromDType(\"bool\", 1));\n    const binaryInputs = { a: x, b: zerosTensorInfo };\n    const result = notEqual4({ inputs: binaryInputs, backend: backend2 });\n    backend2.disposeData(zerosTensorInfo.dataId);\n    return result;\n  }\n  throw new Error(`Error in Cast: failed to cast ${x.dtype} to ${dtype}`);\n}\nvar castConfig4 = {\n  kernelName: Cast,\n  backendName: \"webgpu\",\n  kernelFunc: cast6\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/Ceil.js\nvar ceil4 = unaryKernelFunc3({ opType: UnaryOpType.CEIL, cpuKernelImpl: ceilImplCPU2 });\nvar ceilConfig4 = {\n  kernelName: Ceil,\n  backendName: \"webgpu\",\n  kernelFunc: ceil4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/clip_vec4_webgpu.js\nvar ClipVec4Program = class {\n  constructor(outputShape) {\n    this.variableNames = [\"A\"];\n    this.uniforms = \"minVal : f32, maxVal : f32,\";\n    this.workPerThread = 4;\n    this.workGroupSize = [64, 1, 1];\n    this.isVec4 = true;\n    this.size = true;\n    this.outputShape = outputShape;\n    this.dispatchLayout = flatDispatchLayout(this.outputShape);\n    this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, this.workGroupSize, [this.workPerThread, 1, 1]);\n    this.shaderKey = \"clipVec4\";\n  }\n  getUserCode() {\n    const userCode = `\n      ${getMainHeaderString(\"index\")} {\n        if(index < uniforms.size) {\n          let value = getAByOutputIndex(index);\n          var clampedValue : vec4<f32>;\n          for (var i = 0; i < 4; i = i + 1) {\n            if (isnan(value[i])) {\n              clampedValue[i] = value[i];\n            } else {\n              clampedValue[i] = clamp(value[i], uniforms.minVal, uniforms.maxVal);\n            }\n          }\n\n          setOutputAtIndex(index, clampedValue);\n        }\n      }\n    `;\n    return userCode;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/clip_webgpu.js\nvar ClipProgram2 = class {\n  constructor(outputShape) {\n    this.variableNames = [\"A\"];\n    this.uniforms = \"minVal : f32, maxVal : f32,\";\n    this.workGroupSize = [64, 1, 1];\n    this.size = true;\n    this.outputShape = outputShape;\n    this.dispatchLayout = flatDispatchLayout(this.outputShape);\n    this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, this.workGroupSize);\n    this.shaderKey = \"clip\";\n  }\n  getUserCode() {\n    const userCode = `\n      ${getMainHeaderString(\"index\")} {\n        if(index < uniforms.size) {\n          let value = getAByOutputIndex(index);\n          if (isnan(value)) {\n            setOutputAtIndex(index, value);\n            return;\n          }\n          setOutputAtIndex(index, clamp(value, uniforms.minVal, uniforms.maxVal));\n        }\n      }\n    `;\n    return userCode;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/ClipByValue.js\nfunction clipByValue4(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x } = inputs;\n  const { clipValueMin, clipValueMax } = attrs;\n  let program;\n  const uniformData = [\n    { type: \"float32\", data: [clipValueMin] },\n    { type: \"float32\", data: [clipValueMax] }\n  ];\n  if (util_exports.sizeFromShape(x.shape) % 4 === 0) {\n    program = new ClipVec4Program(x.shape);\n  } else {\n    program = new ClipProgram2(x.shape);\n  }\n  return backend2.runWebGPUProgram(program, [x], x.dtype, uniformData);\n}\nvar clipByValueConfig4 = {\n  kernelName: ClipByValue,\n  backendName: \"webgpu\",\n  kernelFunc: clipByValue4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/concat_webgpu.js\nvar ConcatProgram2 = class {\n  constructor(shapes) {\n    this.uniforms = \"\";\n    this.workPerThread = 4;\n    this.workGroupSize = [64, 1, 1];\n    this.size = true;\n    this.outputShape = backend_util_exports.computeOutShape(shapes, 1);\n    this.variableNames = shapes.map((_, i2) => `T${i2}`);\n    this.dispatchLayout = flatDispatchLayout(this.outputShape);\n    this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, this.workGroupSize, [this.workPerThread, 1, 1]);\n    this.offsetLength = shapes.length - 1;\n    for (let i2 = 0; i2 < this.offsetLength; i2++) {\n      this.uniforms += `offset${i2} : i32,`;\n    }\n    this.shaderKey = \"concat\";\n  }\n  getUserCode() {\n    const snippets = [];\n    if (this.offsetLength > 0) {\n      snippets.push(`if (yC < uniforms.offset0){ setOutputAtCoords(coords.x, coords.y, getT0(yR, yC)); }`);\n      for (let i2 = 1; i2 < this.offsetLength; i2++) {\n        snippets.push(`else if (yC < uniforms.offset${[i2]}){ setOutputAtCoords(coords.x, coords.y, getT${i2}(yR, yC - uniforms.offset${i2 - 1})); }`);\n      }\n      const lastIndex = this.offsetLength;\n      const lastShiftIndex = this.offsetLength - 1;\n      snippets.push(`else { setOutputAtCoords(coords.x, coords.y, getT${lastIndex}(yR, yC - uniforms.offset${lastShiftIndex})); }`);\n    } else {\n      snippets.push(`setOutputAtCoords(coords.x, coords.y, getT0(yR, yC));`);\n    }\n    const userCode = `\n      ${getMainHeaderString(\"index\")} {\n        for(var i = 0; i < ${this.workPerThread}; i = i + 1) {\n          let flatIndex = index * ${this.workPerThread} + i;\n          if(flatIndex < uniforms.size) {\n            let coords = getCoordsFromIndex(flatIndex);\n            let yR = coords.x;\n            let yC = coords.y;\n\n            ${snippets.join(\"\\n        \")}\n          }\n        }\n      }\n    `;\n    return userCode;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/Imag.js\nfunction imag4(args) {\n  const { inputs, backend: backend2 } = args;\n  const { input: input2 } = inputs;\n  const inputData = backend2.tensorMap.get(input2.dataId);\n  return identity5({ inputs: { x: inputData.complexTensorInfos.imag }, backend: backend2 });\n}\nvar imagConfig3 = {\n  kernelName: Imag,\n  backendName: \"webgpu\",\n  kernelFunc: imag4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/Concat_impl.js\nfunction concatImpl3(inputs, axis, backend2) {\n  const dtype = inputs[0].dtype;\n  if (dtype === \"complex64\") {\n    const reals = inputs.map((t2) => real4({ inputs: { input: t2 }, backend: backend2 }));\n    const imags = inputs.map((t2) => imag4({ inputs: { input: t2 }, backend: backend2 }));\n    const realConcated = concatImpl3(reals, axis, backend2);\n    const imagConcated = concatImpl3(imags, axis, backend2);\n    const result = complex4({ inputs: { real: realConcated, imag: imagConcated }, backend: backend2 });\n    reals.forEach((r2) => backend2.disposeData(r2.dataId));\n    imags.forEach((i2) => backend2.disposeData(i2.dataId));\n    backend2.disposeData(realConcated.dataId);\n    backend2.disposeData(imagConcated.dataId);\n    return result;\n  }\n  let runOnCpu = backend2.shouldExecuteOnCPU(inputs);\n  if (dtype === \"string\") {\n    runOnCpu = true;\n  }\n  if (runOnCpu) {\n    const tensors2D2 = inputs.map((t2) => {\n      const innerSize = util_exports.sizeFromShape(t2.shape.slice(axis));\n      const shape = [-1, innerSize];\n      return reshape6({ inputs: { x: t2 }, backend: backend2, attrs: { shape } });\n    });\n    const inputsValShapes = tensors2D2.map((t2) => {\n      return { vals: backend2.readSync(t2.dataId), shape: t2.shape };\n    });\n    const outShape2 = backend_util_exports.computeOutShape(tensors2D2.map((t2) => t2.shape), 1);\n    const simplyConcat = tensors2D2[0].shape[0] === 1;\n    const outVals = concatImplCPU2(inputsValShapes, outShape2, dtype, simplyConcat);\n    const finalOutShape = backend_util_exports.computeOutShape(inputs.map((t2) => t2.shape), axis);\n    const outInfo = backend2.makeTensorInfo(finalOutShape, dtype, outVals);\n    tensors2D2.forEach((t2) => backend2.disposeData(t2.dataId));\n    return outInfo;\n  }\n  const maxInputNum = backend2.device.limits.maxStorageBuffersPerShaderStage - 1;\n  if (inputs.length > maxInputNum) {\n    const reducedInputs = [];\n    for (let i2 = 0; i2 < inputs.length; i2 += maxInputNum) {\n      const subArray = inputs.slice(i2, i2 + maxInputNum);\n      reducedInputs.push(concatImpl3(subArray, axis, backend2));\n    }\n    const result = concatImpl3(reducedInputs, axis, backend2);\n    for (const i2 of reducedInputs) {\n      backend2.disposeData(i2.dataId);\n    }\n    return result;\n  }\n  const { tensors2D, outShape } = computeTensors2D2(inputs, axis, backend2);\n  const shapes = tensors2D.map((t2) => t2.shape);\n  const program = new ConcatProgram2(shapes);\n  const uniformData = [];\n  const offsets = new Array(shapes.length - 1);\n  if (offsets.length > 0) {\n    offsets[0] = shapes[0][1];\n    uniformData.push({ type: \"int32\", data: [offsets[0]] });\n    for (let i2 = 1; i2 < offsets.length; i2++) {\n      offsets[i2] = offsets[i2 - 1] + shapes[i2][1];\n      uniformData.push({ type: \"int32\", data: [offsets[i2]] });\n    }\n  }\n  const res = backend2.runWebGPUProgram(program, tensors2D, tensors2D[0].dtype, uniformData);\n  tensors2D.forEach((r2) => backend2.disposeData(r2.dataId));\n  const reshapedResult = reshape6({ inputs: { x: res }, backend: backend2, attrs: { shape: outShape } });\n  backend2.disposeData(res.dataId);\n  return reshapedResult;\n}\nfunction computeTensors2D2(inputs, axis, backend2) {\n  const outShape = backend_util_exports.computeOutShape(inputs.map((t2) => t2.shape), axis);\n  const tensors2D = inputs.map((t2) => reshape6({\n    inputs: { x: t2 },\n    backend: backend2,\n    attrs: {\n      shape: [\n        util_exports.sizeFromShape(t2.shape.slice(0, axis)),\n        util_exports.sizeFromShape(t2.shape.slice(axis))\n      ]\n    }\n  }));\n  return { tensors2D, outShape };\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/Concat.js\nfunction concat5(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { axis } = attrs;\n  const $axis = util_exports.parseAxisParam(axis, inputs[0].shape)[0];\n  const outShape = backend_util_exports.computeOutShape(inputs.map((t2) => t2.shape), $axis);\n  if (util_exports.sizeFromShape(outShape) === 0) {\n    return backend2.makeTensorInfo(outShape, inputs[0].dtype, []);\n  }\n  const $inputs = inputs.filter((t2) => util_exports.sizeFromShape(t2.shape) > 0);\n  if ($inputs.length === 1) {\n    return identity5({ inputs: { x: $inputs[0] }, backend: backend2 });\n  }\n  const shapes = $inputs.map((t2) => t2.shape);\n  backend_util_exports.assertParamsConsistent(shapes, $axis);\n  return concatImpl3($inputs, $axis, backend2);\n}\nvar concatConfig4 = {\n  kernelName: Concat,\n  backendName: \"webgpu\",\n  kernelFunc: concat5\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/conv2d_mm_webgpu.js\nfunction conv2dCommonSnippet(isChannelsLast, fitAOuter, fitBOuter, fitInner, addBias = false, activation2 = null, hasPreluActivationWeights = false, innerElementSizeX = 4, innerElementSizeW = 4, innerElementSize = 4) {\n  const getXSnippet = (innerElementSize2) => {\n    switch (innerElementSize2) {\n      case 1:\n        return \"resData = x[xIndex];\";\n      case 3:\n        return \"resData = vec3<f32>(x[xIndex], x[xIndex + 1], x[xIndex + 2]);\";\n      case 4:\n        return \"resData = x[xIndex / 4];\";\n      default:\n        throw new Error(`innerElementSize ${innerElementSize2} is not supported.`);\n    }\n  };\n  const getWSnippet = (innerElementSize2) => {\n    switch (innerElementSize2) {\n      case 1:\n        return \"return W[row * uniforms.wShape[3] + colIn];\";\n      case 4:\n        return \"return W[row * uniforms.wShape[3] / 4 + colIn];\";\n      default:\n        throw new Error(`innerElementSize ${innerElementSize2} is not supported.`);\n    }\n  };\n  const coordASnippet = isChannelsLast ? `\n      let coord = vec4<i32>(batch, xRow, xCol, xCh);\n      ` : `\n      let coord = vec4<i32>(batch, xCh, xRow, xCol);\n      `;\n  const coordResSnippet = isChannelsLast ? `\n      let coords = vec4<i32>(\n        batch,\n        row / outWidth,\n        row % outWidth,\n        col);\n      ` : `\n      let coords = vec4<i32>(\n        batch,\n        row,\n        col / outWidth,\n        col % outWidth);\n      `;\n  const xHight = isChannelsLast ? \"uniforms.xShape[1]\" : \"uniforms.xShape[2]\";\n  const xWidth = isChannelsLast ? \"uniforms.xShape[2]\" : \"uniforms.xShape[3]\";\n  const row = isChannelsLast ? \"row\" : \"col\";\n  const col = isChannelsLast ? \"col\" : \"row\";\n  const readXSnippet = `\n      let inChannels = uniforms.wShape[2];\n      let outWidth = ${isChannelsLast ? \"uniforms.outShape[2]\" : \"uniforms.outShape[3]\"};\n      let outRow = ${row} / outWidth;\n      let outCol = ${row} % outWidth;\n\n      let WRow = ${col} / (uniforms.filterDims[1] * inChannels);\n      let WCol = ${col} / inChannels % uniforms.filterDims[1];\n      let xRow = outRow * uniforms.stride[0] + uniforms.dilation[0] * WRow - uniforms.pad[0];\n      let xCol = outCol * uniforms.stride[1] + uniforms.dilation[1] * WCol - uniforms.pad[1];\n      let xCh = ${col} % inChannels;\n      var resData = ${typeSnippet(innerElementSizeX)}(0.0);\n      // The bounds checking is always needed since we use it to pad zero for\n      // the 'same' padding type.\n      if (xRow >= 0 && xRow < ${xHight} && xCol >= 0 && xCol < ${xWidth}) {\n        ${coordASnippet}\n        let xIndex = getIndexFromCoords4D(coord, uniforms.xShape);\n        ${getXSnippet(innerElementSizeX)}\n      }\n      return resData;`;\n  const sampleX = isChannelsLast ? fitAOuter && fitInner ? `\n      let col = colIn * ${innerElementSizeX};\n      ${readXSnippet}` : `\n      let col = colIn * ${innerElementSizeX};\n      if (row < uniforms.dimAOuter && col < uniforms.dimInner) {\n        ${readXSnippet}\n      }\n      return ${typeSnippet(innerElementSizeX)}(0.0);` : fitInner && fitBOuter ? `\n      let col = colIn * ${innerElementSizeX};\n      ${readXSnippet}` : `\n      let col = colIn * ${innerElementSizeX};\n      if (row < uniforms.dimInner && col < uniforms.dimBOuter) {\n        ${readXSnippet}\n      }\n      return ${typeSnippet(innerElementSizeX)}(0.0);`;\n  const sampleW = `${getWSnippet(innerElementSizeW)}`;\n  const resType = typeSnippet(innerElementSize);\n  const aType = isChannelsLast ? typeSnippet(innerElementSizeX) : typeSnippet(innerElementSizeW);\n  const bType = isChannelsLast ? typeSnippet(innerElementSizeW) : typeSnippet(innerElementSizeX);\n  const userCode = `\n      ${activationFnSnippet(activation2, hasPreluActivationWeights, innerElementSize === 4, 4)}\n      fn mm_readA(batch: i32, row : i32, colIn : i32) -> ${aType} {\n        ${isChannelsLast ? sampleX : sampleW}\n      }\n\n      fn mm_readB(batch: i32, row : i32, colIn : i32) -> ${bType} {\n        ${isChannelsLast ? sampleW : sampleX}\n      }\n\n      fn mm_write(batch: i32, row : i32, colIn : i32, valueIn : ${resType}) {\n        let col = colIn * ${innerElementSize};\n        if (row < uniforms.dimAOuter && col < uniforms.dimBOuter)\n        {\n        var value = valueIn;\n        let outWidth = ${isChannelsLast ? \"uniforms.outShape[2]\" : \"uniforms.outShape[3]\"};\n        ${coordResSnippet}\n        ${biasActivationSnippet(addBias, activation2)}\n        setOutputAtCoords(coords[0], coords[1], coords[2], coords[3], value);\n        }\n      }`;\n  return userCode;\n}\nvar Conv2DMMProgram = class {\n  constructor(convInfo, dimAOuter, dimBOuter, dimInner, addBias = false, activation2 = null, hasPreluActivationWeights = false) {\n    this.variableNames = [\"x\", \"W\"];\n    this.uniforms = `filterDims : vec2<i32>, pad : vec2<i32>, stride : vec2<i32>, dilation : vec2<i32>, dimAOuter : i32, dimBOuter : i32, dimInner : i32,`;\n    this.outputShape = convInfo.outShape;\n    this.isChannelsLast = convInfo.dataFormat === \"channelsLast\";\n    this.isVec4 = ((convInfo.inChannels % 4 === 0 || convInfo.inChannels % 3 === 0) && this.isChannelsLast || convInfo.outWidth % 4 === 0 && !this.isChannelsLast) && convInfo.outChannels % 4 === 0;\n    this.dispatchLayout = this.isChannelsLast ? { x: [3], y: [1, 2], z: [0] } : { x: [2, 3], y: [1], z: [0] };\n    this.workGroupSize = computeWorkGroupSizeForConv2d(this.dispatchLayout, this.outputShape, this.isVec4);\n    this.elementsPerThread = computeWorkPerThreadForConv2d(this.dispatchLayout, this.outputShape, this.isVec4);\n    this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, this.workGroupSize, this.elementsPerThread);\n    if (this.isVec4) {\n      if (this.isChannelsLast && convInfo.inChannels % 4 !== 0) {\n        this.innerElementSize = 3;\n        this.variableTypes = [\"f32\", \"vec4<f32>\"];\n      } else {\n        this.innerElementSize = 4;\n        this.variableTypes = [\"vec4<f32>\", \"vec4<f32>\"];\n      }\n      if (addBias) {\n        this.variableNames.push(\"bias\");\n        this.variableTypes.push(\"vec4<f32>\");\n      }\n      if (hasPreluActivationWeights) {\n        this.variableNames.push(\"preluActivationWeights\");\n        this.variableTypes.push(\"vec4<f32>\");\n      }\n    } else {\n      this.innerElementSize = this.elementsPerThread[0];\n      if (addBias) {\n        this.variableNames.push(\"bias\");\n      }\n      if (hasPreluActivationWeights) {\n        this.variableNames.push(\"preluActivationWeights\");\n      }\n    }\n    this.addBias = addBias;\n    this.activation = activation2;\n    this.hasPreluActivationWeights = hasPreluActivationWeights;\n    this.tileAOuter = this.workGroupSize[1] * this.elementsPerThread[1];\n    this.tileBOuter = this.workGroupSize[0] * this.elementsPerThread[0];\n    this.tileInner = Math.max(this.workGroupSize[0] * this.innerElementSize, this.workGroupSize[1]);\n    this.fitAOuter = dimAOuter % this.tileAOuter === 0;\n    this.fitBOuter = dimBOuter % this.tileBOuter === 0;\n    this.fitInner = dimInner % this.tileInner === 0;\n    this.shaderKey = `conv2DMM_${this.elementsPerThread}_${this.activation}}_${this.fitAOuter}_${this.fitBOuter}_${this.fitInner}_${this.isVec4}_${this.innerElementSize}_${this.isChannelsLast}`;\n  }\n  getUserCode() {\n    const matMulSource = this.isVec4 ? makeMatMulPackedVec4Source(this.elementsPerThread, this.workGroupSize, !this.isChannelsLast, this.tileInner) : makeMatMulPackedSource(this.elementsPerThread, this.workGroupSize, !this.isChannelsLast, this.tileInner);\n    const elementsSize = this.isVec4 ? [this.innerElementSize, 4, 4] : [1, 1, 1];\n    const userCode = `\n    ${conv2dCommonSnippet(this.isChannelsLast, this.fitAOuter, this.fitBOuter, this.fitInner, this.addBias, this.activation, this.hasPreluActivationWeights, elementsSize[0], elementsSize[1], elementsSize[2])}\n    ${matMulSource}\n  `;\n    return userCode;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/Conv2D_impl.js\nfunction getShapeForBatchMatMul2(shape, isChannelsLast) {\n  const length = shape.length;\n  if (length >= 3) {\n    return isChannelsLast ? [\n      ...shape.slice(0, -3),\n      shape[length - 3] * shape[length - 2],\n      shape[length - 1]\n    ] : [\n      ...shape.slice(0, -3),\n      shape[length - 3],\n      shape[length - 2] * shape[length - 1]\n    ];\n  } else if (!isChannelsLast && length === 1 && shape[0] > 1) {\n    return [shape[0], 1];\n  } else {\n    return null;\n  }\n}\nfunction conv2dByMatMul2({ x, filter, convInfo, backend: backend2, bias = null, preluActivationWeights = null, leakyreluAlpha = 0, activation: activation2 = null }) {\n  const isChannelsLast = convInfo.dataFormat === \"channelsLast\";\n  const transposeA = isChannelsLast ? false : true;\n  const transposeB = false;\n  const sameSize = isChannelsLast && convInfo.filterHeight === convInfo.inHeight && convInfo.filterWidth === convInfo.inWidth && convInfo.padInfo.type === \"VALID\";\n  const intermediates = [];\n  let xReshaped;\n  let filterReshaped;\n  if (sameSize) {\n    const sharedDim = convInfo.inHeight * convInfo.inWidth * convInfo.inChannels;\n    xReshaped = reshape6({\n      inputs: { x },\n      backend: backend2,\n      attrs: { shape: [1, convInfo.batchSize, sharedDim] }\n    });\n    filterReshaped = reshape6({\n      inputs: { x: filter },\n      backend: backend2,\n      attrs: { shape: [1, sharedDim, convInfo.outChannels] }\n    });\n  } else {\n    xReshaped = reshape6({\n      inputs: { x },\n      backend: backend2,\n      attrs: {\n        shape: isChannelsLast ? [\n          convInfo.batchSize,\n          convInfo.inHeight * convInfo.inWidth,\n          convInfo.inChannels\n        ] : [\n          convInfo.batchSize,\n          convInfo.inChannels,\n          convInfo.inHeight * convInfo.inWidth\n        ]\n      }\n    });\n    filterReshaped = reshape6({\n      inputs: { x: filter },\n      backend: backend2,\n      attrs: { shape: [1, convInfo.inChannels, convInfo.outChannels] }\n    });\n  }\n  intermediates.push(xReshaped);\n  intermediates.push(filterReshaped);\n  if (preluActivationWeights != null) {\n    const targetShape = getShapeForBatchMatMul2(preluActivationWeights.shape, isChannelsLast);\n    if (targetShape != null) {\n      preluActivationWeights = reshape6({\n        inputs: { x: preluActivationWeights },\n        backend: backend2,\n        attrs: { shape: targetShape }\n      });\n      intermediates.push(preluActivationWeights);\n    }\n  }\n  if (bias != null) {\n    const targetShape = getShapeForBatchMatMul2(bias.shape, isChannelsLast);\n    if (targetShape != null) {\n      bias = reshape6({ inputs: { x: bias }, backend: backend2, attrs: { shape: targetShape } });\n      intermediates.push(bias);\n    }\n  }\n  const result = batchMatMulImpl2({\n    a: isChannelsLast ? xReshaped : filterReshaped,\n    b: isChannelsLast ? filterReshaped : xReshaped,\n    transposeA,\n    transposeB,\n    backend: backend2,\n    bias,\n    activation: activation2,\n    preluActivationWeights,\n    leakyreluAlpha\n  });\n  const out = reshape6({ inputs: { x: result }, backend: backend2, attrs: { shape: convInfo.outShape } });\n  intermediates.push(result);\n  for (const i2 of intermediates) {\n    backend2.disposeData(i2.dataId);\n  }\n  return out;\n}\nfunction conv2DImpl({ x, filter, convInfo, backend: backend2, bias = null, preluActivationWeights = null, leakyreluAlpha = 0, activation: activation2 = null }) {\n  const hasBias = bias != null;\n  const hasPreluActivationWeights = preluActivationWeights != null;\n  const isChannelsLast = convInfo.dataFormat === \"channelsLast\";\n  const sameSize = isChannelsLast && convInfo.filterHeight === convInfo.inHeight && convInfo.filterWidth === convInfo.inWidth && convInfo.padInfo.type === \"VALID\";\n  if (sameSize || convInfo.filterHeight === 1 && convInfo.filterWidth === 1 && convInfo.dilationHeight === 1 && convInfo.dilationWidth === 1 && convInfo.strideHeight === 1 && convInfo.strideWidth === 1 && (convInfo.padInfo.type === \"SAME\" || convInfo.padInfo.type === \"VALID\")) {\n    return conv2dByMatMul2({\n      x,\n      filter,\n      convInfo,\n      backend: backend2,\n      bias,\n      activation: activation2,\n      preluActivationWeights,\n      leakyreluAlpha\n    });\n  }\n  const dimAOuter = isChannelsLast ? convInfo.outHeight * convInfo.outWidth : convInfo.outChannels;\n  const dimBOuter = isChannelsLast ? convInfo.outChannels : convInfo.outHeight * convInfo.outWidth;\n  const dimInner = convInfo.filterHeight * convInfo.filterWidth * convInfo.inChannels;\n  const padInfo = [convInfo.padInfo.top, convInfo.padInfo.left];\n  const dimensions = [\n    { type: \"int32\", data: [convInfo.filterHeight, convInfo.filterWidth] },\n    { type: \"int32\", data: [...padInfo] },\n    { type: \"int32\", data: [convInfo.strideHeight, convInfo.strideWidth] },\n    { type: \"int32\", data: [convInfo.dilationHeight, convInfo.dilationWidth] },\n    { type: \"int32\", data: [dimAOuter] },\n    { type: \"int32\", data: [dimBOuter] },\n    { type: \"int32\", data: [dimInner] }\n  ];\n  const program = new Conv2DMMProgram(convInfo, dimAOuter, dimBOuter, dimInner, hasBias, activation2, hasPreluActivationWeights);\n  const intermediates = [];\n  const inputVar = [x, filter];\n  if (hasBias) {\n    if (!isChannelsLast && bias.shape.length === 1) {\n      bias = reshape6({ inputs: { x: bias }, backend: backend2, attrs: { shape: [bias.shape[0], 1, 1] } });\n      intermediates.push(bias);\n    }\n    inputVar.push(bias);\n  }\n  if (hasPreluActivationWeights) {\n    if (!isChannelsLast && preluActivationWeights.shape.length === 1) {\n      preluActivationWeights = reshape6({\n        inputs: { x: preluActivationWeights },\n        backend: backend2,\n        attrs: { shape: [preluActivationWeights.shape[0], 1, 1] }\n      });\n      intermediates.push(preluActivationWeights);\n    }\n    inputVar.push(preluActivationWeights);\n  }\n  if (activation2 === \"leakyrelu\") {\n    dimensions.push({ type: \"float32\", data: [leakyreluAlpha] });\n    program.uniforms += \" alpha : f32,\";\n  }\n  const out = backend2.runWebGPUProgram(program, inputVar, x.dtype, dimensions);\n  for (const i2 of intermediates) {\n    backend2.disposeData(i2.dataId);\n  }\n  return out;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/Conv2D.js\nfunction conv2d6(args) {\n  const { inputs, attrs, backend: backend2 } = args;\n  const { x, filter } = inputs;\n  const { strides, pad: pad3, dataFormat, dilations, dimRoundingMode } = attrs;\n  const $dataFormat = backend_util_exports.convertConv2DDataFormat(dataFormat);\n  const convInfo = backend_util_exports.computeConv2DInfo(x.shape, filter.shape, strides, dilations, pad3, dimRoundingMode, false, $dataFormat);\n  return conv2DImpl({ x, filter, convInfo, backend: backend2 });\n}\nvar conv2DConfig4 = {\n  kernelName: Conv2D,\n  backendName: \"webgpu\",\n  kernelFunc: conv2d6\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/conv_backprop_mm_webgpu.js\nfunction conv2dTransposeCommonSnippet(innerElementSize = 4) {\n  const getWSnippet = (innerElementSize2) => {\n    switch (innerElementSize2) {\n      case 1:\n        return \"return W[getIndexFromCoords4D(coord, uniforms.wShape)];\";\n      case 4:\n        return `\n            let coord1 = vec4<i32>(coordX, coordY, col + 1, rowInner);\n            let coord2 = vec4<i32>(coordX, coordY, col + 2, rowInner);\n            let coord3 = vec4<i32>(coordX, coordY, col + 3, rowInner);\n            let v0 = W[getIndexFromCoords4D(coord, uniforms.wShape)];\n            let v1 = W[getIndexFromCoords4D(coord1, uniforms.wShape)];\n            let v2 = W[getIndexFromCoords4D(coord2, uniforms.wShape)];\n            let v3 = W[getIndexFromCoords4D(coord3, uniforms.wShape)];\n            return vec4<f32>(v0, v1, v2, v3);\n            `;\n      default:\n        throw new Error(`innerElementSize ${innerElementSize2} is not supported.`);\n    }\n  };\n  const readASnippet = `\n      let outRow = row / uniforms.outShape[2];\n      let outCol = row % uniforms.outShape[2];\n\n      let WRow = col / (uniforms.filterDims[1] * uniforms.outBackprop[3]);\n      let WCol = col / uniforms.outBackprop[3] % uniforms.filterDims[1];\n      let xR = f32(outRow - uniforms.pads[0] + WRow) / f32(uniforms.stride[0]);\n      let xC = f32(outCol - uniforms.pads[1] + WCol) / f32(uniforms.stride[1]);\n      if (xR < 0.0 || xR >= f32(uniforms.outBackprop[1]) || fract(xR) > 0.0) {\n        return ${typeSnippet(innerElementSize)}(0.0);\n      }\n      if (xC < 0.0 || xC >= f32(uniforms.outBackprop[2]) || fract(xC) > 0.0) {\n        return ${typeSnippet(innerElementSize)}(0.0);\n      }\n      let coord = vec4<i32>(\n          batch,\n          i32(xR),\n          i32(xC),\n          col % uniforms.outBackprop[3]);\n      return x[getIndexFromCoords4D(coord, uniforms.xShape)/${innerElementSize}];`;\n  const sampleA = `if (row < uniforms.dimAOuter && col < uniforms.dimInner) {\n        ${readASnippet}\n      }\n      return ${typeSnippet(innerElementSize)}(0.0);`;\n  const userCode = `\n  fn mm_readA(batch: i32, row : i32, colIn : i32) -> ${typeSnippet(innerElementSize)} {\n    let col = colIn * ${innerElementSize};\n    ${sampleA}\n  }\n\n  fn mm_readB(batch: i32, row : i32, colIn : i32) -> ${typeSnippet(innerElementSize)} {\n    let col = colIn * ${innerElementSize};\n    let coordX = uniforms.filterDims.x - 1 -\n        row / (uniforms.filterDims[1] * uniforms.outBackprop[3]);\n    let coordY = uniforms.filterDims.y - 1 -\n        (row / uniforms.outBackprop[3]) % uniforms.filterDims[1];\n    if (row < uniforms.dimInner && col < uniforms.dimBOuter &&\n        coordX >= 0 && coordY >= 0) {\n      let rowInner = row % uniforms.outBackprop[3];\n      let coord = vec4<i32>(coordX, coordY, col, rowInner);\n      ${getWSnippet(innerElementSize)}\n    }\n    return ${typeSnippet(innerElementSize)}(0.0);\n  }\n\n  fn mm_write(batch: i32, row : i32, colIn : i32, valueInput : ${typeSnippet(innerElementSize)}) {\n    let col = colIn * ${innerElementSize};\n    if (row < uniforms.dimAOuter && (col + ${innerElementSize - 1}) < uniforms.dimBOuter) {\n      var value = valueInput;\n      let outCoord = vec4<i32>(\n          batch,\n          row / uniforms.outShape[2],\n          row % uniforms.outShape[2],\n          col);\n      result[getIndexFromCoords4D(outCoord, uniforms.outShape)/${innerElementSize}] = value;\n    }\n  }`;\n  return userCode;\n}\nvar Conv2DDerInputMMProgram = class {\n  constructor(convInfo) {\n    this.variableNames = [\"x\", \"W\"];\n    this.uniforms = \"filterDims : vec2<i32>, pads : vec2<i32>, stride : vec2<i32>, outBackprop : vec4<i32>, dimAOuter : i32, dimBOuter : i32, dimInner : i32,\";\n    this.outputShape = convInfo.inShape;\n    util_exports.assert(convInfo.dataFormat === \"channelsLast\", () => \"TODO: NCHW is unimplemented\");\n    this.isVec4 = convInfo.inChannels % 4 === 0 && convInfo.outChannels % 4 === 0;\n    this.dispatchLayout = { x: [3], y: [1, 2], z: [0] };\n    this.workGroupSize = computeWorkGroupSizeForConv2d(this.dispatchLayout, this.outputShape, this.isVec4);\n    this.elementsPerThread = computeWorkPerThreadForConv2d(this.dispatchLayout, this.outputShape, this.isVec4);\n    this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, this.workGroupSize, this.elementsPerThread);\n    if (this.isVec4) {\n      this.variableTypes = [\"vec4<f32>\", \"f32\"];\n    }\n    this.shaderKey = `conv2DDerInputMM_${this.isVec4}_${this.elementsPerThread}`;\n  }\n  getUserCode() {\n    const matMulSource = this.isVec4 ? makeMatMulPackedVec4Source(this.elementsPerThread, this.workGroupSize) : makeMatMulPackedSource(this.elementsPerThread, this.workGroupSize);\n    const userCode = `\n    ${conv2dTransposeCommonSnippet(this.isVec4 ? 4 : 1)}\n    ${matMulSource}\n    `;\n    return userCode;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/conv_backprop_webgpu.js\nvar Conv2DDerInputProgram2 = class {\n  constructor(convInfo) {\n    this.variableNames = [\"dy\", \"W\"];\n    this.uniforms = \"filterDims : vec2<i32>, pads : vec2<i32>, stride : vec2<i32>, outBackprop : vec4<i32>,\";\n    this.workGroupSize = [64, 1, 1];\n    this.size = true;\n    this.outputShape = convInfo.inShape;\n    this.dispatchLayout = flatDispatchLayout(this.outputShape);\n    this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, this.workGroupSize);\n    this.isChannelsLast = convInfo.dataFormat === \"channelsLast\";\n    this.shaderKey = `conv2DDerInput_${this.isChannelsLast}`;\n  }\n  getUserCode() {\n    const rowDim = this.isChannelsLast ? 1 : 2;\n    const colDim = this.isChannelsLast ? 2 : 3;\n    const channelDim = this.isChannelsLast ? 3 : 1;\n    return `\n    ${getMainHeaderString(\"index\")} {\n      if(index < uniforms.size) {\n        let coords = getCoordsFromIndex(index);\n        let batch = coords[0];\n        let d1 = coords[${channelDim}];\n\n        let dyCorner = vec2<i32>(coords[${rowDim}]), coords[${colDim}]) - uniforms.pads;\n        let dyRCorner = dyCorner.x;\n        let dyCCorner = dyCorner.y;\n\n        // Convolve dy(?, ?, d2) with w(:, :, d1, d2) to compute dx(xR, xC, d1).\n        // ? = to be determined. : = across all values in that axis.\n        var dotProd = 0.0;\n        for (var wR = 0; wR < uniforms.filterDims.x; wR = wR + 1) {\n          let dyR = (f32(dyRCorner) + f32(wR)) / f32(uniforms.stride.x);\n          let wRPerm = uniforms.filterDims.x - 1 - wR;\n          if (dyR < 0.0 || dyR >= f32(uniforms.outBackprop[1]) || fract(dyR) > 0.0 ||\n              wRPerm < 0) {\n            continue;\n          }\n          let idyR = dyR;\n\n          for (var wC = 0; wC < uniforms.filterDims.y; wC = wC + 1) {\n            let dyC = (f32(dyCCorner) + f32(wC)) / f32(uniforms.stride.y);\n            let wCPerm = uniforms.filterDims.y - 1 - wC;\n            if (dyC < 0.0 || dyC >= f32(uniforms.outBackprop[2]) ||\n                fract(dyC) > 0.0 || wCPerm < 0) {\n              continue;\n            }\n            let idyC = dyC;\n\n            for (var d2 = 0; d2 < uniforms.outBackprop[3]; d2 = d2 + 1) {\n              if (${this.isChannelsLast}) {\n                let xValue = getDy(batch, idyR, idyC, d2);\n                let wValue = getW(wRPerm, wCPerm, d1, d2);\n                dotProd = dotProd + xValue * wValue;\n              } else {\n                let xValue = getDy(batch, d2, idyR, idyC);\n                let wValue = getW(wRPerm, wCPerm, d1, d2);\n                dotProd = dotProd + xValue * wValue;\n              }\n\n            }\n          }\n        }\n        setOutputAtIndex(index, dotProd);\n      }\n    }\n  `;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/Conv2DBackpropInput.js\nfunction conv2DBackpropInput5(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { dy, filter } = inputs;\n  const { inputShape, strides, pad: pad3, dataFormat, dimRoundingMode } = attrs;\n  const $dataFormat = backend_util_exports.convertConv2DDataFormat(dataFormat);\n  const convInfo = backend_util_exports.computeConv2DInfo(inputShape, filter.shape, strides, 1, pad3, dimRoundingMode, false, $dataFormat);\n  const dimensions = [\n    { type: \"int32\", data: [convInfo.filterHeight, convInfo.filterWidth] },\n    {\n      type: \"int32\",\n      data: [\n        convInfo.filterHeight - 1 - convInfo.padInfo.top,\n        convInfo.filterWidth - 1 - convInfo.padInfo.left\n      ]\n    },\n    { type: \"int32\", data: [convInfo.strideHeight, convInfo.strideWidth] },\n    {\n      type: \"int32\",\n      data: [\n        convInfo.batchSize,\n        convInfo.outHeight,\n        convInfo.outWidth,\n        convInfo.outChannels\n      ]\n    }\n  ];\n  let program;\n  if (env().getBool(\"WEBGPU_USE_NAIVE_CONV2D_TRANSPOSE\")) {\n    program = new Conv2DDerInputProgram2(convInfo);\n  } else {\n    program = new Conv2DDerInputMMProgram(convInfo);\n    const dimAOuter = convInfo.inShape[1] * convInfo.inShape[2];\n    const dimBOuter = convInfo.inShape[3];\n    const dimInner = convInfo.filterHeight * convInfo.filterWidth * convInfo.outChannels;\n    dimensions.push({ type: \"uint32\", data: [dimAOuter] }, { type: \"uint32\", data: [dimBOuter] }, { type: \"uint32\", data: [dimInner] });\n  }\n  return backend2.runWebGPUProgram(program, [dy, filter], \"float32\", dimensions);\n}\nvar conv2DBackpropInputConfig4 = {\n  kernelName: Conv2DBackpropInput,\n  backendName: \"webgpu\",\n  kernelFunc: conv2DBackpropInput5\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/Cos.js\nvar cos4 = unaryKernelFunc3({ opType: UnaryOpType.COS });\nvar cosConfig4 = {\n  kernelName: Cos,\n  backendName: \"webgpu\",\n  kernelFunc: cos4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/Cosh.js\nvar cosh4 = unaryKernelFunc3({ opType: UnaryOpType.COSH });\nvar coshConfig4 = {\n  kernelName: Cosh,\n  backendName: \"webgpu\",\n  kernelFunc: cosh4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/crop_and_resize_webgpu.js\nvar CropAndResizeProgram2 = class {\n  constructor(channnel, boxShape, cropSize, method) {\n    this.variableNames = [\"Image\", \"Boxes\", \"BoxInd\"];\n    this.uniforms = \"extrapolationValue : f32,\";\n    this.workGroupSize = [64, 1, 1];\n    this.size = true;\n    const [numBoxes] = boxShape;\n    this.outputShape = [numBoxes, cropSize[0], cropSize[1], channnel];\n    this.dispatchLayout = flatDispatchLayout(this.outputShape);\n    this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, this.workGroupSize);\n    this.methodId = method === \"bilinear\" ? 1 : 0;\n    this.cropHeightBiggerThan1 = this.outputShape[1] > 1;\n    this.cropWidthBiggerThan1 = this.outputShape[2] > 1;\n    this.shaderKey = `cropAndResize_${this.methodId}_${this.cropHeightBiggerThan1}_${this.cropWidthBiggerThan1}`;\n  }\n  getUserCode() {\n    const [inputHeightFloat, inputWidthFloat] = [`f32(uniforms.imageShape[1] - 1)`, `f32(uniforms.imageShape[2] - 1)`];\n    const [heightRatio, heightScale, inY] = this.cropHeightBiggerThan1 ? [\n      `(${inputHeightFloat} / f32(uniforms.outShape[1] - 1))`,\n      \"(y2-y1) * height_ratio\",\n      `y1*${inputHeightFloat} + f32(y)*(height_scale)`\n    ] : [\n      \"0.0\",\n      \"0.0\",\n      `0.5 * (y1+y2) * ${inputHeightFloat}`\n    ];\n    const [widthRatio, widthScale, inX] = this.cropWidthBiggerThan1 ? [\n      `(${inputWidthFloat} / f32(uniforms.outShape[2] - 1))`,\n      \"(x2-x1) * width_ratio\",\n      `x1*${inputWidthFloat} + f32(x)*(width_scale)`\n    ] : [\n      \"0.0\",\n      \"0.0\",\n      `0.5 * (x1+x2) * ${inputWidthFloat}`\n    ];\n    const userCode = `\n    ${getMainHeaderString(\"index\")} {\n      if (index < uniforms.size) {\n        let coords = getCoordsFromIndex(index);\n        let height_ratio = f32(${heightRatio});\n        let width_ratio = f32(${widthRatio});\n        let b = coords[0];\n        let y = coords[1];\n        let x = coords[2];\n        let d = coords[3];\n        // get box vals\n        let y1 = getBoxes(b, 0);\n        let x1 = getBoxes(b, 1);\n        let y2 = getBoxes(b, 2);\n        let x2 = getBoxes(b, 3);\n        // get image in batch index\n        let bInd = i32(round(getBoxInd(b)));\n        if(bInd < 0 || bInd >= uniforms.outShape[0]) {\n          return;\n        }\n        let height_scale = ${heightScale};\n        let width_scale = ${widthScale};\n        let in_y = ${inY};\n        if( in_y < 0.0 || in_y > ${inputHeightFloat} ) {\n          setOutputAtIndex(index, uniforms.extrapolationValue);\n          return;\n        }\n        let in_x = ${inX};\n        if( in_x < 0.0 || in_x > ${inputWidthFloat} ) {\n          setOutputAtIndex(index, uniforms.extrapolationValue);\n          return;\n        }\n        let sourceFracIndexCR = vec2<f32>(in_x,in_y);\n        if(${this.methodId} == 1) {\n          // Compute the four integer indices.\n          let sourceFloorCR = vec2<i32>(sourceFracIndexCR);\n          let sourceCeilCR = vec2<i32>(ceil(sourceFracIndexCR));\n          let topLeft = getImage(bInd, sourceFloorCR.y, sourceFloorCR.x, d);\n          let bottomLeft = getImage(bInd, sourceCeilCR.y, sourceFloorCR.x, d);\n          let topRight = getImage(bInd, sourceFloorCR.y, sourceCeilCR.x, d);\n          let bottomRight = getImage(bInd, sourceCeilCR.y, sourceCeilCR.x, d);\n          let fracCR = sourceFracIndexCR - vec2<f32>(sourceFloorCR);\n          let top = topLeft + (topRight - topLeft) * fracCR.x;\n          let bottom = bottomLeft + (bottomRight - bottomLeft) * fracCR.x;\n          let newValue = top + (bottom - top) * fracCR.y;\n          setOutputAtIndex(index, newValue);\n        } else {\n          // Compute the coordinators of nearest neighbor point.\n          let sourceNearestCR = vec2<i32>(floor(\n            sourceFracIndexCR + vec2<f32>(0.5,0.5)));\n          let newValue = getImage(\n            bInd, sourceNearestCR.y, sourceNearestCR.x, d);\n          setOutputAtIndex(index, newValue);\n        }\n      }\n    }\n    `;\n    return userCode;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/CropAndResize.js\nvar cropAndResize5 = (args) => {\n  const { inputs, backend: backend2, attrs } = args;\n  const { image: image2, boxes, boxInd } = inputs;\n  const { cropSize, method, extrapolationValue } = attrs;\n  const program = new CropAndResizeProgram2(image2.shape[3], boxes.shape, cropSize, method);\n  const uniformData = [{ type: \"float32\", data: [extrapolationValue] }];\n  return backend2.runWebGPUProgram(program, [image2, boxes, boxInd], \"float32\", uniformData);\n};\nvar cropAndResizeConfig4 = {\n  kernelName: CropAndResize,\n  backendName: \"webgpu\",\n  kernelFunc: cropAndResize5\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/cum_webgpu.js\nvar CumOpType2;\n(function(CumOpType3) {\n  CumOpType3[\"Prod\"] = \"*\";\n  CumOpType3[\"Sum\"] = \"+\";\n})(CumOpType2 || (CumOpType2 = {}));\nvar CumProgram2 = class {\n  constructor(op2, shape, exclusive, reverse5) {\n    this.variableNames = [\"x\"];\n    this.uniforms = \"index : f32,\";\n    this.size = true;\n    const workGroupSizeX = 128;\n    this.workGroupSize = [workGroupSizeX, 1, 1];\n    this.outputShape = shape;\n    this.dispatchLayout = flatDispatchLayout(this.outputShape);\n    this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, this.workGroupSize);\n    this.exclusive = exclusive;\n    this.reverse = reverse5;\n    this.op = op2;\n    this.shaderKey = `cum_${this.op}_${this.exclusive}_${this.reverse}`;\n  }\n  getUserCode() {\n    const rank = this.outputShape.length;\n    const initVal = this.op === CumOpType2.Prod ? \"1.0\" : \"0.0\";\n    const val = this.exclusive ? initVal : `getX(${getCoords4(rank, \"coords\", this.op)})`;\n    const length = this.outputShape[this.outputShape.length - 1];\n    let condition = \"\";\n    let idxString = \"\";\n    if (this.exclusive) {\n      condition = this.reverse ? `end != ${length - 1}` : \"end != 0\";\n      idxString = this.reverse ? \"end + 1\" : \"end - 1\";\n    } else {\n      condition = this.reverse ? `end + pow2 < ${length}` : \"end >= pow2\";\n      idxString = this.reverse ? \"end + pow2\" : \"end - pow2\";\n    }\n    return `\n      ${getMainHeaderString(\"index\")} {\n       if (index < uniforms.size) {\n         var coords = getCoordsFromIndex(index);\n\n         let end = ${getFinalCoord2(rank, \"coords\", this.op)};\n         var val = ${val};\n         let pow2 = i32(pow(2.0, uniforms.index));\n         if (${condition}) {\n           let idx = ${idxString};\n           ${getFinalCoord2(rank, \"coords\", this.op)} = idx;\n           val ${this.op}= getX(${getCoords4(rank, \"coords\", this.op)});\n         }\n         setOutputAtIndex(index, val);\n       }\n      }\n    `;\n  }\n};\nfunction getCoords4(rank, name, op2) {\n  if (rank === 1) {\n    return `${name}`;\n  } else if (rank === 2) {\n    return `${name}.x, ${name}.y`;\n  } else if (rank === 3) {\n    return `${name}.x, ${name}.y, ${name}.z`;\n  } else if (rank === 4) {\n    return `${name}.x, ${name}.y, ${name}.z, ${name}.w`;\n  } else {\n    throw Error(`Cumulative ${op2} for rank ${rank} is not yet supported`);\n  }\n}\nfunction getFinalCoord2(rank, name, op2) {\n  if (rank === 1) {\n    return `${name}`;\n  } else if (rank === 2) {\n    return `${name}.y`;\n  } else if (rank === 3) {\n    return `${name}.z`;\n  } else if (rank === 4) {\n    return `${name}.w`;\n  } else {\n    throw Error(`Cumulative ${op2} for rank ${rank} is not yet supported`);\n  }\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/Cum_impl.js\nfunction cumImpl2(op2, x, backend2, axis, exclusive, reverse5) {\n  const xRank = x.shape.length;\n  const permutation = backend_util_exports.getAxesPermutation([axis], xRank);\n  let permutedX = x;\n  if (permutation != null) {\n    permutedX = transpose5({ inputs: { x }, backend: backend2, attrs: { perm: permutation } });\n  }\n  const permutedAxis = backend_util_exports.getInnerMostAxes(1, xRank)[0];\n  if (permutedAxis !== xRank - 1) {\n    throw new Error(`WebGPU cumprod shader expects an inner-most axis=${x.shape.length - 1} but got axis=${axis}`);\n  }\n  const size = permutedX.shape[permutedAxis];\n  let result = identity5({ inputs: { x: permutedX }, backend: backend2 });\n  for (let i2 = 0; i2 <= Math.ceil(Math.log2(size)) - 1; i2++) {\n    const program = new CumProgram2(op2, permutedX.shape, false, reverse5);\n    const prevResult = result;\n    const uniformData = [{ type: \"float32\", data: [i2] }];\n    result = backend2.runWebGPUProgram(program, [result], result.dtype, uniformData);\n    backend2.disposeData(prevResult.dataId);\n  }\n  if (exclusive) {\n    const program = new CumProgram2(op2, permutedX.shape, exclusive, reverse5);\n    const prevResult = result;\n    const uniformData = [{ type: \"float32\", data: [0] }];\n    result = backend2.runWebGPUProgram(program, [result], result.dtype, uniformData);\n    backend2.disposeData(prevResult.dataId);\n  }\n  if (permutation != null) {\n    const reversePermutation = backend_util_exports.getUndoAxesPermutation(permutation);\n    const reverseTransposedResult = transpose5({ inputs: { x: result }, backend: backend2, attrs: { perm: reversePermutation } });\n    backend2.disposeData(result.dataId);\n    backend2.disposeData(permutedX.dataId);\n    return reverseTransposedResult;\n  }\n  return result;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/Cumprod.js\nfunction cumprod5(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x } = inputs;\n  const { axis, exclusive, reverse: reverse5 } = attrs;\n  return cumImpl2(CumOpType2.Prod, x, backend2, axis, exclusive, reverse5);\n}\nvar cumprodConfig4 = {\n  kernelName: Cumprod,\n  backendName: \"webgpu\",\n  kernelFunc: cumprod5\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/Cumsum.js\nfunction cumsum5(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x } = inputs;\n  const { axis, exclusive, reverse: reverse5 } = attrs;\n  return cumImpl2(CumOpType2.Sum, x, backend2, axis, exclusive, reverse5);\n}\nvar cumsumConfig4 = {\n  kernelName: Cumsum,\n  backendName: \"webgpu\",\n  kernelFunc: cumsum5\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/depth_to_space_webgpu.js\nvar DepthToSpaceProgram2 = class {\n  constructor(outputShape, dataFormat) {\n    this.variableNames = [\"x\"];\n    this.workGroupSize = [64, 1, 1];\n    this.size = true;\n    this.uniforms = \"blockSize : i32,\";\n    this.outputShape = outputShape;\n    this.dispatchLayout = flatDispatchLayout(this.outputShape);\n    this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, this.workGroupSize);\n    this.shaderKey = `depthToSpace_${dataFormat}`;\n    this.dataFormat = dataFormat;\n  }\n  getUserCode() {\n    const userCode = `\n      ${getMainHeaderString(\"index\")} {\n        if (index < uniforms.size) {\n          let coords = getCoordsFromIndex(index);\n          let b = coords[0];\n          let h = ${this.getHeightCoordString()};\n          let w = ${this.getWidthCoordString()};\n          let d = ${this.getDepthCoordString()};\n\n          let in_h = h / uniforms.blockSize;\n          let offset_h = h % uniforms.blockSize;\n          let in_w = w / uniforms.blockSize;\n          let offset_w = w % uniforms.blockSize;\n          let offset_d = (offset_h * uniforms.blockSize + offset_w) *\n            ${this.getOutputDepthSize()};\n          let in_d = d + offset_d;\n\n          let rlt = ${this.getInputSamplingString()};\n          setOutputAtIndex(index, rlt);\n        }\n      }`;\n    return userCode;\n  }\n  getHeightCoordString() {\n    if (this.dataFormat === \"NHWC\") {\n      return `coords[1]`;\n    } else {\n      return `coords[2]`;\n    }\n  }\n  getWidthCoordString() {\n    if (this.dataFormat === \"NHWC\") {\n      return `coords[2]`;\n    } else {\n      return `coords[3]`;\n    }\n  }\n  getDepthCoordString() {\n    if (this.dataFormat === \"NHWC\") {\n      return `coords[3]`;\n    } else {\n      return `coords[1]`;\n    }\n  }\n  getOutputDepthSize() {\n    if (this.dataFormat === \"NHWC\") {\n      return `uniforms.outShape[3]`;\n    } else {\n      return `uniforms.outShape[1]`;\n    }\n  }\n  getInputSamplingString() {\n    if (this.dataFormat === \"NHWC\") {\n      return `getX(b, in_h, in_w, in_d)`;\n    } else {\n      return `getX(b, in_d, in_h, in_w)`;\n    }\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/DepthToSpace.js\nfunction depthToSpace5(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x } = inputs;\n  const { blockSize, dataFormat } = attrs;\n  const batchSize = x.shape[0];\n  const inputHeight = dataFormat === \"NHWC\" ? x.shape[1] : x.shape[2];\n  const inputWidth = dataFormat === \"NHWC\" ? x.shape[2] : x.shape[3];\n  const inputDepth = dataFormat === \"NHWC\" ? x.shape[3] : x.shape[1];\n  const outputHeight = inputHeight * blockSize;\n  const outputWidth = inputWidth * blockSize;\n  const outputDepth = inputDepth / (blockSize * blockSize);\n  const outputShape = dataFormat === \"NHWC\" ? [batchSize, outputHeight, outputWidth, outputDepth] : [batchSize, outputDepth, outputHeight, outputWidth];\n  const uniformData = [\n    { type: \"int32\", data: [blockSize] }\n  ];\n  const program = new DepthToSpaceProgram2(outputShape, dataFormat);\n  return backend2.runWebGPUProgram(program, [x], x.dtype, uniformData);\n}\nvar depthToSpaceConfig4 = {\n  kernelName: DepthToSpace,\n  backendName: \"webgpu\",\n  kernelFunc: depthToSpace5\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/depthwise_conv2d_nchw_shared_webgpu.js\nvar DepthwiseConv2DNCHWSharedProgram = class {\n  constructor(outputShape, filterHeight, filterWidth, addBias = false, activation2 = null, hasPreluActivation = false) {\n    this.variableNames = [\"x\", \"W\"];\n    this.uniforms = `pad : vec2<i32>, inDims : vec2<i32>,`;\n    this.workGroupSize = [16, 16, 1];\n    this.outputShape = outputShape;\n    this.dispatchLayout = { x: [3], y: [2], z: [0, 1] };\n    this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, this.workGroupSize);\n    if (addBias) {\n      this.variableNames.push(\"bias\");\n    }\n    if (hasPreluActivation) {\n      this.variableNames.push(\"preluActivationWeights\");\n    }\n    this.addBias = addBias;\n    this.activation = activation2;\n    this.hasPreluActivation = hasPreluActivation;\n    this.filterHeight = filterHeight;\n    this.filterWidth = filterWidth;\n    this.shaderKey = `depthwiseNCHW_${this.activation}_${this.filterHeight}_${this.filterWidth}`;\n  }\n  getUserCode() {\n    const filterSize = this.filterWidth * this.filterHeight;\n    const workGroupSize = this.workGroupSize[0] * this.workGroupSize[1] * this.workGroupSize[2];\n    const tileAHeight = this.workGroupSize[1] + this.filterHeight - 1;\n    const tileAWidth = this.workGroupSize[0] + this.filterWidth - 1;\n    const userCode = `\n      ${activationFnSnippet(this.activation, this.hasPreluActivation, false, 4)}\n\n      var<workgroup> mm_Asub : array<array<f32, ${tileAWidth}>, ${tileAHeight}>;\n      var<workgroup> mm_Bsub : array<array<f32, ${this.filterWidth}>, ${this.filterHeight}>;\n      fn readX(batch : i32, channel : i32, row : i32, col : i32) -> f32 {\n        var value = 0.0;\n        if (row >=0 && row < uniforms.inDims[0] && col >=0 && col < uniforms.inDims[1])\n        {\n          value = getX(batch, channel, row, col);\n        }\n        return value;\n      }\n\n      ${getWorkGroupSizeString()}\n      fn _start(@builtin(local_invocation_id) LocalId : vec3<u32>,\n                @builtin(global_invocation_id) GlobalId : vec3<u32>,\n                @builtin(local_invocation_index) LocalIndex: u32,\n                @builtin(num_workgroups) NumWorkgroups: vec3<u32>) {\n        localId = LocalId;\n        globalId = GlobalId;\n        let localIndex = i32(LocalIndex);\n        numWorkgroups = NumWorkgroups;\n        let coords = getOutputCoords();\n        let batch = coords[0];\n        let xRCCorner = vec2<i32>(coords.zw) - uniforms.pad;\n        let channelMul = uniforms.wShape[3];\n        let d1 = coords[1] / channelMul;\n        let q = coords[1] % channelMul;\n\n        let inputRowStart = xRCCorner.x;\n        let inputColStart = xRCCorner.y;\n\n        let localRow = i32(localId.y);\n        let localCol = i32(localId.x);\n\n        // Load one tile of X into local memory.\n        for (var inputRow = localRow; inputRow < ${tileAHeight}; inputRow = inputRow + ${this.workGroupSize[1]}) {\n          for (var inputCol = localCol; inputCol < ${tileAWidth}; inputCol = inputCol + ${this.workGroupSize[0]}) {\n            let rowOffset = inputRow - localRow;\n            let colOffset = inputCol - localCol;\n            mm_Asub[inputRow][inputCol] = readX(batch, d1, inputRowStart + rowOffset, inputColStart + colOffset);\n          }\n        }\n\n        // Load one tile of W into local memory.\n        var wIndex = localIndex;\n        ${filterSize < workGroupSize ? `if (wIndex < ${filterSize})` : `for(; wIndex < ${filterSize}; wIndex = wIndex + ${workGroupSize})`}\n\n        {\n          let wRow = wIndex / ${this.filterWidth};\n          let wCol = wIndex % ${this.filterWidth};\n          mm_Bsub[wRow][wCol] = getW(wRow, wCol, d1, q);\n        }\n\n        workgroupBarrier();\n\n        var value = 0.0;\n        for (var wR = 0; wR < ${this.filterHeight}; wR = wR + 1) {\n          for (var wC = 0; wC < ${this.filterWidth}; wC = wC + 1) {\n            let xVal = mm_Asub[localRow + wR][localCol + wC];\n            let wVal = mm_Bsub[wR][wC];\n            value = fma(xVal, wVal, value);\n          }\n        }\n        ${biasActivationSnippet(this.addBias, this.activation)}\n        if (coordsInBounds4D(coords, uniforms.outShape)) {\n          setOutputAtCoords(coords[0], coords[1], coords[2], coords[3], value);\n        }\n      }\n    `;\n    return userCode;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/depthwise_conv2d_vec4_webgpu.js\nvar DepthwiseConv2DVec4Program = class {\n  constructor(convInfo, addBias = false, activation2 = null, hasPreluActivation = false) {\n    this.variableNames = [\"x\", \"W\"];\n    this.uniforms = \"pad : vec2<i32>, inDims : vec2<i32>,\";\n    this.workGroupSize = [4, 4, 4];\n    this.isVec4 = true;\n    this.outputShape = convInfo.outShape;\n    this.dispatchLayout = { x: [3], y: [2], z: [0, 1] };\n    this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, this.workGroupSize, [4, 4, 1]);\n    util_exports.assert(convInfo.dataFormat === \"channelsLast\", () => \"TODO: NCHW is unimplemented\");\n    if (addBias) {\n      this.variableNames.push(\"bias\");\n    }\n    if (hasPreluActivation) {\n      this.variableNames.push(\"preluActivationWeights\");\n    }\n    this.convInfo = convInfo;\n    this.addBias = addBias;\n    this.activation = activation2;\n    this.hasPreluActivation = hasPreluActivation;\n    this.shaderKey = `depthwiseVec4_${activation2}_${this.convInfo.filterHeight}_${this.convInfo.filterWidth}`;\n  }\n  getUserCode() {\n    const xNumber = 4 + this.convInfo.filterWidth - 1;\n    const userCode = `\n      ${activationFnSnippet(this.activation, this.hasPreluActivation, true, 4)}\n      fn readX(batch : i32, row : i32, col : i32, channel : i32) -> vec4<f32> {\n        var value = vec4<f32>(0.0);\n        if (row >=0 && row < uniforms.inDims[0] && col >=0 && col < uniforms.inDims[1])\n        {\n          value = getX(batch, row, col, channel);\n        }\n        return value;\n      }\n      ${getWorkGroupSizeString()}\n      fn _start(@builtin(global_invocation_id) globalId: vec3<u32>) {\n        let batch = i32(globalId.z) / uniforms.outShape[1];\n        let r = i32(globalId.z) % uniforms.outShape[1];\n        let c = i32(globalId.y) * 4;\n        let d1 = i32(globalId.x) * 4;\n        let xRCCorner = vec2<i32>(r, c) - uniforms.pad;\n\n        let xRCorner = xRCCorner.x;\n        let xCCorner = xRCCorner.y;\n        var xVals : array<vec4<f32>, ${xNumber}>;\n        var dotProd : array<vec4<f32>, 4>;\n        dotProd[0] = vec4<f32>(0.0);\n        dotProd[1] = vec4<f32>(0.0);\n        dotProd[2] = vec4<f32>(0.0);\n        dotProd[3] = vec4<f32>(0.0);\n\n        // Use constant instead of uniform can give better performance.\n        for (var wR = 0; wR < ${this.convInfo.filterHeight}; wR = wR + 1) {\n          let xR = xRCorner + wR;\n          for (var i = 0; i < ${xNumber}; i++)\n          {\n            xVals[i] = readX(batch, xR, xCCorner + i, d1);\n          }\n          for (var wC = 0; wC < ${this.convInfo.filterWidth}; wC = wC + 1) {\n            let wValue = getW(wR, wC, d1, 0);\n            dotProd[0] = dotProd[0] + xVals[0 + wC] * wValue;\n            dotProd[1] = dotProd[1] + xVals[1 + wC] * wValue;\n            dotProd[2] = dotProd[2] + xVals[2 + wC] * wValue;\n            dotProd[3] = dotProd[3] + xVals[3 + wC] * wValue;\n          }\n        }\n\n        for (var i = 0; i < 4; i = i + 1) {\n          let coords = vec4<i32>(batch, r, c + i, d1);\n          if (coordsInBounds4D(coords, uniforms.outShape)) {\n            var value = dotProd[i];\n            ${biasActivationSnippet(this.addBias, this.activation)}\n            setOutputAtCoords(coords[0], coords[1], coords[2], coords[3], value);\n          }\n        }\n      }\n    `;\n    return userCode;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/depthwise_conv2d_webgpu.js\nvar DepthwiseConv2DProgram2 = class {\n  constructor(convInfo, addBias = false, activation2 = null, hasPreluActivation = false) {\n    this.variableNames = [\"x\", \"W\"];\n    this.uniforms = `pad : vec2<i32>, inDims : vec2<i32>, filterHeight : i32,\n      filterWidth : i32, stride : vec2<i32>, dilation : vec2<i32>,`;\n    this.workGroupSize = [256, 1, 1];\n    this.outputShape = convInfo.outShape;\n    this.dispatchLayout = flatDispatchLayout(this.outputShape);\n    this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, this.workGroupSize);\n    this.isChannelsLast = convInfo.dataFormat === \"channelsLast\";\n    if (addBias) {\n      this.variableNames.push(\"bias\");\n    }\n    if (hasPreluActivation) {\n      this.variableNames.push(\"preluActivationWeights\");\n    }\n    this.convInfo = convInfo;\n    this.addBias = addBias;\n    this.activation = activation2;\n    this.hasPreluActivation = hasPreluActivation;\n    this.shaderKey = `depthwise_${this.activation}_${this.isChannelsLast}`;\n  }\n  getUserCode() {\n    const getXSnippet = this.isChannelsLast ? \"getX(batch, xR, xC, d1);\" : \"getX(batch, d1, xR, xC);\";\n    const userCode = `\n      ${activationFnSnippet(this.activation, this.hasPreluActivation, false, 4)}\n\n      ${getMainHeaderString()} {\n        let coords = getOutputCoords();\n        let batch = coords[0];\n        let xRCCorner = vec2<i32>(coords.${this.isChannelsLast ? \"yz\" : \"zw\"}) * uniforms.stride - uniforms.pad;\n        let d2 = coords[${this.isChannelsLast ? 3 : 1}];\n        let channelMul = uniforms.wShape[3];\n        let d1 = d2 / channelMul;\n        let q = d2 % channelMul;\n\n        let inputRowStart = xRCCorner.x;\n        let inputColStart = xRCCorner.y;\n        let inputRowEnd = inputRowStart + uniforms.filterHeight *\n            uniforms.dilation[0];\n        let inputColEnd = inputColStart + uniforms.filterWidth *\n            uniforms.dilation[1];\n\n        // Convolve x(?, ?, d1)|x(d1, ?, ?) with w(:, :, d1, q) to get\n        // y(yR, yC, d2)|y(d2, yR, yC). ? = to be determined. : = across all\n        // values in that axis. x(?, ?, d1) and y(yR, yC, d2) is for NHWC.\n        // x(d1, ?, ?) and y(d2, yR, yC) is for NCHW.\n        var value = 0.0;\n\n        // Extract if checking out of for loop for performance.\n        if (inputRowStart >= 0 && inputColStart >= 0 &&\n          inputRowEnd < uniforms.inDims[0] &&\n              inputColEnd < uniforms.inDims[1]) {\n            for (var wR = 0; wR < uniforms.filterHeight; wR = wR + 1) {\n              let xR = inputRowStart + wR * uniforms.dilation[0];\n\n              for (var wC = 0; wC < uniforms.filterWidth; wC = wC + 1) {\n                let xC = inputColStart + wC * uniforms.dilation[1];\n\n                let xVal = ${getXSnippet};\n                let wVal = getW(wR, wC, d1, q);\n                value = value + xVal * wVal;\n              }\n            }\n          } else {\n            for (var wR = 0; wR < uniforms.filterHeight; wR = wR + 1) {\n              let xR = inputRowStart + wR * uniforms.dilation[0];\n\n              if (xR < 0 || xR >= uniforms.inDims[0]) {\n                continue;\n              }\n\n              for (var wC = 0; wC < uniforms.filterWidth; wC = wC + 1) {\n                let xC = inputColStart + wC * uniforms.dilation[1];\n\n                if (xC < 0 || xC >= uniforms.inDims[1]) {\n                  continue;\n                }\n\n                let xVal = ${getXSnippet};\n                let wVal = getW(wR, wC, d1, q);\n                value = value + xVal * wVal;\n              }\n            }\n          }\n          ${biasActivationSnippet(this.addBias, this.activation)}\n        if (coordsInBounds4D(coords, uniforms.outShape)) {\n          setOutputAtCoords(coords[0], coords[1], coords[2], coords[3], value);\n        }\n      }\n    `;\n    return userCode;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/DepthwiseConv2dNative.js\nfunction depthwiseConv2dNative3(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x, filter } = inputs;\n  const { strides, pad: pad3, dataFormat, dilations, dimRoundingMode } = attrs;\n  const $dataFormat = backend_util_exports.convertConv2DDataFormat(dataFormat);\n  let $dilations = dilations;\n  if ($dilations == null) {\n    $dilations = [1, 1];\n  }\n  const convInfo = backend_util_exports.computeConv2DInfo(x.shape, filter.shape, strides, $dilations, pad3, dimRoundingMode, true, $dataFormat);\n  const dimensions = [\n    { type: \"int32\", data: [convInfo.padInfo.top, convInfo.padInfo.left] },\n    { type: \"int32\", data: [convInfo.inHeight, convInfo.inWidth] }\n  ];\n  const isChannelsLast = convInfo.dataFormat === \"channelsLast\";\n  let program;\n  if (!isChannelsLast && convInfo.inHeight > 16 && convInfo.inWidth > 16 && convInfo.strideHeight === 1 && convInfo.strideWidth === 1 && convInfo.dilationWidth === 1 && convInfo.dilationHeight === 1 && convInfo.inChannels === convInfo.outChannels) {\n    program = new DepthwiseConv2DNCHWSharedProgram(convInfo.outShape, convInfo.filterHeight, convInfo.filterWidth);\n  } else if (isChannelsLast && convInfo.inHeight > 4 && convInfo.inWidth > 4 && convInfo.strideHeight === 1 && convInfo.strideWidth === 1 && convInfo.inChannels === convInfo.outChannels && convInfo.dilationHeight === 1 && convInfo.dilationWidth === 1 && convInfo.inChannels % 4 === 0) {\n    program = new DepthwiseConv2DVec4Program(convInfo);\n  } else {\n    program = new DepthwiseConv2DProgram2(convInfo);\n    dimensions.push({ type: \"int32\", data: [convInfo.filterHeight] }, { type: \"int32\", data: [convInfo.filterWidth] }, { type: \"int32\", data: [convInfo.strideHeight, convInfo.strideWidth] }, {\n      type: \"int32\",\n      data: [convInfo.dilationHeight, convInfo.dilationWidth]\n    });\n  }\n  return backend2.runWebGPUProgram(program, [x, filter], x.dtype, dimensions);\n}\nvar depthwiseConv2dNativeConfig4 = {\n  kernelName: DepthwiseConv2dNative,\n  backendName: \"webgpu\",\n  kernelFunc: depthwiseConv2dNative3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/Multiply.js\nvar multiplyKernelFunc = binaryKernelFunc3({\n  opType: BinaryOpType.MUL,\n  cpuKernelImpl: multiplyImplCPU2,\n  supportsComplex: true\n});\nvar multiplyConfig4 = {\n  kernelName: Multiply,\n  backendName: \"webgpu\",\n  kernelFunc: multiplyKernelFunc\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/Sum.js\nfunction sum6(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x } = inputs;\n  const { axis, keepDims } = attrs;\n  return reduce2(x, axis, keepDims, \"sum\", backend2);\n}\nvar sumConfig4 = {\n  kernelName: Sum,\n  backendName: \"webgpu\",\n  kernelFunc: sum6\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/Einsum.js\nfunction einsum4(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { equation } = attrs;\n  const tensors = inputs;\n  const { allDims, summedDims, idDims } = backend_util_exports.decodeEinsumEquation(equation, tensors.length);\n  backend_util_exports.checkEinsumDimSizes(allDims.length, idDims, tensors);\n  const { path, steps } = backend_util_exports.getEinsumComputePath(summedDims, idDims);\n  const nSteps = steps.length;\n  let out = null;\n  let numDimsRemaining = allDims.length;\n  const tensorsToDispose = [];\n  for (let i2 = 0; i2 < nSteps; ++i2) {\n    for (const idTerm of steps[i2]) {\n      const { permutationIndices: perm, expandDims: dimsToExpand } = backend_util_exports.getEinsumPermutation(numDimsRemaining, idDims[idTerm]);\n      let x;\n      if (backend_util_exports.isIdentityPermutation(perm)) {\n        x = tensors[idTerm];\n      } else {\n        x = transpose5({ inputs: { x: tensors[idTerm] }, backend: backend2, attrs: { perm } });\n        tensorsToDispose.push(x);\n      }\n      const targetShape = x.shape.slice();\n      for (let k = 0; k < dimsToExpand.length; ++k) {\n        targetShape.splice(dimsToExpand[k], 0, 1);\n      }\n      if (!util_exports.arraysEqual(x.shape, targetShape)) {\n        x = reshape6({ inputs: { x }, backend: backend2, attrs: { shape: targetShape } });\n        tensorsToDispose.push(x);\n      }\n      if (out === null) {\n        out = x;\n      } else {\n        out = multiplyKernelFunc({ inputs: { a: x, b: out }, backend: backend2 });\n        tensorsToDispose.push(out);\n      }\n    }\n    if (i2 < nSteps - 1) {\n      if (path[i2] >= 0) {\n        out = sum6({\n          inputs: { x: out },\n          backend: backend2,\n          attrs: {\n            axis: path[i2] - (allDims.length - numDimsRemaining),\n            keepDims: false\n          }\n        });\n        tensorsToDispose.push(out);\n      }\n      numDimsRemaining--;\n    }\n  }\n  for (const tensorInfo of tensorsToDispose) {\n    if (tensorInfo === out) {\n      continue;\n    }\n    backend2.disposeData(tensorInfo.dataId);\n  }\n  return out;\n}\nvar einsumConfig3 = {\n  kernelName: Einsum,\n  backendName: \"webgpu\",\n  kernelFunc: einsum4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/Elu.js\nvar elu6 = unaryKernelFunc3({ opType: UnaryOpType.ELU });\nvar eluConfig4 = {\n  kernelName: Elu,\n  backendName: \"webgpu\",\n  kernelFunc: elu6\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/Equal.js\nvar equal4 = binaryKernelFunc3({ opType: BinaryOpType.EQUAL, dtype: \"bool\", cpuKernelImpl: equalImplCPU2 });\nvar equalConfig4 = {\n  kernelName: Equal,\n  backendName: \"webgpu\",\n  kernelFunc: equal4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/Exp.js\nvar exp4 = unaryKernelFunc3({\n  opType: UnaryOpType.EXP,\n  cpuKernelImpl: expImplCPU2,\n  dtype: \"float32\"\n});\nvar expConfig4 = {\n  kernelName: Exp,\n  backendName: \"webgpu\",\n  kernelFunc: exp4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/ExpandDims.js\nfunction expandDims6(args) {\n  const { inputs, attrs, backend: backend2 } = args;\n  const { dim } = attrs;\n  const { input: input2 } = inputs;\n  const inputRank = input2.shape.length;\n  const newShape = input2.shape.slice();\n  let $dim = dim;\n  if (dim < 0) {\n    util_exports.assert(-(inputRank + 1) <= dim, () => `Axis must be in the interval [${-(inputRank + 1)}, ${inputRank}]`);\n    $dim = inputRank + dim + 1;\n  }\n  newShape.splice($dim, 0, 1);\n  return reshape6({ inputs: { x: input2 }, backend: backend2, attrs: { shape: newShape } });\n}\nvar expandDimsConfig4 = {\n  kernelName: ExpandDims,\n  backendName: \"webgpu\",\n  kernelFunc: expandDims6\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/Expm1.js\nvar expm14 = unaryKernelFunc3({ opType: UnaryOpType.EXPM1, cpuKernelImpl: expm1ImplCPU2 });\nvar expm1Config3 = {\n  kernelName: Expm1,\n  backendName: \"webgpu\",\n  kernelFunc: expm14\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/flip_left_right_webgpu.js\nvar FlipLeftRightProgram2 = class {\n  constructor(imageShape) {\n    this.outputShape = [];\n    this.variableNames = [\"x\"];\n    this.workGroupSize = [64, 1, 1];\n    this.size = true;\n    this.outputShape = imageShape;\n    this.dispatchLayout = flatDispatchLayout(this.outputShape);\n    this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, this.workGroupSize);\n    this.shaderKey = \"flipLeftRight\";\n  }\n  getUserCode() {\n    const userCode = `\n      ${getMainHeaderString(\"index\")} {\n        if (index < uniforms.size) {\n          let coords = getCoordsFromIndex(index);\n          let coordX = uniforms.xShape[2] - coords[2] - 1;\n          let outputValue = getX(coords[0], coords[1], coordX, coords[3]);\n          setOutputAtIndex(index, outputValue);\n        }\n      }\n    `;\n    return userCode;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/FlipLeftRight.js\nvar flipLeftRightConfig4 = {\n  kernelName: FlipLeftRight,\n  backendName: \"webgpu\",\n  kernelFunc: ({ inputs, backend: backend2 }) => {\n    const { image: image2 } = inputs;\n    const webgpuBackend = backend2;\n    const program = new FlipLeftRightProgram2(image2.shape);\n    const output = webgpuBackend.runWebGPUProgram(program, [image2], image2.dtype);\n    return output;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/Floor.js\nvar floor4 = unaryKernelFunc3({ opType: UnaryOpType.FLOOR, cpuKernelImpl: floorImplCPU2 });\nvar floorConfig4 = {\n  kernelName: Floor,\n  backendName: \"webgpu\",\n  kernelFunc: floor4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/FloorDiv.js\nvar floorDiv4 = binaryKernelFunc3({ opType: BinaryOpType.INT_DIV, dtype: \"int32\" });\nvar floorDivConfig4 = {\n  kernelName: FloorDiv,\n  backendName: \"webgpu\",\n  kernelFunc: floorDiv4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/from_pixels_webgpu.js\nvar FromPixelsProgram2 = class {\n  constructor(outputShape, numChannels, importVideo = false) {\n    this.isFromPixels = true;\n    this.outputShape = [0];\n    this.variableNames = [];\n    this.workGroupSize = [256, 1, 1];\n    this.outputShape = outputShape;\n    this.dispatchLayout = flatDispatchLayout(this.outputShape);\n    this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, this.workGroupSize, [numChannels, 1, 1]);\n    this.importVideo = importVideo;\n    this.shaderKey = `fromPixels_${this.importVideo}`;\n  }\n  getUserCode() {\n    const textureLoad = this.importVideo ? \"textureLoad(src, vec2<i32>(coords.yx));\" : \"textureLoad(src, vec2<i32>(coords.yx), 0)\";\n    const textureType = this.importVideo ? \"texture_external\" : \"texture_2d<f32>\";\n    return `\n      @binding(1) @group(0) var src: ${textureType};\n      ${getMainHeaderString(\"index\")} {\n        let flatIndex = index * uniforms.numChannels;\n        if (flatIndex < uniforms.size) {\n          let coords = getCoordsFromIndex(flatIndex);\n          let values = ${textureLoad};\n          for (var i = 0; i < uniforms.numChannels; i = i + 1) {\n            result[flatIndex + i] = i32(floor(255.0 * values[i]));\n          }\n        }\n      }\n  `;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/FromPixels.js\nvar fromPixelsConfig2 = {\n  kernelName: FromPixels,\n  backendName: \"webgpu\",\n  kernelFunc: fromPixels3\n};\nvar fromPixels2DContext3;\nvar willReadFrequently2 = env().getBool(\"CANVAS2D_WILL_READ_FREQUENTLY_FOR_GPU\");\nvar videoToTextureMap = /* @__PURE__ */ new Map();\nfunction fromPixels3(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  let { pixels } = inputs;\n  const { numChannels } = attrs;\n  if (pixels == null) {\n    throw new Error(\"pixels passed to tf.browser.fromPixels() can not be null\");\n  }\n  const isVideo = typeof HTMLVideoElement !== \"undefined\" && pixels instanceof HTMLVideoElement;\n  const isImage = typeof HTMLImageElement !== \"undefined\" && pixels instanceof HTMLImageElement;\n  const isCanvas = typeof HTMLCanvasElement !== \"undefined\" && pixels instanceof HTMLCanvasElement || typeof OffscreenCanvas !== \"undefined\" && pixels instanceof OffscreenCanvas;\n  const isImageBitmap = typeof ImageBitmap !== \"undefined\" && pixels instanceof ImageBitmap;\n  const [width, height] = isVideo ? [\n    pixels.videoWidth,\n    pixels.videoHeight\n  ] : [pixels.width, pixels.height];\n  const outputShape = [height, width, numChannels];\n  const importVideo = env().getBool(\"WEBGPU_IMPORT_EXTERNAL_TEXTURE\") && isVideo;\n  const isVideoOrImage = isVideo || isImage;\n  if (isImageBitmap || isCanvas || isVideoOrImage) {\n    let textureInfo;\n    if (importVideo) {\n      const videoElement = pixels;\n      if (!videoToTextureMap.has(videoElement) || videoToTextureMap.get(videoElement).expired) {\n        const externalTextureDescriptor = { source: videoElement };\n        videoToTextureMap.set(videoElement, backend2.device.importExternalTexture(externalTextureDescriptor));\n      }\n      textureInfo = {\n        width,\n        height,\n        format: null,\n        usage: null,\n        texture: videoToTextureMap.get(videoElement)\n      };\n    } else {\n      if (isVideoOrImage) {\n        const newWillReadFrequently = env().getBool(\"CANVAS2D_WILL_READ_FREQUENTLY_FOR_GPU\");\n        if (fromPixels2DContext3 == null || newWillReadFrequently !== willReadFrequently2) {\n          willReadFrequently2 = newWillReadFrequently;\n          fromPixels2DContext3 = document.createElement(\"canvas\").getContext(\"2d\", { willReadFrequently: willReadFrequently2 });\n        }\n        fromPixels2DContext3.canvas.width = width;\n        fromPixels2DContext3.canvas.height = height;\n        fromPixels2DContext3.drawImage(pixels, 0, 0, width, height);\n        pixels = fromPixels2DContext3.canvas;\n      }\n      const usage = GPUTextureUsage.COPY_DST | GPUTextureUsage.RENDER_ATTACHMENT | GPUTextureUsage.TEXTURE_BINDING;\n      const format = \"rgba8unorm\";\n      const texture = backend2.textureManager.acquireTexture(outputShape[1], outputShape[0], format, usage);\n      backend2.queue.copyExternalImageToTexture({ source: pixels }, { texture }, [outputShape[1], outputShape[0]]);\n      textureInfo = { width, height, format, usage, texture };\n    }\n    const size = util_exports.sizeFromShape(outputShape);\n    const strides = util_exports.computeStrides(outputShape);\n    const program = new FromPixelsProgram2(outputShape, numChannels, importVideo);\n    const uniformData = [\n      { type: \"uint32\", data: [size] },\n      { type: \"uint32\", data: [numChannels] },\n      { type: \"uint32\", data: [...strides] }\n    ];\n    const input2 = backend2.makeTensorInfo([height, width], \"int32\");\n    const info = backend2.tensorMap.get(input2.dataId);\n    info.resourceInfo = textureInfo;\n    const result = backend2.runWebGPUProgram(program, [input2], \"int32\", uniformData);\n    backend2.disposeData(input2.dataId);\n    return result;\n  }\n  const imageData = pixels.data;\n  let pixelArray = imageData;\n  if (numChannels != null && numChannels !== 4) {\n    pixelArray = new Uint8Array(pixels.width * pixels.height * numChannels);\n    const dataLength = imageData.length;\n    let j = 0;\n    for (let i2 = 0; i2 < dataLength; i2++) {\n      if (i2 % 4 < numChannels) {\n        pixelArray[j++] = imageData[i2];\n      }\n    }\n  }\n  const output = backend2.makeTensorInfo(outputShape, \"int32\", new Int32Array(pixelArray));\n  backend2.uploadToGPU(output.dataId);\n  return output;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/batchnorm_webgpu.js\nvar BatchNormProgram2 = class {\n  constructor(xShape, meanShape, varianceShape, offsetShape, scaleShape) {\n    this.uniforms = \"varianceEpsilon : f32,\";\n    this.workGroupSize = [128, 1, 1];\n    this.size = true;\n    this.variableNames = [\"x\", \"mean\", \"variance\"];\n    backend_util_exports.assertAndGetBroadcastShape(xShape, meanShape);\n    backend_util_exports.assertAndGetBroadcastShape(xShape, varianceShape);\n    this.outputShape = xShape;\n    this.dispatchLayout = flatDispatchLayout(this.outputShape);\n    this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, this.workGroupSize);\n    if (offsetShape != null) {\n      backend_util_exports.assertAndGetBroadcastShape(xShape, offsetShape);\n      this.variableNames.push(\"offset\");\n    }\n    if (scaleShape != null) {\n      backend_util_exports.assertAndGetBroadcastShape(xShape, scaleShape);\n      this.variableNames.push(\"scale\");\n    }\n    this.offsetShape = offsetShape;\n    this.scaleShape = scaleShape;\n    this.shaderKey = \"batchNorm\";\n  }\n  getUserCode() {\n    let offsetSnippet = \"0.0\";\n    if (this.offsetShape != null) {\n      offsetSnippet = \"getOffsetByOutputIndex(index)\";\n    }\n    let scaleSnippet = \"1.0\";\n    if (this.scaleShape != null) {\n      scaleSnippet = \"getScaleByOutputIndex(index)\";\n    }\n    const userCode = `\n      ${getMainHeaderString(\"index\")} {\n        if (index < uniforms.size)\n        {\n          let xValue = getXByOutputIndex(index);\n          let meanValue = getMeanByOutputIndex(index);\n          let varianValue = getVarianceByOutputIndex(index);\n          let offsetValue = ${offsetSnippet};\n          let scaleValue = ${scaleSnippet};\n          let inv = scaleValue * inverseSqrt(varianValue + f32(uniforms.varianceEpsilon));\n          setOutputAtIndex(index,dot(vec3<f32>(xValue, -meanValue, offsetValue), vec3<f32>(inv, inv, 1.0)));\n        }\n      }\n  `;\n    return userCode;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/FusedBatchNorm.js\nvar fusedBatchNormConfig2 = {\n  kernelName: FusedBatchNorm,\n  backendName: \"webgpu\",\n  kernelFunc: ({ inputs, attrs, backend: backend2 }) => {\n    const { x, scale: scale2, offset, mean: mean5, variance } = inputs;\n    const { varianceEpsilon } = attrs;\n    const webGPUBackend = backend2;\n    const batchNormInputs = [x, mean5, variance];\n    let offsetShape = null;\n    if (offset != null) {\n      offsetShape = offset.shape;\n      batchNormInputs.push(offset);\n    }\n    let scaleShape = null;\n    if (scale2 != null) {\n      scaleShape = scale2.shape;\n      batchNormInputs.push(scale2);\n    }\n    const program = new BatchNormProgram2(x.shape, mean5.shape, variance.shape, offsetShape, scaleShape);\n    const uniformData = [{ type: \"float32\", data: [varianceEpsilon] }];\n    return webGPUBackend.runWebGPUProgram(program, batchNormInputs, x.dtype, uniformData);\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/FusedConv2D.js\nfunction fusedConv2d3(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x, filter, bias, preluActivationWeights } = inputs;\n  const { strides, pad: pad3, dataFormat, dilations, dimRoundingMode, activation: activation2, leakyreluAlpha } = attrs;\n  const $dataFormat = backend_util_exports.convertConv2DDataFormat(dataFormat);\n  const convInfo = backend_util_exports.computeConv2DInfo(x.shape, filter.shape, strides, dilations, pad3, dimRoundingMode, false, $dataFormat);\n  return conv2DImpl({\n    x,\n    filter,\n    convInfo,\n    backend: backend2,\n    bias,\n    preluActivationWeights,\n    leakyreluAlpha,\n    activation: activation2\n  });\n}\nvar fusedConv2DConfig4 = {\n  kernelName: FusedConv2D,\n  backendName: \"webgpu\",\n  kernelFunc: fusedConv2d3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/FusedDepthwiseConv2D.js\nfunction fusedDepthwiseConv2D3(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x, filter, bias, preluActivationWeights } = inputs;\n  const { strides, pad: pad3, dilations, dimRoundingMode, activation: activation2, leakyreluAlpha } = attrs;\n  let $dilations = dilations;\n  if ($dilations == null) {\n    $dilations = [1, 1];\n  }\n  util_exports.assert(backend_util_exports.eitherStridesOrDilationsAreOne(strides, $dilations), () => `Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${strides} and dilations '${$dilations}'`);\n  const convInfo = backend_util_exports.computeConv2DInfo(x.shape, filter.shape, strides, $dilations, pad3, dimRoundingMode, true);\n  const programInputs = [x, filter];\n  const hasBias = bias != null;\n  const hasPreluActivationWeights = preluActivationWeights != null;\n  if (hasBias) {\n    programInputs.push(bias);\n  }\n  if (hasPreluActivationWeights) {\n    programInputs.push(preluActivationWeights);\n  }\n  const dimensions = [\n    { type: \"int32\", data: [convInfo.padInfo.top, convInfo.padInfo.left] },\n    { type: \"int32\", data: [convInfo.inHeight, convInfo.inWidth] }\n  ];\n  let program;\n  if (convInfo.inHeight > 4 && convInfo.inWidth > 4 && convInfo.strideHeight === 1 && convInfo.strideWidth === 1 && convInfo.inChannels === convInfo.outChannels && convInfo.dilationHeight === 1 && convInfo.dilationWidth === 1 && convInfo.inChannels % 4 === 0) {\n    program = new DepthwiseConv2DVec4Program(convInfo, hasBias, activation2, hasPreluActivationWeights);\n  } else {\n    program = new DepthwiseConv2DProgram2(convInfo, hasBias, activation2, hasPreluActivationWeights);\n    dimensions.push({ type: \"int32\", data: [convInfo.filterHeight] }, { type: \"int32\", data: [convInfo.filterWidth] }, { type: \"int32\", data: [convInfo.strideHeight, convInfo.strideWidth] }, {\n      type: \"int32\",\n      data: [convInfo.dilationHeight, convInfo.dilationWidth]\n    });\n  }\n  if (activation2 === \"leakyrelu\") {\n    dimensions.push({ type: \"float32\", data: [leakyreluAlpha] });\n    program.uniforms += \" alpha : f32,\";\n  }\n  const result = backend2.runWebGPUProgram(program, programInputs, \"float32\", dimensions);\n  return result;\n}\nvar fusedDepthwiseConv2DConfig4 = {\n  kernelName: FusedDepthwiseConv2D,\n  backendName: \"webgpu\",\n  kernelFunc: fusedDepthwiseConv2D3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/gather_nd_webgpu.js\nvar GatherNDProgram2 = class {\n  constructor(sliceDim, shape) {\n    this.variableNames = [\"A\", \"indices\"];\n    this.workGroupSize = [64, 1, 1];\n    this.size = true;\n    this.outputShape = shape;\n    this.dispatchLayout = flatDispatchLayout(this.outputShape);\n    this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, this.workGroupSize);\n    this.shaderKey = `gathernd_${sliceDim}`;\n    this.sliceDim = sliceDim;\n    this.uniforms = `sliceDim : i32, strides : ${getCoordsDataType2(sliceDim)},`;\n  }\n  getUserCode() {\n    let strideString;\n    if (this.sliceDim > 1) {\n      strideString = \"uniforms.strides[j]\";\n    } else {\n      strideString = \"uniforms.strides\";\n    }\n    const userCode = `\n      ${getMainHeaderString(\"index\")} {\n        if (index < uniforms.size) {\n          let coords = getCoordsFromIndex(index);\n          var flattenIndex = 0;\n          for (var j = 0; j < uniforms.sliceDim; j = j + 1) {\n            let indexTemp = i32(round(getIndices(coords[0], j)));\n            let strideNum = ${strideString};\n            flattenIndex = flattenIndex + indexTemp * strideNum;\n          }\n\n          setOutputAtIndex(index, getA(flattenIndex, coords[1]));\n        }\n      }\n      `;\n    return userCode;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/GatherNd.js\nfunction gatherNd4(args) {\n  const { inputs, backend: backend2 } = args;\n  const { params, indices } = inputs;\n  const indicesShape = indices.shape;\n  const sliceRank = indicesShape[indicesShape.length - 1];\n  const paramsSize = util_exports.sizeFromShape(params.shape);\n  const [resultShape, numSlices, sliceSize, strides] = backend_util_exports.prepareAndValidate(params, indices);\n  const flattenIndices = reshape6({ inputs: { x: indices }, backend: backend2, attrs: { shape: [numSlices, sliceRank] } });\n  const flattenX = reshape6({\n    inputs: { x: params },\n    backend: backend2,\n    attrs: { shape: [util_exports.sizeFromShape(params.shape) / sliceSize, sliceSize] }\n  });\n  if (backend2.shouldExecuteOnCPU([params, indices]) || params.dtype === \"string\") {\n    const indicesData = backend2.readSync(indices.dataId);\n    const paramsBuf = backend2.bufferSync(params);\n    const outValue = gatherNdImplCPU2(indicesData, paramsBuf, params.dtype, numSlices, sliceRank, sliceSize, strides, params.shape, paramsSize);\n    return backend2.makeTensorInfo(resultShape, params.dtype, outValue.values);\n  }\n  const program = new GatherNDProgram2(sliceRank, [numSlices, sliceSize]);\n  const uniformData = [{ type: \"int32\", data: [sliceRank] }, { type: \"int32\", data: strides }];\n  const res = backend2.runWebGPUProgram(program, [flattenX, flattenIndices], flattenX.dtype, uniformData);\n  const reshaped = reshape6({ inputs: { x: res }, backend: backend2, attrs: { shape: resultShape } });\n  backend2.disposeData(flattenIndices.dataId);\n  backend2.disposeData(flattenX.dataId);\n  backend2.disposeData(res.dataId);\n  return reshaped;\n}\nvar gatherNdConfig4 = {\n  kernelName: GatherNd,\n  backendName: \"webgpu\",\n  kernelFunc: gatherNd4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/gather_webgpu.js\nvar GatherProgram2 = class {\n  constructor(aShape, outputShape) {\n    this.variableNames = [\"A\", \"indices\"];\n    this.workGroupSize = [64, 1, 1];\n    this.size = true;\n    this.outputShape = aShape.slice();\n    this.aShape = aShape;\n    this.outputShape = outputShape;\n    this.dispatchLayout = flatDispatchLayout(this.outputShape);\n    this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, this.workGroupSize);\n    this.shaderKey = `gather`;\n  }\n  getUserCode() {\n    const sourceCoords = getSourceCoords4(this.aShape);\n    const userCode = `\n      ${getMainHeaderString(\"index\")} {\n        if (index < uniforms.size) {\n          let resRC = getCoordsFromIndex(index);\n          let indexZ = i32(getIndices(resRC.x, resRC.z));\n          let inBounds = select(0.0, 1.0, indexZ >= 0 && indexZ < uniforms.aShape[2]);\n          setOutputAtIndex(index, inBounds * getA(${sourceCoords}));\n        }\n      }\n    `;\n    return userCode;\n  }\n};\nfunction getSourceCoords4(aShape) {\n  const currentCoords = [\"resRC.x\", \"resRC.y\", \"resRC.z\", \"resRC.w\"];\n  const sourceCoords = [];\n  for (let i2 = 0; i2 < aShape.length; i2++) {\n    if (i2 === 2) {\n      sourceCoords.push(\"indexZ\");\n    } else {\n      sourceCoords.push(`${currentCoords[i2]}`);\n    }\n  }\n  return sourceCoords.join();\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/GatherV2.js\nfunction gatherV24(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x, indices } = inputs;\n  const { axis, batchDims } = attrs;\n  const parsedAxis = util_exports.parseAxisParam(axis, x.shape)[0];\n  const shapeInfo = backend_util_exports.segment_util.collectGatherOpShapeInfo(x, indices, parsedAxis, batchDims);\n  const indicesSize = util_exports.sizeFromShape(indices.shape);\n  const toDispose = [];\n  const flattenX = reshape6({\n    inputs: { x },\n    backend: backend2,\n    attrs: {\n      shape: [\n        shapeInfo.batchSize,\n        shapeInfo.outerSize,\n        shapeInfo.dimSize,\n        shapeInfo.sliceSize\n      ]\n    }\n  });\n  const flattenIndex = reshape6({\n    inputs: { x: indices },\n    backend: backend2,\n    attrs: { shape: [shapeInfo.batchSize, indicesSize / shapeInfo.batchSize] }\n  });\n  toDispose.push(flattenX);\n  toDispose.push(flattenIndex);\n  const flattenOutputShape = [\n    shapeInfo.batchSize,\n    shapeInfo.outerSize,\n    indicesSize / shapeInfo.batchSize,\n    shapeInfo.sliceSize\n  ];\n  if (backend2.shouldExecuteOnCPU([x, indices])) {\n    const indicesBufferInfo = backend2.tensorMap.get(flattenIndex.dataId);\n    const indicesValues = indicesBufferInfo.values;\n    const indicesBuf = buffer(flattenIndex.shape, flattenIndex.dtype, indicesValues);\n    const xBufferInfo = backend2.tensorMap.get(flattenX.dataId);\n    const xValues = xBufferInfo.values;\n    const xBuf = buffer(flattenX.shape, flattenX.dtype, xValues);\n    const outBuf = gatherV2ImplCPU2(xBuf, indicesBuf, flattenOutputShape);\n    toDispose.forEach((t2) => backend2.disposeData(t2.dataId));\n    return backend2.makeTensorInfo(shapeInfo.outputShape, outBuf.dtype, outBuf.values);\n  }\n  const program = new GatherProgram2(flattenX.shape, flattenOutputShape);\n  const res = backend2.runWebGPUProgram(program, [flattenX, flattenIndex], flattenX.dtype);\n  toDispose.push(res);\n  const reshaped = reshape6({ inputs: { x: res }, backend: backend2, attrs: { shape: shapeInfo.outputShape } });\n  toDispose.forEach((t2) => backend2.disposeData(t2.dataId));\n  return reshaped;\n}\nvar gatherV2Config4 = {\n  kernelName: GatherV2,\n  backendName: \"webgpu\",\n  kernelFunc: gatherV24\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/Greater.js\nvar greater5 = binaryKernelFunc3({\n  opType: BinaryOpType.GREATER,\n  cpuKernelImpl: greaterImplCPU2,\n  dtype: \"bool\"\n});\nvar greaterConfig4 = {\n  kernelName: Greater,\n  backendName: \"webgpu\",\n  kernelFunc: greater5\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/GreaterEqual.js\nvar greaterEqual4 = binaryKernelFunc3({\n  opType: BinaryOpType.GREATER_EQUAL,\n  dtype: \"bool\",\n  cpuKernelImpl: greaterEqualImplCPU2\n});\nvar greaterEqualConfig4 = {\n  kernelName: GreaterEqual,\n  backendName: \"webgpu\",\n  kernelFunc: greaterEqual4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/IsNaN.js\nvar isNaN5 = unaryKernelFunc3({ opType: UnaryOpType.IS_NAN, dtype: \"bool\" });\nvar isNaNConfig3 = {\n  kernelName: IsNan,\n  backendName: \"webgpu\",\n  kernelFunc: isNaN5\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/LeakyRelu.js\nfunction leakyRelu5(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x } = inputs;\n  const { alpha } = attrs;\n  const uniformData = [{ type: \"float32\", data: [alpha] }];\n  const program = new UnaryOpProgram2(x.shape, UnaryOpType.LEAKYRELU);\n  program.uniforms = \"alpha : f32,\";\n  return backend2.runWebGPUProgram(program, [x], \"float32\", uniformData);\n}\nvar leakyReluConfig4 = {\n  kernelName: LeakyRelu,\n  backendName: \"webgpu\",\n  kernelFunc: leakyRelu5\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/Less.js\nvar less5 = binaryKernelFunc3({ opType: BinaryOpType.LESS, dtype: \"bool\", cpuKernelImpl: lessImplCPU2 });\nvar lessConfig4 = {\n  kernelName: Less,\n  backendName: \"webgpu\",\n  kernelFunc: less5\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/LessEqual.js\nvar lessEqual4 = binaryKernelFunc3({\n  opType: BinaryOpType.LESS_EQUAL,\n  dtype: \"bool\",\n  cpuKernelImpl: lessEqualImplCPU2\n});\nvar lessEqualConfig4 = {\n  kernelName: LessEqual,\n  backendName: \"webgpu\",\n  kernelFunc: lessEqual4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/Log.js\nvar log5 = unaryKernelFunc3({ opType: UnaryOpType.LOG, cpuKernelImpl: logImplCPU2 });\nvar logConfig4 = {\n  kernelName: Log,\n  backendName: \"webgpu\",\n  kernelFunc: log5\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/LogicalAnd.js\nvar logicalAnd4 = binaryKernelFunc3({ opType: BinaryOpType.LOGICAL_AND, dtype: \"bool\" });\nvar logicalAndConfig4 = {\n  kernelName: LogicalAnd,\n  backendName: \"webgpu\",\n  kernelFunc: logicalAnd4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/LogicalNot.js\nvar logicalNot4 = unaryKernelFunc3({ opType: UnaryOpType.LOGICAL_NOT });\nvar logicalNotConfig4 = {\n  kernelName: LogicalNot,\n  backendName: \"webgpu\",\n  kernelFunc: logicalNot4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/Maximum.js\nvar maximum5 = binaryKernelFunc3({\n  opType: BinaryOpType.MAX,\n  cpuKernelImpl: maximumImplCPU2\n});\nvar maximumConfig4 = {\n  kernelName: Maximum,\n  backendName: \"webgpu\",\n  kernelFunc: maximum5\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/MaxPool.js\nfunction maxPool5(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x } = inputs;\n  const { filterSize, strides, pad: pad3, dimRoundingMode } = attrs;\n  const dilations = 1;\n  const convInfo = backend_util_exports.computePool2DInfo(x.shape, filterSize, strides, dilations, pad3, dimRoundingMode);\n  return poolImpl(x, convInfo, \"max\", backend2);\n}\nvar maxPoolConfig4 = {\n  kernelName: MaxPool,\n  backendName: \"webgpu\",\n  kernelFunc: maxPool5\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/Min.js\nfunction min6(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x } = inputs;\n  const { axis, keepDims } = attrs;\n  return reduce2(x, axis, keepDims, \"min\", backend2);\n}\nvar minConfig4 = {\n  kernelName: Min,\n  backendName: \"webgpu\",\n  kernelFunc: min6\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/Minimum.js\nvar minimum5 = binaryKernelFunc3({\n  opType: BinaryOpType.MIN,\n  cpuKernelImpl: minimumImplCPU2\n});\nvar minimumConfig4 = {\n  kernelName: Minimum,\n  backendName: \"webgpu\",\n  kernelFunc: minimum5\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/mirror_pad_webgpu.js\nvar MirrorPadProgram2 = class {\n  constructor(xShape, paddings, mode) {\n    this.uniforms = \"\";\n    this.variableNames = [\"x\"];\n    this.workGroupSize = [64, 1, 1];\n    this.size = true;\n    this.outputShape = paddings.map((p2, i2) => p2[0] + xShape[i2] + p2[1]);\n    this.dispatchLayout = flatDispatchLayout(this.outputShape);\n    this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, this.workGroupSize);\n    this.xShape = xShape;\n    paddings.map((_, i2) => {\n      this.uniforms += ` pad${i2} : vec2<i32>,`;\n    });\n    this.offset = mode === \"reflect\" ? 0 : 1;\n    this.shaderKey = `mirrorPad_${mode}`;\n  }\n  getUserCode() {\n    const rank = this.xShape.length;\n    const start = this.xShape.map((_, i2) => `uniforms.pad${i2}[0]`).join(\",\");\n    const end = this.xShape.map((_, i2) => `uniforms.pad${i2}[0] + uniforms.xShape${rank > 1 ? `[${i2}]` : \"\"}`).join(\",\");\n    const shaderStart = rank === 1 ? \"start\" : \"start[i]\";\n    const shaderEnd = rank === 1 ? \"end\" : \"end[i]\";\n    const shaderOutC = rank === 1 ? \"outC\" : \"outC[i]\";\n    const dtype = getCoordsDataType2(rank);\n    const unpackedCoords = rank > 1 ? [\"coords[0]\", \"coords[1]\", \"coords[2]\", \"coords[3]\"].slice(0, rank) : \"coords\";\n    return `\n      ${getMainHeaderString(\"index\")} {\n        if (index < uniforms.size) {\n          let start = ${dtype}(${start});\n          let end = ${dtype}(${end});\n          var outC = getCoordsFromIndex(index);\n          for (var i = 0; i < ${rank}; i = i + 1) {\n            if (${shaderOutC} < ${shaderStart}) {\n              ${shaderOutC} = ${shaderStart} * 2 - ${shaderOutC} - ${this.offset};\n            } else if(${shaderOutC} >= ${shaderEnd}) {\n              ${shaderOutC} = (${shaderEnd} - 1) * 2 - ${shaderOutC} + ${this.offset};\n            }\n          }\n          let coords = outC - start;\n          setOutputAtIndex(index, getX(${unpackedCoords}));\n        }\n      }\n    `;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/MirrorPad.js\nvar mirrorPadConfig4 = {\n  kernelName: MirrorPad,\n  backendName: \"webgpu\",\n  kernelFunc: ({ inputs, attrs, backend: backend2 }) => {\n    const { x } = inputs;\n    const { paddings, mode } = attrs;\n    const webGPUBackend = backend2;\n    const uniformData = paddings.map((p2) => {\n      return { type: \"int32\", data: [p2[0], p2[1]] };\n    });\n    const program = new MirrorPadProgram2(x.shape, paddings, mode);\n    const output = webGPUBackend.runWebGPUProgram(program, [x], x.dtype, uniformData);\n    return output;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/Neg.js\nfunction neg4(args) {\n  const { inputs, backend: backend2 } = args;\n  const { x } = inputs;\n  if (backend2.shouldExecuteOnCPU([x])) {\n    const xData = backend2.tensorMap.get(x.dataId);\n    const [outValues, newShape] = negImplCPU2(xData.values, x.shape, x.dtype);\n    return backend2.makeTensorInfo(newShape, x.dtype, outValues);\n  }\n  const program = new UnaryOpProgram2(x.shape, UnaryOpType.NEG);\n  return backend2.runWebGPUProgram(program, [x], x.dtype);\n}\nvar negConfig4 = {\n  kernelName: Neg,\n  backendName: \"webgpu\",\n  kernelFunc: neg4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/NonMaxSuppressionV3.js\nfunction nonMaxSuppressionV33(args) {\n  console.warn(\"tf.nonMaxSuppression() in webgpu locks the UI thread. Call tf.nonMaxSuppressionAsync() instead\");\n  const { inputs, backend: backend2, attrs } = args;\n  const { boxes, scores } = inputs;\n  const { maxOutputSize, iouThreshold, scoreThreshold } = attrs;\n  const boxesVals = backend2.readSync(boxes.dataId);\n  const scoresVals = backend2.readSync(scores.dataId);\n  const { selectedIndices } = kernel_impls_exports.nonMaxSuppressionV3Impl(boxesVals, scoresVals, maxOutputSize, iouThreshold, scoreThreshold);\n  return backend2.makeTensorInfo([selectedIndices.length], \"int32\", new Int32Array(selectedIndices));\n}\nvar nonMaxSuppressionV3Config4 = {\n  kernelName: NonMaxSuppressionV3,\n  backendName: \"webgpu\",\n  kernelFunc: nonMaxSuppressionV33\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/NonMaxSuppressionV5.js\nfunction nonMaxSuppressionV53(args) {\n  console.warn(\"tf.nonMaxSuppression() in webgpu locks the UI thread. Call tf.nonMaxSuppressionAsync() instead\");\n  const { inputs, backend: backend2, attrs } = args;\n  const { boxes, scores } = inputs;\n  const { maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma } = attrs;\n  const boxesVals = backend2.readSync(boxes.dataId);\n  const scoresVals = backend2.readSync(scores.dataId);\n  const maxOutputSizeVal = maxOutputSize;\n  const iouThresholdVal = iouThreshold;\n  const scoreThresholdVal = scoreThreshold;\n  const softNmsSigmaVal = softNmsSigma;\n  const { selectedIndices, selectedScores } = kernel_impls_exports.nonMaxSuppressionV5Impl(boxesVals, scoresVals, maxOutputSizeVal, iouThresholdVal, scoreThresholdVal, softNmsSigmaVal);\n  return [\n    backend2.makeTensorInfo([selectedIndices.length], \"int32\", new Int32Array(selectedIndices)),\n    backend2.makeTensorInfo([selectedScores.length], \"float32\", new Float32Array(selectedScores))\n  ];\n}\nvar nonMaxSuppressionV5Config4 = {\n  kernelName: NonMaxSuppressionV5,\n  backendName: \"webgpu\",\n  kernelFunc: nonMaxSuppressionV53\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/ZerosLike.js\nfunction zerosLike5(args) {\n  const { inputs, backend: backend2 } = args;\n  const { x } = inputs;\n  if (x.dtype === \"complex64\") {\n    const realPart = real4({ inputs: { input: x }, backend: backend2 });\n    const r2 = zerosLike5({ inputs: { x: realPart }, backend: backend2 });\n    const imagPart = imag4({ inputs: { input: x }, backend: backend2 });\n    const i2 = zerosLike5({ inputs: { x: imagPart }, backend: backend2 });\n    const result = complex4({ inputs: { real: r2, imag: i2 }, backend: backend2 });\n    backend2.disposeData(realPart.dataId);\n    backend2.disposeData(r2.dataId);\n    backend2.disposeData(imagPart.dataId);\n    backend2.disposeData(i2.dataId);\n    return result;\n  } else {\n    return fill5({\n      attrs: {\n        shape: x.shape,\n        dtype: x.dtype,\n        value: x.dtype === \"string\" ? \"\" : 0\n      },\n      backend: backend2\n    });\n  }\n}\nvar zerosLikeConfig4 = {\n  kernelName: ZerosLike,\n  backendName: \"webgpu\",\n  kernelFunc: zerosLike5\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/OnesLike.js\nfunction onesLike5(args) {\n  const { inputs, backend: backend2 } = args;\n  const { x } = inputs;\n  if (x.dtype === \"string\") {\n    throw new Error(\"onesLike is not supported under string dtype\");\n  } else if (x.dtype === \"complex64\") {\n    const realPart = real4({ inputs: { input: x }, backend: backend2 });\n    const r2 = onesLike5({ inputs: { x: realPart }, backend: backend2 });\n    const imagPart = imag4({ inputs: { input: x }, backend: backend2 });\n    const i2 = zerosLike5({ inputs: { x: imagPart }, backend: backend2 });\n    const result = complex4({ inputs: { real: r2, imag: i2 }, backend: backend2 });\n    backend2.disposeData(realPart.dataId);\n    backend2.disposeData(r2.dataId);\n    backend2.disposeData(imagPart.dataId);\n    backend2.disposeData(i2.dataId);\n    return result;\n  } else {\n    return fill5({ attrs: { shape: x.shape, dtype: x.dtype, value: 1 }, backend: backend2 });\n  }\n}\nvar onesLikeConfig4 = {\n  kernelName: OnesLike,\n  backendName: \"webgpu\",\n  kernelFunc: onesLike5\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/Pack.js\nfunction pack4(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { axis } = attrs;\n  if (inputs.length === 1) {\n    return expandDims6({ inputs: { input: inputs[0] }, backend: backend2, attrs: { dim: axis } });\n  }\n  const shape = inputs[0].shape;\n  const dtype = inputs[0].dtype;\n  inputs.forEach((t2) => {\n    util_exports.assertShapesMatch(shape, t2.shape, \"All tensors passed to stack must have matching shapes\");\n    util_exports.assert(dtype === t2.dtype, () => \"All tensors passed to stack must have matching dtypes\");\n  });\n  const intermediateTensorInfos = [];\n  const expandedTensors = inputs.map((t2) => {\n    const expandedT = expandDims6({ inputs: { input: t2 }, backend: backend2, attrs: { dim: axis } });\n    intermediateTensorInfos.push(expandedT);\n    return expandedT;\n  });\n  const result = concat5({ inputs: expandedTensors, backend: backend2, attrs: { axis } });\n  intermediateTensorInfos.forEach((t2) => backend2.disposeData(t2.dataId));\n  return result;\n}\nvar packConfig4 = {\n  kernelName: Pack,\n  backendName: \"webgpu\",\n  kernelFunc: pack4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/pad_webgpu.js\nvar PadProgram2 = class {\n  constructor(xShape, paddings) {\n    this.variableNames = [\"x\"];\n    this.uniforms = \"constantValue : f32,\";\n    this.workGroupSize = [64, 1, 1];\n    this.size = true;\n    this.outputShape = paddings.map((p2, i2) => p2[0] + xShape[i2] + p2[1]);\n    this.dispatchLayout = flatDispatchLayout(this.outputShape);\n    this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, this.workGroupSize);\n    paddings.map((_, i2) => {\n      this.uniforms += ` pad${i2} : vec2<i32>,`;\n    });\n    this.xShape = xShape;\n    this.shaderKey = \"pad\";\n  }\n  getUserCode() {\n    const rank = this.xShape.length;\n    const type = getCoordsDataType2(rank);\n    const start = this.xShape.map((_, i2) => `uniforms.pad${i2}[0]`).join(\",\");\n    const end = this.xShape.map((_, i2) => `uniforms.pad${i2}[0] + uniforms.xShape${rank > 1 ? `[${i2}]` : \"\"}`).join(\",\");\n    const startValue = rank > 1 ? `${type}(${start})` : `${start}`;\n    const endValue = rank > 1 ? `${type}(${end})` : `${end}`;\n    const leftPadCondition = rank > 1 ? `any(outC < start)` : `outC < start`;\n    const rightPadCondition = rank > 1 ? `any(outC >= end)` : `outC >= end`;\n    const unpackedCoords = rank > 1 ? [\"coords[0]\", \"coords[1]\", \"coords[2]\", \"coords[3]\"].slice(0, rank) : \"coords\";\n    const userCode = `\n      ${getMainHeaderString(\"index\")} {\n        if (index < uniforms.size) {\n          let start = ${startValue};\n          let end = ${endValue};\n          let outC = getCoordsFromIndex(index);\n\n          if (${leftPadCondition} || ${rightPadCondition}) {\n            setOutputAtIndex(index, uniforms.constantValue);\n          } else {\n            let coords = outC - start;\n            setOutputAtIndex(index, getX(${unpackedCoords}));\n          }\n        }\n      }\n    `;\n    return userCode;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/PadV2.js\nvar padV23 = (args) => {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x } = inputs;\n  const { paddings, constantValue } = attrs;\n  if (paddings.every((p2) => util_exports.arraysEqual(p2, [0, 0]))) {\n    return identity5({ inputs: { x }, backend: backend2 });\n  }\n  if (util_exports.sizeFromShape(x.shape) === 0) {\n    const outputShape = paddings.map((p2, i2) => p2[0] + x.shape[i2] + p2[1]);\n    return fill5({\n      backend: backend2,\n      attrs: { shape: outputShape, value: constantValue, dtype: x.dtype }\n    });\n  }\n  const uniformData = [{ type: \"float32\", data: [constantValue] }];\n  paddings.map((p2) => uniformData.push({ type: \"int32\", data: [p2[0], p2[1]] }));\n  const program = new PadProgram2(x.shape, paddings);\n  return backend2.runWebGPUProgram(program, [x], x.dtype, uniformData);\n};\nvar padV2Config4 = {\n  kernelName: PadV2,\n  backendName: \"webgpu\",\n  kernelFunc: padV23\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/Pow.js\nvar pow4 = binaryKernelFunc3({\n  opType: BinaryOpType.POW\n});\nvar powConfig4 = {\n  kernelName: Pow,\n  backendName: \"webgpu\",\n  kernelFunc: pow4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/Prelu.js\nfunction prelu6(args) {\n  const { inputs, backend: backend2 } = args;\n  const { x, alpha } = inputs;\n  const program = new BinaryOpProgram2(BinaryOpType.PRELU, x.shape, alpha.shape);\n  return backend2.runWebGPUProgram(program, [x, alpha], \"float32\");\n}\nvar preluConfig4 = {\n  kernelName: Prelu,\n  backendName: \"webgpu\",\n  kernelFunc: prelu6\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/Prod.js\nfunction prod5(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x } = inputs;\n  const { axis, keepDims } = attrs;\n  return reduce2(x, axis, keepDims, \"prod\", backend2);\n}\nvar prodConfig4 = {\n  kernelName: Prod,\n  backendName: \"webgpu\",\n  kernelFunc: prod5\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/Range.js\nvar range6 = (args) => {\n  const { backend: backend2, attrs } = args;\n  const { start, stop, step: step5, dtype } = attrs;\n  const values = rangeImplCPU2(start, stop, step5, dtype);\n  return backend2.makeTensorInfo([values.length], dtype, values);\n};\nvar rangeConfig4 = {\n  kernelName: Range,\n  backendName: \"webgpu\",\n  kernelFunc: range6\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/RealDiv.js\nvar realDiv2 = binaryKernelFunc3({ opType: BinaryOpType.DIV });\nvar realDivConfig4 = {\n  kernelName: RealDiv,\n  backendName: \"webgpu\",\n  kernelFunc: realDiv2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/Reciprocal.js\nvar reciprocal4 = unaryKernelFunc3({ opType: UnaryOpType.RECIPROCAL });\nvar reciprocalConfig3 = {\n  kernelName: Reciprocal,\n  backendName: \"webgpu\",\n  kernelFunc: reciprocal4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/Relu.js\nvar relu4 = unaryKernelFunc3({ opType: UnaryOpType.RELU });\nvar reluConfig4 = {\n  kernelName: Relu,\n  backendName: \"webgpu\",\n  kernelFunc: relu4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/Relu6.js\nvar relu64 = unaryKernelFunc3({ opType: UnaryOpType.RELU6 });\nvar relu6Config4 = {\n  kernelName: Relu6,\n  backendName: \"webgpu\",\n  kernelFunc: relu64\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/resize_bilinear_webgpu.js\nvar ResizeBilinearProgram2 = class {\n  constructor(inputShape, newHeight, newWidth) {\n    this.variableNames = [\"x\"];\n    this.uniforms = \"adjustHeightWidth : vec2<f32>, halfPixelCenters : f32,\";\n    this.workGroupSize = [64, 1, 1];\n    this.size = true;\n    this.outputShape = [inputShape[0], newHeight, newWidth, inputShape[3]];\n    this.dispatchLayout = flatDispatchLayout(this.outputShape);\n    this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, this.workGroupSize);\n    this.shaderKey = `resizeBilinear`;\n  }\n  getUserCode() {\n    const userCode = `\n      ${getMainHeaderString(\"index\")} {\n        if (index < uniforms.size) {\n        let coords = getCoordsFromIndex(index);\n          let b = coords[0];\n          let d = coords[3];\n          let rc = coords.yz;\n\n          let effectiveInSize = vec2<f32>(\n            f32(uniforms.xShape.y) - uniforms.adjustHeightWidth[0],\n            f32(uniforms.xShape.z) - uniforms.adjustHeightWidth[1]);\n\n          let effectiveOutSize = vec2<f32>(\n            f32(uniforms.outShape.y) - uniforms.adjustHeightWidth[0],\n            f32(uniforms.outShape.z) - uniforms.adjustHeightWidth[1]);\n\n          let effectiveInputOverOutputRatioRC =\n              effectiveInSize / effectiveOutSize;\n\n          // Fractional source index\n          let sourceFracIndexRC =\n            (vec2<f32>(rc) + vec2<f32>(uniforms.halfPixelCenters)) *\n            effectiveInputOverOutputRatioRC - vec2<f32>(uniforms.halfPixelCenters);\n\n          // Compute the four integer indices.\n          let sourceFloorRC = vec2<i32>(sourceFracIndexRC);\n          let sourceCeilRC = vec2<i32>(\n            min(vec2<f32>(uniforms.xShape.yz) - vec2<f32>(1.0), ceil(sourceFracIndexRC)));\n\n          let topLeft = getX(b, sourceFloorRC.x, sourceFloorRC.y, d);\n          let bottomLeft = getX(b, sourceCeilRC.x, sourceFloorRC.y, d);\n          let topRight = getX(b, sourceFloorRC.x, sourceCeilRC.y, d);\n          let bottomRight = getX(b, sourceCeilRC.x, sourceCeilRC.y, d);\n\n          let fracRC = sourceFracIndexRC - vec2<f32>(sourceFloorRC);\n\n          let top = topLeft + (topRight - topLeft) * fracRC.y;\n          let bottom = bottomLeft + (bottomRight - bottomLeft) * fracRC.y;\n          let newValue = top + (bottom - top) * fracRC.x;\n\n          setOutputAtIndex(index, newValue);\n        }\n      }\n    `;\n    return userCode;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/ResizeBilinear.js\nfunction resizeBilinear5(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { images } = inputs;\n  const { alignCorners, size, halfPixelCenters } = attrs;\n  const [newHeight, newWidth] = size;\n  const adjustHeight = alignCorners && newHeight > 1 ? 1 : 0;\n  const adjustWidth = alignCorners && newWidth > 1 ? 1 : 0;\n  const halfPixelCentersValue = halfPixelCenters ? 0.5 : 0;\n  const uniformData = [\n    { type: \"float32\", data: [adjustHeight, adjustWidth] },\n    { type: \"float32\", data: [halfPixelCentersValue] }\n  ];\n  const program = new ResizeBilinearProgram2(images.shape, newHeight, newWidth);\n  return backend2.runWebGPUProgram(program, [images], \"float32\", uniformData);\n}\nvar resizeBilinearConfig4 = {\n  kernelName: ResizeBilinear,\n  backendName: \"webgpu\",\n  kernelFunc: resizeBilinear5\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/resize_nearest_neighbor_webgpu.js\nvar ResizeNearestNeighborProgram2 = class {\n  constructor(inputShape, newHeight, newWidth, halfPixelCenters) {\n    this.variableNames = [\"x\"];\n    this.uniforms = \"adjustHeightWidth : vec2<f32>, roundBase : f32,\";\n    this.workGroupSize = [64, 1, 1];\n    this.size = true;\n    this.outputShape = [inputShape[0], newHeight, newWidth, inputShape[3]];\n    this.dispatchLayout = flatDispatchLayout(this.outputShape);\n    this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, this.workGroupSize);\n    this.halfPixelCenters = halfPixelCenters;\n    this.shaderKey = `resizeNearest_${halfPixelCenters}`;\n  }\n  getUserCode() {\n    let sourceFracIndexRC;\n    if (this.halfPixelCenters) {\n      sourceFracIndexRC = `max((vec2<f32>(rc) + vec2<f32>(0.5)) * effectiveInputOverOutputRatioRC, vec2<f32>(0.0))`;\n    } else {\n      sourceFracIndexRC = `vec2<f32>(rc) * effectiveInputOverOutputRatioRC`;\n    }\n    const userCode = `\n      ${getMainHeaderString(\"index\")} {\n        if (index < uniforms.size) {\n          let coords = getCoordsFromIndex(index);\n          let b = coords[0];\n          let d = coords[3];\n          let rc = coords.yz;\n\n          let effectiveInSize = vec2<f32>(\n            f32(uniforms.xShape.y) - uniforms.adjustHeightWidth[0],\n            f32(uniforms.xShape.z) - uniforms.adjustHeightWidth[1]);\n\n          let effectiveOutSize = vec2<f32>(\n            f32(uniforms.outShape.y) - uniforms.adjustHeightWidth[0],\n            f32(uniforms.outShape.z) - uniforms.adjustHeightWidth[1]);\n\n          let effectiveInputOverOutputRatioRC =\n              effectiveInSize / effectiveOutSize;\n\n          // Fractional source index\n          let sourceFracIndexRC = ${sourceFracIndexRC};\n\n          // Compute the coordinators of nearest neighbor point.\n          let inputShapeRC = vec2<f32>(f32(uniforms.xShape.y), f32(uniforms.xShape.z));\n          let sourceNearestRC = vec2<i32>(\n            min(inputShapeRC - 1.0, floor(sourceFracIndexRC + uniforms.roundBase)));\n          let newValue = getX(b, sourceNearestRC.x, sourceNearestRC.y, d);\n\n          setOutputAtIndex(index, newValue);\n        }\n      }\n    `;\n    return userCode;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/ResizeNearestNeighbor.js\nfunction resizeNearestNeighbor5(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { images } = inputs;\n  const { alignCorners, halfPixelCenters, size } = attrs;\n  const [newHeight, newWidth] = size;\n  const adjustHeight = alignCorners && newHeight > 1 ? 1 : 0;\n  const adjustWidth = alignCorners && newWidth > 1 ? 1 : 0;\n  const roundBase = alignCorners ? 0.5 : 0;\n  const uniformData = [\n    { type: \"float32\", data: [adjustHeight, adjustWidth] },\n    { type: \"float32\", data: [roundBase] }\n  ];\n  const program = new ResizeNearestNeighborProgram2(images.shape, newHeight, newWidth, halfPixelCenters);\n  return backend2.runWebGPUProgram(program, [images], images.dtype, uniformData);\n}\nvar resizeNearestNeighborConfig4 = {\n  kernelName: ResizeNearestNeighbor,\n  backendName: \"webgpu\",\n  kernelFunc: resizeNearestNeighbor5\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/rotate_webgpu.js\nvar RotateProgram2 = class {\n  constructor(imageShape, fillValue) {\n    this.outputShape = [];\n    this.variableNames = [\"x\"];\n    this.workGroupSize = [64, 1, 1];\n    this.size = true;\n    this.outputShape = imageShape;\n    this.dispatchLayout = flatDispatchLayout(this.outputShape);\n    this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, this.workGroupSize);\n    this.uniforms = `centerX : f32, centerY : f32, sinRadians : f32,\n          cosRadians : f32,`;\n    this.shaderKey = \"rotate\";\n    this.outputShape = imageShape;\n    if (typeof fillValue === \"number\") {\n      this.uniforms += ` fillValue : f32,`;\n      this.fillSnippet = `var outputValue = uniforms.fillValue;`;\n      this.shaderKey += \"_float\";\n    } else {\n      this.uniforms += ` fillValue : vec3<f32>,`;\n      this.fillSnippet = `var outputValue = uniforms.fillValue[coords[3]];`;\n      this.shaderKey += \"_vec3\";\n    }\n  }\n  getUserCode() {\n    const userCode = `\n        ${getMainHeaderString(\"index\")} {\n          if (index < uniforms.size) {\n            let coords = getCoordsFromIndex(index);\n            let coordXFloat = (f32(coords[2]) - uniforms.centerX) *\n                uniforms.cosRadians - (f32(coords[1]) - uniforms.centerY) *\n                uniforms.sinRadians;\n            let coordYFloat = (f32(coords[2]) - uniforms.centerX) *\n                uniforms.sinRadians + (f32(coords[1]) - uniforms.centerY) *\n                uniforms.cosRadians;\n            let coordX = i32(round(coordXFloat + uniforms.centerX));\n            let coordY = i32(round(coordYFloat + uniforms.centerY));\n            ${this.fillSnippet}\n            if(coordX >= 0 && coordX < uniforms.xShape[2] && coordY >= 0 &&\n                coordY < uniforms.xShape[1]) {\n              outputValue = getX(coords[0], coordY, coordX, coords[3]);\n            }\n            setOutputAtIndex(index, outputValue);\n          }\n        }\n      `;\n    return userCode;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/RotateWithOffset.js\nvar rotateWithOffsetConfig4 = {\n  kernelName: RotateWithOffset,\n  backendName: \"webgpu\",\n  kernelFunc: ({ inputs, attrs, backend: backend2 }) => {\n    const { image: image2 } = inputs;\n    const { radians, fillValue, center } = attrs;\n    const webgpuBackend = backend2;\n    const program = new RotateProgram2(image2.shape, fillValue);\n    const [centerX, centerY] = backend_util_exports.getImageCenter(center, image2.shape[1], image2.shape[2]);\n    const uniformData = [\n      { type: \"float32\", data: [centerX] },\n      { type: \"float32\", data: [centerY] },\n      { type: \"float32\", data: [Math.sin(radians)] },\n      { type: \"float32\", data: [Math.cos(radians)] }\n    ];\n    if (typeof fillValue === \"number\") {\n      uniformData.push({ type: \"float32\", data: [Number.parseFloat(fillValue.toFixed(2))] });\n    } else {\n      uniformData.push({ type: \"float32\", data: fillValue });\n    }\n    const output = webgpuBackend.runWebGPUProgram(program, [image2], image2.dtype, uniformData);\n    return output;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/Rsqrt.js\nvar rsqrt4 = unaryKernelFunc3({ opType: UnaryOpType.RSQRT, cpuKernelImpl: rsqrtImplCPU2 });\nvar rsqrtConfig4 = {\n  kernelName: Rsqrt,\n  backendName: \"webgpu\",\n  kernelFunc: rsqrt4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/scatter_webgpu.js\nvar ScatterProgram2 = class {\n  constructor(flattenXShape, sliceDim, indicesRank, updatesRank, strides, shape, outputDtype, sumDupeIndices = true) {\n    this.variableNames = [\"updates\", \"indices\"];\n    this.workGroupSize = [64, 1, 1];\n    this.atomic = true;\n    this.outputShape = shape;\n    this.type = outputDtype;\n    this.sumDupeIndices = sumDupeIndices;\n    this.dispatchLayout = flatDispatchLayout(flattenXShape);\n    this.dispatch = computeDispatch(this.dispatchLayout, flattenXShape, this.workGroupSize);\n    this.sliceDimGreaterThanOne = sliceDim > 1;\n    this.shaderKey = `scatter_${indicesRank}_${updatesRank}_${this.sliceDimGreaterThanOne}_${outputDtype}_${sumDupeIndices}`;\n    const stridesType = getCoordsDataType2(strides.length);\n    this.uniforms = `sliceDim : i32, strides: ${stridesType}, size: i32,`;\n    this.updatesRank = updatesRank;\n    this.indicesRank = indicesRank;\n  }\n  getUserCode() {\n    let indicesString = \"\";\n    if (this.indicesRank === 1) {\n      indicesString = \"coords[0]\";\n    } else if (this.indicesRank === 2) {\n      indicesString = \"coords[0], j\";\n    }\n    const indicesSnippet = `getIndices(${indicesString})`;\n    const strideString = this.sliceDimGreaterThanOne ? \"uniforms.strides[j]\" : \"uniforms.strides\";\n    let outCoordsString = \"\";\n    let getUpdatesCoordsFromFlatIndex = \"\";\n    if (this.dispatchLayout.x.length === 1) {\n      outCoordsString = \"flattenedIndex\";\n      getUpdatesCoordsFromFlatIndex = `\n      fn getUpdatesCoordsFromFlatIndex(index : i32) -> i32 {\n        return index;\n      }\n      `;\n    } else if (this.dispatchLayout.x.length === 2) {\n      outCoordsString = \"vec2<i32>(flattenedIndex, coords[1])\";\n      getUpdatesCoordsFromFlatIndex = `\n      fn getUpdatesCoordsFromFlatIndex(index : i32) -> vec2<i32> {\n        // N.B. |updates| could be a scalar tensor, conceptually representing a\n        // 2D tensor with all values equal to that. By design, its size must be\n        // the same as |outShape[1]| in one dimension, and |indicesShape[0]|\n        // gives the other.\n        let sliceSize = uniforms.outShape[1];\n        let d0 = index / sliceSize;\n        let d1 = index - d0 * sliceSize;\n        return vec2<i32>(d0, d1);\n      }\n      `;\n    }\n    const updatesString = Array.from({ length: this.updatesRank }, (_, idx) => `coords[${idx}]`);\n    const updatesSnippet = `getUpdates(${updatesString.join(\", \")})`;\n    const atomicRMW = (ptr, val) => {\n      let atomicAddSnippet = `atomicAdd(${ptr}, bitcast<i32>(${val}))`;\n      if (this.type === \"float32\") {\n        atomicAddSnippet = `\n          {\n            var oldBits = 0;\n            var newBits = bitcast<i32>(${val});\n            loop {\n              let info = atomicCompareExchangeWeak(${ptr}, oldBits, newBits);\n              if (info.exchanged) {\n                break;\n              }\n              oldBits = info.old_value;\n              let oldValue = bitcast<f32>(oldBits);\n              let newValue = oldValue + (${val});\n              newBits = bitcast<i32>(newValue);\n            }\n          }\n        `;\n      }\n      const atomicStoreSnippet = `atomicStore(${ptr}, bitcast<i32>(${val}));`;\n      return this.sumDupeIndices ? atomicAddSnippet : atomicStoreSnippet;\n    };\n    const userCode = `\n    ${getUpdatesCoordsFromFlatIndex}\n\n      ${getMainHeaderString(\"index\")} {\n        if (index < uniforms.size) {\n          let coords = getUpdatesCoordsFromFlatIndex(index);\n          var flattenedIndex = 0;\n          for (var j = 0; j < uniforms.sliceDim; j = j + 1) {\n            let indexInside = i32(round(${indicesSnippet}));\n            flattenedIndex = flattenedIndex + indexInside * ${strideString};\n          }\n          let updateValue =\n              ${mapToWgslTypes(this.type, false)}(${updatesSnippet});\n          let flatIndex = getOutputIndexFromCoords(${outCoordsString});\n\n          ${atomicRMW(\"&result[flatIndex]\", \"updateValue\")};\n        }\n      }`;\n    return userCode;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/ScatterNd.js\nfunction scatterNd4(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { indices, updates } = inputs;\n  const { shape } = attrs;\n  const { sliceRank, numUpdates, sliceSize, strides, outputSize } = backend_util_exports.calculateShapes(updates, indices, shape);\n  const flattenShape = [outputSize / sliceSize, sliceSize];\n  if (outputSize === 0) {\n    return backend2.makeTensorInfo(shape, indices.dtype);\n  }\n  const flattenIndices = reshape6({ inputs: { x: indices }, backend: backend2, attrs: { shape: [numUpdates, sliceRank] } });\n  const flattenX = reshape6({ inputs: { x: updates }, backend: backend2, attrs: { shape: [numUpdates, sliceSize] } });\n  const type = flattenX.dtype;\n  const output = fill5({ backend: backend2, attrs: { shape: flattenShape, value: 0, dtype: type } });\n  const size = util_exports.sizeFromShape(flattenX.shape);\n  const uniformData = [\n    { type: \"int32\", data: [sliceRank] },\n    { type: \"int32\", data: strides },\n    { type: \"int32\", data: [size] }\n  ];\n  const program = new ScatterProgram2(flattenX.shape, sliceRank, flattenIndices.shape.length, flattenX.shape.length, strides, flattenShape, type);\n  const res = backend2.runWebGPUProgram(program, [flattenX, flattenIndices], type, uniformData, output);\n  const reshaped = reshape6({ inputs: { x: res }, backend: backend2, attrs: { shape } });\n  backend2.disposeData(flattenIndices.dataId);\n  backend2.disposeData(flattenX.dataId);\n  backend2.disposeData(res.dataId);\n  return reshaped;\n}\nvar scatterNdConfig4 = {\n  kernelName: ScatterNd,\n  backendName: \"webgpu\",\n  kernelFunc: scatterNd4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/select_webgpu.js\nvar SelectProgram2 = class {\n  constructor(cRank, shape, rank) {\n    this.variableNames = [\"c\", \"a\", \"b\"];\n    this.workGroupSize = [64, 1, 1];\n    this.size = true;\n    this.outputShape = shape;\n    this.dispatchLayout = flatDispatchLayout(this.outputShape);\n    this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, this.workGroupSize);\n    this.cRank = cRank;\n    this.rank = rank;\n    this.shaderKey = \"select\";\n  }\n  getUserCode() {\n    let cCoords;\n    let abCoords;\n    if (this.rank > 4) {\n      throw Error(`Where for rank ${this.rank} is not yet supported`);\n    }\n    if (this.rank === 1) {\n      abCoords = `resRC`;\n      cCoords = `resRC`;\n    } else {\n      const currentCoords = [\"resRC.x\", \"resRC.y\", \"resRC.z\", \"resRC.w\"];\n      const cCoordVars = [];\n      const abCoordVars = [];\n      for (let i2 = 0; i2 < this.outputShape.length; i2++) {\n        abCoordVars.push(`${currentCoords[i2]}`);\n        if (i2 < this.cRank) {\n          cCoordVars.push(`${currentCoords[i2]}`);\n        }\n      }\n      cCoords = cCoordVars.join();\n      abCoords = abCoordVars.join();\n    }\n    const userCode = `\n      ${getMainHeaderString(\"index\")} {\n        if (index < uniforms.size) {\n          let resRC = getCoordsFromIndex(index);\n          let cVal = getC(${cCoords});\n          if (cVal >= 1.0) {\n            setOutputAtIndex(index, getA(${abCoords}));\n          } else {\n            setOutputAtIndex(index, getB(${abCoords}));\n          }\n        }\n      }\n    `;\n    return userCode;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/Select.js\nfunction select5(args) {\n  const { inputs, backend: backend2 } = args;\n  const { condition, t: t2, e: e2 } = inputs;\n  const program = new SelectProgram2(condition.shape.length, t2.shape, t2.shape.length);\n  return backend2.runWebGPUProgram(program, [condition, t2, e2], upcastType(t2.dtype, e2.dtype));\n}\nvar selectConfig4 = {\n  kernelName: Select,\n  backendName: \"webgpu\",\n  kernelFunc: select5\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/Sigmoid.js\nvar sigmoid5 = unaryKernelFunc3({ opType: UnaryOpType.SIGMOID });\nvar sigmoidConfig4 = {\n  kernelName: Sigmoid,\n  backendName: \"webgpu\",\n  kernelFunc: sigmoid5\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/Sin.js\nvar sin4 = unaryKernelFunc3({ opType: UnaryOpType.SIN });\nvar sinConfig4 = {\n  kernelName: Sin,\n  backendName: \"webgpu\",\n  kernelFunc: sin4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/Sinh.js\nvar sinh4 = unaryKernelFunc3({ opType: UnaryOpType.SINH });\nvar sinhConfig3 = {\n  kernelName: Sinh,\n  backendName: \"webgpu\",\n  kernelFunc: sinh4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/Sub.js\nvar sub4 = binaryKernelFunc3({ opType: BinaryOpType.SUB, cpuKernelImpl: subImplCPU2, supportsComplex: true });\nvar subConfig4 = {\n  kernelName: Sub,\n  backendName: \"webgpu\",\n  kernelFunc: sub4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/Softmax.js\nfunction softmax6(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { logits } = inputs;\n  const { dim } = attrs;\n  const axes = util_exports.parseAxisParam([dim], logits.shape);\n  const maxLogit = max6({\n    inputs: { x: logits },\n    backend: backend2,\n    attrs: { reductionIndices: axes, keepDims: false }\n  });\n  const expandedShape = backend_util_exports.expandShapeToKeepDim(maxLogit.shape, axes);\n  const maxLogitsReshaped = reshape6({ inputs: { x: maxLogit }, backend: backend2, attrs: { shape: expandedShape } });\n  const a = sub4({ inputs: { a: logits, b: maxLogitsReshaped }, backend: backend2 });\n  const b = exp4({ inputs: { x: a }, backend: backend2 });\n  const sumExp = sum6({ inputs: { x: b }, backend: backend2, attrs: { axis: axes, keepDims: false } });\n  const sumExpReshaped = reshape6({ inputs: { x: sumExp }, backend: backend2, attrs: { shape: expandedShape } });\n  const res = realDiv2({ inputs: { a: b, b: sumExpReshaped }, backend: backend2 });\n  backend2.disposeData(maxLogit.dataId);\n  backend2.disposeData(maxLogitsReshaped.dataId);\n  backend2.disposeData(a.dataId);\n  backend2.disposeData(b.dataId);\n  backend2.disposeData(sumExp.dataId);\n  backend2.disposeData(sumExpReshaped.dataId);\n  return res;\n}\nvar softmaxConfig4 = {\n  kernelName: Softmax,\n  backendName: \"webgpu\",\n  kernelFunc: softmax6\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/SpaceToBatchND.js\nvar spaceToBatchND5 = (args) => {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x } = inputs;\n  const { blockShape, paddings } = attrs;\n  util_exports.assert(x.shape.length <= 4, () => \"spaceToBatchND for rank > 4 with a WebGPU backend not implemented yet\");\n  const prod6 = blockShape.reduce((a, b) => a * b);\n  const completePaddings = [[0, 0]];\n  completePaddings.push(...paddings);\n  for (let i2 = 1 + blockShape.length; i2 < x.shape.length; ++i2) {\n    completePaddings.push([0, 0]);\n  }\n  const toDispose = [];\n  const paddedX = padV23({\n    inputs: { x },\n    backend: backend2,\n    attrs: { paddings: completePaddings, constantValue: 0 }\n  });\n  const reshapedPaddedShape = backend_util_exports.getReshaped(paddedX.shape, blockShape, prod6, false);\n  const permutedReshapedPaddedPermutation = backend_util_exports.getPermuted(reshapedPaddedShape.length, blockShape.length, false);\n  const flattenShape = backend_util_exports.getReshapedPermuted(paddedX.shape, blockShape, prod6, false);\n  const reshapedPaddedX = reshape6({ inputs: { x: paddedX }, backend: backend2, attrs: { shape: reshapedPaddedShape } });\n  const paddedXT = transpose5({\n    inputs: { x: reshapedPaddedX },\n    backend: backend2,\n    attrs: { perm: permutedReshapedPaddedPermutation }\n  });\n  const result = reshape6({ inputs: { x: paddedXT }, backend: backend2, attrs: { shape: flattenShape } });\n  toDispose.push(paddedX);\n  toDispose.push(reshapedPaddedX);\n  toDispose.push(paddedXT);\n  toDispose.forEach((t2) => backend2.disposeData(t2.dataId));\n  return result;\n};\nvar spaceToBatchNDConfig4 = {\n  kernelName: SpaceToBatchND,\n  backendName: \"webgpu\",\n  kernelFunc: spaceToBatchND5\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/tile_webgpu.js\nvar TileProgram2 = class {\n  constructor(aShape, reps) {\n    this.variableNames = [\"A\"];\n    this.workGroupSize = [64, 1, 1];\n    this.size = true;\n    const outputShape = new Array(aShape.length);\n    for (let i2 = 0; i2 < outputShape.length; i2++) {\n      outputShape[i2] = aShape[i2] * reps[i2];\n    }\n    this.outputShape = outputShape;\n    this.dispatchLayout = flatDispatchLayout(this.outputShape);\n    this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, this.workGroupSize);\n    this.rank = this.outputShape.length;\n    this.shaderKey = \"tile\";\n  }\n  getUserCode() {\n    const sourceCoords = getSourceCoords5(this.rank, \"uniforms.\");\n    const userCode = `\n      ${getMainHeaderString(\"index\")} {\n        if (index < uniforms.size) {\n          let resRC = getCoordsFromIndex(index);\n          setOutputAtIndex(index, getA(${sourceCoords}));\n        }\n      }\n    `;\n    return userCode;\n  }\n};\nfunction getSourceCoords5(rank, uniformPrefix = \"\") {\n  if (rank >= 5) {\n    throw Error(`Tile for rank ${rank} is not yet supported`);\n  }\n  if (rank === 1) {\n    return `(resRC % ${uniformPrefix}aShape)`;\n  }\n  const currentCoords = [\"resRC.x\", \"resRC.y\", \"resRC.z\", \"resRC.w\"];\n  const sourceCoords = [];\n  for (let i2 = 0; i2 < rank; i2++) {\n    sourceCoords.push(`(${currentCoords[i2]} % ${uniformPrefix}aShape[${i2}])`);\n  }\n  return sourceCoords.join();\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/Tile.js\nfunction tile6(params) {\n  const { inputs, backend: backend2, attrs } = params;\n  const { x } = inputs;\n  const { reps } = attrs;\n  if (backend2.shouldExecuteOnCPU([x]) || x.dtype === \"string\" || x.shape.length >= 5) {\n    const data = backend2.readSync(x.dataId);\n    const value = x.dtype === \"string\" ? data.map((d) => util_exports.decodeString(d)) : data;\n    const buf = buffer(x.shape, x.dtype, value);\n    const outBuf = tileImplCPU2(buf, reps);\n    return backend2.makeTensorInfo(outBuf.shape, outBuf.dtype, outBuf.values);\n  }\n  const program = new TileProgram2(x.shape, reps);\n  const output = backend2.runWebGPUProgram(program, [x], x.dtype);\n  return output;\n}\nvar tileConfig4 = {\n  kernelName: Tile,\n  backendName: \"webgpu\",\n  kernelFunc: tile6\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/SparseToDense.js\nfunction sparseToDense4(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { sparseIndices, sparseValues, defaultValue } = inputs;\n  const { outputShape } = attrs;\n  const { sliceRank, numUpdates, sliceSize, strides, outputSize } = backend_util_exports.calculateShapes(sparseValues, sparseIndices, outputShape);\n  const sumDupeIndices = false;\n  if (sparseValues.dtype === \"string\") {\n    const indicesBuf = backend2.bufferSync(sparseIndices);\n    const updatesBuf = backend2.bufferSync(sparseValues);\n    const $defaultValue2 = util_exports.decodeString(backend2.readSync(defaultValue.dataId)[0]);\n    const outBuf = scatterImplCPU2(indicesBuf, updatesBuf, outputShape, outputSize, sliceSize, numUpdates, sliceRank, strides, $defaultValue2, sumDupeIndices);\n    return backend2.makeTensorInfo(outputShape, outBuf.dtype, outBuf.values);\n  }\n  const flattenShape = [outputSize / sliceSize, sliceSize];\n  const $sparseIndices = reshape6({\n    inputs: { x: sparseIndices },\n    backend: backend2,\n    attrs: { shape: [numUpdates, sliceRank] }\n  });\n  const $sparseValues = sparseValues.shape.length ? reshape6({\n    inputs: { x: sparseValues },\n    backend: backend2,\n    attrs: { shape: [numUpdates, sliceSize] }\n  }) : identity5({ inputs: { x: sparseValues }, backend: backend2 });\n  const type = $sparseValues.dtype;\n  const zero = backend2.makeTensorInfo([], type, util_exports.makeZerosTypedArray(1, type));\n  const $defaultValue = reshape6({\n    inputs: { x: defaultValue },\n    backend: backend2,\n    attrs: { shape: Array(flattenShape.length).fill(1) }\n  });\n  const $denseValues = tile6({ inputs: { x: $defaultValue }, backend: backend2, attrs: { reps: flattenShape } });\n  const size = util_exports.sizeFromShape([numUpdates, sliceSize]);\n  const uniformData = [\n    { type: \"int32\", data: [sliceRank] },\n    { type: \"int32\", data: strides },\n    { type: \"int32\", data: [size] }\n  ];\n  switch (numUpdates) {\n    case 0:\n      break;\n    case 1:\n      if (true) {\n        const program = new ScatterProgram2([numUpdates, sliceSize], sliceRank, $sparseIndices.shape.length, $sparseValues.shape.length, strides, flattenShape, type, sumDupeIndices);\n        backend2.runWebGPUProgram(program, [$sparseValues, $sparseIndices], type, uniformData, $denseValues);\n      }\n      break;\n    default:\n      if (true) {\n        const program = new ScatterProgram2([numUpdates, sliceSize], sliceRank, $sparseIndices.shape.length, zero.shape.length, strides, flattenShape, type, sumDupeIndices);\n        backend2.runWebGPUProgram(program, [zero, $sparseIndices], type, uniformData, $denseValues);\n      }\n      {\n        const program = new ScatterProgram2([numUpdates, sliceSize], sliceRank, $sparseIndices.shape.length, $sparseValues.shape.length, strides, flattenShape, type);\n        backend2.runWebGPUProgram(program, [$sparseValues, $sparseIndices], type, uniformData, $denseValues);\n      }\n  }\n  const denseValues = reshape6({ inputs: { x: $denseValues }, backend: backend2, attrs: { shape: outputShape } });\n  backend2.disposeData($sparseIndices.dataId);\n  backend2.disposeData($sparseValues.dataId);\n  backend2.disposeData($defaultValue.dataId);\n  backend2.disposeData(zero.dataId);\n  backend2.disposeData($denseValues.dataId);\n  return denseValues;\n}\nvar sparseToDenseConfig3 = {\n  kernelName: SparseToDense,\n  backendName: \"webgpu\",\n  kernelFunc: sparseToDense4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/SplitV.js\nfunction splitV4(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x } = inputs;\n  const { numOrSizeSplits, axis } = attrs;\n  const $axis = util_exports.parseAxisParam(axis, x.shape)[0];\n  const splitSizes = backend_util_exports.prepareSplitSize(x, numOrSizeSplits, $axis);\n  const xRank = x.shape.length;\n  const begin = new Array(xRank).fill(0);\n  const size = x.shape.slice();\n  return splitSizes.map((s2) => {\n    const sliceSize = [...size];\n    sliceSize[$axis] = s2;\n    const sliceT = slice5({ inputs: { x }, backend: backend2, attrs: { begin, size: sliceSize } });\n    begin[$axis] += s2;\n    return sliceT;\n  });\n}\nvar splitVConfig4 = {\n  kernelName: SplitV,\n  backendName: \"webgpu\",\n  kernelFunc: splitV4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/Sqrt.js\nvar sqrt4 = unaryKernelFunc3({ opType: UnaryOpType.SQRT });\nvar sqrtConfig4 = {\n  kernelName: Sqrt,\n  backendName: \"webgpu\",\n  kernelFunc: sqrt4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/Square.js\nvar squareConfig4 = {\n  kernelName: Square,\n  backendName: \"webgpu\",\n  kernelFunc: ({ inputs, backend: backend2 }) => {\n    const { x } = inputs;\n    const webGPUBackend = backend2;\n    const program = new UnaryOpProgram2(x.shape, UnaryOpType.SQUARE);\n    return webGPUBackend.runWebGPUProgram(program, [x], x.dtype);\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/SquaredDifference.js\nvar squaredDifference4 = binaryKernelFunc3({\n  opType: BinaryOpType.SQUARED_DIFFERENCE\n});\nvar squaredDifferenceConfig4 = {\n  kernelName: SquaredDifference,\n  backendName: \"webgpu\",\n  kernelFunc: squaredDifference4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/strided_slice_webgpu.js\nvar StridedSliceProgram2 = class {\n  constructor(destSize) {\n    this.variableNames = [\"x\"];\n    this.workPerThread = 1;\n    this.workGroupSize = [64, 1, 1];\n    this.size = true;\n    this.outputShape = destSize;\n    this.dispatchLayout = flatDispatchLayout(this.outputShape);\n    this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, this.workGroupSize, [this.workPerThread, 1, 1]);\n    const dtype = getCoordsDataType2(this.outputShape.length);\n    this.uniforms = `begin : ${dtype},  strides : ${dtype}, `;\n    this.shaderKey = \"stridedSlice\";\n  }\n  getUserCode() {\n    const rank = this.outputShape.length;\n    let newCoords = \"\";\n    if (rank === 1) {\n      newCoords = \"coords * uniforms.strides + uniforms.begin\";\n    } else {\n      let outputAxis = 0;\n      newCoords = this.outputShape.map((_, i2) => {\n        outputAxis++;\n        return this.outputShape.length === 1 ? `coords * uniforms.strides[${i2}] + uniforms.begin[${i2}]` : `coords[${outputAxis - 1}] * uniforms.strides[${i2}] + uniforms.begin[${i2}]`;\n      }).join(\",\");\n    }\n    const userCode = `\n       ${getMainHeaderString(\"index\")} {\n         if (index < uniforms.size) {\n           let coords = getCoordsFromIndex(index);\n           setOutputAtIndex(index, getX(${newCoords}));\n         }\n       }\n     `;\n    return userCode;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/StridedSlice.js\nfunction stridedSlice5(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x } = inputs;\n  const { begin, end, strides, beginMask, endMask, ellipsisMask, newAxisMask, shrinkAxisMask } = attrs;\n  const { finalShapeSparse, finalShape, isIdentity, sliceDim0, isSimpleSlice, begin: $begin, end: $end, strides: $strides } = slice_util_exports.sliceInfo(x.shape, begin, end, strides, beginMask, endMask, ellipsisMask, newAxisMask, shrinkAxisMask);\n  let result;\n  if (isIdentity) {\n    result = reshape6({ inputs: { x }, backend: backend2, attrs: { shape: finalShape } });\n  } else if (sliceDim0 || isSimpleSlice) {\n    util_exports.assert(x.shape.length >= 1, () => `Input must have rank at least 1, got: ${x.shape.length}`);\n    const size = slice_util_exports.computeOutShape($begin, $end, $strides);\n    const sliced = slice5({ inputs: { x }, backend: backend2, attrs: { begin: $begin, size } });\n    result = reshape6({ inputs: { x: sliced }, backend: backend2, attrs: { shape: finalShape } });\n    backend2.disposeData(sliced.dataId);\n  } else {\n    const shouldExecuteOnCPU = backend2.shouldExecuteOnCPU([x]);\n    if (shouldExecuteOnCPU) {\n      const values = backend2.readSync(x.dataId);\n      const xBuf = buffer(x.shape, x.dtype, values);\n      const resultValues = stridedSliceImplCPU2(finalShapeSparse, xBuf, $strides, $begin);\n      result = backend2.makeTensorInfo(finalShape, x.dtype, resultValues.values);\n    } else {\n      const program = new StridedSliceProgram2(finalShapeSparse);\n      const uniformData = [{ type: \"int32\", data: $begin }, { type: \"int32\", data: $strides }];\n      const resultValues = backend2.runWebGPUProgram(program, [x], x.dtype, uniformData);\n      result = reshape6({ inputs: { x: resultValues }, backend: backend2, attrs: { shape: finalShape } });\n      backend2.disposeData(resultValues.dataId);\n    }\n  }\n  return result;\n}\nvar stridedSliceConfig4 = {\n  kernelName: StridedSlice,\n  backendName: \"webgpu\",\n  kernelFunc: stridedSlice5\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/StringNGrams.js\nfunction stringNGrams5(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { separator, nGramWidths, leftPad, rightPad: rightPad2, padWidth, preserveShortSequences } = attrs;\n  const { data, dataSplits } = inputs;\n  const $data = backend2.readSync(data.dataId);\n  const $dataSplits = backend2.readSync(dataSplits.dataId);\n  const [nGrams, nGramsSplits] = stringNGramsImplCPU2($data, $dataSplits, separator, nGramWidths, leftPad, rightPad2, padWidth, preserveShortSequences);\n  return [\n    backend2.makeTensorInfo([nGrams.length], \"string\", nGrams),\n    backend2.makeTensorInfo(dataSplits.shape, \"int32\", nGramsSplits)\n  ];\n}\nvar stringNGramsConfig4 = {\n  kernelName: StringNGrams,\n  backendName: \"webgpu\",\n  kernelFunc: stringNGrams5\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/Tanh.js\nvar tanh5 = unaryKernelFunc3({ opType: UnaryOpType.TANH });\nvar tanhConfig4 = {\n  kernelName: Tanh,\n  backendName: \"webgpu\",\n  kernelFunc: tanh5\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/top_k_webgpu.js\nvar SwapProgram2 = class {\n  constructor(shape) {\n    this.variableNames = [\"x\", \"indices\"];\n    this.workGroupSize = [256, 1, 1];\n    this.size = true;\n    this.outputShape = shape;\n    this.dispatchLayout = flatDispatchLayout(this.outputShape);\n    this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, this.workGroupSize);\n    this.uniforms = `inputSize : i32, firstPass : i32, negativeInf : f32,\n        dir : i32, inc : i32,`;\n    this.shaderKey = \"swap\";\n  }\n  getUserCode() {\n    const userCode = `\n        ${getMainHeaderString(\"index\")} {\n          if (index < uniforms.size) {\n            let outC = getCoordsFromIndex(index);\n            let batch = outC[0];\n            let elemIdx = outC[1];\n            // We compare elements pair-wise within a group of size 2 * inc.\n            // The comparing rule for each group alternates between ascending\n            // and descending. Within each group, we compare each pair at\n            // positions i and i+inc. To decide whether an element at position i\n            // is x0 or x1, we mod it by 2 * inc, if the result is smaller than\n            // inc, it is in the first half of the group, we denote it as x0,\n            // otherwise we denote it as x1.\n            // For example, as shown in the Bitonic top K paper referenced\n            // above, Figure5(a) shows that element[1] is in the second half of\n            // the group when group size is 2, but it is in the first half of\n            // the group when group size is 4.\n            let isFirstInPair = elemIdx % (2 * uniforms.inc) < uniforms.inc;\n            var i = 0;\n            if (isFirstInPair) {\n              i = elemIdx;\n            } else {\n              i = elemIdx - uniforms.inc;\n            }\n\n            var i0 = 0;\n            if (uniforms.firstPass == 1) {\n              i0 = i;\n            } else {\n              i0 = i32(getIndices(batch, i));\n            }\n\n            var i1 = 0;\n            if (uniforms.firstPass == 1) {\n              i1 = i + uniforms.inc;\n            } else {\n              i1 = i32(getIndices(batch, i + uniforms.inc));\n            }\n\n            var x0 = f32(0.0);\n            var x1 = f32(0.0);\n            if (i0 < uniforms.inputSize) {\n              x0 = getX(batch, i0);\n            } else {\n              x0 = uniforms.negativeInf;\n            }\n            if (i1 < uniforms.inputSize) {\n              x1 = getX(batch, i1);\n            } else {\n              x1 = uniforms.negativeInf;\n            }\n\n            let reverse = elemIdx % (2 * uniforms.dir) >= uniforms.dir;\n            let isGreater = x0 > x1 || (x0 == x1 && i1 > i0);\n            if (reverse == isGreater) {\n              // Elements in opposite order of direction\n              let iTemp = i0;\n              i0 = i1;\n              i1 = iTemp;\n            }\n            if (isFirstInPair) {\n              setOutputAtIndex(index, f32(i0));\n            } else {\n              setOutputAtIndex(index, f32(i1));\n            }\n          }\n        }\n      `;\n    return userCode;\n  }\n};\nvar MergeProgram2 = class {\n  constructor(shape) {\n    this.variableNames = [\"x\", \"indices\"];\n    this.workGroupSize = [256, 1, 1];\n    this.size = true;\n    this.outputShape = shape;\n    this.dispatchLayout = flatDispatchLayout(this.outputShape);\n    this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, this.workGroupSize);\n    this.uniforms = `inputSize : i32, firstPass : i32, k : i32,`;\n    this.shaderKey = \"merge\";\n  }\n  getUserCode() {\n    const userCode = `\n        ${getMainHeaderString(\"index\")} {\n          if (index < uniforms.size) {\n            let outC = getCoordsFromIndex(index);\n            let batch = outC[0];\n            let elemIdx = outC[1];\n            // The output size is half of the previous size.\n            // If the previous sequence is | | | | _ _ _ _  | | | |  _ _ _ _\n            // (k=4), we only need to output the indices at positions |, the\n            // indices at positions _ can be thrown away, see Figure5(b) After\n            // Phase 2 (Merge phase) in the Bitonic Top K paper referenced\n            // above.\n            // For example, the paper shows we only need to output the orange\n            // bars. The output sequence should look like this | | | | | | | |.\n            // Because the sequence is halved, to map the output index back to\n            // the previous sequence to find the corresponding value, we need\n            // to double the index. When we double the index, we basically\n            // interpolate a position, so 2i looks like\n            // | _ | _ | _ | _ | _ | _ | _. We move the | to the first k\n            // position of each 2k positions by - elemIdx % k. E.g. for output\n            // at index 4,5,6,7, we want to get the corresponding element at\n            // original index 8,9,10,11, for output at index 8,9,10,11,\n            // we want to get the corresponding element at original index\n            // 16,17,18,19, so on and so forth.\n\n            var i = 0;\n            if (elemIdx < uniforms.k) {\n              i = elemIdx;\n            } else {\n              i = elemIdx * 2 - elemIdx % uniforms.k;\n            }\n            var i0 = 0;\n            if (uniforms.firstPass == 1) {\n              i0 = i;\n            } else {\n              i0 = i32(getIndices(batch, i));\n            }\n            var i1 = 0;\n            if (uniforms.firstPass == 1) {\n              i1 = i + uniforms.k;\n            } else {\n              i1 = i32(getIndices(batch, i + uniforms.k));\n            }\n\n            let x0 = getX(batch, i0);\n            var x1 = f32(0.0);\n            if (i1 < uniforms.inputSize) {\n              x1 = getX(batch, i1);\n            } else {\n              x1 = x0;\n            }\n\n            if (x0 >= x1) {\n              setOutputAtIndex(index, f32(i0));\n            } else {\n              setOutputAtIndex(index, f32(i1));\n            }\n          }\n        }\n      `;\n    return userCode;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/TopK.js\nfunction disposeIntermediateTensorInfoOrNull2(backend2, tensorInfo) {\n  if (tensorInfo !== null) {\n    backend2.disposeData(tensorInfo.dataId);\n  }\n}\nfunction roundUpToPow22(num) {\n  let pow22 = 1;\n  while (pow22 < num) {\n    pow22 *= 2;\n  }\n  return pow22;\n}\nfunction topK3(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x } = inputs;\n  const { k, sorted } = attrs;\n  const xShape = x.shape;\n  const lastDim = xShape[xShape.length - 1];\n  if (backend2.shouldExecuteOnCPU([x])) {\n    const xVals = backend2.readSync(x.dataId);\n    const [allTopKVals, allTopKIndices] = topKImplCPU2(xVals, xShape, x.dtype, k, sorted);\n    return [\n      backend2.makeTensorInfo(allTopKVals.shape, allTopKVals.dtype, allTopKVals.values),\n      backend2.makeTensorInfo(allTopKIndices.shape, allTopKIndices.dtype, allTopKIndices.values)\n    ];\n  }\n  if (k === 0) {\n    xShape[xShape.length - 1] = 0;\n    return [\n      backend2.makeTensorInfo(xShape, x.dtype, []),\n      backend2.makeTensorInfo(xShape, \"int32\", [])\n    ];\n  }\n  if (lastDim === 1) {\n    return [\n      x,\n      fill5({ attrs: { shape: xShape, dtype: \"int32\", value: 0 }, backend: backend2 })\n    ];\n  }\n  const xSize = util_exports.sizeFromShape(xShape);\n  const batch = xSize / lastDim;\n  const x2D = reshape6({ inputs: { x }, attrs: { shape: [batch, lastDim] }, backend: backend2 });\n  const kPow2 = roundUpToPow22(k);\n  const lastDimPow2 = roundUpToPow22(lastDim);\n  let indices = null;\n  const getInputs = () => indices === null ? [x2D, x2D] : [x2D, indices];\n  const runSwap = (dir, inc, shape) => {\n    const inputs2 = getInputs();\n    const program = new SwapProgram2(shape);\n    const firstPass = indices === null ? 1 : 0;\n    const uniformDataSwap = [\n      { type: \"int32\", data: [lastDim] },\n      { type: \"int32\", data: [firstPass] },\n      { type: \"float32\", data: [Number.NEGATIVE_INFINITY] },\n      { type: \"int32\", data: [dir] },\n      { type: \"int32\", data: [inc] }\n    ];\n    const prevIndices2 = indices;\n    indices = backend2.runWebGPUProgram(program, inputs2, \"int32\", uniformDataSwap);\n    disposeIntermediateTensorInfoOrNull2(backend2, prevIndices2);\n  };\n  for (let len = 1; len < kPow2; len *= 2) {\n    const dir = len * 2;\n    for (let inc = len; inc >= 1; inc /= 2) {\n      runSwap(dir, inc, [batch, lastDimPow2]);\n    }\n  }\n  for (let indicesSize = lastDimPow2; indicesSize > kPow2; indicesSize /= 2) {\n    const inputs2 = getInputs();\n    const mergeProgram = new MergeProgram2([batch, indicesSize / 2]);\n    const firstPass = indices === null ? 1 : 0;\n    const uniformDataMerge = [\n      { type: \"int32\", data: [lastDim] },\n      { type: \"int32\", data: [firstPass] },\n      { type: \"int32\", data: [kPow2] }\n    ];\n    const prevIndices2 = indices;\n    indices = backend2.runWebGPUProgram(mergeProgram, inputs2, \"int32\", uniformDataMerge);\n    disposeIntermediateTensorInfoOrNull2(backend2, prevIndices2);\n    const len = kPow2 / 2;\n    const dir = len * 2;\n    for (let inc = len; inc >= 1; inc /= 2) {\n      runSwap(dir, inc, indices.shape);\n    }\n  }\n  let prevIndices = indices;\n  indices = slice5({ inputs: { x: indices }, backend: backend2, attrs: { begin: 0, size: [batch, k] } });\n  disposeIntermediateTensorInfoOrNull2(backend2, prevIndices);\n  let values = gatherV24({ inputs: { x: x2D, indices }, backend: backend2, attrs: { axis: 1, batchDims: 1 } });\n  disposeIntermediateTensorInfoOrNull2(backend2, x2D);\n  const newShape = xShape.slice(0, -1);\n  newShape.push(k);\n  prevIndices = indices;\n  indices = reshape6({ inputs: { x: indices }, attrs: { shape: newShape }, backend: backend2 });\n  disposeIntermediateTensorInfoOrNull2(backend2, prevIndices);\n  const prevValues = values;\n  values = reshape6({ inputs: { x: values }, attrs: { shape: newShape }, backend: backend2 });\n  disposeIntermediateTensorInfoOrNull2(backend2, prevValues);\n  return [values, indices];\n}\nvar topKConfig4 = {\n  kernelName: TopK,\n  backendName: \"webgpu\",\n  kernelFunc: topK3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/transform_webgpu.js\nvar TransformProgram2 = class {\n  constructor(outShape) {\n    this.variableNames = [\"Image\", \"Transforms\"];\n    this.uniforms = \"interpolationModeId : i32, fillModeId : i32, fillValue : f32,\";\n    this.workGroupSize = [64, 1, 1];\n    this.size = true;\n    this.outputShape = outShape;\n    this.dispatchLayout = flatDispatchLayout(this.outputShape);\n    this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, this.workGroupSize);\n    this.shaderKey = \"transform\";\n  }\n  getUserCode() {\n    const userCode = `\n          fn mapCoord(outCoord : f32, len : f32) -> f32{\n            var inCoord = outCoord;\n            if(uniforms.fillModeId == 2) {\n              if (inCoord < 0.0) {\n                if (len <= 1.0) {\n                  inCoord = 0.0;\n                } else {\n                  let sz2 = 2.0 * len;\n                  if (inCoord < sz2) {\n                    inCoord = sz2 * f32(i32(f32(-inCoord / sz2))) +\n                    inCoord;\n                  }\n                  if (inCoord < -len) {\n                    inCoord = inCoord + sz2;\n                  } else {\n                    inCoord = -inCoord - 1.0;\n                  }\n                }\n              } else if (inCoord > len - 1.0) {\n                if (len <= 1.0) {\n                  inCoord = 0.0;\n                } else {\n                  let sz2 = 2.0 * len;\n                  inCoord = inCoord - sz2 * f32(i32(f32(inCoord / sz2)));\n                  if (inCoord >= len) {\n                    inCoord = sz2 - inCoord - 1.0;\n                  }\n                }\n              }\n              return clamp(inCoord, 0.0, len - 1.0);\n            } else if (uniforms.fillModeId == 3) {\n              if (inCoord < 0.0) {\n                if (len <= 1.0) {\n                  inCoord = 0.0;\n                } else {\n                  let sz = len - 1.0;\n                  inCoord = inCoord + len * (f32(i32(f32(-inCoord / sz))) + 1.0);\n                }\n              } else if (inCoord > len - 1.0) {\n                if (len <= 1.0) {\n                  inCoord = 0.0;\n                } else {\n                  let sz = len - 1.0;\n                  inCoord = inCoord - len * f32(i32(f32(inCoord / sz)));\n                }\n              }\n              return clamp(inCoord, 0.0, len - 1.0);\n            } else if (uniforms.fillModeId == 4) {\n              return clamp(outCoord, 0.0, len - 1.0);\n            }\n            return outCoord;\n          }\n          fn readWithFillValue(batch : i32, coordY : i32, coordX : i32,\n            channel : i32) -> f32 {\n            var outputValue : f32;\n            if (0 <= coordY && coordY < uniforms.imageShape[1] && 0 <= coordX && coordX < uniforms.imageShape[2]) {\n                outputValue = getImage(batch, coordY, coordX, channel);\n            } else {\n              outputValue = uniforms.fillValue;\n            }\n            return outputValue;\n          }\n\n          ${getMainHeaderString(\"index\")} {\n            if (index < uniforms.size) {\n              let coords = getCoordsFromIndex(index);\n              var outputValue : f32;\n              let batch = coords[0];\n              let x = coords[2];\n              let y = coords[1];\n              let channel = coords[3];\n              let xf = f32(x);\n              let yf = f32(y);\n              let a1 = getTransforms(batch, 0);\n              let a2 = getTransforms(batch, 1);\n              let a3 = getTransforms(batch, 2);\n              let b1 = getTransforms(batch, 3);\n              let b2 = getTransforms(batch, 4);\n              let b3 = getTransforms(batch, 5);\n              let c1 = getTransforms(batch, 6);\n              let c2 = getTransforms(batch, 7);\n              let projection = c1 * xf + c2 * yf + 1.0;\n              if (projection == 0.0) {\n                outputValue = uniforms.fillValue;\n              } else {\n                let inX = (a1 * xf + a2 * yf + a3) / projection;\n                let inY = (b1 * xf + b2 * yf + b3) / projection;\n                let mapX = mapCoord(inX, f32(uniforms.imageShape[2]));\n                let mapY = mapCoord(inY, f32(uniforms.imageShape[1]));\n\n                if (uniforms.interpolationModeId == 1) {\n                  let coordY = i32(round(mapY));\n                  let coordX = i32(round(mapX));\n                  outputValue = readWithFillValue(batch, coordY, coordX,\n                    channel);\n                } else {\n                  let yFloor = floor(mapY);\n                  let xFloor = floor(mapX);\n                  let yCeil = yFloor + 1.0;\n                  let xCeil = xFloor + 1.0;\n                  let valueYFloor = (xCeil - mapX) *\n                  readWithFillValue(batch, i32(yFloor), i32(xFloor), channel) +\n                  (mapX - xFloor) *\n                  readWithFillValue(batch, i32(yFloor), i32(xCeil), channel);\n                  let valueYCeil = (xCeil - mapX) *\n                  readWithFillValue(batch, i32(yCeil), i32(xFloor), channel) +\n                  (mapX - xFloor) *\n                  readWithFillValue(batch, i32(yCeil), i32(xCeil), channel);\n                  outputValue = (yCeil - mapY) * valueYFloor +\n                  (mapY - yFloor) * valueYCeil;\n                }\n              }\n              setOutputAtIndex(index, outputValue);\n            }\n          }\n        `;\n    return userCode;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/Transform.js\nfunction transform5(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { image: image2, transforms } = inputs;\n  const { interpolation, fillMode, fillValue, outputShape } = attrs;\n  const [batch, imageHeight, imageWidth, numChannels] = image2.shape;\n  const [outHeight, outWidth] = outputShape != null ? outputShape : [imageHeight, imageWidth];\n  const outShape = [\n    batch,\n    outHeight,\n    outWidth,\n    numChannels\n  ];\n  const program = new TransformProgram2(outShape);\n  const interpolationModeId = interpolation === \"nearest\" ? 1 : 2;\n  let fillModeId;\n  switch (fillMode) {\n    case \"constant\":\n      fillModeId = 1;\n      break;\n    case \"reflect\":\n      fillModeId = 2;\n      break;\n    case \"wrap\":\n      fillModeId = 3;\n      break;\n    case \"nearest\":\n      fillModeId = 4;\n      break;\n    default:\n      fillModeId = 1;\n      break;\n  }\n  const uniformData = [\n    { type: \"int32\", data: [interpolationModeId] },\n    { type: \"int32\", data: [fillModeId] },\n    { type: \"float32\", data: [fillValue] }\n  ];\n  return backend2.runWebGPUProgram(program, [image2, transforms], \"float32\", uniformData);\n}\nvar transformConfig4 = {\n  kernelName: Transform,\n  backendName: \"webgpu\",\n  kernelFunc: transform5\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/Unpack.js\nfunction unpack4(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { value } = inputs;\n  let { axis } = attrs;\n  if (axis < 0) {\n    axis += value.shape.length;\n  }\n  const x = value;\n  const xRank = x.shape.length;\n  const num = value.shape[axis];\n  const outShape = new Array(xRank - 1);\n  let outIndex = 0;\n  for (let i2 = 0; i2 < xRank; i2++) {\n    if (i2 !== axis) {\n      outShape[outIndex++] = x.shape[i2];\n    }\n  }\n  const toDispose = [];\n  const begin = new Array(xRank).fill(0);\n  const size = x.shape.slice();\n  size[axis] = 1;\n  const res = new Array(num);\n  for (let i2 = 0; i2 < res.length; i2++) {\n    begin[axis] = i2;\n    const sliced = slice5({ inputs: { x }, backend: backend2, attrs: { begin, size } });\n    const reshaped = reshape6({ inputs: { x: sliced }, backend: backend2, attrs: { shape: outShape } });\n    res[i2] = reshaped;\n    toDispose.push(sliced);\n  }\n  toDispose.forEach((t2) => backend2.disposeData(t2.dataId));\n  return res;\n}\nvar unpackConfig4 = {\n  kernelName: Unpack,\n  backendName: \"webgpu\",\n  kernelFunc: unpack4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/register_all_kernels.js\nvar kernelConfigs4 = [\n  _fusedMatMulConfig4,\n  absConfig4,\n  addConfig4,\n  addNConfig4,\n  argMaxConfig4,\n  argMinConfig3,\n  atan2Config3,\n  avgPoolConfig4,\n  batchMatMulConfig4,\n  batchToSpaceNDConfig4,\n  castConfig4,\n  ceilConfig4,\n  clipByValueConfig4,\n  complexConfig3,\n  concatConfig4,\n  conv2DConfig4,\n  conv2DBackpropInputConfig4,\n  cosConfig4,\n  coshConfig4,\n  cropAndResizeConfig4,\n  cumprodConfig4,\n  cumsumConfig4,\n  depthToSpaceConfig4,\n  depthwiseConv2dNativeConfig4,\n  einsumConfig3,\n  eluConfig4,\n  equalConfig4,\n  expConfig4,\n  expandDimsConfig4,\n  expm1Config3,\n  fillConfig4,\n  flipLeftRightConfig4,\n  fromPixelsConfig2,\n  floorConfig4,\n  floorDivConfig4,\n  fusedBatchNormConfig2,\n  fusedConv2DConfig4,\n  fusedDepthwiseConv2DConfig4,\n  gatherNdConfig4,\n  gatherV2Config4,\n  greaterConfig4,\n  greaterEqualConfig4,\n  identityConfig4,\n  imagConfig3,\n  isNaNConfig3,\n  leakyReluConfig4,\n  lessConfig4,\n  lessEqualConfig4,\n  logConfig4,\n  logicalAndConfig4,\n  logicalNotConfig4,\n  maxConfig4,\n  maximumConfig4,\n  maxPoolConfig4,\n  meanConfig4,\n  minConfig4,\n  minimumConfig4,\n  mirrorPadConfig4,\n  multiplyConfig4,\n  negConfig4,\n  nonMaxSuppressionV3Config4,\n  nonMaxSuppressionV5Config4,\n  notEqualConfig4,\n  onesLikeConfig4,\n  packConfig4,\n  padV2Config4,\n  powConfig4,\n  preluConfig4,\n  prodConfig4,\n  rangeConfig4,\n  realConfig3,\n  realDivConfig4,\n  reciprocalConfig3,\n  reluConfig4,\n  relu6Config4,\n  reshapeConfig4,\n  resizeBilinearConfig4,\n  resizeNearestNeighborConfig4,\n  rotateWithOffsetConfig4,\n  rsqrtConfig4,\n  scatterNdConfig4,\n  selectConfig4,\n  sigmoidConfig4,\n  sinConfig4,\n  sinhConfig3,\n  sliceConfig4,\n  stridedSliceConfig4,\n  stringNGramsConfig4,\n  softmaxConfig4,\n  spaceToBatchNDConfig4,\n  sparseToDenseConfig3,\n  splitVConfig4,\n  sqrtConfig4,\n  squareConfig4,\n  squaredDifferenceConfig4,\n  subConfig4,\n  sumConfig4,\n  tanhConfig4,\n  tileConfig4,\n  topKConfig4,\n  transformConfig4,\n  transposeConfig4,\n  unpackConfig4,\n  zerosLikeConfig4\n];\nfor (const kernelConfig of kernelConfigs4) {\n  registerKernel(kernelConfig);\n}\n\n// dist/tfjs.version.js\nvar e = \"3.20.0\";\nvar s = \"3.20.0\";\nvar t = \"3.20.0\";\nvar i = \"3.20.0\";\nvar n = \"3.20.0\";\nvar r = \"3.20.0\";\nvar l = \"3.20.0\";\nvar V = { tfjs: e, \"tfjs-core\": s, \"tfjs-data\": t, \"tfjs-layers\": i, \"tfjs-converter\": n, \"tfjs-backend-webgl\": r, \"tfjs-backend-wasm\": l };\nexport {\n  Abs,\n  Acos,\n  Acosh,\n  AdadeltaOptimizer,\n  AdagradOptimizer,\n  AdamOptimizer,\n  AdamaxOptimizer,\n  Add,\n  AddN,\n  All,\n  Any,\n  ArgMax,\n  ArgMin,\n  Asin,\n  Asinh,\n  Atan,\n  Atan2,\n  Atanh,\n  AvgPool,\n  AvgPool3D,\n  AvgPool3DGrad,\n  AvgPoolGrad,\n  BackendWasm,\n  BatchMatMul,\n  BatchToSpaceND,\n  Bincount,\n  BroadcastArgs,\n  BroadcastTo,\n  Callback,\n  CallbackList,\n  Cast,\n  Ceil,\n  ClipByValue,\n  Complex,\n  ComplexAbs,\n  Concat,\n  Conv2D,\n  Conv2DBackpropFilter,\n  Conv2DBackpropInput,\n  Conv3D,\n  Conv3DBackpropFilterV2,\n  Conv3DBackpropInputV2,\n  Cos,\n  Cosh,\n  CropAndResize,\n  Cumprod,\n  Cumsum,\n  CustomCallback,\n  DataStorage,\n  DenseBincount,\n  DepthToSpace,\n  DepthwiseConv2dNative,\n  DepthwiseConv2dNativeBackpropFilter,\n  DepthwiseConv2dNativeBackpropInput,\n  Diag,\n  Dilation2D,\n  Dilation2DBackpropFilter,\n  Dilation2DBackpropInput,\n  ENV,\n  EarlyStopping,\n  Einsum,\n  Elu,\n  EluGrad,\n  Environment,\n  Equal,\n  Erf,\n  Exp,\n  ExpandDims,\n  Expm1,\n  FFT,\n  Fill,\n  FlipLeftRight,\n  Floor,\n  FloorDiv,\n  FromPixels,\n  FusedBatchNorm,\n  FusedConv2D,\n  FusedDepthwiseConv2D,\n  GPGPUContext,\n  GatherNd,\n  GatherV2,\n  GraphModel,\n  Greater,\n  GreaterEqual,\n  History,\n  IFFT,\n  Identity,\n  Imag,\n  InputSpec,\n  IsFinite,\n  IsInf,\n  IsNan,\n  KernelBackend,\n  LRN,\n  LRNGrad,\n  LayerVariable,\n  LayersModel,\n  LeakyRelu,\n  Less,\n  LessEqual,\n  LinSpace,\n  Log,\n  Log1p,\n  LogSoftmax,\n  LogicalAnd,\n  LogicalNot,\n  LogicalOr,\n  LogicalXor,\n  LowerBound,\n  MathBackendWebGL,\n  Max,\n  MaxPool,\n  MaxPool3D,\n  MaxPool3DGrad,\n  MaxPoolGrad,\n  MaxPoolWithArgmax,\n  Maximum,\n  Mean,\n  Min,\n  Minimum,\n  MirrorPad,\n  Mod,\n  MomentumOptimizer,\n  Multinomial,\n  Multiply,\n  Neg,\n  NonMaxSuppressionV3,\n  NonMaxSuppressionV4,\n  NonMaxSuppressionV5,\n  NotEqual,\n  OP_SCOPE_SUFFIX,\n  OneHot,\n  OnesLike,\n  Optimizer,\n  OptimizerConstructors,\n  Pack,\n  PadV2,\n  Pool,\n  Pow,\n  Prelu,\n  Prod,\n  RMSPropOptimizer,\n  RNN,\n  RaggedTensorToTensor,\n  Range,\n  Rank,\n  Real,\n  RealDiv,\n  Reciprocal,\n  Reduction,\n  Relu,\n  Relu6,\n  Reshape,\n  ResizeBilinear,\n  ResizeBilinearGrad,\n  ResizeNearestNeighbor,\n  ResizeNearestNeighborGrad,\n  Reverse,\n  RotateWithOffset,\n  Round,\n  Rsqrt,\n  SGDOptimizer,\n  ScatterNd,\n  SearchSorted,\n  Select,\n  Selu,\n  Sequential,\n  Sigmoid,\n  Sign,\n  Sin,\n  Sinh,\n  Slice,\n  Softmax,\n  Softplus,\n  SpaceToBatchND,\n  SparseFillEmptyRows,\n  SparseReshape,\n  SparseSegmentMean,\n  SparseSegmentSum,\n  SparseToDense,\n  SplitV,\n  Sqrt,\n  Square,\n  SquaredDifference,\n  Step,\n  StridedSlice,\n  StringNGrams,\n  StringSplit,\n  StringToHashBucketFast,\n  Sub,\n  Sum,\n  SymbolicTensor,\n  Tan,\n  Tanh,\n  Tensor,\n  TensorBuffer,\n  Tile,\n  TopK,\n  Transform,\n  Transpose,\n  Unique,\n  Unpack,\n  UnsortedSegmentSum,\n  UpperBound,\n  Variable,\n  WebGPUBackend,\n  ZerosLike,\n  _FusedMatMul,\n  abs,\n  acos,\n  acosh,\n  add2 as add,\n  addN,\n  all,\n  any,\n  argMax,\n  argMin,\n  asin,\n  asinh,\n  atan,\n  atan2,\n  atanh,\n  avgPool,\n  avgPool3d,\n  backend,\n  backend_util_exports as backend_util,\n  basicLSTMCell,\n  batchNorm,\n  batchNorm2d,\n  batchNorm3d,\n  batchNorm4d,\n  batchToSpaceND,\n  bincount,\n  booleanMaskAsync,\n  broadcastArgs,\n  broadcastTo,\n  broadcast_util_exports as broadcast_util,\n  browser_exports as browser,\n  buffer,\n  callbacks,\n  cast,\n  ceil,\n  clipByValue,\n  clone,\n  complex,\n  concat,\n  concat1d,\n  concat2d,\n  concat3d,\n  concat4d,\n  exports_constraints_exports as constraints,\n  conv1d,\n  conv2d,\n  conv2dTranspose,\n  conv3d,\n  conv3dTranspose,\n  copyRegisteredKernels,\n  cos,\n  cosh,\n  cosineWindow,\n  cumprod,\n  cumsum,\n  customGrad,\n  dist_exports2 as data,\n  denseBincount,\n  deprecationWarn,\n  depthToSpace,\n  depthwiseConv2d,\n  deregisterOp,\n  device_util_exports as device_util,\n  diag,\n  dilation2d,\n  disableDeprecationWarnings,\n  dispose,\n  disposeVariables,\n  div,\n  divNoNan,\n  dot,\n  dropout,\n  einsum,\n  elu,\n  enableDebugMode,\n  enableProdMode,\n  enclosingPowerOfTwo,\n  engine,\n  env,\n  equal,\n  erf,\n  euclideanNorm,\n  exp,\n  expandDims,\n  expm1,\n  eye,\n  fft,\n  fill,\n  findBackend,\n  findBackendFactory,\n  floor,\n  floorDiv,\n  forceHalfFloat,\n  fused_ops_exports as fused,\n  gather,\n  gatherND,\n  gather_nd_util_exports as gather_util,\n  getBackend,\n  getGradient,\n  getKernel,\n  getKernelsForBackend,\n  getThreadsCount,\n  gpgpu_util_exports as gpgpu_util,\n  grad,\n  grads,\n  greater,\n  greaterEqual,\n  ifft,\n  imag,\n  image,\n  inTopKAsync,\n  exports_initializers_exports as initializers,\n  input,\n  io_exports as io,\n  irfft,\n  isFinite2 as isFinite,\n  isInf,\n  isNaN2 as isNaN,\n  keep,\n  kernel_impls_exports as kernel_impls,\n  exports_layers_exports as layers,\n  leakyRelu,\n  less,\n  lessEqual,\n  linalg,\n  linspace,\n  loadGraphModel,\n  loadGraphModelSync,\n  loadLayersModel,\n  localResponseNormalization,\n  log2 as log,\n  log1p,\n  logSigmoid,\n  logSoftmax,\n  logSumExp,\n  logicalAnd,\n  logicalNot,\n  logicalOr,\n  logicalXor,\n  losses,\n  lowerBound,\n  matMul,\n  math_exports as math,\n  max,\n  maxPool,\n  maxPool3d,\n  maxPoolWithArgmax,\n  maximum,\n  mean,\n  memory,\n  meshgrid,\n  exports_metrics_exports as metrics,\n  min,\n  minimum,\n  mirrorPad,\n  mod,\n  model,\n  exports_models_exports as models,\n  moments,\n  movingAverage,\n  mul,\n  multiRNNCell,\n  multinomial,\n  neg,\n  nextFrame,\n  norm,\n  notEqual,\n  oneHot,\n  ones2 as ones,\n  onesLike,\n  op,\n  outerProduct,\n  pad,\n  pad1d,\n  pad2d,\n  pad3d,\n  pad4d,\n  pool,\n  pow,\n  prelu,\n  print,\n  prod,\n  profile,\n  raggedTensorToTensor,\n  rand,\n  randomGamma,\n  randomNormal,\n  randomStandardNormal,\n  randomUniform,\n  range,\n  ready,\n  real,\n  reciprocal,\n  registerBackend,\n  registerCallbackConstructor,\n  registerGradient,\n  registerKernel,\n  registerOp,\n  exports_regularizers_exports as regularizers,\n  relu,\n  relu6,\n  removeBackend,\n  reshape,\n  reverse,\n  reverse1d,\n  reverse2d,\n  reverse3d,\n  reverse4d,\n  rfft,\n  round2 as round,\n  rsqrt,\n  scalar,\n  scatterND,\n  scatter_nd_util_exports as scatter_util,\n  searchSorted,\n  selu,\n  separableConv2d,\n  sequential,\n  serialization_exports as serialization,\n  setBackend,\n  setPlatform,\n  setThreadsCount,\n  setWasmPath,\n  setWasmPaths,\n  setWebGLContext,\n  setdiff1dAsync,\n  sigmoid,\n  sign,\n  signal,\n  sin,\n  sinh,\n  slice,\n  slice1d,\n  slice2d,\n  slice3d,\n  slice4d,\n  slice_util_exports as slice_util,\n  softmax,\n  softplus,\n  spaceToBatchND,\n  sparse,\n  sparseToDense,\n  spectral,\n  split,\n  sqrt,\n  square,\n  squaredDifference,\n  squeeze,\n  stack,\n  step,\n  stridedSlice,\n  string,\n  sub,\n  sum2 as sum,\n  sumOutType,\n  tan,\n  tanh2 as tanh,\n  tensor,\n  tensor1d,\n  tensor2d,\n  tensor3d,\n  tensor4d,\n  tensor5d,\n  tensor6d,\n  tensor_util_exports as tensor_util,\n  test_util_exports as test_util,\n  tidy,\n  tile,\n  time,\n  topk,\n  train,\n  transpose,\n  truncatedNormal,\n  unique,\n  unregisterGradient,\n  unregisterKernel,\n  unsortedSegmentSum,\n  unstack,\n  upcastType,\n  upperBound,\n  util_exports as util,\n  valueAndGrad,\n  valueAndGrads,\n  variable,\n  variableGrads,\n  V as version,\n  version3 as version_converter,\n  version as version_core,\n  version2 as version_layers,\n  version8 as version_wasm,\n  version6 as version_webgl,\n  webgl,\n  webgl_util_exports as webgl_util,\n  webgpu_util_exports as webgpu_util,\n  where,\n  whereAsync,\n  zeros,\n  zerosLike\n};\n", "export const vertexIdentity = `\n  precision highp float;\n  attribute vec2 pos;\n  attribute vec2 uv;\n  varying vec2 vUv;\n  uniform float flipY;\n  void main(void) {\n    vUv = uv;\n    gl_Position = vec4(pos.x, pos.y*flipY, 0.0, 1.);\n  }\n`;\n\nexport const fragmentIdentity = `\n  precision highp float;\n  varying vec2 vUv;\n  uniform sampler2D texture;\n  void main(void) {\n    gl_FragColor = texture2D(texture, vUv);\n  }\n`;\n\nexport const colorMatrixWithAlpha = `\n  precision highp float;\n  varying vec2 vUv;\n  uniform sampler2D texture;\n  uniform float m[20];\n  void main(void) {\n    vec4 c = texture2D(texture, vUv);\n    gl_FragColor.r = m[0] * c.r + m[1] * c.g + m[2] * c.b + m[3] * c.a + m[4];\n    gl_FragColor.g = m[5] * c.r + m[6] * c.g + m[7] * c.b + m[8] * c.a + m[9];\n    gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[13] * c.a + m[14];\n    gl_FragColor.a = m[15] * c.r + m[16] * c.g + m[17] * c.b + m[18] * c.a + m[19];\n  }\n`;\n\nexport const colorMatrixWithoutAlpha = `\n  precision highp float;\n  varying vec2 vUv;\n  uniform sampler2D texture;\n  uniform float m[20];\n  void main(void) {\n    vec4 c = texture2D(texture, vUv);\n    gl_FragColor.r = m[0] * c.r + m[1] * c.g + m[2] * c.b + m[4];\n    gl_FragColor.g = m[5] * c.r + m[6] * c.g + m[7] * c.b + m[9];\n    gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[14];\n    gl_FragColor.a = c.a;\n  }\n`;\n\nexport const pixelate = `\n  precision highp float;\n  varying vec2 vUv;\n  uniform vec2 size;\n  uniform sampler2D texture;\n  vec2 pixelate(vec2 coord, vec2 size) {\n    return floor( coord / size ) * size;\n  }\n  void main(void) {\n    gl_FragColor = vec4(0.0);\n    vec2 coord = pixelate(vUv, size);\n    gl_FragColor += texture2D(texture, coord);\n  }\n`;\n\nexport const blur = `\n  precision highp float;\n  varying vec2 vUv;\n  uniform sampler2D texture;\n  uniform vec2 px;\n  void main(void) {\n    gl_FragColor = vec4(0.0);\n    gl_FragColor += texture2D(texture, vUv + vec2(-7.0*px.x, -7.0*px.y))*0.0044299121055113265;\n    gl_FragColor += texture2D(texture, vUv + vec2(-6.0*px.x, -6.0*px.y))*0.00895781211794;\n    gl_FragColor += texture2D(texture, vUv + vec2(-5.0*px.x, -5.0*px.y))*0.0215963866053;\n    gl_FragColor += texture2D(texture, vUv + vec2(-4.0*px.x, -4.0*px.y))*0.0443683338718;\n    gl_FragColor += texture2D(texture, vUv + vec2(-3.0*px.x, -3.0*px.y))*0.0776744219933;\n    gl_FragColor += texture2D(texture, vUv + vec2(-2.0*px.x, -2.0*px.y))*0.115876621105;\n    gl_FragColor += texture2D(texture, vUv + vec2(-1.0*px.x, -1.0*px.y))*0.147308056121;\n    gl_FragColor += texture2D(texture, vUv                             )*0.159576912161;\n    gl_FragColor += texture2D(texture, vUv + vec2( 1.0*px.x,  1.0*px.y))*0.147308056121;\n    gl_FragColor += texture2D(texture, vUv + vec2( 2.0*px.x,  2.0*px.y))*0.115876621105;\n    gl_FragColor += texture2D(texture, vUv + vec2( 3.0*px.x,  3.0*px.y))*0.0776744219933;\n    gl_FragColor += texture2D(texture, vUv + vec2( 4.0*px.x,  4.0*px.y))*0.0443683338718;\n    gl_FragColor += texture2D(texture, vUv + vec2( 5.0*px.x,  5.0*px.y))*0.0215963866053;\n    gl_FragColor += texture2D(texture, vUv + vec2( 6.0*px.x,  6.0*px.y))*0.00895781211794;\n    gl_FragColor += texture2D(texture, vUv + vec2( 7.0*px.x,  7.0*px.y))*0.0044299121055113265;\n  }\n`;\n\nexport const convolution = `\n  precision highp float;\n  varying vec2 vUv;\n  uniform sampler2D texture;\n  uniform vec2 px;\n  uniform float m[9];\n  void main(void) {\n    vec4 c11 = texture2D(texture, vUv - px); // top left\n    vec4 c12 = texture2D(texture, vec2(vUv.x, vUv.y - px.y)); // top center\n    vec4 c13 = texture2D(texture, vec2(vUv.x + px.x, vUv.y - px.y)); // top right\n    vec4 c21 = texture2D(texture, vec2(vUv.x - px.x, vUv.y) ); // mid left\n    vec4 c22 = texture2D(texture, vUv); // mid center\n    vec4 c23 = texture2D(texture, vec2(vUv.x + px.x, vUv.y) ); // mid right\n    vec4 c31 = texture2D(texture, vec2(vUv.x - px.x, vUv.y + px.y) ); // bottom left\n    vec4 c32 = texture2D(texture, vec2(vUv.x, vUv.y + px.y) ); // bottom center\n    vec4 c33 = texture2D(texture, vUv + px ); // bottom right\n    gl_FragColor = \n    c11 * m[0] + c12 * m[1] + c22 * m[2] +\n    c21 * m[3] + c22 * m[4] + c23 * m[5] +\n    c31 * m[6] + c32 * m[7] + c33 * m[8];\n    gl_FragColor.a = c22.a;\n  }\n`;\n", "/**\n * Image Filters in WebGL algoritm implementation\n * Based on: [WebGLImageFilter](https://github.com/phoboslab/WebGLImageFilter)\n */\n\n/* eslint-disable func-names */\n\nimport * as shaders from './imagefxshaders';\nimport { canvas } from './image';\nimport { log } from '../util/util';\n\nconst collect = (source, prefix: string, collection) => {\n  const r = new RegExp('\\\\b' + prefix + ' \\\\w+ (\\\\w+)', 'ig');\n  source.replace(r, (match, name) => {\n    collection[name] = 0;\n    return match;\n  });\n};\n\nclass GLProgram {\n  uniform = {};\n  attribute = {};\n  gl: WebGLRenderingContext;\n  id: WebGLProgram;\n\n  constructor(gl, vertexSource, fragmentSource) {\n    this.gl = gl;\n    const vertexShader = this.compile(vertexSource, this.gl.VERTEX_SHADER);\n    const fragmentShader = this.compile(fragmentSource, this.gl.FRAGMENT_SHADER);\n    this.id = this.gl.createProgram() as WebGLProgram;\n    if (!vertexShader || !fragmentShader) return;\n    if (!this.id) {\n      log('filter: could not create webgl program');\n      return;\n    }\n    this.gl.attachShader(this.id, vertexShader);\n    this.gl.attachShader(this.id, fragmentShader);\n    this.gl.linkProgram(this.id);\n    if (!this.gl.getProgramParameter(this.id, this.gl.LINK_STATUS)) {\n      log(`filter: gl link failed: ${this.gl.getProgramInfoLog(this.id) || 'unknown'}`);\n      return;\n    }\n    this.gl.useProgram(this.id);\n    collect(vertexSource, 'attribute', this.attribute); // Collect attributes\n    for (const a in this.attribute) this.attribute[a] = this.gl.getAttribLocation(this.id, a);\n    collect(vertexSource, 'uniform', this.uniform); // Collect uniforms\n    collect(fragmentSource, 'uniform', this.uniform);\n    for (const u in this.uniform) this.uniform[u] = this.gl.getUniformLocation(this.id, u);\n  }\n\n  compile = (source, type): WebGLShader | null => {\n    const shader = this.gl.createShader(type);\n    if (!shader) {\n      log('filter: could not create shader');\n      return null;\n    }\n    this.gl.shaderSource(shader, source);\n    this.gl.compileShader(shader);\n    if (!this.gl.getShaderParameter(shader, this.gl.COMPILE_STATUS)) {\n      log(`filter: gl compile failed: ${this.gl.getShaderInfoLog(shader) || 'unknown'}`);\n      return null;\n    }\n    return shader;\n  };\n}\n\n// function that is instantiated as class so it has private this members\n/**\n * @class GLImageFilter\n * @property {function} reset reset current filter chain\n * @property {function} add add specified filter to filter chain\n * @property {function} apply execute filter chain and draw result\n * @property {function} draw just draw input to result\n */\n\nexport function GLImageFilter() {\n  let drawCount = 0;\n  let sourceTexture: WebGLTexture | null = null;\n  let lastInChain = false;\n  let currentFramebufferIndex = -1;\n  let tempFramebuffers: [null, null] | [{ fbo: WebGLFramebuffer | null, texture: WebGLTexture | null }] = [null, null];\n  let filterChain: Record<string, unknown>[] = [];\n  let vertexBuffer: WebGLBuffer | null = null;\n  let currentProgram: GLProgram | null = null;\n  const fxcanvas = canvas(100, 100);\n  const shaderProgramCache = { }; // key is the shader program source, value is the compiled program\n  const DRAW = { INTERMEDIATE: 1 };\n  const gl = fxcanvas.getContext('webgl') as WebGLRenderingContext;\n  if (!gl) {\n    log('filter: cannot get webgl context');\n    return;\n  }\n  // @ts-ignore used for sanity checks outside of imagefx\n  this.gl = gl;\n\n  function resize(width, height) {\n    if (width === fxcanvas.width && height === fxcanvas.height) return; // Same width/height? Nothing to do here\n    fxcanvas.width = width;\n    fxcanvas.height = height;\n    if (!vertexBuffer) { // Create the context if we don't have it yet\n      const vertices = new Float32Array([-1, -1, 0, 1, 1, -1, 1, 1, -1, 1, 0, 0, -1, 1, 0, 0, 1, -1, 1, 1, 1, 1, 1, 0]); // Create the vertex buffer for the two triangles [x, y, u, v] * 6\n      vertexBuffer = gl.createBuffer();\n      gl.bindBuffer(gl.ARRAY_BUFFER, vertexBuffer);\n      gl.bufferData(gl.ARRAY_BUFFER, vertices, gl.STATIC_DRAW);\n      gl.pixelStorei(gl.UNPACK_PREMULTIPLY_ALPHA_WEBGL, true);\n    }\n    gl.viewport(0, 0, fxcanvas.width, fxcanvas.height);\n    tempFramebuffers = [null, null]; // Delete old temp framebuffers\n  }\n\n  function createFramebufferTexture(width, height) {\n    const fbo = gl.createFramebuffer();\n    gl.bindFramebuffer(gl.FRAMEBUFFER, fbo);\n    const renderbuffer = gl.createRenderbuffer();\n    gl.bindRenderbuffer(gl.RENDERBUFFER, renderbuffer);\n    const texture = gl.createTexture();\n    gl.bindTexture(gl.TEXTURE_2D, texture);\n    gl.texImage2D(gl.TEXTURE_2D, 0, gl.RGBA, width, height, 0, gl.RGBA, gl.UNSIGNED_BYTE, null);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MAG_FILTER, gl.LINEAR);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MIN_FILTER, gl.LINEAR);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_S, gl.CLAMP_TO_EDGE);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_T, gl.CLAMP_TO_EDGE);\n    gl.framebufferTexture2D(gl.FRAMEBUFFER, gl.COLOR_ATTACHMENT0, gl.TEXTURE_2D, texture, 0);\n    gl.bindTexture(gl.TEXTURE_2D, null);\n    gl.bindFramebuffer(gl.FRAMEBUFFER, null);\n    return { fbo, texture };\n  }\n\n  function getTempFramebuffer(index): { fbo: WebGLFramebuffer | null, texture: WebGLTexture | null } {\n    tempFramebuffers[index] = tempFramebuffers[index] || createFramebufferTexture(fxcanvas.width, fxcanvas.height);\n    return tempFramebuffers[index] as { fbo: WebGLFramebuffer, texture: WebGLTexture };\n  }\n\n  function draw(flags = 0) {\n    if (!currentProgram) return;\n    let source: WebGLTexture | null = null;\n    let target: WebGLFramebuffer | null = null;\n    let flipY = false;\n    if (drawCount === 0) source = sourceTexture; // First draw call - use the source texture\n    else source = getTempFramebuffer(currentFramebufferIndex).texture || null; // All following draw calls use the temp buffer last drawn to\n    drawCount++;\n    if (lastInChain && !(flags & DRAW.INTERMEDIATE)) { // Last filter in our chain - draw directly to the WebGL Canvas. We may also have to flip the image vertically now\n      target = null;\n      flipY = drawCount % 2 === 0;\n    } else {\n      currentFramebufferIndex = (currentFramebufferIndex + 1) % 2;\n      target = getTempFramebuffer(currentFramebufferIndex).fbo || null; // Intermediate draw call - get a temp buffer to draw to\n    }\n    gl.bindTexture(gl.TEXTURE_2D, source); // Bind the source and target and draw the two triangles\n    gl.bindFramebuffer(gl.FRAMEBUFFER, target);\n    gl.uniform1f(currentProgram.uniform['flipY'], (flipY ? -1 : 1));\n    gl.drawArrays(gl.TRIANGLES, 0, 6);\n  }\n\n  function compileShader(fragmentSource): GLProgram | null {\n    if (shaderProgramCache[fragmentSource]) {\n      currentProgram = shaderProgramCache[fragmentSource];\n      gl.useProgram((currentProgram ? currentProgram.id : null) || null);\n      return currentProgram;\n    }\n    currentProgram = new GLProgram(gl, shaders.vertexIdentity, fragmentSource);\n    if (!currentProgram) {\n      log('filter: could not get webgl program');\n      return null;\n    }\n    const floatSize = Float32Array.BYTES_PER_ELEMENT;\n    const vertSize = 4 * floatSize;\n    gl.enableVertexAttribArray(currentProgram.attribute['pos']);\n    gl.vertexAttribPointer(currentProgram.attribute['pos'], 2, gl.FLOAT, false, vertSize, 0 * floatSize);\n    gl.enableVertexAttribArray(currentProgram.attribute['uv']);\n    gl.vertexAttribPointer(currentProgram.attribute['uv'], 2, gl.FLOAT, false, vertSize, 2 * floatSize);\n    shaderProgramCache[fragmentSource] = currentProgram;\n    return currentProgram;\n  }\n\n  const filter = {\n    colorMatrix: (matrix: number[]) => { // general color matrix filter\n      const m = new Float32Array(matrix);\n      m[4] /= 255;\n      m[9] /= 255;\n      m[14] /= 255;\n      m[19] /= 255;\n      const shader = (m[18] === 1 && m[3] === 0 && m[8] === 0 && m[13] === 0 && m[15] === 0 && m[16] === 0 && m[17] === 0 && m[19] === 0) // Can we ignore the alpha value? Makes things a bit faster.\n        ? shaders.colorMatrixWithoutAlpha\n        : shaders.colorMatrixWithAlpha;\n      const program = compileShader(shader);\n      if (!program) return;\n      gl.uniform1fv(program.uniform['m'], m);\n      draw();\n    },\n\n    brightness: (brightness: number) => {\n      const b = (brightness || 0) + 1;\n      filter.colorMatrix([\n        b, 0, 0, 0, 0,\n        0, b, 0, 0, 0,\n        0, 0, b, 0, 0,\n        0, 0, 0, 1, 0,\n      ]);\n    },\n\n    saturation: (amount: number) => {\n      const x = (amount || 0) * 2 / 3 + 1;\n      const y = ((x - 1) * -0.5);\n      filter.colorMatrix([\n        x, y, y, 0, 0,\n        y, x, y, 0, 0,\n        y, y, x, 0, 0,\n        0, 0, 0, 1, 0,\n      ]);\n    },\n\n    desaturate: () => {\n      filter.saturation(-1);\n    },\n\n    contrast: (amount: number) => {\n      const v = (amount || 0) + 1;\n      const o = -128 * (v - 1);\n      filter.colorMatrix([\n        v, 0, 0, 0, o,\n        0, v, 0, 0, o,\n        0, 0, v, 0, o,\n        0, 0, 0, 1, 0,\n      ]);\n    },\n\n    negative: () => {\n      filter.contrast(-2);\n    },\n\n    hue: (rotation: number) => {\n      rotation = (rotation || 0) / 180 * Math.PI;\n      const cos = Math.cos(rotation);\n      const sin = Math.sin(rotation);\n      const lumR = 0.213;\n      const lumG = 0.715;\n      const lumB = 0.072;\n      filter.colorMatrix([\n        lumR + cos * (1 - lumR) + sin * (-lumR), lumG + cos * (-lumG) + sin * (-lumG), lumB + cos * (-lumB) + sin * (1 - lumB), 0, 0,\n        lumR + cos * (-lumR) + sin * (0.143), lumG + cos * (1 - lumG) + sin * (0.140), lumB + cos * (-lumB) + sin * (-0.283), 0, 0,\n        lumR + cos * (-lumR) + sin * (-(1 - lumR)), lumG + cos * (-lumG) + sin * (lumG), lumB + cos * (1 - lumB) + sin * (lumB), 0, 0,\n        0, 0, 0, 1, 0,\n      ]);\n    },\n\n    desaturateLuminance: () => {\n      filter.colorMatrix([\n        0.2764723, 0.9297080, 0.0938197, 0, -37.1,\n        0.2764723, 0.9297080, 0.0938197, 0, -37.1,\n        0.2764723, 0.9297080, 0.0938197, 0, -37.1,\n        0, 0, 0, 1, 0,\n      ]);\n    },\n\n    sepia: () => {\n      filter.colorMatrix([\n        0.393, 0.7689999, 0.18899999, 0, 0,\n        0.349, 0.6859999, 0.16799999, 0, 0,\n        0.272, 0.5339999, 0.13099999, 0, 0,\n        0, 0, 0, 1, 0,\n      ]);\n    },\n\n    brownie: () => {\n      filter.colorMatrix([\n        0.5997023498159715, 0.34553243048391263, -0.2708298674538042, 0, 47.43192855600873,\n        -0.037703249837783157, 0.8609577587992641, 0.15059552388459913, 0, -36.96841498319127,\n        0.24113635128153335, -0.07441037908422492, 0.44972182064877153, 0, -7.562075277591283,\n        0, 0, 0, 1, 0,\n      ]);\n    },\n\n    vintagePinhole: () => {\n      filter.colorMatrix([\n        0.6279345635605994, 0.3202183420819367, -0.03965408211312453, 0, 9.651285835294123,\n        0.02578397704808868, 0.6441188644374771, 0.03259127616149294, 0, 7.462829176470591,\n        0.0466055556782719, -0.0851232987247891, 0.5241648018700465, 0, 5.159190588235296,\n        0, 0, 0, 1, 0,\n      ]);\n    },\n\n    kodachrome: () => {\n      filter.colorMatrix([\n        1.1285582396593525, -0.3967382283601348, -0.03992559172921793, 0, 63.72958762196502,\n        -0.16404339962244616, 1.0835251566291304, -0.05498805115633132, 0, 24.732407896706203,\n        -0.16786010706155763, -0.5603416277695248, 1.6014850761964943, 0, 35.62982807460946,\n        0, 0, 0, 1, 0,\n      ]);\n    },\n\n    technicolor: () => {\n      filter.colorMatrix([\n        1.9125277891456083, -0.8545344976951645, -0.09155508482755585, 0, 11.793603434377337,\n        -0.3087833385928097, 1.7658908555458428, -0.10601743074722245, 0, -70.35205161461398,\n        -0.231103377548616, -0.7501899197440212, 1.847597816108189, 0, 30.950940869491138,\n        0, 0, 0, 1, 0,\n      ]);\n    },\n\n    polaroid: () => {\n      filter.colorMatrix([\n        1.438, -0.062, -0.062, 0, 0,\n        -0.122, 1.378, -0.122, 0, 0,\n        -0.016, -0.016, 1.483, 0, 0,\n        0, 0, 0, 1, 0,\n      ]);\n    },\n\n    shiftToBGR: () => {\n      filter.colorMatrix([\n        0, 0, 1, 0, 0,\n        0, 1, 0, 0, 0,\n        1, 0, 0, 0, 0,\n        0, 0, 0, 1, 0,\n      ]);\n    },\n\n    convolution: (matrix: number[]) => { // general convolution Filter\n      const m = new Float32Array(matrix);\n      const pixelSizeX = 1 / fxcanvas.width;\n      const pixelSizeY = 1 / fxcanvas.height;\n      const program = compileShader(shaders.convolution);\n      if (!program) return;\n      gl.uniform1fv(program.uniform['m'], m);\n      gl.uniform2f(program.uniform['px'], pixelSizeX, pixelSizeY);\n      draw();\n    },\n\n    detectEdges: () => {\n      // @ts-ignore this\n      filter.convolution.call(this, [\n        0, 1, 0,\n        1, -4, 1,\n        0, 1, 0,\n      ]);\n    },\n\n    sobelX: () => {\n      // @ts-ignore this\n      filter.convolution.call(this, [\n        -1, 0, 1,\n        -2, 0, 2,\n        -1, 0, 1,\n      ]);\n    },\n\n    sobelY: () => {\n      // @ts-ignore this\n      filter.convolution.call(this, [\n        -1, -2, -1,\n        0, 0, 0,\n        1, 2, 1,\n      ]);\n    },\n\n    sharpen: (amount) => {\n      const a = amount || 1;\n      // @ts-ignore this\n      filter.convolution.call(this, [\n        0, -1 * a, 0,\n        -1 * a, 1 + 4 * a, -1 * a,\n        0, -1 * a, 0,\n      ]);\n    },\n\n    emboss: (size: number) => {\n      const s = size || 1;\n      // @ts-ignore this\n      filter.convolution.call(this, [\n        -2 * s, -1 * s, 0,\n        -1 * s, 1, 1 * s,\n        0, 1 * s, 2 * s,\n      ]);\n    },\n\n    blur: (size: number) => {\n      const blurSizeX = (size / 7) / fxcanvas.width;\n      const blurSizeY = (size / 7) / fxcanvas.height;\n      const program = compileShader(shaders.blur);\n      if (!program) return;\n      // Vertical\n      gl.uniform2f(program.uniform['px'], 0, blurSizeY);\n      draw(DRAW.INTERMEDIATE);\n      // Horizontal\n      gl.uniform2f(program.uniform['px'], blurSizeX, 0);\n      draw();\n    },\n\n    pixelate: (size: number) => {\n      const blurSizeX = (size) / fxcanvas.width;\n      const blurSizeY = (size) / fxcanvas.height;\n      const program = compileShader(shaders.pixelate);\n      if (!program) return;\n      gl.uniform2f(program.uniform['size'], blurSizeX, blurSizeY);\n      draw();\n    },\n  };\n\n  // @ts-ignore this\n  this.add = function (name) {\n    const args = Array.prototype.slice.call(arguments, 1); // eslint-disable-line prefer-rest-params\n    const func = filter[name];\n    filterChain.push({ func, args });\n  };\n\n  // @ts-ignore this\n  this.reset = function () {\n    filterChain = [];\n  };\n\n  // @ts-ignore this\n  this.get = function () {\n    return filterChain;\n  };\n\n  // @ts-ignore this\n  this.apply = function (image) {\n    resize(image.width, image.height);\n    drawCount = 0;\n    if (!sourceTexture) sourceTexture = gl.createTexture(); // Create the texture for the input image if we haven't yet\n    gl.bindTexture(gl.TEXTURE_2D, sourceTexture);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_S, gl.CLAMP_TO_EDGE);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_T, gl.CLAMP_TO_EDGE);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MIN_FILTER, gl.NEAREST);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MAG_FILTER, gl.NEAREST);\n    gl.texImage2D(gl.TEXTURE_2D, 0, gl.RGBA, gl.RGBA, gl.UNSIGNED_BYTE, image);\n    for (let i = 0; i < filterChain.length; i++) {\n      lastInChain = (i === filterChain.length - 1);\n      const f = filterChain[i];\n      // @ts-ignore function assigment\n      f.func.apply(this, f.args || []);\n    }\n    return fxcanvas;\n  };\n\n  // @ts-ignore this\n  this.draw = function (image) {\n    this.add('brightness', 0);\n    return this.apply(image);\n  };\n}\n", "/**\n * Image enhancements\n */\n\nimport * as tf from '../../dist/tfjs.esm.js';\nimport type { Tensor } from '../exports';\n\nexport async function histogramEqualization(inputImage: Tensor): Promise<Tensor> {\n  // const maxValue = 254; // using 255 results in values slightly larger than 1 due to math rounding errors\n  const squeeze = inputImage.shape.length === 4 ? tf.squeeze(inputImage) : inputImage;\n  const channels = tf.split(squeeze, 3, 2);\n  const min: Tensor[] = [tf.min(channels[0]), tf.min(channels[1]), tf.min(channels[2])];\n  const max: Tensor[] = [tf.max(channels[0]), tf.max(channels[1]), tf.max(channels[2])];\n  const absMax = await Promise.all(max.map((channel) => channel.data()));\n  const maxValue = 0.99 * Math.max(absMax[0][0], absMax[1][0], absMax[2][0]);\n  const sub = [tf.sub(channels[0], min[0]), tf.sub(channels[1], min[1]), tf.sub(channels[2], min[2])];\n  const range = [tf.sub(max[0], min[0]), tf.sub(max[1], min[1]), tf.sub(max[2], min[2])];\n  const fact = [tf.div(maxValue, range[0]), tf.div(maxValue, range[1]), tf.div(maxValue, range[2])];\n  const enh = [tf.mul(sub[0], fact[0]), tf.mul(sub[1], fact[1]), tf.mul(sub[2], fact[2])];\n  const rgb = tf.stack([enh[0], enh[1], enh[2]], 2);\n  const reshape = tf.reshape(rgb, [1, squeeze.shape[0], squeeze.shape[1], 3]);\n  tf.dispose([...channels, ...min, ...max, ...sub, ...range, ...fact, ...enh, rgb, squeeze]);\n  return reshape as Tensor; // output shape is [1, height, width, 3]\n}\n", "/**\n * Image Processing algorithm implementation\n */\n\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as fxImage from './imagefx';\nimport type { Input, AnyCanvas, Tensor, Config } from '../exports';\nimport { env } from '../util/env';\nimport { log } from '../util/util';\nimport * as enhance from './enhance';\n\nconst maxSize = 3840;\n// internal temp canvases\nlet inCanvas: AnyCanvas | null = null; // use global variable to avoid recreating canvas on each frame\nlet outCanvas: AnyCanvas | null = null; // use global variable to avoid recreating canvas on each frame\nlet tmpCanvas: AnyCanvas | null = null; // use global variable to avoid recreating canvas on each frame\n// @ts-ignore // imagefx is js module that should be converted to a class\nlet fx: fxImage.GLImageFilter | null; // instance of imagefx\n\nconst last: { inputSum: number, cacheDiff: number, sumMethod: number, inputTensor: undefined | Tensor } = {\n  inputSum: 0,\n  cacheDiff: 1,\n  sumMethod: 0,\n  inputTensor: undefined,\n};\n\nexport function reset() {\n  last.inputSum = 0;\n  last.cacheDiff = 1;\n  last.sumMethod = 0;\n  last.inputTensor = undefined;\n}\n\nexport function canvas(width: number, height: number): AnyCanvas {\n  let c: AnyCanvas;\n  if (env.browser) { // browser defines canvas object\n    if (env.worker) { // if runing in web  worker use OffscreenCanvas\n      if (typeof OffscreenCanvas === 'undefined') throw new Error('canvas error: attempted to run in web worker but OffscreenCanvas is not supported');\n      c = new OffscreenCanvas(width, height);\n    } else { // otherwise use DOM canvas\n      if (typeof document === 'undefined') throw new Error('canvas error: attempted to run in browser but DOM is not defined');\n      c = document.createElement('canvas');\n      c.width = width;\n      c.height = height;\n    }\n  } else { // if not running in browser, there is no \"default\" canvas object, so we need monkey patch or fail\n    // @ts-ignore // env.canvas is an external monkey-patch\n    if (typeof env.Canvas !== 'undefined') c = new env.Canvas(width, height);\n    else if (typeof globalThis.Canvas !== 'undefined') c = new globalThis.Canvas(width, height);\n    // else throw new Error('canvas error: attempted to use canvas in nodejs without canvas support installed');\n  }\n  // @ts-ignore its either defined or we already threw an error\n  return c;\n}\n\n// helper function to copy canvas from input to output\nexport function copy(input: AnyCanvas, output?: AnyCanvas) {\n  const outputCanvas = output || canvas(input.width, input.height);\n  const ctx = outputCanvas.getContext('2d') as CanvasRenderingContext2D;\n  ctx.drawImage(input, 0, 0);\n  return outputCanvas;\n}\n\n// process input image and return tensor\n// input can be tensor, imagedata, htmlimageelement, htmlvideoelement\n// input is resized and run through imagefx filter\nexport async function process(input: Input, config: Config, getTensor: boolean = true): Promise<{ tensor: Tensor | null, canvas: AnyCanvas | null }> {\n  if (!input) {\n    // throw new Error('input is missing');\n    if (config.debug) log('input error: input is missing');\n    return { tensor: null, canvas: null }; // video may become temporarily unavailable due to onresize\n  }\n  // sanity checks since different browsers do not implement all dom elements\n  if (\n    !(input instanceof tf.Tensor)\n    && !(typeof Image !== 'undefined' && input instanceof Image)\n    && !(typeof env.Canvas !== 'undefined' && input instanceof env.Canvas)\n    && !(typeof globalThis.Canvas !== 'undefined' && input instanceof globalThis.Canvas)\n    && !(typeof ImageData !== 'undefined' && input instanceof ImageData)\n    && !(typeof ImageBitmap !== 'undefined' && input instanceof ImageBitmap)\n    && !(typeof HTMLImageElement !== 'undefined' && input instanceof HTMLImageElement)\n    && !(typeof HTMLMediaElement !== 'undefined' && input instanceof HTMLMediaElement)\n    && !(typeof HTMLVideoElement !== 'undefined' && input instanceof HTMLVideoElement)\n    && !(typeof HTMLCanvasElement !== 'undefined' && input instanceof HTMLCanvasElement)\n    && !(typeof OffscreenCanvas !== 'undefined' && input instanceof OffscreenCanvas)\n  ) {\n    throw new Error('input error: type is not recognized');\n  }\n  if (input instanceof tf.Tensor) { // if input is tensor use as-is without filters but correct shape as needed\n    let tensor: Tensor | null = null;\n    if ((input as Tensor)['isDisposedInternal']) throw new Error('input error: attempted to use tensor but it is disposed');\n    if (!(input as Tensor).shape) throw new Error('input error: attempted to use tensor without a shape');\n    if ((input as Tensor).shape.length === 3) { // [height, width, 3 || 4]\n      if ((input as Tensor).shape[2] === 3) { // [height, width, 3] so add batch\n        tensor = tf.expandDims(input, 0);\n      } else if ((input as Tensor).shape[2] === 4) { // [height, width, 4] so strip alpha and add batch\n        const rgb = tf.slice3d(input, [0, 0, 0], [-1, -1, 3]);\n        tensor = tf.expandDims(rgb, 0);\n        tf.dispose(rgb);\n      }\n    } else if ((input as Tensor).shape.length === 4) { // [1, width, height, 3 || 4]\n      if ((input as Tensor).shape[3] === 3) { // [1, width, height, 3] just clone\n        tensor = tf.clone(input);\n      } else if ((input as Tensor).shape[3] === 4) { // [1, width, height, 4] so strip alpha\n        tensor = tf.slice4d(input, [0, 0, 0, 0], [-1, -1, -1, 3]);\n      }\n    }\n    // at the end shape must be [1, height, width, 3]\n    if (tensor == null || tensor.shape.length !== 4 || tensor.shape[0] !== 1 || tensor.shape[3] !== 3) throw new Error(`input error: attempted to use tensor with unrecognized shape: ${((input as Tensor).shape).toString()}`);\n    if ((tensor).dtype === 'int32') {\n      const cast = tf.cast(tensor, 'float32');\n      tf.dispose(tensor);\n      tensor = cast;\n    }\n    return { tensor, canvas: (config.filter.return ? outCanvas : null) };\n  }\n  // check if resizing will be needed\n  if (typeof input['readyState'] !== 'undefined' && (input as HTMLMediaElement).readyState <= 2) {\n    if (config.debug) log('input stream is not ready');\n    return { tensor: null, canvas: inCanvas }; // video may become temporarily unavailable due to onresize\n  }\n  const originalWidth: number = input['naturalWidth'] || input['videoWidth'] || input['width'] || (input['shape'] && (input['shape'][1] > 0));\n  const originalHeight: number = input['naturalHeight'] || input['videoHeight'] || input['height'] || (input['shape'] && (input['shape'][2] > 0));\n  if (!originalWidth || !originalHeight) {\n    if (config.debug) log('cannot determine input dimensions');\n    return { tensor: null, canvas: inCanvas }; // video may become temporarily unavailable due to onresize\n  }\n  let targetWidth: number = originalWidth;\n  let targetHeight: number = originalHeight;\n  if (targetWidth > maxSize) {\n    targetWidth = maxSize;\n    targetHeight = Math.trunc(targetWidth * originalHeight / originalWidth);\n  }\n  if (targetHeight > maxSize) {\n    targetHeight = maxSize;\n    targetWidth = Math.trunc(targetHeight * originalWidth / originalHeight);\n  }\n\n  // create our canvas and resize it if needed\n  if ((config.filter?.width || 0) > 0) targetWidth = config.filter.width as number;\n  else if ((config.filter?.height || 0) > 0) targetWidth = originalWidth * ((config.filter.height || 0) / originalHeight);\n  if ((config.filter.height || 0) > 0) targetHeight = config.filter.height as number;\n  else if ((config.filter.width || 0) > 0) targetHeight = originalHeight * ((config.filter.width || 0) / originalWidth);\n  if (!targetWidth || !targetHeight) throw new Error('input error: cannot determine dimension');\n  if (!inCanvas || (inCanvas.width !== targetWidth) || (inCanvas.height !== targetHeight)) inCanvas = canvas(targetWidth, targetHeight);\n\n  // draw input to our canvas\n  const inCtx = inCanvas.getContext('2d') as CanvasRenderingContext2D;\n  if ((typeof ImageData !== 'undefined') && (input instanceof ImageData)) {\n    inCtx.putImageData(input, 0, 0);\n  } else {\n    if (config.filter.flip && typeof inCtx.translate !== 'undefined') {\n      inCtx.translate(originalWidth, 0);\n      inCtx.scale(-1, 1);\n      inCtx.drawImage(input as AnyCanvas, 0, 0, originalWidth, originalHeight, 0, 0, inCanvas.width, inCanvas.height);\n      inCtx.setTransform(1, 0, 0, 1, 0, 0); // resets transforms to defaults\n    } else {\n      inCtx.drawImage(input as AnyCanvas, 0, 0, originalWidth, originalHeight, 0, 0, inCanvas.width, inCanvas.height);\n    }\n  }\n\n  if (!outCanvas || (inCanvas.width !== outCanvas.width) || (inCanvas.height !== outCanvas.height)) outCanvas = canvas(inCanvas.width, inCanvas.height); // init output canvas\n\n  // imagefx transforms using gl from input canvas to output canvas\n  if (config.filter.enabled && env.webgl.supported) {\n    if (!fx) fx = env.browser ? new fxImage.GLImageFilter() : null; // && (typeof document !== 'undefined')\n    env.filter = !!fx;\n    if (!fx?.add) {\n      if (config.debug) log('input process error: cannot initialize filters');\n      env.webgl.supported = false;\n      config.filter.enabled = false;\n      copy(inCanvas, outCanvas); // filter failed to initialize\n      // return { tensor: null, canvas: inCanvas };\n    } else {\n      fx.reset();\n      if (config.filter.brightness !== 0) fx.add('brightness', config.filter.brightness);\n      if (config.filter.contrast !== 0) fx.add('contrast', config.filter.contrast);\n      if (config.filter.sharpness !== 0) fx.add('sharpen', config.filter.sharpness);\n      if (config.filter.blur !== 0) fx.add('blur', config.filter.blur);\n      if (config.filter.saturation !== 0) fx.add('saturation', config.filter.saturation);\n      if (config.filter.hue !== 0) fx.add('hue', config.filter.hue);\n      if (config.filter.negative) fx.add('negative');\n      if (config.filter.sepia) fx.add('sepia');\n      if (config.filter.vintage) fx.add('brownie');\n      if (config.filter.sepia) fx.add('sepia');\n      if (config.filter.kodachrome) fx.add('kodachrome');\n      if (config.filter.technicolor) fx.add('technicolor');\n      if (config.filter.polaroid) fx.add('polaroid');\n      if (config.filter.pixelate !== 0) fx.add('pixelate', config.filter.pixelate);\n      if (fx.get() > 0) outCanvas = fx.apply(inCanvas);\n      else outCanvas = fx.draw(inCanvas);\n    }\n  } else {\n    copy(inCanvas, outCanvas); // if no filters applied, output canvas is input canvas\n    if (fx) fx = null;\n    env.filter = !!fx;\n  }\n\n  if (!getTensor) return { tensor: null, canvas: outCanvas }; // just canvas was requested\n  if (!outCanvas) throw new Error('canvas error: cannot create output');\n\n  // create tensor from image unless input was a tensor already\n  let pixels;\n  let depth = 3;\n  if ((typeof ImageData !== 'undefined' && input instanceof ImageData) || ((input as ImageData).data && (input as ImageData).width && (input as ImageData).height)) { // if input is imagedata, just use it\n    if (env.browser && tf.browser) {\n      pixels = tf.browser ? tf.browser.fromPixels(input) : null;\n    } else {\n      depth = (input as ImageData).data.length / (input as ImageData).height / (input as ImageData).width;\n      // const arr = Uint8Array.from(input['data']);\n      const arr = new Uint8Array((input as ImageData).data.buffer);\n      pixels = tf.tensor(arr, [(input as ImageData).height, (input as ImageData).width, depth], 'int32');\n    }\n  } else {\n    if (!tmpCanvas || (outCanvas.width !== tmpCanvas.width) || (outCanvas.height !== tmpCanvas.height)) tmpCanvas = canvas(outCanvas.width, outCanvas.height); // init output canvas\n    if (tf.browser && env.browser) {\n      if (config.backend === 'webgl' || config.backend === 'humangl' || config.backend === 'webgpu') {\n        pixels = tf.browser.fromPixels(outCanvas); // safe to reuse since both backend and context are gl based\n      } else {\n        tmpCanvas = copy(outCanvas); // cannot use output canvas as it already has gl context so we do a silly one more canvas\n        pixels = tf.browser.fromPixels(tmpCanvas);\n      }\n    } else {\n      const tempCanvas = copy(outCanvas); // cannot use output canvas as it already has gl context so we do a silly one more canvas\n      const tempCtx = tempCanvas.getContext('2d') as CanvasRenderingContext2D;\n      const tempData = tempCtx.getImageData(0, 0, targetWidth, targetHeight);\n      depth = tempData.data.length / targetWidth / targetHeight;\n      const arr = new Uint8Array(tempData.data.buffer);\n      pixels = tf.tensor(arr, [targetWidth, targetHeight, depth]);\n    }\n  }\n  if (depth === 4) { // rgba to rgb\n    const rgb = tf.slice3d(pixels, [0, 0, 0], [-1, -1, 3]); // strip alpha channel\n    tf.dispose(pixels);\n    pixels = rgb;\n  }\n  if (!pixels) throw new Error('input error: cannot create tensor');\n  const casted: Tensor = tf.cast(pixels, 'float32');\n  const tensor: Tensor = config.filter.equalization ? await enhance.histogramEqualization(casted) : tf.expandDims(casted, 0);\n  tf.dispose([pixels, casted]);\n  return { tensor, canvas: (config.filter.return ? outCanvas : null) };\n}\n\n/*\nconst checksum = async (input: Tensor): Promise<number> => { // use tf sum or js based sum loop depending on which is faster\n  const resizeFact = 48;\n  const reduced: Tensor = tf.image.resizeBilinear(input, [Math.trunc((input.shape[1] || 1) / resizeFact), Math.trunc((input.shape[2] || 1) / resizeFact)]);\n  const tfSum = async (): Promise<number> => {\n    const sumT = tf.sum(reduced);\n    const sum0 = await sumT.data();\n    tf.dispose(sumT);\n    return sum0[0];\n  };\n  const jsSum = async (): Promise<number> => {\n    const reducedData = await reduced.data(); // raw image rgb array\n    let sum0 = 0;\n    for (let i = 0; i < reducedData.length / 3; i++) sum0 += reducedData[3 * i + 2]; // look only at green value of each pixel\n    return sum0;\n  };\n  if (last.sumMethod === 0) {\n    const t0 = now();\n    await jsSum();\n    const t1 = now();\n    await tfSum();\n    const t2 = now();\n    last.sumMethod = t1 - t0 < t2 - t1 ? 1 : 2;\n  }\n  const res = last.sumMethod === 1 ? await jsSum() : await tfSum();\n  tf.dispose(reduced);\n  return res;\n};\n*/\n\nexport async function skip(config: Partial<Config>, input: Tensor) {\n  let skipFrame = false;\n  if (config.cacheSensitivity === 0 || !input.shape || input.shape.length !== 4 || input.shape[1] > 2048 || input.shape[2] > 2048) return skipFrame; // cache disabled or input is invalid or too large for cache analysis\n\n  /*\n  const checkSum = await checksum(input);\n  const diff = 100 * (Math.max(checkSum, last.inputSum) / Math.min(checkSum, last.inputSum) - 1);\n  last.inputSum = checkSum;\n  // if previous frame was skipped, skip this frame if changed more than cacheSensitivity\n  // if previous frame was not skipped, then look for cacheSensitivity or difference larger than one in previous frame to avoid resetting cache in subsequent frames unnecessarily\n  let skipFrame = diff < Math.max(config.cacheSensitivity, last.cacheDiff);\n  // if difference is above 10x threshold, don't use last value to force reset cache for significant change of scenes or images\n  last.cacheDiff = diff > 10 * config.cacheSensitivity ? 0 : diff;\n  skipFrame = skipFrame && (last.cacheDiff > 0); // if no cached diff value then force no skip\n  */\n\n  if (!last.inputTensor) {\n    last.inputTensor = tf.clone(input);\n  } else if (last.inputTensor.shape[1] !== input.shape[1] || last.inputTensor.shape[2] !== input.shape[2]) { // input resolution changed\n    tf.dispose(last.inputTensor);\n    last.inputTensor = tf.clone(input);\n  } else {\n    const t: Record<string, Tensor> = {};\n    t.diff = tf.sub(input, last.inputTensor);\n    t.squared = tf.mul(t.diff, t.diff);\n    t.sum = tf.sum(t.squared);\n    const diffSum = await t.sum.data();\n    const diffRelative = diffSum[0] / (input.shape[1] || 1) / (input.shape[2] || 1) / 255 / 3; // squared difference relative to input resolution and averaged per channel\n    tf.dispose([last.inputTensor, t.diff, t.squared, t.sum]);\n    last.inputTensor = tf.clone(input);\n    skipFrame = diffRelative <= (config.cacheSensitivity || 0);\n  }\n  return skipFrame;\n}\n\nexport async function compare(config: Partial<Config>, input1: Tensor, input2: Tensor): Promise<number> {\n  const t: Record<string, Tensor> = {};\n  if (!input1 || !input2 || input1.shape.length !== 4 || input1.shape.length !== input2.shape.length) {\n    if (!config.debug) log('invalid input tensor or tensor shapes do not match:', input1.shape, input2.shape);\n    return 0;\n  }\n  if (input1.shape[0] !== 1 || input2.shape[0] !== 1 || input1.shape[3] !== 3 || input2.shape[3] !== 3) {\n    if (!config.debug) log('input tensors must be of shape [1, height, width, 3]:', input1.shape, input2.shape);\n    return 0;\n  }\n  t.input1 = tf.clone(input1);\n  t.input2 = (input1.shape[1] !== input2.shape[1] || input1.shape[2] !== input2.shape[2]) ? tf.image.resizeBilinear(input2, [input1.shape[1], input1.shape[2]]) : tf.clone(input2);\n  t.diff = tf.sub(t.input1, t.input2);\n  t.squared = tf.mul(t.diff, t.diff);\n  t.sum = tf.sum(t.squared);\n  const diffSum = await t.sum.data();\n  const diffRelative = diffSum[0] / (input1.shape[1] || 1) / (input1.shape[2] || 1) / 255 / 3;\n  tf.dispose([t.input1, t.input2, t.diff, t.squared, t.sum]);\n  return diffRelative;\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\nimport * as image from '../image/image';\n\n/** Env class that holds detected capabilities */\nexport class Env {\n  /** Running in Browser */\n  browser: boolean;\n  /** Running in NodeJS */\n  node: boolean;\n  /** Running in WebWorker thread */\n  worker: boolean;\n  /** Detected platform */\n  platform: string = '';\n  /** Detected agent */\n  agent: string = '';\n  /** List of supported backends */\n  backends: string[] = [];\n  /** Has any work been performed so far */\n  initial: boolean;\n  /** Are image filters supported? */\n  filter: boolean | undefined;\n  /** TFJS instance details */\n  tfjs: {\n    version: undefined | string,\n  };\n  /** Is offscreenCanvas supported? */\n  offscreen: undefined | boolean;\n  /** Are performance counter instant values or additive */\n  perfadd: boolean = false;\n  /** If using tfjs-node get version of underlying tensorflow shared library and if gpu acceleration is enabled */\n  tensorflow: {\n    version: undefined | string,\n    gpu: undefined | boolean,\n  } = {\n      version: undefined,\n      gpu: undefined,\n    };\n  /** WASM detected capabilities */\n  wasm: {\n    supported: undefined | boolean,\n    backend: undefined | boolean,\n    simd: undefined | boolean,\n    multithread: undefined | boolean,\n  } = {\n      supported: undefined,\n      backend: undefined,\n      simd: undefined,\n      multithread: undefined,\n    };\n  /** WebGL detected capabilities */\n  webgl: {\n    supported: undefined | boolean,\n    backend: undefined | boolean,\n    version: undefined | string,\n    renderer: undefined | string,\n  } = {\n      supported: undefined,\n      backend: undefined,\n      version: undefined,\n      renderer: undefined,\n    };\n  /** WebGPU detected capabilities */\n  webgpu: {\n    supported: undefined | boolean,\n    backend: undefined | boolean,\n    adapter: undefined | string,\n  } = {\n      supported: undefined,\n      backend: undefined,\n      adapter: undefined,\n    };\n  /** CPU info */\n  cpu: {\n    model: undefined | string,\n    flags: string[],\n  } = {\n      model: undefined,\n      flags: [],\n    };\n  /** List of supported kernels for current backend */\n  kernels: string[] = [];\n  /** MonkeyPatch for Canvas */\n  Canvas: undefined;\n  /** MonkeyPatch for Image */\n  Image: undefined;\n  /** MonkeyPatch for ImageData */\n  ImageData: undefined;\n\n  constructor() {\n    this.browser = typeof navigator !== 'undefined';\n    this.node = (typeof process !== 'undefined') && (typeof process.versions !== 'undefined') && (typeof process.versions.node !== 'undefined');\n    this.tfjs = { version: tf.version['tfjs-core'] };\n    this.offscreen = typeof OffscreenCanvas !== 'undefined';\n    this.initial = true;\n\n    // @ts-ignore WorkerGlobalScope evaluated in browser only\n    this.worker = this.browser && this.offscreen ? (typeof WorkerGlobalScope !== 'undefined') : undefined;\n    if (typeof navigator !== 'undefined') {\n      const raw = navigator.userAgent.match(/\\(([^()]+)\\)/g);\n      if (raw?.[0]) {\n        const platformMatch = raw[0].match(/\\(([^()]+)\\)/g);\n        this.platform = (platformMatch?.[0]) ? platformMatch[0].replace(/\\(|\\)/g, '') : '';\n        this.agent = navigator.userAgent.replace(raw[0], '');\n        if (this.platform[1]) this.agent = this.agent.replace(raw[1], '');\n        this.agent = this.agent.replace(/  /g, ' ');\n        // chrome offscreencanvas gpu memory leak\n        /*\n        const isChrome = env.agent.match(/Chrome\\/.[0-9]/g);\n        const verChrome = isChrome && isChrome[0] ? isChrome[0].split('/')[1] : 0;\n        if (verChrome > 92 && verChrome < 96) {\n          log('disabling offscreenCanvas due to browser error:', isChrome ? isChrome[0] : 'unknown');\n          this.offscreen = false;\n        }\n        */\n      }\n    } else if (typeof process !== 'undefined') {\n      this.platform = `${process.platform} ${process.arch}`;\n      this.agent = `NodeJS ${process.version}`;\n    }\n  }\n\n  /** update backend information */\n  async updateBackend() {\n    // analyze backends\n    this.backends = Object.keys(tf.engine().registryFactory);\n    this.tensorflow = {\n      version: (tf.backend().binding ? tf.backend().binding.TF_Version : undefined),\n      gpu: (tf.backend().binding ? tf.backend().binding.isUsingGpuDevice() : undefined),\n    };\n    this.wasm.supported = typeof WebAssembly !== 'undefined';\n    this.wasm.backend = this.backends.includes('wasm');\n    if (this.wasm.supported && this.wasm.backend && tf.getBackend() === 'wasm') {\n      this.wasm.simd = tf.env().get('WASM_HAS_SIMD_SUPPORT');\n      this.wasm.multithread = tf.env().get('WASM_HAS_MULTITHREAD_SUPPORT');\n    }\n    const c = image.canvas(100, 100);\n    const ctx = c ? c.getContext('webgl2') : undefined; // causes too many gl contexts\n    // const ctx = typeof tf.backend().getGPGPUContext !== undefined ? tf.backend().getGPGPUContext : null;\n    this.webgl.supported = typeof ctx !== 'undefined';\n    this.webgl.backend = this.backends.includes('webgl');\n    if (this.webgl.supported && this.webgl.backend && (tf.getBackend() === 'webgl' || tf.getBackend() === 'humangl')) {\n      const gl = tf.backend().gpgpu !== 'undefined' ? await tf.backend().getGPGPUContext().gl : null;\n      if (gl) {\n        this.webgl.version = gl.getParameter(gl.VERSION);\n        this.webgl.renderer = gl.getParameter(gl.RENDERER);\n      }\n    }\n    this.webgpu.supported = this.browser && typeof navigator.gpu !== 'undefined';\n    this.webgpu.backend = this.backends.includes('webgpu');\n    try {\n      if (this.webgpu.supported) {\n        const adapter = await navigator.gpu.requestAdapter();\n        this.webgpu.adapter = adapter ? adapter.name : undefined;\n      }\n    } catch {\n      this.webgpu.supported = false;\n    }\n    try {\n      this.kernels = tf.getKernelsForBackend(tf.getBackend()).map((kernel) => (kernel.kernelName as string).toLowerCase());\n    } catch { /**/ }\n  }\n\n  /** update cpu information */\n  updateCPU() {\n    const cpu = { model: '', flags: [] };\n    if (this.node && this.platform.startsWith('linux')) {\n      /*\n      const fs = require('fs');\n      try {\n        const data = fs.readFileSync('/proc/cpuinfo').toString();\n        for (const line of data.split('\\n')) {\n          if (line.startsWith('model name')) cpu.model = line.match(/:(.*)/g)[0].replace(':', '').trim();\n          if (line.startsWith('flags')) cpu.flags = line.match(/:(.*)/g)[0].replace(':', '').trim().split(' ').sort();\n        }\n      } catch { }\n      */\n    }\n    if (!this.cpu) Object.defineProperty(this, 'cpu', { value: cpu });\n    else this.cpu = cpu;\n  }\n}\n\nexport const env = new Env();\n", "import { log } from './util';\n\n// const log = (...msg) => console.log('webcam', ...msg); // eslint-disable-line no-console\n\n/** WebCam configuration */\nexport interface WebCamConfig {\n  /**\n   * element can be:\n   * - string which indicates dom element id\n   * - actual HTMLVideo dom element\n   * - undefined in which case a new HTMLVideoElement will be created\n   */\n  element: string | HTMLVideoElement | undefined,\n  /** print messages on console */\n  debug: boolean,\n  /** use front or back camera */\n  mode: 'front' | 'back',\n  /** camera crop mode */\n  crop: boolean,\n  /** desired webcam width */\n  width: number,\n  /** desired webcam height */\n  height: number,\n}\n\nexport class WebCam { // eslint-disable-line @typescript-eslint/no-extraneous-class\n  /** current webcam configuration */\n  config: WebCamConfig;\n  /** instance of dom element associated with webcam stream */\n  element: HTMLVideoElement | undefined;\n  /** active webcam stream */\n  stream: MediaStream | undefined;\n\n  constructor() {\n    this.config = {\n      element: undefined,\n      debug: true,\n      mode: 'front',\n      crop: false,\n      width: 0,\n      height: 0,\n    };\n  }\n\n  /** get active webcam stream track */\n  public get track(): MediaStreamTrack | undefined {\n    if (!this.stream) return undefined;\n    return this.stream.getVideoTracks()[0];\n  }\n\n  /** get webcam capabilities */\n  public get capabilities(): MediaTrackCapabilities | undefined {\n    if (!this.track) return undefined;\n    return this.track.getCapabilities ? this.track.getCapabilities() : undefined;\n  }\n\n  /** get webcam constraints */\n  public get constraints(): MediaTrackConstraints | undefined {\n    if (!this.track) return undefined;\n    return this.track.getConstraints ? this.track.getConstraints() : undefined;\n  }\n\n  /** get webcam settings */\n  public get settings(): MediaTrackSettings | undefined {\n    if (!this.stream) return undefined;\n    const track: MediaStreamTrack = this.stream.getVideoTracks()[0];\n    return track.getSettings ? track.getSettings() : undefined;\n  }\n\n  /** get webcam label */\n  public get label(): string {\n    if (!this.track) return '';\n    return this.track.label;\n  }\n\n  /** is webcam paused */\n  public get paused(): boolean {\n    return this.element?.paused || false;\n  }\n\n  /** webcam current width */\n  public get width(): number {\n    return this.element?.videoWidth || 0;\n  }\n\n  /** webcam current height */\n  public get height(): number {\n    return this.element?.videoHeight || 0;\n  }\n\n  /** start method initializizes webcam stream and associates it with a dom video element */\n  public start = async (webcamConfig?: Partial<WebCamConfig>): Promise<void> => {\n    // set config\n    if (webcamConfig?.debug) this.config.debug = webcamConfig?.debug;\n    if (webcamConfig?.crop) this.config.crop = webcamConfig?.crop;\n    if (webcamConfig?.mode) this.config.mode = webcamConfig?.mode;\n    if (webcamConfig?.width) this.config.width = webcamConfig?.width;\n    if (webcamConfig?.height) this.config.height = webcamConfig?.height;\n\n    // use or create dom element\n    if (webcamConfig?.element) {\n      if (typeof webcamConfig.element === 'string') {\n        const el = document.getElementById(webcamConfig.element);\n        if (el && el instanceof HTMLVideoElement) {\n          this.element = el;\n        } else {\n          if (this.config.debug) log('webcam', 'cannot get dom element', webcamConfig.element);\n          return;\n        }\n      } else if (webcamConfig.element instanceof HTMLVideoElement) {\n        this.element = webcamConfig.element;\n      } else {\n        if (this.config.debug) log('webcam', 'unknown dom element', webcamConfig.element);\n        return;\n      }\n    } else {\n      this.element = document.createElement('video');\n    }\n\n    // set constraints to use\n    const requestedConstraints: DisplayMediaStreamConstraints = {\n      audio: false,\n      video: {\n        facingMode: this.config.mode === 'front' ? 'user' : 'environment',\n        // @ts-ignore // resizeMode is still not defined in tslib\n        resizeMode: this.config.crop ? 'crop-and-scale' : 'none',\n        width: { ideal: this.config.width > 0 ? this.config.width : window.innerWidth },\n        height: { ideal: this.config.height > 0 ? this.config.height : window.innerHeight },\n      },\n    };\n\n    // set default event listeners\n    this.element.addEventListener('play', () => { if (this.config.debug) log('webcam', 'play'); });\n    this.element.addEventListener('pause', () => { if (this.config.debug) log('webcam', 'pause'); });\n    this.element.addEventListener('click', async () => { // pause when clicked on screen and resume on next click\n      if (!this.element || !this.stream) return;\n      if (this.element.paused) await this.element.play();\n      else this.element.pause();\n    });\n\n    // get webcam and set it to run in dom element\n    if (!navigator?.mediaDevices) {\n      if (this.config.debug) log('webcam', 'no devices');\n      return;\n    }\n    try {\n      this.stream = await navigator.mediaDevices.getUserMedia(requestedConstraints); // get stream that satisfies constraints\n    } catch (err) {\n      log('webcam', err);\n      return;\n    }\n    if (!this.stream) {\n      if (this.config.debug) log('webcam', 'no stream');\n      return;\n    }\n    this.element.srcObject = this.stream; // assign it to dom element\n    const ready = new Promise((resolve) => { // wait until stream is ready\n      if (!this.element) resolve(false);\n      else this.element.onloadeddata = () => resolve(true);\n    });\n    await ready;\n    await this.element.play(); // start playing\n\n    if (this.config.debug) {\n      log('webcam', {\n        width: this.width,\n        height: this.height,\n        label: this.label,\n        stream: this.stream,\n        track: this.track,\n        settings: this.settings,\n        constraints: this.constraints,\n        capabilities: this.capabilities,\n      });\n    }\n  };\n\n  /** pause webcam video method */\n  public pause = (): void => {\n    if (this.element) this.element.pause();\n  };\n\n  /** play webcam video method */\n  public play = async (): Promise<void> => {\n    if (this.element) await this.element.play();\n  };\n\n  /** stop method stops active webcam stream track and disconnects webcam */\n  public stop = (): void => {\n    if (this.config.debug) log('webcam', 'stop');\n    if (this.track) this.track.stop();\n  };\n}\n", "/**\n * Loader and Validator for all models used by Human\n */\n\nimport { env } from './util/env';\nimport { log } from './util/util';\nimport * as gear from './gear/gear';\nimport * as ssrnetAge from './gear/ssrnet-age';\nimport * as ssrnetGender from './gear/ssrnet-gender';\nimport * as antispoof from './face/antispoof';\nimport * as blazeface from './face/blazeface';\nimport * as blazepose from './body/blazepose';\nimport * as centernet from './object/centernet';\nimport * as efficientpose from './body/efficientpose';\nimport * as emotion from './gear/emotion';\nimport * as mobilefacenet from './face/mobilefacenet';\nimport * as insightface from './face/insightface';\nimport * as facemesh from './face/facemesh';\nimport * as faceres from './face/faceres';\nimport * as handpose from './hand/handpose';\nimport * as handtrack from './hand/handtrack';\nimport * as iris from './face/iris';\nimport * as liveness from './face/liveness';\nimport * as movenet from './body/movenet';\nimport * as nanodet from './object/nanodet';\nimport * as posenet from './body/posenet';\nimport * as segmentation from './segmentation/segmentation';\nimport { modelStats, ModelInfo } from './tfjs/load';\nimport type { GraphModel } from './tfjs/types';\nimport type { Human } from './human';\n\n/** Instances of all possible TFJS Graph Models used by Human\n * - loaded as needed based on configuration\n * - initialized explictly with `human.load()` method\n * - initialized implicity on first call to `human.detect()`\n * - each model can be `null` if not loaded, instance of `GraphModel` if loaded or `Promise` if loading\n */\nexport class Models {\n  ssrnetage: null | GraphModel | Promise<GraphModel> = null;\n  gear: null | GraphModel | Promise<GraphModel> = null;\n  blazeposedetect: null | GraphModel | Promise<GraphModel> = null;\n  blazepose: null | GraphModel | Promise<GraphModel> = null;\n  centernet: null | GraphModel | Promise<GraphModel> = null;\n  efficientpose: null | GraphModel | Promise<GraphModel> = null;\n  mobilefacenet: null | GraphModel | Promise<GraphModel> = null;\n  insightface: null | GraphModel | Promise<GraphModel> = null;\n  emotion: null | GraphModel | Promise<GraphModel> = null;\n  facedetect: null | GraphModel | Promise<GraphModel> = null;\n  faceiris: null | GraphModel | Promise<GraphModel> = null;\n  facemesh: null | GraphModel | Promise<GraphModel> = null;\n  faceres: null | GraphModel | Promise<GraphModel> = null;\n  ssrnetgender: null | GraphModel | Promise<GraphModel> = null;\n  handpose: null | GraphModel | Promise<GraphModel> = null;\n  handskeleton: null | GraphModel | Promise<GraphModel> = null;\n  handtrack: null | GraphModel | Promise<GraphModel> = null;\n  liveness: null | GraphModel | Promise<GraphModel> = null;\n  movenet: null | GraphModel | Promise<GraphModel> = null;\n  nanodet: null | GraphModel | Promise<GraphModel> = null;\n  posenet: null | GraphModel | Promise<GraphModel> = null;\n  segmentation: null | GraphModel | Promise<GraphModel> = null;\n  antispoof: null | GraphModel | Promise<GraphModel> = null;\n}\n\n/** structure that holds global stats for currently loaded models */\nexport interface ModelStats {\n  numLoadedModels: number,\n  numEnabledModels: undefined,\n  numDefinedModels: number,\n  percentageLoaded: number,\n  totalSizeFromManifest: number,\n  totalSizeWeights: number,\n  totalSizeLoading: number,\n  totalSizeEnabled: undefined,\n  modelStats: ModelInfo[],\n}\n\nlet instance: Human;\n\nexport const getModelStats = (currentInstance: Human): ModelStats => {\n  if (currentInstance) instance = currentInstance;\n  if (!instance) log('instance not registred');\n  let totalSizeFromManifest = 0;\n  let totalSizeWeights = 0;\n  let totalSizeLoading = 0;\n  for (const m of Object.values(modelStats)) {\n    totalSizeFromManifest += m.sizeFromManifest;\n    totalSizeWeights += m.sizeLoadedWeights;\n    totalSizeLoading += m.sizeDesired;\n  }\n  const percentageLoaded = totalSizeLoading > 0 ? totalSizeWeights / totalSizeLoading : 0;\n  return {\n    numLoadedModels: Object.values(modelStats).length,\n    numEnabledModels: undefined,\n    numDefinedModels: Object.keys(instance.models).length,\n    percentageLoaded,\n    totalSizeFromManifest,\n    totalSizeWeights,\n    totalSizeLoading,\n    totalSizeEnabled: undefined,\n    modelStats: Object.values(modelStats),\n  };\n};\n\nexport function reset(currentInstance: Human): void {\n  if (currentInstance) instance = currentInstance;\n  // if (instance.config.debug) log('resetting loaded models');\n  for (const model of Object.keys(instance.models)) instance.models[model as keyof Models] = null;\n}\n\n/** Load method preloads all instance.configured models on-demand */\nexport async function load(currentInstance: Human): Promise<void> {\n  if (currentInstance) instance = currentInstance;\n  if (!instance) log('instance not registred');\n  if (env.initial) reset(instance);\n  if (instance.config.hand.enabled) { // handpose model is a combo that must be loaded as a whole\n    if (!instance.models.handpose && instance.config.hand.detector?.modelPath?.includes('handdetect')) {\n      [instance.models.handpose, instance.models.handskeleton] = await handpose.load(instance.config);\n    }\n    if (!instance.models.handskeleton && instance.config.hand.landmarks && instance.config.hand.detector?.modelPath?.includes('handdetect')) {\n      [instance.models.handpose, instance.models.handskeleton] = await handpose.load(instance.config);\n    }\n  }\n  if (instance.config.body.enabled && !instance.models.blazepose && instance.config.body.modelPath?.includes('blazepose')) instance.models.blazepose = blazepose.loadPose(instance.config);\n  if (instance.config.body.enabled && !instance.models.blazeposedetect && instance.config.body['detector'] && instance.config.body['detector'].modelPath) instance.models.blazeposedetect = blazepose.loadDetect(instance.config);\n  if (instance.config.body.enabled && !instance.models.efficientpose && instance.config.body.modelPath?.includes('efficientpose')) instance.models.efficientpose = efficientpose.load(instance.config);\n  if (instance.config.body.enabled && !instance.models.movenet && instance.config.body.modelPath?.includes('movenet')) instance.models.movenet = movenet.load(instance.config);\n  if (instance.config.body.enabled && !instance.models.posenet && instance.config.body.modelPath?.includes('posenet')) instance.models.posenet = posenet.load(instance.config);\n  if (instance.config.face.enabled && !instance.models.facedetect) instance.models.facedetect = blazeface.load(instance.config);\n  if (instance.config.face.enabled && instance.config.face.antispoof?.enabled && !instance.models.antispoof) instance.models.antispoof = antispoof.load(instance.config);\n  if (instance.config.face.enabled && instance.config.face.liveness?.enabled && !instance.models.liveness) instance.models.liveness = liveness.load(instance.config);\n  if (instance.config.face.enabled && instance.config.face.description?.enabled && !instance.models.faceres) instance.models.faceres = faceres.load(instance.config);\n  if (instance.config.face.enabled && instance.config.face.emotion?.enabled && !instance.models.emotion) instance.models.emotion = emotion.load(instance.config);\n  if (instance.config.face.enabled && instance.config.face.iris?.enabled && !instance.config.face.attention?.enabled && !instance.models.faceiris) instance.models.faceiris = iris.load(instance.config);\n  if (instance.config.face.enabled && instance.config.face.mesh?.enabled && (!instance.models.facemesh)) instance.models.facemesh = facemesh.load(instance.config);\n  if (instance.config.face.enabled && instance.config.face['gear']?.enabled && !instance.models.gear) instance.models.gear = gear.load(instance.config);\n  if (instance.config.face.enabled && instance.config.face['ssrnet']?.enabled && !instance.models.ssrnetage) instance.models.ssrnetage = ssrnetAge.load(instance.config);\n  if (instance.config.face.enabled && instance.config.face['ssrnet']?.enabled && !instance.models.ssrnetgender) instance.models.ssrnetgender = ssrnetGender.load(instance.config);\n  if (instance.config.face.enabled && instance.config.face['mobilefacenet']?.enabled && !instance.models.mobilefacenet) instance.models.mobilefacenet = mobilefacenet.load(instance.config);\n  if (instance.config.face.enabled && instance.config.face['insightface']?.enabled && !instance.models.insightface) instance.models.insightface = insightface.load(instance.config);\n  if (instance.config.hand.enabled && !instance.models.handtrack && instance.config.hand.detector?.modelPath?.includes('handtrack')) instance.models.handtrack = handtrack.loadDetect(instance.config);\n  if (instance.config.hand.enabled && instance.config.hand.landmarks && !instance.models.handskeleton && instance.config.hand.detector?.modelPath?.includes('handtrack')) instance.models.handskeleton = handtrack.loadSkeleton(instance.config);\n  if (instance.config.object.enabled && !instance.models.centernet && instance.config.object.modelPath?.includes('centernet')) instance.models.centernet = centernet.load(instance.config);\n  if (instance.config.object.enabled && !instance.models.nanodet && instance.config.object.modelPath?.includes('nanodet')) instance.models.nanodet = nanodet.load(instance.config);\n  if (instance.config.segmentation.enabled && !instance.models.segmentation) instance.models.segmentation = segmentation.load(instance.config);\n\n  // models are loaded in parallel asynchronously so lets wait until they are actually loaded\n  for await (const model of Object.keys(instance.models)) {\n    if (instance.models[model as keyof Models] && typeof instance.models[model as keyof Models] !== 'undefined') {\n      instance.models[model as keyof Models] = await instance.models[model as keyof Models];\n    }\n  }\n}\n\nexport interface KernelOps { name: string, url: string, missing: string[], ops: string[] }\n\nexport function validateModel(currentInstance: Human | null, model: GraphModel | null, name: string): KernelOps | null {\n  if (!model) return null;\n  if (currentInstance) instance = currentInstance;\n  if (!instance) log('instance not registred');\n  if (!instance?.config?.validateModels) return null;\n  const simpleOps = ['const', 'placeholder', 'noop', 'pad', 'squeeze', 'add', 'sub', 'mul', 'div'];\n  const ignoreOps = ['biasadd', 'fusedbatchnormv3', 'matmul'];\n  const ops: string[] = [];\n  const missing: string[] = [];\n  interface Op { name: string, category: string, op: string }\n  const url = model['modelUrl'] as string;\n  const executor = model['executor'];\n  if (executor?.graph?.nodes) {\n    for (const kernel of Object.values(executor.graph.nodes)) {\n      const op = (kernel as Op).op.toLowerCase();\n      if (!ops.includes(op)) ops.push(op);\n    }\n  } else {\n    if (!executor && instance.config.debug) {\n      log('model not loaded', name);\n    }\n  }\n  for (const op of ops) {\n    if (!simpleOps.includes(op) // exclude simple ops\n      && !ignoreOps.includes(op) // exclude specific ops\n      && !instance.env.kernels.includes(op) // check actual kernel ops\n      && !instance.env.kernels.includes(op.replace('_', '')) // check variation without _\n      && !instance.env.kernels.includes(op.replace('native', '')) // check standard variation\n      && !instance.env.kernels.includes(op.replace('v2', ''))) { // check non-versioned variation\n      missing.push(op);\n    }\n  }\n  if (instance.config.debug && missing.length > 0) log('model validation failed:', name, missing);\n  return missing.length > 0 ? { name, missing, ops, url } : null;\n}\n\nexport function validate(currentInstance: Human): { name: string, missing: string[] }[] {\n  if (currentInstance) instance = currentInstance;\n  if (!instance) log('instance not registred');\n  const missing: KernelOps[] = [];\n  for (const defined of Object.keys(currentInstance.models)) {\n    const model: GraphModel | null = currentInstance.models[defined as keyof Models] as GraphModel | null;\n    if (!model) continue;\n    const res = validateModel(currentInstance, model, defined);\n    if (res) missing.push(res);\n  }\n  return missing;\n}\n", "/**\n * GEAR [gender/emotion/age/race] model implementation\n *\n * Based on: [**GEAR Predictor**](https://github.com/Udolf15/GEAR-Predictor)\n */\n\nimport { log, now } from '../util/util';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { loadModel } from '../tfjs/load';\nimport type { Gender, Race } from '../result';\nimport type { Config } from '../config';\nimport type { GraphModel, Tensor } from '../tfjs/types';\nimport { env } from '../util/env';\n\nexport interface GearType { age: number, gender: Gender, genderScore: number, race: { score: number, race: Race }[] }\nlet model: GraphModel | null;\nconst last: GearType[] = [];\nconst raceNames = ['white', 'black', 'asian', 'indian', 'other'];\nconst ageWeights = [15, 23, 28, 35.5, 45.5, 55.5, 65];\nlet lastCount = 0;\nlet lastTime = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nexport async function load(config: Config) {\n  if (env.initial) model = null;\n  if (!model) model = await loadModel(config.face.gear?.modelPath);\n  else if (config.debug) log('cached model:', model['modelUrl']);\n  return model;\n}\n\nexport async function predict(image: Tensor, config: Config, idx: number, count: number): Promise<GearType> {\n  if (!model) return { age: 0, gender: 'unknown', genderScore: 0, race: [] };\n  const skipFrame = skipped < (config.face.gear?.skipFrames || 0);\n  const skipTime = (config.face.gear?.skipTime || 0) > (now() - lastTime);\n  if (config.skipAllowed && skipTime && skipFrame && (lastCount === count) && last[idx]) {\n    skipped++;\n    return last[idx];\n  }\n  skipped = 0;\n  return new Promise(async (resolve) => {\n    if (!model?.inputs[0].shape) return;\n    const t: Record<string, Tensor> = {};\n    // t.resize = tf.image.resizeBilinear(image, [model?.inputs[0].shape[2], model?.inputs[0].shape[1]], false);\n    const box = [[0.0, 0.10, 0.90, 0.90]]; // empyrical values for top, left, bottom, right\n    t.resize = tf.image.cropAndResize(image, box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]);\n    const obj: GearType = { age: 0, gender: 'unknown', genderScore: 0, race: [] };\n    if (config.face.gear?.enabled) [t.age, t.gender, t.race] = model.execute(t.resize, ['age_output', 'gender_output', 'race_output']) as Tensor[];\n    const gender = await t.gender.data();\n    obj.gender = gender[0] > gender[1] ? 'male' : 'female';\n    obj.genderScore = Math.round(100 * (gender[0] > gender[1] ? gender[0] : gender[1])) / 100;\n    const race = await t.race.data();\n    for (let i = 0; i < race.length; i++) {\n      if (race[i] > (config.face.gear?.minConfidence || 0.2)) obj.race.push({ score: Math.round(100 * race[i]) / 100, race: raceNames[i] as Race });\n    }\n    obj.race.sort((a, b) => b.score - a.score);\n    // {0: 'Below20', 1: '21-25', 2: '26-30', 3: '31-40',4: '41-50', 5: '51-60', 6: 'Above60'}\n    const ageDistribution = Array.from(await t.age.data());\n    const ageSorted = ageDistribution.map((a, i) => [ageWeights[i], a]).sort((a, b) => b[1] - a[1]);\n    let age = ageSorted[0][0]; // pick best starting point\n    for (let i = 1; i < ageSorted.length; i++) age += ageSorted[i][1] * (ageSorted[i][0] - age); // adjust with each other choice by weight\n    obj.age = Math.round(10 * age) / 10;\n    Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n    last[idx] = obj;\n    lastCount = count;\n    lastTime = now();\n    resolve(obj);\n  });\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\nimport type { Tensor } from './types';\n\nexport const constants: Record<string, Tensor | number | number[]> = {\n  tf255: 255.0,\n  tf1: 1.0,\n  tf2: 2.0,\n  tf05: 0.5,\n  tf127: 127.5,\n  rgb: [0.2989, 0.5870, 0.1140],\n};\n\nexport function init() {\n  constants.tf255 = tf.scalar(255.0, 'float32');\n  constants.tf1 = tf.scalar(1.0, 'float32');\n  constants.tf2 = tf.scalar(2.0, 'float32');\n  constants.tf05 = tf.scalar(0.5, 'float32');\n  constants.tf127 = tf.scalar(127.5, 'float32');\n  constants.rgb = tf.tensor1d([0.2989, 0.5870, 0.1140], 'float32'); // factors for red/green/blue colors when converting to grayscale\n}\n", "/**\n * Age model implementation\n *\n * Based on: [**SSR-Net**](https://github.com/shamangary/SSR-Net)\n */\n\nimport { log, now } from '../util/util';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { loadModel } from '../tfjs/load';\nimport { env } from '../util/env';\nimport { constants } from '../tfjs/constants';\nimport type { Config } from '../config';\nimport type { GraphModel, Tensor } from '../tfjs/types';\n\nlet model: GraphModel | null;\nconst last: { age: number }[] = [];\nlet lastCount = 0;\nlet lastTime = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nexport async function load(config: Config) {\n  if (env.initial) model = null;\n  if (!model) model = await loadModel(config.face['ssrnet'].modelPathAge);\n  else if (config.debug) log('cached model:', model['modelUrl']);\n  return model;\n}\n\nexport async function predict(image: Tensor, config: Config, idx: number, count: number): Promise<{ age: number }> {\n  if (!model) return { age: 0 };\n  const skipFrame = skipped < (config.face['ssrnet']?.skipFrames || 0);\n  const skipTime = (config.face['ssrnet']?.skipTime || 0) > (now() - lastTime);\n  if (config.skipAllowed && skipFrame && skipTime && (lastCount === count) && last[idx]?.age && (last[idx]?.age > 0)) {\n    skipped++;\n    return last[idx];\n  }\n  skipped = 0;\n  return new Promise(async (resolve) => {\n    if (!model?.inputs || !model.inputs[0] || !model.inputs[0].shape) return;\n    const t: Record<string, Tensor> = {};\n    t.resize = tf.image.resizeBilinear(image, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false);\n    t.enhance = tf.mul(t.resize, constants.tf255);\n    const obj = { age: 0 };\n    if (config.face['ssrnet']?.enabled) t.age = model.execute(t.enhance) as Tensor;\n    if (t.age) {\n      const data = await t.age.data();\n      obj.age = Math.trunc(10 * data[0]) / 10;\n    }\n    Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n    last[idx] = obj;\n    lastCount = count;\n    lastTime = now();\n    resolve(obj);\n  });\n}\n", "/**\n * Gender model implementation\n *\n * Based on: [**SSR-Net**](https://github.com/shamangary/SSR-Net)\n */\n\nimport { log, now } from '../util/util';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { loadModel } from '../tfjs/load';\nimport { constants } from '../tfjs/constants';\nimport type { Gender } from '../result';\nimport type { Config } from '../config';\nimport type { GraphModel, Tensor } from '../tfjs/types';\nimport { env } from '../util/env';\n\nlet model: GraphModel | null;\nconst last: { gender: Gender, genderScore: number }[] = [];\nlet lastCount = 0;\nlet lastTime = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n\n// tuning values\nconst rgb = [0.2989, 0.5870, 0.1140]; // factors for red/green/blue colors when converting to grayscale\n\nexport async function load(config: Config) {\n  if (env.initial) model = null;\n  if (!model) model = await loadModel(config.face['ssrnet']?.modelPathGender);\n  else if (config.debug) log('cached model:', model['modelUrl']);\n  return model;\n}\n\nexport async function predict(image: Tensor, config: Config, idx, count): Promise<{ gender: Gender, genderScore: number }> {\n  if (!model) return { gender: 'unknown', genderScore: 0 };\n  const skipFrame = skipped < (config.face['ssrnet']?.skipFrames || 0);\n  const skipTime = (config.face['ssrnet']?.skipTime || 0) > (now() - lastTime);\n  if (config.skipAllowed && skipFrame && skipTime && (lastCount === count) && last[idx]?.gender && (last[idx]?.genderScore > 0)) {\n    skipped++;\n    return last[idx];\n  }\n  skipped = 0;\n  return new Promise(async (resolve) => {\n    if (!model?.inputs[0].shape) return;\n    const t: Record<string, Tensor> = {};\n    t.resize = tf.image.resizeBilinear(image, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false);\n    t.enhance = tf.tidy(() => {\n      const [red, green, blue] = tf.split(t.resize, 3, 3);\n      const redNorm = tf.mul(red, rgb[0]);\n      const greenNorm = tf.mul(green, rgb[1]);\n      const blueNorm = tf.mul(blue, rgb[2]);\n      const grayscale = tf.addN([redNorm, greenNorm, blueNorm]);\n      const normalize = tf.mul(tf.sub(grayscale, constants.tf05), 2); // range grayscale:-1..1\n      return normalize;\n    });\n    const obj: { gender: Gender, genderScore: number } = { gender: 'unknown', genderScore: 0 };\n    if (config.face['ssrnet']?.enabled) t.gender = model.execute(t.enhance) as Tensor;\n    const data = await t.gender.data();\n    obj.gender = data[0] > data[1] ? 'female' : 'male'; // returns two values 0..1, bigger one is prediction\n    obj.genderScore = data[0] > data[1] ? (Math.trunc(100 * data[0]) / 100) : (Math.trunc(100 * data[1]) / 100);\n    Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n    last[idx] = obj;\n    lastCount = count;\n    lastTime = now();\n    resolve(obj);\n  });\n}\n", "/**\n * Anti-spoofing model implementation\n */\n\nimport { log, now } from '../util/util';\nimport type { Config } from '../config';\nimport type { GraphModel, Tensor } from '../tfjs/types';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { loadModel } from '../tfjs/load';\nimport { env } from '../util/env';\n\nlet model: GraphModel | null;\nconst cached: number[] = [];\nlet skipped = Number.MAX_SAFE_INTEGER;\nlet lastCount = 0;\nlet lastTime = 0;\n\nexport async function load(config: Config): Promise<GraphModel> {\n  if (env.initial) model = null;\n  if (!model) model = await loadModel(config.face.antispoof?.modelPath);\n  else if (config.debug) log('cached model:', model['modelUrl']);\n  return model;\n}\n\nexport async function predict(image: Tensor, config: Config, idx: number, count: number): Promise<number> {\n  if (!model || !model?.['executor']) return 0;\n  const skipTime = (config.face.antispoof?.skipTime || 0) > (now() - lastTime);\n  const skipFrame = skipped < (config.face.antispoof?.skipFrames || 0);\n  if (config.skipAllowed && skipTime && skipFrame && (lastCount === count) && cached[idx]) {\n    skipped++;\n    return cached[idx];\n  }\n  skipped = 0;\n  return new Promise(async (resolve) => {\n    const resize = tf.image.resizeBilinear(image, [model?.inputs[0].shape ? model.inputs[0].shape[2] : 0, model?.inputs[0].shape ? model.inputs[0].shape[1] : 0], false);\n    const res = model?.execute(resize) as Tensor;\n    const num = (await res.data())[0];\n    cached[idx] = Math.round(100 * num) / 100;\n    lastCount = count;\n    lastTime = now();\n    tf.dispose([resize, res]);\n    resolve(cached[idx]);\n  });\n}\n", "/**\n * BlazeFace, FaceMesh & Iris model implementation\n * See `facemesh.ts` for entry point\n */\n\nexport const meshAnnotations: Record<string, number[]> = {\n  silhouette: [\n    10, 338, 297, 332, 284, 251, 389, 356, 454, 323, 361, 288,\n    397, 365, 379, 378, 400, 377, 152, 148, 176, 149, 150, 136,\n    172, 58, 132, 93, 234, 127, 162, 21, 54, 103, 67, 109,\n  ],\n  // lipsUpperOuter: [61, 185, 40, 39, 37, 0, 267, 269, 270, 409, 291], // 11\n  // lipsLowerOuter: [146, 91, 181, 84, 17, 314, 405, 321, 375, 291], // 10\n  // lipsUpperInner: [78, 191, 80, 81, 82, 13, 312, 311, 310, 415, 308], // 11\n  // lipsLowerInner: [78, 95, 88, 178, 87, 14, 317, 402, 318, 324, 308], // 11\n  lipsUpperOuter: [185, 40, 39, 37, 0, 267, 269, 270, 409],\n  lipsLowerOuter: [61, 146, 91, 181, 84, 17, 314, 405, 321, 375, 291],\n  lipsUpperInner: [191, 80, 81, 82, 13, 312, 311, 310, 415],\n  lipsLowerInner: [78, 95, 88, 178, 87, 14, 317, 402, 318, 324, 308],\n  lipsLowerSemiOuter: [76, 77, 90, 180, 85, 16, 315, 404, 320, 307, 306],\n  lipsUpperSemiOuter: [184, 74, 73, 72, 11, 302, 303, 304, 408],\n  lipsLowerSemiInner: [62, 96, 89, 179, 86, 15, 316, 403, 319, 325, 292],\n  lipsUpperSemiInner: [183, 42, 41, 38, 12, 268, 271, 272, 407],\n  rightEyeUpper0: [246, 161, 160, 159, 158, 157, 173], // 7\n  rightEyeLower0: [33, 7, 163, 144, 145, 153, 154, 155, 133], // 9\n  rightEyeUpper1: [247, 30, 29, 27, 28, 56, 190], // 7\n  rightEyeLower1: [130, 25, 110, 24, 23, 22, 26, 112, 243], // 9\n  rightEyeUpper2: [113, 225, 224, 223, 222, 221, 189], // 7\n  rightEyeLower2: [226, 31, 228, 229, 230, 231, 232, 233, 244], // 9\n  rightEyeLower3: [143, 111, 117, 118, 119, 120, 121, 128, 245], // 9\n  rightEyebrowUpper: [156, 70, 63, 105, 66, 107, 55, 193], // 8\n  rightEyebrowLower: [35, 124, 46, 53, 52, 65], // 6\n  rightEyeIris: [473, 474, 475, 476, 477], // 5\n  leftEyeUpper0: [466, 388, 387, 386, 385, 384, 398],\n  leftEyeLower0: [263, 249, 390, 373, 374, 380, 381, 382, 362],\n  leftEyeUpper1: [467, 260, 259, 257, 258, 286, 414],\n  leftEyeLower1: [359, 255, 339, 254, 253, 252, 256, 341, 463],\n  leftEyeUpper2: [342, 445, 444, 443, 442, 441, 413],\n  leftEyeLower2: [446, 261, 448, 449, 450, 451, 452, 453, 464],\n  leftEyeLower3: [372, 340, 346, 347, 348, 349, 350, 357, 465],\n  leftEyebrowUpper: [383, 300, 293, 334, 296, 336, 285, 417],\n  leftEyebrowLower: [265, 353, 276, 283, 282, 295],\n  leftEyeIris: [468, 469, 470, 471, 472],\n  midwayBetweenEyes: [168],\n  noseTip: [1],\n  noseBottom: [2],\n  noseRightCorner: [98],\n  noseLeftCorner: [327],\n  rightCheek: [205],\n  leftCheek: [425],\n};\n\nexport const meshLandmarks: Record<string, number | number[]> = {\n  count: 468,\n  mouth: 13,\n  symmetryLine: [13, meshAnnotations.midwayBetweenEyes[0]],\n};\n\nexport const blazeFaceLandmarks: Record<string, number | number[]> = {\n  leftEye: 0,\n  rightEye: 1,\n  nose: 2,\n  mouth: 3,\n  leftEar: 4,\n  rightEar: 5,\n  symmetryLine: [3, 2],\n};\n\nexport const irisIndices: { key: string, indices: number[] }[] = [ // A mapping from facemesh model keypoints to iris model keypoints.\n  { key: 'EyeUpper0', indices: [9, 10, 11, 12, 13, 14, 15] }, // 7 x 3d\n  { key: 'EyeUpper1', indices: [25, 26, 27, 28, 29, 30, 31] }, // 7 x 3d\n  { key: 'EyeUpper2', indices: [41, 42, 43, 44, 45, 46, 47] }, // 7 x 3d\n  { key: 'EyeLower0', indices: [0, 1, 2, 3, 4, 5, 6, 7, 8] }, // 7 x 3d\n  { key: 'EyeLower1', indices: [16, 17, 18, 19, 20, 21, 22, 23, 24] }, // 9 x 3d\n  { key: 'EyeLower2', indices: [32, 33, 34, 35, 36, 37, 38, 39, 40] }, // 9 x 3d\n  { key: 'EyeLower3', indices: [54, 55, 56, 57, 58, 59, 60, 61, 62] }, // 9 x 3d\n  { key: 'EyebrowUpper', indices: [63, 64, 65, 66, 67, 68, 69, 70] }, // 8 x 3d\n  { key: 'EyebrowLower', indices: [48, 49, 50, 51, 52, 53] }, // 6 x 3d\n];\n\nexport const UV468: [number, number][] = [\n  [0.499976992607117, 0.652534008026123],\n  [0.500025987625122, 0.547487020492554],\n  [0.499974012374878, 0.602371990680695],\n  [0.482113003730774, 0.471979022026062],\n  [0.500150978565216, 0.527155995368958],\n  [0.499909996986389, 0.498252987861633],\n  [0.499523013830185, 0.40106201171875],\n  [0.289712011814117, 0.380764007568359],\n  [0.499954998493195, 0.312398016452789],\n  [0.499987006187439, 0.269918978214264],\n  [0.500023007392883, 0.107050001621246],\n  [0.500023007392883, 0.666234016418457],\n  [0.5000159740448, 0.679224014282227],\n  [0.500023007392883, 0.692348003387451],\n  [0.499976992607117, 0.695277988910675],\n  [0.499976992607117, 0.70593398809433],\n  [0.499976992607117, 0.719385027885437],\n  [0.499976992607117, 0.737019002437592],\n  [0.499967992305756, 0.781370997428894],\n  [0.499816000461578, 0.562981009483337],\n  [0.473773002624512, 0.573909997940063],\n  [0.104906998574734, 0.254140973091125],\n  [0.365929991006851, 0.409575998783112],\n  [0.338757991790771, 0.41302502155304],\n  [0.311120003461838, 0.409460008144379],\n  [0.274657994508743, 0.389131009578705],\n  [0.393361985683441, 0.403706014156342],\n  [0.345234006643295, 0.344011008739471],\n  [0.370094001293182, 0.346076011657715],\n  [0.319321990013123, 0.347265005111694],\n  [0.297903001308441, 0.353591024875641],\n  [0.24779200553894, 0.410809993743896],\n  [0.396889001131058, 0.842755019664764],\n  [0.280097991228104, 0.375599980354309],\n  [0.106310002505779, 0.399955987930298],\n  [0.2099249958992, 0.391353011131287],\n  [0.355807989835739, 0.534406006336212],\n  [0.471751004457474, 0.65040397644043],\n  [0.474155008792877, 0.680191993713379],\n  [0.439785003662109, 0.657229006290436],\n  [0.414617002010345, 0.66654098033905],\n  [0.450374007225037, 0.680860996246338],\n  [0.428770989179611, 0.682690978050232],\n  [0.374971002340317, 0.727805018424988],\n  [0.486716985702515, 0.547628998756409],\n  [0.485300987958908, 0.527395009994507],\n  [0.257764995098114, 0.314490020275116],\n  [0.401223003864288, 0.455172002315521],\n  [0.429818987846375, 0.548614978790283],\n  [0.421351999044418, 0.533740997314453],\n  [0.276895999908447, 0.532056987285614],\n  [0.483370006084442, 0.499586999416351],\n  [0.33721199631691, 0.282882988452911],\n  [0.296391993761063, 0.293242990970612],\n  [0.169294998049736, 0.193813979625702],\n  [0.447580009698868, 0.302609980106354],\n  [0.392390012741089, 0.353887975215912],\n  [0.354490011930466, 0.696784019470215],\n  [0.067304998636246, 0.730105042457581],\n  [0.442739009857178, 0.572826027870178],\n  [0.457098007202148, 0.584792017936707],\n  [0.381974011659622, 0.694710969924927],\n  [0.392388999462128, 0.694203019142151],\n  [0.277076005935669, 0.271932005882263],\n  [0.422551989555359, 0.563233017921448],\n  [0.385919004678726, 0.281364023685455],\n  [0.383103013038635, 0.255840003490448],\n  [0.331431001424789, 0.119714021682739],\n  [0.229923993349075, 0.232002973556519],\n  [0.364500999450684, 0.189113974571228],\n  [0.229622006416321, 0.299540996551514],\n  [0.173287004232407, 0.278747975826263],\n  [0.472878992557526, 0.666198015213013],\n  [0.446828007698059, 0.668527007102966],\n  [0.422762006521225, 0.673889994621277],\n  [0.445307999849319, 0.580065965652466],\n  [0.388103008270264, 0.693961024284363],\n  [0.403039008378983, 0.706539988517761],\n  [0.403629004955292, 0.693953037261963],\n  [0.460041999816895, 0.557139039039612],\n  [0.431158006191254, 0.692366003990173],\n  [0.452181994915009, 0.692366003990173],\n  [0.475387006998062, 0.692366003990173],\n  [0.465828001499176, 0.779190003871918],\n  [0.472328990697861, 0.736225962638855],\n  [0.473087012767792, 0.717857003211975],\n  [0.473122000694275, 0.704625964164734],\n  [0.473033010959625, 0.695277988910675],\n  [0.427942007780075, 0.695277988910675],\n  [0.426479011774063, 0.703539967536926],\n  [0.423162013292313, 0.711845993995667],\n  [0.4183090031147, 0.720062971115112],\n  [0.390094995498657, 0.639572978019714],\n  [0.013953999616206, 0.560034036636353],\n  [0.499913990497589, 0.58014702796936],\n  [0.413199990987778, 0.69539999961853],\n  [0.409626007080078, 0.701822996139526],\n  [0.468080013990402, 0.601534962654114],\n  [0.422728985548019, 0.585985004901886],\n  [0.463079988956451, 0.593783974647522],\n  [0.37211999297142, 0.47341400384903],\n  [0.334562003612518, 0.496073007583618],\n  [0.411671012639999, 0.546965003013611],\n  [0.242175996303558, 0.14767599105835],\n  [0.290776997804642, 0.201445996761322],\n  [0.327338010072708, 0.256527006626129],\n  [0.399509996175766, 0.748921036720276],\n  [0.441727995872498, 0.261676013469696],\n  [0.429764986038208, 0.187834024429321],\n  [0.412198007106781, 0.108901023864746],\n  [0.288955003023148, 0.398952007293701],\n  [0.218936994671822, 0.435410976409912],\n  [0.41278201341629, 0.398970007896423],\n  [0.257135003805161, 0.355440020561218],\n  [0.427684992551804, 0.437960982322693],\n  [0.448339998722076, 0.536936044692993],\n  [0.178560003638268, 0.45755398273468],\n  [0.247308000922203, 0.457193970680237],\n  [0.286267012357712, 0.467674970626831],\n  [0.332827985286713, 0.460712015628815],\n  [0.368755996227264, 0.447206974029541],\n  [0.398963987827301, 0.432654976844788],\n  [0.476410001516342, 0.405806005001068],\n  [0.189241006970406, 0.523923993110657],\n  [0.228962004184723, 0.348950982093811],\n  [0.490725994110107, 0.562400996685028],\n  [0.404670000076294, 0.485132992267609],\n  [0.019469000399113, 0.401564002037048],\n  [0.426243007183075, 0.420431017875671],\n  [0.396993011236191, 0.548797011375427],\n  [0.266469985246658, 0.376977026462555],\n  [0.439121007919312, 0.51895797252655],\n  [0.032313998788595, 0.644356966018677],\n  [0.419054001569748, 0.387154996395111],\n  [0.462783008813858, 0.505746960639954],\n  [0.238978996872902, 0.779744982719421],\n  [0.198220998048782, 0.831938028335571],\n  [0.107550002634525, 0.540755033493042],\n  [0.183610007166862, 0.740257024765015],\n  [0.134409993886948, 0.333683013916016],\n  [0.385764002799988, 0.883153975009918],\n  [0.490967005491257, 0.579378008842468],\n  [0.382384985685349, 0.508572995662689],\n  [0.174399003386497, 0.397670984268188],\n  [0.318785011768341, 0.39623498916626],\n  [0.343364000320435, 0.400596976280212],\n  [0.396100014448166, 0.710216999053955],\n  [0.187885001301765, 0.588537991046906],\n  [0.430987000465393, 0.944064974784851],\n  [0.318993002176285, 0.898285031318665],\n  [0.266247987747192, 0.869701027870178],\n  [0.500023007392883, 0.190576016902924],\n  [0.499976992607117, 0.954452991485596],\n  [0.366169989109039, 0.398822009563446],\n  [0.393207013607025, 0.39553701877594],\n  [0.410373002290726, 0.391080021858215],\n  [0.194993004202843, 0.342101991176605],\n  [0.388664990663528, 0.362284004688263],\n  [0.365961998701096, 0.355970978736877],\n  [0.343364000320435, 0.355356991291046],\n  [0.318785011768341, 0.35834002494812],\n  [0.301414996385574, 0.363156020641327],\n  [0.058132998645306, 0.319076001644135],\n  [0.301414996385574, 0.387449026107788],\n  [0.499987989664078, 0.618434011936188],\n  [0.415838003158569, 0.624195992946625],\n  [0.445681989192963, 0.566076993942261],\n  [0.465844005346298, 0.620640993118286],\n  [0.49992299079895, 0.351523995399475],\n  [0.288718998432159, 0.819945991039276],\n  [0.335278987884521, 0.852819979190826],\n  [0.440512001514435, 0.902418971061707],\n  [0.128294005990028, 0.791940987110138],\n  [0.408771991729736, 0.373893976211548],\n  [0.455606997013092, 0.451801002025604],\n  [0.499877005815506, 0.908990025520325],\n  [0.375436991453171, 0.924192011356354],\n  [0.11421000212431, 0.615022003650665],\n  [0.448662012815475, 0.695277988910675],\n  [0.4480200111866, 0.704632043838501],\n  [0.447111994028091, 0.715808033943176],\n  [0.444831997156143, 0.730794012546539],\n  [0.430011987686157, 0.766808986663818],\n  [0.406787008047104, 0.685672998428345],\n  [0.400738000869751, 0.681069016456604],\n  [0.392399996519089, 0.677703022956848],\n  [0.367855995893478, 0.663918972015381],\n  [0.247923001646996, 0.601333022117615],\n  [0.452769994735718, 0.420849978923798],\n  [0.43639200925827, 0.359887003898621],\n  [0.416164010763168, 0.368713974952698],\n  [0.413385987281799, 0.692366003990173],\n  [0.228018000721931, 0.683571994304657],\n  [0.468268007040024, 0.352671027183533],\n  [0.411361992359161, 0.804327011108398],\n  [0.499989002943039, 0.469825029373169],\n  [0.479153990745544, 0.442654013633728],\n  [0.499974012374878, 0.439637005329132],\n  [0.432112008333206, 0.493588984012604],\n  [0.499886006116867, 0.866917014122009],\n  [0.49991300702095, 0.821729004383087],\n  [0.456548988819122, 0.819200992584229],\n  [0.344549000263214, 0.745438992977142],\n  [0.37890899181366, 0.574010014533997],\n  [0.374292999505997, 0.780184984207153],\n  [0.319687992334366, 0.570737957954407],\n  [0.357154995203018, 0.604269981384277],\n  [0.295284003019333, 0.621580958366394],\n  [0.447750002145767, 0.862477004528046],\n  [0.410986006259918, 0.508723020553589],\n  [0.31395098567009, 0.775308012962341],\n  [0.354128003120422, 0.812552988529205],\n  [0.324548006057739, 0.703992962837219],\n  [0.189096003770828, 0.646299958229065],\n  [0.279776990413666, 0.71465802192688],\n  [0.1338230073452, 0.682700991630554],\n  [0.336768001317978, 0.644733011722565],\n  [0.429883986711502, 0.466521978378296],\n  [0.455527991056442, 0.548622965812683],\n  [0.437114000320435, 0.558896005153656],\n  [0.467287987470627, 0.529924988746643],\n  [0.414712011814117, 0.335219979286194],\n  [0.37704598903656, 0.322777986526489],\n  [0.344107985496521, 0.320150971412659],\n  [0.312875986099243, 0.32233202457428],\n  [0.283526003360748, 0.333190023899078],\n  [0.241245999932289, 0.382785975933075],\n  [0.102986000478268, 0.468762993812561],\n  [0.267612010240555, 0.424560010433197],\n  [0.297879010438919, 0.433175981044769],\n  [0.333433985710144, 0.433878004550934],\n  [0.366427004337311, 0.426115989685059],\n  [0.396012008190155, 0.416696012020111],\n  [0.420121014118195, 0.41022801399231],\n  [0.007561000064015, 0.480777025222778],\n  [0.432949006557465, 0.569517970085144],\n  [0.458638995885849, 0.479089021682739],\n  [0.473466008901596, 0.545744001865387],\n  [0.476087987422943, 0.563830018043518],\n  [0.468472003936768, 0.555056989192963],\n  [0.433990985155106, 0.582361996173859],\n  [0.483518004417419, 0.562983989715576],\n  [0.482482999563217, 0.57784903049469],\n  [0.42645001411438, 0.389798998832703],\n  [0.438998997211456, 0.39649498462677],\n  [0.450067013502121, 0.400434017181396],\n  [0.289712011814117, 0.368252992630005],\n  [0.276670008897781, 0.363372981548309],\n  [0.517862021923065, 0.471948027610779],\n  [0.710287988185883, 0.380764007568359],\n  [0.526226997375488, 0.573909997940063],\n  [0.895093023777008, 0.254140973091125],\n  [0.634069979190826, 0.409575998783112],\n  [0.661242008209229, 0.41302502155304],\n  [0.688880026340485, 0.409460008144379],\n  [0.725341975688934, 0.389131009578705],\n  [0.606630027294159, 0.40370500087738],\n  [0.654766023159027, 0.344011008739471],\n  [0.629905998706818, 0.346076011657715],\n  [0.680678009986877, 0.347265005111694],\n  [0.702096998691559, 0.353591024875641],\n  [0.75221198797226, 0.410804986953735],\n  [0.602918028831482, 0.842862963676453],\n  [0.719901978969574, 0.375599980354309],\n  [0.893692970275879, 0.399959981441498],\n  [0.790081977844238, 0.391354024410248],\n  [0.643998026847839, 0.534487962722778],\n  [0.528249025344849, 0.65040397644043],\n  [0.525849997997284, 0.680191040039062],\n  [0.560214996337891, 0.657229006290436],\n  [0.585384011268616, 0.66654098033905],\n  [0.549625992774963, 0.680860996246338],\n  [0.57122802734375, 0.682691991329193],\n  [0.624852001667023, 0.72809898853302],\n  [0.513050019741058, 0.547281980514526],\n  [0.51509702205658, 0.527251958847046],\n  [0.742246985435486, 0.314507007598877],\n  [0.598631024360657, 0.454979002475739],\n  [0.570338010787964, 0.548575043678284],\n  [0.578631997108459, 0.533622980117798],\n  [0.723087012767792, 0.532054007053375],\n  [0.516445994377136, 0.499638974666595],\n  [0.662801027297974, 0.282917976379395],\n  [0.70362401008606, 0.293271005153656],\n  [0.830704987049103, 0.193813979625702],\n  [0.552385985851288, 0.302568018436432],\n  [0.607609987258911, 0.353887975215912],\n  [0.645429015159607, 0.696707010269165],\n  [0.932694971561432, 0.730105042457581],\n  [0.557260990142822, 0.572826027870178],\n  [0.542901992797852, 0.584792017936707],\n  [0.6180260181427, 0.694710969924927],\n  [0.607590973377228, 0.694203019142151],\n  [0.722943007946014, 0.271963000297546],\n  [0.577413976192474, 0.563166975975037],\n  [0.614082992076874, 0.281386971473694],\n  [0.616907000541687, 0.255886018276215],\n  [0.668509006500244, 0.119913995265961],\n  [0.770092010498047, 0.232020974159241],\n  [0.635536015033722, 0.189248979091644],\n  [0.77039098739624, 0.299556016921997],\n  [0.826722025871277, 0.278755009174347],\n  [0.527121007442474, 0.666198015213013],\n  [0.553171992301941, 0.668527007102966],\n  [0.577238023281097, 0.673889994621277],\n  [0.554691970348358, 0.580065965652466],\n  [0.611896991729736, 0.693961024284363],\n  [0.59696102142334, 0.706539988517761],\n  [0.596370995044708, 0.693953037261963],\n  [0.539958000183105, 0.557139039039612],\n  [0.568841993808746, 0.692366003990173],\n  [0.547818005084991, 0.692366003990173],\n  [0.52461302280426, 0.692366003990173],\n  [0.534089982509613, 0.779141008853912],\n  [0.527670979499817, 0.736225962638855],\n  [0.526912987232208, 0.717857003211975],\n  [0.526877999305725, 0.704625964164734],\n  [0.526966989040375, 0.695277988910675],\n  [0.572058022022247, 0.695277988910675],\n  [0.573521018028259, 0.703539967536926],\n  [0.57683801651001, 0.711845993995667],\n  [0.581691026687622, 0.720062971115112],\n  [0.609944999217987, 0.639909982681274],\n  [0.986046016216278, 0.560034036636353],\n  [0.5867999792099, 0.69539999961853],\n  [0.590372025966644, 0.701822996139526],\n  [0.531915009021759, 0.601536989212036],\n  [0.577268004417419, 0.585934996604919],\n  [0.536915004253387, 0.593786001205444],\n  [0.627542972564697, 0.473352015018463],\n  [0.665585994720459, 0.495950996875763],\n  [0.588353991508484, 0.546862006187439],\n  [0.757824003696442, 0.14767599105835],\n  [0.709249973297119, 0.201507985591888],\n  [0.672684013843536, 0.256581008434296],\n  [0.600408971309662, 0.74900496006012],\n  [0.55826598405838, 0.261672019958496],\n  [0.570303976535797, 0.187870979309082],\n  [0.588165998458862, 0.109044015407562],\n  [0.711045026779175, 0.398952007293701],\n  [0.781069993972778, 0.435405015945435],\n  [0.587247014045715, 0.398931980133057],\n  [0.742869973182678, 0.355445981025696],\n  [0.572156012058258, 0.437651991844177],\n  [0.55186802148819, 0.536570012569427],\n  [0.821442008018494, 0.457556009292603],\n  [0.752701997756958, 0.457181990146637],\n  [0.71375697851181, 0.467626988887787],\n  [0.66711300611496, 0.460672974586487],\n  [0.631101012229919, 0.447153985500336],\n  [0.6008620262146, 0.432473003864288],\n  [0.523481011390686, 0.405627012252808],\n  [0.810747981071472, 0.523926019668579],\n  [0.771045982837677, 0.348959028720856],\n  [0.509127020835876, 0.562718033790588],\n  [0.595292985439301, 0.485023975372314],\n  [0.980530977249146, 0.401564002037048],\n  [0.573499977588654, 0.420000016689301],\n  [0.602994978427887, 0.548687994480133],\n  [0.733529984951019, 0.376977026462555],\n  [0.560611009597778, 0.519016981124878],\n  [0.967685997486115, 0.644356966018677],\n  [0.580985009670258, 0.387160003185272],\n  [0.537728011608124, 0.505385041236877],\n  [0.760966002941132, 0.779752969741821],\n  [0.801778972148895, 0.831938028335571],\n  [0.892440974712372, 0.54076099395752],\n  [0.816350996494293, 0.740260004997253],\n  [0.865594983100891, 0.333687007427216],\n  [0.614073991775513, 0.883246004581451],\n  [0.508952975273132, 0.579437971115112],\n  [0.617941975593567, 0.508316040039062],\n  [0.825608015060425, 0.397674977779388],\n  [0.681214988231659, 0.39623498916626],\n  [0.656635999679565, 0.400596976280212],\n  [0.603900015354156, 0.710216999053955],\n  [0.81208598613739, 0.588539004325867],\n  [0.56801301240921, 0.944564998149872],\n  [0.681007981300354, 0.898285031318665],\n  [0.733752012252808, 0.869701027870178],\n  [0.633830010890961, 0.398822009563446],\n  [0.606792986392975, 0.39553701877594],\n  [0.589659988880157, 0.391062021255493],\n  [0.805015981197357, 0.342108011245728],\n  [0.611334979534149, 0.362284004688263],\n  [0.634037971496582, 0.355970978736877],\n  [0.656635999679565, 0.355356991291046],\n  [0.681214988231659, 0.35834002494812],\n  [0.698584973812103, 0.363156020641327],\n  [0.941866993904114, 0.319076001644135],\n  [0.698584973812103, 0.387449026107788],\n  [0.584177017211914, 0.624107003211975],\n  [0.554318010807037, 0.566076993942261],\n  [0.534153997898102, 0.62064003944397],\n  [0.711217999458313, 0.819975018501282],\n  [0.664629995822906, 0.852871000766754],\n  [0.559099972248077, 0.902631998062134],\n  [0.871706008911133, 0.791940987110138],\n  [0.591234028339386, 0.373893976211548],\n  [0.544341027736664, 0.451583981513977],\n  [0.624562978744507, 0.924192011356354],\n  [0.88577002286911, 0.615028977394104],\n  [0.551338016986847, 0.695277988910675],\n  [0.551980018615723, 0.704632043838501],\n  [0.552887976169586, 0.715808033943176],\n  [0.555167973041534, 0.730794012546539],\n  [0.569944024085999, 0.767035007476807],\n  [0.593203008174896, 0.685675978660583],\n  [0.599261999130249, 0.681069016456604],\n  [0.607599973678589, 0.677703022956848],\n  [0.631937980651855, 0.663500010967255],\n  [0.752032995223999, 0.601315021514893],\n  [0.547226011753082, 0.420395016670227],\n  [0.563543975353241, 0.359827995300293],\n  [0.583841025829315, 0.368713974952698],\n  [0.586614012718201, 0.692366003990173],\n  [0.771915018558502, 0.683578014373779],\n  [0.531597018241882, 0.352482974529266],\n  [0.588370978832245, 0.804440975189209],\n  [0.52079701423645, 0.442565023899078],\n  [0.567984998226166, 0.493479013442993],\n  [0.543282985687256, 0.819254994392395],\n  [0.655317008495331, 0.745514988899231],\n  [0.621008992195129, 0.574018001556396],\n  [0.625559985637665, 0.78031200170517],\n  [0.680198013782501, 0.570719003677368],\n  [0.64276397228241, 0.604337990283966],\n  [0.704662978649139, 0.621529996395111],\n  [0.552012026309967, 0.862591981887817],\n  [0.589071989059448, 0.508637011051178],\n  [0.685944974422455, 0.775357007980347],\n  [0.645735025405884, 0.812640011310577],\n  [0.675342977046967, 0.703978002071381],\n  [0.810858011245728, 0.646304965019226],\n  [0.72012197971344, 0.714666962623596],\n  [0.866151988506317, 0.682704985141754],\n  [0.663187026977539, 0.644596993923187],\n  [0.570082008838654, 0.466325998306274],\n  [0.544561982154846, 0.548375964164734],\n  [0.562758982181549, 0.558784961700439],\n  [0.531987011432648, 0.530140042304993],\n  [0.585271000862122, 0.335177004337311],\n  [0.622952997684479, 0.32277899980545],\n  [0.655896008014679, 0.320163011550903],\n  [0.687132000923157, 0.322345972061157],\n  [0.716481983661652, 0.333200991153717],\n  [0.758756995201111, 0.382786989212036],\n  [0.897013008594513, 0.468769013881683],\n  [0.732392013072968, 0.424547016620636],\n  [0.70211398601532, 0.433162987232208],\n  [0.66652500629425, 0.433866024017334],\n  [0.633504986763, 0.426087975502014],\n  [0.603875994682312, 0.416586995124817],\n  [0.579657971858978, 0.409945011138916],\n  [0.992439985275269, 0.480777025222778],\n  [0.567192018032074, 0.569419980049133],\n  [0.54136598110199, 0.478899002075195],\n  [0.526564002037048, 0.546118021011353],\n  [0.523913025856018, 0.563830018043518],\n  [0.531529009342194, 0.555056989192963],\n  [0.566035985946655, 0.582329034805298],\n  [0.51631098985672, 0.563053965568542],\n  [0.5174720287323, 0.577877044677734],\n  [0.573594987392426, 0.389806985855103],\n  [0.560697972774506, 0.395331978797913],\n  [0.549755990505219, 0.399751007556915],\n  [0.710287988185883, 0.368252992630005],\n  [0.723330020904541, 0.363372981548309],\n];\n\nexport const TRI468: number[] = [\n  127, 34, 139, 11, 0, 37, 232, 231, 120, 72, 37, 39, 128, 121, 47, 232, 121, 128, 104, 69, 67, 175, 171, 148, 157, 154, 155, 118, 50, 101, 73, 39, 40, 9,\n  151, 108, 48, 115, 131, 194, 204, 211, 74, 40, 185, 80, 42, 183, 40, 92, 186, 230, 229, 118, 202, 212, 214, 83, 18, 17, 76, 61, 146, 160, 29, 30, 56,\n  157, 173, 106, 204, 194, 135, 214, 192, 203, 165, 98, 21, 71, 68, 51, 45, 4, 144, 24, 23, 77, 146, 91, 205, 50, 187, 201, 200, 18, 91, 106, 182, 90, 91,\n  181, 85, 84, 17, 206, 203, 36, 148, 171, 140, 92, 40, 39, 193, 189, 244, 159, 158, 28, 247, 246, 161, 236, 3, 196, 54, 68, 104, 193, 168, 8, 117,\n  228, 31, 189, 193, 55, 98, 97, 99, 126, 47, 100, 166, 79, 218, 155, 154, 26, 209, 49, 131, 135, 136, 150, 47, 126, 217, 223, 52, 53, 45, 51, 134, 211,\n  170, 140, 67, 69, 108, 43, 106, 91, 230, 119, 120, 226, 130, 247, 63, 53, 52, 238, 20, 242, 46, 70, 156, 78, 62, 96, 46, 53, 63, 143, 34, 227, 173,\n  155, 133, 123, 117, 111, 44, 125, 19, 236, 134, 51, 216, 206, 205, 154, 153, 22, 39, 37, 167, 200, 201, 208, 36, 142, 100, 57, 212, 202, 20, 60, 99, 28,\n  158, 157, 35, 226, 113, 160, 159, 27, 204, 202, 210, 113, 225, 46, 43, 202, 204, 62, 76, 77, 137, 123, 116, 41, 38, 72, 203, 129, 142, 64, 98, 240, 49,\n  102, 64, 41, 73, 74, 212, 216, 207, 42, 74, 184, 169, 170, 211, 170, 149, 176, 105, 66, 69, 122, 6, 168, 123, 147, 187, 96, 77, 90, 65, 55, 107, 89,\n  90, 180, 101, 100, 120, 63, 105, 104, 93, 137, 227, 15, 86, 85, 129, 102, 49, 14, 87, 86, 55, 8, 9, 100, 47, 121, 145, 23, 22, 88, 89, 179, 6, 122,\n  196, 88, 95, 96, 138, 172, 136, 215, 58, 172, 115, 48, 219, 42, 80, 81, 195, 3, 51, 43, 146, 61, 171, 175, 199, 81, 82, 38, 53, 46, 225, 144, 163, 110,\n  246, 33, 7, 52, 65, 66, 229, 228, 117, 34, 127, 234, 107, 108, 69, 109, 108, 151, 48, 64, 235, 62, 78, 191, 129, 209, 126, 111, 35, 143, 163, 161, 246,\n  117, 123, 50, 222, 65, 52, 19, 125, 141, 221, 55, 65, 3, 195, 197, 25, 7, 33, 220, 237, 44, 70, 71, 139, 122, 193, 245, 247, 130, 33, 71, 21, 162,\n  153, 158, 159, 170, 169, 150, 188, 174, 196, 216, 186, 92, 144, 160, 161, 2, 97, 167, 141, 125, 241, 164, 167, 37, 72, 38, 12, 145, 159, 160, 38, 82, 13,\n  63, 68, 71, 226, 35, 111, 158, 153, 154, 101, 50, 205, 206, 92, 165, 209, 198, 217, 165, 167, 97, 220, 115, 218, 133, 112, 243, 239, 238, 241, 214,\n  135, 169, 190, 173, 133, 171, 208, 32, 125, 44, 237, 86, 87, 178, 85, 86, 179, 84, 85, 180, 83, 84, 181, 201, 83, 182, 137, 93, 132, 76, 62, 183, 61,\n  76, 184, 57, 61, 185, 212, 57, 186, 214, 207, 187, 34, 143, 156, 79, 239, 237, 123, 137, 177, 44, 1, 4, 201, 194, 32, 64, 102, 129, 213, 215, 138, 59,\n  166, 219, 242, 99, 97, 2, 94, 141, 75, 59, 235, 24, 110, 228, 25, 130, 226, 23, 24, 229, 22, 23, 230, 26, 22, 231, 112, 26, 232, 189, 190, 243, 221, 56,\n  190, 28, 56, 221, 27, 28, 222, 29, 27, 223, 30, 29, 224, 247, 30, 225, 238, 79, 20, 166, 59, 75, 60, 75, 240, 147, 177, 215, 20, 79, 166, 187, 147, 213,\n  112, 233, 244, 233, 128, 245, 128, 114, 188, 114, 217, 174, 131, 115, 220, 217, 198, 236, 198, 131, 134, 177, 132, 58, 143, 35, 124, 110, 163, 7, 228,\n  110, 25, 356, 389, 368, 11, 302, 267, 452, 350, 349, 302, 303, 269, 357, 343, 277, 452, 453, 357, 333, 332, 297, 175, 152, 377, 384, 398, 382, 347,\n  348, 330, 303, 304, 270, 9, 336, 337, 278, 279, 360, 418, 262, 431, 304, 408, 409, 310, 415, 407, 270, 409, 410, 450, 348, 347, 422, 430, 434, 313,\n  314, 17, 306, 307, 375, 387, 388, 260, 286, 414, 398, 335, 406, 418, 364, 367, 416, 423, 358, 327, 251, 284, 298, 281, 5, 4, 373, 374, 253, 307, 320,\n  321, 425, 427, 411, 421, 313, 18, 321, 405, 406, 320, 404, 405, 315, 16, 17, 426, 425, 266, 377, 400, 369, 322, 391, 269, 417, 465, 464, 386, 257, 258,\n  466, 260, 388, 456, 399, 419, 284, 332, 333, 417, 285, 8, 346, 340, 261, 413, 441, 285, 327, 460, 328, 355, 371, 329, 392, 439, 438, 382, 341, 256,\n  429, 420, 360, 364, 394, 379, 277, 343, 437, 443, 444, 283, 275, 440, 363, 431, 262, 369, 297, 338, 337, 273, 375, 321, 450, 451, 349, 446, 342, 467,\n  293, 334, 282, 458, 461, 462, 276, 353, 383, 308, 324, 325, 276, 300, 293, 372, 345, 447, 382, 398, 362, 352, 345, 340, 274, 1, 19, 456, 248, 281, 436,\n  427, 425, 381, 256, 252, 269, 391, 393, 200, 199, 428, 266, 330, 329, 287, 273, 422, 250, 462, 328, 258, 286, 384, 265, 353, 342, 387, 259, 257, 424,\n  431, 430, 342, 353, 276, 273, 335, 424, 292, 325, 307, 366, 447, 345, 271, 303, 302, 423, 266, 371, 294, 455, 460, 279, 278, 294, 271, 272, 304, 432,\n  434, 427, 272, 407, 408, 394, 430, 431, 395, 369, 400, 334, 333, 299, 351, 417, 168, 352, 280, 411, 325, 319, 320, 295, 296, 336, 319, 403, 404, 330,\n  348, 349, 293, 298, 333, 323, 454, 447, 15, 16, 315, 358, 429, 279, 14, 15, 316, 285, 336, 9, 329, 349, 350, 374, 380, 252, 318, 402, 403, 6, 197, 419,\n  318, 319, 325, 367, 364, 365, 435, 367, 397, 344, 438, 439, 272, 271, 311, 195, 5, 281, 273, 287, 291, 396, 428, 199, 311, 271, 268, 283, 444, 445,\n  373, 254, 339, 263, 466, 249, 282, 334, 296, 449, 347, 346, 264, 447, 454, 336, 296, 299, 338, 10, 151, 278, 439, 455, 292, 407, 415, 358, 371, 355,\n  340, 345, 372, 390, 249, 466, 346, 347, 280, 442, 443, 282, 19, 94, 370, 441, 442, 295, 248, 419, 197, 263, 255, 359, 440, 275, 274, 300, 383, 368,\n  351, 412, 465, 263, 467, 466, 301, 368, 389, 380, 374, 386, 395, 378, 379, 412, 351, 419, 436, 426, 322, 373, 390, 388, 2, 164, 393, 370, 462, 461,\n  164, 0, 267, 302, 11, 12, 374, 373, 387, 268, 12, 13, 293, 300, 301, 446, 261, 340, 385, 384, 381, 330, 266, 425, 426, 423, 391, 429, 355, 437, 391,\n  327, 326, 440, 457, 438, 341, 382, 362, 459, 457, 461, 434, 430, 394, 414, 463, 362, 396, 369, 262, 354, 461, 457, 316, 403, 402, 315, 404, 403, 314,\n  405, 404, 313, 406, 405, 421, 418, 406, 366, 401, 361, 306, 408, 407, 291, 409, 408, 287, 410, 409, 432, 436, 410, 434, 416, 411, 264, 368, 383, 309,\n  438, 457, 352, 376, 401, 274, 275, 4, 421, 428, 262, 294, 327, 358, 433, 416, 367, 289, 455, 439, 462, 370, 326, 2, 326, 370, 305, 460, 455, 254,\n  449, 448, 255, 261, 446, 253, 450, 449, 252, 451, 450, 256, 452, 451, 341, 453, 452, 413, 464, 463, 441, 413, 414, 258, 442, 441, 257, 443, 442, 259,\n  444, 443, 260, 445, 444, 467, 342, 445, 459, 458, 250, 289, 392, 290, 290, 328, 460, 376, 433, 435, 250, 290, 392, 411, 416, 433, 341, 463, 464, 453,\n  464, 465, 357, 465, 412, 343, 412, 399, 360, 363, 440, 437, 399, 456, 420, 456, 363, 401, 435, 288, 372, 383, 353, 339, 255, 249, 448, 261, 255, 133,\n  243, 190, 133, 155, 112, 33, 246, 247, 33, 130, 25, 398, 384, 286, 362, 398, 414, 362, 463, 341, 263, 359, 467, 263, 249, 255, 466, 467, 260, 75, 60,\n  166, 238, 239, 79, 162, 127, 139, 72, 11, 37, 121, 232, 120, 73, 72, 39, 114, 128, 47, 233, 232, 128, 103, 104, 67, 152, 175, 148, 173, 157, 155,\n  119, 118, 101, 74, 73, 40, 107, 9, 108, 49, 48, 131, 32, 194, 211, 184, 74, 185, 191, 80, 183, 185, 40, 186, 119, 230, 118, 210, 202, 214, 84, 83, 17,\n  77, 76, 146, 161, 160, 30, 190, 56, 173, 182, 106, 194, 138, 135, 192, 129, 203, 98, 54, 21, 68, 5, 51, 4, 145, 144, 23, 90, 77, 91, 207, 205, 187, 83,\n  201, 18, 181, 91, 182, 180, 90, 181, 16, 85, 17, 205, 206, 36, 176, 148, 140, 165, 92, 39, 245, 193, 244, 27, 159, 28, 30, 247, 161, 174, 236, 196,\n  103, 54, 104, 55, 193, 8, 111, 117, 31, 221, 189, 55, 240, 98, 99, 142, 126, 100, 219, 166, 218, 112, 155, 26, 198, 209, 131, 169, 135, 150, 114, 47,\n  217, 224, 223, 53, 220, 45, 134, 32, 211, 140, 109, 67, 108, 146, 43, 91, 231, 230, 120, 113, 226, 247, 105, 63, 52, 241, 238, 242, 124, 46, 156, 95,\n  78, 96, 70, 46, 63, 116, 143, 227, 116, 123, 111, 1, 44, 19, 3, 236, 51, 207, 216, 205, 26, 154, 22, 165, 39, 167, 199, 200, 208, 101, 36, 100, 43,\n  57, 202, 242, 20, 99, 56, 28, 157, 124, 35, 113, 29, 160, 27, 211, 204, 210, 124, 113, 46, 106, 43, 204, 96, 62, 77, 227, 137, 116, 73, 41, 72, 36, 203,\n  142, 235, 64, 240, 48, 49, 64, 42, 41, 74, 214, 212, 207, 183, 42, 184, 210, 169, 211, 140, 170, 176, 104, 105, 69, 193, 122, 168, 50, 123, 187, 89, 96,\n  90, 66, 65, 107, 179, 89, 180, 119, 101, 120, 68, 63, 104, 234, 93, 227, 16, 15, 85, 209, 129, 49, 15, 14, 86, 107, 55, 9, 120, 100, 121, 153, 145, 22,\n  178, 88, 179, 197, 6, 196, 89, 88, 96, 135, 138, 136, 138, 215, 172, 218, 115, 219, 41, 42, 81, 5, 195, 51, 57, 43, 61, 208, 171, 199, 41, 81, 38,\n  224, 53, 225, 24, 144, 110, 105, 52, 66, 118, 229, 117, 227, 34, 234, 66, 107, 69, 10, 109, 151, 219, 48, 235, 183, 62, 191, 142, 129, 126, 116, 111,\n  143, 7, 163, 246, 118, 117, 50, 223, 222, 52, 94, 19, 141, 222, 221, 65, 196, 3, 197, 45, 220, 44, 156, 70, 139, 188, 122, 245, 139, 71, 162, 145,\n  153, 159, 149, 170, 150, 122, 188, 196, 206, 216, 92, 163, 144, 161, 164, 2, 167, 242, 141, 241, 0, 164, 37, 11, 72, 12, 144, 145, 160, 12, 38, 13, 70,\n  63, 71, 31, 226, 111, 157, 158, 154, 36, 101, 205, 203, 206, 165, 126, 209, 217, 98, 165, 97, 237, 220, 218, 237, 239, 241, 210, 214, 169, 140, 171, 32,\n  241, 125, 237, 179, 86, 178, 180, 85, 179, 181, 84, 180, 182, 83, 181, 194, 201, 182, 177, 137, 132, 184, 76, 183, 185, 61, 184, 186, 57, 185, 216, 212,\n  186, 192, 214, 187, 139, 34, 156, 218, 79, 237, 147, 123, 177, 45, 44, 4, 208, 201, 32, 98, 64, 129, 192, 213, 138, 235, 59, 219, 141, 242, 97, 97, 2,\n  141, 240, 75, 235, 229, 24, 228, 31, 25, 226, 230, 23, 229, 231, 22, 230, 232, 26, 231, 233, 112, 232, 244, 189, 243, 189, 221, 190, 222, 28, 221,\n  223, 27, 222, 224, 29, 223, 225, 30, 224, 113, 247, 225, 99, 60, 240, 213, 147, 215, 60, 20, 166, 192, 187, 213, 243, 112, 244, 244, 233, 245, 245,\n  128, 188, 188, 114, 174, 134, 131, 220, 174, 217, 236, 236, 198, 134, 215, 177, 58, 156, 143, 124, 25, 110, 7, 31, 228, 25, 264, 356, 368, 0, 11, 267,\n  451, 452, 349, 267, 302, 269, 350, 357, 277, 350, 452, 357, 299, 333, 297, 396, 175, 377, 381, 384, 382, 280, 347, 330, 269, 303, 270, 151, 9, 337,\n  344, 278, 360, 424, 418, 431, 270, 304, 409, 272, 310, 407, 322, 270, 410, 449, 450, 347, 432, 422, 434, 18, 313, 17, 291, 306, 375, 259, 387, 260,\n  424, 335, 418, 434, 364, 416, 391, 423, 327, 301, 251, 298, 275, 281, 4, 254, 373, 253, 375, 307, 321, 280, 425, 411, 200, 421, 18, 335, 321, 406,\n  321, 320, 405, 314, 315, 17, 423, 426, 266, 396, 377, 369, 270, 322, 269, 413, 417, 464, 385, 386, 258, 248, 456, 419, 298, 284, 333, 168, 417, 8,\n  448, 346, 261, 417, 413, 285, 326, 327, 328, 277, 355, 329, 309, 392, 438, 381, 382, 256, 279, 429, 360, 365, 364, 379, 355, 277, 437, 282, 443, 283,\n  281, 275, 363, 395, 431, 369, 299, 297, 337, 335, 273, 321, 348, 450, 349, 359, 446, 467, 283, 293, 282, 250, 458, 462, 300, 276, 383, 292, 308, 325,\n  283, 276, 293, 264, 372, 447, 346, 352, 340, 354, 274, 19, 363, 456, 281, 426, 436, 425, 380, 381, 252, 267, 269, 393, 421, 200, 428, 371, 266, 329,\n  432, 287, 422, 290, 250, 328, 385, 258, 384, 446, 265, 342, 386, 387, 257, 422, 424, 430, 445, 342, 276, 422, 273, 424, 306, 292, 307, 352, 366, 345,\n  268, 271, 302, 358, 423, 371, 327, 294, 460, 331, 279, 294, 303, 271, 304, 436, 432, 427, 304, 272, 408, 395, 394, 431, 378, 395, 400, 296, 334, 299,\n  6, 351, 168, 376, 352, 411, 307, 325, 320, 285, 295, 336, 320, 319, 404, 329, 330, 349, 334, 293, 333, 366, 323, 447, 316, 15, 315, 331, 358, 279,\n  317, 14, 316, 8, 285, 9, 277, 329, 350, 253, 374, 252, 319, 318, 403, 351, 6, 419, 324, 318, 325, 397, 367, 365, 288, 435, 397, 278, 344, 439, 310,\n  272, 311, 248, 195, 281, 375, 273, 291, 175, 396, 199, 312, 311, 268, 276, 283, 445, 390, 373, 339, 295, 282, 296, 448, 449, 346, 356, 264, 454, 337,\n  336, 299, 337, 338, 151, 294, 278, 455, 308, 292, 415, 429, 358, 355, 265, 340, 372, 388, 390, 466, 352, 346, 280, 295, 442, 282, 354, 19, 370, 285,\n  441, 295, 195, 248, 197, 457, 440, 274, 301, 300, 368, 417, 351, 465, 251, 301, 389, 385, 380, 386, 394, 395, 379, 399, 412, 419, 410, 436, 322, 387,\n  373, 388, 326, 2, 393, 354, 370, 461, 393, 164, 267, 268, 302, 12, 386, 374, 387, 312, 268, 13, 298, 293, 301, 265, 446, 340, 380, 385, 381, 280, 330,\n  425, 322, 426, 391, 420, 429, 437, 393, 391, 326, 344, 440, 438, 458, 459, 461, 364, 434, 394, 428, 396, 262, 274, 354, 457, 317, 316, 402, 316, 315,\n  403, 315, 314, 404, 314, 313, 405, 313, 421, 406, 323, 366, 361, 292, 306, 407, 306, 291, 408, 291, 287, 409, 287, 432, 410, 427, 434, 411, 372, 264,\n  383, 459, 309, 457, 366, 352, 401, 1, 274, 4, 418, 421, 262, 331, 294, 358, 435, 433, 367, 392, 289, 439, 328, 462, 326, 94, 2, 370, 289, 305, 455, 339,\n  254, 448, 359, 255, 446, 254, 253, 449, 253, 252, 450, 252, 256, 451, 256, 341, 452, 414, 413, 463, 286, 441, 414, 286, 258, 441, 258, 257, 442, 257,\n  259, 443, 259, 260, 444, 260, 467, 445, 309, 459, 250, 305, 289, 290, 305, 290, 460, 401, 376, 435, 309, 250, 392, 376, 411, 433, 453, 341, 464, 357,\n  453, 465, 343, 357, 412, 437, 343, 399, 344, 360, 440, 420, 437, 456, 360, 420, 363, 361, 401, 288, 265, 372, 353, 390, 339, 249, 339, 448, 255];\n\nexport const TRI68: number[] = [0, 1, 36, 0, 36, 17, 1, 2, 41, 1, 41, 36, 2, 3, 31, 2, 31, 41, 3, 4, 48, 3, 48, 31, 4, 5, 48, 5, 6, 48, 6, 7, 59, 6, 59, 48, 7, 8, 58, 7, 58, 59,\n  8, 9, 56, 8, 56, 57, 8, 57, 58, 9, 10, 55, 9, 55, 56, 10, 11, 54, 10, 54, 55, 11, 12, 54, 12, 13, 54, 13, 14, 35, 13, 35, 54, 14, 15, 46, 14, 46, 35, 15, 16,\n  45, 15, 45, 46, 16, 26, 45, 17, 36, 18, 18, 37, 19, 18, 36, 37, 19, 38, 20, 19, 37, 38, 20, 39, 21, 20, 38, 39, 21, 39, 27, 22, 42, 23, 22, 27, 42, 23, 43, 24,\n  23, 42, 43, 24, 44, 25, 24, 43, 44, 25, 45, 26, 25, 44, 45, 27, 39, 28, 27, 28, 42, 28, 39, 29, 28, 29, 42, 29, 31, 30, 29, 30, 35, 29, 40, 31, 29, 35, 47, 29,\n  39, 40, 29, 47, 42, 30, 31, 32, 30, 32, 33, 30, 33, 34, 30, 34, 35, 31, 50, 32, 31, 40, 41, 31, 48, 49, 31, 49, 50, 32, 51, 33, 32, 50, 51, 33, 51, 34, 34, 52,\n  35, 34, 51, 52, 35, 46, 47, 35, 52, 53, 35, 53, 54, 36, 41, 37, 37, 40, 38, 37, 41, 40, 38, 40, 39, 42, 47, 43, 43, 47, 44, 44, 46, 45, 44, 47, 46, 48, 60, 49,\n  48, 59, 60, 49, 61, 50, 49, 60, 61, 50, 62, 51, 50, 61, 62, 51, 62, 52, 52, 63, 53, 52, 62, 63, 53, 64, 54, 53, 63, 64, 54, 64, 55, 55, 65, 56, 55, 64, 65, 56,\n  66, 57, 56, 65, 66, 57, 66, 58, 58, 67, 59, 58, 66, 67, 59, 67, 60, 60, 67, 61, 61, 66, 62, 61, 67, 66, 62, 66, 63, 63, 65, 64, 63, 66, 65, 21, 27, 22];\n\nexport const TRI33: number[] = [\n  /*  eyes  */ 0, 8, 7, 7, 8, 1, 2, 10, 9, 9, 10, 3,\n  /*  brows */ 17, 0, 18, 18, 0, 7, 18, 7, 19, 19, 7, 1, 19, 1, 11, 19, 11, 20, 21, 3, 22, 21, 9, 3, 20, 9, 21, 20, 2, 9, 20, 11, 2,\n  /*  4head */ 23, 17, 18, 25, 21, 22, 24, 19, 20, 24, 18, 19, 24, 20, 21, 24, 23, 18, 24, 21, 25,\n  /*  nose  */ 11, 12, 4, 11, 4, 13, 1, 12, 11, 11, 13, 2, 12, 14, 4, 4, 14, 13,\n  /* up-lip */ 14, 5, 15, 14, 15, 6, 12, 5, 14, 14, 6, 13,\n  /* cheeks */ 8, 12, 1, 2, 13, 10, 8, 26, 12, 10, 13, 27, 26, 5, 12, 13, 6, 27, 0, 26, 8, 10, 27, 3,\n  /*  chin  */ 5, 32, 16, 16, 32, 6, 5, 30, 32, 6, 32, 31,\n  /*  cont  */ 26, 30, 5, 27, 6, 31, 0, 28, 26, 3, 27, 29, 17, 28, 0, 3, 29, 22, 23, 28, 17, 22, 29, 25, 28, 30, 26, 27, 31, 29,\n];\n\nexport const TRI7: number[] = [0, 4, 1, 2, 4, 3, 4, 5, 6];\n\nexport const VTX68: number[] = [\n  /* cont  */ 127, 234, 132, 58, 172, 150, 149, 148, 152, 377, 378, 379, 397, 288, 361, 454, 356,\n  /* brows */ 70, 63, 105, 66, 107, 336, 296, 334, 293, 300,\n  /* nose  */ 168, 6, 195, 4, 98, 97, 2, 326, 327,\n  /* eyes  */ 33, 160, 158, 133, 153, 144, 362, 385, 387, 263, 373, 380,\n  /* lip   */ 57, 40, 37, 0, 267, 270, 287, 321, 314, 17, 84, 91,\n  /* mouth */ 78, 81, 13, 311, 308, 402, 14, 178,\n];\n\nexport const VTX33: number[] = [33, 133, 362, 263, 1, 62, 308, 159, 145, 386, 374, 6, 102, 331, 2, 13, 14, 70, 105, 107, 336, 334, 300, 54, 10, 284, 50, 280, 234, 454, 58, 288, 152];\n\nexport const VTX7: number[] = [33, 133, 362, 263, 1, 78, 308];\n\nexport const UV68 = VTX68.map((x) => UV468[x]);\n\nexport const UV33 = VTX33.map((x) => UV468[x]);\n\nexport const UV7 = VTX7.map((x) => UV468[x]);\n\n// https://github.com/tensorflow/tfjs-models/blob/master/face-landmarks-detection/src/constants.ts\n// https://github.com/google/mediapipe/mediapipe/python/solutions/face_mesh_connections.py\n\ntype PairArray = [number, number][];\n\nfunction connectionsToIndices(connections: PairArray) {\n  const indices = connections.map((connection) => connection[0]);\n  indices.push(connections[connections.length - 1][1]);\n  return indices;\n}\n\nexport const pairsLips: PairArray = [\n  [61, 146], [146, 91], [91, 181], [181, 84], [84, 17], [17, 314], [314, 405], [405, 321], [321, 375], [375, 291], [61, 185], [185, 40], [40, 39], [39, 37], [37, 0], [0, 267], [267, 269], [269, 270], [270, 409], [409, 291],\n  [78, 95], [95, 88], [88, 178], [178, 87], [87, 14], [14, 317], [317, 402], [402, 318], [318, 324], [324, 308], [78, 191], [191, 80], [80, 81], [81, 82], [82, 13], [13, 312], [312, 311], [311, 310], [310, 415], [415, 308],\n];\n\nexport const pairsLeftEye: PairArray = [[263, 249], [249, 390], [390, 373], [373, 374], [374, 380], [380, 381], [381, 382], [382, 362], [263, 466], [466, 388], [388, 387], [387, 386], [386, 385], [385, 384], [384, 398], [398, 362]];\n\nexport const pairsLeftEyebrow: PairArray = [[276, 283], [283, 282], [282, 295], [295, 285], [300, 293], [293, 334], [334, 296], [296, 336]];\n\nexport const pairsLeftIris: PairArray = [[474, 475], [475, 476], [476, 477], [477, 474]];\n\nexport const pairsRightEye: PairArray = [[33, 7], [7, 163], [163, 144], [144, 145], [145, 153], [153, 154], [154, 155], [155, 133], [33, 246], [246, 161], [161, 160], [160, 159], [159, 158], [158, 157], [157, 173], [173, 133]];\n\nexport const pairsRightEyebrow: PairArray = [[46, 53], [53, 52], [52, 65], [65, 55], [70, 63], [63, 105], [105, 66], [66, 107]];\n\nexport const pairsRightIris: PairArray = [[469, 470], [470, 471], [471, 472], [472, 469]];\n\nexport const pairsFaceContour: PairArray = [\n  [10, 338], [338, 297], [297, 332], [332, 284], [284, 251], [251, 389],\n  [389, 356], [356, 454], [454, 323], [323, 361], [361, 288], [288, 397],\n  [397, 365], [365, 379], [379, 378], [378, 400], [400, 377], [377, 152],\n  [152, 148], [148, 176], [176, 149], [149, 150], [150, 136], [136, 172],\n  [172, 58], [58, 132], [132, 93], [93, 234], [234, 127], [127, 162],\n  [162, 21], [21, 54], [54, 103], [103, 67], [67, 109], [109, 10],\n];\n\nexport const contourKeypoints = {\n  lips: connectionsToIndices(pairsLips),\n  leftEye: connectionsToIndices(pairsLeftEye),\n  leftEyebrow: connectionsToIndices(pairsLeftEyebrow),\n  leftIris: connectionsToIndices(pairsLeftIris),\n  rightEye: connectionsToIndices(pairsRightEye),\n  rightEyebrow: connectionsToIndices(pairsRightEyebrow),\n  rightIris: connectionsToIndices(pairsRightIris),\n  faceOval: connectionsToIndices(pairsFaceContour),\n};\n\nexport const pairsFaceMesh: PairArray = [\n  [127, 34], [34, 139], [139, 127], [11, 0], [0, 37], [37, 11],\n  [232, 231], [231, 120], [120, 232], [72, 37], [37, 39], [39, 72],\n  [128, 121], [121, 47], [47, 128], [232, 121], [121, 128], [128, 232],\n  [104, 69], [69, 67], [67, 104], [175, 171], [171, 148], [148, 175],\n  [118, 50], [50, 101], [101, 118], [73, 39], [39, 40], [40, 73],\n  [9, 151], [151, 108], [108, 9], [48, 115], [115, 131], [131, 48],\n  [194, 204], [204, 211], [211, 194], [74, 40], [40, 185], [185, 74],\n  [80, 42], [42, 183], [183, 80], [40, 92], [92, 186], [186, 40],\n  [230, 229], [229, 118], [118, 230], [202, 212], [212, 214], [214, 202],\n  [83, 18], [18, 17], [17, 83], [76, 61], [61, 146], [146, 76],\n  [160, 29], [29, 30], [30, 160], [56, 157], [157, 173], [173, 56],\n  [106, 204], [204, 194], [194, 106], [135, 214], [214, 192], [192, 135],\n  [203, 165], [165, 98], [98, 203], [21, 71], [71, 68], [68, 21],\n  [51, 45], [45, 4], [4, 51], [144, 24], [24, 23], [23, 144],\n  [77, 146], [146, 91], [91, 77], [205, 50], [50, 187], [187, 205],\n  [201, 200], [200, 18], [18, 201], [91, 106], [106, 182], [182, 91],\n  [90, 91], [91, 181], [181, 90], [85, 84], [84, 17], [17, 85],\n  [206, 203], [203, 36], [36, 206], [148, 171], [171, 140], [140, 148],\n  [92, 40], [40, 39], [39, 92], [193, 189], [189, 244], [244, 193],\n  [159, 158], [158, 28], [28, 159], [247, 246], [246, 161], [161, 247],\n  [236, 3], [3, 196], [196, 236], [54, 68], [68, 104], [104, 54],\n  [193, 168], [168, 8], [8, 193], [117, 228], [228, 31], [31, 117],\n  [189, 193], [193, 55], [55, 189], [98, 97], [97, 99], [99, 98],\n  [126, 47], [47, 100], [100, 126], [166, 79], [79, 218], [218, 166],\n  [155, 154], [154, 26], [26, 155], [209, 49], [49, 131], [131, 209],\n  [135, 136], [136, 150], [150, 135], [47, 126], [126, 217], [217, 47],\n  [223, 52], [52, 53], [53, 223], [45, 51], [51, 134], [134, 45],\n  [211, 170], [170, 140], [140, 211], [67, 69], [69, 108], [108, 67],\n  [43, 106], [106, 91], [91, 43], [230, 119], [119, 120], [120, 230],\n  [226, 130], [130, 247], [247, 226], [63, 53], [53, 52], [52, 63],\n  [238, 20], [20, 242], [242, 238], [46, 70], [70, 156], [156, 46],\n  [78, 62], [62, 96], [96, 78], [46, 53], [53, 63], [63, 46],\n  [143, 34], [34, 227], [227, 143], [123, 117], [117, 111], [111, 123],\n  [44, 125], [125, 19], [19, 44], [236, 134], [134, 51], [51, 236],\n  [216, 206], [206, 205], [205, 216], [154, 153], [153, 22], [22, 154],\n  [39, 37], [37, 167], [167, 39], [200, 201], [201, 208], [208, 200],\n  [36, 142], [142, 100], [100, 36], [57, 212], [212, 202], [202, 57],\n  [20, 60], [60, 99], [99, 20], [28, 158], [158, 157], [157, 28],\n  [35, 226], [226, 113], [113, 35], [160, 159], [159, 27], [27, 160],\n  [204, 202], [202, 210], [210, 204], [113, 225], [225, 46], [46, 113],\n  [43, 202], [202, 204], [204, 43], [62, 76], [76, 77], [77, 62],\n  [137, 123], [123, 116], [116, 137], [41, 38], [38, 72], [72, 41],\n  [203, 129], [129, 142], [142, 203], [64, 98], [98, 240], [240, 64],\n  [49, 102], [102, 64], [64, 49], [41, 73], [73, 74], [74, 41],\n  [212, 216], [216, 207], [207, 212], [42, 74], [74, 184], [184, 42],\n  [169, 170], [170, 211], [211, 169], [170, 149], [149, 176], [176, 170],\n  [105, 66], [66, 69], [69, 105], [122, 6], [6, 168], [168, 122],\n  [123, 147], [147, 187], [187, 123], [96, 77], [77, 90], [90, 96],\n  [65, 55], [55, 107], [107, 65], [89, 90], [90, 180], [180, 89],\n  [101, 100], [100, 120], [120, 101], [63, 105], [105, 104], [104, 63],\n  [93, 137], [137, 227], [227, 93], [15, 86], [86, 85], [85, 15],\n  [129, 102], [102, 49], [49, 129], [14, 87], [87, 86], [86, 14],\n  [55, 8], [8, 9], [9, 55], [100, 47], [47, 121], [121, 100],\n  [145, 23], [23, 22], [22, 145], [88, 89], [89, 179], [179, 88],\n  [6, 122], [122, 196], [196, 6], [88, 95], [95, 96], [96, 88],\n  [138, 172], [172, 136], [136, 138], [215, 58], [58, 172], [172, 215],\n  [115, 48], [48, 219], [219, 115], [42, 80], [80, 81], [81, 42],\n  [195, 3], [3, 51], [51, 195], [43, 146], [146, 61], [61, 43],\n  [171, 175], [175, 199], [199, 171], [81, 82], [82, 38], [38, 81],\n  [53, 46], [46, 225], [225, 53], [144, 163], [163, 110], [110, 144],\n  [52, 65], [65, 66], [66, 52], [229, 228], [228, 117], [117, 229],\n  [34, 127], [127, 234], [234, 34], [107, 108], [108, 69], [69, 107],\n  [109, 108], [108, 151], [151, 109], [48, 64], [64, 235], [235, 48],\n  [62, 78], [78, 191], [191, 62], [129, 209], [209, 126], [126, 129],\n  [111, 35], [35, 143], [143, 111], [117, 123], [123, 50], [50, 117],\n  [222, 65], [65, 52], [52, 222], [19, 125], [125, 141], [141, 19],\n  [221, 55], [55, 65], [65, 221], [3, 195], [195, 197], [197, 3],\n  [25, 7], [7, 33], [33, 25], [220, 237], [237, 44], [44, 220],\n  [70, 71], [71, 139], [139, 70], [122, 193], [193, 245], [245, 122],\n  [247, 130], [130, 33], [33, 247], [71, 21], [21, 162], [162, 71],\n  [170, 169], [169, 150], [150, 170], [188, 174], [174, 196], [196, 188],\n  [216, 186], [186, 92], [92, 216], [2, 97], [97, 167], [167, 2],\n  [141, 125], [125, 241], [241, 141], [164, 167], [167, 37], [37, 164],\n  [72, 38], [38, 12], [12, 72], [38, 82], [82, 13], [13, 38],\n  [63, 68], [68, 71], [71, 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454], [454, 447], [447, 323],\n  [15, 16], [16, 315], [315, 15], [358, 429], [429, 279], [279, 358],\n  [14, 15], [15, 316], [316, 14], [285, 336], [336, 9], [9, 285],\n  [329, 349], [349, 350], [350, 329], [374, 380], [380, 252], [252, 374],\n  [318, 402], [402, 403], [403, 318], [6, 197], [197, 419], [419, 6],\n  [318, 319], [319, 325], [325, 318], [367, 364], [364, 365], [365, 367],\n  [435, 367], [367, 397], [397, 435], [344, 438], [438, 439], [439, 344],\n  [272, 271], [271, 311], [311, 272], [195, 5], [5, 281], [281, 195],\n  [273, 287], [287, 291], [291, 273], [396, 428], [428, 199], [199, 396],\n  [311, 271], [271, 268], [268, 311], [283, 444], [444, 445], [445, 283],\n  [373, 254], [254, 339], [339, 373], [282, 334], [334, 296], [296, 282],\n  [449, 347], [347, 346], [346, 449], [264, 447], [447, 454], [454, 264],\n  [336, 296], [296, 299], [299, 336], [338, 10], [10, 151], [151, 338],\n  [278, 439], [439, 455], [455, 278], [292, 407], [407, 415], [415, 292],\n  [358, 371], 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438], [438, 440],\n  [341, 382], [382, 362], [362, 341], [459, 457], [457, 461], [461, 459],\n  [434, 430], [430, 394], [394, 434], [414, 463], [463, 362], [362, 414],\n  [396, 369], [369, 262], [262, 396], [354, 461], [461, 457], [457, 354],\n  [316, 403], [403, 402], [402, 316], [315, 404], [404, 403], [403, 315],\n  [314, 405], [405, 404], [404, 314], [313, 406], [406, 405], [405, 313],\n  [421, 418], [418, 406], [406, 421], [366, 401], [401, 361], [361, 366],\n  [306, 408], [408, 407], [407, 306], [291, 409], [409, 408], [408, 291],\n  [287, 410], [410, 409], [409, 287], [432, 436], [436, 410], [410, 432],\n  [434, 416], [416, 411], [411, 434], [264, 368], [368, 383], [383, 264],\n  [309, 438], [438, 457], [457, 309], [352, 376], [376, 401], [401, 352],\n  [274, 275], [275, 4], [4, 274], [421, 428], [428, 262], [262, 421],\n  [294, 327], [327, 358], [358, 294], [433, 416], [416, 367], [367, 433],\n  [289, 455], [455, 439], [439, 289], [462, 370], [370, 326], [326, 462],\n  [2, 326], [326, 370], [370, 2], [305, 460], [460, 455], [455, 305],\n  [254, 449], [449, 448], [448, 254], [255, 261], [261, 446], [446, 255],\n  [253, 450], [450, 449], [449, 253], [252, 451], [451, 450], [450, 252],\n  [256, 452], [452, 451], [451, 256], [341, 453], [453, 452], [452, 341],\n  [413, 464], [464, 463], [463, 413], [441, 413], [413, 414], [414, 441],\n  [258, 442], [442, 441], [441, 258], [257, 443], [443, 442], [442, 257],\n  [259, 444], [444, 443], [443, 259], [260, 445], [445, 444], [444, 260],\n  [467, 342], [342, 445], [445, 467], [459, 458], [458, 250], [250, 459],\n  [289, 392], [392, 290], [290, 289], [290, 328], [328, 460], [460, 290],\n  [376, 433], [433, 435], [435, 376], [250, 290], [290, 392], [392, 250],\n  [411, 416], [416, 433], [433, 411], [341, 463], [463, 464], [464, 341],\n  [453, 464], [464, 465], [465, 453], [357, 465], [465, 412], [412, 357],\n  [343, 412], [412, 399], [399, 343], [360, 363], [363, 440], [440, 360],\n  [437, 399], [399, 456], [456, 437], [420, 456], [456, 363], [363, 420],\n  [401, 435], [435, 288], [288, 401], [372, 383], [383, 353], [353, 372],\n  [339, 255], [255, 249], [249, 339], [448, 261], [261, 255], [255, 448],\n  [133, 243], [243, 190], [190, 133], [133, 155], [155, 112], [112, 133],\n  [33, 246], [246, 247], [247, 33], [33, 130], [130, 25], [25, 33],\n  [398, 384], [384, 286], [286, 398], [362, 398], [398, 414], [414, 362],\n  [362, 463], [463, 341], [341, 362], [263, 359], [359, 467], [467, 263],\n  [263, 249], [249, 255], [255, 263], [466, 467], [467, 260], [260, 466],\n  [75, 60], [60, 166], [166, 75], [238, 239], [239, 79], [79, 238],\n  [162, 127], [127, 139], [139, 162], [72, 11], [11, 37], [37, 72],\n  [121, 232], [232, 120], [120, 121], [73, 72], [72, 39], [39, 73],\n  [114, 128], [128, 47], [47, 114], [233, 232], [232, 128], [128, 233],\n  [103, 104], [104, 67], [67, 103], [152, 175], [175, 148], [148, 152],\n  [119, 118], [118, 101], [101, 119], [74, 73], [73, 40], [40, 74],\n  [107, 9], [9, 108], [108, 107], [49, 48], [48, 131], [131, 49],\n  [32, 194], [194, 211], [211, 32], [184, 74], [74, 185], [185, 184],\n  [191, 80], [80, 183], [183, 191], [185, 40], [40, 186], [186, 185],\n  [119, 230], [230, 118], [118, 119], [210, 202], [202, 214], [214, 210],\n  [84, 83], [83, 17], [17, 84], [77, 76], [76, 146], [146, 77],\n  [161, 160], [160, 30], [30, 161], [190, 56], [56, 173], [173, 190],\n  [182, 106], [106, 194], [194, 182], [138, 135], [135, 192], [192, 138],\n  [129, 203], [203, 98], [98, 129], [54, 21], [21, 68], [68, 54],\n  [5, 51], [51, 4], [4, 5], [145, 144], [144, 23], [23, 145],\n  [90, 77], [77, 91], [91, 90], [207, 205], [205, 187], [187, 207],\n  [83, 201], [201, 18], [18, 83], [181, 91], [91, 182], [182, 181],\n  [180, 90], [90, 181], [181, 180], [16, 85], [85, 17], [17, 16],\n  [205, 206], [206, 36], [36, 205], [176, 148], [148, 140], [140, 176],\n  [165, 92], [92, 39], [39, 165], [245, 193], [193, 244], [244, 245],\n  [27, 159], [159, 28], [28, 27], [30, 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51], [51, 3],\n  [207, 216], [216, 205], [205, 207], [26, 154], [154, 22], [22, 26],\n  [165, 39], [39, 167], [167, 165], [199, 200], [200, 208], [208, 199],\n  [101, 36], [36, 100], [100, 101], [43, 57], [57, 202], [202, 43],\n  [242, 20], [20, 99], [99, 242], [56, 28], [28, 157], [157, 56],\n  [124, 35], [35, 113], [113, 124], [29, 160], [160, 27], [27, 29],\n  [211, 204], [204, 210], [210, 211], [124, 113], [113, 46], [46, 124],\n  [106, 43], [43, 204], [204, 106], [96, 62], [62, 77], [77, 96],\n  [227, 137], [137, 116], [116, 227], [73, 41], [41, 72], [72, 73],\n  [36, 203], [203, 142], [142, 36], [235, 64], [64, 240], [240, 235],\n  [48, 49], [49, 64], [64, 48], [42, 41], [41, 74], [74, 42],\n  [214, 212], [212, 207], [207, 214], [183, 42], [42, 184], [184, 183],\n  [210, 169], [169, 211], [211, 210], [140, 170], [170, 176], [176, 140],\n  [104, 105], [105, 69], [69, 104], [193, 122], [122, 168], [168, 193],\n  [50, 123], [123, 187], [187, 50], [89, 96], [96, 90], [90, 89],\n  [66, 65], [65, 107], [107, 66], [179, 89], [89, 180], [180, 179],\n  [119, 101], [101, 120], [120, 119], [68, 63], [63, 104], [104, 68],\n  [234, 93], [93, 227], [227, 234], [16, 15], [15, 85], [85, 16],\n  [209, 129], [129, 49], [49, 209], [15, 14], [14, 86], [86, 15],\n  [107, 55], [55, 9], [9, 107], [120, 100], [100, 121], [121, 120],\n  [153, 145], [145, 22], [22, 153], [178, 88], [88, 179], [179, 178],\n  [197, 6], [6, 196], [196, 197], [89, 88], [88, 96], [96, 89],\n  [135, 138], [138, 136], [136, 135], [138, 215], [215, 172], [172, 138],\n  [218, 115], [115, 219], [219, 218], [41, 42], [42, 81], [81, 41],\n  [5, 195], [195, 51], [51, 5], [57, 43], [43, 61], [61, 57],\n  [208, 171], [171, 199], [199, 208], [41, 81], [81, 38], [38, 41],\n  [224, 53], [53, 225], [225, 224], [24, 144], [144, 110], [110, 24],\n  [105, 52], [52, 66], [66, 105], [118, 229], [229, 117], [117, 118],\n  [227, 34], [34, 234], [234, 227], [66, 107], [107, 69], [69, 66],\n  [10, 109], [109, 151], [151, 10], [219, 48], [48, 235], [235, 219],\n  [183, 62], [62, 191], [191, 183], [142, 129], [129, 126], [126, 142],\n  [116, 111], [111, 143], [143, 116], [118, 117], [117, 50], [50, 118],\n  [223, 222], [222, 52], [52, 223], [94, 19], [19, 141], [141, 94],\n  [222, 221], [221, 65], [65, 222], [196, 3], [3, 197], [197, 196],\n  [45, 220], [220, 44], [44, 45], [156, 70], [70, 139], [139, 156],\n  [188, 122], [122, 245], [245, 188], [139, 71], [71, 162], [162, 139],\n  [149, 170], [170, 150], [150, 149], [122, 188], [188, 196], [196, 122],\n  [206, 216], [216, 92], [92, 206], [164, 2], [2, 167], [167, 164],\n  [242, 141], [141, 241], [241, 242], [0, 164], [164, 37], [37, 0],\n  [11, 72], [72, 12], [12, 11], [12, 38], [38, 13], [13, 12],\n  [70, 63], [63, 71], [71, 70], [31, 226], [226, 111], [111, 31],\n  [36, 101], [101, 205], [205, 36], [203, 206], [206, 165], [165, 203],\n  [126, 209], [209, 217], [217, 126], [98, 165], [165, 97], [97, 98],\n  [237, 220], [220, 218], [218, 237], [237, 239], [239, 241], [241, 237],\n  [210, 214], [214, 169], [169, 210], [140, 171], [171, 32], [32, 140],\n  [241, 125], [125, 237], [237, 241], [179, 86], [86, 178], [178, 179],\n  [180, 85], [85, 179], [179, 180], [181, 84], [84, 180], [180, 181],\n  [182, 83], [83, 181], [181, 182], [194, 201], [201, 182], [182, 194],\n  [177, 137], [137, 132], [132, 177], [184, 76], [76, 183], [183, 184],\n  [185, 61], [61, 184], [184, 185], [186, 57], [57, 185], [185, 186],\n  [216, 212], [212, 186], [186, 216], [192, 214], [214, 187], [187, 192],\n  [139, 34], [34, 156], [156, 139], [218, 79], [79, 237], [237, 218],\n  [147, 123], [123, 177], [177, 147], [45, 44], [44, 4], [4, 45],\n  [208, 201], [201, 32], [32, 208], [98, 64], [64, 129], [129, 98],\n  [192, 213], [213, 138], [138, 192], [235, 59], [59, 219], [219, 235],\n  [141, 242], [242, 97], [97, 141], [97, 2], [2, 141], [141, 97],\n  [240, 75], [75, 235], [235, 240], [229, 24], [24, 228], [228, 229],\n  [31, 25], [25, 226], [226, 31], [230, 23], [23, 229], [229, 230],\n  [231, 22], [22, 230], [230, 231], [232, 26], [26, 231], [231, 232],\n  [233, 112], [112, 232], [232, 233], [244, 189], [189, 243], [243, 244],\n  [189, 221], [221, 190], [190, 189], [222, 28], [28, 221], [221, 222],\n  [223, 27], [27, 222], [222, 223], [224, 29], [29, 223], [223, 224],\n  [225, 30], [30, 224], [224, 225], [113, 247], [247, 225], [225, 113],\n  [99, 60], [60, 240], [240, 99], [213, 147], [147, 215], [215, 213],\n  [60, 20], [20, 166], [166, 60], [192, 187], [187, 213], [213, 192],\n  [243, 112], [112, 244], [244, 243], [244, 233], [233, 245], [245, 244],\n  [245, 128], [128, 188], [188, 245], [188, 114], [114, 174], [174, 188],\n  [134, 131], [131, 220], [220, 134], [174, 217], [217, 236], [236, 174],\n  [236, 198], [198, 134], [134, 236], [215, 177], [177, 58], [58, 215],\n  [156, 143], [143, 124], [124, 156], [25, 110], [110, 7], [7, 25],\n  [31, 228], [228, 25], [25, 31], [264, 356], [356, 368], [368, 264],\n  [0, 11], [11, 267], [267, 0], [451, 452], [452, 349], [349, 451],\n  [267, 302], [302, 269], [269, 267], [350, 357], [357, 277], [277, 350],\n  [350, 452], [452, 357], [357, 350], [299, 333], [333, 297], [297, 299],\n  [396, 175], [175, 377], [377, 396], [280, 347], [347, 330], [330, 280],\n  [269, 303], [303, 270], [270, 269], [151, 9], [9, 337], [337, 151],\n  [344, 278], [278, 360], [360, 344], [424, 418], [418, 431], [431, 424],\n  [270, 304], [304, 409], [409, 270], [272, 310], [310, 407], [407, 272],\n  [322, 270], [270, 410], [410, 322], [449, 450], [450, 347], [347, 449],\n  [432, 422], [422, 434], [434, 432], [18, 313], [313, 17], [17, 18],\n  [291, 306], [306, 375], [375, 291], [259, 387], [387, 260], [260, 259],\n  [424, 335], [335, 418], [418, 424], [434, 364], [364, 416], [416, 434],\n  [391, 423], [423, 327], [327, 391], [301, 251], [251, 298], [298, 301],\n  [275, 281], [281, 4], [4, 275], [254, 373], [373, 253], [253, 254],\n  [375, 307], [307, 321], [321, 375], [280, 425], [425, 411], [411, 280],\n  [200, 421], [421, 18], [18, 200], [335, 321], [321, 406], [406, 335],\n  [321, 320], [320, 405], [405, 321], [314, 315], [315, 17], [17, 314],\n  [423, 426], [426, 266], [266, 423], [396, 377], [377, 369], [369, 396],\n  [270, 322], [322, 269], [269, 270], [413, 417], [417, 464], [464, 413],\n  [385, 386], [386, 258], [258, 385], [248, 456], [456, 419], [419, 248],\n  [298, 284], [284, 333], [333, 298], [168, 417], [417, 8], [8, 168],\n  [448, 346], [346, 261], [261, 448], [417, 413], [413, 285], [285, 417],\n  [326, 327], [327, 328], [328, 326], [277, 355], [355, 329], [329, 277],\n  [309, 392], [392, 438], [438, 309], [381, 382], [382, 256], [256, 381],\n  [279, 429], [429, 360], [360, 279], [365, 364], [364, 379], [379, 365],\n  [355, 277], [277, 437], [437, 355], [282, 443], [443, 283], [283, 282],\n  [281, 275], [275, 363], [363, 281], [395, 431], [431, 369], [369, 395],\n  [299, 297], [297, 337], [337, 299], [335, 273], [273, 321], [321, 335],\n  [348, 450], [450, 349], [349, 348], [359, 446], [446, 467], [467, 359],\n  [283, 293], [293, 282], [282, 283], [250, 458], [458, 462], [462, 250],\n  [300, 276], [276, 383], [383, 300], [292, 308], [308, 325], [325, 292],\n  [283, 276], [276, 293], [293, 283], [264, 372], [372, 447], [447, 264],\n  [346, 352], [352, 340], [340, 346], [354, 274], [274, 19], [19, 354],\n  [363, 456], [456, 281], [281, 363], [426, 436], [436, 425], [425, 426],\n  [380, 381], [381, 252], [252, 380], [267, 269], [269, 393], [393, 267],\n  [421, 200], [200, 428], [428, 421], [371, 266], [266, 329], [329, 371],\n  [432, 287], [287, 422], [422, 432], [290, 250], [250, 328], [328, 290],\n  [385, 258], [258, 384], [384, 385], [446, 265], [265, 342], [342, 446],\n  [386, 387], [387, 257], [257, 386], [422, 424], [424, 430], [430, 422],\n  [445, 342], [342, 276], [276, 445], [422, 273], [273, 424], [424, 422],\n  [306, 292], [292, 307], [307, 306], [352, 366], [366, 345], [345, 352],\n  [268, 271], [271, 302], [302, 268], [358, 423], [423, 371], [371, 358],\n  [327, 294], [294, 460], [460, 327], [331, 279], [279, 294], [294, 331],\n  [303, 271], [271, 304], [304, 303], [436, 432], [432, 427], [427, 436],\n  [304, 272], [272, 408], [408, 304], [395, 394], [394, 431], [431, 395],\n  [378, 395], [395, 400], [400, 378], [296, 334], [334, 299], [299, 296],\n  [6, 351], [351, 168], [168, 6], [376, 352], [352, 411], [411, 376],\n  [307, 325], [325, 320], [320, 307], [285, 295], [295, 336], [336, 285],\n  [320, 319], [319, 404], [404, 320], [329, 330], [330, 349], [349, 329],\n  [334, 293], [293, 333], [333, 334], [366, 323], [323, 447], [447, 366],\n  [316, 15], [15, 315], [315, 316], [331, 358], [358, 279], [279, 331],\n  [317, 14], [14, 316], [316, 317], [8, 285], [285, 9], [9, 8],\n  [277, 329], [329, 350], [350, 277], [253, 374], [374, 252], [252, 253],\n  [319, 318], [318, 403], [403, 319], [351, 6], [6, 419], [419, 351],\n  [324, 318], [318, 325], [325, 324], [397, 367], [367, 365], [365, 397],\n  [288, 435], [435, 397], [397, 288], [278, 344], [344, 439], [439, 278],\n  [310, 272], [272, 311], [311, 310], [248, 195], [195, 281], [281, 248],\n  [375, 273], [273, 291], [291, 375], [175, 396], [396, 199], [199, 175],\n  [312, 311], [311, 268], [268, 312], [276, 283], [283, 445], [445, 276],\n  [390, 373], [373, 339], [339, 390], [295, 282], [282, 296], [296, 295],\n  [448, 449], [449, 346], [346, 448], [356, 264], [264, 454], [454, 356],\n  [337, 336], [336, 299], [299, 337], [337, 338], [338, 151], [151, 337],\n  [294, 278], [278, 455], [455, 294], [308, 292], [292, 415], [415, 308],\n  [429, 358], [358, 355], [355, 429], [265, 340], [340, 372], [372, 265],\n  [352, 346], [346, 280], [280, 352], [295, 442], [442, 282], [282, 295],\n  [354, 19], [19, 370], [370, 354], [285, 441], [441, 295], [295, 285],\n  [195, 248], [248, 197], [197, 195], [457, 440], [440, 274], [274, 457],\n  [301, 300], [300, 368], [368, 301], [417, 351], [351, 465], [465, 417],\n  [251, 301], [301, 389], [389, 251], [394, 395], [395, 379], [379, 394],\n  [399, 412], [412, 419], [419, 399], [410, 436], [436, 322], [322, 410],\n  [326, 2], [2, 393], [393, 326], [354, 370], [370, 461], [461, 354],\n  [393, 164], [164, 267], [267, 393], [268, 302], [302, 12], [12, 268],\n  [312, 268], [268, 13], [13, 312], [298, 293], [293, 301], [301, 298],\n  [265, 446], [446, 340], [340, 265], [280, 330], [330, 425], [425, 280],\n  [322, 426], [426, 391], [391, 322], [420, 429], [429, 437], [437, 420],\n  [393, 391], [391, 326], [326, 393], [344, 440], [440, 438], [438, 344],\n  [458, 459], [459, 461], [461, 458], [364, 434], [434, 394], [394, 364],\n  [428, 396], [396, 262], [262, 428], [274, 354], [354, 457], [457, 274],\n  [317, 316], [316, 402], [402, 317], [316, 315], [315, 403], [403, 316],\n  [315, 314], [314, 404], [404, 315], [314, 313], [313, 405], [405, 314],\n  [313, 421], [421, 406], [406, 313], [323, 366], [366, 361], [361, 323],\n  [292, 306], [306, 407], [407, 292], [306, 291], [291, 408], [408, 306],\n  [291, 287], [287, 409], [409, 291], [287, 432], [432, 410], [410, 287],\n  [427, 434], [434, 411], [411, 427], [372, 264], [264, 383], [383, 372],\n  [459, 309], [309, 457], [457, 459], [366, 352], [352, 401], [401, 366],\n  [1, 274], [274, 4], [4, 1], [418, 421], [421, 262], [262, 418],\n  [331, 294], [294, 358], [358, 331], [435, 433], [433, 367], [367, 435],\n  [392, 289], [289, 439], [439, 392], [328, 462], [462, 326], [326, 328],\n  [94, 2], [2, 370], [370, 94], [289, 305], [305, 455], [455, 289],\n  [339, 254], [254, 448], [448, 339], [359, 255], [255, 446], [446, 359],\n  [254, 253], [253, 449], [449, 254], [253, 252], [252, 450], [450, 253],\n  [252, 256], [256, 451], [451, 252], [256, 341], [341, 452], [452, 256],\n  [414, 413], [413, 463], [463, 414], [286, 441], [441, 414], [414, 286],\n  [286, 258], [258, 441], [441, 286], [258, 257], [257, 442], [442, 258],\n  [257, 259], [259, 443], [443, 257], [259, 260], [260, 444], [444, 259],\n  [260, 467], [467, 445], [445, 260], [309, 459], [459, 250], [250, 309],\n  [305, 289], [289, 290], [290, 305], [305, 290], [290, 460], [460, 305],\n  [401, 376], [376, 435], [435, 401], [309, 250], [250, 392], [392, 309],\n  [376, 411], [411, 433], [433, 376], [453, 341], [341, 464], [464, 453],\n  [357, 453], [453, 465], [465, 357], [343, 357], [357, 412], [412, 343],\n  [437, 343], [343, 399], [399, 437], [344, 360], [360, 440], [440, 344],\n  [420, 437], [437, 456], [456, 420], [360, 420], [420, 363], [363, 360],\n  [361, 401], [401, 288], [288, 361], [265, 372], [372, 353], [353, 265],\n  [390, 339], [339, 249], [249, 390], [339, 448], [448, 255], [255, 339],\n];\n", "/**\n * BlazeFace, FaceMesh & Iris model implementation\n * See `facemesh.ts` for entry point\n */\n\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as coords from './facemeshcoords';\nimport { constants } from '../tfjs/constants';\nimport type { Box, Point } from '../result';\nimport { env } from '../util/env';\n\nexport const createBox = (startEndTensor) => ({ startPoint: tf.slice(startEndTensor, [0, 0], [-1, 2]), endPoint: tf.slice(startEndTensor, [0, 2], [-1, 2]) });\n\nexport const disposeBox = (t) => tf.dispose([t.startPoint, t.endPoint]);\n\nexport const getBoxSize = (box): [number, number] => [Math.abs(box.endPoint[0] - box.startPoint[0]), Math.abs(box.endPoint[1] - box.startPoint[1])];\n\nexport const getBoxCenter = (box): [number, number, number] => [box.startPoint[0] + (box.endPoint[0] - box.startPoint[0]) / 2, box.startPoint[1] + (box.endPoint[1] - box.startPoint[1]) / 2, 1];\n\nexport const clampBox = (box, input): Box => (box ? [\n  Math.trunc(Math.max(0, box.startPoint[0])),\n  Math.trunc(Math.max(0, box.startPoint[1])),\n  Math.trunc(Math.min((input.shape[2] || 0), box.endPoint[0]) - Math.max(0, box.startPoint[0])),\n  Math.trunc(Math.min((input.shape[1] || 0), box.endPoint[1]) - Math.max(0, box.startPoint[1])),\n] : [0, 0, 0, 0]);\n\nexport const getRawBox = (box, input): Box => (box ? [\n  box.startPoint[0] / (input.shape[2] || 0),\n  box.startPoint[1] / (input.shape[1] || 0),\n  (box.endPoint[0] - box.startPoint[0]) / (input.shape[2] || 0),\n  (box.endPoint[1] - box.startPoint[1]) / (input.shape[1] || 0),\n] : [0, 0, 0, 0]);\n\nexport const scaleBoxCoordinates = (box, factor) => {\n  const startPoint: Point = [box.startPoint[0] * factor[0], box.startPoint[1] * factor[1]];\n  const endPoint: Point = [box.endPoint[0] * factor[0], box.endPoint[1] * factor[1]];\n  return { startPoint, endPoint, landmarks: box.landmarks, confidence: box.confidence };\n};\n\nexport const cutAndResize = (box, image, cropSize) => {\n  const h = image.shape[1];\n  const w = image.shape[2];\n  const cutBox = [box.startPoint[1] / h, box.startPoint[0] / w, box.endPoint[1] / h, box.endPoint[0] / w];\n  const crop = tf.image.cropAndResize(image, [cutBox], [0], cropSize);\n  const norm = tf.div(crop, constants.tf255);\n  tf.dispose(crop);\n  return norm;\n};\n\nexport const enlargeBox = (box, factor) => {\n  const center = getBoxCenter(box);\n  const size = getBoxSize(box);\n  const halfSize: [number, number] = [factor * size[0] / 2, factor * size[1] / 2];\n  return { startPoint: [center[0] - halfSize[0], center[1] - halfSize[1]] as Point, endPoint: [center[0] + halfSize[0], center[1] + halfSize[1]] as Point, landmarks: box.landmarks, confidence: box.confidence };\n};\n\nexport const squarifyBox = (box) => {\n  const centers = getBoxCenter(box);\n  const size = getBoxSize(box);\n  const halfSize = Math.max(...size) / 2;\n  return { startPoint: [Math.round(centers[0] - halfSize), Math.round(centers[1] - halfSize)] as Point, endPoint: [Math.round(centers[0] + halfSize), Math.round(centers[1] + halfSize)] as Point, landmarks: box.landmarks, confidence: box.confidence };\n};\n\nexport const calculateLandmarksBoundingBox = (landmarks) => {\n  const x = landmarks.map((d) => d[0]);\n  const y = landmarks.map((d) => d[1]);\n  return { startPoint: [Math.min(...x), Math.min(...y)] as Point, endPoint: [Math.max(...x), Math.max(...y)] as Point, landmarks };\n};\n\nexport const fixedRotationMatrix = [[1, 0, 0], [0, 1, 0], [0, 0, 1]];\n\nexport const normalizeRadians = (angle: number) => angle - 2 * Math.PI * Math.floor((angle + Math.PI) / (2 * Math.PI));\n\nexport const computeRotation = (point1, point2) => normalizeRadians(Math.PI / 2 - Math.atan2(-(point2[1] - point1[1]), point2[0] - point1[0]));\n\nexport const radToDegrees = (rad) => rad * 180 / Math.PI;\n\nexport const buildTranslationMatrix = (x, y) => [[1, 0, x], [0, 1, y], [0, 0, 1]];\n\nexport const dot = (v1: number[], v2: number[]) => {\n  let product = 0;\n  for (let i = 0; i < v1.length; i++) product += v1[i] * v2[i];\n  return product;\n};\n\nexport const getColumnFrom2DArr = (arr, columnIndex) => {\n  const column: number[] = [];\n  for (let i = 0; i < arr.length; i++) column.push(arr[i][columnIndex]);\n  return column;\n};\n\nexport const multiplyTransformMatrices = (mat1, mat2) => {\n  const product: number[][] = [];\n  const size = mat1.length;\n  for (let row = 0; row < size; row++) {\n    product.push([]);\n    for (let col = 0; col < size; col++) product[row].push(dot(mat1[row], getColumnFrom2DArr(mat2, col)));\n  }\n  return product;\n};\n\nexport const buildRotationMatrix = (rotation, center) => {\n  const cosA = Math.cos(rotation);\n  const sinA = Math.sin(rotation);\n  const rotationMatrix = [[cosA, -sinA, 0], [sinA, cosA, 0], [0, 0, 1]];\n  const translationMatrix = buildTranslationMatrix(center[0], center[1]);\n  const translationTimesRotation = multiplyTransformMatrices(translationMatrix, rotationMatrix);\n  const negativeTranslationMatrix = buildTranslationMatrix(-center[0], -center[1]);\n  return multiplyTransformMatrices(translationTimesRotation, negativeTranslationMatrix);\n};\n\nexport const invertTransformMatrix = (matrix) => {\n  const rotationComponent = [[matrix[0][0], matrix[1][0]], [matrix[0][1], matrix[1][1]]];\n  const translationComponent = [matrix[0][2], matrix[1][2]];\n  const invertedTranslation = [-dot(rotationComponent[0], translationComponent), -dot(rotationComponent[1], translationComponent)];\n  return [rotationComponent[0].concat(invertedTranslation[0]), rotationComponent[1].concat(invertedTranslation[1]), [0, 0, 1]];\n};\n\nexport const rotatePoint = (homogeneousCoordinate, rotationMatrix) => [dot(homogeneousCoordinate, rotationMatrix[0]), dot(homogeneousCoordinate, rotationMatrix[1])];\n\nexport const xyDistanceBetweenPoints = (a, b) => Math.sqrt(((a[0] - b[0]) ** 2) + ((a[1] - b[1]) ** 2));\n\nexport function generateAnchors(inputSize: number) {\n  const spec = inputSize === 192\n    ? { strides: [4], anchors: [1] } // facemesh-detector\n    : { strides: [inputSize / 16, inputSize / 8], anchors: [2, 6] }; // blazeface\n  const anchors: [number, number][] = [];\n  for (let i = 0; i < spec.strides.length; i++) {\n    const stride = spec.strides[i];\n    const gridRows = Math.floor((inputSize + stride - 1) / stride);\n    const gridCols = Math.floor((inputSize + stride - 1) / stride);\n    const anchorsNum = spec.anchors[i];\n    for (let gridY = 0; gridY < gridRows; gridY++) {\n      const anchorY = stride * (gridY + 0.5);\n      for (let gridX = 0; gridX < gridCols; gridX++) {\n        const anchorX = stride * (gridX + 0.5);\n        for (let n = 0; n < anchorsNum; n++) anchors.push([anchorX, anchorY]);\n      }\n    }\n  }\n  return anchors;\n}\n\nexport function transformRawCoords(coordsRaw, box, angle, rotationMatrix, inputSize) {\n  const boxSize = getBoxSize(box);\n  const coordsScaled = coordsRaw.map((coord) => ([ // scaled around zero-point\n    (boxSize[0] / inputSize) * (coord[0] - (inputSize / 2)),\n    (boxSize[1] / inputSize) * (coord[1] - (inputSize / 2)),\n    (coord[2] || 0),\n  ]));\n  const largeAngle = angle && (angle !== 0) && (Math.abs(angle) > 0.2);\n  const coordsRotationMatrix = largeAngle ? buildRotationMatrix(angle, [0, 0]) : fixedRotationMatrix;\n  const coordsRotated = largeAngle ? coordsScaled.map((coord) => ([...rotatePoint(coord, coordsRotationMatrix), coord[2]])) : coordsScaled;\n  const inverseRotationMatrix = largeAngle ? invertTransformMatrix(rotationMatrix) : fixedRotationMatrix;\n  const boxCenter = getBoxCenter(box);\n  const offsets = [dot(boxCenter, inverseRotationMatrix[0]), dot(boxCenter, inverseRotationMatrix[1])];\n  return coordsRotated.map((coord) => ([\n    Math.trunc(coord[0] + offsets[0]),\n    Math.trunc(coord[1] + offsets[1]),\n    Math.trunc(coord[2] || 0),\n  ]));\n}\n\nexport function correctFaceRotation(rotate, box, input, inputSize) {\n  const symmetryLine = (box.landmarks.length >= coords.meshLandmarks.count)\n    ? coords.meshLandmarks.symmetryLine\n    : coords.blazeFaceLandmarks.symmetryLine;\n  let angle = 0; // default\n  let rotationMatrix = fixedRotationMatrix; // default\n  let face; // default\n\n  if (rotate && env.kernels.includes('rotatewithoffset')) {\n    angle = computeRotation(box.landmarks[symmetryLine[0]], box.landmarks[symmetryLine[1]]);\n    const largeAngle = angle && (angle !== 0) && (Math.abs(angle) > 0.2);\n    if (largeAngle) { // perform rotation only if angle is sufficiently high\n      const center: Point = getBoxCenter(box);\n      const centerRaw: Point = [center[0] / input.shape[2], center[1] / input.shape[1]];\n      const rotated = tf.image.rotateWithOffset(input, angle, 0, centerRaw);\n      rotationMatrix = buildRotationMatrix(-angle, center);\n      face = cutAndResize(box, rotated, [inputSize, inputSize]);\n      tf.dispose(rotated);\n    } else {\n      face = cutAndResize(box, input, [inputSize, inputSize]);\n    }\n  } else {\n    face = cutAndResize(box, input, [inputSize, inputSize]);\n  }\n  return [angle, rotationMatrix, face];\n}\n\nexport const findFaceCenter = (mesh) => {\n  const x = mesh.map((m) => m[0]);\n  const y = mesh.map((m) => m[1]);\n  // weighted center\n  /*\n  const sum = (arr: number[]) => arr.reduce((prev, curr) => prev + curr, 0);\n  return [sum(x) / mesh.length, sum(y) / mesh.length];\n  */\n  // absolute center\n  return [Math.min(...x) + (Math.max(...x) - Math.min(...x)) / 2, Math.min(...y) + (Math.max(...y) - Math.min(...y)) / 2];\n};\n\nexport const calculateFaceBox = (mesh, previousBox) => {\n  const center = findFaceCenter(mesh);\n  const boxSize = getBoxSize(previousBox);\n  const calculatedBox = {\n    startPoint: [center[0] - boxSize[0] / 2, center[1] - boxSize[1] / 2] as Point,\n    endPoint: [center[0] + boxSize[0] / 2, center[1] + boxSize[1] / 2] as Point,\n  };\n  return calculatedBox;\n};\n", "/**\n * BlazeFace, FaceMesh & Iris model implementation\n * See `facemesh.ts` for entry point\n */\n\nimport { log } from '../util/util';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as util from './facemeshutil';\nimport { loadModel } from '../tfjs/load';\nimport { constants } from '../tfjs/constants';\nimport type { Config } from '../config';\nimport type { Tensor, GraphModel } from '../tfjs/types';\nimport { env } from '../util/env';\nimport type { Point } from '../result';\n\nconst keypointsCount = 6;\nconst faceBoxScaleFactor = 1.4;\nlet model: GraphModel | null;\nlet anchors: Tensor | null = null;\nlet inputSize = 0;\nlet inputSizeT: Tensor | null = null;\n\ninterface DetectBox { startPoint: Point, endPoint: Point, landmarks: Point[], confidence: number }\n\nexport const size = () => inputSize;\n\nexport async function load(config: Config): Promise<GraphModel> {\n  if (env.initial) model = null;\n  if (!model) model = await loadModel(config.face.detector?.modelPath);\n  else if (config.debug) log('cached model:', model['modelUrl']);\n  inputSize = (model['executor'] && model.inputs[0].shape) ? model.inputs[0].shape[2] : 256;\n  inputSizeT = tf.scalar(inputSize, 'int32') as Tensor;\n  anchors = tf.tensor2d(util.generateAnchors(inputSize)) as Tensor;\n  return model;\n}\n\nfunction decodeBoxes(boxOutputs: Tensor) {\n  const t: Record<string, Tensor> = {};\n  t.boxStarts = tf.slice(boxOutputs, [0, 1], [-1, 2]);\n  t.centers = tf.add(t.boxStarts, anchors);\n  t.boxSizes = tf.slice(boxOutputs, [0, 3], [-1, 2]);\n  t.boxSizesNormalized = tf.div(t.boxSizes, inputSizeT);\n  t.centersNormalized = tf.div(t.centers, inputSizeT);\n  t.halfBoxSize = tf.div(t.boxSizesNormalized, constants.tf2);\n  t.starts = tf.sub(t.centersNormalized, t.halfBoxSize);\n  t.ends = tf.add(t.centersNormalized, t.halfBoxSize);\n  t.startNormalized = tf.mul(t.starts, inputSizeT);\n  t.endNormalized = tf.mul(t.ends, inputSizeT);\n  const boxes = tf.concat2d([t.startNormalized, t.endNormalized], 1);\n  Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n  return boxes;\n}\n\nexport async function getBoxes(inputImage: Tensor, config: Config) {\n  // sanity check on input\n  if ((!inputImage) || (inputImage['isDisposedInternal']) || (inputImage.shape.length !== 4) || (inputImage.shape[1] < 1) || (inputImage.shape[2] < 1)) return [];\n  const t: Record<string, Tensor> = {};\n  t.resized = tf.image.resizeBilinear(inputImage, [inputSize, inputSize]);\n  t.div = tf.div(t.resized, constants.tf127);\n  t.normalized = tf.sub(t.div, constants.tf05);\n  const res = model?.execute(t.normalized) as Tensor[];\n  if (Array.isArray(res) && res.length > 2) { // pinto converted model?\n    const sorted = res.sort((a, b) => a.size - b.size);\n    t.concat384 = tf.concat([sorted[0], sorted[2]], 2); // dim: 384, 1 + 16\n    t.concat512 = tf.concat([sorted[1], sorted[3]], 2); // dim: 512, 1 + 16\n    t.concat = tf.concat([t.concat512, t.concat384], 1);\n    t.batch = tf.squeeze(t.concat, 0);\n  } else if (Array.isArray(res)) { // new facemesh-detection tfhub model\n    t.batch = tf.squeeze(res[0]);\n  } else { // original blazeface tfhub model\n    t.batch = tf.squeeze(res);\n  }\n  tf.dispose(res);\n  t.boxes = decodeBoxes(t.batch);\n  t.logits = tf.slice(t.batch, [0, 0], [-1, 1]);\n  t.sigmoid = tf.sigmoid(t.logits);\n  t.scores = tf.squeeze(t.sigmoid);\n  t.nms = await tf.image.nonMaxSuppressionAsync(t.boxes, t.scores, (config.face.detector?.maxDetected || 0), (config.face.detector?.iouThreshold || 0), (config.face.detector?.minConfidence || 0));\n  const nms = await t.nms.array() as number[];\n  const boxes: DetectBox[] = [];\n  const scores = await t.scores.data();\n  for (let i = 0; i < nms.length; i++) {\n    const confidence = scores[nms[i]];\n    if (confidence > (config.face.detector?.minConfidence || 0)) {\n      const b: Record<string, Tensor> = {};\n      b.bbox = tf.slice(t.boxes, [nms[i], 0], [1, -1]);\n      b.slice = tf.slice(t.batch, [nms[i], keypointsCount - 1], [1, -1]);\n      b.squeeze = tf.squeeze(b.slice);\n      b.landmarks = tf.reshape(b.squeeze, [keypointsCount, -1]);\n      const points = await b.bbox.data();\n      const rawBox = {\n        startPoint: [points[0], points[1]] as Point,\n        endPoint: [points[2], points[3]] as Point,\n        landmarks: (await b.landmarks.array()) as Point[],\n        confidence,\n      };\n      const scaledBox = util.scaleBoxCoordinates(rawBox, [(inputImage.shape[2] || 0) / inputSize, (inputImage.shape[1] || 0) / inputSize]);\n      const enlargedBox = util.enlargeBox(scaledBox, config.face['scale'] || faceBoxScaleFactor);\n      const squaredBox = util.squarifyBox(enlargedBox);\n      boxes.push(squaredBox);\n      Object.keys(b).forEach((tensor) => tf.dispose(b[tensor]));\n    }\n  }\n  Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n  return boxes;\n}\n", "/* eslint-disable no-multi-spaces */\n\nexport const kpt: string[] = [\n  'nose',            //  0\n  'leftEyeInside',   //  1\n  'leftEye',         //  2\n  'leftEyeOutside',  //  3\n  'rightEyeInside',  //  4\n  'rightEye',        //  5\n  'rightEyeOutside', //  6\n  'leftEar',         //  7\n  'rightEar',        //  8\n  'leftMouth',       //  9\n  'rightMouth',      // 10\n  'leftShoulder',    // 11\n  'rightShoulder',   // 12\n  'leftElbow',       // 13\n  'rightElbow',      // 14\n  'leftWrist',       // 15\n  'rightWrist',      // 16\n  'leftPinky',       // 17\n  'rightPinky',      // 18\n  'leftIndex',       // 19\n  'rightIndex',      // 20\n  'leftThumb',       // 21\n  'rightThumb',      // 22\n  'leftHip',         // 23\n  'rightHip',        // 24\n  'leftKnee',        // 25\n  'rightKnee',       // 26\n  'leftAnkle',       // 27\n  'rightAnkle',      // 28\n  'leftHeel',        // 29\n  'rightHeel',       // 30\n  'leftFoot',        // 31\n  'rightFoot',       // 32\n  'bodyCenter',      // 33\n  'bodyTop',         // 34\n  'leftPalm',        // 35 // z-coord not ok\n  'leftHand',        // 36 // similar to wrist but z-coord not ok\n  'rightPalm',       // 37 // z-coord not ok\n  'rightHand',       // 38 // similar to wrist but z-coord not ok\n];\n\nexport const connected: Record<string, string[]> = {\n  shoulders: ['leftShoulder', 'rightShoulder'],\n  hips: ['rightHip', 'leftHip'],\n  mouth: ['leftMouth', 'rightMouth'],\n  leftLegUpper: ['leftHip', 'leftKnee'],\n  leftLegLower: ['leftKnee', 'leftAnkle'],\n  leftFoot: ['leftAnkle', 'leftHeel', 'leftFoot'],\n  leftTorso: ['leftShoulder', 'leftHip'],\n  leftArmUpper: ['leftShoulder', 'leftElbow'],\n  leftArmLower: ['leftElbow', 'leftWrist'],\n  leftHand: ['leftWrist', 'leftPalm'],\n  leftHandPinky: ['leftPalm', 'leftPinky'],\n  leftHandIndex: ['leftPalm', 'leftIndex'],\n  leftHandThumb: ['leftPalm', 'leftThumb'],\n  leftEyeOutline: ['leftEyeInside', 'leftEyeOutside'],\n  rightLegUpper: ['rightHip', 'rightKnee'],\n  rightLegLower: ['rightKnee', 'rightAnkle'],\n  rightFoot: ['rightAnkle', 'rightHeel', 'rightFoot'],\n  rightTorso: ['rightShoulder', 'rightHip'],\n  rightArmUpper: ['rightShoulder', 'rightElbow'],\n  rightArmLower: ['rightElbow', 'rightWrist'],\n  rightHand: ['rightWrist', 'rightPalm'],\n  rightHandPinky: ['rightPalm', 'rightPinky'],\n  rightHandIndex: ['rightPalm', 'rightIndex'],\n  rightHandThumb: ['rightPalm', 'rightThumb'],\n  rightEyeOutline: ['rightEyeInside', 'rightEyeOutside'],\n};\n", "import * as tf from '../../dist/tfjs.esm.js';\nimport type { Tensor } from '../tfjs/types';\nimport type { Box } from '../result';\nimport type { Config } from '../config';\n\ninterface DetectedBox { box: Box, boxRaw: Box, score: number }\n\nconst inputSize = 224;\nlet anchorTensor: { x, y };\nconst numLayers = 5;\nconst strides = [8, 16, 32, 32, 32];\n\nexport function createAnchors() {\n  const anchors: { x: number, y: number }[] = [];\n  let layerId = 0;\n  while (layerId < numLayers) {\n    let anchorCount = 0;\n    let lastSameStrideLayer = layerId;\n    while (lastSameStrideLayer < strides.length && strides[lastSameStrideLayer] === strides[layerId]) {\n      anchorCount += 2;\n      lastSameStrideLayer++;\n    }\n    const stride = strides[layerId];\n    const featureMapHeight = Math.ceil(inputSize / stride);\n    const featureMapWidth = Math.ceil(inputSize / stride);\n    for (let y = 0; y < featureMapHeight; ++y) {\n      for (let x = 0; x < featureMapWidth; ++x) {\n        for (let anchorId = 0; anchorId < anchorCount; ++anchorId) {\n          anchors.push({ x: (x + 0.5) / featureMapWidth, y: (y + 0.5) / featureMapHeight });\n        }\n      }\n    }\n    layerId = lastSameStrideLayer;\n  }\n  anchorTensor = { x: tf.tensor1d(anchors.map((a) => a.x)), y: tf.tensor1d(anchors.map((a) => a.y)) };\n}\n\nconst cropFactor = [5.0, 5.0];\nfunction decodeBoxes(boxesTensor, anchor): Tensor {\n  return tf.tidy(() => {\n    const split = tf.split(boxesTensor, 12, 1); // first 4 are box data [x,y,w,h] and 4 are keypoints data [x,y] for total of 12\n    let xCenter = tf.squeeze(split[0]);\n    let yCenter = tf.squeeze(split[1]);\n    let width = tf.squeeze(split[2]);\n    let height = tf.squeeze(split[3]);\n    xCenter = tf.add(tf.div(xCenter, inputSize), anchor.x);\n    yCenter = tf.add(tf.div(yCenter, inputSize), anchor.y);\n    width = tf.mul(tf.div(width, inputSize), cropFactor[0]);\n    height = tf.mul(tf.div(height, inputSize), cropFactor[1]);\n    const xMin = tf.sub(xCenter, tf.div(width, 2));\n    const yMin = tf.sub(yCenter, tf.div(height, 2));\n    const boxes = tf.stack([xMin, yMin, width, height], 1);\n    return boxes;\n  });\n}\n\nexport async function decode(boxesTensor: Tensor, logitsTensor: Tensor, config: Config, outputSize: [number, number]): Promise<DetectedBox[]> {\n  const t: Record<string, Tensor> = {};\n  t.boxes = decodeBoxes(boxesTensor, anchorTensor);\n  t.scores = tf.sigmoid(logitsTensor);\n  t.argmax = tf.argMax(t.scores);\n  const i = (await t.argmax.data())[0];\n  const scores = await t.scores.data();\n  const detected: { box: Box, boxRaw: Box, score: number }[] = [];\n  const minScore = config.body?.['detector']?.minConfidence || 0;\n  if (scores[i] >= minScore) {\n    const boxes = await t.boxes.array();\n    const boxRaw: Box = boxes[i];\n    const box: Box = [boxRaw[0] * outputSize[0], boxRaw[1] * outputSize[1], boxRaw[2] * outputSize[0], boxRaw[3] * outputSize[1]];\n    // console.log(box);\n    detected.push({ box, boxRaw, score: scores[i] });\n  }\n  /*\n  t.nms = await tf.image.nonMaxSuppressionAsync(t.boxes, t.scores, 1, config.body.detector?.minConfidence || 0.1, config.body.detector?.iouThreshold || 0.1);\n  const boxes = t.boxes.arraySync();\n  const scores = t.scores.dataSync();\n  const nms = t.nms.dataSync();\n  const detected: Array<DetectedBox> = [];\n  for (const i of Array.from(nms)) {\n    const boxRaw: Box = boxes[i];\n    const box: Box = [boxRaw[0] * outputSize[0], boxRaw[0] * outputSize[1], boxRaw[3] * outputSize[0], boxRaw[2] * outputSize[1]];\n    detected.push({ box, boxRaw, score: scores[i] });\n  }\n  */\n  Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n  return detected;\n}\n", "import type { Point, Box } from '../result';\n\nexport function calc(keypoints: Point[], outputSize: [number, number] = [1, 1]) {\n  const coords = [keypoints.map((pt) => pt[0]), keypoints.map((pt) => pt[1])]; // all x/y coords\n  const min = [Math.min(...coords[0]), Math.min(...coords[1])];\n  const max = [Math.max(...coords[0]), Math.max(...coords[1])];\n  const box: Box = [min[0], min[1], max[0] - min[0], max[1] - min[1]];\n  const boxRaw: Box = [box[0] / outputSize[0], box[1] / outputSize[1], box[2] / outputSize[0], box[3] / outputSize[1]];\n  return { box, boxRaw };\n}\n\nexport function square(keypoints: Point[], outputSize: [number, number] = [1, 1]) {\n  const coords = [keypoints.map((pt) => pt[0]), keypoints.map((pt) => pt[1])]; // all x/y coords\n  const min = [Math.min(...coords[0]), Math.min(...coords[1])];\n  const max = [Math.max(...coords[0]), Math.max(...coords[1])];\n  const center = [(min[0] + max[0]) / 2, (min[1] + max[1]) / 2]; // find center x and y coord of all fingers\n  const dist = Math.max(center[0] - min[0], center[1] - min[1], -center[0] + max[0], -center[1] + max[1]); // largest distance from center in any direction\n  const box: Box = [Math.trunc(center[0] - dist), Math.trunc(center[1] - dist), Math.trunc(2 * dist), Math.trunc(2 * dist)];\n  const boxRaw: Box = [box[0] / outputSize[0], box[1] / outputSize[1], box[2] / outputSize[0], box[3] / outputSize[1]];\n  return { box, boxRaw };\n}\n\nexport function scale(box: Box, scaleFact: number) {\n  const dist = [box[2] * scaleFact, box[3] * scaleFact];\n  const newBox: Box = [\n    box[0] - (dist[0] - box[2]) / 2,\n    box[1] - (dist[1] - box[3]) / 2,\n    dist[0],\n    dist[1],\n  ];\n  return newBox;\n}\n\nexport function crop(box: Box) { // [y1, x1, y2, x2] clamped to 0..1\n  const yxBox: Box = [Math.max(0, box[1]), Math.max(0, box[0]), Math.min(1, box[3] + box[1]), Math.min(1, box[2] + box[0])];\n  return yxBox;\n}\n", "/**\n * BlazePose model implementation\n */\n\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { loadModel } from '../tfjs/load';\nimport { constants } from '../tfjs/constants';\nimport { log, now } from '../util/util';\nimport type { BodyKeypoint, BodyResult, BodyLandmark, Box, Point, BodyAnnotation } from '../result';\nimport type { GraphModel, Tensor } from '../tfjs/types';\nimport type { Config } from '../config';\nimport * as coords from './blazeposecoords';\nimport * as detect from './blazeposedetector';\nimport * as box from '../util/box';\n\nconst env = { initial: true };\n// const models: [GraphModel | null, GraphModel | null] = [null, null];\nconst models: { detector: GraphModel | null, landmarks: GraphModel | null } = { detector: null, landmarks: null };\nconst inputSize: { detector: [number, number], landmarks: [number, number] } = { detector: [224, 224], landmarks: [256, 256] };\nlet skipped = Number.MAX_SAFE_INTEGER;\nconst outputNodes: { detector: string[], landmarks: string[] } = {\n  landmarks: ['ld_3d', 'activation_segmentation', 'activation_heatmap', 'world_3d', 'output_poseflag'],\n  detector: [],\n};\n\nlet cache: BodyResult | null = null;\nlet cropBox: Box | undefined;\nlet padding: [number, number][] = [[0, 0], [0, 0], [0, 0], [0, 0]];\nlet lastTime = 0;\n\nconst sigmoid = (x) => (1 - (1 / (1 + Math.exp(x))));\n\nexport async function loadDetect(config: Config): Promise<GraphModel> {\n  if (env.initial) models.detector = null;\n  if (!models.detector && config.body['detector'] && config.body['detector'].modelPath || '') {\n    models.detector = await loadModel(config.body['detector'].modelPath);\n    const inputs = models.detector?.['executor'] ? Object.values(models.detector.modelSignature['inputs']) : undefined;\n    inputSize.detector[0] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[1].size) : 0;\n    inputSize.detector[1] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 0;\n  } else if (config.debug && models.detector) log('cached model:', models.detector['modelUrl']);\n  detect.createAnchors();\n  return models.detector as GraphModel;\n}\n\nexport async function loadPose(config: Config): Promise<GraphModel> {\n  if (env.initial) models.landmarks = null;\n  if (!models.landmarks) {\n    models.landmarks = await loadModel(config.body.modelPath);\n    const inputs = models.landmarks?.['executor'] ? Object.values(models.landmarks.modelSignature['inputs']) : undefined;\n    inputSize.landmarks[0] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[1].size) : 0;\n    inputSize.landmarks[1] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 0;\n  } else if (config.debug) log('cached model:', models.landmarks['modelUrl']);\n  return models.landmarks;\n}\n\nexport async function load(config: Config): Promise<[GraphModel | null, GraphModel | null]> {\n  if (!models.detector) await loadDetect(config);\n  if (!models.landmarks) await loadPose(config);\n  return [models.detector, models.landmarks];\n}\n\nfunction prepareImage(input: Tensor, size: number): Tensor {\n  const t: Record<string, Tensor> = {};\n  if (!input?.shape?.[1] || !input?.shape?.[2]) return input;\n  let final: Tensor;\n  if (cropBox) {\n    t.cropped = tf.image.cropAndResize(input, [cropBox], [0], [input.shape[1], input.shape[2]]); // if we have cached box use it to crop input\n  }\n  if (input.shape[1] !== input.shape[2]) { // only pad if width different than height\n    const height: [number, number] = [\n      input.shape[2] > input.shape[1] ? Math.trunc((input.shape[2] - input.shape[1]) / 2) : 0,\n      input.shape[2] > input.shape[1] ? Math.trunc((input.shape[2] - input.shape[1]) / 2) : 0,\n    ];\n    const width: [number, number] = [\n      input.shape[1] > input.shape[2] ? Math.trunc((input.shape[1] - input.shape[2]) / 2) : 0,\n      input.shape[1] > input.shape[2] ? Math.trunc((input.shape[1] - input.shape[2]) / 2) : 0,\n    ];\n    padding = [\n      [0, 0], // dont touch batch\n      height, // height before&after\n      width, // width before&after\n      [0, 0], // dont touch rbg\n    ];\n    t.pad = tf.pad(t.cropped || input, padding); // use cropped box if it exists\n    t.resize = tf.image.resizeBilinear(t.pad, [size, size]);\n    final = tf.div(t.resize, constants.tf255);\n  } else if (input.shape[1] !== size) { // if input needs resizing\n    t.resize = tf.image.resizeBilinear(t.cropped || input, [size, size]);\n    final = tf.div(t.resize, constants.tf255);\n  } else { // if input is already in a correct resolution just normalize it\n    final = tf.div(t.cropped || input, constants.tf255);\n  }\n  Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n  return final;\n}\n\nfunction rescaleKeypoints(keypoints: BodyKeypoint[], outputSize: [number, number]): BodyKeypoint[] {\n  for (const kpt of keypoints) { // first rescale due to padding\n    kpt.position = [\n      Math.trunc(kpt.position[0] * (outputSize[0] + padding[2][0] + padding[2][1]) / outputSize[0] - padding[2][0]),\n      Math.trunc(kpt.position[1] * (outputSize[1] + padding[1][0] + padding[1][1]) / outputSize[1] - padding[1][0]),\n      kpt.position[2] as number,\n    ];\n    kpt.positionRaw = [kpt.position[0] / outputSize[0], kpt.position[1] / outputSize[1], 2 * (kpt.position[2] as number) / (outputSize[0] + outputSize[1])];\n  }\n  if (cropBox) { // second rescale due to cropping\n    for (const kpt of keypoints) {\n      kpt.positionRaw = [\n        kpt.positionRaw[0] + cropBox[1], // correct offset due to crop\n        kpt.positionRaw[1] + cropBox[0], // correct offset due to crop\n        kpt.positionRaw[2] as number,\n      ];\n      kpt.position = [\n        Math.trunc(kpt.positionRaw[0] * outputSize[0]),\n        Math.trunc(kpt.positionRaw[1] * outputSize[1]),\n        kpt.positionRaw[2] as number,\n      ];\n    }\n  }\n  return keypoints;\n}\n\nfunction fixKeypoints(keypoints: BodyKeypoint[]) {\n  // palm z-coord is incorrect around near-zero so we approximate it\n  const leftPalm = keypoints.find((k) => k.part === 'leftPalm') as BodyKeypoint;\n  const leftWrist = keypoints.find((k) => k.part === 'leftWrist') as BodyKeypoint;\n  const leftIndex = keypoints.find((k) => k.part === 'leftIndex') as BodyKeypoint;\n  leftPalm.position[2] = ((leftWrist.position[2] || 0) + (leftIndex.position[2] || 0)) / 2;\n  const rightPalm = keypoints.find((k) => k.part === 'rightPalm') as BodyKeypoint;\n  const rightWrist = keypoints.find((k) => k.part === 'rightWrist') as BodyKeypoint;\n  const rightIndex = keypoints.find((k) => k.part === 'rightIndex') as BodyKeypoint;\n  rightPalm.position[2] = ((rightWrist.position[2] || 0) + (rightIndex.position[2] || 0)) / 2;\n}\n\nasync function detectLandmarks(input: Tensor, config: Config, outputSize: [number, number]): Promise<BodyResult | null> {\n  /**\n   * t.ld: 39 keypoints [x,y,z,score,presence] normalized to input size\n   * t.segmentation:\n   * t.heatmap:\n   * t.world: 39 keypoints [x,y,z] normalized to -1..1\n   * t.poseflag: body score\n  */\n  if (!models.landmarks?.['executor']) return null;\n  const t: Record<string, Tensor> = {};\n  [t.ld/* 1,195(39*5) */, t.segmentation/* 1,256,256,1 */, t.heatmap/* 1,64,64,39 */, t.world/* 1,117(39*3) */, t.poseflag/* 1,1 */] = models.landmarks?.execute(input, outputNodes.landmarks) as Tensor[]; // run model\n  const poseScore = (await t.poseflag.data())[0];\n  const points = await t.ld.data();\n  const distances = await t.world.data();\n  Object.keys(t).forEach((tensor) => tf.dispose(t[tensor])); // dont need tensors after this\n  const keypointsRelative: BodyKeypoint[] = [];\n  const depth = 5; // each points has x,y,z,visibility,presence\n  for (let i = 0; i < points.length / depth; i++) {\n    const score = sigmoid(points[depth * i + 3]);\n    const presence = sigmoid(points[depth * i + 4]);\n    const adjScore = Math.trunc(100 * score * presence * poseScore) / 100;\n    const positionRaw: Point = [points[depth * i + 0] / inputSize.landmarks[0], points[depth * i + 1] / inputSize.landmarks[1], points[depth * i + 2] + 0];\n    const position: Point = [Math.trunc(outputSize[0] * positionRaw[0]), Math.trunc(outputSize[1] * positionRaw[1]), positionRaw[2] as number];\n    const distance: Point = [distances[depth * i + 0], distances[depth * i + 1], distances[depth * i + 2] + 0];\n    keypointsRelative.push({ part: coords.kpt[i] as BodyLandmark, positionRaw, position, distance, score: adjScore });\n  }\n  if (poseScore < (config.body.minConfidence || 0)) return null;\n  fixKeypoints(keypointsRelative);\n  const keypoints: BodyKeypoint[] = rescaleKeypoints(keypointsRelative, outputSize); // keypoints were relative to input image which is padded\n  const kpts = keypoints.map((k) => k.position);\n  const boxes = box.calc(kpts, [outputSize[0], outputSize[1]]); // now find boxes based on rescaled keypoints\n  const annotations: Record<BodyAnnotation, Point[][]> = {} as Record<BodyAnnotation, Point[][]>;\n  for (const [name, indexes] of Object.entries(coords.connected)) {\n    const pt: Point[][] = [];\n    for (let i = 0; i < indexes.length - 1; i++) {\n      const pt0 = keypoints.find((kpt) => kpt.part === indexes[i]);\n      const pt1 = keypoints.find((kpt) => kpt.part === indexes[i + 1]);\n      if (pt0 && pt1) pt.push([pt0.position, pt1.position]);\n    }\n    annotations[name] = pt;\n  }\n  const body = { id: 0, score: Math.trunc(100 * poseScore) / 100, box: boxes.box, boxRaw: boxes.boxRaw, keypoints, annotations };\n  return body;\n}\n\n/*\ninterface DetectedBox { box: Box, boxRaw: Box, score: number }\n\nfunction rescaleBoxes(boxes: Array<DetectedBox>, outputSize: [number, number]): Array<DetectedBox> {\n  for (const b of boxes) {\n    b.box = [\n      Math.trunc(b.box[0] * (outputSize[0] + padding[2][0] + padding[2][1]) / outputSize[0]),\n      Math.trunc(b.box[1] * (outputSize[1] + padding[1][0] + padding[1][1]) / outputSize[1]),\n      Math.trunc(b.box[2] * (outputSize[0] + padding[2][0] + padding[2][1]) / outputSize[0]),\n      Math.trunc(b.box[3] * (outputSize[1] + padding[1][0] + padding[1][1]) / outputSize[1]),\n    ];\n    b.boxRaw = [b.box[0] / outputSize[0], b.box[1] / outputSize[1], b.box[2] / outputSize[0], b.box[3] / outputSize[1]];\n  }\n  return boxes;\n}\n\nasync function detectBoxes(input: Tensor, config: Config, outputSize: [number, number]) {\n  const t: Record<string, Tensor> = {};\n  t.res = models.detector?.execute(input, ['Identity']) as Tensor; //\n  t.logitsRaw = tf.slice(t.res, [0, 0, 0], [1, -1, 1]);\n  t.boxesRaw = tf.slice(t.res, [0, 0, 1], [1, -1, -1]);\n  t.logits = tf.squeeze(t.logitsRaw);\n  t.boxes = tf.squeeze(t.boxesRaw);\n  const boxes = await detect.decode(t.boxes, t.logits, config, outputSize);\n  rescaleBoxes(boxes, outputSize);\n  Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n  return boxes;\n}\n*/\n\nexport async function predict(input: Tensor, config: Config): Promise<BodyResult[]> {\n  const outputSize: [number, number] = [input.shape[2] || 0, input.shape[1] || 0];\n  const skipTime = (config.body.skipTime || 0) > (now() - lastTime);\n  const skipFrame = skipped < (config.body.skipFrames || 0);\n  if (config.skipAllowed && skipTime && skipFrame && cache !== null) {\n    skipped++;\n  } else {\n    const t: Record<string, Tensor> = {};\n    /*\n    if (config.body['detector'] && config.body['detector']['enabled']) {\n      t.detector = await prepareImage(input, 224);\n      const boxes = await detectBoxes(t.detector, config, outputSize);\n    }\n    */\n    t.landmarks = prepareImage(input, 256); // padded and resized\n    cache = await detectLandmarks(t.landmarks, config, outputSize);\n    /*\n    cropBox = [0, 0, 1, 1]; // reset crop coordinates\n    if (cache?.boxRaw && config.skipAllowed) {\n      const cx = (2.0 * cache.boxRaw[0] + cache.boxRaw[2]) / 2;\n      const cy = (2.0 * cache.boxRaw[1] + cache.boxRaw[3]) / 2;\n      let size = cache.boxRaw[2] > cache.boxRaw[3] ? cache.boxRaw[2] : cache.boxRaw[3];\n      size = (size * 1.0) / 2; // enlarge and half it\n      if (cx > 0.1 && cx < 0.9 && cy > 0.1 && cy < 0.9 && size > 0.1) { // only update if box is sane\n        const y = 0; // cy - size;\n        const x = cx - size;\n        cropBox = [y, x, y + 1, x + 1]; // [y0,x0,y1,x1] used for cropping but width/height are not yet implemented so we only reposition image to center of body\n      }\n    }\n    */\n    Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n    lastTime = now();\n    skipped = 0;\n  }\n  return cache ? [cache] : [];\n}\n", "/**\n * CoCo Labels used by object detection implementations\n */\nexport const labels = [\n  { class: 1, label: 'person' },\n  { class: 2, label: 'bicycle' },\n  { class: 3, label: 'car' },\n  { class: 4, label: 'motorcycle' },\n  { class: 5, label: 'airplane' },\n  { class: 6, label: 'bus' },\n  { class: 7, label: 'train' },\n  { class: 8, label: 'truck' },\n  { class: 9, label: 'boat' },\n  { class: 10, label: 'traffic light' },\n  { class: 11, label: 'fire hydrant' },\n  { class: 12, label: 'stop sign' },\n  { class: 13, label: 'parking meter' },\n  { class: 14, label: 'bench' },\n  { class: 15, label: 'bird' },\n  { class: 16, label: 'cat' },\n  { class: 17, label: 'dog' },\n  { class: 18, label: 'horse' },\n  { class: 19, label: 'sheep' },\n  { class: 20, label: 'cow' },\n  { class: 21, label: 'elephant' },\n  { class: 22, label: 'bear' },\n  { class: 23, label: 'zebra' },\n  { class: 24, label: 'giraffe' },\n  { class: 25, label: 'backpack' },\n  { class: 26, label: 'umbrella' },\n  { class: 27, label: 'handbag' },\n  { class: 28, label: 'tie' },\n  { class: 29, label: 'suitcase' },\n  { class: 30, label: 'frisbee' },\n  { class: 31, label: 'skis' },\n  { class: 32, label: 'snowboard' },\n  { class: 33, label: 'sports ball' },\n  { class: 34, label: 'kite' },\n  { class: 35, label: 'baseball bat' },\n  { class: 36, label: 'baseball glove' },\n  { class: 37, label: 'skateboard' },\n  { class: 38, label: 'surfboard' },\n  { class: 39, label: 'tennis racket' },\n  { class: 40, label: 'bottle' },\n  { class: 41, label: 'wine glass' },\n  { class: 42, label: 'cup' },\n  { class: 43, label: 'fork' },\n  { class: 44, label: 'knife' },\n  { class: 45, label: 'spoon' },\n  { class: 46, label: 'bowl' },\n  { class: 47, label: 'banana' },\n  { class: 48, label: 'apple' },\n  { class: 49, label: 'sandwich' },\n  { class: 50, label: 'orange' },\n  { class: 51, label: 'broccoli' },\n  { class: 52, label: 'carrot' },\n  { class: 53, label: 'hot dog' },\n  { class: 54, label: 'pizza' },\n  { class: 55, label: 'donut' },\n  { class: 56, label: 'cake' },\n  { class: 57, label: 'chair' },\n  { class: 58, label: 'couch' },\n  { class: 59, label: 'potted plant' },\n  { class: 60, label: 'bed' },\n  { class: 61, label: 'dining table' },\n  { class: 62, label: 'toilet' },\n  { class: 63, label: 'tv' },\n  { class: 64, label: 'laptop' },\n  { class: 65, label: 'mouse' },\n  { class: 66, label: 'remote' },\n  { class: 67, label: 'keyboard' },\n  { class: 68, label: 'cell phone' },\n  { class: 69, label: 'microwave' },\n  { class: 70, label: 'oven' },\n  { class: 71, label: 'toaster' },\n  { class: 72, label: 'sink' },\n  { class: 73, label: 'refrigerator' },\n  { class: 74, label: 'book' },\n  { class: 75, label: 'clock' },\n  { class: 76, label: 'vase' },\n  { class: 77, label: 'scissors' },\n  { class: 78, label: 'teddy bear' },\n  { class: 79, label: 'hair drier' },\n  { class: 80, label: 'toothbrush' },\n];\n", "/**\n * CenterNet object detection model implementation\n *\n * Based on: [**NanoDet**](https://github.com/RangiLyu/nanodet)\n */\n\nimport { log, now } from '../util/util';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { loadModel } from '../tfjs/load';\nimport { labels } from './labels';\nimport type { ObjectResult, ObjectType, Box } from '../result';\nimport type { GraphModel, Tensor } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\n\nlet model: GraphModel | null;\nlet inputSize = 0;\nlet last: ObjectResult[] = [];\nlet lastTime = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nexport async function load(config: Config): Promise<GraphModel> {\n  if (env.initial) model = null;\n  if (!model) {\n    // fakeOps(['floormod'], config);\n    model = await loadModel(config.object.modelPath);\n    const inputs = model?.['executor'] ? Object.values(model.modelSignature['inputs']) : undefined;\n    inputSize = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 0;\n  } else if (config.debug) log('cached model:', model['modelUrl']);\n  return model;\n}\n\nasync function process(res: Tensor | null, outputShape: [number, number], config: Config) {\n  if (!res) return [];\n  const t: Record<string, Tensor> = {};\n  const results: ObjectResult[] = [];\n  const detections = await res.array() as number[][][];\n  t.squeeze = tf.squeeze(res);\n  const arr = tf.split(t.squeeze, 6, 1) as Tensor[]; // x1, y1, x2, y2, score, class\n  t.stack = tf.stack([arr[1], arr[0], arr[3], arr[2]], 1); // reorder dims as tf.nms expects y, x\n  t.boxes = tf.squeeze(t.stack);\n  t.scores = tf.squeeze(arr[4]);\n  t.classes = tf.squeeze(arr[5]);\n  tf.dispose([res, ...arr]);\n  t.nms = await tf.image.nonMaxSuppressionAsync(t.boxes, t.scores, config.object.maxDetected, config.object.iouThreshold, (config.object.minConfidence || 0));\n  const nms = await t.nms.data();\n  let i = 0;\n  for (const id of Array.from(nms)) {\n    const score = Math.trunc(100 * detections[0][id][4]) / 100;\n    const classVal = detections[0][id][5];\n    if (Number.isNaN(classVal)) continue;\n    const label = labels[classVal].label as ObjectType;\n    const [x, y] = [\n      detections[0][id][0] / inputSize,\n      detections[0][id][1] / inputSize,\n    ];\n    const boxRaw: Box = [\n      x,\n      y,\n      detections[0][id][2] / inputSize - x,\n      detections[0][id][3] / inputSize - y,\n    ];\n    const box: Box = [\n      Math.trunc(boxRaw[0] * outputShape[0]),\n      Math.trunc(boxRaw[1] * outputShape[1]),\n      Math.trunc(boxRaw[2] * outputShape[0]),\n      Math.trunc(boxRaw[3] * outputShape[1]),\n    ];\n    results.push({ id: i++, score, class: classVal, label, box, boxRaw });\n  }\n  Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n  return results;\n}\n\nexport async function predict(input: Tensor, config: Config): Promise<ObjectResult[]> {\n  if (!model?.['executor']) return [];\n  const skipTime = (config.object.skipTime || 0) > (now() - lastTime);\n  const skipFrame = skipped < (config.object.skipFrames || 0);\n  if (config.skipAllowed && skipTime && skipFrame && (last.length > 0)) {\n    skipped++;\n    return last;\n  }\n  skipped = 0;\n  return new Promise(async (resolve) => {\n    const outputSize = [input.shape[2] || 0, input.shape[1] || 0] as [number, number];\n    const resize = tf.image.resizeBilinear(input, [inputSize, inputSize]);\n    const objectT = config.object.enabled ? model?.execute(resize, ['tower_0/detections']) as Tensor : null;\n    lastTime = now();\n    tf.dispose(resize);\n\n    const obj = await process(objectT, outputSize, config);\n    last = obj;\n\n    resolve(obj);\n  });\n}\n", "export const kpt: string[] = [\n  'head',\n  'neck',\n  'rightShoulder',\n  'rightElbow',\n  'rightWrist',\n  'chest',\n  'leftShoulder',\n  'leftElbow',\n  'leftWrist',\n  'bodyCenter',\n  'rightHip',\n  'rightKnee',\n  'rightAnkle',\n  'leftHip',\n  'leftKnee',\n  'leftAnkle',\n];\n\nexport const connected: Record<string, string[]> = {\n  leftLeg: ['leftHip', 'leftKnee', 'leftAnkle'],\n  rightLeg: ['rightHip', 'rightKnee', 'rightAnkle'],\n  torso: ['leftShoulder', 'rightShoulder', 'rightHip', 'leftHip', 'leftShoulder'],\n  leftArm: ['leftShoulder', 'leftElbow', 'leftWrist'],\n  rightArm: ['rightShoulder', 'rightElbow', 'rightWrist'],\n  head: [],\n};\n", "/**\n * EfficientPose model implementation\n *\n * Based on: [**EfficientPose**](https://github.com/daniegr/EfficientPose)\n */\n\nimport { log, now } from '../util/util';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { loadModel } from '../tfjs/load';\nimport * as coords from './efficientposecoords';\nimport { constants } from '../tfjs/constants';\nimport type { BodyResult, Point, BodyLandmark, BodyAnnotation } from '../result';\nimport type { GraphModel, Tensor } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\n\nlet model: GraphModel | null;\nlet lastTime = 0;\nconst cache: BodyResult = { id: 0, keypoints: [], box: [0, 0, 0, 0], boxRaw: [0, 0, 0, 0], score: 0, annotations: {} as Record<BodyAnnotation, Point[][]> };\n\n// const keypoints: Array<BodyKeypoint> = [];\n// let box: Box = [0, 0, 0, 0];\n// let boxRaw: Box = [0, 0, 0, 0];\n// let score = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nexport async function load(config: Config): Promise<GraphModel> {\n  if (env.initial) model = null;\n  if (!model) model = await loadModel(config.body.modelPath);\n  else if (config.debug) log('cached model:', model['modelUrl']);\n  return model;\n}\n\n// performs argmax and max functions on a 2d tensor\nasync function max2d(inputs, minScore): Promise<[number, number, number]> {\n  const [width, height] = inputs.shape;\n  const reshaped = tf.reshape(inputs, [height * width]); // combine all data\n  const max = tf.max(reshaped, 0);\n  const newScore: number = (await max.data())[0]; // get highest score\n  if (newScore > minScore) { // skip coordinate calculation is score is too low\n    const coordinates = tf.argMax(reshaped, 0);\n    const mod = tf.mod(coordinates, width);\n    const x = (await mod.data())[0];\n    const div = tf.div(coordinates, width);\n    const y: number = (await div.data())[0];\n    tf.dispose([reshaped, max, coordinates, mod, div]);\n    return [x, y, newScore];\n  }\n  tf.dispose([reshaped, max]);\n  return [0, 0, newScore];\n}\n\nexport async function predict(image: Tensor, config: Config): Promise<BodyResult[]> {\n  if (!model?.['executor']) return [];\n  const skipTime = (config.body.skipTime || 0) > (now() - lastTime);\n  const skipFrame = skipped < (config.body.skipFrames || 0);\n  if (config.skipAllowed && skipTime && skipFrame && Object.keys(cache.keypoints).length > 0) {\n    skipped++;\n    return [cache];\n  }\n  skipped = 0;\n  return new Promise(async (resolve) => {\n    const tensor = tf.tidy(() => {\n      if (!model?.inputs[0].shape) return null;\n      const resize = tf.image.resizeBilinear(image, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false);\n      const enhance = tf.mul(resize, constants.tf2);\n      const norm = tf.sub(enhance, constants.tf1);\n      return norm;\n    });\n    let resT;\n    if (config.body.enabled) resT = model?.execute(tensor);\n    lastTime = now();\n    tf.dispose(tensor);\n\n    if (resT) {\n      cache.keypoints.length = 0;\n      const squeeze = tf.squeeze(resT);\n      tf.dispose(resT);\n      // body parts are basically just a stack of 2d tensors\n      const stack = tf.unstack(squeeze, 2);\n      tf.dispose(squeeze);\n\n      // process each unstacked tensor as a separate body part\n      for (let id = 0; id < stack.length; id++) {\n        // actual processing to get coordinates and score\n        const [x, y, partScore] = await max2d(stack[id], config.body.minConfidence);\n        if (partScore > (config.body.minConfidence || 0)) {\n          cache.keypoints.push({\n            score: Math.round(100 * partScore) / 100,\n            part: coords.kpt[id] as BodyLandmark,\n            positionRaw: [ // normalized to 0..1\n              // @ts-ignore model is not undefined here\n              x / model.inputs[0].shape[2], y / model.inputs[0].shape[1],\n            ],\n            position: [ // normalized to input image size\n              // @ts-ignore model is not undefined here\n              Math.round(image.shape[2] * x / model.inputs[0].shape[2]), Math.round(image.shape[1] * y / model.inputs[0].shape[1]),\n            ],\n          });\n        }\n      }\n      stack.forEach((s) => tf.dispose(s));\n    }\n    cache.score = cache.keypoints.reduce((prev, curr) => (curr.score > prev ? curr.score : prev), 0);\n    const x = cache.keypoints.map((a) => a.position[0]);\n    const y = cache.keypoints.map((a) => a.position[1]);\n    cache.box = [\n      Math.min(...x),\n      Math.min(...y),\n      Math.max(...x) - Math.min(...x),\n      Math.max(...y) - Math.min(...y),\n    ];\n    const xRaw = cache.keypoints.map((a) => a.positionRaw[0]);\n    const yRaw = cache.keypoints.map((a) => a.positionRaw[1]);\n    cache.boxRaw = [\n      Math.min(...xRaw),\n      Math.min(...yRaw),\n      Math.max(...xRaw) - Math.min(...xRaw),\n      Math.max(...yRaw) - Math.min(...yRaw),\n    ];\n    for (const [name, indexes] of Object.entries(coords.connected)) {\n      const pt: Point[][] = [];\n      for (let i = 0; i < indexes.length - 1; i++) {\n        const pt0 = cache.keypoints.find((kpt) => kpt.part === indexes[i]);\n        const pt1 = cache.keypoints.find((kpt) => kpt.part === indexes[i + 1]);\n        if (pt0 && pt1 && pt0.score > (config.body.minConfidence || 0) && pt1.score > (config.body.minConfidence || 0)) pt.push([pt0.position, pt1.position]);\n      }\n      cache.annotations[name] = pt;\n    }\n    resolve([cache]);\n  });\n}\n", "/**\n * Emotion model implementation\n *\n * [**Oarriaga**](https://github.com/oarriaga/face_classification)\n */\n\nimport type { Emotion } from '../result';\nimport { log, now } from '../util/util';\nimport type { Config } from '../config';\nimport type { GraphModel, Tensor } from '../tfjs/types';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { loadModel } from '../tfjs/load';\nimport { env } from '../util/env';\nimport { constants } from '../tfjs/constants';\n\nconst annotations = ['angry', 'disgust', 'fear', 'happy', 'sad', 'surprise', 'neutral'];\nlet model: GraphModel | null;\nconst last: { score: number, emotion: Emotion }[][] = [];\nlet lastCount = 0;\nlet lastTime = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nexport async function load(config: Config): Promise<GraphModel> {\n  if (env.initial) model = null;\n  if (!model) model = await loadModel(config.face.emotion?.modelPath);\n  else if (config.debug) log('cached model:', model['modelUrl']);\n  return model;\n}\n\nexport async function predict(image: Tensor, config: Config, idx: number, count: number): Promise<{ score: number, emotion: Emotion }[]> {\n  if (!model) return [];\n  const skipFrame = skipped < (config.face.emotion?.skipFrames || 0);\n  const skipTime = (config.face.emotion?.skipTime || 0) > (now() - lastTime);\n  if (config.skipAllowed && skipTime && skipFrame && (lastCount === count) && last[idx] && (last[idx].length > 0)) {\n    skipped++;\n    return last[idx];\n  }\n  skipped = 0;\n  return new Promise(async (resolve) => {\n    const obj: { score: number, emotion: Emotion }[] = [];\n    if (config.face.emotion?.enabled) {\n      const t: Record<string, Tensor> = {};\n      const inputSize = model?.inputs[0].shape ? model.inputs[0].shape[2] : 0;\n      t.resize = tf.image.resizeBilinear(image, [inputSize, inputSize], false);\n      // const box = [[0.15, 0.15, 0.85, 0.85]]; // empyrical values for top, left, bottom, right\n      // const resize = tf.image.cropAndResize(image, box, [0], [inputSize, inputSize]);\n      // [t.red, t.green, t.blue] = tf.split(t.resize, 3, 3);\n      // weighted rgb to grayscale: https://www.mathworks.com/help/matlab/ref/rgb2gray.html\n      // t.redNorm = tf.mul(t.red, rgb[0]);\n      // t.greenNorm = tf.mul(t.green, rgb[1]);\n      // t.blueNorm = tf.mul(t.blue, rgb[2]);\n      // t.grayscale = tf.addN([t.redNorm, t.greenNorm, t.blueNorm]);\n      t.channels = tf.mul(t.resize, constants.rgb);\n      t.grayscale = tf.sum(t.channels, 3, true);\n      t.grayscaleSub = tf.sub(t.grayscale, constants.tf05);\n      t.grayscaleMul = tf.mul(t.grayscaleSub, constants.tf2);\n      t.emotion = model?.execute(t.grayscaleMul) as Tensor; // result is already in range 0..1, no need for additional activation\n      lastTime = now();\n      const data = await t.emotion.data();\n      for (let i = 0; i < data.length; i++) {\n        if (data[i] > (config.face.emotion.minConfidence || 0)) obj.push({ score: Math.min(0.99, Math.trunc(100 * data[i]) / 100), emotion: annotations[i] as Emotion });\n      }\n      obj.sort((a, b) => b.score - a.score);\n      Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n    }\n    last[idx] = obj;\n    lastCount = count;\n    resolve(obj);\n  });\n}\n", "/**\n * MobileFaceNet model implementation\n *\n * Based on: [**BecauseofAI MobileFace**](https://github.com/becauseofAI/MobileFace)\n *\n * Obsolete and replaced by `faceres` that performs age/gender/descriptor analysis\n */\n\nimport { log, now } from '../util/util';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { loadModel } from '../tfjs/load';\nimport type { Tensor, GraphModel } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\n\nlet model: GraphModel | null;\nconst last: number[][] = [];\nlet lastCount = 0;\nlet lastTime = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nexport async function load(config: Config): Promise<GraphModel> {\n  if (env.initial) model = null;\n  if (!model) model = await loadModel(config.face['mobilefacenet']?.modelPath);\n  else if (config.debug) log('cached model:', model['modelUrl']);\n  return model;\n}\n\n/*\n// convert to black&white to avoid colorization impact\nconst rgb = [0.2989, 0.5870, 0.1140]; // factors for red/green/blue colors when converting to grayscale: https://www.mathworks.com/help/matlab/ref/rgb2gray.html\nconst [red, green, blue] = tf.split(crop, 3, 3);\nconst redNorm = tf.mul(red, rgb[0]);\nconst greenNorm = tf.mul(green, rgb[1]);\nconst blueNorm = tf.mul(blue, rgb[2]);\nconst grayscale = tf.addN([redNorm, greenNorm, blueNorm]);\nconst merge = tf.stack([grayscale, grayscale, grayscale], 3).squeeze(4);\n\n// optional increase image contrast\n// or do it per-channel so mean is done on each channel\n// or do it based on histogram\nconst mean = merge.mean();\nconst factor = 5;\nconst contrast = merge.sub(mean).mul(factor).add(mean);\n*/\n\nexport async function predict(input: Tensor, config: Config, idx, count): Promise<number[]> {\n  if (!model?.['executor']) return [];\n  const skipFrame = skipped < (config.face['mobilefacenet']?.skipFrames || 0);\n  const skipTime = (config.face['mobilefacenet']?.skipTime || 0) > (now() - lastTime);\n  if (config.skipAllowed && skipTime && skipFrame && (lastCount === count) && last[idx]) {\n    skipped++;\n    return last[idx];\n  }\n  return new Promise(async (resolve) => {\n    let data: number[] = [];\n    if (config.face['mobilefacenet']?.enabled && model?.inputs[0].shape) {\n      const t: Record<string, Tensor> = {};\n      t.crop = tf.image.resizeBilinear(input, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false); // just resize to fit the embedding model\n      // do a tight crop of image and resize it to fit the model\n      // const box = [[0.05, 0.15, 0.85, 0.85]]; // empyrical values for top, left, bottom, right\n      // t.crop = tf.image.cropAndResize(input, box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]);\n      t.data = model.execute(t.crop) as Tensor;\n      /*\n      // optional normalize outputs with l2 normalization\n      const scaled = tf.tidy(() => {\n        const l2 = res.norm('euclidean');\n        const scale = res.div(l2);\n        return scale;\n      });\n\n      // optional reduce feature vector complexity\n      const reshape = tf.reshape(res, [128, 2]); // split 256 vectors into 128 x 2\n      const reduce = reshape.logSumExp(1); // reduce 2nd dimension by calculating logSumExp on it\n      */\n      const output = await t.data.data();\n      data = Array.from(output); // convert typed array to simple array\n      Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n    }\n    last[idx] = data;\n    lastCount = count;\n    lastTime = now();\n    resolve(data);\n  });\n}\n", "/**\n * InsightFace model implementation\n *\n * Based on: [**DeepInsight InsightFace**](https://github.com/deepinsight/insightface)\n *\n * Alternative face embedding detection\n */\n\nimport { log, now } from '../util/util';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { loadModel } from '../tfjs/load';\nimport type { Tensor, GraphModel } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\n\nlet model: GraphModel | null;\nconst last: number[][] = [];\nlet lastCount = 0;\nlet lastTime = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nexport async function load(config: Config): Promise<GraphModel> {\n  if (env.initial) model = null;\n  if (!model) model = await loadModel(config.face['insightface'].modelPath);\n  else if (config.debug) log('cached model:', model['modelUrl']);\n  return model;\n}\n\nexport async function predict(input: Tensor, config: Config, idx, count): Promise<number[]> {\n  if (!model?.['executor']) return [];\n  const skipFrame = skipped < (config.face['insightface']?.skipFrames || 0);\n  const skipTime = (config.face['insightface']?.skipTime || 0) > (now() - lastTime);\n  if (config.skipAllowed && skipTime && skipFrame && (lastCount === count) && last[idx]) {\n    skipped++;\n    return last[idx];\n  }\n  return new Promise(async (resolve) => {\n    let data: number[] = [];\n    if (config.face['insightface']?.enabled && model?.inputs[0].shape) {\n      const t: Record<string, Tensor> = {};\n      t.crop = tf.image.resizeBilinear(input, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false); // just resize to fit the embedding model\n      // do a tight crop of image and resize it to fit the model\n      // const box = [[0.05, 0.15, 0.85, 0.85]]; // empyrical values for top, left, bottom, right\n      // t.crop = tf.image.cropAndResize(input, box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]);\n      t.data = model.execute(t.crop) as Tensor;\n      const output = await t.data.data();\n      data = Array.from(output); // convert typed array to simple array\n      Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n    }\n    last[idx] = data;\n    lastCount = count;\n    lastTime = now();\n    resolve(data);\n  });\n}\n", "import * as coords from './facemeshcoords';\nimport * as util from './facemeshutil';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport type { Tensor, GraphModel } from '../tfjs/types';\nimport { env } from '../util/env';\nimport { log } from '../util/util';\nimport { loadModel } from '../tfjs/load';\nimport type { Config } from '../config';\nimport type { Point } from '../result';\n\nlet model: GraphModel | null;\nlet inputSize = 0;\n\nconst irisEnlarge = 2.3;\n\nconst leftOutline = coords.meshAnnotations.leftEyeLower0;\nconst rightOutline = coords.meshAnnotations.rightEyeLower0;\n\nconst eyeLandmarks = {\n  leftBounds: [leftOutline[0], leftOutline[leftOutline.length - 1]],\n  rightBounds: [rightOutline[0], rightOutline[rightOutline.length - 1]],\n};\n\nconst irisLandmarks = {\n  upperCenter: 3,\n  lowerCenter: 4,\n  index: 71,\n  numCoordinates: 76,\n};\n\nexport async function load(config: Config): Promise<GraphModel> {\n  if (env.initial) model = null;\n  if (!model) model = await loadModel(config.face.iris?.modelPath);\n  else if (config.debug) log('cached model:', model['modelUrl']);\n  inputSize = (model?.['executor'] && model.inputs?.[0].shape) ? model.inputs[0].shape[2] : 0;\n  if (inputSize === -1) inputSize = 64;\n  return model;\n}\n\n// Replace the raw coordinates returned by facemesh with refined iris model coordinates and update the z coordinate to be an average of the original and the new.\nexport function replaceIrisCoords(rawCoords, newCoords, prefix, keys) {\n  for (let i = 0; i < coords.irisIndices.length; i++) {\n    const { key, indices } = coords.irisIndices[i];\n    const originalIndices = coords.meshAnnotations[`${prefix}${key}`];\n    if (!keys || keys.includes(key)) {\n      for (let j = 0; j < indices.length; j++) {\n        const index = indices[j];\n        rawCoords[originalIndices[j]] = [\n          newCoords[index][0],\n          newCoords[index][1],\n          (newCoords[index][2] + rawCoords[originalIndices[j]][2]) / 2,\n        ];\n      }\n    }\n  }\n}\n\nexport const getLeftToRightEyeDepthDifference = (rawCoords) => {\n  const leftEyeZ = rawCoords[eyeLandmarks.leftBounds[0]][2];\n  const rightEyeZ = rawCoords[eyeLandmarks.rightBounds[0]][2];\n  return leftEyeZ - rightEyeZ;\n};\n\n// Returns a box describing a cropped region around the eye fit for passing to the iris model.\nexport const getEyeBox = (rawCoords, face, eyeInnerCornerIndex, eyeOuterCornerIndex, meshSize, flip = false) => {\n  const box = util.squarifyBox(util.enlargeBox(util.calculateLandmarksBoundingBox([rawCoords[eyeInnerCornerIndex], rawCoords[eyeOuterCornerIndex]]), irisEnlarge));\n  const boxSize = util.getBoxSize(box);\n  let crop = tf.image.cropAndResize(face, [[\n    box.startPoint[1] / meshSize,\n    box.startPoint[0] / meshSize, box.endPoint[1] / meshSize,\n    box.endPoint[0] / meshSize,\n  ]], [0], [inputSize, inputSize]);\n  if (flip && env.kernels.includes('flipleftright')) {\n    const flipped = tf.image.flipLeftRight(crop); // flipLeftRight is not defined for tfjs-node\n    tf.dispose(crop);\n    crop = flipped;\n  }\n  return { box, boxSize, crop };\n};\n\n// Given a cropped image of an eye, returns the coordinates of the contours surrounding the eye and the iris.\nexport const getEyeCoords = (eyeData, eyeBox, eyeBoxSize, flip = false) => {\n  const eyeRawCoords: Point[] = [];\n  for (let i = 0; i < irisLandmarks.numCoordinates; i++) {\n    const x = eyeData[i * 3];\n    const y = eyeData[i * 3 + 1];\n    const z = eyeData[i * 3 + 2];\n    eyeRawCoords.push([\n      (flip ? (1 - (x / inputSize)) : (x / inputSize)) * eyeBoxSize[0] + eyeBox.startPoint[0],\n      (y / inputSize) * eyeBoxSize[1] + eyeBox.startPoint[1], z,\n    ]);\n  }\n  return { rawCoords: eyeRawCoords, iris: eyeRawCoords.slice(irisLandmarks.index) };\n};\n\n// The z-coordinates returned for the iris are unreliable, so we take the z values from the surrounding keypoints.\nexport const getAdjustedIrisCoords = (rawCoords, irisCoords, direction) => {\n  const upperCenterZ = rawCoords[coords.meshAnnotations[`${direction}EyeUpper0`][irisLandmarks.upperCenter]][2];\n  const lowerCenterZ = rawCoords[coords.meshAnnotations[`${direction}EyeLower0`][irisLandmarks.lowerCenter]][2];\n  const averageZ = (upperCenterZ + lowerCenterZ) / 2;\n  // Iris indices: 0: center | 1: right | 2: above | 3: left | 4: below\n  return irisCoords.map((coord, i) => {\n    let z = averageZ;\n    if (i === 2) {\n      z = upperCenterZ;\n    } else if (i === 4) {\n      z = lowerCenterZ;\n    }\n    return [coord[0], coord[1], z];\n  });\n};\n\nexport async function augmentIris(rawCoords, face, meshSize) {\n  if (!model?.['executor']) return rawCoords;\n  const { box: leftEyeBox, boxSize: leftEyeBoxSize, crop: leftEyeCrop } = getEyeBox(rawCoords, face, eyeLandmarks.leftBounds[0], eyeLandmarks.leftBounds[1], meshSize, true);\n  const { box: rightEyeBox, boxSize: rightEyeBoxSize, crop: rightEyeCrop } = getEyeBox(rawCoords, face, eyeLandmarks.rightBounds[0], eyeLandmarks.rightBounds[1], meshSize, true);\n  const combined = tf.concat([leftEyeCrop, rightEyeCrop]);\n  tf.dispose(leftEyeCrop);\n  tf.dispose(rightEyeCrop);\n  const eyePredictions = model.execute(combined) as Tensor;\n  tf.dispose(combined);\n  const eyePredictionsData = await eyePredictions.data();\n  tf.dispose(eyePredictions);\n  const leftEyeData = eyePredictionsData.slice(0, irisLandmarks.numCoordinates * 3);\n  const { rawCoords: leftEyeRawCoords, iris: leftIrisRawCoords } = getEyeCoords(leftEyeData, leftEyeBox, leftEyeBoxSize, true);\n  const rightEyeData = eyePredictionsData.slice(irisLandmarks.numCoordinates * 3);\n  const { rawCoords: rightEyeRawCoords, iris: rightIrisRawCoords } = getEyeCoords(rightEyeData, rightEyeBox, rightEyeBoxSize, false);\n  const leftToRightEyeDepthDifference = getLeftToRightEyeDepthDifference(rawCoords);\n  if (Math.abs(leftToRightEyeDepthDifference) < 30) { // User is looking straight ahead.\n    replaceIrisCoords(rawCoords, leftEyeRawCoords, 'left', null);\n    replaceIrisCoords(rawCoords, rightEyeRawCoords, 'right', null);\n    // If the user is looking to the left or to the right, the iris coordinates tend to diverge too much from the mesh coordinates for them to be merged so we only update a single contour line above and below the eye.\n  } else if (leftToRightEyeDepthDifference < 1) { // User is looking towards the right.\n    replaceIrisCoords(rawCoords, leftEyeRawCoords, 'left', ['EyeUpper0', 'EyeLower0']);\n  } else { // User is looking towards the left.\n    replaceIrisCoords(rawCoords, rightEyeRawCoords, 'right', ['EyeUpper0', 'EyeLower0']);\n  }\n  const adjustedLeftIrisCoords = getAdjustedIrisCoords(rawCoords, leftIrisRawCoords, 'left');\n  const adjustedRightIrisCoords = getAdjustedIrisCoords(rawCoords, rightIrisRawCoords, 'right');\n  const newCoords = rawCoords.concat(adjustedLeftIrisCoords).concat(adjustedRightIrisCoords);\n  return newCoords;\n}\n", "// @tensorflow/tfjs-models/face-landmark-detection/src/constants.ts\n// https://github.com/google/mediapipe/mediapipe/python/solutions/face_mesh_connections.py\n\ntype PairArray = [number, number][];\n\nconst LIPS_CONNECTIONS: PairArray = [\n  [61, 146], [146, 91], [91, 181], [181, 84], [84, 17], [17, 314], [314, 405], [405, 321], [321, 375], [375, 291], [61, 185], [185, 40], [40, 39], [39, 37], [37, 0], [0, 267], [267, 269], [269, 270], [270, 409], [409, 291],\n  [78, 95], [95, 88], [88, 178], [178, 87], [87, 14], [14, 317], [317, 402], [402, 318], [318, 324], [324, 308], [78, 191], [191, 80], [80, 81], [81, 82], [82, 13], [13, 312], [312, 311], [311, 310], [310, 415], [415, 308],\n];\n\nconst LEFT_EYE_CONNECTIONS: PairArray = [[263, 249], [249, 390], [390, 373], [373, 374], [374, 380], [380, 381], [381, 382], [382, 362], [263, 466], [466, 388], [388, 387], [387, 386], [386, 385], [385, 384], [384, 398], [398, 362]];\n\nconst LEFT_EYEBROW_CONNECTIONS: PairArray = [[276, 283], [283, 282], [282, 295], [295, 285], [300, 293], [293, 334], [334, 296], [296, 336]];\n\nconst LEFT_IRIS_CONNECTIONS: PairArray = [[474, 475], [475, 476], [476, 477], [477, 474]];\n\nconst RIGHT_EYE_CONNECTIONS: PairArray = [[33, 7], [7, 163], [163, 144], [144, 145], [145, 153], [153, 154], [154, 155], [155, 133], [33, 246], [246, 161], [161, 160], [160, 159], [159, 158], [158, 157], [157, 173], [173, 133]];\n\nconst RIGHT_EYEBROW_CONNECTIONS: PairArray = [[46, 53], [53, 52], [52, 65], [65, 55], [70, 63], [63, 105], [105, 66], [66, 107]];\n\nconst RIGHT_IRIS_CONNECTIONS: PairArray = [[469, 470], [470, 471], [471, 472], [472, 469]];\n\nconst FACE_OVAL_CONNECTIONS: PairArray = [\n  [10, 338], [338, 297], [297, 332], [332, 284], [284, 251], [251, 389], [389, 356], [356, 454], [454, 323], [323, 361], [361, 288], [288, 397], [397, 365], [365, 379], [379, 378], [378, 400], [400, 377], [377, 152],\n  [152, 148], [148, 176], [176, 149], [149, 150], [150, 136], [136, 172], [172, 58], [58, 132], [132, 93], [93, 234], [234, 127], [127, 162], [162, 21], [21, 54], [54, 103], [103, 67], [67, 109], [109, 10],\n];\n\nexport const MEDIAPIPE_FACE_MESH_CONNECTED_KEYPOINTS_PAIRS: PairArray = [\n  [127, 34], [34, 139], [139, 127], [11, 0], [0, 37], [37, 11], [232, 231], [231, 120], [120, 232], [72, 37], [37, 39], [39, 72], [128, 121], [121, 47], [47, 128], [232, 121], [121, 128], [128, 232],\n  [104, 69], [69, 67], [67, 104], [175, 171], [171, 148], [148, 175], [118, 50], [50, 101], [101, 118], [73, 39], [39, 40], [40, 73], [9, 151], [151, 108], [108, 9], [48, 115], [115, 131], [131, 48],\n  [194, 204], [204, 211], [211, 194], [74, 40], [40, 185], [185, 74], [80, 42], [42, 183], [183, 80], [40, 92], [92, 186], [186, 40], [230, 229], [229, 118], [118, 230], [202, 212], [212, 214], [214, 202],\n  [83, 18], [18, 17], [17, 83], [76, 61], [61, 146], [146, 76], [160, 29], [29, 30], [30, 160], [56, 157], [157, 173], [173, 56], [106, 204], [204, 194], [194, 106], [135, 214], [214, 192], [192, 135],\n  [203, 165], [165, 98], [98, 203], [21, 71], [71, 68], [68, 21], [51, 45], [45, 4], [4, 51], [144, 24], [24, 23], [23, 144], [77, 146], [146, 91], [91, 77], [205, 50], [50, 187], [187, 205],\n  [201, 200], [200, 18], [18, 201], [91, 106], [106, 182], [182, 91], [90, 91], [91, 181], [181, 90], [85, 84], [84, 17], [17, 85], [206, 203], [203, 36], [36, 206], [148, 171], [171, 140], [140, 148],\n  [92, 40], [40, 39], [39, 92], [193, 189], [189, 244], [244, 193], [159, 158], [158, 28], [28, 159], [247, 246], [246, 161], [161, 247], [236, 3], [3, 196], [196, 236], [54, 68], [68, 104], [104, 54],\n  [193, 168], [168, 8], [8, 193], [117, 228], [228, 31], [31, 117], [189, 193], [193, 55], [55, 189], [98, 97], [97, 99], [99, 98], [126, 47], [47, 100], [100, 126], [166, 79], [79, 218], [218, 166],\n  [155, 154], [154, 26], [26, 155], [209, 49], [49, 131], [131, 209], [135, 136], [136, 150], [150, 135], [47, 126], [126, 217], [217, 47], [223, 52], [52, 53], [53, 223], [45, 51], [51, 134], [134, 45],\n  [211, 170], [170, 140], [140, 211], [67, 69], [69, 108], [108, 67], [43, 106], [106, 91], [91, 43], [230, 119], [119, 120], [120, 230], [226, 130], [130, 247], [247, 226], [63, 53], [53, 52], [52, 63],\n  [238, 20], [20, 242], [242, 238], [46, 70], [70, 156], [156, 46], [78, 62], [62, 96], [96, 78], [46, 53], [53, 63], [63, 46], [143, 34], [34, 227], [227, 143], [123, 117], [117, 111], [111, 123],\n  [44, 125], [125, 19], [19, 44], [236, 134], [134, 51], [51, 236], [216, 206], [206, 205], [205, 216], [154, 153], [153, 22], [22, 154], [39, 37], [37, 167], [167, 39], [200, 201], [201, 208], [208, 200],\n  [36, 142], [142, 100], [100, 36], [57, 212], [212, 202], [202, 57], [20, 60], [60, 99], [99, 20], [28, 158], [158, 157], [157, 28], [35, 226], [226, 113], [113, 35], [160, 159], [159, 27], [27, 160],\n  [204, 202], [202, 210], [210, 204], [113, 225], [225, 46], [46, 113], [43, 202], [202, 204], [204, 43], [62, 76], [76, 77], [77, 62], [137, 123], [123, 116], [116, 137], [41, 38], [38, 72], [72, 41],\n  [203, 129], [129, 142], [142, 203], [64, 98], [98, 240], [240, 64], [49, 102], [102, 64], [64, 49], [41, 73], [73, 74], [74, 41], [212, 216], [216, 207], [207, 212], [42, 74], [74, 184], [184, 42],\n  [169, 170], [170, 211], [211, 169], [170, 149], [149, 176], [176, 170], [105, 66], [66, 69], [69, 105], [122, 6], [6, 168], [168, 122], [123, 147], [147, 187], [187, 123], [96, 77], [77, 90], [90, 96],\n  [65, 55], [55, 107], [107, 65], [89, 90], [90, 180], [180, 89], [101, 100], [100, 120], [120, 101], [63, 105], [105, 104], [104, 63], [93, 137], [137, 227], [227, 93], [15, 86], [86, 85], [85, 15],\n  [129, 102], [102, 49], [49, 129], [14, 87], [87, 86], [86, 14], [55, 8], [8, 9], [9, 55], [100, 47], [47, 121], [121, 100], [145, 23], [23, 22], [22, 145], [88, 89], [89, 179], [179, 88],\n  [6, 122], [122, 196], [196, 6], [88, 95], [95, 96], [96, 88], [138, 172], [172, 136], [136, 138], [215, 58], [58, 172], [172, 215], [115, 48], [48, 219], [219, 115], [42, 80], [80, 81], [81, 42],\n  [195, 3], [3, 51], [51, 195], [43, 146], [146, 61], [61, 43], [171, 175], [175, 199], [199, 171], [81, 82], [82, 38], [38, 81], [53, 46], [46, 225], [225, 53], [144, 163], [163, 110], [110, 144],\n  [52, 65], [65, 66], [66, 52], [229, 228], [228, 117], [117, 229], [34, 127], [127, 234], [234, 34], [107, 108], [108, 69], [69, 107], [109, 108], [108, 151], [151, 109], [48, 64], [64, 235], [235, 48],\n  [62, 78], [78, 191], [191, 62], [129, 209], [209, 126], [126, 129], [111, 35], [35, 143], [143, 111], [117, 123], [123, 50], [50, 117], [222, 65], [65, 52], [52, 222], [19, 125], [125, 141], [141, 19],\n  [221, 55], [55, 65], [65, 221], [3, 195], [195, 197], [197, 3], [25, 7], [7, 33], [33, 25], [220, 237], [237, 44], [44, 220], [70, 71], [71, 139], [139, 70], [122, 193], [193, 245], [245, 122],\n  [247, 130], [130, 33], [33, 247], [71, 21], [21, 162], [162, 71], [170, 169], [169, 150], [150, 170], [188, 174], [174, 196], [196, 188], [216, 186], [186, 92], [92, 216], [2, 97], [97, 167], [167, 2],\n  [141, 125], [125, 241], [241, 141], [164, 167], [167, 37], [37, 164], [72, 38], [38, 12], [12, 72], [38, 82], [82, 13], [13, 38], [63, 68], [68, 71], [71, 63], [226, 35], [35, 111], [111, 226],\n  [101, 50], [50, 205], [205, 101], [206, 92], [92, 165], [165, 206], [209, 198], [198, 217], [217, 209], [165, 167], [167, 97], [97, 165], [220, 115], [115, 218], [218, 220], [133, 112], [112, 243], [243, 133],\n  [239, 238], [238, 241], [241, 239], [214, 135], [135, 169], [169, 214], [190, 173], [173, 133], [133, 190], [171, 208], [208, 32], [32, 171], [125, 44], [44, 237], [237, 125], [86, 87], [87, 178], [178, 86],\n  [85, 86], [86, 179], [179, 85], [84, 85], [85, 180], [180, 84], [83, 84], [84, 181], [181, 83], [201, 83], [83, 182], [182, 201], [137, 93], [93, 132], [132, 137], [76, 62], [62, 183], [183, 76],\n  [61, 76], [76, 184], [184, 61], [57, 61], [61, 185], [185, 57], [212, 57], [57, 186], [186, 212], [214, 207], [207, 187], [187, 214], [34, 143], [143, 156], [156, 34], [79, 239], [239, 237], [237, 79],\n  [123, 137], [137, 177], [177, 123], [44, 1], [1, 4], [4, 44], [201, 194], [194, 32], [32, 201], [64, 102], [102, 129], [129, 64], [213, 215], [215, 138], [138, 213], [59, 166], [166, 219], [219, 59],\n  [242, 99], [99, 97], [97, 242], [2, 94], [94, 141], [141, 2], [75, 59], [59, 235], [235, 75], [24, 110], [110, 228], [228, 24], [25, 130], [130, 226], [226, 25], [23, 24], [24, 229], [229, 23],\n  [22, 23], [23, 230], [230, 22], [26, 22], [22, 231], [231, 26], [112, 26], [26, 232], [232, 112], [189, 190], [190, 243], [243, 189], [221, 56], [56, 190], [190, 221], [28, 56], [56, 221], [221, 28],\n  [27, 28], [28, 222], [222, 27], [29, 27], [27, 223], [223, 29], [30, 29], [29, 224], [224, 30], [247, 30], [30, 225], [225, 247], [238, 79], [79, 20], [20, 238], [166, 59], [59, 75], [75, 166],\n  [60, 75], [75, 240], [240, 60], [147, 177], [177, 215], [215, 147], [20, 79], [79, 166], [166, 20], [187, 147], [147, 213], [213, 187], [112, 233], [233, 244], [244, 112], [233, 128], [128, 245], [245, 233],\n  [128, 114], [114, 188], [188, 128], [114, 217], [217, 174], [174, 114], [131, 115], [115, 220], [220, 131], [217, 198], [198, 236], [236, 217], [198, 131], [131, 134], [134, 198], [177, 132], [132, 58], [58, 177],\n  [143, 35], [35, 124], [124, 143], [110, 163], [163, 7], [7, 110], [228, 110], [110, 25], [25, 228], [356, 389], [389, 368], [368, 356], [11, 302], [302, 267], [267, 11], [452, 350], [350, 349], [349, 452],\n  [302, 303], [303, 269], [269, 302], [357, 343], [343, 277], [277, 357], [452, 453], [453, 357], [357, 452], [333, 332], [332, 297], [297, 333], [175, 152], [152, 377], [377, 175], [347, 348], [348, 330], [330, 347],\n  [303, 304], [304, 270], [270, 303], [9, 336], [336, 337], [337, 9], [278, 279], [279, 360], [360, 278], [418, 262], [262, 431], [431, 418], [304, 408], [408, 409], [409, 304], [310, 415], [415, 407], [407, 310],\n  [270, 409], [409, 410], [410, 270], [450, 348], [348, 347], [347, 450], [422, 430], [430, 434], [434, 422], [313, 314], [314, 17], [17, 313], [306, 307], [307, 375], [375, 306], [387, 388], [388, 260], [260, 387],\n  [286, 414], [414, 398], [398, 286], [335, 406], [406, 418], [418, 335], [364, 367], [367, 416], [416, 364], [423, 358], [358, 327], [327, 423], [251, 284], [284, 298], [298, 251], [281, 5], [5, 4], [4, 281],\n  [373, 374], [374, 253], [253, 373], [307, 320], [320, 321], [321, 307], [425, 427], [427, 411], [411, 425], [421, 313], [313, 18], [18, 421], [321, 405], [405, 406], [406, 321], [320, 404], [404, 405], [405, 320],\n  [315, 16], [16, 17], [17, 315], [426, 425], [425, 266], [266, 426], [377, 400], [400, 369], [369, 377], [322, 391], [391, 269], [269, 322], [417, 465], [465, 464], [464, 417], [386, 257], [257, 258], [258, 386],\n  [466, 260], [260, 388], [388, 466], [456, 399], [399, 419], [419, 456], [284, 332], [332, 333], [333, 284], [417, 285], [285, 8], [8, 417], [346, 340], [340, 261], [261, 346], [413, 441], [441, 285], [285, 413],\n  [327, 460], [460, 328], [328, 327], [355, 371], [371, 329], [329, 355], [392, 439], [439, 438], [438, 392], [382, 341], [341, 256], [256, 382], [429, 420], [420, 360], [360, 429], [364, 394], [394, 379], [379, 364],\n  [277, 343], [343, 437], [437, 277], [443, 444], [444, 283], [283, 443], [275, 440], [440, 363], [363, 275], [431, 262], [262, 369], [369, 431], [297, 338], [338, 337], [337, 297], [273, 375], [375, 321], [321, 273],\n  [450, 451], [451, 349], [349, 450], [446, 342], [342, 467], [467, 446], [293, 334], [334, 282], [282, 293], [458, 461], [461, 462], [462, 458], [276, 353], [353, 383], [383, 276], [308, 324], [324, 325], [325, 308],\n  [276, 300], [300, 293], [293, 276], [372, 345], [345, 447], [447, 372], [352, 345], [345, 340], [340, 352], [274, 1], [1, 19], [19, 274], [456, 248], [248, 281], [281, 456], [436, 427], [427, 425], [425, 436],\n  [381, 256], [256, 252], [252, 381], [269, 391], [391, 393], [393, 269], [200, 199], [199, 428], [428, 200], [266, 330], [330, 329], [329, 266], [287, 273], [273, 422], [422, 287], [250, 462], [462, 328], [328, 250],\n  [258, 286], [286, 384], [384, 258], [265, 353], [353, 342], [342, 265], [387, 259], [259, 257], [257, 387], [424, 431], [431, 430], [430, 424], [342, 353], [353, 276], [276, 342], [273, 335], [335, 424], [424, 273],\n  [292, 325], [325, 307], [307, 292], [366, 447], [447, 345], [345, 366], [271, 303], [303, 302], [302, 271], [423, 266], [266, 371], [371, 423], [294, 455], [455, 460], [460, 294], [279, 278], [278, 294], [294, 279],\n  [271, 272], [272, 304], [304, 271], [432, 434], [434, 427], [427, 432], [272, 407], [407, 408], [408, 272], [394, 430], [430, 431], [431, 394], [395, 369], [369, 400], [400, 395], [334, 333], [333, 299], [299, 334],\n  [351, 417], [417, 168], [168, 351], [352, 280], [280, 411], [411, 352], [325, 319], [319, 320], [320, 325], [295, 296], [296, 336], [336, 295], [319, 403], [403, 404], [404, 319], [330, 348], [348, 349], [349, 330],\n  [293, 298], [298, 333], [333, 293], [323, 454], [454, 447], [447, 323], [15, 16], [16, 315], [315, 15], [358, 429], [429, 279], [279, 358], [14, 15], [15, 316], [316, 14], [285, 336], [336, 9], [9, 285],\n  [329, 349], [349, 350], [350, 329], [374, 380], [380, 252], [252, 374], [318, 402], [402, 403], [403, 318], [6, 197], [197, 419], [419, 6], [318, 319], [319, 325], [325, 318], [367, 364], [364, 365], [365, 367],\n  [435, 367], [367, 397], [397, 435], [344, 438], [438, 439], [439, 344], [272, 271], [271, 311], [311, 272], [195, 5], [5, 281], [281, 195], [273, 287], [287, 291], [291, 273], [396, 428], [428, 199], [199, 396],\n  [311, 271], [271, 268], [268, 311], [283, 444], [444, 445], [445, 283], [373, 254], [254, 339], [339, 373], [282, 334], [334, 296], [296, 282], [449, 347], [347, 346], [346, 449], [264, 447], [447, 454], [454, 264],\n  [336, 296], [296, 299], [299, 336], [338, 10], [10, 151], [151, 338], [278, 439], [439, 455], [455, 278], [292, 407], [407, 415], [415, 292], [358, 371], [371, 355], [355, 358], [340, 345], [345, 372], [372, 340],\n  [346, 347], [347, 280], [280, 346], [442, 443], [443, 282], [282, 442], [19, 94], [94, 370], [370, 19], [441, 442], [442, 295], [295, 441], [248, 419], [419, 197], [197, 248], [263, 255], [255, 359], [359, 263],\n  [440, 275], [275, 274], [274, 440], [300, 383], [383, 368], [368, 300], [351, 412], [412, 465], [465, 351], [263, 467], [467, 466], [466, 263], [301, 368], [368, 389], [389, 301], [395, 378], [378, 379], [379, 395],\n  [412, 351], [351, 419], [419, 412], [436, 426], [426, 322], [322, 436], [2, 164], [164, 393], [393, 2], [370, 462], [462, 461], [461, 370], [164, 0], [0, 267], [267, 164], [302, 11], [11, 12], [12, 302],\n  [268, 12], [12, 13], [13, 268], [293, 300], [300, 301], [301, 293], [446, 261], [261, 340], [340, 446], [330, 266], [266, 425], [425, 330], [426, 423], [423, 391], [391, 426], [429, 355], [355, 437], [437, 429],\n  [391, 327], [327, 326], [326, 391], [440, 457], [457, 438], [438, 440], [341, 382], [382, 362], [362, 341], [459, 457], [457, 461], [461, 459], [434, 430], [430, 394], [394, 434], [414, 463], [463, 362], [362, 414],\n  [396, 369], [369, 262], [262, 396], [354, 461], [461, 457], [457, 354], [316, 403], [403, 402], [402, 316], [315, 404], [404, 403], [403, 315], [314, 405], [405, 404], [404, 314], [313, 406], [406, 405], [405, 313],\n  [421, 418], [418, 406], [406, 421], [366, 401], [401, 361], [361, 366], [306, 408], [408, 407], [407, 306], [291, 409], [409, 408], [408, 291], [287, 410], [410, 409], [409, 287], [432, 436], [436, 410], [410, 432],\n  [434, 416], [416, 411], [411, 434], [264, 368], [368, 383], [383, 264], [309, 438], [438, 457], [457, 309], [352, 376], [376, 401], [401, 352], [274, 275], [275, 4], [4, 274], [421, 428], [428, 262], [262, 421],\n  [294, 327], [327, 358], [358, 294], [433, 416], [416, 367], [367, 433], [289, 455], [455, 439], [439, 289], [462, 370], [370, 326], [326, 462], [2, 326], [326, 370], [370, 2], [305, 460], [460, 455], [455, 305],\n  [254, 449], [449, 448], [448, 254], [255, 261], [261, 446], [446, 255], [253, 450], [450, 449], [449, 253], [252, 451], [451, 450], [450, 252], [256, 452], [452, 451], [451, 256], [341, 453], [453, 452], [452, 341],\n  [413, 464], [464, 463], [463, 413], [441, 413], [413, 414], [414, 441], [258, 442], [442, 441], [441, 258], [257, 443], [443, 442], [442, 257], [259, 444], [444, 443], [443, 259], [260, 445], [445, 444], [444, 260],\n  [467, 342], [342, 445], [445, 467], [459, 458], [458, 250], [250, 459], [289, 392], [392, 290], [290, 289], [290, 328], [328, 460], [460, 290], [376, 433], [433, 435], [435, 376], [250, 290], [290, 392], [392, 250],\n  [411, 416], [416, 433], [433, 411], [341, 463], [463, 464], [464, 341], [453, 464], [464, 465], [465, 453], [357, 465], [465, 412], [412, 357], [343, 412], [412, 399], [399, 343], [360, 363], [363, 440], [440, 360],\n  [437, 399], [399, 456], [456, 437], [420, 456], [456, 363], [363, 420], [401, 435], [435, 288], [288, 401], [372, 383], [383, 353], [353, 372], [339, 255], [255, 249], [249, 339], [448, 261], [261, 255], [255, 448],\n  [133, 243], [243, 190], [190, 133], [133, 155], [155, 112], [112, 133], [33, 246], [246, 247], [247, 33], [33, 130], [130, 25], [25, 33], [398, 384], [384, 286], [286, 398], [362, 398], [398, 414], [414, 362],\n  [362, 463], [463, 341], [341, 362], [263, 359], [359, 467], [467, 263], [263, 249], [249, 255], [255, 263], [466, 467], [467, 260], [260, 466], [75, 60], [60, 166], [166, 75], [238, 239], [239, 79], [79, 238],\n  [162, 127], [127, 139], [139, 162], [72, 11], [11, 37], [37, 72], [121, 232], [232, 120], [120, 121], [73, 72], [72, 39], [39, 73], [114, 128], [128, 47], [47, 114], [233, 232], [232, 128], [128, 233],\n  [103, 104], [104, 67], [67, 103], [152, 175], [175, 148], [148, 152], [119, 118], [118, 101], [101, 119], [74, 73], [73, 40], [40, 74], [107, 9], [9, 108], [108, 107], [49, 48], [48, 131], [131, 49],\n  [32, 194], [194, 211], [211, 32], [184, 74], [74, 185], [185, 184], [191, 80], [80, 183], [183, 191], [185, 40], [40, 186], [186, 185], [119, 230], [230, 118], [118, 119], [210, 202], [202, 214], [214, 210],\n  [84, 83], [83, 17], [17, 84], [77, 76], [76, 146], [146, 77], [161, 160], [160, 30], [30, 161], [190, 56], [56, 173], [173, 190], [182, 106], [106, 194], [194, 182], [138, 135], [135, 192], [192, 138],\n  [129, 203], [203, 98], [98, 129], [54, 21], [21, 68], [68, 54], [5, 51], [51, 4], [4, 5], [145, 144], [144, 23], [23, 145], [90, 77], [77, 91], [91, 90], [207, 205], [205, 187], [187, 207],\n  [83, 201], [201, 18], [18, 83], [181, 91], [91, 182], [182, 181], [180, 90], [90, 181], [181, 180], [16, 85], [85, 17], [17, 16], [205, 206], [206, 36], [36, 205], [176, 148], [148, 140], [140, 176],\n  [165, 92], [92, 39], [39, 165], [245, 193], [193, 244], [244, 245], [27, 159], [159, 28], [28, 27], [30, 247], [247, 161], [161, 30], [174, 236], [236, 196], [196, 174], [103, 54], [54, 104], [104, 103],\n  [55, 193], [193, 8], [8, 55], [111, 117], [117, 31], [31, 111], [221, 189], [189, 55], [55, 221], [240, 98], [98, 99], [99, 240], [142, 126], [126, 100], [100, 142], [219, 166], [166, 218], [218, 219],\n  [112, 155], [155, 26], [26, 112], [198, 209], [209, 131], [131, 198], [169, 135], [135, 150], [150, 169], [114, 47], [47, 217], [217, 114], [224, 223], [223, 53], [53, 224], [220, 45], [45, 134], [134, 220],\n  [32, 211], [211, 140], [140, 32], [109, 67], [67, 108], [108, 109], [146, 43], [43, 91], [91, 146], [231, 230], [230, 120], [120, 231], [113, 226], [226, 247], [247, 113], [105, 63], [63, 52], [52, 105],\n  [241, 238], [238, 242], [242, 241], [124, 46], [46, 156], [156, 124], [95, 78], [78, 96], [96, 95], [70, 46], [46, 63], [63, 70], [116, 143], [143, 227], [227, 116], [116, 123], [123, 111], [111, 116],\n  [1, 44], [44, 19], [19, 1], [3, 236], [236, 51], [51, 3], [207, 216], [216, 205], [205, 207], [26, 154], [154, 22], [22, 26], [165, 39], [39, 167], [167, 165], [199, 200], [200, 208], [208, 199],\n  [101, 36], [36, 100], [100, 101], [43, 57], [57, 202], [202, 43], [242, 20], [20, 99], [99, 242], [56, 28], [28, 157], [157, 56], [124, 35], [35, 113], [113, 124], [29, 160], [160, 27], [27, 29],\n  [211, 204], [204, 210], [210, 211], [124, 113], [113, 46], [46, 124], [106, 43], [43, 204], [204, 106], [96, 62], [62, 77], [77, 96], [227, 137], [137, 116], [116, 227], [73, 41], [41, 72], [72, 73],\n  [36, 203], [203, 142], [142, 36], [235, 64], [64, 240], [240, 235], [48, 49], [49, 64], [64, 48], [42, 41], [41, 74], [74, 42], [214, 212], [212, 207], [207, 214], [183, 42], [42, 184], [184, 183],\n  [210, 169], [169, 211], [211, 210], [140, 170], [170, 176], [176, 140], [104, 105], [105, 69], [69, 104], [193, 122], [122, 168], [168, 193], [50, 123], [123, 187], [187, 50], [89, 96], [96, 90], [90, 89],\n  [66, 65], [65, 107], [107, 66], [179, 89], [89, 180], [180, 179], [119, 101], [101, 120], [120, 119], [68, 63], [63, 104], [104, 68], [234, 93], [93, 227], [227, 234], [16, 15], [15, 85], [85, 16],\n  [209, 129], [129, 49], [49, 209], [15, 14], [14, 86], [86, 15], [107, 55], [55, 9], [9, 107], [120, 100], [100, 121], [121, 120], [153, 145], [145, 22], [22, 153], [178, 88], [88, 179], [179, 178],\n  [197, 6], [6, 196], [196, 197], [89, 88], [88, 96], [96, 89], [135, 138], [138, 136], [136, 135], [138, 215], [215, 172], [172, 138], [218, 115], [115, 219], [219, 218], [41, 42], [42, 81], [81, 41],\n  [5, 195], [195, 51], [51, 5], [57, 43], [43, 61], [61, 57], [208, 171], [171, 199], [199, 208], [41, 81], [81, 38], [38, 41], [224, 53], [53, 225], [225, 224], [24, 144], [144, 110], [110, 24],\n  [105, 52], [52, 66], [66, 105], [118, 229], [229, 117], [117, 118], [227, 34], [34, 234], [234, 227], [66, 107], [107, 69], [69, 66], [10, 109], [109, 151], [151, 10], [219, 48], [48, 235], [235, 219],\n  [183, 62], [62, 191], [191, 183], [142, 129], [129, 126], [126, 142], [116, 111], [111, 143], [143, 116], [118, 117], [117, 50], [50, 118], [223, 222], [222, 52], [52, 223], [94, 19], [19, 141], [141, 94],\n  [222, 221], [221, 65], [65, 222], [196, 3], [3, 197], [197, 196], [45, 220], [220, 44], [44, 45], [156, 70], [70, 139], [139, 156], [188, 122], [122, 245], [245, 188], [139, 71], [71, 162], [162, 139],\n  [149, 170], [170, 150], [150, 149], [122, 188], [188, 196], [196, 122], [206, 216], [216, 92], [92, 206], [164, 2], [2, 167], [167, 164], [242, 141], [141, 241], [241, 242], [0, 164], [164, 37], [37, 0],\n  [11, 72], [72, 12], [12, 11], [12, 38], [38, 13], [13, 12], [70, 63], [63, 71], [71, 70], [31, 226], [226, 111], [111, 31], [36, 101], [101, 205], [205, 36], [203, 206], [206, 165], [165, 203],\n  [126, 209], [209, 217], [217, 126], [98, 165], [165, 97], [97, 98], [237, 220], [220, 218], [218, 237], [237, 239], [239, 241], [241, 237], [210, 214], [214, 169], [169, 210], [140, 171], [171, 32], [32, 140],\n  [241, 125], [125, 237], [237, 241], [179, 86], [86, 178], [178, 179], [180, 85], [85, 179], [179, 180], [181, 84], [84, 180], [180, 181], [182, 83], [83, 181], [181, 182], [194, 201], [201, 182], [182, 194],\n  [177, 137], [137, 132], [132, 177], [184, 76], [76, 183], [183, 184], [185, 61], [61, 184], [184, 185], [186, 57], [57, 185], [185, 186], [216, 212], [212, 186], [186, 216], [192, 214], [214, 187], [187, 192],\n  [139, 34], [34, 156], [156, 139], [218, 79], [79, 237], [237, 218], [147, 123], [123, 177], [177, 147], [45, 44], [44, 4], [4, 45], [208, 201], [201, 32], [32, 208], [98, 64], [64, 129], [129, 98],\n  [192, 213], [213, 138], [138, 192], [235, 59], [59, 219], [219, 235], [141, 242], [242, 97], [97, 141], [97, 2], [2, 141], [141, 97], [240, 75], [75, 235], [235, 240], [229, 24], [24, 228], [228, 229],\n  [31, 25], [25, 226], [226, 31], [230, 23], [23, 229], [229, 230], [231, 22], [22, 230], [230, 231], [232, 26], [26, 231], [231, 232], [233, 112], [112, 232], [232, 233], [244, 189], [189, 243], [243, 244],\n  [189, 221], [221, 190], [190, 189], [222, 28], [28, 221], [221, 222], [223, 27], [27, 222], [222, 223], [224, 29], [29, 223], [223, 224], [225, 30], [30, 224], [224, 225], [113, 247], [247, 225], [225, 113],\n  [99, 60], [60, 240], [240, 99], [213, 147], [147, 215], [215, 213], [60, 20], [20, 166], [166, 60], [192, 187], [187, 213], [213, 192], [243, 112], [112, 244], [244, 243], [244, 233], [233, 245], [245, 244],\n  [245, 128], [128, 188], [188, 245], [188, 114], [114, 174], [174, 188], [134, 131], [131, 220], [220, 134], [174, 217], [217, 236], [236, 174], [236, 198], [198, 134], [134, 236], [215, 177], [177, 58], [58, 215],\n  [156, 143], [143, 124], [124, 156], [25, 110], [110, 7], [7, 25], [31, 228], [228, 25], [25, 31], [264, 356], [356, 368], [368, 264], [0, 11], [11, 267], [267, 0], [451, 452], [452, 349], [349, 451],\n  [267, 302], [302, 269], [269, 267], [350, 357], [357, 277], [277, 350], [350, 452], [452, 357], [357, 350], [299, 333], [333, 297], [297, 299], [396, 175], [175, 377], [377, 396], [280, 347], [347, 330], [330, 280],\n  [269, 303], [303, 270], [270, 269], [151, 9], [9, 337], [337, 151], [344, 278], [278, 360], [360, 344], [424, 418], [418, 431], [431, 424], [270, 304], [304, 409], [409, 270], [272, 310], [310, 407], [407, 272],\n  [322, 270], [270, 410], [410, 322], [449, 450], [450, 347], [347, 449], [432, 422], [422, 434], [434, 432], [18, 313], [313, 17], [17, 18], [291, 306], [306, 375], [375, 291], [259, 387], [387, 260], [260, 259],\n  [424, 335], [335, 418], [418, 424], [434, 364], [364, 416], [416, 434], [391, 423], [423, 327], [327, 391], [301, 251], [251, 298], [298, 301], [275, 281], [281, 4], [4, 275], [254, 373], [373, 253], [253, 254],\n  [375, 307], [307, 321], [321, 375], [280, 425], [425, 411], [411, 280], [200, 421], [421, 18], [18, 200], [335, 321], [321, 406], [406, 335], [321, 320], [320, 405], [405, 321], [314, 315], [315, 17], [17, 314],\n  [423, 426], [426, 266], [266, 423], [396, 377], [377, 369], [369, 396], [270, 322], [322, 269], [269, 270], [413, 417], [417, 464], [464, 413], [385, 386], [386, 258], [258, 385], [248, 456], [456, 419], [419, 248],\n  [298, 284], [284, 333], [333, 298], [168, 417], [417, 8], [8, 168], [448, 346], [346, 261], [261, 448], [417, 413], [413, 285], [285, 417], [326, 327], [327, 328], [328, 326], [277, 355], [355, 329], [329, 277],\n  [309, 392], [392, 438], [438, 309], [381, 382], [382, 256], [256, 381], [279, 429], [429, 360], [360, 279], [365, 364], [364, 379], [379, 365], [355, 277], [277, 437], [437, 355], [282, 443], [443, 283], [283, 282],\n  [281, 275], [275, 363], [363, 281], [395, 431], [431, 369], [369, 395], [299, 297], [297, 337], [337, 299], [335, 273], [273, 321], [321, 335], [348, 450], [450, 349], [349, 348], [359, 446], [446, 467], [467, 359],\n  [283, 293], [293, 282], [282, 283], [250, 458], [458, 462], [462, 250], [300, 276], [276, 383], [383, 300], [292, 308], [308, 325], [325, 292], [283, 276], [276, 293], [293, 283], [264, 372], [372, 447], [447, 264],\n  [346, 352], [352, 340], [340, 346], [354, 274], [274, 19], [19, 354], [363, 456], [456, 281], [281, 363], [426, 436], [436, 425], [425, 426], [380, 381], [381, 252], [252, 380], [267, 269], [269, 393], [393, 267],\n  [421, 200], [200, 428], [428, 421], [371, 266], [266, 329], [329, 371], [432, 287], [287, 422], [422, 432], [290, 250], [250, 328], [328, 290], [385, 258], [258, 384], [384, 385], [446, 265], [265, 342], [342, 446],\n  [386, 387], [387, 257], [257, 386], [422, 424], [424, 430], [430, 422], [445, 342], [342, 276], [276, 445], [422, 273], [273, 424], [424, 422], [306, 292], [292, 307], [307, 306], [352, 366], [366, 345], [345, 352],\n  [268, 271], [271, 302], [302, 268], [358, 423], [423, 371], [371, 358], [327, 294], [294, 460], [460, 327], [331, 279], [279, 294], [294, 331], [303, 271], [271, 304], [304, 303], [436, 432], [432, 427], [427, 436],\n  [304, 272], [272, 408], [408, 304], [395, 394], [394, 431], [431, 395], [378, 395], [395, 400], [400, 378], [296, 334], [334, 299], [299, 296], [6, 351], [351, 168], [168, 6], [376, 352], [352, 411], [411, 376],\n  [307, 325], [325, 320], [320, 307], [285, 295], [295, 336], [336, 285], [320, 319], [319, 404], [404, 320], [329, 330], [330, 349], [349, 329], [334, 293], [293, 333], [333, 334], [366, 323], [323, 447], [447, 366],\n  [316, 15], [15, 315], [315, 316], [331, 358], [358, 279], [279, 331], [317, 14], [14, 316], [316, 317], [8, 285], [285, 9], [9, 8], [277, 329], [329, 350], [350, 277], [253, 374], [374, 252], [252, 253],\n  [319, 318], [318, 403], [403, 319], [351, 6], [6, 419], [419, 351], [324, 318], [318, 325], [325, 324], [397, 367], [367, 365], [365, 397], [288, 435], [435, 397], [397, 288], [278, 344], [344, 439], [439, 278],\n  [310, 272], [272, 311], [311, 310], [248, 195], [195, 281], [281, 248], [375, 273], [273, 291], [291, 375], [175, 396], [396, 199], [199, 175], [312, 311], [311, 268], [268, 312], [276, 283], [283, 445], [445, 276],\n  [390, 373], [373, 339], [339, 390], [295, 282], [282, 296], [296, 295], [448, 449], [449, 346], [346, 448], [356, 264], [264, 454], [454, 356], [337, 336], [336, 299], [299, 337], [337, 338], [338, 151], [151, 337],\n  [294, 278], [278, 455], [455, 294], [308, 292], [292, 415], [415, 308], [429, 358], [358, 355], [355, 429], [265, 340], [340, 372], [372, 265], [352, 346], [346, 280], [280, 352], [295, 442], [442, 282], [282, 295],\n  [354, 19], [19, 370], [370, 354], [285, 441], [441, 295], [295, 285], [195, 248], [248, 197], [197, 195], [457, 440], [440, 274], [274, 457], [301, 300], [300, 368], [368, 301], [417, 351], [351, 465], [465, 417],\n  [251, 301], [301, 389], [389, 251], [394, 395], [395, 379], [379, 394], [399, 412], [412, 419], [419, 399], [410, 436], [436, 322], [322, 410], [326, 2], [2, 393], [393, 326], [354, 370], [370, 461], [461, 354],\n  [393, 164], [164, 267], [267, 393], [268, 302], [302, 12], [12, 268], [312, 268], [268, 13], [13, 312], [298, 293], [293, 301], [301, 298], [265, 446], [446, 340], [340, 265], [280, 330], [330, 425], [425, 280],\n  [322, 426], [426, 391], [391, 322], [420, 429], [429, 437], [437, 420], [393, 391], [391, 326], [326, 393], [344, 440], [440, 438], [438, 344], [458, 459], [459, 461], [461, 458], [364, 434], [434, 394], [394, 364],\n  [428, 396], [396, 262], [262, 428], [274, 354], [354, 457], [457, 274], [317, 316], [316, 402], [402, 317], [316, 315], [315, 403], [403, 316], [315, 314], [314, 404], [404, 315], [314, 313], [313, 405], [405, 314],\n  [313, 421], [421, 406], [406, 313], [323, 366], [366, 361], [361, 323], [292, 306], [306, 407], [407, 292], [306, 291], [291, 408], [408, 306], [291, 287], [287, 409], [409, 291], [287, 432], [432, 410], [410, 287],\n  [427, 434], [434, 411], [411, 427], [372, 264], [264, 383], [383, 372], [459, 309], [309, 457], [457, 459], [366, 352], [352, 401], [401, 366], [1, 274], [274, 4], [4, 1], [418, 421], [421, 262], [262, 418],\n  [331, 294], [294, 358], [358, 331], [435, 433], [433, 367], [367, 435], [392, 289], [289, 439], [439, 392], [328, 462], [462, 326], [326, 328], [94, 2], [2, 370], [370, 94], [289, 305], [305, 455], [455, 289],\n  [339, 254], [254, 448], [448, 339], [359, 255], [255, 446], [446, 359], [254, 253], [253, 449], [449, 254], [253, 252], [252, 450], [450, 253], [252, 256], [256, 451], [451, 252], [256, 341], [341, 452], [452, 256],\n  [414, 413], [413, 463], [463, 414], [286, 441], [441, 414], [414, 286], [286, 258], [258, 441], [441, 286], [258, 257], [257, 442], [442, 258], [257, 259], [259, 443], [443, 257], [259, 260], [260, 444], [444, 259],\n  [260, 467], [467, 445], [445, 260], [309, 459], [459, 250], [250, 309], [305, 289], [289, 290], [290, 305], [305, 290], [290, 460], [460, 305], [401, 376], [376, 435], [435, 401], [309, 250], [250, 392], [392, 309],\n  [376, 411], [411, 433], [433, 376], [453, 341], [341, 464], [464, 453], [357, 453], [453, 465], [465, 357], [343, 357], [357, 412], [412, 343], [437, 343], [343, 399], [399, 437], [344, 360], [360, 440], [440, 344],\n  [420, 437], [437, 456], [456, 420], [360, 420], [420, 363], [363, 360], [361, 401], [401, 288], [288, 361], [265, 372], [372, 353], [353, 265], [390, 339], [339, 249], [249, 390], [339, 448], [448, 255], [255, 339],\n];\n\nfunction connectionsToIndices(connections: PairArray) {\n  const indices = connections.map((connection) => connection[0]);\n  indices.push(connections[connections.length - 1][1]);\n  return indices;\n}\n\nexport const MEDIAPIPE_FACE_MESH_KEYPOINTS_BY_CONTOUR = {\n  lips: connectionsToIndices(LIPS_CONNECTIONS),\n  leftEye: connectionsToIndices(LEFT_EYE_CONNECTIONS),\n  leftEyebrow: connectionsToIndices(LEFT_EYEBROW_CONNECTIONS),\n  leftIris: connectionsToIndices(LEFT_IRIS_CONNECTIONS),\n  rightEye: connectionsToIndices(RIGHT_EYE_CONNECTIONS),\n  rightEyebrow: connectionsToIndices(RIGHT_EYEBROW_CONNECTIONS),\n  rightIris: connectionsToIndices(RIGHT_IRIS_CONNECTIONS),\n  faceOval: connectionsToIndices(FACE_OVAL_CONNECTIONS),\n};\n\nconst indexLabelPairs: [number, string][] = Object.entries(MEDIAPIPE_FACE_MESH_KEYPOINTS_BY_CONTOUR)\n  .map(([label, indices]) => indices.map((index) => [index, label] as [number, string]))\n  .flat();\n\nexport const MEDIAPIPE_FACE_MESH_KEYPOINTS = new Map(indexLabelPairs);\n\ntype AssignAverage = number[];\nexport interface LandmarksRefinementConfig {\n  indexesMapping: number[]; // Maps indexes of the given set of landmarks to indexes of the resulting set of landmarks. Should be non empty and contain the same amount of indexes as landmarks in the corresponding input\n  zRefinement: 'none'|'copy'|AssignAverage; // Z refinement instructions.\n}\n\nexport const LANDMARKS_REFINEMENT_LIPS_CONFIG = [\n  61, 146, 91, 181, 84, 17, 314, 405, 321, 375, 291, // Lower outer.\n  185, 40, 39, 37, 0, 267, 269, 270, 409, // Upper outer(excluding corners).\n  78, 95, 88, 178, 87, 14, 317, 402, 318, 324, 308, // Lower inner.\n  191, 80, 81, 82, 13, 312, 311, 310, 415, // Upper inner(excluding corners).\n  76, 77, 90, 180, 85, 16, 315, 404, 320, 307, 306, // Lower semi - outer.\n  184, 74, 73, 72, 11, 302, 303, 304, 408, // Upper semi - outer(excluding corners).\n  62, 96, 89, 179, 86, 15, 316, 403, 319, 325, 292, // Lower semi - inner.\n  183, 42, 41, 38, 12, 268, 271, 272, 407, // Upper semi - inner(excluding corners).\n];\n\nexport const LANDMARKS_REFINEMENT_LEFT_EYE_CONFIG = [\n  33, 7, 163, 144, 145, 153, 154, 155, 133, // Lower contour.\n  246, 161, 160, 159, 158, 157, 173, // upper contour (excluding corners).\n  130, 25, 110, 24, 23, 22, 26, 112, 243, // Halo x2 lower contour.\n  247, 30, 29, 27, 28, 56, 190, // Halo x2 upper contour (excluding corners).\n  226, 31, 228, 229, 230, 231, 232, 233, 244, // Halo x3 lower contour.\n  113, 225, 224, 223, 222, 221, 189, // Halo x3 upper contour (excluding corners).\n  35, 124, 46, 53, 52, 65, // Halo x4 upper contour (no lower because of mesh structure) or eyebrow inner contour.\n  143, 111, 117, 118, 119, 120, 121, 128, 245, // Halo x5 lower contour.\n  156, 70, 63, 105, 66, 107, 55, 193, // Halo x5 upper contour (excluding corners) or eyebrow outer contour.\n];\n\nexport const LANDMARKS_REFINEMENT_RIGHT_EYE_CONFIG = [\n  263, 249, 390, 373, 374, 380, 381, 382, 362, // Lower contour.\n  466, 388, 387, 386, 385, 384, 398, // Upper contour (excluding corners).\n  359, 255, 339, 254, 253, 252, 256, 341, 463, // Halo x2 lower contour.\n  467, 260, 259, 257, 258, 286, 414, // Halo x2 upper contour (excluding corners).\n  446, 261, 448, 449, 450, 451, 452, 453, 464, // Halo x3 lower contour.\n  342, 445, 444, 443, 442, 441, 413, // Halo x3 upper contour (excluding corners).\n  265, 353, 276, 283, 282, 295, // Halo x4 upper contour (no lower because of mesh structure) or/ eyebrow inner contour.\n  372, 340, 346, 347, 348, 349, 350, 357, 465, // Halo x5 lower contour.\n  383, 300, 293, 334, 296, 336, 285, 417, // Halo x5 upper contour (excluding corners) or eyebrow outer contour.\n];\n\nexport const LANDMARKS_REFINEMENT_LEFT_IRIS_CONFIG = [\n  468, // Center.\n  469, // Iris right edge.\n  470, // Iris top edge.\n  471, // Iris left edge.\n  472, // Iris bottom edge.\n];\n/*\nzRefinement: [\n  33, 7, 163, 144, 145, 153, 154, 155, 133, // Lower contour.\n  246, 161, 160, 159, 158, 157, 173, // Upper contour (excluding corners).\n];\n*/\n\nexport const LANDMARKS_REFINEMENT_RIGHT_IRIS_CONFIG = [\n  473, // Center.\n  474, // Iris right edge.\n  475, // Iris top edge.\n  476, // Iris left edge.\n  477, // Iris bottom edge.\n];\n/*\nzRefinement: [\n  263, 249, 390, 373, 374, 380, 381, 382, 362, // Lower contour.\n  466, 388, 387, 386, 385, 384, 398, // Upper contour (excluding corners).\n];\n*/\n", "import * as constants from './constants';\nimport type { Tensor } from '../tfjs/types';\n\nexport async function augment(rawCoords, results: Tensor[]) {\n  const t: Record<string, Float32Array> = { // all attention models produce 2d results so it needs to be later augmented with correct z-coords\n    // mesh: results[0], // already have it in rawCoords // output_mesh_identity\n    // flag: results[1], // already processed in parent // conv_faceflag\n    lips: await results.filter((r) => r.size === 160)?.[0]?.data() as Float32Array, // 80 x 2d = 160 // output_lips\n    irisL: await results.filter((r) => r.size === 10)?.[0]?.data() as Float32Array, // 5 x 2d = 10 // output_right_iris\n    eyeL: await results.filter((r) => r.size === 142)?.[0]?.data() as Float32Array, // 71 x 2d = 142 // output_right_eye\n    irisR: await results.filter((r) => r.size === 10)?.[1]?.data() as Float32Array, // 5 x 2d = 10 // output_left_iris\n    eyeR: await results.filter((r) => r.size === 142)?.[1]?.data() as Float32Array, // 71 x 2d = 142// output_left_eye\n  };\n  for (const val of Object.values(t)) {\n    if (!val) return rawCoords; // could not find tensor\n  }\n\n  // augment iris: adds additional 5 keypoints per eye\n  const irisLDepth = constants.LANDMARKS_REFINEMENT_LEFT_EYE_CONFIG.reduce((prev, curr) => prev += rawCoords[curr][2], 0) / constants.LANDMARKS_REFINEMENT_LEFT_EYE_CONFIG.length; // get average z-coord for iris\n  for (let i = 0; i < t.irisL.length / 2; i++) rawCoords.push([t.irisL[2 * i + 0], t.irisL[2 * i + 1], irisLDepth]);\n  const irisRDepth = constants.LANDMARKS_REFINEMENT_RIGHT_EYE_CONFIG.reduce((prev, curr) => prev += rawCoords[curr][2], 0) / constants.LANDMARKS_REFINEMENT_RIGHT_EYE_CONFIG.length; // get average z-coord for iris\n  for (let i = 0; i < t.irisR.length / 2; i++) rawCoords.push([t.irisR[2 * i + 0], t.irisR[2 * i + 1], irisRDepth]);\n\n  // augment eyes: replaces eye keypoints based on heuristic mapping\n  for (let i = 0; i < t.eyeL.length / 2; i++) rawCoords[constants.LANDMARKS_REFINEMENT_LEFT_EYE_CONFIG[i]] = [t.eyeL[2 * i + 0], t.eyeL[2 * i + 1], rawCoords[constants.LANDMARKS_REFINEMENT_LEFT_EYE_CONFIG[i]][2]];\n  for (let i = 0; i < t.eyeR.length / 2; i++) rawCoords[constants.LANDMARKS_REFINEMENT_RIGHT_EYE_CONFIG[i]] = [t.eyeR[2 * i + 0], t.eyeR[2 * i + 1], rawCoords[constants.LANDMARKS_REFINEMENT_RIGHT_EYE_CONFIG[i]][2]];\n\n  // augment lips: replaces eye keypoints based on heuristic mapping\n  for (let i = 0; i < t.lips.length / 2; i++) rawCoords[constants.LANDMARKS_REFINEMENT_LIPS_CONFIG[i]] = [t.lips[2 * i + 0], t.lips[2 * i + 1], rawCoords[constants.LANDMARKS_REFINEMENT_LIPS_CONFIG[i]][2]];\n\n  return rawCoords;\n}\n", "/**\n * BlazeFace, FaceMesh & Iris model implementation\n *\n * Based on:\n * - [**MediaPipe BlazeFace**](https://drive.google.com/file/d/1f39lSzU5Oq-j_OXgS67KfN5wNsoeAZ4V/view)\n * - Facial Spacial Geometry: [**MediaPipe FaceMesh**](https://drive.google.com/file/d/1VFC_wIpw4O7xBOiTgUldl79d9LA-LsnA/view)\n * - Eye Iris Details: [**MediaPipe Iris**](https://drive.google.com/file/d/1bsWbokp9AklH2ANjCfmjqEzzxO1CNbMu/view)\n */\n\nimport { log, now } from '../util/util';\nimport { loadModel } from '../tfjs/load';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as blazeface from './blazeface';\nimport * as util from './facemeshutil';\nimport * as coords from './facemeshcoords';\nimport * as iris from './iris';\nimport * as attention from './attention';\nimport { histogramEqualization } from '../image/enhance';\nimport { env } from '../util/env';\nimport type { GraphModel, Tensor } from '../tfjs/types';\nimport type { FaceResult, FaceLandmark, Point } from '../result';\nimport type { Config } from '../config';\n\ninterface DetectBox { startPoint: Point, endPoint: Point, landmarks: Point[], confidence: number }\n\nconst cache = {\n  boxes: [] as DetectBox[],\n  skipped: Number.MAX_SAFE_INTEGER,\n  timestamp: 0,\n};\n\nlet model: GraphModel | null = null;\nlet inputSize = 0;\n\nexport async function predict(input: Tensor, config: Config): Promise<FaceResult[]> {\n  if (!model?.['executor']) return [];\n  // reset cached boxes\n  const skipTime = (config.face.detector?.skipTime || 0) > (now() - cache.timestamp);\n  const skipFrame = cache.skipped < (config.face.detector?.skipFrames || 0);\n  if (!config.skipAllowed || !skipTime || !skipFrame || cache.boxes.length === 0) {\n    cache.boxes = await blazeface.getBoxes(input, config); // get results from blazeface detector\n    cache.timestamp = now();\n    cache.skipped = 0;\n  } else {\n    cache.skipped++;\n  }\n  const faces: FaceResult[] = [];\n  const newCache: DetectBox[] = [];\n  let id = 0;\n  const size = inputSize;\n  for (let i = 0; i < cache.boxes.length; i++) {\n    const box = cache.boxes[i];\n    let angle = 0;\n    let rotationMatrix;\n    const face: FaceResult = { // init face result\n      id: id++,\n      mesh: [],\n      meshRaw: [],\n      box: [0, 0, 0, 0],\n      boxRaw: [0, 0, 0, 0],\n      score: 0,\n      boxScore: 0,\n      faceScore: 0,\n      // contoursRaw: [],\n      // contours: [],\n      annotations: {} as Record<FaceLandmark, Point[]>,\n    };\n\n    // optional rotation correction based on detector data only if mesh is disabled otherwise perform it later when we have more accurate mesh data. if no rotation correction this function performs crop\n    [angle, rotationMatrix, face.tensor] = util.correctFaceRotation(config.face.detector?.rotation, box, input, config.face.mesh?.enabled ? inputSize : blazeface.size());\n    if (config.filter.equalization) {\n      const equilized = face.tensor ? await histogramEqualization(face.tensor) : undefined;\n      tf.dispose(face.tensor);\n      if (equilized) face.tensor = equilized;\n    }\n    face.boxScore = Math.round(100 * box.confidence) / 100;\n    if (!config.face.mesh?.enabled) { // mesh not enabled, return resuts from detector only\n      face.box = util.clampBox(box, input);\n      face.boxRaw = util.getRawBox(box, input);\n      face.score = face.boxScore;\n      face.mesh = box.landmarks.map((pt) => [\n        ((box.startPoint[0] + box.endPoint[0])) / 2 + ((box.endPoint[0] + box.startPoint[0]) * pt[0] / blazeface.size()),\n        ((box.startPoint[1] + box.endPoint[1])) / 2 + ((box.endPoint[1] + box.startPoint[1]) * pt[1] / blazeface.size()),\n      ]);\n      face.meshRaw = face.mesh.map((pt) => [pt[0] / (input.shape[2] || 0), pt[1] / (input.shape[1] || 0), (pt[2] || 0) / size]);\n      for (const key of Object.keys(coords.blazeFaceLandmarks)) {\n        face.annotations[key] = [face.mesh[coords.blazeFaceLandmarks[key] as number]]; // add annotations\n      }\n    } else if (!model) { // mesh enabled, but not loaded\n      if (config.debug) log('face mesh detection requested, but model is not loaded');\n    } else { // mesh enabled\n      if (config.face.attention?.enabled && !env.kernels.includes('atan2')) {\n        config.face.attention.enabled = false;\n        tf.dispose(face.tensor);\n        return faces;\n      }\n      const results = model.execute(face.tensor as Tensor) as Tensor[];\n      const confidenceT = results.find((t) => t.shape[t.shape.length - 1] === 1) as Tensor;\n      const faceConfidence = await confidenceT.data();\n      face.faceScore = Math.round(100 * faceConfidence[0]) / 100;\n      if (face.faceScore < (config.face.detector?.minConfidence || 1)) { // low confidence in detected mesh\n        box.confidence = face.faceScore; // reset confidence of cached box\n        if (config.face.mesh.keepInvalid) {\n          face.box = util.clampBox(box, input);\n          face.boxRaw = util.getRawBox(box, input);\n          face.score = face.boxScore;\n          face.mesh = box.landmarks.map((pt) => [\n            ((box.startPoint[0] + box.endPoint[0])) / 2 + ((box.endPoint[0] + box.startPoint[0]) * pt[0] / blazeface.size()),\n            ((box.startPoint[1] + box.endPoint[1])) / 2 + ((box.endPoint[1] + box.startPoint[1]) * pt[1] / blazeface.size()),\n          ]);\n          face.meshRaw = face.mesh.map((pt) => [pt[0] / (input.shape[2] || 1), pt[1] / (input.shape[1] || 1), (pt[2] || 0) / size]);\n          for (const key of Object.keys(coords.blazeFaceLandmarks)) {\n            face.annotations[key] = [face.mesh[coords.blazeFaceLandmarks[key] as number]]; // add annotations\n          }\n        }\n      } else {\n        const meshT = results.find((t) => t.shape[t.shape.length - 1] === 1404) as Tensor;\n        const coordsReshaped = tf.reshape(meshT, [-1, 3]);\n        let rawCoords = await coordsReshaped.array();\n        tf.dispose(coordsReshaped);\n        if (config.face.attention?.enabled) {\n          rawCoords = await attention.augment(rawCoords, results); // augment iris results using attention model results\n        } else if (config.face.iris?.enabled) {\n          rawCoords = await iris.augmentIris(rawCoords, face.tensor, inputSize); // run iris model and augment results\n        }\n        face.mesh = util.transformRawCoords(rawCoords, box, angle, rotationMatrix, inputSize); // get processed mesh\n        face.meshRaw = face.mesh.map((pt) => [pt[0] / (input.shape[2] || 0), pt[1] / (input.shape[1] || 0), (pt[2] || 0) / size]);\n        for (const key of Object.keys(coords.meshAnnotations)) face.annotations[key] = coords.meshAnnotations[key].map((index) => face.mesh[index]); // add annotations\n        face.score = face.faceScore;\n        const calculatedBox = { ...util.calculateFaceBox(face.mesh, box), confidence: box.confidence, landmarks: box.landmarks };\n        face.box = util.clampBox(calculatedBox, input);\n        face.boxRaw = util.getRawBox(calculatedBox, input);\n        /*\n        const contoursT = results.find((t) => t.shape[t.shape.length - 1] === 266) as Tensor;\n        const contoursData = contoursT && await contoursT.data(); // 133 x 2d points\n        face.contoursRaw = [];\n        for (let j = 0; j < contoursData.length / 2; j++) face.contoursRaw.push([contoursData[2 * j + 0] / inputSize, contoursData[2 * j + 1] / inputSize]);\n        face.contours = face.contoursRaw.map((c) => [Math.trunc((input.shape[2] || 1) * c[0]), Math.trunc((input.shape[1] || 1) * c[1])]);\n        */\n        newCache.push(calculatedBox);\n      }\n      tf.dispose(results);\n    }\n    if (face.score > (config.face.detector?.minConfidence || 1)) faces.push(face);\n    else tf.dispose(face.tensor);\n  }\n  cache.boxes = newCache; // reset cache\n  return faces;\n}\n\nexport async function load(config: Config): Promise<GraphModel> {\n  if (env.initial) model = null;\n  if (config.face.attention?.enabled && model?.['signature']) {\n    if (Object.keys(model?.['signature']?.outputs || {}).length < 6) model = null;\n  }\n  if (!model) {\n    if (config.face.attention?.enabled) model = await loadModel(config.face.attention.modelPath);\n    else model = await loadModel(config.face.mesh?.modelPath);\n  } else if (config.debug) {\n    log('cached model:', model['modelUrl']);\n  }\n  inputSize = (model['executor'] && model?.inputs?.[0].shape) ? model?.inputs?.[0].shape[2] : 256;\n  return model;\n}\n\nexport const triangulation = coords.TRI468;\nexport const uvmap = coords.UV468;\n", "/**\n * FaceRes model implementation\n *\n * Returns Age, Gender, Descriptor\n * Implements Face simmilarity function\n *\n * Based on: [**HSE-FaceRes**](https://github.com/HSE-asavchenko/HSE_FaceRec_tf)\n */\n\nimport { log, now } from '../util/util';\nimport { env } from '../util/env';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { loadModel } from '../tfjs/load';\nimport { constants } from '../tfjs/constants';\nimport type { Tensor, GraphModel } from '../tfjs/types';\nimport type { Config } from '../config';\nimport type { Gender, Race } from '../result';\n\nexport interface FaceRes { age: number, gender: Gender, genderScore: number, descriptor: number[], race?: { score: number, race: Race }[] }\n\nlet model: GraphModel | null;\nconst last: FaceRes[] = [];\n\nlet lastTime = 0;\nlet lastCount = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nexport async function load(config: Config): Promise<GraphModel> {\n  if (env.initial) model = null;\n  if (!model) model = await loadModel(config.face.description?.modelPath);\n  else if (config.debug) log('cached model:', model['modelUrl']);\n  return model;\n}\n\nexport function enhance(input): Tensor {\n  const tensor = (input.image || input.tensor || input) as Tensor; // input received from detector is already normalized to 0..1, input is also assumed to be straightened\n  if (!model?.inputs[0].shape) return tensor; // model has no shape so no point continuing\n  const crop: Tensor = tf.image.resizeBilinear(tensor, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false);\n  const norm: Tensor = tf.mul(crop, constants.tf255);\n  tf.dispose(crop);\n  return norm;\n  /*\n  // do a tight crop of image and resize it to fit the model\n  const box = [[0.05, 0.15, 0.85, 0.85]]; // empyrical values for top, left, bottom, right\n  const crop = (tensor.shape.length === 3)\n    ? tf.image.cropAndResize(tf.expandDims(tensor, 0), box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]) // add batch dimension if missing\n    : tf.image.cropAndResize(tensor, box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]);\n  */\n  /*\n  // convert to black&white to avoid colorization impact\n  const rgb = [0.2989, 0.5870, 0.1140]; // factors for red/green/blue colors when converting to grayscale: https://www.mathworks.com/help/matlab/ref/rgb2gray.html\n  const [red, green, blue] = tf.split(crop, 3, 3);\n  const redNorm = tf.mul(red, rgb[0]);\n  const greenNorm = tf.mul(green, rgb[1]);\n  const blueNorm = tf.mul(blue, rgb[2]);\n  const grayscale = tf.addN([redNorm, greenNorm, blueNorm]);\n  const merge = tf.stack([grayscale, grayscale, grayscale], 3).squeeze(4);\n  */\n}\n\nexport async function predict(image: Tensor, config: Config, idx: number, count: number): Promise<FaceRes> {\n  const obj: FaceRes = {\n    age: 0 as number,\n    gender: 'unknown' as Gender,\n    genderScore: 0 as number,\n    descriptor: [] as number[],\n  };\n  if (!model?.['executor']) return obj;\n  const skipFrame = skipped < (config.face.description?.skipFrames || 0);\n  const skipTime = (config.face.description?.skipTime || 0) > (now() - lastTime);\n  if (config.skipAllowed && skipFrame && skipTime && (lastCount === count) && (last?.[idx]?.age > 0) && (last?.[idx]?.genderScore > 0)) {\n    skipped++;\n    return last[idx];\n  }\n  skipped = 0;\n  return new Promise(async (resolve) => {\n    if (config.face.description?.enabled) {\n      const enhanced = enhance(image);\n      const resT = model?.execute(enhanced) as Tensor[];\n      lastTime = now();\n      tf.dispose(enhanced);\n      const genderT = resT.find((t) => t.shape[1] === 1) as Tensor;\n      const gender = await genderT.data();\n      const confidence = Math.trunc(200 * Math.abs((gender[0] - 0.5))) / 100;\n      if (confidence > (config.face.description.minConfidence || 0)) {\n        obj.gender = gender[0] <= 0.5 ? 'female' : 'male';\n        obj.genderScore = Math.min(0.99, confidence);\n      }\n      const argmax = tf.argMax(resT.find((t) => t.shape[1] === 100), 1);\n      const ageIdx: number = (await argmax.data())[0];\n      tf.dispose(argmax);\n      const ageT = resT.find((t) => t.shape[1] === 100) as Tensor;\n      const all = await ageT.data();\n      obj.age = Math.round(all[ageIdx - 1] > all[ageIdx + 1] ? 10 * ageIdx - 100 * all[ageIdx - 1] : 10 * ageIdx + 100 * all[ageIdx + 1]) / 10;\n\n      if (Number.isNaN(gender[0]) || Number.isNaN(all[0])) log('faceres error:', { model, result: resT });\n\n      const desc = resT.find((t) => t.shape[1] === 1024);\n      // const reshape = desc.reshape([128, 8]); // reshape large 1024-element descriptor to 128 x 8\n      // const reduce = reshape.logSumExp(1); // reduce 2nd dimension by calculating logSumExp on it which leaves us with 128-element descriptor\n      const descriptor = desc ? await desc.data() : [] as number[];\n      obj.descriptor = Array.from(descriptor);\n      resT.forEach((t) => tf.dispose(t));\n    }\n    last[idx] = obj;\n    lastCount = count;\n    resolve(obj);\n  });\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\nimport type { Point } from '../result';\n\nexport function getBoxSize(box) {\n  return [\n    Math.abs(box.endPoint[0] - box.startPoint[0]),\n    Math.abs(box.endPoint[1] - box.startPoint[1]),\n  ];\n}\n\nexport function getBoxCenter(box) {\n  return [\n    box.startPoint[0] + (box.endPoint[0] - box.startPoint[0]) / 2,\n    box.startPoint[1] + (box.endPoint[1] - box.startPoint[1]) / 2,\n  ];\n}\n\nexport function cutBoxFromImageAndResize(box, image, cropSize) {\n  const h = image.shape[1];\n  const w = image.shape[2];\n  const boxes = [[\n    box.startPoint[1] / h,\n    box.startPoint[0] / w,\n    box.endPoint[1] / h,\n    box.endPoint[0] / w,\n  ]];\n  return tf.image.cropAndResize(image, boxes, [0], cropSize);\n}\n\nexport function scaleBoxCoordinates(box, factor) {\n  const startPoint = [box.startPoint[0] * factor[0], box.startPoint[1] * factor[1]] as Point;\n  const endPoint = [box.endPoint[0] * factor[0], box.endPoint[1] * factor[1]] as Point;\n  const palmLandmarks = box.palmLandmarks.map((coord) => {\n    const scaledCoord = [coord[0] * factor[0], coord[1] * factor[1]];\n    return scaledCoord;\n  });\n  return { startPoint, endPoint, palmLandmarks, confidence: box.confidence };\n}\n\nexport function enlargeBox(box, factor = 1.5) {\n  const center = getBoxCenter(box);\n  const size = getBoxSize(box);\n  const newHalfSize = [factor * size[0] / 2, factor * size[1] / 2];\n  const startPoint = [center[0] - newHalfSize[0], center[1] - newHalfSize[1]] as Point;\n  const endPoint = [center[0] + newHalfSize[0], center[1] + newHalfSize[1]] as Point;\n  return { startPoint, endPoint, palmLandmarks: box.palmLandmarks };\n}\n\nexport function squarifyBox(box) {\n  const centers = getBoxCenter(box);\n  const size = getBoxSize(box);\n  const maxEdge = Math.max(...size);\n  const halfSize = maxEdge / 2;\n  const startPoint = [centers[0] - halfSize, centers[1] - halfSize] as Point;\n  const endPoint = [centers[0] + halfSize, centers[1] + halfSize] as Point;\n  return { startPoint, endPoint, palmLandmarks: box.palmLandmarks };\n}\n\nexport function shiftBox(box, shiftFactor) {\n  const boxSize = [\n    box.endPoint[0] - box.startPoint[0],\n    box.endPoint[1] - box.startPoint[1],\n  ];\n  const shiftVector = [boxSize[0] * shiftFactor[0], boxSize[1] * shiftFactor[1]];\n  const startPoint = [box.startPoint[0] + shiftVector[0], box.startPoint[1] + shiftVector[1]] as Point;\n  const endPoint = [box.endPoint[0] + shiftVector[0], box.endPoint[1] + shiftVector[1]] as Point;\n  return { startPoint, endPoint, palmLandmarks: box.palmLandmarks };\n}\n\nexport function normalizeRadians(angle) {\n  return angle - 2 * Math.PI * Math.floor((angle + Math.PI) / (2 * Math.PI));\n}\n\nexport function computeRotation(point1, point2) {\n  const radians = Math.PI / 2 - Math.atan2(-(point2[1] - point1[1]), point2[0] - point1[0]);\n  return normalizeRadians(radians);\n}\n\nexport const buildTranslationMatrix = (x, y) => [[1, 0, x], [0, 1, y], [0, 0, 1]];\n\nexport function dot(v1, v2) {\n  let product = 0;\n  for (let i = 0; i < v1.length; i++) {\n    product += v1[i] * v2[i];\n  }\n  return product;\n}\n\nexport function getColumnFrom2DArr(arr, columnIndex) {\n  const column: number[] = [];\n  for (let i = 0; i < arr.length; i++) {\n    column.push(arr[i][columnIndex]);\n  }\n  return column;\n}\n\nexport function multiplyTransformMatrices(mat1, mat2) {\n  const product: number[][] = [];\n  const size = mat1.length;\n  for (let row = 0; row < size; row++) {\n    product.push([]);\n    for (let col = 0; col < size; col++) {\n      product[row].push(dot(mat1[row], getColumnFrom2DArr(mat2, col)));\n    }\n  }\n  return product;\n}\n\nexport function buildRotationMatrix(rotation, center) {\n  const cosA = Math.cos(rotation);\n  const sinA = Math.sin(rotation);\n  const rotationMatrix = [[cosA, -sinA, 0], [sinA, cosA, 0], [0, 0, 1]];\n  const translationMatrix = buildTranslationMatrix(center[0], center[1]);\n  const translationTimesRotation = multiplyTransformMatrices(translationMatrix, rotationMatrix);\n  const negativeTranslationMatrix = buildTranslationMatrix(-center[0], -center[1]);\n  return multiplyTransformMatrices(translationTimesRotation, negativeTranslationMatrix);\n}\n\nexport function invertTransformMatrix(matrix) {\n  const rotationComponent = [[matrix[0][0], matrix[1][0]], [matrix[0][1], matrix[1][1]]];\n  const translationComponent = [matrix[0][2], matrix[1][2]];\n  const invertedTranslation = [\n    -dot(rotationComponent[0], translationComponent),\n    -dot(rotationComponent[1], translationComponent),\n  ];\n  return [\n    rotationComponent[0].concat(invertedTranslation[0]),\n    rotationComponent[1].concat(invertedTranslation[1]),\n    [0, 0, 1],\n  ];\n}\n\nexport function rotatePoint(homogeneousCoordinate, rotationMatrix) {\n  return [\n    dot(homogeneousCoordinate, rotationMatrix[0]),\n    dot(homogeneousCoordinate, rotationMatrix[1]),\n  ];\n}\n", "/**\n * HandPose model implementation constants\n * See `handpose.ts` for entry point\n */\n\nexport const anchors = [\n  { x: 0.015625, y: 0.015625 },\n  { x: 0.015625, y: 0.015625 },\n  { x: 0.046875, y: 0.015625 },\n  { x: 0.046875, y: 0.015625 },\n  { x: 0.078125, y: 0.015625 },\n  { x: 0.078125, y: 0.015625 },\n  { x: 0.109375, y: 0.015625 },\n  { x: 0.109375, y: 0.015625 },\n  { x: 0.140625, y: 0.015625 },\n  { x: 0.140625, y: 0.015625 },\n  { x: 0.171875, y: 0.015625 },\n  { x: 0.171875, y: 0.015625 },\n  { x: 0.203125, y: 0.015625 },\n  { x: 0.203125, y: 0.015625 },\n  { x: 0.234375, y: 0.015625 },\n  { x: 0.234375, y: 0.015625 },\n  { x: 0.265625, y: 0.015625 },\n  { x: 0.265625, y: 0.015625 },\n  { x: 0.296875, y: 0.015625 },\n  { x: 0.296875, y: 0.015625 },\n  { x: 0.328125, y: 0.015625 },\n  { x: 0.328125, y: 0.015625 },\n  { x: 0.359375, y: 0.015625 },\n  { x: 0.359375, y: 0.015625 },\n  { x: 0.390625, y: 0.015625 },\n  { x: 0.390625, y: 0.015625 },\n  { x: 0.421875, y: 0.015625 },\n  { x: 0.421875, y: 0.015625 },\n  { x: 0.453125, y: 0.015625 },\n  { x: 0.453125, y: 0.015625 },\n  { x: 0.484375, y: 0.015625 },\n  { x: 0.484375, y: 0.015625 },\n  { x: 0.515625, y: 0.015625 },\n  { x: 0.515625, y: 0.015625 },\n  { x: 0.546875, y: 0.015625 },\n  { x: 0.546875, y: 0.015625 },\n  { x: 0.578125, y: 0.015625 },\n  { x: 0.578125, y: 0.015625 },\n  { x: 0.609375, y: 0.015625 },\n  { x: 0.609375, y: 0.015625 },\n  { x: 0.640625, y: 0.015625 },\n  { x: 0.640625, y: 0.015625 },\n  { x: 0.671875, y: 0.015625 },\n  { x: 0.671875, y: 0.015625 },\n  { x: 0.703125, y: 0.015625 },\n  { x: 0.703125, y: 0.015625 },\n  { x: 0.734375, y: 0.015625 },\n  { x: 0.734375, y: 0.015625 },\n  { x: 0.765625, y: 0.015625 },\n  { x: 0.765625, y: 0.015625 },\n  { x: 0.796875, y: 0.015625 },\n  { x: 0.796875, y: 0.015625 },\n  { x: 0.828125, y: 0.015625 },\n  { x: 0.828125, y: 0.015625 },\n  { x: 0.859375, y: 0.015625 },\n  { x: 0.859375, y: 0.015625 },\n  { x: 0.890625, y: 0.015625 },\n  { x: 0.890625, y: 0.015625 },\n  { x: 0.921875, y: 0.015625 },\n  { x: 0.921875, y: 0.015625 },\n  { x: 0.953125, y: 0.015625 },\n  { x: 0.953125, y: 0.015625 },\n  { x: 0.984375, y: 0.015625 },\n  { x: 0.984375, y: 0.015625 },\n  { x: 0.015625, y: 0.046875 },\n  { x: 0.015625, y: 0.046875 },\n  { x: 0.046875, y: 0.046875 },\n  { x: 0.046875, y: 0.046875 },\n  { x: 0.078125, y: 0.046875 },\n  { x: 0.078125, y: 0.046875 },\n  { x: 0.109375, y: 0.046875 },\n  { x: 0.109375, y: 0.046875 },\n  { x: 0.140625, y: 0.046875 },\n  { x: 0.140625, y: 0.046875 },\n  { x: 0.171875, y: 0.046875 },\n  { x: 0.171875, y: 0.046875 },\n  { x: 0.203125, y: 0.046875 },\n  { x: 0.203125, y: 0.046875 },\n  { x: 0.234375, y: 0.046875 },\n  { x: 0.234375, y: 0.046875 },\n  { x: 0.265625, y: 0.046875 },\n  { x: 0.265625, y: 0.046875 },\n  { x: 0.296875, y: 0.046875 },\n  { x: 0.296875, y: 0.046875 },\n  { x: 0.328125, y: 0.046875 },\n  { x: 0.328125, y: 0.046875 },\n  { x: 0.359375, y: 0.046875 },\n  { x: 0.359375, y: 0.046875 },\n  { x: 0.390625, y: 0.046875 },\n  { x: 0.390625, y: 0.046875 },\n  { x: 0.421875, y: 0.046875 },\n  { x: 0.421875, y: 0.046875 },\n  { x: 0.453125, y: 0.046875 },\n  { x: 0.453125, y: 0.046875 },\n  { x: 0.484375, y: 0.046875 },\n  { x: 0.484375, y: 0.046875 },\n  { x: 0.515625, y: 0.046875 },\n  { x: 0.515625, y: 0.046875 },\n  { x: 0.546875, y: 0.046875 },\n  { x: 0.546875, y: 0.046875 },\n  { x: 0.578125, y: 0.046875 },\n  { x: 0.578125, y: 0.046875 },\n  { x: 0.609375, y: 0.046875 },\n  { x: 0.609375, y: 0.046875 },\n  { x: 0.640625, y: 0.046875 },\n  { x: 0.640625, y: 0.046875 },\n  { x: 0.671875, y: 0.046875 },\n  { x: 0.671875, y: 0.046875 },\n  { x: 0.703125, y: 0.046875 },\n  { x: 0.703125, y: 0.046875 },\n  { x: 0.734375, y: 0.046875 },\n  { x: 0.734375, y: 0.046875 },\n  { x: 0.765625, y: 0.046875 },\n  { x: 0.765625, y: 0.046875 },\n  { x: 0.796875, y: 0.046875 },\n  { x: 0.796875, y: 0.046875 },\n  { x: 0.828125, y: 0.046875 },\n  { x: 0.828125, y: 0.046875 },\n  { x: 0.859375, y: 0.046875 },\n  { x: 0.859375, y: 0.046875 },\n  { x: 0.890625, y: 0.046875 },\n  { x: 0.890625, y: 0.046875 },\n  { x: 0.921875, y: 0.046875 },\n  { x: 0.921875, y: 0.046875 },\n  { x: 0.953125, y: 0.046875 },\n  { x: 0.953125, y: 0.046875 },\n  { x: 0.984375, y: 0.046875 },\n  { x: 0.984375, y: 0.046875 },\n  { x: 0.015625, y: 0.078125 },\n  { x: 0.015625, y: 0.078125 },\n  { x: 0.046875, y: 0.078125 },\n  { x: 0.046875, y: 0.078125 },\n  { x: 0.078125, y: 0.078125 },\n  { x: 0.078125, y: 0.078125 },\n  { x: 0.109375, y: 0.078125 },\n  { x: 0.109375, y: 0.078125 },\n  { x: 0.140625, y: 0.078125 },\n  { x: 0.140625, y: 0.078125 },\n  { x: 0.171875, y: 0.078125 },\n  { x: 0.171875, y: 0.078125 },\n  { x: 0.203125, y: 0.078125 },\n  { x: 0.203125, y: 0.078125 },\n  { x: 0.234375, y: 0.078125 },\n  { x: 0.234375, y: 0.078125 },\n  { x: 0.265625, y: 0.078125 },\n  { x: 0.265625, y: 0.078125 },\n  { x: 0.296875, y: 0.078125 },\n  { x: 0.296875, y: 0.078125 },\n  { x: 0.328125, y: 0.078125 },\n  { x: 0.328125, y: 0.078125 },\n  { x: 0.359375, y: 0.078125 },\n  { x: 0.359375, y: 0.078125 },\n  { x: 0.390625, y: 0.078125 },\n  { x: 0.390625, y: 0.078125 },\n  { x: 0.421875, y: 0.078125 },\n  { x: 0.421875, y: 0.078125 },\n  { x: 0.453125, y: 0.078125 },\n  { x: 0.453125, y: 0.078125 },\n  { x: 0.484375, y: 0.078125 },\n  { x: 0.484375, y: 0.078125 },\n  { x: 0.515625, y: 0.078125 },\n  { x: 0.515625, y: 0.078125 },\n  { x: 0.546875, y: 0.078125 },\n  { x: 0.546875, y: 0.078125 },\n  { x: 0.578125, y: 0.078125 },\n  { x: 0.578125, y: 0.078125 },\n  { x: 0.609375, y: 0.078125 },\n  { x: 0.609375, y: 0.078125 },\n  { x: 0.640625, y: 0.078125 },\n  { x: 0.640625, y: 0.078125 },\n  { x: 0.671875, y: 0.078125 },\n  { x: 0.671875, y: 0.078125 },\n  { x: 0.703125, y: 0.078125 },\n  { x: 0.703125, y: 0.078125 },\n  { x: 0.734375, y: 0.078125 },\n  { x: 0.734375, y: 0.078125 },\n  { x: 0.765625, y: 0.078125 },\n  { x: 0.765625, y: 0.078125 },\n  { x: 0.796875, y: 0.078125 },\n  { x: 0.796875, y: 0.078125 },\n  { x: 0.828125, y: 0.078125 },\n  { x: 0.828125, y: 0.078125 },\n  { x: 0.859375, y: 0.078125 },\n  { x: 0.859375, y: 0.078125 },\n  { x: 0.890625, y: 0.078125 },\n  { x: 0.890625, y: 0.078125 },\n  { x: 0.921875, y: 0.078125 },\n  { x: 0.921875, y: 0.078125 },\n  { x: 0.953125, y: 0.078125 },\n  { x: 0.953125, y: 0.078125 },\n  { x: 0.984375, y: 0.078125 },\n  { x: 0.984375, y: 0.078125 },\n  { x: 0.015625, y: 0.109375 },\n  { x: 0.015625, y: 0.109375 },\n  { x: 0.046875, y: 0.109375 },\n  { x: 0.046875, y: 0.109375 },\n  { x: 0.078125, y: 0.109375 },\n  { x: 0.078125, y: 0.109375 },\n  { x: 0.109375, y: 0.109375 },\n  { x: 0.109375, y: 0.109375 },\n  { x: 0.140625, y: 0.109375 },\n  { x: 0.140625, y: 0.109375 },\n  { x: 0.171875, y: 0.109375 },\n  { x: 0.171875, y: 0.109375 },\n  { x: 0.203125, y: 0.109375 },\n  { x: 0.203125, y: 0.109375 },\n  { x: 0.234375, y: 0.109375 },\n  { x: 0.234375, y: 0.109375 },\n  { x: 0.265625, y: 0.109375 },\n  { x: 0.265625, y: 0.109375 },\n  { x: 0.296875, y: 0.109375 },\n  { x: 0.296875, y: 0.109375 },\n  { x: 0.328125, y: 0.109375 },\n  { x: 0.328125, y: 0.109375 },\n  { x: 0.359375, y: 0.109375 },\n  { x: 0.359375, y: 0.109375 },\n  { x: 0.390625, y: 0.109375 },\n  { x: 0.390625, y: 0.109375 },\n  { x: 0.421875, y: 0.109375 },\n  { x: 0.421875, y: 0.109375 },\n  { x: 0.453125, y: 0.109375 },\n  { x: 0.453125, y: 0.109375 },\n  { x: 0.484375, y: 0.109375 },\n  { x: 0.484375, y: 0.109375 },\n  { x: 0.515625, y: 0.109375 },\n  { x: 0.515625, y: 0.109375 },\n  { x: 0.546875, y: 0.109375 },\n  { x: 0.546875, y: 0.109375 },\n  { x: 0.578125, y: 0.109375 },\n  { x: 0.578125, y: 0.109375 },\n  { x: 0.609375, y: 0.109375 },\n  { x: 0.609375, y: 0.109375 },\n  { x: 0.640625, y: 0.109375 },\n  { x: 0.640625, y: 0.109375 },\n  { x: 0.671875, y: 0.109375 },\n  { x: 0.671875, y: 0.109375 },\n  { x: 0.703125, y: 0.109375 },\n  { x: 0.703125, y: 0.109375 },\n  { x: 0.734375, y: 0.109375 },\n  { x: 0.734375, y: 0.109375 },\n  { x: 0.765625, y: 0.109375 },\n  { x: 0.765625, y: 0.109375 },\n  { x: 0.796875, y: 0.109375 },\n  { x: 0.796875, y: 0.109375 },\n  { x: 0.828125, y: 0.109375 },\n  { x: 0.828125, y: 0.109375 },\n  { x: 0.859375, y: 0.109375 },\n  { x: 0.859375, y: 0.109375 },\n  { x: 0.890625, y: 0.109375 },\n  { x: 0.890625, y: 0.109375 },\n  { x: 0.921875, y: 0.109375 },\n  { x: 0.921875, y: 0.109375 },\n  { x: 0.953125, y: 0.109375 },\n  { x: 0.953125, y: 0.109375 },\n  { x: 0.984375, y: 0.109375 },\n  { x: 0.984375, y: 0.109375 },\n  { x: 0.015625, y: 0.140625 },\n  { x: 0.015625, y: 0.140625 },\n  { x: 0.046875, y: 0.140625 },\n  { x: 0.046875, y: 0.140625 },\n  { x: 0.078125, y: 0.140625 },\n  { x: 0.078125, y: 0.140625 },\n  { x: 0.109375, y: 0.140625 },\n  { x: 0.109375, y: 0.140625 },\n  { x: 0.140625, y: 0.140625 },\n  { x: 0.140625, y: 0.140625 },\n  { x: 0.171875, y: 0.140625 },\n  { x: 0.171875, y: 0.140625 },\n  { x: 0.203125, y: 0.140625 },\n  { x: 0.203125, y: 0.140625 },\n  { x: 0.234375, y: 0.140625 },\n  { x: 0.234375, y: 0.140625 },\n  { x: 0.265625, y: 0.140625 },\n  { x: 0.265625, y: 0.140625 },\n  { x: 0.296875, y: 0.140625 },\n  { x: 0.296875, y: 0.140625 },\n  { x: 0.328125, y: 0.140625 },\n  { x: 0.328125, y: 0.140625 },\n  { x: 0.359375, y: 0.140625 },\n  { x: 0.359375, y: 0.140625 },\n  { x: 0.390625, y: 0.140625 },\n  { x: 0.390625, y: 0.140625 },\n  { x: 0.421875, y: 0.140625 },\n  { x: 0.421875, y: 0.140625 },\n  { x: 0.453125, y: 0.140625 },\n  { x: 0.453125, y: 0.140625 },\n  { x: 0.484375, y: 0.140625 },\n  { x: 0.484375, y: 0.140625 },\n  { x: 0.515625, y: 0.140625 },\n  { x: 0.515625, y: 0.140625 },\n  { x: 0.546875, y: 0.140625 },\n  { x: 0.546875, y: 0.140625 },\n  { x: 0.578125, y: 0.140625 },\n  { x: 0.578125, y: 0.140625 },\n  { x: 0.609375, y: 0.140625 },\n  { x: 0.609375, y: 0.140625 },\n  { x: 0.640625, y: 0.140625 },\n  { x: 0.640625, y: 0.140625 },\n  { x: 0.671875, y: 0.140625 },\n  { x: 0.671875, y: 0.140625 },\n  { x: 0.703125, y: 0.140625 },\n  { x: 0.703125, y: 0.140625 },\n  { x: 0.734375, y: 0.140625 },\n  { x: 0.734375, y: 0.140625 },\n  { x: 0.765625, y: 0.140625 },\n  { x: 0.765625, y: 0.140625 },\n  { x: 0.796875, y: 0.140625 },\n  { x: 0.796875, y: 0.140625 },\n  { x: 0.828125, y: 0.140625 },\n  { x: 0.828125, y: 0.140625 },\n  { x: 0.859375, y: 0.140625 },\n  { x: 0.859375, y: 0.140625 },\n  { x: 0.890625, y: 0.140625 },\n  { x: 0.890625, y: 0.140625 },\n  { x: 0.921875, y: 0.140625 },\n  { x: 0.921875, y: 0.140625 },\n  { x: 0.953125, y: 0.140625 },\n  { x: 0.953125, y: 0.140625 },\n  { x: 0.984375, y: 0.140625 },\n  { x: 0.984375, y: 0.140625 },\n  { x: 0.015625, y: 0.171875 },\n  { x: 0.015625, y: 0.171875 },\n  { x: 0.046875, y: 0.171875 },\n  { x: 0.046875, 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y: 0.90625 },\n  { x: 0.09375, y: 0.90625 },\n  { x: 0.15625, y: 0.90625 },\n  { x: 0.15625, y: 0.90625 },\n  { x: 0.21875, y: 0.90625 },\n  { x: 0.21875, y: 0.90625 },\n  { x: 0.28125, y: 0.90625 },\n  { x: 0.28125, y: 0.90625 },\n  { x: 0.34375, y: 0.90625 },\n  { x: 0.34375, y: 0.90625 },\n  { x: 0.40625, y: 0.90625 },\n  { x: 0.40625, y: 0.90625 },\n  { x: 0.46875, y: 0.90625 },\n  { x: 0.46875, y: 0.90625 },\n  { x: 0.53125, y: 0.90625 },\n  { x: 0.53125, y: 0.90625 },\n  { x: 0.59375, y: 0.90625 },\n  { x: 0.59375, y: 0.90625 },\n  { x: 0.65625, y: 0.90625 },\n  { x: 0.65625, y: 0.90625 },\n  { x: 0.71875, y: 0.90625 },\n  { x: 0.71875, y: 0.90625 },\n  { x: 0.78125, y: 0.90625 },\n  { x: 0.78125, y: 0.90625 },\n  { x: 0.84375, y: 0.90625 },\n  { x: 0.84375, y: 0.90625 },\n  { x: 0.90625, y: 0.90625 },\n  { x: 0.90625, y: 0.90625 },\n  { x: 0.96875, y: 0.90625 },\n  { x: 0.96875, y: 0.90625 },\n  { x: 0.03125, y: 0.96875 },\n  { x: 0.03125, y: 0.96875 },\n  { x: 0.09375, y: 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y: 0.6875 },\n  { x: 0.0625, y: 0.6875 },\n  { x: 0.1875, y: 0.6875 },\n  { x: 0.1875, y: 0.6875 },\n  { x: 0.1875, y: 0.6875 },\n  { x: 0.1875, y: 0.6875 },\n  { x: 0.1875, y: 0.6875 },\n  { x: 0.1875, y: 0.6875 },\n  { x: 0.3125, y: 0.6875 },\n  { x: 0.3125, y: 0.6875 },\n  { x: 0.3125, y: 0.6875 },\n  { x: 0.3125, y: 0.6875 },\n  { x: 0.3125, y: 0.6875 },\n  { x: 0.3125, y: 0.6875 },\n  { x: 0.4375, y: 0.6875 },\n  { x: 0.4375, y: 0.6875 },\n  { x: 0.4375, y: 0.6875 },\n  { x: 0.4375, y: 0.6875 },\n  { x: 0.4375, y: 0.6875 },\n  { x: 0.4375, y: 0.6875 },\n  { x: 0.5625, y: 0.6875 },\n  { x: 0.5625, y: 0.6875 },\n  { x: 0.5625, y: 0.6875 },\n  { x: 0.5625, y: 0.6875 },\n  { x: 0.5625, y: 0.6875 },\n  { x: 0.5625, y: 0.6875 },\n  { x: 0.6875, y: 0.6875 },\n  { x: 0.6875, y: 0.6875 },\n  { x: 0.6875, y: 0.6875 },\n  { x: 0.6875, y: 0.6875 },\n  { x: 0.6875, y: 0.6875 },\n  { x: 0.6875, y: 0.6875 },\n  { x: 0.8125, y: 0.6875 },\n  { x: 0.8125, y: 0.6875 },\n  { x: 0.8125, y: 0.6875 },\n  { x: 0.8125, y: 0.6875 },\n  { x: 0.8125, y: 0.6875 },\n  { x: 0.8125, y: 0.6875 },\n  { x: 0.9375, y: 0.6875 },\n  { x: 0.9375, y: 0.6875 },\n  { x: 0.9375, y: 0.6875 },\n  { x: 0.9375, y: 0.6875 },\n  { x: 0.9375, y: 0.6875 },\n  { x: 0.9375, y: 0.6875 },\n  { x: 0.0625, y: 0.8125 },\n  { x: 0.0625, y: 0.8125 },\n  { x: 0.0625, y: 0.8125 },\n  { x: 0.0625, y: 0.8125 },\n  { x: 0.0625, y: 0.8125 },\n  { x: 0.0625, y: 0.8125 },\n  { x: 0.1875, y: 0.8125 },\n  { x: 0.1875, y: 0.8125 },\n  { x: 0.1875, y: 0.8125 },\n  { x: 0.1875, y: 0.8125 },\n  { x: 0.1875, y: 0.8125 },\n  { x: 0.1875, y: 0.8125 },\n  { x: 0.3125, y: 0.8125 },\n  { x: 0.3125, y: 0.8125 },\n  { x: 0.3125, y: 0.8125 },\n  { x: 0.3125, y: 0.8125 },\n  { x: 0.3125, y: 0.8125 },\n  { x: 0.3125, y: 0.8125 },\n  { x: 0.4375, y: 0.8125 },\n  { x: 0.4375, y: 0.8125 },\n  { x: 0.4375, y: 0.8125 },\n  { x: 0.4375, y: 0.8125 },\n  { x: 0.4375, y: 0.8125 },\n  { x: 0.4375, y: 0.8125 },\n  { x: 0.5625, y: 0.8125 },\n  { x: 0.5625, y: 0.8125 },\n  { x: 0.5625, y: 0.8125 },\n  { x: 0.5625, y: 0.8125 },\n  { x: 0.5625, y: 0.8125 },\n  { x: 0.5625, y: 0.8125 },\n  { x: 0.6875, y: 0.8125 },\n  { x: 0.6875, y: 0.8125 },\n  { x: 0.6875, y: 0.8125 },\n  { x: 0.6875, y: 0.8125 },\n  { x: 0.6875, y: 0.8125 },\n  { x: 0.6875, y: 0.8125 },\n  { x: 0.8125, y: 0.8125 },\n  { x: 0.8125, y: 0.8125 },\n  { x: 0.8125, y: 0.8125 },\n  { x: 0.8125, y: 0.8125 },\n  { x: 0.8125, y: 0.8125 },\n  { x: 0.8125, y: 0.8125 },\n  { x: 0.9375, y: 0.8125 },\n  { x: 0.9375, y: 0.8125 },\n  { x: 0.9375, y: 0.8125 },\n  { x: 0.9375, y: 0.8125 },\n  { x: 0.9375, y: 0.8125 },\n  { x: 0.9375, y: 0.8125 },\n  { x: 0.0625, y: 0.9375 },\n  { x: 0.0625, y: 0.9375 },\n  { x: 0.0625, y: 0.9375 },\n  { x: 0.0625, y: 0.9375 },\n  { x: 0.0625, y: 0.9375 },\n  { x: 0.0625, y: 0.9375 },\n  { x: 0.1875, y: 0.9375 },\n  { x: 0.1875, y: 0.9375 },\n  { x: 0.1875, y: 0.9375 },\n  { x: 0.1875, y: 0.9375 },\n  { x: 0.1875, y: 0.9375 },\n  { x: 0.1875, y: 0.9375 },\n  { x: 0.3125, y: 0.9375 },\n  { x: 0.3125, y: 0.9375 },\n  { x: 0.3125, y: 0.9375 },\n  { x: 0.3125, y: 0.9375 },\n  { x: 0.3125, y: 0.9375 },\n  { x: 0.3125, y: 0.9375 },\n  { x: 0.4375, y: 0.9375 },\n  { x: 0.4375, y: 0.9375 },\n  { x: 0.4375, y: 0.9375 },\n  { x: 0.4375, y: 0.9375 },\n  { x: 0.4375, y: 0.9375 },\n  { x: 0.4375, y: 0.9375 },\n  { x: 0.5625, y: 0.9375 },\n  { x: 0.5625, y: 0.9375 },\n  { x: 0.5625, y: 0.9375 },\n  { x: 0.5625, y: 0.9375 },\n  { x: 0.5625, y: 0.9375 },\n  { x: 0.5625, y: 0.9375 },\n  { x: 0.6875, y: 0.9375 },\n  { x: 0.6875, y: 0.9375 },\n  { x: 0.6875, y: 0.9375 },\n  { x: 0.6875, y: 0.9375 },\n  { x: 0.6875, y: 0.9375 },\n  { x: 0.6875, y: 0.9375 },\n  { x: 0.8125, y: 0.9375 },\n  { x: 0.8125, y: 0.9375 },\n  { x: 0.8125, y: 0.9375 },\n  { x: 0.8125, y: 0.9375 },\n  { x: 0.8125, y: 0.9375 },\n  { x: 0.8125, y: 0.9375 },\n  { x: 0.9375, y: 0.9375 },\n  { x: 0.9375, y: 0.9375 },\n  { x: 0.9375, y: 0.9375 },\n  { x: 0.9375, y: 0.9375 },\n  { x: 0.9375, y: 0.9375 },\n  { x: 0.9375, y: 0.9375 },\n];\n", "/**\n * HandPose model implementation\n * See `handpose.ts` for entry point\n */\n\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as util from './handposeutil';\nimport * as anchors from './handposeanchors';\nimport { constants } from '../tfjs/constants';\nimport type { Tensor, GraphModel } from '../tfjs/types';\nimport type { Point } from '../result';\nimport type { Config } from '../config';\n\nexport class HandDetector {\n  model: GraphModel;\n  anchors: number[][];\n  anchorsTensor: Tensor;\n  inputSize: number;\n  inputSizeTensor: Tensor;\n  doubleInputSizeTensor: Tensor;\n\n  constructor(model: GraphModel) {\n    this.model = model;\n    this.anchors = anchors.anchors.map((anchor) => [anchor.x, anchor.y]);\n    this.anchorsTensor = tf.tensor2d(this.anchors);\n    this.inputSize = this?.model?.inputs?.[0]?.shape?.[2] || 0;\n    this.inputSizeTensor = tf.tensor1d([this.inputSize, this.inputSize]);\n    this.doubleInputSizeTensor = tf.tensor1d([this.inputSize * 2, this.inputSize * 2]);\n  }\n\n  normalizeBoxes(boxes) {\n    const t: Record<string, Tensor> = {};\n    t.boxOffsets = tf.slice(boxes, [0, 0], [-1, 2]);\n    t.boxSizes = tf.slice(boxes, [0, 2], [-1, 2]);\n    t.div = tf.div(t.boxOffsets, this.inputSizeTensor);\n    t.boxCenterPoints = tf.add(t.div, this.anchorsTensor);\n    t.halfBoxSizes = tf.div(t.boxSizes, this.doubleInputSizeTensor);\n    t.sub = tf.sub(t.boxCenterPoints, t.halfBoxSizes);\n    t.startPoints = tf.mul(t.sub, this.inputSizeTensor);\n    t.add = tf.add(t.boxCenterPoints, t.halfBoxSizes);\n    t.endPoints = tf.mul(t.add, this.inputSizeTensor);\n    const res = tf.concat2d([t.startPoints, t.endPoints], 1);\n    Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n    return res as Tensor;\n  }\n\n  normalizeLandmarks(rawPalmLandmarks, index: number) {\n    const t: Record<string, Tensor> = {};\n    t.reshape = tf.reshape(rawPalmLandmarks, [-1, 7, 2]);\n    t.div = tf.div(t.reshape, this.inputSizeTensor);\n    t.landmarks = tf.add(t.div, this.anchors[index] ? this.anchors[index] : 0);\n    const res = tf.mul(t.landmarks, this.inputSizeTensor);\n    Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n    return res as Tensor;\n  }\n\n  async predict(input: Tensor, config: Config): Promise<{ startPoint: Point; endPoint: Point, palmLandmarks: Point[]; confidence: number }[]> {\n    const t: Record<string, Tensor> = {};\n    t.resize = tf.image.resizeBilinear(input, [this.inputSize, this.inputSize]);\n    t.div = tf.div(t.resize, constants.tf127);\n    t.image = tf.sub(t.div, constants.tf1);\n    t.batched = this.model.execute(t.image) as Tensor;\n    t.predictions = tf.squeeze(t.batched);\n    t.slice = tf.slice(t.predictions, [0, 0], [-1, 1]);\n    t.sigmoid = tf.sigmoid(t.slice);\n    t.scores = tf.squeeze(t.sigmoid);\n    const scores = await t.scores.data();\n    t.boxes = tf.slice(t.predictions, [0, 1], [-1, 4]);\n    t.norm = this.normalizeBoxes(t.boxes);\n    // box detection is flaky so we look for 3x boxes than we need results\n    t.nms = await tf.image.nonMaxSuppressionAsync(t.norm, t.scores, 3 * (config.hand?.maxDetected || 1), config.hand.iouThreshold, config.hand.minConfidence);\n    const nms = await t.nms.array() as number[];\n    const hands: { startPoint: Point; endPoint: Point; palmLandmarks: Point[]; confidence: number }[] = [];\n    for (const index of nms) {\n      const p: Record<string, Tensor> = {};\n      p.box = tf.slice(t.norm, [index, 0], [1, -1]);\n      p.slice = tf.slice(t.predictions, [index, 5], [1, 14]);\n      p.norm = this.normalizeLandmarks(p.slice, index);\n      p.palmLandmarks = tf.reshape(p.norm, [-1, 2]);\n      const box = await p.box.data();\n      const startPoint = box.slice(0, 2) as unknown as Point;\n      const endPoint = box.slice(2, 4) as unknown as Point;\n      const palmLandmarks = await p.palmLandmarks.array();\n      const hand = { startPoint, endPoint, palmLandmarks, confidence: scores[index] };\n      const scaled = util.scaleBoxCoordinates(hand, [(input.shape[2] || 1) / this.inputSize, (input.shape[1] || 0) / this.inputSize]);\n      hands.push(scaled);\n      Object.keys(p).forEach((tensor) => tf.dispose(p[tensor]));\n    }\n    Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n    return hands;\n  }\n}\n", "/**\n * HandPose model implementation\n * See `handpose.ts` for entry point\n */\n\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as util from './handposeutil';\nimport type * as detector from './handposedetector';\nimport { constants } from '../tfjs/constants';\nimport type { Tensor, GraphModel } from '../tfjs/types';\nimport { env } from '../util/env';\nimport { now } from '../util/util';\nimport type { Point } from '../result';\n\nconst palmBoxEnlargeFactor = 5; // default 3\nconst handBoxEnlargeFactor = 1.65; // default 1.65\nconst palmLandmarkIds = [0, 5, 9, 13, 17, 1, 2];\nconst palmLandmarksPalmBase = 0;\nconst palmLandmarksMiddleFingerBase = 2;\nlet lastTime = 0;\n\nexport class HandPipeline {\n  handDetector: detector.HandDetector;\n  handPoseModel: GraphModel;\n  inputSize: number;\n  storedBoxes: ({ startPoint: Point; endPoint: Point; palmLandmarks: Point[]; confidence: number } | null)[];\n  skipped: number;\n  detectedHands: number;\n\n  constructor(handDetector, handPoseModel) {\n    this.handDetector = handDetector;\n    this.handPoseModel = handPoseModel;\n    this.inputSize = this.handPoseModel?.inputs?.[0].shape?.[2] || 0;\n    this.storedBoxes = [];\n    this.skipped = Number.MAX_SAFE_INTEGER;\n    this.detectedHands = 0;\n  }\n\n  calculateLandmarksBoundingBox(landmarks) { // eslint-disable-line class-methods-use-this\n    const xs = landmarks.map((d) => d[0]);\n    const ys = landmarks.map((d) => d[1]);\n    const startPoint = [Math.min(...xs), Math.min(...ys)];\n    const endPoint = [Math.max(...xs), Math.max(...ys)];\n    return { startPoint, endPoint };\n  }\n\n  getBoxForPalmLandmarks(palmLandmarks, rotationMatrix) {\n    const rotatedPalmLandmarks = palmLandmarks.map((coord) => util.rotatePoint([...coord, 1], rotationMatrix));\n    const boxAroundPalm = this.calculateLandmarksBoundingBox(rotatedPalmLandmarks);\n    return util.enlargeBox(util.squarifyBox(boxAroundPalm), palmBoxEnlargeFactor);\n  }\n\n  getBoxForHandLandmarks(landmarks) {\n    const boundingBox = this.calculateLandmarksBoundingBox(landmarks);\n    const boxAroundHand = util.enlargeBox(util.squarifyBox(boundingBox), handBoxEnlargeFactor);\n    boxAroundHand.palmLandmarks = [];\n    for (let i = 0; i < palmLandmarkIds.length; i++) {\n      boxAroundHand.palmLandmarks.push(landmarks[palmLandmarkIds[i]].slice(0, 2));\n    }\n    return boxAroundHand;\n  }\n\n  transformRawCoords(rawCoords, box2, angle, rotationMatrix) {\n    const boxSize = util.getBoxSize(box2);\n    const scaleFactor = [boxSize[0] / this.inputSize, boxSize[1] / this.inputSize, (boxSize[0] + boxSize[1]) / this.inputSize / 2];\n    const coordsScaled = rawCoords.map((coord) => [\n      scaleFactor[0] * (coord[0] - this.inputSize / 2),\n      scaleFactor[1] * (coord[1] - this.inputSize / 2),\n      scaleFactor[2] * coord[2],\n    ]);\n    const coordsRotationMatrix = util.buildRotationMatrix(angle, [0, 0]);\n    const coordsRotated = coordsScaled.map((coord) => {\n      const rotated = util.rotatePoint(coord, coordsRotationMatrix);\n      return [...rotated, coord[2]];\n    });\n    const inverseRotationMatrix = util.invertTransformMatrix(rotationMatrix);\n    const boxCenter = [...util.getBoxCenter(box2), 1];\n    const originalBoxCenter = [\n      util.dot(boxCenter, inverseRotationMatrix[0]),\n      util.dot(boxCenter, inverseRotationMatrix[1]),\n    ];\n    return coordsRotated.map((coord) => [\n      Math.trunc(coord[0] + originalBoxCenter[0]),\n      Math.trunc(coord[1] + originalBoxCenter[1]),\n      Math.trunc(coord[2]),\n    ]);\n  }\n\n  async estimateHands(image, config) {\n    let useFreshBox = false;\n\n    // run new detector every skipFrames\n    let boxes;\n    const skipTime = (config.hand.skipTime || 0) > (now() - lastTime);\n    const skipFrame = this.skipped < (config.hand.skipFrames || 0);\n    if (config.skipAllowed && skipTime && skipFrame) {\n      boxes = await this.handDetector.predict(image, config);\n      this.skipped = 0;\n    }\n    if (config.skipAllowed) this.skipped++;\n\n    // if detector result count doesn't match current working set, use it to reset current working set\n    if (boxes && (boxes.length > 0) && ((boxes.length !== this.detectedHands) && (this.detectedHands !== config.hand.maxDetected) || !config.hand.landmarks)) {\n      this.detectedHands = 0;\n      this.storedBoxes = [...boxes];\n      // for (const possible of boxes) this.storedBoxes.push(possible);\n      if (this.storedBoxes.length > 0) useFreshBox = true;\n    }\n    const hands: { landmarks: Point[], confidence: number, boxConfidence: number, fingerConfidence: number, box: { topLeft: Point, bottomRight: Point } }[] = [];\n\n    // go through working set of boxes\n    for (let i = 0; i < this.storedBoxes.length; i++) {\n      const currentBox = this.storedBoxes[i];\n      if (!currentBox) continue;\n      if (config.hand.landmarks) {\n        const angle = config.hand.rotation ? util.computeRotation(currentBox.palmLandmarks[palmLandmarksPalmBase], currentBox.palmLandmarks[palmLandmarksMiddleFingerBase]) : 0;\n        const palmCenter = util.getBoxCenter(currentBox);\n        const palmCenterNormalized = [palmCenter[0] / image.shape[2], palmCenter[1] / image.shape[1]];\n        const rotatedImage = config.hand.rotation && env.kernels.includes('rotatewithoffset') ? tf.image.rotateWithOffset(image, angle, 0, palmCenterNormalized) : image.clone();\n        const rotationMatrix = util.buildRotationMatrix(-angle, palmCenter);\n        const newBox = useFreshBox ? this.getBoxForPalmLandmarks(currentBox.palmLandmarks, rotationMatrix) : currentBox;\n        const croppedInput = util.cutBoxFromImageAndResize(newBox, rotatedImage, [this.inputSize, this.inputSize]);\n        const handImage = tf.div(croppedInput, constants.tf255);\n        tf.dispose(croppedInput);\n        tf.dispose(rotatedImage);\n        const [confidenceT, keypoints] = this.handPoseModel.execute(handImage) as Tensor[];\n        lastTime = now();\n        tf.dispose(handImage);\n        const confidence = (await confidenceT.data())[0];\n        tf.dispose(confidenceT);\n        if (confidence >= config.hand.minConfidence / 4) {\n          const keypointsReshaped = tf.reshape(keypoints, [-1, 3]);\n          const rawCoords = await keypointsReshaped.array();\n          tf.dispose(keypoints);\n          tf.dispose(keypointsReshaped);\n          const coords = this.transformRawCoords(rawCoords, newBox, angle, rotationMatrix);\n          const nextBoundingBox = this.getBoxForHandLandmarks(coords);\n          this.storedBoxes[i] = { ...nextBoundingBox, confidence };\n          const result = {\n            landmarks: coords,\n            confidence,\n            boxConfidence: currentBox.confidence,\n            fingerConfidence: confidence,\n            box: { topLeft: nextBoundingBox.startPoint, bottomRight: nextBoundingBox.endPoint },\n          };\n          hands.push(result);\n        } else {\n          this.storedBoxes[i] = null;\n        }\n        tf.dispose(keypoints);\n      } else {\n        // const enlarged = box.enlargeBox(box.squarifyBox(box.shiftBox(currentBox, HAND_BOX_SHIFT_VECTOR)), handBoxEnlargeFactor);\n        const enlarged = util.enlargeBox(util.squarifyBox(currentBox), handBoxEnlargeFactor);\n        const result = {\n          confidence: currentBox.confidence,\n          boxConfidence: currentBox.confidence,\n          fingerConfidence: 0,\n          box: { topLeft: enlarged.startPoint, bottomRight: enlarged.endPoint },\n          landmarks: [],\n        };\n        hands.push(result);\n      }\n    }\n    this.storedBoxes = this.storedBoxes.filter((a) => a !== null);\n    this.detectedHands = hands.length;\n    if (hands.length > config.hand.maxDetected) hands.length = config.hand.maxDetected;\n    return hands;\n  }\n}\n", "/**\n * FingerPose algorithm implementation\n * See `fingerpose.ts` for entry point\n */\n\nexport const Finger = {\n  thumb: 0,\n  index: 1,\n  middle: 2,\n  ring: 3,\n  pinky: 4,\n  all: [0, 1, 2, 3, 4], // just for convenience\n  nameMapping: { 0: 'thumb', 1: 'index', 2: 'middle', 3: 'ring', 4: 'pinky' },\n  // Describes mapping of joints based on the 21 points returned by handpose.\n  // [0]     Palm\n  // [1-4]   Thumb\n  // [5-8]   Index\n  // [9-12]  Middle\n  // [13-16] Ring\n  // [17-20] Pinky\n  pointsMapping: {\n    0: [[0, 1], [1, 2], [2, 3], [3, 4]],\n    1: [[0, 5], [5, 6], [6, 7], [7, 8]],\n    2: [[0, 9], [9, 10], [10, 11], [11, 12]],\n    3: [[0, 13], [13, 14], [14, 15], [15, 16]],\n    4: [[0, 17], [17, 18], [18, 19], [19, 20]],\n  },\n  getName: (value) => Finger.nameMapping[value],\n  getPoints: (value) => Finger.pointsMapping[value],\n};\n\nexport const FingerCurl = {\n  none: 0,\n  half: 1,\n  full: 2,\n  nameMapping: { 0: 'none', 1: 'half', 2: 'full' },\n  getName: (value) => FingerCurl.nameMapping[value],\n};\n\nexport const FingerDirection = {\n  verticalUp: 0,\n  verticalDown: 1,\n  horizontalLeft: 2,\n  horizontalRight: 3,\n  diagonalUpRight: 4,\n  diagonalUpLeft: 5,\n  diagonalDownRight: 6,\n  diagonalDownLeft: 7,\n  nameMapping: { 0: 'verticalUp', 1: 'verticalDown', 2: 'horizontalLeft', 3: 'horizontalRight', 4: 'diagonalUpRight', 5: 'diagonalUpLeft', 6: 'diagonalDownRight', 7: 'diagonalDownLeft' },\n  getName: (value) => FingerDirection.nameMapping[value],\n};\n\nexport class FingerGesture {\n  name;\n  curls;\n  directions;\n  weights;\n  weightsRelative;\n\n  constructor(name) {\n    // name (should be unique)\n    this.name = name;\n    this.curls = {};\n    this.directions = {};\n    this.weights = [1.0, 1.0, 1.0, 1.0, 1.0];\n    this.weightsRelative = [1.0, 1.0, 1.0, 1.0, 1.0];\n  }\n\n  curl(finger, curl, confidence) {\n    if (typeof this.curls[finger] === 'undefined') this.curls[finger] = [];\n    this.curls[finger].push([curl, confidence]);\n  }\n\n  direction(finger, position, confidence) {\n    if (!this.directions[finger]) this.directions[finger] = [];\n    this.directions[finger].push([position, confidence]);\n  }\n\n  weight(finger, weight) {\n    this.weights[finger] = weight;\n    // recalculate relative weights\n    const total = this.weights.reduce((a, b) => a + b, 0);\n    this.weightsRelative = this.weights.map((el) => el * 5 / total);\n  }\n\n  matchAgainst(detectedCurls, detectedDirections) {\n    let confidence = 0.0;\n    // look at the detected curl of each finger and compare with\n    // the expected curl of this finger inside current gesture\n    for (const fingerIdx in detectedCurls) {\n      const detectedCurl = detectedCurls[fingerIdx];\n      const expectedCurls = this.curls[fingerIdx];\n      if (typeof expectedCurls === 'undefined') {\n        // no curl description available for this finger\n        // add default confidence of \"1\"\n        confidence += this.weightsRelative[fingerIdx];\n        continue;\n      }\n      // compare to each possible curl of this specific finger\n      for (const [expectedCurl, score] of expectedCurls) {\n        if (detectedCurl === expectedCurl) {\n          confidence += score * this.weightsRelative[fingerIdx];\n          break;\n        }\n      }\n    }\n    // same for detected direction of each finger\n    for (const fingerIdx in detectedDirections) {\n      const detectedDirection = detectedDirections[fingerIdx];\n      const expectedDirections = this.directions[fingerIdx];\n      if (typeof expectedDirections === 'undefined') {\n        // no direction description available for this finger\n        // add default confidence of \"1\"\n        confidence += this.weightsRelative[fingerIdx];\n        continue;\n      }\n      // compare to each possible direction of this specific finger\n      for (const [expectedDirection, score] of expectedDirections) {\n        if (detectedDirection === expectedDirection) {\n          confidence += score * this.weightsRelative[fingerIdx];\n          break;\n        }\n      }\n    }\n    return confidence / 10;\n  }\n}\n", "/**\n * FingerPose algorithm implementation\n * See `fingerpose.ts` for entry point\n */\n\nimport { Finger, FingerCurl, FingerDirection, FingerGesture } from './fingerdef';\n\nexport const { thumb, index, middle, ring, pinky } = Finger;\nexport const { none, half, full } = FingerCurl;\nexport const { verticalUp, verticalDown, horizontalLeft, horizontalRight, diagonalUpRight, diagonalUpLeft, diagonalDownRight, diagonalDownLeft } = FingerDirection;\n\n// describe thumbs up gesture \uD83D\uDC4D\nconst ThumbsUp = new FingerGesture('thumbs up');\nThumbsUp.curl(thumb, none, 1.0);\nThumbsUp.direction(thumb, verticalUp, 1.0);\nThumbsUp.direction(thumb, diagonalUpLeft, 0.25);\nThumbsUp.direction(thumb, diagonalUpRight, 0.25);\nfor (const finger of [Finger.index, Finger.middle, Finger.ring, Finger.pinky]) {\n  ThumbsUp.curl(finger, full, 1.0);\n  ThumbsUp.direction(finger, horizontalLeft, 1.0);\n  ThumbsUp.direction(finger, horizontalRight, 1.0);\n}\n\n// describe Victory gesture \u270C\uFE0F\nconst Victory = new FingerGesture('victory');\nVictory.curl(thumb, half, 0.5);\nVictory.curl(thumb, none, 0.5);\nVictory.direction(thumb, verticalUp, 1.0);\nVictory.direction(thumb, diagonalUpLeft, 1.0);\nVictory.curl(index, none, 1.0);\nVictory.direction(index, verticalUp, 0.75);\nVictory.direction(index, diagonalUpLeft, 1.0);\nVictory.curl(middle, none, 1.0);\nVictory.direction(middle, verticalUp, 1.0);\nVictory.direction(middle, diagonalUpLeft, 0.75);\nVictory.curl(ring, full, 1.0);\nVictory.direction(ring, verticalUp, 0.2);\nVictory.direction(ring, diagonalUpLeft, 1.0);\nVictory.direction(ring, horizontalLeft, 0.2);\nVictory.curl(pinky, full, 1.0);\nVictory.direction(pinky, verticalUp, 0.2);\nVictory.direction(pinky, diagonalUpLeft, 1.0);\nVictory.direction(pinky, horizontalLeft, 0.2);\nVictory.weight(index, 2);\nVictory.weight(middle, 2);\n\n// describe Point gesture \u270C\uFE0F\nconst Point = new FingerGesture('point');\nPoint.curl(thumb, full, 1.0);\nPoint.curl(index, none, 0.5);\nPoint.curl(middle, full, 0.5);\nPoint.curl(ring, full, 0.5);\nPoint.curl(pinky, full, 0.5);\nPoint.weight(index, 2);\nPoint.weight(middle, 2);\n\n// describe Point gesture \u270C\uFE0F\nconst MiddleFinger = new FingerGesture('middle finger');\nMiddleFinger.curl(thumb, none, 1.0);\nMiddleFinger.curl(index, full, 0.5);\nMiddleFinger.curl(middle, full, 0.5);\nMiddleFinger.curl(ring, full, 0.5);\nMiddleFinger.curl(pinky, full, 0.5);\nMiddleFinger.weight(index, 2);\nMiddleFinger.weight(middle, 2);\n\n// describe Open Palm gesture \u270C\uFE0F\nconst OpenPalm = new FingerGesture('open palm');\nOpenPalm.curl(thumb, none, 0.75);\nOpenPalm.curl(index, none, 0.75);\nOpenPalm.curl(middle, none, 0.75);\nOpenPalm.curl(ring, none, 0.75);\nOpenPalm.curl(pinky, none, 0.75);\n\nexport default [ThumbsUp, Victory, Point, MiddleFinger, OpenPalm];\n", "/**\n * FingerPose algorithm implementation constants\n *\n * Based on: [**FingerPose***](https://github.com/andypotato/fingerpose)\n */\n\n/* eslint-disable camelcase */\n\nimport { Finger, FingerCurl, FingerDirection } from './fingerdef';\nimport Gestures from '../hand/fingergesture';\n\nconst minConfidence = 0.7;\nconst options = {\n  // curl estimation\n  HALF_CURL_START_LIMIT: 60.0,\n  NO_CURL_START_LIMIT: 130.0,\n  // direction estimation\n  DISTANCE_VOTE_POWER: 1.1,\n  SINGLE_ANGLE_VOTE_POWER: 0.9,\n  TOTAL_ANGLE_VOTE_POWER: 1.6,\n};\n\nfunction calculateSlope(point1x, point1y, point2x, point2y) {\n  const value = (point1y - point2y) / (point1x - point2x);\n  let slope = Math.atan(value) * 180 / Math.PI;\n  if (slope <= 0) slope = -slope;\n  else if (slope > 0) slope = 180 - slope;\n  return slope;\n}\n\n// point1, point2 are 2d or 3d point arrays (xy[z])\n// returns either a single scalar (2d) or array of two slopes (3d)\nfunction getSlopes(point1, point2) {\n  if (!point1 || !point2) return [0, 0];\n  const slopeXY = calculateSlope(point1[0], point1[1], point2[0], point2[1]);\n  if (point1.length === 2) return slopeXY;\n  const slopeYZ = calculateSlope(point1[1], point1[2], point2[1], point2[2]);\n  return [slopeXY, slopeYZ];\n}\n\nfunction angleOrientationAt(angle, weightageAt = 1.0) {\n  let isVertical = 0;\n  let isDiagonal = 0;\n  let isHorizontal = 0;\n  if (angle >= 75.0 && angle <= 105.0) isVertical = 1 * weightageAt;\n  else if (angle >= 25.0 && angle <= 155.0) isDiagonal = 1 * weightageAt;\n  else isHorizontal = 1 * weightageAt;\n  return [isVertical, isDiagonal, isHorizontal];\n}\n\nfunction estimateFingerCurl(startPoint, midPoint, endPoint) {\n  const start_mid_x_dist = startPoint[0] - midPoint[0];\n  const start_end_x_dist = startPoint[0] - endPoint[0];\n  const mid_end_x_dist = midPoint[0] - endPoint[0];\n  const start_mid_y_dist = startPoint[1] - midPoint[1];\n  const start_end_y_dist = startPoint[1] - endPoint[1];\n  const mid_end_y_dist = midPoint[1] - endPoint[1];\n  const start_mid_z_dist = startPoint[2] - midPoint[2];\n  const start_end_z_dist = startPoint[2] - endPoint[2];\n  const mid_end_z_dist = midPoint[2] - endPoint[2];\n  const start_mid_dist = Math.sqrt(start_mid_x_dist * start_mid_x_dist + start_mid_y_dist * start_mid_y_dist + start_mid_z_dist * start_mid_z_dist);\n  const start_end_dist = Math.sqrt(start_end_x_dist * start_end_x_dist + start_end_y_dist * start_end_y_dist + start_end_z_dist * start_end_z_dist);\n  const mid_end_dist = Math.sqrt(mid_end_x_dist * mid_end_x_dist + mid_end_y_dist * mid_end_y_dist + mid_end_z_dist * mid_end_z_dist);\n  let cos_in = (mid_end_dist * mid_end_dist + start_mid_dist * start_mid_dist - start_end_dist * start_end_dist) / (2 * mid_end_dist * start_mid_dist);\n  if (cos_in > 1.0) cos_in = 1.0;\n  else if (cos_in < -1.0) cos_in = -1.0;\n  let angleOfCurve = Math.acos(cos_in);\n  angleOfCurve = (57.2958 * angleOfCurve) % 180;\n  let fingerCurl;\n  if (angleOfCurve > options.NO_CURL_START_LIMIT) fingerCurl = FingerCurl.none;\n  else if (angleOfCurve > options.HALF_CURL_START_LIMIT) fingerCurl = FingerCurl.half;\n  else fingerCurl = FingerCurl.full;\n  return fingerCurl;\n}\n\nfunction estimateHorizontalDirection(start_end_x_dist, start_mid_x_dist, mid_end_x_dist, max_dist_x) {\n  let estimatedDirection;\n  if (max_dist_x === Math.abs(start_end_x_dist)) {\n    if (start_end_x_dist > 0) estimatedDirection = FingerDirection.horizontalLeft;\n    else estimatedDirection = FingerDirection.horizontalRight;\n  } else if (max_dist_x === Math.abs(start_mid_x_dist)) {\n    if (start_mid_x_dist > 0) estimatedDirection = FingerDirection.horizontalLeft;\n    else estimatedDirection = FingerDirection.horizontalRight;\n  } else {\n    if (mid_end_x_dist > 0) estimatedDirection = FingerDirection.horizontalLeft;\n    else estimatedDirection = FingerDirection.horizontalRight;\n  }\n  return estimatedDirection;\n}\n\nfunction estimateVerticalDirection(start_end_y_dist, start_mid_y_dist, mid_end_y_dist, max_dist_y) {\n  let estimatedDirection;\n  if (max_dist_y === Math.abs(start_end_y_dist)) {\n    if (start_end_y_dist < 0) estimatedDirection = FingerDirection.verticalDown;\n    else estimatedDirection = FingerDirection.verticalUp;\n  } else if (max_dist_y === Math.abs(start_mid_y_dist)) {\n    if (start_mid_y_dist < 0) estimatedDirection = FingerDirection.verticalDown;\n    else estimatedDirection = FingerDirection.verticalUp;\n  } else {\n    if (mid_end_y_dist < 0) estimatedDirection = FingerDirection.verticalDown;\n    else estimatedDirection = FingerDirection.verticalUp;\n  }\n  return estimatedDirection;\n}\n\nfunction estimateDiagonalDirection(start_end_y_dist, start_mid_y_dist, mid_end_y_dist, max_dist_y, start_end_x_dist, start_mid_x_dist, mid_end_x_dist, max_dist_x) {\n  let estimatedDirection;\n  const reqd_vertical_direction = estimateVerticalDirection(start_end_y_dist, start_mid_y_dist, mid_end_y_dist, max_dist_y);\n  const reqd_horizontal_direction = estimateHorizontalDirection(start_end_x_dist, start_mid_x_dist, mid_end_x_dist, max_dist_x);\n  if (reqd_vertical_direction === FingerDirection.verticalUp) {\n    if (reqd_horizontal_direction === FingerDirection.horizontalLeft) estimatedDirection = FingerDirection.diagonalUpLeft;\n    else estimatedDirection = FingerDirection.diagonalUpRight;\n  } else {\n    if (reqd_horizontal_direction === FingerDirection.horizontalLeft) estimatedDirection = FingerDirection.diagonalDownLeft;\n    else estimatedDirection = FingerDirection.diagonalDownRight;\n  }\n  return estimatedDirection;\n}\n\nfunction calculateFingerDirection(startPoint, midPoint, endPoint, fingerSlopes) {\n  const start_mid_x_dist = startPoint[0] - midPoint[0];\n  const start_end_x_dist = startPoint[0] - endPoint[0];\n  const mid_end_x_dist = midPoint[0] - endPoint[0];\n  const start_mid_y_dist = startPoint[1] - midPoint[1];\n  const start_end_y_dist = startPoint[1] - endPoint[1];\n  const mid_end_y_dist = midPoint[1] - endPoint[1];\n  const max_dist_x = Math.max(Math.abs(start_mid_x_dist), Math.abs(start_end_x_dist), Math.abs(mid_end_x_dist));\n  const max_dist_y = Math.max(Math.abs(start_mid_y_dist), Math.abs(start_end_y_dist), Math.abs(mid_end_y_dist));\n  let voteVertical = 0.0;\n  let voteDiagonal = 0.0;\n  let voteHorizontal = 0.0;\n  const start_end_x_y_dist_ratio = max_dist_y / (max_dist_x + 0.00001);\n  if (start_end_x_y_dist_ratio > 1.5) voteVertical += options.DISTANCE_VOTE_POWER;\n  else if (start_end_x_y_dist_ratio > 0.66) voteDiagonal += options.DISTANCE_VOTE_POWER;\n  else voteHorizontal += options.DISTANCE_VOTE_POWER;\n  const start_mid_dist = Math.sqrt(start_mid_x_dist * start_mid_x_dist + start_mid_y_dist * start_mid_y_dist);\n  const start_end_dist = Math.sqrt(start_end_x_dist * start_end_x_dist + start_end_y_dist * start_end_y_dist);\n  const mid_end_dist = Math.sqrt(mid_end_x_dist * mid_end_x_dist + mid_end_y_dist * mid_end_y_dist);\n  const max_dist = Math.max(start_mid_dist, start_end_dist, mid_end_dist);\n  let calc_start_point_x = startPoint[0];\n  let calc_start_point_y = startPoint[1];\n  let calc_end_point_x = endPoint[0];\n  let calc_end_point_y = endPoint[1];\n  if (max_dist === start_mid_dist) {\n    calc_end_point_x = endPoint[0];\n    calc_end_point_y = endPoint[1];\n  } else if (max_dist === mid_end_dist) {\n    calc_start_point_x = midPoint[0];\n    calc_start_point_y = midPoint[1];\n  }\n  const calcStartPoint = [calc_start_point_x, calc_start_point_y];\n  const calcEndPoint = [calc_end_point_x, calc_end_point_y];\n  const totalAngle = getSlopes(calcStartPoint, calcEndPoint);\n  const votes = angleOrientationAt(totalAngle, options.TOTAL_ANGLE_VOTE_POWER);\n  voteVertical += votes[0];\n  voteDiagonal += votes[1];\n  voteHorizontal += votes[2];\n  for (const fingerSlope of fingerSlopes) {\n    const fingerVotes = angleOrientationAt(fingerSlope, options.SINGLE_ANGLE_VOTE_POWER);\n    voteVertical += fingerVotes[0];\n    voteDiagonal += fingerVotes[1];\n    voteHorizontal += fingerVotes[2];\n  }\n  // in case of tie, highest preference goes to Vertical,\n  // followed by horizontal and then diagonal\n  let estimatedDirection;\n  if (voteVertical === Math.max(voteVertical, voteDiagonal, voteHorizontal)) {\n    estimatedDirection = estimateVerticalDirection(start_end_y_dist, start_mid_y_dist, mid_end_y_dist, max_dist_y);\n  } else if (voteHorizontal === Math.max(voteDiagonal, voteHorizontal)) {\n    estimatedDirection = estimateHorizontalDirection(start_end_x_dist, start_mid_x_dist, mid_end_x_dist, max_dist_x);\n  } else {\n    estimatedDirection = estimateDiagonalDirection(start_end_y_dist, start_mid_y_dist, mid_end_y_dist, max_dist_y, start_end_x_dist, start_mid_x_dist, mid_end_x_dist, max_dist_x);\n  }\n  return estimatedDirection;\n}\n\nfunction estimate(landmarks) {\n  // step 1: calculate slopes\n  const slopesXY: number[][] = [];\n  const slopesYZ: number[][] = [];\n  const fingerCurls: number[] = [];\n  const fingerDirections: number[] = [];\n  if (!landmarks) return { curls: fingerCurls, directions: fingerDirections };\n\n  // step 1: calculate slopes\n  for (const finger of Finger.all) {\n    const points = Finger.getPoints(finger);\n    const slopeAtXY: number[] = [];\n    const slopeAtYZ: number[] = [];\n    for (const point of points) {\n      const point1 = landmarks[point[0]];\n      const point2 = landmarks[point[1]];\n      // calculate single slope\n      const slopes = getSlopes(point1, point2);\n      const slopeXY = slopes[0];\n      const slopeYZ = slopes[1];\n      slopeAtXY.push(slopeXY);\n      slopeAtYZ.push(slopeYZ);\n    }\n    slopesXY.push(slopeAtXY);\n    slopesYZ.push(slopeAtYZ);\n  }\n\n  // step 2: calculate orientations\n  for (const finger of Finger.all) {\n    // start finger predictions from palm - except for thumb\n    const pointIndexAt = (finger === Finger.thumb) ? 1 : 0;\n    const fingerPointsAt = Finger.getPoints(finger);\n    const startPoint = landmarks[fingerPointsAt[pointIndexAt][0]];\n    const midPoint = landmarks[fingerPointsAt[pointIndexAt + 1][1]];\n    const endPoint = landmarks[fingerPointsAt[3][1]];\n    // check if finger is curled\n    const fingerCurled = estimateFingerCurl(startPoint, midPoint, endPoint);\n    const fingerPosition = calculateFingerDirection(startPoint, midPoint, endPoint, slopesXY[finger].slice(pointIndexAt));\n    fingerCurls[finger] = fingerCurled;\n    fingerDirections[finger] = fingerPosition;\n  }\n  return { curls: fingerCurls, directions: fingerDirections };\n}\n\nexport function analyze(keypoints) { // get estimations of curl / direction for each finger\n  if (!keypoints || keypoints.length === 0) return null;\n  const estimatorRes = estimate(keypoints);\n  const landmarks = {};\n  for (const fingerIdx of Finger.all) {\n    landmarks[Finger.getName(fingerIdx)] = {\n      curl: FingerCurl.getName(estimatorRes.curls[fingerIdx]),\n      direction: FingerDirection.getName(estimatorRes.directions[fingerIdx]),\n    };\n  }\n  return landmarks;\n}\n\nexport function match(keypoints) { // compare gesture description to each known gesture\n  const poses: { name: string, confidence: number }[] = [];\n  if (!keypoints || keypoints.length === 0) return poses;\n  const estimatorRes = estimate(keypoints);\n  for (const gesture of Gestures) {\n    const confidence = gesture.matchAgainst(estimatorRes.curls, estimatorRes.directions);\n    if (confidence >= minConfidence) poses.push({ name: gesture.name, confidence });\n  }\n  return poses;\n}\n", "/**\n * HandPose model implementation\n *\n * Based on: [**MediaPipe HandPose**](https://drive.google.com/file/d/1sv4sSb9BSNVZhLzxXJ0jBv9DqD-4jnAz/view)\n */\n\nimport { log } from '../util/util';\nimport * as handdetector from './handposedetector';\nimport * as handpipeline from './handposepipeline';\nimport * as fingerPose from './fingerpose';\nimport { loadModel } from '../tfjs/load';\nimport type { HandResult, Box, Point } from '../result';\nimport type { Tensor, GraphModel } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\n\nconst meshAnnotations = {\n  thumb: [1, 2, 3, 4],\n  index: [5, 6, 7, 8],\n  middle: [9, 10, 11, 12],\n  ring: [13, 14, 15, 16],\n  pinky: [17, 18, 19, 20],\n  palm: [0],\n};\n\nlet handDetectorModel: GraphModel | null;\nlet handPoseModel: GraphModel | null;\nlet handPipeline: handpipeline.HandPipeline;\n\nexport async function predict(input: Tensor, config: Config): Promise<HandResult[]> {\n  const predictions = await handPipeline.estimateHands(input, config);\n  if (!predictions) return [];\n  const hands: HandResult[] = [];\n  for (let i = 0; i < predictions.length; i++) {\n    const annotations = {};\n    if (predictions[i].landmarks) {\n      for (const key of Object.keys(meshAnnotations)) {\n        annotations[key] = meshAnnotations[key].map((index) => predictions[i].landmarks[index]);\n      }\n    }\n    const keypoints = predictions[i].landmarks as unknown as Point[];\n    let box: Box = [Number.MAX_SAFE_INTEGER, Number.MAX_SAFE_INTEGER, 0, 0]; // maximums so conditionals work\n    let boxRaw: Box = [0, 0, 0, 0];\n    if (keypoints && keypoints.length > 0) { // if we have landmarks, calculate box based on landmarks\n      for (const pt of keypoints) {\n        if (pt[0] < box[0]) box[0] = pt[0];\n        if (pt[1] < box[1]) box[1] = pt[1];\n        if (pt[0] > box[2]) box[2] = pt[0];\n        if (pt[1] > box[3]) box[3] = pt[1];\n      }\n      box[2] -= box[0];\n      box[3] -= box[1];\n      boxRaw = [box[0] / (input.shape[2] || 0), box[1] / (input.shape[1] || 0), box[2] / (input.shape[2] || 0), box[3] / (input.shape[1] || 0)];\n    } else { // otherwise use box from prediction\n      box = predictions[i].box ? [\n        Math.trunc(Math.max(0, predictions[i].box.topLeft[0])),\n        Math.trunc(Math.max(0, predictions[i].box.topLeft[1])),\n        Math.trunc(Math.min((input.shape[2] || 0), predictions[i].box.bottomRight[0]) - Math.max(0, predictions[i].box.topLeft[0])),\n        Math.trunc(Math.min((input.shape[1] || 0), predictions[i].box.bottomRight[1]) - Math.max(0, predictions[i].box.topLeft[1])),\n      ] : [0, 0, 0, 0];\n      boxRaw = [\n        (predictions[i].box.topLeft[0]) / (input.shape[2] || 0),\n        (predictions[i].box.topLeft[1]) / (input.shape[1] || 0),\n        (predictions[i].box.bottomRight[0] - predictions[i].box.topLeft[0]) / (input.shape[2] || 0),\n        (predictions[i].box.bottomRight[1] - predictions[i].box.topLeft[1]) / (input.shape[1] || 0),\n      ];\n    }\n    const landmarks = fingerPose.analyze(keypoints);\n    hands.push({\n      id: i,\n      score: Math.round(100 * predictions[i].confidence) / 100,\n      boxScore: Math.round(100 * predictions[i].boxConfidence) / 100,\n      fingerScore: Math.round(100 * predictions[i].fingerConfidence) / 100,\n      label: 'hand',\n      box,\n      boxRaw,\n      keypoints,\n      annotations: annotations as HandResult['annotations'],\n      landmarks: landmarks as HandResult['landmarks'],\n    });\n  }\n  return hands;\n}\n\nexport async function load(config: Config): Promise<[GraphModel | null, GraphModel | null]> {\n  if (env.initial) {\n    handDetectorModel = null;\n    handPoseModel = null;\n  }\n  if (!handDetectorModel || !handPoseModel) {\n    [handDetectorModel, handPoseModel] = await Promise.all([\n      config.hand.enabled ? loadModel(config.hand.detector?.modelPath) : null,\n      config.hand.landmarks ? loadModel(config.hand.skeleton?.modelPath) : null,\n    ]);\n  } else {\n    if (config.debug) log('cached model:', handDetectorModel['modelUrl']);\n    if (config.debug) log('cached model:', handPoseModel['modelUrl']);\n  }\n  const handDetector = handDetectorModel ? new handdetector.HandDetector(handDetectorModel) : undefined;\n  if (handDetector && handPoseModel) handPipeline = new handpipeline.HandPipeline(handDetector, handPoseModel);\n  return [handDetectorModel, handPoseModel];\n}\n", "/** TFJS custom backend registration */\n\nimport type { Human } from '../human';\nimport { log } from '../util/util';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as image from '../image/image';\nimport * as models from '../models';\nimport type { AnyCanvas } from '../exports';\n// import { env } from '../env';\n\nexport const config = {\n  name: 'humangl',\n  priority: 999,\n  canvas: null as null | AnyCanvas,\n  gl: null as null | WebGL2RenderingContext,\n  extensions: [] as string[] | null,\n  webGLattr: { // https://www.khronos.org/registry/webgl/specs/latest/1.0/#5.2\n    alpha: false,\n    antialias: false,\n    premultipliedAlpha: false,\n    preserveDrawingBuffer: false,\n    depth: false,\n    stencil: false,\n    failIfMajorPerformanceCaveat: false, // default=true\n    desynchronized: true, // default=undefined\n  },\n};\n\nfunction extensions(): void {\n  /*\n  https://www.khronos.org/registry/webgl/extensions/\n  https://webglreport.com/?v=2\n  */\n  const gl = config.gl;\n  if (!gl) return;\n  config.extensions = gl.getSupportedExtensions();\n  // gl.getExtension('KHR_parallel_shader_compile');\n}\n\n/**\n * Registers custom WebGL2 backend to be used by Human library\n *\n * @returns void\n */\nexport function register(instance: Human): void {\n  // force backend reload if gl context is not valid\n  if (instance.config.backend !== 'humangl') return;\n  if ((config.name in tf.engine().registry) && !config?.gl?.getParameter(config.gl.VERSION)) {\n    log('humangl error: backend invalid context');\n    models.reset(instance);\n    /*\n    log('resetting humangl backend');\n    await tf.removeBackend(config.name);\n    await register(instance); // re-register\n    */\n  }\n  if (!tf.findBackend(config.name)) {\n    try {\n      config.canvas = image.canvas(100, 100);\n    } catch (err) {\n      log('humangl error: cannot create canvas:', err);\n      return;\n    }\n    try {\n      config.gl = config.canvas.getContext('webgl2', config.webGLattr);\n      if (!config.gl) {\n        log('humangl error: cannot get webgl context');\n        return;\n      }\n      const glv2 = config.gl.getParameter(config.gl.VERSION).includes('2.0');\n      if (!glv2) {\n        log('backend override: using fallback webgl backend as webgl 2.0 is not detected');\n        instance.config.backend = 'webgl';\n        return;\n      }\n      if (config.canvas) {\n        config.canvas.addEventListener('webglcontextlost', (e) => {\n          log('humangl error:', e.type);\n          log('possible browser memory leak using webgl or conflict with multiple backend registrations');\n          instance.emit('error');\n          throw new Error('backend error: webgl context lost');\n          // log('resetting humangl backend');\n          // env.initial = true;\n          // models.reset(instance);\n          // await tf.removeBackend(config.name);\n          // await register(instance); // re-register\n        });\n        config.canvas.addEventListener('webglcontextrestored', (e) => {\n          log('humangl error: context restored:', e);\n        });\n        config.canvas.addEventListener('webglcontextcreationerror', (e) => {\n          log('humangl error: context create:', e);\n        });\n      }\n    } catch (err) {\n      log('humangl error: cannot get webgl context:', err);\n      return;\n    }\n    try {\n      tf.setWebGLContext(2, config.gl);\n    } catch (err) {\n      log('humangl error: cannot set webgl context:', err);\n      return;\n    }\n    try {\n      const ctx = new tf.GPGPUContext(config.gl);\n      tf.registerBackend(config.name, () => new tf.MathBackendWebGL(ctx), config.priority);\n    } catch (err) {\n      log('humangl error: cannot register webgl backend:', err);\n      return;\n    }\n    try {\n      const kernels = tf.getKernelsForBackend('webgl');\n      kernels.forEach((kernelConfig) => {\n        const newKernelConfig = { ...kernelConfig, backendName: config.name };\n        tf.registerKernel(newKernelConfig);\n      });\n    } catch (err) {\n      log('humangl error: cannot update webgl backend registration:', err);\n      return;\n    }\n    try {\n      if (tf.env().flagRegistry.WEBGL_VERSION) tf.env().set('WEBGL_VERSION', 2);\n    } catch (err) {\n      log('humangl error: cannot set WebGL backend flags:', err);\n      return;\n    }\n    extensions();\n    const current = tf.backend().getGPGPUContext ? tf.backend().getGPGPUContext().gl : null;\n    if (current) {\n      if (instance.config.debug) log('humangl backend registered:', { webgl: current.getParameter(current.VERSION) as string, renderer: current.getParameter(current.RENDERER) as string });\n    } else {\n      log('humangl error: no current gl context:', current, config.gl);\n    }\n  }\n}\n", "/** TFJS backend initialization and customization */\n\nimport type { Human, Config } from '../human';\nimport { log, now } from '../util/util';\nimport { env } from '../util/env';\nimport * as humangl from './humangl';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as constants from './constants';\n\nfunction registerCustomOps(config: Config) {\n  const newKernels: string[] = [];\n  if (!env.kernels.includes('mod')) {\n    const kernelMod = {\n      kernelName: 'Mod',\n      backendName: tf.getBackend(),\n      kernelFunc: (op) => tf.tidy(() => tf.sub(op.inputs.a, tf.mul(tf.div(op.inputs.a, op.inputs.b), op.inputs.b))),\n    };\n    tf.registerKernel(kernelMod);\n    env.kernels.push('mod');\n    newKernels.push('mod');\n  }\n  if (!env.kernels.includes('floormod')) {\n    const kernelFloorMod = {\n      kernelName: 'FloorMod',\n      backendName: tf.getBackend(),\n      kernelFunc: (op) => tf.tidy(() => tf.add(tf.mul(tf.floorDiv(op.inputs.a / op.inputs.b), op.inputs.b), tf.mod(op.inputs.a, op.inputs.b))),\n    };\n    tf.registerKernel(kernelFloorMod);\n    env.kernels.push('floormod');\n    newKernels.push('floormod');\n  }\n  /*\n  if (!env.kernels.includes('atan2') && config.softwareKernels) {\n    const kernelAtan2 = {\n      kernelName: 'Atan2',\n      backendName: tf.getBackend(),\n      kernelFunc: (op) => tf.tidy(() => {\n        const backend = tf.getBackend();\n        tf.setBackend('cpu');\n        const t = tf.atan2(op.inputs.a, op.inputs.b);\n        tf.setBackend(backend);\n        return t;\n      }),\n    };\n    if (config.debug) log('registered kernel:', 'atan2');\n    log('registered kernel:', 'atan2');\n    tf.registerKernel(kernelAtan2);\n    env.kernels.push('atan2');\n    newKernels.push('atan2');\n  }\n  */\n  if (!env.kernels.includes('rotatewithoffset') && config.softwareKernels) {\n    const kernelRotateWithOffset = {\n      kernelName: 'RotateWithOffset',\n      backendName: tf.getBackend(),\n      kernelFunc: (op) => tf.tidy(() => {\n        const backend = tf.getBackend();\n        tf.setBackend('cpu');\n        const t = tf.image.rotateWithOffset(op.inputs.image, op.attrs.radians, op.attrs.fillValue, op.attrs.center);\n        tf.setBackend(backend);\n        return t;\n      }),\n    };\n    tf.registerKernel(kernelRotateWithOffset);\n    env.kernels.push('rotatewithoffset');\n    newKernels.push('rotatewithoffset');\n  }\n  if ((newKernels.length > 0) && config.debug) log('registered kernels:', newKernels);\n}\n\nlet defaultFlags: Record<string, unknown> = {};\n\nexport async function check(instance: Human, force = false) {\n  instance.state = 'backend';\n  if (force || env.initial || (instance.config.backend && (instance.config.backend.length > 0) && (tf.getBackend() !== instance.config.backend))) {\n    const timeStamp = now();\n\n    if (instance.config.backend && instance.config.backend.length > 0) {\n      // detect web worker\n      // @ts-ignore ignore missing type for WorkerGlobalScope as that is the point\n      if (typeof window === 'undefined' && typeof WorkerGlobalScope !== 'undefined' && instance.config.debug) {\n        if (instance.config.debug) log('running inside web worker');\n      }\n\n      // force browser vs node backend\n      if (env.browser && instance.config.backend === 'tensorflow') {\n        if (instance.config.debug) log('override: backend set to tensorflow while running in browser');\n        instance.config.backend = 'webgl';\n      }\n      if (env.node && (instance.config.backend === 'webgl' || instance.config.backend === 'humangl')) {\n        if (instance.config.debug) log(`override: backend set to ${instance.config.backend} while running in nodejs`);\n        instance.config.backend = 'tensorflow';\n      }\n\n      // handle webgpu\n      if (env.browser && instance.config.backend === 'webgpu') {\n        if (typeof navigator === 'undefined' || typeof navigator.gpu === 'undefined') {\n          log('override: backend set to webgpu but browser does not support webgpu');\n          instance.config.backend = 'webgl';\n        } else {\n          const adapter = await navigator.gpu.requestAdapter();\n          if (instance.config.debug) log('enumerated webgpu adapter:', adapter);\n          if (!adapter) {\n            log('override: backend set to webgpu but browser reports no available gpu');\n            instance.config.backend = 'webgl';\n          } else {\n            // @ts-ignore requestAdapterInfo is not in tslib\n            const adapterInfo = 'requestAdapterInfo' in adapter ? await (adapter as GPUAdapter).requestAdapterInfo() : undefined;\n            // if (adapter.features) adapter.features.forEach((feature) => log('webgpu features:', feature));\n            log('webgpu adapter info:', adapterInfo);\n          }\n        }\n      }\n\n      // check available backends\n      let available = Object.keys(tf.engine().registryFactory as Record<string, unknown>);\n      if (instance.config.backend === 'humangl' && !available.includes('humangl')) {\n        humangl.register(instance);\n        available = Object.keys(tf.engine().registryFactory as Record<string, unknown>);\n      }\n      if (instance.config.debug) log('available backends:', available);\n\n      if (!available.includes(instance.config.backend)) {\n        log(`error: backend ${instance.config.backend} not found in registry`);\n        instance.config.backend = env.node ? 'tensorflow' : 'webgl';\n        if (instance.config.debug) log(`override: setting backend ${instance.config.backend}`);\n      }\n\n      if (instance.config.debug) log('setting backend:', [instance.config.backend]);\n\n      // customize wasm\n      if (instance.config.backend === 'wasm') {\n        if (tf.env().flagRegistry.CANVAS2D_WILL_READ_FREQUENTLY) tf.env().set('CANVAS2D_WILL_READ_FREQUENTLY', true);\n        if (instance.config.debug) log('wasm path:', instance.config.wasmPath);\n        if (typeof tf.setWasmPaths !== 'undefined') tf.setWasmPaths(instance.config.wasmPath, instance.config.wasmPlatformFetch);\n        else throw new Error('backend error: attempting to use wasm backend but wasm path is not set');\n        let mt = false;\n        let simd = false;\n        try {\n          mt = await tf.env().getAsync('WASM_HAS_MULTITHREAD_SUPPORT');\n          simd = await tf.env().getAsync('WASM_HAS_SIMD_SUPPORT');\n          if (instance.config.debug) log(`wasm execution: ${simd ? 'simd' : 'no simd'} ${mt ? 'multithreaded' : 'singlethreaded'}`);\n          if (instance.config.debug && !simd) log('warning: wasm simd support is not enabled');\n        } catch {\n          log('wasm detection failed');\n        }\n      }\n\n      try {\n        await tf.setBackend(instance.config.backend);\n        await tf.ready();\n      } catch (err) {\n        log('error: cannot set backend:', instance.config.backend, err);\n        return false;\n      }\n      if (instance.config.debug) defaultFlags = JSON.parse(JSON.stringify(tf.env().flags));\n    }\n\n    // customize humangl\n    if (tf.getBackend() === 'humangl' || tf.getBackend() === 'webgl') {\n      if (tf.env().flagRegistry.WEBGL_USE_SHAPES_UNIFORMS) tf.env().set('WEBGL_USE_SHAPES_UNIFORMS', true); // default=false <https://github.com/tensorflow/tfjs/issues/5205>\n      if (tf.env().flagRegistry.WEBGL_EXP_CONV) tf.env().set('WEBGL_EXP_CONV', true); // default=false <https://github.com/tensorflow/tfjs/issues/6678>\n      // if (tf.env().flagRegistry['WEBGL_PACK_DEPTHWISECONV'])  tf.env().set('WEBGL_PACK_DEPTHWISECONV', false); // default=true <https://github.com/tensorflow/tfjs/pull/4909>\n      // if (tf.env().flagRegistry.USE_SETTIMEOUTCUSTOM) tf.env().set('USE_SETTIMEOUTCUSTOM', true); // default=false <https://github.com/tensorflow/tfjs/issues/6687>\n      // if (tf.env().flagRegistry.CPU_HANDOFF_SIZE_THRESHOLD) tf.env().set('CPU_HANDOFF_SIZE_THRESHOLD', 1024); // default=1000\n      // if (tf.env().flagRegistry['WEBGL_FORCE_F16_TEXTURES'] && !instance.config.object.enabled) tf.env().set('WEBGL_FORCE_F16_TEXTURES', true); // safe to use 16bit precision\n      if (instance.config.debug && typeof instance.config.deallocate !== 'undefined' && instance.config.deallocate) { // hidden param\n        log('changing webgl: WEBGL_DELETE_TEXTURE_THRESHOLD:', true);\n        tf.env().set('WEBGL_DELETE_TEXTURE_THRESHOLD', 0);\n      }\n    }\n\n    // customize webgpu\n    if (tf.getBackend() === 'webgpu') {\n      // if (tf.env().flagRegistry['WEBGPU_CPU_HANDOFF_SIZE_THRESHOLD']) tf.env().set('WEBGPU_CPU_HANDOFF_SIZE_THRESHOLD', 512);\n      // if (tf.env().flagRegistry['WEBGPU_DEFERRED_SUBMIT_BATCH_SIZE']) tf.env().set('WEBGPU_DEFERRED_SUBMIT_BATCH_SIZE', 0);\n      // if (tf.env().flagRegistry['WEBGPU_CPU_FORWARD']) tf.env().set('WEBGPU_CPU_FORWARD', true);\n    }\n\n    if (instance.config.debug) {\n      const newFlags = tf.env().flags;\n      const updatedFlags = {};\n      for (const key of Object.keys(newFlags)) {\n        if (defaultFlags[key] === newFlags[key]) continue;\n        updatedFlags[key] = newFlags[key];\n      }\n      if (instance.config.debug && Object.keys(updatedFlags).length > 0) log('backend:', tf.getBackend(), 'flags:', updatedFlags);\n    }\n\n    if (instance.config.flags && Object.keys(instance.config.flags).length > 0) {\n      if (instance.config.debug) log('flags:', instance.config['flags']);\n      for (const [key, val] of Object.entries(instance.config.flags)) {\n        tf.env().set(key, val);\n      }\n    }\n\n    tf.enableProdMode();\n    constants.init();\n    instance.performance.initBackend = Math.trunc(now() - timeStamp);\n    instance.config.backend = tf.getBackend();\n    await env.updateBackend(); // update env on backend init\n    registerCustomOps(instance.config);\n    // await env.updateBackend(); // update env on backend init\n    env.initial = false;\n  }\n  return true;\n}\n\n// register fake missing tfjs ops\nexport function fakeOps(kernelNames: string[], config) {\n  // if (config.debug) log('registerKernel:', kernelNames);\n  for (const kernelName of kernelNames) {\n    const kernelConfig = {\n      kernelName,\n      backendName: config.backend,\n      kernelFunc: () => { if (config.debug) log('kernelFunc', kernelName, config.backend); },\n      // setupFunc: () => { if (config.debug) log('kernelFunc', kernelName, config.backend); },\n      // disposeFunc: () => { if (config.debug) log('kernelFunc', kernelName, config.backend); },\n    };\n    tf.registerKernel(kernelConfig);\n  }\n  env.kernels = tf.getKernelsForBackend(tf.getBackend()).map((kernel) => (kernel.kernelName as string).toLowerCase()); // re-scan registered ops\n}\n", "/**\n * HandTrack model implementation\n *\n * Based on:\n * - Hand Detection & Skeleton: [**MediaPipe HandPose**](https://drive.google.com/file/d/1sv4sSb9BSNVZhLzxXJ0jBv9DqD-4jnAz/view)\n * - Hand Tracking: [**HandTracking**](https://github.com/victordibia/handtracking)\n */\n\nimport { log, now } from '../util/util';\nimport * as box from '../util/box';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { loadModel } from '../tfjs/load';\nimport type { HandResult, HandType, Box, Point } from '../result';\nimport type { GraphModel, Tensor } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\nimport * as fingerPose from './fingerpose';\nimport { fakeOps } from '../tfjs/backend';\nimport { constants } from '../tfjs/constants';\n\nconst models: [GraphModel | null, GraphModel | null] = [null, null];\nconst modelOutputNodes = ['StatefulPartitionedCall/Postprocessor/Slice', 'StatefulPartitionedCall/Postprocessor/ExpandDims_1'];\n\nconst inputSize = [[0, 0], [0, 0]];\n\nconst classes = ['hand', 'fist', 'pinch', 'point', 'face', 'tip', 'pinchtip'];\nconst faceIndex = 4;\n\nconst boxExpandFact = 1.6;\nconst maxDetectorResolution = 512;\nconst detectorExpandFact = 1.4;\n\nlet skipped = Number.MAX_SAFE_INTEGER;\nlet lastTime = 0;\nlet outputSize: [number, number] = [0, 0];\n\ninterface HandDetectResult {\n  id: number,\n  score: number,\n  box: Box,\n  boxRaw: Box,\n  label: HandType,\n}\n\nconst cache: {\n  boxes: HandDetectResult[],\n  hands: HandResult[];\n} = {\n  boxes: [],\n  hands: [],\n};\n\nconst fingerMap = {\n  /*\n  thumb: [0, 1, 2, 3, 4],\n  index: [0, 5, 6, 7, 8],\n  middle: [0, 9, 10, 11, 12],\n  ring: [0, 13, 14, 15, 16],\n  pinky: [0, 17, 18, 19, 20],\n  palm: [0],\n  */\n  thumb: [1, 2, 3, 4],\n  index: [5, 6, 7, 8],\n  middle: [9, 10, 11, 12],\n  ring: [13, 14, 15, 16],\n  pinky: [17, 18, 19, 20],\n  base: [0],\n  palm: [0, 17, 13, 9, 5, 1, 0],\n};\n\nexport async function loadDetect(config: Config): Promise<GraphModel> {\n  // HandTrack Model: Original: <https://github.com/victordibia/handtracking> TFJS Port: <https://github.com/victordibia/handtrack.js/>\n  if (env.initial) models[0] = null;\n  if (!models[0]) {\n    // handtrack model has some kernel ops defined in model but those are never referenced and non-existent in tfjs\n    // ideally need to prune the model itself\n    fakeOps(['tensorlistreserve', 'enter', 'tensorlistfromtensor', 'merge', 'loopcond', 'switch', 'exit', 'tensorliststack', 'nextiteration', 'tensorlistsetitem', 'tensorlistgetitem', 'reciprocal', 'shape', 'split', 'where'], config);\n    models[0] = await loadModel(config.hand.detector?.modelPath);\n    const inputs = models[0]['executor'] ? Object.values(models[0].modelSignature['inputs']) : undefined;\n    inputSize[0][0] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[1].size) : 0;\n    inputSize[0][1] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 0;\n  } else if (config.debug) log('cached model:', models[0]['modelUrl']);\n  return models[0];\n}\n\nexport async function loadSkeleton(config: Config): Promise<GraphModel> {\n  if (env.initial) models[1] = null;\n  if (!models[1]) {\n    models[1] = await loadModel(config.hand.skeleton?.modelPath);\n    const inputs = models[1]['executor'] ? Object.values(models[1].modelSignature['inputs']) : undefined;\n    inputSize[1][0] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[1].size) : 0;\n    inputSize[1][1] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 0;\n  } else if (config.debug) log('cached model:', models[1]['modelUrl']);\n  return models[1];\n}\n\nexport async function load(config: Config): Promise<[GraphModel | null, GraphModel | null]> {\n  if (!models[0]) await loadDetect(config);\n  if (!models[1]) await loadSkeleton(config);\n  return models;\n}\n\nasync function detectHands(input: Tensor, config: Config): Promise<HandDetectResult[]> {\n  const hands: HandDetectResult[] = [];\n  if (!input || !models[0]) return hands;\n  const t: Record<string, Tensor> = {};\n  const ratio = (input.shape[2] || 1) / (input.shape[1] || 1);\n  const height = Math.min(Math.round((input.shape[1] || 0) / 8) * 8, maxDetectorResolution); // use dynamic input size but cap at 512\n  const width = Math.round(height * ratio / 8) * 8;\n  t.resize = tf.image.resizeBilinear(input, [height, width]); // todo: resize with padding\n  t.cast = tf.cast(t.resize, 'int32');\n  [t.rawScores, t.rawBoxes] = await models[0].executeAsync(t.cast, modelOutputNodes) as Tensor[];\n  t.boxes = tf.squeeze(t.rawBoxes, [0, 2]);\n  t.scores = tf.squeeze(t.rawScores, [0]);\n  const classScores: Tensor[] = tf.unstack(t.scores, 1); // unstack scores based on classes\n  tf.dispose(classScores[faceIndex]);\n  classScores.splice(faceIndex, 1); // remove faces\n  t.filtered = tf.stack(classScores, 1); // restack\n  tf.dispose(classScores);\n  // t.filtered = t.scores;\n  t.max = tf.max(t.filtered, 1); // max overall score\n  t.argmax = tf.argMax(t.filtered, 1); // class index of max overall score\n  let id = 0;\n  t.nms = await tf.image.nonMaxSuppressionAsync(t.boxes, t.max, (config.hand.maxDetected || 0) + 1, config.hand.iouThreshold || 0, config.hand.minConfidence || 1);\n  const nms = await t.nms.data();\n  const scores = await t.max.data();\n  const classNum = await t.argmax.data();\n  for (const nmsIndex of Array.from(nms)) { // generates results for each class\n    const boxSlice = tf.slice(t.boxes, nmsIndex, 1);\n    const boxYX = await boxSlice.data();\n    tf.dispose(boxSlice);\n    const boxData: Box = [boxYX[1], boxYX[0], boxYX[3] - boxYX[1], boxYX[2] - boxYX[0]]; // yx box reshaped to standard box\n    const boxRaw: Box = box.scale(boxData, detectorExpandFact);\n    const boxFull: Box = [Math.trunc(boxData[0] * outputSize[0]), Math.trunc(boxData[1] * outputSize[1]), Math.trunc(boxData[2] * outputSize[0]), Math.trunc(boxData[3] * outputSize[1])];\n    const score = scores[nmsIndex];\n    const label = classes[classNum[nmsIndex]] as HandType;\n    const hand: HandDetectResult = { id: id++, score, box: boxFull, boxRaw, label };\n    hands.push(hand);\n  }\n  Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n  hands.sort((a, b) => b.score - a.score);\n  if (hands.length > (config.hand.maxDetected || 1)) hands.length = (config.hand.maxDetected || 1);\n  return hands;\n}\n\nasync function detectFingers(input: Tensor, h: HandDetectResult, config: Config): Promise<HandResult> {\n  const hand: HandResult = { // initial values inherited from hand detect\n    id: h.id,\n    score: Math.round(100 * h.score) / 100,\n    boxScore: Math.round(100 * h.score) / 100,\n    fingerScore: 0,\n    box: h.box,\n    boxRaw: h.boxRaw,\n    label: h.label,\n    keypoints: [],\n    landmarks: {} as HandResult['landmarks'],\n    annotations: {} as HandResult['annotations'],\n  };\n  if (input && models[1] && config.hand.landmarks && h.score > (config.hand.minConfidence || 0)) {\n    const t: Record<string, Tensor> = {};\n    const boxCrop = [h.boxRaw[1], h.boxRaw[0], h.boxRaw[3] + h.boxRaw[1], h.boxRaw[2] + h.boxRaw[0]] as Box;\n    t.crop = tf.image.cropAndResize(input, [boxCrop], [0], [inputSize[1][0], inputSize[1][1]], 'bilinear');\n    t.div = tf.div(t.crop, constants.tf255);\n    [t.score, t.keypoints] = models[1].execute(t.div, ['Identity_1', 'Identity']) as Tensor[];\n    const rawScore = (await t.score.data())[0];\n    const score = (100 - Math.trunc(100 / (1 + Math.exp(rawScore)))) / 100; // reverse sigmoid value\n    if (score >= (config.hand.minConfidence || 0)) {\n      hand.fingerScore = score;\n      t.reshaped = tf.reshape(t.keypoints, [-1, 3]);\n      const coordsData: Point[] = await t.reshaped.array() as Point[];\n      const coordsRaw: Point[] = coordsData.map((kpt) => [kpt[0] / inputSize[1][1], kpt[1] / inputSize[1][0], (kpt[2] || 0)]);\n      const coordsNorm: Point[] = coordsRaw.map((kpt) => [kpt[0] * h.boxRaw[2], kpt[1] * h.boxRaw[3], (kpt[2] || 0)]);\n      hand.keypoints = (coordsNorm).map((kpt) => [outputSize[0] * (kpt[0] + h.boxRaw[0]), outputSize[1] * (kpt[1] + h.boxRaw[1]), (kpt[2] || 0)]);\n      hand.landmarks = fingerPose.analyze(hand.keypoints) as HandResult['landmarks']; // calculate finger gestures\n      for (const key of Object.keys(fingerMap)) { // map keypoints to per-finger annotations\n        hand.annotations[key] = fingerMap[key].map((index: number) => (hand.landmarks && hand.keypoints[index] ? hand.keypoints[index] : null));\n      }\n    }\n    Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n  }\n  return hand;\n}\n\nexport async function predict(input: Tensor, config: Config): Promise<HandResult[]> {\n  if (!models[0]?.['executor'] || !models[1]?.['executor'] || !models[0].inputs[0].shape || !models[1].inputs[0].shape) return []; // something is wrong with the model\n  outputSize = [input.shape[2] || 0, input.shape[1] || 0];\n  skipped++; // increment skip frames\n  const skipTime = (config.hand.skipTime || 0) > (now() - lastTime);\n  const skipFrame = skipped < (config.hand.skipFrames || 0);\n  if (config.skipAllowed && skipTime && skipFrame) {\n    return cache.hands; // return cached results without running anything\n  }\n  return new Promise(async (resolve) => {\n    const skipTimeExtended = 3 * (config.hand.skipTime || 0) > (now() - lastTime);\n    const skipFrameExtended = skipped < 3 * (config.hand.skipFrames || 0);\n    if (config.skipAllowed && cache.hands.length === config.hand.maxDetected) { // we have all detected hands so we're definitely skipping\n      cache.hands = await Promise.all(cache.boxes.map((handBox) => detectFingers(input, handBox, config)));\n    } else if (config.skipAllowed && skipTimeExtended && skipFrameExtended && cache.hands.length > 0) { // we have some cached results: maybe not enough but anyhow continue for bit longer\n      cache.hands = await Promise.all(cache.boxes.map((handBox) => detectFingers(input, handBox, config)));\n    } else { // finally rerun detector\n      cache.boxes = await detectHands(input, config);\n      lastTime = now();\n      cache.hands = await Promise.all(cache.boxes.map((handBox) => detectFingers(input, handBox, config)));\n      skipped = 0;\n    }\n\n    const oldCache = [...cache.boxes];\n    cache.boxes.length = 0; // reset cache\n    if (config.cacheSensitivity > 0) {\n      for (let i = 0; i < cache.hands.length; i++) {\n        const boxKpt = box.square(cache.hands[i].keypoints, outputSize);\n        if (boxKpt.box[2] / (input.shape[2] || 1) > 0.05 && boxKpt.box[3] / (input.shape[1] || 1) > 0.05 && cache.hands[i].fingerScore && cache.hands[i].fingerScore > (config.hand.minConfidence || 0)) {\n          const boxScale = box.scale(boxKpt.box, boxExpandFact);\n          const boxScaleRaw = box.scale(boxKpt.boxRaw, boxExpandFact);\n          // const boxCrop = box.crop(boxScaleRaw);\n          cache.boxes.push({ ...oldCache[i], box: boxScale, boxRaw: boxScaleRaw });\n        }\n      }\n    }\n    for (let i = 0; i < cache.hands.length; i++) { // replace detected boxes with calculated boxes in final output\n      const bbox = box.calc(cache.hands[i].keypoints, outputSize);\n      cache.hands[i].box = bbox.box;\n      cache.hands[i].boxRaw = bbox.boxRaw;\n    }\n    resolve(cache.hands);\n  });\n}\n", "/**\n * Anti-spoofing model implementation\n */\n\nimport { log, now } from '../util/util';\nimport { loadModel } from '../tfjs/load';\nimport type { Config } from '../config';\nimport type { GraphModel, Tensor } from '../tfjs/types';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { env } from '../util/env';\n\nlet model: GraphModel | null;\nconst cached: number[] = [];\nlet skipped = Number.MAX_SAFE_INTEGER;\nlet lastCount = 0;\nlet lastTime = 0;\n\nexport async function load(config: Config): Promise<GraphModel> {\n  if (env.initial) model = null;\n  if (!model) model = await loadModel(config.face.liveness?.modelPath);\n  else if (config.debug) log('cached model:', model['modelUrl']);\n  return model;\n}\n\nexport async function predict(image: Tensor, config: Config, idx: number, count: number): Promise<number> {\n  if (!model?.['executor']) return 0;\n  const skipTime = (config.face.liveness?.skipTime || 0) > (now() - lastTime);\n  const skipFrame = skipped < (config.face.liveness?.skipFrames || 0);\n  if (config.skipAllowed && skipTime && skipFrame && (lastCount === count) && cached[idx]) {\n    skipped++;\n    return cached[idx];\n  }\n  skipped = 0;\n  return new Promise(async (resolve) => {\n    const resize = tf.image.resizeBilinear(image, [model?.inputs[0].shape ? model.inputs[0].shape[2] : 0, model?.inputs[0].shape ? model.inputs[0].shape[1] : 0], false);\n    const res = model?.execute(resize) as Tensor;\n    const num = (await res.data())[0];\n    cached[idx] = Math.round(100 * num) / 100;\n    lastCount = count;\n    lastTime = now();\n    tf.dispose([resize, res]);\n    resolve(cached[idx]);\n  });\n}\n", "export const kpt: string[] = [ // used to create part labels\n  'nose',\n  'leftEye',\n  'rightEye',\n  'leftEar',\n  'rightEar',\n  'leftShoulder',\n  'rightShoulder',\n  'leftElbow',\n  'rightElbow',\n  'leftWrist',\n  'rightWrist',\n  'leftHip',\n  'rightHip',\n  'leftKnee',\n  'rightKnee',\n  'leftAnkle',\n  'rightAnkle',\n];\n\nexport const horizontal: string[][] = [ // used to fix left vs right\n  ['leftEye', 'rightEye'],\n  ['leftEar', 'rightEar'],\n  ['leftShoulder', 'rightShoulder'],\n  ['leftElbow', 'rightElbow'],\n  ['leftWrist', 'rightWrist'],\n  ['leftHip', 'rightHip'],\n  ['leftKnee', 'rightKnee'],\n  ['leftAnkle', 'rightAnkle'],\n];\n\nexport const vertical: string[][] = [ // used to remove unlikely keypoint positions\n  ['leftKnee', 'leftShoulder'],\n  ['rightKnee', 'rightShoulder'],\n  ['leftAnkle', 'leftKnee'],\n  ['rightAnkle', 'rightKnee'],\n];\n\nexport const relative: string[][][] = [ // used to match relative body parts\n  [['leftHip', 'rightHip'], ['leftShoulder', 'rightShoulder']],\n  [['leftElbow', 'rightElbow'], ['leftShoulder', 'rightShoulder']],\n];\n\nexport const connected: Record<string, string[]> = { // used to create body outline in annotations\n  leftLeg: ['leftHip', 'leftKnee', 'leftAnkle'],\n  rightLeg: ['rightHip', 'rightKnee', 'rightAnkle'],\n  torso: ['leftShoulder', 'rightShoulder', 'rightHip', 'leftHip', 'leftShoulder'],\n  leftArm: ['leftShoulder', 'leftElbow', 'leftWrist'],\n  rightArm: ['rightShoulder', 'rightElbow', 'rightWrist'],\n  head: [],\n};\n", "import type { BodyKeypoint, BodyResult } from '../result';\nimport * as box from '../util/box';\nimport * as coords from './movenetcoords';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport type { Tensor } from '../tfjs/types';\n\nconst maxJitter = 0.005; // default allowed jitter is within 0.5%\n\nconst cache: {\n  keypoints: BodyKeypoint[],\n  padding: [number, number][];\n} = {\n  keypoints: [],\n  padding: [[0, 0], [0, 0], [0, 0], [0, 0]],\n};\n\nexport function bodyParts(body: BodyResult) { // model sometimes mixes up left vs right keypoints so we fix them\n  for (const pair of coords.horizontal) { // fix body parts left vs right\n    const left = body.keypoints.findIndex((kp) => kp.part === pair[0]);\n    const right = body.keypoints.findIndex((kp) => kp.part === pair[1]);\n    if (body.keypoints[left] && body.keypoints[right]) {\n      if (body.keypoints[left].position[0] < body.keypoints[right].position[0]) {\n        const tmp = body.keypoints[left];\n        body.keypoints[left] = body.keypoints[right];\n        body.keypoints[right] = tmp;\n      }\n    }\n  }\n  for (const pair of coords.vertical) { // remove body parts with improbable vertical position\n    const lower = body.keypoints.findIndex((kp) => (kp && kp.part === pair[0]));\n    const higher = body.keypoints.findIndex((kp) => (kp && kp.part === pair[1]));\n    if (body.keypoints[lower] && body.keypoints[higher]) {\n      if (body.keypoints[lower].position[1] < body.keypoints[higher].position[1]) {\n        body.keypoints.splice(lower, 1);\n      }\n    }\n  }\n  for (const [pair, compare] of coords.relative) { // rearrange body parts according to their relative position\n    const left = body.keypoints.findIndex((kp) => (kp && kp.part === pair[0]));\n    const right = body.keypoints.findIndex((kp) => (kp && kp.part === pair[1]));\n    const leftTo = body.keypoints.findIndex((kp) => (kp && kp.part === compare[0]));\n    const rightTo = body.keypoints.findIndex((kp) => (kp && kp.part === compare[1]));\n    if (!body.keypoints[leftTo] || !body.keypoints[rightTo]) continue; // only if we have both compare points\n    const distanceLeft = body.keypoints[left] ? [\n      Math.abs(body.keypoints[leftTo].position[0] - body.keypoints[left].position[0]),\n      Math.abs(body.keypoints[rightTo].position[0] - body.keypoints[left].position[0]),\n    ] : [0, 0];\n    const distanceRight = body.keypoints[right] ? [\n      Math.abs(body.keypoints[rightTo].position[0] - body.keypoints[right].position[0]),\n      Math.abs(body.keypoints[leftTo].position[0] - body.keypoints[right].position[0]),\n    ] : [0, 0];\n    if (distanceLeft[0] > distanceLeft[1] || distanceRight[0] > distanceRight[1]) { // should flip keypoints\n      const tmp = body.keypoints[left];\n      body.keypoints[left] = body.keypoints[right];\n      body.keypoints[right] = tmp;\n    }\n  }\n}\n\nexport function jitter(keypoints: BodyKeypoint[]): BodyKeypoint[] {\n  for (let i = 0; i < keypoints.length; i++) {\n    if (keypoints[i] && cache.keypoints[i]) {\n      const diff = [Math.abs(keypoints[i].positionRaw[0] - cache.keypoints[i].positionRaw[0]), Math.abs(keypoints[i].positionRaw[1] - cache.keypoints[i].positionRaw[1])];\n      if (diff[0] < maxJitter && diff[1] < maxJitter) {\n        keypoints[i] = cache.keypoints[i]; // below jitter so replace keypoint\n      } else {\n        cache.keypoints[i] = keypoints[i]; // above jitter so update cache\n      }\n    } else {\n      cache.keypoints[i] = keypoints[i]; // cache for keypoint doesnt exist so create it here\n    }\n  }\n  return keypoints;\n}\n\nexport function padInput(input: Tensor, inputSize: number): Tensor {\n  const t: Record<string, Tensor> = {};\n  if (!input?.shape?.[1] || !input?.shape?.[2]) return input;\n  cache.padding = [\n    [0, 0], // dont touch batch\n    [input.shape[2] > input.shape[1] ? Math.trunc((input.shape[2] - input.shape[1]) / 2) : 0, input.shape[2] > input.shape[1] ? Math.trunc((input.shape[2] - input.shape[1]) / 2) : 0], // height before&after\n    [input.shape[1] > input.shape[2] ? Math.trunc((input.shape[1] - input.shape[2]) / 2) : 0, input.shape[1] > input.shape[2] ? Math.trunc((input.shape[1] - input.shape[2]) / 2) : 0], // width before&after\n    [0, 0], // dont touch rbg\n  ];\n  t.pad = tf.pad(input, cache.padding);\n  t.resize = tf.image.resizeBilinear(t.pad, [inputSize, inputSize]);\n  const final = tf.cast(t.resize, 'int32');\n  Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n  return final;\n}\n\nexport function rescaleBody(body: BodyResult, outputSize: [number, number]): BodyResult {\n  body.keypoints = body.keypoints.filter((kpt) => kpt?.position); // filter invalid keypoints\n  for (const kpt of body.keypoints) {\n    kpt.position = [\n      kpt.position[0] * (outputSize[0] + cache.padding[2][0] + cache.padding[2][1]) / outputSize[0] - cache.padding[2][0],\n      kpt.position[1] * (outputSize[1] + cache.padding[1][0] + cache.padding[1][1]) / outputSize[1] - cache.padding[1][0],\n    ];\n    kpt.positionRaw = [\n      kpt.position[0] / outputSize[0], kpt.position[1] / outputSize[1],\n    ];\n  }\n  const rescaledBoxes = box.calc(body.keypoints.map((pt) => pt.position), outputSize);\n  body.box = rescaledBoxes.box;\n  body.boxRaw = rescaledBoxes.boxRaw;\n  return body;\n}\n", "/**\n * MoveNet model implementation\n *\n * Based on: [**MoveNet**](https://blog.tensorflow.org/2021/05/next-generation-pose-detection-with-movenet-and-tensorflowjs.html)\n */\n\nimport { log, now } from '../util/util';\nimport * as box from '../util/box';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as coords from './movenetcoords';\nimport * as fix from './movenetfix';\nimport { loadModel } from '../tfjs/load';\nimport type { BodyKeypoint, BodyResult, BodyLandmark, BodyAnnotation, Box, Point } from '../result';\nimport type { GraphModel, Tensor } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { fakeOps } from '../tfjs/backend';\nimport { env } from '../util/env';\n\nlet model: GraphModel | null;\nlet inputSize = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n// const boxExpandFact = 1.5; // increase to 150%\n\nconst cache: {\n  boxes: Box[], // unused\n  bodies: BodyResult[];\n  last: number,\n} = {\n  boxes: [],\n  bodies: [],\n  last: 0,\n};\n\nexport async function load(config: Config): Promise<GraphModel> {\n  if (env.initial) model = null;\n  if (!model) {\n    fakeOps(['size'], config);\n    model = await loadModel(config.body.modelPath);\n  } else if (config.debug) log('cached model:', model['modelUrl']);\n  inputSize = (model?.['executor'] && model?.inputs?.[0].shape) ? model.inputs[0].shape[2] : 0;\n  if (inputSize < 64) inputSize = 256;\n  return model;\n}\n\nfunction parseSinglePose(res, config, image) {\n  const kpt = res[0][0];\n  const keypoints: BodyKeypoint[] = [];\n  let score = 0;\n  for (let id = 0; id < kpt.length; id++) {\n    score = kpt[id][2];\n    if (score > config.body.minConfidence) {\n      const positionRaw: Point = [kpt[id][1], kpt[id][0]];\n      keypoints.push({\n        score: Math.round(100 * score) / 100,\n        part: coords.kpt[id] as BodyLandmark,\n        positionRaw,\n        position: [ // normalized to input image size\n          Math.round((image.shape[2] || 0) * positionRaw[0]),\n          Math.round((image.shape[1] || 0) * positionRaw[1]),\n        ],\n      });\n    }\n  }\n  score = keypoints.reduce((prev, curr) => (curr.score > prev ? curr.score : prev), 0);\n  const bodies: BodyResult[] = [];\n  const newBox = box.calc(keypoints.map((pt) => pt.position), [image.shape[2], image.shape[1]]);\n  const annotations: Record<string, Point[][]> = {};\n  for (const [name, indexes] of Object.entries(coords.connected)) {\n    const pt: Point[][] = [];\n    for (let i = 0; i < indexes.length - 1; i++) {\n      const pt0 = keypoints.find((kp) => kp.part === indexes[i]);\n      const pt1 = keypoints.find((kp) => kp.part === indexes[i + 1]);\n      if (pt0 && pt1 && pt0.score > (config.body.minConfidence || 0) && pt1.score > (config.body.minConfidence || 0)) pt.push([pt0.position, pt1.position]);\n    }\n    annotations[name] = pt;\n  }\n  const body: BodyResult = { id: 0, score, box: newBox.box, boxRaw: newBox.boxRaw, keypoints, annotations };\n  fix.bodyParts(body);\n  bodies.push(body);\n  return bodies;\n}\n\nfunction parseMultiPose(res, config, image) {\n  const bodies: BodyResult[] = [];\n  for (let id = 0; id < res[0].length; id++) {\n    const kpt = res[0][id];\n    const totalScore = Math.round(100 * kpt[51 + 4]) / 100;\n    if (totalScore > config.body.minConfidence) {\n      const keypoints: BodyKeypoint[] = [];\n      for (let i = 0; i < 17; i++) {\n        const score = kpt[3 * i + 2];\n        if (score > config.body.minConfidence) {\n          const positionRaw: Point = [kpt[3 * i + 1], kpt[3 * i + 0]];\n          keypoints.push({\n            part: coords.kpt[i] as BodyLandmark,\n            score: Math.round(100 * score) / 100,\n            positionRaw,\n            position: [Math.round((image.shape[2] || 0) * positionRaw[0]), Math.round((image.shape[1] || 0) * positionRaw[1])],\n          });\n        }\n      }\n      const newBox = box.calc(keypoints.map((pt) => pt.position), [image.shape[2], image.shape[1]]);\n      // movenet-multipose has built-in box details\n      // const boxRaw: Box = [kpt[51 + 1], kpt[51 + 0], kpt[51 + 3] - kpt[51 + 1], kpt[51 + 2] - kpt[51 + 0]];\n      // const box: Box = [Math.trunc(boxRaw[0] * (image.shape[2] || 0)), Math.trunc(boxRaw[1] * (image.shape[1] || 0)), Math.trunc(boxRaw[2] * (image.shape[2] || 0)), Math.trunc(boxRaw[3] * (image.shape[1] || 0))];\n      const annotations: Record<BodyAnnotation, Point[][]> = {} as Record<BodyAnnotation, Point[][]>;\n      for (const [name, indexes] of Object.entries(coords.connected)) {\n        const pt: Point[][] = [];\n        for (let i = 0; i < indexes.length - 1; i++) {\n          const pt0 = keypoints.find((kp) => kp.part === indexes[i]);\n          const pt1 = keypoints.find((kp) => kp.part === indexes[i + 1]);\n          if (pt0 && pt1 && pt0.score > (config.body.minConfidence || 0) && pt1.score > (config.body.minConfidence || 0)) pt.push([pt0.position, pt1.position]);\n        }\n        annotations[name] = pt;\n      }\n      const body: BodyResult = { id, score: totalScore, box: newBox.box, boxRaw: newBox.boxRaw, keypoints: [...keypoints], annotations };\n      fix.bodyParts(body);\n      bodies.push(body);\n    }\n  }\n  bodies.sort((a, b) => b.score - a.score);\n  if (bodies.length > config.body.maxDetected) bodies.length = config.body.maxDetected;\n  return bodies;\n}\n\nexport async function predict(input: Tensor, config: Config): Promise<BodyResult[]> {\n  if (!model?.['executor'] || !model?.inputs?.[0].shape) return []; // something is wrong with the model\n  if (!config.skipAllowed) cache.boxes.length = 0; // allowed to use cache or not\n  skipped++; // increment skip frames\n  const skipTime = (config.body.skipTime || 0) > (now() - cache.last);\n  const skipFrame = skipped < (config.body.skipFrames || 0);\n  if (config.skipAllowed && skipTime && skipFrame) {\n    return cache.bodies; // return cached results without running anything\n  }\n  return new Promise(async (resolve) => {\n    const t: Record<string, Tensor> = {};\n    skipped = 0;\n    // run detection on squared input and cached boxes\n    /*\n    cache.bodies = []; // reset bodies result\n    if (cache.boxes.length >= (config.body.maxDetected || 0)) { // if we have enough cached boxes run detection using cache\n      for (let i = 0; i < cache.boxes.length; i++) { // run detection based on cached boxes\n        t.crop = tf.image.cropAndResize(input, [cache.boxes[i]], [0], [inputSize, inputSize], 'bilinear');\n        t.cast = tf.cast(t.crop, 'int32');\n        // t.input = prepareImage(input);\n        t.res = model?.execute(t.cast) as Tensor;\n        const res = await t.res.array();\n        const newBodies = (t.res.shape[2] === 17) ? await parseSinglePose(res, config, input, cache.boxes[i]) : await parseMultiPose(res, config, input, cache.boxes[i]);\n        cache.bodies = cache.bodies.concat(newBodies);\n        Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n      }\n    }\n    if (cache.bodies.length !== config.body.maxDetected) { // did not find enough bodies based on cached boxes so run detection on full frame\n      t.input = prepareImage(input);\n      t.res = model?.execute(t.input) as Tensor;\n      const res = await t.res.array();\n      cache.bodies = (t.res.shape[2] === 17) ? await parseSinglePose(res, config, input, [0, 0, 1, 1]) : await parseMultiPose(res, config, input, [0, 0, 1, 1]);\n      for (const body of cache.bodies) rescaleBody(body, [input.shape[2] || 1, input.shape[1] || 1]);\n      Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n    }\n    cache.boxes.length = 0; // reset cache\n    for (let i = 0; i < cache.bodies.length; i++) {\n      if (cache.bodies[i].keypoints.length > (coords.kpt.length / 2)) { // only update cache if we detected at least half keypoints\n        const scaledBox = box.scale(cache.bodies[i].boxRaw, boxExpandFact);\n        const cropBox = box.crop(scaledBox);\n        cache.boxes.push(cropBox);\n      }\n    }\n    */\n\n    // run detection on squared input and no cached boxes\n    t.input = fix.padInput(input, inputSize);\n    t.res = model?.execute(t.input) as Tensor;\n    cache.last = now();\n    const res = await t.res.array();\n    cache.bodies = (t.res.shape[2] === 17)\n      ? parseSinglePose(res, config, input)\n      : parseMultiPose(res, config, input);\n    for (const body of cache.bodies) {\n      fix.rescaleBody(body, [input.shape[2] || 1, input.shape[1] || 1]);\n      fix.jitter(body.keypoints);\n    }\n    Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n\n    resolve(cache.bodies);\n  });\n}\n", "/**\n * NanoDet object detection model implementation\n *\n * Based on: [**MB3-CenterNet**](https://github.com/610265158/mobilenetv3_centernet)\n */\n\nimport { log, now } from '../util/util';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { loadModel } from '../tfjs/load';\nimport { constants } from '../tfjs/constants';\nimport { labels } from './labels';\nimport type { ObjectResult, ObjectType, Box } from '../result';\nimport type { GraphModel, Tensor } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\n\nlet model: GraphModel;\nlet last: ObjectResult[] = [];\nlet lastTime = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\nlet inputSize = 0;\n\nconst scaleBox = 2.5; // increase box size\n\nexport async function load(config: Config): Promise<GraphModel> {\n  if (!model || env.initial) {\n    model = await loadModel(config.object.modelPath);\n    const inputs = model?.['executor'] ? Object.values(model.modelSignature['inputs']) : undefined;\n    inputSize = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 416;\n  } else if (config.debug) log('cached model:', model['modelUrl']);\n  return model;\n}\n\nasync function process(res: Tensor[], outputShape: [number, number], config: Config) {\n  let id = 0;\n  let results: ObjectResult[] = [];\n  const size = inputSize;\n  for (const strideSize of [1, 2, 4]) { // try each stride size as it detects large/medium/small objects\n    // find scores, boxes, classes\n    const baseSize = strideSize * 13; // 13x13=169, 26x26=676, 52x52=2704\n    // find boxes and scores output depending on stride\n    const scoresT = tf.squeeze(res.find((a: Tensor) => (a.shape[1] === (baseSize ** 2) && (a.shape[2] || 0) === labels.length)));\n    const scores = await scoresT.array(); // optionally use exponential scores or just as-is\n    const featuresT = tf.squeeze(res.find((a: Tensor) => (a.shape[1] === (baseSize ** 2) && (a.shape[2] || 0) < labels.length)));\n    const boxesMaxT = featuresT.reshape([-1, 4, featuresT.shape[1] / 4]); // reshape [output] to [4, output / 4] where number is number of different features inside each stride\n    const boxIdxT = boxesMaxT.argMax(2); // what we need is indexes of features with highest scores, not values itself\n    const boxIdx = await boxIdxT.array(); // what we need is indexes of features with highest scores, not values itself\n    for (let i = 0; i < scoresT.shape[0]; i++) { // total strides (x * y matrix)\n      for (let j = 0; j < scoresT.shape[1]; j++) { // one score for each class\n        const score = scores[i][j]; // get score for current position\n        if (score > (config.object.minConfidence || 0) && j !== 61) {\n          const cx = (0.5 + Math.trunc(i % baseSize)) / baseSize; // center.x normalized to range 0..1\n          const cy = (0.5 + Math.trunc(i / baseSize)) / baseSize; // center.y normalized to range 0..1\n          const boxOffset = boxIdx[i].map((a: number) => a * (baseSize / strideSize / (size))); // just grab indexes of features with highest scores\n          const [x, y] = [\n            cx - (scaleBox / strideSize * boxOffset[0]),\n            cy - (scaleBox / strideSize * boxOffset[1]),\n          ];\n          const [w, h] = [\n            cx + (scaleBox / strideSize * boxOffset[2]) - x,\n            cy + (scaleBox / strideSize * boxOffset[3]) - y,\n          ];\n          let boxRaw: Box = [x, y, w, h]; // results normalized to range 0..1\n          boxRaw = boxRaw.map((a) => Math.max(0, Math.min(a, 1))) as Box; // fix out-of-bounds coords\n          const box = [ // results normalized to input image pixels\n            boxRaw[0] * outputShape[0],\n            boxRaw[1] * outputShape[1],\n            boxRaw[2] * outputShape[0],\n            boxRaw[3] * outputShape[1],\n          ];\n          const result = {\n            id: id++,\n            // strideSize,\n            score: Math.round(100 * score) / 100,\n            class: j + 1,\n            label: labels[j].label as ObjectType,\n            // center: [Math.trunc(outputShape[0] * cx), Math.trunc(outputShape[1] * cy)],\n            // centerRaw: [cx, cy],\n            box: box.map((a) => Math.trunc(a)) as Box,\n            boxRaw,\n          };\n          results.push(result);\n        }\n      }\n    }\n    tf.dispose([scoresT, featuresT, boxesMaxT, boxIdxT]);\n  }\n\n  // normally nms is run on raw results, but since boxes need to be calculated this way we skip calulcation of\n  // unnecessary boxes and run nms only on good candidates (basically it just does IOU analysis as scores are already filtered)\n  const nmsBoxes = results.map((a) => [a.boxRaw[1], a.boxRaw[0], a.boxRaw[3], a.boxRaw[2]]); // switches coordinates from x,y to y,x as expected by tf.nms\n  const nmsScores = results.map((a) => a.score);\n  let nmsIdx: number[] = [];\n  if (nmsBoxes && nmsBoxes.length > 0) {\n    const nms = await tf.image.nonMaxSuppressionAsync(nmsBoxes, nmsScores, config.object.maxDetected, config.object.iouThreshold, config.object.minConfidence);\n    nmsIdx = await nms.data();\n    tf.dispose(nms);\n  }\n\n  // filter & sort results\n  results = results\n    .filter((_val, idx) => nmsIdx.includes(idx))\n    .sort((a, b) => (b.score - a.score));\n\n  return results;\n}\n\nexport async function predict(image: Tensor, config: Config): Promise<ObjectResult[]> {\n  if (!model?.['executor']) return [];\n  const skipTime = (config.object.skipTime || 0) > (now() - lastTime);\n  const skipFrame = skipped < (config.object.skipFrames || 0);\n  if (config.skipAllowed && skipTime && skipFrame && (last.length > 0)) {\n    skipped++;\n    return last;\n  }\n  skipped = 0;\n  if (!env.kernels.includes('mod') || !env.kernels.includes('sparsetodense')) return last;\n  return new Promise(async (resolve) => {\n    const outputSize = [image.shape[2] || 0, image.shape[1] || 0];\n    const resizeT = tf.image.resizeBilinear(image, [inputSize, inputSize], false);\n    const normT = tf.div(resizeT, constants.tf255);\n    const transposeT = tf.transpose(normT, [0, 3, 1, 2]);\n\n    let objectT;\n    if (config.object.enabled) objectT = model.execute(transposeT);\n    lastTime = now();\n\n    const obj = await process(objectT as Tensor[], outputSize as [number, number], config);\n    last = obj;\n    tf.dispose([resizeT, normT, transposeT, ...objectT]);\n    resolve(obj);\n  });\n}\n", "/**\n * PoseNet body detection model implementation constants\n * See `posenet.ts` for entry point\n */\n\nimport type { Point, BodyResult, BodyAnnotation, BodyLandmark } from '../result';\n\nexport const partNames = [\n  'nose', 'leftEye', 'rightEye', 'leftEar', 'rightEar', 'leftShoulder',\n  'rightShoulder', 'leftElbow', 'rightElbow', 'leftWrist', 'rightWrist',\n  'leftHip', 'rightHip', 'leftKnee', 'rightKnee', 'leftAnkle', 'rightAnkle',\n];\n\nexport const count = partNames.length; // 17 keypoints\n\nexport const partIds = partNames.reduce((result, jointName, i) => {\n  result[jointName] = i;\n  return result;\n}, {});\n\nconst connectedPartNames = [\n  ['leftHip', 'leftShoulder'], ['leftElbow', 'leftShoulder'],\n  ['leftElbow', 'leftWrist'], ['leftHip', 'leftKnee'],\n  ['leftKnee', 'leftAnkle'], ['rightHip', 'rightShoulder'],\n  ['rightElbow', 'rightShoulder'], ['rightElbow', 'rightWrist'],\n  ['rightHip', 'rightKnee'], ['rightKnee', 'rightAnkle'],\n  ['leftShoulder', 'rightShoulder'], ['leftHip', 'rightHip'],\n];\nexport const connectedPartIndices = connectedPartNames.map(([jointNameA, jointNameB]) => ([partIds[jointNameA], partIds[jointNameB]]));\n\nexport const poseChain = [\n  ['nose', 'leftEye'], ['leftEye', 'leftEar'], ['nose', 'rightEye'],\n  ['rightEye', 'rightEar'], ['nose', 'leftShoulder'],\n  ['leftShoulder', 'leftElbow'], ['leftElbow', 'leftWrist'],\n  ['leftShoulder', 'leftHip'], ['leftHip', 'leftKnee'],\n  ['leftKnee', 'leftAnkle'], ['nose', 'rightShoulder'],\n  ['rightShoulder', 'rightElbow'], ['rightElbow', 'rightWrist'],\n  ['rightShoulder', 'rightHip'], ['rightHip', 'rightKnee'],\n  ['rightKnee', 'rightAnkle'],\n];\n\nexport function eitherPointDoesntMeetConfidence(a: number, b: number, minConfidence: number) {\n  return (a < minConfidence || b < minConfidence);\n}\n\nexport function getAdjacentKeyPoints(keypoints, minConfidence: number) {\n  return connectedPartIndices.reduce((result, [leftJoint, rightJoint]) => {\n    if (eitherPointDoesntMeetConfidence(keypoints[leftJoint].score, keypoints[rightJoint].score, minConfidence)) {\n      return result;\n    }\n    result.push([keypoints[leftJoint], keypoints[rightJoint]]);\n    return result;\n  }, []);\n}\n\nexport function getBoundingBox(keypoints): [number, number, number, number] {\n  const coord = keypoints.reduce(({ maxX, maxY, minX, minY }, { position: { x, y } }) => ({\n    maxX: Math.max(maxX, x),\n    maxY: Math.max(maxY, y),\n    minX: Math.min(minX, x),\n    minY: Math.min(minY, y),\n  }), {\n    maxX: Number.NEGATIVE_INFINITY,\n    maxY: Number.NEGATIVE_INFINITY,\n    minX: Number.POSITIVE_INFINITY,\n    minY: Number.POSITIVE_INFINITY,\n  });\n  return [coord.minX, coord.minY, coord.maxX - coord.minX, coord.maxY - coord.minY];\n}\n\nexport function scalePoses(poses, [height, width], [inputResolutionHeight, inputResolutionWidth]): BodyResult[] {\n  const scaleY = height / inputResolutionHeight;\n  const scaleX = width / inputResolutionWidth;\n  const scalePose = (pose, i): BodyResult => ({\n    id: i,\n    score: pose.score,\n    boxRaw: [pose.box[0] / inputResolutionWidth, pose.box[1] / inputResolutionHeight, pose.box[2] / inputResolutionWidth, pose.box[3] / inputResolutionHeight],\n    box: [Math.trunc(pose.box[0] * scaleX), Math.trunc(pose.box[1] * scaleY), Math.trunc(pose.box[2] * scaleX), Math.trunc(pose.box[3] * scaleY)],\n    keypoints: pose.keypoints.map(({ score, part, position }) => ({\n      score: score as number,\n      part: part as BodyLandmark,\n      position: [Math.trunc(position.x * scaleX), Math.trunc(position.y * scaleY)] as Point,\n      positionRaw: [position.x / inputResolutionHeight, position.y / inputResolutionHeight] as Point,\n    })),\n    annotations: {} as Record<BodyAnnotation, Point[][]>,\n  });\n  const scaledPoses = poses.map((pose, i) => scalePose(pose, i));\n  return scaledPoses;\n}\n\n// algorithm based on Coursera Lecture from Algorithms, Part 1: https://www.coursera.org/learn/algorithms-part1/lecture/ZjoSM/heapsort\nexport class MaxHeap {\n  priorityQueue: unknown[]; // don't touch\n  numberOfElements: number;\n  getElementValue: unknown; // function call\n\n  constructor(maxSize, getElementValue) {\n    this.priorityQueue = new Array(maxSize);\n    this.numberOfElements = -1;\n    this.getElementValue = getElementValue;\n  }\n\n  enqueue(x) {\n    this.priorityQueue[++this.numberOfElements] = x;\n    this.swim(this.numberOfElements);\n  }\n\n  dequeue() {\n    const max = this.priorityQueue[0];\n    this.exchange(0, this.numberOfElements--);\n    this.sink(0);\n    this.priorityQueue[this.numberOfElements + 1] = null;\n    return max;\n  }\n\n  empty() { return this.numberOfElements === -1; }\n\n  size() { return this.numberOfElements + 1; }\n\n  all() { return this.priorityQueue.slice(0, this.numberOfElements + 1); }\n\n  max() { return this.priorityQueue[0]; }\n\n  swim(k) {\n    while (k > 0 && this.less(Math.floor(k / 2), k)) {\n      this.exchange(k, Math.floor(k / 2));\n      k = Math.floor(k / 2);\n    }\n  }\n\n  sink(k) {\n    while (2 * k <= this.numberOfElements) {\n      let j = 2 * k;\n      if (j < this.numberOfElements && this.less(j, j + 1)) j++;\n      if (!this.less(k, j)) break;\n      this.exchange(k, j);\n      k = j;\n    }\n  }\n\n  getValueAt(i) {\n    // @ts-ignore getter is of unknown type\n    return this.getElementValue(this.priorityQueue[i]);\n  }\n\n  less(i, j) {\n    return this.getValueAt(i) < this.getValueAt(j);\n  }\n\n  exchange(i, j) {\n    const t = this.priorityQueue[i];\n    this.priorityQueue[i] = this.priorityQueue[j];\n    this.priorityQueue[j] = t;\n  }\n}\n\nexport function getOffsetPoint(y, x, keypoint: number, offsets) {\n  return {\n    y: offsets.get(y, x, keypoint),\n    x: offsets.get(y, x, keypoint + count),\n  };\n}\n\nexport function getImageCoords(part, outputStride: number, offsets) {\n  const { heatmapY, heatmapX, id: keypoint } = part;\n  const { y, x } = getOffsetPoint(heatmapY, heatmapX, keypoint, offsets);\n  return {\n    x: part.heatmapX * outputStride + x,\n    y: part.heatmapY * outputStride + y,\n  };\n}\n\nexport function fillArray(element, size) {\n  const result = new Array(size);\n  for (let i = 0; i < size; i++) {\n    result[i] = element;\n  }\n  return result;\n}\n\nexport function clamp(a, min, max) {\n  if (a < min) return min;\n  if (a > max) return max;\n  return a;\n}\n\nexport function squaredDistance(y1, x1, y2, x2) {\n  const dy = y2 - y1;\n  const dx = x2 - x1;\n  return dy * dy + dx * dx;\n}\n\nexport function addVectors(a: { x: number, y: number }, b: { x: number, y: number }) {\n  return { x: a.x + b.x, y: a.y + b.y };\n}\n\nexport function clampVector(a, min, max) {\n  return { y: clamp(a.y, min, max), x: clamp(a.x, min, max) };\n}\n", "/**\n * PoseNet body detection model implementation\n *\n * Based on: [**PoseNet**](https://medium.com/tensorflow/real-time-human-pose-estimation-in-the-browser-with-tensorflow-js-7dd0bc881cd5)\n */\n\nimport { log } from '../util/util';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { loadModel } from '../tfjs/load';\nimport type { BodyResult, BodyLandmark, Box } from '../result';\nimport type { Tensor, GraphModel } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\nimport * as utils from './posenetutils';\n\nlet model: GraphModel;\nconst poseNetOutputs = ['MobilenetV1/offset_2/BiasAdd'/* offsets */, 'MobilenetV1/heatmap_2/BiasAdd'/* heatmapScores */, 'MobilenetV1/displacement_fwd_2/BiasAdd'/* displacementFwd */, 'MobilenetV1/displacement_bwd_2/BiasAdd'/* displacementBwd */];\nconst localMaximumRadius = 1;\nconst outputStride = 16;\nconst squaredNmsRadius = 50 ** 2;\n\nfunction traverse(edgeId: number, sourceKeypoint, targetId, scores, offsets, displacements, offsetRefineStep = 2) {\n  const getDisplacement = (point) => ({\n    y: displacements.get(point.y, point.x, edgeId),\n    x: displacements.get(point.y, point.x, (displacements.shape[2] / 2) + edgeId),\n  });\n  const getStridedIndexNearPoint = (point, height, width) => ({\n    y: utils.clamp(Math.round(point.y / outputStride), 0, height - 1),\n    x: utils.clamp(Math.round(point.x / outputStride), 0, width - 1),\n  });\n\n  const [height, width] = scores.shape;\n  // Nearest neighbor interpolation for the source->target displacements.\n  const sourceKeypointIndices = getStridedIndexNearPoint(sourceKeypoint.position, height, width);\n  const displacement = getDisplacement(sourceKeypointIndices);\n  const displacedPoint = utils.addVectors(sourceKeypoint.position, displacement);\n  let targetKeypoint = displacedPoint;\n  for (let i = 0; i < offsetRefineStep; i++) {\n    const targetKeypointIndices = getStridedIndexNearPoint(targetKeypoint, height, width);\n    const offsetPoint = utils.getOffsetPoint(targetKeypointIndices.y, targetKeypointIndices.x, targetId, offsets);\n    targetKeypoint = utils.addVectors(\n      { x: targetKeypointIndices.x * outputStride, y: targetKeypointIndices.y * outputStride },\n      { x: offsetPoint.x, y: offsetPoint.y },\n    );\n  }\n  const targetKeyPointIndices = getStridedIndexNearPoint(targetKeypoint, height, width);\n  const score = scores.get(targetKeyPointIndices.y, targetKeyPointIndices.x, targetId);\n  return { position: targetKeypoint, part: utils.partNames[targetId], score };\n}\n\nexport function decodePose(root, scores, offsets, displacementsFwd, displacementsBwd) {\n  const tuples = utils.poseChain.map(([parentJoinName, childJoinName]) => ([utils.partIds[parentJoinName], utils.partIds[childJoinName]]));\n  const edgesFwd = tuples.map(([, childJointId]) => childJointId);\n  const edgesBwd = tuples.map(([parentJointId]) => parentJointId);\n  const numParts = scores.shape[2]; // [21,21,17]\n  const numEdges = edgesFwd.length;\n  const keypoints = new Array(numParts);\n  // Start a new detection instance at the position of the root.\n  const rootPoint = utils.getImageCoords(root.part, outputStride, offsets);\n  keypoints[root.part.id] = {\n    score: root.score,\n    part: utils.partNames[root.part.id] as BodyLandmark,\n    position: rootPoint,\n  };\n  // Decode the part positions upwards in the tree, following the backward displacements.\n  for (let edge = numEdges - 1; edge >= 0; --edge) {\n    const sourceId = edgesFwd[edge];\n    const targetId = edgesBwd[edge];\n    if (keypoints[sourceId] && !keypoints[targetId]) {\n      keypoints[targetId] = traverse(edge, keypoints[sourceId], targetId, scores, offsets, displacementsBwd);\n    }\n  }\n  // Decode the part positions downwards in the tree, following the forward displacements.\n  for (let edge = 0; edge < numEdges; ++edge) {\n    const sourceId = edgesBwd[edge];\n    const targetId = edgesFwd[edge];\n    if (keypoints[sourceId] && !keypoints[targetId]) {\n      keypoints[targetId] = traverse(edge, keypoints[sourceId], targetId, scores, offsets, displacementsFwd);\n    }\n  }\n  return keypoints;\n}\n\nfunction scoreIsMaximumInLocalWindow(keypointId, score: number, heatmapY: number, heatmapX: number, scores) {\n  const [height, width]: [number, number] = scores.shape;\n  let localMaximum = true;\n  const yStart = Math.max(heatmapY - localMaximumRadius, 0);\n  const yEnd = Math.min(heatmapY + localMaximumRadius + 1, height);\n  for (let yCurrent = yStart; yCurrent < yEnd; ++yCurrent) {\n    const xStart = Math.max(heatmapX - localMaximumRadius, 0);\n    const xEnd = Math.min(heatmapX + localMaximumRadius + 1, width);\n    for (let xCurrent = xStart; xCurrent < xEnd; ++xCurrent) {\n      if (scores.get(yCurrent, xCurrent, keypointId) > score) {\n        localMaximum = false;\n        break;\n      }\n    }\n    if (!localMaximum) break;\n  }\n  return localMaximum;\n}\n\nexport function buildPartWithScoreQueue(minConfidence, scores) {\n  const [height, width, numKeypoints] = scores.shape;\n  const queue = new utils.MaxHeap(height * width * numKeypoints, ({ score }) => score);\n  for (let heatmapY = 0; heatmapY < height; ++heatmapY) {\n    for (let heatmapX = 0; heatmapX < width; ++heatmapX) {\n      for (let keypointId = 0; keypointId < numKeypoints; ++keypointId) {\n        const score = scores.get(heatmapY, heatmapX, keypointId);\n        // Only consider parts with score greater or equal to threshold as root candidates.\n        if (score < minConfidence) continue;\n        // Only consider keypoints whose score is maximum in a local window.\n        if (scoreIsMaximumInLocalWindow(keypointId, score, heatmapY, heatmapX, scores)) queue.enqueue({ score, part: { heatmapY, heatmapX, id: keypointId } });\n      }\n    }\n  }\n  return queue;\n}\n\nfunction withinRadius(poses, { x, y }, keypointId) {\n  return poses.some(({ keypoints }) => {\n    const correspondingKeypoint = keypoints[keypointId]?.position;\n    if (!correspondingKeypoint) return false;\n    return utils.squaredDistance(y, x, correspondingKeypoint.y, correspondingKeypoint.x) <= squaredNmsRadius;\n  });\n}\n\nfunction getInstanceScore(existingPoses, keypoints) {\n  const notOverlappedKeypointScores = keypoints.reduce((result, { position, score }, keypointId) => {\n    if (!withinRadius(existingPoses, position, keypointId)) result += score;\n    return result;\n  }, 0.0);\n  return notOverlappedKeypointScores / keypoints.length;\n}\n\nexport function decode(offsets, scores, displacementsFwd, displacementsBwd, maxDetected, minConfidence) {\n  const poses: { keypoints, box: Box, score: number }[] = [];\n  const queue = buildPartWithScoreQueue(minConfidence, scores);\n  // Generate at most maxDetected object instances per image in decreasing root part score order.\n  while (poses.length < maxDetected && !queue.empty()) {\n    // The top element in the queue is the next root candidate.\n    const root = queue.dequeue();\n    // Part-based non-maximum suppression: We reject a root candidate if it is within a disk of `nmsRadius` pixels from the corresponding part of a previously detected instance.\n    // @ts-ignore this one is tree walk\n    const rootImageCoords = utils.getImageCoords(root.part, outputStride, offsets);\n    // @ts-ignore this one is tree walk\n    if (withinRadius(poses, rootImageCoords, root.part.id)) continue;\n    // Else start a new detection instance at the position of the root.\n    let keypoints = decodePose(root, scores, offsets, displacementsFwd, displacementsBwd);\n    keypoints = keypoints.filter((a) => a.score > minConfidence);\n    const score = getInstanceScore(poses, keypoints);\n    const box = utils.getBoundingBox(keypoints);\n    if (score > minConfidence) poses.push({ keypoints, box, score: Math.round(100 * score) / 100 });\n  }\n  return poses;\n}\n\nexport async function predict(input: Tensor, config: Config): Promise<BodyResult[]> {\n  /** posenet is mostly obsolete\n   * caching is not implemented\n   */\n  if (!model?.['executor']) return [];\n  const res = tf.tidy(() => {\n    if (!model.inputs[0].shape) return [];\n    const resized = tf.image.resizeBilinear(input, [model.inputs[0].shape[2], model.inputs[0].shape[1]]);\n    const normalized = tf.sub(tf.div(tf.cast(resized, 'float32'), 127.5), 1.0);\n    const results: Tensor[] = model.execute(normalized, poseNetOutputs) as Tensor[];\n    const results3d = results.map((y) => tf.squeeze(y, [0]));\n    results3d[1] = tf.sigmoid(results3d[1]); // apply sigmoid on scores\n    return results3d;\n  });\n\n  const buffers = await Promise.all(res.map((tensor: Tensor) => tensor.buffer()));\n  for (const t of res) tf.dispose(t);\n\n  const decoded = decode(buffers[0], buffers[1], buffers[2], buffers[3], config.body.maxDetected, config.body.minConfidence);\n  if (!model.inputs[0].shape) return [];\n  const scaled = utils.scalePoses(decoded, [input.shape[1], input.shape[2]], [model.inputs[0].shape[2], model.inputs[0].shape[1]]);\n  return scaled;\n}\n\nexport async function load(config: Config): Promise<GraphModel> {\n  if (!model || env.initial) model = await loadModel(config.body.modelPath);\n  else if (config.debug) log('cached model:', model['modelUrl']);\n  return model;\n}\n", "/**\n * Image segmentation for body detection model\n *\n * Based on:\n * - [**MediaPipe Meet**](https://drive.google.com/file/d/1lnP1bRi9CSqQQXUHa13159vLELYDgDu0/preview)\n * - [**MediaPipe Selfie**](https://drive.google.com/file/d/1dCfozqknMa068vVsO2j_1FgZkW_e3VWv/preview)\n */\n\nimport { log } from '../util/util';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { loadModel } from '../tfjs/load';\nimport * as image from '../image/image';\nimport { constants } from '../tfjs/constants';\nimport type { GraphModel, Tensor } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\nimport type { Input, AnyCanvas } from '../exports';\n\nlet model: GraphModel;\nlet busy = false;\n\nexport async function load(config: Config): Promise<GraphModel> {\n  if (!model || env.initial) model = await loadModel(config.segmentation.modelPath);\n  else if (config.debug) log('cached model:', model['modelUrl']);\n  return model;\n}\n\nexport async function process(input: Input, background: Input | undefined, config: Config)\n: Promise<{ data: number[] | Tensor, canvas: AnyCanvas | null, alpha: AnyCanvas | null }> {\n  if (busy) return { data: [], canvas: null, alpha: null };\n  busy = true;\n  if (!model) await load(config);\n  const inputImage = await image.process(input, config);\n  const width = inputImage.tensor?.shape[2] || 0;\n  const height = inputImage.tensor?.shape[1] || 0;\n  if (!inputImage.tensor) return { data: [], canvas: null, alpha: null };\n  const t: Record<string, Tensor> = {};\n\n  t.resize = tf.image.resizeBilinear(inputImage.tensor, [model.inputs[0].shape ? model.inputs[0].shape[1] : 0, model.inputs[0].shape ? model.inputs[0].shape[2] : 0], false);\n  tf.dispose(inputImage.tensor);\n  t.norm = tf.div(t.resize, constants.tf255);\n  t.res = model.execute(t.norm) as Tensor;\n\n  t.squeeze = tf.squeeze(t.res, 0); // meet.shape:[1,256,256,1], selfie.shape:[1,144,256,2]\n  if (t.squeeze.shape[2] === 2) {\n    t.softmax = tf.softmax(t.squeeze); // model meet has two channels for fg and bg\n    [t.bg, t.fg] = tf.unstack(t.softmax, 2);\n    t.expand = tf.expandDims(t.fg, 2);\n    t.pad = tf.expandDims(t.expand, 0);\n    t.crop = tf.image.cropAndResize(t.pad, [[0, 0, 0.5, 0.5]], [0], [width, height]);\n    // running sofmax before unstack creates 2x2 matrix so we only take upper-left quadrant\n    // otherwise run softmax after unstack and use standard resize\n    // resizeOutput = tf.image.resizeBilinear(expand, [input.tensor?.shape[1], input.tensor?.shape[2]]);\n    t.data = tf.squeeze(t.crop, 0);\n  } else {\n    t.data = tf.image.resizeBilinear(t.squeeze, [height, width]); // model selfie has a single channel that we can use directly\n  }\n  const data = Array.from(await t.data.data());\n\n  if (env.node && !env.Canvas && (typeof ImageData === 'undefined')) {\n    if (config.debug) log('canvas support missing');\n    Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n    return { data, canvas: null, alpha: null }; // running in nodejs so return alpha array as-is\n  }\n\n  const alphaCanvas = image.canvas(width, height);\n  if (tf.browser) await tf.browser.toPixels(t.data, alphaCanvas);\n  const alphaCtx = alphaCanvas.getContext('2d') as CanvasRenderingContext2D;\n  if (config.segmentation.blur && config.segmentation.blur > 0) alphaCtx.filter = `blur(${config.segmentation.blur}px)`; // use css filter for bluring, can be done with gaussian blur manually instead\n  const alphaData = alphaCtx.getImageData(0, 0, width, height);\n\n  const compositeCanvas = image.canvas(width, height);\n  const compositeCtx = compositeCanvas.getContext('2d') as CanvasRenderingContext2D;\n  if (inputImage.canvas) compositeCtx.drawImage(inputImage.canvas, 0, 0);\n  compositeCtx.globalCompositeOperation = 'darken'; // https://developer.mozilla.org/en-US/docs/Web/API/CanvasRenderingContext2D/globalCompositeOperation // best options are: darken, color-burn, multiply\n  if (config.segmentation.blur && config.segmentation.blur > 0) compositeCtx.filter = `blur(${config.segmentation.blur}px)`; // use css filter for bluring, can be done with gaussian blur manually instead\n  compositeCtx.drawImage(alphaCanvas, 0, 0);\n  compositeCtx.globalCompositeOperation = 'source-over'; // reset composite operation\n  compositeCtx.filter = 'none'; // reset css filter\n  const compositeData = compositeCtx.getImageData(0, 0, width, height);\n  for (let i = 0; i < width * height; i++) compositeData.data[4 * i + 3] = alphaData.data[4 * i + 0]; // copy original alpha value to new composite canvas\n  compositeCtx.putImageData(compositeData, 0, 0);\n\n  let mergedCanvas: AnyCanvas | null = null;\n  if (background && compositeCanvas) { // draw background with segmentation as overlay if background is present\n    mergedCanvas = image.canvas(width, height);\n    const bgImage = await image.process(background, config);\n    tf.dispose(bgImage.tensor);\n    const ctxMerge = mergedCanvas.getContext('2d') as CanvasRenderingContext2D;\n    ctxMerge.drawImage(bgImage.canvas as HTMLCanvasElement, 0, 0, mergedCanvas.width, mergedCanvas.height);\n    ctxMerge.drawImage(compositeCanvas, 0, 0);\n  }\n\n  Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n  busy = false;\n  // return { data, canvas: mergedCanvas || compositeCanvas, alpha: alphaCanvas };\n  return { data, canvas: compositeCanvas, alpha: alphaCanvas };\n}\n", "import { log, join } from '../util/util';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport type { GraphModel } from './types';\nimport type { Config } from '../config';\nimport * as modelsDefs from '../../models/models.json';\nimport { validateModel } from '../models';\n\nconst options = {\n  cacheModels: true,\n  cacheSupported: true,\n  verbose: true,\n  debug: false,\n  modelBasePath: '',\n};\n\nexport interface ModelInfo {\n  name: string,\n  inCache: boolean,\n  sizeDesired: number,\n  sizeFromManifest: number,\n  sizeLoadedWeights: number,\n}\n\nexport const modelStats: Record<string, ModelInfo> = {};\n\nasync function httpHandler(url: string, init?: RequestInit): Promise<Response | null> {\n  if (options.debug) log('load model fetch:', url, init);\n  return fetch(url, init);\n}\n\nexport function setModelLoadOptions(config: Config) {\n  options.cacheModels = config.cacheModels;\n  options.verbose = config.debug;\n  options.modelBasePath = config.modelBasePath;\n}\n\nexport async function loadModel(modelPath: string | undefined): Promise<GraphModel> {\n  let modelUrl = join(options.modelBasePath, modelPath || '');\n  if (!modelUrl.toLowerCase().endsWith('.json')) modelUrl += '.json';\n  const modelPathSegments = modelUrl.includes('/') ? modelUrl.split('/') : modelUrl.split('\\\\');\n  const shortModelName = modelPathSegments[modelPathSegments.length - 1].replace('.json', '');\n  const cachedModelName = 'indexeddb://' + shortModelName; // generate short model name for cache\n  modelStats[shortModelName] = {\n    name: shortModelName,\n    sizeFromManifest: 0,\n    sizeLoadedWeights: 0,\n    sizeDesired: modelsDefs[shortModelName],\n    inCache: false,\n  };\n  options.cacheSupported = (typeof indexedDB !== 'undefined'); // check if localStorage and indexedb are available\n  let cachedModels = {};\n  try {\n    cachedModels = (options.cacheSupported && options.cacheModels) ? await tf.io.listModels() : {}; // list all models already in cache // this fails for webview although localStorage is defined\n  } catch {\n    options.cacheSupported = false;\n  }\n  modelStats[shortModelName].inCache = (options.cacheSupported && options.cacheModels) && Object.keys(cachedModels).includes(cachedModelName); // is model found in cache\n  const tfLoadOptions = typeof fetch === 'undefined' ? {} : { fetchFunc: (url: string, init?: RequestInit) => httpHandler(url, init) };\n  let model: GraphModel = new tf.GraphModel(modelStats[shortModelName].inCache ? cachedModelName : modelUrl, tfLoadOptions) as unknown as GraphModel; // create model prototype and decide if load from cache or from original modelurl\n  let loaded = false;\n  try {\n    // @ts-ignore private function\n    model.findIOHandler(); // decide how to actually load a model\n    if (options.debug) log('model load handler:', model['handler']);\n  } catch (err) {\n    log('error finding model i/o handler:', modelUrl, err);\n  }\n  try {\n    // @ts-ignore private property\n    const artifacts = await model.handler?.load() || null; // load manifest\n    modelStats[shortModelName].sizeFromManifest = artifacts?.weightData?.byteLength || 0;\n    if (artifacts) model.loadSync(artifacts); // load weights\n    else model = await tf.loadGraphModel(modelStats[shortModelName].inCache ? cachedModelName : modelUrl, tfLoadOptions) as unknown as GraphModel;\n    // @ts-ignore private property\n    modelStats[shortModelName].sizeLoadedWeights = model.artifacts?.weightData?.byteLength || 0;\n    if (options.verbose) log('load:', { model: shortModelName, url: model['modelUrl'], bytes: modelStats[shortModelName].sizeLoadedWeights });\n    loaded = true;\n  } catch (err) {\n    log('error loading model:', modelUrl, err);\n  }\n  if (loaded && options.cacheModels && options.cacheSupported && !modelStats[shortModelName].inCache) { // save model to cache\n    try {\n      const saveResult = await model.save(cachedModelName);\n      if (options.debug) log('model saved:', cachedModelName, saveResult);\n    } catch (err) {\n      log('error saving model:', modelUrl, err);\n    }\n  }\n  validateModel(null, model, `${modelPath || ''}`);\n  return model;\n}\n", "/**\n * Module that implements helper draw functions, exposed as human.draw\n */\n\nimport { mergeDeep, now } from '../util/util';\nimport { env } from '../util/env';\nimport { getCanvasContext, rect } from './primitives';\nimport { options } from './options';\nimport { face } from './face';\nimport { body } from './body';\nimport { hand } from './hand';\nimport { object } from './object';\nimport { gesture } from './gesture';\nimport type { Result, PersonResult } from '../result';\nimport type { AnyCanvas, DrawOptions } from '../exports';\n\nlet drawTime = 0;\n\nexport { options } from './options';\nexport { face } from './face';\nexport { body } from './body';\nexport { hand } from './hand';\nexport { object } from './object';\nexport { gesture } from './gesture';\n\n/** draw combined person results instead of individual detection result objects */\nexport function person(inCanvas: AnyCanvas, result: PersonResult[], drawOptions?: Partial<DrawOptions>) {\n  const localOptions: DrawOptions = mergeDeep(options, drawOptions);\n  if (!result || !inCanvas) return;\n  const ctx = getCanvasContext(inCanvas);\n  if (!ctx) return;\n  ctx.lineJoin = 'round';\n  ctx.font = localOptions.font;\n\n  for (let i = 0; i < result.length; i++) {\n    if (localOptions.drawBoxes) {\n      ctx.strokeStyle = localOptions.color;\n      ctx.fillStyle = localOptions.color;\n      rect(ctx, result[i].box[0], result[i].box[1], result[i].box[2], result[i].box[3], localOptions);\n      if (localOptions.drawLabels) {\n        const label = `person #${i}`;\n        if (localOptions.shadowColor && localOptions.shadowColor !== '') {\n          ctx.fillStyle = localOptions.shadowColor;\n          ctx.fillText(label, result[i].box[0] + 3, 1 + result[i].box[1] + localOptions.lineHeight, result[i].box[2]);\n        }\n        ctx.fillStyle = localOptions.labelColor;\n        ctx.fillText(label, result[i].box[0] + 2, 0 + result[i].box[1] + localOptions.lineHeight, result[i].box[2]);\n      }\n      ctx.stroke();\n    }\n  }\n}\n\n/** draw processed canvas */\nexport function canvas(input: AnyCanvas | HTMLImageElement | HTMLVideoElement, output: AnyCanvas) {\n  if (!input || !output) return;\n  const ctx = getCanvasContext(output);\n  if (!ctx) return;\n  ctx.drawImage(input, 0, 0);\n}\n\n/** meta-function that performs draw for: canvas, face, body, hand */\nexport async function all(inCanvas: AnyCanvas, result: Result, drawOptions?: Partial<DrawOptions>) {\n  if (!result?.performance || !inCanvas) return null;\n  const timeStamp = now();\n  const localOptions = mergeDeep(options, drawOptions);\n  const promise = Promise.all([\n    face(inCanvas, result.face, localOptions),\n    body(inCanvas, result.body, localOptions),\n    hand(inCanvas, result.hand, localOptions),\n    object(inCanvas, result.object, localOptions),\n    gesture(inCanvas, result.gesture, localOptions), // gestures do not have buffering\n    // person(inCanvas, result.persons, localOptions); // already included above\n  ]);\n  drawTime = env.perfadd ? drawTime + Math.round(now() - timeStamp) : Math.round(now() - timeStamp);\n  result.performance.draw = drawTime;\n  return promise;\n}\n", "import { log } from '../util/util';\nimport type { AnyCanvas } from '../exports';\nimport type { Point } from '../result';\nimport type { DrawOptions } from './options';\n\nexport const getCanvasContext = (input: AnyCanvas) => {\n  if (!input) log('draw error: invalid canvas');\n  else if (!input.getContext) log('draw error: canvas context not defined');\n  else {\n    const ctx = input.getContext('2d');\n    if (!ctx) log('draw error: cannot get canvas context');\n    else return ctx;\n  }\n  return null;\n};\n\nexport const rad2deg = (theta: number) => Math.round((theta * 180) / Math.PI);\n\nexport const colorDepth = (z: number | undefined, opt: DrawOptions): string => { // performance optimization needed\n  if (!opt.useDepth || typeof z === 'undefined') return opt.color;\n  const rgb = Uint8ClampedArray.from([127 + (2 * z), 127 - (2 * z), 255]);\n  return `rgba(${rgb[0]}, ${rgb[1]}, ${rgb[2]}, ${opt.alpha})`;\n};\n\nexport function point(ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D, x: number, y: number, z: number | undefined, localOptions: DrawOptions) {\n  ctx.fillStyle = colorDepth(z, localOptions);\n  ctx.beginPath();\n  ctx.arc(x, y, localOptions.pointSize, 0, 2 * Math.PI);\n  ctx.fill();\n}\n\nexport function rect(ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D, x: number, y: number, width: number, height: number, localOptions: DrawOptions) {\n  ctx.beginPath();\n  ctx.lineWidth = localOptions.lineWidth;\n  if (localOptions.useCurves) {\n    const cx = (x + x + width) / 2;\n    const cy = (y + y + height) / 2;\n    ctx.ellipse(cx, cy, width / 2, height / 2, 0, 0, 2 * Math.PI);\n  } else {\n    ctx.moveTo(x + localOptions.roundRect, y);\n    ctx.lineTo(x + width - localOptions.roundRect, y);\n    ctx.quadraticCurveTo(x + width, y, x + width, y + localOptions.roundRect);\n    ctx.lineTo(x + width, y + height - localOptions.roundRect);\n    ctx.quadraticCurveTo(x + width, y + height, x + width - localOptions.roundRect, y + height);\n    ctx.lineTo(x + localOptions.roundRect, y + height);\n    ctx.quadraticCurveTo(x, y + height, x, y + height - localOptions.roundRect);\n    ctx.lineTo(x, y + localOptions.roundRect);\n    ctx.quadraticCurveTo(x, y, x + localOptions.roundRect, y);\n    ctx.closePath();\n  }\n  ctx.stroke();\n}\n\nexport function lines(ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D, points: Point[], localOptions: DrawOptions) {\n  if (points.length < 2) return;\n  ctx.beginPath();\n  ctx.moveTo(points[0][0], points[0][1]);\n  for (const pt of points) {\n    ctx.strokeStyle = colorDepth(pt[2] || 0, localOptions);\n    ctx.lineTo(Math.trunc(pt[0]), Math.trunc(pt[1]));\n  }\n  ctx.stroke();\n  if (localOptions.fillPolygons) {\n    ctx.closePath();\n    ctx.fill();\n  }\n}\n\nexport function curves(ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D, points: Point[], localOptions: DrawOptions) {\n  if (points.length < 2) return;\n  ctx.lineWidth = localOptions.lineWidth;\n  if (!localOptions.useCurves || points.length <= 2) {\n    lines(ctx, points, localOptions);\n    return;\n  }\n  ctx.moveTo(points[0][0], points[0][1]);\n  for (let i = 0; i < points.length - 2; i++) {\n    const xc = (points[i][0] + points[i + 1][0]) / 2;\n    const yc = (points[i][1] + points[i + 1][1]) / 2;\n    ctx.quadraticCurveTo(points[i][0], points[i][1], xc, yc);\n  }\n  ctx.quadraticCurveTo(points[points.length - 2][0], points[points.length - 2][1], points[points.length - 1][0], points[points.length - 1][1]);\n  ctx.stroke();\n  if (localOptions.fillPolygons) {\n    ctx.closePath();\n    ctx.fill();\n  }\n}\n\nexport function arrow(ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D, from: Point, to: Point, radius = 5) {\n  let angle;\n  let x;\n  let y;\n  ctx.beginPath();\n  ctx.moveTo(from[0], from[1]);\n  ctx.lineTo(to[0], to[1]);\n  angle = Math.atan2(to[1] - from[1], to[0] - from[0]);\n  x = radius * Math.cos(angle) + to[0];\n  y = radius * Math.sin(angle) + to[1];\n  ctx.moveTo(x, y);\n  angle += (1.0 / 3.0) * (2 * Math.PI);\n  x = radius * Math.cos(angle) + to[0];\n  y = radius * Math.sin(angle) + to[1];\n  ctx.lineTo(x, y);\n  angle += (1.0 / 3.0) * (2 * Math.PI);\n  x = radius * Math.cos(angle) + to[0];\n  y = radius * Math.sin(angle) + to[1];\n  ctx.lineTo(x, y);\n  ctx.closePath();\n  ctx.stroke();\n  ctx.fill();\n}\n", "/** Draw Options\n * - Accessed via `human.draw.options` or provided per each draw method as the drawOptions optional parameter\n */\nexport interface DrawOptions {\n  /** draw line color */\n  color: string,\n  /** alpha value used for lines */\n  alpha: number,\n  /** label color */\n  labelColor: string,\n  /** label shadow color */\n  shadowColor: string,\n  /** label font */\n  font: string,\n  /** line spacing between labels */\n  lineHeight: number,\n  /** line width for drawn lines */\n  lineWidth: number,\n  /** size of drawn points */\n  pointSize: number,\n  /** draw rounded boxes by n pixels */\n  roundRect: number,\n  /** should points be drawn? */\n  drawPoints: boolean,\n  /** should labels be drawn? */\n  drawLabels: boolean,\n  /** should face attention keypoints be highlighted */\n  drawAttention: boolean;\n  /** should detected gestures be drawn? */\n  drawGestures: boolean,\n  /** should draw boxes around detection results? */\n  drawBoxes: boolean,\n  /** should draw polygons from detection points? */\n  drawPolygons: boolean,\n  /** should draw gaze arrows? */\n  drawGaze: boolean,\n  /** should fill polygons? */\n  fillPolygons: boolean,\n  /** use z-coordinate when available */\n  useDepth: boolean,\n  /** should lines be curved? */\n  useCurves: boolean,\n}\n\n/** currently set draw options {@link DrawOptions} */\nexport const options: DrawOptions = {\n  color: 'rgba(173, 216, 230, 0.6)' as string, // 'lightblue' with light alpha channel\n  labelColor: 'rgba(173, 216, 230, 1)' as string, // 'lightblue' with dark alpha channel\n  shadowColor: 'black' as string,\n  alpha: 0.5 as number,\n  font: 'small-caps 16px \"Segoe UI\"' as string,\n  lineHeight: 18 as number,\n  lineWidth: 4 as number,\n  pointSize: 2 as number,\n  roundRect: 8 as number,\n  drawPoints: false as boolean,\n  drawLabels: true as boolean,\n  drawBoxes: true as boolean,\n  drawAttention: true as boolean,\n  drawGestures: true as boolean,\n  drawPolygons: true as boolean,\n  drawGaze: true as boolean,\n  fillPolygons: false as boolean,\n  useDepth: true as boolean,\n  useCurves: false as boolean,\n};\n", "import { TRI468 as triangulation } from '../face/facemeshcoords';\nimport { mergeDeep } from '../util/util';\nimport { getCanvasContext, rad2deg, rect, point, lines, arrow } from './primitives';\nimport { options } from './options';\nimport * as facemeshConstants from '../face/constants';\nimport type { FaceResult } from '../result';\nimport type { AnyCanvas, DrawOptions } from '../exports';\n\nlet opt: DrawOptions;\n\nfunction drawLabels(f: FaceResult, ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D) {\n  if (opt.drawLabels) {\n    // silly hack since fillText does not suport new line\n    const labels:string[] = [];\n    labels.push(`face: ${Math.trunc(100 * f.score)}%`);\n    if (f.genderScore) labels.push(`${f.gender || ''} ${Math.trunc(100 * f.genderScore)}%`);\n    if (f.age) labels.push(`age: ${f.age || ''}`);\n    if (f.iris) labels.push(`distance: ${f.iris}`);\n    if (f.real) labels.push(`real: ${Math.trunc(100 * f.real)}%`);\n    if (f.live) labels.push(`live: ${Math.trunc(100 * f.live)}%`);\n    if (f.emotion && f.emotion.length > 0) {\n      const emotion = f.emotion.map((a) => `${Math.trunc(100 * a.score)}% ${a.emotion}`);\n      if (emotion.length > 3) emotion.length = 3;\n      labels.push(emotion.join(' '));\n    }\n    if (f.rotation?.angle && f.rotation?.gaze) {\n      if (f.rotation.angle.roll) labels.push(`roll: ${rad2deg(f.rotation.angle.roll)}\u00B0 yaw:${rad2deg(f.rotation.angle.yaw)}\u00B0 pitch:${rad2deg(f.rotation.angle.pitch)}\u00B0`);\n      if (f.rotation.gaze.bearing) labels.push(`gaze: ${rad2deg(f.rotation.gaze.bearing)}\u00B0`);\n    }\n    if (labels.length === 0) labels.push('face');\n    ctx.fillStyle = opt.color;\n    for (let i = labels.length - 1; i >= 0; i--) {\n      const x = Math.max(f.box[0], 0);\n      const y = i * opt.lineHeight + f.box[1];\n      if (opt.shadowColor && opt.shadowColor !== '') {\n        ctx.fillStyle = opt.shadowColor;\n        ctx.fillText(labels[i], x + 5, y + 16);\n      }\n      ctx.fillStyle = opt.labelColor;\n      ctx.fillText(labels[i], x + 4, y + 15);\n    }\n  }\n}\n\nfunction drawIrisElipse(f: FaceResult, ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D) {\n  // iris: array[center, left, top, right, bottom]\n  if (f.annotations?.leftEyeIris && f.annotations?.leftEyeIris[0]) {\n    ctx.strokeStyle = opt.useDepth ? 'rgba(255, 200, 255, 0.3)' : opt.color;\n    ctx.beginPath();\n    const sizeX = Math.abs(f.annotations.leftEyeIris[3][0] - f.annotations.leftEyeIris[1][0]) / 2;\n    const sizeY = Math.abs(f.annotations.leftEyeIris[4][1] - f.annotations.leftEyeIris[2][1]) / 2;\n    ctx.ellipse(f.annotations.leftEyeIris[0][0], f.annotations.leftEyeIris[0][1], sizeX, sizeY, 0, 0, 2 * Math.PI);\n    ctx.stroke();\n    if (opt.fillPolygons) {\n      ctx.fillStyle = opt.useDepth ? 'rgba(255, 255, 200, 0.3)' : opt.color;\n      ctx.fill();\n    }\n  }\n  if (f.annotations?.rightEyeIris && f.annotations?.rightEyeIris[0]) {\n    ctx.strokeStyle = opt.useDepth ? 'rgba(255, 200, 255, 0.3)' : opt.color;\n    ctx.beginPath();\n    const sizeX = Math.abs(f.annotations.rightEyeIris[3][0] - f.annotations.rightEyeIris[1][0]) / 2;\n    const sizeY = Math.abs(f.annotations.rightEyeIris[4][1] - f.annotations.rightEyeIris[2][1]) / 2;\n    ctx.ellipse(f.annotations.rightEyeIris[0][0], f.annotations.rightEyeIris[0][1], sizeX, sizeY, 0, 0, 2 * Math.PI);\n    ctx.stroke();\n    if (opt.fillPolygons) {\n      ctx.fillStyle = opt.useDepth ? 'rgba(255, 255, 200, 0.3)' : opt.color;\n      ctx.fill();\n    }\n  }\n}\n\nfunction drawGazeSpheres(f: FaceResult, ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D) {\n  if (opt.drawGaze && f.rotation?.angle && typeof Path2D !== 'undefined') {\n    ctx.strokeStyle = 'pink';\n    const valX = (f.box[0] + f.box[2] / 2) - (f.box[3] * rad2deg(f.rotation.angle.yaw) / 90);\n    const valY = (f.box[1] + f.box[3] / 2) + (f.box[2] * rad2deg(f.rotation.angle.pitch) / 90);\n    const pathV = new Path2D(`\n      M ${f.box[0] + f.box[2] / 2} ${f.box[1]}\n      C\n        ${valX} ${f.box[1]},\n        ${valX} ${f.box[1] + f.box[3]},\n        ${f.box[0] + f.box[2] / 2} ${f.box[1] + f.box[3]}\n    `);\n    const pathH = new Path2D(`\n      M ${f.box[0]} ${f.box[1] + f.box[3] / 2}\n      C \n        ${f.box[0]} ${valY},\n        ${f.box[0] + f.box[2]} ${valY},\n        ${f.box[0] + f.box[2]} ${f.box[1] + f.box[3] / 2}\n    `);\n    ctx.stroke(pathH);\n    ctx.stroke(pathV);\n  }\n}\n\nfunction drawGazeArrows(f: FaceResult, ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D) {\n  if (opt.drawGaze && f.rotation?.gaze.strength && f.rotation.gaze.bearing && f.annotations.leftEyeIris && f.annotations.rightEyeIris && f.annotations.leftEyeIris[0] && f.annotations.rightEyeIris[0]) {\n    ctx.strokeStyle = 'pink';\n    ctx.fillStyle = 'pink';\n    const leftGaze = [\n      f.annotations.leftEyeIris[0][0] + (Math.sin(f.rotation.gaze.bearing) * f.rotation.gaze.strength * f.box[3]),\n      f.annotations.leftEyeIris[0][1] + (Math.cos(f.rotation.gaze.bearing) * f.rotation.gaze.strength * f.box[2]),\n    ];\n    arrow(ctx, [f.annotations.leftEyeIris[0][0], f.annotations.leftEyeIris[0][1]], [leftGaze[0], leftGaze[1]], 4);\n    const rightGaze = [\n      f.annotations.rightEyeIris[0][0] + (Math.sin(f.rotation.gaze.bearing) * f.rotation.gaze.strength * f.box[3]),\n      f.annotations.rightEyeIris[0][1] + (Math.cos(f.rotation.gaze.bearing) * f.rotation.gaze.strength * f.box[2]),\n    ];\n    arrow(ctx, [f.annotations.rightEyeIris[0][0], f.annotations.rightEyeIris[0][1]], [rightGaze[0], rightGaze[1]], 4);\n  }\n}\n\nfunction drawFacePolygons(f: FaceResult, ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D) {\n  if (opt.drawPolygons && f.mesh.length >= 468) {\n    ctx.lineWidth = 1;\n    for (let i = 0; i < triangulation.length / 3; i++) {\n      const points = [triangulation[i * 3 + 0], triangulation[i * 3 + 1], triangulation[i * 3 + 2]].map((index) => f.mesh[index]);\n      lines(ctx, points, opt);\n    }\n    drawIrisElipse(f, ctx);\n  }\n  /*\n  if (opt.drawPolygons && f.contours.length > 1) {\n    ctx.lineWidth = 5;\n    lines(ctx, f.contours, opt);\n  }\n  ctx.lineWidth = 1;\n  */\n}\n\nfunction drawFacePoints(f: FaceResult, ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D) {\n  if (opt.drawPoints && f.mesh.length >= 468) {\n    for (let i = 0; i < f.mesh.length; i++) {\n      point(ctx, f.mesh[i][0], f.mesh[i][1], f.mesh[i][2], opt);\n      if (opt.drawAttention) {\n        if (facemeshConstants.LANDMARKS_REFINEMENT_LIPS_CONFIG.includes(i)) point(ctx, f.mesh[i][0], f.mesh[i][1], (f.mesh[i][2] as number) + 127, opt);\n        if (facemeshConstants.LANDMARKS_REFINEMENT_LEFT_EYE_CONFIG.includes(i)) point(ctx, f.mesh[i][0], f.mesh[i][1], (f.mesh[i][2] as number) - 127, opt);\n        if (facemeshConstants.LANDMARKS_REFINEMENT_RIGHT_EYE_CONFIG.includes(i)) point(ctx, f.mesh[i][0], f.mesh[i][1], (f.mesh[i][2] as number) - 127, opt);\n      }\n    }\n  }\n}\n\nfunction drawFaceBoxes(f: FaceResult, ctx) {\n  if (opt.drawBoxes) {\n    rect(ctx, f.box[0], f.box[1], f.box[2], f.box[3], opt);\n  }\n}\n\n/** draw detected faces */\nexport function face(inCanvas: AnyCanvas, result: FaceResult[], drawOptions?: Partial<DrawOptions>) {\n  opt = mergeDeep(options, drawOptions);\n  if (!result || !inCanvas) return;\n  const ctx = getCanvasContext(inCanvas);\n  if (!ctx) return;\n  ctx.font = opt.font;\n  ctx.strokeStyle = opt.color;\n  ctx.fillStyle = opt.color;\n  for (const f of result) {\n    drawFaceBoxes(f, ctx);\n    drawLabels(f, ctx);\n    if (f.mesh && f.mesh.length > 0) {\n      drawFacePoints(f, ctx);\n      drawFacePolygons(f, ctx);\n      drawGazeSpheres(f, ctx);\n      drawGazeArrows(f, ctx);\n    }\n  }\n}\n", "import { mergeDeep } from '../util/util';\nimport { getCanvasContext, rect, point, curves, colorDepth } from './primitives';\nimport { options } from './options';\nimport type { BodyResult } from '../result';\nimport type { AnyCanvas, DrawOptions } from '../exports';\n\n/** draw detected bodies */\nexport function body(inCanvas: AnyCanvas, result: BodyResult[], drawOptions?: Partial<DrawOptions>) {\n  const localOptions: DrawOptions = mergeDeep(options, drawOptions);\n  if (!result || !inCanvas) return;\n  const ctx = getCanvasContext(inCanvas);\n  if (!ctx) return;\n  ctx.lineJoin = 'round';\n  for (let i = 0; i < result.length; i++) {\n    ctx.strokeStyle = localOptions.color;\n    ctx.fillStyle = localOptions.color;\n    ctx.lineWidth = localOptions.lineWidth;\n    ctx.font = localOptions.font;\n    if (localOptions.drawBoxes && result[i].box && result[i].box.length === 4) {\n      rect(ctx, result[i].box[0], result[i].box[1], result[i].box[2], result[i].box[3], localOptions);\n      if (localOptions.drawLabels) {\n        if (localOptions.shadowColor && localOptions.shadowColor !== '') {\n          ctx.fillStyle = localOptions.shadowColor;\n          ctx.fillText(`body ${100 * result[i].score}%`, result[i].box[0] + 3, 1 + result[i].box[1] + localOptions.lineHeight, result[i].box[2]);\n        }\n        ctx.fillStyle = localOptions.labelColor;\n        ctx.fillText(`body ${100 * result[i].score}%`, result[i].box[0] + 2, 0 + result[i].box[1] + localOptions.lineHeight, result[i].box[2]);\n      }\n    }\n    if (localOptions.drawPoints && result[i].keypoints) {\n      for (let pt = 0; pt < result[i].keypoints.length; pt++) {\n        if (!result[i].keypoints[pt].score || (result[i].keypoints[pt].score === 0)) continue;\n        ctx.fillStyle = colorDepth(result[i].keypoints[pt].position[2], localOptions);\n        point(ctx, result[i].keypoints[pt].position[0], result[i].keypoints[pt].position[1], 0, localOptions);\n      }\n    }\n    if (localOptions.drawLabels && result[i].keypoints) {\n      ctx.font = localOptions.font;\n      for (const pt of result[i].keypoints) {\n        if (!pt.score || (pt.score === 0)) continue;\n        ctx.fillStyle = colorDepth(pt.position[2], localOptions);\n        ctx.fillText(`${pt.part} ${Math.trunc(100 * pt.score)}%`, pt.position[0] + 4, pt.position[1] + 4);\n      }\n    }\n    if (localOptions.drawPolygons && result[i].keypoints && result[i].annotations) {\n      for (const part of Object.values(result[i].annotations)) {\n        for (const connected of part) curves(ctx, connected, localOptions);\n      }\n    }\n  }\n}\n", "import { mergeDeep } from '../util/util';\nimport { getCanvasContext, rect, point, colorDepth } from './primitives';\nimport { options } from './options';\nimport type { HandResult } from '../result';\nimport type { AnyCanvas, DrawOptions, Point } from '../exports';\n\n/** draw detected hands */\nexport function hand(inCanvas: AnyCanvas, result: HandResult[], drawOptions?: Partial<DrawOptions>) {\n  const localOptions: DrawOptions = mergeDeep(options, drawOptions);\n  if (!result || !inCanvas) return;\n  const ctx = getCanvasContext(inCanvas);\n  if (!ctx) return;\n  ctx.lineJoin = 'round';\n  ctx.font = localOptions.font;\n  for (const h of result) {\n    if (localOptions.drawBoxes) {\n      ctx.strokeStyle = localOptions.color;\n      ctx.fillStyle = localOptions.color;\n      rect(ctx, h.box[0], h.box[1], h.box[2], h.box[3], localOptions);\n      if (localOptions.drawLabels) {\n        if (localOptions.shadowColor && localOptions.shadowColor !== '') {\n          ctx.fillStyle = localOptions.shadowColor;\n          ctx.fillText(`hand:${Math.trunc(100 * h.score)}%`, h.box[0] + 3, 1 + h.box[1] + localOptions.lineHeight, h.box[2]); // can use h.label\n        }\n        ctx.fillStyle = localOptions.labelColor;\n        ctx.fillText(`hand:${Math.trunc(100 * h.score)}%`, h.box[0] + 2, 0 + h.box[1] + localOptions.lineHeight, h.box[2]); // can use h.label\n      }\n      ctx.stroke();\n    }\n    if (localOptions.drawPoints) {\n      if (h.keypoints && h.keypoints.length > 0) {\n        for (const pt of h.keypoints) {\n          ctx.fillStyle = colorDepth(pt[2], localOptions);\n          point(ctx, pt[0], pt[1], 0, localOptions);\n        }\n      }\n    }\n    if (localOptions.drawLabels && h.annotations) {\n      const addHandLabel = (part: Point[], title: string) => {\n        if (!part || part.length === 0 || !part[0]) return;\n        const z = part[part.length - 1][2] || -256;\n        ctx.fillStyle = colorDepth(z, localOptions);\n        ctx.fillText(title, part[part.length - 1][0] + 4, part[part.length - 1][1] + 4);\n      };\n      ctx.font = localOptions.font;\n      addHandLabel(h.annotations.index, 'index');\n      addHandLabel(h.annotations.middle, 'middle');\n      addHandLabel(h.annotations.ring, 'ring');\n      addHandLabel(h.annotations.pinky, 'pinky');\n      addHandLabel(h.annotations.thumb, 'thumb');\n      addHandLabel(h.annotations.palm, 'palm');\n    }\n    if (localOptions.drawPolygons && h.annotations) {\n      const addHandLine = (part: Point[]) => {\n        if (!part || part.length === 0 || !part[0]) return;\n        for (let i = 0; i < part.length; i++) {\n          ctx.beginPath();\n          const z = part[i][2] || 0;\n          ctx.strokeStyle = colorDepth(i * z, localOptions);\n          ctx.moveTo(part[i > 0 ? i - 1 : 0][0], part[i > 0 ? i - 1 : 0][1]);\n          ctx.lineTo(part[i][0], part[i][1]);\n          ctx.stroke();\n        }\n      };\n      ctx.lineWidth = localOptions.lineWidth;\n      addHandLine(h.annotations.index);\n      addHandLine(h.annotations.middle);\n      addHandLine(h.annotations.ring);\n      addHandLine(h.annotations.pinky);\n      addHandLine(h.annotations.thumb);\n      // addPart(h.annotations.palm);\n    }\n  }\n}\n", "import { mergeDeep } from '../util/util';\nimport { getCanvasContext, rect } from './primitives';\nimport { options } from './options';\nimport type { ObjectResult } from '../result';\nimport type { AnyCanvas, DrawOptions } from '../exports';\n\n/** draw detected objects */\nexport function object(inCanvas: AnyCanvas, result: ObjectResult[], drawOptions?: Partial<DrawOptions>) {\n  const localOptions: DrawOptions = mergeDeep(options, drawOptions);\n  if (!result || !inCanvas) return;\n  const ctx = getCanvasContext(inCanvas);\n  if (!ctx) return;\n  ctx.lineJoin = 'round';\n  ctx.font = localOptions.font;\n  for (const h of result) {\n    if (localOptions.drawBoxes) {\n      ctx.strokeStyle = localOptions.color;\n      ctx.fillStyle = localOptions.color;\n      rect(ctx, h.box[0], h.box[1], h.box[2], h.box[3], localOptions);\n      if (localOptions.drawLabels) {\n        const label = `${h.label} ${Math.round(100 * h.score)}%`;\n        if (localOptions.shadowColor && localOptions.shadowColor !== '') {\n          ctx.fillStyle = localOptions.shadowColor;\n          ctx.fillText(label, h.box[0] + 3, 1 + h.box[1] + localOptions.lineHeight, h.box[2]);\n        }\n        ctx.fillStyle = localOptions.labelColor;\n        ctx.fillText(label, h.box[0] + 2, 0 + h.box[1] + localOptions.lineHeight, h.box[2]);\n      }\n      ctx.stroke();\n    }\n  }\n}\n", "import { mergeDeep } from '../util/util';\nimport { getCanvasContext } from './primitives';\nimport { options } from './options';\nimport type { GestureResult } from '../result';\nimport type { AnyCanvas, DrawOptions } from '../exports';\n\n/** draw detected gestures */\nexport function gesture(inCanvas: AnyCanvas, result: GestureResult[], drawOptions?: Partial<DrawOptions>) {\n  const localOptions: DrawOptions = mergeDeep(options, drawOptions);\n  if (!result || !inCanvas) return;\n  if (localOptions.drawGestures) {\n    const ctx = getCanvasContext(inCanvas);\n    if (!ctx) return;\n    ctx.font = localOptions.font;\n    ctx.fillStyle = localOptions.color;\n    let i = 1;\n    for (let j = 0; j < result.length; j++) {\n      let where: unknown[] = []; // what&where is a record\n      let what: unknown[] = []; // what&where is a record\n      [where, what] = Object.entries(result[j]);\n      if ((what.length > 1) && ((what[1] as string).length > 0)) {\n        const who = where[1] as number > 0 ? `#${where[1]}` : '';\n        const label = `${where[0]} ${who}: ${what[1]}`;\n        if (localOptions.shadowColor && localOptions.shadowColor !== '') {\n          ctx.fillStyle = localOptions.shadowColor;\n          ctx.fillText(label, 8, 2 + (i * localOptions.lineHeight));\n        }\n        ctx.fillStyle = localOptions.labelColor;\n        ctx.fillText(label, 6, 0 + (i * localOptions.lineHeight));\n        i += 1;\n      }\n    }\n  }\n}\n", "import type { Tensor } from '../tfjs/types';\nimport type { FaceResult } from '../result';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { meshAnnotations } from './facemeshcoords';\n\nconst expandFact = 0.1;\nconst alpha = 0.5;\n\n// point inclusion in polygon based on https://wrf.ecse.rpi.edu/Research/Short_Notes/pnpoly.html\nfunction insidePoly(x: number, y: number, polygon: { x: number, y: number }[]): boolean {\n  let inside = false;\n  let j = polygon.length - 1;\n  for (let i = 0; i < polygon.length; j = i++) {\n    if (((polygon[i].y > y) !== (polygon[j].y > y)) && (x < (polygon[j].x - polygon[i].x) * (y - polygon[i].y) / (polygon[j].y - polygon[i].y) + polygon[i].x)) inside = !inside;\n  }\n  return inside;\n}\n\nexport async function mask(face: FaceResult): Promise<Tensor | undefined> {\n  if (!face.tensor) return face.tensor;\n  if (!face.mesh || face.mesh.length < 100) return face.tensor;\n  const width = face.tensor.shape[2] || 0;\n  const height = face.tensor.shape[1] || 0;\n  const buffer = await face.tensor.buffer();\n  let silhouette: { x: number, y: number }[] = [];\n  for (const pt of meshAnnotations.silhouette) silhouette.push({ x: (face.mesh[pt][0] - face.box[0]) / face.box[2], y: (face.mesh[pt][1] - face.box[1]) / face.box[3] }); // add all silhouette points scaled to local box\n  if (expandFact && expandFact > 0) silhouette = silhouette.map((pt) => ({ x: pt.x > 0.5 ? pt.x + expandFact : pt.x - expandFact, y: pt.y > 0.5 ? pt.y + expandFact : pt.y - expandFact })); // expand silhouette\n  for (let x = 0; x < width; x++) {\n    for (let y = 0; y < height; y++) {\n      const inside = insidePoly(x / width, y / width, silhouette);\n      if (!inside) {\n        buffer.set(alpha * buffer.get(0, y, x, 0), 0, y, x, 0);\n        buffer.set(alpha * buffer.get(0, y, x, 1), 0, y, x, 1);\n        buffer.set(alpha * buffer.get(0, y, x, 2), 0, y, x, 2);\n      }\n    }\n  }\n  const output = buffer.toTensor();\n  tf.dispose(buffer);\n  return output;\n}\n", "import type { Point, FaceResult } from '../result';\n\ntype Vector = [number, number, number];\n\nconst calculateGaze = (face: FaceResult): { bearing: number, strength: number } => {\n  const radians = (pt1: Point, pt2: Point) => Math.atan2(pt1[1] - pt2[1], pt1[0] - pt2[0]); // function to calculate angle between any two points\n  if (!face.annotations.rightEyeIris || !face.annotations.leftEyeIris) return { bearing: 0, strength: 0 };\n\n  const offsetIris = [0, -0.1]; // iris center may not align with average of eye extremes\n  const eyeRatio = 1; // factor to normalize changes x vs y\n\n  const left = (face.mesh[33][2] || 0) > (face.mesh[263][2] || 0); // pick left or right eye depending which one is closer bazed on outsize point z axis\n  const irisCenter = left ? face.mesh[473] : face.mesh[468];\n  const eyeCenter = left // eye center is average of extreme points on x axis for both x and y, ignoring y extreme points as eyelids naturally open/close more when gazing up/down so relative point is less precise\n    ? [(face.mesh[133][0] + face.mesh[33][0]) / 2, (face.mesh[133][1] + face.mesh[33][1]) / 2]\n    : [(face.mesh[263][0] + face.mesh[362][0]) / 2, (face.mesh[263][1] + face.mesh[362][1]) / 2];\n  const eyeSize = left // eye size is difference between extreme points for both x and y, used to normalize & squarify eye dimensions\n    ? [face.mesh[133][0] - face.mesh[33][0], face.mesh[23][1] - face.mesh[27][1]]\n    : [face.mesh[263][0] - face.mesh[362][0], face.mesh[253][1] - face.mesh[257][1]];\n  const eyeDiff: Point = [ // x distance between extreme point and center point normalized with eye size\n    (eyeCenter[0] - irisCenter[0]) / eyeSize[0] - offsetIris[0],\n    eyeRatio * (irisCenter[1] - eyeCenter[1]) / eyeSize[1] - offsetIris[1],\n  ];\n  let strength = Math.sqrt((eyeDiff[0] * eyeDiff[0]) + (eyeDiff[1] * eyeDiff[1])); // vector length is a diagonal between two differences\n  strength = Math.min(strength, face.boxRaw[2] / 2, face.boxRaw[3] / 2); // limit strength to half of box size to avoid clipping due to low precision\n  const bearing = (radians([0, 0], eyeDiff) + (Math.PI / 2)) % Math.PI; // using eyeDiff instead eyeCenter/irisCenter combo due to manual adjustments and rotate clockwise 90degrees\n  return { bearing, strength };\n};\n\nexport const calculateFaceAngle = (face: FaceResult, imageSize: [number, number]): {\n  angle: { pitch: number, yaw: number, roll: number },\n  matrix: [number, number, number, number, number, number, number, number, number],\n  gaze: { bearing: number, strength: number },\n} => {\n  // const degrees = (theta) => Math.abs(((theta * 180) / Math.PI) % 360);\n  const normalize = (v: Vector): Vector => { // normalize vector\n    const length = Math.sqrt(v[0] * v[0] + v[1] * v[1] + v[2] * v[2]);\n    v[0] /= length;\n    v[1] /= length;\n    v[2] /= length;\n    return v;\n  };\n  const subVectors = (a: Vector, b: Vector): Vector => { // vector subtraction (a - b)\n    const x = a[0] - b[0];\n    const y = a[1] - b[1];\n    const z = a[2] - b[2];\n    return [x, y, z];\n  };\n  const crossVectors = (a: Vector, b: Vector): Vector => { // vector cross product (a x b)\n    const x = a[1] * b[2] - a[2] * b[1];\n    const y = a[2] * b[0] - a[0] * b[2];\n    const z = a[0] * b[1] - a[1] * b[0];\n    return [x, y, z];\n  };\n  // 3x3 rotation matrix to Euler angles based on https://www.geometrictools.com/Documentation/EulerAngles.pdf\n  const rotationMatrixToEulerAngle = (r: number[]): { pitch: number, yaw: number, roll: number } => {\n    const [r00, _r01, _r02, r10, r11, r12, r20, r21, r22] = r; // eslint-disable-line @typescript-eslint/no-unused-vars\n    let thetaX: number;\n    let thetaY: number;\n    let thetaZ: number;\n    if (r10 < 1) { // YZX calculation\n      if (r10 > -1) {\n        thetaZ = Math.asin(r10);\n        thetaY = Math.atan2(-r20, r00);\n        thetaX = Math.atan2(-r12, r11);\n      } else {\n        thetaZ = -Math.PI / 2;\n        thetaY = -Math.atan2(r21, r22);\n        thetaX = 0;\n      }\n    } else {\n      thetaZ = Math.PI / 2;\n      thetaY = Math.atan2(r21, r22);\n      thetaX = 0;\n    }\n    if (Number.isNaN(thetaX)) thetaX = 0;\n    if (Number.isNaN(thetaY)) thetaY = 0;\n    if (Number.isNaN(thetaZ)) thetaZ = 0;\n    return { pitch: 2 * -thetaX, yaw: 2 * -thetaY, roll: 2 * -thetaZ };\n  };\n\n  /*\n  const meshToEulerAngle = (mesh) => { // simple Euler angle calculation based existing 3D mesh\n    const radians = (a1, a2, b1, b2) => Math.atan2(b2 - a2, b1 - a1);\n    return { // values are in radians in range of -pi/2 to pi/2 which is -90 to +90 degrees, value of 0 means center\n      pitch: radians(mesh[10][1], mesh[10][2], mesh[152][1], mesh[152][2]), // looking at y,z of top and bottom points of the face // pitch is face move up/down\n      yaw: radians(mesh[33][0], mesh[33][2], mesh[263][0], mesh[263][2]), // looking at x,z of outside corners of leftEye and rightEye // yaw is face turn left/right\n      roll: radians(mesh[33][0], mesh[33][1], mesh[263][0], mesh[263][1]), // looking at x,y of outside corners of leftEye and rightEye // roll is face lean left/right\n    };\n  };\n  */\n\n  // initialize gaze and mesh\n  const mesh = face.meshRaw;\n  if (!mesh || mesh.length < 300) return { angle: { pitch: 0, yaw: 0, roll: 0 }, matrix: [1, 0, 0, 0, 1, 0, 0, 0, 1], gaze: { bearing: 0, strength: 0 } };\n\n  const size = Math.max(face.boxRaw[2] * imageSize[0], face.boxRaw[3] * imageSize[1]) / 1.5;\n  // top, bottom, left, right\n  const pts: Point[] = [mesh[10], mesh[152], mesh[234], mesh[454]].map((pt) => [pt[0] * imageSize[0] / size, pt[1] * imageSize[1] / size, pt[2]] as Point); // make the xyz coordinates proportional, independent of the image/box size\n\n  const yAxis = normalize(subVectors(pts[1] as Vector, pts[0] as Vector));\n  let xAxis = normalize(subVectors(pts[3] as Vector, pts[2] as Vector));\n  const zAxis = normalize(crossVectors(xAxis, yAxis));\n  // adjust xAxis to make sure that all axes are perpendicular to each other\n  xAxis = crossVectors(yAxis, zAxis);\n\n  // Rotation Matrix from Axis Vectors - http://renderdan.blogspot.com/2006/05/rotation-matrix-from-axis-vectors.html\n  // 3x3 rotation matrix is flatten to array in row-major order. Note that the rotation represented by this matrix is inverted.\n  const matrix: [number, number, number, number, number, number, number, number, number] = [\n    xAxis[0], xAxis[1], xAxis[2],\n    yAxis[0], yAxis[1], yAxis[2],\n    zAxis[0], zAxis[1], zAxis[2],\n  ];\n  const angle = rotationMatrixToEulerAngle(matrix);\n  // const angle = meshToEulerAngle(mesh);\n\n  // we have iris keypoints so we can calculate gaze direction\n  const gaze = mesh.length === 478 ? calculateGaze(face) : { bearing: 0, strength: 0 };\n\n  return { angle, matrix, gaze };\n};\n", "/**\n * Face algorithm implementation\n * Uses FaceMesh, Emotion and FaceRes models to create a unified pipeline\n */\n\nimport { log, now } from '../util/util';\nimport { env } from '../util/env';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as facemesh from './facemesh';\nimport * as emotion from '../gear/emotion';\nimport * as faceres from './faceres';\nimport * as mask from './mask';\nimport * as antispoof from './antispoof';\nimport * as liveness from './liveness';\nimport * as gear from '../gear/gear';\nimport * as ssrnetAge from '../gear/ssrnet-age';\nimport * as ssrnetGender from '../gear/ssrnet-gender';\nimport * as mobilefacenet from './mobilefacenet';\nimport * as insightface from './insightface';\nimport type { FaceResult, Emotion, Gender, Race } from '../result';\nimport type { Tensor } from '../tfjs/types';\nimport type { Human } from '../human';\nimport { calculateFaceAngle } from './angles';\n\ninterface DescRes { age: number, gender: Gender, genderScore: number, descriptor: number[], race?: { score: number, race: Race }[] }\n\nexport const detectFace = async (instance: Human /* instance of human */, input: Tensor): Promise<FaceResult[]> => {\n  // run facemesh, includes blazeface and iris\n  let timeStamp: number = now();\n  let ageRes: { age: number } | Promise<{ age: number }> | null;\n  let gearRes: gear.GearType | Promise<gear.GearType> | null;\n  let genderRes: { gender: string, genderScore: number } | Promise<{ gender: string, genderScore: number }> | null;\n  let emotionRes: { score: number, emotion: Emotion }[] | Promise<{ score: number, emotion: Emotion }[]>;\n  let mobilefacenetRes: number[] | Promise<number[]> | null;\n  let insightfaceRes: number[] | Promise<number[]> | null;\n  let antispoofRes: number | Promise<number> | null;\n  let livenessRes: number | Promise<number> | null;\n  let descRes: DescRes | Promise<DescRes> | null;\n\n  const faceRes: FaceResult[] = [];\n  instance.state = 'run:face';\n\n  const faces = await facemesh.predict(input, instance.config);\n  instance.performance.face = env.perfadd ? (instance.performance.face || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n  if (!input.shape || input.shape.length !== 4) return [];\n  if (!faces) return [];\n  // for (const face of faces) {\n  for (let i = 0; i < faces.length; i++) {\n    instance.analyze('Get Face');\n\n    // is something went wrong, skip the face\n    // @ts-ignore possibly undefied\n    if (!faces[i].tensor || faces[i].tensor.isDisposedInternal) {\n      log('Face object is disposed:', faces[i].tensor);\n      continue;\n    }\n\n    // optional face mask\n    if (instance.config.face.detector?.mask) {\n      const masked = await mask.mask(faces[i]);\n      tf.dispose(faces[i].tensor);\n      if (masked) faces[i].tensor = masked;\n    }\n\n    // calculate face angles\n    const rotation = faces[i].mesh && (faces[i].mesh.length > 200) ? calculateFaceAngle(faces[i], [input.shape[2], input.shape[1]]) : null;\n\n    // run emotion, inherits face from blazeface\n    instance.analyze('Start Emotion:');\n    if (instance.config.async) {\n      emotionRes = instance.config.face.emotion?.enabled ? emotion.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : [];\n    } else {\n      instance.state = 'run:emotion';\n      timeStamp = now();\n      emotionRes = instance.config.face.emotion?.enabled ? await emotion.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : [];\n      instance.performance.emotion = env.perfadd ? (instance.performance.emotion || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n    }\n    instance.analyze('End Emotion:');\n\n    // run antispoof, inherits face from blazeface\n    instance.analyze('Start AntiSpoof:');\n    if (instance.config.async) {\n      antispoofRes = instance.config.face.antispoof?.enabled ? antispoof.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : 0;\n    } else {\n      instance.state = 'run:antispoof';\n      timeStamp = now();\n      antispoofRes = instance.config.face.antispoof?.enabled ? await antispoof.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : 0;\n      instance.performance.antispoof = env.perfadd ? (instance.performance.antispoof || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n    }\n    instance.analyze('End AntiSpoof:');\n\n    // run liveness, inherits face from blazeface\n    instance.analyze('Start Liveness:');\n    if (instance.config.async) {\n      livenessRes = instance.config.face.liveness?.enabled ? liveness.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : 0;\n    } else {\n      instance.state = 'run:liveness';\n      timeStamp = now();\n      livenessRes = instance.config.face.liveness?.enabled ? await liveness.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : 0;\n      instance.performance.liveness = env.perfadd ? (instance.performance.antispoof || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n    }\n    instance.analyze('End Liveness:');\n\n    // run gear, inherits face from blazeface\n    instance.analyze('Start GEAR:');\n    if (instance.config.async) {\n      gearRes = instance.config.face.gear?.enabled ? gear.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : null;\n    } else {\n      instance.state = 'run:gear';\n      timeStamp = now();\n      gearRes = instance.config.face.gear?.enabled ? await gear.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : null;\n      instance.performance.gear = Math.trunc(now() - timeStamp);\n    }\n    instance.analyze('End GEAR:');\n\n    // run gear, inherits face from blazeface\n    instance.analyze('Start SSRNet:');\n    if (instance.config.async) {\n      ageRes = instance.config.face['ssrnet']?.enabled ? ssrnetAge.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : null;\n      genderRes = instance.config.face['ssrnet']?.enabled ? ssrnetGender.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : null;\n    } else {\n      instance.state = 'run:ssrnet';\n      timeStamp = now();\n      ageRes = instance.config.face['ssrnet']?.enabled ? await ssrnetAge.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : null;\n      genderRes = instance.config.face['ssrnet']?.enabled ? await ssrnetGender.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : null;\n      instance.performance.ssrnet = Math.trunc(now() - timeStamp);\n    }\n    instance.analyze('End SSRNet:');\n\n    // run mobilefacenet alternative, inherits face from blazeface\n    instance.analyze('Start MobileFaceNet:');\n    if (instance.config.async) {\n      mobilefacenetRes = instance.config.face['mobilefacenet']?.enabled ? mobilefacenet.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : null;\n    } else {\n      instance.state = 'run:mobilefacenet';\n      timeStamp = now();\n      mobilefacenetRes = instance.config.face['mobilefacenet']?.enabled ? await mobilefacenet.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : null;\n      instance.performance.mobilefacenet = Math.trunc(now() - timeStamp);\n    }\n    instance.analyze('End MobileFaceNet:');\n\n    // run insightface alternative, inherits face from blazeface\n    instance.analyze('Start InsightFace:');\n    if (instance.config.async) {\n      insightfaceRes = instance.config.face['insightface']?.enabled ? insightface.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : null;\n    } else {\n      instance.state = 'run:mobilefacenet';\n      timeStamp = now();\n      insightfaceRes = instance.config.face['insightface']?.enabled ? await insightface.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : null;\n      instance.performance.mobilefacenet = Math.trunc(now() - timeStamp);\n    }\n    instance.analyze('End InsightFace:');\n\n    // run faceres, inherits face from blazeface\n    instance.analyze('Start Description:');\n    if (instance.config.async) {\n      descRes = faceres.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length);\n    } else {\n      instance.state = 'run:description';\n      timeStamp = now();\n      descRes = await faceres.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length);\n      instance.performance.description = env.perfadd ? (instance.performance.description || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n    }\n    instance.analyze('End Description:');\n\n    // if async wait for results\n    if (instance.config.async) {\n      [ageRes, genderRes, emotionRes, mobilefacenetRes, insightfaceRes, descRes, gearRes, antispoofRes, livenessRes] = await Promise.all([ageRes, genderRes, emotionRes, mobilefacenetRes, insightfaceRes, descRes, gearRes, antispoofRes, livenessRes]);\n    }\n    instance.analyze('Finish Face:');\n\n    if (instance.config.face['ssrnet']?.enabled && ageRes && genderRes) { // override age/gender if ssrnet model is used\n      descRes = {\n        ...(descRes as DescRes),\n        age: (ageRes as { age: number}).age,\n        gender: (genderRes as { gender: Gender, genderScore: number }).gender,\n        genderScore: (genderRes as { gender: Gender, genderScore: number }).genderScore,\n      };\n    }\n    if (instance.config.face.gear?.enabled && gearRes) { // override age/gender/race if gear model is used\n      descRes = {\n        ...(descRes as DescRes),\n        age: (gearRes as gear.GearType).age,\n        gender: (gearRes as gear.GearType).gender,\n        genderScore: (gearRes as gear.GearType).genderScore,\n        race: (gearRes as gear.GearType).race,\n      };\n    }\n    if (instance.config.face['mobilefacenet']?.enabled && mobilefacenetRes) { // override descriptor if mobilefacenet model is used\n      (descRes as DescRes).descriptor = mobilefacenetRes as number[];\n    }\n\n    if (instance.config.face['insightface']?.enabled && insightfaceRes) { // override descriptor if insightface model is used\n      (descRes as DescRes).descriptor = insightfaceRes as number[];\n    }\n\n    // calculate iris distance\n    // iris: array[ center, left, top, right, bottom]\n    if (!instance.config.face.iris?.enabled) {\n      // if (faces[i]?.annotations?.leftEyeIris) delete faces[i].annotations.leftEyeIris;\n      // if (faces[i]?.annotations?.rightEyeIris) delete faces[i].annotations.rightEyeIris;\n    }\n    const irisSize = (faces[i]?.annotations?.leftEyeIris?.[0] && faces[i]?.annotations?.rightEyeIris?.[0]\n      && (faces[i].annotations.leftEyeIris.length > 0) && (faces[i].annotations.rightEyeIris.length > 0)\n      && (faces[i].annotations.leftEyeIris[0] !== null) && (faces[i].annotations.rightEyeIris[0] !== null))\n      ? Math.max(Math.abs(faces[i].annotations.leftEyeIris[3][0] - faces[i].annotations.leftEyeIris[1][0]), Math.abs(faces[i].annotations.rightEyeIris[4][1] - faces[i].annotations.rightEyeIris[2][1])) / input.shape[2]\n      : 0; // note: average human iris size is 11.7mm\n\n    // optionally return tensor\n    const tensor = instance.config.face.detector?.return ? tf.squeeze(faces[i].tensor) : null;\n    // dispose original face tensor\n    tf.dispose(faces[i].tensor);\n    // delete temp face image\n    if (faces[i].tensor) delete faces[i].tensor;\n    // combine results\n    const res: FaceResult = {\n      ...faces[i],\n      id: i,\n    };\n    if ((descRes as DescRes).age) res.age = (descRes as DescRes).age;\n    if ((descRes as DescRes).gender) res.gender = (descRes as DescRes).gender;\n    if ((descRes as DescRes).genderScore) res.genderScore = (descRes as DescRes).genderScore;\n    if ((descRes as DescRes).descriptor) res.embedding = (descRes as DescRes).descriptor;\n    if ((descRes as DescRes).race) res.race = (descRes as DescRes).race as { score: number, race: Race }[];\n    if (emotionRes) res.emotion = emotionRes as { score: number, emotion: Emotion }[];\n    if (antispoofRes) res.real = antispoofRes as number;\n    if (livenessRes) res.live = livenessRes as number;\n    if (irisSize && irisSize !== 0) res.iris = Math.trunc(500 / irisSize / 11.7) / 100;\n    if (rotation) res.rotation = rotation;\n    if (tensor) res.tensor = tensor;\n    faceRes.push(res);\n    instance.analyze('End Face');\n  }\n  instance.analyze('End FaceMesh:');\n  if (instance.config.async) {\n    if (instance.performance.face) delete instance.performance.face;\n    if (instance.performance.age) delete instance.performance.age;\n    if (instance.performance.gender) delete instance.performance.gender;\n    if (instance.performance.emotion) delete instance.performance.emotion;\n  }\n  return faceRes;\n};\n", "/**\n * Gesture detection algorithm\n */\n\nimport type { GestureResult, BodyResult, FaceResult, HandResult, Point } from '../result';\nimport * as fingerPose from '../hand/fingerpose';\n\n/** face gesture type */\nexport type FaceGesture =\n  `facing ${'left' | 'center' | 'right'}`\n  | `blink ${'left' | 'right'} eye`\n  | `mouth ${number}% open`\n  | `head ${'up' | 'down'}`;\n\n/** iris gesture type */\nexport type IrisGesture =\n  'facing center'\n  | `looking ${'left' | 'right' | 'up' | 'down'}`\n  | 'looking center';\n\n/** body gesture type */\nexport type BodyGesture =\n  `leaning ${'left' | 'right'}`\n  | `raise ${'left' | 'right'} hand`\n  | 'i give up';\n\n/** hand gesture type */\nexport type HandGesture =\n  `${'thumb' | 'index' | 'middle' | 'ring' | 'pinky'} forward`\n  | `${'thumb' | 'index' | 'middle' | 'ring' | 'pinky'} up`\n  | 'victory'\n  | 'thumbs up';\n\nexport const body = (res: BodyResult[]): GestureResult[] => {\n  if (!res) return [];\n  const gestures: { body: number, gesture: BodyGesture }[] = [];\n  for (let i = 0; i < res.length; i++) {\n    // raising hands\n    const leftWrist = res[i].keypoints.find((a) => (a.part === 'leftWrist'));\n    const rightWrist = res[i].keypoints.find((a) => (a.part === 'rightWrist'));\n    const nose = res[i].keypoints.find((a) => (a.part === 'nose'));\n    if (nose && leftWrist && rightWrist && (leftWrist.position[1] < nose.position[1]) && (rightWrist.position[1] < nose.position[1])) gestures.push({ body: i, gesture: 'i give up' });\n    else if (nose && leftWrist && (leftWrist.position[1] < nose.position[1])) gestures.push({ body: i, gesture: 'raise left hand' });\n    else if (nose && rightWrist && (rightWrist.position[1] < nose.position[1])) gestures.push({ body: i, gesture: 'raise right hand' });\n\n    // leaning\n    const leftShoulder = res[i].keypoints.find((a) => (a.part === 'leftShoulder'));\n    const rightShoulder = res[i].keypoints.find((a) => (a.part === 'rightShoulder'));\n    if (leftShoulder && rightShoulder && Math.abs(leftShoulder.positionRaw[1] - rightShoulder.positionRaw[1]) > 0.1) {\n      gestures.push({ body: i, gesture: `leaning ${(leftShoulder.position[1] > rightShoulder.position[1]) ? 'left' : 'right'}` });\n    }\n  }\n  return gestures;\n};\n\nexport const face = (res: FaceResult[]): GestureResult[] => {\n  if (!res) return [];\n  const gestures: { face: number, gesture: FaceGesture }[] = [];\n  for (let i = 0; i < res.length; i++) {\n    if (res[i].mesh && res[i].mesh.length > 450) {\n      const zDiff = (res[i].mesh[33][2] || 0) - (res[i].mesh[263][2] || 0);\n      const xDiff = res[i].mesh[33][0] - res[i].mesh[263][0];\n      if (Math.abs(zDiff / xDiff) <= 0.15) gestures.push({ face: i, gesture: 'facing center' });\n      else gestures.push({ face: i, gesture: `facing ${zDiff < 0 ? 'left' : 'right'}` });\n      const openLeft = Math.abs(res[i].mesh[374][1] - res[i].mesh[386][1]) / Math.abs(res[i].mesh[443][1] - res[i].mesh[450][1]); // center of eye inner lid y coord div center of wider eye border y coord\n      if (openLeft < 0.2) gestures.push({ face: i, gesture: 'blink left eye' });\n      const openRight = Math.abs(res[i].mesh[145][1] - res[i].mesh[159][1]) / Math.abs(res[i].mesh[223][1] - res[i].mesh[230][1]); // center of eye inner lid y coord div center of wider eye border y coord\n      if (openRight < 0.2) gestures.push({ face: i, gesture: 'blink right eye' });\n      const mouthOpen = Math.min(100, 500 * Math.abs(res[i].mesh[13][1] - res[i].mesh[14][1]) / Math.abs(res[i].mesh[10][1] - res[i].mesh[152][1]));\n      if (mouthOpen > 10) gestures.push({ face: i, gesture: `mouth ${Math.trunc(mouthOpen)}% open` });\n      const chinDepth = res[i].mesh[152][2] || 0;\n      if (Math.abs(chinDepth) > 10) gestures.push({ face: i, gesture: `head ${chinDepth < 0 ? 'up' : 'down'}` });\n    }\n  }\n  return gestures;\n};\n\nexport const iris = (res: FaceResult[]): GestureResult[] => {\n  if (!res) return [];\n  const gestures: { iris: number, gesture: IrisGesture }[] = [];\n  for (let i = 0; i < res.length; i++) {\n    if (!res[i].annotations?.leftEyeIris?.[0] || !res[i].annotations?.rightEyeIris?.[0]) continue;\n    const sizeXLeft = res[i].annotations.leftEyeIris[3][0] - res[i].annotations.leftEyeIris[1][0];\n    const sizeYLeft = res[i].annotations.leftEyeIris[4][1] - res[i].annotations.leftEyeIris[2][1];\n    const areaLeft = Math.abs(sizeXLeft * sizeYLeft);\n\n    const sizeXRight = res[i].annotations.rightEyeIris[3][0] - res[i].annotations.rightEyeIris[1][0];\n    const sizeYRight = res[i].annotations.rightEyeIris[4][1] - res[i].annotations.rightEyeIris[2][1];\n    const areaRight = Math.abs(sizeXRight * sizeYRight);\n\n    let center = false;\n    const difference = Math.abs(areaLeft - areaRight) / Math.max(areaLeft, areaRight);\n    if (difference < 0.25) {\n      center = true;\n      gestures.push({ iris: i, gesture: 'facing center' });\n    }\n\n    const leftIrisCenterX = Math.abs(res[i].mesh[263][0] - res[i].annotations.leftEyeIris[0][0]) / res[i].box[2];\n    const rightIrisCenterX = Math.abs(res[i].mesh[33][0] - res[i].annotations.rightEyeIris[0][0]) / res[i].box[2];\n    if (leftIrisCenterX > 0.06 || rightIrisCenterX > 0.06) center = false;\n    if (leftIrisCenterX > rightIrisCenterX) { // check eye with bigger offset\n      if (leftIrisCenterX > 0.05) gestures.push({ iris: i, gesture: 'looking right' });\n    } else {\n      if (rightIrisCenterX > 0.05) gestures.push({ iris: i, gesture: 'looking left' });\n    }\n\n    const rightIrisCenterY = Math.abs(res[i].mesh[145][1] - res[i].annotations.rightEyeIris[0][1]) / res[i].box[3];\n    const leftIrisCenterY = Math.abs(res[i].mesh[374][1] - res[i].annotations.leftEyeIris[0][1]) / res[i].box[3];\n    if (leftIrisCenterY < 0.01 || rightIrisCenterY < 0.01 || leftIrisCenterY > 0.022 || rightIrisCenterY > 0.022) center = false;\n    if (leftIrisCenterY < 0.01 || rightIrisCenterY < 0.01) gestures.push({ iris: i, gesture: 'looking down' });\n    if (leftIrisCenterY > 0.022 || rightIrisCenterY > 0.022) gestures.push({ iris: i, gesture: 'looking up' });\n\n    // still center;\n    if (center) gestures.push({ iris: i, gesture: 'looking center' });\n  }\n  return gestures;\n};\n\nexport const hand = (res: HandResult[]): GestureResult[] => {\n  if (!res) return [];\n  const gestures: { hand: number, gesture: HandGesture }[] = [];\n  for (let i = 0; i < res.length; i++) {\n    const fingers: { name: string, position: Point }[] = [];\n    if (res[i].annotations) {\n      for (const [finger, pos] of Object.entries(res[i].annotations)) {\n        if (finger !== 'palmBase' && Array.isArray(pos) && pos[0]) fingers.push({ name: finger.toLowerCase(), position: pos[0] }); // get tip of each finger\n      }\n    }\n    if (fingers && fingers.length > 0) {\n      const closest = fingers.reduce((best, a) => ((best.position[2] || 0) < (a.position[2] || 0) ? best : a));\n      gestures.push({ hand: i, gesture: `${closest.name} forward` as HandGesture });\n      const highest = fingers.reduce((best, a) => (best.position[1] < a.position[1] ? best : a));\n      gestures.push({ hand: i, gesture: `${highest.name} up` as HandGesture });\n    }\n    if (res[i].keypoints) {\n      const poses = fingerPose.match(res[i].keypoints);\n      for (const pose of poses) gestures.push({ hand: i, gesture: pose.name as HandGesture });\n    }\n  }\n  return gestures;\n};\n", "/**\n * Results interpolation for smoothening of video detection results inbetween detected frames\n */\n\nimport type { Result, FaceResult, BodyResult, HandResult, ObjectResult, PersonResult, Box, Point, BodyLandmark, BodyAnnotation } from '../result';\nimport type { Config } from '../config';\n\nimport * as moveNetCoords from '../body/movenetcoords';\nimport * as blazePoseCoords from '../body/blazeposecoords';\nimport * as efficientPoseCoords from '../body/efficientposecoords';\nimport { now } from './util';\nimport { env } from './env';\n\nconst bufferedResult: Result = { face: [], body: [], hand: [], gesture: [], object: [], persons: [], performance: {}, timestamp: 0, error: null };\nlet interpolateTime = 0;\n\nexport function calc(newResult: Result, config: Config): Result {\n  const t0 = now();\n  if (!newResult) return { face: [], body: [], hand: [], gesture: [], object: [], persons: [], performance: {}, timestamp: 0, error: null };\n  // each record is only updated using deep clone when number of detected record changes, otherwise it will converge by itself\n  // otherwise bufferedResult is a shallow clone of result plus updated local calculated values\n  // thus mixing by-reference and by-value assignments to minimize memory operations\n\n  const elapsed = Date.now() - newResult.timestamp;\n\n  /* curve fitted: buffer = 8 - ln(delay)\n     interpolation formula: current = ((buffer - 1) * previous + live) / buffer\n    - at  50ms delay buffer = ~4.1 => 28% towards live data\n    - at 250ms delay buffer = ~2.5 => 40% towards live data\n    - at 500ms delay buffer = ~1.8 => 55% towards live data\n    - at 750ms delay buffer = ~1.4 => 71% towards live data\n    - at  1sec delay buffer = 1 which means live data is used\n  */\n  const bufferedFactor = elapsed < 1000 ? 8 - Math.log(elapsed + 1) : 1;\n\n  if (newResult.canvas) bufferedResult.canvas = newResult.canvas;\n  if (newResult.error) bufferedResult.error = newResult.error;\n\n  // interpolate body results\n  if (!bufferedResult.body || (newResult.body.length !== bufferedResult.body.length)) {\n    bufferedResult.body = JSON.parse(JSON.stringify(newResult.body)) as BodyResult[]; // deep clone once\n  } else {\n    for (let i = 0; i < newResult.body.length; i++) {\n      const box = newResult.body[i].box // update box\n        .map((newBoxCoord, j) => ((bufferedFactor - 1) * bufferedResult.body[i].box[j] + newBoxCoord) / bufferedFactor) as Box;\n      const boxRaw = newResult.body[i].boxRaw // update boxRaw\n        .map((newBoxCoord, j) => ((bufferedFactor - 1) * bufferedResult.body[i].boxRaw[j] + newBoxCoord) / bufferedFactor) as Box;\n      const keypoints = (newResult.body[i].keypoints // update keypoints\n        .map((newKpt, j) => ({\n          score: newKpt.score,\n          part: newKpt.part,\n          position: [\n            bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].position[0] || 0) + (newKpt.position[0] || 0)) / bufferedFactor : newKpt.position[0],\n            bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].position[1] || 0) + (newKpt.position[1] || 0)) / bufferedFactor : newKpt.position[1],\n            bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].position[2] || 0) + (newKpt.position[2] || 0)) / bufferedFactor : newKpt.position[2],\n          ],\n          positionRaw: [\n            bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].positionRaw[0] || 0) + (newKpt.positionRaw[0] || 0)) / bufferedFactor : newKpt.positionRaw[0],\n            bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].positionRaw[1] || 0) + (newKpt.positionRaw[1] || 0)) / bufferedFactor : newKpt.positionRaw[1],\n            bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].positionRaw[2] || 0) + (newKpt.positionRaw[2] || 0)) / bufferedFactor : newKpt.positionRaw[2],\n          ],\n          distance: [\n            bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].distance?.[0] || 0) + (newKpt.distance?.[0] || 0)) / bufferedFactor : newKpt.distance?.[0],\n            bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].distance?.[1] || 0) + (newKpt.distance?.[1] || 0)) / bufferedFactor : newKpt.distance?.[1],\n            bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].distance?.[2] || 0) + (newKpt.distance?.[2] || 0)) / bufferedFactor : newKpt.distance?.[2],\n          ],\n        }))) as { score: number, part: BodyLandmark, position: [number, number, number?], positionRaw: [number, number, number?] }[];\n\n      const annotations: Record<BodyAnnotation, Point[][]> = {} as Record<BodyAnnotation, Point[][]>; // recreate annotations\n      let coords = { connected: {} };\n      if (config.body.modelPath?.includes('efficientpose')) coords = efficientPoseCoords;\n      else if (config.body.modelPath?.includes('blazepose')) coords = blazePoseCoords;\n      else if (config.body.modelPath?.includes('movenet')) coords = moveNetCoords;\n      for (const [name, indexes] of Object.entries(coords.connected as Record<string, string[]>)) {\n        const pt: Point[][] = [];\n        for (let j = 0; j < indexes.length - 1; j++) {\n          const pt0 = keypoints.find((kp) => kp.part === indexes[j]);\n          const pt1 = keypoints.find((kp) => kp.part === indexes[j + 1]);\n          // if (pt0 && pt1 && pt0.score > (config.body.minConfidence || 0) && pt1.score > (config.body.minConfidence || 0)) pt.push([pt0.position, pt1.position]);\n          if (pt0 && pt1) pt.push([pt0.position, pt1.position]);\n        }\n        annotations[name] = pt;\n      }\n      bufferedResult.body[i] = { ...newResult.body[i], box, boxRaw, keypoints, annotations }; // shallow clone plus updated values\n    }\n  }\n\n  // interpolate hand results\n  if (!bufferedResult.hand || (newResult.hand.length !== bufferedResult.hand.length)) {\n    bufferedResult.hand = JSON.parse(JSON.stringify(newResult.hand)); // deep clone once\n  } else {\n    for (let i = 0; i < newResult.hand.length; i++) {\n      const box = (newResult.hand[i].box// update box\n        .map((b, j) => ((bufferedFactor - 1) * bufferedResult.hand[i].box[j] + b) / bufferedFactor)) as Box;\n      const boxRaw = (newResult.hand[i].boxRaw // update boxRaw\n        .map((b, j) => ((bufferedFactor - 1) * bufferedResult.hand[i].boxRaw[j] + b) / bufferedFactor)) as Box;\n      if (bufferedResult.hand[i].keypoints.length !== newResult.hand[i].keypoints.length) bufferedResult.hand[i].keypoints = newResult.hand[i].keypoints; // reset keypoints as previous frame did not have them\n      const keypoints = newResult.hand[i].keypoints && newResult.hand[i].keypoints.length > 0 ? newResult.hand[i].keypoints // update landmarks\n        .map((landmark, j) => landmark\n          .map((coord, k) => (((bufferedFactor - 1) * (bufferedResult.hand[i].keypoints[j][k] || 1) + (coord || 0)) / bufferedFactor)) as Point)\n        : [];\n      let annotations = {};\n      if (Object.keys(bufferedResult.hand[i].annotations).length !== Object.keys(newResult.hand[i].annotations).length) {\n        bufferedResult.hand[i].annotations = newResult.hand[i].annotations; // reset annotations as previous frame did not have them\n        annotations = bufferedResult.hand[i].annotations;\n      } else if (newResult.hand[i].annotations) {\n        for (const key of Object.keys(newResult.hand[i].annotations)) { // update annotations\n          annotations[key] = newResult.hand[i]?.annotations?.[key]?.[0]\n            ? newResult.hand[i].annotations[key]\n              .map((val, j: number) => val\n                .map((coord: number, k: number) => ((bufferedFactor - 1) * bufferedResult.hand[i].annotations[key][j][k] + coord) / bufferedFactor))\n            : null;\n        }\n      }\n      bufferedResult.hand[i] = { ...newResult.hand[i], box, boxRaw, keypoints, annotations: annotations as HandResult['annotations'] }; // shallow clone plus updated values\n    }\n  }\n\n  // interpolate face results\n  if (!bufferedResult.face || (newResult.face.length !== bufferedResult.face.length)) {\n    bufferedResult.face = JSON.parse(JSON.stringify(newResult.face)) as FaceResult[]; // deep clone once\n  } else {\n    for (let i = 0; i < newResult.face.length; i++) {\n      const box = (newResult.face[i].box // update box\n        .map((b, j) => ((bufferedFactor - 1) * bufferedResult.face[i].box[j] + b) / bufferedFactor)) as Box;\n      const boxRaw = (newResult.face[i].boxRaw // update boxRaw\n        .map((b, j) => ((bufferedFactor - 1) * bufferedResult.face[i].boxRaw[j] + b) / bufferedFactor)) as Box;\n      if (newResult.face[i].rotation) {\n        const rotation: {\n          matrix: [number, number, number, number, number, number, number, number, number],\n          angle: { roll: number, yaw: number, pitch: number },\n          gaze: { bearing: number, strength: number }\n        } = { matrix: [0, 0, 0, 0, 0, 0, 0, 0, 0], angle: { roll: 0, yaw: 0, pitch: 0 }, gaze: { bearing: 0, strength: 0 } };\n        rotation.matrix = newResult.face[i].rotation?.matrix as [number, number, number, number, number, number, number, number, number];\n        rotation.angle = {\n          roll: ((bufferedFactor - 1) * (bufferedResult.face[i].rotation?.angle?.roll || 0) + (newResult.face[i].rotation?.angle?.roll || 0)) / bufferedFactor,\n          yaw: ((bufferedFactor - 1) * (bufferedResult.face[i].rotation?.angle?.yaw || 0) + (newResult.face[i].rotation?.angle?.yaw || 0)) / bufferedFactor,\n          pitch: ((bufferedFactor - 1) * (bufferedResult.face[i].rotation?.angle?.pitch || 0) + (newResult.face[i].rotation?.angle?.pitch || 0)) / bufferedFactor,\n        };\n        rotation.gaze = {\n          // not fully correct due projection on circle, also causes wrap-around draw on jump from negative to positive\n          bearing: ((bufferedFactor - 1) * (bufferedResult.face[i].rotation?.gaze.bearing || 0) + (newResult.face[i].rotation?.gaze.bearing || 0)) / bufferedFactor,\n          strength: ((bufferedFactor - 1) * (bufferedResult.face[i].rotation?.gaze.strength || 0) + (newResult.face[i].rotation?.gaze.strength || 0)) / bufferedFactor,\n        };\n        bufferedResult.face[i] = { ...newResult.face[i], rotation, box, boxRaw }; // shallow clone plus updated values\n      } else {\n        bufferedResult.face[i] = { ...newResult.face[i], box, boxRaw }; // shallow clone plus updated values\n      }\n    }\n  }\n\n  // interpolate object detection results\n  if (!bufferedResult.object || (newResult.object.length !== bufferedResult.object.length)) {\n    bufferedResult.object = JSON.parse(JSON.stringify(newResult.object)) as ObjectResult[]; // deep clone once\n  } else {\n    for (let i = 0; i < newResult.object.length; i++) {\n      const box = (newResult.object[i].box // update box\n        .map((b, j) => ((bufferedFactor - 1) * bufferedResult.object[i].box[j] + b) / bufferedFactor)) as Box;\n      const boxRaw = (newResult.object[i].boxRaw // update boxRaw\n        .map((b, j) => ((bufferedFactor - 1) * bufferedResult.object[i].boxRaw[j] + b) / bufferedFactor)) as Box;\n      bufferedResult.object[i] = { ...newResult.object[i], box, boxRaw }; // shallow clone plus updated values\n    }\n  }\n\n  // interpolate person results\n  if (newResult.persons) {\n    const newPersons = newResult.persons; // trigger getter function\n    if (!bufferedResult.persons || (newPersons.length !== bufferedResult.persons.length)) {\n      bufferedResult.persons = JSON.parse(JSON.stringify(newPersons)) as PersonResult[];\n    } else {\n      for (let i = 0; i < newPersons.length; i++) { // update person box, we don't update the rest as it's updated as reference anyhow\n        bufferedResult.persons[i].box = (newPersons[i].box\n          .map((box, j) => ((bufferedFactor - 1) * bufferedResult.persons[i].box[j] + box) / bufferedFactor)) as Box;\n      }\n    }\n  }\n\n  // just copy latest gestures without interpolation\n  if (newResult.gesture) bufferedResult.gesture = newResult.gesture;\n\n  // append interpolation performance data\n  const t1 = now();\n  interpolateTime = env.perfadd ? interpolateTime + Math.round(t1 - t0) : Math.round(t1 - t0);\n  if (newResult.performance) bufferedResult.performance = { ...newResult.performance, interpolate: interpolateTime };\n\n  return bufferedResult;\n}\n", "/** Face descriptor type as number array */\nexport type Descriptor = number[]\nexport type MatchOptions = { order?: number, threshold?: number, multiplier?: number, min?: number, max?: number } | undefined;\n\n/** Calculates distance between two descriptors\n * @param options - calculation options\n * - order - algorithm to use\n *   Euclidean distance if `order` is 2 (default), Minkowski distance algorithm of nth order if `order` is higher than 2\n * - multiplier - by how much to enhance difference analysis in range of 1..100\n *   default is 20 which normalizes results to similarity above 0.5 can be considered a match\n */\nexport function distance(descriptor1: Descriptor, descriptor2: Descriptor, options: MatchOptions = { order: 2, multiplier: 25 }) {\n  // general minkowski distance, euclidean distance is limited case where order is 2\n  if (!descriptor1 || !descriptor1) return Number.MAX_SAFE_INTEGER;\n  let sum = 0;\n  for (let i = 0; i < descriptor1.length; i++) {\n    const diff = (!options.order || options.order === 2) ? (descriptor1[i] - descriptor2[i]) : (Math.abs(descriptor1[i] - descriptor2[i]));\n    sum += (!options.order || options.order === 2) ? (diff * diff) : (diff ** options.order);\n  }\n  return (options.multiplier || 20) * sum;\n}\n\n// invert distance to similarity, normalize to given range and clamp\nconst normalizeDistance = (dist, order, min, max) => {\n  if (dist === 0) return 1; // short circuit for identical inputs\n  const root = order === 2 ? Math.sqrt(dist) : dist ** (1 / order); // take root of distance\n  const norm = (1 - (root / 100) - min) / (max - min); // normalize to range\n  const clamp = Math.max(Math.min(norm, 1), 0); // clamp to 0..1\n  return clamp;\n};\n\n/** Calculates normalized similarity between two face descriptors based on their `distance`\n * @param options - calculation options\n * - order - algorithm to use\n *   Euclidean distance if `order` is 2 (default), Minkowski distance algorithm of nth order if `order` is higher than 2\n * - multiplier - by how much to enhance difference analysis in range of 1..100\n *   default is 20 which normalizes results to similarity above 0.5 can be considered a match\n * - min - normalize similarity result to a given range\n * - max - normalzie similarity resutl to a given range\n *   default is 0.2...0.8\n * Returns similarity between two face descriptors normalized to 0..1 range where 0 is no similarity and 1 is perfect similarity\n */\nexport function similarity(descriptor1: Descriptor, descriptor2: Descriptor, options: MatchOptions = { order: 2, multiplier: 25, min: 0.2, max: 0.8 }) {\n  const dist = distance(descriptor1, descriptor2, options);\n  return normalizeDistance(dist, options.order || 2, options.min || 0, options.max || 1);\n}\n\n/** Matches given descriptor to a closest entry in array of descriptors\n * @param descriptor - face descriptor\n * @param descriptors - array of face descriptors to commpare given descriptor to\n * @param options - see `similarity` method for options description\n * Returns\n * - `index` index array index where best match was found or -1 if no matches\n * - `distance` calculated `distance` of given descriptor to the best match\n * - `similarity` calculated normalized `similarity` of given descriptor to the best match\n*/\nexport function match(descriptor: Descriptor, descriptors: Descriptor[], options: MatchOptions = { order: 2, multiplier: 25, threshold: 0, min: 0.2, max: 0.8 }) {\n  if (!Array.isArray(descriptor) || !Array.isArray(descriptors) || descriptor.length < 64 || descriptors.length === 0) { // validate input\n    return { index: -1, distance: Number.POSITIVE_INFINITY, similarity: 0 };\n  }\n  let lowestDistance = Number.MAX_SAFE_INTEGER;\n  let index = -1;\n  for (let i = 0; i < descriptors.length; i++) {\n    const res = descriptors[i].length === descriptor.length ? distance(descriptor, descriptors[i], options) : Number.MAX_SAFE_INTEGER;\n    if (res < lowestDistance) {\n      lowestDistance = res;\n      index = i;\n    }\n    if (lowestDistance < (options.threshold || 0)) break;\n  }\n  const normalizedSimilarity = normalizeDistance(lowestDistance, options.order || 2, options.min || 0, options.max || 1);\n  return { index, distance: lowestDistance, similarity: normalizedSimilarity };\n}\n", "/**\n * Analyze detection Results and sort&combine them into per-person view\n */\n\nimport type { FaceResult, BodyResult, HandResult, GestureResult, PersonResult, Box } from '../result';\n\nexport function join(faces: FaceResult[], bodies: BodyResult[], hands: HandResult[], gestures: GestureResult[], shape: number[] | undefined): PersonResult[] {\n  let id = 0;\n  const persons: PersonResult[] = [];\n  for (const face of faces) { // person is defined primarily by face and then we append other objects as found\n    const person: PersonResult = { id: id++, face, body: null, hands: { left: null, right: null }, gestures: [], box: [0, 0, 0, 0] };\n    for (const body of bodies) {\n      if (face.box[0] > body.box[0] // x within body\n        && face.box[0] < body.box[0] + body.box[2]\n        && face.box[1] + face.box[3] > body.box[1] // y within body\n        && face.box[1] + face.box[3] < body.box[1] + body.box[3]) {\n        person.body = body;\n      }\n    }\n    if (person.body) { // only try to join hands if body is found\n      for (const hand of hands) {\n        if (hand.box[0] + hand.box[2] > person.body.box[0] // x within body for left hand\n          && hand.box[0] + hand.box[2] < person.body.box[0] + person.body.box[2]\n          && hand.box[1] + hand.box[3] > person.body.box[1] // x within body for left hand\n          && hand.box[1] + hand.box[3] < person.body.box[1] + person.body.box[3]) {\n          if (person.hands) person.hands.left = hand;\n        }\n        if (hand.box[0] < person.body.box[0] + person.body.box[2] // x within body for right hand\n          && hand.box[0] > person.body.box[0]\n          && hand.box[1] + hand.box[3] > person.body.box[1] // x within body for right hand\n          && hand.box[1] + hand.box[3] < person.body.box[1] + person.body.box[3]) {\n          if (person.hands) person.hands.right = hand;\n        }\n      }\n    }\n    for (const gesture of gestures) { // append all gestures according to ids\n      if (gesture['face'] !== undefined && gesture['face'] === face.id) person.gestures.push(gesture);\n      else if (gesture['iris'] !== undefined && gesture['iris'] === face.id) person.gestures.push(gesture);\n      else if (gesture['body'] !== undefined && gesture['body'] === person.body?.id) person.gestures.push(gesture);\n      else if (gesture['hand'] !== undefined && gesture['hand'] === person.hands.left?.id) person.gestures.push(gesture);\n      else if (gesture['hand'] !== undefined && gesture['hand'] === person.hands.right?.id) person.gestures.push(gesture);\n    }\n\n    // create new overarching box from all boxes belonging to person\n    const x: number[] = [];\n    const y: number[] = [];\n    const extractXY = (box: Box | undefined) => { // extract all [x, y] coordinates from boxes [x, y, width, height]\n      if (box && box.length === 4) {\n        x.push(box[0], box[0] + box[2]);\n        y.push(box[1], box[1] + box[3]);\n      }\n    };\n    extractXY(person.face.box);\n    extractXY(person.body?.box);\n    extractXY(person.hands.left?.box);\n    extractXY(person.hands.right?.box);\n    const minX = Math.min(...x);\n    const minY = Math.min(...y);\n    person.box = [minX, minY, Math.max(...x) - minX, Math.max(...y) - minY]; // create new overarching box\n\n    // shape is known so we calculate boxRaw as well\n    if (shape?.[1] && shape?.[2]) person.boxRaw = [person.box[0] / shape[2], person.box[1] / shape[1], person.box[2] / shape[2], person.box[3] / shape[1]];\n\n    persons.push(person);\n  }\n  return persons;\n}\n", "/**\n * Embedded sample images used during warmup in dataURL format\n */\n\n// data:image/jpeg;base64,\nexport const face = 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"/**\n * Warmup algorithm that uses embedded images to exercise loaded models for faster future inference\n */\n\nimport { log, now, mergeDeep } from './util/util';\nimport * as sample from './sample';\nimport * as tf from '../dist/tfjs.esm.js';\nimport * as image from './image/image';\nimport * as backend from './tfjs/backend';\nimport { env } from './util/env';\nimport type { Config } from './config';\nimport type { Result } from './result';\nimport { Human, models } from './human';\nimport type { Tensor } from './exports';\n\nasync function warmupBitmap(instance: Human): Promise<Result | undefined> {\n  const b64toBlob = (base64: string, type = 'application/octet-stream') => fetch(`data:${type};base64,${base64}`).then((res) => res.blob());\n  let blob: Blob | null;\n  let res: Result | undefined;\n  switch (instance.config.warmup) {\n    case 'face': blob = await b64toBlob(sample.face); break;\n    case 'body':\n    case 'full': blob = await b64toBlob(sample.body); break;\n    default: blob = null;\n  }\n  if (blob) {\n    const bitmap = await createImageBitmap(blob);\n    res = await instance.detect(bitmap, instance.config);\n    bitmap.close();\n  }\n  return res;\n}\n\nasync function warmupCanvas(instance: Human): Promise<Result | undefined> {\n  return new Promise((resolve) => {\n    let src: string;\n    // let size = 0;\n    switch (instance.config.warmup) {\n      case 'face':\n        // size = 256;\n        src = 'data:image/jpeg;base64,' + sample.face;\n        break;\n      case 'full':\n      case 'body':\n        // size = 1200;\n        src = 'data:image/jpeg;base64,' + sample.body;\n        break;\n      default:\n        src = '';\n    }\n    // src = encodeURI('../assets/human-sample-upper.jpg');\n    let img: HTMLImageElement;\n    if (typeof Image !== 'undefined') img = new Image();\n    // @ts-ignore env.image is an external monkey-patch\n    else if (env.Image) img = new env.Image();\n    else return;\n    img.onload = async () => {\n      const canvas = image.canvas(img.naturalWidth, img.naturalHeight);\n      if (!canvas) {\n        log('Warmup: Canvas not found');\n        resolve(undefined);\n      } else {\n        const ctx = canvas.getContext('2d');\n        if (ctx) ctx.drawImage(img, 0, 0);\n        // const data = ctx?.getImageData(0, 0, canvas.height, canvas.width);\n        const tensor = await instance.image(canvas);\n        const res = tensor.tensor ? await instance.detect(tensor.tensor, instance.config) : undefined;\n        resolve(res);\n      }\n    };\n    if (src) img.src = src;\n    else resolve(undefined);\n  });\n}\n\nasync function warmupNode(instance: Human): Promise<Result | undefined> {\n  const atob = (str: string) => Buffer.from(str, 'base64');\n  let img;\n  if (instance.config.warmup === 'face') img = atob(sample.face);\n  else img = atob(sample.body);\n  let res: Result;\n  if (('node' in tf) && (tf.getBackend() === 'tensorflow')) {\n    const data: Tensor = tf['node'].decodeJpeg(img); // eslint-disable-line import/namespace\n    const expanded: Tensor = tf.expandDims(data, 0);\n    instance.tf.dispose(data);\n    // log('Input:', expanded);\n    res = await instance.detect(expanded, instance.config);\n    instance.tf.dispose(expanded);\n  } else {\n    if (instance.config.debug) log('Warmup tfjs-node not loaded');\n    /*\n    const input = await canvasJS.loadImage(img);\n    const canvas = canvasJS.createCanvas(input.width, input.height);\n    const ctx = canvas.getContext('2d');\n    ctx.drawImage(img, 0, 0, input.width, input.height);\n    res = await instance.detect(input, instance.config);\n    */\n  }\n  // @ts-ignore\n  return res;\n}\n\nasync function runInference(instance: Human) {\n  let res: Result | undefined;\n  if (typeof createImageBitmap === 'function') res = await warmupBitmap(instance);\n  else if (typeof Image !== 'undefined' || env.Canvas !== undefined) res = await warmupCanvas(instance);\n  else res = await warmupNode(instance);\n  return res;\n}\n\n/** Runs pre-compile on all loaded models */\nexport async function runCompile(instance: Human) {\n  if (!tf.env().flagRegistry.ENGINE_COMPILE_ONLY) return; // tfjs does not support compile-only inference\n  const backendType = tf.getBackend();\n  const webGLBackend = tf.backend();\n  if ((backendType !== 'webgl' && backendType !== 'humangl') || !webGLBackend?.checkCompileCompletion) {\n    // log('compile pass: skip');\n    return;\n  }\n  tf.env().set('ENGINE_COMPILE_ONLY', true);\n  const numTensorsStart = tf.engine().state.numTensors;\n  const compiledModels: string[] = [];\n  for (const [modelName, model] of Object.entries(instance.models).filter(([key, val]) => (key !== null && val !== null))) {\n    const shape = (model.inputs?.[0]?.shape) ? [...model.inputs[0].shape] : [1, 64, 64, 3];\n    const dtype: string = (model.inputs?.[0]?.dtype) ? model.inputs[0].dtype : 'float32';\n    for (let dim = 0; dim < shape.length; dim++) {\n      if (shape[dim] === -1) shape[dim] = dim === 0 ? 1 : 64; // override batch number and any dynamic dimensions\n    }\n    const tensor = tf.zeros(shape, dtype);\n    try {\n      const res = model.execute(tensor);\n      compiledModels.push(modelName);\n      if (Array.isArray(res)) res.forEach((t) => tf.dispose(t));\n      else tf.dispose(res);\n    } catch {\n      log('compile fail model:', modelName);\n    }\n    tf.dispose(tensor);\n  }\n  const kernels = await webGLBackend.checkCompileCompletionAsync();\n  webGLBackend.getUniformLocations();\n  if (instance.config.debug) log('compile pass:', { models: compiledModels, kernels: kernels.length });\n  tf.env().set('ENGINE_COMPILE_ONLY', false);\n  const numTensorsEnd = tf.engine().state.numTensors;\n  if ((numTensorsEnd - numTensorsStart) > 0) log('tensor leak:', numTensorsEnd - numTensorsStart);\n}\n\n/** Warmup method pre-initializes all configured models for faster inference\n * - can take significant time on startup\n * - only used in browser environments for `webgl` and `humangl` backends\n * @param userConfig?: Config\n*/\nexport async function warmup(instance: Human, userConfig?: Partial<Config>): Promise<Result | undefined> {\n  await backend.check(instance, false);\n  const t0 = now();\n  instance.state = 'warmup';\n  if (userConfig) instance.config = mergeDeep(instance.config, userConfig) as Config;\n  if (!instance.config.warmup || instance.config.warmup.length === 0 || instance.config.warmup === 'none') {\n    return { face: [], body: [], hand: [], gesture: [], object: [], performance: instance.performance, timestamp: now(), persons: [], error: null };\n  }\n  return new Promise(async (resolve) => {\n    await models.load(instance);\n    await runCompile(instance);\n    const res = await runInference(instance);\n    const t1 = now();\n    if (instance.config.debug) log('warmup', instance.config.warmup, Math.round(t1 - t0), 'ms');\n    instance.emit('warmup');\n    resolve(res);\n  });\n}\n", "/**\n * Human main module\n * @default Human Library\n * @summary <https://github.com/vladmandic/human>\n * @author <https://github.com/vladmandic>\n * @copyright <https://github.com/vladmandic>\n * @license MIT\n */\n\n// module imports\nimport { log, now, mergeDeep, validate } from './util/util';\nimport { defaults } from './config';\nimport { env, Env } from './util/env';\nimport { WebCam } from './util/webcam';\nimport { setModelLoadOptions } from './tfjs/load';\nimport * as tf from '../dist/tfjs.esm.js';\nimport * as app from '../package.json';\nimport * as backend from './tfjs/backend';\nimport * as blazepose from './body/blazepose';\nimport * as centernet from './object/centernet';\nimport * as draw from './draw/draw';\nimport * as efficientpose from './body/efficientpose';\nimport * as face from './face/face';\nimport * as facemesh from './face/facemesh';\nimport * as faceres from './face/faceres';\nimport * as gesture from './gesture/gesture';\nimport * as handpose from './hand/handpose';\nimport * as handtrack from './hand/handtrack';\nimport * as humangl from './tfjs/humangl';\nimport * as image from './image/image';\nimport * as interpolate from './util/interpolate';\nimport * as match from './face/match';\nimport * as models from './models';\nimport * as movenet from './body/movenet';\nimport * as nanodet from './object/nanodet';\nimport * as persons from './util/persons';\nimport * as posenet from './body/posenet';\nimport * as segmentation from './segmentation/segmentation';\nimport * as warmups from './warmup';\n\n// type definitions\nimport type { Input, Tensor, DrawOptions, Config, Result, FaceResult, HandResult, BodyResult, ObjectResult, GestureResult, PersonResult, AnyCanvas } from './exports';\n// type exports\nexport * from './exports';\n\n/** **Human** library main class\n *\n * All methods and properties are available only as members of Human class\n *\n * - Configuration object definition: {@link Config}\n * - Results object definition: {@link Result}\n * - Possible inputs: {@link Input}\n *\n * @param userConfig - {@link Config}\n * @returns instance of {@link Human}\n */\nexport class Human {\n  /** Current version of Human library in *semver* format */\n  version: string;\n\n  /** Current configuration\n   * - Defaults: [config](https://github.com/vladmandic/human/blob/main/src/config.ts#L262)\n   */\n  config: Config;\n\n  /** Last known result of detect run\n   * - Can be accessed anytime after initial detection\n  */\n  result: Result;\n\n  /** Current state of Human library\n   * - Can be polled to determine operations that are currently executed\n   * - Progresses through: 'config', 'check', 'backend', 'load', 'run:<model>', 'idle'\n   */\n  state: string;\n\n  /** currenty processed image tensor and canvas */\n  process: { tensor: Tensor | null, canvas: AnyCanvas | null };\n\n  /** Instance of TensorFlow/JS used by Human\n   *  - Can be embedded or externally provided\n   * [TFJS API](https://js.tensorflow.org/api/latest/)\n   */\n  tf;\n\n  /** Object containing environment information used for diagnostics */\n  env: Env;\n\n  /** Draw helper classes that can draw detected objects on canvas using specified draw\n   * - canvas: draws input to canvas\n   * - options: are global settings for all draw operations, can be overriden for each draw method {@link DrawOptions}\n   * - face, body, hand, gesture, object, person: draws detected results as overlays on canvas\n   */\n  draw: { canvas: typeof draw.canvas, face: typeof draw.face, body: typeof draw.body, hand: typeof draw.hand, gesture: typeof draw.gesture, object: typeof draw.object, person: typeof draw.person, all: typeof draw.all, options: DrawOptions };\n\n  /** Currently loaded models\n   * @internal\n   * {@link models#Models}\n  */\n  models: models.Models;\n\n  /** Container for events dispatched by Human\n   * Possible events:\n   * - `create`: triggered when Human object is instantiated\n   * - `load`: triggered when models are loaded (explicitly or on-demand)\n   * - `image`: triggered when input image is processed\n   * - `result`: triggered when detection is complete\n   * - `warmup`: triggered when warmup is complete\n   * - `error`: triggered on some errors\n   */\n  events: EventTarget | undefined;\n  /** Reference face triangualtion array of 468 points, used for triangle references between points */\n  faceTriangulation: number[];\n  /** Refernce UV map of 468 values, used for 3D mapping of the face mesh */\n  faceUVMap: [number, number][];\n  /** Performance object that contains values for all recently performed operations */\n  performance: Record<string, number>; // perf members are dynamically defined as needed\n  #numTensors: number;\n  #analyzeMemoryLeaks: boolean;\n  #checkSanity: boolean;\n  /** WebGL debug info */\n  gl: Record<string, unknown>;\n  // definition end\n\n  /** Constructor for **Human** library that is futher used for all operations\n   * @param userConfig - user configuration object {@link Config}\n   */\n  constructor(userConfig?: Partial<Config>) {\n    this.env = env;\n    /*\n    defaults.wasmPath = tf.version['tfjs-core'].includes('-') // custom build or official build\n      ? 'https://vladmandic.github.io/tfjs/dist/'\n      : `https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-backend-wasm@${tf.version_core}/dist/`;\n    */\n    const tfVersion = (tf.version.tfjs || tf.version_core).replace(/-(.*)/, '');\n    defaults.wasmPath = `https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-backend-wasm@${tfVersion}/dist/`;\n    defaults.modelBasePath = env.browser ? '../models/' : 'file://models/';\n    defaults.backend = env.browser ? 'webgl' : 'tensorflow';\n    this.version = app.version; // expose version property on instance of class\n    Object.defineProperty(this, 'version', { value: app.version }); // expose version property directly on class itself\n    this.config = JSON.parse(JSON.stringify(defaults));\n    Object.seal(this.config);\n    this.config.cacheModels = typeof indexedDB !== 'undefined';\n    if (userConfig) this.config = mergeDeep(this.config, userConfig);\n    setModelLoadOptions(this.config);\n    this.tf = tf;\n    this.state = 'idle';\n    this.#numTensors = 0;\n    this.#analyzeMemoryLeaks = false;\n    this.#checkSanity = false;\n    this.performance = {};\n    this.events = (typeof EventTarget !== 'undefined') ? new EventTarget() : undefined;\n    // object that contains all initialized models\n    this.models = new models.Models();\n    // reexport draw methods\n    this.draw = {\n      options: draw.options,\n      canvas: (input: AnyCanvas | HTMLImageElement | HTMLVideoElement, output: AnyCanvas) => draw.canvas(input, output),\n      face: (output: AnyCanvas, result: FaceResult[], options?: Partial<DrawOptions>) => draw.face(output, result, options),\n      body: (output: AnyCanvas, result: BodyResult[], options?: Partial<DrawOptions>) => draw.body(output, result, options),\n      hand: (output: AnyCanvas, result: HandResult[], options?: Partial<DrawOptions>) => draw.hand(output, result, options),\n      gesture: (output: AnyCanvas, result: GestureResult[], options?: Partial<DrawOptions>) => draw.gesture(output, result, options),\n      object: (output: AnyCanvas, result: ObjectResult[], options?: Partial<DrawOptions>) => draw.object(output, result, options),\n      person: (output: AnyCanvas, result: PersonResult[], options?: Partial<DrawOptions>) => draw.person(output, result, options),\n      all: (output: AnyCanvas, result: Result, options?: Partial<DrawOptions>) => draw.all(output, result, options),\n    };\n    this.result = { face: [], body: [], hand: [], gesture: [], object: [], performance: {}, timestamp: 0, persons: [], error: null };\n    // export access to image processing\n    this.process = { tensor: null, canvas: null };\n    // export raw access to underlying models\n    this.faceTriangulation = facemesh.triangulation;\n    this.faceUVMap = facemesh.uvmap;\n    // set gl info\n    this.gl = humangl.config;\n    // init model validation\n    models.validateModel(this, null, '');\n    // include platform info\n    this.emit('create');\n    if (this.config.debug || this.env.browser) log(`version: ${this.version}`);\n    if (this.config.debug) log(`tfjs version: ${this.tf.version['tfjs-core']}`);\n    const envTemp = JSON.parse(JSON.stringify(this.env));\n    delete envTemp.kernels;\n    delete envTemp.initial;\n    delete envTemp.perfadd;\n    if (this.config.debug) log('environment:', envTemp);\n  }\n\n  /** internal function to measure tensor leaks */\n  analyze = (...msg: string[]) => {\n    if (!this.#analyzeMemoryLeaks) return;\n    const currentTensors = this.tf.engine().state.numTensors;\n    const previousTensors = this.#numTensors;\n    this.#numTensors = currentTensors;\n    const leaked = currentTensors - previousTensors;\n    if (leaked !== 0) log(...msg, leaked);\n  };\n\n  /** internal function for quick sanity check on inputs @hidden */\n  #sanity = (input: Input): null | string => {\n    if (!this.#checkSanity) return null;\n    if (!input) return 'input is not defined';\n    if (this.env.node && !(input instanceof tf.Tensor)) return 'input must be a tensor';\n    try {\n      this.tf.getBackend();\n    } catch {\n      return 'backend not loaded';\n    }\n    return null;\n  };\n\n  /** Reset configuration to default values */\n  reset(): void {\n    const currentBackend = this.config.backend; // save backend;\n    this.config = JSON.parse(JSON.stringify(defaults));\n    this.config.backend = currentBackend;\n    image.reset();\n    env.initial = true;\n  }\n\n  /** Validate current configuration schema */\n  validate(userConfig?: Partial<Config>) {\n    const msgs = validate(defaults, userConfig || this.config);\n    if (msgs.length === 0) this.config = mergeDeep(this.config, userConfig) as Config;\n    return msgs;\n  }\n\n  /** Check model for invalid kernel ops for current backend */\n  check() {\n    return models.validate(this);\n  }\n\n  /** Exports face matching methods {@link match#similarity} */\n  public similarity = match.similarity;\n  /** Exports face matching methods {@link match#distance} */\n  public distance = match.distance;\n  /** Exports face matching methods {@link match#match} */\n  public match = match.match;\n\n  /** Utility wrapper for performance.now() */\n  now(): number { // eslint-disable-line class-methods-use-this\n    return now();\n  }\n\n  /** Process input as return canvas and tensor\n   *\n   * @param input - any input {@link Input}\n   * @param getTensor - should image processing also return tensor or just canvas\n   * Returns object with `tensor` and `canvas`\n   */\n  image(input: Input, getTensor: boolean = true) {\n    return image.process(input, this.config, getTensor);\n  }\n\n  /** Segmentation method takes any input and returns processed canvas with body segmentation\n   *  - Segmentation is not triggered as part of detect process\n   * @param input - {@link Input}\n   * @param background - {@link Input}\n   *  - Optional parameter background is used to fill the background with specific input\n   *  Returns:\n   *  - `data` as raw data array with per-pixel segmentation values\n   *  - `canvas` as canvas which is input image filtered with segementation data and optionally merged with background image. canvas alpha values are set to segmentation values for easy merging\n   *  - `alpha` as grayscale canvas that represents segmentation alpha values\n   */\n  async segmentation(input: Input, background?: Input): Promise<{ data: number[] | Tensor, canvas: AnyCanvas | null, alpha: AnyCanvas | null }> {\n    return segmentation.process(input, background, this.config);\n  }\n\n  /** Enhance method performs additional enhacements to face image previously detected for futher processing\n   *\n   * @param input - Tensor as provided in human.result.face[n].tensor\n   * @returns Tensor\n   */\n  enhance(input: Tensor): Tensor | null { // eslint-disable-line class-methods-use-this\n    return faceres.enhance(input);\n  }\n\n  /** Compare two input tensors for pixel simmilarity\n   * - use `human.image` to process any valid input and get a tensor that can be used for compare\n   * - when passing manually generated tensors:\n   *  - both input tensors must be in format [1, height, width, 3]\n   *  - if resolution of tensors does not match, second tensor will be resized to match resolution of the first tensor\n   * - return value is pixel similarity score normalized by input resolution and rgb channels\n  */\n  compare(firstImageTensor: Tensor, secondImageTensor: Tensor): Promise<number> {\n    return image.compare(this.config, firstImageTensor, secondImageTensor);\n  }\n\n  /** Explicit backend initialization\n   *  - Normally done implicitly during initial load phase\n   *  - Call to explictly register and initialize TFJS backend without any other operations\n   *  - Use when changing backend during runtime\n   */\n  async init(): Promise<void> {\n    await backend.check(this, true);\n    await this.tf.ready();\n    image.reset();\n  }\n\n  /** WebCam helper methods\n   *\n   */\n  public webcam = new WebCam();\n\n  /** Load method preloads all configured models on-demand\n   * - Not explicitly required as any required model is load implicitly on it's first run\n   *\n   * @param userConfig - {@link Config}\n  */\n  async load(userConfig?: Partial<Config>): Promise<void> {\n    this.state = 'load';\n    const timeStamp = now();\n    const count = Object.values(this.models).filter((model) => model).length;\n    if (userConfig) this.config = mergeDeep(this.config, userConfig) as Config;\n\n    if (this.env.initial) { // print version info on first run and check for correct backend setup\n      if (!await backend.check(this, false)) log('error: backend check failed');\n      await tf.ready();\n      if (this.env.browser) {\n        if (this.config.debug) log('configuration:', this.config);\n        if (this.config.debug) log('tf flags:', this.tf.ENV.flags);\n      }\n    }\n\n    await models.load(this); // actually loads models\n    if (this.env.initial && this.config.debug) log('tf engine state:', this.tf.engine().state.numBytes, 'bytes', this.tf.engine().state.numTensors, 'tensors'); // print memory stats on first run\n    this.env.initial = false;\n\n    const loaded = Object.values(this.models).filter((model) => model).length;\n    if (loaded !== count) { // number of loaded models changed\n      models.validate(this); // validate kernel ops used by model against current backend\n      this.emit('load');\n    }\n\n    const current = Math.trunc(now() - timeStamp);\n    if (current > (this.performance.loadModels || 0)) this.performance.loadModels = this.env.perfadd ? (this.performance.loadModels || 0) + current : current;\n  }\n\n  /** emit event */\n  emit = (event: string) => {\n    if (this.events?.dispatchEvent) this.events.dispatchEvent(new Event(event));\n  };\n\n  /** Runs interpolation using last known result and returns smoothened result\n   * Interpolation is based on time since last known result so can be called independently\n   *\n   * @param result - {@link Result} optional use specific result set to run interpolation on\n   * @returns result - {@link Result}\n   */\n  next(result: Result = this.result): Result {\n    return interpolate.calc(result, this.config);\n  }\n\n  /** get model loading/loaded stats */\n  getModelStats(): models.ModelStats { return models.getModelStats(this); }\n\n  /** Warmup method pre-initializes all configured models for faster inference\n   * - can take significant time on startup\n   * - only used for `webgl` and `humangl` backends\n   * @param userConfig - {@link Config}\n   * @returns result - {@link Result}\n  */\n  async warmup(userConfig?: Partial<Config>) {\n    const t0 = now();\n    const res = await warmups.warmup(this, userConfig);\n    const t1 = now();\n    this.performance.warmup = Math.trunc(t1 - t0);\n    return res;\n  }\n\n  /** Run detect with tensorflow profiling\n   * - result object will contain total exeuction time information for top-20 kernels\n   * - actual detection object can be accessed via `human.result`\n  */\n  async profile(input: Input, userConfig?: Partial<Config>): Promise<{ kernel: string, time: number, perc: number }[]> {\n    const profile = await this.tf.profile(() => this.detect(input, userConfig));\n    const kernels: Record<string, number> = {};\n    let total = 0;\n    for (const kernel of profile.kernels) { // sum kernel time values per kernel\n      if (kernels[kernel.name]) kernels[kernel.name] += kernel.kernelTimeMs;\n      else kernels[kernel.name] = kernel.kernelTimeMs;\n      total += kernel.kernelTimeMs;\n    }\n    const kernelArr: { kernel: string, time: number, perc: number }[] = [];\n    Object.entries(kernels).forEach((key) => kernelArr.push({ kernel: key[0], time: key[1] as unknown as number, perc: 0 })); // convert to array\n    for (const kernel of kernelArr) {\n      kernel.perc = Math.round(1000 * kernel.time / total) / 1000;\n      kernel.time = Math.round(1000 * kernel.time) / 1000;\n    }\n    kernelArr.sort((a, b) => b.time - a.time); // sort\n    kernelArr.length = 20; // crop\n    return kernelArr;\n  }\n\n  /** Main detection method\n   * - Analyze configuration: {@link Config}\n   * - Pre-process input: {@link Input}\n   * - Run inference for all configured models\n   * - Process and return result: {@link Result}\n   *\n   * @param input - {@link Input}\n   * @param userConfig - {@link Config}\n   * @returns result - {@link Result}\n  */\n  async detect(input: Input, userConfig?: Partial<Config>): Promise<Result> {\n    // detection happens inside a promise\n    this.state = 'detect';\n    return new Promise(async (resolve) => {\n      this.state = 'config';\n      let timeStamp;\n\n      // update configuration\n      this.config = mergeDeep(this.config, userConfig) as Config;\n\n      // sanity checks\n      this.state = 'check';\n      const error = this.#sanity(input);\n      if (error) {\n        log(error, input);\n        this.emit('error');\n        resolve({ face: [], body: [], hand: [], gesture: [], object: [], performance: this.performance, timestamp: now(), persons: [], error });\n      }\n\n      const timeStart = now();\n\n      // load models if enabled\n      await this.load();\n\n      timeStamp = now();\n      this.state = 'image';\n      const img = await image.process(input, this.config) as { canvas: AnyCanvas, tensor: Tensor };\n      this.process = img;\n      this.performance.inputProcess = this.env.perfadd ? (this.performance.inputProcess || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n      this.analyze('Get Image:');\n\n      if (!img.tensor) {\n        if (this.config.debug) log('could not convert input to tensor');\n        this.emit('error');\n        resolve({ face: [], body: [], hand: [], gesture: [], object: [], performance: this.performance, timestamp: now(), persons: [], error: 'could not convert input to tensor' });\n        return;\n      }\n      this.emit('image');\n\n      timeStamp = now();\n      this.config.skipAllowed = await image.skip(this.config, img.tensor);\n      if (!this.performance.totalFrames) this.performance.totalFrames = 0;\n      if (!this.performance.cachedFrames) this.performance.cachedFrames = 0;\n      (this.performance.totalFrames)++;\n      if (this.config.skipAllowed) this.performance.cachedFrames++;\n      this.performance.cacheCheck = this.env.perfadd ? (this.performance.cacheCheck || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n      this.analyze('Check Changed:');\n\n      // prepare where to store model results\n      // keep them with weak typing as it can be promise or not\n      let faceRes: FaceResult[] | Promise<FaceResult[]> | never[] = [];\n      let bodyRes: BodyResult[] | Promise<BodyResult[]> | never[] = [];\n      let handRes: HandResult[] | Promise<HandResult[]> | never[] = [];\n      let objectRes: ObjectResult[] | Promise<ObjectResult[]> | never[] = [];\n\n      // run face detection followed by all models that rely on face bounding box: face mesh, age, gender, emotion\n      this.state = 'detect:face';\n      if (this.config.async) {\n        faceRes = this.config.face.enabled ? face.detectFace(this, img.tensor) : [];\n        if (this.performance.face) delete this.performance.face;\n      } else {\n        timeStamp = now();\n        faceRes = this.config.face.enabled ? await face.detectFace(this, img.tensor) : [];\n        this.performance.face = this.env.perfadd ? (this.performance.face || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n      }\n\n      if (this.config.async && (this.config.body.maxDetected === -1 || this.config.hand.maxDetected === -1)) faceRes = await faceRes; // need face result for auto-detect number of hands or bodies\n\n      // run body: can be posenet, blazepose, efficientpose, movenet\n      this.analyze('Start Body:');\n      this.state = 'detect:body';\n      const bodyConfig = this.config.body.maxDetected === -1 ? mergeDeep(this.config, { body: { maxDetected: this.config.face.enabled ? 1 * (faceRes as FaceResult[]).length : 1 } }) : this.config; // autodetect number of bodies\n      if (this.config.async) {\n        if (this.config.body.modelPath?.includes('posenet')) bodyRes = this.config.body.enabled ? posenet.predict(img.tensor, bodyConfig) : [];\n        else if (this.config.body.modelPath?.includes('blazepose')) bodyRes = this.config.body.enabled ? blazepose.predict(img.tensor, bodyConfig) : [];\n        else if (this.config.body.modelPath?.includes('efficientpose')) bodyRes = this.config.body.enabled ? efficientpose.predict(img.tensor, bodyConfig) : [];\n        else if (this.config.body.modelPath?.includes('movenet')) bodyRes = this.config.body.enabled ? movenet.predict(img.tensor, bodyConfig) : [];\n        if (this.performance.body) delete this.performance.body;\n      } else {\n        timeStamp = now();\n        if (this.config.body.modelPath?.includes('posenet')) bodyRes = this.config.body.enabled ? await posenet.predict(img.tensor, bodyConfig) : [];\n        else if (this.config.body.modelPath?.includes('blazepose')) bodyRes = this.config.body.enabled ? await blazepose.predict(img.tensor, bodyConfig) : [];\n        else if (this.config.body.modelPath?.includes('efficientpose')) bodyRes = this.config.body.enabled ? await efficientpose.predict(img.tensor, bodyConfig) : [];\n        else if (this.config.body.modelPath?.includes('movenet')) bodyRes = this.config.body.enabled ? await movenet.predict(img.tensor, bodyConfig) : [];\n        this.performance.body = this.env.perfadd ? (this.performance.body || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n      }\n      this.analyze('End Body:');\n\n      // run handpose\n      this.analyze('Start Hand:');\n      this.state = 'detect:hand';\n      const handConfig = this.config.hand.maxDetected === -1 ? mergeDeep(this.config, { hand: { maxDetected: this.config.face.enabled ? 2 * (faceRes as FaceResult[]).length : 1 } }) : this.config; // autodetect number of hands\n      if (this.config.async) {\n        if (this.config.hand.detector?.modelPath?.includes('handdetect')) handRes = this.config.hand.enabled ? handpose.predict(img.tensor, handConfig) : [];\n        else if (this.config.hand.detector?.modelPath?.includes('handtrack')) handRes = this.config.hand.enabled ? handtrack.predict(img.tensor, handConfig) : [];\n        if (this.performance.hand) delete this.performance.hand;\n      } else {\n        timeStamp = now();\n        if (this.config.hand.detector?.modelPath?.includes('handdetect')) handRes = this.config.hand.enabled ? await handpose.predict(img.tensor, handConfig) : [];\n        else if (this.config.hand.detector?.modelPath?.includes('handtrack')) handRes = this.config.hand.enabled ? await handtrack.predict(img.tensor, handConfig) : [];\n        this.performance.hand = this.env.perfadd ? (this.performance.hand || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n      }\n      this.analyze('End Hand:');\n\n      // run object detection\n      this.analyze('Start Object:');\n      this.state = 'detect:object';\n      if (this.config.async) {\n        if (this.config.object.modelPath?.includes('nanodet')) objectRes = this.config.object.enabled ? nanodet.predict(img.tensor, this.config) : [];\n        else if (this.config.object.modelPath?.includes('centernet')) objectRes = this.config.object.enabled ? centernet.predict(img.tensor, this.config) : [];\n        if (this.performance.object) delete this.performance.object;\n      } else {\n        timeStamp = now();\n        if (this.config.object.modelPath?.includes('nanodet')) objectRes = this.config.object.enabled ? await nanodet.predict(img.tensor, this.config) : [];\n        else if (this.config.object.modelPath?.includes('centernet')) objectRes = this.config.object.enabled ? await centernet.predict(img.tensor, this.config) : [];\n        this.performance.object = this.env.perfadd ? (this.performance.object || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n      }\n      this.analyze('End Object:');\n\n      // if async wait for results\n      this.state = 'detect:await';\n      if (this.config.async) [faceRes, bodyRes, handRes, objectRes] = await Promise.all([faceRes, bodyRes, handRes, objectRes]);\n\n      // run gesture analysis last\n      this.state = 'detect:gesture';\n      let gestureRes: GestureResult[] = [];\n      if (this.config.gesture.enabled) {\n        timeStamp = now();\n        gestureRes = [...gesture.face(faceRes as FaceResult[]), ...gesture.body(bodyRes as BodyResult[]), ...gesture.hand(handRes as HandResult[]), ...gesture.iris(faceRes as FaceResult[])];\n        if (!this.config.async) this.performance.gesture = this.env.perfadd ? 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(this.performance.total || 0) + Math.trunc(now() - timeStart) : Math.trunc(now() - timeStart);\n      const shape = this.process.tensor?.shape || [];\n      this.result = {\n        face: faceRes as FaceResult[],\n        body: bodyRes as BodyResult[],\n        hand: handRes as HandResult[],\n        gesture: gestureRes,\n        object: objectRes as ObjectResult[],\n        performance: this.performance,\n        canvas: this.process.canvas,\n        timestamp: Date.now(),\n        error: null,\n        get persons() { return persons.join(faceRes as FaceResult[], bodyRes as BodyResult[], handRes as HandResult[], gestureRes, shape); },\n      };\n\n      // finally dispose input tensor\n      tf.dispose(img.tensor);\n\n      // log('Result:', result);\n      this.emit('detect');\n      this.state = 'idle';\n      resolve(this.result);\n    });\n  }\n\n  /** Helper function\n   * @param ms - sleep time in miliseconds\n   */\n  async sleep(ms: number): Promise<void> { // eslint-disable-line class-methods-use-this\n    return new Promise((resolve) => { setTimeout(resolve, ms); });\n  }\n\n  /** internal structure that keeps track of processed videos @hidden */\n  #loops: Record<string, boolean> = {};\n  /** Continously detect video frames\n   * @param element - HTMLVideoElement input\n   * @param run - boolean run continously or stop if already running, default true\n   * @param delay - number delay detection between frames for number of miliseconds, default 0\n   */\n  async video(element: HTMLVideoElement, run: boolean = true, delay: number = 0) {\n    if (run) {\n      if (!this.#loops[element.id]) {\n        if (this.config.debug) log('video start', element.id);\n        this.#loops[element.id] = true;\n      }\n      if (!element.paused && this.#loops[element.id] && (element.readyState >= 2)) await this.detect(element);\n      if (delay > 0) await this.sleep(delay);\n      if (this.#loops[element.id]) requestAnimationFrame(() => this.video(element, run, delay));\n    } else {\n      if (this.config.debug) log('video stop', element.id);\n      this.#loops[element.id] = false;\n    }\n  }\n}\n\n/** Class Human as default export */\n/* eslint no-restricted-exports: [\"off\", { \"restrictedNamedExports\": [\"default\"] }] */\nexport { Human as default, match, draw, models };\n"],
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+  "sources": ["../src/util/util.ts", "../src/config.ts", "tfjs.esm.js", "../src/image/imagefxshaders.ts", "../src/image/imagefx.ts", "../src/image/enhance.ts", "../src/image/image.ts", "../src/util/env.ts", "../src/util/webcam.ts", "../src/tfjs/load.ts", "../src/models.ts", "../src/face/antispoof.ts", "../src/face/facemeshcoords.ts", "../src/tfjs/constants.ts", "../src/face/facemeshutil.ts", "../src/face/blazeface.ts", "../src/body/blazeposecoords.ts", "../src/body/blazeposedetector.ts", "../src/util/box.ts", "../src/body/blazepose.ts", "../src/object/labels.ts", "../src/object/centernet.ts", "../src/body/efficientposecoords.ts", "../src/body/efficientpose.ts", "../src/gear/emotion.ts", "../src/face/iris.ts", "../src/face/constants.ts", "../src/face/attention.ts", "../src/face/facemesh.ts", "../src/face/faceres.ts", "../src/gear/gear.ts", "../src/hand/handposeutil.ts", "../src/hand/handposeanchors.ts", "../src/hand/handposedetector.ts", "../src/hand/handposepipeline.ts", "../src/hand/fingerdef.ts", "../src/hand/fingergesture.ts", "../src/hand/fingerpose.ts", "../src/hand/handpose.ts", "../src/hand/handtrack.ts", "../src/face/insightface.ts", "../src/face/liveness.ts", "../src/segmentation/meet.ts", "../src/face/mobilefacenet.ts", "../src/body/movenetcoords.ts", "../src/body/movenetfix.ts", "../src/body/movenet.ts", "../src/object/nanodet.ts", "../src/body/posenetutils.ts", "../src/body/posenet.ts", "../src/segmentation/rvm.ts", "../src/segmentation/selfie.ts", "../src/gear/ssrnet-age.ts", "../src/gear/ssrnet-gender.ts", "../src/tfjs/humangl.ts", "../src/tfjs/backend.ts", "../src/draw/draw.ts", "../src/draw/primitives.ts", "../src/draw/options.ts", "../src/draw/face.ts", "../src/draw/body.ts", "../src/draw/hand.ts", "../src/draw/object.ts", "../src/draw/gesture.ts", "../src/face/mask.ts", "../src/face/angles.ts", "../src/face/face.ts", "../src/gesture/gesture.ts", "../src/util/interpolate.ts", "../src/face/match.ts", "../src/util/persons.ts", "../src/sample.ts", "../src/warmup.ts", "../src/human.ts"],
+  "sourcesContent": ["import type { Config } from '../exports';\n\n/**\n * Simple helper functions used accross codebase\n */\n\n// helper function: wrapper around console output\nexport function log(...msg): void {\n  const dt = new Date();\n  const ts = `${dt.getHours().toString().padStart(2, '0')}:${dt.getMinutes().toString().padStart(2, '0')}:${dt.getSeconds().toString().padStart(2, '0')}.${dt.getMilliseconds().toString().padStart(3, '0')}`;\n  if (msg) console.log(ts, 'Human:', ...msg); // eslint-disable-line no-console\n}\n\n// helper function: join two paths\nexport function join(folder: string, file: string): string {\n  const separator = folder.endsWith('/') ? '' : '/';\n  const skipJoin = file.startsWith('.') || file.startsWith('/') || file.startsWith('http:') || file.startsWith('https:') || file.startsWith('file:');\n  const path = skipJoin ? `${file}` : `${folder}${separator}${file}`;\n  if (!path.toLocaleLowerCase().includes('.json')) throw new Error(`modelpath error: expecting json file: ${path}`);\n  return path;\n}\n\n// helper function: gets elapsed time on both browser and nodejs\nexport const now = () => {\n  if (typeof performance !== 'undefined') return performance.now();\n  return parseInt((Number(process.hrtime.bigint()) / 1000 / 1000).toString());\n};\n\n// helper function: checks current config validity\nexport function validate(defaults: Partial<Config>, config: Partial<Config>, parent = 'config', msgs: { reason: string, where: string, expected?: string }[] = []) {\n  for (const key of Object.keys(config)) {\n    if (typeof config[key] === 'object') {\n      validate(defaults[key], config[key], key, msgs);\n    } else {\n      const defined = defaults && (typeof defaults[key] !== 'undefined');\n      if (!defined) msgs.push({ reason: 'unknown property', where: `${parent}.${key} = ${config[key]}` });\n      const same = defaults && typeof defaults[key] === typeof config[key];\n      if (defined && !same) msgs.push({ reason: 'property type mismatch', where: `${parent}.${key} = ${config[key]}`, expected: typeof defaults[key] });\n    }\n    // ok = ok && defined && same;\n  }\n  if (config.debug && parent === 'config' && msgs.length > 0) log('invalid configuration', msgs);\n  return msgs;\n}\n\n// helper function: perform deep merge of multiple objects so it allows full inheritance with overrides\nexport function mergeDeep(...objects) {\n  const isObject = (obj) => obj && typeof obj === 'object';\n  return objects.reduce((prev, obj) => {\n    Object.keys(obj || {}).forEach((key) => {\n      const pVal = prev[key];\n      const oVal = obj[key];\n      if (Array.isArray(pVal) && Array.isArray(oVal)) prev[key] = pVal.concat(...oVal);\n      else if (isObject(pVal) && isObject(oVal)) prev[key] = mergeDeep(pVal, oVal);\n      else prev[key] = oVal;\n    });\n    return prev;\n  }, {});\n}\n\n// helper function: return min and max from input array\nexport const minmax = (data: number[]) => data.reduce((acc: number[], val) => {\n  acc[0] = (acc[0] === undefined || val < acc[0]) ? val : acc[0];\n  acc[1] = (acc[1] === undefined || val > acc[1]) ? val : acc[1];\n  return acc;\n}, []);\n\n// helper function: async wait\nexport async function wait(time: number) {\n  const waiting = new Promise((resolve) => { setTimeout(() => resolve(true), time); });\n  await waiting;\n}\n", "/* eslint-disable no-multi-spaces */\n\n/** Possible TensorFlow backends */\nexport type BackendEnum = '' | 'cpu' | 'wasm' | 'webgl' | 'humangl' | 'tensorflow' | 'webgpu';\n\n/** Possible values for `human.warmup` */\nexport type WarmupEnum = '' | 'none' | 'face' | 'full' | 'body';\n\n/** Possible segmentation model behavior */\nexport type SegmentationEnum = 'default' | 'alpha' | 'foreground' | 'state'\n\n/** Generic config type inherited by all module types */\nexport interface GenericConfig {\n  /** is module enabled? */\n  enabled: boolean,\n  /** path to model json file (relative to `modelBasePath` */\n  modelPath: string,\n  /** how many max frames to go without re-running model if cached results are acceptable\n   * for two-phase models such as face and hand caching applies to bounding boxes detection only */\n  skipFrames: number,\n  /** how many max milliseconds to go without re-running model if cached results are acceptable\n  * for two-phase models such as face and hand caching applies to bounding boxes detection only */\n  skipTime: number,\n}\n\n/** Detector part of face configuration */\nexport interface FaceDetectorConfig extends GenericConfig {\n  /** is face rotation correction performed after detecting face?\n   * used to correctly analyze faces under high angles\n  */\n  rotation: boolean,\n  /** maximum number of detected faces */\n  maxDetected: number,\n  /** minimum confidence for a detected face before results are discarded */\n  minConfidence: number,\n  /** minimum overlap between two detected faces before one is discarded */\n  iouThreshold: number,\n  /** should child models perform on masked image of a face */\n  mask: boolean,\n  /** should face detection return processed and cropped face tensor that can with an external model for addtional processing?\n   * if enabled it must be manually deallocated to avoid memory leak */\n  return: boolean,\n}\n\n/** Mesh part of face configuration */\nexport interface FaceMeshConfig extends GenericConfig {\n  /** Keep detected faces that cannot be verified using facemesh */\n  keepInvalid: boolean\n}\n\n/** Iris part of face configuration */\nexport interface FaceIrisConfig extends GenericConfig {}\n\n/** Attention part of face configuration */\nexport interface FaceAttentionConfig extends GenericConfig {}\n\n/** Description or face embedding part of face configuration\n * - also used by age and gender detection\n */\nexport interface FaceDescriptionConfig extends GenericConfig {\n  /** minimum confidence for a detected face before results are discarded */\n  minConfidence: number,\n}\n\n/** Emotion part of face configuration */\nexport interface FaceEmotionConfig extends GenericConfig {\n  /** minimum confidence for a detected face before results are discarded */\n  minConfidence: number,\n}\n\n/** Anti-spoofing part of face configuration */\nexport interface FaceAntiSpoofConfig extends GenericConfig {}\n\n/** Liveness part of face configuration */\nexport interface FaceLivenessConfig extends GenericConfig {}\n\n/** Gear part of face configuration */\nexport interface FaceGearConfig extends GenericConfig {\n  /** minimum confidence for a detected race before results are discarded */\n  minConfidence: number,\n}\n\n/** Configures all face-specific options: face detection, mesh analysis, age, gender, emotion detection and face description */\nexport interface FaceConfig extends GenericConfig {\n  detector: Partial<FaceDetectorConfig>,\n  mesh: Partial<FaceMeshConfig>,\n  attention: Partial<FaceAttentionConfig>,\n  iris: Partial<FaceIrisConfig>,\n  description: Partial<FaceDescriptionConfig>,\n  emotion: Partial<FaceEmotionConfig>,\n  antispoof: Partial<FaceAntiSpoofConfig>,\n  liveness: Partial<FaceLivenessConfig>,\n  gear: Partial<FaceGearConfig>,\n}\n\n/** Configures all body detection specific options */\nexport interface BodyConfig extends GenericConfig {\n  /** maximum number of detected bodies */\n  maxDetected: number,\n  /** minimum confidence for a detected body before results are discarded */\n  minConfidence: number,\n  /* experimental\n  /** experimental: detector used for body model before actual analysis\n  detector?: {\n    /** experimental: enable body detector before body landmarks\n    enabled: boolean,\n    /** experimental: path to optional body detector model json file\n    modelPath: string,\n    /** experimental: minimum confidence for a detected body before results are discarded\n    minConfidence: number,\n    /** experimental: minimum overlap between two detected bodies before one is discarded\n    iouThreshold: number\n  },\n  */\n}\n\n/** Configures all hand detection specific options */\nexport interface HandConfig extends GenericConfig {\n  /** should hand rotation correction be performed after hand detection? */\n  rotation: boolean,\n  /** minimum confidence for a detected hand before results are discarded */\n  minConfidence: number,\n  /** minimum overlap between two detected hands before one is discarded */\n  iouThreshold: number,\n  /** maximum number of detected hands */\n  maxDetected: number,\n  /** should hand landmarks be detected or just return detected hand box */\n  landmarks: boolean,\n  detector: {\n    /** path to hand detector model json */\n    modelPath?: string,\n  },\n  skeleton: {\n    /** path to hand skeleton model json */\n    modelPath?: string,\n  },\n}\n\n/** Configures all object detection specific options */\nexport interface ObjectConfig extends GenericConfig {\n  /** minimum confidence for a detected objects before results are discarded */\n  minConfidence: number,\n  /** minimum overlap between two detected objects before one is discarded */\n  iouThreshold: number,\n  /** maximum number of detected objects */\n  maxDetected: number,\n}\n\n/** Configures all body segmentation module\n * removes background from input containing person\n * if segmentation is enabled it will run as preprocessing task before any other model\n * alternatively leave it disabled and use it on-demand using human.segmentation method which can\n * remove background or replace it with user-provided background\n*/\nexport interface SegmentationConfig extends GenericConfig {\n  /** downsample ratio, adjust to reflect approximately how much of input is taken by body */\n  ratio: number,\n  /** possible rvm segmentation mode */\n  mode: SegmentationEnum,\n}\n\n/** Run input through image filters before inference\n * - available only in Browser environments\n * - image filters run with near-zero latency as they are executed on the GPU using WebGL\n*/\nexport interface FilterConfig {\n  /** are image filters enabled? */\n  enabled: boolean,\n  /** perform image histogram equalization\n   * - equalization is performed on input as a whole and detected face before its passed for further analysis\n  */\n  equalization: boolean,\n  /** resize input width\n  * - if both width and height are set to 0, there is no resizing\n  * - if just one is set, second one is scaled automatically\n  * - if both are set, values are used as-is\n  */\n  width: number,\n  /** resize input height\n  * - if both width and height are set to 0, there is no resizing\n  * - if just one is set, second one is scaled automatically\n  * - if both are set, values are used as-is\n  */\n  height: number,\n  /** return processed canvas imagedata in result */\n  return: boolean,\n  /** flip input as mirror image */\n  flip: boolean,\n  /** range: -1 (darken) to 1 (lighten) */\n  brightness: number,\n  /** range: -1 (reduce contrast) to 1 (increase contrast) */\n  contrast: number,\n  /** range: 0 (no sharpening) to 1 (maximum sharpening) */\n  sharpness: number,\n  /** range: 0 (no blur) to N (blur radius in pixels) */\n  blur: number\n  /** range: -1 (reduce saturation) to 1 (increase saturation) */\n  saturation: number,\n  /** range: 0 (no change) to 360 (hue rotation in degrees) */\n  hue: number,\n  /** image negative */\n  negative: boolean,\n  /** image sepia colors */\n  sepia: boolean,\n  /** image vintage colors */\n  vintage: boolean,\n  /** image kodachrome colors */\n  kodachrome: boolean,\n  /** image technicolor colors */\n  technicolor: boolean,\n  /** image polaroid camera effect */\n  polaroid: boolean,\n  /** range: 0 (no pixelate) to N (number of pixels to pixelate) */\n  pixelate: number,\n}\n\n/** Controlls gesture detection */\nexport interface GestureConfig {\n  /** is gesture detection enabled? */\n  enabled: boolean,\n}\n/**\n * Configuration interface definition for **Human** library\n * Contains all configurable parameters\n * Defaults: [config](https://github.com/vladmandic/human/blob/main/src/config.ts#L262)\n */\nexport interface Config {\n  /** Backend used for TFJS operations\n   * valid build-in backends are:\n   * - Browser: `cpu`, `wasm`, `webgl`, `humangl`, `webgpu`\n   * - NodeJS: `cpu`, `wasm`, `tensorflow`\n   * default: `webgl` for browser and `tensorflow` for nodejs\n  */\n  backend: BackendEnum,\n\n  /** Path to *.wasm files if backend is set to `wasm`\n   *\n   * default: auto-detects to link to CDN `jsdelivr` when running in browser\n  */\n  wasmPath: string,\n\n  /** Force WASM loader to use platform fetch\n   *\n   * default: false\n  */\n  wasmPlatformFetch: boolean,\n\n  /** Print debug statements to console\n   *\n   * default: `true`\n  */\n  debug: boolean,\n\n  /** Perform model loading and inference concurrently or sequentially\n   *\n   * default: `true`\n  */\n  async: boolean,\n\n  /** What to use for `human.warmup()`\n   * - warmup pre-initializes all models for faster inference but can take significant time on startup\n   * - used by `webgl`, `humangl` and `webgpu` backends\n   *\n   * default: `full`\n  */\n  warmup: WarmupEnum,\n\n  /** Base model path (typically starting with file://, http:// or https://) for all models\n   * - individual modelPath values are relative to this path\n   *\n   * default: `../models/` for browsers and `file://models/` for nodejs\n  */\n  modelBasePath: string,\n\n  /** Cache models in IndexDB on first sucessfull load\n   * default: true if indexdb is available (browsers), false if its not (nodejs)\n   */\n  cacheModels: boolean,\n\n  /** Validate kernel ops used in model during model load\n   * default: true\n   * any errors will be printed on console but will be treated as non-fatal\n   */\n  validateModels: boolean,\n\n  /** Cache sensitivity\n   * - values 0..1 where 0.01 means reset cache if input changed more than 1%\n   * - set to 0 to disable caching\n   *\n   * default: 0.7\n  */\n  cacheSensitivity: number;\n\n  /** Explicit flags passed to initialize TFJS */\n  flags: Record<string, unknown>,\n\n  /** Software Kernels\n   * Registers software kernel ops running on CPU when accelerated version of kernel is not found in the current backend\n  */\n  softwareKernels: boolean,\n\n  /** Perform immediate garbage collection on deallocated tensors instead of caching them */\n  deallocate: boolean;\n\n  /** Internal Variable */\n  skipAllowed: boolean;\n\n  /** Filter config {@link FilterConfig} */\n  filter: Partial<FilterConfig>,\n\n  /** Gesture config {@link GestureConfig} */\n  gesture: Partial<GestureConfig>;\n\n  /** Face config {@link FaceConfig} */\n  face: Partial<FaceConfig>,\n\n  /** Body config {@link BodyConfig} */\n  body: Partial<BodyConfig>,\n\n  /** Hand config {@link HandConfig} */\n  hand: Partial<HandConfig>,\n\n  /** Object config {@link ObjectConfig} */\n  object: Partial<ObjectConfig>,\n\n  /** Segmentation config {@link SegmentationConfig} */\n  segmentation: Partial<SegmentationConfig>,\n}\n\n/** - [See all default Config values...](https://github.com/vladmandic/human/blob/main/src/config.ts#L262) */\nconst config: Config = {\n  backend: '',\n  modelBasePath: '',\n  cacheModels: true,\n  validateModels: true,\n  wasmPath: '',\n  wasmPlatformFetch: false,\n  debug: false,\n  async: true,\n  warmup: 'full',\n  cacheSensitivity: 0.70,\n  skipAllowed: false,\n  deallocate: false,\n  flags: {},\n  softwareKernels: false,\n  filter: {\n    enabled: true,\n    equalization: false,\n    width: 0,\n    height: 0,\n    flip: false,\n    return: true,\n    brightness: 0,\n    contrast: 0,\n    sharpness: 0,\n    blur: 0,\n    saturation: 0,\n    hue: 0,\n    negative: false,\n    sepia: false,\n    vintage: false,\n    kodachrome: false,\n    technicolor: false,\n    polaroid: false,\n    pixelate: 0,\n  },\n  gesture: {\n    enabled: true,\n  },\n  face: {\n    enabled: true,\n    detector: {\n      modelPath: 'blazeface.json',\n      rotation: true,\n      maxDetected: 1,\n      skipFrames: 99,\n      skipTime: 2500,\n      minConfidence: 0.2,\n      iouThreshold: 0.1,\n      mask: false,\n      return: false,\n    },\n    mesh: {\n      enabled: true,\n      modelPath: 'facemesh.json',\n      keepInvalid: false,\n    },\n    attention: {\n      enabled: false,\n      modelPath: 'facemesh-attention.json',\n    },\n    iris: {\n      enabled: true,\n      modelPath: 'iris.json',\n    },\n    emotion: {\n      enabled: true,\n      minConfidence: 0.1,\n      skipFrames: 99,\n      skipTime: 1500,\n      modelPath: 'emotion.json',\n    },\n    description: {\n      enabled: true,\n      modelPath: 'faceres.json',\n      skipFrames: 99,\n      skipTime: 3000,\n      minConfidence: 0.1,\n    },\n    antispoof: {\n      enabled: false,\n      skipFrames: 99,\n      skipTime: 4000,\n      modelPath: 'antispoof.json',\n    },\n    liveness: {\n      enabled: false,\n      skipFrames: 99,\n      skipTime: 4000,\n      modelPath: 'liveness.json',\n    },\n  },\n  body: {\n    enabled: true,\n    modelPath: 'movenet-lightning.json',\n    maxDetected: -1,\n    minConfidence: 0.3,\n    skipFrames: 1,\n    skipTime: 200,\n  },\n  hand: {\n    enabled: true,\n    rotation: true,\n    skipFrames: 99,\n    skipTime: 1000,\n    minConfidence: 0.50,\n    iouThreshold: 0.2,\n    maxDetected: -1,\n    landmarks: true,\n    detector: {\n      modelPath: 'handtrack.json',\n    },\n    skeleton: {\n      modelPath: 'handlandmark-full.json',\n    },\n  },\n  object: {\n    enabled: false,\n    modelPath: 'mb3-centernet.json',\n    minConfidence: 0.2,\n    iouThreshold: 0.4,\n    maxDetected: 10,\n    skipFrames: 99,\n    skipTime: 2000,\n  },\n  segmentation: {\n    enabled: false,\n    modelPath: 'rvm.json',\n    ratio: 0.5,\n    mode: 'default',\n  },\n};\n\nexport { config as defaults };\n", "/*\n  Human\n  homepage: <https://github.com/vladmandic/human>\n  author: <https://github.com/vladmandic>'\n*/\n\nvar __create = Object.create;\nvar __defProp = Object.defineProperty;\nvar __getOwnPropDesc = Object.getOwnPropertyDescriptor;\nvar __getOwnPropNames = Object.getOwnPropertyNames;\nvar __getProtoOf = Object.getPrototypeOf;\nvar __hasOwnProp = Object.prototype.hasOwnProperty;\nvar __commonJS = (cb, mod4) => function __require() {\n  return mod4 || (0, cb[__getOwnPropNames(cb)[0]])((mod4 = { exports: {} }).exports, mod4), mod4.exports;\n};\nvar __export = (target, all5) => {\n  for (var name in all5)\n    __defProp(target, name, { get: all5[name], enumerable: true });\n};\nvar __copyProps = (to, from, except, desc) => {\n  if (from && typeof from === \"object\" || typeof from === \"function\") {\n    for (let key of __getOwnPropNames(from))\n      if (!__hasOwnProp.call(to, key) && key !== except)\n        __defProp(to, key, { get: () => from[key], enumerable: !(desc = __getOwnPropDesc(from, key)) || desc.enumerable });\n  }\n  return to;\n};\nvar __toESM = (mod4, isNodeMode, target) => (target = mod4 != null ? __create(__getProtoOf(mod4)) : {}, __copyProps(\n  isNodeMode || !mod4 || !mod4.__esModule ? __defProp(target, \"default\", { value: mod4, enumerable: true }) : target,\n  mod4\n));\n\n// node_modules/.pnpm/long@4.0.0/node_modules/long/src/long.js\nvar require_long = __commonJS({\n  \"node_modules/.pnpm/long@4.0.0/node_modules/long/src/long.js\"(exports, module) {\n    module.exports = Long2;\n    var wasm = null;\n    try {\n      wasm = new WebAssembly.Instance(new WebAssembly.Module(new Uint8Array([\n        0,\n        97,\n        115,\n        109,\n        1,\n        0,\n        0,\n        0,\n        1,\n        13,\n        2,\n        96,\n        0,\n        1,\n        127,\n        96,\n        4,\n        127,\n        127,\n        127,\n        127,\n        1,\n        127,\n        3,\n        7,\n        6,\n        0,\n        1,\n        1,\n        1,\n        1,\n        1,\n        6,\n        6,\n        1,\n        127,\n        1,\n        65,\n        0,\n        11,\n        7,\n        50,\n        6,\n        3,\n        109,\n        117,\n        108,\n        0,\n        1,\n        5,\n        100,\n        105,\n        118,\n        95,\n        115,\n        0,\n        2,\n        5,\n        100,\n        105,\n        118,\n        95,\n        117,\n        0,\n        3,\n        5,\n        114,\n        101,\n        109,\n        95,\n        115,\n        0,\n        4,\n        5,\n        114,\n        101,\n        109,\n        95,\n        117,\n        0,\n        5,\n        8,\n        103,\n        101,\n        116,\n        95,\n        104,\n        105,\n        103,\n        104,\n        0,\n        0,\n        10,\n        191,\n        1,\n        6,\n        4,\n        0,\n        35,\n        0,\n        11,\n        36,\n        1,\n        1,\n        126,\n        32,\n        0,\n        173,\n        32,\n        1,\n        173,\n        66,\n        32,\n        134,\n        132,\n        32,\n        2,\n        173,\n        32,\n        3,\n        173,\n        66,\n        32,\n        134,\n        132,\n        126,\n        34,\n        4,\n        66,\n        32,\n        135,\n        167,\n        36,\n        0,\n        32,\n        4,\n        167,\n        11,\n        36,\n        1,\n        1,\n        126,\n        32,\n        0,\n        173,\n        32,\n        1,\n        173,\n        66,\n        32,\n        134,\n        132,\n        32,\n        2,\n        173,\n        32,\n        3,\n        173,\n        66,\n        32,\n        134,\n        132,\n        127,\n        34,\n        4,\n        66,\n        32,\n        135,\n        167,\n        36,\n        0,\n        32,\n        4,\n        167,\n        11,\n        36,\n        1,\n        1,\n        126,\n        32,\n        0,\n        173,\n        32,\n        1,\n        173,\n        66,\n        32,\n        134,\n        132,\n        32,\n        2,\n        173,\n        32,\n        3,\n        173,\n        66,\n        32,\n        134,\n        132,\n        128,\n        34,\n        4,\n        66,\n        32,\n        135,\n        167,\n        36,\n        0,\n        32,\n        4,\n        167,\n        11,\n        36,\n        1,\n        1,\n        126,\n        32,\n        0,\n        173,\n        32,\n        1,\n        173,\n        66,\n        32,\n        134,\n        132,\n        32,\n        2,\n        173,\n        32,\n        3,\n        173,\n        66,\n        32,\n        134,\n        132,\n        129,\n        34,\n        4,\n        66,\n        32,\n        135,\n        167,\n        36,\n        0,\n        32,\n        4,\n        167,\n        11,\n        36,\n        1,\n        1,\n        126,\n        32,\n        0,\n        173,\n        32,\n        1,\n        173,\n        66,\n        32,\n        134,\n        132,\n        32,\n        2,\n        173,\n        32,\n        3,\n        173,\n        66,\n        32,\n        134,\n        132,\n        130,\n        34,\n        4,\n        66,\n        32,\n        135,\n        167,\n        36,\n        0,\n        32,\n        4,\n        167,\n        11\n      ])), {}).exports;\n    } catch (e) {\n    }\n    function Long2(low, high, unsigned) {\n      this.low = low | 0;\n      this.high = high | 0;\n      this.unsigned = !!unsigned;\n    }\n    Long2.prototype.__isLong__;\n    Object.defineProperty(Long2.prototype, \"__isLong__\", { value: true });\n    function isLong(obj) {\n      return (obj && obj[\"__isLong__\"]) === true;\n    }\n    Long2.isLong = isLong;\n    var INT_CACHE = {};\n    var UINT_CACHE = {};\n    function fromInt(value, unsigned) {\n      var obj, cachedObj, cache;\n      if (unsigned) {\n        value >>>= 0;\n        if (cache = 0 <= value && value < 256) {\n          cachedObj = UINT_CACHE[value];\n          if (cachedObj)\n            return cachedObj;\n        }\n        obj = fromBits(value, (value | 0) < 0 ? -1 : 0, true);\n        if (cache)\n          UINT_CACHE[value] = obj;\n        return obj;\n      } else {\n        value |= 0;\n        if (cache = -128 <= value && value < 128) {\n          cachedObj = INT_CACHE[value];\n          if (cachedObj)\n            return cachedObj;\n        }\n        obj = fromBits(value, value < 0 ? -1 : 0, false);\n        if (cache)\n          INT_CACHE[value] = obj;\n        return obj;\n      }\n    }\n    Long2.fromInt = fromInt;\n    function fromNumber(value, unsigned) {\n      if (isNaN(value))\n        return unsigned ? UZERO : ZERO;\n      if (unsigned) {\n        if (value < 0)\n          return UZERO;\n        if (value >= TWO_PWR_64_DBL)\n          return MAX_UNSIGNED_VALUE;\n      } else {\n        if (value <= -TWO_PWR_63_DBL)\n          return MIN_VALUE;\n        if (value + 1 >= TWO_PWR_63_DBL)\n          return MAX_VALUE;\n      }\n      if (value < 0)\n        return fromNumber(-value, unsigned).neg();\n      return fromBits(value % TWO_PWR_32_DBL | 0, value / TWO_PWR_32_DBL | 0, unsigned);\n    }\n    Long2.fromNumber = fromNumber;\n    function fromBits(lowBits, highBits, unsigned) {\n      return new Long2(lowBits, highBits, unsigned);\n    }\n    Long2.fromBits = fromBits;\n    var pow_dbl = Math.pow;\n    function fromString(str, unsigned, radix) {\n      if (str.length === 0)\n        throw Error(\"empty string\");\n      if (str === \"NaN\" || str === \"Infinity\" || str === \"+Infinity\" || str === \"-Infinity\")\n        return ZERO;\n      if (typeof unsigned === \"number\") {\n        radix = unsigned, unsigned = false;\n      } else {\n        unsigned = !!unsigned;\n      }\n      radix = radix || 10;\n      if (radix < 2 || 36 < radix)\n        throw RangeError(\"radix\");\n      var p2;\n      if ((p2 = str.indexOf(\"-\")) > 0)\n        throw Error(\"interior hyphen\");\n      else if (p2 === 0) {\n        return fromString(str.substring(1), unsigned, radix).neg();\n      }\n      var radixToPower = fromNumber(pow_dbl(radix, 8));\n      var result = ZERO;\n      for (var i = 0; i < str.length; i += 8) {\n        var size = Math.min(8, str.length - i), value = parseInt(str.substring(i, i + size), radix);\n        if (size < 8) {\n          var power = fromNumber(pow_dbl(radix, size));\n          result = result.mul(power).add(fromNumber(value));\n        } else {\n          result = result.mul(radixToPower);\n          result = result.add(fromNumber(value));\n        }\n      }\n      result.unsigned = unsigned;\n      return result;\n    }\n    Long2.fromString = fromString;\n    function fromValue(val, unsigned) {\n      if (typeof val === \"number\")\n        return fromNumber(val, unsigned);\n      if (typeof val === \"string\")\n        return fromString(val, unsigned);\n      return fromBits(val.low, val.high, typeof unsigned === \"boolean\" ? unsigned : val.unsigned);\n    }\n    Long2.fromValue = fromValue;\n    var TWO_PWR_16_DBL = 1 << 16;\n    var TWO_PWR_24_DBL = 1 << 24;\n    var TWO_PWR_32_DBL = TWO_PWR_16_DBL * TWO_PWR_16_DBL;\n    var TWO_PWR_64_DBL = TWO_PWR_32_DBL * TWO_PWR_32_DBL;\n    var TWO_PWR_63_DBL = TWO_PWR_64_DBL / 2;\n    var TWO_PWR_24 = fromInt(TWO_PWR_24_DBL);\n    var ZERO = fromInt(0);\n    Long2.ZERO = ZERO;\n    var UZERO = fromInt(0, true);\n    Long2.UZERO = UZERO;\n    var ONE = fromInt(1);\n    Long2.ONE = ONE;\n    var UONE = fromInt(1, true);\n    Long2.UONE = UONE;\n    var NEG_ONE = fromInt(-1);\n    Long2.NEG_ONE = NEG_ONE;\n    var MAX_VALUE = fromBits(4294967295 | 0, 2147483647 | 0, false);\n    Long2.MAX_VALUE = MAX_VALUE;\n    var MAX_UNSIGNED_VALUE = fromBits(4294967295 | 0, 4294967295 | 0, true);\n    Long2.MAX_UNSIGNED_VALUE = MAX_UNSIGNED_VALUE;\n    var MIN_VALUE = fromBits(0, 2147483648 | 0, false);\n    Long2.MIN_VALUE = MIN_VALUE;\n    var LongPrototype = Long2.prototype;\n    LongPrototype.toInt = function toInt() {\n      return this.unsigned ? this.low >>> 0 : this.low;\n    };\n    LongPrototype.toNumber = function toNumber() {\n      if (this.unsigned)\n        return (this.high >>> 0) * TWO_PWR_32_DBL + (this.low >>> 0);\n      return this.high * TWO_PWR_32_DBL + (this.low >>> 0);\n    };\n    LongPrototype.toString = function toString(radix) {\n      radix = radix || 10;\n      if (radix < 2 || 36 < radix)\n        throw RangeError(\"radix\");\n      if (this.isZero())\n        return \"0\";\n      if (this.isNegative()) {\n        if (this.eq(MIN_VALUE)) {\n          var radixLong = fromNumber(radix), div3 = this.div(radixLong), rem1 = div3.mul(radixLong).sub(this);\n          return div3.toString(radix) + rem1.toInt().toString(radix);\n        } else\n          return \"-\" + this.neg().toString(radix);\n      }\n      var radixToPower = fromNumber(pow_dbl(radix, 6), this.unsigned), rem = this;\n      var result = \"\";\n      while (true) {\n        var remDiv = rem.div(radixToPower), intval = rem.sub(remDiv.mul(radixToPower)).toInt() >>> 0, digits = intval.toString(radix);\n        rem = remDiv;\n        if (rem.isZero())\n          return digits + result;\n        else {\n          while (digits.length < 6)\n            digits = \"0\" + digits;\n          result = \"\" + digits + result;\n        }\n      }\n    };\n    LongPrototype.getHighBits = function getHighBits() {\n      return this.high;\n    };\n    LongPrototype.getHighBitsUnsigned = function getHighBitsUnsigned() {\n      return this.high >>> 0;\n    };\n    LongPrototype.getLowBits = function getLowBits() {\n      return this.low;\n    };\n    LongPrototype.getLowBitsUnsigned = function getLowBitsUnsigned() {\n      return this.low >>> 0;\n    };\n    LongPrototype.getNumBitsAbs = function getNumBitsAbs() {\n      if (this.isNegative())\n        return this.eq(MIN_VALUE) ? 64 : this.neg().getNumBitsAbs();\n      var val = this.high != 0 ? this.high : this.low;\n      for (var bit = 31; bit > 0; bit--)\n        if ((val & 1 << bit) != 0)\n          break;\n      return this.high != 0 ? bit + 33 : bit + 1;\n    };\n    LongPrototype.isZero = function isZero() {\n      return this.high === 0 && this.low === 0;\n    };\n    LongPrototype.eqz = LongPrototype.isZero;\n    LongPrototype.isNegative = function isNegative() {\n      return !this.unsigned && this.high < 0;\n    };\n    LongPrototype.isPositive = function isPositive() {\n      return this.unsigned || this.high >= 0;\n    };\n    LongPrototype.isOdd = function isOdd() {\n      return (this.low & 1) === 1;\n    };\n    LongPrototype.isEven = function isEven2() {\n      return (this.low & 1) === 0;\n    };\n    LongPrototype.equals = function equals(other) {\n      if (!isLong(other))\n        other = fromValue(other);\n      if (this.unsigned !== other.unsigned && this.high >>> 31 === 1 && other.high >>> 31 === 1)\n        return false;\n      return this.high === other.high && this.low === other.low;\n    };\n    LongPrototype.eq = LongPrototype.equals;\n    LongPrototype.notEquals = function notEquals(other) {\n      return !this.eq(other);\n    };\n    LongPrototype.neq = LongPrototype.notEquals;\n    LongPrototype.ne = LongPrototype.notEquals;\n    LongPrototype.lessThan = function lessThan(other) {\n      return this.comp(other) < 0;\n    };\n    LongPrototype.lt = LongPrototype.lessThan;\n    LongPrototype.lessThanOrEqual = function lessThanOrEqual(other) {\n      return this.comp(other) <= 0;\n    };\n    LongPrototype.lte = LongPrototype.lessThanOrEqual;\n    LongPrototype.le = LongPrototype.lessThanOrEqual;\n    LongPrototype.greaterThan = function greaterThan(other) {\n      return this.comp(other) > 0;\n    };\n    LongPrototype.gt = LongPrototype.greaterThan;\n    LongPrototype.greaterThanOrEqual = function greaterThanOrEqual(other) {\n      return this.comp(other) >= 0;\n    };\n    LongPrototype.gte = LongPrototype.greaterThanOrEqual;\n    LongPrototype.ge = LongPrototype.greaterThanOrEqual;\n    LongPrototype.compare = function compare(other) {\n      if (!isLong(other))\n        other = fromValue(other);\n      if (this.eq(other))\n        return 0;\n      var thisNeg = this.isNegative(), otherNeg = other.isNegative();\n      if (thisNeg && !otherNeg)\n        return -1;\n      if (!thisNeg && otherNeg)\n        return 1;\n      if (!this.unsigned)\n        return this.sub(other).isNegative() ? -1 : 1;\n      return other.high >>> 0 > this.high >>> 0 || other.high === this.high && other.low >>> 0 > this.low >>> 0 ? -1 : 1;\n    };\n    LongPrototype.comp = LongPrototype.compare;\n    LongPrototype.negate = function negate() {\n      if (!this.unsigned && this.eq(MIN_VALUE))\n        return MIN_VALUE;\n      return this.not().add(ONE);\n    };\n    LongPrototype.neg = LongPrototype.negate;\n    LongPrototype.add = function add5(addend) {\n      if (!isLong(addend))\n        addend = fromValue(addend);\n      var a48 = this.high >>> 16;\n      var a32 = this.high & 65535;\n      var a16 = this.low >>> 16;\n      var a00 = this.low & 65535;\n      var b48 = addend.high >>> 16;\n      var b32 = addend.high & 65535;\n      var b16 = addend.low >>> 16;\n      var b00 = addend.low & 65535;\n      var c48 = 0, c32 = 0, c16 = 0, c00 = 0;\n      c00 += a00 + b00;\n      c16 += c00 >>> 16;\n      c00 &= 65535;\n      c16 += a16 + b16;\n      c32 += c16 >>> 16;\n      c16 &= 65535;\n      c32 += a32 + b32;\n      c48 += c32 >>> 16;\n      c32 &= 65535;\n      c48 += a48 + b48;\n      c48 &= 65535;\n      return fromBits(c16 << 16 | c00, c48 << 16 | c32, this.unsigned);\n    };\n    LongPrototype.subtract = function subtract(subtrahend) {\n      if (!isLong(subtrahend))\n        subtrahend = fromValue(subtrahend);\n      return this.add(subtrahend.neg());\n    };\n    LongPrototype.sub = LongPrototype.subtract;\n    LongPrototype.multiply = function multiply4(multiplier) {\n      if (this.isZero())\n        return ZERO;\n      if (!isLong(multiplier))\n        multiplier = fromValue(multiplier);\n      if (wasm) {\n        var low = wasm.mul(\n          this.low,\n          this.high,\n          multiplier.low,\n          multiplier.high\n        );\n        return fromBits(low, wasm.get_high(), this.unsigned);\n      }\n      if (multiplier.isZero())\n        return ZERO;\n      if (this.eq(MIN_VALUE))\n        return multiplier.isOdd() ? MIN_VALUE : ZERO;\n      if (multiplier.eq(MIN_VALUE))\n        return this.isOdd() ? MIN_VALUE : ZERO;\n      if (this.isNegative()) {\n        if (multiplier.isNegative())\n          return this.neg().mul(multiplier.neg());\n        else\n          return this.neg().mul(multiplier).neg();\n      } else if (multiplier.isNegative())\n        return this.mul(multiplier.neg()).neg();\n      if (this.lt(TWO_PWR_24) && multiplier.lt(TWO_PWR_24))\n        return fromNumber(this.toNumber() * multiplier.toNumber(), this.unsigned);\n      var a48 = this.high >>> 16;\n      var a32 = this.high & 65535;\n      var a16 = this.low >>> 16;\n      var a00 = this.low & 65535;\n      var b48 = multiplier.high >>> 16;\n      var b32 = multiplier.high & 65535;\n      var b16 = multiplier.low >>> 16;\n      var b00 = multiplier.low & 65535;\n      var c48 = 0, c32 = 0, c16 = 0, c00 = 0;\n      c00 += a00 * b00;\n      c16 += c00 >>> 16;\n      c00 &= 65535;\n      c16 += a16 * b00;\n      c32 += c16 >>> 16;\n      c16 &= 65535;\n      c16 += a00 * b16;\n      c32 += c16 >>> 16;\n      c16 &= 65535;\n      c32 += a32 * b00;\n      c48 += c32 >>> 16;\n      c32 &= 65535;\n      c32 += a16 * b16;\n      c48 += c32 >>> 16;\n      c32 &= 65535;\n      c32 += a00 * b32;\n      c48 += c32 >>> 16;\n      c32 &= 65535;\n      c48 += a48 * b00 + a32 * b16 + a16 * b32 + a00 * b48;\n      c48 &= 65535;\n      return fromBits(c16 << 16 | c00, c48 << 16 | c32, this.unsigned);\n    };\n    LongPrototype.mul = LongPrototype.multiply;\n    LongPrototype.divide = function divide(divisor) {\n      if (!isLong(divisor))\n        divisor = fromValue(divisor);\n      if (divisor.isZero())\n        throw Error(\"division by zero\");\n      if (wasm) {\n        if (!this.unsigned && this.high === -2147483648 && divisor.low === -1 && divisor.high === -1) {\n          return this;\n        }\n        var low = (this.unsigned ? wasm.div_u : wasm.div_s)(\n          this.low,\n          this.high,\n          divisor.low,\n          divisor.high\n        );\n        return fromBits(low, wasm.get_high(), this.unsigned);\n      }\n      if (this.isZero())\n        return this.unsigned ? UZERO : ZERO;\n      var approx, rem, res;\n      if (!this.unsigned) {\n        if (this.eq(MIN_VALUE)) {\n          if (divisor.eq(ONE) || divisor.eq(NEG_ONE))\n            return MIN_VALUE;\n          else if (divisor.eq(MIN_VALUE))\n            return ONE;\n          else {\n            var halfThis = this.shr(1);\n            approx = halfThis.div(divisor).shl(1);\n            if (approx.eq(ZERO)) {\n              return divisor.isNegative() ? ONE : NEG_ONE;\n            } else {\n              rem = this.sub(divisor.mul(approx));\n              res = approx.add(rem.div(divisor));\n              return res;\n            }\n          }\n        } else if (divisor.eq(MIN_VALUE))\n          return this.unsigned ? UZERO : ZERO;\n        if (this.isNegative()) {\n          if (divisor.isNegative())\n            return this.neg().div(divisor.neg());\n          return this.neg().div(divisor).neg();\n        } else if (divisor.isNegative())\n          return this.div(divisor.neg()).neg();\n        res = ZERO;\n      } else {\n        if (!divisor.unsigned)\n          divisor = divisor.toUnsigned();\n        if (divisor.gt(this))\n          return UZERO;\n        if (divisor.gt(this.shru(1)))\n          return UONE;\n        res = UZERO;\n      }\n      rem = this;\n      while (rem.gte(divisor)) {\n        approx = Math.max(1, Math.floor(rem.toNumber() / divisor.toNumber()));\n        var log22 = Math.ceil(Math.log(approx) / Math.LN2), delta = log22 <= 48 ? 1 : pow_dbl(2, log22 - 48), approxRes = fromNumber(approx), approxRem = approxRes.mul(divisor);\n        while (approxRem.isNegative() || approxRem.gt(rem)) {\n          approx -= delta;\n          approxRes = fromNumber(approx, this.unsigned);\n          approxRem = approxRes.mul(divisor);\n        }\n        if (approxRes.isZero())\n          approxRes = ONE;\n        res = res.add(approxRes);\n        rem = rem.sub(approxRem);\n      }\n      return res;\n    };\n    LongPrototype.div = LongPrototype.divide;\n    LongPrototype.modulo = function modulo(divisor) {\n      if (!isLong(divisor))\n        divisor = fromValue(divisor);\n      if (wasm) {\n        var low = (this.unsigned ? wasm.rem_u : wasm.rem_s)(\n          this.low,\n          this.high,\n          divisor.low,\n          divisor.high\n        );\n        return fromBits(low, wasm.get_high(), this.unsigned);\n      }\n      return this.sub(this.div(divisor).mul(divisor));\n    };\n    LongPrototype.mod = LongPrototype.modulo;\n    LongPrototype.rem = LongPrototype.modulo;\n    LongPrototype.not = function not() {\n      return fromBits(~this.low, ~this.high, this.unsigned);\n    };\n    LongPrototype.and = function and(other) {\n      if (!isLong(other))\n        other = fromValue(other);\n      return fromBits(this.low & other.low, this.high & other.high, this.unsigned);\n    };\n    LongPrototype.or = function or(other) {\n      if (!isLong(other))\n        other = fromValue(other);\n      return fromBits(this.low | other.low, this.high | other.high, this.unsigned);\n    };\n    LongPrototype.xor = function xor(other) {\n      if (!isLong(other))\n        other = fromValue(other);\n      return fromBits(this.low ^ other.low, this.high ^ other.high, this.unsigned);\n    };\n    LongPrototype.shiftLeft = function shiftLeft(numBits) {\n      if (isLong(numBits))\n        numBits = numBits.toInt();\n      if ((numBits &= 63) === 0)\n        return this;\n      else if (numBits < 32)\n        return fromBits(this.low << numBits, this.high << numBits | this.low >>> 32 - numBits, this.unsigned);\n      else\n        return fromBits(0, this.low << numBits - 32, this.unsigned);\n    };\n    LongPrototype.shl = LongPrototype.shiftLeft;\n    LongPrototype.shiftRight = function shiftRight(numBits) {\n      if (isLong(numBits))\n        numBits = numBits.toInt();\n      if ((numBits &= 63) === 0)\n        return this;\n      else if (numBits < 32)\n        return fromBits(this.low >>> numBits | this.high << 32 - numBits, this.high >> numBits, this.unsigned);\n      else\n        return fromBits(this.high >> numBits - 32, this.high >= 0 ? 0 : -1, this.unsigned);\n    };\n    LongPrototype.shr = LongPrototype.shiftRight;\n    LongPrototype.shiftRightUnsigned = function shiftRightUnsigned(numBits) {\n      if (isLong(numBits))\n        numBits = numBits.toInt();\n      numBits &= 63;\n      if (numBits === 0)\n        return this;\n      else {\n        var high = this.high;\n        if (numBits < 32) {\n          var low = this.low;\n          return fromBits(low >>> numBits | high << 32 - numBits, high >>> numBits, this.unsigned);\n        } else if (numBits === 32)\n          return fromBits(high, 0, this.unsigned);\n        else\n          return fromBits(high >>> numBits - 32, 0, this.unsigned);\n      }\n    };\n    LongPrototype.shru = LongPrototype.shiftRightUnsigned;\n    LongPrototype.shr_u = LongPrototype.shiftRightUnsigned;\n    LongPrototype.toSigned = function toSigned() {\n      if (!this.unsigned)\n        return this;\n      return fromBits(this.low, this.high, false);\n    };\n    LongPrototype.toUnsigned = function toUnsigned() {\n      if (this.unsigned)\n        return this;\n      return fromBits(this.low, this.high, true);\n    };\n    LongPrototype.toBytes = function toBytes(le) {\n      return le ? this.toBytesLE() : this.toBytesBE();\n    };\n    LongPrototype.toBytesLE = function toBytesLE() {\n      var hi = this.high, lo = this.low;\n      return [\n        lo & 255,\n        lo >>> 8 & 255,\n        lo >>> 16 & 255,\n        lo >>> 24,\n        hi & 255,\n        hi >>> 8 & 255,\n        hi >>> 16 & 255,\n        hi >>> 24\n      ];\n    };\n    LongPrototype.toBytesBE = function toBytesBE() {\n      var hi = this.high, lo = this.low;\n      return [\n        hi >>> 24,\n        hi >>> 16 & 255,\n        hi >>> 8 & 255,\n        hi & 255,\n        lo >>> 24,\n        lo >>> 16 & 255,\n        lo >>> 8 & 255,\n        lo & 255\n      ];\n    };\n    Long2.fromBytes = function fromBytes(bytes, unsigned, le) {\n      return le ? Long2.fromBytesLE(bytes, unsigned) : Long2.fromBytesBE(bytes, unsigned);\n    };\n    Long2.fromBytesLE = function fromBytesLE(bytes, unsigned) {\n      return new Long2(\n        bytes[0] | bytes[1] << 8 | bytes[2] << 16 | bytes[3] << 24,\n        bytes[4] | bytes[5] << 8 | bytes[6] << 16 | bytes[7] << 24,\n        unsigned\n      );\n    };\n    Long2.fromBytesBE = function fromBytesBE(bytes, unsigned) {\n      return new Long2(\n        bytes[4] << 24 | bytes[5] << 16 | bytes[6] << 8 | bytes[7],\n        bytes[0] << 24 | bytes[1] << 16 | bytes[2] << 8 | bytes[3],\n        unsigned\n      );\n    };\n  }\n});\n\n// (disabled):node_modules/.pnpm/node-fetch@2.6.7/node_modules/node-fetch/browser.js\nvar require_browser = __commonJS({\n  \"(disabled):node_modules/.pnpm/node-fetch@2.6.7/node_modules/node-fetch/browser.js\"() {\n  }\n});\n\n// (disabled):util\nvar require_util = __commonJS({\n  \"(disabled):util\"() {\n  }\n});\n\n// node_modules/.pnpm/seedrandom@3.0.5/node_modules/seedrandom/lib/alea.js\nvar require_alea = __commonJS({\n  \"node_modules/.pnpm/seedrandom@3.0.5/node_modules/seedrandom/lib/alea.js\"(exports, module) {\n    (function(global2, module2, define2) {\n      function Alea(seed) {\n        var me = this, mash = Mash();\n        me.next = function() {\n          var t = 2091639 * me.s0 + me.c * 23283064365386963e-26;\n          me.s0 = me.s1;\n          me.s1 = me.s2;\n          return me.s2 = t - (me.c = t | 0);\n        };\n        me.c = 1;\n        me.s0 = mash(\" \");\n        me.s1 = mash(\" \");\n        me.s2 = mash(\" \");\n        me.s0 -= mash(seed);\n        if (me.s0 < 0) {\n          me.s0 += 1;\n        }\n        me.s1 -= mash(seed);\n        if (me.s1 < 0) {\n          me.s1 += 1;\n        }\n        me.s2 -= mash(seed);\n        if (me.s2 < 0) {\n          me.s2 += 1;\n        }\n        mash = null;\n      }\n      function copy(f, t) {\n        t.c = f.c;\n        t.s0 = f.s0;\n        t.s1 = f.s1;\n        t.s2 = f.s2;\n        return t;\n      }\n      function impl(seed, opts) {\n        var xg = new Alea(seed), state = opts && opts.state, prng = xg.next;\n        prng.int32 = function() {\n          return xg.next() * 4294967296 | 0;\n        };\n        prng.double = function() {\n          return prng() + (prng() * 2097152 | 0) * 11102230246251565e-32;\n        };\n        prng.quick = prng;\n        if (state) {\n          if (typeof state == \"object\")\n            copy(state, xg);\n          prng.state = function() {\n            return copy(xg, {});\n          };\n        }\n        return prng;\n      }\n      function Mash() {\n        var n = 4022871197;\n        var mash = function(data) {\n          data = String(data);\n          for (var i = 0; i < data.length; i++) {\n            n += data.charCodeAt(i);\n            var h = 0.02519603282416938 * n;\n            n = h >>> 0;\n            h -= n;\n            h *= n;\n            n = h >>> 0;\n            h -= n;\n            n += h * 4294967296;\n          }\n          return (n >>> 0) * 23283064365386963e-26;\n        };\n        return mash;\n      }\n      if (module2 && module2.exports) {\n        module2.exports = impl;\n      } else if (define2 && define2.amd) {\n        define2(function() {\n          return impl;\n        });\n      } else {\n        this.alea = impl;\n      }\n    })(\n      exports,\n      typeof module == \"object\" && module,\n      typeof define == \"function\" && define\n    );\n  }\n});\n\n// node_modules/.pnpm/seedrandom@3.0.5/node_modules/seedrandom/lib/xor128.js\nvar require_xor128 = __commonJS({\n  \"node_modules/.pnpm/seedrandom@3.0.5/node_modules/seedrandom/lib/xor128.js\"(exports, module) {\n    (function(global2, module2, define2) {\n      function XorGen(seed) {\n        var me = this, strseed = \"\";\n        me.x = 0;\n        me.y = 0;\n        me.z = 0;\n        me.w = 0;\n        me.next = function() {\n          var t = me.x ^ me.x << 11;\n          me.x = me.y;\n          me.y = me.z;\n          me.z = me.w;\n          return me.w ^= me.w >>> 19 ^ t ^ t >>> 8;\n        };\n        if (seed === (seed | 0)) {\n          me.x = seed;\n        } else {\n          strseed += seed;\n        }\n        for (var k = 0; k < strseed.length + 64; k++) {\n          me.x ^= strseed.charCodeAt(k) | 0;\n          me.next();\n        }\n      }\n      function copy(f, t) {\n        t.x = f.x;\n        t.y = f.y;\n        t.z = f.z;\n        t.w = f.w;\n        return t;\n      }\n      function impl(seed, opts) {\n        var xg = new XorGen(seed), state = opts && opts.state, prng = function() {\n          return (xg.next() >>> 0) / 4294967296;\n        };\n        prng.double = function() {\n          do {\n            var top = xg.next() >>> 11, bot = (xg.next() >>> 0) / 4294967296, result = (top + bot) / (1 << 21);\n          } while (result === 0);\n          return result;\n        };\n        prng.int32 = xg.next;\n        prng.quick = prng;\n        if (state) {\n          if (typeof state == \"object\")\n            copy(state, xg);\n          prng.state = function() {\n            return copy(xg, {});\n          };\n        }\n        return prng;\n      }\n      if (module2 && module2.exports) {\n        module2.exports = impl;\n      } else if (define2 && define2.amd) {\n        define2(function() {\n          return impl;\n        });\n      } else {\n        this.xor128 = impl;\n      }\n    })(\n      exports,\n      typeof module == \"object\" && module,\n      typeof define == \"function\" && define\n    );\n  }\n});\n\n// node_modules/.pnpm/seedrandom@3.0.5/node_modules/seedrandom/lib/xorwow.js\nvar require_xorwow = __commonJS({\n  \"node_modules/.pnpm/seedrandom@3.0.5/node_modules/seedrandom/lib/xorwow.js\"(exports, module) {\n    (function(global2, module2, define2) {\n      function XorGen(seed) {\n        var me = this, strseed = \"\";\n        me.next = function() {\n          var t = me.x ^ me.x >>> 2;\n          me.x = me.y;\n          me.y = me.z;\n          me.z = me.w;\n          me.w = me.v;\n          return (me.d = me.d + 362437 | 0) + (me.v = me.v ^ me.v << 4 ^ (t ^ t << 1)) | 0;\n        };\n        me.x = 0;\n        me.y = 0;\n        me.z = 0;\n        me.w = 0;\n        me.v = 0;\n        if (seed === (seed | 0)) {\n          me.x = seed;\n        } else {\n          strseed += seed;\n        }\n        for (var k = 0; k < strseed.length + 64; k++) {\n          me.x ^= strseed.charCodeAt(k) | 0;\n          if (k == strseed.length) {\n            me.d = me.x << 10 ^ me.x >>> 4;\n          }\n          me.next();\n        }\n      }\n      function copy(f, t) {\n        t.x = f.x;\n        t.y = f.y;\n        t.z = f.z;\n        t.w = f.w;\n        t.v = f.v;\n        t.d = f.d;\n        return t;\n      }\n      function impl(seed, opts) {\n        var xg = new XorGen(seed), state = opts && opts.state, prng = function() {\n          return (xg.next() >>> 0) / 4294967296;\n        };\n        prng.double = function() {\n          do {\n            var top = xg.next() >>> 11, bot = (xg.next() >>> 0) / 4294967296, result = (top + bot) / (1 << 21);\n          } while (result === 0);\n          return result;\n        };\n        prng.int32 = xg.next;\n        prng.quick = prng;\n        if (state) {\n          if (typeof state == \"object\")\n            copy(state, xg);\n          prng.state = function() {\n            return copy(xg, {});\n          };\n        }\n        return prng;\n      }\n      if (module2 && module2.exports) {\n        module2.exports = impl;\n      } else if (define2 && define2.amd) {\n        define2(function() {\n          return impl;\n        });\n      } else {\n        this.xorwow = impl;\n      }\n    })(\n      exports,\n      typeof module == \"object\" && module,\n      typeof define == \"function\" && define\n    );\n  }\n});\n\n// node_modules/.pnpm/seedrandom@3.0.5/node_modules/seedrandom/lib/xorshift7.js\nvar require_xorshift7 = __commonJS({\n  \"node_modules/.pnpm/seedrandom@3.0.5/node_modules/seedrandom/lib/xorshift7.js\"(exports, module) {\n    (function(global2, module2, define2) {\n      function XorGen(seed) {\n        var me = this;\n        me.next = function() {\n          var X = me.x, i = me.i, t, v, w;\n          t = X[i];\n          t ^= t >>> 7;\n          v = t ^ t << 24;\n          t = X[i + 1 & 7];\n          v ^= t ^ t >>> 10;\n          t = X[i + 3 & 7];\n          v ^= t ^ t >>> 3;\n          t = X[i + 4 & 7];\n          v ^= t ^ t << 7;\n          t = X[i + 7 & 7];\n          t = t ^ t << 13;\n          v ^= t ^ t << 9;\n          X[i] = v;\n          me.i = i + 1 & 7;\n          return v;\n        };\n        function init2(me2, seed2) {\n          var j, w, X = [];\n          if (seed2 === (seed2 | 0)) {\n            w = X[0] = seed2;\n          } else {\n            seed2 = \"\" + seed2;\n            for (j = 0; j < seed2.length; ++j) {\n              X[j & 7] = X[j & 7] << 15 ^ seed2.charCodeAt(j) + X[j + 1 & 7] << 13;\n            }\n          }\n          while (X.length < 8)\n            X.push(0);\n          for (j = 0; j < 8 && X[j] === 0; ++j)\n            ;\n          if (j == 8)\n            w = X[7] = -1;\n          else\n            w = X[j];\n          me2.x = X;\n          me2.i = 0;\n          for (j = 256; j > 0; --j) {\n            me2.next();\n          }\n        }\n        init2(me, seed);\n      }\n      function copy(f, t) {\n        t.x = f.x.slice();\n        t.i = f.i;\n        return t;\n      }\n      function impl(seed, opts) {\n        if (seed == null)\n          seed = +new Date();\n        var xg = new XorGen(seed), state = opts && opts.state, prng = function() {\n          return (xg.next() >>> 0) / 4294967296;\n        };\n        prng.double = function() {\n          do {\n            var top = xg.next() >>> 11, bot = (xg.next() >>> 0) / 4294967296, result = (top + bot) / (1 << 21);\n          } while (result === 0);\n          return result;\n        };\n        prng.int32 = xg.next;\n        prng.quick = prng;\n        if (state) {\n          if (state.x)\n            copy(state, xg);\n          prng.state = function() {\n            return copy(xg, {});\n          };\n        }\n        return prng;\n      }\n      if (module2 && module2.exports) {\n        module2.exports = impl;\n      } else if (define2 && define2.amd) {\n        define2(function() {\n          return impl;\n        });\n      } else {\n        this.xorshift7 = impl;\n      }\n    })(\n      exports,\n      typeof module == \"object\" && module,\n      typeof define == \"function\" && define\n    );\n  }\n});\n\n// node_modules/.pnpm/seedrandom@3.0.5/node_modules/seedrandom/lib/xor4096.js\nvar require_xor4096 = __commonJS({\n  \"node_modules/.pnpm/seedrandom@3.0.5/node_modules/seedrandom/lib/xor4096.js\"(exports, module) {\n    (function(global2, module2, define2) {\n      function XorGen(seed) {\n        var me = this;\n        me.next = function() {\n          var w = me.w, X = me.X, i = me.i, t, v;\n          me.w = w = w + 1640531527 | 0;\n          v = X[i + 34 & 127];\n          t = X[i = i + 1 & 127];\n          v ^= v << 13;\n          t ^= t << 17;\n          v ^= v >>> 15;\n          t ^= t >>> 12;\n          v = X[i] = v ^ t;\n          me.i = i;\n          return v + (w ^ w >>> 16) | 0;\n        };\n        function init2(me2, seed2) {\n          var t, v, i, j, w, X = [], limit = 128;\n          if (seed2 === (seed2 | 0)) {\n            v = seed2;\n            seed2 = null;\n          } else {\n            seed2 = seed2 + \"\\0\";\n            v = 0;\n            limit = Math.max(limit, seed2.length);\n          }\n          for (i = 0, j = -32; j < limit; ++j) {\n            if (seed2)\n              v ^= seed2.charCodeAt((j + 32) % seed2.length);\n            if (j === 0)\n              w = v;\n            v ^= v << 10;\n            v ^= v >>> 15;\n            v ^= v << 4;\n            v ^= v >>> 13;\n            if (j >= 0) {\n              w = w + 1640531527 | 0;\n              t = X[j & 127] ^= v + w;\n              i = 0 == t ? i + 1 : 0;\n            }\n          }\n          if (i >= 128) {\n            X[(seed2 && seed2.length || 0) & 127] = -1;\n          }\n          i = 127;\n          for (j = 4 * 128; j > 0; --j) {\n            v = X[i + 34 & 127];\n            t = X[i = i + 1 & 127];\n            v ^= v << 13;\n            t ^= t << 17;\n            v ^= v >>> 15;\n            t ^= t >>> 12;\n            X[i] = v ^ t;\n          }\n          me2.w = w;\n          me2.X = X;\n          me2.i = i;\n        }\n        init2(me, seed);\n      }\n      function copy(f, t) {\n        t.i = f.i;\n        t.w = f.w;\n        t.X = f.X.slice();\n        return t;\n      }\n      ;\n      function impl(seed, opts) {\n        if (seed == null)\n          seed = +new Date();\n        var xg = new XorGen(seed), state = opts && opts.state, prng = function() {\n          return (xg.next() >>> 0) / 4294967296;\n        };\n        prng.double = function() {\n          do {\n            var top = xg.next() >>> 11, bot = (xg.next() >>> 0) / 4294967296, result = (top + bot) / (1 << 21);\n          } while (result === 0);\n          return result;\n        };\n        prng.int32 = xg.next;\n        prng.quick = prng;\n        if (state) {\n          if (state.X)\n            copy(state, xg);\n          prng.state = function() {\n            return copy(xg, {});\n          };\n        }\n        return prng;\n      }\n      if (module2 && module2.exports) {\n        module2.exports = impl;\n      } else if (define2 && define2.amd) {\n        define2(function() {\n          return impl;\n        });\n      } else {\n        this.xor4096 = impl;\n      }\n    })(\n      exports,\n      typeof module == \"object\" && module,\n      typeof define == \"function\" && define\n    );\n  }\n});\n\n// node_modules/.pnpm/seedrandom@3.0.5/node_modules/seedrandom/lib/tychei.js\nvar require_tychei = __commonJS({\n  \"node_modules/.pnpm/seedrandom@3.0.5/node_modules/seedrandom/lib/tychei.js\"(exports, module) {\n    (function(global2, module2, define2) {\n      function XorGen(seed) {\n        var me = this, strseed = \"\";\n        me.next = function() {\n          var b = me.b, c = me.c, d = me.d, a = me.a;\n          b = b << 25 ^ b >>> 7 ^ c;\n          c = c - d | 0;\n          d = d << 24 ^ d >>> 8 ^ a;\n          a = a - b | 0;\n          me.b = b = b << 20 ^ b >>> 12 ^ c;\n          me.c = c = c - d | 0;\n          me.d = d << 16 ^ c >>> 16 ^ a;\n          return me.a = a - b | 0;\n        };\n        me.a = 0;\n        me.b = 0;\n        me.c = 2654435769 | 0;\n        me.d = 1367130551;\n        if (seed === Math.floor(seed)) {\n          me.a = seed / 4294967296 | 0;\n          me.b = seed | 0;\n        } else {\n          strseed += seed;\n        }\n        for (var k = 0; k < strseed.length + 20; k++) {\n          me.b ^= strseed.charCodeAt(k) | 0;\n          me.next();\n        }\n      }\n      function copy(f, t) {\n        t.a = f.a;\n        t.b = f.b;\n        t.c = f.c;\n        t.d = f.d;\n        return t;\n      }\n      ;\n      function impl(seed, opts) {\n        var xg = new XorGen(seed), state = opts && opts.state, prng = function() {\n          return (xg.next() >>> 0) / 4294967296;\n        };\n        prng.double = function() {\n          do {\n            var top = xg.next() >>> 11, bot = (xg.next() >>> 0) / 4294967296, result = (top + bot) / (1 << 21);\n          } while (result === 0);\n          return result;\n        };\n        prng.int32 = xg.next;\n        prng.quick = prng;\n        if (state) {\n          if (typeof state == \"object\")\n            copy(state, xg);\n          prng.state = function() {\n            return copy(xg, {});\n          };\n        }\n        return prng;\n      }\n      if (module2 && module2.exports) {\n        module2.exports = impl;\n      } else if (define2 && define2.amd) {\n        define2(function() {\n          return impl;\n        });\n      } else {\n        this.tychei = impl;\n      }\n    })(\n      exports,\n      typeof module == \"object\" && module,\n      typeof define == \"function\" && define\n    );\n  }\n});\n\n// (disabled):crypto\nvar require_crypto = __commonJS({\n  \"(disabled):crypto\"() {\n  }\n});\n\n// node_modules/.pnpm/seedrandom@3.0.5/node_modules/seedrandom/seedrandom.js\nvar require_seedrandom = __commonJS({\n  \"node_modules/.pnpm/seedrandom@3.0.5/node_modules/seedrandom/seedrandom.js\"(exports, module) {\n    (function(global2, pool3, math) {\n      var width = 256, chunks = 6, digits = 52, rngname = \"random\", startdenom = math.pow(width, chunks), significance = math.pow(2, digits), overflow = significance * 2, mask = width - 1, nodecrypto;\n      function seedrandom5(seed, options, callback) {\n        var key = [];\n        options = options == true ? { entropy: true } : options || {};\n        var shortseed = mixkey(flatten4(\n          options.entropy ? [seed, tostring(pool3)] : seed == null ? autoseed() : seed,\n          3\n        ), key);\n        var arc4 = new ARC4(key);\n        var prng = function() {\n          var n = arc4.g(chunks), d = startdenom, x = 0;\n          while (n < significance) {\n            n = (n + x) * width;\n            d *= width;\n            x = arc4.g(1);\n          }\n          while (n >= overflow) {\n            n /= 2;\n            d /= 2;\n            x >>>= 1;\n          }\n          return (n + x) / d;\n        };\n        prng.int32 = function() {\n          return arc4.g(4) | 0;\n        };\n        prng.quick = function() {\n          return arc4.g(4) / 4294967296;\n        };\n        prng.double = prng;\n        mixkey(tostring(arc4.S), pool3);\n        return (options.pass || callback || function(prng2, seed2, is_math_call, state) {\n          if (state) {\n            if (state.S) {\n              copy(state, arc4);\n            }\n            prng2.state = function() {\n              return copy(arc4, {});\n            };\n          }\n          if (is_math_call) {\n            math[rngname] = prng2;\n            return seed2;\n          } else\n            return prng2;\n        })(\n          prng,\n          shortseed,\n          \"global\" in options ? options.global : this == math,\n          options.state\n        );\n      }\n      function ARC4(key) {\n        var t, keylen = key.length, me = this, i = 0, j = me.i = me.j = 0, s = me.S = [];\n        if (!keylen) {\n          key = [keylen++];\n        }\n        while (i < width) {\n          s[i] = i++;\n        }\n        for (i = 0; i < width; i++) {\n          s[i] = s[j = mask & j + key[i % keylen] + (t = s[i])];\n          s[j] = t;\n        }\n        (me.g = function(count2) {\n          var t2, r = 0, i2 = me.i, j2 = me.j, s2 = me.S;\n          while (count2--) {\n            t2 = s2[i2 = mask & i2 + 1];\n            r = r * width + s2[mask & (s2[i2] = s2[j2 = mask & j2 + t2]) + (s2[j2] = t2)];\n          }\n          me.i = i2;\n          me.j = j2;\n          return r;\n        })(width);\n      }\n      function copy(f, t) {\n        t.i = f.i;\n        t.j = f.j;\n        t.S = f.S.slice();\n        return t;\n      }\n      ;\n      function flatten4(obj, depth) {\n        var result = [], typ = typeof obj, prop;\n        if (depth && typ == \"object\") {\n          for (prop in obj) {\n            try {\n              result.push(flatten4(obj[prop], depth - 1));\n            } catch (e) {\n            }\n          }\n        }\n        return result.length ? result : typ == \"string\" ? obj : obj + \"\\0\";\n      }\n      function mixkey(seed, key) {\n        var stringseed = seed + \"\", smear, j = 0;\n        while (j < stringseed.length) {\n          key[mask & j] = mask & (smear ^= key[mask & j] * 19) + stringseed.charCodeAt(j++);\n        }\n        return tostring(key);\n      }\n      function autoseed() {\n        try {\n          var out;\n          if (nodecrypto && (out = nodecrypto.randomBytes)) {\n            out = out(width);\n          } else {\n            out = new Uint8Array(width);\n            (global2.crypto || global2.msCrypto).getRandomValues(out);\n          }\n          return tostring(out);\n        } catch (e) {\n          var browser = global2.navigator, plugins = browser && browser.plugins;\n          return [+new Date(), global2, plugins, global2.screen, tostring(pool3)];\n        }\n      }\n      function tostring(a) {\n        return String.fromCharCode.apply(0, a);\n      }\n      mixkey(math.random(), pool3);\n      if (typeof module == \"object\" && module.exports) {\n        module.exports = seedrandom5;\n        try {\n          nodecrypto = require_crypto();\n        } catch (ex) {\n        }\n      } else if (typeof define == \"function\" && define.amd) {\n        define(function() {\n          return seedrandom5;\n        });\n      } else {\n        math[\"seed\" + rngname] = seedrandom5;\n      }\n    })(\n      typeof self !== \"undefined\" ? self : exports,\n      [],\n      Math\n    );\n  }\n});\n\n// node_modules/.pnpm/seedrandom@3.0.5/node_modules/seedrandom/index.js\nvar require_seedrandom2 = __commonJS({\n  \"node_modules/.pnpm/seedrandom@3.0.5/node_modules/seedrandom/index.js\"(exports, module) {\n    var alea5 = require_alea();\n    var xor128 = require_xor128();\n    var xorwow = require_xorwow();\n    var xorshift7 = require_xorshift7();\n    var xor4096 = require_xor4096();\n    var tychei = require_tychei();\n    var sr = require_seedrandom();\n    sr.alea = alea5;\n    sr.xor128 = xor128;\n    sr.xorwow = xorwow;\n    sr.xorshift7 = xorshift7;\n    sr.xor4096 = xor4096;\n    sr.tychei = tychei;\n    module.exports = sr;\n  }\n});\n\n// (disabled):node_modules/.pnpm/string_decoder@1.3.0/node_modules/string_decoder/lib/string_decoder.js\nvar require_string_decoder = __commonJS({\n  \"(disabled):node_modules/.pnpm/string_decoder@1.3.0/node_modules/string_decoder/lib/string_decoder.js\"() {\n  }\n});\n\n// (disabled):fs\nvar require_fs = __commonJS({\n  \"(disabled):fs\"() {\n  }\n});\n\n// (disabled):path\nvar require_path = __commonJS({\n  \"(disabled):path\"() {\n  }\n});\n\n// (disabled):worker_threads\nvar require_worker_threads = __commonJS({\n  \"(disabled):worker_threads\"() {\n  }\n});\n\n// (disabled):perf_hooks\nvar require_perf_hooks = __commonJS({\n  \"(disabled):perf_hooks\"() {\n  }\n});\n\n// (disabled):os\nvar require_os = __commonJS({\n  \"(disabled):os\"() {\n  }\n});\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm-threaded-simd.js\nvar require_tfjs_backend_wasm_threaded_simd = __commonJS({\n  \"node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm-threaded-simd.js\"(exports, module) {\n    var WasmBackendModuleThreadedSimd2 = (() => {\n      var _scriptDir = typeof document !== \"undefined\" && document.currentScript ? document.currentScript.src : void 0;\n      if (typeof __filename !== \"undefined\")\n        _scriptDir = _scriptDir || __filename;\n      return function(WasmBackendModuleThreadedSimd3) {\n        WasmBackendModuleThreadedSimd3 = WasmBackendModuleThreadedSimd3 || {};\n        function GROWABLE_HEAP_I8() {\n          if (wasmMemory.buffer != buffer2) {\n            updateGlobalBufferAndViews(wasmMemory.buffer);\n          }\n          return HEAP8;\n        }\n        function GROWABLE_HEAP_U8() {\n          if (wasmMemory.buffer != buffer2) {\n            updateGlobalBufferAndViews(wasmMemory.buffer);\n          }\n          return HEAPU8;\n        }\n        function GROWABLE_HEAP_I16() {\n          if (wasmMemory.buffer != buffer2) {\n            updateGlobalBufferAndViews(wasmMemory.buffer);\n          }\n          return HEAP16;\n        }\n        function GROWABLE_HEAP_U16() {\n          if (wasmMemory.buffer != buffer2) {\n            updateGlobalBufferAndViews(wasmMemory.buffer);\n          }\n          return HEAPU16;\n        }\n        function GROWABLE_HEAP_I32() {\n          if (wasmMemory.buffer != buffer2) {\n            updateGlobalBufferAndViews(wasmMemory.buffer);\n          }\n          return HEAP32;\n        }\n        function GROWABLE_HEAP_F32() {\n          if (wasmMemory.buffer != buffer2) {\n            updateGlobalBufferAndViews(wasmMemory.buffer);\n          }\n          return HEAPF32;\n        }\n        function GROWABLE_HEAP_F64() {\n          if (wasmMemory.buffer != buffer2) {\n            updateGlobalBufferAndViews(wasmMemory.buffer);\n          }\n          return HEAPF64;\n        }\n        var Module = typeof WasmBackendModuleThreadedSimd3 !== \"undefined\" ? WasmBackendModuleThreadedSimd3 : {};\n        var readyPromiseResolve, readyPromiseReject;\n        Module[\"ready\"] = new Promise(function(resolve, reject) {\n          readyPromiseResolve = resolve;\n          readyPromiseReject = reject;\n        });\n        var beforeListeners;\n        if (typeof process !== \"undefined\" && process.listeners) {\n          beforeListeners = { uncaughtException: process.listeners(\"uncaughtException\"), unhandledRejection: process.listeners(\"unhandledRejection\") };\n        }\n        var moduleOverrides = Object.assign({}, Module);\n        var arguments_ = [];\n        var thisProgram = \"./this.program\";\n        var quit_ = (status, toThrow) => {\n          throw toThrow;\n        };\n        var ENVIRONMENT_IS_WEB = typeof window === \"object\";\n        var ENVIRONMENT_IS_WORKER = typeof importScripts === \"function\";\n        var ENVIRONMENT_IS_NODE = typeof process === \"object\" && typeof process.versions === \"object\" && typeof process.versions.node === \"string\";\n        var ENVIRONMENT_IS_PTHREAD = Module[\"ENVIRONMENT_IS_PTHREAD\"] || false;\n        var scriptDirectory = \"\";\n        function locateFile(path) {\n          if (Module[\"locateFile\"]) {\n            return Module[\"locateFile\"](path, scriptDirectory);\n          }\n          return scriptDirectory + path;\n        }\n        var read_, readAsync, readBinary, setWindowTitle;\n        function logExceptionOnExit(e) {\n          if (e instanceof ExitStatus)\n            return;\n          let toLog = e;\n          err(\"exiting due to exception: \" + toLog);\n        }\n        var fs;\n        var nodePath;\n        var requireNodeFS;\n        if (ENVIRONMENT_IS_NODE) {\n          if (ENVIRONMENT_IS_WORKER) {\n            scriptDirectory = require_path().dirname(scriptDirectory) + \"/\";\n          } else {\n            scriptDirectory = __dirname + \"/\";\n          }\n          requireNodeFS = () => {\n            if (!nodePath) {\n              fs = require_fs();\n              nodePath = require_path();\n            }\n          };\n          read_ = function shell_read(filename, binary) {\n            requireNodeFS();\n            filename = nodePath[\"normalize\"](filename);\n            return fs.readFileSync(filename, binary ? void 0 : \"utf8\");\n          };\n          readBinary = (filename) => {\n            var ret = read_(filename, true);\n            if (!ret.buffer) {\n              ret = new Uint8Array(ret);\n            }\n            return ret;\n          };\n          readAsync = (filename, onload, onerror) => {\n            requireNodeFS();\n            filename = nodePath[\"normalize\"](filename);\n            fs.readFile(filename, function(err2, data) {\n              if (err2)\n                onerror(err2);\n              else\n                onload(data.buffer);\n            });\n          };\n          if (process[\"argv\"].length > 1) {\n            thisProgram = process[\"argv\"][1].replace(/\\\\/g, \"/\");\n          }\n          arguments_ = process[\"argv\"].slice(2);\n          process[\"on\"](\"uncaughtException\", function(ex) {\n            if (!(ex instanceof ExitStatus)) {\n              throw ex;\n            }\n          });\n          process[\"on\"](\"unhandledRejection\", function(reason) {\n            throw reason;\n          });\n          quit_ = (status, toThrow) => {\n            if (keepRuntimeAlive()) {\n              process[\"exitCode\"] = status;\n              throw toThrow;\n            }\n            logExceptionOnExit(toThrow);\n            process[\"exit\"](status);\n          };\n          Module[\"inspect\"] = function() {\n            return \"[Emscripten Module object]\";\n          };\n          let nodeWorkerThreads;\n          try {\n            nodeWorkerThreads = require_worker_threads();\n          } catch (e) {\n            console.error('The \"worker_threads\" module is not supported in this node.js build - perhaps a newer version is needed?');\n            throw e;\n          }\n          global.Worker = nodeWorkerThreads.Worker;\n        } else if (ENVIRONMENT_IS_WEB || ENVIRONMENT_IS_WORKER) {\n          if (ENVIRONMENT_IS_WORKER) {\n            scriptDirectory = self.location.href;\n          } else if (typeof document !== \"undefined\" && document.currentScript) {\n            scriptDirectory = document.currentScript.src;\n          }\n          if (typeof _scriptDir !== \"undefined\" && _scriptDir) {\n            scriptDirectory = _scriptDir;\n          }\n          if (scriptDirectory.indexOf(\"blob:\") !== 0) {\n            scriptDirectory = scriptDirectory.substr(0, scriptDirectory.replace(/[?#].*/, \"\").lastIndexOf(\"/\") + 1);\n          } else {\n            scriptDirectory = \"\";\n          }\n          if (!ENVIRONMENT_IS_NODE) {\n            read_ = (url) => {\n              var xhr = new XMLHttpRequest();\n              xhr.open(\"GET\", url, false);\n              xhr.send(null);\n              return xhr.responseText;\n            };\n            if (ENVIRONMENT_IS_WORKER) {\n              readBinary = (url) => {\n                var xhr = new XMLHttpRequest();\n                xhr.open(\"GET\", url, false);\n                xhr.responseType = \"arraybuffer\";\n                xhr.send(null);\n                return new Uint8Array(xhr.response);\n              };\n            }\n            readAsync = (url, onload, onerror) => {\n              var xhr = new XMLHttpRequest();\n              xhr.open(\"GET\", url, true);\n              xhr.responseType = \"arraybuffer\";\n              xhr.onload = () => {\n                if (xhr.status == 200 || xhr.status == 0 && xhr.response) {\n                  onload(xhr.response);\n                  return;\n                }\n                onerror();\n              };\n              xhr.onerror = onerror;\n              xhr.send(null);\n            };\n          }\n          setWindowTitle = (title) => document.title = title;\n        } else {\n        }\n        if (ENVIRONMENT_IS_NODE) {\n          if (typeof performance === \"undefined\") {\n            global.performance = require_perf_hooks().performance;\n          }\n        }\n        var defaultPrint = console.log.bind(console);\n        var defaultPrintErr = console.warn.bind(console);\n        if (ENVIRONMENT_IS_NODE) {\n          requireNodeFS();\n          defaultPrint = (str) => fs.writeSync(1, str + \"\\n\");\n          defaultPrintErr = (str) => fs.writeSync(2, str + \"\\n\");\n        }\n        var out = Module[\"print\"] || defaultPrint;\n        var err = Module[\"printErr\"] || defaultPrintErr;\n        Object.assign(Module, moduleOverrides);\n        moduleOverrides = null;\n        if (Module[\"arguments\"])\n          arguments_ = Module[\"arguments\"];\n        if (Module[\"thisProgram\"])\n          thisProgram = Module[\"thisProgram\"];\n        if (Module[\"quit\"])\n          quit_ = Module[\"quit\"];\n        var POINTER_SIZE = 4;\n        function warnOnce(text) {\n          if (!warnOnce.shown)\n            warnOnce.shown = {};\n          if (!warnOnce.shown[text]) {\n            warnOnce.shown[text] = 1;\n            err(text);\n          }\n        }\n        function convertJsFunctionToWasm(func2, sig) {\n          if (typeof WebAssembly.Function === \"function\") {\n            var typeNames = { \"i\": \"i32\", \"j\": \"i64\", \"f\": \"f32\", \"d\": \"f64\" };\n            var type = { parameters: [], results: sig[0] == \"v\" ? [] : [typeNames[sig[0]]] };\n            for (var i = 1; i < sig.length; ++i) {\n              type.parameters.push(typeNames[sig[i]]);\n            }\n            return new WebAssembly.Function(type, func2);\n          }\n          var typeSection = [1, 0, 1, 96];\n          var sigRet = sig.slice(0, 1);\n          var sigParam = sig.slice(1);\n          var typeCodes = { \"i\": 127, \"j\": 126, \"f\": 125, \"d\": 124 };\n          typeSection.push(sigParam.length);\n          for (var i = 0; i < sigParam.length; ++i) {\n            typeSection.push(typeCodes[sigParam[i]]);\n          }\n          if (sigRet == \"v\") {\n            typeSection.push(0);\n          } else {\n            typeSection = typeSection.concat([1, typeCodes[sigRet]]);\n          }\n          typeSection[1] = typeSection.length - 2;\n          var bytes = new Uint8Array([0, 97, 115, 109, 1, 0, 0, 0].concat(typeSection, [2, 7, 1, 1, 101, 1, 102, 0, 0, 7, 5, 1, 1, 102, 0, 0]));\n          var module2 = new WebAssembly.Module(bytes);\n          var instance = new WebAssembly.Instance(module2, { \"e\": { \"f\": func2 } });\n          var wrappedFunc = instance.exports[\"f\"];\n          return wrappedFunc;\n        }\n        var freeTableIndexes = [];\n        var functionsInTableMap;\n        function getEmptyTableSlot() {\n          if (freeTableIndexes.length) {\n            return freeTableIndexes.pop();\n          }\n          try {\n            wasmTable.grow(1);\n          } catch (err2) {\n            if (!(err2 instanceof RangeError)) {\n              throw err2;\n            }\n            throw \"Unable to grow wasm table. Set ALLOW_TABLE_GROWTH.\";\n          }\n          return wasmTable.length - 1;\n        }\n        function updateTableMap(offset, count2) {\n          for (var i = offset; i < offset + count2; i++) {\n            var item = getWasmTableEntry(i);\n            if (item) {\n              functionsInTableMap.set(item, i);\n            }\n          }\n        }\n        var tempRet0 = 0;\n        var setTempRet0 = (value) => {\n          tempRet0 = value;\n        };\n        var Atomics_load = Atomics.load;\n        var Atomics_store = Atomics.store;\n        var Atomics_compareExchange = Atomics.compareExchange;\n        var wasmBinary;\n        if (Module[\"wasmBinary\"])\n          wasmBinary = Module[\"wasmBinary\"];\n        var noExitRuntime = Module[\"noExitRuntime\"] || true;\n        if (typeof WebAssembly !== \"object\") {\n          abort(\"no native wasm support detected\");\n        }\n        var wasmMemory;\n        var wasmModule;\n        var ABORT = false;\n        var EXITSTATUS;\n        function assert3(condition, text) {\n          if (!condition) {\n            abort(text);\n          }\n        }\n        function getCFunc(ident) {\n          var func2 = Module[\"_\" + ident];\n          return func2;\n        }\n        function ccall(ident, returnType, argTypes, args, opts) {\n          var toC = { \"string\": function(str) {\n            var ret2 = 0;\n            if (str !== null && str !== void 0 && str !== 0) {\n              var len = (str.length << 2) + 1;\n              ret2 = stackAlloc(len);\n              stringToUTF8(str, ret2, len);\n            }\n            return ret2;\n          }, \"array\": function(arr) {\n            var ret2 = stackAlloc(arr.length);\n            writeArrayToMemory(arr, ret2);\n            return ret2;\n          } };\n          function convertReturnValue(ret2) {\n            if (returnType === \"string\")\n              return UTF8ToString(ret2);\n            if (returnType === \"boolean\")\n              return Boolean(ret2);\n            return ret2;\n          }\n          var func2 = getCFunc(ident);\n          var cArgs = [];\n          var stack2 = 0;\n          if (args) {\n            for (var i = 0; i < args.length; i++) {\n              var converter = toC[argTypes[i]];\n              if (converter) {\n                if (stack2 === 0)\n                  stack2 = stackSave();\n                cArgs[i] = converter(args[i]);\n              } else {\n                cArgs[i] = args[i];\n              }\n            }\n          }\n          var ret = func2.apply(null, cArgs);\n          function onDone(ret2) {\n            if (stack2 !== 0)\n              stackRestore(stack2);\n            return convertReturnValue(ret2);\n          }\n          ret = onDone(ret);\n          return ret;\n        }\n        function cwrap(ident, returnType, argTypes, opts) {\n          argTypes = argTypes || [];\n          var numericArgs = argTypes.every(function(type) {\n            return type === \"number\";\n          });\n          var numericRet = returnType !== \"string\";\n          if (numericRet && numericArgs && !opts) {\n            return getCFunc(ident);\n          }\n          return function() {\n            return ccall(ident, returnType, argTypes, arguments, opts);\n          };\n        }\n        var ALLOC_STACK = 1;\n        function TextDecoderWrapper(encoding) {\n          var textDecoder = new TextDecoder(encoding);\n          this.decode = (data) => {\n            if (data.buffer instanceof SharedArrayBuffer) {\n              data = new Uint8Array(data);\n            }\n            return textDecoder.decode.call(textDecoder, data);\n          };\n        }\n        var UTF8Decoder = typeof TextDecoder !== \"undefined\" ? new TextDecoderWrapper(\"utf8\") : void 0;\n        function UTF8ArrayToString(heap, idx, maxBytesToRead) {\n          var endIdx = idx + maxBytesToRead;\n          var endPtr = idx;\n          while (heap[endPtr] && !(endPtr >= endIdx))\n            ++endPtr;\n          if (endPtr - idx > 16 && heap.subarray && UTF8Decoder) {\n            return UTF8Decoder.decode(heap.subarray(idx, endPtr));\n          } else {\n            var str = \"\";\n            while (idx < endPtr) {\n              var u0 = heap[idx++];\n              if (!(u0 & 128)) {\n                str += String.fromCharCode(u0);\n                continue;\n              }\n              var u1 = heap[idx++] & 63;\n              if ((u0 & 224) == 192) {\n                str += String.fromCharCode((u0 & 31) << 6 | u1);\n                continue;\n              }\n              var u2 = heap[idx++] & 63;\n              if ((u0 & 240) == 224) {\n                u0 = (u0 & 15) << 12 | u1 << 6 | u2;\n              } else {\n                u0 = (u0 & 7) << 18 | u1 << 12 | u2 << 6 | heap[idx++] & 63;\n              }\n              if (u0 < 65536) {\n                str += String.fromCharCode(u0);\n              } else {\n                var ch = u0 - 65536;\n                str += String.fromCharCode(55296 | ch >> 10, 56320 | ch & 1023);\n              }\n            }\n          }\n          return str;\n        }\n        function UTF8ToString(ptr, maxBytesToRead) {\n          return ptr ? UTF8ArrayToString(GROWABLE_HEAP_U8(), ptr, maxBytesToRead) : \"\";\n        }\n        function stringToUTF8Array(str, heap, outIdx, maxBytesToWrite) {\n          if (!(maxBytesToWrite > 0))\n            return 0;\n          var startIdx = outIdx;\n          var endIdx = outIdx + maxBytesToWrite - 1;\n          for (var i = 0; i < str.length; ++i) {\n            var u = str.charCodeAt(i);\n            if (u >= 55296 && u <= 57343) {\n              var u1 = str.charCodeAt(++i);\n              u = 65536 + ((u & 1023) << 10) | u1 & 1023;\n            }\n            if (u <= 127) {\n              if (outIdx >= endIdx)\n                break;\n              heap[outIdx++] = u;\n            } else if (u <= 2047) {\n              if (outIdx + 1 >= endIdx)\n                break;\n              heap[outIdx++] = 192 | u >> 6;\n              heap[outIdx++] = 128 | u & 63;\n            } else if (u <= 65535) {\n              if (outIdx + 2 >= endIdx)\n                break;\n              heap[outIdx++] = 224 | u >> 12;\n              heap[outIdx++] = 128 | u >> 6 & 63;\n              heap[outIdx++] = 128 | u & 63;\n            } else {\n              if (outIdx + 3 >= endIdx)\n                break;\n              heap[outIdx++] = 240 | u >> 18;\n              heap[outIdx++] = 128 | u >> 12 & 63;\n              heap[outIdx++] = 128 | u >> 6 & 63;\n              heap[outIdx++] = 128 | u & 63;\n            }\n          }\n          heap[outIdx] = 0;\n          return outIdx - startIdx;\n        }\n        function stringToUTF8(str, outPtr, maxBytesToWrite) {\n          return stringToUTF8Array(str, GROWABLE_HEAP_U8(), outPtr, maxBytesToWrite);\n        }\n        function lengthBytesUTF8(str) {\n          var len = 0;\n          for (var i = 0; i < str.length; ++i) {\n            var u = str.charCodeAt(i);\n            if (u >= 55296 && u <= 57343)\n              u = 65536 + ((u & 1023) << 10) | str.charCodeAt(++i) & 1023;\n            if (u <= 127)\n              ++len;\n            else if (u <= 2047)\n              len += 2;\n            else if (u <= 65535)\n              len += 3;\n            else\n              len += 4;\n          }\n          return len;\n        }\n        var UTF16Decoder = typeof TextDecoder !== \"undefined\" ? new TextDecoderWrapper(\"utf-16le\") : void 0;\n        function writeArrayToMemory(array2, buffer3) {\n          GROWABLE_HEAP_I8().set(array2, buffer3);\n        }\n        function writeAsciiToMemory(str, buffer3, dontAddNull) {\n          for (var i = 0; i < str.length; ++i) {\n            GROWABLE_HEAP_I8()[buffer3++ >> 0] = str.charCodeAt(i);\n          }\n          if (!dontAddNull)\n            GROWABLE_HEAP_I8()[buffer3 >> 0] = 0;\n        }\n        function alignUp(x, multiple) {\n          if (x % multiple > 0) {\n            x += multiple - x % multiple;\n          }\n          return x;\n        }\n        var buffer2, HEAP8, HEAPU8, HEAP16, HEAPU16, HEAP32, HEAPU32, HEAPF32, HEAPF64;\n        if (ENVIRONMENT_IS_PTHREAD) {\n          buffer2 = Module[\"buffer\"];\n        }\n        function updateGlobalBufferAndViews(buf) {\n          buffer2 = buf;\n          Module[\"HEAP8\"] = HEAP8 = new Int8Array(buf);\n          Module[\"HEAP16\"] = HEAP16 = new Int16Array(buf);\n          Module[\"HEAP32\"] = HEAP32 = new Int32Array(buf);\n          Module[\"HEAPU8\"] = HEAPU8 = new Uint8Array(buf);\n          Module[\"HEAPU16\"] = HEAPU16 = new Uint16Array(buf);\n          Module[\"HEAPU32\"] = HEAPU32 = new Uint32Array(buf);\n          Module[\"HEAPF32\"] = HEAPF32 = new Float32Array(buf);\n          Module[\"HEAPF64\"] = HEAPF64 = new Float64Array(buf);\n        }\n        var INITIAL_MEMORY = Module[\"INITIAL_MEMORY\"] || 16777216;\n        if (ENVIRONMENT_IS_PTHREAD) {\n          wasmMemory = Module[\"wasmMemory\"];\n          buffer2 = Module[\"buffer\"];\n        } else {\n          if (Module[\"wasmMemory\"]) {\n            wasmMemory = Module[\"wasmMemory\"];\n          } else {\n            wasmMemory = new WebAssembly.Memory({ \"initial\": INITIAL_MEMORY / 65536, \"maximum\": 2147483648 / 65536, \"shared\": true });\n            if (!(wasmMemory.buffer instanceof SharedArrayBuffer)) {\n              err(\"requested a shared WebAssembly.Memory but the returned buffer is not a SharedArrayBuffer, indicating that while the browser has SharedArrayBuffer it does not have WebAssembly threads support - you may need to set a flag\");\n              if (ENVIRONMENT_IS_NODE) {\n                console.log(\"(on node you may need: --experimental-wasm-threads --experimental-wasm-bulk-memory and also use a recent version)\");\n              }\n              throw Error(\"bad memory\");\n            }\n          }\n        }\n        if (wasmMemory) {\n          buffer2 = wasmMemory.buffer;\n        }\n        INITIAL_MEMORY = buffer2.byteLength;\n        updateGlobalBufferAndViews(buffer2);\n        var wasmTable;\n        var __ATPRERUN__ = [];\n        var __ATINIT__ = [];\n        var __ATEXIT__ = [];\n        var __ATPOSTRUN__ = [];\n        var runtimeInitialized = false;\n        var runtimeExited = false;\n        var runtimeKeepaliveCounter = 0;\n        function keepRuntimeAlive() {\n          return noExitRuntime || runtimeKeepaliveCounter > 0;\n        }\n        function preRun() {\n          if (Module[\"preRun\"]) {\n            if (typeof Module[\"preRun\"] == \"function\")\n              Module[\"preRun\"] = [Module[\"preRun\"]];\n            while (Module[\"preRun\"].length) {\n              addOnPreRun(Module[\"preRun\"].shift());\n            }\n          }\n          callRuntimeCallbacks(__ATPRERUN__);\n        }\n        function initRuntime() {\n          runtimeInitialized = true;\n          if (ENVIRONMENT_IS_PTHREAD)\n            return;\n          callRuntimeCallbacks(__ATINIT__);\n        }\n        function exitRuntime() {\n          if (ENVIRONMENT_IS_PTHREAD)\n            return;\n          PThread.terminateAllThreads();\n          runtimeExited = true;\n        }\n        function postRun() {\n          if (ENVIRONMENT_IS_PTHREAD)\n            return;\n          if (Module[\"postRun\"]) {\n            if (typeof Module[\"postRun\"] == \"function\")\n              Module[\"postRun\"] = [Module[\"postRun\"]];\n            while (Module[\"postRun\"].length) {\n              addOnPostRun(Module[\"postRun\"].shift());\n            }\n          }\n          callRuntimeCallbacks(__ATPOSTRUN__);\n        }\n        function addOnPreRun(cb) {\n          __ATPRERUN__.unshift(cb);\n        }\n        function addOnInit(cb) {\n          __ATINIT__.unshift(cb);\n        }\n        function addOnPostRun(cb) {\n          __ATPOSTRUN__.unshift(cb);\n        }\n        var runDependencies = 0;\n        var runDependencyWatcher = null;\n        var dependenciesFulfilled = null;\n        function addRunDependency(id) {\n          runDependencies++;\n          if (Module[\"monitorRunDependencies\"]) {\n            Module[\"monitorRunDependencies\"](runDependencies);\n          }\n        }\n        function removeRunDependency(id) {\n          runDependencies--;\n          if (Module[\"monitorRunDependencies\"]) {\n            Module[\"monitorRunDependencies\"](runDependencies);\n          }\n          if (runDependencies == 0) {\n            if (runDependencyWatcher !== null) {\n              clearInterval(runDependencyWatcher);\n              runDependencyWatcher = null;\n            }\n            if (dependenciesFulfilled) {\n              var callback = dependenciesFulfilled;\n              dependenciesFulfilled = null;\n              callback();\n            }\n          }\n        }\n        Module[\"preloadedImages\"] = {};\n        Module[\"preloadedAudios\"] = {};\n        function abort(what) {\n          if (ENVIRONMENT_IS_PTHREAD) {\n            postMessage({ \"cmd\": \"onAbort\", \"arg\": what });\n          } else {\n            if (Module[\"onAbort\"]) {\n              Module[\"onAbort\"](what);\n            }\n          }\n          what = \"Aborted(\" + what + \")\";\n          err(what);\n          ABORT = true;\n          EXITSTATUS = 1;\n          what += \". Build with -s ASSERTIONS=1 for more info.\";\n          var e = new WebAssembly.RuntimeError(what);\n          readyPromiseReject(e);\n          throw e;\n        }\n        var dataURIPrefix = \"data:application/octet-stream;base64,\";\n        function isDataURI(filename) {\n          return filename.startsWith(dataURIPrefix);\n        }\n        function isFileURI(filename) {\n          return filename.startsWith(\"file://\");\n        }\n        var wasmBinaryFile;\n        wasmBinaryFile = \"tfjs-backend-wasm-threaded-simd.wasm\";\n        if (!isDataURI(wasmBinaryFile)) {\n          wasmBinaryFile = locateFile(wasmBinaryFile);\n        }\n        function getBinary(file) {\n          try {\n            if (file == wasmBinaryFile && wasmBinary) {\n              return new Uint8Array(wasmBinary);\n            }\n            if (readBinary) {\n              return readBinary(file);\n            } else {\n              throw \"both async and sync fetching of the wasm failed\";\n            }\n          } catch (err2) {\n            abort(err2);\n          }\n        }\n        function getBinaryPromise() {\n          if (!wasmBinary && (ENVIRONMENT_IS_WEB || ENVIRONMENT_IS_WORKER)) {\n            if (typeof fetch === \"function\" && !isFileURI(wasmBinaryFile)) {\n              return fetch(wasmBinaryFile, { credentials: \"same-origin\" }).then(function(response) {\n                if (!response[\"ok\"]) {\n                  throw \"failed to load wasm binary file at '\" + wasmBinaryFile + \"'\";\n                }\n                return response[\"arrayBuffer\"]();\n              }).catch(function() {\n                return getBinary(wasmBinaryFile);\n              });\n            } else {\n              if (readAsync) {\n                return new Promise(function(resolve, reject) {\n                  readAsync(wasmBinaryFile, function(response) {\n                    resolve(new Uint8Array(response));\n                  }, reject);\n                });\n              }\n            }\n          }\n          return Promise.resolve().then(function() {\n            return getBinary(wasmBinaryFile);\n          });\n        }\n        function createWasm() {\n          var info = { \"env\": asmLibraryArg, \"wasi_snapshot_preview1\": asmLibraryArg };\n          function receiveInstance(instance, module2) {\n            var exports3 = instance.exports;\n            Module[\"asm\"] = exports3;\n            registerTlsInit(Module[\"asm\"][\"emscripten_tls_init\"]);\n            wasmTable = Module[\"asm\"][\"__indirect_function_table\"];\n            addOnInit(Module[\"asm\"][\"__wasm_call_ctors\"]);\n            wasmModule = module2;\n            if (!ENVIRONMENT_IS_PTHREAD) {\n              var numWorkersToLoad = PThread.unusedWorkers.length;\n              PThread.unusedWorkers.forEach(function(w) {\n                PThread.loadWasmModuleToWorker(w, function() {\n                  if (!--numWorkersToLoad)\n                    removeRunDependency(\"wasm-instantiate\");\n                });\n              });\n            }\n          }\n          if (!ENVIRONMENT_IS_PTHREAD) {\n            addRunDependency(\"wasm-instantiate\");\n          }\n          function receiveInstantiationResult(result) {\n            receiveInstance(result[\"instance\"], result[\"module\"]);\n          }\n          function instantiateArrayBuffer(receiver) {\n            return getBinaryPromise().then(function(binary) {\n              return WebAssembly.instantiate(binary, info);\n            }).then(function(instance) {\n              return instance;\n            }).then(receiver, function(reason) {\n              err(\"failed to asynchronously prepare wasm: \" + reason);\n              abort(reason);\n            });\n          }\n          function instantiateAsync() {\n            if (!wasmBinary && typeof WebAssembly.instantiateStreaming === \"function\" && !isDataURI(wasmBinaryFile) && !isFileURI(wasmBinaryFile) && typeof fetch === \"function\") {\n              return fetch(wasmBinaryFile, { credentials: \"same-origin\" }).then(function(response) {\n                var result = WebAssembly.instantiateStreaming(response, info);\n                return result.then(receiveInstantiationResult, function(reason) {\n                  err(\"wasm streaming compile failed: \" + reason);\n                  err(\"falling back to ArrayBuffer instantiation\");\n                  return instantiateArrayBuffer(receiveInstantiationResult);\n                });\n              });\n            } else {\n              return instantiateArrayBuffer(receiveInstantiationResult);\n            }\n          }\n          if (Module[\"instantiateWasm\"]) {\n            try {\n              var exports2 = Module[\"instantiateWasm\"](info, receiveInstance);\n              return exports2;\n            } catch (e) {\n              err(\"Module.instantiateWasm callback failed with error: \" + e);\n              return false;\n            }\n          }\n          instantiateAsync().catch(readyPromiseReject);\n          return {};\n        }\n        var tempDouble;\n        var tempI64;\n        var ASM_CONSTS = {};\n        function callRuntimeCallbacks(callbacks2) {\n          while (callbacks2.length > 0) {\n            var callback = callbacks2.shift();\n            if (typeof callback == \"function\") {\n              callback(Module);\n              continue;\n            }\n            var func2 = callback.func;\n            if (typeof func2 === \"number\") {\n              if (callback.arg === void 0) {\n                getWasmTableEntry(func2)();\n              } else {\n                getWasmTableEntry(func2)(callback.arg);\n              }\n            } else {\n              func2(callback.arg === void 0 ? null : callback.arg);\n            }\n          }\n        }\n        function withStackSave(f) {\n          var stack2 = stackSave();\n          var ret = f();\n          stackRestore(stack2);\n          return ret;\n        }\n        function demangle(func2) {\n          return func2;\n        }\n        function demangleAll(text) {\n          var regex = /\\b_Z[\\w\\d_]+/g;\n          return text.replace(regex, function(x) {\n            var y = demangle(x);\n            return x === y ? x : y + \" [\" + x + \"]\";\n          });\n        }\n        function killThread(pthread_ptr) {\n          GROWABLE_HEAP_I32()[pthread_ptr >> 2] = 0;\n          var pthread = PThread.pthreads[pthread_ptr];\n          delete PThread.pthreads[pthread_ptr];\n          pthread.worker.terminate();\n          __emscripten_thread_free_data(pthread_ptr);\n          PThread.runningWorkers.splice(PThread.runningWorkers.indexOf(pthread.worker), 1);\n          pthread.worker.pthread = void 0;\n        }\n        function cancelThread(pthread_ptr) {\n          var pthread = PThread.pthreads[pthread_ptr];\n          pthread.worker.postMessage({ \"cmd\": \"cancel\" });\n        }\n        function cleanupThread(pthread_ptr) {\n          var pthread = PThread.pthreads[pthread_ptr];\n          if (pthread) {\n            GROWABLE_HEAP_I32()[pthread_ptr >> 2] = 0;\n            var worker = pthread.worker;\n            PThread.returnWorkerToPool(worker);\n          }\n        }\n        function _exit(status) {\n          exit(status);\n        }\n        function handleException(e) {\n          if (e instanceof ExitStatus || e == \"unwind\") {\n            return EXITSTATUS;\n          }\n          quit_(1, e);\n        }\n        var PThread = { unusedWorkers: [], runningWorkers: [], tlsInitFunctions: [], init: function() {\n          if (ENVIRONMENT_IS_PTHREAD) {\n            PThread.initWorker();\n          } else {\n            PThread.initMainThread();\n          }\n        }, initMainThread: function() {\n          var pthreadPoolSize = 8;\n          for (var i = 0; i < pthreadPoolSize; ++i) {\n            PThread.allocateUnusedWorker();\n          }\n        }, initWorker: function() {\n          noExitRuntime = false;\n        }, pthreads: {}, setExitStatus: function(status) {\n          EXITSTATUS = status;\n        }, terminateAllThreads: function() {\n          for (var t in PThread.pthreads) {\n            var pthread = PThread.pthreads[t];\n            if (pthread && pthread.worker) {\n              PThread.returnWorkerToPool(pthread.worker);\n            }\n          }\n          for (var i = 0; i < PThread.unusedWorkers.length; ++i) {\n            var worker = PThread.unusedWorkers[i];\n            worker.terminate();\n          }\n          PThread.unusedWorkers = [];\n        }, returnWorkerToPool: function(worker) {\n          PThread.runWithoutMainThreadQueuedCalls(function() {\n            delete PThread.pthreads[worker.pthread.threadInfoStruct];\n            PThread.unusedWorkers.push(worker);\n            PThread.runningWorkers.splice(PThread.runningWorkers.indexOf(worker), 1);\n            __emscripten_thread_free_data(worker.pthread.threadInfoStruct);\n            worker.pthread = void 0;\n          });\n        }, runWithoutMainThreadQueuedCalls: function(func2) {\n          GROWABLE_HEAP_I32()[__emscripten_allow_main_runtime_queued_calls >> 2] = 0;\n          try {\n            func2();\n          } finally {\n            GROWABLE_HEAP_I32()[__emscripten_allow_main_runtime_queued_calls >> 2] = 1;\n          }\n        }, receiveObjectTransfer: function(data) {\n        }, threadInit: function() {\n          for (var i in PThread.tlsInitFunctions) {\n            PThread.tlsInitFunctions[i]();\n          }\n        }, loadWasmModuleToWorker: function(worker, onFinishedLoading) {\n          worker.onmessage = (e) => {\n            var d = e[\"data\"];\n            var cmd = d[\"cmd\"];\n            if (worker.pthread)\n              PThread.currentProxiedOperationCallerThread = worker.pthread.threadInfoStruct;\n            if (d[\"targetThread\"] && d[\"targetThread\"] != _pthread_self()) {\n              var thread = PThread.pthreads[d.targetThread];\n              if (thread) {\n                thread.worker.postMessage(d, d[\"transferList\"]);\n              } else {\n                err('Internal error! Worker sent a message \"' + cmd + '\" to target pthread ' + d[\"targetThread\"] + \", but that thread no longer exists!\");\n              }\n              PThread.currentProxiedOperationCallerThread = void 0;\n              return;\n            }\n            if (cmd === \"processQueuedMainThreadWork\") {\n              _emscripten_main_thread_process_queued_calls();\n            } else if (cmd === \"spawnThread\") {\n              spawnThread(d);\n            } else if (cmd === \"cleanupThread\") {\n              cleanupThread(d[\"thread\"]);\n            } else if (cmd === \"killThread\") {\n              killThread(d[\"thread\"]);\n            } else if (cmd === \"cancelThread\") {\n              cancelThread(d[\"thread\"]);\n            } else if (cmd === \"loaded\") {\n              worker.loaded = true;\n              if (onFinishedLoading)\n                onFinishedLoading(worker);\n              if (worker.runPthread) {\n                worker.runPthread();\n                delete worker.runPthread;\n              }\n            } else if (cmd === \"print\") {\n              out(\"Thread \" + d[\"threadId\"] + \": \" + d[\"text\"]);\n            } else if (cmd === \"printErr\") {\n              err(\"Thread \" + d[\"threadId\"] + \": \" + d[\"text\"]);\n            } else if (cmd === \"alert\") {\n              alert(\"Thread \" + d[\"threadId\"] + \": \" + d[\"text\"]);\n            } else if (d.target === \"setimmediate\") {\n              worker.postMessage(d);\n            } else if (cmd === \"onAbort\") {\n              if (Module[\"onAbort\"]) {\n                Module[\"onAbort\"](d[\"arg\"]);\n              }\n            } else {\n              err(\"worker sent an unknown command \" + cmd);\n            }\n            PThread.currentProxiedOperationCallerThread = void 0;\n          };\n          worker.onerror = (e) => {\n            var message = \"worker sent an error!\";\n            err(message + \" \" + e.filename + \":\" + e.lineno + \": \" + e.message);\n            throw e;\n          };\n          if (ENVIRONMENT_IS_NODE) {\n            worker.on(\"message\", function(data) {\n              worker.onmessage({ data });\n            });\n            worker.on(\"error\", function(e) {\n              worker.onerror(e);\n            });\n            worker.on(\"detachedExit\", function() {\n            });\n          }\n          worker.postMessage({ \"cmd\": \"load\", \"urlOrBlob\": Module[\"mainScriptUrlOrBlob\"] || _scriptDir, \"wasmMemory\": wasmMemory, \"wasmModule\": wasmModule });\n        }, allocateUnusedWorker: function() {\n          var pthreadMainJs = locateFile(\"tfjs-backend-wasm-threaded-simd.worker.js\");\n          PThread.unusedWorkers.push(new Worker(pthreadMainJs));\n        }, getNewWorker: function() {\n          if (PThread.unusedWorkers.length == 0) {\n            PThread.allocateUnusedWorker();\n            PThread.loadWasmModuleToWorker(PThread.unusedWorkers[0]);\n          }\n          return PThread.unusedWorkers.pop();\n        } };\n        function establishStackSpace() {\n          var pthread_ptr = _pthread_self();\n          var stackTop = GROWABLE_HEAP_I32()[pthread_ptr + 44 >> 2];\n          var stackSize = GROWABLE_HEAP_I32()[pthread_ptr + 48 >> 2];\n          var stackMax = stackTop - stackSize;\n          _emscripten_stack_set_limits(stackTop, stackMax);\n          stackRestore(stackTop);\n        }\n        Module[\"establishStackSpace\"] = establishStackSpace;\n        function exitOnMainThread(returnCode) {\n          if (ENVIRONMENT_IS_PTHREAD)\n            return _emscripten_proxy_to_main_thread_js(1, 0, returnCode);\n          try {\n            _exit(returnCode);\n          } catch (e) {\n            handleException(e);\n          }\n        }\n        var wasmTableMirror = [];\n        function getWasmTableEntry(funcPtr) {\n          var func2 = wasmTableMirror[funcPtr];\n          if (!func2) {\n            if (funcPtr >= wasmTableMirror.length)\n              wasmTableMirror.length = funcPtr + 1;\n            wasmTableMirror[funcPtr] = func2 = wasmTable.get(funcPtr);\n          }\n          return func2;\n        }\n        function invokeEntryPoint(ptr, arg) {\n          return getWasmTableEntry(ptr)(arg);\n        }\n        Module[\"invokeEntryPoint\"] = invokeEntryPoint;\n        function jsStackTrace() {\n          var error = new Error();\n          if (!error.stack) {\n            try {\n              throw new Error();\n            } catch (e) {\n              error = e;\n            }\n            if (!error.stack) {\n              return \"(no stack trace available)\";\n            }\n          }\n          return error.stack.toString();\n        }\n        function registerTlsInit(tlsInitFunc, moduleExports, metadata) {\n          PThread.tlsInitFunctions.push(tlsInitFunc);\n        }\n        function setWasmTableEntry(idx, func2) {\n          wasmTable.set(idx, func2);\n          wasmTableMirror[idx] = func2;\n        }\n        var _emscripten_get_now;\n        if (ENVIRONMENT_IS_NODE) {\n          _emscripten_get_now = () => {\n            var t = process[\"hrtime\"]();\n            return t[0] * 1e3 + t[1] / 1e6;\n          };\n        } else if (ENVIRONMENT_IS_PTHREAD) {\n          _emscripten_get_now = () => performance.now() - Module[\"__performance_now_clock_drift\"];\n        } else\n          _emscripten_get_now = () => performance.now();\n        var _emscripten_get_now_is_monotonic = true;\n        function setErrNo(value) {\n          GROWABLE_HEAP_I32()[___errno_location() >> 2] = value;\n          return value;\n        }\n        function _clock_gettime(clk_id, tp) {\n          var now2;\n          if (clk_id === 0) {\n            now2 = Date.now();\n          } else if ((clk_id === 1 || clk_id === 4) && _emscripten_get_now_is_monotonic) {\n            now2 = _emscripten_get_now();\n          } else {\n            setErrNo(28);\n            return -1;\n          }\n          GROWABLE_HEAP_I32()[tp >> 2] = now2 / 1e3 | 0;\n          GROWABLE_HEAP_I32()[tp + 4 >> 2] = now2 % 1e3 * 1e3 * 1e3 | 0;\n          return 0;\n        }\n        function ___clock_gettime(a0, a12) {\n          return _clock_gettime(a0, a12);\n        }\n        function ___emscripten_init_main_thread_js(tb) {\n          __emscripten_thread_init(tb, !ENVIRONMENT_IS_WORKER, 1, !ENVIRONMENT_IS_WEB);\n          PThread.threadInit();\n        }\n        function ___emscripten_thread_cleanup(thread) {\n          if (!ENVIRONMENT_IS_PTHREAD)\n            cleanupThread(thread);\n          else\n            postMessage({ \"cmd\": \"cleanupThread\", \"thread\": thread });\n        }\n        function spawnThread(threadParams) {\n          var worker = PThread.getNewWorker();\n          if (!worker) {\n            return 6;\n          }\n          PThread.runningWorkers.push(worker);\n          var pthread = PThread.pthreads[threadParams.pthread_ptr] = { worker, threadInfoStruct: threadParams.pthread_ptr };\n          worker.pthread = pthread;\n          var msg = { \"cmd\": \"run\", \"start_routine\": threadParams.startRoutine, \"arg\": threadParams.arg, \"threadInfoStruct\": threadParams.pthread_ptr };\n          worker.runPthread = () => {\n            msg.time = performance.now();\n            worker.postMessage(msg, threadParams.transferList);\n          };\n          if (worker.loaded) {\n            worker.runPthread();\n            delete worker.runPthread;\n          }\n          return 0;\n        }\n        function ___pthread_create_js(pthread_ptr, attr, start_routine, arg) {\n          if (typeof SharedArrayBuffer === \"undefined\") {\n            err(\"Current environment does not support SharedArrayBuffer, pthreads are not available!\");\n            return 6;\n          }\n          var transferList = [];\n          var error = 0;\n          if (ENVIRONMENT_IS_PTHREAD && (transferList.length === 0 || error)) {\n            return _emscripten_sync_run_in_main_thread_4(687865856, pthread_ptr, attr, start_routine, arg);\n          }\n          if (error)\n            return error;\n          var threadParams = { startRoutine: start_routine, pthread_ptr, arg, transferList };\n          if (ENVIRONMENT_IS_PTHREAD) {\n            threadParams.cmd = \"spawnThread\";\n            postMessage(threadParams, transferList);\n            return 0;\n          }\n          return spawnThread(threadParams);\n        }\n        function __emscripten_default_pthread_stack_size() {\n          return 2097152;\n        }\n        function __emscripten_notify_thread_queue(targetThreadId, mainThreadId) {\n          if (targetThreadId == mainThreadId) {\n            postMessage({ \"cmd\": \"processQueuedMainThreadWork\" });\n          } else if (ENVIRONMENT_IS_PTHREAD) {\n            postMessage({ \"targetThread\": targetThreadId, \"cmd\": \"processThreadQueue\" });\n          } else {\n            var pthread = PThread.pthreads[targetThreadId];\n            var worker = pthread && pthread.worker;\n            if (!worker) {\n              return;\n            }\n            worker.postMessage({ \"cmd\": \"processThreadQueue\" });\n          }\n          return 1;\n        }\n        function _abort() {\n          abort(\"\");\n        }\n        function _emscripten_check_blocking_allowed() {\n          if (ENVIRONMENT_IS_NODE)\n            return;\n          if (ENVIRONMENT_IS_WORKER)\n            return;\n          warnOnce(\"Blocking on the main thread is very dangerous, see https://emscripten.org/docs/porting/pthreads.html#blocking-on-the-main-browser-thread\");\n        }\n        function _emscripten_get_heap_max() {\n          return 2147483648;\n        }\n        function _emscripten_memcpy_big(dest, src, num) {\n          GROWABLE_HEAP_U8().copyWithin(dest, src, src + num);\n        }\n        function _emscripten_num_logical_cores() {\n          if (ENVIRONMENT_IS_NODE)\n            return require_os().cpus().length;\n          return navigator[\"hardwareConcurrency\"];\n        }\n        function _emscripten_proxy_to_main_thread_js(index, sync) {\n          var numCallArgs = arguments.length - 2;\n          var outerArgs = arguments;\n          return withStackSave(function() {\n            var serializedNumCallArgs = numCallArgs;\n            var args = stackAlloc(serializedNumCallArgs * 8);\n            var b = args >> 3;\n            for (var i = 0; i < numCallArgs; i++) {\n              var arg = outerArgs[2 + i];\n              GROWABLE_HEAP_F64()[b + i] = arg;\n            }\n            return _emscripten_run_in_main_runtime_thread_js(index, serializedNumCallArgs, args, sync);\n          });\n        }\n        var _emscripten_receive_on_main_thread_js_callArgs = [];\n        function _emscripten_receive_on_main_thread_js(index, numCallArgs, args) {\n          _emscripten_receive_on_main_thread_js_callArgs.length = numCallArgs;\n          var b = args >> 3;\n          for (var i = 0; i < numCallArgs; i++) {\n            _emscripten_receive_on_main_thread_js_callArgs[i] = GROWABLE_HEAP_F64()[b + i];\n          }\n          var isEmAsmConst = index < 0;\n          var func2 = !isEmAsmConst ? proxiedFunctionTable[index] : ASM_CONSTS[-index - 1];\n          return func2.apply(null, _emscripten_receive_on_main_thread_js_callArgs);\n        }\n        function emscripten_realloc_buffer(size) {\n          try {\n            wasmMemory.grow(size - buffer2.byteLength + 65535 >>> 16);\n            updateGlobalBufferAndViews(wasmMemory.buffer);\n            return 1;\n          } catch (e) {\n          }\n        }\n        function _emscripten_resize_heap(requestedSize) {\n          var oldSize = GROWABLE_HEAP_U8().length;\n          requestedSize = requestedSize >>> 0;\n          if (requestedSize <= oldSize) {\n            return false;\n          }\n          var maxHeapSize = _emscripten_get_heap_max();\n          if (requestedSize > maxHeapSize) {\n            return false;\n          }\n          for (var cutDown = 1; cutDown <= 4; cutDown *= 2) {\n            var overGrownHeapSize = oldSize * (1 + 0.2 / cutDown);\n            overGrownHeapSize = Math.min(overGrownHeapSize, requestedSize + 100663296);\n            var newSize = Math.min(maxHeapSize, alignUp(Math.max(requestedSize, overGrownHeapSize), 65536));\n            var replacement = emscripten_realloc_buffer(newSize);\n            if (replacement) {\n              return true;\n            }\n          }\n          return false;\n        }\n        var JSEvents = { inEventHandler: 0, removeAllEventListeners: function() {\n          for (var i = JSEvents.eventHandlers.length - 1; i >= 0; --i) {\n            JSEvents._removeHandler(i);\n          }\n          JSEvents.eventHandlers = [];\n          JSEvents.deferredCalls = [];\n        }, registerRemoveEventListeners: function() {\n          if (!JSEvents.removeEventListenersRegistered) {\n            __ATEXIT__.push(JSEvents.removeAllEventListeners);\n            JSEvents.removeEventListenersRegistered = true;\n          }\n        }, deferredCalls: [], deferCall: function(targetFunction, precedence, argsList) {\n          function arraysHaveEqualContent(arrA, arrB) {\n            if (arrA.length != arrB.length)\n              return false;\n            for (var i2 in arrA) {\n              if (arrA[i2] != arrB[i2])\n                return false;\n            }\n            return true;\n          }\n          for (var i in JSEvents.deferredCalls) {\n            var call = JSEvents.deferredCalls[i];\n            if (call.targetFunction == targetFunction && arraysHaveEqualContent(call.argsList, argsList)) {\n              return;\n            }\n          }\n          JSEvents.deferredCalls.push({ targetFunction, precedence, argsList });\n          JSEvents.deferredCalls.sort(function(x, y) {\n            return x.precedence < y.precedence;\n          });\n        }, removeDeferredCalls: function(targetFunction) {\n          for (var i = 0; i < JSEvents.deferredCalls.length; ++i) {\n            if (JSEvents.deferredCalls[i].targetFunction == targetFunction) {\n              JSEvents.deferredCalls.splice(i, 1);\n              --i;\n            }\n          }\n        }, canPerformEventHandlerRequests: function() {\n          return JSEvents.inEventHandler && JSEvents.currentEventHandler.allowsDeferredCalls;\n        }, runDeferredCalls: function() {\n          if (!JSEvents.canPerformEventHandlerRequests()) {\n            return;\n          }\n          for (var i = 0; i < JSEvents.deferredCalls.length; ++i) {\n            var call = JSEvents.deferredCalls[i];\n            JSEvents.deferredCalls.splice(i, 1);\n            --i;\n            call.targetFunction.apply(null, call.argsList);\n          }\n        }, eventHandlers: [], removeAllHandlersOnTarget: function(target, eventTypeString) {\n          for (var i = 0; i < JSEvents.eventHandlers.length; ++i) {\n            if (JSEvents.eventHandlers[i].target == target && (!eventTypeString || eventTypeString == JSEvents.eventHandlers[i].eventTypeString)) {\n              JSEvents._removeHandler(i--);\n            }\n          }\n        }, _removeHandler: function(i) {\n          var h = JSEvents.eventHandlers[i];\n          h.target.removeEventListener(h.eventTypeString, h.eventListenerFunc, h.useCapture);\n          JSEvents.eventHandlers.splice(i, 1);\n        }, registerOrRemoveHandler: function(eventHandler) {\n          var jsEventHandler = function jsEventHandler2(event) {\n            ++JSEvents.inEventHandler;\n            JSEvents.currentEventHandler = eventHandler;\n            JSEvents.runDeferredCalls();\n            eventHandler.handlerFunc(event);\n            JSEvents.runDeferredCalls();\n            --JSEvents.inEventHandler;\n          };\n          if (eventHandler.callbackfunc) {\n            eventHandler.eventListenerFunc = jsEventHandler;\n            eventHandler.target.addEventListener(eventHandler.eventTypeString, jsEventHandler, eventHandler.useCapture);\n            JSEvents.eventHandlers.push(eventHandler);\n            JSEvents.registerRemoveEventListeners();\n          } else {\n            for (var i = 0; i < JSEvents.eventHandlers.length; ++i) {\n              if (JSEvents.eventHandlers[i].target == eventHandler.target && JSEvents.eventHandlers[i].eventTypeString == eventHandler.eventTypeString) {\n                JSEvents._removeHandler(i--);\n              }\n            }\n          }\n        }, queueEventHandlerOnThread_iiii: function(targetThread, eventHandlerFunc, eventTypeId, eventData, userData) {\n          withStackSave(function() {\n            var varargs = stackAlloc(12);\n            GROWABLE_HEAP_I32()[varargs >> 2] = eventTypeId;\n            GROWABLE_HEAP_I32()[varargs + 4 >> 2] = eventData;\n            GROWABLE_HEAP_I32()[varargs + 8 >> 2] = userData;\n            _emscripten_dispatch_to_thread_(targetThread, 637534208, eventHandlerFunc, eventData, varargs);\n          });\n        }, getTargetThreadForEventCallback: function(targetThread) {\n          switch (targetThread) {\n            case 1:\n              return 0;\n            case 2:\n              return PThread.currentProxiedOperationCallerThread;\n            default:\n              return targetThread;\n          }\n        }, getNodeNameForTarget: function(target) {\n          if (!target)\n            return \"\";\n          if (target == window)\n            return \"#window\";\n          if (target == screen)\n            return \"#screen\";\n          return target && target.nodeName ? target.nodeName : \"\";\n        }, fullscreenEnabled: function() {\n          return document.fullscreenEnabled || document.webkitFullscreenEnabled;\n        } };\n        function stringToNewUTF8(jsString) {\n          var length = lengthBytesUTF8(jsString) + 1;\n          var cString = _malloc(length);\n          stringToUTF8(jsString, cString, length);\n          return cString;\n        }\n        function _emscripten_set_offscreencanvas_size_on_target_thread_js(targetThread, targetCanvas, width, height) {\n          withStackSave(function() {\n            var varargs = stackAlloc(12);\n            var targetCanvasPtr = 0;\n            if (targetCanvas) {\n              targetCanvasPtr = stringToNewUTF8(targetCanvas);\n            }\n            GROWABLE_HEAP_I32()[varargs >> 2] = targetCanvasPtr;\n            GROWABLE_HEAP_I32()[varargs + 4 >> 2] = width;\n            GROWABLE_HEAP_I32()[varargs + 8 >> 2] = height;\n            _emscripten_dispatch_to_thread_(targetThread, 657457152, 0, targetCanvasPtr, varargs);\n          });\n        }\n        function _emscripten_set_offscreencanvas_size_on_target_thread(targetThread, targetCanvas, width, height) {\n          targetCanvas = targetCanvas ? UTF8ToString(targetCanvas) : \"\";\n          _emscripten_set_offscreencanvas_size_on_target_thread_js(targetThread, targetCanvas, width, height);\n        }\n        function maybeCStringToJsString(cString) {\n          return cString > 2 ? UTF8ToString(cString) : cString;\n        }\n        var specialHTMLTargets = [0, typeof document !== \"undefined\" ? document : 0, typeof window !== \"undefined\" ? window : 0];\n        function findEventTarget(target) {\n          target = maybeCStringToJsString(target);\n          var domElement = specialHTMLTargets[target] || (typeof document !== \"undefined\" ? document.querySelector(target) : void 0);\n          return domElement;\n        }\n        function findCanvasEventTarget(target) {\n          return findEventTarget(target);\n        }\n        function _emscripten_set_canvas_element_size_calling_thread(target, width, height) {\n          var canvas = findCanvasEventTarget(target);\n          if (!canvas)\n            return -4;\n          if (canvas.canvasSharedPtr) {\n            GROWABLE_HEAP_I32()[canvas.canvasSharedPtr >> 2] = width;\n            GROWABLE_HEAP_I32()[canvas.canvasSharedPtr + 4 >> 2] = height;\n          }\n          if (canvas.offscreenCanvas || !canvas.controlTransferredOffscreen) {\n            if (canvas.offscreenCanvas)\n              canvas = canvas.offscreenCanvas;\n            var autoResizeViewport = false;\n            if (canvas.GLctxObject && canvas.GLctxObject.GLctx) {\n              var prevViewport = canvas.GLctxObject.GLctx.getParameter(2978);\n              autoResizeViewport = prevViewport[0] === 0 && prevViewport[1] === 0 && prevViewport[2] === canvas.width && prevViewport[3] === canvas.height;\n            }\n            canvas.width = width;\n            canvas.height = height;\n            if (autoResizeViewport) {\n              canvas.GLctxObject.GLctx.viewport(0, 0, width, height);\n            }\n          } else if (canvas.canvasSharedPtr) {\n            var targetThread = GROWABLE_HEAP_I32()[canvas.canvasSharedPtr + 8 >> 2];\n            _emscripten_set_offscreencanvas_size_on_target_thread(targetThread, target, width, height);\n            return 1;\n          } else {\n            return -4;\n          }\n          return 0;\n        }\n        function _emscripten_set_canvas_element_size_main_thread(target, width, height) {\n          if (ENVIRONMENT_IS_PTHREAD)\n            return _emscripten_proxy_to_main_thread_js(2, 1, target, width, height);\n          return _emscripten_set_canvas_element_size_calling_thread(target, width, height);\n        }\n        function _emscripten_set_canvas_element_size(target, width, height) {\n          var canvas = findCanvasEventTarget(target);\n          if (canvas) {\n            return _emscripten_set_canvas_element_size_calling_thread(target, width, height);\n          } else {\n            return _emscripten_set_canvas_element_size_main_thread(target, width, height);\n          }\n        }\n        function _emscripten_unwind_to_js_event_loop() {\n          throw \"unwind\";\n        }\n        function __webgl_enable_ANGLE_instanced_arrays(ctx) {\n          var ext = ctx.getExtension(\"ANGLE_instanced_arrays\");\n          if (ext) {\n            ctx[\"vertexAttribDivisor\"] = function(index, divisor) {\n              ext[\"vertexAttribDivisorANGLE\"](index, divisor);\n            };\n            ctx[\"drawArraysInstanced\"] = function(mode, first, count2, primcount) {\n              ext[\"drawArraysInstancedANGLE\"](mode, first, count2, primcount);\n            };\n            ctx[\"drawElementsInstanced\"] = function(mode, count2, type, indices, primcount) {\n              ext[\"drawElementsInstancedANGLE\"](mode, count2, type, indices, primcount);\n            };\n            return 1;\n          }\n        }\n        function __webgl_enable_OES_vertex_array_object(ctx) {\n          var ext = ctx.getExtension(\"OES_vertex_array_object\");\n          if (ext) {\n            ctx[\"createVertexArray\"] = function() {\n              return ext[\"createVertexArrayOES\"]();\n            };\n            ctx[\"deleteVertexArray\"] = function(vao) {\n              ext[\"deleteVertexArrayOES\"](vao);\n            };\n            ctx[\"bindVertexArray\"] = function(vao) {\n              ext[\"bindVertexArrayOES\"](vao);\n            };\n            ctx[\"isVertexArray\"] = function(vao) {\n              return ext[\"isVertexArrayOES\"](vao);\n            };\n            return 1;\n          }\n        }\n        function __webgl_enable_WEBGL_draw_buffers(ctx) {\n          var ext = ctx.getExtension(\"WEBGL_draw_buffers\");\n          if (ext) {\n            ctx[\"drawBuffers\"] = function(n, bufs) {\n              ext[\"drawBuffersWEBGL\"](n, bufs);\n            };\n            return 1;\n          }\n        }\n        function __webgl_enable_WEBGL_multi_draw(ctx) {\n          return !!(ctx.multiDrawWebgl = ctx.getExtension(\"WEBGL_multi_draw\"));\n        }\n        var GL = { counter: 1, buffers: [], programs: [], framebuffers: [], renderbuffers: [], textures: [], shaders: [], vaos: [], contexts: {}, offscreenCanvases: {}, queries: [], stringCache: {}, unpackAlignment: 4, recordError: function recordError(errorCode) {\n          if (!GL.lastError) {\n            GL.lastError = errorCode;\n          }\n        }, getNewId: function(table) {\n          var ret = GL.counter++;\n          for (var i = table.length; i < ret; i++) {\n            table[i] = null;\n          }\n          return ret;\n        }, getSource: function(shader, count2, string2, length) {\n          var source = \"\";\n          for (var i = 0; i < count2; ++i) {\n            var len = length ? GROWABLE_HEAP_I32()[length + i * 4 >> 2] : -1;\n            source += UTF8ToString(GROWABLE_HEAP_I32()[string2 + i * 4 >> 2], len < 0 ? void 0 : len);\n          }\n          return source;\n        }, createContext: function(canvas, webGLContextAttributes) {\n          if (!canvas.getContextSafariWebGL2Fixed) {\n            canvas.getContextSafariWebGL2Fixed = canvas.getContext;\n            canvas.getContext = function(ver, attrs) {\n              var gl = canvas.getContextSafariWebGL2Fixed(ver, attrs);\n              return ver == \"webgl\" == gl instanceof WebGLRenderingContext ? gl : null;\n            };\n          }\n          var ctx = canvas.getContext(\"webgl\", webGLContextAttributes);\n          if (!ctx)\n            return 0;\n          var handle = GL.registerContext(ctx, webGLContextAttributes);\n          return handle;\n        }, registerContext: function(ctx, webGLContextAttributes) {\n          var handle = _malloc(8);\n          GROWABLE_HEAP_I32()[handle + 4 >> 2] = _pthread_self();\n          var context = { handle, attributes: webGLContextAttributes, version: webGLContextAttributes.majorVersion, GLctx: ctx };\n          if (ctx.canvas)\n            ctx.canvas.GLctxObject = context;\n          GL.contexts[handle] = context;\n          if (typeof webGLContextAttributes.enableExtensionsByDefault === \"undefined\" || webGLContextAttributes.enableExtensionsByDefault) {\n            GL.initExtensions(context);\n          }\n          return handle;\n        }, makeContextCurrent: function(contextHandle) {\n          GL.currentContext = GL.contexts[contextHandle];\n          Module.ctx = GLctx = GL.currentContext && GL.currentContext.GLctx;\n          return !(contextHandle && !GLctx);\n        }, getContext: function(contextHandle) {\n          return GL.contexts[contextHandle];\n        }, deleteContext: function(contextHandle) {\n          if (GL.currentContext === GL.contexts[contextHandle])\n            GL.currentContext = null;\n          if (typeof JSEvents === \"object\")\n            JSEvents.removeAllHandlersOnTarget(GL.contexts[contextHandle].GLctx.canvas);\n          if (GL.contexts[contextHandle] && GL.contexts[contextHandle].GLctx.canvas)\n            GL.contexts[contextHandle].GLctx.canvas.GLctxObject = void 0;\n          _free(GL.contexts[contextHandle].handle);\n          GL.contexts[contextHandle] = null;\n        }, initExtensions: function(context) {\n          if (!context)\n            context = GL.currentContext;\n          if (context.initExtensionsDone)\n            return;\n          context.initExtensionsDone = true;\n          var GLctx2 = context.GLctx;\n          __webgl_enable_ANGLE_instanced_arrays(GLctx2);\n          __webgl_enable_OES_vertex_array_object(GLctx2);\n          __webgl_enable_WEBGL_draw_buffers(GLctx2);\n          {\n            GLctx2.disjointTimerQueryExt = GLctx2.getExtension(\"EXT_disjoint_timer_query\");\n          }\n          __webgl_enable_WEBGL_multi_draw(GLctx2);\n          var exts = GLctx2.getSupportedExtensions() || [];\n          exts.forEach(function(ext) {\n            if (!ext.includes(\"lose_context\") && !ext.includes(\"debug\")) {\n              GLctx2.getExtension(ext);\n            }\n          });\n        } };\n        var __emscripten_webgl_power_preferences = [\"default\", \"low-power\", \"high-performance\"];\n        function _emscripten_webgl_do_create_context(target, attributes) {\n          var a = attributes >> 2;\n          var powerPreference = GROWABLE_HEAP_I32()[a + (24 >> 2)];\n          var contextAttributes = { \"alpha\": !!GROWABLE_HEAP_I32()[a + (0 >> 2)], \"depth\": !!GROWABLE_HEAP_I32()[a + (4 >> 2)], \"stencil\": !!GROWABLE_HEAP_I32()[a + (8 >> 2)], \"antialias\": !!GROWABLE_HEAP_I32()[a + (12 >> 2)], \"premultipliedAlpha\": !!GROWABLE_HEAP_I32()[a + (16 >> 2)], \"preserveDrawingBuffer\": !!GROWABLE_HEAP_I32()[a + (20 >> 2)], \"powerPreference\": __emscripten_webgl_power_preferences[powerPreference], \"failIfMajorPerformanceCaveat\": !!GROWABLE_HEAP_I32()[a + (28 >> 2)], majorVersion: GROWABLE_HEAP_I32()[a + (32 >> 2)], minorVersion: GROWABLE_HEAP_I32()[a + (36 >> 2)], enableExtensionsByDefault: GROWABLE_HEAP_I32()[a + (40 >> 2)], explicitSwapControl: GROWABLE_HEAP_I32()[a + (44 >> 2)], proxyContextToMainThread: GROWABLE_HEAP_I32()[a + (48 >> 2)], renderViaOffscreenBackBuffer: GROWABLE_HEAP_I32()[a + (52 >> 2)] };\n          var canvas = findCanvasEventTarget(target);\n          if (!canvas) {\n            return 0;\n          }\n          if (contextAttributes.explicitSwapControl) {\n            return 0;\n          }\n          var contextHandle = GL.createContext(canvas, contextAttributes);\n          return contextHandle;\n        }\n        function _emscripten_webgl_create_context(a0, a12) {\n          return _emscripten_webgl_do_create_context(a0, a12);\n        }\n        var SYSCALLS = { mappings: {}, buffers: [null, [], []], printChar: function(stream, curr) {\n          var buffer3 = SYSCALLS.buffers[stream];\n          if (curr === 0 || curr === 10) {\n            (stream === 1 ? out : err)(UTF8ArrayToString(buffer3, 0));\n            buffer3.length = 0;\n          } else {\n            buffer3.push(curr);\n          }\n        }, varargs: void 0, get: function() {\n          SYSCALLS.varargs += 4;\n          var ret = GROWABLE_HEAP_I32()[SYSCALLS.varargs - 4 >> 2];\n          return ret;\n        }, getStr: function(ptr) {\n          var ret = UTF8ToString(ptr);\n          return ret;\n        }, get64: function(low, high) {\n          return low;\n        } };\n        function _fd_close(fd) {\n          if (ENVIRONMENT_IS_PTHREAD)\n            return _emscripten_proxy_to_main_thread_js(3, 1, fd);\n          return 0;\n        }\n        function _fd_seek(fd, offset_low, offset_high, whence, newOffset) {\n          if (ENVIRONMENT_IS_PTHREAD)\n            return _emscripten_proxy_to_main_thread_js(4, 1, fd, offset_low, offset_high, whence, newOffset);\n        }\n        function _fd_write(fd, iov, iovcnt, pnum) {\n          if (ENVIRONMENT_IS_PTHREAD)\n            return _emscripten_proxy_to_main_thread_js(5, 1, fd, iov, iovcnt, pnum);\n          var num = 0;\n          for (var i = 0; i < iovcnt; i++) {\n            var ptr = GROWABLE_HEAP_I32()[iov >> 2];\n            var len = GROWABLE_HEAP_I32()[iov + 4 >> 2];\n            iov += 8;\n            for (var j = 0; j < len; j++) {\n              SYSCALLS.printChar(fd, GROWABLE_HEAP_U8()[ptr + j]);\n            }\n            num += len;\n          }\n          GROWABLE_HEAP_I32()[pnum >> 2] = num;\n          return 0;\n        }\n        function _setTempRet0(val) {\n          setTempRet0(val);\n        }\n        PThread.init();\n        var GLctx;\n        var proxiedFunctionTable = [null, exitOnMainThread, _emscripten_set_canvas_element_size_main_thread, _fd_close, _fd_seek, _fd_write];\n        var ASSERTIONS = false;\n        var asmLibraryArg = { \"__clock_gettime\": ___clock_gettime, \"__emscripten_init_main_thread_js\": ___emscripten_init_main_thread_js, \"__emscripten_thread_cleanup\": ___emscripten_thread_cleanup, \"__pthread_create_js\": ___pthread_create_js, \"_emscripten_default_pthread_stack_size\": __emscripten_default_pthread_stack_size, \"_emscripten_notify_thread_queue\": __emscripten_notify_thread_queue, \"abort\": _abort, \"emscripten_check_blocking_allowed\": _emscripten_check_blocking_allowed, \"emscripten_get_heap_max\": _emscripten_get_heap_max, \"emscripten_get_now\": _emscripten_get_now, \"emscripten_memcpy_big\": _emscripten_memcpy_big, \"emscripten_num_logical_cores\": _emscripten_num_logical_cores, \"emscripten_receive_on_main_thread_js\": _emscripten_receive_on_main_thread_js, \"emscripten_resize_heap\": _emscripten_resize_heap, \"emscripten_set_canvas_element_size\": _emscripten_set_canvas_element_size, \"emscripten_unwind_to_js_event_loop\": _emscripten_unwind_to_js_event_loop, \"emscripten_webgl_create_context\": _emscripten_webgl_create_context, \"exit\": _exit, \"fd_close\": _fd_close, \"fd_seek\": _fd_seek, \"fd_write\": _fd_write, \"memory\": wasmMemory || Module[\"wasmMemory\"], \"setTempRet0\": _setTempRet0 };\n        var asm = createWasm();\n        var ___wasm_call_ctors = Module[\"___wasm_call_ctors\"] = function() {\n          return (___wasm_call_ctors = Module[\"___wasm_call_ctors\"] = Module[\"asm\"][\"__wasm_call_ctors\"]).apply(null, arguments);\n        };\n        var _init = Module[\"_init\"] = function() {\n          return (_init = Module[\"_init\"] = Module[\"asm\"][\"init\"]).apply(null, arguments);\n        };\n        var _init_with_threads_count = Module[\"_init_with_threads_count\"] = function() {\n          return (_init_with_threads_count = Module[\"_init_with_threads_count\"] = Module[\"asm\"][\"init_with_threads_count\"]).apply(null, arguments);\n        };\n        var _get_threads_count = Module[\"_get_threads_count\"] = function() {\n          return (_get_threads_count = Module[\"_get_threads_count\"] = Module[\"asm\"][\"get_threads_count\"]).apply(null, arguments);\n        };\n        var _register_tensor = Module[\"_register_tensor\"] = function() {\n          return (_register_tensor = Module[\"_register_tensor\"] = Module[\"asm\"][\"register_tensor\"]).apply(null, arguments);\n        };\n        var _dispose_data = Module[\"_dispose_data\"] = function() {\n          return (_dispose_data = Module[\"_dispose_data\"] = Module[\"asm\"][\"dispose_data\"]).apply(null, arguments);\n        };\n        var _dispose = Module[\"_dispose\"] = function() {\n          return (_dispose = Module[\"_dispose\"] = Module[\"asm\"][\"dispose\"]).apply(null, arguments);\n        };\n        var _Abs = Module[\"_Abs\"] = function() {\n          return (_Abs = Module[\"_Abs\"] = Module[\"asm\"][\"Abs\"]).apply(null, arguments);\n        };\n        var _Add = Module[\"_Add\"] = function() {\n          return (_Add = Module[\"_Add\"] = Module[\"asm\"][\"Add\"]).apply(null, arguments);\n        };\n        var _AddN = Module[\"_AddN\"] = function() {\n          return (_AddN = Module[\"_AddN\"] = Module[\"asm\"][\"AddN\"]).apply(null, arguments);\n        };\n        var _All = Module[\"_All\"] = function() {\n          return (_All = Module[\"_All\"] = Module[\"asm\"][\"All\"]).apply(null, arguments);\n        };\n        var _Any = Module[\"_Any\"] = function() {\n          return (_Any = Module[\"_Any\"] = Module[\"asm\"][\"Any\"]).apply(null, arguments);\n        };\n        var _ArgMax = Module[\"_ArgMax\"] = function() {\n          return (_ArgMax = Module[\"_ArgMax\"] = Module[\"asm\"][\"ArgMax\"]).apply(null, arguments);\n        };\n        var _AvgPool = Module[\"_AvgPool\"] = function() {\n          return (_AvgPool = Module[\"_AvgPool\"] = Module[\"asm\"][\"AvgPool\"]).apply(null, arguments);\n        };\n        var _BatchMatMul = Module[\"_BatchMatMul\"] = function() {\n          return (_BatchMatMul = Module[\"_BatchMatMul\"] = Module[\"asm\"][\"BatchMatMul\"]).apply(null, arguments);\n        };\n        var _Ceil = Module[\"_Ceil\"] = function() {\n          return (_Ceil = Module[\"_Ceil\"] = Module[\"asm\"][\"Ceil\"]).apply(null, arguments);\n        };\n        var _ClipByValue = Module[\"_ClipByValue\"] = function() {\n          return (_ClipByValue = Module[\"_ClipByValue\"] = Module[\"asm\"][\"ClipByValue\"]).apply(null, arguments);\n        };\n        var _Conv2D = Module[\"_Conv2D\"] = function() {\n          return (_Conv2D = Module[\"_Conv2D\"] = Module[\"asm\"][\"Conv2D\"]).apply(null, arguments);\n        };\n        var _Conv2DBackpropInput = Module[\"_Conv2DBackpropInput\"] = function() {\n          return (_Conv2DBackpropInput = Module[\"_Conv2DBackpropInput\"] = Module[\"asm\"][\"Conv2DBackpropInput\"]).apply(null, arguments);\n        };\n        var _Cos = Module[\"_Cos\"] = function() {\n          return (_Cos = Module[\"_Cos\"] = Module[\"asm\"][\"Cos\"]).apply(null, arguments);\n        };\n        var _Cosh = Module[\"_Cosh\"] = function() {\n          return (_Cosh = Module[\"_Cosh\"] = Module[\"asm\"][\"Cosh\"]).apply(null, arguments);\n        };\n        var _CropAndResize = Module[\"_CropAndResize\"] = function() {\n          return (_CropAndResize = Module[\"_CropAndResize\"] = Module[\"asm\"][\"CropAndResize\"]).apply(null, arguments);\n        };\n        var _Cumprod = Module[\"_Cumprod\"] = function() {\n          return (_Cumprod = Module[\"_Cumprod\"] = Module[\"asm\"][\"Cumprod\"]).apply(null, arguments);\n        };\n        var _Cumsum = Module[\"_Cumsum\"] = function() {\n          return (_Cumsum = Module[\"_Cumsum\"] = Module[\"asm\"][\"Cumsum\"]).apply(null, arguments);\n        };\n        var _DepthToSpace = Module[\"_DepthToSpace\"] = function() {\n          return (_DepthToSpace = Module[\"_DepthToSpace\"] = Module[\"asm\"][\"DepthToSpace\"]).apply(null, arguments);\n        };\n        var _DepthwiseConv2dNative = Module[\"_DepthwiseConv2dNative\"] = function() {\n          return (_DepthwiseConv2dNative = Module[\"_DepthwiseConv2dNative\"] = Module[\"asm\"][\"DepthwiseConv2dNative\"]).apply(null, arguments);\n        };\n        var _Elu = Module[\"_Elu\"] = function() {\n          return (_Elu = Module[\"_Elu\"] = Module[\"asm\"][\"Elu\"]).apply(null, arguments);\n        };\n        var _Equal = Module[\"_Equal\"] = function() {\n          return (_Equal = Module[\"_Equal\"] = Module[\"asm\"][\"Equal\"]).apply(null, arguments);\n        };\n        var _Exp = Module[\"_Exp\"] = function() {\n          return (_Exp = Module[\"_Exp\"] = Module[\"asm\"][\"Exp\"]).apply(null, arguments);\n        };\n        var _FlipLeftRight = Module[\"_FlipLeftRight\"] = function() {\n          return (_FlipLeftRight = Module[\"_FlipLeftRight\"] = Module[\"asm\"][\"FlipLeftRight\"]).apply(null, arguments);\n        };\n        var _Floor = Module[\"_Floor\"] = function() {\n          return (_Floor = Module[\"_Floor\"] = Module[\"asm\"][\"Floor\"]).apply(null, arguments);\n        };\n        var _FloorDiv = Module[\"_FloorDiv\"] = function() {\n          return (_FloorDiv = Module[\"_FloorDiv\"] = Module[\"asm\"][\"FloorDiv\"]).apply(null, arguments);\n        };\n        var _FusedBatchNorm = Module[\"_FusedBatchNorm\"] = function() {\n          return (_FusedBatchNorm = Module[\"_FusedBatchNorm\"] = Module[\"asm\"][\"FusedBatchNorm\"]).apply(null, arguments);\n        };\n        var _FusedConv2D = Module[\"_FusedConv2D\"] = function() {\n          return (_FusedConv2D = Module[\"_FusedConv2D\"] = Module[\"asm\"][\"FusedConv2D\"]).apply(null, arguments);\n        };\n        var _FusedDepthwiseConv2D = Module[\"_FusedDepthwiseConv2D\"] = function() {\n          return (_FusedDepthwiseConv2D = Module[\"_FusedDepthwiseConv2D\"] = Module[\"asm\"][\"FusedDepthwiseConv2D\"]).apply(null, arguments);\n        };\n        var _Gather = Module[\"_Gather\"] = function() {\n          return (_Gather = Module[\"_Gather\"] = Module[\"asm\"][\"Gather\"]).apply(null, arguments);\n        };\n        var _GatherNd = Module[\"_GatherNd\"] = function() {\n          return (_GatherNd = Module[\"_GatherNd\"] = Module[\"asm\"][\"GatherNd\"]).apply(null, arguments);\n        };\n        var _Greater = Module[\"_Greater\"] = function() {\n          return (_Greater = Module[\"_Greater\"] = Module[\"asm\"][\"Greater\"]).apply(null, arguments);\n        };\n        var _GreaterEqual = Module[\"_GreaterEqual\"] = function() {\n          return (_GreaterEqual = Module[\"_GreaterEqual\"] = Module[\"asm\"][\"GreaterEqual\"]).apply(null, arguments);\n        };\n        var _LeakyRelu = Module[\"_LeakyRelu\"] = function() {\n          return (_LeakyRelu = Module[\"_LeakyRelu\"] = Module[\"asm\"][\"LeakyRelu\"]).apply(null, arguments);\n        };\n        var _Less = Module[\"_Less\"] = function() {\n          return (_Less = Module[\"_Less\"] = Module[\"asm\"][\"Less\"]).apply(null, arguments);\n        };\n        var _LessEqual = Module[\"_LessEqual\"] = function() {\n          return (_LessEqual = Module[\"_LessEqual\"] = Module[\"asm\"][\"LessEqual\"]).apply(null, arguments);\n        };\n        var _Log = Module[\"_Log\"] = function() {\n          return (_Log = Module[\"_Log\"] = Module[\"asm\"][\"Log\"]).apply(null, arguments);\n        };\n        var _LogicalAnd = Module[\"_LogicalAnd\"] = function() {\n          return (_LogicalAnd = Module[\"_LogicalAnd\"] = Module[\"asm\"][\"LogicalAnd\"]).apply(null, arguments);\n        };\n        var _LogicalNot = Module[\"_LogicalNot\"] = function() {\n          return (_LogicalNot = Module[\"_LogicalNot\"] = Module[\"asm\"][\"LogicalNot\"]).apply(null, arguments);\n        };\n        var _LogicalOr = Module[\"_LogicalOr\"] = function() {\n          return (_LogicalOr = Module[\"_LogicalOr\"] = Module[\"asm\"][\"LogicalOr\"]).apply(null, arguments);\n        };\n        var _LogicalXor = Module[\"_LogicalXor\"] = function() {\n          return (_LogicalXor = Module[\"_LogicalXor\"] = Module[\"asm\"][\"LogicalXor\"]).apply(null, arguments);\n        };\n        var _Max = Module[\"_Max\"] = function() {\n          return (_Max = Module[\"_Max\"] = Module[\"asm\"][\"Max\"]).apply(null, arguments);\n        };\n        var _MaxPool = Module[\"_MaxPool\"] = function() {\n          return (_MaxPool = Module[\"_MaxPool\"] = Module[\"asm\"][\"MaxPool\"]).apply(null, arguments);\n        };\n        var _Maximum = Module[\"_Maximum\"] = function() {\n          return (_Maximum = Module[\"_Maximum\"] = Module[\"asm\"][\"Maximum\"]).apply(null, arguments);\n        };\n        var _Mean = Module[\"_Mean\"] = function() {\n          return (_Mean = Module[\"_Mean\"] = Module[\"asm\"][\"Mean\"]).apply(null, arguments);\n        };\n        var _Min = Module[\"_Min\"] = function() {\n          return (_Min = Module[\"_Min\"] = Module[\"asm\"][\"Min\"]).apply(null, arguments);\n        };\n        var _Minimum = Module[\"_Minimum\"] = function() {\n          return (_Minimum = Module[\"_Minimum\"] = Module[\"asm\"][\"Minimum\"]).apply(null, arguments);\n        };\n        var _MirrorPad = Module[\"_MirrorPad\"] = function() {\n          return (_MirrorPad = Module[\"_MirrorPad\"] = Module[\"asm\"][\"MirrorPad\"]).apply(null, arguments);\n        };\n        var _Multiply = Module[\"_Multiply\"] = function() {\n          return (_Multiply = Module[\"_Multiply\"] = Module[\"asm\"][\"Multiply\"]).apply(null, arguments);\n        };\n        var _Neg = Module[\"_Neg\"] = function() {\n          return (_Neg = Module[\"_Neg\"] = Module[\"asm\"][\"Neg\"]).apply(null, arguments);\n        };\n        var _NonMaxSuppressionV3 = Module[\"_NonMaxSuppressionV3\"] = function() {\n          return (_NonMaxSuppressionV3 = Module[\"_NonMaxSuppressionV3\"] = Module[\"asm\"][\"NonMaxSuppressionV3\"]).apply(null, arguments);\n        };\n        var _NonMaxSuppressionV4 = Module[\"_NonMaxSuppressionV4\"] = function() {\n          return (_NonMaxSuppressionV4 = Module[\"_NonMaxSuppressionV4\"] = Module[\"asm\"][\"NonMaxSuppressionV4\"]).apply(null, arguments);\n        };\n        var _NonMaxSuppressionV5 = Module[\"_NonMaxSuppressionV5\"] = function() {\n          return (_NonMaxSuppressionV5 = Module[\"_NonMaxSuppressionV5\"] = Module[\"asm\"][\"NonMaxSuppressionV5\"]).apply(null, arguments);\n        };\n        var _NotEqual = Module[\"_NotEqual\"] = function() {\n          return (_NotEqual = Module[\"_NotEqual\"] = Module[\"asm\"][\"NotEqual\"]).apply(null, arguments);\n        };\n        var _OneHot = Module[\"_OneHot\"] = function() {\n          return (_OneHot = Module[\"_OneHot\"] = Module[\"asm\"][\"OneHot\"]).apply(null, arguments);\n        };\n        var _PadV2 = Module[\"_PadV2\"] = function() {\n          return (_PadV2 = Module[\"_PadV2\"] = Module[\"asm\"][\"PadV2\"]).apply(null, arguments);\n        };\n        var _Pow = Module[\"_Pow\"] = function() {\n          return (_Pow = Module[\"_Pow\"] = Module[\"asm\"][\"Pow\"]).apply(null, arguments);\n        };\n        var _Prelu = Module[\"_Prelu\"] = function() {\n          return (_Prelu = Module[\"_Prelu\"] = Module[\"asm\"][\"Prelu\"]).apply(null, arguments);\n        };\n        var _Prod = Module[\"_Prod\"] = function() {\n          return (_Prod = Module[\"_Prod\"] = Module[\"asm\"][\"Prod\"]).apply(null, arguments);\n        };\n        var _RealDiv = Module[\"_RealDiv\"] = function() {\n          return (_RealDiv = Module[\"_RealDiv\"] = Module[\"asm\"][\"RealDiv\"]).apply(null, arguments);\n        };\n        var _Relu = Module[\"_Relu\"] = function() {\n          return (_Relu = Module[\"_Relu\"] = Module[\"asm\"][\"Relu\"]).apply(null, arguments);\n        };\n        var _Relu6 = Module[\"_Relu6\"] = function() {\n          return (_Relu6 = Module[\"_Relu6\"] = Module[\"asm\"][\"Relu6\"]).apply(null, arguments);\n        };\n        var _ResizeBilinear = Module[\"_ResizeBilinear\"] = function() {\n          return (_ResizeBilinear = Module[\"_ResizeBilinear\"] = Module[\"asm\"][\"ResizeBilinear\"]).apply(null, arguments);\n        };\n        var _ResizeNearestNeighbor = Module[\"_ResizeNearestNeighbor\"] = function() {\n          return (_ResizeNearestNeighbor = Module[\"_ResizeNearestNeighbor\"] = Module[\"asm\"][\"ResizeNearestNeighbor\"]).apply(null, arguments);\n        };\n        var _Reverse = Module[\"_Reverse\"] = function() {\n          return (_Reverse = Module[\"_Reverse\"] = Module[\"asm\"][\"Reverse\"]).apply(null, arguments);\n        };\n        var _RotateWithOffset = Module[\"_RotateWithOffset\"] = function() {\n          return (_RotateWithOffset = Module[\"_RotateWithOffset\"] = Module[\"asm\"][\"RotateWithOffset\"]).apply(null, arguments);\n        };\n        var _Round = Module[\"_Round\"] = function() {\n          return (_Round = Module[\"_Round\"] = Module[\"asm\"][\"Round\"]).apply(null, arguments);\n        };\n        var _Rsqrt = Module[\"_Rsqrt\"] = function() {\n          return (_Rsqrt = Module[\"_Rsqrt\"] = Module[\"asm\"][\"Rsqrt\"]).apply(null, arguments);\n        };\n        var _ScatterNd = Module[\"_ScatterNd\"] = function() {\n          return (_ScatterNd = Module[\"_ScatterNd\"] = Module[\"asm\"][\"ScatterNd\"]).apply(null, arguments);\n        };\n        var _SelectV2 = Module[\"_SelectV2\"] = function() {\n          return (_SelectV2 = Module[\"_SelectV2\"] = Module[\"asm\"][\"SelectV2\"]).apply(null, arguments);\n        };\n        var _Sigmoid = Module[\"_Sigmoid\"] = function() {\n          return (_Sigmoid = Module[\"_Sigmoid\"] = Module[\"asm\"][\"Sigmoid\"]).apply(null, arguments);\n        };\n        var _Sin = Module[\"_Sin\"] = function() {\n          return (_Sin = Module[\"_Sin\"] = Module[\"asm\"][\"Sin\"]).apply(null, arguments);\n        };\n        var _Softmax = Module[\"_Softmax\"] = function() {\n          return (_Softmax = Module[\"_Softmax\"] = Module[\"asm\"][\"Softmax\"]).apply(null, arguments);\n        };\n        var _SparseFillEmptyRows = Module[\"_SparseFillEmptyRows\"] = function() {\n          return (_SparseFillEmptyRows = Module[\"_SparseFillEmptyRows\"] = Module[\"asm\"][\"SparseFillEmptyRows\"]).apply(null, arguments);\n        };\n        var _SparseReshape = Module[\"_SparseReshape\"] = function() {\n          return (_SparseReshape = Module[\"_SparseReshape\"] = Module[\"asm\"][\"SparseReshape\"]).apply(null, arguments);\n        };\n        var _SparseSegmentReduction = Module[\"_SparseSegmentReduction\"] = function() {\n          return (_SparseSegmentReduction = Module[\"_SparseSegmentReduction\"] = Module[\"asm\"][\"SparseSegmentReduction\"]).apply(null, arguments);\n        };\n        var _Sqrt = Module[\"_Sqrt\"] = function() {\n          return (_Sqrt = Module[\"_Sqrt\"] = Module[\"asm\"][\"Sqrt\"]).apply(null, arguments);\n        };\n        var _Square = Module[\"_Square\"] = function() {\n          return (_Square = Module[\"_Square\"] = Module[\"asm\"][\"Square\"]).apply(null, arguments);\n        };\n        var _SquaredDifference = Module[\"_SquaredDifference\"] = function() {\n          return (_SquaredDifference = Module[\"_SquaredDifference\"] = Module[\"asm\"][\"SquaredDifference\"]).apply(null, arguments);\n        };\n        var _Step = Module[\"_Step\"] = function() {\n          return (_Step = Module[\"_Step\"] = Module[\"asm\"][\"Step\"]).apply(null, arguments);\n        };\n        var _StridedSlice = Module[\"_StridedSlice\"] = function() {\n          return (_StridedSlice = Module[\"_StridedSlice\"] = Module[\"asm\"][\"StridedSlice\"]).apply(null, arguments);\n        };\n        var _Sub = Module[\"_Sub\"] = function() {\n          return (_Sub = Module[\"_Sub\"] = Module[\"asm\"][\"Sub\"]).apply(null, arguments);\n        };\n        var _Sum = Module[\"_Sum\"] = function() {\n          return (_Sum = Module[\"_Sum\"] = Module[\"asm\"][\"Sum\"]).apply(null, arguments);\n        };\n        var _Tan = Module[\"_Tan\"] = function() {\n          return (_Tan = Module[\"_Tan\"] = Module[\"asm\"][\"Tan\"]).apply(null, arguments);\n        };\n        var _Tanh = Module[\"_Tanh\"] = function() {\n          return (_Tanh = Module[\"_Tanh\"] = Module[\"asm\"][\"Tanh\"]).apply(null, arguments);\n        };\n        var _Tile = Module[\"_Tile\"] = function() {\n          return (_Tile = Module[\"_Tile\"] = Module[\"asm\"][\"Tile\"]).apply(null, arguments);\n        };\n        var _TopK = Module[\"_TopK\"] = function() {\n          return (_TopK = Module[\"_TopK\"] = Module[\"asm\"][\"TopK\"]).apply(null, arguments);\n        };\n        var _Transform = Module[\"_Transform\"] = function() {\n          return (_Transform = Module[\"_Transform\"] = Module[\"asm\"][\"Transform\"]).apply(null, arguments);\n        };\n        var _Transpose = Module[\"_Transpose\"] = function() {\n          return (_Transpose = Module[\"_Transpose\"] = Module[\"asm\"][\"Transpose\"]).apply(null, arguments);\n        };\n        var __FusedMatMul = Module[\"__FusedMatMul\"] = function() {\n          return (__FusedMatMul = Module[\"__FusedMatMul\"] = Module[\"asm\"][\"_FusedMatMul\"]).apply(null, arguments);\n        };\n        var _malloc = Module[\"_malloc\"] = function() {\n          return (_malloc = Module[\"_malloc\"] = Module[\"asm\"][\"malloc\"]).apply(null, arguments);\n        };\n        var _free = Module[\"_free\"] = function() {\n          return (_free = Module[\"_free\"] = Module[\"asm\"][\"free\"]).apply(null, arguments);\n        };\n        var _emscripten_tls_init = Module[\"_emscripten_tls_init\"] = function() {\n          return (_emscripten_tls_init = Module[\"_emscripten_tls_init\"] = Module[\"asm\"][\"emscripten_tls_init\"]).apply(null, arguments);\n        };\n        var ___errno_location = Module[\"___errno_location\"] = function() {\n          return (___errno_location = Module[\"___errno_location\"] = Module[\"asm\"][\"__errno_location\"]).apply(null, arguments);\n        };\n        var _pthread_self = Module[\"_pthread_self\"] = function() {\n          return (_pthread_self = Module[\"_pthread_self\"] = Module[\"asm\"][\"pthread_self\"]).apply(null, arguments);\n        };\n        var _emscripten_main_thread_process_queued_calls = Module[\"_emscripten_main_thread_process_queued_calls\"] = function() {\n          return (_emscripten_main_thread_process_queued_calls = Module[\"_emscripten_main_thread_process_queued_calls\"] = Module[\"asm\"][\"emscripten_main_thread_process_queued_calls\"]).apply(null, arguments);\n        };\n        var __emscripten_thread_crashed = Module[\"__emscripten_thread_crashed\"] = function() {\n          return (__emscripten_thread_crashed = Module[\"__emscripten_thread_crashed\"] = Module[\"asm\"][\"_emscripten_thread_crashed\"]).apply(null, arguments);\n        };\n        var __emscripten_thread_init = Module[\"__emscripten_thread_init\"] = function() {\n          return (__emscripten_thread_init = Module[\"__emscripten_thread_init\"] = Module[\"asm\"][\"_emscripten_thread_init\"]).apply(null, arguments);\n        };\n        var _emscripten_current_thread_process_queued_calls = Module[\"_emscripten_current_thread_process_queued_calls\"] = function() {\n          return (_emscripten_current_thread_process_queued_calls = Module[\"_emscripten_current_thread_process_queued_calls\"] = Module[\"asm\"][\"emscripten_current_thread_process_queued_calls\"]).apply(null, arguments);\n        };\n        var _emscripten_main_browser_thread_id = Module[\"_emscripten_main_browser_thread_id\"] = function() {\n          return (_emscripten_main_browser_thread_id = Module[\"_emscripten_main_browser_thread_id\"] = Module[\"asm\"][\"emscripten_main_browser_thread_id\"]).apply(null, arguments);\n        };\n        var _emscripten_sync_run_in_main_thread_2 = Module[\"_emscripten_sync_run_in_main_thread_2\"] = function() {\n          return (_emscripten_sync_run_in_main_thread_2 = Module[\"_emscripten_sync_run_in_main_thread_2\"] = Module[\"asm\"][\"emscripten_sync_run_in_main_thread_2\"]).apply(null, arguments);\n        };\n        var _emscripten_sync_run_in_main_thread_4 = Module[\"_emscripten_sync_run_in_main_thread_4\"] = function() {\n          return (_emscripten_sync_run_in_main_thread_4 = Module[\"_emscripten_sync_run_in_main_thread_4\"] = Module[\"asm\"][\"emscripten_sync_run_in_main_thread_4\"]).apply(null, arguments);\n        };\n        var _emscripten_run_in_main_runtime_thread_js = Module[\"_emscripten_run_in_main_runtime_thread_js\"] = function() {\n          return (_emscripten_run_in_main_runtime_thread_js = Module[\"_emscripten_run_in_main_runtime_thread_js\"] = Module[\"asm\"][\"emscripten_run_in_main_runtime_thread_js\"]).apply(null, arguments);\n        };\n        var _emscripten_dispatch_to_thread_ = Module[\"_emscripten_dispatch_to_thread_\"] = function() {\n          return (_emscripten_dispatch_to_thread_ = Module[\"_emscripten_dispatch_to_thread_\"] = Module[\"asm\"][\"emscripten_dispatch_to_thread_\"]).apply(null, arguments);\n        };\n        var __emscripten_thread_free_data = Module[\"__emscripten_thread_free_data\"] = function() {\n          return (__emscripten_thread_free_data = Module[\"__emscripten_thread_free_data\"] = Module[\"asm\"][\"_emscripten_thread_free_data\"]).apply(null, arguments);\n        };\n        var __emscripten_thread_exit = Module[\"__emscripten_thread_exit\"] = function() {\n          return (__emscripten_thread_exit = Module[\"__emscripten_thread_exit\"] = Module[\"asm\"][\"_emscripten_thread_exit\"]).apply(null, arguments);\n        };\n        var _memalign = Module[\"_memalign\"] = function() {\n          return (_memalign = Module[\"_memalign\"] = Module[\"asm\"][\"memalign\"]).apply(null, arguments);\n        };\n        var _emscripten_stack_set_limits = Module[\"_emscripten_stack_set_limits\"] = function() {\n          return (_emscripten_stack_set_limits = Module[\"_emscripten_stack_set_limits\"] = Module[\"asm\"][\"emscripten_stack_set_limits\"]).apply(null, arguments);\n        };\n        var stackSave = Module[\"stackSave\"] = function() {\n          return (stackSave = Module[\"stackSave\"] = Module[\"asm\"][\"stackSave\"]).apply(null, arguments);\n        };\n        var stackRestore = Module[\"stackRestore\"] = function() {\n          return (stackRestore = Module[\"stackRestore\"] = Module[\"asm\"][\"stackRestore\"]).apply(null, arguments);\n        };\n        var stackAlloc = Module[\"stackAlloc\"] = function() {\n          return (stackAlloc = Module[\"stackAlloc\"] = Module[\"asm\"][\"stackAlloc\"]).apply(null, arguments);\n        };\n        var dynCall_iijjiiii = Module[\"dynCall_iijjiiii\"] = function() {\n          return (dynCall_iijjiiii = Module[\"dynCall_iijjiiii\"] = Module[\"asm\"][\"dynCall_iijjiiii\"]).apply(null, arguments);\n        };\n        var dynCall_jiji = Module[\"dynCall_jiji\"] = function() {\n          return (dynCall_jiji = Module[\"dynCall_jiji\"] = Module[\"asm\"][\"dynCall_jiji\"]).apply(null, arguments);\n        };\n        var __emscripten_allow_main_runtime_queued_calls = Module[\"__emscripten_allow_main_runtime_queued_calls\"] = 21672;\n        Module[\"cwrap\"] = cwrap;\n        Module[\"keepRuntimeAlive\"] = keepRuntimeAlive;\n        Module[\"PThread\"] = PThread;\n        Module[\"PThread\"] = PThread;\n        Module[\"wasmMemory\"] = wasmMemory;\n        Module[\"ExitStatus\"] = ExitStatus;\n        var calledRun;\n        function ExitStatus(status) {\n          this.name = \"ExitStatus\";\n          this.message = \"Program terminated with exit(\" + status + \")\";\n          this.status = status;\n        }\n        dependenciesFulfilled = function runCaller() {\n          if (!calledRun)\n            run();\n          if (!calledRun)\n            dependenciesFulfilled = runCaller;\n        };\n        function run(args) {\n          args = args || arguments_;\n          if (runDependencies > 0) {\n            return;\n          }\n          if (ENVIRONMENT_IS_PTHREAD) {\n            readyPromiseResolve(Module);\n            initRuntime();\n            postMessage({ \"cmd\": \"loaded\" });\n            return;\n          }\n          preRun();\n          if (runDependencies > 0) {\n            return;\n          }\n          function doRun() {\n            if (calledRun)\n              return;\n            calledRun = true;\n            Module[\"calledRun\"] = true;\n            if (ABORT)\n              return;\n            initRuntime();\n            readyPromiseResolve(Module);\n            if (Module[\"onRuntimeInitialized\"])\n              Module[\"onRuntimeInitialized\"]();\n            postRun();\n          }\n          if (Module[\"setStatus\"]) {\n            Module[\"setStatus\"](\"Running...\");\n            setTimeout(function() {\n              setTimeout(function() {\n                Module[\"setStatus\"](\"\");\n              }, 1);\n              doRun();\n            }, 1);\n          } else {\n            doRun();\n          }\n        }\n        Module[\"run\"] = run;\n        function exit(status, implicit) {\n          EXITSTATUS = status;\n          if (!implicit) {\n            if (ENVIRONMENT_IS_PTHREAD) {\n              exitOnMainThread(status);\n              throw \"unwind\";\n            } else {\n            }\n          }\n          if (keepRuntimeAlive()) {\n          } else {\n            exitRuntime();\n          }\n          procExit(status);\n        }\n        function procExit(code) {\n          EXITSTATUS = code;\n          if (!keepRuntimeAlive()) {\n            PThread.terminateAllThreads();\n            if (Module[\"onExit\"])\n              Module[\"onExit\"](code);\n            ABORT = true;\n          }\n          quit_(code, new ExitStatus(code));\n        }\n        if (Module[\"preInit\"]) {\n          if (typeof Module[\"preInit\"] == \"function\")\n            Module[\"preInit\"] = [Module[\"preInit\"]];\n          while (Module[\"preInit\"].length > 0) {\n            Module[\"preInit\"].pop()();\n          }\n        }\n        run();\n        var listenersAdded;\n        if (beforeListeners) {\n          listenersAdded = { uncaughtException: process.listeners(\"uncaughtException\").filter(function(listener) {\n            return !beforeListeners.uncaughtException.indexOf(listener) > -1;\n          }), unhandledRejection: process.listeners(\"unhandledRejection\").filter(function(listener) {\n            return !beforeListeners.unhandledRejection.indexOf(listener) > -1;\n          }) };\n        }\n        var actualModule;\n        if (typeof WasmBackendModule !== \"undefined\") {\n          actualModule = WasmBackendModule;\n        } else if (typeof WasmBackendModuleThreadedSimd3 !== \"undefined\") {\n          actualModule = WasmBackendModuleThreadedSimd3;\n        } else {\n          throw new Error(\"Could not find wasm module in post.js\");\n        }\n        if (listenersAdded) {\n          var tmpDispose = actualModule[\"_dispose\"];\n          actualModule[\"_dispose\"] = function() {\n            tmpDispose();\n            listenersAdded.uncaughtException.forEach(function(listener) {\n              process.removeListener(\"uncaughtException\", listener);\n            });\n            listenersAdded.unhandledRejection.forEach(function(listener) {\n              process.removeListener(\"unhandledRejection\", listener);\n            });\n          };\n        }\n        return WasmBackendModuleThreadedSimd3.ready;\n      };\n    })();\n    if (typeof exports === \"object\" && typeof module === \"object\")\n      module.exports = WasmBackendModuleThreadedSimd2;\n    else if (typeof define === \"function\" && define[\"amd\"])\n      define([], function() {\n        return WasmBackendModuleThreadedSimd2;\n      });\n    else if (typeof exports === \"object\")\n      exports[\"WasmBackendModuleThreadedSimd\"] = WasmBackendModuleThreadedSimd2;\n  }\n});\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm-threaded-simd.worker.js\nvar require_tfjs_backend_wasm_threaded_simd_worker = __commonJS({\n  \"node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm-threaded-simd.worker.js\"(exports, module) {\n    module.exports.wasmWorkerContents = `\"use strict\";var Module={};var ENVIRONMENT_IS_NODE=typeof process===\"object\"&&typeof process.versions===\"object\"&&typeof process.versions.node===\"string\";if(ENVIRONMENT_IS_NODE){var nodeWorkerThreads=require(\"worker_threads\");var parentPort=nodeWorkerThreads.parentPort;parentPort.on(\"message\",function(data){onmessage({data:data})});var fs=require(\"fs\");Object.assign(global,{self:global,require:require,Module:Module,location:{href:__filename},Worker:nodeWorkerThreads.Worker,importScripts:function(f){(0,eval)(fs.readFileSync(f,\"utf8\"))},postMessage:function(msg){parentPort.postMessage(msg)},performance:global.performance||{now:function(){return Date.now()}}})}function threadPrintErr(){var text=Array.prototype.slice.call(arguments).join(\" \");if(ENVIRONMENT_IS_NODE){fs.writeSync(2,text+\"\n\");return}console.error(text)}function threadAlert(){var text=Array.prototype.slice.call(arguments).join(\" \");postMessage({cmd:\"alert\",text:text,threadId:Module[\"_pthread_self\"]()})}var err=threadPrintErr;self.alert=threadAlert;Module[\"instantiateWasm\"]=((info,receiveInstance)=>{var instance=new WebAssembly.Instance(Module[\"wasmModule\"],info);receiveInstance(instance);Module[\"wasmModule\"]=null;return instance.exports});self.onmessage=(e=>{try{if(e.data.cmd===\"load\"){Module[\"wasmModule\"]=e.data.wasmModule;Module[\"wasmMemory\"]=e.data.wasmMemory;Module[\"buffer\"]=Module[\"wasmMemory\"].buffer;Module[\"ENVIRONMENT_IS_PTHREAD\"]=true;if(typeof e.data.urlOrBlob===\"string\"){importScripts(e.data.urlOrBlob)}else{var objectUrl=URL.createObjectURL(e.data.urlOrBlob);importScripts(objectUrl);URL.revokeObjectURL(objectUrl)}WasmBackendModuleThreadedSimd(Module).then(function(instance){Module=instance})}else if(e.data.cmd===\"run\"){Module[\"__performance_now_clock_drift\"]=performance.now()-e.data.time;Module[\"__emscripten_thread_init\"](e.data.threadInfoStruct,0,0,1);Module[\"establishStackSpace\"]();Module[\"PThread\"].receiveObjectTransfer(e.data);Module[\"PThread\"].threadInit();try{var result=Module[\"invokeEntryPoint\"](e.data.start_routine,e.data.arg);if(Module[\"keepRuntimeAlive\"]()){Module[\"PThread\"].setExitStatus(result)}else{Module[\"__emscripten_thread_exit\"](result)}}catch(ex){if(ex!=\"unwind\"){if(ex instanceof Module[\"ExitStatus\"]){if(Module[\"keepRuntimeAlive\"]()){}else{Module[\"__emscripten_thread_exit\"](ex.status)}}else{throw ex}}}}else if(e.data.cmd===\"cancel\"){if(Module[\"_pthread_self\"]()){Module[\"__emscripten_thread_exit\"](-1)}}else if(e.data.target===\"setimmediate\"){}else if(e.data.cmd===\"processThreadQueue\"){if(Module[\"_pthread_self\"]()){Module[\"_emscripten_current_thread_process_queued_calls\"]()}}else if(e.data.cmd===\"processProxyingQueue\"){if(Module[\"_pthread_self\"]()){Module[\"_emscripten_proxy_execute_queue\"](e.data.queue)}}else{err(\"worker.js received unknown command \"+e.data.cmd);err(e.data)}}catch(ex){err(\"worker.js onmessage() captured an uncaught exception: \"+ex);if(ex&&ex.stack)err(ex.stack);if(Module[\"__emscripten_thread_crashed\"]){Module[\"__emscripten_thread_crashed\"]()}throw ex}});`;\n  }\n});\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm.js\nvar require_tfjs_backend_wasm = __commonJS({\n  \"node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm.js\"(exports, module) {\n    var WasmBackendModule2 = (() => {\n      var _scriptDir = typeof document !== \"undefined\" && document.currentScript ? document.currentScript.src : void 0;\n      if (typeof __filename !== \"undefined\")\n        _scriptDir = _scriptDir || __filename;\n      return function(WasmBackendModule3) {\n        WasmBackendModule3 = WasmBackendModule3 || {};\n        var Module = typeof WasmBackendModule3 !== \"undefined\" ? WasmBackendModule3 : {};\n        var readyPromiseResolve, readyPromiseReject;\n        Module[\"ready\"] = new Promise(function(resolve, reject) {\n          readyPromiseResolve = resolve;\n          readyPromiseReject = reject;\n        });\n        var beforeListeners;\n        if (typeof process !== \"undefined\" && process.listeners) {\n          beforeListeners = { uncaughtException: process.listeners(\"uncaughtException\"), unhandledRejection: process.listeners(\"unhandledRejection\") };\n        }\n        var moduleOverrides = Object.assign({}, Module);\n        var arguments_ = [];\n        var thisProgram = \"./this.program\";\n        var quit_ = (status, toThrow) => {\n          throw toThrow;\n        };\n        var ENVIRONMENT_IS_WEB = typeof window === \"object\";\n        var ENVIRONMENT_IS_WORKER = typeof importScripts === \"function\";\n        var ENVIRONMENT_IS_NODE = typeof process === \"object\" && typeof process.versions === \"object\" && typeof process.versions.node === \"string\";\n        var scriptDirectory = \"\";\n        function locateFile(path) {\n          if (Module[\"locateFile\"]) {\n            return Module[\"locateFile\"](path, scriptDirectory);\n          }\n          return scriptDirectory + path;\n        }\n        var read_, readAsync, readBinary, setWindowTitle;\n        function logExceptionOnExit(e) {\n          if (e instanceof ExitStatus)\n            return;\n          let toLog = e;\n          err(\"exiting due to exception: \" + toLog);\n        }\n        var fs;\n        var nodePath;\n        var requireNodeFS;\n        if (ENVIRONMENT_IS_NODE) {\n          if (ENVIRONMENT_IS_WORKER) {\n            scriptDirectory = require_path().dirname(scriptDirectory) + \"/\";\n          } else {\n            scriptDirectory = __dirname + \"/\";\n          }\n          requireNodeFS = () => {\n            if (!nodePath) {\n              fs = require_fs();\n              nodePath = require_path();\n            }\n          };\n          read_ = function shell_read(filename, binary) {\n            requireNodeFS();\n            filename = nodePath[\"normalize\"](filename);\n            return fs.readFileSync(filename, binary ? void 0 : \"utf8\");\n          };\n          readBinary = (filename) => {\n            var ret = read_(filename, true);\n            if (!ret.buffer) {\n              ret = new Uint8Array(ret);\n            }\n            return ret;\n          };\n          readAsync = (filename, onload, onerror) => {\n            requireNodeFS();\n            filename = nodePath[\"normalize\"](filename);\n            fs.readFile(filename, function(err2, data) {\n              if (err2)\n                onerror(err2);\n              else\n                onload(data.buffer);\n            });\n          };\n          if (process[\"argv\"].length > 1) {\n            thisProgram = process[\"argv\"][1].replace(/\\\\/g, \"/\");\n          }\n          arguments_ = process[\"argv\"].slice(2);\n          process[\"on\"](\"uncaughtException\", function(ex) {\n            if (!(ex instanceof ExitStatus)) {\n              throw ex;\n            }\n          });\n          process[\"on\"](\"unhandledRejection\", function(reason) {\n            throw reason;\n          });\n          quit_ = (status, toThrow) => {\n            if (keepRuntimeAlive()) {\n              process[\"exitCode\"] = status;\n              throw toThrow;\n            }\n            logExceptionOnExit(toThrow);\n            process[\"exit\"](status);\n          };\n          Module[\"inspect\"] = function() {\n            return \"[Emscripten Module object]\";\n          };\n        } else if (ENVIRONMENT_IS_WEB || ENVIRONMENT_IS_WORKER) {\n          if (ENVIRONMENT_IS_WORKER) {\n            scriptDirectory = self.location.href;\n          } else if (typeof document !== \"undefined\" && document.currentScript) {\n            scriptDirectory = document.currentScript.src;\n          }\n          if (_scriptDir) {\n            scriptDirectory = _scriptDir;\n          }\n          if (scriptDirectory.indexOf(\"blob:\") !== 0) {\n            scriptDirectory = scriptDirectory.substr(0, scriptDirectory.replace(/[?#].*/, \"\").lastIndexOf(\"/\") + 1);\n          } else {\n            scriptDirectory = \"\";\n          }\n          {\n            read_ = (url) => {\n              var xhr = new XMLHttpRequest();\n              xhr.open(\"GET\", url, false);\n              xhr.send(null);\n              return xhr.responseText;\n            };\n            if (ENVIRONMENT_IS_WORKER) {\n              readBinary = (url) => {\n                var xhr = new XMLHttpRequest();\n                xhr.open(\"GET\", url, false);\n                xhr.responseType = \"arraybuffer\";\n                xhr.send(null);\n                return new Uint8Array(xhr.response);\n              };\n            }\n            readAsync = (url, onload, onerror) => {\n              var xhr = new XMLHttpRequest();\n              xhr.open(\"GET\", url, true);\n              xhr.responseType = \"arraybuffer\";\n              xhr.onload = () => {\n                if (xhr.status == 200 || xhr.status == 0 && xhr.response) {\n                  onload(xhr.response);\n                  return;\n                }\n                onerror();\n              };\n              xhr.onerror = onerror;\n              xhr.send(null);\n            };\n          }\n          setWindowTitle = (title) => document.title = title;\n        } else {\n        }\n        var out = Module[\"print\"] || console.log.bind(console);\n        var err = Module[\"printErr\"] || console.warn.bind(console);\n        Object.assign(Module, moduleOverrides);\n        moduleOverrides = null;\n        if (Module[\"arguments\"])\n          arguments_ = Module[\"arguments\"];\n        if (Module[\"thisProgram\"])\n          thisProgram = Module[\"thisProgram\"];\n        if (Module[\"quit\"])\n          quit_ = Module[\"quit\"];\n        var POINTER_SIZE = 4;\n        function warnOnce(text) {\n          if (!warnOnce.shown)\n            warnOnce.shown = {};\n          if (!warnOnce.shown[text]) {\n            warnOnce.shown[text] = 1;\n            err(text);\n          }\n        }\n        function convertJsFunctionToWasm(func2, sig) {\n          if (typeof WebAssembly.Function === \"function\") {\n            var typeNames = { \"i\": \"i32\", \"j\": \"i64\", \"f\": \"f32\", \"d\": \"f64\" };\n            var type = { parameters: [], results: sig[0] == \"v\" ? [] : [typeNames[sig[0]]] };\n            for (var i = 1; i < sig.length; ++i) {\n              type.parameters.push(typeNames[sig[i]]);\n            }\n            return new WebAssembly.Function(type, func2);\n          }\n          var typeSection = [1, 0, 1, 96];\n          var sigRet = sig.slice(0, 1);\n          var sigParam = sig.slice(1);\n          var typeCodes = { \"i\": 127, \"j\": 126, \"f\": 125, \"d\": 124 };\n          typeSection.push(sigParam.length);\n          for (var i = 0; i < sigParam.length; ++i) {\n            typeSection.push(typeCodes[sigParam[i]]);\n          }\n          if (sigRet == \"v\") {\n            typeSection.push(0);\n          } else {\n            typeSection = typeSection.concat([1, typeCodes[sigRet]]);\n          }\n          typeSection[1] = typeSection.length - 2;\n          var bytes = new Uint8Array([0, 97, 115, 109, 1, 0, 0, 0].concat(typeSection, [2, 7, 1, 1, 101, 1, 102, 0, 0, 7, 5, 1, 1, 102, 0, 0]));\n          var module2 = new WebAssembly.Module(bytes);\n          var instance = new WebAssembly.Instance(module2, { \"e\": { \"f\": func2 } });\n          var wrappedFunc = instance.exports[\"f\"];\n          return wrappedFunc;\n        }\n        var freeTableIndexes = [];\n        var functionsInTableMap;\n        function getEmptyTableSlot() {\n          if (freeTableIndexes.length) {\n            return freeTableIndexes.pop();\n          }\n          try {\n            wasmTable.grow(1);\n          } catch (err2) {\n            if (!(err2 instanceof RangeError)) {\n              throw err2;\n            }\n            throw \"Unable to grow wasm table. Set ALLOW_TABLE_GROWTH.\";\n          }\n          return wasmTable.length - 1;\n        }\n        function updateTableMap(offset, count2) {\n          for (var i = offset; i < offset + count2; i++) {\n            var item = getWasmTableEntry(i);\n            if (item) {\n              functionsInTableMap.set(item, i);\n            }\n          }\n        }\n        var tempRet0 = 0;\n        var setTempRet0 = (value) => {\n          tempRet0 = value;\n        };\n        var wasmBinary;\n        if (Module[\"wasmBinary\"])\n          wasmBinary = Module[\"wasmBinary\"];\n        var noExitRuntime = Module[\"noExitRuntime\"] || true;\n        if (typeof WebAssembly !== \"object\") {\n          abort(\"no native wasm support detected\");\n        }\n        var wasmMemory;\n        var ABORT = false;\n        var EXITSTATUS;\n        function assert3(condition, text) {\n          if (!condition) {\n            abort(text);\n          }\n        }\n        function getCFunc(ident) {\n          var func2 = Module[\"_\" + ident];\n          return func2;\n        }\n        function ccall(ident, returnType, argTypes, args, opts) {\n          var toC = { \"string\": function(str) {\n            var ret2 = 0;\n            if (str !== null && str !== void 0 && str !== 0) {\n              var len = (str.length << 2) + 1;\n              ret2 = stackAlloc(len);\n              stringToUTF8(str, ret2, len);\n            }\n            return ret2;\n          }, \"array\": function(arr) {\n            var ret2 = stackAlloc(arr.length);\n            writeArrayToMemory(arr, ret2);\n            return ret2;\n          } };\n          function convertReturnValue(ret2) {\n            if (returnType === \"string\")\n              return UTF8ToString(ret2);\n            if (returnType === \"boolean\")\n              return Boolean(ret2);\n            return ret2;\n          }\n          var func2 = getCFunc(ident);\n          var cArgs = [];\n          var stack2 = 0;\n          if (args) {\n            for (var i = 0; i < args.length; i++) {\n              var converter = toC[argTypes[i]];\n              if (converter) {\n                if (stack2 === 0)\n                  stack2 = stackSave();\n                cArgs[i] = converter(args[i]);\n              } else {\n                cArgs[i] = args[i];\n              }\n            }\n          }\n          var ret = func2.apply(null, cArgs);\n          function onDone(ret2) {\n            if (stack2 !== 0)\n              stackRestore(stack2);\n            return convertReturnValue(ret2);\n          }\n          ret = onDone(ret);\n          return ret;\n        }\n        function cwrap(ident, returnType, argTypes, opts) {\n          argTypes = argTypes || [];\n          var numericArgs = argTypes.every(function(type) {\n            return type === \"number\";\n          });\n          var numericRet = returnType !== \"string\";\n          if (numericRet && numericArgs && !opts) {\n            return getCFunc(ident);\n          }\n          return function() {\n            return ccall(ident, returnType, argTypes, arguments, opts);\n          };\n        }\n        var ALLOC_STACK = 1;\n        var UTF8Decoder = typeof TextDecoder !== \"undefined\" ? new TextDecoder(\"utf8\") : void 0;\n        function UTF8ArrayToString(heap, idx, maxBytesToRead) {\n          var endIdx = idx + maxBytesToRead;\n          var endPtr = idx;\n          while (heap[endPtr] && !(endPtr >= endIdx))\n            ++endPtr;\n          if (endPtr - idx > 16 && heap.subarray && UTF8Decoder) {\n            return UTF8Decoder.decode(heap.subarray(idx, endPtr));\n          } else {\n            var str = \"\";\n            while (idx < endPtr) {\n              var u0 = heap[idx++];\n              if (!(u0 & 128)) {\n                str += String.fromCharCode(u0);\n                continue;\n              }\n              var u1 = heap[idx++] & 63;\n              if ((u0 & 224) == 192) {\n                str += String.fromCharCode((u0 & 31) << 6 | u1);\n                continue;\n              }\n              var u2 = heap[idx++] & 63;\n              if ((u0 & 240) == 224) {\n                u0 = (u0 & 15) << 12 | u1 << 6 | u2;\n              } else {\n                u0 = (u0 & 7) << 18 | u1 << 12 | u2 << 6 | heap[idx++] & 63;\n              }\n              if (u0 < 65536) {\n                str += String.fromCharCode(u0);\n              } else {\n                var ch = u0 - 65536;\n                str += String.fromCharCode(55296 | ch >> 10, 56320 | ch & 1023);\n              }\n            }\n          }\n          return str;\n        }\n        function UTF8ToString(ptr, maxBytesToRead) {\n          return ptr ? UTF8ArrayToString(HEAPU8, ptr, maxBytesToRead) : \"\";\n        }\n        function stringToUTF8Array(str, heap, outIdx, maxBytesToWrite) {\n          if (!(maxBytesToWrite > 0))\n            return 0;\n          var startIdx = outIdx;\n          var endIdx = outIdx + maxBytesToWrite - 1;\n          for (var i = 0; i < str.length; ++i) {\n            var u = str.charCodeAt(i);\n            if (u >= 55296 && u <= 57343) {\n              var u1 = str.charCodeAt(++i);\n              u = 65536 + ((u & 1023) << 10) | u1 & 1023;\n            }\n            if (u <= 127) {\n              if (outIdx >= endIdx)\n                break;\n              heap[outIdx++] = u;\n            } else if (u <= 2047) {\n              if (outIdx + 1 >= endIdx)\n                break;\n              heap[outIdx++] = 192 | u >> 6;\n              heap[outIdx++] = 128 | u & 63;\n            } else if (u <= 65535) {\n              if (outIdx + 2 >= endIdx)\n                break;\n              heap[outIdx++] = 224 | u >> 12;\n              heap[outIdx++] = 128 | u >> 6 & 63;\n              heap[outIdx++] = 128 | u & 63;\n            } else {\n              if (outIdx + 3 >= endIdx)\n                break;\n              heap[outIdx++] = 240 | u >> 18;\n              heap[outIdx++] = 128 | u >> 12 & 63;\n              heap[outIdx++] = 128 | u >> 6 & 63;\n              heap[outIdx++] = 128 | u & 63;\n            }\n          }\n          heap[outIdx] = 0;\n          return outIdx - startIdx;\n        }\n        function stringToUTF8(str, outPtr, maxBytesToWrite) {\n          return stringToUTF8Array(str, HEAPU8, outPtr, maxBytesToWrite);\n        }\n        function lengthBytesUTF8(str) {\n          var len = 0;\n          for (var i = 0; i < str.length; ++i) {\n            var u = str.charCodeAt(i);\n            if (u >= 55296 && u <= 57343)\n              u = 65536 + ((u & 1023) << 10) | str.charCodeAt(++i) & 1023;\n            if (u <= 127)\n              ++len;\n            else if (u <= 2047)\n              len += 2;\n            else if (u <= 65535)\n              len += 3;\n            else\n              len += 4;\n          }\n          return len;\n        }\n        var UTF16Decoder = typeof TextDecoder !== \"undefined\" ? new TextDecoder(\"utf-16le\") : void 0;\n        function writeArrayToMemory(array2, buffer3) {\n          HEAP8.set(array2, buffer3);\n        }\n        function writeAsciiToMemory(str, buffer3, dontAddNull) {\n          for (var i = 0; i < str.length; ++i) {\n            HEAP8[buffer3++ >> 0] = str.charCodeAt(i);\n          }\n          if (!dontAddNull)\n            HEAP8[buffer3 >> 0] = 0;\n        }\n        function alignUp(x, multiple) {\n          if (x % multiple > 0) {\n            x += multiple - x % multiple;\n          }\n          return x;\n        }\n        var buffer2, HEAP8, HEAPU8, HEAP16, HEAPU16, HEAP32, HEAPU32, HEAPF32, HEAPF64;\n        function updateGlobalBufferAndViews(buf) {\n          buffer2 = buf;\n          Module[\"HEAP8\"] = HEAP8 = new Int8Array(buf);\n          Module[\"HEAP16\"] = HEAP16 = new Int16Array(buf);\n          Module[\"HEAP32\"] = HEAP32 = new Int32Array(buf);\n          Module[\"HEAPU8\"] = HEAPU8 = new Uint8Array(buf);\n          Module[\"HEAPU16\"] = HEAPU16 = new Uint16Array(buf);\n          Module[\"HEAPU32\"] = HEAPU32 = new Uint32Array(buf);\n          Module[\"HEAPF32\"] = HEAPF32 = new Float32Array(buf);\n          Module[\"HEAPF64\"] = HEAPF64 = new Float64Array(buf);\n        }\n        var INITIAL_MEMORY = Module[\"INITIAL_MEMORY\"] || 16777216;\n        var wasmTable;\n        var __ATPRERUN__ = [];\n        var __ATINIT__ = [];\n        var __ATPOSTRUN__ = [];\n        var runtimeInitialized = false;\n        var runtimeExited = false;\n        var runtimeKeepaliveCounter = 0;\n        function keepRuntimeAlive() {\n          return noExitRuntime || runtimeKeepaliveCounter > 0;\n        }\n        function preRun() {\n          if (Module[\"preRun\"]) {\n            if (typeof Module[\"preRun\"] == \"function\")\n              Module[\"preRun\"] = [Module[\"preRun\"]];\n            while (Module[\"preRun\"].length) {\n              addOnPreRun(Module[\"preRun\"].shift());\n            }\n          }\n          callRuntimeCallbacks(__ATPRERUN__);\n        }\n        function initRuntime() {\n          runtimeInitialized = true;\n          callRuntimeCallbacks(__ATINIT__);\n        }\n        function exitRuntime() {\n          runtimeExited = true;\n        }\n        function postRun() {\n          if (Module[\"postRun\"]) {\n            if (typeof Module[\"postRun\"] == \"function\")\n              Module[\"postRun\"] = [Module[\"postRun\"]];\n            while (Module[\"postRun\"].length) {\n              addOnPostRun(Module[\"postRun\"].shift());\n            }\n          }\n          callRuntimeCallbacks(__ATPOSTRUN__);\n        }\n        function addOnPreRun(cb) {\n          __ATPRERUN__.unshift(cb);\n        }\n        function addOnInit(cb) {\n          __ATINIT__.unshift(cb);\n        }\n        function addOnPostRun(cb) {\n          __ATPOSTRUN__.unshift(cb);\n        }\n        var runDependencies = 0;\n        var runDependencyWatcher = null;\n        var dependenciesFulfilled = null;\n        function addRunDependency(id) {\n          runDependencies++;\n          if (Module[\"monitorRunDependencies\"]) {\n            Module[\"monitorRunDependencies\"](runDependencies);\n          }\n        }\n        function removeRunDependency(id) {\n          runDependencies--;\n          if (Module[\"monitorRunDependencies\"]) {\n            Module[\"monitorRunDependencies\"](runDependencies);\n          }\n          if (runDependencies == 0) {\n            if (runDependencyWatcher !== null) {\n              clearInterval(runDependencyWatcher);\n              runDependencyWatcher = null;\n            }\n            if (dependenciesFulfilled) {\n              var callback = dependenciesFulfilled;\n              dependenciesFulfilled = null;\n              callback();\n            }\n          }\n        }\n        Module[\"preloadedImages\"] = {};\n        Module[\"preloadedAudios\"] = {};\n        function abort(what) {\n          {\n            if (Module[\"onAbort\"]) {\n              Module[\"onAbort\"](what);\n            }\n          }\n          what = \"Aborted(\" + what + \")\";\n          err(what);\n          ABORT = true;\n          EXITSTATUS = 1;\n          what += \". Build with -s ASSERTIONS=1 for more info.\";\n          var e = new WebAssembly.RuntimeError(what);\n          readyPromiseReject(e);\n          throw e;\n        }\n        var dataURIPrefix = \"data:application/octet-stream;base64,\";\n        function isDataURI(filename) {\n          return filename.startsWith(dataURIPrefix);\n        }\n        function isFileURI(filename) {\n          return filename.startsWith(\"file://\");\n        }\n        var wasmBinaryFile;\n        wasmBinaryFile = \"tfjs-backend-wasm.wasm\";\n        if (!isDataURI(wasmBinaryFile)) {\n          wasmBinaryFile = locateFile(wasmBinaryFile);\n        }\n        function getBinary(file) {\n          try {\n            if (file == wasmBinaryFile && wasmBinary) {\n              return new Uint8Array(wasmBinary);\n            }\n            if (readBinary) {\n              return readBinary(file);\n            } else {\n              throw \"both async and sync fetching of the wasm failed\";\n            }\n          } catch (err2) {\n            abort(err2);\n          }\n        }\n        function getBinaryPromise() {\n          if (!wasmBinary && (ENVIRONMENT_IS_WEB || ENVIRONMENT_IS_WORKER)) {\n            if (typeof fetch === \"function\" && !isFileURI(wasmBinaryFile)) {\n              return fetch(wasmBinaryFile, { credentials: \"same-origin\" }).then(function(response) {\n                if (!response[\"ok\"]) {\n                  throw \"failed to load wasm binary file at '\" + wasmBinaryFile + \"'\";\n                }\n                return response[\"arrayBuffer\"]();\n              }).catch(function() {\n                return getBinary(wasmBinaryFile);\n              });\n            } else {\n              if (readAsync) {\n                return new Promise(function(resolve, reject) {\n                  readAsync(wasmBinaryFile, function(response) {\n                    resolve(new Uint8Array(response));\n                  }, reject);\n                });\n              }\n            }\n          }\n          return Promise.resolve().then(function() {\n            return getBinary(wasmBinaryFile);\n          });\n        }\n        function createWasm() {\n          var info = { \"env\": asmLibraryArg, \"wasi_snapshot_preview1\": asmLibraryArg };\n          function receiveInstance(instance, module2) {\n            var exports3 = instance.exports;\n            Module[\"asm\"] = exports3;\n            wasmMemory = Module[\"asm\"][\"memory\"];\n            updateGlobalBufferAndViews(wasmMemory.buffer);\n            wasmTable = Module[\"asm\"][\"__indirect_function_table\"];\n            addOnInit(Module[\"asm\"][\"__wasm_call_ctors\"]);\n            removeRunDependency(\"wasm-instantiate\");\n          }\n          addRunDependency(\"wasm-instantiate\");\n          function receiveInstantiationResult(result) {\n            receiveInstance(result[\"instance\"]);\n          }\n          function instantiateArrayBuffer(receiver) {\n            return getBinaryPromise().then(function(binary) {\n              return WebAssembly.instantiate(binary, info);\n            }).then(function(instance) {\n              return instance;\n            }).then(receiver, function(reason) {\n              err(\"failed to asynchronously prepare wasm: \" + reason);\n              abort(reason);\n            });\n          }\n          function instantiateAsync() {\n            if (!wasmBinary && typeof WebAssembly.instantiateStreaming === \"function\" && !isDataURI(wasmBinaryFile) && !isFileURI(wasmBinaryFile) && typeof fetch === \"function\") {\n              return fetch(wasmBinaryFile, { credentials: \"same-origin\" }).then(function(response) {\n                var result = WebAssembly.instantiateStreaming(response, info);\n                return result.then(receiveInstantiationResult, function(reason) {\n                  err(\"wasm streaming compile failed: \" + reason);\n                  err(\"falling back to ArrayBuffer instantiation\");\n                  return instantiateArrayBuffer(receiveInstantiationResult);\n                });\n              });\n            } else {\n              return instantiateArrayBuffer(receiveInstantiationResult);\n            }\n          }\n          if (Module[\"instantiateWasm\"]) {\n            try {\n              var exports2 = Module[\"instantiateWasm\"](info, receiveInstance);\n              return exports2;\n            } catch (e) {\n              err(\"Module.instantiateWasm callback failed with error: \" + e);\n              return false;\n            }\n          }\n          instantiateAsync().catch(readyPromiseReject);\n          return {};\n        }\n        var tempDouble;\n        var tempI64;\n        function callRuntimeCallbacks(callbacks2) {\n          while (callbacks2.length > 0) {\n            var callback = callbacks2.shift();\n            if (typeof callback == \"function\") {\n              callback(Module);\n              continue;\n            }\n            var func2 = callback.func;\n            if (typeof func2 === \"number\") {\n              if (callback.arg === void 0) {\n                getWasmTableEntry(func2)();\n              } else {\n                getWasmTableEntry(func2)(callback.arg);\n              }\n            } else {\n              func2(callback.arg === void 0 ? null : callback.arg);\n            }\n          }\n        }\n        function demangle(func2) {\n          return func2;\n        }\n        function demangleAll(text) {\n          var regex = /\\b_Z[\\w\\d_]+/g;\n          return text.replace(regex, function(x) {\n            var y = demangle(x);\n            return x === y ? x : y + \" [\" + x + \"]\";\n          });\n        }\n        var wasmTableMirror = [];\n        function getWasmTableEntry(funcPtr) {\n          var func2 = wasmTableMirror[funcPtr];\n          if (!func2) {\n            if (funcPtr >= wasmTableMirror.length)\n              wasmTableMirror.length = funcPtr + 1;\n            wasmTableMirror[funcPtr] = func2 = wasmTable.get(funcPtr);\n          }\n          return func2;\n        }\n        function jsStackTrace() {\n          var error = new Error();\n          if (!error.stack) {\n            try {\n              throw new Error();\n            } catch (e) {\n              error = e;\n            }\n            if (!error.stack) {\n              return \"(no stack trace available)\";\n            }\n          }\n          return error.stack.toString();\n        }\n        function setWasmTableEntry(idx, func2) {\n          wasmTable.set(idx, func2);\n          wasmTableMirror[idx] = func2;\n        }\n        function _abort() {\n          abort(\"\");\n        }\n        function _emscripten_get_heap_max() {\n          return 2147483648;\n        }\n        function _emscripten_memcpy_big(dest, src, num) {\n          HEAPU8.copyWithin(dest, src, src + num);\n        }\n        function emscripten_realloc_buffer(size) {\n          try {\n            wasmMemory.grow(size - buffer2.byteLength + 65535 >>> 16);\n            updateGlobalBufferAndViews(wasmMemory.buffer);\n            return 1;\n          } catch (e) {\n          }\n        }\n        function _emscripten_resize_heap(requestedSize) {\n          var oldSize = HEAPU8.length;\n          requestedSize = requestedSize >>> 0;\n          var maxHeapSize = _emscripten_get_heap_max();\n          if (requestedSize > maxHeapSize) {\n            return false;\n          }\n          for (var cutDown = 1; cutDown <= 4; cutDown *= 2) {\n            var overGrownHeapSize = oldSize * (1 + 0.2 / cutDown);\n            overGrownHeapSize = Math.min(overGrownHeapSize, requestedSize + 100663296);\n            var newSize = Math.min(maxHeapSize, alignUp(Math.max(requestedSize, overGrownHeapSize), 65536));\n            var replacement = emscripten_realloc_buffer(newSize);\n            if (replacement) {\n              return true;\n            }\n          }\n          return false;\n        }\n        var SYSCALLS = { mappings: {}, buffers: [null, [], []], printChar: function(stream, curr) {\n          var buffer3 = SYSCALLS.buffers[stream];\n          if (curr === 0 || curr === 10) {\n            (stream === 1 ? out : err)(UTF8ArrayToString(buffer3, 0));\n            buffer3.length = 0;\n          } else {\n            buffer3.push(curr);\n          }\n        }, varargs: void 0, get: function() {\n          SYSCALLS.varargs += 4;\n          var ret = HEAP32[SYSCALLS.varargs - 4 >> 2];\n          return ret;\n        }, getStr: function(ptr) {\n          var ret = UTF8ToString(ptr);\n          return ret;\n        }, get64: function(low, high) {\n          return low;\n        } };\n        function _fd_close(fd) {\n          return 0;\n        }\n        function _fd_seek(fd, offset_low, offset_high, whence, newOffset) {\n        }\n        function _fd_write(fd, iov, iovcnt, pnum) {\n          var num = 0;\n          for (var i = 0; i < iovcnt; i++) {\n            var ptr = HEAP32[iov >> 2];\n            var len = HEAP32[iov + 4 >> 2];\n            iov += 8;\n            for (var j = 0; j < len; j++) {\n              SYSCALLS.printChar(fd, HEAPU8[ptr + j]);\n            }\n            num += len;\n          }\n          HEAP32[pnum >> 2] = num;\n          return 0;\n        }\n        function _setTempRet0(val) {\n          setTempRet0(val);\n        }\n        var ASSERTIONS = false;\n        var asmLibraryArg = { \"abort\": _abort, \"emscripten_get_heap_max\": _emscripten_get_heap_max, \"emscripten_memcpy_big\": _emscripten_memcpy_big, \"emscripten_resize_heap\": _emscripten_resize_heap, \"fd_close\": _fd_close, \"fd_seek\": _fd_seek, \"fd_write\": _fd_write, \"setTempRet0\": _setTempRet0 };\n        var asm = createWasm();\n        var ___wasm_call_ctors = Module[\"___wasm_call_ctors\"] = function() {\n          return (___wasm_call_ctors = Module[\"___wasm_call_ctors\"] = Module[\"asm\"][\"__wasm_call_ctors\"]).apply(null, arguments);\n        };\n        var _init = Module[\"_init\"] = function() {\n          return (_init = Module[\"_init\"] = Module[\"asm\"][\"init\"]).apply(null, arguments);\n        };\n        var _init_with_threads_count = Module[\"_init_with_threads_count\"] = function() {\n          return (_init_with_threads_count = Module[\"_init_with_threads_count\"] = Module[\"asm\"][\"init_with_threads_count\"]).apply(null, arguments);\n        };\n        var _get_threads_count = Module[\"_get_threads_count\"] = function() {\n          return (_get_threads_count = Module[\"_get_threads_count\"] = Module[\"asm\"][\"get_threads_count\"]).apply(null, arguments);\n        };\n        var _register_tensor = Module[\"_register_tensor\"] = function() {\n          return (_register_tensor = Module[\"_register_tensor\"] = Module[\"asm\"][\"register_tensor\"]).apply(null, arguments);\n        };\n        var _dispose_data = Module[\"_dispose_data\"] = function() {\n          return (_dispose_data = Module[\"_dispose_data\"] = Module[\"asm\"][\"dispose_data\"]).apply(null, arguments);\n        };\n        var _dispose = Module[\"_dispose\"] = function() {\n          return (_dispose = Module[\"_dispose\"] = Module[\"asm\"][\"dispose\"]).apply(null, arguments);\n        };\n        var _Abs = Module[\"_Abs\"] = function() {\n          return (_Abs = Module[\"_Abs\"] = Module[\"asm\"][\"Abs\"]).apply(null, arguments);\n        };\n        var _Add = Module[\"_Add\"] = function() {\n          return (_Add = Module[\"_Add\"] = Module[\"asm\"][\"Add\"]).apply(null, arguments);\n        };\n        var _AddN = Module[\"_AddN\"] = function() {\n          return (_AddN = Module[\"_AddN\"] = Module[\"asm\"][\"AddN\"]).apply(null, arguments);\n        };\n        var _All = Module[\"_All\"] = function() {\n          return (_All = Module[\"_All\"] = Module[\"asm\"][\"All\"]).apply(null, arguments);\n        };\n        var _Any = Module[\"_Any\"] = function() {\n          return (_Any = Module[\"_Any\"] = Module[\"asm\"][\"Any\"]).apply(null, arguments);\n        };\n        var _ArgMax = Module[\"_ArgMax\"] = function() {\n          return (_ArgMax = Module[\"_ArgMax\"] = Module[\"asm\"][\"ArgMax\"]).apply(null, arguments);\n        };\n        var _AvgPool = Module[\"_AvgPool\"] = function() {\n          return (_AvgPool = Module[\"_AvgPool\"] = Module[\"asm\"][\"AvgPool\"]).apply(null, arguments);\n        };\n        var _BatchMatMul = Module[\"_BatchMatMul\"] = function() {\n          return (_BatchMatMul = Module[\"_BatchMatMul\"] = Module[\"asm\"][\"BatchMatMul\"]).apply(null, arguments);\n        };\n        var _Ceil = Module[\"_Ceil\"] = function() {\n          return (_Ceil = Module[\"_Ceil\"] = Module[\"asm\"][\"Ceil\"]).apply(null, arguments);\n        };\n        var _ClipByValue = Module[\"_ClipByValue\"] = function() {\n          return (_ClipByValue = Module[\"_ClipByValue\"] = Module[\"asm\"][\"ClipByValue\"]).apply(null, arguments);\n        };\n        var _Conv2D = Module[\"_Conv2D\"] = function() {\n          return (_Conv2D = Module[\"_Conv2D\"] = Module[\"asm\"][\"Conv2D\"]).apply(null, arguments);\n        };\n        var _Conv2DBackpropInput = Module[\"_Conv2DBackpropInput\"] = function() {\n          return (_Conv2DBackpropInput = Module[\"_Conv2DBackpropInput\"] = Module[\"asm\"][\"Conv2DBackpropInput\"]).apply(null, arguments);\n        };\n        var _Cos = Module[\"_Cos\"] = function() {\n          return (_Cos = Module[\"_Cos\"] = Module[\"asm\"][\"Cos\"]).apply(null, arguments);\n        };\n        var _Cosh = Module[\"_Cosh\"] = function() {\n          return (_Cosh = Module[\"_Cosh\"] = Module[\"asm\"][\"Cosh\"]).apply(null, arguments);\n        };\n        var _CropAndResize = Module[\"_CropAndResize\"] = function() {\n          return (_CropAndResize = Module[\"_CropAndResize\"] = Module[\"asm\"][\"CropAndResize\"]).apply(null, arguments);\n        };\n        var _Cumprod = Module[\"_Cumprod\"] = function() {\n          return (_Cumprod = Module[\"_Cumprod\"] = Module[\"asm\"][\"Cumprod\"]).apply(null, arguments);\n        };\n        var _Cumsum = Module[\"_Cumsum\"] = function() {\n          return (_Cumsum = Module[\"_Cumsum\"] = Module[\"asm\"][\"Cumsum\"]).apply(null, arguments);\n        };\n        var _DepthToSpace = Module[\"_DepthToSpace\"] = function() {\n          return (_DepthToSpace = Module[\"_DepthToSpace\"] = Module[\"asm\"][\"DepthToSpace\"]).apply(null, arguments);\n        };\n        var _DepthwiseConv2dNative = Module[\"_DepthwiseConv2dNative\"] = function() {\n          return (_DepthwiseConv2dNative = Module[\"_DepthwiseConv2dNative\"] = Module[\"asm\"][\"DepthwiseConv2dNative\"]).apply(null, arguments);\n        };\n        var _Elu = Module[\"_Elu\"] = function() {\n          return (_Elu = Module[\"_Elu\"] = Module[\"asm\"][\"Elu\"]).apply(null, arguments);\n        };\n        var _Equal = Module[\"_Equal\"] = function() {\n          return (_Equal = Module[\"_Equal\"] = Module[\"asm\"][\"Equal\"]).apply(null, arguments);\n        };\n        var _Exp = Module[\"_Exp\"] = function() {\n          return (_Exp = Module[\"_Exp\"] = Module[\"asm\"][\"Exp\"]).apply(null, arguments);\n        };\n        var _FlipLeftRight = Module[\"_FlipLeftRight\"] = function() {\n          return (_FlipLeftRight = Module[\"_FlipLeftRight\"] = Module[\"asm\"][\"FlipLeftRight\"]).apply(null, arguments);\n        };\n        var _Floor = Module[\"_Floor\"] = function() {\n          return (_Floor = Module[\"_Floor\"] = Module[\"asm\"][\"Floor\"]).apply(null, arguments);\n        };\n        var _FloorDiv = Module[\"_FloorDiv\"] = function() {\n          return (_FloorDiv = Module[\"_FloorDiv\"] = Module[\"asm\"][\"FloorDiv\"]).apply(null, arguments);\n        };\n        var _FusedBatchNorm = Module[\"_FusedBatchNorm\"] = function() {\n          return (_FusedBatchNorm = Module[\"_FusedBatchNorm\"] = Module[\"asm\"][\"FusedBatchNorm\"]).apply(null, arguments);\n        };\n        var _FusedConv2D = Module[\"_FusedConv2D\"] = function() {\n          return (_FusedConv2D = Module[\"_FusedConv2D\"] = Module[\"asm\"][\"FusedConv2D\"]).apply(null, arguments);\n        };\n        var _FusedDepthwiseConv2D = Module[\"_FusedDepthwiseConv2D\"] = function() {\n          return (_FusedDepthwiseConv2D = Module[\"_FusedDepthwiseConv2D\"] = Module[\"asm\"][\"FusedDepthwiseConv2D\"]).apply(null, arguments);\n        };\n        var _Gather = Module[\"_Gather\"] = function() {\n          return (_Gather = Module[\"_Gather\"] = Module[\"asm\"][\"Gather\"]).apply(null, arguments);\n        };\n        var _GatherNd = Module[\"_GatherNd\"] = function() {\n          return (_GatherNd = Module[\"_GatherNd\"] = Module[\"asm\"][\"GatherNd\"]).apply(null, arguments);\n        };\n        var _Greater = Module[\"_Greater\"] = function() {\n          return (_Greater = Module[\"_Greater\"] = Module[\"asm\"][\"Greater\"]).apply(null, arguments);\n        };\n        var _GreaterEqual = Module[\"_GreaterEqual\"] = function() {\n          return (_GreaterEqual = Module[\"_GreaterEqual\"] = Module[\"asm\"][\"GreaterEqual\"]).apply(null, arguments);\n        };\n        var _LeakyRelu = Module[\"_LeakyRelu\"] = function() {\n          return (_LeakyRelu = Module[\"_LeakyRelu\"] = Module[\"asm\"][\"LeakyRelu\"]).apply(null, arguments);\n        };\n        var _Less = Module[\"_Less\"] = function() {\n          return (_Less = Module[\"_Less\"] = Module[\"asm\"][\"Less\"]).apply(null, arguments);\n        };\n        var _LessEqual = Module[\"_LessEqual\"] = function() {\n          return (_LessEqual = Module[\"_LessEqual\"] = Module[\"asm\"][\"LessEqual\"]).apply(null, arguments);\n        };\n        var _Log = Module[\"_Log\"] = function() {\n          return (_Log = Module[\"_Log\"] = Module[\"asm\"][\"Log\"]).apply(null, arguments);\n        };\n        var _LogicalAnd = Module[\"_LogicalAnd\"] = function() {\n          return (_LogicalAnd = Module[\"_LogicalAnd\"] = Module[\"asm\"][\"LogicalAnd\"]).apply(null, arguments);\n        };\n        var _LogicalNot = Module[\"_LogicalNot\"] = function() {\n          return (_LogicalNot = Module[\"_LogicalNot\"] = Module[\"asm\"][\"LogicalNot\"]).apply(null, arguments);\n        };\n        var _LogicalOr = Module[\"_LogicalOr\"] = function() {\n          return (_LogicalOr = Module[\"_LogicalOr\"] = Module[\"asm\"][\"LogicalOr\"]).apply(null, arguments);\n        };\n        var _LogicalXor = Module[\"_LogicalXor\"] = function() {\n          return (_LogicalXor = Module[\"_LogicalXor\"] = Module[\"asm\"][\"LogicalXor\"]).apply(null, arguments);\n        };\n        var _Max = Module[\"_Max\"] = function() {\n          return (_Max = Module[\"_Max\"] = Module[\"asm\"][\"Max\"]).apply(null, arguments);\n        };\n        var _MaxPool = Module[\"_MaxPool\"] = function() {\n          return (_MaxPool = Module[\"_MaxPool\"] = Module[\"asm\"][\"MaxPool\"]).apply(null, arguments);\n        };\n        var _Maximum = Module[\"_Maximum\"] = function() {\n          return (_Maximum = Module[\"_Maximum\"] = Module[\"asm\"][\"Maximum\"]).apply(null, arguments);\n        };\n        var _Mean = Module[\"_Mean\"] = function() {\n          return (_Mean = Module[\"_Mean\"] = Module[\"asm\"][\"Mean\"]).apply(null, arguments);\n        };\n        var _Min = Module[\"_Min\"] = function() {\n          return (_Min = Module[\"_Min\"] = Module[\"asm\"][\"Min\"]).apply(null, arguments);\n        };\n        var _Minimum = Module[\"_Minimum\"] = function() {\n          return (_Minimum = Module[\"_Minimum\"] = Module[\"asm\"][\"Minimum\"]).apply(null, arguments);\n        };\n        var _MirrorPad = Module[\"_MirrorPad\"] = function() {\n          return (_MirrorPad = Module[\"_MirrorPad\"] = Module[\"asm\"][\"MirrorPad\"]).apply(null, arguments);\n        };\n        var _Multiply = Module[\"_Multiply\"] = function() {\n          return (_Multiply = Module[\"_Multiply\"] = Module[\"asm\"][\"Multiply\"]).apply(null, arguments);\n        };\n        var _Neg = Module[\"_Neg\"] = function() {\n          return (_Neg = Module[\"_Neg\"] = Module[\"asm\"][\"Neg\"]).apply(null, arguments);\n        };\n        var _NonMaxSuppressionV3 = Module[\"_NonMaxSuppressionV3\"] = function() {\n          return (_NonMaxSuppressionV3 = Module[\"_NonMaxSuppressionV3\"] = Module[\"asm\"][\"NonMaxSuppressionV3\"]).apply(null, arguments);\n        };\n        var _NonMaxSuppressionV4 = Module[\"_NonMaxSuppressionV4\"] = function() {\n          return (_NonMaxSuppressionV4 = Module[\"_NonMaxSuppressionV4\"] = Module[\"asm\"][\"NonMaxSuppressionV4\"]).apply(null, arguments);\n        };\n        var _NonMaxSuppressionV5 = Module[\"_NonMaxSuppressionV5\"] = function() {\n          return (_NonMaxSuppressionV5 = Module[\"_NonMaxSuppressionV5\"] = Module[\"asm\"][\"NonMaxSuppressionV5\"]).apply(null, arguments);\n        };\n        var _NotEqual = Module[\"_NotEqual\"] = function() {\n          return (_NotEqual = Module[\"_NotEqual\"] = Module[\"asm\"][\"NotEqual\"]).apply(null, arguments);\n        };\n        var _OneHot = Module[\"_OneHot\"] = function() {\n          return (_OneHot = Module[\"_OneHot\"] = Module[\"asm\"][\"OneHot\"]).apply(null, arguments);\n        };\n        var _PadV2 = Module[\"_PadV2\"] = function() {\n          return (_PadV2 = Module[\"_PadV2\"] = Module[\"asm\"][\"PadV2\"]).apply(null, arguments);\n        };\n        var _Pow = Module[\"_Pow\"] = function() {\n          return (_Pow = Module[\"_Pow\"] = Module[\"asm\"][\"Pow\"]).apply(null, arguments);\n        };\n        var _Prelu = Module[\"_Prelu\"] = function() {\n          return (_Prelu = Module[\"_Prelu\"] = Module[\"asm\"][\"Prelu\"]).apply(null, arguments);\n        };\n        var _Prod = Module[\"_Prod\"] = function() {\n          return (_Prod = Module[\"_Prod\"] = Module[\"asm\"][\"Prod\"]).apply(null, arguments);\n        };\n        var _RealDiv = Module[\"_RealDiv\"] = function() {\n          return (_RealDiv = Module[\"_RealDiv\"] = Module[\"asm\"][\"RealDiv\"]).apply(null, arguments);\n        };\n        var _Relu = Module[\"_Relu\"] = function() {\n          return (_Relu = Module[\"_Relu\"] = Module[\"asm\"][\"Relu\"]).apply(null, arguments);\n        };\n        var _Relu6 = Module[\"_Relu6\"] = function() {\n          return (_Relu6 = Module[\"_Relu6\"] = Module[\"asm\"][\"Relu6\"]).apply(null, arguments);\n        };\n        var _ResizeBilinear = Module[\"_ResizeBilinear\"] = function() {\n          return (_ResizeBilinear = Module[\"_ResizeBilinear\"] = Module[\"asm\"][\"ResizeBilinear\"]).apply(null, arguments);\n        };\n        var _ResizeNearestNeighbor = Module[\"_ResizeNearestNeighbor\"] = function() {\n          return (_ResizeNearestNeighbor = Module[\"_ResizeNearestNeighbor\"] = Module[\"asm\"][\"ResizeNearestNeighbor\"]).apply(null, arguments);\n        };\n        var _Reverse = Module[\"_Reverse\"] = function() {\n          return (_Reverse = Module[\"_Reverse\"] = Module[\"asm\"][\"Reverse\"]).apply(null, arguments);\n        };\n        var _RotateWithOffset = Module[\"_RotateWithOffset\"] = function() {\n          return (_RotateWithOffset = Module[\"_RotateWithOffset\"] = Module[\"asm\"][\"RotateWithOffset\"]).apply(null, arguments);\n        };\n        var _Round = Module[\"_Round\"] = function() {\n          return (_Round = Module[\"_Round\"] = Module[\"asm\"][\"Round\"]).apply(null, arguments);\n        };\n        var _Rsqrt = Module[\"_Rsqrt\"] = function() {\n          return (_Rsqrt = Module[\"_Rsqrt\"] = Module[\"asm\"][\"Rsqrt\"]).apply(null, arguments);\n        };\n        var _ScatterNd = Module[\"_ScatterNd\"] = function() {\n          return (_ScatterNd = Module[\"_ScatterNd\"] = Module[\"asm\"][\"ScatterNd\"]).apply(null, arguments);\n        };\n        var _SelectV2 = Module[\"_SelectV2\"] = function() {\n          return (_SelectV2 = Module[\"_SelectV2\"] = Module[\"asm\"][\"SelectV2\"]).apply(null, arguments);\n        };\n        var _Sigmoid = Module[\"_Sigmoid\"] = function() {\n          return (_Sigmoid = Module[\"_Sigmoid\"] = Module[\"asm\"][\"Sigmoid\"]).apply(null, arguments);\n        };\n        var _Sin = Module[\"_Sin\"] = function() {\n          return (_Sin = Module[\"_Sin\"] = Module[\"asm\"][\"Sin\"]).apply(null, arguments);\n        };\n        var _Softmax = Module[\"_Softmax\"] = function() {\n          return (_Softmax = Module[\"_Softmax\"] = Module[\"asm\"][\"Softmax\"]).apply(null, arguments);\n        };\n        var _SparseFillEmptyRows = Module[\"_SparseFillEmptyRows\"] = function() {\n          return (_SparseFillEmptyRows = Module[\"_SparseFillEmptyRows\"] = Module[\"asm\"][\"SparseFillEmptyRows\"]).apply(null, arguments);\n        };\n        var _SparseReshape = Module[\"_SparseReshape\"] = function() {\n          return (_SparseReshape = Module[\"_SparseReshape\"] = Module[\"asm\"][\"SparseReshape\"]).apply(null, arguments);\n        };\n        var _SparseSegmentReduction = Module[\"_SparseSegmentReduction\"] = function() {\n          return (_SparseSegmentReduction = Module[\"_SparseSegmentReduction\"] = Module[\"asm\"][\"SparseSegmentReduction\"]).apply(null, arguments);\n        };\n        var _Sqrt = Module[\"_Sqrt\"] = function() {\n          return (_Sqrt = Module[\"_Sqrt\"] = Module[\"asm\"][\"Sqrt\"]).apply(null, arguments);\n        };\n        var _Square = Module[\"_Square\"] = function() {\n          return (_Square = Module[\"_Square\"] = Module[\"asm\"][\"Square\"]).apply(null, arguments);\n        };\n        var _SquaredDifference = Module[\"_SquaredDifference\"] = function() {\n          return (_SquaredDifference = Module[\"_SquaredDifference\"] = Module[\"asm\"][\"SquaredDifference\"]).apply(null, arguments);\n        };\n        var _Step = Module[\"_Step\"] = function() {\n          return (_Step = Module[\"_Step\"] = Module[\"asm\"][\"Step\"]).apply(null, arguments);\n        };\n        var _StridedSlice = Module[\"_StridedSlice\"] = function() {\n          return (_StridedSlice = Module[\"_StridedSlice\"] = Module[\"asm\"][\"StridedSlice\"]).apply(null, arguments);\n        };\n        var _Sub = Module[\"_Sub\"] = function() {\n          return (_Sub = Module[\"_Sub\"] = Module[\"asm\"][\"Sub\"]).apply(null, arguments);\n        };\n        var _Sum = Module[\"_Sum\"] = function() {\n          return (_Sum = Module[\"_Sum\"] = Module[\"asm\"][\"Sum\"]).apply(null, arguments);\n        };\n        var _Tan = Module[\"_Tan\"] = function() {\n          return (_Tan = Module[\"_Tan\"] = Module[\"asm\"][\"Tan\"]).apply(null, arguments);\n        };\n        var _Tanh = Module[\"_Tanh\"] = function() {\n          return (_Tanh = Module[\"_Tanh\"] = Module[\"asm\"][\"Tanh\"]).apply(null, arguments);\n        };\n        var _Tile = Module[\"_Tile\"] = function() {\n          return (_Tile = Module[\"_Tile\"] = Module[\"asm\"][\"Tile\"]).apply(null, arguments);\n        };\n        var _TopK = Module[\"_TopK\"] = function() {\n          return (_TopK = Module[\"_TopK\"] = Module[\"asm\"][\"TopK\"]).apply(null, arguments);\n        };\n        var _Transform = Module[\"_Transform\"] = function() {\n          return (_Transform = Module[\"_Transform\"] = Module[\"asm\"][\"Transform\"]).apply(null, arguments);\n        };\n        var _Transpose = Module[\"_Transpose\"] = function() {\n          return (_Transpose = Module[\"_Transpose\"] = Module[\"asm\"][\"Transpose\"]).apply(null, arguments);\n        };\n        var __FusedMatMul = Module[\"__FusedMatMul\"] = function() {\n          return (__FusedMatMul = Module[\"__FusedMatMul\"] = Module[\"asm\"][\"_FusedMatMul\"]).apply(null, arguments);\n        };\n        var _malloc = Module[\"_malloc\"] = function() {\n          return (_malloc = Module[\"_malloc\"] = Module[\"asm\"][\"malloc\"]).apply(null, arguments);\n        };\n        var _free = Module[\"_free\"] = function() {\n          return (_free = Module[\"_free\"] = Module[\"asm\"][\"free\"]).apply(null, arguments);\n        };\n        var ___errno_location = Module[\"___errno_location\"] = function() {\n          return (___errno_location = Module[\"___errno_location\"] = Module[\"asm\"][\"__errno_location\"]).apply(null, arguments);\n        };\n        var _emscripten_main_thread_process_queued_calls = Module[\"_emscripten_main_thread_process_queued_calls\"] = function() {\n          return (_emscripten_main_thread_process_queued_calls = Module[\"_emscripten_main_thread_process_queued_calls\"] = Module[\"asm\"][\"emscripten_main_thread_process_queued_calls\"]).apply(null, arguments);\n        };\n        var stackSave = Module[\"stackSave\"] = function() {\n          return (stackSave = Module[\"stackSave\"] = Module[\"asm\"][\"stackSave\"]).apply(null, arguments);\n        };\n        var stackRestore = Module[\"stackRestore\"] = function() {\n          return (stackRestore = Module[\"stackRestore\"] = Module[\"asm\"][\"stackRestore\"]).apply(null, arguments);\n        };\n        var stackAlloc = Module[\"stackAlloc\"] = function() {\n          return (stackAlloc = Module[\"stackAlloc\"] = Module[\"asm\"][\"stackAlloc\"]).apply(null, arguments);\n        };\n        var dynCall_iijjiiii = Module[\"dynCall_iijjiiii\"] = function() {\n          return (dynCall_iijjiiii = Module[\"dynCall_iijjiiii\"] = Module[\"asm\"][\"dynCall_iijjiiii\"]).apply(null, arguments);\n        };\n        var dynCall_jiji = Module[\"dynCall_jiji\"] = function() {\n          return (dynCall_jiji = Module[\"dynCall_jiji\"] = Module[\"asm\"][\"dynCall_jiji\"]).apply(null, arguments);\n        };\n        Module[\"cwrap\"] = cwrap;\n        var calledRun;\n        function ExitStatus(status) {\n          this.name = \"ExitStatus\";\n          this.message = \"Program terminated with exit(\" + status + \")\";\n          this.status = status;\n        }\n        dependenciesFulfilled = function runCaller() {\n          if (!calledRun)\n            run();\n          if (!calledRun)\n            dependenciesFulfilled = runCaller;\n        };\n        function run(args) {\n          args = args || arguments_;\n          if (runDependencies > 0) {\n            return;\n          }\n          preRun();\n          if (runDependencies > 0) {\n            return;\n          }\n          function doRun() {\n            if (calledRun)\n              return;\n            calledRun = true;\n            Module[\"calledRun\"] = true;\n            if (ABORT)\n              return;\n            initRuntime();\n            readyPromiseResolve(Module);\n            if (Module[\"onRuntimeInitialized\"])\n              Module[\"onRuntimeInitialized\"]();\n            postRun();\n          }\n          if (Module[\"setStatus\"]) {\n            Module[\"setStatus\"](\"Running...\");\n            setTimeout(function() {\n              setTimeout(function() {\n                Module[\"setStatus\"](\"\");\n              }, 1);\n              doRun();\n            }, 1);\n          } else {\n            doRun();\n          }\n        }\n        Module[\"run\"] = run;\n        function procExit(code) {\n          EXITSTATUS = code;\n          if (!keepRuntimeAlive()) {\n            if (Module[\"onExit\"])\n              Module[\"onExit\"](code);\n            ABORT = true;\n          }\n          quit_(code, new ExitStatus(code));\n        }\n        if (Module[\"preInit\"]) {\n          if (typeof Module[\"preInit\"] == \"function\")\n            Module[\"preInit\"] = [Module[\"preInit\"]];\n          while (Module[\"preInit\"].length > 0) {\n            Module[\"preInit\"].pop()();\n          }\n        }\n        run();\n        var listenersAdded;\n        if (beforeListeners) {\n          listenersAdded = { uncaughtException: process.listeners(\"uncaughtException\").filter(function(listener) {\n            return !beforeListeners.uncaughtException.indexOf(listener) > -1;\n          }), unhandledRejection: process.listeners(\"unhandledRejection\").filter(function(listener) {\n            return !beforeListeners.unhandledRejection.indexOf(listener) > -1;\n          }) };\n        }\n        var actualModule;\n        if (typeof WasmBackendModule3 !== \"undefined\") {\n          actualModule = WasmBackendModule3;\n        } else if (typeof WasmBackendModuleThreadedSimd !== \"undefined\") {\n          actualModule = WasmBackendModuleThreadedSimd;\n        } else {\n          throw new Error(\"Could not find wasm module in post.js\");\n        }\n        if (listenersAdded) {\n          var tmpDispose = actualModule[\"_dispose\"];\n          actualModule[\"_dispose\"] = function() {\n            tmpDispose();\n            listenersAdded.uncaughtException.forEach(function(listener) {\n              process.removeListener(\"uncaughtException\", listener);\n            });\n            listenersAdded.unhandledRejection.forEach(function(listener) {\n              process.removeListener(\"unhandledRejection\", listener);\n            });\n          };\n        }\n        return WasmBackendModule3.ready;\n      };\n    })();\n    if (typeof exports === \"object\" && typeof module === \"object\")\n      module.exports = WasmBackendModule2;\n    else if (typeof define === \"function\" && define[\"amd\"])\n      define([], function() {\n        return WasmBackendModule2;\n      });\n    else if (typeof exports === \"object\")\n      exports[\"WasmBackendModule\"] = WasmBackendModule2;\n  }\n});\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/backends/backend.js\nvar EPSILON_FLOAT32 = 1e-7;\nvar EPSILON_FLOAT16 = 1e-4;\nvar DataStorage = class {\n  constructor(backend2, dataMover) {\n    this.backend = backend2;\n    this.dataMover = dataMover;\n    this.data = /* @__PURE__ */ new WeakMap();\n    this.dataIdsCount = 0;\n  }\n  get(dataId) {\n    if (!this.data.has(dataId)) {\n      this.dataMover.moveData(this.backend, dataId);\n    }\n    return this.data.get(dataId);\n  }\n  set(dataId, value) {\n    this.dataIdsCount++;\n    this.data.set(dataId, value);\n  }\n  has(dataId) {\n    return this.data.has(dataId);\n  }\n  delete(dataId) {\n    this.dataIdsCount--;\n    return this.data.delete(dataId);\n  }\n  numDataIds() {\n    return this.dataIdsCount;\n  }\n};\nvar KernelBackend = class {\n  refCount(dataId) {\n    return notYetImplemented(\"refCount\");\n  }\n  incRef(dataId) {\n    return notYetImplemented(\"incRef\");\n  }\n  timerAvailable() {\n    return true;\n  }\n  time(f) {\n    return notYetImplemented(\"time\");\n  }\n  read(dataId) {\n    return notYetImplemented(\"read\");\n  }\n  readSync(dataId) {\n    return notYetImplemented(\"readSync\");\n  }\n  readToGPU(dataId, options) {\n    return notYetImplemented(\"readToGPU\");\n  }\n  numDataIds() {\n    return notYetImplemented(\"numDataIds\");\n  }\n  disposeData(dataId, force) {\n    return notYetImplemented(\"disposeData\");\n  }\n  write(values, shape, dtype) {\n    return notYetImplemented(\"write\");\n  }\n  move(dataId, values, shape, dtype, refCount) {\n    return notYetImplemented(\"move\");\n  }\n  memory() {\n    return notYetImplemented(\"memory\");\n  }\n  floatPrecision() {\n    return notYetImplemented(\"floatPrecision\");\n  }\n  epsilon() {\n    return this.floatPrecision() === 32 ? EPSILON_FLOAT32 : EPSILON_FLOAT16;\n  }\n  dispose() {\n    return notYetImplemented(\"dispose\");\n  }\n};\nfunction notYetImplemented(kernelName) {\n  throw new Error(`'${kernelName}' not yet implemented or not found in the registry. This kernel may not be supported by the tfjs backend you have chosen`);\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/util_base.js\nfunction shuffle(array2) {\n  let counter = array2.length;\n  let index = 0;\n  while (counter > 0) {\n    index = Math.random() * counter | 0;\n    counter--;\n    swap(array2, counter, index);\n  }\n}\nfunction shuffleCombo(array2, array22) {\n  if (array2.length !== array22.length) {\n    throw new Error(`Array sizes must match to be shuffled together First array length was ${array2.length}Second array length was ${array22.length}`);\n  }\n  let counter = array2.length;\n  let index = 0;\n  while (counter > 0) {\n    index = Math.random() * counter | 0;\n    counter--;\n    swap(array2, counter, index);\n    swap(array22, counter, index);\n  }\n}\nfunction clamp(min7, x, max7) {\n  return Math.max(min7, Math.min(x, max7));\n}\nfunction nearestLargerEven(val) {\n  return val % 2 === 0 ? val : val + 1;\n}\nfunction swap(object, left, right) {\n  const temp = object[left];\n  object[left] = object[right];\n  object[right] = temp;\n}\nfunction sum(arr) {\n  let sum7 = 0;\n  for (let i = 0; i < arr.length; i++) {\n    sum7 += arr[i];\n  }\n  return sum7;\n}\nfunction randUniform(a, b) {\n  const r = Math.random();\n  return b * r + (1 - r) * a;\n}\nfunction distSquared(a, b) {\n  let result = 0;\n  for (let i = 0; i < a.length; i++) {\n    const diff = Number(a[i]) - Number(b[i]);\n    result += diff * diff;\n  }\n  return result;\n}\nfunction assert(expr, msg) {\n  if (!expr) {\n    throw new Error(typeof msg === \"string\" ? msg : msg());\n  }\n}\nfunction assertShapesMatch(shapeA, shapeB, errorMessagePrefix = \"\") {\n  assert(arraysEqual(shapeA, shapeB), () => errorMessagePrefix + ` Shapes ${shapeA} and ${shapeB} must match`);\n}\nfunction assertNonNull(a) {\n  assert(a != null, () => `The input to the tensor constructor must be a non-null value.`);\n}\nfunction flatten(arr, result = [], skipTypedArray = false) {\n  if (result == null) {\n    result = [];\n  }\n  if (Array.isArray(arr) || isTypedArray(arr) && !skipTypedArray) {\n    for (let i = 0; i < arr.length; ++i) {\n      flatten(arr[i], result, skipTypedArray);\n    }\n  } else {\n    result.push(arr);\n  }\n  return result;\n}\nfunction sizeFromShape(shape) {\n  if (shape.length === 0) {\n    return 1;\n  }\n  let size = shape[0];\n  for (let i = 1; i < shape.length; i++) {\n    size *= shape[i];\n  }\n  return size;\n}\nfunction isScalarShape(shape) {\n  return shape.length === 0;\n}\nfunction arraysEqual(n1, n2) {\n  if (n1 === n2) {\n    return true;\n  }\n  if (n1 == null || n2 == null) {\n    return false;\n  }\n  if (n1.length !== n2.length) {\n    return false;\n  }\n  for (let i = 0; i < n1.length; i++) {\n    if (n1[i] !== n2[i]) {\n      return false;\n    }\n  }\n  return true;\n}\nfunction isInt(a) {\n  return a % 1 === 0;\n}\nfunction tanh(x) {\n  if (Math.tanh != null) {\n    return Math.tanh(x);\n  }\n  if (x === Infinity) {\n    return 1;\n  } else if (x === -Infinity) {\n    return -1;\n  } else {\n    const e2x = Math.exp(2 * x);\n    return (e2x - 1) / (e2x + 1);\n  }\n}\nfunction sizeToSquarishShape(size) {\n  const width = Math.ceil(Math.sqrt(size));\n  return [width, Math.ceil(size / width)];\n}\nfunction createShuffledIndices(n) {\n  const shuffledIndices = new Uint32Array(n);\n  for (let i = 0; i < n; ++i) {\n    shuffledIndices[i] = i;\n  }\n  shuffle(shuffledIndices);\n  return shuffledIndices;\n}\nfunction rightPad(a, size) {\n  if (size <= a.length) {\n    return a;\n  }\n  return a + \" \".repeat(size - a.length);\n}\nfunction repeatedTry(checkFn, delayFn = (counter) => 0, maxCounter) {\n  return new Promise((resolve, reject) => {\n    let tryCount = 0;\n    const tryFn = () => {\n      if (checkFn()) {\n        resolve();\n        return;\n      }\n      tryCount++;\n      const nextBackoff = delayFn(tryCount);\n      if (maxCounter != null && tryCount >= maxCounter) {\n        reject();\n        return;\n      }\n      setTimeout(tryFn, nextBackoff);\n    };\n    tryFn();\n  });\n}\nfunction inferFromImplicitShape(shape, size) {\n  let shapeProd = 1;\n  let implicitIdx = -1;\n  for (let i = 0; i < shape.length; ++i) {\n    if (shape[i] >= 0) {\n      shapeProd *= shape[i];\n    } else if (shape[i] === -1) {\n      if (implicitIdx !== -1) {\n        throw Error(`Shapes can only have 1 implicit size. Found -1 at dim ${implicitIdx} and dim ${i}`);\n      }\n      implicitIdx = i;\n    } else if (shape[i] < 0) {\n      throw Error(`Shapes can not be < 0. Found ${shape[i]} at dim ${i}`);\n    }\n  }\n  if (implicitIdx === -1) {\n    if (size > 0 && size !== shapeProd) {\n      throw Error(`Size(${size}) must match the product of shape ${shape}`);\n    }\n    return shape;\n  }\n  if (shapeProd === 0) {\n    throw Error(`Cannot infer the missing size in [${shape}] when there are 0 elements`);\n  }\n  if (size % shapeProd !== 0) {\n    throw Error(`The implicit shape can't be a fractional number. Got ${size} / ${shapeProd}`);\n  }\n  const newShape = shape.slice();\n  newShape[implicitIdx] = size / shapeProd;\n  return newShape;\n}\nfunction parseAxisParam(axis, shape) {\n  const rank = shape.length;\n  axis = axis == null ? shape.map((s, i) => i) : [].concat(axis);\n  assert(axis.every((ax) => ax >= -rank && ax < rank), () => `All values in axis param must be in range [-${rank}, ${rank}) but got axis ${axis}`);\n  assert(axis.every((ax) => isInt(ax)), () => `All values in axis param must be integers but got axis ${axis}`);\n  return axis.map((a) => a < 0 ? rank + a : a);\n}\nfunction squeezeShape(shape, axis) {\n  const newShape = [];\n  const keptDims = [];\n  const isEmptyArray = axis != null && Array.isArray(axis) && axis.length === 0;\n  const axes = axis == null || isEmptyArray ? null : parseAxisParam(axis, shape).sort();\n  let j = 0;\n  for (let i = 0; i < shape.length; ++i) {\n    if (axes != null) {\n      if (axes[j] === i && shape[i] !== 1) {\n        throw new Error(`Can't squeeze axis ${i} since its dim '${shape[i]}' is not 1`);\n      }\n      if ((axes[j] == null || axes[j] > i) && shape[i] === 1) {\n        newShape.push(shape[i]);\n        keptDims.push(i);\n      }\n      if (axes[j] <= i) {\n        j++;\n      }\n    }\n    if (shape[i] !== 1) {\n      newShape.push(shape[i]);\n      keptDims.push(i);\n    }\n  }\n  return { newShape, keptDims };\n}\nfunction getTypedArrayFromDType(dtype, size) {\n  let values = null;\n  if (dtype == null || dtype === \"float32\") {\n    values = new Float32Array(size);\n  } else if (dtype === \"int32\") {\n    values = new Int32Array(size);\n  } else if (dtype === \"bool\") {\n    values = new Uint8Array(size);\n  } else {\n    throw new Error(`Unknown data type ${dtype}`);\n  }\n  return values;\n}\nfunction getArrayFromDType(dtype, size) {\n  let values = null;\n  if (dtype == null || dtype === \"float32\") {\n    values = new Float32Array(size);\n  } else if (dtype === \"int32\") {\n    values = new Int32Array(size);\n  } else if (dtype === \"bool\") {\n    values = new Uint8Array(size);\n  } else if (dtype === \"string\") {\n    values = new Array(size);\n  } else {\n    throw new Error(`Unknown data type ${dtype}`);\n  }\n  return values;\n}\nfunction checkConversionForErrors(vals, dtype) {\n  for (let i = 0; i < vals.length; i++) {\n    const num = vals[i];\n    if (isNaN(num) || !isFinite(num)) {\n      throw Error(`A tensor of type ${dtype} being uploaded contains ${num}.`);\n    }\n  }\n}\nfunction isValidDtype(dtype) {\n  return dtype === \"bool\" || dtype === \"complex64\" || dtype === \"float32\" || dtype === \"int32\" || dtype === \"string\";\n}\nfunction hasEncodingLoss(oldType, newType) {\n  if (newType === \"complex64\") {\n    return false;\n  }\n  if (newType === \"float32\" && oldType !== \"complex64\") {\n    return false;\n  }\n  if (newType === \"int32\" && oldType !== \"float32\" && oldType !== \"complex64\") {\n    return false;\n  }\n  if (newType === \"bool\" && oldType === \"bool\") {\n    return false;\n  }\n  return true;\n}\nfunction isTypedArray(a) {\n  return a instanceof Float32Array || a instanceof Int32Array || a instanceof Uint8Array || a instanceof Uint8ClampedArray;\n}\nfunction bytesPerElement(dtype) {\n  if (dtype === \"float32\" || dtype === \"int32\") {\n    return 4;\n  } else if (dtype === \"complex64\") {\n    return 8;\n  } else if (dtype === \"bool\") {\n    return 1;\n  } else {\n    throw new Error(`Unknown dtype ${dtype}`);\n  }\n}\nfunction bytesFromStringArray(arr) {\n  if (arr == null) {\n    return 0;\n  }\n  let bytes = 0;\n  arr.forEach((x) => bytes += x.length);\n  return bytes;\n}\nfunction isString(value) {\n  return typeof value === \"string\" || value instanceof String;\n}\nfunction isBoolean(value) {\n  return typeof value === \"boolean\";\n}\nfunction isNumber(value) {\n  return typeof value === \"number\";\n}\nfunction inferDtype(values) {\n  if (Array.isArray(values)) {\n    return inferDtype(values[0]);\n  }\n  if (values instanceof Float32Array) {\n    return \"float32\";\n  } else if (values instanceof Int32Array || values instanceof Uint8Array || values instanceof Uint8ClampedArray) {\n    return \"int32\";\n  } else if (isNumber(values)) {\n    return \"float32\";\n  } else if (isString(values)) {\n    return \"string\";\n  } else if (isBoolean(values)) {\n    return \"bool\";\n  }\n  return \"float32\";\n}\nfunction isFunction(f) {\n  return !!(f && f.constructor && f.call && f.apply);\n}\nfunction nearestDivisor(size, start) {\n  for (let i = start; i < size; ++i) {\n    if (size % i === 0) {\n      return i;\n    }\n  }\n  return size;\n}\nfunction computeStrides(shape) {\n  const rank = shape.length;\n  if (rank < 2) {\n    return [];\n  }\n  const strides = new Array(rank - 1);\n  strides[rank - 2] = shape[rank - 1];\n  for (let i = rank - 3; i >= 0; --i) {\n    strides[i] = strides[i + 1] * shape[i + 1];\n  }\n  return strides;\n}\nfunction createNestedArray(offset, shape, a, isComplex = false) {\n  const ret = new Array();\n  if (shape.length === 1) {\n    const d = shape[0] * (isComplex ? 2 : 1);\n    for (let i = 0; i < d; i++) {\n      ret[i] = a[offset + i];\n    }\n  } else {\n    const d = shape[0];\n    const rest = shape.slice(1);\n    const len = rest.reduce((acc, c) => acc * c) * (isComplex ? 2 : 1);\n    for (let i = 0; i < d; i++) {\n      ret[i] = createNestedArray(offset + i * len, rest, a, isComplex);\n    }\n  }\n  return ret;\n}\nfunction toNestedArray(shape, a, isComplex = false) {\n  if (shape.length === 0) {\n    return a[0];\n  }\n  const size = shape.reduce((acc, c) => acc * c) * (isComplex ? 2 : 1);\n  if (size === 0) {\n    return [];\n  }\n  if (size !== a.length) {\n    throw new Error(`[${shape}] does not match the input size ${a.length}${isComplex ? \" for a complex tensor\" : \"\"}.`);\n  }\n  return createNestedArray(0, shape, a, isComplex);\n}\nfunction makeOnesTypedArray(size, dtype) {\n  const array2 = makeZerosTypedArray(size, dtype);\n  for (let i = 0; i < array2.length; i++) {\n    array2[i] = 1;\n  }\n  return array2;\n}\nfunction makeZerosTypedArray(size, dtype) {\n  if (dtype == null || dtype === \"float32\" || dtype === \"complex64\") {\n    return new Float32Array(size);\n  } else if (dtype === \"int32\") {\n    return new Int32Array(size);\n  } else if (dtype === \"bool\") {\n    return new Uint8Array(size);\n  } else {\n    throw new Error(`Unknown data type ${dtype}`);\n  }\n}\nfunction makeZerosNestedTypedArray(shape, dtype) {\n  const size = shape.reduce((prev, curr) => prev * curr, 1);\n  if (dtype == null || dtype === \"float32\") {\n    return toNestedArray(shape, new Float32Array(size));\n  } else if (dtype === \"int32\") {\n    return toNestedArray(shape, new Int32Array(size));\n  } else if (dtype === \"bool\") {\n    return toNestedArray(shape, new Uint8Array(size));\n  } else {\n    throw new Error(`Unknown data type ${dtype}`);\n  }\n}\nfunction assertNonNegativeIntegerDimensions(shape) {\n  shape.forEach((dimSize) => {\n    assert(Number.isInteger(dimSize) && dimSize >= 0, () => `Tensor must have a shape comprised of positive integers but got shape [${shape}].`);\n  });\n}\nfunction locToIndex(locs, rank, strides) {\n  if (rank === 0) {\n    return 0;\n  } else if (rank === 1) {\n    return locs[0];\n  }\n  let index = locs[locs.length - 1];\n  for (let i = 0; i < locs.length - 1; ++i) {\n    index += strides[i] * locs[i];\n  }\n  return index;\n}\nfunction indexToLoc(index, rank, strides) {\n  if (rank === 0) {\n    return [];\n  } else if (rank === 1) {\n    return [index];\n  }\n  const locs = new Array(rank);\n  for (let i = 0; i < locs.length - 1; ++i) {\n    locs[i] = Math.floor(index / strides[i]);\n    index -= locs[i] * strides[i];\n  }\n  locs[locs.length - 1] = index;\n  return locs;\n}\nfunction isPromise(object) {\n  return object && object.then && typeof object.then === \"function\";\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/environment.js\nvar TENSORFLOWJS_FLAGS_PREFIX = \"tfjsflags\";\nvar Environment = class {\n  constructor(global2) {\n    this.global = global2;\n    this.flags = {};\n    this.flagRegistry = {};\n    this.urlFlags = {};\n    this.getQueryParams = getQueryParams;\n    this.populateURLFlags();\n  }\n  setPlatform(platformName, platform) {\n    if (this.platform != null) {\n      if (!(env().getBool(\"IS_TEST\") || env().getBool(\"PROD\"))) {\n        console.warn(`Platform ${this.platformName} has already been set. Overwriting the platform with ${platformName}.`);\n      }\n    }\n    this.platformName = platformName;\n    this.platform = platform;\n  }\n  registerFlag(flagName, evaluationFn, setHook) {\n    this.flagRegistry[flagName] = { evaluationFn, setHook };\n    if (this.urlFlags[flagName] != null) {\n      const flagValue = this.urlFlags[flagName];\n      if (!(env().getBool(\"IS_TEST\") || env().getBool(\"PROD\"))) {\n        console.warn(`Setting feature override from URL ${flagName}: ${flagValue}.`);\n      }\n      this.set(flagName, flagValue);\n    }\n  }\n  async getAsync(flagName) {\n    if (flagName in this.flags) {\n      return this.flags[flagName];\n    }\n    this.flags[flagName] = await this.evaluateFlag(flagName);\n    return this.flags[flagName];\n  }\n  get(flagName) {\n    if (flagName in this.flags) {\n      return this.flags[flagName];\n    }\n    const flagValue = this.evaluateFlag(flagName);\n    if (isPromise(flagValue)) {\n      throw new Error(`Flag ${flagName} cannot be synchronously evaluated. Please use getAsync() instead.`);\n    }\n    this.flags[flagName] = flagValue;\n    return this.flags[flagName];\n  }\n  getNumber(flagName) {\n    return this.get(flagName);\n  }\n  getBool(flagName) {\n    return this.get(flagName);\n  }\n  getFlags() {\n    return this.flags;\n  }\n  get features() {\n    return this.flags;\n  }\n  set(flagName, value) {\n    if (this.flagRegistry[flagName] == null) {\n      throw new Error(`Cannot set flag ${flagName} as it has not been registered.`);\n    }\n    this.flags[flagName] = value;\n    if (this.flagRegistry[flagName].setHook != null) {\n      this.flagRegistry[flagName].setHook(value);\n    }\n  }\n  evaluateFlag(flagName) {\n    if (this.flagRegistry[flagName] == null) {\n      throw new Error(`Cannot evaluate flag '${flagName}': no evaluation function found.`);\n    }\n    return this.flagRegistry[flagName].evaluationFn();\n  }\n  setFlags(flags) {\n    this.flags = Object.assign({}, flags);\n  }\n  reset() {\n    this.flags = {};\n    this.urlFlags = {};\n    this.populateURLFlags();\n  }\n  populateURLFlags() {\n    if (typeof this.global === \"undefined\" || typeof this.global.location === \"undefined\" || typeof this.global.location.search === \"undefined\") {\n      return;\n    }\n    const urlParams = this.getQueryParams(this.global.location.search);\n    if (TENSORFLOWJS_FLAGS_PREFIX in urlParams) {\n      const keyValues = urlParams[TENSORFLOWJS_FLAGS_PREFIX].split(\",\");\n      keyValues.forEach((keyValue) => {\n        const [key, value] = keyValue.split(\":\");\n        this.urlFlags[key] = parseValue(key, value);\n      });\n    }\n  }\n};\nfunction getQueryParams(queryString) {\n  const params = {};\n  queryString.replace(/[?&]([^=?&]+)(?:=([^&]*))?/g, (s, ...t) => {\n    decodeParam(params, t[0], t[1]);\n    return t.join(\"=\");\n  });\n  return params;\n}\nfunction decodeParam(params, name, value) {\n  params[decodeURIComponent(name)] = decodeURIComponent(value || \"\");\n}\nfunction parseValue(flagName, value) {\n  value = value.toLowerCase();\n  if (value === \"true\" || value === \"false\") {\n    return value === \"true\";\n  } else if (`${+value}` === value) {\n    return +value;\n  }\n  throw new Error(`Could not parse value flag value ${value} for flag ${flagName}.`);\n}\nfunction env() {\n  return ENV;\n}\nvar ENV = null;\nfunction setEnvironmentGlobal(environment) {\n  ENV = environment;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/global_util.js\nvar globalNameSpace;\nfunction getGlobalNamespace() {\n  if (globalNameSpace == null) {\n    let ns;\n    if (typeof window !== \"undefined\") {\n      ns = window;\n    } else if (typeof global !== \"undefined\") {\n      ns = global;\n    } else if (typeof process !== \"undefined\") {\n      ns = process;\n    } else if (typeof self !== \"undefined\") {\n      ns = self;\n    } else {\n      throw new Error(\"Could not find a global object\");\n    }\n    globalNameSpace = ns;\n  }\n  return globalNameSpace;\n}\nfunction getGlobalMap() {\n  const ns = getGlobalNamespace();\n  if (ns._tfGlobals == null) {\n    ns._tfGlobals = /* @__PURE__ */ new Map();\n  }\n  return ns._tfGlobals;\n}\nfunction getGlobal(key, init2) {\n  const globalMap = getGlobalMap();\n  if (globalMap.has(key)) {\n    return globalMap.get(key);\n  } else {\n    const singleton = init2();\n    globalMap.set(key, singleton);\n    return globalMap.get(key);\n  }\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/kernel_names.js\nvar Abs = \"Abs\";\nvar Acos = \"Acos\";\nvar Acosh = \"Acosh\";\nvar Add = \"Add\";\nvar AddN = \"AddN\";\nvar All = \"All\";\nvar Any = \"Any\";\nvar ArgMax = \"ArgMax\";\nvar ArgMin = \"ArgMin\";\nvar Asin = \"Asin\";\nvar Asinh = \"Asinh\";\nvar Atan = \"Atan\";\nvar Atanh = \"Atanh\";\nvar Atan2 = \"Atan2\";\nvar AvgPool = \"AvgPool\";\nvar AvgPoolGrad = \"AvgPoolGrad\";\nvar AvgPool3D = \"AvgPool3D\";\nvar AvgPool3DGrad = \"AvgPool3DGrad\";\nvar BatchMatMul = \"BatchMatMul\";\nvar BatchToSpaceND = \"BatchToSpaceND\";\nvar Bincount = \"Bincount\";\nvar BroadcastTo = \"BroadcastTo\";\nvar BroadcastArgs = \"BroadcastArgs\";\nvar Cast = \"Cast\";\nvar Ceil = \"Ceil\";\nvar ClipByValue = \"ClipByValue\";\nvar Complex = \"Complex\";\nvar ComplexAbs = \"ComplexAbs\";\nvar Concat = \"Concat\";\nvar Conv2D = \"Conv2D\";\nvar Conv2DBackpropFilter = \"Conv2DBackpropFilter\";\nvar Conv2DBackpropInput = \"Conv2DBackpropInput\";\nvar Conv3D = \"Conv3D\";\nvar Conv3DBackpropFilterV2 = \"Conv3DBackpropFilterV2\";\nvar Conv3DBackpropInputV2 = \"Conv3DBackpropInputV2\";\nvar Cos = \"Cos\";\nvar Cosh = \"Cosh\";\nvar Cumprod = \"Cumprod\";\nvar Cumsum = \"Cumsum\";\nvar CropAndResize = \"CropAndResize\";\nvar DenseBincount = \"DenseBincount\";\nvar DepthToSpace = \"DepthToSpace\";\nvar DepthwiseConv2dNative = \"DepthwiseConv2dNative\";\nvar DepthwiseConv2dNativeBackpropFilter = \"DepthwiseConv2dNativeBackpropFilter\";\nvar DepthwiseConv2dNativeBackpropInput = \"DepthwiseConv2dNativeBackpropInput\";\nvar Diag = \"Diag\";\nvar Dilation2D = \"Dilation2D\";\nvar Dilation2DBackpropInput = \"Dilation2DBackpropInput\";\nvar Dilation2DBackpropFilter = \"Dilation2DBackpropFilter\";\nvar RealDiv = \"RealDiv\";\nvar Einsum = \"Einsum\";\nvar Elu = \"Elu\";\nvar EluGrad = \"EluGrad\";\nvar Erf = \"Erf\";\nvar Equal = \"Equal\";\nvar Exp = \"Exp\";\nvar ExpandDims = \"ExpandDims\";\nvar Expm1 = \"Expm1\";\nvar FFT = \"FFT\";\nvar Fill = \"Fill\";\nvar FlipLeftRight = \"FlipLeftRight\";\nvar Floor = \"Floor\";\nvar FloorDiv = \"FloorDiv\";\nvar FusedBatchNorm = \"FusedBatchNorm\";\nvar GatherV2 = \"GatherV2\";\nvar GatherNd = \"GatherNd\";\nvar Greater = \"Greater\";\nvar GreaterEqual = \"GreaterEqual\";\nvar Identity = \"Identity\";\nvar IFFT = \"IFFT\";\nvar Imag = \"Imag\";\nvar IsFinite = \"IsFinite\";\nvar IsInf = \"IsInf\";\nvar IsNan = \"IsNan\";\nvar LeakyRelu = \"LeakyRelu\";\nvar Less = \"Less\";\nvar LessEqual = \"LessEqual\";\nvar LinSpace = \"LinSpace\";\nvar Log = \"Log\";\nvar Log1p = \"Log1p\";\nvar LogicalAnd = \"LogicalAnd\";\nvar LogicalNot = \"LogicalNot\";\nvar LogicalOr = \"LogicalOr\";\nvar LogicalXor = \"LogicalXor\";\nvar LogSoftmax = \"LogSoftmax\";\nvar LowerBound = \"LowerBound\";\nvar LRN = \"LRN\";\nvar LRNGrad = \"LRNGrad\";\nvar Max = \"Max\";\nvar Maximum = \"Maximum\";\nvar MaxPool = \"MaxPool\";\nvar MaxPoolGrad = \"MaxPoolGrad\";\nvar MaxPool3D = \"MaxPool3D\";\nvar MaxPool3DGrad = \"MaxPool3DGrad\";\nvar MaxPoolWithArgmax = \"MaxPoolWithArgmax\";\nvar Mean = \"Mean\";\nvar Min = \"Min\";\nvar Minimum = \"Minimum\";\nvar MirrorPad = \"MirrorPad\";\nvar Mod = \"Mod\";\nvar Multinomial = \"Multinomial\";\nvar Multiply = \"Multiply\";\nvar Neg = \"Neg\";\nvar NotEqual = \"NotEqual\";\nvar NonMaxSuppressionV3 = \"NonMaxSuppressionV3\";\nvar NonMaxSuppressionV4 = \"NonMaxSuppressionV4\";\nvar NonMaxSuppressionV5 = \"NonMaxSuppressionV5\";\nvar OnesLike = \"OnesLike\";\nvar OneHot = \"OneHot\";\nvar Pack = \"Pack\";\nvar PadV2 = \"PadV2\";\nvar Pool = \"Pool\";\nvar Pow = \"Pow\";\nvar Prelu = \"Prelu\";\nvar Prod = \"Prod\";\nvar RaggedTensorToTensor = \"RaggedTensorToTensor\";\nvar Range = \"Range\";\nvar Real = \"Real\";\nvar Reciprocal = \"Reciprocal\";\nvar Relu = \"Relu\";\nvar Reshape = \"Reshape\";\nvar ResizeNearestNeighbor = \"ResizeNearestNeighbor\";\nvar ResizeNearestNeighborGrad = \"ResizeNearestNeighborGrad\";\nvar ResizeBilinear = \"ResizeBilinear\";\nvar ResizeBilinearGrad = \"ResizeBilinearGrad\";\nvar Relu6 = \"Relu6\";\nvar Reverse = \"Reverse\";\nvar Round = \"Round\";\nvar Rsqrt = \"Rsqrt\";\nvar ScatterNd = \"ScatterNd\";\nvar SearchSorted = \"SearchSorted\";\nvar Select = \"Select\";\nvar Selu = \"Selu\";\nvar Slice = \"Slice\";\nvar Sin = \"Sin\";\nvar Sinh = \"Sinh\";\nvar Sign = \"Sign\";\nvar Sigmoid = \"Sigmoid\";\nvar Softplus = \"Softplus\";\nvar Sqrt = \"Sqrt\";\nvar Sum = \"Sum\";\nvar SpaceToBatchND = \"SpaceToBatchND\";\nvar SplitV = \"SplitV\";\nvar Softmax = \"Softmax\";\nvar SparseFillEmptyRows = \"SparseFillEmptyRows\";\nvar SparseReshape = \"SparseReshape\";\nvar SparseSegmentMean = \"SparseSegmentMean\";\nvar SparseSegmentSum = \"SparseSegmentSum\";\nvar SparseToDense = \"SparseToDense\";\nvar SquaredDifference = \"SquaredDifference\";\nvar Square = \"Square\";\nvar StridedSlice = \"StridedSlice\";\nvar StringNGrams = \"StringNGrams\";\nvar StringSplit = \"StringSplit\";\nvar StringToHashBucketFast = \"StringToHashBucketFast\";\nvar Sub = \"Sub\";\nvar Tan = \"Tan\";\nvar Tanh = \"Tanh\";\nvar Tile = \"Tile\";\nvar TopK = \"TopK\";\nvar Transform = \"Transform\";\nvar Transpose = \"Transpose\";\nvar Unique = \"Unique\";\nvar Unpack = \"Unpack\";\nvar UnsortedSegmentSum = \"UnsortedSegmentSum\";\nvar UpperBound = \"UpperBound\";\nvar ZerosLike = \"ZerosLike\";\nvar Step = \"Step\";\nvar FromPixels = \"FromPixels\";\nvar RotateWithOffset = \"RotateWithOffset\";\nvar _FusedMatMul = \"_FusedMatMul\";\nvar FusedConv2D = \"FusedConv2D\";\nvar FusedDepthwiseConv2D = \"FusedDepthwiseConv2D\";\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/log.js\nfunction warn(...msg) {\n  if (!(env().getBool(\"IS_TEST\") || env().getBool(\"PROD\"))) {\n    console.warn(...msg);\n  }\n}\nfunction log(...msg) {\n  if (!(env().getBool(\"IS_TEST\") || env().getBool(\"PROD\"))) {\n    console.log(...msg);\n  }\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/kernel_registry.js\nvar kernelRegistry = getGlobal(\"kernelRegistry\", () => /* @__PURE__ */ new Map());\nvar gradRegistry = getGlobal(\"gradRegistry\", () => /* @__PURE__ */ new Map());\nfunction getKernel(kernelName, backendName) {\n  const key = makeKey(kernelName, backendName);\n  return kernelRegistry.get(key);\n}\nfunction getGradient(kernelName) {\n  return gradRegistry.get(kernelName);\n}\nfunction getKernelsForBackend(backendName) {\n  const it = kernelRegistry.entries();\n  const result = [];\n  while (true) {\n    const { done, value } = it.next();\n    if (done) {\n      break;\n    }\n    const [key, config] = value;\n    const [backend2] = key.split(\"_\");\n    if (backend2 === backendName) {\n      result.push(config);\n    }\n  }\n  return result;\n}\nfunction registerKernel(config) {\n  const { kernelName, backendName } = config;\n  const key = makeKey(kernelName, backendName);\n  if (kernelRegistry.has(key)) {\n    warn(`The kernel '${kernelName}' for backend '${backendName}' is already registered`);\n  }\n  kernelRegistry.set(key, config);\n}\nfunction registerGradient(config) {\n  const { kernelName } = config;\n  if (gradRegistry.has(kernelName)) {\n    if (env().getBool(\"DEBUG\")) {\n      warn(`Overriding the gradient for '${kernelName}'`);\n    }\n  }\n  gradRegistry.set(kernelName, config);\n}\nfunction unregisterKernel(kernelName, backendName) {\n  const key = makeKey(kernelName, backendName);\n  if (!kernelRegistry.has(key)) {\n    throw new Error(`The kernel '${kernelName}' for backend '${backendName}' is not registered`);\n  }\n  kernelRegistry.delete(key);\n}\nfunction unregisterGradient(kernelName) {\n  if (!gradRegistry.has(kernelName)) {\n    throw new Error(`The gradient '${kernelName}' for backend is not registered`);\n  }\n  gradRegistry.delete(kernelName);\n}\nfunction copyRegisteredKernels(registeredBackendName, newBackendName) {\n  const kernels = getKernelsForBackend(registeredBackendName);\n  kernels.forEach((kernelConfig) => {\n    const newKernelConfig = Object.assign({}, kernelConfig, { backendName: newBackendName });\n    registerKernel(newKernelConfig);\n  });\n}\nfunction makeKey(kernelName, backendName) {\n  return `${backendName}_${kernelName}`;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/util.js\nvar util_exports = {};\n__export(util_exports, {\n  arraysEqual: () => arraysEqual,\n  assert: () => assert,\n  assertNonNegativeIntegerDimensions: () => assertNonNegativeIntegerDimensions,\n  assertNonNull: () => assertNonNull,\n  assertShapesMatch: () => assertShapesMatch,\n  bytesFromStringArray: () => bytesFromStringArray,\n  bytesPerElement: () => bytesPerElement,\n  checkConversionForErrors: () => checkConversionForErrors,\n  clamp: () => clamp,\n  computeStrides: () => computeStrides,\n  createScalarValue: () => createScalarValue,\n  createShuffledIndices: () => createShuffledIndices,\n  decodeString: () => decodeString,\n  distSquared: () => distSquared,\n  encodeString: () => encodeString,\n  fetch: () => fetch3,\n  fingerPrint64: () => fingerPrint64,\n  flatten: () => flatten,\n  getArrayFromDType: () => getArrayFromDType,\n  getTypedArrayFromDType: () => getTypedArrayFromDType,\n  hasEncodingLoss: () => hasEncodingLoss,\n  hexToLong: () => hexToLong,\n  indexToLoc: () => indexToLoc,\n  inferDtype: () => inferDtype,\n  inferFromImplicitShape: () => inferFromImplicitShape,\n  isBoolean: () => isBoolean,\n  isFunction: () => isFunction,\n  isInt: () => isInt,\n  isNumber: () => isNumber,\n  isPromise: () => isPromise,\n  isScalarShape: () => isScalarShape,\n  isString: () => isString,\n  isTypedArray: () => isTypedArray,\n  isValidDtype: () => isValidDtype,\n  locToIndex: () => locToIndex,\n  makeOnesTypedArray: () => makeOnesTypedArray,\n  makeZerosNestedTypedArray: () => makeZerosNestedTypedArray,\n  makeZerosTypedArray: () => makeZerosTypedArray,\n  nearestDivisor: () => nearestDivisor,\n  nearestLargerEven: () => nearestLargerEven,\n  now: () => now,\n  parseAxisParam: () => parseAxisParam,\n  randUniform: () => randUniform,\n  repeatedTry: () => repeatedTry,\n  rightPad: () => rightPad,\n  shuffle: () => shuffle,\n  shuffleCombo: () => shuffleCombo,\n  sizeFromShape: () => sizeFromShape,\n  sizeToSquarishShape: () => sizeToSquarishShape,\n  squeezeShape: () => squeezeShape,\n  sum: () => sum,\n  swap: () => swap,\n  tanh: () => tanh,\n  toNestedArray: () => toNestedArray,\n  toTypedArray: () => toTypedArray\n});\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/hash_util.js\nvar LongExports = __toESM(require_long());\nvar Long = LongExports.default || LongExports;\nfunction hexToLong(hex) {\n  return Long.fromString(hex, true, 16);\n}\nvar k0 = hexToLong(\"c3a5c85c97cb3127\");\nvar k1 = hexToLong(\"b492b66fbe98f273\");\nvar k2 = hexToLong(\"9ae16a3b2f90404f\");\nfunction shiftMix(val) {\n  return val.xor(val.shru(47));\n}\nfunction fetch2(s, offset, numBytes) {\n  const bytes = s.slice(offset, offset + numBytes);\n  return Long.fromBytes(Array.from(bytes), true, true);\n}\nfunction fetch64(s, offset) {\n  return fetch2(s, offset, 8);\n}\nfunction fetch32(s, offset) {\n  return fetch2(s, offset, 4);\n}\nfunction rotate64(val, shift) {\n  return shift === 0 ? val : val.shru(shift).or(val.shl(64 - shift));\n}\nfunction hashLen16(u, v, mul2 = hexToLong(\"9ddfea08eb382d69\")) {\n  let a = u.xor(v).mul(mul2);\n  a = a.xor(a.shru(47));\n  let b = v.xor(a).mul(mul2);\n  b = b.xor(b.shru(47));\n  b = b.mul(mul2);\n  return b;\n}\nfunction weakHashLen32WithSeeds(w, x, y, z, a, b) {\n  a = a.add(w);\n  b = rotate64(b.add(a).add(z), 21);\n  const c = a;\n  a = a.add(x);\n  a = a.add(y);\n  b = b.add(rotate64(a, 44));\n  return [a.add(z), b.add(c)];\n}\nfunction weakHashLen32WithSeedsStr(s, offset, a, b) {\n  return weakHashLen32WithSeeds(fetch64(s, offset), fetch64(s, offset + 8), fetch64(s, offset + 16), fetch64(s, offset + 24), a, b);\n}\nfunction hashLen0to16(s, len = s.length) {\n  if (len >= 8) {\n    const mul2 = k2.add(len * 2);\n    const a = fetch64(s, 0).add(k2);\n    const b = fetch64(s, len - 8);\n    const c = rotate64(b, 37).mul(mul2).add(a);\n    const d = rotate64(a, 25).add(b).mul(mul2);\n    return hashLen16(c, d, mul2);\n  }\n  if (len >= 4) {\n    const mul2 = k2.add(len * 2);\n    const a = fetch32(s, 0);\n    return hashLen16(a.shl(3).add(len), fetch32(s, len - 4), mul2);\n  }\n  if (len > 0) {\n    const a = s[0];\n    const b = s[len >> 1];\n    const c = s[len - 1];\n    const y = a + (b << 8);\n    const z = len + (c << 2);\n    return shiftMix(k2.mul(y).xor(k0.mul(z))).mul(k2);\n  }\n  return k2;\n}\nfunction hashLen17to32(s, len = s.length) {\n  const mul2 = k2.add(len * 2);\n  const a = fetch64(s, 0).mul(k1);\n  const b = fetch64(s, 8);\n  const c = fetch64(s, len - 8).mul(mul2);\n  const d = fetch64(s, len - 16).mul(k2);\n  return hashLen16(rotate64(a.add(b), 43).add(rotate64(c, 30)).add(d), a.add(rotate64(b.add(k2), 18)).add(c), mul2);\n}\nfunction hashLen33to64(s, len = s.length) {\n  const mul2 = k2.add(len * 2);\n  const a = fetch64(s, 0).mul(k2);\n  const b = fetch64(s, 8);\n  const c = fetch64(s, len - 8).mul(mul2);\n  const d = fetch64(s, len - 16).mul(k2);\n  const y = rotate64(a.add(b), 43).add(rotate64(c, 30)).add(d);\n  const z = hashLen16(y, a.add(rotate64(b.add(k2), 18)).add(c), mul2);\n  const e = fetch64(s, 16).mul(mul2);\n  const f = fetch64(s, 24);\n  const g = y.add(fetch64(s, len - 32)).mul(mul2);\n  const h = z.add(fetch64(s, len - 24)).mul(mul2);\n  return hashLen16(rotate64(e.add(f), 43).add(rotate64(g, 30)).add(h), e.add(rotate64(f.add(a), 18)).add(g), mul2);\n}\nfunction fingerPrint64(s, len = s.length) {\n  const seed = Long.fromNumber(81, true);\n  if (len <= 32) {\n    if (len <= 16) {\n      return hashLen0to16(s, len);\n    } else {\n      return hashLen17to32(s, len);\n    }\n  } else if (len <= 64) {\n    return hashLen33to64(s, len);\n  }\n  let x = seed;\n  let y = seed.mul(k1).add(113);\n  let z = shiftMix(y.mul(k2).add(113)).mul(k2);\n  let v = [Long.UZERO, Long.UZERO];\n  let w = [Long.UZERO, Long.UZERO];\n  x = x.mul(k2).add(fetch64(s, 0));\n  let offset = 0;\n  const end = (len - 1 >> 6) * 64;\n  const last64 = end + (len - 1 & 63) - 63;\n  do {\n    x = rotate64(x.add(y).add(v[0]).add(fetch64(s, offset + 8)), 37).mul(k1);\n    y = rotate64(y.add(v[1]).add(fetch64(s, offset + 48)), 42).mul(k1);\n    x = x.xor(w[1]);\n    y = y.add(v[0]).add(fetch64(s, offset + 40));\n    z = rotate64(z.add(w[0]), 33).mul(k1);\n    v = weakHashLen32WithSeedsStr(s, offset, v[1].mul(k1), x.add(w[0]));\n    w = weakHashLen32WithSeedsStr(s, offset + 32, z.add(w[1]), y.add(fetch64(s, offset + 16)));\n    [z, x] = [x, z];\n    offset += 64;\n  } while (offset !== end);\n  const mul2 = k1.add(z.and(255).shl(1));\n  offset = last64;\n  w[0] = w[0].add(len - 1 & 63);\n  v[0] = v[0].add(w[0]);\n  w[0] = w[0].add(v[0]);\n  x = rotate64(x.add(y).add(v[0]).add(fetch64(s, offset + 8)), 37).mul(mul2);\n  y = rotate64(y.add(v[1]).add(fetch64(s, offset + 48)), 42).mul(mul2);\n  x = x.xor(w[1].mul(9));\n  y = y.add(v[0].mul(9).add(fetch64(s, offset + 40)));\n  z = rotate64(z.add(w[0]), 33).mul(mul2);\n  v = weakHashLen32WithSeedsStr(s, offset, v[1].mul(mul2), x.add(w[0]));\n  w = weakHashLen32WithSeedsStr(s, offset + 32, z.add(w[1]), y.add(fetch64(s, offset + 16)));\n  [z, x] = [x, z];\n  return hashLen16(hashLen16(v[0], w[0], mul2).add(shiftMix(y).mul(k0)).add(z), hashLen16(v[1], w[1], mul2).add(x), mul2);\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/util.js\nfunction createScalarValue(value, dtype) {\n  if (dtype === \"string\") {\n    return encodeString(value);\n  }\n  return toTypedArray([value], dtype);\n}\nfunction noConversionNeeded(a, dtype) {\n  return a instanceof Float32Array && dtype === \"float32\" || a instanceof Int32Array && dtype === \"int32\" || a instanceof Uint8Array && dtype === \"bool\";\n}\nfunction toTypedArray(a, dtype) {\n  if (dtype === \"string\") {\n    throw new Error(\"Cannot convert a string[] to a TypedArray\");\n  }\n  if (Array.isArray(a)) {\n    a = flatten(a);\n  }\n  if (env().getBool(\"DEBUG\")) {\n    checkConversionForErrors(a, dtype);\n  }\n  if (noConversionNeeded(a, dtype)) {\n    return a;\n  }\n  if (dtype == null || dtype === \"float32\" || dtype === \"complex64\") {\n    return new Float32Array(a);\n  } else if (dtype === \"int32\") {\n    return new Int32Array(a);\n  } else if (dtype === \"bool\") {\n    const bool = new Uint8Array(a.length);\n    for (let i = 0; i < bool.length; ++i) {\n      if (Math.round(a[i]) !== 0) {\n        bool[i] = 1;\n      }\n    }\n    return bool;\n  } else {\n    throw new Error(`Unknown data type ${dtype}`);\n  }\n}\nfunction now() {\n  return env().platform.now();\n}\nfunction fetch3(path, requestInits) {\n  return env().platform.fetch(path, requestInits);\n}\nfunction encodeString(s, encoding = \"utf-8\") {\n  encoding = encoding || \"utf-8\";\n  return env().platform.encode(s, encoding);\n}\nfunction decodeString(bytes, encoding = \"utf-8\") {\n  encoding = encoding || \"utf-8\";\n  return env().platform.decode(bytes, encoding);\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/profiler.js\nvar Profiler = class {\n  constructor(backendTimer, logger) {\n    this.backendTimer = backendTimer;\n    this.logger = logger;\n    if (logger == null) {\n      this.logger = new Logger();\n    }\n  }\n  profileKernel(kernelName, inputs, f) {\n    let outputs;\n    const holdResultWrapperFn = () => {\n      outputs = f();\n    };\n    let timer;\n    const start = now();\n    if (this.backendTimer.timerAvailable()) {\n      timer = this.backendTimer.time(holdResultWrapperFn);\n    } else {\n      holdResultWrapperFn();\n      for (const output of outputs) {\n        output.dataSync();\n      }\n      timer = Promise.resolve({ kernelMs: now() - start });\n    }\n    if (env().getBool(\"CHECK_COMPUTATION_FOR_ERRORS\")) {\n      for (let i = 0; i < outputs.length; i++) {\n        const output = outputs[i];\n        output.data().then((tensorVals) => {\n          checkComputationForErrors(tensorVals, output.dtype, kernelName);\n        });\n      }\n    }\n    const kernelProfile = {\n      kernelName,\n      outputs,\n      inputs,\n      timeMs: timer.then((timing) => timing.kernelMs),\n      extraInfo: timer.then((timing) => timing.getExtraProfileInfo != null ? timing.getExtraProfileInfo() : \"\")\n    };\n    return kernelProfile;\n  }\n  logKernelProfile(kernelProfile) {\n    const { kernelName, outputs, timeMs, inputs, extraInfo } = kernelProfile;\n    outputs.forEach((result) => {\n      Promise.all([result.data(), timeMs, extraInfo]).then((valueContainer) => {\n        this.logger.logKernelProfile(kernelName, result, valueContainer[0], valueContainer[1], inputs, valueContainer[2]);\n      });\n    });\n  }\n};\nfunction checkComputationForErrors(vals, dtype, kernelName) {\n  if (dtype !== \"float32\") {\n    return false;\n  }\n  for (let i = 0; i < vals.length; i++) {\n    const num = vals[i];\n    if (isNaN(num) || !isFinite(num)) {\n      console.warn(`Found ${num} in the result of '${kernelName}'`);\n      return true;\n    }\n  }\n  return false;\n}\nvar Logger = class {\n  logKernelProfile(name, result, vals, timeMs, inputs, extraInfo) {\n    const time2 = typeof timeMs === \"number\" ? rightPad(`${timeMs}ms`, 9) : timeMs[\"error\"];\n    const paddedName = rightPad(name, 25);\n    const rank = result.rank;\n    const size = result.size;\n    const shape = rightPad(result.shape.toString(), 14);\n    let inputShapesDescription = \"\";\n    for (const name2 in inputs) {\n      const input2 = inputs[name2];\n      if (input2 != null) {\n        const inputShape = input2.shape || result.shape;\n        const inputRank = inputShape.length;\n        inputShapesDescription += `${name2}: ${inputRank}D ${inputRank > 0 ? inputShape : \"\"} `;\n      }\n    }\n    console.log(`%c${paddedName}\t%c${time2}\t%c${rank}D ${shape}\t%c${size}\t%c${inputShapesDescription}\t%c${extraInfo}`, \"font-weight:bold\", \"color:red\", \"color:blue\", \"color: orange\", \"color: green\", \"color: steelblue\");\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/tape.js\nfunction getFilteredNodesXToY(tape, xs, y) {\n  const tensorsFromX = {};\n  const nodesFromX = {};\n  for (let i = 0; i < xs.length; i++) {\n    tensorsFromX[xs[i].id] = true;\n  }\n  for (let i = 0; i < tape.length; i++) {\n    const node = tape[i];\n    const nodeInputs = node.inputs;\n    for (const inputName in nodeInputs) {\n      const input2 = nodeInputs[inputName];\n      let anyInputFromX = false;\n      for (let j = 0; j < xs.length; j++) {\n        if (tensorsFromX[input2.id]) {\n          node.outputs.forEach((output) => tensorsFromX[output.id] = true);\n          anyInputFromX = true;\n          nodesFromX[node.id] = true;\n          break;\n        }\n      }\n      if (anyInputFromX) {\n        break;\n      }\n    }\n  }\n  const tensorsLeadToY = {};\n  tensorsLeadToY[y.id] = true;\n  const nodesToY = {};\n  for (let i = tape.length - 1; i >= 0; i--) {\n    const node = tape[i];\n    const nodeInputs = node.inputs;\n    for (let j = 0; j < node.outputs.length; j++) {\n      if (tensorsLeadToY[node.outputs[j].id]) {\n        for (const inputName in nodeInputs) {\n          tensorsLeadToY[nodeInputs[inputName].id] = true;\n          nodesToY[node.id] = true;\n        }\n        break;\n      }\n    }\n  }\n  const filteredTape = [];\n  for (let i = 0; i < tape.length; i++) {\n    const node = tape[i];\n    if (nodesFromX[node.id] && nodesToY[node.id]) {\n      const prunedInputs = {};\n      for (const inputName in node.inputs) {\n        const nodeInput = node.inputs[inputName];\n        if (tensorsFromX[nodeInput.id]) {\n          prunedInputs[inputName] = nodeInput;\n        }\n      }\n      const prunedNode = Object.assign({}, node);\n      prunedNode.inputs = prunedInputs;\n      prunedNode.outputs = node.outputs;\n      filteredTape.push(prunedNode);\n    }\n  }\n  return filteredTape;\n}\nfunction backpropagateGradients(tensorAccumulatedGradientMap, filteredTape, tidy2, add5) {\n  for (let i = filteredTape.length - 1; i >= 0; i--) {\n    const node = filteredTape[i];\n    const dys = [];\n    node.outputs.forEach((o) => {\n      const gradTensor = tensorAccumulatedGradientMap[o.id];\n      if (gradTensor != null) {\n        dys.push(gradTensor);\n      } else {\n        dys.push(null);\n      }\n    });\n    if (node.gradient == null) {\n      throw new Error(`Cannot compute gradient: gradient function not found for ${node.kernelName}.`);\n    }\n    const inputGradients = node.gradient(dys);\n    for (const inputName in node.inputs) {\n      if (!(inputName in inputGradients)) {\n        throw new Error(`Cannot backprop through input ${inputName}. Available gradients found: ${Object.keys(inputGradients)}.`);\n      }\n      const dx = tidy2(() => inputGradients[inputName]());\n      if (dx.dtype !== \"float32\") {\n        throw new Error(`Error in gradient for op ${node.kernelName}. The gradient of input ${inputName} must have 'float32' dtype, but has '${dx.dtype}'`);\n      }\n      const x = node.inputs[inputName];\n      if (!arraysEqual(dx.shape, x.shape)) {\n        throw new Error(`Error in gradient for op ${node.kernelName}. The gradient of input '${inputName}' has shape '${dx.shape}', which does not match the shape of the input '${x.shape}'`);\n      }\n      if (tensorAccumulatedGradientMap[x.id] == null) {\n        tensorAccumulatedGradientMap[x.id] = dx;\n      } else {\n        const curGradient = tensorAccumulatedGradientMap[x.id];\n        tensorAccumulatedGradientMap[x.id] = add5(curGradient, dx);\n        curGradient.dispose();\n      }\n    }\n  }\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/tensor_format.js\nvar FORMAT_LIMIT_NUM_VALS = 20;\nvar FORMAT_NUM_FIRST_LAST_VALS = 3;\nvar FORMAT_NUM_SIG_DIGITS = 7;\nfunction tensorToString(vals, shape, dtype, verbose) {\n  const strides = computeStrides(shape);\n  const padPerCol = computeMaxSizePerColumn(vals, shape, dtype, strides);\n  const rank = shape.length;\n  const valsLines = subTensorToString(vals, shape, dtype, strides, padPerCol);\n  const lines = [\"Tensor\"];\n  if (verbose) {\n    lines.push(`  dtype: ${dtype}`);\n    lines.push(`  rank: ${rank}`);\n    lines.push(`  shape: [${shape}]`);\n    lines.push(`  values:`);\n  }\n  lines.push(valsLines.map((l) => \"    \" + l).join(\"\\n\"));\n  return lines.join(\"\\n\");\n}\nfunction computeMaxSizePerColumn(vals, shape, dtype, strides) {\n  const n = sizeFromShape(shape);\n  const numCols = strides[strides.length - 1];\n  const padPerCol = new Array(numCols).fill(0);\n  const rank = shape.length;\n  const valuesOrTuples = dtype === \"complex64\" ? createComplexTuples(vals) : vals;\n  if (rank > 1) {\n    for (let row = 0; row < n / numCols; row++) {\n      const offset = row * numCols;\n      for (let j = 0; j < numCols; j++) {\n        padPerCol[j] = Math.max(padPerCol[j], valToString(valuesOrTuples[offset + j], 0, dtype).length);\n      }\n    }\n  }\n  return padPerCol;\n}\nfunction valToString(val, pad3, dtype) {\n  let valStr;\n  if (Array.isArray(val)) {\n    valStr = `${parseFloat(val[0].toFixed(FORMAT_NUM_SIG_DIGITS))} + ${parseFloat(val[1].toFixed(FORMAT_NUM_SIG_DIGITS))}j`;\n  } else if (isString(val)) {\n    valStr = `'${val}'`;\n  } else if (dtype === \"bool\") {\n    valStr = boolNumToString(val);\n  } else {\n    valStr = parseFloat(val.toFixed(FORMAT_NUM_SIG_DIGITS)).toString();\n  }\n  return rightPad(valStr, pad3);\n}\nfunction boolNumToString(v) {\n  return v === 0 ? \"false\" : \"true\";\n}\nfunction subTensorToString(vals, shape, dtype, strides, padPerCol, isLast = true) {\n  const storagePerElement = dtype === \"complex64\" ? 2 : 1;\n  const size = shape[0];\n  const rank = shape.length;\n  if (rank === 0) {\n    if (dtype === \"complex64\") {\n      const complexTuple = createComplexTuples(vals);\n      return [valToString(complexTuple[0], 0, dtype)];\n    }\n    if (dtype === \"bool\") {\n      return [boolNumToString(vals[0])];\n    }\n    return [vals[0].toString()];\n  }\n  if (rank === 1) {\n    if (size > FORMAT_LIMIT_NUM_VALS) {\n      const firstValsSize = FORMAT_NUM_FIRST_LAST_VALS * storagePerElement;\n      let firstVals = Array.from(vals.slice(0, firstValsSize));\n      let lastVals = Array.from(vals.slice((size - FORMAT_NUM_FIRST_LAST_VALS) * storagePerElement, size * storagePerElement));\n      if (dtype === \"complex64\") {\n        firstVals = createComplexTuples(firstVals);\n        lastVals = createComplexTuples(lastVals);\n      }\n      return [\n        \"[\" + firstVals.map((x, i) => valToString(x, padPerCol[i], dtype)).join(\", \") + \", ..., \" + lastVals.map((x, i) => valToString(x, padPerCol[size - FORMAT_NUM_FIRST_LAST_VALS + i], dtype)).join(\", \") + \"]\"\n      ];\n    }\n    const displayVals = dtype === \"complex64\" ? createComplexTuples(vals) : Array.from(vals);\n    return [\n      \"[\" + displayVals.map((x, i) => valToString(x, padPerCol[i], dtype)).join(\", \") + \"]\"\n    ];\n  }\n  const subshape = shape.slice(1);\n  const substrides = strides.slice(1);\n  const stride = strides[0] * storagePerElement;\n  const lines = [];\n  if (size > FORMAT_LIMIT_NUM_VALS) {\n    for (let i = 0; i < FORMAT_NUM_FIRST_LAST_VALS; i++) {\n      const start = i * stride;\n      const end = start + stride;\n      lines.push(...subTensorToString(vals.slice(start, end), subshape, dtype, substrides, padPerCol, false));\n    }\n    lines.push(\"...\");\n    for (let i = size - FORMAT_NUM_FIRST_LAST_VALS; i < size; i++) {\n      const start = i * stride;\n      const end = start + stride;\n      lines.push(...subTensorToString(vals.slice(start, end), subshape, dtype, substrides, padPerCol, i === size - 1));\n    }\n  } else {\n    for (let i = 0; i < size; i++) {\n      const start = i * stride;\n      const end = start + stride;\n      lines.push(...subTensorToString(vals.slice(start, end), subshape, dtype, substrides, padPerCol, i === size - 1));\n    }\n  }\n  const sep = rank === 2 ? \",\" : \"\";\n  lines[0] = \"[\" + lines[0] + sep;\n  for (let i = 1; i < lines.length - 1; i++) {\n    lines[i] = \" \" + lines[i] + sep;\n  }\n  let newLineSep = \",\\n\";\n  for (let i = 2; i < rank; i++) {\n    newLineSep += \"\\n\";\n  }\n  lines[lines.length - 1] = \" \" + lines[lines.length - 1] + \"]\" + (isLast ? \"\" : newLineSep);\n  return lines;\n}\nfunction createComplexTuples(vals) {\n  const complexTuples = [];\n  for (let i = 0; i < vals.length; i += 2) {\n    complexTuples.push([vals[i], vals[i + 1]]);\n  }\n  return complexTuples;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/tensor.js\nvar TensorBuffer = class {\n  constructor(shape, dtype, values) {\n    this.dtype = dtype;\n    this.shape = shape.slice();\n    this.size = sizeFromShape(shape);\n    if (values != null) {\n      const n = values.length;\n      assert(n === this.size, () => `Length of values '${n}' does not match the size inferred by the shape '${this.size}'.`);\n    }\n    if (dtype === \"complex64\") {\n      throw new Error(`complex64 dtype TensorBuffers are not supported. Please create a TensorBuffer for the real and imaginary parts separately and call tf.complex(real, imag).`);\n    }\n    this.values = values || getArrayFromDType(dtype, this.size);\n    this.strides = computeStrides(shape);\n  }\n  set(value, ...locs) {\n    if (locs.length === 0) {\n      locs = [0];\n    }\n    assert(locs.length === this.rank, () => `The number of provided coordinates (${locs.length}) must match the rank (${this.rank})`);\n    const index = this.locToIndex(locs);\n    this.values[index] = value;\n  }\n  get(...locs) {\n    if (locs.length === 0) {\n      locs = [0];\n    }\n    let i = 0;\n    for (const loc of locs) {\n      if (loc < 0 || loc >= this.shape[i]) {\n        const msg = `Requested out of range element at ${locs}.   Buffer shape=${this.shape}`;\n        throw new Error(msg);\n      }\n      i++;\n    }\n    let index = locs[locs.length - 1];\n    for (let i2 = 0; i2 < locs.length - 1; ++i2) {\n      index += this.strides[i2] * locs[i2];\n    }\n    return this.values[index];\n  }\n  locToIndex(locs) {\n    if (this.rank === 0) {\n      return 0;\n    } else if (this.rank === 1) {\n      return locs[0];\n    }\n    let index = locs[locs.length - 1];\n    for (let i = 0; i < locs.length - 1; ++i) {\n      index += this.strides[i] * locs[i];\n    }\n    return index;\n  }\n  indexToLoc(index) {\n    if (this.rank === 0) {\n      return [];\n    } else if (this.rank === 1) {\n      return [index];\n    }\n    const locs = new Array(this.shape.length);\n    for (let i = 0; i < locs.length - 1; ++i) {\n      locs[i] = Math.floor(index / this.strides[i]);\n      index -= locs[i] * this.strides[i];\n    }\n    locs[locs.length - 1] = index;\n    return locs;\n  }\n  get rank() {\n    return this.shape.length;\n  }\n  toTensor() {\n    return trackerFn().makeTensor(this.values, this.shape, this.dtype);\n  }\n};\nvar trackerFn = null;\nvar opHandler = null;\nvar deprecationWarningFn = null;\nfunction setTensorTracker(fn) {\n  trackerFn = fn;\n}\nfunction setOpHandler(handler) {\n  opHandler = handler;\n}\nfunction setDeprecationWarningFn(fn) {\n  deprecationWarningFn = fn;\n}\nvar Tensor = class {\n  constructor(shape, dtype, dataId, id) {\n    this.kept = false;\n    this.isDisposedInternal = false;\n    this.shape = shape.slice();\n    this.dtype = dtype || \"float32\";\n    this.size = sizeFromShape(shape);\n    this.strides = computeStrides(shape);\n    this.dataId = dataId;\n    this.id = id;\n    this.rankType = this.rank < 5 ? this.rank.toString() : \"higher\";\n  }\n  get rank() {\n    return this.shape.length;\n  }\n  async buffer() {\n    const vals = await this.data();\n    return opHandler.buffer(this.shape, this.dtype, vals);\n  }\n  bufferSync() {\n    return opHandler.buffer(this.shape, this.dtype, this.dataSync());\n  }\n  async array() {\n    const vals = await this.data();\n    return toNestedArray(this.shape, vals, this.dtype === \"complex64\");\n  }\n  arraySync() {\n    return toNestedArray(this.shape, this.dataSync(), this.dtype === \"complex64\");\n  }\n  async data() {\n    this.throwIfDisposed();\n    const data = trackerFn().read(this.dataId);\n    if (this.dtype === \"string\") {\n      const bytes = await data;\n      try {\n        return bytes.map((b) => decodeString(b));\n      } catch (_a) {\n        throw new Error(\"Failed to decode the string bytes into utf-8. To get the original bytes, call tensor.bytes().\");\n      }\n    }\n    return data;\n  }\n  dataToGPU(options) {\n    this.throwIfDisposed();\n    return trackerFn().readToGPU(this.dataId, options);\n  }\n  dataSync() {\n    this.throwIfDisposed();\n    const data = trackerFn().readSync(this.dataId);\n    if (this.dtype === \"string\") {\n      try {\n        return data.map((b) => decodeString(b));\n      } catch (_a) {\n        throw new Error(\"Failed to decode the string bytes into utf-8. To get the original bytes, call tensor.bytes().\");\n      }\n    }\n    return data;\n  }\n  async bytes() {\n    this.throwIfDisposed();\n    const data = await trackerFn().read(this.dataId);\n    if (this.dtype === \"string\") {\n      return data;\n    } else {\n      return new Uint8Array(data.buffer);\n    }\n  }\n  dispose() {\n    if (this.isDisposed) {\n      return;\n    }\n    trackerFn().disposeTensor(this);\n    this.isDisposedInternal = true;\n  }\n  get isDisposed() {\n    return this.isDisposedInternal;\n  }\n  throwIfDisposed() {\n    if (this.isDisposed) {\n      throw new Error(`Tensor is disposed.`);\n    }\n  }\n  print(verbose = false) {\n    return opHandler.print(this, verbose);\n  }\n  clone() {\n    this.throwIfDisposed();\n    return opHandler.clone(this);\n  }\n  toString(verbose = false) {\n    const vals = this.dataSync();\n    return tensorToString(vals, this.shape, this.dtype, verbose);\n  }\n  cast(dtype) {\n    this.throwIfDisposed();\n    return opHandler.cast(this, dtype);\n  }\n  variable(trainable = true, name, dtype) {\n    this.throwIfDisposed();\n    return trackerFn().makeVariable(this, trainable, name, dtype);\n  }\n};\nObject.defineProperty(Tensor, Symbol.hasInstance, {\n  value: (instance) => {\n    return !!instance && instance.data != null && instance.dataSync != null && instance.throwIfDisposed != null;\n  }\n});\nfunction getGlobalTensorClass() {\n  return getGlobal(\"Tensor\", () => {\n    return Tensor;\n  });\n}\ngetGlobalTensorClass();\nvar Variable = class extends Tensor {\n  constructor(initialValue, trainable, name, tensorId) {\n    super(initialValue.shape, initialValue.dtype, initialValue.dataId, tensorId);\n    this.trainable = trainable;\n    this.name = name;\n  }\n  assign(newValue) {\n    if (newValue.dtype !== this.dtype) {\n      throw new Error(`dtype of the new value (${newValue.dtype}) and previous value (${this.dtype}) must match`);\n    }\n    if (!arraysEqual(newValue.shape, this.shape)) {\n      throw new Error(`shape of the new value (${newValue.shape}) and previous value (${this.shape}) must match`);\n    }\n    trackerFn().disposeTensor(this);\n    this.dataId = newValue.dataId;\n    trackerFn().incRef(this, null);\n  }\n  dispose() {\n    trackerFn().disposeVariable(this);\n    this.isDisposedInternal = true;\n  }\n};\nObject.defineProperty(Variable, Symbol.hasInstance, {\n  value: (instance) => {\n    return instance instanceof Tensor && instance.assign != null && instance.assign instanceof Function;\n  }\n});\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/tensor_util.js\nvar tensor_util_exports = {};\n__export(tensor_util_exports, {\n  assertTypesMatch: () => assertTypesMatch,\n  getTensorsInContainer: () => getTensorsInContainer,\n  isTensorInList: () => isTensorInList,\n  makeTypesMatch: () => makeTypesMatch\n});\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/types.js\nvar Rank;\n(function(Rank2) {\n  Rank2[\"R0\"] = \"R0\";\n  Rank2[\"R1\"] = \"R1\";\n  Rank2[\"R2\"] = \"R2\";\n  Rank2[\"R3\"] = \"R3\";\n  Rank2[\"R4\"] = \"R4\";\n  Rank2[\"R5\"] = \"R5\";\n  Rank2[\"R6\"] = \"R6\";\n})(Rank || (Rank = {}));\nvar UpcastInt32AndMap;\n(function(UpcastInt32AndMap2) {\n  UpcastInt32AndMap2[\"float32\"] = \"float32\";\n  UpcastInt32AndMap2[\"int32\"] = \"int32\";\n  UpcastInt32AndMap2[\"bool\"] = \"int32\";\n  UpcastInt32AndMap2[\"complex64\"] = \"complex64\";\n})(UpcastInt32AndMap || (UpcastInt32AndMap = {}));\nvar UpcastBoolAndMap;\n(function(UpcastBoolAndMap2) {\n  UpcastBoolAndMap2[\"float32\"] = \"float32\";\n  UpcastBoolAndMap2[\"int32\"] = \"int32\";\n  UpcastBoolAndMap2[\"bool\"] = \"bool\";\n  UpcastBoolAndMap2[\"complex64\"] = \"complex64\";\n})(UpcastBoolAndMap || (UpcastBoolAndMap = {}));\nvar UpcastFloat32AndMap;\n(function(UpcastFloat32AndMap2) {\n  UpcastFloat32AndMap2[\"float32\"] = \"float32\";\n  UpcastFloat32AndMap2[\"int32\"] = \"float32\";\n  UpcastFloat32AndMap2[\"bool\"] = \"float32\";\n  UpcastFloat32AndMap2[\"complex64\"] = \"complex64\";\n})(UpcastFloat32AndMap || (UpcastFloat32AndMap = {}));\nvar UpcastComplex64AndMap;\n(function(UpcastComplex64AndMap2) {\n  UpcastComplex64AndMap2[\"float32\"] = \"complex64\";\n  UpcastComplex64AndMap2[\"int32\"] = \"complex64\";\n  UpcastComplex64AndMap2[\"bool\"] = \"complex64\";\n  UpcastComplex64AndMap2[\"complex64\"] = \"complex64\";\n})(UpcastComplex64AndMap || (UpcastComplex64AndMap = {}));\nvar upcastTypeMap = {\n  \"float32\": UpcastFloat32AndMap,\n  \"int32\": UpcastInt32AndMap,\n  \"bool\": UpcastBoolAndMap,\n  \"complex64\": UpcastComplex64AndMap\n};\nfunction upcastType(typeA, typeB) {\n  if (typeA === \"string\" || typeB === \"string\") {\n    if (typeA === \"string\" && typeB === \"string\") {\n      return \"string\";\n    }\n    throw new Error(`Can not upcast ${typeA} with ${typeB}`);\n  }\n  return upcastTypeMap[typeA][typeB];\n}\nfunction sumOutType(type) {\n  return upcastType(type, \"int32\");\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/tensor_util.js\nfunction makeTypesMatch(a, b) {\n  if (a.dtype === b.dtype) {\n    return [a, b];\n  }\n  const dtype = upcastType(a.dtype, b.dtype);\n  return [a.cast(dtype), b.cast(dtype)];\n}\nfunction assertTypesMatch(a, b) {\n  assert(a.dtype === b.dtype, () => `The dtypes of the first(${a.dtype}) and second(${b.dtype}) input must match`);\n}\nfunction isTensorInList(tensor2, tensorList) {\n  return tensorList.some((x) => x.id === tensor2.id);\n}\nfunction getTensorsInContainer(result) {\n  const list = [];\n  const seen = /* @__PURE__ */ new Set();\n  walkTensorContainer(result, list, seen);\n  return list;\n}\nfunction walkTensorContainer(container, list, seen) {\n  if (container == null) {\n    return;\n  }\n  if (container instanceof Tensor) {\n    list.push(container);\n    return;\n  }\n  if (!isIterable(container)) {\n    return;\n  }\n  const iterable = container;\n  for (const k in iterable) {\n    const val = iterable[k];\n    if (!seen.has(val)) {\n      seen.add(val);\n      walkTensorContainer(val, list, seen);\n    }\n  }\n}\nfunction isIterable(obj) {\n  return Array.isArray(obj) || typeof obj === \"object\";\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/engine.js\nfunction isRegisteredKernelInvocation(kernelInvocation) {\n  return kernelInvocation.kernelName != null;\n}\nvar EngineState = class {\n  constructor() {\n    this.registeredVariables = {};\n    this.nextTapeNodeId = 0;\n    this.numBytes = 0;\n    this.numTensors = 0;\n    this.numStringTensors = 0;\n    this.numDataBuffers = 0;\n    this.gradientDepth = 0;\n    this.kernelDepth = 0;\n    this.scopeStack = [];\n    this.numDataMovesStack = [];\n    this.nextScopeId = 0;\n    this.tensorInfo = /* @__PURE__ */ new WeakMap();\n    this.profiling = false;\n    this.activeProfile = {\n      newBytes: 0,\n      newTensors: 0,\n      peakBytes: 0,\n      kernels: [],\n      result: null,\n      get kernelNames() {\n        return Array.from(new Set(this.kernels.map((k) => k.name)));\n      }\n    };\n  }\n  dispose() {\n    for (const variableName in this.registeredVariables) {\n      this.registeredVariables[variableName].dispose();\n    }\n  }\n};\nvar Engine = class {\n  constructor(ENV8) {\n    this.ENV = ENV8;\n    this.registry = {};\n    this.registryFactory = {};\n    this.pendingBackendInitId = 0;\n    this.state = new EngineState();\n  }\n  async ready() {\n    if (this.pendingBackendInit != null) {\n      return this.pendingBackendInit.then(() => {\n      });\n    }\n    if (this.backendInstance != null) {\n      return;\n    }\n    const sortedBackends = this.getSortedBackends();\n    for (let i = 0; i < sortedBackends.length; i++) {\n      const backendName = sortedBackends[i];\n      const success = await this.initializeBackend(backendName).success;\n      if (success) {\n        await this.setBackend(backendName);\n        return;\n      }\n    }\n    throw new Error(`Could not initialize any backends, all backend initializations failed.`);\n  }\n  get backend() {\n    if (this.pendingBackendInit != null) {\n      throw new Error(`Backend '${this.backendName}' has not yet been initialized. Make sure to await tf.ready() or await tf.setBackend() before calling other methods`);\n    }\n    if (this.backendInstance == null) {\n      const { name, asyncInit } = this.initializeBackendsAndReturnBest();\n      if (asyncInit) {\n        throw new Error(`The highest priority backend '${name}' has not yet been initialized. Make sure to await tf.ready() or await tf.setBackend() before calling other methods`);\n      }\n      this.setBackend(name);\n    }\n    return this.backendInstance;\n  }\n  backendNames() {\n    return Object.keys(this.registryFactory);\n  }\n  findBackend(backendName) {\n    if (!(backendName in this.registry)) {\n      if (backendName in this.registryFactory) {\n        const { asyncInit } = this.initializeBackend(backendName);\n        if (asyncInit) {\n          return null;\n        }\n      } else {\n        return null;\n      }\n    }\n    return this.registry[backendName];\n  }\n  findBackendFactory(backendName) {\n    if (!(backendName in this.registryFactory)) {\n      return null;\n    }\n    return this.registryFactory[backendName].factory;\n  }\n  registerBackend(backendName, factory, priority = 1) {\n    if (backendName in this.registryFactory) {\n      warn(`${backendName} backend was already registered. Reusing existing backend factory.`);\n      return false;\n    }\n    this.registryFactory[backendName] = { factory, priority };\n    return true;\n  }\n  async setBackend(backendName) {\n    if (this.registryFactory[backendName] == null) {\n      throw new Error(`Backend name '${backendName}' not found in registry`);\n    }\n    this.backendName = backendName;\n    if (this.registry[backendName] == null) {\n      this.backendInstance = null;\n      const { success, asyncInit } = this.initializeBackend(backendName);\n      const result = asyncInit ? await success : success;\n      if (!result) {\n        return false;\n      }\n    }\n    this.backendInstance = this.registry[backendName];\n    this.setupRegisteredKernels();\n    this.profiler = new Profiler(this.backendInstance);\n    return true;\n  }\n  setupRegisteredKernels() {\n    const kernels = getKernelsForBackend(this.backendName);\n    kernels.forEach((kernel) => {\n      if (kernel.setupFunc != null) {\n        kernel.setupFunc(this.backendInstance);\n      }\n    });\n  }\n  disposeRegisteredKernels(backendName) {\n    const kernels = getKernelsForBackend(backendName);\n    kernels.forEach((kernel) => {\n      if (kernel.disposeFunc != null) {\n        kernel.disposeFunc(this.registry[backendName]);\n      }\n    });\n  }\n  initializeBackend(backendName) {\n    const registryFactoryEntry = this.registryFactory[backendName];\n    if (registryFactoryEntry == null) {\n      throw new Error(`Cannot initialize backend ${backendName}, no registration found.`);\n    }\n    try {\n      const backend2 = registryFactoryEntry.factory();\n      if (backend2 && !(backend2 instanceof KernelBackend) && typeof backend2.then === \"function\") {\n        const promiseId = ++this.pendingBackendInitId;\n        const success = backend2.then((backendInstance) => {\n          if (promiseId < this.pendingBackendInitId) {\n            return false;\n          }\n          this.registry[backendName] = backendInstance;\n          this.pendingBackendInit = null;\n          return true;\n        }).catch((err) => {\n          if (promiseId < this.pendingBackendInitId) {\n            return false;\n          }\n          this.pendingBackendInit = null;\n          warn(`Initialization of backend ${backendName} failed`);\n          warn(err.stack || err.message);\n          return false;\n        });\n        this.pendingBackendInit = success;\n        return { success, asyncInit: true };\n      } else {\n        this.registry[backendName] = backend2;\n        return { success: true, asyncInit: false };\n      }\n    } catch (err) {\n      warn(`Initialization of backend ${backendName} failed`);\n      warn(err.stack || err.message);\n      return { success: false, asyncInit: false };\n    }\n  }\n  removeBackend(backendName) {\n    if (!(backendName in this.registryFactory)) {\n      throw new Error(`${backendName} backend not found in registry`);\n    }\n    if (this.backendName === backendName && this.pendingBackendInit != null) {\n      this.pendingBackendInitId++;\n    }\n    if (backendName in this.registry) {\n      this.disposeRegisteredKernels(backendName);\n      this.registry[backendName].dispose();\n      delete this.registry[backendName];\n    }\n    delete this.registryFactory[backendName];\n    if (this.backendName === backendName) {\n      this.pendingBackendInit = null;\n      this.backendName = null;\n      this.backendInstance = null;\n    }\n  }\n  getSortedBackends() {\n    if (Object.keys(this.registryFactory).length === 0) {\n      throw new Error(\"No backend found in registry.\");\n    }\n    return Object.keys(this.registryFactory).sort((a, b) => {\n      return this.registryFactory[b].priority - this.registryFactory[a].priority;\n    });\n  }\n  initializeBackendsAndReturnBest() {\n    const sortedBackends = this.getSortedBackends();\n    for (let i = 0; i < sortedBackends.length; i++) {\n      const backendName = sortedBackends[i];\n      const { success, asyncInit } = this.initializeBackend(backendName);\n      if (asyncInit || success) {\n        return { name: backendName, asyncInit };\n      }\n    }\n    throw new Error(`Could not initialize any backends, all backend initializations failed.`);\n  }\n  moveData(backend2, dataId) {\n    const info = this.state.tensorInfo.get(dataId);\n    const srcBackend = info.backend;\n    const values = this.readSync(dataId);\n    const refCount = srcBackend.refCount(dataId);\n    srcBackend.disposeData(dataId, true);\n    info.backend = backend2;\n    backend2.move(dataId, values, info.shape, info.dtype, refCount);\n    if (this.shouldCheckForMemLeaks()) {\n      this.state.numDataMovesStack[this.state.numDataMovesStack.length - 1]++;\n    }\n  }\n  tidy(nameOrFn, fn) {\n    let name = null;\n    if (fn == null) {\n      if (typeof nameOrFn !== \"function\") {\n        throw new Error(\"Please provide a function to tidy()\");\n      }\n      fn = nameOrFn;\n    } else {\n      if (typeof nameOrFn !== \"string\" && !(nameOrFn instanceof String)) {\n        throw new Error(\"When calling with two arguments, the first argument to tidy() must be a string\");\n      }\n      if (typeof fn !== \"function\") {\n        throw new Error(\"When calling with two arguments, the 2nd argument to tidy() must be a function\");\n      }\n      name = nameOrFn;\n    }\n    let result;\n    return this.scopedRun(() => this.startScope(name), () => this.endScope(result), () => {\n      result = fn();\n      if (result instanceof Promise) {\n        console.error(\"Cannot return a Promise inside of tidy.\");\n      }\n      return result;\n    });\n  }\n  scopedRun(start, end, f) {\n    start();\n    try {\n      const res = f();\n      end();\n      return res;\n    } catch (ex) {\n      end();\n      throw ex;\n    }\n  }\n  nextTensorId() {\n    return Engine.nextTensorId++;\n  }\n  nextVariableId() {\n    return Engine.nextVariableId++;\n  }\n  clone(x) {\n    const y = ENGINE.runKernel(Identity, { x });\n    const inputs = { x };\n    const grad2 = (dy) => ({\n      x: () => {\n        const dtype = \"float32\";\n        const gradInputs = { x: dy };\n        const attrs = { dtype };\n        return ENGINE.runKernel(\n          Cast,\n          gradInputs,\n          attrs\n        );\n      }\n    });\n    const saved = [];\n    this.addTapeNode(this.state.activeScope.name, inputs, [y], grad2, saved, {});\n    return y;\n  }\n  runKernel(kernelName, inputs, attrs) {\n    if (this.backendName == null) {\n      this.backend;\n    }\n    const hasKernel = getKernel(kernelName, this.backendName) != null;\n    if (!hasKernel) {\n      throw new Error(`Kernel '${kernelName}' not registered for backend '${this.backendName}'`);\n    }\n    return this.runKernelFunc({ kernelName, inputs, attrs });\n  }\n  shouldCheckForMemLeaks() {\n    return this.ENV.getBool(\"IS_TEST\");\n  }\n  checkKernelForMemLeak(kernelName, numDataIdsBefore, outInfos) {\n    const numDataIdsAfter = this.backend.numDataIds();\n    let numOutputDataIds = 0;\n    outInfos.forEach((info) => {\n      numOutputDataIds += info.dtype === \"complex64\" ? 3 : 1;\n    });\n    const numMoves = this.state.numDataMovesStack[this.state.numDataMovesStack.length - 1];\n    const dataIdsLeaked = numDataIdsAfter - numDataIdsBefore - numOutputDataIds - numMoves;\n    if (dataIdsLeaked > 0) {\n      throw new Error(`Backend '${this.backendName}' has an internal memory leak (${dataIdsLeaked} data ids) after running '${kernelName}'`);\n    }\n  }\n  runKernelFunc(kernelParams) {\n    let outputs;\n    let saved = [];\n    const isTapeOn = this.isTapeOn();\n    const startingBytecount = this.state.numBytes;\n    const startingNumTensors = this.state.numTensors;\n    if (this.shouldCheckForMemLeaks()) {\n      this.state.numDataMovesStack.push(0);\n    }\n    let kernelFunc3;\n    if (this.backendName == null) {\n      this.backend;\n    }\n    let out;\n    const kernelOrScopeName = isRegisteredKernelInvocation(kernelParams) ? kernelParams.kernelName : this.state.activeScope != null ? this.state.activeScope.name : \"\";\n    if (isRegisteredKernelInvocation(kernelParams)) {\n      const { kernelName, inputs: inputs2, attrs: attrs2 } = kernelParams;\n      if (this.backendName == null) {\n        this.backend;\n      }\n      const kernel = getKernel(kernelName, this.backendName);\n      assert(kernel != null, () => `Cannot find registered kernel '${kernelName}' for backend '${this.backendName}'`);\n      kernelFunc3 = () => {\n        const numDataIdsBefore = this.backend.numDataIds();\n        out = kernel.kernelFunc({ inputs: inputs2, attrs: attrs2, backend: this.backend });\n        const outInfos = Array.isArray(out) ? out : [out];\n        if (this.shouldCheckForMemLeaks()) {\n          this.checkKernelForMemLeak(kernelName, numDataIdsBefore, outInfos);\n        }\n        const outTensors = outInfos.map((outInfo) => {\n          if (outInfo.rank != null) {\n            return outInfo;\n          }\n          return this.makeTensorFromTensorInfo(outInfo);\n        });\n        if (isTapeOn) {\n          const tensorsToSave = this.getTensorsForGradient(kernelName, inputs2, outTensors);\n          saved = this.saveTensorsForBackwardMode(tensorsToSave);\n        }\n        return outTensors;\n      };\n    } else {\n      const { forwardFunc } = kernelParams;\n      const saveFunc = (tensors) => {\n        if (!isTapeOn) {\n          return;\n        }\n        saved = tensors.map((tensor2) => this.keep(this.clone(tensor2)));\n      };\n      kernelFunc3 = () => {\n        const numDataIdsBefore = this.backend.numDataIds();\n        out = this.tidy(() => forwardFunc(this.backend, saveFunc));\n        const outs = Array.isArray(out) ? out : [out];\n        if (this.shouldCheckForMemLeaks()) {\n          this.checkKernelForMemLeak(kernelOrScopeName, numDataIdsBefore, outs);\n        }\n        return outs;\n      };\n    }\n    const { inputs, attrs } = kernelParams;\n    const backwardsFunc = isRegisteredKernelInvocation(kernelParams) ? null : kernelParams.backwardsFunc;\n    let kernelProfile;\n    this.scopedRun(\n      () => this.state.kernelDepth++,\n      () => this.state.kernelDepth--,\n      () => {\n        if (!this.ENV.getBool(\"DEBUG\") && !this.state.profiling) {\n          outputs = kernelFunc3();\n        } else {\n          kernelProfile = this.profiler.profileKernel(kernelOrScopeName, inputs, () => kernelFunc3());\n          if (this.ENV.getBool(\"DEBUG\")) {\n            this.profiler.logKernelProfile(kernelProfile);\n          }\n          outputs = kernelProfile.outputs;\n        }\n      }\n    );\n    if (isTapeOn) {\n      this.addTapeNode(kernelOrScopeName, inputs, outputs, backwardsFunc, saved, attrs);\n    }\n    if (this.state.profiling) {\n      this.state.activeProfile.kernels.push({\n        name: kernelOrScopeName,\n        bytesAdded: this.state.numBytes - startingBytecount,\n        totalBytesSnapshot: this.state.numBytes,\n        tensorsAdded: this.state.numTensors - startingNumTensors,\n        totalTensorsSnapshot: this.state.numTensors,\n        inputShapes: Object.keys(inputs).map((key) => inputs[key] != null ? inputs[key].shape : null),\n        outputShapes: outputs.map((item) => item.shape),\n        kernelTimeMs: kernelProfile.timeMs,\n        extraInfo: kernelProfile.extraInfo\n      });\n    }\n    return Array.isArray(out) ? outputs : outputs[0];\n  }\n  saveTensorsForBackwardMode(tensors) {\n    const saved = tensors.map((tensor2) => this.keep(this.clone(tensor2)));\n    return saved;\n  }\n  getTensorsForGradient(kernelName, inputs, outputs) {\n    const gradConfig = getGradient(kernelName);\n    if (gradConfig != null) {\n      const inputsToSave = gradConfig.inputsToSave || [];\n      const outputsToSave = gradConfig.outputsToSave || [];\n      let inputTensorsToSave;\n      if (gradConfig.saveAllInputs) {\n        assert(Array.isArray(inputs), () => \"saveAllInputs is true, expected inputs to be an array.\");\n        inputTensorsToSave = Object.keys(inputs).map((key) => inputs[key]);\n      } else {\n        inputTensorsToSave = inputsToSave.map((inputName) => inputs[inputName]);\n      }\n      const outputTensorsToSave = outputs.filter((_, i) => outputsToSave[i]);\n      return inputTensorsToSave.concat(outputTensorsToSave);\n    }\n    return [];\n  }\n  makeTensor(values, shape, dtype, backend2) {\n    if (values == null) {\n      throw new Error(\"Values passed to engine.makeTensor() are null\");\n    }\n    dtype = dtype || \"float32\";\n    backend2 = backend2 || this.backend;\n    let backendVals = values;\n    if (dtype === \"string\" && isString(values[0])) {\n      backendVals = values.map((d) => encodeString(d));\n    }\n    const dataId = backend2.write(backendVals, shape, dtype);\n    const t = new Tensor(shape, dtype, dataId, this.nextTensorId());\n    this.trackTensor(t, backend2);\n    if (dtype === \"string\") {\n      const info = this.state.tensorInfo.get(dataId);\n      const newBytes = bytesFromStringArray(backendVals);\n      this.state.numBytes += newBytes - info.bytes;\n      info.bytes = newBytes;\n    }\n    return t;\n  }\n  makeTensorFromDataId(dataId, shape, dtype, backend2) {\n    dtype = dtype || \"float32\";\n    const tensorInfo = { dataId, shape, dtype };\n    return this.makeTensorFromTensorInfo(tensorInfo, backend2);\n  }\n  makeTensorFromTensorInfo(tensorInfo, backend2) {\n    const { dataId, shape, dtype } = tensorInfo;\n    const t = new Tensor(shape, dtype, dataId, this.nextTensorId());\n    this.trackTensor(t, backend2);\n    return t;\n  }\n  makeVariable(initialValue, trainable = true, name, dtype) {\n    name = name || this.nextVariableId().toString();\n    if (dtype != null && dtype !== initialValue.dtype) {\n      initialValue = initialValue.cast(dtype);\n    }\n    const v = new Variable(initialValue, trainable, name, this.nextTensorId());\n    if (this.state.registeredVariables[v.name] != null) {\n      throw new Error(`Variable with name ${v.name} was already registered`);\n    }\n    this.state.registeredVariables[v.name] = v;\n    this.incRef(v, this.backend);\n    return v;\n  }\n  trackTensor(a, backend2) {\n    this.state.numTensors++;\n    if (a.dtype === \"string\") {\n      this.state.numStringTensors++;\n    }\n    let bytes = 0;\n    if (a.dtype !== \"complex64\" && a.dtype !== \"string\") {\n      bytes = a.size * bytesPerElement(a.dtype);\n    }\n    this.state.numBytes += bytes;\n    if (!this.state.tensorInfo.has(a.dataId)) {\n      this.state.numDataBuffers++;\n      this.state.tensorInfo.set(a.dataId, {\n        backend: backend2 || this.backend,\n        dtype: a.dtype,\n        shape: a.shape,\n        bytes\n      });\n    }\n    if (!(a instanceof Variable)) {\n      this.track(a);\n    }\n  }\n  incRef(a, backend2) {\n    this.trackTensor(a, backend2);\n    this.backend.incRef(a.dataId);\n  }\n  removeDataId(dataId, backend2) {\n    if (this.state.tensorInfo.has(dataId) && this.state.tensorInfo.get(dataId).backend === backend2) {\n      this.state.tensorInfo.delete(dataId);\n      this.state.numDataBuffers--;\n    }\n  }\n  disposeTensor(a) {\n    if (!this.state.tensorInfo.has(a.dataId)) {\n      return;\n    }\n    const info = this.state.tensorInfo.get(a.dataId);\n    this.state.numTensors--;\n    if (a.dtype === \"string\") {\n      this.state.numStringTensors--;\n      this.state.numBytes -= info.bytes;\n    }\n    if (a.dtype !== \"complex64\" && a.dtype !== \"string\") {\n      const bytes = a.size * bytesPerElement(a.dtype);\n      this.state.numBytes -= bytes;\n    }\n    if (info.backend.disposeData(a.dataId)) {\n      this.removeDataId(a.dataId, info.backend);\n    }\n  }\n  disposeVariables() {\n    for (const varName in this.state.registeredVariables) {\n      const v = this.state.registeredVariables[varName];\n      this.disposeVariable(v);\n    }\n  }\n  disposeVariable(v) {\n    this.disposeTensor(v);\n    if (this.state.registeredVariables[v.name] != null) {\n      delete this.state.registeredVariables[v.name];\n    }\n  }\n  memory() {\n    const info = this.backend.memory();\n    info.numTensors = this.state.numTensors;\n    info.numDataBuffers = this.state.numDataBuffers;\n    info.numBytes = this.state.numBytes;\n    if (this.state.numStringTensors > 0) {\n      info.unreliable = true;\n      if (info.reasons == null) {\n        info.reasons = [];\n      }\n      info.reasons.push(\"Memory usage by string tensors is approximate (2 bytes per character)\");\n    }\n    return info;\n  }\n  async profile(query) {\n    this.state.profiling = true;\n    const startBytes = this.state.numBytes;\n    const startNumTensors = this.state.numTensors;\n    this.state.activeProfile.kernels = [];\n    this.state.activeProfile.result = await query();\n    this.state.profiling = false;\n    this.state.activeProfile.peakBytes = Math.max(...this.state.activeProfile.kernels.map((d) => d.totalBytesSnapshot));\n    this.state.activeProfile.newBytes = this.state.numBytes - startBytes;\n    this.state.activeProfile.newTensors = this.state.numTensors - startNumTensors;\n    for (const kernel of this.state.activeProfile.kernels) {\n      kernel.kernelTimeMs = await kernel.kernelTimeMs;\n      kernel.extraInfo = await kernel.extraInfo;\n    }\n    return this.state.activeProfile;\n  }\n  isTapeOn() {\n    return this.state.gradientDepth > 0 && this.state.kernelDepth === 0;\n  }\n  addTapeNode(kernelName, inputs, outputs, gradientsFunc, saved, attrs) {\n    const tapeNode = { id: this.state.nextTapeNodeId++, kernelName, inputs, outputs, saved };\n    const gradConfig = getGradient(kernelName);\n    if (gradConfig != null) {\n      gradientsFunc = gradConfig.gradFunc;\n    }\n    if (gradientsFunc != null) {\n      tapeNode.gradient = (dys) => {\n        dys = dys.map((dy, i) => {\n          if (dy == null) {\n            const output = outputs[i];\n            const vals = makeZerosTypedArray(output.size, output.dtype);\n            return this.makeTensor(vals, output.shape, output.dtype);\n          }\n          return dy;\n        });\n        return gradientsFunc(dys.length > 1 ? dys : dys[0], saved, attrs);\n      };\n    }\n    this.state.activeTape.push(tapeNode);\n  }\n  keep(result) {\n    result.kept = true;\n    return result;\n  }\n  startTape() {\n    if (this.state.gradientDepth === 0) {\n      this.state.activeTape = [];\n    }\n    this.state.gradientDepth++;\n  }\n  endTape() {\n    this.state.gradientDepth--;\n  }\n  startScope(name) {\n    const scopeInfo = {\n      track: [],\n      name: \"unnamed scope\",\n      id: this.state.nextScopeId++\n    };\n    if (name) {\n      scopeInfo.name = name;\n    }\n    this.state.scopeStack.push(scopeInfo);\n    this.state.activeScope = scopeInfo;\n  }\n  endScope(result) {\n    const tensorsToTrackInParent = getTensorsInContainer(result);\n    const tensorsToTrackInParentSet = new Set(tensorsToTrackInParent.map((t) => t.id));\n    for (let i = 0; i < this.state.activeScope.track.length; i++) {\n      const tensor2 = this.state.activeScope.track[i];\n      if (!tensor2.kept && !tensorsToTrackInParentSet.has(tensor2.id)) {\n        tensor2.dispose();\n      }\n    }\n    const oldScope = this.state.scopeStack.pop();\n    this.state.activeScope = this.state.scopeStack.length === 0 ? null : this.state.scopeStack[this.state.scopeStack.length - 1];\n    tensorsToTrackInParent.forEach((tensor2) => {\n      if (!tensor2.kept && tensor2.scopeId === oldScope.id) {\n        this.track(tensor2);\n      }\n    });\n  }\n  gradients(f, xs, dy, allowNoGradients = false) {\n    assert(xs.length > 0, () => \"gradients() received an empty list of xs.\");\n    if (dy != null && dy.dtype !== \"float32\") {\n      throw new Error(`dy must have 'float32' dtype, but has '${dy.dtype}'`);\n    }\n    const y = this.scopedRun(() => this.startTape(), () => this.endTape(), () => this.tidy(\"forward\", f));\n    assert(y instanceof Tensor, () => \"The result y returned by f() must be a tensor.\");\n    const filteredTape = getFilteredNodesXToY(this.state.activeTape, xs, y);\n    if (!allowNoGradients && filteredTape.length === 0 && xs.length > 0) {\n      throw new Error(\"Cannot compute gradient of y=f(x) with respect to x. Make sure that the f you passed encloses all operations that lead from x to y.\");\n    }\n    return this.tidy(\"backward\", () => {\n      const accumulatedGradientMap = {};\n      accumulatedGradientMap[y.id] = dy == null ? ones(y.shape) : dy;\n      backpropagateGradients(\n        accumulatedGradientMap,\n        filteredTape,\n        (f2) => this.tidy(f2),\n        add\n      );\n      const grads2 = xs.map((x) => accumulatedGradientMap[x.id]);\n      if (this.state.gradientDepth === 0) {\n        this.state.activeTape.forEach((node) => {\n          for (const tensor2 of node.saved) {\n            tensor2.dispose();\n          }\n        });\n        this.state.activeTape = null;\n      }\n      return { value: y, grads: grads2 };\n    });\n  }\n  customGrad(f) {\n    assert(isFunction(f), () => \"The f passed in customGrad(f) must be a function.\");\n    return (...inputs) => {\n      assert(inputs.every((t) => t instanceof Tensor), () => \"The args passed in customGrad(f)(x1, x2,...) must all be tensors\");\n      let res;\n      const inputMap = {};\n      inputs.forEach((input2, i) => {\n        inputMap[i] = input2;\n      });\n      const forwardFunc = (_, save) => {\n        res = f(...[...inputs, save]);\n        assert(res.value instanceof Tensor, () => \"The function f passed in customGrad(f) must return an object where `obj.value` is a tensor\");\n        assert(isFunction(res.gradFunc), () => \"The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function.\");\n        return res.value;\n      };\n      const backwardsFunc = (dy, saved) => {\n        const gradRes = res.gradFunc(dy, saved);\n        const grads2 = Array.isArray(gradRes) ? gradRes : [gradRes];\n        assert(grads2.length === inputs.length, () => \"The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function that returns the same number of tensors as inputs passed to f(...).\");\n        assert(grads2.every((t) => t instanceof Tensor), () => \"The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function that returns a list of only tensors.\");\n        const gradMap = {};\n        grads2.forEach((grad2, i) => {\n          gradMap[i] = () => grad2;\n        });\n        return gradMap;\n      };\n      return this.runKernelFunc({\n        forwardFunc,\n        backwardsFunc,\n        inputs: inputMap\n      });\n    };\n  }\n  readSync(dataId) {\n    const info = this.state.tensorInfo.get(dataId);\n    return info.backend.readSync(dataId);\n  }\n  read(dataId) {\n    const info = this.state.tensorInfo.get(dataId);\n    return info.backend.read(dataId);\n  }\n  readToGPU(dataId, options) {\n    const info = this.state.tensorInfo.get(dataId);\n    return info.backend.readToGPU(dataId, options);\n  }\n  async time(query) {\n    const start = now();\n    const timingInfo = await this.backend.time(query);\n    timingInfo.wallMs = now() - start;\n    return timingInfo;\n  }\n  track(result) {\n    if (this.state.activeScope != null) {\n      result.scopeId = this.state.activeScope.id;\n      this.state.activeScope.track.push(result);\n    }\n    return result;\n  }\n  get registeredVariables() {\n    return this.state.registeredVariables;\n  }\n  reset() {\n    this.pendingBackendInitId++;\n    this.state.dispose();\n    this.ENV.reset();\n    this.state = new EngineState();\n    for (const backendName in this.registry) {\n      this.disposeRegisteredKernels(backendName);\n      this.registry[backendName].dispose();\n      delete this.registry[backendName];\n    }\n    this.backendName = null;\n    this.backendInstance = null;\n    this.pendingBackendInit = null;\n  }\n};\nEngine.nextTensorId = 0;\nEngine.nextVariableId = 0;\nfunction ones(shape) {\n  const values = makeOnesTypedArray(sizeFromShape(shape), \"float32\");\n  return ENGINE.makeTensor(values, shape, \"float32\");\n}\nfunction getOrMakeEngine() {\n  const ns = getGlobalNamespace();\n  if (ns._tfengine == null) {\n    const environment = new Environment(ns);\n    ns._tfengine = new Engine(environment);\n  }\n  setEnvironmentGlobal(ns._tfengine.ENV);\n  setTensorTracker(() => ns._tfengine);\n  return ns._tfengine;\n}\nvar ENGINE = getOrMakeEngine();\nfunction add(a, b) {\n  const inputs = { a, b };\n  return ENGINE.runKernel(Add, inputs);\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/device_util.js\nvar device_util_exports = {};\n__export(device_util_exports, {\n  isBrowser: () => isBrowser,\n  isMobile: () => isMobile,\n  mockIsMobile: () => mockIsMobile\n});\nfunction _isNavigatorDefined() {\n  return typeof navigator !== \"undefined\" && navigator != null;\n}\nvar isMobileMockValue;\nfunction mockIsMobile(value) {\n  isMobileMockValue = value;\n}\nfunction isMobile(nav) {\n  if (isMobileMockValue !== void 0) {\n    return isMobileMockValue;\n  }\n  if (nav || _isNavigatorDefined()) {\n    if (!nav) {\n      nav = navigator;\n    }\n    if (nav.product === \"ReactNative\") {\n      return true;\n    }\n    const a = nav.userAgent || nav.vendor || (typeof window !== \"undefined\" ? window.opera : \"\");\n    if (!a) {\n      const navAny = nav;\n      return navAny.userAgentData && navAny.userAgentData.mobile;\n    }\n    return /(android|bb\\d+|meego).+mobile|avantgo|bada\\/|blackberry|blazer|compal|elaine|fennec|hiptop|iemobile|ip(hone|od)|iris|kindle|lge |maemo|midp|mmp|mobile.+firefox|netfront|opera m(ob|in)i|palm( os)?|phone|p(ixi|re)\\/|plucker|pocket|psp|series(4|6)0|symbian|treo|up\\.(browser|link)|vodafone|wap|windows ce|xda|xiino/i.test(a) || /1207|6310|6590|3gso|4thp|50[1-6]i|770s|802s|a wa|abac|ac(er|oo|s\\-)|ai(ko|rn)|al(av|ca|co)|amoi|an(ex|ny|yw)|aptu|ar(ch|go)|as(te|us)|attw|au(di|\\-m|r |s )|avan|be(ck|ll|nq)|bi(lb|rd)|bl(ac|az)|br(e|v)w|bumb|bw\\-(n|u)|c55\\/|capi|ccwa|cdm\\-|cell|chtm|cldc|cmd\\-|co(mp|nd)|craw|da(it|ll|ng)|dbte|dc\\-s|devi|dica|dmob|do(c|p)o|ds(12|\\-d)|el(49|ai)|em(l2|ul)|er(ic|k0)|esl8|ez([4-7]0|os|wa|ze)|fetc|fly(\\-|_)|g1 u|g560|gene|gf\\-5|g\\-mo|go(\\.w|od)|gr(ad|un)|haie|hcit|hd\\-(m|p|t)|hei\\-|hi(pt|ta)|hp( i|ip)|hs\\-c|ht(c(\\-| |_|a|g|p|s|t)|tp)|hu(aw|tc)|i\\-(20|go|ma)|i230|iac( |\\-|\\/)|ibro|idea|ig01|ikom|im1k|inno|ipaq|iris|ja(t|v)a|jbro|jemu|jigs|kddi|keji|kgt( |\\/)|klon|kpt |kwc\\-|kyo(c|k)|le(no|xi)|lg( g|\\/(k|l|u)|50|54|\\-[a-w])|libw|lynx|m1\\-w|m3ga|m50\\/|ma(te|ui|xo)|mc(01|21|ca)|m\\-cr|me(rc|ri)|mi(o8|oa|ts)|mmef|mo(01|02|bi|de|do|t(\\-| |o|v)|zz)|mt(50|p1|v )|mwbp|mywa|n10[0-2]|n20[2-3]|n30(0|2)|n50(0|2|5)|n7(0(0|1)|10)|ne((c|m)\\-|on|tf|wf|wg|wt)|nok(6|i)|nzph|o2im|op(ti|wv)|oran|owg1|p800|pan(a|d|t)|pdxg|pg(13|\\-([1-8]|c))|phil|pire|pl(ay|uc)|pn\\-2|po(ck|rt|se)|prox|psio|pt\\-g|qa\\-a|qc(07|12|21|32|60|\\-[2-7]|i\\-)|qtek|r380|r600|raks|rim9|ro(ve|zo)|s55\\/|sa(ge|ma|mm|ms|ny|va)|sc(01|h\\-|oo|p\\-)|sdk\\/|se(c(\\-|0|1)|47|mc|nd|ri)|sgh\\-|shar|sie(\\-|m)|sk\\-0|sl(45|id)|sm(al|ar|b3|it|t5)|so(ft|ny)|sp(01|h\\-|v\\-|v )|sy(01|mb)|t2(18|50)|t6(00|10|18)|ta(gt|lk)|tcl\\-|tdg\\-|tel(i|m)|tim\\-|t\\-mo|to(pl|sh)|ts(70|m\\-|m3|m5)|tx\\-9|up(\\.b|g1|si)|utst|v400|v750|veri|vi(rg|te)|vk(40|5[0-3]|\\-v)|vm40|voda|vulc|vx(52|53|60|61|70|80|81|83|85|98)|w3c(\\-| )|webc|whit|wi(g |nc|nw)|wmlb|wonu|x700|yas\\-|your|zeto|zte\\-/i.test(a.substr(0, 4));\n  }\n  return false;\n}\nfunction isBrowser() {\n  return typeof window !== \"undefined\" && window.document != null || typeof WorkerGlobalScope !== \"undefined\";\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/flags.js\nvar ENV2 = env();\nENV2.registerFlag(\"DEBUG\", () => false, (debugValue) => {\n  if (debugValue) {\n    console.warn(\"Debugging mode is ON. The output of every math call will be downloaded to CPU and checked for NaNs. This significantly impacts performance.\");\n  }\n});\nENV2.registerFlag(\"IS_BROWSER\", () => isBrowser());\nENV2.registerFlag(\"IS_NODE\", () => typeof process !== \"undefined\" && typeof process.versions !== \"undefined\" && typeof process.versions.node !== \"undefined\");\nENV2.registerFlag(\"IS_CHROME\", () => typeof navigator !== \"undefined\" && navigator != null && navigator.userAgent != null && /Chrome/.test(navigator.userAgent) && /Google Inc/.test(navigator.vendor));\nENV2.registerFlag(\"PROD\", () => false);\nENV2.registerFlag(\"TENSORLIKE_CHECK_SHAPE_CONSISTENCY\", () => ENV2.getBool(\"DEBUG\"));\nENV2.registerFlag(\"DEPRECATION_WARNINGS_ENABLED\", () => true);\nENV2.registerFlag(\"IS_TEST\", () => false);\nENV2.registerFlag(\"CHECK_COMPUTATION_FOR_ERRORS\", () => true);\nENV2.registerFlag(\"WRAP_TO_IMAGEBITMAP\", () => false);\nENV2.registerFlag(\"ENGINE_COMPILE_ONLY\", () => false);\nENV2.registerFlag(\"CANVAS2D_WILL_READ_FREQUENTLY_FOR_GPU\", () => false);\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/tensor_util_env.js\nfunction inferShape(val, dtype) {\n  let firstElem = val;\n  if (isTypedArray(val)) {\n    return dtype === \"string\" ? [] : [val.length];\n  }\n  if (!Array.isArray(val)) {\n    return [];\n  }\n  const shape = [];\n  while (Array.isArray(firstElem) || isTypedArray(firstElem) && dtype !== \"string\") {\n    shape.push(firstElem.length);\n    firstElem = firstElem[0];\n  }\n  if (Array.isArray(val) && env().getBool(\"TENSORLIKE_CHECK_SHAPE_CONSISTENCY\")) {\n    deepAssertShapeConsistency(val, shape, []);\n  }\n  return shape;\n}\nfunction deepAssertShapeConsistency(val, shape, indices) {\n  indices = indices || [];\n  if (!Array.isArray(val) && !isTypedArray(val)) {\n    assert(shape.length === 0, () => `Element arr[${indices.join(\"][\")}] is a primitive, but should be an array/TypedArray of ${shape[0]} elements`);\n    return;\n  }\n  assert(shape.length > 0, () => `Element arr[${indices.join(\"][\")}] should be a primitive, but is an array of ${val.length} elements`);\n  assert(val.length === shape[0], () => `Element arr[${indices.join(\"][\")}] should have ${shape[0]} elements, but has ${val.length} elements`);\n  const subShape = shape.slice(1);\n  for (let i = 0; i < val.length; ++i) {\n    deepAssertShapeConsistency(val[i], subShape, indices.concat(i));\n  }\n}\nfunction assertDtype(expectedDtype, actualDType, argName, functionName) {\n  if (expectedDtype === \"string_or_numeric\") {\n    return;\n  }\n  if (expectedDtype == null) {\n    throw new Error(`Expected dtype cannot be null.`);\n  }\n  if (expectedDtype !== \"numeric\" && expectedDtype !== actualDType || expectedDtype === \"numeric\" && actualDType === \"string\") {\n    throw new Error(`Argument '${argName}' passed to '${functionName}' must be ${expectedDtype} tensor, but got ${actualDType} tensor`);\n  }\n}\nfunction convertToTensor(x, argName, functionName, parseAsDtype = \"numeric\") {\n  if (x instanceof Tensor) {\n    assertDtype(parseAsDtype, x.dtype, argName, functionName);\n    return x;\n  }\n  let inferredDtype = inferDtype(x);\n  if (inferredDtype !== \"string\" && [\"bool\", \"int32\", \"float32\"].indexOf(parseAsDtype) >= 0) {\n    inferredDtype = parseAsDtype;\n  }\n  assertDtype(parseAsDtype, inferredDtype, argName, functionName);\n  if (x == null || !isTypedArray(x) && !Array.isArray(x) && typeof x !== \"number\" && typeof x !== \"boolean\" && typeof x !== \"string\") {\n    const type = x == null ? \"null\" : x.constructor.name;\n    throw new Error(`Argument '${argName}' passed to '${functionName}' must be a Tensor or TensorLike, but got '${type}'`);\n  }\n  const inferredShape = inferShape(x, inferredDtype);\n  if (!isTypedArray(x) && !Array.isArray(x)) {\n    x = [x];\n  }\n  const skipTypedArray = true;\n  const values = inferredDtype !== \"string\" ? toTypedArray(x, inferredDtype) : flatten(x, [], skipTypedArray);\n  return ENGINE.makeTensor(values, inferredShape, inferredDtype);\n}\nfunction convertToTensorArray(arg, argName, functionName, parseAsDtype = \"numeric\") {\n  if (!Array.isArray(arg)) {\n    throw new Error(`Argument ${argName} passed to ${functionName} must be a \\`Tensor[]\\` or \\`TensorLike[]\\``);\n  }\n  const tensors = arg;\n  return tensors.map((t, i) => convertToTensor(t, `${argName}[${i}]`, functionName, parseAsDtype));\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/operation.js\nvar OP_SCOPE_SUFFIX = \"__op\";\nfunction op(f) {\n  const keys = Object.keys(f);\n  if (keys.length !== 1) {\n    throw new Error(`Please provide an object with a single key (operation name) mapping to a function. Got an object with ${keys.length} keys.`);\n  }\n  let opName = keys[0];\n  const fn = f[opName];\n  if (opName.endsWith(\"_\")) {\n    opName = opName.substring(0, opName.length - 1);\n  }\n  opName = opName + OP_SCOPE_SUFFIX;\n  const f2 = (...args) => {\n    ENGINE.startScope(opName);\n    try {\n      const result = fn(...args);\n      if (isPromise(result)) {\n        console.error(\"Cannot return a Promise inside of tidy.\");\n      }\n      ENGINE.endScope(result);\n      return result;\n    } catch (ex) {\n      ENGINE.endScope(null);\n      throw ex;\n    }\n  };\n  Object.defineProperty(f2, \"name\", { value: opName, configurable: true });\n  return f2;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/complex.js\nfunction complex_(real5, imag5) {\n  const $real = convertToTensor(real5, \"real\", \"complex\");\n  const $imag = convertToTensor(imag5, \"imag\", \"complex\");\n  assertShapesMatch($real.shape, $imag.shape, `real and imag shapes, ${$real.shape} and ${$imag.shape}, must match in call to tf.complex().`);\n  const inputs = { real: $real, imag: $imag };\n  return ENGINE.runKernel(Complex, inputs);\n}\nvar complex = op({ complex_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/tensor_ops_util.js\nfunction makeTensor(values, shape, inferredShape, dtype) {\n  if (dtype == null) {\n    dtype = inferDtype(values);\n  }\n  if (dtype === \"complex64\") {\n    throw new Error(`Cannot construct a complex64 tensor directly. Please use tf.complex(real, imag).`);\n  }\n  if (!isTypedArray(values) && !Array.isArray(values) && typeof values !== \"number\" && typeof values !== \"boolean\" && typeof values !== \"string\") {\n    throw new Error(\"values passed to tensor(values) must be a number/boolean/string or an array of numbers/booleans/strings, or a TypedArray\");\n  }\n  if (shape != null) {\n    assertNonNegativeIntegerDimensions(shape);\n    const providedSize = sizeFromShape(shape);\n    const inferredSize = sizeFromShape(inferredShape);\n    assert(providedSize === inferredSize, () => `Based on the provided shape, [${shape}], the tensor should have ${providedSize} values but has ${inferredSize}`);\n    for (let i = 0; i < inferredShape.length; ++i) {\n      const inferred = inferredShape[i];\n      const flatDimsDontMatch = i === inferredShape.length - 1 ? inferred !== sizeFromShape(shape.slice(i)) : true;\n      assert(inferredShape[i] === shape[i] || !flatDimsDontMatch, () => `Error creating a new Tensor. Inferred shape (${inferredShape}) does not match the provided shape (${shape}). `);\n    }\n  }\n  if (!isTypedArray(values) && !Array.isArray(values)) {\n    values = [values];\n  }\n  shape = shape || inferredShape;\n  values = dtype !== \"string\" ? toTypedArray(values, dtype) : flatten(values, [], true);\n  return ENGINE.makeTensor(values, shape, dtype);\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/tensor.js\nfunction tensor(values, shape, dtype) {\n  const inferredShape = inferShape(values, dtype);\n  return makeTensor(values, shape, inferredShape, dtype);\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/io/types.js\nvar DTYPE_VALUE_SIZE_MAP = {\n  \"float32\": 4,\n  \"float16\": 2,\n  \"int32\": 4,\n  \"uint16\": 2,\n  \"uint8\": 1,\n  \"bool\": 1,\n  \"complex64\": 8\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/io/io_utils.js\nvar NUM_BYTES_STRING_LENGTH = 4;\nasync function encodeWeights(tensors, group) {\n  const specs = [];\n  const dataPromises = [];\n  const names = Array.isArray(tensors) ? tensors.map((tensor2) => tensor2.name) : Object.keys(tensors);\n  for (let i = 0; i < names.length; ++i) {\n    const name = names[i];\n    const t = Array.isArray(tensors) ? tensors[i].tensor : tensors[name];\n    if (t.dtype !== \"float32\" && t.dtype !== \"int32\" && t.dtype !== \"bool\" && t.dtype !== \"string\" && t.dtype !== \"complex64\") {\n      throw new Error(`Unsupported dtype in weight '${name}': ${t.dtype}`);\n    }\n    const spec = { name, shape: t.shape, dtype: t.dtype };\n    if (t.dtype === \"string\") {\n      const utf8bytes = new Promise(async (resolve) => {\n        const vals = await t.bytes();\n        const totalNumBytes = vals.reduce((p2, c) => p2 + c.length, 0) + NUM_BYTES_STRING_LENGTH * vals.length;\n        const bytes = new Uint8Array(totalNumBytes);\n        let offset = 0;\n        for (let i2 = 0; i2 < vals.length; i2++) {\n          const val = vals[i2];\n          const bytesOfLength = new Uint8Array(new Uint32Array([val.length]).buffer);\n          bytes.set(bytesOfLength, offset);\n          offset += NUM_BYTES_STRING_LENGTH;\n          bytes.set(val, offset);\n          offset += val.length;\n        }\n        resolve(bytes);\n      });\n      dataPromises.push(utf8bytes);\n    } else {\n      dataPromises.push(t.data());\n    }\n    if (group != null) {\n      spec.group = group;\n    }\n    specs.push(spec);\n  }\n  const tensorValues = await Promise.all(dataPromises);\n  return { data: concatenateTypedArrays(tensorValues), specs };\n}\nfunction decodeWeights(buffer2, specs) {\n  const out = {};\n  let float16Decode;\n  let offset = 0;\n  for (const spec of specs) {\n    const name = spec.name;\n    const dtype = spec.dtype;\n    const shape = spec.shape;\n    const size = sizeFromShape(shape);\n    let values;\n    if (\"quantization\" in spec) {\n      const quantization = spec.quantization;\n      if (quantization.dtype === \"uint8\" || quantization.dtype === \"uint16\") {\n        if (!(\"min\" in quantization && \"scale\" in quantization)) {\n          throw new Error(`Weight ${spec.name} with quantization ${quantization.dtype} doesn't have corresponding metadata min and scale.`);\n        }\n      } else if (quantization.dtype === \"float16\") {\n        if (dtype !== \"float32\") {\n          throw new Error(`Weight ${spec.name} is quantized with ${quantization.dtype} which only supports weights of type float32 not ${dtype}.`);\n        }\n      } else {\n        throw new Error(`Weight ${spec.name} has unknown quantization dtype ${quantization.dtype}. Supported quantization dtypes are: 'uint8', 'uint16', and 'float16'.`);\n      }\n      const quantizationSizeFactor = DTYPE_VALUE_SIZE_MAP[quantization.dtype];\n      const byteBuffer = buffer2.slice(offset, offset + size * quantizationSizeFactor);\n      const quantizedArray = quantization.dtype === \"uint8\" ? new Uint8Array(byteBuffer) : new Uint16Array(byteBuffer);\n      if (dtype === \"float32\") {\n        if (quantization.dtype === \"uint8\" || quantization.dtype === \"uint16\") {\n          values = new Float32Array(quantizedArray.length);\n          for (let i = 0; i < quantizedArray.length; i++) {\n            const v = quantizedArray[i];\n            values[i] = v * quantization.scale + quantization.min;\n          }\n        } else if (quantization.dtype === \"float16\") {\n          if (float16Decode === void 0) {\n            float16Decode = getFloat16Decoder();\n          }\n          values = float16Decode(quantizedArray);\n        } else {\n          throw new Error(`Unsupported quantization type ${quantization.dtype} for weight type float32.`);\n        }\n      } else if (dtype === \"int32\") {\n        if (quantization.dtype !== \"uint8\" && quantization.dtype !== \"uint16\") {\n          throw new Error(`Unsupported quantization type ${quantization.dtype} for weight type int32.`);\n        }\n        values = new Int32Array(quantizedArray.length);\n        for (let i = 0; i < quantizedArray.length; i++) {\n          const v = quantizedArray[i];\n          values[i] = Math.round(v * quantization.scale + quantization.min);\n        }\n      } else {\n        throw new Error(`Unsupported dtype in weight '${name}': ${dtype}`);\n      }\n      offset += size * quantizationSizeFactor;\n    } else if (dtype === \"string\") {\n      const size2 = sizeFromShape(spec.shape);\n      values = [];\n      for (let i = 0; i < size2; i++) {\n        const byteLength = new Uint32Array(buffer2.slice(offset, offset + NUM_BYTES_STRING_LENGTH))[0];\n        offset += NUM_BYTES_STRING_LENGTH;\n        const bytes = new Uint8Array(buffer2.slice(offset, offset + byteLength));\n        values.push(bytes);\n        offset += byteLength;\n      }\n    } else {\n      const dtypeFactor = DTYPE_VALUE_SIZE_MAP[dtype];\n      const byteBuffer = buffer2.slice(offset, offset + size * dtypeFactor);\n      if (dtype === \"float32\") {\n        values = new Float32Array(byteBuffer);\n      } else if (dtype === \"int32\") {\n        values = new Int32Array(byteBuffer);\n      } else if (dtype === \"bool\") {\n        values = new Uint8Array(byteBuffer);\n      } else if (dtype === \"complex64\") {\n        values = new Float32Array(byteBuffer);\n        const real5 = new Float32Array(values.length / 2);\n        const image2 = new Float32Array(values.length / 2);\n        for (let i = 0; i < real5.length; i++) {\n          real5[i] = values[i * 2];\n          image2[i] = values[i * 2 + 1];\n        }\n        const realTensor = tensor(real5, shape, \"float32\");\n        const imageTensor = tensor(image2, shape, \"float32\");\n        out[name] = complex(realTensor, imageTensor);\n        realTensor.dispose();\n        imageTensor.dispose();\n      } else {\n        throw new Error(`Unsupported dtype in weight '${name}': ${dtype}`);\n      }\n      offset += size * dtypeFactor;\n    }\n    if (dtype !== \"complex64\") {\n      out[name] = tensor(values, shape, dtype);\n    }\n  }\n  return out;\n}\nfunction concatenateTypedArrays(xs) {\n  if (xs === null) {\n    throw new Error(`Invalid input value: ${JSON.stringify(xs)}`);\n  }\n  let totalByteLength = 0;\n  const normalizedXs = [];\n  xs.forEach((x) => {\n    totalByteLength += x.byteLength;\n    normalizedXs.push(x.byteLength === x.buffer.byteLength ? x : new x.constructor(x));\n    if (!(x instanceof Float32Array || x instanceof Int32Array || x instanceof Uint8Array)) {\n      throw new Error(`Unsupported TypedArray subtype: ${x.constructor.name}`);\n    }\n  });\n  const y = new Uint8Array(totalByteLength);\n  let offset = 0;\n  normalizedXs.forEach((x) => {\n    y.set(new Uint8Array(x.buffer), offset);\n    offset += x.byteLength;\n  });\n  return y.buffer;\n}\nvar useNodeBuffer = typeof Buffer !== \"undefined\" && (typeof Blob === \"undefined\" || typeof atob === \"undefined\" || typeof btoa === \"undefined\");\nfunction stringByteLength(str) {\n  if (useNodeBuffer) {\n    return Buffer.byteLength(str);\n  }\n  return new Blob([str]).size;\n}\nfunction arrayBufferToBase64String(buffer2) {\n  if (useNodeBuffer) {\n    return Buffer.from(buffer2).toString(\"base64\");\n  }\n  const buf = new Uint8Array(buffer2);\n  let s = \"\";\n  for (let i = 0, l = buf.length; i < l; i++) {\n    s += String.fromCharCode(buf[i]);\n  }\n  return btoa(s);\n}\nfunction base64StringToArrayBuffer(str) {\n  if (useNodeBuffer) {\n    const buf = Buffer.from(str, \"base64\");\n    return buf.buffer.slice(buf.byteOffset, buf.byteOffset + buf.byteLength);\n  }\n  const s = atob(str);\n  const buffer2 = new Uint8Array(s.length);\n  for (let i = 0; i < s.length; ++i) {\n    buffer2.set([s.charCodeAt(i)], i);\n  }\n  return buffer2.buffer;\n}\nfunction concatenateArrayBuffers(buffers) {\n  if (buffers.length === 1) {\n    return buffers[0];\n  }\n  let totalByteLength = 0;\n  buffers.forEach((buffer2) => {\n    totalByteLength += buffer2.byteLength;\n  });\n  const temp = new Uint8Array(totalByteLength);\n  let offset = 0;\n  buffers.forEach((buffer2) => {\n    temp.set(new Uint8Array(buffer2), offset);\n    offset += buffer2.byteLength;\n  });\n  return temp.buffer;\n}\nfunction basename(path) {\n  const SEPARATOR = \"/\";\n  path = path.trim();\n  while (path.endsWith(SEPARATOR)) {\n    path = path.slice(0, path.length - 1);\n  }\n  const items = path.split(SEPARATOR);\n  return items[items.length - 1];\n}\nfunction getModelJSONForModelArtifacts(artifacts, manifest) {\n  const result = {\n    modelTopology: artifacts.modelTopology,\n    format: artifacts.format,\n    generatedBy: artifacts.generatedBy,\n    convertedBy: artifacts.convertedBy,\n    weightsManifest: manifest\n  };\n  if (artifacts.signature != null) {\n    result.signature = artifacts.signature;\n  }\n  if (artifacts.userDefinedMetadata != null) {\n    result.userDefinedMetadata = artifacts.userDefinedMetadata;\n  }\n  if (artifacts.modelInitializer != null) {\n    result.modelInitializer = artifacts.modelInitializer;\n  }\n  if (artifacts.trainingConfig != null) {\n    result.trainingConfig = artifacts.trainingConfig;\n  }\n  return result;\n}\nasync function getModelArtifactsForJSON(modelJSON, loadWeights2) {\n  const modelArtifacts = {\n    modelTopology: modelJSON.modelTopology,\n    format: modelJSON.format,\n    generatedBy: modelJSON.generatedBy,\n    convertedBy: modelJSON.convertedBy\n  };\n  if (modelJSON.trainingConfig != null) {\n    modelArtifacts.trainingConfig = modelJSON.trainingConfig;\n  }\n  if (modelJSON.weightsManifest != null) {\n    const [weightSpecs, weightData] = await loadWeights2(modelJSON.weightsManifest);\n    modelArtifacts.weightSpecs = weightSpecs;\n    modelArtifacts.weightData = weightData;\n  }\n  if (modelJSON.signature != null) {\n    modelArtifacts.signature = modelJSON.signature;\n  }\n  if (modelJSON.userDefinedMetadata != null) {\n    modelArtifacts.userDefinedMetadata = modelJSON.userDefinedMetadata;\n  }\n  if (modelJSON.modelInitializer != null) {\n    modelArtifacts.modelInitializer = modelJSON.modelInitializer;\n  }\n  return modelArtifacts;\n}\nfunction getModelArtifactsInfoForJSON(modelArtifacts) {\n  if (modelArtifacts.modelTopology instanceof ArrayBuffer) {\n    throw new Error(\"Expected JSON model topology, received ArrayBuffer.\");\n  }\n  return {\n    dateSaved: new Date(),\n    modelTopologyType: \"JSON\",\n    modelTopologyBytes: modelArtifacts.modelTopology == null ? 0 : stringByteLength(JSON.stringify(modelArtifacts.modelTopology)),\n    weightSpecsBytes: modelArtifacts.weightSpecs == null ? 0 : stringByteLength(JSON.stringify(modelArtifacts.weightSpecs)),\n    weightDataBytes: modelArtifacts.weightData == null ? 0 : modelArtifacts.weightData.byteLength\n  };\n}\nfunction computeFloat16MantisaTable() {\n  const convertMantissa = (i) => {\n    let m = i << 13;\n    let e = 0;\n    while ((m & 8388608) === 0) {\n      e -= 8388608;\n      m <<= 1;\n    }\n    m &= ~8388608;\n    e += 947912704;\n    return m | e;\n  };\n  const mantisaTable = new Uint32Array(2048);\n  mantisaTable[0] = 0;\n  for (let i = 1; i < 1024; i++) {\n    mantisaTable[i] = convertMantissa(i);\n  }\n  for (let i = 1024; i < 2048; i++) {\n    mantisaTable[i] = 939524096 + (i - 1024 << 13);\n  }\n  return mantisaTable;\n}\nfunction computeFloat16ExponentTable() {\n  const exponentTable = new Uint32Array(64);\n  exponentTable[0] = 0;\n  exponentTable[31] = 1199570944;\n  exponentTable[32] = 2147483648;\n  exponentTable[63] = 3347054592;\n  for (let i = 1; i < 31; i++) {\n    exponentTable[i] = i << 23;\n  }\n  for (let i = 33; i < 63; i++) {\n    exponentTable[i] = 2147483648 + (i - 32 << 23);\n  }\n  return exponentTable;\n}\nfunction computeFloat16OffsetTable() {\n  const offsetTable = new Uint32Array(64);\n  for (let i = 0; i < 64; i++) {\n    offsetTable[i] = 1024;\n  }\n  offsetTable[0] = offsetTable[32] = 0;\n  return offsetTable;\n}\nfunction getFloat16Decoder() {\n  const mantisaTable = computeFloat16MantisaTable();\n  const exponentTable = computeFloat16ExponentTable();\n  const offsetTable = computeFloat16OffsetTable();\n  return (quantizedArray) => {\n    const buffer2 = new ArrayBuffer(4 * quantizedArray.length);\n    const bufferUint32View = new Uint32Array(buffer2);\n    for (let index = 0; index < quantizedArray.length; index++) {\n      const float16Bits = quantizedArray[index];\n      const float32Bits = mantisaTable[offsetTable[float16Bits >> 10] + (float16Bits & 1023)] + exponentTable[float16Bits >> 10];\n      bufferUint32View[index] = float32Bits;\n    }\n    return new Float32Array(buffer2);\n  };\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/io/router_registry.js\nvar IORouterRegistry = class {\n  constructor() {\n    this.saveRouters = [];\n    this.loadRouters = [];\n  }\n  static getInstance() {\n    if (IORouterRegistry.instance == null) {\n      IORouterRegistry.instance = new IORouterRegistry();\n    }\n    return IORouterRegistry.instance;\n  }\n  static registerSaveRouter(saveRouter) {\n    IORouterRegistry.getInstance().saveRouters.push(saveRouter);\n  }\n  static registerLoadRouter(loadRouter) {\n    IORouterRegistry.getInstance().loadRouters.push(loadRouter);\n  }\n  static getSaveHandlers(url) {\n    return IORouterRegistry.getHandlers(url, \"save\");\n  }\n  static getLoadHandlers(url, loadOptions) {\n    return IORouterRegistry.getHandlers(url, \"load\", loadOptions);\n  }\n  static getHandlers(url, handlerType, loadOptions) {\n    const validHandlers = [];\n    const routers = handlerType === \"load\" ? IORouterRegistry.getInstance().loadRouters : IORouterRegistry.getInstance().saveRouters;\n    routers.forEach((router) => {\n      const handler = router(url, loadOptions);\n      if (handler !== null) {\n        validHandlers.push(handler);\n      }\n    });\n    return validHandlers;\n  }\n};\nvar registerSaveRouter = (loudRouter) => IORouterRegistry.registerSaveRouter(loudRouter);\nvar registerLoadRouter = (loudRouter) => IORouterRegistry.registerLoadRouter(loudRouter);\nvar getSaveHandlers = (url) => IORouterRegistry.getSaveHandlers(url);\nvar getLoadHandlers = (url, loadOptions) => IORouterRegistry.getLoadHandlers(url, loadOptions);\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/io/indexed_db.js\nvar DATABASE_NAME = \"tensorflowjs\";\nvar DATABASE_VERSION = 1;\nvar MODEL_STORE_NAME = \"models_store\";\nvar INFO_STORE_NAME = \"model_info_store\";\nfunction getIndexedDBFactory() {\n  if (!env().getBool(\"IS_BROWSER\")) {\n    throw new Error(\"Failed to obtain IndexedDB factory because the current environmentis not a web browser.\");\n  }\n  const theWindow = typeof window === \"undefined\" ? self : window;\n  const factory = theWindow.indexedDB || theWindow.mozIndexedDB || theWindow.webkitIndexedDB || theWindow.msIndexedDB || theWindow.shimIndexedDB;\n  if (factory == null) {\n    throw new Error(\"The current browser does not appear to support IndexedDB.\");\n  }\n  return factory;\n}\nfunction setUpDatabase(openRequest) {\n  const db = openRequest.result;\n  db.createObjectStore(MODEL_STORE_NAME, { keyPath: \"modelPath\" });\n  db.createObjectStore(INFO_STORE_NAME, { keyPath: \"modelPath\" });\n}\nvar BrowserIndexedDB = class {\n  constructor(modelPath) {\n    this.indexedDB = getIndexedDBFactory();\n    if (modelPath == null || !modelPath) {\n      throw new Error(\"For IndexedDB, modelPath must not be null, undefined or empty.\");\n    }\n    this.modelPath = modelPath;\n  }\n  async save(modelArtifacts) {\n    if (modelArtifacts.modelTopology instanceof ArrayBuffer) {\n      throw new Error(\"BrowserLocalStorage.save() does not support saving model topology in binary formats yet.\");\n    }\n    return this.databaseAction(this.modelPath, modelArtifacts);\n  }\n  async load() {\n    return this.databaseAction(this.modelPath);\n  }\n  databaseAction(modelPath, modelArtifacts) {\n    return new Promise((resolve, reject) => {\n      const openRequest = this.indexedDB.open(DATABASE_NAME, DATABASE_VERSION);\n      openRequest.onupgradeneeded = () => setUpDatabase(openRequest);\n      openRequest.onsuccess = () => {\n        const db = openRequest.result;\n        if (modelArtifacts == null) {\n          const modelTx = db.transaction(MODEL_STORE_NAME, \"readonly\");\n          const modelStore = modelTx.objectStore(MODEL_STORE_NAME);\n          const getRequest = modelStore.get(this.modelPath);\n          getRequest.onsuccess = () => {\n            if (getRequest.result == null) {\n              db.close();\n              return reject(new Error(`Cannot find model with path '${this.modelPath}' in IndexedDB.`));\n            } else {\n              resolve(getRequest.result.modelArtifacts);\n            }\n          };\n          getRequest.onerror = (error) => {\n            db.close();\n            return reject(getRequest.error);\n          };\n          modelTx.oncomplete = () => db.close();\n        } else {\n          const modelArtifactsInfo = getModelArtifactsInfoForJSON(modelArtifacts);\n          const infoTx = db.transaction(INFO_STORE_NAME, \"readwrite\");\n          let infoStore = infoTx.objectStore(INFO_STORE_NAME);\n          const putInfoRequest = infoStore.put({ modelPath: this.modelPath, modelArtifactsInfo });\n          let modelTx;\n          putInfoRequest.onsuccess = () => {\n            modelTx = db.transaction(MODEL_STORE_NAME, \"readwrite\");\n            const modelStore = modelTx.objectStore(MODEL_STORE_NAME);\n            const putModelRequest = modelStore.put({\n              modelPath: this.modelPath,\n              modelArtifacts,\n              modelArtifactsInfo\n            });\n            putModelRequest.onsuccess = () => resolve({ modelArtifactsInfo });\n            putModelRequest.onerror = (error) => {\n              infoStore = infoTx.objectStore(INFO_STORE_NAME);\n              const deleteInfoRequest = infoStore.delete(this.modelPath);\n              deleteInfoRequest.onsuccess = () => {\n                db.close();\n                return reject(putModelRequest.error);\n              };\n              deleteInfoRequest.onerror = (error2) => {\n                db.close();\n                return reject(putModelRequest.error);\n              };\n            };\n          };\n          putInfoRequest.onerror = (error) => {\n            db.close();\n            return reject(putInfoRequest.error);\n          };\n          infoTx.oncomplete = () => {\n            if (modelTx == null) {\n              db.close();\n            } else {\n              modelTx.oncomplete = () => db.close();\n            }\n          };\n        }\n      };\n      openRequest.onerror = (error) => reject(openRequest.error);\n    });\n  }\n};\nBrowserIndexedDB.URL_SCHEME = \"indexeddb://\";\nvar indexedDBRouter = (url) => {\n  if (!env().getBool(\"IS_BROWSER\")) {\n    return null;\n  } else {\n    if (!Array.isArray(url) && url.startsWith(BrowserIndexedDB.URL_SCHEME)) {\n      return browserIndexedDB(url.slice(BrowserIndexedDB.URL_SCHEME.length));\n    } else {\n      return null;\n    }\n  }\n};\nIORouterRegistry.registerSaveRouter(indexedDBRouter);\nIORouterRegistry.registerLoadRouter(indexedDBRouter);\nfunction browserIndexedDB(modelPath) {\n  return new BrowserIndexedDB(modelPath);\n}\nfunction maybeStripScheme(key) {\n  return key.startsWith(BrowserIndexedDB.URL_SCHEME) ? key.slice(BrowserIndexedDB.URL_SCHEME.length) : key;\n}\nvar BrowserIndexedDBManager = class {\n  constructor() {\n    this.indexedDB = getIndexedDBFactory();\n  }\n  async listModels() {\n    return new Promise((resolve, reject) => {\n      const openRequest = this.indexedDB.open(DATABASE_NAME, DATABASE_VERSION);\n      openRequest.onupgradeneeded = () => setUpDatabase(openRequest);\n      openRequest.onsuccess = () => {\n        const db = openRequest.result;\n        const tx = db.transaction(INFO_STORE_NAME, \"readonly\");\n        const store = tx.objectStore(INFO_STORE_NAME);\n        const getAllInfoRequest = store.getAll();\n        getAllInfoRequest.onsuccess = () => {\n          const out = {};\n          for (const item of getAllInfoRequest.result) {\n            out[item.modelPath] = item.modelArtifactsInfo;\n          }\n          resolve(out);\n        };\n        getAllInfoRequest.onerror = (error) => {\n          db.close();\n          return reject(getAllInfoRequest.error);\n        };\n        tx.oncomplete = () => db.close();\n      };\n      openRequest.onerror = (error) => reject(openRequest.error);\n    });\n  }\n  async removeModel(path) {\n    path = maybeStripScheme(path);\n    return new Promise((resolve, reject) => {\n      const openRequest = this.indexedDB.open(DATABASE_NAME, DATABASE_VERSION);\n      openRequest.onupgradeneeded = () => setUpDatabase(openRequest);\n      openRequest.onsuccess = () => {\n        const db = openRequest.result;\n        const infoTx = db.transaction(INFO_STORE_NAME, \"readwrite\");\n        const infoStore = infoTx.objectStore(INFO_STORE_NAME);\n        const getInfoRequest = infoStore.get(path);\n        let modelTx;\n        getInfoRequest.onsuccess = () => {\n          if (getInfoRequest.result == null) {\n            db.close();\n            return reject(new Error(`Cannot find model with path '${path}' in IndexedDB.`));\n          } else {\n            const deleteInfoRequest = infoStore.delete(path);\n            const deleteModelData = () => {\n              modelTx = db.transaction(MODEL_STORE_NAME, \"readwrite\");\n              const modelStore = modelTx.objectStore(MODEL_STORE_NAME);\n              const deleteModelRequest = modelStore.delete(path);\n              deleteModelRequest.onsuccess = () => resolve(getInfoRequest.result.modelArtifactsInfo);\n              deleteModelRequest.onerror = (error) => reject(getInfoRequest.error);\n            };\n            deleteInfoRequest.onsuccess = deleteModelData;\n            deleteInfoRequest.onerror = (error) => {\n              deleteModelData();\n              db.close();\n              return reject(getInfoRequest.error);\n            };\n          }\n        };\n        getInfoRequest.onerror = (error) => {\n          db.close();\n          return reject(getInfoRequest.error);\n        };\n        infoTx.oncomplete = () => {\n          if (modelTx == null) {\n            db.close();\n          } else {\n            modelTx.oncomplete = () => db.close();\n          }\n        };\n      };\n      openRequest.onerror = (error) => reject(openRequest.error);\n    });\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/io/local_storage.js\nvar PATH_SEPARATOR = \"/\";\nvar PATH_PREFIX = \"tensorflowjs_models\";\nvar INFO_SUFFIX = \"info\";\nvar MODEL_TOPOLOGY_SUFFIX = \"model_topology\";\nvar WEIGHT_SPECS_SUFFIX = \"weight_specs\";\nvar WEIGHT_DATA_SUFFIX = \"weight_data\";\nvar MODEL_METADATA_SUFFIX = \"model_metadata\";\nfunction getModelKeys(path) {\n  return {\n    info: [PATH_PREFIX, path, INFO_SUFFIX].join(PATH_SEPARATOR),\n    topology: [PATH_PREFIX, path, MODEL_TOPOLOGY_SUFFIX].join(PATH_SEPARATOR),\n    weightSpecs: [PATH_PREFIX, path, WEIGHT_SPECS_SUFFIX].join(PATH_SEPARATOR),\n    weightData: [PATH_PREFIX, path, WEIGHT_DATA_SUFFIX].join(PATH_SEPARATOR),\n    modelMetadata: [PATH_PREFIX, path, MODEL_METADATA_SUFFIX].join(PATH_SEPARATOR)\n  };\n}\nfunction removeItems(keys) {\n  for (const key of Object.values(keys)) {\n    window.localStorage.removeItem(key);\n  }\n}\nfunction getModelPathFromKey(key) {\n  const items = key.split(PATH_SEPARATOR);\n  if (items.length < 3) {\n    throw new Error(`Invalid key format: ${key}`);\n  }\n  return items.slice(1, items.length - 1).join(PATH_SEPARATOR);\n}\nfunction maybeStripScheme2(key) {\n  return key.startsWith(BrowserLocalStorage.URL_SCHEME) ? key.slice(BrowserLocalStorage.URL_SCHEME.length) : key;\n}\nvar BrowserLocalStorage = class {\n  constructor(modelPath) {\n    if (!env().getBool(\"IS_BROWSER\") || typeof window === \"undefined\" || typeof window.localStorage === \"undefined\") {\n      throw new Error(\"The current environment does not support local storage.\");\n    }\n    this.LS = window.localStorage;\n    if (modelPath == null || !modelPath) {\n      throw new Error(\"For local storage, modelPath must not be null, undefined or empty.\");\n    }\n    this.modelPath = modelPath;\n    this.keys = getModelKeys(this.modelPath);\n  }\n  async save(modelArtifacts) {\n    if (modelArtifacts.modelTopology instanceof ArrayBuffer) {\n      throw new Error(\"BrowserLocalStorage.save() does not support saving model topology in binary formats yet.\");\n    } else {\n      const topology = JSON.stringify(modelArtifacts.modelTopology);\n      const weightSpecs = JSON.stringify(modelArtifacts.weightSpecs);\n      const modelArtifactsInfo = getModelArtifactsInfoForJSON(modelArtifacts);\n      try {\n        this.LS.setItem(this.keys.info, JSON.stringify(modelArtifactsInfo));\n        this.LS.setItem(this.keys.topology, topology);\n        this.LS.setItem(this.keys.weightSpecs, weightSpecs);\n        this.LS.setItem(this.keys.weightData, arrayBufferToBase64String(modelArtifacts.weightData));\n        const metadata = {\n          format: modelArtifacts.format,\n          generatedBy: modelArtifacts.generatedBy,\n          convertedBy: modelArtifacts.convertedBy,\n          signature: modelArtifacts.signature != null ? modelArtifacts.signature : void 0,\n          userDefinedMetadata: modelArtifacts.userDefinedMetadata != null ? modelArtifacts.userDefinedMetadata : void 0,\n          modelInitializer: modelArtifacts.modelInitializer != null ? modelArtifacts.modelInitializer : void 0,\n          trainingConfig: modelArtifacts.trainingConfig != null ? modelArtifacts.trainingConfig : void 0\n        };\n        this.LS.setItem(this.keys.modelMetadata, JSON.stringify(metadata));\n        return { modelArtifactsInfo };\n      } catch (err) {\n        removeItems(this.keys);\n        throw new Error(`Failed to save model '${this.modelPath}' to local storage: size quota being exceeded is a possible cause of this failure: modelTopologyBytes=${modelArtifactsInfo.modelTopologyBytes}, weightSpecsBytes=${modelArtifactsInfo.weightSpecsBytes}, weightDataBytes=${modelArtifactsInfo.weightDataBytes}.`);\n      }\n    }\n  }\n  async load() {\n    const info = JSON.parse(this.LS.getItem(this.keys.info));\n    if (info == null) {\n      throw new Error(`In local storage, there is no model with name '${this.modelPath}'`);\n    }\n    if (info.modelTopologyType !== \"JSON\") {\n      throw new Error(\"BrowserLocalStorage does not support loading non-JSON model topology yet.\");\n    }\n    const out = {};\n    const topology = JSON.parse(this.LS.getItem(this.keys.topology));\n    if (topology == null) {\n      throw new Error(`In local storage, the topology of model '${this.modelPath}' is missing.`);\n    }\n    out.modelTopology = topology;\n    const weightSpecs = JSON.parse(this.LS.getItem(this.keys.weightSpecs));\n    if (weightSpecs == null) {\n      throw new Error(`In local storage, the weight specs of model '${this.modelPath}' are missing.`);\n    }\n    out.weightSpecs = weightSpecs;\n    const metadataString = this.LS.getItem(this.keys.modelMetadata);\n    if (metadataString != null) {\n      const metadata = JSON.parse(metadataString);\n      out.format = metadata.format;\n      out.generatedBy = metadata.generatedBy;\n      out.convertedBy = metadata.convertedBy;\n      if (metadata.signature != null) {\n        out.signature = metadata.signature;\n      }\n      if (metadata.userDefinedMetadata != null) {\n        out.userDefinedMetadata = metadata.userDefinedMetadata;\n      }\n      if (metadata.modelInitializer != null) {\n        out.modelInitializer = metadata.modelInitializer;\n      }\n      if (metadata.trainingConfig != null) {\n        out.trainingConfig = metadata.trainingConfig;\n      }\n    }\n    const weightDataBase64 = this.LS.getItem(this.keys.weightData);\n    if (weightDataBase64 == null) {\n      throw new Error(`In local storage, the binary weight values of model '${this.modelPath}' are missing.`);\n    }\n    out.weightData = base64StringToArrayBuffer(weightDataBase64);\n    return out;\n  }\n};\nBrowserLocalStorage.URL_SCHEME = \"localstorage://\";\nvar localStorageRouter = (url) => {\n  if (!env().getBool(\"IS_BROWSER\")) {\n    return null;\n  } else {\n    if (!Array.isArray(url) && url.startsWith(BrowserLocalStorage.URL_SCHEME)) {\n      return browserLocalStorage(url.slice(BrowserLocalStorage.URL_SCHEME.length));\n    } else {\n      return null;\n    }\n  }\n};\nIORouterRegistry.registerSaveRouter(localStorageRouter);\nIORouterRegistry.registerLoadRouter(localStorageRouter);\nfunction browserLocalStorage(modelPath) {\n  return new BrowserLocalStorage(modelPath);\n}\nvar BrowserLocalStorageManager = class {\n  constructor() {\n    assert(env().getBool(\"IS_BROWSER\"), () => \"Current environment is not a web browser\");\n    assert(typeof window === \"undefined\" || typeof window.localStorage !== \"undefined\", () => \"Current browser does not appear to support localStorage\");\n    this.LS = window.localStorage;\n  }\n  async listModels() {\n    const out = {};\n    const prefix = PATH_PREFIX + PATH_SEPARATOR;\n    const suffix = PATH_SEPARATOR + INFO_SUFFIX;\n    for (let i = 0; i < this.LS.length; ++i) {\n      const key = this.LS.key(i);\n      if (key.startsWith(prefix) && key.endsWith(suffix)) {\n        const modelPath = getModelPathFromKey(key);\n        out[modelPath] = JSON.parse(this.LS.getItem(key));\n      }\n    }\n    return out;\n  }\n  async removeModel(path) {\n    path = maybeStripScheme2(path);\n    const keys = getModelKeys(path);\n    if (this.LS.getItem(keys.info) == null) {\n      throw new Error(`Cannot find model at path '${path}'`);\n    }\n    const info = JSON.parse(this.LS.getItem(keys.info));\n    removeItems(keys);\n    return info;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/io/model_management.js\nvar URL_SCHEME_SUFFIX = \"://\";\nvar ModelStoreManagerRegistry = class {\n  constructor() {\n    this.managers = {};\n  }\n  static getInstance() {\n    if (ModelStoreManagerRegistry.instance == null) {\n      ModelStoreManagerRegistry.instance = new ModelStoreManagerRegistry();\n    }\n    return ModelStoreManagerRegistry.instance;\n  }\n  static registerManager(scheme, manager) {\n    assert(scheme != null, () => \"scheme must not be undefined or null.\");\n    if (scheme.endsWith(URL_SCHEME_SUFFIX)) {\n      scheme = scheme.slice(0, scheme.indexOf(URL_SCHEME_SUFFIX));\n    }\n    assert(scheme.length > 0, () => \"scheme must not be an empty string.\");\n    const registry = ModelStoreManagerRegistry.getInstance();\n    assert(registry.managers[scheme] == null, () => `A model store manager is already registered for scheme '${scheme}'.`);\n    registry.managers[scheme] = manager;\n  }\n  static getManager(scheme) {\n    const manager = ModelStoreManagerRegistry.getInstance().managers[scheme];\n    if (manager == null) {\n      throw new Error(`Cannot find model manager for scheme '${scheme}'`);\n    }\n    return manager;\n  }\n  static getSchemes() {\n    return Object.keys(ModelStoreManagerRegistry.getInstance().managers);\n  }\n};\nfunction parseURL(url) {\n  if (url.indexOf(URL_SCHEME_SUFFIX) === -1) {\n    throw new Error(`The url string provided does not contain a scheme. Supported schemes are: ${ModelStoreManagerRegistry.getSchemes().join(\",\")}`);\n  }\n  return {\n    scheme: url.split(URL_SCHEME_SUFFIX)[0],\n    path: url.split(URL_SCHEME_SUFFIX)[1]\n  };\n}\nasync function cloneModelInternal(sourceURL, destURL, deleteSource = false) {\n  assert(sourceURL !== destURL, () => `Old path and new path are the same: '${sourceURL}'`);\n  const loadHandlers = IORouterRegistry.getLoadHandlers(sourceURL);\n  assert(loadHandlers.length > 0, () => `Copying failed because no load handler is found for source URL ${sourceURL}.`);\n  assert(loadHandlers.length < 2, () => `Copying failed because more than one (${loadHandlers.length}) load handlers for source URL ${sourceURL}.`);\n  const loadHandler = loadHandlers[0];\n  const saveHandlers = IORouterRegistry.getSaveHandlers(destURL);\n  assert(saveHandlers.length > 0, () => `Copying failed because no save handler is found for destination URL ${destURL}.`);\n  assert(saveHandlers.length < 2, () => `Copying failed because more than one (${loadHandlers.length}) save handlers for destination URL ${destURL}.`);\n  const saveHandler = saveHandlers[0];\n  const sourceScheme = parseURL(sourceURL).scheme;\n  const sourcePath = parseURL(sourceURL).path;\n  const sameMedium = sourceScheme === parseURL(sourceURL).scheme;\n  const modelArtifacts = await loadHandler.load();\n  if (deleteSource && sameMedium) {\n    await ModelStoreManagerRegistry.getManager(sourceScheme).removeModel(sourcePath);\n  }\n  const saveResult = await saveHandler.save(modelArtifacts);\n  if (deleteSource && !sameMedium) {\n    await ModelStoreManagerRegistry.getManager(sourceScheme).removeModel(sourcePath);\n  }\n  return saveResult.modelArtifactsInfo;\n}\nasync function listModels() {\n  const schemes = ModelStoreManagerRegistry.getSchemes();\n  const out = {};\n  for (const scheme of schemes) {\n    const schemeOut = await ModelStoreManagerRegistry.getManager(scheme).listModels();\n    for (const path in schemeOut) {\n      const url = scheme + URL_SCHEME_SUFFIX + path;\n      out[url] = schemeOut[path];\n    }\n  }\n  return out;\n}\nasync function removeModel(url) {\n  const schemeAndPath = parseURL(url);\n  const manager = ModelStoreManagerRegistry.getManager(schemeAndPath.scheme);\n  return manager.removeModel(schemeAndPath.path);\n}\nasync function copyModel(sourceURL, destURL) {\n  const deleteSource = false;\n  return cloneModelInternal(sourceURL, destURL, deleteSource);\n}\nasync function moveModel(sourceURL, destURL) {\n  const deleteSource = true;\n  return cloneModelInternal(sourceURL, destURL, deleteSource);\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/platforms/platform_browser.js\nvar PlatformBrowser = class {\n  fetch(path, init2) {\n    return fetch(path, init2);\n  }\n  now() {\n    return performance.now();\n  }\n  encode(text, encoding) {\n    if (encoding !== \"utf-8\" && encoding !== \"utf8\") {\n      throw new Error(`Browser's encoder only supports utf-8, but got ${encoding}`);\n    }\n    if (this.textEncoder == null) {\n      this.textEncoder = new TextEncoder();\n    }\n    return this.textEncoder.encode(text);\n  }\n  decode(bytes, encoding) {\n    return new TextDecoder(encoding).decode(bytes);\n  }\n};\nif (env().get(\"IS_BROWSER\")) {\n  env().setPlatform(\"browser\", new PlatformBrowser());\n  try {\n    ModelStoreManagerRegistry.registerManager(BrowserLocalStorage.URL_SCHEME, new BrowserLocalStorageManager());\n  } catch (err) {\n  }\n  try {\n    ModelStoreManagerRegistry.registerManager(BrowserIndexedDB.URL_SCHEME, new BrowserIndexedDBManager());\n  } catch (err) {\n  }\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/platforms/platform_node.js\nvar getNodeFetch = {\n  importFetch: () => require_browser()\n};\nvar systemFetch;\nvar PlatformNode = class {\n  constructor() {\n    this.util = require_util();\n    this.textEncoder = new this.util.TextEncoder();\n  }\n  fetch(path, requestInits) {\n    if (env().global.fetch != null) {\n      return env().global.fetch(path, requestInits);\n    }\n    if (systemFetch == null) {\n      systemFetch = getNodeFetch.importFetch();\n    }\n    return systemFetch(path, requestInits);\n  }\n  now() {\n    const time2 = process.hrtime();\n    return time2[0] * 1e3 + time2[1] / 1e6;\n  }\n  encode(text, encoding) {\n    if (encoding !== \"utf-8\" && encoding !== \"utf8\") {\n      throw new Error(`Node built-in encoder only supports utf-8, but got ${encoding}`);\n    }\n    return this.textEncoder.encode(text);\n  }\n  decode(bytes, encoding) {\n    if (bytes.length === 0) {\n      return \"\";\n    }\n    return new this.util.TextDecoder(encoding).decode(bytes);\n  }\n};\nif (env().get(\"IS_NODE\") && !env().get(\"IS_BROWSER\")) {\n  env().setPlatform(\"node\", new PlatformNode());\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/buffer.js\nfunction buffer(shape, dtype = \"float32\", values) {\n  dtype = dtype || \"float32\";\n  assertNonNegativeIntegerDimensions(shape);\n  return new TensorBuffer(shape, dtype, values);\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/cast.js\nfunction cast_(x, dtype) {\n  const $x = convertToTensor(x, \"x\", \"cast\");\n  if (!isValidDtype(dtype)) {\n    throw new Error(`Failed to cast to unknown dtype ${dtype}`);\n  }\n  if (dtype === \"string\" && $x.dtype !== \"string\" || dtype !== \"string\" && $x.dtype === \"string\") {\n    throw new Error(\"Only strings can be casted to strings\");\n  }\n  const inputs = { x: $x };\n  const attrs = { dtype };\n  return ENGINE.runKernel(Cast, inputs, attrs);\n}\nvar cast = op({ cast_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/clone.js\nfunction clone_(x) {\n  const $x = convertToTensor(x, \"x\", \"clone\", \"string_or_numeric\");\n  const inputs = { x: $x };\n  return ENGINE.runKernel(Identity, inputs);\n}\nvar clone = op({ clone_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/print.js\nfunction print(x, verbose = false) {\n  console.log(x.toString(verbose));\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/base_side_effects.js\ngetOrMakeEngine();\nvar opHandler2 = {\n  buffer,\n  cast,\n  clone,\n  print\n};\nsetOpHandler(opHandler2);\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/io/io.js\nvar io_exports = {};\n__export(io_exports, {\n  browserFiles: () => browserFiles,\n  browserHTTPRequest: () => browserHTTPRequest,\n  concatenateArrayBuffers: () => concatenateArrayBuffers,\n  copyModel: () => copyModel,\n  decodeWeights: () => decodeWeights,\n  encodeWeights: () => encodeWeights,\n  fromMemory: () => fromMemory,\n  fromMemorySync: () => fromMemorySync,\n  getLoadHandlers: () => getLoadHandlers,\n  getModelArtifactsForJSON: () => getModelArtifactsForJSON,\n  getModelArtifactsInfoForJSON: () => getModelArtifactsInfoForJSON,\n  getSaveHandlers: () => getSaveHandlers,\n  http: () => http,\n  isHTTPScheme: () => isHTTPScheme,\n  listModels: () => listModels,\n  loadWeights: () => loadWeights,\n  moveModel: () => moveModel,\n  registerLoadRouter: () => registerLoadRouter,\n  registerSaveRouter: () => registerSaveRouter,\n  removeModel: () => removeModel,\n  weightsLoaderFactory: () => weightsLoaderFactory,\n  withSaveHandler: () => withSaveHandler,\n  withSaveHandlerSync: () => withSaveHandlerSync\n});\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/io/browser_files.js\nvar DEFAULT_FILE_NAME_PREFIX = \"model\";\nvar DEFAULT_JSON_EXTENSION_NAME = \".json\";\nvar DEFAULT_WEIGHT_DATA_EXTENSION_NAME = \".weights.bin\";\nfunction defer(f) {\n  return new Promise((resolve) => setTimeout(resolve)).then(f);\n}\nvar BrowserDownloads = class {\n  constructor(fileNamePrefix) {\n    if (!env().getBool(\"IS_BROWSER\")) {\n      throw new Error(\"browserDownloads() cannot proceed because the current environment is not a browser.\");\n    }\n    if (fileNamePrefix.startsWith(BrowserDownloads.URL_SCHEME)) {\n      fileNamePrefix = fileNamePrefix.slice(BrowserDownloads.URL_SCHEME.length);\n    }\n    if (fileNamePrefix == null || fileNamePrefix.length === 0) {\n      fileNamePrefix = DEFAULT_FILE_NAME_PREFIX;\n    }\n    this.modelJsonFileName = fileNamePrefix + DEFAULT_JSON_EXTENSION_NAME;\n    this.weightDataFileName = fileNamePrefix + DEFAULT_WEIGHT_DATA_EXTENSION_NAME;\n  }\n  async save(modelArtifacts) {\n    if (typeof document === \"undefined\") {\n      throw new Error(\"Browser downloads are not supported in this environment since `document` is not present\");\n    }\n    const weightsURL = window.URL.createObjectURL(new Blob([modelArtifacts.weightData], { type: \"application/octet-stream\" }));\n    if (modelArtifacts.modelTopology instanceof ArrayBuffer) {\n      throw new Error(\"BrowserDownloads.save() does not support saving model topology in binary formats yet.\");\n    } else {\n      const weightsManifest = [{\n        paths: [\"./\" + this.weightDataFileName],\n        weights: modelArtifacts.weightSpecs\n      }];\n      const modelJSON = getModelJSONForModelArtifacts(modelArtifacts, weightsManifest);\n      const modelJsonURL = window.URL.createObjectURL(new Blob([JSON.stringify(modelJSON)], { type: \"application/json\" }));\n      const jsonAnchor = this.modelJsonAnchor == null ? document.createElement(\"a\") : this.modelJsonAnchor;\n      jsonAnchor.download = this.modelJsonFileName;\n      jsonAnchor.href = modelJsonURL;\n      await defer(() => jsonAnchor.dispatchEvent(new MouseEvent(\"click\")));\n      if (modelArtifacts.weightData != null) {\n        const weightDataAnchor = this.weightDataAnchor == null ? document.createElement(\"a\") : this.weightDataAnchor;\n        weightDataAnchor.download = this.weightDataFileName;\n        weightDataAnchor.href = weightsURL;\n        await defer(() => weightDataAnchor.dispatchEvent(new MouseEvent(\"click\")));\n      }\n      return { modelArtifactsInfo: getModelArtifactsInfoForJSON(modelArtifacts) };\n    }\n  }\n};\nBrowserDownloads.URL_SCHEME = \"downloads://\";\nvar BrowserFiles = class {\n  constructor(files) {\n    if (files == null || files.length < 1) {\n      throw new Error(`When calling browserFiles, at least 1 file is required, but received ${files}`);\n    }\n    this.jsonFile = files[0];\n    this.weightsFiles = files.slice(1);\n  }\n  async load() {\n    return new Promise((resolve, reject) => {\n      const jsonReader = new FileReader();\n      jsonReader.onload = (event) => {\n        const modelJSON = JSON.parse(event.target.result);\n        const modelTopology = modelJSON.modelTopology;\n        if (modelTopology == null) {\n          reject(new Error(`modelTopology field is missing from file ${this.jsonFile.name}`));\n          return;\n        }\n        const weightsManifest = modelJSON.weightsManifest;\n        if (weightsManifest == null) {\n          reject(new Error(`weightManifest field is missing from file ${this.jsonFile.name}`));\n          return;\n        }\n        if (this.weightsFiles.length === 0) {\n          resolve({ modelTopology });\n          return;\n        }\n        const modelArtifactsPromise = getModelArtifactsForJSON(modelJSON, (weightsManifest2) => this.loadWeights(weightsManifest2));\n        resolve(modelArtifactsPromise);\n      };\n      jsonReader.onerror = (error) => reject(`Failed to read model topology and weights manifest JSON from file '${this.jsonFile.name}'. BrowserFiles supports loading Keras-style tf.Model artifacts only.`);\n      jsonReader.readAsText(this.jsonFile);\n    });\n  }\n  loadWeights(weightsManifest) {\n    const weightSpecs = [];\n    const paths = [];\n    for (const entry of weightsManifest) {\n      weightSpecs.push(...entry.weights);\n      paths.push(...entry.paths);\n    }\n    const pathToFile = this.checkManifestAndWeightFiles(weightsManifest);\n    const promises = paths.map((path) => this.loadWeightsFile(path, pathToFile[path]));\n    return Promise.all(promises).then((buffers) => [weightSpecs, concatenateArrayBuffers(buffers)]);\n  }\n  loadWeightsFile(path, file) {\n    return new Promise((resolve, reject) => {\n      const weightFileReader = new FileReader();\n      weightFileReader.onload = (event) => {\n        const weightData = event.target.result;\n        resolve(weightData);\n      };\n      weightFileReader.onerror = (error) => reject(`Failed to weights data from file of path '${path}'.`);\n      weightFileReader.readAsArrayBuffer(file);\n    });\n  }\n  checkManifestAndWeightFiles(manifest) {\n    const basenames = [];\n    const fileNames = this.weightsFiles.map((file) => basename(file.name));\n    const pathToFile = {};\n    for (const group of manifest) {\n      group.paths.forEach((path) => {\n        const pathBasename = basename(path);\n        if (basenames.indexOf(pathBasename) !== -1) {\n          throw new Error(`Duplicate file basename found in weights manifest: '${pathBasename}'`);\n        }\n        basenames.push(pathBasename);\n        if (fileNames.indexOf(pathBasename) === -1) {\n          throw new Error(`Weight file with basename '${pathBasename}' is not provided.`);\n        } else {\n          pathToFile[path] = this.weightsFiles[fileNames.indexOf(pathBasename)];\n        }\n      });\n    }\n    if (basenames.length !== this.weightsFiles.length) {\n      throw new Error(`Mismatch in the number of files in weights manifest (${basenames.length}) and the number of weight files provided (${this.weightsFiles.length}).`);\n    }\n    return pathToFile;\n  }\n};\nvar browserDownloadsRouter = (url) => {\n  if (!env().getBool(\"IS_BROWSER\")) {\n    return null;\n  } else {\n    if (!Array.isArray(url) && url.startsWith(BrowserDownloads.URL_SCHEME)) {\n      return browserDownloads(url.slice(BrowserDownloads.URL_SCHEME.length));\n    } else {\n      return null;\n    }\n  }\n};\nIORouterRegistry.registerSaveRouter(browserDownloadsRouter);\nfunction browserDownloads(fileNamePrefix = \"model\") {\n  return new BrowserDownloads(fileNamePrefix);\n}\nfunction browserFiles(files) {\n  return new BrowserFiles(files);\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/io/progress.js\nfunction monitorPromisesProgress(promises, onProgress, startFraction, endFraction) {\n  checkPromises(promises);\n  startFraction = startFraction == null ? 0 : startFraction;\n  endFraction = endFraction == null ? 1 : endFraction;\n  checkFraction(startFraction, endFraction);\n  let resolvedPromise = 0;\n  const registerMonitor = (promise) => {\n    promise.then((value) => {\n      const fraction = startFraction + ++resolvedPromise / promises.length * (endFraction - startFraction);\n      onProgress(fraction);\n      return value;\n    });\n    return promise;\n  };\n  function checkPromises(promises2) {\n    assert(promises2 != null && Array.isArray(promises2) && promises2.length > 0, () => \"promises must be a none empty array\");\n  }\n  function checkFraction(startFraction2, endFraction2) {\n    assert(startFraction2 >= 0 && startFraction2 <= 1, () => `Progress fraction must be in range [0, 1], but got startFraction ${startFraction2}`);\n    assert(endFraction2 >= 0 && endFraction2 <= 1, () => `Progress fraction must be in range [0, 1], but got endFraction ${endFraction2}`);\n    assert(endFraction2 >= startFraction2, () => `startFraction must be no more than endFraction, but got startFraction ${startFraction2} and endFraction ${endFraction2}`);\n  }\n  return Promise.all(promises.map(registerMonitor));\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/io/weights_loader.js\nasync function loadWeightsAsArrayBuffer(fetchURLs, loadOptions) {\n  if (loadOptions == null) {\n    loadOptions = {};\n  }\n  const fetchFunc = loadOptions.fetchFunc == null ? env().platform.fetch : loadOptions.fetchFunc;\n  const requests = fetchURLs.map((fetchURL) => fetchFunc(fetchURL, loadOptions.requestInit, { isBinary: true }));\n  const fetchStartFraction = 0;\n  const fetchEndFraction = 0.5;\n  const responses = loadOptions.onProgress == null ? await Promise.all(requests) : await monitorPromisesProgress(requests, loadOptions.onProgress, fetchStartFraction, fetchEndFraction);\n  const bufferPromises = responses.map((response) => response.arrayBuffer());\n  const bufferStartFraction = 0.5;\n  const bufferEndFraction = 1;\n  const buffers = loadOptions.onProgress == null ? await Promise.all(bufferPromises) : await monitorPromisesProgress(bufferPromises, loadOptions.onProgress, bufferStartFraction, bufferEndFraction);\n  return buffers;\n}\nasync function loadWeights(manifest, filePathPrefix = \"\", weightNames, requestInit) {\n  const fetchWeights = (fetchUrls) => loadWeightsAsArrayBuffer(fetchUrls, { requestInit });\n  const loadWeights2 = weightsLoaderFactory(fetchWeights);\n  return loadWeights2(manifest, filePathPrefix, weightNames);\n}\nfunction weightsLoaderFactory(fetchWeightsFunction) {\n  return async (manifest, filePathPrefix = \"\", weightNames) => {\n    const groupIndicesToFetchMap = manifest.map(() => false);\n    const groupWeightsToFetch = {};\n    const weightsFound = weightNames != null ? weightNames.map(() => false) : [];\n    const allManifestWeightNames = [];\n    manifest.forEach((manifestGroupConfig, groupIndex) => {\n      let groupOffset = 0;\n      manifestGroupConfig.weights.forEach((weightsEntry) => {\n        const rawDtype = \"quantization\" in weightsEntry ? weightsEntry.quantization.dtype : weightsEntry.dtype;\n        const weightsBytes = DTYPE_VALUE_SIZE_MAP[rawDtype] * sizeFromShape(weightsEntry.shape);\n        const enqueueWeightsForFetchingFn = () => {\n          groupIndicesToFetchMap[groupIndex] = true;\n          if (groupWeightsToFetch[groupIndex] == null) {\n            groupWeightsToFetch[groupIndex] = [];\n          }\n          groupWeightsToFetch[groupIndex].push({\n            manifestEntry: weightsEntry,\n            groupOffset,\n            sizeBytes: weightsBytes\n          });\n        };\n        if (weightNames != null) {\n          weightNames.forEach((weightName, weightIndex) => {\n            if (weightName === weightsEntry.name) {\n              enqueueWeightsForFetchingFn();\n              weightsFound[weightIndex] = true;\n            }\n          });\n        } else {\n          enqueueWeightsForFetchingFn();\n        }\n        allManifestWeightNames.push(weightsEntry.name);\n        groupOffset += weightsBytes;\n      });\n    });\n    if (!weightsFound.every((found) => found)) {\n      const weightsNotFound = weightNames.filter((_, i) => !weightsFound[i]);\n      throw new Error(`Could not find weights in manifest with names: ${weightsNotFound.join(\", \")}. \nManifest JSON has weights with names: ${allManifestWeightNames.join(\", \")}.`);\n    }\n    const groupIndicesToFetch = groupIndicesToFetchMap.reduce((accumulator, shouldFetch, i) => {\n      if (shouldFetch) {\n        accumulator.push(i);\n      }\n      return accumulator;\n    }, []);\n    const fetchUrls = [];\n    groupIndicesToFetch.forEach((i) => {\n      manifest[i].paths.forEach((filepath) => {\n        const fetchUrl = filePathPrefix + (!filePathPrefix.endsWith(\"/\") ? \"/\" : \"\") + filepath;\n        fetchUrls.push(fetchUrl);\n      });\n    });\n    const buffers = await fetchWeightsFunction(fetchUrls);\n    const weightsTensorMap = {};\n    let bufferIndexOffset = 0;\n    groupIndicesToFetch.forEach((i) => {\n      const numBuffers = manifest[i].paths.length;\n      let groupBytes = 0;\n      for (let i2 = 0; i2 < numBuffers; i2++) {\n        groupBytes += buffers[bufferIndexOffset + i2].byteLength;\n      }\n      const groupBuffer = new ArrayBuffer(groupBytes);\n      const groupByteBuffer = new Uint8Array(groupBuffer);\n      let groupBufferOffset = 0;\n      for (let i2 = 0; i2 < numBuffers; i2++) {\n        const buffer2 = new Uint8Array(buffers[bufferIndexOffset + i2]);\n        groupByteBuffer.set(buffer2, groupBufferOffset);\n        groupBufferOffset += buffer2.byteLength;\n      }\n      const weightsEntries = groupWeightsToFetch[i];\n      weightsEntries.forEach((weightsEntry) => {\n        const byteBuffer = groupBuffer.slice(weightsEntry.groupOffset, weightsEntry.groupOffset + weightsEntry.sizeBytes);\n        const nameToTensorMap = decodeWeights(byteBuffer, [weightsEntry.manifestEntry]);\n        for (const name in nameToTensorMap) {\n          weightsTensorMap[name] = nameToTensorMap[name];\n        }\n      });\n      bufferIndexOffset += numBuffers;\n    });\n    return weightsTensorMap;\n  };\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/io/http.js\nvar OCTET_STREAM_MIME_TYPE = \"application/octet-stream\";\nvar JSON_TYPE = \"application/json\";\nvar HTTPRequest = class {\n  constructor(path, loadOptions) {\n    this.DEFAULT_METHOD = \"POST\";\n    if (loadOptions == null) {\n      loadOptions = {};\n    }\n    this.weightPathPrefix = loadOptions.weightPathPrefix;\n    this.onProgress = loadOptions.onProgress;\n    this.weightUrlConverter = loadOptions.weightUrlConverter;\n    if (loadOptions.fetchFunc != null) {\n      assert(typeof loadOptions.fetchFunc === \"function\", () => \"Must pass a function that matches the signature of `fetch` (see https://developer.mozilla.org/en-US/docs/Web/API/Fetch_API)\");\n      this.fetch = loadOptions.fetchFunc;\n    } else {\n      this.fetch = env().platform.fetch;\n    }\n    assert(path != null && path.length > 0, () => \"URL path for http must not be null, undefined or empty.\");\n    if (Array.isArray(path)) {\n      assert(path.length === 2, () => `URL paths for http must have a length of 2, (actual length is ${path.length}).`);\n    }\n    this.path = path;\n    if (loadOptions.requestInit != null && loadOptions.requestInit.body != null) {\n      throw new Error(\"requestInit is expected to have no pre-existing body, but has one.\");\n    }\n    this.requestInit = loadOptions.requestInit || {};\n  }\n  async save(modelArtifacts) {\n    if (modelArtifacts.modelTopology instanceof ArrayBuffer) {\n      throw new Error(\"BrowserHTTPRequest.save() does not support saving model topology in binary formats yet.\");\n    }\n    const init2 = Object.assign({ method: this.DEFAULT_METHOD }, this.requestInit);\n    init2.body = new FormData();\n    const weightsManifest = [{\n      paths: [\"./model.weights.bin\"],\n      weights: modelArtifacts.weightSpecs\n    }];\n    const modelTopologyAndWeightManifest = getModelJSONForModelArtifacts(modelArtifacts, weightsManifest);\n    init2.body.append(\"model.json\", new Blob([JSON.stringify(modelTopologyAndWeightManifest)], { type: JSON_TYPE }), \"model.json\");\n    if (modelArtifacts.weightData != null) {\n      init2.body.append(\"model.weights.bin\", new Blob([modelArtifacts.weightData], { type: OCTET_STREAM_MIME_TYPE }), \"model.weights.bin\");\n    }\n    const response = await this.fetch(this.path, init2);\n    if (response.ok) {\n      return {\n        modelArtifactsInfo: getModelArtifactsInfoForJSON(modelArtifacts),\n        responses: [response]\n      };\n    } else {\n      throw new Error(`BrowserHTTPRequest.save() failed due to HTTP response status ${response.status}.`);\n    }\n  }\n  async load() {\n    const modelConfigRequest = await this.fetch(this.path, this.requestInit);\n    if (!modelConfigRequest.ok) {\n      throw new Error(`Request to ${this.path} failed with status code ${modelConfigRequest.status}. Please verify this URL points to the model JSON of the model to load.`);\n    }\n    let modelJSON;\n    try {\n      modelJSON = await modelConfigRequest.json();\n    } catch (e) {\n      let message = `Failed to parse model JSON of response from ${this.path}.`;\n      if (this.path.endsWith(\".pb\")) {\n        message += \" Your path contains a .pb file extension. Support for .pb models have been removed in TensorFlow.js 1.0 in favor of .json models. You can re-convert your Python TensorFlow model using the TensorFlow.js 1.0 conversion scripts or you can convert your.pb models with the 'pb2json'NPM script in the tensorflow/tfjs-converter repository.\";\n      } else {\n        message += \" Please make sure the server is serving valid JSON for this request.\";\n      }\n      throw new Error(message);\n    }\n    const modelTopology = modelJSON.modelTopology;\n    const weightsManifest = modelJSON.weightsManifest;\n    if (modelTopology == null && weightsManifest == null) {\n      throw new Error(`The JSON from HTTP path ${this.path} contains neither model topology or manifest for weights.`);\n    }\n    return getModelArtifactsForJSON(modelJSON, (weightsManifest2) => this.loadWeights(weightsManifest2));\n  }\n  async loadWeights(weightsManifest) {\n    const weightPath = Array.isArray(this.path) ? this.path[1] : this.path;\n    const [prefix, suffix] = parseUrl(weightPath);\n    const pathPrefix = this.weightPathPrefix || prefix;\n    const weightSpecs = [];\n    for (const entry of weightsManifest) {\n      weightSpecs.push(...entry.weights);\n    }\n    const fetchURLs = [];\n    const urlPromises = [];\n    for (const weightsGroup of weightsManifest) {\n      for (const path of weightsGroup.paths) {\n        if (this.weightUrlConverter != null) {\n          urlPromises.push(this.weightUrlConverter(path));\n        } else {\n          fetchURLs.push(pathPrefix + path + suffix);\n        }\n      }\n    }\n    if (this.weightUrlConverter) {\n      fetchURLs.push(...await Promise.all(urlPromises));\n    }\n    const buffers = await loadWeightsAsArrayBuffer(fetchURLs, {\n      requestInit: this.requestInit,\n      fetchFunc: this.fetch,\n      onProgress: this.onProgress\n    });\n    return [weightSpecs, concatenateArrayBuffers(buffers)];\n  }\n};\nHTTPRequest.URL_SCHEME_REGEX = /^https?:\\/\\//;\nfunction parseUrl(url) {\n  const lastSlash = url.lastIndexOf(\"/\");\n  const lastSearchParam = url.lastIndexOf(\"?\");\n  const prefix = url.substring(0, lastSlash);\n  const suffix = lastSearchParam > lastSlash ? url.substring(lastSearchParam) : \"\";\n  return [prefix + \"/\", suffix];\n}\nfunction isHTTPScheme(url) {\n  return url.match(HTTPRequest.URL_SCHEME_REGEX) != null;\n}\nvar httpRouter = (url, loadOptions) => {\n  if (typeof fetch === \"undefined\" && (loadOptions == null || loadOptions.fetchFunc == null)) {\n    return null;\n  } else {\n    let isHTTP = true;\n    if (Array.isArray(url)) {\n      isHTTP = url.every((urlItem) => isHTTPScheme(urlItem));\n    } else {\n      isHTTP = isHTTPScheme(url);\n    }\n    if (isHTTP) {\n      return http(url, loadOptions);\n    }\n  }\n  return null;\n};\nIORouterRegistry.registerSaveRouter(httpRouter);\nIORouterRegistry.registerLoadRouter(httpRouter);\nfunction http(path, loadOptions) {\n  return new HTTPRequest(path, loadOptions);\n}\nfunction browserHTTPRequest(path, loadOptions) {\n  return http(path, loadOptions);\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/io/passthrough.js\nvar PassthroughLoader = class {\n  constructor(modelArtifacts) {\n    this.modelArtifacts = modelArtifacts;\n  }\n  load() {\n    return this.modelArtifacts;\n  }\n};\nvar PassthroughSaver = class {\n  constructor(saveHandler) {\n    this.saveHandler = saveHandler;\n  }\n  save(modelArtifacts) {\n    return this.saveHandler(modelArtifacts);\n  }\n};\nvar PassthroughAsync = class {\n  constructor(handler) {\n    if (handler.load) {\n      this.load = () => Promise.resolve(handler.load());\n    }\n    if (handler.save) {\n      this.save = (modelArtifacts) => Promise.resolve(handler.save(modelArtifacts));\n    }\n  }\n};\nfunction fromMemory(modelArtifacts, weightSpecs, weightData, trainingConfig) {\n  const args = arguments;\n  return new PassthroughAsync(fromMemorySync(...args));\n}\nfunction fromMemorySync(modelArtifacts, weightSpecs, weightData, trainingConfig) {\n  if (arguments.length === 1) {\n    const isModelArtifacts = modelArtifacts.modelTopology != null || modelArtifacts.weightSpecs != null;\n    if (isModelArtifacts) {\n      return new PassthroughLoader(modelArtifacts);\n    } else {\n      console.warn(\"Please call tf.io.fromMemory() with only one argument. The argument should be of type ModelArtifacts. The multi-argument signature of tf.io.fromMemory() has been deprecated and will be removed in a future release.\");\n      return new PassthroughLoader({ modelTopology: modelArtifacts });\n    }\n  } else {\n    console.warn(\"Please call tf.io.fromMemory() with only one argument. The argument should be of type ModelArtifacts. The multi-argument signature of tf.io.fromMemory() has been deprecated and will be removed in a future release.\");\n    return new PassthroughLoader({\n      modelTopology: modelArtifacts,\n      weightSpecs,\n      weightData,\n      trainingConfig\n    });\n  }\n}\nfunction withSaveHandler(saveHandler) {\n  return new PassthroughSaver(saveHandler);\n}\nfunction withSaveHandlerSync(saveHandler) {\n  return new PassthroughSaver(saveHandler);\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/math.js\nvar math_exports = {};\n__export(math_exports, {\n  confusionMatrix: () => confusionMatrix\n});\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/mat_mul.js\nfunction matMul_(a, b, transposeA = false, transposeB = false) {\n  let $a = convertToTensor(a, \"a\", \"matMul\");\n  let $b = convertToTensor(b, \"b\", \"matMul\");\n  [$a, $b] = makeTypesMatch($a, $b);\n  const inputs = { a: $a, b: $b };\n  const attrs = { transposeA, transposeB };\n  return ENGINE.runKernel(BatchMatMul, inputs, attrs);\n}\nvar matMul = op({ matMul_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/one_hot.js\nfunction oneHot_(indices, depth, onValue = 1, offValue = 0, dtype = \"int32\") {\n  if (depth < 2) {\n    throw new Error(`Error in oneHot: depth must be >=2, but it is ${depth}`);\n  }\n  const $indices = convertToTensor(indices, \"indices\", \"oneHot\", \"int32\");\n  const inputs = { indices: $indices };\n  const attrs = { dtype, depth, onValue, offValue };\n  return ENGINE.runKernel(OneHot, inputs, attrs);\n}\nvar oneHot = op({ oneHot_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/globals.js\nfunction enableProdMode() {\n  env().set(\"PROD\", true);\n}\nfunction enableDebugMode() {\n  env().set(\"DEBUG\", true);\n}\nfunction disableDeprecationWarnings() {\n  env().set(\"DEPRECATION_WARNINGS_ENABLED\", false);\n  console.warn(`TensorFlow.js deprecation warnings have been disabled.`);\n}\nfunction deprecationWarn(msg) {\n  if (env().getBool(\"DEPRECATION_WARNINGS_ENABLED\")) {\n    console.warn(msg + \" You can disable deprecation warnings with tf.disableDeprecationWarnings().\");\n  }\n}\nsetDeprecationWarningFn(deprecationWarn);\nfunction disposeVariables() {\n  ENGINE.disposeVariables();\n}\nfunction engine() {\n  return ENGINE;\n}\nfunction memory() {\n  return ENGINE.memory();\n}\nfunction profile(f) {\n  return ENGINE.profile(f);\n}\nfunction tidy(nameOrFn, fn) {\n  return ENGINE.tidy(nameOrFn, fn);\n}\nfunction dispose(container) {\n  const tensors = getTensorsInContainer(container);\n  tensors.forEach((tensor2) => tensor2.dispose());\n}\nfunction keep(result) {\n  return ENGINE.keep(result);\n}\nfunction time(f) {\n  return ENGINE.time(f);\n}\nfunction setBackend(backendName) {\n  return ENGINE.setBackend(backendName);\n}\nfunction ready() {\n  return ENGINE.ready();\n}\nfunction getBackend() {\n  return ENGINE.backendName;\n}\nfunction removeBackend(name) {\n  ENGINE.removeBackend(name);\n}\nfunction findBackend(name) {\n  return ENGINE.findBackend(name);\n}\nfunction findBackendFactory(name) {\n  return ENGINE.findBackendFactory(name);\n}\nfunction registerBackend(name, factory, priority = 1) {\n  return ENGINE.registerBackend(name, factory, priority);\n}\nfunction backend() {\n  return ENGINE.backend;\n}\nfunction setPlatform(platformName, platform) {\n  env().setPlatform(platformName, platform);\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/imag.js\nfunction imag_(input2) {\n  const $input = convertToTensor(input2, \"input\", \"imag\");\n  const inputs = { input: $input };\n  return ENGINE.runKernel(Imag, inputs);\n}\nvar imag = op({ imag_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/neg.js\nfunction neg_(x) {\n  const $x = convertToTensor(x, \"x\", \"neg\");\n  const inputs = { x: $x };\n  return ENGINE.runKernel(Neg, inputs);\n}\nvar neg = op({ neg_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/real.js\nfunction real_(input2) {\n  const $input = convertToTensor(input2, \"input\", \"real\");\n  const inputs = { input: $input };\n  return ENGINE.runKernel(Real, inputs);\n}\nvar real = op({ real_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/transpose.js\nfunction transpose_(x, perm, conjugate) {\n  const $x = convertToTensor(x, \"x\", \"transpose\");\n  if (perm == null) {\n    perm = $x.shape.map((s, i) => i).reverse();\n  }\n  assert($x.rank === perm.length, () => `Error in transpose: rank of input ${$x.rank} must match length of perm ${perm}.`);\n  perm.forEach((axis) => {\n    assert(axis >= 0 && axis < $x.rank, () => `All entries in 'perm' must be between 0 and ${$x.rank - 1} but got ${perm}`);\n  });\n  if ($x.rank <= 1) {\n    return $x.clone();\n  }\n  const inputs = { x: $x };\n  const attrs = { perm };\n  if ($x.dtype === \"complex64\") {\n    return tidy(() => {\n      let $real = real($x);\n      let $imag = imag($x);\n      $real = ENGINE.runKernel(Transpose, { x: $real }, attrs);\n      $imag = ENGINE.runKernel(Transpose, { x: $imag }, attrs);\n      if (conjugate) {\n        $imag = neg($imag);\n      }\n      return complex($real, $imag);\n    });\n  }\n  return ENGINE.runKernel(Transpose, inputs, attrs);\n}\nvar transpose = op({ transpose_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/confusion_matrix.js\nfunction confusionMatrix_(labels, predictions, numClasses) {\n  const $labels = convertToTensor(labels, \"labels\", \"confusionMatrix\");\n  const $predictions = convertToTensor(predictions, \"predictions\", \"confusionMatrix\");\n  assert(numClasses == null || numClasses > 0 && Number.isInteger(numClasses), () => `If provided, numClasses must be a positive integer, but got ${numClasses}`);\n  assert($labels.rank === 1, () => `Expected the rank of labels to be 1, but got ${$labels.rank}`);\n  assert($predictions.rank === 1, () => `Expected the rank of predictions to be 1, but got ${$predictions.rank}`);\n  assert($labels.shape[0] === $predictions.shape[0], () => `Mismatch in the number of examples: ${$labels.shape[0]} vs. ${$predictions.shape[0]}. Labels and predictions should have the same number of elements.`);\n  assert(numClasses > 0 && Number.isInteger(numClasses), () => `numClasses is required to be a positive integer, but got ${numClasses}`);\n  const oneHotLabels = oneHot(cast($labels, \"int32\"), numClasses);\n  const oneHotPredictions = oneHot(cast($predictions, \"int32\"), numClasses);\n  const oneHotLabelsT = transpose(oneHotLabels);\n  const product = matMul(oneHotLabelsT, oneHotPredictions);\n  return cast(product, \"int32\");\n}\nvar confusionMatrix = op({ confusionMatrix_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/broadcast_util.js\nvar broadcast_util_exports = {};\n__export(broadcast_util_exports, {\n  assertAndGetBroadcastShape: () => assertAndGetBroadcastShape,\n  getBroadcastDims: () => getBroadcastDims,\n  getReductionAxes: () => getReductionAxes\n});\nfunction getBroadcastDims(inShape, outShape) {\n  const inRank = inShape.length;\n  const dims = [];\n  for (let i = 0; i < inRank; i++) {\n    const dim = inRank - 1 - i;\n    const a = inShape[dim] || 1;\n    const b = outShape[outShape.length - 1 - i] || 1;\n    if (b > 1 && a === 1) {\n      dims.unshift(dim);\n    }\n  }\n  return dims;\n}\nfunction getReductionAxes(inShape, outShape) {\n  const result = [];\n  for (let i = 0; i < outShape.length; i++) {\n    const inDim = inShape[inShape.length - i - 1];\n    const outAxis = outShape.length - i - 1;\n    const outDim = outShape[outAxis];\n    if (inDim == null || inDim === 1 && outDim > 1) {\n      result.unshift(outAxis);\n    }\n  }\n  return result;\n}\nfunction assertAndGetBroadcastShape(shapeA, shapeB) {\n  const result = [];\n  const l = Math.max(shapeA.length, shapeB.length);\n  for (let i = 0; i < l; i++) {\n    let a = shapeA[shapeA.length - i - 1];\n    if (a == null) {\n      a = 1;\n    }\n    let b = shapeB[shapeB.length - i - 1];\n    if (b == null) {\n      b = 1;\n    }\n    if (a === 1) {\n      result.unshift(b);\n    } else if (b === 1) {\n      result.unshift(a);\n    } else if (a !== b) {\n      const errMsg = `Operands could not be broadcast together with shapes ${shapeA} and ${shapeB}.`;\n      throw Error(errMsg);\n    } else {\n      result.unshift(a);\n    }\n  }\n  return result;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/browser.js\nvar browser_exports = {};\n__export(browser_exports, {\n  fromPixels: () => fromPixels,\n  fromPixelsAsync: () => fromPixelsAsync,\n  toPixels: () => toPixels\n});\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/tensor3d.js\nfunction tensor3d(values, shape, dtype) {\n  assertNonNull(values);\n  if (shape != null && shape.length !== 3) {\n    throw new Error(\"tensor3d() requires shape to have three numbers\");\n  }\n  const inferredShape = inferShape(values, dtype);\n  if (inferredShape.length !== 3 && inferredShape.length !== 1) {\n    throw new Error(\"tensor3d() requires values to be number[][][] or flat/TypedArray\");\n  }\n  if (inferredShape.length === 1 && shape == null) {\n    throw new Error(\"tensor3d() requires shape to be provided when `values` are a flat array\");\n  }\n  return makeTensor(values, shape, inferredShape, dtype);\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/browser.js\nvar fromPixels2DContext;\nfunction fromPixels_(pixels, numChannels = 3) {\n  if (numChannels > 4) {\n    throw new Error(\"Cannot construct Tensor with more than 4 channels from pixels.\");\n  }\n  if (pixels == null) {\n    throw new Error(\"pixels passed to tf.browser.fromPixels() can not be null\");\n  }\n  let isPixelData2 = false;\n  let isImageData = false;\n  let isVideo = false;\n  let isImage = false;\n  let isCanvasLike = false;\n  let isImageBitmap = false;\n  if (pixels.data instanceof Uint8Array) {\n    isPixelData2 = true;\n  } else if (typeof ImageData !== \"undefined\" && pixels instanceof ImageData) {\n    isImageData = true;\n  } else if (typeof HTMLVideoElement !== \"undefined\" && pixels instanceof HTMLVideoElement) {\n    isVideo = true;\n  } else if (typeof HTMLImageElement !== \"undefined\" && pixels instanceof HTMLImageElement) {\n    isImage = true;\n  } else if (pixels.getContext != null) {\n    isCanvasLike = true;\n  } else if (typeof ImageBitmap !== \"undefined\" && pixels instanceof ImageBitmap) {\n    isImageBitmap = true;\n  } else {\n    throw new Error(`pixels passed to tf.browser.fromPixels() must be either an HTMLVideoElement, HTMLImageElement, HTMLCanvasElement, ImageData in browser, or OffscreenCanvas, ImageData in webworker or {data: Uint32Array, width: number, height: number}, but was ${pixels.constructor.name}`);\n  }\n  const kernel = getKernel(FromPixels, ENGINE.backendName);\n  if (kernel != null) {\n    const inputs = { pixels };\n    const attrs = { numChannels };\n    return ENGINE.runKernel(FromPixels, inputs, attrs);\n  }\n  const [width, height] = isVideo ? [\n    pixels.videoWidth,\n    pixels.videoHeight\n  ] : [pixels.width, pixels.height];\n  let vals;\n  if (isCanvasLike) {\n    vals = pixels.getContext(\"2d\").getImageData(0, 0, width, height).data;\n  } else if (isImageData || isPixelData2) {\n    vals = pixels.data;\n  } else if (isImage || isVideo || isImageBitmap) {\n    if (fromPixels2DContext == null) {\n      if (typeof document === \"undefined\") {\n        if (typeof OffscreenCanvas !== \"undefined\" && typeof OffscreenCanvasRenderingContext2D !== \"undefined\") {\n          fromPixels2DContext = new OffscreenCanvas(1, 1).getContext(\"2d\");\n        } else {\n          throw new Error(\"Cannot parse input in current context. Reason: OffscreenCanvas Context2D rendering is not supported.\");\n        }\n      } else {\n        fromPixels2DContext = document.createElement(\"canvas\").getContext(\"2d\", { willReadFrequently: true });\n      }\n    }\n    fromPixels2DContext.canvas.width = width;\n    fromPixels2DContext.canvas.height = height;\n    fromPixels2DContext.drawImage(pixels, 0, 0, width, height);\n    vals = fromPixels2DContext.getImageData(0, 0, width, height).data;\n  }\n  let values;\n  if (numChannels === 4) {\n    values = new Int32Array(vals);\n  } else {\n    const numPixels = width * height;\n    values = new Int32Array(numPixels * numChannels);\n    for (let i = 0; i < numPixels; i++) {\n      for (let channel = 0; channel < numChannels; ++channel) {\n        values[i * numChannels + channel] = vals[i * 4 + channel];\n      }\n    }\n  }\n  const outShape = [height, width, numChannels];\n  return tensor3d(values, outShape, \"int32\");\n}\nfunction isPixelData(pixels) {\n  return pixels != null && pixels.data instanceof Uint8Array;\n}\nfunction isImageBitmapFullySupported() {\n  return typeof window !== \"undefined\" && typeof ImageBitmap !== \"undefined\" && window.hasOwnProperty(\"createImageBitmap\");\n}\nfunction isNonEmptyPixels(pixels) {\n  return pixels != null && pixels.width !== 0 && pixels.height !== 0;\n}\nfunction canWrapPixelsToImageBitmap(pixels) {\n  return isImageBitmapFullySupported() && !(pixels instanceof ImageBitmap) && isNonEmptyPixels(pixels) && !isPixelData(pixels);\n}\nasync function fromPixelsAsync(pixels, numChannels = 3) {\n  let inputs = null;\n  if (env().getBool(\"WRAP_TO_IMAGEBITMAP\") && canWrapPixelsToImageBitmap(pixels)) {\n    let imageBitmap;\n    try {\n      imageBitmap = await createImageBitmap(pixels, { premultiplyAlpha: \"none\" });\n    } catch (e) {\n      imageBitmap = null;\n    }\n    if (imageBitmap != null && imageBitmap.width === pixels.width && imageBitmap.height === pixels.height) {\n      inputs = imageBitmap;\n    } else {\n      inputs = pixels;\n    }\n  } else {\n    inputs = pixels;\n  }\n  return fromPixels_(inputs, numChannels);\n}\nasync function toPixels(img, canvas) {\n  let $img = convertToTensor(img, \"img\", \"toPixels\");\n  if (!(img instanceof Tensor)) {\n    const originalImgTensor = $img;\n    $img = cast(originalImgTensor, \"int32\");\n    originalImgTensor.dispose();\n  }\n  if ($img.rank !== 2 && $img.rank !== 3) {\n    throw new Error(`toPixels only supports rank 2 or 3 tensors, got rank ${$img.rank}.`);\n  }\n  const [height, width] = $img.shape.slice(0, 2);\n  const depth = $img.rank === 2 ? 1 : $img.shape[2];\n  if (depth > 4 || depth === 2) {\n    throw new Error(`toPixels only supports depth of size 1, 3 or 4 but got ${depth}`);\n  }\n  if ($img.dtype !== \"float32\" && $img.dtype !== \"int32\") {\n    throw new Error(`Unsupported type for toPixels: ${$img.dtype}. Please use float32 or int32 tensors.`);\n  }\n  const data = await $img.data();\n  const multiplier = $img.dtype === \"float32\" ? 255 : 1;\n  const bytes = new Uint8ClampedArray(width * height * 4);\n  for (let i = 0; i < height * width; ++i) {\n    const rgba = [0, 0, 0, 255];\n    for (let d = 0; d < depth; d++) {\n      const value = data[i * depth + d];\n      if ($img.dtype === \"float32\") {\n        if (value < 0 || value > 1) {\n          throw new Error(`Tensor values for a float32 Tensor must be in the range [0 - 1] but encountered ${value}.`);\n        }\n      } else if ($img.dtype === \"int32\") {\n        if (value < 0 || value > 255) {\n          throw new Error(`Tensor values for a int32 Tensor must be in the range [0 - 255] but encountered ${value}.`);\n        }\n      }\n      if (depth === 1) {\n        rgba[0] = value * multiplier;\n        rgba[1] = value * multiplier;\n        rgba[2] = value * multiplier;\n      } else {\n        rgba[d] = value * multiplier;\n      }\n    }\n    const j = i * 4;\n    bytes[j + 0] = Math.round(rgba[0]);\n    bytes[j + 1] = Math.round(rgba[1]);\n    bytes[j + 2] = Math.round(rgba[2]);\n    bytes[j + 3] = Math.round(rgba[3]);\n  }\n  if (canvas != null) {\n    canvas.width = width;\n    canvas.height = height;\n    const ctx = canvas.getContext(\"2d\");\n    const imageData = new ImageData(bytes, width, height);\n    ctx.putImageData(imageData, 0, 0);\n  }\n  if ($img !== img) {\n    $img.dispose();\n  }\n  return bytes;\n}\nvar fromPixels = op({ fromPixels_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/gather_nd_util.js\nvar gather_nd_util_exports = {};\n__export(gather_nd_util_exports, {\n  prepareAndValidate: () => prepareAndValidate\n});\nfunction prepareAndValidate(tensor2, indices) {\n  const tensorRank = tensor2.shape.length;\n  const indicesRank = indices.shape.length;\n  if (tensorRank < 1) {\n    throw new Error(`tf.gatherND() expects the input to be rank 1 or higher, but the rank was ${tensorRank}.`);\n  }\n  if (indicesRank < 1) {\n    throw new Error(`tf.gatherND() expects the indices to be rank 1 or higher, but the rank was ${indicesRank}.`);\n  }\n  if (indices.dtype !== \"int32\") {\n    throw new Error(`tf.gatherND() expects the indices to be int32 type, but the dtype was ${indices.dtype}.`);\n  }\n  if (indices.shape[indicesRank - 1] > tensorRank) {\n    throw new Error(`index innermost dimension length must be <= tensor rank; saw: ${indices.shape[indicesRank - 1]} vs. ${tensorRank}`);\n  }\n  if (sizeFromShape(tensor2.shape) === 0) {\n    throw new Error(`Requested more than 0 entries, but input is empty. Input shape: ${tensor2.shape}.`);\n  }\n  const indicesShape = indices.shape;\n  const sliceRank = indicesShape[indicesShape.length - 1];\n  let nResult = 1;\n  for (let i = 0; i < indicesShape.length - 1; ++i) {\n    nResult *= indicesShape[i];\n  }\n  const inputShape = tensor2.shape;\n  const resultShape = indicesShape.slice();\n  resultShape.pop();\n  let sliceSize = 1;\n  for (let i = sliceRank; i < tensorRank; ++i) {\n    sliceSize *= inputShape[i];\n    resultShape.push(inputShape[i]);\n  }\n  const strides = [\n    ...computeStrides(tensor2.shape).map((stride) => stride / sliceSize),\n    1\n  ].slice(0, sliceRank);\n  return [resultShape, nResult, sliceSize, strides];\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/scatter_nd_util.js\nvar scatter_nd_util_exports = {};\n__export(scatter_nd_util_exports, {\n  calculateShapes: () => calculateShapes,\n  validateInput: () => validateInput,\n  validateUpdateShape: () => validateUpdateShape\n});\nfunction validateUpdateShape(shape, indices, updates) {\n  const sliceDim = indices.rank > 1 ? indices.shape[indices.rank - 1] : 1;\n  const batchDim = indices.rank > 1 ? indices.rank - 1 : 1;\n  const shapeError = `Must have updates.shape = indices.shape[:batchDim] + shape[sliceDim:], got updates.shape: ${updates.shape}, indices.shape: ${indices.shape}, shape: ${shape}, sliceDim: ${sliceDim}, and batchDim: ${batchDim}.`;\n  if (updates.rank < batchDim) {\n    throw new Error(shapeError + ` update.rank < ${batchDim}. `);\n  }\n  if (shape.length < sliceDim + (updates.rank - batchDim)) {\n    throw new Error(shapeError + ` Output shape length < ${sliceDim + (updates.rank - batchDim)}`);\n  }\n  if (updates.rank !== batchDim + shape.length - sliceDim) {\n    throw new Error(shapeError + ` update.rank != ${batchDim + shape.length - sliceDim}`);\n  }\n  for (let d = 0; d < batchDim; ++d) {\n    if (updates.shape[d] !== indices.shape[d]) {\n      throw new Error(shapeError + ` updates.shape[${d}] (${updates.shape[d]}) != indices.shape[${d}] (${indices.shape[d]}).`);\n    }\n  }\n  for (let d = 0; d < updates.rank - batchDim; ++d) {\n    if (updates.shape[d + batchDim] !== shape[d + sliceDim]) {\n      throw new Error(shapeError + ` updates.shape[${d + batchDim}] (${updates.shape[d + batchDim]}) != shape[${d + batchDim}] (${shape[d + batchDim]})`);\n    }\n  }\n}\nfunction validateInput(updates, indices, shape) {\n  if (indices.rank < 1) {\n    throw new Error(`tf.scatterND() expects the indices to be rank 1 or higher, but the rank was ${indices.rank}.`);\n  }\n  if (updates.rank < 1) {\n    throw new Error(`tf.scatterND() expects the updates to be rank 1 or higher, but the rank was ${updates.rank}.`);\n  }\n  if (indices.dtype !== \"int32\") {\n    throw new Error(`The dtype of 'indices' should be int32, but got dtype: ${indices.dtype}`);\n  }\n  if (shape.length < 1) {\n    throw new Error(`Output rank must be greater or equal to 1, but got shape: ${shape}`);\n  }\n  if (shape.length === 0) {\n    if (indices.size === 0) {\n      throw new Error(`Indices specified for empty output. indices shape: ${indices.shape}`);\n    }\n    if (updates.size === 0) {\n      throw new Error(`Updates specified for empty output. updates shape: ${updates.shape}`);\n    }\n  }\n  validateUpdateShape(shape, indices, updates);\n}\nfunction calculateShapes(updates, indices, shape) {\n  const indicesRank = indices.shape.length;\n  const sliceRank = indicesRank > 1 ? indices.shape[indicesRank - 1] : 1;\n  const totalNd = shape.length;\n  let sliceSize = 1;\n  for (let i = sliceRank; i < totalNd; ++i) {\n    sliceSize *= shape[i];\n  }\n  const safeSliceDim = sliceRank < 1 ? 1 : sliceRank;\n  const numUpdates = sizeFromShape(indices.shape) / safeSliceDim;\n  const strides = [...computeStrides(shape.slice(0, sliceRank)), 1];\n  const outputSize = sizeFromShape(shape);\n  return { sliceRank, numUpdates, sliceSize, strides, outputSize };\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/slice_util.js\nvar slice_util_exports = {};\n__export(slice_util_exports, {\n  assertParamsValid: () => assertParamsValid,\n  computeFlatOffset: () => computeFlatOffset,\n  computeOutShape: () => computeOutShape,\n  getNormalizedAxes: () => getNormalizedAxes,\n  isSliceContinous: () => isSliceContinous,\n  maskToAxes: () => maskToAxes,\n  parseSliceParams: () => parseSliceParams,\n  sliceInfo: () => sliceInfo,\n  startForAxis: () => startForAxis,\n  startIndicesWithElidedDims: () => startIndicesWithElidedDims,\n  stopForAxis: () => stopForAxis,\n  stopIndicesWithElidedDims: () => stopIndicesWithElidedDims,\n  stridesForAxis: () => stridesForAxis,\n  stridesWithElidedDims: () => stridesWithElidedDims\n});\nvar NEW_AXIS = -2;\nvar SHRINK_AXIS = -1;\nfunction assertParamsValid(input2, begin, size) {\n  const inputRank = input2.shape.length;\n  assert(inputRank === begin.length, () => `Error in slice${inputRank}D: Length of begin ${begin} must match the rank of the array (${inputRank}).`);\n  assert(inputRank === size.length, () => `Error in slice${inputRank}D: Length of size ${size} must match the rank of the array (${inputRank}).`);\n  for (let i = 0; i < inputRank; ++i) {\n    assert(begin[i] + size[i] <= input2.shape[i], () => `Error in slice${inputRank}D: begin[${i}] + size[${i}] (${begin[i] + size[i]}) would overflow input.shape[${i}] (${input2.shape[i]})`);\n  }\n}\nfunction maskToAxes(mask) {\n  const axes = [];\n  let axis = 0;\n  while (mask > 0) {\n    if (mask & 1) {\n      axes.push(axis);\n    }\n    mask /= 2;\n    axis++;\n  }\n  return axes;\n}\nfunction computeOutShape(begin, end, strides) {\n  const size = [];\n  for (let axis = 0; axis < begin.length; axis++) {\n    size[axis] = Math.ceil((end[axis] - begin[axis]) / strides[axis]);\n  }\n  return size;\n}\nfunction stridesWithElidedDims(strides, ellipsisInsertionIndex, numElidedAxes, inputShape) {\n  const newStrides = [...strides];\n  for (let i = newStrides.length; i < inputShape.length; i++) {\n    newStrides.push(1);\n  }\n  for (let i = 0; i < numElidedAxes; i++) {\n    if (i === 0) {\n      newStrides[ellipsisInsertionIndex] = 1;\n    } else {\n      newStrides.splice(ellipsisInsertionIndex, 0, 1);\n      newStrides.pop();\n    }\n  }\n  return newStrides;\n}\nfunction unnormalizeAxis(ellipsisInsertionIndex, numElidedAxes, normalizedAxis) {\n  if (normalizedAxis <= ellipsisInsertionIndex) {\n    return normalizedAxis;\n  }\n  return normalizedAxis - (numElidedAxes - 1);\n}\nfunction getElidedAxes(numElidedAxes, ellipsisInsertionIndex) {\n  const elidedAxes = [];\n  for (let i = 0; i < numElidedAxes; i++) {\n    elidedAxes.push(ellipsisInsertionIndex + i);\n  }\n  return elidedAxes;\n}\nfunction getNormalizedAxes(inputShape, ellipsisAxes, numInterpolatedAxes, begin, end, strides, beginMask, endMask, ellipsisMask) {\n  const inputRank = inputShape.length;\n  let normalizedBegin = new Array(inputRank), normalizedEnd = new Array(inputRank), normalizedStrides = new Array(inputRank);\n  if (ellipsisAxes.length && numInterpolatedAxes > 0) {\n    const fullIndex = ellipsisAxes[0];\n    const numElidedAxes = numInterpolatedAxes + 1;\n    normalizedBegin = startIndicesWithElidedDims(beginMask, fullIndex, numElidedAxes, begin, inputShape);\n    normalizedEnd = stopIndicesWithElidedDims(endMask, fullIndex, numElidedAxes, end, inputShape);\n    normalizedStrides = stridesWithElidedDims(strides, fullIndex, numElidedAxes, inputShape);\n  } else {\n    for (let axis = 0; axis < inputRank; axis++) {\n      normalizedBegin[axis] = startForAxis(beginMask, begin, strides, inputShape, axis, ellipsisMask);\n      normalizedEnd[axis] = stopForAxis(endMask, end, strides, inputShape, axis, ellipsisMask);\n      normalizedStrides[axis] = stridesForAxis(strides, axis, ellipsisMask);\n    }\n  }\n  return {\n    begin: normalizedBegin,\n    end: normalizedEnd,\n    strides: normalizedStrides\n  };\n}\nfunction startIndicesWithElidedDims(beginMask, ellipsisInsertionIndex, numElidedAxes, originalBegin, inputShape) {\n  const newIndices = [...inputShape];\n  const elidedAxes = getElidedAxes(numElidedAxes, ellipsisInsertionIndex);\n  for (let axis = 0; axis < newIndices.length; axis++) {\n    if (elidedAxes.indexOf(axis) > -1) {\n      newIndices[axis] = 0;\n    } else {\n      const originalAxis = unnormalizeAxis(ellipsisInsertionIndex, numElidedAxes, axis);\n      let originalValue = originalBegin[originalAxis];\n      if (beginMask & 1 << originalAxis) {\n        originalValue = 0;\n      }\n      newIndices[axis] = originalValue;\n    }\n  }\n  return newIndices;\n}\nfunction stopIndicesWithElidedDims(endMask, ellipsisInsertionIndex, numElidedAxes, originalEnd, inputShape) {\n  const newIndices = [...inputShape];\n  const elidedAxes = getElidedAxes(numElidedAxes, ellipsisInsertionIndex);\n  for (let axis = 0; axis < newIndices.length; axis++) {\n    if (elidedAxes.indexOf(axis) > -1) {\n      newIndices[axis] = Number.MAX_SAFE_INTEGER;\n    } else {\n      const originalAxis = unnormalizeAxis(ellipsisInsertionIndex, numElidedAxes, axis);\n      let originalValue = originalEnd[originalAxis];\n      if (endMask & 1 << originalAxis) {\n        originalValue = Number.MAX_SAFE_INTEGER;\n      }\n      newIndices[axis] = originalValue;\n    }\n  }\n  for (let i = 0; i < newIndices.length; i++) {\n    const axisSize = inputShape[i];\n    if (newIndices[i] < 0) {\n      newIndices[i] += axisSize;\n    }\n    newIndices[i] = clamp(0, newIndices[i], inputShape[i]);\n  }\n  return newIndices;\n}\nfunction stridesForAxis(strides, axis, ellipsisMask) {\n  let stride = strides[axis];\n  if (ellipsisMask & 1 << axis || stride == null) {\n    stride = 1;\n  }\n  return stride;\n}\nfunction startForAxis(beginMask, startIndices, strides, inputShape, axis, ellipsisMask) {\n  let start = startIndices[axis];\n  const stride = strides[axis] || 1;\n  if (beginMask & 1 << axis || ellipsisMask & 1 << axis || start == null) {\n    if (stride > 0) {\n      start = Number.MIN_SAFE_INTEGER;\n    } else {\n      start = Number.MAX_SAFE_INTEGER;\n    }\n  }\n  const axisSize = inputShape[axis];\n  if (start < 0) {\n    start += axisSize;\n  }\n  start = clamp(0, start, axisSize - 1);\n  return start;\n}\nfunction stopForAxis(endMask, stopIndices, strides, inputShape, axis, ellipsisMask) {\n  let stop = stopIndices[axis];\n  const stride = strides[axis] || 1;\n  if (endMask & 1 << axis || ellipsisMask & 1 << axis || stop == null) {\n    if (stride > 0) {\n      stop = Number.MAX_SAFE_INTEGER;\n    } else {\n      stop = Number.MIN_SAFE_INTEGER;\n    }\n  }\n  const axisSize = inputShape[axis];\n  if (stop < 0) {\n    stop += axisSize;\n  }\n  if (stride > 0) {\n    stop = clamp(0, stop, axisSize);\n  } else {\n    stop = clamp(-1, stop, axisSize - 1);\n  }\n  return stop;\n}\nfunction isSliceContinous(shape, begin, size) {\n  let firstNonOneAxis = size.length;\n  for (let i = 0; i < size.length; i++) {\n    if (size[i] > 1) {\n      firstNonOneAxis = i;\n      break;\n    }\n  }\n  for (let i = firstNonOneAxis + 1; i < size.length; i++) {\n    if (begin[i] > 0 || size[i] !== shape[i]) {\n      return false;\n    }\n  }\n  return true;\n}\nfunction computeFlatOffset(begin, strides) {\n  let flatOffset = begin.length > 0 ? begin[begin.length - 1] : 1;\n  for (let i = 0; i < begin.length - 1; i++) {\n    flatOffset += begin[i] * strides[i];\n  }\n  return flatOffset;\n}\nfunction parseSliceParams(x, begin, size) {\n  let begin_;\n  const xRank = x.shape.length;\n  if (typeof begin === \"number\") {\n    begin_ = [begin, ...new Array(xRank - 1).fill(0)];\n  } else if (begin.length < xRank) {\n    begin_ = begin.concat(new Array(xRank - begin.length).fill(0));\n  } else {\n    begin_ = begin.slice();\n  }\n  begin_.forEach((d) => {\n    assert(d !== -1, () => \"slice() does not support negative begin indexing.\");\n  });\n  let size_;\n  if (size == null) {\n    size_ = new Array(xRank).fill(-1);\n  } else if (typeof size === \"number\") {\n    size_ = [size, ...new Array(xRank - 1).fill(-1)];\n  } else if (size.length < xRank) {\n    size_ = size.concat(new Array(xRank - size.length).fill(-1));\n  } else {\n    size_ = size;\n  }\n  size_ = size_.map((d, i) => {\n    if (d >= 0) {\n      return d;\n    } else {\n      assert(d === -1, () => `Negative size values should be exactly -1 but got ${d} for the slice() size at index ${i}.`);\n      return x.shape[i] - begin_[i];\n    }\n  });\n  return [begin_, size_];\n}\nfunction sliceInfo(xShape, begin, end, strides, beginMask, endMask, ellipsisMask, newAxisMask, shrinkAxisMask) {\n  let stridesNonNull;\n  if (strides == null) {\n    stridesNonNull = new Array(begin.length);\n    stridesNonNull.fill(1);\n  } else {\n    stridesNonNull = strides;\n  }\n  if (ellipsisMask != null && (ellipsisMask & ellipsisMask - 1) !== 0) {\n    throw new Error(\"Multiple ellipses in slice is not allowed.\");\n  }\n  let ellipsisSeen = false;\n  const sparseSpec = {\n    dims: stridesNonNull.length,\n    numAddAxisAfterEllipsis: 0,\n    begin: begin.slice(),\n    end: end.slice(),\n    strides: stridesNonNull.slice(),\n    beginMask,\n    endMask,\n    ellipsisMask,\n    newAxisMask,\n    shrinkAxisMask\n  };\n  for (let i = 0; i < sparseSpec.dims; i++) {\n    if (ellipsisSeen && (1 << i & newAxisMask) !== 0) {\n      sparseSpec.numAddAxisAfterEllipsis++;\n    }\n    if (1 << i & ellipsisMask) {\n      ellipsisSeen = true;\n    }\n  }\n  if (!ellipsisSeen) {\n    sparseSpec.ellipsisMask |= 1 << sparseSpec.dims;\n    sparseSpec.dims++;\n  }\n  const denseSpec = {\n    dims: xShape.length,\n    beginMask: 0,\n    endMask: 0,\n    beginValid: false,\n    endValid: false\n  };\n  buildDenseSpec(sparseSpec, denseSpec);\n  let isIdentity = true;\n  let sliceDim0 = true;\n  let isSimpleSlice = true;\n  const processingShape = [];\n  const finalShape = [];\n  for (let i = 0; i < xShape.length; ++i) {\n    if (denseSpec.strides[i] === 0) {\n      throw Error(`strides[${i}] must be non-zero`);\n    }\n    const shrinkI = !!(denseSpec.shrinkAxisMask & 1 << i);\n    const dimI = xShape[i];\n    if (dimI === -1) {\n      processingShape.push(shrinkI ? 1 : -1);\n      continue;\n    }\n    const masks = [denseSpec.beginMask & 1 << i, denseSpec.endMask & 1 << i];\n    const validRange = [\n      denseSpec.strides[i] > 0 ? 0 : -1,\n      denseSpec.strides[i] > 0 ? dimI : dimI - 1\n    ];\n    if (shrinkI && denseSpec.strides[i] <= 0) {\n      throw Error(\"only stride 1 allowed on non-range indexing.\");\n    }\n    isSimpleSlice = isSimpleSlice && denseSpec.strides[i] === 1;\n    const beginAndEndMasked = !!(denseSpec.beginMask & 1 << i && denseSpec.endMask & 1 << i);\n    if (denseSpec.beginValid && denseSpec.endValid) {\n      if (shrinkI) {\n        const xFwd = denseSpec.begin[i] < 0 ? dimI + denseSpec.begin[i] : denseSpec.begin[i];\n        denseSpec.begin[i] = xFwd;\n        denseSpec.end[i] = denseSpec.begin[i] + 1;\n        if (xFwd < 0 || xFwd >= dimI) {\n          throw Error(`slice index ${denseSpec.begin[i]} of dimension ${i} out of bounds.`);\n        }\n      } else {\n        denseSpec.begin[i] = canonical(denseSpec.begin[i], 0, denseSpec.strides[i], dimI, masks, validRange);\n        denseSpec.end[i] = canonical(denseSpec.end[i], 1, denseSpec.strides[i], dimI, masks, validRange);\n      }\n      const takeAllInDimension = denseSpec.strides[i] === 1 && denseSpec.begin[i] === 0 && denseSpec.end[i] === dimI;\n      isIdentity = isIdentity && takeAllInDimension;\n      sliceDim0 = sliceDim0 && (i === 0 && denseSpec.strides[i] === 1 || takeAllInDimension);\n    } else {\n      isIdentity = isIdentity && (denseSpec.strides[i] === 1 && beginAndEndMasked);\n      sliceDim0 = sliceDim0 && (i === 0 && denseSpec.strides[i] === 1 || beginAndEndMasked);\n    }\n    let intervalLength;\n    let knownInterval = false;\n    if (denseSpec.beginValid && denseSpec.endValid) {\n      intervalLength = denseSpec.end[i] - denseSpec.begin[i];\n      knownInterval = true;\n    } else if (shrinkI) {\n      intervalLength = 1;\n      knownInterval = true;\n    } else if (beginAndEndMasked) {\n      if (dimI >= 0) {\n        if (denseSpec.strides[i] < 0) {\n          intervalLength = -dimI;\n        } else {\n          intervalLength = dimI;\n        }\n        knownInterval = true;\n      }\n    }\n    if (knownInterval) {\n      let sizeI;\n      if (intervalLength === 0 || intervalLength < 0 !== denseSpec.strides[i] < 0) {\n        sizeI = 0;\n      } else {\n        sizeI = Math.trunc(intervalLength / denseSpec.strides[i]) + (intervalLength % denseSpec.strides[i] !== 0 ? 1 : 0);\n      }\n      processingShape.push(sizeI);\n    } else {\n      processingShape.push(-1);\n    }\n  }\n  for (let denseDim = 0; denseDim < denseSpec.finalShapeGatherIndices.length; ++denseDim) {\n    const gatherIndex = denseSpec.finalShapeGatherIndices[denseDim];\n    if (gatherIndex >= 0) {\n      finalShape.push(processingShape[gatherIndex]);\n    } else if (gatherIndex === NEW_AXIS) {\n      finalShape.push(1);\n    }\n  }\n  const finalShapeSparse = finalShape.filter((dim, i) => denseSpec.finalShapeGatherIndices[i] !== NEW_AXIS);\n  return {\n    finalShapeSparse,\n    finalShape,\n    isIdentity,\n    sliceDim0,\n    isSimpleSlice,\n    begin: denseSpec.begin,\n    end: denseSpec.end,\n    strides: denseSpec.strides\n  };\n}\nfunction buildDenseSpec(sparse2, dense2) {\n  dense2.beginMask = 0;\n  dense2.endMask = 0;\n  dense2.shrinkAxisMask = 0;\n  let fullIndex = 0;\n  dense2.beginValid = sparse2.begin != null;\n  dense2.endValid = sparse2.end != null;\n  dense2.begin = new Array(dense2.dims);\n  dense2.end = new Array(dense2.dims);\n  dense2.strides = new Array(dense2.dims);\n  dense2.finalShapeGatherIndices = [];\n  dense2.finalShapeGatherIndicesSparse = [];\n  dense2.inputShapeGatherIndicesSparse = new Array(dense2.dims);\n  for (let i = 0; i < sparse2.dims; i++) {\n    if (1 << i & sparse2.ellipsisMask) {\n      const nextIndex = Math.min(dense2.dims - (sparse2.dims - i) + 1 + sparse2.numAddAxisAfterEllipsis, dense2.dims);\n      for (; fullIndex < nextIndex; fullIndex++) {\n        dense2.begin[fullIndex] = 0;\n        dense2.end[fullIndex] = 0;\n        dense2.strides[fullIndex] = 1;\n        dense2.beginMask |= 1 << fullIndex;\n        dense2.endMask |= 1 << fullIndex;\n        dense2.finalShapeGatherIndices.push(fullIndex);\n        dense2.finalShapeGatherIndicesSparse.push(-1);\n        dense2.inputShapeGatherIndicesSparse[fullIndex] = i;\n      }\n    } else if (1 << i & sparse2.newAxisMask) {\n      dense2.finalShapeGatherIndices.push(NEW_AXIS);\n      dense2.finalShapeGatherIndicesSparse.push(-1);\n    } else {\n      if (fullIndex === dense2.begin.length) {\n        throw Error(`Index out of range using input dim ${fullIndex}; input has only ${dense2.dims} dims, ${dense2.begin.length}.`);\n      }\n      if (sparse2.begin != null) {\n        dense2.begin[fullIndex] = sparse2.begin[i];\n      }\n      if (sparse2.end != null) {\n        dense2.end[fullIndex] = sparse2.end[i];\n      }\n      dense2.strides[fullIndex] = sparse2.strides[i];\n      if (sparse2.beginMask & 1 << i) {\n        dense2.beginMask |= 1 << fullIndex;\n      }\n      if (sparse2.endMask & 1 << i) {\n        dense2.endMask |= 1 << fullIndex;\n      }\n      if (sparse2.shrinkAxisMask & 1 << i) {\n        dense2.finalShapeGatherIndices.push(SHRINK_AXIS);\n        dense2.finalShapeGatherIndicesSparse.push(-1);\n        dense2.shrinkAxisMask |= 1 << fullIndex;\n      } else {\n        dense2.finalShapeGatherIndices.push(fullIndex);\n        dense2.finalShapeGatherIndicesSparse.push(i);\n      }\n      dense2.inputShapeGatherIndicesSparse[fullIndex] = i;\n      fullIndex++;\n    }\n  }\n}\nfunction canonical(x, c, strideI, dimI, masks, validRange) {\n  if (masks[c]) {\n    return strideI > 0 ? validRange[c] : validRange[c + 1 & 1];\n  } else {\n    const xFwd = x < 0 ? dimI + x : x;\n    return xFwd < validRange[0] ? validRange[0] : xFwd > validRange[1] ? validRange[1] : xFwd;\n  }\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/serialization.js\nvar serialization_exports = {};\n__export(serialization_exports, {\n  Serializable: () => Serializable,\n  SerializationMap: () => SerializationMap,\n  registerClass: () => registerClass\n});\nvar Serializable = class {\n  getClassName() {\n    return this.constructor.className;\n  }\n  static fromConfig(cls, config) {\n    return new cls(config);\n  }\n};\nvar SerializationMap = class {\n  constructor() {\n    this.classNameMap = {};\n  }\n  static getMap() {\n    if (SerializationMap.instance == null) {\n      SerializationMap.instance = new SerializationMap();\n    }\n    return SerializationMap.instance;\n  }\n  static register(cls) {\n    SerializationMap.getMap().classNameMap[cls.className] = [cls, cls.fromConfig];\n  }\n};\nfunction registerClass(cls) {\n  assert(cls.className != null, () => `Class being registered does not have the static className property defined.`);\n  assert(typeof cls.className === \"string\", () => `className is required to be a string, but got type ` + typeof cls.className);\n  assert(cls.className.length > 0, () => `Class being registered has an empty-string as its className, which is disallowed.`);\n  SerializationMap.register(cls);\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/test_util.js\nvar test_util_exports = {};\n__export(test_util_exports, {\n  TEST_EPSILON_FLOAT16: () => TEST_EPSILON_FLOAT16,\n  createVideoElement: () => createVideoElement,\n  encodeStrings: () => encodeStrings,\n  expectArrayBuffersEqual: () => expectArrayBuffersEqual,\n  expectArraysClose: () => expectArraysClose,\n  expectArraysEqual: () => expectArraysEqual,\n  expectNumbersClose: () => expectNumbersClose,\n  expectPromiseToFail: () => expectPromiseToFail,\n  expectValuesInRange: () => expectValuesInRange,\n  play: () => play,\n  testEpsilon: () => testEpsilon\n});\nvar TEST_EPSILON_FLOAT32 = 1e-3;\nvar TEST_EPSILON_FLOAT16 = 0.1;\nfunction expectArraysClose(actual, expected, epsilon3) {\n  if (epsilon3 == null) {\n    epsilon3 = testEpsilon();\n  }\n  return expectArraysPredicate(actual, expected, (a, b) => areClose(a, b, epsilon3));\n}\nfunction testEpsilon() {\n  return ENGINE.backend.floatPrecision() === 32 ? TEST_EPSILON_FLOAT32 : TEST_EPSILON_FLOAT16;\n}\nfunction expectArraysPredicate(actual, expected, predicate) {\n  let checkClassType = true;\n  if (isTypedArray(actual) || isTypedArray(expected)) {\n    checkClassType = false;\n  }\n  if (isTypedArray(actual) && isTypedArray(expected)) {\n    checkClassType = true;\n  }\n  if (checkClassType) {\n    const aType = actual.constructor.name;\n    const bType = expected.constructor.name;\n    if (aType !== bType) {\n      throw new Error(`Arrays are of different type. Actual: ${aType}. Expected: ${bType}`);\n    }\n  }\n  if (Array.isArray(actual) && Array.isArray(expected)) {\n    const actualShape = inferShape(actual);\n    const expectedShape = inferShape(expected);\n    if (!arraysEqual(actualShape, expectedShape)) {\n      throw new Error(`Arrays have different shapes. Actual: [${actualShape}]. Expected: [${expectedShape}]`);\n    }\n  }\n  const actualFlat = isTypedArray(actual) ? actual : flatten(actual);\n  const expectedFlat = isTypedArray(expected) ? expected : flatten(expected);\n  if (actualFlat.length !== expectedFlat.length) {\n    throw new Error(`Arrays have different lengths actual: ${actualFlat.length} vs expected: ${expectedFlat.length}.\nActual:   ${actualFlat}.\nExpected: ${expectedFlat}.`);\n  }\n  for (let i = 0; i < expectedFlat.length; ++i) {\n    const a = actualFlat[i];\n    const e = expectedFlat[i];\n    if (!predicate(a, e)) {\n      throw new Error(`Arrays differ: actual[${i}] = ${a}, expected[${i}] = ${e}.\nActual:   ${actualFlat}.\nExpected: ${expectedFlat}.`);\n    }\n  }\n  if (typeof expect !== \"undefined\") {\n    expect().nothing();\n  }\n}\nfunction expectPromiseToFail(fn, done) {\n  fn().then(() => done.fail(), () => done());\n  if (typeof expect !== \"undefined\") {\n    expect().nothing();\n  }\n}\nfunction expectArraysEqual(actual, expected) {\n  const exp5 = typeof expected === \"string\" || typeof expected === \"number\" || typeof expected === \"boolean\" ? [expected] : expected;\n  if (isString(actual) || isString(actual[0]) || isString(expected) || isString(expected[0])) {\n    return expectArraysPredicate(actual, exp5, (a, b) => a == b);\n  }\n  return expectArraysPredicate(actual, expected, (a, b) => areClose(a, b, 0));\n}\nfunction expectNumbersClose(a, e, epsilon3) {\n  if (epsilon3 == null) {\n    epsilon3 = testEpsilon();\n  }\n  if (!areClose(a, e, epsilon3)) {\n    throw new Error(`Numbers differ: actual === ${a}, expected === ${e}`);\n  }\n  if (typeof expect !== \"undefined\") {\n    expect().nothing();\n  }\n}\nfunction areClose(a, e, epsilon3) {\n  if (!isFinite(a) && !isFinite(e)) {\n    return true;\n  }\n  if (isNaN(a) || isNaN(e) || Math.abs(a - e) > epsilon3) {\n    return false;\n  }\n  return true;\n}\nfunction expectValuesInRange(actual, low, high) {\n  for (let i = 0; i < actual.length; i++) {\n    if (actual[i] < low || actual[i] > high) {\n      throw new Error(`Value out of range:${actual[i]} low: ${low}, high: ${high}`);\n    }\n  }\n}\nfunction expectArrayBuffersEqual(actual, expected) {\n  const actualArray = new Float32Array(actual);\n  const expectedArray = new Float32Array(expected);\n  if (actualArray.length !== expectedArray.length) {\n    throw new Error(`Expected ArrayBuffer to be of length ${expectedArray.length}, but it was ${actualArray.length}`);\n  }\n  for (let i = 0; i < expectedArray.length; i++) {\n    if (actualArray[i] !== expectedArray[i]) {\n      throw new Error(`Expected ArrayBuffer value at ${i} to be ${expectedArray[i]} but got ${actualArray[i]} instead`);\n    }\n  }\n}\nfunction encodeStrings(a) {\n  for (let i = 0; i < a.length; i++) {\n    const val = a[i];\n    if (Array.isArray(val)) {\n      encodeStrings(val);\n    } else {\n      a[i] = encodeString(val);\n    }\n  }\n  return a;\n}\nfunction createVideoElement(source) {\n  const video = document.createElement(\"video\");\n  if (\"playsInline\" in video) {\n    video.playsInline = true;\n  }\n  video.muted = true;\n  video.loop = true;\n  video.style.position = \"fixed\";\n  video.style.left = \"0px\";\n  video.style.top = \"0px\";\n  video.preload = \"auto\";\n  video.appendChild(source);\n  return new Promise((resolve) => {\n    video.addEventListener(\"loadeddata\", (_) => resolve(video));\n    video.load();\n  });\n}\nasync function play(video) {\n  await video.play();\n  if (\"requestVideoFrameCallback\" in video) {\n    await new Promise((resolve) => {\n      video.requestVideoFrameCallback(resolve);\n    });\n  }\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/version.js\nvar version = \"3.20.0\";\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/add.js\nfunction add_(a, b) {\n  let $a = convertToTensor(a, \"a\", \"add\");\n  let $b = convertToTensor(b, \"b\", \"add\");\n  [$a, $b] = makeTypesMatch($a, $b);\n  const inputs = { a: $a, b: $b };\n  return ENGINE.runKernel(Add, inputs);\n}\nvar add2 = op({ add_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/floorDiv.js\nfunction floorDiv_(a, b) {\n  let $a = convertToTensor(a, \"a\", \"floorDiv\");\n  let $b = convertToTensor(b, \"b\", \"floorDiv\");\n  [$a, $b] = makeTypesMatch($a, $b);\n  const inputs = { a: $a, b: $b };\n  return ENGINE.runKernel(FloorDiv, inputs);\n}\nvar floorDiv = op({ floorDiv_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/div.js\nfunction div_(a, b) {\n  let $a = convertToTensor(a, \"a\", \"div\");\n  let $b = convertToTensor(b, \"b\", \"div\");\n  [$a, $b] = makeTypesMatch($a, $b);\n  if ($a.dtype === \"int32\" && $b.dtype === \"int32\") {\n    return floorDiv($a, $b);\n  }\n  const inputs = { a: $a, b: $b };\n  const attrs = {};\n  return ENGINE.runKernel(RealDiv, inputs, attrs);\n}\nvar div = op({ div_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/mul.js\nfunction mul_(a, b) {\n  let $a = convertToTensor(a, \"a\", \"mul\");\n  let $b = convertToTensor(b, \"b\", \"mul\");\n  [$a, $b] = makeTypesMatch($a, $b);\n  const inputs = { a: $a, b: $b };\n  return ENGINE.runKernel(Multiply, inputs);\n}\nvar mul = op({ mul_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/abs.js\nfunction abs_(x) {\n  const $x = convertToTensor(x, \"x\", \"abs\");\n  if ($x.dtype === \"complex64\") {\n    const inputs = { x: $x };\n    return ENGINE.runKernel(ComplexAbs, inputs);\n  } else {\n    const inputs = { x: $x };\n    return ENGINE.runKernel(Abs, inputs);\n  }\n}\nvar abs = op({ abs_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/acos.js\nfunction acos_(x) {\n  const $x = convertToTensor(x, \"x\", \"acos\");\n  const inputs = { x: $x };\n  return ENGINE.runKernel(Acos, inputs);\n}\nvar acos = op({ acos_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/acosh.js\nfunction acosh_(x) {\n  const $x = convertToTensor(x, \"x\", \"acosh\");\n  const inputs = { x: $x };\n  return ENGINE.runKernel(Acosh, inputs);\n}\nvar acosh = op({ acosh_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/add_n.js\nfunction addN_(tensors) {\n  assert(Array.isArray(tensors), () => \"The argument passed to tf.addN() must be a list of tensors\");\n  assert(tensors.length >= 1, () => `Must pass at least one tensor to tf.addN(), but got ${tensors.length}`);\n  const $tensors = tensors.map((t, i) => convertToTensor(t, `tensors${i}`, \"addN\"));\n  const firstTensor = $tensors[0];\n  $tensors.forEach((t) => {\n    if (t.dtype !== firstTensor.dtype) {\n      throw new Error(\"All tensors passed to tf.addN() must have the same dtype\");\n    }\n  });\n  $tensors.forEach((t) => {\n    if (!arraysEqual(t.shape, firstTensor.shape)) {\n      throw new Error(\"All tensors passed to tf.addN() must have the same shape\");\n    }\n  });\n  const inputs = $tensors;\n  return ENGINE.runKernel(AddN, inputs);\n}\nvar addN = op({ addN_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/all.js\nfunction all_(x, axis = null, keepDims = false) {\n  const $x = convertToTensor(x, \"x\", \"all\", \"bool\");\n  const inputs = { x: $x };\n  const attrs = { axis, keepDims };\n  return ENGINE.runKernel(All, inputs, attrs);\n}\nvar all = op({ all_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/any.js\nfunction any_(x, axis = null, keepDims = false) {\n  const $x = convertToTensor(x, \"x\", \"any\", \"bool\");\n  const inputs = { x: $x };\n  const attrs = { axis, keepDims };\n  return ENGINE.runKernel(Any, inputs, attrs);\n}\nvar any = op({ any_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/arg_max.js\nfunction argMax_(x, axis = 0) {\n  const $x = convertToTensor(x, \"x\", \"argMax\");\n  const inputs = { x: $x };\n  const attrs = { axis };\n  return ENGINE.runKernel(ArgMax, inputs, attrs);\n}\nvar argMax = op({ argMax_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/arg_min.js\nfunction argMin_(x, axis = 0) {\n  const $x = convertToTensor(x, \"x\", \"argMin\");\n  const inputs = { x: $x };\n  const attrs = { axis };\n  return ENGINE.runKernel(ArgMin, inputs, attrs);\n}\nvar argMin = op({ argMin_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/asin.js\nfunction asin_(x) {\n  const $x = convertToTensor(x, \"x\", \"asin\");\n  const inputs = { x: $x };\n  return ENGINE.runKernel(Asin, inputs);\n}\nvar asin = op({ asin_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/asinh.js\nfunction asinh_(x) {\n  const $x = convertToTensor(x, \"x\", \"asinh\");\n  const inputs = { x: $x };\n  return ENGINE.runKernel(Asinh, inputs);\n}\nvar asinh = op({ asinh_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/atan.js\nfunction atan_(x) {\n  const $x = convertToTensor(x, \"x\", \"atan\");\n  const inputs = { x: $x };\n  return ENGINE.runKernel(Atan, inputs);\n}\nvar atan = op({ atan_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/atan2.js\nfunction atan2_(a, b) {\n  let $a = convertToTensor(a, \"a\", \"atan2\");\n  let $b = convertToTensor(b, \"b\", \"atan2\");\n  [$a, $b] = makeTypesMatch($a, $b);\n  const inputs = { a: $a, b: $b };\n  return ENGINE.runKernel(Atan2, inputs);\n}\nvar atan2 = op({ atan2_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/atanh.js\nfunction atanh_(x) {\n  const $x = convertToTensor(x, \"x\", \"atanh\");\n  const inputs = { x: $x };\n  return ENGINE.runKernel(Atanh, inputs);\n}\nvar atanh = op({ atanh_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/conv_util.js\nfunction computeDilation2DInfo(inputShape, filterShape, strides, pad3, dataFormat = \"NHWC\", dilations) {\n  const inputChannels = inputShape[3];\n  const $filterShape = [...filterShape, inputChannels];\n  const $dataFormat = convertConv2DDataFormat(dataFormat);\n  return computeConv2DInfo(inputShape, $filterShape, strides, dilations, pad3, null, null, $dataFormat);\n}\nfunction computePool2DInfo(inShape, filterSize, strides, dilations, pad3, roundingMode, dataFormat = \"channelsLast\") {\n  const [filterHeight, filterWidth] = parseTupleParam(filterSize);\n  let filterShape;\n  if (dataFormat === \"channelsLast\") {\n    filterShape = [filterHeight, filterWidth, inShape[3], inShape[3]];\n  } else if (dataFormat === \"channelsFirst\") {\n    filterShape = [filterHeight, filterWidth, inShape[1], inShape[1]];\n  } else {\n    throw new Error(`Unknown dataFormat ${dataFormat}`);\n  }\n  return computeConv2DInfo(inShape, filterShape, strides, dilations, pad3, roundingMode, false, dataFormat);\n}\nfunction computePool3DInfo(inShape, filterSize, strides, dilations, pad3, roundingMode, dataFormat = \"NDHWC\") {\n  const [filterDepth, filterHeight, filterWidth] = parse3TupleParam(filterSize);\n  let filterShape;\n  let $dataFormat;\n  if (dataFormat === \"NDHWC\") {\n    $dataFormat = \"channelsLast\";\n    filterShape = [filterDepth, filterHeight, filterWidth, inShape[4], inShape[4]];\n  } else if (dataFormat === \"NCDHW\") {\n    $dataFormat = \"channelsFirst\";\n    filterShape = [filterDepth, filterHeight, filterWidth, inShape[1], inShape[1]];\n  } else {\n    throw new Error(`Unknown dataFormat ${dataFormat}`);\n  }\n  return computeConv3DInfo(inShape, filterShape, strides, dilations, pad3, false, $dataFormat, roundingMode);\n}\nfunction computeConv2DInfo(inShape, filterShape, strides, dilations, pad3, roundingMode, depthwise = false, dataFormat = \"channelsLast\") {\n  let [batchSize, inHeight, inWidth, inChannels] = [-1, -1, -1, -1];\n  if (dataFormat === \"channelsLast\") {\n    [batchSize, inHeight, inWidth, inChannels] = inShape;\n  } else if (dataFormat === \"channelsFirst\") {\n    [batchSize, inChannels, inHeight, inWidth] = inShape;\n  } else {\n    throw new Error(`Unknown dataFormat ${dataFormat}`);\n  }\n  const [filterHeight, filterWidth, , filterChannels] = filterShape;\n  const [strideHeight, strideWidth] = parseTupleParam(strides);\n  const [dilationHeight, dilationWidth] = parseTupleParam(dilations);\n  const effectiveFilterHeight = getEffectiveFilterSize(filterHeight, dilationHeight);\n  const effectiveFilterWidth = getEffectiveFilterSize(filterWidth, dilationWidth);\n  const { padInfo, outHeight, outWidth } = getPadAndOutInfo(pad3, inHeight, inWidth, strideHeight, strideWidth, effectiveFilterHeight, effectiveFilterWidth, roundingMode, dataFormat);\n  const outChannels = depthwise ? filterChannels * inChannels : filterChannels;\n  let outShape;\n  if (dataFormat === \"channelsFirst\") {\n    outShape = [batchSize, outChannels, outHeight, outWidth];\n  } else if (dataFormat === \"channelsLast\") {\n    outShape = [batchSize, outHeight, outWidth, outChannels];\n  }\n  return {\n    batchSize,\n    dataFormat,\n    inHeight,\n    inWidth,\n    inChannels,\n    outHeight,\n    outWidth,\n    outChannels,\n    padInfo,\n    strideHeight,\n    strideWidth,\n    filterHeight,\n    filterWidth,\n    effectiveFilterHeight,\n    effectiveFilterWidth,\n    dilationHeight,\n    dilationWidth,\n    inShape,\n    outShape,\n    filterShape\n  };\n}\nfunction computeConv3DInfo(inShape, filterShape, strides, dilations, pad3, depthwise = false, dataFormat = \"channelsLast\", roundingMode) {\n  let [batchSize, inDepth, inHeight, inWidth, inChannels] = [-1, -1, -1, -1, -1];\n  if (dataFormat === \"channelsLast\") {\n    [batchSize, inDepth, inHeight, inWidth, inChannels] = inShape;\n  } else if (dataFormat === \"channelsFirst\") {\n    [batchSize, inChannels, inDepth, inHeight, inWidth] = inShape;\n  } else {\n    throw new Error(`Unknown dataFormat ${dataFormat}`);\n  }\n  const [filterDepth, filterHeight, filterWidth, , filterChannels] = filterShape;\n  const [strideDepth, strideHeight, strideWidth] = parse3TupleParam(strides);\n  const [dilationDepth, dilationHeight, dilationWidth] = parse3TupleParam(dilations);\n  const effectiveFilterDepth = getEffectiveFilterSize(filterDepth, dilationDepth);\n  const effectiveFilterHeight = getEffectiveFilterSize(filterHeight, dilationHeight);\n  const effectiveFilterWidth = getEffectiveFilterSize(filterWidth, dilationWidth);\n  const { padInfo, outDepth, outHeight, outWidth } = get3DPadAndOutInfo(pad3, inDepth, inHeight, inWidth, strideDepth, strideHeight, strideWidth, effectiveFilterDepth, effectiveFilterHeight, effectiveFilterWidth, roundingMode);\n  const outChannels = depthwise ? filterChannels * inChannels : filterChannels;\n  let outShape;\n  if (dataFormat === \"channelsFirst\") {\n    outShape = [batchSize, outChannels, outDepth, outHeight, outWidth];\n  } else if (dataFormat === \"channelsLast\") {\n    outShape = [batchSize, outDepth, outHeight, outWidth, outChannels];\n  }\n  return {\n    batchSize,\n    dataFormat,\n    inDepth,\n    inHeight,\n    inWidth,\n    inChannels,\n    outDepth,\n    outHeight,\n    outWidth,\n    outChannels,\n    padInfo,\n    strideDepth,\n    strideHeight,\n    strideWidth,\n    filterDepth,\n    filterHeight,\n    filterWidth,\n    effectiveFilterDepth,\n    effectiveFilterHeight,\n    effectiveFilterWidth,\n    dilationDepth,\n    dilationHeight,\n    dilationWidth,\n    inShape,\n    outShape,\n    filterShape\n  };\n}\nfunction computeOutputShape2D(inShape, fieldSize, stride, zeroPad, roundingMode) {\n  if (zeroPad == null) {\n    zeroPad = computeDefaultPad(inShape, fieldSize, stride);\n  }\n  const inputRows = inShape[0];\n  const inputCols = inShape[1];\n  const outputRows = round((inputRows - fieldSize + 2 * zeroPad) / stride + 1, roundingMode);\n  const outputCols = round((inputCols - fieldSize + 2 * zeroPad) / stride + 1, roundingMode);\n  return [outputRows, outputCols];\n}\nfunction computeOutputShape4D(inShape, fieldSize, outChannels, stride, zeroPad, roundingMode) {\n  if (zeroPad == null) {\n    zeroPad = computeDefaultPad(inShape, fieldSize, stride);\n  }\n  const inputDepth = inShape[0];\n  const inputRows = inShape[1];\n  const inputCols = inShape[2];\n  const outputDepths = round((inputDepth - fieldSize + 2 * zeroPad) / stride + 1, roundingMode);\n  const outputRows = round((inputRows - fieldSize + 2 * zeroPad) / stride + 1, roundingMode);\n  const outputCols = round((inputCols - fieldSize + 2 * zeroPad) / stride + 1, roundingMode);\n  return [outputDepths, outputRows, outputCols, outChannels];\n}\nfunction computeDefaultPad(inputShape, fieldSize, stride, dilation = 1) {\n  const effectiveFieldSize = getEffectiveFilterSize(fieldSize, dilation);\n  return Math.floor((inputShape[0] * (stride - 1) - stride + effectiveFieldSize) / 2);\n}\nfunction parseTupleParam(param) {\n  if (typeof param === \"number\") {\n    return [param, param, param];\n  }\n  if (param.length === 2) {\n    return [param[0], param[1], 1];\n  }\n  return param;\n}\nfunction parse3TupleParam(param) {\n  return typeof param === \"number\" ? [param, param, param] : param;\n}\nfunction getEffectiveFilterSize(filterSize, dilation) {\n  if (dilation <= 1) {\n    return filterSize;\n  }\n  return filterSize + (filterSize - 1) * (dilation - 1);\n}\nfunction getPadAndOutInfo(pad3, inHeight, inWidth, strideHeight, strideWidth, filterHeight, filterWidth, roundingMode, dataFormat) {\n  let padInfo;\n  let outHeight;\n  let outWidth;\n  if (typeof pad3 === \"number\") {\n    const padType = pad3 === 0 ? \"VALID\" : \"NUMBER\";\n    padInfo = { top: pad3, bottom: pad3, left: pad3, right: pad3, type: padType };\n    const outShape = computeOutputShape2D([inHeight, inWidth], filterHeight, strideHeight, pad3, roundingMode);\n    outHeight = outShape[0];\n    outWidth = outShape[1];\n  } else if (pad3 === \"same\") {\n    outHeight = Math.ceil(inHeight / strideHeight);\n    outWidth = Math.ceil(inWidth / strideWidth);\n    const padAlongHeight = Math.max(0, (outHeight - 1) * strideHeight + filterHeight - inHeight);\n    const padAlongWidth = Math.max(0, (outWidth - 1) * strideWidth + filterWidth - inWidth);\n    const top = Math.floor(padAlongHeight / 2);\n    const bottom = padAlongHeight - top;\n    const left = Math.floor(padAlongWidth / 2);\n    const right = padAlongWidth - left;\n    padInfo = { top, bottom, left, right, type: \"SAME\" };\n  } else if (pad3 === \"valid\") {\n    padInfo = { top: 0, bottom: 0, left: 0, right: 0, type: \"VALID\" };\n    outHeight = Math.ceil((inHeight - filterHeight + 1) / strideHeight);\n    outWidth = Math.ceil((inWidth - filterWidth + 1) / strideWidth);\n  } else if (typeof pad3 === \"object\") {\n    const top = dataFormat === \"channelsLast\" ? pad3[1][0] : pad3[2][0];\n    const bottom = dataFormat === \"channelsLast\" ? pad3[1][1] : pad3[2][1];\n    const left = dataFormat === \"channelsLast\" ? pad3[2][0] : pad3[3][0];\n    const right = dataFormat === \"channelsLast\" ? pad3[2][1] : pad3[3][1];\n    const padType = top === 0 && bottom === 0 && left === 0 && right === 0 ? \"VALID\" : \"EXPLICIT\";\n    padInfo = { top, bottom, left, right, type: padType };\n    outHeight = round((inHeight - filterHeight + top + bottom) / strideHeight + 1, roundingMode);\n    outWidth = round((inWidth - filterWidth + left + right) / strideWidth + 1, roundingMode);\n  } else {\n    throw Error(`Unknown padding parameter: ${pad3}`);\n  }\n  return { padInfo, outHeight, outWidth };\n}\nfunction get3DPadAndOutInfo(pad3, inDepth, inHeight, inWidth, strideDepth, strideHeight, strideWidth, filterDepth, filterHeight, filterWidth, roundingMode) {\n  let padInfo;\n  let outDepth;\n  let outHeight;\n  let outWidth;\n  if (typeof pad3 === \"number\") {\n    const padType = pad3 === 0 ? \"VALID\" : \"NUMBER\";\n    padInfo = {\n      top: pad3,\n      bottom: pad3,\n      left: pad3,\n      right: pad3,\n      front: pad3,\n      back: pad3,\n      type: padType\n    };\n    const outShape = computeOutputShape4D([inDepth, inHeight, inWidth, 1], filterDepth, 1, strideDepth, pad3, roundingMode);\n    outDepth = outShape[0];\n    outHeight = outShape[1];\n    outWidth = outShape[2];\n  } else if (pad3 === \"same\") {\n    outDepth = Math.ceil(inDepth / strideDepth);\n    outHeight = Math.ceil(inHeight / strideHeight);\n    outWidth = Math.ceil(inWidth / strideWidth);\n    const padAlongDepth = (outDepth - 1) * strideDepth + filterDepth - inDepth;\n    const padAlongHeight = (outHeight - 1) * strideHeight + filterHeight - inHeight;\n    const padAlongWidth = (outWidth - 1) * strideWidth + filterWidth - inWidth;\n    const front = Math.floor(padAlongDepth / 2);\n    const back = padAlongDepth - front;\n    const top = Math.floor(padAlongHeight / 2);\n    const bottom = padAlongHeight - top;\n    const left = Math.floor(padAlongWidth / 2);\n    const right = padAlongWidth - left;\n    padInfo = { top, bottom, left, right, front, back, type: \"SAME\" };\n  } else if (pad3 === \"valid\") {\n    padInfo = {\n      top: 0,\n      bottom: 0,\n      left: 0,\n      right: 0,\n      front: 0,\n      back: 0,\n      type: \"VALID\"\n    };\n    outDepth = Math.ceil((inDepth - filterDepth + 1) / strideDepth);\n    outHeight = Math.ceil((inHeight - filterHeight + 1) / strideHeight);\n    outWidth = Math.ceil((inWidth - filterWidth + 1) / strideWidth);\n  } else {\n    throw Error(`Unknown padding parameter: ${pad3}`);\n  }\n  return { padInfo, outDepth, outHeight, outWidth };\n}\nfunction round(value, roundingMode) {\n  if (!roundingMode) {\n    return Math.trunc(value);\n  }\n  switch (roundingMode) {\n    case \"round\":\n      return Math.round(value);\n    case \"ceil\":\n      return Math.ceil(value);\n    case \"floor\":\n      return Math.floor(value);\n    default:\n      throw new Error(`Unknown roundingMode ${roundingMode}`);\n  }\n}\nfunction tupleValuesAreOne(param) {\n  const [dimA, dimB, dimC] = parseTupleParam(param);\n  return dimA === 1 && dimB === 1 && dimC === 1;\n}\nfunction eitherStridesOrDilationsAreOne(strides, dilations) {\n  return tupleValuesAreOne(strides) || tupleValuesAreOne(dilations);\n}\nfunction convertConv2DDataFormat(dataFormat) {\n  if (dataFormat === \"NHWC\") {\n    return \"channelsLast\";\n  } else if (dataFormat === \"NCHW\") {\n    return \"channelsFirst\";\n  } else {\n    throw new Error(`Unknown dataFormat ${dataFormat}`);\n  }\n}\nfunction checkPadOnDimRoundingMode(opDesc, pad3, dimRoundingMode) {\n  if (dimRoundingMode != null) {\n    if (typeof pad3 === \"string\") {\n      throw Error(`Error in ${opDesc}: pad must be an integer when using dimRoundingMode ${dimRoundingMode} but got pad ${pad3}.`);\n    } else if (typeof pad3 === \"number\") {\n      assert(isInt(pad3), () => `Error in ${opDesc}: pad must be an integer when using dimRoundingMode ${dimRoundingMode} but got pad ${pad3}.`);\n    } else if (typeof pad3 === \"object\") {\n      pad3.forEach((p2) => {\n        p2.forEach((v) => {\n          assert(isInt(v), () => `Error in ${opDesc}: pad must be an integer when using dimRoundingMode ${dimRoundingMode} but got pad ${v}.`);\n        });\n      });\n    } else {\n      throw Error(`Error in ${opDesc}: Unknown padding parameter: ${pad3}`);\n    }\n  }\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/reshape.js\nfunction reshape_(x, shape) {\n  const $x = convertToTensor(x, \"x\", \"reshape\", \"string_or_numeric\");\n  const inputs = { x: $x };\n  const attrs = { shape };\n  return ENGINE.runKernel(Reshape, inputs, attrs);\n}\nvar reshape = op({ reshape_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/avg_pool.js\nfunction avgPool_(x, filterSize, strides, pad3, dimRoundingMode) {\n  const $x = convertToTensor(x, \"x\", \"avgPool\", \"float32\");\n  const dilations = 1;\n  assert(eitherStridesOrDilationsAreOne(strides, dilations), () => `Error in avgPool: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`);\n  let x4D = $x;\n  let reshapedTo4D = false;\n  if ($x.rank === 3) {\n    reshapedTo4D = true;\n    x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);\n  }\n  assert(x4D.rank === 4, () => `Error in avgPool: x must be rank 4 but got rank ${x4D.rank}.`);\n  checkPadOnDimRoundingMode(\"avgPool\", pad3, dimRoundingMode);\n  const inputs = { x: x4D };\n  const attrs = { filterSize, strides, pad: pad3, dimRoundingMode };\n  let res = ENGINE.runKernel(AvgPool, inputs, attrs);\n  res = cast(res, $x.dtype);\n  if (reshapedTo4D) {\n    return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);\n  }\n  return res;\n}\nvar avgPool = op({ avgPool_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/avg_pool_3d.js\nfunction avgPool3d_(x, filterSize, strides, pad3, dimRoundingMode, dataFormat = \"NDHWC\") {\n  const $x = convertToTensor(x, \"x\", \"avgPool3d\", \"float32\");\n  let x5D = $x;\n  let reshapedTo5D = false;\n  if ($x.rank === 4) {\n    reshapedTo5D = true;\n    x5D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2], $x.shape[3]]);\n  }\n  assert(x5D.rank === 5, () => `Error in avgPool3d: x must be rank 5 but got rank ${x5D.rank}.`);\n  assert(dataFormat === \"NDHWC\", () => `Error in avgPool3d: Only NDHWC is currently supported, but got dataFormat of ${dataFormat}`);\n  checkPadOnDimRoundingMode(\"avgPool3d\", pad3, dimRoundingMode);\n  const inputs = { x: x5D };\n  const attrs = { filterSize, strides, pad: pad3, dimRoundingMode, dataFormat };\n  let res = ENGINE.runKernel(AvgPool3D, inputs, attrs);\n  res = cast(res, x5D.dtype);\n  if (reshapedTo5D) {\n    return reshape(res, [res.shape[1], res.shape[2], res.shape[3], res.shape[4]]);\n  }\n  return res;\n}\nvar avgPool3d = op({ avgPool3d_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/concat.js\nfunction concat_(tensors, axis = 0) {\n  assert(tensors.length >= 1, () => \"Pass at least one tensor to concat\");\n  const $tensors = convertToTensorArray(tensors, \"tensors\", \"concat\", \"string_or_numeric\");\n  if ($tensors[0].dtype === \"complex64\") {\n    $tensors.forEach((tensor2) => {\n      if (tensor2.dtype !== \"complex64\") {\n        throw new Error(`Cannot concatenate complex64 tensors with a tensor\n          with dtype ${tensor2.dtype}. `);\n      }\n    });\n  }\n  if ($tensors.length === 1) {\n    return clone($tensors[0]);\n  }\n  const inputs = $tensors;\n  const attr = { axis };\n  return ENGINE.runKernel(Concat, inputs, attr);\n}\nvar concat = op({ concat_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/sigmoid.js\nfunction sigmoid_(x) {\n  const $x = convertToTensor(x, \"x\", \"sigmoid\", \"float32\");\n  const inputs = { x: $x };\n  return ENGINE.runKernel(Sigmoid, inputs);\n}\nvar sigmoid = op({ sigmoid_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/slice.js\nfunction slice_(x, begin, size) {\n  const $x = convertToTensor(x, \"x\", \"slice\", \"string_or_numeric\");\n  if ($x.rank === 0) {\n    throw new Error(\"Slicing scalar is not possible\");\n  }\n  const inputs = { x: $x };\n  const attrs = { begin, size };\n  return ENGINE.runKernel(Slice, inputs, attrs);\n}\nvar slice = op({ slice_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/tanh.js\nfunction tanh_(x) {\n  const $x = convertToTensor(x, \"x\", \"tanh\", \"float32\");\n  const inputs = { x: $x };\n  return ENGINE.runKernel(Tanh, inputs);\n}\nvar tanh2 = op({ tanh_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/basic_lstm_cell.js\nfunction basicLSTMCell_(forgetBias, lstmKernel, lstmBias, data, c, h) {\n  const $forgetBias = convertToTensor(forgetBias, \"forgetBias\", \"basicLSTMCell\");\n  const $lstmKernel = convertToTensor(lstmKernel, \"lstmKernel\", \"basicLSTMCell\");\n  const $lstmBias = convertToTensor(lstmBias, \"lstmBias\", \"basicLSTMCell\");\n  const $data = convertToTensor(data, \"data\", \"basicLSTMCell\");\n  const $c = convertToTensor(c, \"c\", \"basicLSTMCell\");\n  const $h = convertToTensor(h, \"h\", \"basicLSTMCell\");\n  const combined = concat([$data, $h], 1);\n  const weighted = matMul(combined, $lstmKernel);\n  const res = add2(weighted, $lstmBias);\n  const batchSize = res.shape[0];\n  const sliceCols = res.shape[1] / 4;\n  const sliceSize = [batchSize, sliceCols];\n  const i = slice(res, [0, 0], sliceSize);\n  const j = slice(res, [0, sliceCols], sliceSize);\n  const f = slice(res, [0, sliceCols * 2], sliceSize);\n  const o = slice(res, [0, sliceCols * 3], sliceSize);\n  const newC = add2(mul(sigmoid(i), tanh2(j)), mul($c, sigmoid(add2($forgetBias, f))));\n  const newH = mul(tanh2(newC), sigmoid(o));\n  return [newC, newH];\n}\nvar basicLSTMCell = op({ basicLSTMCell_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/batch_to_space_nd.js\nfunction batchToSpaceND_(x, blockShape, crops) {\n  const $x = convertToTensor(x, \"x\", \"batchToSpaceND\");\n  const prod6 = blockShape.reduce((a, b) => a * b);\n  assert($x.rank >= 1 + blockShape.length, () => `input rank is ${$x.rank} but should be > than blockShape.length ${blockShape.length}`);\n  assert(crops.length === blockShape.length, () => `crops.length is ${crops.length} but should be equal to blockShape.length  ${blockShape.length}`);\n  assert($x.shape[0] % prod6 === 0, () => `input tensor batch is ${$x.shape[0]} but is not divisible by the product of the elements of blockShape ${blockShape.join(\" * \")} === ${prod6}`);\n  const inputs = { x: $x };\n  const attrs = { blockShape, crops };\n  return ENGINE.runKernel(BatchToSpaceND, inputs, attrs);\n}\nvar batchToSpaceND = op({ batchToSpaceND_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/batchnorm_util.js\nfunction xAs4D(x) {\n  let x4D;\n  if (x.rank === 0 || x.rank === 1) {\n    x4D = reshape(x, [1, 1, 1, x.size]);\n  } else if (x.rank === 2) {\n    x4D = reshape(x, [1, 1, x.shape[0], x.shape[1]]);\n  } else if (x.rank === 3) {\n    x4D = reshape(x, [1, x.shape[0], x.shape[1], x.shape[2]]);\n  } else {\n    x4D = x;\n  }\n  return x4D;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/batchnorm.js\nfunction batchNorm_(x, mean5, variance, offset, scale2, varianceEpsilon) {\n  if (varianceEpsilon == null) {\n    varianceEpsilon = 1e-3;\n  }\n  const $x = convertToTensor(x, \"x\", \"batchNorm\");\n  const $mean = convertToTensor(mean5, \"mean\", \"batchNorm\");\n  const $variance = convertToTensor(variance, \"variance\", \"batchNorm\");\n  let $scale;\n  if (scale2 != null) {\n    $scale = convertToTensor(scale2, \"scale\", \"batchNorm\");\n  }\n  let $offset;\n  if (offset != null) {\n    $offset = convertToTensor(offset, \"offset\", \"batchNorm\");\n  }\n  assert($mean.rank === $variance.rank, () => \"Batch normalization gradient requires mean and variance to have equal ranks.\");\n  assert($offset == null || $mean.rank === $offset.rank, () => \"Batch normalization gradient requires mean and offset to have equal ranks.\");\n  assert($scale == null || $mean.rank === $scale.rank, () => \"Batch normalization gradient requires mean and scale to have equal ranks.\");\n  const x4D = xAs4D($x);\n  const inputs = {\n    x: x4D,\n    scale: $scale,\n    offset: $offset,\n    mean: $mean,\n    variance: $variance\n  };\n  const attrs = { varianceEpsilon };\n  const res = ENGINE.runKernel(FusedBatchNorm, inputs, attrs);\n  return reshape(res, $x.shape);\n}\nvar batchNorm = op({ batchNorm_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/batchnorm2d.js\nfunction batchNorm2d_(x, mean5, variance, offset, scale2, varianceEpsilon) {\n  const $x = convertToTensor(x, \"x\", \"batchNorm\");\n  const $mean = convertToTensor(mean5, \"mean\", \"batchNorm\");\n  const $variance = convertToTensor(variance, \"variance\", \"batchNorm\");\n  let $scale;\n  if (scale2 != null) {\n    $scale = convertToTensor(scale2, \"scale\", \"batchNorm\");\n  }\n  let $offset;\n  if (offset != null) {\n    $offset = convertToTensor(offset, \"offset\", \"batchNorm\");\n  }\n  assert($x.rank === 2, () => `Error in batchNorm2D: x must be rank 2 but got rank ${$x.rank}.`);\n  assert($mean.rank === 2 || $mean.rank === 1, () => `Error in batchNorm2D: mean must be rank 2 or rank 1 but got rank ${$mean.rank}.`);\n  assert($variance.rank === 2 || $variance.rank === 1, () => `Error in batchNorm2D: variance must be rank 2 or rank 1 but got rank ${$variance.rank}.`);\n  if ($scale != null) {\n    assert($scale.rank === 2 || $scale.rank === 1, () => `Error in batchNorm2D: scale must be rank 2 or rank 1 but got rank ${$scale.rank}.`);\n  }\n  if ($offset != null) {\n    assert($offset.rank === 2 || $offset.rank === 1, () => `Error in batchNorm2D: offset must be rank 2 or rank 1 but got rank ${$offset.rank}.`);\n  }\n  return batchNorm($x, $mean, $variance, $offset, $scale, varianceEpsilon);\n}\nvar batchNorm2d = op({ batchNorm2d_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/batchnorm3d.js\nfunction batchNorm3d_(x, mean5, variance, offset, scale2, varianceEpsilon) {\n  const $x = convertToTensor(x, \"x\", \"batchNorm\");\n  const $mean = convertToTensor(mean5, \"mean\", \"batchNorm\");\n  const $variance = convertToTensor(variance, \"variance\", \"batchNorm\");\n  let $scale;\n  if (scale2 != null) {\n    $scale = convertToTensor(scale2, \"scale\", \"batchNorm\");\n  }\n  let $offset;\n  if (offset != null) {\n    $offset = convertToTensor(offset, \"offset\", \"batchNorm\");\n  }\n  assert($x.rank === 3, () => `Error in batchNorm3D: x must be rank 3 but got rank ${$x.rank}.`);\n  assert($mean.rank === 3 || $mean.rank === 1, () => `Error in batchNorm3D: mean must be rank 3 or rank 1 but got rank ${$mean.rank}.`);\n  assert($variance.rank === 3 || $variance.rank === 1, () => `Error in batchNorm3D: variance must be rank 3 or rank 1 but got rank ${$variance.rank}.`);\n  if ($scale != null) {\n    assert($scale.rank === 3 || $scale.rank === 1, () => `Error in batchNorm3D: scale must be rank 3 or rank 1 but got rank ${$scale.rank}.`);\n  }\n  if ($offset != null) {\n    assert($offset.rank === 3 || $offset.rank === 1, () => `Error in batchNorm3D: offset must be rank 3 or rank 1 but got rank ${$offset.rank}.`);\n  }\n  return batchNorm($x, $mean, $variance, $offset, $scale, varianceEpsilon);\n}\nvar batchNorm3d = op({ batchNorm3d_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/batchnorm4d.js\nfunction batchNorm4d_(x, mean5, variance, offset, scale2, varianceEpsilon) {\n  const $x = convertToTensor(x, \"x\", \"batchNorm\");\n  const $mean = convertToTensor(mean5, \"mean\", \"batchNorm\");\n  const $variance = convertToTensor(variance, \"variance\", \"batchNorm\");\n  let $scale;\n  if (scale2 != null) {\n    $scale = convertToTensor(scale2, \"scale\", \"batchNorm\");\n  }\n  let $offset;\n  if (offset != null) {\n    $offset = convertToTensor(offset, \"offset\", \"batchNorm\");\n  }\n  assert($x.rank === 4, () => `Error in batchNorm4D: x must be rank 4 but got rank ${$x.rank}.`);\n  assert($mean.rank === 4 || $mean.rank === 1, () => `Error in batchNorm4D: mean must be rank 4 or rank 1 but got rank ${$mean.rank}.`);\n  assert($variance.rank === 4 || $variance.rank === 1, () => `Error in batchNorm4D: variance must be rank 4 or rank 1 but got rank ${$variance.rank}.`);\n  if ($scale != null) {\n    assert($scale.rank === 4 || $scale.rank === 1, () => `Error in batchNorm4D: scale must be rank 4 or rank 1 but got rank ${$scale.rank}.`);\n  }\n  if ($offset != null) {\n    assert($offset.rank === 4 || $offset.rank === 1, () => `Error in batchNorm4D: offset must be rank 4 or rank 1 but got rank ${$offset.rank}.`);\n  }\n  return batchNorm($x, $mean, $variance, $offset, $scale, varianceEpsilon);\n}\nvar batchNorm4d = op({ batchNorm4d_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/bincount.js\nfunction bincount_(x, weights, size) {\n  const $x = convertToTensor(x, \"x\", \"bincount\");\n  const $weights = convertToTensor(weights, \"weights\", \"bincount\");\n  assert($x.dtype === \"int32\", () => `Error in bincount: input dtype must be int32, but got ${$x.dtype}`);\n  assert(size >= 0, () => `size must be non-negative, but got ${size}.`);\n  assert($weights.size === $x.size || $weights.size === 0, () => `Error in bincount: weights must have the same size as input or0-length, but got input shape: ${$x.shape}, weights shape: ${$weights.shape}.`);\n  const inputs = { x: $x, weights: $weights };\n  const attrs = { size };\n  return ENGINE.runKernel(Bincount, inputs, attrs);\n}\nvar bincount = op({ bincount_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/broadcast_args.js\nfunction broadcastArgs_(s0, s1) {\n  const shape1Input = convertToTensor(s0, \"s0\", \"broadcastArgs\", \"int32\");\n  const shape2Input = convertToTensor(s1, \"s1\", \"broadcastArgs\", \"int32\");\n  if (shape1Input.rank !== 1) {\n    throw new Error(`broadcastArgs(): first input must be a vector (rank=1). Has rank ${shape1Input.rank}`);\n  }\n  if (shape2Input.rank !== 1) {\n    throw new Error(`broadcastArgs(): second input must be a vector (rank=1). Has rank ${shape2Input.rank}`);\n  }\n  const inputs = { s0: shape1Input, s1: shape2Input };\n  return ENGINE.runKernel(BroadcastArgs, inputs);\n}\nvar broadcastArgs = op({ broadcastArgs_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/broadcast_to.js\nfunction broadcastTo_(x, shape) {\n  let input2 = convertToTensor(x, \"broadcastTo\", \"x\");\n  const xShape = input2.shape;\n  if (shape.some((d) => !(d > 0) || d % 1 !== 0)) {\n    throw new Error(`broadcastTo(): Invalid broadcast shape [${shape}].`);\n  }\n  if (shape.length < input2.rank) {\n    throw new Error(`broadcastTo(): shape.length=${shape.length} < input.rank=${input2.rank}.`);\n  }\n  if (shape.length > input2.rank) {\n    const newShape = input2.shape.slice();\n    while (newShape.length < shape.length) {\n      newShape.unshift(1);\n    }\n    input2 = reshape(input2, newShape);\n  }\n  const inputShape = input2.shape;\n  const reps = Array.from(shape);\n  for (let i = shape.length - 1; i >= 0; i--) {\n    if (inputShape[i] === shape[i]) {\n      reps[i] = 1;\n    } else if (input2.shape[i] !== 1) {\n      throw new Error(`broadcastTo(): [${xShape}] cannot be broadcast to [${shape}].`);\n    }\n  }\n  const axes = reps.map((n, i) => n > 1 ? i : -1).filter((i) => i >= 0);\n  if (axes.length === 0) {\n    return clone(input2);\n  }\n  const inputs = { x: input2 };\n  const attrs = { reps };\n  return ENGINE.runKernel(Tile, inputs, attrs);\n}\nvar broadcastTo = op({ broadcastTo_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/ceil.js\nfunction ceil_(x) {\n  const $x = convertToTensor(x, \"x\", \"ceil\", \"float32\");\n  const inputs = { x: $x };\n  return ENGINE.runKernel(Ceil, inputs);\n}\nvar ceil = op({ ceil_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/clip_by_value.js\nfunction clipByValue_(x, clipValueMin, clipValueMax) {\n  const $x = convertToTensor(x, \"x\", \"clipByValue\");\n  assert(clipValueMin <= clipValueMax, () => `Error in clip: min (${clipValueMin}) must be less than or equal to max (${clipValueMax}).`);\n  const inputs = { x: $x };\n  const attrs = { clipValueMin, clipValueMax };\n  return ENGINE.runKernel(ClipByValue, inputs, attrs);\n}\nvar clipByValue = op({ clipByValue_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/concat_1d.js\nfunction concat1d_(tensors) {\n  return concat(tensors, 0);\n}\nvar concat1d = op({ concat1d_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/concat_2d.js\nfunction concat2d_(tensors, axis) {\n  return concat(tensors, axis);\n}\nvar concat2d = op({ concat2d_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/concat_3d.js\nfunction concat3d_(tensors, axis) {\n  return concat(tensors, axis);\n}\nvar concat3d = op({ concat3d_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/concat_4d.js\nfunction concat4d_(tensors, axis) {\n  return concat(tensors, axis);\n}\nvar concat4d = op({ concat4d_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/conv2d.js\nfunction conv2d_(x, filter, strides, pad3, dataFormat = \"NHWC\", dilations = [1, 1], dimRoundingMode) {\n  const $x = convertToTensor(x, \"x\", \"conv2d\", \"float32\");\n  const $filter = convertToTensor(filter, \"filter\", \"conv2d\", \"float32\");\n  let x4D = $x;\n  let reshapedTo4D = false;\n  if ($x.rank === 3) {\n    reshapedTo4D = true;\n    x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);\n  }\n  assert(x4D.rank === 4, () => `Error in conv2d: input must be rank 4, but got rank ${x4D.rank}.`);\n  assert($filter.rank === 4, () => `Error in conv2d: filter must be rank 4, but got rank ${$filter.rank}.`);\n  checkPadOnDimRoundingMode(\"conv2d\", pad3, dimRoundingMode);\n  const inDepth = dataFormat === \"NHWC\" ? x4D.shape[3] : x4D.shape[1];\n  assert(inDepth === $filter.shape[2], () => `Error in conv2d: depth of input (${inDepth}) must match input depth for filter ${$filter.shape[2]}.`);\n  assert(eitherStridesOrDilationsAreOne(strides, dilations), () => `Error in conv2D: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`);\n  const inputs = { x: x4D, filter: $filter };\n  const attrs = { strides, pad: pad3, dataFormat, dilations, dimRoundingMode };\n  const res = ENGINE.runKernel(Conv2D, inputs, attrs);\n  if (reshapedTo4D) {\n    return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);\n  }\n  return res;\n}\nvar conv2d = op({ conv2d_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/conv1d.js\nfunction conv1d_(x, filter, stride, pad3, dataFormat = \"NWC\", dilation = 1, dimRoundingMode) {\n  const $x = convertToTensor(x, \"x\", \"conv1d\");\n  const $filter = convertToTensor(filter, \"filter\", \"conv1d\");\n  let x3D = $x;\n  let reshapedTo3D = false;\n  if ($x.rank === 2) {\n    reshapedTo3D = true;\n    x3D = reshape($x, [1, $x.shape[0], $x.shape[1]]);\n  }\n  assert(x3D.rank === 3, () => `Error in conv1d: input must be rank 3, but got rank ${x3D.rank}.`);\n  assert($filter.rank === 3, () => `Error in conv1d: filter must be rank 3, but got rank ${$filter.rank}.`);\n  checkPadOnDimRoundingMode(\"conv1d\", pad3, dimRoundingMode);\n  assert(x3D.shape[2] === $filter.shape[1], () => `Error in conv1d: depth of input (${x3D.shape[2]}) must match input depth for filter ${$filter.shape[1]}.`);\n  assert(eitherStridesOrDilationsAreOne(stride, dilation), () => `Error in conv1D: Either stride or dilation must be 1. Got stride ${stride} and dilation '${dilation}'`);\n  assert(dataFormat === \"NWC\", () => `Error in conv1d: got dataFormat of ${dataFormat} but only NWC is currently supported.`);\n  const filter4D = reshape($filter, [1, $filter.shape[0], $filter.shape[1], $filter.shape[2]]);\n  const input4D = reshape(x3D, [x3D.shape[0], 1, x3D.shape[1], x3D.shape[2]]);\n  const strides = [1, stride];\n  const dilations = [1, dilation];\n  const conv2dDataFormat = \"NHWC\";\n  const res = conv2d(input4D, filter4D, strides, pad3, conv2dDataFormat, dilations, dimRoundingMode);\n  if (reshapedTo3D) {\n    return reshape(res, [res.shape[2], res.shape[3]]);\n  }\n  return reshape(res, [res.shape[0], res.shape[2], res.shape[3]]);\n}\nvar conv1d = op({ conv1d_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/conv2d_backprop_input.js\nfunction conv2DBackpropInput_(xShape, dy, filter, strides, pad3, dataFormat = \"NHWC\", dimRoundingMode) {\n  assert(xShape.length === dy.rank, () => `Length of inShape (${xShape.length}) and rank of dy (${dy.rank}) must match`);\n  let xShape4D = xShape;\n  let dy4D = dy;\n  let reshapedTo4D = false;\n  if (dy.rank === 3) {\n    reshapedTo4D = true;\n    dy4D = reshape(dy, [1, dy.shape[0], dy.shape[1], dy.shape[2]]);\n    xShape4D = [1, xShape[0], xShape[1], xShape[2]];\n  }\n  assert(xShape4D.length === 4, () => `Error in conv2dDerInput: inShape must be length 4, but got length ${xShape4D.length}.`);\n  assert(dy4D.rank === 4, () => `Error in conv2dDerInput: dy must be rank 4, but got rank ${dy4D.rank}`);\n  assert(filter.rank === 4, () => `Error in conv2dDerInput: filter must be rank 4, but got rank ${filter.rank}`);\n  const inDepth = dataFormat === \"NHWC\" ? xShape4D[3] : xShape4D[1];\n  const outDepth = dataFormat === \"NHWC\" ? dy4D.shape[3] : dy4D.shape[1];\n  assert(inDepth === filter.shape[2], () => `Error in conv2dDerInput: depth of input (${inDepth}) must match input depth for filter ${filter.shape[2]}.`);\n  assert(outDepth === filter.shape[3], () => `Error in conv2dDerInput: depth of output (${outDepth}) must match output depth for filter ${filter.shape[3]}.`);\n  checkPadOnDimRoundingMode(\"conv2dDerInput\", pad3, dimRoundingMode);\n  const inputs = { dy: dy4D, filter };\n  const attrs = { strides, pad: pad3, dataFormat, dimRoundingMode, inputShape: xShape4D };\n  const res = ENGINE.runKernel(Conv2DBackpropInput, inputs, attrs);\n  if (reshapedTo4D) {\n    return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);\n  }\n  return res;\n}\nvar conv2DBackpropInput = op({ conv2DBackpropInput_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/conv2d_transpose.js\nfunction conv2dTranspose_(x, filter, outputShape, strides, pad3, dimRoundingMode) {\n  const $x = convertToTensor(x, \"x\", \"conv2dTranspose\");\n  const $filter = convertToTensor(filter, \"filter\", \"conv2dTranspose\");\n  return conv2DBackpropInput(outputShape, $x, $filter, strides, pad3, \"NHWC\", dimRoundingMode);\n}\nvar conv2dTranspose = op({ conv2dTranspose_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/conv3d.js\nfunction conv3d_(x, filter, strides, pad3, dataFormat = \"NDHWC\", dilations = [1, 1, 1]) {\n  const $x = convertToTensor(x, \"x\", \"conv3d\");\n  const $filter = convertToTensor(filter, \"filter\", \"conv3d\");\n  let x5D = $x;\n  let reshapedTo5D = false;\n  if ($x.rank === 4) {\n    reshapedTo5D = true;\n    x5D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2], $x.shape[3]]);\n  }\n  assert(x5D.rank === 5, () => `Error in conv3d: input must be rank 5, but got rank ${x5D.rank}.`);\n  assert($filter.rank === 5, () => `Error in conv3d: filter must be rank 5, but got rank ${$filter.rank}.`);\n  assert(x5D.shape[4] === $filter.shape[3], () => `Error in conv3d: depth of input (${x5D.shape[4]}) must match input depth for filter ${$filter.shape[3]}.`);\n  assert(eitherStridesOrDilationsAreOne(strides, dilations), () => `Error in conv3D: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`);\n  assert(dataFormat === \"NDHWC\", () => `Error in conv3d: got dataFormat of ${dataFormat} but only NDHWC is currently supported.`);\n  const inputs = { x: x5D, filter: $filter };\n  const attrs = { strides, pad: pad3, dataFormat, dilations };\n  const res = ENGINE.runKernel(Conv3D, inputs, attrs);\n  if (reshapedTo5D) {\n    return reshape(res, [res.shape[1], res.shape[2], res.shape[3], res.shape[4]]);\n  }\n  return res;\n}\nvar conv3d = op({ conv3d_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/conv3d_backprop_input.js\nfunction conv3DBackpropInput_(xShape, dy, filter, strides, pad3) {\n  assert(xShape.length === dy.rank, () => `Length of inShape (${xShape.length}) and rank of dy (${dy.rank}) must match`);\n  let xShape5D = xShape;\n  let dy5D = dy;\n  let reshapedTo5D = false;\n  if (dy.rank === 4) {\n    reshapedTo5D = true;\n    dy5D = reshape(dy, [1, dy.shape[0], dy.shape[1], dy.shape[2], dy.shape[3]]);\n    xShape5D = [1, xShape[0], xShape[1], xShape[2], xShape[3]];\n  }\n  const inDepth = xShape5D[4];\n  const outDepth = dy5D.shape[4];\n  assert(xShape5D.length === 5, () => `Error in conv3dDerInput: inShape must be length 5, but got length ${xShape5D.length}.`);\n  assert(dy5D.rank === 5, () => `Error in conv3dDerInput: dy must be rank 5, but got rank ${dy5D.rank}`);\n  assert(filter.rank === 5, () => `Error in conv3dDerInput: filter must be rank 5, but got rank ${filter.rank}`);\n  assert(inDepth === filter.shape[3], () => `Error in conv3dDerInput: depth of input (${inDepth}) must match input depth for filter ${filter.shape[3]}.`);\n  assert(outDepth === filter.shape[4], () => `Error in conv3dDerInput: depth of output (${outDepth}) must match output depth for filter ${filter.shape[4]}.`);\n  const inputs = { dy: dy5D, filter };\n  const attrs = { pad: pad3, strides, inputShape: xShape5D };\n  const res = ENGINE.runKernel(Conv3DBackpropInputV2, inputs, attrs);\n  if (reshapedTo5D) {\n    return reshape(res, [res.shape[1], res.shape[2], res.shape[3], res.shape[4]]);\n  }\n  return res;\n}\nvar conv3DBackpropInput = op({ conv3DBackpropInput_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/conv3d_transpose.js\nfunction conv3dTranspose_(x, filter, outputShape, strides, pad3) {\n  const $x = convertToTensor(x, \"x\", \"conv3dTranspose\");\n  const $filter = convertToTensor(filter, \"filter\", \"conv3dTranspose\");\n  return conv3DBackpropInput(outputShape, $x, $filter, strides, pad3);\n}\nvar conv3dTranspose = op({ conv3dTranspose_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/cos.js\nfunction cos_(x) {\n  const $x = convertToTensor(x, \"x\", \"cos\", \"float32\");\n  const inputs = { x: $x };\n  return ENGINE.runKernel(Cos, inputs);\n}\nvar cos = op({ cos_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/cosh.js\nfunction cosh_(x) {\n  const $x = convertToTensor(x, \"x\", \"cosh\", \"float32\");\n  const inputs = { x: $x };\n  return ENGINE.runKernel(Cosh, inputs);\n}\nvar cosh = op({ cosh_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/cumprod.js\nfunction cumprod_(x, axis = 0, exclusive = false, reverse5 = false) {\n  const $x = convertToTensor(x, \"x\", \"cumprod\");\n  const inputs = { x: $x };\n  const attrs = { axis, exclusive, reverse: reverse5 };\n  return ENGINE.runKernel(Cumprod, inputs, attrs);\n}\nvar cumprod = op({ cumprod_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/cumsum.js\nfunction cumsum_(x, axis = 0, exclusive = false, reverse5 = false) {\n  const $x = convertToTensor(x, \"x\", \"cumsum\");\n  const inputs = { x: $x };\n  const attrs = { axis, exclusive, reverse: reverse5 };\n  return ENGINE.runKernel(Cumsum, inputs, attrs);\n}\nvar cumsum = op({ cumsum_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/dense_bincount.js\nfunction denseBincount_(x, weights, size, binaryOutput = false) {\n  const $x = convertToTensor(x, \"x\", \"denseBincount\");\n  const $weights = convertToTensor(weights, \"weights\", \"denseBincount\");\n  assert($x.dtype === \"int32\", () => `Error in denseBincount: input dtype must be int32, but got ${$x.dtype}`);\n  assert($x.rank <= 2, () => `Error in denseBincount: input must be at most rank 2, but got rank ${$x.rank}.`);\n  assert(size >= 0, () => `size must be non-negative, but got ${size}.`);\n  assert($weights.size === $x.size || $weights.size === 0, () => `Error in denseBincount: weights must have the same shape as x or 0-length, but got x shape: ${$x.shape}, weights shape: ${$weights.shape}.`);\n  const inputs = { x: $x, weights: $weights };\n  const attrs = { size, binaryOutput };\n  return ENGINE.runKernel(DenseBincount, inputs, attrs);\n}\nvar denseBincount = op({ denseBincount_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/depth_to_space.js\nfunction depthToSpace_(x, blockSize, dataFormat = \"NHWC\") {\n  const $x = convertToTensor(x, \"x\", \"depthToSpace\", \"float32\");\n  const inputHeight = dataFormat === \"NHWC\" ? $x.shape[1] : $x.shape[2];\n  const inputWidth = dataFormat === \"NHWC\" ? $x.shape[2] : $x.shape[3];\n  const inputDepth = dataFormat === \"NHWC\" ? $x.shape[3] : $x.shape[1];\n  assert(blockSize > 1, () => `blockSize should be > 1 for depthToSpace, but was: ${blockSize}`);\n  assert(inputHeight * blockSize >= 0, () => `Negative dimension size caused by overflow when multiplying\n    ${inputHeight} and ${blockSize}  for depthToSpace with input shape\n    ${$x.shape}`);\n  assert(inputWidth * blockSize >= 0, () => `Negative dimension size caused by overflow when multiplying\n    ${inputWidth} and ${blockSize} for depthToSpace with input shape\n        ${$x.shape}`);\n  assert(inputDepth % (blockSize * blockSize) === 0, () => `Dimension size must be evenly divisible by ${blockSize * blockSize} but is ${inputDepth} for depthToSpace with input shape ${$x.shape}`);\n  const inputs = { x: $x };\n  const attrs = { blockSize, dataFormat };\n  return ENGINE.runKernel(DepthToSpace, inputs, attrs);\n}\nvar depthToSpace = op({ depthToSpace_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/depthwise_conv2d.js\nfunction depthwiseConv2d_(x, filter, strides, pad3, dataFormat = \"NHWC\", dilations = [1, 1], dimRoundingMode) {\n  const $x = convertToTensor(x, \"x\", \"depthwiseConv2d\", \"float32\");\n  const $filter = convertToTensor(filter, \"filter\", \"depthwiseConv2d\", \"float32\");\n  let x4D = $x;\n  let reshapedTo4D = false;\n  if ($x.rank === 3) {\n    reshapedTo4D = true;\n    x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);\n  }\n  assert(x4D.rank === 4, () => `Error in depthwiseConv2d: input must be rank 4, but got rank ${x4D.rank}.`);\n  assert($filter.rank === 4, () => `Error in depthwiseConv2d: filter must be rank 4, but got rank ${$filter.rank}.`);\n  const inChannels = dataFormat === \"NHWC\" ? x4D.shape[3] : x4D.shape[1];\n  assert(inChannels === $filter.shape[2], () => `Error in depthwiseConv2d: number of input channels (${inChannels}) must match the inChannels dimension in filter ${$filter.shape[2]}.`);\n  checkPadOnDimRoundingMode(\"depthwiseConv2d\", pad3, dimRoundingMode);\n  const inputs = { x: x4D, filter: $filter };\n  const attrs = { strides, pad: pad3, dataFormat, dilations, dimRoundingMode };\n  const res = ENGINE.runKernel(DepthwiseConv2dNative, inputs, attrs);\n  if (reshapedTo4D) {\n    return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);\n  }\n  return res;\n}\nvar depthwiseConv2d = op({ depthwiseConv2d_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/diag.js\nfunction diag_(x) {\n  const $x = convertToTensor(x, \"x\", \"diag\");\n  const inputs = { x: $x };\n  return ENGINE.runKernel(Diag, inputs);\n}\nvar diag = op({ diag_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/dilation2d.js\nfunction dilation2d_(x, filter, strides, pad3, dilations = [1, 1], dataFormat = \"NHWC\") {\n  const $x = convertToTensor(x, \"x\", \"dilation2d\");\n  const $filter = convertToTensor(filter, \"filter\", \"dilation2d\");\n  assert($x.rank === 3 || $x.rank === 4, () => `Error in dilation2d: input must be rank 3 or 4, but got rank ${$x.rank}.`);\n  assert($filter.rank === 3, () => `Error in dilation2d: filter must be rank 3, but got rank ${$filter.rank}.`);\n  assert(dataFormat === \"NHWC\", () => `Error in dilation2d: Only NHWC is currently supported, but got dataFormat of ${dataFormat}`);\n  let x4D = $x;\n  let reshapedTo4D = false;\n  if ($x.rank === 3) {\n    x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);\n    reshapedTo4D = true;\n  }\n  const inputs = { x: x4D, filter: $filter };\n  const attrs = { strides, pad: pad3, dilations };\n  const res = ENGINE.runKernel(Dilation2D, inputs, attrs);\n  if (reshapedTo4D) {\n    return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);\n  }\n  return res;\n}\nvar dilation2d = op({ dilation2d_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/equal.js\nfunction equal_(a, b) {\n  let $a = convertToTensor(a, \"a\", \"equal\", \"string_or_numeric\");\n  let $b = convertToTensor(b, \"b\", \"equal\", \"string_or_numeric\");\n  [$a, $b] = makeTypesMatch($a, $b);\n  assertAndGetBroadcastShape($a.shape, $b.shape);\n  const inputs = { a: $a, b: $b };\n  return ENGINE.runKernel(Equal, inputs);\n}\nvar equal = op({ equal_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/where.js\nfunction where_(condition, a, b) {\n  const $a = convertToTensor(a, \"a\", \"where\");\n  const $b = convertToTensor(b, \"b\", \"where\");\n  const $condition = convertToTensor(condition, \"condition\", \"where\", \"bool\");\n  const broadcastShape = assertAndGetBroadcastShape(assertAndGetBroadcastShape($condition.shape, $a.shape), $b.shape);\n  const $broadcastedCondition = broadcastTo($condition, broadcastShape);\n  const $broadcastedA = broadcastTo($a, broadcastShape);\n  const $broadcastedB = broadcastTo($b, broadcastShape);\n  const inputs = {\n    condition: $broadcastedCondition,\n    t: $broadcastedA,\n    e: $broadcastedB\n  };\n  return ENGINE.runKernel(Select, inputs);\n}\nvar where = op({ where_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/zeros_like.js\nfunction zerosLike_(x) {\n  const $x = convertToTensor(x, \"x\", \"zerosLike\");\n  const inputs = { x: $x };\n  return ENGINE.runKernel(ZerosLike, inputs);\n}\nvar zerosLike = op({ zerosLike_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/div_no_nan.js\nfunction divNoNan_(a, b) {\n  let $a = convertToTensor(a, \"a\", \"div\");\n  let $b = convertToTensor(b, \"b\", \"div\");\n  [$a, $b] = makeTypesMatch($a, $b);\n  const divResult = div($a, $b);\n  const zeros4 = zerosLike(divResult);\n  const bEqualsZero = equal($b, zeros4);\n  return where(bEqualsZero, zeros4, divResult);\n}\nvar divNoNan = op({ divNoNan_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/dot.js\nfunction dot_(t1, t2) {\n  const $t1 = convertToTensor(t1, \"t1\", \"dot\");\n  const $t2 = convertToTensor(t2, \"t2\", \"dot\");\n  assert(($t1.rank === 1 || $t1.rank === 2) && ($t2.rank === 1 || $t2.rank === 2), () => `Error in dot: inputs must all be rank 1 or 2, but got ranks ${$t1.rank} and ${$t2.rank}.`);\n  const t1Inner = $t1.rank === 1 ? $t1.size : $t1.shape[1];\n  const t2Inner = $t2.rank === 1 ? $t2.size : $t2.shape[0];\n  assert(t1Inner === t2Inner, () => `Error in dot: inner dimensions of inputs must match, but got ${t1Inner} and ${t2Inner}.`);\n  if ($t1.rank === 1 && $t2.rank === 1) {\n    const t12D = reshape($t1, [1, -1]);\n    const t22D = reshape($t2, [-1, 1]);\n    const t1t2 = matMul(t12D, t22D);\n    return reshape(t1t2, []);\n  } else if ($t1.rank === 1 && $t2.rank === 2) {\n    const t12D = reshape($t1, [1, -1]);\n    const t22D = reshape($t2, [$t2.shape[0], $t2.shape[1]]);\n    const t1t2 = matMul(t12D, t22D);\n    return reshape(t1t2, [t1t2.size]);\n  } else if ($t1.rank === 2 && $t2.rank === 1) {\n    const t22D = reshape($t2, [-1, 1]);\n    const t1t2 = matMul($t1, t22D);\n    return reshape(t1t2, [t1t2.size]);\n  } else {\n    const t22D = reshape($t2, [$t2.shape[0], $t2.shape[1]]);\n    const t1t2 = matMul($t1, t22D);\n    return t1t2;\n  }\n}\nvar dot = op({ dot_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/einsum.js\nfunction einsum_(equation, ...tensors) {\n  const $tensors = tensors.map((t, i) => convertToTensor(t, `tensors${i}`, \"einsum\"));\n  const attrs = { equation };\n  return ENGINE.runKernel(Einsum, $tensors, attrs);\n}\nvar einsum = op({ einsum_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/elu.js\nfunction elu_(x) {\n  const $x = convertToTensor(x, \"x\", \"elu\", \"float32\");\n  const inputs = { x: $x };\n  return ENGINE.runKernel(Elu, inputs);\n}\nvar elu = op({ elu_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/erf.js\nfunction erf_(x) {\n  let $x = convertToTensor(x, \"x\", \"erf\");\n  assert($x.dtype === \"int32\" || $x.dtype === \"float32\", () => \"Input dtype must be `int32` or `float32`.\");\n  if ($x.dtype === \"int32\") {\n    $x = cast($x, \"float32\");\n  }\n  const inputs = { x: $x };\n  return ENGINE.runKernel(Erf, inputs);\n}\nvar erf = op({ erf_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/axis_util.js\nfunction axesAreInnerMostDims(axes, rank) {\n  for (let i = 0; i < axes.length; ++i) {\n    if (axes[axes.length - i - 1] !== rank - 1 - i) {\n      return false;\n    }\n  }\n  return true;\n}\nfunction combineLocations(outputLoc, reduceLoc, axes) {\n  const rank = outputLoc.length + reduceLoc.length;\n  const loc = [];\n  let outIdx = 0;\n  let reduceIdx = 0;\n  for (let dim = 0; dim < rank; dim++) {\n    if (axes.indexOf(dim) === -1) {\n      loc.push(outputLoc[outIdx++]);\n    } else {\n      loc.push(reduceLoc[reduceIdx++]);\n    }\n  }\n  return loc;\n}\nfunction computeOutAndReduceShapes(aShape, axes) {\n  const outShape = [];\n  const rank = aShape.length;\n  for (let dim = 0; dim < rank; dim++) {\n    if (axes.indexOf(dim) === -1) {\n      outShape.push(aShape[dim]);\n    }\n  }\n  const reduceShape = axes.map((dim) => aShape[dim]);\n  return [outShape, reduceShape];\n}\nfunction expandShapeToKeepDim(shape, axes) {\n  const reduceSubShape = axes.map((x) => 1);\n  return combineLocations(shape, reduceSubShape, axes);\n}\nfunction assertAxesAreInnerMostDims(msg, axes, rank) {\n  assert(axesAreInnerMostDims(axes, rank), () => `${msg} supports only inner-most axes for now. Got axes ${axes} and rank-${rank} input.`);\n}\nfunction getAxesPermutation(axes, rank) {\n  if (axesAreInnerMostDims(axes, rank)) {\n    return null;\n  }\n  const result = [];\n  for (let i = 0; i < rank; ++i) {\n    if (axes.indexOf(i) === -1) {\n      result.push(i);\n    }\n  }\n  axes.forEach((axis) => result.push(axis));\n  return result;\n}\nfunction getUndoAxesPermutation(axes) {\n  return axes.map((axis, i) => [i, axis]).sort((a, b) => a[1] - b[1]).map((x) => x[0]);\n}\nfunction getInnerMostAxes(numAxes, rank) {\n  const res = [];\n  for (let i = rank - numAxes; i < rank; ++i) {\n    res.push(i);\n  }\n  return res;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/max.js\nfunction max_(x, axis = null, keepDims = false) {\n  const $x = convertToTensor(x, \"x\", \"max\");\n  const inputs = { x: $x };\n  const attrs = { reductionIndices: axis, keepDims };\n  return ENGINE.runKernel(Max, inputs, attrs);\n}\nvar max = op({ max_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/min.js\nfunction min_(x, axis = null, keepDims = false) {\n  const $x = convertToTensor(x, \"x\", \"min\");\n  const inputs = { x: $x };\n  const attrs = { axis, keepDims };\n  return ENGINE.runKernel(Min, inputs, attrs);\n}\nvar min = op({ min_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/pow.js\nfunction pow_(base, exp5) {\n  let $base = convertToTensor(base, \"base\", \"pow\");\n  let $exp = convertToTensor(exp5, \"exp\", \"pow\");\n  [$base, $exp] = makeTypesMatch($base, $exp);\n  const inputs = { a: $base, b: $exp };\n  return ENGINE.runKernel(Pow, inputs);\n}\nvar pow = op({ pow_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/scalar.js\nfunction scalar(value, dtype) {\n  if ((isTypedArray(value) && dtype !== \"string\" || Array.isArray(value)) && dtype !== \"complex64\") {\n    throw new Error(\"Error creating a new Scalar: value must be a primitive (number|boolean|string)\");\n  }\n  if (dtype === \"string\" && isTypedArray(value) && !(value instanceof Uint8Array)) {\n    throw new Error(\"When making a scalar from encoded string, the value must be `Uint8Array`.\");\n  }\n  const shape = [];\n  const inferredShape = [];\n  return makeTensor(value, shape, inferredShape, dtype);\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/sqrt.js\nfunction sqrt_(x) {\n  const $x = convertToTensor(x, \"x\", \"sqrt\", \"float32\");\n  const inputs = { x: $x };\n  return ENGINE.runKernel(Sqrt, inputs);\n}\nvar sqrt = op({ sqrt_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/square.js\nfunction square_(x) {\n  const $x = convertToTensor(x, \"x\", \"square\");\n  const attrs = {};\n  return ENGINE.runKernel(\"Square\", { x: $x }, attrs);\n}\nvar square = op({ square_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/sum.js\nfunction sum_(x, axis = null, keepDims = false) {\n  let $x = convertToTensor(x, \"x\", \"sum\");\n  if ($x.dtype === \"bool\") {\n    $x = cast($x, \"int32\");\n  }\n  const inputs = { x: $x };\n  const attrs = { axis, keepDims };\n  return ENGINE.runKernel(Sum, inputs, attrs);\n}\nvar sum2 = op({ sum_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/norm.js\nfunction norm_(x, ord = \"euclidean\", axis = null, keepDims = false) {\n  x = convertToTensor(x, \"x\", \"norm\");\n  const norm2 = normImpl(x, ord, axis);\n  let keepDimsShape = norm2.shape;\n  if (keepDims) {\n    const axes = parseAxisParam(axis, x.shape);\n    keepDimsShape = expandShapeToKeepDim(norm2.shape, axes);\n  }\n  return reshape(norm2, keepDimsShape);\n}\nfunction normImpl(x, p2, axis = null) {\n  if (x.rank === 0) {\n    return abs(x);\n  }\n  if (x.rank !== 1 && axis === null) {\n    return normImpl(reshape(x, [-1]), p2, axis);\n  }\n  if (x.rank === 1 || typeof axis === \"number\" || Array.isArray(axis) && axis.length === 1) {\n    if (p2 === 1) {\n      return sum2(abs(x), axis);\n    }\n    if (p2 === Infinity) {\n      return max(abs(x), axis);\n    }\n    if (p2 === -Infinity) {\n      return min(abs(x), axis);\n    }\n    if (p2 === \"euclidean\" || p2 === 2) {\n      return sqrt(sum2(pow(abs(x), scalar(2, \"int32\")), axis));\n    }\n    throw new Error(`Error in norm: invalid ord value: ${p2}`);\n  }\n  if (Array.isArray(axis) && axis.length === 2) {\n    if (p2 === 1) {\n      return max(sum2(abs(x), axis[0]), axis[1] - 1);\n    }\n    if (p2 === Infinity) {\n      return max(sum2(abs(x), axis[1]), axis[0]);\n    }\n    if (p2 === -Infinity) {\n      return min(sum2(abs(x), axis[1]), axis[0]);\n    }\n    if (p2 === \"fro\" || p2 === \"euclidean\") {\n      return sqrt(sum2(square(x), axis));\n    }\n    throw new Error(`Error in norm: invalid ord value: ${p2}`);\n  }\n  throw new Error(`Error in norm: invalid axis: ${axis}`);\n}\nvar norm = op({ norm_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/euclidean_norm.js\nfunction euclideanNorm_(x, axis = null, keepDims = false) {\n  return norm(x, \"euclidean\", axis, keepDims);\n}\nvar euclideanNorm = op({ euclideanNorm_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/exp.js\nfunction exp_(x) {\n  const $x = convertToTensor(x, \"x\", \"exp\");\n  const inputs = { x: $x };\n  return ENGINE.runKernel(Exp, inputs);\n}\nvar exp = op({ exp_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/expand_dims.js\nfunction expandDims_(x, axis = 0) {\n  const $x = convertToTensor(x, \"x\", \"expandDims\", \"string_or_numeric\");\n  assert(axis <= $x.rank, () => \"Axis must be <= rank of the tensor\");\n  const inputs = { input: $x };\n  const attrs = { dim: axis };\n  return ENGINE.runKernel(ExpandDims, inputs, attrs);\n}\nvar expandDims = op({ expandDims_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/expm1.js\nfunction expm1_(x) {\n  const $x = convertToTensor(x, \"x\", \"expm1\");\n  const inputs = { x: $x };\n  return ENGINE.runKernel(Expm1, inputs);\n}\nvar expm1 = op({ expm1_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/tile.js\nfunction tile_(x, reps) {\n  const $x = convertToTensor(x, \"x\", \"tile\", \"string_or_numeric\");\n  assert($x.rank === reps.length, () => `Error in transpose: rank of input ${$x.rank} must match length of reps ${reps}.`);\n  const inputs = { x: $x };\n  const attrs = { reps };\n  return ENGINE.runKernel(Tile, inputs, attrs);\n}\nvar tile = op({ tile_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/eye.js\nfunction eye_(numRows, numColumns, batchShape, dtype = \"float32\") {\n  if (numColumns == null) {\n    numColumns = numRows;\n  }\n  const buff = buffer([numRows, numColumns], dtype);\n  const n = numRows <= numColumns ? numRows : numColumns;\n  for (let i = 0; i < n; ++i) {\n    buff.set(1, i, i);\n  }\n  const out = reshape(buff.toTensor(), [numRows, numColumns]);\n  if (batchShape == null) {\n    return out;\n  } else {\n    if (batchShape.length === 1) {\n      return tile(expandDims(out, 0), [batchShape[0], 1, 1]);\n    } else if (batchShape.length === 2) {\n      return tile(expandDims(expandDims(out, 0), 0), [batchShape[0], batchShape[1], 1, 1]);\n    } else if (batchShape.length === 3) {\n      return tile(expandDims(expandDims(expandDims(out, 0), 0), 0), [\n        batchShape[0],\n        batchShape[1],\n        batchShape[2],\n        1,\n        1\n      ]);\n    } else {\n      throw new Error(`eye() currently supports only 1D and 2D batchShapes, but received ${batchShape.length}D.`);\n    }\n  }\n}\nvar eye = op({ eye_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/fill.js\nfunction fill(shape, value, dtype) {\n  const attrs = { shape, value, dtype };\n  return ENGINE.runKernel(Fill, {}, attrs);\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/floor.js\nfunction floor_(x) {\n  const $x = convertToTensor(x, \"x\", \"floor\", \"float32\");\n  const inputs = { x: $x };\n  return ENGINE.runKernel(Floor, inputs);\n}\nvar floor = op({ floor_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/gather.js\nfunction gather_(x, indices, axis = 0, batchDims = 0) {\n  const $x = convertToTensor(x, \"x\", \"gather\");\n  const $indices = convertToTensor(indices, \"indices\", \"gather\", \"int32\");\n  const inputs = { x: $x, indices: $indices };\n  const attrs = { axis, batchDims };\n  return ENGINE.runKernel(GatherV2, inputs, attrs);\n}\nvar gather = op({ gather_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/greater.js\nfunction greater_(a, b) {\n  let $a = convertToTensor(a, \"a\", \"greater\", \"string_or_numeric\");\n  let $b = convertToTensor(b, \"b\", \"greater\", \"string_or_numeric\");\n  [$a, $b] = makeTypesMatch($a, $b);\n  assertAndGetBroadcastShape($a.shape, $b.shape);\n  const inputs = { a: $a, b: $b };\n  return ENGINE.runKernel(Greater, inputs);\n}\nvar greater = op({ greater_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/greater_equal.js\nfunction greaterEqual_(a, b) {\n  let $a = convertToTensor(a, \"a\", \"greaterEqual\", \"string_or_numeric\");\n  let $b = convertToTensor(b, \"b\", \"greaterEqual\", \"string_or_numeric\");\n  [$a, $b] = makeTypesMatch($a, $b);\n  assertAndGetBroadcastShape($a.shape, $b.shape);\n  const inputs = { a: $a, b: $b };\n  return ENGINE.runKernel(GreaterEqual, inputs);\n}\nvar greaterEqual = op({ greaterEqual_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/is_finite.js\nfunction isFinite_(x) {\n  const $x = convertToTensor(x, \"x\", \"isFinite\");\n  const inputs = { x: $x };\n  return ENGINE.runKernel(IsFinite, inputs);\n}\nvar isFinite2 = op({ isFinite_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/is_inf.js\nfunction isInf_(x) {\n  const $x = convertToTensor(x, \"x\", \"isInf\");\n  const inputs = { x: $x };\n  return ENGINE.runKernel(IsInf, inputs);\n}\nvar isInf = op({ isInf_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/is_nan.js\nfunction isNaN_(x) {\n  const $x = convertToTensor(x, \"x\", \"isNaN\");\n  const inputs = { x: $x };\n  return ENGINE.runKernel(IsNan, inputs);\n}\nvar isNaN2 = op({ isNaN_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/leaky_relu.js\nfunction leakyRelu_(x, alpha = 0.2) {\n  const $x = convertToTensor(x, \"x\", \"leakyRelu\");\n  const inputs = { x: $x };\n  const attrs = { alpha };\n  return ENGINE.runKernel(LeakyRelu, inputs, attrs);\n}\nvar leakyRelu = op({ leakyRelu_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/less.js\nfunction less_(a, b) {\n  let $a = convertToTensor(a, \"a\", \"less\", \"string_or_numeric\");\n  let $b = convertToTensor(b, \"b\", \"less\", \"string_or_numeric\");\n  [$a, $b] = makeTypesMatch($a, $b);\n  assertAndGetBroadcastShape($a.shape, $b.shape);\n  const inputs = { a: $a, b: $b };\n  return ENGINE.runKernel(Less, inputs);\n}\nvar less = op({ less_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/less_equal.js\nfunction lessEqual_(a, b) {\n  let $a = convertToTensor(a, \"a\", \"lessEqual\", \"string_or_numeric\");\n  let $b = convertToTensor(b, \"b\", \"lessEqual\", \"string_or_numeric\");\n  [$a, $b] = makeTypesMatch($a, $b);\n  assertAndGetBroadcastShape($a.shape, $b.shape);\n  const inputs = { a: $a, b: $b };\n  return ENGINE.runKernel(LessEqual, inputs);\n}\nvar lessEqual = op({ lessEqual_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/linspace.js\nfunction linspace(start, stop, num) {\n  if (num <= 0) {\n    throw new Error(\"The number of values should be positive.\");\n  }\n  const attrs = { start, stop, num };\n  return ENGINE.runKernel(LinSpace, {}, attrs);\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/local_response_normalization.js\nfunction localResponseNormalization_(x, depthRadius = 5, bias = 1, alpha = 1, beta = 0.5) {\n  const $x = convertToTensor(x, \"x\", \"localResponseNormalization\");\n  assert($x.rank === 4 || $x.rank === 3, () => `Error in localResponseNormalization: x must be rank 3 or 4 but got\n               rank ${$x.rank}.`);\n  assert(isInt(depthRadius), () => `Error in localResponseNormalization: depthRadius must be an integer but got depthRadius ${depthRadius}.`);\n  let x4D = $x;\n  let reshapedTo4D = false;\n  if ($x.rank === 3) {\n    reshapedTo4D = true;\n    x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);\n  }\n  const inputs = { x: x4D };\n  const attrs = { depthRadius, bias, alpha, beta };\n  const res = ENGINE.runKernel(LRN, inputs, attrs);\n  if (reshapedTo4D) {\n    return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);\n  } else {\n    return res;\n  }\n}\nvar localResponseNormalization = op({ localResponseNormalization_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/log.js\nfunction log_(x) {\n  const $x = convertToTensor(x, \"x\", \"log\", \"float32\");\n  const inputs = { x: $x };\n  return ENGINE.runKernel(Log, inputs);\n}\nvar log2 = op({ log_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/log1p.js\nfunction log1p_(x) {\n  const $x = convertToTensor(x, \"x\", \"log1p\");\n  const inputs = { x: $x };\n  return ENGINE.runKernel(Log1p, inputs);\n}\nvar log1p = op({ log1p_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/gradients.js\nfunction grad(f) {\n  assert(isFunction(f), () => \"The f passed in grad(f) must be a function\");\n  return (x, dy) => {\n    const $x = convertToTensor(x, \"x\", \"tf.grad\", \"string_or_numeric\");\n    const $dy = dy != null ? convertToTensor(dy, \"dy\", \"tf.grad\") : null;\n    return ENGINE.tidy(() => {\n      const { value, grads: grads2 } = ENGINE.gradients(() => f($x), [$x], $dy);\n      if ($dy != null) {\n        assertShapesMatch(value.shape, $dy.shape, \"The shape of dy passed in grad(f)(x, dy) must match the shape returned by f(x)\");\n      }\n      checkGrads(grads2);\n      return grads2[0];\n    });\n  };\n}\nfunction grads(f) {\n  assert(isFunction(f), () => \"The f passed in grads(f) must be a function\");\n  return (args, dy) => {\n    assert(Array.isArray(args), () => \"The args passed in grads(f)(args) must be an array of `Tensor`s or `TensorLike`s\");\n    const $args = convertToTensorArray(args, \"args\", \"tf.grads\", \"string_or_numeric\");\n    const $dy = dy != null ? convertToTensor(dy, \"dy\", \"tf.grads\") : null;\n    return ENGINE.tidy(() => {\n      const { value, grads: grads2 } = ENGINE.gradients(() => f(...$args), $args, $dy);\n      if ($dy != null) {\n        assertShapesMatch(value.shape, $dy.shape, \"The shape of dy passed in grads(f)([x1,...], dy) must match the shape returned by f([x1,...])\");\n      }\n      checkGrads(grads2);\n      return grads2;\n    });\n  };\n}\nfunction valueAndGrad(f) {\n  assert(isFunction(f), () => \"The f passed in valueAndGrad(f) must be a function\");\n  return (x, dy) => {\n    assert(x instanceof Tensor, () => \"The x passed in valueAndGrad(f)(x) must be a tensor\");\n    assert(dy == null || dy instanceof Tensor, () => \"The dy passed in valueAndGrad(f)(x, dy) must be a tensor\");\n    const { grads: grads2, value } = ENGINE.gradients(() => f(x), [x], dy);\n    checkGrads(grads2);\n    return { grad: grads2[0], value };\n  };\n}\nfunction valueAndGrads(f) {\n  assert(isFunction(f), () => \"The f passed in valueAndGrads(f) must be a function\");\n  return (args, dy) => {\n    assert(Array.isArray(args) && args.every((arg) => arg instanceof Tensor), () => \"The args passed in valueAndGrads(f)(args) must be array of tensors\");\n    assert(dy == null || dy instanceof Tensor, () => \"The dy passed in valueAndGrads(f)(args, dy) must be a tensor\");\n    const res = ENGINE.gradients(() => f(...args), args, dy);\n    if (dy != null) {\n      assertShapesMatch(res.value.shape, dy.shape, \"The shape of dy passed in valueAndGrads(f)([x1,...], dy) must match the shape returned by f([x1,...])\");\n    }\n    checkGrads(res.grads);\n    return res;\n  };\n}\nfunction variableGrads(f, varList) {\n  assert(isFunction(f), () => \"The f passed in variableGrads(f) must be a function\");\n  assert(varList == null || Array.isArray(varList) && varList.every((v) => v instanceof Variable), () => \"The varList passed in variableGrads(f, varList) must be an array of variables\");\n  const specifiedVarList = varList != null;\n  if (!specifiedVarList) {\n    varList = [];\n    for (const varName in ENGINE.registeredVariables) {\n      varList.push(ENGINE.registeredVariables[varName]);\n    }\n  }\n  const specifiedNonTrainable = specifiedVarList ? varList.filter((variable2) => !variable2.trainable) : null;\n  const originalVarCount = varList.length;\n  varList = varList.filter((variable2) => variable2.trainable);\n  assert(varList.length > 0, () => `variableGrads() expects at least one of the input variables to be trainable, but none of the ${originalVarCount} variables is trainable.`);\n  const allowNoGradients = true;\n  const { value, grads: grads2 } = ENGINE.gradients(f, varList, null, allowNoGradients);\n  assert(grads2.some((g) => g != null), () => \"Cannot find a connection between any variable and the result of the loss function y=f(x). Please make sure the operations that use variables are inside the function f passed to minimize().\");\n  assert(value.rank === 0, () => `The f passed in variableGrads(f) must return a scalar, but it returned a rank-${value.rank} tensor`);\n  const namedGrads = {};\n  varList.forEach((v, i) => {\n    if (grads2[i] != null) {\n      namedGrads[v.name] = grads2[i];\n    }\n  });\n  if (specifiedNonTrainable != null) {\n    specifiedNonTrainable.forEach((v) => namedGrads[v.name] = null);\n  }\n  return { value, grads: namedGrads };\n}\nfunction customGrad(f) {\n  return ENGINE.customGrad(f);\n}\nfunction checkGrads(grads2) {\n  const numNullGradients = grads2.filter((g) => g == null).length;\n  if (numNullGradients > 0) {\n    throw new Error(`Cannot compute gradient of y=f(x) with respect to x. Make sure that\n    the f you passed encloses all operations that lead from x to y.`);\n  }\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/softplus.js\nfunction softplus_(x) {\n  const $x = convertToTensor(x, \"x\", \"softplus\");\n  const inputs = { x: $x };\n  return ENGINE.runKernel(Softplus, inputs);\n}\nvar softplus = op({ softplus_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/log_sigmoid.js\nfunction logSigmoid_(x) {\n  const $x = convertToTensor(x, \"x\", \"logSigmoid\");\n  const customOp = customGrad((x2) => {\n    const value = neg(softplus(neg(x2)));\n    const gradFunc = (dy) => {\n      const derX = mul(dy, sigmoid(neg(x2)));\n      return derX;\n    };\n    return { value, gradFunc };\n  });\n  return customOp($x);\n}\nvar logSigmoid = op({ logSigmoid_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/sub.js\nfunction sub_(a, b) {\n  let $a = convertToTensor(a, \"a\", \"sub\");\n  let $b = convertToTensor(b, \"b\", \"sub\");\n  [$a, $b] = makeTypesMatch($a, $b);\n  const inputs = { a: $a, b: $b };\n  return ENGINE.runKernel(Sub, inputs);\n}\nvar sub = op({ sub_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/log_softmax.js\nfunction logSoftmax_(logits, axis = -1) {\n  const $logits = convertToTensor(logits, \"logits\", \"logSoftmax\");\n  if (axis === -1) {\n    axis = $logits.rank - 1;\n  }\n  if (axis !== $logits.rank - 1) {\n    throw Error(`Log Softmax along a non-last dimension is not yet supported. Logits was rank ${$logits.rank} and axis was ${axis}`);\n  }\n  const customOp = customGrad((logits2, save) => {\n    const keepDims = true;\n    const xMax = max(logits2, axis, true);\n    const shifted = sub(logits2, xMax);\n    const value = sub(cast(shifted, \"float32\"), log2(sum2(exp(shifted), axis, keepDims)));\n    save([value]);\n    const gradFunc = (dy, saved) => {\n      const [value2] = saved;\n      const keepDims2 = true;\n      const softmax7 = exp(value2);\n      return sub(dy, mul(sum2(dy, axis, keepDims2), softmax7));\n    };\n    return { value, gradFunc };\n  });\n  return customOp($logits);\n}\nvar logSoftmax = op({ logSoftmax_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/log_sum_exp.js\nfunction logSumExp_(x, axis = null, keepDims = false) {\n  const $x = convertToTensor(x, \"x\", \"logSumExp\");\n  const axes = parseAxisParam(axis, $x.shape);\n  const xMax = max($x, axes, true);\n  const a = sub($x, xMax);\n  const b = exp(a);\n  const c = sum2(b, axes);\n  const d = log2(c);\n  const res = add2(reshape(xMax, d.shape), d);\n  if (keepDims) {\n    const newShape = expandShapeToKeepDim(res.shape, axes);\n    return reshape(res, newShape);\n  }\n  return res;\n}\nvar logSumExp = op({ logSumExp_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/logical_and.js\nfunction logicalAnd_(a, b) {\n  const $a = convertToTensor(a, \"a\", \"logicalAnd\", \"bool\");\n  const $b = convertToTensor(b, \"b\", \"logicalAnd\", \"bool\");\n  assertAndGetBroadcastShape($a.shape, $b.shape);\n  const inputs = { a: $a, b: $b };\n  return ENGINE.runKernel(LogicalAnd, inputs);\n}\nvar logicalAnd = op({ logicalAnd_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/logical_not.js\nfunction logicalNot_(x) {\n  const $x = convertToTensor(x, \"x\", \"logicalNot\", \"bool\");\n  const inputs = { x: $x };\n  return ENGINE.runKernel(LogicalNot, inputs);\n}\nvar logicalNot = op({ logicalNot_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/logical_or.js\nfunction logicalOr_(a, b) {\n  const $a = convertToTensor(a, \"a\", \"logicalOr\", \"bool\");\n  const $b = convertToTensor(b, \"b\", \"logicalOr\", \"bool\");\n  assertAndGetBroadcastShape($a.shape, $b.shape);\n  const inputs = { a: $a, b: $b };\n  return ENGINE.runKernel(LogicalOr, inputs);\n}\nvar logicalOr = op({ logicalOr_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/logical_xor.js\nfunction logicalXor_(a, b) {\n  const $a = convertToTensor(a, \"a\", \"logicalXor\", \"bool\");\n  const $b = convertToTensor(b, \"b\", \"logicalXor\", \"bool\");\n  assertAndGetBroadcastShape($a.shape, $b.shape);\n  return logicalAnd(logicalOr(a, b), logicalNot(logicalAnd(a, b)));\n}\nvar logicalXor = op({ logicalXor_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/search_sorted.js\nvar INT32_MAX = 2147483648;\nfunction searchSorted_(sortedSequence, values, side = \"left\") {\n  const $sortedSequence = convertToTensor(sortedSequence, \"sortedSequence\", \"searchSorted\");\n  const $values = convertToTensor(values, \"values\", \"searchSorted\");\n  const sequenceSize = $sortedSequence.shape[$sortedSequence.shape.length - 1];\n  const valuesSize = $values.shape[$values.shape.length - 1];\n  const $sortedSequence2D = reshape($sortedSequence, [-1, sequenceSize]);\n  const $values2D = reshape($values, [-1, valuesSize]);\n  if ($sortedSequence2D.rank < 2) {\n    throw new Error(`Sorted input argument must be at least 2-dimensional`);\n  }\n  if ($sortedSequence2D.shape[0] !== $values2D.shape[0]) {\n    throw new Error(`Leading dimension of 'sortedSequence' and 'values' must match.`);\n  }\n  if (sizeFromShape($values2D.shape) >= INT32_MAX) {\n    throw new Error(`values tensor size must less than ${INT32_MAX}`);\n  }\n  if ($sortedSequence2D.shape[1] >= INT32_MAX) {\n    throw new Error(`trailing dim_size must less than ${INT32_MAX} for int32 output type, was ${$sortedSequence2D.shape[1]}`);\n  }\n  const inputs = {\n    sortedSequence: $sortedSequence2D,\n    values: $values2D\n  };\n  const attrs = { side };\n  return ENGINE.runKernel(SearchSorted, inputs, attrs);\n}\nvar searchSorted = op({ searchSorted_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/lower_bound.js\nfunction lowerBound(sortedSequence, values) {\n  return searchSorted(sortedSequence, values, \"left\");\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/max_pool.js\nfunction maxPool_(x, filterSize, strides, pad3, dimRoundingMode) {\n  const $x = convertToTensor(x, \"x\", \"maxPool\");\n  const dilations = 1;\n  let x4D = $x;\n  let reshapedTo4D = false;\n  if ($x.rank === 3) {\n    reshapedTo4D = true;\n    x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);\n  }\n  assert(x4D.rank === 4, () => `Error in maxPool: input must be rank 4 but got rank ${x4D.rank}.`);\n  assert(eitherStridesOrDilationsAreOne(strides, dilations), () => `Error in maxPool: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`);\n  checkPadOnDimRoundingMode(\"maxPool\", pad3, dimRoundingMode);\n  const inputs = { x: x4D };\n  const attrs = { filterSize, strides, pad: pad3, dimRoundingMode };\n  const res = ENGINE.runKernel(MaxPool, inputs, attrs);\n  if (reshapedTo4D) {\n    return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);\n  }\n  return res;\n}\nvar maxPool = op({ maxPool_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/max_pool_3d.js\nfunction maxPool3d_(x, filterSize = [1, 1, 1], strides, pad3, dimRoundingMode, dataFormat = \"NDHWC\") {\n  const $x = convertToTensor(x, \"x\", \"maxPool3d\");\n  let x5D = $x;\n  let reshapedTo5D = false;\n  if ($x.rank === 4) {\n    reshapedTo5D = true;\n    x5D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2], $x.shape[3]]);\n  }\n  assert(x5D.rank === 5, () => `Error in maxPool3d: x must be rank 5 but got rank ${x5D.rank}.`);\n  assert(dataFormat === \"NDHWC\", () => `Error in maxPool3d: Only NDHWC is currently supported, but got dataFormat of ${dataFormat}`);\n  checkPadOnDimRoundingMode(\"maxPool3d\", pad3, dimRoundingMode);\n  const inputs = { x: x5D };\n  const attrs = { filterSize, strides, pad: pad3, dimRoundingMode, dataFormat };\n  const res = ENGINE.runKernel(MaxPool3D, inputs, attrs);\n  if (reshapedTo5D) {\n    return reshape(res, [res.shape[1], res.shape[2], res.shape[3], res.shape[4]]);\n  }\n  return res;\n}\nvar maxPool3d = op({ maxPool3d_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/max_pool_with_argmax.js\nfunction maxPoolWithArgmax_(x, filterSize, strides, pad3, includeBatchInIndex = false) {\n  const $x = convertToTensor(x, \"x\", \"maxPoolWithArgmax\");\n  const inputs = { x: $x };\n  const attrs = { filterSize, strides, pad: pad3, includeBatchInIndex };\n  const result = ENGINE.runKernel(MaxPoolWithArgmax, inputs, attrs);\n  return { result: result[0], indexes: result[1] };\n}\nvar maxPoolWithArgmax = op({ maxPoolWithArgmax_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/maximum.js\nfunction maximum_(a, b) {\n  let $a = convertToTensor(a, \"a\", \"maximum\");\n  let $b = convertToTensor(b, \"b\", \"maximum\");\n  [$a, $b] = makeTypesMatch($a, $b);\n  if ($a.dtype === \"bool\") {\n    $a = cast($a, \"int32\");\n    $b = cast($b, \"int32\");\n  }\n  assertAndGetBroadcastShape($a.shape, $b.shape);\n  const inputs = { a: $a, b: $b };\n  return ENGINE.runKernel(Maximum, inputs);\n}\nvar maximum = op({ maximum_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/mean.js\nfunction mean_(x, axis = null, keepDims = false) {\n  const $x = convertToTensor(x, \"x\", \"mean\");\n  const inputs = { x: $x };\n  const attrs = { axis, keepDims };\n  return ENGINE.runKernel(Mean, inputs, attrs);\n}\nvar mean = op({ mean_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/zeros.js\nfunction zeros(shape, dtype = \"float32\") {\n  if (dtype === \"complex64\") {\n    const real5 = zeros(shape, \"float32\");\n    const imag5 = zeros(shape, \"float32\");\n    return complex(real5, imag5);\n  }\n  const values = makeZerosTypedArray(sizeFromShape(shape), dtype);\n  return ENGINE.makeTensor(values, shape, dtype);\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/ones.js\nfunction ones2(shape, dtype = \"float32\") {\n  if (dtype === \"complex64\") {\n    const real5 = ones2(shape, \"float32\");\n    const imag5 = zeros(shape, \"float32\");\n    return complex(real5, imag5);\n  }\n  const values = makeOnesTypedArray(sizeFromShape(shape), dtype);\n  return ENGINE.makeTensor(values, shape, dtype);\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/meshgrid.js\nfunction meshgrid(x, y, { indexing = \"xy\" } = {}) {\n  if (indexing !== \"xy\" && indexing !== \"ij\") {\n    throw new TypeError(`${indexing} is not a valid third argument to meshgrid`);\n  }\n  if (x === void 0) {\n    return [];\n  }\n  let $x = convertToTensor(x, \"x\", \"meshgrid\", x instanceof Tensor ? x.dtype : \"float32\");\n  if (y === void 0) {\n    return [$x];\n  }\n  let $y = convertToTensor(y, \"y\", \"meshgrid\", y instanceof Tensor ? y.dtype : \"float32\");\n  const w = sizeFromShape($x.shape);\n  const h = sizeFromShape($y.shape);\n  if (indexing === \"xy\") {\n    $x = reshape($x, [1, -1]);\n    $y = reshape($y, [-1, 1]);\n    return [\n      matMul(ones2([h, 1], $x.dtype), $x),\n      matMul($y, ones2([1, w], $y.dtype))\n    ];\n  }\n  $x = reshape($x, [-1, 1]);\n  $y = reshape($y, [1, -1]);\n  return [\n    matMul($x, ones2([1, h], $x.dtype)),\n    matMul(ones2([w, 1], $y.dtype), $y)\n  ];\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/minimum.js\nfunction minimum_(a, b) {\n  let $a = convertToTensor(a, \"a\", \"minimum\");\n  let $b = convertToTensor(b, \"b\", \"minimum\");\n  [$a, $b] = makeTypesMatch($a, $b);\n  if ($a.dtype === \"bool\") {\n    $a = cast($a, \"int32\");\n    $b = cast($b, \"int32\");\n  }\n  assertAndGetBroadcastShape($a.shape, $b.shape);\n  const inputs = { a: $a, b: $b };\n  return ENGINE.runKernel(Minimum, inputs);\n}\nvar minimum = op({ minimum_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/mirror_pad.js\nfunction mirrorPad_(x, paddings, mode) {\n  assert(mode === \"reflect\" || mode === \"symmetric\", () => `Invalid mode. Mode must be either reflect or symmetric. Got ${mode}.`);\n  const $x = convertToTensor(x, \"x\", \"mirrorPad\");\n  if ($x.rank === 0) {\n    throw new Error(\"mirrorPad(scalar) is not defined. Pass non-scalar to mirrorPad\");\n  }\n  assert(paddings.length === $x.rank, () => `Padding doesn't match input. Must be ${$x.rank}. Got ${paddings.length}.`);\n  const shapeOffset = mode === \"reflect\" ? 1 : 0;\n  for (let i = 0; i < $x.rank; i++) {\n    assert(paddings[i].length === 2, () => `Invalid number of paddings. Must be length of 2 each.`);\n    assert(paddings[i][0] >= 0 && paddings[i][0] <= $x.shape[i] - shapeOffset && paddings[i][1] >= 0 && paddings[i][1] <= $x.shape[i] - shapeOffset, () => `Padding in dimension ${i} cannot be greater than or equal to ${$x.shape[i] - shapeOffset} or less than 0 for input of shape ${$x.shape}`);\n  }\n  const attrs = { paddings, mode };\n  const inputs = { x: $x };\n  return ENGINE.runKernel(MirrorPad, inputs, attrs);\n}\nvar mirrorPad = op({ mirrorPad_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/mod.js\nfunction mod_(a, b) {\n  let $a = convertToTensor(a, \"a\", \"mod\");\n  let $b = convertToTensor(b, \"b\", \"mod\");\n  [$a, $b] = makeTypesMatch($a, $b);\n  const inputs = { a: $a, b: $b };\n  return ENGINE.runKernel(Mod, inputs);\n}\nvar mod = op({ mod_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/moments.js\nfunction moments_(x, axis = null, keepDims = false) {\n  x = convertToTensor(x, \"x\", \"moments\");\n  const axes = parseAxisParam(axis, x.shape);\n  const xMean = mean(x, axes, keepDims);\n  let keepDimsShape = xMean.shape;\n  if (!keepDims) {\n    keepDimsShape = expandShapeToKeepDim(xMean.shape, axes);\n  }\n  const devSquared = square(sub(cast(x, \"float32\"), reshape(xMean, keepDimsShape)));\n  const variance = mean(devSquared, axes, keepDims);\n  return { mean: xMean, variance };\n}\nvar moments = op({ moments_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/multi_rnn_cell.js\nfunction multiRNNCell_(lstmCells, data, c, h) {\n  const $data = convertToTensor(data, \"data\", \"multiRNNCell\");\n  const $c = convertToTensorArray(c, \"c\", \"multiRNNCell\");\n  const $h = convertToTensorArray(h, \"h\", \"multiRNNCell\");\n  let input2 = $data;\n  const newStates = [];\n  for (let i = 0; i < lstmCells.length; i++) {\n    const output = lstmCells[i](input2, $c[i], $h[i]);\n    newStates.push(output[0]);\n    newStates.push(output[1]);\n    input2 = output[1];\n  }\n  const newC = [];\n  const newH = [];\n  for (let i = 0; i < newStates.length; i += 2) {\n    newC.push(newStates[i]);\n    newH.push(newStates[i + 1]);\n  }\n  return [newC, newH];\n}\nvar multiRNNCell = op({ multiRNNCell_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/multinomial.js\nfunction multinomial_(logits, numSamples, seed, normalized = false) {\n  const $logits = convertToTensor(logits, \"logits\", \"multinomial\");\n  const numOutcomes = $logits.size;\n  const origRank = $logits.rank;\n  if (numOutcomes < 2) {\n    throw new Error(`Error in multinomial: you need at least 2 outcomes, but got ${numOutcomes}.`);\n  }\n  if (origRank > 2) {\n    throw new Error(`Rank of probabilities must be 1 or 2, but is ${origRank}`);\n  }\n  seed = seed || Math.random();\n  const logits2D = origRank === 1 ? reshape($logits, [1, -1]) : $logits;\n  const inputs = { logits: logits2D };\n  const attrs = { numSamples, seed, normalized };\n  const res = ENGINE.runKernel(Multinomial, inputs, attrs);\n  return origRank === 1 ? reshape(res, [res.size]) : res;\n}\nvar multinomial = op({ multinomial_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/not_equal.js\nfunction notEqual_(a, b) {\n  let $a = convertToTensor(a, \"a\", \"notEqual\", \"string_or_numeric\");\n  let $b = convertToTensor(b, \"b\", \"notEqual\", \"string_or_numeric\");\n  [$a, $b] = makeTypesMatch($a, $b);\n  assertAndGetBroadcastShape($a.shape, $b.shape);\n  const inputs = { a: $a, b: $b };\n  return ENGINE.runKernel(NotEqual, inputs);\n}\nvar notEqual = op({ notEqual_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/ones_like.js\nfunction onesLike_(x) {\n  const $x = convertToTensor(x, \"x\", \"onesLike\");\n  const inputs = { x: $x };\n  return ENGINE.runKernel(OnesLike, inputs);\n}\nvar onesLike = op({ onesLike_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/outer_product.js\nfunction outerProduct_(v1, v2) {\n  const $v1 = convertToTensor(v1, \"v1\", \"outerProduct\");\n  const $v2 = convertToTensor(v2, \"v2\", \"outerProduct\");\n  assert($v1.rank === 1 && $v2.rank === 1, () => `Error in outerProduct: inputs must be rank 1, but got ranks ${$v1.rank} and ${$v2.rank}.`);\n  const v12D = reshape($v1, [-1, 1]);\n  const v22D = reshape($v2, [1, -1]);\n  return matMul(v12D, v22D);\n}\nvar outerProduct = op({ outerProduct_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/pad.js\nfunction pad_(x, paddings, constantValue = 0) {\n  const $x = convertToTensor(x, \"x\", \"pad\");\n  if ($x.rank === 0) {\n    throw new Error(\"pad(scalar) is not defined. Pass non-scalar to pad\");\n  }\n  const attrs = { paddings, constantValue };\n  const inputs = { x: $x };\n  return ENGINE.runKernel(PadV2, inputs, attrs);\n}\nvar pad = op({ pad_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/pad1d.js\nfunction pad1d_(x, paddings, constantValue = 0) {\n  assert(paddings.length === 2, () => \"Invalid number of paddings. Must be length of 2.\");\n  return pad(x, [paddings], constantValue);\n}\nvar pad1d = op({ pad1d_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/pad2d.js\nfunction pad2d_(x, paddings, constantValue = 0) {\n  assert(paddings.length === 2 && paddings[0].length === 2 && paddings[1].length === 2, () => \"Invalid number of paddings. Must be length of 2 each.\");\n  return pad(x, paddings, constantValue);\n}\nvar pad2d = op({ pad2d_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/pad3d.js\nfunction pad3d_(x, paddings, constantValue = 0) {\n  assert(paddings.length === 3 && paddings[0].length === 2 && paddings[1].length === 2 && paddings[2].length === 2, () => \"Invalid number of paddings. Must be length of 2 each.\");\n  return pad(x, paddings, constantValue);\n}\nvar pad3d = op({ pad3d_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/pad4d.js\nfunction pad4d_(x, paddings, constantValue = 0) {\n  assert(paddings.length === 4 && paddings[0].length === 2 && paddings[1].length === 2 && paddings[2].length === 2 && paddings[3].length === 2, () => \"Invalid number of paddings. Must be length of 2 each.\");\n  return pad(x, paddings, constantValue);\n}\nvar pad4d = op({ pad4d_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/space_to_batch_nd.js\nfunction spaceToBatchND_(x, blockShape, paddings) {\n  const $x = convertToTensor(x, \"x\", \"spaceToBatchND\");\n  assert($x.rank >= 1 + blockShape.length, () => `input rank ${$x.rank} should be > than [blockShape] ${blockShape.length}`);\n  assert(paddings.length === blockShape.length, () => `paddings.shape[0] ${paddings.length} must be equal to [blockShape] ${blockShape.length}`);\n  assert($x.shape.reduce((a, b, i) => {\n    if (i > 0 && i <= blockShape.length) {\n      return a && (b + paddings[i - 1][0] + paddings[i - 1][1]) % blockShape[i - 1] === 0;\n    }\n    return a;\n  }, true), () => `input spatial dimensions ${$x.shape.slice(1)} with paddings ${paddings.toString()} must be divisible by blockShapes ${blockShape.toString()}`);\n  const inputs = { x: $x };\n  const attrs = { blockShape, paddings };\n  return ENGINE.runKernel(SpaceToBatchND, inputs, attrs);\n}\nvar spaceToBatchND = op({ spaceToBatchND_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/pool.js\nfunction pool_(input2, windowShape, poolingType, pad3, dilations, strides, dimRoundingMode) {\n  if (dilations == null) {\n    dilations = [1, 1];\n  }\n  if (strides == null) {\n    strides = 1;\n  }\n  if (pad3 === 0) {\n    pad3 = \"valid\";\n  }\n  const $x = convertToTensor(input2, \"x\", \"maxPool\");\n  let x4D = $x;\n  let reshapedTo4D = false;\n  if ($x.rank === 3) {\n    reshapedTo4D = true;\n    x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);\n  }\n  assert(eitherStridesOrDilationsAreOne(strides, dilations), () => `Error in pool: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`);\n  const convInfo = computePool2DInfo(x4D.shape, windowShape, strides, dilations, pad3);\n  const dilation = [convInfo.dilationHeight, convInfo.dilationWidth];\n  let basePadding;\n  if (pad3 === \"same\") {\n    basePadding = withSpaceToBatchBasePaddings([convInfo.filterHeight, convInfo.filterWidth], dilation);\n  } else {\n    basePadding = [[0, 0], [0, 0]];\n  }\n  const isDilationOne = dilation[0] === 1 && dilation[1] === 1;\n  const [adjustedPadding, adjustedCrops] = requiredSpaceToBatchPaddings([convInfo.inHeight, convInfo.inWidth], dilation, basePadding);\n  const convertedPad = isDilationOne ? pad3 : \"valid\";\n  const convertedX = isDilationOne ? x4D : spaceToBatchND(x4D, dilation, adjustedPadding);\n  const forwardOp = poolingType === \"avg\" ? () => avgPool(convertedX, windowShape, strides, convertedPad, dimRoundingMode) : () => maxPool(convertedX, windowShape, strides, convertedPad, dimRoundingMode);\n  const y = forwardOp();\n  const res = isDilationOne ? y : batchToSpaceND(y, dilation, adjustedCrops);\n  if (reshapedTo4D) {\n    return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);\n  }\n  return res;\n}\nfunction requiredSpaceToBatchPaddings(inputShape, blockShape, basePadding) {\n  const padStart = basePadding.map((b) => b[0]);\n  const origPadEnd = basePadding.map((b) => b[1]);\n  const fullInputShape = inputShape.concat(padStart, origPadEnd);\n  const padEndExtra = blockShape.map((b, i) => (b - fullInputShape[i] % b) % b);\n  const padEnd = origPadEnd.map((s, i) => s + padEndExtra[i]);\n  const paddings = blockShape.map((_, i) => [padStart[i], padEnd[i]]);\n  const crops = blockShape.map((_, i) => [0, padEndExtra[i]]);\n  return [paddings, crops];\n}\nfunction withSpaceToBatchBasePaddings(filterShape, dilation) {\n  const dilatedFilterShape = filterShape.map((s, i) => {\n    return s + (s - 1) * (dilation[i] - 1);\n  });\n  const padExtraShape = dilatedFilterShape.map((s) => s - 1);\n  const padExtraStart = padExtraShape.map((s) => Math.floor(s / 2));\n  const padExtraEnd = padExtraShape.map((s, i) => s - padExtraStart[i]);\n  return padExtraShape.map((_, i) => {\n    return [padExtraStart[i], padExtraEnd[i]];\n  });\n}\nvar pool = op({ pool_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/prelu.js\nfunction prelu_(x, alpha) {\n  const $x = convertToTensor(x, \"x\", \"prelu\");\n  const $alpha = convertToTensor(alpha, \"alpha\", \"prelu\");\n  const inputs = { x: $x, alpha: $alpha };\n  return ENGINE.runKernel(Prelu, inputs);\n}\nvar prelu = op({ prelu_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/prod.js\nfunction prod_(x, axis = null, keepDims = false) {\n  let $x = convertToTensor(x, \"x\", \"prod\");\n  if ($x.dtype === \"bool\") {\n    $x = cast($x, \"int32\");\n  }\n  const inputs = { x: $x };\n  const attrs = { axis, keepDims };\n  return ENGINE.runKernel(Prod, inputs, attrs);\n}\nvar prod = op({ prod_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/ragged_tensor_to_tensor.js\nfunction raggedTensorToTensor_(shape, values, defaultValue, rowPartitionTensors, rowPartitionTypes) {\n  const $shape = convertToTensor(shape, \"shape\", \"raggedTensorToTensor\", \"int32\");\n  const $values = convertToTensor(values, \"values\", \"raggedTensorToTensor\");\n  const $defaultValue = convertToTensor(defaultValue, \"defaultValue\", \"raggedTensorToTensor\", $values.dtype);\n  const $rowPartitionTensors = rowPartitionTensors.map((t, i) => convertToTensor(t, `tensors${i}`, \"raggedTensorToTensor\", \"int32\"));\n  const inputs = {\n    shape: $shape,\n    values: $values,\n    defaultValue: $defaultValue,\n    rowPartitionTensors: $rowPartitionTensors\n  };\n  const attrs = { rowPartitionTypes };\n  return ENGINE.runKernel(RaggedTensorToTensor, inputs, attrs);\n}\nvar raggedTensorToTensor = op({ raggedTensorToTensor_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/rand.js\nfunction rand_(shape, randFunction, dtype) {\n  const size = sizeFromShape(shape);\n  let values = null;\n  if (dtype == null || dtype === \"float32\") {\n    values = new Float32Array(size);\n  } else if (dtype === \"int32\") {\n    values = new Int32Array(size);\n  } else if (dtype === \"bool\") {\n    values = new Uint8Array(size);\n  } else {\n    throw new Error(`Unknown data type ${dtype}`);\n  }\n  for (let i = 0; i < size; i++) {\n    values[i] = randFunction();\n  }\n  return ENGINE.makeTensor(values, shape, dtype);\n}\nvar rand = op({ rand_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/rand_util.js\nvar seedrandom = __toESM(require_seedrandom2());\nvar MPRandGauss = class {\n  constructor(mean5, stdDeviation, dtype, truncated, seed) {\n    this.mean = mean5;\n    this.stdDev = stdDeviation;\n    this.dtype = dtype;\n    this.nextVal = NaN;\n    this.truncated = truncated;\n    if (this.truncated) {\n      this.upper = this.mean + this.stdDev * 2;\n      this.lower = this.mean - this.stdDev * 2;\n    }\n    const seedValue = seed ? seed : Math.random();\n    this.random = seedrandom.alea(seedValue.toString());\n  }\n  nextValue() {\n    if (!isNaN(this.nextVal)) {\n      const value = this.nextVal;\n      this.nextVal = NaN;\n      return value;\n    }\n    let resultX, resultY;\n    let isValid = false;\n    while (!isValid) {\n      let v1, v2, s;\n      do {\n        v1 = 2 * this.random() - 1;\n        v2 = 2 * this.random() - 1;\n        s = v1 * v1 + v2 * v2;\n      } while (s >= 1 || s === 0);\n      const mul2 = Math.sqrt(-2 * Math.log(s) / s);\n      resultX = this.mean + this.stdDev * v1 * mul2;\n      resultY = this.mean + this.stdDev * v2 * mul2;\n      if (!this.truncated || this.isValidTruncated(resultX)) {\n        isValid = true;\n      }\n    }\n    if (!this.truncated || this.isValidTruncated(resultY)) {\n      this.nextVal = this.convertValue(resultY);\n    }\n    return this.convertValue(resultX);\n  }\n  convertValue(value) {\n    if (this.dtype == null || this.dtype === \"float32\") {\n      return value;\n    }\n    return Math.round(value);\n  }\n  isValidTruncated(value) {\n    return value <= this.upper && value >= this.lower;\n  }\n};\nvar RandGamma = class {\n  constructor(alpha, beta, dtype, seed) {\n    this.alpha = alpha;\n    this.beta = 1 / beta;\n    this.dtype = dtype;\n    const seedValue = seed ? seed : Math.random();\n    this.randu = seedrandom.alea(seedValue.toString());\n    this.randn = new MPRandGauss(0, 1, dtype, false, this.randu());\n    if (alpha < 1) {\n      this.d = alpha + 2 / 3;\n    } else {\n      this.d = alpha - 1 / 3;\n    }\n    this.c = 1 / Math.sqrt(9 * this.d);\n  }\n  nextValue() {\n    let x2, v0, v1, x, u, v;\n    while (true) {\n      do {\n        x = this.randn.nextValue();\n        v = 1 + this.c * x;\n      } while (v <= 0);\n      v *= v * v;\n      x2 = x * x;\n      v0 = 1 - 0.331 * x2 * x2;\n      v1 = 0.5 * x2 + this.d * (1 - v + Math.log(v));\n      u = this.randu();\n      if (u < v0 || Math.log(u) < v1) {\n        break;\n      }\n    }\n    v = 1 / this.beta * this.d * v;\n    if (this.alpha < 1) {\n      v *= Math.pow(this.randu(), 1 / this.alpha);\n    }\n    return this.convertValue(v);\n  }\n  convertValue(value) {\n    if (this.dtype === \"float32\") {\n      return value;\n    }\n    return Math.round(value);\n  }\n};\nvar UniformRandom = class {\n  constructor(min7 = 0, max7 = 1, dtype, seed) {\n    this.canReturnFloat = () => this.dtype == null || this.dtype === \"float32\";\n    this.min = min7;\n    this.range = max7 - min7;\n    this.dtype = dtype;\n    if (seed == null) {\n      seed = Math.random();\n    }\n    if (typeof seed === \"number\") {\n      seed = seed.toString();\n    }\n    if (!this.canReturnFloat() && this.range <= 1) {\n      throw new Error(`The difference between ${min7} - ${max7} <= 1 and dtype is not float`);\n    }\n    this.random = seedrandom.alea(seed);\n  }\n  convertValue(value) {\n    if (this.canReturnFloat()) {\n      return value;\n    }\n    return Math.round(value);\n  }\n  nextValue() {\n    return this.convertValue(this.min + this.range * this.random());\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/random_gamma.js\nfunction randomGamma_(shape, alpha, beta = 1, dtype = \"float32\", seed) {\n  if (beta == null) {\n    beta = 1;\n  }\n  if (dtype == null) {\n    dtype = \"float32\";\n  }\n  if (dtype !== \"float32\" && dtype !== \"int32\") {\n    throw new Error(`Unsupported data type ${dtype}`);\n  }\n  const rgamma = new RandGamma(alpha, beta, dtype, seed);\n  const res = buffer(shape, dtype);\n  for (let i = 0; i < res.values.length; i++) {\n    res.values[i] = rgamma.nextValue();\n  }\n  return res.toTensor();\n}\nvar randomGamma = op({ randomGamma_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/random_normal.js\nfunction randomNormal_(shape, mean5 = 0, stdDev = 1, dtype, seed) {\n  if (dtype != null && dtype === \"bool\") {\n    throw new Error(`Unsupported data type ${dtype}`);\n  }\n  const randGauss = new MPRandGauss(mean5, stdDev, dtype, false, seed);\n  const res = buffer(shape, dtype);\n  for (let i = 0; i < res.values.length; i++) {\n    res.values[i] = randGauss.nextValue();\n  }\n  return res.toTensor();\n}\nvar randomNormal = op({ randomNormal_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/random_standard_normal.js\nfunction randomStandardNormal_(shape, dtype, seed) {\n  if (dtype != null && dtype === \"bool\") {\n    throw new Error(`Unsupported data type ${dtype}`);\n  }\n  return randomNormal(shape, 0, 1, dtype, seed);\n}\nvar randomStandardNormal = op({ randomStandardNormal_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/random_uniform.js\nfunction randomUniform_(shape, minval = 0, maxval = 1, dtype = \"float32\", seed) {\n  const res = buffer(shape, dtype);\n  const random = new UniformRandom(minval, maxval, null, seed);\n  for (let i = 0; i < res.values.length; i++) {\n    res.values[i] = random.nextValue();\n  }\n  return res.toTensor();\n}\nvar randomUniform = op({ randomUniform_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/range.js\nfunction range(start, stop, step5 = 1, dtype = \"float32\") {\n  if (step5 === 0) {\n    throw new Error(\"Cannot have a step of zero\");\n  }\n  const attrs = { start, stop, step: step5, dtype };\n  return ENGINE.runKernel(Range, {}, attrs);\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/reciprocal.js\nfunction reciprocal_(x) {\n  const $x = convertToTensor(x, \"x\", \"reciprocal\");\n  const inputs = { x: $x };\n  return ENGINE.runKernel(Reciprocal, inputs);\n}\nvar reciprocal = op({ reciprocal_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/relu.js\nfunction relu_(x) {\n  const $x = convertToTensor(x, \"x\", \"relu\");\n  const inputs = { x: $x };\n  return ENGINE.runKernel(Relu, inputs);\n}\nvar relu = op({ relu_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/relu6.js\nfunction relu6_(x) {\n  const $x = convertToTensor(x, \"x\", \"relu6\");\n  const inputs = { x: $x };\n  return ENGINE.runKernel(Relu6, inputs);\n}\nvar relu6 = op({ relu6_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/reverse.js\nfunction reverse_(x, axis) {\n  const $x = convertToTensor(x, \"x\", \"reverse\");\n  const inputs = { x: $x };\n  const attrs = { dims: axis };\n  return ENGINE.runKernel(Reverse, inputs, attrs);\n}\nvar reverse = op({ reverse_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/reverse_1d.js\nfunction reverse1d_(x) {\n  const $x = convertToTensor(x, \"x\", \"reverse\");\n  assert($x.rank === 1, () => `Error in reverse1D: x must be rank 1 but got rank ${$x.rank}.`);\n  return reverse($x, 0);\n}\nvar reverse1d = op({ reverse1d_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/reverse_2d.js\nfunction reverse2d_(x, axis) {\n  const $x = convertToTensor(x, \"x\", \"reverse\");\n  assert($x.rank === 2, () => `Error in reverse2D: x must be rank 2 but got rank ${$x.rank}.`);\n  return reverse($x, axis);\n}\nvar reverse2d = op({ reverse2d_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/reverse_3d.js\nfunction reverse3d_(x, axis) {\n  const $x = convertToTensor(x, \"x\", \"reverse\");\n  assert($x.rank === 3, () => `Error in reverse3D: x must be rank 3 but got rank ${$x.rank}.`);\n  return reverse($x, axis);\n}\nvar reverse3d = op({ reverse3d_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/reverse_4d.js\nfunction reverse4d_(x, axis) {\n  const $x = convertToTensor(x, \"x\", \"reverse\");\n  assert($x.rank === 4, () => `Error in reverse4D: x must be rank 4 but got rank ${$x.rank}.`);\n  return reverse($x, axis);\n}\nvar reverse4d = op({ reverse4d_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/round.js\nfunction round_(x) {\n  const $x = convertToTensor(x, \"x\", \"round\");\n  const inputs = { x: $x };\n  return ENGINE.runKernel(Round, inputs);\n}\nvar round2 = op({ round_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/rsqrt.js\nfunction rsqrt_(x) {\n  const $x = convertToTensor(x, \"x\", \"rsqrt\", \"float32\");\n  const inputs = { x: $x };\n  return ENGINE.runKernel(Rsqrt, inputs);\n}\nvar rsqrt = op({ rsqrt_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/selu.js\nfunction selu_(x) {\n  const $x = convertToTensor(x, \"x\", \"selu\");\n  const inputs = { x: $x };\n  return ENGINE.runKernel(Selu, inputs);\n}\nvar selu = op({ selu_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/separable_conv2d.js\nfunction separableConv2d_(x, depthwiseFilter, pointwiseFilter, strides, pad3, dilation = [1, 1], dataFormat = \"NHWC\") {\n  const $x = convertToTensor(x, \"x\", \"separableConv2d\");\n  const $depthwiseFilter = convertToTensor(depthwiseFilter, \"depthwiseFilter\", \"separableConv2d\");\n  const $pointwiseFilter = convertToTensor(pointwiseFilter, \"pointwiseFilter\", \"separableConv2d\");\n  let x4D = $x;\n  let reshapedTo4D = false;\n  if ($x.rank === 3) {\n    reshapedTo4D = true;\n    x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);\n  }\n  if (dataFormat === \"NCHW\") {\n    throw new Error(\"separableConv2d currently does not support dataFormat NCHW; only NHWC is supported\");\n  }\n  assert(x4D.rank === 4, () => `Error in separableConv2d: input must be rank 4, but got rank ${x4D.rank}.`);\n  assert($depthwiseFilter.rank === 4, () => `Error in separableConv2d: depthwise filter must be rank 4, but got rank ${$depthwiseFilter.rank}.`);\n  assert($pointwiseFilter.rank === 4, () => `Error in separableConv2d: pointwise filter must be rank 4, but got rank ${$depthwiseFilter.rank}.`);\n  assert($pointwiseFilter.shape[0] === 1, () => `Error in separableConv2d: the first dimension of pointwise filter  must be 1, but got ${$pointwiseFilter.shape[0]}.`);\n  assert($pointwiseFilter.shape[1] === 1, () => `Error in separableConv2d: the second dimension of pointwise filter must be 1, but got ${$pointwiseFilter.shape[1]}.`);\n  const inChannels = $depthwiseFilter.shape[2];\n  const channelMultiplier = $depthwiseFilter.shape[3];\n  assert($pointwiseFilter.shape[2] === inChannels * channelMultiplier, () => `Error in separableConv2d: the third dimension of pointwise filter must be ${inChannels * channelMultiplier}, but got ${$pointwiseFilter.shape[2]}.`);\n  const depthwise = depthwiseConv2d(x4D, $depthwiseFilter, strides, pad3, dataFormat, dilation);\n  const pointwiseStride = 1;\n  const res = conv2d(depthwise, $pointwiseFilter, pointwiseStride, \"valid\", dataFormat);\n  if (reshapedTo4D) {\n    return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);\n  }\n  return res;\n}\nvar separableConv2d = op({ separableConv2d_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/setdiff1d_async.js\nasync function setdiff1dAsync_(x, y) {\n  const $x = convertToTensor(x, \"x\", \"setdiff1d\");\n  const $y = convertToTensor(y, \"y\", \"setdiff1d\");\n  assert($x.dtype === $y.dtype, () => `x and y should have the same dtype, but got x (${$x.dtype}) and y (${$y.dtype}).`);\n  assert($x.rank === 1, () => `x should be 1D tensor, but got x (${$x.shape}).`);\n  assert($y.rank === 1, () => `y should be 1D tensor, but got y (${$y.shape}).`);\n  const xVals = await $x.data();\n  const yVals = await $y.data();\n  const ySet = new Set(yVals);\n  let outputSize = 0;\n  for (let i = 0; i < xVals.length; i++) {\n    if (!ySet.has(xVals[i])) {\n      outputSize++;\n    }\n  }\n  const buffer2 = new TensorBuffer([outputSize], $x.dtype);\n  const indices = new TensorBuffer([outputSize], \"int32\");\n  for (let i = 0, p2 = 0; i < xVals.length; i++) {\n    if (!ySet.has(xVals[i])) {\n      buffer2.values[p2] = xVals[i];\n      indices.values[p2] = i;\n      p2++;\n    }\n  }\n  return [buffer2.toTensor(), indices.toTensor()];\n}\nvar setdiff1dAsync = setdiff1dAsync_;\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/sign.js\nfunction sign_(x) {\n  const $x = convertToTensor(x, \"x\", \"sign\");\n  const inputs = { x: $x };\n  return ENGINE.runKernel(Sign, inputs);\n}\nvar sign = op({ sign_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/sin.js\nfunction sin_(x) {\n  const $x = convertToTensor(x, \"x\", \"sin\", \"float32\");\n  const inputs = { x: $x };\n  return ENGINE.runKernel(Sin, inputs);\n}\nvar sin = op({ sin_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/sinh.js\nfunction sinh_(x) {\n  const $x = convertToTensor(x, \"x\", \"sinh\");\n  const inputs = { x: $x };\n  return ENGINE.runKernel(Sinh, inputs);\n}\nvar sinh = op({ sinh_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/slice1d.js\nfunction slice1d_(x, begin, size) {\n  const $x = convertToTensor(x, \"x\", \"slice1d\");\n  assert($x.rank === 1, () => `slice1d expects a rank-1 tensor, but got a rank-${$x.rank} tensor`);\n  return slice($x, [begin], [size]);\n}\nvar slice1d = op({ slice1d_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/slice2d.js\nfunction slice2d_(x, begin, size) {\n  const $x = convertToTensor(x, \"x\", \"slice2d\");\n  assert($x.rank === 2, () => `slice2d expects a rank-2 tensor, but got a rank-${$x.rank} tensor`);\n  return slice($x, begin, size);\n}\nvar slice2d = op({ slice2d_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/slice3d.js\nfunction slice3d_(x, begin, size) {\n  const $x = convertToTensor(x, \"x\", \"slice3d\");\n  assert($x.rank === 3, () => `slice3d expects a rank-3 tensor, but got a rank-${$x.rank} tensor`);\n  return slice($x, begin, size);\n}\nvar slice3d = op({ slice3d_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/slice4d.js\nfunction slice4d_(x, begin, size) {\n  const $x = convertToTensor(x, \"x\", \"slice4d\");\n  assert($x.rank === 4, () => `slice4d expects a rank-4 tensor, but got a rank-${$x.rank} tensor`);\n  return slice($x, begin, size);\n}\nvar slice4d = op({ slice4d_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/softmax.js\nfunction softmax_(logits, dim = -1) {\n  const $logits = convertToTensor(logits, \"logits\", \"softmax\", \"float32\");\n  if (dim === -1) {\n    dim = $logits.rank - 1;\n  }\n  if (dim !== $logits.rank - 1) {\n    throw Error(`Softmax along a non-last dimension is not yet supported. Logits was rank ${$logits.rank} and dim was ${dim}`);\n  }\n  const inputs = { logits: $logits };\n  const attrs = { dim };\n  return ENGINE.runKernel(Softmax, inputs, attrs);\n}\nvar softmax = op({ softmax_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/spectral/fft.js\nfunction fft_(input2) {\n  assert(input2.dtype === \"complex64\", () => `The dtype for tf.spectral.fft() must be complex64 but got ${input2.dtype}.`);\n  const inputs = { input: input2 };\n  return ENGINE.runKernel(FFT, inputs);\n}\nvar fft = op({ fft_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/spectral/ifft.js\nfunction ifft_(input2) {\n  assert(input2.dtype === \"complex64\", () => `The dtype for tf.spectral.ifft() must be complex64 but got ${input2.dtype}.`);\n  const inputs = { input: input2 };\n  return ENGINE.runKernel(IFFT, inputs);\n}\nvar ifft = op({ ifft_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/spectral/irfft.js\nfunction irfft_(input2) {\n  const innerDimensionSize = input2.shape[input2.shape.length - 1];\n  const batch = input2.size / innerDimensionSize;\n  let ret;\n  if (innerDimensionSize <= 2) {\n    const complexInput = reshape(input2, [batch, innerDimensionSize]);\n    ret = ifft(complexInput);\n  } else {\n    const outputShape = [batch, 2 * (innerDimensionSize - 1)];\n    const realInput = reshape(real(input2), [batch, innerDimensionSize]);\n    const imagInput = reshape(imag(input2), [batch, innerDimensionSize]);\n    const realConjugate = reverse(slice(realInput, [0, 1], [batch, innerDimensionSize - 2]), 1);\n    const imagConjugate = mul(reverse(slice(imagInput, [0, 1], [batch, innerDimensionSize - 2]), 1), scalar(-1));\n    const r = concat([realInput, realConjugate], 1);\n    const i = concat([imagInput, imagConjugate], 1);\n    const complexInput = reshape(complex(r, i), [outputShape[0], outputShape[1]]);\n    ret = ifft(complexInput);\n  }\n  ret = real(ret);\n  if (input2.rank === 3 && input2.shape[0] !== 0) {\n    const temp = ret;\n    const batch2 = input2.shape[0];\n    ret = reshape(ret, [batch2, ret.shape[0] / batch2, ret.shape[1]]);\n    temp.dispose();\n  }\n  return ret;\n}\nvar irfft = op({ irfft_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/split.js\nfunction split_(x, numOrSizeSplits, axis = 0) {\n  const $x = convertToTensor(x, \"x\", \"split\");\n  const inputs = { x: $x };\n  const attr = { numOrSizeSplits, axis };\n  return ENGINE.runKernel(SplitV, inputs, attr);\n}\nvar split = op({ split_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/spectral/rfft.js\nfunction rfft_(input2, fftLength) {\n  assert(input2.dtype === \"float32\", () => `The dtype for rfft() must be real value but got ${input2.dtype}`);\n  let innerDimensionSize = input2.shape[input2.shape.length - 1];\n  const batch = input2.size / innerDimensionSize;\n  let adjustedInput;\n  if (fftLength != null && fftLength < innerDimensionSize) {\n    const begin = input2.shape.map((v) => 0);\n    const size = input2.shape.map((v) => v);\n    size[input2.shape.length - 1] = fftLength;\n    adjustedInput = slice(input2, begin, size);\n    innerDimensionSize = fftLength;\n  } else if (fftLength != null && fftLength > innerDimensionSize) {\n    const zerosShape = input2.shape.map((v) => v);\n    zerosShape[input2.shape.length - 1] = fftLength - innerDimensionSize;\n    adjustedInput = concat([input2, zeros(zerosShape)], input2.shape.length - 1);\n    innerDimensionSize = fftLength;\n  } else {\n    adjustedInput = input2;\n  }\n  const zerosInput = zerosLike(adjustedInput);\n  const complexInput = reshape(complex(adjustedInput, zerosInput), [batch, innerDimensionSize]);\n  const ret = fft(complexInput);\n  const half = Math.floor(innerDimensionSize / 2) + 1;\n  const realValues = real(ret);\n  const imagValues = imag(ret);\n  const realComplexConjugate = split(realValues, [half, innerDimensionSize - half], realValues.shape.length - 1);\n  const imagComplexConjugate = split(imagValues, [half, innerDimensionSize - half], imagValues.shape.length - 1);\n  const outputShape = adjustedInput.shape.slice();\n  outputShape[adjustedInput.shape.length - 1] = half;\n  return reshape(complex(realComplexConjugate[0], imagComplexConjugate[0]), outputShape);\n}\nvar rfft = op({ rfft_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/squared_difference.js\nfunction squaredDifference_(a, b) {\n  let $a = convertToTensor(a, \"a\", \"squaredDifference\");\n  let $b = convertToTensor(b, \"b\", \"squaredDifference\");\n  [$a, $b] = makeTypesMatch($a, $b);\n  assertAndGetBroadcastShape($a.shape, $b.shape);\n  const inputs = { a: $a, b: $b };\n  const attrs = {};\n  return ENGINE.runKernel(SquaredDifference, inputs, attrs);\n}\nvar squaredDifference = op({ squaredDifference_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/squeeze.js\nfunction squeeze_(x, axis) {\n  const $x = convertToTensor(x, \"x\", \"squeeze\", \"string_or_numeric\");\n  return reshape($x, squeezeShape($x.shape, axis).newShape);\n}\nvar squeeze = op({ squeeze_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/stack.js\nfunction stack_(tensors, axis = 0) {\n  const $tensors = convertToTensorArray(tensors, \"tensors\", \"stack\", \"string_or_numeric\");\n  assert($tensors.length >= 1, () => \"Pass at least one tensor to tf.stack\");\n  if ($tensors.length > 0) {\n    assert(axis <= $tensors[0].rank, () => \"Axis must be <= rank of the tensor\");\n  }\n  const inputs = $tensors;\n  const attrs = { axis };\n  return ENGINE.runKernel(Pack, inputs, attrs);\n}\nvar stack = op({ stack_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/step.js\nfunction step_(x, alpha = 0) {\n  const $x = convertToTensor(x, \"x\", \"step\");\n  const inputs = { x: $x };\n  const attrs = { alpha };\n  return ENGINE.runKernel(Step, inputs, attrs);\n}\nvar step = op({ step_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/strided_slice.js\nfunction stridedSlice_(x, begin, end, strides, beginMask = 0, endMask = 0, ellipsisMask = 0, newAxisMask = 0, shrinkAxisMask = 0) {\n  const $x = convertToTensor(x, \"x\", \"stridedSlice\", \"string_or_numeric\");\n  const inputs = { x: $x };\n  const attrs = {\n    begin,\n    end,\n    strides,\n    beginMask,\n    endMask,\n    ellipsisMask,\n    newAxisMask,\n    shrinkAxisMask\n  };\n  return ENGINE.runKernel(StridedSlice, inputs, attrs);\n}\nvar stridedSlice = op({ stridedSlice_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/tan.js\nfunction tan_(x) {\n  const $x = convertToTensor(x, \"x\", \"tan\", \"float32\");\n  const inputs = { x: $x };\n  return ENGINE.runKernel(Tan, inputs);\n}\nvar tan = op({ tan_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/tensor1d.js\nfunction tensor1d(values, dtype) {\n  assertNonNull(values);\n  const inferredShape = inferShape(values, dtype);\n  if (inferredShape.length !== 1) {\n    throw new Error(\"tensor1d() requires values to be a flat/TypedArray\");\n  }\n  const shape = null;\n  return makeTensor(values, shape, inferredShape, dtype);\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/tensor2d.js\nfunction tensor2d(values, shape, dtype) {\n  assertNonNull(values);\n  if (shape != null && shape.length !== 2) {\n    throw new Error(\"tensor2d() requires shape to have two numbers\");\n  }\n  const inferredShape = inferShape(values, dtype);\n  if (inferredShape.length !== 2 && inferredShape.length !== 1) {\n    throw new Error(\"tensor2d() requires values to be number[][] or flat/TypedArray\");\n  }\n  if (inferredShape.length === 1 && shape == null) {\n    throw new Error(\"tensor2d() requires shape to be provided when `values` are a flat/TypedArray\");\n  }\n  return makeTensor(values, shape, inferredShape, dtype);\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/tensor4d.js\nfunction tensor4d(values, shape, dtype) {\n  assertNonNull(values);\n  if (shape != null && shape.length !== 4) {\n    throw new Error(\"tensor4d() requires shape to have four numbers\");\n  }\n  const inferredShape = inferShape(values, dtype);\n  if (inferredShape.length !== 4 && inferredShape.length !== 1) {\n    throw new Error(\"tensor4d() requires values to be number[][][][] or flat/TypedArray\");\n  }\n  if (inferredShape.length === 1 && shape == null) {\n    throw new Error(\"tensor4d() requires shape to be provided when `values` are a flat array\");\n  }\n  return makeTensor(values, shape, inferredShape, dtype);\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/tensor5d.js\nfunction tensor5d(values, shape, dtype) {\n  assertNonNull(values);\n  if (shape != null && shape.length !== 5) {\n    throw new Error(\"tensor5d() requires shape to have five numbers\");\n  }\n  const inferredShape = inferShape(values, dtype);\n  if (inferredShape.length !== 5 && inferredShape.length !== 1) {\n    throw new Error(\"tensor5d() requires values to be number[][][][][] or flat/TypedArray\");\n  }\n  if (inferredShape.length === 1 && shape == null) {\n    throw new Error(\"tensor5d() requires shape to be provided when `values` are a flat array\");\n  }\n  return makeTensor(values, shape, inferredShape, dtype);\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/tensor6d.js\nfunction tensor6d(values, shape, dtype) {\n  assertNonNull(values);\n  if (shape != null && shape.length !== 6) {\n    throw new Error(\"tensor6d() requires shape to have six numbers\");\n  }\n  const inferredShape = inferShape(values, dtype);\n  if (inferredShape.length !== 6 && inferredShape.length !== 1) {\n    throw new Error(\"tensor6d() requires values to be number[][][][][][] or flat/TypedArray\");\n  }\n  if (inferredShape.length === 1 && shape == null) {\n    throw new Error(\"tensor6d() requires shape to be provided when `values` are a flat array\");\n  }\n  shape = shape || inferredShape;\n  return makeTensor(values, shape, inferredShape, dtype);\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/topk.js\nfunction topk_(x, k = 1, sorted = true) {\n  const $x = convertToTensor(x, \"x\", \"topk\");\n  if ($x.rank === 0) {\n    throw new Error(\"topk() expects the input to be of rank 1 or higher\");\n  }\n  const lastDim = $x.shape[$x.shape.length - 1];\n  if (k < 0) {\n    throw new Error(`'k' passed to topk() must be >= 0 but got ${k}`);\n  }\n  if (k > lastDim) {\n    throw new Error(`'k' passed to topk() must be <= the last dimension (${lastDim}) but got ${k}`);\n  }\n  const inputs = { x: $x };\n  const attrs = { k, sorted };\n  const [values, indices] = ENGINE.runKernel(TopK, inputs, attrs);\n  return { values, indices };\n}\nvar topk = op({ topk_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/truncated_normal.js\nfunction truncatedNormal_(shape, mean5 = 0, stdDev = 1, dtype, seed) {\n  if (dtype != null && dtype === \"bool\") {\n    throw new Error(`Unsupported data type $ { dtype }`);\n  }\n  const randGauss = new MPRandGauss(mean5, stdDev, dtype, true, seed);\n  const res = buffer(shape, dtype);\n  for (let i = 0; i < res.values.length; i++) {\n    res.values[i] = randGauss.nextValue();\n  }\n  return res.toTensor();\n}\nvar truncatedNormal = op({ truncatedNormal_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/unique.js\nfunction unique_(x, axis = 0) {\n  const $x = convertToTensor(x, \"x\", \"unique\", \"string_or_numeric\");\n  assert($x.rank > 0, () => \"The input tensor must be at least 1D\");\n  const inputs = { x: $x };\n  const attrs = { axis };\n  const [values, indices] = ENGINE.runKernel(Unique, inputs, attrs);\n  return { values, indices };\n}\nvar unique = op({ unique_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/unsorted_segment_sum.js\nfunction unsortedSegmentSum_(x, segmentIds, numSegments) {\n  const $x = convertToTensor(x, \"x\", \"unsortedSegmentSum\");\n  const $segmentIds = convertToTensor(segmentIds, \"segmentIds\", \"unsortedSegmentSum\", \"int32\");\n  assert(isInt(numSegments), () => \"numSegments must be of dtype int\");\n  const inputs = { x: $x, segmentIds: $segmentIds };\n  const attrs = { numSegments };\n  return ENGINE.runKernel(UnsortedSegmentSum, inputs, attrs);\n}\nvar unsortedSegmentSum = op({ unsortedSegmentSum_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/unstack.js\nfunction unstack_(x, axis = 0) {\n  const $x = convertToTensor(x, \"x\", \"unstack\", \"string_or_numeric\");\n  assert(axis >= -$x.shape.length && axis < $x.shape.length, () => `Axis = ${axis} is not in [-${$x.shape.length}, ${$x.shape.length})`);\n  const inputs = { value: $x };\n  const attrs = { axis };\n  return ENGINE.runKernel(Unpack, inputs, attrs);\n}\nvar unstack = op({ unstack_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/upper_bound.js\nfunction upperBound(sortedSequence, values) {\n  return searchSorted(sortedSequence, values, \"right\");\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/variable.js\nfunction variable(initialValue, trainable = true, name, dtype) {\n  return ENGINE.makeVariable(initialValue, trainable, name, dtype);\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/backends/where_impl.js\nfunction whereImpl(condShape, condVals) {\n  const indices = [];\n  for (let i = 0; i < condVals.length; i++) {\n    if (condVals[i]) {\n      indices.push(i);\n    }\n  }\n  const inBuffer = buffer(condShape, \"int32\");\n  const out = buffer([indices.length, condShape.length], \"int32\");\n  for (let i = 0; i < indices.length; i++) {\n    const loc = inBuffer.indexToLoc(indices[i]);\n    const offset = i * condShape.length;\n    out.values.set(loc, offset);\n  }\n  return out.toTensor();\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/where_async.js\nasync function whereAsync_(condition) {\n  const $condition = convertToTensor(condition, \"condition\", \"whereAsync\", \"bool\");\n  const vals = await $condition.data();\n  const res = whereImpl($condition.shape, vals);\n  if (condition !== $condition) {\n    $condition.dispose();\n  }\n  return res;\n}\nvar whereAsync = whereAsync_;\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/boolean_mask.js\nasync function booleanMaskAsync_(tensor2, mask, axis) {\n  const $tensor = convertToTensor(tensor2, \"tensor\", \"boolMask\");\n  const $mask = convertToTensor(mask, \"mask\", \"boolMask\", \"bool\");\n  const axisFrom = axis == null ? 0 : axis;\n  const maskDim = $mask.rank;\n  const tensorShape = $tensor.shape;\n  assert(maskDim > 0, () => \"mask cannot be scalar\");\n  assertShapesMatch(tensorShape.slice(axisFrom, axisFrom + maskDim), $mask.shape, `mask's shape must match the first K dimensions of tensor's shape,`);\n  let leadingSize = 1;\n  for (let i = axisFrom; i < axisFrom + maskDim; i++) {\n    leadingSize *= tensorShape[i];\n  }\n  const targetTensorShape = tensorShape.slice(0, axisFrom).concat([leadingSize], tensorShape.slice(axisFrom + maskDim));\n  const reshapedTensor = reshape($tensor, targetTensorShape);\n  const reshapedMask = reshape($mask, [-1]);\n  const positivePositions = await whereAsync(reshapedMask);\n  const indices = squeeze(positivePositions, [1]);\n  const res = gather(reshapedTensor, indices, axisFrom);\n  if (tensor2 !== $tensor) {\n    $tensor.dispose();\n  }\n  if (mask !== $mask) {\n    $mask.dispose();\n  }\n  indices.dispose();\n  reshapedTensor.dispose();\n  reshapedMask.dispose();\n  positivePositions.dispose();\n  return res;\n}\nvar booleanMaskAsync = booleanMaskAsync_;\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/moving_average.js\nfunction movingAverage_(v, x, decay, step5, zeroDebias = true) {\n  const $v = convertToTensor(v, \"v\", \"movingAverage\");\n  const $x = convertToTensor(x, \"x\", \"movingAverage\");\n  const $decay = convertToTensor(decay, \"decay\", \"movingAverage\");\n  assertTypesMatch($v, $x);\n  assert(arraysEqual($v.shape, $x.shape), () => \"Shape mismatch in v and x\");\n  const one = scalar(1);\n  const oneMinusDecay = sub(one, $decay);\n  let update = mul(sub($x, $v), oneMinusDecay);\n  if (zeroDebias) {\n    assert(step5 != null, () => \"When using zeroDebias: true, step is required.\");\n    const $step = convertToTensor(step5, \"step\", \"movingAverage\");\n    update = div(update, sub(one, pow($decay, $step)));\n  }\n  return add2($v, update);\n}\nvar movingAverage = op({ movingAverage_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/scatter_nd.js\nfunction scatterND_(indices, updates, shape) {\n  const $indices = convertToTensor(indices, \"indices\", \"scatterND\", \"int32\");\n  const $updates = convertToTensor(updates, \"updates\", \"scatterND\");\n  validateInput($updates, $indices, shape);\n  const inputs = { indices: $indices, updates: $updates };\n  const attrs = { shape };\n  return ENGINE.runKernel(ScatterNd, inputs, attrs);\n}\nvar scatterND = op({ scatterND_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/sparse_to_dense_util.js\nfunction validateInput2(sparseIndices, sparseValues, outputShape, defaultValues) {\n  if (sparseIndices.dtype !== \"int32\") {\n    throw new Error(`tf.sparseToDense() expects the indices to be int32 type, but the dtype was ${sparseIndices.dtype}.`);\n  }\n  if (sparseIndices.rank > 2) {\n    throw new Error(`sparseIndices should be a scalar, vector, or matrix, but got shape ${sparseIndices.shape}.`);\n  }\n  const numElems = sparseIndices.rank > 0 ? sparseIndices.shape[0] : 1;\n  const numDims = sparseIndices.rank > 1 ? sparseIndices.shape[1] : 1;\n  if (outputShape.length !== numDims) {\n    throw new Error(`outputShape has incorrect number of elements:, ${outputShape.length}, should be: ${numDims}.`);\n  }\n  const numValues = sparseValues.size;\n  if (!(sparseValues.rank === 0 || sparseValues.rank === 1 && numValues === numElems)) {\n    throw new Error(`sparseValues has incorrect shape ${sparseValues.shape}, should be [] or [${numElems}]`);\n  }\n  if (sparseValues.dtype !== defaultValues.dtype) {\n    throw new Error(\"sparseValues.dtype must match defaultValues.dtype\");\n  }\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/sparse_to_dense.js\nfunction sparseToDense_(sparseIndices, sparseValues, outputShape, defaultValue = 0) {\n  const $sparseIndices = convertToTensor(sparseIndices, \"sparseIndices\", \"sparseToDense\", \"int32\");\n  const $sparseValues = convertToTensor(sparseValues, \"sparseValues\", \"sparseToDense\", \"string_or_numeric\");\n  const $defaultValue = convertToTensor(defaultValue, \"defaultValue\", \"sparseToDense\", $sparseValues.dtype);\n  validateInput2($sparseIndices, $sparseValues, outputShape, $defaultValue);\n  const inputs = {\n    sparseIndices: $sparseIndices,\n    sparseValues: $sparseValues,\n    defaultValue: $defaultValue\n  };\n  const attrs = { outputShape };\n  return ENGINE.runKernel(SparseToDense, inputs, attrs);\n}\nvar sparseToDense = op({ sparseToDense_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/gather_nd.js\nfunction gatherND_(x, indices) {\n  const $indices = convertToTensor(indices, \"indices\", \"gatherND\", \"int32\");\n  const $x = convertToTensor(x, \"x\", \"gatherND\", \"string_or_numeric\");\n  const inputs = { params: $x, indices: $indices };\n  return ENGINE.runKernel(GatherNd, inputs);\n}\nvar gatherND = op({ gatherND_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/dropout_util.js\nfunction getNoiseShape(x, noiseShape) {\n  if (noiseShape == null) {\n    return x.shape.slice();\n  }\n  if (arraysEqual(x.shape, noiseShape)) {\n    return noiseShape;\n  }\n  if (x.shape.length === noiseShape.length) {\n    const newDimension = [];\n    for (let i = 0; i < x.shape.length; i++) {\n      if (noiseShape[i] == null && x.shape[i] != null) {\n        newDimension.push(x.shape[i]);\n      } else {\n        newDimension.push(noiseShape[i]);\n      }\n    }\n    return newDimension;\n  }\n  return noiseShape;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/dropout.js\nfunction dropout_(x, rate, noiseShape, seed) {\n  const $x = convertToTensor(x, \"x\", \"dropout\");\n  assert($x.dtype === \"float32\", () => `x has to be a floating point tensor since it's going to be scaled, but got a ${$x.dtype} tensor instead.`);\n  assert(rate >= 0 && rate < 1, () => `rate must be a float in the range [0, 1), but got ${rate}.`);\n  if (rate === 0) {\n    return x instanceof Tensor ? $x.clone() : $x;\n  }\n  const $noiseShape = getNoiseShape($x, noiseShape);\n  const keepProb = 1 - rate;\n  const multiplier = div(floor(add2(randomUniform($noiseShape, 0, 1, \"float32\", seed), keepProb)), keepProb);\n  return mul($x, multiplier);\n}\nvar dropout = op({ dropout_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/signal_ops_util.js\nfunction enclosingPowerOfTwo(value) {\n  return Math.floor(Math.pow(2, Math.ceil(Math.log(value) / Math.log(2))));\n}\nfunction cosineWindow(windowLength, a, b) {\n  const even = 1 - windowLength % 2;\n  const newValues = new Float32Array(windowLength);\n  for (let i = 0; i < windowLength; ++i) {\n    const cosArg = 2 * Math.PI * i / (windowLength + even - 1);\n    newValues[i] = a - b * Math.cos(cosArg);\n  }\n  return tensor1d(newValues, \"float32\");\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/in_top_k.js\nasync function inTopKAsync_(predictions, targets, k = 1) {\n  const $predictions = convertToTensor(predictions, \"predictions\", \"inTopK\");\n  const $targets = convertToTensor(targets, \"targets\", \"inTopK\");\n  assert($predictions.rank > 1, () => `inTopK() expects the predictions to be of rank 2 or higher, but got ${$predictions.rank}`);\n  assert($predictions.rank - 1 === $targets.rank, () => `predictions rank should be 1 larger than targets rank, but got predictions rank ${$predictions.rank} and targets rank ${$targets.rank}`);\n  assertShapesMatch($predictions.shape.slice(0, $predictions.shape.length - 1), $targets.shape, `predictions's shape should be align with the targets' shape, except the last dimension.`);\n  const lastDim = $predictions.shape[$predictions.shape.length - 1];\n  assert(k > 0 && k <= lastDim, () => `'k' passed to inTopK() must be > 0 && <= the predictions last dimension (${lastDim}), but got ${k}`);\n  const predictionsVals = await $predictions.data();\n  const targetsVals = await $targets.data();\n  const [batch, size] = [predictionsVals.length / lastDim, lastDim];\n  const precision3 = getTypedArrayFromDType(\"bool\", batch);\n  for (let b = 0; b < batch; b++) {\n    const offset = b * size;\n    const vals = predictionsVals.subarray(offset, offset + size);\n    const valAndInd = [];\n    for (let i = 0; i < vals.length; i++) {\n      valAndInd.push({ value: vals[i], index: i });\n    }\n    valAndInd.sort((a, b2) => b2.value - a.value);\n    precision3[b] = 0;\n    for (let i = 0; i < k; i++) {\n      if (valAndInd[i].index === targetsVals[b]) {\n        precision3[b] = 1;\n        break;\n      }\n    }\n  }\n  if (predictions !== $predictions) {\n    $predictions.dispose();\n  }\n  if (targets !== $targets) {\n    $targets.dispose();\n  }\n  return tensor(precision3, $targets.shape, \"bool\");\n}\nvar inTopKAsync = inTopKAsync_;\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/fused_ops.js\nvar fused_ops_exports = {};\n__export(fused_ops_exports, {\n  conv2d: () => conv2d2,\n  depthwiseConv2d: () => depthwiseConv2d2,\n  matMul: () => matMul2\n});\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/conv2d_backprop_filter.js\nfunction conv2DBackpropFilter_(x, dy, filterShape, strides, pad3, dataFormat = \"NHWC\", dimRoundingMode) {\n  let x4D = x;\n  if (x.rank === 3) {\n    x4D = reshape(x, [1, x.shape[0], x.shape[1], x.shape[2]]);\n  }\n  let dy4D = dy;\n  if (dy4D.rank === 3) {\n    dy4D = reshape(dy, [1, dy.shape[0], dy.shape[1], dy.shape[2]]);\n  }\n  assert(x4D.rank === 4, () => `Error in conv2dDerFilter: input must be rank 4, but got shape ${x4D.shape}.`);\n  assert(dy4D.rank === 4, () => `Error in conv2dDerFilter: dy must be rank 4, but got shape ${dy4D.shape}.`);\n  assert(filterShape.length === 4, () => `Error in conv2dDerFilter: filterShape must be length 4, but got ${filterShape}.`);\n  const inDepth = dataFormat === \"NHWC\" ? x4D.shape[3] : x4D.shape[1];\n  const outDepth = dataFormat === \"NHWC\" ? dy4D.shape[3] : dy4D.shape[1];\n  assert(inDepth === filterShape[2], () => `Error in conv2dDerFilter: depth of input ${inDepth}) must match input depth in filter (${filterShape[2]}.`);\n  assert(outDepth === filterShape[3], () => `Error in conv2dDerFilter: depth of dy (${outDepth}) must match output depth for filter (${filterShape[3]}).`);\n  checkPadOnDimRoundingMode(\"conv2dDerFilter\", pad3, dimRoundingMode);\n  const inputs = { x: x4D, dy: dy4D };\n  const attrs = { strides, pad: pad3, dataFormat, dimRoundingMode, filterShape };\n  return ENGINE.runKernel(Conv2DBackpropFilter, inputs, attrs);\n}\nvar conv2DBackpropFilter = op({ conv2DBackpropFilter_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/fused_util.js\nfunction getFusedDyActivation(dy, y, activation2) {\n  if (activation2 == null || activation2 === \"linear\") {\n    return dy;\n  }\n  if (activation2 === \"relu\") {\n    return mul(dy, step(y));\n  }\n  throw new Error(`Cannot compute gradient for fused activation ${activation2}.`);\n}\nfunction getFusedBiasGradient(bias, dyActivation) {\n  let res = dyActivation;\n  const reduceAxes = getReductionAxes(bias.shape, dyActivation.shape);\n  if (reduceAxes.length > 0) {\n    res = sum2(res, reduceAxes);\n  }\n  return reshape(res, bias.shape);\n}\nfunction applyActivation(x, activation2, preluActivationWeights, leakyreluAlpha) {\n  if (activation2 === \"linear\") {\n    return x;\n  } else if (activation2 === \"relu\") {\n    return relu(x);\n  } else if (activation2 === \"elu\") {\n    return elu(x);\n  } else if (activation2 === \"relu6\") {\n    return relu6(x);\n  } else if (activation2 === \"prelu\") {\n    return prelu(x, preluActivationWeights);\n  } else if (activation2 === \"leakyrelu\") {\n    return leakyRelu(x, leakyreluAlpha);\n  } else if (activation2 === \"sigmoid\") {\n    return sigmoid(x);\n  }\n  throw new Error(`Unknown fused activation ${activation2}.`);\n}\nvar shouldFuse = (gradientDepth, activation2) => {\n  const gradientMode = gradientDepth > 0;\n  return !gradientMode || activation2 === \"linear\";\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/fused/conv2d.js\nfunction fusedConv2d_({ x, filter, strides, pad: pad3, dataFormat = \"NHWC\", dilations = [1, 1], dimRoundingMode, bias, activation: activation2 = \"linear\", preluActivationWeights, leakyreluAlpha }) {\n  activation2 = activation2 || \"linear\";\n  if (shouldFuse(ENGINE.state.gradientDepth, activation2) === false) {\n    assert(dataFormat === \"NHWC\", () => `Error in fused conv2d: got dataFormat of ${dataFormat} but only NHWC is currently supported for the case of gradient depth is 0 and the activation is not linear.`);\n    let result = conv2d(x, filter, strides, pad3, dataFormat, dilations, dimRoundingMode);\n    if (bias != null) {\n      result = add2(result, bias);\n    }\n    return applyActivation(result, activation2, preluActivationWeights, leakyreluAlpha);\n  }\n  const $x = convertToTensor(x, \"x\", \"conv2d\", \"float32\");\n  const $filter = convertToTensor(filter, \"filter\", \"conv2d\", \"float32\");\n  let x4D = $x;\n  let reshapedTo4D = false;\n  if ($x.rank === 3) {\n    reshapedTo4D = true;\n    x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);\n  }\n  assert(x4D.rank === 4, () => `Error in fused conv2d: input must be rank 4, but got rank ${x4D.rank}.`);\n  assert($filter.rank === 4, () => `Error in fused conv2d: filter must be rank 4, but got rank ${$filter.rank}.`);\n  checkPadOnDimRoundingMode(\"fused conv2d\", pad3, dimRoundingMode);\n  const inputChannels = dataFormat === \"NHWC\" ? x4D.shape[3] : x4D.shape[1];\n  assert($filter.shape[2] === inputChannels, () => `Error in conv2d: depth of input (${inputChannels}) must match input depth for filter ${$filter.shape[2]}.`);\n  assert(eitherStridesOrDilationsAreOne(strides, dilations), () => `Error in conv2D: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`);\n  const convInfo = computeConv2DInfo(x4D.shape, $filter.shape, strides, dilations, pad3, dimRoundingMode);\n  let $bias;\n  if (bias != null) {\n    $bias = convertToTensor(bias, \"bias\", \"fused conv2d\");\n    [$bias] = makeTypesMatch($bias, $x);\n    if (dataFormat === \"NHWC\") {\n      assertAndGetBroadcastShape(convInfo.outShape, $bias.shape);\n    } else {\n      assert($bias.shape.length <= 1, () => `Error in fused conv2d: only supports scalar or 1-D Tensor bias for NCHW format but got the bias of rank-${$bias.shape.length}.`);\n      assert($bias.shape.length === 0 || $bias.shape[0] === convInfo.outChannels || $bias.shape[0] === 1, () => `Error in fused conv2d: bias shape (${$bias.shape}) is not compatible with the number of output channels (${convInfo.outChannels})`);\n    }\n  }\n  let $preluActivationWeights;\n  if (preluActivationWeights != null) {\n    const alphaShape = preluActivationWeights.shape;\n    assert(alphaShape.length <= 1 || alphaShape.length === 3, () => `Error in fused conv2d: only supports scalar, 1-D Tensor or 3-D Tensor PReLU activation weights but got a tensor of rank-${alphaShape.length}.`);\n    if (alphaShape.length === 1) {\n      assert(alphaShape[0] === 1 || alphaShape[0] === convInfo.outChannels, () => `Error in fused conv2d: PReLU activation weights (${alphaShape}) is not compatible with the number of output channels (${convInfo.outChannels}).`);\n    } else if (alphaShape.length === 3) {\n      try {\n        assertAndGetBroadcastShape(alphaShape, convInfo.outShape);\n      } catch (e) {\n        const errMsg = `Error in fused conv2d: PReLU activation weights (${alphaShape}) is not compatible with the output shape of the conv2d (${convInfo.outShape}).`;\n        throw Error(errMsg);\n      }\n    }\n    $preluActivationWeights = convertToTensor(preluActivationWeights, \"prelu weights\", \"fused conv2d\");\n  }\n  const grad2 = (dy, saved) => {\n    assert(dataFormat === \"NHWC\", () => `Error in gradient of fused conv2D: got dataFormat of ${dataFormat} but only NHWC is currently supported.`);\n    const [$filter2, x4D2, y, $bias2] = saved;\n    const dyActivation = getFusedDyActivation(dy, y, activation2);\n    assert(tupleValuesAreOne(dilations), () => `Error in gradient of fused conv2D: dilation rates greater than 1 are not yet supported in gradients. Got dilations '${dilations}'`);\n    const xDer = conv2DBackpropInput(x4D2.shape, dyActivation, $filter2, strides, pad3);\n    const filterDer = conv2DBackpropFilter(x4D2, dyActivation, $filter2.shape, strides, pad3);\n    const der = [xDer, filterDer];\n    if ($bias2 != null) {\n      const biasDer = getFusedBiasGradient($bias2, dyActivation);\n      der.push(biasDer);\n    }\n    return der;\n  };\n  const inputs = {\n    x: x4D,\n    filter: $filter,\n    bias: $bias,\n    preluActivationWeights: $preluActivationWeights\n  };\n  const attrs = {\n    strides,\n    pad: pad3,\n    dataFormat,\n    dilations,\n    dimRoundingMode,\n    activation: activation2,\n    leakyreluAlpha\n  };\n  if (bias == null) {\n    const customOp = customGrad((x4D2, filter2, save) => {\n      let res = ENGINE.runKernel(FusedConv2D, inputs, attrs);\n      save([filter2, x4D2, res]);\n      if (reshapedTo4D) {\n        res = reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);\n      }\n      return { value: res, gradFunc: grad2 };\n    });\n    return customOp(x4D, $filter);\n  } else {\n    const customOpWithBias = customGrad((x4D2, filter2, bias2, save) => {\n      let res = ENGINE.runKernel(FusedConv2D, inputs, attrs);\n      save([filter2, x4D2, res, bias2]);\n      if (reshapedTo4D) {\n        res = reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);\n      }\n      return { value: res, gradFunc: grad2 };\n    });\n    return customOpWithBias(x4D, $filter, $bias);\n  }\n}\nvar conv2d2 = op({ fusedConv2d_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/depthwise_conv2d_native_backprop_filter.js\nfunction depthwiseConv2dNativeBackpropFilter_(x, dy, filterShape, strides, pad3, dilations = [1, 1], dimRoundingMode) {\n  let x4D = x;\n  if (x.rank === 3) {\n    x4D = reshape(x, [1, x.shape[0], x.shape[1], x.shape[2]]);\n  }\n  let dy4D = dy;\n  if (dy4D.rank === 3) {\n    dy4D = reshape(dy, [1, dy.shape[0], dy.shape[1], dy.shape[2]]);\n  }\n  const inputs = { x: x4D, dy: dy4D };\n  const attrs = { strides, pad: pad3, dimRoundingMode, dilations, filterShape };\n  return ENGINE.runKernel(DepthwiseConv2dNativeBackpropFilter, inputs, attrs);\n}\nvar depthwiseConv2dNativeBackpropFilter = op({ depthwiseConv2dNativeBackpropFilter_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/depthwise_conv2d_native_backprop_input.js\nfunction depthwiseConv2dNativeBackpropInput_(xShape, dy, filter, strides, pad3, dilations = [1, 1], dimRoundingMode) {\n  let dy4D = dy;\n  let reshapedTo4D = false;\n  if (dy.rank === 3) {\n    reshapedTo4D = true;\n    dy4D = reshape(dy, [1, dy.shape[0], dy.shape[1], dy.shape[2]]);\n  }\n  const inputs = { dy: dy4D, filter };\n  const attrs = { strides, pad: pad3, dimRoundingMode, dilations, inputShape: xShape };\n  const res = ENGINE.runKernel(DepthwiseConv2dNativeBackpropInput, inputs, attrs);\n  if (reshapedTo4D) {\n    return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);\n  }\n  return res;\n}\nvar depthwiseConv2dNativeBackpropInput = op({ depthwiseConv2dNativeBackpropInput_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/fused/depthwise_conv2d.js\nfunction fusedDepthwiseConv2d_({ x, filter, strides, pad: pad3, dataFormat = \"NHWC\", dilations = [1, 1], dimRoundingMode, bias, activation: activation2 = \"linear\", preluActivationWeights, leakyreluAlpha }) {\n  if (shouldFuse(ENGINE.state.gradientDepth, activation2) === false) {\n    let result = depthwiseConv2d(x, filter, strides, pad3, dataFormat, dilations, dimRoundingMode);\n    if (bias != null) {\n      result = add2(result, bias);\n    }\n    return applyActivation(result, activation2, preluActivationWeights, leakyreluAlpha);\n  }\n  const $x = convertToTensor(x, \"x\", \"depthwiseConv2d\", \"float32\");\n  const $filter = convertToTensor(filter, \"filter\", \"depthwiseConv2d\", \"float32\");\n  let x4D = $x;\n  let reshapedTo4D = false;\n  if ($x.rank === 3) {\n    reshapedTo4D = true;\n    x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);\n  }\n  assert(x4D.rank === 4, () => `Error in fused depthwiseConv2d: input must be rank 4, but got rank ${x4D.rank}.`);\n  assert($filter.rank === 4, () => `Error in fused depthwiseConv2d: filter must be rank 4, but got rank ${$filter.rank}.`);\n  assert(x4D.shape[3] === $filter.shape[2], () => `Error in fused depthwiseConv2d: number of input channels (${x4D.shape[3]}) must match the inChannels dimension in filter ${$filter.shape[2]}.`);\n  if (dilations == null) {\n    dilations = [1, 1];\n  }\n  assert(eitherStridesOrDilationsAreOne(strides, dilations), () => `Error in fused depthwiseConv2d: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`);\n  checkPadOnDimRoundingMode(\"fused depthwiseConv2d\", pad3, dimRoundingMode);\n  const convInfo = computeConv2DInfo(x4D.shape, $filter.shape, strides, dilations, pad3, dimRoundingMode, true);\n  let $bias;\n  if (bias != null) {\n    $bias = convertToTensor(bias, \"bias\", \"fused conv2d\");\n    [$bias] = makeTypesMatch($bias, $x);\n    assertAndGetBroadcastShape(convInfo.outShape, $bias.shape);\n  }\n  let $preluActivationWeights;\n  if (preluActivationWeights != null) {\n    $preluActivationWeights = convertToTensor(preluActivationWeights, \"prelu weights\", \"fused depthwiseConv2d\");\n  }\n  const grad2 = (dy, saved) => {\n    assert(tupleValuesAreOne(dilations), () => `Error in gradient of fused depthwiseConv2d: dilation rates greater than 1 are not yet supported. Got dilations '${dilations}'`);\n    const [$filter2, x4D2, y, bias2] = saved;\n    const dyActivation = getFusedDyActivation(dy, y, activation2);\n    const xDer = depthwiseConv2dNativeBackpropInput(x4D2.shape, dyActivation, $filter2, strides, pad3, dilations, dimRoundingMode);\n    const filterDer = depthwiseConv2dNativeBackpropFilter(x4D2, dyActivation, $filter2.shape, strides, pad3, dilations, dimRoundingMode);\n    if (bias2 != null) {\n      const biasDer = getFusedBiasGradient($bias, dyActivation);\n      return [xDer, filterDer, biasDer];\n    }\n    return [xDer, filterDer];\n  };\n  const inputs = {\n    x: x4D,\n    filter: $filter,\n    bias: $bias,\n    preluActivationWeights: $preluActivationWeights\n  };\n  const attrs = {\n    strides,\n    pad: pad3,\n    dataFormat,\n    dilations,\n    dimRoundingMode,\n    activation: activation2,\n    leakyreluAlpha\n  };\n  if (bias == null) {\n    const customOp = customGrad((x4D2, filter2, save) => {\n      let res = ENGINE.runKernel(FusedDepthwiseConv2D, inputs, attrs);\n      save([filter2, x4D2, res]);\n      if (reshapedTo4D) {\n        res = reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);\n      }\n      return { value: res, gradFunc: grad2 };\n    });\n    return customOp(x4D, $filter);\n  } else {\n    const customOpWithBias = customGrad((x4D2, filter2, bias2, save) => {\n      let res = ENGINE.runKernel(FusedDepthwiseConv2D, inputs, attrs);\n      save([filter2, x4D2, res, bias2]);\n      if (reshapedTo4D) {\n        res = reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);\n      }\n      return { value: res, gradFunc: grad2 };\n    });\n    return customOpWithBias(x4D, $filter, $bias);\n  }\n}\nvar depthwiseConv2d2 = op({ fusedDepthwiseConv2d_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/fused/mat_mul.js\nfunction fusedMatMul_({ a, b, transposeA = false, transposeB = false, bias, activation: activation2 = \"linear\", preluActivationWeights, leakyreluAlpha = 0.2 }) {\n  if (shouldFuse(ENGINE.state.gradientDepth, activation2) === false) {\n    let result = matMul(a, b, transposeA, transposeB);\n    if (bias != null) {\n      result = add2(result, bias);\n    }\n    return applyActivation(result, activation2, preluActivationWeights, leakyreluAlpha);\n  }\n  let $a = convertToTensor(a, \"a\", \"fused matMul\");\n  let $b = convertToTensor(b, \"b\", \"fused matMul\");\n  [$a, $b] = makeTypesMatch($a, $b);\n  const innerShapeA = transposeA ? $a.shape[$a.rank - 2] : $a.shape[$a.rank - 1];\n  const innerShapeB = transposeB ? $b.shape[$b.rank - 1] : $b.shape[$b.rank - 2];\n  const outerShapeA = transposeA ? $a.shape[$a.rank - 1] : $a.shape[$a.rank - 2];\n  const outerShapeB = transposeB ? $b.shape[$b.rank - 2] : $b.shape[$b.rank - 1];\n  const outerDimsA = $a.shape.slice(0, -2);\n  const outerDimsB = $b.shape.slice(0, -2);\n  const batchDimA = sizeFromShape(outerDimsA);\n  const batchDimB = sizeFromShape(outerDimsB);\n  assert(innerShapeA === innerShapeB, () => `Error in fused matMul: inner shapes (${innerShapeA}) and (${innerShapeB}) of Tensors with shapes ${$a.shape} and ${$b.shape} and transposeA=${transposeA} and transposeB=${transposeB} must match.`);\n  const outShapeOuterDims = assertAndGetBroadcastShape($a.shape.slice(0, -2), $b.shape.slice(0, -2));\n  const outShape = outShapeOuterDims.concat([outerShapeA, outerShapeB]);\n  const a3D = transposeA ? reshape($a, [batchDimA, innerShapeA, outerShapeA]) : reshape($a, [batchDimA, outerShapeA, innerShapeA]);\n  const b3D = transposeB ? reshape($b, [batchDimB, outerShapeB, innerShapeB]) : reshape($b, [batchDimB, innerShapeB, outerShapeB]);\n  let $bias;\n  if (bias != null) {\n    $bias = convertToTensor(bias, \"bias\", \"fused matMul\");\n    [$bias] = makeTypesMatch($bias, $a);\n    assertAndGetBroadcastShape(outShape, $bias.shape);\n  }\n  let $preluActivationWeights;\n  if (preluActivationWeights != null) {\n    $preluActivationWeights = convertToTensor(preluActivationWeights, \"prelu weights\", \"fused matMul\");\n  }\n  const grad2 = (dy, saved) => {\n    const [a3D2, b3D2, y, $bias2] = saved;\n    const dyActivation = getFusedDyActivation(reshape(dy, y.shape), y, activation2);\n    let aDer;\n    let bDer;\n    if (!transposeA && !transposeB) {\n      aDer = matMul(dyActivation, b3D2, false, true);\n      bDer = matMul(a3D2, dyActivation, true, false);\n    } else if (!transposeA && transposeB) {\n      aDer = matMul(dyActivation, b3D2, false, false);\n      bDer = matMul(dyActivation, a3D2, true, false);\n    } else if (transposeA && !transposeB) {\n      aDer = matMul(b3D2, dyActivation, false, true);\n      bDer = matMul(a3D2, dyActivation, false, false);\n    } else {\n      aDer = matMul(b3D2, dyActivation, true, true);\n      bDer = matMul(dyActivation, a3D2, true, true);\n    }\n    if (bias != null) {\n      const biasDer = getFusedBiasGradient($bias2, dyActivation);\n      return [aDer, bDer, biasDer];\n    } else {\n      return [aDer, bDer];\n    }\n  };\n  const inputs = {\n    a: a3D,\n    b: b3D,\n    bias: $bias,\n    preluActivationWeights: $preluActivationWeights\n  };\n  const attrs = { transposeA, transposeB, activation: activation2, leakyreluAlpha };\n  if (bias == null) {\n    const customOp = customGrad((a3D2, b3D2, save) => {\n      const res = ENGINE.runKernel(_FusedMatMul, inputs, attrs);\n      save([a3D2, b3D2, res]);\n      return { value: reshape(res, outShape), gradFunc: grad2 };\n    });\n    return customOp(a3D, b3D);\n  } else {\n    const customOpWithBias = customGrad((a3D2, b3D2, $bias2, save) => {\n      const res = ENGINE.runKernel(_FusedMatMul, inputs, attrs);\n      save([a3D2, b3D2, res, $bias2]);\n      return { value: reshape(res, outShape), gradFunc: grad2 };\n    });\n    return customOpWithBias(a3D, b3D, $bias);\n  }\n}\nvar matMul2 = op({ fusedMatMul_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/signal/hamming_window.js\nfunction hammingWindow_(windowLength) {\n  return cosineWindow(windowLength, 0.54, 0.46);\n}\nvar hammingWindow = op({ hammingWindow_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/signal/hann_window.js\nfunction hannWindow_(windowLength) {\n  return cosineWindow(windowLength, 0.5, 0.5);\n}\nvar hannWindow = op({ hannWindow_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/signal/frame.js\nfunction frame_(signal2, frameLength, frameStep, padEnd = false, padValue = 0) {\n  let start = 0;\n  const output = [];\n  while (start + frameLength <= signal2.size) {\n    output.push(slice(signal2, start, frameLength));\n    start += frameStep;\n  }\n  if (padEnd) {\n    while (start < signal2.size) {\n      const padLen = start + frameLength - signal2.size;\n      const pad3 = concat([\n        slice(signal2, start, frameLength - padLen),\n        fill([padLen], padValue)\n      ]);\n      output.push(pad3);\n      start += frameStep;\n    }\n  }\n  if (output.length === 0) {\n    return tensor2d([], [0, frameLength]);\n  }\n  return reshape(concat(output), [output.length, frameLength]);\n}\nvar frame = op({ frame_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/signal/stft.js\nfunction stft_(signal2, frameLength, frameStep, fftLength, windowFn = hannWindow) {\n  if (fftLength == null) {\n    fftLength = enclosingPowerOfTwo(frameLength);\n  }\n  const framedSignal = frame(signal2, frameLength, frameStep);\n  const windowedSignal = mul(framedSignal, windowFn(frameLength));\n  return rfft(windowedSignal, fftLength);\n}\nvar stft = op({ stft_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/image/crop_and_resize.js\nfunction cropAndResize_(image2, boxes, boxInd, cropSize, method = \"bilinear\", extrapolationValue = 0) {\n  const $image = convertToTensor(image2, \"image\", \"cropAndResize\");\n  const $boxes = convertToTensor(boxes, \"boxes\", \"cropAndResize\", \"float32\");\n  const $boxInd = convertToTensor(boxInd, \"boxInd\", \"cropAndResize\", \"int32\");\n  const numBoxes = $boxes.shape[0];\n  assert($image.rank === 4, () => `Error in cropAndResize: image must be rank 4,but got rank ${$image.rank}.`);\n  assert($boxes.rank === 2 && $boxes.shape[1] === 4, () => `Error in cropAndResize: boxes must be have size [${numBoxes},4] but had shape ${$boxes.shape}.`);\n  assert($boxInd.rank === 1 && $boxInd.shape[0] === numBoxes, () => `Error in cropAndResize: boxInd must be have size [${numBoxes}] but had shape ${$boxes.shape}.`);\n  assert(cropSize.length === 2, () => `Error in cropAndResize: cropSize must be of length 2, but got length ${cropSize.length}.`);\n  assert(cropSize[0] >= 1 && cropSize[1] >= 1, () => `cropSize must be atleast [1,1], but was ${cropSize}`);\n  assert(method === \"bilinear\" || method === \"nearest\", () => `method must be bilinear or nearest, but was ${method}`);\n  const inputs = { image: $image, boxes: $boxes, boxInd: $boxInd };\n  const attrs = { method, extrapolationValue, cropSize };\n  const res = ENGINE.runKernel(CropAndResize, inputs, attrs);\n  return res;\n}\nvar cropAndResize = op({ cropAndResize_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/image/flip_left_right.js\nfunction flipLeftRight_(image2) {\n  const $image = convertToTensor(image2, \"image\", \"flipLeftRight\", \"float32\");\n  assert($image.rank === 4, () => `Error in flipLeftRight: image must be rank 4,but got rank ${$image.rank}.`);\n  const inputs = { image: $image };\n  const res = ENGINE.runKernel(FlipLeftRight, inputs, {});\n  return res;\n}\nvar flipLeftRight = op({ flipLeftRight_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/image/grayscale_to_rgb.js\nfunction grayscaleToRGB_(image2) {\n  const $image = convertToTensor(image2, \"image\", \"grayscaleToRGB\");\n  const lastDimsIdx = $image.rank - 1;\n  const lastDims = $image.shape[lastDimsIdx];\n  assert($image.rank >= 2, () => `Error in grayscaleToRGB: images must be at least rank 2, but got rank ${$image.rank}.`);\n  assert(lastDims === 1, () => `Error in grayscaleToRGB: last dimension of a grayscale image should be size 1, but got size ${lastDims}.`);\n  const reps = new Array($image.rank);\n  reps.fill(1, 0, lastDimsIdx);\n  reps[lastDimsIdx] = 3;\n  return tile($image, reps);\n}\nvar grayscaleToRGB = op({ grayscaleToRGB_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/image/rotate_with_offset.js\nfunction rotateWithOffset_(image2, radians, fillValue = 0, center = 0.5) {\n  const $image = convertToTensor(image2, \"image\", \"rotateWithOffset\", \"float32\");\n  assert($image.rank === 4, () => `Error in rotateWithOffset: image must be rank 4,but got rank ${$image.rank}.`);\n  const inputs = { image: $image };\n  const attrs = { radians, fillValue, center };\n  const res = ENGINE.runKernel(RotateWithOffset, inputs, attrs);\n  return res;\n}\nvar rotateWithOffset = op({ rotateWithOffset_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/nonmax_util.js\nfunction nonMaxSuppSanityCheck(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma) {\n  if (iouThreshold == null) {\n    iouThreshold = 0.5;\n  }\n  if (scoreThreshold == null) {\n    scoreThreshold = Number.NEGATIVE_INFINITY;\n  }\n  if (softNmsSigma == null) {\n    softNmsSigma = 0;\n  }\n  const numBoxes = boxes.shape[0];\n  maxOutputSize = Math.min(maxOutputSize, numBoxes);\n  assert(0 <= iouThreshold && iouThreshold <= 1, () => `iouThreshold must be in [0, 1], but was '${iouThreshold}'`);\n  assert(boxes.rank === 2, () => `boxes must be a 2D tensor, but was of rank '${boxes.rank}'`);\n  assert(boxes.shape[1] === 4, () => `boxes must have 4 columns, but 2nd dimension was ${boxes.shape[1]}`);\n  assert(scores.rank === 1, () => \"scores must be a 1D tensor\");\n  assert(scores.shape[0] === numBoxes, () => `scores has incompatible shape with boxes. Expected ${numBoxes}, but was ${scores.shape[0]}`);\n  assert(0 <= softNmsSigma && softNmsSigma <= 1, () => `softNmsSigma must be in [0, 1], but was '${softNmsSigma}'`);\n  return { maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma };\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/image/non_max_suppression.js\nfunction nonMaxSuppression_(boxes, scores, maxOutputSize, iouThreshold = 0.5, scoreThreshold = Number.NEGATIVE_INFINITY) {\n  const $boxes = convertToTensor(boxes, \"boxes\", \"nonMaxSuppression\", \"float32\");\n  const $scores = convertToTensor(scores, \"scores\", \"nonMaxSuppression\", \"float32\");\n  const inputs = nonMaxSuppSanityCheck($boxes, $scores, maxOutputSize, iouThreshold, scoreThreshold);\n  maxOutputSize = inputs.maxOutputSize;\n  iouThreshold = inputs.iouThreshold;\n  scoreThreshold = inputs.scoreThreshold;\n  const attrs = { maxOutputSize, iouThreshold, scoreThreshold };\n  return ENGINE.runKernel(NonMaxSuppressionV3, { boxes: $boxes, scores: $scores }, attrs);\n}\nvar nonMaxSuppression = op({ nonMaxSuppression_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/backends/non_max_suppression_util.js\nfunction binaryInsert(arr, element, comparator) {\n  const index = binarySearch(arr, element, comparator);\n  const insertionPoint = index < 0 ? -(index + 1) : index;\n  arr.splice(insertionPoint, 0, element);\n}\nfunction binarySearch(arr, target, comparator) {\n  return binarySearch_(arr, target, comparator || defaultComparator);\n}\nfunction defaultComparator(a, b) {\n  return a > b ? 1 : a < b ? -1 : 0;\n}\nfunction binarySearch_(arr, target, comparator) {\n  let left = 0;\n  let right = arr.length;\n  let middle = 0;\n  let found = false;\n  while (left < right) {\n    middle = left + (right - left >>> 1);\n    const compareResult = comparator(target, arr[middle]);\n    if (compareResult > 0) {\n      left = middle + 1;\n    } else {\n      right = middle;\n      found = !compareResult;\n    }\n  }\n  return found ? left : -left - 1;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/backends/non_max_suppression_impl.js\nfunction nonMaxSuppressionV3Impl(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold) {\n  return nonMaxSuppressionImpl_(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, 0);\n}\nfunction nonMaxSuppressionV4Impl(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, padToMaxOutputSize) {\n  return nonMaxSuppressionImpl_(\n    boxes,\n    scores,\n    maxOutputSize,\n    iouThreshold,\n    scoreThreshold,\n    0,\n    false,\n    padToMaxOutputSize,\n    true\n  );\n}\nfunction nonMaxSuppressionV5Impl(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma) {\n  return nonMaxSuppressionImpl_(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma, true);\n}\nfunction nonMaxSuppressionImpl_(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma, returnScoresTensor = false, padToMaxOutputSize = false, returnValidOutputs = false) {\n  const candidates = [];\n  for (let i = 0; i < scores.length; i++) {\n    if (scores[i] > scoreThreshold) {\n      candidates.push({ score: scores[i], boxIndex: i, suppressBeginIndex: 0 });\n    }\n  }\n  candidates.sort(ascendingComparator);\n  const scale2 = softNmsSigma > 0 ? -0.5 / softNmsSigma : 0;\n  const selectedIndices = [];\n  const selectedScores = [];\n  while (selectedIndices.length < maxOutputSize && candidates.length > 0) {\n    const candidate = candidates.pop();\n    const { score: originalScore, boxIndex, suppressBeginIndex } = candidate;\n    if (originalScore < scoreThreshold) {\n      break;\n    }\n    let ignoreCandidate = false;\n    for (let j = selectedIndices.length - 1; j >= suppressBeginIndex; --j) {\n      const iou = intersectionOverUnion(boxes, boxIndex, selectedIndices[j]);\n      if (iou >= iouThreshold) {\n        ignoreCandidate = true;\n        break;\n      }\n      candidate.score = candidate.score * suppressWeight(iouThreshold, scale2, iou);\n      if (candidate.score <= scoreThreshold) {\n        break;\n      }\n    }\n    candidate.suppressBeginIndex = selectedIndices.length;\n    if (!ignoreCandidate) {\n      if (candidate.score === originalScore) {\n        selectedIndices.push(boxIndex);\n        selectedScores.push(candidate.score);\n      } else if (candidate.score > scoreThreshold) {\n        binaryInsert(candidates, candidate, ascendingComparator);\n      }\n    }\n  }\n  const validOutputs = selectedIndices.length;\n  const elemsToPad = maxOutputSize - validOutputs;\n  if (padToMaxOutputSize && elemsToPad > 0) {\n    selectedIndices.push(...new Array(elemsToPad).fill(0));\n    selectedScores.push(...new Array(elemsToPad).fill(0));\n  }\n  const result = { selectedIndices };\n  if (returnScoresTensor) {\n    result[\"selectedScores\"] = selectedScores;\n  }\n  if (returnValidOutputs) {\n    result[\"validOutputs\"] = validOutputs;\n  }\n  return result;\n}\nfunction intersectionOverUnion(boxes, i, j) {\n  const iCoord = boxes.subarray(i * 4, i * 4 + 4);\n  const jCoord = boxes.subarray(j * 4, j * 4 + 4);\n  const yminI = Math.min(iCoord[0], iCoord[2]);\n  const xminI = Math.min(iCoord[1], iCoord[3]);\n  const ymaxI = Math.max(iCoord[0], iCoord[2]);\n  const xmaxI = Math.max(iCoord[1], iCoord[3]);\n  const yminJ = Math.min(jCoord[0], jCoord[2]);\n  const xminJ = Math.min(jCoord[1], jCoord[3]);\n  const ymaxJ = Math.max(jCoord[0], jCoord[2]);\n  const xmaxJ = Math.max(jCoord[1], jCoord[3]);\n  const areaI = (ymaxI - yminI) * (xmaxI - xminI);\n  const areaJ = (ymaxJ - yminJ) * (xmaxJ - xminJ);\n  if (areaI <= 0 || areaJ <= 0) {\n    return 0;\n  }\n  const intersectionYmin = Math.max(yminI, yminJ);\n  const intersectionXmin = Math.max(xminI, xminJ);\n  const intersectionYmax = Math.min(ymaxI, ymaxJ);\n  const intersectionXmax = Math.min(xmaxI, xmaxJ);\n  const intersectionArea = Math.max(intersectionYmax - intersectionYmin, 0) * Math.max(intersectionXmax - intersectionXmin, 0);\n  return intersectionArea / (areaI + areaJ - intersectionArea);\n}\nfunction suppressWeight(iouThreshold, scale2, iou) {\n  const weight = Math.exp(scale2 * iou * iou);\n  return iou <= iouThreshold ? weight : 0;\n}\nfunction ascendingComparator(c1, c2) {\n  return c1.score - c2.score || c1.score === c2.score && c2.boxIndex - c1.boxIndex;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/image/non_max_suppression_async.js\nasync function nonMaxSuppressionAsync_(boxes, scores, maxOutputSize, iouThreshold = 0.5, scoreThreshold = Number.NEGATIVE_INFINITY) {\n  const $boxes = convertToTensor(boxes, \"boxes\", \"nonMaxSuppressionAsync\");\n  const $scores = convertToTensor(scores, \"scores\", \"nonMaxSuppressionAsync\");\n  const inputs = nonMaxSuppSanityCheck($boxes, $scores, maxOutputSize, iouThreshold, scoreThreshold);\n  maxOutputSize = inputs.maxOutputSize;\n  iouThreshold = inputs.iouThreshold;\n  scoreThreshold = inputs.scoreThreshold;\n  const boxesAndScores = await Promise.all([$boxes.data(), $scores.data()]);\n  const boxesVals = boxesAndScores[0];\n  const scoresVals = boxesAndScores[1];\n  const { selectedIndices } = nonMaxSuppressionV3Impl(boxesVals, scoresVals, maxOutputSize, iouThreshold, scoreThreshold);\n  if ($boxes !== boxes) {\n    $boxes.dispose();\n  }\n  if ($scores !== scores) {\n    $scores.dispose();\n  }\n  return tensor1d(selectedIndices, \"int32\");\n}\nvar nonMaxSuppressionAsync = nonMaxSuppressionAsync_;\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/image/non_max_suppression_with_score.js\nfunction nonMaxSuppressionWithScore_(boxes, scores, maxOutputSize, iouThreshold = 0.5, scoreThreshold = Number.NEGATIVE_INFINITY, softNmsSigma = 0) {\n  const $boxes = convertToTensor(boxes, \"boxes\", \"nonMaxSuppression\");\n  const $scores = convertToTensor(scores, \"scores\", \"nonMaxSuppression\");\n  const params = nonMaxSuppSanityCheck($boxes, $scores, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma);\n  maxOutputSize = params.maxOutputSize;\n  iouThreshold = params.iouThreshold;\n  scoreThreshold = params.scoreThreshold;\n  softNmsSigma = params.softNmsSigma;\n  const inputs = { boxes: $boxes, scores: $scores };\n  const attrs = { maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma };\n  const result = ENGINE.runKernel(NonMaxSuppressionV5, inputs, attrs);\n  return { selectedIndices: result[0], selectedScores: result[1] };\n}\nvar nonMaxSuppressionWithScore = op({ nonMaxSuppressionWithScore_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/image/non_max_suppression_with_score_async.js\nasync function nonMaxSuppressionWithScoreAsync_(boxes, scores, maxOutputSize, iouThreshold = 0.5, scoreThreshold = Number.NEGATIVE_INFINITY, softNmsSigma = 0) {\n  const $boxes = convertToTensor(boxes, \"boxes\", \"nonMaxSuppressionAsync\");\n  const $scores = convertToTensor(scores, \"scores\", \"nonMaxSuppressionAsync\");\n  const params = nonMaxSuppSanityCheck($boxes, $scores, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma);\n  maxOutputSize = params.maxOutputSize;\n  iouThreshold = params.iouThreshold;\n  scoreThreshold = params.scoreThreshold;\n  softNmsSigma = params.softNmsSigma;\n  const boxesAndScores = await Promise.all([$boxes.data(), $scores.data()]);\n  const boxesVals = boxesAndScores[0];\n  const scoresVals = boxesAndScores[1];\n  const { selectedIndices, selectedScores } = nonMaxSuppressionV5Impl(boxesVals, scoresVals, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma);\n  if ($boxes !== boxes) {\n    $boxes.dispose();\n  }\n  if ($scores !== scores) {\n    $scores.dispose();\n  }\n  return {\n    selectedIndices: tensor1d(selectedIndices, \"int32\"),\n    selectedScores: tensor1d(selectedScores)\n  };\n}\nvar nonMaxSuppressionWithScoreAsync = nonMaxSuppressionWithScoreAsync_;\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/image/non_max_suppression_padded.js\nfunction nonMaxSuppressionPadded_(boxes, scores, maxOutputSize, iouThreshold = 0.5, scoreThreshold = Number.NEGATIVE_INFINITY, padToMaxOutputSize = false) {\n  const $boxes = convertToTensor(boxes, \"boxes\", \"nonMaxSuppression\");\n  const $scores = convertToTensor(scores, \"scores\", \"nonMaxSuppression\");\n  const params = nonMaxSuppSanityCheck($boxes, $scores, maxOutputSize, iouThreshold, scoreThreshold, null);\n  const $maxOutputSize = params.maxOutputSize;\n  const $iouThreshold = params.iouThreshold;\n  const $scoreThreshold = params.scoreThreshold;\n  const inputs = { boxes: $boxes, scores: $scores };\n  const attrs = {\n    maxOutputSize: $maxOutputSize,\n    iouThreshold: $iouThreshold,\n    scoreThreshold: $scoreThreshold,\n    padToMaxOutputSize\n  };\n  const result = ENGINE.runKernel(NonMaxSuppressionV4, inputs, attrs);\n  return { selectedIndices: result[0], validOutputs: result[1] };\n}\nvar nonMaxSuppressionPadded = op({ nonMaxSuppressionPadded_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/image/non_max_suppression_padded_async.js\nasync function nonMaxSuppressionPaddedAsync_(boxes, scores, maxOutputSize, iouThreshold = 0.5, scoreThreshold = Number.NEGATIVE_INFINITY, padToMaxOutputSize = false) {\n  const $boxes = convertToTensor(boxes, \"boxes\", \"nonMaxSuppressionAsync\");\n  const $scores = convertToTensor(scores, \"scores\", \"nonMaxSuppressionAsync\");\n  const params = nonMaxSuppSanityCheck($boxes, $scores, maxOutputSize, iouThreshold, scoreThreshold, null);\n  const $maxOutputSize = params.maxOutputSize;\n  const $iouThreshold = params.iouThreshold;\n  const $scoreThreshold = params.scoreThreshold;\n  const [boxesVals, scoresVals] = await Promise.all([$boxes.data(), $scores.data()]);\n  const { selectedIndices, validOutputs } = nonMaxSuppressionV4Impl(boxesVals, scoresVals, $maxOutputSize, $iouThreshold, $scoreThreshold, padToMaxOutputSize);\n  if ($boxes !== boxes) {\n    $boxes.dispose();\n  }\n  if ($scores !== scores) {\n    $scores.dispose();\n  }\n  return {\n    selectedIndices: tensor1d(selectedIndices, \"int32\"),\n    validOutputs: scalar(validOutputs, \"int32\")\n  };\n}\nvar nonMaxSuppressionPaddedAsync = nonMaxSuppressionPaddedAsync_;\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/image/resize_bilinear.js\nfunction resizeBilinear_(images, size, alignCorners = false, halfPixelCenters = false) {\n  const $images = convertToTensor(images, \"images\", \"resizeBilinear\");\n  assert($images.rank === 3 || $images.rank === 4, () => `Error in resizeBilinear: x must be rank 3 or 4, but got rank ${$images.rank}.`);\n  assert(size.length === 2, () => `Error in resizeBilinear: new shape must 2D, but got shape ${size}.`);\n  assert(halfPixelCenters === false || alignCorners === false, () => `Error in resizeBilinear: If halfPixelCenters is true, alignCorners must be false.`);\n  let batchImages = $images;\n  let reshapedTo4D = false;\n  if ($images.rank === 3) {\n    reshapedTo4D = true;\n    batchImages = reshape($images, [1, $images.shape[0], $images.shape[1], $images.shape[2]]);\n  }\n  const [] = size;\n  const inputs = { images: batchImages };\n  const attrs = { alignCorners, halfPixelCenters, size };\n  const res = ENGINE.runKernel(ResizeBilinear, inputs, attrs);\n  if (reshapedTo4D) {\n    return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);\n  }\n  return res;\n}\nvar resizeBilinear = op({ resizeBilinear_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/image/resize_nearest_neighbor.js\nfunction resizeNearestNeighbor_(images, size, alignCorners = false, halfPixelCenters = false) {\n  const $images = convertToTensor(images, \"images\", \"resizeNearestNeighbor\");\n  assert($images.rank === 3 || $images.rank === 4, () => `Error in resizeNearestNeighbor: x must be rank 3 or 4, but got rank ${$images.rank}.`);\n  assert(size.length === 2, () => `Error in resizeNearestNeighbor: new shape must 2D, but got shape ${size}.`);\n  assert($images.dtype === \"float32\" || $images.dtype === \"int32\", () => \"`images` must have `int32` or `float32` as dtype\");\n  assert(halfPixelCenters === false || alignCorners === false, () => `Error in resizeNearestNeighbor: If halfPixelCenters is true, alignCorners must be false.`);\n  let batchImages = $images;\n  let reshapedTo4D = false;\n  if ($images.rank === 3) {\n    reshapedTo4D = true;\n    batchImages = reshape($images, [1, $images.shape[0], $images.shape[1], $images.shape[2]]);\n  }\n  const [] = size;\n  const inputs = { images: batchImages };\n  const attrs = { alignCorners, halfPixelCenters, size };\n  const res = ENGINE.runKernel(ResizeNearestNeighbor, inputs, attrs);\n  if (reshapedTo4D) {\n    return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);\n  }\n  return res;\n}\nvar resizeNearestNeighbor = op({ resizeNearestNeighbor_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/image/threshold.js\nfunction threshold_(image2, method = \"binary\", inverted = false, threshValue = 0.5) {\n  const $image = convertToTensor(image2, \"image\", \"threshold\");\n  const RED_INTENCITY_COEF = 0.2989;\n  const GREEN_INTENCITY_COEF = 0.587;\n  const BLUE_INTENCITY_COEF = 0.114;\n  const totalPixelsInImage = $image.shape[0] * $image.shape[1];\n  let $threshold = mul(tensor1d([threshValue]), 255);\n  let r, g, b, grayscale;\n  assert($image.rank === 3, () => `Error in threshold: image must be rank 3,but got rank ${$image.rank}.`);\n  assert($image.shape[2] === 3 || $image.shape[2] === 1, () => `Error in threshold: image color channel must be equal to 3 or 1but got ${$image.shape[2]}.`);\n  assert($image.dtype === \"int32\" || $image.dtype === \"float32\", () => `Error in dtype: image dtype must be int32 or float32,but got dtype ${$image.dtype}.`);\n  assert(method === \"otsu\" || method === \"binary\", () => `Method must be binary or otsu, but was ${method}`);\n  if ($image.shape[2] === 3) {\n    [r, g, b] = split($image, [1, 1, 1], -1);\n    const $r = mul(r, RED_INTENCITY_COEF);\n    const $g = mul(g, GREEN_INTENCITY_COEF);\n    const $b = mul(b, BLUE_INTENCITY_COEF);\n    grayscale = add2(add2($r, $g), $b);\n  } else {\n    grayscale = image2;\n  }\n  if (method === \"otsu\") {\n    const $histogram = bincount(cast(round2(grayscale), \"int32\"), tensor([]), 256);\n    $threshold = otsu($histogram, totalPixelsInImage);\n  }\n  const invCondition = inverted ? lessEqual(grayscale, $threshold) : greater(grayscale, $threshold);\n  const result = cast(mul(invCondition, 255), \"int32\");\n  return result;\n}\nfunction otsu(histogram, total) {\n  let bestThresh = tensor1d([-1]);\n  let bestInBetVar = tensor1d([0]);\n  let cInBetVar = tensor1d([0]);\n  let classFirst, classSecond, meanFirst, meanSec, weightForeground, weightBack;\n  for (let index = 0; index < histogram.size - 1; index++) {\n    classFirst = slice(histogram, 0, index + 1);\n    classSecond = slice(histogram, index + 1);\n    weightForeground = div(sum2(classFirst), total);\n    weightBack = div(sum2(classSecond), total);\n    const meanFirstDivA = sum2(mul(classFirst, range(0, classFirst.size)));\n    meanFirst = div(meanFirstDivA, sum2(classFirst));\n    const meanSecFill = fill(classSecond.shape, classFirst.size);\n    const meanSecAdd = add2(range(0, classSecond.size), meanSecFill);\n    const meanSecMul = mul(classSecond, meanSecAdd);\n    meanSec = div(sum2(meanSecMul), sum2(classSecond));\n    const cInBetVarSubA = sub(meanFirst, meanSec);\n    const cInBetVarSubB = sub(meanFirst, meanSec);\n    const cInBetVarMul = mul(weightForeground, weightBack);\n    cInBetVar = mul(mul(cInBetVarMul, cInBetVarSubA), cInBetVarSubB);\n    const condition = greater(cInBetVar, bestInBetVar);\n    bestInBetVar = where(condition, cInBetVar, bestInBetVar);\n    bestThresh = where(condition, tensor1d([index]), bestThresh);\n  }\n  return bestThresh;\n}\nvar threshold = op({ threshold_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/image/transform.js\nfunction transform_(image2, transforms, interpolation = \"nearest\", fillMode = \"constant\", fillValue = 0, outputShape) {\n  const $image = convertToTensor(image2, \"image\", \"transform\", \"float32\");\n  const $transforms = convertToTensor(transforms, \"transforms\", \"transform\", \"float32\");\n  assert($image.rank === 4, () => `Error in transform: image must be rank 4,but got rank ${$image.rank}.`);\n  assert($transforms.rank === 2 && ($transforms.shape[0] === $image.shape[0] || $transforms.shape[0] === 1) && $transforms.shape[1] === 8, () => `Error in transform: Input transform should be batch x 8 or 1 x 8`);\n  assert(outputShape == null || outputShape.length === 2, () => `Error in transform: outputShape must be [height, width] or null, but got ${outputShape}.`);\n  const inputs = { image: $image, transforms: $transforms };\n  const attrs = { interpolation, fillMode, fillValue, outputShape };\n  return ENGINE.runKernel(Transform, inputs, attrs);\n}\nvar transform = op({ transform_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/linalg/band_part.js\nfunction bandPart_(a, numLower, numUpper) {\n  assert(numLower % 1 === 0, () => `bandPart(): numLower must be an integer, got ${numLower}.`);\n  assert(numUpper % 1 === 0, () => `bandPart(): numUpper must be an integer, got ${numUpper}.`);\n  const $a = convertToTensor(a, \"a\", \"bandPart\");\n  assert($a.rank >= 2, () => `bandPart(): Rank must be at least 2, got ${$a.rank}.`);\n  const shape = $a.shape;\n  const [M, N] = $a.shape.slice(-2);\n  if (!(numLower <= M)) {\n    throw new Error(`bandPart(): numLower (${numLower}) must not be greater than the number of rows (${M}).`);\n  }\n  if (!(numUpper <= N)) {\n    throw new Error(`bandPart(): numUpper (${numUpper}) must not be greater than the number of columns (${N}).`);\n  }\n  if (numLower < 0) {\n    numLower = M;\n  }\n  if (numUpper < 0) {\n    numUpper = N;\n  }\n  const i = reshape(range(0, M, 1, \"int32\"), [-1, 1]);\n  const j = range(0, N, 1, \"int32\");\n  const ij = sub(i, j);\n  const inBand = logicalAnd(lessEqual(ij, scalar(+numLower, \"int32\")), greaterEqual(ij, scalar(-numUpper, \"int32\")));\n  const zero = zeros([M, N], $a.dtype);\n  return reshape(stack(unstack(reshape($a, [-1, M, N])).map((mat) => where(inBand, mat, zero))), shape);\n}\nvar bandPart = op({ bandPart_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/linalg/gram_schmidt.js\nfunction gramSchmidt_(xs) {\n  let inputIsTensor2D;\n  if (Array.isArray(xs)) {\n    inputIsTensor2D = false;\n    assert(xs != null && xs.length > 0, () => \"Gram-Schmidt process: input must not be null, undefined, or empty\");\n    const dim = xs[0].shape[0];\n    for (let i = 1; i < xs.length; ++i) {\n      assert(xs[i].shape[0] === dim, () => `Gram-Schmidt: Non-unique lengths found in the input vectors: (${xs[i].shape[0]} vs. ${dim})`);\n    }\n  } else {\n    inputIsTensor2D = true;\n    xs = split(xs, xs.shape[0], 0).map((x) => squeeze(x, [0]));\n  }\n  assert(xs.length <= xs[0].shape[0], () => `Gram-Schmidt: Number of vectors (${xs.length}) exceeds number of dimensions (${xs[0].shape[0]}).`);\n  const ys = [];\n  const xs1d = xs;\n  for (let i = 0; i < xs.length; ++i) {\n    ys.push(ENGINE.tidy(() => {\n      let x = xs1d[i];\n      if (i > 0) {\n        for (let j = 0; j < i; ++j) {\n          const proj = mul(sum2(mul(ys[j], x)), ys[j]);\n          x = sub(x, proj);\n        }\n      }\n      return div(x, norm(x, \"euclidean\"));\n    }));\n  }\n  if (inputIsTensor2D) {\n    return stack(ys, 0);\n  } else {\n    return ys;\n  }\n}\nvar gramSchmidt = op({ gramSchmidt_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/linalg/qr.js\nfunction qr_(x, fullMatrices = false) {\n  assert(x.rank >= 2, () => `qr() requires input tensor to have a rank >= 2, but got rank ${x.rank}`);\n  if (x.rank === 2) {\n    return qr2d(x, fullMatrices);\n  } else {\n    const outerDimsProd = x.shape.slice(0, x.shape.length - 2).reduce((value, prev) => value * prev);\n    const x2ds = unstack(reshape(x, [\n      outerDimsProd,\n      x.shape[x.shape.length - 2],\n      x.shape[x.shape.length - 1]\n    ]), 0);\n    const q2ds = [];\n    const r2ds = [];\n    x2ds.forEach((x2d) => {\n      const [q2d, r2d] = qr2d(x2d, fullMatrices);\n      q2ds.push(q2d);\n      r2ds.push(r2d);\n    });\n    const q = reshape(stack(q2ds, 0), x.shape);\n    const r = reshape(stack(r2ds, 0), x.shape);\n    return [q, r];\n  }\n}\nfunction qr2d(x, fullMatrices = false) {\n  return ENGINE.tidy(() => {\n    assert(x.shape.length === 2, () => `qr2d() requires a 2D Tensor, but got a ${x.shape.length}D Tensor.`);\n    const m = x.shape[0];\n    const n = x.shape[1];\n    let q = eye(m);\n    let r = clone(x);\n    const one2D = tensor2d([[1]], [1, 1]);\n    let w = clone(one2D);\n    const iters = m >= n ? n : m;\n    for (let j = 0; j < iters; ++j) {\n      const rTemp = r;\n      const wTemp = w;\n      const qTemp = q;\n      [w, r, q] = ENGINE.tidy(() => {\n        const rjEnd1 = slice(r, [j, j], [m - j, 1]);\n        const normX = norm(rjEnd1);\n        const rjj = slice(r, [j, j], [1, 1]);\n        const s = where(greater(rjj, 0), tensor2d([[-1]]), tensor2d([[1]]));\n        const u1 = sub(rjj, mul(s, normX));\n        const wPre = div(rjEnd1, u1);\n        if (wPre.shape[0] === 1) {\n          w = clone(one2D);\n        } else {\n          w = concat([\n            one2D,\n            slice(wPre, [1, 0], [wPre.shape[0] - 1, wPre.shape[1]])\n          ], 0);\n        }\n        const tau = neg(div(matMul(s, u1), normX));\n        const rjEndAll = slice(r, [j, 0], [m - j, n]);\n        const tauTimesW = mul(tau, w);\n        const wT = transpose(w);\n        if (j === 0) {\n          r = sub(rjEndAll, matMul(tauTimesW, matMul(wT, rjEndAll)));\n        } else {\n          const rTimesTau = sub(rjEndAll, matMul(tauTimesW, matMul(wT, rjEndAll)));\n          r = concat([slice(r, [0, 0], [j, n]), rTimesTau], 0);\n        }\n        const tawTimesWT = transpose(tauTimesW);\n        const qAllJEnd = slice(q, [0, j], [m, q.shape[1] - j]);\n        if (j === 0) {\n          q = sub(qAllJEnd, matMul(matMul(qAllJEnd, w), tawTimesWT));\n        } else {\n          const qTimesTau = sub(qAllJEnd, matMul(matMul(qAllJEnd, w), tawTimesWT));\n          q = concat([slice(q, [0, 0], [m, j]), qTimesTau], 1);\n        }\n        return [w, r, q];\n      });\n      dispose([rTemp, wTemp, qTemp]);\n    }\n    if (!fullMatrices && m > n) {\n      q = slice(q, [0, 0], [m, n]);\n      r = slice(r, [0, 0], [n, n]);\n    }\n    return [q, r];\n  });\n}\nvar qr = op({ qr_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/loss_ops_utils.js\nvar Reduction;\n(function(Reduction2) {\n  Reduction2[Reduction2[\"NONE\"] = 0] = \"NONE\";\n  Reduction2[Reduction2[\"MEAN\"] = 1] = \"MEAN\";\n  Reduction2[Reduction2[\"SUM\"] = 2] = \"SUM\";\n  Reduction2[Reduction2[\"SUM_BY_NONZERO_WEIGHTS\"] = 3] = \"SUM_BY_NONZERO_WEIGHTS\";\n})(Reduction || (Reduction = {}));\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/losses/compute_weighted_loss.js\nfunction computeWeightedLoss_(losses2, weights, reduction = Reduction.SUM_BY_NONZERO_WEIGHTS) {\n  const $losses = convertToTensor(losses2, \"losses\", \"computeWeightedLoss\");\n  let $weights = null;\n  if (weights != null) {\n    $weights = convertToTensor(weights, \"weights\", \"computeWeightedLoss\");\n  }\n  const weightedLoss = $weights == null ? $losses : mul($losses, $weights);\n  if (reduction === Reduction.NONE) {\n    return weightedLoss;\n  }\n  if (reduction === Reduction.SUM) {\n    return sum2(weightedLoss);\n  }\n  if (reduction === Reduction.MEAN) {\n    if ($weights == null) {\n      return mean(weightedLoss);\n    } else {\n      const broadcastFactor = $losses.size / $weights.size;\n      const result = div(sum2(weightedLoss), sum2($weights));\n      return broadcastFactor > 1 ? div(result, scalar(broadcastFactor)) : result;\n    }\n  }\n  if (reduction === Reduction.SUM_BY_NONZERO_WEIGHTS) {\n    if ($weights == null) {\n      return div(sum2(weightedLoss), scalar($losses.size));\n    } else {\n      const broadcastedWeights = mul($weights, ones2($losses.shape));\n      const numNonZeros = cast(sum2(notEqual(broadcastedWeights, scalar(0))), \"float32\");\n      return div(sum2(weightedLoss), numNonZeros);\n    }\n  }\n  throw Error(`Unknown reduction: ${reduction}`);\n}\nvar computeWeightedLoss = op({ computeWeightedLoss_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/losses/absolute_difference.js\nfunction absoluteDifference_(labels, predictions, weights, reduction = Reduction.SUM_BY_NONZERO_WEIGHTS) {\n  const $labels = convertToTensor(labels, \"labels\", \"absoluteDifference\");\n  const $predictions = convertToTensor(predictions, \"predictions\", \"absoluteDifference\");\n  let $weights = null;\n  if (weights != null) {\n    $weights = convertToTensor(weights, \"weights\", \"absoluteDifference\");\n  }\n  assertShapesMatch($labels.shape, $predictions.shape, \"Error in absoluteDifference: \");\n  const losses2 = abs(sub($labels, $predictions));\n  return computeWeightedLoss(losses2, $weights, reduction);\n}\nvar absoluteDifference = op({ absoluteDifference_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/losses/cosine_distance.js\nfunction cosineDistance_(labels, predictions, axis, weights, reduction = Reduction.SUM_BY_NONZERO_WEIGHTS) {\n  const $labels = convertToTensor(labels, \"labels\", \"cosineDistance\");\n  const $predictions = convertToTensor(predictions, \"predictions\", \"cosineDistance\");\n  let $weights = null;\n  if (weights != null) {\n    $weights = convertToTensor(weights, \"weights\", \"cosineDistance\");\n  }\n  assertShapesMatch($labels.shape, $predictions.shape, \"Error in cosineDistance: \");\n  const one = scalar(1);\n  const losses2 = sub(one, sum2(mul($labels, $predictions), axis, true));\n  return computeWeightedLoss(losses2, $weights, reduction);\n}\nvar cosineDistance = op({ cosineDistance_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/losses/hinge_loss.js\nfunction hingeLoss_(labels, predictions, weights, reduction = Reduction.SUM_BY_NONZERO_WEIGHTS) {\n  let $labels = convertToTensor(labels, \"labels\", \"hingeLoss\");\n  const $predictions = convertToTensor(predictions, \"predictions\", \"hingeLoss\");\n  let $weights = null;\n  if (weights != null) {\n    $weights = convertToTensor(weights, \"weights\", \"hingeLoss\");\n  }\n  assertShapesMatch($labels.shape, $predictions.shape, \"Error in hingeLoss: \");\n  const one = scalar(1);\n  $labels = sub(mul(scalar(2), $labels), one);\n  const losses2 = relu(sub(one, mul($labels, $predictions)));\n  return computeWeightedLoss(losses2, $weights, reduction);\n}\nvar hingeLoss = op({ hingeLoss_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/losses/huber_loss.js\nfunction huberLoss_(labels, predictions, weights, delta = 1, reduction = Reduction.SUM_BY_NONZERO_WEIGHTS) {\n  const $labels = convertToTensor(labels, \"labels\", \"huberLoss\");\n  const $predictions = convertToTensor(predictions, \"predictions\", \"huberLoss\");\n  let $weights = null;\n  if (weights != null) {\n    $weights = convertToTensor(weights, \"weights\", \"huberLoss\");\n  }\n  assertShapesMatch($labels.shape, $predictions.shape, \"Error in huberLoss: \");\n  const deltaScalar = scalar(delta);\n  const error = abs(sub($predictions, $labels));\n  const quadratic = minimum(error, deltaScalar);\n  const linear = sub(error, quadratic);\n  const losses2 = add2(mul(scalar(0.5), square(quadratic)), mul(deltaScalar, linear));\n  return computeWeightedLoss(losses2, $weights, reduction);\n}\nvar huberLoss = op({ huberLoss_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/losses/log_loss.js\nfunction logLoss_(labels, predictions, weights, epsilon3 = 1e-7, reduction = Reduction.SUM_BY_NONZERO_WEIGHTS) {\n  const $labels = convertToTensor(labels, \"labels\", \"logLoss\");\n  const $predictions = convertToTensor(predictions, \"predictions\", \"logLoss\");\n  let $weights = null;\n  if (weights != null) {\n    $weights = convertToTensor(weights, \"weights\", \"logLoss\");\n  }\n  assertShapesMatch($labels.shape, $predictions.shape, \"Error in logLoss: \");\n  const one = scalar(1);\n  const epsilonScalar = scalar(epsilon3);\n  const l13 = neg(mul($labels, log2(add2($predictions, epsilonScalar))));\n  const l23 = mul(sub(one, $labels), log2(add2(sub(one, $predictions), epsilonScalar)));\n  const losses2 = sub(l13, l23);\n  return computeWeightedLoss(losses2, $weights, reduction);\n}\nvar logLoss = op({ logLoss_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/losses/mean_squared_error.js\nfunction meanSquaredError_(labels, predictions, weights, reduction = Reduction.SUM_BY_NONZERO_WEIGHTS) {\n  const $labels = convertToTensor(labels, \"labels\", \"meanSquaredError\");\n  const $predictions = convertToTensor(predictions, \"predictions\", \"meanSquaredError\");\n  let $weights = null;\n  if (weights != null) {\n    $weights = convertToTensor(weights, \"weights\", \"meanSquaredError\");\n  }\n  assertShapesMatch($labels.shape, $predictions.shape, \"Error in meanSquaredError: \");\n  const losses2 = squaredDifference($labels, $predictions);\n  return computeWeightedLoss(losses2, $weights, reduction);\n}\nvar meanSquaredError = op({ meanSquaredError_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/losses/sigmoid_cross_entropy.js\nfunction sigmoidCrossEntropyWithLogits_(labels, logits) {\n  const $labels = convertToTensor(labels, \"labels\", \"sigmoidCrossEntropyWithLogits\");\n  const $logits = convertToTensor(logits, \"logits\", \"sigmoidCrossEntropyWithLogits\");\n  assertShapesMatch($labels.shape, $logits.shape, \"Error in sigmoidCrossEntropyWithLogits: \");\n  const maxOutput = relu($logits);\n  const outputXTarget = mul($logits, $labels);\n  const sigmoidOutput = log1p(exp(neg(abs($logits))));\n  return add2(sub(maxOutput, outputXTarget), sigmoidOutput);\n}\nfunction sigmoidCrossEntropy_(multiClassLabels, logits, weights, labelSmoothing = 0, reduction = Reduction.SUM_BY_NONZERO_WEIGHTS) {\n  let $multiClassLabels = convertToTensor(multiClassLabels, \"multiClassLabels\", \"sigmoidCrossEntropy\");\n  const $logits = convertToTensor(logits, \"logits\", \"sigmoidCrossEntropy\");\n  let $weights = null;\n  if (weights != null) {\n    $weights = convertToTensor(weights, \"weights\", \"sigmoidCrossEntropy\");\n  }\n  assertShapesMatch($multiClassLabels.shape, $logits.shape, \"Error in sigmoidCrossEntropy: \");\n  if (labelSmoothing > 0) {\n    const labelSmoothingScalar = scalar(labelSmoothing);\n    const one = scalar(1);\n    const half = scalar(0.5);\n    $multiClassLabels = add2(mul($multiClassLabels, sub(one, labelSmoothingScalar)), mul(half, labelSmoothingScalar));\n  }\n  const losses2 = sigmoidCrossEntropyWithLogits_($multiClassLabels, $logits);\n  return computeWeightedLoss(losses2, $weights, reduction);\n}\nvar sigmoidCrossEntropy = op({ sigmoidCrossEntropy_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/losses/softmax_cross_entropy.js\nfunction softmaxCrossEntropyWithLogits_(labels, logits, dim = -1) {\n  if (dim === -1) {\n    dim = logits.rank - 1;\n  }\n  if (dim !== logits.rank - 1) {\n    throw Error(`Softmax cross entropy along a non-last dimension is not yet supported. Labels / logits was rank ${logits.rank} and dim was ${dim}`);\n  }\n  const customOp = customGrad((labels2, logits2, save) => {\n    const keepDims = true;\n    const lse = logSumExp(logits2, [dim], keepDims);\n    const logResult = sub(cast(logits2, \"float32\"), lse);\n    save([labels2, logResult]);\n    const costVector = neg(mul(logResult, labels2));\n    const value = sum2(costVector, [dim]);\n    const gradFunc = (dy, saved) => {\n      const [labels3, logResult2] = saved;\n      const dyShape = expandShapeToKeepDim(dy.shape, [dim]);\n      return [\n        mul(reshape(dy, dyShape), sub(cast(labels3, \"float32\"), exp(logResult2))),\n        mul(reshape(dy, dyShape), sub(exp(logResult2), cast(labels3, \"float32\")))\n      ];\n    };\n    return { value, gradFunc };\n  });\n  return customOp(labels, logits);\n}\nfunction softmaxCrossEntropy_(onehotLabels, logits, weights, labelSmoothing = 0, reduction = Reduction.SUM_BY_NONZERO_WEIGHTS) {\n  let $onehotLabels = convertToTensor(onehotLabels, \"onehotLabels\", \"softmaxCrossEntropy\");\n  const $logits = convertToTensor(logits, \"logits\", \"softmaxCrossEntropy\");\n  let $weights = null;\n  if (weights != null) {\n    $weights = convertToTensor(weights, \"weights\", \"softmaxCrossEntropy\");\n  }\n  assertShapesMatch($onehotLabels.shape, $logits.shape, \"Error in softmaxCrossEntropy: \");\n  if (labelSmoothing > 0) {\n    const labelSmoothingScalar = scalar(labelSmoothing);\n    const one = scalar(1);\n    const numClasses = scalar($onehotLabels.shape[1]);\n    $onehotLabels = add2(mul($onehotLabels, sub(one, labelSmoothingScalar)), div(labelSmoothingScalar, numClasses));\n  }\n  const losses2 = softmaxCrossEntropyWithLogits_($onehotLabels, $logits);\n  return computeWeightedLoss(losses2, $weights, reduction);\n}\nvar softmaxCrossEntropy = op({ softmaxCrossEntropy_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/sparse/sparse_fill_empty_rows.js\nfunction sparseFillEmptyRows_(indices, values, denseShape, defaultValue) {\n  const $indices = convertToTensor(indices, \"indices\", \"sparseFillEmptyRows\", \"int32\");\n  const $values = convertToTensor(values, \"values\", \"sparseFillEmptyRows\");\n  const $denseShape = convertToTensor(denseShape, \"denseShape\", \"sparseFillEmptyRows\", \"int32\");\n  const $defaultValue = convertToTensor(defaultValue, \"defaultValue\", \"sparseFillEmptyRows\", $values.dtype);\n  if ($indices.rank !== 2) {\n    throw new Error(`Indices should be Tensor2D but received shape\n        ${$indices.shape}`);\n  }\n  if ($values.rank !== 1) {\n    throw new Error(`Values should be Tensor1D but received shape ${$values.shape}`);\n  }\n  if ($denseShape.rank !== 1) {\n    throw new Error(`Dense shape should be Tensor1D but received shape ${$denseShape.shape}`);\n  }\n  if ($defaultValue.rank !== 0) {\n    throw new Error(`Default value should be a scalar but received shape ${$defaultValue.shape}`);\n  }\n  const inputs = {\n    indices: $indices,\n    values: $values,\n    denseShape: $denseShape,\n    defaultValue: $defaultValue\n  };\n  const result = ENGINE.runKernel(SparseFillEmptyRows, inputs);\n  return {\n    outputIndices: result[0],\n    outputValues: result[1],\n    emptyRowIndicator: result[2],\n    reverseIndexMap: result[3]\n  };\n}\nvar sparseFillEmptyRows = op({ sparseFillEmptyRows_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/sparse/sparse_reshape.js\nfunction sparseReshape_(inputIndices, inputShape, newShape) {\n  const $inputIndices = convertToTensor(inputIndices, \"inputIndices\", \"sparseReshape\", \"int32\");\n  const $inputShape = convertToTensor(inputShape, \"inputShape\", \"sparseReshape\", \"int32\");\n  const $newShape = convertToTensor(newShape, \"newShape\", \"sparseReshape\", \"int32\");\n  if ($inputIndices.rank !== 2) {\n    throw new Error(`Input indices should be Tensor2D but received shape\n        ${$inputIndices.shape}`);\n  }\n  if ($inputShape.rank !== 1) {\n    throw new Error(`Input shape should be Tensor1D but received shape ${$inputShape.shape}`);\n  }\n  if ($newShape.rank !== 1) {\n    throw new Error(`New shape should be Tensor1D but received shape ${$newShape.shape}`);\n  }\n  const inputs = {\n    inputIndices: $inputIndices,\n    inputShape: $inputShape,\n    newShape: $newShape\n  };\n  const result = ENGINE.runKernel(SparseReshape, inputs);\n  return { outputIndices: result[0], outputShape: result[1] };\n}\nvar sparseReshape = op({ sparseReshape_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/sparse/sparse_segment_mean.js\nfunction sparseSegmentMean_(data, indices, segmentIds) {\n  const $data = convertToTensor(data, \"data\", \"sparseSegmentMean\");\n  const $indices = convertToTensor(indices, \"indices\", \"sparseSegmentMean\", \"int32\");\n  const $segmentIds = convertToTensor(segmentIds, \"segmentIds\", \"sparseSegmentMean\", \"int32\");\n  if ($data.rank < 1) {\n    throw new Error(`Data should be at least 1 dimensional but received scalar`);\n  }\n  if ($indices.rank !== 1) {\n    throw new Error(`Indices should be Tensor1D but received shape\n          ${$indices.shape}`);\n  }\n  if ($segmentIds.rank !== 1) {\n    throw new Error(`Segment ids should be Tensor1D but received shape\n          ${$segmentIds.shape}`);\n  }\n  const inputs = {\n    data: $data,\n    indices: $indices,\n    segmentIds: $segmentIds\n  };\n  return ENGINE.runKernel(SparseSegmentMean, inputs);\n}\nvar sparseSegmentMean = op({ sparseSegmentMean_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/sparse/sparse_segment_sum.js\nfunction sparseSegmentSum_(data, indices, segmentIds) {\n  const $data = convertToTensor(data, \"data\", \"sparseSegmentSum\");\n  const $indices = convertToTensor(indices, \"indices\", \"sparseSegmentSum\", \"int32\");\n  const $segmentIds = convertToTensor(segmentIds, \"segmentIds\", \"sparseSegmentSum\", \"int32\");\n  if ($data.rank < 1) {\n    throw new Error(`Data should be at least 1 dimensional but received scalar`);\n  }\n  if ($indices.rank !== 1) {\n    throw new Error(`Indices should be Tensor1D but received shape\n         ${$indices.shape}`);\n  }\n  if ($segmentIds.rank !== 1) {\n    throw new Error(`Segment ids should be Tensor1D but received shape\n         ${$segmentIds.shape}`);\n  }\n  const inputs = {\n    data: $data,\n    indices: $indices,\n    segmentIds: $segmentIds\n  };\n  return ENGINE.runKernel(SparseSegmentSum, inputs);\n}\nvar sparseSegmentSum = op({ sparseSegmentSum_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/string/string_n_grams.js\nfunction stringNGrams_(data, dataSplits, separator, nGramWidths, leftPad, rightPad2, padWidth, preserveShortSequences) {\n  const $data = convertToTensor(data, \"data\", \"stringNGrams\", \"string\");\n  if ($data.dtype !== \"string\") {\n    throw new Error(\"Data must be of datatype string\");\n  }\n  if ($data.shape.length !== 1) {\n    throw new Error(`Data must be a vector, saw: ${$data.shape}`);\n  }\n  const $dataSplits = convertToTensor(dataSplits, \"dataSplits\", \"stringNGrams\");\n  if ($dataSplits.dtype !== \"int32\") {\n    throw new Error(\"Data splits must be of datatype int32\");\n  }\n  const attrs = {\n    separator,\n    nGramWidths,\n    leftPad,\n    rightPad: rightPad2,\n    padWidth,\n    preserveShortSequences\n  };\n  const inputs = { data: $data, dataSplits: $dataSplits };\n  const result = ENGINE.runKernel(StringNGrams, inputs, attrs);\n  return { nGrams: result[0], nGramsSplits: result[1] };\n}\nvar stringNGrams = op({ stringNGrams_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/string/string_split.js\nfunction stringSplit_(input2, delimiter, skipEmpty = true) {\n  const $input = convertToTensor(input2, \"input\", \"stringSplit\", \"string\");\n  const $delimiter = convertToTensor(delimiter, \"delimiter\", \"stringSplit\", \"string\");\n  if ($input.rank !== 1) {\n    throw new Error(`Input should be Tensor1D but received shape ${$input.shape}`);\n  }\n  if ($delimiter.rank !== 0) {\n    throw new Error(`Delimiter should be a scalar but received shape ${$delimiter.shape}`);\n  }\n  const attrs = { skipEmpty };\n  const inputs = { input: $input, delimiter: $delimiter };\n  const result = ENGINE.runKernel(StringSplit, inputs, attrs);\n  return { indices: result[0], values: result[1], shape: result[2] };\n}\nvar stringSplit = op({ stringSplit_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/string/string_to_hash_bucket_fast.js\nfunction stringToHashBucketFast_(input2, numBuckets) {\n  const $input = convertToTensor(input2, \"input\", \"stringToHashBucketFast\", \"string\");\n  const attrs = { numBuckets };\n  if (numBuckets <= 0) {\n    throw new Error(`Number of buckets must be at least 1`);\n  }\n  const inputs = { input: $input };\n  return ENGINE.runKernel(StringToHashBucketFast, inputs, attrs);\n}\nvar stringToHashBucketFast = op({ stringToHashBucketFast_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/ops.js\nvar spectral = {\n  fft,\n  ifft,\n  rfft,\n  irfft\n};\nvar signal = {\n  hammingWindow,\n  hannWindow,\n  frame,\n  stft\n};\nvar image = {\n  flipLeftRight,\n  grayscaleToRGB,\n  resizeNearestNeighbor,\n  resizeBilinear,\n  rotateWithOffset,\n  cropAndResize,\n  nonMaxSuppression,\n  nonMaxSuppressionAsync,\n  nonMaxSuppressionWithScore,\n  nonMaxSuppressionWithScoreAsync,\n  nonMaxSuppressionPadded,\n  nonMaxSuppressionPaddedAsync,\n  threshold,\n  transform\n};\nvar linalg = {\n  bandPart,\n  gramSchmidt,\n  qr\n};\nvar losses = {\n  absoluteDifference,\n  computeWeightedLoss,\n  cosineDistance,\n  hingeLoss,\n  huberLoss,\n  logLoss,\n  meanSquaredError,\n  sigmoidCrossEntropy,\n  softmaxCrossEntropy\n};\nvar sparse = {\n  sparseFillEmptyRows,\n  sparseReshape,\n  sparseSegmentMean,\n  sparseSegmentSum\n};\nvar string = {\n  stringNGrams,\n  stringSplit,\n  stringToHashBucketFast\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/optimizers/optimizer.js\nvar Optimizer = class extends Serializable {\n  minimize(f, returnCost = false, varList) {\n    const { value, grads: grads2 } = this.computeGradients(f, varList);\n    if (varList != null) {\n      const gradArray = varList.map((v) => ({ name: v.name, tensor: grads2[v.name] }));\n      this.applyGradients(gradArray);\n    } else {\n      this.applyGradients(grads2);\n    }\n    dispose(grads2);\n    if (returnCost) {\n      return value;\n    } else {\n      value.dispose();\n      return null;\n    }\n  }\n  get iterations() {\n    if (this.iterations_ == null) {\n      this.iterations_ = 0;\n    }\n    return this.iterations_;\n  }\n  incrementIterations() {\n    this.iterations_ = this.iterations + 1;\n  }\n  computeGradients(f, varList) {\n    return variableGrads(f, varList);\n  }\n  dispose() {\n    if (this.iterations_ != null) {\n      dispose(this.iterations_);\n    }\n  }\n  async saveIterations() {\n    if (this.iterations_ == null) {\n      this.iterations_ = 0;\n    }\n    return {\n      name: \"iter\",\n      tensor: scalar(this.iterations_, \"int32\")\n    };\n  }\n  async getWeights() {\n    throw new Error(\"getWeights() is not implemented for this optimizer yet.\");\n  }\n  async setWeights(weightValues) {\n    throw new Error(`setWeights() is not implemented for this optimizer class ${this.getClassName()}`);\n  }\n  async extractIterations(weightValues) {\n    this.iterations_ = (await weightValues[0].tensor.data())[0];\n    return weightValues.slice(1);\n  }\n};\nObject.defineProperty(Optimizer, Symbol.hasInstance, {\n  value: (instance) => {\n    return instance.minimize != null && instance.computeGradients != null && instance.applyGradients != null;\n  }\n});\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/optimizers/adadelta_optimizer.js\nvar AdadeltaOptimizer = class extends Optimizer {\n  constructor(learningRate, rho, epsilon3 = null) {\n    super();\n    this.learningRate = learningRate;\n    this.rho = rho;\n    this.epsilon = epsilon3;\n    this.accumulatedGrads = [];\n    this.accumulatedUpdates = [];\n    if (epsilon3 == null) {\n      this.epsilon = ENGINE.backend.epsilon();\n    }\n  }\n  applyGradients(variableGradients) {\n    const variableNames = Array.isArray(variableGradients) ? variableGradients.map((item) => item.name) : Object.keys(variableGradients);\n    variableNames.forEach((name, i) => {\n      const value = ENGINE.registeredVariables[name];\n      const trainable = false;\n      if (this.accumulatedGrads[i] == null) {\n        this.accumulatedGrads[i] = {\n          originalName: `${name}/accum_grad`,\n          variable: tidy(() => zerosLike(value).variable(trainable))\n        };\n      }\n      if (this.accumulatedUpdates[i] == null) {\n        this.accumulatedUpdates[i] = {\n          originalName: `${name}/accum_var`,\n          variable: tidy(() => zerosLike(value).variable(trainable))\n        };\n      }\n      const gradient = Array.isArray(variableGradients) ? variableGradients[i].tensor : variableGradients[name];\n      if (gradient == null) {\n        return;\n      }\n      const accumulatedGrad = this.accumulatedGrads[i].variable;\n      const accumulatedUpdate = this.accumulatedUpdates[i].variable;\n      tidy(() => {\n        const newAccumulatedGrad = add2(mul(accumulatedGrad, this.rho), mul(square(gradient), 1 - this.rho));\n        const updates = mul(div(sqrt(add2(accumulatedUpdate, this.epsilon)), sqrt(add2(accumulatedGrad, this.epsilon))), gradient);\n        const newAccumulatedUpdate = add2(mul(accumulatedUpdate, this.rho), mul(square(updates), 1 - this.rho));\n        accumulatedGrad.assign(newAccumulatedGrad);\n        accumulatedUpdate.assign(newAccumulatedUpdate);\n        const newValue = add2(mul(updates, -this.learningRate), value);\n        value.assign(newValue);\n      });\n    });\n    this.incrementIterations();\n  }\n  dispose() {\n    if (this.accumulatedUpdates != null) {\n      dispose(this.accumulatedGrads.map((v) => v.variable));\n      dispose(this.accumulatedUpdates.map((v) => v.variable));\n    }\n  }\n  async getWeights() {\n    const variables = [...this.accumulatedGrads, ...this.accumulatedUpdates];\n    return [await this.saveIterations()].concat(variables.map((v) => ({ name: v.originalName, tensor: v.variable })));\n  }\n  async setWeights(weightValues) {\n    weightValues = await this.extractIterations(weightValues);\n    const variableCount = weightValues.length / 2;\n    const trainable = false;\n    this.accumulatedGrads = weightValues.slice(0, variableCount).map((v) => ({\n      originalName: v.name,\n      variable: v.tensor.variable(trainable)\n    }));\n    this.accumulatedUpdates = weightValues.slice(variableCount, variableCount * 2).map((v) => ({\n      originalName: v.name,\n      variable: v.tensor.variable(trainable)\n    }));\n  }\n  getConfig() {\n    return {\n      \"learningRate\": this.learningRate,\n      \"rho\": this.rho,\n      \"epsilon\": this.epsilon\n    };\n  }\n  static fromConfig(cls, config) {\n    return new cls(config[\"learningRate\"], config[\"rho\"], config[\"epsilon\"]);\n  }\n};\nAdadeltaOptimizer.className = \"Adadelta\";\nregisterClass(AdadeltaOptimizer);\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/optimizers/adagrad_optimizer.js\nvar AdagradOptimizer = class extends Optimizer {\n  constructor(learningRate, initialAccumulatorValue = 0.1) {\n    super();\n    this.learningRate = learningRate;\n    this.initialAccumulatorValue = initialAccumulatorValue;\n    this.accumulatedGrads = [];\n  }\n  applyGradients(variableGradients) {\n    const variableNames = Array.isArray(variableGradients) ? variableGradients.map((item) => item.name) : Object.keys(variableGradients);\n    variableNames.forEach((name, i) => {\n      const value = ENGINE.registeredVariables[name];\n      if (this.accumulatedGrads[i] == null) {\n        const trainable = false;\n        this.accumulatedGrads[i] = {\n          originalName: `${name}/accumulator`,\n          variable: tidy(() => fill(value.shape, this.initialAccumulatorValue).variable(trainable))\n        };\n      }\n      const gradient = Array.isArray(variableGradients) ? variableGradients[i].tensor : variableGradients[name];\n      if (gradient == null) {\n        return;\n      }\n      const accumulatedGrad = this.accumulatedGrads[i].variable;\n      tidy(() => {\n        const newAccumulatedGrad = add2(accumulatedGrad, square(gradient));\n        accumulatedGrad.assign(newAccumulatedGrad);\n        const newValue = add2(mul(div(gradient, sqrt(add2(newAccumulatedGrad, ENGINE.backend.epsilon()))), -this.learningRate), value);\n        value.assign(newValue);\n      });\n    });\n    this.incrementIterations();\n  }\n  dispose() {\n    if (this.accumulatedGrads != null) {\n      dispose(this.accumulatedGrads.map((v) => v.variable));\n    }\n  }\n  async getWeights() {\n    return [await this.saveIterations()].concat(this.accumulatedGrads.map((v) => ({ name: v.originalName, tensor: v.variable })));\n  }\n  async setWeights(weightValues) {\n    weightValues = await this.extractIterations(weightValues);\n    const trainable = false;\n    this.accumulatedGrads = weightValues.map((v) => ({ originalName: v.name, variable: v.tensor.variable(trainable) }));\n  }\n  getConfig() {\n    return {\n      \"learningRate\": this.learningRate,\n      \"initialAccumulatorValue\": this.initialAccumulatorValue\n    };\n  }\n  static fromConfig(cls, config) {\n    return new cls(config[\"learningRate\"], config[\"initialAccumulatorValue\"]);\n  }\n};\nAdagradOptimizer.className = \"Adagrad\";\nregisterClass(AdagradOptimizer);\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/optimizers/adam_optimizer.js\nvar AdamOptimizer = class extends Optimizer {\n  constructor(learningRate, beta1, beta2, epsilon3 = null) {\n    super();\n    this.learningRate = learningRate;\n    this.beta1 = beta1;\n    this.beta2 = beta2;\n    this.epsilon = epsilon3;\n    this.accumulatedFirstMoment = [];\n    this.accumulatedSecondMoment = [];\n    tidy(() => {\n      this.accBeta1 = scalar(beta1).variable();\n      this.accBeta2 = scalar(beta2).variable();\n    });\n    if (epsilon3 == null) {\n      this.epsilon = ENGINE.backend.epsilon();\n    }\n  }\n  applyGradients(variableGradients) {\n    const varNames = Array.isArray(variableGradients) ? variableGradients.map((v) => v.name) : Object.keys(variableGradients);\n    tidy(() => {\n      const oneMinusAccBeta1 = sub(1, this.accBeta1);\n      const oneMinusAccBeta2 = sub(1, this.accBeta2);\n      varNames.forEach((name, i) => {\n        const value = ENGINE.registeredVariables[name];\n        const trainable = false;\n        if (this.accumulatedFirstMoment[i] == null) {\n          this.accumulatedFirstMoment[i] = {\n            originalName: `${name}/m`,\n            variable: tidy(() => zerosLike(value).variable(trainable))\n          };\n        }\n        if (this.accumulatedSecondMoment[i] == null) {\n          this.accumulatedSecondMoment[i] = {\n            originalName: `${name}/v`,\n            variable: tidy(() => zerosLike(value).variable(trainable))\n          };\n        }\n        const gradient = Array.isArray(variableGradients) ? variableGradients[i].tensor : variableGradients[name];\n        if (gradient == null) {\n          return;\n        }\n        const firstMoment = this.accumulatedFirstMoment[i].variable;\n        const secondMoment = this.accumulatedSecondMoment[i].variable;\n        const newFirstMoment = add2(mul(firstMoment, this.beta1), mul(gradient, 1 - this.beta1));\n        const newSecondMoment = add2(mul(secondMoment, this.beta2), mul(square(gradient), 1 - this.beta2));\n        const biasCorrectedFirstMoment = div(newFirstMoment, oneMinusAccBeta1);\n        const biasCorrectedSecondMoment = div(newSecondMoment, oneMinusAccBeta2);\n        firstMoment.assign(newFirstMoment);\n        secondMoment.assign(newSecondMoment);\n        const newValue = add2(mul(div(biasCorrectedFirstMoment, add2(sqrt(biasCorrectedSecondMoment), this.epsilon)), -this.learningRate), value);\n        value.assign(newValue);\n      });\n      this.accBeta1.assign(mul(this.accBeta1, this.beta1));\n      this.accBeta2.assign(mul(this.accBeta2, this.beta2));\n    });\n    this.incrementIterations();\n  }\n  dispose() {\n    this.accBeta1.dispose();\n    this.accBeta2.dispose();\n    if (this.accumulatedFirstMoment != null) {\n      dispose(this.accumulatedFirstMoment.map((v) => v.variable));\n    }\n    if (this.accumulatedSecondMoment != null) {\n      dispose(this.accumulatedSecondMoment.map((v) => v.variable));\n    }\n  }\n  async getWeights() {\n    const variables = [...this.accumulatedFirstMoment, ...this.accumulatedSecondMoment];\n    return [await this.saveIterations()].concat(variables.map((v) => ({ name: v.originalName, tensor: v.variable })));\n  }\n  async setWeights(weightValues) {\n    weightValues = await this.extractIterations(weightValues);\n    tidy(() => {\n      this.accBeta1.assign(pow(this.beta1, this.iterations_ + 1));\n      this.accBeta2.assign(pow(this.beta2, this.iterations_ + 1));\n    });\n    const variableCount = weightValues.length / 2;\n    const trainable = false;\n    this.accumulatedFirstMoment = weightValues.slice(0, variableCount).map((v) => ({\n      originalName: v.name,\n      variable: v.tensor.variable(trainable)\n    }));\n    this.accumulatedSecondMoment = weightValues.slice(variableCount, variableCount * 2).map((v) => ({\n      originalName: v.name,\n      variable: v.tensor.variable(trainable)\n    }));\n  }\n  getConfig() {\n    return {\n      \"learningRate\": this.learningRate,\n      \"beta1\": this.beta1,\n      \"beta2\": this.beta2,\n      \"epsilon\": this.epsilon\n    };\n  }\n  static fromConfig(cls, config) {\n    return new cls(config[\"learningRate\"], config[\"beta1\"], config[\"beta2\"], config[\"epsilon\"]);\n  }\n};\nAdamOptimizer.className = \"Adam\";\nregisterClass(AdamOptimizer);\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/optimizers/adamax_optimizer.js\nvar AdamaxOptimizer = class extends Optimizer {\n  constructor(learningRate, beta1, beta2, epsilon3 = null, decay = 0) {\n    super();\n    this.learningRate = learningRate;\n    this.beta1 = beta1;\n    this.beta2 = beta2;\n    this.epsilon = epsilon3;\n    this.decay = decay;\n    this.accumulatedFirstMoment = [];\n    this.accumulatedWeightedInfNorm = [];\n    tidy(() => {\n      this.iteration = scalar(0).variable();\n      this.accBeta1 = scalar(beta1).variable();\n    });\n    if (epsilon3 == null) {\n      this.epsilon = ENGINE.backend.epsilon();\n    }\n  }\n  applyGradients(variableGradients) {\n    const variableNames = Array.isArray(variableGradients) ? variableGradients.map((item) => item.name) : Object.keys(variableGradients);\n    tidy(() => {\n      const oneMinusAccBeta1 = sub(1, this.accBeta1);\n      const lr = div(-this.learningRate, add2(mul(this.iteration, this.decay), 1));\n      variableNames.forEach((name, i) => {\n        const value = ENGINE.registeredVariables[name];\n        const trainable = false;\n        if (this.accumulatedFirstMoment[i] == null) {\n          this.accumulatedFirstMoment[i] = {\n            originalName: `${name}/m`,\n            variable: zerosLike(value).variable(trainable)\n          };\n        }\n        if (this.accumulatedWeightedInfNorm[i] == null) {\n          this.accumulatedWeightedInfNorm[i] = {\n            originalName: `${name}/v`,\n            variable: zerosLike(value).variable(trainable)\n          };\n        }\n        const gradient = Array.isArray(variableGradients) ? variableGradients[i].tensor : variableGradients[name];\n        if (gradient == null) {\n          return;\n        }\n        const firstMoment = this.accumulatedFirstMoment[i].variable;\n        const weightedInfNorm = this.accumulatedWeightedInfNorm[i].variable;\n        const newFirstMoment = add2(mul(firstMoment, this.beta1), mul(gradient, 1 - this.beta1));\n        const ut0 = mul(weightedInfNorm, this.beta2);\n        const ut1 = abs(gradient);\n        const newWeightedInfNorm = maximum(ut0, ut1);\n        firstMoment.assign(newFirstMoment);\n        weightedInfNorm.assign(newWeightedInfNorm);\n        const newValue = add2(mul(div(lr, oneMinusAccBeta1), div(newFirstMoment, add2(newWeightedInfNorm, this.epsilon))), value);\n        value.assign(newValue);\n      });\n      this.iteration.assign(add2(this.iteration, 1));\n      this.accBeta1.assign(mul(this.accBeta1, this.beta1));\n    });\n    this.incrementIterations();\n  }\n  dispose() {\n    this.accBeta1.dispose();\n    this.iteration.dispose();\n    if (this.accumulatedFirstMoment != null) {\n      dispose(this.accumulatedFirstMoment.map((v) => v.variable));\n    }\n    if (this.accumulatedWeightedInfNorm != null) {\n      dispose(this.accumulatedWeightedInfNorm.map((v) => v.variable));\n    }\n  }\n  async getWeights() {\n    throw new Error(\"getWeights() is not implemented for Adamax yet.\");\n  }\n  async setWeights(weightValues) {\n    throw new Error(\"setWeights() is not implemented for Adamax yet.\");\n  }\n  getConfig() {\n    return {\n      \"learningRate\": this.learningRate,\n      \"beta1\": this.beta1,\n      \"beta2\": this.beta2,\n      \"epsilon\": this.epsilon,\n      \"decay\": this.decay\n    };\n  }\n  static fromConfig(cls, config) {\n    return new cls(config[\"learningRate\"], config[\"beta1\"], config[\"beta2\"], config[\"epsilon\"], config[\"decay\"]);\n  }\n};\nAdamaxOptimizer.className = \"Adamax\";\nregisterClass(AdamaxOptimizer);\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/optimizers/sgd_optimizer.js\nvar SGDOptimizer = class extends Optimizer {\n  constructor(learningRate) {\n    super();\n    this.learningRate = learningRate;\n    this.setLearningRate(learningRate);\n  }\n  applyGradients(variableGradients) {\n    const varNames = Array.isArray(variableGradients) ? variableGradients.map((v) => v.name) : Object.keys(variableGradients);\n    varNames.forEach((name, i) => {\n      const gradient = Array.isArray(variableGradients) ? variableGradients[i].tensor : variableGradients[name];\n      if (gradient == null) {\n        return;\n      }\n      const value = ENGINE.registeredVariables[name];\n      tidy(() => {\n        const newValue = add2(mul(this.c, gradient), value);\n        value.assign(newValue);\n      });\n    });\n    this.incrementIterations();\n  }\n  setLearningRate(learningRate) {\n    this.learningRate = learningRate;\n    if (this.c != null) {\n      this.c.dispose();\n    }\n    this.c = keep(scalar(-learningRate));\n  }\n  dispose() {\n    this.c.dispose();\n  }\n  async getWeights() {\n    return [await this.saveIterations()];\n  }\n  async setWeights(weightValues) {\n    weightValues = await this.extractIterations(weightValues);\n    if (weightValues.length !== 0) {\n      throw new Error(\"SGD optimizer does not have settable weights.\");\n    }\n  }\n  getConfig() {\n    return { \"learningRate\": this.learningRate };\n  }\n  static fromConfig(cls, config) {\n    return new cls(config[\"learningRate\"]);\n  }\n};\nSGDOptimizer.className = \"SGD\";\nregisterClass(SGDOptimizer);\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/optimizers/momentum_optimizer.js\nvar MomentumOptimizer = class extends SGDOptimizer {\n  constructor(learningRate, momentum, useNesterov = false) {\n    super(learningRate);\n    this.learningRate = learningRate;\n    this.momentum = momentum;\n    this.useNesterov = useNesterov;\n    this.accumulations = [];\n    this.m = scalar(this.momentum);\n  }\n  applyGradients(variableGradients) {\n    const variableNames = Array.isArray(variableGradients) ? variableGradients.map((item) => item.name) : Object.keys(variableGradients);\n    variableNames.forEach((name, i) => {\n      const value = ENGINE.registeredVariables[name];\n      if (this.accumulations[i] == null) {\n        const trainable = false;\n        this.accumulations[i] = {\n          originalName: `${name}/momentum`,\n          variable: tidy(() => zerosLike(value).variable(trainable))\n        };\n      }\n      const accumulation = this.accumulations[i].variable;\n      const gradient = Array.isArray(variableGradients) ? variableGradients[i].tensor : variableGradients[name];\n      if (gradient == null) {\n        return;\n      }\n      tidy(() => {\n        let newValue;\n        const newAccumulation = add2(mul(this.m, accumulation), gradient);\n        if (this.useNesterov) {\n          newValue = add2(mul(this.c, add2(gradient, mul(newAccumulation, this.m))), value);\n        } else {\n          newValue = add2(mul(this.c, newAccumulation), value);\n        }\n        accumulation.assign(newAccumulation);\n        value.assign(newValue);\n      });\n    });\n    this.incrementIterations();\n  }\n  dispose() {\n    this.m.dispose();\n    if (this.accumulations != null) {\n      dispose(this.accumulations.map((v) => v.variable));\n    }\n  }\n  setMomentum(momentum) {\n    this.momentum = momentum;\n  }\n  async getWeights() {\n    return [await this.saveIterations()].concat(this.accumulations.map((v) => ({ name: v.originalName, tensor: v.variable })));\n  }\n  async setWeights(weightValues) {\n    weightValues = await this.extractIterations(weightValues);\n    const trainable = false;\n    this.accumulations = weightValues.map((v) => ({ originalName: v.name, variable: v.tensor.variable(trainable) }));\n  }\n  getConfig() {\n    return {\n      \"learningRate\": this.learningRate,\n      \"momentum\": this.momentum,\n      \"useNesterov\": this.useNesterov\n    };\n  }\n  static fromConfig(cls, config) {\n    return new cls(config[\"learningRate\"], config[\"momentum\"], config[\"useNesterov\"]);\n  }\n};\nMomentumOptimizer.className = \"Momentum\";\nregisterClass(MomentumOptimizer);\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/optimizers/rmsprop_optimizer.js\nvar RMSPropOptimizer = class extends Optimizer {\n  constructor(learningRate, decay = 0.9, momentum = 0, epsilon3 = null, centered = false) {\n    super();\n    this.learningRate = learningRate;\n    this.decay = decay;\n    this.momentum = momentum;\n    this.epsilon = epsilon3;\n    this.accumulatedMeanSquares = [];\n    this.accumulatedMoments = [];\n    this.accumulatedMeanGrads = [];\n    this.centered = centered;\n    if (epsilon3 == null) {\n      this.epsilon = ENGINE.backend.epsilon();\n    }\n    if (learningRate == null) {\n      throw new Error(`learningRate for RMSPropOptimizer must be defined.`);\n    }\n  }\n  applyGradients(variableGradients) {\n    const variableNames = Array.isArray(variableGradients) ? variableGradients.map((item) => item.name) : Object.keys(variableGradients);\n    variableNames.forEach((name, i) => {\n      const value = ENGINE.registeredVariables[name];\n      const trainable = false;\n      if (this.accumulatedMeanSquares[i] == null) {\n        this.accumulatedMeanSquares[i] = {\n          originalName: `${name}/rms`,\n          variable: tidy(() => zerosLike(value).variable(trainable))\n        };\n      }\n      if (this.accumulatedMoments[i] == null) {\n        this.accumulatedMoments[i] = {\n          originalName: `${name}/momentum`,\n          variable: tidy(() => zerosLike(value).variable(trainable))\n        };\n      }\n      if (this.accumulatedMeanGrads[i] == null && this.centered) {\n        this.accumulatedMeanGrads[i] = {\n          originalName: `${name}/mg`,\n          variable: tidy(() => zerosLike(value).variable(trainable))\n        };\n      }\n      const gradient = Array.isArray(variableGradients) ? variableGradients[i].tensor : variableGradients[name];\n      if (gradient == null) {\n        return;\n      }\n      const accumulatedMeanSquare = this.accumulatedMeanSquares[i].variable;\n      const accumulatedMoments = this.accumulatedMoments[i].variable;\n      tidy(() => {\n        const newAccumulatedMeanSquare = add2(mul(accumulatedMeanSquare, this.decay), mul(square(gradient), 1 - this.decay));\n        if (this.centered) {\n          const accumulatedMeanGrad = this.accumulatedMeanGrads[i].variable;\n          const newAccumulatedMeanGrad = add2(mul(accumulatedMeanGrad, this.decay), mul(gradient, 1 - this.decay));\n          const gradContribution = div(mul(gradient, this.learningRate), sqrt(sub(newAccumulatedMeanSquare, add2(square(newAccumulatedMeanGrad), this.epsilon))));\n          const newAccumulatedMoments = add2(mul(accumulatedMoments, this.momentum), gradContribution);\n          accumulatedMeanSquare.assign(newAccumulatedMeanSquare);\n          accumulatedMeanGrad.assign(newAccumulatedMeanGrad);\n          accumulatedMoments.assign(newAccumulatedMoments);\n          const newValue = sub(value, newAccumulatedMoments);\n          value.assign(newValue);\n        } else {\n          const newAccumulatedMeanSquare2 = add2(mul(accumulatedMeanSquare, this.decay), mul(square(gradient), 1 - this.decay));\n          const newAccumulatedMoments = add2(mul(accumulatedMoments, this.momentum), div(mul(gradient, this.learningRate), sqrt(add2(newAccumulatedMeanSquare2, this.epsilon))));\n          accumulatedMeanSquare.assign(newAccumulatedMeanSquare2);\n          accumulatedMoments.assign(newAccumulatedMoments);\n          const newValue = sub(value, newAccumulatedMoments);\n          value.assign(newValue);\n        }\n      });\n    });\n    this.incrementIterations();\n  }\n  dispose() {\n    if (this.accumulatedMeanSquares != null) {\n      dispose(this.accumulatedMeanSquares.map((v) => v.variable));\n    }\n    if (this.accumulatedMeanGrads != null && this.centered) {\n      dispose(this.accumulatedMeanGrads.map((v) => v.variable));\n    }\n    if (this.accumulatedMoments != null) {\n      dispose(this.accumulatedMoments.map((v) => v.variable));\n    }\n  }\n  async getWeights() {\n    const variables = [...this.accumulatedMeanSquares, ...this.accumulatedMoments];\n    if (this.centered) {\n      variables.push(...this.accumulatedMeanGrads);\n    }\n    return [await this.saveIterations()].concat(variables.map((v) => ({ name: v.originalName, tensor: v.variable })));\n  }\n  async setWeights(weightValues) {\n    weightValues = await this.extractIterations(weightValues);\n    const variableCount = this.centered ? weightValues.length / 3 : weightValues.length / 2;\n    const trainable = false;\n    this.accumulatedMeanSquares = weightValues.slice(0, variableCount).map((v) => ({\n      originalName: v.name,\n      variable: v.tensor.variable(trainable)\n    }));\n    this.accumulatedMoments = weightValues.slice(variableCount, variableCount * 2).map((v) => ({\n      originalName: v.name,\n      variable: v.tensor.variable(trainable)\n    }));\n    if (this.centered) {\n      this.accumulatedMeanGrads = weightValues.slice(variableCount * 2, variableCount * 3).map((v) => ({\n        originalName: v.name,\n        variable: v.tensor.variable(trainable)\n      }));\n    }\n  }\n  getConfig() {\n    return {\n      \"learningRate\": this.learningRate,\n      \"decay\": this.decay,\n      \"momentum\": this.momentum,\n      \"epsilon\": this.epsilon,\n      \"centered\": this.centered\n    };\n  }\n  static fromConfig(cls, config) {\n    return new cls(config[\"learningRate\"], config[\"decay\"], config[\"momentum\"], config[\"epsilon\"], config[\"centered\"]);\n  }\n};\nRMSPropOptimizer.className = \"RMSProp\";\nregisterClass(RMSPropOptimizer);\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/optimizers/optimizer_constructors.js\nvar OptimizerConstructors = class {\n  static sgd(learningRate) {\n    return new SGDOptimizer(learningRate);\n  }\n  static momentum(learningRate, momentum, useNesterov = false) {\n    return new MomentumOptimizer(learningRate, momentum, useNesterov);\n  }\n  static rmsprop(learningRate, decay = 0.9, momentum = 0, epsilon3 = null, centered = false) {\n    return new RMSPropOptimizer(learningRate, decay, momentum, epsilon3, centered);\n  }\n  static adam(learningRate = 1e-3, beta1 = 0.9, beta2 = 0.999, epsilon3 = null) {\n    return new AdamOptimizer(learningRate, beta1, beta2, epsilon3);\n  }\n  static adadelta(learningRate = 1e-3, rho = 0.95, epsilon3 = null) {\n    return new AdadeltaOptimizer(learningRate, rho, epsilon3);\n  }\n  static adamax(learningRate = 2e-3, beta1 = 0.9, beta2 = 0.999, epsilon3 = null, decay = 0) {\n    return new AdamaxOptimizer(learningRate, beta1, beta2, epsilon3, decay);\n  }\n  static adagrad(learningRate, initialAccumulatorValue = 0.1) {\n    return new AdagradOptimizer(learningRate, initialAccumulatorValue);\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/train.js\nvar train = {\n  sgd: OptimizerConstructors.sgd,\n  momentum: OptimizerConstructors.momentum,\n  adadelta: OptimizerConstructors.adadelta,\n  adagrad: OptimizerConstructors.adagrad,\n  rmsprop: OptimizerConstructors.rmsprop,\n  adamax: OptimizerConstructors.adamax,\n  adam: OptimizerConstructors.adam\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/browser_util.js\nvar delayCallback = (() => {\n  if (typeof requestAnimationFrame !== \"undefined\") {\n    return requestAnimationFrame;\n  } else if (typeof setImmediate !== \"undefined\") {\n    return setImmediate;\n  }\n  return (f) => f();\n})();\nfunction nextFrame() {\n  return new Promise((resolve) => delayCallback(() => resolve()));\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/backends/backend_util.js\nvar backend_util_exports = {};\n__export(backend_util_exports, {\n  ERF_A1: () => ERF_A1,\n  ERF_A2: () => ERF_A2,\n  ERF_A3: () => ERF_A3,\n  ERF_A4: () => ERF_A4,\n  ERF_A5: () => ERF_A5,\n  ERF_P: () => ERF_P,\n  PARALLELIZE_THRESHOLD: () => PARALLELIZE_THRESHOLD,\n  RowPartitionType: () => RowPartitionType,\n  SELU_SCALE: () => SELU_SCALE,\n  SELU_SCALEALPHA: () => SELU_SCALEALPHA,\n  applyActivation: () => applyActivation,\n  assertAndGetBroadcastShape: () => assertAndGetBroadcastShape,\n  assertAxesAreInnerMostDims: () => assertAxesAreInnerMostDims,\n  assertParamsConsistent: () => assertParamsConsistent,\n  assignToTypedArray: () => assignToTypedArray,\n  axesAreInnerMostDims: () => axesAreInnerMostDims,\n  calculateShapes: () => calculateShapes,\n  checkEinsumDimSizes: () => checkEinsumDimSizes,\n  checkPadOnDimRoundingMode: () => checkPadOnDimRoundingMode,\n  combineLocations: () => combineLocations,\n  combineRaggedTensorToTensorShapes: () => combineRaggedTensorToTensorShapes,\n  complexWithEvenIndex: () => complexWithEvenIndex,\n  complexWithOddIndex: () => complexWithOddIndex,\n  computeConv2DInfo: () => computeConv2DInfo,\n  computeConv3DInfo: () => computeConv3DInfo,\n  computeDefaultPad: () => computeDefaultPad,\n  computeDilation2DInfo: () => computeDilation2DInfo,\n  computeOptimalWindowSize: () => computeOptimalWindowSize,\n  computeOutAndReduceShapes: () => computeOutAndReduceShapes,\n  computeOutShape: () => computeOutShape2,\n  computePool2DInfo: () => computePool2DInfo,\n  computePool3DInfo: () => computePool3DInfo,\n  convertConv2DDataFormat: () => convertConv2DDataFormat,\n  decodeEinsumEquation: () => decodeEinsumEquation,\n  eitherStridesOrDilationsAreOne: () => eitherStridesOrDilationsAreOne,\n  expandShapeToKeepDim: () => expandShapeToKeepDim,\n  exponent: () => exponent,\n  exponents: () => exponents,\n  fromStringArrayToUint8: () => fromStringArrayToUint8,\n  fromUint8ToStringArray: () => fromUint8ToStringArray,\n  getAxesPermutation: () => getAxesPermutation,\n  getBroadcastDims: () => getBroadcastDims,\n  getComplexWithIndex: () => getComplexWithIndex,\n  getEinsumComputePath: () => getEinsumComputePath,\n  getEinsumPermutation: () => getEinsumPermutation,\n  getFusedBiasGradient: () => getFusedBiasGradient,\n  getFusedDyActivation: () => getFusedDyActivation,\n  getImageCenter: () => getImageCenter,\n  getInnerMostAxes: () => getInnerMostAxes,\n  getPermuted: () => getPermuted,\n  getRaggedRank: () => getRaggedRank,\n  getReductionAxes: () => getReductionAxes,\n  getReshaped: () => getReshaped,\n  getReshapedPermuted: () => getReshapedPermuted,\n  getRowPartitionTypesHelper: () => getRowPartitionTypesHelper,\n  getSliceBeginCoords: () => getSliceBeginCoords,\n  getSliceSize: () => getSliceSize,\n  getSparseFillEmptyRowsIndicesDenseShapeMismatch: () => getSparseFillEmptyRowsIndicesDenseShapeMismatch,\n  getSparseFillEmptyRowsNegativeIndexErrorMessage: () => getSparseFillEmptyRowsNegativeIndexErrorMessage,\n  getSparseFillEmptyRowsOutOfRangeIndexErrorMessage: () => getSparseFillEmptyRowsOutOfRangeIndexErrorMessage,\n  getSparseReshapeEmptyTensorZeroOutputDimErrorMessage: () => getSparseReshapeEmptyTensorZeroOutputDimErrorMessage,\n  getSparseReshapeInputOutputMismatchErrorMessage: () => getSparseReshapeInputOutputMismatchErrorMessage,\n  getSparseReshapeInputOutputMultipleErrorMessage: () => getSparseReshapeInputOutputMultipleErrorMessage,\n  getSparseReshapeMultipleNegativeOneOutputDimErrorMessage: () => getSparseReshapeMultipleNegativeOneOutputDimErrorMessage,\n  getSparseReshapeNegativeOutputDimErrorMessage: () => getSparseReshapeNegativeOutputDimErrorMessage,\n  getSparseSegmentReductionIndicesOutOfRangeErrorMessage: () => getSparseSegmentReductionIndicesOutOfRangeErrorMessage,\n  getSparseSegmentReductionNegativeSegmentIdsErrorMessage: () => getSparseSegmentReductionNegativeSegmentIdsErrorMessage,\n  getSparseSegmentReductionNonIncreasingSegmentIdsErrorMessage: () => getSparseSegmentReductionNonIncreasingSegmentIdsErrorMessage,\n  getSparseSegmentReductionSegmentIdOutOfRangeErrorMessage: () => getSparseSegmentReductionSegmentIdOutOfRangeErrorMessage,\n  getUndoAxesPermutation: () => getUndoAxesPermutation,\n  isIdentityPermutation: () => isIdentityPermutation,\n  log: () => log,\n  mergeRealAndImagArrays: () => mergeRealAndImagArrays,\n  prepareAndValidate: () => prepareAndValidate,\n  prepareSplitSize: () => prepareSplitSize,\n  segment_util: () => segment_util_exports,\n  shouldFuse: () => shouldFuse,\n  slice_util: () => slice_util_exports,\n  splitRealAndImagArrays: () => splitRealAndImagArrays,\n  tupleValuesAreOne: () => tupleValuesAreOne,\n  upcastType: () => upcastType,\n  validateDefaultValueShape: () => validateDefaultValueShape,\n  validateInput: () => validateInput,\n  validateUpdateShape: () => validateUpdateShape,\n  warn: () => warn\n});\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/concat_util.js\nfunction assertParamsConsistent(shapes, axis) {\n  const rank = shapes[0].length;\n  shapes.forEach((shape, i) => {\n    assert(shape.length === rank, () => `Error in concat${rank}D: rank of tensors[${i}] must be the same as the rank of the rest (${rank})`);\n  });\n  assert(axis >= 0 && axis < rank, () => `Error in concat${rank}D: axis must be between 0 and ${rank - 1}.`);\n  const firstShape = shapes[0];\n  shapes.forEach((shape, i) => {\n    for (let r = 0; r < rank; r++) {\n      assert(r === axis || shape[r] === firstShape[r], () => `Error in concat${rank}D: Shape of tensors[${i}] (${shape}) does not match the shape of the rest (${firstShape}) along the non-concatenated axis ${i}.`);\n    }\n  });\n}\nfunction computeOutShape2(shapes, axis) {\n  const outputShape = shapes[0].slice();\n  for (let i = 1; i < shapes.length; i++) {\n    outputShape[axis] += shapes[i][axis];\n  }\n  return outputShape;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/ragged_to_dense_util.js\nvar RowPartitionType;\n(function(RowPartitionType3) {\n  RowPartitionType3[RowPartitionType3[\"FIRST_DIM_SIZE\"] = 0] = \"FIRST_DIM_SIZE\";\n  RowPartitionType3[RowPartitionType3[\"VALUE_ROWIDS\"] = 1] = \"VALUE_ROWIDS\";\n  RowPartitionType3[RowPartitionType3[\"ROW_LENGTHS\"] = 2] = \"ROW_LENGTHS\";\n  RowPartitionType3[RowPartitionType3[\"ROW_SPLITS\"] = 3] = \"ROW_SPLITS\";\n  RowPartitionType3[RowPartitionType3[\"ROW_LIMITS\"] = 4] = \"ROW_LIMITS\";\n  RowPartitionType3[RowPartitionType3[\"ROW_STARTS\"] = 5] = \"ROW_STARTS\";\n})(RowPartitionType || (RowPartitionType = {}));\nfunction combineRaggedTensorToTensorShapes(raggedRank, shape, valueShape) {\n  let outputShape = new Array();\n  if (valueShape == null && shape == null) {\n    return outputShape;\n  }\n  if (shape == null) {\n    while (outputShape.length < raggedRank + valueShape.length) {\n      outputShape.push(-1);\n    }\n  } else {\n    outputShape = shape.slice();\n  }\n  if (valueShape == null) {\n    return outputShape;\n  }\n  if (raggedRank + valueShape.length !== outputShape.length) {\n    throw new Error(`rt input.shape and shape=${shape} are incompatible: rt input.rank = ${raggedRank + valueShape.length}, but shape.rank = ${outputShape.length}`);\n  }\n  for (let i = 1; i < valueShape.length; ++i) {\n    const valueDim = valueShape[i];\n    const outputShapeDimIndex = outputShape[outputShape.length - valueShape.length + i];\n    const outputShapeDim = outputShape[outputShapeDimIndex];\n    if (valueDim >= 0) {\n      if (outputShapeDim >= 0) {\n        if (outputShapeDim !== valueDim) {\n          throw new Error(`rt input.shape and shape=${shape} are incompatible: rt input.shape[${i + raggedRank}] = ${valueDim} but shape[${i + raggedRank}] = ${outputShapeDim}`);\n        }\n      } else {\n        outputShape[outputShapeDimIndex] = valueDim;\n      }\n    }\n  }\n  return outputShape;\n}\nfunction getRowPartitionTypesHelper(rowPartitionTypeStrings) {\n  const stringToType = {\n    \"FIRST_DIM_SIZE\": RowPartitionType.FIRST_DIM_SIZE,\n    \"VALUE_ROWIDS\": RowPartitionType.VALUE_ROWIDS,\n    \"ROW_LENGTHS\": RowPartitionType.ROW_LENGTHS,\n    \"ROW_SPLITS\": RowPartitionType.ROW_SPLITS,\n    \"ROW_LIMITS\": RowPartitionType.ROW_LIMITS,\n    \"ROW_STARTS\": RowPartitionType.ROW_STARTS\n  };\n  const result = [];\n  for (const typeStr of rowPartitionTypeStrings) {\n    if (typeStr in stringToType) {\n      result.push(stringToType[typeStr]);\n    } else {\n      break;\n    }\n  }\n  return result;\n}\nfunction getRaggedRank(rowPartitionTypes) {\n  if (rowPartitionTypes.length === 0) {\n    return 0;\n  }\n  if (rowPartitionTypes[0] === RowPartitionType.FIRST_DIM_SIZE) {\n    return rowPartitionTypes.length - 1;\n  }\n  return rowPartitionTypes.length;\n}\nfunction validateDefaultValueShape(defaultValueShape, valueShape) {\n  if (defaultValueShape == null || valueShape == null) {\n    return;\n  }\n  const defaultNDims = defaultValueShape.length;\n  const valuesNDims = valueShape.length;\n  if (defaultNDims >= valuesNDims) {\n    throw new Error(`defaultValue.shape=${defaultValueShape} and ragged tensor flatValues.shape=${valueShape}, are incompatible: defaultValue.rank = ${defaultNDims} must be less than ragged tensor input flatValues.rank = ${valuesNDims})`);\n  }\n  for (let i = 0; i < Math.min(defaultNDims, valuesNDims - 1); ++i) {\n    const defaultDim = defaultValueShape[i];\n    const valueDim = valueShape[i + 1];\n    if (defaultDim >= 0 && valueDim >= 0 && defaultDim !== 1 && defaultDim !== valueDim) {\n      throw new Error(`defaultValue.shape=${defaultValueShape}, and ragged tensor input flatValues.shape=${valueShape} are incompatible: defaultValue.shape[${i - defaultValueShape.length}] = ${defaultDim} but ragged tensor input.flatValues.shape[${i - defaultValueShape.length}] = ${valueDim}`);\n    }\n  }\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/reduce_util.js\nvar PARALLELIZE_THRESHOLD = 30;\nfunction computeOptimalWindowSize(inSize) {\n  if (inSize <= PARALLELIZE_THRESHOLD) {\n    return inSize;\n  }\n  return nearestDivisor(inSize, Math.floor(Math.sqrt(inSize)));\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/rotate_util.js\nfunction getImageCenter(center, imageHeight, imageWidth) {\n  const centerX = imageWidth * (typeof center === \"number\" ? center : center[0]);\n  const centerY = imageHeight * (typeof center === \"number\" ? center : center[1]);\n  return [centerX, centerY];\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/array_ops_util.js\nfunction getReshaped(inputShape, blockShape, prod6, batchToSpace = true) {\n  let reshaped = [];\n  if (batchToSpace) {\n    reshaped = reshaped.concat(blockShape.slice(0));\n    reshaped.push(inputShape[0] / prod6);\n    reshaped = reshaped.concat(inputShape.slice(1));\n  } else {\n    reshaped = reshaped.concat(inputShape[0]);\n    const spatialLength = blockShape.length;\n    for (let i = 0; i < spatialLength; ++i) {\n      reshaped = reshaped.concat([inputShape[i + 1] / blockShape[i], blockShape[i]]);\n    }\n    reshaped = reshaped.concat(inputShape.slice(spatialLength + 1));\n  }\n  return reshaped;\n}\nfunction getPermuted(reshapedRank, blockShapeRank, batchToSpace = true) {\n  const permuted = [];\n  if (batchToSpace) {\n    permuted.push(blockShapeRank);\n    for (let i = blockShapeRank + 1; i < reshapedRank; ++i) {\n      if (i <= 2 * blockShapeRank) {\n        permuted.push(i);\n        permuted.push(i - (blockShapeRank + 1));\n      } else {\n        permuted.push(i);\n      }\n    }\n  } else {\n    const permutedBeforeBatch = [];\n    const permutedAfterBatch = [];\n    for (let i = 1; i < reshapedRank; ++i) {\n      if (i >= blockShapeRank * 2 + 1 || i % 2 === 1) {\n        permutedAfterBatch.push(i);\n      } else {\n        permutedBeforeBatch.push(i);\n      }\n    }\n    permuted.push(...permutedBeforeBatch);\n    permuted.push(0);\n    permuted.push(...permutedAfterBatch);\n  }\n  return permuted;\n}\nfunction getReshapedPermuted(inputShape, blockShape, prod6, batchToSpace = true) {\n  const reshapedPermuted = [];\n  if (batchToSpace) {\n    reshapedPermuted.push(inputShape[0] / prod6);\n  } else {\n    reshapedPermuted.push(inputShape[0] * prod6);\n  }\n  for (let i = 1; i < inputShape.length; ++i) {\n    if (i <= blockShape.length) {\n      if (batchToSpace) {\n        reshapedPermuted.push(blockShape[i - 1] * inputShape[i]);\n      } else {\n        reshapedPermuted.push(inputShape[i] / blockShape[i - 1]);\n      }\n    } else {\n      reshapedPermuted.push(inputShape[i]);\n    }\n  }\n  return reshapedPermuted;\n}\nfunction getSliceBeginCoords(crops, blockShape) {\n  const sliceBeginCoords = [0];\n  for (let i = 0; i < blockShape; ++i) {\n    sliceBeginCoords.push(crops[i][0]);\n  }\n  return sliceBeginCoords;\n}\nfunction getSliceSize(uncroppedShape, crops, blockShape) {\n  const sliceSize = uncroppedShape.slice(0, 1);\n  for (let i = 0; i < blockShape; ++i) {\n    sliceSize.push(uncroppedShape[i + 1] - crops[i][0] - crops[i][1]);\n  }\n  return sliceSize;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/selu_util.js\nvar SELU_SCALEALPHA = 1.7580993408473768;\nvar SELU_SCALE = 1.0507009873554805;\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/erf_util.js\nvar ERF_P = 0.3275911;\nvar ERF_A1 = 0.254829592;\nvar ERF_A2 = -0.284496736;\nvar ERF_A3 = 1.421413741;\nvar ERF_A4 = -1.453152027;\nvar ERF_A5 = 1.061405429;\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/backends/complex_util.js\nfunction mergeRealAndImagArrays(real5, imag5) {\n  if (real5.length !== imag5.length) {\n    throw new Error(`Cannot merge real and imag arrays of different lengths. real:${real5.length}, imag: ${imag5.length}.`);\n  }\n  const result = new Float32Array(real5.length * 2);\n  for (let i = 0; i < result.length; i += 2) {\n    result[i] = real5[i / 2];\n    result[i + 1] = imag5[i / 2];\n  }\n  return result;\n}\nfunction splitRealAndImagArrays(complex5) {\n  const real5 = new Float32Array(complex5.length / 2);\n  const imag5 = new Float32Array(complex5.length / 2);\n  for (let i = 0; i < complex5.length; i += 2) {\n    real5[i / 2] = complex5[i];\n    imag5[i / 2] = complex5[i + 1];\n  }\n  return { real: real5, imag: imag5 };\n}\nfunction complexWithEvenIndex(complex5) {\n  const len = Math.ceil(complex5.length / 4);\n  const real5 = new Float32Array(len);\n  const imag5 = new Float32Array(len);\n  for (let i = 0; i < complex5.length; i += 4) {\n    real5[Math.floor(i / 4)] = complex5[i];\n    imag5[Math.floor(i / 4)] = complex5[i + 1];\n  }\n  return { real: real5, imag: imag5 };\n}\nfunction complexWithOddIndex(complex5) {\n  const len = Math.floor(complex5.length / 4);\n  const real5 = new Float32Array(len);\n  const imag5 = new Float32Array(len);\n  for (let i = 2; i < complex5.length; i += 4) {\n    real5[Math.floor(i / 4)] = complex5[i];\n    imag5[Math.floor(i / 4)] = complex5[i + 1];\n  }\n  return { real: real5, imag: imag5 };\n}\nfunction getComplexWithIndex(complex5, index) {\n  const real5 = complex5[index * 2];\n  const imag5 = complex5[index * 2 + 1];\n  return { real: real5, imag: imag5 };\n}\nfunction assignToTypedArray(data, real5, imag5, index) {\n  data[index * 2] = real5;\n  data[index * 2 + 1] = imag5;\n}\nfunction exponents(n, inverse) {\n  const real5 = new Float32Array(n / 2);\n  const imag5 = new Float32Array(n / 2);\n  for (let i = 0; i < Math.ceil(n / 2); i++) {\n    const x = (inverse ? 2 : -2) * Math.PI * (i / n);\n    real5[i] = Math.cos(x);\n    imag5[i] = Math.sin(x);\n  }\n  return { real: real5, imag: imag5 };\n}\nfunction exponent(k, n, inverse) {\n  const x = (inverse ? 2 : -2) * Math.PI * (k / n);\n  const real5 = Math.cos(x);\n  const imag5 = Math.sin(x);\n  return { real: real5, imag: imag5 };\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/backends/einsum_util.js\nvar ARROW = \"->\";\nvar ARROW_REGEX = /->/g;\nvar COMMA = \",\";\nvar ELLIPSIS = \"...\";\nfunction decodeEinsumEquation(equation, numTensors) {\n  equation = equation.replace(/\\s/g, \"\");\n  const numArrows = (equation.length - equation.replace(ARROW_REGEX, \"\").length) / ARROW.length;\n  if (numArrows < 1) {\n    throw new Error(\"Equations without an arrow are not supported.\");\n  } else if (numArrows > 1) {\n    throw new Error(`Equation must contain exactly one arrow (\"${ARROW}\").`);\n  }\n  const [inputString, outputString] = equation.split(ARROW);\n  assert(inputString.indexOf(ELLIPSIS) === -1, () => `The ellipsis notation (\"${ELLIPSIS}\") is not supported yet.`);\n  const inputTerms = inputString.split(COMMA);\n  const numInputs = inputTerms.length;\n  if (numTensors !== numInputs) {\n    throw new Error(`Expected ${numInputs} input tensors, received ${numTensors}`);\n  }\n  if (numInputs > 2) {\n    throw new Error(\"Support for more than 2 input tensors is not implemented yet.\");\n  }\n  const allDims = [];\n  for (let i = 0; i < outputString.length; ++i) {\n    const dimName = outputString[i];\n    if (!inputTerms.some((inputTerm) => inputTerm.indexOf(dimName) !== -1)) {\n      throw new Error(`Output subscripts contain the label ${dimName} not present in the input subscripts.`);\n    }\n    if (allDims.indexOf(dimName) === -1) {\n      allDims.push(dimName);\n    }\n  }\n  for (let i = 0; i < inputString.length; ++i) {\n    const dimName = inputString[i];\n    if (allDims.indexOf(dimName) === -1 && dimName !== COMMA) {\n      allDims.push(dimName);\n    }\n  }\n  const idDims = new Array(inputTerms.length);\n  for (let i = 0; i < numInputs; ++i) {\n    if (new Set(inputTerms[i].split(\"\")).size !== inputTerms[i].length) {\n      throw new Error(`Found duplicate axes in input component ${inputTerms[i]}. Support for duplicate axes in input is not implemented yet.`);\n    }\n    idDims[i] = [];\n    for (let j = 0; j < inputTerms[i].length; ++j) {\n      idDims[i].push(allDims.indexOf(inputTerms[i][j]));\n    }\n  }\n  const numDims = allDims.length;\n  const numOutDims = outputString.length;\n  const summedDims = [];\n  for (let i = numOutDims; i < numDims; ++i) {\n    summedDims.push(i);\n  }\n  return { allDims, summedDims, idDims };\n}\nfunction getEinsumPermutation(nDims, idDims) {\n  let permutationIndices = new Array(nDims);\n  permutationIndices.fill(-1);\n  for (let i = 0; i < idDims.length; ++i) {\n    permutationIndices[idDims[i]] = i;\n  }\n  const expandDims7 = [];\n  for (let i = 0; i < nDims; ++i) {\n    if (permutationIndices[i] === -1) {\n      expandDims7.push(i);\n    }\n  }\n  permutationIndices = permutationIndices.filter((d) => d !== -1);\n  return { permutationIndices, expandDims: expandDims7 };\n}\nfunction checkEinsumDimSizes(nDims, idDims, tensors) {\n  const dimSizes = new Array(nDims);\n  for (let i = 0; i < tensors.length; ++i) {\n    const shape = tensors[i].shape;\n    for (let j = 0; j < idDims[i].length; ++j) {\n      if (dimSizes[idDims[i][j]] === void 0) {\n        dimSizes[idDims[i][j]] = shape[j];\n      } else {\n        assert(dimSizes[idDims[i][j]] === shape[j], () => `Expected dimension ${dimSizes[idDims[i][j]]} at axis ${j} of input shaped ${JSON.stringify(shape)}, but got dimension ${shape[j]}`);\n      }\n    }\n  }\n}\nfunction getEinsumComputePath(summedDims, idDims) {\n  const path = summedDims;\n  const steps = [];\n  let nSteps = 0;\n  if (summedDims.length === 0) {\n    path.push(-1);\n  }\n  nSteps = summedDims.length + 1;\n  for (let i = 0; i < nSteps; ++i) {\n    steps.push([]);\n  }\n  const computedTermIndices = [];\n  for (let i = 0; i < path.length; ++i) {\n    const summedDim = path[i];\n    const termIndices = findTermsWithDim(idDims, summedDim);\n    for (const termIndex of termIndices) {\n      if (computedTermIndices.indexOf(termIndex) === -1) {\n        steps[i].push(termIndex);\n        computedTermIndices.push(termIndex);\n      }\n    }\n  }\n  return { path, steps };\n}\nfunction isIdentityPermutation(perm) {\n  return perm.every((dim, index) => dim === index);\n}\nfunction findTermsWithDim(idDims, dim) {\n  const termIndices = [];\n  for (let i = 0; i < idDims.length; ++i) {\n    if (idDims[i].length === 0 || idDims[i].indexOf(dim) !== -1 || dim === -1) {\n      termIndices.push(i);\n    }\n  }\n  return termIndices;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/split_util.js\nfunction prepareSplitSize(x, numOrSizeSplits, axis = 0) {\n  let splitSizes = [];\n  if (typeof numOrSizeSplits === \"number\") {\n    assert(x.shape[axis] % numOrSizeSplits === 0, () => \"Number of splits must evenly divide the axis.\");\n    splitSizes = new Array(numOrSizeSplits).fill(x.shape[axis] / numOrSizeSplits);\n  } else {\n    const numOfNegs = numOrSizeSplits.reduce((count2, value) => {\n      if (value === -1) {\n        count2 += 1;\n      }\n      return count2;\n    }, 0);\n    assert(numOfNegs <= 1, () => \"There should be only one negative value in split array.\");\n    const negIndex = numOrSizeSplits.indexOf(-1);\n    if (negIndex !== -1) {\n      const total = numOrSizeSplits.reduce((a, b) => b > 0 ? a + b : a);\n      numOrSizeSplits[negIndex] = x.shape[axis] - total;\n    }\n    assert(x.shape[axis] === numOrSizeSplits.reduce((a, b) => a + b), () => \"The sum of sizes must match the size of the axis dimension.\");\n    splitSizes = numOrSizeSplits;\n  }\n  return splitSizes;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/sparse/sparse_fill_empty_rows_util.js\nfunction getSparseFillEmptyRowsIndicesDenseShapeMismatch(indicesLength) {\n  return `Received SparseTensor with denseShape[0] = 0 but\n  indices.shape[0] = ${indicesLength}`;\n}\nfunction getSparseFillEmptyRowsNegativeIndexErrorMessage(index, value) {\n  return `indices(${index}, 0) is invalid: ${value} < 0`;\n}\nfunction getSparseFillEmptyRowsOutOfRangeIndexErrorMessage(index, value, limit) {\n  return `indices(${index}, 0) is invalid: ${value} >= ${limit}`;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/sparse/sparse_reshape_util.js\nfunction getSparseReshapeMultipleNegativeOneOutputDimErrorMessage(dim1, dim2) {\n  return `only one output dimension may be -1, not both ${dim1} and ${dim2}`;\n}\nfunction getSparseReshapeNegativeOutputDimErrorMessage(dim, value) {\n  return `size ${dim} must be non-negative, not ${value}`;\n}\nfunction getSparseReshapeEmptyTensorZeroOutputDimErrorMessage() {\n  return \"reshape cannot infer the missing input size for an empty tensor unless all specified input sizes are non-zero\";\n}\nfunction getSparseReshapeInputOutputMultipleErrorMessage(inputShape, outputShape) {\n  const inputSize = sizeFromShape(inputShape);\n  const outputSize = sizeFromShape(outputShape);\n  return `Input to reshape is a SparseTensor with ${inputSize}\n  dense values, but the requested shape requires a multiple of ${outputSize}. inputShape=${inputShape} outputShape= ${outputShape}`;\n}\nfunction getSparseReshapeInputOutputMismatchErrorMessage(inputShape, outputShape) {\n  const inputSize = sizeFromShape(inputShape);\n  const outputSize = sizeFromShape(outputShape);\n  return `Input to reshape is a tensor with ${inputSize} dense values, but the requested shape has ${outputSize}. inputShape=${inputShape} outputShape=${outputShape}`;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/sparse/sparse_segment_reduction_util.js\nfunction getSparseSegmentReductionNegativeSegmentIdsErrorMessage() {\n  return `segment ids must be >= 0`;\n}\nfunction getSparseSegmentReductionNonIncreasingSegmentIdsErrorMessage() {\n  return `segment ids are not increasing`;\n}\nfunction getSparseSegmentReductionSegmentIdOutOfRangeErrorMessage(segmentId, outputRows) {\n  return `Segment id ${segmentId} out of range [0, ${outputRows}), possibly because segmentIds input is not sorted.`;\n}\nfunction getSparseSegmentReductionIndicesOutOfRangeErrorMessage(index, indexValue, inputRows) {\n  return `Bad: indices[${index}] == ${indexValue} out of range [0, ${inputRows})`;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/segment_util.js\nvar segment_util_exports = {};\n__export(segment_util_exports, {\n  collectGatherOpShapeInfo: () => collectGatherOpShapeInfo,\n  computeOutShape: () => computeOutShape3,\n  segOpComputeOptimalWindowSize: () => segOpComputeOptimalWindowSize\n});\nfunction segOpComputeOptimalWindowSize(inSize, numSegments) {\n  let done = false;\n  let res;\n  if (inSize <= PARALLELIZE_THRESHOLD) {\n    res = inSize;\n    done = true;\n  } else {\n    res = nearestDivisor(inSize, Math.floor(Math.sqrt(inSize)));\n  }\n  while (!done) {\n    if (res > numSegments || res === inSize) {\n      done = true;\n    } else {\n      res = nearestDivisor(inSize, res + 1);\n    }\n  }\n  return res;\n}\nfunction computeOutShape3(aShape, axis, numSegments) {\n  const outShape = [];\n  const rank = aShape.length;\n  for (let dim = 0; dim < rank; dim++) {\n    if (dim !== axis) {\n      outShape.push(aShape[dim]);\n    } else {\n      outShape.push(numSegments);\n    }\n  }\n  return outShape;\n}\nfunction collectGatherOpShapeInfo(x, indices, axis, batchDims) {\n  const indicesRank = indices.shape.length;\n  const xRank = x.shape.length;\n  if (batchDims !== 0) {\n    if (batchDims < -indicesRank || batchDims > indicesRank) {\n      throw new Error(`Expect batchDims in the range of [-${indicesRank}, ${indicesRank}], but got ${batchDims}`);\n    }\n  }\n  if (batchDims < 0) {\n    batchDims += indicesRank;\n  }\n  if (batchDims > xRank) {\n    throw new Error(`batchDims (${batchDims}) must be less than rank(x) (\n    ${xRank}).`);\n  }\n  if (axis < batchDims) {\n    throw new Error(`batchDims (${batchDims}) must be less than or equal to axis (${axis}).`);\n  }\n  for (let i = 0; i < batchDims; ++i) {\n    if (x.shape[i] !== indices.shape[i]) {\n      throw new Error(`x.shape[${i}]: ${x.shape[i]} should be equal to indices.shape[${i}]: ${indices.shape[i]}.`);\n    }\n  }\n  const dimSize = x.shape[axis];\n  const outputShape = [];\n  let batchSize = 1;\n  let outerSize = 1;\n  let sliceSize = 1;\n  for (let i = 0; i < batchDims; ++i) {\n    outputShape.push(x.shape[i]);\n    batchSize *= x.shape[i];\n  }\n  for (let i = batchDims; i < axis; i++) {\n    outputShape.push(x.shape[i]);\n    outerSize *= x.shape[i];\n  }\n  for (let i = batchDims; i < indicesRank; i++) {\n    outputShape.push(indices.shape[i]);\n  }\n  for (let i = axis + 1; i < xRank; i++) {\n    outputShape.push(x.shape[i]);\n    sliceSize *= x.shape[i];\n  }\n  return { batchSize, sliceSize, outerSize, dimSize, outputShape };\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/backends/backend_util.js\nfunction fromUint8ToStringArray(vals) {\n  try {\n    return vals.map((val) => decodeString(val));\n  } catch (err) {\n    throw new Error(`Failed to decode encoded string bytes into utf-8, error: ${err}`);\n  }\n}\nfunction fromStringArrayToUint8(strings) {\n  return strings.map((s) => encodeString(s));\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/backends/kernel_impls.js\nvar kernel_impls_exports = {};\n__export(kernel_impls_exports, {\n  nonMaxSuppressionV3Impl: () => nonMaxSuppressionV3Impl,\n  nonMaxSuppressionV4Impl: () => nonMaxSuppressionV4Impl,\n  nonMaxSuppressionV5Impl: () => nonMaxSuppressionV5Impl,\n  whereImpl: () => whereImpl\n});\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Abs_grad.js\nvar absGradConfig = {\n  kernelName: Abs,\n  inputsToSave: [\"x\"],\n  gradFunc: (dy, saved) => {\n    const [x] = saved;\n    return { x: () => mul(dy, step(cast(x, \"float32\"), -1)) };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Acos_grad.js\nvar acosGradConfig = {\n  kernelName: Acos,\n  inputsToSave: [\"x\"],\n  gradFunc: (dy, saved) => {\n    const [x] = saved;\n    return {\n      x: () => {\n        const a = square(cast(x, \"float32\"));\n        const b = sqrt(sub(scalar(1), a));\n        return neg(div(dy, b));\n      }\n    };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Acosh_grad.js\nvar acoshGradConfig = {\n  kernelName: Acosh,\n  inputsToSave: [\"x\"],\n  gradFunc: (dy, saved) => {\n    const [x] = saved;\n    return {\n      x: () => {\n        const a = sqrt(sub(square(cast(x, \"float32\")), 1));\n        return div(dy, a);\n      }\n    };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Add_grad.js\nvar addGradConfig = {\n  kernelName: Add,\n  inputsToSave: [\"a\", \"b\"],\n  gradFunc: (dy, saved) => {\n    const [a, b] = saved;\n    const outShape = assertAndGetBroadcastShape(a.shape, b.shape);\n    const derA = () => {\n      let res = dy;\n      const reduceAxes = getReductionAxes(a.shape, outShape);\n      if (reduceAxes.length > 0) {\n        res = sum2(res, reduceAxes);\n      }\n      return reshape(res, a.shape);\n    };\n    const derB = () => {\n      let res = dy;\n      const reduceAxes = getReductionAxes(b.shape, outShape);\n      if (reduceAxes.length > 0) {\n        res = sum2(res, reduceAxes);\n      }\n      return reshape(res, b.shape);\n    };\n    return { a: derA, b: derB };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/gradients/AddN_grad.js\nvar addNGradConfig = {\n  kernelName: AddN,\n  saveAllInputs: true,\n  gradFunc: (dy, saved) => {\n    const ders = {};\n    saved.forEach((_, i) => {\n      ders[i] = () => dy.clone();\n    });\n    return ders;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/gradients/ArgMax_grad.js\nvar argMaxGradConfig = {\n  kernelName: ArgMax,\n  inputsToSave: [\"x\"],\n  gradFunc: (dy, saved) => {\n    const [x] = saved;\n    return { x: () => zerosLike(x) };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/gradients/ArgMin_grad.js\nvar argMinGradConfig = {\n  kernelName: ArgMin,\n  inputsToSave: [\"x\"],\n  gradFunc: (dy, saved) => {\n    const [x] = saved;\n    return { x: () => zerosLike(x) };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Asin_grad.js\nvar asinGradConfig = {\n  kernelName: Asin,\n  inputsToSave: [\"x\"],\n  gradFunc: (dy, saved) => {\n    const [x] = saved;\n    return { x: () => div(dy, sqrt(sub(scalar(1), square(cast(x, \"float32\"))))) };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Asinh_grad.js\nvar asinhGradConfig = {\n  kernelName: Asinh,\n  inputsToSave: [\"x\"],\n  gradFunc: (dy, saved) => {\n    const [x] = saved;\n    return {\n      x: () => {\n        const a = sqrt(add2(scalar(1), square(cast(x, \"float32\"))));\n        return div(dy, a);\n      }\n    };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Atan2_grad.js\nvar atan2GradConfig = {\n  kernelName: Atan2,\n  inputsToSave: [\"a\", \"b\"],\n  gradFunc: (dy, saved) => {\n    const [a, b] = saved;\n    const outShape = assertAndGetBroadcastShape(a.shape, b.shape);\n    const derA = () => {\n      const d = add2(square(a), square(b));\n      let res = mul(dy, div(b, d));\n      const reduceAxes = getReductionAxes(a.shape, outShape);\n      if (reduceAxes.length > 0) {\n        res = sum2(res, reduceAxes);\n      }\n      return reshape(res, a.shape);\n    };\n    const derB = () => {\n      const d = add2(square(a), square(b));\n      let res = neg(mul(dy, div(a, d)));\n      const reduceAxes = getReductionAxes(b.shape, outShape);\n      if (reduceAxes.length > 0) {\n        res = sum2(res, reduceAxes);\n      }\n      return reshape(res, b.shape);\n    };\n    return { a: derA, b: derB };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Atan_grad.js\nvar atanGradConfig = {\n  kernelName: Atan,\n  inputsToSave: [\"x\"],\n  gradFunc: (dy, saved) => {\n    const [x] = saved;\n    return { x: () => div(dy, add2(square(cast(x, \"float32\")), 1)) };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Atanh_grad.js\nvar atanhGradConfig = {\n  kernelName: Atanh,\n  inputsToSave: [\"x\"],\n  gradFunc: (dy, saved) => {\n    const [x] = saved;\n    return { x: () => div(dy, sub(scalar(1), square(cast(x, \"float32\")))) };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/avg_pool_3d_grad.js\nfunction avgPool3dGrad_(dy, input2, filterSize, strides, pad3, dimRoundingMode) {\n  const $dy = convertToTensor(dy, \"dy\", \"avgPool3dGrad\");\n  const $input = convertToTensor(input2, \"input\", \"avgPool3dGrad\");\n  let dy5D = $dy;\n  let input5D = $input;\n  let reshapedTo5D = false;\n  if ($input.rank === 4) {\n    reshapedTo5D = true;\n    dy5D = reshape($dy, [1, $dy.shape[0], $dy.shape[1], $dy.shape[2], $dy.shape[3]]);\n    input5D = reshape($input, [\n      1,\n      $input.shape[0],\n      $input.shape[1],\n      $input.shape[2],\n      $input.shape[3]\n    ]);\n  }\n  assert(dy5D.rank === 5, () => `Error in avgPool3dGrad: dy must be rank 5 but got rank ${dy5D.rank}.`);\n  assert(input5D.rank === 5, () => `Error in avgPool3dGrad: input must be rank 5 but got rank ${input5D.rank}.`);\n  checkPadOnDimRoundingMode(\"avgPool3dGrad\", pad3, dimRoundingMode);\n  const inputs = { dy: dy5D, input: input5D };\n  const attrs = { filterSize, strides, pad: pad3, dimRoundingMode };\n  const res = ENGINE.runKernel(AvgPool3DGrad, inputs, attrs);\n  if (reshapedTo5D) {\n    return reshape(res, [res.shape[1], res.shape[2], res.shape[3], res.shape[4]]);\n  }\n  return res;\n}\nvar avgPool3dGrad = op({ avgPool3dGrad_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/gradients/AvgPool3D_grad.js\nvar avgPool3DGradConfig = {\n  kernelName: AvgPool3D,\n  inputsToSave: [\"x\"],\n  gradFunc: (dy, saved, attrs) => {\n    const [x] = saved;\n    const { filterSize, strides, pad: pad3, dimRoundingMode } = attrs;\n    return {\n      x: () => avgPool3dGrad(dy, x, filterSize, strides, pad3, dimRoundingMode)\n    };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/avg_pool_grad.js\nfunction avgPoolGrad_(dy, input2, filterSize, strides, pad3) {\n  const $dy = convertToTensor(dy, \"dy\", \"avgPoolGrad\");\n  const $input = convertToTensor(input2, \"input\", \"avgPoolGrad\");\n  assert($input.rank === $dy.rank, () => `Rank of input (${$input.rank}) does not match rank of dy (${$dy.rank})`);\n  let input4D = $input;\n  let dy4D = $dy;\n  let reshapedTo4D = false;\n  if ($input.rank === 3) {\n    reshapedTo4D = true;\n    input4D = reshape($input, [1, $input.shape[0], $input.shape[1], $input.shape[2]]);\n    dy4D = reshape($dy, [1, $dy.shape[0], $dy.shape[1], $dy.shape[2]]);\n  }\n  assert(dy4D.rank === 4, () => `Error in avgPoolGrad: dy must be rank 4 but got rank ${dy4D.rank}.`);\n  assert(input4D.rank === 4, () => `Error in avgPoolGrad: input must be rank 4 but got rank ${input4D.rank}.`);\n  const inputs = { dy: dy4D, input: input4D };\n  const attrs = { filterSize, strides, pad: pad3 };\n  const res = ENGINE.runKernel(AvgPoolGrad, inputs, attrs);\n  if (reshapedTo4D) {\n    return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);\n  }\n  return res;\n}\nvar avgPoolGrad = op({ avgPoolGrad_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/gradients/AvgPool_grad.js\nvar avgPoolGradConfig = {\n  kernelName: AvgPool,\n  inputsToSave: [\"x\"],\n  gradFunc: (dy, saved, attrs) => {\n    const [x] = saved;\n    const { filterSize, strides, pad: pad3 } = attrs;\n    return { x: () => avgPoolGrad(dy, x, filterSize, strides, pad3) };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/gradients/BatchMatMul_grad.js\nvar batchMatMulGradConfig = {\n  kernelName: BatchMatMul,\n  inputsToSave: [\"a\", \"b\"],\n  gradFunc: (dy, saved, attrs) => {\n    const [a, b] = saved;\n    const { transposeA, transposeB } = attrs;\n    if (!transposeA && !transposeB) {\n      return {\n        a: () => matMul(dy, b, false, true),\n        b: () => matMul(a, dy, true, false)\n      };\n    } else if (!transposeA && transposeB) {\n      return {\n        a: () => matMul(dy, b, false, false),\n        b: () => matMul(dy, a, true, false)\n      };\n    } else if (transposeA && !transposeB) {\n      return {\n        a: () => matMul(b, dy, false, true),\n        b: () => matMul(a, dy, false, false)\n      };\n    } else {\n      return {\n        a: () => matMul(b, dy, true, true),\n        b: () => matMul(dy, a, true, true)\n      };\n    }\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/gradients/BatchToSpaceND_grad.js\nvar batchToSpaceNDGradConfig = {\n  kernelName: BatchToSpaceND,\n  gradFunc: (dy, saved, attrs) => {\n    const { blockShape, crops } = attrs;\n    return { x: () => spaceToBatchND(dy, blockShape, crops) };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/gradients/BroadcastTo_grad.js\nvar broadcastToGradConfig = {\n  kernelName: BroadcastTo,\n  gradFunc: (dy, saved, attrs) => {\n    const broadCastToAttrs = attrs;\n    const inputShape = broadCastToAttrs.inputShape;\n    const outputShape = broadCastToAttrs.shape;\n    const reps = Array.from(outputShape);\n    for (let i = inputShape.length - 1; i >= 0; i--) {\n      if (inputShape[i] === outputShape[i]) {\n        reps[i] = 1;\n      } else if (inputShape[i] !== 1) {\n        throw new Error(`broadcastTo(): [${inputShape}] cannot be broadcast to [${outputShape}].`);\n      }\n    }\n    const axes = [];\n    for (let i = 0; i < reps.length; i++) {\n      if (reps[i] > 1) {\n        axes.push(i);\n      }\n    }\n    return { x: () => sum2(dy, axes, true) };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Cast_grad.js\nvar castGradConfig = {\n  kernelName: Cast,\n  gradFunc: (dy) => {\n    return { x: () => dy.clone() };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Ceil_grad.js\nvar ceilGradConfig = {\n  kernelName: Ceil,\n  gradFunc: (dy) => {\n    return { x: () => zerosLike(dy) };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/gradients/ClipByValue_grad.js\nvar clipByValueGradConfig = {\n  kernelName: ClipByValue,\n  inputsToSave: [\"x\"],\n  gradFunc: (dy, saved, attrs) => {\n    const [x] = saved;\n    const { clipValueMin, clipValueMax } = attrs;\n    return {\n      x: () => where(logicalAnd(greaterEqual(x, clipValueMin), lessEqual(x, clipValueMax)), dy, zerosLike(dy))\n    };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/gradients/ComplexAbs_grad.js\nvar complexAbsGradConfig = {\n  kernelName: ComplexAbs,\n  inputsToSave: [\"x\"],\n  gradFunc: absGradConfig.gradFunc\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Concat_grad.js\nvar concatGradConfig = {\n  kernelName: Concat,\n  saveAllInputs: true,\n  gradFunc: (dy, saved, attrs) => {\n    const shapes = saved.map((t) => t.shape);\n    const { axis } = attrs;\n    const $axis = parseAxisParam(axis, saved[0].shape)[0];\n    const sizeSplits = shapes.map((s) => s[$axis]);\n    const derTensors = split(dy, sizeSplits, $axis);\n    return derTensors.map((t) => () => t);\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Conv2D_grad.js\nvar conv2DGradConfig = {\n  kernelName: Conv2D,\n  inputsToSave: [\"x\", \"filter\"],\n  gradFunc: (dy, saved, attrs) => {\n    const [x4D, $filter] = saved;\n    const { dilations, strides, pad: pad3, dataFormat } = attrs;\n    assert(tupleValuesAreOne(dilations), () => `Error in gradient of conv2D: dilation rates greater than 1 are not yet supported in gradients. Got dilations '${dilations}'`);\n    return {\n      x: () => conv2DBackpropInput(x4D.shape, dy, $filter, strides, pad3, dataFormat),\n      filter: () => conv2DBackpropFilter(x4D, dy, $filter.shape, strides, pad3, dataFormat)\n    };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Conv2DBackpropInput_grad.js\nvar conv2DBackpropInputGradConfig = {\n  kernelName: Conv2DBackpropInput,\n  inputsToSave: [\"dy\", \"filter\"],\n  gradFunc: (ddx, saved, attrs) => {\n    const [dy, filter] = saved;\n    const { strides, pad: pad3, dataFormat, dimRoundingMode } = attrs;\n    return {\n      dy: () => conv2d(ddx, filter, strides, pad3, dataFormat, 1, dimRoundingMode),\n      filter: () => conv2DBackpropFilter(ddx, dy, filter.shape, strides, pad3, dataFormat, dimRoundingMode)\n    };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/conv3d_backprop_filter.js\nfunction conv3DBackpropFilter_(x, dy, filterShape, strides, pad3) {\n  let x5D = x;\n  if (x.rank === 4) {\n    x5D = reshape(x, [1, x.shape[0], x.shape[1], x.shape[2], x.shape[3]]);\n  }\n  let dy5D = dy;\n  if (dy5D.rank === 4) {\n    dy5D = reshape(dy, [1, dy.shape[0], dy.shape[1], dy.shape[2], dy.shape[3]]);\n  }\n  assert(x5D.rank === 5, () => `Error in conv3dDerFilter: input must be rank 5, but got shape ${x5D.shape}.`);\n  assert(dy5D.rank === 5, () => `Error in conv3dDerFilter: dy must be rank 5, but got shape ${dy5D.shape}.`);\n  assert(filterShape.length === 5, () => `Error in conv3dDerFilter: filterShape must be length 5, but got ${filterShape}.`);\n  assert(x5D.shape[4] === filterShape[3], () => `Error in conv3dDerFilter: depth of input ${x5D.shape[4]}) must match input depth in filter (${filterShape[3]}.`);\n  assert(dy5D.shape[4] === filterShape[4], () => `Error in conv3dDerFilter: depth of dy (${dy5D.shape[4]}) must match output depth for filter (${filterShape[4]}).`);\n  const inputs = { x: x5D, dy: dy5D };\n  const attrs = { strides, pad: pad3, filterShape };\n  return ENGINE.runKernel(Conv3DBackpropFilterV2, inputs, attrs);\n}\nvar conv3DBackpropFilter = op({ conv3DBackpropFilter_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Conv3D_grad.js\nvar conv3DGradConfig = {\n  kernelName: Conv3D,\n  inputsToSave: [\"x\", \"filter\"],\n  gradFunc: (dy, saved, attrs) => {\n    const { dilations, strides, pad: pad3 } = attrs;\n    assert(tupleValuesAreOne(dilations), () => `Error in gradient of conv3D: dilation rates greater than 1 are not yet supported in gradients. Got dilations '${dilations}'`);\n    const [x5D, $filter] = saved;\n    return {\n      x: () => conv3DBackpropInput(x5D.shape, dy, $filter, strides, pad3),\n      filter: () => conv3DBackpropFilter(x5D, dy, $filter.shape, strides, pad3)\n    };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Cos_grad.js\nvar cosGradConfig = {\n  kernelName: Cos,\n  inputsToSave: [\"x\"],\n  gradFunc: (dy, saved) => {\n    const [x] = saved;\n    return { x: () => mul(neg(sin(cast(x, \"float32\"))), dy) };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Cosh_grad.js\nvar coshGradConfig = {\n  kernelName: Cosh,\n  inputsToSave: [\"x\"],\n  gradFunc: (dy, saved) => {\n    const [x] = saved;\n    return { x: () => mul(sinh(cast(x, \"float32\")), dy) };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Cumsum_grad.js\nvar cumsumGradConfig = {\n  kernelName: Cumsum,\n  inputsToSave: [\"x\"],\n  gradFunc: (dy, saved, attrs) => {\n    const [x] = saved;\n    const { axis, exclusive, reverse: reverse5 } = attrs;\n    return {\n      x: () => {\n        const permutation = getAxesPermutation([axis], x.rank);\n        let out = cumsum(dy, axis, exclusive, !reverse5);\n        if (permutation != null) {\n          out = transpose(out, permutation);\n        }\n        return out;\n      }\n    };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/gradients/DepthwiseConv2dNative_grad.js\nvar depthwiseConv2dNativeGradConfig = {\n  kernelName: DepthwiseConv2dNative,\n  inputsToSave: [\"x\", \"filter\"],\n  gradFunc: (dy, saved, attrs) => {\n    const { dilations, strides, pad: pad3, dimRoundingMode } = attrs;\n    const $dilations = dilations == null ? [1, 1] : dilations;\n    assert(tupleValuesAreOne($dilations), () => `Error in gradient of depthwiseConv2dNative: dilation rates greater than 1 are not yet supported. Got dilations '${$dilations}'`);\n    const [x, filter] = saved;\n    assert(x.rank === 4, () => `Error in gradient of depthwiseConv2dNative: input must be rank 4, but got rank ${x.rank}.`);\n    assert(filter.rank === 4, () => `Error in gradient of depthwiseConv2dNative: filter must be rank 4, but got rank ${filter.rank}.`);\n    assert(x.shape[3] === filter.shape[2], () => `Error in gradient of depthwiseConv2d: number of input channels (${x.shape[3]}) must match the inChannels dimension in filter ${filter.shape[2]}.`);\n    assert(eitherStridesOrDilationsAreOne(strides, $dilations), () => `Error in gradient of depthwiseConv2d: Either strides or dilations must be  1. Got strides ${strides} and dilations '${$dilations}'.`);\n    checkPadOnDimRoundingMode(\"depthwiseConv2d\", pad3, dimRoundingMode);\n    return {\n      x: () => depthwiseConv2dNativeBackpropInput(x.shape, dy, filter, strides, pad3, $dilations, dimRoundingMode),\n      filter: () => depthwiseConv2dNativeBackpropFilter(x, dy, filter.shape, strides, pad3, $dilations, dimRoundingMode)\n    };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Dilation2D_grad.js\nvar dilation2dGradConfig = {\n  kernelName: Dilation2D,\n  inputsToSave: [\"x\", \"filter\"],\n  gradFunc: (dy, saved, attrs) => {\n    const [x, filter] = saved;\n    const inputInputs = { x, filter, dy };\n    const filterInputs = { x, filter, dy };\n    return {\n      x: () => ENGINE.runKernel(Dilation2DBackpropInput, inputInputs, attrs),\n      filter: () => ENGINE.runKernel(Dilation2DBackpropFilter, filterInputs, attrs)\n    };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Elu_grad.js\nvar eluGradConfig = {\n  kernelName: Elu,\n  outputsToSave: [true],\n  gradFunc: (dy, saved) => {\n    const [y] = saved;\n    const inputs = { dy, y };\n    return { x: () => ENGINE.runKernel(EluGrad, inputs) };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Erf_grad.js\nvar erfGradConfig = {\n  kernelName: Erf,\n  inputsToSave: [\"x\"],\n  gradFunc: (dy, saved) => {\n    const [x] = saved;\n    const a = mul(exp(neg(square(x))), 2 / Math.sqrt(Math.PI));\n    return { x: () => mul(dy, a) };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Exp_grad.js\nvar expGradConfig = {\n  kernelName: Exp,\n  outputsToSave: [true],\n  gradFunc: (dy, saved) => {\n    const [y] = saved;\n    return { x: () => mul(dy, y) };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/gradients/ExpandDims_grad.js\nvar expandDimsGradConfig = {\n  kernelName: ExpandDims,\n  inputsToSave: [\"input\"],\n  gradFunc: (dy, saved) => {\n    const [input2] = saved;\n    return { input: () => reshape(dy, input2.shape) };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Expm1_grad.js\nvar expm1GradConfig = {\n  kernelName: Expm1,\n  inputsToSave: [\"x\"],\n  gradFunc: (dy, saved) => {\n    const [x] = saved;\n    return { x: () => mul(dy, exp(x)) };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Floor_grad.js\nvar floorGradConfig = {\n  kernelName: Floor,\n  gradFunc: (dy) => {\n    return { x: () => zerosLike(dy) };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/gradients/FloorDiv_grad.js\nvar floorDivGradConfig = {\n  kernelName: FloorDiv,\n  inputsToSave: [\"a\", \"b\"],\n  gradFunc: (dy, saved) => {\n    const [a, b] = saved;\n    const outShape = assertAndGetBroadcastShape(a.shape, b.shape);\n    const derA = () => {\n      const res = div(dy, cast(b, \"float32\"));\n      const reduceAxes = getReductionAxes(a.shape, outShape);\n      if (reduceAxes.length > 0) {\n        return reshape(sum2(res, reduceAxes), a.shape);\n      }\n      return res;\n    };\n    const derB = () => {\n      let res = mul(dy, cast(a, \"float32\"));\n      const reduceAxes = getReductionAxes(b.shape, outShape);\n      if (reduceAxes.length > 0) {\n        res = reshape(sum2(res, reduceAxes), b.shape);\n      }\n      const tmp = square(b);\n      return neg(div(res, cast(tmp, \"float32\")));\n    };\n    return { a: derA, b: derB };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/gradients/FusedBatchNorm_grad.js\nvar fusedBatchNormGradConfig = {\n  kernelName: FusedBatchNorm,\n  inputsToSave: [\"x\", \"mean\", \"variance\", \"scale\"],\n  gradFunc: (dy, saved, attrs) => {\n    const { varianceEpsilon } = attrs;\n    const [x, mean5, variance, scale2] = saved;\n    const scaleValue = scale2 == null ? scalar(1) : scale2;\n    const reductionAxes = getReductionAxes(mean5.shape, x.shape);\n    const tileShape = [];\n    if (mean5.rank === 1) {\n      for (let i = 0; i < x.shape.length - 1; ++i) {\n        tileShape.push(x.shape[i]);\n      }\n      tileShape.push(1);\n    }\n    const xMinusMean = sub(x, mean5);\n    const dyTimesScaleValue = mul(dy, scaleValue);\n    const oneOverSqrtVariance = rsqrt(add2(variance, scalar(varianceEpsilon)));\n    const minusHalfRCube = mul(mul(mul(oneOverSqrtVariance, oneOverSqrtVariance), oneOverSqrtVariance), scalar(-0.5));\n    const derX = () => {\n      if (mean5.rank === 1) {\n        return reshape(mul(mul(dy, tile(reshape(oneOverSqrtVariance, [1, 1, 1, mean5.shape[0]]), tileShape)), scaleValue), x.shape);\n      } else {\n        return reshape(mul(mul(dy, oneOverSqrtVariance), scaleValue), x.shape);\n      }\n    };\n    const derMean = () => {\n      let meanDer = mul(mul(oneOverSqrtVariance, scalar(-1)), dyTimesScaleValue);\n      if (mean5.rank === 1) {\n        meanDer = sum2(meanDer, reductionAxes);\n      }\n      return reshape(meanDer, mean5.shape);\n    };\n    const derVariance = () => {\n      let varianceDer = mul(mul(minusHalfRCube, xMinusMean), dyTimesScaleValue);\n      if (mean5.rank === 1) {\n        varianceDer = sum2(varianceDer, reductionAxes);\n      }\n      return reshape(varianceDer, mean5.shape);\n    };\n    const derScale = () => {\n      const xMinusMean2TimesRsqrt = mul(xMinusMean, oneOverSqrtVariance);\n      let scaleDer = mul(dy, xMinusMean2TimesRsqrt);\n      if (mean5.rank === 1) {\n        scaleDer = sum2(scaleDer, reductionAxes);\n      }\n      return reshape(scaleDer, mean5.shape);\n    };\n    const derOffset = () => {\n      let offsetDer = dy;\n      if (mean5.rank === 1) {\n        offsetDer = sum2(offsetDer, reductionAxes);\n      }\n      return reshape(offsetDer, mean5.shape);\n    };\n    return {\n      x: derX,\n      mean: derMean,\n      variance: derVariance,\n      scale: derScale,\n      offset: derOffset\n    };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/gradients/GatherV2_grad.js\nvar gatherGradConfig = {\n  kernelName: GatherV2,\n  inputsToSave: [\"x\", \"indices\"],\n  gradFunc: (dy, saved, attrs) => {\n    const [x, indices] = saved;\n    const { axis } = attrs;\n    const parsedAxis = parseAxisParam(axis, x.shape)[0];\n    const derX = () => {\n      const paramsShape = x.shape;\n      const indicesSize = indices.size;\n      const outerShape = paramsShape.slice(0, parsedAxis);\n      const outerDims = outerShape.length;\n      const innerShape = paramsShape.slice(axis, paramsShape.length).slice(1);\n      const innerDims = innerShape.length;\n      const outerAxesIndices = arrayRange(0, outerDims);\n      const innerAxesIndices = arrayRange(outerDims + 1, outerDims + 1 + innerDims);\n      const valuesShape = arrayConcat([outerShape, [indicesSize], innerShape]);\n      const values = reshape(dy, valuesShape);\n      const reshapedIndices = reshape(indices, [indicesSize]);\n      const transposeDims = arrayConcat([[outerDims], outerAxesIndices, innerAxesIndices]);\n      const valuesTranspose = transpose(values, transposeDims);\n      let paramsGrad = unsortedSegmentSum(valuesTranspose, reshapedIndices, x.shape[parsedAxis]);\n      const invertTransposeDims = getUndoAxesPermutation(transposeDims);\n      paramsGrad = transpose(paramsGrad, invertTransposeDims);\n      return paramsGrad;\n    };\n    return { x: derX, indices: () => indices };\n  }\n};\nfunction arrayRange(start, stop) {\n  const result = [];\n  for (let i = start; i < stop; ++i) {\n    result.push(i);\n  }\n  return result;\n}\nfunction arrayConcat(arrays) {\n  const result = [];\n  for (let i = 0; i < arrays.length; ++i) {\n    for (let j = 0; j < arrays[i].length; ++j) {\n      result.push(arrays[i][j]);\n    }\n  }\n  return result;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/gradients/GreaterEqual_grad.js\nvar greaterEqualGradConfig = {\n  kernelName: GreaterEqual,\n  inputsToSave: [\"a\", \"b\"],\n  gradFunc: (dy, saved) => {\n    const [a, b] = saved;\n    return { a: () => zerosLike(a), b: () => zerosLike(b) };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Identity_grad.js\nvar identityGradConfig = {\n  kernelName: Identity,\n  gradFunc: (dy) => {\n    return { x: () => cast(dy, \"float32\") };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/gradients/IsFinite_grad.js\nvar isFiniteGradConfig = {\n  kernelName: IsFinite,\n  gradFunc: (dy) => {\n    return { x: () => zerosLike(dy) };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/gradients/IsInf_grad.js\nvar isInfGradConfig = {\n  kernelName: IsInf,\n  gradFunc: (dy) => {\n    return { x: () => zerosLike(dy) };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/gradients/IsNan_grad.js\nvar isNanGradConfig = {\n  kernelName: IsNan,\n  gradFunc: (dy) => {\n    return { x: () => zerosLike(dy) };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/gradients/LeakyRelu_grad.js\nvar leakyReluGradConfig = {\n  kernelName: LeakyRelu,\n  inputsToSave: [\"x\"],\n  gradFunc: (dy, saved, attrs) => {\n    const [x] = saved;\n    const { alpha } = attrs;\n    const mask = greater(x, 0);\n    return { x: () => where(mask, dy, mul(dy, alpha)) };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Log1p_grad.js\nvar log1pGradConfig = {\n  kernelName: Log1p,\n  inputsToSave: [\"x\"],\n  gradFunc: (dy, saved) => {\n    const [x] = saved;\n    return { x: () => div(dy, add2(x, 1)) };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Log_grad.js\nvar logGradConfig = {\n  kernelName: Log,\n  inputsToSave: [\"x\"],\n  gradFunc: (dy, saved) => {\n    const [x] = saved;\n    return { x: () => div(dy, cast(x, \"float32\")) };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/gradients/LogSoftmax_grad.js\nvar logSoftmaxGradConfig = {\n  kernelName: LogSoftmax,\n  inputsToSave: [],\n  outputsToSave: [true],\n  gradFunc: (dy, saved, attrs) => {\n    const [value] = saved;\n    const { axis } = attrs;\n    return {\n      logits: () => {\n        const keepDims = true;\n        const softmax7 = exp(value);\n        return sub(dy, mul(sum2(dy, axis, keepDims), softmax7));\n      }\n    };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/local_response_normalization_backprop.js\nfunction localResponseNormalizationBackprop_(x, y, dy, depthRadius = 5, bias = 1, alpha = 1, beta = 0.5) {\n  const inputs = { x, y, dy };\n  const attrs = { depthRadius, bias, alpha, beta };\n  return ENGINE.runKernel(LRNGrad, inputs, attrs);\n}\nvar localResponseNormalizationBackprop = op({ localResponseNormalizationBackprop_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/gradients/LRN_grad.js\nvar lrnGradConfig = {\n  kernelName: LRN,\n  inputsToSave: [\"x\"],\n  outputsToSave: [true],\n  gradFunc: (dy, saved, attrs) => {\n    const [x, y] = saved;\n    const { depthRadius, bias, alpha, beta } = attrs;\n    return {\n      x: () => localResponseNormalizationBackprop(x, y, dy, depthRadius, bias, alpha, beta)\n    };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/gradients/min_max_grad_util.js\nfunction gradForMinAndMax(dy, y, xOrig, origAxes) {\n  if (y.rank < xOrig.rank) {\n    y = reshape(y, expandShapeToKeepDim(y.shape, origAxes));\n  }\n  if (dy.rank < xOrig.rank) {\n    dy = reshape(dy, expandShapeToKeepDim(dy.shape, origAxes));\n  }\n  return {\n    x: () => {\n      const dx = mul(dy, cast(equal(xOrig, y), dy.dtype));\n      return dx;\n    }\n  };\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Max_grad.js\nvar maxGradConfig = {\n  kernelName: Max,\n  inputsToSave: [\"x\"],\n  outputsToSave: [true],\n  gradFunc: (dy, saved, attrs) => {\n    const maxAttrs = attrs;\n    const { reductionIndices } = maxAttrs;\n    const x = saved[0];\n    const y = saved[1];\n    const origAxes = parseAxisParam(reductionIndices, x.shape);\n    const maxGrad = gradForMinAndMax(dy, y, x, origAxes);\n    return {\n      x: () => {\n        return maxGrad[\"x\"]();\n      }\n    };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Maximum_grad.js\nvar maximumGradConfig = {\n  kernelName: Maximum,\n  inputsToSave: [\"a\", \"b\"],\n  gradFunc: (dy, saved) => {\n    const [a, b] = saved;\n    const derA = () => mul(dy, cast(greaterEqual(a, b), \"float32\"));\n    const derB = () => mul(dy, cast(less(a, b), \"float32\"));\n    return { a: derA, b: derB };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/max_pool_3d_grad.js\nfunction maxPool3dGrad_(dy, input2, output, filterSize, strides, pad3, dimRoundingMode) {\n  const $dy = convertToTensor(dy, \"dy\", \"maxPool3dGrad\");\n  const $input = convertToTensor(input2, \"input\", \"maxPool3dGrad\");\n  const $output = convertToTensor(output, \"output\", \"maxPool3dGrad\");\n  let dy5D = $dy;\n  let input5D = $input;\n  let output5D = $output;\n  let reshapedTo5D = false;\n  if ($input.rank === 4) {\n    reshapedTo5D = true;\n    dy5D = reshape($dy, [1, $dy.shape[0], $dy.shape[1], $dy.shape[2], $dy.shape[3]]);\n    input5D = reshape($input, [\n      1,\n      $input.shape[0],\n      $input.shape[1],\n      $input.shape[2],\n      $input.shape[3]\n    ]);\n    output5D = reshape($output, [\n      1,\n      $output.shape[0],\n      $output.shape[1],\n      $output.shape[2],\n      $output.shape[3]\n    ]);\n  }\n  assert(dy5D.rank === 5, () => `Error in maxPool3dGrad: dy must be rank 5 but got rank ${dy5D.rank}.`);\n  assert(input5D.rank === 5, () => `Error in maxPool3dGrad: input must be rank 5 but got rank ${input5D.rank}.`);\n  assert(output5D.rank === 5, () => `Error in maxPool3dGrad: output must be rank 5 but got rank ${output5D.rank}.`);\n  checkPadOnDimRoundingMode(\"maxPool3dGrad\", pad3, dimRoundingMode);\n  const inputs = { dy: dy5D, input: input5D, output: output5D };\n  const attrs = { filterSize, strides, pad: pad3, dimRoundingMode };\n  const res = ENGINE.runKernel(MaxPool3DGrad, inputs, attrs);\n  if (reshapedTo5D) {\n    return reshape(res, [res.shape[1], res.shape[2], res.shape[3], res.shape[4]]);\n  }\n  return res;\n}\nvar maxPool3dGrad = op({ maxPool3dGrad_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/gradients/MaxPool3D_grad.js\nvar maxPool3DGradConfig = {\n  kernelName: MaxPool3D,\n  inputsToSave: [\"x\"],\n  outputsToSave: [true],\n  gradFunc: (dy, saved, attrs) => {\n    const [x, y] = saved;\n    const { filterSize, strides, pad: pad3, dimRoundingMode } = attrs;\n    return {\n      x: () => maxPool3dGrad(dy, x, y, filterSize, strides, pad3, dimRoundingMode)\n    };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/max_pool_grad.js\nfunction maxPoolGrad_(dy, input2, output, filterSize, strides, pad3, dimRoundingMode) {\n  const $dy = convertToTensor(dy, \"dy\", \"maxPoolGrad\");\n  const $input = convertToTensor(input2, \"input\", \"maxPoolGrad\");\n  const $output = convertToTensor(output, \"output\", \"maxPoolGrad\");\n  assert($input.rank === $dy.rank, () => `Rank of input (${$input.rank}) does not match rank of dy (${$dy.rank})`);\n  assert($dy.rank === 4, () => `Error in maxPoolGrad: dy must be rank 4 but got rank ${$dy.rank}.`);\n  assert($input.rank === 4, () => `Error in maxPoolGrad: input must be rank 4 but got rank ${$input.rank}.`);\n  checkPadOnDimRoundingMode(\"maxPoolGrad\", pad3, dimRoundingMode);\n  const inputs = { dy: $dy, input: $input, output: $output };\n  const attrs = { filterSize, strides, pad: pad3, dimRoundingMode };\n  return ENGINE.runKernel(MaxPoolGrad, inputs, attrs);\n}\nvar maxPoolGrad = op({ maxPoolGrad_ });\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/gradients/MaxPool_grad.js\nvar maxPoolGradConfig = {\n  kernelName: MaxPool,\n  inputsToSave: [\"x\"],\n  outputsToSave: [true],\n  gradFunc: (dy, saved, attrs) => {\n    const [x, y] = saved;\n    const { filterSize, strides, pad: pad3 } = attrs;\n    return {\n      x: () => maxPoolGrad(dy, x, y, filterSize, strides, pad3)\n    };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Mean_grad.js\nvar meanGradConfig = {\n  kernelName: Mean,\n  inputsToSave: [\"x\"],\n  gradFunc: (dy, saved, attrs) => {\n    const [x] = saved;\n    const { axis } = attrs;\n    const axes = parseAxisParam(axis, x.shape);\n    const shapes = computeOutAndReduceShapes(x.shape, axes);\n    const reduceShape = shapes[1];\n    const reduceSize = sizeFromShape(reduceShape);\n    const derX = () => {\n      const expandedDyShape = x.shape.slice();\n      axes.forEach((axis2) => {\n        expandedDyShape[axis2] = 1;\n      });\n      const expandedDy = reshape(dy, expandedDyShape);\n      const res = div(mul(expandedDy, ones2(x.shape, \"float32\")), reduceSize);\n      return res;\n    };\n    return { x: derX };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Min_grad.js\nvar minGradConfig = {\n  kernelName: Min,\n  inputsToSave: [\"x\"],\n  outputsToSave: [true],\n  gradFunc: (dy, saved, attrs) => {\n    const minAttrs = attrs;\n    const { axis } = minAttrs;\n    const [x, y] = saved;\n    const origAxes = parseAxisParam(axis, x.shape);\n    const minGrad = gradForMinAndMax(dy, y, x, origAxes);\n    return {\n      x: () => {\n        return minGrad[\"x\"]();\n      }\n    };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Minimum_grad.js\nvar minimumGradConfig = {\n  kernelName: Minimum,\n  inputsToSave: [\"a\", \"b\"],\n  gradFunc: (dy, saved) => {\n    const [a, b] = saved;\n    const derA = () => mul(dy, cast(lessEqual(a, b), \"float32\"));\n    const derB = () => mul(dy, cast(greater(a, b), \"float32\"));\n    return { a: derA, b: derB };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/gradients/MirrorPad_grad.js\nvar mirrorPadGradConfig = {\n  kernelName: MirrorPad,\n  inputsToSave: [\"x\"],\n  gradFunc: (dy, saved, attrs) => {\n    const x = saved[0];\n    const { paddings } = attrs;\n    const begin = paddings.map((p2) => p2[0]);\n    return { x: () => slice(dy, begin, x.shape) };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Mod_grad.js\nvar modGradConfig = {\n  kernelName: Mod,\n  inputsToSave: [\"a\", \"b\"],\n  gradFunc: (dy, saved) => {\n    const [a, b] = saved;\n    const outShape = assertAndGetBroadcastShape(a.shape, b.shape);\n    const derA = () => {\n      const reduceAxes = getReductionAxes(a.shape, outShape);\n      if (reduceAxes.length > 0) {\n        return reshape(sum2(dy, reduceAxes), a.shape);\n      }\n      return dy;\n    };\n    const derB = () => {\n      const res = mul(dy, neg(floor(div(a, b))));\n      const reduceAxes = getReductionAxes(b.shape, outShape);\n      if (reduceAxes.length > 0) {\n        return reshape(sum2(res, reduceAxes), b.shape);\n      }\n      return res;\n    };\n    return { a: derA, b: derB };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Multiply_grad.js\nvar multiplyGradConfig = {\n  kernelName: Multiply,\n  inputsToSave: [\"a\", \"b\"],\n  gradFunc: (dy, saved) => {\n    const [a, b] = saved;\n    const outShape = assertAndGetBroadcastShape(a.shape, b.shape);\n    const derA = () => {\n      const res = mul(dy, cast(b, \"float32\"));\n      const reduceAxes = getReductionAxes(a.shape, outShape);\n      if (reduceAxes.length > 0) {\n        return reshape(sum2(res, reduceAxes), a.shape);\n      }\n      return res;\n    };\n    const derB = () => {\n      const res = mul(dy, cast(a, \"float32\"));\n      const reduceAxes = getReductionAxes(b.shape, outShape);\n      if (reduceAxes.length > 0) {\n        return reshape(sum2(res, reduceAxes), b.shape);\n      }\n      return res;\n    };\n    return { a: derA, b: derB };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Neg_grad.js\nvar negGradConfig = {\n  kernelName: Neg,\n  gradFunc: (dy) => {\n    return { x: () => neg(dy) };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/gradients/OneHot_grad.js\nvar oneHotGradConfig = {\n  kernelName: OneHot,\n  inputsToSave: [\"indices\"],\n  gradFunc: (dy, saved) => {\n    const indices = saved[0];\n    return { indices: () => zeros(indices.shape, \"float32\") };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/gradients/OnesLike_grad.js\nvar onesLikeGradConfig = {\n  kernelName: OnesLike,\n  gradFunc: (dy) => {\n    return { x: () => zerosLike(dy) };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Pack_grad.js\nvar packGradConfig = {\n  kernelName: Pack,\n  saveAllInputs: true,\n  gradFunc: (dy, saved, attrs) => {\n    const { axis } = attrs;\n    const derTensors = unstack(dy, axis);\n    return derTensors.map((t) => () => t);\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/gradients/PadV2_grad.js\nvar padV2GradConfig = {\n  kernelName: PadV2,\n  inputsToSave: [\"x\"],\n  gradFunc: (dy, saved, attrs) => {\n    const x = saved[0];\n    const { paddings } = attrs;\n    const begin = paddings.map((p2) => p2[0]);\n    return { x: () => slice(dy, begin, x.shape) };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Pow_grad.js\nvar powGradConfig = {\n  kernelName: Pow,\n  inputsToSave: [\"a\", \"b\"],\n  outputsToSave: [true],\n  gradFunc: (dy, saved) => {\n    const [a, b, y] = saved;\n    const base = a;\n    const exp5 = b;\n    const outShape = assertAndGetBroadcastShape(base.shape, exp5.shape);\n    const derBase = () => {\n      const expFloat = cast(exp5, \"float32\");\n      let res = mul(dy, mul(expFloat, pow(base, sub(expFloat, scalar(1)))));\n      const reduceAxes = getReductionAxes(base.shape, outShape);\n      if (reduceAxes.length > 0) {\n        res = sum2(res, reduceAxes);\n      }\n      return reshape(res, base.shape);\n    };\n    const derExp = () => {\n      const condition = greater(base, 0);\n      const logBase = where(condition, log2(base), zerosLike(base));\n      let res = mul(dy, mul(y, logBase));\n      const reduceAxes = getReductionAxes(exp5.shape, outShape);\n      if (reduceAxes.length > 0) {\n        res = sum2(res, reduceAxes);\n      }\n      return reshape(res, exp5.shape);\n    };\n    return { a: derBase, b: derExp };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Prelu_grad.js\nvar preluGradConfig = {\n  kernelName: Prelu,\n  inputsToSave: [\"x\", \"alpha\"],\n  gradFunc: (dy, saved) => {\n    const [x, alpha] = saved;\n    const mask = greater(x, 0);\n    return {\n      x: () => where(mask, dy, mul(dy, alpha)),\n      alpha: () => {\n        let res = where(mask, zerosLike(dy), mul(dy, x));\n        const reduceAxes = getReductionAxes(alpha.shape, dy.shape);\n        if (reduceAxes.length > 0) {\n          res = sum2(res, reduceAxes);\n        }\n        return reshape(res, alpha.shape);\n      }\n    };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Prod_grad.js\nfunction prodGradFn_(x, dy, axis) {\n  const expandedYShape = x.shape.slice();\n  expandedYShape[axis] = 1;\n  const expandedDy = reshape(dy, expandedYShape);\n  const xCumProd = cumprod(x, axis, true, false);\n  const xCumRevProd = cumprod(x, axis, true, true);\n  const dx = mul(xCumProd, xCumRevProd);\n  return mul(expandedDy, dx);\n}\nfunction prodsGradFn_(x, dy, axis) {\n  const xRank = x.shape.length;\n  const finalProdAxis = xRank - axis.length;\n  const xPermutation = backend_util_exports.getAxesPermutation(axis, xRank);\n  let permutedX = x;\n  if (xPermutation != null) {\n    permutedX = transpose(x, xPermutation);\n  }\n  const newShape = permutedX.shape.slice();\n  const removedShape = newShape.splice(xRank - axis.length, axis.length);\n  const endPartShape = removedShape.reduce((p2, c) => p2 * c, 1);\n  newShape.push(endPartShape);\n  const reshapedPermutedX = permutedX.reshape(newShape);\n  let prodGrad = prodGradFn_(reshapedPermutedX, dy, finalProdAxis);\n  prodGrad = prodGrad.reshape(permutedX.shape);\n  if (xPermutation != null) {\n    const undoPermutation = backend_util_exports.getUndoAxesPermutation(xPermutation);\n    prodGrad = transpose(prodGrad, undoPermutation);\n  }\n  return prodGrad;\n}\nvar prodGradConfig = {\n  kernelName: Prod,\n  inputsToSave: [\"x\"],\n  gradFunc: (dy, saved, attrs) => {\n    const [x] = saved;\n    const { axis } = attrs;\n    let axisArr = [];\n    if (axis === void 0 || axis === null) {\n      axisArr = x.shape.map((_, i) => i);\n    } else if (typeof axis === \"number\") {\n      axisArr = [axis];\n    } else {\n      axisArr = axis;\n    }\n    return { x: () => prodsGradFn_(x, dy, axisArr) };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/gradients/RealDiv_grad.js\nvar divGradConfig = {\n  kernelName: RealDiv,\n  inputsToSave: [\"a\", \"b\"],\n  gradFunc: (dy, saved) => {\n    const [a, b] = saved;\n    const outShape = assertAndGetBroadcastShape(a.shape, b.shape);\n    const derA = () => {\n      const res = div(dy, cast(b, \"float32\"));\n      const reduceAxes = getReductionAxes(a.shape, outShape);\n      if (reduceAxes.length > 0) {\n        return reshape(sum2(res, reduceAxes), a.shape);\n      }\n      return res;\n    };\n    const derB = () => {\n      let res = mul(dy, cast(a, \"float32\"));\n      const reduceAxes = getReductionAxes(b.shape, outShape);\n      if (reduceAxes.length > 0) {\n        res = reshape(sum2(res, reduceAxes), b.shape);\n      }\n      const tmp = square(b);\n      return neg(div(res, cast(tmp, \"float32\")));\n    };\n    return { a: derA, b: derB };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Reciprocal_grad.js\nvar reciprocalGradConfig = {\n  kernelName: Reciprocal,\n  inputsToSave: [\"x\"],\n  gradFunc: (dy, saved) => {\n    const [x] = saved;\n    return { x: () => div(dy, neg(square(x))) };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Relu6_grad.js\nvar relu6GradConfig = {\n  kernelName: Relu6,\n  inputsToSave: [\"x\"],\n  gradFunc: (dy, saved) => {\n    const [x] = saved;\n    const mask = mul(lessEqual(x, 6), step(x));\n    return { x: () => mul(dy, cast(mask, \"float32\")) };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Relu_grad.js\nvar reluGradConfig = {\n  kernelName: Relu,\n  inputsToSave: [\"x\"],\n  gradFunc: (dy, saved) => {\n    const [x] = saved;\n    return { x: () => mul(dy, cast(step(x), \"float32\")) };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Reshape_grad.js\nvar reshapeGradConfig = {\n  kernelName: Reshape,\n  inputsToSave: [\"x\"],\n  gradFunc: (dy, saved) => {\n    const [x] = saved;\n    return { x: () => reshape(dy, x.shape) };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/gradients/ResizeBilinear_grad.js\nvar resizeBilinearGradConfig = {\n  kernelName: ResizeBilinear,\n  inputsToSave: [\"images\"],\n  gradFunc: (dy, saved, attrs) => {\n    const [images] = saved;\n    const inputs = { dy, images };\n    const imagesDer = () => ENGINE.runKernel(ResizeBilinearGrad, inputs, attrs);\n    return { images: imagesDer };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/gradients/ResizeNearestNeighbor_grad.js\nvar resizeNearestNeighborGradConfig = {\n  kernelName: ResizeNearestNeighbor,\n  inputsToSave: [\"images\"],\n  gradFunc: (dy, saved, attrs) => {\n    const [images] = saved;\n    const inputs = { dy, images };\n    const imagesDer = () => ENGINE.runKernel(ResizeNearestNeighborGrad, inputs, attrs);\n    return { images: imagesDer };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Reverse_grad.js\nvar reverseGradConfig = {\n  kernelName: Reverse,\n  gradFunc: (dy, saved, attrs) => {\n    const { dims } = attrs;\n    const axes = parseAxisParam(dims, dy.shape);\n    return { x: () => reverse(dy, axes) };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Round_grad.js\nvar roundGradConfig = {\n  kernelName: Round,\n  gradFunc: (dy) => {\n    return { x: () => zerosLike(dy) };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Rsqrt_grad.js\nvar rsqrtGradConfig = {\n  kernelName: Rsqrt,\n  inputsToSave: [\"x\"],\n  gradFunc: (dy, saved) => {\n    const [x] = saved;\n    return { x: () => neg(div(dy, mul(pow(x, 1.5), 2))) };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Select_grad.js\nvar selectGradConfig = {\n  kernelName: Select,\n  inputsToSave: [\"condition\"],\n  gradFunc: (dy, saved) => {\n    const [condition] = saved;\n    return {\n      condition: () => cast(zerosLike(condition), \"float32\"),\n      t: () => mul(dy, cast(condition, dy.dtype)),\n      e: () => mul(dy, cast(logicalNot(condition), dy.dtype))\n    };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Selu_grad.js\nvar seluGradConfig = {\n  kernelName: Selu,\n  inputsToSave: [\"x\"],\n  gradFunc: (dy, saved) => {\n    const [x] = saved;\n    return {\n      x: () => {\n        const mask = greater(x, scalar(0));\n        const scaleAlpha2 = scalar(SELU_SCALEALPHA);\n        const scale2 = scalar(SELU_SCALE);\n        const greaterThanZeroDer = mul(dy, scale2);\n        const lessEqualZeroDer = mul(mul(dy, scaleAlpha2), exp(cast(x, \"float32\")));\n        return where(mask, greaterThanZeroDer, lessEqualZeroDer);\n      }\n    };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Sigmoid_grad.js\nvar sigmoidGradConfig = {\n  kernelName: Sigmoid,\n  outputsToSave: [true],\n  gradFunc: (dy, saved) => {\n    const [y] = saved;\n    return { x: () => mul(dy, mul(y, sub(scalar(1), y))) };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Sign_grad.js\nvar signGradConfig = {\n  kernelName: Sign,\n  gradFunc: (dy) => {\n    return { x: () => zerosLike(dy) };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Sin_grad.js\nvar sinGradConfig = {\n  kernelName: Sin,\n  inputsToSave: [\"x\"],\n  gradFunc: (dy, saved) => {\n    const [x] = saved;\n    return { x: () => mul(cos(cast(x, \"float32\")), dy) };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Sinh_grad.js\nvar sinhGradConfig = {\n  kernelName: Sinh,\n  inputsToSave: [\"x\"],\n  gradFunc: (dy, saved) => {\n    const [x] = saved;\n    return { x: () => mul(cosh(cast(x, \"float32\")), dy) };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Slice_grad.js\nvar sliceGradConfig = {\n  kernelName: Slice,\n  inputsToSave: [\"x\"],\n  gradFunc: (dy, saved, attrs) => {\n    const [x] = saved;\n    const { begin, size } = attrs;\n    const inputShape = x.shape;\n    const [begin_, size_] = parseSliceParams(x, begin, size);\n    const paddings = [];\n    for (let i = 0; i < dy.rank; i++) {\n      paddings.push([begin_[i], inputShape[i] - begin_[i] - size_[i]]);\n    }\n    return { x: () => pad(dy, paddings) };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Softmax_grad.js\nvar softmaxGradConfig = {\n  kernelName: Softmax,\n  outputsToSave: [true],\n  gradFunc: (dy, saved, attrs) => {\n    const [y] = saved;\n    const { dim } = attrs;\n    const keepDims = true;\n    const dyTimesY = mul(dy, y);\n    return {\n      logits: () => sub(dyTimesY, mul(sum2(dyTimesY, [dim], keepDims), y))\n    };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Softplus_grad.js\nvar softplusGradConfig = {\n  kernelName: Softplus,\n  inputsToSave: [\"x\"],\n  gradFunc: (dy, saved) => {\n    const [x] = saved;\n    return { x: () => mul(dy, sigmoid(x)) };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/gradients/SpaceToBatchND_grad.js\nvar spaceToBatchNDGradConfig = {\n  kernelName: SpaceToBatchND,\n  gradFunc: (dy, saved, attrs) => {\n    const { blockShape, paddings } = attrs;\n    return { x: () => batchToSpaceND(dy, blockShape, paddings) };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/gradients/SplitV_grad.js\nvar splitVGradConfig = {\n  kernelName: SplitV,\n  gradFunc: (dy, saved, attrs) => {\n    const { axis } = attrs;\n    return { x: () => concat(dy, axis) };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Sqrt_grad.js\nvar sqrtGradConfig = {\n  kernelName: Sqrt,\n  inputsToSave: [\"x\"],\n  gradFunc: (dy, saved) => {\n    const [x] = saved;\n    return { x: () => div(dy, mul(sqrt(cast(x, \"float32\")), 2)) };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Square_grad.js\nvar squareGradConfig = {\n  kernelName: Square,\n  inputsToSave: [\"x\"],\n  gradFunc: (dy, saved) => {\n    const [x] = saved;\n    return { x: () => mul(dy, mul(cast(x, \"float32\"), 2)) };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/gradients/SquaredDifference_grad.js\nvar squaredDifferenceGradConfig = {\n  kernelName: SquaredDifference,\n  inputsToSave: [\"a\", \"b\"],\n  gradFunc: (dy, saved) => {\n    const [a, b] = saved;\n    const two = scalar(2);\n    const derA = () => mul(dy, mul(two, sub(a, b)));\n    const derB = () => mul(dy, mul(two, sub(b, a)));\n    return { a: derA, b: derB };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Step_grad.js\nvar stepGradConfig = {\n  kernelName: Step,\n  gradFunc: (dy) => {\n    return { x: () => zerosLike(dy) };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Sub_grad.js\nvar subGradConfig = {\n  kernelName: Sub,\n  inputsToSave: [\"a\", \"b\"],\n  gradFunc: (dy, saved) => {\n    const [a, b] = saved;\n    const outShape = assertAndGetBroadcastShape(a.shape, b.shape);\n    const derA = () => {\n      let res = dy;\n      const reduceAxes = getReductionAxes(a.shape, outShape);\n      if (reduceAxes.length > 0) {\n        res = sum2(res, reduceAxes);\n      }\n      return reshape(res, a.shape);\n    };\n    const derB = () => {\n      let res = dy;\n      const reduceAxes = getReductionAxes(b.shape, outShape);\n      if (reduceAxes.length > 0) {\n        res = sum2(res, reduceAxes);\n      }\n      return reshape(neg(res), b.shape);\n    };\n    return { a: derA, b: derB };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Sum_grad.js\nvar sumGradConfig = {\n  kernelName: Sum,\n  inputsToSave: [\"x\"],\n  gradFunc: (dy, saved, attrs) => {\n    const [x] = saved;\n    const expandedDyShape = x.shape.slice();\n    const { axis } = attrs;\n    const axes = parseAxisParam(axis, x.shape);\n    axes.forEach((axis2) => {\n      expandedDyShape[axis2] = 1;\n    });\n    const expandedDy = reshape(dy, expandedDyShape);\n    const derX = mul(expandedDy, ones2(x.shape, \"float32\"));\n    return { x: () => derX };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Tan_grad.js\nvar tanGradConfig = {\n  kernelName: Tan,\n  inputsToSave: [\"x\"],\n  gradFunc: (dy, saved) => {\n    const [x] = saved;\n    return { x: () => div(dy, square(cos(x))) };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Tanh_grad.js\nvar tanhGradConfig = {\n  kernelName: Tanh,\n  outputsToSave: [true],\n  gradFunc: (dy, saved) => {\n    const [y] = saved;\n    return { x: () => mul(sub(scalar(1), square(y)), dy) };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Tile_grad.js\nvar tileGradConfig = {\n  kernelName: Tile,\n  inputsToSave: [\"x\"],\n  gradFunc: (dy, saved, attrs) => {\n    const [x] = saved;\n    const { reps } = attrs;\n    const derX = () => {\n      let xGrad = zerosLike(x);\n      if (x.rank === 1) {\n        for (let i = 0; i < reps[0]; ++i) {\n          xGrad = add2(xGrad, slice(dy, [i * x.shape[0]], [x.shape[0]]));\n        }\n      } else if (x.rank === 2) {\n        for (let i = 0; i < reps[0]; ++i) {\n          for (let j = 0; j < reps[1]; ++j) {\n            xGrad = add2(xGrad, slice(dy, [i * x.shape[0], j * x.shape[1]], [\n              x.shape[0],\n              x.shape[1]\n            ]));\n          }\n        }\n      } else if (x.rank === 3) {\n        for (let i = 0; i < reps[0]; ++i) {\n          for (let j = 0; j < reps[1]; ++j) {\n            for (let k = 0; k < reps[2]; ++k) {\n              xGrad = add2(xGrad, slice(dy, [i * x.shape[0], j * x.shape[1], k * x.shape[2]], [x.shape[0], x.shape[1], x.shape[2]]));\n            }\n          }\n        }\n      } else if (x.rank === 4) {\n        for (let i = 0; i < reps[0]; ++i) {\n          for (let j = 0; j < reps[1]; ++j) {\n            for (let k = 0; k < reps[2]; ++k) {\n              for (let l = 0; l < reps[3]; ++l) {\n                xGrad = add2(xGrad, slice(dy, [\n                  i * x.shape[0],\n                  j * x.shape[1],\n                  k * x.shape[2],\n                  l * x.shape[3]\n                ], [x.shape[0], x.shape[1], x.shape[2], x.shape[3]]));\n              }\n            }\n          }\n        }\n      } else {\n        throw new Error(`Gradient for tile operation is not implemented for rank-${x.rank} tensors yet.`);\n      }\n      return xGrad;\n    };\n    return { x: derX };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Transpose_grad.js\nvar transposeGradConfig = {\n  kernelName: Transpose,\n  gradFunc: (dy, saved, attrs) => {\n    const transposeAttrs = attrs;\n    const { perm } = transposeAttrs;\n    const undoPerm = getUndoAxesPermutation(perm);\n    return { x: () => transpose(dy, undoPerm) };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/gradients/Unpack_grad.js\nvar unpackGradConfig = {\n  kernelName: Unpack,\n  gradFunc: (dy, saved, attrs) => {\n    const unpackAttrs = attrs;\n    const { axis } = unpackAttrs;\n    return { value: () => stack(dy, axis) };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/gradients/UnsortedSegmentSum_grad.js\nvar unsortedSegmentSumGradConfig = {\n  kernelName: UnsortedSegmentSum,\n  inputsToSave: [\"segmentIds\"],\n  gradFunc: (dy, saved) => {\n    const [segmentIds] = saved;\n    const derX = () => {\n      return gatherDropNegatives(dy, segmentIds);\n    };\n    return { x: derX };\n  }\n};\nfunction gatherDropNegatives(x, indices) {\n  const zeroClippedIndices = maximum(indices, zerosLike(indices));\n  const gathered = gather(x, zeroClippedIndices);\n  let isPositive = greaterEqual(indices, scalar(0, \"int32\"));\n  const numIters = gathered.rank - isPositive.rank;\n  for (let i = 0; i < numIters; ++i) {\n    isPositive = expandDims(isPositive, i + 1);\n  }\n  isPositive = logicalAnd(isPositive, ones2(gathered.shape, \"bool\"));\n  const zeroSlice = zerosLike(gathered);\n  return where(isPositive, gathered, zeroSlice);\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/gradients/ZerosLike_grad.js\nvar zerosLikeGradConfig = {\n  kernelName: ZerosLike,\n  gradFunc: (dy) => {\n    return { x: () => zerosLike(dy) };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/register_all_gradients.js\nvar gradConfigs = [\n  absGradConfig,\n  acosGradConfig,\n  acoshGradConfig,\n  addGradConfig,\n  addNGradConfig,\n  argMaxGradConfig,\n  argMinGradConfig,\n  asinGradConfig,\n  asinhGradConfig,\n  atan2GradConfig,\n  atanGradConfig,\n  atanhGradConfig,\n  avgPool3DGradConfig,\n  avgPoolGradConfig,\n  batchMatMulGradConfig,\n  batchToSpaceNDGradConfig,\n  broadcastToGradConfig,\n  castGradConfig,\n  ceilGradConfig,\n  clipByValueGradConfig,\n  complexAbsGradConfig,\n  concatGradConfig,\n  conv2DBackpropInputGradConfig,\n  conv2DGradConfig,\n  conv3DGradConfig,\n  cosGradConfig,\n  coshGradConfig,\n  cumsumGradConfig,\n  depthwiseConv2dNativeGradConfig,\n  dilation2dGradConfig,\n  divGradConfig,\n  eluGradConfig,\n  erfGradConfig,\n  expGradConfig,\n  expandDimsGradConfig,\n  expm1GradConfig,\n  floorDivGradConfig,\n  floorGradConfig,\n  fusedBatchNormGradConfig,\n  gatherGradConfig,\n  greaterEqualGradConfig,\n  identityGradConfig,\n  isFiniteGradConfig,\n  isInfGradConfig,\n  isNanGradConfig,\n  leakyReluGradConfig,\n  log1pGradConfig,\n  logGradConfig,\n  logSoftmaxGradConfig,\n  lrnGradConfig,\n  maxGradConfig,\n  maxGradConfig,\n  maximumGradConfig,\n  maxPool3DGradConfig,\n  maxPoolGradConfig,\n  meanGradConfig,\n  minGradConfig,\n  minimumGradConfig,\n  mirrorPadGradConfig,\n  modGradConfig,\n  multiplyGradConfig,\n  negGradConfig,\n  oneHotGradConfig,\n  onesLikeGradConfig,\n  packGradConfig,\n  padV2GradConfig,\n  padV2GradConfig,\n  powGradConfig,\n  preluGradConfig,\n  prodGradConfig,\n  reciprocalGradConfig,\n  relu6GradConfig,\n  reluGradConfig,\n  reshapeGradConfig,\n  resizeBilinearGradConfig,\n  resizeNearestNeighborGradConfig,\n  reverseGradConfig,\n  roundGradConfig,\n  rsqrtGradConfig,\n  selectGradConfig,\n  seluGradConfig,\n  sigmoidGradConfig,\n  signGradConfig,\n  sinGradConfig,\n  sinhGradConfig,\n  sliceGradConfig,\n  softmaxGradConfig,\n  softplusGradConfig,\n  spaceToBatchNDGradConfig,\n  spaceToBatchNDGradConfig,\n  splitVGradConfig,\n  splitVGradConfig,\n  sqrtGradConfig,\n  squaredDifferenceGradConfig,\n  squareGradConfig,\n  stepGradConfig,\n  subGradConfig,\n  sumGradConfig,\n  tanGradConfig,\n  tanhGradConfig,\n  tileGradConfig,\n  transposeGradConfig,\n  unpackGradConfig,\n  unsortedSegmentSumGradConfig,\n  zerosLikeGradConfig\n];\nfor (const gradientConfig of gradConfigs) {\n  registerGradient(gradientConfig);\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/abs.js\ngetGlobalTensorClass().prototype.abs = function() {\n  this.throwIfDisposed();\n  return abs(this);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/acos.js\ngetGlobalTensorClass().prototype.acos = function() {\n  this.throwIfDisposed();\n  return acos(this);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/acosh.js\ngetGlobalTensorClass().prototype.acosh = function() {\n  this.throwIfDisposed();\n  return acosh(this);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/add.js\ngetGlobalTensorClass().prototype.add = function(b) {\n  this.throwIfDisposed();\n  return add2(this, b);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/all.js\ngetGlobalTensorClass().prototype.all = function(axis, keepDims) {\n  this.throwIfDisposed();\n  return all(this, axis, keepDims);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/any.js\ngetGlobalTensorClass().prototype.any = function(axis, keepDims) {\n  this.throwIfDisposed();\n  return any(this, axis, keepDims);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/arg_max.js\ngetGlobalTensorClass().prototype.argMax = function(axis) {\n  this.throwIfDisposed();\n  return argMax(this, axis);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/arg_min.js\ngetGlobalTensorClass().prototype.argMin = function(axis) {\n  this.throwIfDisposed();\n  return argMin(this, axis);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/as_scalar.js\ngetGlobalTensorClass().prototype.asScalar = function() {\n  this.throwIfDisposed();\n  assert(this.size === 1, () => \"The array must have only 1 element.\");\n  return reshape(this, []);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/as_type.js\ngetGlobalTensorClass().prototype.asType = function(dtype) {\n  this.throwIfDisposed();\n  return cast(this, dtype);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/as1d.js\ngetGlobalTensorClass().prototype.as1D = function() {\n  this.throwIfDisposed();\n  return reshape(this, [this.size]);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/as2d.js\ngetGlobalTensorClass().prototype.as2D = function(rows, columns) {\n  this.throwIfDisposed();\n  return reshape(this, [rows, columns]);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/as3d.js\ngetGlobalTensorClass().prototype.as3D = function(rows, columns, depth) {\n  this.throwIfDisposed();\n  return reshape(this, [rows, columns, depth]);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/as4d.js\ngetGlobalTensorClass().prototype.as4D = function(rows, columns, depth, depth2) {\n  this.throwIfDisposed();\n  return reshape(this, [rows, columns, depth, depth2]);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/as5d.js\ngetGlobalTensorClass().prototype.as5D = function(rows, columns, depth, depth2, depth3) {\n  this.throwIfDisposed();\n  return reshape(this, [rows, columns, depth, depth2, depth3]);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/asin.js\ngetGlobalTensorClass().prototype.asin = function() {\n  this.throwIfDisposed();\n  return asin(this);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/asinh.js\ngetGlobalTensorClass().prototype.asinh = function() {\n  this.throwIfDisposed();\n  return asinh(this);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/atan.js\ngetGlobalTensorClass().prototype.atan = function() {\n  this.throwIfDisposed();\n  return atan(this);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/atan2.js\ngetGlobalTensorClass().prototype.atan2 = function(b) {\n  this.throwIfDisposed();\n  return atan2(this, b);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/atanh.js\ngetGlobalTensorClass().prototype.atanh = function() {\n  this.throwIfDisposed();\n  return atanh(this);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/avg_pool.js\ngetGlobalTensorClass().prototype.avgPool = function(filterSize, strides, pad3, dimRoundingMode) {\n  this.throwIfDisposed();\n  return avgPool(this, filterSize, strides, pad3, dimRoundingMode);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/batch_to_space_nd.js\ngetGlobalTensorClass().prototype.batchToSpaceND = function(blockShape, crops) {\n  this.throwIfDisposed();\n  return batchToSpaceND(this, blockShape, crops);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/batchnorm.js\ngetGlobalTensorClass().prototype.batchNorm = function(mean5, variance, offset, scale2, varianceEpsilon) {\n  this.throwIfDisposed();\n  return batchNorm(this, mean5, variance, offset, scale2, varianceEpsilon);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/broadcast_to.js\ngetGlobalTensorClass().prototype.broadcastTo = function(shape) {\n  this.throwIfDisposed();\n  return broadcastTo(this, shape);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/cast.js\ngetGlobalTensorClass().prototype.cast = function(dtype) {\n  this.throwIfDisposed();\n  return cast(this, dtype);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/ceil.js\ngetGlobalTensorClass().prototype.ceil = function() {\n  this.throwIfDisposed();\n  return ceil(this);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/clip_by_value.js\ngetGlobalTensorClass().prototype.clipByValue = function(min7, max7) {\n  this.throwIfDisposed();\n  return clipByValue(this, min7, max7);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/concat.js\ngetGlobalTensorClass().prototype.concat = function(x, axis) {\n  this.throwIfDisposed();\n  if (x instanceof Tensor) {\n    x = [x];\n  }\n  return concat([this, ...x], axis);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/conv1d.js\ngetGlobalTensorClass().prototype.conv1d = function(filter, stride, pad3, dataFormat, dilation, dimRoundingMode) {\n  this.throwIfDisposed();\n  return conv1d(this, filter, stride, pad3, dataFormat, dilation, dimRoundingMode);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/conv2d_transpose.js\ngetGlobalTensorClass().prototype.conv2dTranspose = function(filter, outputShape, strides, pad3, dimRoundingMode) {\n  this.throwIfDisposed();\n  return conv2dTranspose(this, filter, outputShape, strides, pad3, dimRoundingMode);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/conv2d.js\ngetGlobalTensorClass().prototype.conv2d = function(filter, strides, pad3, dataFormat, dilations, dimRoundingMode) {\n  this.throwIfDisposed();\n  return conv2d(this, filter, strides, pad3, dataFormat, dilations, dimRoundingMode);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/cos.js\ngetGlobalTensorClass().prototype.cos = function() {\n  this.throwIfDisposed();\n  return cos(this);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/cosh.js\ngetGlobalTensorClass().prototype.cosh = function() {\n  this.throwIfDisposed();\n  return cosh(this);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/cumprod.js\ngetGlobalTensorClass().prototype.cumprod = function(axis, exclusive, reverse5) {\n  this.throwIfDisposed();\n  return cumprod(this, axis, exclusive, reverse5);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/cumsum.js\ngetGlobalTensorClass().prototype.cumsum = function(axis, exclusive, reverse5) {\n  this.throwIfDisposed();\n  return cumsum(this, axis, exclusive, reverse5);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/depth_to_space.js\ngetGlobalTensorClass().prototype.depthToSpace = function(blockSize, dataFormat) {\n  this.throwIfDisposed();\n  return depthToSpace(this, blockSize, dataFormat);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/depthwise_conv2d.js\ngetGlobalTensorClass().prototype.depthwiseConv2d = function(filter, strides, pad3, dataFormat, dilations, dimRoundingMode) {\n  this.throwIfDisposed();\n  return depthwiseConv2d(this, filter, strides, pad3, dataFormat, dilations, dimRoundingMode);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/dilation2d.js\ngetGlobalTensorClass().prototype.dilation2d = function(filter, strides, pad3, dilations, dataFormat) {\n  this.throwIfDisposed();\n  return dilation2d(this, filter, strides, pad3, dilations, dataFormat);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/div_no_nan.js\ngetGlobalTensorClass().prototype.divNoNan = function(b) {\n  this.throwIfDisposed();\n  return divNoNan(this, b);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/div.js\ngetGlobalTensorClass().prototype.div = function(b) {\n  this.throwIfDisposed();\n  return div(this, b);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/dot.js\ngetGlobalTensorClass().prototype.dot = function(b) {\n  this.throwIfDisposed();\n  return dot(this, b);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/elu.js\ngetGlobalTensorClass().prototype.elu = function() {\n  this.throwIfDisposed();\n  return elu(this);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/equal.js\ngetGlobalTensorClass().prototype.equal = function(b) {\n  this.throwIfDisposed();\n  return equal(this, b);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/erf.js\ngetGlobalTensorClass().prototype.erf = function() {\n  this.throwIfDisposed();\n  return erf(this);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/euclidean_norm.js\ngetGlobalTensorClass().prototype.euclideanNorm = function(axis, keepDims) {\n  this.throwIfDisposed();\n  return euclideanNorm(this, axis, keepDims);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/exp.js\ngetGlobalTensorClass().prototype.exp = function() {\n  this.throwIfDisposed();\n  return exp(this);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/expand_dims.js\ngetGlobalTensorClass().prototype.expandDims = function(axis) {\n  this.throwIfDisposed();\n  return expandDims(this, axis);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/expm1.js\ngetGlobalTensorClass().prototype.expm1 = function() {\n  this.throwIfDisposed();\n  return expm1(this);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/fft.js\ngetGlobalTensorClass().prototype.fft = function() {\n  this.throwIfDisposed();\n  return fft(this);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/flatten.js\ngetGlobalTensorClass().prototype.flatten = function() {\n  this.throwIfDisposed();\n  return reshape(this, [this.size]);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/floor.js\ngetGlobalTensorClass().prototype.floor = function() {\n  this.throwIfDisposed();\n  return floor(this);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/floorDiv.js\ngetGlobalTensorClass().prototype.floorDiv = function(b) {\n  this.throwIfDisposed();\n  return floorDiv(this, b);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/gather.js\ngetGlobalTensorClass().prototype.gather = function(indices, axis) {\n  this.throwIfDisposed();\n  return gather(this, indices, axis);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/greater_equal.js\ngetGlobalTensorClass().prototype.greaterEqual = function(b) {\n  this.throwIfDisposed();\n  return greaterEqual(this, b);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/greater.js\ngetGlobalTensorClass().prototype.greater = function(b) {\n  this.throwIfDisposed();\n  return greater(this, b);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/ifft.js\ngetGlobalTensorClass().prototype.ifft = function() {\n  this.throwIfDisposed();\n  return ifft(this);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/irfft.js\ngetGlobalTensorClass().prototype.irfft = function() {\n  this.throwIfDisposed();\n  return irfft(this);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/is_finite.js\ngetGlobalTensorClass().prototype.isFinite = function() {\n  this.throwIfDisposed();\n  return isFinite2(this);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/is_inf.js\ngetGlobalTensorClass().prototype.isInf = function() {\n  this.throwIfDisposed();\n  return isInf(this);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/is_nan.js\ngetGlobalTensorClass().prototype.isNaN = function() {\n  this.throwIfDisposed();\n  return isNaN2(this);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/leaky_relu.js\ngetGlobalTensorClass().prototype.leakyRelu = function(alpha) {\n  this.throwIfDisposed();\n  return leakyRelu(this, alpha);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/less_equal.js\ngetGlobalTensorClass().prototype.lessEqual = function(b) {\n  this.throwIfDisposed();\n  return lessEqual(this, b);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/less.js\ngetGlobalTensorClass().prototype.less = function(b) {\n  this.throwIfDisposed();\n  return less(this, b);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/local_response_normalization.js\ngetGlobalTensorClass().prototype.localResponseNormalization = function(depthRadius, bias, alpha, beta) {\n  this.throwIfDisposed();\n  return localResponseNormalization(this, depthRadius, bias, alpha, beta);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/log_sigmoid.js\ngetGlobalTensorClass().prototype.logSigmoid = function() {\n  this.throwIfDisposed();\n  return logSigmoid(this);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/log_softmax.js\ngetGlobalTensorClass().prototype.logSoftmax = function(axis) {\n  this.throwIfDisposed();\n  return logSoftmax(this, axis);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/log_sum_exp.js\ngetGlobalTensorClass().prototype.logSumExp = function(axis, keepDims) {\n  this.throwIfDisposed();\n  return logSumExp(this, axis, keepDims);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/log.js\ngetGlobalTensorClass().prototype.log = function() {\n  this.throwIfDisposed();\n  return log2(this);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/log1p.js\ngetGlobalTensorClass().prototype.log1p = function() {\n  this.throwIfDisposed();\n  return log1p(this);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/logical_and.js\ngetGlobalTensorClass().prototype.logicalAnd = function(b) {\n  this.throwIfDisposed();\n  return logicalAnd(this, b);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/logical_not.js\ngetGlobalTensorClass().prototype.logicalNot = function() {\n  this.throwIfDisposed();\n  return logicalNot(this);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/logical_or.js\ngetGlobalTensorClass().prototype.logicalOr = function(b) {\n  this.throwIfDisposed();\n  return logicalOr(this, b);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/logical_xor.js\ngetGlobalTensorClass().prototype.logicalXor = function(b) {\n  this.throwIfDisposed();\n  return logicalXor(this, b);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/mat_mul.js\ngetGlobalTensorClass().prototype.matMul = function(b, transposeA, transposeB) {\n  this.throwIfDisposed();\n  return matMul(this, b, transposeA, transposeB);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/max_pool.js\ngetGlobalTensorClass().prototype.maxPool = function(filterSize, strides, pad3, dimRoundingMode) {\n  this.throwIfDisposed();\n  return maxPool(this, filterSize, strides, pad3, dimRoundingMode);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/max.js\ngetGlobalTensorClass().prototype.max = function(axis, keepDims) {\n  this.throwIfDisposed();\n  return max(this, axis, keepDims);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/maximum.js\ngetGlobalTensorClass().prototype.maximum = function(b) {\n  this.throwIfDisposed();\n  return maximum(this, b);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/mean.js\ngetGlobalTensorClass().prototype.mean = function(axis, keepDims) {\n  this.throwIfDisposed();\n  return mean(this, axis, keepDims);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/min.js\ngetGlobalTensorClass().prototype.min = function(axis, keepDims) {\n  this.throwIfDisposed();\n  return min(this, axis, keepDims);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/minimum.js\ngetGlobalTensorClass().prototype.minimum = function(b) {\n  this.throwIfDisposed();\n  return minimum(this, b);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/mirror_pad.js\ngetGlobalTensorClass().prototype.mirrorPad = function(paddings, mode) {\n  this.throwIfDisposed();\n  return mirrorPad(this, paddings, mode);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/mod.js\ngetGlobalTensorClass().prototype.mod = function(b) {\n  this.throwIfDisposed();\n  return mod(this, b);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/mul.js\ngetGlobalTensorClass().prototype.mul = function(b) {\n  this.throwIfDisposed();\n  return mul(this, b);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/neg.js\ngetGlobalTensorClass().prototype.neg = function() {\n  this.throwIfDisposed();\n  return neg(this);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/norm.js\ngetGlobalTensorClass().prototype.norm = function(ord, axis, keepDims) {\n  this.throwIfDisposed();\n  return norm(this, ord, axis, keepDims);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/not_equal.js\ngetGlobalTensorClass().prototype.notEqual = function(b) {\n  this.throwIfDisposed();\n  return notEqual(this, b);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/one_hot.js\ngetGlobalTensorClass().prototype.oneHot = function(depth, onValue = 1, offValue = 0) {\n  this.throwIfDisposed();\n  return oneHot(this, depth, onValue, offValue);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/ones_like.js\ngetGlobalTensorClass().prototype.onesLike = function() {\n  this.throwIfDisposed();\n  return onesLike(this);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/pad.js\ngetGlobalTensorClass().prototype.pad = function(paddings, constantValue) {\n  this.throwIfDisposed();\n  return pad(this, paddings, constantValue);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/pool.js\ngetGlobalTensorClass().prototype.pool = function(windowShape, poolingType, padding, dilationRate, strides, dimRoundingMode) {\n  this.throwIfDisposed();\n  return pool(this, windowShape, poolingType, padding, dilationRate, strides, dimRoundingMode);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/pow.js\ngetGlobalTensorClass().prototype.pow = function(exp5) {\n  this.throwIfDisposed();\n  return pow(this, exp5);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/prelu.js\ngetGlobalTensorClass().prototype.prelu = function(alpha) {\n  this.throwIfDisposed();\n  return prelu(this, alpha);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/prod.js\ngetGlobalTensorClass().prototype.prod = function(axis, keepDims) {\n  this.throwIfDisposed();\n  return prod(this, axis, keepDims);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/reciprocal.js\ngetGlobalTensorClass().prototype.reciprocal = function() {\n  this.throwIfDisposed();\n  return reciprocal(this);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/relu.js\ngetGlobalTensorClass().prototype.relu = function() {\n  this.throwIfDisposed();\n  return relu(this);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/relu6.js\ngetGlobalTensorClass().prototype.relu6 = function() {\n  this.throwIfDisposed();\n  return relu6(this);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/reshape_as.js\ngetGlobalTensorClass().prototype.reshapeAs = function(x) {\n  this.throwIfDisposed();\n  return reshape(this, x.shape);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/reshape.js\ngetGlobalTensorClass().prototype.reshape = function(shape) {\n  this.throwIfDisposed();\n  return reshape(this, shape);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/resize_bilinear.js\ngetGlobalTensorClass().prototype.resizeBilinear = function(newShape2D, alignCorners, halfPixelCenters) {\n  this.throwIfDisposed();\n  return resizeBilinear(this, newShape2D, alignCorners, halfPixelCenters);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/resize_nearest_neighbor.js\ngetGlobalTensorClass().prototype.resizeNearestNeighbor = function(newShape2D, alignCorners, halfFloatCenters) {\n  this.throwIfDisposed();\n  return resizeNearestNeighbor(this, newShape2D, alignCorners, halfFloatCenters);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/reverse.js\ngetGlobalTensorClass().prototype.reverse = function(axis) {\n  this.throwIfDisposed();\n  return reverse(this, axis);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/rfft.js\ngetGlobalTensorClass().prototype.rfft = function() {\n  this.throwIfDisposed();\n  return rfft(this);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/round.js\ngetGlobalTensorClass().prototype.round = function() {\n  this.throwIfDisposed();\n  return round2(this);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/rsqrt.js\ngetGlobalTensorClass().prototype.rsqrt = function() {\n  this.throwIfDisposed();\n  return rsqrt(this);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/selu.js\ngetGlobalTensorClass().prototype.selu = function() {\n  this.throwIfDisposed();\n  return selu(this);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/separable_conv2d.js\ngetGlobalTensorClass().prototype.separableConv2d = function(depthwiseFilter, pointwiseFilter, strides, pad3, dilation, dataFormat) {\n  this.throwIfDisposed();\n  return separableConv2d(this, depthwiseFilter, pointwiseFilter, strides, pad3, dilation, dataFormat);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/sigmoid.js\ngetGlobalTensorClass().prototype.sigmoid = function() {\n  this.throwIfDisposed();\n  return sigmoid(this);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/sign.js\ngetGlobalTensorClass().prototype.sign = function() {\n  this.throwIfDisposed();\n  return sign(this);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/sin.js\ngetGlobalTensorClass().prototype.sin = function() {\n  this.throwIfDisposed();\n  return sin(this);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/sinh.js\ngetGlobalTensorClass().prototype.sinh = function() {\n  this.throwIfDisposed();\n  return sinh(this);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/slice.js\ngetGlobalTensorClass().prototype.slice = function(begin, size) {\n  this.throwIfDisposed();\n  return slice(this, begin, size);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/softmax.js\ngetGlobalTensorClass().prototype.softmax = function(dim) {\n  this.throwIfDisposed();\n  return softmax(this, dim);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/softplus.js\ngetGlobalTensorClass().prototype.softplus = function() {\n  this.throwIfDisposed();\n  return softplus(this);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/space_to_batch_nd.js\ngetGlobalTensorClass().prototype.spaceToBatchND = function(blockShape, paddings) {\n  this.throwIfDisposed();\n  return spaceToBatchND(this, blockShape, paddings);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/split.js\ngetGlobalTensorClass().prototype.split = function(numOrSizeSplits, axis) {\n  this.throwIfDisposed();\n  return split(this, numOrSizeSplits, axis);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/sqrt.js\ngetGlobalTensorClass().prototype.sqrt = function() {\n  this.throwIfDisposed();\n  return sqrt(this);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/square.js\ngetGlobalTensorClass().prototype.square = function() {\n  this.throwIfDisposed();\n  return square(this);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/squared_difference.js\ngetGlobalTensorClass().prototype.squaredDifference = function(b) {\n  this.throwIfDisposed();\n  return squaredDifference(this, b);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/squeeze.js\ngetGlobalTensorClass().prototype.squeeze = function(axis) {\n  this.throwIfDisposed();\n  return squeeze(this, axis);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/stack.js\ngetGlobalTensorClass().prototype.stack = function(x, axis) {\n  this.throwIfDisposed();\n  const tensorsToBeStacked = x instanceof Tensor ? [this, x] : [this, ...x];\n  return stack(tensorsToBeStacked, axis);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/step.js\ngetGlobalTensorClass().prototype.step = function(alpha) {\n  this.throwIfDisposed();\n  return step(this, alpha);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/strided_slice.js\ngetGlobalTensorClass().prototype.stridedSlice = function(begin, end, strides, beginMask, endMask, ellipsisMask, newAxisMask, shrinkAxisMask) {\n  this.throwIfDisposed();\n  return stridedSlice(this, begin, end, strides, beginMask, endMask, ellipsisMask, newAxisMask, shrinkAxisMask);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/sub.js\ngetGlobalTensorClass().prototype.sub = function(b) {\n  this.throwIfDisposed();\n  return sub(this, b);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/sum.js\ngetGlobalTensorClass().prototype.sum = function(axis, keepDims) {\n  this.throwIfDisposed();\n  return sum2(this, axis, keepDims);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/tan.js\ngetGlobalTensorClass().prototype.tan = function() {\n  this.throwIfDisposed();\n  return tan(this);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/tanh.js\ngetGlobalTensorClass().prototype.tanh = function() {\n  this.throwIfDisposed();\n  return tanh2(this);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/tile.js\ngetGlobalTensorClass().prototype.tile = function(reps) {\n  this.throwIfDisposed();\n  return tile(this, reps);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/to_bool.js\ngetGlobalTensorClass().prototype.toBool = function() {\n  this.throwIfDisposed();\n  return cast(this, \"bool\");\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/to_float.js\ngetGlobalTensorClass().prototype.toFloat = function() {\n  this.throwIfDisposed();\n  return cast(this, \"float32\");\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/to_int.js\ngetGlobalTensorClass().prototype.toInt = function() {\n  this.throwIfDisposed();\n  return cast(this, \"int32\");\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/topk.js\ngetGlobalTensorClass().prototype.topk = function(k, sorted) {\n  this.throwIfDisposed();\n  return topk(this, k, sorted);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/transpose.js\ngetGlobalTensorClass().prototype.transpose = function(perm) {\n  this.throwIfDisposed();\n  return transpose(this, perm);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/unique.js\ngetGlobalTensorClass().prototype.unique = function(axis) {\n  this.throwIfDisposed();\n  return unique(this, axis);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/unsorted_segment_sum.js\ngetGlobalTensorClass().prototype.unsortedSegmentSum = function(segmentIds, numSegments) {\n  this.throwIfDisposed();\n  return unsortedSegmentSum(this, segmentIds, numSegments);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/unstack.js\ngetGlobalTensorClass().prototype.unstack = function(axis) {\n  this.throwIfDisposed();\n  return unstack(this, axis);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/where.js\ngetGlobalTensorClass().prototype.where = function(condition, x) {\n  this.throwIfDisposed();\n  return where(condition, this, x);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/zeros_like.js\ngetGlobalTensorClass().prototype.zerosLike = function() {\n  this.throwIfDisposed();\n  return zerosLike(this);\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-layers@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-layers/dist/errors.js\nvar AttributeError = class extends Error {\n  constructor(message) {\n    super(message);\n    Object.setPrototypeOf(this, AttributeError.prototype);\n  }\n};\nvar RuntimeError = class extends Error {\n  constructor(message) {\n    super(message);\n    Object.setPrototypeOf(this, RuntimeError.prototype);\n  }\n};\nvar ValueError = class extends Error {\n  constructor(message) {\n    super(message);\n    Object.setPrototypeOf(this, ValueError.prototype);\n  }\n};\nvar NotImplementedError = class extends Error {\n  constructor(message) {\n    super(message);\n    Object.setPrototypeOf(this, NotImplementedError.prototype);\n  }\n};\nvar AssertionError = class extends Error {\n  constructor(message) {\n    super(message);\n    Object.setPrototypeOf(this, AssertionError.prototype);\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-layers@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-layers/dist/utils/executor_utils.js\nvar LruCache = class {\n  constructor(maxEntries) {\n    this.maxEntries = maxEntries || 100;\n    this.cache = /* @__PURE__ */ new Map();\n  }\n  get(key) {\n    let entry;\n    if (this.cache.has(key)) {\n      entry = this.cache.get(key);\n      this.cache.delete(key);\n      this.cache.set(key, entry);\n    }\n    return entry;\n  }\n  put(key, value) {\n    if (this.cache.has(key)) {\n      this.cache.delete(key);\n    } else if (this.cache.size >= this.maxEntries) {\n      const keyToDelete = this.cache.keys().next().value;\n      this.cache.delete(keyToDelete);\n    }\n    this.cache.set(key, value);\n  }\n  getMaxEntries() {\n    return this.maxEntries;\n  }\n  setMaxEntries(maxEntries) {\n    if (maxEntries < 0) {\n      throw new Error(`The maxEntries of LRU caches must be at least 0, but got ${maxEntries}.`);\n    }\n    if (this.maxEntries > maxEntries) {\n      for (let i = 0; i < this.maxEntries - maxEntries; i++) {\n        const keyToDelete = this.cache.keys().next().value;\n        this.cache.delete(keyToDelete);\n      }\n    }\n    this.maxEntries = maxEntries;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-layers@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-layers/dist/utils/generic_utils.js\nfunction pyListRepeat(value, numValues) {\n  if (Array.isArray(value)) {\n    let newArray = [];\n    for (let i = 0; i < numValues; i++) {\n      newArray = newArray.concat(value);\n    }\n    return newArray;\n  } else {\n    const newArray = new Array(numValues);\n    newArray.fill(value);\n    return newArray;\n  }\n}\nfunction assert2(val, message) {\n  if (!val) {\n    throw new AssertionError(message);\n  }\n}\nfunction count(array2, refernce) {\n  let counter = 0;\n  for (const item of array2) {\n    if (item === refernce) {\n      counter++;\n    }\n  }\n  return counter;\n}\nfunction singletonOrArray(xs) {\n  if (xs.length === 1) {\n    return xs[0];\n  }\n  return xs;\n}\nfunction toList(x) {\n  if (Array.isArray(x)) {\n    return x;\n  }\n  return [x];\n}\nfunction toSnakeCase(name) {\n  const intermediate = name.replace(/(.)([A-Z][a-z0-9]+)/g, \"$1_$2\");\n  const insecure = intermediate.replace(/([a-z])([A-Z])/g, \"$1_$2\").toLowerCase();\n  if (insecure[0] !== \"_\") {\n    return insecure;\n  }\n  return \"private\" + insecure;\n}\nfunction toCamelCase(identifier) {\n  if (identifier.length <= 1) {\n    return identifier;\n  }\n  if (identifier.indexOf(\"_\") === -1) {\n    return identifier;\n  }\n  return identifier.replace(/[_]+(\\w|$)/g, (m, p1) => p1.toUpperCase());\n}\nvar _GLOBAL_CUSTOM_OBJECTS = {};\nfunction serializeKerasObject(instance) {\n  if (instance === null || instance === void 0) {\n    return null;\n  }\n  const dict = {};\n  dict[\"className\"] = instance.getClassName();\n  dict[\"config\"] = instance.getConfig();\n  return dict;\n}\nfunction convertNDArrayScalarsInConfig(config) {\n  if (config == null || typeof config !== \"object\") {\n    return;\n  } else if (Array.isArray(config)) {\n    config.forEach((configItem) => convertNDArrayScalarsInConfig(configItem));\n  } else {\n    const fields = Object.keys(config);\n    for (const field of fields) {\n      const value = config[field];\n      if (value != null && typeof value === \"object\") {\n        if (!Array.isArray(value) && value[\"type\"] === \"ndarray\" && typeof value[\"value\"] === \"number\") {\n          config[field] = value[\"value\"];\n        } else {\n          convertNDArrayScalarsInConfig(value);\n        }\n      }\n    }\n  }\n}\nfunction deserializeKerasObject(identifier, moduleObjects = {}, customObjects = {}, printableModuleName = \"object\", fastWeightInit = false) {\n  if (typeof identifier === \"string\") {\n    const functionName = identifier;\n    let fn;\n    if (functionName in customObjects) {\n      fn = customObjects[functionName];\n    } else if (functionName in _GLOBAL_CUSTOM_OBJECTS) {\n      fn = _GLOBAL_CUSTOM_OBJECTS[functionName];\n    } else {\n      fn = moduleObjects[functionName];\n      if (fn == null) {\n        throw new ValueError(`Unknown ${printableModuleName}: ${identifier}. This may be due to one of the following reasons:\n1. The ${printableModuleName} is defined in Python, in which case it needs to be ported to TensorFlow.js or your JavaScript code.\n2. The custom ${printableModuleName} is defined in JavaScript, but is not registered properly with tf.serialization.registerClass().`);\n      }\n    }\n    return fn;\n  } else {\n    const config = identifier;\n    if (config[\"className\"] == null || config[\"config\"] == null) {\n      throw new ValueError(`${printableModuleName}: Improper config format: ${JSON.stringify(config)}.\n'className' and 'config' must set.`);\n    }\n    const className = config[\"className\"];\n    let cls, fromConfig;\n    if (className in customObjects) {\n      [cls, fromConfig] = customObjects[className];\n    } else if (className in _GLOBAL_CUSTOM_OBJECTS) {\n      [cls, fromConfig] = _GLOBAL_CUSTOM_OBJECTS[\"className\"];\n    } else if (className in moduleObjects) {\n      [cls, fromConfig] = moduleObjects[className];\n    }\n    if (cls == null) {\n      throw new ValueError(`Unknown ${printableModuleName}: ${className}. This may be due to one of the following reasons:\n1. The ${printableModuleName} is defined in Python, in which case it needs to be ported to TensorFlow.js or your JavaScript code.\n2. The custom ${printableModuleName} is defined in JavaScript, but is not registered properly with tf.serialization.registerClass().`);\n    }\n    if (fromConfig != null) {\n      const customObjectsCombined = {};\n      for (const key of Object.keys(_GLOBAL_CUSTOM_OBJECTS)) {\n        customObjectsCombined[key] = _GLOBAL_CUSTOM_OBJECTS[key];\n      }\n      for (const key of Object.keys(customObjects)) {\n        customObjectsCombined[key] = customObjects[key];\n      }\n      const nestedConfig = config[\"config\"];\n      nestedConfig[\"customObjects\"] = customObjectsCombined;\n      const backupCustomObjects = Object.assign({}, _GLOBAL_CUSTOM_OBJECTS);\n      for (const key of Object.keys(customObjects)) {\n        _GLOBAL_CUSTOM_OBJECTS[key] = customObjects[key];\n      }\n      convertNDArrayScalarsInConfig(config[\"config\"]);\n      const returnObj = fromConfig(cls, config[\"config\"], customObjects, fastWeightInit);\n      _GLOBAL_CUSTOM_OBJECTS = Object.assign({}, backupCustomObjects);\n      return returnObj;\n    } else {\n      const backupCustomObjects = Object.assign({}, _GLOBAL_CUSTOM_OBJECTS);\n      for (const key of Object.keys(customObjects)) {\n        _GLOBAL_CUSTOM_OBJECTS[key] = customObjects[key];\n      }\n      const returnObj = new cls(config[\"config\"]);\n      _GLOBAL_CUSTOM_OBJECTS = Object.assign({}, backupCustomObjects);\n      return returnObj;\n    }\n  }\n}\nfunction numberCompare(a, b) {\n  return a < b ? -1 : a > b ? 1 : 0;\n}\nfunction reverseNumberCompare(a, b) {\n  return -1 * numberCompare(a, b);\n}\nfunction unique2(xs) {\n  if (xs == null) {\n    return xs;\n  }\n  const out = [];\n  for (const x of xs) {\n    if (out.indexOf(x) === -1) {\n      out.push(x);\n    }\n  }\n  return out;\n}\nfunction isObjectEmpty(obj) {\n  if (obj == null) {\n    throw new ValueError(`Invalid value in obj: ${JSON.stringify(obj)}`);\n  }\n  for (const key in obj) {\n    if (obj.hasOwnProperty(key)) {\n      return false;\n    }\n  }\n  return true;\n}\nfunction checkStringTypeUnionValue(values, label, value) {\n  if (value == null) {\n    return;\n  }\n  if (values.indexOf(value) < 0) {\n    throw new ValueError(`${value} is not a valid ${label}.  Valid values are ${values} or null/undefined.`);\n  }\n}\nfunction checkArrayTypeAndLength(x, expectedType, minLength = 0, maxLength = Infinity) {\n  assert2(minLength >= 0);\n  assert2(maxLength >= minLength);\n  return Array.isArray(x) && x.length >= minLength && x.length <= maxLength && x.every((e) => typeof e === expectedType);\n}\nfunction assertPositiveInteger(value, name) {\n  if (Array.isArray(value)) {\n    util_exports.assert(value.length > 0, () => `${name} is unexpectedly an empty array.`);\n    value.forEach((v, i) => assertPositiveInteger(v, `element ${i + 1} of ${name}`));\n  } else {\n    util_exports.assert(Number.isInteger(value) && value > 0, () => `Expected ${name} to be a positive integer, but got ${formatAsFriendlyString(value)}.`);\n  }\n}\nfunction formatAsFriendlyString(value) {\n  if (value === null) {\n    return \"null\";\n  } else if (Array.isArray(value)) {\n    return \"[\" + value.map((v) => formatAsFriendlyString(v)).join(\",\") + \"]\";\n  } else if (typeof value === \"string\") {\n    return `\"${value}\"`;\n  } else {\n    return `${value}`;\n  }\n}\nfunction debounce(f, waitMs, nowFunc) {\n  let lastTime = nowFunc != null ? nowFunc() : util_exports.now();\n  let lastResult;\n  const f2 = (...args) => {\n    const now2 = nowFunc != null ? nowFunc() : util_exports.now();\n    if (now2 - lastTime < waitMs) {\n      return lastResult;\n    }\n    lastTime = now2;\n    lastResult = f(...args);\n    return lastResult;\n  };\n  return f2;\n}\nfunction mapActivationToFusedKernel(activationName) {\n  if (activationName === \"relu\") {\n    return \"relu\";\n  }\n  if (activationName === \"linear\") {\n    return \"linear\";\n  }\n  if (activationName === \"elu\") {\n    return \"elu\";\n  }\n  return null;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-layers@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-layers/dist/backend/state.js\nvar _nextUniqueTensorId = 0;\nfunction getNextUniqueTensorId() {\n  return _nextUniqueTensorId++;\n}\nvar _uidPrefixes = {};\nfunction getUid(prefix = \"\") {\n  if (!(prefix in _uidPrefixes)) {\n    _uidPrefixes[prefix] = 0;\n  }\n  _uidPrefixes[prefix] += 1;\n  return prefix + _uidPrefixes[prefix].toString();\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-layers@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-layers/dist/keras_format/common.js\nvar VALID_DATA_FORMAT_VALUES = [\"channelsFirst\", \"channelsLast\"];\nvar VALID_INTERPOLATION_FORMAT_VALUES = [\"nearest\", \"bilinear\"];\nvar VALID_PADDING_MODE_VALUES = [\"valid\", \"same\", \"causal\"];\nvar VALID_POOL_MODE_VALUES = [\"max\", \"avg\"];\nvar VALID_BIDIRECTIONAL_MERGE_MODES = [\"sum\", \"mul\", \"concat\", \"ave\"];\n\n// node_modules/.pnpm/@tensorflow+tfjs-layers@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-layers/dist/common.js\nvar nameMap = /* @__PURE__ */ new Map();\nfunction checkDataFormat(value) {\n  checkStringTypeUnionValue(VALID_DATA_FORMAT_VALUES, \"DataFormat\", value);\n}\nfunction checkInterpolationFormat(value) {\n  checkStringTypeUnionValue(VALID_INTERPOLATION_FORMAT_VALUES, \"InterpolationFormat\", value);\n}\nfunction checkPaddingMode(value) {\n  checkStringTypeUnionValue(VALID_PADDING_MODE_VALUES, \"PaddingMode\", value);\n}\nfunction checkPoolMode(value) {\n  checkStringTypeUnionValue(VALID_POOL_MODE_VALUES, \"PoolMode\", value);\n}\nvar _nameScopeStack = [];\nvar _nameScopeDivider = \"/\";\nfunction nameScope(name, fn) {\n  _nameScopeStack.push(name);\n  try {\n    const val = fn();\n    _nameScopeStack.pop();\n    return val;\n  } catch (e) {\n    _nameScopeStack.pop();\n    throw e;\n  }\n}\nfunction currentNameScopePrefix() {\n  if (_nameScopeStack.length === 0) {\n    return \"\";\n  } else {\n    return _nameScopeStack.join(_nameScopeDivider) + _nameScopeDivider;\n  }\n}\nfunction getScopedTensorName(tensorName) {\n  if (!isValidTensorName(tensorName)) {\n    throw new Error(\"Not a valid tensor name: '\" + tensorName + \"'\");\n  }\n  return currentNameScopePrefix() + tensorName;\n}\nfunction getUniqueTensorName(scopedName) {\n  if (!isValidTensorName(scopedName)) {\n    throw new Error(\"Not a valid tensor name: '\" + scopedName + \"'\");\n  }\n  if (!nameMap.has(scopedName)) {\n    nameMap.set(scopedName, 0);\n  }\n  const index = nameMap.get(scopedName);\n  nameMap.set(scopedName, nameMap.get(scopedName) + 1);\n  if (index > 0) {\n    const result = `${scopedName}_${index}`;\n    nameMap.set(result, 1);\n    return result;\n  } else {\n    return scopedName;\n  }\n}\nvar tensorNameRegex = new RegExp(/^[A-Za-z0-9][-A-Za-z0-9\\._\\/]*$/);\nfunction isValidTensorName(name) {\n  return !!name.match(tensorNameRegex);\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-layers@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-layers/dist/utils/math_utils.js\nfunction isInteger(x) {\n  return x === parseInt(x.toString(), 10);\n}\nfunction arrayProd(array2, begin, end) {\n  if (begin == null) {\n    begin = 0;\n  }\n  if (end == null) {\n    end = array2.length;\n  }\n  let prod6 = 1;\n  for (let i = begin; i < end; ++i) {\n    prod6 *= array2[i];\n  }\n  return prod6;\n}\nfunction min2(array2) {\n  if (array2.length === 0) {\n    return Number.NaN;\n  }\n  let min7 = Number.POSITIVE_INFINITY;\n  for (let i = 0; i < array2.length; i++) {\n    const value = array2[i];\n    if (value < min7) {\n      min7 = value;\n    }\n  }\n  return min7;\n}\nfunction max2(array2) {\n  if (array2.length === 0) {\n    return Number.NaN;\n  }\n  let max7 = Number.NEGATIVE_INFINITY;\n  for (let i = 0; i < array2.length; i++) {\n    const value = array2[i];\n    if (value > max7) {\n      max7 = value;\n    }\n  }\n  return max7;\n}\nfunction range2(begin, end) {\n  if (end < begin) {\n    throw new ValueError(`end (${end}) < begin (${begin}) is forbidden.`);\n  }\n  const out = [];\n  for (let i = begin; i < end; ++i) {\n    out.push(i);\n  }\n  return out;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-layers@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-layers/dist/backend/common.js\nvar _epsilon;\nfunction epsilon() {\n  if (_epsilon == null) {\n    _epsilon = backend().epsilon();\n  }\n  return _epsilon;\n}\nfunction imageDataFormat() {\n  return \"channelsLast\";\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-layers@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-layers/dist/backend/tfjs_backend.js\nfunction cast2(x, dtype) {\n  return cast(x, dtype);\n}\nfunction expandDims2(x, axis = -1) {\n  const outShape = x.shape.slice();\n  if (axis < 0) {\n    axis = outShape.length + axis + 1;\n  }\n  outShape.splice(axis, 0, 1);\n  return reshape(x, outShape);\n}\nfunction repeat(x, n) {\n  return tidy(() => {\n    if (x.shape.length !== 2) {\n      throw new ValueError(`repeat() expects a rank-2 tensor, but received a rank-${x.shape.length} tensor.`);\n    }\n    const y = expandDims2(x, 1);\n    return tile2(y, [1, n, 1]);\n  });\n}\nfunction flatten2(x) {\n  const newShape = [arrayProd(x.shape)];\n  return reshape(x, newShape);\n}\nfunction batchFlatten(x) {\n  if (x.rank <= 1) {\n    throw new ValueError(`batchFlatten requires a minimum rank of 2. Got rank: ${x.rank}.`);\n  }\n  const newShape = [x.shape[0], arrayProd(x.shape, 1)];\n  return reshape(x, newShape);\n}\nfunction sliceAlongFirstAxis(array2, start, size) {\n  return tidy(() => {\n    switch (array2.rank) {\n      case 1:\n        return slice1d(array2, start, size);\n      case 2:\n        return slice2d(array2, [start, 0], [size, array2.shape[1]]);\n      case 3:\n        return slice3d(array2, [start, 0, 0], [size, array2.shape[1], array2.shape[2]]);\n      case 4:\n        return slice4d(array2, [start, 0, 0, 0], [size, array2.shape[1], array2.shape[2], array2.shape[3]]);\n      case 5:\n        return slice(array2, [start, 0, 0, 0, 0], [\n          size,\n          array2.shape[1],\n          array2.shape[2],\n          array2.shape[3],\n          array2.shape[4]\n        ]);\n      case 6:\n        return slice(array2, [start, 0, 0, 0, 0, 0], [\n          size,\n          array2.shape[1],\n          array2.shape[2],\n          array2.shape[3],\n          array2.shape[4],\n          array2.shape[5]\n        ]);\n      default:\n        throw new ValueError(`sliceAlongFirstAxis() received an unsupported tensor rank: ${array2.rank}`);\n    }\n  });\n}\nfunction sliceAlongLastAxis(array2, start, size) {\n  return tidy(() => {\n    switch (array2.rank) {\n      case 1:\n        return slice1d(array2, start, size);\n      case 2:\n        return slice2d(array2, [0, start], [array2.shape[0], size]);\n      case 3:\n        return slice3d(array2, [0, 0, start], [array2.shape[0], array2.shape[1], size]);\n      case 4:\n        return slice4d(array2, [0, 0, 0, start], [array2.shape[0], array2.shape[1], array2.shape[2], size]);\n      default:\n        throw new ValueError(`sliceAlongLastAxis() received an unsupported tensor rank: ${array2.rank}`);\n    }\n  });\n}\nfunction sliceAlongAxis(array2, start, size, axis) {\n  return tidy(() => {\n    switch (array2.rank) {\n      case 1:\n        return slice1d(array2, start, size);\n      case 2:\n        switch (axis) {\n          case 1:\n            return sliceAlongFirstAxis(array2, start, size);\n          case 2:\n            return sliceAlongLastAxis(array2, start, size);\n          default:\n            throw new ValueError(`The axis is not within the rank of the tensor ${axis}`);\n        }\n      case 3:\n        switch (axis) {\n          case 1:\n            return sliceAlongFirstAxis(array2, start, size);\n          case 2:\n            return slice3d(array2, [0, start, 0], [array2.shape[0], size, array2.shape[2]]);\n          case 3:\n            return sliceAlongLastAxis(array2, start, size);\n          default:\n            throw new ValueError(`The axis is not within the rank of the tensor ${axis}`);\n        }\n      case 4:\n        switch (axis) {\n          case 1:\n            return sliceAlongFirstAxis(array2, start, size);\n          case 2:\n            return slice4d(array2, [0, start, 0, 0], [array2.shape[0], size, array2.shape[2], array2.shape[3]]);\n          case 3:\n            return slice4d(array2, [0, 0, start, 0], [array2.shape[0], array2.shape[1], size, array2.shape[3]]);\n          case 4:\n            return sliceAlongLastAxis(array2, start, size);\n          default:\n            throw new ValueError(`The axis is not within the rank of the tensor ${axis}`);\n        }\n      default:\n        throw new ValueError(`sliceAlongLastAxis() received an unsupported tensor rank: ${array2.rank}`);\n    }\n  });\n}\nfunction concatenate(tensors, axis = -1) {\n  let rank;\n  if (axis < 0) {\n    rank = tensors[0].rank;\n    if (rank !== 0) {\n      axis = rank;\n    } else {\n      axis = 0;\n    }\n  }\n  if (axis === tensors[0].rank) {\n    axis = -1;\n  }\n  return concat(tensors, axis);\n}\nfunction concatAlongFirstAxis(a, b) {\n  switch (a.rank) {\n    case 1:\n      return concat1d([a, b]);\n    case 2:\n      return concat2d([a, b], 0);\n    case 3:\n      return concat3d([a, b], 0);\n    case 4:\n      return concat4d([a, b], 0);\n    default:\n      throw new ValueError(`concatAlongFirstAxis() received an unsupported tensor rank: ${a.rank}`);\n  }\n}\nfunction tile2(x, n) {\n  if (!Array.isArray(n)) {\n    n = [n];\n  }\n  if (x.rank !== n.length) {\n    throw new ValueError(`The length of input n (${n.length}) does not match the number of dimensions in input x (${x.rank})`);\n  }\n  return tile(x, n);\n}\nfunction randomNormal2(shape, mean5 = 0, stddev = 1, dtype, seed) {\n  return randomNormal(shape, mean5, stddev, dtype, seed);\n}\nfunction dot2(a, b, activation2, bias) {\n  if (a.rank < 2 || b.rank < 2) {\n    throw new NotImplementedError(`dot requires both inputs to be rank >= 2 but got x shape = ${a.shape} and y shape = ${b.shape}`);\n  }\n  if (b.rank >= 3) {\n    const xLastDim = a.shape.slice(-1)[0];\n    const ySecondLastDim = b.shape.slice(-2)[0];\n    if (xLastDim !== ySecondLastDim) {\n      throw new NotImplementedError(`If rank y >= 3, then the second last dim of y must equal the last dim of x but got x shape = ${a.shape} and  y shape = ${b.shape}`);\n    }\n  }\n  if (a.rank === 2 && b.rank === 2) {\n    const transposeA = false;\n    const transposeB = false;\n    return fused_ops_exports.matMul({\n      a,\n      b,\n      transposeA,\n      transposeB,\n      bias: bias ? reshapeBias(a.rank, bias, imageDataFormat()) : null,\n      activation: activation2\n    });\n  } else {\n    const aFirstDims = a.shape.slice();\n    const aLastDim = aFirstDims.pop();\n    a = reshape(a, [-1, aLastDim]);\n    const bShape = b.shape.slice();\n    const bLastDim = bShape.pop();\n    const ySecondLastDim = bShape.pop();\n    const yOtherDims = [...bShape, bLastDim];\n    const perm = Array.from({ length: b.rank }, (_, i) => {\n      if (i === 0) {\n        return b.rank - 2;\n      } else if (i <= b.rank - 2) {\n        return i - 1;\n      }\n      return i;\n    });\n    b = reshape(transpose(b, perm), [ySecondLastDim, -1]);\n    const outputShape = [...aFirstDims, ...yOtherDims];\n    const transposeA = false;\n    const transposeB = false;\n    return reshape(fused_ops_exports.matMul({\n      a,\n      b,\n      transposeA,\n      transposeB,\n      bias: bias ? reshapeBias(a.rank, bias, imageDataFormat()) : null,\n      activation: activation2\n    }), outputShape);\n  }\n}\nfunction gather2(reference, indices, axis) {\n  return tidy(() => {\n    if (Array.isArray(indices)) {\n      indices = tensor1d(indices, \"int32\");\n    } else {\n      indices = cast(indices, \"int32\");\n    }\n    return gather(reference, indices, axis);\n  });\n}\nfunction square2(x) {\n  return mul(x, x);\n}\nfunction reshapeBias(xRank, bias, dataFormat) {\n  const biasShape = bias.shape;\n  if (bias.rank !== 1 && bias.rank !== xRank) {\n    throw new ValueError(`Unexpected bias dimensions: ${bias.rank}; expected it to be 1 or ${xRank}`);\n  }\n  if (xRank === 5) {\n    if (dataFormat === \"channelsFirst\") {\n      if (biasShape.length === 1) {\n        return reshape(bias, [1, biasShape[0], 1, 1, 1]);\n      } else {\n        return reshape(bias, [1, biasShape[3], biasShape[0], biasShape[1], biasShape[2]]);\n      }\n    } else if (dataFormat === \"channelsLast\") {\n      if (biasShape.length === 1) {\n        return reshape(bias, [1, 1, 1, 1, biasShape[0]]);\n      } else {\n        return reshape(bias, [1].concat(biasShape));\n      }\n    }\n  } else if (xRank === 4) {\n    if (dataFormat === \"channelsFirst\") {\n      if (biasShape.length === 1) {\n        return reshape(bias, [1, biasShape[0], 1, 1]);\n      } else {\n        return reshape(bias, [1, biasShape[2], biasShape[0], biasShape[1]]);\n      }\n    } else if (dataFormat === \"channelsLast\") {\n      if (biasShape.length === 1) {\n        return reshape(bias, [1, 1, 1, biasShape[0]]);\n      } else {\n        return reshape(bias, [1].concat(biasShape));\n      }\n    }\n  } else if (xRank === 3) {\n    if (dataFormat === \"channelsFirst\") {\n      if (biasShape.length === 1) {\n        return reshape(bias, [1, biasShape[0], 1]);\n      } else {\n        return reshape(bias, [1, biasShape[1], biasShape[0]]);\n      }\n    } else if (dataFormat === \"channelsLast\") {\n      if (biasShape.length === 1) {\n        return reshape(bias, [1, 1, biasShape[0]]);\n      } else {\n        return reshape(bias, [1].concat(biasShape));\n      }\n    }\n  } else if (xRank < 3) {\n    return bias;\n  }\n  throw new ValueError(`Unsupported input rank by biasAdd: ${bias.rank}`);\n}\nfunction biasAdd(x, bias, dataFormat) {\n  return tidy(() => {\n    if (dataFormat == null) {\n      dataFormat = imageDataFormat();\n    }\n    checkDataFormat(dataFormat);\n    return add2(x, reshapeBias(x.rank, bias, dataFormat));\n  });\n}\nfunction elu2(x, alpha = 1) {\n  if (alpha !== 1) {\n    throw new NotImplementedError(`Support for alpha values other than 1 (${alpha}) is not implemented yet.`);\n  }\n  return elu(x);\n}\nfunction softsign(x) {\n  return tidy(() => div(x, add2(abs(x), 1)));\n}\nfunction dropout2(x, level, noiseShape, seed) {\n  return tidy(() => dropout(x, level, noiseShape, seed));\n}\nfunction hardSigmoid(x) {\n  return tidy(() => {\n    const y = add2(0.5, mul(0.2, x));\n    return clipByValue(y, 0, 1);\n  });\n}\nfunction inTrainPhase(x, alt, training = false) {\n  return training ? x() : alt();\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-layers@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-layers/dist/keras_format/initializer_config.js\nvar VALID_FAN_MODE_VALUES = [\"fanIn\", \"fanOut\", \"fanAvg\"];\nvar VALID_DISTRIBUTION_VALUES = [\"normal\", \"uniform\", \"truncatedNormal\"];\n\n// node_modules/.pnpm/@tensorflow+tfjs-layers@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-layers/dist/initializers.js\nfunction checkFanMode(value) {\n  checkStringTypeUnionValue(VALID_FAN_MODE_VALUES, \"FanMode\", value);\n}\nfunction checkDistribution(value) {\n  checkStringTypeUnionValue(VALID_DISTRIBUTION_VALUES, \"Distribution\", value);\n}\nvar Initializer = class extends serialization_exports.Serializable {\n  fromConfigUsesCustomObjects() {\n    return false;\n  }\n  getConfig() {\n    return {};\n  }\n};\nvar Zeros = class extends Initializer {\n  apply(shape, dtype) {\n    return zeros(shape, dtype);\n  }\n};\nZeros.className = \"Zeros\";\nserialization_exports.registerClass(Zeros);\nvar Ones = class extends Initializer {\n  apply(shape, dtype) {\n    return ones2(shape, dtype);\n  }\n};\nOnes.className = \"Ones\";\nserialization_exports.registerClass(Ones);\nvar Constant = class extends Initializer {\n  constructor(args) {\n    super();\n    if (typeof args !== \"object\") {\n      throw new ValueError(`Expected argument of type ConstantConfig but got ${args}`);\n    }\n    if (args.value === void 0) {\n      throw new ValueError(`config must have value set but got ${args}`);\n    }\n    this.value = args.value;\n  }\n  apply(shape, dtype) {\n    return tidy(() => mul(scalar(this.value), ones2(shape, dtype)));\n  }\n  getConfig() {\n    return {\n      value: this.value\n    };\n  }\n};\nConstant.className = \"Constant\";\nserialization_exports.registerClass(Constant);\nvar RandomUniform = class extends Initializer {\n  constructor(args) {\n    super();\n    this.DEFAULT_MINVAL = -0.05;\n    this.DEFAULT_MAXVAL = 0.05;\n    this.minval = args.minval || this.DEFAULT_MINVAL;\n    this.maxval = args.maxval || this.DEFAULT_MAXVAL;\n    this.seed = args.seed;\n  }\n  apply(shape, dtype) {\n    return randomUniform(shape, this.minval, this.maxval, dtype);\n  }\n  getConfig() {\n    return { minval: this.minval, maxval: this.maxval, seed: this.seed };\n  }\n};\nRandomUniform.className = \"RandomUniform\";\nserialization_exports.registerClass(RandomUniform);\nvar RandomNormal = class extends Initializer {\n  constructor(args) {\n    super();\n    this.DEFAULT_MEAN = 0;\n    this.DEFAULT_STDDEV = 0.05;\n    this.mean = args.mean || this.DEFAULT_MEAN;\n    this.stddev = args.stddev || this.DEFAULT_STDDEV;\n    this.seed = args.seed;\n  }\n  apply(shape, dtype) {\n    dtype = dtype || \"float32\";\n    if (dtype !== \"float32\" && dtype !== \"int32\") {\n      throw new NotImplementedError(`randomNormal does not support dType ${dtype}.`);\n    }\n    return randomNormal2(shape, this.mean, this.stddev, dtype, this.seed);\n  }\n  getConfig() {\n    return { mean: this.mean, stddev: this.stddev, seed: this.seed };\n  }\n};\nRandomNormal.className = \"RandomNormal\";\nserialization_exports.registerClass(RandomNormal);\nvar TruncatedNormal = class extends Initializer {\n  constructor(args) {\n    super();\n    this.DEFAULT_MEAN = 0;\n    this.DEFAULT_STDDEV = 0.05;\n    this.mean = args.mean || this.DEFAULT_MEAN;\n    this.stddev = args.stddev || this.DEFAULT_STDDEV;\n    this.seed = args.seed;\n  }\n  apply(shape, dtype) {\n    dtype = dtype || \"float32\";\n    if (dtype !== \"float32\" && dtype !== \"int32\") {\n      throw new NotImplementedError(`truncatedNormal does not support dType ${dtype}.`);\n    }\n    return truncatedNormal(shape, this.mean, this.stddev, dtype, this.seed);\n  }\n  getConfig() {\n    return { mean: this.mean, stddev: this.stddev, seed: this.seed };\n  }\n};\nTruncatedNormal.className = \"TruncatedNormal\";\nserialization_exports.registerClass(TruncatedNormal);\nvar Identity2 = class extends Initializer {\n  constructor(args) {\n    super();\n    this.gain = args.gain != null ? args.gain : 1;\n  }\n  apply(shape, dtype) {\n    return tidy(() => {\n      if (shape.length !== 2 || shape[0] !== shape[1]) {\n        throw new ValueError(\"Identity matrix initializer can only be used for 2D square matrices.\");\n      } else {\n        return mul(this.gain, eye(shape[0]));\n      }\n    });\n  }\n  getConfig() {\n    return { gain: this.gain };\n  }\n};\nIdentity2.className = \"Identity\";\nserialization_exports.registerClass(Identity2);\nfunction computeFans(shape, dataFormat = \"channelsLast\") {\n  let fanIn;\n  let fanOut;\n  checkDataFormat(dataFormat);\n  if (shape.length === 2) {\n    fanIn = shape[0];\n    fanOut = shape[1];\n  } else if ([3, 4, 5].indexOf(shape.length) !== -1) {\n    if (dataFormat === \"channelsFirst\") {\n      const receptiveFieldSize = arrayProd(shape, 2);\n      fanIn = shape[1] * receptiveFieldSize;\n      fanOut = shape[0] * receptiveFieldSize;\n    } else if (dataFormat === \"channelsLast\") {\n      const receptiveFieldSize = arrayProd(shape, 0, shape.length - 2);\n      fanIn = shape[shape.length - 2] * receptiveFieldSize;\n      fanOut = shape[shape.length - 1] * receptiveFieldSize;\n    }\n  } else {\n    const shapeProd = arrayProd(shape);\n    fanIn = Math.sqrt(shapeProd);\n    fanOut = Math.sqrt(shapeProd);\n  }\n  return [fanIn, fanOut];\n}\nvar VarianceScaling = class extends Initializer {\n  constructor(args) {\n    super();\n    if (args.scale < 0) {\n      throw new ValueError(`scale must be a positive float. Got: ${args.scale}`);\n    }\n    this.scale = args.scale == null ? 1 : args.scale;\n    this.mode = args.mode == null ? \"fanIn\" : args.mode;\n    checkFanMode(this.mode);\n    this.distribution = args.distribution == null ? \"normal\" : args.distribution;\n    checkDistribution(this.distribution);\n    this.seed = args.seed;\n  }\n  apply(shape, dtype) {\n    const fans = computeFans(shape);\n    const fanIn = fans[0];\n    const fanOut = fans[1];\n    let scale2 = this.scale;\n    if (this.mode === \"fanIn\") {\n      scale2 /= Math.max(1, fanIn);\n    } else if (this.mode === \"fanOut\") {\n      scale2 /= Math.max(1, fanOut);\n    } else {\n      scale2 /= Math.max(1, (fanIn + fanOut) / 2);\n    }\n    if (this.distribution === \"normal\") {\n      const stddev = Math.sqrt(scale2);\n      dtype = dtype || \"float32\";\n      if (dtype !== \"float32\" && dtype !== \"int32\") {\n        throw new NotImplementedError(`${this.getClassName()} does not support dType ${dtype}.`);\n      }\n      return truncatedNormal(shape, 0, stddev, dtype, this.seed);\n    } else {\n      const limit = Math.sqrt(3 * scale2);\n      return randomUniform(shape, -limit, limit, dtype);\n    }\n  }\n  getConfig() {\n    return {\n      scale: this.scale,\n      mode: this.mode,\n      distribution: this.distribution,\n      seed: this.seed\n    };\n  }\n};\nVarianceScaling.className = \"VarianceScaling\";\nserialization_exports.registerClass(VarianceScaling);\nvar GlorotUniform = class extends VarianceScaling {\n  constructor(args) {\n    super({\n      scale: 1,\n      mode: \"fanAvg\",\n      distribution: \"uniform\",\n      seed: args == null ? null : args.seed\n    });\n  }\n  getClassName() {\n    return VarianceScaling.className;\n  }\n};\nGlorotUniform.className = \"GlorotUniform\";\nserialization_exports.registerClass(GlorotUniform);\nvar GlorotNormal = class extends VarianceScaling {\n  constructor(args) {\n    super({\n      scale: 1,\n      mode: \"fanAvg\",\n      distribution: \"normal\",\n      seed: args == null ? null : args.seed\n    });\n  }\n  getClassName() {\n    return VarianceScaling.className;\n  }\n};\nGlorotNormal.className = \"GlorotNormal\";\nserialization_exports.registerClass(GlorotNormal);\nvar HeNormal = class extends VarianceScaling {\n  constructor(args) {\n    super({\n      scale: 2,\n      mode: \"fanIn\",\n      distribution: \"normal\",\n      seed: args == null ? null : args.seed\n    });\n  }\n  getClassName() {\n    return VarianceScaling.className;\n  }\n};\nHeNormal.className = \"HeNormal\";\nserialization_exports.registerClass(HeNormal);\nvar HeUniform = class extends VarianceScaling {\n  constructor(args) {\n    super({\n      scale: 2,\n      mode: \"fanIn\",\n      distribution: \"uniform\",\n      seed: args == null ? null : args.seed\n    });\n  }\n  getClassName() {\n    return VarianceScaling.className;\n  }\n};\nHeUniform.className = \"HeUniform\";\nserialization_exports.registerClass(HeUniform);\nvar LeCunNormal = class extends VarianceScaling {\n  constructor(args) {\n    super({\n      scale: 1,\n      mode: \"fanIn\",\n      distribution: \"normal\",\n      seed: args == null ? null : args.seed\n    });\n  }\n  getClassName() {\n    return VarianceScaling.className;\n  }\n};\nLeCunNormal.className = \"LeCunNormal\";\nserialization_exports.registerClass(LeCunNormal);\nvar LeCunUniform = class extends VarianceScaling {\n  constructor(args) {\n    super({\n      scale: 1,\n      mode: \"fanIn\",\n      distribution: \"uniform\",\n      seed: args == null ? null : args.seed\n    });\n  }\n  getClassName() {\n    return VarianceScaling.className;\n  }\n};\nLeCunUniform.className = \"LeCunNormal\";\nserialization_exports.registerClass(LeCunUniform);\nvar Orthogonal = class extends Initializer {\n  constructor(args) {\n    super();\n    this.DEFAULT_GAIN = 1;\n    this.gain = args.gain == null ? this.DEFAULT_GAIN : args.gain;\n    this.seed = args.seed;\n    if (this.seed != null) {\n      throw new NotImplementedError(\"Random seed is not implemented for Orthogonal Initializer yet.\");\n    }\n  }\n  apply(shape, dtype) {\n    return tidy(() => {\n      if (shape.length < 2) {\n        throw new NotImplementedError(\"Shape must be at least 2D.\");\n      }\n      if (shape[0] * shape[1] > 2e3) {\n        console.warn(`Orthogonal initializer is being called on a matrix with more than 2000 (${shape[0] * shape[1]}) elements: Slowness may result.`);\n      }\n      const normalizedShape = shape[0] > shape[1] ? [shape[1], shape[0]] : shape;\n      const a = randomNormal2(normalizedShape, 0, 1, \"float32\");\n      let q = linalg.gramSchmidt(a);\n      if (shape[0] > shape[1]) {\n        q = transpose(q);\n      }\n      return mul(this.gain, q);\n    });\n  }\n  getConfig() {\n    return {\n      gain: this.gain,\n      seed: this.seed\n    };\n  }\n};\nOrthogonal.className = \"Orthogonal\";\nserialization_exports.registerClass(Orthogonal);\nvar INITIALIZER_IDENTIFIER_REGISTRY_SYMBOL_MAP = {\n  \"constant\": \"Constant\",\n  \"glorotNormal\": \"GlorotNormal\",\n  \"glorotUniform\": \"GlorotUniform\",\n  \"heNormal\": \"HeNormal\",\n  \"heUniform\": \"HeUniform\",\n  \"identity\": \"Identity\",\n  \"leCunNormal\": \"LeCunNormal\",\n  \"leCunUniform\": \"LeCunUniform\",\n  \"ones\": \"Ones\",\n  \"orthogonal\": \"Orthogonal\",\n  \"randomNormal\": \"RandomNormal\",\n  \"randomUniform\": \"RandomUniform\",\n  \"truncatedNormal\": \"TruncatedNormal\",\n  \"varianceScaling\": \"VarianceScaling\",\n  \"zeros\": \"Zeros\"\n};\nfunction deserializeInitializer(config, customObjects = {}) {\n  return deserializeKerasObject(config, serialization_exports.SerializationMap.getMap().classNameMap, customObjects, \"initializer\");\n}\nfunction serializeInitializer(initializer) {\n  return serializeKerasObject(initializer);\n}\nfunction getInitializer(identifier) {\n  if (typeof identifier === \"string\") {\n    const className = identifier in INITIALIZER_IDENTIFIER_REGISTRY_SYMBOL_MAP ? INITIALIZER_IDENTIFIER_REGISTRY_SYMBOL_MAP[identifier] : identifier;\n    if (className === \"GlorotNormal\") {\n      return new GlorotNormal();\n    } else if (className === \"GlorotUniform\") {\n      return new GlorotUniform();\n    } else if (className === \"HeNormal\") {\n      return new HeNormal();\n    } else if (className === \"HeUniform\") {\n      return new HeUniform();\n    } else if (className === \"LeCunNormal\") {\n      return new LeCunNormal();\n    } else if (className === \"LeCunUniform\") {\n      return new LeCunUniform();\n    } else {\n      const config = {};\n      config[\"className\"] = className;\n      config[\"config\"] = {};\n      return deserializeInitializer(config);\n    }\n  } else if (identifier instanceof Initializer) {\n    return identifier;\n  } else {\n    return deserializeInitializer(identifier);\n  }\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-layers@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-layers/dist/utils/types_utils.js\nfunction isArrayOfShapes(x) {\n  return Array.isArray(x) && Array.isArray(x[0]);\n}\nfunction normalizeShapeList(x) {\n  if (x.length === 0) {\n    return [];\n  }\n  if (!Array.isArray(x[0])) {\n    return [x];\n  }\n  return x;\n}\nfunction getExactlyOneTensor(xs) {\n  let x;\n  if (Array.isArray(xs)) {\n    if (xs.length !== 1) {\n      throw new ValueError(`Expected Tensor length to be 1; got ${xs.length}`);\n    }\n    x = xs[0];\n  } else {\n    x = xs;\n  }\n  return x;\n}\nfunction getExactlyOneShape(shapes) {\n  if (Array.isArray(shapes) && Array.isArray(shapes[0])) {\n    if (shapes.length === 1) {\n      shapes = shapes;\n      return shapes[0];\n    } else {\n      throw new ValueError(`Expected exactly 1 Shape; got ${shapes.length}`);\n    }\n  } else {\n    return shapes;\n  }\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-layers@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-layers/dist/utils/variable_utils.js\nfunction countParamsInWeights(weights) {\n  let count2 = 0;\n  for (const weight of weights) {\n    if (weight.shape.length === 0) {\n      count2 += 1;\n    } else {\n      count2 += weight.shape.reduce((a, b) => a * b);\n    }\n  }\n  return count2;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-layers@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-layers/dist/variables.js\nvar DEFAULT_VARIABLE_NAME_PREFIX = \"Variable\";\nvar LayerVariable = class {\n  constructor(val, dtype = \"float32\", name = DEFAULT_VARIABLE_NAME_PREFIX, trainable = true, constraint = null) {\n    this.dtype = dtype == null ? \"float32\" : dtype;\n    this.shape = val.shape;\n    this.id = getNextUniqueTensorId();\n    name = name == null ? DEFAULT_VARIABLE_NAME_PREFIX : name;\n    this.originalName = getScopedTensorName(name);\n    this.name = getUniqueTensorName(this.originalName);\n    this.trainable_ = trainable;\n    this.constraint = constraint;\n    this.val = variable(val, this.trainable_, this.name, this.dtype);\n  }\n  read() {\n    this.assertNotDisposed();\n    return this.val;\n  }\n  write(newVal) {\n    this.assertNotDisposed();\n    checkShapesMatch(this.val, newVal);\n    if (this.val.id !== newVal.id) {\n      this.val.assign(newVal);\n      if (this.constraint != null) {\n        this.val.assign(this.constraint.apply(this.val));\n      }\n    }\n    return this;\n  }\n  dispose() {\n    this.assertNotDisposed();\n    this.val.dispose();\n  }\n  assertNotDisposed() {\n    if (this.val.isDisposed) {\n      throw new Error(`LayersVariable ${this.name} is already disposed.`);\n    }\n  }\n  get trainable() {\n    return this.trainable_;\n  }\n  set trainable(trainable) {\n    this.trainable_ = trainable;\n    this.val.trainable = trainable;\n  }\n};\nfunction checkShapesMatch(x, y) {\n  if (x.shape.toString() !== y.shape.toString()) {\n    throw new Error(\"Shape mismatch: \" + JSON.stringify(x.shape) + \" vs. \" + JSON.stringify(y.shape));\n  }\n}\nfunction batchGetValue(xs) {\n  return xs.map((x) => x.read());\n}\nfunction batchSetValue(variablesAndValues) {\n  variablesAndValues.forEach((variableAndValue) => {\n    const variable2 = variableAndValue[0];\n    variable2.write(variableAndValue[1]);\n  });\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-layers@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-layers/dist/engine/topology.js\nvar InputSpec = class {\n  constructor(args) {\n    this.dtype = args.dtype;\n    this.shape = args.shape;\n    if (args.shape != null) {\n      this.ndim = args.shape.length;\n    } else {\n      this.ndim = args.ndim;\n    }\n    this.maxNDim = args.maxNDim;\n    this.minNDim = args.minNDim;\n    this.axes = args.axes || {};\n  }\n};\nvar SymbolicTensor = class {\n  constructor(dtype, shape, sourceLayer, inputs, callArgs, name, outputTensorIndex) {\n    this.dtype = dtype;\n    this.shape = shape;\n    this.sourceLayer = sourceLayer;\n    this.inputs = inputs;\n    this.callArgs = callArgs;\n    this.outputTensorIndex = outputTensorIndex;\n    this.id = getNextUniqueTensorId();\n    if (name != null) {\n      this.originalName = getScopedTensorName(name);\n      this.name = getUniqueTensorName(this.originalName);\n    }\n    this.rank = shape.length;\n  }\n};\nvar _nextNodeID = 0;\nvar Node = class {\n  constructor(args, callArgs) {\n    this.callArgs = callArgs;\n    this.id = _nextNodeID++;\n    this.outboundLayer = args.outboundLayer;\n    this.inboundLayers = args.inboundLayers;\n    this.nodeIndices = args.nodeIndices;\n    this.tensorIndices = args.tensorIndices;\n    this.inputTensors = args.inputTensors;\n    this.outputTensors = args.outputTensors;\n    this.inputMasks = args.inputMasks;\n    this.outputMasks = args.outputMasks;\n    this.inputShapes = args.inputShapes;\n    this.outputShapes = args.outputShapes;\n    for (const layer of args.inboundLayers) {\n      if (layer != null) {\n        layer.outboundNodes.push(this);\n      }\n    }\n    args.outboundLayer.inboundNodes.push(this);\n  }\n  getConfig() {\n    const inboundNames = [];\n    for (const layer of this.inboundLayers) {\n      if (layer != null) {\n        inboundNames.push(layer.name);\n      } else {\n        inboundNames.push(null);\n      }\n    }\n    return {\n      outboundLayer: this.outboundLayer ? this.outboundLayer.name : null,\n      inboundLayers: inboundNames,\n      nodeIndices: this.nodeIndices,\n      tensorIndices: this.tensorIndices\n    };\n  }\n};\nvar _nextLayerID = 0;\nvar Layer = class extends serialization_exports.Serializable {\n  constructor(args = {}) {\n    super();\n    this._callHook = null;\n    this._addedWeightNames = [];\n    this._stateful = false;\n    this.id = _nextLayerID++;\n    this.activityRegularizer = null;\n    this.inputSpec = null;\n    this.supportsMasking = false;\n    this._trainableWeights = [];\n    this._nonTrainableWeights = [];\n    this._losses = [];\n    this._updates = [];\n    this._built = false;\n    this.inboundNodes = [];\n    this.outboundNodes = [];\n    let name = args.name;\n    if (!name) {\n      const prefix = this.getClassName();\n      name = toSnakeCase(prefix) + \"_\" + getUid(prefix);\n    }\n    this.name = name;\n    this.trainable_ = args.trainable == null ? true : args.trainable;\n    if (args.inputShape != null || args.batchInputShape != null) {\n      let batchInputShape;\n      if (args.batchInputShape != null) {\n        batchInputShape = args.batchInputShape;\n      } else if (args.inputShape != null) {\n        let batchSize = null;\n        if (args.batchSize != null) {\n          batchSize = args.batchSize;\n        }\n        batchInputShape = [batchSize].concat(args.inputShape);\n      }\n      this.batchInputShape = batchInputShape;\n      let dtype = args.dtype;\n      if (dtype == null) {\n        dtype = args.inputDType;\n      }\n      if (dtype == null) {\n        dtype = \"float32\";\n      }\n      this.dtype = dtype;\n    }\n    if (args.weights != null) {\n      this.initialWeights = args.weights;\n    } else {\n      this.initialWeights = null;\n    }\n    this._refCount = null;\n    this.fastWeightInitDuringBuild = false;\n  }\n  static nodeKey(layer, nodeIndex) {\n    return layer.name + \"_ib-\" + nodeIndex.toString();\n  }\n  getNodeAtIndex(nodeIndex, attrName) {\n    if (this.inboundNodes.length === 0) {\n      throw new RuntimeError(`The layer has never been called and thus has no defined ${attrName}.`);\n    }\n    if (this.inboundNodes.length <= nodeIndex) {\n      throw new ValueError(`Asked to get ${attrName} at node ${nodeIndex}, but the layer has only ${this.inboundNodes.length} inbound nodes.`);\n    }\n    return this.inboundNodes[nodeIndex];\n  }\n  getInputAt(nodeIndex) {\n    return singletonOrArray(this.getNodeAtIndex(nodeIndex, \"input\").inputTensors);\n  }\n  getOutputAt(nodeIndex) {\n    return singletonOrArray(this.getNodeAtIndex(nodeIndex, \"output\").outputTensors);\n  }\n  get input() {\n    if (this.inboundNodes.length > 1) {\n      throw new AttributeError(`Layer ${this.name} has multiple inbound nodes, hence the notion of \"layer input\" is ill-defined. Use \\`getInputAt(nodeIndex)\\` instead.`);\n    } else if (this.inboundNodes.length === 0) {\n      throw new AttributeError(`Layer ${this.name} is not connected, no input to return.`);\n    }\n    return singletonOrArray(this.getNodeAtIndex(0, \"input\").inputTensors);\n  }\n  get output() {\n    if (this.inboundNodes.length === 0) {\n      throw new AttributeError(`Layer ${this.name} has no inbound nodes.`);\n    }\n    if (this.inboundNodes.length > 1) {\n      throw new AttributeError(`Layer ${this.name} has multiple inbound nodes, hence the notion of \"layer output\" is ill-defined. Use \\`getOutputAt(nodeIndex)\\` instead.`);\n    }\n    return singletonOrArray(this.getNodeAtIndex(0, \"output\").outputTensors);\n  }\n  get losses() {\n    return this._losses;\n  }\n  calculateLosses() {\n    return this.losses.map((lossFn) => lossFn());\n  }\n  get updates() {\n    return this._updates;\n  }\n  get built() {\n    return this._built;\n  }\n  set built(built) {\n    this._built = built;\n  }\n  get trainable() {\n    return this.trainable_;\n  }\n  set trainable(trainable) {\n    this._trainableWeights.forEach((w) => w.trainable = trainable);\n    this.trainable_ = trainable;\n  }\n  get trainableWeights() {\n    if (this.trainable_) {\n      return this._trainableWeights.filter((w) => w.trainable);\n    } else {\n      return [];\n    }\n  }\n  set trainableWeights(weights) {\n    this._trainableWeights = weights;\n  }\n  get nonTrainableWeights() {\n    if (this.trainable) {\n      return this._trainableWeights.filter((w) => !w.trainable).concat(this._nonTrainableWeights);\n    } else {\n      return this._trainableWeights.concat(this._nonTrainableWeights);\n    }\n  }\n  set nonTrainableWeights(weights) {\n    this._nonTrainableWeights = weights;\n  }\n  get weights() {\n    return this.trainableWeights.concat(this.nonTrainableWeights);\n  }\n  get stateful() {\n    return this._stateful;\n  }\n  resetStates() {\n    if (!this.stateful) {\n      throw new Error(\"Cannot call the resetStates() method of a non-stateful Layer object.\");\n    }\n  }\n  assertInputCompatibility(inputs) {\n    inputs = toList(inputs);\n    if (this.inputSpec == null || this.inputSpec.length === 0) {\n      return;\n    }\n    const inputSpec = toList(this.inputSpec);\n    if (inputs.length !== inputSpec.length) {\n      throw new ValueError(`Layer ${this.name} expects ${inputSpec.length} inputs, but it received ${inputs.length} input tensors. Input received: ${inputs}`);\n    }\n    for (let inputIndex = 0; inputIndex < inputs.length; inputIndex++) {\n      const x = inputs[inputIndex];\n      const spec = inputSpec[inputIndex];\n      if (spec == null) {\n        continue;\n      }\n      const ndim = x.rank;\n      if (spec.ndim != null) {\n        if (ndim !== spec.ndim) {\n          throw new ValueError(`Input ${inputIndex} is incompatible with layer ${this.name}: expected ndim=${spec.ndim}, found ndim=${ndim}`);\n        }\n      }\n      if (spec.maxNDim != null) {\n        if (ndim > spec.maxNDim) {\n          throw new ValueError(`Input ${inputIndex} is incompatible with layer ${this.name}: expected max_ndim=${spec.maxNDim}, found ndim=${ndim}`);\n        }\n      }\n      if (spec.minNDim != null) {\n        if (ndim < spec.minNDim) {\n          throw new ValueError(`Input ${inputIndex} is incompatible with layer ${this.name}: expected min_ndim=${spec.minNDim}, found ndim=${ndim}.`);\n        }\n      }\n      if (spec.dtype != null) {\n        if (x.dtype !== spec.dtype) {\n          throw new ValueError(`Input ${inputIndex} is incompatible with layer ${this.name} : expected dtype=${spec.dtype}, found dtype=${x.dtype}.`);\n        }\n      }\n      if (spec.axes) {\n        const xShape = x.shape;\n        for (const key in spec.axes) {\n          const axis = Number(key);\n          const value = spec.axes[key];\n          const xShapeAtAxis = axis >= 0 ? xShape[axis] : xShape[xShape.length + axis];\n          if (value != null && [value, null].indexOf(xShapeAtAxis) === -1) {\n            throw new ValueError(`Input ${inputIndex} is incompatible with layer ${this.name}: expected axis ${axis} of input shape to have value ${value} but got shape ${xShape}.`);\n          }\n        }\n      }\n      if (spec.shape != null) {\n        for (let i = 0; i < spec.shape.length; ++i) {\n          const specDim = spec.shape[i];\n          const dim = x.shape[i];\n          if (specDim != null && dim != null) {\n            if (specDim !== dim) {\n              throw new ValueError(`Input ${inputIndex} is incompatible with layer ${this.name}: expected shape=${spec.shape}, found shape=${x.shape}.`);\n            }\n          }\n        }\n      }\n    }\n  }\n  call(inputs, kwargs) {\n    return inputs;\n  }\n  invokeCallHook(inputs, kwargs) {\n    if (this._callHook != null) {\n      this._callHook(inputs, kwargs);\n    }\n  }\n  setCallHook(callHook) {\n    this._callHook = callHook;\n  }\n  clearCallHook() {\n    this._callHook = null;\n  }\n  apply(inputs, kwargs) {\n    kwargs = kwargs || {};\n    this.assertNotDisposed();\n    const inputsList = toList(inputs);\n    let allAreSymbolic = true;\n    for (const input2 of inputsList) {\n      if (!(input2 instanceof SymbolicTensor)) {\n        allAreSymbolic = false;\n        break;\n      }\n    }\n    let noneAreSymbolic = true;\n    for (const input2 of inputsList) {\n      if (input2 instanceof SymbolicTensor) {\n        noneAreSymbolic = false;\n        break;\n      }\n    }\n    if (allAreSymbolic === noneAreSymbolic) {\n      throw new ValueError(\"Arguments to apply() must be all SymbolicTensors or all Tensors\");\n    }\n    return nameScope(this.name, () => {\n      if (!this.built) {\n        this.assertInputCompatibility(inputs);\n        const inputShapes = [];\n        for (const xElem of toList(inputs)) {\n          inputShapes.push(xElem.shape);\n        }\n        this.build(singletonOrArray(inputShapes));\n        this.built = true;\n        if (this.initialWeights) {\n          this.setWeights(this.initialWeights);\n        }\n        if (this._refCount === null && noneAreSymbolic) {\n          this._refCount = 1;\n        }\n      }\n      this.assertInputCompatibility(inputs);\n      if (noneAreSymbolic) {\n        let output = this.call(inputs, kwargs);\n        const outputList = toList(output);\n        const outputListCopy = [];\n        for (let x of outputList) {\n          if (inputsList.indexOf(x) !== -1) {\n            x = x.clone();\n          }\n          outputListCopy.push(x);\n        }\n        output = singletonOrArray(outputListCopy);\n        if (this.activityRegularizer != null) {\n          throw new NotImplementedError(\"Layer invocation in the presence of activity regularizer(s) is not supported yet.\");\n        }\n        return output;\n      } else {\n        const inputShape = collectInputShape(inputs);\n        const outputShape = this.computeOutputShape(inputShape);\n        let output;\n        const outputDType = guessOutputDType(inputs);\n        this.warnOnIncompatibleInputShape(Array.isArray(inputs) ? inputShape[0] : inputShape);\n        if (outputShape != null && outputShape.length > 0 && Array.isArray(outputShape[0])) {\n          output = outputShape.map((shape, index) => new SymbolicTensor(outputDType, shape, this, toList(inputs), kwargs, this.name, index));\n        } else {\n          output = new SymbolicTensor(outputDType, outputShape, this, toList(inputs), kwargs, this.name);\n        }\n        this.addInboundNode(inputs, output, null, null, inputShape, outputShape, kwargs);\n        this._refCount++;\n        if (this.activityRegularizer != null) {\n          throw new NotImplementedError(\"Layer invocation in the presence of activity regularizer(s) is not supported yet.\");\n        }\n        return output;\n      }\n    });\n  }\n  warnOnIncompatibleInputShape(inputShape) {\n    if (this.batchInputShape == null) {\n      return;\n    } else if (inputShape.length !== this.batchInputShape.length) {\n      console.warn(`The rank of the input tensor provided (shape: ${JSON.stringify(inputShape)}) does not match that of the batchInputShape (${JSON.stringify(this.batchInputShape)}) of the layer ${this.name}`);\n    } else {\n      let dimMismatch = false;\n      this.batchInputShape.forEach((dimension, i) => {\n        if (dimension != null && inputShape[i] != null && inputShape[i] !== dimension) {\n          dimMismatch = true;\n        }\n      });\n      if (dimMismatch) {\n        console.warn(`The shape of the input tensor (${JSON.stringify(inputShape)}) does not match the expectation of layer ${this.name}: ${JSON.stringify(this.batchInputShape)}`);\n      }\n    }\n  }\n  get outputShape() {\n    if (this.inboundNodes == null || this.inboundNodes.length === 0) {\n      throw new AttributeError(`The layer ${this.name} has never been called and thus has no defined output shape.`);\n    }\n    const allOutputShapes = [];\n    for (const node of this.inboundNodes) {\n      const shapeString = JSON.stringify(node.outputShapes);\n      if (allOutputShapes.indexOf(shapeString) === -1) {\n        allOutputShapes.push(shapeString);\n      }\n    }\n    if (allOutputShapes.length === 1) {\n      const outputShapes = this.inboundNodes[0].outputShapes;\n      if (Array.isArray(outputShapes) && Array.isArray(outputShapes[0]) && outputShapes.length === 1) {\n        return outputShapes[0];\n      } else {\n        return outputShapes;\n      }\n    } else {\n      throw new AttributeError(`The layer ${this.name} has multiple inbound nodes with different output shapes. Hence the notion of \"output shape\" is ill-defined for the layer.`);\n    }\n  }\n  countParams() {\n    if (!this.built) {\n      throw new RuntimeError(`You tried to call countParams() on ${this.name}, but the layer is not built yet. Build it first by calling build(batchInputShape).`);\n    }\n    return countParamsInWeights(this.weights);\n  }\n  build(inputShape) {\n    this.built = true;\n  }\n  getWeights(trainableOnly = false) {\n    return batchGetValue(trainableOnly ? this.trainableWeights : this.weights);\n  }\n  setWeights(weights) {\n    tidy(() => {\n      const params = this.weights;\n      if (params.length !== weights.length) {\n        throw new ValueError(`You called setWeights(weights) on layer \"${this.name}\" with a weight list of length ${weights.length}, but the layer was expecting ${params.length} weights. Provided weights: ${weights}...`);\n      }\n      if (params.length === 0) {\n        return;\n      }\n      const weightValueTuples = [];\n      const paramValues = batchGetValue(params);\n      for (let i = 0; i < paramValues.length; ++i) {\n        const pv = paramValues[i];\n        const p2 = params[i];\n        const w = weights[i];\n        if (!util_exports.arraysEqual(pv.shape, w.shape)) {\n          throw new ValueError(`Layer weight shape ${pv.shape} not compatible with provided weight shape ${w.shape}`);\n        }\n        weightValueTuples.push([p2, w]);\n      }\n      batchSetValue(weightValueTuples);\n    });\n  }\n  addWeight(name, shape, dtype, initializer, regularizer, trainable, constraint, getInitializerFunc) {\n    if (this._addedWeightNames.indexOf(name) !== -1) {\n      throw new ValueError(`Duplicate weight name ${name} for layer ${this.name}`);\n    }\n    this._addedWeightNames.push(name);\n    if (dtype == null) {\n      dtype = \"float32\";\n    }\n    if (this.fastWeightInitDuringBuild) {\n      initializer = getInitializerFunc != null ? getInitializerFunc() : getInitializer(\"zeros\");\n    }\n    const initValue = initializer.apply(shape, dtype);\n    const weight = new LayerVariable(initValue, dtype, name, trainable, constraint);\n    initValue.dispose();\n    if (regularizer != null) {\n      this.addLoss(() => regularizer.apply(weight.read()));\n    }\n    if (trainable == null) {\n      trainable = true;\n    }\n    if (trainable) {\n      this._trainableWeights.push(weight);\n    } else {\n      this._nonTrainableWeights.push(weight);\n    }\n    return weight;\n  }\n  setFastWeightInitDuringBuild(value) {\n    this.fastWeightInitDuringBuild = value;\n  }\n  addLoss(losses2) {\n    if (losses2 == null || Array.isArray(losses2) && losses2.length === 0) {\n      return;\n    }\n    losses2 = toList(losses2);\n    if (this._losses !== void 0 && this._losses !== null) {\n      this.losses.push(...losses2);\n    }\n  }\n  computeOutputShape(inputShape) {\n    return inputShape;\n  }\n  computeMask(inputs, mask) {\n    if (!this.supportsMasking) {\n      if (mask != null) {\n        if (Array.isArray(mask)) {\n          mask.forEach((maskElement) => {\n            if (maskElement != null) {\n              throw new TypeError(`Layer ${this.name} does not support masking, but was passed an inputMask.`);\n            }\n          });\n        } else {\n          throw new TypeError(`Layer ${this.name} does not support masking, but was passed an inputMask.`);\n        }\n      }\n      return null;\n    }\n    return mask;\n  }\n  addInboundNode(inputTensors, outputTensors, inputMasks, outputMasks, inputShapes, outputShapes, kwargs = null) {\n    const inputTensorList = toList(inputTensors);\n    outputTensors = toList(outputTensors);\n    inputMasks = toList(inputMasks);\n    outputMasks = toList(outputMasks);\n    inputShapes = normalizeShapeList(inputShapes);\n    outputShapes = normalizeShapeList(outputShapes);\n    const inboundLayers = [];\n    const nodeIndices = [];\n    const tensorIndices = [];\n    for (const x of inputTensorList) {\n      inboundLayers.push(x.sourceLayer);\n      nodeIndices.push(x.nodeIndex);\n      tensorIndices.push(x.tensorIndex);\n    }\n    new Node({\n      outboundLayer: this,\n      inboundLayers,\n      nodeIndices,\n      tensorIndices,\n      inputTensors: inputTensorList,\n      outputTensors,\n      inputMasks,\n      outputMasks,\n      inputShapes,\n      outputShapes\n    }, kwargs);\n    for (let i = 0; i < outputTensors.length; i++) {\n      outputTensors[i].sourceLayer = this;\n      outputTensors[i].nodeIndex = this.inboundNodes.length - 1;\n      outputTensors[i].tensorIndex = i;\n    }\n  }\n  getConfig() {\n    const config = { name: this.name, trainable: this.trainable };\n    if (this.batchInputShape != null) {\n      config[\"batchInputShape\"] = this.batchInputShape;\n    }\n    if (this.dtype != null) {\n      config[\"dtype\"] = this.dtype;\n    }\n    return config;\n  }\n  disposeWeights() {\n    this.weights.forEach((weight) => weight.dispose());\n    return this.weights.length;\n  }\n  assertNotDisposed() {\n    if (this._refCount === 0) {\n      throw new Error(`Layer '${this.name}' is already disposed.`);\n    }\n  }\n  dispose() {\n    if (!this.built) {\n      throw new Error(`Cannot dispose Layer ${this.name} because it has not been built yet.`);\n    }\n    if (this._refCount === null) {\n      throw new Error(`Cannot dispose Layer ${this.name} because it has not been used yet.`);\n    }\n    this.assertNotDisposed();\n    let numDisposedVariables = 0;\n    if (--this._refCount === 0) {\n      numDisposedVariables = this.disposeWeights();\n    }\n    return { refCountAfterDispose: this._refCount, numDisposedVariables };\n  }\n};\nfunction collectInputShape(inputTensors) {\n  inputTensors = toList(inputTensors);\n  const shapes = [];\n  for (const x of inputTensors) {\n    shapes.push(x.shape);\n  }\n  return singletonOrArray(shapes);\n}\nfunction guessOutputDType(inputTensors) {\n  return \"float32\";\n}\nfunction getSourceInputs(tensor2, layer, nodeIndex) {\n  if (layer == null || nodeIndex != null && nodeIndex > 0) {\n    layer = tensor2.sourceLayer;\n    nodeIndex = tensor2.nodeIndex;\n  }\n  if (layer.inboundNodes.length === 0) {\n    return [tensor2];\n  } else {\n    const node = layer.inboundNodes[nodeIndex];\n    if (node.inboundLayers.length === 0) {\n      return node.inputTensors;\n    } else {\n      const sourceTensors = [];\n      for (let i = 0; i < node.inboundLayers.length; i++) {\n        const x = node.inputTensors[i];\n        const layer2 = node.inboundLayers[i];\n        const nodeIndex2 = node.nodeIndices[i];\n        const previousSources = getSourceInputs(x, layer2, nodeIndex2);\n        for (const x2 of previousSources) {\n          if (sourceTensors.indexOf(x2) === -1) {\n            sourceTensors.push(x2);\n          }\n        }\n      }\n      return sourceTensors;\n    }\n  }\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-layers@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-layers/dist/engine/input_layer.js\nvar InputLayer = class extends Layer {\n  constructor(args) {\n    super({\n      dtype: args.dtype,\n      name: args.name != null ? args.name : getUid(\"input\").toString()\n    });\n    if (args.batchSize == null) {\n      args.batchSize = null;\n    }\n    if (args.sparse == null) {\n      args.sparse = false;\n    }\n    this.trainable = false;\n    this.built = true;\n    this.sparse = args.sparse;\n    if (args.inputShape != null && args.batchInputShape != null) {\n      throw new ValueError(\"Only provide the inputShape OR batchInputShape argument to inputLayer, not both at the same time.\");\n    }\n    let batchInputShape = args.batchInputShape;\n    if (batchInputShape == null) {\n      if (args.inputShape == null) {\n        throw new ValueError(\"An InputLayer should be passed either a `batchInputShape` or an `inputShape`.\");\n      } else {\n        batchInputShape = [args.batchSize].concat(args.inputShape);\n      }\n    } else {\n      if (args.batchSize != null) {\n        throw new ValueError(\"Cannot specify batchSize if batchInputShape is specified when creating an InputLayer.\");\n      }\n    }\n    const dtype = args.dtype || \"float32\";\n    this.batchInputShape = batchInputShape;\n    this.dtype = dtype;\n    this.inputSpec = [{ shape: batchInputShape }];\n    const inputTensor = new SymbolicTensor(this.dtype, this.batchInputShape, this, [], {}, this.name);\n    inputTensor.nodeIndex = 0;\n    inputTensor.tensorIndex = 0;\n    new Node({\n      outboundLayer: this,\n      inboundLayers: [],\n      nodeIndices: [],\n      tensorIndices: [],\n      inputTensors: [inputTensor],\n      outputTensors: [inputTensor],\n      inputMasks: [null],\n      outputMasks: [null],\n      inputShapes: [batchInputShape],\n      outputShapes: [batchInputShape]\n    });\n  }\n  apply(inputs, kwargs) {\n    throw new ValueError(`Cannot pass any input to an InputLayer's apply() method. InputLayer name: ${this.name}`);\n  }\n  dispose() {\n    return { refCountAfterDispose: this._refCount, numDisposedVariables: 0 };\n  }\n  getConfig() {\n    return {\n      batchInputShape: this.batchInputShape,\n      dtype: this.dtype,\n      sparse: this.sparse,\n      name: this.name\n    };\n  }\n};\nInputLayer.className = \"InputLayer\";\nserialization_exports.registerClass(InputLayer);\nfunction Input(config) {\n  if (config.batchShape == null && config.shape == null) {\n    throw new Error(\"Please provide to Input either a `shape` or a `batchShape` argument. Note that `shape` does not include the batch dimension.\");\n  }\n  if (config.batchShape != null && config.shape != null) {\n    throw new ValueError(\"Please provide either a `shape` or `batchShape` argument to Input, but not both.\");\n  }\n  let batchShape = config.batchShape;\n  if (config.shape != null && batchShape == null) {\n    batchShape = [null].concat(config.shape);\n  }\n  let dtype = config.dtype;\n  if (dtype == null) {\n    dtype = \"float32\";\n  }\n  const inputLayer2 = new InputLayer({\n    batchInputShape: batchShape,\n    name: config.name,\n    dtype,\n    sparse: config.sparse\n  });\n  const outputs = inputLayer2.inboundNodes[0].outputTensors;\n  return outputs[0];\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-layers@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-layers/dist/engine/executor.js\nfunction assertFeedCompatibility(key, val) {\n  if (key.dtype == null || key.dtype === val.dtype) {\n    return val;\n  }\n  try {\n    return cast(val, key.dtype);\n  } catch (err) {\n    throw new ValueError(`The dtype of the feed (${val.dtype}) can not be cast to the dtype of the key '${key.name}' (${key.dtype}).`);\n  }\n}\nvar FeedDict = class {\n  constructor(feeds) {\n    this.id2Value = {};\n    this.id2Mask = {};\n    this.name2Id = {};\n    if (feeds instanceof FeedDict) {\n      for (const id in feeds.id2Value) {\n        this.id2Value[id] = feeds.id2Value[id];\n        if (id in feeds.id2Mask) {\n          this.id2Mask[id] = feeds.id2Mask[id];\n        }\n      }\n    } else {\n      if (feeds == null) {\n        return;\n      }\n      for (const feed of feeds) {\n        this.add(feed.key, feed.value);\n      }\n    }\n  }\n  add(key, value, mask) {\n    if (this.id2Value[key.id] == null) {\n      this.id2Value[key.id] = assertFeedCompatibility(key, value);\n      this.name2Id[key.name] = key.id;\n      if (mask != null) {\n        this.id2Mask[key.id] = mask;\n      }\n    } else {\n      throw new ValueError(`Duplicate key: name=${key.name}, id=${key.id}`);\n    }\n    return this;\n  }\n  addFeed(feed) {\n    this.add(feed.key, feed.value);\n  }\n  hasKey(key) {\n    return this.id2Value[key.id] != null;\n  }\n  names() {\n    return Object.keys(this.name2Id);\n  }\n  getValue(key) {\n    if (key instanceof SymbolicTensor) {\n      if (this.id2Value[key.id] == null) {\n        throw new ValueError(`Nonexistent key: ${key.name}`);\n      } else {\n        return this.id2Value[key.id];\n      }\n    } else {\n      const id = this.name2Id[key];\n      if (id == null) {\n        throw new ValueError(`Feed dict has no SymbolicTensor name: ${key}`);\n      }\n      return this.id2Value[id];\n    }\n  }\n  getMask(key) {\n    if (key instanceof SymbolicTensor) {\n      if (this.id2Value[key.id] == null) {\n        throw new ValueError(`Nonexistent key: ${key.name}`);\n      } else {\n        return this.id2Mask[key.id];\n      }\n    } else {\n      const id = this.name2Id[key];\n      if (id == null) {\n        throw new ValueError(`Feed dict has no SymbolicTensor name: ${key}`);\n      }\n      return this.id2Mask[id];\n    }\n  }\n  disposeMasks() {\n    if (this.id2Mask != null) {\n      dispose(this.id2Mask);\n    }\n  }\n};\nvar cachedSorted = new LruCache();\nvar cachedRecipientCounts = new LruCache();\nfunction updateCacheMaxEntries(maxEntries) {\n  if (cachedSorted != null) {\n    cachedSorted.setMaxEntries(maxEntries);\n  }\n  if (cachedRecipientCounts != null) {\n    cachedRecipientCounts.setMaxEntries(maxEntries);\n  }\n}\nfunction execute(fetches, feedDict, kwargs, probe) {\n  const training = kwargs == null ? false : kwargs[\"training\"];\n  const arrayFetches = Array.isArray(fetches);\n  const fetchArray = arrayFetches ? fetches : [fetches];\n  const outputNames = fetchArray.map((t) => t.name);\n  const finalOutputs = [];\n  const feedNames = feedDict.names();\n  for (const outputName of outputNames) {\n    if (feedNames.indexOf(outputName) !== -1) {\n      finalOutputs.push(feedDict.getValue(outputName));\n    } else {\n      finalOutputs.push(null);\n    }\n  }\n  if (probe != null) {\n    probe.maxNumTensors = -Infinity;\n    probe.minNumTensors = Infinity;\n  }\n  const fetchAndFeedKey = outputNames.join(\",\") + \"|\" + feedDict.names().sort().join(\",\");\n  let sorted = cachedSorted.get(fetchAndFeedKey);\n  let recipientCounts;\n  if (sorted == null) {\n    const out = getTopologicalSortAndRecipientCounts(fetchArray, feedDict);\n    sorted = out.sorted;\n    recipientCounts = out.recipientCounts;\n    cachedSorted.put(fetchAndFeedKey, sorted);\n    cachedRecipientCounts.put(fetchAndFeedKey, recipientCounts);\n  }\n  recipientCounts = {};\n  if (!training) {\n    Object.assign(recipientCounts, cachedRecipientCounts.get(fetchAndFeedKey));\n  }\n  const internalFeedDict = new FeedDict(feedDict);\n  for (let i = 0; i < sorted.length; ++i) {\n    if (probe != null) {\n      const numTensors = memory().numTensors;\n      if (numTensors > probe.maxNumTensors) {\n        probe.maxNumTensors = numTensors;\n      }\n      if (numTensors < probe.minNumTensors) {\n        probe.minNumTensors = numTensors;\n      }\n    }\n    const symbolic = sorted[i];\n    const srcLayer = symbolic.sourceLayer;\n    if (srcLayer instanceof InputLayer) {\n      continue;\n    }\n    const inputValues = [];\n    const inputMasks = [];\n    const tensorsToDispose = [];\n    let maskExists = false;\n    for (const input2 of symbolic.inputs) {\n      const value = internalFeedDict.getValue(input2);\n      const mask = internalFeedDict.getMask(input2);\n      inputValues.push(value);\n      inputMasks.push(mask);\n      if (mask != null) {\n        maskExists = true;\n      }\n      if (!training) {\n        recipientCounts[input2.name]--;\n        if (recipientCounts[input2.name] === 0 && !feedDict.hasKey(input2) && outputNames.indexOf(input2.name) === -1 && !value.isDisposed && input2.sourceLayer.stateful !== true) {\n          tensorsToDispose.push(value);\n        }\n      }\n    }\n    if (maskExists) {\n      kwargs = kwargs || {};\n      kwargs[\"mask\"] = inputMasks[0];\n    }\n    const outputTensors = toList(srcLayer.apply(inputValues, kwargs));\n    let outputMask = null;\n    if (srcLayer.supportsMasking) {\n      outputMask = srcLayer.computeMask(inputValues, inputMasks);\n    }\n    const layerOutputs = getNodeOutputs(symbolic);\n    const outputSymbolicTensors = Array.isArray(layerOutputs) ? layerOutputs : [layerOutputs];\n    for (let i2 = 0; i2 < outputSymbolicTensors.length; ++i2) {\n      if (!internalFeedDict.hasKey(outputSymbolicTensors[i2])) {\n        internalFeedDict.add(outputSymbolicTensors[i2], outputTensors[i2], Array.isArray(outputMask) ? outputMask[0] : outputMask);\n      }\n      const index = outputNames.indexOf(outputSymbolicTensors[i2].name);\n      if (index !== -1) {\n        finalOutputs[index] = outputTensors[i2];\n      }\n    }\n    if (!training) {\n      dispose(tensorsToDispose);\n    }\n  }\n  internalFeedDict.disposeMasks();\n  return arrayFetches ? finalOutputs : finalOutputs[0];\n}\nfunction getTopologicalSortAndRecipientCounts(fetches, feedDict) {\n  util_exports.assert(fetches != null && fetches.length > 0, () => `Expected at least one fetch, got none`);\n  let finalSorted = [];\n  let finalRecipientMap = {};\n  if (fetches.length === 1) {\n    const out = getTopologicalSortAndRecipientCountsForOneFetch(fetches[0], feedDict);\n    finalSorted = out.sorted;\n    finalRecipientMap = out.recipientMap;\n  } else {\n    const visited = /* @__PURE__ */ new Set();\n    for (const fetch4 of fetches) {\n      const { sorted, recipientMap } = getTopologicalSortAndRecipientCountsForOneFetch(fetch4, feedDict);\n      for (const symbolicTensor of sorted) {\n        if (!visited.has(symbolicTensor.name)) {\n          finalSorted.push(symbolicTensor);\n          visited.add(symbolicTensor.name);\n        }\n      }\n      for (const name in recipientMap) {\n        if (finalRecipientMap[name] == null) {\n          finalRecipientMap[name] = /* @__PURE__ */ new Set();\n        }\n        recipientMap[name].forEach((recipient) => finalRecipientMap[name].add(recipient));\n      }\n    }\n  }\n  return {\n    sorted: finalSorted,\n    recipientCounts: recipientMap2Counts(finalRecipientMap)\n  };\n}\nfunction recipientMap2Counts(recipientMap) {\n  const recipientCounts = {};\n  for (const name in recipientMap) {\n    recipientCounts[name] = recipientMap[name].size;\n  }\n  return recipientCounts;\n}\nfunction getTopologicalSortAndRecipientCountsForOneFetch(fetch4, feedDict) {\n  const visited = /* @__PURE__ */ new Set();\n  const sorted = [];\n  const recipientMap = {};\n  for (const key of feedDict.names()) {\n    visited.add(key);\n  }\n  const stack2 = [];\n  const marks = [];\n  stack2.push(fetch4);\n  while (stack2.length > 0) {\n    const top = stack2[stack2.length - 1];\n    if (visited.has(top.name)) {\n      stack2.pop();\n      continue;\n    }\n    const topIsMarked = marks[marks.length - 1] === stack2.length - 1;\n    if (top.inputs.length === 0 || topIsMarked) {\n      stack2.pop();\n      sorted.push(top);\n      visited.add(top.name);\n      if (topIsMarked) {\n        marks.pop();\n      }\n    } else {\n      marks.push(stack2.length - 1);\n      for (const input2 of top.inputs) {\n        if (recipientMap[input2.name] == null) {\n          recipientMap[input2.name] = /* @__PURE__ */ new Set();\n        }\n        recipientMap[input2.name].add(top.name);\n        if (visited.has(input2.name)) {\n          continue;\n        }\n        stack2.push(input2);\n      }\n    }\n  }\n  return { sorted, recipientMap };\n}\nfunction getNodeOutputs(fetch4) {\n  let layerOutputs;\n  if (fetch4.sourceLayer.inboundNodes.length === 1) {\n    layerOutputs = fetch4.sourceLayer.output;\n  } else {\n    let nodeIndex = null;\n    for (let i = 0; i < fetch4.sourceLayer.inboundNodes.length; ++i) {\n      for (const outputTensor of fetch4.sourceLayer.inboundNodes[i].outputTensors) {\n        if (outputTensor.id === fetch4.id) {\n          nodeIndex = i;\n          break;\n        }\n      }\n    }\n    layerOutputs = fetch4.sourceLayer.getOutputAt(nodeIndex);\n  }\n  return layerOutputs;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-layers@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-layers/dist/flags_layers.js\nvar ENV3 = env();\nENV3.registerFlag(\"TOPOLOGICAL_SORT_CACHE_MAX_ENTRIES\", () => 100, updateCacheMaxEntries);\n\n// node_modules/.pnpm/@tensorflow+tfjs-layers@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-layers/dist/exports_constraints.js\nvar exports_constraints_exports = {};\n__export(exports_constraints_exports, {\n  maxNorm: () => maxNorm,\n  minMaxNorm: () => minMaxNorm,\n  nonNeg: () => nonNeg,\n  unitNorm: () => unitNorm\n});\n\n// node_modules/.pnpm/@tensorflow+tfjs-layers@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-layers/dist/constraints.js\nfunction calcL2Norms(w, axis) {\n  return tidy(() => sqrt(sum2(mul(w, w), axis, true)));\n}\nvar Constraint = class extends serialization_exports.Serializable {\n  getConfig() {\n    return {};\n  }\n};\nvar MaxNorm = class extends Constraint {\n  constructor(args) {\n    super();\n    this.defaultMaxValue = 2;\n    this.defaultAxis = 0;\n    this.maxValue = args.maxValue != null ? args.maxValue : this.defaultMaxValue;\n    this.axis = args.axis != null ? args.axis : this.defaultAxis;\n  }\n  apply(w) {\n    return tidy(() => {\n      const norms = calcL2Norms(w, this.axis);\n      const desired = clipByValue(norms, 0, this.maxValue);\n      return mul(w, div(desired, add2(epsilon(), norms)));\n    });\n  }\n  getConfig() {\n    return { maxValue: this.maxValue, axis: this.axis };\n  }\n};\nMaxNorm.className = \"MaxNorm\";\nserialization_exports.registerClass(MaxNorm);\nvar UnitNorm = class extends Constraint {\n  constructor(args) {\n    super();\n    this.defaultAxis = 0;\n    this.axis = args.axis != null ? args.axis : this.defaultAxis;\n  }\n  apply(w) {\n    return tidy(() => div(w, add2(epsilon(), calcL2Norms(w, this.axis))));\n  }\n  getConfig() {\n    return { axis: this.axis };\n  }\n};\nUnitNorm.className = \"UnitNorm\";\nserialization_exports.registerClass(UnitNorm);\nvar NonNeg = class extends Constraint {\n  apply(w) {\n    return relu(w);\n  }\n};\nNonNeg.className = \"NonNeg\";\nserialization_exports.registerClass(NonNeg);\nvar MinMaxNorm = class extends Constraint {\n  constructor(args) {\n    super();\n    this.defaultMinValue = 0;\n    this.defaultMaxValue = 1;\n    this.defaultRate = 1;\n    this.defaultAxis = 0;\n    this.minValue = args.minValue != null ? args.minValue : this.defaultMinValue;\n    this.maxValue = args.maxValue != null ? args.maxValue : this.defaultMaxValue;\n    this.rate = args.rate != null ? args.rate : this.defaultRate;\n    this.axis = args.axis != null ? args.axis : this.defaultAxis;\n  }\n  apply(w) {\n    return tidy(() => {\n      const norms = calcL2Norms(w, this.axis);\n      const desired = add2(mul(this.rate, clipByValue(norms, this.minValue, this.maxValue)), mul(1 - this.rate, norms));\n      return mul(w, div(desired, add2(epsilon(), norms)));\n    });\n  }\n  getConfig() {\n    return {\n      minValue: this.minValue,\n      maxValue: this.maxValue,\n      rate: this.rate,\n      axis: this.axis\n    };\n  }\n};\nMinMaxNorm.className = \"MinMaxNorm\";\nserialization_exports.registerClass(MinMaxNorm);\nvar CONSTRAINT_IDENTIFIER_REGISTRY_SYMBOL_MAP = {\n  \"maxNorm\": \"MaxNorm\",\n  \"minMaxNorm\": \"MinMaxNorm\",\n  \"nonNeg\": \"NonNeg\",\n  \"unitNorm\": \"UnitNorm\"\n};\nfunction serializeConstraint(constraint) {\n  return serializeKerasObject(constraint);\n}\nfunction deserializeConstraint(config, customObjects = {}) {\n  return deserializeKerasObject(config, serialization_exports.SerializationMap.getMap().classNameMap, customObjects, \"constraint\");\n}\nfunction getConstraint(identifier) {\n  if (identifier == null) {\n    return null;\n  }\n  if (typeof identifier === \"string\") {\n    const className = identifier in CONSTRAINT_IDENTIFIER_REGISTRY_SYMBOL_MAP ? CONSTRAINT_IDENTIFIER_REGISTRY_SYMBOL_MAP[identifier] : identifier;\n    const config = { className, config: {} };\n    return deserializeConstraint(config);\n  } else if (identifier instanceof Constraint) {\n    return identifier;\n  } else {\n    return deserializeConstraint(identifier);\n  }\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-layers@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-layers/dist/exports_constraints.js\nfunction maxNorm(args) {\n  return new MaxNorm(args);\n}\nfunction unitNorm(args) {\n  return new UnitNorm(args);\n}\nfunction nonNeg() {\n  return new NonNeg();\n}\nfunction minMaxNorm(config) {\n  return new MinMaxNorm(config);\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-layers@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-layers/dist/exports_initializers.js\nvar exports_initializers_exports = {};\n__export(exports_initializers_exports, {\n  constant: () => constant,\n  glorotNormal: () => glorotNormal,\n  glorotUniform: () => glorotUniform,\n  heNormal: () => heNormal,\n  heUniform: () => heUniform,\n  identity: () => identity,\n  leCunNormal: () => leCunNormal,\n  leCunUniform: () => leCunUniform,\n  ones: () => ones3,\n  orthogonal: () => orthogonal,\n  randomNormal: () => randomNormal3,\n  randomUniform: () => randomUniform2,\n  truncatedNormal: () => truncatedNormal2,\n  varianceScaling: () => varianceScaling,\n  zeros: () => zeros2\n});\nfunction zeros2() {\n  return new Zeros();\n}\nfunction ones3() {\n  return new Ones();\n}\nfunction constant(args) {\n  return new Constant(args);\n}\nfunction randomUniform2(args) {\n  return new RandomUniform(args);\n}\nfunction randomNormal3(args) {\n  return new RandomNormal(args);\n}\nfunction truncatedNormal2(args) {\n  return new TruncatedNormal(args);\n}\nfunction identity(args) {\n  return new Identity2(args);\n}\nfunction varianceScaling(config) {\n  return new VarianceScaling(config);\n}\nfunction glorotUniform(args) {\n  return new GlorotUniform(args);\n}\nfunction glorotNormal(args) {\n  return new GlorotNormal(args);\n}\nfunction heNormal(args) {\n  return new HeNormal(args);\n}\nfunction heUniform(args) {\n  return new HeUniform(args);\n}\nfunction leCunNormal(args) {\n  return new LeCunNormal(args);\n}\nfunction leCunUniform(args) {\n  return new LeCunUniform(args);\n}\nfunction orthogonal(args) {\n  return new Orthogonal(args);\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-layers@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-layers/dist/exports_layers.js\nvar exports_layers_exports = {};\n__export(exports_layers_exports, {\n  Layer: () => Layer,\n  RNN: () => RNN,\n  RNNCell: () => RNNCell,\n  activation: () => activation,\n  add: () => add3,\n  alphaDropout: () => alphaDropout,\n  average: () => average,\n  averagePooling1d: () => averagePooling1d,\n  averagePooling2d: () => averagePooling2d,\n  averagePooling3d: () => averagePooling3d,\n  avgPool1d: () => avgPool1d,\n  avgPool2d: () => avgPool2d,\n  avgPool3d: () => avgPool3d2,\n  avgPooling1d: () => avgPooling1d,\n  avgPooling2d: () => avgPooling2d,\n  avgPooling3d: () => avgPooling3d,\n  batchNormalization: () => batchNormalization2,\n  bidirectional: () => bidirectional,\n  concatenate: () => concatenate2,\n  conv1d: () => conv1d2,\n  conv2d: () => conv2d3,\n  conv2dTranspose: () => conv2dTranspose2,\n  conv3d: () => conv3d2,\n  conv3dTranspose: () => conv3dTranspose2,\n  convLstm2d: () => convLstm2d,\n  convLstm2dCell: () => convLstm2dCell,\n  cropping2D: () => cropping2D,\n  dense: () => dense,\n  depthwiseConv2d: () => depthwiseConv2d4,\n  dot: () => dot3,\n  dropout: () => dropout3,\n  elu: () => elu3,\n  embedding: () => embedding,\n  flatten: () => flatten3,\n  gaussianDropout: () => gaussianDropout,\n  gaussianNoise: () => gaussianNoise,\n  globalAveragePooling1d: () => globalAveragePooling1d,\n  globalAveragePooling2d: () => globalAveragePooling2d,\n  globalMaxPool1d: () => globalMaxPool1d,\n  globalMaxPool2d: () => globalMaxPool2d,\n  globalMaxPooling1d: () => globalMaxPooling1d,\n  globalMaxPooling2d: () => globalMaxPooling2d,\n  gru: () => gru,\n  gruCell: () => gruCell,\n  input: () => input,\n  inputLayer: () => inputLayer,\n  layerNormalization: () => layerNormalization,\n  leakyReLU: () => leakyReLU,\n  lstm: () => lstm,\n  lstmCell: () => lstmCell,\n  masking: () => masking,\n  maxPool1d: () => maxPool1d,\n  maxPool2d: () => maxPool2d,\n  maxPooling1d: () => maxPooling1d,\n  maxPooling2d: () => maxPooling2d,\n  maxPooling3d: () => maxPooling3d,\n  maximum: () => maximum2,\n  minimum: () => minimum2,\n  multiply: () => multiply,\n  permute: () => permute,\n  prelu: () => prelu2,\n  reLU: () => reLU,\n  repeatVector: () => repeatVector,\n  reshape: () => reshape2,\n  rnn: () => rnn2,\n  separableConv2d: () => separableConv2d2,\n  simpleRNN: () => simpleRNN,\n  simpleRNNCell: () => simpleRNNCell,\n  softmax: () => softmax2,\n  spatialDropout1d: () => spatialDropout1d,\n  stackedRNNCells: () => stackedRNNCells,\n  thresholdedReLU: () => thresholdedReLU,\n  timeDistributed: () => timeDistributed,\n  upSampling2d: () => upSampling2d,\n  zeroPadding2d: () => zeroPadding2d\n});\n\n// node_modules/.pnpm/@tensorflow+tfjs-layers@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-layers/dist/logs.js\nasync function resolveScalarsInLogs(logs) {\n  if (logs == null) {\n    return;\n  }\n  const promises = [];\n  const keys = [];\n  const scalarsToDispose = [];\n  for (const key in logs) {\n    const value = logs[key];\n    if (typeof value !== \"number\") {\n      const valueScalar = value;\n      promises.push(valueScalar.data());\n      keys.push(key);\n      scalarsToDispose.push(valueScalar);\n    }\n  }\n  if (promises.length > 0) {\n    const values = await Promise.all(promises);\n    for (let i = 0; i < values.length; ++i) {\n      logs[keys[i]] = values[i][0];\n    }\n    dispose(scalarsToDispose);\n  }\n}\nfunction disposeTensorsInLogs(logs) {\n  if (logs == null) {\n    return;\n  }\n  for (const key in logs) {\n    const value = logs[key];\n    if (typeof value !== \"number\") {\n      value.dispose();\n    }\n  }\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-layers@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-layers/dist/base_callbacks.js\nvar ModelLoggingVerbosity;\n(function(ModelLoggingVerbosity2) {\n  ModelLoggingVerbosity2[ModelLoggingVerbosity2[\"SILENT\"] = 0] = \"SILENT\";\n  ModelLoggingVerbosity2[ModelLoggingVerbosity2[\"VERBOSE\"] = 1] = \"VERBOSE\";\n})(ModelLoggingVerbosity || (ModelLoggingVerbosity = {}));\nvar DEFAULT_YIELD_EVERY_MS = 125;\nvar BaseCallback = class {\n  constructor() {\n    this.validationData = null;\n  }\n  setParams(params) {\n    this.params = params;\n  }\n  async onEpochBegin(epoch, logs) {\n  }\n  async onEpochEnd(epoch, logs) {\n  }\n  async onBatchBegin(batch, logs) {\n  }\n  async onBatchEnd(batch, logs) {\n  }\n  async onTrainBegin(logs) {\n  }\n  async onTrainEnd(logs) {\n  }\n  setModel(model2) {\n  }\n};\nvar CallbackList = class {\n  constructor(callbacks2, queueLength = 10) {\n    if (callbacks2 == null) {\n      callbacks2 = [];\n    }\n    this.callbacks = callbacks2;\n    this.queueLength = queueLength;\n  }\n  append(callback) {\n    this.callbacks.push(callback);\n  }\n  setParams(params) {\n    for (const callback of this.callbacks) {\n      callback.setParams(params);\n    }\n  }\n  setModel(model2) {\n    for (const callback of this.callbacks) {\n      callback.setModel(model2);\n    }\n  }\n  async onEpochBegin(epoch, logs) {\n    if (logs == null) {\n      logs = {};\n    }\n    for (const callback of this.callbacks) {\n      await callback.onEpochBegin(epoch, logs);\n    }\n  }\n  async onEpochEnd(epoch, logs) {\n    if (logs == null) {\n      logs = {};\n    }\n    for (const callback of this.callbacks) {\n      await callback.onEpochEnd(epoch, logs);\n    }\n  }\n  async onBatchBegin(batch, logs) {\n    if (logs == null) {\n      logs = {};\n    }\n    for (const callback of this.callbacks) {\n      await callback.onBatchBegin(batch, logs);\n    }\n  }\n  async onBatchEnd(batch, logs) {\n    if (logs == null) {\n      logs = {};\n    }\n    for (const callback of this.callbacks) {\n      await callback.onBatchEnd(batch, logs);\n    }\n  }\n  async onTrainBegin(logs) {\n    if (logs == null) {\n      logs = {};\n    }\n    for (const callback of this.callbacks) {\n      await callback.onTrainBegin(logs);\n    }\n  }\n  async onTrainEnd(logs) {\n    if (logs == null) {\n      logs = {};\n    }\n    for (const callback of this.callbacks) {\n      await callback.onTrainEnd(logs);\n    }\n  }\n};\nvar BaseLogger = class extends BaseCallback {\n  constructor() {\n    super();\n  }\n  async onEpochBegin(epoch) {\n    this.seen = 0;\n    this.totals = {};\n  }\n  async onBatchEnd(batch, logs) {\n    if (logs == null) {\n      logs = {};\n    }\n    const batchSize = logs[\"size\"] == null ? 0 : logs[\"size\"];\n    this.seen += batchSize;\n    for (const key in logs) {\n      const value = logs[key];\n      if (typeof value === \"number\") {\n        if (!this.totals.hasOwnProperty(key)) {\n          this.totals[key] = 0;\n        }\n        this.totals[key] = this.totals[key] + value * batchSize;\n      } else {\n        let oldTotalsToDispose;\n        if (key in this.totals) {\n          oldTotalsToDispose = this.totals[key];\n        } else {\n          this.totals[key] = 0;\n        }\n        const total = tidy(() => add2(this.totals[key], mul(value, batchSize)));\n        this.totals[key] = total;\n        if (oldTotalsToDispose != null) {\n          oldTotalsToDispose.dispose();\n        }\n      }\n    }\n  }\n  async onEpochEnd(epoch, logs) {\n    if (logs != null) {\n      for (const key of this.params[\"metrics\"]) {\n        if (this.totals[key] == null) {\n          continue;\n        }\n        if (typeof this.totals[key] === \"number\") {\n          logs[key] = this.totals[key] / this.seen;\n        } else {\n          tidy(() => {\n            const log6 = mul(div(1, this.seen), this.totals[key]);\n            logs[key] = log6;\n            this.totals[key].dispose();\n            keep(logs[key]);\n          });\n        }\n      }\n    }\n  }\n};\nvar History = class extends BaseCallback {\n  async onTrainBegin(logs) {\n    this.epoch = [];\n    this.history = {};\n  }\n  async onEpochEnd(epoch, logs) {\n    if (logs == null) {\n      logs = {};\n    }\n    this.epoch.push(epoch);\n    for (const key in logs) {\n      if (this.history[key] == null) {\n        this.history[key] = [];\n      }\n      this.history[key].push(logs[key]);\n    }\n  }\n  async syncData() {\n    const promises = [];\n    const keys = [];\n    const indices = [];\n    for (const key in this.history) {\n      const valueArray = this.history[key];\n      for (let i = 0; i < valueArray.length; ++i) {\n        if (typeof valueArray[i] !== \"number\") {\n          const valueScalar = valueArray[i];\n          promises.push(valueScalar.data());\n          keys.push(key);\n          indices.push(i);\n        }\n      }\n    }\n    const values = await Promise.all(promises);\n    for (let n = 0; n < values.length; ++n) {\n      const tensorToDispose = this.history[keys[n]][indices[n]];\n      tensorToDispose.dispose();\n      this.history[keys[n]][indices[n]] = values[n][0];\n    }\n  }\n};\nvar CustomCallback = class extends BaseCallback {\n  constructor(args, yieldEvery) {\n    super();\n    this.currentEpoch = 0;\n    this.nowFunc = args.nowFunc;\n    this.nextFrameFunc = args.nextFrameFunc || nextFrame;\n    this.yieldEvery = yieldEvery || \"auto\";\n    if (this.yieldEvery === \"auto\") {\n      this.yieldEvery = DEFAULT_YIELD_EVERY_MS;\n    }\n    if (this.yieldEvery === \"never\" && args.onYield != null) {\n      throw new Error(\"yieldEvery is `never` but you provided an `onYield` callback. Either change `yieldEvery` or remove the callback\");\n    }\n    if (util_exports.isNumber(this.yieldEvery)) {\n      this.maybeWait = debounce(this.maybeWait.bind(this), this.yieldEvery, this.nowFunc);\n    }\n    this.trainBegin = args.onTrainBegin;\n    this.trainEnd = args.onTrainEnd;\n    this.epochBegin = args.onEpochBegin;\n    this.epochEnd = args.onEpochEnd;\n    this.batchBegin = args.onBatchBegin;\n    this.batchEnd = args.onBatchEnd;\n    this.yield = args.onYield;\n  }\n  async maybeWait(epoch, batch, logs) {\n    const ps = [];\n    if (this.yield != null) {\n      await resolveScalarsInLogs(logs);\n      ps.push(this.yield(epoch, batch, logs));\n    }\n    ps.push(this.nextFrameFunc());\n    await Promise.all(ps);\n  }\n  async onEpochBegin(epoch, logs) {\n    this.currentEpoch = epoch;\n    if (this.epochBegin != null) {\n      await resolveScalarsInLogs(logs);\n      await this.epochBegin(epoch, logs);\n    }\n  }\n  async onEpochEnd(epoch, logs) {\n    const ps = [];\n    if (this.epochEnd != null) {\n      await resolveScalarsInLogs(logs);\n      ps.push(this.epochEnd(epoch, logs));\n    }\n    if (this.yieldEvery === \"epoch\") {\n      ps.push(this.nextFrameFunc());\n    }\n    await Promise.all(ps);\n  }\n  async onBatchBegin(batch, logs) {\n    if (this.batchBegin != null) {\n      await resolveScalarsInLogs(logs);\n      await this.batchBegin(batch, logs);\n    }\n  }\n  async onBatchEnd(batch, logs) {\n    const ps = [];\n    if (this.batchEnd != null) {\n      await resolveScalarsInLogs(logs);\n      ps.push(this.batchEnd(batch, logs));\n    }\n    if (this.yieldEvery === \"batch\") {\n      ps.push(this.nextFrameFunc());\n    } else if (util_exports.isNumber(this.yieldEvery)) {\n      ps.push(this.maybeWait(this.currentEpoch, batch, logs));\n    }\n    await Promise.all(ps);\n  }\n  async onTrainBegin(logs) {\n    if (this.trainBegin != null) {\n      await resolveScalarsInLogs(logs);\n      await this.trainBegin(logs);\n    }\n  }\n  async onTrainEnd(logs) {\n    if (this.trainEnd != null) {\n      await resolveScalarsInLogs(logs);\n      await this.trainEnd(logs);\n    }\n  }\n};\nfunction standardizeCallbacks(callbacks2, yieldEvery) {\n  if (callbacks2 == null) {\n    callbacks2 = {};\n  }\n  if (callbacks2 instanceof BaseCallback) {\n    return [callbacks2];\n  }\n  if (Array.isArray(callbacks2) && callbacks2[0] instanceof BaseCallback) {\n    return callbacks2;\n  }\n  const callbackConfigs = toList(callbacks2);\n  return callbackConfigs.map((callbackConfig) => new CustomCallback(callbackConfig, yieldEvery));\n}\nvar CallbackConstructorRegistry = class {\n  constructor() {\n  }\n  static registerCallbackConstructor(verbosityLevel, callbackConstructor) {\n    util_exports.assert(verbosityLevel >= 0 && Number.isInteger(verbosityLevel), () => `Verbosity level is expected to be an integer >= 0, but got ${verbosityLevel}`);\n    CallbackConstructorRegistry.checkForDuplicate(callbackConstructor);\n    if (CallbackConstructorRegistry.constructors[verbosityLevel] == null) {\n      CallbackConstructorRegistry.constructors[verbosityLevel] = [];\n    }\n    CallbackConstructorRegistry.constructors[verbosityLevel].push(callbackConstructor);\n  }\n  static checkForDuplicate(callbackConstructor) {\n    for (const levelName in CallbackConstructorRegistry.constructors) {\n      const constructors = CallbackConstructorRegistry.constructors[+levelName];\n      constructors.forEach((ctor) => {\n        if (ctor === callbackConstructor) {\n          throw new ValueError(\"Duplicate callback constructor.\");\n        }\n      });\n    }\n  }\n  static clear() {\n    CallbackConstructorRegistry.constructors = {};\n  }\n  static createCallbacks(verbosityLevel) {\n    const constructors = [];\n    for (const levelName in CallbackConstructorRegistry.constructors) {\n      const level = +levelName;\n      if (verbosityLevel >= level) {\n        constructors.push(...CallbackConstructorRegistry.constructors[level]);\n      }\n    }\n    return constructors.map((ctor) => new ctor());\n  }\n};\nCallbackConstructorRegistry.constructors = {};\nfunction configureCallbacks(callbacks2, verbose, epochs, initialEpoch, numTrainSamples, stepsPerEpoch, batchSize, doValidation, callbackMetrics) {\n  const history = new History();\n  const actualCallbacks = [\n    new BaseLogger(),\n    ...CallbackConstructorRegistry.createCallbacks(verbose)\n  ];\n  if (callbacks2 != null) {\n    actualCallbacks.push(...callbacks2);\n  }\n  actualCallbacks.push(history);\n  const callbackList = new CallbackList(actualCallbacks);\n  callbackList.setParams({\n    epochs,\n    initialEpoch,\n    samples: numTrainSamples,\n    steps: stepsPerEpoch,\n    batchSize,\n    verbose,\n    doValidation,\n    metrics: callbackMetrics\n  });\n  return { callbackList, history };\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-layers@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-layers/dist/layers/serialization.js\nfunction deserialize(config, customObjects = {}, fastWeightInit = false) {\n  return deserializeKerasObject(config, serialization_exports.SerializationMap.getMap().classNameMap, customObjects, \"layer\", fastWeightInit);\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-layers@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-layers/dist/losses.js\nfunction l2Normalize(x, axis) {\n  return tidy(() => {\n    if (x.dtype !== \"float32\") {\n      x = cast(x, \"float32\");\n    }\n    const squareSum = sum2(square2(x), axis, true);\n    const epsilonTensor = fill(squareSum.shape, epsilon());\n    const norm2 = sqrt(maximum(squareSum, epsilonTensor));\n    return div(x, norm2);\n  });\n}\nfunction meanSquaredError2(yTrue, yPred) {\n  return tidy(() => mean(square2(sub(yPred, yTrue)), -1));\n}\nfunction meanAbsoluteError(yTrue, yPred) {\n  return tidy(() => mean(abs(sub(yPred, yTrue)), -1));\n}\nfunction meanAbsolutePercentageError(yTrue, yPred) {\n  return tidy(() => {\n    const diff = sub(yTrue, yPred);\n    const clippedTrue = clipByValue(abs(yTrue), epsilon(), Number.MAX_VALUE);\n    const absResult = abs(div(diff, clippedTrue));\n    return mul(100, mean(absResult, -1));\n  });\n}\nfunction meanSquaredLogarithmicError(yTrue, yPred) {\n  return tidy(() => {\n    const clippedPred = clipByValue(yPred, epsilon(), Number.MAX_VALUE);\n    const firstLog = log2(add2(1, clippedPred));\n    const clippedTrue = clipByValue(yTrue, epsilon(), Number.MAX_VALUE);\n    const secondLog = log2(add2(1, clippedTrue));\n    return mean(square2(sub(firstLog, secondLog)), -1);\n  });\n}\nfunction squaredHinge(yTrue, yPred) {\n  return tidy(() => {\n    const maxResult = maximum(0, sub(1, mul(yTrue, yPred)));\n    return mean(square2(maxResult), -1);\n  });\n}\nfunction hinge(yTrue, yPred) {\n  return tidy(() => {\n    const maxResult = maximum(0, sub(1, mul(yTrue, yPred)));\n    return mean(maxResult, -1);\n  });\n}\nfunction categoricalHinge(yTrue, yPred) {\n  return tidy(() => {\n    const pos = sum2(mul(yTrue, yPred), -1);\n    const neg5 = max(mul(sub(1, yTrue), yPred), -1);\n    return maximum(0, add2(1, sub(neg5, pos)));\n  });\n}\nfunction logcosh(yTrue, yPred) {\n  return tidy(() => {\n    const log22 = Math.log(2);\n    const predictionDiff = sub(yPred, yTrue);\n    const logcoshResult = sub(add2(predictionDiff, softplus(mul(-2, predictionDiff))), log22);\n    return mean(logcoshResult, -1);\n  });\n}\nfunction categoricalCrossentropy(target, output, fromLogits = false) {\n  return tidy(() => {\n    if (fromLogits) {\n      output = softmax(output);\n    } else {\n      const outputSum = sum2(output, output.shape.length - 1, true);\n      output = div(output, outputSum);\n    }\n    output = clipByValue(output, epsilon(), 1 - epsilon());\n    return neg(sum2(mul(cast(target, \"float32\"), log2(output)), output.shape.length - 1));\n  });\n}\nfunction sparseCategoricalCrossentropy(target, output, fromLogits = false) {\n  return tidy(() => {\n    const flatTarget = cast(floor(flatten2(target)), \"int32\");\n    output = clipByValue(output, epsilon(), 1 - epsilon());\n    const outputShape = output.shape;\n    const oneHotTarget = reshape(oneHot(flatTarget, outputShape[outputShape.length - 1]), outputShape);\n    return categoricalCrossentropy(oneHotTarget, output, fromLogits);\n  });\n}\nfunction sigmoidCrossEntropyWithLogits(labels, logits) {\n  if (!util_exports.arraysEqual(labels.shape, logits.shape)) {\n    throw new ValueError(`logits and labels must have the same shape, but got shapes ${JSON.stringify(labels.shape)} and ${JSON.stringify(logits.shape)}`);\n  }\n  return tidy(() => {\n    const reluLogits = relu(logits);\n    const negAbsLogits = neg(abs(logits));\n    return add2(sub(reluLogits, mul(logits, labels)), log1p(exp(negAbsLogits)));\n  });\n}\nfunction binaryCrossentropy(yTrue, yPred) {\n  return tidy(() => {\n    let y;\n    y = clipByValue(yPred, epsilon(), 1 - epsilon());\n    y = log2(div(y, sub(1, y)));\n    return mean(sigmoidCrossEntropyWithLogits(yTrue, y), -1);\n  });\n}\nfunction kullbackLeiblerDivergence(yTrue, yPred) {\n  return tidy(() => {\n    const clippedTrue = clipByValue(yTrue, epsilon(), 1);\n    const clippedPred = clipByValue(yPred, epsilon(), 1);\n    return sum2(mul(yTrue, log2(div(clippedTrue, clippedPred))), -1);\n  });\n}\nfunction poisson(yTrue, yPred) {\n  return tidy(() => {\n    const logPred = log2(add2(epsilon(), yPred));\n    return mean(sub(yPred, mul(yTrue, logPred)), -1);\n  });\n}\nfunction cosineProximity(yTrue, yPred) {\n  return tidy(() => {\n    const trueNormalized = l2Normalize(yTrue, -1);\n    const predNormalized = l2Normalize(yPred, -1);\n    const trueXPred = mul(trueNormalized, predNormalized);\n    return neg(sum2(trueXPred, -1));\n  });\n}\nvar lossesMap = {\n  meanSquaredError: meanSquaredError2,\n  meanAbsoluteError,\n  meanAbsolutePercentageError,\n  meanSquaredLogarithmicError,\n  squaredHinge,\n  hinge,\n  categoricalHinge,\n  logcosh,\n  categoricalCrossentropy,\n  sparseCategoricalCrossentropy,\n  binaryCrossentropy,\n  kullbackLeiblerDivergence,\n  poisson,\n  cosineProximity\n};\nfunction get(identifierOrFn) {\n  if (typeof identifierOrFn === \"string\") {\n    if (identifierOrFn in lossesMap) {\n      return lossesMap[identifierOrFn];\n    }\n    let errMsg = `Unknown loss ${identifierOrFn}`;\n    if (identifierOrFn.toLowerCase().includes(\"softmaxcrossentropy\")) {\n      errMsg = `Unknown loss ${identifierOrFn}. Use \"categoricalCrossentropy\" as the string name for tf.losses.softmaxCrossEntropy`;\n    }\n    throw new ValueError(errMsg);\n  } else {\n    return identifierOrFn;\n  }\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-layers@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-layers/dist/metrics.js\nfunction binaryAccuracy(yTrue, yPred) {\n  return tidy(() => {\n    const threshold3 = mul(0.5, onesLike(yPred));\n    const yPredThresholded = cast2(greater(yPred, threshold3), yTrue.dtype);\n    return mean(equal(yTrue, yPredThresholded), -1);\n  });\n}\nfunction categoricalAccuracy(yTrue, yPred) {\n  return tidy(() => cast2(equal(argMax(yTrue, -1), argMax(yPred, -1)), \"float32\"));\n}\nfunction truePositives(yTrue, yPred) {\n  return tidy(() => {\n    return cast(sum2(logicalAnd(equal(yTrue, 1), equal(yPred, 1))), \"float32\");\n  });\n}\nfunction falseNegatives(yTrue, yPred) {\n  return tidy(() => {\n    return cast(sum2(logicalAnd(equal(yTrue, 1), equal(yPred, 0))), \"float32\");\n  });\n}\nfunction falsePositives(yTrue, yPred) {\n  return tidy(() => {\n    return cast(sum2(logicalAnd(equal(yTrue, 0), equal(yPred, 1))), \"float32\");\n  });\n}\nfunction precision(yTrue, yPred) {\n  return tidy(() => {\n    const tp = truePositives(yTrue, yPred);\n    const fp = falsePositives(yTrue, yPred);\n    const denominator = add2(tp, fp);\n    return cast(where(greater(denominator, 0), div(tp, denominator), 0), \"float32\");\n  });\n}\nfunction recall(yTrue, yPred) {\n  return tidy(() => {\n    const tp = truePositives(yTrue, yPred);\n    const fn = falseNegatives(yTrue, yPred);\n    const denominator = add2(tp, fn);\n    return cast(where(greater(denominator, 0), div(tp, denominator), 0), \"float32\");\n  });\n}\nfunction binaryCrossentropy2(yTrue, yPred) {\n  return binaryCrossentropy(yTrue, yPred);\n}\nfunction sparseCategoricalAccuracy(yTrue, yPred) {\n  if (yTrue.rank === yPred.rank) {\n    yTrue = squeeze(yTrue, [yTrue.rank - 1]);\n  }\n  yPred = argMax(yPred, -1);\n  if (yPred.dtype !== yTrue.dtype) {\n    yPred = cast(yPred, yTrue.dtype);\n  }\n  return cast(equal(yTrue, yPred), \"float32\");\n}\nvar mse = meanSquaredError2;\nvar MSE = meanSquaredError2;\nvar mae = meanAbsoluteError;\nvar MAE = meanAbsoluteError;\nvar mape = meanAbsolutePercentageError;\nvar MAPE = meanAbsolutePercentageError;\nvar categoricalCrossentropy2 = categoricalCrossentropy;\nvar cosine = cosineProximity;\nvar sparseCategoricalCrossentropy2 = sparseCategoricalCrossentropy;\nvar metricsMap = {\n  binaryAccuracy,\n  categoricalAccuracy,\n  precision,\n  categoricalCrossentropy: categoricalCrossentropy2,\n  sparseCategoricalCrossentropy: sparseCategoricalCrossentropy2,\n  mse,\n  MSE,\n  mae,\n  MAE,\n  mape,\n  MAPE,\n  cosine\n};\nfunction get2(identifier) {\n  if (typeof identifier === \"string\" && identifier in metricsMap) {\n    return metricsMap[identifier];\n  } else if (typeof identifier !== \"string\" && identifier != null) {\n    return identifier;\n  } else {\n    throw new ValueError(`Unknown metric ${identifier}`);\n  }\n}\nfunction getLossOrMetricName(fn) {\n  assert2(fn !== null, `Unknown LossOrMetricFn ${fn}`);\n  if (typeof fn === \"string\") {\n    return fn;\n  } else {\n    let fnName;\n    for (const key of Object.keys(lossesMap)) {\n      if (lossesMap[key] === fn) {\n        fnName = key;\n        break;\n      }\n    }\n    if (fnName !== void 0) {\n      return fnName;\n    }\n    for (const key of Object.keys(metricsMap)) {\n      if (metricsMap[key] === fn) {\n        fnName = key;\n        break;\n      }\n    }\n    if (fnName !== void 0) {\n      return fnName;\n    }\n    return fn.name;\n  }\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-layers@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-layers/dist/optimizers.js\nfunction getOptimizer(identifier) {\n  const optimizerMap = {\n    \"Adagrad\": () => train.adagrad(0.01),\n    \"Adadelta\": () => train.adadelta(1, 0.95, epsilon()),\n    \"Adam\": () => train.adam(1e-3, 0.9, 0.999, epsilon()),\n    \"Adamax\": () => train.adamax(2e-3, 0.9, 0.999, epsilon(), 0),\n    \"RMSProp\": () => train.rmsprop(1e-3, 0.9, 0, epsilon()),\n    \"SGD\": () => train.sgd(0.01)\n  };\n  optimizerMap[\"adagrad\"] = optimizerMap[\"Adagrad\"];\n  optimizerMap[\"adadelta\"] = optimizerMap[\"Adadelta\"];\n  optimizerMap[\"adam\"] = optimizerMap[\"Adam\"];\n  optimizerMap[\"adamax\"] = optimizerMap[\"Adamax\"];\n  optimizerMap[\"rmsprop\"] = optimizerMap[\"RMSProp\"];\n  optimizerMap[\"sgd\"] = optimizerMap[\"SGD\"];\n  if (identifier in optimizerMap) {\n    return optimizerMap[identifier]();\n  }\n  throw new ValueError(`Unknown Optimizer ${identifier}`);\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-layers@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-layers/dist/user_defined_metadata.js\nvar MAX_USER_DEFINED_METADATA_SERIALIZED_LENGTH = 1 * 1024 * 1024;\nfunction checkUserDefinedMetadata(userDefinedMetadata, modelName, checkSize = false) {\n  if (userDefinedMetadata == null || typeof userDefinedMetadata !== \"object\" || Object.getPrototypeOf(userDefinedMetadata) !== Object.prototype || !plainObjectCheck(userDefinedMetadata)) {\n    throw new Error(\"User-defined metadata is expected to be a JSON object, but is not.\");\n  }\n  if (checkSize) {\n    const out = JSON.stringify(userDefinedMetadata);\n    if (out.length > MAX_USER_DEFINED_METADATA_SERIALIZED_LENGTH) {\n      console.warn(`User-defined metadata of model \"${modelName}\" is too large in size (length=${out.length} when serialized). It is not recommended to store such large objects in user-defined metadata. Please make sure its serialized length is <= ${MAX_USER_DEFINED_METADATA_SERIALIZED_LENGTH}.`);\n    }\n  }\n}\nfunction plainObjectCheck(x) {\n  if (x === null) {\n    return true;\n  } else if (typeof x === \"object\") {\n    if (Object.getPrototypeOf(x) === Object.prototype) {\n      const keys = Object.keys(x);\n      for (const key of keys) {\n        if (typeof key !== \"string\") {\n          return false;\n        }\n        if (!plainObjectCheck(x[key])) {\n          return false;\n        }\n      }\n      return true;\n    } else {\n      if (Array.isArray(x)) {\n        for (const item of x) {\n          if (!plainObjectCheck(item)) {\n            return false;\n          }\n        }\n        return true;\n      } else {\n        return false;\n      }\n    }\n  } else {\n    const xType = typeof x;\n    return xType === \"string\" || xType === \"number\" || xType === \"boolean\";\n  }\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-layers@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-layers/dist/utils/layer_utils.js\nfunction printSummary(model2, lineLength, positions, printFn = console.log) {\n  const sequentialLike = isModelSequentialLike(model2);\n  const toDisplay = [\"Layer (type)\", \"Input Shape\", \"Output shape\", \"Param #\"];\n  if (sequentialLike) {\n    lineLength = lineLength || 90;\n    positions = positions || [0.32, 0.61, 0.89, 1];\n  } else {\n    lineLength = lineLength || 115;\n    positions = positions || [0.24, 0.48, 0.7, 0.8, 1];\n  }\n  if (positions[positions.length - 1] <= 1) {\n    positions = positions.map((p2) => Math.floor(lineLength * p2));\n  }\n  let relevantNodes;\n  if (!sequentialLike) {\n    toDisplay.push(\"Receives inputs\");\n    relevantNodes = [];\n    for (const depth in model2.nodesByDepth) {\n      relevantNodes.push(...model2.nodesByDepth[depth]);\n    }\n  }\n  printFn(\"_\".repeat(lineLength));\n  printRow(toDisplay, positions, printFn);\n  printFn(\"=\".repeat(lineLength));\n  const layers = model2.layers;\n  for (let i = 0; i < layers.length; ++i) {\n    if (sequentialLike) {\n      printLayerSummary(layers[i], positions, printFn);\n    } else {\n      printLayerSummaryWithConnections(layers[i], positions, relevantNodes, printFn);\n    }\n    printFn((i === layers.length - 1 ? \"=\" : \"_\").repeat(lineLength));\n  }\n  model2.checkTrainableWeightsConsistency();\n  const trainableCount = countTrainableParams(model2);\n  const nonTrainableCount = countParamsInWeights(model2.nonTrainableWeights);\n  printFn(`Total params: ${trainableCount + nonTrainableCount}`);\n  printFn(`Trainable params: ${trainableCount}`);\n  printFn(`Non-trainable params: ${nonTrainableCount}`);\n  printFn(\"_\".repeat(lineLength));\n}\nfunction countTrainableParams(model2) {\n  let trainableCount;\n  if (model2.collectedTrainableWeights != null) {\n    trainableCount = countParamsInWeights(model2.collectedTrainableWeights);\n  } else {\n    trainableCount = countParamsInWeights(model2.trainableWeights);\n  }\n  return trainableCount;\n}\nfunction isModelSequentialLike(model2) {\n  let sequentialLike = true;\n  const nodesByDepth = [];\n  const nodes = [];\n  for (const depth in model2.nodesByDepth) {\n    nodesByDepth.push(model2.nodesByDepth[depth]);\n  }\n  for (const depthNodes of nodesByDepth) {\n    if (depthNodes.length > 1 || depthNodes.length === 1 && depthNodes[0].inboundLayers.length > 1) {\n      sequentialLike = false;\n      break;\n    }\n    nodes.push(...depthNodes);\n  }\n  if (sequentialLike) {\n    for (const layer of model2.layers) {\n      let flag = false;\n      for (const node of layer.inboundNodes) {\n        if (nodes.indexOf(node) !== -1) {\n          if (flag) {\n            sequentialLike = false;\n            break;\n          } else {\n            flag = true;\n          }\n        }\n      }\n      if (!sequentialLike) {\n        break;\n      }\n    }\n  }\n  return sequentialLike;\n}\nfunction printRow(fields, positions, printFn = console.log) {\n  let line = \"\";\n  for (let i = 0; i < fields.length; ++i) {\n    if (i > 0) {\n      line = line.slice(0, line.length - 1) + \" \";\n    }\n    line += fields[i];\n    line = line.slice(0, positions[i]);\n    line += \" \".repeat(positions[i] - line.length);\n  }\n  printFn(line);\n}\nfunction printLayerSummary(layer, positions, printFn) {\n  let outputShape;\n  let inputShape;\n  try {\n    inputShape = layer.inboundNodes.map((x) => JSON.stringify(x.inputShapes)).join(\",\");\n  } catch (err) {\n    inputShape = \"multiple\";\n  }\n  try {\n    outputShape = JSON.stringify(layer.outputShape);\n  } catch (err) {\n    outputShape = \"multiple\";\n  }\n  const name = layer.name;\n  const className = layer.getClassName();\n  const fields = [\n    `${name} (${className})`,\n    inputShape,\n    outputShape,\n    layer.countParams().toString()\n  ];\n  printRow(fields, positions, printFn);\n}\nfunction printLayerSummaryWithConnections(layer, positions, relevantNodes, printFn) {\n  let outputShape;\n  let inputShape;\n  try {\n    inputShape = layer.inboundNodes.map((x) => JSON.stringify(x.inputShapes)).join(\",\");\n  } catch (err) {\n    inputShape = \"multiple\";\n  }\n  try {\n    outputShape = JSON.stringify(layer.outputShape);\n  } catch (err) {\n    outputShape = \"multiple\";\n  }\n  const connections = [];\n  for (const node of layer.inboundNodes) {\n    if (relevantNodes != null && relevantNodes.length > 0 && relevantNodes.indexOf(node) === -1) {\n      continue;\n    }\n    for (let i = 0; i < node.inboundLayers.length; ++i) {\n      const inboundLayer = node.inboundLayers[i].name;\n      const inboundLayerIndex = node.nodeIndices[i];\n      const inboundTensorIndex = node.tensorIndices[i];\n      connections.push(`${inboundLayer}[${inboundLayerIndex}][${inboundTensorIndex}]`);\n    }\n  }\n  const name = layer.name;\n  const className = layer.getClassName();\n  const firstConnection = connections.length === 0 ? \"\" : connections[0];\n  const fields = [\n    `${name} (${className})`,\n    inputShape,\n    outputShape,\n    layer.countParams().toString(),\n    firstConnection\n  ];\n  printRow(fields, positions, printFn);\n  for (let i = 1; i < connections.length; ++i) {\n    printRow([\"\", \"\", \"\", \"\", connections[i]], positions, printFn);\n  }\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-layers@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-layers/dist/utils/serialization_utils.js\nfunction isArrayItemInputOrOutputName(key, index, value) {\n  return (key === \"inboundNodes\" || key === \"outputLayers\" || key === \"inputLayers\") && index === 0 && typeof value === \"string\";\n}\nfunction convertPythonicToTs(pythonicConfig, key) {\n  if (pythonicConfig === null) {\n    return null;\n  } else if (typeof pythonicConfig === \"string\") {\n    return toCamelCase(pythonicConfig);\n  } else if (typeof pythonicConfig === \"number\" || typeof pythonicConfig === \"boolean\") {\n    return pythonicConfig;\n  } else if (pythonicConfig instanceof Array) {\n    const tsArray = [];\n    const arrayLength = pythonicConfig.length;\n    for (let i = 0; i < arrayLength; ++i) {\n      const item = pythonicConfig[i];\n      if (isArrayItemInputOrOutputName(key, i, item)) {\n        tsArray.push(item);\n      } else {\n        tsArray.push(convertPythonicToTs(item, key));\n      }\n    }\n    return tsArray;\n  } else {\n    const tsDict = {};\n    for (const pythonicKey of Object.keys(pythonicConfig)) {\n      const pythonicValue = pythonicConfig[pythonicKey];\n      if (pythonicKey === \"name\" && typeof pythonicValue === \"string\") {\n        tsDict[pythonicKey] = pythonicValue;\n      } else {\n        const tsKey = toCamelCase(pythonicKey);\n        tsDict[tsKey] = convertPythonicToTs(pythonicValue, tsKey);\n      }\n    }\n    return tsDict;\n  }\n}\nfunction convertTsToPythonic(tsConfig, key) {\n  if (tsConfig === null || tsConfig === void 0) {\n    return null;\n  } else if (typeof tsConfig === \"string\") {\n    return toSnakeCase(tsConfig);\n  } else if (typeof tsConfig === \"number\" || typeof tsConfig === \"boolean\") {\n    return tsConfig;\n  } else if (tsConfig instanceof Array) {\n    const pyArray = [];\n    const arrayLength = tsConfig.length;\n    for (let i = 0; i < arrayLength; ++i) {\n      const item = tsConfig[i];\n      if (isArrayItemInputOrOutputName(key, i, item)) {\n        pyArray.push(item);\n      } else {\n        pyArray.push(convertTsToPythonic(item, key));\n      }\n    }\n    return pyArray;\n  } else {\n    const pyDict = {};\n    for (const tsKey of Object.keys(tsConfig)) {\n      const tsValue = tsConfig[tsKey];\n      const pyKey = toSnakeCase(tsKey);\n      if ((tsKey === \"name\" || tsKey === \"className\") && typeof tsValue === \"string\") {\n        pyDict[pyKey] = tsValue;\n      } else {\n        pyDict[pyKey] = convertTsToPythonic(tsValue, tsKey);\n      }\n    }\n    return pyDict;\n  }\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-layers@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-layers/dist/version.js\nvar version2 = \"3.20.0\";\n\n// node_modules/.pnpm/@tensorflow+tfjs-layers@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-layers/dist/engine/container.js\nvar Container = class extends Layer {\n  constructor(args) {\n    super({});\n    this.containerNodes = /* @__PURE__ */ new Set();\n    this.name = args.name;\n    if (this.name == null) {\n      const prefix = this.getClassName().toLowerCase();\n      this.name = getUid(prefix);\n    }\n    this.supportsMasking = false;\n    this.trainable_ = true;\n    if (Array.isArray(args.inputs)) {\n      this.inputs = args.inputs.slice();\n    } else {\n      this.inputs = [args.inputs];\n    }\n    if (Array.isArray(args.outputs)) {\n      this.outputs = args.outputs.slice();\n    } else {\n      this.outputs = [args.outputs];\n    }\n    if (unique2(this.inputs).length !== this.inputs.length) {\n      throw new ValueError(`The list of inputs passed to the model is redundant. All inputs should only appear once. Found: ${this.inputs.map((x) => x.name)}`);\n    }\n    if (unique2(this.outputs).length !== this.outputs.length) {\n      console.warn(`The list of outputs passed to the model is redundant. All outputs should only appear once. Found: ${this.outputs.map((x) => x.name)}`);\n    }\n    this.inputLayers = [];\n    this.inputLayersNodeIndices = [];\n    this.inputLayersTensorIndices = [];\n    this.outputLayers = [];\n    this.outputLayersNodeIndices = [];\n    this.outputLayersTensorIndices = [];\n    this.layers = [];\n    this.internalContainerRefs = [];\n    for (const x of this.outputs) {\n      const layer = x.sourceLayer;\n      const nodeIndex = x.nodeIndex;\n      const tensorIndex = x.tensorIndex;\n      this.outputLayers.push(layer);\n      this.outputLayersNodeIndices.push(nodeIndex);\n      this.outputLayersTensorIndices.push(tensorIndex);\n    }\n    for (const x of this.inputs) {\n      const layer = x.sourceLayer;\n      const nodeIndex = x.nodeIndex;\n      const tensorIndex = x.tensorIndex;\n      assert2(nodeIndex === 0, \"input layer has >1 nodes\");\n      assert2(tensorIndex === 0, \"input layer has >1 tensors\");\n      this.inputLayers.push(layer);\n      this.inputLayersNodeIndices.push(nodeIndex);\n      this.inputLayersTensorIndices.push(tensorIndex);\n    }\n    this.inputNames = [];\n    this.outputNames = [];\n    this.feedInputShapes = [];\n    this.feedInputNames = [];\n    this.feedOutputNames = [];\n    for (let i = 0; i < this.inputLayers.length; i++) {\n      const layer = this.inputLayers[i];\n      if (!(layer instanceof InputLayer)) {\n        throw new TypeError(`Input layers to a LayersModel must be InputLayer objects. Received inputs: ${args.inputs}. Input ${i} (0-based) originates from layer type ${layer.getClassName()}.`);\n      }\n      this.inputNames.push(layer.name);\n      this.feedInputShapes.push(layer.batchInputShape);\n      this.feedInputNames.push(layer.name);\n    }\n    for (const layer of this.outputLayers) {\n      this.outputNames.push(layer.name);\n    }\n    this.internalInputShapes = this.inputs.map((x) => x.shape);\n    this.internalOutputShapes = this.outputs.map((x) => x.shape);\n    const nodesDepths = {};\n    const nodeIDToNode = {};\n    const layersDepths = {};\n    const layerIDToLayer = {};\n    const layerIndices = {};\n    const nodesInDecreasingDepth = [];\n    const buildMapOfGraph = (tensor2, finishedNodes2, nodesInProgress2, layer, nodeIndex, tensorIndex) => {\n      if (layer == null || nodeIndex == null || tensorIndex == null) {\n        layer = tensor2.sourceLayer;\n        nodeIndex = tensor2.nodeIndex;\n        tensorIndex = tensor2.tensorIndex;\n      }\n      const node = layer.inboundNodes[nodeIndex];\n      if (nodesInProgress2.indexOf(node) !== -1) {\n        throw new RuntimeError(`The tensor ${tensor2.name} at layer \"${layer.name}\" is part of a cycle.`);\n      }\n      if (finishedNodes2.indexOf(node) !== -1) {\n        return;\n      }\n      this.containerNodes.add(Container.nodeKey(layer, nodeIndex));\n      if (!(layer.id in layerIndices)) {\n        layerIndices[layer.id] = Object.keys(layerIndices).length;\n      }\n      if (nodesInProgress2.indexOf(node) === -1) {\n        nodesInProgress2.push(node);\n      }\n      const numInboundLayers = node.inboundLayers.length;\n      for (let i = 0; i < numInboundLayers; i++) {\n        const x = node.inputTensors[i];\n        const layer2 = node.inboundLayers[i];\n        const nodeIndex2 = node.nodeIndices[i];\n        const tensorIndex2 = node.tensorIndices[i];\n        buildMapOfGraph(x, finishedNodes2, nodesInProgress2, layer2, nodeIndex2, tensorIndex2);\n      }\n      finishedNodes2.push(node);\n      while (nodesInProgress2.indexOf(node) >= 0) {\n        nodesInProgress2.splice(nodesInProgress2.indexOf(node), 1);\n      }\n      nodesInDecreasingDepth.push(node);\n    };\n    const finishedNodes = [];\n    const nodesInProgress = [];\n    for (const x of this.outputs) {\n      buildMapOfGraph(x, finishedNodes, nodesInProgress);\n    }\n    const reversedNodesInDecreasingDepth = nodesInDecreasingDepth.slice().reverse();\n    for (const node of reversedNodesInDecreasingDepth) {\n      nodeIDToNode[node.id] = node;\n      if (!(node.id in nodesDepths)) {\n        nodesDepths[node.id] = 0;\n      }\n      let depth = nodesDepths[node.id];\n      const previousDepth = layersDepths[node.outboundLayer.id] == null ? 0 : layersDepths[node.outboundLayer.id];\n      depth = Math.max(depth, previousDepth);\n      layersDepths[node.outboundLayer.id] = depth;\n      layerIDToLayer[node.outboundLayer.id] = node.outboundLayer;\n      nodesDepths[node.id] = depth;\n      for (let i = 0; i < node.inboundLayers.length; i++) {\n        const inboundLayer = node.inboundLayers[i];\n        const nodeIndex = node.nodeIndices[i];\n        const inboundNode = inboundLayer.inboundNodes[nodeIndex];\n        const previousDepth2 = nodesDepths[inboundNode.id] == null ? 0 : nodesDepths[inboundNode.id];\n        nodesDepths[inboundNode.id] = Math.max(depth + 1, previousDepth2);\n        nodeIDToNode[inboundNode.id] = inboundNode;\n      }\n    }\n    const nodesByDepth = {};\n    for (const nodeID in nodesDepths) {\n      const depth = nodesDepths[nodeID];\n      if (!(depth in nodesByDepth)) {\n        nodesByDepth[depth] = [];\n      }\n      nodesByDepth[depth].push(nodeIDToNode[nodeID]);\n    }\n    const layersByDepth = {};\n    for (const layerID in layersDepths) {\n      const depth = layersDepths[layerID];\n      if (!(depth in layersByDepth)) {\n        layersByDepth[depth] = [];\n      }\n      layersByDepth[depth].push(layerIDToLayer[layerID]);\n    }\n    let depthKeys = Object.keys(layersByDepth).map((x) => parseInt(x, 10)).sort(reverseNumberCompare);\n    this.layers = [];\n    for (const depth of depthKeys) {\n      const layersForDepth = layersByDepth[depth];\n      layersForDepth.sort((a, b) => {\n        const aIndex = layerIndices[a.id];\n        const bIndex = layerIndices[b.id];\n        if (aIndex < bIndex) {\n          return -1;\n        }\n        if (aIndex > bIndex) {\n          return 1;\n        }\n        return 0;\n      });\n      for (const layer of layersForDepth) {\n        if (layer instanceof Container) {\n          this.internalContainerRefs.push(layer);\n        }\n        this.layers.push(layer);\n      }\n    }\n    this.layersByDepth = layersByDepth;\n    depthKeys = Object.keys(nodesByDepth).map((x) => parseInt(x, 10)).sort(reverseNumberCompare);\n    const computableTensors = this.inputs.slice();\n    const layersWithCompleteInput = [];\n    for (const depth of depthKeys) {\n      for (const node of nodesByDepth[depth]) {\n        const layer = node.outboundLayer;\n        if (layer != null) {\n          for (const x of node.inputTensors) {\n            if (computableTensors.indexOf(x) === -1) {\n              throw new RuntimeError(`Graph disconnected: cannot obtain value for tensor ${x} at layer \"${layer.name}\". The following previous layers were accessed without issue: ${layersWithCompleteInput}`);\n            }\n          }\n          for (const x of node.outputTensors) {\n            computableTensors.push(x);\n          }\n          layersWithCompleteInput.push(layer.name);\n        }\n      }\n    }\n    this.nodesByDepth = nodesByDepth;\n    const allNames = this.layers.map((x) => x.name);\n    for (const name of allNames) {\n      const numOccurrences = allNames.filter((x) => x === name).length;\n      if (numOccurrences !== 1) {\n        throw new RuntimeError(`The name \"${name}\" is used ${numOccurrences} times in the model. All layer names should be unique. Layer names: ` + JSON.stringify(allNames));\n      }\n    }\n    this.outboundNodes = [];\n    this.inboundNodes = [];\n    new Node({\n      outboundLayer: this,\n      inboundLayers: [],\n      nodeIndices: [],\n      tensorIndices: [],\n      inputTensors: this.inputs,\n      outputTensors: this.outputs,\n      inputMasks: this.inputs.map((x) => null),\n      outputMasks: this.outputs.map((x) => null),\n      inputShapes: this.inputs.map((x) => x.shape),\n      outputShapes: this.outputs.map((x) => x.shape)\n    });\n    this.built = true;\n    this._refCount = 1;\n  }\n  assertNotDisposed() {\n    if (this._refCount === 0) {\n      throw new Error(`Container '${this.name}' is already disposed.`);\n    }\n  }\n  dispose() {\n    this.assertNotDisposed();\n    const result = { refCountAfterDispose: null, numDisposedVariables: 0 };\n    if (--this._refCount === 0) {\n      for (const layer of this.layers) {\n        result.numDisposedVariables += layer.dispose().numDisposedVariables;\n      }\n      for (const container of this.internalContainerRefs) {\n        result.numDisposedVariables += container.dispose().numDisposedVariables;\n      }\n    }\n    result.refCountAfterDispose = this._refCount;\n    return result;\n  }\n  get trainable() {\n    return this.trainable_;\n  }\n  set trainable(trainable) {\n    this.layers.forEach((layer) => {\n      layer._trainableWeights.forEach((w) => w.trainable = trainable);\n    });\n    this.trainable_ = trainable;\n  }\n  get trainableWeights() {\n    if (this._trainableWeights.length > 0) {\n      throw new ValueError(\"Container instance unexpectedly contains _trainableWeights.The trainable weights of a Container are a union of the trainable weights of its consituent Layers. Its own _trainableWeights must remain an empty Array.\");\n    }\n    if (!this.trainable) {\n      return [];\n    }\n    let weights = [];\n    for (const layer of this.layers) {\n      weights = weights.concat(layer.trainableWeights);\n    }\n    return weights;\n  }\n  get nonTrainableWeights() {\n    const weights = [];\n    for (const layer of this.layers) {\n      weights.push(...layer.nonTrainableWeights);\n    }\n    if (!this.trainable) {\n      const trainableWeights = [];\n      for (const layer of this.layers) {\n        trainableWeights.push(...layer.trainableWeights);\n      }\n      return trainableWeights.concat(weights);\n    }\n    return weights;\n  }\n  get weights() {\n    return this.trainableWeights.concat(this.nonTrainableWeights);\n  }\n  loadWeights(weights, strict = true) {\n    const nameToWeight = {};\n    let totalWeightsCount = 0;\n    for (const layer of this.layers) {\n      for (const weight of layer.weights) {\n        if (nameToWeight[weight.originalName] != null) {\n          throw new ValueError(`Duplicate weight name: ${weight.originalName}`);\n        }\n        nameToWeight[weight.originalName] = weight;\n        totalWeightsCount++;\n      }\n    }\n    const weightValueTuples = [];\n    for (const name in weights) {\n      let validatedName = name;\n      if (nameToWeight[name] == null) {\n        const tokens = name.split(\"/\");\n        const shortenNameArray = tokens.slice(0, -2).concat([tokens[tokens.length - 1]]);\n        validatedName = shortenNameArray.join(\"/\");\n      }\n      if (nameToWeight[validatedName] != null) {\n        weightValueTuples.push([nameToWeight[validatedName], weights[name]]);\n      } else if (strict) {\n        throw new ValueError(`Provided weight data has no target variable: ${name}`);\n      }\n      delete nameToWeight[validatedName];\n    }\n    if (strict) {\n      const unsetNames = [];\n      for (const name in nameToWeight) {\n        unsetNames.push(name);\n      }\n      if (unsetNames.length > 0) {\n        throw new ValueError(`${unsetNames.length} of ${totalWeightsCount} weights are not set: ${unsetNames}`);\n      }\n    }\n    batchSetValue(weightValueTuples);\n  }\n  updatedConfig() {\n    const theConfig = this.getConfig();\n    const modelConfig = {};\n    modelConfig[\"className\"] = this.getClassName();\n    modelConfig[\"config\"] = theConfig;\n    modelConfig[\"kerasVersion\"] = `tfjs-layers ${version2}`;\n    modelConfig[\"backend\"] = \"TensorFlow.js\";\n    return modelConfig;\n  }\n  toJSON(unused, returnString = true) {\n    const modelConfig = convertTsToPythonic(this.updatedConfig());\n    return returnString ? JSON.stringify(modelConfig) : modelConfig;\n  }\n  call(inputs, kwargs) {\n    return tidy(() => {\n      inputs = toList(inputs);\n      const feedDict = new FeedDict();\n      for (let i = 0; i < this.inputs.length; ++i) {\n        feedDict.add(this.inputs[i], inputs[i]);\n      }\n      return execute(this.outputs, feedDict, kwargs);\n    });\n  }\n  computeMask(inputs, mask) {\n    return tidy(() => {\n      inputs = toList(inputs);\n      let masks;\n      if (mask == null) {\n        masks = pyListRepeat(null, inputs.length);\n      } else {\n        masks = toList(mask);\n      }\n      return this.runInternalGraph(inputs, masks)[1];\n    });\n  }\n  computeOutputShape(inputShape) {\n    const inputShapes = normalizeShapeList(inputShape);\n    if (inputShapes.length !== this.inputLayers.length) {\n      throw new ValueError(`Invalid inputShape argument ${inputShape}: model has ${this.inputLayers.length} tensor inputs.`);\n    }\n    const layersToOutputShapes = {};\n    for (let i = 0; i < inputShapes.length; i++) {\n      const layer = this.inputLayers[i];\n      const inputShape2 = inputShapes[i];\n      const shapeKey = layer.name + \"_0_0\";\n      layersToOutputShapes[shapeKey] = inputShape2;\n    }\n    const depthKeys = Object.keys(this.nodesByDepth).map((x) => parseInt(x, 10)).sort(reverseNumberCompare);\n    if (depthKeys.length > 1) {\n      for (const depth of depthKeys) {\n        const nodes = this.nodesByDepth[depth];\n        for (const node of nodes) {\n          const layer = node.outboundLayer;\n          if (this.inputLayers.map((x) => x.id).indexOf(layer.id) !== -1) {\n            continue;\n          }\n          const inputShapes2 = [];\n          for (let j = 0; j < node.inboundLayers.length; j++) {\n            const inboundLayer = node.inboundLayers[j];\n            const nodeIndex2 = node.nodeIndices[j];\n            const tensorIndex = node.tensorIndices[j];\n            const shapeKey = `${inboundLayer.name}_${nodeIndex2}_${tensorIndex}`;\n            const inputShape2 = layersToOutputShapes[shapeKey];\n            inputShapes2.push(inputShape2);\n          }\n          const outputShape = layer.computeOutputShape(singletonOrArray(inputShapes2));\n          const outputShapes2 = normalizeShapeList(outputShape);\n          const nodeIndex = layer.inboundNodes.indexOf(node);\n          for (let j = 0; j < outputShapes2.length; j++) {\n            const shapeKey = `${layer.name}_${nodeIndex}_${j}`;\n            layersToOutputShapes[shapeKey] = outputShapes2[j];\n          }\n        }\n      }\n    }\n    const outputShapes = [];\n    const outputShapeKeys = [];\n    for (let i = 0; i < this.outputLayers.length; i++) {\n      const layer = this.outputLayers[i];\n      const nodeIndex = this.outputLayersNodeIndices[i];\n      const tensorIndex = this.outputLayersTensorIndices[i];\n      const shapeKey = `${layer.name}_${nodeIndex}_${tensorIndex}`;\n      outputShapeKeys.push(shapeKey);\n    }\n    for (let i = 0; i < outputShapeKeys.length; i++) {\n      const key = outputShapeKeys[i];\n      assert2(key in layersToOutputShapes);\n      outputShapes.push(layersToOutputShapes[key]);\n    }\n    return singletonOrArray(outputShapes);\n  }\n  runInternalGraph(inputs, masks) {\n    if (masks == null) {\n      masks = pyListRepeat(null, inputs.length);\n    }\n    const tensorMap = {};\n    for (let i = 0; i < this.inputs.length; ++i) {\n      const x = this.inputs[i];\n      const y = inputs[i];\n      const mask = masks[i];\n      tensorMap[x.id] = [y, mask];\n    }\n    const depthKeys = Object.keys(this.nodesByDepth).map((x) => parseInt(x, 10)).sort(reverseNumberCompare);\n    for (const depth of depthKeys) {\n      const nodes = this.nodesByDepth[depth];\n      for (const node of nodes) {\n        const layer = node.outboundLayer;\n        const referenceInputTensors = node.inputTensors;\n        const referenceOutputTensors = node.outputTensors;\n        const computedData = new Array();\n        for (const x of referenceInputTensors) {\n          if (x.id in tensorMap) {\n            computedData.push(tensorMap[x.id]);\n          }\n        }\n        if (computedData.length === referenceInputTensors.length) {\n          let kwargs = {};\n          let computedTensors;\n          let computedMasks;\n          let outputTensors2;\n          let outputMasks2;\n          if (node.callArgs != null) {\n            kwargs = node.callArgs;\n          }\n          if (computedData.length === 1) {\n            const [computedTensor, computedMask] = computedData[0];\n            if (kwargs[\"mask\"] == null) {\n              kwargs[\"mask\"] = computedMask;\n            }\n            outputTensors2 = toList(layer.call(computedTensor, kwargs));\n            outputMasks2 = toList(layer.computeMask(computedTensor, computedMask));\n            computedTensors = [computedTensor];\n            computedMasks = [computedMask];\n          } else {\n            computedTensors = computedData.map((x) => x[0]);\n            computedMasks = computedData.map((x) => x[1]);\n            if (kwargs[\"mask\"] == null) {\n              kwargs[\"mask\"] = computedMasks;\n            }\n            outputTensors2 = toList(layer.call(computedTensors, kwargs));\n            outputMasks2 = toList(layer.computeMask(computedTensors, computedMasks));\n          }\n          if (layer.activityRegularizer) {\n            throw new NotImplementedError(\"LayersModel invocation with concrete Tensor value(s) in the presence of activity regularizer(s) is not supported yet.\");\n          }\n          for (let i = 0; i < referenceOutputTensors.length; ++i) {\n            const x = referenceOutputTensors[i];\n            const y = outputTensors2[i];\n            const mask = outputMasks2[i];\n            tensorMap[x.id] = [y, mask];\n          }\n        }\n      }\n    }\n    const outputTensors = [];\n    const outputMasks = [];\n    const outputShapes = [];\n    for (const x of this.outputs) {\n      assert2(x.id in tensorMap, `Could not compute output ${x.name} : ${x.id}`);\n      const [tensor2, mask] = tensorMap[x.id];\n      outputShapes.push(tensor2.shape);\n      outputTensors.push(tensor2);\n      outputMasks.push(mask);\n    }\n    return [outputTensors, outputMasks, outputShapes];\n  }\n  buildNodeConversionMap(layers) {\n    const nodeConversionMap = {};\n    let keptNodes;\n    for (const layer of this.layers) {\n      keptNodes = layer instanceof Container ? 1 : 0;\n      for (let originalNodeIndex = 0; originalNodeIndex < layer.inboundNodes.length; originalNodeIndex++) {\n        const nodeKey = Container.nodeKey(layer, originalNodeIndex);\n        if (this.containerNodes.has(nodeKey)) {\n          nodeConversionMap[nodeKey] = keptNodes;\n          keptNodes += 1;\n        }\n      }\n    }\n    return nodeConversionMap;\n  }\n  getLayer(name, index) {\n    if (index != null) {\n      if (this.layers.length <= index) {\n        throw new ValueError(`Was asked to retrieve layer at index ${index}, but model only has ${this.layers.length} layer(s).`);\n      } else {\n        return this.layers[index];\n      }\n    } else {\n      if (name == null) {\n        throw new ValueError(\"Provide either a layer name or layer index\");\n      }\n    }\n    for (const layer of this.layers) {\n      if (layer.name === name) {\n        return layer;\n      }\n    }\n    throw new ValueError(`No such layer: ${name}`);\n  }\n  calculateLosses() {\n    return tidy(() => {\n      const losses2 = [];\n      for (const layer of this.layers) {\n        for (let nodeIndex = 0; nodeIndex < layer.inboundNodes.length; ++nodeIndex) {\n          const nodeKey = Container.nodeKey(layer, nodeIndex);\n          if (this.containerNodes.has(nodeKey)) {\n            losses2.push(...layer.calculateLosses());\n          }\n        }\n      }\n      return losses2;\n    });\n  }\n  getConfig() {\n    const config = { name: this.name };\n    const nodeConversionMap = this.buildNodeConversionMap(this.layers);\n    const layerConfigs = [];\n    for (const layer of this.layers) {\n      const layerClassName = layer.getClassName();\n      const layerConfig = layer.getConfig();\n      const filteredInboundNodes = [];\n      for (let originalNodeIndex = 0; originalNodeIndex < layer.inboundNodes.length; originalNodeIndex++) {\n        const node = layer.inboundNodes[originalNodeIndex];\n        const nodeKey = Container.nodeKey(layer, originalNodeIndex);\n        let kwargs = {};\n        if (this.containerNodes.has(nodeKey)) {\n          if (node.callArgs) {\n            try {\n              JSON.stringify(node.callArgs);\n              kwargs = node.callArgs;\n            } catch (err) {\n              console.warn(`Layer ${layer.name} was passed non-serializable keyword arguments: ${node.callArgs}. They will not be included in the serialized model (and thus will be missing at deserialization time).`);\n              kwargs = {};\n            }\n          }\n          if (node.inboundLayers.length > 0) {\n            const nodeData = [];\n            for (let i = 0; i < node.inboundLayers.length; i++) {\n              const inboundLayer = node.inboundLayers[i];\n              const nodeIndex = node.nodeIndices[i];\n              const tensorIndex = node.tensorIndices[i];\n              const nodeKey2 = Container.nodeKey(inboundLayer, nodeIndex);\n              let newNodeIndex = nodeConversionMap[nodeKey2];\n              if (newNodeIndex == null) {\n                newNodeIndex = 0;\n              }\n              nodeData.push([inboundLayer.name, newNodeIndex, tensorIndex, kwargs]);\n            }\n            filteredInboundNodes.push(nodeData);\n          }\n        }\n      }\n      const dict = {};\n      dict[\"name\"] = layer.name;\n      dict[\"className\"] = layerClassName;\n      dict[\"config\"] = layerConfig;\n      dict[\"inboundNodes\"] = filteredInboundNodes;\n      layerConfigs.push(dict);\n    }\n    config[\"layers\"] = layerConfigs;\n    const modelInputs = [];\n    for (let i = 0; i < this.inputLayers.length; i++) {\n      const layer = this.inputLayers[i];\n      const nodeIndex = this.inputLayersNodeIndices[i];\n      const nodeKey = Container.nodeKey(layer, nodeIndex);\n      if (!this.containerNodes.has(nodeKey)) {\n        continue;\n      }\n      let newNodeIndex = nodeConversionMap[nodeKey];\n      if (newNodeIndex === null || newNodeIndex === void 0) {\n        newNodeIndex = 0;\n      }\n      const tensorIndex = this.inputLayersTensorIndices[i];\n      modelInputs.push([layer.name, newNodeIndex, tensorIndex]);\n    }\n    config[\"inputLayers\"] = modelInputs;\n    const modelOutputs = [];\n    for (let i = 0; i < this.outputLayers.length; i++) {\n      const layer = this.outputLayers[i];\n      const nodeIndex = this.outputLayersNodeIndices[i];\n      const nodeKey = Container.nodeKey(layer, nodeIndex);\n      if (!this.containerNodes.has(nodeKey)) {\n        continue;\n      }\n      let newNodeIndex = nodeConversionMap[nodeKey];\n      if (newNodeIndex === null || newNodeIndex === void 0) {\n        newNodeIndex = 0;\n      }\n      const tensorIndex = this.outputLayersTensorIndices[i];\n      modelOutputs.push([layer.name, newNodeIndex, tensorIndex]);\n    }\n    config[\"outputLayers\"] = modelOutputs;\n    return config;\n  }\n  static fromConfig(cls, config, customObjects = {}, fastWeightInit = false) {\n    const createdLayers = {};\n    const unprocessedNodes = {};\n    function addUnprocessedNode(layer, nodeData) {\n      if (!(layer.name in unprocessedNodes)) {\n        unprocessedNodes[layer.name] = [nodeData];\n      } else {\n        unprocessedNodes[layer.name].push(nodeData);\n      }\n    }\n    function processNode(layer, nodeData) {\n      const inputTensors2 = [];\n      let kwargs;\n      for (const inputData of nodeData) {\n        const inboundLayerName = inputData[0];\n        const inboundNodeIndex = inputData[1];\n        const inboundTensorIndex = inputData[2];\n        kwargs = inputData[3] == null ? {} : inputData[3];\n        if (!(inboundLayerName in createdLayers)) {\n          addUnprocessedNode(layer, nodeData);\n          return;\n        }\n        const inboundLayer = createdLayers[inboundLayerName];\n        if (inboundLayer.inboundNodes.length <= inboundNodeIndex) {\n          addUnprocessedNode(layer, nodeData);\n          return;\n        }\n        const inboundNode = inboundLayer.inboundNodes[inboundNodeIndex];\n        inputTensors2.push(inboundNode.outputTensors[inboundTensorIndex]);\n      }\n      if (inputTensors2.length > 0) {\n        layer.apply(singletonOrArray(inputTensors2), kwargs);\n      }\n    }\n    function processLayer(layerData) {\n      const layerName = layerData[\"name\"];\n      const layer = deserialize(layerData, config[\"customObjects\"] != null ? config[\"customObjects\"] : {});\n      layer.setFastWeightInitDuringBuild(fastWeightInit);\n      createdLayers[layerName] = layer;\n      const inboundNodesData = layerData[\"inboundNodes\"];\n      inboundNodesData.forEach((nodeData) => {\n        if (!(nodeData instanceof Array)) {\n          throw new ValueError(`Corrupted configuration, expected array for nodeData: ${nodeData}`);\n        }\n        addUnprocessedNode(layer, nodeData);\n      });\n    }\n    const name = config[\"name\"];\n    const layersFromConfig = config[\"layers\"];\n    for (const layerData of layersFromConfig) {\n      processLayer(layerData);\n    }\n    while (!isObjectEmpty(unprocessedNodes)) {\n      for (const layerData of layersFromConfig) {\n        const layer = createdLayers[layerData[\"name\"]];\n        if (layer.name in unprocessedNodes) {\n          const currentUnprocessedNodesForLayer = unprocessedNodes[layer.name];\n          delete unprocessedNodes[layer.name];\n          for (const nodeData of currentUnprocessedNodesForLayer) {\n            processNode(layer, nodeData);\n          }\n        }\n      }\n    }\n    const inputTensors = [];\n    const outputTensors = [];\n    const inputLayersFromConfig = config[\"inputLayers\"];\n    for (const layerData of inputLayersFromConfig) {\n      const layerName = layerData[0];\n      const nodeIndex = layerData[1];\n      const tensorIndex = layerData[2];\n      assert2(layerName in createdLayers);\n      const layer = createdLayers[layerName];\n      const layerOutputTensors = layer.inboundNodes[nodeIndex].outputTensors;\n      inputTensors.push(layerOutputTensors[tensorIndex]);\n    }\n    const outputLayersFromConfig = config[\"outputLayers\"];\n    for (const layerData of outputLayersFromConfig) {\n      const layerName = layerData[0];\n      const nodeIndex = layerData[1];\n      const tensorIndex = layerData[2];\n      assert2(layerName in createdLayers);\n      const layer = createdLayers[layerName];\n      const layerOutputTensors = layer.inboundNodes[nodeIndex].outputTensors;\n      outputTensors.push(layerOutputTensors[tensorIndex]);\n    }\n    return new cls({ inputs: inputTensors, outputs: outputTensors, name });\n  }\n  get stateful() {\n    if (this._stateful) {\n      throw new ValueError(\"Container instance unexpectedly has _stateful = true. The statefulness of a Container is determined by the Layers it contains. Its _stateful property must remain the default false.\");\n    }\n    for (const layer of this.layers) {\n      if (layer.stateful) {\n        return true;\n      }\n    }\n    return false;\n  }\n  resetStates() {\n    tidy(() => {\n      this.layers.forEach((layer) => {\n        if (layer.stateful) {\n          layer.resetStates();\n        }\n      });\n    });\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-layers@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-layers/dist/engine/training_utils.js\nfunction standardizeSampleOrClassWeights(xWeight, outputNames, weightType) {\n  const numOutputs = outputNames.length;\n  if (xWeight == null || Array.isArray(xWeight) && xWeight.length === 0) {\n    return outputNames.map((name) => null);\n  }\n  if (numOutputs === 1) {\n    if (Array.isArray(xWeight) && xWeight.length === 1) {\n      return xWeight;\n    } else if (typeof xWeight === \"object\" && outputNames[0] in xWeight) {\n      return [xWeight[outputNames[0]]];\n    } else {\n      return [xWeight];\n    }\n  }\n  if (Array.isArray(xWeight)) {\n    if (xWeight.length !== numOutputs) {\n      throw new Error(`Provided ${weightType} is an array of ${xWeight.length} element(s), but the model has ${numOutputs} outputs. Make sure a set of weights is provided for each model output.`);\n    }\n    return xWeight;\n  } else if (typeof xWeight === \"object\" && Object.keys(xWeight).length > 0 && typeof xWeight[Object.keys(xWeight)[0]] === \"object\") {\n    const output = [];\n    outputNames.forEach((outputName) => {\n      if (outputName in xWeight) {\n        output.push(xWeight[outputName]);\n      } else {\n        output.push(null);\n      }\n    });\n    return output;\n  } else {\n    throw new Error(`The model has multiple (${numOutputs}) outputs, so ${weightType} must be either an array with ${numOutputs} elements or an object with ${outputNames} keys. Provided ${weightType} not understood: ${JSON.stringify(xWeight)}`);\n  }\n}\nfunction standardizeClassWeights(classWeight, outputNames) {\n  return standardizeSampleOrClassWeights(classWeight, outputNames, \"classWeight\");\n}\nasync function standardizeWeights(y, sampleWeight, classWeight, sampleWeightMode) {\n  if (sampleWeight != null || sampleWeightMode != null) {\n    throw new Error(\"Support sampleWeight is not implemented yet\");\n  }\n  if (classWeight != null) {\n    const yClasses = tidy(() => {\n      if (y.shape.length === 1) {\n        return clone(y);\n      } else if (y.shape.length === 2) {\n        if (y.shape[1] > 1) {\n          const axis = 1;\n          return argMax(y, axis);\n        } else if (y.shape[1] === 1) {\n          return reshape(y, [y.shape[0]]);\n        } else {\n          throw new Error(`Encountered unexpected last-dimension size (${y.shape[1]}) during handling of class weights. The size is expected to be >= 1.`);\n        }\n      } else {\n        throw new Error(`Unexpected rank of target (y) tensor (${y.rank}) during handling of class weights. The rank is expected to be 1 or 2.`);\n      }\n    });\n    const yClassIndices = Array.from(await yClasses.data());\n    dispose(yClasses);\n    const classSampleWeight = [];\n    yClassIndices.forEach((classIndex) => {\n      if (classWeight[classIndex] == null) {\n        throw new Error(`classWeight must contain all classes in the training data. The class ${classIndex} exists in the data but not in classWeight`);\n      } else {\n        classSampleWeight.push(classWeight[classIndex]);\n      }\n    });\n    return tensor1d(classSampleWeight, \"float32\");\n  } else {\n    return null;\n  }\n}\nfunction computeWeightedLoss2(losses2, sampleWeights) {\n  return mul(losses2, sampleWeights);\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-layers@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-layers/dist/engine/training_dataset.js\nvar DEFAULT_VALIDATION_BATCH_SIZE = 32;\nfunction standardizeDataIteratorOutput(model2, iteratorOut) {\n  let xs;\n  let ys;\n  const iteratorOutObj = iteratorOut;\n  xs = iteratorOutObj[\"xs\"];\n  ys = iteratorOutObj[\"ys\"];\n  util_exports.assert(xs != null && ys != null, () => `A Dataset iterator for fitDataset() is expected to generate objects of the form \\`{xs: xVal, ys: yVal}\\`, where the two values may be \\`tf.Tensor\\`, an array of Tensors, or a map of string to Tensor.  The provided Dataset instead generates ${iteratorOut}`);\n  const flattenedXs = flattenTensorOrArrayOrMap(\"input\", model2.inputNames, xs);\n  const flattenedYs = flattenTensorOrArrayOrMap(\"output\", model2.outputNames, ys);\n  const batchSize = flattenedXs[0].shape[0];\n  util_exports.assert(flattenedXs.length === model2.inputs.length, () => `LayersModel has ${model2.inputs.length} inputs, but the dataset provides ${flattenedXs.length} inputs.  (Expected input keys: ${JSON.stringify(model2.inputNames)})`);\n  util_exports.assert(flattenedYs.length === model2.outputs.length, () => `LayersModel has ${model2.outputs.length} outputs, but the dataset provides ${flattenedYs.length} outputs.  (Expected output keys: ${JSON.stringify(model2.outputNames)})`);\n  for (let xIndex = 0; xIndex < flattenedXs.length; xIndex++) {\n    util_exports.assert(flattenedXs[xIndex].shape[0] === batchSize, () => `Batch size mismatch: input ${model2.inputNames[xIndex]} has ${flattenedXs[xIndex].shape[0]}; expected  ${batchSize} based on input ${model2.inputNames[0]}.`);\n  }\n  for (let yIndex = 0; yIndex < flattenedYs.length; yIndex++) {\n    util_exports.assert(flattenedYs[yIndex].shape[0] === batchSize, () => `Batch size mismatch: output ${model2.outputNames[yIndex]} has ${flattenedYs[yIndex].shape[0]}; expected  ${batchSize} based on input ${model2.inputNames[0]}.`);\n  }\n  return { xs: flattenedXs, ys: flattenedYs };\n}\nfunction flattenTensorOrArrayOrMap(inputOrOutput, names, values) {\n  if (values instanceof Tensor) {\n    return [values];\n  } else if (Array.isArray(values)) {\n    util_exports.assert(values.length === names.length, () => `Received an array of ${values.length} Tensors, but expected ${names.length} to match the ${inputOrOutput} keys ${names}.`);\n    return values;\n  } else {\n    const result = [];\n    for (const name of names) {\n      if (values[name] == null) {\n        throw new ValueError(`The feature data generated by the dataset lacks the required ${inputOrOutput} key '${name}'.`);\n      }\n      result.push(values[name]);\n    }\n    return result;\n  }\n}\nfunction standardizeTensorValidationData(data) {\n  if (data.length === 3) {\n    throw new NotImplementedError(\"Validation with sample weights is not implemented yet.\");\n  }\n  return { xs: data[0], ys: data[1] };\n}\nasync function fitDataset(model2, dataset, args) {\n  const hasBatchesPerEpoch = args.batchesPerEpoch != null;\n  util_exports.assert(model2.optimizer != null, () => \"You must compile a model before training/testing. Use LayersModel.compile(modelCompileConfig).\");\n  util_exports.assert(args != null, () => `For fitDataset(), the 2nd argument (config) is required, but it is not provided in this call.`);\n  util_exports.assert(args.epochs != null && args.epochs > 0 && Number.isInteger(args.epochs), () => `For fitDataset(), config.epochs is expected to be a positive integer, but got ${args.epochs}`);\n  util_exports.assert(!hasBatchesPerEpoch || args.batchesPerEpoch > 0 && Number.isInteger(args.batchesPerEpoch), () => `For fitDataset(), config.batchesPerEpoch is expected to be a positive integer if specified, but got ${args.batchesPerEpoch}`);\n  util_exports.assert(\n    args[\"validationSplit\"] == null,\n    () => \"`validationSplit` is not supported by `fitDataset()`. Use validationData instead.\"\n  );\n  if (model2.isTraining) {\n    throw new Error(\"Cannot start training because another fit() call is ongoing.\");\n  }\n  model2.isTraining = true;\n  try {\n    const doValidation = args.validationData != null;\n    let valXs;\n    let valYs;\n    if (doValidation) {\n      if (isDatasetObject(args.validationData)) {\n        util_exports.assert(args.validationBatches == null || args.validationBatches > 0 && Number.isInteger(args.validationBatches), () => `For fitDataset() with dataset-based validation, config.validationBatches is expected not to be provided, or to be a positive integer, but got ${args.validationBatches}`);\n      } else {\n        const validationData = standardizeTensorValidationData(args.validationData);\n        valXs = validationData.xs;\n        valYs = validationData.ys;\n      }\n    }\n    const trainFunction = model2.makeTrainFunction();\n    const outLabels = model2.getDedupedMetricsNames();\n    let callbackMetrics;\n    if (doValidation) {\n      callbackMetrics = outLabels.slice().concat(outLabels.map((n) => \"val_\" + n));\n    } else {\n      callbackMetrics = outLabels.slice();\n    }\n    const callbacks2 = standardizeCallbacks(args.callbacks, args.yieldEvery);\n    const verbose = args.verbose == null ? 1 : args.verbose;\n    const { callbackList, history } = configureCallbacks(\n      callbacks2,\n      verbose,\n      args.epochs,\n      null,\n      null,\n      getStepsPerEpoch(dataset, args),\n      null,\n      doValidation,\n      callbackMetrics\n    );\n    callbackList.setModel(model2);\n    model2.history = history;\n    await callbackList.onTrainBegin();\n    model2.stopTraining_ = false;\n    let epoch = args.initialEpoch == null ? 0 : args.initialEpoch;\n    let dataIterator = await dataset.iterator();\n    while (epoch < args.epochs) {\n      const epochLogs = {};\n      await callbackList.onEpochBegin(epoch);\n      let stepsDone = 0;\n      let batchIndex = 0;\n      if (!hasBatchesPerEpoch) {\n        dataIterator = await dataset.iterator();\n      }\n      while (hasBatchesPerEpoch ? stepsDone < args.batchesPerEpoch : true) {\n        const iteratorOut = await dataIterator.next();\n        if (hasBatchesPerEpoch && iteratorOut.done) {\n          console.warn(`You provided \\`batchesPerEpoch\\` as ${args.batchesPerEpoch}, but your dataset iterator ran out of data after ${stepsDone} batches; interrupting training. Make sure that your dataset can generate at least \\`batchesPerEpoch * epochs\\` batches (in this case, ${args.batchesPerEpoch * args.epochs} batches). You may need to use the repeat() function when building your dataset.`);\n          break;\n        }\n        if (iteratorOut.value != null) {\n          const { xs, ys } = standardizeDataIteratorOutput(model2, iteratorOut.value);\n          const batchLogs = {};\n          batchLogs[\"batch\"] = batchIndex;\n          batchLogs[\"size\"] = xs[0].shape[0];\n          await callbackList.onBatchBegin(batchIndex, batchLogs);\n          const sampleWeights = [];\n          if (args.classWeight != null) {\n            const standardClassWeights = standardizeClassWeights(args.classWeight, model2.outputNames);\n            for (let i = 0; i < standardClassWeights.length; ++i) {\n              sampleWeights.push(await standardizeWeights(ys[i], null, standardClassWeights[i]));\n            }\n          }\n          const ins = xs.concat(ys).concat(sampleWeights);\n          const outs = trainFunction(ins);\n          dispose(ins);\n          for (let i = 0; i < outLabels.length; ++i) {\n            const label = outLabels[i];\n            const out = outs[i];\n            batchLogs[label] = out;\n            keep(out);\n          }\n          await callbackList.onBatchEnd(batchIndex, batchLogs);\n          disposeTensorsInLogs(batchLogs);\n          batchIndex++;\n          stepsDone++;\n        }\n        if (hasBatchesPerEpoch ? stepsDone >= args.batchesPerEpoch : iteratorOut.done) {\n          if (doValidation) {\n            let valOuts;\n            if (isDatasetObject(args.validationData)) {\n              valOuts = toList(await model2.evaluateDataset(args.validationData, { batches: args.validationBatches }));\n            } else {\n              valOuts = toList(model2.evaluate(valXs, valYs, {\n                batchSize: args.validationBatchSize == null ? DEFAULT_VALIDATION_BATCH_SIZE : args.validationBatchSize,\n                verbose: 0\n              }));\n            }\n            for (let i = 0; i < model2.metricsNames.length; ++i) {\n              epochLogs[`val_${model2.metricsNames[i]}`] = valOuts[i];\n            }\n          }\n          break;\n        }\n        if (model2.stopTraining_) {\n          break;\n        }\n      }\n      await callbackList.onEpochEnd(epoch, epochLogs);\n      epoch++;\n      if (model2.stopTraining_) {\n        break;\n      }\n    }\n    await callbackList.onTrainEnd();\n    await model2.history.syncData();\n    return model2.history;\n  } finally {\n    model2.isTraining = false;\n  }\n}\nfunction getStepsPerEpoch(dataset, args) {\n  let stepsPerEpoch = null;\n  if (args.batchesPerEpoch != null) {\n    stepsPerEpoch = args.batchesPerEpoch;\n  } else if (Number.isFinite(dataset.size)) {\n    stepsPerEpoch = dataset.size;\n  }\n  return stepsPerEpoch;\n}\nfunction isDatasetObject(dataset) {\n  return typeof dataset.iterator === \"function\";\n}\nfunction isLazyIteratorObject(iterator) {\n  return typeof iterator.next === \"function\";\n}\nasync function evaluateDataset(model2, dataset, args) {\n  args = args || {};\n  const hasBatches = args.batches != null;\n  const f = model2.testFunction;\n  let outs = [];\n  if (args.verbose > 0) {\n    throw new NotImplementedError(\"Verbose mode is not implemented yet.\");\n  }\n  util_exports.assert(!hasBatches || args.batches > 0 && Number.isInteger(args.batches), () => `Test loop expects \\`batches\\` to be a positive integer, but received ${JSON.stringify(args.batches)}`);\n  const dataIterator = isLazyIteratorObject(dataset) ? dataset : await dataset.iterator();\n  let numExamples = 0;\n  let batch = 0;\n  while (hasBatches ? batch < args.batches : true) {\n    const iteratorOut = await dataIterator.next();\n    outs = tidy(() => {\n      if (iteratorOut.value) {\n        const { xs, ys } = standardizeDataIteratorOutput(model2, iteratorOut.value);\n        const xsAndYs = xs.concat(ys);\n        const batchOuts = tidy(() => f(xsAndYs));\n        dispose(xsAndYs);\n        if (batch === 0) {\n          for (let i = 0; i < batchOuts.length; ++i) {\n            outs.push(scalar(0));\n          }\n        }\n        const batchSize = xsAndYs[0].shape[0];\n        for (let i = 0; i < batchOuts.length; ++i) {\n          const batchOut = batchOuts[i];\n          const oldScalar = outs[i];\n          outs[i] = tidy(() => add2(outs[i], mul(batchSize, batchOut)));\n          if (batch > 0) {\n            dispose(oldScalar);\n          }\n        }\n        dispose(batchOuts);\n        numExamples += batchSize;\n        ++batch;\n      }\n      return outs;\n    });\n    if (iteratorOut.done) {\n      if (hasBatches) {\n        console.warn(`Your dataset iterator ran out of data during evaluateDataset(). Interrupting evalution. Make sure that your dataset can generate at least \\`batches\\` batches (in this case, ${args.batches} batches). You may need to use the repeat() function when building your dataset.`);\n      }\n      break;\n    }\n  }\n  for (let i = 0; i < outs.length; ++i) {\n    const oldScalar = outs[i];\n    outs[i] = div(outs[i], numExamples);\n    dispose(oldScalar);\n  }\n  return singletonOrArray(outs);\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-layers@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-layers/dist/engine/training_tensors.js\nfunction checkBatchSize(batchSize) {\n  util_exports.assert(batchSize > 0 && Number.isInteger(batchSize), () => `batchSize is required to be a positive integer, but got ${batchSize}`);\n}\nfunction sliceArrays(arrays, start, stop) {\n  if (arrays == null) {\n    return [null];\n  } else if (Array.isArray(arrays)) {\n    return arrays.map((array2) => sliceAlongFirstAxis(array2, start, stop - start));\n  } else {\n    return sliceAlongFirstAxis(arrays, start, stop - start);\n  }\n}\nfunction sliceArraysByIndices(arrays, indices) {\n  return tidy(() => {\n    if (arrays == null) {\n      return null;\n    } else if (Array.isArray(arrays)) {\n      return arrays.map((array2) => sliceArraysByIndices(array2, indices));\n    } else {\n      return gather2(arrays, indices.dtype === \"int32\" ? indices : cast(indices, \"int32\"));\n    }\n  });\n}\nfunction makeBatches(size, batchSize) {\n  const output = [];\n  let batchStart = 0;\n  let batchEnd = null;\n  while (batchStart < size) {\n    batchEnd = batchStart + batchSize;\n    if (batchEnd >= size) {\n      batchEnd = size;\n    }\n    output.push([batchStart, batchEnd]);\n    batchStart = batchEnd;\n  }\n  return output;\n}\nasync function fitLoop(model2, f, ins, outLabels, batchSize, epochs, verbose, callbacks2, valF, valIns, shuffle2, callbackMetrics, initialEpoch, stepsPerEpoch, validationSteps) {\n  if (batchSize == null) {\n    batchSize = 32;\n  }\n  if (epochs == null) {\n    epochs = 1;\n  }\n  if (shuffle2 == null) {\n    shuffle2 = true;\n  }\n  if (initialEpoch == null) {\n    initialEpoch = 0;\n  }\n  let doValidation = false;\n  if (valF != null && valIns != null) {\n    doValidation = true;\n  }\n  if (validationSteps != null) {\n    doValidation = true;\n    if (stepsPerEpoch == null) {\n      throw new ValueError(\"Can only use `validationSteps` when doing step-wise training, i.e., `stepsPerEpoch` must be set.\");\n    }\n  }\n  const numTrainSamples = model2.checkNumSamples(ins, batchSize, stepsPerEpoch, \"steps_per_epoch\");\n  let indexArray;\n  if (numTrainSamples != null) {\n    indexArray = range2(0, numTrainSamples);\n  }\n  if (verbose == null) {\n    verbose = 1;\n  }\n  const { callbackList, history } = configureCallbacks(callbacks2, verbose, epochs, initialEpoch, numTrainSamples, stepsPerEpoch, batchSize, doValidation, callbackMetrics);\n  callbackList.setModel(model2);\n  model2.history = history;\n  await callbackList.onTrainBegin();\n  model2.stopTraining_ = false;\n  for (let epoch = initialEpoch; epoch < epochs; ++epoch) {\n    await callbackList.onEpochBegin(epoch);\n    const epochLogs = {};\n    if (stepsPerEpoch != null) {\n      throw new NotImplementedError(\"stepsPerEpoch mode is not implemented yet.\");\n    } else {\n      if (shuffle2 === \"batch\") {\n        throw new NotImplementedError(\"batch shuffling is not implemneted yet\");\n      } else if (shuffle2) {\n        util_exports.shuffle(indexArray);\n      }\n      const epochIndexArray1D = tensor1d(indexArray);\n      const batches = makeBatches(numTrainSamples, batchSize);\n      for (let batchIndex = 0; batchIndex < batches.length; ++batchIndex) {\n        const batchLogs = {};\n        await callbackList.onBatchBegin(batchIndex, batchLogs);\n        tidy(() => {\n          const batchStart = batches[batchIndex][0];\n          const batchEnd = batches[batchIndex][1];\n          const batchIds = sliceAlongFirstAxis(epochIndexArray1D, batchStart, batchEnd - batchStart);\n          batchLogs[\"batch\"] = batchIndex;\n          batchLogs[\"size\"] = batchEnd - batchStart;\n          const insBatch = sliceArraysByIndices(ins, batchIds);\n          const outs = f(insBatch);\n          for (let i = 0; i < outLabels.length; ++i) {\n            const label = outLabels[i];\n            const out = outs[i];\n            batchLogs[label] = out;\n            keep(out);\n          }\n          if (batchIndex === batches.length - 1) {\n            if (doValidation) {\n              const valOuts = model2.testLoop(valF, valIns, batchSize);\n              for (let i = 0; i < outLabels.length; ++i) {\n                const label = outLabels[i];\n                const out = valOuts[i];\n                keep(out);\n                epochLogs[\"val_\" + label] = out;\n              }\n            }\n          }\n        });\n        await callbackList.onBatchEnd(batchIndex, batchLogs);\n        disposeTensorsInLogs(batchLogs);\n        if (model2.stopTraining_) {\n          break;\n        }\n      }\n      epochIndexArray1D.dispose();\n    }\n    await callbackList.onEpochEnd(epoch, epochLogs);\n    if (model2.stopTraining_) {\n      break;\n    }\n  }\n  await callbackList.onTrainEnd();\n  await model2.history.syncData();\n  return model2.history;\n}\nasync function fitTensors(model2, x, y, args = {}) {\n  if (model2.isTraining) {\n    throw new Error(\"Cannot start training because another fit() call is ongoing.\");\n  }\n  model2.isTraining = true;\n  let inputs;\n  let targets;\n  let originalInputs;\n  let originalTargets;\n  let inputValX;\n  let inputValY;\n  let valX;\n  let valY;\n  let sampleWeights;\n  try {\n    const batchSize = args.batchSize == null ? 32 : args.batchSize;\n    checkBatchSize(batchSize);\n    const checkBatchAxis = false;\n    const standardizedOuts = await model2.standardizeUserData(x, y, args.sampleWeight, args.classWeight, checkBatchAxis, batchSize);\n    inputs = standardizedOuts[0];\n    targets = standardizedOuts[1];\n    sampleWeights = standardizedOuts[2];\n    let doValidation = false;\n    let valIns;\n    if (args.validationData != null && args.validationData.length > 0) {\n      doValidation = true;\n      if (args.validationData.length === 2) {\n        inputValX = args.validationData[0];\n        inputValY = args.validationData[1];\n      } else if (args.validationData.length === 3) {\n        throw new NotImplementedError(\"validationData including sample weights is not supported yet.\");\n      } else {\n        throw new ValueError(`When passing validation data, it must contain 2 (valX, valY) or 3 (valX, valY, valSampleWeight) items; ${args.validationData} is invalid.`);\n      }\n      const checkBatchAxis2 = true;\n      const valStandardized = await model2.standardizeUserData(inputValX, inputValY, null, null, checkBatchAxis2, batchSize);\n      valX = valStandardized[0];\n      valY = valStandardized[1];\n      valIns = valX.concat(valY);\n    } else if (args.validationSplit != null && args.validationSplit > 0 && args.validationSplit < 1) {\n      doValidation = true;\n      const splitAt = Math.floor(inputs[0].shape[0] * (1 - args.validationSplit));\n      const originalBatchSize = inputs[0].shape[0];\n      valX = sliceArrays(inputs, splitAt, originalBatchSize);\n      originalInputs = inputs;\n      inputs = sliceArrays(inputs, 0, splitAt);\n      valY = sliceArrays(targets, splitAt, originalBatchSize);\n      originalTargets = targets;\n      targets = sliceArrays(targets, 0, splitAt);\n      valIns = valX.concat(valY);\n    } else if (args.validationSteps != null) {\n      doValidation = true;\n    }\n    const ins = inputs.concat(targets).concat(sampleWeights);\n    model2.checkTrainableWeightsConsistency();\n    const trainFunction = model2.makeTrainFunction();\n    const outLabels = model2.getDedupedMetricsNames();\n    let valFunction;\n    let callbackMetrics;\n    if (doValidation) {\n      model2.makeTestFunction();\n      valFunction = model2.testFunction;\n      callbackMetrics = outLabels.slice().concat(outLabels.map((n) => \"val_\" + n));\n    } else {\n      valFunction = null;\n      valIns = [];\n      callbackMetrics = outLabels.slice();\n    }\n    const callbacks2 = standardizeCallbacks(args.callbacks, args.yieldEvery);\n    const out = await fitLoop(model2, trainFunction, ins, outLabels, batchSize, args.epochs, args.verbose, callbacks2, valFunction, valIns, args.shuffle, callbackMetrics, args.initialEpoch, null, null);\n    return out;\n  } finally {\n    model2.isTraining = false;\n    disposeNewTensors(inputs, x);\n    disposeNewTensors(targets, y);\n    disposeNewTensors(originalInputs, x);\n    disposeNewTensors(originalTargets, y);\n    disposeNewTensors(valX, inputValX);\n    disposeNewTensors(valY, inputValY);\n    if (sampleWeights != null) {\n      dispose(sampleWeights);\n    }\n  }\n}\nfunction ensureTensorsRank2OrHigher(tensors) {\n  const outs = [];\n  if (tensors instanceof Tensor) {\n    tensors = [tensors];\n  }\n  for (let i = 0; i < tensors.length; ++i) {\n    const tensor2 = tensors[i];\n    if (tensor2.rank === 1) {\n      outs.push(expandDims2(tensor2, 1));\n    } else if (tensor2.rank === 0) {\n      throw new Error(\"Expected tensor to be at least 1D, but received a 0D tensor (scalar).\");\n    } else {\n      outs.push(tensor2);\n    }\n  }\n  return outs;\n}\nfunction disposeNewTensors(tensors, refTensors) {\n  if (tensors == null) {\n    return;\n  }\n  const oldTensorIds = [];\n  if (refTensors instanceof Tensor) {\n    oldTensorIds.push(refTensors.id);\n  } else if (Array.isArray(refTensors)) {\n    refTensors.forEach((t) => oldTensorIds.push(t.id));\n  } else if (refTensors != null) {\n    for (const name in refTensors) {\n      const oldTensor = refTensors[name];\n      oldTensorIds.push(oldTensor.id);\n    }\n  }\n  const tensorsToDispose = [];\n  if (tensors instanceof Tensor) {\n    if (oldTensorIds.indexOf(tensors.id) === -1) {\n      tensorsToDispose.push(tensors);\n    }\n  } else if (Array.isArray(tensors)) {\n    tensors.forEach((t) => {\n      if (oldTensorIds.indexOf(t.id) === -1) {\n        tensorsToDispose.push(t);\n      }\n    });\n  } else if (tensors != null) {\n    for (const name in tensors) {\n      const tensor2 = tensors[name];\n      if (oldTensorIds.indexOf(tensor2.id) === -1) {\n        tensorsToDispose.push(tensor2);\n      }\n    }\n  }\n  tensorsToDispose.forEach((t) => {\n    if (!t.isDisposed) {\n      t.dispose();\n    }\n  });\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-layers@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-layers/dist/engine/training.js\nfunction isDataTensor(x) {\n  return x instanceof Tensor;\n}\nfunction isDataArray(x) {\n  return Array.isArray(x);\n}\nfunction isDataDict(x) {\n  return !isDataTensor(x) && !isDataArray(x);\n}\nfunction standardizeInputData(data, names, shapes, checkBatchAxis = true, exceptionPrefix = \"\") {\n  if (names == null || names.length === 0) {\n    if (data != null) {\n      let gotUnexpectedData = false;\n      if (isDataArray(data) && data.length > 0) {\n        gotUnexpectedData = true;\n      } else if (isDataDict(data)) {\n        for (const key in data) {\n          if (data.hasOwnProperty(key)) {\n            gotUnexpectedData = true;\n            break;\n          }\n        }\n      } else {\n        gotUnexpectedData = true;\n      }\n      if (gotUnexpectedData) {\n        throw new ValueError(`Error when checking model ${exceptionPrefix} expected no data, but got ${data}`);\n      }\n    }\n    return [];\n  }\n  if (data == null) {\n    return names.map((name) => null);\n  }\n  let arrays;\n  if (isDataDict(data)) {\n    data = data;\n    arrays = [];\n    for (const name of names) {\n      if (data[name] == null) {\n        throw new ValueError(`No data provided for \"${name}\". Need data for each key in: ${names}`);\n      }\n      arrays.push(data[name]);\n    }\n  } else if (isDataArray(data)) {\n    data = data;\n    if (data.length !== names.length) {\n      throw new ValueError(`Error when checking model ${exceptionPrefix}: the Array of Tensors that you are passing to your model is not the size the model expected. Expected to see ${names.length} Tensor(s), but instead got the following list of Tensor(s): ${data}`);\n    }\n    arrays = data;\n  } else {\n    data = data;\n    if (names.length > 1) {\n      throw new ValueError(`The model ${exceptionPrefix} expects ${names.length} Tensor(s), but only received one Tensor. Found: Tensor with shape ${data.shape}`);\n    }\n    arrays = [data];\n  }\n  arrays = ensureTensorsRank2OrHigher(arrays);\n  if (shapes != null) {\n    for (let i = 0; i < names.length; ++i) {\n      if (shapes[i] == null) {\n        continue;\n      }\n      const array2 = arrays[i];\n      if (array2.shape.length !== shapes[i].length) {\n        throw new ValueError(`Error when checking ${exceptionPrefix}: expected ${names[i]} to have ${shapes[i].length} dimension(s). but got array with shape ${array2.shape}`);\n      }\n      for (let j = 0; j < shapes[i].length; ++j) {\n        if (j === 0 && !checkBatchAxis) {\n          continue;\n        }\n        const dim = array2.shape[j];\n        const refDim = shapes[i][j];\n        if (refDim != null && refDim >= 0 && dim !== refDim) {\n          throw new ValueError(`${exceptionPrefix} expected a batch of elements where each example has shape [${shapes[i].slice(1, shapes[i].length)}] (i.e.,tensor shape [*,${shapes[i].slice(1, shapes[i].length)}]) but the ${exceptionPrefix} received an input with ${array2.shape[0]} examples, each with shape [${array2.shape.slice(1, array2.shape.length)}] (tensor shape [${array2.shape}])`);\n        }\n      }\n    }\n  }\n  return arrays;\n}\nfunction checkArrayLengths(inputs, targets, weights) {\n  const setX = unique2(inputs.map((input2) => input2.shape[0]));\n  setX.sort();\n  const setY = unique2(targets.map((target) => target.shape[0]));\n  setY.sort();\n  if (setX.length > 1) {\n    throw new ValueError(`All input Tensors (x) should have the same number of samples. Got array shapes: ${JSON.stringify(inputs.map((input2) => input2.shape))}`);\n  }\n  if (setY.length > 1) {\n    throw new ValueError(`All target Tensors (y) should have the same number of samples. Got array shapes: ${JSON.stringify(targets.map((target) => target.shape))}`);\n  }\n  if (setX.length > 0 && setY.length > 0 && !util_exports.arraysEqual(setX, setY)) {\n    throw new ValueError(`Input Tensors should have the same number of samples as target Tensors. Found ${setX[0]} input sample(s) and ${setY[0]} target sample(s).`);\n  }\n}\nfunction checkLossAndTargetCompatibility(targets, lossFns, outputShapes) {\n  const keyLosses = [\n    meanSquaredError2,\n    binaryCrossentropy,\n    categoricalCrossentropy\n  ];\n  for (let i = 0; i < targets.length; ++i) {\n    const y = targets[i];\n    const loss = lossFns[i];\n    const shape = outputShapes[i];\n    if (loss == null) {\n      continue;\n    }\n    if (loss === categoricalCrossentropy) {\n      if (y.shape[y.shape.length - 1] === 1) {\n        throw new ValueError(`You are passing a target array of shape ${y.shape} while using a loss 'categorical_crossentropy'. 'categorical_crossentropy'expects targets to be binary matrices (1s and 0s) of shape [samples, classes].`);\n      }\n    }\n    if (keyLosses.indexOf(loss) !== -1) {\n      const slicedYShape = y.shape.slice(1);\n      const slicedShape = shape.slice(1);\n      for (let j = 0; j < slicedYShape.length; ++j) {\n        const targetDim = slicedYShape[j];\n        const outDim = slicedShape[j];\n        if (outDim != null && targetDim !== outDim) {\n          throw new ValueError(`A target Tensor with shape ${y.shape} was passed for an output of shape ${shape}, while using a loss function that expects targets to have the same shape as the output.`);\n        }\n      }\n    }\n  }\n}\nfunction checkInputData(data, names, shapes, checkBatchAxis = true, exceptionPrefix = \"\") {\n  let arrays;\n  if (Array.isArray(data)) {\n    if (data.length !== names.length) {\n      throw new ValueError(`Error when checking model ${exceptionPrefix}: the Array of Tensors that you are passing to your model is not the size the the model expected. Expected to see ${names.length} Tensor(s), but instead got ${data.length} Tensors(s).`);\n    }\n    arrays = data;\n  } else {\n    if (names.length > 1) {\n      throw new ValueError(`The model expects ${names.length} ${exceptionPrefix} Tensors, but only received one Tensor. Found: array with shape ${JSON.stringify(data.shape)}.`);\n    }\n    arrays = [data];\n  }\n  if (shapes != null) {\n    for (let i = 0; i < names.length; ++i) {\n      if (shapes[i] == null) {\n        continue;\n      }\n      const array2 = arrays[i];\n      if (array2.shape.length !== shapes[i].length) {\n        throw new ValueError(`Error when checking ${exceptionPrefix}: expected ${names[i]} to have ${shapes[i].length} dimension(s), but got array with shape ${JSON.stringify(array2.shape)}`);\n      }\n      for (let j = 0; j < shapes[i].length; ++j) {\n        if (j === 0 && !checkBatchAxis) {\n          continue;\n        }\n        const dim = array2.shape[j];\n        const refDim = shapes[i][j];\n        if (refDim != null) {\n          if (refDim !== dim) {\n            throw new ValueError(`Error when checking ${exceptionPrefix}: expected ${names[i]} to have shape ${JSON.stringify(shapes[i])} but got array with shape ${JSON.stringify(array2.shape)}.`);\n          }\n        }\n      }\n    }\n  }\n}\nfunction collectMetrics(metrics, outputNames) {\n  if (metrics == null || Array.isArray(metrics) && metrics.length === 0) {\n    return outputNames.map((name) => []);\n  }\n  let wrappedMetrics;\n  if (typeof metrics === \"string\" || typeof metrics === \"function\") {\n    wrappedMetrics = [metrics];\n  } else if (Array.isArray(metrics) || typeof metrics === \"object\") {\n    wrappedMetrics = metrics;\n  } else {\n    throw new TypeError(`Type of metrics argument not understood. Expected an string,function, Array, or Object, found: ${metrics}`);\n  }\n  if (Array.isArray(wrappedMetrics)) {\n    return outputNames.map((name) => wrappedMetrics);\n  } else {\n    const nestedMetrics = [];\n    for (const name of outputNames) {\n      let outputMetrics = wrappedMetrics.hasOwnProperty(name) ? wrappedMetrics[name] : [];\n      if (!Array.isArray(outputMetrics)) {\n        outputMetrics = [outputMetrics];\n      }\n      nestedMetrics.push(outputMetrics);\n    }\n    return nestedMetrics;\n  }\n}\nvar LAYERS_MODEL_FORMAT_NAME = \"layers-model\";\nvar LayersModel = class extends Container {\n  constructor(args) {\n    super(args);\n    this.isTraining = false;\n  }\n  summary(lineLength, positions, printFn = console.log) {\n    if (!this.built) {\n      throw new ValueError(`This model has never been called, thus its weights have not been created yet. So no summary can be displayed. Build the model first (e.g., by calling it on some test data).`);\n    }\n    printSummary(this, lineLength, positions, printFn);\n  }\n  compile(args) {\n    if (args.loss == null) {\n      args.loss = [];\n    }\n    this.loss = args.loss;\n    if (typeof args.optimizer === \"string\") {\n      this.optimizer_ = getOptimizer(args.optimizer);\n      this.isOptimizerOwned = true;\n    } else {\n      if (!(args.optimizer instanceof Optimizer)) {\n        throw new ValueError(`User-defined optimizer must be an instance of tf.Optimizer.`);\n      }\n      this.optimizer_ = args.optimizer;\n      this.isOptimizerOwned = false;\n    }\n    let lossFunctions = [];\n    if (!Array.isArray(args.loss) && typeof args.loss !== \"string\" && typeof args.loss !== \"function\") {\n      args.loss = args.loss;\n      for (const name in args.loss) {\n        if (this.outputNames.indexOf(name) === -1) {\n          throw new ValueError(`Unknown entry in loss dictionary: \"${name}\". Only expected the following keys: ${this.outputNames}`);\n        }\n      }\n      for (const name of this.outputNames) {\n        if (args.loss[name] == null) {\n          console.warn(`Output \"${name}\" is missing from loss dictionary. We assume this was done on purpose, and we will not be expecting data to be passed to ${name} during training`);\n        }\n        lossFunctions.push(get(args.loss[name]));\n      }\n    } else if (Array.isArray(args.loss)) {\n      if (args.loss.length !== this.outputs.length) {\n        throw new ValueError(`When passing an Array as loss, it should have one entry per model output. The model has ${this.outputs.length} output(s), but you passed loss=${args.loss}.`);\n      }\n      const theLosses = args.loss;\n      lossFunctions = theLosses.map((l) => get(l));\n    } else {\n      const lossFunction = get(args.loss);\n      this.outputs.forEach((_) => {\n        lossFunctions.push(lossFunction);\n      });\n    }\n    this.lossFunctions = lossFunctions;\n    this.feedOutputNames = [];\n    this.feedOutputShapes = [];\n    this.feedLossFns = [];\n    for (let i = 0; i < this.outputs.length; ++i) {\n      const shape = this.internalOutputShapes[i];\n      const name = this.outputNames[i];\n      this.feedOutputNames.push(name);\n      this.feedOutputShapes.push(shape);\n      this.feedLossFns.push(this.lossFunctions[i]);\n    }\n    const skipTargetIndices = [];\n    this.metrics = args.metrics;\n    this.metricsNames = [\"loss\"];\n    this.metricsTensors = [];\n    nameScope(\"loss\", () => {\n      for (let i = 0; i < this.outputs.length; ++i) {\n        if (skipTargetIndices.indexOf(i) !== -1) {\n          continue;\n        }\n        const weightedLoss = this.lossFunctions[i];\n        if (this.outputs.length > 1) {\n          this.metricsTensors.push([weightedLoss, i]);\n          this.metricsNames.push(this.outputNames[i] + \"_loss\");\n        }\n      }\n    });\n    const nestedMetrics = collectMetrics(args.metrics, this.outputNames);\n    const appendMetric = (outputIndex, metricName, metricTensor) => {\n      if (this.outputNames.length > 1) {\n        metricName = this.outputNames[outputIndex] + \"_\" + metricName;\n      }\n      this.metricsNames.push(metricName);\n      this.metricsTensors.push([metricTensor, outputIndex]);\n    };\n    nameScope(\"metric\", () => {\n      for (let i = 0; i < this.outputs.length; ++i) {\n        if (skipTargetIndices.indexOf(i) !== -1) {\n          continue;\n        }\n        const outputMetrics = nestedMetrics[i];\n        const handleMetrics = (metrics) => {\n          const metricNamePrefix = \"\";\n          let metricName;\n          let accFn;\n          let weightedMetricFn;\n          for (const metric of metrics) {\n            if (typeof metric === \"string\" && [\"accuracy\", \"acc\", \"crossentropy\", \"ce\"].indexOf(metric) !== -1) {\n              const outputShape = this.internalOutputShapes[i];\n              if (outputShape[outputShape.length - 1] === 1 || this.lossFunctions[i] === binaryCrossentropy) {\n                if ([\"accuracy\", \"acc\"].indexOf(metric) !== -1) {\n                  accFn = binaryAccuracy;\n                } else if ([\"crossentropy\", \"ce\"].indexOf(metric) !== -1) {\n                  accFn = binaryCrossentropy2;\n                }\n              } else if (this.lossFunctions[i] === sparseCategoricalCrossentropy) {\n                if ([\"accuracy\", \"acc\"].indexOf(metric) !== -1) {\n                  accFn = sparseCategoricalAccuracy;\n                } else if ([\"crossentropy\", \"ce\"].indexOf(metric) !== -1) {\n                  accFn = sparseCategoricalCrossentropy2;\n                }\n              } else {\n                if ([\"accuracy\", \"acc\"].indexOf(metric) !== -1) {\n                  accFn = categoricalAccuracy;\n                } else if ([\"crossentropy\", \"ce\"].indexOf(metric) !== -1) {\n                  accFn = categoricalCrossentropy2;\n                }\n              }\n              let suffix;\n              if ([\"accuracy\", \"acc\"].indexOf(metric) !== -1) {\n                suffix = \"acc\";\n              } else if ([\"crossentropy\", \"ce\"].indexOf(metric) !== -1) {\n                suffix = \"ce\";\n              }\n              weightedMetricFn = accFn;\n              metricName = metricNamePrefix + suffix;\n            } else {\n              const metricFn = get2(metric);\n              weightedMetricFn = metricFn;\n              metricName = metricNamePrefix + getLossOrMetricName(metric);\n            }\n            let metricResult;\n            nameScope(metricName, () => {\n              metricResult = weightedMetricFn;\n            });\n            appendMetric(i, metricName, metricResult);\n          }\n        };\n        handleMetrics(outputMetrics);\n      }\n    });\n    this.collectedTrainableWeights = this.trainableWeights;\n  }\n  checkTrainableWeightsConsistency() {\n    if (this.collectedTrainableWeights == null) {\n      return;\n    }\n    if (this.trainableWeights.length !== this.collectedTrainableWeights.length) {\n      console.warn(\"Discrepancy between trainableweights and collected trainable weights. Did you set `model.trainable` without calling `model.compile()` afterwards?\");\n    }\n  }\n  evaluate(x, y, args = {}) {\n    const batchSize = args.batchSize == null ? 32 : args.batchSize;\n    checkBatchSize(batchSize);\n    const checkBatchAxis = true;\n    const standardizedOuts = this.standardizeUserDataXY(x, y, checkBatchAxis, batchSize);\n    try {\n      const ins = standardizedOuts[0].concat(standardizedOuts[1]);\n      this.makeTestFunction();\n      const f = this.testFunction;\n      const testOuts = this.testLoop(f, ins, batchSize, args.verbose, args.steps);\n      return singletonOrArray(testOuts);\n    } finally {\n      disposeNewTensors(standardizedOuts[0], x);\n      disposeNewTensors(standardizedOuts[1], y);\n    }\n  }\n  async evaluateDataset(dataset, args) {\n    this.makeTestFunction();\n    return evaluateDataset(this, dataset, args);\n  }\n  checkNumSamples(ins, batchSize, steps, stepsName = \"steps\") {\n    let numSamples;\n    if (steps != null) {\n      numSamples = null;\n      if (batchSize != null) {\n        throw new ValueError(`If ${stepsName} is set, batchSize must be null or undefined.Got batchSize = ${batchSize}`);\n      }\n    } else if (ins != null) {\n      if (Array.isArray(ins)) {\n        numSamples = ins[0].shape[0];\n      } else {\n        numSamples = ins.shape[0];\n      }\n    } else {\n      throw new ValueError(`Either the input data should have a defined shape, or ${stepsName} shoud be specified.`);\n    }\n    return numSamples;\n  }\n  execute(inputs, outputs) {\n    if (Array.isArray(outputs) && outputs.length === 0) {\n      throw new ValueError(\"`outputs` is an empty Array, which is not allowed.\");\n    }\n    const outputsIsArray = Array.isArray(outputs);\n    const outputNames = outputsIsArray ? outputs : [outputs];\n    const outputSymbolicTensors = this.retrieveSymbolicTensors(outputNames);\n    const feedDict = new FeedDict();\n    if (inputs instanceof Tensor) {\n      inputs = [inputs];\n    }\n    if (Array.isArray(inputs)) {\n      if (inputs.length !== this.inputs.length) {\n        throw new ValueError(`The number of inputs provided (${inputs.length}) does not match the number of inputs of this model (${this.inputs.length}).`);\n      }\n      for (let i = 0; i < this.inputs.length; ++i) {\n        feedDict.add(this.inputs[i], inputs[i]);\n      }\n    } else {\n      for (const input2 of this.inputs) {\n        const tensorValue = inputs[input2.name];\n        if (tensorValue == null) {\n          throw new ValueError(`No value is provided for the model's input ${input2.name}`);\n        }\n        feedDict.add(input2, tensorValue);\n      }\n    }\n    const executeOutputs = execute(outputSymbolicTensors, feedDict);\n    return outputsIsArray ? executeOutputs : executeOutputs[0];\n  }\n  retrieveSymbolicTensors(symbolicTensorNames) {\n    const outputSymbolicTensors = pyListRepeat(null, symbolicTensorNames.length);\n    let outputsRemaining = symbolicTensorNames.length;\n    for (const layer of this.layers) {\n      const layerOutputs = Array.isArray(layer.output) ? layer.output : [layer.output];\n      const layerOutputNames = layerOutputs.map((output) => output.name);\n      for (let i = 0; i < symbolicTensorNames.length; ++i) {\n        const index = layerOutputNames.indexOf(symbolicTensorNames[i]);\n        if (index !== -1) {\n          outputSymbolicTensors[i] = layerOutputs[index];\n          outputsRemaining--;\n        }\n        if (outputsRemaining === 0) {\n          break;\n        }\n      }\n      if (outputsRemaining === 0) {\n        break;\n      }\n    }\n    if (outputsRemaining > 0) {\n      const remainingNames = [];\n      outputSymbolicTensors.forEach((tensor2, i) => {\n        if (tensor2 == null) {\n          remainingNames.push(symbolicTensorNames[i]);\n        }\n      });\n      throw new ValueError(`Cannot find SymbolicTensors for output name(s): ${JSON.stringify(remainingNames)}`);\n    }\n    return outputSymbolicTensors;\n  }\n  predictLoop(ins, batchSize = 32, verbose = false) {\n    return tidy(() => {\n      const numSamples = this.checkNumSamples(ins);\n      if (verbose) {\n        throw new NotImplementedError(\"Verbose predictLoop() is not implemented yet.\");\n      }\n      const batches = makeBatches(numSamples, batchSize);\n      const outsBatches = this.outputs.map((output) => []);\n      for (let batchIndex = 0; batchIndex < batches.length; ++batchIndex) {\n        const batchOuts = tidy(() => {\n          const batchStart = batches[batchIndex][0];\n          const batchEnd = batches[batchIndex][1];\n          const insBatch = sliceArrays(ins, batchStart, batchEnd);\n          const feeds = [];\n          if (Array.isArray(insBatch)) {\n            for (let i = 0; i < insBatch.length; ++i) {\n              feeds.push({ key: this.inputs[i], value: insBatch[i] });\n            }\n          } else {\n            feeds.push({ key: this.inputs[0], value: insBatch });\n          }\n          const feedDict = new FeedDict(feeds);\n          return execute(this.outputs, feedDict);\n        });\n        batchOuts.forEach((batchOut, i) => outsBatches[i].push(batchOut));\n      }\n      return singletonOrArray(outsBatches.map((batches2) => concat(batches2, 0)));\n    });\n  }\n  predict(x, args = {}) {\n    const xsRank2OrHigher = ensureTensorsRank2OrHigher(x);\n    checkInputData(xsRank2OrHigher, this.inputNames, this.feedInputShapes, false);\n    try {\n      const batchSize = args.batchSize == null ? 32 : args.batchSize;\n      checkBatchSize(batchSize);\n      return this.predictLoop(xsRank2OrHigher, batchSize);\n    } finally {\n      disposeNewTensors(xsRank2OrHigher, x);\n    }\n  }\n  predictOnBatch(x) {\n    checkInputData(x, this.inputNames, this.feedInputShapes, true);\n    const batchSize = (Array.isArray(x) ? x[0] : x).shape[0];\n    return this.predictLoop(x, batchSize);\n  }\n  standardizeUserDataXY(x, y, checkBatchAxis = true, batchSize) {\n    if (this.optimizer_ == null) {\n      throw new RuntimeError(\"You must compile a model before training/testing. Use LayersModel.compile(modelCompileArgs).\");\n    }\n    const outputShapes = [];\n    for (let i = 0; i < this.feedOutputShapes.length; ++i) {\n      const outputShape = this.feedOutputShapes[i];\n      const lossFn = this.feedLossFns[i];\n      if (lossFn === sparseCategoricalCrossentropy) {\n        outputShapes.push(outputShape.slice(0, outputShape.length - 1).concat([1]));\n      } else {\n        outputShapes.push(outputShape);\n      }\n    }\n    x = standardizeInputData(x, this.feedInputNames, this.feedInputShapes, false, \"input\");\n    y = standardizeInputData(y, this.feedOutputNames, outputShapes, false, \"target\");\n    checkArrayLengths(x, y, null);\n    checkLossAndTargetCompatibility(y, this.feedLossFns, this.feedOutputShapes);\n    if (this.stateful && batchSize != null && batchSize > 0) {\n      if (x[0].shape[0] % batchSize !== 0) {\n        throw new ValueError(`In a stateful network, you should only pass inputs with a number of samples that is divisible by the batch size ${batchSize}. Found: ${x[0].shape[0]} sample(s).`);\n      }\n    }\n    return [x, y];\n  }\n  async standardizeUserData(x, y, sampleWeight, classWeight, checkBatchAxis = true, batchSize) {\n    const [standardXs, standardYs] = this.standardizeUserDataXY(x, y, checkBatchAxis, batchSize);\n    if (sampleWeight != null) {\n      throw new Error(\"sample weight is not supported yet.\");\n    }\n    let standardSampleWeights = null;\n    if (classWeight != null) {\n      const classWeights = standardizeClassWeights(classWeight, this.outputNames);\n      standardSampleWeights = [];\n      for (let i = 0; i < classWeights.length; ++i) {\n        standardSampleWeights.push(await standardizeWeights(standardYs[i], null, classWeights[i]));\n      }\n    }\n    return [standardXs, standardYs, standardSampleWeights];\n  }\n  testLoop(f, ins, batchSize, verbose = 0, steps) {\n    return tidy(() => {\n      const numSamples = this.checkNumSamples(ins, batchSize, steps, \"steps\");\n      const outs = [];\n      if (verbose > 0) {\n        throw new NotImplementedError(\"Verbose mode is not implemented yet.\");\n      }\n      if (steps != null) {\n        throw new NotImplementedError(\"steps mode in testLoop() is not implemented yet\");\n      } else {\n        const batches = makeBatches(numSamples, batchSize);\n        const indexArray = tensor1d(range2(0, numSamples));\n        for (let batchIndex = 0; batchIndex < batches.length; ++batchIndex) {\n          const batchStart = batches[batchIndex][0];\n          const batchEnd = batches[batchIndex][1];\n          const batchIds = sliceAlongFirstAxis(indexArray, batchStart, batchEnd - batchStart);\n          const insBatch = sliceArraysByIndices(ins, batchIds);\n          const batchOuts = f(insBatch);\n          if (batchIndex === 0) {\n            for (let i = 0; i < batchOuts.length; ++i) {\n              outs.push(scalar(0));\n            }\n          }\n          for (let i = 0; i < batchOuts.length; ++i) {\n            const batchOut = batchOuts[i];\n            outs[i] = add2(outs[i], mul(batchEnd - batchStart, batchOut));\n          }\n        }\n        for (let i = 0; i < outs.length; ++i) {\n          outs[i] = div(outs[i], numSamples);\n        }\n      }\n      return outs;\n    });\n  }\n  getDedupedMetricsNames() {\n    const outLabels = this.metricsNames;\n    const dedupedOutLabels = [];\n    for (let i = 0; i < outLabels.length; ++i) {\n      const label = outLabels[i];\n      let newLabel = label;\n      if (count(outLabels, label) > 1) {\n        const dupIndex = count(outLabels.slice(0, i), label);\n        newLabel += `_${dupIndex}`;\n      }\n      dedupedOutLabels.push(newLabel);\n    }\n    return dedupedOutLabels;\n  }\n  makeTrainFunction() {\n    return (data) => {\n      const lossValues = [];\n      const inputs = data.slice(0, this.inputs.length);\n      const targets = data.slice(this.inputs.length, this.inputs.length + this.outputs.length);\n      const sampleWeights = data.slice(this.inputs.length + this.outputs.length, this.inputs.length + this.outputs.length * 2);\n      const metricsValues = [];\n      const totalLossFunction = () => {\n        const feeds = [];\n        for (let i = 0; i < this.inputs.length; ++i) {\n          feeds.push({ key: this.inputs[i], value: inputs[i] });\n        }\n        const feedDict = new FeedDict(feeds);\n        const outputs = execute(this.outputs, feedDict, { \"training\": true });\n        let totalLoss;\n        for (let i = 0; i < this.lossFunctions.length; ++i) {\n          const lossFunction = this.lossFunctions[i];\n          let loss = lossFunction(targets[i], outputs[i]);\n          if (sampleWeights[i] != null) {\n            loss = computeWeightedLoss2(loss, sampleWeights[i]);\n          }\n          const meanLoss = mean(loss);\n          lossValues.push(meanLoss);\n          if (i === 0) {\n            totalLoss = loss;\n          } else {\n            totalLoss = add2(totalLoss, loss);\n          }\n        }\n        for (let i = 0; i < this.metricsTensors.length; ++i) {\n          let weightedMetric;\n          if (this.outputs.length > 1 && i < this.outputs.length) {\n            weightedMetric = lossValues[i];\n          } else {\n            const metric = this.metricsTensors[i][0];\n            const outputIndex = this.metricsTensors[i][1];\n            weightedMetric = mean(metric(targets[outputIndex], outputs[outputIndex]));\n          }\n          keep(weightedMetric);\n          metricsValues.push(weightedMetric);\n        }\n        totalLoss = mean(totalLoss);\n        this.calculateLosses().forEach((regularizerLoss) => {\n          totalLoss = add2(totalLoss, regularizerLoss);\n        });\n        return totalLoss;\n      };\n      const variables = this.collectedTrainableWeights.map((param) => param.read());\n      const returnCost = true;\n      const totalLossValue = this.optimizer_.minimize(totalLossFunction, returnCost, variables);\n      return [totalLossValue].concat(metricsValues);\n    };\n  }\n  makeTestFunction() {\n    this.testFunction = (data) => {\n      return tidy(() => {\n        const valOutputs = [];\n        let totalLoss;\n        const inputs = data.slice(0, this.inputs.length);\n        const targets = data.slice(this.inputs.length, this.inputs.length + this.outputs.length);\n        const feeds = [];\n        for (let i = 0; i < this.inputs.length; ++i) {\n          feeds.push({ key: this.inputs[i], value: inputs[i] });\n        }\n        const feedDict = new FeedDict(feeds);\n        const outputs = execute(this.outputs, feedDict);\n        for (let i = 0; i < this.lossFunctions.length; ++i) {\n          const lossFunction = this.lossFunctions[i];\n          const loss = mean(lossFunction(targets[i], outputs[i]));\n          if (i === 0) {\n            totalLoss = loss;\n          } else {\n            totalLoss = add2(totalLoss, loss);\n          }\n          valOutputs.push(totalLoss);\n        }\n        for (let i = 0; i < this.metricsTensors.length; ++i) {\n          const metric = this.metricsTensors[i][0];\n          const outputIndex = this.metricsTensors[i][1];\n          const meanMetric = mean(metric(targets[outputIndex], outputs[outputIndex]));\n          valOutputs.push(meanMetric);\n        }\n        return valOutputs;\n      });\n    };\n  }\n  async fit(x, y, args = {}) {\n    return fitTensors(this, x, y, args);\n  }\n  async fitDataset(dataset, args) {\n    return fitDataset(this, dataset, args);\n  }\n  async trainOnBatch(x, y) {\n    const standardizeOut = await this.standardizeUserData(x, y);\n    const inputs = standardizeOut[0];\n    const targets = standardizeOut[1];\n    const trainFunction = this.makeTrainFunction();\n    const losses2 = trainFunction(inputs.concat(targets));\n    const lossValues = [];\n    for (const loss of losses2) {\n      const v = await loss.data();\n      lossValues.push(v[0]);\n    }\n    dispose(losses2);\n    disposeNewTensors(standardizeOut[0], x);\n    disposeNewTensors(standardizeOut[1], y);\n    return singletonOrArray(lossValues);\n  }\n  getNamedWeights(config) {\n    const namedWeights = [];\n    const trainableOnly = config != null && config.trainableOnly;\n    const weights = trainableOnly ? this.trainableWeights : this.weights;\n    const weightValues = this.getWeights(trainableOnly);\n    for (let i = 0; i < weights.length; ++i) {\n      if (trainableOnly && !weights[i].trainable) {\n        continue;\n      }\n      namedWeights.push({ name: weights[i].originalName, tensor: weightValues[i] });\n    }\n    return namedWeights;\n  }\n  set stopTraining(stop) {\n    this.stopTraining_ = stop;\n  }\n  get stopTraining() {\n    return this.stopTraining_;\n  }\n  get optimizer() {\n    return this.optimizer_;\n  }\n  set optimizer(optimizer) {\n    if (this.optimizer_ !== optimizer) {\n      this.optimizer_ = optimizer;\n      this.isOptimizerOwned = false;\n    }\n  }\n  dispose() {\n    const result = super.dispose();\n    if (result.refCountAfterDispose === 0 && this.optimizer != null && this.isOptimizerOwned) {\n      const numTensorsBeforeOptmizerDisposal = memory().numTensors;\n      this.optimizer_.dispose();\n      result.numDisposedVariables += numTensorsBeforeOptmizerDisposal - memory().numTensors;\n    }\n    return result;\n  }\n  getLossIdentifiers() {\n    let lossNames;\n    if (typeof this.loss === \"string\") {\n      lossNames = toSnakeCase(this.loss);\n    } else if (Array.isArray(this.loss)) {\n      for (const loss of this.loss) {\n        if (typeof loss !== \"string\") {\n          throw new Error(\"Serialization of non-string loss is not supported.\");\n        }\n      }\n      lossNames = this.loss.map((name) => toSnakeCase(name));\n    } else {\n      const outputNames = Object.keys(this.loss);\n      lossNames = {};\n      const losses2 = this.loss;\n      for (const outputName of outputNames) {\n        if (typeof losses2[outputName] === \"string\") {\n          lossNames[outputName] = toSnakeCase(losses2[outputName]);\n        } else {\n          throw new Error(\"Serialization of non-string loss is not supported.\");\n        }\n      }\n    }\n    return lossNames;\n  }\n  getMetricIdentifiers() {\n    if (typeof this.metrics === \"string\" || typeof this.metrics === \"function\") {\n      return [toSnakeCase(getLossOrMetricName(this.metrics))];\n    } else if (Array.isArray(this.metrics)) {\n      return this.metrics.map((metric) => toSnakeCase(getLossOrMetricName(metric)));\n    } else {\n      const metricsIdentifiers = {};\n      for (const key in this.metrics) {\n        metricsIdentifiers[key] = toSnakeCase(getLossOrMetricName(this.metrics[key]));\n      }\n      return metricsIdentifiers;\n    }\n  }\n  getTrainingConfig() {\n    return {\n      loss: this.getLossIdentifiers(),\n      metrics: this.getMetricIdentifiers(),\n      optimizer_config: {\n        class_name: this.optimizer.getClassName(),\n        config: this.optimizer.getConfig()\n      }\n    };\n  }\n  loadTrainingConfig(trainingConfig) {\n    if (trainingConfig.weighted_metrics != null) {\n      throw new Error(\"Loading weight_metrics is not supported yet.\");\n    }\n    if (trainingConfig.loss_weights != null) {\n      throw new Error(\"Loading loss_weights is not supported yet.\");\n    }\n    if (trainingConfig.sample_weight_mode != null) {\n      throw new Error(\"Loading sample_weight_mode is not supported yet.\");\n    }\n    const tsConfig = convertPythonicToTs(trainingConfig.optimizer_config);\n    const optimizer = deserialize(tsConfig);\n    let loss;\n    if (typeof trainingConfig.loss === \"string\") {\n      loss = toCamelCase(trainingConfig.loss);\n    } else if (Array.isArray(trainingConfig.loss)) {\n      loss = trainingConfig.loss.map((lossEntry) => toCamelCase(lossEntry));\n    } else if (trainingConfig.loss != null) {\n      loss = {};\n      for (const key in trainingConfig.loss) {\n        loss[key] = toCamelCase(trainingConfig.loss[key]);\n      }\n    }\n    let metrics;\n    if (Array.isArray(trainingConfig.metrics)) {\n      metrics = trainingConfig.metrics.map((metric) => toCamelCase(metric));\n    } else if (trainingConfig.metrics != null) {\n      metrics = {};\n      for (const key in trainingConfig.metrics) {\n        metrics[key] = toCamelCase(trainingConfig.metrics[key]);\n      }\n    }\n    this.compile({ loss, metrics, optimizer });\n  }\n  async save(handlerOrURL, config) {\n    if (typeof handlerOrURL === \"string\") {\n      const handlers = io_exports.getSaveHandlers(handlerOrURL);\n      if (handlers.length === 0) {\n        throw new ValueError(`Cannot find any save handlers for URL '${handlerOrURL}'`);\n      } else if (handlers.length > 1) {\n        throw new ValueError(`Found more than one (${handlers.length}) save handlers for URL '${handlerOrURL}'`);\n      }\n      handlerOrURL = handlers[0];\n    }\n    if (handlerOrURL.save == null) {\n      throw new ValueError(\"LayersModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.\");\n    }\n    const weightDataAndSpecs = await io_exports.encodeWeights(this.getNamedWeights(config));\n    const returnString = false;\n    const unusedArg = null;\n    const modelConfig = this.toJSON(unusedArg, returnString);\n    const modelArtifacts = {\n      modelTopology: modelConfig,\n      format: LAYERS_MODEL_FORMAT_NAME,\n      generatedBy: `TensorFlow.js tfjs-layers v${version2}`,\n      convertedBy: null\n    };\n    const includeOptimizer = config == null ? false : config.includeOptimizer;\n    if (includeOptimizer && this.optimizer != null) {\n      modelArtifacts.trainingConfig = this.getTrainingConfig();\n      const weightType = \"optimizer\";\n      const { data: optimizerWeightData, specs: optimizerWeightSpecs } = await io_exports.encodeWeights(await this.optimizer.getWeights(), weightType);\n      weightDataAndSpecs.specs.push(...optimizerWeightSpecs);\n      weightDataAndSpecs.data = io_exports.concatenateArrayBuffers([weightDataAndSpecs.data, optimizerWeightData]);\n    }\n    if (this.userDefinedMetadata != null) {\n      const checkSize = true;\n      checkUserDefinedMetadata(this.userDefinedMetadata, this.name, checkSize);\n      modelArtifacts.userDefinedMetadata = this.userDefinedMetadata;\n    }\n    modelArtifacts.weightData = weightDataAndSpecs.data;\n    modelArtifacts.weightSpecs = weightDataAndSpecs.specs;\n    return handlerOrURL.save(modelArtifacts);\n  }\n  setUserDefinedMetadata(userDefinedMetadata) {\n    checkUserDefinedMetadata(userDefinedMetadata, this.name);\n    this.userDefinedMetadata = userDefinedMetadata;\n  }\n  getUserDefinedMetadata() {\n    return this.userDefinedMetadata;\n  }\n};\nLayersModel.className = \"Model\";\nserialization_exports.registerClass(LayersModel);\nvar Functional = class extends LayersModel {\n};\nFunctional.className = \"Functional\";\nserialization_exports.registerClass(Functional);\n\n// node_modules/.pnpm/@tensorflow+tfjs-layers@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-layers/dist/models.js\nasync function modelFromJSON(modelAndWeightsConfig, customObjects) {\n  if (!(\"modelTopology\" in modelAndWeightsConfig)) {\n    modelAndWeightsConfig = { modelTopology: modelAndWeightsConfig };\n  }\n  modelAndWeightsConfig = modelAndWeightsConfig;\n  let modelTopology = modelAndWeightsConfig.modelTopology;\n  if (modelTopology[\"model_config\"] != null) {\n    modelTopology = modelTopology[\"model_config\"];\n  }\n  const tsConfig = convertPythonicToTs(modelTopology);\n  const model2 = deserialize(tsConfig, customObjects);\n  if (modelAndWeightsConfig.weightsManifest != null) {\n    const weightValues = await io_exports.loadWeights(modelAndWeightsConfig.weightsManifest, modelAndWeightsConfig.pathPrefix, model2.weights.map((weight) => weight.originalName));\n    const uniqueWeightValues = {};\n    for (const weight of model2.weights) {\n      uniqueWeightValues[weight.originalName] = weightValues[weight.originalName];\n    }\n    model2.loadWeights(uniqueWeightValues);\n    dispose(weightValues);\n  }\n  return model2;\n}\nasync function loadLayersModelInternal(pathOrIOHandler, options) {\n  if (options == null) {\n    options = {};\n  }\n  if (typeof pathOrIOHandler === \"string\") {\n    const handlers = io_exports.getLoadHandlers(pathOrIOHandler, options);\n    if (handlers.length === 0) {\n      handlers.push(io_exports.browserHTTPRequest(pathOrIOHandler, options));\n    } else if (handlers.length > 1) {\n      throw new ValueError(`Found more than one (${handlers.length}) load handlers for URL '${pathOrIOHandler}'`);\n    }\n    pathOrIOHandler = handlers[0];\n  }\n  return loadLayersModelFromIOHandler(pathOrIOHandler, void 0, options);\n}\nasync function loadLayersModelFromIOHandler(handler, customObjects, options) {\n  if (options == null) {\n    options = {};\n  }\n  if (handler.load == null) {\n    throw new ValueError(\"Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.\");\n  }\n  const artifacts = await handler.load();\n  let modelTopology = artifacts.modelTopology;\n  if (modelTopology[\"model_config\"] != null) {\n    modelTopology = modelTopology[\"model_config\"];\n  }\n  const strict = options.strict == null ? true : options.strict;\n  const fastWeightInit = artifacts.weightData != null && artifacts.weightSpecs != null && strict;\n  const model2 = deserialize(convertPythonicToTs(modelTopology), customObjects, fastWeightInit);\n  const trainingConfig = artifacts.trainingConfig;\n  if (trainingConfig != null) {\n    model2.loadTrainingConfig(trainingConfig);\n  }\n  if (artifacts.userDefinedMetadata != null) {\n    model2.setUserDefinedMetadata(artifacts.userDefinedMetadata);\n  }\n  if (artifacts.weightData != null) {\n    if (artifacts.weightSpecs == null) {\n      throw new ValueError(\"LayersModel artifacts contains weight data, but not weight specs. Therefore loading of weights cannot proceed.\");\n    }\n    const { modelWeights, optimizerWeights } = decodeModelAndOptimizerWeights(artifacts.weightData, artifacts.weightSpecs);\n    model2.loadWeights(modelWeights, strict);\n    if (model2.optimizer != null && optimizerWeights.length > 0) {\n      await model2.optimizer.setWeights(optimizerWeights);\n    }\n    dispose(modelWeights);\n    dispose(optimizerWeights.map((w) => w.tensor));\n  }\n  return model2;\n}\nfunction decodeModelAndOptimizerWeights(buffer2, specs) {\n  const name2Tensor = io_exports.decodeWeights(buffer2, specs);\n  const modelWeights = {};\n  const optimizerWeights = [];\n  specs.forEach((spec) => {\n    if (spec.group === \"optimizer\") {\n      optimizerWeights.push({ name: spec.name, tensor: name2Tensor[spec.name] });\n    } else {\n      modelWeights[spec.name] = name2Tensor[spec.name];\n    }\n  });\n  return { modelWeights, optimizerWeights };\n}\nvar Sequential = class extends LayersModel {\n  constructor(args) {\n    super({ inputs: [], outputs: [] });\n    args = args || {};\n    this.trainable = true;\n    this.built = false;\n    this.name = args.name != null ? args.name : getUid(\"sequential_\");\n    if (args.layers != null) {\n      for (const layer of args.layers) {\n        this.add(layer);\n      }\n    }\n  }\n  checkShape(layer) {\n    const shape = layer.inboundNodes[0].outputTensors[0].shape;\n    if (shape.some((x) => x < 0)) {\n      throw new ValueError(`Negative dimension size caused by adding layer ${layer.name} with input shape [${layer.inboundNodes[0].inputTensors[0].shape}]`);\n    }\n  }\n  add(layer) {\n    const isLayerModelInstance = layer instanceof Sequential || layer instanceof LayersModel;\n    let modelLayer;\n    if (isLayerModelInstance) {\n      modelLayer = layer;\n      if (modelLayer.outputs.length !== 1) {\n        throw new ValueError(\"All layers in a Sequential model should have a single output tensor. For multi-output layers, use the functional API.\");\n      }\n      if (modelLayer.inputs.length !== 1) {\n        throw new ValueError(\"All layers in a Sequential model should have a single input tensor. For multi-input layers, use the functional API.\");\n      }\n    }\n    if (this.outputs.length === 0) {\n      if (layer.inboundNodes.length === 0) {\n        if (layer.batchInputShape == null) {\n          throw new ValueError(\"The first layer in a Sequential model must get an `inputShape` or `batchInputShape` argument.\");\n        }\n        const x = Input({\n          batchShape: layer.batchInputShape,\n          dtype: layer.dtype,\n          name: layer.name + \"_input\"\n        });\n        layer.apply(x);\n      }\n      if (isLayerModelInstance) {\n        this.outputs = modelLayer.outputs;\n        this.inputs = modelLayer.inputs;\n      } else {\n        if (layer.inboundNodes.length !== 1) {\n          throw new ValueError(`A layer added to a Sequential model must not already be connected somewhere else. LayersModel received layer ${layer.name} which has ${layer.inboundNodes.length} pre-existing inbound connections.`);\n        }\n        if (layer.inboundNodes[0].outputTensors.length !== 1) {\n          throw new ValueError(\"All layers in a Sequential model should have a single output tensor. For multi-output layers, use the functional API.\");\n        }\n        this.checkShape(layer);\n        this.outputs = [layer.inboundNodes[0].outputTensors[0]];\n        this.inputs = getSourceInputs(this.outputs[0]);\n      }\n      this.inboundNodes = [];\n      new Node({\n        outboundLayer: this,\n        inboundLayers: [],\n        nodeIndices: [],\n        tensorIndices: [],\n        inputTensors: this.inputs,\n        outputTensors: this.outputs,\n        inputMasks: pyListRepeat(null, this.inputs.length),\n        outputMasks: [null],\n        inputShapes: this.inputs.map((x) => x.shape),\n        outputShapes: this.outputs[0].shape\n      });\n    } else {\n      const outputTensor = layer.apply(this.outputs[0]);\n      if (Array.isArray(outputTensor)) {\n        throw new TypeError(\"All layers in a Sequential model should have a single output tensor. For multi-output layers, use the functional API.\");\n      }\n      this.checkShape(layer);\n      this.outputs = [outputTensor];\n      this.inboundNodes[0].outputTensors = this.outputs;\n      this.inboundNodes[0].outputShapes = [this.outputs[0].shape];\n    }\n    this.layers.push(layer);\n    this.built = false;\n  }\n  pop() {\n    if (this.layers.length === 0) {\n      throw new TypeError(\"There are no layers in the model.\");\n    }\n    this.layers.pop();\n    if (this.layers.length === 0) {\n      this.outputs = [];\n      this.inboundNodes = [];\n      this.outboundNodes = [];\n    } else {\n      const lastLayerIndex = this.layers.length - 1;\n      this.layers[lastLayerIndex].outboundNodes = [];\n      this.outputs = [this.layers[lastLayerIndex].output];\n      this.inboundNodes[0].outputTensors = this.outputs;\n      this.inboundNodes[0].outputShapes = [this.outputs[0].shape];\n    }\n  }\n  call(inputs, kwargs) {\n    if (this.model == null) {\n      this.build();\n    }\n    return this.model.call(inputs, kwargs);\n  }\n  build(inputShape) {\n    getExactlyOneShape(inputShape);\n    if (this.inputs.length === 0 || this.outputs.length === 0) {\n      throw new TypeError(\"Sequential model cannot be built: model is empty. Add some layers first.\");\n    }\n    this.model = new LayersModel({\n      inputs: this.inputs,\n      outputs: this.outputs[0],\n      name: this.name + \"_model\"\n    });\n    this.model.trainable = this.trainable;\n    this.supportsMasking = this.model.supportsMasking;\n    this.inputLayers = this.model.inputLayers;\n    this.inputLayersNodeIndices = this.model.inputLayersNodeIndices;\n    this.inputLayersTensorIndices = this.model.inputLayersTensorIndices;\n    this.outputLayers = this.model.outputLayers;\n    this.outputLayersNodeIndices = this.model.outputLayersNodeIndices;\n    this.outputLayersTensorIndices = this.model.outputLayersTensorIndices;\n    this.nodesByDepth = this.model.nodesByDepth;\n    this.containerNodes = this.model.containerNodes;\n    this.outputNames = this.model.outputNames;\n    this.inputNames = this.model.inputNames;\n    this.built = true;\n  }\n  countParams() {\n    if (!this.built) {\n      this.build();\n    }\n    return super.countParams();\n  }\n  summary(lineLength, positions, printFn = console.log) {\n    if (!this.built) {\n      this.build();\n    }\n    super.summary(lineLength, positions, printFn);\n  }\n  setWeights(weights) {\n    if (this.model == null) {\n      this.build();\n    }\n    this.model.setWeights(weights);\n  }\n  evaluate(x, y, args = {}) {\n    if (!this.built) {\n      throw new RuntimeError(\"The model needs to be compiled before being used.\");\n    }\n    return this.model.evaluate(x, y, args);\n  }\n  async evaluateDataset(dataset, args) {\n    if (!this.built) {\n      throw new RuntimeError(\"The model needs to be compiled before being used.\");\n    }\n    return this.model.evaluateDataset(dataset, args);\n  }\n  predict(x, args = {}) {\n    if (this.model == null) {\n      this.build();\n    }\n    return this.model.predict(x, args);\n  }\n  predictOnBatch(x) {\n    if (this.model == null) {\n      this.build();\n    }\n    return this.model.predictOnBatch(x);\n  }\n  compile(args) {\n    this.build();\n    this.model.compile(args);\n    this.optimizer_ = this.model.optimizer;\n    this.isOptimizerOwned = this.model.isOptimizerOwned;\n    this.loss = this.model.loss;\n    this.metrics = this.model.metrics;\n    this.metricsTensors = this.model.metricsTensors;\n    this.metricsNames = this.model.metricsNames;\n  }\n  get optimizer() {\n    return this.model == null ? void 0 : this.model.optimizer;\n  }\n  set optimizer(optimizer) {\n    this.model.optimizer = optimizer;\n  }\n  async fit(x, y, args = {}) {\n    if (!this.built) {\n      throw new RuntimeError(\"The model needs to be compiled before being used.\");\n    }\n    return this.model.fit(x, y, args);\n  }\n  async fitDataset(dataset, args) {\n    if (!this.built) {\n      throw new RuntimeError(\"The model needs to be compiled before being used.\");\n    }\n    return this.model.fitDataset(dataset, args);\n  }\n  async trainOnBatch(x, y) {\n    return this.model.trainOnBatch(x, y);\n  }\n  static fromConfig(cls, config, customObjects = {}, fastWeightInit = false) {\n    let configArray;\n    let extraModelConfig = {};\n    if (config instanceof Array) {\n      if (!(config[0].className != null) || config[0][\"className\"] === \"Merge\") {\n        throw new ValueError(\"Legacy serialization format not supported yet.\");\n      }\n      configArray = config;\n    } else {\n      util_exports.assert(config[\"layers\"] != null, () => `When the config data for a Sequential model is not an Array, it must be an Object that contains the 'layers' field.`);\n      configArray = config[\"layers\"];\n      delete config[\"layers\"];\n      extraModelConfig = config;\n    }\n    const model2 = new cls(extraModelConfig);\n    if (!(model2 instanceof Sequential)) {\n      throw new NotImplementedError(`Sequential.fromConfig called on non-Sequential input: ${model2}`);\n    }\n    for (const conf of configArray) {\n      const customObjects2 = void 0;\n      const layer = deserialize(conf, customObjects2, fastWeightInit);\n      if (fastWeightInit) {\n        layer.setFastWeightInitDuringBuild(true);\n      }\n      model2.add(layer);\n    }\n    return model2;\n  }\n  set stopTraining(stop) {\n    if (this.model == null) {\n      throw new ValueError(\"Cannot set the stopTraining property of a sequential model before it is compiled.\");\n    }\n    this.model.stopTraining = stop;\n  }\n  get stopTraining() {\n    if (this.model == null) {\n      throw new ValueError(\"Cannot get the stopTraining property of a sequential model before it is compiled.\");\n    }\n    return this.model.stopTraining;\n  }\n  getConfig() {\n    const layers = [];\n    for (const layer of this.layers) {\n      const dict = {};\n      dict[\"className\"] = layer.getClassName();\n      dict[\"config\"] = layer.getConfig();\n      layers.push(dict);\n    }\n    return { name: this.name, layers };\n  }\n};\nSequential.className = \"Sequential\";\nserialization_exports.registerClass(Sequential);\n\n// node_modules/.pnpm/@tensorflow+tfjs-layers@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-layers/dist/exports.js\nfunction model(args) {\n  return new LayersModel(args);\n}\nfunction sequential(config) {\n  return new Sequential(config);\n}\nfunction loadLayersModel(pathOrIOHandler, options) {\n  if (options == null) {\n    options = {};\n  }\n  return loadLayersModelInternal(pathOrIOHandler, options);\n}\nfunction input(config) {\n  return Input(config);\n}\nfunction registerCallbackConstructor(verbosityLevel, callbackConstructor) {\n  CallbackConstructorRegistry.registerCallbackConstructor(verbosityLevel, callbackConstructor);\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-layers@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-layers/dist/activations.js\nvar Activation = class extends serialization_exports.Serializable {\n  getConfig() {\n    return {};\n  }\n};\nvar Elu2 = class extends Activation {\n  apply(x, alpha = 1) {\n    return elu2(x, alpha);\n  }\n};\nElu2.className = \"elu\";\nserialization_exports.registerClass(Elu2);\nvar Selu2 = class extends Activation {\n  apply(x) {\n    return selu(x);\n  }\n};\nSelu2.className = \"selu\";\nserialization_exports.registerClass(Selu2);\nvar Relu2 = class extends Activation {\n  apply(x) {\n    return relu(x);\n  }\n};\nRelu2.className = \"relu\";\nserialization_exports.registerClass(Relu2);\nvar Relu62 = class extends Activation {\n  apply(x) {\n    return tidy(() => minimum(6, relu(x)));\n  }\n};\nRelu62.className = \"relu6\";\nserialization_exports.registerClass(Relu62);\nvar Linear = class extends Activation {\n  apply(x) {\n    return x;\n  }\n};\nLinear.className = \"linear\";\nserialization_exports.registerClass(Linear);\nvar Sigmoid2 = class extends Activation {\n  apply(x) {\n    return sigmoid(x);\n  }\n};\nSigmoid2.className = \"sigmoid\";\nserialization_exports.registerClass(Sigmoid2);\nvar HardSigmoid = class extends Activation {\n  apply(x) {\n    return hardSigmoid(x);\n  }\n};\nHardSigmoid.className = \"hardSigmoid\";\nserialization_exports.registerClass(HardSigmoid);\nvar Softplus2 = class extends Activation {\n  apply(x) {\n    return softplus(x);\n  }\n};\nSoftplus2.className = \"softplus\";\nserialization_exports.registerClass(Softplus2);\nvar Softsign = class extends Activation {\n  apply(x) {\n    return softsign(x);\n  }\n};\nSoftsign.className = \"softsign\";\nserialization_exports.registerClass(Softsign);\nvar Tanh2 = class extends Activation {\n  apply(x) {\n    return tanh2(x);\n  }\n};\nTanh2.className = \"tanh\";\nserialization_exports.registerClass(Tanh2);\nvar Softmax2 = class extends Activation {\n  apply(x, axis = -1) {\n    return softmax(x, axis);\n  }\n};\nSoftmax2.className = \"softmax\";\nserialization_exports.registerClass(Softmax2);\nvar LogSoftmax2 = class extends Activation {\n  apply(x, axis = -1) {\n    return logSoftmax(x, axis);\n  }\n};\nLogSoftmax2.className = \"logSoftmax\";\nserialization_exports.registerClass(LogSoftmax2);\nvar Swish = class extends Activation {\n  apply(x, alpha = 1) {\n    return tidy(() => mul(sigmoid(mul(x, alpha)), x));\n  }\n};\nSwish.className = \"swish\";\nserialization_exports.registerClass(Swish);\nvar Mish = class extends Activation {\n  apply(x) {\n    return tidy(() => mul(x, tanh2(softplus(x))));\n  }\n};\nMish.className = \"mish\";\nserialization_exports.registerClass(Mish);\nfunction serializeActivation(activation2) {\n  return activation2.getClassName();\n}\nfunction deserializeActivation(config, customObjects = {}) {\n  return deserializeKerasObject(config, serialization_exports.SerializationMap.getMap().classNameMap, customObjects, \"activation\");\n}\nfunction getActivation(identifier) {\n  if (identifier == null) {\n    const config = {};\n    config[\"className\"] = \"linear\";\n    config[\"config\"] = {};\n    return deserializeActivation(config);\n  }\n  if (typeof identifier === \"string\") {\n    const config = {};\n    config[\"className\"] = identifier;\n    config[\"config\"] = {};\n    return deserializeActivation(config);\n  } else if (identifier instanceof Activation) {\n    return identifier;\n  } else {\n    return deserializeActivation(identifier);\n  }\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-layers@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-layers/dist/regularizers.js\nfunction assertObjectArgs(args) {\n  if (args != null && typeof args !== \"object\") {\n    throw new Error(`Argument to L1L2 regularizer's constructor is expected to be an object, but received: ${args}`);\n  }\n}\nvar Regularizer = class extends serialization_exports.Serializable {\n};\nvar L1L2 = class extends Regularizer {\n  constructor(args) {\n    super();\n    assertObjectArgs(args);\n    this.l1 = args == null || args.l1 == null ? 0.01 : args.l1;\n    this.l2 = args == null || args.l2 == null ? 0.01 : args.l2;\n    this.hasL1 = this.l1 !== 0;\n    this.hasL2 = this.l2 !== 0;\n  }\n  apply(x) {\n    return tidy(() => {\n      let regularization = zeros([1]);\n      if (this.hasL1) {\n        regularization = add2(regularization, sum2(mul(this.l1, abs(x))));\n      }\n      if (this.hasL2) {\n        regularization = add2(regularization, sum2(mul(this.l2, square2(x))));\n      }\n      return reshape(regularization, []);\n    });\n  }\n  getConfig() {\n    return { \"l1\": this.l1, \"l2\": this.l2 };\n  }\n  static fromConfig(cls, config) {\n    return new cls({ l1: config[\"l1\"], l2: config[\"l2\"] });\n  }\n};\nL1L2.className = \"L1L2\";\nserialization_exports.registerClass(L1L2);\nfunction l1(args) {\n  assertObjectArgs(args);\n  return new L1L2({ l1: args != null ? args.l1 : null, l2: 0 });\n}\nfunction l2(args) {\n  assertObjectArgs(args);\n  return new L1L2({ l2: args != null ? args.l2 : null, l1: 0 });\n}\nvar REGULARIZER_IDENTIFIER_REGISTRY_SYMBOL_MAP = {\n  \"l1l2\": \"L1L2\"\n};\nfunction serializeRegularizer(constraint) {\n  return serializeKerasObject(constraint);\n}\nfunction deserializeRegularizer(config, customObjects = {}) {\n  return deserializeKerasObject(config, serialization_exports.SerializationMap.getMap().classNameMap, customObjects, \"regularizer\");\n}\nfunction getRegularizer(identifier) {\n  if (identifier == null) {\n    return null;\n  }\n  if (typeof identifier === \"string\") {\n    const className = identifier in REGULARIZER_IDENTIFIER_REGISTRY_SYMBOL_MAP ? REGULARIZER_IDENTIFIER_REGISTRY_SYMBOL_MAP[identifier] : identifier;\n    const config = { className, config: {} };\n    return deserializeRegularizer(config);\n  } else if (identifier instanceof Regularizer) {\n    return identifier;\n  } else {\n    return deserializeRegularizer(identifier);\n  }\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-layers@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-layers/dist/layers/advanced_activations.js\nvar ReLU = class extends Layer {\n  constructor(args) {\n    super(args == null ? {} : args);\n    this.supportsMasking = true;\n    if (args != null) {\n      this.maxValue = args.maxValue;\n    }\n  }\n  call(inputs, kwargs) {\n    inputs = getExactlyOneTensor(inputs);\n    let output = relu(inputs);\n    if (this.maxValue != null) {\n      output = clipByValue(output, 0, this.maxValue);\n    }\n    return output;\n  }\n  computeOutputShape(inputShape) {\n    return inputShape;\n  }\n  getConfig() {\n    const config = { maxValue: this.maxValue };\n    const baseConfig = super.getConfig();\n    Object.assign(config, baseConfig);\n    return config;\n  }\n};\nReLU.className = \"ReLU\";\nserialization_exports.registerClass(ReLU);\nvar LeakyReLU = class extends Layer {\n  constructor(args) {\n    super(args == null ? {} : args);\n    this.DEFAULT_ALPHA = 0.3;\n    if (args == null) {\n      args = {};\n    }\n    this.alpha = args.alpha == null ? this.DEFAULT_ALPHA : args.alpha;\n  }\n  call(inputs, kwargs) {\n    const x = getExactlyOneTensor(inputs);\n    return leakyRelu(x, this.alpha);\n  }\n  computeOutputShape(inputShape) {\n    return inputShape;\n  }\n  getConfig() {\n    const config = { alpha: this.alpha };\n    const baseConfig = super.getConfig();\n    Object.assign(config, baseConfig);\n    return config;\n  }\n};\nLeakyReLU.className = \"LeakyReLU\";\nserialization_exports.registerClass(LeakyReLU);\nvar PReLU = class extends Layer {\n  constructor(args) {\n    super(args == null ? {} : args);\n    this.DEFAULT_ALPHA_INITIALIZER = \"zeros\";\n    if (args == null) {\n      args = {};\n    }\n    this.supportsMasking = true;\n    this.alphaInitializer = getInitializer(args.alphaInitializer || this.DEFAULT_ALPHA_INITIALIZER);\n    this.alphaRegularizer = getRegularizer(args.alphaRegularizer);\n    this.alphaConstraint = getConstraint(args.alphaConstraint);\n    if (args.sharedAxes == null) {\n      this.sharedAxes = null;\n    } else if (Array.isArray(args.sharedAxes)) {\n      this.sharedAxes = args.sharedAxes;\n    } else if (typeof args.sharedAxes === \"number\") {\n      this.sharedAxes = [args.sharedAxes];\n    } else {\n      throw new ValueError(`Expected sharedAxes to be a number or an array of numbers, but got ${args.sharedAxes}`);\n    }\n  }\n  build(inputShape) {\n    inputShape = getExactlyOneShape(inputShape);\n    const paramShape = inputShape.slice(1);\n    if (this.sharedAxes != null) {\n      for (const i of this.sharedAxes) {\n        paramShape[i - 1] = 1;\n      }\n    }\n    this.alpha = this.addWeight(\"alpha\", paramShape, \"float32\", this.alphaInitializer, this.alphaRegularizer, true, this.alphaConstraint);\n    const axes = {};\n    if (this.sharedAxes != null) {\n      for (let i = 1; i < inputShape.length; ++i) {\n        axes[i] = inputShape[i];\n      }\n    }\n    this.inputSpec = [new InputSpec({\n      ndim: inputShape.length,\n      axes\n    })];\n    this.built = true;\n  }\n  call(inputs, kwargs) {\n    inputs = getExactlyOneTensor(inputs);\n    return prelu(inputs, this.alpha.read());\n  }\n  getConfig() {\n    const config = {\n      alphaInitializer: serializeInitializer(this.alphaInitializer),\n      alphaRegularizer: serializeRegularizer(this.alphaRegularizer),\n      alphaConstraint: serializeConstraint(this.alphaConstraint),\n      sharedAxes: this.sharedAxes\n    };\n    const baseConfig = super.getConfig();\n    Object.assign(config, baseConfig);\n    return config;\n  }\n};\nPReLU.className = \"PReLU\";\nserialization_exports.registerClass(PReLU);\nvar ELU = class extends Layer {\n  constructor(args) {\n    super(args == null ? {} : args);\n    this.DEFAULT_ALPHA = 1;\n    if (args == null) {\n      args = {};\n    }\n    if (args.alpha != null && args.alpha !== this.DEFAULT_ALPHA) {\n      throw new NotImplementedError(`Non-default alpha value (${args.alpha}) is not supported by the ELU layer yet.`);\n    }\n    this.alpha = args.alpha == null ? this.DEFAULT_ALPHA : args.alpha;\n  }\n  call(inputs, kwargs) {\n    const x = getExactlyOneTensor(inputs);\n    return elu(x);\n  }\n  computeOutputShape(inputShape) {\n    return inputShape;\n  }\n  getConfig() {\n    const config = { alpha: this.alpha };\n    const baseConfig = super.getConfig();\n    Object.assign(config, baseConfig);\n    return config;\n  }\n};\nELU.className = \"ELU\";\nserialization_exports.registerClass(ELU);\nvar ThresholdedReLU = class extends Layer {\n  constructor(args) {\n    super(args == null ? {} : args);\n    this.DEFAULT_THETA = 1;\n    if (args == null) {\n      args = {};\n    }\n    this.theta = args.theta == null ? this.DEFAULT_THETA : args.theta;\n  }\n  call(inputs, kwargs) {\n    const x = getExactlyOneTensor(inputs);\n    return mul(x, cast(greater(x, this.theta), \"float32\"));\n  }\n  computeOutputShape(inputShape) {\n    return inputShape;\n  }\n  getConfig() {\n    const config = { theta: this.theta };\n    const baseConfig = super.getConfig();\n    Object.assign(config, baseConfig);\n    return config;\n  }\n};\nThresholdedReLU.className = \"ThresholdedReLU\";\nserialization_exports.registerClass(ThresholdedReLU);\nvar Softmax3 = class extends Layer {\n  constructor(args) {\n    super(args == null ? {} : args);\n    this.DEFAULT_AXIS = 1;\n    if (args == null) {\n      args = {};\n    }\n    this.softmax = new Softmax2().apply;\n    this.axis = args.axis == null ? this.DEFAULT_AXIS : args.axis;\n  }\n  call(inputs, kwargs) {\n    const x = getExactlyOneTensor(inputs);\n    return this.softmax(x, this.axis);\n  }\n  computeOutputShape(inputShape) {\n    return inputShape;\n  }\n  getConfig() {\n    const config = { axis: this.axis };\n    const baseConfig = super.getConfig();\n    Object.assign(config, baseConfig);\n    return config;\n  }\n};\nSoftmax3.className = \"Softmax\";\nserialization_exports.registerClass(Softmax3);\n\n// node_modules/.pnpm/@tensorflow+tfjs-layers@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-layers/dist/utils/conv_utils.js\nfunction normalizeArray(value, n, name) {\n  if (typeof value === \"number\") {\n    return pyListRepeat(value, n);\n  } else {\n    if (value.length !== n) {\n      throw new ValueError(`The ${name} argument must be an integer or tuple of ${n} integers. Received: ${value.length} elements.`);\n    }\n    for (let i = 0; i < n; ++i) {\n      const singleValue = value[i];\n      if (!isInteger(singleValue)) {\n        throw new ValueError(`The ${name} argument must be an integer or tuple of ${n} integers. Received: ${JSON.stringify(value)} including a non-integer number ${singleValue}`);\n      }\n    }\n    return value;\n  }\n}\nfunction convOutputLength(inputLength, filterSize, padding, stride, dilation = 1) {\n  if (inputLength == null) {\n    return inputLength;\n  }\n  const dilatedFilterSize = filterSize + (filterSize - 1) * (dilation - 1);\n  let outputLength;\n  if (padding === \"same\") {\n    outputLength = inputLength;\n  } else {\n    outputLength = inputLength - dilatedFilterSize + 1;\n  }\n  return Math.floor((outputLength + stride - 1) / stride);\n}\nfunction deconvLength(dimSize, strideSize, kernelSize, padding) {\n  if (dimSize == null) {\n    return null;\n  }\n  if (padding === \"valid\") {\n    dimSize = dimSize * strideSize + max2([kernelSize - strideSize, 0]);\n  } else if (padding === \"same\") {\n    dimSize = dimSize * strideSize;\n  } else {\n    throw new ValueError(`Unsupport padding mode: ${padding}.`);\n  }\n  return dimSize;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-layers@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-layers/dist/layers/convolutional.js\nfunction preprocessConv2DInput(x, dataFormat) {\n  return tidy(() => {\n    checkDataFormat(dataFormat);\n    if (dataFormat === \"channelsFirst\") {\n      return transpose(x, [0, 2, 3, 1]);\n    } else {\n      return x;\n    }\n  });\n}\nfunction preprocessConv3DInput(x, dataFormat) {\n  return tidy(() => {\n    checkDataFormat(dataFormat);\n    if (dataFormat === \"channelsFirst\") {\n      return transpose(x, [0, 2, 3, 4, 1]);\n    } else {\n      return x;\n    }\n  });\n}\nfunction conv1dWithBias(x, kernel, bias, strides = 1, padding = \"valid\", dataFormat, dilationRate = 1) {\n  return tidy(() => {\n    if (dataFormat == null) {\n      dataFormat = imageDataFormat();\n    }\n    checkDataFormat(dataFormat);\n    if (x.shape.length !== 3) {\n      throw new ValueError(`The input of a conv1dWithBias operation should be 3, but is ${x.shape.length} instead.`);\n    }\n    if (kernel.shape.length !== 3) {\n      throw new ValueError(`The kernel for a conv1dWithBias operation should be 3, but is ${kernel.shape.length} instead`);\n    }\n    if (bias != null && bias.shape.length !== 1) {\n      throw new ValueError(`The bias for a conv1dWithBias operation should be 1, but is ${kernel.shape.length} instead`);\n    }\n    if (dataFormat === \"channelsFirst\") {\n      x = transpose(x, [0, 2, 1]);\n    }\n    if (padding === \"causal\") {\n      throw new NotImplementedError(\"The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.\");\n    }\n    let y = conv1d(x, kernel, strides, padding === \"same\" ? \"same\" : \"valid\", \"NWC\", dilationRate);\n    if (bias != null) {\n      y = biasAdd(y, bias);\n    }\n    return y;\n  });\n}\nfunction conv2dWithBiasActivation(x, kernel, bias, strides = [1, 1], padding = \"valid\", dataFormat, dilationRate, activation2 = null) {\n  return tidy(() => {\n    if (dataFormat == null) {\n      dataFormat = imageDataFormat();\n    }\n    checkDataFormat(dataFormat);\n    if (x.rank !== 3 && x.rank !== 4) {\n      throw new ValueError(`conv2dWithBiasActivation expects input to be of rank 3 or 4, but received ${x.rank}.`);\n    }\n    if (kernel.rank !== 3 && kernel.rank !== 4) {\n      throw new ValueError(`conv2dWithBiasActivation expects kernel to be of rank 3 or 4, but received ${x.rank}.`);\n    }\n    let y = preprocessConv2DInput(x, dataFormat);\n    if (padding === \"causal\") {\n      throw new NotImplementedError(\"The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.\");\n    }\n    y = fused_ops_exports.conv2d({\n      x: y,\n      filter: kernel,\n      strides,\n      pad: padding === \"same\" ? \"same\" : \"valid\",\n      dilations: dilationRate,\n      dataFormat: \"NHWC\",\n      bias,\n      activation: activation2\n    });\n    if (dataFormat === \"channelsFirst\") {\n      y = transpose(y, [0, 3, 1, 2]);\n    }\n    return y;\n  });\n}\nfunction conv3dWithBias(x, kernel, bias, strides = [1, 1, 1], padding = \"valid\", dataFormat, dilationRate) {\n  return tidy(() => {\n    if (dataFormat == null) {\n      dataFormat = imageDataFormat();\n    }\n    checkDataFormat(dataFormat);\n    if (x.rank !== 4 && x.rank !== 5) {\n      throw new ValueError(`conv3dWithBias expects input to be of rank 4 or 5, but received ${x.rank}.`);\n    }\n    if (kernel.rank !== 4 && kernel.rank !== 5) {\n      throw new ValueError(`conv3dWithBias expects kernel to be of rank 4 or 5, but received ${x.rank}.`);\n    }\n    let y = preprocessConv3DInput(x, dataFormat);\n    if (padding === \"causal\") {\n      throw new NotImplementedError(\"The support for CAUSAL padding mode in conv3dWithBias is not implemented yet.\");\n    }\n    y = conv3d(y, kernel, strides, padding === \"same\" ? \"same\" : \"valid\", \"NDHWC\", dilationRate);\n    if (bias != null) {\n      y = biasAdd(y, bias);\n    }\n    if (dataFormat === \"channelsFirst\") {\n      y = transpose(y, [0, 4, 1, 2, 3]);\n    }\n    return y;\n  });\n}\nvar BaseConv = class extends Layer {\n  constructor(rank, args) {\n    super(args);\n    this.bias = null;\n    this.DEFAULT_KERNEL_INITIALIZER = \"glorotNormal\";\n    this.DEFAULT_BIAS_INITIALIZER = \"zeros\";\n    BaseConv.verifyArgs(args);\n    this.rank = rank;\n    assertPositiveInteger(this.rank, \"rank\");\n    if (this.rank !== 1 && this.rank !== 2 && this.rank !== 3) {\n      throw new NotImplementedError(`Convolution layer for rank other than 1, 2, or 3 (${this.rank}) is not implemented yet.`);\n    }\n    this.kernelSize = normalizeArray(args.kernelSize, rank, \"kernelSize\");\n    this.strides = normalizeArray(args.strides == null ? 1 : args.strides, rank, \"strides\");\n    this.padding = args.padding == null ? \"valid\" : args.padding;\n    checkPaddingMode(this.padding);\n    this.dataFormat = args.dataFormat == null ? \"channelsLast\" : args.dataFormat;\n    checkDataFormat(this.dataFormat);\n    this.activation = getActivation(args.activation);\n    this.useBias = args.useBias == null ? true : args.useBias;\n    this.biasInitializer = getInitializer(args.biasInitializer || this.DEFAULT_BIAS_INITIALIZER);\n    this.biasConstraint = getConstraint(args.biasConstraint);\n    this.biasRegularizer = getRegularizer(args.biasRegularizer);\n    this.activityRegularizer = getRegularizer(args.activityRegularizer);\n    this.dilationRate = normalizeArray(args.dilationRate == null ? 1 : args.dilationRate, rank, \"dilationRate\");\n    if (this.rank === 1 && (Array.isArray(this.dilationRate) && this.dilationRate.length !== 1)) {\n      throw new ValueError(`dilationRate must be a number or an array of a single number for 1D convolution, but received ${JSON.stringify(this.dilationRate)}`);\n    } else if (this.rank === 2) {\n      if (typeof this.dilationRate === \"number\") {\n        this.dilationRate = [this.dilationRate, this.dilationRate];\n      } else if (this.dilationRate.length !== 2) {\n        throw new ValueError(`dilationRate must be a number or array of two numbers for 2D convolution, but received ${JSON.stringify(this.dilationRate)}`);\n      }\n    } else if (this.rank === 3) {\n      if (typeof this.dilationRate === \"number\") {\n        this.dilationRate = [this.dilationRate, this.dilationRate, this.dilationRate];\n      } else if (this.dilationRate.length !== 3) {\n        throw new ValueError(`dilationRate must be a number or array of three numbers for 3D convolution, but received ${JSON.stringify(this.dilationRate)}`);\n      }\n    }\n  }\n  static verifyArgs(args) {\n    assert2(\"kernelSize\" in args, `required key 'kernelSize' not in config`);\n    if (typeof args.kernelSize !== \"number\" && !checkArrayTypeAndLength(args.kernelSize, \"number\", 1, 3)) {\n      throw new ValueError(`BaseConv expects config.kernelSize to be number or number[] with length 1, 2, or 3, but received ${JSON.stringify(args.kernelSize)}.`);\n    }\n  }\n  getConfig() {\n    const config = {\n      kernelSize: this.kernelSize,\n      strides: this.strides,\n      padding: this.padding,\n      dataFormat: this.dataFormat,\n      dilationRate: this.dilationRate,\n      activation: serializeActivation(this.activation),\n      useBias: this.useBias,\n      biasInitializer: serializeInitializer(this.biasInitializer),\n      biasRegularizer: serializeRegularizer(this.biasRegularizer),\n      activityRegularizer: serializeRegularizer(this.activityRegularizer),\n      biasConstraint: serializeConstraint(this.biasConstraint)\n    };\n    const baseConfig = super.getConfig();\n    Object.assign(config, baseConfig);\n    return config;\n  }\n};\nvar Conv = class extends BaseConv {\n  constructor(rank, args) {\n    super(rank, args);\n    this.kernel = null;\n    Conv.verifyArgs(args);\n    this.filters = args.filters;\n    assertPositiveInteger(this.filters, \"filters\");\n    this.kernelInitializer = getInitializer(args.kernelInitializer || this.DEFAULT_KERNEL_INITIALIZER);\n    this.kernelConstraint = getConstraint(args.kernelConstraint);\n    this.kernelRegularizer = getRegularizer(args.kernelRegularizer);\n  }\n  build(inputShape) {\n    inputShape = getExactlyOneShape(inputShape);\n    const channelAxis = this.dataFormat === \"channelsFirst\" ? 1 : inputShape.length - 1;\n    if (inputShape[channelAxis] == null) {\n      throw new ValueError(`The channel dimension of the input should be defined. Found ${inputShape[channelAxis]}`);\n    }\n    const inputDim = inputShape[channelAxis];\n    const kernelShape = this.kernelSize.concat([inputDim, this.filters]);\n    this.kernel = this.addWeight(\"kernel\", kernelShape, null, this.kernelInitializer, this.kernelRegularizer, true, this.kernelConstraint);\n    if (this.useBias) {\n      this.bias = this.addWeight(\"bias\", [this.filters], null, this.biasInitializer, this.biasRegularizer, true, this.biasConstraint);\n    }\n    this.inputSpec = [{ ndim: this.rank + 2, axes: { [channelAxis]: inputDim } }];\n    this.built = true;\n  }\n  call(inputs, kwargs) {\n    return tidy(() => {\n      inputs = getExactlyOneTensor(inputs);\n      let outputs;\n      const biasValue = this.bias == null ? null : this.bias.read();\n      const fusedActivationName = mapActivationToFusedKernel(this.activation.getClassName());\n      if (fusedActivationName != null && this.rank === 2) {\n        outputs = conv2dWithBiasActivation(inputs, this.kernel.read(), biasValue, this.strides, this.padding, this.dataFormat, this.dilationRate, fusedActivationName);\n      } else {\n        if (this.rank === 1) {\n          outputs = conv1dWithBias(inputs, this.kernel.read(), biasValue, this.strides[0], this.padding, this.dataFormat, this.dilationRate[0]);\n        } else if (this.rank === 2) {\n          outputs = conv2dWithBiasActivation(inputs, this.kernel.read(), biasValue, this.strides, this.padding, this.dataFormat, this.dilationRate);\n        } else if (this.rank === 3) {\n          outputs = conv3dWithBias(inputs, this.kernel.read(), biasValue, this.strides, this.padding, this.dataFormat, this.dilationRate);\n        } else {\n          throw new NotImplementedError(\"convolutions greater than 3D are not implemented yet.\");\n        }\n        if (this.activation != null) {\n          outputs = this.activation.apply(outputs);\n        }\n      }\n      return outputs;\n    });\n  }\n  computeOutputShape(inputShape) {\n    inputShape = getExactlyOneShape(inputShape);\n    const newSpace = [];\n    const space = this.dataFormat === \"channelsLast\" ? inputShape.slice(1, inputShape.length - 1) : inputShape.slice(2);\n    for (let i = 0; i < space.length; ++i) {\n      const newDim = convOutputLength(space[i], this.kernelSize[i], this.padding, this.strides[i], typeof this.dilationRate === \"number\" ? this.dilationRate : this.dilationRate[i]);\n      newSpace.push(newDim);\n    }\n    let outputShape = [inputShape[0]];\n    if (this.dataFormat === \"channelsLast\") {\n      outputShape = outputShape.concat(newSpace);\n      outputShape.push(this.filters);\n    } else {\n      outputShape.push(this.filters);\n      outputShape = outputShape.concat(newSpace);\n    }\n    return outputShape;\n  }\n  getConfig() {\n    const config = {\n      filters: this.filters,\n      kernelInitializer: serializeInitializer(this.kernelInitializer),\n      kernelRegularizer: serializeRegularizer(this.kernelRegularizer),\n      kernelConstraint: serializeConstraint(this.kernelConstraint)\n    };\n    const baseConfig = super.getConfig();\n    Object.assign(config, baseConfig);\n    return config;\n  }\n  static verifyArgs(args) {\n    if (!(\"filters\" in args) || typeof args.filters !== \"number\" || args.filters < 1) {\n      throw new ValueError(`Convolution layer expected config.filters to be a 'number' > 0 but got ${JSON.stringify(args.filters)}`);\n    }\n  }\n};\nvar Conv2D2 = class extends Conv {\n  constructor(args) {\n    super(2, args);\n    Conv2D2.verifyArgs(args);\n  }\n  getConfig() {\n    const config = super.getConfig();\n    delete config[\"rank\"];\n    return config;\n  }\n  static verifyArgs(args) {\n    if (typeof args.kernelSize !== \"number\" && !checkArrayTypeAndLength(args.kernelSize, \"number\", 1, 2)) {\n      throw new ValueError(`Conv2D expects config.kernelSize to be number or number[] with length 1 or 2, but received ${JSON.stringify(args.kernelSize)}.`);\n    }\n  }\n};\nConv2D2.className = \"Conv2D\";\nserialization_exports.registerClass(Conv2D2);\nvar Conv3D2 = class extends Conv {\n  constructor(args) {\n    super(3, args);\n    Conv3D2.verifyArgs(args);\n  }\n  getConfig() {\n    const config = super.getConfig();\n    delete config[\"rank\"];\n    return config;\n  }\n  static verifyArgs(args) {\n    if (typeof args.kernelSize !== \"number\") {\n      if (!(Array.isArray(args.kernelSize) && (args.kernelSize.length === 1 || args.kernelSize.length === 3))) {\n        throw new ValueError(`Conv3D expects config.kernelSize to be number or [number, number, number], but received ${JSON.stringify(args.kernelSize)}.`);\n      }\n    }\n  }\n};\nConv3D2.className = \"Conv3D\";\nserialization_exports.registerClass(Conv3D2);\nvar Conv2DTranspose = class extends Conv2D2 {\n  constructor(args) {\n    super(args);\n    this.inputSpec = [new InputSpec({ ndim: 4 })];\n    if (this.padding !== \"same\" && this.padding !== \"valid\") {\n      throw new ValueError(`Conv2DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`);\n    }\n  }\n  build(inputShape) {\n    inputShape = getExactlyOneShape(inputShape);\n    if (inputShape.length !== 4) {\n      throw new ValueError(\"Input should have rank 4; Received input shape: \" + JSON.stringify(inputShape));\n    }\n    const channelAxis = this.dataFormat === \"channelsFirst\" ? 1 : inputShape.length - 1;\n    if (inputShape[channelAxis] == null) {\n      throw new ValueError(\"The channel dimension of the inputs should be defined. Found `None`.\");\n    }\n    const inputDim = inputShape[channelAxis];\n    const kernelShape = this.kernelSize.concat([this.filters, inputDim]);\n    this.kernel = this.addWeight(\"kernel\", kernelShape, \"float32\", this.kernelInitializer, this.kernelRegularizer, true, this.kernelConstraint);\n    if (this.useBias) {\n      this.bias = this.addWeight(\"bias\", [this.filters], \"float32\", this.biasInitializer, this.biasRegularizer, true, this.biasConstraint);\n    }\n    this.inputSpec = [new InputSpec({ ndim: 4, axes: { [channelAxis]: inputDim } })];\n    this.built = true;\n  }\n  call(inputs, kwargs) {\n    return tidy(() => {\n      let input2 = getExactlyOneTensor(inputs);\n      if (input2.shape.length !== 4) {\n        throw new ValueError(`Conv2DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${input2.shape.length}`);\n      }\n      const inputShape = input2.shape;\n      const batchSize = inputShape[0];\n      let hAxis;\n      let wAxis;\n      if (this.dataFormat === \"channelsFirst\") {\n        hAxis = 2;\n        wAxis = 3;\n      } else {\n        hAxis = 1;\n        wAxis = 2;\n      }\n      const height = inputShape[hAxis];\n      const width = inputShape[wAxis];\n      const kernelH = this.kernelSize[0];\n      const kernelW = this.kernelSize[1];\n      const strideH = this.strides[0];\n      const strideW = this.strides[1];\n      const outHeight = deconvLength(height, strideH, kernelH, this.padding);\n      const outWidth = deconvLength(width, strideW, kernelW, this.padding);\n      const outputShape = [batchSize, outHeight, outWidth, this.filters];\n      if (this.dataFormat !== \"channelsLast\") {\n        input2 = transpose(input2, [0, 2, 3, 1]);\n      }\n      let outputs = conv2dTranspose(input2, this.kernel.read(), outputShape, this.strides, this.padding);\n      if (this.dataFormat !== \"channelsLast\") {\n        outputs = transpose(outputs, [0, 3, 1, 2]);\n      }\n      if (this.bias != null) {\n        outputs = biasAdd(outputs, this.bias.read(), this.dataFormat);\n      }\n      if (this.activation != null) {\n        outputs = this.activation.apply(outputs);\n      }\n      return outputs;\n    });\n  }\n  computeOutputShape(inputShape) {\n    inputShape = getExactlyOneShape(inputShape);\n    const outputShape = inputShape.slice();\n    let channelAxis;\n    let heightAxis;\n    let widthAxis;\n    if (this.dataFormat === \"channelsFirst\") {\n      channelAxis = 1;\n      heightAxis = 2;\n      widthAxis = 3;\n    } else {\n      channelAxis = 3;\n      heightAxis = 1;\n      widthAxis = 2;\n    }\n    const kernelH = this.kernelSize[0];\n    const kernelW = this.kernelSize[1];\n    const strideH = this.strides[0];\n    const strideW = this.strides[1];\n    outputShape[channelAxis] = this.filters;\n    outputShape[heightAxis] = deconvLength(outputShape[heightAxis], strideH, kernelH, this.padding);\n    outputShape[widthAxis] = deconvLength(outputShape[widthAxis], strideW, kernelW, this.padding);\n    return outputShape;\n  }\n  getConfig() {\n    const config = super.getConfig();\n    delete config[\"dilationRate\"];\n    return config;\n  }\n};\nConv2DTranspose.className = \"Conv2DTranspose\";\nserialization_exports.registerClass(Conv2DTranspose);\nvar Conv3DTranspose = class extends Conv3D2 {\n  constructor(args) {\n    super(args);\n    this.inputSpec = [new InputSpec({ ndim: 5 })];\n    if (this.padding !== \"same\" && this.padding !== \"valid\") {\n      throw new ValueError(`Conv3DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`);\n    }\n  }\n  build(inputShape) {\n    inputShape = getExactlyOneShape(inputShape);\n    if (inputShape.length !== 5) {\n      throw new ValueError(\"Input should have rank 5; Received input shape: \" + JSON.stringify(inputShape));\n    }\n    const channelAxis = this.dataFormat === \"channelsFirst\" ? 1 : inputShape.length - 1;\n    if (inputShape[channelAxis] == null) {\n      throw new ValueError(\"The channel dimension of the inputs should be defined. Found `None`.\");\n    }\n    const inputDim = inputShape[channelAxis];\n    const kernelShape = this.kernelSize.concat([this.filters, inputDim]);\n    this.kernel = this.addWeight(\"kernel\", kernelShape, \"float32\", this.kernelInitializer, this.kernelRegularizer, true, this.kernelConstraint);\n    if (this.useBias) {\n      this.bias = this.addWeight(\"bias\", [this.filters], \"float32\", this.biasInitializer, this.biasRegularizer, true, this.biasConstraint);\n    }\n    this.inputSpec = [new InputSpec({ ndim: 5, axes: { [channelAxis]: inputDim } })];\n    this.built = true;\n  }\n  call(inputs, kwargs) {\n    return tidy(() => {\n      let input2 = getExactlyOneTensor(inputs);\n      if (input2.shape.length !== 5) {\n        throw new ValueError(`Conv3DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${input2.shape.length}`);\n      }\n      const inputShape = input2.shape;\n      const batchSize = inputShape[0];\n      let hAxis;\n      let wAxis;\n      let dAxis;\n      if (this.dataFormat === \"channelsFirst\") {\n        dAxis = 2;\n        hAxis = 3;\n        wAxis = 4;\n      } else {\n        dAxis = 1;\n        hAxis = 2;\n        wAxis = 3;\n      }\n      const depth = inputShape[dAxis];\n      const height = inputShape[hAxis];\n      const width = inputShape[wAxis];\n      const kernelD = this.kernelSize[0];\n      const kernelH = this.kernelSize[1];\n      const kernelW = this.kernelSize[2];\n      const strideD = this.strides[0];\n      const strideH = this.strides[1];\n      const strideW = this.strides[2];\n      const outDepth = deconvLength(depth, strideD, kernelD, this.padding);\n      const outHeight = deconvLength(height, strideH, kernelH, this.padding);\n      const outWidth = deconvLength(width, strideW, kernelW, this.padding);\n      const outputShape = [batchSize, outDepth, outHeight, outWidth, this.filters];\n      if (this.dataFormat !== \"channelsLast\") {\n        input2 = transpose(input2, [0, 2, 3, 4, 1]);\n      }\n      let outputs = conv3dTranspose(input2, this.kernel.read(), outputShape, this.strides, this.padding);\n      if (this.dataFormat !== \"channelsLast\") {\n        outputs = transpose(outputs, [0, 4, 1, 2, 3]);\n      }\n      if (this.bias !== null) {\n        outputs = biasAdd(outputs, this.bias.read(), this.dataFormat);\n      }\n      if (this.activation !== null) {\n        outputs = this.activation.apply(outputs);\n      }\n      return outputs;\n    });\n  }\n  computeOutputShape(inputShape) {\n    inputShape = getExactlyOneShape(inputShape);\n    const outputShape = inputShape.slice();\n    let channelAxis;\n    let depthAxis;\n    let heightAxis;\n    let widthAxis;\n    if (this.dataFormat === \"channelsFirst\") {\n      channelAxis = 1;\n      depthAxis = 2;\n      heightAxis = 3;\n      widthAxis = 4;\n    } else {\n      channelAxis = 4;\n      depthAxis = 1;\n      heightAxis = 2;\n      widthAxis = 3;\n    }\n    const kernelD = this.kernelSize[0];\n    const kernelH = this.kernelSize[1];\n    const kernelW = this.kernelSize[2];\n    const strideD = this.strides[0];\n    const strideH = this.strides[1];\n    const strideW = this.strides[2];\n    outputShape[channelAxis] = this.filters;\n    outputShape[depthAxis] = deconvLength(outputShape[depthAxis], strideD, kernelD, this.padding);\n    outputShape[heightAxis] = deconvLength(outputShape[heightAxis], strideH, kernelH, this.padding);\n    outputShape[widthAxis] = deconvLength(outputShape[widthAxis], strideW, kernelW, this.padding);\n    return outputShape;\n  }\n  getConfig() {\n    const config = super.getConfig();\n    delete config[\"dilationRate\"];\n    return config;\n  }\n};\nConv3DTranspose.className = \"Conv3DTranspose\";\nserialization_exports.registerClass(Conv3DTranspose);\nvar SeparableConv = class extends Conv {\n  constructor(rank, config) {\n    super(rank, config);\n    this.DEFAULT_DEPTHWISE_INITIALIZER = \"glorotUniform\";\n    this.DEFAULT_POINTWISE_INITIALIZER = \"glorotUniform\";\n    this.depthwiseKernel = null;\n    this.pointwiseKernel = null;\n    if (config.filters == null) {\n      throw new ValueError(\"The `filters` configuration field is required by SeparableConv, but is unspecified.\");\n    }\n    if (config.kernelInitializer != null || config.kernelRegularizer != null || config.kernelConstraint != null) {\n      throw new ValueError(\"Fields kernelInitializer, kernelRegularizer and kernelConstraint are invalid for SeparableConv2D. Use depthwiseInitializer, depthwiseRegularizer, depthwiseConstraint, pointwiseInitializer, pointwiseRegularizer and pointwiseConstraint instead.\");\n    }\n    if (config.padding != null && config.padding !== \"same\" && config.padding !== \"valid\") {\n      throw new ValueError(`SeparableConv${this.rank}D supports only padding modes: 'same' and 'valid', but received ${JSON.stringify(config.padding)}`);\n    }\n    this.depthMultiplier = config.depthMultiplier == null ? 1 : config.depthMultiplier;\n    this.depthwiseInitializer = getInitializer(config.depthwiseInitializer || this.DEFAULT_DEPTHWISE_INITIALIZER);\n    this.depthwiseRegularizer = getRegularizer(config.depthwiseRegularizer);\n    this.depthwiseConstraint = getConstraint(config.depthwiseConstraint);\n    this.pointwiseInitializer = getInitializer(config.depthwiseInitializer || this.DEFAULT_POINTWISE_INITIALIZER);\n    this.pointwiseRegularizer = getRegularizer(config.pointwiseRegularizer);\n    this.pointwiseConstraint = getConstraint(config.pointwiseConstraint);\n  }\n  build(inputShape) {\n    inputShape = getExactlyOneShape(inputShape);\n    if (inputShape.length < this.rank + 2) {\n      throw new ValueError(`Inputs to SeparableConv${this.rank}D should have rank ${this.rank + 2}, but received input shape: ${JSON.stringify(inputShape)}`);\n    }\n    const channelAxis = this.dataFormat === \"channelsFirst\" ? 1 : inputShape.length - 1;\n    if (inputShape[channelAxis] == null || inputShape[channelAxis] < 0) {\n      throw new ValueError(`The channel dimension of the inputs should be defined, but found ${JSON.stringify(inputShape[channelAxis])}`);\n    }\n    const inputDim = inputShape[channelAxis];\n    const depthwiseKernelShape = this.kernelSize.concat([inputDim, this.depthMultiplier]);\n    const pointwiseKernelShape = [];\n    for (let i = 0; i < this.rank; ++i) {\n      pointwiseKernelShape.push(1);\n    }\n    pointwiseKernelShape.push(inputDim * this.depthMultiplier, this.filters);\n    const trainable = true;\n    this.depthwiseKernel = this.addWeight(\"depthwise_kernel\", depthwiseKernelShape, \"float32\", this.depthwiseInitializer, this.depthwiseRegularizer, trainable, this.depthwiseConstraint);\n    this.pointwiseKernel = this.addWeight(\"pointwise_kernel\", pointwiseKernelShape, \"float32\", this.pointwiseInitializer, this.pointwiseRegularizer, trainable, this.pointwiseConstraint);\n    if (this.useBias) {\n      this.bias = this.addWeight(\"bias\", [this.filters], \"float32\", this.biasInitializer, this.biasRegularizer, trainable, this.biasConstraint);\n    } else {\n      this.bias = null;\n    }\n    this.inputSpec = [new InputSpec({ ndim: this.rank + 2, axes: { [channelAxis]: inputDim } })];\n    this.built = true;\n  }\n  call(inputs, kwargs) {\n    return tidy(() => {\n      inputs = getExactlyOneTensor(inputs);\n      let output;\n      if (this.rank === 1) {\n        throw new NotImplementedError(\"1D separable convolution is not implemented yet.\");\n      } else if (this.rank === 2) {\n        if (this.dataFormat === \"channelsFirst\") {\n          inputs = transpose(inputs, [0, 2, 3, 1]);\n        }\n        output = separableConv2d(inputs, this.depthwiseKernel.read(), this.pointwiseKernel.read(), this.strides, this.padding, this.dilationRate, \"NHWC\");\n      }\n      if (this.useBias) {\n        output = biasAdd(output, this.bias.read(), this.dataFormat);\n      }\n      if (this.activation != null) {\n        output = this.activation.apply(output);\n      }\n      if (this.dataFormat === \"channelsFirst\") {\n        output = transpose(output, [0, 3, 1, 2]);\n      }\n      return output;\n    });\n  }\n  getConfig() {\n    const config = super.getConfig();\n    delete config[\"rank\"];\n    delete config[\"kernelInitializer\"];\n    delete config[\"kernelRegularizer\"];\n    delete config[\"kernelConstraint\"];\n    config[\"depthwiseInitializer\"] = serializeInitializer(this.depthwiseInitializer);\n    config[\"pointwiseInitializer\"] = serializeInitializer(this.pointwiseInitializer);\n    config[\"depthwiseRegularizer\"] = serializeRegularizer(this.depthwiseRegularizer);\n    config[\"pointwiseRegularizer\"] = serializeRegularizer(this.pointwiseRegularizer);\n    config[\"depthwiseConstraint\"] = serializeConstraint(this.depthwiseConstraint);\n    config[\"pointwiseConstraint\"] = serializeConstraint(this.pointwiseConstraint);\n    return config;\n  }\n};\nSeparableConv.className = \"SeparableConv\";\nvar SeparableConv2D = class extends SeparableConv {\n  constructor(args) {\n    super(2, args);\n  }\n};\nSeparableConv2D.className = \"SeparableConv2D\";\nserialization_exports.registerClass(SeparableConv2D);\nvar Conv1D = class extends Conv {\n  constructor(args) {\n    super(1, args);\n    Conv1D.verifyArgs(args);\n    this.inputSpec = [{ ndim: 3 }];\n  }\n  getConfig() {\n    const config = super.getConfig();\n    delete config[\"rank\"];\n    delete config[\"dataFormat\"];\n    return config;\n  }\n  static verifyArgs(args) {\n    if (typeof args.kernelSize !== \"number\" && !checkArrayTypeAndLength(args.kernelSize, \"number\", 1, 1)) {\n      throw new ValueError(`Conv1D expects config.kernelSize to be number or number[] with length 1, but received ${JSON.stringify(args.kernelSize)}.`);\n    }\n  }\n};\nConv1D.className = \"Conv1D\";\nserialization_exports.registerClass(Conv1D);\nvar Cropping2D = class extends Layer {\n  constructor(args) {\n    super(args);\n    if (typeof args.cropping === \"number\") {\n      this.cropping = [[args.cropping, args.cropping], [args.cropping, args.cropping]];\n    } else if (typeof args.cropping[0] === \"number\") {\n      this.cropping = [\n        [args.cropping[0], args.cropping[0]],\n        [args.cropping[1], args.cropping[1]]\n      ];\n    } else {\n      this.cropping = args.cropping;\n    }\n    this.dataFormat = args.dataFormat === void 0 ? \"channelsLast\" : args.dataFormat;\n    this.inputSpec = [{ ndim: 4 }];\n  }\n  computeOutputShape(inputShape) {\n    if (this.dataFormat === \"channelsFirst\") {\n      return [\n        inputShape[0],\n        inputShape[1],\n        inputShape[2] - this.cropping[0][0] - this.cropping[0][1],\n        inputShape[3] - this.cropping[1][0] - this.cropping[1][1]\n      ];\n    } else {\n      return [\n        inputShape[0],\n        inputShape[1] - this.cropping[0][0] - this.cropping[0][1],\n        inputShape[2] - this.cropping[1][0] - this.cropping[1][1],\n        inputShape[3]\n      ];\n    }\n  }\n  call(inputs, kwargs) {\n    return tidy(() => {\n      inputs = getExactlyOneTensor(inputs);\n      if (this.dataFormat === \"channelsLast\") {\n        const hSliced = sliceAlongAxis(inputs, this.cropping[0][0], inputs.shape[1] - this.cropping[0][0] - this.cropping[0][1], 2);\n        return sliceAlongAxis(hSliced, this.cropping[1][0], inputs.shape[2] - this.cropping[1][1] - this.cropping[1][0], 3);\n      } else {\n        const hSliced = sliceAlongAxis(inputs, this.cropping[0][0], inputs.shape[2] - this.cropping[0][0] - this.cropping[0][1], 3);\n        return sliceAlongAxis(hSliced, this.cropping[1][0], inputs.shape[3] - this.cropping[1][1] - this.cropping[1][0], 4);\n      }\n    });\n  }\n  getConfig() {\n    const config = { cropping: this.cropping, dataFormat: this.dataFormat };\n    const baseConfig = super.getConfig();\n    Object.assign(config, baseConfig);\n    return config;\n  }\n};\nCropping2D.className = \"Cropping2D\";\nserialization_exports.registerClass(Cropping2D);\nvar UpSampling2D = class extends Layer {\n  constructor(args) {\n    super(args);\n    this.DEFAULT_SIZE = [2, 2];\n    this.inputSpec = [{ ndim: 4 }];\n    this.size = args.size == null ? this.DEFAULT_SIZE : args.size;\n    this.dataFormat = args.dataFormat == null ? \"channelsLast\" : args.dataFormat;\n    checkDataFormat(this.dataFormat);\n    this.interpolation = args.interpolation == null ? \"nearest\" : args.interpolation;\n    checkInterpolationFormat(this.interpolation);\n  }\n  computeOutputShape(inputShape) {\n    if (this.dataFormat === \"channelsFirst\") {\n      const height = inputShape[2] == null ? null : this.size[0] * inputShape[2];\n      const width = inputShape[3] == null ? null : this.size[1] * inputShape[3];\n      return [inputShape[0], inputShape[1], height, width];\n    } else {\n      const height = inputShape[1] == null ? null : this.size[0] * inputShape[1];\n      const width = inputShape[2] == null ? null : this.size[1] * inputShape[2];\n      return [inputShape[0], height, width, inputShape[3]];\n    }\n  }\n  call(inputs, kwargs) {\n    return tidy(() => {\n      let input2 = getExactlyOneTensor(inputs);\n      const inputShape = input2.shape;\n      if (this.dataFormat === \"channelsFirst\") {\n        input2 = transpose(input2, [0, 2, 3, 1]);\n        const height = this.size[0] * inputShape[2];\n        const width = this.size[1] * inputShape[3];\n        const resized = this.interpolation === \"nearest\" ? image.resizeNearestNeighbor(input2, [height, width]) : image.resizeBilinear(input2, [height, width]);\n        return transpose(resized, [0, 3, 1, 2]);\n      } else {\n        const height = this.size[0] * inputShape[1];\n        const width = this.size[1] * inputShape[2];\n        return this.interpolation === \"nearest\" ? image.resizeNearestNeighbor(input2, [height, width]) : image.resizeBilinear(input2, [height, width]);\n      }\n    });\n  }\n  getConfig() {\n    const config = {\n      size: this.size,\n      dataFormat: this.dataFormat,\n      interpolation: this.interpolation\n    };\n    const baseConfig = super.getConfig();\n    Object.assign(config, baseConfig);\n    return config;\n  }\n};\nUpSampling2D.className = \"UpSampling2D\";\nserialization_exports.registerClass(UpSampling2D);\n\n// node_modules/.pnpm/@tensorflow+tfjs-layers@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-layers/dist/layers/convolutional_depthwise.js\nfunction depthwiseConv2d3(x, depthwiseKernel, strides = [1, 1], padding = \"valid\", dataFormat, dilationRate) {\n  return tidy(() => {\n    if (dataFormat == null) {\n      dataFormat = imageDataFormat();\n    }\n    checkDataFormat(dataFormat);\n    let y = preprocessConv2DInput(x, dataFormat);\n    if (x.rank !== 4) {\n      throw new ValueError(`Input for depthwiseConv2d is required to be 4-D, but is instead ${x.rank}-D`);\n    }\n    if (depthwiseKernel.rank !== 4) {\n      throw new ValueError(`depthwiseKernel is required to be 4-D, but is instead ${depthwiseKernel.rank}-D`);\n    }\n    y = depthwiseConv2d(y, depthwiseKernel, strides, padding === \"same\" ? \"same\" : \"valid\", \"NHWC\", dilationRate);\n    if (dataFormat === \"channelsFirst\") {\n      y = transpose(y, [0, 3, 1, 2]);\n    }\n    return y;\n  });\n}\nvar DepthwiseConv2D = class extends BaseConv {\n  constructor(args) {\n    super(2, args);\n    this.depthwiseKernel = null;\n    this.depthMultiplier = args.depthMultiplier == null ? 1 : args.depthMultiplier;\n    this.depthwiseInitializer = getInitializer(args.depthwiseInitializer || this.DEFAULT_KERNEL_INITIALIZER);\n    this.depthwiseConstraint = getConstraint(args.depthwiseConstraint);\n    this.depthwiseRegularizer = getRegularizer(args.depthwiseRegularizer);\n  }\n  build(inputShape) {\n    inputShape = getExactlyOneShape(inputShape);\n    if (inputShape.length < 4) {\n      throw new ValueError(`Inputs to DepthwiseConv2D should have rank 4. Received input shape: ${JSON.stringify(inputShape)}.`);\n    }\n    const channelAxis = this.dataFormat === \"channelsFirst\" ? 1 : 3;\n    if (inputShape[channelAxis] == null || inputShape[channelAxis] < 0) {\n      throw new ValueError(`The channel dimension of the inputs to DepthwiseConv2D should be defined, but is not (${inputShape[channelAxis]}).`);\n    }\n    const inputDim = inputShape[channelAxis];\n    const depthwiseKernelShape = [\n      this.kernelSize[0],\n      this.kernelSize[1],\n      inputDim,\n      this.depthMultiplier\n    ];\n    this.depthwiseKernel = this.addWeight(\"depthwise_kernel\", depthwiseKernelShape, null, this.depthwiseInitializer, this.depthwiseRegularizer, true, this.depthwiseConstraint);\n    if (this.useBias) {\n      this.bias = this.addWeight(\"bias\", [inputDim * this.depthMultiplier], null, this.biasInitializer, this.biasRegularizer, true, this.biasConstraint);\n    } else {\n      this.bias = null;\n    }\n    this.built = true;\n  }\n  call(inputs, kwargs) {\n    return tidy(() => {\n      inputs = getExactlyOneTensor(inputs);\n      let outputs = depthwiseConv2d3(inputs, this.depthwiseKernel.read(), this.strides, this.padding, this.dataFormat, null);\n      if (this.useBias) {\n        outputs = biasAdd(outputs, this.bias.read(), this.dataFormat);\n      }\n      if (this.activation != null) {\n        outputs = this.activation.apply(outputs);\n      }\n      return outputs;\n    });\n  }\n  computeOutputShape(inputShape) {\n    inputShape = getExactlyOneShape(inputShape);\n    const rows = this.dataFormat === \"channelsFirst\" ? inputShape[2] : inputShape[1];\n    const cols = this.dataFormat === \"channelsFirst\" ? inputShape[3] : inputShape[2];\n    const outFilters = this.dataFormat === \"channelsFirst\" ? inputShape[1] * this.depthMultiplier : inputShape[3] * this.depthMultiplier;\n    const outRows = convOutputLength(rows, this.kernelSize[0], this.padding, this.strides[0]);\n    const outCols = convOutputLength(cols, this.kernelSize[1], this.padding, this.strides[1]);\n    if (this.dataFormat === \"channelsFirst\") {\n      return [inputShape[0], outFilters, outRows, outCols];\n    } else {\n      return [inputShape[0], outRows, outCols, outFilters];\n    }\n  }\n  getConfig() {\n    const config = super.getConfig();\n    config[\"depthMultiplier\"] = this.depthMultiplier;\n    config[\"depthwiseInitializer\"] = serializeInitializer(this.depthwiseInitializer);\n    config[\"depthwiseRegularizer\"] = serializeRegularizer(this.depthwiseRegularizer);\n    config[\"depthwiseConstraint\"] = serializeConstraint(this.depthwiseRegularizer);\n    return config;\n  }\n};\nDepthwiseConv2D.className = \"DepthwiseConv2D\";\nserialization_exports.registerClass(DepthwiseConv2D);\n\n// node_modules/.pnpm/@tensorflow+tfjs-layers@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-layers/dist/layers/recurrent.js\nfunction standardizeArgs(inputs, initialState, constants, numConstants) {\n  if (Array.isArray(inputs)) {\n    if (initialState != null || constants != null) {\n      throw new ValueError(\"When inputs is an array, neither initialState or constants should be provided\");\n    }\n    if (numConstants != null) {\n      constants = inputs.slice(inputs.length - numConstants, inputs.length);\n      inputs = inputs.slice(0, inputs.length - numConstants);\n    }\n    if (inputs.length > 1) {\n      initialState = inputs.slice(1, inputs.length);\n    }\n    inputs = inputs[0];\n  }\n  function toListOrNull(x) {\n    if (x == null || Array.isArray(x)) {\n      return x;\n    } else {\n      return [x];\n    }\n  }\n  initialState = toListOrNull(initialState);\n  constants = toListOrNull(constants);\n  return { inputs, initialState, constants };\n}\nfunction rnn(stepFunction, inputs, initialStates, goBackwards = false, mask, constants, unroll = false, needPerStepOutputs = false) {\n  return tidy(() => {\n    const ndim = inputs.shape.length;\n    if (ndim < 3) {\n      throw new ValueError(`Input should be at least 3D, but is ${ndim}D.`);\n    }\n    const axes = [1, 0].concat(range2(2, ndim));\n    inputs = transpose(inputs, axes);\n    if (constants != null) {\n      throw new NotImplementedError(\"The rnn() functoin of the deeplearn.js backend does not support constants yet.\");\n    }\n    if (unroll) {\n      console.warn(\"Backend rnn(): the unroll = true option is not applicable to the imperative deeplearn.js backend.\");\n    }\n    if (mask != null) {\n      mask = cast(cast(mask, \"bool\"), \"float32\");\n      if (mask.rank === ndim - 1) {\n        mask = expandDims(mask, -1);\n      }\n      mask = transpose(mask, axes);\n    }\n    if (goBackwards) {\n      inputs = reverse(inputs, 0);\n      if (mask != null) {\n        mask = reverse(mask, 0);\n      }\n    }\n    const perStepOutputs = [];\n    let lastOutput;\n    let states = initialStates;\n    const timeSteps = inputs.shape[0];\n    const perStepInputs = unstack(inputs);\n    let perStepMasks;\n    if (mask != null) {\n      perStepMasks = unstack(mask);\n    }\n    for (let t = 0; t < timeSteps; ++t) {\n      const currentInput = perStepInputs[t];\n      const stepOutputs = tidy(() => stepFunction(currentInput, states));\n      if (mask == null) {\n        lastOutput = stepOutputs[0];\n        states = stepOutputs[1];\n      } else {\n        const maskedOutputs = tidy(() => {\n          const stepMask = perStepMasks[t];\n          const negStepMask = sub(onesLike(stepMask), stepMask);\n          const output = add2(mul(stepOutputs[0], stepMask), mul(states[0], negStepMask));\n          const newStates = states.map((state, i) => {\n            return add2(mul(stepOutputs[1][i], stepMask), mul(state, negStepMask));\n          });\n          return { output, newStates };\n        });\n        lastOutput = maskedOutputs.output;\n        states = maskedOutputs.newStates;\n      }\n      if (needPerStepOutputs) {\n        perStepOutputs.push(lastOutput);\n      }\n    }\n    let outputs;\n    if (needPerStepOutputs) {\n      const axis = 1;\n      outputs = stack(perStepOutputs, axis);\n    }\n    return [lastOutput, outputs, states];\n  });\n}\nvar RNN = class extends Layer {\n  constructor(args) {\n    super(args);\n    let cell;\n    if (args.cell == null) {\n      throw new ValueError(\"cell property is missing for the constructor of RNN.\");\n    } else if (Array.isArray(args.cell)) {\n      cell = new StackedRNNCells({ cells: args.cell });\n    } else {\n      cell = args.cell;\n    }\n    if (cell.stateSize == null) {\n      throw new ValueError(\"The RNN cell should have an attribute `stateSize` (tuple of integers, one integer per RNN state).\");\n    }\n    this.cell = cell;\n    this.returnSequences = args.returnSequences == null ? false : args.returnSequences;\n    this.returnState = args.returnState == null ? false : args.returnState;\n    this.goBackwards = args.goBackwards == null ? false : args.goBackwards;\n    this._stateful = args.stateful == null ? false : args.stateful;\n    this.unroll = args.unroll == null ? false : args.unroll;\n    this.supportsMasking = true;\n    this.inputSpec = [new InputSpec({ ndim: 3 })];\n    this.stateSpec = null;\n    this.states_ = null;\n    this.numConstants = null;\n    this.keptStates = [];\n  }\n  getStates() {\n    if (this.states_ == null) {\n      const numStates = Array.isArray(this.cell.stateSize) ? this.cell.stateSize.length : 1;\n      return range2(0, numStates).map((x) => null);\n    } else {\n      return this.states_;\n    }\n  }\n  setStates(states) {\n    this.states_ = states;\n  }\n  computeOutputShape(inputShape) {\n    if (isArrayOfShapes(inputShape)) {\n      inputShape = inputShape[0];\n    }\n    inputShape = inputShape;\n    let stateSize = this.cell.stateSize;\n    if (!Array.isArray(stateSize)) {\n      stateSize = [stateSize];\n    }\n    const outputDim = stateSize[0];\n    let outputShape;\n    if (this.returnSequences) {\n      outputShape = [inputShape[0], inputShape[1], outputDim];\n    } else {\n      outputShape = [inputShape[0], outputDim];\n    }\n    if (this.returnState) {\n      const stateShape = [];\n      for (const dim of stateSize) {\n        stateShape.push([inputShape[0], dim]);\n      }\n      return [outputShape].concat(stateShape);\n    } else {\n      return outputShape;\n    }\n  }\n  computeMask(inputs, mask) {\n    return tidy(() => {\n      if (Array.isArray(mask)) {\n        mask = mask[0];\n      }\n      const outputMask = this.returnSequences ? mask : null;\n      if (this.returnState) {\n        const stateMask = this.states.map((s) => null);\n        return [outputMask].concat(stateMask);\n      } else {\n        return outputMask;\n      }\n    });\n  }\n  get states() {\n    if (this.states_ == null) {\n      const numStates = Array.isArray(this.cell.stateSize) ? this.cell.stateSize.length : 1;\n      const output = [];\n      for (let i = 0; i < numStates; ++i) {\n        output.push(null);\n      }\n      return output;\n    } else {\n      return this.states_;\n    }\n  }\n  set states(s) {\n    this.states_ = s;\n  }\n  build(inputShape) {\n    const constantShape = null;\n    if (this.numConstants != null) {\n      throw new NotImplementedError(\"Constants support is not implemented in RNN yet.\");\n    }\n    if (isArrayOfShapes(inputShape)) {\n      inputShape = inputShape[0];\n    }\n    inputShape = inputShape;\n    const batchSize = this.stateful ? inputShape[0] : null;\n    const inputDim = inputShape.slice(2);\n    this.inputSpec[0] = new InputSpec({ shape: [batchSize, null, ...inputDim] });\n    const stepInputShape = [inputShape[0]].concat(inputShape.slice(2));\n    if (constantShape != null) {\n      throw new NotImplementedError(\"Constants support is not implemented in RNN yet.\");\n    } else {\n      this.cell.build(stepInputShape);\n    }\n    let stateSize;\n    if (Array.isArray(this.cell.stateSize)) {\n      stateSize = this.cell.stateSize;\n    } else {\n      stateSize = [this.cell.stateSize];\n    }\n    if (this.stateSpec != null) {\n      if (!util_exports.arraysEqual(this.stateSpec.map((spec) => spec.shape[spec.shape.length - 1]), stateSize)) {\n        throw new ValueError(`An initialState was passed that is not compatible with cell.stateSize. Received stateSpec=${this.stateSpec}; However cell.stateSize is ${this.cell.stateSize}`);\n      }\n    } else {\n      this.stateSpec = stateSize.map((dim) => new InputSpec({ shape: [null, dim] }));\n    }\n    if (this.stateful) {\n      this.resetStates();\n    }\n  }\n  resetStates(states, training = false) {\n    tidy(() => {\n      if (!this.stateful) {\n        throw new AttributeError(\"Cannot call resetStates() on an RNN Layer that is not stateful.\");\n      }\n      const batchSize = this.inputSpec[0].shape[0];\n      if (batchSize == null) {\n        throw new ValueError(\"If an RNN is stateful, it needs to know its batch size. Specify the batch size of your input tensors: \\n- If using a Sequential model, specify the batch size by passing a `batchInputShape` option to your first layer.\\n- If using the functional API, specify the batch size by passing a `batchShape` option to your Input layer.\");\n      }\n      if (this.states_ == null) {\n        if (Array.isArray(this.cell.stateSize)) {\n          this.states_ = this.cell.stateSize.map((dim) => zeros([batchSize, dim]));\n        } else {\n          this.states_ = [zeros([batchSize, this.cell.stateSize])];\n        }\n      } else if (states == null) {\n        dispose(this.states_);\n        if (this.keptStates != null) {\n          dispose(this.keptStates);\n          this.keptStates = [];\n        }\n        if (Array.isArray(this.cell.stateSize)) {\n          this.states_ = this.cell.stateSize.map((dim) => zeros([batchSize, dim]));\n        } else {\n          this.states_[0] = zeros([batchSize, this.cell.stateSize]);\n        }\n      } else {\n        if (!Array.isArray(states)) {\n          states = [states];\n        }\n        if (states.length !== this.states_.length) {\n          throw new ValueError(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${states.length} state value(s). Input received: ${states}`);\n        }\n        if (training === true) {\n          this.keptStates.push(this.states_.slice());\n        } else {\n          dispose(this.states_);\n        }\n        for (let index = 0; index < this.states_.length; ++index) {\n          const value = states[index];\n          const dim = Array.isArray(this.cell.stateSize) ? this.cell.stateSize[index] : this.cell.stateSize;\n          const expectedShape = [batchSize, dim];\n          if (!util_exports.arraysEqual(value.shape, expectedShape)) {\n            throw new ValueError(`State ${index} is incompatible with layer ${this.name}: expected shape=${expectedShape}, received shape=${value.shape}`);\n          }\n          this.states_[index] = value;\n        }\n      }\n      this.states_ = this.states_.map((state) => keep(state.clone()));\n    });\n  }\n  apply(inputs, kwargs) {\n    let initialState = kwargs == null ? null : kwargs[\"initialState\"];\n    let constants = kwargs == null ? null : kwargs[\"constants\"];\n    if (kwargs == null) {\n      kwargs = {};\n    }\n    const standardized = standardizeArgs(inputs, initialState, constants, this.numConstants);\n    inputs = standardized.inputs;\n    initialState = standardized.initialState;\n    constants = standardized.constants;\n    let additionalInputs = [];\n    let additionalSpecs = [];\n    if (initialState != null) {\n      kwargs[\"initialState\"] = initialState;\n      additionalInputs = additionalInputs.concat(initialState);\n      this.stateSpec = [];\n      for (const state of initialState) {\n        this.stateSpec.push(new InputSpec({ shape: state.shape }));\n      }\n      additionalSpecs = additionalSpecs.concat(this.stateSpec);\n    }\n    if (constants != null) {\n      kwargs[\"constants\"] = constants;\n      additionalInputs = additionalInputs.concat(constants);\n      this.numConstants = constants.length;\n    }\n    const isTensor = additionalInputs[0] instanceof SymbolicTensor;\n    if (isTensor) {\n      const fullInput = [inputs].concat(additionalInputs);\n      const fullInputSpec = this.inputSpec.concat(additionalSpecs);\n      const originalInputSpec = this.inputSpec;\n      this.inputSpec = fullInputSpec;\n      const output = super.apply(fullInput, kwargs);\n      this.inputSpec = originalInputSpec;\n      return output;\n    } else {\n      return super.apply(inputs, kwargs);\n    }\n  }\n  call(inputs, kwargs) {\n    return tidy(() => {\n      const mask = kwargs == null ? null : kwargs[\"mask\"];\n      const training = kwargs == null ? null : kwargs[\"training\"];\n      let initialState = kwargs == null ? null : kwargs[\"initialState\"];\n      inputs = getExactlyOneTensor(inputs);\n      if (initialState == null) {\n        if (this.stateful) {\n          initialState = this.states_;\n        } else {\n          initialState = this.getInitialState(inputs);\n        }\n      }\n      const numStates = Array.isArray(this.cell.stateSize) ? this.cell.stateSize.length : 1;\n      if (initialState.length !== numStates) {\n        throw new ValueError(`RNN Layer has ${numStates} state(s) but was passed ${initialState.length} initial state(s).`);\n      }\n      if (this.unroll) {\n        console.warn(\"Ignoring unroll = true for RNN layer, due to imperative backend.\");\n      }\n      const cellCallKwargs = { training };\n      const step5 = (inputs2, states2) => {\n        const outputs2 = this.cell.call([inputs2].concat(states2), cellCallKwargs);\n        return [outputs2[0], outputs2.slice(1)];\n      };\n      const rnnOutputs = rnn(step5, inputs, initialState, this.goBackwards, mask, null, this.unroll, this.returnSequences);\n      const lastOutput = rnnOutputs[0];\n      const outputs = rnnOutputs[1];\n      const states = rnnOutputs[2];\n      if (this.stateful) {\n        this.resetStates(states, training);\n      }\n      const output = this.returnSequences ? outputs : lastOutput;\n      if (this.returnState) {\n        return [output].concat(states);\n      } else {\n        return output;\n      }\n    });\n  }\n  getInitialState(inputs) {\n    return tidy(() => {\n      let initialState = zeros(inputs.shape);\n      initialState = sum2(initialState, [1, 2]);\n      initialState = expandDims2(initialState);\n      if (Array.isArray(this.cell.stateSize)) {\n        return this.cell.stateSize.map((dim) => dim > 1 ? tile2(initialState, [1, dim]) : initialState);\n      } else {\n        return this.cell.stateSize > 1 ? [tile2(initialState, [1, this.cell.stateSize])] : [initialState];\n      }\n    });\n  }\n  get trainableWeights() {\n    if (!this.trainable) {\n      return [];\n    }\n    return this.cell.trainableWeights;\n  }\n  get nonTrainableWeights() {\n    if (!this.trainable) {\n      return this.cell.weights;\n    }\n    return this.cell.nonTrainableWeights;\n  }\n  setFastWeightInitDuringBuild(value) {\n    super.setFastWeightInitDuringBuild(value);\n    if (this.cell != null) {\n      this.cell.setFastWeightInitDuringBuild(value);\n    }\n  }\n  getConfig() {\n    const baseConfig = super.getConfig();\n    const config = {\n      returnSequences: this.returnSequences,\n      returnState: this.returnState,\n      goBackwards: this.goBackwards,\n      stateful: this.stateful,\n      unroll: this.unroll\n    };\n    if (this.numConstants != null) {\n      config[\"numConstants\"] = this.numConstants;\n    }\n    const cellConfig = this.cell.getConfig();\n    if (this.getClassName() === RNN.className) {\n      config[\"cell\"] = {\n        \"className\": this.cell.getClassName(),\n        \"config\": cellConfig\n      };\n    }\n    return Object.assign({}, cellConfig, baseConfig, config);\n  }\n  static fromConfig(cls, config, customObjects = {}) {\n    const cellConfig = config[\"cell\"];\n    const cell = deserialize(cellConfig, customObjects);\n    return new cls(Object.assign(config, { cell }));\n  }\n};\nRNN.className = \"RNN\";\nserialization_exports.registerClass(RNN);\nvar RNNCell = class extends Layer {\n};\nvar SimpleRNNCell = class extends RNNCell {\n  constructor(args) {\n    super(args);\n    this.DEFAULT_ACTIVATION = \"tanh\";\n    this.DEFAULT_KERNEL_INITIALIZER = \"glorotNormal\";\n    this.DEFAULT_RECURRENT_INITIALIZER = \"orthogonal\";\n    this.DEFAULT_BIAS_INITIALIZER = \"zeros\";\n    this.units = args.units;\n    assertPositiveInteger(this.units, `units`);\n    this.activation = getActivation(args.activation == null ? this.DEFAULT_ACTIVATION : args.activation);\n    this.useBias = args.useBias == null ? true : args.useBias;\n    this.kernelInitializer = getInitializer(args.kernelInitializer || this.DEFAULT_KERNEL_INITIALIZER);\n    this.recurrentInitializer = getInitializer(args.recurrentInitializer || this.DEFAULT_RECURRENT_INITIALIZER);\n    this.biasInitializer = getInitializer(args.biasInitializer || this.DEFAULT_BIAS_INITIALIZER);\n    this.kernelRegularizer = getRegularizer(args.kernelRegularizer);\n    this.recurrentRegularizer = getRegularizer(args.recurrentRegularizer);\n    this.biasRegularizer = getRegularizer(args.biasRegularizer);\n    this.kernelConstraint = getConstraint(args.kernelConstraint);\n    this.recurrentConstraint = getConstraint(args.recurrentConstraint);\n    this.biasConstraint = getConstraint(args.biasConstraint);\n    this.dropout = min2([1, max2([0, args.dropout == null ? 0 : args.dropout])]);\n    this.recurrentDropout = min2([\n      1,\n      max2([0, args.recurrentDropout == null ? 0 : args.recurrentDropout])\n    ]);\n    this.dropoutFunc = args.dropoutFunc;\n    this.stateSize = this.units;\n    this.dropoutMask = null;\n    this.recurrentDropoutMask = null;\n  }\n  build(inputShape) {\n    inputShape = getExactlyOneShape(inputShape);\n    this.kernel = this.addWeight(\"kernel\", [inputShape[inputShape.length - 1], this.units], null, this.kernelInitializer, this.kernelRegularizer, true, this.kernelConstraint);\n    this.recurrentKernel = this.addWeight(\"recurrent_kernel\", [this.units, this.units], null, this.recurrentInitializer, this.recurrentRegularizer, true, this.recurrentConstraint);\n    if (this.useBias) {\n      this.bias = this.addWeight(\"bias\", [this.units], null, this.biasInitializer, this.biasRegularizer, true, this.biasConstraint);\n    } else {\n      this.bias = null;\n    }\n    this.built = true;\n  }\n  call(inputs, kwargs) {\n    return tidy(() => {\n      inputs = inputs;\n      if (inputs.length !== 2) {\n        throw new ValueError(`SimpleRNNCell expects 2 input Tensors, got ${inputs.length}.`);\n      }\n      let prevOutput = inputs[1];\n      inputs = inputs[0];\n      const training = kwargs[\"training\"] == null ? false : kwargs[\"training\"];\n      if (0 < this.dropout && this.dropout < 1 && this.dropoutMask == null) {\n        this.dropoutMask = generateDropoutMask({\n          ones: () => onesLike(inputs),\n          rate: this.dropout,\n          training,\n          dropoutFunc: this.dropoutFunc\n        });\n      }\n      if (0 < this.recurrentDropout && this.recurrentDropout < 1 && this.recurrentDropoutMask == null) {\n        this.recurrentDropoutMask = generateDropoutMask({\n          ones: () => onesLike(prevOutput),\n          rate: this.recurrentDropout,\n          training,\n          dropoutFunc: this.dropoutFunc\n        });\n      }\n      let h;\n      const dpMask = this.dropoutMask;\n      const recDpMask = this.recurrentDropoutMask;\n      if (dpMask != null) {\n        h = dot2(mul(inputs, dpMask), this.kernel.read());\n      } else {\n        h = dot2(inputs, this.kernel.read());\n      }\n      if (this.bias != null) {\n        h = biasAdd(h, this.bias.read());\n      }\n      if (recDpMask != null) {\n        prevOutput = mul(prevOutput, recDpMask);\n      }\n      let output = add2(h, dot2(prevOutput, this.recurrentKernel.read()));\n      if (this.activation != null) {\n        output = this.activation.apply(output);\n      }\n      return [output, output];\n    });\n  }\n  getConfig() {\n    const baseConfig = super.getConfig();\n    const config = {\n      units: this.units,\n      activation: serializeActivation(this.activation),\n      useBias: this.useBias,\n      kernelInitializer: serializeInitializer(this.kernelInitializer),\n      recurrentInitializer: serializeInitializer(this.recurrentInitializer),\n      biasInitializer: serializeInitializer(this.biasInitializer),\n      kernelRegularizer: serializeRegularizer(this.kernelRegularizer),\n      recurrentRegularizer: serializeRegularizer(this.recurrentRegularizer),\n      biasRegularizer: serializeRegularizer(this.biasRegularizer),\n      activityRegularizer: serializeRegularizer(this.activityRegularizer),\n      kernelConstraint: serializeConstraint(this.kernelConstraint),\n      recurrentConstraint: serializeConstraint(this.recurrentConstraint),\n      biasConstraint: serializeConstraint(this.biasConstraint),\n      dropout: this.dropout,\n      recurrentDropout: this.recurrentDropout\n    };\n    return Object.assign({}, baseConfig, config);\n  }\n};\nSimpleRNNCell.className = \"SimpleRNNCell\";\nserialization_exports.registerClass(SimpleRNNCell);\nvar SimpleRNN = class extends RNN {\n  constructor(args) {\n    args.cell = new SimpleRNNCell(args);\n    super(args);\n  }\n  call(inputs, kwargs) {\n    return tidy(() => {\n      if (this.cell.dropoutMask != null) {\n        dispose(this.cell.dropoutMask);\n        this.cell.dropoutMask = null;\n      }\n      if (this.cell.recurrentDropoutMask != null) {\n        dispose(this.cell.recurrentDropoutMask);\n        this.cell.recurrentDropoutMask = null;\n      }\n      const mask = kwargs == null ? null : kwargs[\"mask\"];\n      const training = kwargs == null ? null : kwargs[\"training\"];\n      const initialState = kwargs == null ? null : kwargs[\"initialState\"];\n      return super.call(inputs, { mask, training, initialState });\n    });\n  }\n  static fromConfig(cls, config) {\n    return new cls(config);\n  }\n};\nSimpleRNN.className = \"SimpleRNN\";\nserialization_exports.registerClass(SimpleRNN);\nvar GRUCell = class extends RNNCell {\n  constructor(args) {\n    super(args);\n    this.DEFAULT_ACTIVATION = \"tanh\";\n    this.DEFAULT_RECURRENT_ACTIVATION = \"hardSigmoid\";\n    this.DEFAULT_KERNEL_INITIALIZER = \"glorotNormal\";\n    this.DEFAULT_RECURRENT_INITIALIZER = \"orthogonal\";\n    this.DEFAULT_BIAS_INITIALIZER = \"zeros\";\n    if (args.resetAfter) {\n      throw new ValueError(`GRUCell does not support reset_after parameter set to true.`);\n    }\n    this.units = args.units;\n    assertPositiveInteger(this.units, \"units\");\n    this.activation = getActivation(args.activation === void 0 ? this.DEFAULT_ACTIVATION : args.activation);\n    this.recurrentActivation = getActivation(args.recurrentActivation === void 0 ? this.DEFAULT_RECURRENT_ACTIVATION : args.recurrentActivation);\n    this.useBias = args.useBias == null ? true : args.useBias;\n    this.kernelInitializer = getInitializer(args.kernelInitializer || this.DEFAULT_KERNEL_INITIALIZER);\n    this.recurrentInitializer = getInitializer(args.recurrentInitializer || this.DEFAULT_RECURRENT_INITIALIZER);\n    this.biasInitializer = getInitializer(args.biasInitializer || this.DEFAULT_BIAS_INITIALIZER);\n    this.kernelRegularizer = getRegularizer(args.kernelRegularizer);\n    this.recurrentRegularizer = getRegularizer(args.recurrentRegularizer);\n    this.biasRegularizer = getRegularizer(args.biasRegularizer);\n    this.kernelConstraint = getConstraint(args.kernelConstraint);\n    this.recurrentConstraint = getConstraint(args.recurrentConstraint);\n    this.biasConstraint = getConstraint(args.biasConstraint);\n    this.dropout = min2([1, max2([0, args.dropout == null ? 0 : args.dropout])]);\n    this.recurrentDropout = min2([\n      1,\n      max2([0, args.recurrentDropout == null ? 0 : args.recurrentDropout])\n    ]);\n    this.dropoutFunc = args.dropoutFunc;\n    this.implementation = args.implementation;\n    this.stateSize = this.units;\n    this.dropoutMask = null;\n    this.recurrentDropoutMask = null;\n  }\n  build(inputShape) {\n    inputShape = getExactlyOneShape(inputShape);\n    const inputDim = inputShape[inputShape.length - 1];\n    this.kernel = this.addWeight(\"kernel\", [inputDim, this.units * 3], null, this.kernelInitializer, this.kernelRegularizer, true, this.kernelConstraint);\n    this.recurrentKernel = this.addWeight(\"recurrent_kernel\", [this.units, this.units * 3], null, this.recurrentInitializer, this.recurrentRegularizer, true, this.recurrentConstraint);\n    if (this.useBias) {\n      this.bias = this.addWeight(\"bias\", [this.units * 3], null, this.biasInitializer, this.biasRegularizer, true, this.biasConstraint);\n    } else {\n      this.bias = null;\n    }\n    this.built = true;\n  }\n  call(inputs, kwargs) {\n    return tidy(() => {\n      inputs = inputs;\n      if (inputs.length !== 2) {\n        throw new ValueError(`GRUCell expects 2 input Tensors (inputs, h, c), got ${inputs.length}.`);\n      }\n      const training = kwargs[\"training\"] == null ? false : kwargs[\"training\"];\n      let hTMinus1 = inputs[1];\n      inputs = inputs[0];\n      if (0 < this.dropout && this.dropout < 1 && this.dropoutMask == null) {\n        this.dropoutMask = generateDropoutMask({\n          ones: () => onesLike(inputs),\n          rate: this.dropout,\n          training,\n          count: 3,\n          dropoutFunc: this.dropoutFunc\n        });\n      }\n      if (0 < this.recurrentDropout && this.recurrentDropout < 1 && this.recurrentDropoutMask == null) {\n        this.recurrentDropoutMask = generateDropoutMask({\n          ones: () => onesLike(hTMinus1),\n          rate: this.recurrentDropout,\n          training,\n          count: 3,\n          dropoutFunc: this.dropoutFunc\n        });\n      }\n      const dpMask = this.dropoutMask;\n      const recDpMask = this.recurrentDropoutMask;\n      let z;\n      let r;\n      let hh;\n      if (0 < this.dropout && this.dropout < 1) {\n        inputs = mul(inputs, dpMask[0]);\n      }\n      let matrixX = dot2(inputs, this.kernel.read());\n      if (this.useBias) {\n        matrixX = biasAdd(matrixX, this.bias.read());\n      }\n      if (0 < this.recurrentDropout && this.recurrentDropout < 1) {\n        hTMinus1 = mul(hTMinus1, recDpMask[0]);\n      }\n      const recurrentKernelValue = this.recurrentKernel.read();\n      const [rk1, rk2] = split(recurrentKernelValue, [2 * this.units, this.units], recurrentKernelValue.rank - 1);\n      const matrixInner = dot2(hTMinus1, rk1);\n      const [xZ, xR, xH] = split(matrixX, 3, matrixX.rank - 1);\n      const [recurrentZ, recurrentR] = split(matrixInner, 2, matrixInner.rank - 1);\n      z = this.recurrentActivation.apply(add2(xZ, recurrentZ));\n      r = this.recurrentActivation.apply(add2(xR, recurrentR));\n      const recurrentH = dot2(mul(r, hTMinus1), rk2);\n      hh = this.activation.apply(add2(xH, recurrentH));\n      const h = add2(mul(z, hTMinus1), mul(add2(1, neg(z)), hh));\n      return [h, h];\n    });\n  }\n  getConfig() {\n    const baseConfig = super.getConfig();\n    const config = {\n      units: this.units,\n      activation: serializeActivation(this.activation),\n      recurrentActivation: serializeActivation(this.recurrentActivation),\n      useBias: this.useBias,\n      kernelInitializer: serializeInitializer(this.kernelInitializer),\n      recurrentInitializer: serializeInitializer(this.recurrentInitializer),\n      biasInitializer: serializeInitializer(this.biasInitializer),\n      kernelRegularizer: serializeRegularizer(this.kernelRegularizer),\n      recurrentRegularizer: serializeRegularizer(this.recurrentRegularizer),\n      biasRegularizer: serializeRegularizer(this.biasRegularizer),\n      activityRegularizer: serializeRegularizer(this.activityRegularizer),\n      kernelConstraint: serializeConstraint(this.kernelConstraint),\n      recurrentConstraint: serializeConstraint(this.recurrentConstraint),\n      biasConstraint: serializeConstraint(this.biasConstraint),\n      dropout: this.dropout,\n      recurrentDropout: this.recurrentDropout,\n      implementation: this.implementation,\n      resetAfter: false\n    };\n    return Object.assign({}, baseConfig, config);\n  }\n};\nGRUCell.className = \"GRUCell\";\nserialization_exports.registerClass(GRUCell);\nvar GRU = class extends RNN {\n  constructor(args) {\n    if (args.implementation === 0) {\n      console.warn(\"`implementation=0` has been deprecated, and now defaults to `implementation=1`. Please update your layer call.\");\n    }\n    args.cell = new GRUCell(args);\n    super(args);\n  }\n  call(inputs, kwargs) {\n    return tidy(() => {\n      if (this.cell.dropoutMask != null) {\n        dispose(this.cell.dropoutMask);\n        this.cell.dropoutMask = null;\n      }\n      if (this.cell.recurrentDropoutMask != null) {\n        dispose(this.cell.recurrentDropoutMask);\n        this.cell.recurrentDropoutMask = null;\n      }\n      const mask = kwargs == null ? null : kwargs[\"mask\"];\n      const training = kwargs == null ? null : kwargs[\"training\"];\n      const initialState = kwargs == null ? null : kwargs[\"initialState\"];\n      return super.call(inputs, { mask, training, initialState });\n    });\n  }\n  static fromConfig(cls, config) {\n    if (config[\"implmentation\"] === 0) {\n      config[\"implementation\"] = 1;\n    }\n    return new cls(config);\n  }\n};\nGRU.className = \"GRU\";\nserialization_exports.registerClass(GRU);\nvar LSTMCell = class extends RNNCell {\n  constructor(args) {\n    super(args);\n    this.DEFAULT_ACTIVATION = \"tanh\";\n    this.DEFAULT_RECURRENT_ACTIVATION = \"hardSigmoid\";\n    this.DEFAULT_KERNEL_INITIALIZER = \"glorotNormal\";\n    this.DEFAULT_RECURRENT_INITIALIZER = \"orthogonal\";\n    this.DEFAULT_BIAS_INITIALIZER = \"zeros\";\n    this.units = args.units;\n    assertPositiveInteger(this.units, \"units\");\n    this.activation = getActivation(args.activation === void 0 ? this.DEFAULT_ACTIVATION : args.activation);\n    this.recurrentActivation = getActivation(args.recurrentActivation === void 0 ? this.DEFAULT_RECURRENT_ACTIVATION : args.recurrentActivation);\n    this.useBias = args.useBias == null ? true : args.useBias;\n    this.kernelInitializer = getInitializer(args.kernelInitializer || this.DEFAULT_KERNEL_INITIALIZER);\n    this.recurrentInitializer = getInitializer(args.recurrentInitializer || this.DEFAULT_RECURRENT_INITIALIZER);\n    this.biasInitializer = getInitializer(args.biasInitializer || this.DEFAULT_BIAS_INITIALIZER);\n    this.unitForgetBias = args.unitForgetBias;\n    this.kernelRegularizer = getRegularizer(args.kernelRegularizer);\n    this.recurrentRegularizer = getRegularizer(args.recurrentRegularizer);\n    this.biasRegularizer = getRegularizer(args.biasRegularizer);\n    this.kernelConstraint = getConstraint(args.kernelConstraint);\n    this.recurrentConstraint = getConstraint(args.recurrentConstraint);\n    this.biasConstraint = getConstraint(args.biasConstraint);\n    this.dropout = min2([1, max2([0, args.dropout == null ? 0 : args.dropout])]);\n    this.recurrentDropout = min2([\n      1,\n      max2([0, args.recurrentDropout == null ? 0 : args.recurrentDropout])\n    ]);\n    this.dropoutFunc = args.dropoutFunc;\n    this.implementation = args.implementation;\n    this.stateSize = [this.units, this.units];\n    this.dropoutMask = null;\n    this.recurrentDropoutMask = null;\n  }\n  build(inputShape) {\n    var _a;\n    inputShape = getExactlyOneShape(inputShape);\n    const inputDim = inputShape[inputShape.length - 1];\n    this.kernel = this.addWeight(\"kernel\", [inputDim, this.units * 4], null, this.kernelInitializer, this.kernelRegularizer, true, this.kernelConstraint);\n    this.recurrentKernel = this.addWeight(\"recurrent_kernel\", [this.units, this.units * 4], null, this.recurrentInitializer, this.recurrentRegularizer, true, this.recurrentConstraint);\n    let biasInitializer;\n    if (this.useBias) {\n      if (this.unitForgetBias) {\n        const capturedBiasInit = this.biasInitializer;\n        const capturedUnits = this.units;\n        biasInitializer = new (_a = class CustomInit extends Initializer {\n          apply(shape, dtype) {\n            const bI = capturedBiasInit.apply([capturedUnits]);\n            const bF = new Ones().apply([capturedUnits]);\n            const bCAndH = capturedBiasInit.apply([capturedUnits * 2]);\n            return concatAlongFirstAxis(concatAlongFirstAxis(bI, bF), bCAndH);\n          }\n        }, _a.className = \"CustomInit\", _a)();\n      } else {\n        biasInitializer = this.biasInitializer;\n      }\n      this.bias = this.addWeight(\"bias\", [this.units * 4], null, biasInitializer, this.biasRegularizer, true, this.biasConstraint);\n    } else {\n      this.bias = null;\n    }\n    this.built = true;\n  }\n  call(inputs, kwargs) {\n    return tidy(() => {\n      const training = kwargs[\"training\"] == null ? false : kwargs[\"training\"];\n      inputs = inputs;\n      if (inputs.length !== 3) {\n        throw new ValueError(`LSTMCell expects 3 input Tensors (inputs, h, c), got ${inputs.length}.`);\n      }\n      let hTMinus1 = inputs[1];\n      const cTMinus1 = inputs[2];\n      inputs = inputs[0];\n      if (0 < this.dropout && this.dropout < 1 && this.dropoutMask == null) {\n        this.dropoutMask = generateDropoutMask({\n          ones: () => onesLike(inputs),\n          rate: this.dropout,\n          training,\n          count: 4,\n          dropoutFunc: this.dropoutFunc\n        });\n      }\n      if (0 < this.recurrentDropout && this.recurrentDropout < 1 && this.recurrentDropoutMask == null) {\n        this.recurrentDropoutMask = generateDropoutMask({\n          ones: () => onesLike(hTMinus1),\n          rate: this.recurrentDropout,\n          training,\n          count: 4,\n          dropoutFunc: this.dropoutFunc\n        });\n      }\n      const dpMask = this.dropoutMask;\n      const recDpMask = this.recurrentDropoutMask;\n      let i;\n      let f;\n      let c;\n      let o;\n      if (0 < this.dropout && this.dropout < 1) {\n        inputs = mul(inputs, dpMask[0]);\n      }\n      let z = dot2(inputs, this.kernel.read());\n      if (0 < this.recurrentDropout && this.recurrentDropout < 1) {\n        hTMinus1 = mul(hTMinus1, recDpMask[0]);\n      }\n      z = add2(z, dot2(hTMinus1, this.recurrentKernel.read()));\n      if (this.useBias) {\n        z = biasAdd(z, this.bias.read());\n      }\n      const [z0, z1, z2, z3] = split(z, 4, z.rank - 1);\n      i = this.recurrentActivation.apply(z0);\n      f = this.recurrentActivation.apply(z1);\n      c = add2(mul(f, cTMinus1), mul(i, this.activation.apply(z2)));\n      o = this.recurrentActivation.apply(z3);\n      const h = mul(o, this.activation.apply(c));\n      return [h, h, c];\n    });\n  }\n  getConfig() {\n    const baseConfig = super.getConfig();\n    const config = {\n      units: this.units,\n      activation: serializeActivation(this.activation),\n      recurrentActivation: serializeActivation(this.recurrentActivation),\n      useBias: this.useBias,\n      kernelInitializer: serializeInitializer(this.kernelInitializer),\n      recurrentInitializer: serializeInitializer(this.recurrentInitializer),\n      biasInitializer: serializeInitializer(this.biasInitializer),\n      unitForgetBias: this.unitForgetBias,\n      kernelRegularizer: serializeRegularizer(this.kernelRegularizer),\n      recurrentRegularizer: serializeRegularizer(this.recurrentRegularizer),\n      biasRegularizer: serializeRegularizer(this.biasRegularizer),\n      activityRegularizer: serializeRegularizer(this.activityRegularizer),\n      kernelConstraint: serializeConstraint(this.kernelConstraint),\n      recurrentConstraint: serializeConstraint(this.recurrentConstraint),\n      biasConstraint: serializeConstraint(this.biasConstraint),\n      dropout: this.dropout,\n      recurrentDropout: this.recurrentDropout,\n      implementation: this.implementation\n    };\n    return Object.assign({}, baseConfig, config);\n  }\n};\nLSTMCell.className = \"LSTMCell\";\nserialization_exports.registerClass(LSTMCell);\nvar LSTM = class extends RNN {\n  constructor(args) {\n    if (args.implementation === 0) {\n      console.warn(\"`implementation=0` has been deprecated, and now defaults to `implementation=1`. Please update your layer call.\");\n    }\n    args.cell = new LSTMCell(args);\n    super(args);\n  }\n  call(inputs, kwargs) {\n    return tidy(() => {\n      if (this.cell.dropoutMask != null) {\n        dispose(this.cell.dropoutMask);\n        this.cell.dropoutMask = null;\n      }\n      if (this.cell.recurrentDropoutMask != null) {\n        dispose(this.cell.recurrentDropoutMask);\n        this.cell.recurrentDropoutMask = null;\n      }\n      const mask = kwargs == null ? null : kwargs[\"mask\"];\n      const training = kwargs == null ? null : kwargs[\"training\"];\n      const initialState = kwargs == null ? null : kwargs[\"initialState\"];\n      return super.call(inputs, { mask, training, initialState });\n    });\n  }\n  static fromConfig(cls, config) {\n    if (config[\"implmentation\"] === 0) {\n      config[\"implementation\"] = 1;\n    }\n    return new cls(config);\n  }\n};\nLSTM.className = \"LSTM\";\nserialization_exports.registerClass(LSTM);\nvar StackedRNNCells = class extends RNNCell {\n  constructor(args) {\n    super(args);\n    this.cells = args.cells;\n  }\n  get stateSize() {\n    const stateSize = [];\n    for (const cell of this.cells.slice().reverse()) {\n      if (Array.isArray(cell.stateSize)) {\n        stateSize.push(...cell.stateSize);\n      } else {\n        stateSize.push(cell.stateSize);\n      }\n    }\n    return stateSize;\n  }\n  call(inputs, kwargs) {\n    return tidy(() => {\n      inputs = inputs;\n      let states = inputs.slice(1);\n      const nestedStates = [];\n      for (const cell of this.cells.slice().reverse()) {\n        if (Array.isArray(cell.stateSize)) {\n          nestedStates.push(states.splice(0, cell.stateSize.length));\n        } else {\n          nestedStates.push(states.splice(0, 1));\n        }\n      }\n      nestedStates.reverse();\n      const newNestedStates = [];\n      let callInputs;\n      for (let i = 0; i < this.cells.length; ++i) {\n        const cell = this.cells[i];\n        states = nestedStates[i];\n        if (i === 0) {\n          callInputs = [inputs[0]].concat(states);\n        } else {\n          callInputs = [callInputs[0]].concat(states);\n        }\n        callInputs = cell.call(callInputs, kwargs);\n        newNestedStates.push(callInputs.slice(1));\n      }\n      states = [];\n      for (const cellStates of newNestedStates.slice().reverse()) {\n        states.push(...cellStates);\n      }\n      return [callInputs[0]].concat(states);\n    });\n  }\n  build(inputShape) {\n    if (isArrayOfShapes(inputShape)) {\n      inputShape = inputShape[0];\n    }\n    inputShape = inputShape;\n    let outputDim;\n    this.cells.forEach((cell, i) => {\n      nameScope(`RNNCell_${i}`, () => {\n        cell.build(inputShape);\n        if (Array.isArray(cell.stateSize)) {\n          outputDim = cell.stateSize[0];\n        } else {\n          outputDim = cell.stateSize;\n        }\n        inputShape = [inputShape[0], outputDim];\n      });\n    });\n    this.built = true;\n  }\n  getConfig() {\n    const baseConfig = super.getConfig();\n    const getCellConfig = (cell) => {\n      return {\n        \"className\": cell.getClassName(),\n        \"config\": cell.getConfig()\n      };\n    };\n    const cellConfigs = this.cells.map(getCellConfig);\n    const config = { \"cells\": cellConfigs };\n    return Object.assign({}, baseConfig, config);\n  }\n  static fromConfig(cls, config, customObjects = {}) {\n    const cells = [];\n    for (const cellConfig of config[\"cells\"]) {\n      cells.push(deserialize(cellConfig, customObjects));\n    }\n    return new cls({ cells });\n  }\n  get trainableWeights() {\n    if (!this.trainable) {\n      return [];\n    }\n    const weights = [];\n    for (const cell of this.cells) {\n      weights.push(...cell.trainableWeights);\n    }\n    return weights;\n  }\n  get nonTrainableWeights() {\n    const weights = [];\n    for (const cell of this.cells) {\n      weights.push(...cell.nonTrainableWeights);\n    }\n    if (!this.trainable) {\n      const trainableWeights = [];\n      for (const cell of this.cells) {\n        trainableWeights.push(...cell.trainableWeights);\n      }\n      return trainableWeights.concat(weights);\n    }\n    return weights;\n  }\n  getWeights() {\n    const weights = [];\n    for (const cell of this.cells) {\n      weights.push(...cell.weights);\n    }\n    return batchGetValue(weights);\n  }\n  setWeights(weights) {\n    const tuples = [];\n    for (const cell of this.cells) {\n      const numParams = cell.weights.length;\n      const inputWeights = weights.splice(numParams);\n      for (let i = 0; i < cell.weights.length; ++i) {\n        tuples.push([cell.weights[i], inputWeights[i]]);\n      }\n    }\n    batchSetValue(tuples);\n  }\n};\nStackedRNNCells.className = \"StackedRNNCells\";\nserialization_exports.registerClass(StackedRNNCells);\nfunction generateDropoutMask(args) {\n  const { ones: ones4, rate, training = false, count: count2 = 1, dropoutFunc } = args;\n  const droppedInputs = () => dropoutFunc != null ? dropoutFunc(ones4(), rate) : dropout2(ones4(), rate);\n  const createMask = () => inTrainPhase(droppedInputs, ones4, training);\n  if (!count2 || count2 <= 1) {\n    return keep(createMask().clone());\n  }\n  const masks = Array(count2).fill(void 0).map(createMask);\n  return masks.map((m) => keep(m.clone()));\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-layers@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-layers/dist/layers/convolutional_recurrent.js\nvar __rest = function(s, e) {\n  var t = {};\n  for (var p2 in s)\n    if (Object.prototype.hasOwnProperty.call(s, p2) && e.indexOf(p2) < 0)\n      t[p2] = s[p2];\n  if (s != null && typeof Object.getOwnPropertySymbols === \"function\")\n    for (var i = 0, p2 = Object.getOwnPropertySymbols(s); i < p2.length; i++) {\n      if (e.indexOf(p2[i]) < 0 && Object.prototype.propertyIsEnumerable.call(s, p2[i]))\n        t[p2[i]] = s[p2[i]];\n    }\n  return t;\n};\nvar ConvRNN2D = class extends RNN {\n  constructor(args) {\n    if (args.unroll) {\n      throw new NotImplementedError(\"Unrolling is not possible with convolutional RNNs.\");\n    }\n    if (Array.isArray(args.cell)) {\n      throw new NotImplementedError(\"It is not possible at the moment to stack convolutional cells.\");\n    }\n    super(args);\n    this.inputSpec = [new InputSpec({ ndim: 5 })];\n  }\n  call(inputs, kwargs) {\n    return tidy(() => {\n      if (this.cell.dropoutMask != null) {\n        dispose(this.cell.dropoutMask);\n        this.cell.dropoutMask = null;\n      }\n      if (this.cell.recurrentDropoutMask != null) {\n        dispose(this.cell.recurrentDropoutMask);\n        this.cell.recurrentDropoutMask = null;\n      }\n      if (kwargs && kwargs[\"constants\"]) {\n        throw new ValueError(\"ConvRNN2D cell does not support constants\");\n      }\n      const mask = kwargs == null ? null : kwargs[\"mask\"];\n      const training = kwargs == null ? null : kwargs[\"training\"];\n      const initialState = kwargs == null ? null : kwargs[\"initialState\"];\n      return super.call(inputs, { mask, training, initialState });\n    });\n  }\n  computeOutputShape(inputShape) {\n    let outShape = this.computeSingleOutputShape(inputShape);\n    if (!this.returnSequences) {\n      outShape = [outShape[0], ...outShape.slice(2)];\n    }\n    if (this.returnState) {\n      outShape = [outShape, ...Array(2).fill([inputShape[0], ...outShape.slice(-3)])];\n    }\n    return outShape;\n  }\n  getInitialState(inputs) {\n    return tidy(() => {\n      const { stateSize } = this.cell;\n      const inputShape = inputs.shape;\n      const outputShape = this.computeSingleOutputShape(inputShape);\n      const stateShape = [outputShape[0], ...outputShape.slice(2)];\n      const initialState = zeros(stateShape);\n      if (Array.isArray(stateSize)) {\n        return Array(stateSize.length).fill(initialState);\n      }\n      return [initialState];\n    });\n  }\n  resetStates(states, training = false) {\n    tidy(() => {\n      if (!this.stateful) {\n        throw new AttributeError(\"Cannot call resetStates() on an RNN Layer that is not stateful.\");\n      }\n      const inputShape = this.inputSpec[0].shape;\n      const outputShape = this.computeSingleOutputShape(inputShape);\n      const stateShape = [outputShape[0], ...outputShape.slice(2)];\n      const batchSize = inputShape[0];\n      if (batchSize == null) {\n        throw new ValueError(\"If an RNN is stateful, it needs to know its batch size. Specify the batch size of your input tensors: \\n- If using a Sequential model, specify the batch size by passing a `batchInputShape` option to your first layer.\\n- If using the functional API, specify the batch size by passing a `batchShape` option to your Input layer.\");\n      }\n      if (this.getStates() == null) {\n        if (Array.isArray(this.cell.stateSize)) {\n          this.states_ = this.cell.stateSize.map(() => zeros(stateShape));\n        } else {\n          this.states_ = [zeros(stateShape)];\n        }\n      } else if (states == null) {\n        dispose(this.states_);\n        if (this.keptStates != null) {\n          dispose(this.keptStates);\n          this.keptStates = [];\n        }\n        if (Array.isArray(this.cell.stateSize)) {\n          this.states_ = this.cell.stateSize.map(() => zeros(stateShape));\n        } else {\n          this.states_[0] = zeros(stateShape);\n        }\n      } else {\n        if (!Array.isArray(states)) {\n          states = [states];\n        }\n        if (states.length !== this.states_.length) {\n          throw new ValueError(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${states.length} state value(s). Input received: ${states}`);\n        }\n        if (training) {\n          this.keptStates.push(this.states_.slice());\n        } else {\n          dispose(this.states_);\n        }\n        for (let index = 0; index < this.states_.length; ++index) {\n          const value = states[index];\n          const expectedShape = stateShape;\n          if (!util_exports.arraysEqual(value.shape, expectedShape)) {\n            throw new ValueError(`State ${index} is incompatible with layer ${this.name}: expected shape=${expectedShape}, received shape=${value.shape}`);\n          }\n          this.states_[index] = value;\n        }\n      }\n      this.states_ = this.states_.map((state) => keep(state.clone()));\n    });\n  }\n  computeSingleOutputShape(inputShape) {\n    const { dataFormat, filters, kernelSize, padding, strides, dilationRate } = this.cell;\n    const isChannelsFirst = dataFormat === \"channelsFirst\";\n    const h = inputShape[isChannelsFirst ? 3 : 2];\n    const w = inputShape[isChannelsFirst ? 4 : 3];\n    const hOut = convOutputLength(h, kernelSize[0], padding, strides[0], dilationRate[0]);\n    const wOut = convOutputLength(w, kernelSize[1], padding, strides[1], dilationRate[1]);\n    const outShape = [\n      ...inputShape.slice(0, 2),\n      ...isChannelsFirst ? [filters, hOut, wOut] : [hOut, wOut, filters]\n    ];\n    return outShape;\n  }\n};\nConvRNN2D.className = \"ConvRNN2D\";\nvar ConvLSTM2DCell = class extends LSTMCell {\n  constructor(args) {\n    const { filters, kernelSize, strides, padding, dataFormat, dilationRate } = args;\n    super(Object.assign({}, args, { units: filters }));\n    this.filters = filters;\n    assertPositiveInteger(this.filters, \"filters\");\n    this.kernelSize = normalizeArray(kernelSize, 2, \"kernelSize\");\n    this.kernelSize.forEach((size) => assertPositiveInteger(size, \"kernelSize\"));\n    this.strides = normalizeArray(strides || 1, 2, \"strides\");\n    this.strides.forEach((stride) => assertPositiveInteger(stride, \"strides\"));\n    this.padding = padding || \"valid\";\n    checkPaddingMode(this.padding);\n    this.dataFormat = dataFormat || \"channelsLast\";\n    checkDataFormat(this.dataFormat);\n    this.dilationRate = normalizeArray(dilationRate || 1, 2, \"dilationRate\");\n    this.dilationRate.forEach((rate) => assertPositiveInteger(rate, \"dilationRate\"));\n  }\n  build(inputShape) {\n    var _a;\n    inputShape = getExactlyOneShape(inputShape);\n    const channelAxis = this.dataFormat === \"channelsFirst\" ? 1 : inputShape.length - 1;\n    if (inputShape[channelAxis] == null) {\n      throw new ValueError(`The channel dimension of the input should be defined. Found ${inputShape[channelAxis]}`);\n    }\n    const inputDim = inputShape[channelAxis];\n    const numOfKernels = 4;\n    const kernelShape = this.kernelSize.concat([inputDim, this.filters * numOfKernels]);\n    this.kernel = this.addWeight(\"kernel\", kernelShape, null, this.kernelInitializer, this.kernelRegularizer, true, this.kernelConstraint);\n    const recurrentKernelShape = this.kernelSize.concat([this.filters, this.filters * numOfKernels]);\n    this.recurrentKernel = this.addWeight(\"recurrent_kernel\", recurrentKernelShape, null, this.recurrentInitializer, this.recurrentRegularizer, true, this.recurrentConstraint);\n    if (this.useBias) {\n      let biasInitializer;\n      if (this.unitForgetBias) {\n        const init2 = this.biasInitializer;\n        const filters = this.filters;\n        biasInitializer = new (_a = class CustomInit extends Initializer {\n          apply(shape, dtype) {\n            const biasI = init2.apply([filters]);\n            const biasF = ones2([filters]);\n            const biasCAndO = init2.apply([filters * 2]);\n            return concatenate([biasI, biasF, biasCAndO]);\n          }\n        }, _a.className = \"CustomInit\", _a)();\n      } else {\n        biasInitializer = this.biasInitializer;\n      }\n      this.bias = this.addWeight(\"bias\", [this.filters * numOfKernels], null, biasInitializer, this.biasRegularizer, true, this.biasConstraint);\n    }\n    this.built = true;\n  }\n  call(inputs, kwargs) {\n    return tidy(() => {\n      if (inputs.length !== 3) {\n        throw new ValueError(`ConvLSTM2DCell expects 3 input Tensors (inputs, h, c), got ${inputs.length}.`);\n      }\n      const training = kwargs[\"training\"] || false;\n      const x = inputs[0];\n      const hTMinus1 = inputs[1];\n      const cTMinus1 = inputs[2];\n      const numOfKernels = 4;\n      if (0 < this.dropout && this.dropout < 1 && this.dropoutMask == null) {\n        this.dropoutMask = generateDropoutMask({\n          ones: () => onesLike(x),\n          rate: this.dropout,\n          training,\n          count: numOfKernels,\n          dropoutFunc: this.dropoutFunc\n        });\n      }\n      const dropoutMask = this.dropoutMask;\n      const applyDropout = (x2, mask, index) => {\n        if (!mask || !mask[index]) {\n          return x2;\n        }\n        return mul(mask[index], x2);\n      };\n      let xI = applyDropout(x, dropoutMask, 0);\n      let xF = applyDropout(x, dropoutMask, 1);\n      let xC = applyDropout(x, dropoutMask, 2);\n      let xO = applyDropout(x, dropoutMask, 3);\n      if (0 < this.recurrentDropout && this.recurrentDropout < 1 && this.recurrentDropoutMask == null) {\n        this.recurrentDropoutMask = generateDropoutMask({\n          ones: () => onesLike(hTMinus1),\n          rate: this.recurrentDropout,\n          training,\n          count: numOfKernels,\n          dropoutFunc: this.dropoutFunc\n        });\n      }\n      const recDropoutMask = this.recurrentDropoutMask;\n      let hI = applyDropout(hTMinus1, recDropoutMask, 0);\n      let hF = applyDropout(hTMinus1, recDropoutMask, 1);\n      let hC = applyDropout(hTMinus1, recDropoutMask, 2);\n      let hO = applyDropout(hTMinus1, recDropoutMask, 3);\n      const kernelChannelAxis = 3;\n      const [kernelI, kernelF, kernelC, kernelO] = split(this.kernel.read(), numOfKernels, kernelChannelAxis);\n      const [biasI, biasF, biasC, biasO] = this.useBias ? split(this.bias.read(), numOfKernels) : [null, null, null, null];\n      xI = this.inputConv(xI, kernelI, biasI, this.padding);\n      xF = this.inputConv(xF, kernelF, biasF, this.padding);\n      xC = this.inputConv(xC, kernelC, biasC, this.padding);\n      xO = this.inputConv(xO, kernelO, biasO, this.padding);\n      const [recKernelI, recKernelF, recKernelC, recKernelO] = split(this.recurrentKernel.read(), numOfKernels, kernelChannelAxis);\n      hI = this.recurrentConv(hI, recKernelI);\n      hF = this.recurrentConv(hF, recKernelF);\n      hC = this.recurrentConv(hC, recKernelC);\n      hO = this.recurrentConv(hO, recKernelO);\n      const i = this.recurrentActivation.apply(add2(xI, hI));\n      const f = this.recurrentActivation.apply(add2(xF, hF));\n      const c = add2(mul(f, cTMinus1), mul(i, this.activation.apply(add2(xC, hC))));\n      const h = mul(this.recurrentActivation.apply(add2(xO, hO)), this.activation.apply(c));\n      return [h, h, c];\n    });\n  }\n  getConfig() {\n    const _a = super.getConfig(), { \"units\": _ } = _a, baseConfig = __rest(_a, [\"units\"]);\n    const config = {\n      filters: this.filters,\n      kernelSize: this.kernelSize,\n      padding: this.padding,\n      dataFormat: this.dataFormat,\n      dilationRate: this.dilationRate,\n      strides: this.strides\n    };\n    return Object.assign({}, baseConfig, config);\n  }\n  inputConv(x, w, b, padding) {\n    const out = conv2d(x, w, this.strides, padding || \"valid\", this.dataFormat === \"channelsFirst\" ? \"NCHW\" : \"NHWC\", this.dilationRate);\n    if (b) {\n      return biasAdd(out, b, this.dataFormat);\n    }\n    return out;\n  }\n  recurrentConv(x, w) {\n    const strides = 1;\n    return conv2d(x, w, strides, \"same\", this.dataFormat === \"channelsFirst\" ? \"NCHW\" : \"NHWC\");\n  }\n};\nConvLSTM2DCell.className = \"ConvLSTM2DCell\";\nserialization_exports.registerClass(ConvLSTM2DCell);\nvar ConvLSTM2D = class extends ConvRNN2D {\n  constructor(args) {\n    const cell = new ConvLSTM2DCell(args);\n    super(Object.assign({}, args, { cell }));\n  }\n  static fromConfig(cls, config) {\n    return new cls(config);\n  }\n};\nConvLSTM2D.className = \"ConvLSTM2D\";\nserialization_exports.registerClass(ConvLSTM2D);\n\n// node_modules/.pnpm/@tensorflow+tfjs-layers@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-layers/dist/layers/core.js\nvar Dropout = class extends Layer {\n  constructor(args) {\n    super(args);\n    this.rate = Math.max(Math.min(args.rate, 1), 0);\n    this.noiseShape = args.noiseShape;\n    this.seed = args.seed;\n    this.supportsMasking = true;\n  }\n  getNoiseShape(input2) {\n    if (this.noiseShape == null) {\n      return this.noiseShape;\n    }\n    const inputShape = input2.shape;\n    const noiseShape = [];\n    for (let i = 0; i < this.noiseShape.length; ++i) {\n      noiseShape.push(this.noiseShape[i] == null ? inputShape[i] : this.noiseShape[i]);\n    }\n    return noiseShape;\n  }\n  call(inputs, kwargs) {\n    return tidy(() => {\n      this.invokeCallHook(inputs, kwargs);\n      const input2 = getExactlyOneTensor(inputs);\n      if (0 < this.rate && this.rate < 1) {\n        const training = kwargs[\"training\"] == null ? false : kwargs[\"training\"];\n        const noiseShape = this.getNoiseShape(input2);\n        const output = inTrainPhase(() => dropout2(input2, this.rate, noiseShape, this.seed), () => input2, training);\n        return output;\n      }\n      return inputs;\n    });\n  }\n  getConfig() {\n    const config = {\n      rate: this.rate,\n      noiseShape: this.noiseShape,\n      seed: this.seed\n    };\n    const baseConfig = super.getConfig();\n    Object.assign(config, baseConfig);\n    return config;\n  }\n  dispose() {\n    return super.dispose();\n  }\n};\nDropout.className = \"Dropout\";\nserialization_exports.registerClass(Dropout);\nvar SpatialDropout1D = class extends Dropout {\n  constructor(args) {\n    super(args);\n    this.inputSpec = [{ ndim: 3 }];\n  }\n  getNoiseShape(input2) {\n    const inputShape = input2.shape;\n    return [inputShape[0], 1, inputShape[2]];\n  }\n};\nSpatialDropout1D.className = \"SpatialDropout1D\";\nserialization_exports.registerClass(SpatialDropout1D);\nvar Dense = class extends Layer {\n  constructor(args) {\n    super(args);\n    this.activation = null;\n    this.useBias = true;\n    this.kernel = null;\n    this.bias = null;\n    this.DEFAULT_KERNEL_INITIALIZER = \"glorotNormal\";\n    this.DEFAULT_BIAS_INITIALIZER = \"zeros\";\n    if (args.batchInputShape == null && args.inputShape == null && args.inputDim != null) {\n      let batchSize = null;\n      if (args.batchSize != null) {\n        batchSize = args.batchSize;\n      }\n      this.batchInputShape = [batchSize, args.inputDim];\n    }\n    this.units = args.units;\n    assertPositiveInteger(this.units, \"units\");\n    this.activation = getActivation(args.activation);\n    if (args.useBias != null) {\n      this.useBias = args.useBias;\n    }\n    this.kernelInitializer = getInitializer(args.kernelInitializer || this.DEFAULT_KERNEL_INITIALIZER);\n    this.biasInitializer = getInitializer(args.biasInitializer || this.DEFAULT_BIAS_INITIALIZER);\n    this.kernelConstraint = getConstraint(args.kernelConstraint);\n    this.biasConstraint = getConstraint(args.biasConstraint);\n    this.kernelRegularizer = getRegularizer(args.kernelRegularizer);\n    this.biasRegularizer = getRegularizer(args.biasRegularizer);\n    this.activityRegularizer = getRegularizer(args.activityRegularizer);\n    this.supportsMasking = true;\n    this.inputSpec = [{ minNDim: 2 }];\n  }\n  build(inputShape) {\n    inputShape = getExactlyOneShape(inputShape);\n    const inputLastDim = inputShape[inputShape.length - 1];\n    if (this.kernel == null) {\n      this.kernel = this.addWeight(\"kernel\", [inputLastDim, this.units], null, this.kernelInitializer, this.kernelRegularizer, true, this.kernelConstraint);\n      if (this.useBias) {\n        this.bias = this.addWeight(\"bias\", [this.units], null, this.biasInitializer, this.biasRegularizer, true, this.biasConstraint);\n      }\n    }\n    this.inputSpec = [{ minNDim: 2, axes: { [-1]: inputLastDim } }];\n    this.built = true;\n  }\n  computeOutputShape(inputShape) {\n    inputShape = getExactlyOneShape(inputShape);\n    const outputShape = inputShape.slice();\n    outputShape[outputShape.length - 1] = this.units;\n    return outputShape;\n  }\n  call(inputs, kwargs) {\n    return tidy(() => {\n      this.invokeCallHook(inputs, kwargs);\n      const input2 = getExactlyOneTensor(inputs);\n      const fusedActivationName = mapActivationToFusedKernel(this.activation.getClassName());\n      let output;\n      if (fusedActivationName != null) {\n        output = dot2(input2, this.kernel.read(), fusedActivationName, this.bias ? this.bias.read() : null);\n      } else {\n        output = dot2(input2, this.kernel.read());\n        if (this.bias != null) {\n          output = biasAdd(output, this.bias.read());\n        }\n        if (this.activation != null) {\n          output = this.activation.apply(output);\n        }\n      }\n      return output;\n    });\n  }\n  getConfig() {\n    const config = {\n      units: this.units,\n      activation: serializeActivation(this.activation),\n      useBias: this.useBias,\n      kernelInitializer: serializeInitializer(this.kernelInitializer),\n      biasInitializer: serializeInitializer(this.biasInitializer),\n      kernelRegularizer: serializeRegularizer(this.kernelRegularizer),\n      biasRegularizer: serializeRegularizer(this.biasRegularizer),\n      activityRegularizer: serializeRegularizer(this.activityRegularizer),\n      kernelConstraint: serializeConstraint(this.kernelConstraint),\n      biasConstraint: serializeConstraint(this.biasConstraint)\n    };\n    const baseConfig = super.getConfig();\n    Object.assign(config, baseConfig);\n    return config;\n  }\n};\nDense.className = \"Dense\";\nserialization_exports.registerClass(Dense);\nvar Flatten = class extends Layer {\n  constructor(args) {\n    args = args || {};\n    super(args);\n    this.inputSpec = [{ minNDim: 3 }];\n    this.dataFormat = args.dataFormat;\n  }\n  computeOutputShape(inputShape) {\n    inputShape = getExactlyOneShape(inputShape);\n    for (const dim of inputShape.slice(1)) {\n      if (dim == null) {\n        throw new ValueError(`The shape of the input to \"Flatten\" is not fully defined (got ${inputShape.slice(1)}). Make sure to pass a complete \"input_shape\" or \"batch_input_shape\" argument to the first layer in your model.`);\n      }\n    }\n    return [inputShape[0], arrayProd(inputShape, 1)];\n  }\n  call(inputs, kwargs) {\n    return tidy(() => {\n      this.invokeCallHook(inputs, kwargs);\n      let input2 = getExactlyOneTensor(inputs);\n      if (this.dataFormat === \"channelsFirst\" && input2.rank > 1) {\n        const permutation = [0];\n        for (let i = 2; i < input2.rank; ++i) {\n          permutation.push(i);\n        }\n        permutation.push(1);\n        input2 = transpose(input2, permutation);\n      }\n      return batchFlatten(input2);\n    });\n  }\n  getConfig() {\n    const config = {};\n    if (this.dataFormat != null) {\n      config[\"dataFormat\"] = this.dataFormat;\n    }\n    const baseConfig = super.getConfig();\n    Object.assign(config, baseConfig);\n    return config;\n  }\n};\nFlatten.className = \"Flatten\";\nserialization_exports.registerClass(Flatten);\nvar Activation2 = class extends Layer {\n  constructor(args) {\n    super(args);\n    this.supportsMasking = true;\n    this.activation = getActivation(args.activation);\n  }\n  call(inputs, kwargs) {\n    return tidy(() => {\n      this.invokeCallHook(inputs, kwargs);\n      const input2 = getExactlyOneTensor(inputs);\n      return this.activation.apply(input2);\n    });\n  }\n  getConfig() {\n    const config = { activation: serializeActivation(this.activation) };\n    const baseConfig = super.getConfig();\n    Object.assign(config, baseConfig);\n    return config;\n  }\n};\nActivation2.className = \"Activation\";\nserialization_exports.registerClass(Activation2);\nvar RepeatVector = class extends Layer {\n  constructor(args) {\n    super(args);\n    this.n = args.n;\n    this.inputSpec = [{ ndim: 2 }];\n  }\n  computeOutputShape(inputShape) {\n    return [inputShape[0], this.n, inputShape[1]];\n  }\n  call(inputs, kwargs) {\n    return tidy(() => {\n      inputs = getExactlyOneTensor(inputs);\n      return repeat(inputs, this.n);\n    });\n  }\n  getConfig() {\n    const config = {\n      n: this.n\n    };\n    const baseConfig = super.getConfig();\n    Object.assign(config, baseConfig);\n    return config;\n  }\n};\nRepeatVector.className = \"RepeatVector\";\nserialization_exports.registerClass(RepeatVector);\nvar Reshape2 = class extends Layer {\n  constructor(args) {\n    super(args);\n    this.targetShape = args.targetShape;\n    for (let i = 0; i < this.targetShape.length; ++i) {\n      if (this.isUnknown(this.targetShape[i])) {\n        this.targetShape[i] = null;\n      }\n    }\n  }\n  isUnknown(dim) {\n    return dim < 0 || dim == null;\n  }\n  fixUnknownDimension(inputShape, outputShape) {\n    const errorMsg = \"Total size of new array must be unchanged.\";\n    const finalShape = outputShape.slice();\n    let known = 1;\n    let unknown = null;\n    for (let i = 0; i < finalShape.length; ++i) {\n      const dim = finalShape[i];\n      if (this.isUnknown(dim)) {\n        if (unknown === null) {\n          unknown = i;\n        } else {\n          throw new ValueError(\"Can only specifiy one unknown dimension.\");\n        }\n      } else {\n        known *= dim;\n      }\n    }\n    const originalSize = arrayProd(inputShape);\n    if (unknown !== null) {\n      if (known === 0 || originalSize % known !== 0) {\n        throw new ValueError(errorMsg);\n      }\n      finalShape[unknown] = originalSize / known;\n    } else if (originalSize !== known) {\n      throw new ValueError(errorMsg);\n    }\n    return finalShape;\n  }\n  computeOutputShape(inputShape) {\n    let anyUnknownDims = false;\n    for (let i = 0; i < inputShape.length; ++i) {\n      if (this.isUnknown(inputShape[i])) {\n        anyUnknownDims = true;\n        break;\n      }\n    }\n    if (anyUnknownDims) {\n      return inputShape.slice(0, 1).concat(this.targetShape);\n    } else {\n      return inputShape.slice(0, 1).concat(this.fixUnknownDimension(inputShape.slice(1), this.targetShape));\n    }\n  }\n  call(inputs, kwargs) {\n    return tidy(() => {\n      this.invokeCallHook(inputs, kwargs);\n      const input2 = getExactlyOneTensor(inputs);\n      const inputShape = input2.shape;\n      const outputShape = inputShape.slice(0, 1).concat(this.fixUnknownDimension(inputShape.slice(1), this.targetShape));\n      return reshape(input2, outputShape);\n    });\n  }\n  getConfig() {\n    const config = {\n      targetShape: this.targetShape\n    };\n    const baseConfig = super.getConfig();\n    Object.assign(config, baseConfig);\n    return config;\n  }\n};\nReshape2.className = \"Reshape\";\nserialization_exports.registerClass(Reshape2);\nvar Permute = class extends Layer {\n  constructor(args) {\n    super(args);\n    if (args.dims == null) {\n      throw new Error(\"Required configuration field `dims` is missing during Permute constructor call.\");\n    }\n    if (!Array.isArray(args.dims)) {\n      throw new Error(`Permute constructor requires \\`dims\\` to be an Array, but received ${args.dims} instead.`);\n    }\n    const expectedSortedIndices = range2(1, args.dims.length + 1);\n    if (!util_exports.arraysEqual(args.dims.slice().sort(), expectedSortedIndices)) {\n      throw new Error(\"Invalid permutation `dims`: \" + JSON.stringify(args.dims) + \" `dims` must contain consecutive integers starting from 1.\");\n    }\n    this.dims = args.dims;\n    this.dimsIncludingBatch = [0].concat(this.dims);\n    this.inputSpec = [new InputSpec({ ndim: this.dims.length + 1 })];\n  }\n  computeOutputShape(inputShape) {\n    inputShape = getExactlyOneShape(inputShape);\n    const outputShape = inputShape.slice();\n    this.dims.forEach((dim, i) => {\n      outputShape[i + 1] = inputShape[dim];\n    });\n    return outputShape;\n  }\n  call(inputs, kwargs) {\n    return transpose(getExactlyOneTensor(inputs), this.dimsIncludingBatch);\n  }\n  getConfig() {\n    const config = {\n      dims: this.dims\n    };\n    const baseConfig = super.getConfig();\n    Object.assign(config, baseConfig);\n    return config;\n  }\n};\nPermute.className = \"Permute\";\nserialization_exports.registerClass(Permute);\nvar Masking = class extends Layer {\n  constructor(args) {\n    super(args == null ? {} : args);\n    this.supportsMasking = true;\n    if (args != null) {\n      this.maskValue = args.maskValue == null ? 0 : args.maskValue;\n    } else {\n      this.maskValue = 0;\n    }\n  }\n  computeOutputShape(inputShape) {\n    return inputShape;\n  }\n  getConfig() {\n    const baseConfig = super.getConfig();\n    const config = { maskValue: this.maskValue };\n    Object.assign(config, baseConfig);\n    return config;\n  }\n  computeMask(inputs, mask) {\n    const input2 = getExactlyOneTensor(inputs);\n    const axis = -1;\n    return any(notEqual(input2, this.maskValue), axis);\n  }\n  call(inputs, kwargs) {\n    return tidy(() => {\n      this.invokeCallHook(inputs, kwargs);\n      const input2 = getExactlyOneTensor(inputs);\n      const axis = -1;\n      const keepDims = true;\n      const booleanMask = any(notEqual(input2, this.maskValue), axis, keepDims);\n      const output = mul(input2, cast(booleanMask, input2.dtype));\n      return output;\n    });\n  }\n};\nMasking.className = \"Masking\";\nserialization_exports.registerClass(Masking);\n\n// node_modules/.pnpm/@tensorflow+tfjs-layers@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-layers/dist/layers/embeddings.js\nvar Embedding = class extends Layer {\n  constructor(args) {\n    super(args);\n    this.embeddings = null;\n    this.DEFAULT_EMBEDDINGS_INITIALIZER = \"randomUniform\";\n    if (args.batchInputShape == null && args.inputShape == null) {\n      let batchSize = null;\n      if (args.batchSize != null) {\n        batchSize = args.batchSize;\n      }\n      if (args.inputLength == null) {\n        this.batchInputShape = [batchSize, null];\n      } else {\n        this.batchInputShape = [batchSize].concat(toList(args.inputLength));\n      }\n    }\n    this.inputDim = args.inputDim;\n    assertPositiveInteger(this.inputDim, \"inputDim\");\n    this.outputDim = args.outputDim;\n    assertPositiveInteger(this.outputDim, \"outputDim\");\n    this.embeddingsInitializer = getInitializer(args.embeddingsInitializer || this.DEFAULT_EMBEDDINGS_INITIALIZER);\n    this.embeddingsRegularizer = getRegularizer(args.embeddingsRegularizer);\n    this.activityRegularizer = getRegularizer(args.activityRegularizer);\n    this.embeddingsConstraint = getConstraint(args.embeddingsConstraint);\n    this.maskZero = args.maskZero;\n    this.supportsMasking = args.maskZero;\n    this.inputLength = args.inputLength;\n  }\n  build(inputShape) {\n    this.embeddings = this.addWeight(\"embeddings\", [this.inputDim, this.outputDim], this.dtype, this.embeddingsInitializer, this.embeddingsRegularizer, true, this.embeddingsConstraint);\n    this.built = true;\n  }\n  warnOnIncompatibleInputShape(inputShape) {\n  }\n  computeMask(inputs, mask) {\n    return tidy(() => {\n      if (!this.maskZero) {\n        return null;\n      } else {\n        inputs = getExactlyOneTensor(inputs);\n        return notEqual(inputs, zerosLike(inputs));\n      }\n    });\n  }\n  computeOutputShape(inputShape) {\n    inputShape = getExactlyOneShape(inputShape);\n    if (this.inputLength == null) {\n      return [...inputShape, this.outputDim];\n    }\n    const inLens = toList(this.inputLength);\n    if (inLens.length !== inputShape.length - 1) {\n      throw new ValueError(`\"inputLength\" is ${this.inputLength}, but received input shape has shape ${inputShape}`);\n    } else {\n      let i = 0;\n      for (let k = 0; k < inLens.length; ++k) {\n        const s1 = inLens[k];\n        const s2 = inputShape[k + 1];\n        if (s1 != null && s2 != null && s1 !== s2) {\n          throw new ValueError(`\"inputLength\" is ${this.inputLength}, but received input shape has shape ${inputShape}`);\n        } else if (s1 == null) {\n          inLens[i] = s2;\n        }\n        i++;\n      }\n    }\n    return [inputShape[0], ...inLens, this.outputDim];\n  }\n  call(inputs, kwargs) {\n    return tidy(() => {\n      this.invokeCallHook(inputs, kwargs);\n      let input2 = getExactlyOneTensor(inputs);\n      if (input2.dtype !== \"int32\") {\n        input2 = cast2(input2, \"int32\");\n      }\n      const output = gather2(this.embeddings.read(), reshape(input2, [input2.size]));\n      return reshape(output, getExactlyOneShape(this.computeOutputShape(input2.shape)));\n    });\n  }\n  getConfig() {\n    const config = {\n      inputDim: this.inputDim,\n      outputDim: this.outputDim,\n      embeddingsInitializer: serializeInitializer(this.embeddingsInitializer),\n      embeddingsRegularizer: serializeRegularizer(this.embeddingsRegularizer),\n      activityRegularizer: serializeRegularizer(this.activityRegularizer),\n      embeddingsConstraint: serializeConstraint(this.embeddingsConstraint),\n      maskZero: this.maskZero,\n      inputLength: this.inputLength\n    };\n    const baseConfig = super.getConfig();\n    Object.assign(config, baseConfig);\n    return config;\n  }\n};\nEmbedding.className = \"Embedding\";\nserialization_exports.registerClass(Embedding);\n\n// node_modules/.pnpm/@tensorflow+tfjs-layers@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-layers/dist/layers/merge.js\nvar Merge = class extends Layer {\n  constructor(args) {\n    super(args || {});\n    this.supportsMasking = true;\n  }\n  mergeFunction(inputs) {\n    throw new NotImplementedError();\n  }\n  computeElementwiseOpOutputShape(shape1, shape2) {\n    if (shape1 == null || shape2 == null) {\n      return null;\n    } else if (shape1.length < shape2.length) {\n      return this.computeElementwiseOpOutputShape(shape2, shape1);\n    } else if (shape2.length === 0) {\n      return shape1;\n    }\n    const outputShape = shape1.slice(0, shape1.length - shape2.length);\n    for (let k = 0; k < shape2.length; ++k) {\n      const i = shape1[shape1.length - shape2.length + k];\n      const j = shape2[k];\n      if (i == null || j == null || i < 0 || j < 0) {\n        outputShape.push(null);\n      } else if (i === 1) {\n        outputShape.push(j);\n      } else if (j === 1) {\n        outputShape.push(i);\n      } else {\n        if (i !== j) {\n          throw new ValueError(\"Operands could not be broadcast together with shapes \" + JSON.stringify(shape1) + \" \" + JSON.stringify(shape2));\n        }\n        outputShape.push(i);\n      }\n    }\n    return outputShape;\n  }\n  build(inputShape) {\n    if (Array.isArray(inputShape) && !Array.isArray(inputShape[0])) {\n      inputShape = [getExactlyOneShape(inputShape)];\n    }\n    inputShape = inputShape;\n    if (inputShape.length < 2) {\n      throw new ValueError(`A merge layer should be called on an Array of at least 2 inputs. Got ${inputShape.length} input(s).`);\n    }\n    let batchSizes = [];\n    for (const shape of inputShape) {\n      if (shape != null && shape[0] !== null) {\n        batchSizes.push(shape[0]);\n      }\n    }\n    batchSizes = unique2(batchSizes);\n    if (batchSizes.length > 1) {\n      throw new ValueError(`Can not merge tensors with different batch sizes. Got tensors with shapes: ${JSON.stringify(inputShape)}.`);\n    }\n    let outputShape = inputShape[0] == null ? null : inputShape[0].slice(1);\n    for (let i = 1; i < inputShape.length; ++i) {\n      const shape = inputShape[i] == null ? null : inputShape[i].slice(1);\n      outputShape = this.computeElementwiseOpOutputShape(outputShape, shape);\n    }\n    const allRanks = inputShape.map((shape) => shape.length);\n    if (inputShape.indexOf(null) === -1 && unique2(allRanks).length === 1) {\n      this.reshapeRequired = false;\n    } else {\n      this.reshapeRequired = true;\n    }\n  }\n  call(inputs, kwargs) {\n    return tidy(() => {\n      inputs = inputs;\n      if (this.reshapeRequired) {\n        const reshapedInputs = [];\n        const inputDims = inputs.map((input2) => input2.rank);\n        if (inputDims.indexOf(null) === -1) {\n          const maxNDim = max2(inputDims);\n          for (let x of inputs) {\n            const xNDim = x.rank;\n            for (let k = 0; k < maxNDim - xNDim; ++k) {\n              x = expandDims2(x, 1);\n            }\n            reshapedInputs.push(x);\n          }\n          return this.mergeFunction(reshapedInputs);\n        } else {\n          let transposed = false;\n          for (const x of inputs) {\n            const xNDim = x.rank;\n            if (xNDim == null) {\n              const xShape = x.shape;\n              const batchSize = xShape[0];\n              const newShape = xShape.slice(1).concat([batchSize]);\n              let xTransposed = reshape(x, [batchSize].concat(arrayProd(xShape.slice(1))));\n              xTransposed = transpose(xTransposed, [1, 0]);\n              xTransposed = reshape(xTransposed, newShape);\n              reshapedInputs.push(xTransposed);\n              transposed = true;\n            } else if (xNDim > 1) {\n              const dims = range2(1, xNDim).concat([0]);\n              reshapedInputs.push(transpose(x, dims));\n              transposed = true;\n            } else {\n              reshapedInputs.push(x);\n            }\n          }\n          let y = this.mergeFunction(reshapedInputs);\n          const yNDim = y.rank;\n          if (transposed) {\n            if (yNDim == null) {\n              const yShape = y.shape;\n              const yNDim2 = yShape.length;\n              const batchSize = yShape[yNDim2 - 1];\n              const newShape = [batchSize].concat(yShape.slice(0, yShape.length - 1));\n              y = reshape(transpose(reshape(y, [-1, batchSize]), [1, 0]), newShape);\n            } else if (yNDim > 1) {\n              const dims = [yNDim - 1].concat(range2(0, yNDim - 1));\n              y = transpose(y, dims);\n            }\n          }\n          return y;\n        }\n      } else {\n        return this.mergeFunction(inputs);\n      }\n    });\n  }\n  computeOutputShape(inputShape) {\n    inputShape = inputShape;\n    let outputShape;\n    if (inputShape[0] == null) {\n      outputShape = null;\n    } else {\n      outputShape = inputShape[0].slice(1);\n    }\n    for (let i = 1; i < inputShape.length; ++i) {\n      const shape = inputShape[i] == null ? null : inputShape[i].slice(1);\n      outputShape = this.computeElementwiseOpOutputShape(outputShape, shape);\n    }\n    let batchSizes = [];\n    for (const shape of inputShape) {\n      if (shape != null && shape[0] !== null) {\n        batchSizes.push(shape[0]);\n      }\n    }\n    batchSizes = unique2(batchSizes);\n    if (batchSizes.length === 1) {\n      outputShape = batchSizes.concat(outputShape);\n    } else {\n      outputShape = [null].concat(outputShape);\n    }\n    return outputShape;\n  }\n  computeMask(inputs, mask) {\n    return tidy(() => {\n      if (mask == null) {\n        return null;\n      }\n      if (!Array.isArray(mask)) {\n        throw new ValueError(\"`mask` should be an Array\");\n      }\n      if (!Array.isArray(inputs)) {\n        throw new ValueError(\"`inputs` should be an Array\");\n      }\n      if (mask.length !== inputs.length) {\n        throw new ValueError(`The Array 'inputs' and 'mask' are expected to have the same length, but have different lengths (${inputs.length} vs ${mask.length})`);\n      }\n      if (mask.every((m) => m == null)) {\n        return null;\n      }\n      mask = mask.map((m) => m == null ? m : expandDims(m, 0));\n      let output = mask[0];\n      for (let i = 1; i < mask.length - 1; ++i) {\n        output = logicalAnd(output, mask[i]);\n      }\n      return output;\n    });\n  }\n};\nvar Add2 = class extends Merge {\n  constructor(args) {\n    super(args);\n  }\n  mergeFunction(inputs) {\n    return tidy(() => {\n      let output = inputs[0].clone();\n      for (let i = 1; i < inputs.length; ++i) {\n        output = add2(output, inputs[i]);\n      }\n      return output;\n    });\n  }\n};\nAdd2.className = \"Add\";\nserialization_exports.registerClass(Add2);\nvar Multiply2 = class extends Merge {\n  constructor(args) {\n    super(args);\n  }\n  mergeFunction(inputs) {\n    return tidy(() => {\n      let output = inputs[0].clone();\n      for (let i = 1; i < inputs.length; ++i) {\n        output = mul(output, inputs[i]);\n      }\n      return output;\n    });\n  }\n};\nMultiply2.className = \"Multiply\";\nserialization_exports.registerClass(Multiply2);\nvar Average = class extends Merge {\n  constructor(args) {\n    super(args);\n  }\n  mergeFunction(inputs) {\n    return tidy(() => {\n      let output = inputs[0].clone();\n      for (let i = 1; i < inputs.length; ++i) {\n        output = add2(output, inputs[i]);\n      }\n      return mul(1 / inputs.length, output);\n    });\n  }\n};\nAverage.className = \"Average\";\nserialization_exports.registerClass(Average);\nvar Maximum2 = class extends Merge {\n  constructor(args) {\n    super(args);\n  }\n  mergeFunction(inputs) {\n    return tidy(() => {\n      let output = inputs[0];\n      for (let i = 1; i < inputs.length; ++i) {\n        output = maximum(output, inputs[i]);\n      }\n      return output;\n    });\n  }\n};\nMaximum2.className = \"Maximum\";\nserialization_exports.registerClass(Maximum2);\nvar Minimum2 = class extends Merge {\n  constructor(args) {\n    super(args);\n  }\n  mergeFunction(inputs) {\n    return tidy(() => {\n      let output = inputs[0];\n      for (let i = 1; i < inputs.length; ++i) {\n        output = minimum(output, inputs[i]);\n      }\n      return output;\n    });\n  }\n};\nMinimum2.className = \"Minimum\";\nserialization_exports.registerClass(Minimum2);\nvar Concatenate = class extends Merge {\n  constructor(args) {\n    super(args);\n    this.DEFAULT_AXIS = -1;\n    if (args == null) {\n      args = {};\n    }\n    this.axis = args.axis == null ? this.DEFAULT_AXIS : args.axis;\n    this.supportsMasking = true;\n    this.reshapeRequired = false;\n  }\n  build(inputShape) {\n    if (!(Array.isArray(inputShape) && Array.isArray(inputShape[0])) || inputShape.length === 1) {\n      throw new ValueError(\"A `Concatenate` layer should be called on a list of at least 2 inputs\");\n    }\n    inputShape = inputShape;\n    let allNoneShape = true;\n    for (const shape of inputShape) {\n      if (shape != null) {\n        allNoneShape = false;\n        break;\n      }\n    }\n    if (allNoneShape) {\n      return;\n    }\n    const shapeSet = [];\n    for (let i = 0; i < inputShape.length; ++i) {\n      const shapeWithoutConcatAxis = inputShape[i].slice();\n      shapeWithoutConcatAxis.splice(this.axis, 1);\n      let exists = false;\n      for (const shape of shapeSet) {\n        if (util_exports.arraysEqual(shape, shapeWithoutConcatAxis)) {\n          exists = true;\n          break;\n        }\n      }\n      if (!exists) {\n        shapeSet.push(shapeWithoutConcatAxis);\n      }\n    }\n    if (shapeSet.length > 1) {\n      throw new ValueError(\"A `Concatenate` layer requires inputs with matching shapes except for the concat axis. Got input shapes: \" + JSON.stringify(inputShape));\n    }\n  }\n  mergeFunction(inputs) {\n    return tidy(() => {\n      return concatenate(inputs, this.axis);\n    });\n  }\n  computeOutputShape(inputShape) {\n    if (!(Array.isArray(inputShape) && Array.isArray(inputShape[0]))) {\n      throw new ValueError(\"A `Concatenate` layer should be called on a list of inputs.\");\n    }\n    const inputShapes = inputShape;\n    const outputShape = inputShapes[0].slice();\n    const axis = this.axis < 0 ? outputShape.length + this.axis : this.axis;\n    for (const shape of inputShapes.slice(1)) {\n      if (outputShape[axis] == null || shape[axis] == null) {\n        outputShape[axis] = null;\n        break;\n      }\n      outputShape[axis] += shape[axis];\n    }\n    return outputShape;\n  }\n  computeMask(inputs, mask) {\n    if (mask == null) {\n      return null;\n    }\n    if (!Array.isArray(mask)) {\n      throw new ValueError(\"`mask` should be an array for Concatenate\");\n    }\n    if (!Array.isArray(inputs)) {\n      throw new ValueError(\"`inputs` should be an array for Concatenate\");\n    }\n    if (mask.length !== inputs.length) {\n      throw new ValueError(`Mismatch in the length of mask (${mask.length}) and the legnth of inputs (${inputs.length})`);\n    }\n    return tidy(() => {\n      let allNullMasks = true;\n      mask.forEach((m) => {\n        if (m != null) {\n          allNullMasks = false;\n          return;\n        }\n      });\n      if (allNullMasks) {\n        return null;\n      }\n      const outputMasks = [];\n      for (let i = 0; i < inputs.length; ++i) {\n        if (mask[i] == null) {\n          outputMasks.push(cast(onesLike(inputs[i]), \"bool\"));\n        } else if (mask[i].rank < inputs[i].rank) {\n          outputMasks.push(expandDims(mask[i], -1));\n        } else {\n          outputMasks.push(mask[i]);\n        }\n      }\n      const concatenatedMasks = concat(outputMasks, this.axis);\n      return all(concatenatedMasks, -1, false);\n    });\n  }\n  getConfig() {\n    const config = {\n      \"axis\": this.axis\n    };\n    const baseConfig = super.getConfig();\n    Object.assign(config, baseConfig);\n    return config;\n  }\n};\nConcatenate.className = \"Concatenate\";\nserialization_exports.registerClass(Concatenate);\nfunction interpretAxis(axis, dim) {\n  while (axis < 0) {\n    axis += dim;\n  }\n  return axis;\n}\nfunction batchDot(x, y, axes) {\n  if (x.shape.length > 3 || y.shape.length > 3) {\n    throw new NotImplementedError(\"batchDot is not implemented for tensors of 4D or higher rank yet\");\n  }\n  util_exports.assert(x.shape.length >= 2, () => `batchDot requires the rank of x to be >= 2, but got ${x.shape.length}`);\n  util_exports.assert(x.shape.length >= 2, () => `batchDot requires the rank of y to be >= 2, but got ${y.shape.length}`);\n  if (typeof axes === \"number\") {\n    axes = [axes, axes];\n  }\n  if (x.dtype === \"complex64\" || y.dtype === \"complex64\") {\n    throw new NotImplementedError(\"batchDot is not implemented for complex64-type Tensors yet.\");\n  }\n  const xNDim = x.shape.length;\n  const yNDim = y.shape.length;\n  if (axes == null) {\n    axes = [xNDim - 1, yNDim - 2];\n  }\n  const axesArray = axes;\n  return tidy(() => {\n    let diff;\n    if (xNDim > yNDim) {\n      diff = xNDim - yNDim;\n      const diffShape = [];\n      for (let i = 0; i < diff; ++i) {\n        diffShape.push(1);\n      }\n      y = reshape(y, y.shape.concat(diffShape));\n    } else if (yNDim > xNDim) {\n      diff = yNDim - xNDim;\n      const diffShape = [];\n      for (let i = 0; i < diff; ++i) {\n        diffShape.push(1);\n      }\n      x = reshape(x, x.shape.concat(diffShape));\n    } else {\n      diff = 0;\n    }\n    let out;\n    if (x.shape.length === 2 && y.shape.length === 2) {\n      if (axesArray[0] === axesArray[1]) {\n        out = sum2(mul(x, y), axesArray[0]);\n      } else {\n        out = sum2(mul(transpose(x, [1, 0]), y), axesArray[1]);\n      }\n    } else {\n      const adjX = axesArray[0] !== x.shape.length - 1;\n      const adjY = axesArray[1] === y.shape.length - 1;\n      out = matMul(x, y, adjX, adjY);\n    }\n    if (diff > 0) {\n      let idx;\n      if (xNDim > yNDim) {\n        idx = xNDim + yNDim - 3;\n      } else {\n        idx = xNDim - 1;\n      }\n      const squeezeAxes = [];\n      for (let i = idx; i < idx + diff; ++i) {\n        squeezeAxes.push(i);\n      }\n      out = squeeze(out, squeezeAxes);\n    }\n    if (out.shape.length === 1) {\n      out = expandDims(out, 1);\n    }\n    return out;\n  });\n}\nvar Dot = class extends Merge {\n  constructor(args) {\n    super(args);\n    this.axes = args.axes;\n    this.normalize = args.normalize == null ? false : args.normalize;\n    this.supportsMasking = true;\n    this.reshapeRequired = false;\n  }\n  build(inputShape) {\n    util_exports.assert(Array.isArray(inputShape) && inputShape.length === 2 && Array.isArray(inputShape[0]) && Array.isArray(inputShape[1]), () => \"A `Dot` layer should be called on a list of exactly 2 inputs.\");\n    const shape1 = inputShape[0];\n    const shape2 = inputShape[1];\n    if (shape1.length > 3 || shape2.length > 3) {\n      throw new NotImplementedError(\"Dot layer does not support tensors of 4D or higher rank yet.\");\n    }\n    const axes = this.interpretAxes(shape1, shape2);\n    if (shape1[axes[0]] !== shape2[axes[1]]) {\n      throw new ValueError(`Dimension incompatibility: ${shape1[axes[0]]} !== ${shape2[axes[1]]}`);\n    }\n  }\n  mergeFunction(inputs) {\n    if (inputs.length !== 2) {\n      throw new ValueError(`A \\`Dot\\` layer must be called on exactly 2 inputs, but received ${inputs.length} input(s).`);\n    }\n    let x1 = inputs[0];\n    let x2 = inputs[1];\n    let axes;\n    if (!Array.isArray(this.axes)) {\n      axes = [\n        interpretAxis(this.axes, x1.shape.length),\n        interpretAxis(this.axes, x2.shape.length)\n      ];\n    } else {\n      axes = this.axes.map((axis, i) => interpretAxis(axis, inputs[i].shape.length));\n    }\n    if (this.normalize) {\n      x1 = l2Normalize(x1, axes[0]);\n      x2 = l2Normalize(x2, axes[1]);\n    }\n    return batchDot(x1, x2, axes);\n  }\n  interpretAxes(shape1, shape2) {\n    let axes;\n    if (!Array.isArray(this.axes)) {\n      axes = [\n        interpretAxis(this.axes, shape1.length),\n        interpretAxis(this.axes, shape2.length)\n      ];\n    } else {\n      axes = this.axes;\n    }\n    return axes;\n  }\n  computeOutputShape(inputShape) {\n    util_exports.assert(Array.isArray(inputShape) && inputShape.length === 2 && Array.isArray(inputShape[0]) && Array.isArray(inputShape[1]), () => \"A `Dot` layer should be called on a list of exactly 2 inputs.\");\n    const shape1 = inputShape[0].slice();\n    const shape2 = inputShape[1].slice();\n    if (shape1.length > 3 || shape2.length > 3) {\n      throw new NotImplementedError(\"Dot layer does not support tensors of 4D or higher rank yet.\");\n    }\n    const axes = this.interpretAxes(shape1, shape2);\n    shape1.splice(axes[0], 1);\n    shape2.splice(axes[1], 1);\n    shape2.splice(0, 1);\n    const outputShape = shape1.concat(shape2);\n    if (outputShape.length === 1) {\n      outputShape.push(1);\n    }\n    return outputShape;\n  }\n  computeMask(inputs, mask) {\n    return null;\n  }\n  getConfig() {\n    const config = {\n      \"axes\": this.axes,\n      \"normalize\": this.normalize\n    };\n    const baseConfig = super.getConfig();\n    Object.assign(config, baseConfig);\n    return config;\n  }\n};\nDot.className = \"Dot\";\nserialization_exports.registerClass(Dot);\n\n// node_modules/.pnpm/@tensorflow+tfjs-layers@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-layers/dist/layers/noise.js\nvar GaussianNoise = class extends Layer {\n  constructor(args) {\n    super(args);\n    this.supportsMasking = true;\n    this.stddev = args.stddev;\n  }\n  computeOutputShape(inputShape) {\n    return inputShape;\n  }\n  getConfig() {\n    const baseConfig = super.getConfig();\n    const config = { stddev: this.stddev };\n    Object.assign(config, baseConfig);\n    return config;\n  }\n  call(inputs, kwargs) {\n    return tidy(() => {\n      this.invokeCallHook(inputs, kwargs);\n      const input2 = getExactlyOneTensor(inputs);\n      const noised = () => add2(randomNormal2(input2.shape, 0, this.stddev), input2);\n      const output = inTrainPhase(noised, () => input2, kwargs[\"training\"] || false);\n      return output;\n    });\n  }\n};\nGaussianNoise.className = \"GaussianNoise\";\nserialization_exports.registerClass(GaussianNoise);\nvar GaussianDropout = class extends Layer {\n  constructor(args) {\n    super(args);\n    this.supportsMasking = true;\n    this.rate = args.rate;\n  }\n  computeOutputShape(inputShape) {\n    return inputShape;\n  }\n  getConfig() {\n    const baseConfig = super.getConfig();\n    const config = { rate: this.rate };\n    Object.assign(config, baseConfig);\n    return config;\n  }\n  call(inputs, kwargs) {\n    return tidy(() => {\n      this.invokeCallHook(inputs, kwargs);\n      const input2 = getExactlyOneTensor(inputs);\n      if (this.rate > 0 && this.rate < 1) {\n        const noised = () => {\n          const stddev = Math.sqrt(this.rate / (1 - this.rate));\n          return mul(input2, randomNormal2(input2.shape, 1, stddev));\n        };\n        return inTrainPhase(noised, () => input2, kwargs[\"training\"] || false);\n      }\n      return input2;\n    });\n  }\n};\nGaussianDropout.className = \"GaussianDropout\";\nserialization_exports.registerClass(GaussianDropout);\nvar AlphaDropout = class extends Layer {\n  constructor(args) {\n    super(args);\n    this.supportsMasking = true;\n    this.rate = args.rate;\n    this.noiseShape = args.noiseShape;\n  }\n  _getNoiseShape(inputs) {\n    return this.noiseShape || getExactlyOneTensor(inputs).shape;\n  }\n  computeOutputShape(inputShape) {\n    return inputShape;\n  }\n  getConfig() {\n    const baseConfig = super.getConfig();\n    const config = { rate: this.rate };\n    Object.assign(config, baseConfig);\n    return config;\n  }\n  call(inputs, kwargs) {\n    return tidy(() => {\n      if (this.rate < 1 && this.rate > 0) {\n        const noiseShape = this._getNoiseShape(inputs);\n        const droppedInputs = () => {\n          const input2 = getExactlyOneTensor(inputs);\n          const alpha = 1.6732632423543772;\n          const scale2 = 1.0507009873554805;\n          const alphaP = -alpha * scale2;\n          let keptIdx = greaterEqual(randomUniform(noiseShape), this.rate);\n          keptIdx = cast2(keptIdx, \"float32\");\n          const a = ((1 - this.rate) * (1 + this.rate * alphaP ** 2)) ** -0.5;\n          const b = -a * alphaP * this.rate;\n          const x = add2(mul(input2, keptIdx), mul(add2(keptIdx, -1), alphaP));\n          return add2(mul(x, a), b);\n        };\n        return inTrainPhase(droppedInputs, () => getExactlyOneTensor(inputs), kwargs[\"training\"] || false);\n      }\n      return inputs;\n    });\n  }\n};\nAlphaDropout.className = \"AlphaDropout\";\nserialization_exports.registerClass(AlphaDropout);\n\n// node_modules/.pnpm/@tensorflow+tfjs-layers@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-layers/dist/layers/normalization.js\nfunction batchNormalization(x, mean5, variance, beta, gamma, epsilon3 = 1e-3) {\n  let out;\n  if (x.rank === 2) {\n    out = batchNorm2d(x, mean5, variance, beta, gamma, epsilon3);\n  } else if (x.rank === 3) {\n    out = batchNorm3d(x, mean5, variance, beta, gamma, epsilon3);\n  } else if (x.rank === 4) {\n    out = batchNorm4d(x, mean5, variance, beta, gamma, epsilon3);\n  } else {\n    throw new NotImplementedError(`batchNormalization is not implemented for array of rank ${x.rank} yet`);\n  }\n  return out;\n}\nfunction regularNormalizeBatchInTraining(x, gamma, beta, reductionAxes, epsilon3 = 1e-3) {\n  return tidy(() => {\n    const meanAndVariance = moments(x, reductionAxes);\n    const mean5 = meanAndVariance.mean;\n    const variance = meanAndVariance.variance;\n    const normed = batchNormalization(x, mean5, variance, beta, gamma, epsilon3);\n    return [normed, mean5, variance];\n  });\n}\nfunction broadcastNormalizeBatchInTraining(x, gamma, beta, reductionAxes, epsilon3 = 1e-3) {\n  return tidy(() => {\n    const meanAndVariance = moments(x, reductionAxes);\n    const mean5 = meanAndVariance.mean;\n    const variance = meanAndVariance.variance;\n    const targetShape = [];\n    for (const axis of range2(0, x.rank)) {\n      if (reductionAxes.indexOf(axis) !== -1) {\n        targetShape.push(1);\n      } else {\n        targetShape.push(x.shape[axis]);\n      }\n    }\n    const broadcastMean = reshape(mean5, targetShape);\n    const broadcastVariance = reshape(variance, targetShape);\n    const broadcastGamma = gamma == null ? null : reshape(gamma, targetShape);\n    const broadcastBeta = beta == null ? null : reshape(beta, targetShape);\n    const normed = batchNormalization(x, broadcastMean, broadcastVariance, broadcastBeta, broadcastGamma, epsilon3);\n    return [normed, mean5, variance];\n  });\n}\nfunction normalizeBatchInTraining(x, gamma, beta, reductionAxes, epsilon3 = 1e-3) {\n  if (util_exports.arraysEqual(reductionAxes.slice().sort(), range2(0, x.rank - 1))) {\n    return regularNormalizeBatchInTraining(x, gamma, beta, reductionAxes, epsilon3);\n  } else {\n    return broadcastNormalizeBatchInTraining(x, gamma, beta, reductionAxes, epsilon3);\n  }\n}\nvar BatchNormalization = class extends Layer {\n  constructor(args) {\n    if (args == null) {\n      args = {};\n    }\n    super(args);\n    this.supportsMasking = true;\n    this.axis = args.axis == null ? -1 : args.axis;\n    this.momentum = args.momentum == null ? 0.99 : args.momentum;\n    this.epsilon = args.epsilon == null ? 1e-3 : args.epsilon;\n    this.center = args.center == null ? true : args.center;\n    this.scale = args.scale == null ? true : args.scale;\n    this.betaInitializer = getInitializer(args.betaInitializer || \"zeros\");\n    this.gammaInitializer = getInitializer(args.gammaInitializer || \"ones\");\n    this.movingMeanInitializer = getInitializer(args.movingMeanInitializer || \"zeros\");\n    this.movingVarianceInitializer = getInitializer(args.movingVarianceInitializer || \"ones\");\n    this.betaConstraint = getConstraint(args.betaConstraint);\n    this.gammaConstraint = getConstraint(args.gammaConstraint);\n    this.betaRegularizer = getRegularizer(args.betaRegularizer);\n    this.gammaRegularizer = getRegularizer(args.gammaRegularizer);\n  }\n  build(inputShape) {\n    inputShape = getExactlyOneShape(inputShape);\n    const axis = this.axis >= 0 ? this.axis : this.axis + inputShape.length;\n    const dim = inputShape[axis];\n    if (dim == null) {\n      throw new ValueError(`Axis ${axis} of input tensor should have a defined dimension but the layer received an input with shape ${JSON.stringify(inputShape)}.`);\n    }\n    this.inputSpec = [new InputSpec({ ndim: inputShape.length, axes: { [axis]: dim } })];\n    const shape = [dim];\n    if (this.scale) {\n      this.gamma = this.addWeight(\"gamma\", shape, null, this.gammaInitializer, this.gammaRegularizer, true, this.gammaConstraint);\n    }\n    if (this.center) {\n      this.beta = this.addWeight(\"beta\", shape, null, this.betaInitializer, this.betaRegularizer, true, this.betaConstraint);\n    }\n    this.movingMean = this.addWeight(\"moving_mean\", shape, null, this.movingMeanInitializer, null, false);\n    this.movingVariance = this.addWeight(\"moving_variance\", shape, null, this.movingVarianceInitializer, null, false);\n    this.built = true;\n  }\n  call(inputs, kwargs) {\n    return tidy(() => {\n      const training = kwargs[\"training\"] == null ? false : kwargs[\"training\"];\n      const input2 = getExactlyOneTensor(inputs);\n      const inputShape = input2.shape;\n      const ndim = inputShape.length;\n      const reductionAxes = range2(0, ndim);\n      const axis = this.axis >= 0 ? this.axis : this.axis + ndim;\n      reductionAxes.splice(axis, 1);\n      const broadcastShape = pyListRepeat(1, ndim);\n      broadcastShape[axis] = inputShape[axis];\n      const sortedReductionAxes = reductionAxes.slice();\n      sortedReductionAxes.sort();\n      const needsBroadcasting = !util_exports.arraysEqual(sortedReductionAxes, range2(0, ndim).slice(0, ndim - 1));\n      const normalizeInference = () => {\n        if (needsBroadcasting) {\n          const broadcastMovingMean = reshape(this.movingMean.read(), broadcastShape);\n          const broadcastMovingVariance = reshape(this.movingVariance.read(), broadcastShape);\n          const broadcastBeta = this.center ? reshape(this.beta.read(), broadcastShape) : null;\n          const broadcastGamma = this.scale ? reshape(this.gamma.read(), broadcastShape) : null;\n          return batchNormalization(input2, broadcastMovingMean, broadcastMovingVariance, broadcastBeta, broadcastGamma, this.epsilon);\n        } else {\n          return batchNormalization(input2, this.movingMean.read(), this.movingVariance.read(), this.beta == null ? null : this.beta.read(), this.gamma == null ? null : this.gamma.read(), this.epsilon);\n        }\n      };\n      if (!training) {\n        return normalizeInference();\n      }\n      const [normedTraining, mean5, variance] = normalizeBatchInTraining(input2, this.gamma.read(), this.beta.read(), reductionAxes, this.epsilon);\n      const doMovingAverage = (variable2, value, momentum) => {\n        tidy(() => {\n          const decay = 1 - momentum;\n          const origValue = variable2.read();\n          const updateDelta = mul(sub(origValue, value), decay);\n          variable2.write(sub(origValue, updateDelta));\n        });\n      };\n      const updateMovingMeanAndVariance = () => {\n        doMovingAverage(this.movingMean, mean5, this.momentum);\n        doMovingAverage(this.movingVariance, variance, this.momentum);\n      };\n      updateMovingMeanAndVariance();\n      return normedTraining;\n    });\n  }\n  getConfig() {\n    const config = {\n      axis: this.axis,\n      momentum: this.momentum,\n      epsilon: this.epsilon,\n      center: this.center,\n      scale: this.scale,\n      betaInitializer: serializeInitializer(this.betaInitializer),\n      gammaInitializer: serializeInitializer(this.gammaInitializer),\n      movingMeanInitializer: serializeInitializer(this.movingMeanInitializer),\n      movingVarianceInitializer: serializeInitializer(this.movingVarianceInitializer),\n      betaRegularizer: serializeRegularizer(this.betaRegularizer),\n      gammaRegularizer: serializeRegularizer(this.gammaRegularizer),\n      betaConstraint: serializeConstraint(this.betaConstraint),\n      gammaConstraint: serializeConstraint(this.gammaConstraint)\n    };\n    const baseConfig = super.getConfig();\n    Object.assign(config, baseConfig);\n    return config;\n  }\n};\nBatchNormalization.className = \"BatchNormalization\";\nserialization_exports.registerClass(BatchNormalization);\nvar LayerNormalization = class extends Layer {\n  constructor(args) {\n    if (args == null) {\n      args = {};\n    }\n    super(args);\n    this.axis = args.axis == null ? -1 : args.axis;\n    if (typeof this.axis === \"number\") {\n      if (!Number.isInteger(this.axis)) {\n        throw new Error(`Expected axis to be an integer, but received ${this.axis}`);\n      }\n    } else if (Array.isArray(this.axis)) {\n      for (const axis of this.axis) {\n        if (!Number.isInteger(axis)) {\n          throw new Error(`Expected axis to be an array of integers, but received ${JSON.stringify(this.axis)}`);\n        }\n      }\n    } else {\n      throw new Error(`Expected axis to be an integer or an array of integers, but received ${JSON.stringify(this.axis)}`);\n    }\n    this.epsilon = args.epsilon == null ? 1e-3 : args.epsilon;\n    this.center = args.center == null ? true : args.center;\n    this.scale = args.scale == null ? true : args.scale;\n    this.betaInitializer = getInitializer(args.betaInitializer || \"zeros\");\n    this.gammaInitializer = getInitializer(args.gammaInitializer || \"ones\");\n    this.betaRegularizer = getRegularizer(args.betaRegularizer);\n    this.gammaRegularizer = getRegularizer(args.gammaRegularizer);\n    this.supportsMasking = true;\n  }\n  build(inputShape) {\n    inputShape = getExactlyOneShape(inputShape);\n    const nDims = inputShape.length;\n    if (typeof this.axis === \"number\") {\n      this.axis = [this.axis];\n    }\n    for (let i = 0; i < this.axis.length; ++i) {\n      if (this.axis[i] < 0) {\n        this.axis[i] += nDims;\n      }\n    }\n    for (const axis of this.axis) {\n      if (axis < 0 || axis >= nDims) {\n        throw new Error(`Invalid axis: ${axis}`);\n      }\n    }\n    if (this.axis.length !== unique2(this.axis).length) {\n      throw new Error(`Found duplicate axes in: ${this.axis}`);\n    }\n    const paramShape = this.axis.map((axis) => inputShape[axis]);\n    const trainable = true;\n    if (this.scale) {\n      this.gamma = this.addWeight(\"gamma\", paramShape, \"float32\", this.gammaInitializer, this.gammaRegularizer, trainable);\n    } else {\n      this.gamma = null;\n    }\n    if (this.center) {\n      this.beta = this.addWeight(\"beta\", paramShape, \"float32\", this.betaInitializer, this.betaRegularizer, trainable);\n    } else {\n      this.beta = null;\n    }\n    this.built = true;\n  }\n  call(inputs, kwargs) {\n    const input2 = getExactlyOneTensor(inputs);\n    const inputShape = input2.shape;\n    const nDims = inputShape.length;\n    return tidy(() => {\n      const keepDims = true;\n      let { mean: mean5, variance } = moments(input2, this.axis, keepDims);\n      const broadcastShape = pyListRepeat(1, nDims);\n      for (const dim of this.axis) {\n        broadcastShape[dim] = inputShape[dim];\n      }\n      const broadcast = (v) => {\n        if (v != null && v.shape.length !== nDims) {\n          return reshape(v, broadcastShape);\n        } else {\n          return v;\n        }\n      };\n      let scale2 = this.scale ? broadcast(this.gamma.read()) : null;\n      let offset = this.center ? broadcast(this.beta.read()) : null;\n      const momentsTiling = [];\n      const scaleOffsetTiling = [];\n      for (let i = 0; i < nDims; ++i) {\n        if (this.axis.indexOf(i) !== -1) {\n          momentsTiling.push(inputShape[i]);\n          scaleOffsetTiling.push(1);\n        } else {\n          momentsTiling.push(1);\n          scaleOffsetTiling.push(inputShape[i]);\n        }\n      }\n      mean5 = tile(mean5, momentsTiling);\n      variance = tile(variance, momentsTiling);\n      if (scale2 != null) {\n        scale2 = tile(scale2, scaleOffsetTiling);\n      }\n      if (offset != null) {\n        offset = tile(offset, scaleOffsetTiling);\n      }\n      return batchNormalization(input2, mean5, variance, offset, scale2, this.epsilon);\n    });\n  }\n  getConfig() {\n    const config = {\n      axis: this.axis,\n      epsilon: this.epsilon,\n      center: this.center,\n      scale: this.scale,\n      betaInitializer: serializeInitializer(this.betaInitializer),\n      gammaInitializer: serializeInitializer(this.gammaInitializer),\n      betaRegularizer: serializeRegularizer(this.betaRegularizer),\n      gammaRegularizer: serializeRegularizer(this.gammaRegularizer)\n    };\n    const baseConfig = super.getConfig();\n    Object.assign(config, baseConfig);\n    return config;\n  }\n};\nLayerNormalization.className = \"LayerNormalization\";\nserialization_exports.registerClass(LayerNormalization);\n\n// node_modules/.pnpm/@tensorflow+tfjs-layers@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-layers/dist/layers/padding.js\nfunction spatial2dPadding(x, padding, dataFormat) {\n  return tidy(() => {\n    if (x.rank !== 4) {\n      throw new ValueError(`temporalPadding expects input tensor to be 4-D, but received a ${x.rank}-D tensor.`);\n    }\n    if (padding == null) {\n      padding = [[1, 1], [1, 1]];\n    }\n    if (padding.length !== 2 || padding[0].length !== 2 || padding[1].length !== 2) {\n      throw new ValueError(\"spatial2dPadding expects `padding` to be an Array of two Arrays, each of which is an Array of two integers.\");\n    }\n    if (dataFormat == null) {\n      dataFormat = imageDataFormat();\n    }\n    if (dataFormat !== \"channelsLast\" && dataFormat !== \"channelsFirst\") {\n      throw new ValueError(`Unknown data format: ${dataFormat}. Supported data formats are 'channelsLast' and 'channelsFirst.`);\n    }\n    let pattern;\n    if (dataFormat === \"channelsFirst\") {\n      pattern = [[0, 0], [0, 0], padding[0], padding[1]];\n    } else {\n      pattern = [[0, 0], padding[0], padding[1], [0, 0]];\n    }\n    return pad(x, pattern);\n  });\n}\nvar ZeroPadding2D = class extends Layer {\n  constructor(args) {\n    if (args == null) {\n      args = {};\n    }\n    super(args);\n    this.dataFormat = args.dataFormat == null ? imageDataFormat() : args.dataFormat;\n    if (args.padding == null) {\n      this.padding = [[1, 1], [1, 1]];\n    } else if (typeof args.padding === \"number\") {\n      this.padding = [[args.padding, args.padding], [args.padding, args.padding]];\n    } else {\n      args.padding = args.padding;\n      if (args.padding.length !== 2) {\n        throw new ValueError(`ZeroPadding2D expects padding to be a length-2 array, but received a length-${args.padding.length} array.`);\n      }\n      let heightPadding;\n      let widthPadding;\n      if (typeof args.padding[0] === \"number\") {\n        heightPadding = [args.padding[0], args.padding[0]];\n        widthPadding = [args.padding[1], args.padding[1]];\n      } else {\n        args.padding = args.padding;\n        if (args.padding[0].length !== 2) {\n          throw new ValueError(`ZeroPadding2D expects height padding to be a length-2 array, but received a length-${args.padding[0].length} array.`);\n        }\n        heightPadding = args.padding[0];\n        if (args.padding[1].length !== 2) {\n          throw new ValueError(`ZeroPadding2D expects width padding to be a length-2 array, but received a length-${args.padding[1].length} array.`);\n        }\n        widthPadding = args.padding[1];\n      }\n      this.padding = [heightPadding, widthPadding];\n    }\n    this.inputSpec = [new InputSpec({ ndim: 4 })];\n  }\n  computeOutputShape(inputShape) {\n    inputShape = getExactlyOneShape(inputShape);\n    let rows;\n    let cols;\n    if (this.dataFormat === \"channelsFirst\") {\n      if (inputShape[2] != null && inputShape[2] >= 0) {\n        rows = inputShape[2] + this.padding[0][0] + this.padding[0][1];\n      } else {\n        rows = null;\n      }\n      if (inputShape[3] != null && inputShape[3] >= 0) {\n        cols = inputShape[3] + this.padding[1][0] + this.padding[1][1];\n      } else {\n        cols = null;\n      }\n      return [inputShape[0], inputShape[1], rows, cols];\n    } else {\n      if (inputShape[1] != null && inputShape[1] >= 0) {\n        rows = inputShape[1] + this.padding[0][0] + this.padding[0][1];\n      } else {\n        rows = null;\n      }\n      if (inputShape[2] != null && inputShape[2] >= 0) {\n        cols = inputShape[2] + this.padding[1][0] + this.padding[1][1];\n      } else {\n        cols = null;\n      }\n      return [inputShape[0], rows, cols, inputShape[3]];\n    }\n  }\n  call(inputs, kwargs) {\n    return tidy(() => spatial2dPadding(getExactlyOneTensor(inputs), this.padding, this.dataFormat));\n  }\n  getConfig() {\n    const config = {\n      padding: this.padding,\n      dataFormat: this.dataFormat\n    };\n    const baseConfig = super.getConfig();\n    Object.assign(config, baseConfig);\n    return config;\n  }\n};\nZeroPadding2D.className = \"ZeroPadding2D\";\nserialization_exports.registerClass(ZeroPadding2D);\n\n// node_modules/.pnpm/@tensorflow+tfjs-layers@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-layers/dist/layers/pooling.js\nfunction pool2d(x, poolSize, strides, padding, dataFormat, poolMode) {\n  return tidy(() => {\n    checkDataFormat(dataFormat);\n    checkPoolMode(poolMode);\n    checkPaddingMode(padding);\n    if (strides == null) {\n      strides = [1, 1];\n    }\n    if (padding == null) {\n      padding = \"valid\";\n    }\n    if (dataFormat == null) {\n      dataFormat = imageDataFormat();\n    }\n    if (poolMode == null) {\n      poolMode = \"max\";\n    }\n    x = preprocessConv2DInput(x, dataFormat);\n    let y;\n    const paddingString = padding === \"same\" ? \"same\" : \"valid\";\n    if (poolMode === \"max\") {\n      y = maxPool(x, poolSize, strides, paddingString);\n    } else {\n      y = avgPool(\n        x,\n        poolSize,\n        strides,\n        paddingString\n      );\n    }\n    if (dataFormat === \"channelsFirst\") {\n      y = transpose(y, [0, 3, 1, 2]);\n    }\n    return y;\n  });\n}\nfunction pool3d(x, poolSize, strides, padding, dataFormat, poolMode) {\n  return tidy(() => {\n    checkDataFormat(dataFormat);\n    checkPoolMode(poolMode);\n    checkPaddingMode(padding);\n    if (strides == null) {\n      strides = [1, 1, 1];\n    }\n    if (padding == null) {\n      padding = \"valid\";\n    }\n    if (dataFormat == null) {\n      dataFormat = imageDataFormat();\n    }\n    if (poolMode == null) {\n      poolMode = \"max\";\n    }\n    x = preprocessConv3DInput(x, dataFormat);\n    let y;\n    const paddingString = padding === \"same\" ? \"same\" : \"valid\";\n    if (poolMode === \"max\") {\n      y = maxPool3d(x, poolSize, strides, paddingString);\n    } else {\n      y = avgPool3d(x, poolSize, strides, paddingString);\n    }\n    if (dataFormat === \"channelsFirst\") {\n      y = transpose(y, [0, 4, 1, 2, 3]);\n    }\n    return y;\n  });\n}\nvar Pooling1D = class extends Layer {\n  constructor(args) {\n    if (args.poolSize == null) {\n      args.poolSize = 2;\n    }\n    super(args);\n    if (typeof args.poolSize === \"number\") {\n      this.poolSize = [args.poolSize];\n    } else if (Array.isArray(args.poolSize) && args.poolSize.length === 1 && typeof args.poolSize[0] === \"number\") {\n      this.poolSize = args.poolSize;\n    } else {\n      throw new ValueError(`poolSize for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(args.poolSize)}`);\n    }\n    assertPositiveInteger(this.poolSize, \"poolSize\");\n    if (args.strides == null) {\n      this.strides = this.poolSize;\n    } else {\n      if (typeof args.strides === \"number\") {\n        this.strides = [args.strides];\n      } else if (Array.isArray(args.strides) && args.strides.length === 1 && typeof args.strides[0] === \"number\") {\n        this.strides = args.strides;\n      } else {\n        throw new ValueError(`strides for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(args.strides)}`);\n      }\n    }\n    assertPositiveInteger(this.strides, \"strides\");\n    this.padding = args.padding == null ? \"valid\" : args.padding;\n    checkPaddingMode(this.padding);\n    this.inputSpec = [new InputSpec({ ndim: 3 })];\n  }\n  computeOutputShape(inputShape) {\n    inputShape = getExactlyOneShape(inputShape);\n    const length = convOutputLength(inputShape[1], this.poolSize[0], this.padding, this.strides[0]);\n    return [inputShape[0], length, inputShape[2]];\n  }\n  call(inputs, kwargs) {\n    return tidy(() => {\n      this.invokeCallHook(inputs, kwargs);\n      inputs = expandDims2(getExactlyOneTensor(inputs), 2);\n      const output = this.poolingFunction(getExactlyOneTensor(inputs), [this.poolSize[0], 1], [this.strides[0], 1], this.padding, \"channelsLast\");\n      return squeeze(output, [2]);\n    });\n  }\n  getConfig() {\n    const config = {\n      poolSize: this.poolSize,\n      padding: this.padding,\n      strides: this.strides\n    };\n    const baseConfig = super.getConfig();\n    Object.assign(config, baseConfig);\n    return config;\n  }\n};\nvar MaxPooling1D = class extends Pooling1D {\n  constructor(args) {\n    super(args);\n  }\n  poolingFunction(inputs, poolSize, strides, padding, dataFormat) {\n    checkDataFormat(dataFormat);\n    checkPaddingMode(padding);\n    return pool2d(inputs, poolSize, strides, padding, dataFormat, \"max\");\n  }\n};\nMaxPooling1D.className = \"MaxPooling1D\";\nserialization_exports.registerClass(MaxPooling1D);\nvar AveragePooling1D = class extends Pooling1D {\n  constructor(args) {\n    super(args);\n  }\n  poolingFunction(inputs, poolSize, strides, padding, dataFormat) {\n    checkDataFormat(dataFormat);\n    checkPaddingMode(padding);\n    return pool2d(inputs, poolSize, strides, padding, dataFormat, \"avg\");\n  }\n};\nAveragePooling1D.className = \"AveragePooling1D\";\nserialization_exports.registerClass(AveragePooling1D);\nvar Pooling2D = class extends Layer {\n  constructor(args) {\n    if (args.poolSize == null) {\n      args.poolSize = [2, 2];\n    }\n    super(args);\n    this.poolSize = Array.isArray(args.poolSize) ? args.poolSize : [args.poolSize, args.poolSize];\n    if (args.strides == null) {\n      this.strides = this.poolSize;\n    } else if (Array.isArray(args.strides)) {\n      if (args.strides.length !== 2) {\n        throw new ValueError(`If the strides property of a 2D pooling layer is an Array, it is expected to have a length of 2, but received length ${args.strides.length}.`);\n      }\n      this.strides = args.strides;\n    } else {\n      this.strides = [args.strides, args.strides];\n    }\n    assertPositiveInteger(this.poolSize, \"poolSize\");\n    assertPositiveInteger(this.strides, \"strides\");\n    this.padding = args.padding == null ? \"valid\" : args.padding;\n    this.dataFormat = args.dataFormat == null ? \"channelsLast\" : args.dataFormat;\n    checkDataFormat(this.dataFormat);\n    checkPaddingMode(this.padding);\n    this.inputSpec = [new InputSpec({ ndim: 4 })];\n  }\n  computeOutputShape(inputShape) {\n    inputShape = getExactlyOneShape(inputShape);\n    let rows = this.dataFormat === \"channelsFirst\" ? inputShape[2] : inputShape[1];\n    let cols = this.dataFormat === \"channelsFirst\" ? inputShape[3] : inputShape[2];\n    rows = convOutputLength(rows, this.poolSize[0], this.padding, this.strides[0]);\n    cols = convOutputLength(cols, this.poolSize[1], this.padding, this.strides[1]);\n    if (this.dataFormat === \"channelsFirst\") {\n      return [inputShape[0], inputShape[1], rows, cols];\n    } else {\n      return [inputShape[0], rows, cols, inputShape[3]];\n    }\n  }\n  call(inputs, kwargs) {\n    return tidy(() => {\n      this.invokeCallHook(inputs, kwargs);\n      return this.poolingFunction(getExactlyOneTensor(inputs), this.poolSize, this.strides, this.padding, this.dataFormat);\n    });\n  }\n  getConfig() {\n    const config = {\n      poolSize: this.poolSize,\n      padding: this.padding,\n      strides: this.strides,\n      dataFormat: this.dataFormat\n    };\n    const baseConfig = super.getConfig();\n    Object.assign(config, baseConfig);\n    return config;\n  }\n};\nvar MaxPooling2D = class extends Pooling2D {\n  constructor(args) {\n    super(args);\n  }\n  poolingFunction(inputs, poolSize, strides, padding, dataFormat) {\n    checkDataFormat(dataFormat);\n    checkPaddingMode(padding);\n    return pool2d(inputs, poolSize, strides, padding, dataFormat, \"max\");\n  }\n};\nMaxPooling2D.className = \"MaxPooling2D\";\nserialization_exports.registerClass(MaxPooling2D);\nvar AveragePooling2D = class extends Pooling2D {\n  constructor(args) {\n    super(args);\n  }\n  poolingFunction(inputs, poolSize, strides, padding, dataFormat) {\n    checkDataFormat(dataFormat);\n    checkPaddingMode(padding);\n    return pool2d(inputs, poolSize, strides, padding, dataFormat, \"avg\");\n  }\n};\nAveragePooling2D.className = \"AveragePooling2D\";\nserialization_exports.registerClass(AveragePooling2D);\nvar Pooling3D = class extends Layer {\n  constructor(args) {\n    if (args.poolSize == null) {\n      args.poolSize = [2, 2, 2];\n    }\n    super(args);\n    this.poolSize = Array.isArray(args.poolSize) ? args.poolSize : [args.poolSize, args.poolSize, args.poolSize];\n    if (args.strides == null) {\n      this.strides = this.poolSize;\n    } else if (Array.isArray(args.strides)) {\n      if (args.strides.length !== 3) {\n        throw new ValueError(`If the strides property of a 3D pooling layer is an Array, it is expected to have a length of 3, but received length ${args.strides.length}.`);\n      }\n      this.strides = args.strides;\n    } else {\n      this.strides = [args.strides, args.strides, args.strides];\n    }\n    assertPositiveInteger(this.poolSize, \"poolSize\");\n    assertPositiveInteger(this.strides, \"strides\");\n    this.padding = args.padding == null ? \"valid\" : args.padding;\n    this.dataFormat = args.dataFormat == null ? \"channelsLast\" : args.dataFormat;\n    checkDataFormat(this.dataFormat);\n    checkPaddingMode(this.padding);\n    this.inputSpec = [new InputSpec({ ndim: 5 })];\n  }\n  computeOutputShape(inputShape) {\n    inputShape = getExactlyOneShape(inputShape);\n    let depths = this.dataFormat === \"channelsFirst\" ? inputShape[2] : inputShape[1];\n    let rows = this.dataFormat === \"channelsFirst\" ? inputShape[3] : inputShape[2];\n    let cols = this.dataFormat === \"channelsFirst\" ? inputShape[4] : inputShape[3];\n    depths = convOutputLength(depths, this.poolSize[0], this.padding, this.strides[0]);\n    rows = convOutputLength(rows, this.poolSize[1], this.padding, this.strides[1]);\n    cols = convOutputLength(cols, this.poolSize[2], this.padding, this.strides[2]);\n    if (this.dataFormat === \"channelsFirst\") {\n      return [inputShape[0], inputShape[1], depths, rows, cols];\n    } else {\n      return [inputShape[0], depths, rows, cols, inputShape[4]];\n    }\n  }\n  call(inputs, kwargs) {\n    return tidy(() => {\n      this.invokeCallHook(inputs, kwargs);\n      return this.poolingFunction(getExactlyOneTensor(inputs), this.poolSize, this.strides, this.padding, this.dataFormat);\n    });\n  }\n  getConfig() {\n    const config = {\n      poolSize: this.poolSize,\n      padding: this.padding,\n      strides: this.strides,\n      dataFormat: this.dataFormat\n    };\n    const baseConfig = super.getConfig();\n    Object.assign(config, baseConfig);\n    return config;\n  }\n};\nvar MaxPooling3D = class extends Pooling3D {\n  constructor(args) {\n    super(args);\n  }\n  poolingFunction(inputs, poolSize, strides, padding, dataFormat) {\n    checkDataFormat(dataFormat);\n    checkPaddingMode(padding);\n    return pool3d(inputs, poolSize, strides, padding, dataFormat, \"max\");\n  }\n};\nMaxPooling3D.className = \"MaxPooling3D\";\nserialization_exports.registerClass(MaxPooling3D);\nvar AveragePooling3D = class extends Pooling3D {\n  constructor(args) {\n    super(args);\n  }\n  poolingFunction(inputs, poolSize, strides, padding, dataFormat) {\n    checkDataFormat(dataFormat);\n    checkPaddingMode(padding);\n    return pool3d(inputs, poolSize, strides, padding, dataFormat, \"avg\");\n  }\n};\nAveragePooling3D.className = \"AveragePooling3D\";\nserialization_exports.registerClass(AveragePooling3D);\nvar GlobalPooling1D = class extends Layer {\n  constructor(args) {\n    super(args);\n    this.inputSpec = [new InputSpec({ ndim: 3 })];\n  }\n  computeOutputShape(inputShape) {\n    return [inputShape[0], inputShape[2]];\n  }\n  call(inputs, kwargs) {\n    throw new NotImplementedError();\n  }\n};\nvar GlobalAveragePooling1D = class extends GlobalPooling1D {\n  constructor(args) {\n    super(args || {});\n  }\n  call(inputs, kwargs) {\n    return tidy(() => {\n      const input2 = getExactlyOneTensor(inputs);\n      return mean(input2, 1);\n    });\n  }\n};\nGlobalAveragePooling1D.className = \"GlobalAveragePooling1D\";\nserialization_exports.registerClass(GlobalAveragePooling1D);\nvar GlobalMaxPooling1D = class extends GlobalPooling1D {\n  constructor(args) {\n    super(args || {});\n  }\n  call(inputs, kwargs) {\n    return tidy(() => {\n      const input2 = getExactlyOneTensor(inputs);\n      return max(input2, 1);\n    });\n  }\n};\nGlobalMaxPooling1D.className = \"GlobalMaxPooling1D\";\nserialization_exports.registerClass(GlobalMaxPooling1D);\nvar GlobalPooling2D = class extends Layer {\n  constructor(args) {\n    super(args);\n    this.dataFormat = args.dataFormat == null ? \"channelsLast\" : args.dataFormat;\n    checkDataFormat(this.dataFormat);\n    this.inputSpec = [new InputSpec({ ndim: 4 })];\n  }\n  computeOutputShape(inputShape) {\n    inputShape = inputShape;\n    if (this.dataFormat === \"channelsLast\") {\n      return [inputShape[0], inputShape[3]];\n    } else {\n      return [inputShape[0], inputShape[1]];\n    }\n  }\n  call(inputs, kwargs) {\n    throw new NotImplementedError();\n  }\n  getConfig() {\n    const config = { dataFormat: this.dataFormat };\n    const baseConfig = super.getConfig();\n    Object.assign(config, baseConfig);\n    return config;\n  }\n};\nvar GlobalAveragePooling2D = class extends GlobalPooling2D {\n  call(inputs, kwargs) {\n    return tidy(() => {\n      const input2 = getExactlyOneTensor(inputs);\n      if (this.dataFormat === \"channelsLast\") {\n        return mean(input2, [1, 2]);\n      } else {\n        return mean(input2, [2, 3]);\n      }\n    });\n  }\n};\nGlobalAveragePooling2D.className = \"GlobalAveragePooling2D\";\nserialization_exports.registerClass(GlobalAveragePooling2D);\nvar GlobalMaxPooling2D = class extends GlobalPooling2D {\n  call(inputs, kwargs) {\n    return tidy(() => {\n      const input2 = getExactlyOneTensor(inputs);\n      if (this.dataFormat === \"channelsLast\") {\n        return max(input2, [1, 2]);\n      } else {\n        return max(input2, [2, 3]);\n      }\n    });\n  }\n};\nGlobalMaxPooling2D.className = \"GlobalMaxPooling2D\";\nserialization_exports.registerClass(GlobalMaxPooling2D);\n\n// node_modules/.pnpm/@tensorflow+tfjs-layers@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-layers/dist/layers/wrappers.js\nvar Wrapper = class extends Layer {\n  constructor(args) {\n    super(args);\n    this.layer = args.layer;\n  }\n  build(inputShape) {\n    this.built = true;\n  }\n  get trainable() {\n    if (this.layer != null) {\n      return this.layer.trainable;\n    } else {\n      return false;\n    }\n  }\n  set trainable(value) {\n    if (this.layer != null) {\n      this.layer.trainable = value;\n    }\n  }\n  get trainableWeights() {\n    return this.layer.trainableWeights;\n  }\n  get nonTrainableWeights() {\n    return this.layer.nonTrainableWeights;\n  }\n  get updates() {\n    return this.layer._updates;\n  }\n  get losses() {\n    return this.layer.losses;\n  }\n  getWeights() {\n    return this.layer.getWeights();\n  }\n  setWeights(weights) {\n    this.layer.setWeights(weights);\n  }\n  getConfig() {\n    const config = {\n      \"layer\": {\n        \"className\": this.layer.getClassName(),\n        \"config\": this.layer.getConfig()\n      }\n    };\n    const baseConfig = super.getConfig();\n    Object.assign(config, baseConfig);\n    return config;\n  }\n  setFastWeightInitDuringBuild(value) {\n    super.setFastWeightInitDuringBuild(value);\n    if (this.layer != null) {\n      this.layer.setFastWeightInitDuringBuild(value);\n    }\n  }\n  static fromConfig(cls, config, customObjects = {}) {\n    const layerConfig = config[\"layer\"];\n    const layer = deserialize(layerConfig, customObjects);\n    delete config[\"layer\"];\n    const newConfig = { layer };\n    Object.assign(newConfig, config);\n    return new cls(newConfig);\n  }\n};\nvar TimeDistributed = class extends Wrapper {\n  constructor(args) {\n    super(args);\n    this.supportsMasking = true;\n  }\n  build(inputShape) {\n    inputShape = getExactlyOneShape(inputShape);\n    if (inputShape.length < 3) {\n      throw new ValueError(`TimeDistributed layer expects an input shape >= 3D, but received input shape ${JSON.stringify(inputShape)}`);\n    }\n    this.inputSpec = [{ shape: inputShape }];\n    const childInputShape = [inputShape[0]].concat(inputShape.slice(2));\n    if (!this.layer.built) {\n      this.layer.build(childInputShape);\n      this.layer.built = true;\n    }\n    super.build(inputShape);\n  }\n  computeOutputShape(inputShape) {\n    inputShape = getExactlyOneShape(inputShape);\n    const childInputShape = [inputShape[0]].concat(inputShape.slice(2));\n    const childOutputShape = this.layer.computeOutputShape(childInputShape);\n    const timesteps = inputShape[1];\n    return [childOutputShape[0], timesteps].concat(childOutputShape.slice(1));\n  }\n  call(inputs, kwargs) {\n    return tidy(() => {\n      inputs = getExactlyOneTensor(inputs);\n      const step5 = (inputs2, states) => {\n        const output = getExactlyOneTensor(this.layer.call(inputs2, kwargs));\n        return [output, []];\n      };\n      const rnnOutputs = rnn(step5, inputs, [], false, null, null, false, true);\n      const y = rnnOutputs[1];\n      return y;\n    });\n  }\n};\nTimeDistributed.className = \"TimeDistributed\";\nserialization_exports.registerClass(TimeDistributed);\nfunction checkBidirectionalMergeMode(value) {\n  checkStringTypeUnionValue(VALID_BIDIRECTIONAL_MERGE_MODES, \"BidirectionalMergeMode\", value);\n}\nvar DEFAULT_BIDIRECTIONAL_MERGE_MODE = \"concat\";\nvar Bidirectional = class extends Wrapper {\n  constructor(args) {\n    super(args);\n    const layerConfig = args.layer.getConfig();\n    const forwDict = {};\n    forwDict[\"className\"] = args.layer.getClassName();\n    forwDict[\"config\"] = layerConfig;\n    this.forwardLayer = deserialize(forwDict);\n    layerConfig[\"goBackwards\"] = layerConfig[\"goBackwards\"] === true ? false : true;\n    const backDict = {};\n    backDict[\"className\"] = args.layer.getClassName();\n    backDict[\"config\"] = layerConfig;\n    this.backwardLayer = deserialize(backDict);\n    this.forwardLayer.name = \"forward_\" + this.forwardLayer.name;\n    this.backwardLayer.name = \"backward_\" + this.backwardLayer.name;\n    this.mergeMode = args.mergeMode === void 0 ? DEFAULT_BIDIRECTIONAL_MERGE_MODE : args.mergeMode;\n    checkBidirectionalMergeMode(this.mergeMode);\n    if (args.weights) {\n      throw new NotImplementedError(\"weights support is not implemented for Bidirectional layer yet.\");\n    }\n    this._stateful = args.layer.stateful;\n    this.returnSequences = args.layer.returnSequences;\n    this.returnState = args.layer.returnState;\n    this.supportsMasking = true;\n    this._trainable = true;\n    this.inputSpec = args.layer.inputSpec;\n    this.numConstants = null;\n  }\n  get trainable() {\n    return this._trainable;\n  }\n  set trainable(value) {\n    this._trainable = value;\n    if (this.forwardLayer != null) {\n      this.forwardLayer.trainable = value;\n    }\n    if (this.backwardLayer != null) {\n      this.backwardLayer.trainable = value;\n    }\n  }\n  getWeights() {\n    return this.forwardLayer.getWeights().concat(this.backwardLayer.getWeights());\n  }\n  setWeights(weights) {\n    const numWeights = weights.length;\n    const numeightsOver2 = Math.floor(numWeights / 2);\n    this.forwardLayer.setWeights(weights.slice(0, numeightsOver2));\n    this.backwardLayer.setWeights(weights.slice(numeightsOver2));\n  }\n  computeOutputShape(inputShape) {\n    let layerShapes = this.forwardLayer.computeOutputShape(inputShape);\n    if (!(Array.isArray(layerShapes) && Array.isArray(layerShapes[0]))) {\n      layerShapes = [layerShapes];\n    }\n    layerShapes = layerShapes;\n    let outputShape;\n    let outputShapes;\n    let stateShape;\n    if (this.returnState) {\n      stateShape = layerShapes.slice(1);\n      outputShape = layerShapes[0];\n    } else {\n      outputShape = layerShapes[0];\n    }\n    outputShape = outputShape;\n    if (this.mergeMode === \"concat\") {\n      outputShape[outputShape.length - 1] *= 2;\n      outputShapes = [outputShape];\n    } else if (this.mergeMode == null) {\n      outputShapes = [outputShape, outputShape.slice()];\n    } else {\n      outputShapes = [outputShape];\n    }\n    if (this.returnState) {\n      if (this.mergeMode == null) {\n        return outputShapes.concat(stateShape).concat(stateShape.slice());\n      }\n      return [outputShape].concat(stateShape).concat(stateShape.slice());\n    }\n    return singletonOrArray(outputShapes);\n  }\n  apply(inputs, kwargs) {\n    let initialState = kwargs == null ? null : kwargs[\"initialState\"];\n    let constants = kwargs == null ? null : kwargs[\"constants\"];\n    if (kwargs == null) {\n      kwargs = {};\n    }\n    const standardized = standardizeArgs(inputs, initialState, constants, this.numConstants);\n    inputs = standardized.inputs;\n    initialState = standardized.initialState;\n    constants = standardized.constants;\n    if (Array.isArray(inputs)) {\n      initialState = inputs.slice(1);\n      inputs = inputs[0];\n    }\n    if ((initialState == null || initialState.length === 0) && constants == null) {\n      return super.apply(inputs, kwargs);\n    }\n    const additionalInputs = [];\n    const additionalSpecs = [];\n    if (initialState != null) {\n      const numStates = initialState.length;\n      if (numStates % 2 > 0) {\n        throw new ValueError(\"When passing `initialState` to a Bidrectional RNN, the state should be an Array containing the states of the underlying RNNs.\");\n      }\n      kwargs[\"initialState\"] = initialState;\n      additionalInputs.push(...initialState);\n      const stateSpecs = initialState.map((state) => new InputSpec({ shape: state.shape }));\n      this.forwardLayer.stateSpec = stateSpecs.slice(0, numStates / 2);\n      this.backwardLayer.stateSpec = stateSpecs.slice(numStates / 2);\n      additionalSpecs.push(...stateSpecs);\n    }\n    if (constants != null) {\n      throw new NotImplementedError(\"Support for constants in Bidirectional layers is not implemented yet.\");\n    }\n    const isSymbolicTensor = additionalInputs[0] instanceof SymbolicTensor;\n    for (const tensor2 of additionalInputs) {\n      if (tensor2 instanceof SymbolicTensor !== isSymbolicTensor) {\n        throw new ValueError(\"The initial state of a Bidirectional layer cannot be specified as a mix of symbolic and non-symbolic tensors\");\n      }\n    }\n    if (isSymbolicTensor) {\n      const fullInput = [inputs].concat(additionalInputs);\n      const fullInputSpec = this.inputSpec.concat(additionalSpecs);\n      const originalInputSpec = this.inputSpec;\n      this.inputSpec = fullInputSpec;\n      const output = super.apply(fullInput, kwargs);\n      this.inputSpec = originalInputSpec;\n      return output;\n    } else {\n      return super.apply(inputs, kwargs);\n    }\n  }\n  call(inputs, kwargs) {\n    return tidy(() => {\n      const initialState = kwargs[\"initialState\"];\n      let y;\n      let yRev;\n      if (initialState == null) {\n        y = this.forwardLayer.call(inputs, kwargs);\n        yRev = this.backwardLayer.call(inputs, kwargs);\n      } else {\n        const forwardState = initialState.slice(0, initialState.length / 2);\n        const backwardState = initialState.slice(initialState.length / 2);\n        y = this.forwardLayer.call(inputs, Object.assign(kwargs, { initialState: forwardState }));\n        yRev = this.backwardLayer.call(inputs, Object.assign(kwargs, { initialState: backwardState }));\n      }\n      let states;\n      if (this.returnState) {\n        if (Array.isArray(y)) {\n          states = y.slice(1).concat(yRev.slice(1));\n        } else {\n        }\n        y = y[0];\n        yRev = yRev[0];\n      }\n      if (this.returnSequences) {\n        yRev = reverse(yRev, 1);\n      }\n      let output;\n      if (this.mergeMode === \"concat\") {\n        output = concatenate([y, yRev]);\n      } else if (this.mergeMode === \"sum\") {\n        output = add2(y, yRev);\n      } else if (this.mergeMode === \"ave\") {\n        output = mul(0.5, add2(y, yRev));\n      } else if (this.mergeMode === \"mul\") {\n        output = mul(y, yRev);\n      } else if (this.mergeMode == null) {\n        output = [y, yRev];\n      }\n      if (this.returnState) {\n        if (this.mergeMode == null) {\n          return output.concat(states);\n        }\n        return [output].concat(states);\n      }\n      return output;\n    });\n  }\n  resetStates(states) {\n    this.forwardLayer.resetStates();\n    this.backwardLayer.resetStates();\n  }\n  build(inputShape) {\n    nameScope(this.forwardLayer.name, () => {\n      this.forwardLayer.build(inputShape);\n    });\n    nameScope(this.backwardLayer.name, () => {\n      this.backwardLayer.build(inputShape);\n    });\n    this.built = true;\n  }\n  computeMask(inputs, mask) {\n    if (Array.isArray(mask)) {\n      mask = mask[0];\n    }\n    let outputMask;\n    if (this.returnSequences) {\n      if (this.mergeMode == null) {\n        outputMask = [mask, mask];\n      } else {\n        outputMask = mask;\n      }\n    } else {\n      if (this.mergeMode == null) {\n        outputMask = [null, null];\n      } else {\n        outputMask = null;\n      }\n    }\n    if (this.returnState) {\n      const states = this.forwardLayer.states;\n      const stateMask = states.map((state) => null);\n      if (Array.isArray(outputMask)) {\n        return outputMask.concat(stateMask).concat(stateMask);\n      } else {\n        return [outputMask].concat(stateMask).concat(stateMask);\n      }\n    } else {\n      return outputMask;\n    }\n  }\n  get trainableWeights() {\n    return this.forwardLayer.trainableWeights.concat(this.backwardLayer.trainableWeights);\n  }\n  get nonTrainableWeights() {\n    return this.forwardLayer.nonTrainableWeights.concat(this.backwardLayer.nonTrainableWeights);\n  }\n  setFastWeightInitDuringBuild(value) {\n    super.setFastWeightInitDuringBuild(value);\n    if (this.forwardLayer != null) {\n      this.forwardLayer.setFastWeightInitDuringBuild(value);\n    }\n    if (this.backwardLayer != null) {\n      this.backwardLayer.setFastWeightInitDuringBuild(value);\n    }\n  }\n  getConfig() {\n    const config = {\n      \"mergeMode\": this.mergeMode\n    };\n    const baseConfig = super.getConfig();\n    Object.assign(config, baseConfig);\n    return config;\n  }\n  static fromConfig(cls, config) {\n    const rnnLayer = deserialize(config[\"layer\"]);\n    delete config[\"layer\"];\n    if (config[\"numConstants\"] != null) {\n      throw new NotImplementedError(`Deserialization of a Bidirectional layer with numConstants present is not supported yet.`);\n    }\n    const newConfig = config;\n    newConfig[\"layer\"] = rnnLayer;\n    return new cls(newConfig);\n  }\n};\nBidirectional.className = \"Bidirectional\";\nserialization_exports.registerClass(Bidirectional);\n\n// node_modules/.pnpm/@tensorflow+tfjs-layers@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-layers/dist/exports_layers.js\nfunction inputLayer(args) {\n  return new InputLayer(args);\n}\nfunction elu3(args) {\n  return new ELU(args);\n}\nfunction reLU(args) {\n  return new ReLU(args);\n}\nfunction leakyReLU(args) {\n  return new LeakyReLU(args);\n}\nfunction prelu2(args) {\n  return new PReLU(args);\n}\nfunction softmax2(args) {\n  return new Softmax3(args);\n}\nfunction thresholdedReLU(args) {\n  return new ThresholdedReLU(args);\n}\nfunction conv1d2(args) {\n  return new Conv1D(args);\n}\nfunction conv2d3(args) {\n  return new Conv2D2(args);\n}\nfunction conv2dTranspose2(args) {\n  return new Conv2DTranspose(args);\n}\nfunction conv3d2(args) {\n  return new Conv3D2(args);\n}\nfunction conv3dTranspose2(args) {\n  return new Conv3DTranspose(args);\n}\nfunction separableConv2d2(args) {\n  return new SeparableConv2D(args);\n}\nfunction cropping2D(args) {\n  return new Cropping2D(args);\n}\nfunction upSampling2d(args) {\n  return new UpSampling2D(args);\n}\nfunction depthwiseConv2d4(args) {\n  return new DepthwiseConv2D(args);\n}\nfunction activation(args) {\n  return new Activation2(args);\n}\nfunction dense(args) {\n  return new Dense(args);\n}\nfunction dropout3(args) {\n  return new Dropout(args);\n}\nfunction spatialDropout1d(args) {\n  return new SpatialDropout1D(args);\n}\nfunction flatten3(args) {\n  return new Flatten(args);\n}\nfunction repeatVector(args) {\n  return new RepeatVector(args);\n}\nfunction reshape2(args) {\n  return new Reshape2(args);\n}\nfunction permute(args) {\n  return new Permute(args);\n}\nfunction embedding(args) {\n  return new Embedding(args);\n}\nfunction add3(args) {\n  return new Add2(args);\n}\nfunction average(args) {\n  return new Average(args);\n}\nfunction concatenate2(args) {\n  return new Concatenate(args);\n}\nfunction maximum2(args) {\n  return new Maximum2(args);\n}\nfunction minimum2(args) {\n  return new Minimum2(args);\n}\nfunction multiply(args) {\n  return new Multiply2(args);\n}\nfunction dot3(args) {\n  return new Dot(args);\n}\nfunction batchNormalization2(args) {\n  return new BatchNormalization(args);\n}\nfunction layerNormalization(args) {\n  return new LayerNormalization(args);\n}\nfunction zeroPadding2d(args) {\n  return new ZeroPadding2D(args);\n}\nfunction averagePooling1d(args) {\n  return new AveragePooling1D(args);\n}\nfunction avgPool1d(args) {\n  return averagePooling1d(args);\n}\nfunction avgPooling1d(args) {\n  return averagePooling1d(args);\n}\nfunction averagePooling2d(args) {\n  return new AveragePooling2D(args);\n}\nfunction avgPool2d(args) {\n  return averagePooling2d(args);\n}\nfunction avgPooling2d(args) {\n  return averagePooling2d(args);\n}\nfunction averagePooling3d(args) {\n  return new AveragePooling3D(args);\n}\nfunction avgPool3d2(args) {\n  return averagePooling3d(args);\n}\nfunction avgPooling3d(args) {\n  return averagePooling3d(args);\n}\nfunction globalAveragePooling1d(args) {\n  return new GlobalAveragePooling1D(args);\n}\nfunction globalAveragePooling2d(args) {\n  return new GlobalAveragePooling2D(args);\n}\nfunction globalMaxPooling1d(args) {\n  return new GlobalMaxPooling1D(args);\n}\nfunction globalMaxPooling2d(args) {\n  return new GlobalMaxPooling2D(args);\n}\nfunction maxPooling1d(args) {\n  return new MaxPooling1D(args);\n}\nfunction maxPooling2d(args) {\n  return new MaxPooling2D(args);\n}\nfunction maxPooling3d(args) {\n  return new MaxPooling3D(args);\n}\nfunction gru(args) {\n  return new GRU(args);\n}\nfunction gruCell(args) {\n  return new GRUCell(args);\n}\nfunction lstm(args) {\n  return new LSTM(args);\n}\nfunction lstmCell(args) {\n  return new LSTMCell(args);\n}\nfunction simpleRNN(args) {\n  return new SimpleRNN(args);\n}\nfunction simpleRNNCell(args) {\n  return new SimpleRNNCell(args);\n}\nfunction convLstm2d(args) {\n  return new ConvLSTM2D(args);\n}\nfunction convLstm2dCell(args) {\n  return new ConvLSTM2DCell(args);\n}\nfunction rnn2(args) {\n  return new RNN(args);\n}\nfunction stackedRNNCells(args) {\n  return new StackedRNNCells(args);\n}\nfunction bidirectional(args) {\n  return new Bidirectional(args);\n}\nfunction timeDistributed(args) {\n  return new TimeDistributed(args);\n}\nvar globalMaxPool1d = globalMaxPooling1d;\nvar globalMaxPool2d = globalMaxPooling2d;\nvar maxPool1d = maxPooling1d;\nvar maxPool2d = maxPooling2d;\nfunction gaussianNoise(args) {\n  return new GaussianNoise(args);\n}\nfunction gaussianDropout(args) {\n  return new GaussianDropout(args);\n}\nfunction alphaDropout(args) {\n  return new AlphaDropout(args);\n}\nfunction masking(args) {\n  return new Masking(args);\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-layers@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-layers/dist/exports_metrics.js\nvar exports_metrics_exports = {};\n__export(exports_metrics_exports, {\n  MAPE: () => MAPE2,\n  MSE: () => MSE2,\n  binaryAccuracy: () => binaryAccuracy2,\n  binaryCrossentropy: () => binaryCrossentropy3,\n  categoricalAccuracy: () => categoricalAccuracy2,\n  categoricalCrossentropy: () => categoricalCrossentropy3,\n  cosineProximity: () => cosineProximity2,\n  mape: () => mape2,\n  meanAbsoluteError: () => meanAbsoluteError2,\n  meanAbsolutePercentageError: () => meanAbsolutePercentageError2,\n  meanSquaredError: () => meanSquaredError3,\n  mse: () => mse2,\n  precision: () => precision2,\n  recall: () => recall2,\n  sparseCategoricalAccuracy: () => sparseCategoricalAccuracy2\n});\nfunction binaryAccuracy2(yTrue, yPred) {\n  return binaryAccuracy(yTrue, yPred);\n}\nfunction binaryCrossentropy3(yTrue, yPred) {\n  return binaryCrossentropy2(yTrue, yPred);\n}\nfunction sparseCategoricalAccuracy2(yTrue, yPred) {\n  return sparseCategoricalAccuracy(yTrue, yPred);\n}\nfunction categoricalAccuracy2(yTrue, yPred) {\n  return categoricalAccuracy(yTrue, yPred);\n}\nfunction categoricalCrossentropy3(yTrue, yPred) {\n  return categoricalCrossentropy2(yTrue, yPred);\n}\nfunction precision2(yTrue, yPred) {\n  return precision(yTrue, yPred);\n}\nfunction recall2(yTrue, yPred) {\n  return recall(yTrue, yPred);\n}\nfunction cosineProximity2(yTrue, yPred) {\n  return cosineProximity(yTrue, yPred);\n}\nfunction meanAbsoluteError2(yTrue, yPred) {\n  return meanAbsoluteError(yTrue, yPred);\n}\nfunction meanAbsolutePercentageError2(yTrue, yPred) {\n  return meanAbsolutePercentageError(yTrue, yPred);\n}\nfunction MAPE2(yTrue, yPred) {\n  return meanAbsolutePercentageError(yTrue, yPred);\n}\nfunction mape2(yTrue, yPred) {\n  return meanAbsolutePercentageError(yTrue, yPred);\n}\nfunction meanSquaredError3(yTrue, yPred) {\n  return meanSquaredError2(yTrue, yPred);\n}\nfunction MSE2(yTrue, yPred) {\n  return meanSquaredError2(yTrue, yPred);\n}\nfunction mse2(yTrue, yPred) {\n  return meanSquaredError2(yTrue, yPred);\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-layers@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-layers/dist/exports_models.js\nvar exports_models_exports = {};\n__export(exports_models_exports, {\n  modelFromJSON: () => modelFromJSON\n});\n\n// node_modules/.pnpm/@tensorflow+tfjs-layers@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-layers/dist/exports_regularizers.js\nvar exports_regularizers_exports = {};\n__export(exports_regularizers_exports, {\n  l1: () => l12,\n  l1l2: () => l1l2,\n  l2: () => l22\n});\nfunction l1l2(config) {\n  return new L1L2(config);\n}\nfunction l12(config) {\n  return l1(config);\n}\nfunction l22(config) {\n  return l2(config);\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-layers@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-layers/dist/callbacks.js\nvar Callback = class extends BaseCallback {\n  constructor() {\n    super(...arguments);\n    this.model = null;\n  }\n  setModel(model2) {\n    if (!(model2 instanceof LayersModel)) {\n      throw new Error(\"model must be a LayersModel, not some other Container\");\n    }\n    this.model = model2;\n  }\n};\nfunction less2(currVal, prevVal) {\n  return currVal < prevVal;\n}\nfunction greater2(currVal, prevVal) {\n  return currVal > prevVal;\n}\nvar EarlyStopping = class extends Callback {\n  constructor(args) {\n    super();\n    if (args == null) {\n      args = {};\n    }\n    if (args.restoreBestWeights) {\n      throw new NotImplementedError(\"restoreBestWeights = True is not implemented in EarlyStopping yet.\");\n    }\n    this.monitor = args.monitor || \"val_loss\";\n    this.minDelta = Math.abs(args.minDelta || 0);\n    this.patience = args.patience || 0;\n    this.verbose = args.verbose || 0;\n    this.mode = args.mode || \"auto\";\n    this.baseline = args.baseline;\n    if ([\"auto\", \"min\", \"max\"].indexOf(this.mode) === -1) {\n      console.warn(`EarlyStopping mode '${this.mode}' is invalid. Falling back to mode 'auto'.`);\n      this.mode = \"auto\";\n    }\n    if (this.mode === \"min\") {\n      this.monitorFunc = less2;\n    } else if (this.mode === \"max\") {\n      this.monitorFunc = greater2;\n    } else {\n      if (this.monitor.indexOf(\"acc\") !== -1) {\n        this.monitorFunc = greater2;\n      } else {\n        this.monitorFunc = less2;\n      }\n    }\n    if (this.monitorFunc === less2) {\n      this.minDelta *= -1;\n    }\n  }\n  async onTrainBegin(logs) {\n    this.wait = 0;\n    this.stoppedEpoch = 0;\n    if (this.baseline != null) {\n      this.best = this.baseline;\n    } else {\n      this.best = this.monitorFunc === less2 ? Infinity : -Infinity;\n    }\n  }\n  async onEpochEnd(epoch, logs) {\n    await resolveScalarsInLogs(logs);\n    const current = this.getMonitorValue(logs);\n    if (current == null) {\n      return;\n    }\n    if (this.monitorFunc(current - this.minDelta, this.best)) {\n      this.best = current;\n      this.wait = 0;\n    } else {\n      this.wait++;\n      if (this.wait >= this.patience) {\n        this.stoppedEpoch = epoch;\n        this.model.stopTraining = true;\n      }\n    }\n  }\n  async onTrainEnd(logs) {\n    if (this.stoppedEpoch > 0 && this.verbose) {\n      console.log(`Epoch ${this.stoppedEpoch}: early stopping.`);\n    }\n  }\n  getMonitorValue(logs) {\n    if (logs == null) {\n      logs = {};\n    }\n    const monitorValue = logs[this.monitor];\n    if (monitorValue == null) {\n      console.warn(`Metric for EarlyStopping ${this.monitor} is not available. Available metrics are: ${Object.keys(logs)}`);\n    }\n    return monitorValue;\n  }\n};\nfunction earlyStopping(args) {\n  return new EarlyStopping(args);\n}\nvar callbacks = { earlyStopping };\n\n// node_modules/.pnpm/@tensorflow+tfjs-converter@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-converter/dist/flags.js\nvar ENV4 = env();\nENV4.registerFlag(\"KEEP_INTERMEDIATE_TENSORS\", () => false, (debugValue) => {\n  if (debugValue) {\n    console.warn(\"Keep intermediate tensors is ON. This will print the values of all intermediate tensors during model inference. Not all models support this mode. For details, check e2e/benchmarks/ model_config.js. This significantly impacts performance.\");\n  }\n});\n\n// node_modules/.pnpm/@tensorflow+tfjs-converter@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-converter/dist/data/compiled_api.js\nvar DataType;\n(function(DataType2) {\n  DataType2[DataType2[\"DT_INVALID\"] = 0] = \"DT_INVALID\";\n  DataType2[DataType2[\"DT_FLOAT\"] = 1] = \"DT_FLOAT\";\n  DataType2[DataType2[\"DT_DOUBLE\"] = 2] = \"DT_DOUBLE\";\n  DataType2[DataType2[\"DT_INT32\"] = 3] = \"DT_INT32\";\n  DataType2[DataType2[\"DT_UINT8\"] = 4] = \"DT_UINT8\";\n  DataType2[DataType2[\"DT_INT16\"] = 5] = \"DT_INT16\";\n  DataType2[DataType2[\"DT_INT8\"] = 6] = \"DT_INT8\";\n  DataType2[DataType2[\"DT_STRING\"] = 7] = \"DT_STRING\";\n  DataType2[DataType2[\"DT_COMPLEX64\"] = 8] = \"DT_COMPLEX64\";\n  DataType2[DataType2[\"DT_INT64\"] = 9] = \"DT_INT64\";\n  DataType2[DataType2[\"DT_BOOL\"] = 10] = \"DT_BOOL\";\n  DataType2[DataType2[\"DT_QINT8\"] = 11] = \"DT_QINT8\";\n  DataType2[DataType2[\"DT_QUINT8\"] = 12] = \"DT_QUINT8\";\n  DataType2[DataType2[\"DT_QINT32\"] = 13] = \"DT_QINT32\";\n  DataType2[DataType2[\"DT_BFLOAT16\"] = 14] = \"DT_BFLOAT16\";\n  DataType2[DataType2[\"DT_QINT16\"] = 15] = \"DT_QINT16\";\n  DataType2[DataType2[\"DT_QUINT16\"] = 16] = \"DT_QUINT16\";\n  DataType2[DataType2[\"DT_UINT16\"] = 17] = \"DT_UINT16\";\n  DataType2[DataType2[\"DT_COMPLEX128\"] = 18] = \"DT_COMPLEX128\";\n  DataType2[DataType2[\"DT_HALF\"] = 19] = \"DT_HALF\";\n  DataType2[DataType2[\"DT_RESOURCE\"] = 20] = \"DT_RESOURCE\";\n  DataType2[DataType2[\"DT_VARIANT\"] = 21] = \"DT_VARIANT\";\n  DataType2[DataType2[\"DT_UINT32\"] = 22] = \"DT_UINT32\";\n  DataType2[DataType2[\"DT_UINT64\"] = 23] = \"DT_UINT64\";\n  DataType2[DataType2[\"DT_FLOAT_REF\"] = 101] = \"DT_FLOAT_REF\";\n  DataType2[DataType2[\"DT_DOUBLE_REF\"] = 102] = \"DT_DOUBLE_REF\";\n  DataType2[DataType2[\"DT_INT32_REF\"] = 103] = \"DT_INT32_REF\";\n  DataType2[DataType2[\"DT_UINT8_REF\"] = 104] = \"DT_UINT8_REF\";\n  DataType2[DataType2[\"DT_INT16_REF\"] = 105] = \"DT_INT16_REF\";\n  DataType2[DataType2[\"DT_INT8_REF\"] = 106] = \"DT_INT8_REF\";\n  DataType2[DataType2[\"DT_STRING_REF\"] = 107] = \"DT_STRING_REF\";\n  DataType2[DataType2[\"DT_COMPLEX64_REF\"] = 108] = \"DT_COMPLEX64_REF\";\n  DataType2[DataType2[\"DT_INT64_REF\"] = 109] = \"DT_INT64_REF\";\n  DataType2[DataType2[\"DT_BOOL_REF\"] = 110] = \"DT_BOOL_REF\";\n  DataType2[DataType2[\"DT_QINT8_REF\"] = 111] = \"DT_QINT8_REF\";\n  DataType2[DataType2[\"DT_QUINT8_REF\"] = 112] = \"DT_QUINT8_REF\";\n  DataType2[DataType2[\"DT_QINT32_REF\"] = 113] = \"DT_QINT32_REF\";\n  DataType2[DataType2[\"DT_BFLOAT16_REF\"] = 114] = \"DT_BFLOAT16_REF\";\n  DataType2[DataType2[\"DT_QINT16_REF\"] = 115] = \"DT_QINT16_REF\";\n  DataType2[DataType2[\"DT_QUINT16_REF\"] = 116] = \"DT_QUINT16_REF\";\n  DataType2[DataType2[\"DT_UINT16_REF\"] = 117] = \"DT_UINT16_REF\";\n  DataType2[DataType2[\"DT_COMPLEX128_REF\"] = 118] = \"DT_COMPLEX128_REF\";\n  DataType2[DataType2[\"DT_HALF_REF\"] = 119] = \"DT_HALF_REF\";\n  DataType2[DataType2[\"DT_RESOURCE_REF\"] = 120] = \"DT_RESOURCE_REF\";\n  DataType2[DataType2[\"DT_VARIANT_REF\"] = 121] = \"DT_VARIANT_REF\";\n  DataType2[DataType2[\"DT_UINT32_REF\"] = 122] = \"DT_UINT32_REF\";\n  DataType2[DataType2[\"DT_UINT64_REF\"] = 123] = \"DT_UINT64_REF\";\n})(DataType || (DataType = {}));\nvar SaverDef;\n(function(SaverDef2) {\n  let CheckpointFormatVersion;\n  (function(CheckpointFormatVersion2) {\n    CheckpointFormatVersion2[CheckpointFormatVersion2[\"LEGACY\"] = 0] = \"LEGACY\";\n    CheckpointFormatVersion2[CheckpointFormatVersion2[\"V1\"] = 1] = \"V1\";\n    CheckpointFormatVersion2[CheckpointFormatVersion2[\"V2\"] = 2] = \"V2\";\n  })(CheckpointFormatVersion = SaverDef2.CheckpointFormatVersion || (SaverDef2.CheckpointFormatVersion = {}));\n})(SaverDef || (SaverDef = {}));\n\n// node_modules/.pnpm/@tensorflow+tfjs-converter@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-converter/dist/operations/custom_op/register.js\nvar CUSTOM_OPS = {};\nfunction registerOp(name, opFunc) {\n  const opMapper = {\n    tfOpName: name,\n    category: \"custom\",\n    inputs: [],\n    attrs: [],\n    customExecutor: opFunc\n  };\n  CUSTOM_OPS[name] = opMapper;\n}\nfunction getRegisteredOp(name) {\n  return CUSTOM_OPS[name];\n}\nfunction deregisterOp(name) {\n  delete CUSTOM_OPS[name];\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-converter@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-converter/dist/operations/executors/utils.js\nfunction getParamValue(paramName, node, tensorMap, context, resourceManager) {\n  const inputParam = node.inputParams[paramName];\n  if (inputParam && inputParam.inputIndexStart !== void 0) {\n    const start = inputParam.inputIndexStart;\n    const end = inputParam.inputIndexEnd === 0 ? void 0 : inputParam.inputIndexEnd === void 0 ? start + 1 : inputParam.inputIndexEnd;\n    if (inputParam.type === \"tensor\") {\n      return getTensor(node.inputNames[inputParam.inputIndexStart], tensorMap, context, resourceManager);\n    }\n    if (inputParam.type === \"tensors\") {\n      const inputs = node.inputNames.slice(start, end);\n      return inputs.map((name) => getTensor(name, tensorMap, context, resourceManager));\n    }\n    const tensor2 = getTensor(node.inputNames.slice(start)[0], tensorMap, context, resourceManager);\n    const data = tensor2.dataSync();\n    return inputParam.type === \"number\" ? data[0] : util_exports.toNestedArray(tensor2.shape, data);\n  }\n  const attrParam = node.attrParams[paramName];\n  return attrParam && attrParam.value;\n}\nfunction getTensor(name, tensorsMap, context, resourceManager) {\n  const [nodeName, index] = parseNodeName(name);\n  if (resourceManager != null) {\n    const tensor2 = resourceManager.getHashTableHandleByName(nodeName);\n    if (tensor2 != null) {\n      return tensor2;\n    }\n  }\n  const contextId = context.currentContextIds.find((contextId2) => {\n    return !!tensorsMap[getNodeNameWithContextId(nodeName, contextId2)];\n  });\n  return contextId !== void 0 ? tensorsMap[getNodeNameWithContextId(nodeName, contextId)][index] : void 0;\n}\nfunction getTensorsForCurrentContenxt(name, tensorsMap, context) {\n  return tensorsMap[getNodeNameWithContextId(name, context.currentContextId)];\n}\nfunction getNodeNameAndIndex(inputName, context) {\n  const [nodeName, index, outputName] = parseNodeName(inputName);\n  return [\n    getNodeNameWithContextId(nodeName, context && context.currentContextId),\n    index,\n    outputName\n  ];\n}\nfunction getNodeNameWithContextId(name, contextId) {\n  return !!contextId ? `${name}-${contextId}` : name;\n}\nfunction parseNodeName(name) {\n  const parts = name.split(\":\");\n  if (parts.length === 1) {\n    return [name, 0, void 0];\n  }\n  const nodeName = parts[0];\n  const outputName = parts.length === 3 ? parts[1] : void 0;\n  const index = Number(parts[parts.length - 1]);\n  return [nodeName, index, outputName];\n}\nfunction getPadding(node, tensorMap, context) {\n  let pad3 = getParamValue(\"pad\", node, tensorMap, context);\n  if (pad3 === \"explicit\") {\n    pad3 = getParamValue(\"explicitPaddings\", node, tensorMap, context);\n    const explicitPadding = [[0, 0], [0, 0], [0, 0], [0, 0]];\n    for (let i = 0; i < 4; i++) {\n      explicitPadding[i][0] = pad3[i * 2];\n      explicitPadding[i][1] = pad3[i * 2 + 1];\n    }\n    return explicitPadding;\n  }\n  return pad3;\n}\nfunction cloneTensor(tensor2) {\n  return tensor2.kept ? tensor2 : clone(tensor2);\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-converter@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-converter/dist/operations/op_list/arithmetic.js\nvar arithmetic_exports = {};\n__export(arithmetic_exports, {\n  json: () => json\n});\nvar json = [\n  {\n    \"tfOpName\": \"Add\",\n    \"category\": \"arithmetic\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"a\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"b\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"T\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"AddV2\",\n    \"category\": \"arithmetic\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"a\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"b\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"T\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"AddN\",\n    \"category\": \"arithmetic\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"end\": 0,\n        \"name\": \"tensors\",\n        \"type\": \"tensors\"\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"BiasAdd\",\n    \"category\": \"arithmetic\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"a\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"b\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"T\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      },\n      {\n        \"tfName\": \"data_format\",\n        \"name\": \"dataFormat\",\n        \"type\": \"string\",\n        \"notSupported\": true\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"Sub\",\n    \"category\": \"arithmetic\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"a\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"b\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"T\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"RealDiv\",\n    \"category\": \"arithmetic\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"a\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"b\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"T\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"Div\",\n    \"category\": \"arithmetic\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"a\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"b\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"T\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"DivNoNan\",\n    \"category\": \"arithmetic\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"a\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"b\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"T\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"FloorDiv\",\n    \"category\": \"arithmetic\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"a\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"b\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"T\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"Mul\",\n    \"category\": \"arithmetic\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"a\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"b\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"T\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"Maximum\",\n    \"category\": \"arithmetic\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"a\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"b\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"T\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"Minimum\",\n    \"category\": \"arithmetic\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"a\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"b\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"T\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"Pow\",\n    \"category\": \"arithmetic\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"a\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"b\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"T\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"SquaredDifference\",\n    \"category\": \"arithmetic\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"a\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"b\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"T\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"Mod\",\n    \"category\": \"arithmetic\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"a\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"b\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"T\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"FloorMod\",\n    \"category\": \"arithmetic\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"a\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"b\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"T\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      }\n    ]\n  }\n];\n\n// node_modules/.pnpm/@tensorflow+tfjs-converter@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-converter/dist/operations/op_list/basic_math.js\nvar basic_math_exports = {};\n__export(basic_math_exports, {\n  json: () => json2\n});\nvar json2 = [\n  {\n    \"tfOpName\": \"Abs\",\n    \"category\": \"basic_math\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"T\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"Acos\",\n    \"category\": \"basic_math\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"T\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"Asin\",\n    \"category\": \"basic_math\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"T\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"Atan\",\n    \"category\": \"basic_math\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"T\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"Atan2\",\n    \"category\": \"basic_math\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"y\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"T\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"Ceil\",\n    \"category\": \"basic_math\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"T\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"ClipByValue\",\n    \"category\": \"basic_math\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"clipValueMin\",\n        \"type\": \"number\"\n      },\n      {\n        \"start\": 2,\n        \"name\": \"clipValueMax\",\n        \"type\": \"number\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"T\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"Complex\",\n    \"category\": \"basic_math\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"real\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"imag\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"T\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"ComplexAbs\",\n    \"category\": \"basic_math\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"T\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"Cos\",\n    \"category\": \"basic_math\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"T\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"Cosh\",\n    \"category\": \"basic_math\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"T\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"Elu\",\n    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 \"type\": \"shape\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"numElements\",\n        \"type\": \"number\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"element_dtype\",\n        \"name\": \"elementDType\",\n        \"type\": \"dtype\"\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"TensorListFromTensor\",\n    \"category\": \"control\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"tensor\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"elementShape\",\n        \"type\": \"shape\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"element_dtype\",\n        \"name\": \"elementDType\",\n        \"type\": \"dtype\"\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"TensorListStack\",\n    \"category\": \"control\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"tensorListId\",\n        \"type\": \"tensor\"\n      },\n      {\n        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\"category\": \"control\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"tensorListId\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"element_shape\",\n        \"name\": \"elementShape\",\n        \"type\": \"shape\"\n      },\n      {\n        \"tfName\": \"element_dtype\",\n        \"name\": \"elementDType\",\n        \"type\": \"dtype\"\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"TensorListConcatV2\",\n    \"category\": \"control\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"tensorListId\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"element_shape\",\n        \"name\": \"elementShape\",\n        \"type\": \"shape\"\n      },\n      {\n        \"tfName\": \"element_dtype\",\n        \"name\": \"elementDType\",\n        \"type\": \"dtype\"\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"TensorListPopBack\",\n    \"category\": \"control\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"tensorListId\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"elementShape\",\n        \"type\": \"shape\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"element_dtype\",\n        \"name\": \"elementDType\",\n        \"type\": \"dtype\"\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"TensorListPushBack\",\n    \"category\": \"control\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"tensorListId\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"tensor\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"element_dtype\",\n        \"name\": \"elementDType\",\n        \"type\": \"dtype\"\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"TensorListLength\",\n    \"category\": \"control\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"tensorListId\",\n        \"type\": \"tensor\"\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"TensorListResize\",\n    \"category\": \"control\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"tensorListId\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"size\",\n        \"type\": \"number\"\n      }\n    ]\n  }\n];\n\n// node_modules/.pnpm/@tensorflow+tfjs-converter@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-converter/dist/operations/op_list/convolution.js\nvar convolution_exports = {};\n__export(convolution_exports, {\n  json: () => json4\n});\nvar json4 = [\n  {\n    \"tfOpName\": \"AvgPool\",\n    \"category\": \"convolution\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"strides\",\n        \"name\": \"strides\",\n        \"type\": \"number[]\"\n      },\n      {\n        \"tfName\": \"padding\",\n        \"name\": \"pad\",\n        \"type\": \"string\"\n      },\n      {\n        \"tfName\": \"data_format\",\n        \"name\": \"dataFormat\",\n        \"type\": \"string\",\n        \"notSupported\": true\n      },\n      {\n        \"tfName\": \"ksize\",\n        \"name\": \"kernelSize\",\n        \"type\": \"number[]\"\n      },\n      {\n        \"tfName\": \"T\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"MaxPool\",\n    \"category\": \"convolution\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"strides\",\n        \"name\": \"strides\",\n        \"type\": \"number[]\"\n      },\n      {\n        \"tfName\": \"padding\",\n        \"name\": \"pad\",\n        \"type\": \"string\"\n      },\n      {\n        \"tfName\": \"data_format\",\n        \"name\": \"dataFormat\",\n        \"type\": \"string\",\n        \"notSupported\": true\n      },\n      {\n        \"tfName\": \"ksize\",\n        \"name\": \"kernelSize\",\n        \"type\": \"number[]\"\n      },\n      {\n        \"tfName\": \"explicit_paddings\",\n        \"name\": \"explicitPaddings\",\n        \"type\": \"number[]\",\n        \"defaultValue\": [],\n        \"notSupported\": true\n      },\n      {\n        \"tfName\": \"T\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"MaxPoolWithArgmax\",\n    \"category\": \"convolution\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"strides\",\n        \"name\": \"strides\",\n        \"type\": \"number[]\"\n      },\n      {\n        \"tfName\": \"padding\",\n        \"name\": \"pad\",\n        \"type\": \"string\"\n      },\n      {\n        \"tfName\": \"ksize\",\n        \"name\": \"kernelSize\",\n        \"type\": \"number[]\"\n      },\n      {\n        \"tfName\": \"include_batch_in_index\",\n        \"name\": \"includeBatchInIndex\",\n        \"type\": \"bool\"\n      },\n      {\n        \"tfName\": \"T\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"AvgPool3D\",\n    \"category\": \"convolution\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"strides\",\n        \"name\": \"strides\",\n        \"type\": \"number[]\"\n      },\n      {\n        \"tfName\": \"padding\",\n        \"name\": \"pad\",\n        \"type\": \"string\"\n      },\n      {\n        \"tfName\": \"data_format\",\n        \"name\": \"dataFormat\",\n        \"type\": \"string\",\n        \"notSupported\": true\n      },\n      {\n        \"tfName\": \"ksize\",\n        \"name\": \"kernelSize\",\n        \"type\": \"number[]\"\n      },\n      {\n        \"tfName\": \"T\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"MaxPool3D\",\n    \"category\": \"convolution\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"strides\",\n        \"name\": \"strides\",\n        \"type\": \"number[]\"\n      },\n      {\n        \"tfName\": \"padding\",\n        \"name\": \"pad\",\n        \"type\": \"string\"\n      },\n      {\n        \"tfName\": \"data_format\",\n        \"name\": \"dataFormat\",\n        \"type\": \"string\",\n        \"notSupported\": true\n      },\n      {\n        \"tfName\": \"ksize\",\n        \"name\": \"kernelSize\",\n        \"type\": \"number[]\"\n      },\n      {\n        \"tfName\": \"T\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"Conv1D\",\n    \"category\": \"convolution\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"filter\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"stride\",\n        \"name\": \"stride\",\n        \"type\": \"number\"\n      },\n      {\n        \"tfName\": \"padding\",\n        \"name\": \"pad\",\n        \"type\": \"string\"\n      },\n      {\n        \"tfName\": \"data_format\",\n        \"name\": \"dataFormat\",\n        \"type\": \"string\",\n        \"defaultValue\": \"NWC\"\n      },\n      {\n        \"tfName\": \"T\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      },\n      {\n        \"tfName\": \"dilation\",\n        \"name\": \"dilation\",\n        \"type\": \"number\",\n        \"defaultValue\": 1\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"Conv2D\",\n    \"category\": \"convolution\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"filter\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"T\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      },\n      {\n        \"tfName\": \"strides\",\n        \"name\": \"strides\",\n        \"type\": \"number[]\"\n      },\n      {\n        \"tfName\": \"padding\",\n        \"name\": \"pad\",\n        \"type\": \"string\"\n      },\n      {\n        \"tfName\": \"useCudnnOnGpu\",\n        \"name\": \"useCudnnOnGpu\",\n        \"type\": \"bool\"\n      },\n      {\n        \"tfName\": \"data_format\",\n        \"name\": \"dataFormat\",\n        \"type\": \"string\",\n        \"defaultValue\": \"NHWC\"\n      },\n      {\n        \"tfName\": \"explicit_paddings\",\n        \"name\": \"explicitPaddings\",\n        \"type\": \"number[]\",\n        \"defaultValue\": []\n      },\n      {\n        \"tfName\": \"dilations\",\n        \"name\": \"dilations\",\n        \"type\": \"number[]\"\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"_FusedConv2D\",\n    \"category\": \"convolution\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"filter\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 2,\n        \"end\": 0,\n        \"name\": \"args\",\n        \"type\": \"tensors\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"num_args\",\n        \"name\": \"numArgs\",\n        \"type\": \"number\"\n      },\n      {\n        \"tfName\": \"T\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      },\n      {\n        \"tfName\": \"strides\",\n        \"name\": \"strides\",\n        \"type\": \"number[]\"\n      },\n      {\n        \"tfName\": \"padding\",\n        \"name\": \"pad\",\n        \"type\": \"string\"\n      },\n      {\n        \"tfName\": \"explicit_paddings\",\n        \"name\": \"explicitPaddings\",\n        \"type\": \"number[]\",\n        \"defaultValue\": []\n      },\n      {\n        \"tfName\": \"use_cudnn_on_gpu\",\n        \"name\": \"useCudnnOnGpu\",\n        \"type\": \"bool\",\n        \"defaultValue\": true\n      },\n      {\n        \"tfName\": \"data_format\",\n        \"name\": \"dataFormat\",\n        \"type\": \"string\",\n        \"defaultValue\": \"NHWC\"\n      },\n      {\n        \"tfName\": \"dilations\",\n        \"name\": \"dilations\",\n        \"type\": \"number[]\",\n        \"defaultValue\": [\n          1,\n          1,\n          1,\n          1\n        ]\n      },\n      {\n        \"tfName\": \"fused_ops\",\n        \"name\": \"fusedOps\",\n        \"type\": \"string[]\",\n        \"defaultValue\": []\n      },\n      {\n        \"tfName\": \"epsilon\",\n        \"name\": \"epsilon\",\n        \"type\": \"number\",\n        \"defaultValue\": 1e-4\n      },\n      {\n        \"tfName\": \"leakyrelu_alpha\",\n        \"name\": \"leakyreluAlpha\",\n        \"type\": \"number\",\n        \"defaultValue\": 0.2\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"Conv2DBackpropInput\",\n    \"category\": \"convolution\",\n    \"inputs\": [\n      {\n        \"start\": 2,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"filter\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 0,\n        \"name\": \"outputShape\",\n        \"type\": \"number[]\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"strides\",\n        \"name\": \"strides\",\n        \"type\": \"number[]\"\n      },\n      {\n        \"tfName\": \"padding\",\n        \"name\": \"pad\",\n        \"type\": \"string\"\n      },\n      {\n        \"tfName\": \"data_format\",\n        \"name\": \"dataFormat\",\n        \"type\": \"string\",\n        \"notSupported\": true\n      },\n      {\n        \"tfName\": \"explicit_paddings\",\n        \"name\": \"explicitPaddings\",\n        \"type\": \"number[]\",\n        \"defaultValue\": []\n      },\n      {\n        \"tfName\": \"dilations\",\n        \"name\": \"dilations\",\n        \"type\": \"number[]\",\n        \"notSupported\": true\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"DepthwiseConv2d\",\n    \"category\": \"convolution\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"input\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"filter\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"strides\",\n        \"name\": \"strides\",\n        \"type\": \"number[]\"\n      },\n      {\n        \"tfName\": \"padding\",\n        \"name\": \"pad\",\n        \"type\": \"string\"\n      },\n      {\n        \"tfName\": \"data_format\",\n        \"name\": \"dataFormat\",\n        \"type\": \"string\",\n        \"defaultValue\": \"NHWC\"\n      },\n      {\n        \"tfName\": \"explicit_paddings\",\n        \"name\": \"explicitPaddings\",\n        \"type\": \"number[]\",\n        \"defaultValue\": []\n      },\n      {\n        \"tfName\": \"dilations\",\n        \"name\": \"dilations\",\n        \"type\": \"number[]\"\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"DepthwiseConv2dNative\",\n    \"category\": \"convolution\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"input\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"filter\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"strides\",\n        \"name\": \"strides\",\n        \"type\": \"number[]\"\n      },\n      {\n        \"tfName\": \"padding\",\n        \"name\": \"pad\",\n        \"type\": \"string\"\n      },\n      {\n        \"tfName\": \"data_format\",\n        \"name\": \"dataFormat\",\n        \"type\": \"string\",\n        \"defaultValue\": \"NHWC\"\n      },\n      {\n        \"tfName\": \"explicit_paddings\",\n        \"name\": \"explicitPaddings\",\n        \"type\": \"number[]\",\n        \"defaultValue\": []\n      },\n      {\n        \"tfName\": \"dilations\",\n        \"name\": \"dilations\",\n        \"type\": \"number[]\"\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"FusedDepthwiseConv2dNative\",\n    \"category\": \"convolution\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"filter\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 2,\n        \"end\": 0,\n        \"name\": \"args\",\n        \"type\": \"tensors\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"num_args\",\n        \"name\": \"numArgs\",\n        \"type\": \"number\"\n      },\n      {\n        \"tfName\": \"T\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      },\n      {\n        \"tfName\": \"strides\",\n        \"name\": \"strides\",\n        \"type\": \"number[]\"\n      },\n      {\n        \"tfName\": \"padding\",\n        \"name\": \"pad\",\n        \"type\": \"string\"\n      },\n      {\n        \"tfName\": \"data_format\",\n        \"name\": \"dataFormat\",\n        \"type\": \"string\",\n        \"defaultValue\": \"NHWC\"\n      },\n      {\n        \"tfName\": \"dilations\",\n        \"name\": \"dilations\",\n        \"type\": \"number[]\",\n        \"defaultValue\": [\n          1,\n          1,\n          1,\n          1\n        ]\n      },\n      {\n        \"tfName\": \"fused_ops\",\n        \"name\": \"fusedOps\",\n        \"type\": \"string[]\",\n        \"defaultValue\": []\n      },\n      {\n        \"tfName\": \"explicit_paddings\",\n        \"name\": \"explicitPaddings\",\n        \"type\": \"number[]\",\n        \"defaultValue\": []\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"Conv3D\",\n    \"category\": \"convolution\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"filter\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"strides\",\n        \"name\": \"strides\",\n        \"type\": \"number[]\"\n      },\n      {\n        \"tfName\": \"padding\",\n        \"name\": \"pad\",\n        \"type\": \"string\"\n      },\n      {\n        \"tfName\": \"data_format\",\n        \"name\": \"dataFormat\",\n        \"type\": \"string\",\n        \"defaultValue\": \"NHWC\"\n      },\n      {\n        \"tfName\": \"dilations\",\n        \"name\": \"dilations\",\n        \"type\": \"number[]\"\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"Dilation2D\",\n    \"category\": \"convolution\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"filter\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"strides\",\n        \"name\": \"strides\",\n        \"type\": \"number[]\"\n      },\n      {\n        \"tfName\": \"rates\",\n        \"name\": \"dilations\",\n        \"type\": \"number[]\"\n      },\n      {\n        \"tfName\": \"padding\",\n        \"name\": \"pad\",\n        \"type\": \"string\"\n      }\n    ]\n  }\n];\n\n// node_modules/.pnpm/@tensorflow+tfjs-converter@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-converter/dist/operations/op_list/creation.js\nvar creation_exports = {};\n__export(creation_exports, {\n  json: () => json5\n});\nvar json5 = [\n  {\n    \"tfOpName\": \"Fill\",\n    \"category\": \"creation\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"shape\",\n        \"type\": \"number[]\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"value\",\n        \"type\": \"number\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"T\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\"\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"LinSpace\",\n    \"category\": \"creation\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"start\",\n        \"type\": \"number\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"stop\",\n        \"type\": \"number\"\n      },\n      {\n        \"start\": 2,\n        \"name\": \"num\",\n        \"type\": \"number\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"T\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"OneHot\",\n    \"category\": \"creation\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"indices\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"depth\",\n        \"type\": \"number\"\n      },\n      {\n        \"start\": 2,\n        \"name\": \"onValue\",\n        \"type\": \"number\",\n        \"defaultValue\": 1\n      },\n      {\n        \"start\": 3,\n        \"name\": \"offValue\",\n        \"type\": \"number\",\n        \"defaultValue\": 0\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"axis\",\n        \"name\": \"axis\",\n        \"type\": \"number\",\n        \"notSupported\": true\n      },\n      {\n        \"tfName\": \"T\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\"\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"Ones\",\n    \"category\": \"creation\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"shape\",\n        \"type\": \"number[]\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"T\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\"\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"OnesLike\",\n    \"category\": \"creation\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"dtype\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\"\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"RandomStandardNormal\",\n    \"category\": \"creation\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"shape\",\n        \"type\": \"number[]\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"seed\",\n        \"name\": \"seed\",\n        \"type\": \"number\",\n        \"defaultValue\": 0\n      },\n      {\n        \"tfName\": \"seed2\",\n        \"name\": \"seed2\",\n        \"type\": \"number\",\n        \"defaultValue\": 0,\n        \"notSupported\": true\n      },\n      {\n        \"tfName\": \"dtype\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\"\n      },\n      {\n        \"tfName\": \"T\",\n        \"name\": \"T\",\n        \"type\": \"number\",\n        \"notSupported\": true\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"RandomUniform\",\n    \"category\": \"creation\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"shape\",\n        \"type\": \"number[]\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"minval\",\n        \"name\": \"minval\",\n        \"type\": \"number\",\n        \"defaultValue\": 0\n      },\n      {\n        \"tfName\": \"maxval\",\n        \"name\": \"maxval\",\n        \"type\": \"number\",\n        \"defaultValue\": 1\n      },\n      {\n        \"tfName\": \"dtype\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\"\n      },\n      {\n        \"tfName\": \"seed\",\n        \"name\": \"seed\",\n        \"type\": \"number\",\n        \"defaultValue\": 0\n      },\n      {\n        \"tfName\": \"seed2\",\n        \"name\": \"seed2\",\n        \"type\": \"number\",\n        \"defaultValue\": 0,\n        \"notSupported\": true\n      },\n      {\n        \"tfName\": \"T\",\n        \"name\": \"T\",\n        \"type\": \"number\",\n        \"notSupported\": true\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"Range\",\n    \"category\": \"creation\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"start\",\n        \"type\": \"number\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"stop\",\n        \"type\": \"number\"\n      },\n      {\n        \"start\": 2,\n        \"name\": \"step\",\n        \"type\": \"number\",\n        \"defaultValue\": 0\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"Tidx\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\"\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"TruncatedNormal\",\n    \"category\": \"creation\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"shape\",\n        \"type\": \"number[]\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"means\",\n        \"name\": \"mean\",\n        \"type\": \"number\",\n        \"defaultValue\": 0\n      },\n      {\n        \"tfName\": \"stddev\",\n        \"name\": \"stdDev\",\n        \"type\": \"number\",\n        \"defaultValue\": 1\n      },\n      {\n        \"tfName\": \"seed\",\n        \"name\": \"seed\",\n        \"type\": \"number\"\n      },\n      {\n        \"tfName\": \"seed2\",\n        \"name\": \"seed2\",\n        \"type\": \"number\",\n        \"defaultValue\": 0,\n        \"notSupported\": true\n      },\n      {\n        \"tfName\": \"dtype\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\"\n      },\n      {\n        \"tfName\": \"T\",\n        \"name\": \"T\",\n        \"type\": \"number\",\n        \"notSupported\": true\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"Zeros\",\n    \"category\": \"creation\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"shape\",\n        \"type\": \"number[]\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"T\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\"\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"ZerosLike\",\n    \"category\": \"creation\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"T\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\"\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"Multinomial\",\n    \"category\": \"creation\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"logits\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"numSamples\",\n        \"type\": \"number\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"seed\",\n        \"name\": \"seed\",\n        \"type\": \"number\"\n      },\n      {\n        \"tfName\": \"seed2\",\n        \"name\": \"seed2\",\n        \"type\": \"number\"\n      },\n      {\n        \"tfName\": \"T\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\"\n      },\n      {\n        \"tfName\": \"output_dtype\",\n        \"name\": \"output_dtype\",\n        \"type\": \"dtype\"\n      }\n    ]\n  }\n];\n\n// node_modules/.pnpm/@tensorflow+tfjs-converter@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-converter/dist/operations/op_list/dynamic.js\nvar dynamic_exports = {};\n__export(dynamic_exports, {\n  json: () => json6\n});\nvar json6 = [\n  {\n    \"tfOpName\": \"NonMaxSuppressionV2\",\n    \"category\": \"dynamic\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"boxes\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"scores\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 2,\n        \"name\": \"maxOutputSize\",\n        \"type\": \"number\"\n      },\n      {\n        \"start\": 3,\n        \"name\": \"iouThreshold\",\n        \"type\": \"number\"\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"NonMaxSuppressionV3\",\n    \"category\": \"dynamic\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"boxes\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"scores\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 2,\n        \"name\": \"maxOutputSize\",\n        \"type\": \"number\"\n      },\n      {\n        \"start\": 3,\n        \"name\": \"iouThreshold\",\n        \"type\": \"number\"\n      },\n      {\n        \"start\": 4,\n        \"name\": \"scoreThreshold\",\n        \"type\": \"number\"\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"NonMaxSuppressionV4\",\n    \"category\": \"dynamic\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"boxes\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"scores\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 2,\n        \"name\": \"maxOutputSize\",\n        \"type\": \"number\"\n      },\n      {\n        \"start\": 3,\n        \"name\": \"iouThreshold\",\n        \"type\": \"number\"\n      },\n      {\n        \"start\": 4,\n        \"name\": \"scoreThreshold\",\n        \"type\": \"number\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"T\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      },\n      {\n        \"tfName\": \"T_threshold\",\n        \"name\": \"threshold\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      },\n      {\n        \"tfName\": \"pad_to_max_output_size\",\n        \"name\": \"padToMaxOutputSize\",\n        \"type\": \"bool\"\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"NonMaxSuppressionV5\",\n    \"category\": \"dynamic\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"boxes\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"scores\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 2,\n        \"name\": \"maxOutputSize\",\n        \"type\": \"number\"\n      },\n      {\n        \"start\": 3,\n        \"name\": \"iouThreshold\",\n        \"type\": \"number\"\n      },\n      {\n        \"start\": 4,\n        \"name\": \"scoreThreshold\",\n        \"type\": \"number\"\n      },\n      {\n        \"start\": 5,\n        \"name\": \"softNmsSigma\",\n        \"type\": \"number\"\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"Where\",\n    \"category\": \"dynamic\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"condition\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"T\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"ListDiff\",\n    \"category\": \"dynamic\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"y\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"T\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      }\n    ]\n  }\n];\n\n// node_modules/.pnpm/@tensorflow+tfjs-converter@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-converter/dist/operations/op_list/evaluation.js\nvar evaluation_exports = {};\n__export(evaluation_exports, {\n  json: () => json7\n});\nvar json7 = [\n  {\n    \"tfOpName\": \"LowerBound\",\n    \"category\": \"evaluation\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"sortedSequence\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"values\",\n        \"type\": \"tensor\"\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"TopKV2\",\n    \"category\": \"evaluation\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"k\",\n        \"type\": \"number\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"sorted\",\n        \"name\": \"sorted\",\n        \"type\": \"bool\"\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"UpperBound\",\n    \"category\": \"evaluation\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"sortedSequence\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"values\",\n        \"type\": \"tensor\"\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"Unique\",\n    \"category\": \"evaluation\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"UniqueV2\",\n    \"category\": \"evaluation\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"axis\",\n        \"type\": \"number\"\n      }\n    ]\n  }\n];\n\n// node_modules/.pnpm/@tensorflow+tfjs-converter@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-converter/dist/operations/op_list/graph.js\nvar graph_exports = {};\n__export(graph_exports, {\n  json: () => json8\n});\nvar json8 = [\n  {\n    \"tfOpName\": \"PlaceholderWithDefault\",\n    \"category\": \"graph\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"default\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"shape\",\n        \"name\": \"shape\",\n        \"type\": \"shape\"\n      },\n      {\n        \"tfName\": \"dtype\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\"\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"Placeholder\",\n    \"category\": \"graph\",\n    \"attrs\": [\n      {\n        \"tfName\": \"shape\",\n        \"name\": \"shape\",\n        \"type\": \"shape\"\n      },\n      {\n        \"tfName\": \"dtype\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\"\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"Const\",\n    \"category\": \"graph\"\n  },\n  {\n    \"tfOpName\": \"Identity\",\n    \"category\": \"graph\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"IdentityN\",\n    \"category\": \"graph\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"end\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensors\"\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"Snapshot\",\n    \"category\": \"graph\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"Rank\",\n    \"category\": \"graph\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"Size\",\n    \"category\": \"graph\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"Shape\",\n    \"category\": \"graph\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"ShapeN\",\n    \"category\": \"graph\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"end\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensors\"\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"Print\",\n    \"category\": \"graph\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"data\",\n        \"type\": \"tensors\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"message\",\n        \"name\": \"message\",\n        \"type\": \"string\"\n      },\n      {\n        \"tfName\": \"first_n\",\n        \"name\": \"firstN\",\n        \"type\": \"number\",\n        \"notSupported\": true\n      },\n      {\n        \"tfName\": \"summarize\",\n        \"name\": \"summarize\",\n        \"type\": \"number\",\n        \"defaultValue\": 3\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"NoOp\",\n    \"category\": \"graph\",\n    \"inputs\": []\n  },\n  {\n    \"tfOpName\": \"StopGradient\",\n    \"category\": \"graph\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"FakeQuantWithMinMaxVars\",\n    \"category\": \"graph\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"min\",\n        \"name\": \"min\",\n        \"type\": \"number\"\n      },\n      {\n        \"tfName\": \"max\",\n        \"name\": \"max\",\n        \"type\": \"number\"\n      }\n    ]\n  }\n];\n\n// node_modules/.pnpm/@tensorflow+tfjs-converter@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-converter/dist/operations/op_list/hash_table.js\nvar hash_table_exports = {};\n__export(hash_table_exports, {\n  json: () => json9\n});\nvar json9 = [\n  {\n    \"tfOpName\": \"HashTable\",\n    \"category\": \"hash_table\",\n    \"inputs\": [],\n    \"attrs\": [\n      {\n        \"tfName\": \"shared_name\",\n        \"name\": \"sharedName\",\n        \"type\": \"string\"\n      },\n      {\n        \"tfName\": \"use_node_name_sharing\",\n        \"name\": \"useNodeNameSharing\",\n        \"type\": \"bool\"\n      },\n      {\n        \"tfName\": \"key_dtype\",\n        \"name\": \"keyDType\",\n        \"type\": \"dtype\"\n      },\n      {\n        \"tfName\": \"value_dtype\",\n        \"name\": \"valueDType\",\n        \"type\": \"dtype\"\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"HashTableV2\",\n    \"category\": \"hash_table\",\n    \"inputs\": [],\n    \"attrs\": [\n      {\n        \"tfName\": \"shared_name\",\n        \"name\": \"sharedName\",\n        \"type\": \"string\"\n      },\n      {\n        \"tfName\": \"use_node_name_sharing\",\n        \"name\": \"useNodeNameSharing\",\n        \"type\": \"bool\"\n      },\n      {\n        \"tfName\": \"key_dtype\",\n        \"name\": \"keyDType\",\n        \"type\": \"dtype\"\n      },\n      {\n        \"tfName\": \"value_dtype\",\n        \"name\": \"valueDType\",\n        \"type\": \"dtype\"\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"LookupTableImport\",\n    \"category\": \"hash_table\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"tableHandle\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"keys\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 2,\n        \"name\": \"values\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"Tin\",\n        \"name\": \"tIn\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      },\n      {\n        \"tfName\": \"Tout\",\n        \"name\": \"tOut\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"LookupTableImportV2\",\n    \"category\": \"hash_table\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"tableHandle\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"keys\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 2,\n        \"name\": \"values\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"Tin\",\n        \"name\": \"tIn\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      },\n      {\n        \"tfName\": \"Tout\",\n        \"name\": \"tOut\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"LookupTableFind\",\n    \"category\": \"hash_table\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"tableHandle\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"keys\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 2,\n        \"name\": \"defaultValue\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"Tin\",\n        \"name\": \"tIn\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      },\n      {\n        \"tfName\": \"Tout\",\n        \"name\": \"tOut\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"LookupTableFindV2\",\n    \"category\": \"hash_table\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"tableHandle\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"keys\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 2,\n        \"name\": \"defaultValue\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"Tin\",\n        \"name\": \"tIn\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      },\n      {\n        \"tfName\": \"Tout\",\n        \"name\": \"tOut\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"LookupTableSize\",\n    \"category\": \"hash_table\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"tableHandle\",\n        \"type\": \"tensor\"\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"LookupTableSizeV2\",\n    \"category\": \"hash_table\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"tableHandle\",\n        \"type\": \"tensor\"\n      }\n    ]\n  }\n];\n\n// node_modules/.pnpm/@tensorflow+tfjs-converter@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-converter/dist/operations/op_list/image.js\nvar image_exports = {};\n__export(image_exports, {\n  json: () => json10\n});\nvar json10 = [\n  {\n    \"tfOpName\": \"ResizeBilinear\",\n    \"category\": \"image\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"images\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"size\",\n        \"type\": \"number[]\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"align_corners\",\n        \"name\": \"alignCorners\",\n        \"type\": \"bool\"\n      },\n      {\n        \"tfName\": \"half_pixel_centers\",\n        \"name\": \"halfPixelCenters\",\n        \"type\": \"bool\"\n      },\n      {\n        \"tfName\": \"T\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"ResizeNearestNeighbor\",\n    \"category\": \"image\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"images\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"size\",\n        \"type\": \"number[]\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"align_corners\",\n        \"name\": \"alignCorners\",\n        \"type\": \"bool\"\n      },\n      {\n        \"tfName\": \"half_pixel_centers\",\n        \"name\": \"halfPixelCenters\",\n        \"type\": \"bool\"\n      },\n      {\n        \"tfName\": \"T\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"CropAndResize\",\n    \"category\": \"image\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"image\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"boxes\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 2,\n        \"name\": \"boxInd\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 3,\n        \"name\": \"cropSize\",\n        \"type\": \"number[]\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"method\",\n        \"name\": \"method\",\n        \"type\": \"string\"\n      },\n      {\n        \"tfName\": \"extrapolation_value\",\n        \"name\": \"extrapolationValue\",\n        \"type\": \"number\"\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"ImageProjectiveTransformV3\",\n    \"category\": \"image\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"images\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"transforms\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 2,\n        \"name\": \"outputShape\",\n        \"type\": \"number[]\"\n      },\n      {\n        \"start\": 3,\n        \"name\": \"fillValue\",\n        \"type\": \"number\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"interpolation\",\n        \"name\": \"interpolation\",\n        \"type\": \"string\"\n      },\n      {\n        \"tfName\": \"fill_mode\",\n        \"name\": \"fillMode\",\n        \"type\": \"string\"\n      }\n    ]\n  }\n];\n\n// node_modules/.pnpm/@tensorflow+tfjs-converter@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-converter/dist/operations/op_list/logical.js\nvar logical_exports = {};\n__export(logical_exports, {\n  json: () => json11\n});\nvar json11 = [\n  {\n    \"tfOpName\": \"Equal\",\n    \"category\": \"logical\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"a\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"b\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"T\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"NotEqual\",\n    \"category\": \"logical\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"a\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"b\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"T\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"Greater\",\n    \"category\": \"logical\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"a\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"b\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"T\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"GreaterEqual\",\n    \"category\": \"logical\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"a\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"b\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"T\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"Less\",\n    \"category\": \"logical\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"a\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"b\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"T\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"LessEqual\",\n    \"category\": \"logical\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"a\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"b\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"T\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"LogicalAnd\",\n    \"category\": \"logical\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"a\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"b\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"T\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"LogicalNot\",\n    \"category\": \"logical\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"a\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"T\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"LogicalOr\",\n    \"category\": \"logical\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"a\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"b\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"T\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"Select\",\n    \"category\": \"logical\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"condition\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"a\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 2,\n        \"name\": \"b\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"T\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"SelectV2\",\n    \"category\": \"logical\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"condition\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"a\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 2,\n        \"name\": \"b\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"T\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      }\n    ]\n  }\n];\n\n// node_modules/.pnpm/@tensorflow+tfjs-converter@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-converter/dist/operations/op_list/matrices.js\nvar matrices_exports = {};\n__export(matrices_exports, {\n  json: () => json12\n});\nvar json12 = [\n  {\n    \"tfOpName\": \"_FusedMatMul\",\n    \"category\": \"matrices\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"a\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"b\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 2,\n        \"end\": 0,\n        \"name\": \"args\",\n        \"type\": \"tensors\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"num_args\",\n        \"name\": \"numArgs\",\n        \"type\": \"number\"\n      },\n      {\n        \"tfName\": \"fused_ops\",\n        \"name\": \"fusedOps\",\n        \"type\": \"string[]\",\n        \"defaultValue\": []\n      },\n      {\n        \"tfName\": \"epsilon\",\n        \"name\": \"epsilon\",\n        \"type\": \"number\",\n        \"defaultValue\": 1e-4\n      },\n      {\n        \"tfName\": \"transpose_a\",\n        \"name\": \"transposeA\",\n        \"type\": \"bool\",\n        \"defaultValue\": false\n      },\n      {\n        \"tfName\": \"transpose_b\",\n        \"name\": \"transposeB\",\n        \"type\": \"bool\",\n        \"defaultValue\": false\n      },\n      {\n        \"tfName\": \"leakyrelu_alpha\",\n        \"name\": \"leakyreluAlpha\",\n        \"type\": \"number\",\n        \"defaultValue\": 0.2\n      },\n      {\n        \"tfName\": \"T\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"MatMul\",\n    \"category\": \"matrices\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"a\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"b\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"transpose_a\",\n        \"name\": \"transposeA\",\n        \"type\": \"bool\",\n        \"defaultValue\": false\n      },\n      {\n        \"tfName\": \"transpose_b\",\n        \"name\": \"transposeB\",\n        \"type\": \"bool\",\n        \"defaultValue\": false\n      },\n      {\n        \"tfName\": \"T\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"BatchMatMul\",\n    \"category\": \"matrices\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"a\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"b\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"adj_x\",\n        \"name\": \"transposeA\",\n        \"type\": \"bool\",\n        \"defaultValue\": false\n      },\n      {\n        \"tfName\": \"adj_y\",\n        \"name\": \"transposeB\",\n        \"type\": \"bool\",\n        \"defaultValue\": false\n      },\n      {\n        \"tfName\": \"T\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"BatchMatMulV2\",\n    \"category\": \"matrices\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"a\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"b\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"adj_x\",\n        \"name\": \"transposeA\",\n        \"type\": \"bool\",\n        \"defaultValue\": false\n      },\n      {\n        \"tfName\": \"adj_y\",\n        \"name\": \"transposeB\",\n        \"type\": \"bool\",\n        \"defaultValue\": false\n      },\n      {\n        \"tfName\": \"T\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"Transpose\",\n    \"category\": \"matrices\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"perm\",\n        \"type\": \"number[]\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"T\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"Einsum\",\n    \"category\": \"matrices\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"end\": 0,\n        \"name\": \"tensors\",\n        \"type\": \"tensors\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"equation\",\n        \"name\": \"equation\",\n        \"type\": \"string\"\n      },\n      {\n        \"tfName\": \"N\",\n        \"name\": \"n\",\n        \"type\": \"number\",\n        \"defaultValue\": 2\n      },\n      {\n        \"tfName\": \"T\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\"\n      }\n    ]\n  }\n];\n\n// node_modules/.pnpm/@tensorflow+tfjs-converter@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-converter/dist/operations/op_list/normalization.js\nvar normalization_exports = {};\n__export(normalization_exports, {\n  json: () => json13\n});\nvar json13 = [\n  {\n    \"tfOpName\": \"EuclideanNorm\",\n    \"category\": \"normalization\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"axis\",\n        \"type\": \"number[]\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"keep_dims\",\n        \"name\": \"keepDims\",\n        \"type\": \"bool\",\n        \"defaultValue\": false\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"FusedBatchNorm\",\n    \"category\": \"normalization\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"scale\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 2,\n        \"name\": \"offset\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 3,\n        \"name\": \"mean\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 4,\n        \"name\": \"variance\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"epsilon\",\n        \"name\": \"epsilon\",\n        \"type\": \"number\",\n        \"defaultValue\": 1e-3\n      },\n      {\n        \"tfName\": \"data_format\",\n        \"name\": \"dataFormat\",\n        \"type\": \"string\",\n        \"notSupported\": true\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"FusedBatchNormV2\",\n    \"category\": \"normalization\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"scale\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 2,\n        \"name\": \"offset\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 3,\n        \"name\": \"mean\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 4,\n        \"name\": \"variance\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"epsilon\",\n        \"name\": \"epsilon\",\n        \"type\": \"number\",\n        \"defaultValue\": 1e-3\n      },\n      {\n        \"tfName\": \"data_format\",\n        \"name\": \"dataFormat\",\n        \"type\": \"string\",\n        \"notSupported\": true\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"FusedBatchNormV3\",\n    \"category\": \"normalization\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"scale\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 2,\n        \"name\": \"offset\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 3,\n        \"name\": \"mean\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 4,\n        \"name\": \"variance\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"epsilon\",\n        \"name\": \"epsilon\",\n        \"type\": \"number\",\n        \"defaultValue\": 1e-3\n      },\n      {\n        \"tfName\": \"data_format\",\n        \"name\": \"dataFormat\",\n        \"type\": \"string\",\n        \"notSupported\": true\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"LRN\",\n    \"category\": \"normalization\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"depth_radius\",\n        \"name\": \"radius\",\n        \"type\": \"number\",\n        \"defaultValue\": 5\n      },\n      {\n        \"tfName\": \"bias\",\n        \"name\": \"bias\",\n        \"type\": \"number\",\n        \"defaultValue\": 1\n      },\n      {\n        \"tfName\": \"alpha\",\n        \"name\": \"alpha\",\n        \"type\": \"number\",\n        \"defaultValue\": 1\n      },\n      {\n        \"tfName\": \"beta\",\n        \"name\": \"beta\",\n        \"type\": \"number\",\n        \"defaultValue\": 0.5\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"Softmax\",\n    \"category\": \"normalization\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"LogSoftmax\",\n    \"category\": \"normalization\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"SparseToDense\",\n    \"category\": \"normalization\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"sparseIndices\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"outputShape\",\n        \"type\": \"number[]\"\n      },\n      {\n        \"start\": 2,\n        \"name\": \"sparseValues\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 3,\n        \"name\": \"defaultValue\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"validate_indices\",\n        \"name\": \"validateIndices\",\n        \"type\": \"bool\",\n        \"defaultValue\": true,\n        \"notSupported\": true\n      }\n    ]\n  }\n];\n\n// node_modules/.pnpm/@tensorflow+tfjs-converter@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-converter/dist/operations/op_list/reduction.js\nvar reduction_exports = {};\n__export(reduction_exports, {\n  json: () => json14\n});\nvar json14 = [\n  {\n    \"tfOpName\": \"Bincount\",\n    \"category\": \"reduction\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"size\",\n        \"type\": \"number\"\n      },\n      {\n        \"start\": 2,\n        \"name\": \"weights\",\n        \"type\": \"tensor\"\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"DenseBincount\",\n    \"category\": \"reduction\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"size\",\n        \"type\": \"number\"\n      },\n      {\n        \"start\": 2,\n        \"name\": \"weights\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"binary_output\",\n        \"name\": \"binaryOutput\",\n        \"type\": \"bool\"\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"Max\",\n    \"category\": \"reduction\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"axis\",\n        \"type\": \"number[]\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"keep_dims\",\n        \"name\": \"keepDims\",\n        \"type\": \"bool\"\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"Mean\",\n    \"category\": \"reduction\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"axis\",\n        \"type\": \"number[]\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"keep_dims\",\n        \"name\": \"keepDims\",\n        \"type\": \"bool\"\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"Min\",\n    \"category\": \"reduction\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"axis\",\n        \"type\": \"number[]\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"keep_dims\",\n        \"name\": \"keepDims\",\n        \"type\": \"bool\"\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"Sum\",\n    \"category\": \"reduction\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"axis\",\n        \"type\": \"number[]\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"keep_dims\",\n        \"name\": \"keepDims\",\n        \"type\": \"bool\"\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"All\",\n    \"category\": \"reduction\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"axis\",\n        \"type\": \"number[]\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"keep_dims\",\n        \"name\": \"keepDims\",\n        \"type\": \"bool\"\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"Any\",\n    \"category\": \"reduction\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"axis\",\n        \"type\": \"number[]\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"keep_dims\",\n        \"name\": \"keepDims\",\n        \"type\": \"bool\"\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"ArgMax\",\n    \"category\": \"reduction\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"axis\",\n        \"type\": \"number\"\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"ArgMin\",\n    \"category\": \"reduction\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"axis\",\n        \"type\": \"number\"\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"Prod\",\n    \"category\": \"reduction\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"axis\",\n        \"type\": \"number[]\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"keep_dims\",\n        \"name\": \"keepDims\",\n        \"type\": \"bool\"\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"Cumprod\",\n    \"category\": \"reduction\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"axis\",\n        \"type\": \"number\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"exclusive\",\n        \"name\": \"exclusive\",\n        \"type\": \"bool\"\n      },\n      {\n        \"tfName\": \"reverse\",\n        \"name\": \"reverse\",\n        \"type\": \"bool\"\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"Cumsum\",\n    \"category\": \"reduction\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"axis\",\n        \"type\": \"number\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"exclusive\",\n        \"name\": \"exclusive\",\n        \"type\": \"bool\"\n      },\n      {\n        \"tfName\": \"reverse\",\n        \"name\": \"reverse\",\n        \"type\": \"bool\"\n      }\n    ]\n  }\n];\n\n// node_modules/.pnpm/@tensorflow+tfjs-converter@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-converter/dist/operations/op_list/slice_join.js\nvar slice_join_exports = {};\n__export(slice_join_exports, {\n  json: () => json15\n});\nvar json15 = [\n  {\n    \"tfOpName\": \"ConcatV2\",\n    \"category\": \"slice_join\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"end\": -1,\n        \"name\": \"tensors\",\n        \"type\": \"tensors\"\n      },\n      {\n        \"start\": -1,\n        \"name\": \"axis\",\n        \"type\": \"number\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"N\",\n        \"name\": \"n\",\n        \"type\": \"number\",\n        \"defaultValue\": 2\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"Concat\",\n    \"category\": \"slice_join\",\n    \"inputs\": [\n      {\n        \"start\": 1,\n        \"end\": 0,\n        \"name\": \"tensors\",\n        \"type\": \"tensors\"\n      },\n      {\n        \"start\": 0,\n        \"name\": \"axis\",\n        \"type\": \"number\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"N\",\n        \"name\": \"n\",\n        \"type\": \"number\",\n        \"defaultValue\": 2\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"GatherV2\",\n    \"category\": \"slice_join\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"indices\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 2,\n        \"name\": \"axis\",\n        \"type\": \"number\",\n        \"defaultValue\": 0\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"batch_dims\",\n        \"name\": \"batchDims\",\n        \"type\": \"number\",\n        \"defaultValue\": 0\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"Gather\",\n    \"category\": \"slice_join\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"indices\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"validate_indices\",\n        \"name\": \"validateIndices\",\n        \"type\": \"bool\",\n        \"notSupported\": true\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"Reverse\",\n    \"category\": \"slice_join\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"dims\",\n        \"type\": \"bool[]\"\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"ReverseV2\",\n    \"category\": \"slice_join\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"axis\",\n        \"type\": \"number[]\"\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"Slice\",\n    \"category\": \"slice_join\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"begin\",\n        \"type\": \"number[]\"\n      },\n      {\n        \"start\": 2,\n        \"name\": \"size\",\n        \"type\": \"number[]\"\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"StridedSlice\",\n    \"category\": \"slice_join\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"begin\",\n        \"type\": \"number[]\"\n      },\n      {\n        \"start\": 2,\n        \"name\": \"end\",\n        \"type\": \"number[]\"\n      },\n      {\n        \"start\": 3,\n        \"name\": \"strides\",\n        \"type\": \"number[]\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"begin_mask\",\n        \"name\": \"beginMask\",\n        \"type\": \"number\",\n        \"defaultValue\": 0\n      },\n      {\n        \"tfName\": \"end_mask\",\n        \"name\": \"endMask\",\n        \"type\": \"number\",\n        \"defaultValue\": 0\n      },\n      {\n        \"tfName\": \"new_axis_mask\",\n        \"name\": \"newAxisMask\",\n        \"type\": \"number\",\n        \"defaultValue\": 0\n      },\n      {\n        \"tfName\": \"ellipsis_mask\",\n        \"name\": \"ellipsisMask\",\n        \"type\": \"number\",\n        \"defaultValue\": 0\n      },\n      {\n        \"tfName\": \"shrink_axis_mask\",\n        \"name\": \"shrinkAxisMask\",\n        \"type\": \"number\",\n        \"defaultValue\": 0\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"Pack\",\n    \"category\": \"slice_join\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"end\": 0,\n        \"name\": \"tensors\",\n        \"type\": \"tensors\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"axis\",\n        \"name\": \"axis\",\n        \"type\": \"number\",\n        \"defaultValue\": 0\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"Unpack\",\n    \"category\": \"slice_join\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"tensor\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"axis\",\n        \"name\": \"axis\",\n        \"type\": \"number\",\n        \"defaultValue\": 0\n      },\n      {\n        \"tfName\": \"num\",\n        \"name\": \"num\",\n        \"type\": \"number\",\n        \"defaultValue\": 0,\n        \"notSupported\": true\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"Tile\",\n    \"category\": \"slice_join\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"reps\",\n        \"type\": \"number[]\"\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"Split\",\n    \"category\": \"slice_join\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"axis\",\n        \"type\": \"number\",\n        \"defaultValue\": 0\n      },\n      {\n        \"start\": 1,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"num_split\",\n        \"name\": \"numOrSizeSplits\",\n        \"type\": \"number\",\n        \"defaultValue\": 1\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"SplitV\",\n    \"category\": \"slice_join\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"numOrSizeSplits\",\n        \"type\": \"number[]\"\n      },\n      {\n        \"start\": 2,\n        \"name\": \"axis\",\n        \"type\": \"number\",\n        \"defaultValue\": 0\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"ScatterNd\",\n    \"category\": \"slice_join\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"indices\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"values\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 2,\n        \"name\": \"shape\",\n        \"type\": \"number[]\"\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"GatherNd\",\n    \"category\": \"slice_join\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"indices\",\n        \"type\": \"tensor\"\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"SparseToDense\",\n    \"category\": \"slice_join\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"sparseIndices\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"outputShape\",\n        \"type\": \"number[]\"\n      },\n      {\n        \"start\": 2,\n        \"name\": \"sparseValues\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 3,\n        \"name\": \"defaultValue\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"validate_indices\",\n        \"name\": \"validateIndices\",\n        \"type\": \"bool\",\n        \"defaultValue\": false,\n        \"notSupported\": true\n      }\n    ]\n  }\n];\n\n// node_modules/.pnpm/@tensorflow+tfjs-converter@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-converter/dist/operations/op_list/sparse.js\nvar sparse_exports = {};\n__export(sparse_exports, {\n  json: () => json16\n});\nvar json16 = [\n  {\n    \"tfOpName\": \"SparseFillEmptyRows\",\n    \"category\": \"sparse\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"indices\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"values\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 2,\n        \"name\": \"denseShape\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 3,\n        \"name\": \"defaultValue\",\n        \"type\": \"tensor\"\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"SparseReshape\",\n    \"category\": \"sparse\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"inputIndices\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"inputShape\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 2,\n        \"name\": \"newShape\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"T\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"SparseSegmentMean\",\n    \"category\": \"sparse\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"data\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"indices\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 2,\n        \"name\": \"segmentIds\",\n        \"type\": \"tensor\"\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"SparseSegmentSum\",\n    \"category\": \"sparse\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"data\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"indices\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 2,\n        \"name\": \"segmentIds\",\n        \"type\": \"tensor\"\n      }\n    ]\n  }\n];\n\n// node_modules/.pnpm/@tensorflow+tfjs-converter@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-converter/dist/operations/op_list/spectral.js\nvar spectral_exports = {};\n__export(spectral_exports, {\n  json: () => json17\n});\nvar json17 = [\n  {\n    \"tfOpName\": \"FFT\",\n    \"category\": \"spectral\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"IFFT\",\n    \"category\": \"spectral\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"RFFT\",\n    \"category\": \"spectral\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"fft_length\",\n        \"type\": \"number\",\n        \"notSupported\": true\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"IRFFT\",\n    \"category\": \"spectral\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"fft_length\",\n        \"type\": \"number\",\n        \"notSupported\": true\n      }\n    ]\n  }\n];\n\n// node_modules/.pnpm/@tensorflow+tfjs-converter@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-converter/dist/operations/op_list/string.js\nvar string_exports = {};\n__export(string_exports, {\n  json: () => json18\n});\nvar json18 = [\n  {\n    \"tfOpName\": \"StringNGrams\",\n    \"category\": \"string\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"data\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"dataSplits\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"separator\",\n        \"name\": \"separator\",\n        \"type\": \"string\"\n      },\n      {\n        \"tfName\": \"ngram_widths\",\n        \"name\": \"nGramWidths\",\n        \"type\": \"number[]\"\n      },\n      {\n        \"tfName\": \"left_pad\",\n        \"name\": \"leftPad\",\n        \"type\": \"string\"\n      },\n      {\n        \"tfName\": \"right_pad\",\n        \"name\": \"rightPad\",\n        \"type\": \"string\"\n      },\n      {\n        \"tfName\": \"pad_width\",\n        \"name\": \"padWidth\",\n        \"type\": \"number\"\n      },\n      {\n        \"tfName\": \"preserve_short_sequences\",\n        \"name\": \"preserveShortSequences\",\n        \"type\": \"bool\"\n      }\n    ],\n    \"outputs\": [\n      \"ngrams\",\n      \"ngrams_splits\"\n    ]\n  },\n  {\n    \"tfOpName\": \"StringSplit\",\n    \"category\": \"string\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"input\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"delimiter\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"skip_empty\",\n        \"name\": \"skipEmpty\",\n        \"type\": \"bool\"\n      }\n    ],\n    \"outputs\": [\n      \"indices\",\n      \"values\",\n      \"shape\"\n    ]\n  },\n  {\n    \"tfOpName\": \"StringToHashBucketFast\",\n    \"category\": \"string\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"input\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"num_buckets\",\n        \"name\": \"numBuckets\",\n        \"type\": \"number\"\n      }\n    ]\n  }\n];\n\n// node_modules/.pnpm/@tensorflow+tfjs-converter@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-converter/dist/operations/op_list/transformation.js\nvar transformation_exports = {};\n__export(transformation_exports, {\n  json: () => json19\n});\nvar json19 = [\n  {\n    \"tfOpName\": \"Cast\",\n    \"category\": \"transformation\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"SrcT\",\n        \"name\": \"sdtype\",\n        \"type\": \"dtype\",\n        \"notSupported\": true\n      },\n      {\n        \"tfName\": \"DstT\",\n        \"name\": \"dtype\",\n        \"type\": \"dtype\"\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"ExpandDims\",\n    \"category\": \"transformation\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"axis\",\n        \"type\": \"number\"\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"MirrorPad\",\n    \"category\": \"transformation\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"padding\",\n        \"type\": \"number[]\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"mode\",\n        \"name\": \"mode\",\n        \"type\": \"string\"\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"Pad\",\n    \"category\": \"transformation\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"padding\",\n        \"type\": \"number[]\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"constant_value\",\n        \"name\": \"constantValue\",\n        \"type\": \"number\",\n        \"defaultValue\": 0\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"PadV2\",\n    \"category\": \"transformation\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"padding\",\n        \"type\": \"number[]\"\n      },\n      {\n        \"start\": 2,\n        \"name\": \"constantValue\",\n        \"type\": \"number\",\n        \"defaultValue\": 0\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"Reshape\",\n    \"category\": \"transformation\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"shape\",\n        \"type\": \"number[]\"\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"Squeeze\",\n    \"category\": \"transformation\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"axis\",\n        \"tfDeprecatedName\": \"squeeze_dims\",\n        \"name\": \"axis\",\n        \"type\": \"number[]\"\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"SpaceToBatchND\",\n    \"category\": \"transformation\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"blockShape\",\n        \"type\": \"number[]\"\n      },\n      {\n        \"start\": 2,\n        \"name\": \"paddings\",\n        \"type\": \"number[]\"\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"BatchToSpaceND\",\n    \"category\": \"transformation\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"blockShape\",\n        \"type\": \"number[]\"\n      },\n      {\n        \"start\": 2,\n        \"name\": \"crops\",\n        \"type\": \"number[]\"\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"DepthToSpace\",\n    \"category\": \"transformation\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": [\n      {\n        \"tfName\": \"block_size\",\n        \"name\": \"blockSize\",\n        \"type\": \"number\"\n      },\n      {\n        \"tfName\": \"data_format\",\n        \"name\": \"dataFormat\",\n        \"type\": \"string\"\n      }\n    ]\n  },\n  {\n    \"tfOpName\": \"BroadcastTo\",\n    \"category\": \"transformation\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"x\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"shape\",\n        \"type\": \"number[]\"\n      }\n    ],\n    \"attrs\": []\n  },\n  {\n    \"tfOpName\": \"BroadcastArgs\",\n    \"category\": \"transformation\",\n    \"inputs\": [\n      {\n        \"start\": 0,\n        \"name\": \"s0\",\n        \"type\": \"tensor\"\n      },\n      {\n        \"start\": 1,\n        \"name\": \"s1\",\n        \"type\": \"tensor\"\n      }\n    ],\n    \"attrs\": []\n  }\n];\n\n// node_modules/.pnpm/@tensorflow+tfjs-converter@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-converter/dist/operations/operation_mapper.js\nvar OperationMapper = class {\n  static get Instance() {\n    return this._instance || (this._instance = new this());\n  }\n  constructor() {\n    const ops = [\n      arithmetic_exports,\n      basic_math_exports,\n      control_exports,\n      convolution_exports,\n      creation_exports,\n      dynamic_exports,\n      evaluation_exports,\n      graph_exports,\n      hash_table_exports,\n      image_exports,\n      logical_exports,\n      matrices_exports,\n      normalization_exports,\n      reduction_exports,\n      slice_join_exports,\n      sparse_exports,\n      spectral_exports,\n      string_exports,\n      transformation_exports\n    ];\n    const mappersJson = [].concat(...ops.map((op2) => op2.json));\n    this.opMappers = mappersJson.reduce((map, mapper) => {\n      map[mapper.tfOpName] = mapper;\n      return map;\n    }, {});\n  }\n  transformGraph(graph, signature = {}) {\n    const tfNodes = graph.node;\n    const placeholders = [];\n    const weights = [];\n    const initNodes = [];\n    const nodes = tfNodes.reduce((map, node) => {\n      map[node.name] = this.mapNode(node);\n      if (node.op.startsWith(\"Placeholder\")) {\n        placeholders.push(map[node.name]);\n      } else if (node.op === \"Const\") {\n        weights.push(map[node.name]);\n      } else if (node.input == null || node.input.length === 0) {\n        initNodes.push(map[node.name]);\n      }\n      return map;\n    }, {});\n    let inputs = [];\n    const outputs = [];\n    let inputNodeNameToKey = {};\n    let outputNodeNameToKey = {};\n    if (signature != null) {\n      inputNodeNameToKey = this.mapSignatureEntries(signature.inputs);\n      outputNodeNameToKey = this.mapSignatureEntries(signature.outputs);\n    }\n    const allNodes = Object.keys(nodes);\n    allNodes.forEach((key) => {\n      const node = nodes[key];\n      node.inputNames.forEach((name, index) => {\n        const [nodeName, , outputName] = getNodeNameAndIndex(name);\n        const inputNode = nodes[nodeName];\n        if (inputNode.outputs != null) {\n          const outputIndex = inputNode.outputs.indexOf(outputName);\n          if (outputIndex !== -1) {\n            const inputName = `${nodeName}:${outputIndex}`;\n            node.inputNames[index] = inputName;\n          }\n        }\n        node.inputs.push(inputNode);\n        inputNode.children.push(node);\n      });\n    });\n    if (Object.keys(outputNodeNameToKey).length === 0) {\n      allNodes.forEach((key) => {\n        const node = nodes[key];\n        if (node.children.length === 0) {\n          outputs.push(node);\n        }\n      });\n    } else {\n      Object.keys(outputNodeNameToKey).forEach((name) => {\n        const [nodeName] = getNodeNameAndIndex(name);\n        const node = nodes[nodeName];\n        if (node != null) {\n          node.signatureKey = outputNodeNameToKey[name];\n          outputs.push(node);\n        }\n      });\n    }\n    if (Object.keys(inputNodeNameToKey).length > 0) {\n      Object.keys(inputNodeNameToKey).forEach((name) => {\n        const [nodeName] = getNodeNameAndIndex(name);\n        const node = nodes[nodeName];\n        if (node) {\n          node.signatureKey = inputNodeNameToKey[name];\n          inputs.push(node);\n        }\n      });\n    } else {\n      inputs = placeholders;\n    }\n    let functions = {};\n    if (graph.library != null && graph.library.function != null) {\n      functions = graph.library.function.reduce((functions2, func2) => {\n        functions2[func2.signature.name] = this.mapFunction(func2);\n        return functions2;\n      }, {});\n    }\n    const result = { nodes, inputs, outputs, weights, placeholders, signature, functions };\n    if (initNodes.length > 0) {\n      result.initNodes = initNodes;\n    }\n    return result;\n  }\n  mapSignatureEntries(entries) {\n    return Object.keys(entries || {}).reduce((prev, curr) => {\n      prev[entries[curr].name] = curr;\n      return prev;\n    }, {});\n  }\n  mapNode(node) {\n    const mapper = getRegisteredOp(node.op) || this.opMappers[node.op] || {};\n    if (node.attr == null) {\n      node.attr = {};\n    }\n    const newNode = {\n      name: node.name,\n      op: node.op,\n      category: mapper.category,\n      inputNames: (node.input || []).map((input2) => input2.startsWith(\"^\") ? input2.slice(1) : input2),\n      inputs: [],\n      children: [],\n      inputParams: {},\n      attrParams: {},\n      rawAttrs: node.attr,\n      outputs: mapper.outputs\n    };\n    if (mapper.inputs != null) {\n      newNode.inputParams = mapper.inputs.reduce((map, param) => {\n        map[param.name] = {\n          type: param.type,\n          inputIndexStart: param.start,\n          inputIndexEnd: param.end\n        };\n        return map;\n      }, {});\n    }\n    if (mapper.attrs != null) {\n      newNode.attrParams = mapper.attrs.reduce((map, param) => {\n        const type = param.type;\n        let value = void 0;\n        switch (param.type) {\n          case \"string\":\n            value = getStringParam(node.attr, param.tfName, param.defaultValue);\n            if (value === void 0 && !!param.tfDeprecatedName) {\n              value = getStringParam(node.attr, param.tfDeprecatedName, param.defaultValue);\n            }\n            break;\n          case \"string[]\":\n            value = getStringArrayParam(node.attr, param.tfName, param.defaultValue);\n            if (value === void 0 && !!param.tfDeprecatedName) {\n              value = getStringArrayParam(node.attr, param.tfDeprecatedName, param.defaultValue);\n            }\n            break;\n          case \"number\":\n            value = getNumberParam(node.attr, param.tfName, param.defaultValue || 0);\n            if (value === void 0 && !!param.tfDeprecatedName) {\n              value = getNumberParam(node.attr, param.tfDeprecatedName, param.defaultValue);\n            }\n            break;\n          case \"number[]\":\n            value = getNumericArrayParam(node.attr, param.tfName, param.defaultValue);\n            if (value === void 0 && !!param.tfDeprecatedName) {\n              value = getNumericArrayParam(node.attr, param.tfDeprecatedName, param.defaultValue);\n            }\n            break;\n          case \"bool\":\n            value = getBoolParam(node.attr, param.tfName, param.defaultValue);\n            if (value === void 0 && !!param.tfDeprecatedName) {\n              value = getBoolParam(node.attr, param.tfDeprecatedName, param.defaultValue);\n            }\n            break;\n          case \"bool[]\":\n            value = getBoolArrayParam(node.attr, param.tfName, param.defaultValue);\n            if (value === void 0 && !!param.tfDeprecatedName) {\n              value = getBoolArrayParam(node.attr, param.tfDeprecatedName, param.defaultValue);\n            }\n            break;\n          case \"shape\":\n            value = getTensorShapeParam(node.attr, param.tfName, param.defaultValue);\n            if (value === void 0 && !!param.tfDeprecatedName) {\n              value = getTensorShapeParam(node.attr, param.tfDeprecatedName, param.defaultValue);\n            }\n            break;\n          case \"shape[]\":\n            value = getTensorShapeArrayParam(node.attr, param.tfName, param.defaultValue);\n            if (value === void 0 && !!param.tfDeprecatedName) {\n              value = getTensorShapeArrayParam(node.attr, param.tfDeprecatedName, param.defaultValue);\n            }\n            break;\n          case \"dtype\":\n            value = getDtypeParam(node.attr, param.tfName, param.defaultValue);\n            if (value === void 0 && !!param.tfDeprecatedName) {\n              value = getDtypeParam(node.attr, param.tfDeprecatedName, param.defaultValue);\n            }\n            break;\n          case \"dtype[]\":\n            value = getDtypeArrayParam(node.attr, param.tfName, param.defaultValue);\n            if (value === void 0 && !!param.tfDeprecatedName) {\n              value = getDtypeArrayParam(node.attr, param.tfDeprecatedName, param.defaultValue);\n            }\n            break;\n          case \"func\":\n            value = getFuncParam(node.attr, param.tfName, param.defaultValue);\n            if (value === void 0 && !!param.tfDeprecatedName) {\n              value = getFuncParam(node.attr, param.tfDeprecatedName, param.defaultValue);\n            }\n            break;\n          case \"tensor\":\n          case \"tensors\":\n            break;\n          default:\n            throw new Error(`Unsupported param type: ${param.type} for op: ${node.op}`);\n        }\n        map[param.name] = { value, type };\n        return map;\n      }, {});\n    }\n    return newNode;\n  }\n  mapFunction(functionDef) {\n    const tfNodes = functionDef.nodeDef;\n    const placeholders = [];\n    const weights = [];\n    let nodes = {};\n    if (tfNodes != null) {\n      nodes = tfNodes.reduce((map, node) => {\n        map[node.name] = this.mapNode(node);\n        if (node.op === \"Const\") {\n          weights.push(map[node.name]);\n        }\n        return map;\n      }, {});\n    }\n    const inputs = [];\n    const outputs = [];\n    functionDef.signature.inputArg.forEach((arg) => {\n      const [nodeName] = getNodeNameAndIndex(arg.name);\n      const node = {\n        name: nodeName,\n        op: \"Placeholder\",\n        inputs: [],\n        inputNames: [],\n        category: \"graph\",\n        inputParams: {},\n        attrParams: { dtype: { value: parseDtypeParam(arg.type), type: \"dtype\" } },\n        children: []\n      };\n      node.signatureKey = arg.name;\n      inputs.push(node);\n      nodes[nodeName] = node;\n    });\n    const allNodes = Object.keys(nodes);\n    allNodes.forEach((key) => {\n      const node = nodes[key];\n      node.inputNames.forEach((name, index) => {\n        const [nodeName, , outputName] = getNodeNameAndIndex(name);\n        const inputNode = nodes[nodeName];\n        if (inputNode.outputs != null) {\n          const outputIndex = inputNode.outputs.indexOf(outputName);\n          if (outputIndex !== -1) {\n            const inputName = `${nodeName}:${outputIndex}`;\n            node.inputNames[index] = inputName;\n          }\n        }\n        node.inputs.push(inputNode);\n        inputNode.children.push(node);\n      });\n    });\n    const returnNodeMap = functionDef.ret;\n    functionDef.signature.outputArg.forEach((output) => {\n      const [nodeName, index] = getNodeNameAndIndex(returnNodeMap[output.name]);\n      const node = nodes[nodeName];\n      if (node != null) {\n        node.defaultOutput = index;\n        outputs.push(node);\n      }\n    });\n    const signature = this.mapArgsToSignature(functionDef);\n    return { nodes, inputs, outputs, weights, placeholders, signature };\n  }\n  mapArgsToSignature(functionDef) {\n    return {\n      methodName: functionDef.signature.name,\n      inputs: functionDef.signature.inputArg.reduce((map, arg) => {\n        map[arg.name] = this.mapArgToTensorInfo(arg);\n        return map;\n      }, {}),\n      outputs: functionDef.signature.outputArg.reduce((map, arg) => {\n        map[arg.name] = this.mapArgToTensorInfo(arg, functionDef.ret);\n        return map;\n      }, {})\n    };\n  }\n  mapArgToTensorInfo(arg, nameMap2) {\n    let name = arg.name;\n    if (nameMap2 != null) {\n      name = nameMap2[name];\n    }\n    return { name, dtype: arg.type };\n  }\n};\nfunction decodeBase64(text) {\n  const global2 = env().global;\n  if (typeof global2.atob !== \"undefined\") {\n    return global2.atob(text);\n  } else if (typeof Buffer !== \"undefined\") {\n    return new Buffer(text, \"base64\").toString();\n  } else {\n    throw new Error(\"Unable to decode base64 in this environment. Missing built-in atob() or Buffer()\");\n  }\n}\nfunction parseStringParam(s, keepCase) {\n  const value = Array.isArray(s) ? String.fromCharCode.apply(null, s) : decodeBase64(s);\n  return keepCase ? value : value.toLowerCase();\n}\nfunction getStringParam(attrs, name, def, keepCase = false) {\n  const param = attrs[name];\n  if (param != null) {\n    return parseStringParam(param.s, keepCase);\n  }\n  return def;\n}\nfunction getBoolParam(attrs, name, def) {\n  const param = attrs[name];\n  return param ? param.b : def;\n}\nfunction getNumberParam(attrs, name, def) {\n  const param = attrs[name] || {};\n  const value = param[\"i\"] != null ? param[\"i\"] : param[\"f\"] != null ? param[\"f\"] : def;\n  return typeof value === \"number\" ? value : parseInt(value, 10);\n}\nfunction parseDtypeParam(value) {\n  if (typeof value === \"string\") {\n    value = DataType[value];\n  }\n  switch (value) {\n    case DataType.DT_FLOAT:\n    case DataType.DT_HALF:\n      return \"float32\";\n    case DataType.DT_INT32:\n    case DataType.DT_INT64:\n    case DataType.DT_INT8:\n    case DataType.DT_UINT8:\n      return \"int32\";\n    case DataType.DT_BOOL:\n      return \"bool\";\n    case DataType.DT_DOUBLE:\n      return \"float32\";\n    case DataType.DT_STRING:\n      return \"string\";\n    default:\n      return null;\n  }\n}\nfunction getFuncParam(attrs, name, def) {\n  const param = attrs[name];\n  if (param && param.func) {\n    return param.func.name;\n  }\n  return def;\n}\nfunction getDtypeParam(attrs, name, def) {\n  const param = attrs[name];\n  if (param && param.type) {\n    return parseDtypeParam(param.type);\n  }\n  return def;\n}\nfunction getDtypeArrayParam(attrs, name, def) {\n  const param = attrs[name];\n  if (param && param.list && param.list.type) {\n    return param.list.type.map((v) => parseDtypeParam(v));\n  }\n  return def;\n}\nfunction parseTensorShapeParam(shape) {\n  if (shape.unknownRank) {\n    return void 0;\n  }\n  if (shape.dim != null) {\n    return shape.dim.map((dim) => typeof dim.size === \"number\" ? dim.size : parseInt(dim.size, 10));\n  }\n  return [];\n}\nfunction getTensorShapeParam(attrs, name, def) {\n  const param = attrs[name];\n  if (param && param.shape) {\n    return parseTensorShapeParam(param.shape);\n  }\n  return def;\n}\nfunction getNumericArrayParam(attrs, name, def) {\n  const param = attrs[name];\n  if (param) {\n    return ((param.list.f && param.list.f.length ? param.list.f : param.list.i) || []).map((v) => typeof v === \"number\" ? v : parseInt(v, 10));\n  }\n  return def;\n}\nfunction getStringArrayParam(attrs, name, def, keepCase = false) {\n  const param = attrs[name];\n  if (param && param.list && param.list.s) {\n    return param.list.s.map((v) => {\n      return parseStringParam(v, keepCase);\n    });\n  }\n  return def;\n}\nfunction getTensorShapeArrayParam(attrs, name, def) {\n  const param = attrs[name];\n  if (param && param.list && param.list.shape) {\n    return param.list.shape.map((v) => {\n      return parseTensorShapeParam(v);\n    });\n  }\n  return def;\n}\nfunction getBoolArrayParam(attrs, name, def) {\n  const param = attrs[name];\n  if (param && param.list && param.list.b) {\n    return param.list.b;\n  }\n  return def;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-converter@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-converter/dist/operations/custom_op/node_value_impl.js\nvar NodeValueImpl = class {\n  constructor(node, tensorMap, context) {\n    this.node = node;\n    this.tensorMap = tensorMap;\n    this.context = context;\n    this.inputs = [];\n    this.attrs = {};\n    this.inputs = node.inputNames.map((name) => this.getInput(name));\n    if (node.rawAttrs != null) {\n      this.attrs = Object.keys(node.rawAttrs).reduce((attrs, key) => {\n        attrs[key] = this.getAttr(key);\n        return attrs;\n      }, {});\n    }\n  }\n  getInput(name) {\n    return getTensor(name, this.tensorMap, this.context);\n  }\n  getAttr(name, defaultValue) {\n    const value = this.node.rawAttrs[name];\n    if (value.tensor != null) {\n      return getTensor(name, this.tensorMap, this.context);\n    }\n    if (value.i != null || value.f != null) {\n      return getNumberParam(this.node.rawAttrs, name, defaultValue);\n    }\n    if (value.s != null) {\n      return getStringParam(this.node.rawAttrs, name, defaultValue);\n    }\n    if (value.b != null) {\n      return getBoolParam(this.node.rawAttrs, name, defaultValue);\n    }\n    if (value.shape != null) {\n      return getTensorShapeParam(this.node.rawAttrs, name, defaultValue);\n    }\n    if (value.type != null) {\n      return getDtypeParam(this.node.rawAttrs, name, defaultValue);\n    }\n    if (value.list != null) {\n      if (value.list.i != null || value.list.f != null) {\n        return getNumericArrayParam(this.node.rawAttrs, name, defaultValue);\n      }\n      if (value.list.s != null) {\n        return getStringArrayParam(this.node.rawAttrs, name, defaultValue);\n      }\n      if (value.list.shape != null) {\n        return getTensorShapeArrayParam(this.node.rawAttrs, name, defaultValue);\n      }\n      if (value.list.b != null) {\n        return getBoolArrayParam(this.node.rawAttrs, name, defaultValue);\n      }\n      if (value.list.type != null) {\n        return getDtypeArrayParam(this.node.rawAttrs, name, defaultValue);\n      }\n    }\n    return defaultValue;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/ops_for_converter.js\nvar ops_for_converter_exports = {};\n__export(ops_for_converter_exports, {\n  OP_SCOPE_SUFFIX: () => OP_SCOPE_SUFFIX,\n  abs: () => abs,\n  acos: () => acos,\n  acosh: () => acosh,\n  add: () => add2,\n  addN: () => addN,\n  all: () => all,\n  any: () => any,\n  argMax: () => argMax,\n  argMin: () => argMin,\n  asin: () => asin,\n  asinh: () => asinh,\n  atan: () => atan,\n  atan2: () => atan2,\n  atanh: () => atanh,\n  avgPool: () => avgPool,\n  avgPool3d: () => avgPool3d,\n  basicLSTMCell: () => basicLSTMCell,\n  batchNorm: () => batchNorm,\n  batchNorm2d: () => batchNorm2d,\n  batchNorm3d: () => batchNorm3d,\n  batchNorm4d: () => batchNorm4d,\n  batchToSpaceND: () => batchToSpaceND,\n  bincount: () => bincount,\n  booleanMaskAsync: () => booleanMaskAsync,\n  broadcastArgs: () => broadcastArgs,\n  broadcastTo: () => broadcastTo,\n  buffer: () => buffer,\n  cast: () => cast,\n  ceil: () => ceil,\n  clipByValue: () => clipByValue,\n  clone: () => clone,\n  complex: () => complex,\n  concat: () => concat,\n  concat1d: () => concat1d,\n  concat2d: () => concat2d,\n  concat3d: () => concat3d,\n  concat4d: () => concat4d,\n  conv1d: () => conv1d,\n  conv2d: () => conv2d,\n  conv2dTranspose: () => conv2dTranspose,\n  conv3d: () => conv3d,\n  conv3dTranspose: () => conv3dTranspose,\n  cos: () => cos,\n  cosh: () => cosh,\n  cosineWindow: () => cosineWindow,\n  cumprod: () => cumprod,\n  cumsum: () => cumsum,\n  denseBincount: () => denseBincount,\n  depthToSpace: () => depthToSpace,\n  depthwiseConv2d: () => depthwiseConv2d,\n  diag: () => diag,\n  dilation2d: () => dilation2d,\n  div: () => div,\n  divNoNan: () => divNoNan,\n  dot: () => dot,\n  dropout: () => dropout,\n  einsum: () => einsum,\n  elu: () => elu,\n  enclosingPowerOfTwo: () => enclosingPowerOfTwo,\n  equal: () => equal,\n  erf: () => erf,\n  euclideanNorm: () => euclideanNorm,\n  exp: () => exp,\n  expandDims: () => expandDims,\n  expm1: () => expm1,\n  eye: () => eye,\n  fft: () => fft,\n  fill: () => fill,\n  floor: () => floor,\n  floorDiv: () => floorDiv,\n  fused: () => fused_ops_exports,\n  gather: () => gather,\n  gatherND: () => gatherND,\n  greater: () => greater,\n  greaterEqual: () => greaterEqual,\n  ifft: () => ifft,\n  imag: () => imag,\n  image: () => image,\n  inTopKAsync: () => inTopKAsync,\n  irfft: () => irfft,\n  isFinite: () => isFinite2,\n  isInf: () => isInf,\n  isNaN: () => isNaN2,\n  leakyRelu: () => leakyRelu,\n  less: () => less,\n  lessEqual: () => lessEqual,\n  linalg: () => linalg,\n  linspace: () => linspace,\n  localResponseNormalization: () => localResponseNormalization,\n  log: () => log2,\n  log1p: () => log1p,\n  logSigmoid: () => logSigmoid,\n  logSoftmax: () => logSoftmax,\n  logSumExp: () => logSumExp,\n  logicalAnd: () => logicalAnd,\n  logicalNot: () => logicalNot,\n  logicalOr: () => logicalOr,\n  logicalXor: () => logicalXor,\n  losses: () => losses,\n  lowerBound: () => lowerBound,\n  matMul: () => matMul,\n  max: () => max,\n  maxPool: () => maxPool,\n  maxPool3d: () => maxPool3d,\n  maxPoolWithArgmax: () => maxPoolWithArgmax,\n  maximum: () => maximum,\n  mean: () => mean,\n  meshgrid: () => meshgrid,\n  min: () => min,\n  minimum: () => minimum,\n  mirrorPad: () => mirrorPad,\n  mod: () => mod,\n  moments: () => moments,\n  movingAverage: () => movingAverage,\n  mul: () => mul,\n  multiRNNCell: () => multiRNNCell,\n  multinomial: () => multinomial,\n  neg: () => neg,\n  norm: () => norm,\n  notEqual: () => notEqual,\n  oneHot: () => oneHot,\n  ones: () => ones2,\n  onesLike: () => onesLike,\n  op: () => op,\n  outerProduct: () => outerProduct,\n  pad: () => pad,\n  pad1d: () => pad1d,\n  pad2d: () => pad2d,\n  pad3d: () => pad3d,\n  pad4d: () => pad4d,\n  pool: () => pool,\n  pow: () => pow,\n  prelu: () => prelu,\n  print: () => print,\n  prod: () => prod,\n  raggedTensorToTensor: () => raggedTensorToTensor,\n  rand: () => rand,\n  randomGamma: () => randomGamma,\n  randomNormal: () => randomNormal,\n  randomStandardNormal: () => randomStandardNormal,\n  randomUniform: () => randomUniform,\n  range: () => range,\n  real: () => real,\n  reciprocal: () => reciprocal,\n  relu: () => relu,\n  relu6: () => relu6,\n  reshape: () => reshape,\n  reverse: () => reverse,\n  reverse1d: () => reverse1d,\n  reverse2d: () => reverse2d,\n  reverse3d: () => reverse3d,\n  reverse4d: () => reverse4d,\n  rfft: () => rfft,\n  round: () => round2,\n  rsqrt: () => rsqrt,\n  scalar: () => scalar,\n  scatterND: () => scatterND,\n  searchSorted: () => searchSorted,\n  selu: () => selu,\n  separableConv2d: () => separableConv2d,\n  setdiff1dAsync: () => setdiff1dAsync,\n  sigmoid: () => sigmoid,\n  sign: () => sign,\n  signal: () => signal,\n  sin: () => sin,\n  sinh: () => sinh,\n  slice: () => slice,\n  slice1d: () => slice1d,\n  slice2d: () => slice2d,\n  slice3d: () => slice3d,\n  slice4d: () => slice4d,\n  softmax: () => softmax,\n  softplus: () => softplus,\n  spaceToBatchND: () => spaceToBatchND,\n  sparse: () => sparse,\n  sparseToDense: () => sparseToDense,\n  spectral: () => spectral,\n  split: () => split,\n  sqrt: () => sqrt,\n  square: () => square,\n  squaredDifference: () => squaredDifference,\n  squeeze: () => squeeze,\n  stack: () => stack,\n  step: () => step,\n  stridedSlice: () => stridedSlice,\n  string: () => string,\n  sub: () => sub,\n  sum: () => sum2,\n  tan: () => tan,\n  tanh: () => tanh2,\n  tensor: () => tensor,\n  tensor1d: () => tensor1d,\n  tensor2d: () => tensor2d,\n  tensor3d: () => tensor3d,\n  tensor4d: () => tensor4d,\n  tensor5d: () => tensor5d,\n  tensor6d: () => tensor6d,\n  tile: () => tile,\n  topk: () => topk,\n  transpose: () => transpose,\n  truncatedNormal: () => truncatedNormal,\n  unique: () => unique,\n  unsortedSegmentSum: () => unsortedSegmentSum,\n  unstack: () => unstack,\n  upperBound: () => upperBound,\n  variable: () => variable,\n  where: () => where,\n  whereAsync: () => whereAsync,\n  zeros: () => zeros,\n  zerosLike: () => zerosLike\n});\n\n// node_modules/.pnpm/@tensorflow+tfjs-converter@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-converter/dist/operations/executors/arithmetic_executor.js\nvar executeOp = (node, tensorMap, context, ops = ops_for_converter_exports) => {\n  switch (node.op) {\n    case \"BiasAdd\":\n    case \"AddV2\":\n    case \"Add\": {\n      return [ops.add(getParamValue(\"a\", node, tensorMap, context), getParamValue(\"b\", node, tensorMap, context))];\n    }\n    case \"AddN\": {\n      return [ops.addN(getParamValue(\"tensors\", node, tensorMap, context))];\n    }\n    case \"FloorMod\":\n    case \"Mod\":\n      return [ops.mod(getParamValue(\"a\", node, tensorMap, context), getParamValue(\"b\", node, tensorMap, context))];\n    case \"Mul\":\n      return [ops.mul(getParamValue(\"a\", node, tensorMap, context), getParamValue(\"b\", node, tensorMap, context))];\n    case \"RealDiv\":\n    case \"Div\": {\n      return [ops.div(getParamValue(\"a\", node, tensorMap, context), getParamValue(\"b\", node, tensorMap, context))];\n    }\n    case \"DivNoNan\": {\n      return [ops.divNoNan(getParamValue(\"a\", node, tensorMap, context), getParamValue(\"b\", node, tensorMap, context))];\n    }\n    case \"FloorDiv\": {\n      return [ops.floorDiv(getParamValue(\"a\", node, tensorMap, context), getParamValue(\"b\", node, tensorMap, context))];\n    }\n    case \"Sub\": {\n      return [ops.sub(getParamValue(\"a\", node, tensorMap, context), getParamValue(\"b\", node, tensorMap, context))];\n    }\n    case \"Minimum\": {\n      return [ops.minimum(getParamValue(\"a\", node, tensorMap, context), getParamValue(\"b\", node, tensorMap, context))];\n    }\n    case \"Maximum\": {\n      return [ops.maximum(getParamValue(\"a\", node, tensorMap, context), getParamValue(\"b\", node, tensorMap, context))];\n    }\n    case \"Pow\": {\n      return [ops.pow(getParamValue(\"a\", node, tensorMap, context), getParamValue(\"b\", node, tensorMap, context))];\n    }\n    case \"SquaredDifference\": {\n      return [ops.squaredDifference(getParamValue(\"a\", node, tensorMap, context), getParamValue(\"b\", node, tensorMap, context))];\n    }\n    default:\n      throw TypeError(`Node type ${node.op} is not implemented`);\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-converter@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-converter/dist/operations/executors/basic_math_executor.js\nvar executeOp2 = (node, tensorMap, context, ops = ops_for_converter_exports) => {\n  switch (node.op) {\n    case \"Abs\":\n    case \"ComplexAbs\":\n      return [ops.abs(getParamValue(\"x\", node, tensorMap, context))];\n    case \"Acos\":\n      return [ops.acos(getParamValue(\"x\", node, tensorMap, context))];\n    case \"Acosh\":\n      return [ops.acosh(getParamValue(\"x\", node, tensorMap, context))];\n    case \"Asin\":\n      return [ops.asin(getParamValue(\"x\", node, tensorMap, context))];\n    case \"Asinh\":\n      return [ops.asinh(getParamValue(\"x\", node, tensorMap, context))];\n    case \"Atan\":\n      return [ops.atan(getParamValue(\"x\", node, tensorMap, context))];\n    case \"Atan2\":\n      return [ops.atan2(getParamValue(\"x\", node, tensorMap, context), getParamValue(\"y\", node, tensorMap, context))];\n    case \"Atanh\":\n      return [ops.atanh(getParamValue(\"x\", node, tensorMap, context))];\n    case \"Ceil\":\n      return [ops.ceil(getParamValue(\"x\", node, tensorMap, context))];\n    case \"Complex\":\n      return [ops.complex(getParamValue(\"real\", node, tensorMap, context), getParamValue(\"imag\", node, tensorMap, context))];\n    case \"Cos\":\n      return [ops.cos(getParamValue(\"x\", node, tensorMap, context))];\n    case \"Cosh\":\n      return [ops.cosh(getParamValue(\"x\", node, tensorMap, context))];\n    case \"Elu\":\n      return [ops.elu(getParamValue(\"x\", node, tensorMap, context))];\n    case \"Erf\":\n      return [ops.erf(getParamValue(\"x\", node, tensorMap, context))];\n    case \"Exp\":\n      return [ops.exp(getParamValue(\"x\", node, tensorMap, context))];\n    case \"Expm1\": {\n      return [ops.expm1(getParamValue(\"x\", node, tensorMap, context))];\n    }\n    case \"Floor\":\n      return [ops.floor(getParamValue(\"x\", node, tensorMap, context))];\n    case \"Log\":\n      return [ops.log(getParamValue(\"x\", node, tensorMap, context))];\n    case \"Log1p\": {\n      return [ops.log1p(getParamValue(\"x\", node, tensorMap, context))];\n    }\n    case \"Imag\":\n      return [ops.imag(getParamValue(\"x\", node, tensorMap, context))];\n    case \"Neg\":\n      return [ops.neg(getParamValue(\"x\", node, tensorMap, context))];\n    case \"Reciprocal\": {\n      return [ops.reciprocal(getParamValue(\"x\", node, tensorMap, context))];\n    }\n    case \"Real\":\n      return [ops.real(getParamValue(\"x\", node, tensorMap, context))];\n    case \"Relu\":\n      return [ops.relu(getParamValue(\"x\", node, tensorMap, context))];\n    case \"Round\": {\n      return [ops.round(getParamValue(\"x\", node, tensorMap, context))];\n    }\n    case \"Selu\":\n      return [ops.selu(getParamValue(\"x\", node, tensorMap, context))];\n    case \"Sigmoid\":\n      return [ops.sigmoid(getParamValue(\"x\", node, tensorMap, context))];\n    case \"Sin\":\n      return [ops.sin(getParamValue(\"x\", node, tensorMap, context))];\n    case \"Sign\": {\n      return [ops.sign(getParamValue(\"x\", node, tensorMap, context))];\n    }\n    case \"Sinh\": {\n      return [ops.sinh(getParamValue(\"x\", node, tensorMap, context))];\n    }\n    case \"Softplus\": {\n      return [ops.softplus(getParamValue(\"x\", node, tensorMap, context))];\n    }\n    case \"Sqrt\": {\n      return [ops.sqrt(getParamValue(\"x\", node, tensorMap, context))];\n    }\n    case \"Square\": {\n      return [ops.square(getParamValue(\"x\", node, tensorMap, context))];\n    }\n    case \"Tanh\": {\n      return [ops.tanh(getParamValue(\"x\", node, tensorMap, context))];\n    }\n    case \"Tan\":\n      return [ops.tan(getParamValue(\"x\", node, tensorMap, context))];\n    case \"ClipByValue\":\n      return [ops.clipByValue(getParamValue(\"x\", node, tensorMap, context), getParamValue(\"clipValueMin\", node, tensorMap, context), getParamValue(\"clipValueMax\", node, tensorMap, context))];\n    case \"Relu6\":\n      return [ops.relu6(getParamValue(\"x\", node, tensorMap, context))];\n    case \"Rsqrt\":\n      return [ops.rsqrt(getTensor(node.inputNames[0], tensorMap, context))];\n    case \"Prod\":\n      return [ops.prod(getParamValue(\"x\", node, tensorMap, context), getParamValue(\"axes\", node, tensorMap, context))];\n    case \"LeakyRelu\":\n      return [ops.leakyRelu(getParamValue(\"x\", node, tensorMap, context), getParamValue(\"alpha\", node, tensorMap, context))];\n    case \"Prelu\":\n      return [ops.prelu(getParamValue(\"x\", node, tensorMap, context), getParamValue(\"alpha\", node, tensorMap, context))];\n    case \"IsNan\":\n      return [ops.isNaN(getTensor(node.inputNames[0], tensorMap, context))];\n    default:\n      throw TypeError(`Node type ${node.op} is not implemented`);\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-converter@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-converter/dist/executor/tensor_utils.js\nfunction assertShapesMatchAllowUndefinedSize(shapeA, shapeB, errorMessagePrefix = \"\") {\n  if (typeof shapeA === \"number\" || typeof shapeB === \"number\") {\n    return;\n  }\n  util_exports.assert(shapeA.length === shapeB.length, () => errorMessagePrefix + ` Shapes ${shapeA} and ${shapeB} must match`);\n  for (let i = 0; i < shapeA.length; i++) {\n    const dim0 = shapeA[i];\n    const dim1 = shapeB[i];\n    util_exports.assert(dim0 < 0 || dim1 < 0 || dim0 === dim1, () => errorMessagePrefix + ` Shapes ${shapeA} and ${shapeB} must match`);\n  }\n}\nfunction fullDefinedShape(elementShape) {\n  if (typeof elementShape === \"number\" || elementShape.some((dim) => dim < 0)) {\n    return false;\n  }\n  return true;\n}\nfunction inferElementShape(listElementShape, tensors, elementShape) {\n  let partialShape = mergeElementShape(listElementShape, elementShape);\n  const notfullDefinedShape = !fullDefinedShape(partialShape);\n  if (notfullDefinedShape && tensors.length === 0) {\n    throw new Error(`Tried to calculate elements of an empty list with non-fully-defined elementShape: ${partialShape}`);\n  }\n  if (notfullDefinedShape) {\n    tensors.forEach((tensor2) => {\n      partialShape = mergeElementShape(tensor2.shape, partialShape);\n    });\n  }\n  if (!fullDefinedShape(partialShape)) {\n    throw new Error(`Non-fully-defined elementShape: ${partialShape}`);\n  }\n  return partialShape;\n}\nfunction mergeElementShape(elementShapeA, elementShapeB) {\n  if (typeof elementShapeA === \"number\") {\n    return elementShapeB;\n  }\n  if (typeof elementShapeB === \"number\") {\n    return elementShapeA;\n  }\n  if (elementShapeA.length !== elementShapeB.length) {\n    throw new Error(`Incompatible ranks during merge: ${elementShapeA} vs. ${elementShapeB}`);\n  }\n  const result = [];\n  for (let i = 0; i < elementShapeA.length; ++i) {\n    const dim0 = elementShapeA[i];\n    const dim1 = elementShapeB[i];\n    if (dim0 >= 0 && dim1 >= 0 && dim0 !== dim1) {\n      throw new Error(`Incompatible shape during merge: ${elementShapeA} vs. ${elementShapeB}`);\n    }\n    result[i] = dim0 >= 0 ? dim0 : dim1;\n  }\n  return result;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-converter@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-converter/dist/executor/tensor_array.js\nvar TensorArray = class {\n  constructor(name, dtype, maxSize, elementShape, identicalElementShapes, dynamicSize, clearAfterRead) {\n    this.name = name;\n    this.dtype = dtype;\n    this.maxSize = maxSize;\n    this.elementShape = elementShape;\n    this.identicalElementShapes = identicalElementShapes;\n    this.dynamicSize = dynamicSize;\n    this.clearAfterRead = clearAfterRead;\n    this.tensors = [];\n    this.closed_ = false;\n    this.idTensor = scalar(0);\n    keep(this.idTensor);\n  }\n  get id() {\n    return this.idTensor.id;\n  }\n  get closed() {\n    return this.closed_;\n  }\n  clearAndClose(keepIds) {\n    this.tensors.forEach((tensor2) => {\n      if (keepIds == null || !keepIds.has(tensor2.tensor.id)) {\n        tensor2.tensor.dispose();\n      }\n    });\n    this.tensors = [];\n    this.closed_ = true;\n    this.idTensor.dispose();\n  }\n  size() {\n    return this.tensors.length;\n  }\n  read(index) {\n    if (this.closed_) {\n      throw new Error(`TensorArray ${this.name} has already been closed.`);\n    }\n    if (index < 0 || index >= this.size()) {\n      throw new Error(`Tried to read from index ${index}, but array size is: ${this.size()}`);\n    }\n    const tensorWithState = this.tensors[index];\n    if (tensorWithState.cleared) {\n      throw new Error(`TensorArray ${this.name}: Could not read index ${index} twice because it was cleared after a previous read (perhaps try setting clear_after_read = false?).`);\n    }\n    if (this.clearAfterRead) {\n      tensorWithState.cleared = true;\n    }\n    tensorWithState.read = true;\n    return tensorWithState.tensor;\n  }\n  readMany(indices) {\n    return indices.map((index) => this.read(index));\n  }\n  write(index, tensor2) {\n    if (this.closed_) {\n      throw new Error(`TensorArray ${this.name} has already been closed.`);\n    }\n    if (index < 0 || !this.dynamicSize && index >= this.maxSize) {\n      throw new Error(`Tried to write to index ${index}, but array is not resizeable and size is: ${this.maxSize}`);\n    }\n    const t = this.tensors[index] || {};\n    if (tensor2.dtype !== this.dtype) {\n      throw new Error(`TensorArray ${this.name}: Could not write to TensorArray index ${index},\n          because the value dtype is ${tensor2.dtype}, but TensorArray dtype is ${this.dtype}.`);\n    }\n    if (this.size() === 0 && (this.elementShape == null || this.elementShape.length === 0)) {\n      this.elementShape = tensor2.shape;\n    }\n    assertShapesMatchAllowUndefinedSize(this.elementShape, tensor2.shape, `TensorArray ${this.name}: Could not write to TensorArray index ${index}.`);\n    if (t.read) {\n      throw new Error(`TensorArray ${this.name}: Could not write to TensorArray index ${index}, because it has already been read.`);\n    }\n    if (t.written) {\n      throw new Error(`TensorArray ${this.name}: Could not write to TensorArray index ${index}, because it has already been written.`);\n    }\n    t.tensor = tensor2;\n    keep(tensor2);\n    t.written = true;\n    this.tensors[index] = t;\n  }\n  writeMany(indices, tensors) {\n    if (indices.length !== tensors.length) {\n      throw new Error(`TensorArray ${this.name}: could not write multiple tensors,because the index size: ${indices.length} is not the same as tensors size: ${tensors.length}.`);\n    }\n    indices.forEach((i, index) => this.write(i, tensors[index]));\n  }\n  gather(indices, dtype) {\n    if (!!dtype && dtype !== this.dtype) {\n      throw new Error(`TensorArray dtype is ${this.dtype} but gather requested dtype ${dtype}`);\n    }\n    if (!indices) {\n      indices = [];\n      for (let i = 0; i < this.size(); i++) {\n        indices.push(i);\n      }\n    } else {\n      indices = indices.slice(0, this.size());\n    }\n    if (indices.length === 0) {\n      return tensor([], [0].concat(this.elementShape));\n    }\n    const tensors = this.readMany(indices);\n    assertShapesMatchAllowUndefinedSize(this.elementShape, tensors[0].shape, \"TensorArray shape mismatch: \");\n    return stack(tensors, 0);\n  }\n  concat(dtype) {\n    if (!!dtype && dtype !== this.dtype) {\n      throw new Error(`TensorArray dtype is ${this.dtype} but concat requested dtype ${dtype}`);\n    }\n    if (this.size() === 0) {\n      return tensor([], [0].concat(this.elementShape));\n    }\n    const indices = [];\n    for (let i = 0; i < this.size(); i++) {\n      indices.push(i);\n    }\n    const tensors = this.readMany(indices);\n    assertShapesMatchAllowUndefinedSize(this.elementShape, tensors[0].shape, `TensorArray shape mismatch: tensor array shape (${this.elementShape}) vs first tensor shape (${tensors[0].shape})`);\n    return concat(tensors, 0);\n  }\n  scatter(indices, tensor2) {\n    if (tensor2.dtype !== this.dtype) {\n      throw new Error(`TensorArray dtype is ${this.dtype} but tensor has dtype ${tensor2.dtype}`);\n    }\n    if (indices.length !== tensor2.shape[0]) {\n      throw new Error(`Expected len(indices) == tensor.shape[0], but saw: ${indices.length} vs. ${tensor2.shape[0]}`);\n    }\n    const maxIndex = Math.max(...indices);\n    if (!this.dynamicSize && maxIndex >= this.maxSize) {\n      throw new Error(`Max index must be < array size (${maxIndex}  vs. ${this.maxSize})`);\n    }\n    this.writeMany(indices, unstack(tensor2, 0));\n  }\n  split(length, tensor2) {\n    if (tensor2.dtype !== this.dtype) {\n      throw new Error(`TensorArray dtype is ${this.dtype} but tensor has dtype ${tensor2.dtype}`);\n    }\n    let totalLength = 0;\n    const cumulativeLengths = length.map((len) => {\n      totalLength += len;\n      return totalLength;\n    });\n    if (totalLength !== tensor2.shape[0]) {\n      throw new Error(`Expected sum of lengths to be equal to\n          tensor.shape[0], but sum of lengths is\n        ${totalLength}, and tensor's shape is: ${tensor2.shape}`);\n    }\n    if (!this.dynamicSize && length.length !== this.maxSize) {\n      throw new Error(`TensorArray's size is not equal to the size of lengths (${this.maxSize} vs. ${length.length}), and the TensorArray is not marked as dynamically resizeable`);\n    }\n    const elementPerRow = totalLength === 0 ? 0 : tensor2.size / totalLength;\n    const tensors = [];\n    tidy(() => {\n      tensor2 = reshape(tensor2, [1, totalLength, elementPerRow]);\n      for (let i = 0; i < length.length; ++i) {\n        const previousLength = i === 0 ? 0 : cumulativeLengths[i - 1];\n        const indices2 = [0, previousLength, 0];\n        const sizes = [1, length[i], elementPerRow];\n        tensors[i] = reshape(slice(tensor2, indices2, sizes), this.elementShape);\n      }\n      return tensors;\n    });\n    const indices = [];\n    for (let i = 0; i < length.length; i++) {\n      indices[i] = i;\n    }\n    this.writeMany(indices, tensors);\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-converter@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-converter/dist/executor/tensor_list.js\nvar TensorList = class {\n  constructor(tensors, elementShape, elementDtype, maxNumElements = -1) {\n    this.tensors = tensors;\n    this.elementShape = elementShape;\n    this.elementDtype = elementDtype;\n    if (tensors != null) {\n      tensors.forEach((tensor2) => {\n        if (elementDtype !== tensor2.dtype) {\n          throw new Error(`Invalid data types; op elements ${elementDtype}, but list elements ${tensor2.dtype}`);\n        }\n        assertShapesMatchAllowUndefinedSize(elementShape, tensor2.shape, \"TensorList shape mismatch: \");\n        keep(tensor2);\n      });\n    }\n    this.idTensor = scalar(0);\n    this.maxNumElements = maxNumElements;\n    keep(this.idTensor);\n  }\n  get id() {\n    return this.idTensor.id;\n  }\n  copy() {\n    return new TensorList([...this.tensors], this.elementShape, this.elementDtype);\n  }\n  clearAndClose(keepIds) {\n    this.tensors.forEach((tensor2) => {\n      if (keepIds == null || !keepIds.has(tensor2.id)) {\n        tensor2.dispose();\n      }\n    });\n    this.tensors.length = 0;\n    this.idTensor.dispose();\n  }\n  size() {\n    return this.tensors.length;\n  }\n  stack(elementShape, elementDtype, numElements = -1) {\n    if (elementDtype !== this.elementDtype) {\n      throw new Error(`Invalid data types; op elements ${elementDtype}, but list elements ${this.elementDtype}`);\n    }\n    if (numElements !== -1 && this.tensors.length !== numElements) {\n      throw new Error(`Operation expected a list with ${numElements} elements but got a list with ${this.tensors.length} elements.`);\n    }\n    assertShapesMatchAllowUndefinedSize(elementShape, this.elementShape, \"TensorList shape mismatch: \");\n    const outputElementShape = inferElementShape(this.elementShape, this.tensors, elementShape);\n    return tidy(() => {\n      const reshapedTensors = this.tensors.map((tensor2) => reshape(tensor2, outputElementShape));\n      return stack(reshapedTensors, 0);\n    });\n  }\n  popBack(elementShape, elementDtype) {\n    if (elementDtype !== this.elementDtype) {\n      throw new Error(`Invalid data types; op elements ${elementDtype}, but list elements ${this.elementDtype}`);\n    }\n    if (this.size() === 0) {\n      throw new Error(\"Trying to pop from an empty list.\");\n    }\n    const outputElementShape = inferElementShape(this.elementShape, this.tensors, elementShape);\n    const tensor2 = this.tensors.pop();\n    tensor2.kept = false;\n    assertShapesMatchAllowUndefinedSize(tensor2.shape, elementShape, \"TensorList shape mismatch: \");\n    return reshape(tensor2, outputElementShape);\n  }\n  pushBack(tensor2) {\n    if (tensor2.dtype !== this.elementDtype) {\n      throw new Error(`Invalid data types; op elements ${tensor2.dtype}, but list elements ${this.elementDtype}`);\n    }\n    assertShapesMatchAllowUndefinedSize(tensor2.shape, this.elementShape, \"TensorList shape mismatch: \");\n    if (this.maxNumElements === this.size()) {\n      throw new Error(`Trying to push element into a full list.`);\n    }\n    keep(tensor2);\n    this.tensors.push(tensor2);\n  }\n  resize(size) {\n    if (size < 0) {\n      throw new Error(`TensorListResize expects size to be non-negative. Got: ${size}`);\n    }\n    if (this.maxNumElements !== -1 && size > this.maxNumElements) {\n      throw new Error(`TensorListResize input size ${size} is greater maxNumElement ${this.maxNumElements}.`);\n    }\n    const destTensorList = new TensorList([], this.elementShape, this.elementDtype, this.maxNumElements);\n    destTensorList.tensors.length = size;\n    for (let i = 0; i < Math.min(this.tensors.length, size); ++i) {\n      destTensorList.tensors[i] = this.tensors[i];\n    }\n    return destTensorList;\n  }\n  getItem(elementIndex, elementShape, elementDtype) {\n    if (elementDtype !== this.elementDtype) {\n      throw new Error(`Invalid data types; op elements ${elementDtype}, but list elements ${this.elementDtype}`);\n    }\n    if (elementIndex < 0 || elementIndex > this.tensors.length) {\n      throw new Error(`Trying to access element ${elementIndex} in a list with ${this.tensors.length} elements.`);\n    }\n    if (this.tensors[elementIndex] == null) {\n      throw new Error(`element at index ${elementIndex} is null.`);\n    }\n    assertShapesMatchAllowUndefinedSize(this.tensors[elementIndex].shape, elementShape, \"TensorList shape mismatch: \");\n    const outputElementShape = inferElementShape(this.elementShape, this.tensors, elementShape);\n    return reshape(this.tensors[elementIndex], outputElementShape);\n  }\n  setItem(elementIndex, tensor2) {\n    if (tensor2.dtype !== this.elementDtype) {\n      throw new Error(`Invalid data types; op elements ${tensor2.dtype}, but list elements ${this.elementDtype}`);\n    }\n    if (elementIndex < 0 || this.maxNumElements !== -1 && elementIndex >= this.maxNumElements) {\n      throw new Error(`Trying to set element ${elementIndex} in a list with max ${this.maxNumElements} elements.`);\n    }\n    assertShapesMatchAllowUndefinedSize(this.elementShape, tensor2.shape, \"TensorList shape mismatch: \");\n    keep(tensor2);\n    if (this.tensors[elementIndex] != null) {\n      this.tensors[elementIndex].kept = false;\n    }\n    this.tensors[elementIndex] = tensor2;\n  }\n  gather(indices, elementDtype, elementShape) {\n    if (elementDtype !== this.elementDtype) {\n      throw new Error(`Invalid data types; op elements ${elementDtype}, but list elements ${this.elementDtype}`);\n    }\n    assertShapesMatchAllowUndefinedSize(this.elementShape, elementShape, \"TensorList shape mismatch: \");\n    indices = indices.slice(0, this.size());\n    const outputElementShape = inferElementShape(this.elementShape, this.tensors, elementShape);\n    if (indices.length === 0) {\n      return tensor([], [0].concat(outputElementShape));\n    }\n    return tidy(() => {\n      const tensors = indices.map((i) => reshape(this.tensors[i], outputElementShape));\n      return stack(tensors, 0);\n    });\n  }\n  concat(elementDtype, elementShape) {\n    if (!!elementDtype && elementDtype !== this.elementDtype) {\n      throw new Error(`TensorList dtype is ${this.elementDtype} but concat requested dtype ${elementDtype}`);\n    }\n    assertShapesMatchAllowUndefinedSize(this.elementShape, elementShape, \"TensorList shape mismatch: \");\n    const outputElementShape = inferElementShape(this.elementShape, this.tensors, elementShape);\n    if (this.size() === 0) {\n      return tensor([], [0].concat(outputElementShape));\n    }\n    return tidy(() => {\n      const tensors = this.tensors.map((t) => reshape(t, outputElementShape));\n      return concat(tensors, 0);\n    });\n  }\n};\nfunction fromTensor(tensor2, elementShape, elementDtype) {\n  const dtype = tensor2.dtype;\n  if (tensor2.shape.length < 1) {\n    throw new Error(`Tensor must be at least a vector, but saw shape: ${tensor2.shape}`);\n  }\n  if (tensor2.dtype !== elementDtype) {\n    throw new Error(`Invalid data types; op elements ${tensor2.dtype}, but list elements ${elementDtype}`);\n  }\n  const tensorElementShape = tensor2.shape.slice(1);\n  assertShapesMatchAllowUndefinedSize(tensorElementShape, elementShape, \"TensorList shape mismatch: \");\n  const tensorList = unstack(tensor2);\n  return new TensorList(tensorList, elementShape, dtype);\n}\nfunction reserve(elementShape, elementDtype, numElements, maxNumElements) {\n  return new TensorList([], elementShape, elementDtype, maxNumElements);\n}\nfunction scatter(tensor2, indices, elementShape, numElements) {\n  if (indices.length !== tensor2.shape[0]) {\n    throw new Error(`Expected len(indices) == tensor.shape[0], but saw: ${indices.length} vs. ${tensor2.shape[0]}`);\n  }\n  const maxIndex = Math.max(...indices);\n  if (numElements != null && numElements !== -1 && maxIndex >= numElements) {\n    throw new Error(`Max index must be < array size (${maxIndex}  vs. ${numElements})`);\n  }\n  const list = new TensorList([], elementShape, tensor2.dtype, numElements);\n  const tensors = unstack(tensor2, 0);\n  indices.forEach((value, index) => {\n    list.setItem(value, tensors[index]);\n  });\n  return list;\n}\nfunction split2(tensor2, length, elementShape) {\n  let totalLength = 0;\n  const cumulativeLengths = length.map((len) => {\n    totalLength += len;\n    return totalLength;\n  });\n  if (totalLength !== tensor2.shape[0]) {\n    throw new Error(`Expected sum of lengths to be equal to\n          tensor.shape[0], but sum of lengths is\n        ${totalLength}, and tensor's shape is: ${tensor2.shape}`);\n  }\n  const shapeWithoutFirstDim = tensor2.shape.slice(1);\n  const outputElementShape = mergeElementShape(shapeWithoutFirstDim, elementShape);\n  const elementPerRow = totalLength === 0 ? 0 : tensor2.size / totalLength;\n  const tensors = tidy(() => {\n    const tensors2 = [];\n    tensor2 = reshape(tensor2, [1, totalLength, elementPerRow]);\n    for (let i = 0; i < length.length; ++i) {\n      const previousLength = i === 0 ? 0 : cumulativeLengths[i - 1];\n      const indices = [0, previousLength, 0];\n      const sizes = [1, length[i], elementPerRow];\n      tensors2[i] = reshape(slice(tensor2, indices, sizes), outputElementShape);\n    }\n    tensor2.dispose();\n    return tensors2;\n  });\n  const list = new TensorList([], elementShape, tensor2.dtype, length.length);\n  for (let i = 0; i < tensors.length; i++) {\n    list.setItem(i, tensors[i]);\n  }\n  return list;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-converter@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-converter/dist/operations/executors/control_executor.js\nvar executeOp3 = async (node, tensorMap, context) => {\n  switch (node.op) {\n    case \"If\":\n    case \"StatelessIf\": {\n      const thenFunc = getParamValue(\"thenBranch\", node, tensorMap, context);\n      const elseFunc = getParamValue(\"elseBranch\", node, tensorMap, context);\n      const cond = getParamValue(\"cond\", node, tensorMap, context);\n      const args = getParamValue(\"args\", node, tensorMap, context);\n      const condValue = await cond.data();\n      if (condValue[0]) {\n        return context.functionMap[thenFunc].executeFunctionAsync(args, context.tensorArrayMap, context.tensorListMap);\n      } else {\n        return context.functionMap[elseFunc].executeFunctionAsync(args, context.tensorArrayMap, context.tensorListMap);\n      }\n    }\n    case \"While\":\n    case \"StatelessWhile\": {\n      const bodyFunc = getParamValue(\"body\", node, tensorMap, context);\n      const condFunc = getParamValue(\"cond\", node, tensorMap, context);\n      const args = getParamValue(\"args\", node, tensorMap, context);\n      const condResult = await context.functionMap[condFunc].executeFunctionAsync(args, context.tensorArrayMap, context.tensorListMap);\n      const argIds = args.map((tensor2) => tensor2.id);\n      let condValue = await condResult[0].data();\n      condResult.forEach((tensor2) => {\n        if (!tensor2.kept && argIds.indexOf(tensor2.id) === -1) {\n          tensor2.dispose();\n        }\n      });\n      let result = args;\n      while (condValue[0]) {\n        const origResult = result;\n        result = await context.functionMap[bodyFunc].executeFunctionAsync(result, context.tensorArrayMap, context.tensorListMap);\n        const resultIds = result.map((tensor2) => tensor2.id);\n        origResult.forEach((tensor2) => {\n          if (!tensor2.kept && argIds.indexOf(tensor2.id) === -1 && resultIds.indexOf(tensor2.id) === -1) {\n            tensor2.dispose();\n          }\n        });\n        const condResult2 = await context.functionMap[condFunc].executeFunctionAsync(result, context.tensorArrayMap, context.tensorListMap);\n        condValue = await condResult2[0].data();\n        condResult2.forEach((tensor2) => {\n          if (!tensor2.kept && argIds.indexOf(tensor2.id) === -1 && resultIds.indexOf(tensor2.id) === -1) {\n            tensor2.dispose();\n          }\n        });\n      }\n      return result;\n    }\n    case \"LoopCond\": {\n      const pred = getParamValue(\"pred\", node, tensorMap, context);\n      return [cloneTensor(pred)];\n    }\n    case \"Switch\": {\n      const pred = getParamValue(\"pred\", node, tensorMap, context);\n      let data = getParamValue(\"data\", node, tensorMap, context);\n      if (!data.kept) {\n        data = cloneTensor(data);\n      }\n      return (await pred.data())[0] ? [void 0, data] : [data, void 0];\n    }\n    case \"Merge\": {\n      const inputName = node.inputNames.find((name) => getTensor(name, tensorMap, context) !== void 0);\n      if (inputName) {\n        const data = getTensor(inputName, tensorMap, context);\n        return [cloneTensor(data)];\n      }\n      return void 0;\n    }\n    case \"Enter\": {\n      const frameId = getParamValue(\"frameName\", node, tensorMap, context);\n      const data = getParamValue(\"tensor\", node, tensorMap, context);\n      context.enterFrame(frameId);\n      return [cloneTensor(data)];\n    }\n    case \"Exit\": {\n      const data = getParamValue(\"tensor\", node, tensorMap, context);\n      context.exitFrame();\n      return [cloneTensor(data)];\n    }\n    case \"NextIteration\": {\n      const data = getParamValue(\"tensor\", node, tensorMap, context);\n      context.nextIteration();\n      return [cloneTensor(data)];\n    }\n    case \"TensorArrayV3\": {\n      const size = getParamValue(\"size\", node, tensorMap, context);\n      const dtype = getParamValue(\"dtype\", node, tensorMap, context);\n      const elementShape = getParamValue(\"elementShape\", node, tensorMap, context);\n      const dynamicSize = getParamValue(\"dynamicSize\", node, tensorMap, context);\n      const clearAfterRead = getParamValue(\"clearAfterRead\", node, tensorMap, context);\n      const identicalElementShapes = getParamValue(\"identicalElementShapes\", node, tensorMap, context);\n      const name = getParamValue(\"name\", node, tensorMap, context);\n      const tensorArray = new TensorArray(name, dtype, size, elementShape, identicalElementShapes, dynamicSize, clearAfterRead);\n      context.addTensorArray(tensorArray);\n      return [tensorArray.idTensor, scalar(1)];\n    }\n    case \"TensorArrayWriteV3\": {\n      const id = getParamValue(\"tensorArrayId\", node, tensorMap, context);\n      const index = getParamValue(\"index\", node, tensorMap, context);\n      const writeTensor = getParamValue(\"tensor\", node, tensorMap, context);\n      const writeTensorArray = context.getTensorArray(id.id);\n      writeTensorArray.write(index, writeTensor);\n      return [writeTensorArray.idTensor];\n    }\n    case \"TensorArrayReadV3\": {\n      const readId = getParamValue(\"tensorArrayId\", node, tensorMap, context);\n      const readIndex = getParamValue(\"index\", node, tensorMap, context);\n      const readTensorArray = context.getTensorArray(readId.id);\n      return [readTensorArray.read(readIndex)];\n    }\n    case \"TensorArrayGatherV3\": {\n      const gatherId = getParamValue(\"tensorArrayId\", node, tensorMap, context);\n      const gatherIndices = getParamValue(\"indices\", node, tensorMap, context);\n      const gatherDtype = getParamValue(\"dtype\", node, tensorMap, context);\n      const gatherTensorArray = context.getTensorArray(gatherId.id);\n      return [gatherTensorArray.gather(gatherIndices, gatherDtype)];\n    }\n    case \"TensorArrayScatterV3\": {\n      const scatterId = getParamValue(\"tensorArrayId\", node, tensorMap, context);\n      const scatterIndices = getParamValue(\"indices\", node, tensorMap, context);\n      const scatterTensor = getParamValue(\"tensor\", node, tensorMap, context);\n      const scatterTensorArray = context.getTensorArray(scatterId.id);\n      scatterTensorArray.scatter(scatterIndices, scatterTensor);\n      return [scatterTensorArray.idTensor];\n    }\n    case \"TensorArrayConcatV3\": {\n      const concatId = getParamValue(\"tensorArrayId\", node, tensorMap, context);\n      const concatTensorArray = context.getTensorArray(concatId.id);\n      const concatDtype = getParamValue(\"dtype\", node, tensorMap, context);\n      return [concatTensorArray.concat(concatDtype)];\n    }\n    case \"TensorArraySplitV3\": {\n      const splitId = getParamValue(\"tensorArrayId\", node, tensorMap, context);\n      const splitTensor = getParamValue(\"tensor\", node, tensorMap, context);\n      const lengths = getParamValue(\"lengths\", node, tensorMap, context);\n      const splitTensorArray = context.getTensorArray(splitId.id);\n      splitTensorArray.split(lengths, splitTensor);\n      return [splitTensorArray.idTensor];\n    }\n    case \"TensorArraySizeV3\": {\n      const sizeId = getParamValue(\"tensorArrayId\", node, tensorMap, context);\n      const sizeTensorArray = context.getTensorArray(sizeId.id);\n      return [scalar(sizeTensorArray.size(), \"int32\")];\n    }\n    case \"TensorArrayCloseV3\": {\n      const closeId = getParamValue(\"tensorArrayId\", node, tensorMap, context);\n      const closeTensorArray = context.getTensorArray(closeId.id);\n      closeTensorArray.clearAndClose();\n      return [closeTensorArray.idTensor];\n    }\n    case \"TensorListSetItem\": {\n      const idTensor = getParamValue(\"tensorListId\", node, tensorMap, context);\n      const index = getParamValue(\"index\", node, tensorMap, context);\n      const writeTensor = getParamValue(\"tensor\", node, tensorMap, context);\n      const tensorList = context.getTensorList(idTensor.id);\n      tensorList.setItem(index, writeTensor);\n      return [tensorList.idTensor];\n    }\n    case \"TensorListGetItem\": {\n      const idTensor = getParamValue(\"tensorListId\", node, tensorMap, context);\n      const readIndex = getParamValue(\"index\", node, tensorMap, context);\n      const elementShape = getParamValue(\"elementShape\", node, tensorMap, context);\n      const elementDType = getParamValue(\"elementDType\", node, tensorMap, context);\n      const tensorList = context.getTensorList(idTensor.id);\n      return [tensorList.getItem(readIndex, elementShape, elementDType)];\n    }\n    case \"TensorListScatterV2\":\n    case \"TensorListScatter\": {\n      const scatterIndices = getParamValue(\"indices\", node, tensorMap, context);\n      const scatterTensor = getParamValue(\"tensor\", node, tensorMap, context);\n      const elementShape = getParamValue(\"elementShape\", node, tensorMap, context);\n      const numElements = getParamValue(\"numElements\", node, tensorMap, context);\n      const tensorList = scatter(scatterTensor, scatterIndices, elementShape, numElements);\n      context.addTensorList(tensorList);\n      return [tensorList.idTensor];\n    }\n    case \"TensorListReserve\":\n    case \"EmptyTensorList\": {\n      const elementShape = getParamValue(\"elementShape\", node, tensorMap, context);\n      const elementDtype = getParamValue(\"elementDType\", node, tensorMap, context);\n      let numElementsParam;\n      if (node.op === \"TensorListReserve\") {\n        numElementsParam = \"numElements\";\n      } else {\n        numElementsParam = \"maxNumElements\";\n      }\n      const numElements = getParamValue(numElementsParam, node, tensorMap, context);\n      const maxNumElements = node.op === \"TensorListReserve\" ? -1 : numElements;\n      const tensorList = reserve(elementShape, elementDtype, numElements, maxNumElements);\n      context.addTensorList(tensorList);\n      return [tensorList.idTensor];\n    }\n    case \"TensorListGather\": {\n      const gatherId = getParamValue(\"tensorListId\", node, tensorMap, context);\n      const gatherIndices = getParamValue(\"indices\", node, tensorMap, context);\n      const elementShape = getParamValue(\"elementShape\", node, tensorMap, context);\n      const elementDtype = getParamValue(\"elementDType\", node, tensorMap, context);\n      const tensorList = context.getTensorList(gatherId.id);\n      return [tensorList.gather(gatherIndices, elementDtype, elementShape)];\n    }\n    case \"TensorListStack\": {\n      const idTensor = getParamValue(\"tensorListId\", node, tensorMap, context);\n      const elementShape = getParamValue(\"elementShape\", node, tensorMap, context);\n      const elementDtype = getParamValue(\"elementDType\", node, tensorMap, context);\n      const numElements = getParamValue(\"numElements\", node, tensorMap, context);\n      const tensorList = context.getTensorList(idTensor.id);\n      return [tensorList.stack(elementShape, elementDtype, numElements)];\n    }\n    case \"TensorListFromTensor\": {\n      const tensor2 = getParamValue(\"tensor\", node, tensorMap, context);\n      const elementShape = getParamValue(\"elementShape\", node, tensorMap, context);\n      const elementDtype = getParamValue(\"elementDType\", node, tensorMap, context);\n      const tensorList = fromTensor(tensor2, elementShape, elementDtype);\n      context.addTensorList(tensorList);\n      return [tensorList.idTensor];\n    }\n    case \"TensorListConcat\":\n    case \"TensorListConcatV2\": {\n      const concatId = getParamValue(\"tensorListId\", node, tensorMap, context);\n      const tensorList = context.getTensorList(concatId.id);\n      const concatDtype = getParamValue(\"dtype\", node, tensorMap, context);\n      const elementShape = getParamValue(\"elementShape\", node, tensorMap, context);\n      return [tensorList.concat(concatDtype, elementShape)];\n    }\n    case \"TensorListPushBack\": {\n      const idTensor = getParamValue(\"tensorListId\", node, tensorMap, context);\n      const writeTensor = getParamValue(\"tensor\", node, tensorMap, context);\n      const tensorList = context.getTensorList(idTensor.id);\n      tensorList.pushBack(writeTensor);\n      return [tensorList.idTensor];\n    }\n    case \"TensorListPopBack\": {\n      const idTensor = getParamValue(\"tensorListId\", node, tensorMap, context);\n      const elementShape = getParamValue(\"elementShape\", node, tensorMap, context);\n      const elementDType = getParamValue(\"elementDType\", node, tensorMap, context);\n      const tensorList = context.getTensorList(idTensor.id);\n      return [tensorList.popBack(elementShape, elementDType)];\n    }\n    case \"TensorListSplit\": {\n      const splitTensor = getParamValue(\"tensor\", node, tensorMap, context);\n      const elementShape = getParamValue(\"elementShape\", node, tensorMap, context);\n      const lengths = getParamValue(\"lengths\", node, tensorMap, context);\n      const tensorList = split2(splitTensor, lengths, elementShape);\n      context.addTensorList(tensorList);\n      return [tensorList.idTensor];\n    }\n    case \"TensorListLength\": {\n      const idTensor = getParamValue(\"tensorListId\", node, tensorMap, context);\n      const tensorList = context.getTensorList(idTensor.id);\n      return [scalar(tensorList.size(), \"int32\")];\n    }\n    case \"TensorListResize\": {\n      const idTensor = getParamValue(\"tensorListId\", node, tensorMap, context);\n      const size = getParamValue(\"size\", node, tensorMap, context);\n      const srcTensorList = context.getTensorList(idTensor.id);\n      const destTensorList = srcTensorList.resize(size);\n      context.addTensorList(destTensorList);\n      return [destTensorList.idTensor];\n    }\n    default:\n      throw TypeError(`Node type ${node.op} is not implemented`);\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-converter@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-converter/dist/operations/executors/convolution_executor.js\nfunction fusedConvAndDepthWiseParams(node, tensorMap, context) {\n  const [extraOp, activationFunc] = getParamValue(\"fusedOps\", node, tensorMap, context);\n  const isBiasAdd = extraOp === \"biasadd\";\n  const noBiasAdd = !isBiasAdd;\n  const isPrelu = activationFunc === \"prelu\";\n  const isBatchNorm = extraOp === \"fusedbatchnorm\";\n  const numArgs = getParamValue(\"numArgs\", node, tensorMap, context);\n  if (isBiasAdd) {\n    if (isPrelu && numArgs !== 2) {\n      throw new Error(\"FusedConv2d and DepthwiseConv2d with BiasAdd and Prelu must have two extra arguments: bias and alpha.\");\n    }\n    if (!isPrelu && isBiasAdd && numArgs !== 1) {\n      throw new Error(\"FusedConv2d and DepthwiseConv2d with BiasAdd must have one extra argument: bias.\");\n    }\n  }\n  if (isBatchNorm) {\n    throw new Error(\"FusedConv2d and DepthwiseConv2d with FusedBatchNorm is not supported\");\n  }\n  const stride = getParamValue(\"strides\", node, tensorMap, context);\n  const pad3 = getPadding(node, tensorMap, context);\n  const dataFormat = getParamValue(\"dataFormat\", node, tensorMap, context).toUpperCase();\n  const dilations = getParamValue(\"dilations\", node, tensorMap, context);\n  let [biasArg, preluArg] = getParamValue(\"args\", node, tensorMap, context);\n  if (noBiasAdd) {\n    preluArg = biasArg;\n    biasArg = void 0;\n  }\n  const leakyreluAlpha = getParamValue(\"leakyreluAlpha\", node, tensorMap, context);\n  return {\n    stride,\n    pad: pad3,\n    dataFormat,\n    dilations,\n    biasArg,\n    preluArg,\n    activationFunc,\n    leakyreluAlpha\n  };\n}\nvar executeOp4 = (node, tensorMap, context, ops = ops_for_converter_exports) => {\n  switch (node.op) {\n    case \"Conv1D\": {\n      const stride = getParamValue(\"stride\", node, tensorMap, context);\n      const pad3 = getParamValue(\"pad\", node, tensorMap, context);\n      const dataFormat = getParamValue(\"dataFormat\", node, tensorMap, context).toUpperCase();\n      const dilation = getParamValue(\"dilation\", node, tensorMap, context);\n      return [ops.conv1d(getParamValue(\"x\", node, tensorMap, context), getParamValue(\"filter\", node, tensorMap, context), stride, pad3, dataFormat, dilation)];\n    }\n    case \"Conv2D\": {\n      const stride = getParamValue(\"strides\", node, tensorMap, context);\n      const pad3 = getPadding(node, tensorMap, context);\n      const dataFormat = getParamValue(\"dataFormat\", node, tensorMap, context).toUpperCase();\n      const dilations = getParamValue(\"dilations\", node, tensorMap, context);\n      return [ops.conv2d(getParamValue(\"x\", node, tensorMap, context), getParamValue(\"filter\", node, tensorMap, context), [stride[1], stride[2]], pad3, dataFormat, [dilations[1], dilations[2]])];\n    }\n    case \"_FusedConv2D\": {\n      const { stride, pad: pad3, dataFormat, dilations, biasArg, preluArg, activationFunc, leakyreluAlpha } = fusedConvAndDepthWiseParams(node, tensorMap, context);\n      return [ops.fused.conv2d({\n        x: getParamValue(\"x\", node, tensorMap, context),\n        filter: getParamValue(\"filter\", node, tensorMap, context),\n        strides: [stride[1], stride[2]],\n        pad: pad3,\n        dataFormat,\n        dilations: [dilations[1], dilations[2]],\n        bias: biasArg,\n        activation: activationFunc,\n        preluActivationWeights: preluArg,\n        leakyreluAlpha\n      })];\n    }\n    case \"FusedDepthwiseConv2dNative\": {\n      const { stride, pad: pad3, dataFormat, dilations, biasArg, preluArg, activationFunc, leakyreluAlpha } = fusedConvAndDepthWiseParams(node, tensorMap, context);\n      return [ops.fused.depthwiseConv2d({\n        x: getParamValue(\"x\", node, tensorMap, context),\n        filter: getParamValue(\"filter\", node, tensorMap, context),\n        strides: [stride[1], stride[2]],\n        pad: pad3,\n        dataFormat,\n        dilations: [dilations[1], dilations[2]],\n        bias: biasArg,\n        activation: activationFunc,\n        preluActivationWeights: preluArg,\n        leakyreluAlpha\n      })];\n    }\n    case \"Conv2DBackpropInput\":\n    case \"Conv2dTranspose\": {\n      const shape = getParamValue(\"outputShape\", node, tensorMap, context);\n      const stride = getParamValue(\"strides\", node, tensorMap, context);\n      const pad3 = getPadding(node, tensorMap, context);\n      return [ops.conv2dTranspose(getParamValue(\"x\", node, tensorMap, context), getParamValue(\"filter\", node, tensorMap, context), shape, [stride[1], stride[2]], pad3)];\n    }\n    case \"DepthwiseConv2dNative\":\n    case \"DepthwiseConv2d\": {\n      const stride = getParamValue(\"strides\", node, tensorMap, context);\n      const pad3 = getPadding(node, tensorMap, context);\n      const dilations = getParamValue(\"dilations\", node, tensorMap, context);\n      const dataFormat = getParamValue(\"dataFormat\", node, tensorMap, context).toUpperCase();\n      return [ops.depthwiseConv2d(getParamValue(\"input\", node, tensorMap, context), getParamValue(\"filter\", node, tensorMap, context), [stride[1], stride[2]], pad3, dataFormat, [dilations[1], dilations[2]])];\n    }\n    case \"Conv3D\": {\n      const stride = getParamValue(\"strides\", node, tensorMap, context);\n      const pad3 = getParamValue(\"pad\", node, tensorMap, context);\n      const dataFormat = getParamValue(\"dataFormat\", node, tensorMap, context).toUpperCase();\n      const dilations = getParamValue(\"dilations\", node, tensorMap, context);\n      return [ops.conv3d(getParamValue(\"x\", node, tensorMap, context), getParamValue(\"filter\", node, tensorMap, context), [stride[1], stride[2], stride[3]], pad3, dataFormat, [dilations[1], dilations[2], dilations[3]])];\n    }\n    case \"AvgPool\": {\n      const stride = getParamValue(\"strides\", node, tensorMap, context);\n      const pad3 = getParamValue(\"pad\", node, tensorMap, context);\n      const kernelSize = getParamValue(\"kernelSize\", node, tensorMap, context);\n      return [ops.avgPool(getParamValue(\"x\", node, tensorMap, context), [kernelSize[1], kernelSize[2]], [stride[1], stride[2]], pad3)];\n    }\n    case \"MaxPool\": {\n      const stride = getParamValue(\"strides\", node, tensorMap, context);\n      const pad3 = getParamValue(\"pad\", node, tensorMap, context);\n      const kernelSize = getParamValue(\"kernelSize\", node, tensorMap, context);\n      return [ops.maxPool(getParamValue(\"x\", node, tensorMap, context), [kernelSize[1], kernelSize[2]], [stride[1], stride[2]], pad3)];\n    }\n    case \"MaxPoolWithArgmax\": {\n      const stride = getParamValue(\"strides\", node, tensorMap, context);\n      const pad3 = getParamValue(\"pad\", node, tensorMap, context);\n      const kernelSize = getParamValue(\"kernelSize\", node, tensorMap, context);\n      const includeBatchInIndex = getParamValue(\"includeBatchInIndex\", node, tensorMap, context);\n      const { result, indexes } = ops.maxPoolWithArgmax(getParamValue(\"x\", node, tensorMap, context), [kernelSize[1], kernelSize[2]], [stride[1], stride[2]], pad3, includeBatchInIndex);\n      return [result, indexes];\n    }\n    case \"AvgPool3D\": {\n      const stride = getParamValue(\"strides\", node, tensorMap, context);\n      const pad3 = getParamValue(\"pad\", node, tensorMap, context);\n      const kernelSize = getParamValue(\"kernelSize\", node, tensorMap, context);\n      return [ops.avgPool3d(getParamValue(\"x\", node, tensorMap, context), [kernelSize[1], kernelSize[2], kernelSize[3]], [stride[1], stride[2], stride[3]], pad3)];\n    }\n    case \"MaxPool3D\": {\n      const stride = getParamValue(\"strides\", node, tensorMap, context);\n      const pad3 = getParamValue(\"pad\", node, tensorMap, context);\n      const kernelSize = getParamValue(\"kernelSize\", node, tensorMap, context);\n      return [ops.maxPool3d(getParamValue(\"x\", node, tensorMap, context), [kernelSize[1], kernelSize[2], kernelSize[3]], [stride[1], stride[2], stride[3]], pad3)];\n    }\n    case \"Dilation2D\": {\n      const strides = getParamValue(\"strides\", node, tensorMap, context);\n      const pad3 = getParamValue(\"pad\", node, tensorMap, context);\n      const dilations = getParamValue(\"dilations\", node, tensorMap, context);\n      const strideHeight = strides[1];\n      const strideWidth = strides[2];\n      const dilationHeight = dilations[1];\n      const dilationWidth = dilations[2];\n      return [ops.dilation2d(getParamValue(\"x\", node, tensorMap, context), getParamValue(\"filter\", node, tensorMap, context), [strideHeight, strideWidth], pad3, [dilationHeight, dilationWidth], \"NHWC\")];\n    }\n    default:\n      throw TypeError(`Node type ${node.op} is not implemented`);\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-converter@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-converter/dist/operations/executors/creation_executor.js\nvar executeOp5 = (node, tensorMap, context, ops = ops_for_converter_exports) => {\n  switch (node.op) {\n    case \"Fill\": {\n      const shape = getParamValue(\"shape\", node, tensorMap, context);\n      const dtype = getParamValue(\"dtype\", node, tensorMap, context);\n      const value = getParamValue(\"value\", node, tensorMap, context);\n      return [ops.fill(shape, value, dtype)];\n    }\n    case \"LinSpace\": {\n      const start = getParamValue(\"start\", node, tensorMap, context);\n      const stop = getParamValue(\"stop\", node, tensorMap, context);\n      const num = getParamValue(\"num\", node, tensorMap, context);\n      return [ops.linspace(start, stop, num)];\n    }\n    case \"Multinomial\": {\n      const logits = getParamValue(\"logits\", node, tensorMap, context);\n      const numSamples = getParamValue(\"numSamples\", node, tensorMap, context);\n      const seed = getParamValue(\"seed\", node, tensorMap, context);\n      return [ops.multinomial(logits, numSamples, seed)];\n    }\n    case \"OneHot\": {\n      const indices = getParamValue(\"indices\", node, tensorMap, context);\n      const depth = getParamValue(\"depth\", node, tensorMap, context);\n      const onValue = getParamValue(\"onValue\", node, tensorMap, context);\n      const offValue = getParamValue(\"offValue\", node, tensorMap, context);\n      const dtype = getParamValue(\"dtype\", node, tensorMap, context);\n      return [ops.oneHot(indices, depth, onValue, offValue, dtype)];\n    }\n    case \"Ones\": {\n      return [ops.ones(getParamValue(\"shape\", node, tensorMap, context), getParamValue(\"dtype\", node, tensorMap, context))];\n    }\n    case \"OnesLike\": {\n      return [ops.onesLike(getParamValue(\"x\", node, tensorMap, context))];\n    }\n    case \"RandomStandardNormal\": {\n      return [ops.randomStandardNormal(getParamValue(\"shape\", node, tensorMap, context), getParamValue(\"dtype\", node, tensorMap, context), getParamValue(\"seed\", node, tensorMap, context))];\n    }\n    case \"RandomUniform\": {\n      return [ops.randomUniform(\n        getParamValue(\"shape\", node, tensorMap, context),\n        getParamValue(\"minval\", node, tensorMap, context),\n        getParamValue(\"maxval\", node, tensorMap, context),\n        getParamValue(\"dtype\", node, tensorMap, context)\n      )];\n    }\n    case \"Range\": {\n      const start = getParamValue(\"start\", node, tensorMap, context);\n      const stop = getParamValue(\"stop\", node, tensorMap, context);\n      const step5 = getParamValue(\"step\", node, tensorMap, context);\n      return [ops.range(start, stop, step5, getParamValue(\"dtype\", node, tensorMap, context))];\n    }\n    case \"TruncatedNormal\": {\n      const shape = getParamValue(\"shape\", node, tensorMap, context);\n      const mean5 = getParamValue(\"mean\", node, tensorMap, context);\n      const stdDev = getParamValue(\"stdDev\", node, tensorMap, context);\n      const seed = getParamValue(\"seed\", node, tensorMap, context);\n      return [ops.truncatedNormal(shape, mean5, stdDev, getParamValue(\"dtype\", node, tensorMap, context), seed)];\n    }\n    case \"Zeros\": {\n      return [ops.zeros(getParamValue(\"shape\", node, tensorMap, context), getParamValue(\"dtype\", node, tensorMap, context))];\n    }\n    case \"ZerosLike\": {\n      return [ops.zerosLike(getParamValue(\"x\", node, tensorMap, context))];\n    }\n    default:\n      throw TypeError(`Node type ${node.op} is not implemented`);\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-converter@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-converter/dist/operations/executors/dynamic_executor.js\nfunction nmsParams(node, tensorMap, context) {\n  const boxes = getParamValue(\"boxes\", node, tensorMap, context);\n  const scores = getParamValue(\"scores\", node, tensorMap, context);\n  const maxOutputSize = getParamValue(\"maxOutputSize\", node, tensorMap, context);\n  const iouThreshold = getParamValue(\"iouThreshold\", node, tensorMap, context);\n  const scoreThreshold = getParamValue(\"scoreThreshold\", node, tensorMap, context);\n  const softNmsSigma = getParamValue(\"softNmsSigma\", node, tensorMap, context);\n  return {\n    boxes,\n    scores,\n    maxOutputSize,\n    iouThreshold,\n    scoreThreshold,\n    softNmsSigma\n  };\n}\nvar executeOp6 = async (node, tensorMap, context, resourceManager, ops = ops_for_converter_exports) => {\n  switch (node.op) {\n    case \"NonMaxSuppressionV5\": {\n      const { boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma } = nmsParams(node, tensorMap, context);\n      const result = await ops.image.nonMaxSuppressionWithScoreAsync(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma);\n      return [result.selectedIndices, result.selectedScores];\n    }\n    case \"NonMaxSuppressionV4\": {\n      const { boxes, scores, maxOutputSize, iouThreshold, scoreThreshold } = nmsParams(node, tensorMap, context);\n      const padToMaxOutputSize = getParamValue(\"padToMaxOutputSize\", node, tensorMap, context);\n      const result = await ops.image.nonMaxSuppressionPaddedAsync(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, padToMaxOutputSize);\n      return [result.selectedIndices, result.validOutputs];\n    }\n    case \"NonMaxSuppressionV3\":\n    case \"NonMaxSuppressionV2\": {\n      const { boxes, scores, maxOutputSize, iouThreshold, scoreThreshold } = nmsParams(node, tensorMap, context);\n      return [await ops.image.nonMaxSuppressionAsync(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold)];\n    }\n    case \"Where\": {\n      const condition = ops.cast(getParamValue(\"condition\", node, tensorMap, context), \"bool\");\n      const result = [await ops.whereAsync(condition)];\n      condition.dispose();\n      return result;\n    }\n    case \"ListDiff\": {\n      return ops.setdiff1dAsync(getParamValue(\"x\", node, tensorMap, context), getParamValue(\"y\", node, tensorMap, context));\n    }\n    default:\n      throw TypeError(`Node type ${node.op} is not implemented`);\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-converter@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-converter/dist/operations/executors/evaluation_executor.js\nvar executeOp7 = (node, tensorMap, context, ops = ops_for_converter_exports) => {\n  switch (node.op) {\n    case \"LowerBound\": {\n      const sortedSequence = getParamValue(\"sortedSequence\", node, tensorMap, context);\n      const values = getParamValue(\"values\", node, tensorMap, context);\n      return [ops.lowerBound(sortedSequence, values)];\n    }\n    case \"TopKV2\": {\n      const x = getParamValue(\"x\", node, tensorMap, context);\n      const k = getParamValue(\"k\", node, tensorMap, context);\n      const sorted = getParamValue(\"sorted\", node, tensorMap, context);\n      const result = ops.topk(x, k, sorted);\n      return [result.values, result.indices];\n    }\n    case \"UpperBound\": {\n      const sortedSequence = getParamValue(\"sortedSequence\", node, tensorMap, context);\n      const values = getParamValue(\"values\", node, tensorMap, context);\n      return [ops.upperBound(sortedSequence, values)];\n    }\n    case \"Unique\": {\n      const x = getParamValue(\"x\", node, tensorMap, context);\n      const result = ops.unique(x);\n      return [result.values, result.indices];\n    }\n    case \"UniqueV2\": {\n      const x = getParamValue(\"x\", node, tensorMap, context);\n      const axis = getParamValue(\"axis\", node, tensorMap, context);\n      const result = ops.unique(x, axis);\n      return [result.values, result.indices];\n    }\n    default:\n      throw TypeError(`Node type ${node.op} is not implemented`);\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-converter@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-converter/dist/operations/executors/graph_executor.js\nvar executeOp8 = (node, tensorMap, context, ops = ops_for_converter_exports) => {\n  switch (node.op) {\n    case \"Const\": {\n      return tensorMap[node.name];\n    }\n    case \"PlaceholderWithDefault\":\n      const def = getParamValue(\"default\", node, tensorMap, context);\n      return [getTensor(node.name, tensorMap, context) || def];\n    case \"Placeholder\":\n      return [getTensor(node.name, tensorMap, context)];\n    case \"Identity\":\n    case \"StopGradient\":\n    case \"FakeQuantWithMinMaxVars\": {\n      const data2 = getParamValue(\"x\", node, tensorMap, context);\n      return [cloneTensor(data2)];\n    }\n    case \"IdentityN\":\n      return getParamValue(\"x\", node, tensorMap, context).map((t) => cloneTensor(t));\n    case \"Snapshot\":\n      const snapshot = getParamValue(\"x\", node, tensorMap, context);\n      return [cloneTensor(snapshot)];\n    case \"Shape\":\n      return [ops.tensor1d(getParamValue(\"x\", node, tensorMap, context).shape, \"int32\")];\n    case \"ShapeN\":\n      return getParamValue(\"x\", node, tensorMap, context).map((t) => ops.tensor1d(t.shape));\n    case \"Size\":\n      return [ops.scalar(getParamValue(\"x\", node, tensorMap, context).size, \"int32\")];\n    case \"Rank\":\n      return [ops.scalar(getParamValue(\"x\", node, tensorMap, context).rank, \"int32\")];\n    case \"NoOp\":\n      return [ops.scalar(1)];\n    case \"Print\":\n      const input2 = getParamValue(\"x\", node, tensorMap, context);\n      const data = getParamValue(\"data\", node, tensorMap, context);\n      const message = getParamValue(\"message\", node, tensorMap, context);\n      const summarize = getParamValue(\"summarize\", node, tensorMap, context);\n      console.warn(\"The graph has a tf.print() operation,usually used for debugging, which slows down performance.\");\n      console.log(message);\n      for (let i = 0; i < data.length; i++) {\n        console.log(Array.prototype.slice.call(data[i].dataSync()).slice(0, summarize));\n      }\n      return [input2];\n    default:\n      throw TypeError(`Node type ${node.op} is not implemented`);\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-converter@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-converter/dist/executor/hash_table.js\nvar HashTable = class {\n  constructor(keyDType, valueDType) {\n    this.keyDType = keyDType;\n    this.valueDType = valueDType;\n    this.handle = scalar(0);\n    this.tensorMap = /* @__PURE__ */ new Map();\n    keep(this.handle);\n  }\n  get id() {\n    return this.handle.id;\n  }\n  clearAndClose() {\n    this.tensorMap.forEach((value) => value.dispose());\n    this.tensorMap.clear();\n    this.handle.dispose();\n  }\n  size() {\n    return this.tensorMap.size;\n  }\n  tensorSize() {\n    return scalar(this.size(), \"int32\");\n  }\n  async import(keys, values) {\n    this.checkKeyAndValueTensor(keys, values);\n    const $keys = await keys.data();\n    this.tensorMap.forEach((value) => value.dispose());\n    this.tensorMap.clear();\n    return tidy(() => {\n      const $values = unstack(values);\n      const keysLength = $keys.length;\n      const valuesLength = $values.length;\n      util_exports.assert(keysLength === valuesLength, () => `The number of elements doesn't match, keys has ${keysLength} elements, the values has ${valuesLength} elements.`);\n      for (let i = 0; i < keysLength; i++) {\n        const key = $keys[i];\n        const value = $values[i];\n        keep(value);\n        this.tensorMap.set(key, value);\n      }\n      return this.handle;\n    });\n  }\n  async find(keys, defaultValue) {\n    this.checkKeyAndValueTensor(keys, defaultValue);\n    const $keys = await keys.data();\n    return tidy(() => {\n      const result = [];\n      for (let i = 0; i < $keys.length; i++) {\n        const key = $keys[i];\n        const value = this.findWithDefault(key, defaultValue);\n        result.push(value);\n      }\n      return stack(result);\n    });\n  }\n  findWithDefault(key, defaultValue) {\n    const result = this.tensorMap.get(key);\n    return result != null ? result : defaultValue;\n  }\n  checkKeyAndValueTensor(key, value) {\n    if (key.dtype !== this.keyDType) {\n      throw new Error(`Expect key dtype ${this.keyDType}, but got ${key.dtype}`);\n    }\n    if (value.dtype !== this.valueDType) {\n      throw new Error(`Expect value dtype ${this.valueDType}, but got ${value.dtype}`);\n    }\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-converter@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-converter/dist/operations/executors/hash_table_executor.js\nvar executeOp9 = async (node, tensorMap, context, resourceManager) => {\n  switch (node.op) {\n    case \"HashTable\":\n    case \"HashTableV2\": {\n      const keyDType = getParamValue(\"keyDType\", node, tensorMap, context);\n      const valueDType = getParamValue(\"valueDType\", node, tensorMap, context);\n      const hashTable = new HashTable(keyDType, valueDType);\n      resourceManager.addHashTable(node.name, hashTable);\n      return [hashTable.handle];\n    }\n    case \"LookupTableImport\":\n    case \"LookupTableImportV2\": {\n      const handle = getParamValue(\"tableHandle\", node, tensorMap, context, resourceManager);\n      const keys = getParamValue(\"keys\", node, tensorMap, context);\n      const values = getParamValue(\"values\", node, tensorMap, context);\n      const hashTable = resourceManager.getHashTableById(handle.id);\n      return [await hashTable.import(keys, values)];\n    }\n    case \"LookupTableFind\":\n    case \"LookupTableFindV2\": {\n      const handle = getParamValue(\"tableHandle\", node, tensorMap, context, resourceManager);\n      const keys = getParamValue(\"keys\", node, tensorMap, context);\n      const defaultValue = getParamValue(\"defaultValue\", node, tensorMap, context);\n      const hashTable = resourceManager.getHashTableById(handle.id);\n      return [await hashTable.find(keys, defaultValue)];\n    }\n    case \"LookupTableSize\":\n    case \"LookupTableSizeV2\": {\n      const handle = getParamValue(\"tableHandle\", node, tensorMap, context, resourceManager);\n      const hashTable = resourceManager.getHashTableById(handle.id);\n      return [hashTable.tensorSize()];\n    }\n    default:\n      throw TypeError(`Node type ${node.op} is not implemented`);\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-converter@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-converter/dist/operations/executors/image_executor.js\nvar executeOp10 = (node, tensorMap, context, ops = ops_for_converter_exports) => {\n  switch (node.op) {\n    case \"ResizeBilinear\": {\n      const images = getParamValue(\"images\", node, tensorMap, context);\n      const size = getParamValue(\"size\", node, tensorMap, context);\n      const alignCorners = getParamValue(\"alignCorners\", node, tensorMap, context);\n      const halfPixelCenters = getParamValue(\"halfPixelCenters\", node, tensorMap, context);\n      return [ops.image.resizeBilinear(images, [size[0], size[1]], alignCorners, halfPixelCenters)];\n    }\n    case \"ResizeNearestNeighbor\": {\n      const images = getParamValue(\"images\", node, tensorMap, context);\n      const size = getParamValue(\"size\", node, tensorMap, context);\n      const alignCorners = getParamValue(\"alignCorners\", node, tensorMap, context);\n      const halfPixelCenters = getParamValue(\"halfPixelCenters\", node, tensorMap, context);\n      return [ops.image.resizeNearestNeighbor(images, [size[0], size[1]], alignCorners, halfPixelCenters)];\n    }\n    case \"CropAndResize\": {\n      const image2 = getParamValue(\"image\", node, tensorMap, context);\n      const boxes = getParamValue(\"boxes\", node, tensorMap, context);\n      const boxInd = getParamValue(\"boxInd\", node, tensorMap, context);\n      const cropSize = getParamValue(\"cropSize\", node, tensorMap, context);\n      const method = getParamValue(\"method\", node, tensorMap, context);\n      const extrapolationValue = getParamValue(\"extrapolationValue\", node, tensorMap, context);\n      return [ops.image.cropAndResize(image2, boxes, boxInd, cropSize, method, extrapolationValue)];\n    }\n    case \"ImageProjectiveTransformV3\": {\n      const images = getParamValue(\"images\", node, tensorMap, context);\n      const transforms = getParamValue(\"transforms\", node, tensorMap, context);\n      const outputShape = getParamValue(\"outputShape\", node, tensorMap, context);\n      const fillValue = getParamValue(\"fillValue\", node, tensorMap, context);\n      const interpolation = getParamValue(\"interpolation\", node, tensorMap, context);\n      const fillMode = getParamValue(\"fillMode\", node, tensorMap, context);\n      return [ops.image.transform(images, transforms, interpolation.toLowerCase(), fillMode.toLowerCase(), fillValue, outputShape)];\n    }\n    default:\n      throw TypeError(`Node type ${node.op} is not implemented`);\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-converter@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-converter/dist/operations/executors/logical_executor.js\nvar executeOp11 = (node, tensorMap, context, ops = ops_for_converter_exports) => {\n  switch (node.op) {\n    case \"Equal\": {\n      return [ops.equal(getParamValue(\"a\", node, tensorMap, context), getParamValue(\"b\", node, tensorMap, context))];\n    }\n    case \"NotEqual\": {\n      return [ops.notEqual(getParamValue(\"a\", node, tensorMap, context), getParamValue(\"b\", node, tensorMap, context))];\n    }\n    case \"Greater\": {\n      return [ops.greater(getParamValue(\"a\", node, tensorMap, context), getParamValue(\"b\", node, tensorMap, context))];\n    }\n    case \"GreaterEqual\": {\n      return [ops.greaterEqual(getParamValue(\"a\", node, tensorMap, context), getParamValue(\"b\", node, tensorMap, context))];\n    }\n    case \"Less\": {\n      return [ops.less(getParamValue(\"a\", node, tensorMap, context), getParamValue(\"b\", node, tensorMap, context))];\n    }\n    case \"LessEqual\": {\n      return [ops.lessEqual(getParamValue(\"a\", node, tensorMap, context), getParamValue(\"b\", node, tensorMap, context))];\n    }\n    case \"LogicalAnd\": {\n      return [ops.logicalAnd(getParamValue(\"a\", node, tensorMap, context), getParamValue(\"b\", node, tensorMap, context))];\n    }\n    case \"LogicalNot\": {\n      return [ops.logicalNot(getParamValue(\"a\", node, tensorMap, context))];\n    }\n    case \"LogicalOr\": {\n      return [ops.logicalOr(getParamValue(\"a\", node, tensorMap, context), getParamValue(\"b\", node, tensorMap, context))];\n    }\n    case \"Select\":\n    case \"SelectV2\": {\n      return [ops.where(getParamValue(\"condition\", node, tensorMap, context), getParamValue(\"a\", node, tensorMap, context), getParamValue(\"b\", node, tensorMap, context))];\n    }\n    default:\n      throw TypeError(`Node type ${node.op} is not implemented`);\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-converter@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-converter/dist/operations/executors/matrices_executor.js\nvar executeOp12 = (node, tensorMap, context, ops = ops_for_converter_exports) => {\n  switch (node.op) {\n    case \"BatchMatMul\":\n    case \"BatchMatMulV2\":\n    case \"MatMul\":\n      return [ops.matMul(getParamValue(\"a\", node, tensorMap, context), getParamValue(\"b\", node, tensorMap, context), getParamValue(\"transposeA\", node, tensorMap, context), getParamValue(\"transposeB\", node, tensorMap, context))];\n    case \"Einsum\":\n      return [ops.einsum(getParamValue(\"equation\", node, tensorMap, context), ...getParamValue(\"tensors\", node, tensorMap, context))];\n    case \"Transpose\":\n      return [ops.transpose(getParamValue(\"x\", node, tensorMap, context), getParamValue(\"perm\", node, tensorMap, context))];\n    case \"_FusedMatMul\":\n      const [extraOp, activationFunc] = getParamValue(\"fusedOps\", node, tensorMap, context);\n      const isBiasAdd = extraOp === \"biasadd\";\n      const isPrelu = activationFunc === \"prelu\";\n      const numArgs = getParamValue(\"numArgs\", node, tensorMap, context);\n      const leakyreluAlpha = getParamValue(\"leakyreluAlpha\", node, tensorMap, context);\n      if (isBiasAdd) {\n        if (isPrelu && numArgs !== 2) {\n          throw new Error(\"Fused MatMul with BiasAdd and Prelu must have two extra arguments: bias and alpha.\");\n        }\n        if (!isPrelu && numArgs !== 1) {\n          throw new Error(\"Fused MatMul with BiasAdd must have one extra argument: bias.\");\n        }\n      }\n      const [biasArg, preluArg] = getParamValue(\"args\", node, tensorMap, context);\n      return [ops.fused.matMul({\n        a: getParamValue(\"a\", node, tensorMap, context),\n        b: getParamValue(\"b\", node, tensorMap, context),\n        transposeA: getParamValue(\"transposeA\", node, tensorMap, context),\n        transposeB: getParamValue(\"transposeB\", node, tensorMap, context),\n        bias: biasArg,\n        activation: activationFunc,\n        preluActivationWeights: preluArg,\n        leakyreluAlpha\n      })];\n    default:\n      throw TypeError(`Node type ${node.op} is not implemented`);\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-converter@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-converter/dist/operations/executors/normalization_executor.js\nvar executeOp13 = (node, tensorMap, context, ops = ops_for_converter_exports) => {\n  switch (node.op) {\n    case \"EuclideanNorm\":\n      return [ops.euclideanNorm(getParamValue(\"x\", node, tensorMap, context), getParamValue(\"axis\", node, tensorMap, context), getParamValue(\"keepDims\", node, tensorMap, context))];\n    case \"FusedBatchNorm\":\n    case \"FusedBatchNormV2\": {\n      return [ops.batchNorm(getParamValue(\"x\", node, tensorMap, context), getParamValue(\"mean\", node, tensorMap, context), getParamValue(\"variance\", node, tensorMap, context), getParamValue(\"offset\", node, tensorMap, context), getParamValue(\"scale\", node, tensorMap, context), getParamValue(\"epsilon\", node, tensorMap, context))];\n    }\n    case \"FusedBatchNormV3\": {\n      return [ops.batchNorm(getParamValue(\"x\", node, tensorMap, context), getParamValue(\"mean\", node, tensorMap, context), getParamValue(\"variance\", node, tensorMap, context), getParamValue(\"offset\", node, tensorMap, context), getParamValue(\"scale\", node, tensorMap, context), getParamValue(\"epsilon\", node, tensorMap, context))];\n    }\n    case \"LRN\": {\n      return [ops.localResponseNormalization(getParamValue(\"x\", node, tensorMap, context), getParamValue(\"radius\", node, tensorMap, context), getParamValue(\"bias\", node, tensorMap, context), getParamValue(\"alpha\", node, tensorMap, context), getParamValue(\"beta\", node, tensorMap, context))];\n    }\n    case \"Softmax\": {\n      return [ops.softmax(getParamValue(\"x\", node, tensorMap, context))];\n    }\n    case \"LogSoftmax\": {\n      return [ops.logSoftmax(getParamValue(\"x\", node, tensorMap, context))];\n    }\n    case \"SparseToDense\": {\n      return [ops.sparseToDense(getParamValue(\"sparseIndices\", node, tensorMap, context), getParamValue(\"outputShape\", node, tensorMap, context), getParamValue(\"sparseValues\", node, tensorMap, context), getParamValue(\"defaultValue\", node, tensorMap, context))];\n    }\n    default:\n      throw TypeError(`Node type ${node.op} is not implemented`);\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-converter@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-converter/dist/operations/executors/reduction_executor.js\nvar executeOp14 = (node, tensorMap, context, ops = ops_for_converter_exports) => {\n  switch (node.op) {\n    case \"Max\": {\n      const axis = getParamValue(\"axis\", node, tensorMap, context);\n      const keepDims = getParamValue(\"keepDims\", node, tensorMap, context);\n      return [ops.max(getParamValue(\"x\", node, tensorMap, context), axis, keepDims)];\n    }\n    case \"Mean\": {\n      const axis = getParamValue(\"axis\", node, tensorMap, context);\n      const keepDims = getParamValue(\"keepDims\", node, tensorMap, context);\n      return [ops.mean(getParamValue(\"x\", node, tensorMap, context), axis, keepDims)];\n    }\n    case \"Min\": {\n      const axis = getParamValue(\"axis\", node, tensorMap, context);\n      const keepDims = getParamValue(\"keepDims\", node, tensorMap, context);\n      return [ops.min(getParamValue(\"x\", node, tensorMap, context), axis, keepDims)];\n    }\n    case \"Sum\": {\n      const axis = getParamValue(\"axis\", node, tensorMap, context);\n      const keepDims = getParamValue(\"keepDims\", node, tensorMap, context);\n      return [ops.sum(getParamValue(\"x\", node, tensorMap, context), axis, keepDims)];\n    }\n    case \"All\": {\n      const axis = getParamValue(\"axis\", node, tensorMap, context);\n      const keepDims = getParamValue(\"keepDims\", node, tensorMap, context);\n      return [ops.all(getParamValue(\"x\", node, tensorMap, context), axis, keepDims)];\n    }\n    case \"Any\": {\n      const axis = getParamValue(\"axis\", node, tensorMap, context);\n      const keepDims = getParamValue(\"keepDims\", node, tensorMap, context);\n      return [ops.any(getParamValue(\"x\", node, tensorMap, context), axis, keepDims)];\n    }\n    case \"ArgMax\": {\n      const axis = getParamValue(\"axis\", node, tensorMap, context);\n      return [ops.argMax(getParamValue(\"x\", node, tensorMap, context), axis)];\n    }\n    case \"ArgMin\": {\n      const axis = getParamValue(\"axis\", node, tensorMap, context);\n      return [ops.argMin(getParamValue(\"x\", node, tensorMap, context), axis)];\n    }\n    case \"Prod\": {\n      const axis = getParamValue(\"axis\", node, tensorMap, context);\n      const keepDims = getParamValue(\"keepDims\", node, tensorMap, context);\n      return [ops.prod(getParamValue(\"x\", node, tensorMap, context), axis, keepDims)];\n    }\n    case \"Cumprod\": {\n      const axis = getParamValue(\"axis\", node, tensorMap, context);\n      const exclusive = getParamValue(\"exclusive\", node, tensorMap, context);\n      const reverse5 = getParamValue(\"reverse\", node, tensorMap, context);\n      return [ops.cumprod(getParamValue(\"x\", node, tensorMap, context), axis, exclusive, reverse5)];\n    }\n    case \"Cumsum\": {\n      const axis = getParamValue(\"axis\", node, tensorMap, context);\n      const exclusive = getParamValue(\"exclusive\", node, tensorMap, context);\n      const reverse5 = getParamValue(\"reverse\", node, tensorMap, context);\n      return [ops.cumsum(getParamValue(\"x\", node, tensorMap, context), axis, exclusive, reverse5)];\n    }\n    case \"Bincount\":\n      const x = getParamValue(\"x\", node, tensorMap, context);\n      const weights = getParamValue(\"weights\", node, tensorMap, context);\n      const size = getParamValue(\"size\", node, tensorMap, context);\n      return [ops.bincount(x, weights, size)];\n    case \"DenseBincount\": {\n      const x2 = getParamValue(\"x\", node, tensorMap, context);\n      const weights2 = getParamValue(\"weights\", node, tensorMap, context);\n      const size2 = getParamValue(\"size\", node, tensorMap, context);\n      const binaryOutput = getParamValue(\"binaryOutput\", node, tensorMap, context);\n      return [ops.denseBincount(x2, weights2, size2, binaryOutput)];\n    }\n    default:\n      throw TypeError(`Node type ${node.op} is not implemented`);\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-converter@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-converter/dist/operations/executors/slice_join_executor.js\nvar executeOp15 = (node, tensorMap, context, ops = ops_for_converter_exports) => {\n  switch (node.op) {\n    case \"ConcatV2\":\n    case \"Concat\": {\n      const n = getParamValue(\"n\", node, tensorMap, context);\n      const axis = getParamValue(\"axis\", node, tensorMap, context);\n      let inputs = getParamValue(\"tensors\", node, tensorMap, context);\n      inputs = inputs.slice(0, n);\n      return [ops.concat(inputs, axis)];\n    }\n    case \"Gather\": {\n      const input2 = getParamValue(\"x\", node, tensorMap, context);\n      const indices = getParamValue(\"indices\", node, tensorMap, context);\n      return [ops.gather(input2, ops.cast(indices, \"int32\"), 0)];\n    }\n    case \"GatherV2\": {\n      const axis = getParamValue(\"axis\", node, tensorMap, context);\n      const batchDims = getParamValue(\"batchDims\", node, tensorMap, context);\n      const input2 = getParamValue(\"x\", node, tensorMap, context);\n      const indices = getParamValue(\"indices\", node, tensorMap, context);\n      return [ops.gather(input2, ops.cast(indices, \"int32\"), axis, batchDims)];\n    }\n    case \"Reverse\": {\n      const dims = getParamValue(\"dims\", node, tensorMap, context);\n      const axis = [];\n      for (let i = 0; i < dims.length; i++) {\n        if (dims[i]) {\n          axis.push(i);\n        }\n      }\n      const input2 = getParamValue(\"x\", node, tensorMap, context);\n      return [ops.reverse(input2, axis)];\n    }\n    case \"ReverseV2\": {\n      const axis = getParamValue(\"axis\", node, tensorMap, context);\n      const input2 = getParamValue(\"x\", node, tensorMap, context);\n      return [ops.reverse(input2, axis)];\n    }\n    case \"Slice\": {\n      const begin = getParamValue(\"begin\", node, tensorMap, context);\n      const size = getParamValue(\"size\", node, tensorMap, context);\n      return [ops.slice(getParamValue(\"x\", node, tensorMap, context), begin, size)];\n    }\n    case \"StridedSlice\": {\n      const begin = getParamValue(\"begin\", node, tensorMap, context);\n      const end = getParamValue(\"end\", node, tensorMap, context);\n      const strides = getParamValue(\"strides\", node, tensorMap, context);\n      const beginMask = getParamValue(\"beginMask\", node, tensorMap, context);\n      const endMask = getParamValue(\"endMask\", node, tensorMap, context);\n      const ellipsisMask = getParamValue(\"ellipsisMask\", node, tensorMap, context);\n      const newAxisMask = getParamValue(\"newAxisMask\", node, tensorMap, context);\n      const shrinkAxisMask = getParamValue(\"shrinkAxisMask\", node, tensorMap, context);\n      const tensor2 = getParamValue(\"x\", node, tensorMap, context);\n      return [ops.stridedSlice(tensor2, begin, end, strides, beginMask, endMask, ellipsisMask, newAxisMask, shrinkAxisMask)];\n    }\n    case \"Pack\": {\n      return tidy(() => {\n        const axis = getParamValue(\"axis\", node, tensorMap, context);\n        const tensors = getParamValue(\"tensors\", node, tensorMap, context);\n        const shape = tensors[0].shape;\n        const squeezedShape = ops.squeeze(tensors[0]).shape;\n        const mapped = tensors.map((tensor2) => {\n          const sameShape = util_exports.arraysEqual(tensor2.shape, shape);\n          if (!sameShape && !util_exports.arraysEqual(ops.squeeze(tensor2).shape, squeezedShape)) {\n            throw new Error(\"the input tensors shape does not match\");\n          }\n          return sameShape ? tensor2 : ops.reshape(tensor2, shape);\n        });\n        return [ops.stack(mapped, axis)];\n      });\n    }\n    case \"Unpack\": {\n      const axis = getParamValue(\"axis\", node, tensorMap, context);\n      const tensor2 = getParamValue(\"tensor\", node, tensorMap, context);\n      return ops.unstack(tensor2, axis);\n    }\n    case \"Tile\": {\n      const reps = getParamValue(\"reps\", node, tensorMap, context);\n      return [ops.tile(getParamValue(\"x\", node, tensorMap, context), reps)];\n    }\n    case \"Split\":\n    case \"SplitV\": {\n      const axis = getParamValue(\"axis\", node, tensorMap, context);\n      const numOrSizeSplits = getParamValue(\"numOrSizeSplits\", node, tensorMap, context);\n      const tensor2 = getParamValue(\"x\", node, tensorMap, context);\n      return ops.split(tensor2, numOrSizeSplits, axis);\n    }\n    case \"ScatterNd\": {\n      const indices = getParamValue(\"indices\", node, tensorMap, context);\n      const values = getParamValue(\"values\", node, tensorMap, context);\n      const shape = getParamValue(\"shape\", node, tensorMap, context);\n      return [ops.scatterND(indices, values, shape)];\n    }\n    case \"GatherNd\": {\n      const x = getParamValue(\"x\", node, tensorMap, context);\n      const indices = getParamValue(\"indices\", node, tensorMap, context);\n      return [ops.gatherND(x, indices)];\n    }\n    case \"SparseToDense\": {\n      const indices = getParamValue(\"sparseIndices\", node, tensorMap, context);\n      const shape = getParamValue(\"outputShape\", node, tensorMap, context);\n      const sparseValues = getParamValue(\"sparseValues\", node, tensorMap, context);\n      const defaultValue = getParamValue(\"defaultValue\", node, tensorMap, context);\n      return [ops.sparseToDense(indices, sparseValues, shape, sparseValues.dtype === defaultValue.dtype ? defaultValue : ops.cast(defaultValue, sparseValues.dtype))];\n    }\n    default:\n      throw TypeError(`Node type ${node.op} is not implemented`);\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-converter@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-converter/dist/operations/executors/sparse_executor.js\nvar executeOp16 = (node, tensorMap, context, ops = ops_for_converter_exports) => {\n  switch (node.op) {\n    case \"SparseFillEmptyRows\": {\n      const { outputIndices, outputValues, emptyRowIndicator, reverseIndexMap } = ops.sparse.sparseFillEmptyRows(getParamValue(\"indices\", node, tensorMap, context), getParamValue(\"values\", node, tensorMap, context), getParamValue(\"denseShape\", node, tensorMap, context), getParamValue(\"defaultValue\", node, tensorMap, context));\n      return [\n        outputIndices,\n        outputValues,\n        emptyRowIndicator,\n        reverseIndexMap\n      ];\n    }\n    case \"SparseReshape\": {\n      const { outputIndices, outputShape } = ops.sparse.sparseReshape(getParamValue(\"inputIndices\", node, tensorMap, context), getParamValue(\"inputShape\", node, tensorMap, context), getParamValue(\"newShape\", node, tensorMap, context));\n      return [outputIndices, outputShape];\n    }\n    case \"SparseSegmentMean\": {\n      const outputData = ops.sparse.sparseSegmentMean(getParamValue(\"data\", node, tensorMap, context), getParamValue(\"indices\", node, tensorMap, context), getParamValue(\"segmentIds\", node, tensorMap, context));\n      return [outputData];\n    }\n    case \"SparseSegmentSum\": {\n      const outputData = ops.sparse.sparseSegmentSum(getParamValue(\"data\", node, tensorMap, context), getParamValue(\"indices\", node, tensorMap, context), getParamValue(\"segmentIds\", node, tensorMap, context));\n      return [outputData];\n    }\n    default:\n      throw TypeError(`Node type ${node.op} is not implemented`);\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-converter@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-converter/dist/operations/executors/spectral_executor.js\nvar executeOp17 = (node, tensorMap, context, ops = ops_for_converter_exports) => {\n  switch (node.op) {\n    case \"FFT\": {\n      return [ops.fft(getParamValue(\"x\", node, tensorMap, context))];\n    }\n    case \"IFFT\": {\n      return [ops.ifft(getParamValue(\"x\", node, tensorMap, context))];\n    }\n    case \"RFFT\": {\n      return [ops.rfft(getParamValue(\"x\", node, tensorMap, context))];\n    }\n    case \"IRFFT\": {\n      return [ops.irfft(getParamValue(\"x\", node, tensorMap, context))];\n    }\n    default:\n      throw TypeError(`Node type ${node.op} is not implemented`);\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-converter@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-converter/dist/operations/executors/string_executor.js\nvar executeOp18 = (node, tensorMap, context, ops = ops_for_converter_exports) => {\n  switch (node.op) {\n    case \"StringNGrams\": {\n      const { nGrams, nGramsSplits } = ops.string.stringNGrams(getParamValue(\"data\", node, tensorMap, context), getParamValue(\"dataSplits\", node, tensorMap, context), getParamValue(\"separator\", node, tensorMap, context), getParamValue(\"nGramWidths\", node, tensorMap, context), getParamValue(\"leftPad\", node, tensorMap, context), getParamValue(\"rightPad\", node, tensorMap, context), getParamValue(\"padWidth\", node, tensorMap, context), getParamValue(\"preserveShortSequences\", node, tensorMap, context));\n      return [nGrams, nGramsSplits];\n    }\n    case \"StringSplit\": {\n      const { indices, values, shape } = ops.string.stringSplit(getParamValue(\"input\", node, tensorMap, context), getParamValue(\"delimiter\", node, tensorMap, context), getParamValue(\"skipEmpty\", node, tensorMap, context));\n      return [indices, values, shape];\n    }\n    case \"StringToHashBucketFast\": {\n      const output = ops.string.stringToHashBucketFast(getParamValue(\"input\", node, tensorMap, context), getParamValue(\"numBuckets\", node, tensorMap, context));\n      return [output];\n    }\n    default:\n      throw TypeError(`Node type ${node.op} is not implemented`);\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-converter@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-converter/dist/operations/executors/transformation_executor.js\nvar executeOp19 = (node, tensorMap, context, ops = ops_for_converter_exports) => {\n  switch (node.op) {\n    case \"Cast\": {\n      return [ops.cast(getParamValue(\"x\", node, tensorMap, context), getParamValue(\"dtype\", node, tensorMap, context))];\n    }\n    case \"ExpandDims\": {\n      const axis = getParamValue(\"axis\", node, tensorMap, context);\n      return [ops.expandDims(getParamValue(\"x\", node, tensorMap, context), axis)];\n    }\n    case \"Squeeze\": {\n      const axis = getParamValue(\"axis\", node, tensorMap, context);\n      return [ops.squeeze(getParamValue(\"x\", node, tensorMap, context), axis)];\n    }\n    case \"Reshape\": {\n      return [ops.reshape(getParamValue(\"x\", node, tensorMap, context), getParamValue(\"shape\", node, tensorMap, context))];\n    }\n    case \"MirrorPad\": {\n      return [ops.mirrorPad(getParamValue(\"x\", node, tensorMap, context), getParamValue(\"padding\", node, tensorMap, context), getParamValue(\"mode\", node, tensorMap, context))];\n    }\n    case \"PadV2\":\n    case \"Pad\": {\n      return [ops.pad(getParamValue(\"x\", node, tensorMap, context), getParamValue(\"padding\", node, tensorMap, context), getParamValue(\"constantValue\", node, tensorMap, context))];\n    }\n    case \"SpaceToBatchND\": {\n      const blockShape = getParamValue(\"blockShape\", node, tensorMap, context);\n      const paddings = getParamValue(\"paddings\", node, tensorMap, context);\n      return [ops.spaceToBatchND(getParamValue(\"x\", node, tensorMap, context), blockShape, paddings)];\n    }\n    case \"BatchToSpaceND\": {\n      const blockShape = getParamValue(\"blockShape\", node, tensorMap, context);\n      const crops = getParamValue(\"crops\", node, tensorMap, context);\n      return [ops.batchToSpaceND(getParamValue(\"x\", node, tensorMap, context), blockShape, crops)];\n    }\n    case \"DepthToSpace\": {\n      const blockSize = getParamValue(\"blockSize\", node, tensorMap, context);\n      const dataFormat = getParamValue(\"dataFormat\", node, tensorMap, context).toUpperCase();\n      return [ops.depthToSpace(getParamValue(\"x\", node, tensorMap, context), blockSize, dataFormat)];\n    }\n    case \"BroadcastTo\": {\n      return [ops.broadcastTo(getParamValue(\"x\", node, tensorMap, context), getParamValue(\"shape\", node, tensorMap, context))];\n    }\n    case \"BroadcastArgs\": {\n      return [ops.broadcastArgs(getParamValue(\"s0\", node, tensorMap, context), getParamValue(\"s1\", node, tensorMap, context))];\n    }\n    default:\n      throw TypeError(`Node type ${node.op} is not implemented`);\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-converter@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-converter/dist/operations/operation_executor.js\nfunction executeOp20(node, tensorMap, context, resourceManager, tidy2 = tidy) {\n  const value = ((node2, tensorMap2, context2) => {\n    switch (node2.category) {\n      case \"arithmetic\":\n        return tidy2(() => executeOp(node2, tensorMap2, context2));\n      case \"basic_math\":\n        return tidy2(() => executeOp2(node2, tensorMap2, context2));\n      case \"control\":\n        return executeOp3(node2, tensorMap2, context2);\n      case \"convolution\":\n        return tidy2(() => executeOp4(node2, tensorMap2, context2));\n      case \"creation\":\n        return tidy2(() => executeOp5(node2, tensorMap2, context2));\n      case \"dynamic\":\n        return executeOp6(node2, tensorMap2, context2);\n      case \"evaluation\":\n        return tidy2(() => executeOp7(node2, tensorMap2, context2));\n      case \"image\":\n        return tidy2(() => executeOp10(node2, tensorMap2, context2));\n      case \"graph\":\n        return tidy2(() => executeOp8(node2, tensorMap2, context2));\n      case \"logical\":\n        return tidy2(() => executeOp11(node2, tensorMap2, context2));\n      case \"matrices\":\n        return tidy2(() => executeOp12(node2, tensorMap2, context2));\n      case \"normalization\":\n        return tidy2(() => executeOp13(node2, tensorMap2, context2));\n      case \"reduction\":\n        return tidy2(() => executeOp14(node2, tensorMap2, context2));\n      case \"slice_join\":\n        return tidy2(() => executeOp15(node2, tensorMap2, context2));\n      case \"sparse\":\n        return tidy2(() => executeOp16(node2, tensorMap2, context2));\n      case \"spectral\":\n        return tidy2(() => executeOp17(node2, tensorMap2, context2));\n      case \"string\":\n        return tidy2(() => executeOp18(node2, tensorMap2, context2));\n      case \"transformation\":\n        return tidy2(() => executeOp19(node2, tensorMap2, context2));\n      case \"hash_table\":\n        return executeOp9(node2, tensorMap2, context2, resourceManager);\n      case \"custom\":\n        const opMapper = getRegisteredOp(node2.op);\n        if (opMapper && opMapper.customExecutor) {\n          return opMapper.customExecutor(new NodeValueImpl(node2, tensorMap2, context2));\n        } else {\n          throw TypeError(`Custom op ${node2.op} is not registered.`);\n        }\n      default:\n        throw TypeError(`Unknown op '${node2.op}'. File an issue at https://github.com/tensorflow/tfjs/issues so we can add it, or register a custom execution with tf.registerOp()`);\n    }\n  })(node, tensorMap, context);\n  if (util_exports.isPromise(value)) {\n    return value.then((data) => [].concat(data));\n  }\n  return [].concat(value);\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-converter@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-converter/dist/executor/execution_context.js\nvar ExecutionContext = class {\n  constructor(weightMap = {}, tensorArrayMap = {}, tensorListMap = {}, functionMap = {}) {\n    this.weightMap = weightMap;\n    this.tensorArrayMap = tensorArrayMap;\n    this.tensorListMap = tensorListMap;\n    this.functionMap = functionMap;\n    this.rootContext = { id: 0, frameName: \"\", iterationId: 0 };\n    this.contexts = [this.rootContext];\n    this.lastId = 0;\n    this.generateCurrentContextIds();\n  }\n  newFrame(id, frameName) {\n    return { id, frameName, iterationId: 0 };\n  }\n  set currentContext(contexts2) {\n    if (this.contexts !== contexts2) {\n      this.contexts = contexts2;\n      this.generateCurrentContextIds();\n    }\n  }\n  get currentContext() {\n    return this.contexts;\n  }\n  get currentContextId() {\n    return this._currentContextIds[0];\n  }\n  get currentContextIds() {\n    return this._currentContextIds;\n  }\n  generateCurrentContextIds() {\n    const names = [];\n    for (let i = 0; i < this.contexts.length - 1; i++) {\n      const contexts2 = this.contexts.slice(0, this.contexts.length - i);\n      names.push(this.contextIdforContexts(contexts2));\n    }\n    names.push(\"\");\n    this._currentContextIds = names;\n  }\n  contextIdforContexts(contexts2) {\n    return contexts2 ? contexts2.map((context) => context.id === 0 && context.iterationId === 0 ? \"\" : `${context.frameName}-${context.iterationId}`).join(\"/\") : \"\";\n  }\n  enterFrame(frameId) {\n    if (this.contexts) {\n      this.lastId++;\n      this.contexts = this.contexts.slice();\n      this.contexts.push(this.newFrame(this.lastId, frameId));\n      this._currentContextIds.unshift(this.contextIdforContexts(this.contexts));\n    }\n  }\n  exitFrame() {\n    if (this.contexts && this.contexts.length > 1) {\n      this.contexts = this.contexts.slice();\n      this.contexts.splice(-1);\n      this.currentContextIds.shift();\n    } else {\n      throw new Error(\"Cannot exit frame, the context is empty\");\n    }\n  }\n  nextIteration() {\n    if (this.contexts && this.contexts.length > 0) {\n      this.contexts = this.contexts.slice();\n      this.lastId++;\n      const context = Object.assign({}, this.contexts[this.contexts.length - 1]);\n      context.iterationId += 1;\n      context.id = this.lastId;\n      this.contexts.splice(-1, 1, context);\n      this._currentContextIds.splice(0, 1, this.contextIdforContexts(this.contexts));\n    } else {\n      throw new Error(\"Cannot increase frame iteration, the context is empty\");\n    }\n  }\n  getWeight(name) {\n    return this.weightMap[name];\n  }\n  addTensorArray(tensorArray) {\n    this.tensorArrayMap[tensorArray.id] = tensorArray;\n  }\n  getTensorArray(id) {\n    return this.tensorArrayMap[id];\n  }\n  addTensorList(tensorList) {\n    this.tensorListMap[tensorList.id] = tensorList;\n  }\n  getTensorList(id) {\n    return this.tensorListMap[id];\n  }\n  dispose(keepIds) {\n    for (const key in this.tensorArrayMap) {\n      this.tensorArrayMap[key].clearAndClose(keepIds);\n    }\n    for (const key in this.tensorListMap) {\n      this.tensorListMap[key].clearAndClose(keepIds);\n    }\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-converter@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-converter/dist/executor/model_analysis.js\nfunction getExecutionSubgraph(inputs, outputs, weightMap, initNodes) {\n  const usedNodes = /* @__PURE__ */ new Set();\n  const missingInputs = [];\n  let dynamicNode = null;\n  let syncInputs = null;\n  const seen = /* @__PURE__ */ new Set();\n  const inputNodeNames = Object.keys(inputs).map((name) => parseNodeName(name)[0]);\n  let initNodeNames = [];\n  if (initNodes != null) {\n    initNodeNames = initNodes.map((node) => parseNodeName(node.name)[0]);\n  }\n  const frontier = [...outputs];\n  while (frontier.length > 0) {\n    const node = frontier.pop();\n    if (isControlFlow(node) || isDynamicShape(node) || isHashTable(node)) {\n      if (dynamicNode == null) {\n        dynamicNode = node;\n        syncInputs = dynamicNode.children.map((child) => child.name).filter((name) => usedNodes.has(name));\n      }\n    }\n    usedNodes.add(node.name);\n    if (weightMap[node.name] != null) {\n      continue;\n    }\n    if (inputNodeNames.indexOf(node.name) !== -1) {\n      continue;\n    }\n    if (initNodeNames.indexOf(node.name) !== -1) {\n      continue;\n    }\n    if (node.inputs.length === 0) {\n      missingInputs.push(node.name);\n      continue;\n    }\n    node.inputs.forEach((input2) => {\n      if (seen.has(input2.name)) {\n        return;\n      }\n      seen.add(input2.name);\n      frontier.push(input2);\n    });\n  }\n  return { inputs, outputs, usedNodes, missingInputs, dynamicNode, syncInputs };\n}\nfunction getNodesInTopologicalOrder(graph, weightMap, executionInfo) {\n  const { usedNodes, inputs } = executionInfo;\n  const frontier = [];\n  const inputNodes = Object.keys(inputs).map((name) => parseNodeName(name)[0]).map((name) => graph.nodes[name]);\n  const initNodes = graph.initNodes;\n  inputNodes.forEach((input2) => {\n    if (usedNodes.has(input2.name)) {\n      frontier.push(input2);\n    }\n  });\n  graph.weights.forEach((weight) => {\n    if (usedNodes.has(weight.name)) {\n      frontier.push(weight);\n    }\n  });\n  if (initNodes != null) {\n    initNodes.forEach((node) => {\n      if (usedNodes.has(node.name)) {\n        frontier.push(node);\n      }\n    });\n  }\n  const seen = /* @__PURE__ */ new Set();\n  const orderedNodes = [];\n  while (frontier.length > 0) {\n    const node = frontier.pop();\n    seen.add(node.name);\n    if (!weightMap[node.name]) {\n      orderedNodes.push(node);\n    }\n    node.children.forEach((child) => {\n      if (!seen.has(child.name) && usedNodes.has(child.name) && child.inputs.every((input2) => seen.has(input2.name))) {\n        frontier.push(child);\n      }\n    });\n  }\n  return orderedNodes;\n}\nvar CONTROL_FLOW_OPS = [\n  \"Switch\",\n  \"Merge\",\n  \"Enter\",\n  \"Exit\",\n  \"NextIteration\",\n  \"StatelessIf\",\n  \"StatelessWhile\",\n  \"if\",\n  \"While\"\n];\nvar DYNAMIC_SHAPE_OPS = [\n  \"NonMaxSuppressionV2\",\n  \"NonMaxSuppressionV3\",\n  \"NonMaxSuppressionV5\",\n  \"Where\"\n];\nvar HASH_TABLE_OPS = [\n  \"HashTable\",\n  \"HashTableV2\",\n  \"LookupTableImport\",\n  \"LookupTableImportV2\",\n  \"LookupTableFind\",\n  \"LookupTableFindV2\",\n  \"LookupTableSize\",\n  \"LookupTableSizeV2\"\n];\nfunction isControlFlow(node) {\n  return CONTROL_FLOW_OPS.indexOf(node.op) >= 0;\n}\nfunction isDynamicShape(node) {\n  return DYNAMIC_SHAPE_OPS.indexOf(node.op) >= 0;\n}\nfunction isHashTable(node) {\n  return HASH_TABLE_OPS.indexOf(node.op) >= 0;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-converter@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-converter/dist/executor/graph_executor.js\nvar GraphExecutor = class {\n  constructor(graph, parent) {\n    this.graph = graph;\n    this.parent = parent;\n    this.compiledMap = /* @__PURE__ */ new Map();\n    this._weightMap = {};\n    this.SEPERATOR = \",\";\n    this._functions = {};\n    this._functionExecutorMap = {};\n    this.intermediateTensors = {};\n    this.keepTensorForDebug = false;\n    this._outputs = graph.outputs;\n    this._inputs = graph.inputs;\n    this._initNodes = graph.initNodes;\n    this._signature = graph.signature;\n    this._functions = graph.functions;\n    if (graph.functions != null) {\n      Object.keys(graph.functions).forEach((name) => {\n        this._functionExecutorMap[name] = new GraphExecutor(graph.functions[name], this);\n      });\n    }\n  }\n  get weightIds() {\n    return this.parent ? this.parent.weightIds : this._weightIds;\n  }\n  get functionExecutorMap() {\n    return this.parent ? this.parent.functionExecutorMap : this._functionExecutorMap;\n  }\n  get weightMap() {\n    return this.parent ? this.parent.weightMap : this._weightMap;\n  }\n  set weightMap(weightMap) {\n    const weightIds = Object.keys(weightMap).map((key) => weightMap[key].map((tensor2) => tensor2.id));\n    this._weightIds = [].concat(...weightIds);\n    this._weightMap = weightMap;\n  }\n  set resourceManager(resourceManager) {\n    this._resourceManager = resourceManager;\n  }\n  get inputs() {\n    return this._inputs.map((node) => {\n      return {\n        name: node.name,\n        shape: node.attrParams[\"shape\"] ? node.attrParams[\"shape\"].value : void 0,\n        dtype: node.attrParams[\"dtype\"] ? node.attrParams[\"dtype\"].value : void 0\n      };\n    });\n  }\n  get outputs() {\n    return this._outputs.map((node) => {\n      return {\n        name: node.name,\n        shape: node.attrParams[\"shape\"] ? node.attrParams[\"shape\"].value : void 0,\n        dtype: node.attrParams[\"dtype\"] ? node.attrParams[\"dtype\"].value : void 0\n      };\n    });\n  }\n  get inputNodes() {\n    return this._inputs.map((node) => node.signatureKey || node.name);\n  }\n  get outputNodes() {\n    return this._outputs.map((node) => {\n      const name = node.signatureKey || node.name;\n      return node.defaultOutput ? `${name}:${node.defaultOutput}` : name;\n    });\n  }\n  get functions() {\n    return Object.keys(this._functions).reduce((map, key) => {\n      map[key] = this._functions[key].signature;\n      return map;\n    }, {});\n  }\n  getCompilationKey(inputs, outputs) {\n    const sortedInputs = inputs.map((node) => node.name).sort();\n    const sortedOutputs = outputs.map((node) => node.name).sort();\n    return sortedInputs.join(this.SEPERATOR) + \"--\" + sortedOutputs.join(this.SEPERATOR);\n  }\n  compile(inputs, outputs) {\n    const executionInfo = getExecutionSubgraph(inputs, outputs, this.weightMap, this._initNodes);\n    const { missingInputs, dynamicNode, syncInputs } = executionInfo;\n    if (dynamicNode != null) {\n      throw new Error(`This execution contains the node '${dynamicNode.name}', which has the dynamic op '${dynamicNode.op}'. Please use model.executeAsync() instead. Alternatively, to avoid the dynamic ops, specify the inputs [${syncInputs}]`);\n    }\n    if (missingInputs.length > 0) {\n      const outNames = outputs.map((n) => n.name);\n      const inNames = Object.keys(inputs);\n      throw new Error(`Cannot compute the outputs [${outNames}] from the provided inputs [${inNames}]. Missing the following inputs: [${missingInputs}]`);\n    }\n    return getNodesInTopologicalOrder(this.graph, this.weightMap, executionInfo);\n  }\n  execute(inputs, outputs) {\n    inputs = this.mapInputs(inputs);\n    const names = Object.keys(inputs).sort();\n    this.checkInputs(inputs);\n    this.checkInputShapeAndType(inputs);\n    outputs = this.mapOutputs(outputs);\n    this.checkOutputs(outputs);\n    const inputNodes = names.map((name) => this.graph.nodes[parseNodeName(name)[0]]);\n    const outputNodeNames = outputs.map((name) => parseNodeName(name)[0]);\n    let outputNodes = outputNodeNames.map((name) => this.graph.nodes[name]);\n    this.resetIntermediateTensors();\n    if (outputNodes.length === 0) {\n      outputNodes = this._outputs;\n    }\n    const compilationKey = this.getCompilationKey(inputNodes, outputNodes);\n    let orderedNodes = this.compiledMap.get(compilationKey);\n    if (orderedNodes == null) {\n      orderedNodes = this.compile(inputs, outputNodes);\n      this.compiledMap.set(compilationKey, orderedNodes);\n    }\n    const tensorArrayMap = {};\n    const tensorListMap = {};\n    return tidy(() => {\n      const context = new ExecutionContext(this.weightMap, tensorArrayMap, tensorListMap, this.functionExecutorMap);\n      const tensorsMap = Object.assign({}, this.weightMap);\n      Object.keys(inputs).forEach((name) => {\n        const [nodeName, index] = parseNodeName(name);\n        const tensors = [];\n        tensors[index] = inputs[name];\n        tensorsMap[nodeName] = tensors;\n      });\n      const tensorsToKeep = this.getFrozenTensorIds(tensorsMap);\n      const intermediateTensorConsumerCount = {};\n      for (let i = 0; i < orderedNodes.length; i++) {\n        const node = orderedNodes[i];\n        if (!tensorsMap[node.name]) {\n          const tensors = executeOp20(node, tensorsMap, context, this._resourceManager);\n          if (util_exports.isPromise(tensors)) {\n            throw new Error(`The execution of the op '${node.op}' returned a promise. Please use model.executeAsync() instead.`);\n          }\n          tensorsMap[node.name] = tensors;\n          this.checkTensorForDisposal(node.name, node, tensorsMap, context, tensorsToKeep, outputNodeNames, intermediateTensorConsumerCount);\n        }\n      }\n      if (this.parent == null) {\n        context.dispose(tensorsToKeep);\n      }\n      return outputs.map((name) => getTensor(name, tensorsMap, context));\n    });\n  }\n  getFrozenTensorIds(tensorMap) {\n    const ids = [].concat.apply([], Object.keys(tensorMap).map((key) => tensorMap[key]).map((tensors) => tensors.map((tensor2) => tensor2.id)));\n    return new Set(ids);\n  }\n  checkTensorForDisposal(nodeName, node, tensorMap, context, tensorsToKeep, outputNames, intermediateTensorConsumerCount) {\n    if (node.category === \"control\" || outputNames.indexOf(nodeName) !== -1) {\n      return;\n    }\n    tensorMap[nodeName].forEach((tensor2) => {\n      if (tensor2 != null) {\n        intermediateTensorConsumerCount[tensor2.id] = (intermediateTensorConsumerCount[tensor2.id] || 0) + node.children.length;\n      }\n    });\n    node.inputs.forEach((input2) => {\n      if (input2.category !== \"control\") {\n        const tensors = getTensorsForCurrentContenxt(input2.name, tensorMap, context);\n        if (tensors != null) {\n          tensors.forEach((tensor2) => {\n            if (tensor2 && !tensor2.kept && !tensorsToKeep.has(tensor2.id)) {\n              const count2 = intermediateTensorConsumerCount[tensor2.id];\n              if (count2 === 1) {\n                if (!this.keepTensorForDebug) {\n                  tensor2.dispose();\n                } else {\n                  const [nodeName2, index] = getNodeNameAndIndex(node.name, context);\n                  if (this.intermediateTensors[nodeName2]) {\n                    this.intermediateTensors[nodeName2][index] = tensor2;\n                  } else {\n                    this.intermediateTensors[nodeName2] = [];\n                    this.intermediateTensors[nodeName2][index] = tensor2;\n                  }\n                }\n                delete intermediateTensorConsumerCount[tensor2.id];\n              } else if (count2 != null) {\n                intermediateTensorConsumerCount[tensor2.id]--;\n              }\n            }\n          });\n        }\n      }\n    });\n  }\n  async executeAsync(inputs, outputs) {\n    return this._executeAsync(inputs, outputs);\n  }\n  disposeIntermediateTensors() {\n    if (!this.intermediateTensors) {\n      return;\n    }\n    Object.keys(this.intermediateTensors).forEach((key) => this.intermediateTensors[key].forEach((tensor2) => tensor2.dispose()));\n    this.disposeTensorsMap();\n  }\n  disposeTensorsMap() {\n    if (!this.tensorsMap) {\n      return;\n    }\n    Object.keys(this.tensorsMap).forEach((key) => {\n      const tensorArray = this.tensorsMap[key];\n      tensorArray.forEach((tensor2) => {\n        if (tensor2 && !tensor2.kept && !tensor2.isDisposed && !this.keepIds.has(tensor2.id)) {\n          tensor2.dispose();\n        }\n      });\n    });\n  }\n  getIntermediateTensors() {\n    return this.tensorsMap;\n  }\n  resetIntermediateTensors() {\n    for (const key in this.intermediateTensors) {\n      this.intermediateTensors[key].forEach((tensor2) => tensor2.dispose());\n      delete this.intermediateTensors[key];\n    }\n  }\n  async _executeAsync(inputs, outputs, isFunctionExecution = false, tensorArrayMap = {}, tensorListMap = {}) {\n    if (!isFunctionExecution) {\n      inputs = this.mapInputs(inputs);\n      this.checkInputs(inputs);\n      this.checkInputShapeAndType(inputs);\n      outputs = this.mapOutputs(outputs);\n      this.checkOutputs(outputs);\n    }\n    try {\n      this.keepTensorForDebug = env().getBool(\"KEEP_INTERMEDIATE_TENSORS\");\n    } catch (e) {\n      console.warn(e.message);\n    }\n    this.resetIntermediateTensors();\n    const context = new ExecutionContext(this.weightMap, tensorArrayMap, tensorListMap, this.functionExecutorMap);\n    this.tensorsMap = await this.executeWithControlFlow(inputs, context, outputs, isFunctionExecution);\n    const results = outputs.map((name) => getTensor(name, this.tensorsMap, context));\n    const outputIds = results.map((t) => t.id);\n    const inputIds = Object.keys(inputs).map((name) => inputs[name].id);\n    this.keepIds = /* @__PURE__ */ new Set([...outputIds, ...inputIds, ...this.weightIds]);\n    if (!this.keepTensorForDebug) {\n      this.disposeTensorsMap();\n    }\n    if (this.parent == null) {\n      context.dispose(this.keepIds);\n    }\n    return results;\n  }\n  async executeFunctionAsync(inputs, tensorArrayMap, tensorListMap) {\n    const mappedInputs = inputs.reduce((map, tensor2, index) => {\n      map[this.inputs[index].name] = tensor2;\n      return map;\n    }, {});\n    return this._executeAsync(mappedInputs, this.outputNodes, true, tensorArrayMap, tensorListMap);\n  }\n  async executeWithControlFlow(inputs, context, outputNames, isFunctionExecution) {\n    const names = Object.keys(inputs);\n    const inputNodes = names.map((name) => this.graph.nodes[parseNodeName(name)[0]]);\n    const outputNodeNames = outputNames.map((name) => parseNodeName(name)[0]);\n    let outputNodes = outputNodeNames.map((name) => this.graph.nodes[name]);\n    if (outputNodes.length === 0) {\n      outputNodes = this._outputs;\n    }\n    const { usedNodes, missingInputs, dynamicNode, syncInputs } = getExecutionSubgraph(inputs, outputNodes, this.weightMap, this._initNodes);\n    const stack2 = [\n      ...inputNodes,\n      ...this.graph.weights,\n      ...this._initNodes || []\n    ].map((node) => {\n      return { node, contexts: context.currentContext };\n    });\n    const tensorsMap = Object.assign({}, this.weightMap);\n    Object.keys(inputs).forEach((name) => {\n      const [nodeName, index] = parseNodeName(name);\n      const tensors = [];\n      tensors[index] = inputs[name];\n      tensorsMap[nodeName] = tensors;\n    });\n    const intermediateTensorConsumerCount = {};\n    const tensorsToKeep = this.getFrozenTensorIds(tensorsMap);\n    const added = {};\n    while (stack2.length > 0) {\n      const promises = this.processStack(inputNodes, stack2, context, tensorsMap, added, tensorsToKeep, outputNodeNames, intermediateTensorConsumerCount, usedNodes);\n      await Promise.all(promises);\n    }\n    if (dynamicNode == null && !isFunctionExecution) {\n      console.warn(`This model execution did not contain any nodes with control flow or dynamic output shapes. You can use model.execute() instead.`);\n    }\n    const missingOutputs = outputNodes.filter((node) => !isControlFlow(node) && !getTensor(node.name, tensorsMap, context)).map((node) => node.name);\n    if (missingOutputs.length > 0) {\n      let alternativeMsg = \"\";\n      if (dynamicNode != null) {\n        alternativeMsg = `Alternatively, to avoid the dynamic ops, use model.execute() and specify the inputs [${syncInputs}]`;\n      }\n      throw new Error(`Cannot compute the outputs [${missingOutputs}] from the provided inputs [${names}]. Consider providing the following inputs: [${missingInputs}]. ${alternativeMsg}`);\n    }\n    return tensorsMap;\n  }\n  processStack(inputNodes, stack2, context, tensorMap, added, tensorsToKeep, outputNames, intermediateTensorConsumerCount, usedNodes) {\n    const promises = [];\n    while (stack2.length > 0) {\n      const item = stack2.pop();\n      context.currentContext = item.contexts;\n      let nodeName = \"\";\n      if (item.node.op === \"Enter\" && getParamValue(\"isConstant\", item.node, tensorMap, context)) {\n        [nodeName] = getNodeNameAndIndex(item.node.name, context);\n      }\n      if (tensorMap[item.node.name] == null) {\n        const tensors = executeOp20(item.node, tensorMap, context, this._resourceManager);\n        if (!nodeName) {\n          [nodeName] = getNodeNameAndIndex(item.node.name, context);\n        }\n        const currentContext = context.currentContext;\n        if (util_exports.isPromise(tensors)) {\n          promises.push(tensors.then((t) => {\n            tensorMap[nodeName] = t;\n            context.currentContext = currentContext;\n            this.checkTensorForDisposal(nodeName, item.node, tensorMap, context, tensorsToKeep, outputNames, intermediateTensorConsumerCount);\n            this.processChildNodes(item.node, stack2, context, tensorMap, added, usedNodes);\n            return t;\n          }));\n        } else {\n          tensorMap[nodeName] = tensors;\n          this.checkTensorForDisposal(nodeName, item.node, tensorMap, context, tensorsToKeep, outputNames, intermediateTensorConsumerCount);\n          this.processChildNodes(item.node, stack2, context, tensorMap, added, usedNodes);\n        }\n      } else {\n        this.processChildNodes(item.node, stack2, context, tensorMap, added, usedNodes);\n      }\n    }\n    return promises;\n  }\n  processChildNodes(node, stack2, context, tensorMap, added, usedNodes) {\n    node.children.forEach((childNode) => {\n      const [nodeName] = getNodeNameAndIndex(childNode.name, context);\n      if (added[nodeName] || !usedNodes.has(childNode.name)) {\n        return;\n      }\n      if (childNode.op === \"Merge\") {\n        if (childNode.inputNames.some((name) => {\n          return !!getTensor(name, tensorMap, context);\n        })) {\n          added[nodeName] = true;\n          stack2.push({ contexts: context.currentContext, node: childNode });\n        }\n      } else if (childNode.inputNames.every((name) => {\n        return !!getTensor(name, tensorMap, context);\n      })) {\n        added[nodeName] = true;\n        stack2.push({ contexts: context.currentContext, node: childNode });\n      }\n    });\n  }\n  dispose() {\n    Object.keys(this.weightMap).forEach((key) => this.weightMap[key].forEach((tensor2) => tensor2.dispose()));\n  }\n  checkInputShapeAndType(inputs) {\n    Object.keys(inputs).forEach((name) => {\n      const input2 = inputs[name];\n      const [nodeName] = parseNodeName(name);\n      const node = this.graph.nodes[nodeName];\n      if (node.attrParams[\"shape\"] && node.attrParams[\"shape\"].value) {\n        const shape = node.attrParams[\"shape\"].value;\n        const match = shape.length === input2.shape.length && input2.shape.every((dim, index) => shape[index] === -1 || shape[index] === dim);\n        util_exports.assert(match, () => `The shape of dict['${node.name}'] provided in model.execute(dict) must be [${shape}], but was [${input2.shape}]`);\n      }\n      if (node.attrParams[\"dtype\"] && node.attrParams[\"dtype\"].value) {\n        util_exports.assert(input2.dtype === node.attrParams[\"dtype\"].value, () => `The dtype of dict['${node.name}'] provided in model.execute(dict) must be ${node.attrParams[\"dtype\"].value}, but was ${input2.dtype}`);\n      }\n    });\n  }\n  mapInputs(inputs) {\n    const result = {};\n    for (const inputName in inputs) {\n      if (this._signature != null && this._signature.inputs != null && this._signature.inputs[inputName] != null) {\n        const tensor2 = this._signature.inputs[inputName];\n        result[tensor2.name] = inputs[inputName];\n      } else {\n        result[inputName] = inputs[inputName];\n      }\n    }\n    return result;\n  }\n  checkInputs(inputs) {\n    const notInGraph = Object.keys(inputs).filter((name) => {\n      const [nodeName] = parseNodeName(name);\n      return this.graph.nodes[nodeName] == null;\n    });\n    if (notInGraph.length > 0) {\n      throw new Error(`The dict provided in model.execute(dict) has keys: [${notInGraph}] that are not part of graph`);\n    }\n  }\n  mapOutputs(outputs) {\n    return outputs.map((name) => {\n      if (this._signature != null && this._signature.outputs != null && this._signature.outputs[name] != null) {\n        const tensor2 = this._signature.outputs[name];\n        return tensor2.name;\n      }\n      return name;\n    }, {});\n  }\n  checkOutputs(outputs) {\n    outputs.forEach((name) => {\n      const [normalizedName] = parseNodeName(name);\n      if (!this.graph.nodes[normalizedName]) {\n        throw new Error(`The output '${name}' is not found in the graph`);\n      }\n    });\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-converter@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-converter/dist/executor/resource_manager.js\nvar ResourceManager = class {\n  constructor(hashTableNameToHandle = {}, hashTableMap = {}) {\n    this.hashTableNameToHandle = hashTableNameToHandle;\n    this.hashTableMap = hashTableMap;\n  }\n  addHashTable(name, hashTable) {\n    this.hashTableNameToHandle[name] = hashTable.handle;\n    this.hashTableMap[hashTable.id] = hashTable;\n  }\n  getHashTableHandleByName(name) {\n    return this.hashTableNameToHandle[name];\n  }\n  getHashTableById(id) {\n    return this.hashTableMap[id];\n  }\n  dispose() {\n    for (const key in this.hashTableMap) {\n      this.hashTableMap[key].clearAndClose();\n      delete this.hashTableMap[key];\n    }\n    for (const name in this.hashTableNameToHandle) {\n      this.hashTableNameToHandle[name].dispose();\n      delete this.hashTableNameToHandle[name];\n    }\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-converter@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-converter/dist/executor/graph_model.js\nvar TFHUB_SEARCH_PARAM = \"?tfjs-format=file\";\nvar DEFAULT_MODEL_NAME = \"model.json\";\nvar GraphModel = class {\n  constructor(modelUrl, loadOptions = {}, tfio = io_exports) {\n    this.modelUrl = modelUrl;\n    this.loadOptions = loadOptions;\n    this.version = \"n/a\";\n    this.io = tfio;\n    if (loadOptions == null) {\n      this.loadOptions = {};\n    }\n    this.resourceManager = new ResourceManager();\n  }\n  get modelVersion() {\n    return this.version;\n  }\n  get inputNodes() {\n    return this.executor.inputNodes;\n  }\n  get outputNodes() {\n    return this.executor.outputNodes;\n  }\n  get inputs() {\n    return this.executor.inputs;\n  }\n  get outputs() {\n    return this.executor.outputs;\n  }\n  get weights() {\n    return this.executor.weightMap;\n  }\n  get metadata() {\n    return this.artifacts.userDefinedMetadata;\n  }\n  get modelSignature() {\n    return this.signature;\n  }\n  get modelStructuredOutputKeys() {\n    return this.structuredOutputKeys;\n  }\n  findIOHandler() {\n    const path = this.modelUrl;\n    if (path.load != null) {\n      this.handler = path;\n    } else if (this.loadOptions.requestInit != null) {\n      this.handler = this.io.browserHTTPRequest(path, this.loadOptions);\n    } else {\n      const handlers = this.io.getLoadHandlers(path, this.loadOptions);\n      if (handlers.length === 0) {\n        handlers.push(this.io.browserHTTPRequest(path, this.loadOptions));\n      } else if (handlers.length > 1) {\n        throw new Error(`Found more than one (${handlers.length}) load handlers for URL '${[path]}'`);\n      }\n      this.handler = handlers[0];\n    }\n  }\n  load() {\n    this.findIOHandler();\n    if (this.handler.load == null) {\n      throw new Error(\"Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.\");\n    }\n    const loadResult = this.handler.load();\n    if (util_exports.isPromise(loadResult)) {\n      return loadResult.then((artifacts) => this.loadSync(artifacts));\n    }\n    return this.loadSync(loadResult);\n  }\n  loadSync(artifacts) {\n    this.artifacts = artifacts;\n    const graph = this.artifacts.modelTopology;\n    let signature = this.artifacts.signature;\n    if (this.artifacts.userDefinedMetadata != null) {\n      const metadata = this.artifacts.userDefinedMetadata;\n      if (metadata.signature != null) {\n        signature = metadata.signature;\n      }\n      if (metadata.structuredOutputKeys != null) {\n        this.structuredOutputKeys = metadata.structuredOutputKeys;\n      }\n    }\n    this.signature = signature;\n    this.version = `${graph.versions.producer}.${graph.versions.minConsumer}`;\n    const weightMap = this.io.decodeWeights(this.artifacts.weightData, this.artifacts.weightSpecs);\n    this.executor = new GraphExecutor(OperationMapper.Instance.transformGraph(graph, this.signature));\n    this.executor.weightMap = this.convertTensorMapToTensorsMap(weightMap);\n    this.executor.resourceManager = this.resourceManager;\n    if (artifacts.modelInitializer != null && artifacts.modelInitializer.node != null) {\n      const initializer = OperationMapper.Instance.transformGraph(artifacts.modelInitializer);\n      this.initializer = new GraphExecutor(initializer);\n      this.initializer.weightMap = this.executor.weightMap;\n      this.initializer.resourceManager = this.resourceManager;\n      this.initializer.executeAsync({}, []);\n    }\n    return true;\n  }\n  async save(handlerOrURL, config) {\n    if (typeof handlerOrURL === \"string\") {\n      const handlers = this.io.getSaveHandlers(handlerOrURL);\n      if (handlers.length === 0) {\n        throw new Error(`Cannot find any save handlers for URL '${handlerOrURL}'`);\n      } else if (handlers.length > 1) {\n        throw new Error(`Found more than one (${handlers.length}) save handlers for URL '${handlerOrURL}'`);\n      }\n      handlerOrURL = handlers[0];\n    }\n    if (handlerOrURL.save == null) {\n      throw new Error(\"GraphModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.\");\n    }\n    return handlerOrURL.save(this.artifacts);\n  }\n  predict(inputs, config) {\n    const outputTensors = this.execute(inputs, this.outputNodes);\n    if (this.structuredOutputKeys) {\n      const outputTensorsArray = outputTensors instanceof Tensor ? [outputTensors] : outputTensors;\n      const outputTensorMap = {};\n      outputTensorsArray.forEach((outputTensor, i) => outputTensorMap[this.structuredOutputKeys[i]] = outputTensor);\n      return outputTensorMap;\n    }\n    return outputTensors;\n  }\n  normalizeInputs(inputs) {\n    if (!(inputs instanceof Tensor) && !Array.isArray(inputs)) {\n      return inputs;\n    }\n    inputs = Array.isArray(inputs) ? inputs : [inputs];\n    if (inputs.length !== this.inputNodes.length) {\n      throw new Error(`Input tensor count mismatch,the graph model has ${this.inputNodes.length} placeholders, while there are ${inputs.length} input tensors.`);\n    }\n    return this.inputNodes.reduce((map, inputName, i) => {\n      map[inputName] = inputs[i];\n      return map;\n    }, {});\n  }\n  normalizeOutputs(outputs) {\n    outputs = outputs || this.outputNodes;\n    return !Array.isArray(outputs) ? [outputs] : outputs;\n  }\n  execute(inputs, outputs) {\n    inputs = this.normalizeInputs(inputs);\n    outputs = this.normalizeOutputs(outputs);\n    const result = this.executor.execute(inputs, outputs);\n    return result.length > 1 ? result : result[0];\n  }\n  async executeAsync(inputs, outputs) {\n    inputs = this.normalizeInputs(inputs);\n    outputs = this.normalizeOutputs(outputs);\n    const result = await this.executor.executeAsync(inputs, outputs);\n    return result.length > 1 ? result : result[0];\n  }\n  getIntermediateTensors() {\n    return this.executor.getIntermediateTensors();\n  }\n  disposeIntermediateTensors() {\n    this.executor.disposeIntermediateTensors();\n  }\n  convertTensorMapToTensorsMap(map) {\n    return Object.keys(map).reduce((newMap, key) => {\n      newMap[key] = [map[key]];\n      return newMap;\n    }, {});\n  }\n  dispose() {\n    this.executor.dispose();\n    if (this.initializer) {\n      this.initializer.dispose();\n    }\n    this.resourceManager.dispose();\n  }\n};\nasync function loadGraphModel(modelUrl, options = {}, tfio = io_exports) {\n  if (modelUrl == null) {\n    throw new Error(\"modelUrl in loadGraphModel() cannot be null. Please provide a url or an IOHandler that loads the model\");\n  }\n  if (options == null) {\n    options = {};\n  }\n  if (options.fromTFHub && typeof modelUrl === \"string\") {\n    modelUrl = getTFHubUrl(modelUrl);\n  }\n  const model2 = new GraphModel(modelUrl, options, tfio);\n  await model2.load();\n  return model2;\n}\nfunction loadGraphModelSync(modelSource) {\n  if (modelSource == null) {\n    throw new Error(\"modelUrl in loadGraphModelSync() cannot be null. Please provide a url or an IOHandler that loads the model\");\n  }\n  if (!modelSource.load) {\n    throw new Error(`modelUrl IO Handler ${modelSource} has no load function`);\n  }\n  const model2 = new GraphModel(modelSource);\n  model2.load();\n  return model2;\n}\nfunction getTFHubUrl(modelUrl) {\n  if (!modelUrl.endsWith(\"/\")) {\n    modelUrl = modelUrl + \"/\";\n  }\n  return `${modelUrl}${DEFAULT_MODEL_NAME}${TFHUB_SEARCH_PARAM}`;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-converter@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-converter/dist/version.js\nvar version3 = \"3.20.0\";\n\n// node_modules/.pnpm/@tensorflow+tfjs-data@3.20.0_k7dauiu3y265wd6lcplf62oi7i/node_modules/@tensorflow/tfjs-data/dist/index.js\nvar dist_exports2 = {};\n__export(dist_exports2, {\n  CSVDataset: () => CSVDataset,\n  Dataset: () => Dataset,\n  FileDataSource: () => FileDataSource,\n  TextLineDataset: () => TextLineDataset,\n  URLDataSource: () => URLDataSource,\n  array: () => array,\n  csv: () => csv,\n  func: () => func,\n  generator: () => generator,\n  microphone: () => microphone,\n  version_data: () => version4,\n  webcam: () => webcam,\n  zip: () => zip\n});\n\n// node_modules/.pnpm/@tensorflow+tfjs-data@3.20.0_k7dauiu3y265wd6lcplf62oi7i/node_modules/@tensorflow/tfjs-data/dist/dataset.js\nvar seedrandom3 = __toESM(require_seedrandom2());\n\n// node_modules/.pnpm/@tensorflow+tfjs-data@3.20.0_k7dauiu3y265wd6lcplf62oi7i/node_modules/@tensorflow/tfjs-data/dist/iterators/lazy_iterator.js\nvar seedrandom2 = __toESM(require_seedrandom2());\n\n// node_modules/.pnpm/@tensorflow+tfjs-data@3.20.0_k7dauiu3y265wd6lcplf62oi7i/node_modules/@tensorflow/tfjs-data/dist/util/deep_map.js\nfunction deepMap(input2, mapFn) {\n  return deepMapInternal(input2, mapFn);\n}\nfunction deepMapInternal(input2, mapFn, seen = /* @__PURE__ */ new Map(), containedIn = /* @__PURE__ */ new Set()) {\n  if (input2 == null) {\n    return null;\n  }\n  if (typeof Blob === \"function\" && input2 instanceof Blob) {\n    return input2.slice();\n  }\n  if (containedIn.has(input2)) {\n    throw new Error(\"Circular references are not supported.\");\n  }\n  if (seen.has(input2)) {\n    return seen.get(input2);\n  }\n  const result = mapFn(input2);\n  if (result.recurse && result.value !== null) {\n    throw new Error(\"A deep map function may not return both a value and recurse=true.\");\n  }\n  if (!result.recurse) {\n    seen.set(input2, result.value);\n    return result.value;\n  } else if (isIterable2(input2)) {\n    const mappedIterable = Array.isArray(input2) ? [] : {};\n    containedIn.add(input2);\n    for (const k in input2) {\n      const child = input2[k];\n      const childResult = deepMapInternal(child, mapFn, seen, containedIn);\n      mappedIterable[k] = childResult;\n    }\n    containedIn.delete(input2);\n    if (input2.__proto__) {\n      mappedIterable.__proto__ = input2.__proto__;\n    }\n    return mappedIterable;\n  } else {\n    throw new Error(`Can't recurse into non-iterable type: ${input2}`);\n  }\n}\nfunction deepZip(inputs, zipFn = zipToList) {\n  return deepZipInternal(inputs, zipFn);\n}\nfunction deepZipInternal(inputs, zipFn, containedIn = /* @__PURE__ */ new Set()) {\n  const input2 = inputs[0];\n  if (containedIn.has(input2)) {\n    throw new Error(\"Circular references are not supported.\");\n  }\n  const result = zipFn(inputs);\n  if (result.recurse && result.value !== null) {\n    throw new Error(\"A deep zip function may not return both a value and recurse=true.\");\n  }\n  if (!result.recurse) {\n    return result.value;\n  } else if (isIterable2(input2)) {\n    const mappedIterable = Array.isArray(input2) ? [] : {};\n    containedIn.add(input2);\n    for (const k in input2) {\n      const children = inputs.map((x) => x[k]);\n      const childResult = deepZipInternal(children, zipFn, containedIn);\n      mappedIterable[k] = childResult;\n    }\n    containedIn.delete(input2);\n    return mappedIterable;\n  } else {\n    throw new Error(`Can't recurse into non-iterable type: ${input2}`);\n  }\n}\nfunction zipToList(x) {\n  if (x === null) {\n    return null;\n  }\n  if (isIterable2(x[0])) {\n    return { value: null, recurse: true };\n  } else {\n    return { value: x, recurse: false };\n  }\n}\nasync function deepMapAndAwaitAll(input2, mapFn) {\n  const seen = /* @__PURE__ */ new Map();\n  deepMapInternal(input2, mapFn, seen);\n  for (const key of Array.from(seen.keys())) {\n    const value = seen.get(key);\n    if (util_exports.isPromise(value)) {\n      const mappedValue = await value;\n      seen.set(key, mappedValue);\n    }\n  }\n  const result = deepMapInternal(input2, mapFn, seen);\n  return result;\n}\nfunction isIterable2(obj) {\n  let isTextDecoder = false;\n  if (env().get(\"IS_BROWSER\")) {\n    isTextDecoder = obj instanceof TextDecoder;\n  } else {\n    const { StringDecoder } = require_string_decoder();\n    isTextDecoder = obj instanceof StringDecoder;\n  }\n  return obj != null && !ArrayBuffer.isView(obj) && (Array.isArray(obj) || typeof obj === \"object\" && !(obj instanceof Tensor) && !(obj instanceof Promise) && !isTextDecoder);\n}\nfunction canTensorify(obj) {\n  return obj == null || isPrimitive(obj) || Array.isArray(obj) || typeof obj === \"object\" && obj instanceof Tensor || util_exports.isTypedArray(obj);\n}\nfunction isPrimitive(value) {\n  return value === null || typeof value !== \"object\" && typeof value !== \"function\";\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-data@3.20.0_k7dauiu3y265wd6lcplf62oi7i/node_modules/@tensorflow/tfjs-data/dist/util/deep_clone.js\nfunction deepClone(container) {\n  return deepMap(container, cloneIfTensor);\n}\nfunction cloneIfTensor(item) {\n  if (item instanceof Tensor) {\n    return { value: item.clone(), recurse: false };\n  } else if (isIterable2(item)) {\n    return { value: null, recurse: true };\n  } else {\n    return { value: item, recurse: false };\n  }\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-data@3.20.0_k7dauiu3y265wd6lcplf62oi7i/node_modules/@tensorflow/tfjs-data/dist/util/ring_buffer.js\nvar RingBuffer = class {\n  constructor(capacity) {\n    this.capacity = capacity;\n    this.begin = 0;\n    this.end = 0;\n    if (capacity == null) {\n      throw new RangeError(\"Can't create a ring buffer of unknown capacity.\");\n    }\n    if (capacity < 1) {\n      throw new RangeError(\"Can't create ring buffer of capacity < 1.\");\n    }\n    this.data = new Array(capacity);\n    this.doubledCapacity = 2 * capacity;\n  }\n  wrap(index) {\n    while (index < 0) {\n      index += this.doubledCapacity;\n    }\n    return index % this.doubledCapacity;\n  }\n  get(index) {\n    if (index < 0) {\n      throw new RangeError(\"Can't get item at a negative index.\");\n    }\n    return this.data[index % this.capacity];\n  }\n  set(index, value) {\n    if (index < 0) {\n      throw new RangeError(\"Can't set item at a negative index.\");\n    }\n    this.data[index % this.capacity] = value;\n  }\n  length() {\n    let length = this.end - this.begin;\n    if (length < 0) {\n      length = this.doubledCapacity + length;\n    }\n    return length;\n  }\n  isFull() {\n    return this.length() === this.capacity;\n  }\n  isEmpty() {\n    return this.length() === 0;\n  }\n  push(value) {\n    if (this.isFull()) {\n      throw new RangeError(\"Ring buffer is full.\");\n    }\n    this.set(this.end, value);\n    this.end = this.wrap(this.end + 1);\n  }\n  pushAll(values) {\n    for (const value of values) {\n      this.push(value);\n    }\n  }\n  pop() {\n    if (this.isEmpty()) {\n      throw new RangeError(\"Ring buffer is empty.\");\n    }\n    this.end = this.wrap(this.end - 1);\n    const result = this.get(this.end);\n    this.set(this.end, void 0);\n    return result;\n  }\n  unshift(value) {\n    if (this.isFull()) {\n      throw new RangeError(\"Ring buffer is full.\");\n    }\n    this.begin = this.wrap(this.begin - 1);\n    this.set(this.begin, value);\n  }\n  shift() {\n    if (this.isEmpty()) {\n      throw new RangeError(\"Ring buffer is empty.\");\n    }\n    const result = this.get(this.begin);\n    this.set(this.begin, void 0);\n    this.begin = this.wrap(this.begin + 1);\n    return result;\n  }\n  shuffleExcise(relativeIndex) {\n    if (this.isEmpty()) {\n      throw new RangeError(\"Ring buffer is empty.\");\n    }\n    const index = this.wrap(this.begin + relativeIndex);\n    const result = this.get(index);\n    this.set(index, this.pop());\n    return result;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-data@3.20.0_k7dauiu3y265wd6lcplf62oi7i/node_modules/@tensorflow/tfjs-data/dist/util/growing_ring_buffer.js\nvar GrowingRingBuffer = class extends RingBuffer {\n  constructor() {\n    super(GrowingRingBuffer.INITIAL_CAPACITY);\n  }\n  isFull() {\n    return false;\n  }\n  push(value) {\n    if (super.isFull()) {\n      this.expand();\n    }\n    super.push(value);\n  }\n  unshift(value) {\n    if (super.isFull()) {\n      this.expand();\n    }\n    super.unshift(value);\n  }\n  expand() {\n    const newCapacity = this.capacity * 2;\n    const newData = new Array(newCapacity);\n    const len = this.length();\n    for (let i = 0; i < len; i++) {\n      newData[i] = this.get(this.wrap(this.begin + i));\n    }\n    this.data = newData;\n    this.capacity = newCapacity;\n    this.doubledCapacity = 2 * this.capacity;\n    this.begin = 0;\n    this.end = len;\n  }\n};\nGrowingRingBuffer.INITIAL_CAPACITY = 32;\n\n// node_modules/.pnpm/@tensorflow+tfjs-data@3.20.0_k7dauiu3y265wd6lcplf62oi7i/node_modules/@tensorflow/tfjs-data/dist/iterators/lazy_iterator.js\nfunction iteratorFromItems(items) {\n  return new ArrayIterator(items);\n}\nfunction iteratorFromFunction(func2) {\n  return new FunctionCallIterator(func2);\n}\nfunction iteratorFromConcatenated(baseIterators, baseErrorHandler) {\n  return new ChainedIterator(baseIterators, baseErrorHandler);\n}\nfunction iteratorFromZipped(iterators, mismatchMode = ZipMismatchMode.FAIL) {\n  return new ZipIterator(iterators, mismatchMode);\n}\nvar LazyIterator = class {\n  async toArray() {\n    const result = [];\n    let x = await this.next();\n    while (!x.done) {\n      result.push(x.value);\n      x = await this.next();\n    }\n    return result;\n  }\n  async toArrayForTest() {\n    const stream = this.prefetch(100);\n    const result = [];\n    let x = await stream.next();\n    while (!x.done) {\n      result.push(x.value);\n      x = await stream.next();\n    }\n    return result;\n  }\n  async resolveFully() {\n    let x = await this.next();\n    while (!x.done) {\n      x = await this.next();\n    }\n  }\n  async resolveWhile(predicate) {\n    let x = await this.next();\n    let shouldContinue = predicate(x.value);\n    while (!x.done && shouldContinue) {\n      x = await this.next();\n      shouldContinue = predicate(x.value);\n    }\n  }\n  handleErrors(handler) {\n    return new ErrorHandlingLazyIterator(this, handler);\n  }\n  filter(predicate) {\n    return new FilterIterator(this, predicate);\n  }\n  map(transform6) {\n    return new MapIterator(this, transform6);\n  }\n  mapAsync(transform6) {\n    return new AsyncMapIterator(this, transform6);\n  }\n  serialMapAsync(transform6) {\n    return new AsyncMapIterator(this, transform6).serial();\n  }\n  flatmap(transform6) {\n    return new FlatmapIterator(this, transform6);\n  }\n  async forEachAsync(f) {\n    return this.map(f).resolveFully();\n  }\n  async serialForEach(f) {\n    return this.serialMapAsync(f).resolveWhile((x) => x === true);\n  }\n  rowMajorBatch(batchSize, smallLastBatch = true) {\n    return new RowMajorBatchIterator(this, batchSize, smallLastBatch);\n  }\n  columnMajorBatch(batchSize, smallLastBatch = true, zipFn = zipToList) {\n    const rowBatches = this.rowMajorBatch(batchSize, smallLastBatch);\n    return rowBatches.map((x) => deepZip(x, zipFn));\n  }\n  concatenate(iterator, baseErrorHandler) {\n    return new ChainedIterator(iteratorFromItems([this, iterator]), baseErrorHandler);\n  }\n  take(count2) {\n    if (count2 < 0 || count2 == null) {\n      return this;\n    }\n    return new TakeIterator(this, count2);\n  }\n  skip(count2) {\n    if (count2 < 0 || count2 == null) {\n      return this;\n    }\n    return new SkipIterator(this, count2);\n  }\n  prefetch(bufferSize) {\n    return new PrefetchIterator(this, bufferSize);\n  }\n  shuffle(windowSize, seed) {\n    return new ShuffleIterator(this, windowSize, seed);\n  }\n  serial() {\n    return new SerialIterator(this);\n  }\n};\nvar ArrayIterator = class extends LazyIterator {\n  constructor(items) {\n    super();\n    this.items = items;\n    this.trav = 0;\n  }\n  summary() {\n    return `Array of ${this.items.length} items`;\n  }\n  async next() {\n    if (this.trav >= this.items.length) {\n      return { value: null, done: true };\n    }\n    const item = this.items[this.trav];\n    this.trav++;\n    return { value: deepClone(item), done: false };\n  }\n};\nvar FunctionCallIterator = class extends LazyIterator {\n  constructor(nextFn) {\n    super();\n    this.nextFn = nextFn;\n  }\n  summary() {\n    return `Function call`;\n  }\n  async next() {\n    try {\n      return this.nextFn();\n    } catch (e) {\n      e.message = `Error thrown while iterating through a dataset: ${e.message}`;\n      throw e;\n    }\n  }\n};\nvar SerialIterator = class extends LazyIterator {\n  constructor(upstream) {\n    super();\n    this.upstream = upstream;\n    this.lastRead = Promise.resolve({ value: null, done: false });\n  }\n  summary() {\n    return `${this.upstream.summary()} -> Serial`;\n  }\n  async next() {\n    this.lastRead = this.lastRead.then(() => this.serialNext());\n    return this.lastRead;\n  }\n  async serialNext() {\n    return this.upstream.next();\n  }\n};\nvar SkipIterator = class extends LazyIterator {\n  constructor(upstream, maxCount) {\n    super();\n    this.upstream = upstream;\n    this.maxCount = maxCount;\n    this.count = 0;\n    this.lastRead = Promise.resolve({ value: null, done: false });\n  }\n  summary() {\n    return `${this.upstream.summary()} -> Skip`;\n  }\n  async next() {\n    this.lastRead = this.lastRead.then(() => this.serialNext());\n    return this.lastRead;\n  }\n  async serialNext() {\n    while (this.count++ < this.maxCount) {\n      const skipped = await this.upstream.next();\n      if (skipped.done) {\n        return skipped;\n      }\n      dispose(skipped.value);\n    }\n    return this.upstream.next();\n  }\n};\nvar TakeIterator = class extends LazyIterator {\n  constructor(upstream, maxCount) {\n    super();\n    this.upstream = upstream;\n    this.maxCount = maxCount;\n    this.count = 0;\n  }\n  summary() {\n    return `${this.upstream.summary()} -> Take`;\n  }\n  async next() {\n    if (this.count++ >= this.maxCount) {\n      return { value: null, done: true };\n    }\n    return this.upstream.next();\n  }\n};\nvar RowMajorBatchIterator = class extends LazyIterator {\n  constructor(upstream, batchSize, enableSmallLastBatch = true) {\n    super();\n    this.upstream = upstream;\n    this.batchSize = batchSize;\n    this.enableSmallLastBatch = enableSmallLastBatch;\n    this.lastRead = Promise.resolve({ value: null, done: false });\n  }\n  summary() {\n    return `${this.upstream.summary()} -> RowMajorBatch`;\n  }\n  async next() {\n    this.lastRead = this.lastRead.then(() => this.serialNext());\n    return this.lastRead;\n  }\n  async serialNext() {\n    const batch = [];\n    while (batch.length < this.batchSize) {\n      const item = await this.upstream.next();\n      if (item.done) {\n        if (this.enableSmallLastBatch && batch.length > 0) {\n          return { value: batch, done: false };\n        }\n        return { value: null, done: true };\n      }\n      batch.push(item.value);\n    }\n    return { value: batch, done: false };\n  }\n};\nvar FilterIterator = class extends LazyIterator {\n  constructor(upstream, predicate) {\n    super();\n    this.upstream = upstream;\n    this.predicate = predicate;\n    this.lastRead = Promise.resolve({ value: null, done: false });\n  }\n  summary() {\n    return `${this.upstream.summary()} -> Filter`;\n  }\n  async next() {\n    this.lastRead = this.lastRead.then(() => this.serialNext());\n    return this.lastRead;\n  }\n  async serialNext() {\n    while (true) {\n      const item = await this.upstream.next();\n      if (item.done || this.predicate(item.value)) {\n        return item;\n      }\n      dispose(item.value);\n    }\n  }\n};\nvar MapIterator = class extends LazyIterator {\n  constructor(upstream, transform6) {\n    super();\n    this.upstream = upstream;\n    this.transform = transform6;\n  }\n  summary() {\n    return `${this.upstream.summary()} -> Map`;\n  }\n  async next() {\n    const item = await this.upstream.next();\n    if (item.done) {\n      return { value: null, done: true };\n    }\n    const inputTensors = tensor_util_exports.getTensorsInContainer(item.value);\n    const mapped = this.transform(item.value);\n    const outputTensors = tensor_util_exports.getTensorsInContainer(mapped);\n    for (const t of inputTensors) {\n      if (!tensor_util_exports.isTensorInList(t, outputTensors)) {\n        t.dispose();\n      }\n    }\n    return { value: mapped, done: false };\n  }\n};\nvar ErrorHandlingLazyIterator = class extends LazyIterator {\n  constructor(upstream, handler) {\n    super();\n    this.upstream = upstream;\n    this.handler = handler;\n    this.count = 0;\n    this.lastRead = Promise.resolve({ value: null, done: false });\n  }\n  summary() {\n    return `${this.upstream.summary()} -> handleErrors`;\n  }\n  async next() {\n    this.lastRead = this.lastRead.then(() => this.serialNext());\n    return this.lastRead;\n  }\n  async serialNext() {\n    while (true) {\n      try {\n        return await this.upstream.next();\n      } catch (e) {\n        if (!this.handler(e)) {\n          return { value: null, done: true };\n        }\n      }\n    }\n  }\n};\nvar AsyncMapIterator = class extends LazyIterator {\n  constructor(upstream, transform6) {\n    super();\n    this.upstream = upstream;\n    this.transform = transform6;\n  }\n  summary() {\n    return `${this.upstream.summary()} -> AsyncMap`;\n  }\n  async next() {\n    const item = await this.upstream.next();\n    if (item.done) {\n      return { value: null, done: true };\n    }\n    const inputTensors = tensor_util_exports.getTensorsInContainer(item.value);\n    const mapped = await this.transform(item.value);\n    const outputTensors = tensor_util_exports.getTensorsInContainer(mapped);\n    for (const t of inputTensors) {\n      if (!tensor_util_exports.isTensorInList(t, outputTensors)) {\n        t.dispose();\n      }\n    }\n    return { value: mapped, done: false };\n  }\n};\nvar OneToManyIterator = class extends LazyIterator {\n  constructor() {\n    super();\n    this.outputQueue = new GrowingRingBuffer();\n    this.lastRead = Promise.resolve({ value: null, done: false });\n  }\n  async next() {\n    this.lastRead = this.lastRead.then(() => this.serialNext());\n    return this.lastRead;\n  }\n  async serialNext() {\n    while (this.outputQueue.length() === 0) {\n      if (!await this.pump()) {\n        return { value: null, done: true };\n      }\n    }\n    return { value: this.outputQueue.shift(), done: false };\n  }\n};\nvar FlatmapIterator = class extends OneToManyIterator {\n  constructor(upstream, transform6) {\n    super();\n    this.upstream = upstream;\n    this.transform = transform6;\n  }\n  summary() {\n    return `${this.upstream.summary()} -> Flatmap`;\n  }\n  async pump() {\n    const item = await this.upstream.next();\n    if (item.done) {\n      return false;\n    }\n    const inputTensors = tensor_util_exports.getTensorsInContainer(item.value);\n    const mappedArray = this.transform(item.value);\n    const outputTensors = tensor_util_exports.getTensorsInContainer(mappedArray);\n    this.outputQueue.pushAll(mappedArray);\n    for (const t of inputTensors) {\n      if (!tensor_util_exports.isTensorInList(t, outputTensors)) {\n        t.dispose();\n      }\n    }\n    return true;\n  }\n};\nvar ChainedIterator = class extends LazyIterator {\n  constructor(iterators, baseErrorHandler) {\n    super();\n    this.baseErrorHandler = baseErrorHandler;\n    this.lastRead = null;\n    this.iterator = null;\n    this.moreIterators = iterators;\n  }\n  summary() {\n    const upstreamSummaries = \"TODO: fill in upstream of chained summaries\";\n    return `${upstreamSummaries} -> Chained`;\n  }\n  async next() {\n    this.lastRead = this.readFromChain(this.lastRead);\n    return this.lastRead;\n  }\n  async readFromChain(lastRead) {\n    await lastRead;\n    if (this.iterator == null) {\n      const iteratorResult = await this.moreIterators.next();\n      if (iteratorResult.done) {\n        return { value: null, done: true };\n      }\n      this.iterator = iteratorResult.value;\n      if (this.baseErrorHandler != null) {\n        this.iterator = this.iterator.handleErrors(this.baseErrorHandler);\n      }\n    }\n    const itemResult = await this.iterator.next();\n    if (itemResult.done) {\n      this.iterator = null;\n      return this.readFromChain(lastRead);\n    }\n    return itemResult;\n  }\n};\nvar ZipMismatchMode;\n(function(ZipMismatchMode2) {\n  ZipMismatchMode2[ZipMismatchMode2[\"FAIL\"] = 0] = \"FAIL\";\n  ZipMismatchMode2[ZipMismatchMode2[\"SHORTEST\"] = 1] = \"SHORTEST\";\n  ZipMismatchMode2[ZipMismatchMode2[\"LONGEST\"] = 2] = \"LONGEST\";\n})(ZipMismatchMode || (ZipMismatchMode = {}));\nvar ZipIterator = class extends LazyIterator {\n  constructor(iterators, mismatchMode = ZipMismatchMode.FAIL) {\n    super();\n    this.iterators = iterators;\n    this.mismatchMode = mismatchMode;\n    this.count = 0;\n    this.currentPromise = null;\n  }\n  summary() {\n    const upstreamSummaries = \"TODO: fill in upstream of zip summaries\";\n    return `{${upstreamSummaries}} -> Zip`;\n  }\n  async nextState(afterState) {\n    await afterState;\n    let numIterators = 0;\n    let iteratorsDone = 0;\n    function getNext(container) {\n      if (container instanceof LazyIterator) {\n        const result = container.next();\n        return {\n          value: result.then((x) => {\n            numIterators++;\n            if (x.done) {\n              iteratorsDone++;\n            }\n            return x.value;\n          }),\n          recurse: false\n        };\n      } else {\n        return { value: null, recurse: true };\n      }\n    }\n    const mapped = await deepMapAndAwaitAll(this.iterators, getNext);\n    if (numIterators === iteratorsDone) {\n      return { value: null, done: true };\n    }\n    if (iteratorsDone > 0) {\n      switch (this.mismatchMode) {\n        case ZipMismatchMode.FAIL:\n          throw new Error(`Zipped streams should have the same length. Mismatched at element ${this.count}.`);\n        case ZipMismatchMode.SHORTEST:\n          return { value: null, done: true };\n        case ZipMismatchMode.LONGEST:\n        default:\n      }\n    }\n    this.count++;\n    return { value: mapped, done: false };\n  }\n  async next() {\n    this.currentPromise = this.nextState(this.currentPromise);\n    return this.currentPromise;\n  }\n};\nvar PrefetchIterator = class extends LazyIterator {\n  constructor(upstream, bufferSize) {\n    super();\n    this.upstream = upstream;\n    this.bufferSize = bufferSize;\n    this.buffer = new RingBuffer(bufferSize);\n  }\n  summary() {\n    return `${this.upstream.summary()} -> Prefetch`;\n  }\n  refill() {\n    while (!this.buffer.isFull()) {\n      const v = this.upstream.next();\n      this.buffer.push(v);\n    }\n  }\n  next() {\n    this.refill();\n    return this.buffer.shift();\n  }\n};\nvar ShuffleIterator = class extends PrefetchIterator {\n  constructor(upstream, windowSize, seed) {\n    super(upstream, windowSize);\n    this.upstream = upstream;\n    this.windowSize = windowSize;\n    this.upstreamExhausted = false;\n    this.random = seedrandom2.alea(seed || util_exports.now().toString());\n    this.lastRead = Promise.resolve({ value: null, done: false });\n  }\n  async next() {\n    this.lastRead = this.lastRead.then(() => this.serialNext());\n    return this.lastRead;\n  }\n  randomInt(max7) {\n    return Math.floor(this.random() * max7);\n  }\n  chooseIndex() {\n    return this.randomInt(this.buffer.length());\n  }\n  async serialNext() {\n    if (!this.upstreamExhausted) {\n      this.refill();\n    }\n    while (!this.buffer.isEmpty()) {\n      const chosenIndex = this.chooseIndex();\n      const result = await this.buffer.shuffleExcise(chosenIndex);\n      if (result.done) {\n        this.upstreamExhausted = true;\n      } else {\n        this.refill();\n        return result;\n      }\n    }\n    return { value: null, done: true };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-data@3.20.0_k7dauiu3y265wd6lcplf62oi7i/node_modules/@tensorflow/tfjs-data/dist/dataset.js\nvar Dataset = class {\n  constructor() {\n    this.size = null;\n  }\n  batch(batchSize, smallLastBatch = true) {\n    const base = this;\n    util_exports.assert(batchSize > 0, () => `batchSize needs to be positive, but it is\n      ${batchSize}`);\n    let size;\n    if (this.size === Infinity || this.size == null) {\n      size = this.size;\n    } else if (smallLastBatch) {\n      size = Math.ceil(this.size / batchSize);\n    } else {\n      size = Math.floor(this.size / batchSize);\n    }\n    return datasetFromIteratorFn(async () => {\n      return (await base.iterator()).columnMajorBatch(batchSize, smallLastBatch, deepBatchConcat);\n    }, size);\n  }\n  concatenate(dataset) {\n    const base = this;\n    let size;\n    if (this.size === Infinity || dataset.size === Infinity) {\n      size = Infinity;\n    } else if (this.size != null && dataset.size != null) {\n      size = this.size + dataset.size;\n    } else {\n      size = null;\n    }\n    return datasetFromIteratorFn(async () => (await base.iterator()).concatenate(await dataset.iterator()), size);\n  }\n  filter(predicate) {\n    const base = this;\n    let size;\n    if (this.size === Infinity) {\n      size = Infinity;\n    } else {\n      size = null;\n    }\n    return datasetFromIteratorFn(async () => {\n      return (await base.iterator()).filter((x) => tidy(() => predicate(x)));\n    }, size);\n  }\n  async forEachAsync(f) {\n    return (await this.iterator()).forEachAsync(f);\n  }\n  map(transform6) {\n    const base = this;\n    return datasetFromIteratorFn(async () => {\n      return (await base.iterator()).map((x) => tidy(() => transform6(x)));\n    }, this.size);\n  }\n  mapAsync(transform6) {\n    const base = this;\n    return datasetFromIteratorFn(async () => {\n      return (await base.iterator()).mapAsync(transform6);\n    }, this.size);\n  }\n  prefetch(bufferSize) {\n    if (bufferSize == null) {\n      throw new RangeError(\"`Dataset.prefetch()` requires bufferSize to be specified.\");\n    }\n    const base = this;\n    return datasetFromIteratorFn(async () => (await base.iterator()).prefetch(bufferSize), this.size);\n  }\n  repeat(count2) {\n    const base = this;\n    let size;\n    if (this.size != null && count2 > 0) {\n      size = this.size * count2;\n    } else if (count2 === 0) {\n      size = 0;\n    } else if (this.size != null && (count2 === void 0 || count2 < 0)) {\n      size = Infinity;\n    } else {\n      size = null;\n    }\n    return datasetFromIteratorFn(async () => {\n      const iteratorIterator = iteratorFromFunction(async () => ({ value: await base.iterator(), done: false }));\n      return iteratorFromConcatenated(iteratorIterator.take(count2));\n    }, size);\n  }\n  skip(count2) {\n    const base = this;\n    let size;\n    if (this.size != null && count2 >= 0 && this.size >= count2) {\n      size = this.size - count2;\n    } else if (this.size != null && (this.size < count2 || count2 === void 0 || count2 < 0)) {\n      size = 0;\n    } else {\n      size = null;\n    }\n    return datasetFromIteratorFn(async () => (await base.iterator()).skip(count2), size);\n  }\n  shuffle(bufferSize, seed, reshuffleEachIteration = true) {\n    if (bufferSize == null || bufferSize < 0) {\n      if (this.size == null) {\n        throw new RangeError(\"`Dataset.shuffle()` requires bufferSize to be specified.\");\n      } else {\n        throw new RangeError(`\\`Dataset.shuffle()\\` requires bufferSize to be specified.  If your data fits in main memory (for regular JS objects), and/or GPU memory (for \\`tf.Tensor\\`s), consider setting bufferSize to the dataset size (${this.size} elements)`);\n      }\n    }\n    const base = this;\n    const random = seedrandom3.alea(seed || util_exports.now().toString());\n    return datasetFromIteratorFn(async () => {\n      let seed2 = random.int32();\n      if (reshuffleEachIteration) {\n        seed2 += random.int32();\n      }\n      return (await base.iterator()).shuffle(bufferSize, seed2.toString());\n    }, this.size);\n  }\n  take(count2) {\n    const base = this;\n    let size;\n    if (this.size != null && this.size > count2) {\n      size = count2;\n    } else if (this.size != null && this.size <= count2) {\n      size = this.size;\n    } else {\n      size = null;\n    }\n    return datasetFromIteratorFn(async () => (await base.iterator()).take(count2), size);\n  }\n  async toArray() {\n    if (this.size === Infinity) {\n      throw new Error(\"Can not convert infinite data stream to array.\");\n    }\n    return (await this.iterator()).toArray();\n  }\n  async toArrayForTest() {\n    if (this.size === Infinity) {\n      throw new Error(\"Can not convert infinite data stream to array.\");\n    }\n    return (await this.iterator()).toArrayForTest();\n  }\n};\nDataset.MAX_BUFFER_SIZE = 1e4;\nfunction datasetFromIteratorFn(iteratorFn, size = null) {\n  return new class extends Dataset {\n    constructor() {\n      super(...arguments);\n      this.size = size;\n    }\n    async iterator() {\n      return iteratorFn();\n    }\n  }();\n}\nfunction array(items) {\n  return datasetFromIteratorFn(async () => iteratorFromItems(items), items.length);\n}\nfunction zip(datasets) {\n  if (!isIterable2(datasets)) {\n    throw new Error(\"The argument to zip() must be an object or array.\");\n  }\n  let size;\n  if (Array.isArray(datasets)) {\n    for (let i = 0; i < datasets.length; i++) {\n      size = size == null ? datasets[i].size : Math.min(size, datasets[i].size);\n    }\n  } else if (datasets instanceof Object) {\n    for (const ds in datasets) {\n      size = size == null ? datasets[ds].size : Math.min(size, datasets[ds].size);\n    }\n  }\n  return datasetFromIteratorFn(async () => {\n    const streams = await deepMapAndAwaitAll(datasets, (d) => {\n      if (d instanceof Dataset) {\n        return { value: d.iterator(), recurse: false };\n      } else if (isIterable2(d)) {\n        return { value: null, recurse: true };\n      } else {\n        throw new Error(\"Leaves of the structure passed to zip() must be Datasets, not primitives.\");\n      }\n    });\n    return iteratorFromZipped(streams, ZipMismatchMode.SHORTEST);\n  }, size);\n}\nfunction deepBatchConcat(rows) {\n  if (rows === null) {\n    return null;\n  }\n  const exampleRow = rows[0];\n  if (canTensorify(exampleRow)) {\n    const value = batchConcat(rows);\n    return { value, recurse: false };\n  }\n  return { value: null, recurse: true };\n}\nfunction batchConcat(arrays) {\n  if (arrays.length === 0) {\n    throw new Error(\"Can't make a batch of zero elements.\");\n  }\n  if (arrays[0] instanceof Tensor) {\n    return stack(arrays);\n  } else {\n    return tensor(arrays);\n  }\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-data@3.20.0_k7dauiu3y265wd6lcplf62oi7i/node_modules/@tensorflow/tfjs-data/dist/datasets/text_line_dataset.js\nvar TextLineDataset = class extends Dataset {\n  constructor(input2) {\n    super();\n    this.input = input2;\n  }\n  async iterator() {\n    const inputIterator = await this.input.iterator();\n    const utf8Iterator = inputIterator.decodeUTF8();\n    const lineIterator = utf8Iterator.split(\"\\n\").map((line) => {\n      if (line.endsWith(\"\\r\")) {\n        line = line.slice(0, -1);\n      }\n      return line;\n    });\n    return lineIterator;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-data@3.20.0_k7dauiu3y265wd6lcplf62oi7i/node_modules/@tensorflow/tfjs-data/dist/datasets/csv_dataset.js\nvar CODE_QUOTE = '\"';\nvar STATE_OUT = Symbol(\"out\");\nvar STATE_FIELD = Symbol(\"field\");\nvar STATE_QUOTE = Symbol(\"quote\");\nvar STATE_QUOTE_AFTER_QUOTE = Symbol(\"quoteafterquote\");\nvar STATE_WITHIN_QUOTE_IN_QUOTE = Symbol(\"quoteinquote\");\nvar CSVDataset = class extends Dataset {\n  constructor(input2, csvConfig) {\n    super();\n    this.input = input2;\n    this.hasHeader = true;\n    this.fullColumnNames = null;\n    this.columnNamesValidated = false;\n    this.columnConfigs = null;\n    this.configuredColumnsOnly = false;\n    this.delimiter = \",\";\n    this.delimWhitespace = false;\n    this.base = new TextLineDataset(input2);\n    if (!csvConfig) {\n      csvConfig = {};\n    }\n    this.hasHeader = csvConfig.hasHeader === false ? false : true;\n    this.fullColumnNames = csvConfig.columnNames;\n    this.columnConfigs = csvConfig.columnConfigs;\n    this.configuredColumnsOnly = csvConfig.configuredColumnsOnly;\n    if (csvConfig.delimWhitespace) {\n      util_exports.assert(csvConfig.delimiter == null, () => \"Delimiter should not be provided when delimWhitespace is true.\");\n      this.delimWhitespace = true;\n      this.delimiter = \" \";\n    } else {\n      this.delimiter = csvConfig.delimiter ? csvConfig.delimiter : \",\";\n    }\n  }\n  async columnNames() {\n    if (!this.columnNamesValidated) {\n      await this.setColumnNames();\n    }\n    return this.configuredColumnsOnly ? Object.keys(this.columnConfigs) : this.fullColumnNames;\n  }\n  async setColumnNames() {\n    const columnNamesFromFile = await this.maybeReadHeaderLine();\n    if (!this.fullColumnNames && !columnNamesFromFile) {\n      throw new Error(\"Column names must be provided if there is no header line.\");\n    } else if (this.fullColumnNames && columnNamesFromFile) {\n      util_exports.assert(columnNamesFromFile.length === this.fullColumnNames.length, () => \"The length of provided columnNames (\" + this.fullColumnNames.length.toString() + \") does not match the length of the header line read from file (\" + columnNamesFromFile.length.toString() + \").\");\n    }\n    if (!this.fullColumnNames) {\n      this.fullColumnNames = columnNamesFromFile;\n    }\n    const counts = this.fullColumnNames.reduce((countAcc, name) => {\n      countAcc[name] = countAcc[name] + 1 || 1;\n      return countAcc;\n    }, {});\n    const duplicateNames = Object.keys(counts).filter((name) => counts[name] > 1);\n    util_exports.assert(duplicateNames.length === 0, () => \"Duplicate column names found: \" + duplicateNames.toString());\n    if (this.columnConfigs) {\n      for (const key of Object.keys(this.columnConfigs)) {\n        const index = this.fullColumnNames.indexOf(key);\n        if (index === -1) {\n          throw new Error('The key \"' + key + '\" provided in columnConfigs does not match any of the column names (' + this.fullColumnNames.toString() + \").\");\n        }\n      }\n    }\n    this.columnNamesValidated = true;\n  }\n  async maybeReadHeaderLine() {\n    if (this.hasHeader) {\n      const iter = await this.base.iterator();\n      const firstElement = await iter.next();\n      if (firstElement.done) {\n        throw new Error(\"No data was found for CSV parsing.\");\n      }\n      const firstLine = firstElement.value;\n      const headers = this.parseRow(firstLine, false);\n      return headers;\n    } else {\n      return null;\n    }\n  }\n  async iterator() {\n    if (!this.columnNamesValidated) {\n      await this.setColumnNames();\n    }\n    let lines = await this.base.iterator();\n    if (this.hasHeader) {\n      lines = lines.skip(1);\n    }\n    return lines.map((x) => this.makeDataElement(x));\n  }\n  makeDataElement(line) {\n    const values = this.parseRow(line);\n    const features = {};\n    const labels = {};\n    for (let i = 0; i < this.fullColumnNames.length; i++) {\n      const key = this.fullColumnNames[i];\n      const config = this.columnConfigs ? this.columnConfigs[key] : null;\n      if (this.configuredColumnsOnly && !config) {\n        continue;\n      } else {\n        const value = values[i];\n        let parsedValue = null;\n        if (value === \"\") {\n          if (config && config.default !== void 0) {\n            parsedValue = config.default;\n          } else if (config && (config.required || config.isLabel)) {\n            throw new Error(`Required column ${key} is empty in this line: ${line}`);\n          } else {\n            parsedValue = void 0;\n          }\n        } else {\n          const valueAsNum = Number(value);\n          if (isNaN(valueAsNum)) {\n            if (config && config.dtype === \"bool\") {\n              parsedValue = this.getBoolean(value);\n            } else {\n              parsedValue = value;\n            }\n          } else if (!config || !config.dtype) {\n            parsedValue = valueAsNum;\n          } else {\n            switch (config.dtype) {\n              case \"float32\":\n                parsedValue = valueAsNum;\n                break;\n              case \"int32\":\n                parsedValue = Math.floor(valueAsNum);\n                break;\n              case \"bool\":\n                parsedValue = this.getBoolean(value);\n                break;\n              default:\n                parsedValue = valueAsNum;\n            }\n          }\n        }\n        config && config.isLabel ? labels[key] = parsedValue : features[key] = parsedValue;\n      }\n    }\n    if (Object.keys(labels).length === 0) {\n      return features;\n    } else {\n      return { xs: features, ys: labels };\n    }\n  }\n  getBoolean(value) {\n    if (value === \"1\" || value.toLowerCase() === \"true\") {\n      return 1;\n    } else {\n      return 0;\n    }\n  }\n  parseRow(line, validateElementCount = true) {\n    const result = [];\n    let readOffset = 0;\n    const readLength = line.length;\n    let currentState = STATE_OUT;\n    for (let i = 0; i < readLength; i++) {\n      switch (currentState) {\n        case STATE_OUT:\n          switch (line.charAt(i)) {\n            case CODE_QUOTE:\n              readOffset = i + 1;\n              currentState = STATE_QUOTE;\n              break;\n            case this.delimiter:\n              readOffset = i + 1;\n              if (this.delimiter === \" \" && this.delimWhitespace) {\n                break;\n              }\n              result.push(\"\");\n              currentState = STATE_OUT;\n              break;\n            default:\n              currentState = STATE_FIELD;\n              readOffset = i;\n              break;\n          }\n          break;\n        case STATE_FIELD:\n          switch (line.charAt(i)) {\n            case this.delimiter:\n              result.push(line.substring(readOffset, i));\n              currentState = STATE_OUT;\n              readOffset = i + 1;\n              break;\n            default:\n          }\n          break;\n        case STATE_QUOTE:\n          switch (line.charAt(i)) {\n            case CODE_QUOTE:\n              currentState = STATE_QUOTE_AFTER_QUOTE;\n              break;\n            default:\n          }\n          break;\n        case STATE_QUOTE_AFTER_QUOTE:\n          switch (line.charAt(i)) {\n            case this.delimiter:\n              result.push(line.substring(readOffset, i - 1));\n              currentState = STATE_OUT;\n              readOffset = i + 1;\n              break;\n            case CODE_QUOTE:\n              currentState = STATE_QUOTE;\n              break;\n            default:\n              currentState = STATE_WITHIN_QUOTE_IN_QUOTE;\n              break;\n          }\n          break;\n        case STATE_WITHIN_QUOTE_IN_QUOTE:\n          switch (line.charAt(i)) {\n            case CODE_QUOTE:\n              currentState = STATE_QUOTE;\n              break;\n            default:\n          }\n          break;\n        default:\n      }\n    }\n    if (currentState === STATE_QUOTE_AFTER_QUOTE) {\n      result.push(line.substring(readOffset, readLength - 1));\n    } else {\n      result.push(line.substring(readOffset));\n    }\n    if (validateElementCount && result.length !== this.fullColumnNames.length) {\n      throw new Error(`Invalid row in csv file. Should have ${this.fullColumnNames.length} elements in a row, but got ${result}`);\n    }\n    return result;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-data@3.20.0_k7dauiu3y265wd6lcplf62oi7i/node_modules/@tensorflow/tfjs-data/dist/iterators/microphone_iterator.js\nvar MicrophoneIterator = class extends LazyIterator {\n  constructor(microphoneConfig) {\n    super();\n    this.microphoneConfig = microphoneConfig;\n    this.isClosed = false;\n    this.fftSize = microphoneConfig.fftSize || 1024;\n    const fftSizeLog2 = Math.log2(this.fftSize);\n    if (this.fftSize < 0 || fftSizeLog2 < 4 || fftSizeLog2 > 14 || !Number.isInteger(fftSizeLog2)) {\n      throw new Error(`Invalid fftSize: it must be a power of 2 between 2 to 4 and 2 to 14, but got ${this.fftSize}`);\n    }\n    this.numFrames = microphoneConfig.numFramesPerSpectrogram || 43;\n    this.sampleRateHz = microphoneConfig.sampleRateHz;\n    this.columnTruncateLength = microphoneConfig.columnTruncateLength || this.fftSize;\n    this.audioTrackConstraints = microphoneConfig.audioTrackConstraints;\n    this.smoothingTimeConstant = microphoneConfig.smoothingTimeConstant || 0;\n    this.includeSpectrogram = microphoneConfig.includeSpectrogram === false ? false : true;\n    this.includeWaveform = microphoneConfig.includeWaveform === true ? true : false;\n    if (!this.includeSpectrogram && !this.includeWaveform) {\n      throw new Error(\"Both includeSpectrogram and includeWaveform are false. At least one type of data should be returned.\");\n    }\n  }\n  summary() {\n    return `microphone`;\n  }\n  static async create(microphoneConfig = {}) {\n    if (!env().get(\"IS_BROWSER\")) {\n      throw new Error(\"microphone API is only supported in browser environment.\");\n    }\n    const microphoneIterator = new MicrophoneIterator(microphoneConfig);\n    await microphoneIterator.start();\n    return microphoneIterator;\n  }\n  async start() {\n    try {\n      this.stream = await navigator.mediaDevices.getUserMedia({\n        audio: this.audioTrackConstraints == null ? true : this.audioTrackConstraints,\n        video: false\n      });\n    } catch (e) {\n      throw new Error(`Error thrown while initializing video stream: ${e.message}`);\n    }\n    if (!this.stream) {\n      throw new Error(\"Could not obtain audio from microphone.\");\n    }\n    const ctxConstructor = window.AudioContext || window.webkitAudioContext;\n    this.audioContext = new ctxConstructor();\n    if (!this.sampleRateHz) {\n      this.sampleRateHz = this.audioContext.sampleRate;\n    } else if (this.audioContext.sampleRate !== this.sampleRateHz) {\n      throw new Error(`Mismatch in sampling rate: Expected: ${this.sampleRateHz}; Actual: ${this.audioContext.sampleRate}`);\n    }\n    const streamSource = this.audioContext.createMediaStreamSource(this.stream);\n    this.analyser = this.audioContext.createAnalyser();\n    this.analyser.fftSize = this.fftSize * 2;\n    this.analyser.smoothingTimeConstant = this.smoothingTimeConstant;\n    streamSource.connect(this.analyser);\n    this.freqData = new Float32Array(this.fftSize);\n    this.timeData = new Float32Array(this.fftSize);\n    return;\n  }\n  async next() {\n    if (this.isClosed) {\n      return { value: null, done: true };\n    }\n    let spectrogramTensor;\n    let waveformTensor;\n    const audioDataQueue = await this.getAudioData();\n    if (this.includeSpectrogram) {\n      const freqData = this.flattenQueue(audioDataQueue.freqDataQueue);\n      spectrogramTensor = this.getTensorFromAudioDataArray(freqData, [this.numFrames, this.columnTruncateLength, 1]);\n    }\n    if (this.includeWaveform) {\n      const timeData = this.flattenQueue(audioDataQueue.timeDataQueue);\n      waveformTensor = this.getTensorFromAudioDataArray(timeData, [this.numFrames * this.fftSize, 1]);\n    }\n    return {\n      value: { \"spectrogram\": spectrogramTensor, \"waveform\": waveformTensor },\n      done: false\n    };\n  }\n  async capture() {\n    return (await this.next()).value;\n  }\n  async getAudioData() {\n    const freqDataQueue = [];\n    const timeDataQueue = [];\n    let currentFrames = 0;\n    return new Promise((resolve) => {\n      const intervalID = setInterval(() => {\n        if (this.includeSpectrogram) {\n          this.analyser.getFloatFrequencyData(this.freqData);\n          if (this.freqData[0] === -Infinity) {\n            resolve({ freqDataQueue, timeDataQueue });\n          }\n          freqDataQueue.push(this.freqData.slice(0, this.columnTruncateLength));\n        }\n        if (this.includeWaveform) {\n          this.analyser.getFloatTimeDomainData(this.timeData);\n          timeDataQueue.push(this.timeData.slice());\n        }\n        if (++currentFrames === this.numFrames) {\n          clearInterval(intervalID);\n          resolve({ freqDataQueue, timeDataQueue });\n        }\n      }, this.fftSize / this.sampleRateHz * 1e3);\n    });\n  }\n  stop() {\n    if (!this.isClosed) {\n      this.isClosed = true;\n      this.analyser.disconnect();\n      this.audioContext.close();\n      if (this.stream != null && this.stream.getTracks().length > 0) {\n        this.stream.getTracks()[0].stop();\n      }\n    }\n  }\n  toArray() {\n    throw new Error(\"Can not convert infinite audio stream to array.\");\n  }\n  getSampleRate() {\n    return this.sampleRateHz;\n  }\n  flattenQueue(queue) {\n    const frameSize = queue[0].length;\n    const freqData = new Float32Array(queue.length * frameSize);\n    queue.forEach((data, i) => freqData.set(data, i * frameSize));\n    return freqData;\n  }\n  getTensorFromAudioDataArray(freqData, shape) {\n    const vals = new Float32Array(util_exports.sizeFromShape(shape));\n    vals.set(freqData, vals.length - freqData.length);\n    return tensor(vals, shape);\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-data@3.20.0_k7dauiu3y265wd6lcplf62oi7i/node_modules/@tensorflow/tfjs-data/dist/iterators/webcam_iterator.js\nvar WebcamIterator = class extends LazyIterator {\n  constructor(webcamVideoElement, webcamConfig) {\n    super();\n    this.webcamVideoElement = webcamVideoElement;\n    this.webcamConfig = webcamConfig;\n    this.isClosed = true;\n    this.resize = false;\n    if (this.needToResize()) {\n      this.resize = true;\n      this.cropSize = [this.webcamConfig.resizeHeight, this.webcamConfig.resizeWidth];\n      this.cropBoxInd = tensor1d([0], \"int32\");\n      if (this.webcamConfig.centerCrop) {\n        const widthCroppingRatio = this.webcamConfig.resizeWidth * 1 / this.webcamVideoElement.width;\n        const heightCroppingRatio = this.webcamConfig.resizeHeight * 1 / this.webcamVideoElement.height;\n        const widthCropStart = (1 - widthCroppingRatio) / 2;\n        const heightCropStart = (1 - heightCroppingRatio) / 2;\n        const widthCropEnd = widthCropStart + widthCroppingRatio;\n        const heightCropEnd = heightCroppingRatio + heightCropStart;\n        this.cropBox = tensor2d([heightCropStart, widthCropStart, heightCropEnd, widthCropEnd], [1, 4]);\n      } else {\n        this.cropBox = tensor2d([0, 0, 1, 1], [1, 4]);\n      }\n    }\n  }\n  summary() {\n    return `webcam`;\n  }\n  static async create(webcamVideoElement, webcamConfig = {}) {\n    if (!env().get(\"IS_BROWSER\")) {\n      throw new Error(\"tf.data.webcam is only supported in browser environment.\");\n    }\n    if (!webcamVideoElement) {\n      webcamVideoElement = document.createElement(\"video\");\n      if (!webcamConfig.resizeWidth || !webcamConfig.resizeHeight) {\n        throw new Error(\"Please provide webcam video element, or resizeWidth and resizeHeight to create a hidden video element.\");\n      }\n      webcamVideoElement.width = webcamConfig.resizeWidth;\n      webcamVideoElement.height = webcamConfig.resizeHeight;\n    }\n    const webcamIterator = new WebcamIterator(webcamVideoElement, webcamConfig);\n    await webcamIterator.start();\n    return webcamIterator;\n  }\n  async start() {\n    if (this.webcamConfig.facingMode) {\n      util_exports.assert(this.webcamConfig.facingMode === \"user\" || this.webcamConfig.facingMode === \"environment\", () => `Invalid webcam facing mode: ${this.webcamConfig.facingMode}. Please provide 'user' or 'environment'`);\n    }\n    try {\n      this.stream = await navigator.mediaDevices.getUserMedia({\n        video: {\n          deviceId: this.webcamConfig.deviceId,\n          facingMode: this.webcamConfig.facingMode ? this.webcamConfig.facingMode : \"user\",\n          width: this.webcamVideoElement.width,\n          height: this.webcamVideoElement.height\n        }\n      });\n    } catch (e) {\n      e.message = `Error thrown while initializing video stream: ${e.message}`;\n      throw e;\n    }\n    if (!this.stream) {\n      throw new Error(\"Could not obtain video from webcam.\");\n    }\n    try {\n      this.webcamVideoElement.srcObject = this.stream;\n    } catch (error) {\n      console.log(error);\n      this.webcamVideoElement.src = window.URL.createObjectURL(this.stream);\n    }\n    this.webcamVideoElement.play();\n    this.isClosed = false;\n    return new Promise((resolve) => {\n      this.webcamVideoElement.onloadedmetadata = () => {\n        resolve();\n      };\n    });\n  }\n  async next() {\n    if (this.isClosed) {\n      return { value: null, done: true };\n    }\n    let img;\n    try {\n      img = browser_exports.fromPixels(this.webcamVideoElement);\n    } catch (e) {\n      throw new Error(`Error thrown converting video to pixels: ${JSON.stringify(e)}`);\n    }\n    if (this.resize) {\n      try {\n        return { value: this.cropAndResizeFrame(img), done: false };\n      } catch (e) {\n        throw new Error(`Error thrown cropping the video: ${e.message}`);\n      } finally {\n        img.dispose();\n      }\n    } else {\n      return { value: img, done: false };\n    }\n  }\n  needToResize() {\n    if (this.webcamConfig.resizeWidth && this.webcamConfig.resizeHeight && (this.webcamVideoElement.width !== this.webcamConfig.resizeWidth || this.webcamVideoElement.height !== this.webcamConfig.resizeHeight)) {\n      return true;\n    }\n    return false;\n  }\n  cropAndResizeFrame(img) {\n    return tidy(() => {\n      const expandedImage = expandDims(cast(img, \"float32\"), 0);\n      let resizedImage;\n      resizedImage = image.cropAndResize(expandedImage, this.cropBox, this.cropBoxInd, this.cropSize, \"bilinear\");\n      const shape = resizedImage.shape;\n      return reshape(resizedImage, shape.slice(1));\n    });\n  }\n  async capture() {\n    return (await this.next()).value;\n  }\n  stop() {\n    const tracks = this.stream.getTracks();\n    tracks.forEach((track) => track.stop());\n    try {\n      this.webcamVideoElement.srcObject = null;\n    } catch (error) {\n      console.log(error);\n      this.webcamVideoElement.src = null;\n    }\n    this.isClosed = true;\n  }\n  toArray() {\n    throw new Error(\"Can not convert infinite video stream to array.\");\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-data@3.20.0_k7dauiu3y265wd6lcplf62oi7i/node_modules/@tensorflow/tfjs-data/dist/datasource.js\nvar DataSource = class {\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-data@3.20.0_k7dauiu3y265wd6lcplf62oi7i/node_modules/@tensorflow/tfjs-data/dist/iterators/string_iterator.js\nvar StringIterator = class extends LazyIterator {\n  split(separator) {\n    return new SplitIterator(this, separator);\n  }\n};\nvar SplitIterator = class extends StringIterator {\n  constructor(upstream, separator) {\n    super();\n    this.upstream = upstream;\n    this.impl = new SplitIteratorImpl(upstream, separator);\n  }\n  summary() {\n    return this.impl.summary();\n  }\n  async next() {\n    return this.impl.next();\n  }\n};\nvar SplitIteratorImpl = class extends OneToManyIterator {\n  constructor(upstream, separator) {\n    super();\n    this.upstream = upstream;\n    this.separator = separator;\n    this.carryover = \"\";\n  }\n  summary() {\n    return `${this.upstream.summary()} -> Split('${this.separator}')`;\n  }\n  async pump() {\n    const chunkResult = await this.upstream.next();\n    if (chunkResult.done) {\n      if (this.carryover === \"\") {\n        return false;\n      }\n      this.outputQueue.push(this.carryover);\n      this.carryover = \"\";\n      return true;\n    }\n    const lines = chunkResult.value.split(this.separator);\n    lines[0] = this.carryover + lines[0];\n    for (const line of lines.slice(0, -1)) {\n      this.outputQueue.push(line);\n    }\n    this.carryover = lines[lines.length - 1];\n    return true;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-data@3.20.0_k7dauiu3y265wd6lcplf62oi7i/node_modules/@tensorflow/tfjs-data/dist/iterators/byte_chunk_iterator.js\nvar ByteChunkIterator = class extends LazyIterator {\n  decodeUTF8() {\n    return new Utf8Iterator(this);\n  }\n};\nvar Utf8Iterator = class extends StringIterator {\n  constructor(upstream) {\n    super();\n    this.upstream = upstream;\n    this.impl = new Utf8IteratorImpl(upstream);\n  }\n  summary() {\n    return this.impl.summary();\n  }\n  async next() {\n    return this.impl.next();\n  }\n};\nvar Utf8IteratorImpl = class extends OneToManyIterator {\n  constructor(upstream) {\n    super();\n    this.upstream = upstream;\n    if (env().get(\"IS_BROWSER\")) {\n      this.decoder = new TextDecoder(\"utf-8\");\n    } else {\n      const { StringDecoder } = require_string_decoder();\n      this.decoder = new StringDecoder(\"utf8\");\n    }\n  }\n  summary() {\n    return `${this.upstream.summary()} -> Utf8`;\n  }\n  async pump() {\n    const chunkResult = await this.upstream.next();\n    let chunk;\n    if (chunkResult.done) {\n      return false;\n    } else {\n      chunk = chunkResult.value;\n    }\n    let text;\n    if (env().get(\"IS_BROWSER\")) {\n      text = this.decoder.decode(chunk, { stream: true });\n    } else {\n      text = this.decoder.write(Buffer.from(chunk.buffer));\n    }\n    this.outputQueue.push(text);\n    return true;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-data@3.20.0_k7dauiu3y265wd6lcplf62oi7i/node_modules/@tensorflow/tfjs-data/dist/iterators/file_chunk_iterator.js\nvar FileChunkIterator = class extends ByteChunkIterator {\n  constructor(file, options = {}) {\n    super();\n    this.file = file;\n    this.options = options;\n    util_exports.assert(file instanceof Uint8Array || (env().get(\"IS_BROWSER\") ? file instanceof File || file instanceof Blob : false), () => \"FileChunkIterator only supports File, Blob and Uint8Array right now.\");\n    this.offset = options.offset || 0;\n    this.chunkSize = options.chunkSize || 1024 * 1024;\n  }\n  summary() {\n    return `FileChunks ${this.file}`;\n  }\n  async next() {\n    if (this.offset >= (this.file instanceof Uint8Array ? this.file.byteLength : this.file.size)) {\n      return { value: null, done: true };\n    }\n    const chunk = new Promise((resolve, reject) => {\n      const end = this.offset + this.chunkSize;\n      if (this.file instanceof Uint8Array) {\n        resolve(new Uint8Array(this.file.slice(this.offset, end)));\n      } else {\n        const fileReader = new FileReader();\n        fileReader.onload = (event) => {\n          let data = fileReader.result;\n          if (data instanceof ArrayBuffer) {\n            data = new Uint8Array(data);\n          }\n          if (!(data instanceof Uint8Array)) {\n            return reject(new TypeError(\"FileReader returned unknown type.\"));\n          }\n          resolve(data);\n        };\n        fileReader.onabort = (event) => {\n          return reject(new Error(\"Aborted\"));\n        };\n        fileReader.onerror = (event) => {\n          return reject(new Error(event.type));\n        };\n        const slice6 = this.file.slice(this.offset, end);\n        fileReader.readAsArrayBuffer(slice6);\n      }\n      this.offset = end;\n    });\n    return { value: await chunk, done: false };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-data@3.20.0_k7dauiu3y265wd6lcplf62oi7i/node_modules/@tensorflow/tfjs-data/dist/iterators/url_chunk_iterator.js\nasync function urlChunkIterator(url, options = {}, fetchFunc) {\n  let urlString;\n  let requestInit;\n  if (typeof url === \"string\") {\n    urlString = url;\n  } else {\n    urlString = url.url;\n    requestInit = getRequestInitFromRequest(url);\n  }\n  const response = await (fetchFunc || util_exports.fetch)(urlString, requestInit);\n  if (response.ok) {\n    const uint8Array = new Uint8Array(await response.arrayBuffer());\n    return new FileChunkIterator(uint8Array, options);\n  } else {\n    throw new Error(response.statusText);\n  }\n}\nvar getRequestInitFromRequest = (request) => {\n  const init2 = {\n    method: request.method,\n    headers: request.headers,\n    body: request.body,\n    mode: request.mode,\n    credentials: request.credentials,\n    cache: request.cache,\n    redirect: request.redirect,\n    referrer: request.referrer,\n    integrity: request.integrity\n  };\n  return init2;\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-data@3.20.0_k7dauiu3y265wd6lcplf62oi7i/node_modules/@tensorflow/tfjs-data/dist/util/source_util.js\nfunction isLocalPath(source) {\n  return typeof source === \"string\" && source.slice(0, 7) === \"file://\";\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-data@3.20.0_k7dauiu3y265wd6lcplf62oi7i/node_modules/@tensorflow/tfjs-data/dist/sources/file_data_source.js\nvar FileDataSource = class extends DataSource {\n  constructor(input2, options = {}) {\n    super();\n    this.input = input2;\n    this.options = options;\n  }\n  async iterator() {\n    if (isLocalPath(this.input) && env().get(\"IS_NODE\")) {\n      const fs = require_fs();\n      this.input = fs.readFileSync(this.input.slice(7));\n    }\n    return new FileChunkIterator(this.input, this.options);\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-data@3.20.0_k7dauiu3y265wd6lcplf62oi7i/node_modules/@tensorflow/tfjs-data/dist/sources/url_data_source.js\nvar URLDataSource = class extends DataSource {\n  constructor(url, fileOptions = {}) {\n    super();\n    this.url = url;\n    this.fileOptions = fileOptions;\n  }\n  async iterator() {\n    if (isLocalPath(this.url)) {\n      return new FileDataSource(this.url, this.fileOptions).iterator();\n    } else {\n      return urlChunkIterator(this.url, this.fileOptions);\n    }\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-data@3.20.0_k7dauiu3y265wd6lcplf62oi7i/node_modules/@tensorflow/tfjs-data/dist/readers.js\nfunction csv(source, csvConfig = {}) {\n  return new CSVDataset(new URLDataSource(source), csvConfig);\n}\nfunction func(f) {\n  const iter = iteratorFromFunction(f);\n  return datasetFromIteratorFn(async () => iter);\n}\nfunction generator(generator2) {\n  return datasetFromIteratorFn(async () => {\n    const gen = await generator2();\n    return iteratorFromFunction(() => gen.next());\n  });\n}\nasync function webcam(webcamVideoElement, webcamConfig) {\n  return WebcamIterator.create(webcamVideoElement, webcamConfig);\n}\nasync function microphone(microphoneConfig) {\n  return MicrophoneIterator.create(microphoneConfig);\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-data@3.20.0_k7dauiu3y265wd6lcplf62oi7i/node_modules/@tensorflow/tfjs-data/dist/version.js\nvar version4 = \"3.20.0\";\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/cpu_util.js\nfunction assertNotComplex(tensor2, opName) {\n  if (!Array.isArray(tensor2)) {\n    tensor2 = [tensor2];\n  }\n  tensor2.forEach((t) => {\n    if (t != null) {\n      util_exports.assert(t.dtype !== \"complex64\", () => `${opName} does not support complex64 tensors in the CPU backend.`);\n    }\n  });\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/backend_cpu.js\nvar whereImpl2 = kernel_impls_exports.whereImpl;\nvar MathBackendCPU = class extends KernelBackend {\n  constructor() {\n    super();\n    this.blockSize = 48;\n    this.firstUse = true;\n    this.data = new DataStorage(this, engine());\n  }\n  nextDataId() {\n    return MathBackendCPU.nextDataId++;\n  }\n  write(values, shape, dtype) {\n    if (this.firstUse) {\n      this.firstUse = false;\n      if (env().get(\"IS_NODE\")) {\n        backend_util_exports.warn(\"\\n============================\\nHi, looks like you are running TensorFlow.js in Node.js. To speed things up dramatically, install our node backend, visit https://github.com/tensorflow/tfjs-node for more details. \\n============================\");\n      }\n    }\n    const dataId = { id: this.nextDataId() };\n    this.data.set(dataId, { values, dtype, refCount: 1 });\n    return dataId;\n  }\n  makeTensorInfo(shape, dtype, values) {\n    let outId;\n    if (dtype === \"string\" && values != null && values.length > 0 && util_exports.isString(values[0])) {\n      const encodedValues = values.map((d) => util_exports.encodeString(d));\n      outId = this.write(encodedValues, shape, dtype);\n    } else {\n      outId = this.write(values, shape, dtype);\n    }\n    return { dataId: outId, shape, dtype };\n  }\n  refCount(dataId) {\n    if (this.data.has(dataId)) {\n      const tensorData = this.data.get(dataId);\n      return tensorData.refCount;\n    }\n    return 0;\n  }\n  incRef(dataId) {\n    const tensorData = this.data.get(dataId);\n    tensorData.refCount++;\n  }\n  decRef(dataId) {\n    if (this.data.has(dataId)) {\n      const tensorData = this.data.get(dataId);\n      tensorData.refCount--;\n    }\n  }\n  move(dataId, values, shape, dtype, refCount) {\n    this.data.set(dataId, { values, dtype, refCount });\n  }\n  numDataIds() {\n    return this.data.numDataIds();\n  }\n  async read(dataId) {\n    return this.readSync(dataId);\n  }\n  readSync(dataId) {\n    const { dtype, complexTensorInfos } = this.data.get(dataId);\n    if (dtype === \"complex64\") {\n      const realValues = this.readSync(complexTensorInfos.real.dataId);\n      const imagValues = this.readSync(complexTensorInfos.imag.dataId);\n      return backend_util_exports.mergeRealAndImagArrays(realValues, imagValues);\n    }\n    return this.data.get(dataId).values;\n  }\n  bufferSync(t) {\n    const data = this.readSync(t.dataId);\n    if (t.dtype === \"string\") {\n      try {\n        const strings = data.map((d) => util_exports.decodeString(d));\n        return buffer(t.shape, t.dtype, strings);\n      } catch (_a) {\n        throw new Error(\"Failed to decode encoded string bytes into utf-8\");\n      }\n    }\n    return buffer(t.shape, t.dtype, data);\n  }\n  makeOutput(values, shape, dtype) {\n    return engine().makeTensorFromTensorInfo(this.makeTensorInfo(shape, dtype, values), this);\n  }\n  disposeData(dataId, force = false) {\n    if (this.data.has(dataId)) {\n      this.data.get(dataId).refCount--;\n      if (!force && this.data.get(dataId).refCount > 0) {\n        return false;\n      }\n      const { complexTensorInfos } = this.data.get(dataId);\n      if (complexTensorInfos != null) {\n        this.disposeData(complexTensorInfos.real.dataId, true);\n        this.disposeData(complexTensorInfos.imag.dataId, true);\n      }\n      this.data.delete(dataId);\n    }\n    return true;\n  }\n  disposeIntermediateTensorInfo(tensorInfo) {\n    this.disposeData(tensorInfo.dataId);\n  }\n  async time(f) {\n    const start = util_exports.now();\n    f();\n    const kernelMs = util_exports.now() - start;\n    return { kernelMs };\n  }\n  memory() {\n    return {\n      unreliable: true,\n      reasons: [\"The reported memory is an upper bound. Due to automatic garbage collection, the true allocated memory may be less.\"]\n    };\n  }\n  where(condition) {\n    assertNotComplex([condition], \"where\");\n    const condVals = this.readSync(condition.dataId);\n    return whereImpl2(condition.shape, condVals);\n  }\n  dispose() {\n  }\n  floatPrecision() {\n    return 32;\n  }\n  epsilon() {\n    return super.epsilon();\n  }\n};\nMathBackendCPU.nextDataId = 0;\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/shared.js\nvar shared_exports = {};\n__export(shared_exports, {\n  addImpl: () => addImpl,\n  bincountImpl: () => bincountImpl,\n  bincountReduceImpl: () => bincountReduceImpl,\n  castImpl: () => castImpl,\n  ceilImpl: () => ceilImpl,\n  concatImpl: () => concatImpl,\n  equalImpl: () => equalImpl,\n  expImpl: () => expImpl,\n  expm1Impl: () => expm1Impl,\n  floorImpl: () => floorImpl,\n  gatherNdImpl: () => gatherNdImpl,\n  gatherV2Impl: () => gatherV2Impl,\n  greaterEqualImpl: () => greaterEqualImpl,\n  greaterImpl: () => greaterImpl,\n  lessEqualImpl: () => lessEqualImpl,\n  lessImpl: () => lessImpl,\n  linSpaceImpl: () => linSpaceImpl,\n  logImpl: () => logImpl,\n  maxImpl: () => maxImpl,\n  maximumImpl: () => maximumImpl,\n  minimumImpl: () => minimumImpl,\n  multiplyImpl: () => multiplyImpl,\n  negImpl: () => negImpl,\n  notEqualImpl: () => notEqualImpl,\n  prodImpl: () => prodImpl,\n  raggedTensorToTensorImpl: () => raggedTensorToTensorImpl,\n  rangeImpl: () => rangeImpl,\n  rsqrtImpl: () => rsqrtImpl,\n  scatterImpl: () => scatterImpl,\n  sigmoidImpl: () => sigmoidImpl,\n  simpleAbsImpl: () => simpleAbsImpl,\n  sliceImpl: () => sliceImpl,\n  sparseFillEmptyRowsImpl: () => sparseFillEmptyRowsImpl,\n  sparseReshapeImpl: () => sparseReshapeImpl,\n  sparseSegmentReductionImpl: () => sparseSegmentReductionImpl,\n  sqrtImpl: () => sqrtImpl,\n  squaredDifferenceImpl: () => squaredDifferenceImpl,\n  stridedSliceImpl: () => stridedSliceImpl,\n  stringNGramsImpl: () => stringNGramsImpl,\n  stringSplitImpl: () => stringSplitImpl,\n  stringToHashBucketFastImpl: () => stringToHashBucketFastImpl,\n  subImpl: () => subImpl,\n  tileImpl: () => tileImpl,\n  topKImpl: () => topKImpl,\n  transposeImpl: () => transposeImpl,\n  uniqueImpl: () => uniqueImpl\n});\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Abs.js\nfunction simpleAbsImpl(vals) {\n  const resultValues = new Float32Array(vals.length);\n  for (let i = 0; i < vals.length; ++i) {\n    resultValues[i] = Math.abs(vals[i]);\n  }\n  return resultValues;\n}\nvar abs2 = (args) => {\n  const { x } = args.inputs;\n  const cpuBackend = args.backend;\n  assertNotComplex(x, \"abs\");\n  let resultValues = new Float32Array(util_exports.sizeFromShape(x.shape));\n  const values = cpuBackend.data.get(x.dataId).values;\n  resultValues = simpleAbsImpl(values);\n  return cpuBackend.makeOutput(resultValues, x.shape, x.dtype);\n};\nvar absConfig = {\n  kernelName: Abs,\n  backendName: \"cpu\",\n  kernelFunc: abs2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/utils/binary_impl.js\nfunction createSimpleBinaryKernelImpl(op2) {\n  return (aShape, bShape, aVals, bVals, dtype) => {\n    const newShape = backend_util_exports.assertAndGetBroadcastShape(aShape, bShape);\n    const resultRank = newShape.length;\n    const resultStrides = util_exports.computeStrides(newShape);\n    const resultSize = util_exports.sizeFromShape(newShape);\n    const result = util_exports.getTypedArrayFromDType(dtype, resultSize);\n    const aRank = aShape.length;\n    const bRank = bShape.length;\n    const aStrides = util_exports.computeStrides(aShape);\n    const bStrides = util_exports.computeStrides(bShape);\n    const aBroadcastDims = backend_util_exports.getBroadcastDims(aShape, newShape);\n    const bBroadcastDims = backend_util_exports.getBroadcastDims(bShape, newShape);\n    if (aBroadcastDims.length + bBroadcastDims.length === 0) {\n      for (let i = 0; i < result.length; ++i) {\n        result[i] = op2(aVals[i % aVals.length], bVals[i % bVals.length]);\n      }\n    } else {\n      for (let i = 0; i < result.length; ++i) {\n        const loc = util_exports.indexToLoc(i, resultRank, resultStrides);\n        const aLoc = loc.slice(-aRank);\n        aBroadcastDims.forEach((d) => aLoc[d] = 0);\n        const aIndex = util_exports.locToIndex(aLoc, aRank, aStrides);\n        const bLoc = loc.slice(-bRank);\n        bBroadcastDims.forEach((d) => bLoc[d] = 0);\n        const bIndex = util_exports.locToIndex(bLoc, bRank, bStrides);\n        result[i] = op2(aVals[aIndex], bVals[bIndex]);\n      }\n    }\n    return [result, newShape];\n  };\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Complex.js\nfunction complex2(args) {\n  const { inputs, backend: backend2 } = args;\n  const { real: real5, imag: imag5 } = inputs;\n  const realVals = backend2.data.get(real5.dataId).values;\n  const imagVals = backend2.data.get(imag5.dataId).values;\n  const complexInfo = backend2.makeTensorInfo(real5.shape, \"complex64\");\n  const complex5 = backend2.data.get(complexInfo.dataId);\n  complex5.complexTensorInfos = {\n    real: backend2.makeTensorInfo(real5.shape, \"float32\", realVals),\n    imag: backend2.makeTensorInfo(imag5.shape, \"float32\", imagVals)\n  };\n  return complexInfo;\n}\nvar complexConfig = {\n  kernelName: Complex,\n  backendName: \"cpu\",\n  kernelFunc: complex2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/utils/zeros_impl.js\nfunction zeros3(backend2, shape, dtype = \"float32\") {\n  if (dtype === \"complex64\") {\n    const real5 = zeros3(backend2, shape, \"float32\");\n    const imag5 = zeros3(backend2, shape, \"float32\");\n    return complex2({ inputs: { real: real5, imag: imag5 }, backend: backend2 });\n  }\n  const values = util_exports.makeZerosTypedArray(util_exports.sizeFromShape(shape), dtype);\n  return backend2.makeTensorInfo(shape, dtype, values);\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Identity.js\nfunction identity2(args) {\n  const { inputs, backend: backend2 } = args;\n  const { x } = inputs;\n  backend2.incRef(x.dataId);\n  return { dataId: x.dataId, shape: x.shape, dtype: x.dtype };\n}\nvar identityConfig = {\n  kernelName: Identity,\n  backendName: \"cpu\",\n  kernelFunc: identity2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Real.js\nfunction real2(args) {\n  const { inputs, backend: backend2 } = args;\n  const { input: input2 } = inputs;\n  const real5 = backend2.data.get(input2.dataId).complexTensorInfos.real;\n  const realVal = backend2.data.get(real5.dataId).values;\n  return backend2.makeTensorInfo(real5.shape, real5.dtype, realVal);\n}\nvar realConfig = {\n  kernelName: Real,\n  backendName: \"cpu\",\n  kernelFunc: real2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Cast.js\nfunction castImpl(values, shape, inputType, dtype) {\n  if (dtype === \"int32\") {\n    const resultValues = Int32Array.from(values);\n    return [shape, \"int32\", resultValues];\n  }\n  if (dtype === \"bool\") {\n    const zero = util_exports.toTypedArray([0], inputType);\n    const [resultData, resultShape] = createSimpleBinaryKernelImpl((a, b) => a !== b ? 1 : 0)(shape, [], values, zero, \"bool\");\n    return [resultShape, \"bool\", resultData];\n  }\n  throw new Error(`Error in Cast: failed to cast ${inputType} to ${dtype}`);\n}\nfunction cast3(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x } = inputs;\n  const { dtype } = attrs;\n  if (dtype === \"complex64\") {\n    if (x.dtype === \"complex64\") {\n      return identity2({ inputs: { x }, backend: backend2 });\n    }\n    const zerosTensorInfo = zeros3(backend2, x.shape, x.dtype);\n    const floatX = cast3({ inputs: { x }, backend: backend2, attrs: { dtype: \"float32\" } });\n    const result = complex2({ inputs: { real: floatX, imag: zerosTensorInfo }, backend: backend2 });\n    backend2.disposeIntermediateTensorInfo(zerosTensorInfo);\n    backend2.disposeIntermediateTensorInfo(floatX);\n    return result;\n  }\n  if (x.dtype === \"complex64\") {\n    const realPart = real2({ inputs: { input: x }, backend: backend2 });\n    const result = cast3({ inputs: { x: realPart }, backend: backend2, attrs: { dtype } });\n    backend2.disposeIntermediateTensorInfo(realPart);\n    return result;\n  }\n  if (!util_exports.hasEncodingLoss(x.dtype, dtype)) {\n    const result = identity2({ inputs: { x }, backend: backend2 });\n    return { dataId: result.dataId, shape: result.shape, dtype };\n  }\n  const values = backend2.data.get(x.dataId).values;\n  const [resultShape, resultType, resultData] = castImpl(values, x.shape, x.dtype, dtype);\n  return backend2.makeTensorInfo(resultShape, resultType, resultData);\n}\nvar castConfig = {\n  kernelName: Cast,\n  backendName: \"cpu\",\n  kernelFunc: cast3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/utils/binary_utils.js\nfunction binaryKernelFunc(name, simpleImpl, complexImpl, dtype) {\n  if (complexImpl == null) {\n    return ({ inputs, backend: backend2 }) => {\n      const { a, b } = inputs;\n      const cpuBackend = backend2;\n      assertNotComplex([a, b], name);\n      const aVals = cpuBackend.data.get(a.dataId).values;\n      const bVals = cpuBackend.data.get(b.dataId).values;\n      const decodedAVals = a.dtype === \"string\" ? backend_util_exports.fromUint8ToStringArray(aVals) : aVals;\n      const decodedBVals = a.dtype === \"string\" ? backend_util_exports.fromUint8ToStringArray(bVals) : bVals;\n      const $dtype = dtype || a.dtype;\n      const [resultData, resultShape] = simpleImpl(a.shape, b.shape, decodedAVals, decodedBVals, $dtype);\n      return cpuBackend.makeTensorInfo(resultShape, $dtype, resultData);\n    };\n  }\n  return ({ inputs, backend: backend2 }) => {\n    const { a, b } = inputs;\n    const cpuBackend = backend2;\n    if (a.dtype === \"complex64\" || b.dtype === \"complex64\") {\n      const $aComplex = cast3({ inputs: { x: a }, backend: cpuBackend, attrs: { dtype: \"complex64\" } });\n      const $aComplexVals = cpuBackend.data.get($aComplex.dataId);\n      const aReal = $aComplexVals.complexTensorInfos.real;\n      const aImag = $aComplexVals.complexTensorInfos.imag;\n      const aRealVals = cpuBackend.data.get(aReal.dataId).values;\n      const aImagVals = cpuBackend.data.get(aImag.dataId).values;\n      const $bComplex = cast3({ inputs: { x: b }, backend: cpuBackend, attrs: { dtype: \"complex64\" } });\n      const $bComplexVals = cpuBackend.data.get($bComplex.dataId);\n      const bReal = $bComplexVals.complexTensorInfos.real;\n      const bImag = $bComplexVals.complexTensorInfos.imag;\n      const bRealVals = cpuBackend.data.get(bReal.dataId).values;\n      const bImagVals = cpuBackend.data.get(bImag.dataId).values;\n      const [resultRealData, resultImagData, resultShape] = complexImpl(a.shape, b.shape, aRealVals, aImagVals, bRealVals, bImagVals);\n      const resultReal = cpuBackend.makeTensorInfo(resultShape, \"float32\", resultRealData);\n      const resultImag = cpuBackend.makeTensorInfo(resultShape, \"float32\", resultImagData);\n      const result = complex2({ inputs: { real: resultReal, imag: resultImag }, backend: cpuBackend });\n      cpuBackend.disposeIntermediateTensorInfo($aComplex);\n      cpuBackend.disposeIntermediateTensorInfo($bComplex);\n      cpuBackend.disposeIntermediateTensorInfo(resultReal);\n      cpuBackend.disposeIntermediateTensorInfo(resultImag);\n      return result;\n    } else {\n      const aVals = cpuBackend.data.get(a.dataId).values;\n      const bVals = cpuBackend.data.get(b.dataId).values;\n      const $dtype = dtype || a.dtype;\n      const [resultData, resultShape] = simpleImpl(a.shape, b.shape, aVals, bVals, $dtype);\n      return cpuBackend.makeTensorInfo(resultShape, $dtype, resultData);\n    }\n  };\n}\nfunction createComplexBinaryKernelImpl(op2) {\n  return (aShape, bShape, aRealVals, aImagVals, bRealVals, bImagVals) => {\n    const resultShape = backend_util_exports.assertAndGetBroadcastShape(aShape, bShape);\n    const resultSize = util_exports.sizeFromShape(resultShape);\n    const resultRank = resultShape.length;\n    const resultStrides = util_exports.computeStrides(resultShape);\n    const resultRealVals = util_exports.getTypedArrayFromDType(\"float32\", resultSize);\n    const resultImagVals = util_exports.getTypedArrayFromDType(\"float32\", resultSize);\n    const aBroadcastDims = backend_util_exports.getBroadcastDims(aShape, resultShape);\n    const bBroadcastDims = backend_util_exports.getBroadcastDims(bShape, resultShape);\n    const aVals = backend_util_exports.mergeRealAndImagArrays(aRealVals, aImagVals);\n    const bVals = backend_util_exports.mergeRealAndImagArrays(bRealVals, bImagVals);\n    const aRank = aShape.length;\n    const aStrides = util_exports.computeStrides(aShape);\n    const bRank = bShape.length;\n    const bStrides = util_exports.computeStrides(bShape);\n    if (aBroadcastDims.length + bBroadcastDims.length === 0) {\n      for (let i = 0; i < resultRealVals.length; i++) {\n        const aIdx = i % aVals.length;\n        const bIdx = i % bVals.length;\n        const result = op2(aVals[aIdx * 2], aVals[aIdx * 2 + 1], bVals[bIdx * 2], bVals[bIdx * 2 + 1]);\n        resultRealVals[i] = result.real;\n        resultImagVals[i] = result.imag;\n      }\n    } else {\n      for (let i = 0; i < resultRealVals.length; i++) {\n        const loc = util_exports.indexToLoc(i, resultRank, resultStrides);\n        const aLoc = loc.slice(-aRank);\n        aBroadcastDims.forEach((d) => aLoc[d] = 0);\n        const aIndex = util_exports.locToIndex(aLoc, aRank, aStrides);\n        const bLoc = loc.slice(-bRank);\n        bBroadcastDims.forEach((d) => bLoc[d] = 0);\n        const bIndex = util_exports.locToIndex(bLoc, bRank, bStrides);\n        const opResult = op2(aVals[aIndex * 2], aVals[aIndex * 2 + 1], bVals[bIndex * 2], bVals[bIndex * 2 + 1]);\n        resultRealVals[i] = opResult.real;\n        resultImagVals[i] = opResult.imag;\n      }\n    }\n    return [resultRealVals, resultImagVals, resultShape];\n  };\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Add.js\nvar addImpl = createSimpleBinaryKernelImpl((a, b) => a + b);\nvar addComplexImpl = createComplexBinaryKernelImpl((aReal, aImag, bReal, bImag) => {\n  return { real: aReal + bReal, imag: aImag + bImag };\n});\nvar add4 = binaryKernelFunc(Add, addImpl, addComplexImpl);\nvar addConfig = {\n  kernelName: Add,\n  backendName: \"cpu\",\n  kernelFunc: add4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Bincount_impl.js\nfunction bincountImpl(xVals, weightsVals, weightsDtype, weightsShape, size) {\n  const weightsSize = util_exports.sizeFromShape(weightsShape);\n  const outVals = util_exports.makeZerosTypedArray(size, weightsDtype);\n  for (let i = 0; i < xVals.length; i++) {\n    const value = xVals[i];\n    if (value < 0) {\n      throw new Error(\"Input x must be non-negative!\");\n    }\n    if (value >= size) {\n      continue;\n    }\n    if (weightsSize > 0) {\n      outVals[value] += weightsVals[i];\n    } else {\n      outVals[value] += 1;\n    }\n  }\n  return outVals;\n}\nfunction bincountReduceImpl(xBuf, weightsBuf, size, binaryOutput = false) {\n  const numRows = xBuf.shape[0];\n  const numCols = xBuf.shape[1];\n  const outBuf = buffer([numRows, size], weightsBuf.dtype);\n  for (let i = 0; i < numRows; i++) {\n    for (let j = 0; j < numCols; j++) {\n      const value = xBuf.get(i, j);\n      if (value < 0) {\n        throw new Error(\"Input x must be non-negative!\");\n      }\n      if (value >= size) {\n        continue;\n      }\n      if (binaryOutput) {\n        outBuf.set(1, i, value);\n      } else {\n        if (weightsBuf.size > 0) {\n          outBuf.set(outBuf.get(i, value) + weightsBuf.get(i, j), i, value);\n        } else {\n          outBuf.set(outBuf.get(i, value) + 1, i, value);\n        }\n      }\n    }\n  }\n  return outBuf;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/utils/unary_impl.js\nfunction createSimpleUnaryImpl(op2) {\n  return (values, dtype, attrs) => {\n    const newValues = util_exports.getTypedArrayFromDType(dtype, values.length);\n    for (let i = 0; i < values.length; ++i) {\n      newValues[i] = op2(values[i], attrs);\n    }\n    return newValues;\n  };\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/utils/unary_utils.js\nfunction unaryKernelFunc(name, op2, dtype) {\n  return ({ inputs, attrs, backend: backend2 }) => {\n    const { x } = inputs;\n    assertNotComplex(x, name);\n    if (x.dtype === \"string\" || dtype === \"string\") {\n      throw new Error(\"unaryKernelFunc does not support string input/output\");\n    }\n    const cpuBackend = backend2;\n    const values = cpuBackend.data.get(x.dataId).values;\n    const xSize = util_exports.sizeFromShape(x.shape);\n    const $dtype = dtype || x.dtype;\n    const newValues = util_exports.getArrayFromDType($dtype, xSize);\n    for (let i = 0; i < xSize; ++i) {\n      newValues[i] = op2(values[i], attrs);\n    }\n    return cpuBackend.makeTensorInfo(x.shape, $dtype, newValues);\n  };\n}\nfunction unaryKernelFuncFromImpl(name, unaryImpl, dtype) {\n  return ({ inputs, attrs, backend: backend2 }) => {\n    const { x } = inputs;\n    assertNotComplex(x, name);\n    if (x.dtype === \"string\" || dtype === \"string\") {\n      throw new Error(\"unaryKernelFunc does not support string input/output\");\n    }\n    const cpuBackend = backend2;\n    const values = cpuBackend.data.get(x.dataId).values;\n    const $dtype = dtype || x.dtype;\n    const newValues = unaryImpl(values, $dtype, attrs);\n    return cpuBackend.makeTensorInfo(x.shape, $dtype, newValues);\n  };\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Ceil.js\nvar ceilImpl = createSimpleUnaryImpl((xi) => Math.ceil(xi));\nvar ceil2 = unaryKernelFuncFromImpl(Ceil, ceilImpl);\nvar ceilConfig = {\n  kernelName: Ceil,\n  backendName: \"cpu\",\n  kernelFunc: ceil2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Concat_impl.js\nfunction concatImpl(inputs, outShape, dtype, simplyConcat) {\n  const outVals = util_exports.getArrayFromDType(dtype, util_exports.sizeFromShape(outShape));\n  if (simplyConcat && dtype !== \"string\") {\n    let offset = 0;\n    inputs.forEach((input2) => {\n      const size = util_exports.sizeFromShape(input2.shape);\n      outVals.set(input2.vals, offset);\n      offset += size;\n    });\n  } else {\n    let colOffset = 0;\n    inputs.forEach((input2) => {\n      const decodedData = dtype === \"string\" ? backend_util_exports.fromUint8ToStringArray(input2.vals) : input2.vals;\n      let tIdx = 0;\n      for (let row = 0; row < input2.shape[0]; ++row) {\n        const resIdx = row * outShape[1] + colOffset;\n        for (let col = 0; col < input2.shape[1]; ++col) {\n          outVals[resIdx + col] = decodedData[tIdx++];\n        }\n      }\n      colOffset += input2.shape[1];\n    });\n  }\n  return outVals;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Equal.js\nvar equalImpl = createSimpleBinaryKernelImpl((a, b) => a === b ? 1 : 0);\nvar equal2 = binaryKernelFunc(Equal, equalImpl, null, \"bool\");\nvar equalConfig = {\n  kernelName: Equal,\n  backendName: \"cpu\",\n  kernelFunc: equal2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Exp.js\nvar expImpl = createSimpleUnaryImpl((xi) => Math.exp(xi));\nvar exp2 = unaryKernelFuncFromImpl(Exp, expImpl, \"float32\");\nvar expConfig = {\n  kernelName: Exp,\n  backendName: \"cpu\",\n  kernelFunc: exp2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Expm1.js\nvar expm1Impl = createSimpleUnaryImpl((xi) => Math.expm1(xi));\nvar expm12 = unaryKernelFuncFromImpl(Expm1, expm1Impl);\nvar expm1Config = {\n  kernelName: Expm1,\n  backendName: \"cpu\",\n  kernelFunc: expm12\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Floor.js\nvar floorImpl = createSimpleUnaryImpl((xi) => Math.floor(xi));\nvar floor2 = unaryKernelFuncFromImpl(Floor, floorImpl);\nvar floorConfig = {\n  kernelName: Floor,\n  backendName: \"cpu\",\n  kernelFunc: floor2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/GatherNd_Impl.js\nfunction gatherNdImpl(indicesData, paramsBuf, dtype, numSlices, sliceRank, sliceSize, strides, paramsShape, paramsSize) {\n  const outBuf = buffer([numSlices, sliceSize], dtype);\n  for (let i = 0; i < numSlices; i++) {\n    const index = [];\n    let flattenIndex = 0;\n    for (let j = 0; j < sliceRank; j++) {\n      const dim = indicesData[i * sliceRank + j];\n      flattenIndex += dim * strides[j];\n      index.push(dim);\n    }\n    if (flattenIndex < 0 || flattenIndex >= paramsSize / sliceSize) {\n      throw new Error(`Invalid indices: ${index} does not index into ${paramsShape}`);\n    }\n    for (let k = 0; k < sliceSize; k++) {\n      outBuf.values[i * sliceSize + k] = paramsBuf.get(...paramsBuf.indexToLoc(flattenIndex * sliceSize + k));\n    }\n  }\n  return outBuf;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/GatherV2_impl.js\nfunction gatherV2Impl(xBuf, indicesBuf, flattenOutputShape) {\n  const outBuf = buffer(flattenOutputShape, xBuf.dtype);\n  for (let i = 0; i < outBuf.size; ++i) {\n    const newLoc = outBuf.indexToLoc(i);\n    const originalLoc = newLoc.slice();\n    const batchIdx = originalLoc[0];\n    const indicesIdx = originalLoc[2];\n    const indicesIndex = indicesBuf.locToIndex([batchIdx, indicesIdx]);\n    originalLoc[2] = indicesBuf.values[indicesIndex];\n    const originalIndex = xBuf.locToIndex(originalLoc);\n    if (0 <= originalIndex && originalIndex < xBuf.values.length) {\n      outBuf.values[i] = xBuf.values[originalIndex];\n    }\n  }\n  return outBuf;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Greater.js\nvar greaterImpl = createSimpleBinaryKernelImpl((a, b) => a > b ? 1 : 0);\nvar greater3 = binaryKernelFunc(Greater, greaterImpl, null, \"bool\");\nvar greaterConfig = {\n  kernelName: Greater,\n  backendName: \"cpu\",\n  kernelFunc: greater3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/GreaterEqual.js\nvar greaterEqualImpl = createSimpleBinaryKernelImpl((a, b) => a >= b ? 1 : 0);\nvar greaterEqual2 = binaryKernelFunc(GreaterEqual, greaterEqualImpl, null, \"bool\");\nvar greaterEqualConfig = {\n  kernelName: GreaterEqual,\n  backendName: \"cpu\",\n  kernelFunc: greaterEqual2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Less.js\nvar lessImpl = createSimpleBinaryKernelImpl((a, b) => a < b ? 1 : 0);\nvar less3 = binaryKernelFunc(Less, lessImpl, null, \"bool\");\nvar lessConfig = {\n  kernelName: Less,\n  backendName: \"cpu\",\n  kernelFunc: less3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/LessEqual.js\nvar lessEqualImpl = createSimpleBinaryKernelImpl((a, b) => a <= b ? 1 : 0);\nvar lessEqual2 = binaryKernelFunc(LessEqual, lessEqualImpl, null, \"bool\");\nvar lessEqualConfig = {\n  kernelName: LessEqual,\n  backendName: \"cpu\",\n  kernelFunc: lessEqual2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/LinSpace_impl.js\nfunction linSpaceImpl(start, stop, num) {\n  const step5 = (stop - start) / (num - 1);\n  const values = util_exports.makeZerosTypedArray(num, \"float32\");\n  values[0] = start;\n  for (let i = 1; i < values.length; i++) {\n    values[i] = values[i - 1] + step5;\n  }\n  return values;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Log.js\nvar logImpl = createSimpleUnaryImpl((xi) => Math.log(xi));\nvar log3 = unaryKernelFuncFromImpl(Log, logImpl);\nvar logConfig = {\n  kernelName: Log,\n  backendName: \"cpu\",\n  kernelFunc: log3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Max_impl.js\nfunction maxImpl(aVals, reduceSize, outShape, dtype) {\n  const vals = util_exports.getTypedArrayFromDType(dtype, util_exports.sizeFromShape(outShape));\n  for (let i = 0; i < vals.length; ++i) {\n    const offset = i * reduceSize;\n    let max7 = aVals[offset];\n    for (let j = 0; j < reduceSize; ++j) {\n      const value = aVals[offset + j];\n      if (Number.isNaN(value) || value > max7) {\n        max7 = value;\n      }\n    }\n    vals[i] = max7;\n  }\n  return vals;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Maximum.js\nvar maximumImpl = createSimpleBinaryKernelImpl((aValue, bValue) => Math.max(aValue, bValue));\nvar maximum3 = binaryKernelFunc(Maximum, maximumImpl);\nvar maximumConfig = {\n  kernelName: Maximum,\n  backendName: \"cpu\",\n  kernelFunc: maximum3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Minimum.js\nvar minimumImpl = createSimpleBinaryKernelImpl((aValue, bValue) => Math.min(aValue, bValue));\nvar minimum3 = binaryKernelFunc(Minimum, minimumImpl);\nvar minimumConfig = {\n  kernelName: Minimum,\n  backendName: \"cpu\",\n  kernelFunc: minimum3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Multiply.js\nvar multiplyImpl = createSimpleBinaryKernelImpl((aValue, bValue) => aValue * bValue);\nvar multiplyComplexImpl = createComplexBinaryKernelImpl((aReal, aImag, bReal, bImag) => {\n  return {\n    real: aReal * bReal - aImag * bImag,\n    imag: aReal * bImag + aImag * bReal\n  };\n});\nvar multiply2 = binaryKernelFunc(Multiply, multiplyImpl, multiplyComplexImpl);\nvar multiplyConfig = {\n  kernelName: Multiply,\n  backendName: \"cpu\",\n  kernelFunc: multiply2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Neg.js\nfunction negImpl(xVals, xShape, xDtype) {\n  const minusOne = util_exports.createScalarValue(-1, xDtype);\n  return multiplyImpl([], xShape, minusOne, xVals, xDtype);\n}\nfunction neg2(args) {\n  const { inputs, backend: backend2 } = args;\n  const { x } = inputs;\n  assertNotComplex(x, \"neg\");\n  const xVals = backend2.data.get(x.dataId).values;\n  const [res, newShape] = negImpl(xVals, x.shape, x.dtype);\n  return backend2.makeTensorInfo(newShape, x.dtype, res);\n}\nvar negConfig = {\n  kernelName: Neg,\n  backendName: \"cpu\",\n  kernelFunc: neg2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/NotEqual.js\nvar notEqualImpl = createSimpleBinaryKernelImpl((a, b) => a !== b ? 1 : 0);\nvar notEqual2 = binaryKernelFunc(NotEqual, notEqualImpl, null, \"bool\");\nvar notEqualConfig = {\n  kernelName: NotEqual,\n  backendName: \"cpu\",\n  kernelFunc: notEqual2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Transpose_impl.js\nfunction transposeImpl(xVals, xShape, dtype, perm, newShape) {\n  const xRank = xShape.length;\n  const xSize = util_exports.sizeFromShape(xShape);\n  const xStrides = util_exports.computeStrides(xShape);\n  const newStrides = util_exports.computeStrides(newShape);\n  const result = util_exports.getTypedArrayFromDType(dtype, util_exports.sizeFromShape(newShape));\n  for (let i = 0; i < xSize; ++i) {\n    const loc = util_exports.indexToLoc(i, xRank, xStrides);\n    const newLoc = new Array(loc.length);\n    for (let i2 = 0; i2 < newLoc.length; i2++) {\n      newLoc[i2] = loc[perm[i2]];\n    }\n    const newIndex = util_exports.locToIndex(newLoc, xRank, newStrides);\n    result[newIndex] = xVals[i];\n  }\n  return result;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Transpose.js\nfunction transpose2(args) {\n  const { inputs, attrs, backend: backend2 } = args;\n  const { x } = inputs;\n  const { perm } = attrs;\n  assertNotComplex(x, \"transpose\");\n  const xRank = x.shape.length;\n  const newShape = new Array(xRank);\n  for (let i = 0; i < newShape.length; i++) {\n    newShape[i] = x.shape[perm[i]];\n  }\n  const values = backend2.data.get(x.dataId).values;\n  const result = transposeImpl(values, x.shape, x.dtype, perm, newShape);\n  const dataId = backend2.write(result, newShape, x.dtype);\n  return { dataId, shape: newShape, dtype: x.dtype };\n}\nvar transposeConfig = {\n  kernelName: Transpose,\n  backendName: \"cpu\",\n  kernelFunc: transpose2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Prod.js\nfunction prodImpl(xShape, xDtype, xVals, reductionAxes) {\n  const [outShape, reduceShape] = backend_util_exports.computeOutAndReduceShapes(xShape, reductionAxes);\n  const outDtype = upcastType(xDtype, \"int32\");\n  const outVals = util_exports.makeZerosTypedArray(util_exports.sizeFromShape(outShape), outDtype);\n  const reduceSize = util_exports.sizeFromShape(reduceShape);\n  for (let i = 0; i < outVals.length; ++i) {\n    const offset = i * reduceSize;\n    let prod6 = 1;\n    for (let j = 0; j < reduceSize; ++j) {\n      prod6 *= xVals[offset + j];\n    }\n    outVals[i] = prod6;\n  }\n  return { outVals, outShape, outDtype };\n}\nfunction prod2(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x } = inputs;\n  const { axis, keepDims } = attrs;\n  assertNotComplex(x, \"prod\");\n  const xRank = x.shape.length;\n  const axes = util_exports.parseAxisParam(axis, x.shape);\n  const permutation = backend_util_exports.getAxesPermutation(axes, xRank);\n  let reductionAxes = axes;\n  let permutedX = x;\n  const intermediateTensorInfos = [];\n  if (permutation != null) {\n    permutedX = transpose2({ inputs: { x }, backend: backend2, attrs: { perm: permutation } });\n    intermediateTensorInfos.push(permutedX);\n    reductionAxes = backend_util_exports.getInnerMostAxes(reductionAxes.length, xRank);\n  }\n  const xVals = backend2.data.get(permutedX.dataId).values;\n  const { outVals, outShape, outDtype } = prodImpl(permutedX.shape, permutedX.dtype, xVals, reductionAxes);\n  let resultShape = outShape;\n  if (keepDims) {\n    resultShape = backend_util_exports.expandShapeToKeepDim(outShape, axes);\n  }\n  intermediateTensorInfos.forEach((t) => backend2.disposeIntermediateTensorInfo(t));\n  return backend2.makeTensorInfo(resultShape, outDtype, outVals);\n}\nvar prodConfig = {\n  kernelName: Prod,\n  backendName: \"cpu\",\n  kernelFunc: prod2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/RaggedTensorToTensor_impl.js\nvar RowPartitionType2 = backend_util_exports.RowPartitionType;\nvar RaggedTensorToTensorOp = class {\n  constructor(shape, shapeShape, values, valuesShape, valuesDType, defaultValue, defaultValueShape, rowPartitionValues, rowPartitionValuesShapes, rowPartitionTypeStrings) {\n    this.shape = shape;\n    this.shapeShape = shapeShape;\n    this.values = values;\n    this.valuesShape = valuesShape;\n    this.valuesDType = valuesDType;\n    this.defaultValue = defaultValue;\n    this.defaultValueShape = defaultValueShape;\n    this.rowPartitionValues = rowPartitionValues;\n    this.rowPartitionValuesShapes = rowPartitionValuesShapes;\n    this.rowPartitionTypes = backend_util_exports.getRowPartitionTypesHelper(rowPartitionTypeStrings);\n    this.raggedRank = backend_util_exports.getRaggedRank(this.rowPartitionTypes);\n  }\n  getRowPartitionTypeByDimension(dimension) {\n    if (this.rowPartitionTypes[0] === RowPartitionType2.FIRST_DIM_SIZE) {\n      return this.rowPartitionTypes[dimension + 1];\n    } else {\n      return this.rowPartitionTypes[dimension];\n    }\n  }\n  getRowPartitionTensor(dimension) {\n    if (this.rowPartitionTypes[0] === RowPartitionType2.FIRST_DIM_SIZE) {\n      return this.rowPartitionValues[dimension + 1];\n    } else {\n      return this.rowPartitionValues[dimension];\n    }\n  }\n  getMaxWidth(dimension) {\n    const rowPartitionTensor = this.getRowPartitionTensor(dimension - 1);\n    switch (this.getRowPartitionTypeByDimension(dimension - 1)) {\n      case RowPartitionType2.VALUE_ROWIDS:\n        return RaggedTensorToTensorOp.getMaxWidthValueRowID(rowPartitionTensor);\n      case RowPartitionType2.ROW_SPLITS:\n        return RaggedTensorToTensorOp.getMaxWidthRowSplit(rowPartitionTensor);\n      default:\n        throw new Error(`Cannot handle partition type ${RowPartitionType2[this.getRowPartitionTypeByDimension(dimension - 1)]}`);\n    }\n  }\n  static getMaxWidthRowSplit(rowSplit) {\n    const tensorLength = rowSplit.length;\n    if (tensorLength === 0 || tensorLength === 1) {\n      return 0;\n    }\n    let maxWidth = 0;\n    for (let i = 0; i < tensorLength - 1; ++i) {\n      const currentWidth = rowSplit[i + 1] - rowSplit[i];\n      if (currentWidth > maxWidth) {\n        maxWidth = currentWidth;\n      }\n    }\n    return maxWidth;\n  }\n  static getMaxWidthValueRowID(valueRowIds) {\n    const indexLength = valueRowIds.length;\n    if (indexLength === 0) {\n      return 0;\n    }\n    let firstEqualIndex = 0;\n    let firstEqualIndexValue = valueRowIds[0];\n    let maxWidth = 0;\n    for (let i = 1; i < indexLength; ++i) {\n      const value = valueRowIds[i];\n      if (value !== firstEqualIndexValue) {\n        firstEqualIndexValue = value;\n        maxWidth = Math.max(i - firstEqualIndex, maxWidth);\n        firstEqualIndex = i;\n      }\n    }\n    return Math.max(indexLength - firstEqualIndex, maxWidth);\n  }\n  tensorShapeFromTensor(t, tShape, isPartial = true) {\n    if (tShape.length === 0) {\n      if (t[0] === -1) {\n        return [];\n      }\n      throw new Error(`The only valid scalar shape tensor is the fully unknown shape specified as -1.`);\n    }\n    return makeShape(t, isPartial);\n  }\n  calculateOutputSize(firstDim) {\n    const valueShape = this.valuesShape;\n    const defaultValueShape = this.defaultValueShape;\n    backend_util_exports.validateDefaultValueShape(defaultValueShape, valueShape);\n    const shape = this.tensorShapeFromTensor(this.shape, this.shapeShape);\n    const outputShape = backend_util_exports.combineRaggedTensorToTensorShapes(this.raggedRank, shape, valueShape);\n    const result = outputShape;\n    if (result[0] < 0) {\n      result[0] = firstDim;\n    }\n    for (let i = 1; i <= this.raggedRank; ++i) {\n      if (result[i] < 0) {\n        result[i] = this.getMaxWidth(i);\n      }\n    }\n    return result;\n  }\n  calculateFirstParentOutputIndex(firstDimension, outputIndexMultiplier, firstDimensionOutput) {\n    const minDimension = Math.min(firstDimension, firstDimensionOutput);\n    const result = [];\n    let currentOutputIndex = 0;\n    for (let i = 0; i < minDimension; ++i, currentOutputIndex += outputIndexMultiplier) {\n      result.push(currentOutputIndex);\n    }\n    for (let i = minDimension; i < firstDimension; ++i) {\n      result.push(-1);\n    }\n    util_exports.assert(result.length === firstDimension, () => \"Final length of result must be equal to firstDimension.\");\n    return result;\n  }\n  calculateOutputIndexRowSplit(rowSplit, parentOutputIndex, outputIndexMultiplier, outputSize) {\n    const rowSplitSize = rowSplit.length;\n    const result = [];\n    for (let i = 0; i < rowSplitSize - 1; ++i) {\n      const rowLength = rowSplit[i + 1] - rowSplit[i];\n      let realLength = Math.min(outputSize, rowLength);\n      let parentOutputIndexCurrent = parentOutputIndex[i];\n      if (parentOutputIndexCurrent === -1) {\n        realLength = 0;\n      }\n      for (let j = 0; j < realLength; ++j) {\n        result.push(parentOutputIndexCurrent);\n        parentOutputIndexCurrent += outputIndexMultiplier;\n      }\n      for (let j = 0; j < rowLength - realLength; ++j) {\n        result.push(-1);\n      }\n    }\n    if (rowSplitSize > 0 && result.length !== rowSplit[rowSplitSize - 1]) {\n      throw new Error(\"Invalid row split size.\");\n    }\n    return result;\n  }\n  calculateOutputIndexValueRowID(valueRowIds, parentOutputIndex, outputIndexMultiplier, outputSize) {\n    const indexSize = valueRowIds.length;\n    const result = [];\n    if (indexSize === 0) {\n      return [];\n    }\n    let currentOutputColumn = 0;\n    let currentValueRowId = valueRowIds[0];\n    if (currentValueRowId >= parentOutputIndex.length) {\n      throw new Error(`Got currentValueRowId=${currentValueRowId}, which is not less than ${parentOutputIndex.length}`);\n    }\n    let currentOutputIndex = parentOutputIndex[currentValueRowId];\n    result.push(currentOutputIndex);\n    for (let i = 1; i < indexSize; ++i) {\n      const nextValueRowId = valueRowIds[i];\n      if (nextValueRowId === currentValueRowId) {\n        if (currentOutputIndex >= 0) {\n          ++currentOutputColumn;\n          if (currentOutputColumn < outputSize) {\n            currentOutputIndex += outputIndexMultiplier;\n          } else {\n            currentOutputIndex = -1;\n          }\n        }\n      } else {\n        currentOutputColumn = 0;\n        currentValueRowId = nextValueRowId;\n        if (nextValueRowId >= parentOutputIndex.length) {\n          throw new Error(`Got nextValueRowId=${nextValueRowId} which is not less than ${parentOutputIndex.length}`);\n        }\n        currentOutputIndex = parentOutputIndex[nextValueRowId];\n      }\n      result.push(currentOutputIndex);\n    }\n    if (result.length !== valueRowIds.length) {\n      throw new Error(\"Invalid row ids.\");\n    }\n    return result;\n  }\n  calculateOutputIndex(dimension, parentOutputIndex, outputIndexMultiplier, outputSize) {\n    const rowPartitionTensor = this.getRowPartitionTensor(dimension);\n    const partitionType = this.getRowPartitionTypeByDimension(dimension);\n    switch (partitionType) {\n      case RowPartitionType2.VALUE_ROWIDS:\n        return this.calculateOutputIndexValueRowID(rowPartitionTensor, parentOutputIndex, outputIndexMultiplier, outputSize);\n      case RowPartitionType2.ROW_SPLITS:\n        if (rowPartitionTensor.length - 1 > parentOutputIndex.length) {\n          throw new Error(`Row partition size is greater than output size: ${rowPartitionTensor.length - 1} > ${parentOutputIndex.length}`);\n        }\n        return this.calculateOutputIndexRowSplit(rowPartitionTensor, parentOutputIndex, outputIndexMultiplier, outputSize);\n      default:\n        throw new Error(`Unsupported partition type: ${RowPartitionType2[partitionType]}`);\n    }\n  }\n  getFirstDimensionSize() {\n    const firstPartitionTensor = this.rowPartitionValues[0];\n    if (this.rowPartitionTypes.length === 0) {\n      throw new Error(\"No row_partition_types given.\");\n    }\n    const firstPartitionType = this.rowPartitionTypes[0];\n    switch (firstPartitionType) {\n      case RowPartitionType2.FIRST_DIM_SIZE:\n        return firstPartitionTensor[0];\n      case RowPartitionType2.VALUE_ROWIDS:\n        throw new Error(\"Cannot handle VALUE_ROWIDS in first dimension.\");\n      case RowPartitionType2.ROW_SPLITS:\n        return this.rowPartitionValuesShapes[0][0] - 1;\n      default:\n        throw new Error(`Cannot handle type ${RowPartitionType2[firstPartitionType]}`);\n    }\n  }\n  compute() {\n    const firstPartitionTensor = this.rowPartitionValues[0];\n    if (firstPartitionTensor.length <= 0) {\n      throw new Error(\"Invalid first partition input. Tensor requires at least one element.\");\n    }\n    const firstDimension = this.getFirstDimensionSize();\n    const outputSize = this.calculateOutputSize(firstDimension);\n    const multiplier = new Array(this.raggedRank + 1);\n    multiplier[multiplier.length - 1] = 1;\n    for (let i = multiplier.length - 2; i >= 0; --i) {\n      multiplier[i] = multiplier[i + 1] * outputSize[i + 1];\n    }\n    const outputShape = makeShape(outputSize, false);\n    const outputTensor = util_exports.getArrayFromDType(this.valuesDType, util_exports.sizeFromShape(outputShape));\n    const fullSize = multiplier[0] * outputSize[0];\n    if (fullSize > 0) {\n      let outputIndex = this.calculateFirstParentOutputIndex(firstDimension, multiplier[0], outputSize[0]);\n      for (let i = 1; i <= this.raggedRank; ++i) {\n        const newOutputIndex = this.calculateOutputIndex(i - 1, outputIndex, multiplier[i], outputSize[i]);\n        outputIndex = newOutputIndex;\n      }\n      this.setOutput(this.raggedRank, outputIndex, outputTensor, outputShape);\n    }\n    return [outputShape, outputTensor];\n  }\n  setOutput(raggedRank, outputIndex, outputTensor, outputShape) {\n    if (outputTensor.length === 0) {\n      return;\n    }\n    const valuesBase = this.values;\n    const outputBase = outputTensor;\n    let elementShape = outputShape.slice();\n    elementShape = elementShape.slice(raggedRank + 1);\n    const valueElementSize = util_exports.sizeFromShape(elementShape);\n    const outputIndexSize = outputIndex.length;\n    let defaultValue = this.defaultValue;\n    if (defaultValue.length !== valueElementSize && defaultValue.length !== 1) {\n      const srcShape = this.defaultValueShape;\n      tidy(() => {\n        const defaultValueTensor = reshape(defaultValue, srcShape);\n        const bCastDefault = broadcastTo(defaultValueTensor, elementShape);\n        defaultValue = bCastDefault.dataSync();\n      });\n    }\n    let srcStart = 0;\n    let dstStart = 0;\n    let dstEnd = 0;\n    for (let srcI = 0; srcI <= outputIndexSize; ++srcI) {\n      let dstI = srcI < outputIndexSize ? outputIndex[srcI] : -1;\n      if (dstI === dstEnd) {\n        ++dstEnd;\n        continue;\n      }\n      if (dstStart < dstEnd) {\n        const src = valuesBase.subarray(srcStart * valueElementSize);\n        const dst = outputBase.subarray(dstStart * valueElementSize);\n        const nVals = (dstEnd - dstStart) * valueElementSize;\n        copyArray(dst, src, nVals);\n      }\n      if (srcI >= outputIndexSize) {\n        const outputSize = outputTensor.length;\n        dstI = Math.floor(outputSize / valueElementSize);\n      }\n      if (dstI > dstEnd) {\n        if (this.defaultValue.length === 1) {\n          outputBase.subarray(dstEnd * valueElementSize, dstI * valueElementSize).fill(this.defaultValue[0]);\n          dstEnd = dstI;\n        } else {\n          while (dstI > dstEnd) {\n            const dst = outputBase.slice(dstEnd * valueElementSize);\n            copyArray(dst, defaultValue, valueElementSize);\n            ++dstEnd;\n          }\n        }\n      }\n      if (dstI < 0) {\n        srcStart = srcI + 1;\n        dstStart = dstEnd;\n      } else {\n        srcStart = srcI;\n        dstStart = dstEnd;\n        dstEnd = dstStart + 1;\n      }\n    }\n  }\n};\nfunction copyArray(dst, src, size) {\n  for (let i = 0; i < size; i++) {\n    dst[i] = src[i];\n  }\n}\nfunction makeShape(shape, isPartial) {\n  const out = [];\n  for (let dim of shape) {\n    if (dim < 0) {\n      if (!isPartial) {\n        throw new Error(`Dimension ${dim} must be >= 0`);\n      }\n      if (dim < -1) {\n        throw new Error(`Dimension ${dim} must be >= -1`);\n      }\n      dim = -1;\n    }\n    out.push(dim);\n  }\n  return out;\n}\nfunction raggedTensorToTensorImpl(shape, shapesShape, values, valuesShape, valuesDType, defaultValue, defaultValueShape, rowPartitionValues, rowPartitionValuesShapes, rowPartitionTypes) {\n  return new RaggedTensorToTensorOp(shape, shapesShape, values, valuesShape, valuesDType, defaultValue, defaultValueShape, rowPartitionValues, rowPartitionValuesShapes, rowPartitionTypes).compute();\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Range_impl.js\nfunction rangeImpl(start, stop, step5, dtype) {\n  const sameStartStop = start === stop;\n  const increasingRangeNegativeStep = start < stop && step5 < 0;\n  const decreasingRangePositiveStep = stop < start && step5 > 1;\n  if (sameStartStop || increasingRangeNegativeStep || decreasingRangePositiveStep) {\n    return util_exports.makeZerosTypedArray(0, dtype);\n  }\n  const numElements = Math.abs(Math.ceil((stop - start) / step5));\n  const values = util_exports.makeZerosTypedArray(numElements, dtype);\n  if (stop < start && step5 === 1) {\n    step5 = -1;\n  }\n  values[0] = start;\n  for (let i = 1; i < values.length; i++) {\n    values[i] = values[i - 1] + step5;\n  }\n  return values;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Rsqrt.js\nvar rsqrtImpl = createSimpleUnaryImpl((xi) => 1 / Math.sqrt(xi));\nvar rsqrt2 = unaryKernelFuncFromImpl(Rsqrt, rsqrtImpl);\nvar rsqrtConfig = {\n  kernelName: Rsqrt,\n  backendName: \"cpu\",\n  kernelFunc: rsqrt2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Scatter_impl.js\nfunction scatterImpl(indices, updates, shape, outputSize, sliceSize, numUpdates, sliceRank, strides, defaultValue, sumDupeIndices) {\n  const flattenShape = [outputSize / sliceSize, sliceSize];\n  const indicesData = indices.values;\n  const updatesData = updates.values;\n  if (outputSize === 0) {\n    return buffer(shape, updates.dtype);\n  }\n  const outBuf = buffer(flattenShape, updates.dtype);\n  if (typeof defaultValue === \"string\") {\n    outBuf.values.fill(defaultValue);\n  } else if (typeof defaultValue === \"number\") {\n    outBuf.values.fill(defaultValue);\n  } else if (typeof defaultValue === \"boolean\") {\n    outBuf.values.fill(+defaultValue);\n  }\n  for (let i = 0; i < numUpdates; i++) {\n    const index = [];\n    let flattenIndex = 0;\n    for (let j = 0; j < sliceRank; j++) {\n      const dim = indicesData[i * sliceRank + j];\n      index.push(dim);\n      flattenIndex += dim * strides[j];\n    }\n    if (flattenIndex < 0 || flattenIndex >= outputSize / sliceSize) {\n      throw new Error(`Invalid indices: ${index} does not index into ${shape}`);\n    }\n    for (let k = 0; k < sliceSize; k++) {\n      if (sumDupeIndices) {\n        outBuf.values[flattenIndex * sliceSize + k] += updatesData[i * sliceSize + k];\n      } else {\n        outBuf.values[flattenIndex * sliceSize + k] = updates.rank === 0 ? updatesData[0] : updatesData[i * sliceSize + k];\n      }\n    }\n  }\n  return outBuf;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Sigmoid.js\nvar sigmoidImpl = createSimpleUnaryImpl((xi) => 1 / (1 + Math.exp(-xi)));\nvar sigmoid2 = unaryKernelFunc(Sigmoid, (xi) => 1 / (1 + Math.exp(-xi)));\nvar sigmoidConfig = {\n  kernelName: Sigmoid,\n  backendName: \"cpu\",\n  kernelFunc: sigmoid2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Slice.js\nfunction sliceImpl(vals, begin, size, shape, dtype) {\n  const isContinous = slice_util_exports.isSliceContinous(shape, begin, size);\n  const length = util_exports.sizeFromShape(size);\n  const xStrides = util_exports.computeStrides(shape);\n  if (isContinous) {\n    const flatOffset = slice_util_exports.computeFlatOffset(begin, xStrides);\n    if (dtype === \"string\") {\n      return vals.slice(flatOffset, flatOffset + length);\n    }\n    return vals.subarray(flatOffset, flatOffset + length);\n  }\n  const decodedData = dtype === \"string\" ? backend_util_exports.fromUint8ToStringArray(vals) : vals;\n  const inBuf = buffer(shape, dtype, decodedData);\n  const outBuf = buffer(size, dtype);\n  for (let i = 0; i < outBuf.size; ++i) {\n    const outLoc = outBuf.indexToLoc(i);\n    const inLoc = outLoc.map((idx, j) => idx + begin[j]);\n    outBuf.set(inBuf.get(...inLoc), ...outLoc);\n  }\n  if (dtype === \"string\") {\n    return backend_util_exports.fromStringArrayToUint8(outBuf.values);\n  }\n  return outBuf.values;\n}\nfunction slice2(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x } = inputs;\n  const { begin, size } = attrs;\n  assertNotComplex(x, \"slice\");\n  const [$begin, $size] = slice_util_exports.parseSliceParams(x, begin, size);\n  slice_util_exports.assertParamsValid(x, $begin, $size);\n  const vals = backend2.data.get(x.dataId).values;\n  const outVals = sliceImpl(vals, $begin, $size, x.shape, x.dtype);\n  return backend2.makeTensorInfo($size, x.dtype, outVals);\n}\nvar sliceConfig = {\n  kernelName: Slice,\n  backendName: \"cpu\",\n  kernelFunc: slice2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/SparseFillEmptyRows_impl.js\nfunction sparseFillEmptyRowsImpl(indices, indicesShape, indicesDType, values, valuesDType, denseShape, defaultValue) {\n  const indicesCount = indicesShape[0];\n  const denseRows = denseShape[0];\n  const emptyRowIndicator = new Array(denseRows);\n  const reverseIndexMap = new Array(indicesCount);\n  const rank = indicesShape[1];\n  if (denseRows === 0) {\n    if (indicesCount !== 0) {\n      throw new Error(backend_util_exports.getSparseFillEmptyRowsIndicesDenseShapeMismatch(indicesCount));\n    }\n    const outputIndices = util_exports.getArrayFromDType(indicesDType, 0);\n    const outputValues = util_exports.getArrayFromDType(valuesDType, 0);\n    return [\n      outputIndices,\n      [0, rank],\n      outputValues,\n      emptyRowIndicator,\n      reverseIndexMap\n    ];\n  }\n  let rowsAreOrdered = true;\n  let lastIndicesRow = 0;\n  const csrOffset = new Array(denseRows).fill(0);\n  for (let i = 0; i < indicesCount; ++i) {\n    const row = indices[i * rank];\n    if (row < 0) {\n      throw new Error(backend_util_exports.getSparseFillEmptyRowsNegativeIndexErrorMessage(i, row));\n    }\n    if (row >= denseRows) {\n      throw new Error(backend_util_exports.getSparseFillEmptyRowsOutOfRangeIndexErrorMessage(i, row, denseRows));\n    }\n    ++csrOffset[row];\n    rowsAreOrdered = rowsAreOrdered && row >= lastIndicesRow;\n    lastIndicesRow = row;\n  }\n  let allRowsFull = true;\n  for (let row = 0; row < denseRows; ++row) {\n    const rowEmpty = csrOffset[row] === 0;\n    emptyRowIndicator[row] = rowEmpty;\n    allRowsFull = allRowsFull && !rowEmpty;\n    csrOffset[row] = Math.max(csrOffset[row], 1);\n    if (row > 0) {\n      csrOffset[row] += csrOffset[row - 1];\n    }\n  }\n  if (allRowsFull && rowsAreOrdered) {\n    const outputIndices = indices;\n    const outputValues = values;\n    for (let i = 0; i < indicesCount; ++i) {\n      reverseIndexMap[i] = i;\n    }\n    return [\n      outputIndices,\n      [indicesCount, rank],\n      outputValues,\n      emptyRowIndicator,\n      reverseIndexMap\n    ];\n  } else {\n    const fullIndicesCount = csrOffset[denseRows - 1];\n    const outputIndices = util_exports.getArrayFromDType(indicesDType, fullIndicesCount * rank);\n    const outputValues = util_exports.getArrayFromDType(valuesDType, fullIndicesCount);\n    const filledCount = new Array(denseRows).fill(0);\n    for (let i = 0; i < indicesCount; ++i) {\n      const row = indices[i * rank];\n      const offset = filledCount[row];\n      const outputI = (row === 0 ? 0 : csrOffset[row - 1]) + offset;\n      filledCount[row]++;\n      for (let j = 0; j < rank; ++j) {\n        outputIndices[outputI * rank + j] = indices[i * rank + j];\n      }\n      outputValues[outputI] = values[i];\n      reverseIndexMap[i] = outputI;\n    }\n    for (let row = 0; row < denseRows; ++row) {\n      const rowCount = filledCount[row];\n      if (rowCount === 0) {\n        const startingIndex = row === 0 ? 0 : csrOffset[row - 1];\n        outputIndices[startingIndex * rank + 0] = row;\n        for (let col = 1; col < rank; ++col) {\n          outputIndices[startingIndex * rank + col] = 0;\n        }\n        outputValues[startingIndex] = defaultValue;\n      }\n    }\n    return [\n      outputIndices,\n      [fullIndicesCount, rank],\n      outputValues,\n      emptyRowIndicator,\n      reverseIndexMap\n    ];\n  }\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/SparseReshape_impl.js\nfunction sparseReshapeImpl(inputIndices, inputIndicesShape, inputDType, inputShape, targetShape) {\n  const denseSize = util_exports.sizeFromShape(inputShape);\n  const nnz = inputIndicesShape[0];\n  const outputRank = targetShape.length;\n  const outputShape = [];\n  let product = 1;\n  let unknownIndex = -1;\n  for (let d = 0; d < outputRank; ++d) {\n    const size = targetShape[d];\n    if (size === -1) {\n      if (unknownIndex !== -1) {\n        throw new Error(backend_util_exports.getSparseReshapeMultipleNegativeOneOutputDimErrorMessage(unknownIndex, d));\n      }\n      unknownIndex = d;\n      outputShape.push(1);\n    } else {\n      if (size < 0) {\n        throw new Error(backend_util_exports.getSparseReshapeNegativeOutputDimErrorMessage(d, size));\n      }\n      product *= size;\n      outputShape.push(size);\n    }\n  }\n  if (unknownIndex !== -1) {\n    if (product <= 0) {\n      throw new Error(backend_util_exports.getSparseReshapeEmptyTensorZeroOutputDimErrorMessage());\n    }\n    const missing = Math.trunc(denseSize / product);\n    if (product * missing !== denseSize) {\n      throw new Error(backend_util_exports.getSparseReshapeInputOutputMultipleErrorMessage(inputShape, outputShape));\n    }\n    outputShape[unknownIndex] = missing;\n  }\n  const outputSize = util_exports.sizeFromShape(outputShape);\n  if (outputSize !== denseSize) {\n    throw new Error(backend_util_exports.getSparseReshapeInputOutputMismatchErrorMessage(inputShape, outputShape));\n  }\n  const inputRank = inputShape.length;\n  const inputStrides = [];\n  if (inputRank > 0) {\n    inputStrides[inputRank - 1] = 1;\n    for (let d = inputRank - 2; d >= 0; --d) {\n      inputStrides[d] = inputStrides[d + 1] * inputShape[d + 1];\n    }\n  }\n  const outputStrides = [];\n  if (outputRank > 0) {\n    outputStrides[outputRank - 1] = 1;\n    for (let d = outputRank - 2; d >= 0; --d) {\n      outputStrides[d] = outputStrides[d + 1] * outputShape[d + 1];\n    }\n  }\n  const newIndices = util_exports.getArrayFromDType(inputDType, nnz * outputRank);\n  for (let i = 0; i < nnz; ++i) {\n    let id = 0;\n    for (let j = 0; j < inputRank; ++j) {\n      id += inputIndices[i * inputRank + j] * inputStrides[j];\n    }\n    for (let j = 0; j < outputRank; ++j) {\n      newIndices[i * outputRank + j] = Math.trunc(id / outputStrides[j]);\n      id %= outputStrides[j];\n    }\n  }\n  return [newIndices, [nnz, outputRank], outputShape];\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/SparseSegmentReduction_impl.js\nfunction sparseSegmentReductionImpl(input2, inputShape, inputDType, indices, segmentIds, isMean = false, defaultValue = 0) {\n  const numIndices = indices.length;\n  const inputFlat = [inputShape[0], input2.length / inputShape[0]];\n  const numCol = inputFlat[1];\n  const lastSegmentIdPlusOne = numIndices > 0 ? segmentIds[numIndices - 1] + 1 : 0;\n  const outputRows = lastSegmentIdPlusOne;\n  if (outputRows < 0) {\n    throw new Error(backend_util_exports.getSparseSegmentReductionNegativeSegmentIdsErrorMessage());\n  }\n  const outputShape = inputShape.slice();\n  outputShape[0] = outputRows;\n  const outputLength = outputShape.reduce((product, value) => product * value, 1);\n  const output = util_exports.getArrayFromDType(inputDType, outputLength);\n  if (numIndices === 0) {\n    if (outputRows > 0) {\n      output.fill(defaultValue);\n    }\n    return [output, outputShape];\n  }\n  if (outputRows <= 0) {\n    throw new Error(backend_util_exports.getSparseSegmentReductionNegativeSegmentIdsErrorMessage());\n  }\n  let start = 0, end = 1;\n  let uninitializedIndex = 0;\n  let outIndex = segmentIds[start];\n  while (true) {\n    let nextIndex = 0;\n    if (end < numIndices) {\n      nextIndex = segmentIds[end];\n      if (outIndex === nextIndex) {\n        ++end;\n        continue;\n      }\n      if (outIndex >= nextIndex) {\n        throw new Error(backend_util_exports.getSparseSegmentReductionNonIncreasingSegmentIdsErrorMessage());\n      }\n    }\n    if (outIndex < 0 || outIndex >= outputRows) {\n      throw new Error(backend_util_exports.getSparseSegmentReductionSegmentIdOutOfRangeErrorMessage(outIndex, outputRows));\n    }\n    if (outIndex > uninitializedIndex) {\n      output.fill(defaultValue, uninitializedIndex * numCol, outIndex * numCol);\n    }\n    for (let i = start; i < end; ++i) {\n      const index = indices[i];\n      if (index < 0 || index >= inputFlat[0]) {\n        throw new Error(backend_util_exports.getSparseSegmentReductionIndicesOutOfRangeErrorMessage(i, indices[i], inputFlat[0]));\n      }\n      for (let j = 0; j < numCol; j++) {\n        output[outIndex * numCol + j] += input2[index * numCol + j];\n      }\n    }\n    if (isMean) {\n      for (let j = 0; j < numCol; j++) {\n        output[outIndex * numCol + j] /= end - start;\n      }\n    }\n    start = end;\n    ++end;\n    uninitializedIndex = outIndex + 1;\n    outIndex = nextIndex;\n    if (end > numIndices) {\n      break;\n    }\n  }\n  if (uninitializedIndex < outputRows) {\n    output.fill(defaultValue, uninitializedIndex * numCol, outputRows * numCol);\n  }\n  return [output, outputShape];\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Sqrt.js\nvar sqrtImpl = createSimpleUnaryImpl((xi) => Math.sqrt(xi));\nvar sqrt2 = unaryKernelFunc(Sqrt, (xi) => Math.sqrt(xi));\nvar sqrtConfig = {\n  kernelName: Sqrt,\n  backendName: \"cpu\",\n  kernelFunc: sqrt2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/SquaredDifference.js\nvar squaredDifferenceImpl = createSimpleBinaryKernelImpl((a, b) => {\n  const diff = a - b;\n  return diff * diff;\n});\nvar squaredDifference2 = binaryKernelFunc(SquaredDifference, squaredDifferenceImpl);\nvar squaredDifferenceConfig = {\n  kernelName: SquaredDifference,\n  backendName: \"cpu\",\n  kernelFunc: squaredDifference2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/StridedSlice_impl.js\nfunction stridedSliceImpl(outShape, xBuf, strides, begin) {\n  const outBuf = buffer(outShape, xBuf.dtype);\n  for (let i = 0; i < outBuf.size; i++) {\n    const loc = outBuf.indexToLoc(i);\n    const newLoc = new Array(loc.length);\n    for (let j = 0; j < newLoc.length; j++) {\n      newLoc[j] = loc[j] * strides[j] + begin[j];\n    }\n    outBuf.set(xBuf.get(...newLoc), ...loc);\n  }\n  return outBuf;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/StringNGrams_impl.js\nvar StringNGramsOp = class {\n  constructor(separator, nGramWidths, leftPad, rightPad2, padWidth, preserveShortSequences) {\n    this.separator = util_exports.encodeString(separator);\n    this.nGramWidths = nGramWidths;\n    this.leftPad = util_exports.encodeString(leftPad);\n    this.rightPad = util_exports.encodeString(rightPad2);\n    this.padWidth = padWidth;\n    this.preserveShort = preserveShortSequences;\n  }\n  getPadWidth(nGramWidth) {\n    return Math.min(this.padWidth < 0 ? nGramWidth - 1 : this.padWidth, nGramWidth - 1);\n  }\n  getNumNGrams(length, nGramWidth) {\n    const padWidth = this.getPadWidth(nGramWidth);\n    return Math.max(0, length + 2 * padWidth - nGramWidth + 1);\n  }\n  createNGrams(data, splitIndex, output, outputStartIndex, numNGrams, nGramWidth) {\n    for (let nGramIndex = 0; nGramIndex < numNGrams; ++nGramIndex) {\n      const padWidth = this.getPadWidth(nGramWidth);\n      const leftPadding = Math.max(0, padWidth - nGramIndex);\n      const rightPadding = Math.max(0, padWidth - (numNGrams - (nGramIndex + 1)));\n      const numTokens = nGramWidth - (leftPadding + rightPadding);\n      const dataStartIndex = splitIndex + (leftPadding > 0 ? 0 : nGramIndex - padWidth);\n      let nGramSize = 0;\n      nGramSize += leftPadding * this.leftPad.length;\n      for (let n = 0; n < numTokens; ++n) {\n        nGramSize += data[dataStartIndex + n].length;\n      }\n      nGramSize += rightPadding * this.rightPad.length;\n      const numSeparators = leftPadding + rightPadding + numTokens - 1;\n      nGramSize += numSeparators * this.separator.length;\n      output[outputStartIndex + nGramIndex] = new Uint8Array(nGramSize);\n      const nGram = output[outputStartIndex + nGramIndex];\n      let nextNGramIndex = 0;\n      const appendToNGram = (str) => str.forEach((value) => nGram[nextNGramIndex++] = value);\n      for (let n = 0; n < leftPadding; ++n) {\n        appendToNGram(this.leftPad);\n        appendToNGram(this.separator);\n      }\n      for (let n = 0; n < numTokens - 1; ++n) {\n        appendToNGram(data[dataStartIndex + n]);\n        appendToNGram(this.separator);\n      }\n      if (numTokens > 0) {\n        appendToNGram(data[dataStartIndex + numTokens - 1]);\n        for (let n = 0; n < rightPadding; ++n) {\n          appendToNGram(this.separator);\n          appendToNGram(this.rightPad);\n        }\n      } else {\n        for (let n = 0; n < rightPadding - 1; ++n) {\n          appendToNGram(this.rightPad);\n          appendToNGram(this.separator);\n        }\n        appendToNGram(this.rightPad);\n      }\n    }\n  }\n  compute(data, splits) {\n    const inputDataSize = data.length;\n    const splitsSize = splits.length;\n    if (splitsSize > 0) {\n      let prevSplit = splits[0];\n      if (prevSplit !== 0) {\n        throw new Error(`First split value must be 0, got ${prevSplit}`);\n      }\n      for (let i = 1; i < splitsSize; ++i) {\n        let validSplits = splits[i] >= prevSplit;\n        validSplits = validSplits && splits[i] <= inputDataSize;\n        if (!validSplits) {\n          throw new Error(`Invalid split value ${splits[i]}, must be in [${prevSplit}, ${inputDataSize}]`);\n        }\n        prevSplit = splits[i];\n      }\n      if (prevSplit !== inputDataSize) {\n        throw new Error(`Last split value must be data size. Expected ${inputDataSize}, got ${prevSplit}`);\n      }\n    }\n    const numBatchItems = splitsSize - 1;\n    const nGramsSplits = util_exports.getArrayFromDType(\"int32\", splitsSize);\n    if (inputDataSize === 0 || splitsSize === 0) {\n      const empty = new Array(inputDataSize);\n      for (let i = 0; i <= numBatchItems; ++i) {\n        nGramsSplits[i] = 0;\n      }\n      return [empty, nGramsSplits];\n    }\n    nGramsSplits[0] = 0;\n    for (let i = 1; i <= numBatchItems; ++i) {\n      const length = splits[i] - splits[i - 1];\n      let numNGrams = 0;\n      this.nGramWidths.forEach((nGramWidth) => {\n        numNGrams += this.getNumNGrams(length, nGramWidth);\n      });\n      if (this.preserveShort && length > 0 && numNGrams === 0) {\n        numNGrams = 1;\n      }\n      nGramsSplits[i] = nGramsSplits[i - 1] + numNGrams;\n    }\n    const nGrams = new Array(nGramsSplits[numBatchItems]);\n    for (let i = 0; i < numBatchItems; ++i) {\n      const splitIndex = splits[i];\n      let outputStartIdx = nGramsSplits[i];\n      this.nGramWidths.forEach((nGramWidth) => {\n        const length = splits[i + 1] - splits[i];\n        const numNGrams = this.getNumNGrams(length, nGramWidth);\n        this.createNGrams(data, splitIndex, nGrams, outputStartIdx, numNGrams, nGramWidth);\n        outputStartIdx += numNGrams;\n      });\n      if (this.preserveShort && outputStartIdx === nGramsSplits[i]) {\n        const dataLength = splits[i + 1] - splits[i];\n        if (dataLength === 0) {\n          continue;\n        }\n        const nGramWidth = dataLength + 2 * this.padWidth;\n        const numNGrams = 1;\n        this.createNGrams(data, splitIndex, nGrams, outputStartIdx, numNGrams, nGramWidth);\n      }\n    }\n    return [nGrams, nGramsSplits];\n  }\n};\nfunction stringNGramsImpl(data, dataSplits, separator, nGramWidths, leftPad, rightPad2, padWidth, preserveShortSequences) {\n  return new StringNGramsOp(separator, nGramWidths, leftPad, rightPad2, padWidth, preserveShortSequences).compute(data, dataSplits);\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/StringSplit_impl.js\nfunction split3(str, delimiters, skipEmpty, result) {\n  if (!str.length) {\n    return;\n  }\n  if (delimiters.length === 0) {\n    for (let i = 0; i < str.length; ++i) {\n      result.push(str.subarray(i, i + 1));\n    }\n    return;\n  }\n  if (delimiters.length === 1) {\n    const delimiter = delimiters[0];\n    let f = str.indexOf(delimiter);\n    while (f !== -1) {\n      const token = str.subarray(0, f);\n      if (!skipEmpty || token.length !== 0) {\n        result.push(token);\n      }\n      str = str.subarray(f + 1);\n      f = str.indexOf(delimiter);\n    }\n    if (!skipEmpty || str.length !== 0) {\n      result.push(str);\n    }\n    return;\n  }\n  let tokenStart = 0;\n  for (let i = 0; i < str.length + 1; i++) {\n    if (i === str.length || delimiters.indexOf(str[i]) !== -1) {\n      const token = str.subarray(tokenStart, i);\n      if (!skipEmpty || token.length !== 0) {\n        result.push(token);\n      }\n      tokenStart = i + 1;\n    }\n  }\n}\nfunction stringSplitImpl(input2, delimiter, skipEmpty) {\n  const batchSize = input2.length;\n  const tokens = [];\n  let outputSize = 0;\n  let maxNumEntries = 0;\n  const numIndices = new Array(batchSize);\n  for (let i = 0; i < batchSize; ++i) {\n    const prevTokensLength = tokens.length;\n    split3(input2[i], delimiter, skipEmpty, tokens);\n    const nEntries = tokens.length - prevTokensLength;\n    numIndices[i] = nEntries;\n    outputSize += nEntries;\n    maxNumEntries = Math.max(maxNumEntries, nEntries);\n  }\n  const indices = util_exports.getArrayFromDType(\"int32\", outputSize * 2);\n  const values = new Array(outputSize);\n  const shape = [batchSize, maxNumEntries];\n  let c = 0;\n  for (let i = 0; i < batchSize; ++i) {\n    for (let j = 0; j < numIndices[i]; ++j) {\n      indices[c * 2] = i;\n      indices[c * 2 + 1] = j;\n      values[c] = tokens[c];\n      ++c;\n    }\n  }\n  return [indices, values, shape];\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/StringToHashBucketFast_impl.js\nfunction stringToHashBucketFastImpl(input2, numBuckets) {\n  const output = util_exports.getArrayFromDType(\"int32\", input2.length);\n  for (let i = 0; i < input2.length; ++i) {\n    output[i] = util_exports.fingerPrint64(input2[i]).modulo(numBuckets).getLowBitsUnsigned();\n  }\n  return output;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Sub.js\nvar subImpl = createSimpleBinaryKernelImpl((aValue, bValue) => aValue - bValue);\nvar subComplexImpl = createComplexBinaryKernelImpl((aReal, aImag, bReal, bImag) => {\n  return { real: aReal - bReal, imag: aImag - bImag };\n});\nvar sub2 = binaryKernelFunc(Sub, subImpl, subComplexImpl);\nvar subConfig = {\n  kernelName: Sub,\n  backendName: \"cpu\",\n  kernelFunc: sub2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Tile_impl.js\nfunction tileImpl(xBuf, reps) {\n  const newShape = new Array(xBuf.rank);\n  for (let i = 0; i < newShape.length; i++) {\n    newShape[i] = xBuf.shape[i] * reps[i];\n  }\n  const result = buffer(newShape, xBuf.dtype);\n  for (let i = 0; i < result.values.length; ++i) {\n    const newLoc = result.indexToLoc(i);\n    const originalLoc = new Array(xBuf.rank);\n    for (let j = 0; j < originalLoc.length; j++) {\n      originalLoc[j] = newLoc[j] % xBuf.shape[j];\n    }\n    const originalIndex = xBuf.locToIndex(originalLoc);\n    result.values[i] = xBuf.values[originalIndex];\n  }\n  return result;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/TopK_impl.js\nvar comparePair = (a, b) => {\n  const valueDiff = b.value - a.value;\n  return valueDiff === 0 ? a.index - b.index : valueDiff;\n};\nfunction select(array2, k, left = 0, right = array2.length - 1) {\n  while (right > left) {\n    if (right - left > 600) {\n      const n = right - left + 1;\n      const i2 = k - left + 1;\n      const z = Math.log(n);\n      const s = 0.5 * Math.exp(2 * z / 3);\n      const sd = 0.5 * Math.sqrt(z * s * (n - s) / n) * Math.sign(i2 - n / 2);\n      const newLeft = Math.max(left, Math.floor(k - i2 * s / n + sd));\n      const newRight = Math.min(right, Math.floor(k + (n - i2) * s / n + sd));\n      select(array2, k, newLeft, newRight);\n    }\n    const t = array2[k];\n    let i = left;\n    let j = right;\n    util_exports.swap(array2, left, k);\n    if (comparePair(array2[right], t) > 0) {\n      util_exports.swap(array2, left, right);\n    }\n    while (i < j) {\n      util_exports.swap(array2, i, j);\n      i++;\n      j--;\n      while (comparePair(array2[i], t) < 0) {\n        i = i + 1;\n      }\n      while (comparePair(array2[j], t) > 0) {\n        j = j - 1;\n      }\n    }\n    if (comparePair(array2[left], t) === 0) {\n      util_exports.swap(array2, left, j);\n    } else {\n      j = j + 1;\n      util_exports.swap(array2, j, right);\n    }\n    if (j <= k) {\n      left = j + 1;\n    }\n    if (k <= j) {\n      right = j - 1;\n    }\n  }\n}\nfunction topKImpl(x, xShape, xDtype, k, sorted) {\n  const lastDim = xShape[xShape.length - 1];\n  const [batch, size] = [x.length / lastDim, lastDim];\n  const allTopKVals = util_exports.getTypedArrayFromDType(xDtype, batch * k);\n  const allTopKIndices = util_exports.getTypedArrayFromDType(\"int32\", batch * k);\n  for (let b = 0; b < batch; b++) {\n    const offset = b * size;\n    const vals = x.subarray(offset, offset + size);\n    let valAndInd = new Array(vals.length);\n    vals.forEach((value, index) => valAndInd[index] = { value, index });\n    if (k < valAndInd.length) {\n      select(valAndInd, k);\n      valAndInd = valAndInd.slice(0, k);\n    }\n    if (sorted) {\n      valAndInd.sort(comparePair);\n    }\n    const outOffset = b * k;\n    const topKVals = allTopKVals.subarray(outOffset, outOffset + k);\n    const topKIndices = allTopKIndices.subarray(outOffset, outOffset + k);\n    for (let i = 0; i < k; i++) {\n      topKVals[i] = valAndInd[i].value;\n      topKIndices[i] = valAndInd[i].index;\n    }\n  }\n  const outputShape = xShape.slice();\n  outputShape[outputShape.length - 1] = k;\n  return [\n    buffer(outputShape, xDtype, allTopKVals),\n    buffer(outputShape, \"int32\", allTopKIndices)\n  ];\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Unique_impl.js\nfunction uniqueImpl(values, axis, shape, dtype) {\n  const $axis = util_exports.parseAxisParam(axis, shape)[0];\n  const newShape = [1, shape[0], 1];\n  for (let i = 0; i < $axis; i++) {\n    newShape[0] *= shape[i];\n  }\n  newShape[1] = shape[$axis];\n  for (let i = $axis + 1; i < shape.length; i++) {\n    newShape[2] *= shape[i];\n  }\n  const uniqueElements = {};\n  const indices = new Int32Array(shape[$axis]);\n  const inputBuffer = new TensorBuffer(newShape, dtype, values);\n  const uniqueIndices = [];\n  const is1DTensor = newShape[0] === 1 && newShape[2] === 1;\n  for (let i = 0; i < shape[$axis]; i++) {\n    let element;\n    if (is1DTensor) {\n      element = values[i].toString();\n    } else {\n      const axisValues = [];\n      for (let m = 0; m < newShape[0]; m++) {\n        for (let n = 0; n < newShape[2]; n++) {\n          axisValues.push(inputBuffer.get(m, i, n));\n        }\n      }\n      element = axisValues.join(\",\");\n    }\n    if (uniqueElements[element] !== void 0) {\n      indices[i] = uniqueElements[element];\n    } else {\n      const uniqueIndex = Object.keys(uniqueElements).length;\n      uniqueElements[element] = uniqueIndex;\n      indices[i] = uniqueIndex;\n      uniqueIndices.push(i);\n    }\n  }\n  const outputTmpShape = newShape.slice();\n  outputTmpShape[1] = Object.keys(uniqueElements).length;\n  const outputBuffer = new TensorBuffer(outputTmpShape, dtype);\n  uniqueIndices.forEach((uniqueElementIndex, i) => {\n    for (let m = 0; m < newShape[0]; m++) {\n      for (let n = 0; n < newShape[2]; n++) {\n        outputBuffer.set(inputBuffer.get(m, uniqueElementIndex, n), m, i, n);\n      }\n    }\n  });\n  const outputShape = shape.slice();\n  outputShape[$axis] = outputTmpShape[1];\n  return {\n    outputValues: outputBuffer.values,\n    outputShape,\n    indices\n  };\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/base.js\nregisterBackend(\"cpu\", () => new MathBackendCPU(), 1);\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Elu.js\nvar elu4 = unaryKernelFunc(Elu, (xi) => xi >= 0 ? xi : Math.exp(xi) - 1);\nvar eluConfig = {\n  kernelName: Elu,\n  backendName: \"cpu\",\n  kernelFunc: elu4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/LeakyRelu.js\nfunction leakyRelu2(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x } = inputs;\n  const { alpha } = attrs;\n  assertNotComplex([x], \"leakyRelu\");\n  const xSize = util_exports.sizeFromShape(x.shape);\n  const xVals = backend2.data.get(x.dataId).values;\n  const outVals = util_exports.getTypedArrayFromDType(\"float32\", xSize);\n  for (let i = 0; i < xVals.length; i++) {\n    outVals[i] = xVals[i] < 0 ? alpha * xVals[i] : xVals[i];\n  }\n  return backend2.makeTensorInfo(x.shape, \"float32\", outVals);\n}\nvar leakyReluConfig = {\n  kernelName: LeakyRelu,\n  backendName: \"cpu\",\n  kernelFunc: leakyRelu2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Prelu.js\nvar preluImpl = createSimpleBinaryKernelImpl((xValue, aValue) => xValue < 0 ? aValue * xValue : xValue);\nfunction prelu3(args) {\n  const { inputs, backend: backend2 } = args;\n  const { x, alpha } = inputs;\n  assertNotComplex([x, alpha], \"prelu\");\n  const aVals = backend2.data.get(x.dataId).values;\n  const bVals = backend2.data.get(alpha.dataId).values;\n  const [resultData, resultShape] = preluImpl(x.shape, alpha.shape, aVals, bVals, \"float32\");\n  return backend2.makeTensorInfo(resultShape, \"float32\", resultData);\n}\nvar preluConfig = {\n  kernelName: Prelu,\n  backendName: \"cpu\",\n  kernelFunc: prelu3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Relu.js\nvar relu2 = unaryKernelFunc(Relu, (xi) => Math.max(0, xi));\nvar reluConfig = {\n  kernelName: Relu,\n  backendName: \"cpu\",\n  kernelFunc: relu2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Relu6.js\nvar relu62 = unaryKernelFunc(Relu6, (xi) => Math.min(Math.max(0, xi), 6));\nvar relu6Config = {\n  kernelName: Relu6,\n  backendName: \"cpu\",\n  kernelFunc: relu62\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/utils/fused_utils.js\nfunction applyActivation2(backend2, x, activation2, preluActivationWeights, leakyreluAlpha) {\n  if (activation2 === \"linear\") {\n    return identity2({ inputs: { x }, backend: backend2 });\n  } else if (activation2 === \"relu\") {\n    return relu2({ inputs: { x }, backend: backend2 });\n  } else if (activation2 === \"elu\") {\n    return elu4({ inputs: { x }, backend: backend2 });\n  } else if (activation2 === \"relu6\") {\n    return relu62({ inputs: { x }, backend: backend2 });\n  } else if (activation2 === \"prelu\") {\n    return prelu3({ inputs: { x, alpha: preluActivationWeights }, backend: backend2 });\n  } else if (activation2 === \"leakyrelu\") {\n    return leakyRelu2({ inputs: { x }, backend: backend2, attrs: { alpha: leakyreluAlpha } });\n  } else if (activation2 === \"sigmoid\") {\n    return sigmoid2({ inputs: { x }, backend: backend2 });\n  }\n  throw new Error(`Activation ${activation2} has not been implemented for the CPU backend.`);\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Reshape.js\nfunction reshape3(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x } = inputs;\n  const { shape } = attrs;\n  const xSize = util_exports.sizeFromShape(x.shape);\n  const $shape = util_exports.inferFromImplicitShape(shape, xSize);\n  const $xSize = util_exports.sizeFromShape($shape);\n  util_exports.assert(xSize === $xSize, () => `The new shape (${$shape}) has ${$xSize} elements and the old shape (${x.shape}) has ${xSize} elements. The new shape and old shape must have the same number of elements.`);\n  backend2.incRef(x.dataId);\n  const xData = backend2.data.get(x.dataId);\n  if (xData.complexTensorInfos != null) {\n    const real5 = xData.complexTensorInfos.real;\n    const imag5 = xData.complexTensorInfos.imag;\n    real5.shape = $shape;\n    imag5.shape = $shape;\n  }\n  return { dataId: x.dataId, shape: $shape, dtype: x.dtype };\n}\nvar reshapeConfig = {\n  kernelName: Reshape,\n  backendName: \"cpu\",\n  kernelFunc: reshape3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/BatchMatMul.js\nfunction batchMatMul(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { a, b } = inputs;\n  const { transposeA, transposeB } = attrs;\n  assertNotComplex([a, b], \"matMul\");\n  const aRank = a.shape.length;\n  const bRank = b.shape.length;\n  const innerShapeA = transposeA ? a.shape[aRank - 2] : a.shape[aRank - 1];\n  const innerShapeB = transposeB ? b.shape[bRank - 1] : b.shape[bRank - 2];\n  const outerShapeA = transposeA ? a.shape[aRank - 1] : a.shape[aRank - 2];\n  const outerShapeB = transposeB ? b.shape[bRank - 2] : b.shape[bRank - 1];\n  const outerDimsA = a.shape.slice(0, -2);\n  const outerDimsB = b.shape.slice(0, -2);\n  const batchDimA = util_exports.sizeFromShape(outerDimsA);\n  const batchDimB = util_exports.sizeFromShape(outerDimsB);\n  const outShapeOuterDims = broadcast_util_exports.assertAndGetBroadcastShape(a.shape.slice(0, -2), b.shape.slice(0, -2));\n  const outShape = outShapeOuterDims.concat([outerShapeA, outerShapeB]);\n  util_exports.assert(innerShapeA === innerShapeB, () => `Error in matMul: inner shapes (${innerShapeA}) and (${innerShapeB}) of Tensors with shapes ${a.shape} and ${b.shape} and transposeA=${transposeA} and transposeB=${transposeB} must match.`);\n  const a3dShape = transposeA ? [batchDimA, innerShapeA, outerShapeA] : [batchDimA, outerShapeA, innerShapeA];\n  const b3dShape = transposeB ? [batchDimB, outerShapeB, innerShapeB] : [batchDimB, innerShapeB, outerShapeB];\n  const a3d = reshape3({ inputs: { x: a }, backend: backend2, attrs: { shape: a3dShape } });\n  const b3d = reshape3({ inputs: { x: b }, backend: backend2, attrs: { shape: b3dShape } });\n  const sharedDim = transposeA ? a3d.shape[1] : a3d.shape[2];\n  const leftDim = transposeA ? a3d.shape[2] : a3d.shape[1];\n  const rightDim = transposeB ? b3d.shape[1] : b3d.shape[2];\n  const batchDim = Math.max(batchDimA, batchDimB);\n  const a3dValues = backend2.data.get(a3d.dataId).values;\n  const b3dValues = backend2.data.get(b3d.dataId).values;\n  const a3dStrides = util_exports.computeStrides(a3d.shape);\n  const b3dStrides = util_exports.computeStrides(b3d.shape);\n  const [aBatch, aOuterStep, aInnerStep] = transposeA ? [a3dStrides[0], 1, a3dStrides[1]] : [a3dStrides[0], a3dStrides[1], 1];\n  const [bInnerStep, bOuterStep, bBatch] = transposeB ? [1, b3dStrides[1], b3dStrides[0]] : [b3dStrides[1], 1, b3dStrides[0]];\n  const size = leftDim * rightDim;\n  const result = buffer([batchDim, leftDim, rightDim], a3d.dtype);\n  const resVals = result.values;\n  const blockSize = backend2.blockSize;\n  for (let bi = 0; bi < batchDim; bi++) {\n    for (let i0 = 0; i0 < leftDim; i0 += blockSize) {\n      for (let j0 = 0; j0 < rightDim; j0 += blockSize) {\n        for (let k02 = 0; k02 < sharedDim; k02 += blockSize) {\n          const iBlock = Math.min(i0 + blockSize, leftDim);\n          const jBlock = Math.min(j0 + blockSize, rightDim);\n          const kBlock = Math.min(k02 + blockSize, sharedDim);\n          for (let i = i0; i < iBlock; i++) {\n            for (let j = j0; j < jBlock; j++) {\n              let sum7 = 0;\n              for (let k = k02; k < kBlock; k++) {\n                const batchOffsetA = Math.min(bi, batchDimA - 1) * aBatch;\n                const batchOffsetB = Math.min(bi, batchDimB - 1) * bBatch;\n                const aVal = a3dValues[batchOffsetA + i * aOuterStep + k * aInnerStep];\n                const bVal = b3dValues[k * bInnerStep + j * bOuterStep + batchOffsetB];\n                sum7 += aVal * bVal;\n              }\n              resVals[bi * size + (i * rightDim + j)] += sum7;\n            }\n          }\n        }\n      }\n    }\n  }\n  backend2.disposeIntermediateTensorInfo(a3d);\n  backend2.disposeIntermediateTensorInfo(b3d);\n  return backend2.makeTensorInfo(outShape, result.dtype, result.values);\n}\nvar batchMatMulConfig = {\n  kernelName: BatchMatMul,\n  backendName: \"cpu\",\n  kernelFunc: batchMatMul\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/_FusedMatMul.js\nfunction _fusedMatMul(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { a, b, bias, preluActivationWeights } = inputs;\n  const { transposeA, transposeB, activation: activation2, leakyreluAlpha } = attrs;\n  let current;\n  let addRes;\n  let activationRes;\n  const intermediates = [];\n  const matMulRes = batchMatMul({ inputs: { a, b }, attrs: { transposeA, transposeB }, backend: backend2 });\n  current = matMulRes;\n  if (bias) {\n    addRes = add4({ inputs: { a: current, b: bias }, backend: backend2 });\n    intermediates.push(current);\n    current = addRes;\n  }\n  if (activation2) {\n    activationRes = applyActivation2(backend2, current, activation2, preluActivationWeights, leakyreluAlpha);\n    intermediates.push(current);\n    current = activationRes;\n  }\n  for (const i of intermediates) {\n    backend2.disposeIntermediateTensorInfo(i);\n  }\n  return current;\n}\nvar _fusedMatMulConfig = {\n  kernelName: _FusedMatMul,\n  backendName: \"cpu\",\n  kernelFunc: _fusedMatMul\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Acos.js\nvar acos2 = unaryKernelFunc(Acos, (xi) => Math.acos(xi));\nvar acosConfig = {\n  kernelName: Acos,\n  backendName: \"cpu\",\n  kernelFunc: acos2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Acosh.js\nvar acosh2 = unaryKernelFunc(Acosh, (xi) => Math.acosh(xi));\nvar acoshConfig = {\n  kernelName: Acosh,\n  backendName: \"cpu\",\n  kernelFunc: acosh2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/AddN.js\nfunction addN2(args) {\n  const { inputs, backend: backend2 } = args;\n  const tensors = inputs;\n  assertNotComplex(inputs, \"addN\");\n  const vals = tensors.map((t) => backend2.data.get(t.dataId).values);\n  const outBuf = buffer(tensors[0].shape, tensors[0].dtype);\n  const outVals = outBuf.values;\n  for (let i = 0; i < tensors.length; i++) {\n    const currVals = vals[i];\n    for (let j = 0; j < outVals.length; j++) {\n      outVals[j] += currVals[j];\n    }\n  }\n  return backend2.makeTensorInfo(outBuf.shape, outBuf.dtype, outBuf.values);\n}\nvar addNConfig = {\n  kernelName: AddN,\n  backendName: \"cpu\",\n  kernelFunc: addN2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/All.js\nfunction all2(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x } = inputs;\n  const { axis, keepDims } = attrs;\n  assertNotComplex(x, \"all\");\n  const origAxes = util_exports.parseAxisParam(axis, x.shape);\n  let axes = origAxes;\n  const permutedAxes = backend_util_exports.getAxesPermutation(axes, x.shape.length);\n  let $x = x;\n  if (permutedAxes != null) {\n    $x = transpose2({ inputs: { x }, backend: backend2, attrs: { perm: permutedAxes } });\n    axes = backend_util_exports.getInnerMostAxes(axes.length, x.shape.length);\n  }\n  backend_util_exports.assertAxesAreInnerMostDims(\"all\", axes, $x.shape.length);\n  const [outShape, reduceShape] = backend_util_exports.computeOutAndReduceShapes($x.shape, axes);\n  const reduceSize = util_exports.sizeFromShape(reduceShape);\n  const vals = util_exports.makeZerosTypedArray(util_exports.sizeFromShape(outShape), $x.dtype);\n  const aVals = backend2.data.get($x.dataId).values;\n  for (let i = 0; i < vals.length; ++i) {\n    const offset = i * reduceSize;\n    let all5 = aVals[offset];\n    for (let j = 0; j < reduceSize; ++j) {\n      const value = aVals[offset + j];\n      all5 = all5 && value;\n    }\n    vals[i] = all5;\n  }\n  if (permutedAxes != null) {\n    backend2.disposeIntermediateTensorInfo($x);\n  }\n  const result = backend2.makeTensorInfo(outShape, $x.dtype, vals);\n  if (keepDims) {\n    const expandedShape = backend_util_exports.expandShapeToKeepDim(outShape, origAxes);\n    const reshapedResult = reshape3({ inputs: { x: result }, backend: backend2, attrs: { shape: expandedShape } });\n    backend2.disposeIntermediateTensorInfo(result);\n    return reshapedResult;\n  }\n  return result;\n}\nvar allConfig = {\n  kernelName: All,\n  backendName: \"cpu\",\n  kernelFunc: all2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Any.js\nfunction any2(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x } = inputs;\n  const { axis, keepDims } = attrs;\n  assertNotComplex(x, \"any\");\n  const origAxes = util_exports.parseAxisParam(axis, x.shape);\n  let axes = origAxes;\n  const permutedAxes = backend_util_exports.getAxesPermutation(axes, x.shape.length);\n  let $x = x;\n  if (permutedAxes != null) {\n    $x = transpose2({ inputs: { x }, backend: backend2, attrs: { perm: permutedAxes } });\n    axes = backend_util_exports.getInnerMostAxes(axes.length, x.shape.length);\n  }\n  backend_util_exports.assertAxesAreInnerMostDims(\"any\", axes, $x.shape.length);\n  const [outShape, reduceShape] = backend_util_exports.computeOutAndReduceShapes($x.shape, axes);\n  const reduceSize = util_exports.sizeFromShape(reduceShape);\n  const vals = util_exports.makeZerosTypedArray(util_exports.sizeFromShape(outShape), $x.dtype);\n  const aVals = backend2.data.get($x.dataId).values;\n  for (let i = 0; i < vals.length; ++i) {\n    const offset = i * reduceSize;\n    let anyVal = aVals[offset];\n    for (let j = 0; j < reduceSize; ++j) {\n      const value = aVals[offset + j];\n      anyVal = anyVal || value;\n    }\n    vals[i] = anyVal;\n  }\n  if (permutedAxes != null) {\n    backend2.disposeIntermediateTensorInfo($x);\n  }\n  const result = backend2.makeTensorInfo(outShape, $x.dtype, vals);\n  if (keepDims) {\n    const expandedShape = backend_util_exports.expandShapeToKeepDim(outShape, origAxes);\n    const reshapedResult = reshape3({ inputs: { x: result }, backend: backend2, attrs: { shape: expandedShape } });\n    backend2.disposeIntermediateTensorInfo(result);\n    return reshapedResult;\n  }\n  return result;\n}\nvar anyConfig = {\n  kernelName: Any,\n  backendName: \"cpu\",\n  kernelFunc: any2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/ArgMax.js\nfunction argMax2(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x } = inputs;\n  const { axis } = attrs;\n  assertNotComplex(x, \"argMax\");\n  let axes = util_exports.parseAxisParam(axis, x.shape);\n  const permutedAxes = backend_util_exports.getAxesPermutation(axes, x.shape.length);\n  let $x = x;\n  const intermediateTensorInfos = [];\n  if (permutedAxes != null) {\n    $x = transpose2({ inputs: { x }, backend: backend2, attrs: { perm: permutedAxes } });\n    intermediateTensorInfos.push($x);\n    axes = backend_util_exports.getInnerMostAxes(axes.length, $x.shape.length);\n  }\n  axes = [axes[0]];\n  backend_util_exports.assertAxesAreInnerMostDims(\"argMax\", axes, $x.shape.length);\n  const [outShape, reduceShape] = backend_util_exports.computeOutAndReduceShapes($x.shape, axes);\n  const outSize = util_exports.sizeFromShape(outShape);\n  const vals = util_exports.makeZerosTypedArray(outSize, \"int32\");\n  const reduceSize = util_exports.sizeFromShape(reduceShape);\n  const aVals = backend2.data.get($x.dataId).values;\n  for (let i = 0; i < vals.length; ++i) {\n    const offset = i * reduceSize;\n    let max7 = aVals[offset];\n    let maxIndex = 0;\n    for (let j = 0; j < reduceSize; ++j) {\n      const value = aVals[offset + j];\n      if (value > max7) {\n        max7 = value;\n        maxIndex = j;\n      }\n    }\n    vals[i] = maxIndex;\n  }\n  intermediateTensorInfos.forEach((t) => backend2.disposeIntermediateTensorInfo(t));\n  return backend2.makeTensorInfo(outShape, \"int32\", vals);\n}\nvar argMaxConfig = {\n  kernelName: ArgMax,\n  backendName: \"cpu\",\n  kernelFunc: argMax2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/ArgMin.js\nfunction argMin2(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x } = inputs;\n  const { axis } = attrs;\n  assertNotComplex(x, \"argMin\");\n  let axes = util_exports.parseAxisParam(axis, x.shape);\n  const permutedAxes = backend_util_exports.getAxesPermutation(axes, x.shape.length);\n  let $x = x;\n  const intermediateTensorInfos = [];\n  if (permutedAxes != null) {\n    $x = transpose2({ inputs: { x }, backend: backend2, attrs: { perm: permutedAxes } });\n    intermediateTensorInfos.push($x);\n    axes = backend_util_exports.getInnerMostAxes(axes.length, $x.shape.length);\n  }\n  axes = [axes[0]];\n  backend_util_exports.assertAxesAreInnerMostDims(\"argMin\", axes, $x.shape.length);\n  const [outShape, reduceShape] = backend_util_exports.computeOutAndReduceShapes($x.shape, axes);\n  const outSize = util_exports.sizeFromShape(outShape);\n  const vals = util_exports.makeZerosTypedArray(outSize, \"int32\");\n  const reduceSize = util_exports.sizeFromShape(reduceShape);\n  const aVals = backend2.data.get($x.dataId).values;\n  for (let i = 0; i < vals.length; ++i) {\n    const offset = i * reduceSize;\n    let min7 = aVals[offset];\n    let minIndex = 0;\n    for (let j = 0; j < reduceSize; ++j) {\n      const value = aVals[offset + j];\n      if (value < min7) {\n        min7 = value;\n        minIndex = j;\n      }\n    }\n    vals[i] = minIndex;\n  }\n  intermediateTensorInfos.forEach((t) => backend2.disposeIntermediateTensorInfo(t));\n  return backend2.makeTensorInfo(outShape, \"int32\", vals);\n}\nvar argMinConfig = {\n  kernelName: ArgMin,\n  backendName: \"cpu\",\n  kernelFunc: argMin2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Asin.js\nvar asin2 = unaryKernelFunc(Asin, (xi) => Math.asin(xi));\nvar asinConfig = {\n  kernelName: Asin,\n  backendName: \"cpu\",\n  kernelFunc: asin2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Asinh.js\nvar asinh2 = unaryKernelFunc(Asinh, (xi) => Math.asinh(xi));\nvar asinhConfig = {\n  kernelName: Asinh,\n  backendName: \"cpu\",\n  kernelFunc: asinh2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Atan.js\nvar atan3 = unaryKernelFunc(Atan, (xi) => Math.atan(xi));\nvar atanConfig = {\n  kernelName: Atan,\n  backendName: \"cpu\",\n  kernelFunc: atan3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Atan2.js\nvar atan2Impl = createSimpleBinaryKernelImpl((aValue, bValue) => Math.atan2(aValue, bValue));\nvar atan22 = binaryKernelFunc(Atan2, atan2Impl);\nvar atan2Config = {\n  kernelName: Atan2,\n  backendName: \"cpu\",\n  kernelFunc: atan22\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Atanh.js\nvar atanh2 = unaryKernelFunc(Atanh, (xi) => Math.atanh(xi));\nvar atanhConfig = {\n  kernelName: Atanh,\n  backendName: \"cpu\",\n  kernelFunc: atanh2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/utils/pool_utils.js\nfunction pool2(xValues, xShape, dtype, strides, convInfo, poolType) {\n  const strideHeight = convInfo.strideHeight;\n  const strideWidth = convInfo.strideWidth;\n  const dilationHeight = convInfo.dilationHeight;\n  const dilationWidth = convInfo.dilationWidth;\n  const effectiveFilterHeight = convInfo.effectiveFilterHeight;\n  const effectiveFilterWidth = convInfo.effectiveFilterWidth;\n  const padTop = convInfo.padInfo.top;\n  const padLeft = convInfo.padInfo.left;\n  const initialValue = poolType === \"max\" ? Number.NEGATIVE_INFINITY : Number.POSITIVE_INFINITY;\n  const output = buffer(convInfo.outShape, dtype);\n  const outputVals = output.values;\n  const outputBatchStrides = convInfo.outShape[1] * convInfo.outShape[2] * convInfo.outShape[3];\n  const outputRowStrides = convInfo.outShape[2] * convInfo.outShape[3];\n  const outputColStrides = convInfo.outShape[3];\n  for (let b = 0; b < convInfo.batchSize; ++b) {\n    const outputBatchOffset = b * outputBatchStrides;\n    const inputBatchOffset = b * strides[0];\n    for (let d = 0; d < convInfo.inChannels; ++d) {\n      for (let yR = 0; yR < convInfo.outHeight; ++yR) {\n        const xRCorner = yR * strideHeight - padTop;\n        const xRMin = Math.max(0, xRCorner);\n        const xRMax = Math.min(convInfo.inHeight, effectiveFilterHeight + xRCorner);\n        const outputRowOffset = outputBatchOffset + yR * outputRowStrides;\n        for (let yC = 0; yC < convInfo.outWidth; ++yC) {\n          const xCCorner = yC * strideWidth - padLeft;\n          const xCMin = Math.max(0, xCCorner);\n          const xCMax = Math.min(convInfo.inWidth, effectiveFilterWidth + xCCorner);\n          let minMaxValue = initialValue;\n          let avgValue = 0;\n          let count2 = 0;\n          for (let xR = xRMin; xR < xRMax; xR += dilationHeight) {\n            const xROffset = inputBatchOffset + xR * strides[1];\n            for (let xC = xCMin; xC < xCMax; xC += dilationWidth) {\n              const xCOffset = xROffset + xC * strides[2];\n              const pixel = xValues[xCOffset + d];\n              if (poolType === \"max\" && pixel > minMaxValue) {\n                minMaxValue = pixel;\n              } else if (poolType === \"avg\") {\n                avgValue += pixel;\n                count2++;\n              }\n            }\n            if (isNaN(minMaxValue)) {\n              break;\n            }\n          }\n          const outputOffset = outputRowOffset + yC * outputColStrides + d;\n          outputVals[outputOffset] = poolType === \"avg\" ? avgValue / count2 : minMaxValue;\n        }\n      }\n    }\n  }\n  return output;\n}\nfunction maxPoolPositions(xValues, xShape, dtype, convInfo, flattenPositions = false, includeBatchInIndex = false) {\n  const maxPositions = buffer(convInfo.outShape, \"int32\");\n  const strideHeight = convInfo.strideHeight;\n  const strideWidth = convInfo.strideWidth;\n  const dilationHeight = convInfo.dilationHeight;\n  const dilationWidth = convInfo.dilationWidth;\n  const effectiveFilterHeight = convInfo.effectiveFilterHeight;\n  const effectiveFilterWidth = convInfo.effectiveFilterWidth;\n  const padTop = convInfo.padInfo.top;\n  const padLeft = convInfo.padInfo.left;\n  const xBuf = buffer(xShape, dtype, xValues);\n  for (let b = 0; b < convInfo.batchSize; ++b) {\n    for (let d = 0; d < convInfo.inChannels; ++d) {\n      for (let yR = 0; yR < convInfo.outHeight; ++yR) {\n        const xRCorner = yR * strideHeight - padTop;\n        let xRMin = xRCorner;\n        while (xRMin < 0) {\n          xRMin += dilationHeight;\n        }\n        const xRMax = Math.min(convInfo.inHeight, effectiveFilterHeight + xRCorner);\n        for (let yC = 0; yC < convInfo.outWidth; ++yC) {\n          const xCCorner = yC * strideWidth - padLeft;\n          let xCMin = xCCorner;\n          while (xCMin < 0) {\n            xCMin += dilationWidth;\n          }\n          const xCMax = Math.min(convInfo.inWidth, effectiveFilterWidth + xCCorner);\n          let maxValue = Number.NEGATIVE_INFINITY;\n          let maxPosition = -1;\n          for (let xR = xRMin; xR < xRMax; xR += dilationHeight) {\n            const wR = xR - xRCorner;\n            for (let xC = xCMin; xC < xCMax; xC += dilationWidth) {\n              const wC = xC - xCCorner;\n              const pixel = xBuf.get(b, xR, xC, d);\n              if (pixel > maxValue) {\n                maxValue = pixel;\n                if (flattenPositions) {\n                  maxPosition = includeBatchInIndex ? ((b * convInfo.inHeight + xR) * convInfo.inWidth + xC) * convInfo.inChannels + d : (xR * convInfo.inWidth + xC) * convInfo.inChannels + d;\n                } else {\n                  maxPosition = wR * effectiveFilterWidth + wC;\n                }\n              }\n            }\n          }\n          maxPositions.set(maxPosition, b, yR, yC, d);\n        }\n      }\n    }\n  }\n  return maxPositions;\n}\nfunction pool3d2(xValues, xShape, dtype, strides, convInfo, poolType) {\n  const strideDepth = convInfo.strideDepth;\n  const strideHeight = convInfo.strideHeight;\n  const strideWidth = convInfo.strideWidth;\n  const dilationDepth = convInfo.dilationDepth;\n  const dilationHeight = convInfo.dilationHeight;\n  const dilationWidth = convInfo.dilationWidth;\n  const effectiveFilterDepth = convInfo.effectiveFilterDepth;\n  const effectiveFilterHeight = convInfo.effectiveFilterHeight;\n  const effectiveFilterWidth = convInfo.effectiveFilterWidth;\n  const padFront = convInfo.padInfo.front;\n  const padTop = convInfo.padInfo.top;\n  const padLeft = convInfo.padInfo.left;\n  const initialValue = poolType === \"max\" ? Number.NEGATIVE_INFINITY : Number.POSITIVE_INFINITY;\n  const output = buffer(convInfo.outShape, dtype);\n  const outputVals = output.values;\n  const outputBatchStrides = convInfo.outShape[1] * convInfo.outShape[2] * convInfo.outShape[3] * convInfo.outShape[4];\n  const outputDepthStrides = convInfo.outShape[2] * convInfo.outShape[3] * convInfo.outShape[4];\n  const outputRowStrides = convInfo.outShape[3] * convInfo.outShape[4];\n  const outputColStrides = convInfo.outShape[4];\n  for (let batch = 0; batch < convInfo.batchSize; ++batch) {\n    const outputBatchOffset = batch * outputBatchStrides;\n    const inputBatchOffset = batch * strides[0];\n    for (let channel = 0; channel < convInfo.inChannels; ++channel) {\n      for (let yDepth = 0; yDepth < convInfo.outDepth; ++yDepth) {\n        const xDepthCorner = yDepth * strideDepth - padFront;\n        let xDepthMin = xDepthCorner;\n        while (xDepthMin < 0) {\n          xDepthMin += dilationDepth;\n        }\n        const xDepthMax = Math.min(convInfo.inDepth, effectiveFilterDepth + xDepthCorner);\n        const outputDepthOffset = outputBatchOffset + yDepth * outputDepthStrides;\n        for (let yRow = 0; yRow < convInfo.outHeight; ++yRow) {\n          const xRowCorner = yRow * strideHeight - padTop;\n          let xRowMin = xRowCorner;\n          while (xRowMin < 0) {\n            xRowMin += dilationHeight;\n          }\n          const xRowMax = Math.min(convInfo.inHeight, effectiveFilterHeight + xRowCorner);\n          const outputRowOffset = outputDepthOffset + yRow * outputRowStrides;\n          for (let yCol = 0; yCol < convInfo.outWidth; ++yCol) {\n            const xColCorner = yCol * strideWidth - padLeft;\n            let xColMin = xColCorner;\n            while (xColMin < 0) {\n              xColMin += dilationWidth;\n            }\n            const xColMax = Math.min(convInfo.inWidth, effectiveFilterWidth + xColCorner);\n            const outputColOffset = outputRowOffset + yCol * outputColStrides;\n            let minMaxValue = initialValue;\n            let avgValue = 0;\n            let count2 = 0;\n            for (let xDepth = xDepthMin; xDepth < xDepthMax; xDepth += dilationDepth) {\n              const xDepthOffset = inputBatchOffset + xDepth * strides[1];\n              for (let xRow = xRowMin; xRow < xRowMax; xRow += dilationHeight) {\n                const xRowOffset = xDepthOffset + xRow * strides[2];\n                for (let xCol = xColMin; xCol < xColMax; xCol += dilationWidth) {\n                  const xColOffset = xRowOffset + xCol * strides[3];\n                  const pixel = xValues[xColOffset + channel];\n                  if (poolType === \"max\" && pixel > minMaxValue) {\n                    minMaxValue = pixel;\n                  } else if (poolType === \"avg\") {\n                    avgValue += pixel;\n                    count2++;\n                  }\n                  if (isNaN(minMaxValue)) {\n                    break;\n                  }\n                }\n                if (isNaN(minMaxValue)) {\n                  break;\n                }\n              }\n              if (isNaN(minMaxValue)) {\n                break;\n              }\n            }\n            const outputOffset = outputColOffset + channel;\n            outputVals[outputOffset] = poolType === \"avg\" ? avgValue / count2 : minMaxValue;\n          }\n        }\n      }\n    }\n  }\n  return output;\n}\nfunction maxPool3dPositions(xBuf, convInfo) {\n  const maxPositions = buffer(convInfo.outShape, \"int32\");\n  const strideDepth = convInfo.strideDepth;\n  const strideHeight = convInfo.strideHeight;\n  const strideWidth = convInfo.strideWidth;\n  const dilationDepth = convInfo.dilationDepth;\n  const dilationHeight = convInfo.dilationHeight;\n  const dilationWidth = convInfo.dilationWidth;\n  const effectiveFilterDepth = convInfo.effectiveFilterDepth;\n  const effectiveFilterHeight = convInfo.effectiveFilterHeight;\n  const effectiveFilterWidth = convInfo.effectiveFilterWidth;\n  const padFront = convInfo.padInfo.front;\n  const padTop = convInfo.padInfo.top;\n  const padLeft = convInfo.padInfo.left;\n  for (let batch = 0; batch < convInfo.batchSize; ++batch) {\n    for (let channel = 0; channel < convInfo.inChannels; ++channel) {\n      for (let yDepth = 0; yDepth < convInfo.outDepth; ++yDepth) {\n        const xDepthCorner = yDepth * strideDepth - padFront;\n        let xDepthMin = xDepthCorner;\n        while (xDepthMin < 0) {\n          xDepthMin += dilationDepth;\n        }\n        const xDepthMax = Math.min(convInfo.inDepth, effectiveFilterDepth + xDepthCorner);\n        for (let yRow = 0; yRow < convInfo.outHeight; ++yRow) {\n          const xRowCorner = yRow * strideHeight - padTop;\n          let xRowMin = xRowCorner;\n          while (xRowMin < 0) {\n            xRowMin += dilationHeight;\n          }\n          const xRowMax = Math.min(convInfo.inHeight, effectiveFilterHeight + xRowCorner);\n          for (let yCol = 0; yCol < convInfo.outWidth; ++yCol) {\n            const xColCorner = yCol * strideWidth - padLeft;\n            let xColMin = xColCorner;\n            while (xColMin < 0) {\n              xColMin += dilationWidth;\n            }\n            const xColMax = Math.min(convInfo.inWidth, effectiveFilterWidth + xColCorner);\n            let maxValue = Number.NEGATIVE_INFINITY;\n            let maxPosition = -1;\n            for (let xDepth = xDepthMin; xDepth < xDepthMax; xDepth += dilationDepth) {\n              const wDepth = xDepth - xDepthCorner;\n              for (let xRow = xRowMin; xRow < xRowMax; xRow += dilationHeight) {\n                const wRow = xRow - xRowCorner;\n                for (let xCol = xColMin; xCol < xColMax; xCol += dilationWidth) {\n                  const wCol = xCol - xColCorner;\n                  const pixel = xBuf.get(batch, xDepth, xRow, xCol, channel);\n                  if (pixel >= maxValue) {\n                    maxValue = pixel;\n                    maxPosition = wDepth * effectiveFilterHeight * effectiveFilterWidth + wRow * effectiveFilterHeight + wCol;\n                  }\n                }\n              }\n            }\n            maxPositions.set(maxPosition, batch, yDepth, yRow, yCol, channel);\n          }\n        }\n      }\n    }\n  }\n  return maxPositions;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/AvgPool.js\nfunction avgPool2(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x } = inputs;\n  assertNotComplex(x, \"avgPool\");\n  const { filterSize, strides, pad: pad3, dimRoundingMode } = attrs;\n  const dilations = 1;\n  util_exports.assert(backend_util_exports.eitherStridesOrDilationsAreOne(strides, dilations), () => `Error in avgPool: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`);\n  const convInfo = backend_util_exports.computePool2DInfo(x.shape, filterSize, strides, dilations, pad3, dimRoundingMode);\n  let res;\n  if (convInfo.filterWidth === 1 && convInfo.filterHeight === 1 && util_exports.arraysEqual(convInfo.inShape, convInfo.outShape)) {\n    res = identity2({ inputs: { x }, backend: backend2 });\n  } else {\n    const xValues = backend2.data.get(x.dataId).values;\n    const strides2 = util_exports.computeStrides(x.shape);\n    const buffer2 = pool2(xValues, x.shape, x.dtype, strides2, convInfo, \"avg\");\n    res = backend2.makeTensorInfo(convInfo.outShape, x.dtype, buffer2.values);\n  }\n  return res;\n}\nvar avgPoolConfig = {\n  kernelName: AvgPool,\n  backendName: \"cpu\",\n  kernelFunc: avgPool2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/AvgPool3D.js\nfunction avgPool3D(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x } = inputs;\n  const { filterSize, strides, pad: pad3, dimRoundingMode, dataFormat } = attrs;\n  assertNotComplex(x, \"avgPool3d\");\n  const convInfo = backend_util_exports.computePool3DInfo(x.shape, filterSize, strides, 1, pad3, dimRoundingMode, dataFormat);\n  const xValues = backend2.data.get(x.dataId).values;\n  const outBuf = pool3d2(xValues, x.shape, x.dtype, util_exports.computeStrides(x.shape), convInfo, \"avg\");\n  return backend2.makeTensorInfo(outBuf.shape, \"float32\", outBuf.values);\n}\nvar avgPool3DConfig = {\n  kernelName: AvgPool3D,\n  backendName: \"cpu\",\n  kernelFunc: avgPool3D\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/AvgPool3DGrad.js\nfunction avgPool3DGrad(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { dy, input: input2 } = inputs;\n  const { filterSize, strides, pad: pad3, dimRoundingMode } = attrs;\n  assertNotComplex([dy, input2], \"avgPool3DGrad\");\n  const convInfo = backend_util_exports.computePool3DInfo(input2.shape, filterSize, strides, 1, pad3, dimRoundingMode);\n  const strideDepth = convInfo.strideDepth;\n  const strideHeight = convInfo.strideHeight;\n  const strideWidth = convInfo.strideWidth;\n  const filterDepth = convInfo.filterDepth;\n  const filterHeight = convInfo.filterHeight;\n  const filterWidth = convInfo.filterWidth;\n  const dilationDepth = convInfo.dilationDepth;\n  const dilationHeight = convInfo.dilationHeight;\n  const dilationWidth = convInfo.dilationWidth;\n  const effectiveFilterDepth = convInfo.effectiveFilterDepth;\n  const effectiveFilterHeight = convInfo.effectiveFilterHeight;\n  const effectiveFilterWidth = convInfo.effectiveFilterWidth;\n  const padFront = effectiveFilterDepth - 1 - convInfo.padInfo.front;\n  const padLeft = effectiveFilterWidth - 1 - convInfo.padInfo.left;\n  const padTop = effectiveFilterHeight - 1 - convInfo.padInfo.top;\n  const dx = buffer(input2.shape, \"float32\");\n  const avgMultiplier = 1 / (filterDepth * filterHeight * filterWidth);\n  const dyBuf = backend2.bufferSync(dy);\n  for (let batch = 0; batch < convInfo.batchSize; ++batch) {\n    for (let channel = 0; channel < convInfo.inChannels; ++channel) {\n      for (let dxDepth = 0; dxDepth < convInfo.inDepth; ++dxDepth) {\n        for (let dxRow = 0; dxRow < convInfo.inHeight; ++dxRow) {\n          for (let dxCol = 0; dxCol < convInfo.inWidth; ++dxCol) {\n            const dyDepthCorner = dxDepth - padFront;\n            const dyRowCorner = dxRow - padTop;\n            const dyColCorner = dxCol - padLeft;\n            let dotProd = 0;\n            for (let wDepth = 0; wDepth < effectiveFilterDepth; wDepth += dilationDepth) {\n              const dyDepth = (dyDepthCorner + wDepth) / strideDepth;\n              if (dyDepth < 0 || dyDepth >= convInfo.outDepth || Math.floor(dyDepth) !== dyDepth) {\n                continue;\n              }\n              for (let wRow = 0; wRow < effectiveFilterHeight; wRow += dilationHeight) {\n                const dyRow = (dyRowCorner + wRow) / strideHeight;\n                if (dyRow < 0 || dyRow >= convInfo.outHeight || Math.floor(dyRow) !== dyRow) {\n                  continue;\n                }\n                for (let wCol = 0; wCol < effectiveFilterWidth; wCol += dilationWidth) {\n                  const dyCol = (dyColCorner + wCol) / strideWidth;\n                  if (dyCol < 0 || dyCol >= convInfo.outWidth || Math.floor(dyCol) !== dyCol) {\n                    continue;\n                  }\n                  const pixel = dyBuf.get(batch, dyDepth, dyRow, dyCol, channel);\n                  dotProd += pixel;\n                }\n              }\n            }\n            dx.set(dotProd * avgMultiplier, batch, dxDepth, dxRow, dxCol, channel);\n          }\n        }\n      }\n    }\n  }\n  return backend2.makeTensorInfo(dx.shape, dx.dtype, dx.values);\n}\nvar avgPool3DGradConfig2 = {\n  kernelName: AvgPool3DGrad,\n  backendName: \"cpu\",\n  kernelFunc: avgPool3DGrad\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/AvgPoolGrad.js\nfunction avgPoolGrad2(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { dy, input: input2 } = inputs;\n  const x = input2;\n  assertNotComplex([dy, input2], \"avgPoolGrad\");\n  const { filterSize, strides, pad: pad3 } = attrs;\n  const convInfo = backend_util_exports.computePool2DInfo(x.shape, filterSize, strides, 1, pad3);\n  const strideHeight = convInfo.strideHeight;\n  const strideWidth = convInfo.strideWidth;\n  const filterHeight = convInfo.filterHeight;\n  const filterWidth = convInfo.filterWidth;\n  const dilationHeight = convInfo.dilationHeight;\n  const dilationWidth = convInfo.dilationWidth;\n  const effectiveFilterHeight = convInfo.effectiveFilterHeight;\n  const effectiveFilterWidth = convInfo.effectiveFilterWidth;\n  const padLeft = effectiveFilterWidth - 1 - convInfo.padInfo.left;\n  const padTop = effectiveFilterHeight - 1 - convInfo.padInfo.top;\n  const dx = buffer(x.shape, \"float32\");\n  const avgMultiplier = 1 / (filterHeight * filterWidth);\n  const dyData = backend2.data.get(dy.dataId).values;\n  const dyBuf = buffer(dy.shape, \"float32\", dyData);\n  for (let b = 0; b < convInfo.batchSize; ++b) {\n    for (let d = 0; d < convInfo.inChannels; ++d) {\n      for (let dxR = 0; dxR < convInfo.inHeight; ++dxR) {\n        for (let dxC = 0; dxC < convInfo.inWidth; ++dxC) {\n          const dyRCorner = dxR - padTop;\n          const dyCCorner = dxC - padLeft;\n          let dotProd = 0;\n          for (let wR = 0; wR < effectiveFilterHeight; wR += dilationHeight) {\n            const dyR = (dyRCorner + wR) / strideHeight;\n            if (dyR < 0 || dyR >= convInfo.outHeight || Math.floor(dyR) !== dyR) {\n              continue;\n            }\n            for (let wC = 0; wC < effectiveFilterWidth; wC += dilationWidth) {\n              const dyC = (dyCCorner + wC) / strideWidth;\n              if (dyC < 0 || dyC >= convInfo.outWidth || Math.floor(dyC) !== dyC) {\n                continue;\n              }\n              const pixel = dyBuf.get(b, dyR, dyC, d);\n              dotProd += pixel;\n            }\n          }\n          dx.set(dotProd * avgMultiplier, b, dxR, dxC, d);\n        }\n      }\n    }\n  }\n  return backend2.makeTensorInfo(dx.shape, dx.dtype, dx.values);\n}\nvar avgPoolGradConfig2 = {\n  kernelName: AvgPoolGrad,\n  backendName: \"cpu\",\n  kernelFunc: avgPoolGrad2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/BatchNorm.js\nfunction batchNorm2(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x, scale: scale2, offset, mean: mean5, variance } = inputs;\n  util_exports.assert(mean5.shape.length === variance.shape.length, () => \"Batch normalization gradient requires mean and variance to have equal ranks.\");\n  util_exports.assert(offset == null || mean5.shape.length === offset.shape.length, () => \"Batch normalization gradient requires mean and offset to have equal ranks.\");\n  util_exports.assert(scale2 == null || mean5.shape.length === scale2.shape.length, () => \"Batch normalization gradient requires mean and scale to have equal ranks.\");\n  assertNotComplex([x, mean5, variance, scale2, offset], \"batchNorm\");\n  let { varianceEpsilon } = attrs;\n  if (varianceEpsilon == null) {\n    varianceEpsilon = 1e-3;\n  }\n  const xVals = backend2.data.get(x.dataId).values;\n  const mVals = backend2.data.get(mean5.dataId).values;\n  const varVals = backend2.data.get(variance.dataId).values;\n  const sVals = scale2 ? backend2.data.get(scale2.dataId).values : new Float32Array([1]);\n  const offVals = offset ? backend2.data.get(offset.dataId).values : new Float32Array([0]);\n  const outVals = new Float32Array(xVals.length);\n  const offValsLength = offVals.length;\n  const sValsLength = sVals.length;\n  const varValsLength = varVals.length;\n  const mValsLength = mVals.length;\n  let offi = 0;\n  let mi = 0;\n  let si = 0;\n  let vi = 0;\n  for (let i = 0; i < xVals.length; ++i) {\n    outVals[i] = offVals[offi++] + (xVals[i] - mVals[mi++]) * sVals[si++] / Math.sqrt(varVals[vi++] + varianceEpsilon);\n    if (offi >= offValsLength) {\n      offi = 0;\n    }\n    if (mi >= mValsLength) {\n      mi = 0;\n    }\n    if (si >= sValsLength) {\n      si = 0;\n    }\n    if (vi >= varValsLength) {\n      vi = 0;\n    }\n  }\n  return backend2.makeTensorInfo(x.shape, x.dtype, outVals);\n}\nvar batchNormConfig = {\n  kernelName: FusedBatchNorm,\n  backendName: \"cpu\",\n  kernelFunc: batchNorm2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/BatchToSpaceND.js\nfunction batchToSpaceND2(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x } = inputs;\n  const { blockShape, crops } = attrs;\n  assertNotComplex([x], \"batchToSpaceND\");\n  const prod6 = blockShape.reduce((a, b) => a * b);\n  const reshaped = backend_util_exports.getReshaped(x.shape, blockShape, prod6);\n  const permuted = backend_util_exports.getPermuted(reshaped.length, blockShape.length);\n  const reshapedPermuted = backend_util_exports.getReshapedPermuted(x.shape, blockShape, prod6);\n  const sliceBeginCoords = backend_util_exports.getSliceBeginCoords(crops, blockShape.length);\n  const sliceSize = backend_util_exports.getSliceSize(reshapedPermuted, crops, blockShape.length);\n  const xReshaped = reshape3({ inputs: { x }, backend: backend2, attrs: { shape: reshaped } });\n  const xTransposed = transpose2({ inputs: { x: xReshaped }, backend: backend2, attrs: { perm: permuted } });\n  const xTransposedReshaped = reshape3({ inputs: { x: xTransposed }, backend: backend2, attrs: { shape: reshapedPermuted } });\n  const result = slice2({\n    inputs: { x: xTransposedReshaped },\n    backend: backend2,\n    attrs: { begin: sliceBeginCoords, size: sliceSize }\n  });\n  backend2.disposeIntermediateTensorInfo(xReshaped);\n  backend2.disposeIntermediateTensorInfo(xTransposed);\n  backend2.disposeIntermediateTensorInfo(xTransposedReshaped);\n  return result;\n}\nvar batchToSpaceNDConfig = {\n  kernelName: BatchToSpaceND,\n  backendName: \"cpu\",\n  kernelFunc: batchToSpaceND2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Bincount.js\nfunction bincount2(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x, weights } = inputs;\n  const { size } = attrs;\n  const xVals = backend2.data.get(x.dataId).values;\n  const weightsVals = backend2.data.get(weights.dataId).values;\n  const outVals = bincountImpl(xVals, weightsVals, weights.dtype, weights.shape, size);\n  return backend2.makeTensorInfo([size], weights.dtype, outVals);\n}\nvar bincountConfig = {\n  kernelName: Bincount,\n  backendName: \"cpu\",\n  kernelFunc: bincount2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/BroadcastArgs.js\nfunction broadcastArgs2(args) {\n  const { inputs, backend: backend2 } = args;\n  const { s0, s1 } = inputs;\n  const s0Vals = backend2.data.get(s0.dataId).values;\n  const s1Vals = backend2.data.get(s1.dataId).values;\n  const broadcastShape = backend_util_exports.assertAndGetBroadcastShape(Array.from(s0Vals), Array.from(s1Vals));\n  return backend2.makeTensorInfo([broadcastShape.length], \"int32\", Int32Array.from(broadcastShape));\n}\nvar broadcastArgsConfig = {\n  kernelName: BroadcastArgs,\n  backendName: \"cpu\",\n  kernelFunc: broadcastArgs2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/ClipByValue.js\nvar clipByValue2 = unaryKernelFunc(ClipByValue, (xi, attrs) => {\n  const clipAttrs = attrs;\n  if (xi > clipAttrs.clipValueMax) {\n    return clipAttrs.clipValueMax;\n  }\n  return xi < clipAttrs.clipValueMin ? clipAttrs.clipValueMin : xi;\n});\nvar clipByValueConfig = {\n  kernelName: ClipByValue,\n  backendName: \"cpu\",\n  kernelFunc: clipByValue2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/ComplexAbs.js\nvar complexAbs = (args) => {\n  const { x } = args.inputs;\n  const cpuBackend = args.backend;\n  const resultValues = new Float32Array(util_exports.sizeFromShape(x.shape));\n  const complexVals = cpuBackend.data.get(x.dataId);\n  const real5 = complexVals.complexTensorInfos.real;\n  const imag5 = complexVals.complexTensorInfos.imag;\n  const realVals = cpuBackend.data.get(real5.dataId).values;\n  const imagVals = cpuBackend.data.get(imag5.dataId).values;\n  for (let i = 0; i < realVals.length; i++) {\n    const real6 = realVals[i];\n    const imag6 = imagVals[i];\n    resultValues[i] = Math.hypot(real6, imag6);\n  }\n  return cpuBackend.makeOutput(resultValues, x.shape, \"float32\");\n};\nvar complexAbsConfig = {\n  kernelName: ComplexAbs,\n  backendName: \"cpu\",\n  kernelFunc: complexAbs\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Imag.js\nfunction imag2(args) {\n  const { inputs, backend: backend2 } = args;\n  const { input: input2 } = inputs;\n  const imag5 = backend2.data.get(input2.dataId).complexTensorInfos.imag;\n  const imagVal = backend2.data.get(imag5.dataId).values;\n  return backend2.makeTensorInfo(imag5.shape, imag5.dtype, imagVal);\n}\nvar imagConfig = {\n  kernelName: Imag,\n  backendName: \"cpu\",\n  kernelFunc: imag2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Concat.js\nfunction concat2(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { axis } = attrs;\n  const $axis = util_exports.parseAxisParam(axis, inputs[0].shape)[0];\n  let outShape = backend_util_exports.computeOutShape(inputs.map((t) => t.shape), $axis);\n  if (util_exports.sizeFromShape(outShape) === 0) {\n    return backend2.makeTensorInfo(outShape, inputs[0].dtype, []);\n  }\n  const $inputs = inputs.filter((t) => util_exports.sizeFromShape(t.shape) > 0);\n  if ($inputs.length === 1) {\n    return identity2({ inputs: { x: $inputs[0] }, backend: backend2 });\n  }\n  const shapes = $inputs.map((t) => t.shape);\n  backend_util_exports.assertParamsConsistent(shapes, $axis);\n  if ($inputs[0].dtype === \"complex64\") {\n    const reals = $inputs.map((t) => real2({ inputs: { input: t }, backend: backend2 }));\n    const imags = $inputs.map((t) => imag2({ inputs: { input: t }, backend: backend2 }));\n    const realConcated = concat2({ inputs: reals, backend: backend2, attrs: { axis: $axis } });\n    const imagConcated = concat2({ inputs: imags, backend: backend2, attrs: { axis: $axis } });\n    const result = complex2({ inputs: { real: realConcated, imag: imagConcated }, backend: backend2 });\n    reals.forEach((r) => backend2.disposeIntermediateTensorInfo(r));\n    imags.forEach((i) => backend2.disposeIntermediateTensorInfo(i));\n    backend2.disposeIntermediateTensorInfo(realConcated);\n    backend2.disposeIntermediateTensorInfo(imagConcated);\n    return result;\n  }\n  const inputs2D = $inputs.map((t) => {\n    const innerSize = util_exports.sizeFromShape(t.shape.slice($axis));\n    const shape = [-1, innerSize];\n    return reshape3({ inputs: { x: t }, backend: backend2, attrs: { shape } });\n  });\n  const inputsValShapes = inputs2D.map((t) => {\n    return { vals: backend2.data.get(t.dataId).values, shape: t.shape };\n  });\n  outShape = backend_util_exports.computeOutShape(inputs2D.map((t) => t.shape), 1);\n  const simplyConcat = inputs2D[0].shape[0] === 1;\n  const outVals = concatImpl(inputsValShapes, outShape, inputs[0].dtype, simplyConcat);\n  const finalOutShape = backend_util_exports.computeOutShape($inputs.map((t) => t.shape), $axis);\n  const outInfo = backend2.makeTensorInfo(finalOutShape, inputs[0].dtype, outVals);\n  inputs2D.forEach((t) => backend2.disposeIntermediateTensorInfo(t));\n  return outInfo;\n}\nvar concatConfig = {\n  kernelName: Concat,\n  backendName: \"cpu\",\n  kernelFunc: concat2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Conv2D.js\nfunction conv2D(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x, filter } = inputs;\n  const { strides, pad: pad3, dataFormat, dilations, dimRoundingMode } = attrs;\n  assertNotComplex([x, filter], \"conv2d\");\n  const $dataFormat = backend_util_exports.convertConv2DDataFormat(dataFormat);\n  const convInfo = backend_util_exports.computeConv2DInfo(x.shape, filter.shape, strides, dilations, pad3, dimRoundingMode, false, $dataFormat);\n  const filterHeight = convInfo.filterHeight;\n  const filterWidth = convInfo.filterWidth;\n  const dilationHeight = convInfo.dilationHeight;\n  const dilationWidth = convInfo.dilationWidth;\n  const padLeft = convInfo.padInfo.left;\n  const padTop = convInfo.padInfo.top;\n  const isChannelsLast = convInfo.dataFormat === \"channelsLast\";\n  const y = new TensorBuffer(convInfo.outShape, x.dtype);\n  const xStrides = util_exports.computeStrides(x.shape);\n  const filterStrides = util_exports.computeStrides(filter.shape);\n  const xBatchStride = xStrides[0];\n  const xRowStride = isChannelsLast ? xStrides[1] : xStrides[2];\n  const xColStride = isChannelsLast ? xStrides[2] : 1;\n  const xChannelStride = isChannelsLast ? 1 : xStrides[1];\n  const yBatchStride = y.strides[0];\n  const yRowStride = isChannelsLast ? y.strides[1] : y.strides[2];\n  const yColStride = isChannelsLast ? y.strides[2] : 1;\n  const yChannelStride = isChannelsLast ? 1 : y.strides[1];\n  const xVals = backend2.data.get(x.dataId).values;\n  const wVals = backend2.data.get(filter.dataId).values;\n  const yVals = y.values;\n  for (let b = 0; b < convInfo.batchSize; ++b) {\n    const xOffset1 = b * xBatchStride;\n    const yOffset1 = b * yBatchStride;\n    for (let yR = 0; yR < convInfo.outHeight; ++yR) {\n      const yOffset2 = yOffset1 + yR * yRowStride;\n      const xRCorner = yR * convInfo.strideHeight - padTop;\n      for (let wR = 0; wR < filterHeight; ++wR) {\n        const xR = xRCorner + wR * dilationHeight;\n        if (xR < 0 || xR >= convInfo.inHeight) {\n          continue;\n        }\n        const wOffset1 = wR * filterStrides[0];\n        const xOffset2 = xOffset1 + xR * xRowStride;\n        for (let yC = 0; yC < convInfo.outWidth; ++yC) {\n          const yOffset3 = yOffset2 + yC * yColStride;\n          const xCCorner = yC * convInfo.strideWidth - padLeft;\n          for (let wC = 0; wC < filterWidth; ++wC) {\n            const xC = xCCorner + wC * dilationWidth;\n            if (xC < 0 || xC >= convInfo.inWidth) {\n              continue;\n            }\n            const wOffset2 = wOffset1 + wC * filterStrides[1];\n            const xOffset3 = xOffset2 + xC * xColStride;\n            let wOffset3 = wOffset2;\n            for (let d1 = 0; d1 < convInfo.inChannels; ++d1) {\n              const xVal = xVals[xOffset3 + d1 * xChannelStride];\n              for (let d2 = 0; d2 < convInfo.outChannels; ++d2) {\n                yVals[yOffset3 + d2 * yChannelStride] += xVal * wVals[wOffset3 + d2];\n              }\n              wOffset3 += convInfo.outChannels;\n            }\n          }\n        }\n      }\n    }\n  }\n  return backend2.makeTensorInfo(y.shape, y.dtype, yVals);\n}\nvar conv2DConfig = {\n  kernelName: Conv2D,\n  backendName: \"cpu\",\n  kernelFunc: conv2D\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Conv2DBackpropFilter.js\nfunction conv2DBackpropFilter2(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x, dy } = inputs;\n  const { strides, pad: pad3, dataFormat, dimRoundingMode, filterShape } = attrs;\n  assertNotComplex([x, dy], \"conv2dBackpropFilter\");\n  const $dataFormat = backend_util_exports.convertConv2DDataFormat(dataFormat);\n  const convInfo = backend_util_exports.computeConv2DInfo(x.shape, filterShape, strides, 1, pad3, dimRoundingMode, false, $dataFormat);\n  const { strideHeight, strideWidth, filterHeight, filterWidth } = convInfo;\n  const isChannelsLast = convInfo.dataFormat === \"channelsLast\";\n  const dW = new TensorBuffer(convInfo.filterShape, \"float32\");\n  const leftPad = convInfo.padInfo.left;\n  const topPad = convInfo.padInfo.top;\n  const xVals = backend2.data.get(x.dataId).values;\n  const dyVals = backend2.data.get(dy.dataId).values;\n  const xBuf = new TensorBuffer(x.shape, x.dtype, xVals);\n  const dyBuf = new TensorBuffer(dy.shape, dy.dtype, dyVals);\n  for (let wR = 0; wR < filterHeight; ++wR) {\n    const yRMin = Math.max(0, Math.ceil((topPad - wR) / strideHeight));\n    const yRMax = Math.min(convInfo.outHeight, (convInfo.inHeight + topPad - wR) / strideHeight);\n    for (let wC = 0; wC < filterWidth; ++wC) {\n      const yCMin = Math.max(0, Math.ceil((leftPad - wC) / strideWidth));\n      const yCMax = Math.min(convInfo.outWidth, (convInfo.inWidth + leftPad - wC) / strideWidth);\n      for (let d1 = 0; d1 < convInfo.inChannels; ++d1) {\n        for (let d2 = 0; d2 < convInfo.outChannels; ++d2) {\n          let dotProd = 0;\n          for (let b = 0; b < convInfo.batchSize; ++b) {\n            for (let yR = yRMin; yR < yRMax; ++yR) {\n              const xR = wR + yR * strideHeight - topPad;\n              for (let yC = yCMin; yC < yCMax; ++yC) {\n                const xC = wC + yC * strideWidth - leftPad;\n                if (isChannelsLast) {\n                  dotProd += xBuf.get(b, xR, xC, d1) * dyBuf.get(b, yR, yC, d2);\n                } else {\n                  dotProd += xBuf.get(b, d1, xR, xC) * dyBuf.get(b, d2, yR, yC);\n                }\n              }\n            }\n          }\n          dW.set(dotProd, wR, wC, d1, d2);\n        }\n      }\n    }\n  }\n  return backend2.makeTensorInfo(dW.shape, dW.dtype, dW.values);\n}\nvar conv2DBackpropFilterConfig = {\n  kernelName: Conv2DBackpropFilter,\n  backendName: \"cpu\",\n  kernelFunc: conv2DBackpropFilter2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Conv2DBackpropInput.js\nfunction conv2DBackpropInput2(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { dy, filter } = inputs;\n  const { inputShape, strides, pad: pad3, dataFormat, dimRoundingMode } = attrs;\n  assertNotComplex([dy, filter], \"conv2dBackpropInput\");\n  const filterStrides = util_exports.computeStrides(filter.shape);\n  const dyStrides = util_exports.computeStrides(dy.shape);\n  let $dataFormat = backend_util_exports.convertConv2DDataFormat(dataFormat);\n  const convInfo = backend_util_exports.computeConv2DInfo(inputShape, filter.shape, strides, 1, pad3, dimRoundingMode, false, $dataFormat);\n  const dx = new TensorBuffer(convInfo.inShape, \"float32\");\n  const dxValues = dx.values;\n  const dyValues = backend2.data.get(dy.dataId).values;\n  const fltValues = backend2.data.get(filter.dataId).values;\n  const [fltS0, fltS1, fltS2] = filterStrides;\n  const { batchSize, filterHeight, filterWidth, inChannels, inHeight, inWidth, outChannels, outHeight, outWidth, strideHeight, strideWidth } = convInfo;\n  $dataFormat = convInfo.dataFormat;\n  const topPad = filterHeight - 1 - convInfo.padInfo.top;\n  const leftPad = filterWidth - 1 - convInfo.padInfo.left;\n  const isChannelsLast = $dataFormat === \"channelsLast\";\n  const xBatchStride = dx.strides[0];\n  const xRowStride = isChannelsLast ? dx.strides[1] : dx.strides[2];\n  const xColStride = isChannelsLast ? dx.strides[2] : 1;\n  const xChannelStride = isChannelsLast ? 1 : dx.strides[1];\n  const yBatchStride = dyStrides[0];\n  const yRowStride = isChannelsLast ? dyStrides[1] : dyStrides[2];\n  const yColStride = isChannelsLast ? dyStrides[2] : 1;\n  const yChannelStride = isChannelsLast ? 1 : dyStrides[1];\n  for (let b = 0; b < batchSize; ++b) {\n    for (let d1 = 0; d1 < inChannels; ++d1) {\n      for (let xR = 0; xR < inHeight; ++xR) {\n        const xRCorner = xR - topPad;\n        const xRMin = Math.max(0, Math.ceil(xRCorner / strideHeight));\n        const yRMax = Math.min(outHeight, (filterHeight + xRCorner) / strideHeight);\n        for (let xC = 0; xC < inWidth; ++xC) {\n          const xCCorner = xC - leftPad;\n          const xCMin = Math.max(0, Math.ceil(xCCorner / strideWidth));\n          const yCMax = Math.min(outWidth, (filterWidth + xCCorner) / strideWidth);\n          let dotProd = 0;\n          for (let yR = xRMin; yR < yRMax; ++yR) {\n            const wR = yR * strideHeight - xRCorner;\n            for (let yC = xCMin; yC < yCMax; ++yC) {\n              const wC = yC * strideWidth - xCCorner;\n              const dyOffset = yBatchStride * b + yRowStride * yR + yColStride * yC;\n              const fltOffset = fltS0 * (filterHeight - 1 - wR) + fltS1 * (filterWidth - 1 - wC) + fltS2 * d1;\n              for (let d2 = 0; d2 < outChannels; ++d2) {\n                const pixel = dyValues[dyOffset + yChannelStride * d2];\n                const weight = fltValues[fltOffset + d2];\n                dotProd += pixel * weight;\n              }\n            }\n          }\n          const dxOffset = xBatchStride * b + xRowStride * xR + xColStride * xC + xChannelStride * d1;\n          dxValues[dxOffset] = dotProd;\n        }\n      }\n    }\n  }\n  return backend2.makeTensorInfo(dx.shape, dx.dtype, dx.values);\n}\nvar conv2DBackpropInputConfig = {\n  kernelName: Conv2DBackpropInput,\n  backendName: \"cpu\",\n  kernelFunc: conv2DBackpropInput2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Conv3D.js\nfunction conv3D(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x, filter } = inputs;\n  const { strides, pad: pad3, dilations } = attrs;\n  assertNotComplex([x, filter], \"conv3d\");\n  const convInfo = backend_util_exports.computeConv3DInfo(x.shape, filter.shape, strides, dilations, pad3);\n  const { filterDepth, filterHeight, filterWidth, dilationDepth, dilationHeight, dilationWidth, padInfo } = convInfo;\n  const padFront = padInfo.front;\n  const padLeft = padInfo.left;\n  const padTop = padInfo.top;\n  const y = new TensorBuffer(convInfo.outShape, x.dtype);\n  const xVals = backend2.data.get(x.dataId).values;\n  const wVals = backend2.data.get(filter.dataId).values;\n  const yVals = y.values;\n  const xStrides = util_exports.computeStrides(x.shape);\n  const filterStrides = util_exports.computeStrides(filter.shape);\n  for (let b = 0; b < convInfo.batchSize; ++b) {\n    const xOffset1 = b * xStrides[0];\n    const yOffset1 = b * y.strides[0];\n    for (let yF = 0; yF < convInfo.outDepth; ++yF) {\n      const yOffset2 = yOffset1 + yF * y.strides[1];\n      const xFCorner = yF * convInfo.strideDepth - padFront;\n      for (let wF = 0; wF < filterDepth; ++wF) {\n        const xF = xFCorner + wF * dilationDepth;\n        if (xF < 0 || xF >= convInfo.inDepth) {\n          continue;\n        }\n        const wOffset1 = wF * filterStrides[0];\n        const xOffset2 = xOffset1 + xF * xStrides[1];\n        for (let yR = 0; yR < convInfo.outHeight; ++yR) {\n          const yOffset3 = yOffset2 + yR * y.strides[2];\n          const xRCorner = yR * convInfo.strideHeight - padTop;\n          for (let wR = 0; wR < filterHeight; ++wR) {\n            const xR = xRCorner + wR * dilationHeight;\n            if (xR < 0 || xR >= convInfo.inHeight) {\n              continue;\n            }\n            const wOffset2 = wOffset1 + wR * filterStrides[1];\n            const xOffset3 = xOffset2 + xR * xStrides[2];\n            for (let yC = 0; yC < convInfo.outWidth; ++yC) {\n              const yOffset4 = yOffset3 + yC * convInfo.outChannels;\n              const xCCorner = yC * convInfo.strideWidth - padLeft;\n              for (let wC = 0; wC < filterWidth; ++wC) {\n                const xC = xCCorner + wC * dilationWidth;\n                if (xC < 0 || xC >= convInfo.inWidth) {\n                  continue;\n                }\n                const wOffset3 = wOffset2 + wC * filterStrides[2];\n                const xOffset4 = xOffset3 + xC * convInfo.inChannels;\n                let wOffset4 = wOffset3;\n                for (let d1 = 0; d1 < convInfo.inChannels; ++d1) {\n                  const xVal = xVals[xOffset4 + d1];\n                  for (let d2 = 0; d2 < convInfo.outChannels; ++d2) {\n                    yVals[yOffset4 + d2] += xVal * wVals[wOffset4 + d2];\n                  }\n                  wOffset4 += convInfo.outChannels;\n                }\n              }\n            }\n          }\n        }\n      }\n    }\n  }\n  return backend2.makeTensorInfo(y.shape, y.dtype, y.values);\n}\nvar conv3DConfig = {\n  kernelName: Conv3D,\n  backendName: \"cpu\",\n  kernelFunc: conv3D\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Conv3DBackpropFilterV2.js\nfunction conv3DBackpropFilterV2(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x, dy } = inputs;\n  const { strides, pad: pad3, filterShape } = attrs;\n  assertNotComplex([x, dy], \"conv3dBackpropFilterV2\");\n  const xStrides = util_exports.computeStrides(x.shape);\n  const dyStrides = util_exports.computeStrides(dy.shape);\n  const convInfo = backend_util_exports.computeConv3DInfo(x.shape, filterShape, strides, 1, pad3);\n  const strideDepth = convInfo.strideDepth;\n  const strideHeight = convInfo.strideHeight;\n  const strideWidth = convInfo.strideWidth;\n  const filterDepth = convInfo.filterDepth;\n  const filterHeight = convInfo.filterHeight;\n  const filterWidth = convInfo.filterWidth;\n  const dw = new TensorBuffer(convInfo.filterShape, \"float32\");\n  const dwValues = dw.values;\n  const [dwS0, dwS1, dwS2, dwS3] = dw.strides;\n  const dyValues = backend2.data.get(dy.dataId).values;\n  const [dyS0, dyS1, dyS2, dyS3] = dyStrides;\n  const xValues = backend2.data.get(x.dataId).values;\n  const [xS0, xS1, xS2, xS3] = xStrides;\n  const frontPad = convInfo.padInfo.front;\n  const leftPad = convInfo.padInfo.left;\n  const topPad = convInfo.padInfo.top;\n  for (let wF = 0; wF < filterDepth; ++wF) {\n    const yFMin = Math.max(0, Math.ceil((frontPad - wF) / strideDepth));\n    const yFMax = Math.min(convInfo.outDepth, (convInfo.inDepth + frontPad - wF) / strideDepth);\n    const wOffset1 = wF * dwS0;\n    for (let wR = 0; wR < filterHeight; ++wR) {\n      const yRMin = Math.max(0, Math.ceil((topPad - wR) / strideHeight));\n      const yRMax = Math.min(convInfo.outHeight, (convInfo.inHeight + topPad - wR) / strideHeight);\n      const wOffset2 = wR * dwS1 + wOffset1;\n      for (let wC = 0; wC < filterWidth; ++wC) {\n        const yCMin = Math.max(0, Math.ceil((leftPad - wC) / strideWidth));\n        const yCMax = Math.min(convInfo.outWidth, (convInfo.inWidth + leftPad - wC) / strideWidth);\n        const wOffset3 = wC * dwS2 + wOffset2;\n        for (let d1 = 0; d1 < convInfo.inChannels; ++d1) {\n          const wOffset4 = d1 * dwS3 + wOffset3;\n          for (let d2 = 0; d2 < convInfo.outChannels; ++d2) {\n            let dotProd = 0;\n            for (let b = 0; b < convInfo.batchSize; ++b) {\n              const xOffset1 = b * xS0;\n              const yOffset1 = b * dyS0;\n              for (let yF = yFMin; yF < yFMax; ++yF) {\n                const xF = wF + yF * strideDepth - frontPad;\n                const xOffset2 = xF * xS1 + xOffset1;\n                const yOffset2 = yF * dyS1 + yOffset1;\n                for (let yR = yRMin; yR < yRMax; ++yR) {\n                  const xR = wR + yR * strideHeight - topPad;\n                  const xOffset3 = xR * xS2 + xOffset2;\n                  const yOffset3 = yR * dyS2 + yOffset2;\n                  for (let yC = yCMin; yC < yCMax; ++yC) {\n                    const xC = wC + yC * strideWidth - leftPad;\n                    const xOffset4 = xC * xS3 + xOffset3;\n                    const yOffset4 = yC * dyS3 + yOffset3;\n                    dotProd += xValues[xOffset4 + d1] * dyValues[yOffset4 + d2];\n                  }\n                }\n              }\n            }\n            dwValues[wOffset4 + d2] = dotProd;\n          }\n        }\n      }\n    }\n  }\n  return backend2.makeTensorInfo(dw.shape, dw.dtype, dw.values);\n}\nvar conv3DBackpropFilterV2Config = {\n  kernelName: Conv3DBackpropFilterV2,\n  backendName: \"cpu\",\n  kernelFunc: conv3DBackpropFilterV2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Conv3DBackpropInputV2.js\nfunction conv3DBackpropInputV2(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { dy, filter } = inputs;\n  const { pad: pad3, strides, inputShape } = attrs;\n  assertNotComplex([dy], \"conv3dBackpropInputV2\");\n  const dyStrides = util_exports.computeStrides(dy.shape);\n  const filterStrides = util_exports.computeStrides(filter.shape);\n  const convInfo = backend_util_exports.computeConv3DInfo(inputShape, filter.shape, strides, 1, pad3);\n  const dx = new TensorBuffer(convInfo.inShape, \"float32\");\n  const dxValues = dx.values;\n  const [dxS0, dxS1, dxS2, dxS3] = dx.strides;\n  const dyValues = backend2.data.get(dy.dataId).values;\n  const [dyS0, dyS1, dyS2, dyS3] = dyStrides;\n  const fltValues = backend2.data.get(filter.dataId).values;\n  const [fltS0, fltS1, fltS2, fltS3] = filterStrides;\n  const { batchSize, filterDepth, filterHeight, filterWidth, inChannels, inDepth, inHeight, inWidth, outChannels, outDepth, outHeight, outWidth, strideDepth, strideHeight, strideWidth } = convInfo;\n  const frontPad = filterDepth - 1 - convInfo.padInfo.front;\n  const topPad = filterHeight - 1 - convInfo.padInfo.top;\n  const leftPad = filterWidth - 1 - convInfo.padInfo.left;\n  for (let b = 0; b < batchSize; ++b) {\n    for (let d1 = 0; d1 < inChannels; ++d1) {\n      for (let xF = 0; xF < inDepth; ++xF) {\n        const xFCorner = xF - frontPad;\n        const xFMin = Math.max(0, Math.ceil(xFCorner / strideDepth));\n        const yFMax = Math.min(outDepth, (filterDepth + xFCorner) / strideDepth);\n        for (let xR = 0; xR < inHeight; ++xR) {\n          const xRCorner = xR - topPad;\n          const xRMin = Math.max(0, Math.ceil(xRCorner / strideHeight));\n          const yRMax = Math.min(outHeight, (filterHeight + xRCorner) / strideHeight);\n          for (let xC = 0; xC < inWidth; ++xC) {\n            const xCCorner = xC - leftPad;\n            const xCMin = Math.max(0, Math.ceil(xCCorner / strideWidth));\n            const yCMax = Math.min(outWidth, (filterWidth + xCCorner) / strideWidth);\n            let dotProd = 0;\n            for (let yF = xFMin; yF < yFMax; ++yF) {\n              const wF = yF * strideDepth - xFCorner;\n              for (let yR = xRMin; yR < yRMax; ++yR) {\n                const wR = yR * strideHeight - xRCorner;\n                for (let yC = xCMin; yC < yCMax; ++yC) {\n                  const wC = yC * strideWidth - xCCorner;\n                  const dyOffset = dyS0 * b + dyS1 * yF + dyS2 * yR + dyS3 * yC;\n                  const fltOffset = fltS0 * (filterDepth - 1 - wF) + fltS1 * (filterHeight - 1 - wR) + fltS2 * (filterWidth - 1 - wC) + fltS3 * d1;\n                  for (let d2 = 0; d2 < outChannels; ++d2) {\n                    const pixel = dyValues[dyOffset + d2];\n                    const weight = fltValues[fltOffset + d2];\n                    dotProd += pixel * weight;\n                  }\n                }\n              }\n            }\n            dxValues[dxS0 * b + dxS1 * xF + dxS2 * xR + dxS3 * xC + d1] = dotProd;\n          }\n        }\n      }\n    }\n  }\n  return backend2.makeTensorInfo(dx.shape, dx.dtype, dx.values);\n}\nvar conv3DBackpropInputV2Config = {\n  kernelName: Conv3DBackpropInputV2,\n  backendName: \"cpu\",\n  kernelFunc: conv3DBackpropInputV2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Cos.js\nvar cos2 = unaryKernelFunc(Cos, (xi) => Math.cos(xi));\nvar cosConfig = {\n  kernelName: Cos,\n  backendName: \"cpu\",\n  kernelFunc: cos2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Cosh.js\nvar cosh2 = unaryKernelFunc(Cosh, (xi) => Math.cosh(xi));\nvar coshConfig = {\n  kernelName: Cosh,\n  backendName: \"cpu\",\n  kernelFunc: cosh2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/CropAndResize.js\nfunction cropAndResize2(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { image: image2, boxes, boxInd } = inputs;\n  const { cropSize, method, extrapolationValue } = attrs;\n  const [batch, imageHeight, imageWidth, numChannels] = image2.shape;\n  const numBoxes = boxes.shape[0];\n  const [cropHeight, cropWidth] = cropSize;\n  const output = buffer([numBoxes, cropHeight, cropWidth, numChannels], \"float32\");\n  const boxVals = backend2.data.get(boxes.dataId).values;\n  const boxIndVals = backend2.data.get(boxInd.dataId).values;\n  const imageVals = backend2.data.get(image2.dataId).values;\n  const inStride = util_exports.computeStrides(image2.shape);\n  const outStride = util_exports.computeStrides(output.shape);\n  for (let b = 0; b < numBoxes; b++) {\n    const startInd = b * 4;\n    const y1 = boxVals[startInd];\n    const x1 = boxVals[startInd + 1];\n    const y2 = boxVals[startInd + 2];\n    const x2 = boxVals[startInd + 3];\n    const bInd = boxIndVals[b];\n    if (bInd >= batch) {\n      continue;\n    }\n    const heightScale = cropHeight > 1 ? (y2 - y1) * (imageHeight - 1) / (cropHeight - 1) : 0;\n    const widthScale = cropWidth > 1 ? (x2 - x1) * (imageWidth - 1) / (cropWidth - 1) : 0;\n    for (let y = 0; y < cropHeight; y++) {\n      const yInd = cropHeight > 1 ? y1 * (imageHeight - 1) + y * heightScale : 0.5 * (y1 + y2) * (imageHeight - 1);\n      if (yInd < 0 || yInd > imageHeight - 1) {\n        for (let x = 0; x < cropWidth; x++) {\n          for (let c = 0; c < numChannels; c++) {\n            const ind = c + x * outStride[2] + y * outStride[1] + b * outStride[0];\n            output.values[ind] = extrapolationValue;\n          }\n        }\n        continue;\n      }\n      if (method === \"bilinear\") {\n        const topInd = Math.floor(yInd);\n        const bottomInd = Math.ceil(yInd);\n        const yLerp = yInd - topInd;\n        for (let x = 0; x < cropWidth; x++) {\n          const xInd = cropWidth > 1 ? x1 * (imageWidth - 1) + x * widthScale : 0.5 * (x1 + x2) * (imageWidth - 1);\n          if (xInd < 0 || xInd > imageWidth - 1) {\n            for (let c = 0; c < numChannels; c++) {\n              const ind = c + x * outStride[2] + y * outStride[1] + b * outStride[0];\n              output.values[ind] = extrapolationValue;\n            }\n            continue;\n          }\n          const leftInd = Math.floor(xInd);\n          const rightInd = Math.ceil(xInd);\n          const xLerp = xInd - leftInd;\n          for (let c = 0; c < numChannels; c++) {\n            let ind = c + leftInd * inStride[2] + topInd * inStride[1] + bInd * inStride[0];\n            const topLeft = imageVals[ind];\n            ind = c + rightInd * inStride[2] + topInd * inStride[1] + bInd * inStride[0];\n            const topRight = imageVals[ind];\n            ind = c + leftInd * inStride[2] + bottomInd * inStride[1] + bInd * inStride[0];\n            const bottomLeft = imageVals[ind];\n            ind = c + rightInd * inStride[2] + bottomInd * inStride[1] + bInd * inStride[0];\n            const bottomRight = imageVals[ind];\n            const top = topLeft + (topRight - topLeft) * xLerp;\n            const bottom = bottomLeft + (bottomRight - bottomLeft) * xLerp;\n            ind = c + x * outStride[2] + y * outStride[1] + b * outStride[0];\n            output.values[ind] = top + (bottom - top) * yLerp;\n          }\n        }\n      } else {\n        for (let x = 0; x < cropWidth; ++x) {\n          const xInd = cropWidth > 1 ? x1 * (imageWidth - 1) + x * widthScale : 0.5 * (x1 + x2) * (imageWidth - 1);\n          if (xInd < 0 || xInd > imageWidth - 1) {\n            for (let c = 0; c < numChannels; c++) {\n              const ind = c + x * outStride[2] + y * outStride[1] + b * outStride[0];\n              output.values[ind] = extrapolationValue;\n            }\n            continue;\n          }\n          const closestX = Math.round(xInd);\n          const closestY = Math.round(yInd);\n          for (let c = 0; c < numChannels; c++) {\n            const inInd = c + closestX * inStride[2] + closestY * inStride[1] + bInd * inStride[0];\n            const outInd = c + x * outStride[2] + y * outStride[1] + b * outStride[0];\n            output.values[outInd] = imageVals[inInd];\n          }\n        }\n      }\n    }\n  }\n  return backend2.makeTensorInfo(output.shape, output.dtype, output.values);\n}\nvar cropAndResizeConfig = {\n  kernelName: CropAndResize,\n  backendName: \"cpu\",\n  kernelFunc: cropAndResize2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Cumprod.js\nfunction cumprod2(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x } = inputs;\n  const { axis, exclusive, reverse: reverse5 } = attrs;\n  assertNotComplex(x, \"cumprod\");\n  const permutation = backend_util_exports.getAxesPermutation([axis], x.shape.length);\n  let $x = x;\n  if (permutation != null) {\n    $x = transpose2({ inputs: { x }, backend: backend2, attrs: { perm: permutation } });\n  }\n  const permutedAxis = backend_util_exports.getInnerMostAxes(1, x.shape.length)[0];\n  if (permutedAxis !== $x.shape.length - 1) {\n    throw new Error(`backend.cumprod in CPU expects an inner-most axis=${$x.shape.length - 1} but got axis=${permutedAxis}`);\n  }\n  const resultDtype = upcastType($x.dtype, \"int32\");\n  const vals = util_exports.makeOnesTypedArray(util_exports.sizeFromShape($x.shape), resultDtype);\n  const aVals = backend2.data.get($x.dataId).values;\n  const finalDim = $x.shape[$x.shape.length - 1];\n  const indexAdjuster = reverse5 ? (i, j) => i + finalDim - j - 1 : (i, j) => i + j;\n  for (let i = 0; i < aVals.length; i += finalDim) {\n    for (let j = 0; j < finalDim; j++) {\n      const idx = indexAdjuster(i, j);\n      if (j === 0) {\n        vals[idx] = exclusive ? 1 : aVals[idx];\n      } else {\n        const prevIdx = indexAdjuster(i, j - 1);\n        vals[idx] = exclusive ? aVals[prevIdx] * vals[prevIdx] : aVals[idx] * vals[prevIdx];\n      }\n    }\n  }\n  const result = backend2.makeTensorInfo($x.shape, resultDtype, vals);\n  if (permutation != null) {\n    const reversePermutation = backend_util_exports.getUndoAxesPermutation(permutation);\n    const reverseTransposedResult = transpose2({ inputs: { x: result }, backend: backend2, attrs: { perm: reversePermutation } });\n    backend2.disposeIntermediateTensorInfo(result);\n    backend2.disposeIntermediateTensorInfo($x);\n    return reverseTransposedResult;\n  }\n  return result;\n}\nvar cumprodConfig = {\n  kernelName: Cumprod,\n  backendName: \"cpu\",\n  kernelFunc: cumprod2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Cumsum.js\nfunction cumsum2(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x } = inputs;\n  const { axis, exclusive, reverse: reverse5 } = attrs;\n  assertNotComplex(x, \"cumsum\");\n  const permutation = backend_util_exports.getAxesPermutation([axis], x.shape.length);\n  let $x = x;\n  if (permutation != null) {\n    $x = transpose2({ inputs: { x }, backend: backend2, attrs: { perm: permutation } });\n  }\n  const permutedAxis = backend_util_exports.getInnerMostAxes(1, x.shape.length)[0];\n  if (permutedAxis !== $x.shape.length - 1) {\n    throw new Error(`backend.cumsum in CPU expects an inner-most axis=${$x.shape.length - 1} but got axis=${permutedAxis}`);\n  }\n  const resultDtype = upcastType($x.dtype, \"int32\");\n  const vals = util_exports.makeZerosTypedArray(util_exports.sizeFromShape($x.shape), resultDtype);\n  const aVals = backend2.data.get($x.dataId).values;\n  const finalDim = $x.shape[$x.shape.length - 1];\n  const indexAdjuster = reverse5 ? (i, j) => i + finalDim - j - 1 : (i, j) => i + j;\n  for (let i = 0; i < aVals.length; i += finalDim) {\n    for (let j = 0; j < finalDim; j++) {\n      const idx = indexAdjuster(i, j);\n      if (j === 0) {\n        vals[idx] = exclusive ? 0 : aVals[idx];\n      } else {\n        const prevIdx = indexAdjuster(i, j - 1);\n        vals[idx] = exclusive ? aVals[prevIdx] + vals[prevIdx] : aVals[idx] + vals[prevIdx];\n      }\n    }\n  }\n  const result = backend2.makeTensorInfo($x.shape, resultDtype, vals);\n  if (permutation != null) {\n    const reversePermutation = backend_util_exports.getUndoAxesPermutation(permutation);\n    const reverseTransposedResult = transpose2({ inputs: { x: result }, backend: backend2, attrs: { perm: reversePermutation } });\n    backend2.disposeIntermediateTensorInfo(result);\n    backend2.disposeIntermediateTensorInfo($x);\n    return reverseTransposedResult;\n  }\n  return result;\n}\nvar cumsumConfig = {\n  kernelName: Cumsum,\n  backendName: \"cpu\",\n  kernelFunc: cumsum2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/DenseBincount.js\nfunction denseBincount2(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x, weights } = inputs;\n  const { size, binaryOutput } = attrs;\n  if (x.shape.length === 1) {\n    const xVals = backend2.data.get(x.dataId).values;\n    const weightsVals = backend2.data.get(weights.dataId).values;\n    const outVals = bincountImpl(xVals, weightsVals, weights.dtype, weights.shape, size);\n    return backend2.makeTensorInfo([size], weights.dtype, outVals);\n  } else if (x.shape.length === 2) {\n    const xBuf = backend2.bufferSync(x);\n    const weightsBuf = backend2.bufferSync(weights);\n    const outBuf = bincountReduceImpl(xBuf, weightsBuf, size, binaryOutput);\n    return backend2.makeTensorInfo(outBuf.shape, weights.dtype, outBuf.values);\n  }\n  throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${x.shape.length}.`);\n}\nvar denseBincountConfig = {\n  kernelName: DenseBincount,\n  backendName: \"cpu\",\n  kernelFunc: denseBincount2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/DepthToSpace.js\nfunction depthToSpace2(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x } = inputs;\n  const { blockSize, dataFormat } = attrs;\n  util_exports.assert(dataFormat === \"NHWC\", () => `Only NHWC dataFormat supported on CPU for depthToSpace. Got ${dataFormat}`);\n  const batchSize = x.shape[0];\n  const inputHeight = x.shape[1];\n  const inputWidth = x.shape[2];\n  const inputDepth = x.shape[3];\n  const outputHeight = inputHeight * blockSize;\n  const outputWidth = inputWidth * blockSize;\n  const outputDepth = inputDepth / (blockSize * blockSize);\n  const xValues = backend2.data.get(x.dataId).values;\n  const result = new Float32Array(batchSize * outputHeight * outputWidth * outputDepth);\n  let outputIdx = 0;\n  for (let b = 0; b < batchSize; ++b) {\n    for (let h = 0; h < outputHeight; ++h) {\n      const inH = Math.floor(h / blockSize);\n      const offsetH = h % blockSize;\n      for (let w = 0; w < outputWidth; ++w) {\n        const inW = Math.floor(w / blockSize);\n        const offsetW = w % blockSize;\n        const offsetD = (offsetH * blockSize + offsetW) * outputDepth;\n        for (let d = 0; d < outputDepth; ++d) {\n          const inD = d + offsetD;\n          const inputIdx = inD + inputDepth * (inW + inputWidth * (inH + inputHeight * b));\n          result[outputIdx++] = xValues[inputIdx];\n        }\n      }\n    }\n  }\n  return backend2.makeTensorInfo([batchSize, outputHeight, outputWidth, outputDepth], x.dtype, result);\n}\nvar depthToSpaceConfig = {\n  kernelName: DepthToSpace,\n  backendName: \"cpu\",\n  kernelFunc: depthToSpace2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/DepthwiseConv2dNative.js\nfunction depthwiseConv2dNative(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x, filter } = inputs;\n  const { strides, pad: pad3, dilations, dimRoundingMode } = attrs;\n  assertNotComplex([x, filter], \"depthwiseConv2DNative\");\n  const xStrides = util_exports.computeStrides(x.shape);\n  const filterStrides = util_exports.computeStrides(filter.shape);\n  let $dilations = dilations;\n  if ($dilations == null) {\n    $dilations = [1, 1];\n  }\n  util_exports.assert(backend_util_exports.eitherStridesOrDilationsAreOne(strides, $dilations), () => `Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${strides} and dilations '${$dilations}'`);\n  const convInfo = backend_util_exports.computeConv2DInfo(x.shape, filter.shape, strides, $dilations, pad3, dimRoundingMode, true);\n  const { filterHeight, filterWidth, dilationHeight, dilationWidth, padInfo } = convInfo;\n  const padLeft = padInfo.left;\n  const padTop = padInfo.top;\n  const chMul = convInfo.outChannels / convInfo.inChannels;\n  const y = new TensorBuffer(convInfo.outShape, x.dtype);\n  const xVals = backend2.data.get(x.dataId).values;\n  const wVals = backend2.data.get(filter.dataId).values;\n  const yVals = y.values;\n  for (let b = 0; b < convInfo.batchSize; ++b) {\n    const xOffset1 = b * xStrides[0];\n    const yOffset1 = b * y.strides[0];\n    for (let yR = 0; yR < convInfo.outHeight; ++yR) {\n      const yOffset2 = yOffset1 + yR * y.strides[1];\n      const xRCorner = yR * convInfo.strideHeight - padTop;\n      for (let wR = 0; wR < filterHeight; ++wR) {\n        const xR = xRCorner + wR * dilationHeight;\n        if (xR < 0 || xR >= convInfo.inHeight) {\n          continue;\n        }\n        const wOffset1 = wR * filterStrides[0];\n        const xOffset2 = xOffset1 + xR * xStrides[1];\n        for (let yC = 0; yC < convInfo.outWidth; ++yC) {\n          const yOffset3 = yOffset2 + yC * y.strides[2];\n          const xCCorner = yC * convInfo.strideWidth - padLeft;\n          for (let wC = 0; wC < filterWidth; ++wC) {\n            const xC = xCCorner + wC * dilationWidth;\n            if (xC < 0 || xC >= convInfo.inWidth) {\n              continue;\n            }\n            const wOffset2 = wOffset1 + wC * filterStrides[1];\n            const xOffset3 = xOffset2 + xC * convInfo.inChannels;\n            let yOffset4 = yOffset3;\n            let wOffset3 = wOffset2;\n            for (let d1 = 0; d1 < convInfo.inChannels; ++d1) {\n              const xVal = xVals[xOffset3 + d1];\n              for (let q = 0; q < chMul; ++q) {\n                yVals[yOffset4 + q] += xVal * wVals[wOffset3 + q];\n              }\n              yOffset4 += chMul;\n              wOffset3 += chMul;\n            }\n          }\n        }\n      }\n    }\n  }\n  return backend2.makeTensorInfo(y.shape, y.dtype, y.values);\n}\nvar depthwiseConv2dNativeConfig = {\n  kernelName: DepthwiseConv2dNative,\n  backendName: \"cpu\",\n  kernelFunc: depthwiseConv2dNative\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/DepthwiseConv2dNativeBackpropFilter.js\nfunction depthwiseConv2dNativeBackpropFilter2(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x, dy } = inputs;\n  const { strides, dilations, pad: pad3, dimRoundingMode, filterShape } = attrs;\n  assertNotComplex([x, dy], \"depthwiseConv2dNativeBackpropFilter\");\n  const convInfo = backend_util_exports.computeConv2DInfo(x.shape, filterShape, strides, dilations, pad3, dimRoundingMode, true);\n  const { strideHeight, strideWidth, filterHeight, filterWidth } = convInfo;\n  const dW = new TensorBuffer(convInfo.filterShape, \"float32\");\n  const leftPad = convInfo.padInfo.left;\n  const topPad = convInfo.padInfo.top;\n  const chMul = convInfo.outChannels / convInfo.inChannels;\n  const xVals = backend2.data.get(x.dataId).values;\n  const xBuf = new TensorBuffer(x.shape, x.dtype, xVals);\n  const dyVals = backend2.data.get(dy.dataId).values;\n  const dyBuf = new TensorBuffer(dy.shape, dy.dtype, dyVals);\n  for (let wR = 0; wR < filterHeight; ++wR) {\n    const yRMin = Math.max(0, Math.ceil((topPad - wR) / strideHeight));\n    const yRMax = Math.min(convInfo.outHeight, (convInfo.inHeight + topPad - wR) / strideHeight);\n    for (let wC = 0; wC < filterWidth; ++wC) {\n      const yCMin = Math.max(0, Math.ceil((leftPad - wC) / strideWidth));\n      const yCMax = Math.min(convInfo.outWidth, (convInfo.inWidth + leftPad - wC) / strideWidth);\n      for (let d2 = 0; d2 < convInfo.outChannels; ++d2) {\n        const d1 = Math.trunc(d2 / chMul);\n        const dm = d2 % chMul;\n        let dotProd = 0;\n        for (let b = 0; b < convInfo.batchSize; ++b) {\n          for (let yR = yRMin; yR < yRMax; ++yR) {\n            const xR = wR + yR * strideHeight - topPad;\n            for (let yC = yCMin; yC < yCMax; ++yC) {\n              const xC = wC + yC * strideWidth - leftPad;\n              dotProd += xBuf.get(b, xR, xC, d1) * dyBuf.get(b, yR, yC, d2);\n            }\n          }\n        }\n        dW.set(dotProd, wR, wC, d1, dm);\n      }\n    }\n  }\n  return backend2.makeTensorInfo(dW.shape, dW.dtype, dW.values);\n}\nvar depthwiseConv2dNativeBackpropFilterConfig = {\n  kernelName: DepthwiseConv2dNativeBackpropFilter,\n  backendName: \"cpu\",\n  kernelFunc: depthwiseConv2dNativeBackpropFilter2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/DepthwiseConv2dNativeBackpropInput.js\nfunction depthwiseConv2dNativeBackpropInput2(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { dy, filter } = inputs;\n  const { strides, dilations, pad: pad3, dimRoundingMode, inputShape } = attrs;\n  assertNotComplex([dy, filter], \"depthwiseConv2DNativeBackpropInput\");\n  const dyStrides = util_exports.computeStrides(dy.shape);\n  const filterStrides = util_exports.computeStrides(filter.shape);\n  const convInfo = backend_util_exports.computeConv2DInfo(inputShape, filter.shape, strides, dilations, pad3, dimRoundingMode, true);\n  const dx = new TensorBuffer(convInfo.inShape, \"float32\");\n  const dxValues = dx.values;\n  const [dxS0, dxS1, dxS2] = dx.strides;\n  const dyValues = backend2.data.get(dy.dataId).values;\n  const [dyS0, dyS1, dyS2] = dyStrides;\n  const fltValues = backend2.data.get(filter.dataId).values;\n  const [fltS0, fltS1, fltS2] = filterStrides;\n  const { batchSize, filterHeight, filterWidth, inChannels, inHeight, inWidth, outChannels, outHeight, outWidth, strideHeight, strideWidth } = convInfo;\n  const topPad = filterHeight - 1 - convInfo.padInfo.top;\n  const leftPad = filterWidth - 1 - convInfo.padInfo.left;\n  const chMul = outChannels / inChannels;\n  for (let b = 0; b < batchSize; ++b) {\n    for (let d1 = 0; d1 < inChannels; ++d1) {\n      for (let xR = 0; xR < inHeight; ++xR) {\n        const xRCorner = xR - topPad;\n        const xRMin = Math.max(0, Math.ceil(xRCorner / strideHeight));\n        const yRMax = Math.min(outHeight, (filterHeight + xRCorner) / strideHeight);\n        for (let xC = 0; xC < inWidth; ++xC) {\n          const xCCorner = xC - leftPad;\n          const xCMin = Math.max(0, Math.ceil(xCCorner / strideWidth));\n          const yCMax = Math.min(outWidth, (filterWidth + xCCorner) / strideWidth);\n          let dotProd = 0;\n          for (let yR = xRMin; yR < yRMax; ++yR) {\n            const wR = yR * strideHeight - xRCorner;\n            for (let yC = xCMin; yC < yCMax; ++yC) {\n              const wC = yC * strideWidth - xCCorner;\n              const dyOffset = dyS0 * b + dyS1 * yR + dyS2 * yC;\n              const fltOffset = fltS0 * (filterHeight - 1 - wR) + fltS1 * (filterWidth - 1 - wC) + fltS2 * d1;\n              for (let dm = 0; dm < chMul; ++dm) {\n                const d2 = d1 * chMul + dm;\n                const pixel = dyValues[dyOffset + d2];\n                const weight = fltValues[fltOffset + dm];\n                dotProd += pixel * weight;\n              }\n            }\n          }\n          dxValues[dxS0 * b + dxS1 * xR + dxS2 * xC + d1] = dotProd;\n        }\n      }\n    }\n  }\n  return backend2.makeTensorInfo(dx.shape, dx.dtype, dx.values);\n}\nvar depthwiseConv2dNativeBackpropInputConfig = {\n  kernelName: DepthwiseConv2dNativeBackpropInput,\n  backendName: \"cpu\",\n  kernelFunc: depthwiseConv2dNativeBackpropInput2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Diag.js\nfunction diag2(args) {\n  const { inputs, backend: backend2 } = args;\n  const { x } = inputs;\n  const xSize = util_exports.sizeFromShape(x.shape);\n  const xVals = backend2.data.get(x.dataId).values;\n  const outBuf = buffer([xSize, xSize], x.dtype);\n  const vals = outBuf.values;\n  for (let i = 0; i < xVals.length; i++) {\n    vals[i * xSize + i] = xVals[i];\n  }\n  const outShape = [...x.shape, ...x.shape];\n  return backend2.makeTensorInfo(outShape, outBuf.dtype, outBuf.values);\n}\nvar diagConfig = {\n  kernelName: Diag,\n  backendName: \"cpu\",\n  kernelFunc: diag2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Dilation2D.js\nvar dilation2DConfig = {\n  kernelName: Dilation2D,\n  backendName: \"cpu\",\n  kernelFunc: ({ inputs, backend: backend2, attrs }) => {\n    const { x, filter } = inputs;\n    const { strides, pad: pad3, dilations } = attrs;\n    const cpuBackend = backend2;\n    const xVals = cpuBackend.data.get(x.dataId).values;\n    const xRank = x.shape.length;\n    const filterVals = cpuBackend.data.get(filter.dataId).values;\n    const filterRank = filter.shape.length;\n    const { batchSize, inHeight, inWidth, inChannels, outHeight, outWidth, padInfo, strideHeight, strideWidth, filterHeight, filterWidth, dilationHeight, dilationWidth, outShape } = backend_util_exports.computeDilation2DInfo(x.shape, filter.shape, strides, pad3, \"NHWC\", dilations);\n    const outSize = util_exports.sizeFromShape(outShape);\n    const outRank = outShape.length;\n    const outputVals = util_exports.getArrayFromDType(x.dtype, outSize);\n    for (let b = 0; b < batchSize; ++b) {\n      for (let hOut = 0; hOut < outHeight; ++hOut) {\n        const hBeg = hOut * strideHeight - padInfo.top;\n        for (let wOut = 0; wOut < outWidth; ++wOut) {\n          const wBeg = wOut * strideWidth - padInfo.left;\n          for (let d = 0; d < inChannels; ++d) {\n            let curVal = Number.MIN_SAFE_INTEGER;\n            for (let h = 0; h < filterHeight; ++h) {\n              const hIn = hBeg + h * dilationHeight;\n              if (hIn >= 0 && hIn < inHeight) {\n                for (let w = 0; w < filterWidth; ++w) {\n                  const wIn = wBeg + w * dilationWidth;\n                  if (wIn >= 0 && wIn < inWidth) {\n                    const xIndex = util_exports.locToIndex([b, hIn, wIn, d], xRank, util_exports.computeStrides(x.shape));\n                    const filterIndex = util_exports.locToIndex([h, w, d], filterRank, util_exports.computeStrides(filter.shape));\n                    const val = xVals[xIndex] + filterVals[filterIndex];\n                    if (val > curVal) {\n                      curVal = val;\n                    }\n                  }\n                }\n              }\n            }\n            const outputIndex = util_exports.locToIndex([b, hOut, wOut, d], outRank, util_exports.computeStrides(outShape));\n            outputVals[outputIndex] = curVal;\n          }\n        }\n      }\n    }\n    const dataId = cpuBackend.write(util_exports.toTypedArray(outputVals, x.dtype), outShape, x.dtype);\n    return { dataId, shape: outShape, dtype: x.dtype };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Dilation2DBackpropFilter.js\nvar dilation2DBackpropFilterConfig = {\n  kernelName: Dilation2DBackpropFilter,\n  backendName: \"cpu\",\n  kernelFunc: ({ inputs, backend: backend2, attrs }) => {\n    const { x, filter, dy } = inputs;\n    const { strides, pad: pad3, dilations } = attrs;\n    const cpuBackend = backend2;\n    const $x = util_exports.toNestedArray(x.shape, cpuBackend.data.get(x.dataId).values);\n    const $filter = util_exports.toNestedArray(filter.shape, cpuBackend.data.get(filter.dataId).values);\n    const { batchSize, inHeight, inWidth, inChannels, outHeight, outWidth, padInfo, strideHeight, strideWidth, filterHeight, filterWidth, dilationHeight, dilationWidth, outShape } = backend_util_exports.computeDilation2DInfo(x.shape, filter.shape, strides, pad3, \"NHWC\", dilations);\n    util_exports.assert(dy.rank === outShape.length, () => `Error in ${Dilation2DBackpropFilter}, dy must have the same rank as output ${outShape.length}, but got ${dy.rank}`);\n    const $dy = util_exports.toNestedArray(outShape, cpuBackend.data.get(dy.dataId).values);\n    const gradients = util_exports.makeZerosNestedTypedArray(filter.shape, filter.dtype);\n    for (let b = 0; b < batchSize; ++b) {\n      for (let hOut = 0; hOut < outHeight; ++hOut) {\n        const hBeg = hOut * strideHeight - padInfo.top;\n        for (let wOut = 0; wOut < outWidth; ++wOut) {\n          const wBeg = wOut * strideWidth - padInfo.left;\n          for (let d = 0; d < inChannels; ++d) {\n            let curVal = Number.MIN_SAFE_INTEGER;\n            let hMax = 0;\n            let wMax = 0;\n            for (let h = 0; h < filterHeight; ++h) {\n              const hIn = hBeg + h * dilationHeight;\n              if (hIn >= 0 && hIn < inHeight) {\n                for (let w = 0; w < filterWidth; ++w) {\n                  const wIn = wBeg + w * dilationWidth;\n                  if (wIn >= 0 && wIn < inWidth) {\n                    const val = $x[b][hIn][wIn][d] + $filter[h][w][d];\n                    if (val > curVal) {\n                      curVal = val;\n                      hMax = h;\n                      wMax = w;\n                    }\n                  }\n                }\n              }\n            }\n            gradients[hMax][wMax][d] += $dy[b][hOut][wOut][d];\n          }\n        }\n      }\n    }\n    const dataId = cpuBackend.write(util_exports.toTypedArray(gradients, x.dtype), filter.shape, filter.dtype);\n    return { dataId, shape: filter.shape, dtype: filter.dtype };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Dilation2DBackpropInput.js\nvar dilation2DBackpropInputConfig = {\n  kernelName: Dilation2DBackpropInput,\n  backendName: \"cpu\",\n  kernelFunc: ({ inputs, backend: backend2, attrs }) => {\n    const { x, filter, dy } = inputs;\n    const { strides, pad: pad3, dilations } = attrs;\n    const cpuBackend = backend2;\n    const $x = util_exports.toNestedArray(x.shape, cpuBackend.data.get(x.dataId).values);\n    const $filter = util_exports.toNestedArray(filter.shape, cpuBackend.data.get(filter.dataId).values);\n    const { batchSize, inHeight, inWidth, inChannels, outHeight, outWidth, padInfo, strideHeight, strideWidth, filterHeight, filterWidth, dilationHeight, dilationWidth, outShape } = backend_util_exports.computeDilation2DInfo(x.shape, filter.shape, strides, pad3, \"NHWC\", dilations);\n    util_exports.assert(dy.rank === outShape.length, () => `Error in ${Dilation2DBackpropInput}, dy must have the same rank as output ${outShape.length}, but got ${dy.rank}`);\n    const $dy = util_exports.toNestedArray(outShape, cpuBackend.data.get(dy.dataId).values);\n    const gradients = util_exports.makeZerosNestedTypedArray(x.shape, x.dtype);\n    for (let b = 0; b < batchSize; ++b) {\n      for (let hOut = 0; hOut < outHeight; ++hOut) {\n        const hBeg = hOut * strideHeight - padInfo.top;\n        for (let wOut = 0; wOut < outWidth; ++wOut) {\n          const wBeg = wOut * strideWidth - padInfo.left;\n          for (let d = 0; d < inChannels; ++d) {\n            let curVal = Number.MIN_SAFE_INTEGER;\n            let hInMax = hBeg < 0 ? 0 : hBeg;\n            let wInMax = wBeg < 0 ? 0 : wBeg;\n            for (let h = 0; h < filterHeight; ++h) {\n              const hIn = hBeg + h * dilationHeight;\n              if (hIn >= 0 && hIn < inHeight) {\n                for (let w = 0; w < filterWidth; ++w) {\n                  const wIn = wBeg + w * dilationWidth;\n                  if (wIn >= 0 && wIn < inWidth) {\n                    const val = $x[b][hIn][wIn][d] + $filter[h][w][d];\n                    if (val > curVal) {\n                      curVal = val;\n                      hInMax = hIn;\n                      wInMax = wIn;\n                    }\n                  }\n                }\n              }\n            }\n            gradients[b][hInMax][wInMax][d] += $dy[b][hOut][wOut][d];\n          }\n        }\n      }\n    }\n    const dataId = cpuBackend.write(util_exports.toTypedArray(gradients, x.dtype), x.shape, x.dtype);\n    return { dataId, shape: x.shape, dtype: x.dtype };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Sum.js\nfunction sum3(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x } = inputs;\n  const { axis, keepDims } = attrs;\n  assertNotComplex(x, \"sum\");\n  let $x;\n  if (x.dtype === \"bool\") {\n    $x = cast3({ inputs: { x }, backend: backend2, attrs: { dtype: \"int32\" } });\n  } else {\n    $x = identity2({ inputs: { x }, backend: backend2 });\n  }\n  const xRank = $x.shape.length;\n  const axes = util_exports.parseAxisParam(axis, $x.shape);\n  const permutation = backend_util_exports.getAxesPermutation(axes, xRank);\n  let reductionAxes = axes;\n  let permutedX = $x;\n  if (permutation != null) {\n    permutedX = transpose2({ inputs: { x: $x }, backend: backend2, attrs: { perm: permutation } });\n    reductionAxes = backend_util_exports.getInnerMostAxes(reductionAxes.length, xRank);\n  }\n  backend_util_exports.assertAxesAreInnerMostDims(\"sum\", reductionAxes, permutedX.shape.length);\n  const [outShape, reduceShape] = backend_util_exports.computeOutAndReduceShapes(permutedX.shape, reductionAxes);\n  const resultDtype = backend_util_exports.upcastType(permutedX.dtype, \"int32\");\n  let result = zeros3(backend2, outShape, resultDtype);\n  const reduceSize = util_exports.sizeFromShape(reduceShape);\n  const vals = backend2.data.get(result.dataId).values;\n  const aVals = backend2.data.get(permutedX.dataId).values;\n  for (let i = 0; i < vals.length; ++i) {\n    const offset = i * reduceSize;\n    let sum7 = 0;\n    for (let j = 0; j < reduceSize; ++j) {\n      sum7 += aVals[offset + j];\n    }\n    vals[i] = sum7;\n  }\n  if (keepDims) {\n    const newShape = backend_util_exports.expandShapeToKeepDim(result.shape, axes);\n    const oldResult = result;\n    result = reshape3({ inputs: { x: result }, backend: backend2, attrs: { shape: newShape } });\n    backend2.disposeIntermediateTensorInfo(oldResult);\n  }\n  backend2.disposeIntermediateTensorInfo($x);\n  if (permutation != null) {\n    backend2.disposeIntermediateTensorInfo(permutedX);\n  }\n  return result;\n}\nvar sumConfig = {\n  kernelName: Sum,\n  backendName: \"cpu\",\n  kernelFunc: sum3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Einsum.js\nfunction einsum2(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { equation } = attrs;\n  const tensors = inputs;\n  const { allDims, summedDims, idDims } = backend_util_exports.decodeEinsumEquation(equation, tensors.length);\n  backend_util_exports.checkEinsumDimSizes(allDims.length, idDims, tensors);\n  const { path, steps } = backend_util_exports.getEinsumComputePath(summedDims, idDims);\n  const nSteps = steps.length;\n  let out = null;\n  let numDimsRemaining = allDims.length;\n  const tensorsToDispose = [];\n  for (let i = 0; i < nSteps; ++i) {\n    for (const idTerm of steps[i]) {\n      const { permutationIndices: perm, expandDims: dimsToExpand } = backend_util_exports.getEinsumPermutation(numDimsRemaining, idDims[idTerm]);\n      let x;\n      if (backend_util_exports.isIdentityPermutation(perm)) {\n        x = tensors[idTerm];\n      } else {\n        x = transpose2({ inputs: { x: tensors[idTerm] }, backend: backend2, attrs: { perm } });\n        tensorsToDispose.push(x);\n      }\n      const targetShape = x.shape.slice();\n      for (let k = 0; k < dimsToExpand.length; ++k) {\n        targetShape.splice(dimsToExpand[k], 0, 1);\n      }\n      if (!util_exports.arraysEqual(x.shape, targetShape)) {\n        x = reshape3({ inputs: { x }, backend: backend2, attrs: { shape: targetShape } });\n        tensorsToDispose.push(x);\n      }\n      if (out === null) {\n        out = x;\n      } else {\n        out = multiply2({ inputs: { a: x, b: out }, backend: backend2 });\n        tensorsToDispose.push(out);\n      }\n    }\n    if (i < nSteps - 1) {\n      if (path[i] >= 0) {\n        out = sum3({\n          inputs: { x: out },\n          backend: backend2,\n          attrs: {\n            axis: path[i] - (allDims.length - numDimsRemaining),\n            keepDims: false\n          }\n        });\n        tensorsToDispose.push(out);\n      }\n      numDimsRemaining--;\n    }\n  }\n  for (const tensorInfo of tensorsToDispose) {\n    if (tensorInfo === out) {\n      continue;\n    }\n    backend2.disposeIntermediateTensorInfo(tensorInfo);\n  }\n  return out;\n}\nvar einsumConfig = {\n  kernelName: Einsum,\n  backendName: \"cpu\",\n  kernelFunc: einsum2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/EluGrad.js\nfunction eluGrad(args) {\n  const { inputs, backend: backend2 } = args;\n  const { dy, y } = inputs;\n  assertNotComplex([dy, y], \"eluGrad\");\n  const resultValues = new Float32Array(util_exports.sizeFromShape(y.shape));\n  const values = backend2.data.get(y.dataId).values;\n  const dyValues = backend2.data.get(dy.dataId).values;\n  for (let i = 0; i < values.length; ++i) {\n    const v = values[i];\n    if (v >= 1) {\n      resultValues[i] = dyValues[i];\n    } else {\n      resultValues[i] = dyValues[i] * (v + 1);\n    }\n  }\n  return backend2.makeTensorInfo(y.shape, \"float32\", resultValues);\n}\nvar eluGradConfig2 = {\n  kernelName: EluGrad,\n  backendName: \"cpu\",\n  kernelFunc: eluGrad\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Erf.js\nvar p = backend_util_exports.ERF_P;\nvar a1 = backend_util_exports.ERF_A1;\nvar a2 = backend_util_exports.ERF_A2;\nvar a3 = backend_util_exports.ERF_A3;\nvar a4 = backend_util_exports.ERF_A4;\nvar a5 = backend_util_exports.ERF_A5;\nvar erf2 = unaryKernelFunc(Erf, (xi) => {\n  const sign4 = Math.sign(xi);\n  const v = Math.abs(xi);\n  const t = 1 / (1 + p * v);\n  return sign4 * (1 - ((((a5 * t + a4) * t + a3) * t + a2) * t + a1) * t * Math.exp(-v * v));\n});\nvar erfConfig = {\n  kernelName: Erf,\n  backendName: \"cpu\",\n  kernelFunc: erf2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/ExpandDims.js\nfunction expandDims3(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { input: input2 } = inputs;\n  const { dim } = attrs;\n  const inputRank = input2.shape.length;\n  const newShape = input2.shape.slice();\n  let $dim = dim;\n  if (dim < 0) {\n    util_exports.assert(-(inputRank + 1) <= dim, () => `Axis must be in the interval [${-(inputRank + 1)}, ${inputRank}]`);\n    $dim = inputRank + dim + 1;\n  }\n  newShape.splice($dim, 0, 1);\n  return reshape3({ inputs: { x: input2 }, backend: backend2, attrs: { shape: newShape } });\n}\nvar expandDimsConfig = {\n  kernelName: ExpandDims,\n  backendName: \"cpu\",\n  kernelFunc: expandDims3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/RealDiv.js\nvar realDivImpl = createSimpleBinaryKernelImpl((a, b) => a / b);\nvar div2 = binaryKernelFunc(RealDiv, realDivImpl);\nvar realDivConfig = {\n  kernelName: RealDiv,\n  backendName: \"cpu\",\n  kernelFunc: div2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/utils/fft_utils.js\nfunction fftBatch(input2, inverse, cpuBackend) {\n  const inputShape = input2.shape;\n  const batch = inputShape[0];\n  const innerDim = inputShape[1];\n  const inputVals = cpuBackend.data.get(input2.dataId);\n  const real2D = inputVals.complexTensorInfos.real;\n  const imag2D = inputVals.complexTensorInfos.imag;\n  const resultShape = [batch, innerDim];\n  const resultSize = util_exports.sizeFromShape(resultShape);\n  const resultReal = util_exports.getTypedArrayFromDType(\"float32\", resultSize);\n  const resultImag = util_exports.getTypedArrayFromDType(\"float32\", resultSize);\n  for (let b = 0; b < batch; b++) {\n    const r = slice2({\n      inputs: { x: real2D },\n      backend: cpuBackend,\n      attrs: { begin: [b, 0], size: [1, innerDim] }\n    });\n    const i = slice2({\n      inputs: { x: imag2D },\n      backend: cpuBackend,\n      attrs: { begin: [b, 0], size: [1, innerDim] }\n    });\n    const input3 = complex2({ inputs: { real: r, imag: i }, backend: cpuBackend });\n    const { real: real5, imag: imag5 } = fftImpl(input3, inverse, cpuBackend);\n    const res = backend_util_exports.mergeRealAndImagArrays(real5, imag5);\n    for (let d = 0; d < innerDim; d++) {\n      const c = backend_util_exports.getComplexWithIndex(res, d);\n      resultReal[b * innerDim + d] = c.real;\n      resultImag[b * innerDim + d] = c.imag;\n    }\n    cpuBackend.disposeIntermediateTensorInfo(r);\n    cpuBackend.disposeIntermediateTensorInfo(i);\n    cpuBackend.disposeIntermediateTensorInfo(input3);\n  }\n  const $realInfo = cpuBackend.makeTensorInfo(resultShape, \"float32\", resultReal);\n  const $imagInfo = cpuBackend.makeTensorInfo(resultShape, \"float32\", resultImag);\n  const result = complex2({ inputs: { real: $realInfo, imag: $imagInfo }, backend: cpuBackend });\n  cpuBackend.disposeIntermediateTensorInfo($realInfo);\n  cpuBackend.disposeIntermediateTensorInfo($imagInfo);\n  return result;\n}\nfunction fftImpl(input2, inverse, cpuBackend) {\n  const inputSize = util_exports.sizeFromShape(input2.shape);\n  const inputVals = cpuBackend.data.get(input2.dataId);\n  const realVals = cpuBackend.data.get(inputVals.complexTensorInfos.real.dataId).values;\n  const imagVals = cpuBackend.data.get(inputVals.complexTensorInfos.imag.dataId).values;\n  if (isExponentOf2(inputSize)) {\n    const result = fftRadix2(realVals, imagVals, inputSize, inverse, cpuBackend);\n    const resultShape = [input2.shape[0], input2.shape[1]];\n    if (inverse) {\n      const realInfo = cpuBackend.makeTensorInfo(resultShape, \"float32\", result.real);\n      const imagInfo = cpuBackend.makeTensorInfo(resultShape, \"float32\", result.imag);\n      const sizeInfo = cpuBackend.makeTensorInfo([], \"float32\", util_exports.createScalarValue(inputSize, \"float32\"));\n      const sizeInfoCopy = identity2({ inputs: { x: sizeInfo }, backend: cpuBackend });\n      const divRealInfo = realDivConfig.kernelFunc({ inputs: { a: realInfo, b: sizeInfo }, backend: cpuBackend });\n      const divImagInfo = realDivConfig.kernelFunc({ inputs: { a: imagInfo, b: sizeInfoCopy }, backend: cpuBackend });\n      const divRealVals = cpuBackend.data.get(divRealInfo.dataId).values;\n      const divImagVals = cpuBackend.data.get(divImagInfo.dataId).values;\n      cpuBackend.disposeIntermediateTensorInfo(realInfo);\n      cpuBackend.disposeIntermediateTensorInfo(imagInfo);\n      cpuBackend.disposeIntermediateTensorInfo(sizeInfo);\n      cpuBackend.disposeIntermediateTensorInfo(sizeInfoCopy);\n      cpuBackend.disposeIntermediateTensorInfo(divRealInfo);\n      cpuBackend.disposeIntermediateTensorInfo(divImagInfo);\n      return { real: divRealVals, imag: divImagVals };\n    }\n    return result;\n  } else {\n    const data = backend_util_exports.mergeRealAndImagArrays(realVals, imagVals);\n    const rawOutput = fourierTransformByMatmul(data, inputSize, inverse);\n    return backend_util_exports.splitRealAndImagArrays(rawOutput);\n  }\n}\nfunction isExponentOf2(size) {\n  return (size & size - 1) === 0;\n}\nfunction fftRadix2(realVals, imagVals, size, inverse, cpuBackend) {\n  if (size === 1) {\n    return { real: realVals, imag: imagVals };\n  }\n  const data = backend_util_exports.mergeRealAndImagArrays(realVals, imagVals);\n  const half = size / 2;\n  const evenComplex = backend_util_exports.complexWithEvenIndex(data);\n  const evenRealVals = evenComplex.real;\n  const evenImagVals = evenComplex.imag;\n  const evenShape = [evenRealVals.length];\n  const evenRealInfo = cpuBackend.makeTensorInfo(evenShape, \"float32\", evenRealVals);\n  const evenImagInfo = cpuBackend.makeTensorInfo(evenShape, \"float32\", evenImagVals);\n  const evenTensorInfo = complex2({ inputs: { real: evenRealInfo, imag: evenImagInfo }, backend: cpuBackend });\n  const oddComplex = backend_util_exports.complexWithOddIndex(data);\n  const oddRealVals = oddComplex.real;\n  const oddImagVals = oddComplex.imag;\n  const oddShape = [oddRealVals.length];\n  const oddRealInfo = cpuBackend.makeTensorInfo(oddShape, \"float32\", oddRealVals);\n  const oddImagInfo = cpuBackend.makeTensorInfo(oddShape, \"float32\", oddImagVals);\n  const oddTensorInfo = complex2({ inputs: { real: oddRealInfo, imag: oddImagInfo }, backend: cpuBackend });\n  const $evenComplex = fftRadix2(evenRealVals, evenImagVals, half, inverse, cpuBackend);\n  const $evenRealVals = $evenComplex.real;\n  const $evenImagVals = $evenComplex.imag;\n  const $evenShape = [$evenRealVals.length];\n  const $evenRealInfo = cpuBackend.makeTensorInfo($evenShape, \"float32\", $evenRealVals);\n  const $evenImagInfo = cpuBackend.makeTensorInfo($evenShape, \"float32\", $evenImagVals);\n  const $evenTensorInfo = complex2({\n    inputs: { real: $evenRealInfo, imag: $evenImagInfo },\n    backend: cpuBackend\n  });\n  const $oddComplex = fftRadix2(oddRealVals, oddImagVals, half, inverse, cpuBackend);\n  const $oddRealVals = $oddComplex.real;\n  const $oddImagVals = $oddComplex.imag;\n  const $oddShape = [$oddRealVals.length];\n  const $oddRealInfo = cpuBackend.makeTensorInfo($oddShape, \"float32\", $oddRealVals);\n  const $oddImagInfo = cpuBackend.makeTensorInfo($oddShape, \"float32\", $oddImagVals);\n  const $oddTensorInfo = complex2({ inputs: { real: $oddRealInfo, imag: $oddImagInfo }, backend: cpuBackend });\n  const e = backend_util_exports.exponents(size, inverse);\n  const eShape = [e.real.length];\n  const eRealInfo = cpuBackend.makeTensorInfo(eShape, \"float32\", e.real);\n  const eImagInfo = cpuBackend.makeTensorInfo(eShape, \"float32\", e.imag);\n  const complexInfo = complex2({ inputs: { real: eRealInfo, imag: eImagInfo }, backend: cpuBackend });\n  const exponentInfo = multiply2({ inputs: { a: complexInfo, b: $oddTensorInfo }, backend: cpuBackend });\n  const addPart = add4({\n    inputs: { a: $evenTensorInfo, b: exponentInfo },\n    backend: cpuBackend\n  });\n  const subPart = sub2({\n    inputs: { a: $evenTensorInfo, b: exponentInfo },\n    backend: cpuBackend\n  });\n  const addPartReal = real2({ inputs: { input: addPart }, backend: cpuBackend });\n  const subPartReal = real2({ inputs: { input: subPart }, backend: cpuBackend });\n  const addPartImag = imag2({ inputs: { input: addPart }, backend: cpuBackend });\n  const subPartImag = imag2({ inputs: { input: subPart }, backend: cpuBackend });\n  const $real = concat2({\n    inputs: [addPartReal, subPartReal],\n    backend: cpuBackend,\n    attrs: { axis: 0 }\n  });\n  const $imag = concat2({\n    inputs: [addPartImag, subPartImag],\n    backend: cpuBackend,\n    attrs: { axis: 0 }\n  });\n  const $realVals = cpuBackend.data.get($real.dataId).values;\n  const $imagVals = cpuBackend.data.get($imag.dataId).values;\n  cpuBackend.disposeIntermediateTensorInfo(evenRealInfo);\n  cpuBackend.disposeIntermediateTensorInfo(evenImagInfo);\n  cpuBackend.disposeIntermediateTensorInfo(evenTensorInfo);\n  cpuBackend.disposeIntermediateTensorInfo(oddRealInfo);\n  cpuBackend.disposeIntermediateTensorInfo(oddImagInfo);\n  cpuBackend.disposeIntermediateTensorInfo(oddTensorInfo);\n  cpuBackend.disposeIntermediateTensorInfo($evenRealInfo);\n  cpuBackend.disposeIntermediateTensorInfo($evenImagInfo);\n  cpuBackend.disposeIntermediateTensorInfo($evenTensorInfo);\n  cpuBackend.disposeIntermediateTensorInfo($oddRealInfo);\n  cpuBackend.disposeIntermediateTensorInfo($oddImagInfo);\n  cpuBackend.disposeIntermediateTensorInfo($oddTensorInfo);\n  cpuBackend.disposeIntermediateTensorInfo(eRealInfo);\n  cpuBackend.disposeIntermediateTensorInfo(eImagInfo);\n  cpuBackend.disposeIntermediateTensorInfo(complexInfo);\n  cpuBackend.disposeIntermediateTensorInfo(exponentInfo);\n  cpuBackend.disposeIntermediateTensorInfo(addPart);\n  cpuBackend.disposeIntermediateTensorInfo(subPart);\n  cpuBackend.disposeIntermediateTensorInfo(addPartReal);\n  cpuBackend.disposeIntermediateTensorInfo(addPartImag);\n  cpuBackend.disposeIntermediateTensorInfo(subPartReal);\n  cpuBackend.disposeIntermediateTensorInfo(subPartImag);\n  cpuBackend.disposeIntermediateTensorInfo($real);\n  cpuBackend.disposeIntermediateTensorInfo($imag);\n  return { real: $realVals, imag: $imagVals };\n}\nfunction fourierTransformByMatmul(data, size, inverse) {\n  const ret = new Float32Array(size * 2);\n  for (let r = 0; r < size; r++) {\n    let real5 = 0;\n    let imag5 = 0;\n    for (let c = 0; c < size; c++) {\n      const e = backend_util_exports.exponent(r * c, size, inverse);\n      const term = backend_util_exports.getComplexWithIndex(data, c);\n      real5 += term.real * e.real - term.imag * e.imag;\n      imag5 += term.real * e.imag + term.imag * e.real;\n    }\n    if (inverse) {\n      real5 /= size;\n      imag5 /= size;\n    }\n    backend_util_exports.assignToTypedArray(ret, real5, imag5, r);\n  }\n  return ret;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/FFT.js\nfunction fft2(args) {\n  const { inputs, backend: backend2 } = args;\n  const { input: input2 } = inputs;\n  const inputSize = util_exports.sizeFromShape(input2.shape);\n  const innerDimensionSize = input2.shape[input2.shape.length - 1];\n  const batch = inputSize / innerDimensionSize;\n  const input2D = reshape3({\n    inputs: { x: input2 },\n    backend: backend2,\n    attrs: { shape: [batch, innerDimensionSize] }\n  });\n  const result = fftBatch(input2D, false, backend2);\n  const resultReshaped = reshape3({ inputs: { x: result }, backend: backend2, attrs: { shape: input2.shape } });\n  backend2.disposeIntermediateTensorInfo(input2D);\n  backend2.disposeIntermediateTensorInfo(result);\n  return resultReshaped;\n}\nvar fftConfig = {\n  kernelName: FFT,\n  backendName: \"cpu\",\n  kernelFunc: fft2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Fill.js\nfunction fill2(args) {\n  const { backend: backend2, attrs } = args;\n  const { shape, value, dtype } = attrs;\n  const $dtype = dtype || util_exports.inferDtype(value);\n  const values = util_exports.getArrayFromDType($dtype, util_exports.sizeFromShape(shape));\n  fillValues(values, value, $dtype);\n  return backend2.makeTensorInfo(shape, $dtype, values);\n}\nvar fillConfig = {\n  kernelName: Fill,\n  backendName: \"cpu\",\n  kernelFunc: fill2\n};\nfunction fillValues(values, value, dtype) {\n  if (dtype === \"string\") {\n    values.fill(value);\n  } else {\n    values.fill(value);\n  }\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/FlipLeftRight.js\nvar flipLeftRightConfig = {\n  kernelName: FlipLeftRight,\n  backendName: \"cpu\",\n  kernelFunc: ({ inputs, attrs, backend: backend2 }) => {\n    const { image: image2 } = inputs;\n    const cpuBackend = backend2;\n    const output = util_exports.getTypedArrayFromDType(image2.dtype, util_exports.sizeFromShape(image2.shape));\n    const [batch, imageHeight, imageWidth, numChannels] = image2.shape;\n    const imageVals = cpuBackend.data.get(image2.dataId).values;\n    for (let batchIdx = 0; batchIdx < batch; batchIdx++) {\n      const batchOffset = batchIdx * imageWidth * imageHeight * numChannels;\n      for (let row = 0; row < imageHeight; row++) {\n        const rowOffset = row * (imageWidth * numChannels);\n        for (let col = 0; col < imageWidth; col++) {\n          const colOffset = col * numChannels;\n          for (let channel = 0; channel < numChannels; channel++) {\n            const coordX = Math.round(imageWidth - col - 1);\n            const outIdx = batchOffset + rowOffset + colOffset + channel;\n            let outputValue = imageVals[outIdx];\n            if (coordX >= 0 && coordX < imageWidth) {\n              const rotatedColOffset = coordX * numChannels;\n              const imageIdx = batchOffset + rowOffset + rotatedColOffset + channel;\n              outputValue = imageVals[imageIdx];\n            }\n            output[outIdx] = outputValue;\n          }\n        }\n      }\n    }\n    const dataId = cpuBackend.write(output, image2.shape, image2.dtype);\n    return { dataId, shape: image2.shape, dtype: image2.dtype };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/FloorDiv.js\nvar floorDivImpl = createSimpleBinaryKernelImpl((a, b) => Math.floor(a / b));\nvar floorDiv2 = binaryKernelFunc(FloorDiv, floorDivImpl, null, \"int32\");\nvar floorDivConfig = {\n  kernelName: FloorDiv,\n  backendName: \"cpu\",\n  kernelFunc: floorDiv2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/FusedConv2D.js\nfunction fusedConv2D(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x, filter, bias, preluActivationWeights } = inputs;\n  const { strides, pad: pad3, dataFormat, dilations, dimRoundingMode, activation: activation2, leakyreluAlpha } = attrs;\n  let result = conv2D({\n    inputs: { x, filter },\n    backend: backend2,\n    attrs: { strides, pad: pad3, dataFormat, dilations, dimRoundingMode }\n  });\n  if (bias) {\n    const resultOld = result;\n    if (dataFormat === \"NCHW\" && bias.shape.length === 1 && bias.shape[0] !== 1) {\n      const reshapedBias = reshape3({ inputs: { x: bias }, backend: backend2, attrs: { shape: [bias.shape[0], 1, 1] } });\n      result = add4({ inputs: { a: result, b: reshapedBias }, backend: backend2 });\n      backend2.disposeIntermediateTensorInfo(reshapedBias);\n    } else {\n      result = add4({ inputs: { a: result, b: bias }, backend: backend2 });\n    }\n    backend2.disposeIntermediateTensorInfo(resultOld);\n  }\n  if (activation2) {\n    const resultOld = result;\n    if (dataFormat === \"NCHW\" && activation2 === \"prelu\" && preluActivationWeights.shape.length === 1 && preluActivationWeights.shape[0] !== 1) {\n      const reshapedAlpha = reshape3({\n        inputs: { x: preluActivationWeights },\n        backend: backend2,\n        attrs: { shape: [preluActivationWeights.shape[0], 1, 1] }\n      });\n      result = applyActivation2(backend2, result, activation2, reshapedAlpha, leakyreluAlpha);\n      backend2.disposeIntermediateTensorInfo(reshapedAlpha);\n    } else {\n      result = applyActivation2(backend2, result, activation2, preluActivationWeights, leakyreluAlpha);\n    }\n    backend2.disposeIntermediateTensorInfo(resultOld);\n  }\n  return result;\n}\nvar fusedConv2DConfig = {\n  kernelName: FusedConv2D,\n  backendName: \"cpu\",\n  kernelFunc: fusedConv2D\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/FusedDepthwiseConv2D.js\nfunction fusedDepthwiseConv2D(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x, filter, bias, preluActivationWeights } = inputs;\n  const { strides, pad: pad3, dataFormat, dilations, dimRoundingMode, activation: activation2, leakyreluAlpha } = attrs;\n  let result = depthwiseConv2dNative({\n    inputs: { x, filter },\n    backend: backend2,\n    attrs: { strides, pad: pad3, dataFormat, dilations, dimRoundingMode }\n  });\n  if (bias) {\n    const oldResult = result;\n    result = add4({ inputs: { a: result, b: bias }, backend: backend2 });\n    backend2.disposeIntermediateTensorInfo(oldResult);\n  }\n  if (activation2) {\n    const oldResult = result;\n    result = applyActivation2(backend2, result, activation2, preluActivationWeights, leakyreluAlpha);\n    backend2.disposeIntermediateTensorInfo(oldResult);\n  }\n  return result;\n}\nvar fusedDepthwiseConv2DConfig = {\n  kernelName: FusedDepthwiseConv2D,\n  backendName: \"cpu\",\n  kernelFunc: fusedDepthwiseConv2D\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/GatherNd.js\nfunction gatherNd(args) {\n  const { inputs, backend: backend2 } = args;\n  const { params, indices } = inputs;\n  const paramsSize = util_exports.sizeFromShape(params.shape);\n  const indicesShape = indices.shape;\n  const sliceRank = indicesShape[indicesShape.length - 1];\n  const [resultShape, numSlices, sliceSize, strides] = backend_util_exports.prepareAndValidate(params, indices);\n  if (numSlices === 0) {\n    return backend2.makeTensorInfo(resultShape, params.dtype, []);\n  }\n  const indicesData = backend2.data.get(indices.dataId).values;\n  const paramsBuf = backend2.bufferSync(params);\n  const outBuf = gatherNdImpl(indicesData, paramsBuf, params.dtype, numSlices, sliceRank, sliceSize, strides, params.shape, paramsSize);\n  return backend2.makeTensorInfo(resultShape, params.dtype, outBuf.values);\n}\nvar gatherNdConfig = {\n  kernelName: GatherNd,\n  backendName: \"cpu\",\n  kernelFunc: gatherNd\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/GatherV2.js\nfunction gatherV2(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x, indices } = inputs;\n  const { axis, batchDims } = attrs;\n  assertNotComplex([x, indices], \"gatherV2\");\n  const parsedAxis = util_exports.parseAxisParam(axis, x.shape)[0];\n  const indicesVals = backend2.data.get(indices.dataId).values;\n  const axisDim = x.shape[parsedAxis];\n  for (let i = 0; i < indicesVals.length; ++i) {\n    const index = indicesVals[i];\n    util_exports.assert(index <= axisDim - 1 && index >= 0, () => `GatherV2: the index value ${index} is not in [0, ${axisDim - 1}]`);\n  }\n  let $batchDims = batchDims;\n  if (batchDims == null) {\n    $batchDims = 0;\n  }\n  const indicesSize = util_exports.sizeFromShape(indices.shape);\n  const shapeInfo = backend_util_exports.segment_util.collectGatherOpShapeInfo(x, indices, parsedAxis, $batchDims);\n  const flattenX = reshape3({\n    inputs: { x },\n    backend: backend2,\n    attrs: {\n      shape: [\n        shapeInfo.batchSize,\n        shapeInfo.outerSize,\n        shapeInfo.dimSize,\n        shapeInfo.sliceSize\n      ]\n    }\n  });\n  const flattenIndex = reshape3({\n    inputs: { x: indices },\n    backend: backend2,\n    attrs: { shape: [shapeInfo.batchSize, indicesSize / shapeInfo.batchSize] }\n  });\n  const flattenOutputShape = [\n    shapeInfo.batchSize,\n    shapeInfo.outerSize,\n    indicesSize / shapeInfo.batchSize,\n    shapeInfo.sliceSize\n  ];\n  const indicesBuf = backend2.bufferSync(flattenIndex);\n  const xBuf = backend2.bufferSync(flattenX);\n  const outBuf = gatherV2Impl(xBuf, indicesBuf, flattenOutputShape);\n  backend2.disposeIntermediateTensorInfo(flattenX);\n  backend2.disposeIntermediateTensorInfo(flattenIndex);\n  return backend2.makeTensorInfo(shapeInfo.outputShape, outBuf.dtype, outBuf.values);\n}\nvar gatherV2Config = {\n  kernelName: GatherV2,\n  backendName: \"cpu\",\n  kernelFunc: gatherV2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/IFFT.js\nfunction ifft2(args) {\n  const { inputs, backend: backend2 } = args;\n  const { input: input2 } = inputs;\n  const inputSize = util_exports.sizeFromShape(input2.shape);\n  const innerDimensionSize = input2.shape[input2.shape.length - 1];\n  const batch = inputSize / innerDimensionSize;\n  const input2D = reshape3({\n    inputs: { x: input2 },\n    backend: backend2,\n    attrs: { shape: [batch, innerDimensionSize] }\n  });\n  const result = fftBatch(input2D, true, backend2);\n  const resultReshaped = reshape3({ inputs: { x: result }, backend: backend2, attrs: { shape: input2.shape } });\n  backend2.disposeIntermediateTensorInfo(input2D);\n  backend2.disposeIntermediateTensorInfo(result);\n  return resultReshaped;\n}\nvar ifftConfig = {\n  kernelName: IFFT,\n  backendName: \"cpu\",\n  kernelFunc: ifft2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/IsFinite.js\nvar isFinite3 = unaryKernelFunc(IsFinite, (xi) => Number.isFinite(xi) ? 1 : 0, \"bool\");\nvar isFiniteConfig = {\n  kernelName: IsFinite,\n  backendName: \"cpu\",\n  kernelFunc: isFinite3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/IsInf.js\nvar isInf2 = unaryKernelFunc(IsInf, (xi) => Math.abs(xi) === Infinity ? 1 : 0, \"bool\");\nvar isInfConfig = {\n  kernelName: IsInf,\n  backendName: \"cpu\",\n  kernelFunc: isInf2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/IsNaN.js\nvar isNaN3 = unaryKernelFunc(IsNan, (xi) => Number.isNaN(xi) ? 1 : 0, \"bool\");\nvar isNaNConfig = {\n  kernelName: IsNan,\n  backendName: \"cpu\",\n  kernelFunc: isNaN3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/LinSpace.js\nfunction linSpace(args) {\n  const { backend: backend2, attrs } = args;\n  const { start, stop, num } = attrs;\n  const outVals = linSpaceImpl(start, stop, num);\n  return backend2.makeTensorInfo([outVals.length], \"float32\", outVals);\n}\nvar linSpaceConfig = {\n  kernelName: LinSpace,\n  backendName: \"cpu\",\n  kernelFunc: linSpace\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Log1p.js\nvar log1p2 = unaryKernelFunc(Log1p, (xi) => Math.log1p(xi));\nvar log1pConfig = {\n  kernelName: Log1p,\n  backendName: \"cpu\",\n  kernelFunc: log1p2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/LogicalAnd.js\nvar logicalAndImpl = createSimpleBinaryKernelImpl((a, b) => a && b);\nvar logicalAnd2 = binaryKernelFunc(LogicalAnd, logicalAndImpl, null, \"bool\");\nvar logicalAndConfig = {\n  kernelName: LogicalAnd,\n  backendName: \"cpu\",\n  kernelFunc: logicalAnd2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/LogicalNot.js\nvar logicalNot2 = unaryKernelFunc(LogicalNot, (xi) => xi ? 0 : 1, \"bool\");\nvar logicalNotConfig = {\n  kernelName: LogicalNot,\n  backendName: \"cpu\",\n  kernelFunc: logicalNot2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/LogicalOr.js\nvar logicalOrImpl = createSimpleBinaryKernelImpl((a, b) => a || b);\nvar logicalOr2 = binaryKernelFunc(LogicalOr, logicalOrImpl, null, \"bool\");\nvar logicalOrConfig = {\n  kernelName: LogicalOr,\n  backendName: \"cpu\",\n  kernelFunc: logicalOr2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/LRN.js\nfunction lRN(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x } = inputs;\n  const { depthRadius, bias, alpha, beta } = attrs;\n  assertNotComplex(x, \"LRN\");\n  const channels = x.shape[3];\n  const maxD = channels - 1;\n  const xValues = backend2.data.get(x.dataId).values;\n  const size = util_exports.sizeFromShape(x.shape);\n  const result = new Float32Array(size);\n  function sumAcrossChannels(offset) {\n    const currentChannel = offset % channels;\n    let beginSumOffset = offset - currentChannel + Math.max(0, currentChannel - depthRadius);\n    const endSumOffset = offset - currentChannel + Math.min(currentChannel + depthRadius, maxD);\n    let sum7 = 0;\n    for (; beginSumOffset <= endSumOffset; beginSumOffset++) {\n      const z = xValues[beginSumOffset];\n      sum7 += z * z;\n    }\n    return sum7;\n  }\n  for (let offset = 0; offset < size; offset++) {\n    const sum7 = sumAcrossChannels(offset);\n    const val = xValues[offset] * Math.pow(bias + alpha * sum7, -beta);\n    result[offset] = val;\n  }\n  return backend2.makeTensorInfo(x.shape, x.dtype, result);\n}\nvar LRNConfig = {\n  kernelName: LRN,\n  backendName: \"cpu\",\n  kernelFunc: lRN\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/LRNGrad.js\nfunction lRNGrad(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x, y, dy } = inputs;\n  const { depthRadius, bias, alpha, beta } = attrs;\n  assertNotComplex(dy, \"LRNGrad\");\n  const dySize = util_exports.sizeFromShape(dy.shape);\n  const channels = dy.shape[3];\n  const dyValues = backend2.data.get(dy.dataId).values;\n  const xValues = backend2.data.get(x.dataId).values;\n  const yValues = backend2.data.get(y.dataId).values;\n  const result = new Float32Array(dySize);\n  const size = dySize;\n  for (let offset = 0; offset < size; offset++) {\n    const currentChannel = offset % channels;\n    const depthBegin = offset - currentChannel + Math.max(0, currentChannel - depthRadius);\n    const depthEnd = offset - currentChannel + Math.min(channels, currentChannel + depthRadius + 1);\n    let norm2 = 0;\n    for (let k = depthBegin; k < depthEnd; k++) {\n      norm2 += Math.pow(xValues[k], 2);\n    }\n    norm2 = alpha * norm2 + bias;\n    for (let k = depthBegin; k < depthEnd; k++) {\n      let dyi = -2 * alpha * beta * xValues[k] * yValues[offset] / norm2;\n      if (offset === k) {\n        dyi += Math.pow(norm2, -beta);\n      }\n      dyi *= dyValues[offset];\n      result[k] += dyi;\n    }\n  }\n  return backend2.makeTensorInfo(dy.shape, x.dtype, result);\n}\nvar LRNGradConfig = {\n  kernelName: LRNGrad,\n  backendName: \"cpu\",\n  kernelFunc: lRNGrad\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Max.js\nfunction max3(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x } = inputs;\n  const { reductionIndices, keepDims } = attrs;\n  const cpuBackend = backend2;\n  let xShape = x.shape;\n  const xRank = xShape.length;\n  const origAxes = util_exports.parseAxisParam(reductionIndices, xShape);\n  let axes = origAxes;\n  const permutedAxes = backend_util_exports.getAxesPermutation(axes, xRank);\n  let xVals = cpuBackend.data.get(x.dataId).values;\n  if (permutedAxes != null) {\n    const newShape = new Array(xRank);\n    for (let i = 0; i < newShape.length; i++) {\n      newShape[i] = xShape[permutedAxes[i]];\n    }\n    xVals = transposeImpl(xVals, xShape, x.dtype, permutedAxes, newShape);\n    axes = backend_util_exports.getInnerMostAxes(axes.length, xRank);\n    xShape = newShape;\n  }\n  assertNotComplex(x, \"max\");\n  backend_util_exports.assertAxesAreInnerMostDims(\"max\", axes, xRank);\n  const [maxOutShape, reduceShape] = backend_util_exports.computeOutAndReduceShapes(xShape, axes);\n  const reduceSize = util_exports.sizeFromShape(reduceShape);\n  const result = maxImpl(xVals, reduceSize, maxOutShape, x.dtype);\n  const dataId = cpuBackend.write(result, maxOutShape, x.dtype);\n  let outShape = maxOutShape;\n  if (keepDims) {\n    const newShape = backend_util_exports.expandShapeToKeepDim(maxOutShape, origAxes);\n    outShape = newShape;\n  }\n  return { dataId, shape: outShape, dtype: x.dtype };\n}\nvar maxConfig = {\n  kernelName: Max,\n  backendName: \"cpu\",\n  kernelFunc: max3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/MaxPool.js\nfunction maxPool2(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x } = inputs;\n  assertNotComplex(x, \"maxPool\");\n  const { filterSize, strides, pad: pad3, dimRoundingMode } = attrs;\n  const dilations = 1;\n  util_exports.assert(backend_util_exports.eitherStridesOrDilationsAreOne(strides, dilations), () => `Error in maxPool: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`);\n  const convInfo = backend_util_exports.computePool2DInfo(x.shape, filterSize, strides, dilations, pad3, dimRoundingMode);\n  let res;\n  if (convInfo.filterWidth === 1 && convInfo.filterHeight === 1 && util_exports.arraysEqual(convInfo.inShape, convInfo.outShape)) {\n    res = identity2({ inputs: { x }, backend: backend2 });\n  } else {\n    const xValues = backend2.data.get(x.dataId).values;\n    const strides2 = util_exports.computeStrides(x.shape);\n    const buffer2 = pool2(xValues, x.shape, x.dtype, strides2, convInfo, \"max\");\n    res = backend2.makeTensorInfo(convInfo.outShape, x.dtype, buffer2.values);\n  }\n  return res;\n}\nvar maxPoolConfig = {\n  kernelName: MaxPool,\n  backendName: \"cpu\",\n  kernelFunc: maxPool2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/MaxPool3D.js\nfunction maxPool3D(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x } = inputs;\n  const { filterSize, strides, pad: pad3, dimRoundingMode, dataFormat } = attrs;\n  assertNotComplex(x, \"maxPool3d\");\n  const convInfo = backend_util_exports.computePool3DInfo(x.shape, filterSize, strides, 1, pad3, dimRoundingMode, dataFormat);\n  const xValues = backend2.data.get(x.dataId).values;\n  const outBuf = pool3d2(xValues, x.shape, x.dtype, util_exports.computeStrides(x.shape), convInfo, \"max\");\n  return backend2.makeTensorInfo(outBuf.shape, \"float32\", outBuf.values);\n}\nvar maxPool3DConfig = {\n  kernelName: MaxPool3D,\n  backendName: \"cpu\",\n  kernelFunc: maxPool3D\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/MaxPool3DGrad.js\nfunction maxPool3DGrad(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { dy, input: input2 } = inputs;\n  const { filterSize, strides, pad: pad3, dimRoundingMode } = attrs;\n  assertNotComplex([dy, input2], \"maxPool3DGrad\");\n  const convInfo = backend_util_exports.computePool3DInfo(input2.shape, filterSize, strides, 1, pad3, dimRoundingMode);\n  const inputBuf = backend2.bufferSync(input2);\n  const maxPosBuf = maxPool3dPositions(inputBuf, convInfo);\n  const strideDepth = convInfo.strideDepth;\n  const strideHeight = convInfo.strideHeight;\n  const strideWidth = convInfo.strideWidth;\n  const dilationDepth = convInfo.dilationDepth;\n  const dilationHeight = convInfo.dilationHeight;\n  const dilationWidth = convInfo.dilationWidth;\n  const effectiveFilterDepth = convInfo.effectiveFilterDepth;\n  const effectiveFilterHeight = convInfo.effectiveFilterHeight;\n  const effectiveFilterWidth = convInfo.effectiveFilterWidth;\n  const padFront = effectiveFilterDepth - 1 - convInfo.padInfo.front;\n  const padLeft = effectiveFilterWidth - 1 - convInfo.padInfo.left;\n  const padTop = effectiveFilterHeight - 1 - convInfo.padInfo.top;\n  const dx = buffer(input2.shape, \"float32\");\n  const dyBuf = backend2.bufferSync(dy);\n  for (let batch = 0; batch < convInfo.batchSize; ++batch) {\n    for (let channel = 0; channel < convInfo.inChannels; ++channel) {\n      for (let dxDepth = 0; dxDepth < convInfo.inDepth; ++dxDepth) {\n        for (let dxRow = 0; dxRow < convInfo.inHeight; ++dxRow) {\n          for (let dxCol = 0; dxCol < convInfo.inWidth; ++dxCol) {\n            const dyDepthCorner = dxDepth - padFront;\n            const dyRowCorner = dxRow - padTop;\n            const dyColCorner = dxCol - padLeft;\n            let dotProd = 0;\n            for (let wDepth = 0; wDepth < effectiveFilterDepth; wDepth += dilationDepth) {\n              const dyDepth = (dyDepthCorner + wDepth) / strideDepth;\n              if (dyDepth < 0 || dyDepth >= convInfo.outDepth || Math.floor(dyDepth) !== dyDepth) {\n                continue;\n              }\n              for (let wRow = 0; wRow < effectiveFilterHeight; wRow += dilationHeight) {\n                const dyRow = (dyRowCorner + wRow) / strideHeight;\n                if (dyRow < 0 || dyRow >= convInfo.outHeight || Math.floor(dyRow) !== dyRow) {\n                  continue;\n                }\n                for (let wCol = 0; wCol < effectiveFilterWidth; wCol += dilationWidth) {\n                  const dyCol = (dyColCorner + wCol) / strideWidth;\n                  if (dyCol < 0 || dyCol >= convInfo.outWidth || Math.floor(dyCol) !== dyCol) {\n                    continue;\n                  }\n                  const maxPos = effectiveFilterDepth * effectiveFilterHeight * effectiveFilterWidth - 1 - maxPosBuf.get(batch, dyDepth, dyRow, dyCol, channel);\n                  const curPos = wDepth * effectiveFilterHeight * effectiveFilterWidth + wRow * effectiveFilterWidth + wCol;\n                  const mask = maxPos === curPos ? 1 : 0;\n                  if (mask === 0) {\n                    continue;\n                  }\n                  const pixel = dyBuf.get(batch, dyDepth, dyRow, dyCol, channel);\n                  dotProd += pixel * mask;\n                }\n              }\n            }\n            dx.set(dotProd, batch, dxDepth, dxRow, dxCol, channel);\n          }\n        }\n      }\n    }\n  }\n  return backend2.makeTensorInfo(dx.shape, dx.dtype, dx.values);\n}\nvar maxPool3DGradConfig2 = {\n  kernelName: MaxPool3DGrad,\n  backendName: \"cpu\",\n  kernelFunc: maxPool3DGrad\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/MaxPoolGrad.js\nfunction maxPoolGrad2(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { dy, input: input2, output } = inputs;\n  const x = input2;\n  assertNotComplex([input2, output], \"maxPoolGrad\");\n  const { filterSize, strides, pad: pad3, dimRoundingMode } = attrs;\n  const convInfo = backend_util_exports.computePool2DInfo(x.shape, filterSize, strides, 1, pad3, dimRoundingMode);\n  const xValues = backend2.data.get(x.dataId).values;\n  const maxPosBuf = buffer(convInfo.outShape, x.dtype, maxPoolPositions(xValues, x.shape, x.dtype, convInfo).values);\n  const strideHeight = convInfo.strideHeight;\n  const strideWidth = convInfo.strideWidth;\n  const dilationHeight = convInfo.dilationHeight;\n  const dilationWidth = convInfo.dilationWidth;\n  const effectiveFilterHeight = convInfo.effectiveFilterHeight;\n  const effectiveFilterWidth = convInfo.effectiveFilterWidth;\n  const padLeft = effectiveFilterWidth - 1 - convInfo.padInfo.left;\n  const padTop = effectiveFilterHeight - 1 - convInfo.padInfo.top;\n  const dx = buffer(x.shape, \"float32\");\n  const dyData = backend2.data.get(dy.dataId).values;\n  const dyBuf = buffer(dy.shape, \"float32\", dyData);\n  for (let b = 0; b < convInfo.batchSize; ++b) {\n    for (let d = 0; d < convInfo.inChannels; ++d) {\n      for (let dxR = 0; dxR < convInfo.inHeight; ++dxR) {\n        for (let dxC = 0; dxC < convInfo.inWidth; ++dxC) {\n          const dyRCorner = dxR - padTop;\n          const dyCCorner = dxC - padLeft;\n          let dotProd = 0;\n          for (let wR = 0; wR < effectiveFilterHeight; wR += dilationHeight) {\n            const dyR = (dyRCorner + wR) / strideHeight;\n            if (dyR < 0 || dyR >= convInfo.outHeight || Math.floor(dyR) !== dyR) {\n              continue;\n            }\n            for (let wC = 0; wC < effectiveFilterWidth; wC += dilationWidth) {\n              const dyC = (dyCCorner + wC) / strideWidth;\n              if (dyC < 0 || dyC >= convInfo.outWidth || Math.floor(dyC) !== dyC) {\n                continue;\n              }\n              const maxPos = effectiveFilterHeight * effectiveFilterWidth - 1 - maxPosBuf.get(b, dyR, dyC, d);\n              const curPos = wR * effectiveFilterWidth + wC;\n              const mask = maxPos === curPos ? 1 : 0;\n              if (mask === 0) {\n                continue;\n              }\n              const pixel = dyBuf.get(b, dyR, dyC, d);\n              dotProd += pixel * mask;\n            }\n          }\n          dx.set(dotProd, b, dxR, dxC, d);\n        }\n      }\n    }\n  }\n  return backend2.makeTensorInfo(dx.shape, dx.dtype, dx.values);\n}\nvar maxPoolGradConfig2 = {\n  kernelName: MaxPoolGrad,\n  backendName: \"cpu\",\n  kernelFunc: maxPoolGrad2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/MaxPoolWithArgmax_impl.js\nfunction maxPoolWithArgmaxImpl(xValues, xShape, dtype, includeBatchInIndex, convInfo) {\n  const strides = util_exports.computeStrides(xShape);\n  const maxPools = pool2(xValues, xShape, dtype, strides, convInfo, \"max\");\n  const maxPositions = maxPoolPositions(xValues, xShape, dtype, convInfo, true, includeBatchInIndex);\n  return [maxPools.values, maxPositions.values];\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/MaxPoolWithArgmax.js\nvar maxPoolWithArgmaxConfig = {\n  kernelName: MaxPoolWithArgmax,\n  backendName: \"cpu\",\n  kernelFunc: ({ inputs, attrs, backend: backend2 }) => {\n    const { x } = inputs;\n    const { filterSize, strides, pad: pad3, includeBatchInIndex } = attrs;\n    const cpuBackend = backend2;\n    assertNotComplex(x, \"MaxPoolWithArgmax\");\n    const values = cpuBackend.data.get(x.dataId).values;\n    const convInfo = backend_util_exports.computePool2DInfo(x.shape, filterSize, strides, [1, 1], pad3);\n    const [pooled, indexes] = maxPoolWithArgmaxImpl(values, x.shape, x.dtype, includeBatchInIndex, convInfo);\n    const pooledDataId = cpuBackend.write(pooled, convInfo.outShape, x.dtype);\n    const indexesDataId = cpuBackend.write(indexes, convInfo.outShape, x.dtype);\n    return [\n      { dataId: pooledDataId, shape: convInfo.outShape, dtype: x.dtype },\n      { dataId: indexesDataId, shape: convInfo.outShape, dtype: \"int32\" }\n    ];\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Mean.js\nfunction mean2(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x } = inputs;\n  const { axis, keepDims } = attrs;\n  const axes = util_exports.parseAxisParam(axis, x.shape);\n  const shapes = backend_util_exports.computeOutAndReduceShapes(x.shape, axes);\n  const reduceShape = shapes[1];\n  const reduceSize = util_exports.sizeFromShape(reduceShape);\n  const toDispose = [];\n  const reduceSizeScalar = backend2.makeTensorInfo([], \"float32\", new Float32Array([reduceSize]));\n  toDispose.push(reduceSizeScalar);\n  const $x = cast3({ inputs: { x }, backend: backend2, attrs: { dtype: \"float32\" } });\n  toDispose.push($x);\n  const res = div2({ inputs: { a: $x, b: reduceSizeScalar }, backend: backend2 });\n  toDispose.push(res);\n  const result = sum3({ inputs: { x: res }, backend: backend2, attrs: { axis, keepDims } });\n  toDispose.forEach((t) => backend2.disposeIntermediateTensorInfo(t));\n  return result;\n}\nvar meanConfig = {\n  kernelName: Mean,\n  backendName: \"cpu\",\n  kernelFunc: mean2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Min.js\nfunction min3(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x } = inputs;\n  const { axis, keepDims } = attrs;\n  assertNotComplex(x, \"min\");\n  const origAxes = util_exports.parseAxisParam(axis, x.shape);\n  let axes = origAxes;\n  const permutedAxes = backend_util_exports.getAxesPermutation(axes, x.shape.length);\n  let $x = x;\n  if (permutedAxes != null) {\n    $x = transpose2({ inputs: { x }, backend: backend2, attrs: { perm: permutedAxes } });\n    axes = backend_util_exports.getInnerMostAxes(axes.length, x.shape.length);\n  }\n  backend_util_exports.assertAxesAreInnerMostDims(\"min\", axes, $x.shape.length);\n  const [outShape, reduceShape] = backend_util_exports.computeOutAndReduceShapes($x.shape, axes);\n  const reduceSize = util_exports.sizeFromShape(reduceShape);\n  const vals = util_exports.makeZerosTypedArray(util_exports.sizeFromShape(outShape), $x.dtype);\n  const aVals = backend2.data.get($x.dataId).values;\n  for (let i = 0; i < vals.length; ++i) {\n    const offset = i * reduceSize;\n    let min7 = aVals[offset];\n    for (let j = 0; j < reduceSize; ++j) {\n      const value = aVals[offset + j];\n      if (Number.isNaN(value) || value < min7) {\n        min7 = value;\n      }\n    }\n    vals[i] = min7;\n  }\n  if (permutedAxes != null) {\n    backend2.disposeIntermediateTensorInfo($x);\n  }\n  const result = backend2.makeTensorInfo(outShape, $x.dtype, vals);\n  if (keepDims) {\n    const expandedShape = backend_util_exports.expandShapeToKeepDim(outShape, origAxes);\n    const reshapedResult = reshape3({ inputs: { x: result }, backend: backend2, attrs: { shape: expandedShape } });\n    backend2.disposeIntermediateTensorInfo(result);\n    return reshapedResult;\n  }\n  return result;\n}\nvar minConfig = {\n  kernelName: Min,\n  backendName: \"cpu\",\n  kernelFunc: min3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/MirrorPad.js\nfunction mirrorPad2(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x } = inputs;\n  const { paddings, mode } = attrs;\n  assertNotComplex(x, \"mirrorPad\");\n  const outShape = paddings.map((p2, i) => p2[0] + x.shape[i] + p2[1]);\n  const start = paddings.map((p2) => p2[0]);\n  const end = paddings.map((p2, i) => p2[0] + x.shape[i]);\n  const offset = mode === \"reflect\" ? 0 : 1;\n  const xVals = backend2.data.get(x.dataId).values;\n  const xRank = x.shape.length;\n  const xStrides = util_exports.computeStrides(x.shape);\n  const resultSize = util_exports.sizeFromShape(outShape);\n  const resultRank = outShape.length;\n  const resultStrides = util_exports.computeStrides(outShape);\n  const resVals = util_exports.getTypedArrayFromDType(x.dtype, resultSize);\n  for (let i = 0; i < resultSize; i++) {\n    let coords3 = util_exports.indexToLoc(i, resultRank, resultStrides);\n    for (let i2 = 0; i2 < resultRank; i2++) {\n      if (coords3[i2] < start[i2]) {\n        coords3[i2] = start[i2] * 2 - coords3[i2] - offset;\n      } else if (coords3[i2] >= end[i2]) {\n        coords3[i2] = (end[i2] - 1) * 2 - coords3[i2] + offset;\n      }\n    }\n    coords3 = coords3.map((c, i2) => c - start[i2]);\n    const inIndex = util_exports.locToIndex(coords3, xRank, xStrides);\n    resVals[i] = xVals[inIndex];\n  }\n  const outId = backend2.write(resVals, outShape, x.dtype);\n  return { dataId: outId, shape: outShape, dtype: x.dtype };\n}\nvar mirrorPadConfig = {\n  kernelName: MirrorPad,\n  backendName: \"cpu\",\n  kernelFunc: mirrorPad2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Mod.js\nvar modImpl = createSimpleBinaryKernelImpl((aValue, bValue) => {\n  const rem = aValue % bValue;\n  if (aValue < 0 && bValue < 0 || aValue >= 0 && bValue >= 0) {\n    return rem;\n  } else {\n    return (rem + bValue) % bValue;\n  }\n});\nvar mod2 = binaryKernelFunc(Mod, modImpl);\nvar modConfig = {\n  kernelName: Mod,\n  backendName: \"cpu\",\n  kernelFunc: mod2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Multinomial.js\nvar seedrandom4 = __toESM(require_seedrandom2());\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Softmax.js\nfunction softmax3(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { logits } = inputs;\n  const { dim } = attrs;\n  const logitsRank = logits.shape.length;\n  let $dim = dim;\n  if ($dim === -1) {\n    $dim = logitsRank - 1;\n  }\n  if ($dim !== logitsRank - 1) {\n    throw Error(`Softmax along a non-last dimension is not yet supported. Logits was rank ${logitsRank} and dim was ${$dim}`);\n  }\n  const axes = util_exports.parseAxisParam([$dim], logits.shape);\n  const maxLogit = max3({\n    inputs: { x: logits },\n    backend: backend2,\n    attrs: { reductionIndices: axes, keepDims: false }\n  });\n  const expandedShape = backend_util_exports.expandShapeToKeepDim(maxLogit.shape, axes);\n  const maxLogitReshaped = reshape3({ inputs: { x: maxLogit }, backend: backend2, attrs: { shape: expandedShape } });\n  const a = sub2({ inputs: { a: logits, b: maxLogitReshaped }, backend: backend2 });\n  const b = exp2({ inputs: { x: a }, backend: backend2 });\n  const sumExp = sum3({ inputs: { x: b }, backend: backend2, attrs: { axis: axes, keepDims: false } });\n  const sumReshaped = reshape3({ inputs: { x: sumExp }, backend: backend2, attrs: { shape: expandedShape } });\n  const result = div2({ inputs: { a: b, b: sumReshaped }, backend: backend2 });\n  backend2.disposeIntermediateTensorInfo(maxLogit);\n  backend2.disposeIntermediateTensorInfo(maxLogitReshaped);\n  backend2.disposeIntermediateTensorInfo(a);\n  backend2.disposeIntermediateTensorInfo(b);\n  backend2.disposeIntermediateTensorInfo(sumExp);\n  backend2.disposeIntermediateTensorInfo(sumReshaped);\n  return result;\n}\nvar softmaxConfig = {\n  kernelName: Softmax,\n  backendName: \"cpu\",\n  kernelFunc: softmax3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Multinomial.js\nfunction multinomial2(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { logits } = inputs;\n  const { numSamples, seed, normalized } = attrs;\n  assertNotComplex(logits, \"multinomial\");\n  const probabilities = normalized ? logits : softmax3({ inputs: { logits }, backend: backend2, attrs: { dim: -1 } });\n  const batchSize = probabilities.shape[0];\n  const numEvents = probabilities.shape[1];\n  const probVals = backend2.data.get(probabilities.dataId).values;\n  const resShape = [batchSize, numSamples];\n  const resVals = util_exports.makeZerosTypedArray(util_exports.sizeFromShape(resShape), \"int32\");\n  for (let b = 0; b < batchSize; ++b) {\n    const offset = b * numEvents;\n    const cdf = new Float32Array(numEvents - 1);\n    cdf[0] = probVals[offset];\n    for (let event = 1; event < cdf.length; ++event) {\n      cdf[event] = cdf[event - 1] + probVals[offset + event];\n    }\n    const random = seedrandom4.alea(seed.toString());\n    const outOffset = b * numSamples;\n    for (let sampleId = 0; sampleId < numSamples; ++sampleId) {\n      const r = random();\n      resVals[outOffset + sampleId] = cdf.length;\n      for (let event = 0; event < cdf.length; event++) {\n        if (r < cdf[event]) {\n          resVals[outOffset + sampleId] = event;\n          break;\n        }\n      }\n    }\n  }\n  if (!normalized) {\n    backend2.disposeIntermediateTensorInfo(probabilities);\n  }\n  return backend2.makeTensorInfo(resShape, \"int32\", resVals);\n}\nvar multinomialConfig = {\n  kernelName: Multinomial,\n  backendName: \"cpu\",\n  kernelFunc: multinomial2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/NonMaxSuppressionV3.js\nvar nonMaxSuppressionV3Impl2 = kernel_impls_exports.nonMaxSuppressionV3Impl;\nfunction nonMaxSuppressionV3(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { boxes, scores } = inputs;\n  const { maxOutputSize, iouThreshold, scoreThreshold } = attrs;\n  assertNotComplex(boxes, \"NonMaxSuppression\");\n  const boxesVals = backend2.data.get(boxes.dataId).values;\n  const scoresVals = backend2.data.get(scores.dataId).values;\n  const { selectedIndices } = nonMaxSuppressionV3Impl2(boxesVals, scoresVals, maxOutputSize, iouThreshold, scoreThreshold);\n  return backend2.makeTensorInfo([selectedIndices.length], \"int32\", new Int32Array(selectedIndices));\n}\nvar nonMaxSuppressionV3Config = {\n  kernelName: NonMaxSuppressionV3,\n  backendName: \"cpu\",\n  kernelFunc: nonMaxSuppressionV3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/NonMaxSuppressionV4.js\nvar nonMaxSuppressionV4Impl2 = kernel_impls_exports.nonMaxSuppressionV4Impl;\nfunction nonMaxSuppressionV4(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { boxes, scores } = inputs;\n  const { maxOutputSize, iouThreshold, scoreThreshold, padToMaxOutputSize } = attrs;\n  assertNotComplex(boxes, \"NonMaxSuppressionPadded\");\n  const boxesVals = backend2.data.get(boxes.dataId).values;\n  const scoresVals = backend2.data.get(scores.dataId).values;\n  const { selectedIndices, validOutputs } = nonMaxSuppressionV4Impl2(boxesVals, scoresVals, maxOutputSize, iouThreshold, scoreThreshold, padToMaxOutputSize);\n  return [\n    backend2.makeTensorInfo([selectedIndices.length], \"int32\", new Int32Array(selectedIndices)),\n    backend2.makeTensorInfo([], \"int32\", new Int32Array([validOutputs]))\n  ];\n}\nvar nonMaxSuppressionV4Config = {\n  kernelName: NonMaxSuppressionV4,\n  backendName: \"cpu\",\n  kernelFunc: nonMaxSuppressionV4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/NonMaxSuppressionV5.js\nvar nonMaxSuppressionV5Impl2 = kernel_impls_exports.nonMaxSuppressionV5Impl;\nfunction nonMaxSuppressionV5(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { boxes, scores } = inputs;\n  const { maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma } = attrs;\n  assertNotComplex(boxes, \"NonMaxSuppressionWithScore\");\n  const boxesVals = backend2.data.get(boxes.dataId).values;\n  const scoresVals = backend2.data.get(scores.dataId).values;\n  const maxOutputSizeVal = maxOutputSize;\n  const iouThresholdVal = iouThreshold;\n  const scoreThresholdVal = scoreThreshold;\n  const softNmsSigmaVal = softNmsSigma;\n  const { selectedIndices, selectedScores } = nonMaxSuppressionV5Impl2(boxesVals, scoresVals, maxOutputSizeVal, iouThresholdVal, scoreThresholdVal, softNmsSigmaVal);\n  return [\n    backend2.makeTensorInfo([selectedIndices.length], \"int32\", new Int32Array(selectedIndices)),\n    backend2.makeTensorInfo([selectedScores.length], \"float32\", new Float32Array(selectedScores))\n  ];\n}\nvar nonMaxSuppressionV5Config = {\n  kernelName: NonMaxSuppressionV5,\n  backendName: \"cpu\",\n  kernelFunc: nonMaxSuppressionV5\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/OneHot.js\nfunction oneHot2(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { indices } = inputs;\n  const { dtype, depth, onValue, offValue } = attrs;\n  assertNotComplex(indices, \"oneHot\");\n  const indicesSize = util_exports.sizeFromShape(indices.shape);\n  const res = new Float32Array(indicesSize * depth);\n  res.fill(offValue);\n  const indicesVal = backend2.data.get(indices.dataId).values;\n  for (let event = 0; event < indicesSize; ++event) {\n    if (indicesVal[event] >= 0 && indicesVal[event] < depth) {\n      res[event * depth + indicesVal[event]] = onValue;\n    }\n  }\n  return backend2.makeTensorInfo([...indices.shape, depth], dtype, res);\n}\nvar oneHotConfig = {\n  kernelName: OneHot,\n  backendName: \"cpu\",\n  kernelFunc: oneHot2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/ZerosLike.js\nfunction zerosLike2(args) {\n  const { inputs, backend: backend2 } = args;\n  const { x } = inputs;\n  if (x.dtype === \"string\") {\n    throw new Error(\"zerosLike is not supported for string tensors\");\n  } else if (x.dtype === \"complex64\") {\n    const realPart = real2({ inputs: { input: x }, backend: backend2 });\n    const r = zerosLike2({ inputs: { x: realPart }, backend: backend2 });\n    const imagPart = imag2({ inputs: { input: x }, backend: backend2 });\n    const i = zerosLike2({ inputs: { x: imagPart }, backend: backend2 });\n    const result = complex2({ inputs: { real: r, imag: i }, backend: backend2 });\n    backend2.disposeIntermediateTensorInfo(realPart);\n    backend2.disposeIntermediateTensorInfo(r);\n    backend2.disposeIntermediateTensorInfo(imagPart);\n    backend2.disposeIntermediateTensorInfo(i);\n    return result;\n  } else {\n    return fill2({ backend: backend2, attrs: { shape: x.shape, value: 0, dtype: x.dtype } });\n  }\n}\nvar zerosLikeConfig = {\n  kernelName: ZerosLike,\n  backendName: \"cpu\",\n  kernelFunc: zerosLike2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/OnesLike.js\nfunction onesLike2(args) {\n  const { inputs, backend: backend2 } = args;\n  const { x } = inputs;\n  if (x.dtype === \"string\") {\n    throw new Error(\"onesLike is not supported for string tensors\");\n  } else if (x.dtype === \"complex64\") {\n    const realPart = real2({ inputs: { input: x }, backend: backend2 });\n    const r = onesLike2({ inputs: { x: realPart }, backend: backend2 });\n    const imagPart = imag2({ inputs: { input: x }, backend: backend2 });\n    const i = zerosLike2({ inputs: { x: imagPart }, backend: backend2 });\n    const result = complex2({ inputs: { real: r, imag: i }, backend: backend2 });\n    backend2.disposeIntermediateTensorInfo(realPart);\n    backend2.disposeIntermediateTensorInfo(r);\n    backend2.disposeIntermediateTensorInfo(imagPart);\n    backend2.disposeIntermediateTensorInfo(i);\n    return result;\n  } else {\n    return fill2({ backend: backend2, attrs: { shape: x.shape, value: 1, dtype: x.dtype } });\n  }\n}\nvar onesLikeConfig = {\n  kernelName: OnesLike,\n  backendName: \"cpu\",\n  kernelFunc: onesLike2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Pack.js\nfunction pack(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { axis } = attrs;\n  if (inputs.length === 1) {\n    return expandDims3({ inputs: { input: inputs[0] }, backend: backend2, attrs: { dim: axis } });\n  }\n  const shape = inputs[0].shape;\n  const dtype = inputs[0].dtype;\n  inputs.forEach((t) => {\n    util_exports.assertShapesMatch(shape, t.shape, \"All tensors passed to stack must have matching shapes\");\n    util_exports.assert(dtype === t.dtype, () => \"All tensors passed to stack must have matching dtypes\");\n  });\n  const intermediateTensorInfos = [];\n  const expandedTensors = inputs.map((t) => {\n    const expandedT = expandDims3({ inputs: { input: t }, backend: backend2, attrs: { dim: axis } });\n    intermediateTensorInfos.push(expandedT);\n    return expandedT;\n  });\n  const result = concat2({ inputs: expandedTensors, backend: backend2, attrs: { axis } });\n  intermediateTensorInfos.forEach((t) => backend2.disposeIntermediateTensorInfo(t));\n  return result;\n}\nvar packConfig = {\n  kernelName: Pack,\n  backendName: \"cpu\",\n  kernelFunc: pack\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/PadV2.js\nfunction padV2(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x } = inputs;\n  const { paddings, constantValue } = attrs;\n  assertNotComplex(x, \"pad\");\n  const outShape = paddings.map((p2, i) => p2[0] + x.shape[i] + p2[1]);\n  const start = paddings.map((p2) => p2[0]);\n  const xVals = backend2.data.get(x.dataId).values;\n  const xSize = util_exports.sizeFromShape(x.shape);\n  const xRank = x.shape.length;\n  const xStrides = util_exports.computeStrides(x.shape);\n  const resultSize = util_exports.sizeFromShape(outShape);\n  const resultRank = outShape.length;\n  const resultStrides = util_exports.computeStrides(outShape);\n  const resVals = util_exports.getTypedArrayFromDType(x.dtype, resultSize);\n  if (constantValue !== 0) {\n    resVals.fill(constantValue);\n  }\n  for (let i = 0; i < xSize; i++) {\n    const coords3 = util_exports.indexToLoc(i, xRank, xStrides);\n    const outCoords = coords3.map((c, i2) => c + start[i2]);\n    const outIndex = util_exports.locToIndex(outCoords, resultRank, resultStrides);\n    resVals[outIndex] = xVals[i];\n  }\n  const outId = backend2.write(resVals, outShape, x.dtype);\n  return { dataId: outId, shape: outShape, dtype: x.dtype };\n}\nvar padV2Config = {\n  kernelName: PadV2,\n  backendName: \"cpu\",\n  kernelFunc: padV2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Pow.js\nvar powImpl = createSimpleBinaryKernelImpl((a, b) => Math.pow(a, b));\nvar pow2 = binaryKernelFunc(Pow, powImpl);\nvar powConfig = {\n  kernelName: Pow,\n  backendName: \"cpu\",\n  kernelFunc: pow2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/RaggedTensorToTensor.js\nfunction raggedTensorToTensor2(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { shape, values, defaultValue, rowPartitionTensors } = inputs;\n  const { rowPartitionTypes } = attrs;\n  const $shape = backend2.data.get(shape.dataId).values;\n  const $values = backend2.data.get(values.dataId).values;\n  const $defaultValue = backend2.data.get(defaultValue.dataId).values;\n  const $rowPartitionValues = rowPartitionTensors.map((t) => backend2.data.get(t.dataId).values);\n  const rowPartitionValuesShapes = rowPartitionTensors.map((t) => t.shape);\n  const [outputShape, output] = raggedTensorToTensorImpl($shape, shape.shape, $values, values.shape, values.dtype, $defaultValue, defaultValue.shape, $rowPartitionValues, rowPartitionValuesShapes, rowPartitionTypes);\n  return backend2.makeTensorInfo(outputShape, values.dtype, output);\n}\nvar raggedTensorToTensorConfig = {\n  kernelName: RaggedTensorToTensor,\n  backendName: \"cpu\",\n  kernelFunc: raggedTensorToTensor2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Range.js\nfunction range3(args) {\n  const { backend: backend2, attrs } = args;\n  const { start, stop, dtype, step: step5 } = attrs;\n  const values = rangeImpl(start, stop, step5, dtype);\n  return backend2.makeTensorInfo([values.length], dtype, values);\n}\nvar rangeConfig = {\n  kernelName: Range,\n  backendName: \"cpu\",\n  kernelFunc: range3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Reciprocal.js\nvar reciprocal2 = unaryKernelFunc(Reciprocal, (xi) => 1 / xi);\nvar reciprocalConfig = {\n  kernelName: Reciprocal,\n  backendName: \"cpu\",\n  kernelFunc: reciprocal2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/ResizeBilinear.js\nfunction resizeBilinear2(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { images } = inputs;\n  const { alignCorners, halfPixelCenters, size } = attrs;\n  assertNotComplex(images, \"resizeBilinear\");\n  const imagesStrides = util_exports.computeStrides(images.shape);\n  const [newHeight, newWidth] = size;\n  const [batch, oldHeight, oldWidth, numChannels] = images.shape;\n  const xValues = backend2.data.get(images.dataId).values;\n  const result = new Float32Array(util_exports.sizeFromShape([batch, newHeight, newWidth, numChannels]));\n  const effectiveInputSize = [\n    alignCorners && newHeight > 1 ? oldHeight - 1 : oldHeight,\n    alignCorners && newWidth > 1 ? oldWidth - 1 : oldWidth\n  ];\n  const effectiveOutputSize = [\n    alignCorners && newHeight > 1 ? newHeight - 1 : newHeight,\n    alignCorners && newWidth > 1 ? newWidth - 1 : newWidth\n  ];\n  let outputIdx = 0;\n  const effectiveRowSizeRatio = effectiveInputSize[0] / effectiveOutputSize[0];\n  const effectiveColSizeRatio = effectiveInputSize[1] / effectiveOutputSize[1];\n  for (let b = 0; b < batch; b++) {\n    for (let r = 0; r < newHeight; r++) {\n      let sourceFracRow;\n      if (halfPixelCenters) {\n        sourceFracRow = effectiveRowSizeRatio * (r + 0.5) - 0.5;\n      } else {\n        sourceFracRow = effectiveRowSizeRatio * r;\n      }\n      const sourceRowFloor = Math.max(0, Math.floor(sourceFracRow));\n      const rowFrac = sourceFracRow - sourceRowFloor;\n      const sourceRowCeil = Math.min(oldHeight - 1, Math.ceil(sourceFracRow));\n      const topRowOffset = b * imagesStrides[0] + sourceRowFloor * imagesStrides[1];\n      const botRowOffset = b * imagesStrides[0] + sourceRowCeil * imagesStrides[1];\n      for (let c = 0; c < newWidth; c++) {\n        let sourceFracCol;\n        if (halfPixelCenters) {\n          sourceFracCol = effectiveColSizeRatio * (c + 0.5) - 0.5;\n        } else {\n          sourceFracCol = effectiveColSizeRatio * c;\n        }\n        const sourceColFloor = Math.max(0, Math.floor(sourceFracCol));\n        const colFrac = sourceFracCol - sourceColFloor;\n        const sourceColCeil = Math.min(oldWidth - 1, Math.ceil(sourceFracCol));\n        const topLeftOffest = topRowOffset + sourceColFloor * imagesStrides[2];\n        const botLeftOffset = botRowOffset + sourceColFloor * imagesStrides[2];\n        const topRightOffset = topRowOffset + sourceColCeil * imagesStrides[2];\n        const botRightOffest = botRowOffset + sourceColCeil * imagesStrides[2];\n        for (let d = 0; d < numChannels; d++) {\n          const topLeft = xValues[topLeftOffest + d];\n          const bottomLeft = xValues[botLeftOffset + d];\n          const topRight = xValues[topRightOffset + d];\n          const bottomRight = xValues[botRightOffest + d];\n          const top = topLeft + (topRight - topLeft) * colFrac;\n          const bottom = bottomLeft + (bottomRight - bottomLeft) * colFrac;\n          const newValue = top + (bottom - top) * rowFrac;\n          result[outputIdx++] = newValue;\n        }\n      }\n    }\n  }\n  return backend2.makeTensorInfo([batch, newHeight, newWidth, numChannels], \"float32\", result);\n}\nvar resizeBilinearConfig = {\n  kernelName: ResizeBilinear,\n  backendName: \"cpu\",\n  kernelFunc: resizeBilinear2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/ResizeBilinearGrad.js\nfunction resizeBilinearGrad(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { images, dy } = inputs;\n  const { alignCorners } = attrs;\n  assertNotComplex([dy, images], \"resizeBilinearGrad\");\n  const imagesStrides = util_exports.computeStrides(images.shape);\n  const [batch, xHeight, xWidth, depth] = images.shape;\n  const [, yHeight, yWidth] = dy.shape;\n  const output = new Float32Array(batch * xHeight * xWidth * depth);\n  const effectiveXSize = [\n    alignCorners && yHeight > 1 ? xHeight - 1 : xHeight,\n    alignCorners && yWidth > 1 ? xWidth - 1 : xWidth\n  ];\n  const effectiveYSize = [\n    alignCorners && yHeight > 1 ? yHeight - 1 : yHeight,\n    alignCorners && yWidth > 1 ? yWidth - 1 : yWidth\n  ];\n  const heightScale = effectiveXSize[0] / effectiveYSize[0];\n  const widthScale = effectiveXSize[1] / effectiveYSize[1];\n  const dyValues = backend2.data.get(dy.dataId).values;\n  let offset = 0;\n  for (let b = 0; b < batch; b++) {\n    const bOffset = b * imagesStrides[0];\n    for (let r = 0; r < yHeight; r++) {\n      const dxR = r * heightScale;\n      const topDxRIndex = Math.floor(dxR);\n      const bottomDxRIndex = Math.min(Math.ceil(dxR), xHeight - 1);\n      const topDxROffset = bOffset + topDxRIndex * imagesStrides[1];\n      const bottomDxROffset = bOffset + bottomDxRIndex * imagesStrides[1];\n      const dxRLerp = dxR - topDxRIndex;\n      const inverseDxRLerp = 1 - dxRLerp;\n      for (let c = 0; c < yWidth; c++) {\n        const dxC = c * widthScale;\n        const leftDxCIndex = Math.floor(dxC);\n        const rightDxCIndex = Math.min(Math.ceil(dxC), xWidth - 1);\n        const dxCLerp = dxC - leftDxCIndex;\n        const inverseDxCLerp = 1 - dxCLerp;\n        const topLeftRCOffset = topDxROffset + leftDxCIndex * imagesStrides[2];\n        const topRightRCOffset = topDxROffset + rightDxCIndex * imagesStrides[2];\n        const bottomLeftRCOffset = bottomDxROffset + leftDxCIndex * imagesStrides[2];\n        const bottomRightRCOffset = bottomDxROffset + rightDxCIndex * imagesStrides[2];\n        const inverseDxRLerpTimesInverseDxCLerp = inverseDxRLerp * inverseDxCLerp;\n        const inverseDxRLerpTimesDxCLerp = inverseDxRLerp * dxCLerp;\n        const dxRLerpTimesInverseDxCLerp = dxRLerp * inverseDxCLerp;\n        const dxRLerpTimesDxCLerp = dxRLerp * dxCLerp;\n        for (let d = 0; d < depth; d++) {\n          const dyVal = dyValues[offset++];\n          output[topLeftRCOffset + d] += dyVal * inverseDxRLerpTimesInverseDxCLerp;\n          output[topRightRCOffset + d] += dyVal * inverseDxRLerpTimesDxCLerp;\n          output[bottomLeftRCOffset + d] += dyVal * dxRLerpTimesInverseDxCLerp;\n          output[bottomRightRCOffset + d] += dyVal * dxRLerpTimesDxCLerp;\n        }\n      }\n    }\n  }\n  return backend2.makeTensorInfo([batch, xWidth, xHeight, depth], \"float32\", output);\n}\nvar resizeBilinearGradConfig2 = {\n  kernelName: ResizeBilinearGrad,\n  backendName: \"cpu\",\n  kernelFunc: resizeBilinearGrad\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/ResizeNearestNeighbor.js\nfunction resizeNearestNeighbor2(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { images } = inputs;\n  const { alignCorners, halfPixelCenters, size } = attrs;\n  assertNotComplex(images, \"resizeNearestNeighbor\");\n  const imagesStrides = util_exports.computeStrides(images.shape);\n  const [newHeight, newWidth] = size;\n  const [batch, oldHeight, oldWidth, numChannels] = images.shape;\n  const xValues = backend2.data.get(images.dataId).values;\n  const output = new Float32Array(batch * newHeight * newWidth * numChannels);\n  const effectiveInputSize = [\n    alignCorners && newHeight > 1 ? oldHeight - 1 : oldHeight,\n    alignCorners && newWidth > 1 ? oldWidth - 1 : oldWidth\n  ];\n  const effectiveOutputSize = [\n    alignCorners && newHeight > 1 ? newHeight - 1 : newHeight,\n    alignCorners && newWidth > 1 ? newWidth - 1 : newWidth\n  ];\n  const effectiveRowSizeRatio = effectiveInputSize[0] / effectiveOutputSize[0];\n  const effectiveColSizeRatio = effectiveInputSize[1] / effectiveOutputSize[1];\n  let outputOffset = 0;\n  for (let b = 0; b < batch; b++) {\n    const batchOffset = b * imagesStrides[0];\n    for (let r = 0; r < newHeight; r++) {\n      const sourceFracRow = halfPixelCenters ? effectiveRowSizeRatio * (r + 0.5) : effectiveRowSizeRatio * r;\n      let sourceNearestRow = Math.min(oldHeight - 1, alignCorners ? Math.round(sourceFracRow) : Math.floor(sourceFracRow));\n      if (halfPixelCenters) {\n        sourceNearestRow = Math.max(0, sourceNearestRow);\n      }\n      const rowOffset = batchOffset + sourceNearestRow * imagesStrides[1];\n      for (let c = 0; c < newWidth; c++) {\n        const sourceFracCol = halfPixelCenters ? effectiveColSizeRatio * (c + 0.5) : effectiveColSizeRatio * c;\n        let sourceNearestCol = Math.min(oldWidth - 1, alignCorners ? Math.round(sourceFracCol) : Math.floor(sourceFracCol));\n        if (halfPixelCenters) {\n          sourceNearestCol = Math.max(0, sourceNearestCol);\n        }\n        const colOffset = rowOffset + sourceNearestCol * imagesStrides[2];\n        for (let d = 0; d < numChannels; d++) {\n          const newVal = xValues[colOffset + d];\n          output[outputOffset++] = newVal;\n        }\n      }\n    }\n  }\n  return backend2.makeTensorInfo([batch, newHeight, newWidth, numChannels], images.dtype, output);\n}\nvar resizeNearestNeighborConfig = {\n  kernelName: ResizeNearestNeighbor,\n  backendName: \"cpu\",\n  kernelFunc: resizeNearestNeighbor2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/ResizeNearestNeighborGrad.js\nfunction resizeNearestNeighborGrad(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { images, dy } = inputs;\n  const { alignCorners } = attrs;\n  assertNotComplex([dy, images], \"resizeNearestNeighborGrad\");\n  const imagesStrides = util_exports.computeStrides(images.shape);\n  const dyStrides = util_exports.computeStrides(dy.shape);\n  const [batch, xHeight, xWidth, depth] = images.shape;\n  const [, yHeight, yWidth] = dy.shape;\n  const output = new Float32Array(batch * xHeight * xWidth * depth);\n  const dyValues = backend2.data.get(dy.dataId).values;\n  const effectiveXSize = [\n    alignCorners && yHeight > 1 ? xHeight - 1 : xHeight,\n    alignCorners && yWidth > 1 ? xWidth - 1 : xWidth\n  ];\n  const effectiveYSize = [\n    alignCorners && yHeight > 1 ? yHeight - 1 : yHeight,\n    alignCorners && yWidth > 1 ? yWidth - 1 : yWidth\n  ];\n  const heightScale = effectiveXSize[0] / effectiveYSize[0];\n  const widthScale = effectiveXSize[1] / effectiveYSize[1];\n  const invHeightScale = 1 / heightScale;\n  const invWidthScale = 1 / widthScale;\n  const winHeight = Math.ceil(invHeightScale) * 2 + 2;\n  const winWidth = Math.ceil(invWidthScale) * 2 + 2;\n  for (let b = 0; b < batch; b++) {\n    const batchOffset = b * imagesStrides[0];\n    for (let r = 0; r < xHeight; r++) {\n      const rowOffset = batchOffset + r * imagesStrides[1];\n      const startRLerp = Math.floor(r * invHeightScale);\n      const startDyR = Math.floor(startRLerp - winHeight / 2);\n      for (let c = 0; c < xWidth; c++) {\n        const colOffset = rowOffset + c * imagesStrides[2];\n        const startCLerp = Math.floor(c * invWidthScale);\n        const startDyC = Math.floor(startCLerp - winWidth / 2);\n        for (let d = 0; d < depth; d++) {\n          let accum = 0;\n          for (let dyRIndex = 0; dyRIndex < winHeight; dyRIndex++) {\n            const dyR = dyRIndex + startDyR;\n            if (dyR < 0 || dyR >= yHeight) {\n              continue;\n            }\n            const dyROffset = batchOffset + dyR * dyStrides[1];\n            const sourceFracRow = dyR * heightScale;\n            const sourceNearestRow = Math.min(xHeight - 1, alignCorners ? Math.round(sourceFracRow) : Math.floor(sourceFracRow));\n            if (r !== sourceNearestRow) {\n              continue;\n            }\n            for (let dyCIndex = 0; dyCIndex < winWidth; dyCIndex++) {\n              const dyC = dyCIndex + startDyC;\n              if (dyC < 0 || dyC >= yWidth) {\n                continue;\n              }\n              const dyCOffset = dyROffset + dyC * dyStrides[2];\n              const sourceFracCol = dyC * widthScale;\n              const sourceNearestCol = Math.min(xWidth - 1, alignCorners ? Math.round(sourceFracCol) : Math.floor(sourceFracCol));\n              if (c === sourceNearestCol) {\n                accum += dyValues[dyCOffset + d];\n              }\n            }\n          }\n          output[colOffset + d] = accum;\n        }\n      }\n    }\n  }\n  return backend2.makeTensorInfo(images.shape, images.dtype, output);\n}\nvar resizeNearestNeighborGradConfig2 = {\n  kernelName: ResizeNearestNeighborGrad,\n  backendName: \"cpu\",\n  kernelFunc: resizeNearestNeighborGrad\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Reverse.js\nfunction reverse2(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x } = inputs;\n  const { dims } = attrs;\n  assertNotComplex(x, \"reverse\");\n  const xRank = x.shape.length;\n  const $dims = util_exports.parseAxisParam(dims, x.shape);\n  if (xRank === 0) {\n    return identity2({ inputs: { x }, backend: backend2 });\n  }\n  const outBuf = new TensorBuffer(x.shape, x.dtype);\n  const xBuf = backend2.bufferSync(x);\n  for (let i = 0; i < outBuf.size; i++) {\n    const outLoc = outBuf.indexToLoc(i);\n    const inLoc = outLoc.slice();\n    $dims.forEach((d) => inLoc[d] = x.shape[d] - 1 - inLoc[d]);\n    outBuf.set(xBuf.get(...inLoc), ...outLoc);\n  }\n  return backend2.makeTensorInfo(outBuf.shape, outBuf.dtype, outBuf.values);\n}\nvar reverseConfig = {\n  kernelName: Reverse,\n  backendName: \"cpu\",\n  kernelFunc: reverse2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/RotateWithOffset.js\nvar rotateWithOffsetConfig = {\n  kernelName: RotateWithOffset,\n  backendName: \"cpu\",\n  kernelFunc: ({ inputs, attrs, backend: backend2 }) => {\n    const { image: image2 } = inputs;\n    const { radians, fillValue, center } = attrs;\n    const cpuBackend = backend2;\n    const output = util_exports.getTypedArrayFromDType(image2.dtype, util_exports.sizeFromShape(image2.shape));\n    const [batch, imageHeight, imageWidth, numChannels] = image2.shape;\n    const [centerX, centerY] = backend_util_exports.getImageCenter(center, imageHeight, imageWidth);\n    const fullOpacityValue = 255;\n    const sinFactor = Math.sin(radians);\n    const cosFactor = Math.cos(radians);\n    const imageVals = cpuBackend.data.get(image2.dataId).values;\n    for (let batchIdx = 0; batchIdx < batch; batchIdx++) {\n      const batchOffset = batchIdx * imageWidth * imageHeight * numChannels;\n      for (let row = 0; row < imageHeight; row++) {\n        const rowOffset = row * (imageWidth * numChannels);\n        for (let col = 0; col < imageWidth; col++) {\n          const colOffset = col * numChannels;\n          for (let channel = 0; channel < numChannels; channel++) {\n            const coords3 = [batch, row, col, channel];\n            const x = coords3[2];\n            const y = coords3[1];\n            let coordX = (x - centerX) * cosFactor - (y - centerY) * sinFactor;\n            let coordY = (x - centerX) * sinFactor + (y - centerY) * cosFactor;\n            coordX = Math.round(coordX + centerX);\n            coordY = Math.round(coordY + centerY);\n            let outputValue = fillValue;\n            if (typeof fillValue !== \"number\") {\n              if (channel === 3) {\n                outputValue = fullOpacityValue;\n              } else {\n                outputValue = fillValue[channel];\n              }\n            }\n            if (coordX >= 0 && coordX < imageWidth && coordY >= 0 && coordY < imageHeight) {\n              const rotatedRowOffset = coordY * (imageWidth * numChannels);\n              const rotatedColOffset = coordX * numChannels;\n              const imageIdx = batchOffset + rotatedRowOffset + rotatedColOffset + channel;\n              outputValue = imageVals[imageIdx];\n            }\n            const outIdx = batchOffset + rowOffset + colOffset + channel;\n            output[outIdx] = outputValue;\n          }\n        }\n      }\n    }\n    const dataId = cpuBackend.write(output, image2.shape, image2.dtype);\n    return { dataId, shape: image2.shape, dtype: image2.dtype };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Round.js\nvar round3 = unaryKernelFunc(Round, (xi) => {\n  const base = Math.floor(xi);\n  if (xi - base < 0.5) {\n    return Math.floor(xi);\n  } else if (xi - base > 0.5) {\n    return Math.ceil(xi);\n  } else {\n    if (base % 2 === 0) {\n      return base;\n    } else {\n      return base + 1;\n    }\n  }\n});\nvar roundConfig = {\n  kernelName: Round,\n  backendName: \"cpu\",\n  kernelFunc: round3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/ScatterNd.js\nfunction scatterNd(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { indices, updates } = inputs;\n  const { shape } = attrs;\n  const { sliceRank, numUpdates, sliceSize, strides, outputSize } = backend_util_exports.calculateShapes(updates, indices, shape);\n  const sumDupeIndices = true;\n  const indicesBuf = backend2.bufferSync(indices);\n  const updatesBuf = backend2.bufferSync(updates);\n  const outBuf = scatterImpl(indicesBuf, updatesBuf, shape, outputSize, sliceSize, numUpdates, sliceRank, strides, 0, sumDupeIndices);\n  return backend2.makeTensorInfo(shape, outBuf.dtype, outBuf.values);\n}\nvar scatterNdConfig = {\n  kernelName: ScatterNd,\n  backendName: \"cpu\",\n  kernelFunc: scatterNd\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/SearchSorted_impl.js\nfunction lowerBound2(array2, value) {\n  let left = 0;\n  let right = array2.length;\n  let mid = 0;\n  while (left < right) {\n    mid = Math.floor((left + right) / 2);\n    if (array2[mid] < value) {\n      left = mid + 1;\n    } else {\n      right = mid;\n    }\n  }\n  return right;\n}\nfunction upperBound2(array2, value) {\n  let left = 0;\n  let right = array2.length;\n  let mid = 0;\n  while (left < right) {\n    mid = Math.floor((left + right) / 2);\n    if (array2[mid] <= value) {\n      left = mid + 1;\n    } else {\n      right = mid;\n    }\n  }\n  return right;\n}\nfunction searchSortedImpl(sortedInputs, values, batchSize, numInputs, numValues, side) {\n  const output = util_exports.getArrayFromDType(\"int32\", batchSize * numValues);\n  for (let b = 0; b < batchSize; ++b) {\n    const sortedInputsSlice = sortedInputs.slice(b * numInputs, (b + 1) * numInputs);\n    const outputOffset = b * numValues;\n    for (let i = 0; i < numValues; ++i) {\n      output[outputOffset + i] = side === \"left\" ? lowerBound2(sortedInputsSlice, values[i + outputOffset]) : upperBound2(sortedInputsSlice, values[i + outputOffset]);\n    }\n  }\n  return output;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/SearchSorted.js\nfunction searchSorted2(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { sortedSequence, values } = inputs;\n  const { side } = attrs;\n  const $sortedSequence = backend2.data.get(sortedSequence.dataId).values;\n  const $values = backend2.data.get(values.dataId).values;\n  const output = searchSortedImpl($sortedSequence, $values, sortedSequence.shape[0], sortedSequence.shape[1], values.shape[1], side);\n  return backend2.makeTensorInfo(values.shape, \"int32\", output);\n}\nvar searchSortedConfig = {\n  kernelName: SearchSorted,\n  backendName: \"cpu\",\n  kernelFunc: searchSorted2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Select.js\nfunction select2(args) {\n  const { inputs, backend: backend2 } = args;\n  const { condition, t, e } = inputs;\n  assertNotComplex([condition, t, e], \"select\");\n  const conditionRank = condition.shape.length;\n  const values = backend2.data.get(condition.dataId).values;\n  const tValues = backend2.data.get(t.dataId).values;\n  const eValues = backend2.data.get(e.dataId).values;\n  const resultDtype = upcastType(t.dtype, e.dtype);\n  const newValues = util_exports.makeZerosTypedArray(util_exports.sizeFromShape(t.shape), resultDtype);\n  let index = 0;\n  const offset = conditionRank === 0 || conditionRank > 1 || t.shape.length === 1 ? 1 : util_exports.sizeFromShape(t.shape.slice(1));\n  for (let i = 0; i < values.length; i++) {\n    for (let j = 0; j < offset; j++) {\n      if (values[i] === 1) {\n        newValues[index++] = tValues[i];\n      } else {\n        newValues[index++] = eValues[i];\n      }\n    }\n  }\n  return backend2.makeTensorInfo(t.shape, resultDtype, newValues);\n}\nvar selectConfig = {\n  kernelName: Select,\n  backendName: \"cpu\",\n  kernelFunc: select2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Selu.js\nvar scaleAlpha = backend_util_exports.SELU_SCALEALPHA;\nvar scale = backend_util_exports.SELU_SCALE;\nvar selu2 = unaryKernelFunc(Selu, (xi) => {\n  if (xi >= 0) {\n    return scale * xi;\n  } else {\n    return scaleAlpha * (Math.exp(xi) - 1);\n  }\n});\nvar seluConfig = {\n  kernelName: Selu,\n  backendName: \"cpu\",\n  kernelFunc: selu2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Sign.js\nvar sign2 = unaryKernelFunc(Sign, (xi) => {\n  if (xi < 0) {\n    return -1;\n  } else if (xi > 0) {\n    return 1;\n  } else {\n    return 0;\n  }\n});\nvar signConfig = {\n  kernelName: Sign,\n  backendName: \"cpu\",\n  kernelFunc: sign2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Sin.js\nvar sin2 = unaryKernelFunc(Sin, (xi) => Math.sin(xi));\nvar sinConfig = {\n  kernelName: Sin,\n  backendName: \"cpu\",\n  kernelFunc: sin2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Sinh.js\nvar sinh2 = unaryKernelFunc(Sinh, (xi) => Math.sinh(xi));\nvar sinhConfig = {\n  kernelName: Sinh,\n  backendName: \"cpu\",\n  kernelFunc: sinh2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Softplus.js\nvar epsilon2 = 11920928955078125e-23;\nvar threshold2 = Math.log(epsilon2) + 2;\nvar softplus2 = unaryKernelFunc(Softplus, (xi) => {\n  const tooLarge = xi > -threshold2;\n  const tooSmall = xi < threshold2;\n  const expX = Math.exp(xi);\n  let result;\n  if (tooSmall) {\n    result = expX;\n  } else if (tooLarge) {\n    result = xi;\n  } else {\n    result = Math.log(1 + expX);\n  }\n  return result;\n});\nvar softplusConfig = {\n  kernelName: Softplus,\n  backendName: \"cpu\",\n  kernelFunc: softplus2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/SpaceToBatchND.js\nfunction spaceToBatchND2(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x } = inputs;\n  const { blockShape, paddings } = attrs;\n  assertNotComplex([x], \"spaceToBatchND\");\n  const prod6 = util_exports.sizeFromShape(blockShape);\n  const completePaddings = [[0, 0]];\n  completePaddings.push(...paddings);\n  for (let i = 1 + blockShape.length; i < x.shape.length; ++i) {\n    completePaddings.push([0, 0]);\n  }\n  const paddedX = padV2Config.kernelFunc({\n    inputs: { x },\n    backend: backend2,\n    attrs: { paddings: completePaddings, constantValue: 0 }\n  });\n  const reshapedPaddedShape = backend_util_exports.getReshaped(paddedX.shape, blockShape, prod6, false);\n  const permutedReshapedPaddedPermutation = backend_util_exports.getPermuted(reshapedPaddedShape.length, blockShape.length, false);\n  const flattenShape = backend_util_exports.getReshapedPermuted(paddedX.shape, blockShape, prod6, false);\n  const reshapeInputs = { x: paddedX };\n  const reshapeAttrs = { shape: reshapedPaddedShape };\n  const paddedXReshaped = reshape3({ inputs: reshapeInputs, backend: backend2, attrs: reshapeAttrs });\n  const transposeInputs = { x: paddedXReshaped };\n  const transposeAttrs = { perm: permutedReshapedPaddedPermutation };\n  const paddedXT = transpose2({ inputs: transposeInputs, backend: backend2, attrs: transposeAttrs });\n  const resultReshapeInputs = { x: paddedXT };\n  const resultReshapeAttrs = { shape: flattenShape };\n  const result = reshape3({ inputs: resultReshapeInputs, backend: backend2, attrs: resultReshapeAttrs });\n  backend2.disposeIntermediateTensorInfo(paddedX);\n  backend2.disposeIntermediateTensorInfo(paddedXReshaped);\n  backend2.disposeIntermediateTensorInfo(paddedXT);\n  return result;\n}\nvar spaceToBatchNDConfig = {\n  kernelName: SpaceToBatchND,\n  backendName: \"cpu\",\n  kernelFunc: spaceToBatchND2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/SparseFillEmptyRows.js\nfunction sparseFillEmptyRows2(args) {\n  const { inputs, backend: backend2 } = args;\n  const { indices, values, denseShape, defaultValue } = inputs;\n  if (denseShape.shape.length !== 1) {\n    throw new Error(`Dense shape must be a vector, saw:\n        ${denseShape.shape}`);\n  }\n  if (indices.shape.length !== 2) {\n    throw new Error(`Indices must be a matrix, saw:\n        ${indices.shape}`);\n  }\n  if (values.shape.length !== 1) {\n    throw new Error(`Values must be a vector, saw:\n        ${values.shape}`);\n  }\n  if (defaultValue.shape.length !== 0) {\n    throw new Error(`Default value must be a scalar, saw:\n        ${defaultValue.shape}`);\n  }\n  const $indices = backend2.data.get(indices.dataId).values;\n  const $values = backend2.data.get(values.dataId).values;\n  const $denseShape = backend2.data.get(denseShape.dataId).values;\n  const $defaultValue = backend2.data.get(defaultValue.dataId).values[0];\n  const [outputIndices, outputIndicesShape, outputValues, emptyRowIndicator, reverseIndexMap] = sparseFillEmptyRowsImpl($indices, indices.shape, indices.dtype, $values, values.dtype, $denseShape, $defaultValue);\n  return [\n    backend2.makeTensorInfo(outputIndicesShape, indices.dtype, outputIndices),\n    backend2.makeTensorInfo([outputIndicesShape[0]], values.dtype, outputValues),\n    backend2.makeTensorInfo([emptyRowIndicator.length], \"bool\", new Uint8Array(emptyRowIndicator.map((value) => Number(value)))),\n    backend2.makeTensorInfo([reverseIndexMap.length], indices.dtype, new Int32Array(reverseIndexMap))\n  ];\n}\nvar sparseFillEmptyRowsConfig = {\n  kernelName: SparseFillEmptyRows,\n  backendName: \"cpu\",\n  kernelFunc: sparseFillEmptyRows2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/SparseReshape.js\nfunction sparseReshape2(args) {\n  const { inputs, backend: backend2 } = args;\n  const { inputIndices, inputShape, newShape } = inputs;\n  if (inputIndices.shape.length !== 2) {\n    throw new Error(`Input indices should be a matrix but received shape\n        ${inputIndices.shape}`);\n  }\n  if (inputShape.shape.length !== 1) {\n    throw new Error(`Input shape should be a vector but received shape\n        ${inputShape.shape}`);\n  }\n  if (newShape.shape.length !== 1) {\n    throw new Error(`Target shape should be a vector but received shape ${newShape.shape}`);\n  }\n  const $inputShape = Array.from(backend2.data.get(inputShape.dataId).values);\n  const $inputIndices = backend2.data.get(inputIndices.dataId).values;\n  const targetShape = Array.from(backend2.data.get(newShape.dataId).values);\n  const [newIndices, indicesShape, outputShape] = sparseReshapeImpl($inputIndices, inputIndices.shape, inputIndices.dtype, $inputShape, targetShape);\n  return [\n    backend2.makeTensorInfo(indicesShape, inputIndices.dtype, newIndices),\n    backend2.makeTensorInfo([outputShape.length], newShape.dtype, new Int32Array(outputShape))\n  ];\n}\nvar sparseReshapeConfig = {\n  kernelName: SparseReshape,\n  backendName: \"cpu\",\n  kernelFunc: sparseReshape2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/SparseSegmentMean.js\nfunction sparseSegmentMean2(args) {\n  const { inputs, backend: backend2 } = args;\n  const { data, indices, segmentIds } = inputs;\n  if (data.shape.length < 1) {\n    throw new Error(`Data should be at least 1 dimensional but received scalar`);\n  }\n  if (indices.shape.length !== 1) {\n    throw new Error(`Indices should be a vector but received shape\n          ${indices.shape}`);\n  }\n  if (segmentIds.shape.length !== 1) {\n    throw new Error(`Segment ids should be a vector but received shape\n          ${segmentIds.shape}`);\n  }\n  if (indices.shape[0] !== segmentIds.shape[0]) {\n    throw new Error(`segmentIds and indices should have same size.`);\n  }\n  const $data = backend2.data.get(data.dataId).values;\n  const $indices = backend2.data.get(indices.dataId).values;\n  const $segmentIds = backend2.data.get(segmentIds.dataId).values;\n  const [outputData, outputDataShape] = sparseSegmentReductionImpl($data, data.shape, data.dtype, $indices, $segmentIds, true);\n  return backend2.makeTensorInfo(outputDataShape, data.dtype, outputData);\n}\nvar sparseSegmentMeanConfig = {\n  kernelName: SparseSegmentMean,\n  backendName: \"cpu\",\n  kernelFunc: sparseSegmentMean2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/SparseSegmentSum.js\nfunction sparseSegmentSum2(args) {\n  const { inputs, backend: backend2 } = args;\n  const { data, indices, segmentIds } = inputs;\n  if (data.shape.length < 1) {\n    throw new Error(`Data should be at least 1 dimensional but received scalar`);\n  }\n  if (indices.shape.length !== 1) {\n    throw new Error(`Indices should be a vector but received shape\n         ${indices.shape}`);\n  }\n  if (segmentIds.shape.length !== 1) {\n    throw new Error(`Segment ids should be a vector but received shape\n         ${segmentIds.shape}`);\n  }\n  if (indices.shape[0] !== segmentIds.shape[0]) {\n    throw new Error(`segmentIds and indices should have same size.`);\n  }\n  const $data = backend2.data.get(data.dataId).values;\n  const $indices = backend2.data.get(indices.dataId).values;\n  const $segmentIds = backend2.data.get(segmentIds.dataId).values;\n  const [outputData, outputDataShape] = sparseSegmentReductionImpl($data, data.shape, data.dtype, $indices, $segmentIds);\n  return backend2.makeTensorInfo(outputDataShape, data.dtype, outputData);\n}\nvar sparseSegmentSumConfig = {\n  kernelName: SparseSegmentSum,\n  backendName: \"cpu\",\n  kernelFunc: sparseSegmentSum2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/SparseToDense.js\nfunction sparseToDense2(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { sparseIndices, sparseValues, defaultValue } = inputs;\n  const { outputShape } = attrs;\n  const { sliceRank, numUpdates, sliceSize, strides, outputSize } = backend_util_exports.calculateShapes(sparseValues, sparseIndices, outputShape);\n  const sumDupeIndices = false;\n  const indicesBuf = backend2.bufferSync(sparseIndices);\n  let outBuf;\n  switch (sparseValues.dtype) {\n    case \"bool\": {\n      const updatesBuf = backend2.bufferSync(sparseValues);\n      const $defaultValue = Boolean(backend2.data.get(defaultValue.dataId).values[0]);\n      outBuf = scatterImpl(indicesBuf, updatesBuf, outputShape, outputSize, sliceSize, numUpdates, sliceRank, strides, $defaultValue, sumDupeIndices);\n      break;\n    }\n    case \"float32\": {\n      const updatesBuf = backend2.bufferSync(sparseValues);\n      const $defaultValue = backend2.data.get(defaultValue.dataId).values[0];\n      outBuf = scatterImpl(indicesBuf, updatesBuf, outputShape, outputSize, sliceSize, numUpdates, sliceRank, strides, $defaultValue, sumDupeIndices);\n      break;\n    }\n    case \"int32\": {\n      const updatesBuf = backend2.bufferSync(sparseValues);\n      const $defaultValue = backend2.data.get(defaultValue.dataId).values[0];\n      outBuf = scatterImpl(indicesBuf, updatesBuf, outputShape, outputSize, sliceSize, numUpdates, sliceRank, strides, $defaultValue, sumDupeIndices);\n      break;\n    }\n    case \"string\": {\n      const updatesBuf = backend2.bufferSync(sparseValues);\n      const $defaultValue = util_exports.decodeString(backend2.data.get(defaultValue.dataId).values[0]);\n      outBuf = scatterImpl(indicesBuf, updatesBuf, outputShape, outputSize, sliceSize, numUpdates, sliceRank, strides, $defaultValue, sumDupeIndices);\n      break;\n    }\n    default:\n      throw new Error(`Unsupported type ${sparseValues.dtype}`);\n  }\n  return backend2.makeTensorInfo(outputShape, outBuf.dtype, outBuf.values);\n}\nvar sparseToDenseConfig = {\n  kernelName: SparseToDense,\n  backendName: \"cpu\",\n  kernelFunc: sparseToDense2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/SplitV.js\nfunction splitV(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x } = inputs;\n  const { numOrSizeSplits, axis } = attrs;\n  const $axis = util_exports.parseAxisParam(axis, x.shape)[0];\n  const splitSizes = backend_util_exports.prepareSplitSize(x, numOrSizeSplits, $axis);\n  const begin = new Array(x.shape.length).fill(0);\n  const size = x.shape.slice();\n  return splitSizes.map((s) => {\n    const sliceSize = [...size];\n    sliceSize[$axis] = s;\n    const sliceT = slice2({ inputs: { x }, backend: backend2, attrs: { begin, size: sliceSize } });\n    begin[$axis] += s;\n    return sliceT;\n  });\n}\nvar splitVConfig = {\n  kernelName: SplitV,\n  backendName: \"cpu\",\n  kernelFunc: splitV\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Square.js\nvar squareConfig = {\n  kernelName: Square,\n  backendName: \"cpu\",\n  kernelFunc: ({ inputs, backend: backend2 }) => {\n    const { x } = inputs;\n    const cpuBackend = backend2;\n    assertNotComplex(x, \"square\");\n    const values = cpuBackend.data.get(x.dataId).values;\n    const newValues = new Float32Array(values.length);\n    for (let i = 0; i < values.length; ++i) {\n      const value = values[i];\n      newValues[i] = value * value;\n    }\n    const dataId = cpuBackend.write(newValues, x.shape, x.dtype);\n    return { dataId, shape: x.shape, dtype: x.dtype };\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Step.js\nvar step2 = unaryKernelFunc(Step, (xi, attrs) => {\n  const stepAttrs = attrs;\n  if (isNaN(xi)) {\n    return NaN;\n  } else {\n    return xi > 0 ? 1 : stepAttrs.alpha;\n  }\n});\nvar stepConfig = {\n  kernelName: Step,\n  backendName: \"cpu\",\n  kernelFunc: step2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/StridedSlice.js\nfunction stridedSlice2(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x } = inputs;\n  const { begin, end, strides, beginMask, endMask, ellipsisMask, newAxisMask, shrinkAxisMask } = attrs;\n  assertNotComplex(x, \"stridedSlice\");\n  const { finalShapeSparse, finalShape, isIdentity, sliceDim0, isSimpleSlice, begin: $begin, end: $end, strides: $strides } = slice_util_exports.sliceInfo(x.shape, begin, end, strides, beginMask, endMask, ellipsisMask, newAxisMask, shrinkAxisMask);\n  let result;\n  if (isIdentity) {\n    result = reshape3({ inputs: { x }, backend: backend2, attrs: { shape: finalShape } });\n  } else if (sliceDim0 || isSimpleSlice) {\n    util_exports.assert(x.shape.length >= 1, () => `Input must have rank at least 1, got: ${x.shape.length}`);\n    const size = slice_util_exports.computeOutShape($begin, $end, $strides);\n    const sliced = slice2({ inputs: { x }, backend: backend2, attrs: { begin: $begin, size } });\n    result = reshape3({ inputs: { x: sliced }, backend: backend2, attrs: { shape: finalShape } });\n    backend2.disposeIntermediateTensorInfo(sliced);\n  } else {\n    const xBuf = backend2.bufferSync(x);\n    const outBuf = stridedSliceImpl(finalShapeSparse, xBuf, $strides, $begin);\n    result = backend2.makeTensorInfo(finalShape, outBuf.dtype, outBuf.values);\n  }\n  return result;\n}\nvar stridedSliceConfig = {\n  kernelName: StridedSlice,\n  backendName: \"cpu\",\n  kernelFunc: stridedSlice2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/StringNGrams.js\nfunction stringNGrams2(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { separator, nGramWidths, leftPad, rightPad: rightPad2, padWidth, preserveShortSequences } = attrs;\n  const { data, dataSplits } = inputs;\n  const $data = backend2.data.get(data.dataId).values;\n  const $dataSplits = backend2.data.get(dataSplits.dataId).values;\n  const [nGrams, nGramsSplits] = stringNGramsImpl($data, $dataSplits, separator, nGramWidths, leftPad, rightPad2, padWidth, preserveShortSequences);\n  return [\n    backend2.makeTensorInfo([nGrams.length], \"string\", nGrams),\n    backend2.makeTensorInfo(dataSplits.shape, \"int32\", nGramsSplits)\n  ];\n}\nvar stringNGramsConfig = {\n  kernelName: StringNGrams,\n  backendName: \"cpu\",\n  kernelFunc: stringNGrams2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/StringSplit.js\nfunction stringSplit2(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { skipEmpty } = attrs;\n  const { input: input2, delimiter } = inputs;\n  if (input2.dtype !== \"string\") {\n    throw new Error(\"Input must be of datatype string\");\n  }\n  if (input2.shape.length !== 1) {\n    throw new Error(`Input must be a vector, got shape: ${input2.shape}`);\n  }\n  if (delimiter.shape.length !== 0) {\n    throw new Error(`Delimiter must be a scalar, got shape: ${delimiter.shape}`);\n  }\n  const $input = backend2.data.get(input2.dataId).values;\n  const $delimiter = backend2.data.get(delimiter.dataId).values[0];\n  const [indices, values, shape] = stringSplitImpl($input, $delimiter, skipEmpty);\n  const outputSize = values.length;\n  return [\n    backend2.makeTensorInfo([outputSize, 2], \"int32\", indices),\n    backend2.makeTensorInfo([outputSize], \"string\", values),\n    backend2.makeTensorInfo([2], \"int32\", new Int32Array(shape))\n  ];\n}\nvar stringSplitConfig = {\n  kernelName: StringSplit,\n  backendName: \"cpu\",\n  kernelFunc: stringSplit2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/StringToHashBucketFast.js\nfunction stringToHashBucketFast2(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { numBuckets } = attrs;\n  const { input: input2 } = inputs;\n  if (input2.dtype !== \"string\") {\n    throw new Error(\"Input must be of datatype string\");\n  }\n  if (numBuckets <= 0) {\n    throw new Error(`Number of buckets must be at least 1`);\n  }\n  const $input = backend2.data.get(input2.dataId).values;\n  const output = stringToHashBucketFastImpl($input, numBuckets);\n  return backend2.makeTensorInfo(input2.shape, \"int32\", output);\n}\nvar stringToHashBucketFastConfig = {\n  kernelName: StringToHashBucketFast,\n  backendName: \"cpu\",\n  kernelFunc: stringToHashBucketFast2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Tan.js\nvar tan2 = unaryKernelFunc(Tan, (xi) => Math.tan(xi));\nvar tanConfig = {\n  kernelName: Tan,\n  backendName: \"cpu\",\n  kernelFunc: tan2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Tanh.js\nvar tanh3 = unaryKernelFunc(Tanh, (xi) => Math.tanh(xi));\nvar tanhConfig = {\n  kernelName: Tanh,\n  backendName: \"cpu\",\n  kernelFunc: tanh3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Tile.js\nfunction tile3(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x } = inputs;\n  const { reps } = attrs;\n  assertNotComplex(x, \"tile\");\n  const outBuf = tileImpl(backend2.bufferSync(x), reps);\n  return backend2.makeTensorInfo(outBuf.shape, outBuf.dtype, outBuf.values);\n}\nvar tileConfig = {\n  kernelName: Tile,\n  backendName: \"cpu\",\n  kernelFunc: tile3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/TopK.js\nfunction topK(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x } = inputs;\n  const { k, sorted } = attrs;\n  assertNotComplex(x, \"topk\");\n  const xVals = backend2.data.get(x.dataId).values;\n  const [allTopKVals, allTopKIndices] = topKImpl(xVals, x.shape, x.dtype, k, sorted);\n  return [\n    backend2.makeTensorInfo(allTopKVals.shape, allTopKVals.dtype, allTopKVals.values),\n    backend2.makeTensorInfo(allTopKIndices.shape, allTopKIndices.dtype, allTopKIndices.values)\n  ];\n}\nvar topKConfig = {\n  kernelName: TopK,\n  backendName: \"cpu\",\n  kernelFunc: topK\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Transform.js\nfunction transform2(args) {\n  const { inputs, attrs, backend: backend2 } = args;\n  const { image: image2, transforms } = inputs;\n  const { interpolation, fillMode, fillValue, outputShape } = attrs;\n  const [batch, imageHeight, imageWidth, numChannels] = image2.shape;\n  const [outHeight, outWidth] = outputShape != null ? outputShape : [imageHeight, imageWidth];\n  const outShape = [batch, outHeight, outWidth, numChannels];\n  const inStrides = util_exports.computeStrides(image2.shape);\n  const batchInStride = inStrides[0];\n  const rowInStride = inStrides[1];\n  const colInStride = inStrides[2];\n  const outStrides = util_exports.computeStrides(outShape);\n  const batchOutStride = outStrides[0];\n  const rowOutStride = outStrides[1];\n  const colOutStride = outStrides[2];\n  const outVals = util_exports.getTypedArrayFromDType(image2.dtype, util_exports.sizeFromShape(outShape));\n  outVals.fill(fillValue);\n  const imageVals = backend2.data.get(image2.dataId).values;\n  const transformVals = backend2.data.get(transforms.dataId).values;\n  for (let b = 0; b < batch; ++b) {\n    const transform6 = transforms.shape[0] === 1 ? transformVals : transformVals.subarray(b * 8, b * 8 + 8);\n    for (let outY = 0; outY < outHeight; ++outY) {\n      for (let outX = 0; outX < outWidth; ++outX) {\n        for (let channel = 0; channel < numChannels; ++channel) {\n          let val;\n          const projection = transform6[6] * outX + transform6[7] * outY + 1;\n          if (projection === 0) {\n            continue;\n          }\n          const inX = (transform6[0] * outX + transform6[1] * outY + transform6[2]) / projection;\n          const inY = (transform6[3] * outX + transform6[4] * outY + transform6[5]) / projection;\n          const x = mapCoord(inX, imageWidth, fillMode);\n          const y = mapCoord(inY, imageHeight, fillMode);\n          switch (interpolation) {\n            case \"nearest\":\n              val = nearestInterpolation(imageVals, imageHeight, imageWidth, batchInStride, rowInStride, colInStride, b, y, x, channel, fillValue);\n              break;\n            case \"bilinear\":\n              val = bilinearInterpolation(imageVals, imageHeight, imageWidth, batchInStride, rowInStride, colInStride, b, y, x, channel, fillValue);\n              break;\n            default:\n              throw new Error(`Error in Transform: Expect 'nearest' or 'bilinear', but got ${interpolation}`);\n          }\n          const ind = b * batchOutStride + outY * rowOutStride + outX * colOutStride + channel;\n          outVals[ind] = val;\n        }\n      }\n    }\n    return backend2.makeTensorInfo(outShape, image2.dtype, outVals);\n  }\n  const dataId = backend2.write(outVals, outShape, image2.dtype);\n  return { dataId, shape: image2.shape, dtype: image2.dtype };\n}\nvar transformConfig = {\n  kernelName: Transform,\n  backendName: \"cpu\",\n  kernelFunc: transform2\n};\nfunction mapCoord(outCoord, len, mode) {\n  switch (mode) {\n    case \"reflect\":\n      return mapCoordReflect(outCoord, len);\n    case \"wrap\":\n      return mapCoordWrap(outCoord, len);\n    case \"nearest\":\n      return mapCoordNearest(outCoord, len);\n    case \"constant\":\n    default:\n      return mapCoordConstant(outCoord, len);\n  }\n}\nfunction mapCoordReflect(outCoord, len) {\n  let inCoord = outCoord;\n  if (inCoord < 0) {\n    if (len <= 1) {\n      inCoord = 0;\n    } else {\n      const sz2 = 2 * len;\n      if (inCoord < sz2) {\n        inCoord = sz2 * Math.trunc(-inCoord / sz2) + inCoord;\n      }\n      inCoord = inCoord < -len ? inCoord + sz2 : -inCoord - 1;\n    }\n  } else if (inCoord > len - 1) {\n    if (len <= 1) {\n      inCoord = 0;\n    } else {\n      const sz2 = 2 * len;\n      inCoord -= sz2 * Math.trunc(inCoord / sz2);\n      if (inCoord >= len) {\n        inCoord = sz2 - inCoord - 1;\n      }\n    }\n  }\n  return util_exports.clamp(0, inCoord, len - 1);\n}\nfunction mapCoordWrap(outCoord, len) {\n  let inCoord = outCoord;\n  if (inCoord < 0) {\n    if (len <= 1) {\n      inCoord = 0;\n    } else {\n      const sz = len - 1;\n      inCoord += len * (Math.trunc(-inCoord / sz) + 1);\n    }\n  } else if (inCoord > len - 1) {\n    if (len <= 1) {\n      inCoord = 0;\n    } else {\n      const sz = len - 1;\n      inCoord -= len * Math.trunc(inCoord / sz);\n    }\n  }\n  return util_exports.clamp(0, inCoord, len - 1);\n}\nfunction mapCoordConstant(outCoord, len) {\n  return outCoord;\n}\nfunction mapCoordNearest(outCoord, len) {\n  return util_exports.clamp(0, outCoord, len - 1);\n}\nfunction readWithFillValue(imageVals, imageHeight, imageWidth, batchStride, rowStride, colStride, batch, y, x, channel, fillValue) {\n  const ind = batch * batchStride + y * rowStride + x * colStride + channel;\n  if (0 <= y && y < imageHeight && 0 <= x && x < imageWidth) {\n    return imageVals[ind];\n  } else {\n    return fillValue;\n  }\n}\nfunction nearestInterpolation(imageVals, imageHeight, imageWidth, batchStride, rowStride, colStride, batch, y, x, channel, fillValue) {\n  const $y = Math.round(y);\n  const $x = Math.round(x);\n  return readWithFillValue(imageVals, imageHeight, imageWidth, batchStride, rowStride, colStride, batch, $y, $x, channel, fillValue);\n}\nfunction bilinearInterpolation(imageVals, imageHeight, imageWidth, batchStride, rowStride, colStride, batch, y, x, channel, fillValue) {\n  const yFloor = Math.floor(y);\n  const xFloor = Math.floor(x);\n  const yCeil = yFloor + 1;\n  const xCeil = xFloor + 1;\n  const valueYFloor = (xCeil - x) * readWithFillValue(imageVals, imageHeight, imageWidth, batchStride, rowStride, colStride, batch, yFloor, xFloor, channel, fillValue) + (x - xFloor) * readWithFillValue(imageVals, imageHeight, imageWidth, batchStride, rowStride, colStride, batch, yFloor, xCeil, channel, fillValue);\n  const valueYCeil = (xCeil - x) * readWithFillValue(imageVals, imageHeight, imageWidth, batchStride, rowStride, colStride, batch, yCeil, xFloor, channel, fillValue) + (x - xFloor) * readWithFillValue(imageVals, imageHeight, imageWidth, batchStride, rowStride, colStride, batch, yCeil, xCeil, channel, fillValue);\n  return (yCeil - y) * valueYFloor + (y - yFloor) * valueYCeil;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Unique.js\nfunction unique3(args) {\n  const { inputs, attrs, backend: backend2 } = args;\n  const { axis } = attrs;\n  const { x } = inputs;\n  assertNotComplex(x, \"unique\");\n  const values = backend2.data.get(x.dataId).values;\n  const { outputValues, outputShape, indices } = uniqueImpl(values, axis, x.shape, x.dtype);\n  return [\n    backend2.makeTensorInfo(outputShape, x.dtype, outputValues),\n    backend2.makeTensorInfo([indices.length], \"int32\", indices)\n  ];\n}\nvar uniqueConfig = {\n  kernelName: Unique,\n  backendName: \"cpu\",\n  kernelFunc: unique3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Unpack.js\nfunction unpack(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { value } = inputs;\n  let { axis } = attrs;\n  if (axis < 0) {\n    axis += value.shape.length;\n  }\n  const valueRank = value.shape.length;\n  const num = value.shape[axis];\n  const outShape = new Array(valueRank - 1);\n  let outIndex = 0;\n  for (let i = 0; i < valueRank; i++) {\n    if (i !== axis) {\n      outShape[outIndex++] = value.shape[i];\n    }\n  }\n  const begin = new Array(valueRank).fill(0);\n  const size = value.shape.slice();\n  size[axis] = 1;\n  const res = new Array(num);\n  for (let i = 0; i < res.length; i++) {\n    begin[axis] = i;\n    const tempRes = slice2({ inputs: { x: value }, backend: backend2, attrs: { begin, size } });\n    res[i] = reshape3({ inputs: { x: tempRes }, backend: backend2, attrs: { shape: outShape } });\n    backend2.disposeIntermediateTensorInfo(tempRes);\n  }\n  return res;\n}\nvar unpackConfig = {\n  kernelName: Unpack,\n  backendName: \"cpu\",\n  kernelFunc: unpack\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/UnsortedSegmentSum.js\nfunction unsortedSegmentSum2(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x, segmentIds } = inputs;\n  const { numSegments } = attrs;\n  assertNotComplex(x, \"unsortedSegmentSum\");\n  const xRank = x.shape.length;\n  const segmentIdsRank = segmentIds.shape.length;\n  const res = [];\n  const intermediates = [];\n  const numIters = xRank - segmentIdsRank;\n  let $segmentIds = segmentIds;\n  for (let i = 0; i < numIters; ++i) {\n    const expanded = expandDims3({ inputs: { input: $segmentIds }, backend: backend2, attrs: { dim: i + 1 } });\n    $segmentIds = expanded;\n    intermediates.push(expanded);\n  }\n  for (let i = 0; i < numSegments; ++i) {\n    const scalarValue = util_exports.createScalarValue(i, \"int32\");\n    const segmentId = backend2.makeTensorInfo([], \"int32\", scalarValue);\n    const mask = equal2({ inputs: { a: segmentId, b: $segmentIds }, backend: backend2 });\n    const maskCasted = cast3({ inputs: { x: mask }, backend: backend2, attrs: { dtype: \"float32\" } });\n    const mul2 = multiply2({ inputs: { a: maskCasted, b: x }, backend: backend2 });\n    const sumTensorInfo = sum3({ inputs: { x: mul2 }, backend: backend2, attrs: { axis: 0, keepDims: false } });\n    res.push(sumTensorInfo);\n    intermediates.push(segmentId);\n    intermediates.push(mask);\n    intermediates.push(maskCasted);\n    intermediates.push(mul2);\n    intermediates.push(sumTensorInfo);\n  }\n  const result = pack({ inputs: res, backend: backend2, attrs: { axis: 0 } });\n  intermediates.forEach((t) => backend2.disposeIntermediateTensorInfo(t));\n  return result;\n}\nvar unsortedSegmentSumConfig = {\n  kernelName: UnsortedSegmentSum,\n  backendName: \"cpu\",\n  kernelFunc: unsortedSegmentSum2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/register_all_kernels.js\nvar kernelConfigs = [\n  _fusedMatMulConfig,\n  absConfig,\n  acosConfig,\n  acoshConfig,\n  addConfig,\n  addNConfig,\n  allConfig,\n  anyConfig,\n  argMaxConfig,\n  argMinConfig,\n  asinConfig,\n  asinhConfig,\n  atanConfig,\n  atan2Config,\n  atanhConfig,\n  avgPoolConfig,\n  avgPool3DConfig,\n  avgPool3DGradConfig2,\n  avgPoolGradConfig2,\n  batchMatMulConfig,\n  batchNormConfig,\n  batchToSpaceNDConfig,\n  bincountConfig,\n  broadcastArgsConfig,\n  castConfig,\n  ceilConfig,\n  clipByValueConfig,\n  complexConfig,\n  complexAbsConfig,\n  concatConfig,\n  conv2DConfig,\n  conv2DBackpropFilterConfig,\n  conv2DBackpropInputConfig,\n  conv3DConfig,\n  conv3DBackpropFilterV2Config,\n  conv3DBackpropInputV2Config,\n  cosConfig,\n  coshConfig,\n  cropAndResizeConfig,\n  cumprodConfig,\n  cumsumConfig,\n  denseBincountConfig,\n  depthToSpaceConfig,\n  depthwiseConv2dNativeConfig,\n  depthwiseConv2dNativeBackpropFilterConfig,\n  depthwiseConv2dNativeBackpropInputConfig,\n  diagConfig,\n  dilation2DConfig,\n  dilation2DBackpropFilterConfig,\n  dilation2DBackpropInputConfig,\n  einsumConfig,\n  eluConfig,\n  eluGradConfig2,\n  equalConfig,\n  erfConfig,\n  expConfig,\n  expandDimsConfig,\n  expm1Config,\n  fftConfig,\n  fillConfig,\n  flipLeftRightConfig,\n  floorConfig,\n  floorDivConfig,\n  fusedConv2DConfig,\n  fusedDepthwiseConv2DConfig,\n  gatherNdConfig,\n  gatherV2Config,\n  greaterConfig,\n  greaterEqualConfig,\n  identityConfig,\n  ifftConfig,\n  imagConfig,\n  isFiniteConfig,\n  isInfConfig,\n  isNaNConfig,\n  leakyReluConfig,\n  lessConfig,\n  lessEqualConfig,\n  linSpaceConfig,\n  logConfig,\n  log1pConfig,\n  logicalAndConfig,\n  logicalNotConfig,\n  logicalOrConfig,\n  LRNConfig,\n  LRNGradConfig,\n  maxConfig,\n  maximumConfig,\n  maxPoolConfig,\n  maxPool3DConfig,\n  maxPool3DGradConfig2,\n  maxPoolGradConfig2,\n  maxPoolWithArgmaxConfig,\n  meanConfig,\n  minConfig,\n  minimumConfig,\n  mirrorPadConfig,\n  modConfig,\n  multinomialConfig,\n  multiplyConfig,\n  negConfig,\n  nonMaxSuppressionV3Config,\n  nonMaxSuppressionV4Config,\n  nonMaxSuppressionV5Config,\n  notEqualConfig,\n  oneHotConfig,\n  onesLikeConfig,\n  packConfig,\n  padV2Config,\n  powConfig,\n  preluConfig,\n  prodConfig,\n  raggedTensorToTensorConfig,\n  rangeConfig,\n  realConfig,\n  realDivConfig,\n  reciprocalConfig,\n  reluConfig,\n  relu6Config,\n  reshapeConfig,\n  resizeBilinearConfig,\n  resizeBilinearGradConfig2,\n  resizeNearestNeighborConfig,\n  resizeNearestNeighborGradConfig2,\n  reverseConfig,\n  rotateWithOffsetConfig,\n  roundConfig,\n  rsqrtConfig,\n  scatterNdConfig,\n  searchSortedConfig,\n  selectConfig,\n  seluConfig,\n  sigmoidConfig,\n  signConfig,\n  sinConfig,\n  sinhConfig,\n  sliceConfig,\n  softmaxConfig,\n  softplusConfig,\n  spaceToBatchNDConfig,\n  sparseFillEmptyRowsConfig,\n  sparseReshapeConfig,\n  sparseSegmentMeanConfig,\n  sparseSegmentSumConfig,\n  sparseToDenseConfig,\n  splitVConfig,\n  sqrtConfig,\n  squareConfig,\n  squaredDifferenceConfig,\n  stepConfig,\n  stridedSliceConfig,\n  stringNGramsConfig,\n  stringSplitConfig,\n  stringToHashBucketFastConfig,\n  subConfig,\n  sumConfig,\n  tanConfig,\n  tanhConfig,\n  tileConfig,\n  topKConfig,\n  transformConfig,\n  transposeConfig,\n  uniqueConfig,\n  unpackConfig,\n  unsortedSegmentSumConfig,\n  zerosLikeConfig\n];\nfor (const kernelConfig of kernelConfigs) {\n  registerKernel(kernelConfig);\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/webgl_util.js\nvar webgl_util_exports = {};\n__export(webgl_util_exports, {\n  assertNotComplex: () => assertNotComplex2,\n  bindCanvasToFramebuffer: () => bindCanvasToFramebuffer,\n  bindColorTextureToFramebuffer: () => bindColorTextureToFramebuffer,\n  bindTextureToProgramUniformSampler: () => bindTextureToProgramUniformSampler,\n  bindTextureUnit: () => bindTextureUnit,\n  bindVertexBufferToProgramAttribute: () => bindVertexBufferToProgramAttribute,\n  callAndCheck: () => callAndCheck,\n  canBeRepresented: () => canBeRepresented,\n  createFragmentShader: () => createFragmentShader,\n  createFramebuffer: () => createFramebuffer,\n  createProgram: () => createProgram,\n  createStaticIndexBuffer: () => createStaticIndexBuffer,\n  createStaticVertexBuffer: () => createStaticVertexBuffer,\n  createTexture: () => createTexture,\n  createVertexShader: () => createVertexShader,\n  getBatchDim: () => getBatchDim,\n  getExtensionOrThrow: () => getExtensionOrThrow,\n  getFramebufferErrorMessage: () => getFramebufferErrorMessage,\n  getMaxTexturesInShader: () => getMaxTexturesInShader,\n  getNumChannels: () => getNumChannels,\n  getProgramUniformLocation: () => getProgramUniformLocation,\n  getProgramUniformLocationOrThrow: () => getProgramUniformLocationOrThrow,\n  getRowsCols: () => getRowsCols,\n  getShapeAs3D: () => getShapeAs3D,\n  getTextureShapeFromLogicalShape: () => getTextureShapeFromLogicalShape,\n  getWebGLDisjointQueryTimerVersion: () => getWebGLDisjointQueryTimerVersion,\n  getWebGLErrorMessage: () => getWebGLErrorMessage,\n  getWebGLMaxTextureSize: () => getWebGLMaxTextureSize,\n  hasExtension: () => hasExtension,\n  isCapableOfRenderingToFloatTexture: () => isCapableOfRenderingToFloatTexture,\n  isDownloadFloatTextureEnabled: () => isDownloadFloatTextureEnabled,\n  isReshapeFree: () => isReshapeFree,\n  isWebGLFenceEnabled: () => isWebGLFenceEnabled,\n  isWebGLVersionEnabled: () => isWebGLVersionEnabled,\n  linkProgram: () => linkProgram,\n  logShaderSourceAndInfoLog: () => logShaderSourceAndInfoLog,\n  resetMaxTextureSize: () => resetMaxTextureSize,\n  resetMaxTexturesInShader: () => resetMaxTexturesInShader,\n  unbindColorTextureFromFramebuffer: () => unbindColorTextureFromFramebuffer,\n  unbindTextureUnit: () => unbindTextureUnit,\n  validateFramebuffer: () => validateFramebuffer,\n  validateProgram: () => validateProgram,\n  validateTextureSize: () => validateTextureSize\n});\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/canvas_util.js\nvar contexts = {};\nvar WEBGL_ATTRIBUTES = {\n  alpha: false,\n  antialias: false,\n  premultipliedAlpha: false,\n  preserveDrawingBuffer: false,\n  depth: false,\n  stencil: false,\n  failIfMajorPerformanceCaveat: true\n};\nfunction setWebGLContext(webGLVersion, gl) {\n  contexts[webGLVersion] = gl;\n}\nfunction getWebGLContext(webGLVersion, customCanvas) {\n  if (!(webGLVersion in contexts) || customCanvas != null) {\n    const newCtx = getWebGLRenderingContext(webGLVersion, customCanvas);\n    if (newCtx !== null) {\n      contexts[webGLVersion] = newCtx;\n    } else {\n      console.log(\"Could not get context for WebGL version\", webGLVersion);\n      return null;\n    }\n  }\n  const gl = contexts[webGLVersion];\n  if (gl == null || gl.isContextLost()) {\n    delete contexts[webGLVersion];\n    return getWebGLContext(webGLVersion);\n  }\n  gl.disable(gl.DEPTH_TEST);\n  gl.disable(gl.STENCIL_TEST);\n  gl.disable(gl.BLEND);\n  gl.disable(gl.DITHER);\n  gl.disable(gl.POLYGON_OFFSET_FILL);\n  gl.disable(gl.SAMPLE_COVERAGE);\n  gl.enable(gl.SCISSOR_TEST);\n  gl.enable(gl.CULL_FACE);\n  gl.cullFace(gl.BACK);\n  return contexts[webGLVersion];\n}\nfunction createCanvas(webGLVersion) {\n  if (typeof OffscreenCanvas !== \"undefined\" && webGLVersion === 2) {\n    return new OffscreenCanvas(300, 150);\n  } else if (typeof document !== \"undefined\") {\n    return document.createElement(\"canvas\");\n  } else {\n    throw new Error(\"Cannot create a canvas in this context\");\n  }\n}\nfunction getWebGLRenderingContext(webGLVersion, customCanvas) {\n  if (webGLVersion !== 1 && webGLVersion !== 2) {\n    throw new Error(\"Cannot get WebGL rendering context, WebGL is disabled.\");\n  }\n  const canvas = customCanvas == null ? createCanvas(webGLVersion) : customCanvas;\n  canvas.addEventListener(\"webglcontextlost\", (ev) => {\n    ev.preventDefault();\n    delete contexts[webGLVersion];\n  }, false);\n  if (env().getBool(\"SOFTWARE_WEBGL_ENABLED\")) {\n    WEBGL_ATTRIBUTES.failIfMajorPerformanceCaveat = false;\n  }\n  if (webGLVersion === 1) {\n    return canvas.getContext(\"webgl\", WEBGL_ATTRIBUTES) || canvas.getContext(\"experimental-webgl\", WEBGL_ATTRIBUTES);\n  }\n  return canvas.getContext(\"webgl2\", WEBGL_ATTRIBUTES);\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/tex_util.js\nvar PackingScheme;\n(function(PackingScheme2) {\n  PackingScheme2[PackingScheme2[\"DENSE\"] = 0] = \"DENSE\";\n  PackingScheme2[PackingScheme2[\"SHARED_BATCH\"] = 1] = \"SHARED_BATCH\";\n})(PackingScheme || (PackingScheme = {}));\nvar TextureUsage;\n(function(TextureUsage2) {\n  TextureUsage2[TextureUsage2[\"RENDER\"] = 0] = \"RENDER\";\n  TextureUsage2[TextureUsage2[\"UPLOAD\"] = 1] = \"UPLOAD\";\n  TextureUsage2[TextureUsage2[\"PIXELS\"] = 2] = \"PIXELS\";\n  TextureUsage2[TextureUsage2[\"DOWNLOAD\"] = 3] = \"DOWNLOAD\";\n})(TextureUsage || (TextureUsage = {}));\nvar PhysicalTextureType;\n(function(PhysicalTextureType2) {\n  PhysicalTextureType2[PhysicalTextureType2[\"UNPACKED_FLOAT16\"] = 0] = \"UNPACKED_FLOAT16\";\n  PhysicalTextureType2[PhysicalTextureType2[\"UNPACKED_FLOAT32\"] = 1] = \"UNPACKED_FLOAT32\";\n  PhysicalTextureType2[PhysicalTextureType2[\"PACKED_4X1_UNSIGNED_BYTE\"] = 2] = \"PACKED_4X1_UNSIGNED_BYTE\";\n  PhysicalTextureType2[PhysicalTextureType2[\"PACKED_2X2_FLOAT32\"] = 3] = \"PACKED_2X2_FLOAT32\";\n  PhysicalTextureType2[PhysicalTextureType2[\"PACKED_2X2_FLOAT16\"] = 4] = \"PACKED_2X2_FLOAT16\";\n})(PhysicalTextureType || (PhysicalTextureType = {}));\nfunction getUnpackedMatrixTextureShapeWidthHeight(rows, columns) {\n  return [columns, rows];\n}\nfunction getUnpackedArraySizeFromMatrixSize(matrixSize, channelsPerTexture) {\n  return matrixSize * channelsPerTexture;\n}\nfunction getDenseTexShape(shape) {\n  const size = util_exports.sizeFromShape(shape);\n  const texelsNeeded = Math.ceil(size / 4);\n  return util_exports.sizeToSquarishShape(texelsNeeded);\n}\nfunction getPackedMatrixTextureShapeWidthHeight(rows, columns) {\n  return [\n    Math.max(1, Math.ceil(columns / 2)),\n    Math.max(1, Math.ceil(rows / 2))\n  ];\n}\nfunction getPackedRGBAArraySizeFromMatrixShape(rows, columns) {\n  const [w, h] = getPackedMatrixTextureShapeWidthHeight(rows, columns);\n  return w * h * 4;\n}\nfunction getTextureConfig(gl, textureHalfFloatExtension) {\n  const glany = gl;\n  let internalFormatFloat;\n  let internalFormatHalfFloat;\n  let internalFormatPackedHalfFloat;\n  let internalFormatPackedFloat;\n  let textureFormatFloat;\n  let downloadTextureFormat;\n  let downloadUnpackNumChannels;\n  let defaultNumChannels;\n  let textureTypeHalfFloat;\n  let textureTypeFloat;\n  if (env().getNumber(\"WEBGL_VERSION\") === 2) {\n    internalFormatFloat = glany.R32F;\n    internalFormatHalfFloat = glany.R16F;\n    internalFormatPackedHalfFloat = glany.RGBA16F;\n    internalFormatPackedFloat = glany.RGBA32F;\n    textureFormatFloat = glany.RED;\n    downloadUnpackNumChannels = 4;\n    defaultNumChannels = 1;\n    textureTypeHalfFloat = glany.HALF_FLOAT;\n    textureTypeFloat = glany.FLOAT;\n    downloadTextureFormat = glany.RGBA8;\n  } else {\n    internalFormatFloat = gl.RGBA;\n    internalFormatHalfFloat = gl.RGBA;\n    internalFormatPackedHalfFloat = gl.RGBA;\n    internalFormatPackedFloat = glany.RGBA;\n    textureFormatFloat = gl.RGBA;\n    downloadUnpackNumChannels = 4;\n    defaultNumChannels = 4;\n    textureTypeHalfFloat = textureHalfFloatExtension != null ? textureHalfFloatExtension.HALF_FLOAT_OES : null;\n    textureTypeFloat = gl.FLOAT;\n    downloadTextureFormat = gl.RGBA;\n  }\n  return {\n    internalFormatFloat,\n    internalFormatHalfFloat,\n    internalFormatPackedHalfFloat,\n    internalFormatPackedFloat,\n    textureFormatFloat,\n    downloadTextureFormat,\n    downloadUnpackNumChannels,\n    defaultNumChannels,\n    textureTypeHalfFloat,\n    textureTypeFloat\n  };\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/webgl_util.js\nfunction callAndCheck(gl, func2) {\n  const returnValue = func2();\n  if (env().getBool(\"DEBUG\")) {\n    checkWebGLError(gl);\n  }\n  return returnValue;\n}\nfunction checkWebGLError(gl) {\n  const error = gl.getError();\n  if (error !== gl.NO_ERROR) {\n    throw new Error(\"WebGL Error: \" + getWebGLErrorMessage(gl, error));\n  }\n}\nvar MIN_FLOAT16 = 596e-10;\nvar MAX_FLOAT16 = 65504;\nfunction canBeRepresented(num) {\n  if (env().getBool(\"WEBGL_RENDER_FLOAT32_ENABLED\") || num === 0 || MIN_FLOAT16 < Math.abs(num) && Math.abs(num) < MAX_FLOAT16) {\n    return true;\n  }\n  return false;\n}\nfunction getWebGLErrorMessage(gl, status) {\n  switch (status) {\n    case gl.NO_ERROR:\n      return \"NO_ERROR\";\n    case gl.INVALID_ENUM:\n      return \"INVALID_ENUM\";\n    case gl.INVALID_VALUE:\n      return \"INVALID_VALUE\";\n    case gl.INVALID_OPERATION:\n      return \"INVALID_OPERATION\";\n    case gl.INVALID_FRAMEBUFFER_OPERATION:\n      return \"INVALID_FRAMEBUFFER_OPERATION\";\n    case gl.OUT_OF_MEMORY:\n      return \"OUT_OF_MEMORY\";\n    case gl.CONTEXT_LOST_WEBGL:\n      return \"CONTEXT_LOST_WEBGL\";\n    default:\n      return `Unknown error code ${status}`;\n  }\n}\nfunction getExtensionOrThrow(gl, extensionName) {\n  return throwIfNull(gl, () => gl.getExtension(extensionName), 'Extension \"' + extensionName + '\" not supported on this browser.');\n}\nfunction createVertexShader(gl, vertexShaderSource) {\n  const vertexShader = throwIfNull(gl, () => gl.createShader(gl.VERTEX_SHADER), \"Unable to create vertex WebGLShader.\");\n  callAndCheck(gl, () => gl.shaderSource(vertexShader, vertexShaderSource));\n  callAndCheck(gl, () => gl.compileShader(vertexShader));\n  if (gl.getShaderParameter(vertexShader, gl.COMPILE_STATUS) === false) {\n    console.log(gl.getShaderInfoLog(vertexShader));\n    throw new Error(\"Failed to compile vertex shader.\");\n  }\n  return vertexShader;\n}\nfunction createFragmentShader(gl, fragmentShaderSource) {\n  const fragmentShader = throwIfNull(gl, () => gl.createShader(gl.FRAGMENT_SHADER), \"Unable to create fragment WebGLShader.\");\n  callAndCheck(gl, () => gl.shaderSource(fragmentShader, fragmentShaderSource));\n  callAndCheck(gl, () => gl.compileShader(fragmentShader));\n  if (env().get(\"ENGINE_COMPILE_ONLY\")) {\n    return fragmentShader;\n  }\n  if (gl.getShaderParameter(fragmentShader, gl.COMPILE_STATUS) === false) {\n    logShaderSourceAndInfoLog(fragmentShaderSource, gl.getShaderInfoLog(fragmentShader));\n    throw new Error(\"Failed to compile fragment shader.\");\n  }\n  return fragmentShader;\n}\nvar lineNumberRegex = /ERROR: [0-9]+:([0-9]+):/g;\nfunction logShaderSourceAndInfoLog(shaderSource, shaderInfoLog) {\n  const lineNumberRegexResult = lineNumberRegex.exec(shaderInfoLog);\n  if (lineNumberRegexResult == null) {\n    console.log(`Couldn't parse line number in error: ${shaderInfoLog}`);\n    console.log(shaderSource);\n    return;\n  }\n  const lineNumber = +lineNumberRegexResult[1];\n  const shaderLines = shaderSource.split(\"\\n\");\n  const pad3 = shaderLines.length.toString().length + 2;\n  const linesWithLineNumbers = shaderLines.map((line, lineNumber2) => util_exports.rightPad((lineNumber2 + 1).toString(), pad3) + line);\n  let maxLineLength = 0;\n  for (let i = 0; i < linesWithLineNumbers.length; i++) {\n    maxLineLength = Math.max(linesWithLineNumbers[i].length, maxLineLength);\n  }\n  const beforeErrorLines = linesWithLineNumbers.slice(0, lineNumber - 1);\n  const errorLine = linesWithLineNumbers.slice(lineNumber - 1, lineNumber);\n  const afterErrorLines = linesWithLineNumbers.slice(lineNumber);\n  console.log(beforeErrorLines.join(\"\\n\"));\n  console.log(shaderInfoLog.split(\"\\n\")[0]);\n  console.log(`%c ${util_exports.rightPad(errorLine[0], maxLineLength)}`, \"border:1px solid red; background-color:#e3d2d2; color:#a61717\");\n  console.log(afterErrorLines.join(\"\\n\"));\n}\nfunction createProgram(gl) {\n  return throwIfNull(gl, () => gl.createProgram(), \"Unable to create WebGLProgram.\");\n}\nfunction linkProgram(gl, program) {\n  callAndCheck(gl, () => gl.linkProgram(program));\n  if (env().get(\"ENGINE_COMPILE_ONLY\")) {\n    return;\n  }\n  if (gl.getProgramParameter(program, gl.LINK_STATUS) === false) {\n    console.log(gl.getProgramInfoLog(program));\n    throw new Error(\"Failed to link vertex and fragment shaders.\");\n  }\n}\nfunction validateProgram(gl, program) {\n  callAndCheck(gl, () => gl.validateProgram(program));\n  if (gl.getProgramParameter(program, gl.VALIDATE_STATUS) === false) {\n    console.log(gl.getProgramInfoLog(program));\n    throw new Error(\"Shader program validation failed.\");\n  }\n}\nfunction createStaticVertexBuffer(gl, data) {\n  const buffer2 = throwIfNull(gl, () => gl.createBuffer(), \"Unable to create WebGLBuffer\");\n  callAndCheck(gl, () => gl.bindBuffer(gl.ARRAY_BUFFER, buffer2));\n  callAndCheck(gl, () => gl.bufferData(gl.ARRAY_BUFFER, data, gl.STATIC_DRAW));\n  return buffer2;\n}\nfunction createStaticIndexBuffer(gl, data) {\n  const buffer2 = throwIfNull(gl, () => gl.createBuffer(), \"Unable to create WebGLBuffer\");\n  callAndCheck(gl, () => gl.bindBuffer(gl.ELEMENT_ARRAY_BUFFER, buffer2));\n  callAndCheck(gl, () => gl.bufferData(gl.ELEMENT_ARRAY_BUFFER, data, gl.STATIC_DRAW));\n  return buffer2;\n}\nfunction getNumChannels() {\n  if (env().getNumber(\"WEBGL_VERSION\") === 2) {\n    return 1;\n  }\n  return 4;\n}\nfunction createTexture(gl) {\n  return throwIfNull(gl, () => gl.createTexture(), \"Unable to create WebGLTexture.\");\n}\nfunction validateTextureSize(width, height) {\n  const maxTextureSize = env().getNumber(\"WEBGL_MAX_TEXTURE_SIZE\");\n  if (width <= 0 || height <= 0) {\n    const requested = `[${width}x${height}]`;\n    throw new Error(\"Requested texture size \" + requested + \" is invalid.\");\n  }\n  if (width > maxTextureSize || height > maxTextureSize) {\n    const requested = `[${width}x${height}]`;\n    const max7 = `[${maxTextureSize}x${maxTextureSize}]`;\n    throw new Error(\"Requested texture size \" + requested + \" greater than WebGL maximum on this browser / GPU \" + max7 + \".\");\n  }\n}\nfunction createFramebuffer(gl) {\n  return throwIfNull(gl, () => gl.createFramebuffer(), \"Unable to create WebGLFramebuffer.\");\n}\nfunction bindVertexBufferToProgramAttribute(gl, program, attribute, buffer2, arrayEntriesPerItem, itemStrideInBytes, itemOffsetInBytes) {\n  const loc = gl.getAttribLocation(program, attribute);\n  if (loc === -1) {\n    return false;\n  }\n  callAndCheck(gl, () => gl.bindBuffer(gl.ARRAY_BUFFER, buffer2));\n  callAndCheck(gl, () => gl.vertexAttribPointer(loc, arrayEntriesPerItem, gl.FLOAT, false, itemStrideInBytes, itemOffsetInBytes));\n  callAndCheck(gl, () => gl.enableVertexAttribArray(loc));\n  return true;\n}\nfunction bindTextureUnit(gl, texture, textureUnit) {\n  validateTextureUnit(gl, textureUnit);\n  callAndCheck(gl, () => gl.activeTexture(gl.TEXTURE0 + textureUnit));\n  callAndCheck(gl, () => gl.bindTexture(gl.TEXTURE_2D, texture));\n}\nfunction unbindTextureUnit(gl, textureUnit) {\n  validateTextureUnit(gl, textureUnit);\n  callAndCheck(gl, () => gl.activeTexture(gl.TEXTURE0 + textureUnit));\n  callAndCheck(gl, () => gl.bindTexture(gl.TEXTURE_2D, null));\n}\nfunction getProgramUniformLocationOrThrow(gl, program, uniformName) {\n  return throwIfNull(gl, () => gl.getUniformLocation(program, uniformName), 'uniform \"' + uniformName + '\" not present in program.');\n}\nfunction getProgramUniformLocation(gl, program, uniformName) {\n  return gl.getUniformLocation(program, uniformName);\n}\nfunction bindTextureToProgramUniformSampler(gl, texture, uniformSamplerLocation, textureUnit) {\n  callAndCheck(gl, () => bindTextureUnit(gl, texture, textureUnit));\n  callAndCheck(gl, () => gl.uniform1i(uniformSamplerLocation, textureUnit));\n}\nfunction bindCanvasToFramebuffer(gl) {\n  callAndCheck(gl, () => gl.bindFramebuffer(gl.FRAMEBUFFER, null));\n  callAndCheck(gl, () => gl.viewport(0, 0, gl.canvas.width, gl.canvas.height));\n  callAndCheck(gl, () => gl.scissor(0, 0, gl.canvas.width, gl.canvas.height));\n}\nfunction bindColorTextureToFramebuffer(gl, texture, framebuffer) {\n  callAndCheck(gl, () => gl.bindFramebuffer(gl.FRAMEBUFFER, framebuffer));\n  callAndCheck(gl, () => gl.framebufferTexture2D(gl.FRAMEBUFFER, gl.COLOR_ATTACHMENT0, gl.TEXTURE_2D, texture, 0));\n}\nfunction unbindColorTextureFromFramebuffer(gl, framebuffer) {\n  callAndCheck(gl, () => gl.bindFramebuffer(gl.FRAMEBUFFER, framebuffer));\n  callAndCheck(gl, () => gl.framebufferTexture2D(gl.FRAMEBUFFER, gl.COLOR_ATTACHMENT0, gl.TEXTURE_2D, null, 0));\n}\nfunction validateFramebuffer(gl) {\n  const status = gl.checkFramebufferStatus(gl.FRAMEBUFFER);\n  if (status !== gl.FRAMEBUFFER_COMPLETE) {\n    throw new Error(\"Error binding framebuffer: \" + getFramebufferErrorMessage(gl, status));\n  }\n}\nfunction getFramebufferErrorMessage(gl, status) {\n  switch (status) {\n    case gl.FRAMEBUFFER_INCOMPLETE_ATTACHMENT:\n      return \"FRAMEBUFFER_INCOMPLETE_ATTACHMENT\";\n    case gl.FRAMEBUFFER_INCOMPLETE_MISSING_ATTACHMENT:\n      return \"FRAMEBUFFER_INCOMPLETE_MISSING_ATTACHMENT\";\n    case gl.FRAMEBUFFER_INCOMPLETE_DIMENSIONS:\n      return \"FRAMEBUFFER_INCOMPLETE_DIMENSIONS\";\n    case gl.FRAMEBUFFER_UNSUPPORTED:\n      return \"FRAMEBUFFER_UNSUPPORTED\";\n    default:\n      return `unknown error ${status}`;\n  }\n}\nfunction throwIfNull(gl, returnTOrNull, failureMessage) {\n  const tOrNull = callAndCheck(gl, () => returnTOrNull());\n  if (tOrNull == null) {\n    throw new Error(failureMessage);\n  }\n  return tOrNull;\n}\nfunction validateTextureUnit(gl, textureUnit) {\n  const maxTextureUnit = gl.MAX_COMBINED_TEXTURE_IMAGE_UNITS - 1;\n  const glTextureUnit = textureUnit + gl.TEXTURE0;\n  if (glTextureUnit < gl.TEXTURE0 || glTextureUnit > maxTextureUnit) {\n    const textureUnitRange = `[gl.TEXTURE0, gl.TEXTURE${maxTextureUnit}]`;\n    throw new Error(`textureUnit must be in ${textureUnitRange}.`);\n  }\n}\nfunction getBatchDim(shape, dimsToSkip = 2) {\n  return util_exports.sizeFromShape(shape.slice(0, shape.length - dimsToSkip));\n}\nfunction getRowsCols(shape) {\n  if (shape.length === 0) {\n    throw Error(\"Cannot get rows and columns of an empty shape array.\");\n  }\n  return [\n    shape.length > 1 ? shape[shape.length - 2] : 1,\n    shape[shape.length - 1]\n  ];\n}\nfunction getShapeAs3D(shape) {\n  let shapeAs3D = [1, 1, 1];\n  const isScalar = shape.length === 0 || shape.length === 1 && shape[0] === 1;\n  if (!isScalar) {\n    shapeAs3D = [getBatchDim(shape), ...getRowsCols(shape)];\n  }\n  return shapeAs3D;\n}\nfunction getTextureShapeFromLogicalShape(logShape, isPacked = false) {\n  let maxTexSize = env().getNumber(\"WEBGL_MAX_TEXTURE_SIZE\");\n  if (isPacked) {\n    maxTexSize = maxTexSize * 2;\n    logShape = logShape.map((d, i) => i >= logShape.length - 2 ? util_exports.nearestLargerEven(logShape[i]) : logShape[i]);\n    if (logShape.length === 1) {\n      logShape = [2, logShape[0]];\n    }\n  }\n  if (logShape.length !== 2) {\n    const squeezeResult = util_exports.squeezeShape(logShape);\n    logShape = squeezeResult.newShape;\n  }\n  let size = util_exports.sizeFromShape(logShape);\n  if (logShape.length <= 1 && size <= maxTexSize) {\n    return [1, size];\n  } else if (logShape.length === 2 && logShape[0] <= maxTexSize && logShape[1] <= maxTexSize) {\n    return logShape;\n  } else if (logShape.length === 3 && logShape[0] * logShape[1] <= maxTexSize && logShape[2] <= maxTexSize) {\n    return [logShape[0] * logShape[1], logShape[2]];\n  } else if (logShape.length === 3 && logShape[0] <= maxTexSize && logShape[1] * logShape[2] <= maxTexSize) {\n    return [logShape[0], logShape[1] * logShape[2]];\n  } else if (logShape.length === 4 && logShape[0] * logShape[1] * logShape[2] <= maxTexSize && logShape[3] <= maxTexSize) {\n    return [logShape[0] * logShape[1] * logShape[2], logShape[3]];\n  } else if (logShape.length === 4 && logShape[0] <= maxTexSize && logShape[1] * logShape[2] * logShape[3] <= maxTexSize) {\n    return [logShape[0], logShape[1] * logShape[2] * logShape[3]];\n  } else {\n    if (isPacked) {\n      const batchDim = getBatchDim(logShape);\n      let rows = 2, cols = 2;\n      if (logShape.length) {\n        [rows, cols] = getRowsCols(logShape);\n      }\n      size = batchDim * (rows / 2) * (cols / 2);\n      return util_exports.sizeToSquarishShape(size).map((d) => d * 2);\n    }\n    return util_exports.sizeToSquarishShape(size);\n  }\n}\nfunction isEven(n) {\n  return n % 2 === 0;\n}\nfunction isReshapeFree(shape1, shape2) {\n  shape1 = shape1.slice(-2);\n  shape2 = shape2.slice(-2);\n  if (util_exports.arraysEqual(shape1, shape2)) {\n    return true;\n  }\n  if (!shape1.length || !shape2.length) {\n    return true;\n  }\n  if (shape1[0] === 0 || shape1[1] === 0 || shape2[0] === 0 || shape2[1] === 0) {\n    return true;\n  }\n  if (shape1.length !== shape2.length) {\n    const shape1Cols = shape1.slice(-1)[0];\n    const shape2Cols = shape2.slice(-1)[0];\n    if (shape1Cols === shape2Cols) {\n      return true;\n    }\n    if (isEven(shape1Cols) && isEven(shape2Cols) && (shape1[0] === 1 || shape2[0] === 1)) {\n      return true;\n    }\n  }\n  return shape1[1] === shape2[1] && isEven(shape1[0]) && isEven(shape2[0]);\n}\nvar MAX_TEXTURE_SIZE;\nvar MAX_TEXTURES_IN_SHADER;\nfunction getWebGLMaxTextureSize(webGLVersion) {\n  if (MAX_TEXTURE_SIZE == null) {\n    const gl = getWebGLContext(webGLVersion);\n    MAX_TEXTURE_SIZE = gl.getParameter(gl.MAX_TEXTURE_SIZE);\n  }\n  return MAX_TEXTURE_SIZE;\n}\nfunction resetMaxTextureSize() {\n  MAX_TEXTURE_SIZE = null;\n}\nfunction resetMaxTexturesInShader() {\n  MAX_TEXTURES_IN_SHADER = null;\n}\nfunction getMaxTexturesInShader(webGLVersion) {\n  if (MAX_TEXTURES_IN_SHADER == null) {\n    const gl = getWebGLContext(webGLVersion);\n    MAX_TEXTURES_IN_SHADER = gl.getParameter(gl.MAX_TEXTURE_IMAGE_UNITS);\n  }\n  return Math.min(16, MAX_TEXTURES_IN_SHADER);\n}\nfunction getWebGLDisjointQueryTimerVersion(webGLVersion) {\n  if (webGLVersion === 0) {\n    return 0;\n  }\n  let queryTimerVersion;\n  const gl = getWebGLContext(webGLVersion);\n  if (hasExtension(gl, \"EXT_disjoint_timer_query_webgl2\") && webGLVersion === 2) {\n    queryTimerVersion = 2;\n  } else if (hasExtension(gl, \"EXT_disjoint_timer_query\")) {\n    queryTimerVersion = 1;\n  } else {\n    queryTimerVersion = 0;\n  }\n  return queryTimerVersion;\n}\nfunction hasExtension(gl, extensionName) {\n  const ext = gl.getExtension(extensionName);\n  return ext != null;\n}\nfunction isWebGLVersionEnabled(webGLVersion) {\n  try {\n    const gl = getWebGLContext(webGLVersion);\n    if (gl != null) {\n      return true;\n    }\n  } catch (e) {\n    console.log(\"Error when getting WebGL context: \", e);\n    return false;\n  }\n  return false;\n}\nfunction isCapableOfRenderingToFloatTexture(webGLVersion) {\n  if (webGLVersion === 0) {\n    return false;\n  }\n  const gl = getWebGLContext(webGLVersion);\n  if (webGLVersion === 1) {\n    if (!hasExtension(gl, \"OES_texture_float\")) {\n      return false;\n    }\n  } else {\n    if (!hasExtension(gl, \"EXT_color_buffer_float\")) {\n      return false;\n    }\n  }\n  const isFrameBufferComplete = createFloatTextureAndBindToFramebuffer(gl);\n  return isFrameBufferComplete;\n}\nfunction isDownloadFloatTextureEnabled(webGLVersion) {\n  if (webGLVersion === 0) {\n    return false;\n  }\n  const gl = getWebGLContext(webGLVersion);\n  if (webGLVersion === 1) {\n    if (!hasExtension(gl, \"OES_texture_float\")) {\n      return false;\n    }\n    if (!hasExtension(gl, \"WEBGL_color_buffer_float\")) {\n      return false;\n    }\n  } else {\n    if (hasExtension(gl, \"EXT_color_buffer_float\")) {\n      return createFloatTextureAndBindToFramebuffer(gl);\n    }\n    const COLOR_BUFFER_HALF_FLOAT = \"EXT_color_buffer_half_float\";\n    if (hasExtension(gl, COLOR_BUFFER_HALF_FLOAT)) {\n      const textureHalfFloatExtension = gl.getExtension(COLOR_BUFFER_HALF_FLOAT);\n      return createHalfFloatTextureAndBindToFramebuffer(gl, textureHalfFloatExtension);\n    }\n    return false;\n  }\n  const isFrameBufferComplete = createFloatTextureAndBindToFramebuffer(gl);\n  return isFrameBufferComplete;\n}\nfunction createFloatTextureAndBindToFramebuffer(gl) {\n  const texConfig = getTextureConfig(gl);\n  const texture = gl.createTexture();\n  gl.bindTexture(gl.TEXTURE_2D, texture);\n  const width = 1;\n  const height = 1;\n  gl.texImage2D(gl.TEXTURE_2D, 0, texConfig.internalFormatFloat, width, height, 0, texConfig.textureFormatFloat, texConfig.textureTypeFloat, null);\n  const frameBuffer = gl.createFramebuffer();\n  gl.bindFramebuffer(gl.FRAMEBUFFER, frameBuffer);\n  gl.framebufferTexture2D(gl.FRAMEBUFFER, gl.COLOR_ATTACHMENT0, gl.TEXTURE_2D, texture, 0);\n  const isFrameBufferComplete = gl.checkFramebufferStatus(gl.FRAMEBUFFER) === gl.FRAMEBUFFER_COMPLETE;\n  gl.bindTexture(gl.TEXTURE_2D, null);\n  gl.bindFramebuffer(gl.FRAMEBUFFER, null);\n  gl.deleteTexture(texture);\n  gl.deleteFramebuffer(frameBuffer);\n  return isFrameBufferComplete;\n}\nfunction createHalfFloatTextureAndBindToFramebuffer(gl, textureHalfFloatExtension) {\n  const texConfig = getTextureConfig(gl, textureHalfFloatExtension);\n  const texture = gl.createTexture();\n  gl.bindTexture(gl.TEXTURE_2D, texture);\n  const width = 1;\n  const height = 1;\n  gl.texImage2D(gl.TEXTURE_2D, 0, texConfig.internalFormatHalfFloat, width, height, 0, texConfig.textureFormatFloat, texConfig.textureTypeHalfFloat, null);\n  const frameBuffer = gl.createFramebuffer();\n  gl.bindFramebuffer(gl.FRAMEBUFFER, frameBuffer);\n  gl.framebufferTexture2D(gl.FRAMEBUFFER, gl.COLOR_ATTACHMENT0, gl.TEXTURE_2D, texture, 0);\n  const isFrameBufferComplete = gl.checkFramebufferStatus(gl.FRAMEBUFFER) === gl.FRAMEBUFFER_COMPLETE;\n  gl.bindTexture(gl.TEXTURE_2D, null);\n  gl.bindFramebuffer(gl.FRAMEBUFFER, null);\n  gl.deleteTexture(texture);\n  gl.deleteFramebuffer(frameBuffer);\n  return isFrameBufferComplete;\n}\nfunction isWebGLFenceEnabled(webGLVersion) {\n  if (webGLVersion !== 2) {\n    return false;\n  }\n  const gl = getWebGLContext(webGLVersion);\n  const isEnabled = gl.fenceSync != null;\n  return isEnabled;\n}\nfunction assertNotComplex2(tensor2, opName) {\n  if (!Array.isArray(tensor2)) {\n    tensor2 = [tensor2];\n  }\n  tensor2.forEach((t) => {\n    if (t != null) {\n      util_exports.assert(t.dtype !== \"complex64\", () => `${opName} does not support complex64 tensors in the WebGL backend.`);\n    }\n  });\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/flags_webgl.js\nvar ENV5 = env();\nENV5.registerFlag(\"HAS_WEBGL\", () => ENV5.getNumber(\"WEBGL_VERSION\") > 0);\nENV5.registerFlag(\"WEBGL_VERSION\", () => {\n  if (isWebGLVersionEnabled(2)) {\n    return 2;\n  } else if (isWebGLVersionEnabled(1)) {\n    return 1;\n  }\n  return 0;\n});\nENV5.registerFlag(\"WEBGL_CHECK_NUMERICAL_PROBLEMS\", () => false);\nENV5.registerFlag(\"WEBGL_BUFFER_SUPPORTED\", () => ENV5.get(\"WEBGL_VERSION\") === 2);\nENV5.registerFlag(\"WEBGL_CPU_FORWARD\", () => true);\nENV5.registerFlag(\"WEBGL_FORCE_F16_TEXTURES\", () => false);\nENV5.registerFlag(\"WEBGL_PACK\", () => ENV5.getBool(\"HAS_WEBGL\"));\nENV5.registerFlag(\"WEBGL_PACK_NORMALIZATION\", () => ENV5.getBool(\"WEBGL_PACK\"));\nENV5.registerFlag(\"WEBGL_PACK_CLIP\", () => ENV5.getBool(\"WEBGL_PACK\"));\nENV5.registerFlag(\"WEBGL_PACK_DEPTHWISECONV\", () => ENV5.getBool(\"WEBGL_PACK\"));\nENV5.registerFlag(\"WEBGL_PACK_BINARY_OPERATIONS\", () => ENV5.getBool(\"WEBGL_PACK\"));\nENV5.registerFlag(\"WEBGL_PACK_UNARY_OPERATIONS\", () => ENV5.getBool(\"WEBGL_PACK\"));\nENV5.registerFlag(\"WEBGL_PACK_ARRAY_OPERATIONS\", () => ENV5.getBool(\"WEBGL_PACK\"));\nENV5.registerFlag(\"WEBGL_PACK_IMAGE_OPERATIONS\", () => ENV5.getBool(\"WEBGL_PACK\"));\nENV5.registerFlag(\"WEBGL_PACK_REDUCE\", () => ENV5.getBool(\"WEBGL_PACK\"));\nENV5.registerFlag(\"WEBGL_LAZILY_UNPACK\", () => ENV5.getBool(\"WEBGL_PACK\"));\nENV5.registerFlag(\"WEBGL_CONV_IM2COL\", () => ENV5.getBool(\"WEBGL_PACK\"));\nENV5.registerFlag(\"WEBGL_MAX_TEXTURE_SIZE\", () => getWebGLMaxTextureSize(ENV5.getNumber(\"WEBGL_VERSION\")));\nENV5.registerFlag(\"WEBGL_MAX_TEXTURES_IN_SHADER\", () => getMaxTexturesInShader(ENV5.getNumber(\"WEBGL_VERSION\")));\nENV5.registerFlag(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION\", () => {\n  const webGLVersion = ENV5.getNumber(\"WEBGL_VERSION\");\n  if (webGLVersion === 0) {\n    return 0;\n  }\n  return getWebGLDisjointQueryTimerVersion(webGLVersion);\n});\nENV5.registerFlag(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE\", () => ENV5.getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION\") > 0 && !device_util_exports.isMobile());\nENV5.registerFlag(\"WEBGL_RENDER_FLOAT32_CAPABLE\", () => isCapableOfRenderingToFloatTexture(ENV5.getNumber(\"WEBGL_VERSION\")));\nENV5.registerFlag(\"WEBGL_RENDER_FLOAT32_ENABLED\", () => {\n  return ENV5.getBool(\"WEBGL_FORCE_F16_TEXTURES\") ? false : ENV5.getBool(\"WEBGL_RENDER_FLOAT32_CAPABLE\");\n});\nENV5.registerFlag(\"WEBGL_DOWNLOAD_FLOAT_ENABLED\", () => isDownloadFloatTextureEnabled(ENV5.getNumber(\"WEBGL_VERSION\")));\nENV5.registerFlag(\"WEBGL_FENCE_API_ENABLED\", () => isWebGLFenceEnabled(ENV5.getNumber(\"WEBGL_VERSION\")));\nENV5.registerFlag(\"WEBGL_SIZE_UPLOAD_UNIFORM\", () => {\n  const useUniforms = ENV5.getBool(\"WEBGL_RENDER_FLOAT32_ENABLED\");\n  return useUniforms ? 4 : 0;\n});\nENV5.registerFlag(\"WEBGL_DELETE_TEXTURE_THRESHOLD\", () => {\n  return -1;\n}, (threshold3) => {\n  if (threshold3 < 0 && threshold3 !== -1) {\n    throw new Error(`WEBGL_DELETE_TEXTURE_THRESHOLD must be -1 (indicating never delete) or at least 0, but got ${threshold3}.`);\n  }\n});\nENV5.registerFlag(\"WEBGL_FLUSH_THRESHOLD\", () => {\n  return device_util_exports.isMobile() ? 1 : -1;\n}, (threshold3) => {\n  if (threshold3 < 0 && threshold3 !== -1) {\n    throw new Error(`WEBGL_FLUSH_THRESHOLD must be -1 (indicating never manual flush) or at least 0, but got ${threshold3}.`);\n  }\n});\nENV5.registerFlag(\"CPU_HANDOFF_SIZE_THRESHOLD\", () => 128);\nENV5.registerFlag(\"WEBGL_USE_SHAPES_UNIFORMS\", () => false);\nENV5.registerFlag(\"TOPK_LAST_DIM_CPU_HANDOFF_SIZE_THRESHOLD\", () => 1e5);\nENV5.registerFlag(\"TOPK_K_CPU_HANDOFF_THRESHOLD\", () => 128);\nENV5.registerFlag(\"WEBGL_EXP_CONV\", () => false);\nENV5.registerFlag(\"SOFTWARE_WEBGL_ENABLED\", () => ENV5.getBool(\"IS_TEST\"));\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/glsl_version.js\nfunction getGlslDifferences() {\n  let version10;\n  let attribute;\n  let varyingVs;\n  let varyingFs;\n  let texture2D;\n  let output;\n  let defineOutput;\n  let defineSpecialNaN;\n  let defineSpecialInf;\n  let defineRound;\n  if (env().getNumber(\"WEBGL_VERSION\") === 2) {\n    version10 = \"#version 300 es\";\n    attribute = \"in\";\n    varyingVs = \"out\";\n    varyingFs = \"in\";\n    texture2D = \"texture\";\n    output = \"outputColor\";\n    defineOutput = \"out vec4 outputColor;\";\n    defineSpecialNaN = `\n      bool isnan_custom(float val) {\n        uint floatToUint = floatBitsToUint(val);\n        return (floatToUint & 0x7fffffffu) > 0x7f800000u;\n      }\n\n      bvec4 isnan_custom(vec4 val) {\n        return bvec4(isnan_custom(val.x),\n          isnan_custom(val.y), isnan_custom(val.z), isnan_custom(val.w));\n      }\n\n      #define isnan(value) isnan_custom(value)\n    `;\n    defineSpecialInf = ``;\n    defineRound = `\n      #define round(value) newRound(value)\n      int newRound(float value) {\n        return int(floor(value + 0.5));\n      }\n\n      ivec4 newRound(vec4 value) {\n        return ivec4(floor(value + vec4(0.5)));\n      }\n    `;\n  } else {\n    version10 = \"\";\n    attribute = \"attribute\";\n    varyingVs = \"varying\";\n    varyingFs = \"varying\";\n    texture2D = \"texture2D\";\n    output = \"gl_FragColor\";\n    defineOutput = \"\";\n    defineSpecialNaN = `\n      #define isnan(value) isnan_custom(value)\n      bool isnan_custom(float val) {\n        return (val > 0. || val < 1. || val == 0.) ? false : true;\n      }\n      bvec4 isnan_custom(vec4 val) {\n        return bvec4(isnan(val.x), isnan(val.y), isnan(val.z), isnan(val.w));\n      }\n    `;\n    defineSpecialInf = `\n      uniform float INFINITY;\n\n      bool isinf(float val) {\n        return abs(val) == INFINITY;\n      }\n      bvec4 isinf(vec4 val) {\n        return equal(abs(val), vec4(INFINITY));\n      }\n    `;\n    defineRound = `\n      int round(float value) {\n        return int(floor(value + 0.5));\n      }\n\n      ivec4 round(vec4 value) {\n        return ivec4(floor(value + vec4(0.5)));\n      }\n    `;\n  }\n  return {\n    version: version10,\n    attribute,\n    varyingVs,\n    varyingFs,\n    texture2D,\n    output,\n    defineOutput,\n    defineSpecialNaN,\n    defineSpecialInf,\n    defineRound\n  };\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/shader_compiler_util.js\nfunction getLogicalCoordinatesFromFlatIndex(coords3, shape, index = \"index\") {\n  const strides = util_exports.computeStrides(shape);\n  return strides.map((stride, i) => {\n    const line1 = `int ${coords3[i]} = ${index} / ${stride}`;\n    const line2 = i === strides.length - 1 ? `int ${coords3[i + 1]} = ${index} - ${coords3[i]} * ${stride}` : `index -= ${coords3[i]} * ${stride}`;\n    return `${line1}; ${line2};`;\n  }).join(\"\");\n}\nfunction getOutputLogicalCoordinatesFromFlatIndexByUniform(coords3, shape, index = \"index\") {\n  const strides = util_exports.computeStrides(shape);\n  return strides.map((_, i) => {\n    const line1 = `int ${coords3[i]} = ${index} / outShapeStrides[${i}]`;\n    const line2 = i === strides.length - 1 ? `int ${coords3[i + 1]} = ${index} - ${coords3[i]} * outShapeStrides[${i}]` : `index -= ${coords3[i]} * outShapeStrides[${i}]`;\n    return `${line1}; ${line2};`;\n  }).join(\"\");\n}\nfunction symbolicallyComputeStrides(indicesArr, variableName) {\n  const numCoords = indicesArr.length;\n  const shape = indicesArr.map((d) => `${variableName}[${d}]`);\n  const strides = new Array(numCoords - 1);\n  strides[numCoords - 2] = shape[numCoords - 1];\n  for (let i = numCoords - 3; i >= 0; --i) {\n    strides[i] = `(${strides[i + 1]} * ${shape[i + 1]})`;\n  }\n  return strides;\n}\nfunction getLogicalCoordinatesFromFlatIndexByUniform(coords3, variableName, index = \"index\") {\n  const indicesArray = coords3.map((_, i) => i);\n  const strides = symbolicallyComputeStrides(indicesArray, variableName);\n  return strides.map((_, i) => {\n    const line1 = `int ${coords3[i]} = ${index} / ${strides[i]}`;\n    const line2 = i === strides.length - 1 ? `int ${coords3[i + 1]} = ${index} - ${coords3[i]} * ${strides[i]}` : `index -= ${coords3[i]} * ${strides[i]}`;\n    return `${line1}; ${line2};`;\n  }).join(\"\");\n}\nfunction getFlatIndexFrom3D(shape) {\n  const strides = util_exports.computeStrides(shape).map((d) => d.toString());\n  return `\n  int getFlatIndex(ivec3 coords) {\n    return coords.x * ${strides[0]} + coords.y * ${strides[1]} + coords.z;\n  }\n`;\n}\nfunction getFlatIndexFrom3DOutput() {\n  return `\n  int getFlatIndex(ivec3 coords) {\n    return coords.x * outShapeStrides[0] + coords.y * outShapeStrides[1] + coords.z;\n  }\n`;\n}\nvar ENCODE_FLOAT_SNIPPET = `\n  const float FLOAT_MAX = 1.70141184e38;\n  const float FLOAT_MIN = 1.17549435e-38;\n\n  lowp vec4 encode_float(highp float v) {\n    if (isnan(v)) {\n      return vec4(255, 255, 255, 255);\n    }\n\n    highp float av = abs(v);\n\n    if(av < FLOAT_MIN) {\n      return vec4(0.0, 0.0, 0.0, 0.0);\n    } else if(v > FLOAT_MAX) {\n      return vec4(0.0, 0.0, 128.0, 127.0) / 255.0;\n    } else if(v < -FLOAT_MAX) {\n      return vec4(0.0, 0.0,  128.0, 255.0) / 255.0;\n    }\n\n    highp vec4 c = vec4(0,0,0,0);\n\n    highp float e = floor(log2(av));\n    highp float m = exp2(fract(log2(av))) - 1.0;\n\n    c[2] = floor(128.0 * m);\n    m -= c[2] / 128.0;\n    c[1] = floor(32768.0 * m);\n    m -= c[1] / 32768.0;\n    c[0] = floor(8388608.0 * m);\n\n    highp float ebias = e + 127.0;\n    c[3] = floor(ebias / 2.0);\n    ebias -= c[3] * 2.0;\n    c[2] += floor(ebias) * 128.0;\n\n    c[3] += 128.0 * step(0.0, -v);\n\n    return c / 255.0;\n  }\n`;\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/shader_compiler.js\nvar { getBroadcastDims: getBroadcastDims2 } = backend_util_exports;\nfunction makeShader(inputsInfo, outputShape, program) {\n  const prefixSnippets = [];\n  inputsInfo.forEach((x) => {\n    const size = util_exports.sizeFromShape(x.shapeInfo.logicalShape);\n    if (x.shapeInfo.isUniform) {\n      prefixSnippets.push(`uniform float ${x.name}${size > 1 ? `[${size}]` : \"\"};`);\n    } else {\n      prefixSnippets.push(`uniform sampler2D ${x.name};`);\n      prefixSnippets.push(`uniform int offset${x.name};`);\n    }\n    if (program.enableShapeUniforms) {\n      const { uniformShape } = getUniformInfoFromShape(program.packedInputs, x.shapeInfo.logicalShape, x.shapeInfo.texShape);\n      switch (uniformShape.length) {\n        case 1:\n          prefixSnippets.push(`uniform int ${x.name}Shape;`);\n          break;\n        case 2:\n          prefixSnippets.push(`uniform ivec2 ${x.name}Shape;`);\n          break;\n        case 3:\n          prefixSnippets.push(`uniform ivec3 ${x.name}Shape;`);\n          break;\n        case 4:\n          prefixSnippets.push(`uniform ivec4 ${x.name}Shape;`);\n          break;\n        default:\n          break;\n      }\n      prefixSnippets.push(`uniform ivec2 ${x.name}TexShape;`);\n    }\n  });\n  if (program.enableShapeUniforms) {\n    switch (outputShape.logicalShape.length) {\n      case 1:\n        prefixSnippets.push(`uniform int outShape;`);\n        break;\n      case 2:\n        prefixSnippets.push(`uniform ivec2 outShape;`);\n        prefixSnippets.push(`uniform int outShapeStrides;`);\n        break;\n      case 3:\n        prefixSnippets.push(`uniform ivec3 outShape;`);\n        prefixSnippets.push(`uniform ivec2 outShapeStrides;`);\n        break;\n      case 4:\n        prefixSnippets.push(`uniform ivec4 outShape;`);\n        prefixSnippets.push(`uniform ivec3 outShapeStrides;`);\n        break;\n      default:\n        break;\n    }\n    prefixSnippets.push(`uniform ivec2 outTexShape;`);\n  }\n  if (program.customUniforms) {\n    program.customUniforms.forEach((d) => {\n      prefixSnippets.push(`uniform ${d.type} ${d.name}${d.arrayIndex ? `[${d.arrayIndex}]` : \"\"};`);\n    });\n  }\n  const inputPrefixSnippet = prefixSnippets.join(\"\\n\");\n  const inputSamplingSnippet = inputsInfo.map((x) => getInputSamplingSnippet(x, outputShape, program.packedInputs, program.enableShapeUniforms)).join(\"\\n\");\n  const outTexShape = outputShape.texShape;\n  const glsl = getGlslDifferences();\n  const floatTextureSampleSnippet = getFloatTextureSampleSnippet(glsl);\n  let outputSamplingSnippet;\n  let floatTextureSetOutputSnippet;\n  let shaderPrefix = getShaderPrefix(glsl);\n  if (outputShape.isPacked) {\n    outputSamplingSnippet = getPackedOutputSamplingSnippet(outputShape.logicalShape, outTexShape, program.enableShapeUniforms);\n    floatTextureSetOutputSnippet = getFloatTextureSetRGBASnippet(glsl);\n  } else {\n    outputSamplingSnippet = getOutputSamplingSnippet(outputShape.logicalShape, outTexShape, program.enableShapeUniforms);\n    floatTextureSetOutputSnippet = getFloatTextureSetRSnippet(glsl);\n  }\n  if (program.packedInputs) {\n    shaderPrefix += SHADER_PACKED_PREFIX;\n  }\n  const source = [\n    shaderPrefix,\n    floatTextureSampleSnippet,\n    floatTextureSetOutputSnippet,\n    inputPrefixSnippet,\n    outputSamplingSnippet,\n    inputSamplingSnippet,\n    program.userCode\n  ].join(\"\\n\");\n  return source;\n}\nfunction getSamplerFromInInfo(inInfo, enableShapeUniforms = false) {\n  const shape = inInfo.shapeInfo.logicalShape;\n  switch (shape.length) {\n    case 0:\n      return getSamplerScalar(inInfo, enableShapeUniforms);\n    case 1:\n      return getSampler1D(inInfo, enableShapeUniforms);\n    case 2:\n      return getSampler2D(inInfo, enableShapeUniforms);\n    case 3:\n      return getSampler3D(inInfo, enableShapeUniforms);\n    case 4:\n      return getSampler4D(inInfo, enableShapeUniforms);\n    case 5:\n      return getSampler5D(inInfo);\n    case 6:\n      return getSampler6D(inInfo);\n    default:\n      throw new Error(`${shape.length}-D input sampling is not yet supported`);\n  }\n}\nfunction getPackedSamplerFromInInfo(inInfo, enableShapeUniforms) {\n  const shape = inInfo.shapeInfo.logicalShape;\n  switch (shape.length) {\n    case 0:\n      return getPackedSamplerScalar(inInfo);\n    case 1:\n      return getPackedSampler1D(inInfo, enableShapeUniforms);\n    case 2:\n      return getPackedSampler2D(inInfo, enableShapeUniforms);\n    case 3:\n      return getPackedSampler3D(inInfo, enableShapeUniforms);\n    default:\n      return getPackedSamplerND(inInfo, enableShapeUniforms);\n  }\n}\nfunction getInputSamplingSnippet(inInfo, outShapeInfo, usesPackedTextures = false, enableShapeUniforms) {\n  let res = \"\";\n  if (usesPackedTextures) {\n    res += getPackedSamplerFromInInfo(inInfo, enableShapeUniforms);\n  } else {\n    res += getSamplerFromInInfo(inInfo, enableShapeUniforms);\n  }\n  const inShape = inInfo.shapeInfo.logicalShape;\n  const outShape = outShapeInfo.logicalShape;\n  if (inShape.length <= outShape.length) {\n    if (usesPackedTextures) {\n      res += getPackedSamplerAtOutputCoords(inInfo, outShapeInfo);\n    } else {\n      res += getSamplerAtOutputCoords(inInfo, outShapeInfo);\n    }\n  }\n  return res;\n}\nfunction getPackedOutputSamplingSnippet(outShape, outTexShape, enableShapeUniforms) {\n  switch (outShape.length) {\n    case 0:\n      return getOutputScalarCoords();\n    case 1:\n      return getOutputPacked1DCoords(outShape, outTexShape, enableShapeUniforms);\n    case 2:\n      return getOutputPacked2DCoords(outShape, outTexShape, enableShapeUniforms);\n    case 3:\n      return getOutputPacked3DCoords(outShape, outTexShape, enableShapeUniforms);\n    default:\n      return getOutputPackedNDCoords(outShape, outTexShape, enableShapeUniforms);\n  }\n}\nfunction getOutputSamplingSnippet(outShape, outTexShape, enableShapeUniforms) {\n  switch (outShape.length) {\n    case 0:\n      return getOutputScalarCoords();\n    case 1:\n      return getOutput1DCoords(outShape, outTexShape, enableShapeUniforms);\n    case 2:\n      return getOutput2DCoords(outShape, outTexShape, enableShapeUniforms);\n    case 3:\n      return getOutput3DCoords(outShape, outTexShape, enableShapeUniforms);\n    case 4:\n      return getOutput4DCoords(outShape, outTexShape, enableShapeUniforms);\n    case 5:\n      return getOutput5DCoords(outShape, outTexShape);\n    case 6:\n      return getOutput6DCoords(outShape, outTexShape);\n    default:\n      throw new Error(`${outShape.length}-D output sampling is not yet supported`);\n  }\n}\nfunction getFloatTextureSampleSnippet(glsl) {\n  return `\n    float sampleTexture(sampler2D textureSampler, vec2 uv) {\n      return ${glsl.texture2D}(textureSampler, uv).r;\n    }\n  `;\n}\nfunction getFloatTextureSetRSnippet(glsl) {\n  return `\n    void setOutput(float val) {\n      ${glsl.output} = vec4(val, 0, 0, 0);\n    }\n  `;\n}\nfunction getFloatTextureSetRGBASnippet(glsl) {\n  return `\n    void setOutput(vec4 val) {\n      ${glsl.output} = val;\n    }\n  `;\n}\nfunction getShaderPrefix(glsl) {\n  const SHADER_PREFIX = `${glsl.version}\n    precision highp float;\n    precision highp int;\n    precision highp sampler2D;\n    ${glsl.varyingFs} vec2 resultUV;\n    ${glsl.defineOutput}\n    const vec2 halfCR = vec2(0.5, 0.5);\n\n    struct ivec5\n    {\n      int x;\n      int y;\n      int z;\n      int w;\n      int u;\n    };\n\n    struct ivec6\n    {\n      int x;\n      int y;\n      int z;\n      int w;\n      int u;\n      int v;\n    };\n\n    uniform float NAN;\n    ${glsl.defineSpecialNaN}\n    ${glsl.defineSpecialInf}\n    ${glsl.defineRound}\n\n    int imod(int x, int y) {\n      return x - y * (x / y);\n    }\n\n    int idiv(int a, int b, float sign) {\n      int res = a / b;\n      int mod = imod(a, b);\n      if (sign < 0. && mod != 0) {\n        res -= 1;\n      }\n      return res;\n    }\n\n    //Based on the work of Dave Hoskins\n    //https://www.shadertoy.com/view/4djSRW\n    #define HASHSCALE1 443.8975\n    float random(float seed){\n      vec2 p = resultUV * seed;\n      vec3 p3  = fract(vec3(p.xyx) * HASHSCALE1);\n      p3 += dot(p3, p3.yzx + 19.19);\n      return fract((p3.x + p3.y) * p3.z);\n    }\n\n    ${SAMPLE_1D_SNIPPET}\n    ${SAMPLE_2D_SNIPPET}\n    ${SAMPLE_3D_SNIPPET}\n  `;\n  return SHADER_PREFIX;\n}\nvar SAMPLE_1D_SNIPPET = `\nvec2 uvFromFlat(int texNumR, int texNumC, int index) {\n  int texR = index / texNumC;\n  int texC = index - texR * texNumC;\n  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n}\nvec2 packedUVfrom1D(int texNumR, int texNumC, int index) {\n  int texelIndex = index / 2;\n  int texR = texelIndex / texNumC;\n  int texC = texelIndex - texR * texNumC;\n  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n}\n`;\nvar SAMPLE_2D_SNIPPET = `\nvec2 packedUVfrom2D(int texelsInLogicalRow, int texNumR,\n  int texNumC, int row, int col) {\n  int texelIndex = (row / 2) * texelsInLogicalRow + (col / 2);\n  int texR = texelIndex / texNumC;\n  int texC = texelIndex - texR * texNumC;\n  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n}\n`;\nvar SAMPLE_3D_SNIPPET = `\nvec2 packedUVfrom3D(int texNumR, int texNumC,\n    int texelsInBatch, int texelsInLogicalRow, int b,\n    int row, int col) {\n  int index = b * texelsInBatch + (row / 2) * texelsInLogicalRow + (col / 2);\n  int texR = index / texNumC;\n  int texC = index - texR * texNumC;\n  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n}\n`;\nvar SHADER_PACKED_PREFIX = `\n  float getChannel(vec4 frag, vec2 innerDims) {\n    vec2 modCoord = mod(innerDims, 2.);\n    return modCoord.x == 0. ?\n      (modCoord.y == 0. ? frag.r : frag.g) :\n      (modCoord.y == 0. ? frag.b : frag.a);\n  }\n  float getChannel(vec4 frag, int dim) {\n    float modCoord = mod(float(dim), 2.);\n    return modCoord == 0. ? frag.r : frag.g;\n  }\n`;\nfunction getOutputScalarCoords() {\n  return `\n    int getOutputCoords() {\n      return 0;\n    }\n  `;\n}\nfunction getOutputPacked1DCoords(shape, texShape, enableShapeUniforms) {\n  const packedTexShape = [Math.ceil(texShape[0] / 2), Math.ceil(texShape[1] / 2)];\n  if (packedTexShape[0] === 1) {\n    if (enableShapeUniforms) {\n      return `\n      int getOutputCoords() {\n        return 2 * int(resultUV.x * ceil(float(outTexShape[1]) / 2.0));\n      }\n    `;\n    }\n    return `\n      int getOutputCoords() {\n        return 2 * int(resultUV.x * ${packedTexShape[1]}.0);\n      }\n    `;\n  }\n  if (packedTexShape[1] === 1) {\n    if (enableShapeUniforms) {\n      return `\n      int getOutputCoords() {\n        return 2 * int(resultUV.y * ceil(float(outTexShape[0]) / 2.0));\n      }\n    `;\n    }\n    return `\n      int getOutputCoords() {\n        return 2 * int(resultUV.y * ${packedTexShape[0]}.0);\n      }\n    `;\n  }\n  if (enableShapeUniforms) {\n    return `\n    int getOutputCoords() {\n      ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(packedTexShape[0], packedTexShape[1]));\n      return 2 * (resTexRC.x * packedTexShape[1] + resTexRC.y);\n    }\n  `;\n  }\n  return `\n    int getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(${packedTexShape[0]}, ${packedTexShape[1]}));\n      return 2 * (resTexRC.x * ${packedTexShape[1]} + resTexRC.y);\n    }\n  `;\n}\nfunction getOutput1DCoords(shape, texShape, enableShapeUniforms) {\n  if (texShape[0] === 1) {\n    if (enableShapeUniforms) {\n      return `\n      int getOutputCoords() {\n        return int(resultUV.x * float(outTexShape[1]));\n      }\n    `;\n    }\n    return `\n      int getOutputCoords() {\n        return int(resultUV.x * ${texShape[1]}.0);\n      }\n    `;\n  }\n  if (texShape[1] === 1) {\n    if (enableShapeUniforms) {\n      return `\n      int getOutputCoords() {\n        return int(resultUV.y * float(outTexShape[0]));\n      }\n    `;\n    }\n    return `\n      int getOutputCoords() {\n        return int(resultUV.y * ${texShape[0]}.0);\n      }\n    `;\n  }\n  if (enableShapeUniforms) {\n    return `\n    int getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(outTexShape[0], outTexShape[1]));\n      return resTexRC.x * outTexShape[1] + resTexRC.y;\n    }\n  `;\n  }\n  return `\n    int getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(${texShape[0]}, ${texShape[1]}));\n      return resTexRC.x * ${texShape[1]} + resTexRC.y;\n    }\n  `;\n}\nfunction getOutputPacked3DCoords(shape, texShape, enableShapeUniforms) {\n  if (enableShapeUniforms) {\n    return `\n    ivec3 getOutputCoords() {\n      ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));\n      int texelsInLogicalRow = int(ceil(float(outShape[2]) / 2.0));\n      int texelsInBatch = texelsInLogicalRow * int(ceil(float(outShape[1]) / 2.0));\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(packedTexShape[0], packedTexShape[1]));\n      int index = resTexRC.x * packedTexShape[1] + resTexRC.y;\n\n      int b = index / texelsInBatch;\n      index -= b * texelsInBatch;\n\n      int r = 2 * (index / texelsInLogicalRow);\n      int c = imod(index, texelsInLogicalRow) * 2;\n\n      return ivec3(b, r, c);\n    }\n  `;\n  }\n  const packedTexShape = [Math.ceil(texShape[0] / 2), Math.ceil(texShape[1] / 2)];\n  const texelsInLogicalRow = Math.ceil(shape[2] / 2);\n  const texelsInBatch = texelsInLogicalRow * Math.ceil(shape[1] / 2);\n  return `\n    ivec3 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(${packedTexShape[0]}, ${packedTexShape[1]}));\n      int index = resTexRC.x * ${packedTexShape[1]} + resTexRC.y;\n\n      int b = index / ${texelsInBatch};\n      index -= b * ${texelsInBatch};\n\n      int r = 2 * (index / ${texelsInLogicalRow});\n      int c = imod(index, ${texelsInLogicalRow}) * 2;\n\n      return ivec3(b, r, c);\n    }\n  `;\n}\nfunction getOutput3DCoords(shape, texShape, enableShapeUniforms) {\n  if (enableShapeUniforms) {\n    const coordsFromIndexSnippet2 = getOutputLogicalCoordinatesFromFlatIndexByUniform([\"r\", \"c\", \"d\"], shape);\n    return `\n  ivec3 getOutputCoords() {\n    ivec2 resTexRC = ivec2(resultUV.yx *\n                           vec2(outTexShape[0], outTexShape[1]));\n    int index = resTexRC.x * outTexShape[1] + resTexRC.y;\n    ${coordsFromIndexSnippet2}\n    return ivec3(r, c, d);\n  }\n`;\n  }\n  const coordsFromIndexSnippet = getLogicalCoordinatesFromFlatIndex([\"r\", \"c\", \"d\"], shape);\n  return `\n    ivec3 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(${texShape[0]}, ${texShape[1]}));\n      int index = resTexRC.x * ${texShape[1]} + resTexRC.y;\n      ${coordsFromIndexSnippet}\n      return ivec3(r, c, d);\n    }\n  `;\n}\nfunction getOutputPackedNDCoords(shape, texShape, enableShapeUniforms) {\n  if (enableShapeUniforms) {\n    return `\n    ivec4 getOutputCoords() {\n      ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(packedTexShape[0], packedTexShape[1]));\n      int index = resTexRC.x * packedTexShape[1] + resTexRC.y;\n\n      int texelsInLogicalRow = int(ceil(float(outShape[3]) / 2.0));\n      int texelsInBatch = texelsInLogicalRow * int(ceil(float(outShape[2]) / 2.0));\n      int texelsInBatchN = texelsInBatch * outShape[1];\n\n      int b2 = index / texelsInBatchN;\n      index -= b2 * texelsInBatchN;\n\n      int b = index / texelsInBatch;\n      index -= b * texelsInBatch;\n\n      int r = 2 * (index / texelsInLogicalRow);\n      int c = imod(index, texelsInLogicalRow) * 2;\n\n      return ivec4(b2, b, r, c);\n    }\n  `;\n  }\n  const packedTexShape = [Math.ceil(texShape[0] / 2), Math.ceil(texShape[1] / 2)];\n  const texelsInLogicalRow = Math.ceil(shape[shape.length - 1] / 2);\n  const texelsInBatch = texelsInLogicalRow * Math.ceil(shape[shape.length - 2] / 2);\n  let texelsInBatchN = texelsInBatch;\n  let batches = ``;\n  let coords3 = \"b, r, c\";\n  for (let b = 2; b < shape.length - 1; b++) {\n    texelsInBatchN *= shape[shape.length - b - 1];\n    batches = `\n      int b${b} = index / ${texelsInBatchN};\n      index -= b${b} * ${texelsInBatchN};\n    ` + batches;\n    coords3 = `b${b}, ` + coords3;\n  }\n  return `\n    ivec${shape.length} getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(${packedTexShape[0]}, ${packedTexShape[1]}));\n      int index = resTexRC.x * ${packedTexShape[1]} + resTexRC.y;\n\n      ${batches}\n\n      int b = index / ${texelsInBatch};\n      index -= b * ${texelsInBatch};\n\n      int r = 2 * (index / ${texelsInLogicalRow});\n      int c = imod(index, ${texelsInLogicalRow}) * 2;\n\n      return ivec${shape.length}(${coords3});\n    }\n  `;\n}\nfunction getOutput4DCoords(shape, texShape, enableShapeUniforms) {\n  if (enableShapeUniforms) {\n    const coordsFromIndexSnippet2 = getOutputLogicalCoordinatesFromFlatIndexByUniform([\"r\", \"c\", \"d\", \"d2\"], shape);\n    return `\n    ivec4 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n        vec2(outTexShape[0], outTexShape[1]));\n      int index = resTexRC.x * outTexShape[1] + resTexRC.y;\n      ${coordsFromIndexSnippet2}\n      return ivec4(r, c, d, d2);\n    }\n  `;\n  }\n  const coordsFromIndexSnippet = getLogicalCoordinatesFromFlatIndex([\"r\", \"c\", \"d\", \"d2\"], shape);\n  return `\n    ivec4 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n        vec2(${texShape[0]}, ${texShape[1]}));\n      int index = resTexRC.x * ${texShape[1]} + resTexRC.y;\n      ${coordsFromIndexSnippet}\n      return ivec4(r, c, d, d2);\n    }\n  `;\n}\nfunction getOutput5DCoords(shape, texShape) {\n  const coordsFromIndexSnippet = getLogicalCoordinatesFromFlatIndex([\"r\", \"c\", \"d\", \"d2\", \"d3\"], shape);\n  return `\n    ivec5 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx * vec2(${texShape[0]},\n                             ${texShape[1]}));\n\n      int index = resTexRC.x * ${texShape[1]} + resTexRC.y;\n\n      ${coordsFromIndexSnippet}\n\n      ivec5 outShape = ivec5(r, c, d, d2, d3);\n      return outShape;\n    }\n  `;\n}\nfunction getOutput6DCoords(shape, texShape) {\n  const coordsFromIndexSnippet = getLogicalCoordinatesFromFlatIndex([\"r\", \"c\", \"d\", \"d2\", \"d3\", \"d4\"], shape);\n  return `\n    ivec6 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n        vec2(${texShape[0]}, ${texShape[1]}));\n      int index = resTexRC.x * ${texShape[1]} + resTexRC.y;\n\n      ${coordsFromIndexSnippet}\n\n      ivec6 result = ivec6(r, c, d, d2, d3, d4);\n      return result;\n    }\n  `;\n}\nfunction getOutputPacked2DCoords(shape, texShape, enableShapeUniforms) {\n  const packedTexShape = [Math.ceil(texShape[0] / 2), Math.ceil(texShape[1] / 2)];\n  if (util_exports.arraysEqual(shape, texShape)) {\n    if (enableShapeUniforms) {\n      return `\n      ivec2 getOutputCoords() {\n        ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));\n        return 2 * ivec2(resultUV.yx * vec2(packedTexShape[0], packedTexShape[1]));\n      }\n    `;\n    }\n    return `\n      ivec2 getOutputCoords() {\n        return 2 * ivec2(resultUV.yx * vec2(${packedTexShape[0]}, ${packedTexShape[1]}));\n      }\n    `;\n  }\n  const texelsInLogicalRow = Math.ceil(shape[1] / 2);\n  if (enableShapeUniforms) {\n    return `\n    ivec2 getOutputCoords() {\n      ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));\n      int texelsInLogicalRow = int(ceil(float(outShape[1]) / 2.0));\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(packedTexShape[0], packedTexShape[1]));\n\n      int index = resTexRC.x * packedTexShape[1] + resTexRC.y;\n      int r = 2 * (index / texelsInLogicalRow);\n      int c = imod(index, texelsInLogicalRow) * 2;\n\n      return ivec2(r, c);\n    }\n  `;\n  }\n  return `\n    ivec2 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(${packedTexShape[0]}, ${packedTexShape[1]}));\n\n      int index = resTexRC.x * ${packedTexShape[1]} + resTexRC.y;\n      int r = 2 * (index / ${texelsInLogicalRow});\n      int c = imod(index, ${texelsInLogicalRow}) * 2;\n\n      return ivec2(r, c);\n    }\n  `;\n}\nfunction getOutput2DCoords(shape, texShape, enableShapeUniforms) {\n  if (util_exports.arraysEqual(shape, texShape)) {\n    if (enableShapeUniforms) {\n      return `\n      ivec2 getOutputCoords() {\n        return ivec2(resultUV.yx * vec2(outTexShape[0], outTexShape[1]));\n      }\n    `;\n    }\n    return `\n      ivec2 getOutputCoords() {\n        return ivec2(resultUV.yx * vec2(${texShape[0]}, ${texShape[1]}));\n      }\n    `;\n  }\n  if (shape[1] === 1) {\n    if (enableShapeUniforms) {\n      return `\n      ivec2 getOutputCoords() {\n        ivec2 resTexRC = ivec2(resultUV.yx *\n                               vec2(outTexShape[0], outTexShape[1]));\n        int index = resTexRC.x * outTexShape[1] + resTexRC.y;\n        return ivec2(index, 0);\n      }\n    `;\n    }\n    return `\n      ivec2 getOutputCoords() {\n        ivec2 resTexRC = ivec2(resultUV.yx *\n                               vec2(${texShape[0]}, ${texShape[1]}));\n        int index = resTexRC.x * ${texShape[1]} + resTexRC.y;\n        return ivec2(index, 0);\n      }\n    `;\n  }\n  if (shape[0] === 1) {\n    if (enableShapeUniforms) {\n      return `\n      ivec2 getOutputCoords() {\n        ivec2 resTexRC = ivec2(resultUV.yx *\n                               vec2(outTexShape[0], outTexShape[1]));\n        int index = resTexRC.x * outTexShape[1] + resTexRC.y;\n        return ivec2(0, index);\n      }\n    `;\n    }\n    return `\n      ivec2 getOutputCoords() {\n        ivec2 resTexRC = ivec2(resultUV.yx *\n                               vec2(${texShape[0]}, ${texShape[1]}));\n        int index = resTexRC.x * ${texShape[1]} + resTexRC.y;\n        return ivec2(0, index);\n      }\n    `;\n  }\n  if (enableShapeUniforms) {\n    return `\n    ivec2 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(outTexShape[0], outTexShape[1]));\n      int index = resTexRC.x * outTexShape[1] + resTexRC.y;\n      int r = index / outShape[1];\n      int c = index - r * outShape[1];\n      return ivec2(r, c);\n    }\n  `;\n  }\n  return `\n    ivec2 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(${texShape[0]}, ${texShape[1]}));\n      int index = resTexRC.x * ${texShape[1]} + resTexRC.y;\n      int r = index / ${shape[1]};\n      int c = index - r * ${shape[1]};\n      return ivec2(r, c);\n    }\n  `;\n}\nfunction getFlatOffsetUniformName(texName) {\n  return `offset${texName}`;\n}\nfunction getPackedSamplerScalar(inputInfo) {\n  const texName = inputInfo.name;\n  const funcName = \"get\" + texName.charAt(0).toUpperCase() + texName.slice(1);\n  const glsl = getGlslDifferences();\n  return `\n    vec4 ${funcName}() {\n      return ${glsl.texture2D}(${texName}, halfCR);\n    }\n  `;\n}\nfunction getSamplerScalar(inputInfo, enableShapeUniforms) {\n  const texName = inputInfo.name;\n  const funcName = \"get\" + texName.charAt(0).toUpperCase() + texName.slice(1);\n  if (inputInfo.shapeInfo.isUniform) {\n    return `float ${funcName}() {return ${texName};}`;\n  }\n  const [texNumR, texNumC] = inputInfo.shapeInfo.texShape;\n  if (texNumR === 1 && texNumC === 1) {\n    return `\n      float ${funcName}() {\n        return sampleTexture(${texName}, halfCR);\n      }\n    `;\n  }\n  const offset = getFlatOffsetUniformName(texName);\n  if (enableShapeUniforms) {\n    return `\n    float ${funcName}() {\n      vec2 uv = uvFromFlat(${texName}TexShape[0], ${texName}TexShape[1], ${offset});\n      return sampleTexture(${texName}, uv);\n    }\n  `;\n  }\n  const [tNumR, tNumC] = inputInfo.shapeInfo.texShape;\n  return `\n    float ${funcName}() {\n      vec2 uv = uvFromFlat(${tNumR}, ${tNumC}, ${offset});\n      return sampleTexture(${texName}, uv);\n    }\n  `;\n}\nfunction getPackedSampler1D(inputInfo, enableShapeUniforms) {\n  const texName = inputInfo.name;\n  const funcName = \"get\" + texName.charAt(0).toUpperCase() + texName.slice(1);\n  const texShape = inputInfo.shapeInfo.texShape;\n  const glsl = getGlslDifferences();\n  if (enableShapeUniforms) {\n    return `\n    vec4 ${funcName}(int index) {\n      ivec2 packedTexShape = ivec2(ceil(float(${texName}TexShape[0]) / 2.0), ceil(float(${texName}TexShape[1]) / 2.0));\n      vec2 uv = packedUVfrom1D(\n        packedTexShape[0], packedTexShape[1], index);\n      return ${glsl.texture2D}(${texName}, uv);\n    }\n  `;\n  }\n  const packedTexShape = [Math.ceil(texShape[0] / 2), Math.ceil(texShape[1] / 2)];\n  return `\n    vec4 ${funcName}(int index) {\n      vec2 uv = packedUVfrom1D(\n        ${packedTexShape[0]}, ${packedTexShape[1]}, index);\n      return ${glsl.texture2D}(${texName}, uv);\n    }\n  `;\n}\nfunction getSampler1D(inputInfo, enableShapeUniforms) {\n  const texName = inputInfo.name;\n  const funcName = \"get\" + texName.charAt(0).toUpperCase() + texName.slice(1);\n  if (inputInfo.shapeInfo.isUniform) {\n    return `\n      float ${funcName}(int index) {\n        ${getUniformSampler(inputInfo)}\n      }\n    `;\n  }\n  const texShape = inputInfo.shapeInfo.texShape;\n  const tNumR = texShape[0];\n  const tNumC = texShape[1];\n  if (tNumC === 1 && tNumR === 1) {\n    return `\n      float ${funcName}(int index) {\n        return sampleTexture(${texName}, halfCR);\n      }\n    `;\n  }\n  const offset = getFlatOffsetUniformName(texName);\n  if (tNumC === 1) {\n    if (enableShapeUniforms) {\n      return `\n      float ${funcName}(int index) {\n        vec2 uv = vec2(0.5, (float(index + ${offset}) + 0.5) / float(${texName}TexShape[0]));\n        return sampleTexture(${texName}, uv);\n      }\n    `;\n    }\n    return `\n      float ${funcName}(int index) {\n        vec2 uv = vec2(0.5, (float(index + ${offset}) + 0.5) / ${tNumR}.0);\n        return sampleTexture(${texName}, uv);\n      }\n    `;\n  }\n  if (tNumR === 1) {\n    if (enableShapeUniforms) {\n      return `\n      float ${funcName}(int index) {\n        vec2 uv = vec2((float(index + ${offset}) + 0.5) / float(${texName}TexShape[1]), 0.5);\n        return sampleTexture(${texName}, uv);\n      }\n    `;\n    }\n    return `\n      float ${funcName}(int index) {\n        vec2 uv = vec2((float(index + ${offset}) + 0.5) / ${tNumC}.0, 0.5);\n        return sampleTexture(${texName}, uv);\n      }\n    `;\n  }\n  if (enableShapeUniforms) {\n    return `\n    float ${funcName}(int index) {\n      vec2 uv = uvFromFlat(${texName}TexShape[0], ${texName}TexShape[1], index + ${offset});\n      return sampleTexture(${texName}, uv);\n    }\n  `;\n  }\n  return `\n    float ${funcName}(int index) {\n      vec2 uv = uvFromFlat(${tNumR}, ${tNumC}, index + ${offset});\n      return sampleTexture(${texName}, uv);\n    }\n  `;\n}\nfunction getPackedSampler2D(inputInfo, enableShapeUniforms) {\n  const shape = inputInfo.shapeInfo.logicalShape;\n  const texName = inputInfo.name;\n  const funcName = \"get\" + texName.charAt(0).toUpperCase() + texName.slice(1);\n  const texShape = inputInfo.shapeInfo.texShape;\n  const texNumR = texShape[0];\n  const texNumC = texShape[1];\n  const glsl = getGlslDifferences();\n  if (texShape != null && util_exports.arraysEqual(shape, texShape)) {\n    if (enableShapeUniforms) {\n      return `\n      vec4 ${funcName}(int row, int col) {\n        vec2 uv = (vec2(col, row) + halfCR) / vec2(${texName}TexShape[1], ${texName}TexShape[0]);\n\n        return ${glsl.texture2D}(${texName}, uv);\n      }\n    `;\n    }\n    return `\n      vec4 ${funcName}(int row, int col) {\n        vec2 uv = (vec2(col, row) + halfCR) / vec2(${texNumC}.0, ${texNumR}.0);\n\n        return ${glsl.texture2D}(${texName}, uv);\n      }\n    `;\n  }\n  if (enableShapeUniforms) {\n    return `\n    vec4 ${funcName}(int row, int col) {\n      ivec2 packedTexShape = ivec2(ceil(float(${texName}TexShape[0]) / 2.0), ceil(float(${texName}TexShape[1]) / 2.0));\n      int valuesPerRow = int(ceil(float(${texName}Shape[1]) / 2.0));\n      vec2 uv = packedUVfrom2D(valuesPerRow, packedTexShape[0], packedTexShape[1], row, col);\n      return ${glsl.texture2D}(${texName}, uv);\n    }\n  `;\n  }\n  const packedTexShape = [Math.ceil(texShape[0] / 2), Math.ceil(texShape[1] / 2)];\n  const valuesPerRow = Math.ceil(shape[1] / 2);\n  return `\n    vec4 ${funcName}(int row, int col) {\n      vec2 uv = packedUVfrom2D(${valuesPerRow}, ${packedTexShape[0]}, ${packedTexShape[1]}, row, col);\n      return ${glsl.texture2D}(${texName}, uv);\n    }\n  `;\n}\nfunction getSampler2D(inputInfo, enableShapeUniforms) {\n  const shape = inputInfo.shapeInfo.logicalShape;\n  const texName = inputInfo.name;\n  const funcName = \"get\" + texName.charAt(0).toUpperCase() + texName.slice(1);\n  const texShape = inputInfo.shapeInfo.texShape;\n  if (texShape != null && util_exports.arraysEqual(shape, texShape)) {\n    if (enableShapeUniforms) {\n      return `\n      float ${funcName}(int row, int col) {\n        vec2 uv = (vec2(col, row) + halfCR) / vec2(${texName}TexShape[1], ${texName}TexShape[0]);\n        return sampleTexture(${texName}, uv);\n      }\n    `;\n    }\n    const texNumR2 = texShape[0];\n    const texNumC2 = texShape[1];\n    return `\n    float ${funcName}(int row, int col) {\n      vec2 uv = (vec2(col, row) + halfCR) / vec2(${texNumC2}.0, ${texNumR2}.0);\n      return sampleTexture(${texName}, uv);\n    }\n  `;\n  }\n  const { newShape, keptDims } = util_exports.squeezeShape(shape);\n  const squeezedShape = newShape;\n  if (squeezedShape.length < shape.length) {\n    const newInputInfo = squeezeInputInfo(inputInfo, squeezedShape);\n    const params = [\"row\", \"col\"];\n    return `\n      ${getSamplerFromInInfo(newInputInfo, enableShapeUniforms)}\n      float ${funcName}(int row, int col) {\n        return ${funcName}(${getSqueezedParams(params, keptDims)});\n      }\n    `;\n  }\n  if (inputInfo.shapeInfo.isUniform) {\n    return `\n      float ${funcName}(int row, int col) {\n        int index = round(dot(vec2(row, col), vec2(${shape[1]}, 1)));\n        ${getUniformSampler(inputInfo)}\n      }\n    `;\n  }\n  const texNumR = texShape[0];\n  const texNumC = texShape[1];\n  const offset = getFlatOffsetUniformName(texName);\n  if (texNumC === 1) {\n    if (enableShapeUniforms) {\n      return `\n      float ${funcName}(int row, int col) {\n        float index = dot(vec3(row, col, ${offset}), vec3(${texName}Shape[1], 1, 1));\n        vec2 uv = vec2(0.5, (index + 0.5) / float(${texName}TexShape[0]));\n        return sampleTexture(${texName}, uv);\n      }\n    `;\n    }\n    return `\n    float ${funcName}(int row, int col) {\n      float index = dot(vec3(row, col, ${offset}), vec3(${shape[1]}, 1, 1));\n      vec2 uv = vec2(0.5, (index + 0.5) / ${texNumR}.0);\n      return sampleTexture(${texName}, uv);\n    }\n  `;\n  }\n  if (texNumR === 1) {\n    if (enableShapeUniforms) {\n      return `\n      float ${funcName}(int row, int col) {\n        float index = dot(vec3(row, col, ${offset}), vec3(${texName}Shape[1], 1, 1));\n        vec2 uv = vec2((index + 0.5) / float(${texName}TexShape[1]), 0.5);\n        return sampleTexture(${texName}, uv);\n      }\n    `;\n    }\n    return `\n    float ${funcName}(int row, int col) {\n      float index = dot(vec3(row, col, ${offset}), vec3(${shape[1]}, 1, 1));\n      vec2 uv = vec2((index + 0.5) / ${texNumC}.0, 0.5);\n      return sampleTexture(${texName}, uv);\n    }\n  `;\n  }\n  if (enableShapeUniforms) {\n    return `\n      float ${funcName}(int row, int col) {\n        // Explicitly use integer operations as dot() only works on floats.\n        int index = row * ${texName}Shape[1] + col + ${offset};\n        vec2 uv = uvFromFlat(${texName}TexShape[0], ${texName}TexShape[1], index);\n        return sampleTexture(${texName}, uv);\n      }\n    `;\n  }\n  return `\n  float ${funcName}(int row, int col) {\n    // Explicitly use integer operations as dot() only works on floats.\n    int index = row * ${shape[1]} + col + ${offset};\n    vec2 uv = uvFromFlat(${texNumR}, ${texNumC}, index);\n    return sampleTexture(${texName}, uv);\n  }\n`;\n}\nfunction getPackedSampler3D(inputInfo, enableShapeUniforms) {\n  const shape = inputInfo.shapeInfo.logicalShape;\n  const texName = inputInfo.name;\n  const funcName = \"get\" + texName.charAt(0).toUpperCase() + texName.slice(1);\n  const texShape = inputInfo.shapeInfo.texShape;\n  const packedTexShape = [Math.ceil(texShape[0] / 2), Math.ceil(texShape[1] / 2)];\n  if (shape[0] === 1) {\n    const squeezedShape = shape.slice(1);\n    const keptDims = [1, 2];\n    const newInputInfo = squeezeInputInfo(inputInfo, squeezedShape);\n    const params = [\"b\", \"row\", \"col\"];\n    return `\n        ${getPackedSamplerFromInInfo(newInputInfo, enableShapeUniforms)}\n        vec4 ${funcName}(int b, int row, int col) {\n          return ${funcName}(${getSqueezedParams(params, keptDims)});\n        }\n      `;\n  }\n  const glsl = getGlslDifferences();\n  if (enableShapeUniforms) {\n    return `\n    vec4 ${funcName}(int b, int row, int col) {\n      ivec2 packedTexShape = ivec2(ceil(float(${texName}TexShape[0]) / 2.0), ceil(float(${texName}TexShape[1]) / 2.0));\n      int valuesPerRow = int(ceil(float(${texName}Shape[2]) / 2.0));\n      int texelsInBatch = valuesPerRow * int(ceil(float(${texName}Shape[1]) / 2.0));\n      vec2 uv = packedUVfrom3D(\n        packedTexShape[0], packedTexShape[1], texelsInBatch, valuesPerRow, b, row, col);\n      return ${glsl.texture2D}(${texName}, uv);\n    }\n  `;\n  }\n  const texNumR = packedTexShape[0];\n  const texNumC = packedTexShape[1];\n  const valuesPerRow = Math.ceil(shape[2] / 2);\n  const texelsInBatch = valuesPerRow * Math.ceil(shape[1] / 2);\n  return `\n    vec4 ${funcName}(int b, int row, int col) {\n      vec2 uv = packedUVfrom3D(\n        ${texNumR}, ${texNumC}, ${texelsInBatch}, ${valuesPerRow}, b, row, col);\n      return ${glsl.texture2D}(${texName}, uv);\n    }\n  `;\n}\nfunction getSampler3D(inputInfo, enableShapeUniforms) {\n  const shape = inputInfo.shapeInfo.logicalShape;\n  const texName = inputInfo.name;\n  const funcName = \"get\" + texName.charAt(0).toUpperCase() + texName.slice(1);\n  const stride0 = shape[1] * shape[2];\n  const stride1 = shape[2];\n  const { newShape, keptDims } = util_exports.squeezeShape(shape);\n  const squeezedShape = newShape;\n  if (squeezedShape.length < shape.length) {\n    const newInputInfo = squeezeInputInfo(inputInfo, squeezedShape);\n    const params = [\"row\", \"col\", \"depth\"];\n    return `\n        ${getSamplerFromInInfo(newInputInfo, enableShapeUniforms)}\n        float ${funcName}(int row, int col, int depth) {\n          return ${funcName}(${getSqueezedParams(params, keptDims)});\n        }\n      `;\n  }\n  if (inputInfo.shapeInfo.isUniform) {\n    return `\n      float ${funcName}(int row, int col, int depth) {\n        int index = round(dot(vec3(row, col, depth),\n                          vec3(${stride0}, ${stride1}, 1)));\n        ${getUniformSampler(inputInfo)}\n      }\n    `;\n  }\n  const texShape = inputInfo.shapeInfo.texShape;\n  const texNumR = texShape[0];\n  const texNumC = texShape[1];\n  const flatOffset = inputInfo.shapeInfo.flatOffset;\n  if (texNumC === stride0 && flatOffset == null) {\n    if (enableShapeUniforms) {\n      return `\n      float ${funcName}(int row, int col, int depth) {\n        int stride1 = ${texName}Shape[2];\n        float texR = float(row);\n        float texC = dot(vec2(col, depth), vec2(stride1, 1));\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                   vec2(${texName}TexShape[1], ${texName}TexShape[0]);\n        return sampleTexture(${texName}, uv);\n      }\n    `;\n    }\n    return `\n        float ${funcName}(int row, int col, int depth) {\n          float texR = float(row);\n          float texC = dot(vec2(col, depth), vec2(${stride1}, 1));\n          vec2 uv = (vec2(texC, texR) + halfCR) /\n                     vec2(${texNumC}.0, ${texNumR}.0);\n          return sampleTexture(${texName}, uv);\n        }\n      `;\n  }\n  if (texNumC === stride1 && flatOffset == null) {\n    if (enableShapeUniforms) {\n      return `\n      float ${funcName}(int row, int col, int depth) {\n        float texR = dot(vec2(row, col), vec2(${texName}Shape[1], 1));\n        float texC = float(depth);\n        vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${texName}TexShape[1], ${texName}TexShape[0]);\n        return sampleTexture(${texName}, uv);\n      }\n    `;\n    }\n    return `\n    float ${funcName}(int row, int col, int depth) {\n      float texR = dot(vec2(row, col), vec2(${shape[1]}, 1));\n      float texC = float(depth);\n      vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${texNumC}.0, ${texNumR}.0);\n      return sampleTexture(${texName}, uv);\n    }\n  `;\n  }\n  const offset = getFlatOffsetUniformName(texName);\n  if (enableShapeUniforms) {\n    return `\n    float ${funcName}(int row, int col, int depth) {\n      // Explicitly use integer operations as dot() only works on floats.\n      int stride0 = ${texName}Shape[1] * ${texName}Shape[2];\n      int stride1 = ${texName}Shape[2];\n      int index = row * ${stride0} + col * ${stride1} + depth + ${offset};\n      vec2 uv = uvFromFlat(${texName}TexShape[0], ${texName}TexShape[1], index);\n      return sampleTexture(${texName}, uv);\n    }\n    `;\n  }\n  return `\n      float ${funcName}(int row, int col, int depth) {\n        // Explicitly use integer operations as dot() only works on floats.\n        int index = row * ${stride0} + col * ${stride1} + depth + ${offset};\n        vec2 uv = uvFromFlat(${texNumR}, ${texNumC}, index);\n        return sampleTexture(${texName}, uv);\n      }\n  `;\n}\nfunction getPackedSamplerND(inputInfo, enableShapeUniforms) {\n  const texName = inputInfo.name;\n  const funcName = \"get\" + texName.charAt(0).toUpperCase() + texName.slice(1);\n  const glsl = getGlslDifferences();\n  if (enableShapeUniforms) {\n    return `\n    vec4 ${funcName}(int b2, int b, int row, int col) {\n      int valuesPerRow = int(ceil(float(${texName}Shape[3]) / 2.0));\n      int texelsInBatch = valuesPerRow * int(ceil(float(${texName}Shape[2]) / 2.0));\n      int index = b * texelsInBatch + (row / 2) * valuesPerRow + (col / 2);\n      texelsInBatch *= ${texName}Shape[1];\n      index = b2 * texelsInBatch + index;\n      ivec2 packedTexShape = ivec2(ceil(float(${texName}TexShape[0]) / 2.0), ceil(float(${texName}TexShape[1]) / 2.0));\n      int texR = index / packedTexShape[1];\n      int texC = index - texR * packedTexShape[1];\n      vec2 uv = (vec2(texC, texR) + halfCR) / vec2(packedTexShape[1], packedTexShape[0]); return ${glsl.texture2D}(${texName}, uv);\n    }\n  `;\n  }\n  const shape = inputInfo.shapeInfo.logicalShape;\n  const rank = shape.length;\n  const texShape = inputInfo.shapeInfo.texShape;\n  const packedTexShape = [Math.ceil(texShape[0] / 2), Math.ceil(texShape[1] / 2)];\n  const texNumR = packedTexShape[0];\n  const texNumC = packedTexShape[1];\n  const valuesPerRow = Math.ceil(shape[rank - 1] / 2);\n  let texelsInBatch = valuesPerRow * Math.ceil(shape[rank - 2] / 2);\n  let params = `int b, int row, int col`;\n  let index = `b * ${texelsInBatch} + (row / 2) * ${valuesPerRow} + (col / 2)`;\n  for (let b = 2; b < rank - 1; b++) {\n    params = `int b${b}, ` + params;\n    texelsInBatch *= shape[rank - b - 1];\n    index = `b${b} * ${texelsInBatch} + ` + index;\n  }\n  return `\n    vec4 ${funcName}(${params}) {\n      int index = ${index};\n      int texR = index / ${texNumC};\n      int texC = index - texR * ${texNumC};\n      vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${texNumC}, ${texNumR});\n      return ${glsl.texture2D}(${texName}, uv);\n    }\n  `;\n}\nfunction getSampler4D(inputInfo, enableShapeUniforms) {\n  const shape = inputInfo.shapeInfo.logicalShape;\n  const texName = inputInfo.name;\n  const funcName = \"get\" + texName.charAt(0).toUpperCase() + texName.slice(1);\n  const stride2 = shape[3];\n  const stride1 = shape[2] * stride2;\n  const stride0 = shape[1] * stride1;\n  const { newShape, keptDims } = util_exports.squeezeShape(shape);\n  if (newShape.length < shape.length) {\n    const newInputInfo = squeezeInputInfo(inputInfo, newShape);\n    const params = [\"row\", \"col\", \"depth\", \"depth2\"];\n    return `\n      ${getSamplerFromInInfo(newInputInfo, enableShapeUniforms)}\n      float ${funcName}(int row, int col, int depth, int depth2) {\n        return ${funcName}(${getSqueezedParams(params, keptDims)});\n      }\n    `;\n  }\n  if (inputInfo.shapeInfo.isUniform) {\n    return `\n      float ${funcName}(int row, int col, int depth, int depth2) {\n        int index = round(dot(vec4(row, col, depth, depth2),\n                          vec4(${stride0}, ${stride1}, ${stride2}, 1)));\n        ${getUniformSampler(inputInfo)}\n      }\n    `;\n  }\n  const flatOffset = inputInfo.shapeInfo.flatOffset;\n  const texShape = inputInfo.shapeInfo.texShape;\n  const texNumR = texShape[0];\n  const texNumC = texShape[1];\n  const stride2Str = `int stride2 = ${texName}Shape[3];`;\n  const stride1Str = `int stride1 = ${texName}Shape[2] * stride2;`;\n  const stride0Str = `int stride0 = ${texName}Shape[1] * stride1;`;\n  if (texNumC === stride0 && flatOffset == null) {\n    if (enableShapeUniforms) {\n      return `\n      float ${funcName}(int row, int col, int depth, int depth2) {\n        ${stride2Str}\n        ${stride1Str}\n        float texR = float(row);\n        float texC =\n            dot(vec3(col, depth, depth2),\n                vec3(stride1, stride2, 1));\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                   vec2(${texName}TexShape[1], ${texName}TexShape[0]);\n        return sampleTexture(${texName}, uv);\n      }\n    `;\n    }\n    return `\n      float ${funcName}(int row, int col, int depth, int depth2) {\n        float texR = float(row);\n        float texC =\n            dot(vec3(col, depth, depth2),\n                vec3(${stride1}, ${stride2}, 1));\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                   vec2(${texNumC}.0, ${texNumR}.0);\n        return sampleTexture(${texName}, uv);\n      }\n    `;\n  }\n  if (texNumC === stride2 && flatOffset == null) {\n    if (enableShapeUniforms) {\n      return `\n      float ${funcName}(int row, int col, int depth, int depth2) {\n        float texR = dot(vec3(row, col, depth),\n                         vec3(${texName}Shape[1] * ${texName}Shape[2], ${texName}Shape[2], 1));\n        float texC = float(depth2);\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                  vec2(${texName}TexShape[1], ${texName}TexShape[0]);\n        return sampleTexture(${texName}, uv);\n      }\n    `;\n    }\n    return `\n      float ${funcName}(int row, int col, int depth, int depth2) {\n        float texR = dot(vec3(row, col, depth),\n                         vec3(${shape[1] * shape[2]}, ${shape[2]}, 1));\n        float texC = float(depth2);\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                  vec2(${texNumC}.0, ${texNumR}.0);\n        return sampleTexture(${texName}, uv);\n      }\n    `;\n  }\n  const offset = getFlatOffsetUniformName(texName);\n  if (enableShapeUniforms) {\n    return `\n    float ${funcName}(int row, int col, int depth, int depth2) {\n      // Explicitly use integer operations as dot() only works on floats.\n      ${stride2Str}\n      ${stride1Str}\n      ${stride0Str}\n      int index = row * stride0 + col * stride1 +\n          depth * stride2 + depth2;\n      vec2 uv = uvFromFlat(${texName}TexShape[0], ${texName}TexShape[1], index + ${offset});\n      return sampleTexture(${texName}, uv);\n    }\n  `;\n  }\n  return `\n    float ${funcName}(int row, int col, int depth, int depth2) {\n      // Explicitly use integer operations as dot() only works on floats.\n      int index = row * ${stride0} + col * ${stride1} +\n          depth * ${stride2} + depth2;\n      vec2 uv = uvFromFlat(${texNumR}, ${texNumC}, index + ${offset});\n      return sampleTexture(${texName}, uv);\n    }\n  `;\n}\nfunction getSampler5D(inputInfo) {\n  const shape = inputInfo.shapeInfo.logicalShape;\n  const texName = inputInfo.name;\n  const funcName = \"get\" + texName.charAt(0).toUpperCase() + texName.slice(1);\n  const stride3 = shape[4];\n  const stride2 = shape[3] * stride3;\n  const stride1 = shape[2] * stride2;\n  const stride0 = shape[1] * stride1;\n  const { newShape, keptDims } = util_exports.squeezeShape(shape);\n  if (newShape.length < shape.length) {\n    const newInputInfo = squeezeInputInfo(inputInfo, newShape);\n    const params = [\"row\", \"col\", \"depth\", \"depth2\", \"depth3\"];\n    return `\n      ${getSamplerFromInInfo(newInputInfo)}\n      float ${funcName}(int row, int col, int depth, int depth2, int depth3) {\n        return ${funcName}(${getSqueezedParams(params, keptDims)});\n      }\n    `;\n  }\n  if (inputInfo.shapeInfo.isUniform) {\n    return `\n      float ${funcName}(int row, int col, int depth, int depth2, int depth3) {\n        float index = dot(\n          vec4(row, col, depth, depth2),\n          vec4(${stride0}, ${stride1}, ${stride2}, ${stride3})) +\n          depth3;\n        ${getUniformSampler(inputInfo)}\n      }\n    `;\n  }\n  const flatOffset = inputInfo.shapeInfo.flatOffset;\n  const texShape = inputInfo.shapeInfo.texShape;\n  const texNumR = texShape[0];\n  const texNumC = texShape[1];\n  if (texNumC === stride0 && flatOffset == null) {\n    return `\n      float ${funcName}(int row, int col, int depth, int depth2, int depth3) {\n        int texR = row;\n        float texC = dot(vec4(col, depth, depth2, depth3),\n                         vec4(${stride1}, ${stride2}, ${stride3}, 1));\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                   vec2(${texNumC}.0, ${texNumR}.0);\n        return sampleTexture(${texName}, uv);\n      }\n    `;\n  }\n  if (texNumC === stride3 && flatOffset == null) {\n    return `\n      float ${funcName}(int row, int col, int depth, int depth2, int depth3) {\n        float texR = dot(\n          vec4(row, col, depth, depth2),\n          vec4(${shape[1] * shape[2] * shape[3]},\n               ${shape[2] * shape[3]}, ${shape[3]}, 1));\n        int texC = depth3;\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                  vec2(${texNumC}.0, ${texNumR}.0);\n        return sampleTexture(${texName}, uv);\n      }\n    `;\n  }\n  const offset = getFlatOffsetUniformName(texName);\n  return `\n    float ${funcName}(int row, int col, int depth, int depth2, int depth3) {\n      // Explicitly use integer operations as dot() only works on floats.\n      int index = row * ${stride0} + col * ${stride1} + depth * ${stride2} +\n          depth2 * ${stride3} + depth3 + ${offset};\n      vec2 uv = uvFromFlat(${texNumR}, ${texNumC}, index);\n      return sampleTexture(${texName}, uv);\n    }\n  `;\n}\nfunction getSampler6D(inputInfo) {\n  const shape = inputInfo.shapeInfo.logicalShape;\n  const texName = inputInfo.name;\n  const funcName = \"get\" + texName.charAt(0).toUpperCase() + texName.slice(1);\n  const { newShape, keptDims } = util_exports.squeezeShape(shape);\n  if (newShape.length < shape.length) {\n    const newInputInfo = squeezeInputInfo(inputInfo, newShape);\n    const params = [\"row\", \"col\", \"depth\", \"depth2\", \"depth3\", \"depth4\"];\n    return `\n      ${getSamplerFromInInfo(newInputInfo)}\n      float ${funcName}(int row, int col, int depth,\n                    int depth2, int depth3, int depth4) {\n        return ${funcName}(${getSqueezedParams(params, keptDims)});\n      }\n    `;\n  }\n  const stride4 = shape[5];\n  const stride3 = shape[4] * stride4;\n  const stride2 = shape[3] * stride3;\n  const stride1 = shape[2] * stride2;\n  const stride0 = shape[1] * stride1;\n  if (inputInfo.shapeInfo.isUniform) {\n    return `\n      float ${funcName}(int row, int col, int depth,\n                  int depth2, int depth3, int depth4) {\n        int index = round(dot(\n          vec4(row, col, depth, depth2),\n          vec4(${stride0}, ${stride1}, ${stride2}, ${stride3})) +\n          dot(\n            vec2(depth3, depth4),\n            vec2(${stride4}, 1)));\n        ${getUniformSampler(inputInfo)}\n      }\n    `;\n  }\n  const flatOffset = inputInfo.shapeInfo.flatOffset;\n  const texShape = inputInfo.shapeInfo.texShape;\n  const texNumR = texShape[0];\n  const texNumC = texShape[1];\n  if (texNumC === stride0 && flatOffset == null) {\n    return `\n      float ${funcName}(int row, int col, int depth,\n                    int depth2, int depth3, int depth4) {\n        int texR = row;\n        float texC = dot(vec4(col, depth, depth2, depth3),\n          vec4(${stride1}, ${stride2}, ${stride3}, ${stride4})) +\n               float(depth4);\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                   vec2(${texNumC}.0, ${texNumR}.0);\n        return sampleTexture(${texName}, uv);\n      }\n    `;\n  }\n  if (texNumC === stride4 && flatOffset == null) {\n    return `\n      float ${funcName}(int row, int col, int depth,\n                    int depth2, int depth3, int depth4) {\n        float texR = dot(vec4(row, col, depth, depth2),\n          vec4(${shape[1] * shape[2] * shape[3] * shape[4]},\n               ${shape[2] * shape[3] * shape[4]},\n               ${shape[3] * shape[4]},\n               ${shape[4]})) + float(depth3);\n        int texC = depth4;\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                  vec2(${texNumC}.0, ${texNumR}.0);\n        return sampleTexture(${texName}, uv);\n      }\n    `;\n  }\n  const offset = getFlatOffsetUniformName(texName);\n  return `\n    float ${funcName}(int row, int col, int depth,\n                  int depth2, int depth3, int depth4) {\n      // Explicitly use integer operations as dot() only works on floats.\n      int index = row * ${stride0} + col * ${stride1} + depth * ${stride2} +\n          depth2 * ${stride3} + depth3 * ${stride4} + depth4 + ${offset};\n      vec2 uv = uvFromFlat(${texNumR}, ${texNumC}, index);\n      return sampleTexture(${texName}, uv);\n    }\n  `;\n}\nfunction getUniformSampler(inputInfo) {\n  const texName = inputInfo.name;\n  const inSize = util_exports.sizeFromShape(inputInfo.shapeInfo.logicalShape);\n  if (inSize < 2) {\n    return `return ${texName};`;\n  }\n  return `\n    for (int i = 0; i < ${inSize}; i++) {\n      if (i == index) {\n        return ${texName}[i];\n      }\n    }\n  `;\n}\nfunction getPackedSamplerAtOutputCoords(inputInfo, outShapeInfo) {\n  const texName = inputInfo.name;\n  const texFuncSnippet = texName.charAt(0).toUpperCase() + texName.slice(1);\n  const funcName = \"get\" + texFuncSnippet + \"AtOutCoords\";\n  const inRank = inputInfo.shapeInfo.logicalShape.length;\n  const outRank = outShapeInfo.logicalShape.length;\n  const broadcastDims = getBroadcastDims2(inputInfo.shapeInfo.logicalShape, outShapeInfo.logicalShape);\n  const type = getCoordsDataType(outRank);\n  const rankDiff = outRank - inRank;\n  let coordsSnippet;\n  const fields = [\"x\", \"y\", \"z\", \"w\", \"u\", \"v\"];\n  if (inRank === 0) {\n    coordsSnippet = \"\";\n  } else if (outRank < 2 && broadcastDims.length >= 1) {\n    coordsSnippet = \"coords = 0;\";\n  } else {\n    coordsSnippet = broadcastDims.map((d) => `coords.${fields[d + rankDiff]} = 0;`).join(\"\\n\");\n  }\n  let unpackedCoordsSnippet = \"\";\n  if (outRank < 2 && inRank > 0) {\n    unpackedCoordsSnippet = \"coords\";\n  } else {\n    unpackedCoordsSnippet = inputInfo.shapeInfo.logicalShape.map((s, i) => `coords.${fields[i + rankDiff]}`).join(\", \");\n  }\n  let output = `return outputValue;`;\n  const inSize = util_exports.sizeFromShape(inputInfo.shapeInfo.logicalShape);\n  const isInputScalar = inSize === 1;\n  const outSize = util_exports.sizeFromShape(outShapeInfo.logicalShape);\n  const isOutputScalar = outSize === 1;\n  if (inRank === 1 && !isInputScalar && !isOutputScalar) {\n    output = `\n      return vec4(outputValue.xy, outputValue.xy);\n    `;\n  } else if (isInputScalar && !isOutputScalar) {\n    if (outRank === 1) {\n      output = `\n        return vec4(outputValue.x, outputValue.x, 0., 0.);\n      `;\n    } else {\n      output = `\n        return vec4(outputValue.x);\n      `;\n    }\n  } else if (broadcastDims.length) {\n    const rows = inRank - 2;\n    const cols = inRank - 1;\n    if (broadcastDims.indexOf(rows) > -1 && broadcastDims.indexOf(cols) > -1) {\n      output = `return vec4(outputValue.x);`;\n    } else if (broadcastDims.indexOf(rows) > -1) {\n      output = `return vec4(outputValue.x, outputValue.y, outputValue.x, outputValue.y);`;\n    } else if (broadcastDims.indexOf(cols) > -1) {\n      output = `return vec4(outputValue.xx, outputValue.zz);`;\n    }\n  }\n  return `\n    vec4 ${funcName}() {\n      ${type} coords = getOutputCoords();\n      ${coordsSnippet}\n      vec4 outputValue = get${texFuncSnippet}(${unpackedCoordsSnippet});\n      ${output}\n    }\n  `;\n}\nfunction getSamplerAtOutputCoords(inputInfo, outShapeInfo) {\n  const texName = inputInfo.name;\n  const texFuncSnippet = texName.charAt(0).toUpperCase() + texName.slice(1);\n  const funcName = \"get\" + texFuncSnippet + \"AtOutCoords\";\n  const outTexShape = outShapeInfo.texShape;\n  const inTexShape = inputInfo.shapeInfo.texShape;\n  const inRank = inputInfo.shapeInfo.logicalShape.length;\n  const outRank = outShapeInfo.logicalShape.length;\n  if (!inputInfo.shapeInfo.isUniform && inRank === outRank && inputInfo.shapeInfo.flatOffset == null && util_exports.arraysEqual(inTexShape, outTexShape)) {\n    return `\n      float ${funcName}() {\n        return sampleTexture(${texName}, resultUV);\n      }\n    `;\n  }\n  const type = getCoordsDataType(outRank);\n  const broadcastDims = getBroadcastDims2(inputInfo.shapeInfo.logicalShape, outShapeInfo.logicalShape);\n  const rankDiff = outRank - inRank;\n  let coordsSnippet;\n  const fields = [\"x\", \"y\", \"z\", \"w\", \"u\", \"v\"];\n  if (inRank === 0) {\n    coordsSnippet = \"\";\n  } else if (outRank < 2 && broadcastDims.length >= 1) {\n    coordsSnippet = \"coords = 0;\";\n  } else {\n    coordsSnippet = broadcastDims.map((d) => `coords.${fields[d + rankDiff]} = 0;`).join(\"\\n\");\n  }\n  let unpackedCoordsSnippet = \"\";\n  if (outRank < 2 && inRank > 0) {\n    unpackedCoordsSnippet = \"coords\";\n  } else {\n    unpackedCoordsSnippet = inputInfo.shapeInfo.logicalShape.map((s, i) => `coords.${fields[i + rankDiff]}`).join(\", \");\n  }\n  return `\n    float ${funcName}() {\n      ${type} coords = getOutputCoords();\n      ${coordsSnippet}\n      return get${texFuncSnippet}(${unpackedCoordsSnippet});\n    }\n  `;\n}\nfunction getCoordsDataType(rank) {\n  if (rank <= 1) {\n    return \"int\";\n  } else if (rank === 2) {\n    return \"ivec2\";\n  } else if (rank === 3) {\n    return \"ivec3\";\n  } else if (rank === 4) {\n    return \"ivec4\";\n  } else if (rank === 5) {\n    return \"ivec5\";\n  } else if (rank === 6) {\n    return \"ivec6\";\n  } else {\n    throw Error(`GPU for rank ${rank} is not yet supported`);\n  }\n}\nfunction getUniformInfoFromShape(isPacked, shape, texShape) {\n  const { newShape, keptDims } = util_exports.squeezeShape(shape);\n  const rank = shape.length;\n  const useSqueezePackedShape = isPacked && rank === 3 && shape[0] === 1;\n  const squeezeShape2 = useSqueezePackedShape ? shape.slice(1) : newShape;\n  const useSqueezeShape = !isPacked && rank > 1 && !util_exports.arraysEqual(shape, texShape) && newShape.length < rank || useSqueezePackedShape;\n  const uniformShape = useSqueezeShape ? squeezeShape2 : shape;\n  return { useSqueezeShape, uniformShape, keptDims };\n}\nfunction squeezeInputInfo(inInfo, squeezedShape) {\n  const newInputInfo = JSON.parse(JSON.stringify(inInfo));\n  newInputInfo.shapeInfo.logicalShape = squeezedShape;\n  return newInputInfo;\n}\nfunction getSqueezedParams(params, keptDims) {\n  return keptDims.map((d) => params[d]).join(\", \");\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/gpgpu_math.js\nfunction compileProgram(gpgpu, program, inputs, output) {\n  const inputInfos = inputs.map((input2, i) => {\n    const shapeInfo = {\n      logicalShape: input2.shape,\n      texShape: input2.isUniform ? null : input2.texData.texShape,\n      isUniform: input2.isUniform,\n      isPacked: input2.isUniform ? false : input2.texData.isPacked,\n      flatOffset: null\n    };\n    if (input2.texData != null && input2.texData.slice != null && input2.texData.slice.flatOffset > 0) {\n      shapeInfo.flatOffset = input2.texData.slice.flatOffset;\n    }\n    return { name: program.variableNames[i], shapeInfo };\n  });\n  const inShapeInfos = inputInfos.map((x) => x.shapeInfo);\n  const outShapeInfo = {\n    logicalShape: output.shape,\n    texShape: output.texData.texShape,\n    isUniform: false,\n    isPacked: output.texData.isPacked,\n    flatOffset: null\n  };\n  const source = makeShader(inputInfos, outShapeInfo, program);\n  const fragmentShader = createFragmentShader(gpgpu.gl, source);\n  const webGLProgram = gpgpu.createProgram(fragmentShader);\n  if (!env().get(\"ENGINE_COMPILE_ONLY\")) {\n    return Object.assign({\n      program,\n      fragmentShader,\n      source,\n      webGLProgram,\n      inShapeInfos,\n      outShapeInfo\n    }, getUniformLocations(gpgpu, program, webGLProgram));\n  } else {\n    return {\n      program,\n      fragmentShader,\n      source,\n      webGLProgram,\n      inShapeInfos,\n      outShapeInfo,\n      uniformLocations: null,\n      customUniformLocations: null,\n      infLoc: null,\n      nanLoc: null,\n      inShapesLocations: null,\n      inTexShapesLocations: null,\n      outShapeLocation: null,\n      outShapeStridesLocation: null,\n      outTexShapeLocation: null\n    };\n  }\n}\nfunction getUniformLocations(gpgpu, program, webGLProgram) {\n  const uniformLocations = {};\n  const inShapesLocations = {};\n  const inTexShapesLocations = {};\n  const customUniformLocations = [];\n  let outShapeLocation;\n  let outTexShapeLocation;\n  let outShapeStridesLocation;\n  let infLoc = null;\n  let nanLoc = null;\n  nanLoc = gpgpu.getUniformLocation(webGLProgram, \"NAN\", false);\n  if (env().getNumber(\"WEBGL_VERSION\") === 1) {\n    infLoc = gpgpu.getUniformLocation(webGLProgram, \"INFINITY\", false);\n  }\n  const shouldThrow = false;\n  for (let i = 0; i < program.variableNames.length; i++) {\n    const varName = program.variableNames[i];\n    uniformLocations[varName] = gpgpu.getUniformLocation(webGLProgram, varName, shouldThrow);\n    uniformLocations[`offset${varName}`] = gpgpu.getUniformLocation(webGLProgram, `offset${varName}`, shouldThrow);\n    if (program.enableShapeUniforms) {\n      inShapesLocations[`${varName}Shape`] = gpgpu.getUniformLocation(webGLProgram, `${varName}Shape`, shouldThrow);\n      inTexShapesLocations[`${varName}TexShape`] = gpgpu.getUniformLocation(webGLProgram, `${varName}TexShape`, shouldThrow);\n    }\n  }\n  if (program.enableShapeUniforms) {\n    outShapeLocation = gpgpu.getUniformLocation(webGLProgram, \"outShape\", shouldThrow);\n    outShapeStridesLocation = gpgpu.getUniformLocation(webGLProgram, \"outShapeStrides\", shouldThrow);\n    outTexShapeLocation = gpgpu.getUniformLocation(webGLProgram, \"outTexShape\", shouldThrow);\n  }\n  if (program.customUniforms) {\n    program.customUniforms.forEach((d, i) => {\n      customUniformLocations[i] = gpgpu.getUniformLocation(webGLProgram, d.name, shouldThrow);\n    });\n  }\n  return {\n    uniformLocations,\n    customUniformLocations,\n    infLoc,\n    nanLoc,\n    inShapesLocations,\n    inTexShapesLocations,\n    outShapeLocation,\n    outShapeStridesLocation,\n    outTexShapeLocation\n  };\n}\nfunction validateBinaryAndProgram(shapeInfos, inputs) {\n  if (shapeInfos.length !== inputs.length) {\n    throw Error(`Binary was compiled with ${shapeInfos.length} inputs, but was executed with ${inputs.length} inputs`);\n  }\n  shapeInfos.forEach((s, i) => {\n    const shapeA = s.logicalShape;\n    const input2 = inputs[i];\n    const shapeB = input2.shape;\n    if (!util_exports.arraysEqual(shapeA, shapeB)) {\n      throw Error(`Binary was compiled with different shapes than the current args. Shapes ${shapeA} and ${shapeB} must match`);\n    }\n    if (s.isUniform && input2.isUniform) {\n      return;\n    }\n    const texShapeA = s.texShape;\n    const texShapeB = input2.isUniform ? null : input2.texData.texShape;\n    if (!util_exports.arraysEqual(texShapeA, texShapeB)) {\n      throw Error(`Binary was compiled with different texture shapes than the current args. Shape ${texShapeA} and ${texShapeB} must match`);\n    }\n  });\n}\nfunction runProgram(gpgpu, binary, inputs, output, customUniformValues) {\n  if (!binary.program.enableShapeUniforms) {\n    validateBinaryAndProgram(binary.inShapeInfos, inputs);\n    validateBinaryAndProgram([binary.outShapeInfo], [output]);\n  }\n  const outTex = output.texData.texture;\n  const outTexShape = output.texData.texShape;\n  if (output.texData.isPacked) {\n    gpgpu.setOutputPackedMatrixTexture(outTex.texture, outTexShape[0], outTexShape[1]);\n  } else {\n    gpgpu.setOutputMatrixTexture(outTex.texture, outTexShape[0], outTexShape[1]);\n  }\n  gpgpu.setProgram(binary.webGLProgram);\n  if (env().getNumber(\"WEBGL_VERSION\") === 1) {\n    if (binary.infLoc !== null) {\n      gpgpu.gl.uniform1f(binary.infLoc, Infinity);\n    }\n  }\n  if (binary.nanLoc !== null) {\n    gpgpu.gl.uniform1f(binary.nanLoc, NaN);\n  }\n  inputs.forEach((input2, i) => {\n    const varName = binary.program.variableNames[i];\n    const varLoc = binary.uniformLocations[varName];\n    const varOffsetLoc = binary.uniformLocations[`offset${varName}`];\n    const varShapeLoc = binary.inShapesLocations[`${varName}Shape`];\n    const varTexShapeLoc = binary.inTexShapesLocations[`${varName}TexShape`];\n    if (varShapeLoc) {\n      const { uniformShape } = getUniformInfoFromShape(binary.program.packedInputs, input2.shape, input2.texData.texShape);\n      switch (uniformShape.length) {\n        case 1:\n          gpgpu.gl.uniform1iv(varShapeLoc, new Int32Array(uniformShape));\n          break;\n        case 2:\n          gpgpu.gl.uniform2iv(varShapeLoc, new Int32Array(uniformShape));\n          break;\n        case 3:\n          gpgpu.gl.uniform3iv(varShapeLoc, new Int32Array(uniformShape));\n          break;\n        case 4:\n          gpgpu.gl.uniform4iv(varShapeLoc, new Int32Array(uniformShape));\n          break;\n        default:\n          break;\n      }\n    }\n    if (varTexShapeLoc) {\n      gpgpu.gl.uniform2i(varTexShapeLoc, input2.texData.texShape[0], input2.texData.texShape[1]);\n    }\n    if (varLoc == null) {\n      return;\n    }\n    if (input2.isUniform) {\n      if (util_exports.sizeFromShape(input2.shape) < 2) {\n        gpgpu.gl.uniform1f(varLoc, input2.uniformValues[0]);\n      } else {\n        let vals = input2.uniformValues;\n        if (!(vals instanceof Float32Array)) {\n          vals = new Float32Array(vals);\n        }\n        gpgpu.gl.uniform1fv(varLoc, vals);\n      }\n      return;\n    }\n    if (input2.texData.slice != null && varOffsetLoc != null) {\n      gpgpu.gl.uniform1i(varOffsetLoc, input2.texData.slice.flatOffset);\n    }\n    gpgpu.setInputMatrixTexture(input2.texData.texture.texture, varLoc, i);\n  });\n  const outShapeLoc = binary.outShapeLocation;\n  if (outShapeLoc) {\n    switch (output.shape.length) {\n      case 1:\n        gpgpu.gl.uniform1iv(outShapeLoc, new Int32Array(output.shape));\n        break;\n      case 2:\n        gpgpu.gl.uniform2iv(outShapeLoc, new Int32Array(output.shape));\n        break;\n      case 3:\n        gpgpu.gl.uniform3iv(outShapeLoc, new Int32Array(output.shape));\n        break;\n      case 4:\n        gpgpu.gl.uniform4iv(outShapeLoc, new Int32Array(output.shape));\n        break;\n      default:\n        break;\n    }\n  }\n  if (binary.outShapeStridesLocation) {\n    const strides = util_exports.computeStrides(output.shape);\n    switch (output.shape.length) {\n      case 2:\n        gpgpu.gl.uniform1iv(binary.outShapeStridesLocation, new Int32Array(strides));\n        break;\n      case 3:\n        gpgpu.gl.uniform2iv(binary.outShapeStridesLocation, new Int32Array(strides));\n        break;\n      case 4:\n        gpgpu.gl.uniform3iv(binary.outShapeStridesLocation, new Int32Array(strides));\n        break;\n      default:\n        break;\n    }\n  }\n  if (binary.outTexShapeLocation) {\n    gpgpu.gl.uniform2i(binary.outTexShapeLocation, output.texData.texShape[0], output.texData.texShape[1]);\n  }\n  if (binary.program.customUniforms && customUniformValues) {\n    binary.program.customUniforms.forEach((d, i) => {\n      const customLoc = binary.customUniformLocations[i];\n      const customValue = customUniformValues[i];\n      if (d.type === \"float\") {\n        gpgpu.gl.uniform1fv(customLoc, customValue);\n      } else if (d.type === \"vec2\") {\n        gpgpu.gl.uniform2fv(customLoc, customValue);\n      } else if (d.type === \"vec3\") {\n        gpgpu.gl.uniform3fv(customLoc, customValue);\n      } else if (d.type === \"vec4\") {\n        gpgpu.gl.uniform4fv(customLoc, customValue);\n      } else if (d.type === \"int\") {\n        gpgpu.gl.uniform1iv(customLoc, customValue);\n      } else if (d.type === \"ivec2\") {\n        gpgpu.gl.uniform2iv(customLoc, customValue);\n      } else if (d.type === \"ivec3\") {\n        gpgpu.gl.uniform3iv(customLoc, customValue);\n      } else if (d.type === \"ivec4\") {\n        gpgpu.gl.uniform4iv(customLoc, customValue);\n      } else {\n        throw Error(`uniform type ${d.type} is not supported yet.`);\n      }\n    });\n  }\n  gpgpu.executeProgram();\n}\nfunction makeShaderKey(program, inputs, output) {\n  let keyInputs = \"\";\n  inputs.concat(output).forEach((x) => {\n    const hasOffset = x.texData != null && x.texData.slice != null && x.texData.slice.flatOffset > 0;\n    if (program.enableShapeUniforms && !x.isUniform) {\n      const xTexShape = x.texData.texShape;\n      const { useSqueezeShape, uniformShape, keptDims } = getUniformInfoFromShape(program.packedInputs, x.shape, xTexShape);\n      let rank1 = \"\", rank2 = \"\", rank34 = \"\";\n      if (uniformShape.length === 1 && program.packedInputs) {\n        const packedTexShape = [Math.ceil(xTexShape[0] / 2), Math.ceil(xTexShape[1] / 2)];\n        rank1 = `${packedTexShape[0] > 1}_${packedTexShape[1] > 1}`;\n      } else if (uniformShape.length === 2 && !program.packedInputs) {\n        rank2 = `${uniformShape[0] > 1}_${uniformShape[1] > 1}`;\n      } else if (uniformShape.length > 2 && !program.packedInputs) {\n        const strides = util_exports.computeStrides(uniformShape);\n        rank34 = `${strides[0] === xTexShape[1]}_${strides[strides.length - 1] === xTexShape[1]}`;\n      }\n      const xRank = x.shape.length;\n      const isLogicalShapTexShapeEqual = uniformShape.length === 2 && util_exports.arraysEqual(x.shape, xTexShape);\n      const isScalar = util_exports.sizeFromShape(x.shape) === 1;\n      const broadcastDims = backend_util_exports.getBroadcastDims(x.shape, output.shape);\n      const isInOutTexShapeEqual = !program.packedInputs && xRank === output.shape.length && util_exports.arraysEqual(xTexShape, output.texData.texShape);\n      const isTexShapeGreaterThanOne = program.packedInputs || uniformShape.length > 2 ? \"\" : `${xTexShape[0] > 1}_${xTexShape[1] > 1}`;\n      keyInputs += `${xRank}_${isInOutTexShapeEqual}_${useSqueezeShape ? keptDims : \"\"}_${uniformShape.length}_${isScalar}_${broadcastDims}_${isLogicalShapTexShapeEqual}_${rank1}_${rank2}_${rank34}_${isTexShapeGreaterThanOne}_${hasOffset}`;\n    } else {\n      const texShape = x.isUniform ? \"uniform\" : x.texData.texShape;\n      keyInputs += `${x.shape}_${texShape}_${hasOffset}`;\n    }\n  });\n  const keyUserCode = program.userCode;\n  let key = program.constructor.name;\n  key += \"_\" + keyInputs + \"_\" + keyUserCode + `${env().getNumber(\"WEBGL_VERSION\")}`;\n  return key;\n}\nfunction useShapeUniforms(rank) {\n  return env().getBool(\"WEBGL_USE_SHAPES_UNIFORMS\") && rank <= 4;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/decode_matrix_gpu.js\nvar DecodeMatrixProgram = class {\n  constructor(outputShape) {\n    this.variableNames = [\"A\"];\n    this.packedInputs = false;\n    this.packedOutput = true;\n    this.outPackingScheme = PackingScheme.DENSE;\n    this.customUniforms = [{ name: \"texShape\", type: \"ivec2\" }];\n    const glsl = getGlslDifferences();\n    this.outputShape = outputShape;\n    this.enableShapeUniforms = useShapeUniforms(this.outputShape.length);\n    this.userCode = `\n      ivec3 outCoordsFromFlatIndex(int index) {\n        ${this.enableShapeUniforms ? getOutputLogicalCoordinatesFromFlatIndexByUniform([\"r\", \"c\", \"d\"], outputShape) : getLogicalCoordinatesFromFlatIndex([\"r\", \"c\", \"d\"], outputShape)}\n        return ivec3(r, c, d);\n      }\n\n      void main() {\n        ivec2 resTexRC = ivec2(resultUV.yx * vec2(texShape[0], texShape[1]));\n        int index = 4 * (resTexRC.x * texShape[1] + resTexRC.y);\n\n        vec4 result = vec4(0.);\n\n        for (int i=0; i<4; i++) {\n          int flatIndex = index + i;\n          ivec3 rc = outCoordsFromFlatIndex(flatIndex);\n          result[i] = getA(rc.x, rc.y, rc.z);\n        }\n\n        ${glsl.output} = result;\n      }\n    `;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/decode_matrix_packed_gpu.js\nvar DecodeMatrixPackedProgram = class {\n  constructor(outputShape) {\n    this.variableNames = [\"A\"];\n    this.packedInputs = true;\n    this.packedOutput = true;\n    this.outPackingScheme = PackingScheme.DENSE;\n    this.customUniforms = [{ name: \"texShape\", type: \"ivec2\" }];\n    const glsl = getGlslDifferences();\n    this.outputShape = outputShape;\n    this.enableShapeUniforms = useShapeUniforms(this.outputShape.length);\n    this.userCode = `\n      ivec3 outCoordsFromFlatIndex(int index) {\n        ${this.enableShapeUniforms ? getOutputLogicalCoordinatesFromFlatIndexByUniform([\"r\", \"c\", \"d\"], outputShape) : getLogicalCoordinatesFromFlatIndex([\"r\", \"c\", \"d\"], outputShape)}\n        return ivec3(r, c, d);\n      }\n\n      void main() {\n        ivec2 resTexRC = ivec2(resultUV.yx * vec2(texShape[0], texShape[1]));\n        int index = 4 * (resTexRC.x * texShape[1] + resTexRC.y);\n\n        vec4 result = vec4(0.);\n\n        for (int i=0; i<4; i++) {\n          int flatIndex = index + i;\n          ivec3 rc = outCoordsFromFlatIndex(flatIndex);\n          result[i] = getChannel(getA(rc.x, rc.y, rc.z), vec2(rc.y, rc.z));\n        }\n\n        ${glsl.output} = result;\n      }\n    `;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/encode_float_gpu.js\nvar EncodeFloatProgram = class {\n  constructor(outputShape) {\n    this.variableNames = [\"A\"];\n    this.outTexUsage = TextureUsage.DOWNLOAD;\n    const glsl = getGlslDifferences();\n    this.outputShape = outputShape;\n    this.userCode = `\n      ${ENCODE_FLOAT_SNIPPET}\n\n      void main() {\n        float x = getAAtOutCoords();\n        ${glsl.output} = encode_float(x);\n      }\n    `;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/encode_float_packed_gpu.js\nvar EncodeFloatPackedProgram = class {\n  constructor(outputShape) {\n    this.variableNames = [\"A\"];\n    this.packedInputs = true;\n    this.packedOutput = false;\n    this.outTexUsage = TextureUsage.DOWNLOAD;\n    const glsl = getGlslDifferences();\n    this.outputShape = outputShape;\n    this.userCode = `\n      ${ENCODE_FLOAT_SNIPPET}\n\n      void main() {\n        ivec3 coords = getOutputCoords();\n        float x = getChannel(getAAtOutCoords(), vec2(coords.y, coords.z));\n        ${glsl.output} = encode_float(x);\n      }\n    `;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/encode_matrix_gpu.js\nvar EncodeMatrixProgram = class {\n  constructor(outputShape, inputIsUnsignedByte = false) {\n    this.variableNames = [\"A\"];\n    this.customUniforms = [{ name: \"texShape\", type: \"ivec2\" }];\n    const glsl = getGlslDifferences();\n    this.outputShape = outputShape;\n    this.enableShapeUniforms = useShapeUniforms(this.outputShape.length);\n    let output = `result`;\n    if (inputIsUnsignedByte) {\n      output = `floor(result * 255. + 0.5)`;\n    }\n    this.userCode = `\n      ${this.enableShapeUniforms ? getFlatIndexFrom3DOutput() : getFlatIndexFrom3D(outputShape)}\n\n      void main() {\n        ivec3 coords = getOutputCoords();\n\n        int flatIndex = getFlatIndex(coords);\n        int offset = imod(flatIndex, 4);\n\n        flatIndex = idiv(flatIndex, 4, 1.);\n\n        int r = flatIndex / texShape[1];\n        int c = imod(flatIndex, texShape[1]);\n        vec2 uv = (vec2(c, r) + halfCR) / vec2(texShape[1], texShape[0]);\n        vec4 values = ${glsl.texture2D}(A, uv);\n\n        float result;\n\n        if(offset == 0) {\n          result = values[0];\n        } else if(offset == 1) {\n          result = values[1];\n        } else if(offset == 2) {\n          result = values[2];\n        } else {\n          result = values[3];\n        }\n\n        ${glsl.output} = vec4(${output}, 0., 0., 0.);\n      }\n    `;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/encode_matrix_packed_gpu.js\nvar EncodeMatrixPackedProgram = class {\n  constructor(outputShape, inputIsUnsignedByte = false) {\n    this.variableNames = [\"A\"];\n    this.packedInputs = false;\n    this.packedOutput = true;\n    this.customUniforms = [{ name: \"texShape\", type: \"ivec2\" }];\n    const glsl = getGlslDifferences();\n    this.outputShape = outputShape;\n    this.enableShapeUniforms = useShapeUniforms(this.outputShape.length);\n    let mainLoop = \"\";\n    let output = \"result\";\n    if (inputIsUnsignedByte) {\n      output = \"floor(result * 255. + 0.5)\";\n    }\n    for (let row = 0; row <= 1; row++) {\n      for (let col = 0; col <= 1; col++) {\n        const channel = row * 2 + col;\n        mainLoop += `\n          localCoords = coords;\n          if(localCoords[2] + ${col} < ${this.enableShapeUniforms ? \"outShape[2]\" : `${outputShape[2]}`}) {\n          localCoords[2] += ${col};\n          if (localCoords[1] + ${row} < ${this.enableShapeUniforms ? \"outShape[1]\" : `${outputShape[1]}`}) {\n            localCoords[1] += ${row};\n\n            flatIndex = getFlatIndex(localCoords);\n            offset = imod(flatIndex, 4);\n\n            flatIndex = idiv(flatIndex, 4, 1.);\n\n            int r = flatIndex / texShape[1];\n            int c = imod(flatIndex, texShape[1]);\n            vec2 uv = (vec2(c, r) + halfCR) / vec2(texShape[1], texShape[0]);\n            values = ${glsl.texture2D}(A, uv);\n\n            if (offset == 0) {\n              result[${channel}] = values[0];\n            } else if (offset == 1) {\n              result[${channel}] = values[1];\n            } else if (offset == 2) {\n              result[${channel}] = values[2];\n            } else {\n              result[${channel}] = values[3];\n            }\n          }\n        }\n        `;\n      }\n    }\n    this.userCode = `\n        ${this.enableShapeUniforms ? getFlatIndexFrom3DOutput() : getFlatIndexFrom3D(outputShape)}\n\n        void main() {\n          ivec3 coords = getOutputCoords();\n\n          vec4 result = vec4(0.);\n          int flatIndex, r, c, offset;\n          ivec3 localCoords;\n          vec2 uv;\n          vec4 values;\n\n          ${mainLoop}\n\n          ${glsl.output} = ${output};\n        }\n    `;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/gpgpu_util.js\nvar gpgpu_util_exports = {};\n__export(gpgpu_util_exports, {\n  bindVertexProgramAttributeStreams: () => bindVertexProgramAttributeStreams,\n  createBufferFromOutputTexture: () => createBufferFromOutputTexture,\n  createFloat16MatrixTexture: () => createFloat16MatrixTexture,\n  createFloat16PackedMatrixTexture: () => createFloat16PackedMatrixTexture,\n  createFloat32MatrixTexture: () => createFloat32MatrixTexture,\n  createIndexBuffer: () => createIndexBuffer,\n  createPackedMatrixTexture: () => createPackedMatrixTexture,\n  createUnsignedBytesMatrixTexture: () => createUnsignedBytesMatrixTexture,\n  createVertexBuffer: () => createVertexBuffer,\n  createVertexShader: () => createVertexShader2,\n  downloadByteEncodedFloatMatrixFromOutputTexture: () => downloadByteEncodedFloatMatrixFromOutputTexture,\n  downloadFloat32MatrixFromBuffer: () => downloadFloat32MatrixFromBuffer,\n  downloadMatrixFromPackedOutputTexture: () => downloadMatrixFromPackedOutputTexture,\n  downloadPackedMatrixFromBuffer: () => downloadPackedMatrixFromBuffer,\n  getInternalFormatForFloat16MatrixTexture: () => getInternalFormatForFloat16MatrixTexture,\n  getInternalFormatForFloat16PackedMatrixTexture: () => getInternalFormatForFloat16PackedMatrixTexture,\n  getInternalFormatForFloat32MatrixTexture: () => getInternalFormatForFloat32MatrixTexture,\n  getInternalFormatForPackedMatrixTexture: () => getInternalFormatForPackedMatrixTexture,\n  getInternalFormatForUnsignedBytesMatrixTexture: () => getInternalFormatForUnsignedBytesMatrixTexture,\n  uploadDenseMatrixToTexture: () => uploadDenseMatrixToTexture,\n  uploadPixelDataToTexture: () => uploadPixelDataToTexture\n});\nfunction createVertexShader2(gl) {\n  const glsl = getGlslDifferences();\n  const vertexShaderSource = `${glsl.version}\n    precision highp float;\n    ${glsl.attribute} vec3 clipSpacePos;\n    ${glsl.attribute} vec2 uv;\n    ${glsl.varyingVs} vec2 resultUV;\n\n    void main() {\n      gl_Position = vec4(clipSpacePos, 1);\n      resultUV = uv;\n    }`;\n  return createVertexShader(gl, vertexShaderSource);\n}\nfunction createVertexBuffer(gl) {\n  const vertexArray = new Float32Array([-1, 1, 0, 0, 1, -1, -1, 0, 0, 0, 1, 1, 0, 1, 1, 1, -1, 0, 1, 0]);\n  return createStaticVertexBuffer(gl, vertexArray);\n}\nfunction createIndexBuffer(gl) {\n  const triangleVertexIndices = new Uint16Array([0, 1, 2, 2, 1, 3]);\n  return createStaticIndexBuffer(gl, triangleVertexIndices);\n}\nfunction createAndConfigureTexture(gl, width, height, internalFormat, textureFormat, textureType) {\n  validateTextureSize(width, height);\n  const texture = createTexture(gl);\n  const tex2d = gl.TEXTURE_2D;\n  callAndCheck(gl, () => gl.bindTexture(tex2d, texture));\n  callAndCheck(gl, () => gl.texParameteri(tex2d, gl.TEXTURE_WRAP_S, gl.CLAMP_TO_EDGE));\n  callAndCheck(gl, () => gl.texParameteri(tex2d, gl.TEXTURE_WRAP_T, gl.CLAMP_TO_EDGE));\n  callAndCheck(gl, () => gl.texParameteri(tex2d, gl.TEXTURE_MIN_FILTER, gl.NEAREST));\n  callAndCheck(gl, () => gl.texParameteri(tex2d, gl.TEXTURE_MAG_FILTER, gl.NEAREST));\n  if (env().getNumber(\"WEBGL_VERSION\") === 1) {\n    callAndCheck(gl, () => gl.texImage2D(tex2d, 0, internalFormat, width, height, 0, textureFormat, textureType, null));\n  } else {\n    callAndCheck(gl, () => gl.texStorage2D(tex2d, 1, internalFormat, width, height));\n  }\n  callAndCheck(gl, () => gl.bindTexture(gl.TEXTURE_2D, null));\n  return { texture, texShape: [height, width] };\n}\nfunction getInternalFormatForFloat32MatrixTexture(textureConfig) {\n  return textureConfig.internalFormatFloat;\n}\nfunction createFloat32MatrixTexture(gl, rows, columns, textureConfig) {\n  const [width, height] = getUnpackedMatrixTextureShapeWidthHeight(rows, columns);\n  return createAndConfigureTexture(gl, width, height, getInternalFormatForFloat32MatrixTexture(textureConfig), textureConfig.textureFormatFloat, gl.FLOAT);\n}\nfunction getInternalFormatForFloat16MatrixTexture(textureConfig) {\n  return textureConfig.internalFormatHalfFloat;\n}\nfunction createFloat16MatrixTexture(gl, rows, columns, textureConfig) {\n  const [width, height] = getUnpackedMatrixTextureShapeWidthHeight(rows, columns);\n  return createAndConfigureTexture(gl, width, height, getInternalFormatForFloat16MatrixTexture(textureConfig), textureConfig.textureFormatFloat, textureConfig.textureTypeHalfFloat);\n}\nfunction getInternalFormatForUnsignedBytesMatrixTexture(textureConfig) {\n  return textureConfig.downloadTextureFormat;\n}\nfunction createUnsignedBytesMatrixTexture(gl, rows, columns, textureConfig) {\n  const [width, height] = getUnpackedMatrixTextureShapeWidthHeight(rows, columns);\n  return createAndConfigureTexture(gl, width, height, getInternalFormatForUnsignedBytesMatrixTexture(textureConfig), gl.RGBA, gl.UNSIGNED_BYTE);\n}\nfunction getInternalFormatForPackedMatrixTexture(textureConfig) {\n  return textureConfig.internalFormatPackedFloat;\n}\nfunction createPackedMatrixTexture(gl, rows, columns, textureConfig) {\n  const [width, height] = getPackedMatrixTextureShapeWidthHeight(rows, columns);\n  return createAndConfigureTexture(gl, width, height, getInternalFormatForPackedMatrixTexture(textureConfig), gl.RGBA, gl.FLOAT);\n}\nfunction getInternalFormatForFloat16PackedMatrixTexture(textureConfig) {\n  return textureConfig.internalFormatPackedHalfFloat;\n}\nfunction createFloat16PackedMatrixTexture(gl, rows, columns, textureConfig) {\n  const [width, height] = getPackedMatrixTextureShapeWidthHeight(rows, columns);\n  return createAndConfigureTexture(gl, width, height, getInternalFormatForFloat16PackedMatrixTexture(textureConfig), gl.RGBA, textureConfig.textureTypeHalfFloat);\n}\nfunction bindVertexProgramAttributeStreams(gl, program, vertexBuffer) {\n  const posOffset = 0;\n  const uvOffset = 3 * 4;\n  const stride = 3 * 4 + 2 * 4;\n  callAndCheck(gl, () => gl.bindBuffer(gl.ARRAY_BUFFER, vertexBuffer));\n  const success = bindVertexBufferToProgramAttribute(gl, program, \"clipSpacePos\", vertexBuffer, 3, stride, posOffset);\n  return success && bindVertexBufferToProgramAttribute(gl, program, \"uv\", vertexBuffer, 2, stride, uvOffset);\n}\nfunction uploadDenseMatrixToTexture(gl, texture, width, height, data, textureConfig) {\n  callAndCheck(gl, () => gl.bindTexture(gl.TEXTURE_2D, texture));\n  let dataForUpload, texelDataType, internalFormat;\n  if (data instanceof Uint8Array) {\n    dataForUpload = new Uint8Array(width * height * 4);\n    texelDataType = gl.UNSIGNED_BYTE;\n    internalFormat = gl.RGBA;\n  } else {\n    dataForUpload = new Float32Array(width * height * 4);\n    texelDataType = gl.FLOAT;\n    internalFormat = textureConfig.internalFormatPackedFloat;\n  }\n  dataForUpload.set(data);\n  if (env().getNumber(\"WEBGL_VERSION\") === 2) {\n    callAndCheck(gl, () => gl.texSubImage2D(gl.TEXTURE_2D, 0, 0, 0, width, height, gl.RGBA, texelDataType, dataForUpload));\n  } else {\n    callAndCheck(gl, () => gl.texImage2D(gl.TEXTURE_2D, 0, internalFormat, width, height, 0, gl.RGBA, texelDataType, dataForUpload));\n  }\n  callAndCheck(gl, () => gl.bindTexture(gl.TEXTURE_2D, null));\n}\nfunction uploadPixelDataToTexture(gl, texture, pixels) {\n  callAndCheck(gl, () => gl.bindTexture(gl.TEXTURE_2D, texture));\n  if (pixels.data instanceof Uint8Array) {\n    if (env().getNumber(\"WEBGL_VERSION\") === 2) {\n      callAndCheck(gl, () => gl.texSubImage2D(gl.TEXTURE_2D, 0, 0, 0, pixels.width, pixels.height, gl.RGBA, gl.UNSIGNED_BYTE, pixels.data));\n    } else {\n      callAndCheck(gl, () => gl.texImage2D(gl.TEXTURE_2D, 0, gl.RGBA, pixels.width, pixels.height, 0, gl.RGBA, gl.UNSIGNED_BYTE, pixels.data));\n    }\n  } else {\n    if (env().getNumber(\"WEBGL_VERSION\") === 2) {\n      callAndCheck(gl, () => gl.texSubImage2D(gl.TEXTURE_2D, 0, 0, 0, gl.RGBA, gl.UNSIGNED_BYTE, pixels));\n    } else {\n      callAndCheck(gl, () => gl.texImage2D(gl.TEXTURE_2D, 0, gl.RGBA, gl.RGBA, gl.UNSIGNED_BYTE, pixels));\n    }\n  }\n  callAndCheck(gl, () => gl.bindTexture(gl.TEXTURE_2D, null));\n}\nfunction createBufferFromOutputTexture(gl2, rows, columns, textureConfig) {\n  const buffer2 = gl2.createBuffer();\n  callAndCheck(gl2, () => gl2.bindBuffer(gl2.PIXEL_PACK_BUFFER, buffer2));\n  const bytesPerFloat = 4;\n  const valuesPerTexel = 4;\n  const bufferSizeBytes = bytesPerFloat * valuesPerTexel * rows * columns;\n  callAndCheck(gl2, () => gl2.bufferData(gl2.PIXEL_PACK_BUFFER, bufferSizeBytes, gl2.STREAM_READ));\n  callAndCheck(gl2, () => gl2.readPixels(0, 0, columns, rows, gl2.RGBA, gl2.FLOAT, 0));\n  callAndCheck(gl2, () => gl2.bindBuffer(gl2.PIXEL_PACK_BUFFER, null));\n  return buffer2;\n}\nfunction downloadFloat32MatrixFromBuffer(gl, buffer2, size) {\n  const gl2 = gl;\n  const downloadTarget = new Float32Array(size);\n  gl2.bindBuffer(gl2.PIXEL_PACK_BUFFER, buffer2);\n  gl2.getBufferSubData(gl2.PIXEL_PACK_BUFFER, 0, downloadTarget);\n  gl2.bindBuffer(gl2.PIXEL_PACK_BUFFER, null);\n  return downloadTarget;\n}\nfunction downloadByteEncodedFloatMatrixFromOutputTexture(gl, rows, columns, textureConfig) {\n  const [w, h] = getUnpackedMatrixTextureShapeWidthHeight(rows, columns);\n  const numChannels = 4;\n  const downloadTarget = new Uint8Array(getUnpackedArraySizeFromMatrixSize(rows * columns, numChannels));\n  callAndCheck(gl, () => gl.readPixels(0, 0, w, h, textureConfig.downloadTextureFormat, gl.UNSIGNED_BYTE, downloadTarget));\n  return new Float32Array(downloadTarget.buffer);\n}\nfunction downloadPackedMatrixFromBuffer(gl, buffer2, batch, rows, cols, physicalRows, physicalCols, textureConfig) {\n  const gl2 = gl;\n  const downloadTarget = new Float32Array(getPackedRGBAArraySizeFromMatrixShape(physicalRows, physicalCols));\n  gl2.bindBuffer(gl2.PIXEL_PACK_BUFFER, buffer2);\n  gl2.getBufferSubData(gl2.PIXEL_PACK_BUFFER, 0, downloadTarget);\n  gl2.bindBuffer(gl2.PIXEL_PACK_BUFFER, null);\n  return downloadTarget;\n}\nfunction downloadMatrixFromPackedOutputTexture(gl, physicalRows, physicalCols) {\n  const packedRGBA = new Float32Array(physicalRows * physicalCols * 4);\n  callAndCheck(gl, () => gl.readPixels(0, 0, physicalCols, physicalRows, gl.RGBA, gl.FLOAT, packedRGBA));\n  return packedRGBA;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/gpgpu_context.js\nvar GPGPUContext = class {\n  constructor(gl) {\n    this.outputTexture = null;\n    this.program = null;\n    this.disposed = false;\n    this.vertexAttrsAreBound = false;\n    this.itemsToPoll = [];\n    const glVersion = env().getNumber(\"WEBGL_VERSION\");\n    if (gl != null) {\n      this.gl = gl;\n      setWebGLContext(glVersion, gl);\n    } else {\n      this.gl = getWebGLContext(glVersion);\n    }\n    let COLOR_BUFFER_FLOAT = \"WEBGL_color_buffer_float\";\n    const COLOR_BUFFER_HALF_FLOAT = \"EXT_color_buffer_half_float\";\n    this.parallelCompilationExtension = this.gl.getExtension(\"KHR_parallel_shader_compile\");\n    if (env().getNumber(\"WEBGL_VERSION\") === 1) {\n      const TEXTURE_FLOAT = \"OES_texture_float\";\n      const TEXTURE_HALF_FLOAT = \"OES_texture_half_float\";\n      this.textureFloatExtension = getExtensionOrThrow(this.gl, TEXTURE_FLOAT);\n      if (hasExtension(this.gl, TEXTURE_HALF_FLOAT)) {\n        this.textureHalfFloatExtension = getExtensionOrThrow(this.gl, TEXTURE_HALF_FLOAT);\n      } else if (env().get(\"WEBGL_FORCE_F16_TEXTURES\")) {\n        throw new Error(\"GL context does not support half float textures, yet the environment flag WEBGL_FORCE_F16_TEXTURES is set to true.\");\n      }\n      this.colorBufferFloatExtension = this.gl.getExtension(COLOR_BUFFER_FLOAT);\n      if (hasExtension(this.gl, COLOR_BUFFER_HALF_FLOAT)) {\n        this.colorBufferHalfFloatExtension = getExtensionOrThrow(this.gl, COLOR_BUFFER_HALF_FLOAT);\n      } else if (env().get(\"WEBGL_FORCE_F16_TEXTURES\")) {\n        throw new Error(\"GL context does not support color renderable half floats, yet the environment flag WEBGL_FORCE_F16_TEXTURES is set to true.\");\n      }\n    } else {\n      COLOR_BUFFER_FLOAT = \"EXT_color_buffer_float\";\n      if (hasExtension(this.gl, COLOR_BUFFER_FLOAT)) {\n        this.colorBufferFloatExtension = this.gl.getExtension(COLOR_BUFFER_FLOAT);\n      } else if (hasExtension(this.gl, COLOR_BUFFER_HALF_FLOAT)) {\n        this.colorBufferHalfFloatExtension = this.gl.getExtension(COLOR_BUFFER_HALF_FLOAT);\n      } else {\n        throw new Error(\"GL context does not support color renderable floats\");\n      }\n    }\n    this.vertexBuffer = createVertexBuffer(this.gl);\n    this.indexBuffer = createIndexBuffer(this.gl);\n    this.framebuffer = createFramebuffer(this.gl);\n    this.textureConfig = getTextureConfig(this.gl, this.textureHalfFloatExtension);\n  }\n  get debug() {\n    return env().getBool(\"DEBUG\");\n  }\n  dispose() {\n    if (this.disposed) {\n      return;\n    }\n    if (this.program != null) {\n      console.warn(\"Disposing a GPGPUContext that still has a bound WebGLProgram. This is probably a resource leak, delete the program with GPGPUContext.deleteProgram before disposing.\");\n    }\n    if (this.outputTexture != null) {\n      console.warn(\"Disposing a GPGPUContext that still has a bound output matrix texture.  This is probably a resource leak, delete the output matrix texture with GPGPUContext.deleteMatrixTexture before disposing.\");\n    }\n    const gl = this.gl;\n    callAndCheck(gl, () => gl.finish());\n    callAndCheck(gl, () => gl.bindFramebuffer(gl.FRAMEBUFFER, null));\n    callAndCheck(gl, () => gl.deleteFramebuffer(this.framebuffer));\n    callAndCheck(gl, () => gl.bindBuffer(gl.ARRAY_BUFFER, null));\n    callAndCheck(gl, () => gl.bindBuffer(gl.ELEMENT_ARRAY_BUFFER, null));\n    callAndCheck(gl, () => gl.deleteBuffer(this.indexBuffer));\n    this.disposed = true;\n  }\n  createFloat32MatrixTexture(rows, columns) {\n    this.throwIfDisposed();\n    return createFloat32MatrixTexture(this.gl, rows, columns, this.textureConfig);\n  }\n  createFloat16MatrixTexture(rows, columns) {\n    this.throwIfDisposed();\n    return createFloat16MatrixTexture(this.gl, rows, columns, this.textureConfig);\n  }\n  createUnsignedBytesMatrixTexture(rows, columns) {\n    this.throwIfDisposed();\n    return createUnsignedBytesMatrixTexture(this.gl, rows, columns, this.textureConfig);\n  }\n  uploadPixelDataToTexture(texture, pixels) {\n    this.throwIfDisposed();\n    uploadPixelDataToTexture(this.gl, texture, pixels);\n  }\n  uploadDenseMatrixToTexture(texture, width, height, data) {\n    this.throwIfDisposed();\n    uploadDenseMatrixToTexture(this.gl, texture, width, height, data, this.textureConfig);\n  }\n  createFloat16PackedMatrixTexture(rows, columns) {\n    this.throwIfDisposed();\n    return createFloat16PackedMatrixTexture(this.gl, rows, columns, this.textureConfig);\n  }\n  createPackedMatrixTexture(rows, columns) {\n    this.throwIfDisposed();\n    return createPackedMatrixTexture(this.gl, rows, columns, this.textureConfig);\n  }\n  deleteMatrixTexture(texture) {\n    this.throwIfDisposed();\n    if (this.outputTexture === texture) {\n      unbindColorTextureFromFramebuffer(this.gl, this.framebuffer);\n      this.outputTexture = null;\n    }\n    callAndCheck(this.gl, () => this.gl.deleteTexture(texture));\n  }\n  downloadByteEncodedFloatMatrixFromOutputTexture(texture, rows, columns) {\n    return this.downloadMatrixDriver(texture, () => downloadByteEncodedFloatMatrixFromOutputTexture(this.gl, rows, columns, this.textureConfig));\n  }\n  downloadPackedMatrixFromBuffer(buffer2, batch, rows, columns, physicalRows, physicalCols) {\n    return downloadPackedMatrixFromBuffer(this.gl, buffer2, batch, rows, columns, physicalRows, physicalCols, this.textureConfig);\n  }\n  downloadFloat32MatrixFromBuffer(buffer2, size) {\n    return downloadFloat32MatrixFromBuffer(this.gl, buffer2, size);\n  }\n  createBufferFromTexture(texture, rows, columns) {\n    this.bindTextureToFrameBuffer(texture);\n    const result = createBufferFromOutputTexture(this.gl, rows, columns, this.textureConfig);\n    this.unbindTextureToFrameBuffer();\n    return result;\n  }\n  createAndWaitForFence() {\n    const fenceContext = this.createFence(this.gl);\n    return this.pollFence(fenceContext);\n  }\n  createFence(gl) {\n    let query;\n    let isFencePassed;\n    if (env().getBool(\"WEBGL_FENCE_API_ENABLED\")) {\n      const gl2 = gl;\n      const sync = gl2.fenceSync(gl2.SYNC_GPU_COMMANDS_COMPLETE, 0);\n      gl.flush();\n      isFencePassed = () => {\n        const status = gl2.clientWaitSync(sync, 0, 0);\n        return status === gl2.ALREADY_SIGNALED || status === gl2.CONDITION_SATISFIED;\n      };\n      query = sync;\n    } else if (env().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION\") > 0) {\n      query = this.beginQuery();\n      this.endQuery();\n      isFencePassed = () => this.isQueryAvailable(query, env().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION\"));\n    } else {\n      isFencePassed = () => true;\n    }\n    return { query, isFencePassed };\n  }\n  downloadMatrixFromPackedTexture(texture, physicalRows, physicalCols) {\n    return this.downloadMatrixDriver(texture, () => downloadMatrixFromPackedOutputTexture(this.gl, physicalRows, physicalCols));\n  }\n  createProgram(fragmentShader) {\n    this.throwIfDisposed();\n    const gl = this.gl;\n    if (this.vertexShader == null) {\n      this.vertexShader = createVertexShader2(gl);\n    }\n    const program = createProgram(gl);\n    callAndCheck(gl, () => gl.attachShader(program, this.vertexShader));\n    callAndCheck(gl, () => gl.attachShader(program, fragmentShader));\n    linkProgram(gl, program);\n    if (this.debug) {\n      validateProgram(gl, program);\n    }\n    if (!this.vertexAttrsAreBound) {\n      this.setProgram(program);\n      this.vertexAttrsAreBound = bindVertexProgramAttributeStreams(gl, this.program, this.vertexBuffer);\n    }\n    return program;\n  }\n  deleteProgram(program) {\n    this.throwIfDisposed();\n    if (program === this.program) {\n      this.program = null;\n    }\n    if (program != null) {\n      callAndCheck(this.gl, () => this.gl.deleteProgram(program));\n    }\n  }\n  setProgram(program) {\n    this.throwIfDisposed();\n    this.program = program;\n    if (this.program != null && this.debug) {\n      validateProgram(this.gl, this.program);\n    }\n    callAndCheck(this.gl, () => this.gl.useProgram(program));\n  }\n  getUniformLocation(program, uniformName, shouldThrow = true) {\n    this.throwIfDisposed();\n    if (shouldThrow) {\n      return getProgramUniformLocationOrThrow(this.gl, program, uniformName);\n    } else {\n      return getProgramUniformLocation(this.gl, program, uniformName);\n    }\n  }\n  getAttributeLocation(program, attribute) {\n    this.throwIfDisposed();\n    return callAndCheck(this.gl, () => this.gl.getAttribLocation(program, attribute));\n  }\n  getUniformLocationNoThrow(program, uniformName) {\n    this.throwIfDisposed();\n    return this.gl.getUniformLocation(program, uniformName);\n  }\n  setInputMatrixTexture(inputMatrixTexture, uniformLocation, textureUnit) {\n    this.throwIfDisposed();\n    this.throwIfNoProgram();\n    bindTextureToProgramUniformSampler(this.gl, inputMatrixTexture, uniformLocation, textureUnit);\n  }\n  setOutputMatrixTexture(outputMatrixTexture, rows, columns) {\n    this.setOutputMatrixTextureDriver(outputMatrixTexture, columns, rows);\n  }\n  setOutputPackedMatrixTexture(outputPackedMatrixTexture, rows, columns) {\n    this.throwIfDisposed();\n    const [width, height] = getPackedMatrixTextureShapeWidthHeight(rows, columns);\n    this.setOutputMatrixTextureDriver(outputPackedMatrixTexture, width, height);\n  }\n  setOutputMatrixWriteRegion(startRow, numRows, startColumn, numColumns) {\n    this.setOutputMatrixWriteRegionDriver(startColumn, startRow, numColumns, numRows);\n  }\n  setOutputPackedMatrixWriteRegion(startRow, numRows, startColumn, numColumns) {\n    throw new Error(\"setOutputPackedMatrixWriteRegion not implemented.\");\n  }\n  debugValidate() {\n    if (this.program != null) {\n      validateProgram(this.gl, this.program);\n    }\n    validateFramebuffer(this.gl);\n  }\n  executeProgram() {\n    this.throwIfDisposed();\n    this.throwIfNoProgram();\n    const gl = this.gl;\n    if (this.debug) {\n      this.debugValidate();\n    }\n    callAndCheck(gl, () => gl.drawElements(gl.TRIANGLES, 6, gl.UNSIGNED_SHORT, 0));\n  }\n  blockUntilAllProgramsCompleted() {\n    this.throwIfDisposed();\n    callAndCheck(this.gl, () => this.gl.finish());\n  }\n  getQueryTimerExtension() {\n    if (this.disjointQueryTimerExtension == null) {\n      this.disjointQueryTimerExtension = getExtensionOrThrow(this.gl, env().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION\") === 2 ? \"EXT_disjoint_timer_query_webgl2\" : \"EXT_disjoint_timer_query\");\n    }\n    return this.disjointQueryTimerExtension;\n  }\n  getQueryTimerExtensionWebGL2() {\n    return this.getQueryTimerExtension();\n  }\n  getQueryTimerExtensionWebGL1() {\n    return this.getQueryTimerExtension();\n  }\n  beginQuery() {\n    if (env().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION\") === 2) {\n      const gl2 = this.gl;\n      const ext2 = this.getQueryTimerExtensionWebGL2();\n      const query2 = gl2.createQuery();\n      gl2.beginQuery(ext2.TIME_ELAPSED_EXT, query2);\n      return query2;\n    }\n    const ext = this.getQueryTimerExtensionWebGL1();\n    const query = ext.createQueryEXT();\n    ext.beginQueryEXT(ext.TIME_ELAPSED_EXT, query);\n    return query;\n  }\n  endQuery() {\n    if (env().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION\") === 2) {\n      const gl2 = this.gl;\n      const ext2 = this.getQueryTimerExtensionWebGL2();\n      gl2.endQuery(ext2.TIME_ELAPSED_EXT);\n      return;\n    }\n    const ext = this.getQueryTimerExtensionWebGL1();\n    ext.endQueryEXT(ext.TIME_ELAPSED_EXT);\n  }\n  async waitForQueryAndGetTime(query) {\n    await util_exports.repeatedTry(() => this.disposed || this.isQueryAvailable(query, env().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION\")));\n    return this.getQueryTime(query, env().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION\"));\n  }\n  getQueryTime(query, queryTimerVersion) {\n    if (queryTimerVersion === 0) {\n      return null;\n    }\n    if (queryTimerVersion === 2) {\n      const gl2 = this.gl;\n      const timeElapsedNanos = gl2.getQueryParameter(query, gl2.QUERY_RESULT);\n      return timeElapsedNanos / 1e6;\n    } else {\n      const ext = this.getQueryTimerExtensionWebGL1();\n      const timeElapsedNanos = ext.getQueryObjectEXT(query, ext.QUERY_RESULT_EXT);\n      return timeElapsedNanos / 1e6;\n    }\n  }\n  isQueryAvailable(query, queryTimerVersion) {\n    if (queryTimerVersion === 0) {\n      return true;\n    }\n    if (queryTimerVersion === 2) {\n      const gl2 = this.gl;\n      const ext = this.getQueryTimerExtensionWebGL2();\n      const available = gl2.getQueryParameter(query, gl2.QUERY_RESULT_AVAILABLE);\n      if (this.disjoint == null) {\n        this.disjoint = this.gl.getParameter(ext.GPU_DISJOINT_EXT);\n      }\n      return available && !this.disjoint;\n    } else {\n      const ext = this.getQueryTimerExtensionWebGL1();\n      const available = ext.getQueryObjectEXT(query, ext.QUERY_RESULT_AVAILABLE_EXT);\n      if (this.disjoint == null) {\n        this.disjoint = this.gl.getParameter(ext.GPU_DISJOINT_EXT);\n      }\n      return available && !this.disjoint;\n    }\n  }\n  pollFence(fenceContext) {\n    return new Promise((resolve) => {\n      this.addItemToPoll(() => fenceContext.isFencePassed(), () => resolve());\n    });\n  }\n  pollItems() {\n    const index = linearSearchLastTrue(this.itemsToPoll.map((x) => x.isDoneFn));\n    for (let i = 0; i <= index; ++i) {\n      const { resolveFn } = this.itemsToPoll[i];\n      resolveFn();\n    }\n    this.itemsToPoll = this.itemsToPoll.slice(index + 1);\n  }\n  addItemToPoll(isDoneFn, resolveFn) {\n    this.itemsToPoll.push({ isDoneFn, resolveFn });\n    if (this.itemsToPoll.length > 1) {\n      return;\n    }\n    util_exports.repeatedTry(() => {\n      this.pollItems();\n      return this.itemsToPoll.length === 0;\n    });\n  }\n  bindTextureToFrameBuffer(texture) {\n    this.throwIfDisposed();\n    bindColorTextureToFramebuffer(this.gl, texture, this.framebuffer);\n    if (this.debug) {\n      validateFramebuffer(this.gl);\n    }\n  }\n  unbindTextureToFrameBuffer() {\n    if (this.outputTexture != null) {\n      bindColorTextureToFramebuffer(this.gl, this.outputTexture, this.framebuffer);\n      if (this.debug) {\n        validateFramebuffer(this.gl);\n      }\n    } else {\n      unbindColorTextureFromFramebuffer(this.gl, this.framebuffer);\n    }\n  }\n  downloadMatrixDriver(texture, downloadAndDecode) {\n    this.bindTextureToFrameBuffer(texture);\n    const result = downloadAndDecode();\n    this.unbindTextureToFrameBuffer();\n    return result;\n  }\n  setOutputMatrixTextureDriver(outputMatrixTextureMaybePacked, width, height) {\n    this.throwIfDisposed();\n    const gl = this.gl;\n    bindColorTextureToFramebuffer(gl, outputMatrixTextureMaybePacked, this.framebuffer);\n    if (this.debug) {\n      validateFramebuffer(gl);\n    }\n    this.outputTexture = outputMatrixTextureMaybePacked;\n    callAndCheck(gl, () => gl.viewport(0, 0, width, height));\n    callAndCheck(gl, () => gl.scissor(0, 0, width, height));\n  }\n  setOutputMatrixWriteRegionDriver(x, y, width, height) {\n    this.throwIfDisposed();\n    callAndCheck(this.gl, () => this.gl.scissor(x, y, width, height));\n  }\n  throwIfDisposed() {\n    if (this.disposed) {\n      throw new Error(\"Attempted to use disposed GPGPUContext.\");\n    }\n  }\n  throwIfNoProgram() {\n    if (this.program == null) {\n      throw new Error(\"No GPU program is currently set.\");\n    }\n  }\n};\nfunction linearSearchLastTrue(arr) {\n  let i = 0;\n  for (; i < arr.length; ++i) {\n    const isDone = arr[i]();\n    if (!isDone) {\n      break;\n    }\n  }\n  return i - 1;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernel_utils/shared.js\nvar { addImpl: addImplCPU, bincountImpl: bincountImplCPU, bincountReduceImpl: bincountReduceImplCPU, castImpl: castImplCPU, ceilImpl: ceilImplCPU, concatImpl: concatImplCPU, equalImpl: equalImplCPU, expImpl: expImplCPU, expm1Impl: expm1ImplCPU, floorImpl: floorImplCPU, gatherNdImpl: gatherNdImplCPU, gatherV2Impl: gatherV2ImplCPU, greaterImpl: greaterImplCPU, greaterEqualImpl: greaterEqualImplCPU, lessImpl: lessImplCPU, lessEqualImpl: lessEqualImplCPU, linSpaceImpl: linSpaceImplCPU, logImpl: logImplCPU, maxImpl: maxImplCPU, maximumImpl: maximumImplCPU, minimumImpl: minimumImplCPU, multiplyImpl: multiplyImplCPU, negImpl: negImplCPU, notEqualImpl: notEqualImplCPU, prodImpl: prodImplCPU, raggedTensorToTensorImpl: raggedTensorToTensorImplCPU, rangeImpl: rangeImplCPU, rsqrtImpl: rsqrtImplCPU, scatterImpl: scatterImplCPU, sigmoidImpl: sigmoidImplCPU, simpleAbsImpl: simpleAbsImplCPU, sliceImpl: sliceImplCPU, sparseFillEmptyRowsImpl: sparseFillEmptyRowsImplCPU, sparseReshapeImpl: sparseReshapeImplCPU, sparseSegmentReductionImpl: sparseSegmentReductionImplCPU, sqrtImpl: sqrtImplCPU, stridedSliceImpl: stridedSliceImplCPU, stringNGramsImpl: stringNGramsImplCPU, stringSplitImpl: stringSplitImplCPU, stringToHashBucketFastImpl: stringToHashBucketFastImplCPU, subImpl: subImplCPU, tileImpl: tileImplCPU, topKImpl: topKImplCPU, transposeImpl: transposeImplCPU, uniqueImpl: uniqueImplCPU } = shared_exports;\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/packing_util.js\nfunction getVecChannels(name, rank) {\n  return [\"x\", \"y\", \"z\", \"w\", \"u\", \"v\"].slice(0, rank).map((d) => `${name}.${d}`);\n}\nfunction getChannels(name, rank) {\n  if (rank === 1) {\n    return [name];\n  }\n  return getVecChannels(name, rank);\n}\nfunction getSourceCoords(rank, dims) {\n  if (rank === 1) {\n    return \"rc\";\n  }\n  let coords3 = \"\";\n  for (let i = 0; i < rank; i++) {\n    coords3 += dims[i];\n    if (i < rank - 1) {\n      coords3 += \",\";\n    }\n  }\n  return coords3;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/pack_gpu.js\nvar PackProgram = class {\n  constructor(outputShape) {\n    this.variableNames = [\"A\"];\n    this.packedInputs = false;\n    this.packedOutput = true;\n    this.outputShape = outputShape;\n    this.rank = outputShape.length;\n    this.enableShapeUniforms = useShapeUniforms(this.outputShape.length);\n    if (this.rank === 0) {\n      this.userCode = `\n        void main() {\n          setOutput(vec4(getA(), 0., 0., 0.));\n        }\n      `;\n    } else {\n      const channels = getChannels(\"rc\", this.rank);\n      const dtype = getCoordsDataType(this.rank);\n      const outOfBoundsCondition = this.getOutOfBoundsCondition(channels);\n      const setup51 = this.getSetup(channels);\n      const output = this.getOutput(channels);\n      this.userCode = `\n        void main() {\n          ${dtype} rc = getOutputCoords();\n\n          if(${outOfBoundsCondition}) {\n            setOutput(vec4(0));\n          } else {\n            ${setup51}\n\n            setOutput(vec4(${output}));\n          }\n        }\n      `;\n    }\n  }\n  getSourceCoordsArr(dims) {\n    const coords3 = [];\n    for (let row = 0; row <= 1; row++) {\n      for (let col = 0; col <= 1; col++) {\n        let coord = `${row === 0 ? \"r\" : \"rp1\"}, ${col === 0 ? \"c\" : \"cp1\"}`;\n        for (let d = 2; d < this.rank; d++) {\n          coord = `${dims[dims.length - 1 - d]},` + coord;\n        }\n        coords3.push(coord);\n      }\n    }\n    return coords3;\n  }\n  getOutOfBoundsCondition(dims) {\n    if (this.rank === 1) {\n      return `rc > ${this.enableShapeUniforms ? \"outShape\" : this.outputShape[0]}`;\n    }\n    let cond = \"\";\n    for (let i = this.rank - 2; i < this.rank; i++) {\n      cond += `${dims[i]} >= ${this.enableShapeUniforms ? `outShape[${i}]` : this.outputShape[i]}`;\n      if (i < this.rank - 1) {\n        cond += \"||\";\n      }\n    }\n    return cond;\n  }\n  getSetup(dims) {\n    if (this.rank === 1) {\n      return \"\";\n    }\n    const innerDims = dims.slice(-2);\n    const col = this.enableShapeUniforms ? `outShape[${this.rank} - 1]` : this.outputShape[this.rank - 1];\n    const row = this.enableShapeUniforms ? `outShape[${this.rank} - 2]` : this.outputShape[this.rank - 2];\n    return `\n      int r = ${innerDims[0]};\n      int c = ${innerDims[1]};\n      int rp1 = r + 1;\n      int cp1 = c + 1;\n\n      bool cEdge = cp1 >= ${col};\n      bool rEdge = rp1 >= ${row};\n    `;\n  }\n  getOutput(dims) {\n    const sourceCoords = this.getSourceCoordsArr(dims);\n    if (this.rank === 1) {\n      const outShape = this.enableShapeUniforms ? \"outShape\" : this.outputShape[0];\n      return `getA(rc), (rc + 1 >= ${outShape} ? 0. : getA(rc + 1)), 0, 0`;\n    }\n    return `getA(${sourceCoords[0]}),\n            cEdge ? 0. : getA(${sourceCoords[1]}),\n            rEdge ? 0. : getA(${sourceCoords[2]}),\n            rEdge || cEdge ? 0. : getA(${sourceCoords[3]})`;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/reshape_packed_gpu.js\nvar ReshapePackedProgram = class {\n  constructor(outputShape, inputShape) {\n    this.variableNames = [\"A\"];\n    this.packedInputs = true;\n    this.packedOutput = true;\n    this.customUniforms = [{ name: \"inputShape\", type: \"ivec3\" }];\n    this.outputShape = outputShape;\n    this.enableShapeUniforms = useShapeUniforms(this.outputShape.length);\n    let mainLoop = ``;\n    for (let i = 0; i < 4; i++) {\n      let thisRC = `thisRC = rc;`;\n      if (i % 2 === 1) {\n        thisRC += `thisRC.z += 1;`;\n      }\n      if (i > 1) {\n        thisRC += `thisRC.y += 1;`;\n      }\n      mainLoop += `\n        ${thisRC}\n        ${i > 0 ? `if(thisRC.y < rows && thisRC.z < cols){` : \"\"}\n          int flatIndex = getFlatIndex(thisRC);\n\n          ivec3 inputRC = inputCoordsFromReshapedOutCoords(flatIndex);\n          vec2 inputRCInnerDims = vec2(float(inputRC.y),float(inputRC.z));\n\n          result[${i}] =\n            getChannel(getA(inputRC.x, inputRC.y, inputRC.z), inputRCInnerDims);\n        ${i > 0 ? \"}\" : \"\"}\n      `;\n    }\n    this.userCode = `\n      ${getReshapedInputCoords(inputShape, this.enableShapeUniforms)}\n      ${this.enableShapeUniforms ? getFlatIndexFrom3DOutput() : getFlatIndexFrom3D(outputShape)}\n\n      void main() {\n        ivec3 rc = getOutputCoords();\n\n        vec4 result = vec4(0.);\n\n        ivec3 thisRC;\n        int rows = ${this.enableShapeUniforms ? \"outShape[1]\" : outputShape[1]};\n        int cols = ${this.enableShapeUniforms ? \"outShape[2]\" : outputShape[2]};\n\n        ${mainLoop}\n\n        setOutput(result);\n      }\n    `;\n  }\n};\nfunction getReshapedInputCoords(shape, enableShapeUniforms) {\n  const coordsFromIndexSnippet = enableShapeUniforms ? getLogicalCoordinatesFromFlatIndexByUniform([\"r\", \"c\", \"d\"], \"inputShape\") : getLogicalCoordinatesFromFlatIndex([\"r\", \"c\", \"d\"], shape);\n  return `\n    ivec3 inputCoordsFromReshapedOutCoords(int index) {\n      ${coordsFromIndexSnippet}\n      return ivec3(r, c, d);\n    }\n  `;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/texture_manager.js\nvar TextureManager = class {\n  constructor(gpgpu) {\n    this.gpgpu = gpgpu;\n    this.numUsedTextures = 0;\n    this.numFreeTextures = 0;\n    this._numBytesAllocated = 0;\n    this._numBytesFree = 0;\n    this.freeTextures = {};\n    this.logEnabled = false;\n    this.usedTextures = {};\n  }\n  acquireTexture(shapeRC, usage, isPacked) {\n    const physicalTexType = getPhysicalFromLogicalTextureType(usage, isPacked);\n    const shapeKey = getKeyFromTextureShape(shapeRC, physicalTexType, isPacked);\n    if (!(shapeKey in this.freeTextures)) {\n      this.freeTextures[shapeKey] = [];\n    }\n    if (!(shapeKey in this.usedTextures)) {\n      this.usedTextures[shapeKey] = [];\n    }\n    const texBytes = computeBytes(shapeRC, physicalTexType, this.gpgpu.gl, this.gpgpu.textureConfig, isPacked);\n    if (this.freeTextures[shapeKey].length > 0) {\n      this.numFreeTextures--;\n      this.numUsedTextures++;\n      this._numBytesFree -= texBytes;\n      this.log();\n      const newTexture2 = this.freeTextures[shapeKey].shift();\n      this.usedTextures[shapeKey].push(newTexture2);\n      return newTexture2;\n    }\n    let newTexture;\n    if (physicalTexType === PhysicalTextureType.PACKED_2X2_FLOAT32) {\n      newTexture = this.gpgpu.createPackedMatrixTexture(shapeRC[0], shapeRC[1]);\n    } else if (physicalTexType === PhysicalTextureType.PACKED_2X2_FLOAT16) {\n      newTexture = this.gpgpu.createFloat16PackedMatrixTexture(shapeRC[0], shapeRC[1]);\n    } else if (physicalTexType === PhysicalTextureType.UNPACKED_FLOAT32) {\n      newTexture = this.gpgpu.createFloat32MatrixTexture(shapeRC[0], shapeRC[1]);\n    } else if (physicalTexType === PhysicalTextureType.UNPACKED_FLOAT16) {\n      newTexture = this.gpgpu.createFloat16MatrixTexture(shapeRC[0], shapeRC[1]);\n    } else if (physicalTexType === PhysicalTextureType.PACKED_4X1_UNSIGNED_BYTE) {\n      newTexture = this.gpgpu.createUnsignedBytesMatrixTexture(shapeRC[0], shapeRC[1]);\n    }\n    this.usedTextures[shapeKey].push(newTexture);\n    this.numUsedTextures++;\n    this._numBytesAllocated += texBytes;\n    this.log();\n    return newTexture;\n  }\n  releaseTexture(texture, shape, logicalTexType, isPacked) {\n    if (this.freeTextures == null) {\n      return;\n    }\n    const physicalTexType = getPhysicalFromLogicalTextureType(logicalTexType, isPacked);\n    const shapeKey = getKeyFromTextureShape(shape, physicalTexType, isPacked);\n    if (!(shapeKey in this.freeTextures)) {\n      this.freeTextures[shapeKey] = [];\n    }\n    const texBytes = computeBytes(shape, physicalTexType, this.gpgpu.gl, this.gpgpu.textureConfig, isPacked);\n    const deleteTexThreshold = env().get(\"WEBGL_DELETE_TEXTURE_THRESHOLD\");\n    if (deleteTexThreshold !== -1 && this._numBytesAllocated > deleteTexThreshold) {\n      this.gpgpu.deleteMatrixTexture(texture.texture);\n      this._numBytesAllocated -= texBytes;\n    } else {\n      this.freeTextures[shapeKey].push(texture);\n      this.numFreeTextures++;\n      this._numBytesFree += texBytes;\n    }\n    this.numUsedTextures--;\n    const texList = this.usedTextures[shapeKey];\n    const texIndex = texList.indexOf(texture);\n    if (texIndex < 0) {\n      throw new Error(\"Cannot release a texture that was never provided by this texture manager\");\n    }\n    texList.splice(texIndex, 1);\n    this.log();\n  }\n  log() {\n    if (!this.logEnabled) {\n      return;\n    }\n    const total = this.numFreeTextures + this.numUsedTextures;\n    console.log(\"Free/Used\", `${this.numFreeTextures} / ${this.numUsedTextures}`, `(${total})`);\n    const freeRatio = this._numBytesFree / this._numBytesAllocated;\n    console.log(`Bytes allocated: ${this._numBytesAllocated}`);\n    console.log(`Bytes unused: ${this._numBytesFree} (${Math.round(100 * freeRatio)}%)`);\n  }\n  get numBytesAllocated() {\n    return this._numBytesAllocated;\n  }\n  get numBytesFree() {\n    return this._numBytesFree;\n  }\n  getNumUsedTextures() {\n    return this.numUsedTextures;\n  }\n  getNumFreeTextures() {\n    return this.numFreeTextures;\n  }\n  dispose() {\n    if (this.freeTextures == null) {\n      return;\n    }\n    for (const texShape in this.freeTextures) {\n      this.freeTextures[texShape].forEach((tex) => {\n        this.gpgpu.deleteMatrixTexture(tex.texture);\n      });\n    }\n    for (const texShape in this.usedTextures) {\n      this.usedTextures[texShape].forEach((tex) => {\n        this.gpgpu.deleteMatrixTexture(tex.texture);\n      });\n    }\n    this.freeTextures = null;\n    this.usedTextures = null;\n    this.numUsedTextures = 0;\n    this.numFreeTextures = 0;\n    this._numBytesAllocated = 0;\n    this._numBytesFree = 0;\n  }\n};\nfunction numBytesForInternalFormat(gl, internalFormat) {\n  const glany = gl;\n  if (internalFormat === glany.R32F) {\n    return 4;\n  } else if (internalFormat === glany.R16F) {\n    return 2;\n  } else if (internalFormat === glany.RGBA32F) {\n    return 16;\n  } else if (internalFormat === gl.RGBA) {\n    return 16;\n  } else if (internalFormat === glany.RGBA16F) {\n    return 8;\n  } else if (internalFormat === glany.RGBA8) {\n    return 4;\n  }\n  throw new Error(`Unknown internal format ${internalFormat}`);\n}\nfunction computeBytes(shape, physicalTexType, gl, textureConfig, isPacked) {\n  const internalFormat = internalFormatForPhysicalTexType(physicalTexType, textureConfig);\n  let numElements;\n  if (isPacked) {\n    const [packedWidth, packedHeight] = getPackedMatrixTextureShapeWidthHeight(shape[0], shape[1]);\n    numElements = packedWidth * packedHeight;\n  } else {\n    const [width, height] = getUnpackedMatrixTextureShapeWidthHeight(shape[0], shape[1]);\n    numElements = width * height;\n  }\n  const bytesPerElement2 = numBytesForInternalFormat(gl, internalFormat);\n  return numElements * bytesPerElement2;\n}\nfunction internalFormatForPhysicalTexType(physicalTexType, textureConfig) {\n  switch (physicalTexType) {\n    case PhysicalTextureType.PACKED_2X2_FLOAT32:\n      return getInternalFormatForPackedMatrixTexture(textureConfig);\n    case PhysicalTextureType.PACKED_2X2_FLOAT16:\n      return getInternalFormatForFloat16PackedMatrixTexture(textureConfig);\n    case PhysicalTextureType.UNPACKED_FLOAT32:\n      return getInternalFormatForFloat32MatrixTexture(textureConfig);\n    case PhysicalTextureType.UNPACKED_FLOAT16:\n      return getInternalFormatForFloat16MatrixTexture(textureConfig);\n    case PhysicalTextureType.PACKED_4X1_UNSIGNED_BYTE:\n      return getInternalFormatForUnsignedBytesMatrixTexture(textureConfig);\n    default:\n      throw new Error(`Unknown physical texture type ${physicalTexType}`);\n  }\n}\nfunction getPhysicalTextureForRendering(isPacked) {\n  if (env().getBool(\"WEBGL_RENDER_FLOAT32_ENABLED\")) {\n    if (isPacked) {\n      return PhysicalTextureType.PACKED_2X2_FLOAT32;\n    }\n    return PhysicalTextureType.UNPACKED_FLOAT32;\n  }\n  if (isPacked) {\n    return PhysicalTextureType.PACKED_2X2_FLOAT16;\n  }\n  return PhysicalTextureType.UNPACKED_FLOAT16;\n}\nfunction getPhysicalFromLogicalTextureType(logicalTexType, isPacked) {\n  if (logicalTexType === TextureUsage.UPLOAD) {\n    return PhysicalTextureType.PACKED_2X2_FLOAT32;\n  } else if (logicalTexType === TextureUsage.RENDER || logicalTexType == null) {\n    return getPhysicalTextureForRendering(isPacked);\n  } else if (logicalTexType === TextureUsage.DOWNLOAD || logicalTexType === TextureUsage.PIXELS) {\n    return PhysicalTextureType.PACKED_4X1_UNSIGNED_BYTE;\n  }\n  throw new Error(`Unknown logical texture type ${logicalTexType}`);\n}\nfunction getKeyFromTextureShape(shapeRowsCol, physicalTexType, isPacked) {\n  return `${shapeRowsCol[0]}_${shapeRowsCol[1]}_${physicalTexType}_${isPacked}`;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/unaryop_gpu.js\nvar UnaryOpProgram = class {\n  constructor(aShape, opSnippet) {\n    this.variableNames = [\"A\"];\n    this.outputShape = aShape;\n    this.enableShapeUniforms = useShapeUniforms(this.outputShape.length);\n    this.userCode = `\n      float unaryOperation(float x) {\n        ${opSnippet}\n      }\n\n      void main() {\n        float x = getAAtOutCoords();\n        float y = unaryOperation(x);\n\n        setOutput(y);\n      }\n    `;\n  }\n};\nvar CHECK_NAN_SNIPPET = `if (isnan(x)) return x;`;\nvar LINEAR = `return x;`;\nvar ABS = `return abs(x);`;\nvar ELU2 = `return (x >= 0.0) ? x : (exp(x) - 1.0);`;\nvar RELU = CHECK_NAN_SNIPPET + `\n  return (x < 0.0) ? 0.0 : x;\n`;\nvar RELU6 = CHECK_NAN_SNIPPET + `\n  return (x < 0.0) ? 0.0 : min(6.0, x);\n`;\nvar CLONE = \"return x;\";\nvar SIGMOID = `return 1.0 / (1.0 + exp(-1.0 * x));`;\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/unaryop_packed_gpu.js\nvar LINEAR2 = `return x;`;\nvar ELU3 = `\n  vec4 result;\n\n  result.r = (x.r >= 0.0) ? x.r : (exp(x.r) - 1.0);\n  result.g = (x.g >= 0.0) ? x.g : (exp(x.g) - 1.0);\n  result.b = (x.b >= 0.0) ? x.b : (exp(x.b) - 1.0);\n  result.a = (x.a >= 0.0) ? x.a : (exp(x.a) - 1.0);\n\n  return result;\n`;\nvar RELU2 = `\n  vec4 result = x * vec4(greaterThanEqual(x, vec4(0.0)));\n  bvec4 isNaN = isnan(x);\n\n  result.r = isNaN.r ? x.r : result.r;\n  result.g = isNaN.g ? x.g : result.g;\n  result.b = isNaN.b ? x.b : result.b;\n  result.a = isNaN.a ? x.a : result.a;\n\n  return result;\n`;\nvar RELU62 = `\n  vec4 result = min(x, vec4(6.)) * vec4(greaterThanEqual(x, vec4(0.0)));\n  bvec4 isNaN = isnan(x);\n\n  result.r = isNaN.r ? x.r : result.r;\n  result.g = isNaN.g ? x.g : result.g;\n  result.b = isNaN.b ? x.b : result.b;\n  result.a = isNaN.a ? x.a : result.a;\n\n  return result;\n`;\nvar SIGMOID2 = `return 1.0 / (1.0 + exp(-1.0 * x));`;\nvar UnaryOpPackedProgram = class {\n  constructor(aShape, opSnippet) {\n    this.variableNames = [\"A\"];\n    this.packedInputs = true;\n    this.packedOutput = true;\n    this.outputShape = aShape;\n    this.enableShapeUniforms = useShapeUniforms(this.outputShape.length);\n    this.userCode = `\n      vec4 unaryOperation(vec4 x) {\n        ${opSnippet}\n      }\n\n      void main() {\n        vec4 x = getAAtOutCoords();\n        vec4 y = unaryOperation(x);\n\n        setOutput(y);\n      }\n    `;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/unpack_gpu.js\nvar UnpackProgram = class {\n  constructor(outputShape) {\n    this.variableNames = [\"A\"];\n    this.packedInputs = true;\n    this.packedOutput = false;\n    this.outputShape = outputShape;\n    this.enableShapeUniforms = useShapeUniforms(this.outputShape.length);\n    const rank = outputShape.length;\n    const channels = getChannels(\"rc\", rank);\n    const dtype = getCoordsDataType(rank);\n    const sourceCoords = getSourceCoords(rank, channels);\n    const innerDims = channels.slice(-2);\n    const coords3 = rank <= 1 ? \"rc\" : `vec2(${innerDims.join(\",\")})`;\n    this.userCode = `\n      void main() {\n        ${dtype} rc = getOutputCoords();\n        vec4 packedInput = getA(${sourceCoords});\n\n        setOutput(getChannel(packedInput, ${coords3}));\n      }\n    `;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/backend_webgl.js\nvar whereImpl3 = kernel_impls_exports.whereImpl;\nvar EPSILON_FLOAT322 = 1e-7;\nvar EPSILON_FLOAT162 = 1e-4;\nvar binaryCaches = {};\nfunction getBinaryCache(webGLVersion) {\n  if (webGLVersion in binaryCaches) {\n    return binaryCaches[webGLVersion];\n  }\n  binaryCaches[webGLVersion] = {};\n  return binaryCaches[webGLVersion];\n}\nvar CPU_HANDOFF_SIZE_THRESHOLD = env().getNumber(\"CPU_HANDOFF_SIZE_THRESHOLD\");\nvar BEFORE_PAGING_CONSTANT = 600;\nfunction numMBBeforeWarning() {\n  if (env().global.screen == null) {\n    return 1024;\n  }\n  return env().global.screen.height * env().global.screen.width * window.devicePixelRatio * BEFORE_PAGING_CONSTANT / 1024 / 1024;\n}\nvar MathBackendWebGL = class extends KernelBackend {\n  constructor(gpuResource) {\n    super();\n    this.pendingRead = /* @__PURE__ */ new WeakMap();\n    this.pendingDisposal = /* @__PURE__ */ new WeakSet();\n    this.dataRefCount = /* @__PURE__ */ new WeakMap();\n    this.numBytesInGPU = 0;\n    this.uploadWaitMs = 0;\n    this.downloadWaitMs = 0;\n    this.lastGlFlushTime = 0;\n    this.warnedAboutMemory = false;\n    this.pendingDeletes = 0;\n    this.disposed = false;\n    if (!env().getBool(\"HAS_WEBGL\")) {\n      throw new Error(\"WebGL is not supported on this device\");\n    }\n    let newGPGPU;\n    if (gpuResource != null) {\n      if (gpuResource instanceof GPGPUContext) {\n        newGPGPU = gpuResource;\n      } else {\n        const gl = getWebGLContext(env().getNumber(\"WEBGL_VERSION\"), gpuResource);\n        newGPGPU = new GPGPUContext(gl);\n      }\n      this.binaryCache = {};\n      this.gpgpuCreatedLocally = false;\n    } else {\n      const gl = getWebGLContext(env().getNumber(\"WEBGL_VERSION\"));\n      newGPGPU = new GPGPUContext(gl);\n      this.binaryCache = getBinaryCache(env().getNumber(\"WEBGL_VERSION\"));\n      this.gpgpuCreatedLocally = true;\n    }\n    this.gpgpu = newGPGPU;\n    this.canvas = this.gpgpu.gl.canvas;\n    this.textureManager = new TextureManager(this.gpgpu);\n    this.numMBBeforeWarning = numMBBeforeWarning();\n    this.texData = new DataStorage(this, engine());\n  }\n  nextDataId() {\n    return MathBackendWebGL.nextDataId++;\n  }\n  numDataIds() {\n    return this.texData.numDataIds() - this.pendingDeletes;\n  }\n  write(values, shape, dtype) {\n    if (env().getBool(\"WEBGL_CHECK_NUMERICAL_PROBLEMS\") || env().getBool(\"DEBUG\")) {\n      this.checkNumericalProblems(values);\n    }\n    if (dtype === \"complex64\" && values != null) {\n      throw new Error(`Cannot write to a complex64 dtype. Please use tf.complex(real, imag).`);\n    }\n    const dataId = { id: this.nextDataId() };\n    this.texData.set(dataId, { shape, dtype, values, usage: TextureUsage.UPLOAD, refCount: 1 });\n    return dataId;\n  }\n  refCount(dataId) {\n    if (this.texData.has(dataId)) {\n      const tensorData = this.texData.get(dataId);\n      return tensorData.refCount;\n    }\n    return 0;\n  }\n  incRef(dataId) {\n    const texData = this.texData.get(dataId);\n    texData.refCount++;\n  }\n  decRef(dataId) {\n    if (this.texData.has(dataId)) {\n      const texData = this.texData.get(dataId);\n      texData.refCount--;\n    }\n  }\n  move(dataId, values, shape, dtype, refCount) {\n    if (env().getBool(\"DEBUG\")) {\n      this.checkNumericalProblems(values);\n    }\n    if (dtype === \"complex64\") {\n      throw new Error(`Cannot write to a complex64 dtype. Please use tf.complex(real, imag).`);\n    }\n    this.texData.set(dataId, { shape, dtype, values, usage: TextureUsage.UPLOAD, refCount });\n  }\n  disposeIntermediateTensorInfo(tensorInfo) {\n    this.disposeData(tensorInfo.dataId);\n  }\n  readSync(dataId) {\n    const texData = this.texData.get(dataId);\n    const { values, dtype, complexTensorInfos, slice: slice6, shape, isPacked } = texData;\n    if (slice6 != null) {\n      let program;\n      if (isPacked) {\n        program = new UnaryOpPackedProgram(shape, CLONE);\n      } else {\n        program = new UnaryOpProgram(shape, CLONE);\n      }\n      const res = this.runWebGLProgram(program, [{ dataId, shape, dtype }], dtype);\n      const data = this.readSync(res.dataId);\n      this.disposeIntermediateTensorInfo(res);\n      return data;\n    }\n    if (values != null) {\n      return this.convertAndCacheOnCPU(dataId);\n    }\n    if (dtype === \"string\") {\n      return values;\n    }\n    const shouldTimeProgram = this.activeTimers != null;\n    let start;\n    if (shouldTimeProgram) {\n      start = util_exports.now();\n    }\n    let result;\n    if (dtype === \"complex64\") {\n      const realValues = this.readSync(complexTensorInfos.real.dataId);\n      const imagValues = this.readSync(complexTensorInfos.imag.dataId);\n      result = backend_util_exports.mergeRealAndImagArrays(realValues, imagValues);\n    } else {\n      result = this.getValuesFromTexture(dataId);\n    }\n    if (shouldTimeProgram) {\n      this.downloadWaitMs += util_exports.now() - start;\n    }\n    return this.convertAndCacheOnCPU(dataId, result);\n  }\n  async read(dataId) {\n    if (this.pendingRead.has(dataId)) {\n      const subscribers2 = this.pendingRead.get(dataId);\n      return new Promise((resolve) => subscribers2.push(resolve));\n    }\n    const texData = this.texData.get(dataId);\n    const { values, shape, slice: slice6, dtype, complexTensorInfos, isPacked } = texData;\n    if (slice6 != null) {\n      let program;\n      if (isPacked) {\n        program = new UnaryOpPackedProgram(shape, CLONE);\n      } else {\n        program = new UnaryOpProgram(shape, CLONE);\n      }\n      const res = this.runWebGLProgram(program, [{ dataId, shape, dtype }], dtype);\n      const data = this.read(res.dataId);\n      this.disposeIntermediateTensorInfo(res);\n      return data;\n    }\n    if (values != null) {\n      return this.convertAndCacheOnCPU(dataId);\n    }\n    if (env().getBool(\"DEBUG\")) {\n      if (!env().getBool(\"WEBGL_DOWNLOAD_FLOAT_ENABLED\") && env().getNumber(\"WEBGL_VERSION\") === 2) {\n        throw new Error(`tensor.data() with WEBGL_DOWNLOAD_FLOAT_ENABLED=false and WEBGL_VERSION=2 not yet supported.`);\n      }\n    }\n    let buffer2 = null;\n    let tmpDownloadTarget;\n    if (dtype !== \"complex64\" && env().get(\"WEBGL_BUFFER_SUPPORTED\")) {\n      tmpDownloadTarget = this.decode(dataId);\n      const tmpData = this.texData.get(tmpDownloadTarget.dataId);\n      buffer2 = this.gpgpu.createBufferFromTexture(tmpData.texture.texture, ...getDenseTexShape(shape));\n    }\n    this.pendingRead.set(dataId, []);\n    if (dtype !== \"complex64\") {\n      await this.gpgpu.createAndWaitForFence();\n    }\n    let vals;\n    if (dtype === \"complex64\") {\n      const ps = await Promise.all([\n        this.read(complexTensorInfos.real.dataId),\n        this.read(complexTensorInfos.imag.dataId)\n      ]);\n      const realValues = ps[0];\n      const imagValues = ps[1];\n      vals = backend_util_exports.mergeRealAndImagArrays(realValues, imagValues);\n    } else if (buffer2 == null) {\n      vals = this.getValuesFromTexture(dataId);\n    } else {\n      const size = util_exports.sizeFromShape(shape);\n      vals = this.gpgpu.downloadFloat32MatrixFromBuffer(buffer2, size);\n    }\n    if (tmpDownloadTarget != null) {\n      this.disposeIntermediateTensorInfo(tmpDownloadTarget);\n    }\n    if (buffer2 != null) {\n      const gl = this.gpgpu.gl;\n      callAndCheck(gl, () => gl.deleteBuffer(buffer2));\n    }\n    const dTypeVals = this.convertAndCacheOnCPU(dataId, vals);\n    const subscribers = this.pendingRead.get(dataId);\n    this.pendingRead.delete(dataId);\n    subscribers.forEach((resolve) => resolve(dTypeVals));\n    if (this.pendingDisposal.has(dataId)) {\n      this.pendingDisposal.delete(dataId);\n      if (this.disposeData(dataId)) {\n        engine().removeDataId(dataId, this);\n      }\n      this.pendingDeletes--;\n    }\n    return dTypeVals;\n  }\n  readToGPU(dataId, options = {}) {\n    const texData = this.texData.get(dataId);\n    const { values, shape, slice: slice6, dtype, isPacked, texture } = texData;\n    if (dtype === \"complex64\") {\n      throw new Error(\"Does not support reading texture for complex64 dtype.\");\n    }\n    if (slice6 != null) {\n      let program;\n      if (isPacked) {\n        program = new UnaryOpPackedProgram(shape, CLONE);\n      } else {\n        program = new UnaryOpProgram(shape, CLONE);\n      }\n      const res = this.runWebGLProgram(program, [{ dataId, shape, dtype }], dtype);\n      const gpuResouorce = this.readToGPU(res, options);\n      this.disposeIntermediateTensorInfo(res);\n      return gpuResouorce;\n    }\n    if (texture == null) {\n      if (values != null) {\n        throw new Error(\"Data is not on GPU but on CPU.\");\n      } else {\n        throw new Error(\"There is no data on GPU or CPU.\");\n      }\n    }\n    const tmpTarget = this.decode(dataId, options.customTexShape);\n    const tensorRef = engine().makeTensorFromTensorInfo(tmpTarget);\n    const tmpData = this.texData.get(tmpTarget.dataId);\n    return Object.assign({ tensorRef }, tmpData.texture);\n  }\n  bufferSync(t) {\n    const data = this.readSync(t.dataId);\n    if (t.dtype === \"string\") {\n      try {\n        const strings = data.map((d) => util_exports.decodeString(d));\n        return buffer(t.shape, t.dtype, strings);\n      } catch (_a) {\n        throw new Error(\"Failed to decode encoded string bytes into utf-8\");\n      }\n    }\n    return buffer(t.shape, t.dtype, data);\n  }\n  checkNumericalProblems(values) {\n    if (values == null) {\n      return;\n    }\n    for (let i = 0; i < values.length; i++) {\n      const num = values[i];\n      if (!canBeRepresented(num)) {\n        if (env().getBool(\"WEBGL_RENDER_FLOAT32_CAPABLE\")) {\n          throw Error(`The value ${num} cannot be represented with your current settings. Consider enabling float32 rendering: 'tf.env().set('WEBGL_RENDER_FLOAT32_ENABLED', true);'`);\n        }\n        throw Error(`The value ${num} cannot be represented on this device.`);\n      }\n    }\n  }\n  getValuesFromTexture(dataId) {\n    const { shape, dtype, isPacked } = this.texData.get(dataId);\n    const size = util_exports.sizeFromShape(shape);\n    if (env().getBool(\"WEBGL_DOWNLOAD_FLOAT_ENABLED\")) {\n      const tmpTarget = this.decode(dataId);\n      const tmpData2 = this.texData.get(tmpTarget.dataId);\n      const vals2 = this.gpgpu.downloadMatrixFromPackedTexture(tmpData2.texture.texture, ...getDenseTexShape(shape)).subarray(0, size);\n      this.disposeIntermediateTensorInfo(tmpTarget);\n      return vals2;\n    }\n    const shouldUsePackedProgram = env().getBool(\"WEBGL_PACK\") && isPacked === true;\n    const outputShape = shouldUsePackedProgram ? getShapeAs3D(shape) : shape;\n    const program = shouldUsePackedProgram ? new EncodeFloatPackedProgram(outputShape) : new EncodeFloatProgram(outputShape);\n    const output = this.runWebGLProgram(program, [{ shape: outputShape, dtype, dataId }], \"float32\");\n    const tmpData = this.texData.get(output.dataId);\n    const vals = this.gpgpu.downloadByteEncodedFloatMatrixFromOutputTexture(tmpData.texture.texture, tmpData.texShape[0], tmpData.texShape[1]).subarray(0, size);\n    this.disposeIntermediateTensorInfo(output);\n    return vals;\n  }\n  timerAvailable() {\n    return env().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE\") > 0;\n  }\n  time(f) {\n    const oldActiveTimers = this.activeTimers;\n    const newActiveTimers = [];\n    let outerMostTime = false;\n    if (this.programTimersStack == null) {\n      this.programTimersStack = newActiveTimers;\n      outerMostTime = true;\n    } else {\n      this.activeTimers.push(newActiveTimers);\n    }\n    this.activeTimers = newActiveTimers;\n    f();\n    const flattenedActiveTimerQueries = util_exports.flatten(this.activeTimers.map((d) => d.query)).filter((d) => d != null);\n    const flattenedActiveTimerNames = util_exports.flatten(this.activeTimers.map((d) => d.name)).filter((d) => d != null);\n    this.activeTimers = oldActiveTimers;\n    if (outerMostTime) {\n      this.programTimersStack = null;\n    }\n    const res = {\n      uploadWaitMs: this.uploadWaitMs,\n      downloadWaitMs: this.downloadWaitMs,\n      kernelMs: null,\n      wallMs: null\n    };\n    return (async () => {\n      if (env().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE\") > 0) {\n        const kernelMs = await Promise.all(flattenedActiveTimerQueries);\n        res[\"kernelMs\"] = util_exports.sum(kernelMs);\n        res[\"getExtraProfileInfo\"] = () => kernelMs.map((d, i) => ({ name: flattenedActiveTimerNames[i], ms: d })).map((d) => `${d.name}: ${d.ms}`).join(\", \");\n      } else {\n        res[\"kernelMs\"] = {\n          error: \"WebGL query timers are not supported in this environment.\"\n        };\n      }\n      this.uploadWaitMs = 0;\n      this.downloadWaitMs = 0;\n      return res;\n    })();\n  }\n  memory() {\n    return {\n      unreliable: false,\n      numBytesInGPU: this.numBytesInGPU,\n      numBytesInGPUAllocated: this.textureManager.numBytesAllocated,\n      numBytesInGPUFree: this.textureManager.numBytesFree\n    };\n  }\n  startTimer() {\n    if (env().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE\") > 0) {\n      return this.gpgpu.beginQuery();\n    }\n    return { startMs: util_exports.now(), endMs: null };\n  }\n  endTimer(query) {\n    if (env().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE\") > 0) {\n      this.gpgpu.endQuery();\n      return query;\n    }\n    query.endMs = util_exports.now();\n    return query;\n  }\n  async getQueryTime(query) {\n    if (env().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE\") > 0) {\n      return this.gpgpu.waitForQueryAndGetTime(query);\n    }\n    const timerQuery = query;\n    return timerQuery.endMs - timerQuery.startMs;\n  }\n  disposeData(dataId, force = false) {\n    if (this.pendingDisposal.has(dataId)) {\n      return false;\n    }\n    if (!this.texData.has(dataId)) {\n      return true;\n    }\n    if (force) {\n      this.texData.get(dataId).refCount = 0;\n    } else {\n      this.texData.get(dataId).refCount--;\n    }\n    if (!force && this.texData.get(dataId).refCount > 0) {\n      return false;\n    }\n    if (this.pendingRead.has(dataId)) {\n      this.pendingDisposal.add(dataId);\n      this.pendingDeletes++;\n      return false;\n    }\n    this.releaseGPUData(dataId);\n    const { complexTensorInfos } = this.texData.get(dataId);\n    if (complexTensorInfos != null) {\n      this.disposeData(complexTensorInfos.real.dataId, force);\n      this.disposeData(complexTensorInfos.imag.dataId, force);\n    }\n    this.texData.delete(dataId);\n    return true;\n  }\n  releaseGPUData(dataId) {\n    const { texture, dtype, texShape, usage, isPacked, slice: slice6 } = this.texData.get(dataId);\n    const key = slice6 && slice6.origDataId || dataId;\n    const refCount = this.dataRefCount.get(key);\n    if (refCount > 1) {\n      this.dataRefCount.set(key, refCount - 1);\n    } else {\n      this.dataRefCount.delete(key);\n      if (texture != null) {\n        this.numBytesInGPU -= this.computeBytes(texShape, dtype);\n        this.textureManager.releaseTexture(texture, texShape, usage, isPacked);\n      }\n    }\n    const texData = this.texData.get(dataId);\n    texData.texture = null;\n    texData.texShape = null;\n    texData.isPacked = false;\n    texData.slice = null;\n  }\n  getTexture(dataId) {\n    this.uploadToGPU(dataId);\n    return this.texData.get(dataId).texture.texture;\n  }\n  getDataInfo(dataId) {\n    return this.texData.get(dataId);\n  }\n  shouldExecuteOnCPU(inputs, sizeThreshold = CPU_HANDOFF_SIZE_THRESHOLD) {\n    return env().getBool(\"WEBGL_CPU_FORWARD\") && inputs.every((input2) => this.texData.get(input2.dataId).texture == null && util_exports.sizeFromShape(input2.shape) < sizeThreshold);\n  }\n  getGPGPUContext() {\n    return this.gpgpu;\n  }\n  where(condition) {\n    backend_util_exports.warn(\"tf.where() in webgl locks the UI thread. Call tf.whereAsync() instead\");\n    const condVals = condition.dataSync();\n    return whereImpl3(condition.shape, condVals);\n  }\n  packedUnaryOp(x, op2, dtype) {\n    const program = new UnaryOpPackedProgram(x.shape, op2);\n    const outInfo = this.compileAndRun(program, [x], dtype);\n    return engine().makeTensorFromTensorInfo(outInfo);\n  }\n  abs(x) {\n    if (this.shouldExecuteOnCPU([x]) && x.dtype !== \"complex64\") {\n      const outValues = simpleAbsImplCPU(this.texData.get(x.dataId).values);\n      return this.makeOutput(x.shape, x.dtype, outValues);\n    }\n    if (env().getBool(\"WEBGL_PACK_UNARY_OPERATIONS\")) {\n      return this.packedUnaryOp(x, ABS, x.dtype);\n    }\n    const program = new UnaryOpProgram(x.shape, ABS);\n    const outInfo = this.compileAndRun(program, [x]);\n    return engine().makeTensorFromTensorInfo(outInfo);\n  }\n  makeTensorInfo(shape, dtype, values) {\n    let dataId;\n    if (dtype === \"string\" && values != null && values.length > 0 && util_exports.isString(values[0])) {\n      const encodedValues = values.map((d) => util_exports.encodeString(d));\n      dataId = this.write(encodedValues, shape, dtype);\n    } else {\n      dataId = this.write(values, shape, dtype);\n    }\n    this.texData.get(dataId).usage = null;\n    return { dataId, shape, dtype };\n  }\n  makeOutput(shape, dtype, values) {\n    return engine().makeTensorFromTensorInfo(this.makeTensorInfo(shape, dtype, values), this);\n  }\n  unpackTensor(input2) {\n    const program = new UnpackProgram(input2.shape);\n    return this.runWebGLProgram(program, [input2], input2.dtype);\n  }\n  packTensor(input2) {\n    const program = new PackProgram(input2.shape);\n    const preventEagerUnpackingOutput = true;\n    return this.runWebGLProgram(program, [input2], input2.dtype, null, preventEagerUnpackingOutput);\n  }\n  packedReshape(input2, afterShape) {\n    const input3DShape = [\n      getBatchDim(input2.shape),\n      ...getRowsCols(input2.shape)\n    ];\n    const input3D = {\n      dtype: input2.dtype,\n      shape: input3DShape,\n      dataId: input2.dataId\n    };\n    const afterShapeAs3D = [\n      getBatchDim(afterShape),\n      ...getRowsCols(afterShape)\n    ];\n    const program = new ReshapePackedProgram(afterShapeAs3D, input3DShape);\n    const preventEagerUnpackingOfOutput = true;\n    const customValues = [input3DShape];\n    const output = this.runWebGLProgram(program, [input3D], input2.dtype, customValues, preventEagerUnpackingOfOutput);\n    return { dataId: output.dataId, shape: afterShape, dtype: output.dtype };\n  }\n  decode(dataId, customTexShape) {\n    const texData = this.texData.get(dataId);\n    const { isPacked, shape, dtype } = texData;\n    if (customTexShape != null) {\n      const size = util_exports.sizeFromShape(shape);\n      const texSize = customTexShape[0] * customTexShape[1] * 4;\n      util_exports.assert(size <= texSize, () => \"customTexShape is too small. Row * Column * 4 should be equal or larger than the size of the tensor data.\");\n    }\n    const shapeAs3D = getShapeAs3D(shape);\n    let program;\n    if (isPacked) {\n      program = new DecodeMatrixPackedProgram(shapeAs3D);\n    } else {\n      program = new DecodeMatrixProgram(shapeAs3D);\n    }\n    const preventEagerUnpackingOfOutput = true;\n    const customValues = [customTexShape != null ? customTexShape : getDenseTexShape(shapeAs3D)];\n    const out = this.runWebGLProgram(program, [{ shape: shapeAs3D, dtype, dataId }], dtype, customValues, preventEagerUnpackingOfOutput, customTexShape);\n    return { dtype, shape, dataId: out.dataId };\n  }\n  runWebGLProgram(program, inputs, outputDtype, customUniformValues, preventEagerUnpackingOfOutput = false, customTexShape) {\n    const output = this.makeTensorInfo(program.outputShape, outputDtype);\n    const outData = this.texData.get(output.dataId);\n    if (program.packedOutput) {\n      outData.isPacked = true;\n    }\n    if (program.outPackingScheme === PackingScheme.DENSE) {\n      const texelShape = customTexShape != null ? customTexShape : getDenseTexShape(program.outputShape);\n      outData.texShape = texelShape.map((d) => d * 2);\n    }\n    if (program.outTexUsage != null) {\n      outData.usage = program.outTexUsage;\n    }\n    if (util_exports.sizeFromShape(output.shape) === 0) {\n      outData.values = util_exports.getTypedArrayFromDType(output.dtype, 0);\n      return output;\n    }\n    const dataToDispose = [];\n    const inputsData = inputs.map((input2) => {\n      if (input2.dtype === \"complex64\") {\n        throw new Error(`GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.`);\n      }\n      let texData = this.texData.get(input2.dataId);\n      if (texData.texture == null) {\n        if (!program.packedInputs && util_exports.sizeFromShape(input2.shape) <= env().getNumber(\"WEBGL_SIZE_UPLOAD_UNIFORM\")) {\n          return {\n            shape: input2.shape,\n            texData: null,\n            isUniform: true,\n            uniformValues: texData.values\n          };\n        }\n        if (program.packedInputs) {\n          texData.isPacked = true;\n          texData.shape = input2.shape;\n        }\n      }\n      this.uploadToGPU(input2.dataId);\n      if (!!texData.isPacked !== !!program.packedInputs) {\n        input2 = texData.isPacked ? this.unpackTensor(input2) : this.packTensor(input2);\n        dataToDispose.push(input2);\n        texData = this.texData.get(input2.dataId);\n      } else if (texData.isPacked && !isReshapeFree(texData.shape, input2.shape)) {\n        const savedInput = input2;\n        const targetShape = input2.shape;\n        input2.shape = texData.shape;\n        input2 = this.packedReshape(input2, targetShape);\n        dataToDispose.push(input2);\n        texData = this.texData.get(input2.dataId);\n        savedInput.shape = targetShape;\n      }\n      return { shape: input2.shape, texData, isUniform: false };\n    });\n    this.uploadToGPU(output.dataId);\n    const outputData = { shape: output.shape, texData: outData, isUniform: false };\n    const key = makeShaderKey(program, inputsData, outputData);\n    const binary = this.getAndSaveBinary(key, () => {\n      return compileProgram(this.gpgpu, program, inputsData, outputData);\n    });\n    const shouldTimeProgram = this.activeTimers != null;\n    let query;\n    if (shouldTimeProgram) {\n      query = this.startTimer();\n    }\n    if (!env().get(\"ENGINE_COMPILE_ONLY\")) {\n      runProgram(this.gpgpu, binary, inputsData, outputData, customUniformValues);\n    }\n    dataToDispose.forEach((info) => this.disposeIntermediateTensorInfo(info));\n    if (shouldTimeProgram) {\n      query = this.endTimer(query);\n      this.activeTimers.push({ name: program.constructor.name, query: this.getQueryTime(query) });\n    }\n    const glFlushThreshold = env().get(\"WEBGL_FLUSH_THRESHOLD\");\n    if (glFlushThreshold > 0) {\n      const time2 = util_exports.now();\n      if (time2 - this.lastGlFlushTime > glFlushThreshold) {\n        this.gpgpu.gl.flush();\n        this.lastGlFlushTime = time2;\n      }\n    }\n    if (!env().getBool(\"WEBGL_LAZILY_UNPACK\") && outData.isPacked && preventEagerUnpackingOfOutput === false) {\n      const unpacked = this.unpackTensor(output);\n      this.disposeIntermediateTensorInfo(output);\n      return unpacked;\n    }\n    return output;\n  }\n  compileAndRun(program, inputs, outputDtype, customUniformValues, preventEagerUnpackingOfOutput = false) {\n    outputDtype = outputDtype || inputs[0].dtype;\n    const outInfo = this.runWebGLProgram(program, inputs, outputDtype, customUniformValues, preventEagerUnpackingOfOutput);\n    return outInfo;\n  }\n  getAndSaveBinary(key, getBinary) {\n    if (!(key in this.binaryCache)) {\n      this.binaryCache[key] = getBinary();\n    }\n    return this.binaryCache[key];\n  }\n  getTextureManager() {\n    return this.textureManager;\n  }\n  dispose() {\n    if (this.disposed) {\n      return;\n    }\n    if (!env().getBool(\"IS_TEST\")) {\n      const allKeys = Object.keys(this.binaryCache);\n      allKeys.forEach((key) => {\n        this.gpgpu.deleteProgram(this.binaryCache[key].webGLProgram);\n        delete this.binaryCache[key];\n      });\n    }\n    this.textureManager.dispose();\n    if (this.canvas != null && (typeof HTMLCanvasElement !== \"undefined\" && this.canvas instanceof HTMLCanvasElement)) {\n      this.canvas.remove();\n    } else {\n      this.canvas = null;\n    }\n    if (this.gpgpuCreatedLocally) {\n      this.gpgpu.program = null;\n      this.gpgpu.dispose();\n    }\n    this.disposed = true;\n  }\n  floatPrecision() {\n    if (this.floatPrecisionValue == null) {\n      this.floatPrecisionValue = tidy(() => {\n        if (!env().get(\"WEBGL_RENDER_FLOAT32_ENABLED\")) {\n          const debugFlag = env().getBool(\"DEBUG\");\n          env().set(\"DEBUG\", false);\n          const underflowCheckValue = this.abs(scalar(1e-8)).dataSync()[0];\n          env().set(\"DEBUG\", debugFlag);\n          if (underflowCheckValue > 0) {\n            return 32;\n          }\n        }\n        return 16;\n      });\n    }\n    return this.floatPrecisionValue;\n  }\n  epsilon() {\n    return this.floatPrecision() === 32 ? EPSILON_FLOAT322 : EPSILON_FLOAT162;\n  }\n  uploadToGPU(dataId) {\n    const texData = this.texData.get(dataId);\n    const { shape, dtype, values, texture, usage, isPacked } = texData;\n    if (texture != null) {\n      return;\n    }\n    const shouldTimeProgram = this.activeTimers != null;\n    let start;\n    if (shouldTimeProgram) {\n      start = util_exports.now();\n    }\n    let texShape = texData.texShape;\n    if (texShape == null) {\n      texShape = getTextureShapeFromLogicalShape(shape, isPacked);\n      texData.texShape = texShape;\n    }\n    if (values != null) {\n      const shapeAs3D = getShapeAs3D(shape);\n      let program;\n      let width = texShape[1], height = texShape[0];\n      const isByteArray = values instanceof Uint8Array || values instanceof Uint8ClampedArray;\n      if (isPacked || !isByteArray) {\n        [width, height] = getPackedMatrixTextureShapeWidthHeight(texShape[0], texShape[1]);\n      }\n      if (isPacked) {\n        program = new EncodeMatrixPackedProgram(shapeAs3D, isByteArray);\n      } else {\n        program = new EncodeMatrixProgram(shapeAs3D, isByteArray);\n      }\n      const tempDenseInputTexShape = isByteArray ? [height, width] : texShape;\n      const tempDenseInputHandle = this.makeTensorInfo(tempDenseInputTexShape, dtype);\n      const tempDenseInputTexData = this.texData.get(tempDenseInputHandle.dataId);\n      if (isByteArray) {\n        tempDenseInputTexData.usage = TextureUsage.PIXELS;\n      } else {\n        tempDenseInputTexData.usage = TextureUsage.UPLOAD;\n      }\n      tempDenseInputTexData.texShape = tempDenseInputTexShape;\n      this.gpgpu.uploadDenseMatrixToTexture(this.getTexture(tempDenseInputHandle.dataId), width, height, values);\n      const customValues = [[height, width]];\n      const preventEagerUnpacking = true;\n      const encodedOutputTarget = this.runWebGLProgram(program, [tempDenseInputHandle], dtype, customValues, preventEagerUnpacking);\n      const outputTexData = this.texData.get(encodedOutputTarget.dataId);\n      texData.texShape = outputTexData.texShape;\n      texData.isPacked = outputTexData.isPacked;\n      texData.usage = outputTexData.usage;\n      if (!env().get(\"ENGINE_COMPILE_ONLY\")) {\n        texData.texture = outputTexData.texture;\n        texData.values = null;\n        this.texData.delete(encodedOutputTarget.dataId);\n      } else {\n        this.disposeData(encodedOutputTarget.dataId);\n      }\n      this.disposeIntermediateTensorInfo(tempDenseInputHandle);\n      if (shouldTimeProgram) {\n        this.uploadWaitMs += util_exports.now() - start;\n      }\n    } else {\n      const newTexture = this.acquireTexture(texShape, usage, dtype, isPacked);\n      texData.texture = newTexture;\n    }\n  }\n  convertAndCacheOnCPU(dataId, float32Values) {\n    const texData = this.texData.get(dataId);\n    const { dtype } = texData;\n    this.releaseGPUData(dataId);\n    if (float32Values != null) {\n      texData.values = float32ToTypedArray(float32Values, dtype);\n    }\n    return texData.values;\n  }\n  acquireTexture(texShape, texType, dtype, isPacked) {\n    this.numBytesInGPU += this.computeBytes(texShape, dtype);\n    if (!this.warnedAboutMemory && this.numBytesInGPU > this.numMBBeforeWarning * 1024 * 1024) {\n      const mb = (this.numBytesInGPU / 1024 / 1024).toFixed(2);\n      this.warnedAboutMemory = true;\n      console.warn(`High memory usage in GPU: ${mb} MB, most likely due to a memory leak`);\n    }\n    return this.textureManager.acquireTexture(texShape, texType, isPacked);\n  }\n  computeBytes(shape, dtype) {\n    return shape[0] * shape[1] * util_exports.bytesPerElement(dtype);\n  }\n  checkCompileCompletion() {\n    for (const [, binary] of Object.entries(this.binaryCache)) {\n      this.checkCompletion_(binary);\n    }\n  }\n  async checkCompileCompletionAsync() {\n    const ps = [];\n    if (this.gpgpu.parallelCompilationExtension) {\n      for (const [, binary] of Object.entries(this.binaryCache)) {\n        ps.push(this.checkCompletionAsync_(binary));\n      }\n      return Promise.all(ps);\n    } else {\n      for (const [, binary] of Object.entries(this.binaryCache)) {\n        const p2 = new Promise((resolve) => {\n          try {\n            this.checkCompletion_(binary);\n            resolve(true);\n          } catch (error) {\n            throw error;\n          }\n        });\n        ps.push(p2);\n      }\n      return Promise.all(ps);\n    }\n  }\n  async checkCompletionAsync_(binary) {\n    if (this.gpgpu.gl.getProgramParameter(binary.webGLProgram, this.gpgpu.parallelCompilationExtension.COMPLETION_STATUS_KHR)) {\n      return this.checkCompletion_(binary);\n    } else {\n      await nextFrame();\n      return this.checkCompletionAsync_(binary);\n    }\n  }\n  checkCompletion_(binary) {\n    if (this.gpgpu.gl.getProgramParameter(binary.webGLProgram, this.gpgpu.gl.LINK_STATUS) === false) {\n      console.log(this.gpgpu.gl.getProgramInfoLog(binary.webGLProgram));\n      if (this.gpgpu.gl.getShaderParameter(binary.fragmentShader, this.gpgpu.gl.COMPILE_STATUS) === false) {\n        logShaderSourceAndInfoLog(binary.source, this.gpgpu.gl.getShaderInfoLog(binary.fragmentShader));\n        throw new Error(\"Failed to compile fragment shader.\");\n      }\n      throw new Error(\"Failed to link vertex and fragment shaders.\");\n    }\n    return true;\n  }\n  getUniformLocations() {\n    for (const [, binary] of Object.entries(this.binaryCache)) {\n      const { uniformLocations, customUniformLocations, infLoc, nanLoc, inShapesLocations, inTexShapesLocations, outShapeLocation, outShapeStridesLocation, outTexShapeLocation } = getUniformLocations(this.gpgpu, binary.program, binary.webGLProgram);\n      binary.uniformLocations = uniformLocations;\n      binary.customUniformLocations = customUniformLocations;\n      binary.infLoc = infLoc;\n      binary.nanLoc = nanLoc;\n      binary.inShapesLocations = inShapesLocations;\n      binary.inTexShapesLocations = inTexShapesLocations;\n      binary.outShapeLocation = outShapeLocation;\n      binary.outShapeStridesLocation = outShapeStridesLocation;\n      binary.outTexShapeLocation = outTexShapeLocation;\n    }\n  }\n};\nMathBackendWebGL.nextDataId = 0;\nfunction float32ToTypedArray(a, dtype) {\n  if (dtype === \"float32\" || dtype === \"complex64\") {\n    return a;\n  } else if (dtype === \"int32\" || dtype === \"bool\") {\n    const result = dtype === \"int32\" ? new Int32Array(a.length) : new Uint8Array(a.length);\n    for (let i = 0; i < result.length; ++i) {\n      result[i] = Math.round(a[i]);\n    }\n    return result;\n  } else {\n    throw new Error(`Unknown dtype ${dtype}`);\n  }\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/version.js\nvar version6 = \"3.20.0\";\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/webgl.js\nfunction forceHalfFloat() {\n  env().set(\"WEBGL_FORCE_F16_TEXTURES\", true);\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/base.js\nif (device_util_exports.isBrowser()) {\n  registerBackend(\"webgl\", () => new MathBackendWebGL(), 2);\n}\nvar webgl = { forceHalfFloat };\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/binaryop_gpu.js\nvar CHECK_NAN_SNIPPET2 = `\n  if (isnan(a)) return a;\n  if (isnan(b)) return b;\n`;\nvar BinaryOpProgram = class {\n  constructor(op2, aShape, bShape) {\n    this.variableNames = [\"A\", \"B\"];\n    this.outputShape = backend_util_exports.assertAndGetBroadcastShape(aShape, bShape);\n    this.enableShapeUniforms = useShapeUniforms(this.outputShape.length);\n    this.userCode = `\n      float binaryOperation(float a, float b) {\n        ${op2}\n      }\n\n      void main() {\n        float a = getAAtOutCoords();\n        float b = getBAtOutCoords();\n        setOutput(binaryOperation(a, b));\n      }\n    `;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/binaryop_packed_gpu.js\nvar CHECK_NAN_SNIPPET3 = `\n  result.r = isNaN.r > 0. ? NAN : result.r;\n  result.g = isNaN.g > 0. ? NAN : result.g;\n  result.b = isNaN.b > 0. ? NAN : result.b;\n  result.a = isNaN.a > 0. ? NAN : result.a;\n`;\nvar BinaryOpPackedProgram = class {\n  constructor(op2, aShape, bShape, checkOutOfBounds = false) {\n    this.variableNames = [\"A\", \"B\"];\n    this.supportsBroadcasting = true;\n    this.packedInputs = true;\n    this.packedOutput = true;\n    this.outputShape = backend_util_exports.assertAndGetBroadcastShape(aShape, bShape);\n    const rank = this.outputShape.length;\n    this.enableShapeUniforms = useShapeUniforms(rank);\n    let checkOutOfBoundsString = \"\";\n    if (checkOutOfBounds) {\n      if (rank === 0 || util_exports.sizeFromShape(this.outputShape) === 1) {\n        checkOutOfBoundsString = `\n          result.y = 0.;\n          result.z = 0.;\n          result.w = 0.;\n        `;\n      } else {\n        const dtype = getCoordsDataType(rank);\n        checkOutOfBoundsString = `\n          ${dtype} coords = getOutputCoords();\n        `;\n        if (rank === 1) {\n          if (this.enableShapeUniforms) {\n            checkOutOfBoundsString += `\n            result.y = (coords + 1) >= outShape ? 0. : result.y;\n            result.z = 0.;\n            result.w = 0.;\n          `;\n          } else {\n            checkOutOfBoundsString += `\n            result.y = (coords + 1) >= ${this.outputShape[0]} ? 0. : result.y;\n            result.z = 0.;\n            result.w = 0.;\n          `;\n          }\n        } else {\n          const channels = getChannels(\"coords\", rank);\n          if (this.enableShapeUniforms) {\n            checkOutOfBoundsString += `\n            bool nextRowOutOfBounds =\n              (${channels[rank - 2]} + 1) >= outShape[${rank} - 2];\n            bool nextColOutOfBounds =\n              (${channels[rank - 1]} + 1) >= outShape[${rank} - 1];\n            result.y = nextColOutOfBounds ? 0. : result.y;\n            result.z = nextRowOutOfBounds ? 0. : result.z;\n            result.w = nextColOutOfBounds || nextRowOutOfBounds ? 0. : result.w;\n          `;\n          } else {\n            checkOutOfBoundsString += `\n            bool nextRowOutOfBounds =\n              (${channels[rank - 2]} + 1) >= ${this.outputShape[rank - 2]};\n            bool nextColOutOfBounds =\n              (${channels[rank - 1]} + 1) >= ${this.outputShape[rank - 1]};\n            result.y = nextColOutOfBounds ? 0. : result.y;\n            result.z = nextRowOutOfBounds ? 0. : result.z;\n            result.w = nextColOutOfBounds || nextRowOutOfBounds ? 0. : result.w;\n          `;\n          }\n        }\n      }\n    }\n    this.userCode = `\n      vec4 binaryOperation(vec4 a, vec4 b) {\n        ${op2}\n      }\n\n      void main() {\n        vec4 a = getAAtOutCoords();\n        vec4 b = getBAtOutCoords();\n\n        vec4 result = binaryOperation(a, b);\n        ${checkOutOfBoundsString}\n\n        setOutput(result);\n      }\n    `;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Identity.js\nfunction identity3(args) {\n  const { inputs, backend: backend2 } = args;\n  const { x } = inputs;\n  backend2.incRef(x.dataId);\n  return { dataId: x.dataId, shape: x.shape, dtype: x.dtype };\n}\nvar identityConfig2 = {\n  kernelName: Identity,\n  backendName: \"webgl\",\n  kernelFunc: identity3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Complex.js\nfunction complex3(args) {\n  const { inputs, backend: backend2 } = args;\n  const { real: real5, imag: imag5 } = inputs;\n  const complexInfo = backend2.makeTensorInfo(real5.shape, \"complex64\");\n  const complex5 = backend2.texData.get(complexInfo.dataId);\n  const realTensorInfo = identity3({ inputs: { x: real5 }, backend: backend2 });\n  const imagTensorInfo = identity3({ inputs: { x: imag5 }, backend: backend2 });\n  complex5.complexTensorInfos = { real: realTensorInfo, imag: imagTensorInfo };\n  return complexInfo;\n}\nvar complexConfig2 = {\n  kernelName: Complex,\n  backendName: \"webgl\",\n  kernelFunc: complex3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/LeakyRelu.js\nvar LEAKYRELU = `return (a < 0.) ? b * a : a;`;\nvar LEAKYRELU_PACKED = `\n  vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));\n  return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);\n`;\nfunction leakyRelu3(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x } = inputs;\n  const { alpha } = attrs;\n  const $alpha = backend2.makeTensorInfo([], \"float32\", util_exports.createScalarValue(alpha, \"float32\"));\n  const program = env().getBool(\"WEBGL_PACK_BINARY_OPERATIONS\") ? new BinaryOpPackedProgram(LEAKYRELU_PACKED, x.shape, $alpha.shape) : new BinaryOpProgram(LEAKYRELU, x.shape, $alpha.shape);\n  const result = backend2.runWebGLProgram(program, [x, $alpha], \"float32\");\n  backend2.disposeIntermediateTensorInfo($alpha);\n  return result;\n}\nvar leakyReluConfig2 = {\n  kernelName: LeakyRelu,\n  backendName: \"webgl\",\n  kernelFunc: leakyRelu3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Prelu.js\nvar PRELU = `return (a < 0.) ? b * a : a;`;\nvar PRELU_PACKED = `\n  vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));\n  return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);\n`;\nfunction prelu4(args) {\n  const { inputs, backend: backend2 } = args;\n  const { x, alpha } = inputs;\n  const program = env().getBool(\"WEBGL_PACK_BINARY_OPERATIONS\") ? new BinaryOpPackedProgram(PRELU_PACKED, x.shape, alpha.shape) : new BinaryOpProgram(PRELU, x.shape, alpha.shape);\n  return backend2.runWebGLProgram(program, [x, alpha], \"float32\");\n}\nvar preluConfig2 = {\n  kernelName: Prelu,\n  backendName: \"webgl\",\n  kernelFunc: prelu4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernel_utils/kernel_funcs_utils.js\nvar CHECK_NAN_SNIPPET_UNARY = `if (isnan(x)) return x;`;\nvar CHECK_NAN_SNIPPET_BINARY = `\n  if (isnan(a)) return a;\n  if (isnan(b)) return b;\n`;\nvar CHECK_NAN_SNIPPET_BINARY_PACKED = `\n  result.r = isNaN.r > 0. ? NAN : result.r;\n  result.g = isNaN.g > 0. ? NAN : result.g;\n  result.b = isNaN.b > 0. ? NAN : result.b;\n  result.a = isNaN.a > 0. ? NAN : result.a;\n`;\nfunction unaryKernelFunc2({ opSnippet, packedOpSnippet, cpuKernelImpl, dtype }) {\n  return ({ inputs, backend: backend2 }) => {\n    const { x } = inputs;\n    const webglBackend = backend2;\n    const $dtype = dtype || x.dtype;\n    if (webglBackend.shouldExecuteOnCPU([x]) && cpuKernelImpl != null) {\n      const xData = webglBackend.texData.get(x.dataId);\n      const outValues = cpuKernelImpl(xData.values, $dtype);\n      return webglBackend.makeTensorInfo(x.shape, $dtype, outValues);\n    }\n    const shouldUsePackedProgram = env().getBool(\"WEBGL_PACK_UNARY_OPERATIONS\") && packedOpSnippet != null;\n    let program;\n    if (shouldUsePackedProgram) {\n      program = new UnaryOpPackedProgram(x.shape, packedOpSnippet);\n    } else {\n      program = new UnaryOpProgram(x.shape, opSnippet);\n    }\n    return webglBackend.runWebGLProgram(program, [x], $dtype);\n  };\n}\nfunction binaryKernelFunc2({ opSnippet, packedOpSnippet, checkOutOfBounds = false, supportsComplex = false, cpuKernelImpl, dtype }) {\n  return ({ inputs, backend: backend2 }) => {\n    const { a, b } = inputs;\n    const webglBackend = backend2;\n    if (supportsComplex && a.dtype === \"complex64\") {\n      const aData = webglBackend.texData.get(a.dataId);\n      const bData = webglBackend.texData.get(b.dataId);\n      const [real5, imag5] = [\n        [aData.complexTensorInfos.real, bData.complexTensorInfos.real],\n        [aData.complexTensorInfos.imag, bData.complexTensorInfos.imag]\n      ].map((complexParts) => {\n        const [aPart, bPart] = complexParts;\n        const aHandle = {\n          dataId: aPart.dataId,\n          dtype: aPart.dtype,\n          shape: a.shape\n        };\n        const bHandle = {\n          dataId: bPart.dataId,\n          dtype: bPart.dtype,\n          shape: b.shape\n        };\n        const program2 = new BinaryOpProgram(opSnippet, a.shape, b.shape);\n        return webglBackend.runWebGLProgram(program2, [aHandle, bHandle], upcastType(aPart.dtype, bPart.dtype));\n      });\n      const complexOutput = complex3({ inputs: { real: real5, imag: imag5 }, backend: webglBackend });\n      webglBackend.disposeIntermediateTensorInfo(real5);\n      webglBackend.disposeIntermediateTensorInfo(imag5);\n      return complexOutput;\n    }\n    const $dtype = dtype || upcastType(a.dtype, b.dtype);\n    if ((a.dtype === \"string\" || b.dtype === \"string\" || webglBackend.shouldExecuteOnCPU([a, b])) && cpuKernelImpl != null) {\n      const aVals = webglBackend.texData.get(a.dataId).values;\n      const bVals = webglBackend.texData.get(b.dataId).values;\n      const decodedAVals = a.dtype === \"string\" ? backend_util_exports.fromUint8ToStringArray(aVals) : aVals;\n      const decodedBVals = a.dtype === \"string\" ? backend_util_exports.fromUint8ToStringArray(bVals) : bVals;\n      const [outValues, outShape] = cpuKernelImpl(a.shape, b.shape, decodedAVals, decodedBVals, $dtype);\n      const out = webglBackend.makeTensorInfo(outShape, $dtype);\n      const outData = webglBackend.texData.get(out.dataId);\n      outData.values = outValues;\n      return out;\n    }\n    const shouldUsePackedProgram = env().getBool(\"WEBGL_PACK_BINARY_OPERATIONS\") && packedOpSnippet != null;\n    let program;\n    if (shouldUsePackedProgram) {\n      program = new BinaryOpPackedProgram(packedOpSnippet, a.shape, b.shape, checkOutOfBounds);\n    } else {\n      program = new BinaryOpProgram(opSnippet, a.shape, b.shape);\n    }\n    return webglBackend.runWebGLProgram(program, [a, b], $dtype);\n  };\n}\nfunction mapActivationToShaderProgram(activation2, packed = false) {\n  if (activation2 === \"linear\") {\n    if (packed) {\n      return LINEAR2;\n    }\n    return LINEAR;\n  } else if (activation2 === \"relu\") {\n    if (packed) {\n      return RELU2;\n    }\n    return RELU;\n  } else if (activation2 === \"elu\") {\n    if (packed) {\n      return ELU3;\n    }\n    return ELU2;\n  } else if (activation2 === \"relu6\") {\n    if (packed) {\n      return RELU62;\n    }\n    return RELU6;\n  } else if (activation2 === \"prelu\") {\n    if (packed) {\n      return PRELU_PACKED;\n    }\n    return PRELU;\n  } else if (activation2 === \"leakyrelu\") {\n    if (packed) {\n      return LEAKYRELU_PACKED;\n    }\n    return LEAKYRELU;\n  } else if (activation2 === \"sigmoid\") {\n    if (packed) {\n      return SIGMOID2;\n    }\n    return SIGMOID;\n  }\n  throw new Error(`Activation ${activation2} has not been implemented for the WebGL backend.`);\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/mulmat_packed_gpu.js\nvar MatMulPackedProgram = class {\n  constructor(aShape, bShape, outputShape, transposeA = false, transposeB = false, addBias = false, activation2 = null, hasPreluActivation = false, hasLeakyreluActivation = false) {\n    this.variableNames = [\"matrixA\", \"matrixB\"];\n    this.packedInputs = true;\n    this.packedOutput = true;\n    this.outputShape = outputShape;\n    this.enableShapeUniforms = useShapeUniforms(this.outputShape.length);\n    const sharedDim = transposeA ? aShape[1] : aShape[2];\n    const sharedDimensionPacked = Math.ceil(sharedDim / 2);\n    const aSample = transposeA ? \"i * 2, rc.y\" : \"rc.y, i * 2\";\n    const bSample = transposeB ? \"rc.z, i * 2\" : \"i * 2, rc.z\";\n    const aSwizzle = transposeA ? [\"a.xxyy\", \"a.zzww\"] : [\"a.xxzz\", \"a.yyww\"];\n    const bSwizzle = transposeB ? [\"b.xzxz\", \"b.ywyw\"] : [\"b.xyxy\", \"b.zwzw\"];\n    let activationSnippet = \"\", applyActivationSnippet = \"\";\n    if (activation2) {\n      if (hasPreluActivation) {\n        activationSnippet = `vec4 activation(vec4 a) {\n          vec4 b = getPreluActivationWeightsAtOutCoords();\n          ${activation2}\n        }`;\n      } else if (hasLeakyreluActivation) {\n        activationSnippet = `vec4 activation(vec4 a) {\n          vec4 b = getLeakyreluAlphaAtOutCoords();\n          ${activation2}\n        }`;\n      } else {\n        activationSnippet = `vec4 activation(vec4 x) {\n          ${activation2}\n        }`;\n      }\n      applyActivationSnippet = `result = activation(result);`;\n    }\n    const addBiasSnippet = addBias ? \"result += getBiasAtOutCoords();\" : \"\";\n    if (addBias) {\n      this.variableNames.push(\"bias\");\n    }\n    if (hasPreluActivation) {\n      this.variableNames.push(\"preluActivationWeights\");\n    }\n    if (hasLeakyreluActivation) {\n      this.variableNames.push(\"leakyreluAlpha\");\n    }\n    let batchASnippet = \"rc.x\";\n    let batchBSnippet = \"rc.x\";\n    if (aShape[0] < bShape[0]) {\n      batchASnippet = `int(min(float(rc.x), ${aShape[0] - 1}.))`;\n    } else if (bShape[0] < aShape[0]) {\n      batchBSnippet = `int(min(float(rc.x), ${bShape[0] - 1}.))`;\n    }\n    this.userCode = `\n      ${activationSnippet}\n      // Don't use uniform for sharedDimensionPacked for performance.\n      const float sharedDimension = ${sharedDimensionPacked}.0;\n\n      vec4 dot2x2ARowBCol(ivec3 rc) {\n        vec4 result = vec4(0);\n        for (int i = 0; i < ${sharedDimensionPacked}; i++) {\n          int batchA = ${batchASnippet};\n          int batchB = ${batchBSnippet};\n          vec4 a = getMatrixA(batchA, ${aSample});\n          vec4 b = getMatrixB(batchB, ${bSample});\n\n          // These swizzled products need to be separately added.\n          // See: https://github.com/tensorflow/tfjs/issues/1735\n          result += (${aSwizzle[0]} * ${bSwizzle[0]});\n          result += (${aSwizzle[1]} * ${bSwizzle[1]});\n        }\n        return result;\n      }\n\n      void main() {\n        ivec3 rc = getOutputCoords();\n        vec4 result = dot2x2ARowBCol(rc);\n\n        ${addBiasSnippet}\n\n        ${applyActivationSnippet}\n\n        setOutput(result);\n      }\n    `;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/binaryop_complex_gpu.js\nvar COMPLEX_MULTIPLY = {\n  REAL: \"return areal * breal - aimag * bimag;\",\n  IMAG: \"return areal * bimag + aimag * breal;\"\n};\nvar BinaryOpComplexProgram = class {\n  constructor(op2, aShape, bShape) {\n    this.variableNames = [\"AReal\", \"AImag\", \"BReal\", \"BImag\"];\n    this.outputShape = backend_util_exports.assertAndGetBroadcastShape(aShape, bShape);\n    this.userCode = `\n      float binaryOpComplex(\n          float areal, float aimag, float breal, float bimag) {\n        ${op2}\n      }\n\n      void main() {\n        float areal = getARealAtOutCoords();\n        float aimag = getAImagAtOutCoords();\n        float breal = getBRealAtOutCoords();\n        float bimag = getBImagAtOutCoords();\n        setOutput(binaryOpComplex(areal, aimag, breal, bimag));\n      }\n    `;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Multiply.js\nvar MUL = \"return a * b;\";\nfunction multiply3(args) {\n  const { inputs, backend: backend2 } = args;\n  const { a, b } = inputs;\n  const dtype = backend_util_exports.upcastType(a.dtype, b.dtype);\n  if (a.dtype === \"complex64\") {\n    const aData = backend2.texData.get(a.dataId);\n    const bData = backend2.texData.get(b.dataId);\n    const realProgram = new BinaryOpComplexProgram(COMPLEX_MULTIPLY.REAL, a.shape, b.shape);\n    const imagProgram = new BinaryOpComplexProgram(COMPLEX_MULTIPLY.IMAG, a.shape, b.shape);\n    const inputs2 = [\n      {\n        dataId: aData.complexTensorInfos.real.dataId,\n        dtype: aData.complexTensorInfos.real.dtype,\n        shape: a.shape\n      },\n      {\n        dataId: aData.complexTensorInfos.imag.dataId,\n        dtype: aData.complexTensorInfos.imag.dtype,\n        shape: a.shape\n      },\n      {\n        dataId: bData.complexTensorInfos.real.dataId,\n        dtype: bData.complexTensorInfos.real.dtype,\n        shape: b.shape\n      },\n      {\n        dataId: bData.complexTensorInfos.imag.dataId,\n        dtype: bData.complexTensorInfos.imag.dtype,\n        shape: b.shape\n      }\n    ];\n    const realPart = backend2.runWebGLProgram(realProgram, inputs2, \"float32\");\n    const imagPart = backend2.runWebGLProgram(imagProgram, inputs2, \"float32\");\n    const complexOutput = complex3({ inputs: { real: realPart, imag: imagPart }, backend: backend2 });\n    backend2.disposeIntermediateTensorInfo(realPart);\n    backend2.disposeIntermediateTensorInfo(imagPart);\n    return complexOutput;\n  }\n  if (backend2.shouldExecuteOnCPU([a, b])) {\n    const aData = backend2.texData.get(a.dataId);\n    const bData = backend2.texData.get(b.dataId);\n    const [outValues, outShape] = multiplyImplCPU(a.shape, b.shape, aData.values, bData.values, dtype);\n    const out = backend2.makeTensorInfo(outShape, dtype);\n    const outData = backend2.texData.get(out.dataId);\n    outData.values = outValues;\n    return out;\n  }\n  let program;\n  if (env().getBool(\"WEBGL_PACK_BINARY_OPERATIONS\")) {\n    program = new BinaryOpPackedProgram(MUL, a.shape, b.shape);\n  } else {\n    program = new BinaryOpProgram(MUL, a.shape, b.shape);\n  }\n  return backend2.runWebGLProgram(program, [a, b], dtype);\n}\nvar multiplyConfig2 = {\n  kernelName: Multiply,\n  backendName: \"webgl\",\n  kernelFunc: multiply3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernel_utils/reshape.js\nfunction packedReshape(input2, afterShape, backend2) {\n  const input3DShape = [\n    getBatchDim(input2.shape),\n    ...getRowsCols(input2.shape)\n  ];\n  const input3D = {\n    dtype: input2.dtype,\n    shape: input3DShape,\n    dataId: input2.dataId\n  };\n  const afterShapeAs3D = [\n    getBatchDim(afterShape),\n    ...getRowsCols(afterShape)\n  ];\n  const program = new ReshapePackedProgram(afterShapeAs3D, input3DShape);\n  const preventEagerUnpackingOfOutput = true;\n  const customValues = [input3DShape];\n  const output = backend2.runWebGLProgram(program, [input3D], input2.dtype, customValues, preventEagerUnpackingOfOutput);\n  return { dataId: output.dataId, shape: afterShape, dtype: output.dtype };\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Reshape.js\nfunction reshape4(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x } = inputs;\n  const { shape } = attrs;\n  const webglBackend = backend2;\n  const xSize = util_exports.sizeFromShape(x.shape);\n  const $shape = util_exports.inferFromImplicitShape(shape, xSize);\n  const $xSize = util_exports.sizeFromShape($shape);\n  util_exports.assert(xSize === $xSize, () => `The new shape (${$shape}) has ${$xSize} elements and the old shape (${x.shape}) has ${xSize} elements. The new shape and old shape must have the same number of elements.`);\n  const xTexData = webglBackend.texData.get(x.dataId);\n  if (xTexData.isPacked && !isReshapeFree(x.shape, $shape) && !(xTexData.texture !== null && isReshapeFree(xTexData.shape, $shape))) {\n    return packedReshape(x, $shape, webglBackend);\n  }\n  webglBackend.incRef(x.dataId);\n  return { dataId: x.dataId, shape: $shape, dtype: x.dtype };\n}\nvar reshapeConfig2 = {\n  kernelName: Reshape,\n  backendName: \"webgl\",\n  kernelFunc: reshape4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/mean_gpu.js\nvar MeanProgram = class {\n  constructor(reduceInfo, divisor) {\n    this.variableNames = [\"x\"];\n    const { windowSize, batchSize, inSize, outSize } = reduceInfo;\n    this.outputShape = [batchSize, outSize];\n    const windowSizeNearestVec4 = Math.floor(windowSize / 4) * 4;\n    const windowSizeVec4Remainder = windowSize % 4;\n    let updateSnippet = `sumValue += dot(values, ones);`;\n    if (divisor != null) {\n      const denominator = 1 / divisor;\n      updateSnippet = `sumValue += dot(values * ${util_exports.isInt(denominator) ? denominator.toPrecision(2) : denominator}, ones);`;\n    }\n    let checkOutOfBounds = \"\";\n    if (inSize % windowSize > 0) {\n      checkOutOfBounds = `\n        if (inIdx < 0 || inIdx >= ${inSize}) {\n          return 0.0;\n        }\n      `;\n    }\n    this.userCode = `\n      const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);\n\n      float getValue(int batch, int inIdx) {\n        ${checkOutOfBounds}\n        return getX(batch, inIdx);\n      }\n\n      void main() {\n        ivec2 coords = getOutputCoords();\n        int batch = coords[0];\n        int outIdx = coords[1];\n        int inOffset = outIdx * ${windowSize};\n\n        float sumValue = 0.0;\n\n        for (int i = 0; i < ${windowSizeNearestVec4}; i += 4) {\n          int inIdx = inOffset + i;\n          vec4 values = vec4(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            getValue(batch, inIdx + 2),\n            getValue(batch, inIdx + 3)\n          );\n\n          ${updateSnippet}\n        }\n\n        int inIdx = inOffset + ${windowSizeNearestVec4};\n        if (${windowSizeVec4Remainder === 1}) {\n          vec4 values = vec4(getValue(batch, inIdx), 0.0, 0.0, 0.0);\n\n          ${updateSnippet}\n        } else if (${windowSizeVec4Remainder === 2}) {\n          vec4 values = vec4(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1), 0.0, 0.0);\n\n          ${updateSnippet}\n        } else if (${windowSizeVec4Remainder === 3}) {\n          vec4 values = vec4(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            getValue(batch, inIdx + 2), 0.0);\n\n          ${updateSnippet}\n        }\n        setOutput(sumValue);\n      }\n    `;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/reduce_gpu.js\nvar ReduceProgram = class {\n  constructor(reduceInfo, reduceType) {\n    this.variableNames = [\"x\"];\n    const { windowSize, batchSize, inSize, outSize } = reduceInfo;\n    this.outputShape = [batchSize, outSize];\n    let initializationValue = \"0.0\";\n    let compareOp = ``;\n    if (reduceType === \"prod\") {\n      initializationValue = \"1.0\";\n    } else if (reduceType === \"min\") {\n      initializationValue = \"1.0 / 1e-20\";\n      compareOp = `min`;\n    } else if (reduceType === \"max\") {\n      initializationValue = \"-1.0 / 1e-20\";\n      compareOp = `max`;\n    }\n    let returnValue = `${reduceType}(${reduceType}(${reduceType}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;\n    if (reduceType === \"sum\") {\n      returnValue = `sumValue`;\n    } else if (reduceType === \"prod\") {\n      returnValue = `prodValue`;\n    } else if (reduceType === \"all\") {\n      returnValue = `allValue`;\n    } else if (reduceType === \"any\") {\n      returnValue = `anyValue`;\n    }\n    const windowSizeNearestVec4 = Math.floor(windowSize / 4) * 4;\n    const windowSizeVec4Remainder = windowSize % 4;\n    let updateSnippet = `\n      if (${reduceType === \"sum\"}) {\n        sumValue += dot(values, ones);\n      } else if (${reduceType === \"prod\"}) {\n        vec2 tmp = vec2(values[0], values[1]) * vec2(values[2], values[3]);\n        prodValue *= tmp[0] * tmp[1];\n      } else {\n        minMaxValue = ${compareOp}(values, minMaxValue);\n        if (${reduceType === \"min\"} || ${reduceType === \"max\"}) {\n          minMaxValue = ${compareOp}(values, minMaxValue);\n          bvec4 isNaN = isnan(values);\n          if (isNaN.r || isNaN.g || isNaN.b || isNaN.a) {\n            minMaxValue = vec4(NAN);\n          }\n        }\n      }\n    `;\n    let vecType = `vec4`;\n    if (reduceType === \"all\") {\n      initializationValue = \"1.0\";\n      updateSnippet = `\n        bool reducedAllValue = all(values);\n        float floatedReducedAllValue = float(reducedAllValue);\n        allValue = float(allValue >= 1.0 && floatedReducedAllValue >= 1.0);\n      `;\n      vecType = `bvec4`;\n    } else if (reduceType === \"any\") {\n      initializationValue = \"0.0\";\n      updateSnippet = `\n        bool reducedAnyValue = any(values);\n        float floatedReducedAnyValue = float(reducedAnyValue);\n        anyValue = float(anyValue >= 1.0 || floatedReducedAnyValue >= 1.0);\n      `;\n      vecType = `bvec4`;\n    }\n    let checkOutOfBounds = \"\";\n    if (inSize % windowSize > 0) {\n      checkOutOfBounds = `\n        if (inIdx < 0 || inIdx >= ${inSize}) {\n          return initializationValue;\n        }\n      `;\n    }\n    this.userCode = `\n      const float initializationValue = ${initializationValue};\n      const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);\n\n      float getValue(int batch, int inIdx) {\n        ${checkOutOfBounds}\n        return getX(batch, inIdx);\n      }\n\n      void main() {\n        ivec2 coords = getOutputCoords();\n        int batch = coords[0];\n        int outIdx = coords[1];\n        int inOffset = outIdx * ${windowSize};\n\n        vec4 minMaxValue = vec4(${initializationValue});\n        float prodValue = 1.0;\n        float sumValue = 0.0;\n        float allValue = 1.0;\n        float anyValue = 0.0;\n\n        for (int i = 0; i < ${windowSizeNearestVec4}; i += 4) {\n          int inIdx = inOffset + i;\n          ${vecType} values = ${vecType}(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            getValue(batch, inIdx + 2),\n            getValue(batch, inIdx + 3)\n          );\n\n          ${updateSnippet}\n        }\n\n        int inIdx = inOffset + ${windowSizeNearestVec4};\n        if (${windowSizeVec4Remainder === 1}) {\n          ${vecType} values = ${vecType}(\n            getValue(batch, inIdx),\n            initializationValue,\n            initializationValue,\n            initializationValue\n          );\n\n          ${updateSnippet}\n        } else if (${windowSizeVec4Remainder === 2}) {\n          ${vecType} values = ${vecType}(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            initializationValue,\n            initializationValue\n          );\n\n          ${updateSnippet}\n        } else if (${windowSizeVec4Remainder === 3}) {\n          ${vecType} values = ${vecType}(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            getValue(batch, inIdx + 2),\n            initializationValue\n          );\n\n          ${updateSnippet}\n        }\n        setOutput(${returnValue});\n      }\n    `;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernel_utils/reduce.js\nfunction getReductionStages(inShape) {\n  const stages = [];\n  while (stages.length === 0 || stages[stages.length - 1].outSize !== 1) {\n    const outSize = stages.length ? stages[stages.length - 1].outSize : inShape[1];\n    const windowSize = backend_util_exports.computeOptimalWindowSize(outSize);\n    stages.push({\n      inSize: outSize,\n      windowSize,\n      outSize: Math.ceil(outSize / windowSize)\n    });\n  }\n  return stages;\n}\nfunction reduce(x, dtype, reductionType, backend2) {\n  const reductionStages = getReductionStages(x.shape);\n  let result = x;\n  for (let i = 0; i < reductionStages.length; i++) {\n    const { inSize, windowSize, outSize } = reductionStages[i];\n    let program;\n    let previousResult;\n    if (reductionType === \"mean\") {\n      program = i === 0 ? new MeanProgram({ windowSize, inSize, batchSize: x.shape[0], outSize }, inSize) : new MeanProgram({ windowSize, inSize, batchSize: x.shape[0], outSize });\n    } else {\n      program = new ReduceProgram({ windowSize, inSize, batchSize: x.shape[0], outSize }, reductionType);\n    }\n    previousResult = result;\n    result = backend2.runWebGLProgram(program, [result], dtype);\n    if (previousResult.dataId !== x.dataId) {\n      backend2.disposeIntermediateTensorInfo(previousResult);\n    }\n  }\n  return result;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/transpose_gpu.js\nvar TransposeProgram = class {\n  constructor(aShape, newDim) {\n    this.variableNames = [\"A\"];\n    const outputShape = new Array(aShape.length);\n    for (let i = 0; i < outputShape.length; i++) {\n      outputShape[i] = aShape[newDim[i]];\n    }\n    this.outputShape = outputShape;\n    this.rank = outputShape.length;\n    const dtype = getCoordsDataType(this.rank);\n    const switched = getSwitchedCoords(newDim);\n    this.userCode = `\n    void main() {\n      ${dtype} resRC = getOutputCoords();\n      setOutput(getA(${switched}));\n    }\n    `;\n  }\n};\nfunction getSwitchedCoords(newDim) {\n  const rank = newDim.length;\n  if (rank > 6) {\n    throw Error(`Transpose for rank ${rank} is not yet supported`);\n  }\n  const originalOrder = [\"resRC.x\", \"resRC.y\", \"resRC.z\", \"resRC.w\", \"resRC.u\", \"resRC.v\"];\n  const switchedCoords = new Array(rank);\n  for (let i = 0; i < newDim.length; i++) {\n    switchedCoords[newDim[i]] = originalOrder[i];\n  }\n  return switchedCoords.join();\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/transpose_packed_gpu.js\nvar TransposePackedProgram = class {\n  constructor(aShape, newDim) {\n    this.variableNames = [\"A\"];\n    this.packedInputs = true;\n    this.packedOutput = true;\n    const outputShape = new Array(aShape.length);\n    for (let i = 0; i < outputShape.length; i++) {\n      outputShape[i] = aShape[newDim[i]];\n    }\n    this.outputShape = outputShape;\n    this.rank = outputShape.length;\n    if (this.rank > 6) {\n      throw Error(`Packed transpose for rank ${this.rank} is not yet supported.`);\n    }\n    const dtype = getCoordsDataType(this.rank);\n    const outputOrder = getVecChannels(\"rc\", this.rank);\n    const switchedOrder = new Array(this.rank);\n    for (let i = 0; i < newDim.length; i++) {\n      switchedOrder[newDim[i]] = outputOrder[i];\n    }\n    const innerDims = `vec2(${switchedOrder.slice(-2).join()})`;\n    const nextColumn = `++${outputOrder[this.rank - 1]} < ${outputShape[this.rank - 1]}`;\n    const getc = `getChannel(getA(${switchedOrder.join()}), ${innerDims})`;\n    this.userCode = `\n    void main() {\n      ${dtype} rc = getOutputCoords();\n      vec4 result = vec4(0.);\n      result[0] = ${getc};\n      if(${nextColumn}) {\n        result[1] = ${getc};\n      }\n      --${outputOrder[this.rank - 1]};\n      if(++${outputOrder[this.rank - 2]} < ${outputShape[this.rank - 2]}) {\n        result[2] = ${getc};\n        if(${nextColumn}) {\n          result[3] = ${getc};\n        }\n      }\n      setOutput(result);\n    }\n    `;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Transpose_impl.js\nfunction transposeImpl2(x, perm, backend2) {\n  const program = env().getBool(\"WEBGL_PACK_ARRAY_OPERATIONS\") ? new TransposePackedProgram(x.shape, perm) : new TransposeProgram(x.shape, perm);\n  return backend2.runWebGLProgram(program, [x], x.dtype);\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Sum_impl.js\nfunction sumImpl(x, axis, keepDims, backend2) {\n  const reductionIndices = axis;\n  const xRank = x.shape.length;\n  const origAxes = util_exports.parseAxisParam(reductionIndices, x.shape);\n  let axes = origAxes;\n  const permutedAxes = backend_util_exports.getAxesPermutation(axes, xRank);\n  const sumInputIsTransposed = permutedAxes != null;\n  let sumInput = x;\n  if (sumInputIsTransposed) {\n    sumInput = transposeImpl2(x, permutedAxes, backend2);\n    axes = backend_util_exports.getInnerMostAxes(axes.length, xRank);\n  }\n  backend_util_exports.assertAxesAreInnerMostDims(\"sum\", axes, xRank);\n  const [sumOutShape, reduceShape] = backend_util_exports.computeOutAndReduceShapes(sumInput.shape, axes);\n  let outShape = sumOutShape;\n  if (keepDims) {\n    outShape = backend_util_exports.expandShapeToKeepDim(sumOutShape, origAxes);\n  }\n  const inSize = util_exports.sizeFromShape(reduceShape);\n  const xSize = util_exports.sizeFromShape(x.shape);\n  const batchSize = xSize / inSize;\n  const reshapedInput = reshape4({ inputs: { x: sumInput }, attrs: { shape: [batchSize, inSize] }, backend: backend2 });\n  const outType = sumOutType(x.dtype);\n  const reduced = reduce(reshapedInput, outType, \"sum\", backend2);\n  const out = reshape4({ inputs: { x: reduced }, attrs: { shape: outShape }, backend: backend2 });\n  backend2.disposeIntermediateTensorInfo(reshapedInput);\n  backend2.disposeIntermediateTensorInfo(reduced);\n  if (sumInputIsTransposed) {\n    backend2.disposeIntermediateTensorInfo(sumInput);\n  }\n  return out;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Sum.js\nfunction sum4(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x } = inputs;\n  const { axis, keepDims } = attrs;\n  return sumImpl(x, axis, keepDims, backend2);\n}\nvar sumConfig2 = {\n  kernelName: Sum,\n  backendName: \"webgl\",\n  kernelFunc: sum4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Transpose.js\nfunction transpose3(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x } = inputs;\n  const { perm } = attrs;\n  const webglBackend = backend2;\n  const xRank = x.shape.length;\n  const newShape = new Array(xRank);\n  for (let i = 0; i < newShape.length; i++) {\n    newShape[i] = x.shape[perm[i]];\n  }\n  let out;\n  if (webglBackend.shouldExecuteOnCPU([x])) {\n    const xTexData = webglBackend.texData.get(x.dataId);\n    const values = xTexData.values;\n    const outValues = transposeImplCPU(values, x.shape, x.dtype, perm, newShape);\n    out = webglBackend.makeTensorInfo(newShape, x.dtype);\n    const outData = webglBackend.texData.get(out.dataId);\n    outData.values = outValues;\n  } else {\n    out = transposeImpl2(x, perm, webglBackend);\n  }\n  return out;\n}\nvar transposeConfig2 = {\n  kernelName: Transpose,\n  backendName: \"webgl\",\n  kernelFunc: transpose3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/BatchMatMul_impl.js\nvar MATMUL_SHARED_DIM_THRESHOLD = 1e3;\nfunction batchMatMulImpl({ a, b, transposeA, transposeB, backend: backend2, bias = null, preluActivationWeights = null, leakyreluAlpha = 0, activation: activation2 = null }) {\n  const aRank = a.shape.length;\n  const bRank = b.shape.length;\n  const innerShapeA = transposeA ? a.shape[aRank - 2] : a.shape[aRank - 1];\n  const innerShapeB = transposeB ? b.shape[bRank - 1] : b.shape[bRank - 2];\n  const outerShapeA = transposeA ? a.shape[aRank - 1] : a.shape[aRank - 2];\n  const outerShapeB = transposeB ? b.shape[bRank - 2] : b.shape[bRank - 1];\n  const outerDimsA = a.shape.slice(0, -2);\n  const outerDimsB = b.shape.slice(0, -2);\n  const batchDimA = util_exports.sizeFromShape(outerDimsA);\n  const batchDimB = util_exports.sizeFromShape(outerDimsB);\n  const outShapeOuterDims = broadcast_util_exports.assertAndGetBroadcastShape(a.shape.slice(0, -2), b.shape.slice(0, -2));\n  const outShape = outShapeOuterDims.concat([outerShapeA, outerShapeB]);\n  util_exports.assert(innerShapeA === innerShapeB, () => `Error in matMul: inner shapes (${innerShapeA}) and (${innerShapeB}) of Tensors with shapes ${a.shape} and ${b.shape} and transposeA=${transposeA} and transposeB=${transposeB} must match.`);\n  const a3dShape = transposeA ? [batchDimA, innerShapeA, outerShapeA] : [batchDimA, outerShapeA, innerShapeA];\n  const b3dShape = transposeB ? [batchDimB, outerShapeB, innerShapeB] : [batchDimB, innerShapeB, outerShapeB];\n  const a3d = reshape4({ inputs: { x: a }, backend: backend2, attrs: { shape: a3dShape } });\n  const b3d = reshape4({ inputs: { x: b }, backend: backend2, attrs: { shape: b3dShape } });\n  const intermediates = [a3d, b3d];\n  const batchDim = Math.max(batchDimA, batchDimB);\n  const sharedDim = transposeA ? a3d.shape[1] : a3d.shape[2];\n  const hasBias = bias != null;\n  const hasPreluActivationWeights = preluActivationWeights != null;\n  const hasLeakyreluAlpha = activation2 === \"leakyrelu\";\n  const fusedActivation = activation2 != null ? mapActivationToShaderProgram(activation2, true) : null;\n  const containsFusedOps = hasBias || hasPreluActivationWeights || hasLeakyreluAlpha || fusedActivation != null;\n  let out;\n  if ((outerShapeA === 1 || outerShapeB === 1) && sharedDim > MATMUL_SHARED_DIM_THRESHOLD && containsFusedOps === false) {\n    let aVec = a3d;\n    let bVec = b3d;\n    if (transposeA) {\n      aVec = transpose3({ inputs: { x: a3d }, backend: backend2, attrs: { perm: [0, 2, 1] } });\n      intermediates.push(aVec);\n    }\n    if (transposeB) {\n      bVec = transpose3({ inputs: { x: b3d }, backend: backend2, attrs: { perm: [0, 2, 1] } });\n      intermediates.push(bVec);\n    }\n    const shouldReshapeA = outerShapeB !== 1;\n    const shouldReshapeB = outerShapeB === 1;\n    let aVec3d = aVec;\n    if (shouldReshapeA) {\n      aVec3d = reshape4({\n        inputs: { x: aVec },\n        backend: backend2,\n        attrs: { shape: [batchDim, sharedDim, 1] }\n      });\n      intermediates.push(aVec3d);\n    }\n    const axis = outerShapeB === 1 ? 2 : 1;\n    let bVec3d = bVec;\n    if (shouldReshapeB) {\n      bVec3d = reshape4({\n        inputs: { x: bVec },\n        backend: backend2,\n        attrs: { shape: [batchDim, 1, sharedDim] }\n      });\n      intermediates.push(bVec3d);\n    }\n    const product = multiply3({ inputs: { a: aVec3d, b: bVec3d }, backend: backend2 });\n    out = sum4({ inputs: { x: product }, backend: backend2, attrs: { axis, keepDims: true } });\n    intermediates.push(product);\n  } else {\n    const dtype = upcastType(a.dtype, b.dtype);\n    const program = new MatMulPackedProgram(a3dShape, b3dShape, [batchDim, outerShapeA, outerShapeB], transposeA, transposeB, hasBias, fusedActivation, hasPreluActivationWeights, hasLeakyreluAlpha);\n    const inputs = [a3d, b3d];\n    if (bias != null) {\n      inputs.push(bias);\n    }\n    if (hasPreluActivationWeights) {\n      inputs.push(preluActivationWeights);\n    }\n    if (hasLeakyreluAlpha) {\n      const $leakyreluAlpha = backend2.makeTensorInfo([], \"float32\", util_exports.createScalarValue(leakyreluAlpha, \"float32\"));\n      inputs.push($leakyreluAlpha);\n      intermediates.push($leakyreluAlpha);\n    }\n    out = backend2.runWebGLProgram(program, inputs, dtype);\n  }\n  const outReshaped = reshape4({ inputs: { x: out }, backend: backend2, attrs: { shape: outShape } });\n  intermediates.push(out);\n  for (const i of intermediates) {\n    backend2.disposeIntermediateTensorInfo(i);\n  }\n  return outReshaped;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/_FusedMatMul.js\nfunction _fusedMatMul2(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { a, b, bias, preluActivationWeights } = inputs;\n  const { transposeA, transposeB, activation: activation2, leakyreluAlpha } = attrs;\n  return batchMatMulImpl({\n    a,\n    b,\n    transposeA,\n    transposeB,\n    backend: backend2,\n    bias,\n    preluActivationWeights,\n    leakyreluAlpha,\n    activation: activation2\n  });\n}\nvar _fusedMatMulConfig2 = {\n  kernelName: _FusedMatMul,\n  backendName: \"webgl\",\n  kernelFunc: _fusedMatMul2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Abs.js\nvar ABS2 = `return abs(x);`;\nfunction abs3(args) {\n  const { inputs, backend: backend2 } = args;\n  const { x } = inputs;\n  if (backend2.shouldExecuteOnCPU([x]) && x.dtype !== \"complex64\") {\n    const xData = backend2.texData.get(x.dataId);\n    const outValues = simpleAbsImplCPU(xData.values);\n    return backend2.makeTensorInfo(x.shape, x.dtype, outValues);\n  }\n  let program;\n  if (env().getBool(\"WEBGL_PACK_UNARY_OPERATIONS\")) {\n    program = new UnaryOpPackedProgram(x.shape, ABS2);\n  } else {\n    program = new UnaryOpProgram(x.shape, ABS2);\n  }\n  return backend2.runWebGLProgram(program, [x], x.dtype);\n}\nvar absConfig2 = {\n  kernelName: Abs,\n  backendName: \"webgl\",\n  kernelFunc: abs3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Acos.js\nvar ACOS = CHECK_NAN_SNIPPET + `\n  if (abs(x) > 1.) {\n    return NAN;\n  }\n  return acos(x);\n`;\nvar acos3 = unaryKernelFunc2({ opSnippet: ACOS });\nvar acosConfig2 = {\n  kernelName: Acos,\n  backendName: \"webgl\",\n  kernelFunc: acos3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Acosh.js\nvar ACOSH = CHECK_NAN_SNIPPET + `\n  if (x < 1.0) return NAN;\nreturn log(x + sqrt(x * x - 1.0));`;\nvar acosh3 = unaryKernelFunc2({ opSnippet: ACOSH });\nvar acoshConfig2 = {\n  kernelName: Acosh,\n  backendName: \"webgl\",\n  kernelFunc: acosh3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Add.js\nvar ADD = \"return a + b;\";\nvar addKernelFunc = binaryKernelFunc2({\n  opSnippet: ADD,\n  packedOpSnippet: ADD,\n  supportsComplex: true,\n  cpuKernelImpl: addImplCPU\n});\nvar addConfig2 = {\n  kernelName: Add,\n  backendName: \"webgl\",\n  kernelFunc: addKernelFunc\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/addn_gpu.js\nvar AddNProgram = class {\n  constructor(outputShape, shapes) {\n    this.outputShape = [];\n    this.outputShape = outputShape;\n    this.variableNames = shapes.map((_, i) => `T${i}`);\n    const snippets = [];\n    this.variableNames.forEach((variable2) => {\n      snippets.push(`float v${variable2} = get${variable2}AtOutCoords();`);\n    });\n    const operation = this.variableNames.map((variable2) => {\n      return `v${variable2}`;\n    }).join(\" + \");\n    this.userCode = `\n      void main() {\n        ${snippets.join(\"\\n        \")}\n\n        float result = ${operation};\n        setOutput(result);\n      }\n    `;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/addn_packed_gpu.js\nvar AddNPackedProgram = class {\n  constructor(outputShape, shapes) {\n    this.outputShape = [];\n    this.packedInputs = true;\n    this.packedOutput = true;\n    this.outputShape = outputShape;\n    this.variableNames = shapes.map((_, i) => `T${i}`);\n    const snippets = [];\n    this.variableNames.forEach((variable2) => {\n      snippets.push(`vec4 v${variable2} = get${variable2}AtOutCoords();`);\n    });\n    const operation = this.variableNames.map((variable2) => {\n      return `v${variable2}`;\n    }).join(\" + \");\n    this.userCode = `\n      void main() {\n        ${snippets.join(\"\\n        \")}\n\n        vec4 result = ${operation};\n        setOutput(result);\n      }\n    `;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/AddN.js\nfunction addN3(args) {\n  const { inputs, backend: backend2 } = args;\n  const tensors = inputs;\n  if (tensors.length === 1) {\n    return identity3({ inputs: { x: tensors[0] }, backend: backend2 });\n  }\n  if (tensors.length > env().get(\"WEBGL_MAX_TEXTURES_IN_SHADER\")) {\n    const midIndex = Math.floor(tensors.length / 2);\n    const leftSide = addN3({ inputs: tensors.slice(0, midIndex), backend: backend2 });\n    const rightSide = addN3({ inputs: tensors.slice(midIndex), backend: backend2 });\n    return addN3({ inputs: [leftSide, rightSide], backend: backend2 });\n  }\n  const dtype = tensors.map((t) => t.dtype).reduce((d1, d2) => upcastType(d1, d2));\n  const shapes = tensors.map((t) => t.shape);\n  const usePackedOp = env().getBool(\"WEBGL_PACK\");\n  const program = usePackedOp ? new AddNPackedProgram(tensors[0].shape, shapes) : new AddNProgram(tensors[0].shape, shapes);\n  return backend2.runWebGLProgram(program, tensors, dtype);\n}\nvar addNConfig2 = {\n  kernelName: AddN,\n  backendName: \"webgl\",\n  kernelFunc: addN3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/All.js\nfunction all3(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x } = inputs;\n  const { axis, keepDims } = attrs;\n  const xRank = x.shape.length;\n  const origAxes = util_exports.parseAxisParam(axis, x.shape);\n  let axes = origAxes;\n  const permutedAxes = backend_util_exports.getAxesPermutation(axes, xRank);\n  let permutedX = x;\n  if (permutedAxes != null) {\n    permutedX = transpose3({ inputs: { x }, backend: backend2, attrs: { perm: permutedAxes } });\n    axes = backend_util_exports.getInnerMostAxes(axes.length, xRank);\n  }\n  backend_util_exports.assertAxesAreInnerMostDims(\"all\", axes, xRank);\n  const [outShape, reduceShape] = backend_util_exports.computeOutAndReduceShapes(permutedX.shape, axes);\n  const inSize = util_exports.sizeFromShape(reduceShape);\n  const a2D = reshape4({ inputs: { x: permutedX }, backend: backend2, attrs: { shape: [-1, inSize] } });\n  const reduced = reduce(a2D, a2D.dtype, \"all\", backend2);\n  let res;\n  if (keepDims) {\n    const newShape = backend_util_exports.expandShapeToKeepDim(outShape, origAxes);\n    res = reshape4({ inputs: { x: reduced }, backend: backend2, attrs: { shape: newShape } });\n  } else {\n    res = reshape4({ inputs: { x: reduced }, backend: backend2, attrs: { shape: outShape } });\n  }\n  backend2.disposeIntermediateTensorInfo(a2D);\n  backend2.disposeIntermediateTensorInfo(reduced);\n  if (permutedAxes != null) {\n    backend2.disposeIntermediateTensorInfo(permutedX);\n  }\n  return res;\n}\nvar allConfig2 = {\n  kernelName: All,\n  backendName: \"webgl\",\n  kernelFunc: all3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Any.js\nfunction any3(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x } = inputs;\n  const { axis, keepDims } = attrs;\n  const xRank = x.shape.length;\n  const origAxes = util_exports.parseAxisParam(axis, x.shape);\n  let axes = origAxes;\n  const permutedAxes = backend_util_exports.getAxesPermutation(axes, xRank);\n  let permutedX = x;\n  if (permutedAxes != null) {\n    permutedX = transpose3({ inputs: { x }, backend: backend2, attrs: { perm: permutedAxes } });\n    axes = backend_util_exports.getInnerMostAxes(axes.length, xRank);\n  }\n  backend_util_exports.assertAxesAreInnerMostDims(\"any\", axes, xRank);\n  const [outShape, reduceShape] = backend_util_exports.computeOutAndReduceShapes(permutedX.shape, axes);\n  const inSize = util_exports.sizeFromShape(reduceShape);\n  const a2D = reshape4({ inputs: { x: permutedX }, backend: backend2, attrs: { shape: [-1, inSize] } });\n  const reduced = reduce(a2D, a2D.dtype, \"any\", backend2);\n  let res;\n  if (keepDims) {\n    const newShape = backend_util_exports.expandShapeToKeepDim(outShape, origAxes);\n    res = reshape4({ inputs: { x: reduced }, backend: backend2, attrs: { shape: newShape } });\n  } else {\n    res = reshape4({ inputs: { x: reduced }, backend: backend2, attrs: { shape: outShape } });\n  }\n  backend2.disposeIntermediateTensorInfo(a2D);\n  backend2.disposeIntermediateTensorInfo(reduced);\n  if (permutedAxes != null) {\n    backend2.disposeIntermediateTensorInfo(permutedX);\n  }\n  return res;\n}\nvar anyConfig2 = {\n  kernelName: Any,\n  backendName: \"webgl\",\n  kernelFunc: any3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/argminmax_gpu.js\nvar ArgMinMaxProgram = class {\n  constructor(reduceInfo, op2, firstPass) {\n    this.variableNames = [\"A\"];\n    const { windowSize, batchSize, outSize } = reduceInfo;\n    if (!firstPass) {\n      this.variableNames.push(\"bestIndicesA\");\n    }\n    this.outputShape = [batchSize, outSize];\n    const compOp = op2 === \"max\" ? \">\" : \"<\";\n    const indexSnippet = firstPass ? \"inOffset + i;\" : \"round(getBestIndicesA(batch, inOffset + i));\";\n    this.userCode = `\n      void main() {\n        ivec2 coords = getOutputCoords();\n        int batch = coords[0];\n        int outIdx = coords[1];\n        int inOffset = outIdx * ${windowSize};\n\n        int bestIndex = inOffset;\n        float bestValue = getA(batch, bestIndex);\n\n        for (int i = 0; i < ${windowSize}; i++) {\n          int inIdx = ${indexSnippet};\n          float candidate = getA(batch, inIdx);\n          if (candidate ${compOp} bestValue) {\n            bestValue = candidate;\n            bestIndex = inIdx;\n          }\n        }\n        setOutput(float(bestIndex));\n      }\n    `;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/argminmax_packed_gpu.js\nvar ArgMinMaxPackedProgram = class {\n  constructor(shape, windowSize, op2, firstPass) {\n    this.variableNames = [\"A\"];\n    this.packedInputs = true;\n    this.packedOutput = true;\n    util_exports.assert(shape.length > 2, () => `Packed arg${op2.charAt(0).toUpperCase() + op2.slice(1)} supports only inputs with rank above 2.`);\n    const inSize = shape[shape.length - 1];\n    const outSize = Math.ceil(inSize / windowSize);\n    this.outputShape = shape.slice(0, -1);\n    if (outSize > 1) {\n      this.outputShape.push(outSize);\n    }\n    if (!firstPass) {\n      this.variableNames.push(\"bestIndicesA\");\n    }\n    const outShape = this.outputShape;\n    const rank = outShape.length;\n    const dtype = getCoordsDataType(rank);\n    const coords3 = getChannels(\"coords\", rank);\n    let sourceLocSetup;\n    let sourceRank;\n    if (outSize === 1) {\n      sourceRank = rank + 1;\n      const sourceLocDType = getCoordsDataType(sourceRank);\n      sourceLocSetup = `\n        ${sourceLocDType} sourceLocR = ${sourceLocDType}(${coords3.join()}, 0);\n        ++${coords3[rank - 1]};\n        ${sourceLocDType} sourceLocG = ${sourceLocDType}(${coords3.join()}, 0);\n        ++${coords3[rank - 2]};\n        ${sourceLocDType} sourceLocA = ${sourceLocDType}(${coords3.join()}, 0);\n        --${coords3[rank - 1]};\n        ${sourceLocDType} sourceLocB = ${sourceLocDType}(${coords3.join()}, 0);\n        --${coords3[rank - 2]};`;\n    } else {\n      sourceRank = rank;\n      sourceLocSetup = `\n        ${dtype} sourceLocR = coords;\n        ++${coords3[rank - 1]};\n        ${dtype} sourceLocG = coords;\n        ++${coords3[rank - 2]};\n        ${dtype} sourceLocA = coords;\n        --${coords3[rank - 1]};\n        ${dtype} sourceLocB = coords;\n        --${coords3[rank - 2]};`;\n    }\n    const channels = [\"x\", \"y\", \"z\", \"w\", \"u\", \"v\"].slice(0, sourceRank);\n    const inChannel = \".\" + channels[sourceRank - 1];\n    const intChannels = channels.map((x) => \"int \" + x);\n    const srcRCoords = getChannels(\"sourceLocR\", sourceRank - 1).concat(\"inIdx.r\");\n    const srcGCoords = getChannels(\"sourceLocG\", sourceRank - 1).concat(\"inIdx.g\");\n    const srcBCoords = getChannels(\"sourceLocB\", sourceRank - 1).concat(\"inIdx.b\");\n    const srcACoords = getChannels(\"sourceLocA\", sourceRank - 1).concat(\"inIdx.a\");\n    const compOp = op2 === \"max\" ? \"greaterThan\" : \"lessThan\";\n    const fetchCandidateIdx = firstPass ? \"\" : `\n          inIdx = round(vec4(getBestIndicesAChannel(${srcRCoords.join()}),\n                             getBestIndicesAChannel(${srcGCoords.join()}),\n                             getBestIndicesAChannel(${srcBCoords.join()}),\n                             getBestIndicesAChannel(${srcACoords.join()})));`;\n    const fetchValue = `vec4(\n            getAChannel(${srcRCoords.join()}),\n            hasNextCol ? getAChannel(${srcGCoords.join()}) : 0.,\n            hasNextRow ? getAChannel(${srcBCoords.join()}) : 0.,\n            hasNextRow && hasNextCol ? getAChannel(${srcACoords.join()}) : 0.)`;\n    const getBestIndicesAChannelSnippet = firstPass ? \"\" : `\n      float getBestIndicesAChannel(${intChannels.join()}) {\n        return getChannel(getBestIndicesA(${channels.join()}),\n                                          vec2(${channels.slice(-2).join()}));\n      }`;\n    this.userCode = `\n      float getAChannel(${intChannels.join()}) {\n        return getChannel(getA(${channels.join()}),\n                               vec2(${channels.slice(-2).join()}));\n      }\n      ${getBestIndicesAChannelSnippet}\n      void main() {\n        ${dtype} coords = getOutputCoords();\n        bool hasNextCol = ${coords3[rank - 1]} < ${outShape[rank - 1] - 1};\n        bool hasNextRow = ${coords3[rank - 2]} < ${outShape[rank - 2] - 1};\n        ${sourceLocSetup}\n        ivec4 srcIdx = ivec4(sourceLocR${inChannel}, sourceLocG${inChannel},\n          sourceLocB${inChannel}, sourceLocA${inChannel}) * ${windowSize};\n        ivec4 inIdx = srcIdx;\n        vec4 bestIndex = vec4(inIdx);\n        vec4 bestValue = ${fetchValue};\n\n        for (int i = 0; i < ${windowSize}; i++) {\n          inIdx = srcIdx;\n          ${fetchCandidateIdx}\n          vec4 candidate = ${fetchValue};\n          bvec4 nan = isnan(candidate);\n          bvec4 replace = bvec4(\n            vec4(${compOp}(candidate, bestValue)) * (vec4(1.0) - vec4(nan)));\n\n          bestValue = vec4(replace.x  ? candidate.x : bestValue.x,\n                           replace.y  ? candidate.y : bestValue.y,\n                           replace.z  ? candidate.z : bestValue.z,\n                           replace.w  ? candidate.w : bestValue.w);\n          bestIndex = mix(bestIndex, vec4(inIdx), vec4(replace));\n          srcIdx++;\n        }\n        setOutput(bestIndex);\n      }\n    `;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernel_utils/arg_min_max.js\nfunction argReduce(backend2, x, reduceType, bestIndicesA = null) {\n  let batchSize = x.shape[0];\n  let inSize = x.shape[1];\n  if (bestIndicesA != null) {\n    batchSize = bestIndicesA.shape[0];\n    inSize = bestIndicesA.shape[1];\n  }\n  const windowSize = backend_util_exports.computeOptimalWindowSize(inSize);\n  const reduceInfo = { windowSize, inSize, batchSize, outSize: Math.ceil(inSize / windowSize) };\n  const program = new ArgMinMaxProgram(reduceInfo, reduceType, bestIndicesA == null);\n  const inputs = [x];\n  if (bestIndicesA != null) {\n    inputs.push(bestIndicesA);\n  }\n  const output = backend2.runWebGLProgram(program, inputs, \"int32\");\n  if (output.shape[1] === 1) {\n    return output;\n  }\n  const result = argReduce(backend2, x, reduceType, output);\n  backend2.disposeIntermediateTensorInfo(output);\n  return result;\n}\nfunction argReducePacked(backend2, x, reduceType, bestIndicesA = null) {\n  const inShape = bestIndicesA != null ? bestIndicesA.shape : x.shape;\n  const inSize = inShape[inShape.length - 1];\n  const windowSize = backend_util_exports.computeOptimalWindowSize(inSize);\n  const program = new ArgMinMaxPackedProgram(inShape, windowSize, reduceType, bestIndicesA == null);\n  const inputs = bestIndicesA == null ? [x] : [x, bestIndicesA];\n  const output = backend2.runWebGLProgram(program, inputs, \"int32\");\n  if (output.shape.length === x.shape.length) {\n    const result = argReducePacked(backend2, x, reduceType, output);\n    backend2.disposeIntermediateTensorInfo(output);\n    return result;\n  }\n  return output;\n}\nfunction argMinMaxReduce(backend2, x, axis, reduceType) {\n  const axes = [axis];\n  backend_util_exports.assertAxesAreInnerMostDims(\"arg\" + reduceType.charAt(0).toUpperCase() + reduceType.slice(1), axes, x.shape.length);\n  if (!env().getBool(\"WEBGL_PACK_REDUCE\") || x.shape.length <= 2) {\n    const intermediateTensorInfos = [];\n    const xtexData = backend2.texData.get(x.dataId);\n    const xIsPacked = xtexData !== null && xtexData.isPacked;\n    let xUnPacked = x;\n    if (xIsPacked) {\n      xUnPacked = backend2.unpackTensor(x);\n      intermediateTensorInfos.push(xUnPacked);\n    }\n    const [outShape, reduceShape] = backend_util_exports.computeOutAndReduceShapes(xUnPacked.shape, axes);\n    const inSize = util_exports.sizeFromShape(reduceShape);\n    const a2D = reshape4({ inputs: { x: xUnPacked }, backend: backend2, attrs: { shape: [-1, inSize] } });\n    intermediateTensorInfos.push(a2D);\n    const reduced = argReduce(backend2, a2D, reduceType);\n    intermediateTensorInfos.push(reduced);\n    const reshaped = reshape4({ inputs: { x: reduced }, backend: backend2, attrs: { shape: outShape } });\n    intermediateTensorInfos.forEach((t) => backend2.disposeIntermediateTensorInfo(t));\n    return reshaped;\n  }\n  return argReducePacked(backend2, x, reduceType);\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/ArgMax.js\nfunction argMax3(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x } = inputs;\n  const { axis } = attrs;\n  let axes = util_exports.parseAxisParam(axis, x.shape);\n  const permutedAxes = backend_util_exports.getAxesPermutation(axes, x.shape.length);\n  let $x = x;\n  const intermediateTensorInfos = [];\n  if (permutedAxes != null) {\n    $x = transpose3({ inputs: { x }, backend: backend2, attrs: { perm: permutedAxes } });\n    intermediateTensorInfos.push($x);\n    axes = backend_util_exports.getInnerMostAxes(axes.length, $x.shape.length);\n  }\n  backend_util_exports.assertAxesAreInnerMostDims(\"argMax\", [axes[0]], $x.shape.length);\n  const out = argMinMaxReduce(backend2, $x, axes[0], \"max\");\n  intermediateTensorInfos.forEach((t) => backend2.disposeIntermediateTensorInfo(t));\n  return out;\n}\nvar argMaxConfig2 = {\n  kernelName: ArgMax,\n  backendName: \"webgl\",\n  kernelFunc: argMax3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/ArgMin.js\nfunction argMin3(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x } = inputs;\n  const { axis } = attrs;\n  let axes = util_exports.parseAxisParam(axis, x.shape);\n  const permutedAxes = backend_util_exports.getAxesPermutation(axes, x.shape.length);\n  let $x = x;\n  const intermediateTensorInfos = [];\n  if (permutedAxes != null) {\n    $x = transpose3({ inputs: { x }, backend: backend2, attrs: { perm: permutedAxes } });\n    intermediateTensorInfos.push($x);\n    axes = backend_util_exports.getInnerMostAxes(axes.length, $x.shape.length);\n  }\n  backend_util_exports.assertAxesAreInnerMostDims(\"argMin\", [axes[0]], $x.shape.length);\n  const out = argMinMaxReduce(backend2, $x, axes[0], \"min\");\n  intermediateTensorInfos.forEach((t) => backend2.disposeIntermediateTensorInfo(t));\n  return out;\n}\nvar argMinConfig2 = {\n  kernelName: ArgMin,\n  backendName: \"webgl\",\n  kernelFunc: argMin3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Asin.js\nvar ASIN = CHECK_NAN_SNIPPET + `\n  if (abs(x) > 1.) {\n    return NAN;\n  }\n  return asin(x);\n`;\nvar asin3 = unaryKernelFunc2({ opSnippet: ASIN });\nvar asinConfig2 = {\n  kernelName: Asin,\n  backendName: \"webgl\",\n  kernelFunc: asin3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Asinh.js\nvar ASINH = CHECK_NAN_SNIPPET + `return log(x + sqrt(x * x + 1.0));`;\nvar asinh3 = unaryKernelFunc2({ opSnippet: ASINH });\nvar asinhConfig2 = {\n  kernelName: Asinh,\n  backendName: \"webgl\",\n  kernelFunc: asinh3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Atan.js\nvar ATAN = CHECK_NAN_SNIPPET + `\n  return atan(x);\n`;\nvar atan4 = unaryKernelFunc2({ opSnippet: ATAN });\nvar atanConfig2 = {\n  kernelName: Atan,\n  backendName: \"webgl\",\n  kernelFunc: atan4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Atan2.js\nvar ATAN2 = CHECK_NAN_SNIPPET_BINARY + `\n  return atan(a, b);\n`;\nvar ATAN2_PACKED = `\n  vec4 result = atan(a, b);\n  vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));\n  ` + CHECK_NAN_SNIPPET_BINARY_PACKED + `\n  return result;\n`;\nvar atan23 = binaryKernelFunc2({ opSnippet: ATAN2, packedOpSnippet: ATAN2_PACKED });\nvar atan2Config2 = {\n  kernelName: Atan2,\n  backendName: \"webgl\",\n  kernelFunc: atan23\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Atanh.js\nvar ATANH = CHECK_NAN_SNIPPET + `\n  if ((x < -1.0) || (x > 1.0)) return NAN;\nreturn (log(1.0 + x) - log(1.0 - x)) / 2.0;`;\nvar atanh3 = unaryKernelFunc2({ opSnippet: ATANH });\nvar atanhConfig2 = {\n  kernelName: Atanh,\n  backendName: \"webgl\",\n  kernelFunc: atanh3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/pool_gpu.js\nvar Pool2DProgram = class {\n  constructor(convInfo, poolType, computePositions, flattenPositions = false, includeBatchInIndex = false) {\n    this.variableNames = [\"x\"];\n    if (poolType === \"avg\" && computePositions) {\n      throw new Error(\"Cannot compute positions for average pool.\");\n    }\n    const filterWidth = convInfo.filterWidth;\n    const strideHeight = convInfo.strideHeight;\n    const strideWidth = convInfo.strideWidth;\n    const dilationHeight = convInfo.dilationHeight;\n    const dilationWidth = convInfo.dilationWidth;\n    const effectiveFilterHeight = convInfo.effectiveFilterHeight;\n    const effectiveFilterWidth = convInfo.effectiveFilterWidth;\n    const padTop = convInfo.padInfo.top;\n    const padLeft = convInfo.padInfo.left;\n    this.outputShape = convInfo.outShape;\n    const isAvgPool = poolType === \"avg\";\n    const batchFlattenPositionStr = `((batch  * ${convInfo.inHeight} + xR) * ${convInfo.inWidth} + xC) * ${convInfo.inChannels} + d`;\n    const flattenPositionStr = `(xR * ${convInfo.inWidth} + xC) * ${convInfo.inChannels} + d`;\n    let initializationValue = \"0.0\";\n    if (!isAvgPool) {\n      initializationValue = \"-1.0 / 1e-20\";\n    }\n    if (computePositions) {\n      const compareOp2 = \">=\";\n      this.userCode = `\n        const ivec2 strides = ivec2(${strideHeight}, ${strideWidth});\n        const ivec2 pads = ivec2(${padTop}, ${padLeft});\n\n        void main() {\n          ivec4 coords = getOutputCoords();\n          int batch = coords[0];\n          int d = coords[3];\n\n          ivec2 xRCCorner = coords.yz * strides - pads;\n          int xRCorner = xRCCorner.x;\n          int xCCorner = xRCCorner.y;\n\n          // max/min x(?, ?, d) to get y(yR, yC, d).\n          // ? = to be determined\n          float minMaxValue = 0.0;\n          float minMaxValueFound = 0.0;\n          int minMaxPosition = 0;\n          float avgValue = 0.0;\n\n          for (int wR = 0; wR < ${effectiveFilterHeight};\n              wR += ${dilationHeight}) {\n            int xR = xRCorner + wR;\n\n            if (xR < 0 || xR >= ${convInfo.inHeight}) {\n              continue;\n            }\n\n            for (int wC = 0; wC < ${effectiveFilterWidth};\n                wC += ${dilationWidth}) {\n              int xC = xCCorner + wC;\n\n              if (xC < 0 || xC >= ${convInfo.inWidth}) {\n                continue;\n              }\n\n              float value = getX(batch, xR, xC, d);\n\n              // If a min / max value has already been found, use it. If not,\n              // use the current value.\n              float currMinMaxValue = mix(\n                  value, minMaxValue, minMaxValueFound);\n              if (value ${compareOp2} currMinMaxValue) {\n                minMaxValue = value;\n                minMaxValueFound = 1.0;\n                minMaxPosition = ${flattenPositions ? includeBatchInIndex ? batchFlattenPositionStr : flattenPositionStr : `wR * ${effectiveFilterWidth} + wC`};\n              }\n            }\n          }\n          setOutput(float(minMaxPosition));\n        }\n      `;\n      return;\n    }\n    const compareOp = \"max\";\n    let returnValue = `${poolType}(${poolType}(${poolType}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;\n    if (poolType === \"avg\") {\n      returnValue = `avgValue / count`;\n    }\n    const filterWidthNearestVec4 = Math.floor(filterWidth / 4) * 4;\n    const filterWidthVec4Remainder = filterWidth % 4;\n    const updateSnippet = `\n      if (${isAvgPool}) {\n        avgValue += dot(values, ones);\n      } else {\n        minMaxValue = ${compareOp}(values, minMaxValue);\n      }\n    `;\n    this.userCode = `\n      const ivec2 strides = ivec2(${strideHeight}, ${strideWidth});\n      const ivec2 pads = ivec2(${padTop}, ${padLeft});\n      const float initializationValue = ${initializationValue};\n      const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);\n\n      float count = 0.0;\n\n      float getValue(int batch, int xR, int xC, int d) {\n        if (xC < 0 || xC >= ${convInfo.inWidth}) {\n          return initializationValue;\n        }\n        count += 1.0;\n        return getX(batch, xR, xC, d);\n      }\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int batch = coords[0];\n        int d = coords[3];\n\n        ivec2 xRCCorner = coords.yz * strides - pads;\n        int xRCorner = xRCCorner.x;\n        int xCCorner = xRCCorner.y;\n\n        // max/min x(?, ?, d) to get y(yR, yC, d).\n        // ? = to be determined\n        vec4 minMaxValue = vec4(${initializationValue});\n        float avgValue = 0.0;\n        count = 0.0;\n\n        for (int wR = 0; wR < ${effectiveFilterHeight};\n            wR += ${dilationHeight}) {\n          int xR = xRCorner + wR;\n\n          if (xR < 0 || xR >= ${convInfo.inHeight}) {\n            continue;\n          }\n\n          for (int wC = 0; wC < ${filterWidthNearestVec4}; wC += 4) {\n            int xC = xCCorner + wC * ${dilationWidth};\n\n            vec4 values = vec4(\n              getValue(batch, xR, xC, d),\n              getValue(batch, xR, xC + ${dilationWidth}, d),\n              getValue(batch, xR, xC + 2 * ${dilationWidth}, d),\n              getValue(batch, xR, xC + 3 * ${dilationWidth}, d)\n            );\n\n            ${updateSnippet}\n          }\n\n          int xC = xCCorner + ${filterWidthNearestVec4};\n          if (${filterWidthVec4Remainder === 1}) {\n            vec4 values = vec4(\n              getValue(batch, xR, xC, d),\n              initializationValue,\n              initializationValue,\n              initializationValue\n            );\n\n            ${updateSnippet}\n          } else if (${filterWidthVec4Remainder === 2}) {\n            vec4 values = vec4(\n              getValue(batch, xR, xC, d),\n              getValue(batch, xR, xC + ${dilationWidth}, d),\n              initializationValue,\n              initializationValue\n            );\n\n            ${updateSnippet}\n          } else if (${filterWidthVec4Remainder === 3}) {\n            vec4 values = vec4(\n              getValue(batch, xR, xC, d),\n              getValue(batch, xR, xC + ${dilationWidth}, d),\n              getValue(batch, xR, xC + 2 * ${dilationWidth}, d),\n              initializationValue\n            );\n\n            ${updateSnippet}\n          }\n        }\n        setOutput(${returnValue});\n      }\n    `;\n  }\n};\nvar Pool3DProgram = class {\n  constructor(convInfo, poolType, computePositions, flattenPositions = false, includeBatchInIndex = false) {\n    this.variableNames = [\"x\"];\n    if (poolType === \"avg\" && computePositions) {\n      throw new Error(\"Cannot compute positions for average pool.\");\n    }\n    const filterWidth = convInfo.filterWidth;\n    const strideDepth = convInfo.strideDepth;\n    const strideHeight = convInfo.strideHeight;\n    const strideWidth = convInfo.strideWidth;\n    const dilationDepth = convInfo.dilationDepth;\n    const dilationHeight = convInfo.dilationHeight;\n    const dilationWidth = convInfo.dilationWidth;\n    const effectiveFilterDepth = convInfo.effectiveFilterDepth;\n    const effectiveFilterHeight = convInfo.effectiveFilterHeight;\n    const effectiveFilterWidth = convInfo.effectiveFilterWidth;\n    const padFront = convInfo.padInfo.front;\n    const padTop = convInfo.padInfo.top;\n    const padLeft = convInfo.padInfo.left;\n    this.outputShape = convInfo.outShape;\n    const isAvgPool = poolType === \"avg\";\n    let initializationValue = \"0.0\";\n    if (!isAvgPool) {\n      initializationValue = \"-1.0 / 1e-20\";\n    }\n    if (computePositions) {\n      const compareOp2 = \">=\";\n      this.userCode = `\n        const ivec3 strides =\n            ivec3(${strideDepth}, ${strideHeight}, ${strideWidth});\n        const ivec3 pads = ivec3(${padFront}, ${padTop}, ${padLeft});\n\n        void main() {\n          ivec5 coords = getOutputCoords();\n          int batch = coords.x;\n          int ch = coords.u;\n\n          ivec3 xCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;\n          int xDCorner = xCorner.x;\n          int xRCorner = xCorner.y;\n          int xCCorner = xCorner.z;\n\n          // max/min x(?, ?, ?, ch) to get y(yD, yR, yC, ch).\n          // ? = to be determined\n          float minMaxValue = 0.0;\n          float minMaxValueFound = 0.0;\n          int minMaxPosition = 0;\n\n          for (int wD = 0; wD < ${effectiveFilterDepth};\n              wD += ${dilationDepth}) {\n            int xD = xDCorner + wD;\n\n            if (xD < 0 || xD >= ${convInfo.inDepth}) {\n              continue;\n            }\n\n            for (int wR = 0; wR < ${effectiveFilterHeight};\n                wR += ${dilationHeight}) {\n              int xR = xRCorner + wR;\n\n              if (xR < 0 || xR >= ${convInfo.inHeight}) {\n                continue;\n              }\n\n              for (int wC = 0; wC < ${effectiveFilterWidth};\n                  wC += ${dilationWidth}) {\n                int xC = xCCorner + wC;\n\n                if (xC < 0 || xC >= ${convInfo.inWidth}) {\n                  continue;\n                }\n\n                float value = getX(batch, xD, xR, xC, ch);\n\n                // If a min / max value has already been found, use it. If not,\n                // use the current value.\n                float currMinMaxValue = mix(\n                    value, minMaxValue, minMaxValueFound);\n                if (value ${compareOp2} currMinMaxValue) {\n                  minMaxValue = value;\n                  minMaxValueFound = 1.0;\n                  minMaxPosition = ${flattenPositions ? includeBatchInIndex ? `(((batch * ${convInfo.inDepth} + xD) * ${convInfo.inHeight} + xR) * ${convInfo.inWidth} + xC) * ${convInfo.inChannels} + ch` : `((xD * ${convInfo.inHeight} + xR) * ${convInfo.inWidth} + xC) * ${convInfo.inChannels} + ch` : `wD * ${effectiveFilterHeight} * ${effectiveFilterWidth} +\n                      wR * ${effectiveFilterWidth} + wC`};\n                }\n              }\n            }\n          }\n          setOutput(float(minMaxPosition));\n        }\n      `;\n      return;\n    }\n    const compareOp = \"max\";\n    let returnValue = `${poolType}(${poolType}(${poolType}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;\n    if (poolType === \"avg\") {\n      returnValue = `avgValue / count`;\n    }\n    const filterWidthNearestVec4 = Math.floor(filterWidth / 4) * 4;\n    const filterWidthVec4Remainder = filterWidth % 4;\n    const updateSnippet = `\n      if (${isAvgPool}) {\n        avgValue += dot(values, ones);\n      } else {\n        minMaxValue = ${compareOp}(values, minMaxValue);\n      }\n    `;\n    this.userCode = `\n      const ivec3 strides =\n        ivec3(${strideDepth}, ${strideHeight}, ${strideWidth});\n      const ivec3 pads = ivec3(${padFront}, ${padTop}, ${padLeft});\n      const float initializationValue = ${initializationValue};\n      const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);\n\n      float count = 0.0;\n\n      float getValue(int batch, int xD, int xR, int xC, int ch) {\n        if (xC < 0 || xC >= ${convInfo.inWidth}) {\n          return initializationValue;\n        }\n        count += 1.0;\n        return getX(batch, xD, xR, xC, ch);\n      }\n\n      void main() {\n        ivec5 coords = getOutputCoords();\n        int batch = coords.x;\n        int ch = coords.u;\n\n        ivec3 xCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;\n        int xDCorner = xCorner.x;\n        int xRCorner = xCorner.y;\n        int xCCorner = xCorner.z;\n\n        // max/min x(?, ?, ?, d) to get y(yD, yR, yC, ch).\n        // ? = to be determined\n        vec4 minMaxValue = vec4(${initializationValue});\n        float avgValue = 0.0;\n        count = 0.0;\n\n        for (int wD = 0; wD < ${effectiveFilterDepth};\n            wD += ${dilationDepth}) {\n          int xD = xDCorner + wD;\n\n          if (xD < 0 || xD >= ${convInfo.inDepth}) {\n            continue;\n          }\n\n          for (int wR = 0; wR < ${effectiveFilterHeight};\n            wR += ${dilationHeight}) {\n            int xR = xRCorner + wR;\n\n            if (xR < 0 || xR >= ${convInfo.inHeight}) {\n              continue;\n            }\n\n            for (int wC = 0; wC < ${filterWidthNearestVec4}; wC += 4) {\n              int xC = xCCorner + wC * ${dilationWidth};\n\n              vec4 values = vec4(\n                getValue(batch, xD, xR, xC, ch),\n                getValue(batch, xD, xR, xC + ${dilationWidth}, ch),\n                getValue(batch, xD, xR, xC + 2 * ${dilationWidth}, ch),\n                getValue(batch, xD, xR, xC + 3 * ${dilationWidth}, ch)\n              );\n\n              ${updateSnippet}\n            }\n\n            int xC = xCCorner + ${filterWidthNearestVec4};\n            if (${filterWidthVec4Remainder === 1}) {\n              vec4 values = vec4(\n                getValue(batch, xD, xR, xC, ch),\n                initializationValue,\n                initializationValue,\n                initializationValue\n              );\n\n              ${updateSnippet}\n            } else if (${filterWidthVec4Remainder === 2}) {\n              vec4 values = vec4(\n                getValue(batch, xD, xR, xC, ch),\n                getValue(batch, xD, xR, xC + ${dilationWidth}, ch),\n                initializationValue,\n                initializationValue\n              );\n\n              ${updateSnippet}\n            } else if (${filterWidthVec4Remainder === 3}) {\n              vec4 values = vec4(\n                getValue(batch, xD, xR, xC, ch),\n                getValue(batch, xD, xR, xC + ${dilationWidth}, ch),\n                getValue(batch, xD, xR, xC + 2 * ${dilationWidth}, ch),\n                initializationValue\n              );\n\n              ${updateSnippet}\n            }\n          }\n          setOutput(${returnValue});\n        }\n      }\n    `;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/AvgPool.js\nfunction avgPool3(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x } = inputs;\n  assertNotComplex2(x, \"avgPool\");\n  const { filterSize, strides, pad: pad3, dimRoundingMode } = attrs;\n  const dilations = 1;\n  util_exports.assert(backend_util_exports.eitherStridesOrDilationsAreOne(strides, dilations), () => `Error in avgPool: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`);\n  const convInfo = backend_util_exports.computePool2DInfo(x.shape, filterSize, strides, dilations, pad3, dimRoundingMode);\n  if (convInfo.filterWidth === 1 && convInfo.filterHeight === 1 && util_exports.arraysEqual(convInfo.inShape, convInfo.outShape)) {\n    return identity3({ inputs: { x }, backend: backend2 });\n  }\n  const avgPoolProgram = new Pool2DProgram(convInfo, \"avg\", false);\n  return backend2.runWebGLProgram(avgPoolProgram, [x], \"float32\");\n}\nvar avgPoolConfig2 = {\n  kernelName: AvgPool,\n  backendName: \"webgl\",\n  kernelFunc: avgPool3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/AvgPool3D.js\nfunction avgPool3D2(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x } = inputs;\n  const { filterSize, strides, pad: pad3, dimRoundingMode, dataFormat } = attrs;\n  const dilations = [1, 1, 1];\n  const convInfo = backend_util_exports.computePool3DInfo(x.shape, filterSize, strides, dilations, pad3, dimRoundingMode, dataFormat);\n  const avgPoolProgram = new Pool3DProgram(convInfo, \"avg\", false);\n  return backend2.runWebGLProgram(avgPoolProgram, [x], \"float32\");\n}\nvar avgPool3DConfig2 = {\n  kernelName: AvgPool3D,\n  backendName: \"webgl\",\n  kernelFunc: avgPool3D2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/avg_pool_backprop_gpu.js\nvar AvgPool2DBackpropProgram = class {\n  constructor(convInfo) {\n    this.variableNames = [\"dy\"];\n    this.outputShape = convInfo.inShape;\n    const filterHeight = convInfo.filterHeight;\n    const filterWidth = convInfo.filterWidth;\n    const strideHeight = convInfo.strideHeight;\n    const strideWidth = convInfo.strideWidth;\n    const dilationHeight = convInfo.dilationHeight;\n    const dilationWidth = convInfo.dilationWidth;\n    const effectiveFilterHeight = convInfo.effectiveFilterHeight;\n    const effectiveFilterWidth = convInfo.effectiveFilterWidth;\n    const padTop = effectiveFilterHeight - 1 - convInfo.padInfo.top;\n    const padLeft = effectiveFilterWidth - 1 - convInfo.padInfo.left;\n    const avgMultiplier = 1 / (filterHeight * filterWidth);\n    this.userCode = `\n      const ivec2 pads = ivec2(${padTop}, ${padLeft});\n      const float avgMultiplier = float(${avgMultiplier});\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n\n        ivec2 dyRCCorner = coords.yz - pads;\n        int dyRCorner = dyRCCorner.x;\n        int dyCCorner = dyRCCorner.y;\n\n        // Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n        for (int wR = 0; wR < ${effectiveFilterHeight};\n            wR += ${dilationHeight}) {\n          float dyR = float(dyRCorner + wR) / ${strideHeight}.0;\n\n          if (dyR < 0.0 || dyR >= ${convInfo.outHeight}.0 || fract(dyR) > 0.0) {\n            continue;\n          }\n          int idyR = int(dyR);\n\n          for (int wC = 0; wC < ${effectiveFilterWidth};\n            wC+= ${dilationWidth}) {\n            float dyC = float(dyCCorner + wC) / ${strideWidth}.0;\n\n            if (dyC < 0.0 || dyC >= ${convInfo.outWidth}.0 ||\n                fract(dyC) > 0.0) {\n              continue;\n            }\n            int idyC = int(dyC);\n\n            float dyValue = getDy(b, idyR, idyC, d);\n\n            dotProd += dyValue * avgMultiplier;\n          }\n        }\n        setOutput(dotProd);\n      }\n    `;\n  }\n};\nvar AvgPool3DBackpropProgram = class {\n  constructor(convInfo) {\n    this.variableNames = [\"dy\"];\n    this.outputShape = convInfo.inShape;\n    const filterDepth = convInfo.filterDepth;\n    const filterHeight = convInfo.filterHeight;\n    const filterWidth = convInfo.filterWidth;\n    const strideDepth = convInfo.strideDepth;\n    const strideHeight = convInfo.strideHeight;\n    const strideWidth = convInfo.strideWidth;\n    const dilationDepth = convInfo.dilationDepth;\n    const dilationHeight = convInfo.dilationHeight;\n    const dilationWidth = convInfo.dilationWidth;\n    const effectiveFilterDepth = convInfo.effectiveFilterDepth;\n    const effectiveFilterHeight = convInfo.effectiveFilterHeight;\n    const effectiveFilterWidth = convInfo.effectiveFilterWidth;\n    const padFront = effectiveFilterDepth - 1 - convInfo.padInfo.front;\n    const padTop = effectiveFilterHeight - 1 - convInfo.padInfo.top;\n    const padLeft = effectiveFilterWidth - 1 - convInfo.padInfo.left;\n    const avgMultiplier = 1 / (filterDepth * filterHeight * filterWidth);\n    this.userCode = `\n      const ivec3 pads = ivec3(${padFront}, ${padTop}, ${padLeft});\n      const float avgMultiplier = float(${avgMultiplier});\n\n      void main() {\n        ivec5 coords = getOutputCoords();\n        int batch = coords.x;\n        int ch = coords.u;\n\n        ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;\n        int dyDCorner = dyCorner.x;\n        int dyRCorner = dyCorner.y;\n        int dyCCorner = dyCorner.z;\n\n        // Convolve dy(?, ?, ?, d) with pos mask(:, :, :, ch) to get\n        // dx(xD, xR, xC, ch).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n\n        for (int wD = 0; wD < ${effectiveFilterDepth};\n            wD += ${dilationDepth}) {\n          float dyD = float(dyDCorner + wD) / ${strideDepth}.0;\n\n          if (dyD < 0.0 || dyD >= ${convInfo.outDepth}.0 || fract(dyD) > 0.0) {\n            continue;\n          }\n          int idyD = int(dyD);\n\n          for (int wR = 0; wR < ${effectiveFilterHeight};\n              wR += ${dilationHeight}) {\n            float dyR = float(dyRCorner + wR) / ${strideHeight}.0;\n\n            if (dyR < 0.0 || dyR >= ${convInfo.outHeight}.0 ||\n                fract(dyR) > 0.0) {\n              continue;\n            }\n            int idyR = int(dyR);\n\n            for (int wC = 0; wC < ${effectiveFilterWidth};\n                wC += ${dilationWidth}) {\n              float dyC = float(dyCCorner + wC) / ${strideWidth}.0;\n\n              if (dyC < 0.0 || dyC >= ${convInfo.outWidth}.0 ||\n                  fract(dyC) > 0.0) {\n                continue;\n              }\n              int idyC = int(dyC);\n\n              float dyValue = getDy(batch, idyD, idyR, idyC, ch);\n\n              dotProd += dyValue * avgMultiplier;\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    `;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/AvgPool3DGrad.js\nfunction avgPool3DGrad2(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { dy, input: input2 } = inputs;\n  const x = input2;\n  const { filterSize, strides, pad: pad3, dimRoundingMode } = attrs;\n  const dilations = [1, 1, 1];\n  const convInfo = backend_util_exports.computePool3DInfo(x.shape, filterSize, strides, dilations, pad3, dimRoundingMode);\n  const avgPoolBackpropProgram = new AvgPool3DBackpropProgram(convInfo);\n  return backend2.runWebGLProgram(avgPoolBackpropProgram, [dy], x.dtype);\n}\nvar avgPool3DGradConfig3 = {\n  kernelName: AvgPool3DGrad,\n  backendName: \"webgl\",\n  kernelFunc: avgPool3DGrad2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/AvgPoolGrad.js\nfunction avgPoolGrad3(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { dy, input: input2 } = inputs;\n  const x = input2;\n  assertNotComplex2([dy, input2], \"avgPoolGrad\");\n  const { filterSize, strides, pad: pad3 } = attrs;\n  const convInfo = backend_util_exports.computePool2DInfo(x.shape, filterSize, strides, 1, pad3);\n  const avgPoolBackpropProgram = new AvgPool2DBackpropProgram(convInfo);\n  return backend2.runWebGLProgram(avgPoolBackpropProgram, [dy], x.dtype);\n}\nvar avgPoolGradConfig3 = {\n  kernelName: AvgPoolGrad,\n  backendName: \"webgl\",\n  kernelFunc: avgPoolGrad3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/BatchMatMul.js\nfunction batchMatMul2(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { a, b } = inputs;\n  const { transposeA, transposeB } = attrs;\n  return batchMatMulImpl({ a, b, transposeA, transposeB, backend: backend2 });\n}\nvar batchMatMulConfig2 = {\n  kernelName: BatchMatMul,\n  backendName: \"webgl\",\n  kernelFunc: batchMatMul2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/batchnorm_gpu.js\nvar BatchNormProgram = class {\n  constructor(xShape, meanShape, varianceShape, offsetShape, scaleShape, varianceEpsilon) {\n    this.outputShape = [];\n    this.variableNames = [\"x\", \"mean\", \"variance\"];\n    backend_util_exports.assertAndGetBroadcastShape(xShape, meanShape);\n    backend_util_exports.assertAndGetBroadcastShape(xShape, varianceShape);\n    let offsetSnippet = \"0.0\";\n    if (offsetShape != null) {\n      backend_util_exports.assertAndGetBroadcastShape(xShape, offsetShape);\n      this.variableNames.push(\"offset\");\n      offsetSnippet = \"getOffsetAtOutCoords()\";\n    }\n    let scaleSnippet = \"1.0\";\n    if (scaleShape != null) {\n      backend_util_exports.assertAndGetBroadcastShape(xShape, scaleShape);\n      this.variableNames.push(\"scale\");\n      scaleSnippet = \"getScaleAtOutCoords()\";\n    }\n    this.outputShape = xShape;\n    this.userCode = `\n      void main() {\n        float x = getXAtOutCoords();\n        float mean = getMeanAtOutCoords();\n        float variance = getVarianceAtOutCoords();\n        float offset = ${offsetSnippet};\n        float scale = ${scaleSnippet};\n        float inv = scale * inversesqrt(variance + float(${varianceEpsilon}));\n        setOutput(dot(vec3(x, -mean, offset), vec3(inv, inv, 1)));\n      }\n    `;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/batchnorm_packed_gpu.js\nvar BatchNormPackedProgram = class {\n  constructor(xShape, meanShape, varianceShape, offsetShape, scaleShape, varianceEpsilon) {\n    this.packedInputs = true;\n    this.packedOutput = true;\n    this.variableNames = [\"x\", \"mean\", \"variance\"];\n    backend_util_exports.assertAndGetBroadcastShape(xShape, meanShape);\n    backend_util_exports.assertAndGetBroadcastShape(xShape, varianceShape);\n    let offsetSnippet = \"vec4(0.0)\";\n    if (offsetShape != null) {\n      backend_util_exports.assertAndGetBroadcastShape(xShape, offsetShape);\n      this.variableNames.push(\"offset\");\n      offsetSnippet = \"getOffsetAtOutCoords()\";\n    }\n    let scaleSnippet = \"vec4(1.0)\";\n    if (scaleShape != null) {\n      backend_util_exports.assertAndGetBroadcastShape(xShape, scaleShape);\n      this.variableNames.push(\"scale\");\n      scaleSnippet = \"getScaleAtOutCoords()\";\n    }\n    this.outputShape = xShape;\n    this.userCode = `\n      void main() {\n        vec4 offset = ${offsetSnippet};\n        vec4 scale = ${scaleSnippet};\n\n        vec4 x = getXAtOutCoords();\n        vec4 mean = getMeanAtOutCoords();\n        vec4 variance = getVarianceAtOutCoords();\n\n        vec4 inv = scale * inversesqrt(variance + vec4(${varianceEpsilon}));\n\n        setOutput((x - mean) * inv + offset);\n      }\n    `;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/BatchNorm.js\nvar batchNorm3 = ({ inputs, backend: backend2, attrs }) => {\n  const { x, mean: mean5, variance, offset, scale: scale2 } = inputs;\n  util_exports.assert(mean5.shape.length === variance.shape.length, () => \"Batch normalization gradient requires mean and variance to have equal ranks.\");\n  util_exports.assert(offset == null || mean5.shape.length === offset.shape.length, () => \"Batch normalization gradient requires mean and offset to have equal ranks.\");\n  util_exports.assert(scale2 == null || mean5.shape.length === scale2.shape.length, () => \"Batch normalization gradient requires mean and scale to have equal ranks.\");\n  let { varianceEpsilon } = attrs;\n  if (varianceEpsilon == null) {\n    varianceEpsilon = 1e-3;\n  }\n  const finalInputs = [x, mean5, variance];\n  let offsetShape = null;\n  if (offset != null) {\n    offsetShape = offset.shape;\n    finalInputs.push(offset);\n  }\n  let scaleShape = null;\n  if (scale2 != null) {\n    scaleShape = scale2.shape;\n    finalInputs.push(scale2);\n  }\n  const program = env().getBool(\"WEBGL_PACK_NORMALIZATION\") ? new BatchNormPackedProgram(x.shape, mean5.shape, variance.shape, offsetShape, scaleShape, varianceEpsilon) : new BatchNormProgram(x.shape, mean5.shape, variance.shape, offsetShape, scaleShape, varianceEpsilon);\n  const output = backend2.runWebGLProgram(program, finalInputs, finalInputs[0].dtype);\n  return output;\n};\nvar batchNormConfig2 = {\n  kernelName: FusedBatchNorm,\n  backendName: \"webgl\",\n  kernelFunc: batchNorm3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/slice_gpu.js\nvar SliceProgram = class {\n  constructor(destSize) {\n    this.variableNames = [\"source\"];\n    this.outputShape = destSize;\n    this.rank = destSize.length;\n    const dtype = getCoordsDataType(this.rank);\n    this.customUniforms = [{ name: \"start\", arrayIndex: this.rank, type: \"int\" }];\n    const sourceCoords = getCoords(this.rank);\n    let body;\n    const coordSum = destSize.map((_, i) => {\n      return `sourceLoc.${coords[i]} = start[${i}] + coords.${coords[i]};`;\n    });\n    body = `\n        ${dtype} sourceLoc;\n        ${dtype} coords = getOutputCoords();\n        ${coordSum.join(\"\\n\")}\n      `;\n    this.userCode = `\n      void main() {\n        ${body}\n        setOutput(getSource(${sourceCoords}));\n      }\n    `;\n  }\n};\nvar coords = [\"x\", \"y\", \"z\", \"w\", \"u\", \"v\"];\nfunction getCoords(rank) {\n  if (rank === 1) {\n    return \"sourceLoc\";\n  } else if (rank <= 6) {\n    return coords.slice(0, rank).map((x) => \"sourceLoc.\" + x).join(\",\");\n  } else {\n    throw Error(`Slicing for rank ${rank} is not yet supported`);\n  }\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/slice_packed_gpu.js\nvar SlicePackedProgram = class {\n  constructor(destSize) {\n    this.variableNames = [\"source\"];\n    this.packedInputs = true;\n    this.packedOutput = true;\n    this.outputShape = destSize;\n    this.rank = destSize.length;\n    this.customUniforms = [{ name: \"start\", arrayIndex: this.rank, type: \"int\" }];\n    const dtype = getCoordsDataType(this.rank);\n    const coords3 = getChannels(\"coords\", this.rank);\n    const sourceLoc = getChannels(\"sourceLoc\", this.rank);\n    const innerDims = this.rank === 1 ? \"sourceLoc\" : `vec2(${sourceLoc.slice(-2).join()})`;\n    const getChannel = `getChannel(getSource(${sourceLoc.join()}), ${innerDims})`;\n    const upperRow = `\n      result.x = ${getChannel};\n      if (++${coords3[this.rank - 1]} < ${destSize[this.rank - 1]}) {\n        ++${sourceLoc[this.rank - 1]};\n        result.y = ${getChannel};\n        --${sourceLoc[this.rank - 1]};\n      }\n    `;\n    const lowerRow = this.rank === 1 ? \"\" : `\n      --${coords3[this.rank - 1]};\n      if (++${coords3[this.rank - 2]} < ${destSize[this.rank - 2]}) {\n        ++${sourceLoc[this.rank - 2]};\n        result.z = ${getChannel};\n        if (++${coords3[this.rank - 1]} < ${destSize[this.rank - 1]}) {\n          ++${sourceLoc[this.rank - 1]};\n          result.w = ${getChannel};\n        }\n      }\n    `;\n    const sourceLocSetup = this.rank <= 4 ? `sourceLoc = coords +\n            ${dtype}(${destSize.map((_, i) => `start[${i}]`).join()});` : destSize.map((_, i) => `${sourceLoc[i]} = ${coords3[i]} + start[${i}];`).join(\"\\n\");\n    this.userCode = `\n      void main() {\n        ${dtype} coords = getOutputCoords();\n        ${dtype} sourceLoc;\n        ${sourceLocSetup}\n        vec4 result = vec4(0.);\n        ${upperRow}\n        ${lowerRow}\n        setOutput(result);\n      }\n    `;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Slice.js\nfunction shallowSlice(x, begin, size, backend2) {\n  const xTexData = backend2.texData.get(x.dataId);\n  const t = backend2.makeTensorInfo(size, x.dtype);\n  const newTexData = backend2.texData.get(t.dataId);\n  Object.assign(newTexData, xTexData);\n  newTexData.refCount = 1;\n  newTexData.shape = size;\n  newTexData.dtype = x.dtype;\n  let flatOffset = slice_util_exports.computeFlatOffset(begin, util_exports.computeStrides(x.shape));\n  if (xTexData.slice) {\n    flatOffset += xTexData.slice.flatOffset;\n  }\n  newTexData.slice = {\n    flatOffset,\n    origDataId: xTexData.slice && xTexData.slice.origDataId || x.dataId\n  };\n  const refCount = backend2.dataRefCount.get(newTexData.slice.origDataId) || 1;\n  backend2.dataRefCount.set(newTexData.slice.origDataId, refCount + 1);\n  return t;\n}\nfunction slice3(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x } = inputs;\n  const { begin, size } = attrs;\n  const [$begin, $size] = slice_util_exports.parseSliceParams(x, begin, size);\n  slice_util_exports.assertParamsValid(x, $begin, $size);\n  if (util_exports.sizeFromShape($size) === 0) {\n    return backend2.makeTensorInfo($size, x.dtype, []);\n  }\n  if (backend2.shouldExecuteOnCPU([x]) || x.dtype === \"string\") {\n    const xTexData = backend2.texData.get(x.dataId);\n    const outValues = sliceImplCPU(xTexData.values, $begin, $size, x.shape, x.dtype);\n    return backend2.makeTensorInfo($size, x.dtype, outValues);\n  }\n  const { isPacked } = backend2.texData.get(x.dataId);\n  const isContinous = slice_util_exports.isSliceContinous(x.shape, $begin, $size);\n  if (isPacked || !isContinous) {\n    const program = env().getBool(\"WEBGL_PACK_ARRAY_OPERATIONS\") ? new SlicePackedProgram($size) : new SliceProgram($size);\n    const customValues = [$begin];\n    return backend2.runWebGLProgram(program, [x], x.dtype, customValues);\n  }\n  backend2.uploadToGPU(x.dataId);\n  return shallowSlice(x, $begin, $size, backend2);\n}\nvar sliceConfig2 = {\n  kernelName: Slice,\n  backendName: \"webgl\",\n  kernelFunc: slice3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/BatchToSpaceND.js\nvar batchToSpaceND3 = (args) => {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x } = inputs;\n  const { blockShape, crops } = attrs;\n  util_exports.assert(x.shape.length <= 4, () => \"batchToSpaceND for rank > 4 with a WebGL backend not implemented yet\");\n  const prod6 = blockShape.reduce((a, b) => a * b);\n  const reshaped = backend_util_exports.getReshaped(x.shape, blockShape, prod6);\n  const permuted = backend_util_exports.getPermuted(reshaped.length, blockShape.length);\n  const reshapedPermuted = backend_util_exports.getReshapedPermuted(x.shape, blockShape, prod6);\n  const sliceBeginCoords = backend_util_exports.getSliceBeginCoords(crops, blockShape.length);\n  const sliceSize = backend_util_exports.getSliceSize(reshapedPermuted, crops, blockShape.length);\n  const toDispose = [];\n  const reshapedIntermediate = reshape4({ inputs: { x }, backend: backend2, attrs: { shape: reshaped } });\n  const transposedIntermediate = transpose3({ inputs: { x: reshapedIntermediate }, backend: backend2, attrs: { perm: permuted } });\n  const reshapedIntermediate2 = reshape4({\n    inputs: { x: transposedIntermediate },\n    backend: backend2,\n    attrs: { shape: reshapedPermuted }\n  });\n  const sliced = slice3({\n    inputs: { x: reshapedIntermediate2 },\n    backend: backend2,\n    attrs: { begin: sliceBeginCoords, size: sliceSize }\n  });\n  toDispose.push(reshapedIntermediate);\n  toDispose.push(transposedIntermediate);\n  toDispose.push(reshapedIntermediate2);\n  toDispose.forEach((t) => backend2.disposeIntermediateTensorInfo(t));\n  return sliced;\n};\nvar batchToSpaceNDConfig2 = {\n  kernelName: BatchToSpaceND,\n  backendName: \"webgl\",\n  kernelFunc: batchToSpaceND3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Bincount.js\nfunction bincount3(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x, weights } = inputs;\n  const { size } = attrs;\n  const xVals = backend2.readSync(x.dataId);\n  const weightsVals = backend2.readSync(weights.dataId);\n  const outVals = bincountImplCPU(xVals, weightsVals, weights.dtype, weights.shape, size);\n  return backend2.makeTensorInfo([size], weights.dtype, outVals);\n}\nvar bincountConfig2 = {\n  kernelName: Bincount,\n  backendName: \"webgl\",\n  kernelFunc: bincount3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/BroadcastArgs.js\nfunction broadcastArgs3(args) {\n  const { inputs, backend: backend2 } = args;\n  const { s0, s1 } = inputs;\n  const s0Vals = backend2.readSync(s0.dataId);\n  const s1Vals = backend2.readSync(s1.dataId);\n  const broadcastShape = backend_util_exports.assertAndGetBroadcastShape(Array.from(s0Vals), Array.from(s1Vals));\n  return backend2.makeTensorInfo([broadcastShape.length], \"int32\", Int32Array.from(broadcastShape));\n}\nvar broadcastArgsConfig2 = {\n  kernelName: BroadcastArgs,\n  backendName: \"webgl\",\n  kernelFunc: broadcastArgs3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/NotEqual.js\nvar NOT_EQUAL = `return float(a != b);`;\nvar notEqual3 = binaryKernelFunc2({ opSnippet: NOT_EQUAL, cpuKernelImpl: notEqualImplCPU, dtype: \"bool\" });\nvar notEqualConfig2 = {\n  kernelName: NotEqual,\n  backendName: \"webgl\",\n  kernelFunc: notEqual3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Real.js\nfunction real3(args) {\n  const { inputs, backend: backend2 } = args;\n  const { input: input2 } = inputs;\n  const inputData = backend2.texData.get(input2.dataId);\n  return identity3({ inputs: { x: inputData.complexTensorInfos.real }, backend: backend2 });\n}\nvar realConfig2 = {\n  kernelName: Real,\n  backendName: \"webgl\",\n  kernelFunc: real3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernel_utils/int.js\nvar TO_INT = `return float(int(x));`;\nfunction int(input2, backend2) {\n  const program = new UnaryOpProgram(input2.shape, TO_INT);\n  const output = backend2.runWebGLProgram(program, [input2], \"int32\");\n  return { dataId: output.dataId, shape: output.shape, dtype: output.dtype };\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Cast.js\nfunction cast4(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x } = inputs;\n  const { dtype } = attrs;\n  if (dtype === \"complex64\") {\n    if (x.dtype === \"complex64\") {\n      return identity3({ inputs: { x }, backend: backend2 });\n    }\n    const zerosTensor = zeros(x.shape);\n    const floatX = cast4({ inputs: { x }, backend: backend2, attrs: { dtype: \"float32\" } });\n    const result = complex3({ inputs: { real: floatX, imag: zerosTensor }, backend: backend2 });\n    zerosTensor.dispose();\n    backend2.disposeIntermediateTensorInfo(floatX);\n    return result;\n  }\n  if (x.dtype === \"complex64\") {\n    const realPart = real3({ inputs: { input: x }, backend: backend2 });\n    const result = cast4({ inputs: { x: realPart }, backend: backend2, attrs: { dtype } });\n    backend2.disposeIntermediateTensorInfo(realPart);\n    return result;\n  }\n  if (!util_exports.hasEncodingLoss(x.dtype, dtype)) {\n    const result = identity3({ inputs: { x }, backend: backend2 });\n    return { dataId: result.dataId, shape: result.shape, dtype };\n  }\n  if (backend2.shouldExecuteOnCPU([x])) {\n    const values = backend2.texData.get(x.dataId).values;\n    const [resultShape, resultType, resultData] = castImplCPU(values, x.shape, x.dtype, dtype);\n    return backend2.makeTensorInfo(resultShape, resultType, resultData);\n  }\n  if (dtype === \"int32\") {\n    return int(x, backend2);\n  }\n  if (dtype === \"bool\") {\n    const zerosTensorInfo = backend2.makeTensorInfo([], \"bool\", util_exports.getTypedArrayFromDType(\"bool\", 1));\n    const binaryInputs = { a: x, b: zerosTensorInfo };\n    const result = notEqual3({ inputs: binaryInputs, backend: backend2 });\n    backend2.disposeIntermediateTensorInfo(zerosTensorInfo);\n    return result;\n  }\n  throw new Error(`Error in Cast: failed to cast ${x.dtype} to ${dtype}`);\n}\nvar castConfig2 = {\n  kernelName: Cast,\n  backendName: \"webgl\",\n  kernelFunc: cast4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Ceil.js\nvar CEIL = `return ceil(x);`;\nvar ceil3 = unaryKernelFunc2({ opSnippet: CEIL, packedOpSnippet: CEIL, cpuKernelImpl: ceilImplCPU });\nvar ceilConfig2 = {\n  kernelName: Ceil,\n  backendName: \"webgl\",\n  kernelFunc: ceil3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/clip_gpu.js\nvar ClipProgram = class {\n  constructor(aShape) {\n    this.variableNames = [\"A\"];\n    this.customUniforms = [\n      { name: \"minVal\", type: \"float\" },\n      { name: \"maxVal\", type: \"float\" }\n    ];\n    this.outputShape = aShape;\n    this.userCode = `\n\n      void main() {\n        float value = getAAtOutCoords();\n        if (isnan(value)) {\n          setOutput(value);\n          return;\n        }\n\n        setOutput(clamp(value, minVal, maxVal));\n      }\n    `;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/clip_packed_gpu.js\nvar ClipPackedProgram = class {\n  constructor(aShape) {\n    this.variableNames = [\"A\"];\n    this.packedInputs = true;\n    this.packedOutput = true;\n    this.customUniforms = [\n      { name: \"minVal\", type: \"float\" },\n      { name: \"maxVal\", type: \"float\" }\n    ];\n    this.outputShape = aShape;\n    this.userCode = `\n      void main() {\n        vec4 value = getAAtOutCoords();\n\n        if (any(isnan(value))) {\n          setOutput(value);\n          return;\n        }\n\n        setOutput(clamp(value, vec4(minVal), vec4(maxVal)));\n      }\n    `;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/ClipByValue.js\nfunction clipByValue3(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x } = inputs;\n  const { clipValueMin, clipValueMax } = attrs;\n  let program;\n  if (env().getBool(\"WEBGL_PACK_CLIP\")) {\n    program = new ClipPackedProgram(x.shape);\n  } else {\n    program = new ClipProgram(x.shape);\n  }\n  const customValues = [[clipValueMin], [clipValueMax]];\n  return backend2.runWebGLProgram(program, [x], x.dtype, customValues);\n}\nvar clipByValueConfig2 = {\n  kernelName: ClipByValue,\n  backendName: \"webgl\",\n  kernelFunc: clipByValue3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/complex_abs_gpu.js\nvar ComplexAbsProgram = class {\n  constructor(shape) {\n    this.variableNames = [\"real\", \"imag\"];\n    this.outputShape = shape;\n    this.userCode = `\n      void main() {\n        float re = abs(getRealAtOutCoords());\n        float im = abs(getImagAtOutCoords());\n        float mx = max(re, im);\n\n        // sadly the length function in glsl is not underflow-safe\n        // (at least not on Intel GPUs). So the safe solution is\n        // to ensure underflow-safety in all cases.\n        setOutput(\n          mx == 0.0 ? 0.0 : mx * length(vec2(1, min(re, im)/mx))\n        );\n      }\n    `;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/ComplexAbs.js\nfunction makeComplexComponentTensorInfo(complexTensor, complexPart) {\n  return {\n    dataId: complexPart.dataId,\n    dtype: complexPart.dtype,\n    shape: complexTensor.shape\n  };\n}\nfunction complexAbs2(args) {\n  const { inputs, backend: backend2 } = args;\n  const { x } = inputs;\n  const xData = backend2.texData.get(x.dataId);\n  const program = new ComplexAbsProgram(x.shape);\n  const programInputs = [\n    makeComplexComponentTensorInfo(x, xData.complexTensorInfos.real),\n    makeComplexComponentTensorInfo(x, xData.complexTensorInfos.imag)\n  ];\n  return backend2.runWebGLProgram(program, programInputs, programInputs[0].dtype);\n}\nvar complexAbsConfig2 = {\n  kernelName: ComplexAbs,\n  backendName: \"webgl\",\n  kernelFunc: complexAbs2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/concat_gpu.js\nvar ConcatProgram = class {\n  constructor(shapes) {\n    this.outputShape = [];\n    this.outputShape = backend_util_exports.computeOutShape(shapes, 1);\n    this.variableNames = shapes.map((_, i) => `T${i}`);\n    const offsets = new Array(shapes.length - 1);\n    offsets[0] = shapes[0][1];\n    for (let i = 1; i < offsets.length; i++) {\n      offsets[i] = offsets[i - 1] + shapes[i][1];\n    }\n    const snippets = [`if (yC < ${offsets[0]}) setOutput(getT0(yR, yC));`];\n    for (let i = 1; i < offsets.length; i++) {\n      const shift = offsets[i - 1];\n      snippets.push(`else if (yC < ${offsets[i]}) setOutput(getT${i}(yR, yC-${shift}));`);\n    }\n    const lastIndex = offsets.length;\n    const lastShift = offsets[offsets.length - 1];\n    snippets.push(`else setOutput(getT${lastIndex}(yR, yC-${lastShift}));`);\n    this.userCode = `\n      void main() {\n        ivec2 coords = getOutputCoords();\n        int yR = coords.x;\n        int yC = coords.y;\n\n        ${snippets.join(\"\\n        \")}\n      }\n    `;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/concat_packed_gpu.js\nvar ConcatPackedProgram = class {\n  constructor(shapes, axis) {\n    this.packedInputs = true;\n    this.packedOutput = true;\n    this.outputShape = [];\n    this.outputShape = backend_util_exports.computeOutShape(shapes, axis);\n    const shape = this.outputShape;\n    const rank = shape.length;\n    const dtype = getCoordsDataType(rank);\n    const coords3 = getChannels(\"coords\", rank);\n    const channels = [\"x\", \"y\", \"z\", \"w\", \"u\", \"v\"].slice(0, rank);\n    this.variableNames = shapes.map((_, i) => `T${i}`);\n    const offsets = new Array(shapes.length - 1);\n    offsets[0] = shapes[0][axis];\n    for (let i = 1; i < offsets.length; i++) {\n      offsets[i] = offsets[i - 1] + shapes[i][axis];\n    }\n    const channel = channels[axis];\n    const lastChannels = channels.slice(-2);\n    const allChannels = channels.join();\n    let getValueSnippet = `if (${channel} < ${offsets[0]}) {\n        return getChannel(\n            getT0(${allChannels}), vec2(${lastChannels.join()}));\n        }`;\n    for (let i = 1; i < offsets.length; i++) {\n      const shift2 = offsets[i - 1];\n      getValueSnippet += `\n        if (${channel} < ${offsets[i]}  && ${channel} >= ${offsets[i - 1]}) {\n          return getChannel(\n            getT${i}(${shiftedChannels(channels, channel, shift2)}),\n            vec2(${shiftedChannels(lastChannels, channel, shift2)}));\n        }`;\n    }\n    const lastIndex = offsets.length;\n    const shift = offsets[offsets.length - 1];\n    getValueSnippet += `\n        return getChannel(\n          getT${lastIndex}(${shiftedChannels(channels, channel, shift)}),\n          vec2(${shiftedChannels(lastChannels, channel, shift)}));`;\n    this.userCode = `\n      float getValue(${channels.map((x) => \"int \" + x)}) {\n        ${getValueSnippet}\n      }\n\n      void main() {\n        ${dtype} coords = getOutputCoords();\n        vec4 result = vec4(getValue(${coords3}), 0., 0., 0.);\n\n        ${coords3[rank - 1]} = ${coords3[rank - 1]} + 1;\n        if (${coords3[rank - 1]} < ${shape[rank - 1]}) {\n          result.g = getValue(${coords3});\n        }\n\n        ${coords3[rank - 2]} = ${coords3[rank - 2]} + 1;\n        if (${coords3[rank - 2]} < ${shape[rank - 2]}) {\n          result.a = getValue(${coords3});\n        }\n\n        ${coords3[rank - 1]} = ${coords3[rank - 1]} - 1;\n        if (${coords3[rank - 2]} < ${shape[rank - 2]} &&\n            ${coords3[rank - 1]} < ${shape[rank - 1]}) {\n          result.b = getValue(${coords3});\n        }\n        setOutput(result);\n      }\n    `;\n  }\n};\nfunction shiftedChannels(channels, channel, shift) {\n  const channelIdx = channels.indexOf(channel);\n  const res = channels.map((c, idx) => {\n    if (idx === channelIdx) {\n      return `${c} - ${shift}`;\n    } else {\n      return c;\n    }\n  });\n  return res.join();\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Imag.js\nfunction imag3(args) {\n  const { inputs, backend: backend2 } = args;\n  const { input: input2 } = inputs;\n  const inputData = backend2.texData.get(input2.dataId);\n  return identity3({ inputs: { x: inputData.complexTensorInfos.imag }, backend: backend2 });\n}\nvar imagConfig2 = {\n  kernelName: Imag,\n  backendName: \"webgl\",\n  kernelFunc: imag3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Concat_impl.js\nfunction concatImpl2(inputs, axis, backend2) {\n  const dtype = inputs[0].dtype;\n  if (dtype === \"complex64\") {\n    const reals = inputs.map((t) => real3({ inputs: { input: t }, backend: backend2 }));\n    const imags = inputs.map((t) => imag3({ inputs: { input: t }, backend: backend2 }));\n    const realConcated = concatImpl2(reals, axis, backend2);\n    const imagConcated = concatImpl2(imags, axis, backend2);\n    const result2 = complex3({ inputs: { real: realConcated, imag: imagConcated }, backend: backend2 });\n    reals.forEach((r) => backend2.disposeIntermediateTensorInfo(r));\n    imags.forEach((i) => backend2.disposeIntermediateTensorInfo(i));\n    backend2.disposeIntermediateTensorInfo(realConcated);\n    backend2.disposeIntermediateTensorInfo(imagConcated);\n    return result2;\n  }\n  let runOnCpu = backend2.shouldExecuteOnCPU(inputs);\n  if (dtype === \"string\") {\n    runOnCpu = true;\n  }\n  if (runOnCpu) {\n    const tensors2D2 = inputs.map((t) => {\n      const innerSize = util_exports.sizeFromShape(t.shape.slice(axis));\n      const shape = [-1, innerSize];\n      return reshape4({ inputs: { x: t }, backend: backend2, attrs: { shape } });\n    });\n    const inputsValShapes = tensors2D2.map((t) => {\n      return { vals: backend2.readSync(t.dataId), shape: t.shape };\n    });\n    const outShape2 = backend_util_exports.computeOutShape(tensors2D2.map((t) => t.shape), 1);\n    const simplyConcat = tensors2D2[0].shape[0] === 1;\n    const outVals = concatImplCPU(inputsValShapes, outShape2, dtype, simplyConcat);\n    const finalOutShape = backend_util_exports.computeOutShape(inputs.map((t) => t.shape), axis);\n    const outInfo = backend2.makeTensorInfo(finalOutShape, dtype, outVals);\n    tensors2D2.forEach((t) => backend2.disposeIntermediateTensorInfo(t));\n    return outInfo;\n  }\n  const maxTexturesInShader = env().getNumber(\"WEBGL_MAX_TEXTURES_IN_SHADER\");\n  if (inputs.length > maxTexturesInShader) {\n    const reducedInputs = [];\n    for (let i = 0; i < inputs.length; i += maxTexturesInShader) {\n      const subArray = inputs.slice(i, i + maxTexturesInShader);\n      reducedInputs.push(concatImpl2(subArray, axis, backend2));\n    }\n    const result2 = concatImpl2(reducedInputs, axis, backend2);\n    for (const i of reducedInputs) {\n      backend2.disposeIntermediateTensorInfo(i);\n    }\n    return result2;\n  }\n  if (env().getBool(\"WEBGL_PACK_ARRAY_OPERATIONS\") && inputs[0].shape.length > 1) {\n    const program2 = new ConcatPackedProgram(inputs.map((t) => t.shape), axis);\n    return backend2.runWebGLProgram(program2, inputs, dtype);\n  }\n  const { tensors2D, outShape } = computeTensors2D(inputs, axis, backend2);\n  const program = new ConcatProgram(tensors2D.map((t) => t.shape));\n  const result = backend2.runWebGLProgram(program, tensors2D, dtype);\n  tensors2D.forEach((r) => backend2.disposeIntermediateTensorInfo(r));\n  const reshapedResult = reshape4({ inputs: { x: result }, attrs: { shape: outShape }, backend: backend2 });\n  backend2.disposeIntermediateTensorInfo(result);\n  return reshapedResult;\n}\nfunction computeTensors2D(inputs, axis, backend2) {\n  const outShape = backend_util_exports.computeOutShape(inputs.map((t) => t.shape), axis);\n  const tensors2D = inputs.map((x) => reshape4({\n    inputs: { x },\n    attrs: { shape: [-1, util_exports.sizeFromShape(x.shape.slice(axis))] },\n    backend: backend2\n  }));\n  return { tensors2D, outShape };\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Concat.js\nfunction concat3(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { axis } = attrs;\n  const $axis = util_exports.parseAxisParam(axis, inputs[0].shape)[0];\n  const outShape = backend_util_exports.computeOutShape(inputs.map((t) => t.shape), $axis);\n  if (util_exports.sizeFromShape(outShape) === 0) {\n    return backend2.makeTensorInfo(outShape, inputs[0].dtype, []);\n  }\n  const $inputs = inputs.filter((t) => util_exports.sizeFromShape(t.shape) > 0);\n  if ($inputs.length === 1) {\n    return identity3({ inputs: { x: $inputs[0] }, backend: backend2 });\n  }\n  const shapes = $inputs.map((t) => t.shape);\n  backend_util_exports.assertParamsConsistent(shapes, $axis);\n  return concatImpl2($inputs, $axis, backend2);\n}\nvar concatConfig2 = {\n  kernelName: Concat,\n  backendName: \"webgl\",\n  kernelFunc: concat3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/conv_gpu.js\nvar Conv2DProgram = class {\n  constructor(convInfo, addBias = false, activation2 = null, hasPreluActivationWeights = false, hasLeakyreluAlpha = false) {\n    this.variableNames = [\"x\", \"W\"];\n    this.outputShape = convInfo.outShape;\n    const padTop = convInfo.padInfo.top;\n    const padLeft = convInfo.padInfo.left;\n    const strideHeight = convInfo.strideHeight;\n    const strideWidth = convInfo.strideWidth;\n    const dilationHeight = convInfo.dilationHeight;\n    const dilationWidth = convInfo.dilationWidth;\n    const filterHeight = convInfo.filterHeight;\n    const filterWidth = convInfo.filterWidth;\n    const inputDepthNearestVec4 = Math.floor(convInfo.inChannels / 4) * 4;\n    const inputDepthVec4Remainder = convInfo.inChannels % 4;\n    const isChannelsLast = convInfo.dataFormat === \"channelsLast\";\n    const rowDim = isChannelsLast ? 1 : 2;\n    const colDim = isChannelsLast ? 2 : 3;\n    const channelDim = isChannelsLast ? 3 : 1;\n    let activationSnippet = \"\", applyActivationSnippet = \"\";\n    if (activation2) {\n      if (hasPreluActivationWeights) {\n        activationSnippet = `float activation(float a) {\n          float b = getPreluActivationWeightsAtOutCoords();\n          ${activation2}\n        }`;\n      } else if (hasLeakyreluAlpha) {\n        activationSnippet = `float activation(float a) {\n          float b = getLeakyreluAlphaAtOutCoords();\n          ${activation2}\n        }`;\n      } else {\n        activationSnippet = `\n          float activation(float x) {\n            ${activation2}\n          }\n        `;\n      }\n      applyActivationSnippet = `result = activation(result);`;\n    }\n    const addBiasSnippet = addBias ? \"result += getBiasAtOutCoords();\" : \"\";\n    if (addBias) {\n      this.variableNames.push(\"bias\");\n    }\n    if (hasPreluActivationWeights) {\n      this.variableNames.push(\"preluActivationWeights\");\n    }\n    if (hasLeakyreluAlpha) {\n      this.variableNames.push(\"leakyreluAlpha\");\n    }\n    this.userCode = `\n      ${activationSnippet}\n\n      const ivec2 strides = ivec2(${strideHeight}, ${strideWidth});\n      const ivec2 pads = ivec2(${padTop}, ${padLeft});\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int batch = coords[0];\n        int d2 = coords[${channelDim}];\n\n        ivec2 xRCCorner =\n            ivec2(coords[${rowDim}], coords[${colDim}]) * strides - pads;\n        int xRCorner = xRCCorner.x;\n        int xCCorner = xRCCorner.y;\n\n        // Convolve x(?, ?, d1) with w(:, :, d1, d2) to get y(yR, yC, d2).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n        for (int wR = 0; wR < ${filterHeight}; wR++) {\n          int xR = xRCorner + wR * ${dilationHeight};\n\n          if (xR < 0 || xR >= ${convInfo.inHeight}) {\n            continue;\n          }\n\n          for (int wC = 0; wC < ${filterWidth}; wC++) {\n            int xC = xCCorner + wC * ${dilationWidth};\n\n            if (xC < 0 || xC >= ${convInfo.inWidth}) {\n              continue;\n            }\n\n            for (int d1 = 0; d1 < ${inputDepthNearestVec4}; d1 += 4) {\n              vec4 wValues = vec4(\n                getW(wR, wC, d1, d2),\n                getW(wR, wC, d1 + 1, d2),\n                getW(wR, wC, d1 + 2, d2),\n                getW(wR, wC, d1 + 3, d2)\n              );\n\n              if (${isChannelsLast}) {\n                vec4 xValues = vec4(\n                  getX(batch, xR, xC, d1),\n                  getX(batch, xR, xC, d1 + 1),\n                  getX(batch, xR, xC, d1 + 2),\n                  getX(batch, xR, xC, d1 + 3)\n                );\n                dotProd += dot(xValues, wValues);\n              } else {\n                vec4 xValues = vec4(\n                  getX(batch, d1, xR, xC),\n                  getX(batch, d1 + 1, xR, xC),\n                  getX(batch, d1 + 2, xR, xC),\n                  getX(batch, d1 + 3, xR, xC)\n                );\n                dotProd += dot(xValues, wValues);\n              }\n            }\n\n            if (${inputDepthVec4Remainder === 1}) {\n\n              if (${isChannelsLast}) {\n                dotProd +=\n                    getX(batch, xR, xC, ${inputDepthNearestVec4}) *\n                    getW(wR, wC, ${inputDepthNearestVec4}, d2);\n              } else {\n                dotProd +=\n                    getX(batch, ${inputDepthNearestVec4}, xR, xC) *\n                    getW(wR, wC, ${inputDepthNearestVec4}, d2);\n              }\n\n            } else if (${inputDepthVec4Remainder === 2}) {\n              vec2 wValues = vec2(\n                getW(wR, wC, ${inputDepthNearestVec4}, d2),\n                getW(wR, wC, ${inputDepthNearestVec4} + 1, d2)\n              );\n\n              if (${isChannelsLast}) {\n                vec2 xValues = vec2(\n                  getX(batch, xR, xC, ${inputDepthNearestVec4}),\n                  getX(batch, xR, xC, ${inputDepthNearestVec4} + 1)\n                );\n                dotProd += dot(xValues, wValues);\n              } else {\n                vec2 xValues = vec2(\n                  getX(batch, ${inputDepthNearestVec4}, xR, xC),\n                  getX(batch, ${inputDepthNearestVec4} + 1, xR, xC)\n                );\n                dotProd += dot(xValues, wValues);\n              }\n\n            } else if (${inputDepthVec4Remainder === 3}) {\n              vec3 wValues = vec3(\n                getW(wR, wC, ${inputDepthNearestVec4}, d2),\n                getW(wR, wC, ${inputDepthNearestVec4} + 1, d2),\n                getW(wR, wC, ${inputDepthNearestVec4} + 2, d2)\n              );\n\n              if (${isChannelsLast}) {\n                vec3 xValues = vec3(\n                  getX(batch, xR, xC, ${inputDepthNearestVec4}),\n                  getX(batch, xR, xC, ${inputDepthNearestVec4} + 1),\n                  getX(batch, xR, xC, ${inputDepthNearestVec4} + 2)\n                );\n                dotProd += dot(xValues, wValues);\n              } else {\n                vec3 xValues = vec3(\n                  getX(batch, ${inputDepthNearestVec4}, xR, xC),\n                  getX(batch, ${inputDepthNearestVec4} + 1, xR, xC),\n                  getX(batch, ${inputDepthNearestVec4} + 2, xR, xC)\n                );\n                dotProd += dot(xValues, wValues);\n              }\n\n            }\n          }\n        }\n\n        float result = dotProd;\n        ${addBiasSnippet}\n        ${applyActivationSnippet}\n        setOutput(result);\n      }\n    `;\n  }\n};\nvar Conv3DProgram = class {\n  constructor(convInfo) {\n    this.variableNames = [\"x\", \"W\"];\n    this.outputShape = convInfo.outShape;\n    const padFront = convInfo.padInfo.front;\n    const padTop = convInfo.padInfo.top;\n    const padLeft = convInfo.padInfo.left;\n    const strideDepth = convInfo.strideDepth;\n    const strideHeight = convInfo.strideHeight;\n    const strideWidth = convInfo.strideWidth;\n    const dilationDepth = convInfo.dilationDepth;\n    const dilationHeight = convInfo.dilationHeight;\n    const dilationWidth = convInfo.dilationWidth;\n    const filterDepth = convInfo.filterDepth;\n    const filterHeight = convInfo.filterHeight;\n    const filterWidth = convInfo.filterWidth;\n    const inputDepthNearestVec4 = Math.floor(convInfo.inChannels / 4) * 4;\n    const inputDepthVec4Remainder = convInfo.inChannels % 4;\n    this.userCode = `\n      const ivec3 strides = ivec3(${strideDepth}, ${strideHeight}, ${strideWidth});\n      const ivec3 pads = ivec3(${padFront}, ${padTop}, ${padLeft});\n\n      void main() {\n        ivec5 coords = getOutputCoords();\n        int batch = coords.x;\n        int d2 = coords.u;\n\n        ivec3 xFRCCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;\n        int xFCorner = xFRCCorner.x;\n        int xRCorner = xFRCCorner.y;\n        int xCCorner = xFRCCorner.z;\n\n        // Convolve x(?, ?, ?, d1) with w(:, :, :, d1, d2) to get\n        // y(yF, yR, yC, d2). ? = to be determined. : = across all\n        // values in that axis.\n        float dotProd = 0.0;\n        for (int wF = 0; wF < ${filterDepth}; wF++) {\n          int xF = xFCorner + wF * ${dilationDepth};\n\n          if (xF < 0 || xF >= ${convInfo.inDepth}) {\n            continue;\n          }\n\n          for (int wR = 0; wR < ${filterHeight}; wR++) {\n            int xR = xRCorner + wR * ${dilationHeight};\n\n            if (xR < 0 || xR >= ${convInfo.inHeight}) {\n              continue;\n            }\n\n            for (int wC = 0; wC < ${filterWidth}; wC++) {\n              int xC = xCCorner + wC * ${dilationWidth};\n\n              if (xC < 0 || xC >= ${convInfo.inWidth}) {\n                continue;\n              }\n\n              for (int d1 = 0; d1 < ${inputDepthNearestVec4}; d1 += 4) {\n                vec4 xValues = vec4(\n                  getX(batch, xF, xR, xC, d1),\n                  getX(batch, xF, xR, xC, d1 + 1),\n                  getX(batch, xF, xR, xC, d1 + 2),\n                  getX(batch, xF, xR, xC, d1 + 3)\n                );\n                vec4 wValues = vec4(\n                  getW(wF, wR, wC, d1, d2),\n                  getW(wF, wR, wC, d1 + 1, d2),\n                  getW(wF, wR, wC, d1 + 2, d2),\n                  getW(wF, wR, wC, d1 + 3, d2)\n                );\n\n                dotProd += dot(xValues, wValues);\n              }\n\n              if (${inputDepthVec4Remainder === 1}) {\n                dotProd +=\n                  getX(batch, xF, xR, xC, ${inputDepthNearestVec4}) *\n                  getW(wF, wR, wC, ${inputDepthNearestVec4}, d2);\n              } else if (${inputDepthVec4Remainder === 2}) {\n                vec2 xValues = vec2(\n                  getX(batch, xF, xR, xC, ${inputDepthNearestVec4}),\n                  getX(batch, xF, xR, xC, ${inputDepthNearestVec4} + 1)\n                );\n                vec2 wValues = vec2(\n                  getW(wF, wR, wC, ${inputDepthNearestVec4}, d2),\n                  getW(wF, wR, wC, ${inputDepthNearestVec4} + 1, d2)\n                );\n                dotProd += dot(xValues, wValues);\n              } else if (${inputDepthVec4Remainder === 3}) {\n                vec3 xValues = vec3(\n                  getX(batch, xF, xR, xC, ${inputDepthNearestVec4}),\n                  getX(batch, xF, xR, xC, ${inputDepthNearestVec4} + 1),\n                  getX(batch, xF, xR, xC, ${inputDepthNearestVec4} + 2)\n                );\n                vec3 wValues = vec3(\n                  getW(wF, wR, wC, ${inputDepthNearestVec4}, d2),\n                  getW(wF, wR, wC, ${inputDepthNearestVec4} + 1, d2),\n                  getW(wF, wR, wC, ${inputDepthNearestVec4} + 2, d2)\n                );\n                dotProd += dot(xValues, wValues);\n              }\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    `;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/conv_packed_gpu.js\nvar Conv2DPackedProgram = class {\n  constructor(convInfo, addBias = false, activation2 = null, hasPreluActivation = false, hasLeakyReluAlpha = false) {\n    this.variableNames = [\"x\", \"W\"];\n    this.packedInputs = true;\n    this.packedOutput = true;\n    this.customUniforms = [\n      { name: \"pads\", type: \"ivec2\" },\n      { name: \"strides\", type: \"ivec2\" },\n      { name: \"dilations\", type: \"ivec2\" },\n      { name: \"inDims\", type: \"ivec2\" }\n    ];\n    this.outputShape = convInfo.outShape;\n    this.enableShapeUniforms = useShapeUniforms(this.outputShape.length);\n    const padLeft = convInfo.padInfo.left;\n    const strideWidth = convInfo.strideWidth;\n    const dilationWidth = convInfo.dilationWidth;\n    const filterHeight = convInfo.filterHeight;\n    const filterWidth = convInfo.filterWidth;\n    const texelsAcross = filterWidth;\n    let mainLoop = `\n       int xR; int xC; int xCOffset;\n       vec4 wTexel; vec4 previous; vec4 final;`;\n    for (let c = 0; c < filterWidth; c++) {\n      mainLoop += `\n           vec4 xTexelC${c * 2};\n           int xTexelC${c * 2}Ready;\n           vec4 xTexelC${c * 2 + 1};\n           int xTexelC${c * 2 + 1}Ready;\n           vec4 xC${c};`;\n    }\n    mainLoop += `\n     for (int r = 0; r < ${filterHeight}; r++) {\n      for (int d1 = 0; d1 < ${convInfo.inChannels}; d1 += 2) {\n       `;\n    for (let c = 0; c < filterWidth; c++) {\n      mainLoop += `\n           xTexelC${c * 2} = vec4(0.0);\n           xTexelC${c * 2}Ready = 0;\n           xTexelC${c * 2 + 1} = vec4(0.0);\n           xTexelC${c * 2 + 1}Ready = 0;\n           xC${c} = vec4(0.0);`;\n    }\n    mainLoop += `\n         xR = xRCorner + r * dilations[0];\n         if (xR >=0 && xR < inDims[0]) {\n       `;\n    for (let texelC = 0; texelC < (texelsAcross + 1) / 2; texelC++) {\n      const colIndex = texelC * 2;\n      mainLoop += `\n           xC = xCCorner + ${colIndex * dilationWidth};\n           `;\n      if (strideWidth === 1) {\n        if (colIndex < filterWidth) {\n          if (padLeft % 2 === 1) {\n            mainLoop += `\n                 xCOffset = xC + 1;\n                 if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${colIndex}Ready == 0) {\n                   xTexelC${colIndex} = getX(batch, xR, xCOffset, d1);\n\n                   // Need to manually clear unused channels in case\n                   // we're reading from recycled texture.\n                   if (xCOffset + 1 >= inDims[1]) {\n                     xTexelC${colIndex}.zw = vec2(0.0);\n                   }\n                   xTexelC${colIndex}Ready = 1;\n                 }\n               `;\n            if (dilationWidth === 1 && colIndex > 0) {\n              mainLoop += `\n                 xC${colIndex} = vec4(xTexelC${colIndex - 2}.zw, xTexelC${colIndex}.xy);\n                 `;\n            } else {\n              mainLoop += `\n                   xCOffset = xC + 1 - 2;\n\n                   if (xCOffset >= 0 && xCOffset < inDims[1]) {\n                     previous = getX(batch, xR, xCOffset, d1);\n\n                     // Need to manually clear unused channels in case\n                     // we're reading from recycled texture.\n                     if (xCOffset + 1 >= inDims[1]) {\n                       previous.zw = vec2(0.0);\n                     }\n\n                     xC${colIndex} = vec4(previous.zw, xTexelC${colIndex}.xy);\n                   } else {\n                     xC${colIndex} = vec4(0.0, 0.0, xTexelC${colIndex}.xy);\n                   }\n                   `;\n            }\n          } else {\n            mainLoop += `\n                 if (xC >= 0 && xC < inDims[1] && xTexelC${colIndex}Ready == 0) {\n                   xTexelC${colIndex} = getX(batch, xR, xC, d1);\n                   if (xC + 1 >= inDims[1]) {\n                     xTexelC${colIndex}.zw = vec2(0.0);\n                   }\n                   xTexelC${colIndex}Ready = 1;\n                 }\n\n                 xC${colIndex} = xTexelC${colIndex};\n                 `;\n          }\n          if (colIndex + 1 < filterWidth) {\n            const nextTexelOffset = padLeft % 2 === 0 ? util_exports.nearestLargerEven(dilationWidth) : dilationWidth;\n            if (dilationWidth % 2 === 0 && padLeft % 2 === 1 || dilationWidth % 2 !== 0 && padLeft % 2 !== 1) {\n              mainLoop += `\n                   xCOffset = xC + imod(pads[1], 2) + ${nextTexelOffset};\n\n                   if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${colIndex + 1}Ready == 0) {\n                     xTexelC${colIndex + 1} = getX(batch, xR, xCOffset, d1);\n\n                     // Need to manually clear unused channels in case\n                     // we're reading from recycled texture.\n                     if (xCOffset + 1 >= inDims[1]) {\n                       xTexelC${colIndex + 1}.zw = vec2(0.0);\n                     }\n                     xTexelC${colIndex + 1}Ready = 1;\n                   }\n                   `;\n              if (dilationWidth > 1) {\n                mainLoop += `\n                     xCOffset -= 2;\n                     if (xCOffset >= 0 && xCOffset < inDims[1]) {\n                      previous = getX(batch, xR, xCOffset, d1);\n                      xC${colIndex + 1} = vec4(previous.zw, xTexelC${colIndex + 1}.xy);\n                     } else {\n                      xC${colIndex + 1} = vec4(0.0, 0.0, xTexelC${colIndex + 1}.xy);\n                     }\n                     `;\n              } else {\n                mainLoop += `\n                     xC${colIndex + 1} = vec4(xTexelC${colIndex}.zw, xTexelC${colIndex + 1}.xy);\n                     `;\n              }\n            } else {\n              if (nextTexelOffset === 1) {\n                mainLoop += `\n                     xC${colIndex + 1} = xTexelC${colIndex};\n                     `;\n              } else {\n                mainLoop += `\n                     xCOffset = xC + ${nextTexelOffset};\n\n                     if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${colIndex + 1}Ready == 0) {\n                       xTexelC${colIndex + 1} = getX(batch, xR, xCOffset, d1);\n                       if (xCOffset + 1 >= inDims[1]) {\n                         xTexelC${colIndex + 1}.zw = vec2(0.0);\n                       }\n                       xTexelC${colIndex + 1}Ready = 1;\n                     }\n\n                     xC${colIndex + 1} = xTexelC${colIndex + 1};\n                     `;\n              }\n            }\n          }\n        }\n      } else {\n        if (colIndex < filterWidth) {\n          if (padLeft % 2 === 1) {\n            mainLoop += `\n                 xCOffset = xC + 1 - strides[1];\n                 if(xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${colIndex}Ready == 0) {\n                   xTexelC${colIndex} = getX(batch, xR, xCOffset, d1);\n                   // Need to manually clear unused channels in case\n                   // we're reading from recycled texture.\n                   if (xCOffset + 1 >= inDims[1]) {\n                     xTexelC${colIndex}.zw = vec2(0.0);\n                   }\n                   xTexelC${colIndex}Ready = 1;\n                 }\n\n                 if(xC + 1 >= 0 && xC + 1 < inDims[1] && xTexelC${colIndex + 1}Ready == 0) {\n                   xTexelC${colIndex + 1} = getX(batch, xR, xC + 1, d1);\n                   // Need to manually clear unused channels in case\n                   // we're reading from recycled texture.\n                   if (xC + 2 >= inDims[1]) {\n                     xTexelC${colIndex + 1}.zw = vec2(0.0);\n                   }\n                   xTexelC${colIndex + 1}Ready = 1;\n                 }\n\n                 xC${colIndex} = vec4(xTexelC${colIndex}.zw, xTexelC${colIndex + 1}.zw);\n               `;\n            if (colIndex + 1 < filterWidth) {\n              mainLoop += `\n                   final = vec4(0.0);\n                   xCOffset = xC + 1 + strides[1];\n                   if(xCOffset >= 0 && xCOffset < inDims[1]) {\n                     final = getX(batch, xR, xCOffset, d1);\n                   }\n                   xC${colIndex + 1} = vec4(xTexelC${colIndex + 1}.xy, final.xy);\n                 `;\n            }\n          } else {\n            mainLoop += `\n                 if(xC >= 0 && xC < inDims[1] && xTexelC${colIndex}Ready == 0) {\n                   xTexelC${colIndex} = getX(batch, xR, xC, d1);\n                   if (xC + 1 >= inDims[1]) {\n                     xTexelC${colIndex}.zw = vec2(0.0);\n                   }\n                   xTexelC${colIndex}Ready = 1;\n                 }\n\n                 xCOffset = xC + strides[1];\n                 if(xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${colIndex + 1}Ready == 0) {\n                   xTexelC${colIndex + 1} = getX(batch, xR, xCOffset, d1);\n                   if (xCOffset + 1 >= inDims[1]) {\n                     xTexelC${colIndex + 1}.zw = vec2(0.);\n                   }\n                   xTexelC${colIndex + 1}Ready = 1;\n                 }\n\n                 xC${colIndex} = vec4(\n                   xTexelC${colIndex}.xy, xTexelC${colIndex + 1}.xy);\n               `;\n            if (colIndex + 1 < filterWidth) {\n              mainLoop += `\n                   xC${colIndex + 1} = vec4(xTexelC${colIndex}.zw, xTexelC${colIndex + 1}.zw);\n                 `;\n            }\n          }\n        }\n      }\n      if (colIndex < filterWidth) {\n        mainLoop += `\n             wTexel = getW(r, ${colIndex}, d1, d2);\n             dotProd += xC${colIndex}.xxzz * vec4(wTexel.xy, wTexel.xy);\n             if(d1 + 1 < ${convInfo.inChannels}) {\n               dotProd += xC${colIndex}.yyww * vec4(wTexel.zw, wTexel.zw);\n             }\n           `;\n        if (colIndex + 1 < filterWidth) {\n          mainLoop += `\n               wTexel = getW(r, ${colIndex + 1}, d1, d2);\n               dotProd += xC${colIndex + 1}.xxzz * vec4(wTexel.xy, wTexel.xy);\n               if(d1 + 1 < ${convInfo.inChannels}) {\n                 dotProd += xC${colIndex + 1}.yyww * vec4(wTexel.zw, wTexel.zw);\n               }\n             `;\n        }\n      }\n    }\n    mainLoop += `\n     }\n   `;\n    mainLoop += `\n     }\n   `;\n    mainLoop += `\n     }\n   `;\n    let activationSnippet = \"\", applyActivationSnippet = \"\";\n    if (activation2) {\n      if (hasPreluActivation) {\n        activationSnippet = `vec4 activation(vec4 a) {\n           vec4 b = getPreluActivationWeightsAtOutCoords();\n           ${activation2}\n         }`;\n      } else if (hasLeakyReluAlpha) {\n        activationSnippet = `vec4 activation(vec4 a) {\n           vec4 b = getLeakyreluAlphaAtOutCoords();\n           ${activation2}\n         }`;\n      } else {\n        activationSnippet = `vec4 activation(vec4 x) {\n           ${activation2}\n         }`;\n      }\n      applyActivationSnippet = `result = activation(result);`;\n    }\n    const addBiasSnippet = addBias ? \"result += getBiasAtOutCoords();\" : \"\";\n    if (addBias) {\n      this.variableNames.push(\"bias\");\n    }\n    if (hasPreluActivation) {\n      this.variableNames.push(\"preluActivationWeights\");\n    }\n    if (hasLeakyReluAlpha) {\n      this.variableNames.push(\"leakyreluAlpha\");\n    }\n    this.userCode = `\n       ${activationSnippet}\n\n       void main() {\n         ivec4 coords = getOutputCoords();\n         int batch = coords.x;\n         ivec2 xRCCorner = coords.yz * strides - pads;\n         int d2 = coords.w;\n         int xRCorner = xRCCorner.x;\n         int xCCorner = xRCCorner.y;\n\n         //intialize dotProd with a small epsilon seems to reduce GPU accuracy loss.\n         vec4 dotProd = vec4(0.000000000000001);\n\n         ${mainLoop}\n\n         vec4 result = dotProd - vec4(0.000000000000001);\n         ${addBiasSnippet}\n         ${applyActivationSnippet}\n         setOutput(result);\n       }\n     `;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/im2col_packed_gpu.js\nvar Im2ColPackedProgram = class {\n  constructor(outputShape, convInfo) {\n    this.variableNames = [\"A\"];\n    this.packedInputs = true;\n    this.packedOutput = true;\n    this.customUniforms = [\n      { name: \"inputShape\", type: \"ivec4\" },\n      { name: \"pad\", type: \"ivec2\" },\n      { name: \"stride\", type: \"ivec2\" },\n      { name: \"dilation\", type: \"ivec2\" },\n      { name: \"inChannels\", type: \"int\" },\n      { name: \"itemsPerBlockRow\", type: \"int\" },\n      { name: \"outWidth\", type: \"int\" }\n    ];\n    this.outputShape = outputShape;\n    this.enableShapeUniforms = useShapeUniforms(this.outputShape.length);\n    const { dataFormat } = convInfo;\n    const glsl = getGlslDifferences();\n    const isChannelsLast = dataFormat === \"channelsLast\";\n    const rowDim = isChannelsLast ? 1 : 2;\n    const colDim = isChannelsLast ? 2 : 3;\n    const boundsCheckingSnippet = this.enableShapeUniforms ? \"if(blockIndex < outShape[2] && pos < outShape[1]) {\" : `if(blockIndex < ${outputShape[2]} && pos < ${outputShape[1]}) {`;\n    let unrolled = ``;\n    for (let row = 0; row <= 1; row++) {\n      for (let col = 0; col <= 1; col++) {\n        unrolled += `\n          blockIndex = rc.z + ${col};\n          pos = rc.y + ${row};\n\n          ${boundsCheckingSnippet}\n            offsetY = int(blockIndex / outWidth) * stride[0] - pad[0];\n            d0 = offsetY + dilation[0] * (pos / itemsPerBlockRow);\n\n            if(d0 < inputShape[${rowDim}] && d0 >= 0) {\n              // Use custom imod instead mod. On Intel GPU, mod may generate\n              // unexpected value.\n              // https://github.com/tensorflow/tfjs/issues/5447\n              offsetX = imod(blockIndex, outWidth) * stride[1] - pad[1];\n              d1 = offsetX + dilation[1] * (imod(pos, itemsPerBlockRow) /\n                  inChannels);\n\n              if(d1 < inputShape[${colDim}] && d1 >= 0) {\n\n                ch = imod(pos, inChannels);\n\n                if (${isChannelsLast}) {\n                  innerDims = vec2(d1, ch);\n                  result[${row * 2 + col}] = getChannel(\n                    getA(rc.x, d0, int(innerDims.x),\n                    int(innerDims.y)), innerDims);\n                } else {\n                  innerDims = vec2(d0, d1);\n                  result[${row * 2 + col}] = getChannel(\n                    getA(rc.x, ch, int(innerDims.x),\n                    int(innerDims.y)), innerDims);\n                }\n              }\n            }\n          }\n        `;\n      }\n    }\n    this.userCode = `\n      void main() {\n        ivec3 rc = getOutputCoords();\n\n        vec4 result = vec4(0);\n\n        int blockIndex, pos, offsetY, d0, offsetX, d1, ch;\n        vec2 innerDims;\n\n        ${unrolled}\n\n        ${glsl.output} = result;\n      }\n    `;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Conv2D_impl.js\nfunction getShapeForBatchMatMul(shape, isChannelsLast) {\n  const length = shape.length;\n  if (length >= 3) {\n    return isChannelsLast ? [\n      ...shape.slice(0, -3),\n      shape[length - 3] * shape[length - 2],\n      shape[length - 1]\n    ] : [\n      ...shape.slice(0, -3),\n      shape[length - 3],\n      shape[length - 2] * shape[length - 1]\n    ];\n  } else if (!isChannelsLast && length === 1 && shape[0] > 1) {\n    return [shape[0], 1];\n  } else {\n    return null;\n  }\n}\nfunction conv2dByMatMul({ x, filter, convInfo, backend: backend2, bias = null, preluActivationWeights = null, leakyreluAlpha = 0, activation: activation2 = null }) {\n  const xShape = x.shape;\n  const xTexData = backend2.texData.get(x.dataId);\n  const sharedMatMulDim = convInfo.inChannels;\n  const outerShapeX = xShape[0] * xShape[1] * xShape[2];\n  const outerShapeFilter = convInfo.outChannels;\n  const isChannelsLast = convInfo.dataFormat === \"channelsLast\";\n  const transposeA = false;\n  const transposeB = false;\n  let out;\n  const intermediates = [];\n  if (preluActivationWeights != null) {\n    const targetShape = getShapeForBatchMatMul(preluActivationWeights.shape, isChannelsLast);\n    if (targetShape != null) {\n      preluActivationWeights = reshape4({\n        inputs: { x: preluActivationWeights },\n        backend: backend2,\n        attrs: { shape: targetShape }\n      });\n      intermediates.push(preluActivationWeights);\n    }\n  }\n  if (bias != null) {\n    const targetShape = getShapeForBatchMatMul(bias.shape, isChannelsLast);\n    if (targetShape != null) {\n      bias = reshape4({ inputs: { x: bias }, backend: backend2, attrs: { shape: targetShape } });\n      intermediates.push(bias);\n    }\n  }\n  const batchMatMulWillBeUnpacked = (outerShapeX === 1 || outerShapeFilter === 1) && sharedMatMulDim > MATMUL_SHARED_DIM_THRESHOLD;\n  const canOptimize = !batchMatMulWillBeUnpacked && xTexData.isPacked && isChannelsLast && xTexData.texture != null && xShape[2] % 2 !== 0 && util_exports.arraysEqual(xTexData.shape.slice(-3), xShape.slice(-3));\n  if (canOptimize) {\n    const targetShape = xShape[0] * xShape[1] * (xShape[2] + 1);\n    const xReshaped = {\n      dataId: x.dataId,\n      shape: [1, targetShape, convInfo.inChannels],\n      dtype: x.dtype\n    };\n    const originalXTexDataShape = xTexData.shape;\n    xTexData.shape = xTexData.shape.slice();\n    xTexData.shape[xTexData.shape.length - 2]++;\n    util_exports.assert(isReshapeFree(xTexData.shape, xReshaped.shape), () => `packed reshape ${xTexData.shape} to ${xReshaped.shape} isn't free`);\n    const filterReshaped = reshape4({\n      inputs: { x: filter },\n      backend: backend2,\n      attrs: { shape: [1, convInfo.inChannels, convInfo.outChannels] }\n    });\n    intermediates.push(filterReshaped);\n    const pointwiseConv = batchMatMulImpl({\n      a: xReshaped,\n      b: filterReshaped,\n      backend: backend2,\n      transposeA,\n      transposeB,\n      bias,\n      activation: activation2,\n      preluActivationWeights,\n      leakyreluAlpha\n    });\n    const pointwiseConvTexData = backend2.texData.get(pointwiseConv.dataId);\n    util_exports.assert(pointwiseConvTexData.isPacked, () => \"batchMatMul result is expected to be packed\");\n    xTexData.shape = originalXTexDataShape;\n    pointwiseConvTexData.shape = convInfo.outShape;\n    out = identity3({ inputs: { x: pointwiseConv }, backend: backend2 });\n    out.shape = convInfo.outShape;\n    intermediates.push(pointwiseConv);\n  } else {\n    const numCols = convInfo.outHeight * convInfo.outWidth;\n    const xReshaped = reshape4({\n      inputs: { x },\n      backend: backend2,\n      attrs: {\n        shape: isChannelsLast ? [convInfo.batchSize, numCols, convInfo.inChannels] : [convInfo.batchSize, convInfo.inChannels, numCols]\n      }\n    });\n    const filterReshaped = reshape4({\n      inputs: { x: filter },\n      backend: backend2,\n      attrs: { shape: [1, convInfo.inChannels, convInfo.outChannels] }\n    });\n    const result = batchMatMulImpl({\n      a: isChannelsLast ? xReshaped : filterReshaped,\n      b: isChannelsLast ? filterReshaped : xReshaped,\n      transposeA: !isChannelsLast,\n      transposeB,\n      backend: backend2,\n      bias,\n      activation: activation2,\n      preluActivationWeights,\n      leakyreluAlpha\n    });\n    out = reshape4({ inputs: { x: result }, backend: backend2, attrs: { shape: convInfo.outShape } });\n    intermediates.push(xReshaped);\n    intermediates.push(filterReshaped);\n    intermediates.push(result);\n  }\n  for (const i of intermediates) {\n    backend2.disposeIntermediateTensorInfo(i);\n  }\n  return out;\n}\nfunction conv2dWithIm2Row({ x, filter, convInfo, backend: backend2, bias = null, preluActivationWeights = null, leakyreluAlpha = 0, activation: activation2 = null }) {\n  const { filterWidth, filterHeight, inChannels, outWidth, outHeight, dataFormat } = convInfo;\n  const isChannelsLast = dataFormat === \"channelsLast\";\n  const sharedDim = filterWidth * filterHeight * inChannels;\n  const numCols = outHeight * outWidth;\n  const x2ColShape = [convInfo.batchSize, sharedDim, numCols];\n  const transposeA = true;\n  const transposeB = false;\n  const intermediates = [];\n  if (preluActivationWeights != null) {\n    const targetShape = getShapeForBatchMatMul(preluActivationWeights.shape, isChannelsLast);\n    if (targetShape != null) {\n      preluActivationWeights = reshape4({\n        inputs: { x: preluActivationWeights },\n        backend: backend2,\n        attrs: { shape: targetShape }\n      });\n      intermediates.push(preluActivationWeights);\n    }\n  }\n  if (bias != null) {\n    const targetShape = getShapeForBatchMatMul(bias.shape, isChannelsLast);\n    if (targetShape != null) {\n      bias = reshape4({ inputs: { x: bias }, backend: backend2, attrs: { shape: targetShape } });\n      intermediates.push(bias);\n    }\n  }\n  const w2Row = reshape4({\n    inputs: { x: filter },\n    backend: backend2,\n    attrs: { shape: [1, sharedDim, util_exports.sizeFromShape(filter.shape) / sharedDim] }\n  });\n  intermediates.push(w2Row);\n  const im2ColProgram = new Im2ColPackedProgram(x2ColShape, convInfo);\n  const customValues = [\n    x.shape,\n    [convInfo.padInfo.top, convInfo.padInfo.left],\n    [convInfo.strideHeight, convInfo.strideWidth],\n    [convInfo.dilationHeight, convInfo.dilationWidth],\n    [convInfo.inChannels],\n    [convInfo.filterWidth * convInfo.inChannels],\n    [convInfo.outWidth]\n  ];\n  const im2Col = backend2.runWebGLProgram(im2ColProgram, [x], \"float32\", customValues);\n  const im2ColReshaped = reshape4({ inputs: { x: im2Col }, backend: backend2, attrs: { shape: x2ColShape } });\n  intermediates.push(im2Col);\n  intermediates.push(im2ColReshaped);\n  const hasBias = bias != null;\n  const hasPreluActivationWeights = preluActivationWeights != null;\n  const hasLeakyreluAlpha = activation2 === \"leakyrelu\";\n  const fusedActivation = activation2 ? mapActivationToShaderProgram(activation2, true) : null;\n  const matmulProgram = new MatMulPackedProgram(isChannelsLast ? im2ColReshaped.shape : w2Row.shape, isChannelsLast ? w2Row.shape : im2ColReshaped.shape, isChannelsLast ? [convInfo.batchSize, numCols, convInfo.outChannels] : [convInfo.batchSize, convInfo.outChannels, numCols], transposeA, transposeB, hasBias, fusedActivation, hasPreluActivationWeights, hasLeakyreluAlpha);\n  const inputs = isChannelsLast ? [im2ColReshaped, w2Row] : [w2Row, im2ColReshaped];\n  if (bias) {\n    inputs.push(bias);\n  }\n  if (hasPreluActivationWeights) {\n    inputs.push(preluActivationWeights);\n  }\n  if (hasLeakyreluAlpha) {\n    const $leakyreluAlpha = backend2.makeTensorInfo([], \"float32\", util_exports.createScalarValue(leakyreluAlpha, \"float32\"));\n    inputs.push($leakyreluAlpha);\n    intermediates.push($leakyreluAlpha);\n  }\n  const product = backend2.runWebGLProgram(matmulProgram, inputs, \"float32\");\n  const out = reshape4({ inputs: { x: product }, backend: backend2, attrs: { shape: convInfo.outShape } });\n  intermediates.push(product);\n  for (const i of intermediates) {\n    backend2.disposeIntermediateTensorInfo(i);\n  }\n  return out;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Conv2D.js\nfunction conv2d4(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x, filter } = inputs;\n  const { strides, pad: pad3, dataFormat, dilations, dimRoundingMode } = attrs;\n  const $dataFormat = backend_util_exports.convertConv2DDataFormat(dataFormat);\n  const convInfo = backend_util_exports.computeConv2DInfo(x.shape, filter.shape, strides, dilations, pad3, dimRoundingMode, false, $dataFormat);\n  let out;\n  if (convInfo.filterHeight === 1 && convInfo.filterWidth === 1 && convInfo.dilationHeight === 1 && convInfo.dilationWidth === 1 && convInfo.strideHeight === 1 && convInfo.strideWidth === 1 && (convInfo.padInfo.type === \"SAME\" || convInfo.padInfo.type === \"VALID\")) {\n    out = conv2dByMatMul({ x, filter, convInfo, backend: backend2 });\n  } else if (convInfo.strideWidth <= 2 && $dataFormat === \"channelsLast\" && env().getBool(\"WEBGL_EXP_CONV\")) {\n    const program = new Conv2DPackedProgram(convInfo);\n    const customValues = [\n      [convInfo.padInfo.top, convInfo.padInfo.left],\n      [convInfo.strideHeight, convInfo.strideWidth],\n      [convInfo.dilationHeight, convInfo.dilationWidth],\n      [convInfo.inHeight, convInfo.inWidth]\n    ];\n    out = backend2.runWebGLProgram(program, [x, filter], \"float32\", customValues);\n  } else if (env().getBool(\"WEBGL_CONV_IM2COL\")) {\n    out = conv2dWithIm2Row({ x, filter, convInfo, backend: backend2 });\n  } else {\n    const program = new Conv2DProgram(convInfo);\n    out = backend2.runWebGLProgram(program, [x, filter], \"float32\");\n  }\n  const outReshaped = reshape4({ inputs: { x: out }, backend: backend2, attrs: { shape: convInfo.outShape } });\n  backend2.disposeIntermediateTensorInfo(out);\n  return outReshaped;\n}\nvar conv2DConfig2 = {\n  kernelName: Conv2D,\n  backendName: \"webgl\",\n  kernelFunc: conv2d4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/conv_backprop_gpu.js\nvar Conv2DDerFilterProgram = class {\n  constructor(convInfo) {\n    this.variableNames = [\"x\", \"dy\"];\n    this.outputShape = convInfo.filterShape;\n    const strideHeight = convInfo.strideHeight;\n    const strideWidth = convInfo.strideWidth;\n    const padTop = convInfo.padInfo.top;\n    const padLeft = convInfo.padInfo.left;\n    const isChannelsLast = convInfo.dataFormat === \"channelsLast\";\n    this.userCode = `\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int wR = coords.x;\n        int wC = coords.y;\n        int d1 = coords.z;\n        int d2 = coords.w;\n\n        // Convolve x(?, ?, d1) with dy(:, :, d2) to get dw(wR, wC, d1, d2).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n\n        for (int b = 0; b < ${convInfo.batchSize}; b++) {\n          for (int yR = 0; yR < ${convInfo.outHeight}; yR++) {\n            int xR = wR + yR * ${strideHeight} - ${padTop};\n\n            if (xR < 0 || xR >= ${convInfo.inHeight}) {\n              continue;\n            }\n\n            for (int yC = 0; yC < ${convInfo.outWidth}; yC++) {\n              int xC = wC + yC * ${strideWidth} - ${padLeft};\n\n              if (xC < 0 || xC >= ${convInfo.inWidth}) {\n                continue;\n              }\n\n              if (${isChannelsLast}) {\n                float dyValue = getDy(b, yR, yC, d2);\n                float xValue = getX(b, xR, xC, d1);\n                dotProd += (xValue * dyValue);\n              } else {\n                float dyValue = getDy(b, d2, yR, yC);\n                float xValue = getX(b, d1, xR, xC);\n                dotProd += (xValue * dyValue);\n              }\n\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    `;\n  }\n};\nvar Conv2DDerInputProgram = class {\n  constructor(convInfo) {\n    this.variableNames = [\"dy\", \"W\"];\n    this.outputShape = convInfo.inShape;\n    const filterHeight = convInfo.filterHeight;\n    const filterWidth = convInfo.filterWidth;\n    const strideHeight = convInfo.strideHeight;\n    const strideWidth = convInfo.strideWidth;\n    const isChannelsLast = convInfo.dataFormat === \"channelsLast\";\n    const padTop = filterHeight - 1 - convInfo.padInfo.top;\n    const padLeft = filterWidth - 1 - convInfo.padInfo.left;\n    const rowDim = isChannelsLast ? 1 : 2;\n    const colDim = isChannelsLast ? 2 : 3;\n    const channelDim = isChannelsLast ? 3 : 1;\n    this.userCode = `\n      const ivec2 pads = ivec2(${padTop}, ${padLeft});\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int batch = coords[0];\n        int d1 = coords[${channelDim}];\n\n        ivec2 dyCorner = ivec2(coords[${rowDim}], coords[${colDim}]) - pads;\n        int dyRCorner = dyCorner.x;\n        int dyCCorner = dyCorner.y;\n\n        // Convolve dy(?, ?, d2) with w(:, :, d1, d2) to compute dx(xR, xC, d1).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n        for (int wR = 0; wR < ${filterHeight}; wR++) {\n          float dyR = float(dyRCorner + wR) / ${strideHeight}.0;\n\n          if (dyR < 0.0 || dyR >= ${convInfo.outHeight}.0 || fract(dyR) > 0.0) {\n            continue;\n          }\n          int idyR = int(dyR);\n\n          int wRPerm = ${filterHeight} - 1 - wR;\n\n          for (int wC = 0; wC < ${filterWidth}; wC++) {\n            float dyC = float(dyCCorner + wC) / ${strideWidth}.0;\n\n            if (dyC < 0.0 || dyC >= ${convInfo.outWidth}.0 ||\n                fract(dyC) > 0.0) {\n              continue;\n            }\n            int idyC = int(dyC);\n\n            int wCPerm = ${filterWidth} - 1 - wC;\n\n            for (int d2 = 0; d2 < ${convInfo.outChannels}; d2++) {\n\n              if (${isChannelsLast}) {\n                float xValue = getDy(batch, idyR, idyC, d2);\n                float wValue = getW(wRPerm, wCPerm, d1, d2);\n                dotProd += xValue * wValue;\n              } else {\n                float xValue = getDy(batch, d2, idyR, idyC);\n                float wValue = getW(wRPerm, wCPerm, d1, d2);\n                dotProd += xValue * wValue;\n              }\n\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    `;\n  }\n};\nvar Conv3DDerFilterProgram = class {\n  constructor(convInfo) {\n    this.variableNames = [\"x\", \"dy\"];\n    this.outputShape = convInfo.filterShape;\n    const strideDepth = convInfo.strideDepth;\n    const strideHeight = convInfo.strideHeight;\n    const strideWidth = convInfo.strideWidth;\n    const padFront = convInfo.padInfo.front;\n    const padTop = convInfo.padInfo.top;\n    const padLeft = convInfo.padInfo.left;\n    this.userCode = `\n      void main() {\n        ivec5 coords = getOutputCoords();\n        int wF = coords.x;\n        int wR = coords.y;\n        int wC = coords.z;\n        int d1 = coords.w;\n        int d2 = coords.u;\n\n        float dotProd = 0.0;\n\n        for (int b = 0; b < ${convInfo.batchSize}; b++) {\n          for (int yF = 0; yF < ${convInfo.outDepth}; yF++) {\n            int xF = wF + yF * ${strideDepth} - ${padFront};\n\n            if (xF < 0 || xF >= ${convInfo.inDepth}) {\n              continue;\n            }\n\n            for (int yR = 0; yR < ${convInfo.outHeight}; yR++) {\n              int xR = wR + yR * ${strideHeight} - ${padTop};\n\n              if (xR < 0 || xR >= ${convInfo.inHeight}) {\n                continue;\n              }\n\n              for (int yC = 0; yC < ${convInfo.outWidth}; yC++) {\n                int xC = wC + yC * ${strideWidth} - ${padLeft};\n\n                if (xC < 0 || xC >= ${convInfo.inWidth}) {\n                  continue;\n                }\n\n                float dyValue = getDy(b, yF, yR, yC, d2);\n                float xValue = getX(b, xF, xR, xC, d1);\n                dotProd += (xValue * dyValue);\n              }\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    `;\n  }\n};\nvar Conv3DDerInputProgram = class {\n  constructor(convInfo) {\n    this.variableNames = [\"dy\", \"W\"];\n    this.outputShape = convInfo.inShape;\n    const filterDepth = convInfo.filterDepth;\n    const filterHeight = convInfo.filterHeight;\n    const filterWidth = convInfo.filterWidth;\n    const strideDepth = convInfo.strideDepth;\n    const strideHeight = convInfo.strideHeight;\n    const strideWidth = convInfo.strideWidth;\n    const padFront = filterDepth - 1 - convInfo.padInfo.front;\n    const padTop = filterHeight - 1 - convInfo.padInfo.top;\n    const padLeft = filterWidth - 1 - convInfo.padInfo.left;\n    this.userCode = `\n      const ivec3 pads = ivec3(${padFront}, ${padTop}, ${padLeft});\n\n      void main() {\n        ivec5 coords = getOutputCoords();\n        int batch = coords.x;\n        int d1 = coords.u;\n\n\n        ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;\n        int dyFCorner = dyCorner.x;\n        int dyRCorner = dyCorner.y;\n        int dyCCorner = dyCorner.z;\n\n        float dotProd = 0.0;\n        for (int wF = 0; wF < ${filterDepth}; wF++) {\n          float dyF = float(dyFCorner + wF) / ${strideDepth}.0;\n\n          if (dyF < 0.0 || dyF >= ${convInfo.outDepth}.0 || fract(dyF) > 0.0) {\n            continue;\n          }\n          int idyF = int(dyF);\n\n          int wFPerm = ${filterDepth} - 1 - wF;\n\n          for (int wR = 0; wR < ${filterHeight}; wR++) {\n            float dyR = float(dyRCorner + wR) / ${strideHeight}.0;\n\n            if (dyR < 0.0 || dyR >= ${convInfo.outHeight}.0 ||\n              fract(dyR) > 0.0) {\n              continue;\n            }\n            int idyR = int(dyR);\n\n            int wRPerm = ${filterHeight} - 1 - wR;\n\n            for (int wC = 0; wC < ${filterWidth}; wC++) {\n              float dyC = float(dyCCorner + wC) / ${strideWidth}.0;\n\n              if (dyC < 0.0 || dyC >= ${convInfo.outWidth}.0 ||\n                  fract(dyC) > 0.0) {\n                continue;\n              }\n              int idyC = int(dyC);\n\n              int wCPerm = ${filterWidth} - 1 - wC;\n\n              for (int d2 = 0; d2 < ${convInfo.outChannels}; d2++) {\n                float xValue = getDy(batch, idyF, idyR, idyC, d2);\n                float wValue = getW(wFPerm, wRPerm, wCPerm, d1, d2);\n                dotProd += xValue * wValue;\n              }\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    `;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Conv2DBackpropFilter.js\nfunction conv2DBackpropFilter3(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x, dy } = inputs;\n  const { strides, pad: pad3, dataFormat, dimRoundingMode, filterShape } = attrs;\n  const $dataFormat = backend_util_exports.convertConv2DDataFormat(dataFormat);\n  const convInfo = backend_util_exports.computeConv2DInfo(x.shape, filterShape, strides, 1, pad3, dimRoundingMode, false, $dataFormat);\n  const program = new Conv2DDerFilterProgram(convInfo);\n  return backend2.runWebGLProgram(program, [x, dy], \"float32\");\n}\nvar conv2DBackpropFilterConfig2 = {\n  kernelName: Conv2DBackpropFilter,\n  backendName: \"webgl\",\n  kernelFunc: conv2DBackpropFilter3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Conv2DBackpropInput.js\nfunction conv2DBackpropInput3(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { dy, filter } = inputs;\n  const { inputShape, strides, pad: pad3, dataFormat, dimRoundingMode } = attrs;\n  const $dataFormat = backend_util_exports.convertConv2DDataFormat(dataFormat);\n  const convInfo = backend_util_exports.computeConv2DInfo(inputShape, filter.shape, strides, 1, pad3, dimRoundingMode, false, $dataFormat);\n  const program = new Conv2DDerInputProgram(convInfo);\n  return backend2.runWebGLProgram(program, [dy, filter], \"float32\");\n}\nvar conv2DBackpropInputConfig2 = {\n  kernelName: Conv2DBackpropInput,\n  backendName: \"webgl\",\n  kernelFunc: conv2DBackpropInput3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Conv3D.js\nfunction conv3D2(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x, filter } = inputs;\n  const { strides, pad: pad3, dilations } = attrs;\n  const convInfo = backend_util_exports.computeConv3DInfo(x.shape, filter.shape, strides, dilations, pad3);\n  const program = new Conv3DProgram(convInfo);\n  return backend2.runWebGLProgram(program, [x, filter], \"float32\");\n}\nvar conv3DConfig2 = {\n  kernelName: Conv3D,\n  backendName: \"webgl\",\n  kernelFunc: conv3D2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Conv3DBackpropFilterV2.js\nfunction conv3DBackpropFilterV22(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x, dy } = inputs;\n  const { strides, pad: pad3, filterShape } = attrs;\n  const convInfo = backend_util_exports.computeConv3DInfo(x.shape, filterShape, strides, 1, pad3);\n  const program = new Conv3DDerFilterProgram(convInfo);\n  return backend2.runWebGLProgram(program, [x, dy], \"float32\");\n}\nvar conv3DBackpropFilterV2Config2 = {\n  kernelName: Conv3DBackpropFilterV2,\n  backendName: \"webgl\",\n  kernelFunc: conv3DBackpropFilterV22\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Conv3DBackpropInputV2.js\nfunction conv3DBackpropInput2(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { dy, filter } = inputs;\n  const { pad: pad3, strides, inputShape } = attrs;\n  const convInfo = backend_util_exports.computeConv3DInfo(inputShape, filter.shape, strides, 1, pad3);\n  const program = new Conv3DDerInputProgram(convInfo);\n  return backend2.runWebGLProgram(program, [dy, filter], \"float32\");\n}\nvar conv3DBackpropInputConfig = {\n  kernelName: Conv3DBackpropInputV2,\n  backendName: \"webgl\",\n  kernelFunc: conv3DBackpropInput2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Cos.js\nvar COS = CHECK_NAN_SNIPPET_UNARY + `\n  return cos(x);\n`;\nvar cos3 = unaryKernelFunc2({ opSnippet: COS });\nvar cosConfig2 = {\n  kernelName: Cos,\n  backendName: \"webgl\",\n  kernelFunc: cos3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Cosh.js\nvar COSH = `\n  float e2x = exp(-x);\n  return (e2x + 1.0 / e2x) / 2.0;\n`;\nvar cosh3 = unaryKernelFunc2({ opSnippet: COSH });\nvar coshConfig2 = {\n  kernelName: Cosh,\n  backendName: \"webgl\",\n  kernelFunc: cosh3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/crop_and_resize_gpu.js\nvar CropAndResizeProgram = class {\n  constructor(imageShape, boxShape, cropSize, method, extrapolationValue) {\n    this.variableNames = [\"Image\", \"Boxes\", \"BoxInd\"];\n    this.outputShape = [];\n    const [batch, imageHeight, imageWidth, depth] = imageShape;\n    const [numBoxes] = boxShape;\n    const [cropHeight, cropWidth] = cropSize;\n    this.outputShape = [numBoxes, cropHeight, cropWidth, depth];\n    const methodId = method === \"bilinear\" ? 1 : 0;\n    const [inputHeightFloat, inputWidthFloat] = [`${imageHeight - 1}.0`, `${imageWidth - 1}.0`];\n    const [heightRatio, heightScale, inY] = cropHeight > 1 ? [\n      `${(imageHeight - 1) / (cropHeight - 1)}`,\n      \"(y2-y1) * height_ratio\",\n      `y1*${inputHeightFloat} + float(y)*(height_scale)`\n    ] : [\n      \"0.0\",\n      \"0.0\",\n      `0.5 * (y1+y2) * ${inputHeightFloat}`\n    ];\n    const [widthRatio, widthScale, inX] = cropWidth > 1 ? [\n      `${(imageWidth - 1) / (cropWidth - 1)}`,\n      \"(x2-x1) * width_ratio\",\n      `x1*${inputWidthFloat} + float(x)*(width_scale)`\n    ] : [\n      \"0.0\",\n      \"0.0\",\n      `0.5 * (x1+x2) * ${inputWidthFloat}`\n    ];\n    this.userCode = `\n      const float height_ratio = float(${heightRatio});\n      const float width_ratio = float(${widthRatio});\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int y = coords[1];\n        int x = coords[2];\n        int d = coords[3];\n\n        // get box vals\n        float y1 = getBoxes(b,0);\n        float x1 = getBoxes(b,1);\n        float y2 = getBoxes(b,2);\n        float x2 = getBoxes(b,3);\n\n        // get image in batch index\n        int bInd = round(getBoxInd(b));\n        if(bInd < 0 || bInd >= ${batch}) {\n          return;\n        }\n\n        float height_scale = ${heightScale};\n        float width_scale = ${widthScale};\n\n        float in_y = ${inY};\n        if( in_y < 0.0 || in_y > ${inputHeightFloat} ) {\n          setOutput(float(${extrapolationValue}));\n          return;\n        }\n        float in_x = ${inX};\n        if( in_x < 0.0 || in_x > ${inputWidthFloat} ) {\n          setOutput(float(${extrapolationValue}));\n          return;\n        }\n\n        vec2 sourceFracIndexCR = vec2(in_x,in_y);\n        if(${methodId} == 1) {\n          // Compute the four integer indices.\n          ivec2 sourceFloorCR = ivec2(sourceFracIndexCR);\n          ivec2 sourceCeilCR = ivec2(ceil(sourceFracIndexCR));\n\n          float topLeft = getImage(b, sourceFloorCR.y, sourceFloorCR.x, d);\n          float bottomLeft = getImage(b, sourceCeilCR.y, sourceFloorCR.x, d);\n          float topRight = getImage(b, sourceFloorCR.y, sourceCeilCR.x, d);\n          float bottomRight = getImage(b, sourceCeilCR.y, sourceCeilCR.x, d);\n\n          vec2 fracCR = sourceFracIndexCR - vec2(sourceFloorCR);\n\n          float top = topLeft + (topRight - topLeft) * fracCR.x;\n          float bottom = bottomLeft + (bottomRight - bottomLeft) * fracCR.x;\n          float newValue = top + (bottom - top) * fracCR.y;\n          setOutput(newValue);\n        } else {\n          // Compute the coordinators of nearest neighbor point.\n          ivec2 sourceNearestCR = ivec2(floor(\n            sourceFracIndexCR + vec2(0.5,0.5)));\n          float newValue = getImage(b, sourceNearestCR.y, sourceNearestCR.x, d);\n          setOutput(newValue);\n        }\n      }\n    `;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/CropAndResize.js\nvar cropAndResize3 = (args) => {\n  const { inputs, backend: backend2, attrs } = args;\n  const { image: image2, boxes, boxInd } = inputs;\n  const { cropSize, method, extrapolationValue } = attrs;\n  const program = new CropAndResizeProgram(image2.shape, boxes.shape, cropSize, method, extrapolationValue);\n  return backend2.runWebGLProgram(program, [image2, boxes, boxInd], \"float32\");\n};\nvar cropAndResizeConfig2 = {\n  kernelName: CropAndResize,\n  backendName: \"webgl\",\n  kernelFunc: cropAndResize3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/cum_gpu.js\nvar CumOpType;\n(function(CumOpType3) {\n  CumOpType3[\"Prod\"] = \"*\";\n  CumOpType3[\"Sum\"] = \"+\";\n})(CumOpType || (CumOpType = {}));\nvar CumProgram = class {\n  constructor(op2, outputShape, exclusive, reverse5) {\n    this.op = op2;\n    this.outputShape = outputShape;\n    this.variableNames = [\"x\"];\n    this.customUniforms = [{ name: \"index\", type: \"float\" }];\n    const rank = this.outputShape.length;\n    const initVal = this.op === CumOpType.Prod ? \"1.0\" : \"0.0\";\n    const val = exclusive ? initVal : `getX(${getCoords2(rank, \"coords\", this.op)})`;\n    const length = this.outputShape[this.outputShape.length - 1];\n    let condition = \"\";\n    let idxString = \"\";\n    if (exclusive) {\n      condition = reverse5 ? `end != ${length - 1}` : \"end != 0\";\n      idxString = reverse5 ? \"end + 1\" : \"end - 1\";\n    } else {\n      condition = reverse5 ? `end + pow2 < ${length}` : \"end >= pow2\";\n      idxString = reverse5 ? \"end + pow2\" : \"end - pow2\";\n    }\n    this.userCode = `\n      void main() {\n        ${getCoordsDataType(rank)} coords = getOutputCoords();\n        int end = ${getFinalCoord(rank, \"coords\", this.op)};\n        float val = ${val};\n        int pow2 = int(pow(2.0, index));\n        if (${condition}) {\n          int idx = ${idxString};\n          ${getFinalCoord(rank, \"coords\", this.op)} = idx;\n          val ${this.op}= getX(${getCoords2(rank, \"coords\", this.op)});\n        }\n        setOutput(val);\n      }\n    `;\n  }\n};\nfunction getCoords2(rank, name, op2) {\n  if (rank === 1) {\n    return `${name}`;\n  } else if (rank === 2) {\n    return `${name}.x, ${name}.y`;\n  } else if (rank === 3) {\n    return `${name}.x, ${name}.y, ${name}.z`;\n  } else if (rank === 4) {\n    return `${name}.x, ${name}.y, ${name}.z, ${name}.w`;\n  } else {\n    throw new Error(`Cumulative ${op2} for rank ${rank} is not yet supported`);\n  }\n}\nfunction getFinalCoord(rank, name, op2) {\n  if (rank === 1) {\n    return `${name}`;\n  } else if (rank === 2) {\n    return `${name}.y`;\n  } else if (rank === 3) {\n    return `${name}.z`;\n  } else if (rank === 4) {\n    return `${name}.w`;\n  } else {\n    throw new Error(`Cumulative ${op2} for rank ${rank} is not yet supported`);\n  }\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Cum_impl.js\nfunction cumImpl(op2, x, backend2, axis, exclusive, reverse5) {\n  const xRank = x.shape.length;\n  const permutation = backend_util_exports.getAxesPermutation([axis], xRank);\n  let permutedX = x;\n  if (permutation != null) {\n    permutedX = transpose3({ inputs: { x }, backend: backend2, attrs: { perm: permutation } });\n  }\n  const permutedAxis = backend_util_exports.getInnerMostAxes(1, xRank)[0];\n  if (permutedAxis !== xRank - 1) {\n    throw new Error(`WebGL cumprod shader expects an inner-most axis=${x.shape.length - 1} but got axis=${axis}`);\n  }\n  const size = permutedX.shape[permutedAxis];\n  let result = identity3({ inputs: { x: permutedX }, backend: backend2 });\n  for (let i = 0; i <= Math.ceil(Math.log2(size)) - 1; i++) {\n    const program = new CumProgram(op2, permutedX.shape, false, reverse5);\n    const customValues = [[i]];\n    const prevResult = result;\n    result = backend2.runWebGLProgram(program, [result], result.dtype, customValues);\n    backend2.disposeIntermediateTensorInfo(prevResult);\n  }\n  if (exclusive) {\n    const program = new CumProgram(op2, permutedX.shape, exclusive, reverse5);\n    const prevResult = result;\n    result = backend2.runWebGLProgram(program, [result], result.dtype);\n    backend2.disposeIntermediateTensorInfo(prevResult);\n  }\n  if (permutation != null) {\n    const reversePermutation = backend_util_exports.getUndoAxesPermutation(permutation);\n    const reverseTransposedResult = transpose3({ inputs: { x: result }, backend: backend2, attrs: { perm: reversePermutation } });\n    backend2.disposeIntermediateTensorInfo(result);\n    backend2.disposeIntermediateTensorInfo(permutedX);\n    return reverseTransposedResult;\n  }\n  return result;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Cumprod.js\nfunction cumprod3(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x } = inputs;\n  const { axis, exclusive, reverse: reverse5 } = attrs;\n  return cumImpl(CumOpType.Prod, x, backend2, axis, exclusive, reverse5);\n}\nvar cumprodConfig2 = {\n  kernelName: Cumprod,\n  backendName: \"webgl\",\n  kernelFunc: cumprod3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Cumsum.js\nfunction cumsum3(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x } = inputs;\n  const { axis, exclusive, reverse: reverse5 } = attrs;\n  return cumImpl(CumOpType.Sum, x, backend2, axis, exclusive, reverse5);\n}\nvar cumsumConfig2 = {\n  kernelName: Cumsum,\n  backendName: \"webgl\",\n  kernelFunc: cumsum3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/DenseBincount.js\nfunction denseBincount3(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x, weights } = inputs;\n  const { size, binaryOutput } = attrs;\n  if (x.shape.length === 1) {\n    const xVals = backend2.readSync(x.dataId);\n    const weightsVals = backend2.readSync(weights.dataId);\n    const outVals = bincountImplCPU(xVals, weightsVals, weights.dtype, weights.shape, size);\n    return backend2.makeTensorInfo([size], weights.dtype, outVals);\n  } else if (x.shape.length === 2) {\n    const xBuf = backend2.bufferSync(x);\n    const weightsBuf = backend2.bufferSync(weights);\n    const outBuf = bincountReduceImplCPU(xBuf, weightsBuf, size, binaryOutput);\n    return backend2.makeTensorInfo(outBuf.shape, weights.dtype, outBuf.values);\n  }\n  throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${x.shape.length}.`);\n}\nvar denseBincountConfig2 = {\n  kernelName: DenseBincount,\n  backendName: \"webgl\",\n  kernelFunc: denseBincount3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/depth_to_space_gpu.js\nvar DepthToSpaceProgram = class {\n  constructor(outputShape, blockSize, dataFormat) {\n    this.variableNames = [\"x\"];\n    this.outputShape = [];\n    this.outputShape = outputShape;\n    this.blockSize = blockSize;\n    this.dataFormat = dataFormat;\n    this.userCode = `\n    void main() {\n      ivec4 coords = getOutputCoords();\n      int b = coords[0];\n      int h = ${this.getHeightCoordString()};\n      int w = ${this.getWidthCoordString()};\n      int d = ${this.getDepthCoordString()};\n\n      int in_h = h / ${blockSize};\n      int offset_h = imod(h, ${blockSize});\n      int in_w = w / ${blockSize};\n      int offset_w = imod(w, ${blockSize});\n      int offset_d = (offset_h * ${blockSize} + offset_w) *\n        ${this.getOutputDepthSize()};\n      int in_d = d + offset_d;\n\n      float result = ${this.getInputSamplingString()};\n      setOutput(result);\n    }\n  `;\n  }\n  getHeightCoordString() {\n    if (this.dataFormat === \"NHWC\") {\n      return `coords[1]`;\n    } else {\n      return `coords[2]`;\n    }\n  }\n  getWidthCoordString() {\n    if (this.dataFormat === \"NHWC\") {\n      return `coords[2]`;\n    } else {\n      return `coords[3]`;\n    }\n  }\n  getDepthCoordString() {\n    if (this.dataFormat === \"NHWC\") {\n      return `coords[3]`;\n    } else {\n      return `coords[1]`;\n    }\n  }\n  getOutputDepthSize() {\n    if (this.dataFormat === \"NHWC\") {\n      return this.outputShape[3];\n    } else {\n      return this.outputShape[1];\n    }\n  }\n  getInputSamplingString() {\n    if (this.dataFormat === \"NHWC\") {\n      return `getX(b, in_h, in_w, in_d)`;\n    } else {\n      return `getX(b, in_d, in_h, in_w)`;\n    }\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/DepthToSpace.js\nfunction depthToSpace3(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x } = inputs;\n  const { blockSize, dataFormat } = attrs;\n  const batchSize = x.shape[0];\n  const inputHeight = dataFormat === \"NHWC\" ? x.shape[1] : x.shape[2];\n  const inputWidth = dataFormat === \"NHWC\" ? x.shape[2] : x.shape[3];\n  const inputDepth = dataFormat === \"NHWC\" ? x.shape[3] : x.shape[1];\n  const outputHeight = inputHeight * blockSize;\n  const outputWidth = inputWidth * blockSize;\n  const outputDepth = inputDepth / (blockSize * blockSize);\n  const outputShape = dataFormat === \"NHWC\" ? [batchSize, outputHeight, outputWidth, outputDepth] : [batchSize, outputDepth, outputHeight, outputWidth];\n  const program = new DepthToSpaceProgram(outputShape, blockSize, dataFormat);\n  return backend2.runWebGLProgram(program, [x], x.dtype);\n}\nvar depthToSpaceConfig2 = {\n  kernelName: DepthToSpace,\n  backendName: \"webgl\",\n  kernelFunc: depthToSpace3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/conv_gpu_depthwise.js\nvar DepthwiseConv2DProgram = class {\n  constructor(convInfo, addBias = false, activation2 = null, hasPreluActivation = false, hasLeakyReluAlpha = false) {\n    this.variableNames = [\"x\", \"W\"];\n    this.customUniforms = [\n      { name: \"pads\", type: \"ivec2\" },\n      { name: \"strides\", type: \"ivec2\" },\n      { name: \"dilations\", type: \"ivec2\" },\n      { name: \"inDims\", type: \"ivec2\" }\n    ];\n    this.outputShape = convInfo.outShape;\n    this.enableShapeUniforms = useShapeUniforms(this.outputShape.length);\n    const filterHeight = convInfo.filterHeight;\n    const filterWidth = convInfo.filterWidth;\n    const channelMul = convInfo.outChannels / convInfo.inChannels;\n    let activationSnippet = \"\", applyActivationSnippet = \"\";\n    if (activation2) {\n      if (hasPreluActivation) {\n        activationSnippet = `float activation(float a) {\n          float b = getPreluActivationWeightsAtOutCoords();\n          ${activation2}\n        }`;\n      } else if (hasLeakyReluAlpha) {\n        activationSnippet = `float activation(float a) {\n          float b = getLeakyreluAlphaAtOutCoords();\n          ${activation2}\n        }`;\n      } else {\n        activationSnippet = `\n          float activation(float x) {\n            ${activation2}\n          }\n        `;\n      }\n      applyActivationSnippet = `result = activation(result);`;\n    }\n    const addBiasSnippet = addBias ? \"result += getBiasAtOutCoords();\" : \"\";\n    if (addBias) {\n      this.variableNames.push(\"bias\");\n    }\n    if (hasPreluActivation) {\n      this.variableNames.push(\"preluActivationWeights\");\n    }\n    if (hasLeakyReluAlpha) {\n      this.variableNames.push(\"leakyreluAlpha\");\n    }\n    this.userCode = `\n      ${activationSnippet}\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int batch = coords.x;\n        ivec2 xRCCorner = coords.yz * strides - pads;\n        int d2 = coords.w;\n        int d1 = d2 / ${channelMul};\n        int q = d2 - d1 * ${channelMul};\n\n        int xRCorner = xRCCorner.x;\n        int xCCorner = xRCCorner.y;\n\n        // Convolve x(?, ?, d1) with w(:, :, d1, q) to get y(yR, yC, d2).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n        // TO DO(dsmilkov): Flatten the two for loops and vec4 the operations.\n        for (int wR = 0; wR < ${filterHeight}; wR++) {\n          int xR = xRCorner + wR * dilations[0];\n\n          if (xR < 0 || xR >= inDims[0]) {\n            continue;\n          }\n\n          for (int wC = 0; wC < ${filterWidth}; wC++) {\n            int xC = xCCorner + wC * dilations[1];\n\n            if (xC < 0 || xC >= inDims[1]) {\n              continue;\n            }\n\n            float xVal = getX(batch, xR, xC, d1);\n            float wVal = getW(wR, wC, d1, q);\n            dotProd += xVal * wVal;\n          }\n        }\n\n        float result = dotProd;\n        ${addBiasSnippet}\n        ${applyActivationSnippet}\n        setOutput(result);\n      }\n    `;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/conv_packed_gpu_depthwise.js\nvar DepthwiseConvPacked2DProgram = class {\n  constructor(convInfo, addBias = false, activation2 = null, hasPreluActivation = false, hasLeakyReluAlpha = false) {\n    this.variableNames = [\"x\", \"W\"];\n    this.packedInputs = true;\n    this.packedOutput = true;\n    this.customUniforms = [\n      { name: \"pads\", type: \"ivec2\" },\n      { name: \"strides\", type: \"ivec2\" },\n      { name: \"dilations\", type: \"ivec2\" },\n      { name: \"inDims\", type: \"ivec2\" }\n    ];\n    this.outputShape = convInfo.outShape;\n    this.enableShapeUniforms = useShapeUniforms(this.outputShape.length);\n    const channelMul = convInfo.outChannels / convInfo.inChannels;\n    const padLeft = convInfo.padInfo.left;\n    const strideWidth = convInfo.strideWidth;\n    const dilationWidth = convInfo.dilationWidth;\n    const filterHeight = convInfo.filterHeight;\n    const filterWidth = convInfo.filterWidth;\n    const texelsAcross = filterWidth;\n    let mainLoop = `\n      int xR; int xC; int xCOffset;\n      vec4 wTexel; vec4 previous; vec4 final;`;\n    for (let c = 0; c < filterWidth; c++) {\n      mainLoop += `\n          vec4 xTexelC${c * 2};\n          int xTexelC${c * 2}Ready;\n          vec4 xTexelC${c * 2 + 1};\n          int xTexelC${c * 2 + 1}Ready;\n          vec4 xC${c};`;\n    }\n    mainLoop += `\n    for (int r = 0; r < ${filterHeight}; r++) {\n      `;\n    for (let c = 0; c < filterWidth; c++) {\n      mainLoop += `\n          xTexelC${c * 2} = vec4(0.0);\n          xTexelC${c * 2}Ready = 0;\n          xTexelC${c * 2 + 1} = vec4(0.0);\n          xTexelC${c * 2 + 1}Ready = 0;\n          xC${c} = vec4(0.0);`;\n    }\n    mainLoop += `\n        xR = xRCorner + r * dilations[0];\n        if (xR >=0 && xR < inDims[0]) {\n      `;\n    for (let texelC = 0; texelC < (texelsAcross + 1) / 2; texelC++) {\n      const colIndex = texelC * 2;\n      mainLoop += `\n          xC = xCCorner + ${colIndex * dilationWidth};\n          `;\n      if (strideWidth === 1) {\n        if (colIndex < filterWidth) {\n          if (padLeft % 2 === 1) {\n            mainLoop += `\n                xCOffset = xC + 1;\n                if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${colIndex}Ready == 0) {\n                  xTexelC${colIndex} = getX(batch, xR, xCOffset, d1);\n\n                  // Need to manually clear unused channels in case\n                  // we're reading from recycled texture.\n                  if (xCOffset + 1 >= inDims[1]) {\n                    xTexelC${colIndex}.zw = vec2(0.0);\n                  }\n                  xTexelC${colIndex}Ready = 1;\n                }\n              `;\n            if (dilationWidth === 1 && colIndex > 0) {\n              mainLoop += `\n                xC${colIndex} = vec4(xTexelC${colIndex - 2}.zw, xTexelC${colIndex}.xy);\n                `;\n            } else {\n              mainLoop += `\n                  xCOffset = xC + 1 - 2;\n\n                  if (xCOffset >= 0 && xCOffset < inDims[1]) {\n                    previous = getX(batch, xR, xCOffset, d1);\n\n                    // Need to manually clear unused channels in case\n                    // we're reading from recycled texture.\n                    if (xCOffset + 1 >= inDims[1]) {\n                      previous.zw = vec2(0.0);\n                    }\n\n                    xC${colIndex} = vec4(previous.zw, xTexelC${colIndex}.xy);\n                  } else {\n                    xC${colIndex} = vec4(0.0, 0.0, xTexelC${colIndex}.xy);\n                  }\n                  `;\n            }\n          } else {\n            mainLoop += `\n                if (xC >= 0 && xC < inDims[1] && xTexelC${colIndex}Ready == 0) {\n                  xTexelC${colIndex} = getX(batch, xR, xC, d1);\n                  if (xC + 1 >= inDims[1]) {\n                    xTexelC${colIndex}.zw = vec2(0.0);\n                  }\n                  xTexelC${colIndex}Ready = 1;\n                }\n\n                xC${colIndex} = xTexelC${colIndex};\n                `;\n          }\n          if (colIndex + 1 < filterWidth) {\n            const nextTexelOffset = padLeft % 2 === 0 ? util_exports.nearestLargerEven(dilationWidth) : dilationWidth;\n            if (dilationWidth % 2 === 0 && padLeft % 2 === 1 || dilationWidth % 2 !== 0 && padLeft % 2 !== 1) {\n              mainLoop += `\n                  xCOffset = xC + imod(pads[1], 2) + ${nextTexelOffset};\n\n                  if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${colIndex + 1}Ready == 0) {\n                    xTexelC${colIndex + 1} = getX(batch, xR, xCOffset, d1);\n\n                    // Need to manually clear unused channels in case\n                    // we're reading from recycled texture.\n                    if (xCOffset + 1 >= inDims[1]) {\n                      xTexelC${colIndex + 1}.zw = vec2(0.0);\n                    }\n                    xTexelC${colIndex + 1}Ready = 1;\n                  }\n                  `;\n              if (dilationWidth > 1) {\n                mainLoop += `\n                    xCOffset -= 2;\n                    if (xCOffset >= 0 && xCOffset < inDims[1]) {\n                     previous = getX(batch, xR, xCOffset, d1);\n                     xC${colIndex + 1} = vec4(previous.zw, xTexelC${colIndex + 1}.xy);\n                    } else {\n                     xC${colIndex + 1} = vec4(0.0, 0.0, xTexelC${colIndex + 1}.xy);\n                    }\n                    `;\n              } else {\n                mainLoop += `\n                    xC${colIndex + 1} = vec4(xTexelC${colIndex}.zw, xTexelC${colIndex + 1}.xy);\n                    `;\n              }\n            } else {\n              if (nextTexelOffset === 1) {\n                mainLoop += `\n                    xC${colIndex + 1} = xTexelC${colIndex};\n                    `;\n              } else {\n                mainLoop += `\n                    xCOffset = xC + ${nextTexelOffset};\n\n                    if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${colIndex + 1}Ready == 0) {\n                      xTexelC${colIndex + 1} = getX(batch, xR, xCOffset, d1);\n                      if (xCOffset + 1 >= inDims[1]) {\n                        xTexelC${colIndex + 1}.zw = vec2(0.0);\n                      }\n                      xTexelC${colIndex + 1}Ready = 1;\n                    }\n\n                    xC${colIndex + 1} = xTexelC${colIndex + 1};\n                    `;\n              }\n            }\n          }\n        }\n      } else {\n        if (colIndex < filterWidth) {\n          if (padLeft % 2 === 1) {\n            mainLoop += `\n                xCOffset = xC + 1 - strides[1];\n                if(xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${colIndex}Ready == 0) {\n                  xTexelC${colIndex} = getX(batch, xR, xCOffset, d1);\n                  // Need to manually clear unused channels in case\n                  // we're reading from recycled texture.\n                  if (xCOffset + 1 >= inDims[1]) {\n                    xTexelC${colIndex}.zw = vec2(0.0);\n                  }\n                  xTexelC${colIndex}Ready = 1;\n                }\n\n                if(xC + 1 >= 0 && xC + 1 < inDims[1] && xTexelC${colIndex + 1}Ready == 0) {\n                  xTexelC${colIndex + 1} = getX(batch, xR, xC + 1, d1);\n                  // Need to manually clear unused channels in case\n                  // we're reading from recycled texture.\n                  if (xC + 2 >= inDims[1]) {\n                    xTexelC${colIndex + 1}.zw = vec2(0.0);\n                  }\n                  xTexelC${colIndex + 1}Ready = 1;\n                }\n\n                xC${colIndex} = vec4(xTexelC${colIndex}.zw, xTexelC${colIndex + 1}.zw);\n              `;\n            if (colIndex + 1 < filterWidth) {\n              mainLoop += `\n                  final = vec4(0.0);\n                  xCOffset = xC + 1 + strides[1];\n                  if(xCOffset >= 0 && xCOffset < inDims[1]) {\n                    final = getX(batch, xR, xCOffset, d1);\n                  }\n                  xC${colIndex + 1} = vec4(xTexelC${colIndex + 1}.xy, final.xy);\n                `;\n            }\n          } else {\n            mainLoop += `\n                if(xC >= 0 && xC < inDims[1] && xTexelC${colIndex}Ready == 0) {\n                  xTexelC${colIndex} = getX(batch, xR, xC, d1);\n                  if (xC + 1 >= inDims[1]) {\n                    xTexelC${colIndex}.zw = vec2(0.0);\n                  }\n                  xTexelC${colIndex}Ready = 1;\n                }\n\n                xCOffset = xC + strides[1];\n                if(xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${colIndex + 1}Ready == 0) {\n                  xTexelC${colIndex + 1} = getX(batch, xR, xCOffset, d1);\n                  if (xCOffset + 1 >= inDims[1]) {\n                    xTexelC${colIndex + 1}.zw = vec2(0.);\n                  }\n                  xTexelC${colIndex + 1}Ready = 1;\n                }\n\n                xC${colIndex} = vec4(\n                  xTexelC${colIndex}.xy, xTexelC${colIndex + 1}.xy);\n              `;\n            if (colIndex + 1 < filterWidth) {\n              mainLoop += `\n                  xC${colIndex + 1} = vec4(xTexelC${colIndex}.zw, xTexelC${colIndex + 1}.zw);\n                `;\n            }\n          }\n        }\n      }\n      if (colIndex < filterWidth) {\n        mainLoop += `\n            wTexel = getW(r, ${colIndex}, d1, q);\n            dotProd += xC${colIndex} * vec4(wTexel.xz, wTexel.xz);\n          `;\n        if (colIndex + 1 < filterWidth) {\n          mainLoop += `\n              wTexel = getW(r, ${colIndex + 1}, d1, q);\n              dotProd += xC${colIndex + 1} * vec4(wTexel.xz, wTexel.xz);\n            `;\n        }\n      }\n    }\n    mainLoop += `\n    }\n  `;\n    mainLoop += `\n      }\n    `;\n    let activationSnippet = \"\", applyActivationSnippet = \"\";\n    if (activation2) {\n      if (hasPreluActivation) {\n        activationSnippet = `vec4 activation(vec4 a) {\n          vec4 b = getPreluActivationWeightsAtOutCoords();\n          ${activation2}\n        }`;\n      } else if (hasLeakyReluAlpha) {\n        activationSnippet = `vec4 activation(vec4 a) {\n          vec4 b = getLeakyreluAlphaAtOutCoords();\n          ${activation2}\n        }`;\n      } else {\n        activationSnippet = `vec4 activation(vec4 x) {\n          ${activation2}\n        }`;\n      }\n      applyActivationSnippet = `result = activation(result);`;\n    }\n    const addBiasSnippet = addBias ? \"result += getBiasAtOutCoords();\" : \"\";\n    if (addBias) {\n      this.variableNames.push(\"bias\");\n    }\n    if (hasPreluActivation) {\n      this.variableNames.push(\"preluActivationWeights\");\n    }\n    if (hasLeakyReluAlpha) {\n      this.variableNames.push(\"leakyreluAlpha\");\n    }\n    this.userCode = `\n      ${activationSnippet}\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int batch = coords.x;\n        ivec2 xRCCorner = coords.yz * strides - pads;\n        int d2 = coords.w;\n        int d1 = d2 / ${channelMul};\n        int q = d2 - d1 * ${channelMul};\n        int xRCorner = xRCCorner.x;\n        int xCCorner = xRCCorner.y;\n\n        //intialize dotProd with a small epsilon seems to reduce GPU accuracy loss.\n        vec4 dotProd = vec4(0.000000000000001);\n\n        ${mainLoop}\n\n        vec4 result = dotProd - vec4(0.000000000000001);\n        ${addBiasSnippet}\n        ${applyActivationSnippet}\n        setOutput(result);\n      }\n    `;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/DepthwiseConv2dNative.js\nfunction depthwiseConv2dNative2(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x, filter } = inputs;\n  const { strides, pad: pad3, dilations, dimRoundingMode } = attrs;\n  let $dilations = dilations;\n  if ($dilations == null) {\n    $dilations = [1, 1];\n  }\n  util_exports.assert(backend_util_exports.eitherStridesOrDilationsAreOne(strides, $dilations), () => `Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${strides} and dilations '${$dilations}'`);\n  const convInfo = backend_util_exports.computeConv2DInfo(x.shape, filter.shape, strides, $dilations, pad3, dimRoundingMode, true);\n  let program;\n  if (env().getBool(\"WEBGL_PACK_DEPTHWISECONV\") && convInfo.strideWidth <= 2 && convInfo.outChannels / convInfo.inChannels === 1) {\n    program = new DepthwiseConvPacked2DProgram(convInfo);\n  } else {\n    program = new DepthwiseConv2DProgram(convInfo);\n  }\n  const customValues = [\n    [convInfo.padInfo.top, convInfo.padInfo.left],\n    [convInfo.strideHeight, convInfo.strideWidth],\n    [convInfo.dilationHeight, convInfo.dilationWidth],\n    [convInfo.inHeight, convInfo.inWidth]\n  ];\n  return backend2.runWebGLProgram(program, [x, filter], \"float32\", customValues);\n}\nvar depthwiseConv2dNativeConfig2 = {\n  kernelName: DepthwiseConv2dNative,\n  backendName: \"webgl\",\n  kernelFunc: depthwiseConv2dNative2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/conv_backprop_gpu_depthwise.js\nvar DepthwiseConv2DDerFilterProgram = class {\n  constructor(convInfo) {\n    this.variableNames = [\"x\", \"dy\"];\n    this.outputShape = convInfo.filterShape;\n    const strideHeight = convInfo.strideHeight;\n    const strideWidth = convInfo.strideWidth;\n    const padTop = convInfo.padInfo.top;\n    const padLeft = convInfo.padInfo.left;\n    const channelMul = convInfo.outChannels / convInfo.inChannels;\n    this.userCode = `\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int wR = coords.x;\n        int wC = coords.y;\n        int d1 = coords.z;\n        int dm = coords.w;\n        int d2 = d1 * ${channelMul} + dm;\n\n        float dotProd = 0.0;\n\n        // TO DO: Vec4 over the batch size\n        for (int b = 0; b < ${convInfo.batchSize}; b++) {\n          for (int yR = 0; yR < ${convInfo.outHeight}; yR++) {\n            int xR = wR + yR * ${strideHeight} - ${padTop};\n\n            if (xR < 0 || xR >= ${convInfo.inHeight}) {\n              continue;\n            }\n\n            for (int yC = 0; yC < ${convInfo.outWidth}; yC++) {\n              int xC = wC + yC * ${strideWidth} - ${padLeft};\n\n              if (xC < 0 || xC >= ${convInfo.inWidth}) {\n                continue;\n              }\n\n              float dyValue = getDy(b, yR, yC, d2);\n              float xValue = getX(b, xR, xC, d1);\n              dotProd += (xValue * dyValue);\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    `;\n  }\n};\nvar DepthwiseConv2DDerInputProgram = class {\n  constructor(convInfo) {\n    this.variableNames = [\"dy\", \"W\"];\n    this.outputShape = convInfo.inShape;\n    const filterHeight = convInfo.filterHeight;\n    const filterWidth = convInfo.filterWidth;\n    const strideHeight = convInfo.strideHeight;\n    const strideWidth = convInfo.strideWidth;\n    const padTop = filterHeight - 1 - convInfo.padInfo.top;\n    const padLeft = filterWidth - 1 - convInfo.padInfo.left;\n    const channelMul = convInfo.outChannels / convInfo.inChannels;\n    this.userCode = `\n      const ivec2 pads = ivec2(${padTop}, ${padLeft});\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int batch = coords[0];\n        int d1 = coords[3];\n        ivec2 dyCorner = coords.yz - pads;\n        int dyRCorner = dyCorner.x;\n        int dyCCorner = dyCorner.y;\n\n        float dotProd = 0.0;\n\n        for (int wR = 0; wR < ${filterHeight}; wR++) {\n          float dyR = float(dyRCorner + wR) / ${strideHeight}.0;\n\n          if (dyR < 0.0 || dyR >= ${convInfo.outHeight}.0 || fract(dyR) > 0.0) {\n            continue;\n          }\n          int idyR = int(dyR);\n\n          int wRPerm = ${filterHeight} - 1 - wR;\n\n          for (int wC = 0; wC < ${filterWidth}; wC++) {\n            float dyC = float(dyCCorner + wC) / ${strideWidth}.0;\n\n            if (dyC < 0.0 || dyC >= ${convInfo.outWidth}.0 ||\n                fract(dyC) > 0.0) {\n              continue;\n            }\n            int idyC = int(dyC);\n\n            int wCPerm = ${filterWidth} - 1 - wC;\n\n            // TO DO: Vec4 over the channelMul\n            for (int dm = 0; dm < ${channelMul}; dm++) {\n              int d2 = d1 * ${channelMul} + dm;\n              float xValue = getDy(batch, idyR, idyC, d2);\n              float wValue = getW(wRPerm, wCPerm, d1, dm);\n              dotProd += xValue * wValue;\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    `;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/DepthwiseConv2dNativeBackpropFilter.js\nfunction depthwiseConv2dNativeBackpropFilter3(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x, dy } = inputs;\n  const { strides, dilations, pad: pad3, dimRoundingMode, filterShape } = attrs;\n  const convInfo = backend_util_exports.computeConv2DInfo(x.shape, filterShape, strides, dilations, pad3, dimRoundingMode, true);\n  const program = new DepthwiseConv2DDerFilterProgram(convInfo);\n  return backend2.runWebGLProgram(program, [x, dy], \"float32\");\n}\nvar depthwiseConv2dNativeBackpropFilterConfig2 = {\n  kernelName: DepthwiseConv2dNativeBackpropFilter,\n  backendName: \"webgl\",\n  kernelFunc: depthwiseConv2dNativeBackpropFilter3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/DepthwiseConv2dNativeBackpropInput.js\nfunction depthwiseConv2dNativeBackpropInput3(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { dy, filter } = inputs;\n  const { strides, dilations, pad: pad3, dimRoundingMode, inputShape } = attrs;\n  const convInfo = backend_util_exports.computeConv2DInfo(inputShape, filter.shape, strides, dilations, pad3, dimRoundingMode, true);\n  const program = new DepthwiseConv2DDerInputProgram(convInfo);\n  return backend2.runWebGLProgram(program, [dy, filter], \"float32\");\n}\nvar depthwiseConv2dNativeBackpropInputConfig2 = {\n  kernelName: DepthwiseConv2dNativeBackpropInput,\n  backendName: \"webgl\",\n  kernelFunc: depthwiseConv2dNativeBackpropInput3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/diag_gpu.js\nvar DiagProgram = class {\n  constructor(size) {\n    this.variableNames = [\"X\"];\n    this.outputShape = [size, size];\n    this.userCode = `\n      void main() {\n          ivec2 coords = getOutputCoords();\n          float val = coords[0] == coords[1] ? getX(coords[0]) : 0.0;\n          setOutput(val);\n      }\n    `;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Diag.js\nfunction diag3(args) {\n  const { inputs, backend: backend2 } = args;\n  const { x } = inputs;\n  const outShape = [...x.shape, ...x.shape];\n  const xSize = util_exports.sizeFromShape(x.shape);\n  const flat = reshape4({ inputs: { x }, backend: backend2, attrs: { shape: [xSize] } });\n  const program = new DiagProgram(xSize);\n  const res = backend2.runWebGLProgram(program, [flat], flat.dtype);\n  const out = reshape4({ inputs: { x: res }, backend: backend2, attrs: { shape: outShape } });\n  backend2.disposeIntermediateTensorInfo(flat);\n  backend2.disposeIntermediateTensorInfo(res);\n  return out;\n}\nvar diagConfig2 = {\n  kernelName: Diag,\n  backendName: \"webgl\",\n  kernelFunc: diag3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/dilation_gpu.js\nvar Dilation2DProgram = class {\n  constructor(convInfo) {\n    this.variableNames = [\"x\", \"W\"];\n    this.outputShape = convInfo.outShape;\n    const { inHeight, inWidth, padInfo, strideHeight, strideWidth, filterHeight, filterWidth, dilationHeight, dilationWidth } = convInfo;\n    const { top: padTop, left: padLeft } = padInfo;\n    this.userCode = `\n      const ivec2 strides = ivec2(${strideHeight}, ${strideWidth});\n      const ivec2 pads = ivec2(${padTop}, ${padLeft});\n      const float neg_infinity = -3.4e38;\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int batch = coords.x;\n        int d1 = coords.w;\n        ivec2 outTopLeftCorner =\n            coords.yz * strides - pads;\n        int hBeg = outTopLeftCorner.x;\n        int wBeg = outTopLeftCorner.y;\n\n        float curVal = neg_infinity;\n        for (int h = 0; h < ${filterHeight}; h++) {\n          int hIn = hBeg + h * ${dilationHeight};\n\n          if (hIn >= 0 && hIn < ${inHeight}) {\n            for (int w = 0; w < ${filterWidth}; w++) {\n              int wIn = wBeg + w * ${dilationWidth};\n\n              if (wIn >= 0 && wIn < ${inWidth}) {\n                float xVal = getX(batch, hIn, wIn, d1);\n                float wVal = getW(h, w, d1);\n\n                float val = xVal + wVal;\n                if (val > curVal) {\n                  curVal = val;\n                }\n              }\n            }\n          }\n        }\n\n        float result = curVal;\n        setOutput(result);\n      }\n    `;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Dilation2D.js\nfunction dilation2D(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x, filter } = inputs;\n  const { strides, pad: pad3, dilations } = attrs;\n  const convInfo = backend_util_exports.computeDilation2DInfo(x.shape, filter.shape, strides, pad3, \"NHWC\", dilations);\n  let out;\n  const program = new Dilation2DProgram(convInfo);\n  out = backend2.runWebGLProgram(program, [x, filter], \"float32\");\n  const outReshaped = reshape4({ inputs: { x: out }, backend: backend2, attrs: { shape: convInfo.outShape } });\n  backend2.disposeIntermediateTensorInfo(out);\n  return outReshaped;\n}\nvar dilation2DConfig2 = {\n  kernelName: Dilation2D,\n  backendName: \"webgl\",\n  kernelFunc: dilation2D\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Einsum.js\nfunction einsum3(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { equation } = attrs;\n  const tensors = inputs;\n  const { allDims, summedDims, idDims } = backend_util_exports.decodeEinsumEquation(equation, tensors.length);\n  backend_util_exports.checkEinsumDimSizes(allDims.length, idDims, tensors);\n  const { path, steps } = backend_util_exports.getEinsumComputePath(summedDims, idDims);\n  const nSteps = steps.length;\n  let out = null;\n  let numDimsRemaining = allDims.length;\n  const tensorsToDispose = [];\n  for (let i = 0; i < nSteps; ++i) {\n    for (const idTerm of steps[i]) {\n      const { permutationIndices: perm, expandDims: dimsToExpand } = backend_util_exports.getEinsumPermutation(numDimsRemaining, idDims[idTerm]);\n      let x;\n      if (backend_util_exports.isIdentityPermutation(perm)) {\n        x = tensors[idTerm];\n      } else {\n        x = transpose3({ inputs: { x: tensors[idTerm] }, backend: backend2, attrs: { perm } });\n        tensorsToDispose.push(x);\n      }\n      const targetShape = x.shape.slice();\n      for (let k = 0; k < dimsToExpand.length; ++k) {\n        targetShape.splice(dimsToExpand[k], 0, 1);\n      }\n      if (!util_exports.arraysEqual(x.shape, targetShape)) {\n        x = reshape4({ inputs: { x }, backend: backend2, attrs: { shape: targetShape } });\n        tensorsToDispose.push(x);\n      }\n      if (out === null) {\n        out = x;\n      } else {\n        out = multiply3({ inputs: { a: x, b: out }, backend: backend2 });\n        tensorsToDispose.push(out);\n      }\n    }\n    if (i < nSteps - 1) {\n      if (path[i] >= 0) {\n        out = sum4({\n          inputs: { x: out },\n          backend: backend2,\n          attrs: {\n            axis: path[i] - (allDims.length - numDimsRemaining),\n            keepDims: false\n          }\n        });\n        tensorsToDispose.push(out);\n      }\n      numDimsRemaining--;\n    }\n  }\n  for (const tensorInfo of tensorsToDispose) {\n    if (tensorInfo === out) {\n      continue;\n    }\n    backend2.disposeIntermediateTensorInfo(tensorInfo);\n  }\n  return out;\n}\nvar einsumConfig2 = {\n  kernelName: Einsum,\n  backendName: \"webgl\",\n  kernelFunc: einsum3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Elu.js\nvar ELU4 = `return (x >= 0.0) ? x : (exp(x) - 1.0);`;\nvar ELU_PACKED = `\n  vec4 result;\n\n  result.r = (x.r >= 0.0) ? x.r : (exp(x.r) - 1.0);\n  result.g = (x.g >= 0.0) ? x.g : (exp(x.g) - 1.0);\n  result.b = (x.b >= 0.0) ? x.b : (exp(x.b) - 1.0);\n  result.a = (x.a >= 0.0) ? x.a : (exp(x.a) - 1.0);\n\n  return result;\n`;\nvar elu5 = unaryKernelFunc2({ opSnippet: ELU4, packedOpSnippet: ELU_PACKED });\nvar eluConfig2 = {\n  kernelName: Elu,\n  backendName: \"webgl\",\n  kernelFunc: elu5\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/EluGrad.js\nvar ELU_DER = `return (b >= 1.0) ? a : a * (b + 1.0);`;\nvar ELU_DER_PACKED = `\n  vec4 bGTEZero = vec4(greaterThanEqual(b, vec4(0.)));\n  return (bGTEZero * a) + ((vec4(1.0) - bGTEZero) * (a * (b + vec4(1.0))));\n`;\nvar eluGrad2 = (args) => {\n  const { inputs, backend: backend2 } = args;\n  const { dy, y } = inputs;\n  const program = env().getBool(\"WEBGL_PACK_BINARY_OPERATIONS\") ? new BinaryOpPackedProgram(ELU_DER_PACKED, dy.shape, y.shape) : new BinaryOpProgram(ELU_DER, dy.shape, y.shape);\n  return backend2.runWebGLProgram(program, [dy, y], dy.dtype);\n};\nvar eluGradConfig3 = {\n  kernelName: EluGrad,\n  backendName: \"webgl\",\n  kernelFunc: eluGrad2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Equal.js\nvar PACKED_EQUAL = `\n  return vec4(equal(a, b));\n`;\nvar EQUAL = `return float(a == b);`;\nvar equal3 = binaryKernelFunc2({\n  opSnippet: EQUAL,\n  packedOpSnippet: PACKED_EQUAL,\n  dtype: \"bool\",\n  cpuKernelImpl: equalImplCPU\n});\nvar equalConfig2 = {\n  kernelName: Equal,\n  backendName: \"webgl\",\n  kernelFunc: equal3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Erf.js\nvar ERF = `\n  // Error function is calculated approximately with elementary function.\n  // See \"Handbook of Mathematical Functions with Formulas,\n  // Graphs, and Mathematical Tables\", Abramowitz and Stegun.\n  float p = ${backend_util_exports.ERF_P};\n  float a1 = ${backend_util_exports.ERF_A1};\n  float a2 = ${backend_util_exports.ERF_A2};\n  float a3 = ${backend_util_exports.ERF_A3};\n  float a4 = ${backend_util_exports.ERF_A4};\n  float a5 = ${backend_util_exports.ERF_A5};\n\n  float sign = sign(x);\n  x = abs(x);\n  float t = 1.0 / (1.0 + p * x);\n  return sign * (1.0 - (((((a5*t + a4)*t) + a3)*t + a2)*t + a1)*t*exp(-x*x));\n`;\nvar erf3 = unaryKernelFunc2({ opSnippet: ERF });\nvar erfConfig2 = {\n  kernelName: Erf,\n  backendName: \"webgl\",\n  kernelFunc: erf3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Exp.js\nvar EXP = CHECK_NAN_SNIPPET_UNARY + `\n  return exp(x);\n`;\nvar EXP_PACKED = `\n  vec4 result = exp(x);\n  bvec4 isNaN = isnan(x);\n  result.r = isNaN.r ? x.r : result.r;\n  result.g = isNaN.g ? x.g : result.g;\n  result.b = isNaN.b ? x.b : result.b;\n  result.a = isNaN.a ? x.a : result.a;\n\n  return result;\n`;\nvar exp3 = unaryKernelFunc2({\n  opSnippet: EXP,\n  packedOpSnippet: EXP_PACKED,\n  cpuKernelImpl: expImplCPU,\n  dtype: \"float32\"\n});\nvar expConfig2 = {\n  kernelName: Exp,\n  backendName: \"webgl\",\n  kernelFunc: exp3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/ExpandDims.js\nfunction expandDims4(args) {\n  const { inputs, attrs, backend: backend2 } = args;\n  const { dim } = attrs;\n  const { input: input2 } = inputs;\n  const inputRank = input2.shape.length;\n  const newShape = input2.shape.slice();\n  let $dim = dim;\n  if (dim < 0) {\n    util_exports.assert(-(inputRank + 1) <= dim, () => `Axis must be in the interval [${-(inputRank + 1)}, ${inputRank}]`);\n    $dim = inputRank + dim + 1;\n  }\n  newShape.splice($dim, 0, 1);\n  return reshape4({ inputs: { x: input2 }, backend: backend2, attrs: { shape: newShape } });\n}\nvar expandDimsConfig2 = {\n  kernelName: ExpandDims,\n  backendName: \"webgl\",\n  kernelFunc: expandDims4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Expm1.js\nvar EXPM1 = `return exp(x) - 1.0;`;\nvar expm13 = unaryKernelFunc2({ opSnippet: EXPM1, packedOpSnippet: EXPM1, cpuKernelImpl: expm1ImplCPU });\nvar expm1Config2 = {\n  kernelName: Expm1,\n  backendName: \"webgl\",\n  kernelFunc: expm13\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/fft_gpu.js\nvar FFTProgram = class {\n  constructor(component, inputShape, inverse) {\n    this.variableNames = [\"real\", \"imag\"];\n    const innerDim = inputShape[1];\n    this.outputShape = inputShape;\n    const exponentMultiplierSnippet = inverse ? `2.0 * ${Math.PI}` : `-2.0 * ${Math.PI}`;\n    const resultDenominator = inverse ? `${innerDim}.0` : \"1.0\";\n    let opString;\n    if (component === \"real\") {\n      opString = \"return real * expR - imag * expI;\";\n    } else if (component === \"imag\") {\n      opString = \"return real * expI + imag * expR;\";\n    } else {\n      throw new Error(`FFT component must be either \"real\" or \"imag\", got ${component}.`);\n    }\n    this.userCode = `\n      const float exponentMultiplier = ${exponentMultiplierSnippet};\n\n      float unaryOpComplex(float real, float expR, float imag, float expI) {\n        ${opString}\n      }\n\n      float mulMatDFT(int batch, int index) {\n        float indexRatio = float(index) / float(${innerDim});\n        float exponentMultiplierTimesIndexRatio =\n            exponentMultiplier * indexRatio;\n\n        float result = 0.0;\n\n        for (int i = 0; i < ${innerDim}; i++) {\n          // x = (-2|2 * PI / N) * index * i;\n          float x = exponentMultiplierTimesIndexRatio * float(i);\n          float expR = cos(x);\n          float expI = sin(x);\n          float real = getReal(batch, i);\n          float imag = getImag(batch, i);\n\n          result +=\n              unaryOpComplex(real, expR, imag, expI) / ${resultDenominator};\n        }\n\n        return result;\n      }\n\n      void main() {\n        ivec2 coords = getOutputCoords();\n        setOutput(mulMatDFT(coords[0], coords[1]));\n      }\n    `;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/FFT_impl.js\nfunction fftImpl2(x, inverse, backend2) {\n  const xData = backend2.texData.get(x.dataId);\n  const inputSize = util_exports.sizeFromShape(x.shape);\n  const innerDimensionSize = x.shape[x.shape.length - 1];\n  const batch = inputSize / innerDimensionSize;\n  const input2D = reshape4({ inputs: { x }, backend: backend2, attrs: { shape: [batch, innerDimensionSize] } });\n  const xShape = input2D.shape;\n  const realProgram = new FFTProgram(\"real\", xShape, inverse);\n  const imagProgram = new FFTProgram(\"imag\", xShape, inverse);\n  const inputs = [\n    {\n      dataId: xData.complexTensorInfos.real.dataId,\n      dtype: xData.complexTensorInfos.real.dtype,\n      shape: xShape\n    },\n    {\n      dataId: xData.complexTensorInfos.imag.dataId,\n      dtype: xData.complexTensorInfos.imag.dtype,\n      shape: xShape\n    }\n  ];\n  const realPart = backend2.runWebGLProgram(realProgram, inputs, \"float32\");\n  const imagPart = backend2.runWebGLProgram(imagProgram, inputs, \"float32\");\n  const complexOutput = complex3({ inputs: { real: realPart, imag: imagPart }, backend: backend2 });\n  backend2.disposeIntermediateTensorInfo(realPart);\n  backend2.disposeIntermediateTensorInfo(imagPart);\n  const complexOutputReshaped = reshape4({ inputs: { x: complexOutput }, backend: backend2, attrs: { shape: x.shape } });\n  backend2.disposeIntermediateTensorInfo(input2D);\n  backend2.disposeIntermediateTensorInfo(complexOutput);\n  return complexOutputReshaped;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/FFT.js\nfunction fft3(args) {\n  const { inputs, backend: backend2 } = args;\n  const { input: input2 } = inputs;\n  return fftImpl2(input2, false, backend2);\n}\nvar fftConfig2 = {\n  kernelName: FFT,\n  backendName: \"webgl\",\n  kernelFunc: fft3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/fill_gpu.js\nvar FillProgram = class {\n  constructor(shape, value) {\n    this.outputShape = [];\n    this.customUniforms = [{ name: \"value\", type: \"float\" }];\n    this.variableNames = [\"x\"];\n    this.outputShape = shape;\n    this.userCode = `\n      void main() {\n        // Input can be obtained from uniform value.\n        setOutput(value);\n      }\n    `;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Fill.js\nfunction fill3(args) {\n  const { backend: backend2, attrs } = args;\n  const { shape, value } = attrs;\n  let { dtype } = attrs;\n  dtype = dtype || util_exports.inferDtype(value);\n  if (dtype === \"string\") {\n    const values = util_exports.getArrayFromDType(dtype, util_exports.sizeFromShape(shape));\n    values.fill(value);\n    return backend2.makeTensorInfo(shape, dtype, values);\n  } else {\n    const program = new FillProgram(shape, value);\n    const customValues = [[value]];\n    return backend2.runWebGLProgram(program, [], dtype, customValues);\n  }\n}\nvar fillConfig2 = {\n  kernelName: Fill,\n  backendName: \"webgl\",\n  kernelFunc: fill3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/flip_left_right_gpu.js\nvar FlipLeftRightProgram = class {\n  constructor(imageShape) {\n    this.variableNames = [\"Image\"];\n    this.outputShape = [];\n    const imageWidth = imageShape[2];\n    this.outputShape = imageShape;\n    this.userCode = `\n        void main() {\n          ivec4 coords = getOutputCoords();\n          int x = coords[2];\n\n          int coordX = ${imageWidth} - x - 1;\n          float outputValue;\n          if(coordX >= 0 && coordX < ${imageWidth}) {\n            outputValue = getImage(coords[0], coords[1], coordX, coords[3]);\n          } else {\n            outputValue = getImage(coords[0], coords[1], coords[2], coords[3]);\n          }\n          setOutput(outputValue);\n        }\n    `;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/FlipLeftRight.js\nvar flipLeftRightConfig2 = {\n  kernelName: FlipLeftRight,\n  backendName: \"webgl\",\n  kernelFunc: ({ inputs, backend: backend2 }) => {\n    const { image: image2 } = inputs;\n    const webglBackend = backend2;\n    const program = new FlipLeftRightProgram(image2.shape);\n    const output = webglBackend.runWebGLProgram(program, [image2], image2.dtype);\n    return output;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Floor.js\nvar FLOOR = `return floor(x);`;\nvar floor3 = unaryKernelFunc2({ opSnippet: FLOOR, packedOpSnippet: FLOOR, cpuKernelImpl: floorImplCPU });\nvar floorConfig2 = {\n  kernelName: Floor,\n  backendName: \"webgl\",\n  kernelFunc: floor3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/FloorDiv.js\nvar INT_DIV = `\n  float s = sign(a) * sign(b);\n  int ia = round(a);\n  int ib = round(b);\n  if (ib != 0) {\n    // Windows (D3D) wants guaranteed non-zero int division at compile-time.\n    return float(idiv(ia, ib, s));\n  } else {\n    return NAN;\n  }\n`;\nvar INT_DIV_PACKED = `\n  ivec4 ia = round(a);\n  ivec4 ib = round(b);\n  bvec4 cond = notEqual(ib, ivec4(0));\n  ivec4 result = ivec4(0);\n  vec4 s = sign(a) * sign(b);\n\n  // Windows (D3D) wants guaranteed non-zero int division at compile-time.\n  if (cond[0]) {\n    result[0] = idiv(ia[0], ib[0], s[0]);\n  }\n  if (cond[1]) {\n    result[1] = idiv(ia[1], ib[1], s[1]);\n  }\n  if (cond[2]) {\n    result[2] = idiv(ia[2], ib[2], s[2]);\n  }\n  if (cond[3]) {\n    result[3] = idiv(ia[3], ib[3], s[3]);\n  }\n  return vec4(result);\n`;\nvar floorDiv3 = binaryKernelFunc2({ opSnippet: INT_DIV, packedOpSnippet: INT_DIV_PACKED, dtype: \"int32\" });\nvar floorDivConfig2 = {\n  kernelName: FloorDiv,\n  backendName: \"webgl\",\n  kernelFunc: floorDiv3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/FromPixels_utils/from_pixels_gpu.js\nvar FromPixelsProgram = class {\n  constructor(outputShape) {\n    this.variableNames = [\"A\"];\n    const glsl = getGlslDifferences();\n    const [height, width] = outputShape;\n    this.outputShape = outputShape;\n    this.userCode = `\n      void main() {\n        ivec3 coords = getOutputCoords();\n        int texR = coords[0];\n        int texC = coords[1];\n        int depth = coords[2];\n        vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${width}.0, ${height}.0);\n\n        vec4 values = ${glsl.texture2D}(A, uv);\n        float value;\n        if (depth == 0) {\n          value = values.r;\n        } else if (depth == 1) {\n          value = values.g;\n        } else if (depth == 2) {\n          value = values.b;\n        } else if (depth == 3) {\n          value = values.a;\n        }\n\n        setOutput(floor(value * 255.0 + 0.5));\n      }\n    `;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/FromPixels_utils/from_pixels_packed_gpu.js\nvar FromPixelsPackedProgram = class {\n  constructor(outputShape) {\n    this.variableNames = [\"A\"];\n    this.packedInputs = false;\n    this.packedOutput = true;\n    const glsl = getGlslDifferences();\n    const [height, width] = outputShape;\n    this.outputShape = outputShape;\n    this.userCode = `\n      void main() {\n        ivec3 coords = getOutputCoords();\n        int texR = coords[0];\n        int texC = coords[1];\n        int depth = coords[2];\n\n        vec4 result = vec4(0.);\n\n        for(int row=0; row<=1; row++) {\n          for(int col=0; col<=1; col++) {\n            texC = coords[1] + row;\n            depth = coords[2] + col;\n\n            vec2 uv = (vec2(texC, texR) + halfCR) /\n                       vec2(${width}.0, ${height}.0);\n            vec4 values = ${glsl.texture2D}(A, uv);\n            float value;\n            if (depth == 0) {\n              value = values.r;\n            } else if (depth == 1) {\n              value = values.g;\n            } else if (depth == 2) {\n              value = values.b;\n            } else if (depth == 3) {\n              value = values.a;\n            }\n\n            result[row * 2 + col] = floor(value * 255.0 + 0.5);\n          }\n        }\n\n        ${glsl.output} = result;\n      }\n    `;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/FromPixels.js\nvar fromPixelsConfig = {\n  kernelName: FromPixels,\n  backendName: \"webgl\",\n  kernelFunc: fromPixels2\n};\nvar fromPixels2DContext2;\nvar willReadFrequently = env().getBool(\"CANVAS2D_WILL_READ_FREQUENTLY_FOR_GPU\");\nfunction fromPixels2(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  let { pixels } = inputs;\n  const { numChannels } = attrs;\n  const isVideo = typeof HTMLVideoElement !== \"undefined\" && pixels instanceof HTMLVideoElement;\n  const isImage = typeof HTMLImageElement !== \"undefined\" && pixels instanceof HTMLImageElement;\n  const [width, height] = isVideo ? [\n    pixels.videoWidth,\n    pixels.videoHeight\n  ] : [pixels.width, pixels.height];\n  const texShape = [height, width];\n  const outShape = [height, width, numChannels];\n  if (isImage || isVideo) {\n    const newWillReadFrequently = env().getBool(\"CANVAS2D_WILL_READ_FREQUENTLY_FOR_GPU\");\n    if (fromPixels2DContext2 == null || newWillReadFrequently !== willReadFrequently) {\n      willReadFrequently = newWillReadFrequently;\n      fromPixels2DContext2 = document.createElement(\"canvas\").getContext(\"2d\", { willReadFrequently });\n    }\n    fromPixels2DContext2.canvas.width = width;\n    fromPixels2DContext2.canvas.height = height;\n    fromPixels2DContext2.drawImage(pixels, 0, 0, width, height);\n    pixels = fromPixels2DContext2.canvas;\n  }\n  const tempPixelHandle = backend2.makeTensorInfo(texShape, \"int32\");\n  backend2.texData.get(tempPixelHandle.dataId).usage = TextureUsage.PIXELS;\n  backend2.gpgpu.uploadPixelDataToTexture(backend2.getTexture(tempPixelHandle.dataId), pixels);\n  const program = env().getBool(\"WEBGL_PACK\") ? new FromPixelsPackedProgram(outShape) : new FromPixelsProgram(outShape);\n  const res = backend2.runWebGLProgram(program, [tempPixelHandle], \"int32\");\n  backend2.disposeData(tempPixelHandle.dataId);\n  return res;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/FusedConv2D.js\nfunction fusedConv2d(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x, filter, bias, preluActivationWeights } = inputs;\n  const { strides, pad: pad3, dataFormat, dilations, dimRoundingMode, activation: activation2, leakyreluAlpha } = attrs;\n  const $dataFormat = backend_util_exports.convertConv2DDataFormat(dataFormat);\n  const convInfo = backend_util_exports.computeConv2DInfo(x.shape, filter.shape, strides, dilations, pad3, dimRoundingMode, false, $dataFormat);\n  let out;\n  const intermediates = [];\n  const hasBias = bias != null;\n  const hasPreluActivationWeights = preluActivationWeights != null;\n  const hasLeakyreluAlpha = activation2 === \"leakyrelu\";\n  const prepareInputs = () => {\n    const inputs2 = [x, filter];\n    const alignInputWithDataFormat = (input2, dataFormat2) => {\n      if (dataFormat2 === \"NCHW\" && input2.shape.length === 1 && input2.shape[0] !== 1) {\n        const alignedInput = reshape4({\n          inputs: { x: input2 },\n          backend: backend2,\n          attrs: { shape: [input2.shape[0], 1, 1] }\n        });\n        intermediates.push(alignedInput);\n        return alignedInput;\n      }\n      return input2;\n    };\n    if (hasBias) {\n      inputs2.push(alignInputWithDataFormat(bias, dataFormat));\n    }\n    if (hasPreluActivationWeights) {\n      inputs2.push(alignInputWithDataFormat(preluActivationWeights, dataFormat));\n    }\n    if (hasLeakyreluAlpha) {\n      const $leakyreluAlpha = backend2.makeTensorInfo([], \"float32\", util_exports.createScalarValue(leakyreluAlpha, \"float32\"));\n      inputs2.push($leakyreluAlpha);\n      intermediates.push($leakyreluAlpha);\n    }\n    return inputs2;\n  };\n  if (convInfo.filterHeight === 1 && convInfo.filterWidth === 1 && convInfo.dilationHeight === 1 && convInfo.dilationWidth === 1 && convInfo.strideHeight === 1 && convInfo.strideWidth === 1 && (convInfo.padInfo.type === \"SAME\" || convInfo.padInfo.type === \"VALID\")) {\n    out = conv2dByMatMul({\n      x,\n      filter,\n      convInfo,\n      backend: backend2,\n      bias,\n      activation: activation2,\n      preluActivationWeights,\n      leakyreluAlpha\n    });\n  } else if (convInfo.strideWidth <= 2 && $dataFormat === \"channelsLast\" && env().getBool(\"WEBGL_EXP_CONV\")) {\n    const fusedActivation = activation2 ? mapActivationToShaderProgram(activation2, true) : null;\n    const program = new Conv2DPackedProgram(convInfo, hasBias, fusedActivation, hasPreluActivationWeights, hasLeakyreluAlpha);\n    const customValues = [\n      [convInfo.padInfo.top, convInfo.padInfo.left],\n      [convInfo.strideHeight, convInfo.strideWidth],\n      [convInfo.dilationHeight, convInfo.dilationWidth],\n      [convInfo.inHeight, convInfo.inWidth]\n    ];\n    const inputs2 = prepareInputs();\n    out = backend2.runWebGLProgram(program, inputs2, \"float32\", customValues);\n  } else if (env().getBool(\"WEBGL_CONV_IM2COL\")) {\n    out = conv2dWithIm2Row({\n      x,\n      filter,\n      convInfo,\n      backend: backend2,\n      bias,\n      activation: activation2,\n      preluActivationWeights,\n      leakyreluAlpha\n    });\n  } else {\n    const fusedActivation = activation2 ? mapActivationToShaderProgram(activation2, false) : null;\n    const program = new Conv2DProgram(convInfo, hasBias, fusedActivation, hasPreluActivationWeights, hasLeakyreluAlpha);\n    const inputs2 = prepareInputs();\n    out = backend2.runWebGLProgram(program, inputs2, \"float32\");\n  }\n  const outReshaped = reshape4({ inputs: { x: out }, backend: backend2, attrs: { shape: convInfo.outShape } });\n  intermediates.push(out);\n  intermediates.forEach((t) => backend2.disposeIntermediateTensorInfo(t));\n  return outReshaped;\n}\nvar fusedConv2DConfig2 = {\n  kernelName: FusedConv2D,\n  backendName: \"webgl\",\n  kernelFunc: fusedConv2d\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/FusedDepthwiseConv2D.js\nfunction fusedDepthwiseConv2D2(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x, filter, bias, preluActivationWeights } = inputs;\n  const { strides, pad: pad3, dilations, dimRoundingMode, activation: activation2, leakyreluAlpha } = attrs;\n  const intermediates = [];\n  let $dilations = dilations;\n  if ($dilations == null) {\n    $dilations = [1, 1];\n  }\n  util_exports.assert(backend_util_exports.eitherStridesOrDilationsAreOne(strides, $dilations), () => `Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${strides} and dilations '${$dilations}'`);\n  const convInfo = backend_util_exports.computeConv2DInfo(x.shape, filter.shape, strides, $dilations, pad3, dimRoundingMode, true);\n  const shouldPackDepthwiseConv = env().getBool(\"WEBGL_PACK_DEPTHWISECONV\") && convInfo.strideWidth <= 2 && convInfo.outChannels / convInfo.inChannels === 1;\n  const fusedActivation = activation2 ? mapActivationToShaderProgram(activation2, shouldPackDepthwiseConv) : null;\n  const programInputs = [x, filter];\n  const hasBias = bias != null;\n  const hasPreluActivationWeights = preluActivationWeights != null;\n  const hasLeakyreluAlpha = activation2 === \"leakyrelu\";\n  if (hasBias) {\n    programInputs.push(bias);\n  }\n  if (hasPreluActivationWeights) {\n    programInputs.push(preluActivationWeights);\n  }\n  if (hasLeakyreluAlpha) {\n    const $leakyreluAlpha = backend2.makeTensorInfo([], \"float32\", util_exports.createScalarValue(leakyreluAlpha, \"float32\"));\n    programInputs.push($leakyreluAlpha);\n    intermediates.push($leakyreluAlpha);\n  }\n  let program;\n  if (shouldPackDepthwiseConv) {\n    program = new DepthwiseConvPacked2DProgram(convInfo, hasBias, fusedActivation, hasPreluActivationWeights, hasLeakyreluAlpha);\n  } else {\n    program = new DepthwiseConv2DProgram(convInfo, hasBias, fusedActivation, hasPreluActivationWeights, hasLeakyreluAlpha);\n  }\n  const customValues = [\n    [convInfo.padInfo.top, convInfo.padInfo.left],\n    [convInfo.strideHeight, convInfo.strideWidth],\n    [convInfo.dilationHeight, convInfo.dilationWidth],\n    [convInfo.inHeight, convInfo.inWidth]\n  ];\n  const result = backend2.runWebGLProgram(program, programInputs, \"float32\", customValues);\n  intermediates.forEach((t) => backend2.disposeIntermediateTensorInfo(t));\n  return result;\n}\nvar fusedDepthwiseConv2DConfig2 = {\n  kernelName: FusedDepthwiseConv2D,\n  backendName: \"webgl\",\n  kernelFunc: fusedDepthwiseConv2D2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/gather_nd_gpu.js\nvar GatherNDProgram = class {\n  constructor(sliceDim, strides, shape, paramsShape) {\n    this.sliceDim = sliceDim;\n    this.strides = strides;\n    this.paramsShape = paramsShape;\n    this.variableNames = [\"x\", \"indices\"];\n    this.outputShape = shape;\n    const stridesType = getCoordsDataType(strides.length);\n    const dtype = getCoordsDataType(shape.length);\n    const strideString = this.sliceDim > 1 ? \"strides[j]\" : \"strides\";\n    const paramsShapeType = getCoordsDataType(paramsShape.length);\n    const paramsShapeString = paramsShape.length > 1 ? \"paramsShape[j]\" : \"paramsShape\";\n    this.userCode = `\n        ${stridesType} strides = ${stridesType}(${this.strides});\n        ${paramsShapeType} paramsShape = ${paramsShapeType}(${this.paramsShape});\n         void main() {\n          ${dtype} coords = getOutputCoords();\n          int flattenIndex = 0;\n          bool out_of_bounds = false;\n          for (int j = 0; j < ${this.sliceDim}; j++) {\n            int index = round(getIndices(coords[0], j));\n            out_of_bounds = out_of_bounds || index < 0;\n            out_of_bounds = out_of_bounds || index >= ${paramsShapeString};\n            flattenIndex += index * ${strideString};\n          }\n          setOutput(out_of_bounds ? 0.0 : getX(flattenIndex, coords[1]));\n        }\n      `;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/GatherNd.js\nfunction gatherNd2(args) {\n  const { inputs, backend: backend2 } = args;\n  const { params, indices } = inputs;\n  const indicesShape = indices.shape;\n  const sliceRank = indicesShape[indicesShape.length - 1];\n  const paramsSize = util_exports.sizeFromShape(params.shape);\n  const [resultShape, numSlices, sliceSize, strides] = backend_util_exports.prepareAndValidate(params, indices);\n  const flattenIndices = reshape4({ inputs: { x: indices }, backend: backend2, attrs: { shape: [numSlices, sliceRank] } });\n  const flattenX = reshape4({\n    inputs: { x: params },\n    backend: backend2,\n    attrs: { shape: [util_exports.sizeFromShape(params.shape) / sliceSize, sliceSize] }\n  });\n  if (backend2.shouldExecuteOnCPU([params, indices]) || params.dtype === \"string\") {\n    const indicesData = backend2.readSync(indices.dataId);\n    const paramsBuf = backend2.bufferSync(params);\n    const outValue = gatherNdImplCPU(indicesData, paramsBuf, params.dtype, numSlices, sliceRank, sliceSize, strides, params.shape, paramsSize);\n    return backend2.makeTensorInfo(resultShape, params.dtype, outValue.values);\n  }\n  const program = new GatherNDProgram(sliceRank, strides, [numSlices, sliceSize], params.shape);\n  const res = backend2.runWebGLProgram(program, [flattenX, flattenIndices], flattenX.dtype);\n  const reshaped = reshape4({ inputs: { x: res }, backend: backend2, attrs: { shape: resultShape } });\n  backend2.disposeIntermediateTensorInfo(flattenIndices);\n  backend2.disposeIntermediateTensorInfo(flattenX);\n  backend2.disposeIntermediateTensorInfo(res);\n  return reshaped;\n}\nvar gatherNdConfig2 = {\n  kernelName: GatherNd,\n  backendName: \"webgl\",\n  kernelFunc: gatherNd2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/gather_gpu.js\nvar GatherProgram = class {\n  constructor(aShape, outputShape) {\n    this.variableNames = [\"A\", \"indices\"];\n    this.outputShape = outputShape;\n    this.rank = outputShape.length;\n    const dtype = getCoordsDataType(this.rank);\n    const sourceCoords = getSourceCoords2(aShape, 2);\n    this.userCode = `\n      void main() {\n        ${dtype} resRC = getOutputCoords();\n        int index = int(getIndices(resRC.x, resRC.z));\n        float inBounds = (index >= 0) && (index < ${aShape[2]}) ? 1.0 : 0.0;\n        setOutput(inBounds * getA(${sourceCoords}));\n      }\n    `;\n  }\n};\nfunction getSourceCoords2(aShape, axis) {\n  const currentCoords = [\"resRC.x\", \"resRC.y\", \"resRC.z\", \"resRC.w\"];\n  const sourceCoords = [];\n  for (let i = 0; i < aShape.length; i++) {\n    if (i === 2) {\n      sourceCoords.push(\"index\");\n    } else {\n      sourceCoords.push(`${currentCoords[i]}`);\n    }\n  }\n  return sourceCoords.join();\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/GatherV2.js\nfunction gatherV22(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x, indices } = inputs;\n  const { axis, batchDims } = attrs;\n  const parsedAxis = util_exports.parseAxisParam(axis, x.shape)[0];\n  if (env().get(\"DEBUG\")) {\n    const indicesVals = backend2.readSync(indices.dataId);\n    const axisDim = x.shape[parsedAxis];\n    for (let i = 0; i < indicesVals.length; ++i) {\n      const index = indicesVals[i];\n      util_exports.assert(index <= axisDim - 1 && index >= 0, () => `GatherV2: the index value ${index} is not in [0, ${axisDim - 1}]`);\n    }\n  }\n  const shapeInfo = backend_util_exports.segment_util.collectGatherOpShapeInfo(x, indices, parsedAxis, batchDims);\n  const indicesSize = util_exports.sizeFromShape(indices.shape);\n  const toDispose = [];\n  const flattenX = reshape4({\n    inputs: { x },\n    backend: backend2,\n    attrs: {\n      shape: [\n        shapeInfo.batchSize,\n        shapeInfo.outerSize,\n        shapeInfo.dimSize,\n        shapeInfo.sliceSize\n      ]\n    }\n  });\n  const flattenIndex = reshape4({\n    inputs: { x: indices },\n    backend: backend2,\n    attrs: { shape: [shapeInfo.batchSize, indicesSize / shapeInfo.batchSize] }\n  });\n  toDispose.push(flattenX);\n  toDispose.push(flattenIndex);\n  const flattenOutputShape = [\n    shapeInfo.batchSize,\n    shapeInfo.outerSize,\n    indicesSize / shapeInfo.batchSize,\n    shapeInfo.sliceSize\n  ];\n  if (backend2.shouldExecuteOnCPU([x, indices]) || x.dtype === \"string\") {\n    const indicesBuf = backend2.bufferSync(flattenIndex);\n    const xBuf = backend2.bufferSync(flattenX);\n    const outBuf = gatherV2ImplCPU(xBuf, indicesBuf, flattenOutputShape);\n    toDispose.forEach((t) => backend2.disposeIntermediateTensorInfo(t));\n    return backend2.makeTensorInfo(shapeInfo.outputShape, outBuf.dtype, outBuf.values);\n  }\n  const program = new GatherProgram(flattenX.shape, flattenOutputShape);\n  const res = backend2.runWebGLProgram(program, [flattenX, flattenIndex], flattenX.dtype);\n  toDispose.push(res);\n  const reshaped = reshape4({ inputs: { x: res }, backend: backend2, attrs: { shape: shapeInfo.outputShape } });\n  toDispose.forEach((t) => backend2.disposeIntermediateTensorInfo(t));\n  return reshaped;\n}\nvar gatherV2Config2 = {\n  kernelName: GatherV2,\n  backendName: \"webgl\",\n  kernelFunc: gatherV22\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Greater.js\nvar GREATER = `return float(a > b);`;\nvar GREATER_PACKED = `\n  return vec4(greaterThan(a, b));\n`;\nvar greater4 = binaryKernelFunc2({\n  opSnippet: GREATER,\n  packedOpSnippet: GREATER_PACKED,\n  cpuKernelImpl: greaterImplCPU,\n  dtype: \"bool\"\n});\nvar greaterConfig2 = {\n  kernelName: Greater,\n  backendName: \"webgl\",\n  kernelFunc: greater4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/GreaterEqual.js\nvar GREATER_EQUAL = `return float(a >= b);`;\nvar GREATER_EQUAL_PACKED = `\n  return vec4(greaterThanEqual(a, b));\n`;\nvar greaterEqual3 = binaryKernelFunc2({\n  opSnippet: GREATER_EQUAL,\n  packedOpSnippet: GREATER_EQUAL_PACKED,\n  dtype: \"bool\",\n  cpuKernelImpl: greaterEqualImplCPU\n});\nvar greaterEqualConfig2 = {\n  kernelName: GreaterEqual,\n  backendName: \"webgl\",\n  kernelFunc: greaterEqual3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/IFFT.js\nfunction ifft3(args) {\n  const { inputs, backend: backend2 } = args;\n  const { input: input2 } = inputs;\n  return fftImpl2(input2, true, backend2);\n}\nvar ifftConfig2 = {\n  kernelName: IFFT,\n  backendName: \"webgl\",\n  kernelFunc: ifft3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/IsFinite.js\nvar IS_FINITE = `return float(!isnan(x) && !isinf(x));`;\nvar isFinite4 = unaryKernelFunc2({ opSnippet: IS_FINITE, dtype: \"bool\" });\nvar isFiniteConfig2 = {\n  kernelName: IsFinite,\n  backendName: \"webgl\",\n  kernelFunc: isFinite4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/IsInf.js\nvar IS_INF = `return float(isinf(x));`;\nvar isInf3 = unaryKernelFunc2({ opSnippet: IS_INF, dtype: \"bool\" });\nvar isInfConfig2 = {\n  kernelName: IsInf,\n  backendName: \"webgl\",\n  kernelFunc: isInf3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/IsNaN.js\nvar IS_NAN = `return float(isnan(x));`;\nvar isNaN4 = unaryKernelFunc2({ opSnippet: IS_NAN, dtype: \"bool\" });\nvar isNaNConfig2 = {\n  kernelName: IsNan,\n  backendName: \"webgl\",\n  kernelFunc: isNaN4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Less.js\nvar LESS = `return float(a < b);`;\nvar LESS_PACKED = `\n  return vec4(lessThan(a, b));\n`;\nvar less4 = binaryKernelFunc2({\n  opSnippet: LESS,\n  packedOpSnippet: LESS_PACKED,\n  cpuKernelImpl: lessImplCPU,\n  dtype: \"bool\"\n});\nvar lessConfig2 = {\n  kernelName: Less,\n  backendName: \"webgl\",\n  kernelFunc: less4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/LessEqual.js\nvar LESS_EQUAL = `return float(a <= b);`;\nvar LESS_EQUAL_PACKED = `\n  return vec4(lessThanEqual(a, b));\n`;\nvar lessEqual3 = binaryKernelFunc2({\n  opSnippet: LESS_EQUAL,\n  packedOpSnippet: LESS_EQUAL_PACKED,\n  cpuKernelImpl: lessEqualImplCPU,\n  dtype: \"bool\"\n});\nvar lessEqualConfig2 = {\n  kernelName: LessEqual,\n  backendName: \"webgl\",\n  kernelFunc: lessEqual3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/LinSpace.js\nfunction linSpace2(args) {\n  const { backend: backend2, attrs } = args;\n  const { start, stop, num } = attrs;\n  const outVals = linSpaceImplCPU(start, stop, num);\n  return backend2.makeTensorInfo([outVals.length], \"float32\", outVals);\n}\nvar linSpaceConfig2 = {\n  kernelName: LinSpace,\n  backendName: \"webgl\",\n  kernelFunc: linSpace2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Log.js\nvar LOG = CHECK_NAN_SNIPPET_UNARY + `\n  return x < 0.0 ? 0./0. : log(x);\n`;\nvar LOG_PACKED = `\n  vec4 result = log(x);\n  bvec4 isNaN = isnan(x);\n  result.r = isNaN.r ? x.r : (x.r < 0.0 ? 0./0. : result.r);\n  result.g = isNaN.g ? x.g : (x.g < 0.0 ? 0./0. : result.g);\n  result.b = isNaN.b ? x.b : (x.b < 0.0 ? 0./0. : result.b);\n  result.a = isNaN.a ? x.a : (x.a < 0.0 ? 0./0. : result.a);\n  return result;\n`;\nvar log4 = unaryKernelFunc2({ opSnippet: LOG, packedOpSnippet: LOG_PACKED, cpuKernelImpl: logImplCPU });\nvar logConfig2 = {\n  kernelName: Log,\n  backendName: \"webgl\",\n  kernelFunc: log4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Log1p.js\nvar LOG1P = CHECK_NAN_SNIPPET_UNARY + `\n  return log(1.0 + x);\n`;\nvar log1p3 = unaryKernelFunc2({ opSnippet: LOG1P });\nvar log1pConfig2 = {\n  kernelName: Log1p,\n  backendName: \"webgl\",\n  kernelFunc: log1p3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/LogicalAnd.js\nvar LOGICAL_AND = `return float(a >= 1.0 && b >= 1.0);`;\nvar LOGICAL_AND_PACKED = `\n  return vec4(\n    vec4(greaterThanEqual(a, vec4(1.0))) *\n    vec4(greaterThanEqual(b, vec4(1.0))));\n`;\nvar logicalAnd3 = binaryKernelFunc2({\n  opSnippet: LOGICAL_AND,\n  packedOpSnippet: LOGICAL_AND_PACKED,\n  dtype: \"bool\"\n});\nvar logicalAndConfig2 = {\n  kernelName: LogicalAnd,\n  backendName: \"webgl\",\n  kernelFunc: logicalAnd3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/LogicalNot.js\nvar LOGICAL_NOT = `return float(!(x >= 1.0));`;\nvar logicalNot3 = unaryKernelFunc2({ opSnippet: LOGICAL_NOT });\nvar logicalNotConfig2 = {\n  kernelName: LogicalNot,\n  backendName: \"webgl\",\n  kernelFunc: logicalNot3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/LogicalOr.js\nvar LOGICAL_OR = `return float(a >= 1.0 || b >= 1.0);`;\nvar LOGICAL_OR_PACKED = `\n  return min(\n    vec4(greaterThanEqual(a, vec4(1.0))) +\n    vec4(greaterThanEqual(b, vec4(1.0))),\n    vec4(1.0));\n`;\nvar logicalOr3 = binaryKernelFunc2({ opSnippet: LOGICAL_OR, packedOpSnippet: LOGICAL_OR_PACKED, dtype: \"bool\" });\nvar logicalOrConfig2 = {\n  kernelName: LogicalOr,\n  backendName: \"webgl\",\n  kernelFunc: logicalOr3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/lrn_gpu.js\nvar LRNProgram = class {\n  constructor(xShape, radius, bias, alpha, beta) {\n    this.variableNames = [\"x\"];\n    this.outputShape = [];\n    const rad = radius;\n    const maxD = xShape[3] - 1;\n    this.outputShape = xShape;\n    let powOperator;\n    const basis = `float(${bias}) + float(${alpha}) * sum`;\n    if (beta === 0.5) {\n      powOperator = `inversesqrt(${basis})`;\n    } else if (beta === 1) {\n      powOperator = `1.0/(${basis})`;\n    } else {\n      powOperator = `exp(log(${basis}) * float(-${beta}));`;\n    }\n    this.userCode = `\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int r = coords[1];\n        int c = coords[2];\n        int d = coords[3];\n        float x = getX(b, r, c, d);\n        float sum = 0.0;\n        for (int j = -${rad}; j <= ${rad}; j++) {\n          int idx = d + j;\n          if (idx >= 0 && idx <=  ${maxD}) {\n            float z = getX(b, r, c, idx);\n            sum += z * z;\n          }\n        }\n        float val = x * ${powOperator};\n        setOutput(val);\n      }\n    `;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/lrn_packed_gpu.js\nvar LRNPackedProgram = class {\n  constructor(xShape, radius, bias, alpha, beta) {\n    this.variableNames = [\"x\"];\n    this.outputShape = [];\n    this.packedInputs = true;\n    this.packedOutput = true;\n    const rad = radius;\n    const maxD = xShape[3] - 1;\n    this.outputShape = xShape;\n    let powOperator;\n    const basis = `float(${bias}) + float(${alpha}) * sum`;\n    if (beta === 0.5) {\n      powOperator = `inversesqrt(${basis})`;\n    } else if (beta === 1) {\n      powOperator = `1.0/(${basis})`;\n    } else {\n      powOperator = `exp(log(${basis}) * float(-${beta}));`;\n    }\n    this.userCode = `\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords.x;\n        int r = coords.y;\n        int c = coords.z;\n        int d = coords.w;\n\n        bool hasNextCol = d < ${this.outputShape[3]};\n        bool hasNextRow = c < ${this.outputShape[2]};\n\n        vec4 sum = vec4(0.);\n        vec4 xFragAtOutputCoords = getX(b, r, c, d);\n\n        vec4 xAtOutputCoords = vec4(\n          getChannel(xFragAtOutputCoords, vec2(c, d)),\n          hasNextCol ?\n            getChannel(xFragAtOutputCoords, vec2(c, d + 1)) : 0.0,\n          hasNextRow ?\n            getChannel(xFragAtOutputCoords , vec2(c + 1, d)) : 0.0,\n          (hasNextRow && hasNextCol) ?\n            getChannel(xFragAtOutputCoords, vec2(c + 1, d + 1)) : 0.0\n        );\n\n        int firstChannel = d - ${rad};\n        vec2 cache = vec2(0.);\n        if(firstChannel >= 0){\n          vec4 firstChannelFrag = getX(b, r, c, firstChannel);\n          cache.x = getChannel(firstChannelFrag, vec2(c, firstChannel));\n            if(hasNextRow){\n              cache.y = getChannel(firstChannelFrag, vec2(c + 1, firstChannel));\n            }\n        }\n\n        ivec2 depth = ivec2(d, d + 1);\n        for (int j = - ${rad}; j <= ${rad}; j++) {\n          ivec2 idx = depth + j;\n          bvec2 aboveLowerBound = greaterThanEqual(idx, ivec2(0));\n          bvec2 belowUpperBound = lessThanEqual(idx, ivec2(${maxD}));\n\n          bool depthInRange = aboveLowerBound.x && belowUpperBound.x;\n          bool depthPlusOneInRange = aboveLowerBound.y && belowUpperBound.y;\n\n          if(depthInRange || depthPlusOneInRange){\n            vec4 z = vec4(0.);\n            vec4 xFragAtCurrentDepth;\n            z.xz = cache.xy;\n            if(depthPlusOneInRange && hasNextCol){\n              xFragAtCurrentDepth = idx.y != d ?\n                getX(b, r, c, idx.y) : xFragAtOutputCoords;\n              z.y = getChannel(xFragAtCurrentDepth, vec2(c, idx.y));\n              if(hasNextRow){\n                z.w = getChannel(xFragAtCurrentDepth, vec2(c + 1, idx.y));\n              }\n            }\n            cache.xy = z.yw;\n            sum += z * z;\n          }\n        }\n        vec4 result = xAtOutputCoords * ${powOperator};\n        setOutput(result);\n      }\n    `;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/LRN.js\nvar lrn = (args) => {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x } = inputs;\n  const { depthRadius, bias, alpha, beta } = attrs;\n  const program = env().getBool(\"WEBGL_PACK_NORMALIZATION\") ? new LRNPackedProgram(x.shape, depthRadius, bias, alpha, beta) : new LRNProgram(x.shape, depthRadius, bias, alpha, beta);\n  return backend2.runWebGLProgram(program, [x], x.dtype);\n};\nvar LRNConfig2 = {\n  kernelName: LRN,\n  backendName: \"webgl\",\n  kernelFunc: lrn\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/lrn_grad_gpu.js\nvar LRNGradProgram = class {\n  constructor(inputShape, depthRadius, bias, alpha, beta) {\n    this.variableNames = [\"inputImage\", \"outputImage\", \"dy\"];\n    this.outputShape = [];\n    this.outputShape = inputShape;\n    this.depth = inputShape[3];\n    this.depthRadius = depthRadius;\n    this.bias = bias;\n    this.alpha = alpha;\n    this.beta = beta;\n    this.userCode = `\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int r = coords[1];\n        int c = coords[2];\n\n        float result = 0.0;\n        for (int d = 0; d < ${this.depth}; ++d) {\n          int depthBegin = int(max(0.0, float(d - ${depthRadius})));\n          int depthEnd = int(min(float(${this.depth}),\n              float(d + ${depthRadius} + 1)));\n\n          const int MIN_DEPTH_BEGIN = 0;\n          const int MAX_DEPTH_END = ${this.depth};\n\n          float norm = 0.0;\n          for (int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k) {\n            if (k < depthBegin){\n              continue;\n            }\n            else if (k >= depthBegin && k < depthEnd) {\n              norm += getInputImage(b, r, c, k) * getInputImage(b, r, c, k);\n            }\n            else {\n              break;\n            }\n          }\n\n          norm = float(${alpha}) * norm + float(${bias});\n\n          for(int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k){\n            if (k < depthBegin){\n              continue;\n            }\n            else if (k >= depthBegin && k < depthEnd){\n              float dyi = -2.0 * float(${alpha})\n                * float(${beta})\n                * getInputImage(b ,r ,c, k) * getOutputImage(b, r, c, d)\n                / norm;\n              if (k == d) {\n                dyi += pow(norm, -1.0 * ${beta});\n              }\n              if (k == coords[3]) {\n                dyi *= getDy(b, r, c, d);\n                result += dyi;\n              }\n            }\n            else {\n              break;\n            }\n          }\n      }\n      setOutput(result);\n      }\n    `;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/LRNGrad.js\nvar lrnGrad = (args) => {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x, y, dy } = inputs;\n  const { depthRadius, bias, alpha, beta } = attrs;\n  const program = new LRNGradProgram(x.shape, depthRadius, bias, alpha, beta);\n  return backend2.runWebGLProgram(program, [x, y, dy], x.dtype);\n};\nvar LRNGradConfig2 = {\n  kernelName: LRNGrad,\n  backendName: \"webgl\",\n  kernelFunc: lrnGrad\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Max_impl.js\nfunction maxImpl2(x, reduceShape, outShape, backend2) {\n  const inSize = util_exports.sizeFromShape(reduceShape);\n  const xSize = util_exports.sizeFromShape(x.shape);\n  const batchSize = xSize / inSize;\n  const reshapedInput = reshape4({ inputs: { x }, attrs: { shape: [batchSize, inSize] }, backend: backend2 });\n  const reduced = reduce(reshapedInput, x.dtype, \"max\", backend2);\n  const reshapedOutput = reshape4({ inputs: { x: reduced }, attrs: { shape: outShape }, backend: backend2 });\n  backend2.disposeIntermediateTensorInfo(reshapedInput);\n  backend2.disposeIntermediateTensorInfo(reduced);\n  return reshapedOutput;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Max.js\nfunction max4(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x } = inputs;\n  const { reductionIndices, keepDims } = attrs;\n  const xRank = x.shape.length;\n  const origAxes = util_exports.parseAxisParam(reductionIndices, x.shape);\n  let axes = origAxes;\n  const permutedAxes = backend_util_exports.getAxesPermutation(axes, xRank);\n  const maxInputIsTransposed = permutedAxes != null;\n  const shouldExecuteOnCPU = backend2.shouldExecuteOnCPU([x]);\n  let maxInput = x;\n  if (maxInputIsTransposed) {\n    if (shouldExecuteOnCPU) {\n      const xTexData = backend2.texData.get(maxInput.dataId);\n      const values = xTexData.values;\n      const newShape = new Array(xRank);\n      for (let i = 0; i < newShape.length; i++) {\n        newShape[i] = x.shape[permutedAxes[i]];\n      }\n      const maxInputValues = transposeImplCPU(values, x.shape, x.dtype, permutedAxes, newShape);\n      maxInput = backend2.makeTensorInfo(newShape, x.dtype);\n      const maxInputData = backend2.texData.get(maxInput.dataId);\n      maxInputData.values = maxInputValues;\n    } else {\n      maxInput = transposeImpl2(x, permutedAxes, backend2);\n    }\n    axes = backend_util_exports.getInnerMostAxes(axes.length, xRank);\n  }\n  backend_util_exports.assertAxesAreInnerMostDims(\"max\", axes, xRank);\n  const [maxOutShape, reduceShape] = backend_util_exports.computeOutAndReduceShapes(maxInput.shape, axes);\n  let outShape = maxOutShape;\n  if (keepDims) {\n    outShape = backend_util_exports.expandShapeToKeepDim(maxOutShape, origAxes);\n  }\n  let out;\n  if (shouldExecuteOnCPU) {\n    const xTexData = backend2.texData.get(maxInput.dataId);\n    const values = xTexData.values;\n    const outValues = maxImplCPU(values, util_exports.sizeFromShape(reduceShape), outShape, x.dtype);\n    out = backend2.makeTensorInfo(outShape, x.dtype);\n    const outData = backend2.texData.get(out.dataId);\n    outData.values = outValues;\n  } else {\n    out = maxImpl2(maxInput, reduceShape, outShape, backend2);\n  }\n  if (maxInputIsTransposed) {\n    backend2.disposeIntermediateTensorInfo(maxInput);\n  }\n  return out;\n}\nvar maxConfig2 = {\n  kernelName: Max,\n  backendName: \"webgl\",\n  kernelFunc: max4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Maximum.js\nvar MAXIMUM = CHECK_NAN_SNIPPET2 + `\n  return max(a, b);\n`;\nvar MAXIMUM_PACKED = `\n  vec4 result = vec4(max(a, b));\n  vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));\n  ` + CHECK_NAN_SNIPPET3 + `\n  return result;\n`;\nvar maximum4 = binaryKernelFunc2({\n  opSnippet: MAXIMUM,\n  packedOpSnippet: MAXIMUM_PACKED,\n  cpuKernelImpl: maximumImplCPU\n});\nvar maximumConfig2 = {\n  kernelName: Maximum,\n  backendName: \"webgl\",\n  kernelFunc: maximum4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/MaxPool.js\nfunction maxPool3(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x } = inputs;\n  assertNotComplex2(x, \"maxPool\");\n  const { filterSize, strides, pad: pad3, dimRoundingMode } = attrs;\n  const dilations = 1;\n  util_exports.assert(backend_util_exports.eitherStridesOrDilationsAreOne(strides, dilations), () => `Error in maxPool: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`);\n  const convInfo = backend_util_exports.computePool2DInfo(x.shape, filterSize, strides, dilations, pad3, dimRoundingMode);\n  if (convInfo.filterWidth === 1 && convInfo.filterHeight === 1 && util_exports.arraysEqual(convInfo.inShape, convInfo.outShape)) {\n    return identity3({ inputs: { x }, backend: backend2 });\n  }\n  const maxPoolProgram = new Pool2DProgram(convInfo, \"max\", false);\n  return backend2.runWebGLProgram(maxPoolProgram, [x], x.dtype);\n}\nvar maxPoolConfig2 = {\n  kernelName: MaxPool,\n  backendName: \"webgl\",\n  kernelFunc: maxPool3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/MaxPool3D.js\nfunction maxPool3d2(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x } = inputs;\n  const { filterSize, strides, pad: pad3, dataFormat, dimRoundingMode } = attrs;\n  const dilations = [1, 1, 1];\n  const convInfo = backend_util_exports.computePool3DInfo(x.shape, filterSize, strides, dilations, pad3, dimRoundingMode, dataFormat);\n  const maxPoolProgram = new Pool3DProgram(convInfo, \"max\", false);\n  return backend2.runWebGLProgram(maxPoolProgram, [x], x.dtype);\n}\nvar maxPool3DConfig2 = {\n  kernelName: MaxPool3D,\n  backendName: \"webgl\",\n  kernelFunc: maxPool3d2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/max_pool_backprop_gpu.js\nvar MaxPool2DBackpropProgram = class {\n  constructor(convInfo) {\n    this.variableNames = [\"dy\", \"maxPos\"];\n    this.outputShape = convInfo.inShape;\n    const strideHeight = convInfo.strideHeight;\n    const strideWidth = convInfo.strideWidth;\n    const dilationHeight = convInfo.dilationHeight;\n    const effectiveFilterHeight = convInfo.effectiveFilterHeight;\n    const effectiveFilterWidth = convInfo.effectiveFilterWidth;\n    const padTop = effectiveFilterHeight - 1 - convInfo.padInfo.top;\n    const padLeft = effectiveFilterWidth - 1 - convInfo.padInfo.left;\n    const lastIndex = effectiveFilterHeight * effectiveFilterWidth - 1;\n    this.userCode = `\n      const ivec2 pads = ivec2(${padTop}, ${padLeft});\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n\n        ivec2 dyRCCorner = coords.yz - pads;\n        int dyRCorner = dyRCCorner.x;\n        int dyCCorner = dyRCCorner.y;\n\n        // Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n        for (int wR = 0; wR < ${effectiveFilterHeight};\n          wR += ${dilationHeight}) {\n          float dyR = float(dyRCorner + wR) / ${strideHeight}.0;\n\n          if (dyR < 0.0 || dyR >= ${convInfo.outHeight}.0 || fract(dyR) > 0.0) {\n            continue;\n          }\n          int idyR = int(dyR);\n\n          for (int wC = 0; wC < ${effectiveFilterWidth}; wC++) {\n            float dyC = float(dyCCorner + wC) / ${strideWidth}.0;\n\n            if (dyC < 0.0 || dyC >= ${convInfo.outWidth}.0 ||\n                fract(dyC) > 0.0) {\n              continue;\n            }\n            int idyC = int(dyC);\n\n            float dyValue = getDy(b, idyR, idyC, d);\n            int maxPosValue = ${lastIndex} - int(getMaxPos(b, idyR, idyC, d));\n\n            // Get the current value, check it against the value from the\n            // position matrix.\n            int curPosValue = wR * ${effectiveFilterWidth} + wC;\n            float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);\n\n            dotProd += dyValue * mask;\n          }\n        }\n        setOutput(dotProd);\n      }\n    `;\n  }\n};\nvar MaxPool3DBackpropProgram = class {\n  constructor(convInfo) {\n    this.variableNames = [\"dy\", \"maxPos\"];\n    this.outputShape = convInfo.inShape;\n    const strideDepth = convInfo.strideDepth;\n    const strideHeight = convInfo.strideHeight;\n    const strideWidth = convInfo.strideWidth;\n    const dilationDepth = convInfo.dilationDepth;\n    const dilationHeight = convInfo.dilationHeight;\n    const dilationWidth = convInfo.dilationWidth;\n    const effectiveFilterDepth = convInfo.effectiveFilterDepth;\n    const effectiveFilterHeight = convInfo.effectiveFilterHeight;\n    const effectiveFilterWidth = convInfo.effectiveFilterWidth;\n    const padFront = effectiveFilterDepth - 1 - convInfo.padInfo.front;\n    const padTop = effectiveFilterHeight - 1 - convInfo.padInfo.top;\n    const padLeft = effectiveFilterWidth - 1 - convInfo.padInfo.left;\n    const lastIndex = effectiveFilterDepth * effectiveFilterHeight * effectiveFilterWidth - 1;\n    this.userCode = `\n      const ivec3 pads = ivec3(${padFront}, ${padTop}, ${padLeft});\n\n      void main() {\n        ivec5 coords = getOutputCoords();\n        int batch = coords.x;\n        int ch = coords.u;\n\n        ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;\n        int dyDCorner = dyCorner.x;\n        int dyRCorner = dyCorner.y;\n        int dyCCorner = dyCorner.z;\n\n        // Convolve dy(?, ?, ?, ch) with pos mask(:, :, :, d) to get\n        // dx(xD, xR, xC, ch).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n\n        for (int wD = 0; wD < ${effectiveFilterDepth};\n           wD += ${dilationDepth}) {\n          float dyD = float(dyDCorner + wD) / ${strideDepth}.0;\n\n          if (dyD < 0.0 || dyD >= ${convInfo.outDepth}.0 || fract(dyD) > 0.0) {\n            continue;\n          }\n          int idyD = int(dyD);\n\n          for (int wR = 0; wR < ${effectiveFilterHeight};\n              wR += ${dilationHeight}) {\n            float dyR = float(dyRCorner + wR) / ${strideHeight}.0;\n\n            if (dyR < 0.0 || dyR >= ${convInfo.outHeight}.0 ||\n                fract(dyR) > 0.0) {\n              continue;\n            }\n            int idyR = int(dyR);\n\n            for (int wC = 0; wC < ${effectiveFilterWidth};\n                wC += ${dilationWidth}) {\n              float dyC = float(dyCCorner + wC) / ${strideWidth}.0;\n\n              if (dyC < 0.0 || dyC >= ${convInfo.outWidth}.0 ||\n                  fract(dyC) > 0.0) {\n                continue;\n              }\n              int idyC = int(dyC);\n\n              float dyValue = getDy(batch, idyD, idyR, idyC, ch);\n              int maxPosValue = ${lastIndex} -\n                  int(getMaxPos(batch, idyD, idyR, idyC, ch));\n\n              // Get the current value, check it against the value from the\n              // position matrix.\n              int curPosValue =\n                  wD * ${effectiveFilterHeight} * ${effectiveFilterWidth} +\n                  wR * ${effectiveFilterWidth} + wC;\n              float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);\n\n              dotProd += dyValue * mask;\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    `;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/MaxPool3DGrad.js\nfunction maxPool3DGrad2(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { dy, input: input2 } = inputs;\n  const x = input2;\n  const { filterSize, strides, pad: pad3, dimRoundingMode } = attrs;\n  const dilations = [1, 1, 1];\n  const convInfo = backend_util_exports.computePool3DInfo(x.shape, filterSize, strides, dilations, pad3, dimRoundingMode);\n  const maxPool3dPositionsProgram = new Pool3DProgram(convInfo, \"max\", true);\n  const maxPool3dPositions2 = backend2.runWebGLProgram(maxPool3dPositionsProgram, [x], x.dtype);\n  const maxPoolBackpropProgram = new MaxPool3DBackpropProgram(convInfo);\n  const result = backend2.runWebGLProgram(maxPoolBackpropProgram, [dy, maxPool3dPositions2], x.dtype);\n  backend2.disposeIntermediateTensorInfo(maxPool3dPositions2);\n  return result;\n}\nvar maxPool3DGradConfig3 = {\n  kernelName: MaxPool3DGrad,\n  backendName: \"webgl\",\n  kernelFunc: maxPool3DGrad2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/MaxPoolGrad.js\nfunction maxPoolGrad3(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { dy, input: input2, output } = inputs;\n  const x = input2;\n  assertNotComplex2([input2, output], \"maxPoolGrad\");\n  const { filterSize, strides, pad: pad3, dimRoundingMode } = attrs;\n  const convInfo = backend_util_exports.computePool2DInfo(x.shape, filterSize, strides, 1, pad3, dimRoundingMode);\n  const getPositions = true;\n  const maxPoolPositionsProgram = new Pool2DProgram(convInfo, \"max\", getPositions);\n  const maxPoolPositions2 = backend2.runWebGLProgram(maxPoolPositionsProgram, [x], x.dtype);\n  const maxPoolBackPropProgram = new MaxPool2DBackpropProgram(convInfo);\n  const result = backend2.runWebGLProgram(maxPoolBackPropProgram, [dy, maxPoolPositions2], x.dtype);\n  backend2.disposeIntermediateTensorInfo(maxPoolPositions2);\n  return result;\n}\nvar maxPoolGradConfig3 = {\n  kernelName: MaxPoolGrad,\n  backendName: \"webgl\",\n  kernelFunc: maxPoolGrad3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/MaxPoolWithArgmax_impl.js\nfunction maxPoolWithArgmaxImpl2(x, includeBatchInIndex, convInfo, backend2) {\n  let program = new Pool2DProgram(convInfo, \"max\", false);\n  const poolOutput = backend2.runWebGLProgram(program, [x], \"float32\");\n  program = new Pool2DProgram(convInfo, \"max\", true, true, includeBatchInIndex);\n  const indexOutput = backend2.runWebGLProgram(program, [x], \"float32\");\n  return [poolOutput, indexOutput];\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/MaxPoolWithArgmax.js\nvar maxPoolWithArgmaxConfig2 = {\n  kernelName: MaxPoolWithArgmax,\n  backendName: \"webgl\",\n  kernelFunc: ({ inputs, attrs, backend: backend2 }) => {\n    const { x } = inputs;\n    const { filterSize, strides, pad: pad3, includeBatchInIndex } = attrs;\n    const webglBackend = backend2;\n    util_exports.assert(x.shape.length === 4, () => `Error in maxPool: input must be rank 4 but got rank ${x.shape.length}.`);\n    const dilations = [1, 1];\n    util_exports.assert(backend_util_exports.eitherStridesOrDilationsAreOne(strides, dilations), () => `Error in maxPool: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`);\n    const convInfo = backend_util_exports.computePool2DInfo(x.shape, filterSize, strides, dilations, pad3);\n    const [result, indexes] = maxPoolWithArgmaxImpl2(x, includeBatchInIndex, convInfo, webglBackend);\n    return [result, indexes];\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Mean_impl.js\nfunction meanImpl(x, reduceShape, outShape, backend2) {\n  const inSize = util_exports.sizeFromShape(reduceShape);\n  const xSize = util_exports.sizeFromShape(x.shape);\n  const batchSize = xSize / inSize;\n  const reshapedInput = reshape4({ inputs: { x }, attrs: { shape: [batchSize, inSize] }, backend: backend2 });\n  const reduced = reduce(reshapedInput, \"float32\", \"mean\", backend2);\n  const reshapedOutput = reshape4({ inputs: { x: reduced }, attrs: { shape: outShape }, backend: backend2 });\n  backend2.disposeIntermediateTensorInfo(reshapedInput);\n  backend2.disposeIntermediateTensorInfo(reduced);\n  return reshapedOutput;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Mean.js\nvar meanConfig2 = {\n  kernelName: Mean,\n  backendName: \"webgl\",\n  kernelFunc: ({ inputs, attrs, backend: backend2 }) => {\n    const { x } = inputs;\n    const { keepDims, axis } = attrs;\n    const webglBackend = backend2;\n    const xRank = x.shape.length;\n    const origAxes = util_exports.parseAxisParam(axis, x.shape);\n    let axes = origAxes;\n    const permutedAxes = backend_util_exports.getAxesPermutation(axes, xRank);\n    const meanInputIsTransposed = permutedAxes != null;\n    const shouldExecuteOnCPU = webglBackend.shouldExecuteOnCPU([x]);\n    const intermediates = [];\n    let meanInput = x;\n    if (meanInputIsTransposed) {\n      if (shouldExecuteOnCPU) {\n        const xTexData = webglBackend.texData.get(meanInput.dataId);\n        const values = xTexData.values;\n        const newShape = new Array(xRank);\n        for (let i = 0; i < newShape.length; i++) {\n          newShape[i] = x.shape[permutedAxes[i]];\n        }\n        const meanInputValues = transposeImplCPU(values, x.shape, x.dtype, permutedAxes, newShape);\n        meanInput = webglBackend.makeTensorInfo(newShape, x.dtype);\n        const meanInputData = webglBackend.texData.get(meanInput.dataId);\n        meanInputData.values = meanInputValues;\n      } else {\n        meanInput = transposeImpl2(x, permutedAxes, webglBackend);\n      }\n      intermediates.push(meanInput);\n      axes = backend_util_exports.getInnerMostAxes(axes.length, xRank);\n    }\n    backend_util_exports.assertAxesAreInnerMostDims(\"sum\", axes, xRank);\n    const [meanOutShape, reduceShape] = backend_util_exports.computeOutAndReduceShapes(meanInput.shape, axes);\n    let outShape = meanOutShape;\n    if (keepDims) {\n      outShape = backend_util_exports.expandShapeToKeepDim(meanOutShape, origAxes);\n    }\n    const out = meanImpl(meanInput, reduceShape, outShape, webglBackend);\n    for (const i of intermediates) {\n      webglBackend.disposeIntermediateTensorInfo(i);\n    }\n    return out;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Min.js\nfunction min4(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x } = inputs;\n  const { axis, keepDims } = attrs;\n  const xRank = x.shape.length;\n  const origAxes = util_exports.parseAxisParam(axis, x.shape);\n  let axes = origAxes;\n  const permutedAxes = backend_util_exports.getAxesPermutation(axes, xRank);\n  let permutedX = x;\n  if (permutedAxes != null) {\n    permutedX = transpose3({ inputs: { x }, backend: backend2, attrs: { perm: permutedAxes } });\n    axes = backend_util_exports.getInnerMostAxes(axes.length, x.shape.length);\n  }\n  backend_util_exports.assertAxesAreInnerMostDims(\"min\", axes, xRank);\n  const [outShape, reduceShape] = backend_util_exports.computeOutAndReduceShapes(permutedX.shape, axes);\n  const inSize = util_exports.sizeFromShape(reduceShape);\n  const a2D = reshape4({ inputs: { x: permutedX }, backend: backend2, attrs: { shape: [-1, inSize] } });\n  const reduced = reduce(a2D, a2D.dtype, \"min\", backend2);\n  let res;\n  if (keepDims) {\n    const newShape = backend_util_exports.expandShapeToKeepDim(outShape, origAxes);\n    res = reshape4({ inputs: { x: reduced }, backend: backend2, attrs: { shape: newShape } });\n  } else {\n    res = reshape4({ inputs: { x: reduced }, backend: backend2, attrs: { shape: outShape } });\n  }\n  backend2.disposeIntermediateTensorInfo(a2D);\n  backend2.disposeIntermediateTensorInfo(reduced);\n  if (permutedAxes != null) {\n    backend2.disposeIntermediateTensorInfo(permutedX);\n  }\n  return res;\n}\nvar minConfig2 = {\n  kernelName: Min,\n  backendName: \"webgl\",\n  kernelFunc: min4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Minimum.js\nvar MINIMUM = CHECK_NAN_SNIPPET2 + `\n  return min(a, b);\n`;\nvar MINIMUM_PACKED = `\n  vec4 result = vec4(min(a, b));\n  vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));\n  ` + CHECK_NAN_SNIPPET3 + `\n  return result;\n`;\nvar minimum4 = binaryKernelFunc2({\n  opSnippet: MINIMUM,\n  packedOpSnippet: MINIMUM_PACKED,\n  cpuKernelImpl: minimumImplCPU\n});\nvar minimumConfig2 = {\n  kernelName: Minimum,\n  backendName: \"webgl\",\n  kernelFunc: minimum4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/mirror_pad_gpu.js\nvar MirrorPadProgram = class {\n  constructor(xShape, paddings, mode) {\n    this.variableNames = [\"x\"];\n    this.outputShape = paddings.map((p2, i) => p2[0] + xShape[i] + p2[1]);\n    const rank = xShape.length;\n    const dtype = getCoordsDataType(rank);\n    const start = paddings.map((p2) => p2[0]).join(\",\");\n    const end = paddings.map((p2, i) => p2[0] + xShape[i]).join(\",\");\n    const unpackedCoords = [\"coords[0]\", \"coords[1]\", \"coords[2]\", \"coords[3]\"].slice(0, rank);\n    const offset = mode === \"reflect\" ? 0 : 1;\n    if (rank === 1) {\n      this.userCode = `\n        int start = ${start};\n        int end = ${end};\n\n        void main() {\n          int outC = getOutputCoords();\n          if (outC < start) {\n            outC = start * 2 - outC - ${offset};\n          } else if(outC >= end) {\n            outC = (end - 1) * 2 - outC + ${offset};\n          }\n          setOutput(getX(outC - start));\n        }\n      `;\n      return;\n    }\n    this.userCode = `\n      ${dtype} start = ${dtype}(${start});\n      ${dtype} end = ${dtype}(${end});\n\n      void main() {\n        ${dtype} outC = getOutputCoords();\n        for (int i = 0; i < ${rank}; i++) {\n          if (outC[i] < start[i]) {\n            outC[i] = start[i] * 2 - outC[i] - ${offset};\n          } else if(outC[i] >= end[i]) {\n            outC[i] = (end[i] - 1) * 2 - outC[i] + ${offset};\n          }\n        }\n        ${dtype} coords = outC - start;\n        setOutput(getX(${unpackedCoords}));\n      }\n    `;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/mirror_pad_packed_gpu.js\nvar MirrorPadPackedProgram = class {\n  constructor(xShape, paddings, mode) {\n    this.variableNames = [\"x\"];\n    this.packedInputs = true;\n    this.packedOutput = true;\n    this.outputShape = paddings.map((p2, i) => p2[0] + xShape[i] + p2[1]);\n    const rank = xShape.length;\n    const dtype = getCoordsDataType(rank);\n    const start = paddings.map((p2) => p2[0]).join(\",\");\n    const end = paddings.map((p2, i) => p2[0] + xShape[i]).join(\",\");\n    const coords3 = getChannels(\"rc\", rank);\n    const source = getChannels(\"source\", rank);\n    const cLimit = `${coords3[rank - 1]} < ${this.outputShape[rank - 1]}`;\n    const innerDims = rank === 1 ? \"source\" : `vec2(${source.slice(-2).join()})`;\n    const offset = mode === \"reflect\" ? 0 : 1;\n    let mainLoop = \"\";\n    if (rank === 1) {\n      const padSetup = `\n        ${dtype} source = rc;\n        if (source < start) {\n          source = start * 2 - source - ${offset};\n        } else if (source >= end) {\n          source = (end - 1) * 2 - source + ${offset};\n        }\n        source -= start;\n      `;\n      mainLoop = `\n        ${dtype} rc = outputLoc;\n        ${padSetup}\n        result[0] = getChannel(getX(${source.join()}), ${innerDims});\n        ${coords3[rank - 1]} += 1;\n        if(${cLimit}) {\n          ${padSetup}\n          result[1] = getChannel(getX(${source.join()}), ${innerDims});\n        }\n      `;\n    } else {\n      const padSetup = `\n        ${dtype} source = rc;\n        ${dtype} lt = ${dtype}(lessThan(source, start));\n        ${dtype} gte = ${dtype}(greaterThanEqual(source, end));\n        ${dtype} orig = 1 - (lt + gte);\n        source = orig * source +\n                lt * (start * 2 - source - ${offset}) +\n                gte * ((end - 1) * 2 - source + ${offset});\n        source -= start;\n      `;\n      mainLoop = `\n        ${dtype} rc = outputLoc;\n        ${padSetup}\n        result[0] = getChannel(getX(${source.join()}), ${innerDims});\n        ${coords3[rank - 1]} += 1;\n        if(${cLimit}) {\n          ${padSetup}\n          result[1] = getChannel(getX(${source.join()}), ${innerDims});\n        }\n        rc = outputLoc;\n        ${coords3[rank - 2]} += 1;\n        if(${coords3[rank - 2]} < ${this.outputShape[rank - 2]}) {\n          ${padSetup}\n          result[2] = getChannel(getX(${source.join()}), ${innerDims});\n          ${coords3[rank - 1]} += 1;\n          if(${cLimit}) {\n            ${padSetup}\n            result[3] = getChannel(getX(${source.join()}), ${innerDims});\n          }\n        }\n      `;\n    }\n    this.userCode = `\n      const ${dtype} start = ${dtype}(${start});\n      const ${dtype} end = ${dtype}(${end});\n\n      void main() {\n        ${dtype} outputLoc = getOutputCoords();\n        vec4 result = vec4(0.);\n        ${mainLoop}\n        setOutput(result);\n      }\n    `;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/MirrorPad.js\nvar mirrorPadKernelFunc = ({ inputs, backend: backend2, attrs }) => {\n  const { x } = inputs;\n  const { paddings, mode } = attrs;\n  const program = env().getBool(\"WEBGL_PACK_ARRAY_OPERATIONS\") ? new MirrorPadPackedProgram(x.shape, paddings, mode) : new MirrorPadProgram(x.shape, paddings, mode);\n  const output = backend2.runWebGLProgram(program, [x], x.dtype);\n  return output;\n};\nvar mirrorPadConfig2 = {\n  kernelName: MirrorPad,\n  backendName: \"webgl\",\n  kernelFunc: mirrorPadKernelFunc\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Mod.js\nvar MOD = `if (b == 0.0) return NAN;\n  return mod(a, b);`;\nvar MOD_PACKED = `\n  vec4 result = mod(a, b);\n  vec4 isNaN = vec4(equal(b, vec4(0.0)));\n  ` + CHECK_NAN_SNIPPET3 + `\n  return result;\n`;\nvar mod3 = binaryKernelFunc2({\n  opSnippet: MOD,\n  packedOpSnippet: MOD_PACKED\n});\nvar modConfig2 = {\n  kernelName: Mod,\n  backendName: \"webgl\",\n  kernelFunc: mod3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/multinomial_gpu.js\nvar MultinomialProgram = class {\n  constructor(batchSize, numOutcomes, numSamples) {\n    this.variableNames = [\"probs\"];\n    this.customUniforms = [{ name: \"seed\", type: \"float\" }];\n    this.outputShape = [batchSize, numSamples];\n    this.userCode = `\n      void main() {\n        ivec2 coords = getOutputCoords();\n        int batch = coords[0];\n\n        float r = random(seed);\n        float cdf = 0.0;\n\n        for (int i = 0; i < ${numOutcomes - 1}; i++) {\n          cdf += getProbs(batch, i);\n\n          if (r < cdf) {\n            setOutput(float(i));\n            return;\n          }\n        }\n\n        // If no other event happened, last event happened.\n        setOutput(float(${numOutcomes - 1}));\n      }\n    `;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/RealDiv.js\nvar DIV = `\nif (a == b) {\n  return 1.0;\n};\nreturn a / b;`;\nvar DIV_PACKED = `\n  // vec4 one = vec4(equal(a, b));\n  // return one + (vec4(1.0) - one) * a / b;\n  vec4 result = a / b;\n  if(a.x == b.x) {\n    result.x = 1.;\n  }\n  if(a.y == b.y) {\n    result.y = 1.;\n  }\n  if(a.z == b.z) {\n    result.z = 1.;\n  }\n  if(a.w == b.w) {\n    result.w = 1.;\n  }\n\n  return result;\n`;\nvar realDiv = binaryKernelFunc2({ opSnippet: DIV, packedOpSnippet: DIV_PACKED, checkOutOfBounds: true });\nvar realDivConfig2 = {\n  kernelName: RealDiv,\n  backendName: \"webgl\",\n  kernelFunc: realDiv\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Sub.js\nvar SUB = \"return a - b;\";\nvar sub3 = binaryKernelFunc2({\n  opSnippet: SUB,\n  packedOpSnippet: SUB,\n  supportsComplex: true,\n  cpuKernelImpl: subImplCPU\n});\nvar subConfig2 = {\n  kernelName: Sub,\n  backendName: \"webgl\",\n  kernelFunc: sub3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Softmax.js\nfunction softmax4(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { logits } = inputs;\n  const { dim } = attrs;\n  const axes = util_exports.parseAxisParam([dim], logits.shape);\n  const maxLogit = max4({\n    inputs: { x: logits },\n    backend: backend2,\n    attrs: { reductionIndices: axes, keepDims: false }\n  });\n  const expandedShape = backend_util_exports.expandShapeToKeepDim(maxLogit.shape, axes);\n  const maxLogitsReshaped = reshape4({ inputs: { x: maxLogit }, backend: backend2, attrs: { shape: expandedShape } });\n  const a = sub3({ inputs: { a: logits, b: maxLogitsReshaped }, backend: backend2 });\n  const b = exp3({ inputs: { x: a }, backend: backend2 });\n  const sumExp = sum4({ inputs: { x: b }, backend: backend2, attrs: { axis: axes, keepDims: false } });\n  const sumExpReshaped = reshape4({ inputs: { x: sumExp }, backend: backend2, attrs: { shape: expandedShape } });\n  const res = realDiv({ inputs: { a: b, b: sumExpReshaped }, backend: backend2 });\n  backend2.disposeIntermediateTensorInfo(maxLogit);\n  backend2.disposeIntermediateTensorInfo(maxLogitsReshaped);\n  backend2.disposeIntermediateTensorInfo(a);\n  backend2.disposeIntermediateTensorInfo(b);\n  backend2.disposeIntermediateTensorInfo(sumExp);\n  backend2.disposeIntermediateTensorInfo(sumExpReshaped);\n  return res;\n}\nvar softmaxConfig2 = {\n  kernelName: Softmax,\n  backendName: \"webgl\",\n  kernelFunc: softmax4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Multinomial.js\nfunction multinomial3(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { logits } = inputs;\n  const { numSamples, seed, normalized } = attrs;\n  const probs = normalized ? logits : softmax4({ inputs: { logits }, backend: backend2, attrs: { dim: logits.shape.length - 1 } });\n  const batchSize = probs.shape[0];\n  const numOutcomes = probs.shape[1];\n  const program = new MultinomialProgram(batchSize, numOutcomes, numSamples);\n  const customValues = [[seed]];\n  const res = backend2.runWebGLProgram(program, [probs], \"int32\", customValues);\n  if (!normalized) {\n    backend2.disposeIntermediateTensorInfo(probs);\n  }\n  return res;\n}\nvar multinomialConfig2 = {\n  kernelName: Multinomial,\n  backendName: \"webgl\",\n  kernelFunc: multinomial3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Neg.js\nvar NEG = CHECK_NAN_SNIPPET + `\n  return -x;\n`;\nvar NEG_PACKED = `\n  vec4 result = -x;\n  bvec4 isNaN = isnan(x);\n\n  result.r = isNaN.r ? x.r : result.r;\n  result.g = isNaN.g ? x.g : result.g;\n  result.b = isNaN.b ? x.b : result.b;\n  result.a = isNaN.a ? x.a : result.a;\n\n  return result;\n`;\nfunction neg3(args) {\n  const { inputs, backend: backend2 } = args;\n  const { x } = inputs;\n  if (backend2.shouldExecuteOnCPU([x])) {\n    const xData = backend2.texData.get(x.dataId);\n    const [outValues, newShape] = negImplCPU(xData.values, x.shape, x.dtype);\n    return backend2.makeTensorInfo(newShape, x.dtype, outValues);\n  }\n  let program;\n  if (env().getBool(\"WEBGL_PACK_UNARY_OPERATIONS\")) {\n    program = new UnaryOpPackedProgram(x.shape, NEG_PACKED);\n  } else {\n    program = new UnaryOpProgram(x.shape, NEG);\n  }\n  return backend2.runWebGLProgram(program, [x], x.dtype);\n}\nvar negConfig2 = {\n  kernelName: Neg,\n  backendName: \"webgl\",\n  kernelFunc: neg3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/NonMaxSuppressionV3.js\nvar nonMaxSuppressionV3Impl3 = kernel_impls_exports.nonMaxSuppressionV3Impl;\nfunction nonMaxSuppressionV32(args) {\n  backend_util_exports.warn(\"tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead\");\n  const { inputs, backend: backend2, attrs } = args;\n  const { boxes, scores } = inputs;\n  const { maxOutputSize, iouThreshold, scoreThreshold } = attrs;\n  const boxesVals = backend2.readSync(boxes.dataId);\n  const scoresVals = backend2.readSync(scores.dataId);\n  const { selectedIndices } = nonMaxSuppressionV3Impl3(boxesVals, scoresVals, maxOutputSize, iouThreshold, scoreThreshold);\n  return backend2.makeTensorInfo([selectedIndices.length], \"int32\", new Int32Array(selectedIndices));\n}\nvar nonMaxSuppressionV3Config2 = {\n  kernelName: NonMaxSuppressionV3,\n  backendName: \"webgl\",\n  kernelFunc: nonMaxSuppressionV32\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/NonMaxSuppressionV4.js\nvar nonMaxSuppressionV4Impl3 = kernel_impls_exports.nonMaxSuppressionV4Impl;\nfunction nonMaxSuppressionV42(args) {\n  backend_util_exports.warn(\"tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead\");\n  const { inputs, backend: backend2, attrs } = args;\n  const { boxes, scores } = inputs;\n  const { maxOutputSize, iouThreshold, scoreThreshold, padToMaxOutputSize } = attrs;\n  const boxesVals = backend2.readSync(boxes.dataId);\n  const scoresVals = backend2.readSync(scores.dataId);\n  const { selectedIndices, validOutputs } = nonMaxSuppressionV4Impl3(boxesVals, scoresVals, maxOutputSize, iouThreshold, scoreThreshold, padToMaxOutputSize);\n  return [\n    backend2.makeTensorInfo([selectedIndices.length], \"int32\", new Int32Array(selectedIndices)),\n    backend2.makeTensorInfo([], \"int32\", new Int32Array([validOutputs]))\n  ];\n}\nvar nonMaxSuppressionV4Config2 = {\n  kernelName: NonMaxSuppressionV4,\n  backendName: \"webgl\",\n  kernelFunc: nonMaxSuppressionV42\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/NonMaxSuppressionV5.js\nvar nonMaxSuppressionV5Impl3 = kernel_impls_exports.nonMaxSuppressionV5Impl;\nfunction nonMaxSuppressionV52(args) {\n  backend_util_exports.warn(\"tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead\");\n  const { inputs, backend: backend2, attrs } = args;\n  const { boxes, scores } = inputs;\n  const { maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma } = attrs;\n  const boxesVals = backend2.readSync(boxes.dataId);\n  const scoresVals = backend2.readSync(scores.dataId);\n  const maxOutputSizeVal = maxOutputSize;\n  const iouThresholdVal = iouThreshold;\n  const scoreThresholdVal = scoreThreshold;\n  const softNmsSigmaVal = softNmsSigma;\n  const { selectedIndices, selectedScores } = nonMaxSuppressionV5Impl3(boxesVals, scoresVals, maxOutputSizeVal, iouThresholdVal, scoreThresholdVal, softNmsSigmaVal);\n  return [\n    backend2.makeTensorInfo([selectedIndices.length], \"int32\", new Int32Array(selectedIndices)),\n    backend2.makeTensorInfo([selectedScores.length], \"float32\", new Float32Array(selectedScores))\n  ];\n}\nvar nonMaxSuppressionV5Config2 = {\n  kernelName: NonMaxSuppressionV5,\n  backendName: \"webgl\",\n  kernelFunc: nonMaxSuppressionV52\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/onehot_gpu.js\nvar OneHotProgram = class {\n  constructor(numIndices, depth, onValue, offValue) {\n    this.variableNames = [\"indices\"];\n    this.outputShape = [numIndices, depth];\n    this.userCode = `\n      void main() {\n        ivec2 coords = getOutputCoords();\n        int index = round(getIndices(coords.x));\n        setOutput(mix(float(${offValue}), float(${onValue}),\n                      float(index == coords.y)));\n      }\n    `;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/OneHot.js\nvar oneHot3 = (args) => {\n  const { inputs, backend: backend2, attrs } = args;\n  const { indices } = inputs;\n  const { dtype, depth, onValue, offValue } = attrs;\n  const indicesSize = util_exports.sizeFromShape(indices.shape);\n  const program = new OneHotProgram(indicesSize, depth, onValue, offValue);\n  const reshaped = reshape4({ inputs: { x: indices }, backend: backend2, attrs: { shape: [indicesSize] } });\n  const result = backend2.runWebGLProgram(program, [reshaped], dtype);\n  backend2.disposeIntermediateTensorInfo(reshaped);\n  const outShape = [...indices.shape, depth];\n  const out = reshape4({ inputs: { x: result }, backend: backend2, attrs: { shape: outShape } });\n  backend2.disposeIntermediateTensorInfo(result);\n  return out;\n};\nvar oneHotConfig2 = {\n  kernelName: OneHot,\n  backendName: \"webgl\",\n  kernelFunc: oneHot3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/ZerosLike.js\nfunction zerosLike3(args) {\n  const { inputs, backend: backend2 } = args;\n  const { x } = inputs;\n  if (x.dtype === \"complex64\") {\n    const realPart = real3({ inputs: { input: x }, backend: backend2 });\n    const r = zerosLike3({ inputs: { x: realPart }, backend: backend2 });\n    const imagPart = imag3({ inputs: { input: x }, backend: backend2 });\n    const i = zerosLike3({ inputs: { x: imagPart }, backend: backend2 });\n    const result = complex3({ inputs: { real: r, imag: i }, backend: backend2 });\n    backend2.disposeIntermediateTensorInfo(realPart);\n    backend2.disposeIntermediateTensorInfo(r);\n    backend2.disposeIntermediateTensorInfo(imagPart);\n    backend2.disposeIntermediateTensorInfo(i);\n    return result;\n  } else {\n    return fill3({\n      attrs: {\n        shape: x.shape,\n        dtype: x.dtype,\n        value: x.dtype === \"string\" ? \"\" : 0\n      },\n      backend: backend2\n    });\n  }\n}\nvar zerosLikeConfig2 = {\n  kernelName: ZerosLike,\n  backendName: \"webgl\",\n  kernelFunc: zerosLike3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/OnesLike.js\nfunction onesLike3(args) {\n  const { inputs, backend: backend2 } = args;\n  const { x } = inputs;\n  if (x.dtype === \"string\") {\n    throw new Error(\"onesLike is not supported under string dtype\");\n  } else if (x.dtype === \"complex64\") {\n    const realPart = real3({ inputs: { input: x }, backend: backend2 });\n    const r = onesLike3({ inputs: { x: realPart }, backend: backend2 });\n    const imagPart = imag3({ inputs: { input: x }, backend: backend2 });\n    const i = zerosLike3({ inputs: { x: imagPart }, backend: backend2 });\n    const result = complex3({ inputs: { real: r, imag: i }, backend: backend2 });\n    backend2.disposeIntermediateTensorInfo(realPart);\n    backend2.disposeIntermediateTensorInfo(r);\n    backend2.disposeIntermediateTensorInfo(imagPart);\n    backend2.disposeIntermediateTensorInfo(i);\n    return result;\n  } else {\n    return fill3({ attrs: { shape: x.shape, dtype: x.dtype, value: 1 }, backend: backend2 });\n  }\n}\nvar onesLikeConfig2 = {\n  kernelName: OnesLike,\n  backendName: \"webgl\",\n  kernelFunc: onesLike3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Pack.js\nfunction pack2(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { axis } = attrs;\n  if (inputs.length === 1) {\n    return expandDims4({ inputs: { input: inputs[0] }, backend: backend2, attrs: { dim: axis } });\n  }\n  const shape = inputs[0].shape;\n  const dtype = inputs[0].dtype;\n  inputs.forEach((t) => {\n    util_exports.assertShapesMatch(shape, t.shape, \"All tensors passed to stack must have matching shapes\");\n    util_exports.assert(dtype === t.dtype, () => \"All tensors passed to stack must have matching dtypes\");\n  });\n  const intermediateTensorInfos = [];\n  const expandedTensors = inputs.map((t) => {\n    const expandedT = expandDims4({ inputs: { input: t }, backend: backend2, attrs: { dim: axis } });\n    intermediateTensorInfos.push(expandedT);\n    return expandedT;\n  });\n  const result = concat3({ inputs: expandedTensors, backend: backend2, attrs: { axis } });\n  intermediateTensorInfos.forEach((t) => backend2.disposeIntermediateTensorInfo(t));\n  return result;\n}\nvar packConfig2 = {\n  kernelName: Pack,\n  backendName: \"webgl\",\n  kernelFunc: pack2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/pad_gpu.js\nvar PadProgram = class {\n  constructor(xShape, paddings, constantValue) {\n    this.variableNames = [\"x\"];\n    this.customUniforms = [{ name: \"value\", type: \"float\" }];\n    this.outputShape = paddings.map((p2, i) => p2[0] + xShape[i] + p2[1]);\n    const rank = xShape.length;\n    const type = getCoordsDataType(rank);\n    const start = paddings.map((p2) => p2[0]).join(\",\");\n    const end = paddings.map((p2, i) => p2[0] + xShape[i]).join(\",\");\n    const unpackedCoords = [\"coords[0]\", \"coords[1]\", \"coords[2]\", \"coords[3]\"].slice(0, rank);\n    if (rank === 1) {\n      this.userCode = `\n        int start = ${start};\n        int end = ${end};\n\n        void main() {\n          int outC = getOutputCoords();\n          if (outC < start || outC >= end) {\n            setOutput(value);\n          } else {\n            setOutput(getX(outC - start));\n          }\n        }\n      `;\n      return;\n    }\n    this.userCode = `\n      ${type} start = ${type}(${start});\n      ${type} end = ${type}(${end});\n\n      void main() {\n        ${type} outC = getOutputCoords();\n        if (any(lessThan(outC, start)) || any(greaterThanEqual(outC, end))) {\n          setOutput(value);\n        } else {\n          ${type} coords = outC - start;\n          setOutput(getX(${unpackedCoords}));\n        }\n      }\n    `;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/pad_packed_gpu.js\nvar PadPackedProgram = class {\n  constructor(xShape, paddings, constantValue) {\n    this.variableNames = [\"x\"];\n    this.packedInputs = true;\n    this.packedOutput = true;\n    this.customUniforms = [{ name: \"value\", type: \"float\" }];\n    this.outputShape = paddings.map((p2, i) => p2[0] + xShape[i] + p2[1]);\n    const rank = xShape.length;\n    const dtype = getCoordsDataType(rank);\n    const start = paddings.map((p2) => p2[0]).join(\",\");\n    const end = paddings.map((p2, i) => p2[0] + xShape[i]).join(\",\");\n    const coords3 = getChannels(\"rc\", rank);\n    const source = getChannels(\"source\", rank);\n    const cLimit = `${coords3[rank - 1]} < ${this.outputShape[rank - 1]}`;\n    const innerDims = rank === 1 ? \"source\" : `vec2(${source.slice(-2).join()})`;\n    const componentSetup = [\n      `${dtype} rc = outputLoc;`,\n      `${coords3[rank - 1]} += 1;\n       if(${cLimit}) {\n      `,\n      rank === 1 ? \"\" : `}\n       rc = outputLoc;\n       ${coords3[rank - 2]} += 1;\n       if(${coords3[rank - 2]} < ${this.outputShape[rank - 2]}) {`,\n      rank === 1 ? \"\" : `  ${coords3[rank - 1]} += 1;\n         if(${cLimit}) {`\n    ];\n    const paddingArea = rank === 1 ? \"rc < start || rc >= end\" : \"any(lessThan(rc, start)) || any(greaterThanEqual(rc, end))\";\n    let mainLoop = \"\";\n    for (let i = 0, j = rank === 1 ? 2 : 4; i < j; i++) {\n      mainLoop += `\n        ${componentSetup[i]}\n        if (${paddingArea}) {\n          result[${i}] = float(value);\n        } else {\n          ${dtype} source = rc - start;\n          result[${i}] = getChannel(getX(${source.join()}), ${innerDims});\n        }\n      `;\n    }\n    mainLoop += rank === 1 ? `} ` : `}}`;\n    this.userCode = `\n      const ${dtype} start = ${dtype}(${start});\n      const ${dtype} end = ${dtype}(${end});\n\n      void main() {\n        ${dtype} outputLoc = getOutputCoords();\n        vec4 result = vec4(0.);\n        ${mainLoop}\n        setOutput(result);\n      }\n    `;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/PadV2.js\nvar padV22 = (args) => {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x } = inputs;\n  const { paddings, constantValue } = attrs;\n  if (util_exports.sizeFromShape(x.shape) === 0) {\n    const outputShape = paddings.map((p2, i) => p2[0] + x.shape[i] + p2[1]);\n    return fill3({\n      backend: backend2,\n      attrs: { shape: outputShape, value: constantValue, dtype: x.dtype }\n    });\n  }\n  const program = env().getBool(\"WEBGL_PACK_ARRAY_OPERATIONS\") ? new PadPackedProgram(x.shape, paddings, constantValue) : new PadProgram(x.shape, paddings, constantValue);\n  const customValues = [[constantValue]];\n  return backend2.runWebGLProgram(program, [x], x.dtype, customValues);\n};\nvar padV2Config2 = {\n  kernelName: PadV2,\n  backendName: \"webgl\",\n  kernelFunc: padV22\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Pow.js\nvar POW = `\n  if(a < 0.0 && floor(b) < b){\n    return NAN;\n  }\n  if (b == 0.0) {\n    return 1.0;\n  }\n  return (round(mod(b, 2.0)) != 1) ?\n      pow(abs(a), b) : sign(a) * pow(abs(a), b);\n`;\nvar POW_PACKED = `\n  // isModRound1 has 1 for components with round(mod(b, 2.0)) == 1, 0 otherwise.\n  vec4 isModRound1 = vec4(equal(round(mod(b, 2.0)), ivec4(1)));\n  vec4 multiplier = sign(a) * isModRound1 + (vec4(1.0) - isModRound1);\n  vec4 result = multiplier * pow(abs(a), b);\n\n  // Ensure that a^0 = 1, including 0^0 = 1 as this correspond to TF and JS\n  bvec4 isExpZero = equal(b, vec4(0.0));\n  result.r = isExpZero.r ? 1.0 : result.r;\n  result.g = isExpZero.g ? 1.0 : result.g;\n  result.b = isExpZero.b ? 1.0 : result.b;\n  result.a = isExpZero.a ? 1.0 : result.a;\n\n  vec4 isNaN = vec4(lessThan(a, vec4(0.0))) * vec4(lessThan(floor(b), b));\n  ` + CHECK_NAN_SNIPPET3 + `\n  return result;\n`;\nvar pow3 = binaryKernelFunc2({ opSnippet: POW, packedOpSnippet: POW_PACKED });\nvar powConfig2 = {\n  kernelName: Pow,\n  backendName: \"webgl\",\n  kernelFunc: pow3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Prod.js\nfunction prod3(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x } = inputs;\n  const { axis, keepDims } = attrs;\n  const xRank = x.shape.length;\n  const toDispose = [];\n  const origAxes = util_exports.parseAxisParam(axis, x.shape);\n  let axes = origAxes;\n  const permutedAxes = backend_util_exports.getAxesPermutation(axes, xRank);\n  let permutedX = x;\n  if (permutedAxes != null) {\n    permutedX = transpose3({ inputs: { x }, backend: backend2, attrs: { perm: permutedAxes } });\n    axes = backend_util_exports.getInnerMostAxes(axes.length, xRank);\n    toDispose.push(permutedX);\n  }\n  backend_util_exports.assertAxesAreInnerMostDims(\"prod\", axes, xRank);\n  let res;\n  if (backend2.shouldExecuteOnCPU([permutedX])) {\n    const xVals = backend2.texData.get(permutedX.dataId).values;\n    const { outVals, outShape, outDtype } = prodImplCPU(permutedX.shape, permutedX.dtype, xVals, axes);\n    res = backend2.makeTensorInfo(outShape, outDtype, outVals);\n  } else {\n    const [outShape, reduceShape] = backend_util_exports.computeOutAndReduceShapes(permutedX.shape, axes);\n    const inSize = util_exports.sizeFromShape(reduceShape);\n    const a2D = reshape4({ inputs: { x: permutedX }, backend: backend2, attrs: { shape: [-1, inSize] } });\n    const outputDType = sumOutType(x.dtype);\n    const reduced = reduce(a2D, outputDType, \"prod\", backend2);\n    res = reshape4({ inputs: { x: reduced }, backend: backend2, attrs: { shape: outShape } });\n    toDispose.push(a2D);\n    toDispose.push(reduced);\n  }\n  if (keepDims) {\n    toDispose.push(res);\n    const newShape = backend_util_exports.expandShapeToKeepDim(res.shape, origAxes);\n    res = reshape4({ inputs: { x: res }, backend: backend2, attrs: { shape: newShape } });\n  }\n  toDispose.forEach((t) => backend2.disposeIntermediateTensorInfo(t));\n  return res;\n}\nvar prodConfig2 = {\n  kernelName: Prod,\n  backendName: \"webgl\",\n  kernelFunc: prod3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/RaggedTensorToTensor.js\nfunction raggedTensorToTensor3(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { shape, values, defaultValue, rowPartitionTensors } = inputs;\n  const { rowPartitionTypes } = attrs;\n  const $shape = backend2.readSync(shape.dataId);\n  const $values = backend2.readSync(values.dataId);\n  const $defaultValue = backend2.readSync(defaultValue.dataId);\n  const $rowPartitionValues = rowPartitionTensors.map((t) => backend2.readSync(t.dataId));\n  const rowPartitionValuesShapes = rowPartitionTensors.map((t) => t.shape);\n  const [outputShape, output] = raggedTensorToTensorImplCPU($shape, shape.shape, $values, values.shape, values.dtype, $defaultValue, defaultValue.shape, $rowPartitionValues, rowPartitionValuesShapes, rowPartitionTypes);\n  return backend2.makeTensorInfo(outputShape, values.dtype, output);\n}\nvar raggedTensorToTensorConfig2 = {\n  kernelName: RaggedTensorToTensor,\n  backendName: \"webgl\",\n  kernelFunc: raggedTensorToTensor3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Range.js\nvar range4 = (args) => {\n  const { backend: backend2, attrs } = args;\n  const { start, stop, step: step5, dtype } = attrs;\n  const values = rangeImplCPU(start, stop, step5, dtype);\n  return backend2.makeTensorInfo([values.length], dtype, values);\n};\nvar rangeConfig2 = {\n  kernelName: Range,\n  backendName: \"webgl\",\n  kernelFunc: range4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Reciprocal.js\nvar RECIPROCAL = `return 1.0 / x;`;\nvar reciprocal3 = unaryKernelFunc2({ opSnippet: RECIPROCAL });\nvar reciprocalConfig2 = {\n  kernelName: Reciprocal,\n  backendName: \"webgl\",\n  kernelFunc: reciprocal3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Relu.js\nvar RELU3 = CHECK_NAN_SNIPPET + `\n  return (x < 0.0) ? 0.0 : x;\n`;\nvar RELU_PACKED = `\n  vec4 result = x * vec4(greaterThanEqual(x, vec4(0.0)));\n  bvec4 isNaN = isnan(x);\n\n  result.r = isNaN.r ? x.r : result.r;\n  result.g = isNaN.g ? x.g : result.g;\n  result.b = isNaN.b ? x.b : result.b;\n  result.a = isNaN.a ? x.a : result.a;\n\n  return result;\n`;\nvar relu3 = unaryKernelFunc2({ opSnippet: RELU3, packedOpSnippet: RELU_PACKED });\nvar reluConfig2 = {\n  kernelName: Relu,\n  backendName: \"webgl\",\n  kernelFunc: relu3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Relu6.js\nvar RELU63 = CHECK_NAN_SNIPPET + `\n  return (x < 0.0) ? 0.0 : min(6.0, x);\n`;\nvar RELU6_PACKED = `\n  vec4 result = min(x, vec4(6.)) * vec4(greaterThanEqual(x, vec4(0.0)));\n  bvec4 isNaN = isnan(x);\n\n  result.r = isNaN.r ? x.r : result.r;\n  result.g = isNaN.g ? x.g : result.g;\n  result.b = isNaN.b ? x.b : result.b;\n  result.a = isNaN.a ? x.a : result.a;\n\n  return result;\n`;\nvar relu63 = unaryKernelFunc2({ opSnippet: RELU63, packedOpSnippet: RELU6_PACKED });\nvar relu6Config2 = {\n  kernelName: Relu6,\n  backendName: \"webgl\",\n  kernelFunc: relu63\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/resize_bilinear_gpu.js\nvar ResizeBilinearProgram = class {\n  constructor(inputShape, newHeight, newWidth, alignCorners, halfPixelCenters) {\n    this.variableNames = [\"A\"];\n    this.outputShape = [];\n    const [batch, oldHeight, oldWidth, depth] = inputShape;\n    this.outputShape = [batch, newHeight, newWidth, depth];\n    const effectiveInSize = [\n      alignCorners && newHeight > 1 ? oldHeight - 1 : oldHeight,\n      alignCorners && newWidth > 1 ? oldWidth - 1 : oldWidth\n    ];\n    const effectiveOutSize = [\n      alignCorners && newHeight > 1 ? newHeight - 1 : newHeight,\n      alignCorners && newWidth > 1 ? newWidth - 1 : newWidth\n    ];\n    let sourceFracIndexRC;\n    if (halfPixelCenters) {\n      sourceFracIndexRC = `(vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC - vec2(0.5)`;\n    } else {\n      sourceFracIndexRC = `vec2(yRC) * effectiveInputOverOutputRatioRC`;\n    }\n    this.userCode = `\n      const vec2 effectiveInputOverOutputRatioRC = vec2(\n          ${effectiveInSize[0] / effectiveOutSize[0]},\n          ${effectiveInSize[1] / effectiveOutSize[1]});\n      const vec2 inputShapeRC = vec2(${oldHeight}.0, ${oldWidth}.0);\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n        ivec2 yRC = coords.yz;\n\n        // Fractional source index.\n        vec2 sourceFracIndexRC = ${sourceFracIndexRC};\n\n        // Compute the four integer indices.\n        ivec2 sourceFloorRC = ivec2(max(sourceFracIndexRC, vec2(0.0)));\n        ivec2 sourceCeilRC = ivec2(\n          min(inputShapeRC - 1.0, ceil(sourceFracIndexRC)));\n\n        float topLeft = getA(b, sourceFloorRC.x, sourceFloorRC.y, d);\n        float bottomLeft = getA(b, sourceCeilRC.x, sourceFloorRC.y, d);\n        float topRight = getA(b, sourceFloorRC.x, sourceCeilRC.y, d);\n        float bottomRight = getA(b, sourceCeilRC.x, sourceCeilRC.y, d);\n\n        vec2 fracRC = sourceFracIndexRC - vec2(sourceFloorRC);\n\n        float top = topLeft + (topRight - topLeft) * fracRC.y;\n        float bottom = bottomLeft + (bottomRight - bottomLeft) * fracRC.y;\n        float newValue = top + (bottom - top) * fracRC.x;\n\n        setOutput(newValue);\n      }\n    `;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/resize_bilinear_packed_gpu.js\nvar ResizeBilinearPackedProgram = class {\n  constructor(inputShape, newHeight, newWidth, alignCorners, halfPixelCenters) {\n    this.variableNames = [\"A\"];\n    this.packedInputs = true;\n    this.packedOutput = true;\n    this.outputShape = [];\n    const [batch, oldHeight, oldWidth, depth] = inputShape;\n    this.outputShape = [batch, newHeight, newWidth, depth];\n    const effectiveInSize = [\n      alignCorners && newHeight > 1 ? oldHeight - 1 : oldHeight,\n      alignCorners && newWidth > 1 ? oldWidth - 1 : oldWidth\n    ];\n    const effectiveOutSize = [\n      alignCorners && newHeight > 1 ? newHeight - 1 : newHeight,\n      alignCorners && newWidth > 1 ? newWidth - 1 : newWidth\n    ];\n    let sourceFracIndexRC;\n    if (halfPixelCenters) {\n      sourceFracIndexRC = `(vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC - vec3(0.5)`;\n    } else {\n      sourceFracIndexRC = `vec3(yRC) * effectiveInputOverOutputRatioRC`;\n    }\n    this.userCode = `\n      const vec3 effectiveInputOverOutputRatioRC = vec3(\n          ${effectiveInSize[0] / effectiveOutSize[0]},\n          ${effectiveInSize[1] / effectiveOutSize[1]},\n          ${effectiveInSize[1] / effectiveOutSize[1]});\n      const vec3 inputShapeRC = vec3(${oldHeight}.0, ${oldWidth}.0,\n                                     ${oldWidth}.0);\n\n      float getAValue(int b, int r, int c, int d) {\n        return getChannel(getA(b, r, c, d), vec2(c, d));\n      }\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n        // Calculate values for next column in yRC.z.\n        ivec3 yRC = coords.yzz + ivec3(0, 0, 1);\n\n        // Fractional source index.\n        vec3 sourceFracIndexRC = ${sourceFracIndexRC};\n\n        // Compute the four integer indices.\n        ivec3 sourceFloorRC = ivec3(max(sourceFracIndexRC, vec3(0.0)));\n        ivec3 sourceCeilRC = ivec3(\n          min(inputShapeRC - 1.0, ceil(sourceFracIndexRC)));\n\n        // Should we calculate next column and row elements in 2x2 packed cell.\n        bool hasNextCol = d < ${depth - 1};\n        bool hasNextRow = coords.z < ${newWidth - 1};\n\n        // In parallel, construct four corners for all four components in\n        // packed 2x2 cell.\n        vec4 topLeft = vec4(\n          getAValue(b, sourceFloorRC.x, sourceFloorRC.y, d),\n          hasNextCol ? getAValue(b, sourceFloorRC.x, sourceFloorRC.y, d + 1)\n                     : 0.0,\n          hasNextRow ? getAValue(b, sourceFloorRC.x, sourceFloorRC.z, d)\n                     : 0.0,\n          (hasNextRow && hasNextCol) ?\n            getAValue(b, sourceFloorRC.x, sourceFloorRC.z, d + 1) : 0.0);\n\n        vec4 bottomLeft = vec4(\n          getAValue(b, sourceCeilRC.x, sourceFloorRC.y, d),\n          hasNextCol ? getAValue(b, sourceCeilRC.x, sourceFloorRC.y, d + 1)\n                     : 0.0,\n          hasNextRow ? getAValue(b, sourceCeilRC.x, sourceFloorRC.z, d)\n                     : 0.0,\n          (hasNextRow && hasNextCol) ?\n            getAValue(b, sourceCeilRC.x, sourceFloorRC.z, d + 1) : 0.0);\n\n        vec4 topRight = vec4(\n          getAValue(b, sourceFloorRC.x, sourceCeilRC.y, d),\n          hasNextCol ? getAValue(b, sourceFloorRC.x, sourceCeilRC.y, d + 1)\n                     : 0.0,\n          hasNextRow ? getAValue(b, sourceFloorRC.x, sourceCeilRC.z, d)\n                     : 0.0,\n          (hasNextRow && hasNextCol) ?\n            getAValue(b, sourceFloorRC.x, sourceCeilRC.z, d + 1) : 0.0);\n\n        vec4 bottomRight = vec4(\n          getAValue(b, sourceCeilRC.x, sourceCeilRC.y, d),\n          hasNextCol ? getAValue(b, sourceCeilRC.x, sourceCeilRC.y, d + 1)\n                     : 0.0,\n          hasNextRow ? getAValue(b, sourceCeilRC.x, sourceCeilRC.z, d)\n                     : 0.0,\n          (hasNextRow && hasNextCol) ?\n            getAValue(b, sourceCeilRC.x, sourceCeilRC.z, d + 1) : 0.0);\n\n        vec3 fracRC = sourceFracIndexRC - vec3(sourceFloorRC);\n\n        vec4 top = mix(topLeft, topRight, fracRC.yyzz);\n        vec4 bottom = mix(bottomLeft, bottomRight, fracRC.yyzz);\n        vec4 newValue = mix(top, bottom, fracRC.x);\n\n        setOutput(newValue);\n      }\n    `;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/ResizeBilinear.js\nfunction resizeBilinear3(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { images } = inputs;\n  const { alignCorners, halfPixelCenters, size } = attrs;\n  const [newHeight, newWidth] = size;\n  const program = env().getBool(\"WEBGL_PACK_IMAGE_OPERATIONS\") ? new ResizeBilinearPackedProgram(images.shape, newHeight, newWidth, alignCorners, halfPixelCenters) : new ResizeBilinearProgram(images.shape, newHeight, newWidth, alignCorners, halfPixelCenters);\n  return backend2.runWebGLProgram(program, [images], \"float32\");\n}\nvar resizeBilinearConfig2 = {\n  kernelName: ResizeBilinear,\n  backendName: \"webgl\",\n  kernelFunc: resizeBilinear3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/resize_bilinear_backprop_gpu.js\nvar ResizeBilinearBackpropProgram = class {\n  constructor(dyShape, inputShape, alignCorners) {\n    this.variableNames = [\"dy\"];\n    this.outputShape = [];\n    this.outputShape = inputShape;\n    const [, xHeight, xWidth] = inputShape;\n    const [, yHeight, yWidth] = dyShape;\n    const effectiveXSize = [\n      alignCorners && yHeight > 1 ? xHeight - 1 : xHeight,\n      alignCorners && yWidth > 1 ? xWidth - 1 : xWidth\n    ];\n    const effectiveYSize = [\n      alignCorners && yHeight > 1 ? yHeight - 1 : yHeight,\n      alignCorners && yWidth > 1 ? yWidth - 1 : yWidth\n    ];\n    const heightScale = effectiveXSize[0] / effectiveYSize[0];\n    const widthScale = effectiveXSize[1] / effectiveYSize[1];\n    const invHeightScale = 1 / heightScale;\n    const invWidthScale = 1 / widthScale;\n    const winHeight = Math.ceil(invHeightScale) * 2 + 2;\n    const winWidth = Math.ceil(invWidthScale) * 2 + 2;\n    this.userCode = `\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n        int r = coords[1];\n        int c = coords[2];\n\n        float accumulator = 0.0;\n\n        const float heightScale = float(${heightScale});\n        const float widthScale = float(${widthScale});\n\n        const float invHeightScale = float(${invHeightScale});\n        const float invWidthScale = float(${invWidthScale});\n\n        const int winHeight = int(${winHeight});\n        const int winWidth = int(${winWidth});\n\n        // Compute bounds for where in dy we will look\n        float startRLerp = floor(float(r) * invHeightScale);\n        int startDyR = int(startRLerp - float(winHeight / 2));\n\n        float startCLerp = floor(float(c) * invWidthScale);\n        int startDyC = int(startCLerp - float(winWidth / 2));\n\n        // Loop over dy\n        for (int dyROffset = 0; dyROffset < winHeight; dyROffset++) {\n          int dyR = dyROffset + startDyR;\n\n          // Guard against the window exceeding the bounds of dy\n          if (dyR < 0 || dyR >= ${yHeight}) {\n            continue;\n          }\n\n          for (int dyCOffset = 0; dyCOffset < winWidth; dyCOffset++) {\n            int dyC = dyCOffset + startDyC;\n\n            // Guard against the window exceeding the bounds of dy\n            if (dyC < 0 || dyC >= ${yWidth}) {\n              continue;\n            }\n\n            float dxR = float(dyR) * heightScale;\n            int topDxRIndex = int(floor(dxR));\n            int bottomDxRIndex = int(min(ceil(dxR), ${xHeight - 1}.0));\n            float dxRLerp = dxR - float(topDxRIndex);\n            float inverseDxRLerp = 1.0 - dxRLerp;\n\n            float dxC = float(dyC) * widthScale;\n            int leftDxCIndex = int(floor(dxC));\n            int rightDxCIndex = int(min(ceil(dxC), ${xWidth - 1}.0));\n            float dxCLerp = dxC - float(leftDxCIndex);\n            float inverseDxCLerp = 1.0 - dxCLerp;\n\n            if (r == topDxRIndex && c == leftDxCIndex) {\n              // topLeft\n              accumulator +=\n                getDy(b, dyR, dyC, d) * inverseDxRLerp * inverseDxCLerp;\n            }\n\n            if (r == topDxRIndex && c == rightDxCIndex) {\n              // topRight\n              accumulator += getDy(b, dyR, dyC, d) * inverseDxRLerp * dxCLerp;\n            }\n\n            if (r == bottomDxRIndex && c == leftDxCIndex) {\n              // bottomLeft\n              accumulator += getDy(b, dyR, dyC, d) * dxRLerp * inverseDxCLerp;\n            }\n\n            if (r == bottomDxRIndex && c == rightDxCIndex) {\n              // bottomRight\n              accumulator += getDy(b, dyR, dyC, d) * dxRLerp * dxCLerp;\n            }\n          }\n        }\n        // End loop over dy\n\n        setOutput(accumulator);\n      }\n    `;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/ResizeBilinearGrad.js\nfunction resizeBilinearGrad2(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { images, dy } = inputs;\n  const { alignCorners } = attrs;\n  const program = new ResizeBilinearBackpropProgram(dy.shape, images.shape, alignCorners);\n  return backend2.runWebGLProgram(program, [dy], dy.dtype);\n}\nvar resizeBilinearGradConfig3 = {\n  kernelName: ResizeBilinearGrad,\n  backendName: \"webgl\",\n  kernelFunc: resizeBilinearGrad2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/resize_nearest_neighbor_gpu.js\nvar ResizeNearestNeighborProgram = class {\n  constructor(inputShape, newHeight, newWidth, alignCorners, halfPixelCenters) {\n    this.variableNames = [\"A\"];\n    this.outputShape = [];\n    const [batch, oldHeight, oldWidth, depth] = inputShape;\n    this.outputShape = [batch, newHeight, newWidth, depth];\n    const effectiveInSize = [\n      alignCorners && newHeight > 1 ? oldHeight - 1 : oldHeight,\n      alignCorners && newWidth > 1 ? oldWidth - 1 : oldWidth\n    ];\n    const effectiveOutSize = [\n      alignCorners && newHeight > 1 ? newHeight - 1 : newHeight,\n      alignCorners && newWidth > 1 ? newWidth - 1 : newWidth\n    ];\n    const roundBase = alignCorners ? \"0.5\" : \"0.0\";\n    let sourceFracIndexRC;\n    if (halfPixelCenters) {\n      sourceFracIndexRC = `max((vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC, vec2(0.0))`;\n    } else {\n      sourceFracIndexRC = `vec2(yRC) * effectiveInputOverOutputRatioRC`;\n    }\n    this.userCode = `\n      const vec2 effectiveInputOverOutputRatioRC = vec2(\n          ${effectiveInSize[0] / effectiveOutSize[0]},\n          ${effectiveInSize[1] / effectiveOutSize[1]});\n      const vec2 inputShapeRC = vec2(${oldHeight}.0, ${oldWidth}.0);\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n        ivec2 yRC = coords.yz;\n\n        // Fractional source index.\n        vec2 sourceFracIndexRC = ${sourceFracIndexRC};\n\n        // Compute the coordinators of nearest neighbor point.\n        ivec2 sourceNearestRC = ivec2(\n          min(inputShapeRC - 1.0, floor(sourceFracIndexRC + ${roundBase})));\n        float newValue = getA(b, sourceNearestRC.x, sourceNearestRC.y, d);\n\n        setOutput(newValue);\n      }\n    `;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/resize_nearest_neighbor_packed_gpu.js\nvar ResizeNearestNeighborPackedProgram = class {\n  constructor(inputShape, newHeight, newWidth, alignCorners, halfPixelCenters) {\n    this.variableNames = [\"A\"];\n    this.packedInputs = true;\n    this.packedOutput = true;\n    this.outputShape = [];\n    const [batch, oldHeight, oldWidth, depth] = inputShape;\n    this.outputShape = [batch, newHeight, newWidth, depth];\n    const effectiveInSize = [\n      alignCorners && newHeight > 1 ? oldHeight - 1 : oldHeight,\n      alignCorners && newWidth > 1 ? oldWidth - 1 : oldWidth\n    ];\n    const effectiveOutSize = [\n      alignCorners && newHeight > 1 ? newHeight - 1 : newHeight,\n      alignCorners && newWidth > 1 ? newWidth - 1 : newWidth\n    ];\n    const roundBase = alignCorners ? \"0.5\" : \"0.0\";\n    let sourceFracIndexRC;\n    if (halfPixelCenters) {\n      sourceFracIndexRC = `max((vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC, vec3(0.0))`;\n    } else {\n      sourceFracIndexRC = `vec3(yRC) * effectiveInputOverOutputRatioRC`;\n    }\n    this.userCode = `\n      const vec3 effectiveInputOverOutputRatioRC = vec3(\n          ${effectiveInSize[0] / effectiveOutSize[0]},\n          ${effectiveInSize[1] / effectiveOutSize[1]},\n          ${effectiveInSize[1] / effectiveOutSize[1]});\n      const vec3 inputShapeRC = vec3(${oldHeight}.0, ${oldWidth}.0,\n                                     ${oldWidth}.0);\n\n      float getAValue(int b, int r, int c, int d) {\n        return getChannel(getA(b, r, c, d), vec2(c, d));\n      }\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n        // Calculate values for next column in yRC.z.\n        ivec3 yRC = coords.yzz + ivec3(0, 0, 1);\n\n        // Fractional source index.\n        vec3 sourceFracIndexRC = ${sourceFracIndexRC};\n\n        // Compute the coordinators of nearest neighbor point.\n        ivec3 sourceNearestRC = ivec3(\n          min(inputShapeRC - 1.0, floor(sourceFracIndexRC + ${roundBase})));\n\n        // Should we calculate next column and row elements in 2x2 packed cell.\n        bool hasNextCol = d < ${depth - 1};\n        bool hasNextRow = coords.z < ${newWidth - 1};\n\n        vec4 newValue = vec4(\n          getAValue(b, sourceNearestRC.x, sourceNearestRC.y, d),\n          hasNextCol ? getAValue(b, sourceNearestRC.x, sourceNearestRC.y, d + 1)\n                     : 0.0,\n          hasNextRow ? getAValue(b, sourceNearestRC.x, sourceNearestRC.z, d)\n                     : 0.0,\n          (hasNextRow && hasNextCol) ?\n            getAValue(b, sourceNearestRC.x, sourceNearestRC.z, d + 1) : 0.0);\n\n        setOutput(newValue);\n      }\n    `;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/ResizeNearestNeighbor.js\nfunction resizeNearestNeighbor3(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { images } = inputs;\n  const { alignCorners, halfPixelCenters, size } = attrs;\n  const [newHeight, newWidth] = size;\n  const program = env().getBool(\"WEBGL_PACK_IMAGE_OPERATIONS\") ? new ResizeNearestNeighborPackedProgram(images.shape, newHeight, newWidth, alignCorners, halfPixelCenters) : new ResizeNearestNeighborProgram(images.shape, newHeight, newWidth, alignCorners, halfPixelCenters);\n  return backend2.runWebGLProgram(program, [images], images.dtype);\n}\nvar resizeNearestNeighborConfig2 = {\n  kernelName: ResizeNearestNeighbor,\n  backendName: \"webgl\",\n  kernelFunc: resizeNearestNeighbor3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/resize_nearest_neighbor_backprop_gpu.js\nvar ResizeNearestNeigborBackpropProgram = class {\n  constructor(dyShape, inputShape, alignCorners) {\n    this.variableNames = [\"dy\"];\n    this.outputShape = [];\n    this.outputShape = inputShape;\n    const [, xHeight, xWidth] = inputShape;\n    const [, yHeight, yWidth] = dyShape;\n    const effectiveXSize = [\n      alignCorners && yHeight > 1 ? xHeight - 1 : xHeight,\n      alignCorners && yWidth > 1 ? xWidth - 1 : xWidth\n    ];\n    const effectiveYSize = [\n      alignCorners && yHeight > 1 ? yHeight - 1 : yHeight,\n      alignCorners && yWidth > 1 ? yWidth - 1 : yWidth\n    ];\n    const heightScale = effectiveXSize[0] / effectiveYSize[0];\n    const widthScale = effectiveXSize[1] / effectiveYSize[1];\n    const invHeightScale = 1 / heightScale;\n    const invWidthScale = 1 / widthScale;\n    const winHeight = Math.ceil(invHeightScale) * 2 + 2;\n    const winWidth = Math.ceil(invWidthScale) * 2 + 2;\n    this.userCode = `\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n        int r = coords[1];\n        int c = coords[2];\n\n        float accumulator = 0.0;\n\n        const float heightScale = float(${heightScale});\n        const float widthScale = float(${widthScale});\n\n        const float invHeightScale = float(${invHeightScale});\n        const float invWidthScale = float(${invWidthScale});\n\n        const int winHeight = int(${winHeight});\n        const int winWidth = int(${winWidth});\n\n        // Compute bounds for where in dy we will look\n        float startRLerp = floor(float(r) * invHeightScale);\n        int startDyR = int(floor(startRLerp - float(winHeight / 2)));\n\n        float startCLerp = floor(float(c) * invWidthScale);\n        int startDyC = int(floor(startCLerp - float(winWidth / 2)));\n\n        // Loop over dy\n        for (int dyROffset = 0; dyROffset < winHeight; dyROffset++) {\n          int dyR = dyROffset + startDyR;\n\n          // Guard against the window exceeding the bounds of dy\n          if (dyR < 0 || dyR >= ${yHeight}) {\n            continue;\n          }\n\n          for (int dyCOffset = 0; dyCOffset < winWidth; dyCOffset++) {\n            int dyC = dyCOffset + startDyC;\n\n            // Guard against the window exceeding the bounds of dy\n            if (dyC < 0 || dyC >= ${yWidth}) {\n              continue;\n            }\n\n            float sourceFracRow =\n              float(${effectiveXSize[0]}) *\n                (float(dyR) / float(${effectiveYSize[0]}));\n\n            float sourceFracCol =\n                float(${effectiveXSize[1]}) *\n                  (float(dyC) / float(${effectiveYSize[1]}));\n\n            int sourceNearestRow = int(min(\n                float(int(${xHeight}) - 1),\n                ${alignCorners} ? float(round(sourceFracRow)) :\n                                  float(floor(sourceFracRow))));\n\n            int sourceNearestCol = int(min(\n                float(int(${xWidth}) - 1),\n                ${alignCorners} ? float(round(sourceFracCol)) :\n                                  float(floor(sourceFracCol))));\n\n            if (r == sourceNearestRow && c == sourceNearestCol) {\n              accumulator += getDy(b, dyR, dyC, d);\n            }\n          }\n        }\n        // End loop over dy\n\n        setOutput(accumulator);\n      }\n    `;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/ResizeNearestNeighborGrad.js\nfunction resizeNearestNeighborGrad2(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { images, dy } = inputs;\n  const { alignCorners } = attrs;\n  const program = new ResizeNearestNeigborBackpropProgram(dy.shape, images.shape, alignCorners);\n  return backend2.runWebGLProgram(program, [dy], dy.dtype);\n}\nvar resizeNearestNeighborGradConfig3 = {\n  kernelName: ResizeNearestNeighborGrad,\n  backendName: \"webgl\",\n  kernelFunc: resizeNearestNeighborGrad2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/reverse_gpu.js\nvar ReverseProgram = class {\n  constructor(xShape, axis) {\n    this.variableNames = [\"x\"];\n    const rank = xShape.length;\n    if (rank > 4) {\n      throw new Error(`WebGL backend: Reverse of rank-${rank} tensor is not yet supported`);\n    }\n    this.outputShape = xShape;\n    if (rank === 1) {\n      this.userCode = `\n        void main() {\n          int coord = getOutputCoords();\n          setOutput(getX(${xShape[0]} - coord - 1));\n        }\n      `;\n      return;\n    }\n    const getInCoord = (i) => {\n      if (axis.indexOf(i) !== -1 && xShape[i] !== 1) {\n        return `${xShape[i]} - coords[${i}] - 1`;\n      }\n      return `coords[${i}]`;\n    };\n    const inCoords = xShape.map((_, i) => getInCoord(i)).join(\",\");\n    const type = getCoordsDataType(rank);\n    this.userCode = `\n      void main() {\n        ${type} coords = getOutputCoords();\n        setOutput(getX(${inCoords}));\n      }\n    `;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/reverse_packed_gpu.js\nvar ReversePackedProgram = class {\n  constructor(xShape, axis) {\n    this.variableNames = [\"x\"];\n    this.packedInputs = true;\n    this.packedOutput = true;\n    const rank = xShape.length;\n    if (rank > 4) {\n      throw new Error(`WebGL backend: Reverse of rank-${rank} tensor is not yet supported`);\n    }\n    this.outputShape = xShape;\n    const channels = getChannels(\"rc\", rank);\n    const nextColumn = `${channels[rank - 1]} + 1 < ${this.outputShape[rank - 1]}`;\n    const nextRow = `${channels[rank - 2]} + 1 < ${this.outputShape[rank - 2]}`;\n    const type = getCoordsDataType(rank);\n    if (rank === 1) {\n      this.userCode = `\n        void main(){\n          int rc = getOutputCoords();\n          vec4 result = vec4(0.);\n          result.r = getChannel(getX(${xShape[0]} - rc - 1),\n            ${xShape[0]} - rc - 1);\n          if(${nextColumn}){\n              result.g = getChannel(getX(${xShape[0]} - (rc  + 1) - 1),\n                ${xShape[0]} - (rc  + 1) - 1);\n          }\n          setOutput(result);\n        }\n      `;\n    } else {\n      this.userCode = `\n        void main() {\n          ${type} rc = getOutputCoords();\n          vec4 result = vec4(0.);\n          result.r = ${getR(channels.slice())};\n          if(${nextColumn}){\n            result.g = ${getG(channels.slice())};\n          }\n          if(${nextRow}) {\n            result.b = ${getB(channels.slice())};\n            if(${nextColumn}) {\n              result.a = ${getA(channels.slice())};\n            }\n          }\n          setOutput(result);\n        }\n    `;\n    }\n    function getR(channels2) {\n      return getChannel(channels2);\n    }\n    function getG(channels2) {\n      channels2[rank - 1] = \"(\" + channels2[rank - 1] + ` + 1)`;\n      return getChannel(channels2);\n    }\n    function getB(channels2) {\n      channels2[rank - 2] = \"(\" + channels2[rank - 2] + ` + 1)`;\n      return getChannel(channels2);\n    }\n    function getA(channels2) {\n      channels2[rank - 1] = \"(\" + channels2[rank - 1] + ` + 1)`;\n      channels2[rank - 2] = \"(\" + channels2[rank - 2] + ` + 1)`;\n      return getChannel(channels2);\n    }\n    function getChannel(channels2) {\n      const inCoordsArray = xShape.map((_, i) => getInCoord(i, channels2));\n      const inCoords = inCoordsArray.join(\",\");\n      const innerDims = inCoordsArray.slice(-2).join(\",\");\n      return `getChannel(getX(${inCoords}), vec2(${innerDims}))`;\n    }\n    function getInCoord(i, channels1) {\n      if (axis.indexOf(i) !== -1 && xShape[i] !== 1) {\n        return `${xShape[i]} - ${channels1[i]} - 1`;\n      } else {\n        return `${channels1[i]}`;\n      }\n    }\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Reverse.js\nfunction reverse3(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x } = inputs;\n  const { dims } = attrs;\n  const xRank = x.shape.length;\n  const $dims = util_exports.parseAxisParam(dims, x.shape);\n  if (xRank === 0) {\n    return identity3({ inputs: { x }, backend: backend2 });\n  }\n  const program = env().getBool(\"WEBGL_PACK_ARRAY_OPERATIONS\") ? new ReversePackedProgram(x.shape, $dims) : new ReverseProgram(x.shape, $dims);\n  return backend2.runWebGLProgram(program, [x], x.dtype);\n}\nvar reverseConfig2 = {\n  kernelName: Reverse,\n  backendName: \"webgl\",\n  kernelFunc: reverse3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/rotate_gpu.js\nvar RotateProgram = class {\n  constructor(imageShape, fillValue) {\n    this.variableNames = [\"Image\"];\n    this.outputShape = [];\n    this.customUniforms = [{ name: \"params\", type: \"vec4\" }];\n    const imageHeight = imageShape[1];\n    const imageWidth = imageShape[2];\n    this.outputShape = imageShape;\n    let fillSnippet = \"\";\n    if (typeof fillValue === \"number\") {\n      fillSnippet = `float outputValue = ${fillValue.toFixed(2)};`;\n    } else {\n      fillSnippet = `\n        vec3 fill = vec3(${fillValue.join(\",\")});\n        float outputValue = fill[coords[3]];`;\n    }\n    this.userCode = `\n        void main() {\n          ivec4 coords = getOutputCoords();\n          int x = coords[2];\n          int y = coords[1];\n          float coordXFloat = (float(x) - params[0]) * params[3] -\n            (float(y) - params[1]) * params[2];\n          float coordYFloat = (float(x) - params[0]) * params[2] +\n            (float(y) - params[1]) * params[3];\n          int coordX = int(round(coordXFloat + params[0]));\n          int coordY = int(round(coordYFloat + params[1]));\n          ${fillSnippet}\n          if(coordX >= 0 && coordX < ${imageWidth} && coordY >= 0 && coordY < ${imageHeight}) {\n            outputValue = getImage(coords[0], coordY, coordX, coords[3]);\n          }\n          setOutput(outputValue);\n        }\n    `;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/RotateWithOffset.js\nvar rotateWithOffsetConfig2 = {\n  kernelName: RotateWithOffset,\n  backendName: \"webgl\",\n  kernelFunc: ({ inputs, attrs, backend: backend2 }) => {\n    const { image: image2 } = inputs;\n    const { radians, fillValue, center } = attrs;\n    const webglBackend = backend2;\n    const program = new RotateProgram(image2.shape, fillValue);\n    const [centerX, centerY] = backend_util_exports.getImageCenter(center, image2.shape[1], image2.shape[2]);\n    const customValues = [[centerX, centerY, Math.sin(radians), Math.cos(radians)]];\n    const output = webglBackend.runWebGLProgram(program, [image2], image2.dtype, customValues);\n    return output;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Round.js\nvar ROUND = `\n  // OpenGL ES does not support round function.\n  // The algorithm is based on banker's rounding.\n  float base = floor(x);\n  if ((x - base) < 0.5) {\n    return floor(x);\n  } else if ((x - base) > 0.5) {\n    return ceil(x);\n  } else {\n    if (mod(base, 2.0) == 0.0) {\n      return base;\n    } else {\n      return base + 1.0;\n    }\n  }\n`;\nvar round4 = unaryKernelFunc2({ opSnippet: ROUND });\nvar roundConfig2 = {\n  kernelName: Round,\n  backendName: \"webgl\",\n  kernelFunc: round4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Rsqrt.js\nvar RSQRT = `return inversesqrt(x);`;\nvar rsqrt3 = unaryKernelFunc2({ opSnippet: RSQRT, cpuKernelImpl: rsqrtImplCPU });\nvar rsqrtConfig2 = {\n  kernelName: Rsqrt,\n  backendName: \"webgl\",\n  kernelFunc: rsqrt3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/scatter_gpu.js\nvar ScatterProgram = class {\n  constructor(updateSize, sliceDim, indicesRank, updatesRank, strides, shape, summingDupeIndex = true) {\n    this.variableNames = [\"updates\", \"indices\", \"defaultValue\"];\n    this.outputShape = shape;\n    const stridesType = getCoordsDataType(strides.length);\n    const dtype = getCoordsDataType(shape.length);\n    let indicesString = \"\";\n    if (indicesRank === 1) {\n      indicesString = \"i\";\n    } else if (indicesRank === 2) {\n      indicesString = \"i, j\";\n    }\n    const indicesSnippet = `getIndices(${indicesString})`;\n    let updatesString = \"\";\n    if (updatesRank === 1) {\n      updatesString = \"i\";\n    } else if (updatesRank === 2) {\n      updatesString = \"i, coords[1]\";\n    }\n    const updatesSnippet = `getUpdates(${updatesString})`;\n    const strideString = sliceDim > 1 ? \"strides[j]\" : \"strides\";\n    this.userCode = `\n        ${stridesType} strides = ${stridesType}(${strides});\n\n        void main() {\n          ${dtype} coords = getOutputCoords();\n          float sum = 0.0;\n          bool found = false;\n          for (int i = 0; i < ${updateSize}; i++) {\n            int flattenedIndex = 0;\n            for (int j = 0; j < ${sliceDim}; j++) {\n              int index = round(${indicesSnippet});\n              flattenedIndex += index * ${strideString};\n            }\n            if (flattenedIndex == coords[0]) {\n              sum += ${updatesSnippet};\n              found = true;\n            }\n          }\n          setOutput(mix(getDefaultValue(), sum, float(found)));\n        }\n      `;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/ScatterNd.js\nfunction scatterNd2(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { indices, updates } = inputs;\n  const { shape } = attrs;\n  const { sliceRank, numUpdates, sliceSize, strides, outputSize } = backend_util_exports.calculateShapes(updates, indices, shape);\n  const flattenShape = [outputSize / sliceSize, sliceSize];\n  if (outputSize === 0) {\n    return backend2.makeTensorInfo(shape, indices.dtype);\n  }\n  const flattenIndices = reshape4({ inputs: { x: indices }, backend: backend2, attrs: { shape: [numUpdates, sliceRank] } });\n  const flattenX = reshape4({ inputs: { x: updates }, backend: backend2, attrs: { shape: [numUpdates, sliceSize] } });\n  const defaultValue = backend2.makeTensorInfo([], \"float32\", new Float32Array([0]));\n  const program = new ScatterProgram(numUpdates, sliceRank, flattenIndices.shape.length, flattenX.shape.length, strides, flattenShape);\n  const res = backend2.runWebGLProgram(program, [flattenX, flattenIndices, defaultValue], flattenX.dtype);\n  const reshaped = reshape4({ inputs: { x: res }, backend: backend2, attrs: { shape } });\n  backend2.disposeIntermediateTensorInfo(flattenIndices);\n  backend2.disposeIntermediateTensorInfo(flattenX);\n  backend2.disposeIntermediateTensorInfo(res);\n  backend2.disposeIntermediateTensorInfo(defaultValue);\n  return reshaped;\n}\nvar scatterNdConfig2 = {\n  kernelName: ScatterNd,\n  backendName: \"webgl\",\n  kernelFunc: scatterNd2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/search_sorted_gpu.js\nvar SearchSortedProgram = class {\n  constructor(batchSize, numInputs, numValues, side) {\n    this.variableNames = [\"sortedSequence\", \"values\"];\n    this.customUniforms = [{ name: \"numInputs\", type: \"int\" }];\n    this.outputShape = [batchSize, numValues];\n    const webGL2LoopHead = \"while (left < right) {\";\n    const webGL1LoopHead = `for (int i = 0; i < ${Math.ceil(Math.log2(numInputs + 1))}; ++i) { if (left >= right) break;`;\n    const loopHead = env().getNumber(\"WEBGL_VERSION\") === 2 ? webGL2LoopHead : webGL1LoopHead;\n    const boundComparator = side === \"left\" ? \"<\" : \"<=\";\n    this.userCode = `\n       int findBound(int batch, float value) {\n         int left = 0;\n         int right = numInputs;\n         int mid;\n         ${loopHead}\n           mid = (left + right) / 2;\n           if (getSortedSequence(batch, mid) ${boundComparator} value) {\n             left = mid + 1;\n           } else {\n             right = mid;\n           }\n         }\n         return right;\n       }\n\n       void main() {\n         ivec2 coords = getOutputCoords();\n         int batch = coords[0];\n         int valueIndex = coords[1];\n\n         float value = getValues(batch, valueIndex);\n\n         setOutput(float(findBound(batch, value)));\n       }\n     `;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/SearchSorted.js\nfunction searchSorted3(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { sortedSequence, values } = inputs;\n  const { side } = attrs;\n  const program = new SearchSortedProgram(sortedSequence.shape[0], sortedSequence.shape[1], values.shape[1], side);\n  const customValues = [[sortedSequence.shape[1]]];\n  return backend2.runWebGLProgram(program, [sortedSequence, values], \"int32\", customValues);\n}\nvar searchSortedConfig2 = {\n  kernelName: SearchSorted,\n  backendName: \"webgl\",\n  kernelFunc: searchSorted3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/select_gpu.js\nvar SelectProgram = class {\n  constructor(cRank, shape, rank) {\n    this.variableNames = [\"c\", \"a\", \"b\"];\n    this.outputShape = shape;\n    let cCoords;\n    let abCoords;\n    if (rank > 4) {\n      throw Error(`Where for rank ${rank} is not yet supported`);\n    }\n    if (rank === 1) {\n      abCoords = `resRC`;\n      cCoords = `resRC`;\n    } else {\n      const currentCoords = [\"resRC.x\", \"resRC.y\", \"resRC.z\", \"resRC.w\"];\n      const cCoordVars = [];\n      const abCoordVars = [];\n      for (let i = 0; i < shape.length; i++) {\n        abCoordVars.push(`${currentCoords[i]}`);\n        if (i < cRank) {\n          cCoordVars.push(`${currentCoords[i]}`);\n        }\n      }\n      cCoords = cCoordVars.join();\n      abCoords = abCoordVars.join();\n    }\n    const dtype = getCoordsDataType(rank);\n    this.userCode = `\n      void main() {\n        ${dtype} resRC = getOutputCoords();\n        float cVal = getC(${cCoords});\n        if (cVal >= 1.0) {\n          setOutput(getA(${abCoords}));\n        } else {\n          setOutput(getB(${abCoords}));\n        }\n      }\n    `;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Select.js\nfunction select3(args) {\n  const { inputs, backend: backend2 } = args;\n  const { condition, t, e } = inputs;\n  const program = new SelectProgram(condition.shape.length, t.shape, t.shape.length);\n  return backend2.runWebGLProgram(program, [condition, t, e], upcastType(t.dtype, e.dtype));\n}\nvar selectConfig2 = {\n  kernelName: Select,\n  backendName: \"webgl\",\n  kernelFunc: select3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Selu.js\nvar SELU = `\n  // Stable and Attracting Fixed Point (0, 1) for Normalized Weights.\n  // see: https://arxiv.org/abs/1706.02515\n  float scaleAlpha = ${backend_util_exports.SELU_SCALEALPHA};\n  float scale = ${backend_util_exports.SELU_SCALE};\n  return (x >= 0.0) ? scale * x : scaleAlpha * (exp(x) - 1.0);\n`;\nvar selu3 = unaryKernelFunc2({ opSnippet: SELU });\nvar seluConfig2 = {\n  kernelName: Selu,\n  backendName: \"webgl\",\n  kernelFunc: selu3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Sigmoid.js\nvar SIGMOID3 = CHECK_NAN_SNIPPET_UNARY + `\n  return 1.0 / (1.0 + exp(-1.0 * x));\n`;\nvar SIGMOID_PACKED = `\n  vec4 result = 1.0 / (1.0 + exp(-1.0 * x));\n  bvec4 isNaN = isnan(x);\n\n  result.r = isNaN.r ? x.r : result.r;\n  result.g = isNaN.g ? x.g : result.g;\n  result.b = isNaN.b ? x.b : result.b;\n  result.a = isNaN.a ? x.a : result.a;\n\n  return result;\n`;\nvar sigmoid3 = unaryKernelFunc2({\n  opSnippet: SIGMOID3,\n  packedOpSnippet: SIGMOID_PACKED,\n  cpuKernelImpl: sigmoidImplCPU\n});\nvar sigmoidConfig2 = {\n  kernelName: Sigmoid,\n  backendName: \"webgl\",\n  kernelFunc: sigmoid3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Sign.js\nvar SIGN = `\n  if (isnan(x)) { return 0.0; }\n  return sign(x);\n`;\nvar sign3 = unaryKernelFunc2({ opSnippet: SIGN });\nvar signConfig2 = {\n  kernelName: Sign,\n  backendName: \"webgl\",\n  kernelFunc: sign3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Sin.js\nvar SIN = CHECK_NAN_SNIPPET_UNARY + `\n  return sin(x);\n`;\nvar sin3 = unaryKernelFunc2({ opSnippet: SIN });\nvar sinConfig2 = {\n  kernelName: Sin,\n  backendName: \"webgl\",\n  kernelFunc: sin3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Sinh.js\nvar SINH = `\n  float e2x = exp(x);\n  return (e2x - 1.0 / e2x) / 2.0;\n`;\nvar sinh3 = unaryKernelFunc2({ opSnippet: SINH });\nvar sinhConfig2 = {\n  kernelName: Sinh,\n  backendName: \"webgl\",\n  kernelFunc: sinh3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Softplus.js\nvar SOFTPLUS = `\n  float epsilon = 1.1920928955078125e-7;\n  float threshold = log(epsilon) + 2.0;\n\n  bool too_large = x > -threshold;\n  bool too_small = x < threshold;\n\n  float result;\n  float exp_x = exp(x);\n\n  if (too_large){\n    result = x;\n  }\n  else if (too_small){\n    result = exp_x;\n  }\n  else{\n    result = log(exp_x + 1.0);\n  }\n  return result;\n`;\nvar softplus3 = unaryKernelFunc2({ opSnippet: SOFTPLUS });\nvar softplusConfig2 = {\n  kernelName: Softplus,\n  backendName: \"webgl\",\n  kernelFunc: softplus3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/SpaceToBatchND.js\nvar spaceToBatchND3 = (args) => {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x } = inputs;\n  const { blockShape, paddings } = attrs;\n  util_exports.assert(x.shape.length <= 4, () => \"spaceToBatchND for rank > 4 with a WebGL backend not implemented yet\");\n  const prod6 = blockShape.reduce((a, b) => a * b);\n  const completePaddings = [[0, 0]];\n  completePaddings.push(...paddings);\n  for (let i = 1 + blockShape.length; i < x.shape.length; ++i) {\n    completePaddings.push([0, 0]);\n  }\n  const toDispose = [];\n  const paddedX = padV22({\n    inputs: { x },\n    backend: backend2,\n    attrs: { paddings: completePaddings, constantValue: 0 }\n  });\n  const reshapedPaddedShape = backend_util_exports.getReshaped(paddedX.shape, blockShape, prod6, false);\n  const permutedReshapedPaddedPermutation = backend_util_exports.getPermuted(reshapedPaddedShape.length, blockShape.length, false);\n  const flattenShape = backend_util_exports.getReshapedPermuted(paddedX.shape, blockShape, prod6, false);\n  const reshapedPaddedX = reshape4({ inputs: { x: paddedX }, backend: backend2, attrs: { shape: reshapedPaddedShape } });\n  const paddedXT = transpose3({\n    inputs: { x: reshapedPaddedX },\n    backend: backend2,\n    attrs: { perm: permutedReshapedPaddedPermutation }\n  });\n  const result = reshape4({ inputs: { x: paddedXT }, backend: backend2, attrs: { shape: flattenShape } });\n  toDispose.push(paddedX);\n  toDispose.push(reshapedPaddedX);\n  toDispose.push(paddedXT);\n  toDispose.forEach((t) => backend2.disposeIntermediateTensorInfo(t));\n  return result;\n};\nvar spaceToBatchNDConfig2 = {\n  kernelName: SpaceToBatchND,\n  backendName: \"webgl\",\n  kernelFunc: spaceToBatchND3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/SparseFillEmptyRows.js\nfunction sparseFillEmptyRows3(args) {\n  const { inputs, backend: backend2 } = args;\n  const { indices, values, denseShape, defaultValue } = inputs;\n  if (denseShape.shape.length !== 1) {\n    throw new Error(`Dense shape must be a vector, saw:\n         ${denseShape.shape}`);\n  }\n  if (indices.shape.length !== 2) {\n    throw new Error(`Indices must be a matrix, saw:\n         ${indices.shape}`);\n  }\n  if (values.shape.length !== 1) {\n    throw new Error(`Values must be a vector, saw:\n         ${values.shape}`);\n  }\n  if (defaultValue.shape.length !== 0) {\n    throw new Error(`Default value must be a scalar, saw:\n        ${defaultValue.shape}`);\n  }\n  const $indices = backend2.readSync(indices.dataId);\n  const $values = backend2.readSync(values.dataId);\n  const $denseShape = backend2.readSync(denseShape.dataId);\n  const $defaultValue = backend2.readSync(defaultValue.dataId)[0];\n  const [outputIndices, outputIndicesShape, outputValues, emptyRowIndicator, reverseIndexMap] = sparseFillEmptyRowsImplCPU($indices, indices.shape, indices.dtype, $values, values.dtype, $denseShape, $defaultValue);\n  return [\n    backend2.makeTensorInfo(outputIndicesShape, indices.dtype, outputIndices),\n    backend2.makeTensorInfo([outputIndicesShape[0]], values.dtype, outputValues),\n    backend2.makeTensorInfo([emptyRowIndicator.length], \"bool\", new Uint8Array(emptyRowIndicator.map((value) => Number(value)))),\n    backend2.makeTensorInfo([reverseIndexMap.length], indices.dtype, new Int32Array(reverseIndexMap))\n  ];\n}\nvar sparseFillEmptyRowsConfig2 = {\n  kernelName: SparseFillEmptyRows,\n  backendName: \"webgl\",\n  kernelFunc: sparseFillEmptyRows3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/SparseReshape.js\nfunction sparseReshape3(args) {\n  const { inputs, backend: backend2 } = args;\n  const { inputIndices, inputShape, newShape } = inputs;\n  if (inputIndices.shape.length !== 2) {\n    throw new Error(`Input indices should be a matrix but received shape ${inputIndices.shape}`);\n  }\n  if (inputShape.shape.length !== 1) {\n    throw new Error(`Input shape should be a vector but received shape ${inputShape.shape}`);\n  }\n  if (newShape.shape.length !== 1) {\n    throw new Error(`Target shape should be a vector but received shape ${newShape.shape}`);\n  }\n  const $inputShape = Array.from(backend2.readSync(inputShape.dataId));\n  const $inputIndices = backend2.readSync(inputIndices.dataId);\n  const targetShape = Array.from(backend2.readSync(newShape.dataId));\n  const [newIndices, indicesShape, outputShape] = sparseReshapeImplCPU($inputIndices, inputIndices.shape, inputIndices.dtype, $inputShape, targetShape);\n  return [\n    backend2.makeTensorInfo(indicesShape, inputIndices.dtype, newIndices),\n    backend2.makeTensorInfo([outputShape.length], newShape.dtype, new Int32Array(outputShape))\n  ];\n}\nvar sparseReshapeConfig2 = {\n  kernelName: SparseReshape,\n  backendName: \"webgl\",\n  kernelFunc: sparseReshape3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/SparseSegmentMean.js\nfunction sparseSegmentMean3(args) {\n  const { inputs, backend: backend2 } = args;\n  const { data, indices, segmentIds } = inputs;\n  if (data.shape.length < 1) {\n    throw new Error(`Data should be at least 1 dimensional but received scalar`);\n  }\n  if (indices.shape.length !== 1) {\n    throw new Error(`Indices should be a vector but received shape\n              ${indices.shape}`);\n  }\n  if (segmentIds.shape.length !== 1) {\n    throw new Error(`Segment ids should be a vector but received shape\n              ${segmentIds.shape}`);\n  }\n  const $data = backend2.readSync(data.dataId);\n  const $indices = backend2.readSync(indices.dataId);\n  const $segmentIds = backend2.readSync(segmentIds.dataId);\n  const [outputData, outputDataShape] = sparseSegmentReductionImplCPU($data, data.shape, data.dtype, $indices, $segmentIds, true);\n  return backend2.makeTensorInfo(outputDataShape, data.dtype, outputData);\n}\nvar sparseSegmentMeanConfig2 = {\n  kernelName: SparseSegmentMean,\n  backendName: \"webgl\",\n  kernelFunc: sparseSegmentMean3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/SparseSegmentSum.js\nfunction sparseSegmentSum3(args) {\n  const { inputs, backend: backend2 } = args;\n  const { data, indices, segmentIds } = inputs;\n  if (data.shape.length < 1) {\n    throw new Error(`Data should be at least 1 dimensional but received scalar`);\n  }\n  if (indices.shape.length !== 1) {\n    throw new Error(`Indices should be a vector but received shape\n             ${indices.shape}`);\n  }\n  if (segmentIds.shape.length !== 1) {\n    throw new Error(`Segment ids should be a vector but received shape\n             ${segmentIds.shape}`);\n  }\n  const $data = backend2.readSync(data.dataId);\n  const $indices = backend2.readSync(indices.dataId);\n  const $segmentIds = backend2.readSync(segmentIds.dataId);\n  const [outputData, outputDataShape] = sparseSegmentReductionImplCPU($data, data.shape, data.dtype, $indices, $segmentIds);\n  return backend2.makeTensorInfo(outputDataShape, data.dtype, outputData);\n}\nvar sparseSegmentSumConfig2 = {\n  kernelName: SparseSegmentSum,\n  backendName: \"webgl\",\n  kernelFunc: sparseSegmentSum3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/SparseToDense.js\nfunction sparseToDense3(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { sparseIndices, sparseValues, defaultValue } = inputs;\n  const { outputShape } = attrs;\n  const { sliceRank, numUpdates, sliceSize, strides, outputSize } = backend_util_exports.calculateShapes(sparseValues, sparseIndices, outputShape);\n  const sumDupeIndices = false;\n  if (sparseValues.dtype === \"string\") {\n    const indicesBuf = backend2.bufferSync(sparseIndices);\n    const updatesBuf = backend2.bufferSync(sparseValues);\n    const $defaultValue = util_exports.decodeString(backend2.readSync(defaultValue.dataId)[0]);\n    const outBuf = scatterImplCPU(indicesBuf, updatesBuf, outputShape, outputSize, sliceSize, numUpdates, sliceRank, strides, $defaultValue, sumDupeIndices);\n    return backend2.makeTensorInfo(outputShape, outBuf.dtype, outBuf.values);\n  }\n  const program = new ScatterProgram(numUpdates, sliceRank, sparseIndices.shape.length, sparseValues.shape.length, strides, [outputSize, 1], sumDupeIndices);\n  const res = backend2.runWebGLProgram(program, [sparseValues, sparseIndices, defaultValue], sparseValues.dtype);\n  const reshaped = reshape4({ inputs: { x: res }, backend: backend2, attrs: { shape: outputShape } });\n  backend2.disposeIntermediateTensorInfo(res);\n  return reshaped;\n}\nvar sparseToDenseConfig2 = {\n  kernelName: SparseToDense,\n  backendName: \"webgl\",\n  kernelFunc: sparseToDense3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/SplitV.js\nfunction splitV2(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x } = inputs;\n  const { numOrSizeSplits, axis } = attrs;\n  const $axis = util_exports.parseAxisParam(axis, x.shape)[0];\n  const splitSizes = backend_util_exports.prepareSplitSize(x, numOrSizeSplits, $axis);\n  const xRank = x.shape.length;\n  const begin = new Array(xRank).fill(0);\n  const size = x.shape.slice();\n  return splitSizes.map((s) => {\n    const sliceSize = [...size];\n    sliceSize[$axis] = s;\n    const sliceT = slice3({ inputs: { x }, backend: backend2, attrs: { begin, size: sliceSize } });\n    begin[$axis] += s;\n    return sliceT;\n  });\n}\nvar splitVConfig2 = {\n  kernelName: SplitV,\n  backendName: \"webgl\",\n  kernelFunc: splitV2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Sqrt.js\nvar SQRT = `return sqrt(x);`;\nvar sqrt3 = unaryKernelFunc2({ opSnippet: SQRT, packedOpSnippet: SQRT, cpuKernelImpl: sqrtImplCPU });\nvar sqrtConfig2 = {\n  kernelName: Sqrt,\n  backendName: \"webgl\",\n  kernelFunc: sqrt3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Square.js\nvar SQUARE = `return x * x;`;\nvar square3 = unaryKernelFunc2({ opSnippet: SQUARE });\nvar squareConfig2 = {\n  kernelName: Square,\n  backendName: \"webgl\",\n  kernelFunc: square3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/SquaredDifference.js\nvar SQUARED_DIFFERENCE = \"return (a - b) * (a - b);\";\nvar squaredDifference3 = binaryKernelFunc2({ opSnippet: SQUARED_DIFFERENCE, packedOpSnippet: SQUARED_DIFFERENCE });\nvar squaredDifferenceConfig2 = {\n  kernelName: SquaredDifference,\n  backendName: \"webgl\",\n  kernelFunc: squaredDifference3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Step.js\nfunction step3({ inputs, attrs, backend: backend2 }) {\n  const { x } = inputs;\n  const opSnippet = CHECK_NAN_SNIPPET + `\n    return x > 0.0 ? 1.0 : float(${attrs.alpha});\n  `;\n  const program = new UnaryOpProgram(x.shape, opSnippet);\n  return backend2.runWebGLProgram(program, [x], x.dtype);\n}\nvar stepConfig2 = {\n  kernelName: Step,\n  backendName: \"webgl\",\n  kernelFunc: step3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/strided_slice_gpu.js\nvar StridedSliceProgram = class {\n  constructor(begin, strides, size) {\n    this.variableNames = [\"x\"];\n    this.outputShape = size;\n    const rank = size.length;\n    const inputDtype = getCoordsDataType(size.length);\n    const dtype = getCoordsDataType(size.length);\n    let newCoords = \"\";\n    if (rank === 1) {\n      newCoords = \"coords * strides + begin\";\n    } else {\n      let outputAxis = 0;\n      newCoords = size.map((_, i) => {\n        outputAxis++;\n        return size.length === 1 ? `coords * strides[${i}] + begin[${i}]` : `coords[${outputAxis - 1}] * strides[${i}] + begin[${i}]`;\n      }).join(\",\");\n    }\n    this.userCode = `\n      ${inputDtype} begin = ${inputDtype}(${begin});\n      ${inputDtype} strides = ${inputDtype}(${strides});\n\n      void main() {\n        ${dtype} coords = getOutputCoords();\n        setOutput(getX(${newCoords}));\n      }\n    `;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/StridedSlice.js\nfunction stridedSlice3(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x } = inputs;\n  const { begin, end, strides, beginMask, endMask, ellipsisMask, newAxisMask, shrinkAxisMask } = attrs;\n  const { finalShapeSparse, finalShape, isIdentity, sliceDim0, isSimpleSlice, begin: $begin, end: $end, strides: $strides } = slice_util_exports.sliceInfo(x.shape, begin, end, strides, beginMask, endMask, ellipsisMask, newAxisMask, shrinkAxisMask);\n  let result;\n  if (isIdentity) {\n    result = reshape4({ inputs: { x }, backend: backend2, attrs: { shape: finalShape } });\n  } else if (sliceDim0 || isSimpleSlice) {\n    util_exports.assert(x.shape.length >= 1, () => `Input must have rank at least 1, got: ${x.shape.length}`);\n    const size = slice_util_exports.computeOutShape($begin, $end, $strides);\n    const sliced = slice3({ inputs: { x }, backend: backend2, attrs: { begin: $begin, size } });\n    result = reshape4({ inputs: { x: sliced }, backend: backend2, attrs: { shape: finalShape } });\n    backend2.disposeIntermediateTensorInfo(sliced);\n  } else {\n    const shouldExecuteOnCPU = backend2.shouldExecuteOnCPU([x]);\n    if (shouldExecuteOnCPU) {\n      const values = backend2.readSync(x.dataId);\n      const xBuf = buffer(x.shape, x.dtype, values);\n      const resultValues = stridedSliceImplCPU(finalShapeSparse, xBuf, $strides, $begin);\n      result = backend2.makeTensorInfo(finalShape, x.dtype, resultValues.values);\n    } else {\n      const program = new StridedSliceProgram($begin, $strides, finalShapeSparse);\n      result = backend2.runWebGLProgram(program, [x], x.dtype);\n    }\n  }\n  const resultReshaped = reshape4({ inputs: { x: result }, backend: backend2, attrs: { shape: finalShape } });\n  backend2.disposeIntermediateTensorInfo(result);\n  return resultReshaped;\n}\nvar stridedSliceConfig2 = {\n  kernelName: StridedSlice,\n  backendName: \"webgl\",\n  kernelFunc: stridedSlice3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/StringNGrams.js\nfunction stringNGrams3(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { separator, nGramWidths, leftPad, rightPad: rightPad2, padWidth, preserveShortSequences } = attrs;\n  const { data, dataSplits } = inputs;\n  const $data = backend2.readSync(data.dataId);\n  const $dataSplits = backend2.readSync(dataSplits.dataId);\n  const [nGrams, nGramsSplits] = stringNGramsImplCPU($data, $dataSplits, separator, nGramWidths, leftPad, rightPad2, padWidth, preserveShortSequences);\n  return [\n    backend2.makeTensorInfo([nGrams.length], \"string\", nGrams),\n    backend2.makeTensorInfo(dataSplits.shape, \"int32\", nGramsSplits)\n  ];\n}\nvar stringNGramsConfig2 = {\n  kernelName: StringNGrams,\n  backendName: \"webgl\",\n  kernelFunc: stringNGrams3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/StringSplit.js\nfunction stringSplit3(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { skipEmpty } = attrs;\n  const { input: input2, delimiter } = inputs;\n  if (input2.dtype !== \"string\") {\n    throw new Error(\"Input must be of datatype string\");\n  }\n  if (input2.shape.length !== 1) {\n    throw new Error(`Input must be a vector, got shape: ${input2.shape}`);\n  }\n  if (delimiter.shape.length !== 0) {\n    throw new Error(`Delimiter must be a scalar, got shape: ${delimiter.shape}`);\n  }\n  const $input = backend2.readSync(input2.dataId);\n  const $delimiter = backend2.readSync(delimiter.dataId)[0];\n  const [indices, values, shape] = stringSplitImplCPU($input, $delimiter, skipEmpty);\n  const outputSize = values.length;\n  return [\n    backend2.makeTensorInfo([outputSize, 2], \"int32\", indices),\n    backend2.makeTensorInfo([outputSize], \"string\", values),\n    backend2.makeTensorInfo([2], \"int32\", new Int32Array(shape))\n  ];\n}\nvar stringSplitConfig2 = {\n  kernelName: StringSplit,\n  backendName: \"webgl\",\n  kernelFunc: stringSplit3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/StringToHashBucketFast.js\nfunction stringToHashBucketFast3(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { numBuckets } = attrs;\n  const { input: input2 } = inputs;\n  if (input2.dtype !== \"string\") {\n    throw new Error(\"Input must be of datatype string\");\n  }\n  if (numBuckets <= 0) {\n    throw new Error(`Number of buckets must be at least 1`);\n  }\n  const $input = backend2.readSync(input2.dataId);\n  const output = stringToHashBucketFastImplCPU($input, numBuckets);\n  return backend2.makeTensorInfo(input2.shape, \"int32\", output);\n}\nvar stringToHashBucketFastConfig2 = {\n  kernelName: StringToHashBucketFast,\n  backendName: \"webgl\",\n  kernelFunc: stringToHashBucketFast3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Tan.js\nvar TAN = `return tan(x);`;\nvar tan3 = unaryKernelFunc2({ opSnippet: TAN });\nvar tanConfig2 = {\n  kernelName: Tan,\n  backendName: \"webgl\",\n  kernelFunc: tan3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Tanh.js\nvar TANH = `\n  float e2x = exp(-2.0 * abs(x));\n  return sign(x) * (1.0 - e2x) / (1.0 + e2x);\n`;\nvar tanh4 = unaryKernelFunc2({ opSnippet: TANH });\nvar tanhConfig2 = {\n  kernelName: Tanh,\n  backendName: \"webgl\",\n  kernelFunc: tanh4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/tile_gpu.js\nvar TileProgram = class {\n  constructor(aShape, reps) {\n    this.variableNames = [\"A\"];\n    const outputShape = new Array(aShape.length);\n    for (let i = 0; i < outputShape.length; i++) {\n      outputShape[i] = aShape[i] * reps[i];\n    }\n    this.outputShape = outputShape;\n    this.rank = outputShape.length;\n    const dtype = getCoordsDataType(this.rank);\n    const sourceCoords = getSourceCoords3(aShape);\n    this.userCode = `\n      void main() {\n        ${dtype} resRC = getOutputCoords();\n        setOutput(getA(${sourceCoords}));\n      }\n    `;\n  }\n};\nfunction getSourceCoords3(aShape) {\n  const rank = aShape.length;\n  if (rank > 5) {\n    throw Error(`Tile for rank ${rank} is not yet supported`);\n  }\n  if (rank === 1) {\n    return `imod(resRC, ${aShape[0]})`;\n  }\n  const currentCoords = [\"resRC.x\", \"resRC.y\", \"resRC.z\", \"resRC.w\", \"resRC.u\"];\n  const sourceCoords = [];\n  for (let i = 0; i < aShape.length; i++) {\n    sourceCoords.push(`imod(${currentCoords[i]}, ${aShape[i]})`);\n  }\n  return sourceCoords.join();\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Tile.js\nfunction tile4(params) {\n  const { inputs, backend: backend2, attrs } = params;\n  const { x } = inputs;\n  const { reps } = attrs;\n  if (x.dtype === \"string\" || x.shape.length > 5) {\n    const data = backend2.readSync(x.dataId);\n    const value = x.dtype === \"string\" ? data.map((d) => util_exports.decodeString(d)) : data;\n    const buf = buffer(x.shape, x.dtype, value);\n    const outBuf = tileImplCPU(buf, reps);\n    return backend2.makeTensorInfo(outBuf.shape, outBuf.dtype, outBuf.values);\n  }\n  const program = new TileProgram(x.shape, reps);\n  const output = backend2.runWebGLProgram(program, [x], x.dtype);\n  return output;\n}\nvar tileConfig2 = {\n  kernelName: Tile,\n  backendName: \"webgl\",\n  kernelFunc: tile4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/top_k_gpu.js\nvar SwapProgram = class {\n  constructor(shape) {\n    this.variableNames = [\"x\", \"indices\"];\n    this.customUniforms = [\n      { name: \"n\", type: \"int\" },\n      { name: \"firstPass\", type: \"int\" },\n      { name: \"negativeInf\", type: \"float\" },\n      { name: \"dir\", type: \"int\" },\n      { name: \"inc\", type: \"int\" }\n    ];\n    this.outputShape = shape;\n    this.userCode = `\n       void main() {\n         ivec2 coords = getOutputCoords();\n         int batch = coords[0];\n         int elemIdx = coords[1];\n\n         // We compare elements pair-wise within a group of size 2 * inc.\n         // The comparing rule for each group alternates between ascending\n         // and descending. Within each group, we compare each pair at\n         // positions i and i+inc. To decide whether an element at position i\n         // is x0 or x1, we mod it by 2 * inc, if the result is smaller than\n         // inc, it is in the first half of the group, we denote it as x0,\n         // otherwise we denote it as x1.\n         // For example, as shown in the Bitonic top K paper referenced above,\n         // Figure5(a) shows that element[1] is in the\n         // second half of the group when group size is 2, but it is in the\n         // first half of the group when group size is 4.\n\n         bool isFirstInPair = imod(elemIdx, 2 * inc) < inc;\n         int i = isFirstInPair ? elemIdx : elemIdx - inc;\n\n         int i0 = firstPass == 1 ? i : int(getIndices(batch, i));\n         int i1 = firstPass == 1 ? i + inc : int(getIndices(batch, i + inc));\n         float x0 = i0 < n ? getX(batch, i0) : negativeInf;\n         float x1 = i1 < n ? getX(batch, i1) : negativeInf;\n\n         // Denotes which direction indices are in (ascending or descending).\n         bool reverse = imod(elemIdx, 2 * dir) >= dir;\n         bool isGreater = x0 > x1 || (x0 == x1 && i1 > i0);\n         if (reverse == isGreater) { // Elements in opposite order of direction\n           int iTemp = i0;\n           i0 = i1;\n           i1 = iTemp;\n         }\n         if (isFirstInPair) {\n            setOutput(float(i0));\n         } else {\n            setOutput(float(i1));\n         }\n       }\n     `;\n  }\n};\nvar MergeProgram = class {\n  constructor(shape) {\n    this.variableNames = [\"x\", \"indices\"];\n    this.customUniforms = [\n      { name: \"n\", type: \"int\" },\n      { name: \"firstPass\", type: \"int\" },\n      { name: \"k\", type: \"int\" }\n    ];\n    this.outputShape = shape;\n    this.userCode = `\n    void main() {\n         // Takes max of indices (0, k), (1, k + 1), (2, k + 2) ...\n         ivec2 coords = getOutputCoords();\n         int batch = coords[0];\n         int elemIdx = coords[1];\n\n         // The output size is half of the previous size.\n         // If the previous sequence is | | | | _ _ _ _  | | | |  _ _ _ _ (k=4),\n         // we only need to output the indices at positions |, the indices at\n         // positions _ can be thrown away, see Figure5(b) After Phase 2\n         // (Merge phase) in the Bitonic Top K paper referenced above.\n         // For example, the paper shows we only need to output the orange bars.\n         // The output sequence should look like this | | | | | | | |.\n         // Because the sequence is halved, to map the output index back\n         // to the previous sequence to find the corresponding value,\n         // we need to double the index. When we double the index,\n         // we basically interpolate a position, so 2i looks like\n         // | _ | _ | _ | _ | _ | _ | _. We move the | to the first k position\n         // of each 2k positions by - elemIdx % k. E.g. for output at\n         // index 4,5,6,7, we want to get the corresponding element at\n         // original index 8,9,10,11, for output at index 8,9,10,11,\n         // we want to get the corresponding element at original index\n         // 16,17,18,19, so on and so forth.\n\n         int i = elemIdx < k ? elemIdx : (elemIdx * 2 - imod(elemIdx, k));\n         int i0 = firstPass == 1 ? i : int(getIndices(batch, i));\n         int i1 = firstPass == 1 ? i + k : int(getIndices(batch, i + k));\n\n         float x0 = getX(batch, i0);\n         float x1 = i1 < n ? getX(batch, i1) : x0;\n\n         setOutput(x0 >= x1 ? float(i0) : float(i1));\n       }\n     `;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/TopK.js\nfunction disposeIntermediateTensorInfoOrNull(backend2, tensorInfo) {\n  if (tensorInfo !== null) {\n    backend2.disposeIntermediateTensorInfo(tensorInfo);\n  }\n}\nfunction roundUpToPow2(num) {\n  let pow22 = 1;\n  while (pow22 < num) {\n    pow22 *= 2;\n  }\n  return pow22;\n}\nfunction topK2(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x } = inputs;\n  const { k, sorted } = attrs;\n  const TOPK_LAST_DIM_CPU_HANDOFF_SIZE_THRESHOLD = env().getNumber(\"TOPK_LAST_DIM_CPU_HANDOFF_SIZE_THRESHOLD\");\n  const TOPK_K_CPU_HANDOFF_THRESHOLD = env().getNumber(\"TOPK_K_CPU_HANDOFF_THRESHOLD\");\n  const xShape = x.shape;\n  const lastDim = xShape[xShape.length - 1];\n  if (backend2.shouldExecuteOnCPU([x]) || lastDim < TOPK_LAST_DIM_CPU_HANDOFF_SIZE_THRESHOLD || k > TOPK_K_CPU_HANDOFF_THRESHOLD) {\n    const xVals = backend2.readSync(x.dataId);\n    const [allTopKVals, allTopKIndices] = topKImplCPU(xVals, xShape, x.dtype, k, sorted);\n    return [\n      backend2.makeTensorInfo(allTopKVals.shape, allTopKVals.dtype, allTopKVals.values),\n      backend2.makeTensorInfo(allTopKIndices.shape, allTopKIndices.dtype, allTopKIndices.values)\n    ];\n  }\n  if (k === 0) {\n    xShape[xShape.length - 1] = 0;\n    return [\n      backend2.makeTensorInfo(xShape, x.dtype, []),\n      backend2.makeTensorInfo(xShape, \"int32\", [])\n    ];\n  }\n  if (lastDim === 1) {\n    return [\n      x,\n      fill3({ attrs: { shape: xShape, dtype: \"int32\", value: 0 }, backend: backend2 })\n    ];\n  }\n  const xtexData = backend2.texData.get(x.dataId);\n  const xIsPacked = xtexData !== null && xtexData.isPacked;\n  const xUnPacked = xIsPacked ? backend2.unpackTensor(x) : x;\n  const xSize = util_exports.sizeFromShape(xShape);\n  const batch = xSize / lastDim;\n  const x2D = reshape4({ inputs: { x: xUnPacked }, attrs: { shape: [batch, lastDim] }, backend: backend2 });\n  if (xIsPacked) {\n    disposeIntermediateTensorInfoOrNull(backend2, xUnPacked);\n  }\n  const kPow2 = roundUpToPow2(k);\n  const lastDimPow2 = roundUpToPow2(lastDim);\n  let indices = null;\n  const getInputs = () => indices === null ? [x2D, x2D] : [x2D, indices];\n  const runSwap = (dir, inc, shape) => {\n    const inputs2 = getInputs();\n    const program = new SwapProgram(shape);\n    const fistPass = indices === null ? 1 : 0;\n    const customValues = [[lastDim], [fistPass], [Number.NEGATIVE_INFINITY], [dir], [inc]];\n    const prevIndices2 = indices;\n    indices = backend2.runWebGLProgram(program, inputs2, \"int32\", customValues);\n    disposeIntermediateTensorInfoOrNull(backend2, prevIndices2);\n  };\n  for (let len = 1; len < kPow2; len *= 2) {\n    const dir = len * 2;\n    for (let inc = len; inc >= 1; inc /= 2) {\n      runSwap(dir, inc, [batch, lastDimPow2]);\n    }\n  }\n  for (let indicesSize = lastDimPow2; indicesSize > kPow2; indicesSize /= 2) {\n    const inputs2 = getInputs();\n    const mergeProgram = new MergeProgram([batch, indicesSize / 2]);\n    const firstPass = indices === null ? 1 : 0;\n    const customValues = [[lastDim], [firstPass], [kPow2]];\n    const prevIndices2 = indices;\n    indices = backend2.runWebGLProgram(mergeProgram, inputs2, \"int32\", customValues);\n    disposeIntermediateTensorInfoOrNull(backend2, prevIndices2);\n    const len = kPow2 / 2;\n    const dir = len * 2;\n    for (let inc = len; inc >= 1; inc /= 2) {\n      runSwap(dir, inc, indices.shape);\n    }\n  }\n  let prevIndices = indices;\n  indices = slice3({ inputs: { x: indices }, backend: backend2, attrs: { begin: 0, size: [batch, k] } });\n  disposeIntermediateTensorInfoOrNull(backend2, prevIndices);\n  let values = gatherV22({ inputs: { x: x2D, indices }, backend: backend2, attrs: { axis: 1, batchDims: 1 } });\n  disposeIntermediateTensorInfoOrNull(backend2, x2D);\n  const newShape = xShape.slice(0, -1);\n  newShape.push(k);\n  prevIndices = indices;\n  indices = reshape4({ inputs: { x: indices }, attrs: { shape: newShape }, backend: backend2 });\n  disposeIntermediateTensorInfoOrNull(backend2, prevIndices);\n  const prevValues = values;\n  values = reshape4({ inputs: { x: values }, attrs: { shape: newShape }, backend: backend2 });\n  disposeIntermediateTensorInfoOrNull(backend2, prevValues);\n  return [values, indices];\n}\nvar topKConfig2 = {\n  kernelName: TopK,\n  backendName: \"webgl\",\n  kernelFunc: topK2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/transform_gpu.js\nvar TransformProgram = class {\n  constructor(imageHeight, imageWidth, interpolation, fillMode, fillValue, outShape) {\n    this.variableNames = [\"Image\", \"Transforms\"];\n    this.outputShape = outShape;\n    const interpolationModeId = interpolation === \"nearest\" ? 1 : 2;\n    let fillModeId;\n    switch (fillMode) {\n      case \"constant\":\n        fillModeId = 1;\n        break;\n      case \"reflect\":\n        fillModeId = 2;\n        break;\n      case \"wrap\":\n        fillModeId = 3;\n        break;\n      case \"nearest\":\n        fillModeId = 4;\n        break;\n      default:\n        fillModeId = 1;\n        break;\n    }\n    this.userCode = `\n            float mapCoord(float outCoord, float len) {\n              float inCoord = outCoord;\n              if(${fillModeId} == 2) {\n                if (inCoord < 0.0) {\n                  if (len <= 1.0) {\n                    inCoord = 0.0;\n                  } else {\n                    float sz2 = 2.0 * len;\n                    if (inCoord < sz2) {\n                      inCoord = sz2 * float(int(float(-inCoord / sz2))) +\n                      inCoord;\n                    }\n                    inCoord = inCoord < -len ? inCoord + sz2 : -inCoord - 1.0;\n                  }\n                } else if (inCoord > len - 1.0) {\n                  if (len <= 1.0) {\n                    inCoord = 0.0;\n                  } else {\n                    float sz2 = 2.0 * len;\n                    inCoord -= sz2 * float(int(float(inCoord / sz2)));\n                    if (inCoord >= len) {\n                      inCoord = sz2 - inCoord - 1.0;\n                    }\n                  }\n                }\n                return clamp(inCoord, 0.0, len - 1.0);\n              } else if (${fillModeId} == 3) {\n                if (inCoord < 0.0) {\n                  if (len <= 1.0) {\n                    inCoord = 0.0;\n                  } else {\n                    float sz = len - 1.0;\n                    inCoord += len * (float(int(float(-inCoord / sz))) + 1.0);\n                  }\n                } else if (inCoord > len - 1.0) {\n                  if (len <= 1.0) {\n                    inCoord = 0.0;\n                  } else {\n                    float sz = len - 1.0;\n                    inCoord -= len * float(int(float(inCoord / sz)));\n                  }\n                }\n                return clamp(inCoord, 0.0, len - 1.0);\n              } else if (${fillModeId} == 4) {\n                return clamp(outCoord, 0.0, len - 1.0);\n              } else {\n                return outCoord;\n              }\n            }\n\n            float readWithFillValue(int batch, int coordY, int coordX,\n              int channel) {\n              float outputValue;\n              if (0 <= coordY && coordY < ${imageHeight} && 0 <= coordX && coordX < ${imageWidth}) {\n                  outputValue = getImage(batch, coordY, coordX, channel);\n              } else {\n                outputValue = float(${fillValue});\n              }\n              return outputValue;\n            }\n\n            void main() {\n              ivec4 coords = getOutputCoords();\n              float outputValue;\n              int batch = coords[0];\n              int x = coords[2];\n              int y = coords[1];\n              int channel = coords[3];\n              float xf = float(x);\n              float yf = float(y);\n              float a1 = getTransforms(batch, 0);\n              float a2 = getTransforms(batch, 1);\n              float a3 = getTransforms(batch, 2);\n              float b1 = getTransforms(batch, 3);\n              float b2 = getTransforms(batch, 4);\n              float b3 = getTransforms(batch, 5);\n              float c1 = getTransforms(batch, 6);\n              float c2 = getTransforms(batch, 7);\n              float projection = c1 * xf + c2 * yf + 1.0;\n              if (projection == 0.0) {\n                outputValue = float(${fillValue});\n              } else {\n                float inX = (a1 * xf + a2 * yf + a3) / projection;\n                float inY = (b1 * xf + b2 * yf + b3) / projection;\n                float mapX = mapCoord(inX, float(${imageWidth}));\n                float mapY = mapCoord(inY, float(${imageHeight}));\n\n                if (${interpolationModeId} == 1) {\n                  int coordY = int(round(mapY));\n                  int coordX = int(round(mapX));\n                  outputValue = readWithFillValue(batch, coordY, coordX,\n                    channel);\n                } else {\n                  float yFloor = floor(mapY);\n                  float xFloor = floor(mapX);\n                  float yCeil = yFloor + 1.0;\n                  float xCeil = xFloor + 1.0;\n                  float valueYFloor = (xCeil - mapX) *\n                  readWithFillValue(batch, int(yFloor), int(xFloor), channel) +\n                  (mapX - xFloor) *\n                  readWithFillValue(batch, int(yFloor), int(xCeil), channel);\n                  float valueYCeil = (xCeil - mapX) *\n                  readWithFillValue(batch, int(yCeil), int(xFloor), channel) +\n                  (mapX - xFloor) *\n                  readWithFillValue(batch, int(yCeil), int(xCeil), channel);\n                  outputValue = (yCeil - mapY) * valueYFloor +\n                  (mapY - yFloor) * valueYCeil;\n                }\n              }\n              setOutput(outputValue);\n            }\n        `;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Transform.js\nfunction transform3(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { image: image2, transforms } = inputs;\n  const { interpolation, fillMode, fillValue, outputShape } = attrs;\n  const [batch, imageHeight, imageWidth, numChannels] = image2.shape;\n  const [outHeight, outWidth] = outputShape != null ? outputShape : [imageHeight, imageWidth];\n  const outShape = [\n    batch,\n    outHeight,\n    outWidth,\n    numChannels\n  ];\n  const program = new TransformProgram(imageHeight, imageWidth, interpolation, fillMode, fillValue, outShape);\n  return backend2.runWebGLProgram(program, [image2, transforms], \"float32\");\n}\nvar transformConfig2 = {\n  kernelName: Transform,\n  backendName: \"webgl\",\n  kernelFunc: transform3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Unique.js\nfunction unique4(args) {\n  const { inputs, attrs, backend: backend2 } = args;\n  const { axis } = attrs;\n  const { x } = inputs;\n  assertNotComplex2(x, \"unique\");\n  console.warn(\"WARNING: \", \"UI might be locked temporarily as data is being downloaded\");\n  const values = backend2.readSync(x.dataId);\n  const { outputValues, outputShape, indices } = uniqueImplCPU(values, axis, x.shape, x.dtype);\n  return [\n    backend2.makeTensorInfo(outputShape, x.dtype, outputValues),\n    backend2.makeTensorInfo([indices.length], \"int32\", indices)\n  ];\n}\nvar uniqueConfig2 = {\n  kernelName: Unique,\n  backendName: \"webgl\",\n  kernelFunc: unique4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Unpack.js\nfunction unpack2(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { value } = inputs;\n  let { axis } = attrs;\n  if (axis < 0) {\n    axis += value.shape.length;\n  }\n  const x = value;\n  const xRank = x.shape.length;\n  const num = value.shape[axis];\n  const outShape = new Array(xRank - 1);\n  let outIndex = 0;\n  for (let i = 0; i < xRank; i++) {\n    if (i !== axis) {\n      outShape[outIndex++] = x.shape[i];\n    }\n  }\n  const toDispose = [];\n  const begin = new Array(xRank).fill(0);\n  const size = x.shape.slice();\n  size[axis] = 1;\n  const res = new Array(num);\n  for (let i = 0; i < res.length; i++) {\n    begin[axis] = i;\n    const sliced = slice3({ inputs: { x }, backend: backend2, attrs: { begin, size } });\n    const reshaped = reshape4({ inputs: { x: sliced }, backend: backend2, attrs: { shape: outShape } });\n    res[i] = reshaped;\n    toDispose.push(sliced);\n  }\n  toDispose.forEach((t) => backend2.disposeIntermediateTensorInfo(t));\n  return res;\n}\nvar unpackConfig2 = {\n  kernelName: Unpack,\n  backendName: \"webgl\",\n  kernelFunc: unpack2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/segment_gpu.js\nvar SegmentOpProgram = class {\n  constructor(segOpInfo, segOpType) {\n    this.variableNames = [\"x\", \"segmentIds\"];\n    const windowSize = segOpInfo.windowSize;\n    const batchSize = segOpInfo.batchSize;\n    const inSize = segOpInfo.inSize;\n    const numSegments = segOpInfo.numSegments;\n    const outSize = numSegments * Math.ceil(inSize / windowSize);\n    this.outputShape = [batchSize, outSize];\n    const initializationValue = \"0.0\";\n    const returnValue = `sumValue`;\n    const windowSizeNearestVec4 = Math.floor(windowSize / 4) * 4;\n    const windowSizeVec4Remainder = windowSize % 4;\n    const updateSnippet = `\n        sumValue += dot(values, segFilter);\n    `;\n    let checkValueOutOfBounds = \"\";\n    if (inSize % windowSize > 0) {\n      checkValueOutOfBounds = `\n        if (inIdx < 0 || inIdx >= ${inSize}) {\n          return initializationValue;\n        }\n      `;\n    }\n    let checkSegmentIdOutOfBounds = \"\";\n    if (inSize % windowSize > 0) {\n      checkSegmentIdOutOfBounds = `\n        if (inIdx < 0 || inIdx >= ${inSize}) {\n          return -1.0;\n        }\n      `;\n    }\n    this.userCode = `\n      const float initializationValue = ${initializationValue};\n\n      float getValue(int batch, int inIdx) {\n        ${checkValueOutOfBounds}\n        return getX(batch, inIdx);\n      }\n\n      float getSegmentIdAtIndex(int inIdx) {\n        ${checkSegmentIdOutOfBounds}\n        return getSegmentIds(inIdx);\n      }\n\n      void main() {\n        ivec2 coords = getOutputCoords();\n        int batch = coords[0];\n        int outIdx = coords[1];\n        int inOffset = int(floor(float(outIdx) / float(\n          ${numSegments})) * float(${windowSize}));\n        int currentSeg = int(mod(float(outIdx), float(${numSegments})));\n\n        float sumValue = 0.0;\n\n        for (int i = 0; i < ${windowSizeNearestVec4}; i += 4) {\n          int inIdx = inOffset + i;\n          vec4 values = vec4(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            getValue(batch, inIdx + 2),\n            getValue(batch, inIdx + 3)\n          );\n\n          vec4 segFilter = vec4(\n            int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,\n            int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,\n            int(getSegmentIdAtIndex(inIdx + 2)) == currentSeg ? 1 : 0,\n            int(getSegmentIdAtIndex(inIdx + 3)) == currentSeg ? 1 : 0\n          );\n\n          ${updateSnippet}\n        }\n\n        int inIdx = inOffset + ${windowSizeNearestVec4};\n        if (${windowSizeVec4Remainder === 1}) {\n          vec4 values = vec4(\n            getValue(batch, inIdx),\n            initializationValue,\n            initializationValue,\n            initializationValue\n          );\n\n          int inIdxSeg = int(getSegmentIdAtIndex(inIdx));\n\n          vec4 segFilter = vec4(\n            int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,\n            0,\n            0,\n            0\n          );\n\n          ${updateSnippet}\n        } else if (${windowSizeVec4Remainder === 2}) {\n          vec4 values = vec4(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            initializationValue,\n            initializationValue\n          );\n\n          vec4 segFilter = vec4(\n            int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,\n            int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,\n              0,\n              0\n          );\n\n          ${updateSnippet}\n        } else if (${windowSizeVec4Remainder === 3}) {\n          vec4 values = vec4(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            getValue(batch, inIdx + 2),\n            initializationValue\n          );\n\n          vec4 segFilter = vec4(\n            int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,\n            int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,\n            int(getSegmentIdAtIndex(inIdx + 2)) == currentSeg ? 1 : 0,\n            0\n          );\n\n          ${updateSnippet}\n        }\n        setOutput(${returnValue});\n      }\n    `;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/UnsortedSegmentSum.js\nfunction unsortedSegmentSum3(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x, segmentIds } = inputs;\n  const { numSegments } = attrs;\n  const xRank = x.shape.length;\n  const toDispose = [];\n  let axis = 0;\n  const permutation = backend_util_exports.getAxesPermutation([axis], xRank);\n  let permutedX = x;\n  if (permutation != null) {\n    permutedX = transpose3({ inputs: { x }, backend: backend2, attrs: { perm: permutation } });\n    toDispose.push(permutedX);\n    axis = backend_util_exports.getInnerMostAxes(1, xRank)[0];\n  }\n  const outShape = backend_util_exports.segment_util.computeOutShape(permutedX.shape, axis, numSegments);\n  const inSize = util_exports.sizeFromShape([permutedX.shape[axis]]);\n  const a2D = reshape4({ inputs: { x: permutedX }, backend: backend2, attrs: { shape: [-1, inSize] } });\n  toDispose.push(a2D);\n  const outputDType = sumOutType(x.dtype);\n  const segOpCompute = (x2, segOpType, segmentIds2, dtype, numSegments2) => {\n    const batchSize = x2.shape[0];\n    const inSize2 = x2.shape[1];\n    const windowSize = backend_util_exports.segment_util.segOpComputeOptimalWindowSize(inSize2, numSegments2);\n    const segOpInfo = { windowSize, inSize: inSize2, batchSize, numSegments: numSegments2 };\n    const program = new SegmentOpProgram(segOpInfo, segOpType);\n    const output = backend2.compileAndRun(program, [x2, segmentIds2], dtype);\n    toDispose.push(output);\n    if (output.shape[1] === numSegments2) {\n      return output;\n    }\n    const rangeInfo = range4({\n      backend: backend2,\n      attrs: { start: 0, stop: numSegments2, step: 1, dtype: \"float32\" }\n    });\n    const tileInfo = tile4({\n      inputs: { x: rangeInfo },\n      backend: backend2,\n      attrs: { reps: [inSize2 / windowSize] }\n    });\n    toDispose.push(rangeInfo);\n    toDispose.push(tileInfo);\n    const result2 = segOpCompute(output, segOpType, tileInfo, dtype, numSegments2);\n    return result2;\n  };\n  const segOpResult = segOpCompute(a2D, \"unsortedSegmentSum\", segmentIds, outputDType, numSegments);\n  const reshaped = reshape4({ inputs: { x: segOpResult }, backend: backend2, attrs: { shape: outShape } });\n  let result = reshaped;\n  if (permutation != null) {\n    toDispose.push(reshaped);\n    const perm = backend_util_exports.getUndoAxesPermutation(permutation);\n    result = transpose3({ inputs: { x: result }, backend: backend2, attrs: { perm } });\n  }\n  toDispose.forEach((t) => backend2.disposeIntermediateTensorInfo(t));\n  return result;\n}\nvar unsortedSegmentSumConfig2 = {\n  kernelName: UnsortedSegmentSum,\n  backendName: \"webgl\",\n  kernelFunc: unsortedSegmentSum3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/register_all_kernels.js\nvar kernelConfigs2 = [\n  _fusedMatMulConfig2,\n  absConfig2,\n  acosConfig2,\n  acoshConfig2,\n  addConfig2,\n  addNConfig2,\n  allConfig2,\n  anyConfig2,\n  argMaxConfig2,\n  argMinConfig2,\n  asinConfig2,\n  asinhConfig2,\n  atanConfig2,\n  atan2Config2,\n  atanhConfig2,\n  avgPoolConfig2,\n  avgPool3DConfig2,\n  avgPool3DGradConfig3,\n  avgPoolGradConfig3,\n  batchMatMulConfig2,\n  batchNormConfig2,\n  batchToSpaceNDConfig2,\n  bincountConfig2,\n  broadcastArgsConfig2,\n  castConfig2,\n  ceilConfig2,\n  clipByValueConfig2,\n  complexConfig2,\n  complexAbsConfig2,\n  concatConfig2,\n  conv2DConfig2,\n  conv2DBackpropFilterConfig2,\n  conv2DBackpropInputConfig2,\n  conv3DConfig2,\n  conv3DBackpropFilterV2Config2,\n  conv3DBackpropInputConfig,\n  cosConfig2,\n  coshConfig2,\n  cropAndResizeConfig2,\n  cumprodConfig2,\n  cumsumConfig2,\n  denseBincountConfig2,\n  depthToSpaceConfig2,\n  depthwiseConv2dNativeConfig2,\n  depthwiseConv2dNativeBackpropFilterConfig2,\n  depthwiseConv2dNativeBackpropInputConfig2,\n  diagConfig2,\n  dilation2DConfig2,\n  einsumConfig2,\n  eluConfig2,\n  eluGradConfig3,\n  equalConfig2,\n  erfConfig2,\n  expConfig2,\n  expandDimsConfig2,\n  expm1Config2,\n  fftConfig2,\n  fillConfig2,\n  flipLeftRightConfig2,\n  floorConfig2,\n  floorDivConfig2,\n  fromPixelsConfig,\n  fusedConv2DConfig2,\n  fusedDepthwiseConv2DConfig2,\n  gatherNdConfig2,\n  gatherV2Config2,\n  greaterConfig2,\n  greaterEqualConfig2,\n  identityConfig2,\n  ifftConfig2,\n  imagConfig2,\n  isFiniteConfig2,\n  isInfConfig2,\n  isNaNConfig2,\n  leakyReluConfig2,\n  lessConfig2,\n  lessEqualConfig2,\n  linSpaceConfig2,\n  logConfig2,\n  log1pConfig2,\n  logicalAndConfig2,\n  logicalNotConfig2,\n  logicalOrConfig2,\n  LRNConfig2,\n  LRNGradConfig2,\n  maxConfig2,\n  maximumConfig2,\n  maxPoolConfig2,\n  maxPool3DConfig2,\n  maxPool3DGradConfig3,\n  maxPoolGradConfig3,\n  maxPoolWithArgmaxConfig2,\n  meanConfig2,\n  minConfig2,\n  minimumConfig2,\n  mirrorPadConfig2,\n  modConfig2,\n  multinomialConfig2,\n  multiplyConfig2,\n  negConfig2,\n  nonMaxSuppressionV3Config2,\n  nonMaxSuppressionV4Config2,\n  nonMaxSuppressionV5Config2,\n  notEqualConfig2,\n  oneHotConfig2,\n  onesLikeConfig2,\n  packConfig2,\n  padV2Config2,\n  powConfig2,\n  preluConfig2,\n  prodConfig2,\n  raggedTensorToTensorConfig2,\n  rangeConfig2,\n  realConfig2,\n  realDivConfig2,\n  reciprocalConfig2,\n  reluConfig2,\n  relu6Config2,\n  reshapeConfig2,\n  resizeBilinearConfig2,\n  resizeBilinearGradConfig3,\n  resizeNearestNeighborConfig2,\n  resizeNearestNeighborGradConfig3,\n  reverseConfig2,\n  rotateWithOffsetConfig2,\n  roundConfig2,\n  rsqrtConfig2,\n  scatterNdConfig2,\n  searchSortedConfig2,\n  selectConfig2,\n  seluConfig2,\n  sigmoidConfig2,\n  signConfig2,\n  sinConfig2,\n  sinhConfig2,\n  sliceConfig2,\n  softmaxConfig2,\n  softplusConfig2,\n  spaceToBatchNDConfig2,\n  sparseFillEmptyRowsConfig2,\n  sparseReshapeConfig2,\n  sparseSegmentMeanConfig2,\n  sparseSegmentSumConfig2,\n  sparseToDenseConfig2,\n  splitVConfig2,\n  sqrtConfig2,\n  squareConfig2,\n  squaredDifferenceConfig2,\n  stepConfig2,\n  stridedSliceConfig2,\n  stringNGramsConfig2,\n  stringSplitConfig2,\n  stringToHashBucketFastConfig2,\n  subConfig2,\n  sumConfig2,\n  tanConfig2,\n  tanhConfig2,\n  tileConfig2,\n  topKConfig2,\n  transformConfig2,\n  transposeConfig2,\n  uniqueConfig2,\n  unpackConfig2,\n  unsortedSegmentSumConfig2,\n  zerosLikeConfig2\n];\nfor (const kernelConfig of kernelConfigs2) {\n  registerKernel(kernelConfig);\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/types.js\nvar CppDType;\n(function(CppDType2) {\n  CppDType2[CppDType2[\"float32\"] = 0] = \"float32\";\n  CppDType2[CppDType2[\"int32\"] = 1] = \"int32\";\n  CppDType2[CppDType2[\"bool\"] = 2] = \"bool\";\n  CppDType2[CppDType2[\"string\"] = 3] = \"string\";\n  CppDType2[CppDType2[\"complex64\"] = 4] = \"complex64\";\n})(CppDType || (CppDType = {}));\nvar FusableActivation;\n(function(FusableActivation2) {\n  FusableActivation2[FusableActivation2[\"linear\"] = 0] = \"linear\";\n  FusableActivation2[FusableActivation2[\"relu\"] = 1] = \"relu\";\n  FusableActivation2[FusableActivation2[\"relu6\"] = 2] = \"relu6\";\n  FusableActivation2[FusableActivation2[\"prelu\"] = 3] = \"prelu\";\n  FusableActivation2[FusableActivation2[\"leakyrelu\"] = 4] = \"leakyrelu\";\n  FusableActivation2[FusableActivation2[\"sigmoid\"] = 5] = \"sigmoid\";\n  FusableActivation2[FusableActivation2[\"elu\"] = 6] = \"elu\";\n})(FusableActivation || (FusableActivation = {}));\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/_FusedMatMul.js\nvar wasmFusedMatMul;\nfunction setup(backend2) {\n  wasmFusedMatMul = backend2.wasm.cwrap(_FusedMatMul, null, [\n    \"number\",\n    \"array\",\n    \"number\",\n    \"number\",\n    \"array\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\"\n  ]);\n}\nfunction fusedBatchMatMul(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { a, b, bias, preluActivationWeights } = inputs;\n  if (a.dtype !== \"float32\" || b.dtype !== \"float32\") {\n    throw new Error(`_FusedMatMul for non non-float32 tensors not yet supported.`);\n  }\n  const { transposeA, transposeB, activation: activation2, leakyreluAlpha } = attrs;\n  const aId = backend2.dataIdMap.get(a.dataId).id;\n  const bId = backend2.dataIdMap.get(b.dataId).id;\n  let biasId = 0;\n  if (bias != null) {\n    const biasData = backend2.dataIdMap.get(bias.dataId);\n    if (biasData.shape.length !== 1) {\n      throw new Error(`_FusedMatMul only supports rank-1 bias but got rank ${biasData.shape.length}.`);\n    }\n    biasId = biasData.id;\n  }\n  const preluActivationWeightsId = preluActivationWeights == null ? 0 : backend2.dataIdMap.get(preluActivationWeights.dataId).id;\n  const fusedActivation = FusableActivation[activation2];\n  if (fusedActivation == null) {\n    throw new Error(`${activation2} activation not yet supported for FusedConv2D in the wasm backend.`);\n  }\n  const leftDim = transposeA ? a.shape[2] : a.shape[1];\n  const rightDim = transposeB ? b.shape[1] : b.shape[2];\n  const batchDims = broadcast_util_exports.assertAndGetBroadcastShape(a.shape.slice(0, -2), b.shape.slice(0, -2));\n  const out = backend2.makeOutput([...batchDims, leftDim, rightDim], a.dtype);\n  const outId = backend2.dataIdMap.get(out.dataId).id;\n  const aShapeBytes = new Uint8Array(new Int32Array(a.shape).buffer);\n  const bShapeBytes = new Uint8Array(new Int32Array(b.shape).buffer);\n  wasmFusedMatMul(aId, aShapeBytes, a.shape.length, bId, bShapeBytes, b.shape.length, transposeA, transposeB, fusedActivation, biasId, preluActivationWeightsId, leakyreluAlpha || 0, outId);\n  return out;\n}\nvar _fusedMatMulConfig3 = {\n  kernelName: _FusedMatMul,\n  backendName: \"wasm\",\n  setupFunc: setup,\n  kernelFunc: fusedBatchMatMul\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/unary_kernel.js\nfunction createUnaryKernelConfig(kernelName, outType) {\n  let wasmFunc9;\n  function setupFunc3(backend2) {\n    wasmFunc9 = backend2.wasm.cwrap(kernelName, null, [\n      \"number\",\n      \"number\",\n      \"number\"\n    ]);\n  }\n  function kernelFunc3(args) {\n    const { backend: backend2, inputs: { x } } = args;\n    const xId = backend2.dataIdMap.get(x.dataId).id;\n    const out = backend2.makeOutput(x.shape, outType || x.dtype);\n    const outId = backend2.dataIdMap.get(out.dataId).id;\n    if (util_exports.sizeFromShape(out.shape) === 0) {\n      return out;\n    }\n    wasmFunc9(xId, CppDType[x.dtype], outId);\n    return out;\n  }\n  return { kernelName, backendName: \"wasm\", setupFunc: setupFunc3, kernelFunc: kernelFunc3 };\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Abs.js\nvar absConfig3 = createUnaryKernelConfig(Abs);\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/binary_kernel.js\nfunction createBinaryKernelConfig(kernelName, supportsFullBroadcast19, dtype) {\n  let wasmFunc9;\n  function setupFunc3(backend2) {\n    wasmFunc9 = backend2.wasm.cwrap(kernelName, null, [\n      \"number\",\n      \"array\",\n      \"number\",\n      \"number\",\n      \"array\",\n      \"number\",\n      \"number\",\n      \"number\"\n    ]);\n  }\n  function kernelFunc3(args) {\n    const { backend: backend2, inputs } = args;\n    const { a, b } = inputs;\n    const aId = backend2.dataIdMap.get(a.dataId).id;\n    const bId = backend2.dataIdMap.get(b.dataId).id;\n    const outputType = dtype != null ? dtype : a.dtype;\n    const newShape = backend_util_exports.assertAndGetBroadcastShape(a.shape, b.shape);\n    const out = backend2.makeOutput(newShape, outputType);\n    if (util_exports.sizeFromShape(newShape) === 0) {\n      return out;\n    }\n    const aShapeBytes = new Uint8Array(new Int32Array(a.shape).buffer);\n    const bShapeBytes = new Uint8Array(new Int32Array(b.shape).buffer);\n    const outId = backend2.dataIdMap.get(out.dataId).id;\n    const kernelFunc4 = () => wasmFunc9(aId, aShapeBytes, a.shape.length, bId, bShapeBytes, b.shape.length, CppDType[a.dtype], outId);\n    kernelFunc4();\n    return out;\n  }\n  return { kernelName, backendName: \"wasm\", setupFunc: setupFunc3, kernelFunc: kernelFunc3 };\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Add.js\nvar supportsFullBroadcast = true;\nvar addConfig3 = createBinaryKernelConfig(Add, supportsFullBroadcast);\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/AddN.js\nvar wasmFunc;\nfunction setupFunc(backend2) {\n  wasmFunc = backend2.wasm.cwrap(AddN, null, [\n    \"array\",\n    \"number\",\n    \"number\",\n    \"number\"\n  ]);\n}\nfunction addn(args) {\n  const { inputs, backend: backend2 } = args;\n  const out = backend2.makeOutput(inputs[0].shape, inputs[0].dtype);\n  if (util_exports.sizeFromShape(out.shape) === 0) {\n    return out;\n  }\n  const inputIds = inputs.map((x) => backend2.dataIdMap.get(x.dataId).id);\n  const inputIdsBytes = new Uint8Array(new Int32Array(inputIds).buffer);\n  const outId = backend2.dataIdMap.get(out.dataId).id;\n  wasmFunc(inputIdsBytes, inputIds.length, CppDType[out.dtype], outId);\n  return out;\n}\nvar addNConfig3 = {\n  kernelName: AddN,\n  backendName: \"wasm\",\n  setupFunc,\n  kernelFunc: addn\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Identity.js\nfunction identity4(args) {\n  const { inputs: { x }, backend: backend2 } = args;\n  const out = backend2.makeOutput(x.shape, x.dtype);\n  const inVals = backend2.typedArrayFromHeap(x);\n  const outVals = backend2.typedArrayFromHeap(out);\n  outVals.set(inVals);\n  return out;\n}\nvar identityConfig3 = {\n  kernelName: Identity,\n  backendName: \"wasm\",\n  kernelFunc: identity4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Transpose.js\nvar wasmTranspose;\nfunction setup2(backend2) {\n  wasmTranspose = backend2.wasm.cwrap(Transpose, null, [\n    \"number\",\n    \"array\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"array\",\n    \"number\"\n  ]);\n}\nfunction transpose4(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const [reducedShape, perm] = removeOneSizeDims(inputs.x.shape, attrs.perm);\n  let permIsNoOp = true;\n  for (let i = 0; i < perm.length; i++) {\n    if (perm[i] !== i) {\n      permIsNoOp = false;\n    }\n  }\n  const outShape = computeOutShape4(inputs.x.shape, attrs.perm);\n  const x = {\n    dataId: inputs.x.dataId,\n    shape: reducedShape,\n    dtype: inputs.x.dtype\n  };\n  if (permIsNoOp) {\n    const cloned = identity4({ inputs, backend: backend2 });\n    cloned.shape = outShape;\n    return cloned;\n  }\n  const out = backend2.makeOutput(outShape, x.dtype);\n  const xId = backend2.dataIdMap.get(x.dataId).id;\n  const outId = backend2.dataIdMap.get(out.dataId).id;\n  const permBytes = new Uint8Array(new Int32Array(perm).buffer);\n  const xShapeBytes = new Uint8Array(new Int32Array(x.shape).buffer);\n  wasmTranspose(xId, xShapeBytes, x.shape.length, CppDType[x.dtype], outId, permBytes, perm.length);\n  return out;\n}\nfunction computeOutShape4(inShape, perm) {\n  const outShape = new Array(inShape.length);\n  for (let i = 0; i < outShape.length; i++) {\n    outShape[i] = inShape[perm[i]];\n  }\n  return outShape;\n}\nfunction removeOneSizeDims(shape, perm) {\n  const newShape = [];\n  const newPerm = [];\n  for (let i = 0; i < shape.length; ++i) {\n    if (shape[i] !== 1) {\n      newShape.push(shape[i]);\n    }\n    if (shape[perm[i]] !== 1) {\n      newPerm.push(perm[i]);\n    }\n  }\n  for (let i = 0; i < newPerm.length; ++i) {\n    let minValIdx = -1;\n    for (let j = 0; j < newPerm.length; ++j) {\n      if (newPerm[j] >= i && (minValIdx === -1 || newPerm[minValIdx] > newPerm[j])) {\n        minValIdx = j;\n      }\n    }\n    newPerm[minValIdx] = i;\n  }\n  return [newShape, newPerm];\n}\nvar transposeConfig3 = {\n  kernelName: Transpose,\n  backendName: \"wasm\",\n  kernelFunc: transpose4,\n  setupFunc: setup2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/kernel_utils.js\nfunction permuteAxesAndTranspose(x, axis, backend2) {\n  const xShape = x.shape;\n  const xRank = x.shape.length;\n  const originalAxes = util_exports.parseAxisParam(axis, xShape);\n  let axes = originalAxes;\n  const permutedAxes = backend_util_exports.getAxesPermutation(axes, xRank);\n  let xTransposed = null;\n  let inputWasTransposed = false;\n  if (permutedAxes != null) {\n    const newShape = new Array(xRank);\n    for (let i = 0; i < newShape.length; i++) {\n      newShape[i] = xShape[permutedAxes[i]];\n    }\n    axes = backend_util_exports.getInnerMostAxes(axes.length, xRank);\n    xTransposed = transpose4({ inputs: { x }, attrs: { perm: permutedAxes }, backend: backend2 });\n    const xId = backend2.dataIdMap.get(x.dataId).id;\n    const transposedId = backend2.dataIdMap.get(xTransposed.dataId).id;\n    if (transposedId !== xId) {\n      inputWasTransposed = true;\n    }\n  }\n  return { transposed: xTransposed, originalAxes, axes, inputWasTransposed };\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/All.js\nvar wasmAll;\nfunction setup3(backend2) {\n  wasmAll = backend2.wasm.cwrap(All, null, [\"number, number, number\"]);\n}\nfunction all4(args) {\n  const { backend: backend2, inputs, attrs } = args;\n  const { axis, keepDims } = attrs;\n  const { x } = inputs;\n  const xId = backend2.dataIdMap.get(x.dataId).id;\n  let inputId = xId;\n  let input2 = x;\n  const { transposed, axes, originalAxes, inputWasTransposed } = permuteAxesAndTranspose(x, axis, backend2);\n  if (inputWasTransposed) {\n    const transposedId = backend2.dataIdMap.get(transposed.dataId).id;\n    input2 = transposed;\n    inputId = transposedId;\n  }\n  const inputRank = input2.shape.length;\n  backend_util_exports.assertAxesAreInnerMostDims(\"all\", axes, inputRank);\n  const [outShape, reduceShape] = backend_util_exports.computeOutAndReduceShapes(input2.shape, axes);\n  const reduceSize = util_exports.sizeFromShape(reduceShape);\n  const out = backend2.makeOutput(outShape, x.dtype);\n  if (util_exports.sizeFromShape(input2.shape) !== 0) {\n    const outId = backend2.dataIdMap.get(out.dataId).id;\n    wasmAll(inputId, reduceSize, outId);\n  }\n  if (inputWasTransposed) {\n    backend2.disposeData(transposed.dataId);\n  }\n  if (keepDims) {\n    const newShape = backend_util_exports.expandShapeToKeepDim(out.shape, originalAxes);\n    out.shape = newShape;\n  }\n  return out;\n}\nvar allConfig3 = {\n  kernelName: All,\n  backendName: \"wasm\",\n  setupFunc: setup3,\n  kernelFunc: all4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Any.js\nvar wasmAny;\nfunction setup4(backend2) {\n  wasmAny = backend2.wasm.cwrap(Any, null, [\"number, number, number\"]);\n}\nfunction any4(args) {\n  const { backend: backend2, inputs, attrs } = args;\n  const { axis, keepDims } = attrs;\n  const { x } = inputs;\n  const xId = backend2.dataIdMap.get(x.dataId).id;\n  let inputId = xId;\n  let input2 = x;\n  const { transposed, axes, originalAxes, inputWasTransposed } = permuteAxesAndTranspose(x, axis, backend2);\n  if (inputWasTransposed) {\n    const transposedId = backend2.dataIdMap.get(transposed.dataId).id;\n    input2 = transposed;\n    inputId = transposedId;\n  }\n  const inputRank = input2.shape.length;\n  backend_util_exports.assertAxesAreInnerMostDims(\"any\", axes, inputRank);\n  const [outShape, reduceShape] = backend_util_exports.computeOutAndReduceShapes(input2.shape, axes);\n  const reduceSize = util_exports.sizeFromShape(reduceShape);\n  const out = backend2.makeOutput(outShape, x.dtype);\n  if (util_exports.sizeFromShape(input2.shape) !== 0) {\n    const outId = backend2.dataIdMap.get(out.dataId).id;\n    wasmAny(inputId, reduceSize, outId);\n  }\n  if (inputWasTransposed) {\n    backend2.disposeData(transposed.dataId);\n  }\n  if (keepDims) {\n    const newShape = backend_util_exports.expandShapeToKeepDim(out.shape, originalAxes);\n    out.shape = newShape;\n  }\n  return out;\n}\nvar anyConfig3 = {\n  kernelName: Any,\n  backendName: \"wasm\",\n  setupFunc: setup4,\n  kernelFunc: any4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/ArgMax.js\nvar wasmFunc2;\nfunction setup5(backend2) {\n  wasmFunc2 = backend2.wasm.cwrap(ArgMax, null, [\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\"\n  ]);\n}\nfunction argmax(args) {\n  const { backend: backend2, inputs, attrs } = args;\n  const { axis } = attrs;\n  const { x } = inputs;\n  const xId = backend2.dataIdMap.get(x.dataId).id;\n  let inputId = xId;\n  let input2 = x;\n  const { transposed, axes, inputWasTransposed } = permuteAxesAndTranspose(x, axis, backend2);\n  if (inputWasTransposed) {\n    const transposedId = backend2.dataIdMap.get(transposed.dataId).id;\n    if (transposedId !== xId) {\n      input2 = transposed;\n      inputId = transposedId;\n    }\n  }\n  const outShape = input2.shape.slice(0, -1);\n  const out = backend2.makeOutput(outShape, \"int32\");\n  const outId = backend2.dataIdMap.get(out.dataId).id;\n  const outerSize = util_exports.sizeFromShape(out.shape);\n  const innerSize = input2.shape[axes[0]];\n  wasmFunc2(inputId, CppDType[input2.dtype], outerSize, innerSize, outId);\n  if (inputWasTransposed) {\n    backend2.disposeData(transposed.dataId);\n  }\n  return out;\n}\nvar argMaxConfig3 = {\n  kernelName: ArgMax,\n  backendName: \"wasm\",\n  kernelFunc: argmax,\n  setupFunc: setup5\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/AvgPool.js\nvar wasmAvgPool;\nfunction setup6(backend2) {\n  wasmAvgPool = backend2.wasm.cwrap(AvgPool, null, [\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\"\n  ]);\n}\nfunction avgPool4(args) {\n  const { inputs, attrs, backend: backend2 } = args;\n  const x = inputs.x;\n  const xId = backend2.dataIdMap.get(x.dataId).id;\n  const { filterSize, strides, pad: pad3, dimRoundingMode } = attrs;\n  const convInfo = backend_util_exports.computePool2DInfo(x.shape, filterSize, strides, 1, pad3, dimRoundingMode);\n  const filterHeight = convInfo.filterHeight;\n  const filterWidth = convInfo.filterWidth;\n  const padTop = convInfo.padInfo.top;\n  const padRight = convInfo.padInfo.right;\n  const padBottom = convInfo.padInfo.bottom;\n  const padLeft = convInfo.padInfo.left;\n  const strideHeight = convInfo.strideHeight;\n  const strideWidth = convInfo.strideWidth;\n  const channels = convInfo.inChannels;\n  if (convInfo.dataFormat !== \"channelsLast\") {\n    throw new Error(`wasm backend does not support dataFormat:'${convInfo.dataFormat}'. Please use 'channelsLast'.`);\n  }\n  if (convInfo.dilationWidth !== 1 || convInfo.dilationHeight !== 1) {\n    throw new Error(`was backend only supports average pooling with dilation = [1, 1], got [${convInfo.dilationHeight}, ${convInfo.dilationWidth}].`);\n  }\n  const out = backend2.makeOutput(convInfo.outShape, \"float32\");\n  const outId = backend2.dataIdMap.get(out.dataId).id;\n  wasmAvgPool(xId, x.shape[0], x.shape[1], x.shape[2], filterHeight, filterWidth, padTop, padRight, padBottom, padLeft, strideHeight, strideWidth, channels, outId);\n  return out;\n}\nvar avgPoolConfig3 = {\n  kernelName: AvgPool,\n  backendName: \"wasm\",\n  setupFunc: setup6,\n  kernelFunc: avgPool4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Reshape.js\nfunction reshape5(args) {\n  const { inputs, attrs } = args;\n  const { x } = inputs;\n  const { shape } = attrs;\n  const xSize = util_exports.sizeFromShape(x.shape);\n  const $shape = util_exports.inferFromImplicitShape(shape, xSize);\n  util_exports.assert(xSize === util_exports.sizeFromShape($shape), () => `new shape: ${$shape}, old shape: ${x.shape}. New shape and old shape must have the same number of elements.`);\n  args.backend.incRef(x.dataId);\n  return { dataId: x.dataId, shape: $shape, dtype: x.dtype };\n}\nvar reshapeConfig3 = {\n  kernelName: Reshape,\n  backendName: \"wasm\",\n  kernelFunc: reshape5\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/BatchMatMul.js\nvar wasmBatchMatMul;\nfunction setup7(backend2) {\n  wasmBatchMatMul = backend2.wasm.cwrap(BatchMatMul, null, [\n    \"number\",\n    \"array\",\n    \"number\",\n    \"number\",\n    \"array\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\"\n  ]);\n}\nfunction batchMatMul3(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { a, b } = inputs;\n  const { transposeA, transposeB } = attrs;\n  if (a.dtype !== \"float32\" || b.dtype !== \"float32\") {\n    throw new Error(`BatchMatMul for non non-float32 tensors not yet supported.`);\n  }\n  const aRank = a.shape.length;\n  const bRank = b.shape.length;\n  const innerShapeA = transposeA ? a.shape[aRank - 2] : a.shape[aRank - 1];\n  const innerShapeB = transposeB ? b.shape[bRank - 1] : b.shape[bRank - 2];\n  const outerShapeA = transposeA ? a.shape[aRank - 1] : a.shape[aRank - 2];\n  const outerShapeB = transposeB ? b.shape[bRank - 2] : b.shape[bRank - 1];\n  const outerDimsA = a.shape.slice(0, -2);\n  const outerDimsB = b.shape.slice(0, -2);\n  const batchDimA = util_exports.sizeFromShape(outerDimsA);\n  const batchDimB = util_exports.sizeFromShape(outerDimsB);\n  const outShapeOuterDims = broadcast_util_exports.assertAndGetBroadcastShape(a.shape.slice(0, -2), b.shape.slice(0, -2));\n  const outShape = outShapeOuterDims.concat([outerShapeA, outerShapeB]);\n  util_exports.assert(innerShapeA === innerShapeB, () => `Error in matMul: inner shapes (${innerShapeA}) and (${innerShapeB}) of Tensors with shapes ${a.shape} and ${b.shape} and transposeA=${transposeA} and transposeB=${transposeB} must match.`);\n  const a3dShape = transposeA ? [batchDimA, innerShapeA, outerShapeA] : [batchDimA, outerShapeA, innerShapeA];\n  const b3dShape = transposeB ? [batchDimB, outerShapeB, innerShapeB] : [batchDimB, innerShapeB, outerShapeB];\n  const a3d = reshape5({ inputs: { x: a }, backend: backend2, attrs: { shape: a3dShape } });\n  const b3d = reshape5({ inputs: { x: b }, backend: backend2, attrs: { shape: b3dShape } });\n  const a3dId = backend2.dataIdMap.get(a3d.dataId).id;\n  const b3dId = backend2.dataIdMap.get(b3d.dataId).id;\n  const leftDim = transposeA ? a3d.shape[2] : a3d.shape[1];\n  const rightDim = transposeB ? b3d.shape[1] : b3d.shape[2];\n  const batchDim = Math.max(batchDimA, batchDimB);\n  const out = backend2.makeOutput([batchDim, leftDim, rightDim], a3d.dtype);\n  const outId = backend2.dataIdMap.get(out.dataId).id;\n  const aShapeBytes = new Uint8Array(new Int32Array(a3d.shape).buffer);\n  const bShapeBytes = new Uint8Array(new Int32Array(b3d.shape).buffer);\n  wasmBatchMatMul(a3dId, aShapeBytes, a3d.shape.length, b3dId, bShapeBytes, b3d.shape.length, transposeA, transposeB, outId);\n  backend2.disposeData(a3d.dataId);\n  backend2.disposeData(b3d.dataId);\n  out.shape = outShape;\n  return out;\n}\nvar batchMatMulConfig3 = {\n  kernelName: BatchMatMul,\n  backendName: \"wasm\",\n  setupFunc: setup7,\n  kernelFunc: batchMatMul3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Slice.js\nfunction slice4(args) {\n  const { inputs: { x }, attrs: { begin, size }, backend: backend2 } = args;\n  const [begin_, size_] = slice_util_exports.parseSliceParams(x, begin, size);\n  const isContinous = slice_util_exports.isSliceContinous(x.shape, begin_, size_);\n  const xVals = backend2.readSync(x.dataId);\n  const out = backend2.makeOutput(size_, x.dtype);\n  const xStrides = util_exports.computeStrides(x.shape);\n  const outData = backend2.dataIdMap.get(out.dataId);\n  if (isContinous) {\n    const flatOffset = slice_util_exports.computeFlatOffset(begin_, xStrides);\n    if (x.dtype === \"string\") {\n      outData.stringBytes = xVals.slice(flatOffset, flatOffset + util_exports.sizeFromShape(size_));\n    } else {\n      const outVals2 = backend2.typedArrayFromHeap(out);\n      outVals2.set(xVals.subarray(flatOffset, flatOffset + util_exports.sizeFromShape(size_)));\n    }\n    return out;\n  }\n  if (x.dtype === \"string\") {\n    const res = sliceImpl(xVals, begin_, size_, x.shape, x.dtype);\n    outData.stringBytes = res;\n    return out;\n  }\n  const outVals = backend2.typedArrayFromHeap(out);\n  const rank = x.shape.length;\n  if (rank === 2) {\n    slice2d2(xVals, xStrides[0], outVals, begin_, size_);\n  } else if (rank === 3) {\n    slice3d2(xVals, xStrides[0], xStrides[1], outVals, begin_, size_);\n  } else if (rank === 4) {\n    slice4d2(xVals, xStrides[0], xStrides[1], xStrides[2], outVals, begin_, size_);\n  } else {\n    const res = sliceImpl(xVals, begin_, size_, x.shape, x.dtype);\n    outVals.set(res);\n  }\n  return out;\n}\nfunction slice2d2(xVals, xStride, outVals, begin, size) {\n  let outOffset = 0;\n  const beginI = begin[0];\n  const beginJ = begin[1];\n  const endI = beginI + size[0];\n  for (let i = beginI; i < endI; i++) {\n    const xOffset = i * xStride + beginJ;\n    outVals.set(xVals.subarray(xOffset, xOffset + size[1]), outOffset);\n    outOffset += size[1];\n  }\n}\nfunction slice3d2(xVals, xStride1, xStride2, outVals, begin, size) {\n  let outOffset = 0;\n  const beginI = begin[0];\n  const beginJ = begin[1];\n  const beginK = begin[2];\n  const endI = beginI + size[0];\n  const endJ = beginJ + size[1];\n  for (let i = beginI; i < endI; i++) {\n    for (let j = beginJ; j < endJ; j++) {\n      const xOffset = i * xStride1 + j * xStride2 + beginK;\n      outVals.set(xVals.subarray(xOffset, xOffset + size[2]), outOffset);\n      outOffset += size[2];\n    }\n  }\n}\nfunction slice4d2(xVals, xStride1, xStride2, xStride3, outVals, begin, size) {\n  let outOffset = 0;\n  const beginI = begin[0];\n  const beginJ = begin[1];\n  const beginK = begin[2];\n  const endI = beginI + size[0];\n  const endJ = beginJ + size[1];\n  const endK = beginK + size[2];\n  const beginL = begin[3];\n  for (let i = beginI; i < endI; i++) {\n    for (let j = beginJ; j < endJ; j++) {\n      for (let k = beginK; k < endK; k++) {\n        const xOffset = i * xStride1 + j * xStride2 + k * xStride3 + beginL;\n        outVals.set(xVals.subarray(xOffset, xOffset + size[3]), outOffset);\n        outOffset += size[3];\n      }\n    }\n  }\n}\nvar sliceConfig3 = {\n  kernelName: Slice,\n  backendName: \"wasm\",\n  kernelFunc: slice4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/BatchToSpaceND.js\nfunction batchToSpaceND4(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x } = inputs;\n  const { blockShape, crops } = attrs;\n  const prod6 = blockShape.reduce((a, b) => a * b);\n  const reshaped = backend_util_exports.getReshaped(x.shape, blockShape, prod6);\n  const permuted = backend_util_exports.getPermuted(reshaped.length, blockShape.length);\n  const reshapedPermuted = backend_util_exports.getReshapedPermuted(x.shape, blockShape, prod6);\n  const sliceBeginCoords = backend_util_exports.getSliceBeginCoords(crops, blockShape.length);\n  const sliceSize = backend_util_exports.getSliceSize(reshapedPermuted, crops, blockShape.length);\n  const xReshaped = reshape5({ inputs: { x }, backend: backend2, attrs: { shape: reshaped } });\n  const xTransposed = transpose4({ inputs: { x: xReshaped }, backend: backend2, attrs: { perm: permuted } });\n  const xTransposedReshaped = reshape5({ inputs: { x: xTransposed }, backend: backend2, attrs: { shape: reshapedPermuted } });\n  const result = slice4({\n    inputs: { x: xTransposedReshaped },\n    backend: backend2,\n    attrs: { begin: sliceBeginCoords, size: sliceSize }\n  });\n  backend2.disposeData(xReshaped.dataId);\n  backend2.disposeData(xTransposed.dataId);\n  backend2.disposeData(xReshaped.dataId);\n  return result;\n}\nvar batchToSpaceNDConfig3 = {\n  kernelName: BatchToSpaceND,\n  backendName: \"wasm\",\n  kernelFunc: batchToSpaceND4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Cast.js\nfunction cast5(args) {\n  const { inputs: { x }, attrs: { dtype }, backend: backend2 } = args;\n  const out = backend2.makeOutput(x.shape, dtype);\n  const inVals = backend2.typedArrayFromHeap(x);\n  const outVals = backend2.typedArrayFromHeap(out);\n  outVals.set(inVals);\n  return out;\n}\nvar castConfig3 = {\n  kernelName: Cast,\n  backendName: \"wasm\",\n  kernelFunc: cast5\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Ceil.js\nvar ceilConfig3 = createUnaryKernelConfig(Ceil);\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/ClipByValue.js\nvar wasmClip;\nfunction setup8(backend2) {\n  wasmClip = backend2.wasm.cwrap(ClipByValue, null, [\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\"\n  ]);\n}\nfunction clip(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x } = inputs;\n  const { clipValueMin, clipValueMax } = attrs;\n  const xId = backend2.dataIdMap.get(x.dataId).id;\n  const out = backend2.makeOutput(x.shape, x.dtype);\n  const outId = backend2.dataIdMap.get(out.dataId).id;\n  wasmClip(xId, clipValueMin, clipValueMax, outId);\n  return out;\n}\nvar clipByValueConfig3 = {\n  kernelName: ClipByValue,\n  backendName: \"wasm\",\n  setupFunc: setup8,\n  kernelFunc: clip\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Concat.js\nfunction concat4(args) {\n  const { inputs, backend: backend2 } = args;\n  const axis = util_exports.parseAxisParam(args.attrs.axis, inputs[0].shape)[0];\n  let outShape = backend_util_exports.computeOutShape(inputs.map((t) => t.shape), axis);\n  const $inputs = inputs.filter((t) => util_exports.sizeFromShape(t.shape) > 0);\n  if ($inputs.length === 1) {\n    return identity4({ inputs: { x: $inputs[0] }, backend: backend2 });\n  }\n  const out = backend2.makeOutput(outShape, inputs[0].dtype);\n  if (util_exports.sizeFromShape(outShape) === 0) {\n    return out;\n  }\n  const shapes = $inputs.map((t) => t.shape);\n  backend_util_exports.assertParamsConsistent(shapes, axis);\n  if ($inputs[0].dtype === \"string\") {\n    const inputs2D = $inputs.map((t) => {\n      const innerSize = util_exports.sizeFromShape(t.shape.slice(axis));\n      const shape = [-1, innerSize];\n      return reshape5({ inputs: { x: t }, backend: backend2, attrs: { shape } });\n    });\n    const inputsValShapes = inputs2D.map((t) => {\n      return { vals: backend2.readSync(t.dataId), shape: t.shape };\n    });\n    outShape = backend_util_exports.computeOutShape(inputs2D.map((t) => t.shape), 1);\n    const simplyConcat = inputs2D[0].shape[0] === 1;\n    const outVals2 = concatImpl(inputsValShapes, outShape, inputs[0].dtype, simplyConcat);\n    const finalOutShape = backend_util_exports.computeOutShape($inputs.map((t) => t.shape), axis);\n    out.shape = finalOutShape;\n    const outData = backend2.dataIdMap.get(out.dataId);\n    outData.stringBytes = backend_util_exports.fromStringArrayToUint8(outVals2);\n    inputs2D.forEach((t) => backend2.disposeData(t.dataId));\n    return out;\n  }\n  const batchDim = util_exports.sizeFromShape($inputs[0].shape.slice(0, axis));\n  let sumInnerDims = 0;\n  const innerDims = $inputs.map((input2) => {\n    const innerDim = util_exports.sizeFromShape(input2.shape.slice(axis));\n    sumInnerDims += innerDim;\n    return innerDim;\n  });\n  const inVals = $inputs.map((input2) => backend2.typedArrayFromHeap(input2));\n  const outVals = backend2.typedArrayFromHeap(out);\n  for (let b = 0; b < batchDim; b++) {\n    let outOffset = b * sumInnerDims;\n    for (let i = 0; i < inVals.length; i++) {\n      const innerDim = innerDims[i];\n      const inOffset = b * innerDim;\n      const vals = inVals[i].subarray(inOffset, inOffset + innerDim);\n      outVals.set(vals, outOffset);\n      outOffset += innerDim;\n    }\n  }\n  return out;\n}\nvar concatConfig3 = {\n  kernelName: Concat,\n  backendName: \"wasm\",\n  kernelFunc: concat4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Conv2D.js\nvar wasmConv2d;\nfunction setup9(backend2) {\n  wasmConv2d = backend2.wasm.cwrap(Conv2D, null, [\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\"\n  ]);\n}\nfunction conv2d5(args) {\n  const { inputs, attrs, backend: backend2 } = args;\n  const { x, filter } = inputs;\n  const xId = backend2.dataIdMap.get(x.dataId).id;\n  const filterId = backend2.dataIdMap.get(filter.dataId).id;\n  const { strides, dilations, pad: pad3, dimRoundingMode, dataFormat } = attrs;\n  const $dataFormat = backend_util_exports.convertConv2DDataFormat(dataFormat);\n  const convInfo = backend_util_exports.computeConv2DInfo(x.shape, filter.shape, strides, dilations, pad3, dimRoundingMode, false, $dataFormat);\n  const filterHeight = convInfo.filterHeight;\n  const filterWidth = convInfo.filterWidth;\n  const padTop = convInfo.padInfo.top;\n  const padRight = convInfo.padInfo.right;\n  const padBottom = convInfo.padInfo.bottom;\n  const padLeft = convInfo.padInfo.left;\n  const dilationHeight = convInfo.dilationHeight;\n  const dilationWidth = convInfo.dilationWidth;\n  const strideHeight = convInfo.strideHeight;\n  const strideWidth = convInfo.strideWidth;\n  const inputChannels = convInfo.inChannels;\n  const outputChannels = convInfo.outChannels;\n  const isSamePad = convInfo.padInfo.type === \"SAME\" ? 1 : 0;\n  if (convInfo.dataFormat !== \"channelsLast\") {\n    throw new Error(`wasm backend Conv2D does not support dataFormat:'${convInfo.dataFormat}'. Please use 'channelsLast'.`);\n  }\n  const out = backend2.makeOutput(convInfo.outShape, \"float32\");\n  const outId = backend2.dataIdMap.get(out.dataId).id;\n  wasmConv2d(xId, x.shape[0], x.shape[1], x.shape[2], filterId, filterHeight, filterWidth, padTop, padRight, padBottom, padLeft, isSamePad, dilationHeight, dilationWidth, strideHeight, strideWidth, inputChannels, outputChannels, outId);\n  return out;\n}\nvar conv2DConfig3 = {\n  kernelName: Conv2D,\n  backendName: \"wasm\",\n  setupFunc: setup9,\n  kernelFunc: conv2d5\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Conv2DBackpropInput.js\nvar wasmConv2DBackpropInput;\nfunction setup10(backend2) {\n  wasmConv2DBackpropInput = backend2.wasm.cwrap(Conv2DBackpropInput, null, [\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\"\n  ]);\n}\nfunction conv2DBackpropInput4(args) {\n  const { backend: backend2, inputs, attrs } = args;\n  const { dy, filter } = inputs;\n  const { strides, pad: pad3, dataFormat, dimRoundingMode, inputShape } = attrs;\n  const dilations = 1;\n  const $dataFormat = backend_util_exports.convertConv2DDataFormat(dataFormat);\n  const convInfo = backend_util_exports.computeConv2DInfo(inputShape, filter.shape, strides, dilations, pad3, dimRoundingMode, false, $dataFormat);\n  const { batchSize, filterHeight, filterWidth, inChannels, inHeight, inWidth, outChannels, outHeight, outWidth, strideHeight, strideWidth } = convInfo;\n  const topPad = filterHeight - 1 - convInfo.padInfo.top;\n  const leftPad = filterWidth - 1 - convInfo.padInfo.left;\n  const isChannelsLast = convInfo.dataFormat === \"channelsLast\";\n  const dxStrides = util_exports.computeStrides(convInfo.inShape);\n  const dyStrides = util_exports.computeStrides(dy.shape);\n  const [fltS0, fltS1, fltS2] = util_exports.computeStrides(filter.shape);\n  const xBatchStride = dxStrides[0];\n  const xRowStride = isChannelsLast ? dxStrides[1] : dxStrides[2];\n  const xColStride = isChannelsLast ? dxStrides[2] : 1;\n  const xChannelStride = isChannelsLast ? 1 : dxStrides[1];\n  const yBatchStride = dyStrides[0];\n  const yRowStride = isChannelsLast ? dyStrides[1] : dyStrides[2];\n  const yColStride = isChannelsLast ? dyStrides[2] : 1;\n  const yChannelStride = isChannelsLast ? 1 : dyStrides[1];\n  const out = backend2.makeOutput(convInfo.inShape, \"float32\");\n  const outId = backend2.dataIdMap.get(out.dataId).id;\n  const dyId = backend2.dataIdMap.get(dy.dataId).id;\n  const filterId = backend2.dataIdMap.get(filter.dataId).id;\n  wasmConv2DBackpropInput(dyId, filterId, batchSize, filterHeight, filterWidth, inHeight, inWidth, inChannels, outHeight, outWidth, outChannels, strideHeight, strideWidth, topPad, leftPad, fltS0, fltS1, fltS2, xBatchStride, xRowStride, xColStride, xChannelStride, yBatchStride, yRowStride, yColStride, yChannelStride, outId);\n  return out;\n}\nvar conv2DBackpropInputConfig3 = {\n  kernelName: Conv2DBackpropInput,\n  backendName: \"wasm\",\n  setupFunc: setup10,\n  kernelFunc: conv2DBackpropInput4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Cos.js\nvar cosConfig3 = createUnaryKernelConfig(Cos);\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Cosh.js\nvar coshConfig3 = createUnaryKernelConfig(Cosh);\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/CropAndResize.js\nvar InterpolationMethod;\n(function(InterpolationMethod2) {\n  InterpolationMethod2[InterpolationMethod2[\"bilinear\"] = 0] = \"bilinear\";\n  InterpolationMethod2[InterpolationMethod2[\"nearest\"] = 1] = \"nearest\";\n})(InterpolationMethod || (InterpolationMethod = {}));\nvar wasmCropAndResize;\nfunction setup11(backend2) {\n  wasmCropAndResize = backend2.wasm.cwrap(CropAndResize, null, [\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"array\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\"\n  ]);\n}\nfunction cropAndResize4(args) {\n  const { backend: backend2, inputs, attrs } = args;\n  const { method, extrapolationValue, cropSize } = attrs;\n  const { image: image2, boxes, boxInd } = inputs;\n  const numBoxes = boxes.shape[0];\n  const [cropHeight, cropWidth] = cropSize;\n  const outShape = [numBoxes, cropHeight, cropWidth, image2.shape[3]];\n  let imagesData = backend2.dataIdMap.get(image2.dataId);\n  let castedData;\n  if (image2.dtype !== \"float32\") {\n    castedData = cast5({ backend: backend2, inputs: { x: image2 }, attrs: { dtype: \"float32\" } });\n    imagesData = backend2.dataIdMap.get(castedData.dataId);\n  }\n  const imagesId = imagesData.id;\n  const boxesId = backend2.dataIdMap.get(boxes.dataId).id;\n  const boxIndId = backend2.dataIdMap.get(boxInd.dataId).id;\n  const out = backend2.makeOutput(outShape, \"float32\");\n  const outId = backend2.dataIdMap.get(out.dataId).id;\n  const imagesShapeBytes = new Uint8Array(new Int32Array(image2.shape).buffer);\n  wasmCropAndResize(imagesId, boxesId, boxIndId, numBoxes, imagesShapeBytes, cropHeight, cropWidth, InterpolationMethod[method], extrapolationValue, outId);\n  if (castedData != null) {\n    backend2.disposeData(castedData.dataId);\n  }\n  return out;\n}\nvar cropAndResizeConfig3 = {\n  kernelName: CropAndResize,\n  backendName: \"wasm\",\n  setupFunc: setup11,\n  kernelFunc: cropAndResize4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Cumprod.js\nvar wasmCumprod;\nfunction setup12(backend2) {\n  wasmCumprod = backend2.wasm.cwrap(Cumprod, null, [\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\"\n  ]);\n}\nfunction cumprod4(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x } = inputs;\n  const { axis, exclusive, reverse: reverse5 } = attrs;\n  const xRank = x.shape.length;\n  util_exports.assert(x.dtype === \"float32\" || x.dtype === \"int32\", () => `cumprod does not support ${x.dtype} tensors in the WASM backend`);\n  const permutation = backend_util_exports.getAxesPermutation([axis], xRank);\n  let permutedX = x;\n  if (permutation !== null) {\n    permutedX = transpose4({ inputs: { x }, attrs: { perm: permutation }, backend: backend2 });\n  }\n  const permutedAxis = backend_util_exports.getInnerMostAxes(1, xRank)[0];\n  backend_util_exports.assertAxesAreInnerMostDims(\"cumprod\", [permutedAxis], xRank);\n  const permutedOut = backend2.makeOutput(permutedX.shape, permutedX.dtype);\n  const finalDim = permutedX.shape[permutedAxis];\n  const permutedXId = backend2.dataIdMap.get(permutedX.dataId).id;\n  const permutedOutId = backend2.dataIdMap.get(permutedOut.dataId).id;\n  wasmCumprod(permutedXId, exclusive ? 1 : 0, reverse5 ? 1 : 0, finalDim, permutedOutId, CppDType[x.dtype]);\n  let out = permutedOut;\n  if (permutation !== null) {\n    const undoPermutation = backend_util_exports.getUndoAxesPermutation(permutation);\n    out = transpose4({ inputs: { x: permutedOut }, attrs: { perm: undoPermutation }, backend: backend2 });\n    backend2.disposeData(permutedX.dataId);\n    backend2.disposeData(permutedOut.dataId);\n  }\n  return out;\n}\nvar cumprodConfig3 = {\n  kernelName: Cumprod,\n  backendName: \"wasm\",\n  setupFunc: setup12,\n  kernelFunc: cumprod4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Cumsum.js\nvar wasmCumsum;\nfunction setup13(backend2) {\n  wasmCumsum = backend2.wasm.cwrap(Cumsum, null, [\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\"\n  ]);\n}\nfunction cumsum4(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x } = inputs;\n  const { axis, exclusive, reverse: reverse5 } = attrs;\n  const xRank = x.shape.length;\n  util_exports.assert(x.dtype === \"float32\" || x.dtype === \"int32\", () => `cumsum does not support ${x.dtype} tensors in the WASM backend`);\n  const permutation = backend_util_exports.getAxesPermutation([axis], xRank);\n  let permutedX = x;\n  if (permutation !== null) {\n    permutedX = transpose4({ inputs: { x }, attrs: { perm: permutation }, backend: backend2 });\n  }\n  const permutedAxis = backend_util_exports.getInnerMostAxes(1, xRank)[0];\n  backend_util_exports.assertAxesAreInnerMostDims(\"cumsum\", [permutedAxis], xRank);\n  const permutedOut = backend2.makeOutput(permutedX.shape, permutedX.dtype);\n  const finalDim = permutedX.shape[permutedAxis];\n  const permutedXId = backend2.dataIdMap.get(permutedX.dataId).id;\n  const permutedOutId = backend2.dataIdMap.get(permutedOut.dataId).id;\n  wasmCumsum(permutedXId, exclusive ? 1 : 0, reverse5 ? 1 : 0, finalDim, permutedOutId, CppDType[x.dtype]);\n  let out = permutedOut;\n  if (permutation !== null) {\n    const undoPermutation = backend_util_exports.getUndoAxesPermutation(permutation);\n    out = transpose4({ inputs: { x: permutedOut }, attrs: { perm: undoPermutation }, backend: backend2 });\n    backend2.disposeData(permutedX.dataId);\n    backend2.disposeData(permutedOut.dataId);\n  }\n  return out;\n}\nvar cumsumConfig3 = {\n  kernelName: Cumsum,\n  backendName: \"wasm\",\n  setupFunc: setup13,\n  kernelFunc: cumsum4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/DepthToSpace.js\nvar wasmDepthToSpace;\nfunction setup14(backend2) {\n  wasmDepthToSpace = backend2.wasm.cwrap(DepthToSpace, null, [\n    \"number\",\n    \"number\",\n    \"number\",\n    \"array\",\n    \"number\",\n    \"array\",\n    \"array\",\n    \"number\",\n    \"number\"\n  ]);\n}\nfunction depthToSpace4(args) {\n  const { backend: backend2, inputs, attrs } = args;\n  const { x } = inputs;\n  const { blockSize, dataFormat } = attrs;\n  const batchSize = x.shape[0];\n  const inputHeight = dataFormat === \"NHWC\" ? x.shape[1] : x.shape[2];\n  const inputWidth = dataFormat === \"NHWC\" ? x.shape[2] : x.shape[3];\n  const inputDepth = dataFormat === \"NHWC\" ? x.shape[3] : x.shape[1];\n  const outputHeight = inputHeight * blockSize;\n  const outputWidth = inputWidth * blockSize;\n  const outputDepth = inputDepth / (blockSize * blockSize);\n  const outputShape = dataFormat === \"NHWC\" ? [batchSize, outputHeight, outputWidth, outputDepth] : [batchSize, outputDepth, outputHeight, outputWidth];\n  const out = backend2.makeOutput(outputShape, \"float32\");\n  const xData = backend2.dataIdMap.get(x.dataId);\n  const xId = xData.id;\n  const xStridesBytes = new Uint8Array(new Int32Array(util_exports.computeStrides(x.shape)).buffer);\n  const outputShapeBytes = new Uint8Array(new Int32Array(outputShape).buffer);\n  const outStridesBytes = new Uint8Array(new Int32Array(util_exports.computeStrides(outputShape)).buffer);\n  const outId = backend2.dataIdMap.get(out.dataId).id;\n  const channelsLast = dataFormat === \"NHWC\" ? 1 : 0;\n  wasmDepthToSpace(xId, blockSize, channelsLast, xStridesBytes, x.shape.length - 1, outputShapeBytes, outStridesBytes, outputShape.length, outId);\n  return out;\n}\nvar depthToSpaceConfig3 = {\n  kernelName: DepthToSpace,\n  backendName: \"wasm\",\n  setupFunc: setup14,\n  kernelFunc: depthToSpace4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/DepthwiseConv2dNative.js\nvar wasmDepthwiseConv2d;\nfunction setup15(backend2) {\n  wasmDepthwiseConv2d = backend2.wasm.cwrap(DepthwiseConv2dNative, null, [\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\"\n  ]);\n}\nfunction depthwiseConv2d5(args) {\n  const { inputs, attrs, backend: backend2 } = args;\n  const { x, filter } = inputs;\n  const xId = backend2.dataIdMap.get(x.dataId).id;\n  const filterId = backend2.dataIdMap.get(filter.dataId).id;\n  const { strides, dilations, pad: pad3, dimRoundingMode } = attrs;\n  const $dilations = dilations == null ? [1, 1] : dilations;\n  const convInfo = backend_util_exports.computeConv2DInfo(x.shape, filter.shape, strides, $dilations, pad3, dimRoundingMode, true);\n  const filterHeight = convInfo.filterHeight;\n  const filterWidth = convInfo.filterWidth;\n  const padTop = convInfo.padInfo.top;\n  const padRight = convInfo.padInfo.right;\n  const padBottom = convInfo.padInfo.bottom;\n  const padLeft = convInfo.padInfo.left;\n  const dilationHeight = convInfo.dilationHeight;\n  const dilationWidth = convInfo.dilationWidth;\n  const strideHeight = convInfo.strideHeight;\n  const strideWidth = convInfo.strideWidth;\n  const inputChannels = convInfo.inChannels;\n  const outputChannels = convInfo.outChannels;\n  const isSamePad = convInfo.padInfo.type === \"SAME\" ? 1 : 0;\n  if (convInfo.dataFormat !== \"channelsLast\") {\n    throw new Error(`wasm backend DepthwiseConv2dNative does not support dataFormat:'${convInfo.dataFormat}'. Please use 'channelsLast'.`);\n  }\n  const out = backend2.makeOutput(convInfo.outShape, \"float32\");\n  const outId = backend2.dataIdMap.get(out.dataId).id;\n  wasmDepthwiseConv2d(xId, x.shape[0], x.shape[1], x.shape[2], filterId, filterHeight, filterWidth, padTop, padRight, padBottom, padLeft, isSamePad, dilationHeight, dilationWidth, strideHeight, strideWidth, inputChannels, outputChannels, outId);\n  return out;\n}\nvar depthwiseConv2dNativeConfig3 = {\n  kernelName: DepthwiseConv2dNative,\n  backendName: \"wasm\",\n  setupFunc: setup15,\n  kernelFunc: depthwiseConv2d5\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Elu.js\nvar eluConfig3 = createUnaryKernelConfig(Elu);\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Equal.js\nvar supportsFullBroadcast2 = false;\nvar equalConfig3 = createBinaryKernelConfig(Equal, supportsFullBroadcast2, \"bool\");\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Exp.js\nvar expConfig3 = createUnaryKernelConfig(Exp, \"float32\");\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/ExpandDims.js\nfunction expandDims5(args) {\n  const { inputs, attrs, backend: backend2 } = args;\n  const { input: input2 } = inputs;\n  const { dim } = attrs;\n  const inputRank = input2.shape.length;\n  const newShape = input2.shape.slice();\n  let $dim = dim;\n  if (dim < 0) {\n    util_exports.assert(-(inputRank + 1) <= dim, () => `Axis must be in the interval [${-(inputRank + 1)}, ${inputRank}]`);\n    $dim = inputRank + dim + 1;\n  }\n  newShape.splice($dim, 0, 1);\n  return reshape5({ inputs: { x: input2 }, backend: backend2, attrs: { shape: newShape } });\n}\nvar expandDimsConfig3 = {\n  kernelName: ExpandDims,\n  backendName: \"wasm\",\n  kernelFunc: expandDims5\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Fill.js\nfunction fill4(args) {\n  const { attrs: { shape, value, dtype }, backend: backend2 } = args;\n  const out = backend2.makeOutput(shape, dtype);\n  const outVals = backend2.typedArrayFromHeap(out);\n  outVals.fill(value);\n  return out;\n}\nvar fillConfig3 = {\n  kernelName: Fill,\n  backendName: \"wasm\",\n  kernelFunc: fill4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/FlipLeftRight.js\nvar wasmFlipLeftRight;\nfunction setup16(backend2) {\n  wasmFlipLeftRight = backend2.wasm.cwrap(FlipLeftRight, null, [\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\"\n  ]);\n}\nfunction flipLeftRight2(args) {\n  const { inputs, backend: backend2 } = args;\n  const { image: image2 } = inputs;\n  const out = backend2.makeOutput(image2.shape, image2.dtype);\n  const imageId = backend2.dataIdMap.get(image2.dataId).id;\n  const outId = backend2.dataIdMap.get(out.dataId).id;\n  const [batch, imageHeight, imageWidth, numChannels] = image2.shape;\n  wasmFlipLeftRight(imageId, batch, imageHeight, imageWidth, numChannels, outId);\n  return out;\n}\nvar flipLeftRightConfig3 = {\n  kernelName: FlipLeftRight,\n  backendName: \"wasm\",\n  kernelFunc: flipLeftRight2,\n  setupFunc: setup16\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Floor.js\nvar floorConfig3 = createUnaryKernelConfig(Floor);\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/FloorDiv.js\nvar supportsFullBroadcast3 = false;\nvar floorDivConfig3 = createBinaryKernelConfig(FloorDiv, supportsFullBroadcast3);\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/FusedBatchNorm.js\nvar wasmBatchNorm;\nfunction setup17(backend2) {\n  wasmBatchNorm = backend2.wasm.cwrap(FusedBatchNorm, null, [\"number\", \"number\", \"number\", \"number\", \"number\", \"number\", \"number\"]);\n}\nfunction fusedBatchNorm(args) {\n  const { backend: backend2, inputs, attrs } = args;\n  const { varianceEpsilon } = attrs;\n  const { x, mean: mean5, variance, offset, scale: scale2 } = inputs;\n  const xId = backend2.dataIdMap.get(x.dataId).id;\n  const meanId = backend2.dataIdMap.get(mean5.dataId).id;\n  const varianceId = backend2.dataIdMap.get(variance.dataId).id;\n  const offsetId = offset != null ? backend2.dataIdMap.get(offset.dataId).id : 0;\n  const scaleId = scale2 != null ? backend2.dataIdMap.get(scale2.dataId).id : 0;\n  const out = backend2.makeOutput(x.shape, x.dtype);\n  if (util_exports.sizeFromShape(x.shape) === 0) {\n    return out;\n  }\n  const outId = backend2.dataIdMap.get(out.dataId).id;\n  wasmBatchNorm(xId, meanId, varianceId, offsetId, scaleId, varianceEpsilon, outId);\n  return out;\n}\nvar fusedBatchNormConfig = {\n  kernelName: FusedBatchNorm,\n  backendName: \"wasm\",\n  setupFunc: setup17,\n  kernelFunc: fusedBatchNorm\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/FusedConv2D.js\nvar wasmFusedConv2d;\nfunction setup18(backend2) {\n  wasmFusedConv2d = backend2.wasm.cwrap(FusedConv2D, null, [\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\"\n  ]);\n}\nfunction fusedConv2d2(args) {\n  const { inputs, attrs, backend: backend2 } = args;\n  const { x, filter, bias, preluActivationWeights } = inputs;\n  const { strides, pad: pad3, dilations, dataFormat, dimRoundingMode, activation: activation2, leakyreluAlpha } = attrs;\n  const convInfo = backend_util_exports.computeConv2DInfo(x.shape, filter.shape, strides, dilations, pad3, dimRoundingMode);\n  const fusedActivation = FusableActivation[activation2];\n  if (fusedActivation == null) {\n    throw new Error(`${activation2} activation not yet supported for FusedConv2D in the wasm backend.`);\n  }\n  const xId = backend2.dataIdMap.get(x.dataId).id;\n  const filterId = backend2.dataIdMap.get(filter.dataId).id;\n  const outputChannels = convInfo.outChannels;\n  let biasId = 0;\n  if (bias != null) {\n    const biasData = backend2.dataIdMap.get(bias.dataId);\n    if (biasData.shape.length !== 1) {\n      throw new Error(`FusedConv2D only supports rank-1 bias but got rank ${biasData.shape.length}.`);\n    }\n    if (biasData.shape[0] !== outputChannels) {\n      throw new Error(`FusedConv2D bias shape (${biasData.shape}) does not match the number of output channels (${outputChannels})`);\n    }\n    biasId = biasData.id;\n  }\n  const filterHeight = convInfo.filterHeight;\n  const filterWidth = convInfo.filterWidth;\n  const padTop = convInfo.padInfo.top;\n  const padRight = convInfo.padInfo.right;\n  const padBottom = convInfo.padInfo.bottom;\n  const padLeft = convInfo.padInfo.left;\n  const dilationHeight = convInfo.dilationHeight;\n  const dilationWidth = convInfo.dilationWidth;\n  const strideHeight = convInfo.strideHeight;\n  const strideWidth = convInfo.strideWidth;\n  const inputChannels = convInfo.inChannels;\n  const isSamePad = convInfo.padInfo.type === \"SAME\" ? 1 : 0;\n  const batchSize = convInfo.batchSize;\n  const inHeight = convInfo.inHeight;\n  const inWidth = convInfo.inWidth;\n  if (dataFormat !== \"NHWC\") {\n    throw new Error(`wasm backend FusedConv2D does not support dataFormat:'${dataFormat}'. Please use 'NHWC'.`);\n  }\n  const out = backend2.makeOutput(convInfo.outShape, \"float32\");\n  const outId = backend2.dataIdMap.get(out.dataId).id;\n  const preluActivationWeightsId = preluActivationWeights == null ? 0 : backend2.dataIdMap.get(preluActivationWeights.dataId).id;\n  wasmFusedConv2d(xId, batchSize, inHeight, inWidth, filterId, filterHeight, filterWidth, biasId, padTop, padRight, padBottom, padLeft, isSamePad, dilationHeight, dilationWidth, strideHeight, strideWidth, inputChannels, outputChannels, fusedActivation, preluActivationWeightsId, leakyreluAlpha || 0, outId);\n  return out;\n}\nvar fusedConv2DConfig3 = {\n  kernelName: FusedConv2D,\n  backendName: \"wasm\",\n  setupFunc: setup18,\n  kernelFunc: fusedConv2d2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/FusedDepthwiseConv2D.js\nvar wasmFusedDepthwiseConv2d;\nfunction setup19(backend2) {\n  wasmFusedDepthwiseConv2d = backend2.wasm.cwrap(FusedDepthwiseConv2D, null, [\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\"\n  ]);\n}\nfunction fusedDepthwiseConv2d(args) {\n  const { inputs, attrs, backend: backend2 } = args;\n  const { x, filter, bias, preluActivationWeights } = inputs;\n  const { strides, pad: pad3, dilations, dataFormat, dimRoundingMode, activation: activation2, leakyreluAlpha } = attrs;\n  const convInfo = backend_util_exports.computeConv2DInfo(x.shape, filter.shape, strides, dilations, pad3, dimRoundingMode, true);\n  const fusedActivation = FusableActivation[activation2];\n  if (fusedActivation == null) {\n    throw new Error(`${activation2} activation not yet supported for FusedDepthwiseConv2D in the wasm backend.`);\n  }\n  const xId = backend2.dataIdMap.get(x.dataId).id;\n  const filterId = backend2.dataIdMap.get(filter.dataId).id;\n  const outputChannels = convInfo.outChannels;\n  let biasId = 0;\n  if (bias != null) {\n    const biasData = backend2.dataIdMap.get(bias.dataId);\n    if (biasData.shape.length !== 1) {\n      throw new Error(`FusedDepthwiseConv2D only supports rank-1 bias but got rank ${biasData.shape.length}.`);\n    }\n    if (biasData.shape[0] !== outputChannels) {\n      throw new Error(`FusedDepthwiseConv2D bias shape (${biasData.shape}) does not match the number of output channels (${outputChannels})`);\n    }\n    biasId = biasData.id;\n  }\n  const filterHeight = convInfo.filterHeight;\n  const filterWidth = convInfo.filterWidth;\n  const padTop = convInfo.padInfo.top;\n  const padRight = convInfo.padInfo.right;\n  const padBottom = convInfo.padInfo.bottom;\n  const padLeft = convInfo.padInfo.left;\n  const dilationHeight = convInfo.dilationHeight;\n  const dilationWidth = convInfo.dilationWidth;\n  const strideHeight = convInfo.strideHeight;\n  const strideWidth = convInfo.strideWidth;\n  const inputChannels = convInfo.inChannels;\n  const isSamePad = convInfo.padInfo.type === \"SAME\" ? 1 : 0;\n  const batchSize = convInfo.batchSize;\n  const inHeight = convInfo.inHeight;\n  const inWidth = convInfo.inWidth;\n  if (dataFormat !== \"NHWC\") {\n    throw new Error(`wasm backend FusedDepthwiseConv2D does not support dataFormat:'${dataFormat}'. Please use 'NHWC'.`);\n  }\n  const out = backend2.makeOutput(convInfo.outShape, \"float32\");\n  const outId = backend2.dataIdMap.get(out.dataId).id;\n  const preluActivationWeightsId = preluActivationWeights == null ? 0 : backend2.dataIdMap.get(preluActivationWeights.dataId).id;\n  wasmFusedDepthwiseConv2d(xId, batchSize, inHeight, inWidth, filterId, filterHeight, filterWidth, biasId, padTop, padRight, padBottom, padLeft, isSamePad, dilationHeight, dilationWidth, strideHeight, strideWidth, inputChannels, outputChannels, fusedActivation, preluActivationWeightsId, leakyreluAlpha || 0, outId);\n  return out;\n}\nvar fusedDepthwiseConv2DConfig3 = {\n  kernelName: FusedDepthwiseConv2D,\n  backendName: \"wasm\",\n  setupFunc: setup19,\n  kernelFunc: fusedDepthwiseConv2d\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/GatherNd.js\nvar wasmGatherNd;\nfunction setup20(backend2) {\n  wasmGatherNd = backend2.wasm.cwrap(GatherNd, null, [\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"array\",\n    \"number\"\n  ]);\n}\nfunction gatherNd3(args) {\n  const { backend: backend2, inputs } = args;\n  const { params, indices } = inputs;\n  const [resultShape, numSlices, sliceSize, strides] = gather_nd_util_exports.prepareAndValidate(params, indices);\n  const out = backend2.makeOutput(resultShape, params.dtype);\n  if (numSlices === 0) {\n    return out;\n  }\n  const indicesShape = indices.shape;\n  const sliceRank = indicesShape[indicesShape.length - 1];\n  const xData = backend2.dataIdMap.get(params.dataId);\n  const xId = xData.id;\n  const indicesData = backend2.dataIdMap.get(indices.dataId);\n  const indicesId = indicesData.id;\n  const stridesBytes = new Uint8Array(new Int32Array(strides).buffer);\n  const outId = backend2.dataIdMap.get(out.dataId).id;\n  wasmGatherNd(xId, CppDType[params.dtype], indicesId, numSlices, sliceRank, sliceSize, stridesBytes, outId);\n  return out;\n}\nvar gatherNdConfig3 = {\n  kernelName: GatherNd,\n  backendName: \"wasm\",\n  setupFunc: setup20,\n  kernelFunc: gatherNd3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/GatherV2.js\nvar wasmGather;\nfunction setup21(backend2) {\n  wasmGather = backend2.wasm.cwrap(\"Gather\", null, [\n    \"number\",\n    \"number\",\n    \"array\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"array\",\n    \"number\"\n  ]);\n}\nfunction gatherV23(args) {\n  const { backend: backend2, inputs, attrs } = args;\n  const { x, indices } = inputs;\n  const { axis, batchDims } = attrs;\n  const parsedAxis = util_exports.parseAxisParam(axis, x.shape)[0];\n  const indicesVals = backend2.readSync(indices.dataId);\n  const axisDim = x.shape[parsedAxis];\n  for (let i = 0; i < indicesVals.length; ++i) {\n    const index = indicesVals[i];\n    util_exports.assert(index <= axisDim - 1 && index >= 0, () => `GatherV2: the index value ${index} is not in [0, ${axisDim - 1}]`);\n  }\n  const shapeInfo = backend_util_exports.segment_util.collectGatherOpShapeInfo(x, indices, parsedAxis, batchDims);\n  const flattenX = reshape5({\n    inputs: { x },\n    attrs: {\n      shape: [\n        shapeInfo.batchSize,\n        shapeInfo.outerSize,\n        shapeInfo.dimSize,\n        shapeInfo.sliceSize\n      ]\n    },\n    backend: backend2\n  });\n  const indicesSize = util_exports.sizeFromShape(indices.shape);\n  const flattenIndex = reshape5({\n    inputs: { x: indices },\n    attrs: { shape: [shapeInfo.batchSize, indicesSize / shapeInfo.batchSize] },\n    backend: backend2\n  });\n  const flattenOutputShape = [\n    shapeInfo.batchSize,\n    shapeInfo.outerSize,\n    indicesSize / shapeInfo.batchSize,\n    shapeInfo.sliceSize\n  ];\n  const out = backend2.makeOutput(flattenOutputShape, x.dtype);\n  if (util_exports.sizeFromShape(x.shape) === 0) {\n    return out;\n  }\n  const stridesSize = flattenX.shape.length - 1;\n  const xData = backend2.dataIdMap.get(flattenX.dataId);\n  const xId = xData.id;\n  const indicesData = backend2.dataIdMap.get(flattenIndex.dataId);\n  const indicesId = indicesData.id;\n  const outId = backend2.dataIdMap.get(out.dataId).id;\n  const xStridesBytes = new Uint8Array(new Int32Array(util_exports.computeStrides(flattenX.shape)).buffer);\n  const outStridesBytes = new Uint8Array(new Int32Array(util_exports.computeStrides(flattenOutputShape)).buffer);\n  wasmGather(xId, CppDType[x.dtype], xStridesBytes, stridesSize, indicesId, shapeInfo.batchSize, outStridesBytes, outId);\n  backend2.disposeData(flattenX.dataId);\n  backend2.disposeData(flattenIndex.dataId);\n  out.shape = shapeInfo.outputShape;\n  return out;\n}\nvar gatherV2Config3 = {\n  kernelName: GatherV2,\n  backendName: \"wasm\",\n  setupFunc: setup21,\n  kernelFunc: gatherV23\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Greater.js\nvar supportsFullBroadcast4 = false;\nvar greaterConfig3 = createBinaryKernelConfig(Greater, supportsFullBroadcast4, \"bool\");\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/GreaterEqual.js\nvar supportsFullBroadcast5 = false;\nvar greaterEqualConfig3 = createBinaryKernelConfig(GreaterEqual, supportsFullBroadcast5, \"bool\");\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/LeakyRelu.js\nvar wasmFunc3;\nfunction setupFunc2(backend2) {\n  wasmFunc3 = backend2.wasm.cwrap(LeakyRelu, null, [\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\"\n  ]);\n}\nfunction leakyRelu4(args) {\n  const { inputs: { x }, attrs: { alpha }, backend: backend2 } = args;\n  const xId = backend2.dataIdMap.get(x.dataId).id;\n  const out = backend2.makeOutput(x.shape, \"float32\");\n  if (util_exports.sizeFromShape(x.shape) !== 0) {\n    const outId = backend2.dataIdMap.get(out.dataId).id;\n    wasmFunc3(xId, CppDType[x.dtype], alpha, outId);\n  }\n  return out;\n}\nvar leakyReluConfig3 = {\n  kernelName: LeakyRelu,\n  backendName: \"wasm\",\n  setupFunc: setupFunc2,\n  kernelFunc: leakyRelu4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Less.js\nvar supportsFullBroadcast6 = false;\nvar lessConfig3 = createBinaryKernelConfig(Less, supportsFullBroadcast6, \"bool\");\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/LessEqual.js\nvar supportsFullBroadcast7 = false;\nvar lessEqualConfig3 = createBinaryKernelConfig(LessEqual, supportsFullBroadcast7, \"bool\");\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Log.js\nvar logConfig3 = createUnaryKernelConfig(Log);\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/LogicalAnd.js\nvar supportsFullBroadcast8 = false;\nvar logicalAndConfig3 = createBinaryKernelConfig(LogicalAnd, supportsFullBroadcast8, \"bool\");\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/LogicalNot.js\nvar logicalNotConfig3 = createUnaryKernelConfig(LogicalNot);\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/LogicalOr.js\nvar supportsFullBroadcast9 = false;\nvar logicalOrConfig3 = createBinaryKernelConfig(LogicalOr, supportsFullBroadcast9, \"bool\");\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/LogicalXor.js\nvar supportsFullBroadcast10 = false;\nvar logicalXorConfig = createBinaryKernelConfig(LogicalXor, supportsFullBroadcast10, \"bool\");\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Max.js\nvar wasmMax;\nfunction setup22(backend2) {\n  wasmMax = backend2.wasm.cwrap(Max, null, [\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\"\n  ]);\n}\nfunction max5(args) {\n  const { backend: backend2, inputs, attrs } = args;\n  const { reductionIndices: axis, keepDims } = attrs;\n  const { x } = inputs;\n  const xId = backend2.dataIdMap.get(x.dataId).id;\n  let inputId = xId;\n  let input2 = x;\n  const { transposed, axes, originalAxes, inputWasTransposed } = permuteAxesAndTranspose(x, axis, backend2);\n  if (inputWasTransposed) {\n    const transposedId = backend2.dataIdMap.get(transposed.dataId).id;\n    input2 = transposed;\n    inputId = transposedId;\n  }\n  const inputRank = input2.shape.length;\n  backend_util_exports.assertAxesAreInnerMostDims(\"max\", axes, inputRank);\n  const [outShape, reduceShape] = backend_util_exports.computeOutAndReduceShapes(input2.shape, axes);\n  const reduceSize = util_exports.sizeFromShape(reduceShape);\n  const out = backend2.makeOutput(outShape, x.dtype);\n  if (util_exports.sizeFromShape(input2.shape) !== 0) {\n    const outId = backend2.dataIdMap.get(out.dataId).id;\n    wasmMax(inputId, CppDType[x.dtype], reduceSize, outId);\n  }\n  if (inputWasTransposed) {\n    backend2.disposeData(transposed.dataId);\n  }\n  if (keepDims) {\n    const newShape = backend_util_exports.expandShapeToKeepDim(out.shape, originalAxes);\n    out.shape = newShape;\n  }\n  return out;\n}\nvar maxConfig3 = {\n  kernelName: Max,\n  backendName: \"wasm\",\n  setupFunc: setup22,\n  kernelFunc: max5\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Maximum.js\nvar supportsFullBroadcast11 = false;\nvar maximumConfig3 = createBinaryKernelConfig(Maximum, supportsFullBroadcast11);\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/MaxPool.js\nvar wasmMaxPool;\nfunction setup23(backend2) {\n  wasmMaxPool = backend2.wasm.cwrap(MaxPool, null, [\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\"\n  ]);\n}\nfunction maxPool4(args) {\n  const { inputs, attrs, backend: backend2 } = args;\n  const x = inputs.x;\n  const xId = backend2.dataIdMap.get(x.dataId).id;\n  util_exports.assert(x.dtype === \"float32\", () => `Error in MaxPool: only float32 input is supported. Got ${x.dtype}.`);\n  const { filterSize, strides, pad: pad3, dimRoundingMode } = attrs;\n  const convInfo = backend_util_exports.computePool2DInfo(x.shape, filterSize, strides, 1, pad3, dimRoundingMode);\n  const filterHeight = convInfo.filterHeight;\n  const filterWidth = convInfo.filterWidth;\n  const padTop = convInfo.padInfo.top;\n  const padRight = convInfo.padInfo.right;\n  const padBottom = convInfo.padInfo.bottom;\n  const padLeft = convInfo.padInfo.left;\n  const dilationHeight = convInfo.dilationHeight;\n  const dilationWidth = convInfo.dilationWidth;\n  const strideHeight = convInfo.strideHeight;\n  const strideWidth = convInfo.strideWidth;\n  const inputChannels = convInfo.inChannels;\n  const outputChannels = convInfo.outChannels;\n  if (convInfo.dataFormat !== \"channelsLast\") {\n    throw new Error(`wasm backend does not support dataFormat:'${convInfo.dataFormat}'. Please use 'channelsLast'.`);\n  }\n  const out = backend2.makeOutput(convInfo.outShape, \"float32\");\n  const outId = backend2.dataIdMap.get(out.dataId).id;\n  wasmMaxPool(xId, x.shape[0], x.shape[1], x.shape[2], filterHeight, filterWidth, padTop, padRight, padBottom, padLeft, dilationHeight, dilationWidth, strideHeight, strideWidth, inputChannels, outputChannels, outId);\n  return out;\n}\nvar maxPoolConfig3 = {\n  kernelName: MaxPool,\n  backendName: \"wasm\",\n  setupFunc: setup23,\n  kernelFunc: maxPool4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Mean.js\nvar wasmMean;\nfunction setup24(backend2) {\n  wasmMean = backend2.wasm.cwrap(Mean, null, [\"number, number, number\"]);\n}\nfunction mean3(args) {\n  const { backend: backend2, inputs, attrs } = args;\n  const { axis, keepDims } = attrs;\n  const { x } = inputs;\n  const xId = backend2.dataIdMap.get(x.dataId).id;\n  let inputId = xId;\n  let input2 = x;\n  const { transposed, axes, originalAxes, inputWasTransposed } = permuteAxesAndTranspose(x, axis, backend2);\n  let reductionAxes = axes;\n  if (inputWasTransposed) {\n    const transposedId = backend2.dataIdMap.get(transposed.dataId).id;\n    if (transposedId !== xId) {\n      input2 = transposed;\n      inputId = transposedId;\n      reductionAxes = backend_util_exports.getInnerMostAxes(reductionAxes.length, input2.shape.length);\n    }\n  }\n  backend_util_exports.assertAxesAreInnerMostDims(\"mean\", reductionAxes, input2.shape.length);\n  const [outShape, reduceShape] = backend_util_exports.computeOutAndReduceShapes(input2.shape, reductionAxes);\n  const reduceSize = util_exports.sizeFromShape(reduceShape);\n  let castedInput = input2;\n  if (input2.dtype !== \"float32\") {\n    castedInput = cast5({ backend: backend2, inputs: { x: input2 }, attrs: { dtype: \"float32\" } });\n    inputId = backend2.dataIdMap.get(castedInput.dataId).id;\n  }\n  const out = backend2.makeOutput(outShape, \"float32\");\n  if (util_exports.sizeFromShape(input2.shape) !== 0) {\n    const outId = backend2.dataIdMap.get(out.dataId).id;\n    wasmMean(inputId, reduceSize, outId);\n  }\n  if (inputWasTransposed) {\n    backend2.disposeData(transposed.dataId);\n  }\n  if (keepDims) {\n    const newShape = backend_util_exports.expandShapeToKeepDim(out.shape, originalAxes);\n    out.shape = newShape;\n  }\n  if (input2.dtype !== \"float32\") {\n    backend2.disposeData(castedInput.dataId);\n  }\n  return out;\n}\nvar meanConfig3 = {\n  kernelName: Mean,\n  backendName: \"wasm\",\n  setupFunc: setup24,\n  kernelFunc: mean3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Min.js\nvar wasmMin;\nfunction setup25(backend2) {\n  wasmMin = backend2.wasm.cwrap(Min, null, [\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\"\n  ]);\n}\nfunction min5(args) {\n  const { backend: backend2, inputs, attrs } = args;\n  const { axis, keepDims } = attrs;\n  const { x } = inputs;\n  const xId = backend2.dataIdMap.get(x.dataId).id;\n  let inputId = xId;\n  let input2 = x;\n  const { transposed, axes, originalAxes, inputWasTransposed } = permuteAxesAndTranspose(x, axis, backend2);\n  if (inputWasTransposed) {\n    const transposedId = backend2.dataIdMap.get(transposed.dataId).id;\n    if (transposedId !== xId) {\n      input2 = transposed;\n      inputId = transposedId;\n    }\n  }\n  const inputRank = input2.shape.length;\n  backend_util_exports.assertAxesAreInnerMostDims(\"min\", axes, inputRank);\n  const [outShape, reduceShape] = backend_util_exports.computeOutAndReduceShapes(input2.shape, axes);\n  const reduceSize = util_exports.sizeFromShape(reduceShape);\n  const out = backend2.makeOutput(outShape, input2.dtype);\n  if (util_exports.sizeFromShape(input2.shape) !== 0) {\n    const outId = backend2.dataIdMap.get(out.dataId).id;\n    wasmMin(inputId, CppDType[x.dtype], reduceSize, outId);\n  }\n  if (inputWasTransposed) {\n    backend2.disposeData(transposed.dataId);\n  }\n  if (keepDims) {\n    const newShape = backend_util_exports.expandShapeToKeepDim(out.shape, originalAxes);\n    out.shape = newShape;\n  }\n  return out;\n}\nvar minConfig3 = {\n  kernelName: Min,\n  backendName: \"wasm\",\n  setupFunc: setup25,\n  kernelFunc: min5\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Minimum.js\nvar supportsFullBroadcast12 = false;\nvar minimumConfig3 = createBinaryKernelConfig(Minimum, supportsFullBroadcast12);\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/MirrorPad.js\nvar MirrorPaddingMode;\n(function(MirrorPaddingMode2) {\n  MirrorPaddingMode2[MirrorPaddingMode2[\"reflect\"] = 0] = \"reflect\";\n  MirrorPaddingMode2[MirrorPaddingMode2[\"symmetric\"] = 1] = \"symmetric\";\n})(MirrorPaddingMode || (MirrorPaddingMode = {}));\nvar wasmMirrorPad;\nfunction setup26(backend2) {\n  wasmMirrorPad = backend2.wasm.cwrap(MirrorPad, null, [\n    \"number\",\n    \"array\",\n    \"number\",\n    \"number\",\n    \"array\",\n    \"array\",\n    \"number\",\n    \"number\"\n  ]);\n}\nfunction mirrorPad3(args) {\n  const { inputs: { x }, backend: backend2, attrs: { paddings, mode } } = args;\n  const outShape = paddings.map((p2, i) => p2[0] + x.shape[i] + p2[1]);\n  const xId = backend2.dataIdMap.get(x.dataId).id;\n  const out = backend2.makeOutput(outShape, x.dtype);\n  const outId = backend2.dataIdMap.get(out.dataId).id;\n  const xShapeBytes = new Uint8Array(new Int32Array(x.shape).buffer);\n  const prePaddingsFlat = paddings.map((padTuple) => padTuple[0]);\n  const postPaddingsFlat = paddings.map((padTuple) => padTuple[1]);\n  const prePaddingsBytes = new Uint8Array(new Int32Array(prePaddingsFlat).buffer);\n  const postPaddingsBytes = new Uint8Array(new Int32Array(postPaddingsFlat).buffer);\n  wasmMirrorPad(xId, xShapeBytes, x.shape.length, CppDType[x.dtype], prePaddingsBytes, postPaddingsBytes, MirrorPaddingMode[mode], outId);\n  return out;\n}\nvar mirrorPadConfig3 = {\n  kernelName: MirrorPad,\n  backendName: \"wasm\",\n  kernelFunc: mirrorPad3,\n  setupFunc: setup26\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Multiply.js\nvar supportsFullBroadcast13 = true;\nvar multiplyConfig3 = createBinaryKernelConfig(Multiply, supportsFullBroadcast13);\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Neg.js\nvar negConfig3 = createUnaryKernelConfig(Neg);\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/NonMaxSuppression_util.js\nfunction parseResultStruct(backend2, resOffset) {\n  const result = new Int32Array(backend2.wasm.HEAPU8.buffer, resOffset, 4);\n  const pSelectedIndices = result[0];\n  const selectedSize = result[1];\n  const pSelectedScores = result[2];\n  const pValidOutputs = result[3];\n  backend2.wasm._free(resOffset);\n  return { pSelectedIndices, selectedSize, pSelectedScores, pValidOutputs };\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/NonMaxSuppressionV3.js\nvar wasmFunc4;\nfunction setup27(backend2) {\n  wasmFunc4 = backend2.wasm.cwrap(\n    NonMaxSuppressionV3,\n    \"number\",\n    [\n      \"number\",\n      \"number\",\n      \"number\",\n      \"number\",\n      \"number\"\n    ]\n  );\n}\nfunction kernelFunc(args) {\n  const { backend: backend2, inputs, attrs } = args;\n  const { iouThreshold, maxOutputSize, scoreThreshold } = attrs;\n  const { boxes, scores } = inputs;\n  const boxesId = backend2.dataIdMap.get(boxes.dataId).id;\n  const scoresId = backend2.dataIdMap.get(scores.dataId).id;\n  const resOffset = wasmFunc4(boxesId, scoresId, maxOutputSize, iouThreshold, scoreThreshold);\n  const { pSelectedIndices, selectedSize, pSelectedScores, pValidOutputs } = parseResultStruct(backend2, resOffset);\n  backend2.wasm._free(pSelectedScores);\n  backend2.wasm._free(pValidOutputs);\n  const selectedIndicesTensor = backend2.makeOutput([selectedSize], \"int32\", pSelectedIndices);\n  return selectedIndicesTensor;\n}\nvar nonMaxSuppressionV3Config3 = {\n  kernelName: NonMaxSuppressionV3,\n  backendName: \"wasm\",\n  setupFunc: setup27,\n  kernelFunc\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/NonMaxSuppressionV4.js\nvar wasmFunc5;\nfunction setup28(backend2) {\n  wasmFunc5 = backend2.wasm.cwrap(\n    NonMaxSuppressionV4,\n    \"number\",\n    [\n      \"number\",\n      \"number\",\n      \"number\",\n      \"number\",\n      \"number\",\n      \"bool\"\n    ]\n  );\n}\nfunction nonMaxSuppressionV43(args) {\n  const { backend: backend2, inputs, attrs } = args;\n  const { iouThreshold, maxOutputSize, scoreThreshold, padToMaxOutputSize } = attrs;\n  const { boxes, scores } = inputs;\n  const boxesId = backend2.dataIdMap.get(boxes.dataId).id;\n  const scoresId = backend2.dataIdMap.get(scores.dataId).id;\n  const resOffset = wasmFunc5(boxesId, scoresId, maxOutputSize, iouThreshold, scoreThreshold, padToMaxOutputSize);\n  const { pSelectedIndices, selectedSize, pSelectedScores, pValidOutputs } = parseResultStruct(backend2, resOffset);\n  backend2.wasm._free(pSelectedScores);\n  const selectedIndicesTensor = backend2.makeOutput([selectedSize], \"int32\", pSelectedIndices);\n  const validOutputsTensor = backend2.makeOutput([], \"int32\", pValidOutputs);\n  return [selectedIndicesTensor, validOutputsTensor];\n}\nvar nonMaxSuppressionV4Config3 = {\n  kernelName: NonMaxSuppressionV4,\n  backendName: \"wasm\",\n  setupFunc: setup28,\n  kernelFunc: nonMaxSuppressionV43\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/NonMaxSuppressionV5.js\nvar wasmFunc6;\nfunction setup29(backend2) {\n  wasmFunc6 = backend2.wasm.cwrap(\n    NonMaxSuppressionV5,\n    \"number\",\n    [\n      \"number\",\n      \"number\",\n      \"number\",\n      \"number\",\n      \"number\",\n      \"number\"\n    ]\n  );\n}\nfunction kernelFunc2(args) {\n  const { backend: backend2, inputs, attrs } = args;\n  const { iouThreshold, maxOutputSize, scoreThreshold, softNmsSigma } = attrs;\n  const { boxes, scores } = inputs;\n  const boxesId = backend2.dataIdMap.get(boxes.dataId).id;\n  const scoresId = backend2.dataIdMap.get(scores.dataId).id;\n  const resOffset = wasmFunc6(boxesId, scoresId, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma);\n  const { pSelectedIndices, selectedSize, pSelectedScores, pValidOutputs } = parseResultStruct(backend2, resOffset);\n  backend2.wasm._free(pValidOutputs);\n  const selectedIndicesTensor = backend2.makeOutput([selectedSize], \"int32\", pSelectedIndices);\n  const selectedScoresTensor = backend2.makeOutput([selectedSize], \"float32\", pSelectedScores);\n  return [selectedIndicesTensor, selectedScoresTensor];\n}\nvar nonMaxSuppressionV5Config3 = {\n  kernelName: NonMaxSuppressionV5,\n  backendName: \"wasm\",\n  setupFunc: setup29,\n  kernelFunc: kernelFunc2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/NotEqual.js\nvar supportsFullBroadcast14 = false;\nvar notEqualConfig3 = createBinaryKernelConfig(NotEqual, supportsFullBroadcast14, \"bool\");\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/OneHot.js\nvar wasmOneHot;\nfunction setup30(backend2) {\n  wasmOneHot = backend2.wasm.cwrap(OneHot, null, [\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\"\n  ]);\n}\nfunction oneHot4(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { indices } = inputs;\n  const { dtype, depth, onValue, offValue } = attrs;\n  const out = backend2.makeOutput([...indices.shape, depth], dtype);\n  const outId = backend2.dataIdMap.get(out.dataId).id;\n  const indicesData = backend2.dataIdMap.get(indices.dataId);\n  const indicesId = indicesData.id;\n  wasmOneHot(indicesId, depth, onValue, offValue, outId);\n  return out;\n}\nvar oneHotConfig3 = {\n  kernelName: OneHot,\n  backendName: \"wasm\",\n  setupFunc: setup30,\n  kernelFunc: oneHot4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/OnesLike.js\nfunction onesLike4(args) {\n  const { inputs: { x }, backend: backend2 } = args;\n  const out = backend2.makeOutput(x.shape, x.dtype);\n  const outVals = backend2.typedArrayFromHeap(out);\n  outVals.fill(1);\n  return out;\n}\nvar onesLikeConfig3 = {\n  kernelName: OnesLike,\n  backendName: \"wasm\",\n  kernelFunc: onesLike4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Pack.js\nfunction pack3(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { axis } = attrs;\n  if (inputs.length === 1) {\n    return expandDims5({ inputs: { input: inputs[0] }, backend: backend2, attrs: { dim: axis } });\n  }\n  const shape = inputs[0].shape;\n  const dtype = inputs[0].dtype;\n  inputs.forEach((t) => {\n    util_exports.assertShapesMatch(shape, t.shape, \"All tensors passed to stack must have matching shapes\");\n    util_exports.assert(dtype === t.dtype, () => \"All tensors passed to stack must have matching dtypes\");\n  });\n  const intermediateTensorInfos = [];\n  const expandedTensors = inputs.map((t) => {\n    const expandedT = expandDims5({ inputs: { input: t }, backend: backend2, attrs: { dim: axis } });\n    intermediateTensorInfos.push(expandedT);\n    return expandedT;\n  });\n  const result = concat4({ inputs: expandedTensors, backend: backend2, attrs: { axis } });\n  intermediateTensorInfos.forEach((t) => backend2.disposeData(t.dataId));\n  return result;\n}\nvar packConfig3 = {\n  kernelName: Pack,\n  backendName: \"wasm\",\n  kernelFunc: pack3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/PadV2.js\nvar wasmPadV2;\nfunction setup31(backend2) {\n  wasmPadV2 = backend2.wasm.cwrap(PadV2, null, [\n    \"number\",\n    \"array\",\n    \"number\",\n    \"number\",\n    \"array\",\n    \"array\",\n    \"number\",\n    \"number\"\n  ]);\n}\nfunction pad2(args) {\n  const { inputs: { x }, backend: backend2, attrs: { paddings, constantValue } } = args;\n  const outShape = paddings.map((p2, i) => p2[0] + x.shape[i] + p2[1]);\n  if (util_exports.sizeFromShape(x.shape) === 0) {\n    return fill4({\n      backend: backend2,\n      attrs: { shape: outShape, value: constantValue, dtype: x.dtype }\n    });\n  }\n  const xId = backend2.dataIdMap.get(x.dataId).id;\n  const out = backend2.makeOutput(outShape, x.dtype);\n  const outTensorData = backend2.dataIdMap.get(out.dataId);\n  const outId = outTensorData.id;\n  const xShapeBytes = new Uint8Array(new Int32Array(x.shape).buffer);\n  const prePaddingsFlat = paddings.map((padTuple) => padTuple[0]);\n  const postPaddingsFlat = paddings.map((padTuple) => padTuple[1]);\n  const prePaddingsBytes = new Uint8Array(new Int32Array(prePaddingsFlat).buffer);\n  const postPaddingsBytes = new Uint8Array(new Int32Array(postPaddingsFlat).buffer);\n  wasmPadV2(xId, xShapeBytes, x.shape.length, CppDType[x.dtype], prePaddingsBytes, postPaddingsBytes, constantValue, outId);\n  return out;\n}\nvar padV2Config3 = {\n  kernelName: PadV2,\n  backendName: \"wasm\",\n  kernelFunc: pad2,\n  setupFunc: setup31\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Pow.js\nvar supportsFullBroadcast15 = false;\nvar powConfig3 = createBinaryKernelConfig(Pow, supportsFullBroadcast15);\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Prelu.js\nvar wasmPrelu;\nfunction setup32(backend2) {\n  wasmPrelu = backend2.wasm.cwrap(Prelu, null, [\n    \"number\",\n    \"number\",\n    \"number\"\n  ]);\n}\nfunction prelu5(args) {\n  const { inputs, backend: backend2 } = args;\n  const { x, alpha } = inputs;\n  const xId = backend2.dataIdMap.get(x.dataId).id;\n  const weightsId = backend2.dataIdMap.get(alpha.dataId).id;\n  let inputId = xId;\n  const input2 = x;\n  let castedInput = input2;\n  if (input2.dtype !== \"float32\") {\n    castedInput = cast5({ backend: backend2, inputs: { x }, attrs: { dtype: \"float32\" } });\n    inputId = backend2.dataIdMap.get(castedInput.dataId).id;\n  }\n  const out = backend2.makeOutput(x.shape, \"float32\");\n  const outId = backend2.dataIdMap.get(out.dataId).id;\n  wasmPrelu(inputId, weightsId, outId);\n  if (input2.dtype !== \"float32\") {\n    backend2.disposeData(castedInput.dataId);\n  }\n  return out;\n}\nvar preluConfig3 = {\n  kernelName: Prelu,\n  backendName: \"wasm\",\n  setupFunc: setup32,\n  kernelFunc: prelu5\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Prod.js\nvar wasmProd;\nfunction setup33(backend2) {\n  wasmProd = backend2.wasm.cwrap(Prod, null, [\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\"\n  ]);\n}\nfunction prod4(args) {\n  const { backend: backend2, inputs, attrs } = args;\n  const { axis, keepDims } = attrs;\n  const { x } = inputs;\n  const xId = backend2.dataIdMap.get(x.dataId).id;\n  let inputId = xId;\n  let input2 = x;\n  const { transposed, axes, originalAxes, inputWasTransposed } = permuteAxesAndTranspose(x, axis, backend2);\n  let reductionAxes = axes;\n  if (inputWasTransposed) {\n    const transposedId = backend2.dataIdMap.get(transposed.dataId).id;\n    if (transposedId !== xId) {\n      input2 = transposed;\n      inputId = transposedId;\n      reductionAxes = backend_util_exports.getInnerMostAxes(reductionAxes.length, input2.shape.length);\n    }\n  }\n  backend_util_exports.assertAxesAreInnerMostDims(\"prod\", reductionAxes, input2.shape.length);\n  const [outShape, reduceShape] = backend_util_exports.computeOutAndReduceShapes(input2.shape, reductionAxes);\n  const reduceSize = util_exports.sizeFromShape(reduceShape);\n  const out = backend2.makeOutput(outShape, input2.dtype);\n  if (util_exports.sizeFromShape(input2.shape) !== 0) {\n    const outId = backend2.dataIdMap.get(out.dataId).id;\n    wasmProd(inputId, reduceSize, CppDType[out.dtype], outId);\n  }\n  if (inputWasTransposed) {\n    backend2.disposeData(transposed.dataId);\n  }\n  if (keepDims) {\n    const newShape = backend_util_exports.expandShapeToKeepDim(out.shape, originalAxes);\n    out.shape = newShape;\n  }\n  return out;\n}\nvar prodConfig3 = {\n  kernelName: Prod,\n  backendName: \"wasm\",\n  setupFunc: setup33,\n  kernelFunc: prod4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Range.js\nvar range5 = (args) => {\n  const { backend: backend2, attrs } = args;\n  const { start, stop, step: step5, dtype } = attrs;\n  const values = rangeImpl(start, stop, step5, dtype);\n  const out = backend2.makeOutput([values.length], dtype);\n  const outVals = backend2.typedArrayFromHeap(out);\n  outVals.set(values);\n  return out;\n};\nvar rangeConfig3 = {\n  kernelName: Range,\n  backendName: \"wasm\",\n  kernelFunc: range5\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/RealDiv.js\nvar supportsFullBroadcast16 = true;\nvar realDivConfig3 = createBinaryKernelConfig(RealDiv, supportsFullBroadcast16);\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Relu.js\nvar reluConfig3 = createUnaryKernelConfig(Relu);\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Relu6.js\nvar relu6Config3 = createUnaryKernelConfig(Relu6);\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/ResizeBilinear.js\nvar wasmResizeBilinear;\nfunction setup34(backend2) {\n  wasmResizeBilinear = backend2.wasm.cwrap(ResizeBilinear, null, [\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\"\n  ]);\n}\nfunction resizeBilinear4(args) {\n  const { backend: backend2, inputs, attrs } = args;\n  const { images } = inputs;\n  const { alignCorners, halfPixelCenters, size } = attrs;\n  const [newHeight, newWidth] = size;\n  const [batch, oldHeight, oldWidth, numChannels] = images.shape;\n  const outShape = [batch, newHeight, newWidth, numChannels];\n  let xData = backend2.dataIdMap.get(images.dataId);\n  let castedData;\n  if (xData.dtype !== \"float32\") {\n    castedData = cast5({ backend: backend2, inputs: { x: images }, attrs: { dtype: \"float32\" } });\n    xData = backend2.dataIdMap.get(castedData.dataId);\n  }\n  const xId = xData.id;\n  const out = backend2.makeOutput(outShape, \"float32\");\n  if (util_exports.sizeFromShape(images.shape) === 0) {\n    return out;\n  }\n  const outId = backend2.dataIdMap.get(out.dataId).id;\n  wasmResizeBilinear(xId, batch, oldHeight, oldWidth, numChannels, newHeight, newWidth, alignCorners ? 1 : 0, halfPixelCenters ? 1 : 0, outId);\n  if (castedData != null) {\n    backend2.disposeData(castedData.dataId);\n  }\n  return out;\n}\nvar resizeBilinearConfig3 = {\n  kernelName: ResizeBilinear,\n  backendName: \"wasm\",\n  setupFunc: setup34,\n  kernelFunc: resizeBilinear4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/ResizeNearestNeighbor.js\nvar wasmResizeNearestNeighbor;\nfunction setup35(backend2) {\n  wasmResizeNearestNeighbor = backend2.wasm.cwrap(ResizeNearestNeighbor, null, [\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\"\n  ]);\n}\nfunction resizeNearestNeighbor4(args) {\n  const { backend: backend2, inputs, attrs } = args;\n  const { images } = inputs;\n  const { alignCorners, halfPixelCenters, size } = attrs;\n  const [newHeight, newWidth] = size;\n  const [batch, oldHeight, oldWidth, numChannels] = images.shape;\n  const outShape = [batch, newHeight, newWidth, numChannels];\n  const out = backend2.makeOutput(outShape, \"float32\");\n  if (util_exports.sizeFromShape(images.shape) === 0) {\n    return out;\n  }\n  let xData = backend2.dataIdMap.get(images.dataId);\n  let castedData;\n  if (xData.dtype !== \"float32\") {\n    castedData = cast5({\n      backend: backend2,\n      inputs: { x: images },\n      attrs: { dtype: \"float32\" }\n    });\n    xData = backend2.dataIdMap.get(castedData.dataId);\n  }\n  const xId = xData.id;\n  const outId = backend2.dataIdMap.get(out.dataId).id;\n  wasmResizeNearestNeighbor(xId, batch, oldHeight, oldWidth, numChannels, newHeight, newWidth, alignCorners ? 1 : 0, halfPixelCenters ? 1 : 0, outId);\n  if (castedData != null) {\n    backend2.disposeData(castedData.dataId);\n  }\n  return out;\n}\nvar resizeNearestNeighborConfig3 = {\n  kernelName: ResizeNearestNeighbor,\n  backendName: \"wasm\",\n  setupFunc: setup35,\n  kernelFunc: resizeNearestNeighbor4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Reverse.js\nvar wasmReverse;\nfunction setup36(backend2) {\n  wasmReverse = backend2.wasm.cwrap(Reverse, null, [\n    \"number\",\n    \"array\",\n    \"number\",\n    \"array\",\n    \"number\",\n    \"number\"\n  ]);\n}\nfunction reverse4(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x } = inputs;\n  const { dims } = attrs;\n  const axes = util_exports.parseAxisParam(dims, x.shape);\n  if (x.shape.length === 0) {\n    return identity4({ inputs: { x }, backend: backend2 });\n  }\n  const out = backend2.makeOutput(x.shape, x.dtype);\n  const xId = backend2.dataIdMap.get(x.dataId).id;\n  const outId = backend2.dataIdMap.get(out.dataId).id;\n  const axesBytes = new Uint8Array(new Int32Array(axes).buffer);\n  const outShapeBytes = new Uint8Array(new Int32Array(x.shape).buffer);\n  wasmReverse(xId, axesBytes, axes.length, outShapeBytes, x.shape.length, outId);\n  const reshaped = reshape5({ inputs: { x: out }, attrs: { shape: x.shape }, backend: backend2 });\n  backend2.disposeData(out.dataId);\n  return reshaped;\n}\nvar reverseConfig3 = {\n  kernelName: Reverse,\n  backendName: \"wasm\",\n  kernelFunc: reverse4,\n  setupFunc: setup36\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/RotateWithOffset.js\nvar wasmRotate;\nfunction setup37(backend2) {\n  wasmRotate = backend2.wasm.cwrap(RotateWithOffset, null, [\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"array\",\n    \"number\",\n    \"number\"\n  ]);\n}\nfunction rotateWithOffset2(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { image: image2 } = inputs;\n  const { radians, fillValue, center } = attrs;\n  const out = backend2.makeOutput(image2.shape, image2.dtype);\n  const imageId = backend2.dataIdMap.get(image2.dataId).id;\n  const outId = backend2.dataIdMap.get(out.dataId).id;\n  const [batch, imageHeight, imageWidth, numChannels] = image2.shape;\n  const [centerX, centerY] = backend_util_exports.getImageCenter(center, imageHeight, imageWidth);\n  const fillIsBlack = fillValue === 0;\n  const fullOpacityValue = 255;\n  const fillValues2 = typeof fillValue === \"number\" ? [fillValue, fillValue, fillValue, fillIsBlack ? 0 : fullOpacityValue] : [...fillValue, fullOpacityValue];\n  const fillBytes = new Uint8Array(new Int32Array(fillValues2).buffer);\n  wasmRotate(imageId, batch, imageHeight, imageWidth, numChannels, radians, centerX, centerY, fillBytes, fillValues2.length, outId);\n  return out;\n}\nvar rotateWithOffsetConfig3 = {\n  kernelName: RotateWithOffset,\n  backendName: \"wasm\",\n  kernelFunc: rotateWithOffset2,\n  setupFunc: setup37\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Round.js\nvar roundConfig3 = createUnaryKernelConfig(Round);\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Rsqrt.js\nvar rsqrtConfig3 = createUnaryKernelConfig(Rsqrt);\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/ScatterNd.js\nvar wasmScatterNd;\nfunction setup38(backend2) {\n  wasmScatterNd = backend2.wasm.cwrap(ScatterNd, null, [\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"array\",\n    \"number\",\n    \"number\"\n  ]);\n}\nfunction scatterNd3(args) {\n  const { backend: backend2, inputs, attrs } = args;\n  const { indices, updates } = inputs;\n  const { shape } = attrs;\n  const out = backend2.makeOutput(shape, updates.dtype);\n  if (util_exports.sizeFromShape(shape) === 0) {\n    return out;\n  }\n  const { sliceRank, numUpdates, sliceSize, strides, outputSize } = scatter_nd_util_exports.calculateShapes(updates, indices, shape);\n  const indicesData = backend2.dataIdMap.get(indices.dataId);\n  const indicesId = indicesData.id;\n  const updatesData = backend2.dataIdMap.get(updates.dataId);\n  const updatesId = updatesData.id;\n  const stridesBytes = new Uint8Array(new Int32Array(strides).buffer);\n  const outId = backend2.dataIdMap.get(out.dataId).id;\n  wasmScatterNd(indicesId, updatesId, CppDType[updates.dtype], sliceRank, numUpdates, sliceSize, stridesBytes, outputSize, outId);\n  return out;\n}\nvar scatterNdConfig3 = {\n  kernelName: ScatterNd,\n  backendName: \"wasm\",\n  setupFunc: setup38,\n  kernelFunc: scatterNd3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Select.js\nvar wasmSelect;\nfunction setup39(backend2) {\n  wasmSelect = backend2.wasm.cwrap(\"SelectV2\", null, [\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\"\n  ]);\n}\nfunction select4(args) {\n  const { inputs, backend: backend2 } = args;\n  const { condition, t, e } = inputs;\n  const conditionId = backend2.dataIdMap.get(condition.dataId).id;\n  const tId = backend2.dataIdMap.get(t.dataId).id;\n  const eId = backend2.dataIdMap.get(e.dataId).id;\n  const out = backend2.makeOutput(t.shape, t.dtype);\n  const outId = backend2.dataIdMap.get(out.dataId).id;\n  const cRank = condition.shape.length;\n  const tRank = t.shape.length;\n  const offset = cRank === 0 || cRank > 1 || tRank === 1 ? 1 : util_exports.sizeFromShape(t.shape.slice(1));\n  wasmSelect(conditionId, tId, eId, offset, outId);\n  return out;\n}\nvar selectConfig3 = {\n  kernelName: Select,\n  backendName: \"wasm\",\n  kernelFunc: select4,\n  setupFunc: setup39\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Sigmoid.js\nvar wasmFunc7;\nfunction setup40(backend2) {\n  wasmFunc7 = backend2.wasm.cwrap(Sigmoid, null, [\"number\", \"number\"]);\n}\nfunction sigmoid4(args) {\n  const { backend: backend2, inputs: { x } } = args;\n  const xId = backend2.dataIdMap.get(x.dataId).id;\n  const out = backend2.makeOutput(x.shape, x.dtype);\n  const outId = backend2.dataIdMap.get(out.dataId).id;\n  if (util_exports.sizeFromShape(out.shape) === 0) {\n    return out;\n  }\n  wasmFunc7(xId, outId);\n  return out;\n}\nvar sigmoidConfig3 = {\n  kernelName: \"Sigmoid\",\n  backendName: \"wasm\",\n  setupFunc: setup40,\n  kernelFunc: sigmoid4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Sin.js\nvar sinConfig3 = createUnaryKernelConfig(Sin);\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Softmax.js\nvar wasmFunc8;\nfunction setup41(backend2) {\n  wasmFunc8 = backend2.wasm.cwrap(Softmax, null, [\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\"\n  ]);\n}\nfunction softmax5(args) {\n  const { backend: backend2, inputs: { logits }, attrs: { dim } } = args;\n  const xId = backend2.dataIdMap.get(logits.dataId).id;\n  const out = backend2.makeOutput(logits.shape, logits.dtype);\n  const outId = backend2.dataIdMap.get(out.dataId).id;\n  const channels = logits.shape[dim];\n  const batch = util_exports.sizeFromShape(logits.shape) / channels;\n  if (util_exports.sizeFromShape(out.shape) === 0) {\n    return out;\n  }\n  wasmFunc8(xId, outId, channels, batch);\n  return out;\n}\nvar softmaxConfig3 = {\n  kernelName: Softmax,\n  backendName: \"wasm\",\n  setupFunc: setup41,\n  kernelFunc: softmax5\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/SpaceToBatchND.js\nfunction spaceToBatchND4(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x } = inputs;\n  const { blockShape, paddings } = attrs;\n  const prod6 = util_exports.sizeFromShape(blockShape);\n  const completePaddings = [[0, 0]];\n  completePaddings.push(...paddings);\n  for (let i = 1 + blockShape.length; i < x.shape.length; ++i) {\n    completePaddings.push([0, 0]);\n  }\n  const paddedX = padV2Config3.kernelFunc({\n    inputs: { x },\n    backend: backend2,\n    attrs: { paddings: completePaddings, constantValue: 0 }\n  });\n  const reshapedPaddedShape = backend_util_exports.getReshaped(paddedX.shape, blockShape, prod6, false);\n  const permutedReshapedPaddedPermutation = backend_util_exports.getPermuted(reshapedPaddedShape.length, blockShape.length, false);\n  const flattenShape = backend_util_exports.getReshapedPermuted(paddedX.shape, blockShape, prod6, false);\n  const reshapeInputs = { x: paddedX };\n  const reshapeAttrs = { shape: reshapedPaddedShape };\n  const paddedXReshaped = reshape5({ inputs: reshapeInputs, backend: backend2, attrs: reshapeAttrs });\n  const transposeInputs = { x: paddedXReshaped };\n  const transposeAttrs = { perm: permutedReshapedPaddedPermutation };\n  const paddedXT = transpose4({ inputs: transposeInputs, backend: backend2, attrs: transposeAttrs });\n  const resultReshapeInputs = { x: paddedXT };\n  const resultReshapeAttrs = { shape: flattenShape };\n  const result = reshape5({ inputs: resultReshapeInputs, backend: backend2, attrs: resultReshapeAttrs });\n  backend2.disposeData(paddedX.dataId);\n  backend2.disposeData(paddedXReshaped.dataId);\n  backend2.disposeData(paddedXT.dataId);\n  return result;\n}\nvar spaceToBatchNDConfig3 = {\n  kernelName: SpaceToBatchND,\n  backendName: \"wasm\",\n  kernelFunc: spaceToBatchND4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/SparseFillEmptyRows.js\nvar wasmSparseFillEmptyRows;\nfunction setup42(backend2) {\n  wasmSparseFillEmptyRows = backend2.wasm.cwrap(\"SparseFillEmptyRows\", \"number\", [\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\"\n  ]);\n}\nfunction sparseFillEmptyRows4(args) {\n  const { backend: backend2, inputs } = args;\n  const { indices, values, denseShape, defaultValue } = inputs;\n  const indicesCount = indices.shape[0];\n  const rank = indices.shape[1];\n  const denseRows = backend2.readSync(denseShape.dataId)[0];\n  const maxOutputIndicesShape = [indicesCount + denseRows, rank];\n  const indicesId = backend2.dataIdMap.get(indices.dataId).id;\n  const valuesId = backend2.dataIdMap.get(values.dataId).id;\n  const defaultValueId = backend2.dataIdMap.get(defaultValue.dataId).id;\n  const outputIndices = backend2.makeOutput(maxOutputIndicesShape, indices.dtype);\n  const outputIndicesId = backend2.dataIdMap.get(outputIndices.dataId).id;\n  const outputValues = backend2.makeOutput(maxOutputIndicesShape.slice(0, 1), values.dtype);\n  const outputValuesId = backend2.dataIdMap.get(outputValues.dataId).id;\n  const emptyRowIndicator = backend2.makeOutput([denseRows], \"bool\");\n  const emptyRowIndicatorId = backend2.dataIdMap.get(emptyRowIndicator.dataId).id;\n  const reverseIndexMap = backend2.makeOutput([indicesCount], indices.dtype);\n  const reverseIndexMapId = backend2.dataIdMap.get(reverseIndexMap.dataId).id;\n  const exceptionValues = backend2.makeOutput([4], \"int32\");\n  const exceptionValuesId = backend2.dataIdMap.get(exceptionValues.dataId).id;\n  const outputRows = wasmSparseFillEmptyRows(indicesId, valuesId, CppDType[values.dtype], indicesCount, denseRows, rank, defaultValueId, outputIndicesId, outputValuesId, emptyRowIndicatorId, reverseIndexMapId, exceptionValuesId);\n  const exceptionValuesArray = backend2.readSync(exceptionValues.dataId);\n  let exceptionMessage;\n  switch (exceptionValuesArray[0]) {\n    case 1: {\n      exceptionMessage = backend_util_exports.getSparseFillEmptyRowsIndicesDenseShapeMismatch(exceptionValuesArray[1]);\n      break;\n    }\n    case 2: {\n      exceptionMessage = backend_util_exports.getSparseFillEmptyRowsNegativeIndexErrorMessage(exceptionValuesArray[1], exceptionValuesArray[2]);\n      break;\n    }\n    case 3:\n      exceptionMessage = backend_util_exports.getSparseFillEmptyRowsOutOfRangeIndexErrorMessage(exceptionValuesArray[1], exceptionValuesArray[2], exceptionValuesArray[3]);\n      break;\n    default:\n      exceptionMessage = \"\";\n  }\n  backend2.disposeData(exceptionValues.dataId);\n  if (exceptionMessage) {\n    backend2.disposeData(outputIndices.dataId);\n    backend2.disposeData(outputValues.dataId);\n    backend2.disposeData(emptyRowIndicator.dataId);\n    backend2.disposeData(reverseIndexMap.dataId);\n    throw new Error(exceptionMessage);\n  }\n  let resizedIndices = outputIndices;\n  let resizedValues = outputValues;\n  if (outputRows !== maxOutputIndicesShape[0]) {\n    resizedIndices = slice4({\n      inputs: { x: outputIndices },\n      attrs: { begin: 0, size: [outputRows, rank] },\n      backend: backend2\n    });\n    resizedValues = slice4({\n      inputs: { x: outputValues },\n      attrs: { begin: 0, size: outputRows },\n      backend: backend2\n    });\n    backend2.disposeData(outputIndices.dataId);\n    backend2.disposeData(outputValues.dataId);\n  }\n  return [resizedIndices, resizedValues, emptyRowIndicator, reverseIndexMap];\n}\nvar sparseFillEmptyRowsConfig3 = {\n  kernelName: SparseFillEmptyRows,\n  backendName: \"wasm\",\n  setupFunc: setup42,\n  kernelFunc: sparseFillEmptyRows4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/SparseReshape.js\nvar wasmSparseReshape;\nfunction setup43(backend2) {\n  wasmSparseReshape = backend2.wasm.cwrap(SparseReshape, null, [\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\"\n  ]);\n}\nfunction sparseReshape4(args) {\n  const { backend: backend2, inputs } = args;\n  const { inputIndices, inputShape, newShape } = inputs;\n  if (inputIndices.shape.length !== 2) {\n    throw new Error(`Input indices should be a matrix but received shape\n        ${inputIndices.shape}`);\n  }\n  if (inputShape.shape.length !== 1) {\n    throw new Error(`Input shape should be a vector but received shape\n        ${inputShape.shape}`);\n  }\n  if (newShape.shape.length !== 1) {\n    throw new Error(`Target shape should be a vector but received shape ${newShape.shape}`);\n  }\n  const inputIndicesId = backend2.dataIdMap.get(inputIndices.dataId).id;\n  const inputShapeId = backend2.dataIdMap.get(inputShape.dataId).id;\n  const newShapeId = backend2.dataIdMap.get(newShape.dataId).id;\n  const nnz = inputIndices.shape[0];\n  const outputRank = util_exports.sizeFromShape(newShape.shape);\n  const newIndices = backend2.makeOutput([nnz, outputRank], inputIndices.dtype);\n  const newIndicesId = backend2.dataIdMap.get(newIndices.dataId).id;\n  const outputShape = backend2.makeOutput([outputRank], newShape.dtype);\n  const outputShapeId = backend2.dataIdMap.get(outputShape.dataId).id;\n  const exceptionValues = backend2.makeOutput([3], \"int32\");\n  const exceptionValuesId = backend2.dataIdMap.get(exceptionValues.dataId).id;\n  wasmSparseReshape(inputIndicesId, inputShapeId, newShapeId, nnz, newIndicesId, outputShapeId, exceptionValuesId);\n  const exceptionValuesArray = backend2.readSync(exceptionValues.dataId);\n  let exceptionMessage;\n  switch (exceptionValuesArray[0]) {\n    case 0: {\n      exceptionMessage = backend_util_exports.getSparseReshapeMultipleNegativeOneOutputDimErrorMessage(exceptionValuesArray[1], exceptionValuesArray[2]);\n      break;\n    }\n    case 1: {\n      exceptionMessage = backend_util_exports.getSparseReshapeNegativeOutputDimErrorMessage(exceptionValuesArray[1], exceptionValuesArray[2]);\n      break;\n    }\n    case 2:\n      exceptionMessage = backend_util_exports.getSparseReshapeEmptyTensorZeroOutputDimErrorMessage();\n      break;\n    case 3: {\n      const inputShapeValues = Array.from(backend2.readSync(inputShape.dataId)), outputShapeValues = Array.from(backend2.readSync(outputShape.dataId));\n      exceptionMessage = backend_util_exports.getSparseReshapeInputOutputMultipleErrorMessage(inputShapeValues, outputShapeValues);\n      break;\n    }\n    case 4: {\n      const inputShapeValues = Array.from(backend2.readSync(inputShape.dataId)), outputShapeValues = Array.from(backend2.readSync(outputShape.dataId));\n      exceptionMessage = backend_util_exports.getSparseReshapeInputOutputMismatchErrorMessage(inputShapeValues, outputShapeValues);\n      break;\n    }\n    default:\n      exceptionMessage = \"\";\n  }\n  backend2.disposeData(exceptionValues.dataId);\n  if (exceptionMessage) {\n    backend2.disposeData(newIndices.dataId);\n    backend2.disposeData(outputShape.dataId);\n    throw new Error(exceptionMessage);\n  }\n  return [newIndices, outputShape];\n}\nvar sparseReshapeConfig3 = {\n  kernelName: SparseReshape,\n  backendName: \"wasm\",\n  setupFunc: setup43,\n  kernelFunc: sparseReshape4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/SparseSegmentReduction.js\nvar wasmSparseSegmentReduction;\nfunction setup44(backend2) {\n  wasmSparseSegmentReduction = backend2.wasm.cwrap(\"SparseSegmentReduction\", null, [\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\"\n  ]);\n}\nfunction sparseSegmentReduction(args, isMean) {\n  const { backend: backend2, inputs } = args;\n  const { data, indices, segmentIds } = inputs;\n  const numIndices = indices.shape[0];\n  const segmentIdsBack = backend2.readSync(segmentIds.dataId, numIndices - 1, numIndices)[0];\n  const lastSegmentIdPlusOne = numIndices > 0 ? segmentIdsBack + 1 : 0;\n  const outputRows = lastSegmentIdPlusOne;\n  if (outputRows < 0) {\n    throw new Error(backend_util_exports.getSparseSegmentReductionNegativeSegmentIdsErrorMessage());\n  }\n  const outputShape = data.shape.slice();\n  outputShape[0] = outputRows;\n  const dataId = backend2.dataIdMap.get(data.dataId).id;\n  const indicesId = backend2.dataIdMap.get(indices.dataId).id;\n  const segmentIdsId = backend2.dataIdMap.get(segmentIds.dataId).id;\n  const output = backend2.makeOutput(outputShape, data.dtype);\n  const outputId = backend2.dataIdMap.get(output.dataId).id;\n  const exceptionValues = backend2.makeOutput([4], \"int32\");\n  const exceptionValuesId = backend2.dataIdMap.get(exceptionValues.dataId).id;\n  wasmSparseSegmentReduction(dataId, CppDType[data.dtype], data.shape[0], indicesId, segmentIdsId, outputId, exceptionValuesId, isMean, 0);\n  const exceptionValuesArray = backend2.readSync(exceptionValues.dataId);\n  let exceptionMessage;\n  switch (exceptionValuesArray[0]) {\n    case 0: {\n      exceptionMessage = backend_util_exports.getSparseSegmentReductionNegativeSegmentIdsErrorMessage();\n      break;\n    }\n    case 1: {\n      exceptionMessage = backend_util_exports.getSparseSegmentReductionNonIncreasingSegmentIdsErrorMessage();\n      break;\n    }\n    case 2:\n      exceptionMessage = backend_util_exports.getSparseSegmentReductionSegmentIdOutOfRangeErrorMessage(exceptionValuesArray[1], exceptionValuesArray[2]);\n      break;\n    case 3:\n      exceptionMessage = backend_util_exports.getSparseSegmentReductionIndicesOutOfRangeErrorMessage(exceptionValuesArray[1], exceptionValuesArray[2], exceptionValuesArray[3]);\n      break;\n    default:\n      exceptionMessage = \"\";\n  }\n  backend2.disposeData(exceptionValues.dataId);\n  if (exceptionMessage) {\n    backend2.disposeData(output.dataId);\n    throw new Error(exceptionMessage);\n  }\n  return output;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/SparseSegmentMean.js\nfunction sparseSegmentMean4(args) {\n  return sparseSegmentReduction(args, true);\n}\nvar sparseSegmentMeanConfig3 = {\n  kernelName: SparseSegmentMean,\n  backendName: \"wasm\",\n  setupFunc: setup44,\n  kernelFunc: sparseSegmentMean4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/SparseSegmentSum.js\nfunction sparseSegmentSum4(args) {\n  return sparseSegmentReduction(args, false);\n}\nvar sparseSegmentSumConfig3 = {\n  kernelName: SparseSegmentSum,\n  backendName: \"wasm\",\n  setupFunc: setup44,\n  kernelFunc: sparseSegmentSum4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/SplitV.js\nfunction splitV3(args) {\n  const { inputs, attrs, backend: backend2 } = args;\n  const { x } = inputs;\n  const { numOrSizeSplits, axis } = attrs;\n  const $axis = util_exports.parseAxisParam(axis, x.shape)[0];\n  const splitSizes = backend_util_exports.prepareSplitSize(x, numOrSizeSplits, $axis);\n  const begin = new Array(x.shape.length).fill(0);\n  const size = x.shape.slice();\n  return splitSizes.map((s) => {\n    const xSliceSize = [...size];\n    xSliceSize[$axis] = s;\n    const xSlice = slice4({ inputs: { x }, attrs: { begin, size: xSliceSize }, backend: backend2 });\n    begin[$axis] += s;\n    return xSlice;\n  });\n}\nvar splitVConfig3 = {\n  kernelName: SplitV,\n  backendName: \"wasm\",\n  kernelFunc: splitV3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Sqrt.js\nvar sqrtConfig3 = createUnaryKernelConfig(Sqrt);\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Square.js\nvar squareConfig3 = createUnaryKernelConfig(Square);\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/SquaredDifference.js\nvar supportsFullBroadcast17 = true;\nvar squaredDifferenceConfig3 = createBinaryKernelConfig(SquaredDifference, supportsFullBroadcast17);\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Step.js\nvar wasmStep;\nfunction setup45(backend2) {\n  wasmStep = backend2.wasm.cwrap(Step, null, [\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\"\n  ]);\n}\nfunction step4(args) {\n  const { backend: backend2, inputs, attrs } = args;\n  const { alpha } = attrs;\n  const { x } = inputs;\n  const xId = backend2.dataIdMap.get(x.dataId).id;\n  const out = backend2.makeOutput(x.shape, x.dtype);\n  const outId = backend2.dataIdMap.get(out.dataId).id;\n  wasmStep(xId, alpha, CppDType[x.dtype], outId);\n  return out;\n}\nvar stepConfig3 = {\n  kernelName: Step,\n  backendName: \"wasm\",\n  setupFunc: setup45,\n  kernelFunc: step4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/StridedSlice.js\nvar wasmStridedSlice;\nfunction setup46(backend2) {\n  wasmStridedSlice = backend2.wasm.cwrap(StridedSlice, null, [\n    \"number\",\n    \"array\",\n    \"number\",\n    \"array\",\n    \"array\",\n    \"array\",\n    \"array\",\n    \"array\",\n    \"number\",\n    \"number\"\n  ]);\n}\nfunction stridedSlice4(args) {\n  const { backend: backend2, inputs, attrs } = args;\n  const { x } = inputs;\n  const { begin, end, strides, beginMask, endMask, ellipsisMask, newAxisMask, shrinkAxisMask } = attrs;\n  const { finalShapeSparse, finalShape, isIdentity, sliceDim0, isSimpleSlice, begin: $begin, end: $end, strides: $strides } = slice_util_exports.sliceInfo(x.shape, begin, end, strides, beginMask, endMask, ellipsisMask, newAxisMask, shrinkAxisMask);\n  let result;\n  if (isIdentity) {\n    result = reshape5({ inputs: { x }, backend: backend2, attrs: { shape: finalShape } });\n  } else if (sliceDim0 || isSimpleSlice) {\n    util_exports.assert(x.shape.length >= 1, () => `Input must have rank at least 1, got: ${x.shape.length}`);\n    const size = slice_util_exports.computeOutShape($begin, $end, $strides);\n    const sliced = slice4({ inputs: { x }, backend: backend2, attrs: { begin: $begin, size } });\n    result = reshape5({ inputs: { x: sliced }, backend: backend2, attrs: { shape: finalShape } });\n    backend2.disposeData(sliced.dataId);\n  } else {\n    const out = backend2.makeOutput(finalShapeSparse, \"float32\");\n    const xId = backend2.dataIdMap.get(x.dataId).id;\n    const xStridesBytes = new Uint8Array(new Int32Array(util_exports.computeStrides(x.shape)).buffer);\n    const beginBytes = new Uint8Array(new Int32Array($begin).buffer);\n    const endBytes = new Uint8Array(new Int32Array($end).buffer);\n    const stridesBytes = new Uint8Array(new Int32Array($strides).buffer);\n    const outputShapeBytes = new Uint8Array(new Int32Array(finalShapeSparse).buffer);\n    const outStridesBytes = new Uint8Array(new Int32Array(util_exports.computeStrides(finalShapeSparse)).buffer);\n    const outId = backend2.dataIdMap.get(out.dataId).id;\n    wasmStridedSlice(xId, xStridesBytes, x.shape.length, beginBytes, endBytes, stridesBytes, outputShapeBytes, outStridesBytes, finalShapeSparse.length, outId);\n    result = reshape5({ inputs: { x: out }, backend: backend2, attrs: { shape: finalShape } });\n    backend2.disposeData(out.dataId);\n  }\n  return result;\n}\nvar stridedSliceConfig3 = {\n  kernelName: StridedSlice,\n  backendName: \"wasm\",\n  setupFunc: setup46,\n  kernelFunc: stridedSlice4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/StringNGrams.js\nfunction stringNGrams4(args) {\n  const { backend: backend2, inputs, attrs } = args;\n  const { data, dataSplits } = inputs;\n  const { separator, nGramWidths, leftPad, rightPad: rightPad2, padWidth, preserveShortSequences } = attrs;\n  const $data = backend2.readSync(data.dataId);\n  const $dataSplits = backend2.readSync(dataSplits.dataId);\n  const [nGrams, nGramsSplits] = stringNGramsImpl($data, $dataSplits, separator, nGramWidths, leftPad, rightPad2, padWidth, preserveShortSequences);\n  const nGramsOut = backend2.makeOutput([nGrams.length], \"string\");\n  const nGramsOutData = backend2.dataIdMap.get(nGramsOut.dataId);\n  nGramsOutData.stringBytes = nGrams;\n  const nGramsSplitsOut = backend2.makeOutput(dataSplits.shape, \"int32\");\n  const nGramsSplitsOutVals = backend2.typedArrayFromHeap(nGramsSplitsOut);\n  nGramsSplitsOutVals.set(nGramsSplits);\n  return [nGramsOut, nGramsSplitsOut];\n}\nvar stringNGramsConfig3 = {\n  kernelName: StringNGrams,\n  backendName: \"wasm\",\n  kernelFunc: stringNGrams4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/StringSplit.js\nfunction stringSplit4(args) {\n  const { backend: backend2, inputs, attrs } = args;\n  const { input: input2, delimiter } = inputs;\n  const { skipEmpty } = attrs;\n  const inputVals = backend2.readSync(input2.dataId);\n  const delimiterVals = backend2.readSync(delimiter.dataId);\n  const [indices, values, shape] = stringSplitImpl(inputVals, delimiterVals[0], skipEmpty);\n  const outputSize = values.length;\n  const indicesOut = backend2.makeOutput([outputSize, 2], \"int32\");\n  const indicesOutVals = backend2.typedArrayFromHeap(indicesOut);\n  indicesOutVals.set(indices);\n  const valuesOut = backend2.makeOutput([outputSize], \"string\");\n  const valuesOutData = backend2.dataIdMap.get(valuesOut.dataId);\n  valuesOutData.stringBytes = values;\n  const shapeOut = backend2.makeOutput([2], \"int32\");\n  const shapeOutVals = backend2.typedArrayFromHeap(shapeOut);\n  shapeOutVals.set(shape);\n  return [indicesOut, valuesOut, shapeOut];\n}\nvar stringSplitConfig3 = {\n  kernelName: StringSplit,\n  backendName: \"wasm\",\n  kernelFunc: stringSplit4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/StringToHashBucketFast.js\nfunction stringToHashBucketFast4(args) {\n  const { backend: backend2, inputs, attrs } = args;\n  const { input: input2 } = inputs;\n  const { numBuckets } = attrs;\n  const inputVals = backend2.readSync(input2.dataId);\n  const values = stringToHashBucketFastImpl(inputVals, numBuckets);\n  const out = backend2.makeOutput(input2.shape, \"int32\");\n  const outVals = backend2.typedArrayFromHeap(out);\n  outVals.set(values);\n  return out;\n}\nvar stringToHashBucketFastConfig3 = {\n  kernelName: StringToHashBucketFast,\n  backendName: \"wasm\",\n  kernelFunc: stringToHashBucketFast4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Sub.js\nvar supportsFullBroadcast18 = true;\nvar subConfig3 = createBinaryKernelConfig(Sub, supportsFullBroadcast18);\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Sum.js\nvar wasmSum;\nfunction setup47(backend2) {\n  wasmSum = backend2.wasm.cwrap(Sum, null, [\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\"\n  ]);\n}\nfunction sum5(args) {\n  const { backend: backend2, inputs, attrs } = args;\n  const { axis, keepDims } = attrs;\n  const { x } = inputs;\n  const xId = backend2.dataIdMap.get(x.dataId).id;\n  let inputId = xId;\n  let input2 = x;\n  const { transposed, axes, originalAxes, inputWasTransposed } = permuteAxesAndTranspose(x, axis, backend2);\n  let reductionAxes = axes;\n  if (inputWasTransposed) {\n    const transposedId = backend2.dataIdMap.get(transposed.dataId).id;\n    if (transposedId !== xId) {\n      input2 = transposed;\n      inputId = transposedId;\n      reductionAxes = backend_util_exports.getInnerMostAxes(reductionAxes.length, input2.shape.length);\n    }\n  }\n  backend_util_exports.assertAxesAreInnerMostDims(\"sum\", reductionAxes, input2.shape.length);\n  const [outShape, reduceShape] = backend_util_exports.computeOutAndReduceShapes(input2.shape, reductionAxes);\n  const reduceSize = util_exports.sizeFromShape(reduceShape);\n  const out = backend2.makeOutput(outShape, input2.dtype);\n  if (util_exports.sizeFromShape(input2.shape) !== 0) {\n    const outId = backend2.dataIdMap.get(out.dataId).id;\n    wasmSum(inputId, reduceSize, CppDType[out.dtype], outId);\n  }\n  if (inputWasTransposed) {\n    backend2.disposeData(transposed.dataId);\n  }\n  if (keepDims) {\n    const newShape = backend_util_exports.expandShapeToKeepDim(out.shape, originalAxes);\n    out.shape = newShape;\n  }\n  return out;\n}\nvar sumConfig3 = {\n  kernelName: Sum,\n  backendName: \"wasm\",\n  setupFunc: setup47,\n  kernelFunc: sum5\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Tan.js\nvar tanConfig3 = createUnaryKernelConfig(Tan);\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Tanh.js\nvar tanhConfig3 = createUnaryKernelConfig(Tanh);\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Tile.js\nvar wasmTile;\nfunction setup48(backend2) {\n  wasmTile = backend2.wasm.cwrap(Tile, null, [\n    \"number\",\n    \"array\",\n    \"number\",\n    \"array\",\n    \"number\",\n    \"number\"\n  ]);\n}\nfunction tile5(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x } = inputs;\n  const xId = backend2.dataIdMap.get(x.dataId).id;\n  const { reps } = attrs;\n  const newShape = new Array(x.shape.length);\n  for (let i = 0; i < newShape.length; i++) {\n    newShape[i] = x.shape[i] * reps[i];\n  }\n  const xShapeBytes = new Uint8Array(new Int32Array(x.shape).buffer);\n  const newShapeBytes = new Uint8Array(new Int32Array(newShape).buffer);\n  const out = backend2.makeOutput(newShape, x.dtype);\n  const outId = backend2.dataIdMap.get(out.dataId).id;\n  wasmTile(xId, xShapeBytes, x.shape.length, newShapeBytes, newShape.length, CppDType[out.dtype], outId);\n  return out;\n}\nvar tileConfig3 = {\n  kernelName: Tile,\n  backendName: \"wasm\",\n  setupFunc: setup48,\n  kernelFunc: tile5\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/TopK.js\nvar wasmTopK;\nfunction setup49(backend2) {\n  wasmTopK = backend2.wasm.cwrap(TopK, null, [\n    \"number\",\n    \"array\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"bool\",\n    \"number\",\n    \"number\"\n  ]);\n}\nvar topk2 = ({ inputs, backend: backend2, attrs }) => {\n  const { x } = inputs;\n  const { k, sorted } = attrs;\n  const xId = backend2.dataIdMap.get(x.dataId).id;\n  const xShapeBytes = new Uint8Array(new Int32Array(x.shape).buffer);\n  const outputShape = x.shape.slice();\n  outputShape[outputShape.length - 1] = k;\n  const outValues = backend2.makeOutput(outputShape, x.dtype);\n  const outValuesId = backend2.dataIdMap.get(outValues.dataId).id;\n  const outIndices = backend2.makeOutput(outputShape, \"int32\");\n  const outIndicesId = backend2.dataIdMap.get(outIndices.dataId).id;\n  wasmTopK(xId, xShapeBytes, x.shape.length, CppDType[x.dtype], k, sorted, outValuesId, outIndicesId);\n  return [outValues, outIndices];\n};\nvar topKConfig3 = {\n  kernelName: TopK,\n  backendName: \"wasm\",\n  setupFunc: setup49,\n  kernelFunc: topk2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Transform.js\nvar wasmTransform;\nfunction setup50(backend2) {\n  wasmTransform = backend2.wasm.cwrap(Transform, null, [\n    \"number\",\n    \"number\",\n    \"bool\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"array\",\n    \"number\",\n    \"array\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\",\n    \"number\"\n  ]);\n}\nfunction transform4(args) {\n  const { backend: backend2, inputs, attrs } = args;\n  const { image: image2, transforms } = inputs;\n  const { interpolation, fillMode, fillValue, outputShape } = attrs;\n  const [batch, imageHeight, imageWidth, numChannels] = image2.shape;\n  const [outHeight, outWidth] = outputShape != null ? outputShape : [imageHeight, imageWidth];\n  const outShape = [\n    batch,\n    outHeight,\n    outWidth,\n    numChannels\n  ];\n  const inputStrides = new Uint8Array(new Int32Array(util_exports.computeStrides(image2.shape)).buffer);\n  const outputStrides = new Uint8Array(new Int32Array(util_exports.computeStrides(outShape)).buffer);\n  const out = backend2.makeOutput(outShape, image2.dtype);\n  const outId = backend2.dataIdMap.get(out.dataId).id;\n  const imageData = backend2.dataIdMap.get(image2.dataId);\n  const imageId = imageData.id;\n  const transformsData = backend2.dataIdMap.get(transforms.dataId);\n  const transformsId = transformsData.id;\n  const interpolationModeId = interpolation === \"nearest\" ? 1 : 2;\n  let fillModeId;\n  switch (fillMode) {\n    case \"constant\":\n      fillModeId = 1;\n      break;\n    case \"reflect\":\n      fillModeId = 2;\n      break;\n    case \"wrap\":\n      fillModeId = 3;\n      break;\n    case \"nearest\":\n      fillModeId = 4;\n      break;\n    default:\n      fillModeId = 1;\n      break;\n  }\n  wasmTransform(imageId, transformsId, transforms.shape[0] > 1, batch, outHeight, outWidth, numChannels, imageWidth, imageHeight, inputStrides, image2.shape.length - 1, outputStrides, outShape.length - 1, interpolationModeId, fillModeId, fillValue, outId);\n  return out;\n}\nvar transformConfig3 = {\n  kernelName: Transform,\n  backendName: \"wasm\",\n  setupFunc: setup50,\n  kernelFunc: transform4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/Unpack.js\nfunction unpack3(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { value } = inputs;\n  let { axis } = attrs;\n  if (axis < 0) {\n    axis += value.shape.length;\n  }\n  const numOutputs = value.shape[axis];\n  const rank = value.shape.length;\n  const outShape = new Array(rank - 1);\n  let outIndex = 0;\n  for (let i = 0; i < rank; i++) {\n    if (i !== axis) {\n      outShape[outIndex++] = value.shape[i];\n    }\n  }\n  const outs = new Array(numOutputs);\n  const begin = new Array(rank).fill(0);\n  const size = value.shape.slice();\n  size[axis] = 1;\n  for (let i = 0; i < outs.length; i++) {\n    begin[axis] = i;\n    outs[i] = slice4({ inputs: { x: value }, attrs: { begin, size }, backend: backend2 });\n  }\n  return outs.map(({ dataId, dtype }) => ({ dataId, dtype, shape: outShape }));\n}\nvar unpackConfig3 = {\n  kernelName: Unpack,\n  backendName: \"wasm\",\n  kernelFunc: unpack3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-wasm/dist/kernels/ZerosLike.js\nfunction zerosLike4(args) {\n  const { inputs: { x }, backend: backend2 } = args;\n  const out = backend2.makeOutput(x.shape, x.dtype);\n  const outVals = backend2.typedArrayFromHeap(out);\n  outVals.fill(0);\n  return out;\n}\nvar zerosLikeConfig3 = {\n  kernelName: ZerosLike,\n  backendName: \"wasm\",\n  kernelFunc: zerosLike4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-wasm/dist/register_all_kernels.js\nvar kernelConfigs3 = [\n  _fusedMatMulConfig3,\n  absConfig3,\n  addConfig3,\n  addNConfig3,\n  allConfig3,\n  anyConfig3,\n  argMaxConfig3,\n  avgPoolConfig3,\n  batchMatMulConfig3,\n  batchToSpaceNDConfig3,\n  castConfig3,\n  ceilConfig3,\n  clipByValueConfig3,\n  concatConfig3,\n  conv2DConfig3,\n  conv2DBackpropInputConfig3,\n  cosConfig3,\n  coshConfig3,\n  cropAndResizeConfig3,\n  cumprodConfig3,\n  cumsumConfig3,\n  depthToSpaceConfig3,\n  depthwiseConv2dNativeConfig3,\n  eluConfig3,\n  equalConfig3,\n  expConfig3,\n  expandDimsConfig3,\n  fillConfig3,\n  flipLeftRightConfig3,\n  floorConfig3,\n  floorDivConfig3,\n  fusedBatchNormConfig,\n  fusedConv2DConfig3,\n  fusedDepthwiseConv2DConfig3,\n  gatherNdConfig3,\n  gatherV2Config3,\n  greaterConfig3,\n  greaterEqualConfig3,\n  identityConfig3,\n  leakyReluConfig3,\n  lessConfig3,\n  lessEqualConfig3,\n  logConfig3,\n  logicalAndConfig3,\n  logicalNotConfig3,\n  logicalOrConfig3,\n  logicalXorConfig,\n  maxConfig3,\n  maximumConfig3,\n  maxPoolConfig3,\n  meanConfig3,\n  minConfig3,\n  minimumConfig3,\n  mirrorPadConfig3,\n  multiplyConfig3,\n  negConfig3,\n  nonMaxSuppressionV3Config3,\n  nonMaxSuppressionV4Config3,\n  nonMaxSuppressionV5Config3,\n  notEqualConfig3,\n  oneHotConfig3,\n  onesLikeConfig3,\n  packConfig3,\n  padV2Config3,\n  powConfig3,\n  preluConfig3,\n  prodConfig3,\n  rangeConfig3,\n  realDivConfig3,\n  reluConfig3,\n  relu6Config3,\n  reshapeConfig3,\n  resizeBilinearConfig3,\n  resizeNearestNeighborConfig3,\n  reverseConfig3,\n  rotateWithOffsetConfig3,\n  roundConfig3,\n  rsqrtConfig3,\n  scatterNdConfig3,\n  selectConfig3,\n  sigmoidConfig3,\n  sinConfig3,\n  sliceConfig3,\n  softmaxConfig3,\n  spaceToBatchNDConfig3,\n  sparseFillEmptyRowsConfig3,\n  sparseReshapeConfig3,\n  sparseSegmentMeanConfig3,\n  sparseSegmentSumConfig3,\n  splitVConfig3,\n  sqrtConfig3,\n  squareConfig3,\n  squaredDifferenceConfig3,\n  stepConfig3,\n  stridedSliceConfig3,\n  stringNGramsConfig3,\n  stringSplitConfig3,\n  stringToHashBucketFastConfig3,\n  subConfig3,\n  sumConfig3,\n  tanConfig3,\n  tanhConfig3,\n  tileConfig3,\n  topKConfig3,\n  transformConfig3,\n  transposeConfig3,\n  unpackConfig3,\n  zerosLikeConfig3\n];\nfor (const kernelConfig of kernelConfigs3) {\n  registerKernel(kernelConfig);\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-wasm/dist/flags_wasm.js\nvar ENV6 = env();\nENV6.registerFlag(\n  \"WASM_HAS_SIMD_SUPPORT\",\n  async () => WebAssembly.validate(new Uint8Array([\n    0,\n    97,\n    115,\n    109,\n    1,\n    0,\n    0,\n    0,\n    1,\n    4,\n    1,\n    96,\n    0,\n    0,\n    3,\n    2,\n    1,\n    0,\n    10,\n    9,\n    1,\n    7,\n    0,\n    65,\n    0,\n    253,\n    15,\n    26,\n    11\n  ]))\n);\nENV6.registerFlag(\"WASM_HAS_MULTITHREAD_SUPPORT\", async () => {\n  if (ENV6.get(\"IS_NODE\")) {\n    return false;\n  }\n  try {\n    new MessageChannel().port1.postMessage(new SharedArrayBuffer(1));\n    return WebAssembly.validate(new Uint8Array([\n      0,\n      97,\n      115,\n      109,\n      1,\n      0,\n      0,\n      0,\n      1,\n      4,\n      1,\n      96,\n      0,\n      0,\n      3,\n      2,\n      1,\n      0,\n      5,\n      4,\n      1,\n      3,\n      1,\n      1,\n      10,\n      11,\n      1,\n      9,\n      0,\n      65,\n      0,\n      254,\n      16,\n      2,\n      0,\n      26,\n      11\n    ]));\n  } catch (e) {\n    return false;\n  }\n});\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-wasm/dist/backend_wasm.js\nvar wasmFactoryThreadedSimd_import = __toESM(require_tfjs_backend_wasm_threaded_simd());\nvar import_tfjs_backend_wasm_threaded_simd_worker = __toESM(require_tfjs_backend_wasm_threaded_simd_worker());\nvar wasmFactory_import = __toESM(require_tfjs_backend_wasm());\nvar wasmFactoryThreadedSimd = wasmFactoryThreadedSimd_import.default || wasmFactoryThreadedSimd_import;\nvar wasmFactory = wasmFactory_import.default || wasmFactory_import;\nvar BackendWasm = class extends KernelBackend {\n  constructor(wasm) {\n    super();\n    this.wasm = wasm;\n    this.dataIdNextNumber = 1;\n    this.wasm.tfjs.initWithThreadsCount(threadsCount);\n    actualThreadsCount = this.wasm.tfjs.getThreadsCount();\n    this.dataIdMap = new DataStorage(this, engine());\n  }\n  write(values, shape, dtype) {\n    const dataId = { id: this.dataIdNextNumber++ };\n    this.move(dataId, values, shape, dtype, 1);\n    return dataId;\n  }\n  numDataIds() {\n    return this.dataIdMap.numDataIds();\n  }\n  async time(f) {\n    const start = util_exports.now();\n    f();\n    const kernelMs = util_exports.now() - start;\n    return { kernelMs };\n  }\n  move(dataId, values, shape, dtype, refCount) {\n    const id = this.dataIdNextNumber++;\n    if (dtype === \"string\") {\n      const stringBytes = values;\n      this.dataIdMap.set(dataId, { id, stringBytes, shape, dtype, memoryOffset: null, refCount });\n      return;\n    }\n    const size = util_exports.sizeFromShape(shape);\n    const numBytes = size * util_exports.bytesPerElement(dtype);\n    const memoryOffset = this.wasm._malloc(numBytes);\n    this.dataIdMap.set(dataId, { id, memoryOffset, shape, dtype, refCount });\n    this.wasm.tfjs.registerTensor(id, size, memoryOffset);\n    if (values != null) {\n      this.wasm.HEAPU8.set(new Uint8Array(values.buffer, values.byteOffset, numBytes), memoryOffset);\n    }\n  }\n  async read(dataId) {\n    return this.readSync(dataId);\n  }\n  readSync(dataId, start, end) {\n    const { memoryOffset, dtype, shape, stringBytes } = this.dataIdMap.get(dataId);\n    if (dtype === \"string\") {\n      if ((start == null || start === 0) && (end == null || end >= stringBytes.length)) {\n        return stringBytes;\n      }\n      return stringBytes.slice(start, end);\n    }\n    start = start || 0;\n    end = end || util_exports.sizeFromShape(shape);\n    const bytesPerElement2 = util_exports.bytesPerElement(dtype);\n    const bytes = this.wasm.HEAPU8.slice(memoryOffset + start * bytesPerElement2, memoryOffset + end * bytesPerElement2);\n    return typedArrayFromBuffer(bytes.buffer, dtype);\n  }\n  disposeData(dataId, force = false) {\n    if (this.dataIdMap.has(dataId)) {\n      const data = this.dataIdMap.get(dataId);\n      data.refCount--;\n      if (!force && data.refCount > 0) {\n        return false;\n      }\n      this.wasm._free(data.memoryOffset);\n      this.wasm.tfjs.disposeData(data.id);\n      this.dataIdMap.delete(dataId);\n    }\n    return true;\n  }\n  refCount(dataId) {\n    if (this.dataIdMap.has(dataId)) {\n      const tensorData = this.dataIdMap.get(dataId);\n      return tensorData.refCount;\n    }\n    return 0;\n  }\n  incRef(dataId) {\n    const data = this.dataIdMap.get(dataId);\n    if (data != null) {\n      data.refCount++;\n    }\n  }\n  floatPrecision() {\n    return 32;\n  }\n  getMemoryOffset(dataId) {\n    return this.dataIdMap.get(dataId).memoryOffset;\n  }\n  dispose() {\n    this.wasm.tfjs.dispose();\n    if (\"PThread\" in this.wasm) {\n      this.wasm.PThread.terminateAllThreads();\n    }\n    this.wasm = null;\n  }\n  memory() {\n    return { unreliable: false };\n  }\n  makeOutput(shape, dtype, memoryOffset) {\n    let dataId;\n    if (memoryOffset == null) {\n      dataId = this.write(null, shape, dtype);\n    } else {\n      const id = this.dataIdNextNumber++;\n      dataId = { id };\n      this.dataIdMap.set(dataId, { id, memoryOffset, shape, dtype, refCount: 1 });\n      const size = util_exports.sizeFromShape(shape);\n      this.wasm.tfjs.registerTensor(id, size, memoryOffset);\n    }\n    return { dataId, shape, dtype };\n  }\n  typedArrayFromHeap({ shape, dtype, dataId }) {\n    const buffer2 = this.wasm.HEAPU8.buffer;\n    const { memoryOffset } = this.dataIdMap.get(dataId);\n    const size = util_exports.sizeFromShape(shape);\n    switch (dtype) {\n      case \"float32\":\n        return new Float32Array(buffer2, memoryOffset, size);\n      case \"int32\":\n        return new Int32Array(buffer2, memoryOffset, size);\n      case \"bool\":\n        return new Uint8Array(buffer2, memoryOffset, size);\n      default:\n        throw new Error(`Unknown dtype ${dtype}`);\n    }\n  }\n};\nfunction createInstantiateWasmFunc(path) {\n  return (imports, callback) => {\n    util_exports.fetch(path, { credentials: \"same-origin\" }).then((response) => {\n      if (!response[\"ok\"]) {\n        imports.env.a(`failed to load wasm binary file at '${path}'`);\n      }\n      response.arrayBuffer().then((binary) => {\n        WebAssembly.instantiate(binary, imports).then((output) => {\n          callback(output.instance, output.module);\n        });\n      });\n    });\n    return {};\n  };\n}\nfunction getPathToWasmBinary(simdSupported, threadsSupported, wasmModuleFolder) {\n  if (wasmPath != null) {\n    return wasmPath;\n  }\n  let path = \"tfjs-backend-wasm.wasm\";\n  if (simdSupported && threadsSupported) {\n    path = \"tfjs-backend-wasm-threaded-simd.wasm\";\n  } else if (simdSupported) {\n    path = \"tfjs-backend-wasm-simd.wasm\";\n  }\n  if (wasmFileMap != null) {\n    if (wasmFileMap[path] != null) {\n      return wasmFileMap[path];\n    }\n  }\n  return wasmModuleFolder + path;\n}\nasync function init() {\n  const [simdSupported, threadsSupported] = await Promise.all([\n    env().getAsync(\"WASM_HAS_SIMD_SUPPORT\"),\n    env().getAsync(\"WASM_HAS_MULTITHREAD_SUPPORT\")\n  ]);\n  return new Promise((resolve, reject) => {\n    const factoryConfig = {};\n    factoryConfig.locateFile = (path, prefix) => {\n      if (path.endsWith(\".worker.js\")) {\n        const response = import_tfjs_backend_wasm_threaded_simd_worker.wasmWorkerContents.replace(/\\n/g, \"\\\\n\");\n        const blob = new Blob([response], { type: \"application/javascript\" });\n        return URL.createObjectURL(blob);\n      }\n      if (path.endsWith(\".wasm\")) {\n        return getPathToWasmBinary(simdSupported, threadsSupported, wasmPathPrefix != null ? wasmPathPrefix : prefix);\n      }\n      return prefix + path;\n    };\n    if (customFetch) {\n      factoryConfig.instantiateWasm = createInstantiateWasmFunc(getPathToWasmBinary(simdSupported, threadsSupported, wasmPathPrefix != null ? wasmPathPrefix : \"\"));\n    }\n    let initialized = false;\n    factoryConfig.onAbort = () => {\n      if (initialized) {\n        return;\n      }\n      if (initAborted) {\n        return;\n      }\n      initAborted = true;\n      const rejectMsg = \"Make sure the server can serve the `.wasm` file relative to the bundled js file. For more details see https://github.com/tensorflow/tfjs/blob/master/tfjs-backend-wasm/README.md#using-bundlers\";\n      reject({ message: rejectMsg });\n    };\n    let wasm;\n    if (threadsSupported && simdSupported && wasmPath == null) {\n      factoryConfig.mainScriptUrlOrBlob = new Blob([`var WasmBackendModuleThreadedSimd = ` + wasmFactoryThreadedSimd.toString()], { type: \"text/javascript\" });\n      wasm = wasmFactoryThreadedSimd(factoryConfig);\n    } else {\n      wasm = wasmFactory(factoryConfig);\n    }\n    wasm.then((module) => {\n      initialized = true;\n      initAborted = false;\n      const voidReturnType = null;\n      module.tfjs = {\n        init: module.cwrap(\"init\", null, []),\n        initWithThreadsCount: module.cwrap(\"init_with_threads_count\", null, [\"number\"]),\n        getThreadsCount: module.cwrap(\"get_threads_count\", \"number\", []),\n        registerTensor: module.cwrap(\"register_tensor\", null, [\n          \"number\",\n          \"number\",\n          \"number\"\n        ]),\n        disposeData: module.cwrap(\"dispose_data\", voidReturnType, [\"number\"]),\n        dispose: module.cwrap(\"dispose\", voidReturnType, [])\n      };\n      resolve({ wasm: module });\n    }).catch(reject);\n  });\n}\nfunction typedArrayFromBuffer(buffer2, dtype) {\n  switch (dtype) {\n    case \"float32\":\n      return new Float32Array(buffer2);\n    case \"int32\":\n      return new Int32Array(buffer2);\n    case \"bool\":\n      return new Uint8Array(buffer2);\n    default:\n      throw new Error(`Unknown dtype ${dtype}`);\n  }\n}\nvar wasmBinaryNames = [\n  \"tfjs-backend-wasm.wasm\",\n  \"tfjs-backend-wasm-simd.wasm\",\n  \"tfjs-backend-wasm-threaded-simd.wasm\"\n];\nvar wasmPath = null;\nvar wasmPathPrefix = null;\nvar wasmFileMap = {};\nvar initAborted = false;\nvar customFetch = false;\nfunction setWasmPath(path, usePlatformFetch = false) {\n  deprecationWarn(\"setWasmPath has been deprecated in favor of setWasmPaths and will be removed in a future release.\");\n  if (initAborted) {\n    throw new Error(\"The WASM backend was already initialized. Make sure you call `setWasmPath()` before you call `tf.setBackend()` or `tf.ready()`\");\n  }\n  wasmPath = path;\n  customFetch = usePlatformFetch;\n}\nfunction setWasmPaths(prefixOrFileMap, usePlatformFetch = false) {\n  if (initAborted) {\n    throw new Error(\"The WASM backend was already initialized. Make sure you call `setWasmPaths()` before you call `tf.setBackend()` or `tf.ready()`\");\n  }\n  if (typeof prefixOrFileMap === \"string\") {\n    wasmPathPrefix = prefixOrFileMap;\n  } else {\n    wasmFileMap = prefixOrFileMap;\n    const missingPaths = wasmBinaryNames.filter((name) => wasmFileMap[name] == null);\n    if (missingPaths.length > 0) {\n      throw new Error(`There were no entries found for the following binaries: ${missingPaths.join(\",\")}. Please either call setWasmPaths with a map providing a path for each binary, or with a string indicating the directory where all the binaries can be found.`);\n    }\n  }\n  customFetch = usePlatformFetch;\n}\nvar threadsCount = -1;\nvar actualThreadsCount = -1;\nfunction setThreadsCount(numThreads) {\n  threadsCount = numThreads;\n}\nfunction getThreadsCount() {\n  if (actualThreadsCount === -1) {\n    throw new Error(`WASM backend not initialized.`);\n  }\n  return actualThreadsCount;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-wasm/dist/version.js\nvar version8 = \"3.20.0\";\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-wasm/dist/base.js\nvar WASM_PRIORITY = 2;\nregisterBackend(\"wasm\", async () => {\n  const { wasm } = await init();\n  return new BackendWasm(wasm);\n}, WASM_PRIORITY);\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/flags_webgpu.js\nvar ENV7 = env();\nENV7.registerFlag(\"WEBGPU_DEFERRED_SUBMIT_BATCH_SIZE\", () => 15);\nENV7.registerFlag(\"WEBGPU_CPU_FORWARD\", () => true);\nENV7.registerFlag(\"WEBGPU_MATMUL_PROGRAM_TYPE\", () => -1);\nENV7.registerFlag(\"WEBGPU_USE_NAIVE_CONV2D_TRANSPOSE\", () => false);\nENV7.registerFlag(\"WEBGPU_USE_LOW_POWER_GPU\", () => false);\nENV7.registerFlag(\"WEBGPU_CPU_HANDOFF_SIZE_THRESHOLD\", () => 1e3);\nENV7.registerFlag(\"WEBGPU_USE_PROFILE_TOOL\", () => false);\nENV7.registerFlag(\"WEBGPU_IMPORT_EXTERNAL_TEXTURE\", () => true);\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/buffer_manager.js\nvar BufferManager = class {\n  constructor(device) {\n    this.device = device;\n    this.numUsedBuffers = 0;\n    this.numFreeBuffers = 0;\n    this.freeBuffers = /* @__PURE__ */ new Map();\n    this.usedBuffers = /* @__PURE__ */ new Map();\n    this.numBytesUsed = 0;\n    this.numBytesAllocated = 0;\n  }\n  acquireUploadBuffer(size, usage) {\n    return this.acquireBuffer(size, usage, true);\n  }\n  acquireBuffer(size, usage, mappedAtCreation = false) {\n    const key = getBufferKey(size, usage);\n    if (!this.freeBuffers.has(key)) {\n      this.freeBuffers.set(key, []);\n    }\n    if (!this.usedBuffers.has(key)) {\n      this.usedBuffers.set(key, []);\n    }\n    this.numBytesUsed += size;\n    this.numUsedBuffers++;\n    if (this.freeBuffers.get(key).length > 0) {\n      this.numFreeBuffers--;\n      const newBuffer2 = this.freeBuffers.get(key).shift();\n      this.usedBuffers.get(key).push(newBuffer2);\n      return newBuffer2;\n    }\n    this.numBytesAllocated += size;\n    const newBuffer = this.device.createBuffer({ size, usage, mappedAtCreation });\n    this.usedBuffers.get(key).push(newBuffer);\n    return newBuffer;\n  }\n  releaseBuffer(buffer2, size, usage) {\n    if (this.freeBuffers.size === 0) {\n      return;\n    }\n    const key = getBufferKey(size, usage);\n    if (!this.freeBuffers.has(key)) {\n      this.freeBuffers.set(key, []);\n    }\n    this.freeBuffers.get(key).push(buffer2);\n    this.numFreeBuffers++;\n    this.numUsedBuffers--;\n    const bufferList = this.usedBuffers.get(key);\n    const bufferIndex = bufferList.indexOf(buffer2);\n    if (bufferIndex < 0) {\n      throw new Error(\"Cannot release a buffer that was never provided by this buffer manager\");\n    }\n    bufferList.splice(bufferIndex, 1);\n    this.numBytesUsed -= size;\n  }\n  releaseUploadBuffer(buffer2, size, usage) {\n    buffer2.mapAsync(GPUMapMode.WRITE).then(() => {\n      this.releaseBuffer(buffer2, size, usage);\n    }, (err) => {\n    });\n  }\n  getNumUsedBuffers() {\n    return this.numUsedBuffers;\n  }\n  getNumFreeBuffers() {\n    return this.numFreeBuffers;\n  }\n  dispose() {\n    this.freeBuffers.forEach((buffers, key) => {\n      buffers.forEach((buffer2) => {\n        buffer2.destroy();\n      });\n    });\n    this.usedBuffers.forEach((buffers, key) => {\n      buffers.forEach((buffer2) => {\n        buffer2.destroy();\n      });\n    });\n    this.freeBuffers = /* @__PURE__ */ new Map();\n    this.usedBuffers = /* @__PURE__ */ new Map();\n    this.numUsedBuffers = 0;\n    this.numFreeBuffers = 0;\n    this.numBytesUsed = 0;\n    this.numBytesAllocated = 0;\n  }\n};\nfunction getBufferKey(size, usage) {\n  return `${size}_${usage}`;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/texture_manager.js\nvar TextureManager2 = class {\n  constructor(device) {\n    this.device = device;\n    this.numUsedTextures = 0;\n    this.numFreeTextures = 0;\n    this.freeTextures = /* @__PURE__ */ new Map();\n    this.usedTextures = /* @__PURE__ */ new Map();\n    this.numBytesUsed = 0;\n    this.numBytesAllocated = 0;\n  }\n  acquireTexture(width, height, format, usage) {\n    const bytesPerElement2 = getBytesPerElement(format);\n    const byteSize = width * height * bytesPerElement2;\n    const key = getTextureKey(width, height, format, usage);\n    if (!this.freeTextures.has(key)) {\n      this.freeTextures.set(key, []);\n    }\n    if (!this.usedTextures.has(key)) {\n      this.usedTextures.set(key, []);\n    }\n    this.numBytesUsed += byteSize;\n    this.numUsedTextures++;\n    if (this.freeTextures.get(key).length > 0) {\n      this.numFreeTextures--;\n      const newTexture2 = this.freeTextures.get(key).shift();\n      this.usedTextures.get(key).push(newTexture2);\n      return newTexture2;\n    }\n    this.numBytesAllocated += byteSize;\n    const newTexture = this.device.createTexture({\n      size: [width, height],\n      format,\n      usage\n    });\n    this.usedTextures.get(key).push(newTexture);\n    return newTexture;\n  }\n  releaseTexture(texture, width, height, format, usage) {\n    if (this.freeTextures.size === 0) {\n      return;\n    }\n    const key = getTextureKey(width, height, format, usage);\n    if (!this.freeTextures.has(key)) {\n      this.freeTextures.set(key, []);\n    }\n    this.freeTextures.get(key).push(texture);\n    this.numFreeTextures++;\n    this.numUsedTextures--;\n    const textureList = this.usedTextures.get(key);\n    const textureIndex = textureList.indexOf(texture);\n    if (textureIndex < 0) {\n      throw new Error(\"Cannot release a texture that was never provided by this texture manager\");\n    }\n    textureList.splice(textureIndex, 1);\n    const bytesPerElement2 = getBytesPerElement(format);\n    const byteSize = width * height * bytesPerElement2;\n    this.numBytesUsed -= byteSize;\n  }\n  getNumUsedTextures() {\n    return this.numUsedTextures;\n  }\n  getNumFreeTextures() {\n    return this.numFreeTextures;\n  }\n  dispose() {\n    this.freeTextures.forEach((textures, key) => {\n      textures.forEach((texture) => {\n        texture.destroy();\n      });\n    });\n    this.usedTextures.forEach((textures, key) => {\n      textures.forEach((texture) => {\n        texture.destroy();\n      });\n    });\n    this.freeTextures = /* @__PURE__ */ new Map();\n    this.usedTextures = /* @__PURE__ */ new Map();\n    this.numUsedTextures = 0;\n    this.numFreeTextures = 0;\n    this.numBytesUsed = 0;\n    this.numBytesAllocated = 0;\n  }\n};\nfunction getTextureKey(width, height, format, usage) {\n  return `${width}_${height}_${format}_${usage}`;\n}\nfunction getBytesPerElement(format) {\n  if (format === \"rgba8unorm\") {\n    return 16;\n  } else {\n    throw new Error(`${format} is not supported!`);\n  }\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/shader_util.js\nfunction symbolicallyComputeStrides2(indicesArr, variableName) {\n  if (Math.max(...indicesArr) > 3) {\n    throw new Error(\"Cannot symbolically compute strides for rank > 4 tensor.\");\n  }\n  const numCoords = indicesArr.length;\n  const shape = indicesArr.map((d) => `${variableName}[${d}]`);\n  const strides = new Array(numCoords - 1);\n  strides[numCoords - 2] = shape[numCoords - 1];\n  for (let i = numCoords - 3; i >= 0; --i) {\n    strides[i] = `(${strides[i + 1]} * ${shape[i + 1]})`;\n  }\n  return strides;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/webgpu_program.js\nvar compileProgram2 = (device, program, inputsData, output) => {\n  const outputData = { dtype: output.dtype, shape: output.shape };\n  const source = makeShader2(inputsData, outputData, program);\n  const module = device.createShaderModule({ code: source, label: program.constructor.name });\n  const pipeline = device.createComputePipeline({\n    compute: { module, entryPoint: \"_start\" },\n    label: program.constructor.name,\n    layout: \"auto\"\n  });\n  return pipeline;\n};\nfunction getCoordsDataType2(rank) {\n  if (rank <= 1) {\n    return \"i32\";\n  } else if (rank === 2) {\n    return `vec2<i32>`;\n  } else if (rank === 3) {\n    return `vec3<i32>`;\n  } else if (rank === 4) {\n    return `vec4<i32>`;\n  } else if (rank === 5) {\n    return `vec5`;\n  } else if (rank === 6) {\n    return `vec6`;\n  } else {\n    throw Error(`GPU for rank ${rank} is not yet supported`);\n  }\n}\nfunction getCoordsXYZ(index) {\n  if (index === 0) {\n    return \"x\";\n  } else if (index === 1) {\n    return \"y\";\n  } else if (index === 2) {\n    return \"z\";\n  } else if (index === 3) {\n    return \"w\";\n  } else if (index === 4) {\n    return \"u\";\n  } else if (index === 5) {\n    return \"v\";\n  } else {\n    throw Error(`Index ${index} is not yet supported`);\n  }\n}\nfunction getMainHeaderString(...params) {\n  let snippet;\n  switch (params.length) {\n    case 0:\n      snippet = `\n        ${getWorkGroupSizeString()}\n        fn _start(@builtin(local_invocation_id) LocalId : vec3<u32>,\n                  @builtin(global_invocation_id) GlobalId : vec3<u32>,\n                  @builtin(num_workgroups) NumWorkgroups : vec3<u32>) {\n          localId = LocalId;\n          globalId = GlobalId;\n          numWorkgroups = NumWorkgroups;\n          main();\n        }\n\n        fn main()\n      `;\n      break;\n    case 1:\n      snippet = `\n        ${getWorkGroupSizeString()}\n        fn _start(@builtin(local_invocation_id) LocalId : vec3<u32>,\n                  @builtin(global_invocation_id) GlobalId : vec3<u32>,\n                  @builtin(num_workgroups) NumWorkgroups : vec3<u32>) {\n          localId = LocalId;\n          globalId = GlobalId;\n          numWorkgroups = NumWorkgroups;\n          main(getGlobalIndex());\n        }\n\n        fn main(${params[0]} : i32)\n      `;\n      break;\n    default:\n      throw Error(\"Unreachable\");\n  }\n  return snippet;\n}\nfunction getWorkGroupSizeString() {\n  return `\n  @compute @workgroup_size(workGroupSizeX, workGroupSizeY, workGroupSizeZ)\n`;\n}\nfunction makeShader2(inputInfo, outputData, program) {\n  const prefixSnippets = [];\n  prefixSnippets.push(`\n      const workGroupSizeX = ${program.workGroupSize[0]}u;\n      const workGroupSizeY = ${program.workGroupSize[1]}u;\n      const workGroupSizeZ = ${program.workGroupSize[2]}u;\n\n      var<private> localId: vec3<u32>;\n      var<private> globalId: vec3<u32>;\n      var<private> numWorkgroups: vec3<u32>;\n\n      // Only used when the y/z dimension of workgroup size is 1.\n      fn getGlobalIndex() -> i32 {\n        ${isFlatDispatch(program) ? `  return i32(globalId.x);` : `  let localInvocationIndex = localId.z * workGroupSizeX * workGroupSizeY +\n                   localId.y * workGroupSizeX + localId.x;\n               let workGroupID = (globalId - localId)/vec3<u32>(\n                   workGroupSizeX, workGroupSizeY, workGroupSizeZ);\n\n               return i32((workGroupID.z * numWorkgroups.x * numWorkgroups.y +\n                   workGroupID.y * numWorkgroups.x + workGroupID.x) *\n                   (workGroupSizeX * workGroupSizeY * workGroupSizeZ) +\n                   localInvocationIndex);\n        `}\n      }\n    `);\n  if (program.isFromPixels) {\n    prefixSnippets.push(`\n        struct Uniform {\n          size            : i32,\n          numChannels     : i32,\n          outShapeStrides : vec2<i32>,\n        };\n\n        @group(0) @binding(0) var<storage, read_write> result: array<${mapToWgslTypes(outputData.dtype, program.isVec4)}>;\n        @group(0) @binding(2) var<uniform> uniforms: Uniform;\n      `);\n    return [\n      commonSnippet,\n      prefixSnippets.join(\"\\n\"),\n      getCoordsFromIndexSnippet(outputData.shape),\n      program.getUserCode()\n    ].join(\"\\n\");\n  }\n  let uniformDeclaration = \"struct Uniforms { NAN : f32, \";\n  program.variableNames.forEach((x, i) => {\n    const perDataType = getCoordsDataType2(inputInfo[i].shape.length);\n    uniformDeclaration += `${x.charAt(0).toLowerCase() + x.slice(1)}Shape : ${perDataType}, `;\n  });\n  const outputDataType = getCoordsDataType2(outputData.shape.length);\n  uniformDeclaration += `outShape : ${outputDataType}, `;\n  const stridesLength = outputData.shape.length - 1;\n  const stridesDataType = getCoordsDataType2(stridesLength);\n  uniformDeclaration += `\n         outShapeStrides: ${stridesDataType}, `;\n  if (program.size) {\n    uniformDeclaration += \"size : i32, \";\n  }\n  if (program.uniforms) {\n    uniformDeclaration += program.uniforms;\n  }\n  uniformDeclaration += \"};\";\n  uniformDeclaration = insertAlignment(uniformDeclaration);\n  prefixSnippets.push(uniformDeclaration);\n  if (program.atomic) {\n    prefixSnippets.push(`\n      @group(0) @binding(0) var<storage, read_write> result: array<atomic<i32>>;\n    `);\n  } else {\n    prefixSnippets.push(`\n      @group(0) @binding(0) var<storage, read_write> result: array<${mapToWgslTypes(outputData.dtype, program.isVec4)}>;\n    `);\n  }\n  program.variableNames.forEach((x, i) => {\n    prefixSnippets.push(`\n      @group(0) @binding(${1 + i}) var<storage, read> ${x}: array<${program.variableTypes ? program.variableTypes[i] : mapToWgslTypes(inputInfo[i].dtype, program.isVec4)}>;\n        `);\n  });\n  if (uniformDeclaration !== \"\") {\n    prefixSnippets.push(`\n      @group(0) @binding(${1 + program.variableNames.length}) var<uniform> uniforms: Uniforms;\n      `);\n  }\n  const coordsSnippet = getOutputCoordsSnippet(outputData.shape, program.dispatchLayout);\n  const sources = [\n    commonSnippet,\n    prefixSnippets.join(\"\\n\"),\n    getCoordsFromIndexSnippet(outputData.shape),\n    coordsSnippet,\n    getOutputIndexFromCoordsSnippet(outputData.shape.length)\n  ];\n  if (!program.atomic) {\n    sources.push(setOutputSnippet(outputData.shape, outputData.dtype, program.isVec4));\n  }\n  const inputSnippet = inputInfo.map((x, i) => getInputSnippet(x, outputData.shape, program.variableTypes ? program.variableTypes[i] === \"vec4<f32>\" : program.isVec4, program.dispatchLayout.x.length === outputData.shape.length)).join(\"\\n\");\n  sources.push(inputSnippet);\n  sources.push(program.getUserCode());\n  const source = sources.join(\"\\n\");\n  return source;\n}\nfunction makeShaderKey2(program, shapes, inputsData, output) {\n  let key = program.shaderKey;\n  if (program.isFromPixels) {\n    return key;\n  }\n  const types = inputsData.map((d) => d.dtype).concat(output.dtype);\n  const broadcastDims = inputsData.map((d) => backend_util_exports.getBroadcastDims(d.shape, output.shape));\n  const inputShapesEqualsOutShape = inputsData.map((d) => util_exports.arraysEqual(d.shape, output.shape)).join(\"_\");\n  const broadcastDimsKey = broadcastDims.map((d) => d.join(\"_\")).join(\";\");\n  const flatDispatchString = isFlatDispatch(program) ? \"flatDispatch\" : \"\";\n  key += \"_\" + (program.workGroupSize ? program.workGroupSize.join(\",\") : \"\") + shapes.map((shape) => shape.length).join(\",\") + types.join(\",\") + program.variableNames.join(\",\") + broadcastDimsKey + inputShapesEqualsOutShape + flatDispatchString;\n  return key;\n}\nvar commonSnippet = `\n  struct vec5 {x: i32, y: i32, z: i32, w: i32, u: i32};\n  struct vec6 {x: i32, y: i32, z: i32, w: i32, u: i32, v: i32};\n\n  // Checks whether coordinates lie within the bounds of the shape.\n  fn coordsInBounds2D(coord : vec2<i32>, shape : vec2<i32>) -> bool {\n    return all(coord >= vec2<i32>(0)) && all(coord < shape);\n  }\n  fn coordsInBounds3D(coord : vec3<i32>, shape : vec3<i32>) -> bool {\n    return all(coord >= vec3<i32>(0)) && all(coord < shape);\n  }\n  fn coordsInBounds4D(coord : vec4<i32>, shape : vec4<i32>) -> bool {\n    return all(coord >= vec4<i32>(0)) && all(coord < shape);\n  }\n\n  fn getIndexFromCoords1D(coord : i32, shape : i32) -> i32 {\n    return coord;\n  }\n  fn getIndexFromCoords2D(coords : vec2<i32>, shape : vec2<i32>) -> i32 {\n    return dot(coords, vec2<i32>(shape.y, 1));\n  }\n  fn getIndexFromCoords3D(coords : vec3<i32>, shape : vec3<i32>) -> i32 {\n    return dot(coords, vec3<i32>(shape.y * shape.z, shape.z, 1));\n  }\n  fn getIndexFromCoords4D(coords : vec4<i32>, shape : vec4<i32>) -> i32 {\n    return dot(coords, vec4<i32>(\n        shape.y * shape.z * shape.w, shape.z * shape.w, shape.w, 1));\n  }\n  fn getIndexFromCoords5D(coords : vec5, shape : vec5) -> i32 {\n    let shapeStrides: vec5 = vec5(shape.y * shape.z * shape.w * shape.u, shape.z * shape.w * shape.u, shape.w * shape.u, shape.u, 1);\n    return coords.x*shapeStrides.x + coords.y*shapeStrides.y + coords.z*shapeStrides.z + coords.w*shapeStrides.w + coords.u*shapeStrides.u;\n  }\n  fn getIndexFromCoords6D(coords : vec6, shape : vec6) -> i32 {\n    let shapeStrides: vec6 = vec6(shape.y * shape.z * shape.w * shape.u * shape.v, shape.z * shape.w * shape.u * shape.v, shape.w * shape.u * shape.v, shape.u * shape.v, shape.v, 1);\n    return coords.x*shapeStrides.x + coords.y*shapeStrides.y + coords.z*shapeStrides.z + coords.w*shapeStrides.w + coords.u*shapeStrides.u + coords.v*shapeStrides.v;\n  }\n\n  fn idiv(a: i32, b: i32, sign: f32) -> i32 {\n    var res: i32 = a / b;\n    let modulo: i32 = a % b;\n    if (sign < 0. && modulo != 0) {\n      res = res - 1;\n    }\n    return res;\n  }\n\n  // NaN defination in IEEE 754-1985 is :\n  //   - sign = either 0 or 1.\n  //   - biased exponent = all 1 bits.\n  //   - fraction = anything except all 0 bits (since all 0 bits represents infinity).\n  // https://en.wikipedia.org/wiki/IEEE_754-1985#Representation_of_non-numbers\n  fn isnan(val: f32) -> bool {\n    let floatToUint: u32 = bitcast<u32>(val);\n    return (floatToUint & 0x7fffffffu) > 0x7f800000u;\n  }\n  fn isnanVec4(val : vec4<f32>) -> vec4<bool> {\n    return vec4<bool>(isnan(val[0]), isnan(val[1]), isnan(val[2]), isnan(val[3]));\n  }\n`;\nfunction getCoordsFromIndexSnippet(shape) {\n  const rank = shape.length;\n  if (rank <= 1) {\n    return `fn getCoordsFromIndex(index : i32) -> i32 { return index; }`;\n  }\n  const strides = util_exports.computeStrides(shape);\n  const dtype = getCoordsDataType2(rank);\n  const coords3 = [];\n  for (let i = 0; i < rank; i++) {\n    coords3.push(`d${i}`);\n  }\n  if (strides.length === 1) {\n    return `    fn getCoordsFromIndex(index : i32) -> vec2<i32> {\n      let d0 = index / uniforms.outShapeStrides; let d1 = index - d0 * uniforms.outShapeStrides;\n      return vec2<i32>(d0, d1);\n    }`;\n  }\n  let snippet;\n  snippet = \"var index2 = index;\" + strides.map((_, i) => {\n    const line1 = `let ${coords3[i]} = index2 / uniforms.outShapeStrides.${getCoordsXYZ(i)}`;\n    const line2 = i === strides.length - 1 ? `let ${coords3[i + 1]} = index2 - ${coords3[i]} * uniforms.outShapeStrides.${getCoordsXYZ(i)}` : `index2 = index2 - ${coords3[i]} * uniforms.outShapeStrides.${getCoordsXYZ(i)}`;\n    return `${line1}; ${line2};`;\n  }).join(\"\");\n  return `\n    fn getCoordsFromIndex(index : i32) -> ${dtype} {\n      ${snippet}\n      return ${dtype}(${coords3.join(\",\")});\n    }\n  `;\n}\nfunction getInputAtCoordsSnippet(inputInfo, isVec4) {\n  const texName = inputInfo.name;\n  const rank = inputInfo.shape.length;\n  const type = getCoordsDataType2(rank);\n  const funcName = \"get\" + texName.charAt(0).toUpperCase() + texName.slice(1);\n  const dims = [\"d0\", \"d1\", \"d2\", \"d3\", \"d4\", \"d5\"].slice(0, rank);\n  const inputs = dims.map((d) => `${d} : i32`).join(\", \");\n  if (rank < 1) {\n    if (isVec4) {\n      return `\n        fn ${funcName}() -> vec4<f32> {\n          return vec4<f32>(${texName}[0]);\n        }\n      `;\n    }\n    return `\n      fn ${funcName}() ->f32 {\n        return f32(${texName}[0]);\n      }\n    `;\n  }\n  const shapeStr = `uniforms.${texName.charAt(0).toLowerCase() + texName.slice(1)}Shape`;\n  let rankStr = `${rank}D`;\n  if (rank === 0) {\n    rankStr = \"1D\";\n  }\n  if (isVec4) {\n    return `\n      fn ${funcName}(${inputs}) -> vec4<f32> {\n        return vec4<f32>(${texName}[getIndexFromCoords${rankStr}(${type}(${dims.join(\",\")}),\n          ${shapeStr}) / 4]);\n      }\n      `;\n  }\n  return `\n    fn ${funcName}(${inputs}) -> f32 {\n      return f32(${texName}[getIndexFromCoords${rankStr}(${type}(${dims.join(\",\")}),\n        ${shapeStr})]);\n    }\n   `;\n}\nfunction getInputByOutputSnippet(inputInfo, outShape, isVec4, isFlatDispatchLayout) {\n  const texName = inputInfo.name;\n  const texFuncSnippet = texName.charAt(0).toUpperCase() + texName.slice(1);\n  const funcName = \"get\" + texFuncSnippet + \"ByOutput\";\n  const inRank = inputInfo.shape.length;\n  const outRank = outShape.length;\n  const type = getCoordsDataType2(outRank);\n  if (util_exports.arraysEqual(inputInfo.shape, outShape) && isFlatDispatchLayout) {\n    if (isVec4) {\n      return `\n      fn ${funcName}Index(globalIndex : i32) -> vec4<f32> {\n        return vec4<f32>(${texName}[globalIndex]);\n      }\n\n      fn ${funcName}Coords(coords : ${type}) -> vec4<f32> {\n        return vec4<f32>(${texName}[${outRank > 1 ? \"getOutputIndexFromCoords(coords)\" : \"coords\"} / 4]);\n      }\n      `;\n    } else {\n      return `\n    fn ${funcName}Index(globalIndex : i32) -> f32 {\n      return f32(${texName}[globalIndex]);\n    }\n\n    fn ${funcName}Coords(coords : ${type}) -> f32 {\n      return f32(${texName}[${outRank > 1 ? \"getOutputIndexFromCoords(coords)\" : \"coords\"}]);\n    }\n    `;\n    }\n  }\n  const broadcastDims = backend_util_exports.getBroadcastDims(inputInfo.shape, outShape);\n  const rankDiff = outRank - inRank;\n  let coordsSnippet = \"\";\n  if (inRank === 0) {\n    if (isVec4) {\n      return `\n    fn ${funcName}Index(globalIndex : i32) -> vec4<f32> {\n      return get${texFuncSnippet}();\n    }\n\n    fn ${funcName}Coords(coords : ${type}) -> vec4<f32> {\n      return get${texFuncSnippet}();\n    }\n  `;\n    }\n    return `\n    fn ${funcName}Index(globalIndex : i32) -> f32{\n      return get${texFuncSnippet}();\n    }\n\n    fn ${funcName}Coords(coords : ${type}) -> f32{\n      return get${texFuncSnippet}();\n    }\n  `;\n  } else {\n    if (outRank < 2 && broadcastDims.length >= 1) {\n      coordsSnippet = \"coords = 0;\";\n    } else {\n      coordsSnippet = broadcastDims.map((d) => `coords.${getCoordsXYZ(d + rankDiff)} = 0;`).join(\"\\n\");\n    }\n  }\n  let unpackedCoordsSnippet = \"\";\n  if (outRank < 2 && inRank > 0) {\n    unpackedCoordsSnippet = \"coords\";\n  } else {\n    if (outRank > 1) {\n      const coordsType = getCoordsDataType2(inRank);\n      const coordsValues = inputInfo.shape.map((s, i) => `coords.${getCoordsXYZ(i + rankDiff)}`).join(\", \");\n      unpackedCoordsSnippet = `${coordsType}(${coordsValues})`;\n    } else {\n      unpackedCoordsSnippet = \"coords\";\n    }\n  }\n  const shapeStr = `uniforms.${texName.charAt(0).toLowerCase() + texName.slice(1)}Shape`;\n  const rankStr = `${inRank}D`;\n  if (isVec4) {\n    return `\n    fn ${funcName}Index(globalIndex : i32) -> vec4<f32> {\n      var coords = getCoordsFromIndex(globalIndex);\n      ${coordsSnippet}\n      return ${texName}[getIndexFromCoords${rankStr}(${unpackedCoordsSnippet}, ${shapeStr}) / 4];\n    }\n\n    fn ${funcName}Coords(coordsIn : ${type}) -> vec4<f32> {\n      var coords = coordsIn;\n      ${coordsSnippet}\n      return ${texName}[getIndexFromCoords${rankStr}(${unpackedCoordsSnippet}, ${shapeStr}) / 4];\n    }\n  `;\n  }\n  return `\n  fn ${funcName}Index(globalIndex : i32) -> f32 {\n    var coords = getCoordsFromIndex(globalIndex);\n    ${coordsSnippet}\n    return f32(${texName}[getIndexFromCoords${rankStr}(${unpackedCoordsSnippet}, ${shapeStr})]);\n  }\n\n  fn ${funcName}Coords(coordsIn : ${type}) -> f32 {\n    var coords = coordsIn;\n    ${coordsSnippet}\n    return f32(${texName}[getIndexFromCoords${rankStr}(${unpackedCoordsSnippet}, ${shapeStr})]);\n  }\n`;\n}\nfunction getInputSnippet(inputInfo, outShape, isVec4, isFlatDispatchLayout) {\n  let res = getInputAtCoordsSnippet(inputInfo, isVec4);\n  const inShape = inputInfo.shape;\n  if (inShape.length <= outShape.length) {\n    res += getInputByOutputSnippet(inputInfo, outShape, isVec4, isFlatDispatchLayout);\n  }\n  return res;\n}\nfunction getOutputCoordsSnippet(outShape, dispatchLayout) {\n  const { x, y = [], z = [] } = dispatchLayout;\n  const outRank = outShape.length;\n  if (x.length === outRank) {\n    const dtype2 = getCoordsDataType2(outRank);\n    const snippet2 = `fn getOutputCoords() -> ${dtype2}{\n    let globalIndex = getGlobalIndex();\n    return getCoordsFromIndex(globalIndex);\n  }\n  `;\n    return snippet2;\n  }\n  let gatherDimensionsStr = \"\";\n  const dims = [x, y, z];\n  let rank = 0;\n  for (let i = 0; i < dims.length; i++) {\n    const arr = dims[i];\n    if (arr.length === 0) {\n      continue;\n    }\n    rank += arr.length;\n    if (arr.length === 1) {\n      gatherDimensionsStr += `let d${arr[0]} = i32(globalId[${i}]);`;\n    } else {\n      const strides = symbolicallyComputeStrides2(arr, \"uniforms.outShape\");\n      gatherDimensionsStr += `var index${i} = i32(globalId[${i}]);`;\n      for (let j = 0; j < strides.length; j++) {\n        gatherDimensionsStr += `let d${arr[j]} = index${i} / ${strides[j]};`;\n        if (j === strides.length - 1) {\n          gatherDimensionsStr += `let d${arr[j + 1]} = index${i} - d${arr[j]} * ${strides[j]};`;\n        } else {\n          gatherDimensionsStr += `index${i} = index${i} - d${arr[j]} * ${strides[j]};`;\n        }\n      }\n    }\n  }\n  const dimensions = [];\n  for (let i = 0; i < rank; i++) {\n    dimensions.push(`d${i}`);\n  }\n  const dtype = getCoordsDataType2(rank);\n  let snippet = `fn getOutputCoords() -> ${dtype} {\n  ${gatherDimensionsStr}\n`;\n  if (dimensions.length === 0) {\n    snippet += `return ${dtype}(0); }`;\n  } else {\n    snippet += `return ${dtype}(${dimensions.join(\",\")}); }`;\n  }\n  return snippet;\n}\nfunction getOutputIndexFromCoordsSnippet(outRank) {\n  let snippet = \"\";\n  switch (outRank) {\n    case 0:\n    case 1:\n      snippet += `\n        fn getOutputIndexFromCoords(coords : i32) -> i32 {\n          return coords;\n        }\n        `;\n      break;\n    case 2:\n      snippet += `\n        fn getOutputIndexFromCoords(coords : vec2<i32>) -> i32 {\n          return dot(coords, vec2<i32>(uniforms.outShapeStrides, 1));\n        }\n        `;\n      break;\n    case 3:\n      snippet += `\n        fn getOutputIndexFromCoords(coords : vec3<i32>) -> i32 {\n          return dot(coords, vec3<i32>(uniforms.outShapeStrides.x, uniforms.outShapeStrides.y, 1));\n        }\n        `;\n      break;\n    case 4:\n      snippet += `\n        fn getOutputIndexFromCoords(coords : vec4<i32>) -> i32 {\n          return dot(coords, vec4<i32>(\n            uniforms.outShapeStrides.x, uniforms.outShapeStrides.y, uniforms.outShapeStrides.z, 1));\n        }\n        `;\n      break;\n    case 5:\n      snippet += `\n        fn getOutputIndexFromCoords(coords : vec5) -> i32 {\n          return coords.x * uniforms.outShapeStrides.x +\n              coords.y * uniforms.outShapeStrides.y +\n              coords.z * uniforms.outShapeStrides.z +\n              coords.w * uniforms.outShapeStrides.w +\n              coords.u;\n        }\n        `;\n      break;\n    case 6:\n      snippet += `\n        fn getOutputIndexFromCoords(coords : vec6) -> i32 {\n          return coords.x * uniforms.outShapeStrides.x +\n              coords.y * uniforms.outShapeStrides.y +\n              coords.z * uniforms.outShapeStrides.z +\n              coords.w * uniforms.outShapeStrides.w +\n              coords.u * uniforms.outShapeStrides.u +\n              coords.v;\n        }\n        `;\n      break;\n    default:\n      util_exports.assert(false, () => `Unsupported ${outRank}D shape`);\n      break;\n  }\n  return snippet;\n}\nfunction isFlatDispatch(program) {\n  return program.dispatch[1] === 1 && program.dispatch[2] === 1;\n}\nfunction mapToWgslTypes(type, isVec4) {\n  if (type === \"float32\") {\n    return isVec4 ? \"vec4<f32>\" : \"f32\";\n  } else if (type === \"int32\") {\n    return isVec4 ? \"vec4<i32>\" : \"i32\";\n  } else if (type === \"bool\") {\n    return isVec4 ? \"vec4<i32>\" : \"i32\";\n  }\n  return type;\n}\nfunction setOutputSnippet(outShape, outBufferType, isVec4) {\n  const outRank = outShape.length;\n  const wgslType = mapToWgslTypes(outBufferType, isVec4);\n  let snippet;\n  if (isVec4) {\n    snippet = `fn setOutputAtIndex(flatIndex : i32, value : vec4<f32>) {\n      result[flatIndex] = ${wgslType}(value);\n    }\n    fn setOutputAtIndexI32(flatIndex : i32, value : vec4<i32>) {\n      result[flatIndex] = ${wgslType}(value);\n    }`;\n  } else {\n    snippet = `fn setOutputAtIndex(flatIndex : i32, value : f32) {\n      result[flatIndex] = ${wgslType}(value);\n    }\n    fn setOutputAtIndexI32(flatIndex : i32, value : i32) {\n      result[flatIndex] = ${wgslType}(value);\n    }`;\n  }\n  if (outRank >= 2) {\n    const dims = [\"d0\", \"d1\", \"d2\", \"d3\", \"d4\", \"d5\"].slice(0, outRank);\n    const type = getCoordsDataType2(outRank);\n    if (isVec4) {\n      snippet += `\n      fn setOutputAtCoords(${dims.map((d) => `${d} : i32`).join(\", \")}, value : vec4<f32>) {\n        let flatIndex = getOutputIndexFromCoords(${type}(${dims.join(\", \")}));\n        setOutputAtIndex(flatIndex / 4, value);\n      }\n      fn setOutputAtCoordsI32(${dims.map((d) => `${d} : i32`).join(\", \")}, value : vec4<i32>) {\n        let flatIndex = getOutputIndexFromCoords(${type}(${dims.join(\", \")}));\n        setOutputAtIndexI32(flatIndex / 4, value);\n      }\n    `;\n    } else {\n      snippet += `\n      fn setOutputAtCoords(${dims.map((d) => `${d} : i32`).join(\", \")}, value : f32) {\n        let flatIndex = getOutputIndexFromCoords(${type}(${dims.join(\", \")}));\n        setOutputAtIndex(flatIndex, value);\n      }\n      fn setOutputAtCoordsI32(${dims.map((d) => `${d} : i32`).join(\", \")}, value : i32) {\n        let flatIndex = getOutputIndexFromCoords(${type}(${dims.join(\", \")}));\n        setOutputAtIndexI32(flatIndex, value);\n      }\n    `;\n    }\n  }\n  return snippet;\n}\nfunction insertAlignment(uniformShader) {\n  const curInsertRe = /(\\w+)\\s*:\\s*vec(5|6)/g;\n  uniformShader = uniformShader.replace(curInsertRe, (match) => {\n    return \"@align(16) \" + match;\n  });\n  const preInsertRe = /vec(5|6)\\s*,\\s*(\\w+)/g;\n  uniformShader = uniformShader.replace(preInsertRe, (_, p1, p2) => {\n    return `vec${p1}, @align(16) ${p2}`;\n  });\n  return uniformShader;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/webgpu_util.js\nvar webgpu_util_exports = {};\n__export(webgpu_util_exports, {\n  ArrayBufferToTypedArray: () => ArrayBufferToTypedArray,\n  GPUBytesPerElement: () => GPUBytesPerElement,\n  MatMulProgramType: () => MatMulProgramType,\n  computeDispatch: () => computeDispatch,\n  computeWorkGroupInfoForMatMul: () => computeWorkGroupInfoForMatMul,\n  computeWorkGroupSizeForConv2d: () => computeWorkGroupSizeForConv2d,\n  computeWorkPerThreadForConv2d: () => computeWorkPerThreadForConv2d,\n  flatDispatchLayout: () => flatDispatchLayout,\n  isWebGPUSupported: () => isWebGPUSupported,\n  tilesFitEvenlyIntoShape: () => tilesFitEvenlyIntoShape\n});\nvar arrayProduct = (arr) => {\n  let product = 1;\n  for (let i = 0; i < arr.length; i++) {\n    product *= arr[i];\n  }\n  return product;\n};\nfunction tilesFitEvenlyIntoShape(tileSize, shape) {\n  if (tileSize.length !== shape.length) {\n    throw new Error(`Cannot compute whether rank ${tileSize.length} tiles fit evenly into rank ${shape.length} shape - ranks must match.`);\n  }\n  return shape.every((dim, dimIdx) => dim % tileSize[dimIdx] === 0);\n}\nfunction computeDispatch(layout, outputShape, workGroupSize = [1, 1, 1], elementsPerThread = [1, 1, 1]) {\n  const [dispatchX, dispatchY, dispatchZ] = [\n    Math.ceil(arrayProduct(layout.x.map((d) => outputShape[d])) / (workGroupSize[0] * elementsPerThread[0])),\n    layout.y ? Math.ceil(arrayProduct(layout.y.map((d) => outputShape[d])) / (workGroupSize[1] * elementsPerThread[1])) : 1,\n    layout.z ? Math.ceil(arrayProduct(layout.z.map((d) => outputShape[d])) / (workGroupSize[2] * elementsPerThread[2])) : 1\n  ];\n  return [dispatchX, dispatchY, dispatchZ];\n}\nfunction computeWorkGroupInfoForMatMul(dimAOuter, dimInner, dimBOuter, transposeA = false) {\n  const workGroupSize = [8, 8, 1];\n  const elementsPerThread = [4, 4, 1];\n  if (!transposeA) {\n    if (dimAOuter <= 8) {\n      elementsPerThread[1] = 1;\n    }\n    if (dimInner <= 16 && dimBOuter <= 16) {\n      workGroupSize[0] = 4;\n    }\n  }\n  return { workGroupSize, elementsPerThread };\n}\nfunction computeWorkGroupSizeForConv2d(layout, outputShape, isVec4 = false) {\n  if (isVec4) {\n    return [8, 8, 1];\n  }\n  const dim0 = arrayProduct(layout.x.map((d) => outputShape[d]));\n  const dim1 = arrayProduct(layout.y.map((d) => outputShape[d]));\n  if (dim0 <= 4) {\n    return [4, 16, 1];\n  }\n  if (dim1 <= 4) {\n    return [16, 4, 1];\n  }\n  return [16, 16, 1];\n}\nfunction computeWorkPerThreadForConv2d(layout, outputShape, isVec4 = false) {\n  if (isVec4) {\n    return [4, 4, 1];\n  }\n  const dim0 = arrayProduct(layout.x.map((d) => outputShape[d]));\n  const dim1 = arrayProduct(layout.y.map((d) => outputShape[d]));\n  if (dim0 <= 4) {\n    return [1, 2, 1];\n  }\n  if (dim1 <= 4) {\n    return [2, 1, 1];\n  }\n  return [2, 2, 1];\n}\nfunction flatDispatchLayout(shape) {\n  return { x: shape.map((d, i) => i) };\n}\nfunction GPUBytesPerElement(dtype) {\n  if (dtype === \"float32\" || dtype === \"int32\" || dtype === \"bool\" || dtype === \"string\") {\n    return 4;\n  } else if (dtype === \"complex64\") {\n    return 8;\n  } else {\n    throw new Error(`Unknown dtype ${dtype}`);\n  }\n}\nfunction ArrayBufferToTypedArray(data, dtype) {\n  if (dtype === \"float32\") {\n    return new Float32Array(data);\n  } else if (dtype === \"int32\") {\n    return new Int32Array(data);\n  } else if (dtype === \"bool\" || dtype === \"string\") {\n    return Uint8Array.from(new Int32Array(data));\n  } else {\n    throw new Error(`Unknown dtype ${dtype}`);\n  }\n}\nfunction isWebGPUSupported() {\n  return (typeof window !== \"undefined\" || typeof WorkerGlobalScope !== \"undefined\") && !!navigator.gpu;\n}\nvar MatMulProgramType;\n(function(MatMulProgramType2) {\n  MatMulProgramType2[MatMulProgramType2[\"MatMulReduceProgram\"] = 0] = \"MatMulReduceProgram\";\n  MatMulProgramType2[MatMulProgramType2[\"MatMulSplitKProgram\"] = 1] = \"MatMulSplitKProgram\";\n  MatMulProgramType2[MatMulProgramType2[\"MatMulSmallOutputSizeProgram\"] = 2] = \"MatMulSmallOutputSizeProgram\";\n  MatMulProgramType2[MatMulProgramType2[\"MatMulPackedProgram\"] = 3] = \"MatMulPackedProgram\";\n  MatMulProgramType2[MatMulProgramType2[\"MatMulMax\"] = 4] = \"MatMulMax\";\n})(MatMulProgramType || (MatMulProgramType = {}));\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/backend_webgpu.js\nvar CPU_HANDOFF_SIZE_THRESHOLD2 = env().getNumber(\"WEBGPU_CPU_HANDOFF_SIZE_THRESHOLD\");\nvar reshapeDispatch = (device, program) => {\n  const MAX_COMPUTE_PER_DIMENSION_DISPATCH_SIZE = device.limits.maxComputeWorkgroupsPerDimension;\n  const layout = program[\"dispatchLayout\"];\n  const dispatch = program[\"dispatch\"];\n  if (dispatch.every((d) => d <= MAX_COMPUTE_PER_DIMENSION_DISPATCH_SIZE)) {\n    return dispatch;\n  }\n  util_exports.assert(dispatch[0] > MAX_COMPUTE_PER_DIMENSION_DISPATCH_SIZE && layout.y === void 0 && layout.z === void 0, () => \"Dispatch size exceeds WebGPU limits in Y or Z dimension.\");\n  let dispatchAverage = Math.ceil(Math.sqrt(dispatch[0]));\n  if (dispatchAverage > MAX_COMPUTE_PER_DIMENSION_DISPATCH_SIZE) {\n    dispatchAverage = Math.ceil(Math.cbrt(dispatch[0]));\n    util_exports.assert(dispatchAverage <= MAX_COMPUTE_PER_DIMENSION_DISPATCH_SIZE, () => \"Total dispatch size exceeds WebGPU maximum.\");\n    return [dispatchAverage, dispatchAverage, dispatchAverage];\n  } else {\n    return [dispatchAverage, dispatchAverage, 1];\n  }\n};\nvar WebGPUBackend = class extends KernelBackend {\n  constructor(device) {\n    super();\n    this.commandQueueOwnedIds = /* @__PURE__ */ new WeakSet();\n    this.dispatchNumberInEncoder = 0;\n    this.disposed = false;\n    this.downloadWaitMs = 0;\n    this.tensorDataPendingDisposal = [];\n    this.stagingPendingDisposal = [];\n    this.uniformPendingDisposal = [];\n    this.uploadWaitMs = 0;\n    if (!isWebGPUSupported()) {\n      throw new Error(\"WebGPU is not supported on this device\");\n    }\n    this.pipelineCache = {};\n    this.device = device;\n    this.queue = device.queue;\n    this.currentCommandEncoder = null;\n    this.currentComputePass = null;\n    this.supportTimeQuery = device.features.has(\"timestamp-query\");\n    this.bufferManager = new BufferManager(this.device);\n    this.textureManager = new TextureManager2(this.device);\n    this.tensorMap = new DataStorage(this, engine());\n    if (this.supportTimeQuery) {\n      this.querySet = this.device.createQuerySet({\n        type: \"timestamp\",\n        count: 2\n      });\n    }\n    if (env().getBool(\"WEBGPU_USE_PROFILE_TOOL\")) {\n      this.dummyCanvas = document.createElement(\"canvas\");\n      this.dummyCanvas.width = 1;\n      this.dummyCanvas.height = 1;\n      this.dummyContext = this.dummyCanvas.getContext(\"webgpu\");\n      this.dummyContext.configure({\n        device,\n        format: \"bgra8unorm\"\n      });\n      document.body.appendChild(this.dummyCanvas);\n    }\n  }\n  nextDataId() {\n    return WebGPUBackend.nextDataId++;\n  }\n  floatPrecision() {\n    return 32;\n  }\n  defaultGpuBufferUsage() {\n    return GPUBufferUsage.STORAGE | GPUBufferUsage.COPY_SRC | GPUBufferUsage.COPY_DST;\n  }\n  disposeData(dataId, force = false) {\n    if (this.tensorDataPendingDisposal.indexOf(dataId) >= 0) {\n      return false;\n    }\n    if (!this.tensorMap.has(dataId)) {\n      return true;\n    }\n    const tensorData = this.tensorMap.get(dataId);\n    this.decRef(dataId);\n    if (!force && tensorData.refCount > 0) {\n      return false;\n    }\n    if (this.commandQueueOwnedIds.has(dataId)) {\n      this.tensorDataPendingDisposal.push(dataId);\n      return false;\n    }\n    const { complexTensorInfos } = this.tensorMap.get(dataId);\n    if (complexTensorInfos != null) {\n      this.disposeData(complexTensorInfos.real.dataId, force);\n      this.disposeData(complexTensorInfos.imag.dataId, force);\n    }\n    this.releaseResource(dataId);\n    this.tensorMap.delete(dataId);\n    return true;\n  }\n  memory() {\n    return {\n      numBytesInGPU: this.bufferManager.numBytesUsed,\n      numBytesAllocatedInGPU: this.bufferManager.numBytesAllocated,\n      unreliable: false\n    };\n  }\n  releaseResource(dataId) {\n    const tensorData = this.tensorMap.get(dataId);\n    if (!tensorData || !tensorData.resourceInfo) {\n      return;\n    }\n    if (\"texture\" in tensorData.resourceInfo) {\n      const textureInfo = tensorData.resourceInfo;\n      if (textureInfo.texture instanceof GPUTexture) {\n        this.textureManager.releaseTexture(textureInfo.texture, textureInfo.width, textureInfo.height, textureInfo.format, textureInfo.usage);\n      }\n      textureInfo.texture = null;\n    } else {\n      const bufferInfo = tensorData.resourceInfo;\n      this.bufferManager.releaseBuffer(bufferInfo.buffer, bufferInfo.size, bufferInfo.usage);\n      bufferInfo.buffer = null;\n    }\n    tensorData.resourceInfo = null;\n  }\n  refCount(dataId) {\n    if (this.tensorMap.has(dataId)) {\n      const tensorData = this.tensorMap.get(dataId);\n      return tensorData.refCount;\n    }\n    return 0;\n  }\n  incRef(dataId) {\n    const tensorData = this.tensorMap.get(dataId);\n    tensorData.refCount++;\n  }\n  decRef(dataId) {\n    if (this.tensorMap.has(dataId)) {\n      const tensorData = this.tensorMap.get(dataId);\n      tensorData.refCount--;\n    }\n  }\n  write(values, shape, dtype) {\n    if (dtype === \"complex64\" && values != null) {\n      throw new Error(`Cannot write to a complex64 dtype. Please use tf.complex(real, imag).`);\n    }\n    const dataId = { id: this.nextDataId() };\n    this.tensorMap.set(dataId, { dtype, shape, values, refCount: 1 });\n    return dataId;\n  }\n  move(dataId, values, shape, dtype, refCount) {\n    if (dtype === \"complex64\") {\n      throw new Error(`Cannot write to a complex64 dtype. Please use tf.complex(real, imag).`);\n    }\n    this.tensorMap.set(dataId, { dtype, shape, values, refCount });\n  }\n  submitQueue() {\n    this.ensureComputePassEnded();\n    this.queue.submit([this.currentCommandEncoder.finish()]);\n    this.currentCommandEncoder = null;\n    this.dispatchNumberInEncoder = 0;\n    this.commandQueueOwnedIds = /* @__PURE__ */ new WeakSet();\n    this.tensorDataPendingDisposal.forEach((d) => {\n      this.releaseResource(d);\n      this.tensorMap.delete(d);\n    });\n    this.uniformPendingDisposal.forEach((d) => this.bufferManager.releaseBuffer(d.buffer, d.size, d.usage));\n    this.stagingPendingDisposal.forEach((d) => this.bufferManager.releaseUploadBuffer(d.buffer, d.size, d.usage));\n    this.tensorDataPendingDisposal = [];\n    this.uniformPendingDisposal = [];\n    this.stagingPendingDisposal = [];\n  }\n  ensureCommandEncoderReady() {\n    if (!this.currentCommandEncoder) {\n      this.currentCommandEncoder = this.device.createCommandEncoder();\n    }\n  }\n  ensureComputePassEnded() {\n    if (this.currentComputePass) {\n      this.currentComputePass.end();\n      this.currentComputePass = null;\n    }\n  }\n  getComputePass() {\n    if (!this.currentComputePass) {\n      this.currentComputePass = this.currentCommandEncoder.beginComputePass();\n    }\n    return this.currentComputePass;\n  }\n  async getBufferData(buffer2, size) {\n    const staging = this.bufferManager.acquireBuffer(size, GPUBufferUsage.COPY_DST | GPUBufferUsage.MAP_READ);\n    this.ensureCommandEncoderReady();\n    this.ensureComputePassEnded();\n    this.currentCommandEncoder.copyBufferToBuffer(buffer2, 0, staging, 0, size);\n    this.submitQueue();\n    await staging.mapAsync(GPUMapMode.READ);\n    const values = staging.getMappedRange().slice(0);\n    staging.unmap();\n    if (staging != null) {\n      this.bufferManager.releaseBuffer(staging, size, GPUBufferUsage.COPY_DST | GPUBufferUsage.MAP_READ);\n    }\n    if (env().getBool(\"WEBGPU_USE_PROFILE_TOOL\")) {\n      util_exports.assert(this.dummyContext !== void 0, () => `Fail to get context for profiling tool`);\n      this.dummyContext.getCurrentTexture();\n    }\n    return values;\n  }\n  convertAndCacheOnCPU(dataId, data) {\n    const tensorData = this.tensorMap.get(dataId);\n    this.releaseResource(dataId);\n    tensorData.values = data;\n    return tensorData.values;\n  }\n  readSync(dataId) {\n    const tensorData = this.tensorMap.get(dataId);\n    const { values } = tensorData;\n    if (values == null) {\n      throw new Error(\"WebGPU readSync is only available for CPU-resident tensors.\");\n    }\n    return values;\n  }\n  async read(dataId) {\n    if (!this.tensorMap.has(dataId)) {\n      throw new Error(`Tensor ${dataId} was not registered!`);\n    }\n    const tensorData = this.tensorMap.get(dataId);\n    const { values } = tensorData;\n    if (values != null) {\n      return this.convertAndCacheOnCPU(dataId, values);\n    }\n    let vals;\n    if (tensorData.dtype === \"complex64\") {\n      const ps = await Promise.all([\n        this.read(tensorData.complexTensorInfos.real.dataId),\n        this.read(tensorData.complexTensorInfos.imag.dataId)\n      ]);\n      const realValues = ps[0];\n      const imagValues = ps[1];\n      vals = backend_util_exports.mergeRealAndImagArrays(realValues, imagValues);\n    } else {\n      const bufferInfo = tensorData.resourceInfo;\n      const data = await this.getBufferData(bufferInfo.buffer, bufferInfo.size);\n      vals = ArrayBufferToTypedArray(data, tensorData.dtype);\n    }\n    this.convertAndCacheOnCPU(dataId, vals);\n    return vals;\n  }\n  readToGPU(dataId) {\n    const srcTensorData = this.tensorMap.get(dataId);\n    const { values, dtype, shape, resourceInfo } = srcTensorData;\n    if (dtype === \"complex64\") {\n      throw new Error(\"Does not support reading buffer for complex64 dtype.\");\n    }\n    if (resourceInfo == null) {\n      if (values != null) {\n        throw new Error(\"Data is not on GPU but on CPU.\");\n      } else {\n        throw new Error(\"There is no data on GPU or CPU.\");\n      }\n    }\n    const size = resourceInfo.size;\n    const buffer2 = this.bufferManager.acquireBuffer(size, resourceInfo.usage);\n    this.ensureCommandEncoderReady();\n    this.ensureComputePassEnded();\n    this.currentCommandEncoder.copyBufferToBuffer(resourceInfo.buffer, 0, buffer2, 0, size);\n    this.submitQueue();\n    const tensorInfo = this.makeTensorInfo(shape, dtype);\n    const tensorRef = engine().makeTensorFromTensorInfo(tensorInfo);\n    const tensorData = this.tensorMap.get(tensorInfo.dataId);\n    tensorData.resourceInfo = { size, usage: this.defaultGpuBufferUsage(), buffer: buffer2 };\n    return { tensorRef, buffer: buffer2, bufSize: size };\n  }\n  bufferSync(t) {\n    const data = this.readSync(t.dataId);\n    if (t.dtype === \"string\") {\n      try {\n        const strings = data.map((d) => util_exports.decodeString(d));\n        return buffer(t.shape, t.dtype, strings);\n      } catch (_a) {\n        throw new Error(\"Failed to decode encoded string bytes into utf-8\");\n      }\n    }\n    return buffer(t.shape, t.dtype, data);\n  }\n  async time(f) {\n    if (!this.supportTimeQuery) {\n      console.warn(`This device doesn't support timestamp-query extension. Start Chrome browser with flag --disable-dawn-features=disallow_unsafe_apis then try again. Otherwise, zero will be shown for the kernel time when profiling mode is enabled. Using performance.now is not workable for webgpu since it doesn't support synchronous data read from GPU.`);\n    }\n    const oldActiveTimers = this.activeTimers;\n    const newActiveTimers = [];\n    let outerMostTime = false;\n    if (this.programTimersStack == null) {\n      this.programTimersStack = newActiveTimers;\n      outerMostTime = true;\n    } else {\n      this.activeTimers.push(newActiveTimers);\n    }\n    this.activeTimers = newActiveTimers;\n    f();\n    const flattenedActiveTimerQueries = util_exports.flatten(this.activeTimers.map((d) => d.query)).filter((d) => d != null);\n    const flattenedActiveTimerNames = util_exports.flatten(this.activeTimers.map((d) => d.name)).filter((d) => d != null);\n    this.activeTimers = oldActiveTimers;\n    if (outerMostTime) {\n      this.programTimersStack = null;\n    }\n    const res = {\n      uploadWaitMs: this.uploadWaitMs,\n      downloadWaitMs: this.downloadWaitMs,\n      kernelMs: null,\n      wallMs: null\n    };\n    const kernelMs = await Promise.all(flattenedActiveTimerQueries);\n    res[\"kernelMs\"] = util_exports.sum(kernelMs);\n    res[\"getExtraProfileInfo\"] = () => kernelMs.map((d, i) => ({ name: flattenedActiveTimerNames[i], ms: d })).map((d) => `${d.name}: ${d.ms}`).join(\", \");\n    this.uploadWaitMs = 0;\n    this.downloadWaitMs = 0;\n    return res;\n  }\n  makeTensorInfo(shape, dtype, values) {\n    if (dtype === \"string\" && values != null && values.length > 0 && util_exports.isString(values[0])) {\n      values = values.map((d) => util_exports.encodeString(d));\n    }\n    const dataId = this.write(values, shape, dtype);\n    return { dataId, shape, dtype };\n  }\n  tensorToBinding(tensor2) {\n    if (!tensor2) {\n      return null;\n    }\n    const tensorData = this.tensorMap.get(tensor2.dataId);\n    if (\"texture\" in tensorData.resourceInfo) {\n      const info = tensorData.resourceInfo;\n      if (info.texture instanceof GPUExternalTexture) {\n        return info.texture;\n      } else {\n        return info.texture.createView();\n      }\n    }\n    const bufferInfo = tensorData.resourceInfo;\n    return { offset: 0, size: bufferInfo.size, buffer: bufferInfo.buffer };\n  }\n  async getQueryTime(query) {\n    if (this.supportTimeQuery) {\n      return this.getTimeFromQuerySet(query);\n    } else {\n      return 0;\n    }\n  }\n  uploadToGPU(dataId) {\n    const tensorData = this.tensorMap.get(dataId);\n    if (tensorData.resourceInfo) {\n      return;\n    }\n    const size = GPUBytesPerElement(tensorData.dtype) * util_exports.sizeFromShape(tensorData.shape);\n    const buffer2 = this.bufferManager.acquireBuffer(size, this.defaultGpuBufferUsage());\n    tensorData.resourceInfo = { size, usage: this.defaultGpuBufferUsage(), buffer: buffer2 };\n    if (tensorData.values) {\n      const stagingBuffer = this.bufferManager.acquireUploadBuffer(size, GPUBufferUsage.MAP_WRITE | GPUBufferUsage.COPY_SRC);\n      const arrayBuffer = stagingBuffer.getMappedRange();\n      if (tensorData.dtype === \"int32\" || tensorData.dtype === \"bool\") {\n        new Int32Array(arrayBuffer).set(tensorData.values);\n      } else {\n        new Float32Array(arrayBuffer).set(tensorData.values);\n      }\n      stagingBuffer.unmap();\n      this.ensureCommandEncoderReady();\n      this.ensureComputePassEnded();\n      this.currentCommandEncoder.copyBufferToBuffer(stagingBuffer, 0, buffer2, 0, size);\n      const stagingInfo = {\n        size,\n        usage: GPUBufferUsage.MAP_WRITE | GPUBufferUsage.COPY_SRC,\n        buffer: stagingBuffer\n      };\n      this.stagingPendingDisposal.push(stagingInfo);\n    }\n  }\n  makeUniforms(programUniform) {\n    let currentOffset = 0;\n    let preLength = 0;\n    const offsets = [];\n    programUniform.forEach((d) => {\n      if (d.data.length === 0) {\n        d.data = [1];\n      }\n      let baseAlignment;\n      switch (d.data.length) {\n        case 1:\n          baseAlignment = 4;\n          break;\n        case 2:\n          baseAlignment = 8;\n          break;\n        case 3:\n          baseAlignment = 16;\n          break;\n        case 4:\n          baseAlignment = 16;\n          break;\n        case 5:\n          baseAlignment = 16;\n          break;\n        case 6:\n          baseAlignment = 16;\n          break;\n        default:\n          util_exports.assert(false, () => `Unsupported ${d.data.length}D shape`);\n      }\n      if (preLength === 5 || preLength === 6) {\n        baseAlignment = 16;\n      }\n      currentOffset = Math.ceil(currentOffset / baseAlignment) * baseAlignment;\n      preLength = d.data.length;\n      offsets.push(currentOffset);\n      currentOffset += d.data.length * 4;\n    });\n    const arrayBuffer = new ArrayBuffer(currentOffset);\n    programUniform.forEach((d, i) => {\n      const offset = offsets[i];\n      if (d.type === \"int32\") {\n        new Int32Array(arrayBuffer, offset, d.data.length).set(d.data);\n      } else if (d.type === \"uint32\") {\n        new Uint32Array(arrayBuffer, offset, d.data.length).set(d.data);\n      } else {\n        new Float32Array(arrayBuffer, offset, d.data.length).set(d.data);\n      }\n    });\n    const uniformBuffer = this.bufferManager.acquireBuffer(currentOffset, GPUBufferUsage.COPY_DST | GPUBufferUsage.UNIFORM);\n    this.queue.writeBuffer(uniformBuffer, 0, arrayBuffer, 0, currentOffset);\n    const uniformInfo = {\n      size: currentOffset,\n      usage: GPUBufferUsage.COPY_DST | GPUBufferUsage.UNIFORM,\n      buffer: uniformBuffer\n    };\n    this.uniformPendingDisposal.push(uniformInfo);\n    return { offset: 0, size: currentOffset, buffer: uniformBuffer };\n  }\n  runWebGPUProgram(program, inputs, outputDtype, programDefinedUniform, output) {\n    if (!output) {\n      output = this.makeTensorInfo(program.outputShape, outputDtype);\n    }\n    if (util_exports.sizeFromShape(output.shape) === 0) {\n      this.tensorMap.get(output.dataId).values = util_exports.getTypedArrayFromDType(output.dtype, 0);\n      return output;\n    }\n    this.uploadToGPU(output.dataId);\n    program.dispatch = reshapeDispatch(this.device, program);\n    let programUniform = [];\n    let bufferShapes = [];\n    if (!program.isFromPixels) {\n      programUniform.push({ type: \"float32\", data: [NaN] });\n      bufferShapes = inputs.concat(output).map((d) => d.shape);\n      const uniformsType = \"int32\";\n      bufferShapes.map((d) => {\n        programUniform.push({ type: uniformsType, data: d });\n      });\n      const strides = util_exports.computeStrides(output.shape);\n      programUniform.push({ type: uniformsType, data: strides });\n      if (program.size) {\n        const size = util_exports.sizeFromShape(program.outputShape);\n        programUniform.push({ type: uniformsType, data: [program.isVec4 ? size / 4 : size] });\n      }\n    }\n    const inputsData = inputs.map((input2, i) => {\n      if (input2.dtype === \"complex64\") {\n        throw new Error(`GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.`);\n      }\n      this.uploadToGPU(input2.dataId);\n      return {\n        dtype: this.tensorMap.get(input2.dataId).dtype,\n        shape: input2.shape,\n        name: program.variableNames[i]\n      };\n    });\n    const key = makeShaderKey2(program, bufferShapes, inputsData, output);\n    let pipeline;\n    if (key in this.pipelineCache) {\n      pipeline = this.pipelineCache[key];\n    } else {\n      pipeline = compileProgram2(this.device, program, inputsData, output);\n      this.pipelineCache[key] = pipeline;\n    }\n    if (programDefinedUniform) {\n      programUniform = [...programUniform, ...programDefinedUniform];\n    }\n    const bindings = [\n      this.tensorToBinding(output),\n      ...inputs.map((t) => this.tensorToBinding(t)),\n      this.makeUniforms(programUniform)\n    ];\n    const bindGroup = this.device.createBindGroup({\n      layout: pipeline.getBindGroupLayout(0),\n      entries: bindings.map((b, i) => ({ binding: i, resource: b }))\n    });\n    this.ensureCommandEncoderReady();\n    const pass = this.getComputePass();\n    const shouldTimeProgram = this.activeTimers != null;\n    if (shouldTimeProgram) {\n      if (this.supportTimeQuery) {\n        pass.writeTimestamp(this.querySet, 0);\n      }\n    }\n    pass.setPipeline(pipeline);\n    pass.setBindGroup(0, bindGroup);\n    pass.dispatchWorkgroups(program.dispatch[0], program.dispatch[1], program.dispatch[2]);\n    if (shouldTimeProgram) {\n      if (this.supportTimeQuery) {\n        pass.writeTimestamp(this.querySet, 1);\n      }\n    }\n    this.dispatchNumberInEncoder++;\n    inputs.forEach((input2) => {\n      this.commandQueueOwnedIds.add(input2.dataId);\n    });\n    this.commandQueueOwnedIds.add(output.dataId);\n    if (env().get(\"WEBGPU_DEFERRED_SUBMIT_BATCH_SIZE\") <= this.dispatchNumberInEncoder) {\n      this.submitQueue();\n    }\n    if (shouldTimeProgram) {\n      this.activeTimers.push({\n        name: program.constructor.name,\n        query: this.getQueryTime(this.querySet)\n      });\n    }\n    return output;\n  }\n  async getTimeFromQuerySet(querySet) {\n    const queryBuffer = this.bufferManager.acquireBuffer(16, GPUBufferUsage.COPY_SRC | GPUBufferUsage.QUERY_RESOLVE);\n    const dst = this.bufferManager.acquireBuffer(16, GPUBufferUsage.MAP_READ | GPUBufferUsage.COPY_DST);\n    this.ensureCommandEncoderReady();\n    this.ensureComputePassEnded();\n    this.currentCommandEncoder.resolveQuerySet(querySet, 0, 2, queryBuffer, 0);\n    this.currentCommandEncoder.copyBufferToBuffer(queryBuffer, 0, dst, 0, 16);\n    this.submitQueue();\n    await dst.mapAsync(GPUMapMode.READ);\n    const arrayBuf = new BigUint64Array(dst.getMappedRange());\n    const timeElapsedNanos = Number(arrayBuf[1] - arrayBuf[0]);\n    dst.unmap();\n    this.bufferManager.releaseBuffer(dst, 16, GPUBufferUsage.MAP_READ | GPUBufferUsage.COPY_DST);\n    this.bufferManager.releaseBuffer(queryBuffer, 16, GPUBufferUsage.COPY_SRC | GPUBufferUsage.QUERY_RESOLVE);\n    return timeElapsedNanos / 1e6;\n  }\n  shouldExecuteOnCPU(inputs, sizeThreshold = CPU_HANDOFF_SIZE_THRESHOLD2) {\n    return env().getBool(\"WEBGPU_CPU_FORWARD\") && inputs.every((input2) => this.tensorMap.get(input2.dataId).resourceInfo == null && util_exports.sizeFromShape(input2.shape) < sizeThreshold);\n  }\n  numDataIds() {\n    return this.tensorMap.numDataIds() - this.tensorDataPendingDisposal.length;\n  }\n  dispose() {\n    if (this.disposed) {\n      return;\n    }\n    this.bufferManager.dispose();\n    this.textureManager.dispose();\n    this.disposed = true;\n  }\n};\nWebGPUBackend.nextDataId = 0;\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/base.js\nif (isWebGPUSupported()) {\n  registerBackend(\"webgpu\", async () => {\n    env().set(\"CHECK_COMPUTATION_FOR_ERRORS\", false);\n    const gpuDescriptor = {\n      powerPreference: env().get(\"WEBGPU_USE_LOW_POWER_GPU\") ? \"low-power\" : \"high-performance\"\n    };\n    const adapter = await navigator.gpu.requestAdapter(gpuDescriptor);\n    const adapterLimits = adapter.limits;\n    const deviceDescriptor = {};\n    const supportTimeQuery = adapter.features.has(\"timestamp-query\");\n    deviceDescriptor.requiredLimits = {\n      \"maxComputeWorkgroupStorageSize\": adapterLimits.maxComputeWorkgroupStorageSize,\n      \"maxComputeWorkgroupsPerDimension\": adapterLimits.maxComputeWorkgroupsPerDimension,\n      \"maxStorageBufferBindingSize\": adapterLimits.maxStorageBufferBindingSize\n    };\n    if (supportTimeQuery) {\n      deviceDescriptor.requiredFeatures = [\"timestamp-query\"];\n    }\n    const device = await adapter.requestDevice(deviceDescriptor);\n    return new WebGPUBackend(device);\n  }, 3);\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/binary_op_util.js\nvar BinaryOpType;\n(function(BinaryOpType2) {\n  BinaryOpType2[BinaryOpType2[\"MUL\"] = 0] = \"MUL\";\n  BinaryOpType2[BinaryOpType2[\"ADD\"] = 1] = \"ADD\";\n  BinaryOpType2[BinaryOpType2[\"ATAN2\"] = 2] = \"ATAN2\";\n  BinaryOpType2[BinaryOpType2[\"SUB\"] = 3] = \"SUB\";\n  BinaryOpType2[BinaryOpType2[\"DIV\"] = 4] = \"DIV\";\n  BinaryOpType2[BinaryOpType2[\"EQUAL\"] = 5] = \"EQUAL\";\n  BinaryOpType2[BinaryOpType2[\"GREATER\"] = 6] = \"GREATER\";\n  BinaryOpType2[BinaryOpType2[\"GREATER_EQUAL\"] = 7] = \"GREATER_EQUAL\";\n  BinaryOpType2[BinaryOpType2[\"LESS\"] = 8] = \"LESS\";\n  BinaryOpType2[BinaryOpType2[\"LESS_EQUAL\"] = 9] = \"LESS_EQUAL\";\n  BinaryOpType2[BinaryOpType2[\"LOGICAL_AND\"] = 10] = \"LOGICAL_AND\";\n  BinaryOpType2[BinaryOpType2[\"NOT_EQUAL\"] = 11] = \"NOT_EQUAL\";\n  BinaryOpType2[BinaryOpType2[\"SQUARED_DIFFERENCE\"] = 12] = \"SQUARED_DIFFERENCE\";\n  BinaryOpType2[BinaryOpType2[\"INT_DIV\"] = 13] = \"INT_DIV\";\n  BinaryOpType2[BinaryOpType2[\"POW\"] = 14] = \"POW\";\n  BinaryOpType2[BinaryOpType2[\"PRELU\"] = 15] = \"PRELU\";\n  BinaryOpType2[BinaryOpType2[\"MAX\"] = 16] = \"MAX\";\n  BinaryOpType2[BinaryOpType2[\"MIN\"] = 17] = \"MIN\";\n  BinaryOpType2[BinaryOpType2[\"COMPLEX_MULTIPLY_REAL\"] = 18] = \"COMPLEX_MULTIPLY_REAL\";\n  BinaryOpType2[BinaryOpType2[\"COMPLEX_MULTIPLY_IMAG\"] = 19] = \"COMPLEX_MULTIPLY_IMAG\";\n})(BinaryOpType || (BinaryOpType = {}));\nvar CHECK_NAN_SNIPPET4 = `\n  if (isnan(a)) { return a; }\n  if (isnan(b)) { return b; }\n  `;\nvar CHECK_NAN_SNIPPET_VEC4_INNER = `\n  if (isNaN.r) {\n    resultTemp.r = valueForNaN;\n  }\n  if (isNaN.g) {\n    resultTemp.g = valueForNaN;\n  }\n  if (isNaN.b) {\n    resultTemp.b = valueForNaN;\n  }\n  if (isNaN.a) {\n    resultTemp.a = valueForNaN;\n  }\n  `;\nvar CHECK_NAN_SNIPPET_VEC4 = `\n  let isNaN = isnanVec4(a) | isnanVec4(b);\n  ${CHECK_NAN_SNIPPET_VEC4_INNER}\n  `;\nvar ADD2 = \"return a + b;\";\nvar COMPLEX_MULTIPLY_REAL = \"return areal * breal - aimag * bimag;\";\nvar COMPLEX_MULTIPLY_IMAG = \"return areal * bimag + aimag * breal;\";\nvar DIV2 = \"return a / b;\";\nvar MUL2 = \"return a * b;\";\nvar SQUARED_DIFFERENCE2 = \"return (a - b) * (a - b);\";\nvar SUB2 = \"return a - b;\";\nvar EQUAL2 = \"return f32(a == b);\";\nvar EQUAL_VEC4 = \"return vec4<f32>(a == b);\";\nvar GREATER2 = \"return f32(a > b);\";\nvar GREATER_VEC4 = \"return vec4<f32>(a > b);\";\nvar GREATER_EQUAL2 = \"return f32(a >= b);\";\nvar GREATER_EQUAL_VEC4 = \"return vec4<f32>(a >= b);\";\nvar LESS2 = \"return f32(a < b);\";\nvar LESS_VEC4 = \"return vec4<f32>(a < b);\";\nvar LESS_EQUAL2 = \"return f32(a <= b);\";\nvar LESS_EQUAL_VEC4 = \"return vec4<f32>(a <= b);\";\nvar LOGICAL_AND2 = \"return f32(f32(a) >= 1.0 && f32(b) >= 1.0);\";\nvar LOGICAL_AND_VEC4 = `return (vec4<f32>(a >= vec4<f32>(1.0)) *\n  vec4<f32>(b >= vec4<f32>(1.0)));`;\nvar INT_DIV2 = `\n  let s = sign(a) * sign(b);\n  let ia = i32(round(a));\n  let ib = i32(round(b));\n  return f32(idiv(ia, ib, s));\n  `;\nvar INT_DIV_VEC4 = `\n  let ia = vec4<i32>(round(a));\n  let ib = vec4<i32>(round(b));\n  let cond = ib != vec4<i32>(0);\n  var resultTemp = vec4<i32>(0);\n  let s = sign(a) * sign(b);\n\n  // Windows (D3D) wants guaranteed non-zero int division at compile-time.\n  if (cond[0]) {\n    resultTemp[0] = idiv(ia[0], ib[0], s[0]);\n  }\n  if (cond[1]) {\n    resultTemp[1] = idiv(ia[1], ib[1], s[1]);\n  }\n  if (cond[2]) {\n    resultTemp[2] = idiv(ia[2], ib[2], s[2]);\n  }\n  if (cond[3]) {\n    resultTemp[3] = idiv(ia[3], ib[3], s[3]);\n  }\n  return vec4<f32>(resultTemp);\n  `;\nvar NOT_EQUAL2 = `\n  if (isnan(a) || isnan(b)) {\n    return 1.0;\n  }\n  return f32(a != b);\n`;\nvar NOT_EQUAL_VEC4 = `\n  var resultTemp = vec4<f32>(a != b);\n  let valueForNaN = 1.0;\n  ${CHECK_NAN_SNIPPET_VEC4}\n\n  return resultTemp;\n`;\nvar POW2 = `\n  if(a < 0.0 && floor(b) < b) {\n    return uniforms.NAN;\n  }\n  if (b == 0.0) {\n    return 1.0;\n  }\n  if (round(abs(b) % 2.0) != 1.0) {\n    return pow(abs(a), b);\n  }\n  return sign(a) * pow(abs(a), b);\n  `;\nvar POW_VEC4 = `\n  let isModRound1Bool = vec4<i32>(round(abs(b) % vec4<f32>(2.0))) == vec4<i32>(1);\n  let isModRound1 = vec4<f32>(isModRound1Bool);\n  let multiplier = sign(a) * isModRound1 + (vec4<f32>(1.0) - isModRound1);\n  var resultTemp = multiplier * pow(abs(a), b);\n\n  // Ensure that a^0 = 1, including 0^0 = 1 as this correspond to TF and JS\n  let isExpZero = b == vec4<f32>(0.0);\n  if (isExpZero.r) {\n    resultTemp.r = 1.0;\n  }\n  if (isExpZero.g) {\n    resultTemp.g = 1.0;\n  }\n  if (isExpZero.b) {\n    resultTemp.b = 1.0;\n  }\n  if (isExpZero.a) {\n    resultTemp.a = 1.0;\n  }\n  let isNaN = a < vec4<f32>(0.0) & floor(b) < b;\n  let valueForNaN = uniforms.NAN;\n  ${CHECK_NAN_SNIPPET_VEC4_INNER}\n  return resultTemp;\n  `;\nvar PRELU2 = `if (a < 0.0) { return b * a; }  return a;`;\nvar PRELU_VEC4 = `\n  let aLessThanZero = vec4<f32>(a < vec4<f32>(0.0));\n  return (aLessThanZero * (b * a)) + ((vec4<f32>(1.0) - aLessThanZero) * a);\n  `;\nfunction getBinaryWithNanString(op2, useVec4, valueForNaN = \"uniforms.NAN\") {\n  const checkNanSnippet = useVec4 ? CHECK_NAN_SNIPPET_VEC4 : CHECK_NAN_SNIPPET4;\n  return useVec4 ? `\n    let valueForNaN = ${valueForNaN};\n    var resultTemp = vec4<f32>(${op2}(a, b));\n    ` + checkNanSnippet + `\n    return resultTemp;\n  ` : checkNanSnippet + `\n    return ${op2}(a, b);\n  `;\n}\nfunction getBinaryOpString(type, useVec4) {\n  switch (type) {\n    case BinaryOpType.MUL:\n      return MUL2;\n    case BinaryOpType.ADD:\n      return ADD2;\n    case BinaryOpType.ATAN2:\n      return getBinaryWithNanString(\"atan2\", useVec4);\n    case BinaryOpType.SUB:\n      return SUB2;\n    case BinaryOpType.DIV:\n      return DIV2;\n    case BinaryOpType.EQUAL:\n      return useVec4 ? EQUAL_VEC4 : EQUAL2;\n    case BinaryOpType.GREATER:\n      return useVec4 ? GREATER_VEC4 : GREATER2;\n    case BinaryOpType.GREATER_EQUAL:\n      return useVec4 ? GREATER_EQUAL_VEC4 : GREATER_EQUAL2;\n    case BinaryOpType.LESS:\n      return useVec4 ? LESS_VEC4 : LESS2;\n    case BinaryOpType.LESS_EQUAL:\n      return useVec4 ? LESS_EQUAL_VEC4 : LESS_EQUAL2;\n    case BinaryOpType.LOGICAL_AND:\n      return useVec4 ? LOGICAL_AND_VEC4 : LOGICAL_AND2;\n    case BinaryOpType.NOT_EQUAL:\n      return useVec4 ? NOT_EQUAL_VEC4 : NOT_EQUAL2;\n    case BinaryOpType.SQUARED_DIFFERENCE:\n      return SQUARED_DIFFERENCE2;\n    case BinaryOpType.INT_DIV:\n      return useVec4 ? INT_DIV_VEC4 : INT_DIV2;\n    case BinaryOpType.PRELU:\n      return useVec4 ? PRELU_VEC4 : PRELU2;\n    case BinaryOpType.MAX:\n      return getBinaryWithNanString(\"max\", useVec4);\n    case BinaryOpType.MIN:\n      return getBinaryWithNanString(\"min\", useVec4);\n    case BinaryOpType.POW:\n      return useVec4 ? POW_VEC4 : POW2;\n    case BinaryOpType.COMPLEX_MULTIPLY_REAL:\n      return COMPLEX_MULTIPLY_REAL;\n    case BinaryOpType.COMPLEX_MULTIPLY_IMAG:\n      return COMPLEX_MULTIPLY_IMAG;\n    default:\n      throw new Error(`BinaryType ${type} is not implemented!`);\n  }\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/unary_op_util.js\nvar UnaryOpType;\n(function(UnaryOpType2) {\n  UnaryOpType2[UnaryOpType2[\"ABS\"] = 0] = \"ABS\";\n  UnaryOpType2[UnaryOpType2[\"CEIL\"] = 1] = \"CEIL\";\n  UnaryOpType2[UnaryOpType2[\"COS\"] = 2] = \"COS\";\n  UnaryOpType2[UnaryOpType2[\"COSH\"] = 3] = \"COSH\";\n  UnaryOpType2[UnaryOpType2[\"ELU\"] = 4] = \"ELU\";\n  UnaryOpType2[UnaryOpType2[\"EXP\"] = 5] = \"EXP\";\n  UnaryOpType2[UnaryOpType2[\"EXPM1\"] = 6] = \"EXPM1\";\n  UnaryOpType2[UnaryOpType2[\"FLOOR\"] = 7] = \"FLOOR\";\n  UnaryOpType2[UnaryOpType2[\"IS_NAN\"] = 8] = \"IS_NAN\";\n  UnaryOpType2[UnaryOpType2[\"LINEAR\"] = 9] = \"LINEAR\";\n  UnaryOpType2[UnaryOpType2[\"LOG\"] = 10] = \"LOG\";\n  UnaryOpType2[UnaryOpType2[\"LOGICAL_NOT\"] = 11] = \"LOGICAL_NOT\";\n  UnaryOpType2[UnaryOpType2[\"NEG\"] = 12] = \"NEG\";\n  UnaryOpType2[UnaryOpType2[\"RELU\"] = 13] = \"RELU\";\n  UnaryOpType2[UnaryOpType2[\"RELU6\"] = 14] = \"RELU6\";\n  UnaryOpType2[UnaryOpType2[\"LEAKYRELU\"] = 15] = \"LEAKYRELU\";\n  UnaryOpType2[UnaryOpType2[\"RECIPROCAL\"] = 16] = \"RECIPROCAL\";\n  UnaryOpType2[UnaryOpType2[\"RSQRT\"] = 17] = \"RSQRT\";\n  UnaryOpType2[UnaryOpType2[\"SIN\"] = 18] = \"SIN\";\n  UnaryOpType2[UnaryOpType2[\"SINH\"] = 19] = \"SINH\";\n  UnaryOpType2[UnaryOpType2[\"SIGMOID\"] = 20] = \"SIGMOID\";\n  UnaryOpType2[UnaryOpType2[\"SQRT\"] = 21] = \"SQRT\";\n  UnaryOpType2[UnaryOpType2[\"SQUARE\"] = 22] = \"SQUARE\";\n  UnaryOpType2[UnaryOpType2[\"TANH\"] = 23] = \"TANH\";\n  UnaryOpType2[UnaryOpType2[\"TO_INT\"] = 24] = \"TO_INT\";\n})(UnaryOpType || (UnaryOpType = {}));\nvar ABS3 = `return abs(a);`;\nvar CEIL2 = `return ceil(a);`;\nvar COS2 = `return cos(a);`;\nvar COSH2 = `\n  let e2x = exp(-a);\n  return (e2x + 1.0 / e2x) / 2.0;\n`;\nvar EXPM12 = `return exp(a) - 1.0;`;\nvar ELU5 = `if (a >= 0.0) { return a; }  return (exp(a) - 1.0);`;\nvar ELU_VEC4 = `\n  var resFloat = exp(a) - vec4<f32>(1.0);\n  if (a.r >= 0.0) {\n    resFloat.r = a.r;\n  }\n  if (a.g >= 0.0) {\n    resFloat.g = a.g;\n  }\n  if (a.b >= 0.0) {\n    resFloat.b = a.b;\n  }\n  if (a.a >= 0.0) {\n    resFloat.a = a.a;\n  }\n  return resFloat;\n`;\nvar EXP2 = `return exp(a);`;\nvar FLOOR2 = `return floor(a);`;\nvar IS_NAN2 = `return f32(isnan(a));`;\nvar LINEAR3 = `return a;`;\nvar LOG2 = `if (a < 0.0) { return 1.0/0.0; }\n  return log(a);`;\nvar LOGICAL_NOT2 = `return f32(!(a >= 1.0));`;\nvar NEG2 = `return -a;`;\nvar LEAKYRELU2 = `if (a < 0.0) { return uniforms.alpha * a; } return a;`;\nvar LEAKYRELU_VEC4 = `\n  let aLessThanZero = vec4<f32>(a < vec4<f32>(0.0));\n  return (aLessThanZero * (uniforms.alpha * a)) + ((vec4<f32>(1.0) - aLessThanZero) * a);\n`;\nvar RECIPROCAL2 = `return 1.0 / a;`;\nvar RELU4 = `return select(a, 0.0, a < 0.0);`;\nvar RELU64 = \"return clamp(a, 0.0, 6.0);\";\nvar RELU6_VEC4 = \"return clamp(a, vec4<f32>(0.0, 0.0, 0.0, 0.0), vec4<f32>(6.0, 6.0, 6.0, 6.0));\";\nvar RELU_VEC4 = `\n  return select(a, vec4<f32>(0.0), a < vec4<f32>(0.0));\n`;\nvar RSQRT2 = `return 1.0/sqrt(a);`;\nvar SIGMOID4 = `return 1.0 / (1.0 + exp(-1.0 * a));`;\nvar SIN2 = `return sin(a);`;\nvar SINH2 = `\n  let e2x = exp(a);\n  return (e2x - 1.0 / e2x) / 2.0;\n`;\nvar SQRT2 = `return sqrt(a);`;\nvar SQUARE2 = `return a * a;`;\nvar TANH2 = `\n  let e2x = exp(-2.0 * abs(a));\n  return sign(a) * (1.0 - e2x) / (1.0 + e2x);\n`;\nvar TO_INT2 = `return f32(i32((a)));`;\nfunction getUnaryOpString(type, useVec4) {\n  switch (type) {\n    case UnaryOpType.ABS:\n      return ABS3;\n    case UnaryOpType.COS:\n      return COS2;\n    case UnaryOpType.COSH:\n      return COSH2;\n    case UnaryOpType.CEIL:\n      return CEIL2;\n    case UnaryOpType.ELU:\n      return useVec4 ? ELU_VEC4 : ELU5;\n    case UnaryOpType.EXP:\n      return EXP2;\n    case UnaryOpType.EXPM1:\n      return EXPM12;\n    case UnaryOpType.FLOOR:\n      return FLOOR2;\n    case UnaryOpType.IS_NAN:\n      return IS_NAN2;\n    case UnaryOpType.LINEAR:\n      return LINEAR3;\n    case UnaryOpType.LOG:\n      return LOG2;\n    case UnaryOpType.LOGICAL_NOT:\n      return LOGICAL_NOT2;\n    case UnaryOpType.NEG:\n      return NEG2;\n    case UnaryOpType.LEAKYRELU:\n      return useVec4 ? LEAKYRELU_VEC4 : LEAKYRELU2;\n    case UnaryOpType.RECIPROCAL:\n      return RECIPROCAL2;\n    case UnaryOpType.RELU:\n      return useVec4 ? RELU_VEC4 : RELU4;\n    case UnaryOpType.RELU6:\n      return useVec4 ? RELU6_VEC4 : RELU64;\n    case UnaryOpType.RSQRT:\n      return RSQRT2;\n    case UnaryOpType.SIGMOID:\n      return SIGMOID4;\n    case UnaryOpType.SIN:\n      return SIN2;\n    case UnaryOpType.SINH:\n      return SINH2;\n    case UnaryOpType.SQRT:\n      return SQRT2;\n    case UnaryOpType.SQUARE:\n      return SQUARE2;\n    case UnaryOpType.TANH:\n      return TANH2;\n    case UnaryOpType.TO_INT:\n      return TO_INT2;\n    default:\n      throw new Error(`BinaryType ${type} is not implemented!`);\n  }\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/activation_util.js\nvar typeSnippet = (component) => {\n  switch (component) {\n    case 1:\n      return \"f32\";\n    case 2:\n      return \"vec2<f32>\";\n    case 3:\n      return \"vec3<f32>\";\n    case 4:\n      return \"vec4<f32>\";\n    default:\n      throw new Error(`${component}-component is not supported.`);\n  }\n};\nfunction activationFnSnippet(activation2, hasPreluActivationWeights = false, packed = false, coordsLength = 3) {\n  if (activation2 === null) {\n    return \"\";\n  }\n  let activationOpSnippet = \"\";\n  if (activation2 === \"linear\") {\n    activationOpSnippet = getUnaryOpString(UnaryOpType.LINEAR);\n  } else if (activation2 === \"relu\") {\n    activationOpSnippet = getUnaryOpString(UnaryOpType.RELU, packed);\n  } else if (activation2 === \"elu\") {\n    activationOpSnippet = getUnaryOpString(UnaryOpType.ELU, packed);\n  } else if (activation2 === \"relu6\") {\n    activationOpSnippet = getUnaryOpString(UnaryOpType.RELU6, packed);\n  } else if (activation2 === \"prelu\") {\n    activationOpSnippet = getBinaryOpString(BinaryOpType.PRELU, packed);\n  } else if (activation2 === \"sigmoid\") {\n    activationOpSnippet = getUnaryOpString(UnaryOpType.SIGMOID, packed);\n  } else if (activation2 === \"leakyrelu\") {\n    activationOpSnippet = getUnaryOpString(UnaryOpType.LEAKYRELU, packed);\n  } else {\n    throw new Error(`Activation ${activation2} has not been implemented for the WebGPU backend.`);\n  }\n  const elementSize = packed ? 4 : 1;\n  const dataType = typeSnippet(elementSize);\n  let activationFnSnippet2 = \"\";\n  if (hasPreluActivationWeights) {\n    activationFnSnippet2 = `\n      fn activation(a : ${dataType}, coords : vec${coordsLength}<i32>) -> ${dataType} {\n        let b = getPreluActivationWeightsByOutputCoords(coords);\n        ${activationOpSnippet}\n      }`;\n  } else {\n    activationFnSnippet2 = `\n      fn activation(a : ${dataType}, coords : vec${coordsLength}<i32>) -> ${dataType} {\n        ${activationOpSnippet}\n      }`;\n  }\n  return activationFnSnippet2;\n}\nfunction biasActivationSnippet(hasBias, activation2) {\n  return `\n      ${hasBias ? \"value = value + getBiasByOutputCoords(coords);\" : \"\"}\n      ${activation2 ? \"value = activation(value, coords);\" : \"\"}\n      `;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/matmul_packed_webgpu.js\nfunction matMulReadFnSource(batchAEqualOne, batchBEqualOne, transposeA, transposeB, fitAOuter = false, fitBOuter = false, fitInner = false, component = 1) {\n  util_exports.assert(transposeA && component === 1 || !transposeA, () => `transposeA ${transposeA} is not compatible with component size ${component}`);\n  const sampleA = `\n      let batch = ${batchAEqualOne ? \"0\" : \"batchIn\"};\n      let batchASize = uniforms.aShape[1] * uniforms.aShape[2];\n      ${transposeA ? `value = A[(batch * batchASize + col * uniforms.aShape[2] + row) / ${component}];` : `value = A[(batch * batchASize + row * uniforms.aShape[2] + col) / ${component}];`}\n\n    `;\n  let sampleB;\n  if (transposeB === false) {\n    sampleB = `value = B[(batch * batchBSize + row * uniforms.bShape[2] + col) / ${component}];`;\n  } else {\n    sampleB = `value = B[(batch * batchBSize + col * uniforms.bShape[2] + row) / ${component}];`;\n  }\n  return `\n  fn mm_readA(batchIn: i32, row: i32, colIn: i32) -> ${typeSnippet(component)} {\n    var value = ${typeSnippet(component)}(0.0);\n    let col = colIn * ${component};\n    ${fitAOuter && fitInner ? sampleA : `\n    ${transposeA ? `if(row < uniforms.dimAOuter && col < uniforms.dimInner)` : `if(row < uniforms.aShape[1] && col < uniforms.aShape[2])`}\n    {\n      ${sampleA}\n    }\n    `}\n    return value;\n  }\n\n  fn mm_readB(batchIn: i32, row: i32, colIn: i32) -> ${typeSnippet(component)} {\n    let col = colIn * ${component};\n    let batch = ${batchBEqualOne ? \"0\" : \"batchIn\"};\n    let batchBSize = uniforms.bShape[1] * uniforms.bShape[2];\n    var value = ${typeSnippet(component)}(0.0);\n    ${sampleB}\n    return value;\n  }\n  `;\n}\nfunction matMulReadWriteFnSource(hasBias, activation2, batchAEqualOne, batchBEqualOne, transposeA, transposeB, fitAOuter = false, fitBOuter = false, fitInner = false, component = 1) {\n  return `\n  ${matMulReadFnSource(batchAEqualOne, batchBEqualOne, transposeA, transposeB, fitAOuter, fitBOuter, fitInner, component)}\n  fn mm_write(batch: i32, row: i32, colIn: i32, valueIn: ${typeSnippet(component)}) {\n    let col = colIn * ${component};\n    ${fitAOuter && fitBOuter ? \"\" : \"if (row < uniforms.dimAOuter && col < uniforms.dimBOuter)\"}\n    {\n      var value = valueIn;\n      let coords = vec3<i32>(batch, row, col);\n      ${biasActivationSnippet(hasBias, activation2)}\n      setOutputAtCoords(coords[0], coords[1], coords[2], value);\n    }\n  }\n  `;\n}\nvar writeDataToSubAVec4Snippet = (transpose6) => {\n  if (transpose6) {\n    return `\n        mm_Asub[inputRow][inputCol] = mm_readA(batch,\n          kStart + inputRow,\n          globalRowStart / InnerElementSize + inputCol);\n        `;\n  } else {\n    return `\n        mm_Asub[inputRow][inputCol] = mm_readA(batch,\n          globalRow + innerRow,\n          kStart / InnerElementSize + inputCol);\n        `;\n  }\n};\nvar calculateResultSnippet = (transposeA, innerElementSize) => {\n  if (transposeA) {\n    return `\n        let ACached0 = mm_Asub[k * InnerElementSize][localRow];\n        let ACached1 = mm_Asub[k * InnerElementSize + 1][localRow];\n        let ACached2 = mm_Asub[k * InnerElementSize + 2][localRow];\n        ${innerElementSize === 3 ? \"\" : \"let ACached3 = mm_Asub[k * InnerElementSize + 3][localRow];\"}\n        for (var i = 0; i < RowPerThread; i = i + 1) {\n          acc[i] = BCached0 * ACached0[i] + acc[i];\n          acc[i] = BCached1 * ACached1[i] + acc[i];\n          acc[i] = BCached2 * ACached2[i] + acc[i];\n          ${innerElementSize === 3 ? \"\" : \"acc[i] = BCached3 * ACached3[i] + acc[i];\"}\n        }`;\n  } else {\n    return `\n        for (var i = 0; i < RowPerThread; i = i + 1) {\n          let ACached = mm_Asub[tileRow + i][k];\n          acc[i] = BCached0 * ACached.x + acc[i];\n          acc[i] = BCached1 * ACached.y + acc[i];\n          acc[i] = BCached2 * ACached.z + acc[i];\n          ${innerElementSize === 3 ? \"\" : \"acc[i] = BCached3 * ACached.w + acc[i];\"}\n        }`;\n  }\n};\nfunction makeMatMulPackedVec4Source(workPerThread, workGroupSize, transposeA = false, tileInner = 32, splitK = false, splitedDimInner = 32, isVectorA = false) {\n  const tileAOuter = workGroupSize[1] * workPerThread[1];\n  const tileBOuter = workGroupSize[0] * workPerThread[0];\n  const tileAWidth = transposeA ? tileAOuter : tileInner;\n  const tileAHight = transposeA ? tileInner : tileAOuter;\n  const innerElementSize = tileAWidth / workGroupSize[0];\n  const rowPerThreadB = tileInner / workGroupSize[1];\n  util_exports.assert((transposeA && innerElementSize === 4 && workPerThread[1] === 4 || !transposeA && (innerElementSize === 3 || innerElementSize === 4)) && tileAWidth % workGroupSize[0] === 0 && tileInner % workGroupSize[1] === 0 && workPerThread[0] === 4, () => `If transposeA ${transposeA} is true, innerElementSize ${innerElementSize} and workPerThread[1] ${workPerThread[1]} must be 4.\n          Otherwise, innerElementSize ${innerElementSize} must be 3 or 4.\n      tileAWidth ${tileAWidth} must be divisible by workGroupSize[0]${workGroupSize[0]}. tileInner ${tileInner} must be divisible by workGroupSize[1] ${workGroupSize[1]}. ColPerThread ${workPerThread[0]} must be 4.`);\n  return `\n  var<workgroup> mm_Asub : array<array<vec${innerElementSize}<f32>, ${tileAWidth / innerElementSize}>, ${tileAHight}>;\n  var<workgroup> mm_Bsub : array<array<vec4<f32>, ${tileBOuter / workPerThread[0]}>, ${tileInner}>;\n\n  const RowPerThread = ${workPerThread[1]};\n  const ColPerThread = ${workPerThread[0]};\n  const InnerElementSize = ${innerElementSize};\n  const TileInner = ${tileInner};\n\n  @compute @workgroup_size(workGroupSizeX, workGroupSizeY, workGroupSizeZ)\n  fn _start(@builtin(local_invocation_id) LocalId : vec3<u32>,\n            @builtin(global_invocation_id) GlobalId : vec3<u32>,\n            @builtin(num_workgroups) NumWorkgroups: vec3<u32>,\n            @builtin(workgroup_id) workgroupId: vec3<u32>) {\n    localId = LocalId;\n    globalId = GlobalId;\n    numWorkgroups = NumWorkgroups;\n\n    let localRow = i32(localId.y);\n    let tileRow = ${isVectorA ? \"0\" : \"localRow * RowPerThread\"};\n    let tileCol = i32(localId.x);\n\n    let globalRow = ${isVectorA ? \"0\" : \"i32(globalId.y) * RowPerThread\"};\n    let globalCol = i32(globalId.x);\n    let batch = ${splitK ? \"0\" : \"i32(globalId.z)\"};\n    let globalRowStart = i32(workgroupId.y) * ${tileAOuter};\n\n    let numTiles = ${splitK ? `${Math.ceil(splitedDimInner / tileInner)}` : \"(uniforms.dimInner - 1) / TileInner + 1\"};\n    var kStart = ${splitK ? `i32(globalId.z) * ${splitedDimInner}` : \"0\"};\n\n    var acc: array<vec4<f32>, RowPerThread>;\n\n    // Loop over shared dimension.\n    let tileRowB = localRow * ${rowPerThreadB};\n    for (var t = 0; t < numTiles; t = t + 1) {\n        // Load one tile of A into local memory.\n        for (var innerRow = 0; innerRow < RowPerThread; innerRow = innerRow + 1) {\n            let inputRow = tileRow + innerRow;\n            let inputCol = tileCol;\n            ${writeDataToSubAVec4Snippet(transposeA)}\n        }\n\n        // Load one tile of B into local memory.\n        for (var innerRow = 0; innerRow < ${rowPerThreadB}; innerRow = innerRow + 1) {\n            let inputRow = tileRowB + innerRow;\n            let inputCol = tileCol;\n            mm_Bsub[inputRow][inputCol] = mm_readB(batch, kStart + inputRow, globalCol);\n        }\n        kStart = kStart + TileInner;\n        workgroupBarrier();\n\n        // Compute acc values for a single thread.\n        for (var k = 0; k < TileInner / InnerElementSize; k = k + 1) {\n            let BCached0 = mm_Bsub[k * InnerElementSize][tileCol];\n            let BCached1 = mm_Bsub[k * InnerElementSize + 1][tileCol];\n            let BCached2 = mm_Bsub[k * InnerElementSize + 2][tileCol];\n            ${innerElementSize === 3 ? \"\" : \"let BCached3 = mm_Bsub[k * InnerElementSize + 3][tileCol];\"}\n\n            ${calculateResultSnippet(transposeA, innerElementSize)}\n        }\n\n        workgroupBarrier();\n    }\n\n    for (var innerRow = 0; innerRow < RowPerThread; innerRow = innerRow + 1) {\n        mm_write(batch, globalRow + innerRow, globalCol, acc[innerRow]);\n    }\n  }`;\n}\nvar writeDataToSubASnippet = (transpose6) => {\n  if (transpose6) {\n    return `\n        mm_Asub[inputRow][inputCol] = mm_readA(batch,\n          kStart + inputRow,\n          globalRowStart + inputCol);\n        `;\n  } else {\n    return `\n        mm_Asub[inputRow][inputCol] = mm_readA(batch,\n          globalRowStart + inputRow,\n          kStart + inputCol);\n        `;\n  }\n};\nvar readDataFromSubASnippet = (transposeA) => {\n  return transposeA ? \"let ACached = mm_Asub[k][tileRow + innerRow];\" : \"let ACached = mm_Asub[tileRow + innerRow][k];\";\n};\nfunction makeMatMulPackedSource(workPerThread, workGroupSize, transposeA = false, tileInner = 32, splitK = false, splitedDimInner = 32) {\n  const tileAOuter = workPerThread[1] * workGroupSize[1];\n  const tileBOuter = workPerThread[0] * workGroupSize[0];\n  const tileAWidth = transposeA ? tileAOuter : tileInner;\n  const tileAHight = transposeA ? tileInner : tileAOuter;\n  util_exports.assert(tileAHight % workGroupSize[1] === 0 && tileAWidth % workGroupSize[0] === 0 && tileInner % workGroupSize[1] === 0, () => `tileAHight ${tileAHight} must be divisible by workGroupSize[1]${workGroupSize[1]}, tileAWidth ${tileAWidth} must be divisible by workGroupSize[0]${workGroupSize[0]}, tileInner ${tileInner} must be divisible by workGroupSize[1]${workGroupSize[1]}`);\n  const rowPerThreadA = tileAHight / workGroupSize[1];\n  const colPerThreadA = tileAWidth / workGroupSize[0];\n  const rowPerThreadB = tileInner / workGroupSize[1];\n  return `\n    var<workgroup> mm_Asub : array<array<f32, ${tileAWidth}>, ${tileAHight}>;\n    var<workgroup> mm_Bsub : array<array<f32, ${tileBOuter}>, ${tileInner}>;\n    const RowPerThread = ${workPerThread[1]};\n    const ColPerThread = ${workPerThread[0]};\n    const TileInner = ${tileInner};\n\n    @compute @workgroup_size(workGroupSizeX, workGroupSizeY, workGroupSizeZ)\n    fn _start(@builtin(local_invocation_id) LocalId : vec3<u32>,\n              @builtin(global_invocation_id) GlobalId : vec3<u32>,\n              @builtin(num_workgroups) NumWorkgroups: vec3<u32>,\n              @builtin(workgroup_id) workgroupId: vec3<u32>) {\n      localId = LocalId;\n      globalId = GlobalId;\n      numWorkgroups = NumWorkgroups;\n\n      let tileRow = i32(localId.y) * RowPerThread;\n      let tileCol = i32(localId.x) * ColPerThread;\n\n      let globalRow = i32(globalId.y) * RowPerThread;\n      let globalCol = i32(globalId.x) * ColPerThread;\n      let batch = ${splitK ? \"0\" : \"i32(globalId.z)\"};\n      let globalRowStart = i32(workgroupId.y) * ${tileAOuter};\n\n      let numTiles = ${splitK ? `${Math.ceil(splitedDimInner / tileInner)}` : \"(uniforms.dimInner - 1) / TileInner + 1\"};\n      var kStart = ${splitK ? `i32(globalId.z) * ${splitedDimInner}` : \"0\"};\n\n      var acc : array<array<f32, ColPerThread>, RowPerThread>;\n\n      // Without this initialization strange values show up in acc.\n      for (var innerRow = 0; innerRow < RowPerThread; innerRow = innerRow + 1) {\n        for (var innerCol = 0; innerCol < ColPerThread; innerCol = innerCol + 1) {\n          acc[innerRow][innerCol] = 0.0;\n        }\n      }\n\n      let tileRowA = i32(localId.y) * ${rowPerThreadA};\n      let tileColA = i32(localId.x) * ${colPerThreadA};\n      let tileRowB = i32(localId.y) * ${rowPerThreadB};\n      // Loop over shared dimension.\n      for (var t = 0; t < numTiles; t = t + 1) {\n        // Load one tile of A into local memory.\n        for (var innerRow = 0; innerRow < ${rowPerThreadA}; innerRow = innerRow + 1) {\n          for (var innerCol = 0; innerCol < ${colPerThreadA}; innerCol = innerCol + 1) {\n            let inputRow = tileRowA + innerRow;\n            let inputCol = tileColA + innerCol;\n            ${writeDataToSubASnippet(transposeA)}\n          }\n        }\n\n        // Load one tile of B into local memory.\n        for (var innerRow = 0; innerRow < ${rowPerThreadB}; innerRow = innerRow + 1) {\n          for (var innerCol = 0; innerCol < ColPerThread; innerCol = innerCol + 1) {\n            let inputRow = tileRowB + innerRow;\n            let inputCol = tileCol + innerCol;\n            mm_Bsub[inputRow][inputCol] = mm_readB(batch,\n              kStart + inputRow,\n              globalCol + innerCol);\n          }\n        }\n        kStart = kStart + TileInner;\n        workgroupBarrier();\n\n        // Compute acc values for a single thread.\n        var BCached : array<f32, ColPerThread>;\n        for (var k = 0; k < TileInner; k = k + 1) {\n          for (var inner = 0; inner < ColPerThread; inner = inner + 1) {\n            BCached[inner] = mm_Bsub[k][tileCol + inner];\n          }\n\n          for (var innerRow = 0; innerRow < RowPerThread; innerRow = innerRow + 1) {\n            ${readDataFromSubASnippet(transposeA)}\n            for (var innerCol = 0; innerCol < ColPerThread; innerCol = innerCol + 1) {\n              acc[innerRow][innerCol] = acc[innerRow][innerCol] + ACached * BCached[innerCol];\n            }\n          }\n        }\n\n        workgroupBarrier();\n      }\n\n      for (var innerRow = 0; innerRow < RowPerThread; innerRow = innerRow + 1) {\n        for (var innerCol = 0; innerCol < ColPerThread; innerCol = innerCol + 1) {\n          mm_write(batch, globalRow + innerRow, globalCol + innerCol,\n              acc[innerRow][innerCol]);\n        }\n      }\n    }\n  `;\n}\nvar readVectorASnippet = (transpose6) => {\n  return transpose6 ? `\n      mm_readA(batch, colA, globalRow),\n      mm_readA(batch, colA + 1, globalRow),\n      mm_readA(batch, colA + 2, globalRow),\n      mm_readA(batch, colA + 3, globalRow)\n  ` : `\n      mm_readA(batch, globalRow, colA),\n      mm_readA(batch, globalRow, colA + 1),\n      mm_readA(batch, globalRow, colA + 2),\n      mm_readA(batch, globalRow, colA + 3)\n  `;\n};\nfunction makeVectorMatrixProductSource(workGroupSize, transposeA = false) {\n  util_exports.assert(workGroupSize[1] === 1 && workGroupSize[2] === 1, () => `A linear work group size is required. But got ${workGroupSize}.`);\n  return `\n    const TileSize = ${workGroupSize[0] * 4};\n    var<workgroup> mm_Asub : array<vec4<f32>, ${workGroupSize[0]}>;\n\n    ${getMainHeaderString()} {\n      let tileCol = i32(localId.x);\n      let globalCol = i32(globalId.x);\n      let globalRow = i32(globalId.y);\n\n      let numTiles = (uniforms.dimInner - 1) / TileSize + 1;\n      let batch = i32(globalId.z);\n      // Without this initialization strange values show up in acc.\n      var acc = 0.0;\n\n      // Loop over shared dimension.\n      for (var t = 0; t < numTiles; t = t + 1) {\n        // Load one tile of A into local memory.\n        let colA = t * TileSize + tileCol * 4;\n        mm_Asub[tileCol] = vec4<f32>(${readVectorASnippet(transposeA)});\n        workgroupBarrier();\n\n        // Compute acc values for a single thread.\n        for (var k = 0; k < TileSize / 4; k = k + 1) {\n          let rowB = t * TileSize + k * 4;\n          let BCached = vec4<f32>(mm_readB(batch, rowB, globalCol),\n                              mm_readB(batch, rowB + 1, globalCol),\n                              mm_readB(batch, rowB + 2, globalCol),\n                              mm_readB(batch, rowB + 3, globalCol));\n\n          let ACached = mm_Asub[k];\n          acc = acc + dot(ACached, BCached);\n        }\n\n        workgroupBarrier();\n      }\n\n      mm_write(batch, globalRow, globalCol, acc);\n    }\n  `;\n}\nvar MatMulPackedProgram2 = class {\n  constructor(aShape, outputShape, batchAEqualOne, batchBEqualOne, transposeA = false, transposeB = false, bias = null, activation2 = null, preluActivationWeights = null) {\n    this.variableNames = [\"A\", \"B\"];\n    this.uniforms = `dimAOuter : i32, dimBOuter : i32, dimInner : i32,`;\n    this.outputShape = outputShape;\n    this.dispatchLayout = { x: [2], y: [1], z: [0] };\n    const dimInner = transposeA ? aShape[1] : aShape[2];\n    this.isVec4 = (dimInner % 4 === 0 && !transposeA || outputShape[1] % 4 === 0 && transposeA) && outputShape[2] % 4 === 0 && !transposeB;\n    this.isVectorA = outputShape[1] === 1 && !transposeA;\n    if (!this.isVec4 && this.isVectorA) {\n      this.elementsPerThread = [1, 1, 1];\n      this.workGroupSize = [32, 1, 1];\n    } else {\n      const workGroupInfo = computeWorkGroupInfoForMatMul(outputShape[1], dimInner, outputShape[2], transposeA);\n      this.workGroupSize = workGroupInfo.workGroupSize;\n      this.elementsPerThread = workGroupInfo.elementsPerThread;\n    }\n    this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, this.workGroupSize, this.elementsPerThread);\n    const addBias = bias != null;\n    const hasPreluActivationWeights = preluActivationWeights != null;\n    if (addBias) {\n      this.variableNames.push(\"bias\");\n    }\n    if (hasPreluActivationWeights) {\n      this.variableNames.push(\"preluActivationWeights\");\n    }\n    this.transposeA = transposeA;\n    this.transposeB = transposeB;\n    this.addBias = addBias;\n    this.activation = activation2;\n    this.hasPreluActivationWeights = hasPreluActivationWeights;\n    this.batchAEqualOne = batchAEqualOne;\n    this.batchBEqualOne = batchBEqualOne;\n    [this.fitAOuter, this.fitBOuter, this.fitInner] = this.getShapeFit(outputShape[1], outputShape[2], dimInner);\n    this.shaderKey = `matMulPacked_${this.elementsPerThread}_${transposeA}_${transposeB}_${this.activation}_${this.fitAOuter}_${this.fitBOuter}_${this.fitInner}_${this.isVec4}_${this.isVectorA}_${this.batchAEqualOne}_${this.batchBEqualOne}`;\n  }\n  getShapeFit(dimAOuter, dimBOuter, dimInner) {\n    const tileAOuter = this.workGroupSize[1] * this.elementsPerThread[1];\n    const tileBOuter = this.workGroupSize[0] * this.elementsPerThread[0];\n    if (!this.isVec4 && this.isVectorA) {\n      this.tileInner = this.workGroupSize[0] * 4;\n    } else {\n      this.tileInner = tileBOuter;\n    }\n    const fitAOuter = dimAOuter % tileAOuter === 0;\n    const fitBOuter = dimBOuter % tileBOuter === 0;\n    const fitInner = dimInner % this.tileInner === 0;\n    return [fitAOuter, fitBOuter, fitInner];\n  }\n  getUserCode() {\n    const userCode = `\n      ${activationFnSnippet(this.activation, this.hasPreluActivationWeights, this.isVec4)}\n      ${matMulReadWriteFnSource(this.addBias, this.activation, this.batchAEqualOne, this.batchBEqualOne, false, this.transposeB, this.fitAOuter, this.fitBOuter, this.fitInner, this.isVec4 ? 4 : 1)}\n      ${this.isVec4 ? makeMatMulPackedVec4Source(this.elementsPerThread, this.workGroupSize, this.transposeA, this.tileInner, false, null, this.isVectorA) : this.isVectorA ? makeVectorMatrixProductSource(this.workGroupSize, this.transposeA) : makeMatMulPackedSource(this.elementsPerThread, this.workGroupSize, this.transposeA, this.tileInner)}\n    `;\n    return userCode;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/matmul_reduce_webgpu.js\nfunction makeMatMulReduceSource() {\n  return `\n    var<workgroup> sumValues : array<f32, workGroupSizeX>;\n    ${getMainHeaderString()} {\n      let coords = getOutputCoords();\n      let batch = coords[0];\n      let row = coords[1];\n      let col = coords[2];\n      var sum = 0.0;\n      let Length = uniforms.dimInner;\n      for (var k = i32(localId.x); k < Length; k = k + i32(workGroupSizeX)) {\n        let dataA = mm_readA(batch, row, k);\n        let dataB = mm_readB(batch, k, col);\n        sum = sum + dataA * dataB;\n      }\n      sumValues[localId.x] = sum;\n      workgroupBarrier();\n\n      for(var currentSize = workGroupSizeX / 2u; currentSize > 1u;\n          currentSize = currentSize / 2u) {\n        if (localId.x < currentSize)\n        {\n          sumValues[localId.x] = sumValues[localId.x] + sumValues[localId.x + currentSize];\n        }\n        workgroupBarrier();\n      }\n\n      if (localId.x == 0u) {\n        sum = sumValues[0] + sumValues[1];\n        mm_write(batch, row, col, sum);\n      }\n    }\n  `;\n}\nvar MatMulReduceProgram = class {\n  constructor(outputShape, batchAEqualOne, batchBEqualOne, transposeA = false, transposeB = false, bias = null, activation2 = null, preluActivationWeights = null) {\n    this.variableNames = [\"A\", \"B\"];\n    this.uniforms = `dimAOuter : i32, dimBOuter : i32, dimInner : i32,`;\n    this.workGroupSize = [256, 1, 1];\n    this.outputShape = outputShape;\n    this.dispatchLayout = { x: [], y: [1, 2], z: [0] };\n    this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, this.workGroupSize);\n    const addBias = bias != null;\n    const hasPreluActivationWeights = preluActivationWeights != null;\n    if (addBias) {\n      this.variableNames.push(\"bias\");\n    }\n    if (hasPreluActivationWeights) {\n      this.variableNames.push(\"preluActivationWeights\");\n    }\n    this.transposeA = transposeA;\n    this.transposeB = transposeB;\n    this.addBias = addBias;\n    this.activation = activation2;\n    this.hasPreluActivationWeights = hasPreluActivationWeights;\n    this.batchAEqualOne = batchAEqualOne;\n    this.batchBEqualOne = batchBEqualOne;\n    this.shaderKey = `matMulReduce_${this.activation}_${transposeA}_${transposeB}_${this.batchAEqualOne}_${this.batchBEqualOne}`;\n  }\n  getUserCode() {\n    const userCode = `\n      ${activationFnSnippet(this.activation, this.hasPreluActivationWeights)}\n      ${matMulReadWriteFnSource(this.addBias, this.activation, this.batchAEqualOne, this.batchBEqualOne, this.transposeA, this.transposeB)}\n      ${makeMatMulReduceSource()}\n    `;\n    return userCode;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/matmul_small_output_size_webgpu.js\nfunction makeMatMulSmallOutputSizeSource(workGroupSize) {\n  const tileAOuter = workGroupSize[1];\n  const tileBOuter = workGroupSize[0];\n  const tileInner = tileAOuter > tileBOuter ? tileAOuter : tileBOuter;\n  return `\n  var<workgroup> mm_Asub : array<array<f32, ${tileInner}>, ${tileAOuter}>;\n  var<workgroup> mm_Bsub : array<array<f32, ${tileBOuter}>, ${tileInner}>;\n\n  // If the output size is small for matrix multiplication, avoid to use vec4\n  // and handle some elements per thread to optimally utilize the ALU.\n  // Read data from global memory to registers firstly, then store them into\n  // shared memory, so it is instruction-Level parallelism for arithmetic\n  // operations and others handle IO operations between barrier api, makes ALU\n  // and load/store units work simultaneously, could improves the performance.\n  ${getMainHeaderString()} {\n    let tileRow = i32(localId.y);\n    let tileCol = i32(localId.x);\n    let globalRow = i32(globalId.y);\n    let globalCol = i32(globalId.x);\n    let batch = i32(globalId.z);\n\n    // uniforms.dimInner should be greater than 0.\n    let numTiles = (uniforms.dimInner - 1) / ${tileInner} + 1;\n    var acc = 0.0;\n\n    var globalColA = tileCol;\n    var globalRowB = 0;\n    var regA = mm_readA(batch, globalRow, globalColA);\n    var regB0 = mm_readB(batch, globalRowB + 2 * tileRow, globalCol);\n    var regB1 = mm_readB(batch, globalRowB + 2 * tileRow + 1, globalCol);\n    globalColA = globalColA + ${tileInner};\n    globalRowB = globalRowB + ${tileInner};\n\n    for (var t = 0; t < numTiles; t = t + 1) {\n      mm_Asub[tileRow][tileCol] = regA;\n      mm_Bsub[2 * tileRow][tileCol] = regB0;\n      mm_Bsub[2 * tileRow + 1][tileCol] = regB1;\n\n      workgroupBarrier();\n\n      regA = mm_readA(batch, globalRow, globalColA);\n      regB0 = mm_readB(batch, globalRowB + 2 * tileRow, globalCol);\n      regB1 = mm_readB(batch, globalRowB + 2 * tileRow + 1, globalCol);\n      globalColA = globalColA + ${tileInner};\n      globalRowB = globalRowB + ${tileInner};\n\n      for (var k = 0; k < ${tileInner}; k = k + 1) {\n        acc = acc + mm_Asub[tileRow][k] * mm_Bsub[k][tileCol];\n      }\n      workgroupBarrier();\n    }\n\n    mm_write(batch, globalRow, globalCol, acc);\n  }\n  `;\n}\nvar MatMulSmallOutputSizeProgram = class {\n  constructor(aShape, bShape, outputShape, transposeA = false, transposeB = false, bias = null, activation2 = null, preluActivationWeights = null) {\n    this.variableNames = [\"A\", \"B\"];\n    this.uniforms = `dimAOuter : i32, dimBOuter : i32, dimInner : i32,`;\n    this.workGroupSize = [16, 8, 1];\n    this.outputShape = outputShape;\n    this.dispatchLayout = { x: [2], y: [1], z: [0] };\n    this.dispatch = [\n      Math.ceil(outputShape[2] / this.workGroupSize[0]),\n      Math.ceil(outputShape[1] / this.workGroupSize[1]),\n      outputShape[0]\n    ];\n    const addBias = bias != null;\n    if (addBias) {\n      this.variableNames.push(\"bias\");\n    }\n    const hasPreluActivationWeights = preluActivationWeights != null;\n    if (hasPreluActivationWeights) {\n      this.variableNames.push(\"preluActivationWeights\");\n    }\n    this.transposeA = transposeA;\n    this.transposeB = transposeB;\n    this.addBias = addBias;\n    this.activation = activation2;\n    this.hasPreluActivationWeights = hasPreluActivationWeights;\n    this.batchAEqualOne = aShape[0] === 1;\n    this.batchBEqualOne = bShape[0] === 1;\n    this.shaderKey = `matMulSmallOutputSize_${this.activation}_${transposeA}_${transposeB}_${this.batchAEqualOne}_${this.batchBEqualOne}`;\n  }\n  getUserCode() {\n    const userCode = `\n      ${activationFnSnippet(this.activation, this.hasPreluActivationWeights)}\n      ${matMulReadWriteFnSource(this.addBias, this.activation, this.batchAEqualOne, this.batchBEqualOne, this.transposeA, this.transposeB)}\n      ${makeMatMulSmallOutputSizeSource(this.workGroupSize)}\n    `;\n    return userCode;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/matmul_splitK_webgpu.js\nvar MatMulSplitKProgram = class {\n  constructor(outputShape, dimInner, batchAEqualOne, batchBEqualOne, transposeA = false, transposeB = false) {\n    this.variableNames = [\"A\", \"B\"];\n    this.uniforms = `dimAOuter : i32, dimBOuter : i32, dimInner : i32,`;\n    this.workGroupSize = [8, 8, 1];\n    this.atomic = true;\n    this.isVec4 = false;\n    this.splitedDimInner = 128;\n    util_exports.assert(outputShape[0] === 1, () => \"MatMulSplitKProgram only supports batch = 1.\");\n    this.outputShape = outputShape;\n    this.dispatchLayout = { x: [2], y: [1], z: [0, 3] };\n    this.isVec4 = (transposeA && this.outputShape[1] % 4 === 0 || !transposeA && dimInner % 4 === 0) && this.outputShape[2] % 4 === 0;\n    this.elementsPerThread = [4, 4, this.splitedDimInner];\n    if (!this.isVec4) {\n      if (this.outputShape[1] < 16) {\n        this.elementsPerThread[1] = 1;\n      }\n      if (this.outputShape[2] < 16) {\n        this.elementsPerThread[0] = 1;\n      }\n    }\n    this.dispatch = computeDispatch(this.dispatchLayout, [\n      this.outputShape[0],\n      this.outputShape[1],\n      this.outputShape[2],\n      dimInner\n    ], this.workGroupSize, this.elementsPerThread);\n    this.transposeA = transposeA;\n    this.transposeB = transposeB;\n    this.batchAEqualOne = batchAEqualOne;\n    this.batchBEqualOne = batchBEqualOne;\n    this.shaderKey = `matMulSplitK_${transposeA}_${transposeB}_${batchAEqualOne}_${batchBEqualOne}_${this.elementsPerThread}_${this.isVec4}`;\n  }\n  getUserCode() {\n    const atomicAddSnippet = (component2) => {\n      return `\n      for (var i = 0; i < ${component2}; i = i + 1)\n      {\n        var oldValue = atomicLoad(&(result[flatIndex + i]));\n        var exchanged = false;\n        for (; !exchanged;) {\n          let newValueF32 = bitcast<f32>(oldValue) + ${component2 > 1 ? \"value[i]\" : \"value\"};\n          let newValue = bitcast<i32>(newValueF32);\n          let res = atomicCompareExchangeWeak(&(result[flatIndex + i]), oldValue, newValue);\n          oldValue = res.old_value;\n          exchanged = res.exchanged;\n        }\n      }\n      `;\n    };\n    const component = this.isVec4 ? 4 : 1;\n    const userCode = `\n      ${matMulReadFnSource(this.batchAEqualOne, this.batchBEqualOne, false, this.transposeB, false, false, false, component)}\n      fn mm_write(batch: i32, row : i32, colIn : i32, value : ${typeSnippet(component)}) {\n        let col = colIn * ${component};\n        if (row < uniforms.dimAOuter && col < uniforms.dimBOuter) {\n          let coords = vec3<i32>(batch, row, col);\n          let flatIndex = getOutputIndexFromCoords(coords);\n          // The problem is that we should initialize output to zero before using.\n          // Otherwise, the original value will be added to the result.\n          ${atomicAddSnippet(component)}\n        }\n      }\n      ${this.isVec4 ? makeMatMulPackedVec4Source(this.elementsPerThread, this.workGroupSize, this.transposeA, 32, true, this.splitedDimInner) : makeMatMulPackedSource(this.elementsPerThread, this.workGroupSize, this.transposeA, 32, true, this.splitedDimInner)}\n    `;\n    return userCode;\n  }\n};\nvar BiasActivationProgram = class {\n  constructor(outputShape, bias = null, activation2 = null, preluActivationWeights = null) {\n    this.uniforms = \"\";\n    this.variableNames = [\"x\"];\n    this.workGroupSize = [64, 1, 1];\n    this.size = true;\n    this.outputShape = outputShape;\n    this.dispatchLayout = flatDispatchLayout(this.outputShape);\n    this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, this.workGroupSize);\n    this.addBias = bias != null;\n    this.hasPreluActivationWeights = preluActivationWeights != null;\n    this.activation = activation2;\n    if (this.addBias) {\n      this.variableNames.push(\"bias\");\n    }\n    if (this.hasPreluActivationWeights) {\n      this.variableNames.push(\"preluActivationWeights\");\n    }\n    this.shaderKey = `biasActivation_${activation2}`;\n  }\n  getUserCode() {\n    return `\n    ${activationFnSnippet(this.activation, this.hasPreluActivationWeights)}\n    ${getMainHeaderString(\"index\")} {\n      if (index < uniforms.size) {\n        let coords = getCoordsFromIndex(index);\n        var value = getXByOutputIndex(index);\n        ${biasActivationSnippet(this.addBias, this.activation)}\n        setOutputAtIndex(index, value);\n      }\n    }\n    `;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/fill_webgpu.js\nvar FillProgram2 = class {\n  constructor(shape) {\n    this.variableNames = [];\n    this.outputShape = [];\n    this.uniforms = \"value : f32,\";\n    this.workGroupSize = [64, 1, 1];\n    this.size = true;\n    this.outputShape = shape;\n    this.dispatchLayout = flatDispatchLayout(this.outputShape);\n    this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, this.workGroupSize);\n    this.shaderKey = \"fill\";\n  }\n  getUserCode() {\n    const userCode = `\n    ${getMainHeaderString(\"index\")} {\n      if (index < uniforms.size) {\n        setOutputAtIndex(index, uniforms.value);\n      }\n    }\n  `;\n    return userCode;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/Fill.js\nfunction fill5(args) {\n  const { backend: backend2, attrs } = args;\n  const { shape, value } = attrs;\n  let { dtype } = attrs;\n  dtype = dtype || util_exports.inferDtype(value);\n  if (dtype === \"string\") {\n    const values = util_exports.getArrayFromDType(dtype, util_exports.sizeFromShape(shape));\n    values.fill(value);\n    return backend2.makeTensorInfo(shape, dtype, values);\n  } else {\n    const program = new FillProgram2(shape);\n    const uniformData = [{ type: \"float32\", data: [value] }];\n    return backend2.runWebGPUProgram(program, [], dtype, uniformData);\n  }\n}\nvar fillConfig4 = {\n  kernelName: Fill,\n  backendName: \"webgpu\",\n  kernelFunc: fill5\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/Reshape.js\nfunction reshape6(args) {\n  const { inputs, attrs } = args;\n  const { x } = inputs;\n  const { shape } = attrs;\n  const xSize = util_exports.sizeFromShape(x.shape);\n  const $shape = util_exports.inferFromImplicitShape(shape, xSize);\n  const $xSize = util_exports.sizeFromShape($shape);\n  util_exports.assert(xSize === $xSize, () => `The new shape (${$shape}) has ${$xSize} elements and the old shape (${x.shape}) has ${xSize} elements. The new shape and old shape must have the same number of elements.`);\n  args.backend.incRef(x.dataId);\n  return { dataId: x.dataId, shape: $shape, dtype: x.dtype };\n}\nvar reshapeConfig4 = {\n  kernelName: Reshape,\n  backendName: \"webgpu\",\n  kernelFunc: reshape6\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/BatchMatMul_impl.js\nfunction batchMatMulImpl2({ a, b, transposeA, transposeB, backend: backend2, bias = null, preluActivationWeights = null, leakyreluAlpha = 0, activation: activation2 = null }) {\n  const aRank = a.shape.length;\n  const bRank = b.shape.length;\n  const innerShapeA = transposeA ? a.shape[aRank - 2] : a.shape[aRank - 1];\n  const innerShapeB = transposeB ? b.shape[bRank - 1] : b.shape[bRank - 2];\n  const outerShapeA = transposeA ? a.shape[aRank - 1] : a.shape[aRank - 2];\n  const outerShapeB = transposeB ? b.shape[bRank - 2] : b.shape[bRank - 1];\n  const outerDimsA = a.shape.slice(0, -2);\n  const outerDimsB = b.shape.slice(0, -2);\n  const batchDimA = util_exports.sizeFromShape(outerDimsA);\n  const batchDimB = util_exports.sizeFromShape(outerDimsB);\n  const outShapeOuterDims = broadcast_util_exports.assertAndGetBroadcastShape(a.shape.slice(0, -2), b.shape.slice(0, -2));\n  const outShape = outShapeOuterDims.concat([outerShapeA, outerShapeB]);\n  util_exports.assert(innerShapeA === innerShapeB, () => `Error in matMul: inner shapes (${innerShapeA}) and (${innerShapeB}) of Tensors with shapes ${a.shape} and ${b.shape} and transposeA=${transposeA} and transposeB=${transposeB} must match.`);\n  const a3dShape = transposeA ? [batchDimA, innerShapeA, outerShapeA] : [batchDimA, outerShapeA, innerShapeA];\n  const b3dShape = transposeB ? [batchDimB, outerShapeB, innerShapeB] : [batchDimB, innerShapeB, outerShapeB];\n  const a3d = reshape6({ inputs: { x: a }, backend: backend2, attrs: { shape: a3dShape } });\n  const b3d = reshape6({ inputs: { x: b }, backend: backend2, attrs: { shape: b3dShape } });\n  const intermediates = [a3d, b3d];\n  const batchDim = Math.max(batchDimA, batchDimB);\n  const batchAEqualOne = batchDimA === 1;\n  const batchBEqualOne = batchDimB === 1;\n  const inputs = [a3d, b3d];\n  const dimensions = [\n    { type: \"int32\", data: [outerShapeA] },\n    { type: \"int32\", data: [outerShapeB] },\n    { type: \"int32\", data: [innerShapeA] }\n  ];\n  let program;\n  let out;\n  const outputShape = [batchDim, outerShapeA, outerShapeB];\n  let matmulProgramType = env().get(\"WEBGPU_MATMUL_PROGRAM_TYPE\");\n  if (matmulProgramType < 0) {\n    if (outerShapeA * outerShapeB <= 128) {\n      matmulProgramType = MatMulProgramType.MatMulReduceProgram;\n    } else if (batchDim === 1 && outerShapeA <= 128 && outerShapeB <= 48 && innerShapeB >= 2e3) {\n      matmulProgramType = MatMulProgramType.MatMulSplitKProgram;\n    } else if (outerShapeA <= 16 && (outerShapeB <= 512 || innerShapeB >= 2 * outerShapeB) || outerShapeB <= 16 && (outerShapeA <= 512 || innerShapeA >= 2 * outerShapeA)) {\n      matmulProgramType = MatMulProgramType.MatMulSmallOutputSizeProgram;\n    } else {\n      matmulProgramType = MatMulProgramType.MatMulPackedProgram;\n    }\n  }\n  switch (matmulProgramType) {\n    case MatMulProgramType.MatMulReduceProgram:\n      program = new MatMulReduceProgram(outputShape, batchAEqualOne, batchBEqualOne, transposeA, transposeB, bias, activation2, preluActivationWeights);\n      break;\n    case MatMulProgramType.MatMulSplitKProgram: {\n      out = fill5({ backend: backend2, attrs: { shape: outputShape, value: 0, dtype: a.dtype } });\n      program = new MatMulSplitKProgram(outputShape, innerShapeB, batchAEqualOne, batchBEqualOne, transposeA, transposeB);\n      if (bias || activation2) {\n        out = backend2.runWebGPUProgram(program, inputs, a.dtype, dimensions, out);\n        const biasActivationProgram = new BiasActivationProgram(out.shape, bias, activation2, preluActivationWeights);\n        let uniformData = null;\n        const activationInputs = [out];\n        if (bias) {\n          activationInputs.push(bias);\n        }\n        if (preluActivationWeights) {\n          activationInputs.push(preluActivationWeights);\n        }\n        if (activation2 === \"leakyrelu\") {\n          uniformData = [{ type: \"float32\", data: [leakyreluAlpha] }];\n          biasActivationProgram.uniforms += \" alpha : f32,\";\n        }\n        const outActivated = backend2.runWebGPUProgram(biasActivationProgram, activationInputs, out.dtype, uniformData);\n        intermediates.push(out);\n        const outReshaped2 = reshape6({ inputs: { x: outActivated }, backend: backend2, attrs: { shape: outShape } });\n        intermediates.push(outActivated);\n        for (const i of intermediates) {\n          backend2.disposeData(i.dataId);\n        }\n        return outReshaped2;\n      }\n      break;\n    }\n    case MatMulProgramType.MatMulSmallOutputSizeProgram:\n      program = new MatMulSmallOutputSizeProgram(a3dShape, b3dShape, outputShape, transposeA, transposeB, bias, activation2, preluActivationWeights);\n      break;\n    case MatMulProgramType.MatMulPackedProgram:\n      program = new MatMulPackedProgram2(a3dShape, outputShape, batchAEqualOne, batchBEqualOne, transposeA, transposeB, bias, activation2, preluActivationWeights);\n      break;\n    default:\n      throw new Error(`Unsupported MatMulProgramType ${matmulProgramType}.`);\n  }\n  if (bias) {\n    inputs.push(bias);\n  }\n  if (preluActivationWeights) {\n    inputs.push(preluActivationWeights);\n  }\n  if (activation2 === \"leakyrelu\") {\n    dimensions.push({ type: \"float32\", data: [leakyreluAlpha] });\n    program.uniforms += \" alpha : f32,\";\n  }\n  out = backend2.runWebGPUProgram(program, inputs, a.dtype, dimensions, out);\n  const outReshaped = reshape6({ inputs: { x: out }, backend: backend2, attrs: { shape: outShape } });\n  intermediates.push(out);\n  for (const i of intermediates) {\n    backend2.disposeData(i.dataId);\n  }\n  return outReshaped;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/_FusedMatMul.js\nfunction _fusedMatMul3(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { a, b, bias, preluActivationWeights } = inputs;\n  const { transposeA, transposeB, activation: activation2, leakyreluAlpha } = attrs;\n  return batchMatMulImpl2({\n    a,\n    b,\n    transposeA,\n    transposeB,\n    backend: backend2,\n    bias,\n    preluActivationWeights,\n    leakyreluAlpha,\n    activation: activation2\n  });\n}\nvar _fusedMatMulConfig4 = {\n  kernelName: _FusedMatMul,\n  backendName: \"webgpu\",\n  kernelFunc: _fusedMatMul3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/binary_op_complex_webgpu.js\nvar BinaryOpComplexProgram2 = class {\n  constructor(op2, aShape, bShape) {\n    this.variableNames = [\"AReal\", \"AImag\", \"BReal\", \"BImag\"];\n    this.workGroupSize = [128, 1, 1];\n    this.size = true;\n    this.outputShape = backend_util_exports.assertAndGetBroadcastShape(aShape, bShape);\n    this.dispatchLayout = flatDispatchLayout(this.outputShape);\n    this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, this.workGroupSize);\n    this.shaderKey = `binaryOpComplex_${op2}`;\n    this.op = op2;\n  }\n  getUserCode() {\n    const opStr = getBinaryOpString(this.op, false);\n    const userCode = `\n      fn binaryOpComplex(\n          areal : f32, aimag : f32, breal : f32, bimag : f32) -> f32 {\n        ${opStr}\n      }\n\n      ${getMainHeaderString(\"index\")} {\n        if(index < uniforms.size) {\n          let areal = getARealByOutputIndex(index);\n          let aimag = getAImagByOutputIndex(index);\n          let breal = getBRealByOutputIndex(index);\n          let bimag = getBImagByOutputIndex(index);\n          setOutputAtIndex(index, binaryOpComplex(areal, aimag, breal, bimag));\n        }\n      }\n    `;\n    return userCode;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/binary_op_webgpu.js\nvar BinaryOpProgram2 = class {\n  constructor(op2, aShape, bShape) {\n    this.size = true;\n    this.variableNames = [\"A\", \"B\"];\n    this.outputShape = backend_util_exports.assertAndGetBroadcastShape(aShape, bShape);\n    this.dispatchLayout = flatDispatchLayout(this.outputShape);\n    this.op = op2;\n    this.useSharedMemoryWithA = aShape.length === 1 && bShape.length > 1 && aShape[0] < 1024;\n    this.useSharedMemoryWithB = bShape.length === 1 && aShape.length > 1 && bShape[0] < 1024;\n    if (this.useSharedMemoryWithA || this.useSharedMemoryWithB) {\n      this.isVec4 = false;\n      this.lastDimensionSize = this.useSharedMemoryWithB ? bShape[0] : aShape[0];\n      this.shaderKey = `binary_${this.type}_${op2}_${this.lastDimensionSize}_${this.useSharedMemoryWithB}`;\n      this.type = \"shared\";\n      this.workGroupSize = [256, 1, 1];\n      if (this.lastDimensionSize < 256) {\n        this.workPerThread = 1;\n      } else if (this.lastDimensionSize < 512) {\n        this.workPerThread = 2;\n      } else {\n        this.workPerThread = 4;\n      }\n    } else {\n      if (util_exports.arraysEqual(aShape, bShape) && util_exports.sizeFromShape(aShape) % 4 === 0) {\n        this.isVec4 = true;\n        this.type = \"vec4\";\n        this.workPerThread = 4;\n      } else {\n        this.isVec4 = false;\n        this.type = \"plain\";\n        this.workPerThread = 1;\n      }\n      this.shaderKey = `binary_${this.type}_${op2}`;\n      this.workGroupSize = [128, 1, 1];\n    }\n    this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, this.workGroupSize, [this.workPerThread, 1, 1]);\n  }\n  getUserCode() {\n    let userCode;\n    if (this.type === \"shared\") {\n      const sharedIndexSnippet = this.lastDimensionSize > 1 ? `coords[${this.outputShape.length - 1}]` : \"0\";\n      const accessDataSnippet = this.useSharedMemoryWithB ? `let a = getAByOutputCoords(coords);\n          let b = sharedBuf[${sharedIndexSnippet}];` : `let a = sharedBuf[${sharedIndexSnippet}];\n          let b = getBByOutputCoords(coords);`;\n      const opStr = getBinaryOpString(this.op, this.isVec4);\n      userCode = `\n        fn binaryOperation(a : f32, b : f32) -> f32 {\n          ${opStr}\n        }\n        var<workgroup> sharedBuf : array<f32, ${this.lastDimensionSize}>;\n        ${getMainHeaderString(\"index\")} {\n          // Fill in the shared memory buffer. Here we need a loop to make sure\n          // that all data in A|B are uploaded when |sharedMemorySize| is larger\n          // than work group size.\n          for(var localIndex = i32(localId.x); localIndex < ${this.lastDimensionSize}; localIndex = localIndex + ${this.workGroupSize[0]}) {\n            sharedBuf[localIndex] = f32(${this.useSharedMemoryWithB ? \"B\" : \"A\"}[localIndex]);\n          }\n          workgroupBarrier();\n\n          for(var i = 0; i < ${this.workPerThread}; i = i + 1) {\n            let flatIndex = index * ${this.workPerThread} + i;\n            if(flatIndex < uniforms.size) {\n              let coords = getCoordsFromIndex(flatIndex);\n\n              ${accessDataSnippet}\n              setOutputAtIndex(flatIndex, binaryOperation(a, b));\n            }\n          }\n        }\n        `;\n    } else {\n      const dType = this.type === \"vec4\" ? \"vec4<f32>\" : \"f32\";\n      const opStr = getBinaryOpString(this.op, this.isVec4);\n      userCode = `\n       fn binaryOperation(a : ${dType}, b : ${dType}) -> ${dType} {\n         ${opStr}\n       }\n       ${getMainHeaderString(\"index\")} {\n         if (index < uniforms.size) {\n           let a = getAByOutputIndex(index);\n           let b = getBByOutputIndex(index);\n           setOutputAtIndex(index, binaryOperation(a, b));\n         }\n       }\n       `;\n    }\n    return userCode;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/Identity.js\nfunction identity5(args) {\n  const { inputs } = args;\n  const { x } = inputs;\n  args.backend.incRef(x.dataId);\n  return { dataId: x.dataId, shape: x.shape, dtype: x.dtype };\n}\nvar identityConfig4 = {\n  kernelName: Identity,\n  backendName: \"webgpu\",\n  kernelFunc: identity5\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/Complex.js\nfunction complex4(args) {\n  const { inputs, backend: backend2 } = args;\n  const { real: real5, imag: imag5 } = inputs;\n  const complexInfo = backend2.makeTensorInfo(real5.shape, \"complex64\");\n  const complex5 = backend2.tensorMap.get(complexInfo.dataId);\n  const realTensorInfo = identity5({ inputs: { x: real5 }, backend: backend2 });\n  const imagTensorInfo = identity5({ inputs: { x: imag5 }, backend: backend2 });\n  complex5.complexTensorInfos = { real: realTensorInfo, imag: imagTensorInfo };\n  return complexInfo;\n}\nvar complexConfig3 = {\n  kernelName: Complex,\n  backendName: \"webgpu\",\n  kernelFunc: complex4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/unary_op_webgpu.js\nvar UnaryOpProgram2 = class {\n  constructor(outputShape, op2) {\n    this.variableNames = [\"A\"];\n    this.size = true;\n    const workGroupSizeX = 128;\n    this.workGroupSize = [workGroupSizeX, 1, 1];\n    this.outputShape = outputShape;\n    this.dispatchLayout = flatDispatchLayout(this.outputShape);\n    this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, this.workGroupSize);\n    this.op = op2;\n    this.shaderKey = `unary_${op2}`;\n  }\n  getUserCode() {\n    return `\n      fn unaryOperation(a : f32) -> f32 {\n        ${getUnaryOpString(this.op, false)}\n      }\n      ${getMainHeaderString(\"index\")} {\n        if (index < uniforms.size) {\n          let a = getAByOutputIndex(index);\n          setOutputAtIndex(index, unaryOperation(a));\n        }\n      }\n      `;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernel_utils/kernel_funcs_utils.js\nfunction unaryKernelFunc3({ opType, cpuKernelImpl, dtype }) {\n  return ({ inputs, backend: backend2 }) => {\n    const { x } = inputs;\n    const webgpuBackend = backend2;\n    const $dtype = dtype || x.dtype;\n    if (webgpuBackend.shouldExecuteOnCPU([x]) && cpuKernelImpl != null) {\n      const xData = webgpuBackend.tensorMap.get(x.dataId);\n      const outValues = cpuKernelImpl(xData.values, $dtype);\n      return webgpuBackend.makeTensorInfo(x.shape, $dtype, outValues);\n    }\n    const program = new UnaryOpProgram2(x.shape, opType);\n    return webgpuBackend.runWebGPUProgram(program, [x], $dtype);\n  };\n}\nfunction binaryKernelFunc3({ opType, cpuKernelImpl, supportsComplex = false, dtype }) {\n  return ({ inputs, backend: backend2 }) => {\n    const { a, b } = inputs;\n    const webgpuBackend = backend2;\n    if (supportsComplex && a.dtype === \"complex64\") {\n      const aData = webgpuBackend.tensorMap.get(a.dataId);\n      const bData = webgpuBackend.tensorMap.get(b.dataId);\n      let real5, imag5;\n      if (opType !== BinaryOpType.MUL) {\n        [real5, imag5] = [\n          [aData.complexTensorInfos.real, bData.complexTensorInfos.real],\n          [aData.complexTensorInfos.imag, bData.complexTensorInfos.imag]\n        ].map((complexParts) => {\n          const [aPart, bPart] = complexParts;\n          const aHandle = {\n            dataId: aPart.dataId,\n            dtype: aPart.dtype,\n            shape: a.shape\n          };\n          const bHandle = {\n            dataId: bPart.dataId,\n            dtype: bPart.dtype,\n            shape: b.shape\n          };\n          const program2 = new BinaryOpProgram2(opType, a.shape, b.shape);\n          return webgpuBackend.runWebGPUProgram(program2, [aHandle, bHandle], upcastType(aPart.dtype, bPart.dtype));\n        });\n      } else {\n        const realProgram = new BinaryOpComplexProgram2(BinaryOpType.COMPLEX_MULTIPLY_REAL, a.shape, b.shape);\n        const imagProgram = new BinaryOpComplexProgram2(BinaryOpType.COMPLEX_MULTIPLY_IMAG, a.shape, b.shape);\n        const inputs2 = [\n          {\n            dataId: aData.complexTensorInfos.real.dataId,\n            dtype: aData.complexTensorInfos.real.dtype,\n            shape: a.shape\n          },\n          {\n            dataId: aData.complexTensorInfos.imag.dataId,\n            dtype: aData.complexTensorInfos.imag.dtype,\n            shape: a.shape\n          },\n          {\n            dataId: bData.complexTensorInfos.real.dataId,\n            dtype: bData.complexTensorInfos.real.dtype,\n            shape: b.shape\n          },\n          {\n            dataId: bData.complexTensorInfos.imag.dataId,\n            dtype: bData.complexTensorInfos.imag.dtype,\n            shape: b.shape\n          }\n        ];\n        real5 = webgpuBackend.runWebGPUProgram(realProgram, inputs2, \"float32\");\n        imag5 = webgpuBackend.runWebGPUProgram(imagProgram, inputs2, \"float32\");\n      }\n      const complexOutput = complex4({ inputs: { real: real5, imag: imag5 }, backend: webgpuBackend });\n      webgpuBackend.disposeData(real5.dataId);\n      webgpuBackend.disposeData(imag5.dataId);\n      return complexOutput;\n    }\n    const $dtype = dtype || upcastType(a.dtype, b.dtype);\n    if ((a.dtype === \"string\" || b.dtype === \"string\" || webgpuBackend.shouldExecuteOnCPU([a, b])) && cpuKernelImpl != null) {\n      const aData = webgpuBackend.tensorMap.get(a.dataId).values;\n      const bData = webgpuBackend.tensorMap.get(b.dataId).values;\n      const decodedAVals = a.dtype === \"string\" ? backend_util_exports.fromUint8ToStringArray(aData) : aData;\n      const decodedBVals = a.dtype === \"string\" ? backend_util_exports.fromUint8ToStringArray(bData) : bData;\n      const [outValues, outShape] = cpuKernelImpl(a.shape, b.shape, decodedAVals, decodedBVals, $dtype);\n      return webgpuBackend.makeTensorInfo(outShape, $dtype, outValues);\n    }\n    const program = new BinaryOpProgram2(opType, a.shape, b.shape);\n    return webgpuBackend.runWebGPUProgram(program, [a, b], $dtype);\n  };\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernel_utils/shared.js\nvar { addImpl: addImplCPU2, castImpl: castImplCPU2, ceilImpl: ceilImplCPU2, concatImpl: concatImplCPU2, equalImpl: equalImplCPU2, expImpl: expImplCPU2, expm1Impl: expm1ImplCPU2, floorImpl: floorImplCPU2, gatherNdImpl: gatherNdImplCPU2, gatherV2Impl: gatherV2ImplCPU2, greaterEqualImpl: greaterEqualImplCPU2, greaterImpl: greaterImplCPU2, lessEqualImpl: lessEqualImplCPU2, lessImpl: lessImplCPU2, logImpl: logImplCPU2, maxImpl: maxImplCPU2, maximumImpl: maximumImplCPU2, minimumImpl: minimumImplCPU2, multiplyImpl: multiplyImplCPU2, negImpl: negImplCPU2, notEqualImpl: notEqualImplCPU2, prodImpl: prodImplCPU2, rangeImpl: rangeImplCPU2, rsqrtImpl: rsqrtImplCPU2, scatterImpl: scatterImplCPU2, simpleAbsImpl: simpleAbsImplCPU2, sliceImpl: sliceImplCPU2, stridedSliceImpl: stridedSliceImplCPU2, stringNGramsImpl: stringNGramsImplCPU2, subImpl: subImplCPU2, tileImpl: tileImplCPU2, topKImpl: topKImplCPU2, transposeImpl: transposeImplCPU2, uniqueImpl: uniqueImplCPU2 } = shared_exports;\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/Abs.js\nvar abs4 = unaryKernelFunc3({ opType: UnaryOpType.ABS, cpuKernelImpl: simpleAbsImplCPU2 });\nvar absConfig4 = {\n  kernelName: Abs,\n  backendName: \"webgpu\",\n  kernelFunc: abs4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/Add.js\nvar addKernelFunc2 = binaryKernelFunc3({ opType: BinaryOpType.ADD, cpuKernelImpl: addImplCPU2, supportsComplex: true });\nvar addConfig4 = {\n  kernelName: Add,\n  backendName: \"webgpu\",\n  kernelFunc: addKernelFunc2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/addn_packed_webgpu.js\nvar AddNPackedProgram2 = class {\n  constructor(shapes) {\n    this.workPerThread = 4;\n    this.workGroupSize = [64, 1, 1];\n    this.size = true;\n    this.outputShape = shapes[0];\n    this.variableNames = shapes.map((_, i) => `T${i}`);\n    this.dispatchLayout = flatDispatchLayout(this.outputShape);\n    this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, this.workGroupSize, [this.workPerThread, 1, 1]);\n    this.shaderKey = \"addN\";\n  }\n  getUserCode() {\n    const snippets = [];\n    this.variableNames.forEach((variable2) => {\n      snippets.push(`let v${variable2} = get${variable2}ByOutputCoords(coords);`);\n    });\n    const operation = this.variableNames.map((variable2) => {\n      return `v${variable2}`;\n    }).join(\" + \");\n    const userCode = `\n      ${getMainHeaderString(\"index\")} {\n        for (var i = 0; i < ${this.workPerThread}; i = i + 1) {\n          let flatIndex = index * ${this.workPerThread} + i;\n          if (flatIndex < uniforms.size) {\n            let coords = getCoordsFromIndex(flatIndex);\n            ${snippets.join(\"\\n        \")}\n            setOutputAtIndex(flatIndex, ${operation});\n          }\n        }\n      }\n    `;\n    return userCode;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/AddN.js\nfunction addN4(args) {\n  const { inputs, backend: backend2 } = args;\n  const tensors = inputs;\n  if (tensors.length === 1) {\n    return identity5({ inputs: { x: tensors[0] }, backend: backend2 });\n  }\n  const dtype = tensors.map((t) => t.dtype).reduce((d1, d2) => upcastType(d1, d2));\n  const shapes = tensors.map((t) => t.shape);\n  const program = new AddNPackedProgram2(shapes);\n  return backend2.runWebGPUProgram(program, tensors, dtype);\n}\nvar addNConfig4 = {\n  kernelName: AddN,\n  backendName: \"webgpu\",\n  kernelFunc: addN4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/argminmax_webgpu.js\nvar ArgMinMaxProgram2 = class {\n  constructor(inputShape, axis, reduceType) {\n    this.workGroupSize = [64, 1, 1];\n    this.variableNames = [\"x\"];\n    this.uniforms = \"infinityValue : f32,\";\n    this.size = true;\n    const axes = [axis];\n    this.op = reduceType === \"min\" ? \"<\" : \">\";\n    const [outputShape, reduceShape] = backend_util_exports.computeOutAndReduceShapes(inputShape, axes);\n    this.outputShape = outputShape.length === 0 ? [1] : outputShape;\n    this.dispatchLayout = flatDispatchLayout(this.outputShape);\n    if (util_exports.sizeFromShape(reduceShape) < 32 || util_exports.sizeFromShape(outputShape) > 1e3) {\n      this.type = \"plain\";\n      this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, this.workGroupSize);\n    } else {\n      this.type = \"shared\";\n      this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, [1, 1, 1]);\n    }\n    this.inputShape = inputShape;\n    this.shaderKey = `argMinMax_${this.op}_${this.type}`;\n  }\n  getUserCode() {\n    const getInputShapeLastDim = () => {\n      if (this.inputShape.length === 1) {\n        return \"uniforms.xShape\";\n      } else {\n        return `uniforms.xShape.${getCoordsXYZ(this.inputShape.length - 1)}`;\n      }\n    };\n    const splitOutputCoords = () => {\n      let snippet = \"\";\n      if (this.outputShape.length === 1) {\n        if (this.inputShape.length !== 1) {\n          snippet += \"outputCoords,\";\n        }\n      } else {\n        for (let i = 0; i < this.outputShape.length; i++) {\n          snippet += `outputCoords.${getCoordsXYZ(i)},`;\n        }\n      }\n      return snippet;\n    };\n    if (this.type === \"shared\") {\n      const sharedMemorySnippet = `\n      var<workgroup> xBestIndices : array<i32, ${this.workGroupSize[0]}>;\n      var<workgroup> xBestValues : array<f32, ${this.workGroupSize[0]}>;\n    `;\n      const userCode = `\n      fn DIV_CEIL(a : u32, b : u32) -> u32 {\n        return ((a - 1u) / b + 1u);\n      }\n\n      ${sharedMemorySnippet}\n\n      ${getMainHeaderString(\"index\")} {\n        let outputIndex = index / i32(workGroupSizeX);\n        let reduceLength = ${getInputShapeLastDim()};\n\n        var bestIndex = i32(localId.x);\n        var bestValue = uniforms.infinityValue;\n        let outputCoords = getCoordsFromIndex(outputIndex);\n        for (var k = i32(localId.x); k < reduceLength && outputIndex < uniforms.size;\n            k = k + i32(workGroupSizeX)) {\n          let candidate = getX(${splitOutputCoords()} k);\n          if (!isnan(candidate) && candidate ${this.op} bestValue) {\n            bestValue = candidate;\n            bestIndex = k;\n          }\n        }\n        xBestValues[localId.x] = bestValue;\n        xBestIndices[localId.x] = bestIndex;\n        workgroupBarrier();\n\n        var reduceSize = min(u32(reduceLength), workGroupSizeX);\n        for (var currentSize = reduceSize / 2u; reduceSize > 1u;\n            currentSize = reduceSize / 2u) {\n          let interval = DIV_CEIL(reduceSize, 2u);\n          if (localId.x < currentSize) {\n            let candidate = xBestValues[localId.x + interval];\n            if (candidate ${this.op} bestValue) {\n              bestValue = candidate;\n              xBestValues[localId.x] = bestValue;\n              xBestIndices[localId.x] = xBestIndices[localId.x + interval];\n            }\n          }\n          reduceSize = interval;\n          workgroupBarrier();\n        }\n\n        if (localId.x == 0u && outputIndex < uniforms.size) {\n          setOutputAtIndexI32(outputIndex, xBestIndices[localId.x]);\n        }\n      }\n    `;\n      return userCode;\n    } else {\n      const userCode = `\n      ${getMainHeaderString(\"index\")} {\n        if (index < uniforms.size) {\n          let outputCoords = getCoordsFromIndex(index);\n          var bestIndex = 0;\n          var bestValue = getX(${splitOutputCoords()} 0);\n          let reduceLength = ${getInputShapeLastDim()};\n          for (var i = 1; i < reduceLength; i++) {\n            let candidate = getX(${splitOutputCoords()} i);\n            if (candidate ${this.op} bestValue) {\n              bestValue = candidate;\n              bestIndex = i;\n            }\n          }\n          setOutputAtIndexI32(index, bestIndex);\n        }\n      }\n      `;\n      return userCode;\n    }\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/transpose_shared_webgpu.js\nvar TransposeSharedProgram = class {\n  constructor(aShape, newDim) {\n    this.variableNames = [\"A\"];\n    this.workGroupSize = [16, 16, 1];\n    const outputShape = new Array(aShape.length);\n    for (let i = 0; i < outputShape.length; i++) {\n      outputShape[i] = aShape[newDim[i]];\n    }\n    this.outputShape = outputShape;\n    this.dispatchLayout = { x: [0], y: [1] };\n    this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, this.workGroupSize, [1, 1, 1]);\n    this.shaderKey = \"transposeShared\";\n  }\n  getUserCode() {\n    const userCode = `\n      const TILE_DIM = ${this.workGroupSize[0]};\n      var<workgroup> tile : array<array<f32, ${this.workGroupSize[0] + 1}>, ${this.workGroupSize[0]}>;\n      ${getWorkGroupSizeString()}\n      fn _start(@builtin(local_invocation_id) localId : vec3<u32>,\n                @builtin(workgroup_id) workgroupId : vec3<u32>) {\n        var x = i32(workgroupId.x) * TILE_DIM + i32(localId.x);\n        var y = i32(workgroupId.y) * TILE_DIM + i32(localId.y);\n        let width = uniforms.outShape[0];\n        let height = uniforms.outShape[1];\n        if (x < width && y < height) {\n          tile[localId.y][localId.x] = A[y * width + x];\n        }\n        workgroupBarrier();\n\n        x = i32(workgroupId.y) * TILE_DIM + i32(localId.x);\n        y = i32(workgroupId.x) * TILE_DIM + i32(localId.y);\n        if (x < height && y < width) {\n          setOutputAtIndex((y * height + x), tile[localId.x]\n            [localId.y]);\n        }\n      }\n    `;\n    return userCode;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/transpose_webgpu.js\nvar TransposeProgram2 = class {\n  constructor(aShape, newDim) {\n    this.variableNames = [\"A\"];\n    this.workPerThread = 4;\n    this.workGroupSize = [64, 1, 1];\n    this.size = true;\n    const outputShape = new Array(aShape.length);\n    for (let i = 0; i < outputShape.length; i++) {\n      outputShape[i] = aShape[newDim[i]];\n    }\n    this.outputShape = outputShape;\n    this.dispatchLayout = flatDispatchLayout(this.outputShape);\n    this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, this.workGroupSize, [this.workPerThread, 1, 1]);\n    this.newDim = newDim;\n    this.shaderKey = `transpose_${newDim}`;\n  }\n  getUserCode() {\n    const dtype = getCoordsDataType2(this.outputShape.length);\n    const switched = getSwitchedCoords2(this.newDim);\n    const userCode = `\n      ${getMainHeaderString(\"index\")} {\n        for(var i = 0; i < ${this.workPerThread}; i = i + 1) {\n          let flatIndex = index * ${this.workPerThread} + i;\n          if(flatIndex < uniforms.size) {\n            let resRC = getCoordsFromIndex(flatIndex);\n            setOutputAtIndex(flatIndex, A[getIndexFromCoords${this.outputShape.length}D(\n              ${dtype}(${switched}), uniforms.aShape)]);\n          }\n        }\n      }\n    `;\n    return userCode;\n  }\n};\nfunction getSwitchedCoords2(newDim) {\n  const rank = newDim.length;\n  if (rank > 6) {\n    throw Error(`Transpose for rank ${rank} is not yet supported`);\n  }\n  const switchedCoords = new Array(rank);\n  for (let i = 0; i < newDim.length; i++) {\n    switchedCoords[newDim[i]] = `resRC.${getCoordsXYZ(i)}`;\n  }\n  return switchedCoords.join();\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/Transpose.js\nfunction transpose5(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x } = inputs;\n  const { perm } = attrs;\n  const webgpuBackend = backend2;\n  const xRank = x.shape.length;\n  const newShape = new Array(xRank);\n  for (let i = 0; i < newShape.length; i++) {\n    newShape[i] = x.shape[perm[i]];\n  }\n  if (backend2.shouldExecuteOnCPU([x])) {\n    const xData = webgpuBackend.tensorMap.get(x.dataId);\n    const values = xData.values;\n    const outValues = transposeImplCPU2(values, x.shape, x.dtype, perm, newShape);\n    return backend2.makeTensorInfo(newShape, x.dtype, outValues);\n  }\n  if (x.shape.length === 2 && util_exports.arraysEqual(perm, [1, 0])) {\n    const program2 = new TransposeSharedProgram(x.shape, perm);\n    return webgpuBackend.runWebGPUProgram(program2, [x], x.dtype);\n  }\n  const program = new TransposeProgram2(x.shape, perm);\n  return webgpuBackend.runWebGPUProgram(program, [x], x.dtype);\n}\nvar transposeConfig4 = {\n  kernelName: Transpose,\n  backendName: \"webgpu\",\n  kernelFunc: transpose5\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/ArgMax.js\nfunction argMax4(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x } = inputs;\n  const { axis } = attrs;\n  let axes = util_exports.parseAxisParam(axis, x.shape);\n  const permutedAxes = backend_util_exports.getAxesPermutation(axes, x.shape.length);\n  let $x = x;\n  const intermediateTensorInfos = [];\n  if (permutedAxes != null) {\n    $x = transpose5({ inputs: { x }, backend: backend2, attrs: { perm: permutedAxes } });\n    intermediateTensorInfos.push($x);\n    axes = backend_util_exports.getInnerMostAxes(axes.length, $x.shape.length);\n  }\n  backend_util_exports.assertAxesAreInnerMostDims(\"argMax\", [axes[0]], $x.shape.length);\n  const program = new ArgMinMaxProgram2($x.shape, axes[0], \"max\");\n  const uniformData = [{ type: \"float32\", data: [Number.NEGATIVE_INFINITY] }];\n  const out = backend2.runWebGPUProgram(program, [$x], \"int32\", uniformData);\n  intermediateTensorInfos.forEach((t) => backend2.disposeData(t.dataId));\n  return out;\n}\nvar argMaxConfig4 = {\n  kernelName: ArgMax,\n  backendName: \"webgpu\",\n  kernelFunc: argMax4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/ArgMin.js\nfunction argMin4(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x } = inputs;\n  const { axis } = attrs;\n  let axes = util_exports.parseAxisParam(axis, x.shape);\n  const permutedAxes = backend_util_exports.getAxesPermutation(axes, x.shape.length);\n  let $x = x;\n  const intermediateTensorInfos = [];\n  if (permutedAxes != null) {\n    $x = transpose5({ inputs: { x }, backend: backend2, attrs: { perm: permutedAxes } });\n    intermediateTensorInfos.push($x);\n    axes = backend_util_exports.getInnerMostAxes(axes.length, $x.shape.length);\n  }\n  backend_util_exports.assertAxesAreInnerMostDims(\"argMin\", [axes[0]], $x.shape.length);\n  const program = new ArgMinMaxProgram2($x.shape, axes[0], \"min\");\n  const uniformData = [{ type: \"float32\", data: [Number.POSITIVE_INFINITY] }];\n  const out = backend2.runWebGPUProgram(program, [$x], \"int32\", uniformData);\n  intermediateTensorInfos.forEach((t) => backend2.disposeData(t.dataId));\n  return out;\n}\nvar argMinConfig3 = {\n  kernelName: ArgMin,\n  backendName: \"webgpu\",\n  kernelFunc: argMin4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/Atan2.js\nvar atan24 = binaryKernelFunc3({ opType: BinaryOpType.ATAN2 });\nvar atan2Config3 = {\n  kernelName: Atan2,\n  backendName: \"webgpu\",\n  kernelFunc: atan24\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/pool2d_webgpu.js\nvar Pool2DProgram2 = class {\n  constructor(convInfo, poolType) {\n    this.variableNames = [\"x\"];\n    this.uniforms = `stride : vec2<i32>, pad : vec2<i32>, dilation : vec2<i32>, convDims : vec2<i32>, filterDims : vec2<i32>,`;\n    this.workGroupSize = [128, 1, 1];\n    this.size = true;\n    this.outputShape = convInfo.outShape;\n    this.dispatchLayout = flatDispatchLayout(this.outputShape);\n    this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, this.workGroupSize);\n    this.shaderKey = `pool2D_${poolType}`;\n    this.poolType = poolType;\n  }\n  getUserCode() {\n    let updateSnippet = `resultValue = max(value, resultValue);`;\n    if (this.poolType === \"avg\") {\n      updateSnippet = `resultValue = resultValue + value; count = count + 1.0;`;\n    }\n    let returnValue = `resultValue`;\n    if (this.poolType === \"avg\") {\n      returnValue = `resultValue / count`;\n    }\n    const userCode = `\n      ${getMainHeaderString(\"index\")} {\n      if (index < uniforms.size) {\n        let coords = getCoordsFromIndex(index);\n          let batch = coords[0];\n          let xRCCorner = vec2<i32>(coords.yz) * uniforms.stride - uniforms.pad;\n          let xRCorner = xRCCorner.x;\n          let xCCorner = xRCCorner.y;\n\n          var resultValue = ${this.poolType === \"avg\" ? \"0.0\" : \"-1.0 / pow(10.0, -20.0)\"};\n          var count = 0.0;\n\n          for (var wR = 0; wR < uniforms.filterDims.x; wR = wR + uniforms.dilation.x) {\n            let xR = xRCorner + wR;\n\n            if (xR < 0 || xR >= uniforms.convDims.x) {\n              continue;\n            }\n\n            for (var wC = 0; wC < uniforms.filterDims.y; wC = wC + uniforms.dilation.y) {\n              let xC = xCCorner + wC;\n              if (xC < 0 || xC >= uniforms.convDims.y) {\n                continue;\n              }\n\n              let value = getX(batch, xR, xC, coords[3]);\n              ${updateSnippet}\n            }\n          }\n\n          setOutputAtIndex(index, ${returnValue});\n        }\n      }\n    `;\n    return userCode;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/pool_filtersizeone_webgpu.js\nvar PoolWithFilterSizeEqualsOneProgram = class {\n  constructor(convInfo) {\n    this.variableNames = [\"x\"];\n    this.uniforms = `stride : vec2<i32>,`;\n    this.workGroupSize = [256, 1, 1];\n    this.size = true;\n    this.outputShape = convInfo.outShape;\n    this.dispatchLayout = flatDispatchLayout(this.outputShape);\n    this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, this.workGroupSize);\n    this.shaderKey = \"poolWithFilterSizeEqualsOne\";\n  }\n  getUserCode() {\n    const userCode = `\n      ${getMainHeaderString(\"index\")} {\n        if (index < uniforms.size) {\n          let coords = getCoordsFromIndex(index);\n          let batch = coords[0];\n          let d = coords[3];\n\n          let xRCCorner = coords.yz * uniforms.stride;\n          let xRCorner = xRCCorner.x;\n          let xCCorner = xRCCorner.y;\n\n          let value = getX(batch, xRCorner, xCCorner, d);\n          setOutputAtIndex(index, value);\n        }\n      }\n    `;\n    return userCode;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/reduce_webgpu.js\nvar ReduceProgram2 = class {\n  constructor(reduceInfo, reduceType) {\n    this.workGroupSize = [64, 1, 1];\n    this.variableNames = [\"x\"];\n    this.uniforms = \"reduceSize : i32,\";\n    this.size = true;\n    this.inputShape = [reduceInfo.batchSize, reduceInfo.inSize];\n    const [outputShape] = backend_util_exports.computeOutAndReduceShapes(this.inputShape, [1]);\n    this.outputShape = outputShape.length === 0 ? [1] : outputShape;\n    this.dispatchLayout = flatDispatchLayout(this.outputShape);\n    this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, [1, 1, 1]);\n    this.reduceType = reduceType;\n    this.shaderKey = `reduce_${reduceType}`;\n  }\n  getUserCode() {\n    let reduceOp = ``;\n    let initValue = \"0.0\";\n    if (this.reduceType === \"min\" || this.reduceType === \"max\") {\n      reduceOp = `\n         if (isnan(candidate)) {\n          bestValue = uniforms.NAN;\n         } else if (!isnan(bestValue) && candidate ${this.reduceType === \"min\" ? \"<\" : \">\"} bestValue)\n           {  bestValue = candidate; }`;\n      initValue = \"f32(x[offset])\";\n    } else if (this.reduceType === \"sum\" || this.reduceType === \"mean\") {\n      reduceOp = \" bestValue = bestValue + candidate; \";\n    } else if (this.reduceType === \"prod\") {\n      reduceOp = \" bestValue = bestValue * candidate; \";\n      initValue = \"1.0\";\n    }\n    const outputSnippet = this.reduceType === \"mean\" ? `setOutputAtIndex(outputIndex, bestValue / f32(uniforms.reduceSize));` : `setOutputAtIndex(outputIndex, bestValue);`;\n    const sharedMemorySnippet = `\n         var<workgroup> xBestValues : array<f32, ${this.workGroupSize[0]}>;\n       `;\n    const userCode = `\n       fn DIV_CEIL(a : u32, b : u32) -> u32 {\n        return ((a - 1u) / b + 1u);\n       }\n\n       ${sharedMemorySnippet}\n       fn getOffset(outputIndex : i32) -> i32 {\n         let outputCoords = getCoordsFromIndex(outputIndex);\n         let offset = ${this.outputShape.length === 1 ? \"outputCoords\" : \"outputCoords[0]\"} * uniforms.reduceSize;\n          return offset;\n       }\n       ${getMainHeaderString(\"index\")} {\n         let outputIndex = index / i32(workGroupSizeX);\n         let offset = getOffset(outputIndex);\n         var bestValue = ${initValue};\n         let Length = uniforms.reduceSize;\n         let WorkPerThread = DIV_CEIL(u32(Length), workGroupSizeX);\n         for (var k = i32(localId.x); k < Length && outputIndex < uniforms.size;\n             k = k + i32(workGroupSizeX)) {\n           let candidate = f32(x[offset + k]);\n           ${reduceOp}\n         }\n         xBestValues[localId.x] = bestValue;\n         workgroupBarrier();\n\n         var reduceSize = min(u32(Length), workGroupSizeX);\n         for (var currentSize = reduceSize / 2u; reduceSize > 1u;\n             currentSize = reduceSize / 2u) {\n           let interval = DIV_CEIL(reduceSize, 2u);\n           if (localId.x < currentSize) {\n            let candidate = xBestValues[localId.x + interval];\n            ${reduceOp}\n            xBestValues[localId.x] = bestValue;\n           }\n           reduceSize = interval;\n           workgroupBarrier();\n         }\n\n         if (localId.x == 0u && outputIndex < uniforms.size) {\n          ${outputSnippet}\n        }\n       }\n     `;\n    return userCode;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernel_utils/reduce.js\nfunction reduce2(x, axis, keepDims, reduceType, backend2) {\n  const xRank = x.shape.length;\n  const toDispose = [];\n  const origAxes = util_exports.parseAxisParam(axis, x.shape);\n  let axes = origAxes;\n  const permutedAxes = backend_util_exports.getAxesPermutation(axes, xRank);\n  let input2 = x;\n  if (permutedAxes != null) {\n    input2 = transpose5({ inputs: { x }, attrs: { perm: permutedAxes }, backend: backend2 });\n    axes = backend_util_exports.getInnerMostAxes(axes.length, xRank);\n    toDispose.push(input2);\n  }\n  backend_util_exports.assertAxesAreInnerMostDims(reduceType, axes, xRank);\n  const [reduceOutShape, reduceShape] = backend_util_exports.computeOutAndReduceShapes(input2.shape, axes);\n  let resOutShape = reduceOutShape;\n  if (keepDims) {\n    resOutShape = backend_util_exports.expandShapeToKeepDim(reduceOutShape, origAxes);\n  }\n  let res;\n  if ((reduceType === \"max\" || reduceType === \"prod\") && backend2.shouldExecuteOnCPU([input2])) {\n    const xVals = backend2.tensorMap.get(input2.dataId).values;\n    switch (reduceType) {\n      case \"max\":\n        const outValues = maxImplCPU2(xVals, util_exports.sizeFromShape(reduceShape), resOutShape, x.dtype);\n        res = backend2.makeTensorInfo(resOutShape, x.dtype, outValues);\n        break;\n      case \"prod\":\n        const { outVals, outShape, outDtype } = prodImplCPU2(input2.shape, input2.dtype, xVals, axes);\n        res = backend2.makeTensorInfo(outShape, outDtype, outVals);\n        break;\n      default:\n        throw new Error(`${reduceType} CPU implementation is not yet supported.`);\n    }\n  } else {\n    const inSize = util_exports.sizeFromShape(reduceShape);\n    const xSize = util_exports.sizeFromShape(input2.shape);\n    const batchSize = xSize / inSize;\n    const reduceInfo = { windowSize: inSize, inSize, batchSize, outSize: 1 };\n    const dtype = reduceType === \"mean\" ? \"float32\" : sumOutType(x.dtype);\n    const uniformData = [\n      { type: \"int32\", data: [inSize] }\n    ];\n    const program = new ReduceProgram2(reduceInfo, reduceType);\n    const reduced = backend2.runWebGPUProgram(program, [input2], dtype, uniformData);\n    toDispose.push(reduced);\n    res = reshape6({ inputs: { x: reduced }, attrs: { shape: resOutShape }, backend: backend2 });\n  }\n  toDispose.forEach((t) => backend2.disposeData(t.dataId));\n  return res;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/Max.js\nfunction max6(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x } = inputs;\n  const { reductionIndices, keepDims } = attrs;\n  return reduce2(x, reductionIndices, keepDims, \"max\", backend2);\n}\nvar maxConfig4 = {\n  kernelName: Max,\n  backendName: \"webgpu\",\n  kernelFunc: max6\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/Mean.js\nfunction mean4(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x } = inputs;\n  const { keepDims, axis } = attrs;\n  return reduce2(x, axis, keepDims, \"mean\", backend2);\n}\nvar meanConfig4 = {\n  kernelName: Mean,\n  backendName: \"webgpu\",\n  kernelFunc: mean4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/Pool_impl.js\nfunction poolImpl(x, convInfo, poolType, backend2) {\n  if (convInfo.filterWidth === 1 && convInfo.filterHeight === 1 && util_exports.arraysEqual(convInfo.inShape, convInfo.outShape)) {\n    return identity5({ inputs: { x }, backend: backend2 });\n  }\n  if (convInfo.filterWidth === convInfo.inWidth && convInfo.filterHeight === convInfo.inHeight && convInfo.batchSize === 1 && convInfo.padInfo.type === \"VALID\") {\n    const length = x.shape.length;\n    const reshapeX = reshape6({\n      inputs: { x },\n      backend: backend2,\n      attrs: {\n        shape: [\n          x.shape[length - 3] * x.shape[length - 2],\n          x.shape[length - 1]\n        ]\n      }\n    });\n    let reduceX;\n    if (poolType === \"avg\") {\n      reduceX = mean4({ inputs: { x: reshapeX }, backend: backend2, attrs: { axis: 0, keepDims: false } });\n    } else {\n      util_exports.assert(poolType === \"max\", () => `Invalid pool type ${poolType}`);\n      reduceX = max6({\n        inputs: { x: reshapeX },\n        backend: backend2,\n        attrs: { reductionIndices: 0, keepDims: false }\n      });\n    }\n    const result = reshape6({ inputs: { x: reduceX }, backend: backend2, attrs: { shape: convInfo.outShape } });\n    backend2.disposeData(reshapeX.dataId);\n    backend2.disposeData(reduceX.dataId);\n    return result;\n  }\n  let program;\n  const dimensions = [{ type: \"int32\", data: [convInfo.strideHeight, convInfo.strideWidth] }];\n  if (convInfo.filterHeight === 1 && convInfo.filterWidth === 1) {\n    program = new PoolWithFilterSizeEqualsOneProgram(convInfo);\n  } else {\n    if (poolType === \"avg\") {\n      program = new Pool2DProgram2(convInfo, \"avg\");\n    } else {\n      util_exports.assert(poolType === \"max\", () => `Invalid pool type ${poolType}`);\n      program = new Pool2DProgram2(convInfo, \"max\");\n    }\n    dimensions.push({ type: \"int32\", data: [convInfo.padInfo.top, convInfo.padInfo.left] }, {\n      type: \"int32\",\n      data: [convInfo.dilationHeight, convInfo.dilationWidth]\n    }, { type: \"int32\", data: [convInfo.inHeight, convInfo.inWidth] }, {\n      type: \"int32\",\n      data: [convInfo.effectiveFilterHeight, convInfo.effectiveFilterWidth]\n    });\n  }\n  return backend2.runWebGPUProgram(program, [x], x.dtype, dimensions);\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/AvgPool.js\nfunction avgPool5(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x } = inputs;\n  const { filterSize, strides, pad: pad3, dimRoundingMode } = attrs;\n  const dilations = 1;\n  const convInfo = backend_util_exports.computePool2DInfo(x.shape, filterSize, strides, dilations, pad3, dimRoundingMode);\n  return poolImpl(x, convInfo, \"avg\", backend2);\n}\nvar avgPoolConfig4 = {\n  kernelName: AvgPool,\n  backendName: \"webgpu\",\n  kernelFunc: avgPool5\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/BatchMatMul.js\nfunction batchMatMul4(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { a, b } = inputs;\n  const { transposeA, transposeB } = attrs;\n  return batchMatMulImpl2({ a, b, transposeA, transposeB, backend: backend2 });\n}\nvar batchMatMulConfig4 = {\n  kernelName: BatchMatMul,\n  backendName: \"webgpu\",\n  kernelFunc: batchMatMul4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/slice_webgpu.js\nvar SliceProgram2 = class {\n  constructor(start, destSize) {\n    this.variableNames = [\"source\"];\n    this.workPerThread = 1;\n    this.workGroupSize = [64, 1, 1];\n    this.size = true;\n    this.outputShape = destSize;\n    this.rank = destSize.length;\n    this.dispatchLayout = flatDispatchLayout(this.outputShape);\n    this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, this.workGroupSize, [this.workPerThread, 1, 1]);\n    this.start = start;\n    this.uniforms = `start : ${getCoordsDataType2(start.length)}, `;\n    this.shaderKey = \"slice\";\n  }\n  getUserCode() {\n    const dtype = getCoordsDataType2(this.rank);\n    const sourceCoords = getCoords3(this.rank);\n    let coordSum;\n    if (this.start.length === 1) {\n      coordSum = this.outputShape.map((_, i) => {\n        return `sourceLoc = uniforms.start + coords;`;\n      });\n    } else {\n      coordSum = this.outputShape.map((_, i) => {\n        return `sourceLoc.${coords2[i]} = uniforms.start.${getCoordsXYZ(i)} + coords.${coords2[i]};`;\n      });\n    }\n    const userCode = `\n      ${getMainHeaderString(\"index\")} {\n        if (index < uniforms.size) {\n          var sourceLoc : ${dtype};\n          let coords = getCoordsFromIndex(index);\n          ${coordSum.join(\"\\n\")}\n          setOutputAtIndex(index, getSource(${sourceCoords}));\n        }\n      }\n    `;\n    return userCode;\n  }\n};\nvar coords2 = [\"x\", \"y\", \"z\", \"w\", \"u\", \"v\"];\nfunction getCoords3(rank) {\n  if (rank === 1) {\n    return \"sourceLoc\";\n  } else if (rank <= 6) {\n    return coords2.slice(0, rank).map((coord) => `sourceLoc.${coord}`).join(\",\");\n  } else {\n    throw Error(`Slicing for rank ${rank} is not yet supported`);\n  }\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/Slice.js\nfunction slice5(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x } = inputs;\n  const { begin, size } = attrs;\n  const [$begin, $size] = slice_util_exports.parseSliceParams(x, begin, size);\n  slice_util_exports.assertParamsValid(x, $begin, $size);\n  if (backend2.shouldExecuteOnCPU([x]) || x.dtype === \"string\") {\n    const xBufferInfo = backend2.tensorMap.get(x.dataId);\n    const outValues = sliceImplCPU2(xBufferInfo.values, $begin, $size, x.shape, x.dtype);\n    return backend2.makeTensorInfo($size, x.dtype, outValues);\n  }\n  if (util_exports.sizeFromShape($size) === 0) {\n    return backend2.makeTensorInfo($size, x.dtype, []);\n  }\n  const program = new SliceProgram2($begin, $size);\n  const uniformData = [{ type: \"int32\", data: $begin }];\n  return backend2.runWebGPUProgram(program, [x], x.dtype, uniformData);\n}\nvar sliceConfig4 = {\n  kernelName: Slice,\n  backendName: \"webgpu\",\n  kernelFunc: slice5\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/BatchToSpaceND.js\nvar batchToSpaceND5 = (args) => {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x } = inputs;\n  const { blockShape, crops } = attrs;\n  util_exports.assert(x.shape.length <= 4, () => \"batchToSpaceND for rank > 4 with a WebGPU backend not implemented yet\");\n  const prod6 = blockShape.reduce((a, b) => a * b);\n  const reshaped = backend_util_exports.getReshaped(x.shape, blockShape, prod6);\n  const permuted = backend_util_exports.getPermuted(reshaped.length, blockShape.length);\n  const reshapedPermuted = backend_util_exports.getReshapedPermuted(x.shape, blockShape, prod6);\n  const sliceBeginCoords = backend_util_exports.getSliceBeginCoords(crops, blockShape.length);\n  const sliceSize = backend_util_exports.getSliceSize(reshapedPermuted, crops, blockShape.length);\n  const toDispose = [];\n  const reshapedIntermediate = reshape6({ inputs: { x }, backend: backend2, attrs: { shape: reshaped } });\n  const transposedIntermediate = transpose5({ inputs: { x: reshapedIntermediate }, backend: backend2, attrs: { perm: permuted } });\n  const reshapedIntermediate2 = reshape6({\n    inputs: { x: transposedIntermediate },\n    backend: backend2,\n    attrs: { shape: reshapedPermuted }\n  });\n  const sliced = slice5({\n    inputs: { x: reshapedIntermediate2 },\n    backend: backend2,\n    attrs: { begin: sliceBeginCoords, size: sliceSize }\n  });\n  toDispose.push(reshapedIntermediate);\n  toDispose.push(transposedIntermediate);\n  toDispose.push(reshapedIntermediate2);\n  toDispose.forEach((t) => backend2.disposeData(t.dataId));\n  return sliced;\n};\nvar batchToSpaceNDConfig4 = {\n  kernelName: BatchToSpaceND,\n  backendName: \"webgpu\",\n  kernelFunc: batchToSpaceND5\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/NotEqual.js\nvar notEqual4 = binaryKernelFunc3({\n  opType: BinaryOpType.NOT_EQUAL,\n  dtype: \"bool\",\n  cpuKernelImpl: notEqualImplCPU2\n});\nvar notEqualConfig4 = {\n  kernelName: NotEqual,\n  backendName: \"webgpu\",\n  kernelFunc: notEqual4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/Real.js\nfunction real4(args) {\n  const { inputs, backend: backend2 } = args;\n  const { input: input2 } = inputs;\n  const inputData = backend2.tensorMap.get(input2.dataId);\n  return identity5({ inputs: { x: inputData.complexTensorInfos.real }, backend: backend2 });\n}\nvar realConfig3 = {\n  kernelName: Real,\n  backendName: \"webgpu\",\n  kernelFunc: real4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernel_utils/int.js\nfunction int2(input2, backend2) {\n  const program = new UnaryOpProgram2(input2.shape, UnaryOpType.TO_INT);\n  const output = backend2.runWebGPUProgram(program, [input2], \"int32\");\n  return { dataId: output.dataId, shape: output.shape, dtype: output.dtype };\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/Cast.js\nfunction cast6(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x } = inputs;\n  const { dtype } = attrs;\n  if (dtype === \"complex64\") {\n    if (x.dtype === \"complex64\") {\n      return identity5({ inputs: { x }, backend: backend2 });\n    }\n    const zerosTensor = zeros(x.shape);\n    const floatX = cast6({ inputs: { x }, backend: backend2, attrs: { dtype: \"float32\" } });\n    const result = complex4({ inputs: { real: floatX, imag: zerosTensor }, backend: backend2 });\n    zerosTensor.dispose();\n    backend2.disposeData(floatX.dataId);\n    return result;\n  }\n  if (x.dtype === \"complex64\") {\n    const realPart = real4({ inputs: { input: x }, backend: backend2 });\n    const result = cast6({ inputs: { x: realPart }, backend: backend2, attrs: { dtype } });\n    backend2.disposeData(realPart.dataId);\n    return result;\n  }\n  if (!util_exports.hasEncodingLoss(x.dtype, dtype)) {\n    const result = identity5({ inputs: { x }, backend: backend2 });\n    return { dataId: result.dataId, shape: result.shape, dtype };\n  }\n  if (backend2.shouldExecuteOnCPU([x])) {\n    const values = backend2.tensorMap.get(x.dataId).values;\n    const [resultShape, resultType, resultData] = castImplCPU2(values, x.shape, x.dtype, dtype);\n    return backend2.makeTensorInfo(resultShape, resultType, resultData);\n  }\n  if (dtype === \"int32\") {\n    return int2(x, backend2);\n  }\n  if (dtype === \"bool\") {\n    const zerosTensorInfo = backend2.makeTensorInfo([], \"bool\", util_exports.getTypedArrayFromDType(\"bool\", 1));\n    const binaryInputs = { a: x, b: zerosTensorInfo };\n    const result = notEqual4({ inputs: binaryInputs, backend: backend2 });\n    backend2.disposeData(zerosTensorInfo.dataId);\n    return result;\n  }\n  throw new Error(`Error in Cast: failed to cast ${x.dtype} to ${dtype}`);\n}\nvar castConfig4 = {\n  kernelName: Cast,\n  backendName: \"webgpu\",\n  kernelFunc: cast6\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/Ceil.js\nvar ceil4 = unaryKernelFunc3({ opType: UnaryOpType.CEIL, cpuKernelImpl: ceilImplCPU2 });\nvar ceilConfig4 = {\n  kernelName: Ceil,\n  backendName: \"webgpu\",\n  kernelFunc: ceil4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/clip_vec4_webgpu.js\nvar ClipVec4Program = class {\n  constructor(outputShape) {\n    this.variableNames = [\"A\"];\n    this.uniforms = \"minVal : f32, maxVal : f32,\";\n    this.workPerThread = 4;\n    this.workGroupSize = [64, 1, 1];\n    this.isVec4 = true;\n    this.size = true;\n    this.outputShape = outputShape;\n    this.dispatchLayout = flatDispatchLayout(this.outputShape);\n    this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, this.workGroupSize, [this.workPerThread, 1, 1]);\n    this.shaderKey = \"clipVec4\";\n  }\n  getUserCode() {\n    const userCode = `\n      ${getMainHeaderString(\"index\")} {\n        if(index < uniforms.size) {\n          let value = getAByOutputIndex(index);\n          var clampedValue : vec4<f32>;\n          for (var i = 0; i < 4; i = i + 1) {\n            if (isnan(value[i])) {\n              clampedValue[i] = value[i];\n            } else {\n              clampedValue[i] = clamp(value[i], uniforms.minVal, uniforms.maxVal);\n            }\n          }\n\n          setOutputAtIndex(index, clampedValue);\n        }\n      }\n    `;\n    return userCode;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/clip_webgpu.js\nvar ClipProgram2 = class {\n  constructor(outputShape) {\n    this.variableNames = [\"A\"];\n    this.uniforms = \"minVal : f32, maxVal : f32,\";\n    this.workGroupSize = [64, 1, 1];\n    this.size = true;\n    this.outputShape = outputShape;\n    this.dispatchLayout = flatDispatchLayout(this.outputShape);\n    this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, this.workGroupSize);\n    this.shaderKey = \"clip\";\n  }\n  getUserCode() {\n    const userCode = `\n      ${getMainHeaderString(\"index\")} {\n        if(index < uniforms.size) {\n          let value = getAByOutputIndex(index);\n          if (isnan(value)) {\n            setOutputAtIndex(index, value);\n            return;\n          }\n          setOutputAtIndex(index, clamp(value, uniforms.minVal, uniforms.maxVal));\n        }\n      }\n    `;\n    return userCode;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/ClipByValue.js\nfunction clipByValue4(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x } = inputs;\n  const { clipValueMin, clipValueMax } = attrs;\n  let program;\n  const uniformData = [\n    { type: \"float32\", data: [clipValueMin] },\n    { type: \"float32\", data: [clipValueMax] }\n  ];\n  if (util_exports.sizeFromShape(x.shape) % 4 === 0) {\n    program = new ClipVec4Program(x.shape);\n  } else {\n    program = new ClipProgram2(x.shape);\n  }\n  return backend2.runWebGPUProgram(program, [x], x.dtype, uniformData);\n}\nvar clipByValueConfig4 = {\n  kernelName: ClipByValue,\n  backendName: \"webgpu\",\n  kernelFunc: clipByValue4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/concat_webgpu.js\nvar ConcatProgram2 = class {\n  constructor(shapes) {\n    this.uniforms = \"\";\n    this.workPerThread = 4;\n    this.workGroupSize = [64, 1, 1];\n    this.size = true;\n    this.outputShape = backend_util_exports.computeOutShape(shapes, 1);\n    this.variableNames = shapes.map((_, i) => `T${i}`);\n    this.dispatchLayout = flatDispatchLayout(this.outputShape);\n    this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, this.workGroupSize, [this.workPerThread, 1, 1]);\n    this.offsetLength = shapes.length - 1;\n    for (let i = 0; i < this.offsetLength; i++) {\n      this.uniforms += `offset${i} : i32,`;\n    }\n    this.shaderKey = \"concat\";\n  }\n  getUserCode() {\n    const snippets = [];\n    if (this.offsetLength > 0) {\n      snippets.push(`if (yC < uniforms.offset0){ setOutputAtCoords(coords.x, coords.y, getT0(yR, yC)); }`);\n      for (let i = 1; i < this.offsetLength; i++) {\n        snippets.push(`else if (yC < uniforms.offset${[i]}){ setOutputAtCoords(coords.x, coords.y, getT${i}(yR, yC - uniforms.offset${i - 1})); }`);\n      }\n      const lastIndex = this.offsetLength;\n      const lastShiftIndex = this.offsetLength - 1;\n      snippets.push(`else { setOutputAtCoords(coords.x, coords.y, getT${lastIndex}(yR, yC - uniforms.offset${lastShiftIndex})); }`);\n    } else {\n      snippets.push(`setOutputAtCoords(coords.x, coords.y, getT0(yR, yC));`);\n    }\n    const userCode = `\n      ${getMainHeaderString(\"index\")} {\n        for(var i = 0; i < ${this.workPerThread}; i = i + 1) {\n          let flatIndex = index * ${this.workPerThread} + i;\n          if(flatIndex < uniforms.size) {\n            let coords = getCoordsFromIndex(flatIndex);\n            let yR = coords.x;\n            let yC = coords.y;\n\n            ${snippets.join(\"\\n        \")}\n          }\n        }\n      }\n    `;\n    return userCode;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/Imag.js\nfunction imag4(args) {\n  const { inputs, backend: backend2 } = args;\n  const { input: input2 } = inputs;\n  const inputData = backend2.tensorMap.get(input2.dataId);\n  return identity5({ inputs: { x: inputData.complexTensorInfos.imag }, backend: backend2 });\n}\nvar imagConfig3 = {\n  kernelName: Imag,\n  backendName: \"webgpu\",\n  kernelFunc: imag4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/Concat_impl.js\nfunction concatImpl3(inputs, axis, backend2) {\n  const dtype = inputs[0].dtype;\n  if (dtype === \"complex64\") {\n    const reals = inputs.map((t) => real4({ inputs: { input: t }, backend: backend2 }));\n    const imags = inputs.map((t) => imag4({ inputs: { input: t }, backend: backend2 }));\n    const realConcated = concatImpl3(reals, axis, backend2);\n    const imagConcated = concatImpl3(imags, axis, backend2);\n    const result = complex4({ inputs: { real: realConcated, imag: imagConcated }, backend: backend2 });\n    reals.forEach((r) => backend2.disposeData(r.dataId));\n    imags.forEach((i) => backend2.disposeData(i.dataId));\n    backend2.disposeData(realConcated.dataId);\n    backend2.disposeData(imagConcated.dataId);\n    return result;\n  }\n  let runOnCpu = backend2.shouldExecuteOnCPU(inputs);\n  if (dtype === \"string\") {\n    runOnCpu = true;\n  }\n  if (runOnCpu) {\n    const tensors2D2 = inputs.map((t) => {\n      const innerSize = util_exports.sizeFromShape(t.shape.slice(axis));\n      const shape = [-1, innerSize];\n      return reshape6({ inputs: { x: t }, backend: backend2, attrs: { shape } });\n    });\n    const inputsValShapes = tensors2D2.map((t) => {\n      return { vals: backend2.readSync(t.dataId), shape: t.shape };\n    });\n    const outShape2 = backend_util_exports.computeOutShape(tensors2D2.map((t) => t.shape), 1);\n    const simplyConcat = tensors2D2[0].shape[0] === 1;\n    const outVals = concatImplCPU2(inputsValShapes, outShape2, dtype, simplyConcat);\n    const finalOutShape = backend_util_exports.computeOutShape(inputs.map((t) => t.shape), axis);\n    const outInfo = backend2.makeTensorInfo(finalOutShape, dtype, outVals);\n    tensors2D2.forEach((t) => backend2.disposeData(t.dataId));\n    return outInfo;\n  }\n  const maxInputNum = backend2.device.limits.maxStorageBuffersPerShaderStage - 1;\n  if (inputs.length > maxInputNum) {\n    const reducedInputs = [];\n    for (let i = 0; i < inputs.length; i += maxInputNum) {\n      const subArray = inputs.slice(i, i + maxInputNum);\n      reducedInputs.push(concatImpl3(subArray, axis, backend2));\n    }\n    const result = concatImpl3(reducedInputs, axis, backend2);\n    for (const i of reducedInputs) {\n      backend2.disposeData(i.dataId);\n    }\n    return result;\n  }\n  const { tensors2D, outShape } = computeTensors2D2(inputs, axis, backend2);\n  const shapes = tensors2D.map((t) => t.shape);\n  const program = new ConcatProgram2(shapes);\n  const uniformData = [];\n  const offsets = new Array(shapes.length - 1);\n  if (offsets.length > 0) {\n    offsets[0] = shapes[0][1];\n    uniformData.push({ type: \"int32\", data: [offsets[0]] });\n    for (let i = 1; i < offsets.length; i++) {\n      offsets[i] = offsets[i - 1] + shapes[i][1];\n      uniformData.push({ type: \"int32\", data: [offsets[i]] });\n    }\n  }\n  const res = backend2.runWebGPUProgram(program, tensors2D, tensors2D[0].dtype, uniformData);\n  tensors2D.forEach((r) => backend2.disposeData(r.dataId));\n  const reshapedResult = reshape6({ inputs: { x: res }, backend: backend2, attrs: { shape: outShape } });\n  backend2.disposeData(res.dataId);\n  return reshapedResult;\n}\nfunction computeTensors2D2(inputs, axis, backend2) {\n  const outShape = backend_util_exports.computeOutShape(inputs.map((t) => t.shape), axis);\n  const tensors2D = inputs.map((t) => reshape6({\n    inputs: { x: t },\n    backend: backend2,\n    attrs: {\n      shape: [\n        util_exports.sizeFromShape(t.shape.slice(0, axis)),\n        util_exports.sizeFromShape(t.shape.slice(axis))\n      ]\n    }\n  }));\n  return { tensors2D, outShape };\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/Concat.js\nfunction concat5(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { axis } = attrs;\n  const $axis = util_exports.parseAxisParam(axis, inputs[0].shape)[0];\n  const outShape = backend_util_exports.computeOutShape(inputs.map((t) => t.shape), $axis);\n  if (util_exports.sizeFromShape(outShape) === 0) {\n    return backend2.makeTensorInfo(outShape, inputs[0].dtype, []);\n  }\n  const $inputs = inputs.filter((t) => util_exports.sizeFromShape(t.shape) > 0);\n  if ($inputs.length === 1) {\n    return identity5({ inputs: { x: $inputs[0] }, backend: backend2 });\n  }\n  const shapes = $inputs.map((t) => t.shape);\n  backend_util_exports.assertParamsConsistent(shapes, $axis);\n  return concatImpl3($inputs, $axis, backend2);\n}\nvar concatConfig4 = {\n  kernelName: Concat,\n  backendName: \"webgpu\",\n  kernelFunc: concat5\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/conv2d_mm_webgpu.js\nfunction conv2dCommonSnippet(isChannelsLast, fitAOuter, fitBOuter, fitInner, addBias = false, activation2 = null, hasPreluActivationWeights = false, innerElementSizeX = 4, innerElementSizeW = 4, innerElementSize = 4) {\n  const getXSnippet = (innerElementSize2) => {\n    switch (innerElementSize2) {\n      case 1:\n        return \"resData = x[xIndex];\";\n      case 3:\n        return \"resData = vec3<f32>(x[xIndex], x[xIndex + 1], x[xIndex + 2]);\";\n      case 4:\n        return \"resData = x[xIndex / 4];\";\n      default:\n        throw new Error(`innerElementSize ${innerElementSize2} is not supported.`);\n    }\n  };\n  const getWSnippet = (innerElementSize2) => {\n    switch (innerElementSize2) {\n      case 1:\n        return \"return W[row * uniforms.wShape[3] + colIn];\";\n      case 4:\n        return \"return W[row * uniforms.wShape[3] / 4 + colIn];\";\n      default:\n        throw new Error(`innerElementSize ${innerElementSize2} is not supported.`);\n    }\n  };\n  const coordASnippet = isChannelsLast ? `\n      let coord = vec4<i32>(batch, xRow, xCol, xCh);\n      ` : `\n      let coord = vec4<i32>(batch, xCh, xRow, xCol);\n      `;\n  const coordResSnippet = isChannelsLast ? `\n      let coords = vec4<i32>(\n        batch,\n        row / outWidth,\n        row % outWidth,\n        col);\n      ` : `\n      let coords = vec4<i32>(\n        batch,\n        row,\n        col / outWidth,\n        col % outWidth);\n      `;\n  const xHight = isChannelsLast ? \"uniforms.xShape[1]\" : \"uniforms.xShape[2]\";\n  const xWidth = isChannelsLast ? \"uniforms.xShape[2]\" : \"uniforms.xShape[3]\";\n  const row = isChannelsLast ? \"row\" : \"col\";\n  const col = isChannelsLast ? \"col\" : \"row\";\n  const readXSnippet = `\n      let inChannels = uniforms.wShape[2];\n      let outWidth = ${isChannelsLast ? \"uniforms.outShape[2]\" : \"uniforms.outShape[3]\"};\n      let outRow = ${row} / outWidth;\n      let outCol = ${row} % outWidth;\n\n      let WRow = ${col} / (uniforms.filterDims[1] * inChannels);\n      let WCol = ${col} / inChannels % uniforms.filterDims[1];\n      let xRow = outRow * uniforms.stride[0] + uniforms.dilation[0] * WRow - uniforms.pad[0];\n      let xCol = outCol * uniforms.stride[1] + uniforms.dilation[1] * WCol - uniforms.pad[1];\n      let xCh = ${col} % inChannels;\n      var resData = ${typeSnippet(innerElementSizeX)}(0.0);\n      // The bounds checking is always needed since we use it to pad zero for\n      // the 'same' padding type.\n      if (xRow >= 0 && xRow < ${xHight} && xCol >= 0 && xCol < ${xWidth}) {\n        ${coordASnippet}\n        let xIndex = getIndexFromCoords4D(coord, uniforms.xShape);\n        ${getXSnippet(innerElementSizeX)}\n      }\n      return resData;`;\n  const sampleX = isChannelsLast ? fitAOuter && fitInner ? `\n      let col = colIn * ${innerElementSizeX};\n      ${readXSnippet}` : `\n      let col = colIn * ${innerElementSizeX};\n      if (row < uniforms.dimAOuter && col < uniforms.dimInner) {\n        ${readXSnippet}\n      }\n      return ${typeSnippet(innerElementSizeX)}(0.0);` : fitInner && fitBOuter ? `\n      let col = colIn * ${innerElementSizeX};\n      ${readXSnippet}` : `\n      let col = colIn * ${innerElementSizeX};\n      if (row < uniforms.dimInner && col < uniforms.dimBOuter) {\n        ${readXSnippet}\n      }\n      return ${typeSnippet(innerElementSizeX)}(0.0);`;\n  const sampleW = `${getWSnippet(innerElementSizeW)}`;\n  const resType = typeSnippet(innerElementSize);\n  const aType = isChannelsLast ? typeSnippet(innerElementSizeX) : typeSnippet(innerElementSizeW);\n  const bType = isChannelsLast ? typeSnippet(innerElementSizeW) : typeSnippet(innerElementSizeX);\n  const userCode = `\n      ${activationFnSnippet(activation2, hasPreluActivationWeights, innerElementSize === 4, 4)}\n      fn mm_readA(batch: i32, row : i32, colIn : i32) -> ${aType} {\n        ${isChannelsLast ? sampleX : sampleW}\n      }\n\n      fn mm_readB(batch: i32, row : i32, colIn : i32) -> ${bType} {\n        ${isChannelsLast ? sampleW : sampleX}\n      }\n\n      fn mm_write(batch: i32, row : i32, colIn : i32, valueIn : ${resType}) {\n        let col = colIn * ${innerElementSize};\n        if (row < uniforms.dimAOuter && col < uniforms.dimBOuter)\n        {\n        var value = valueIn;\n        let outWidth = ${isChannelsLast ? \"uniforms.outShape[2]\" : \"uniforms.outShape[3]\"};\n        ${coordResSnippet}\n        ${biasActivationSnippet(addBias, activation2)}\n        setOutputAtCoords(coords[0], coords[1], coords[2], coords[3], value);\n        }\n      }`;\n  return userCode;\n}\nvar Conv2DMMProgram = class {\n  constructor(convInfo, dimAOuter, dimBOuter, dimInner, addBias = false, activation2 = null, hasPreluActivationWeights = false) {\n    this.variableNames = [\"x\", \"W\"];\n    this.uniforms = `filterDims : vec2<i32>, pad : vec2<i32>, stride : vec2<i32>, dilation : vec2<i32>, dimAOuter : i32, dimBOuter : i32, dimInner : i32,`;\n    this.outputShape = convInfo.outShape;\n    this.isChannelsLast = convInfo.dataFormat === \"channelsLast\";\n    this.isVec4 = ((convInfo.inChannels % 4 === 0 || convInfo.inChannels % 3 === 0) && this.isChannelsLast || convInfo.outWidth % 4 === 0 && !this.isChannelsLast) && convInfo.outChannels % 4 === 0;\n    this.dispatchLayout = this.isChannelsLast ? { x: [3], y: [1, 2], z: [0] } : { x: [2, 3], y: [1], z: [0] };\n    this.workGroupSize = computeWorkGroupSizeForConv2d(this.dispatchLayout, this.outputShape, this.isVec4);\n    this.elementsPerThread = computeWorkPerThreadForConv2d(this.dispatchLayout, this.outputShape, this.isVec4);\n    this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, this.workGroupSize, this.elementsPerThread);\n    if (this.isVec4) {\n      if (this.isChannelsLast && convInfo.inChannels % 4 !== 0) {\n        this.innerElementSize = 3;\n        this.variableTypes = [\"f32\", \"vec4<f32>\"];\n      } else {\n        this.innerElementSize = 4;\n        this.variableTypes = [\"vec4<f32>\", \"vec4<f32>\"];\n      }\n      if (addBias) {\n        this.variableNames.push(\"bias\");\n        this.variableTypes.push(\"vec4<f32>\");\n      }\n      if (hasPreluActivationWeights) {\n        this.variableNames.push(\"preluActivationWeights\");\n        this.variableTypes.push(\"vec4<f32>\");\n      }\n    } else {\n      this.innerElementSize = this.elementsPerThread[0];\n      if (addBias) {\n        this.variableNames.push(\"bias\");\n      }\n      if (hasPreluActivationWeights) {\n        this.variableNames.push(\"preluActivationWeights\");\n      }\n    }\n    this.addBias = addBias;\n    this.activation = activation2;\n    this.hasPreluActivationWeights = hasPreluActivationWeights;\n    this.tileAOuter = this.workGroupSize[1] * this.elementsPerThread[1];\n    this.tileBOuter = this.workGroupSize[0] * this.elementsPerThread[0];\n    this.tileInner = Math.max(this.workGroupSize[0] * this.innerElementSize, this.workGroupSize[1]);\n    this.fitAOuter = dimAOuter % this.tileAOuter === 0;\n    this.fitBOuter = dimBOuter % this.tileBOuter === 0;\n    this.fitInner = dimInner % this.tileInner === 0;\n    this.shaderKey = `conv2DMM_${this.elementsPerThread}_${this.activation}}_${this.fitAOuter}_${this.fitBOuter}_${this.fitInner}_${this.isVec4}_${this.innerElementSize}_${this.isChannelsLast}`;\n  }\n  getUserCode() {\n    const matMulSource = this.isVec4 ? makeMatMulPackedVec4Source(this.elementsPerThread, this.workGroupSize, !this.isChannelsLast, this.tileInner) : makeMatMulPackedSource(this.elementsPerThread, this.workGroupSize, !this.isChannelsLast, this.tileInner);\n    const elementsSize = this.isVec4 ? [this.innerElementSize, 4, 4] : [1, 1, 1];\n    const userCode = `\n    ${conv2dCommonSnippet(this.isChannelsLast, this.fitAOuter, this.fitBOuter, this.fitInner, this.addBias, this.activation, this.hasPreluActivationWeights, elementsSize[0], elementsSize[1], elementsSize[2])}\n    ${matMulSource}\n  `;\n    return userCode;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/Conv2D_impl.js\nfunction getShapeForBatchMatMul2(shape, isChannelsLast) {\n  const length = shape.length;\n  if (length >= 3) {\n    return isChannelsLast ? [\n      ...shape.slice(0, -3),\n      shape[length - 3] * shape[length - 2],\n      shape[length - 1]\n    ] : [\n      ...shape.slice(0, -3),\n      shape[length - 3],\n      shape[length - 2] * shape[length - 1]\n    ];\n  } else if (!isChannelsLast && length === 1 && shape[0] > 1) {\n    return [shape[0], 1];\n  } else {\n    return null;\n  }\n}\nfunction conv2dByMatMul2({ x, filter, convInfo, backend: backend2, bias = null, preluActivationWeights = null, leakyreluAlpha = 0, activation: activation2 = null }) {\n  const isChannelsLast = convInfo.dataFormat === \"channelsLast\";\n  const transposeA = isChannelsLast ? false : true;\n  const transposeB = false;\n  const sameSize = isChannelsLast && convInfo.filterHeight === convInfo.inHeight && convInfo.filterWidth === convInfo.inWidth && convInfo.padInfo.type === \"VALID\";\n  const intermediates = [];\n  let xReshaped;\n  let filterReshaped;\n  if (sameSize) {\n    const sharedDim = convInfo.inHeight * convInfo.inWidth * convInfo.inChannels;\n    xReshaped = reshape6({\n      inputs: { x },\n      backend: backend2,\n      attrs: { shape: [1, convInfo.batchSize, sharedDim] }\n    });\n    filterReshaped = reshape6({\n      inputs: { x: filter },\n      backend: backend2,\n      attrs: { shape: [1, sharedDim, convInfo.outChannels] }\n    });\n  } else {\n    xReshaped = reshape6({\n      inputs: { x },\n      backend: backend2,\n      attrs: {\n        shape: isChannelsLast ? [\n          convInfo.batchSize,\n          convInfo.inHeight * convInfo.inWidth,\n          convInfo.inChannels\n        ] : [\n          convInfo.batchSize,\n          convInfo.inChannels,\n          convInfo.inHeight * convInfo.inWidth\n        ]\n      }\n    });\n    filterReshaped = reshape6({\n      inputs: { x: filter },\n      backend: backend2,\n      attrs: { shape: [1, convInfo.inChannels, convInfo.outChannels] }\n    });\n  }\n  intermediates.push(xReshaped);\n  intermediates.push(filterReshaped);\n  if (preluActivationWeights != null) {\n    const targetShape = getShapeForBatchMatMul2(preluActivationWeights.shape, isChannelsLast);\n    if (targetShape != null) {\n      preluActivationWeights = reshape6({\n        inputs: { x: preluActivationWeights },\n        backend: backend2,\n        attrs: { shape: targetShape }\n      });\n      intermediates.push(preluActivationWeights);\n    }\n  }\n  if (bias != null) {\n    const targetShape = getShapeForBatchMatMul2(bias.shape, isChannelsLast);\n    if (targetShape != null) {\n      bias = reshape6({ inputs: { x: bias }, backend: backend2, attrs: { shape: targetShape } });\n      intermediates.push(bias);\n    }\n  }\n  const result = batchMatMulImpl2({\n    a: isChannelsLast ? xReshaped : filterReshaped,\n    b: isChannelsLast ? filterReshaped : xReshaped,\n    transposeA,\n    transposeB,\n    backend: backend2,\n    bias,\n    activation: activation2,\n    preluActivationWeights,\n    leakyreluAlpha\n  });\n  const out = reshape6({ inputs: { x: result }, backend: backend2, attrs: { shape: convInfo.outShape } });\n  intermediates.push(result);\n  for (const i of intermediates) {\n    backend2.disposeData(i.dataId);\n  }\n  return out;\n}\nfunction conv2DImpl({ x, filter, convInfo, backend: backend2, bias = null, preluActivationWeights = null, leakyreluAlpha = 0, activation: activation2 = null }) {\n  const hasBias = bias != null;\n  const hasPreluActivationWeights = preluActivationWeights != null;\n  const isChannelsLast = convInfo.dataFormat === \"channelsLast\";\n  const sameSize = isChannelsLast && convInfo.filterHeight === convInfo.inHeight && convInfo.filterWidth === convInfo.inWidth && convInfo.padInfo.type === \"VALID\";\n  if (sameSize || convInfo.filterHeight === 1 && convInfo.filterWidth === 1 && convInfo.dilationHeight === 1 && convInfo.dilationWidth === 1 && convInfo.strideHeight === 1 && convInfo.strideWidth === 1 && (convInfo.padInfo.type === \"SAME\" || convInfo.padInfo.type === \"VALID\")) {\n    return conv2dByMatMul2({\n      x,\n      filter,\n      convInfo,\n      backend: backend2,\n      bias,\n      activation: activation2,\n      preluActivationWeights,\n      leakyreluAlpha\n    });\n  }\n  const dimAOuter = isChannelsLast ? convInfo.outHeight * convInfo.outWidth : convInfo.outChannels;\n  const dimBOuter = isChannelsLast ? convInfo.outChannels : convInfo.outHeight * convInfo.outWidth;\n  const dimInner = convInfo.filterHeight * convInfo.filterWidth * convInfo.inChannels;\n  const padInfo = [convInfo.padInfo.top, convInfo.padInfo.left];\n  const dimensions = [\n    { type: \"int32\", data: [convInfo.filterHeight, convInfo.filterWidth] },\n    { type: \"int32\", data: [...padInfo] },\n    { type: \"int32\", data: [convInfo.strideHeight, convInfo.strideWidth] },\n    { type: \"int32\", data: [convInfo.dilationHeight, convInfo.dilationWidth] },\n    { type: \"int32\", data: [dimAOuter] },\n    { type: \"int32\", data: [dimBOuter] },\n    { type: \"int32\", data: [dimInner] }\n  ];\n  const program = new Conv2DMMProgram(convInfo, dimAOuter, dimBOuter, dimInner, hasBias, activation2, hasPreluActivationWeights);\n  const intermediates = [];\n  const inputVar = [x, filter];\n  if (hasBias) {\n    if (!isChannelsLast && bias.shape.length === 1) {\n      bias = reshape6({ inputs: { x: bias }, backend: backend2, attrs: { shape: [bias.shape[0], 1, 1] } });\n      intermediates.push(bias);\n    }\n    inputVar.push(bias);\n  }\n  if (hasPreluActivationWeights) {\n    if (!isChannelsLast && preluActivationWeights.shape.length === 1) {\n      preluActivationWeights = reshape6({\n        inputs: { x: preluActivationWeights },\n        backend: backend2,\n        attrs: { shape: [preluActivationWeights.shape[0], 1, 1] }\n      });\n      intermediates.push(preluActivationWeights);\n    }\n    inputVar.push(preluActivationWeights);\n  }\n  if (activation2 === \"leakyrelu\") {\n    dimensions.push({ type: \"float32\", data: [leakyreluAlpha] });\n    program.uniforms += \" alpha : f32,\";\n  }\n  const out = backend2.runWebGPUProgram(program, inputVar, x.dtype, dimensions);\n  for (const i of intermediates) {\n    backend2.disposeData(i.dataId);\n  }\n  return out;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/Conv2D.js\nfunction conv2d6(args) {\n  const { inputs, attrs, backend: backend2 } = args;\n  const { x, filter } = inputs;\n  const { strides, pad: pad3, dataFormat, dilations, dimRoundingMode } = attrs;\n  const $dataFormat = backend_util_exports.convertConv2DDataFormat(dataFormat);\n  const convInfo = backend_util_exports.computeConv2DInfo(x.shape, filter.shape, strides, dilations, pad3, dimRoundingMode, false, $dataFormat);\n  return conv2DImpl({ x, filter, convInfo, backend: backend2 });\n}\nvar conv2DConfig4 = {\n  kernelName: Conv2D,\n  backendName: \"webgpu\",\n  kernelFunc: conv2d6\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/conv_backprop_mm_webgpu.js\nfunction conv2dTransposeCommonSnippet(innerElementSize = 4) {\n  const getWSnippet = (innerElementSize2) => {\n    switch (innerElementSize2) {\n      case 1:\n        return \"return W[getIndexFromCoords4D(coord, uniforms.wShape)];\";\n      case 4:\n        return `\n            let coord1 = vec4<i32>(coordX, coordY, col + 1, rowInner);\n            let coord2 = vec4<i32>(coordX, coordY, col + 2, rowInner);\n            let coord3 = vec4<i32>(coordX, coordY, col + 3, rowInner);\n            let v0 = W[getIndexFromCoords4D(coord, uniforms.wShape)];\n            let v1 = W[getIndexFromCoords4D(coord1, uniforms.wShape)];\n            let v2 = W[getIndexFromCoords4D(coord2, uniforms.wShape)];\n            let v3 = W[getIndexFromCoords4D(coord3, uniforms.wShape)];\n            return vec4<f32>(v0, v1, v2, v3);\n            `;\n      default:\n        throw new Error(`innerElementSize ${innerElementSize2} is not supported.`);\n    }\n  };\n  const readASnippet = `\n      let outRow = row / uniforms.outShape[2];\n      let outCol = row % uniforms.outShape[2];\n\n      let WRow = col / (uniforms.filterDims[1] * uniforms.outBackprop[3]);\n      let WCol = col / uniforms.outBackprop[3] % uniforms.filterDims[1];\n      let xR = f32(outRow - uniforms.pads[0] + WRow) / f32(uniforms.stride[0]);\n      let xC = f32(outCol - uniforms.pads[1] + WCol) / f32(uniforms.stride[1]);\n      if (xR < 0.0 || xR >= f32(uniforms.outBackprop[1]) || fract(xR) > 0.0) {\n        return ${typeSnippet(innerElementSize)}(0.0);\n      }\n      if (xC < 0.0 || xC >= f32(uniforms.outBackprop[2]) || fract(xC) > 0.0) {\n        return ${typeSnippet(innerElementSize)}(0.0);\n      }\n      let coord = vec4<i32>(\n          batch,\n          i32(xR),\n          i32(xC),\n          col % uniforms.outBackprop[3]);\n      return x[getIndexFromCoords4D(coord, uniforms.xShape)/${innerElementSize}];`;\n  const sampleA = `if (row < uniforms.dimAOuter && col < uniforms.dimInner) {\n        ${readASnippet}\n      }\n      return ${typeSnippet(innerElementSize)}(0.0);`;\n  const userCode = `\n  fn mm_readA(batch: i32, row : i32, colIn : i32) -> ${typeSnippet(innerElementSize)} {\n    let col = colIn * ${innerElementSize};\n    ${sampleA}\n  }\n\n  fn mm_readB(batch: i32, row : i32, colIn : i32) -> ${typeSnippet(innerElementSize)} {\n    let col = colIn * ${innerElementSize};\n    let coordX = uniforms.filterDims.x - 1 -\n        row / (uniforms.filterDims[1] * uniforms.outBackprop[3]);\n    let coordY = uniforms.filterDims.y - 1 -\n        (row / uniforms.outBackprop[3]) % uniforms.filterDims[1];\n    if (row < uniforms.dimInner && col < uniforms.dimBOuter &&\n        coordX >= 0 && coordY >= 0) {\n      let rowInner = row % uniforms.outBackprop[3];\n      let coord = vec4<i32>(coordX, coordY, col, rowInner);\n      ${getWSnippet(innerElementSize)}\n    }\n    return ${typeSnippet(innerElementSize)}(0.0);\n  }\n\n  fn mm_write(batch: i32, row : i32, colIn : i32, valueInput : ${typeSnippet(innerElementSize)}) {\n    let col = colIn * ${innerElementSize};\n    if (row < uniforms.dimAOuter && (col + ${innerElementSize - 1}) < uniforms.dimBOuter) {\n      var value = valueInput;\n      let outCoord = vec4<i32>(\n          batch,\n          row / uniforms.outShape[2],\n          row % uniforms.outShape[2],\n          col);\n      result[getIndexFromCoords4D(outCoord, uniforms.outShape)/${innerElementSize}] = value;\n    }\n  }`;\n  return userCode;\n}\nvar Conv2DDerInputMMProgram = class {\n  constructor(convInfo) {\n    this.variableNames = [\"x\", \"W\"];\n    this.uniforms = \"filterDims : vec2<i32>, pads : vec2<i32>, stride : vec2<i32>, outBackprop : vec4<i32>, dimAOuter : i32, dimBOuter : i32, dimInner : i32,\";\n    this.outputShape = convInfo.inShape;\n    util_exports.assert(convInfo.dataFormat === \"channelsLast\", () => \"TODO: NCHW is unimplemented\");\n    this.isVec4 = convInfo.inChannels % 4 === 0 && convInfo.outChannels % 4 === 0;\n    this.dispatchLayout = { x: [3], y: [1, 2], z: [0] };\n    this.workGroupSize = computeWorkGroupSizeForConv2d(this.dispatchLayout, this.outputShape, this.isVec4);\n    this.elementsPerThread = computeWorkPerThreadForConv2d(this.dispatchLayout, this.outputShape, this.isVec4);\n    this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, this.workGroupSize, this.elementsPerThread);\n    if (this.isVec4) {\n      this.variableTypes = [\"vec4<f32>\", \"f32\"];\n    }\n    this.shaderKey = `conv2DDerInputMM_${this.isVec4}_${this.elementsPerThread}`;\n  }\n  getUserCode() {\n    const matMulSource = this.isVec4 ? makeMatMulPackedVec4Source(this.elementsPerThread, this.workGroupSize) : makeMatMulPackedSource(this.elementsPerThread, this.workGroupSize);\n    const userCode = `\n    ${conv2dTransposeCommonSnippet(this.isVec4 ? 4 : 1)}\n    ${matMulSource}\n    `;\n    return userCode;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/conv_backprop_webgpu.js\nvar Conv2DDerInputProgram2 = class {\n  constructor(convInfo) {\n    this.variableNames = [\"dy\", \"W\"];\n    this.uniforms = \"filterDims : vec2<i32>, pads : vec2<i32>, stride : vec2<i32>, outBackprop : vec4<i32>,\";\n    this.workGroupSize = [64, 1, 1];\n    this.size = true;\n    this.outputShape = convInfo.inShape;\n    this.dispatchLayout = flatDispatchLayout(this.outputShape);\n    this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, this.workGroupSize);\n    this.isChannelsLast = convInfo.dataFormat === \"channelsLast\";\n    this.shaderKey = `conv2DDerInput_${this.isChannelsLast}`;\n  }\n  getUserCode() {\n    const rowDim = this.isChannelsLast ? 1 : 2;\n    const colDim = this.isChannelsLast ? 2 : 3;\n    const channelDim = this.isChannelsLast ? 3 : 1;\n    return `\n    ${getMainHeaderString(\"index\")} {\n      if(index < uniforms.size) {\n        let coords = getCoordsFromIndex(index);\n        let batch = coords[0];\n        let d1 = coords[${channelDim}];\n\n        let dyCorner = vec2<i32>(coords[${rowDim}]), coords[${colDim}]) - uniforms.pads;\n        let dyRCorner = dyCorner.x;\n        let dyCCorner = dyCorner.y;\n\n        // Convolve dy(?, ?, d2) with w(:, :, d1, d2) to compute dx(xR, xC, d1).\n        // ? = to be determined. : = across all values in that axis.\n        var dotProd = 0.0;\n        for (var wR = 0; wR < uniforms.filterDims.x; wR = wR + 1) {\n          let dyR = (f32(dyRCorner) + f32(wR)) / f32(uniforms.stride.x);\n          let wRPerm = uniforms.filterDims.x - 1 - wR;\n          if (dyR < 0.0 || dyR >= f32(uniforms.outBackprop[1]) || fract(dyR) > 0.0 ||\n              wRPerm < 0) {\n            continue;\n          }\n          let idyR = dyR;\n\n          for (var wC = 0; wC < uniforms.filterDims.y; wC = wC + 1) {\n            let dyC = (f32(dyCCorner) + f32(wC)) / f32(uniforms.stride.y);\n            let wCPerm = uniforms.filterDims.y - 1 - wC;\n            if (dyC < 0.0 || dyC >= f32(uniforms.outBackprop[2]) ||\n                fract(dyC) > 0.0 || wCPerm < 0) {\n              continue;\n            }\n            let idyC = dyC;\n\n            for (var d2 = 0; d2 < uniforms.outBackprop[3]; d2 = d2 + 1) {\n              if (${this.isChannelsLast}) {\n                let xValue = getDy(batch, idyR, idyC, d2);\n                let wValue = getW(wRPerm, wCPerm, d1, d2);\n                dotProd = dotProd + xValue * wValue;\n              } else {\n                let xValue = getDy(batch, d2, idyR, idyC);\n                let wValue = getW(wRPerm, wCPerm, d1, d2);\n                dotProd = dotProd + xValue * wValue;\n              }\n\n            }\n          }\n        }\n        setOutputAtIndex(index, dotProd);\n      }\n    }\n  `;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/Conv2DBackpropInput.js\nfunction conv2DBackpropInput5(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { dy, filter } = inputs;\n  const { inputShape, strides, pad: pad3, dataFormat, dimRoundingMode } = attrs;\n  const $dataFormat = backend_util_exports.convertConv2DDataFormat(dataFormat);\n  const convInfo = backend_util_exports.computeConv2DInfo(inputShape, filter.shape, strides, 1, pad3, dimRoundingMode, false, $dataFormat);\n  const dimensions = [\n    { type: \"int32\", data: [convInfo.filterHeight, convInfo.filterWidth] },\n    {\n      type: \"int32\",\n      data: [\n        convInfo.filterHeight - 1 - convInfo.padInfo.top,\n        convInfo.filterWidth - 1 - convInfo.padInfo.left\n      ]\n    },\n    { type: \"int32\", data: [convInfo.strideHeight, convInfo.strideWidth] },\n    {\n      type: \"int32\",\n      data: [\n        convInfo.batchSize,\n        convInfo.outHeight,\n        convInfo.outWidth,\n        convInfo.outChannels\n      ]\n    }\n  ];\n  let program;\n  if (env().getBool(\"WEBGPU_USE_NAIVE_CONV2D_TRANSPOSE\")) {\n    program = new Conv2DDerInputProgram2(convInfo);\n  } else {\n    program = new Conv2DDerInputMMProgram(convInfo);\n    const dimAOuter = convInfo.inShape[1] * convInfo.inShape[2];\n    const dimBOuter = convInfo.inShape[3];\n    const dimInner = convInfo.filterHeight * convInfo.filterWidth * convInfo.outChannels;\n    dimensions.push({ type: \"uint32\", data: [dimAOuter] }, { type: \"uint32\", data: [dimBOuter] }, { type: \"uint32\", data: [dimInner] });\n  }\n  return backend2.runWebGPUProgram(program, [dy, filter], \"float32\", dimensions);\n}\nvar conv2DBackpropInputConfig4 = {\n  kernelName: Conv2DBackpropInput,\n  backendName: \"webgpu\",\n  kernelFunc: conv2DBackpropInput5\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/Cos.js\nvar cos4 = unaryKernelFunc3({ opType: UnaryOpType.COS });\nvar cosConfig4 = {\n  kernelName: Cos,\n  backendName: \"webgpu\",\n  kernelFunc: cos4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/Cosh.js\nvar cosh4 = unaryKernelFunc3({ opType: UnaryOpType.COSH });\nvar coshConfig4 = {\n  kernelName: Cosh,\n  backendName: \"webgpu\",\n  kernelFunc: cosh4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/crop_and_resize_webgpu.js\nvar CropAndResizeProgram2 = class {\n  constructor(channnel, boxShape, cropSize, method) {\n    this.variableNames = [\"Image\", \"Boxes\", \"BoxInd\"];\n    this.uniforms = \"extrapolationValue : f32,\";\n    this.workGroupSize = [64, 1, 1];\n    this.size = true;\n    const [numBoxes] = boxShape;\n    this.outputShape = [numBoxes, cropSize[0], cropSize[1], channnel];\n    this.dispatchLayout = flatDispatchLayout(this.outputShape);\n    this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, this.workGroupSize);\n    this.methodId = method === \"bilinear\" ? 1 : 0;\n    this.cropHeightBiggerThan1 = this.outputShape[1] > 1;\n    this.cropWidthBiggerThan1 = this.outputShape[2] > 1;\n    this.shaderKey = `cropAndResize_${this.methodId}_${this.cropHeightBiggerThan1}_${this.cropWidthBiggerThan1}`;\n  }\n  getUserCode() {\n    const [inputHeightFloat, inputWidthFloat] = [`f32(uniforms.imageShape[1] - 1)`, `f32(uniforms.imageShape[2] - 1)`];\n    const [heightRatio, heightScale, inY] = this.cropHeightBiggerThan1 ? [\n      `(${inputHeightFloat} / f32(uniforms.outShape[1] - 1))`,\n      \"(y2-y1) * height_ratio\",\n      `y1*${inputHeightFloat} + f32(y)*(height_scale)`\n    ] : [\n      \"0.0\",\n      \"0.0\",\n      `0.5 * (y1+y2) * ${inputHeightFloat}`\n    ];\n    const [widthRatio, widthScale, inX] = this.cropWidthBiggerThan1 ? [\n      `(${inputWidthFloat} / f32(uniforms.outShape[2] - 1))`,\n      \"(x2-x1) * width_ratio\",\n      `x1*${inputWidthFloat} + f32(x)*(width_scale)`\n    ] : [\n      \"0.0\",\n      \"0.0\",\n      `0.5 * (x1+x2) * ${inputWidthFloat}`\n    ];\n    const userCode = `\n    ${getMainHeaderString(\"index\")} {\n      if (index < uniforms.size) {\n        let coords = getCoordsFromIndex(index);\n        let height_ratio = f32(${heightRatio});\n        let width_ratio = f32(${widthRatio});\n        let b = coords[0];\n        let y = coords[1];\n        let x = coords[2];\n        let d = coords[3];\n        // get box vals\n        let y1 = getBoxes(b, 0);\n        let x1 = getBoxes(b, 1);\n        let y2 = getBoxes(b, 2);\n        let x2 = getBoxes(b, 3);\n        // get image in batch index\n        let bInd = i32(round(getBoxInd(b)));\n        if(bInd < 0 || bInd >= uniforms.outShape[0]) {\n          return;\n        }\n        let height_scale = ${heightScale};\n        let width_scale = ${widthScale};\n        let in_y = ${inY};\n        if( in_y < 0.0 || in_y > ${inputHeightFloat} ) {\n          setOutputAtIndex(index, uniforms.extrapolationValue);\n          return;\n        }\n        let in_x = ${inX};\n        if( in_x < 0.0 || in_x > ${inputWidthFloat} ) {\n          setOutputAtIndex(index, uniforms.extrapolationValue);\n          return;\n        }\n        let sourceFracIndexCR = vec2<f32>(in_x,in_y);\n        if(${this.methodId} == 1) {\n          // Compute the four integer indices.\n          let sourceFloorCR = vec2<i32>(sourceFracIndexCR);\n          let sourceCeilCR = vec2<i32>(ceil(sourceFracIndexCR));\n          let topLeft = getImage(bInd, sourceFloorCR.y, sourceFloorCR.x, d);\n          let bottomLeft = getImage(bInd, sourceCeilCR.y, sourceFloorCR.x, d);\n          let topRight = getImage(bInd, sourceFloorCR.y, sourceCeilCR.x, d);\n          let bottomRight = getImage(bInd, sourceCeilCR.y, sourceCeilCR.x, d);\n          let fracCR = sourceFracIndexCR - vec2<f32>(sourceFloorCR);\n          let top = topLeft + (topRight - topLeft) * fracCR.x;\n          let bottom = bottomLeft + (bottomRight - bottomLeft) * fracCR.x;\n          let newValue = top + (bottom - top) * fracCR.y;\n          setOutputAtIndex(index, newValue);\n        } else {\n          // Compute the coordinators of nearest neighbor point.\n          let sourceNearestCR = vec2<i32>(floor(\n            sourceFracIndexCR + vec2<f32>(0.5,0.5)));\n          let newValue = getImage(\n            bInd, sourceNearestCR.y, sourceNearestCR.x, d);\n          setOutputAtIndex(index, newValue);\n        }\n      }\n    }\n    `;\n    return userCode;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/CropAndResize.js\nvar cropAndResize5 = (args) => {\n  const { inputs, backend: backend2, attrs } = args;\n  const { image: image2, boxes, boxInd } = inputs;\n  const { cropSize, method, extrapolationValue } = attrs;\n  const program = new CropAndResizeProgram2(image2.shape[3], boxes.shape, cropSize, method);\n  const uniformData = [{ type: \"float32\", data: [extrapolationValue] }];\n  return backend2.runWebGPUProgram(program, [image2, boxes, boxInd], \"float32\", uniformData);\n};\nvar cropAndResizeConfig4 = {\n  kernelName: CropAndResize,\n  backendName: \"webgpu\",\n  kernelFunc: cropAndResize5\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/cum_webgpu.js\nvar CumOpType2;\n(function(CumOpType3) {\n  CumOpType3[\"Prod\"] = \"*\";\n  CumOpType3[\"Sum\"] = \"+\";\n})(CumOpType2 || (CumOpType2 = {}));\nvar CumProgram2 = class {\n  constructor(op2, shape, exclusive, reverse5) {\n    this.variableNames = [\"x\"];\n    this.uniforms = \"index : f32,\";\n    this.size = true;\n    const workGroupSizeX = 128;\n    this.workGroupSize = [workGroupSizeX, 1, 1];\n    this.outputShape = shape;\n    this.dispatchLayout = flatDispatchLayout(this.outputShape);\n    this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, this.workGroupSize);\n    this.exclusive = exclusive;\n    this.reverse = reverse5;\n    this.op = op2;\n    this.shaderKey = `cum_${this.op}_${this.exclusive}_${this.reverse}`;\n  }\n  getUserCode() {\n    const rank = this.outputShape.length;\n    const initVal = this.op === CumOpType2.Prod ? \"1.0\" : \"0.0\";\n    const val = this.exclusive ? initVal : `getX(${getCoords4(rank, \"coords\", this.op)})`;\n    const length = this.outputShape[this.outputShape.length - 1];\n    let condition = \"\";\n    let idxString = \"\";\n    if (this.exclusive) {\n      condition = this.reverse ? `end != ${length - 1}` : \"end != 0\";\n      idxString = this.reverse ? \"end + 1\" : \"end - 1\";\n    } else {\n      condition = this.reverse ? `end + pow2 < ${length}` : \"end >= pow2\";\n      idxString = this.reverse ? \"end + pow2\" : \"end - pow2\";\n    }\n    return `\n      ${getMainHeaderString(\"index\")} {\n       if (index < uniforms.size) {\n         var coords = getCoordsFromIndex(index);\n\n         let end = ${getFinalCoord2(rank, \"coords\", this.op)};\n         var val = ${val};\n         let pow2 = i32(pow(2.0, uniforms.index));\n         if (${condition}) {\n           let idx = ${idxString};\n           ${getFinalCoord2(rank, \"coords\", this.op)} = idx;\n           val ${this.op}= getX(${getCoords4(rank, \"coords\", this.op)});\n         }\n         setOutputAtIndex(index, val);\n       }\n      }\n    `;\n  }\n};\nfunction getCoords4(rank, name, op2) {\n  if (rank === 1) {\n    return `${name}`;\n  } else if (rank === 2) {\n    return `${name}.x, ${name}.y`;\n  } else if (rank === 3) {\n    return `${name}.x, ${name}.y, ${name}.z`;\n  } else if (rank === 4) {\n    return `${name}.x, ${name}.y, ${name}.z, ${name}.w`;\n  } else {\n    throw Error(`Cumulative ${op2} for rank ${rank} is not yet supported`);\n  }\n}\nfunction getFinalCoord2(rank, name, op2) {\n  if (rank === 1) {\n    return `${name}`;\n  } else if (rank === 2) {\n    return `${name}.y`;\n  } else if (rank === 3) {\n    return `${name}.z`;\n  } else if (rank === 4) {\n    return `${name}.w`;\n  } else {\n    throw Error(`Cumulative ${op2} for rank ${rank} is not yet supported`);\n  }\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/Cum_impl.js\nfunction cumImpl2(op2, x, backend2, axis, exclusive, reverse5) {\n  const xRank = x.shape.length;\n  const permutation = backend_util_exports.getAxesPermutation([axis], xRank);\n  let permutedX = x;\n  if (permutation != null) {\n    permutedX = transpose5({ inputs: { x }, backend: backend2, attrs: { perm: permutation } });\n  }\n  const permutedAxis = backend_util_exports.getInnerMostAxes(1, xRank)[0];\n  if (permutedAxis !== xRank - 1) {\n    throw new Error(`WebGPU cumprod shader expects an inner-most axis=${x.shape.length - 1} but got axis=${axis}`);\n  }\n  const size = permutedX.shape[permutedAxis];\n  let result = identity5({ inputs: { x: permutedX }, backend: backend2 });\n  for (let i = 0; i <= Math.ceil(Math.log2(size)) - 1; i++) {\n    const program = new CumProgram2(op2, permutedX.shape, false, reverse5);\n    const prevResult = result;\n    const uniformData = [{ type: \"float32\", data: [i] }];\n    result = backend2.runWebGPUProgram(program, [result], result.dtype, uniformData);\n    backend2.disposeData(prevResult.dataId);\n  }\n  if (exclusive) {\n    const program = new CumProgram2(op2, permutedX.shape, exclusive, reverse5);\n    const prevResult = result;\n    const uniformData = [{ type: \"float32\", data: [0] }];\n    result = backend2.runWebGPUProgram(program, [result], result.dtype, uniformData);\n    backend2.disposeData(prevResult.dataId);\n  }\n  if (permutation != null) {\n    const reversePermutation = backend_util_exports.getUndoAxesPermutation(permutation);\n    const reverseTransposedResult = transpose5({ inputs: { x: result }, backend: backend2, attrs: { perm: reversePermutation } });\n    backend2.disposeData(result.dataId);\n    backend2.disposeData(permutedX.dataId);\n    return reverseTransposedResult;\n  }\n  return result;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/Cumprod.js\nfunction cumprod5(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x } = inputs;\n  const { axis, exclusive, reverse: reverse5 } = attrs;\n  return cumImpl2(CumOpType2.Prod, x, backend2, axis, exclusive, reverse5);\n}\nvar cumprodConfig4 = {\n  kernelName: Cumprod,\n  backendName: \"webgpu\",\n  kernelFunc: cumprod5\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/Cumsum.js\nfunction cumsum5(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x } = inputs;\n  const { axis, exclusive, reverse: reverse5 } = attrs;\n  return cumImpl2(CumOpType2.Sum, x, backend2, axis, exclusive, reverse5);\n}\nvar cumsumConfig4 = {\n  kernelName: Cumsum,\n  backendName: \"webgpu\",\n  kernelFunc: cumsum5\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/depth_to_space_webgpu.js\nvar DepthToSpaceProgram2 = class {\n  constructor(outputShape, dataFormat) {\n    this.variableNames = [\"x\"];\n    this.workGroupSize = [64, 1, 1];\n    this.size = true;\n    this.uniforms = \"blockSize : i32,\";\n    this.outputShape = outputShape;\n    this.dispatchLayout = flatDispatchLayout(this.outputShape);\n    this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, this.workGroupSize);\n    this.shaderKey = `depthToSpace_${dataFormat}`;\n    this.dataFormat = dataFormat;\n  }\n  getUserCode() {\n    const userCode = `\n      ${getMainHeaderString(\"index\")} {\n        if (index < uniforms.size) {\n          let coords = getCoordsFromIndex(index);\n          let b = coords[0];\n          let h = ${this.getHeightCoordString()};\n          let w = ${this.getWidthCoordString()};\n          let d = ${this.getDepthCoordString()};\n\n          let in_h = h / uniforms.blockSize;\n          let offset_h = h % uniforms.blockSize;\n          let in_w = w / uniforms.blockSize;\n          let offset_w = w % uniforms.blockSize;\n          let offset_d = (offset_h * uniforms.blockSize + offset_w) *\n            ${this.getOutputDepthSize()};\n          let in_d = d + offset_d;\n\n          let rlt = ${this.getInputSamplingString()};\n          setOutputAtIndex(index, rlt);\n        }\n      }`;\n    return userCode;\n  }\n  getHeightCoordString() {\n    if (this.dataFormat === \"NHWC\") {\n      return `coords[1]`;\n    } else {\n      return `coords[2]`;\n    }\n  }\n  getWidthCoordString() {\n    if (this.dataFormat === \"NHWC\") {\n      return `coords[2]`;\n    } else {\n      return `coords[3]`;\n    }\n  }\n  getDepthCoordString() {\n    if (this.dataFormat === \"NHWC\") {\n      return `coords[3]`;\n    } else {\n      return `coords[1]`;\n    }\n  }\n  getOutputDepthSize() {\n    if (this.dataFormat === \"NHWC\") {\n      return `uniforms.outShape[3]`;\n    } else {\n      return `uniforms.outShape[1]`;\n    }\n  }\n  getInputSamplingString() {\n    if (this.dataFormat === \"NHWC\") {\n      return `getX(b, in_h, in_w, in_d)`;\n    } else {\n      return `getX(b, in_d, in_h, in_w)`;\n    }\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/DepthToSpace.js\nfunction depthToSpace5(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x } = inputs;\n  const { blockSize, dataFormat } = attrs;\n  const batchSize = x.shape[0];\n  const inputHeight = dataFormat === \"NHWC\" ? x.shape[1] : x.shape[2];\n  const inputWidth = dataFormat === \"NHWC\" ? x.shape[2] : x.shape[3];\n  const inputDepth = dataFormat === \"NHWC\" ? x.shape[3] : x.shape[1];\n  const outputHeight = inputHeight * blockSize;\n  const outputWidth = inputWidth * blockSize;\n  const outputDepth = inputDepth / (blockSize * blockSize);\n  const outputShape = dataFormat === \"NHWC\" ? [batchSize, outputHeight, outputWidth, outputDepth] : [batchSize, outputDepth, outputHeight, outputWidth];\n  const uniformData = [\n    { type: \"int32\", data: [blockSize] }\n  ];\n  const program = new DepthToSpaceProgram2(outputShape, dataFormat);\n  return backend2.runWebGPUProgram(program, [x], x.dtype, uniformData);\n}\nvar depthToSpaceConfig4 = {\n  kernelName: DepthToSpace,\n  backendName: \"webgpu\",\n  kernelFunc: depthToSpace5\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/depthwise_conv2d_nchw_shared_webgpu.js\nvar DepthwiseConv2DNCHWSharedProgram = class {\n  constructor(outputShape, filterHeight, filterWidth, addBias = false, activation2 = null, hasPreluActivation = false) {\n    this.variableNames = [\"x\", \"W\"];\n    this.uniforms = `pad : vec2<i32>, inDims : vec2<i32>,`;\n    this.workGroupSize = [16, 16, 1];\n    this.outputShape = outputShape;\n    this.dispatchLayout = { x: [3], y: [2], z: [0, 1] };\n    this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, this.workGroupSize);\n    if (addBias) {\n      this.variableNames.push(\"bias\");\n    }\n    if (hasPreluActivation) {\n      this.variableNames.push(\"preluActivationWeights\");\n    }\n    this.addBias = addBias;\n    this.activation = activation2;\n    this.hasPreluActivation = hasPreluActivation;\n    this.filterHeight = filterHeight;\n    this.filterWidth = filterWidth;\n    this.shaderKey = `depthwiseNCHW_${this.activation}_${this.filterHeight}_${this.filterWidth}`;\n  }\n  getUserCode() {\n    const filterSize = this.filterWidth * this.filterHeight;\n    const workGroupSize = this.workGroupSize[0] * this.workGroupSize[1] * this.workGroupSize[2];\n    const tileAHeight = this.workGroupSize[1] + this.filterHeight - 1;\n    const tileAWidth = this.workGroupSize[0] + this.filterWidth - 1;\n    const userCode = `\n      ${activationFnSnippet(this.activation, this.hasPreluActivation, false, 4)}\n\n      var<workgroup> mm_Asub : array<array<f32, ${tileAWidth}>, ${tileAHeight}>;\n      var<workgroup> mm_Bsub : array<array<f32, ${this.filterWidth}>, ${this.filterHeight}>;\n      fn readX(batch : i32, channel : i32, row : i32, col : i32) -> f32 {\n        var value = 0.0;\n        if (row >=0 && row < uniforms.inDims[0] && col >=0 && col < uniforms.inDims[1])\n        {\n          value = getX(batch, channel, row, col);\n        }\n        return value;\n      }\n\n      ${getWorkGroupSizeString()}\n      fn _start(@builtin(local_invocation_id) LocalId : vec3<u32>,\n                @builtin(global_invocation_id) GlobalId : vec3<u32>,\n                @builtin(local_invocation_index) LocalIndex: u32,\n                @builtin(num_workgroups) NumWorkgroups: vec3<u32>) {\n        localId = LocalId;\n        globalId = GlobalId;\n        let localIndex = i32(LocalIndex);\n        numWorkgroups = NumWorkgroups;\n        let coords = getOutputCoords();\n        let batch = coords[0];\n        let xRCCorner = vec2<i32>(coords.zw) - uniforms.pad;\n        let channelMul = uniforms.wShape[3];\n        let d1 = coords[1] / channelMul;\n        let q = coords[1] % channelMul;\n\n        let inputRowStart = xRCCorner.x;\n        let inputColStart = xRCCorner.y;\n\n        let localRow = i32(localId.y);\n        let localCol = i32(localId.x);\n\n        // Load one tile of X into local memory.\n        for (var inputRow = localRow; inputRow < ${tileAHeight}; inputRow = inputRow + ${this.workGroupSize[1]}) {\n          for (var inputCol = localCol; inputCol < ${tileAWidth}; inputCol = inputCol + ${this.workGroupSize[0]}) {\n            let rowOffset = inputRow - localRow;\n            let colOffset = inputCol - localCol;\n            mm_Asub[inputRow][inputCol] = readX(batch, d1, inputRowStart + rowOffset, inputColStart + colOffset);\n          }\n        }\n\n        // Load one tile of W into local memory.\n        var wIndex = localIndex;\n        ${filterSize < workGroupSize ? `if (wIndex < ${filterSize})` : `for(; wIndex < ${filterSize}; wIndex = wIndex + ${workGroupSize})`}\n\n        {\n          let wRow = wIndex / ${this.filterWidth};\n          let wCol = wIndex % ${this.filterWidth};\n          mm_Bsub[wRow][wCol] = getW(wRow, wCol, d1, q);\n        }\n\n        workgroupBarrier();\n\n        var value = 0.0;\n        for (var wR = 0; wR < ${this.filterHeight}; wR = wR + 1) {\n          for (var wC = 0; wC < ${this.filterWidth}; wC = wC + 1) {\n            let xVal = mm_Asub[localRow + wR][localCol + wC];\n            let wVal = mm_Bsub[wR][wC];\n            value = fma(xVal, wVal, value);\n          }\n        }\n        ${biasActivationSnippet(this.addBias, this.activation)}\n        if (coordsInBounds4D(coords, uniforms.outShape)) {\n          setOutputAtCoords(coords[0], coords[1], coords[2], coords[3], value);\n        }\n      }\n    `;\n    return userCode;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/depthwise_conv2d_vec4_webgpu.js\nvar DepthwiseConv2DVec4Program = class {\n  constructor(convInfo, addBias = false, activation2 = null, hasPreluActivation = false) {\n    this.variableNames = [\"x\", \"W\"];\n    this.uniforms = \"pad : vec2<i32>, inDims : vec2<i32>,\";\n    this.workGroupSize = [4, 4, 4];\n    this.isVec4 = true;\n    this.outputShape = convInfo.outShape;\n    this.dispatchLayout = { x: [3], y: [2], z: [0, 1] };\n    this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, this.workGroupSize, [4, 4, 1]);\n    util_exports.assert(convInfo.dataFormat === \"channelsLast\", () => \"TODO: NCHW is unimplemented\");\n    if (addBias) {\n      this.variableNames.push(\"bias\");\n    }\n    if (hasPreluActivation) {\n      this.variableNames.push(\"preluActivationWeights\");\n    }\n    this.convInfo = convInfo;\n    this.addBias = addBias;\n    this.activation = activation2;\n    this.hasPreluActivation = hasPreluActivation;\n    this.shaderKey = `depthwiseVec4_${activation2}_${this.convInfo.filterHeight}_${this.convInfo.filterWidth}`;\n  }\n  getUserCode() {\n    const xNumber = 4 + this.convInfo.filterWidth - 1;\n    const userCode = `\n      ${activationFnSnippet(this.activation, this.hasPreluActivation, true, 4)}\n      fn readX(batch : i32, row : i32, col : i32, channel : i32) -> vec4<f32> {\n        var value = vec4<f32>(0.0);\n        if (row >=0 && row < uniforms.inDims[0] && col >=0 && col < uniforms.inDims[1])\n        {\n          value = getX(batch, row, col, channel);\n        }\n        return value;\n      }\n      ${getWorkGroupSizeString()}\n      fn _start(@builtin(global_invocation_id) globalId: vec3<u32>) {\n        let batch = i32(globalId.z) / uniforms.outShape[1];\n        let r = i32(globalId.z) % uniforms.outShape[1];\n        let c = i32(globalId.y) * 4;\n        let d1 = i32(globalId.x) * 4;\n        let xRCCorner = vec2<i32>(r, c) - uniforms.pad;\n\n        let xRCorner = xRCCorner.x;\n        let xCCorner = xRCCorner.y;\n        var xVals : array<vec4<f32>, ${xNumber}>;\n        var dotProd : array<vec4<f32>, 4>;\n        dotProd[0] = vec4<f32>(0.0);\n        dotProd[1] = vec4<f32>(0.0);\n        dotProd[2] = vec4<f32>(0.0);\n        dotProd[3] = vec4<f32>(0.0);\n\n        // Use constant instead of uniform can give better performance.\n        for (var wR = 0; wR < ${this.convInfo.filterHeight}; wR = wR + 1) {\n          let xR = xRCorner + wR;\n          for (var i = 0; i < ${xNumber}; i++)\n          {\n            xVals[i] = readX(batch, xR, xCCorner + i, d1);\n          }\n          for (var wC = 0; wC < ${this.convInfo.filterWidth}; wC = wC + 1) {\n            let wValue = getW(wR, wC, d1, 0);\n            dotProd[0] = dotProd[0] + xVals[0 + wC] * wValue;\n            dotProd[1] = dotProd[1] + xVals[1 + wC] * wValue;\n            dotProd[2] = dotProd[2] + xVals[2 + wC] * wValue;\n            dotProd[3] = dotProd[3] + xVals[3 + wC] * wValue;\n          }\n        }\n\n        for (var i = 0; i < 4; i = i + 1) {\n          let coords = vec4<i32>(batch, r, c + i, d1);\n          if (coordsInBounds4D(coords, uniforms.outShape)) {\n            var value = dotProd[i];\n            ${biasActivationSnippet(this.addBias, this.activation)}\n            setOutputAtCoords(coords[0], coords[1], coords[2], coords[3], value);\n          }\n        }\n      }\n    `;\n    return userCode;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/depthwise_conv2d_webgpu.js\nvar DepthwiseConv2DProgram2 = class {\n  constructor(convInfo, addBias = false, activation2 = null, hasPreluActivation = false) {\n    this.variableNames = [\"x\", \"W\"];\n    this.uniforms = `pad : vec2<i32>, inDims : vec2<i32>, filterHeight : i32,\n      filterWidth : i32, stride : vec2<i32>, dilation : vec2<i32>,`;\n    this.workGroupSize = [256, 1, 1];\n    this.outputShape = convInfo.outShape;\n    this.dispatchLayout = flatDispatchLayout(this.outputShape);\n    this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, this.workGroupSize);\n    this.isChannelsLast = convInfo.dataFormat === \"channelsLast\";\n    if (addBias) {\n      this.variableNames.push(\"bias\");\n    }\n    if (hasPreluActivation) {\n      this.variableNames.push(\"preluActivationWeights\");\n    }\n    this.convInfo = convInfo;\n    this.addBias = addBias;\n    this.activation = activation2;\n    this.hasPreluActivation = hasPreluActivation;\n    this.shaderKey = `depthwise_${this.activation}_${this.isChannelsLast}`;\n  }\n  getUserCode() {\n    const getXSnippet = this.isChannelsLast ? \"getX(batch, xR, xC, d1);\" : \"getX(batch, d1, xR, xC);\";\n    const userCode = `\n      ${activationFnSnippet(this.activation, this.hasPreluActivation, false, 4)}\n\n      ${getMainHeaderString()} {\n        let coords = getOutputCoords();\n        let batch = coords[0];\n        let xRCCorner = vec2<i32>(coords.${this.isChannelsLast ? \"yz\" : \"zw\"}) * uniforms.stride - uniforms.pad;\n        let d2 = coords[${this.isChannelsLast ? 3 : 1}];\n        let channelMul = uniforms.wShape[3];\n        let d1 = d2 / channelMul;\n        let q = d2 % channelMul;\n\n        let inputRowStart = xRCCorner.x;\n        let inputColStart = xRCCorner.y;\n        let inputRowEnd = inputRowStart + uniforms.filterHeight *\n            uniforms.dilation[0];\n        let inputColEnd = inputColStart + uniforms.filterWidth *\n            uniforms.dilation[1];\n\n        // Convolve x(?, ?, d1)|x(d1, ?, ?) with w(:, :, d1, q) to get\n        // y(yR, yC, d2)|y(d2, yR, yC). ? = to be determined. : = across all\n        // values in that axis. x(?, ?, d1) and y(yR, yC, d2) is for NHWC.\n        // x(d1, ?, ?) and y(d2, yR, yC) is for NCHW.\n        var value = 0.0;\n\n        // Extract if checking out of for loop for performance.\n        if (inputRowStart >= 0 && inputColStart >= 0 &&\n          inputRowEnd < uniforms.inDims[0] &&\n              inputColEnd < uniforms.inDims[1]) {\n            for (var wR = 0; wR < uniforms.filterHeight; wR = wR + 1) {\n              let xR = inputRowStart + wR * uniforms.dilation[0];\n\n              for (var wC = 0; wC < uniforms.filterWidth; wC = wC + 1) {\n                let xC = inputColStart + wC * uniforms.dilation[1];\n\n                let xVal = ${getXSnippet};\n                let wVal = getW(wR, wC, d1, q);\n                value = value + xVal * wVal;\n              }\n            }\n          } else {\n            for (var wR = 0; wR < uniforms.filterHeight; wR = wR + 1) {\n              let xR = inputRowStart + wR * uniforms.dilation[0];\n\n              if (xR < 0 || xR >= uniforms.inDims[0]) {\n                continue;\n              }\n\n              for (var wC = 0; wC < uniforms.filterWidth; wC = wC + 1) {\n                let xC = inputColStart + wC * uniforms.dilation[1];\n\n                if (xC < 0 || xC >= uniforms.inDims[1]) {\n                  continue;\n                }\n\n                let xVal = ${getXSnippet};\n                let wVal = getW(wR, wC, d1, q);\n                value = value + xVal * wVal;\n              }\n            }\n          }\n          ${biasActivationSnippet(this.addBias, this.activation)}\n        if (coordsInBounds4D(coords, uniforms.outShape)) {\n          setOutputAtCoords(coords[0], coords[1], coords[2], coords[3], value);\n        }\n      }\n    `;\n    return userCode;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/DepthwiseConv2dNative.js\nfunction depthwiseConv2dNative3(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x, filter } = inputs;\n  const { strides, pad: pad3, dataFormat, dilations, dimRoundingMode } = attrs;\n  const $dataFormat = backend_util_exports.convertConv2DDataFormat(dataFormat);\n  let $dilations = dilations;\n  if ($dilations == null) {\n    $dilations = [1, 1];\n  }\n  const convInfo = backend_util_exports.computeConv2DInfo(x.shape, filter.shape, strides, $dilations, pad3, dimRoundingMode, true, $dataFormat);\n  const dimensions = [\n    { type: \"int32\", data: [convInfo.padInfo.top, convInfo.padInfo.left] },\n    { type: \"int32\", data: [convInfo.inHeight, convInfo.inWidth] }\n  ];\n  const isChannelsLast = convInfo.dataFormat === \"channelsLast\";\n  let program;\n  if (!isChannelsLast && convInfo.inHeight > 16 && convInfo.inWidth > 16 && convInfo.strideHeight === 1 && convInfo.strideWidth === 1 && convInfo.dilationWidth === 1 && convInfo.dilationHeight === 1 && convInfo.inChannels === convInfo.outChannels) {\n    program = new DepthwiseConv2DNCHWSharedProgram(convInfo.outShape, convInfo.filterHeight, convInfo.filterWidth);\n  } else if (isChannelsLast && convInfo.inHeight > 4 && convInfo.inWidth > 4 && convInfo.strideHeight === 1 && convInfo.strideWidth === 1 && convInfo.inChannels === convInfo.outChannels && convInfo.dilationHeight === 1 && convInfo.dilationWidth === 1 && convInfo.inChannels % 4 === 0) {\n    program = new DepthwiseConv2DVec4Program(convInfo);\n  } else {\n    program = new DepthwiseConv2DProgram2(convInfo);\n    dimensions.push({ type: \"int32\", data: [convInfo.filterHeight] }, { type: \"int32\", data: [convInfo.filterWidth] }, { type: \"int32\", data: [convInfo.strideHeight, convInfo.strideWidth] }, {\n      type: \"int32\",\n      data: [convInfo.dilationHeight, convInfo.dilationWidth]\n    });\n  }\n  return backend2.runWebGPUProgram(program, [x, filter], x.dtype, dimensions);\n}\nvar depthwiseConv2dNativeConfig4 = {\n  kernelName: DepthwiseConv2dNative,\n  backendName: \"webgpu\",\n  kernelFunc: depthwiseConv2dNative3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/Multiply.js\nvar multiplyKernelFunc = binaryKernelFunc3({\n  opType: BinaryOpType.MUL,\n  cpuKernelImpl: multiplyImplCPU2,\n  supportsComplex: true\n});\nvar multiplyConfig4 = {\n  kernelName: Multiply,\n  backendName: \"webgpu\",\n  kernelFunc: multiplyKernelFunc\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/Sum.js\nfunction sum6(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x } = inputs;\n  const { axis, keepDims } = attrs;\n  return reduce2(x, axis, keepDims, \"sum\", backend2);\n}\nvar sumConfig4 = {\n  kernelName: Sum,\n  backendName: \"webgpu\",\n  kernelFunc: sum6\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/Einsum.js\nfunction einsum4(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { equation } = attrs;\n  const tensors = inputs;\n  const { allDims, summedDims, idDims } = backend_util_exports.decodeEinsumEquation(equation, tensors.length);\n  backend_util_exports.checkEinsumDimSizes(allDims.length, idDims, tensors);\n  const { path, steps } = backend_util_exports.getEinsumComputePath(summedDims, idDims);\n  const nSteps = steps.length;\n  let out = null;\n  let numDimsRemaining = allDims.length;\n  const tensorsToDispose = [];\n  for (let i = 0; i < nSteps; ++i) {\n    for (const idTerm of steps[i]) {\n      const { permutationIndices: perm, expandDims: dimsToExpand } = backend_util_exports.getEinsumPermutation(numDimsRemaining, idDims[idTerm]);\n      let x;\n      if (backend_util_exports.isIdentityPermutation(perm)) {\n        x = tensors[idTerm];\n      } else {\n        x = transpose5({ inputs: { x: tensors[idTerm] }, backend: backend2, attrs: { perm } });\n        tensorsToDispose.push(x);\n      }\n      const targetShape = x.shape.slice();\n      for (let k = 0; k < dimsToExpand.length; ++k) {\n        targetShape.splice(dimsToExpand[k], 0, 1);\n      }\n      if (!util_exports.arraysEqual(x.shape, targetShape)) {\n        x = reshape6({ inputs: { x }, backend: backend2, attrs: { shape: targetShape } });\n        tensorsToDispose.push(x);\n      }\n      if (out === null) {\n        out = x;\n      } else {\n        out = multiplyKernelFunc({ inputs: { a: x, b: out }, backend: backend2 });\n        tensorsToDispose.push(out);\n      }\n    }\n    if (i < nSteps - 1) {\n      if (path[i] >= 0) {\n        out = sum6({\n          inputs: { x: out },\n          backend: backend2,\n          attrs: {\n            axis: path[i] - (allDims.length - numDimsRemaining),\n            keepDims: false\n          }\n        });\n        tensorsToDispose.push(out);\n      }\n      numDimsRemaining--;\n    }\n  }\n  for (const tensorInfo of tensorsToDispose) {\n    if (tensorInfo === out) {\n      continue;\n    }\n    backend2.disposeData(tensorInfo.dataId);\n  }\n  return out;\n}\nvar einsumConfig3 = {\n  kernelName: Einsum,\n  backendName: \"webgpu\",\n  kernelFunc: einsum4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/Elu.js\nvar elu6 = unaryKernelFunc3({ opType: UnaryOpType.ELU });\nvar eluConfig4 = {\n  kernelName: Elu,\n  backendName: \"webgpu\",\n  kernelFunc: elu6\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/Equal.js\nvar equal4 = binaryKernelFunc3({ opType: BinaryOpType.EQUAL, dtype: \"bool\", cpuKernelImpl: equalImplCPU2 });\nvar equalConfig4 = {\n  kernelName: Equal,\n  backendName: \"webgpu\",\n  kernelFunc: equal4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/Exp.js\nvar exp4 = unaryKernelFunc3({\n  opType: UnaryOpType.EXP,\n  cpuKernelImpl: expImplCPU2,\n  dtype: \"float32\"\n});\nvar expConfig4 = {\n  kernelName: Exp,\n  backendName: \"webgpu\",\n  kernelFunc: exp4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/ExpandDims.js\nfunction expandDims6(args) {\n  const { inputs, attrs, backend: backend2 } = args;\n  const { dim } = attrs;\n  const { input: input2 } = inputs;\n  const inputRank = input2.shape.length;\n  const newShape = input2.shape.slice();\n  let $dim = dim;\n  if (dim < 0) {\n    util_exports.assert(-(inputRank + 1) <= dim, () => `Axis must be in the interval [${-(inputRank + 1)}, ${inputRank}]`);\n    $dim = inputRank + dim + 1;\n  }\n  newShape.splice($dim, 0, 1);\n  return reshape6({ inputs: { x: input2 }, backend: backend2, attrs: { shape: newShape } });\n}\nvar expandDimsConfig4 = {\n  kernelName: ExpandDims,\n  backendName: \"webgpu\",\n  kernelFunc: expandDims6\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/Expm1.js\nvar expm14 = unaryKernelFunc3({ opType: UnaryOpType.EXPM1, cpuKernelImpl: expm1ImplCPU2 });\nvar expm1Config3 = {\n  kernelName: Expm1,\n  backendName: \"webgpu\",\n  kernelFunc: expm14\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/flip_left_right_webgpu.js\nvar FlipLeftRightProgram2 = class {\n  constructor(imageShape) {\n    this.outputShape = [];\n    this.variableNames = [\"x\"];\n    this.workGroupSize = [64, 1, 1];\n    this.size = true;\n    this.outputShape = imageShape;\n    this.dispatchLayout = flatDispatchLayout(this.outputShape);\n    this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, this.workGroupSize);\n    this.shaderKey = \"flipLeftRight\";\n  }\n  getUserCode() {\n    const userCode = `\n      ${getMainHeaderString(\"index\")} {\n        if (index < uniforms.size) {\n          let coords = getCoordsFromIndex(index);\n          let coordX = uniforms.xShape[2] - coords[2] - 1;\n          let outputValue = getX(coords[0], coords[1], coordX, coords[3]);\n          setOutputAtIndex(index, outputValue);\n        }\n      }\n    `;\n    return userCode;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/FlipLeftRight.js\nvar flipLeftRightConfig4 = {\n  kernelName: FlipLeftRight,\n  backendName: \"webgpu\",\n  kernelFunc: ({ inputs, backend: backend2 }) => {\n    const { image: image2 } = inputs;\n    const webgpuBackend = backend2;\n    const program = new FlipLeftRightProgram2(image2.shape);\n    const output = webgpuBackend.runWebGPUProgram(program, [image2], image2.dtype);\n    return output;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/Floor.js\nvar floor4 = unaryKernelFunc3({ opType: UnaryOpType.FLOOR, cpuKernelImpl: floorImplCPU2 });\nvar floorConfig4 = {\n  kernelName: Floor,\n  backendName: \"webgpu\",\n  kernelFunc: floor4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/FloorDiv.js\nvar floorDiv4 = binaryKernelFunc3({ opType: BinaryOpType.INT_DIV, dtype: \"int32\" });\nvar floorDivConfig4 = {\n  kernelName: FloorDiv,\n  backendName: \"webgpu\",\n  kernelFunc: floorDiv4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/from_pixels_webgpu.js\nvar FromPixelsProgram2 = class {\n  constructor(outputShape, numChannels, importVideo = false) {\n    this.isFromPixels = true;\n    this.outputShape = [0];\n    this.variableNames = [];\n    this.workGroupSize = [256, 1, 1];\n    this.outputShape = outputShape;\n    this.dispatchLayout = flatDispatchLayout(this.outputShape);\n    this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, this.workGroupSize, [numChannels, 1, 1]);\n    this.importVideo = importVideo;\n    this.shaderKey = `fromPixels_${this.importVideo}`;\n  }\n  getUserCode() {\n    const textureLoad = this.importVideo ? \"textureLoad(src, vec2<i32>(coords.yx));\" : \"textureLoad(src, vec2<i32>(coords.yx), 0)\";\n    const textureType = this.importVideo ? \"texture_external\" : \"texture_2d<f32>\";\n    return `\n      @binding(1) @group(0) var src: ${textureType};\n      ${getMainHeaderString(\"index\")} {\n        let flatIndex = index * uniforms.numChannels;\n        if (flatIndex < uniforms.size) {\n          let coords = getCoordsFromIndex(flatIndex);\n          let values = ${textureLoad};\n          for (var i = 0; i < uniforms.numChannels; i = i + 1) {\n            result[flatIndex + i] = i32(floor(255.0 * values[i]));\n          }\n        }\n      }\n  `;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/FromPixels.js\nvar fromPixelsConfig2 = {\n  kernelName: FromPixels,\n  backendName: \"webgpu\",\n  kernelFunc: fromPixels3\n};\nvar fromPixels2DContext3;\nvar willReadFrequently2 = env().getBool(\"CANVAS2D_WILL_READ_FREQUENTLY_FOR_GPU\");\nvar videoToTextureMap = /* @__PURE__ */ new Map();\nfunction fromPixels3(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  let { pixels } = inputs;\n  const { numChannels } = attrs;\n  if (pixels == null) {\n    throw new Error(\"pixels passed to tf.browser.fromPixels() can not be null\");\n  }\n  const isVideo = typeof HTMLVideoElement !== \"undefined\" && pixels instanceof HTMLVideoElement;\n  const isImage = typeof HTMLImageElement !== \"undefined\" && pixels instanceof HTMLImageElement;\n  const isCanvas = typeof HTMLCanvasElement !== \"undefined\" && pixels instanceof HTMLCanvasElement || typeof OffscreenCanvas !== \"undefined\" && pixels instanceof OffscreenCanvas;\n  const isImageBitmap = typeof ImageBitmap !== \"undefined\" && pixels instanceof ImageBitmap;\n  const [width, height] = isVideo ? [\n    pixels.videoWidth,\n    pixels.videoHeight\n  ] : [pixels.width, pixels.height];\n  const outputShape = [height, width, numChannels];\n  const importVideo = env().getBool(\"WEBGPU_IMPORT_EXTERNAL_TEXTURE\") && isVideo;\n  const isVideoOrImage = isVideo || isImage;\n  if (isImageBitmap || isCanvas || isVideoOrImage) {\n    let textureInfo;\n    if (importVideo) {\n      const videoElement = pixels;\n      if (!videoToTextureMap.has(videoElement) || videoToTextureMap.get(videoElement).expired) {\n        const externalTextureDescriptor = { source: videoElement };\n        videoToTextureMap.set(videoElement, backend2.device.importExternalTexture(externalTextureDescriptor));\n      }\n      textureInfo = {\n        width,\n        height,\n        format: null,\n        usage: null,\n        texture: videoToTextureMap.get(videoElement)\n      };\n    } else {\n      if (isVideoOrImage) {\n        const newWillReadFrequently = env().getBool(\"CANVAS2D_WILL_READ_FREQUENTLY_FOR_GPU\");\n        if (fromPixels2DContext3 == null || newWillReadFrequently !== willReadFrequently2) {\n          willReadFrequently2 = newWillReadFrequently;\n          fromPixels2DContext3 = document.createElement(\"canvas\").getContext(\"2d\", { willReadFrequently: willReadFrequently2 });\n        }\n        fromPixels2DContext3.canvas.width = width;\n        fromPixels2DContext3.canvas.height = height;\n        fromPixels2DContext3.drawImage(pixels, 0, 0, width, height);\n        pixels = fromPixels2DContext3.canvas;\n      }\n      const usage = GPUTextureUsage.COPY_DST | GPUTextureUsage.RENDER_ATTACHMENT | GPUTextureUsage.TEXTURE_BINDING;\n      const format = \"rgba8unorm\";\n      const texture = backend2.textureManager.acquireTexture(outputShape[1], outputShape[0], format, usage);\n      backend2.queue.copyExternalImageToTexture({ source: pixels }, { texture }, [outputShape[1], outputShape[0]]);\n      textureInfo = { width, height, format, usage, texture };\n    }\n    const size = util_exports.sizeFromShape(outputShape);\n    const strides = util_exports.computeStrides(outputShape);\n    const program = new FromPixelsProgram2(outputShape, numChannels, importVideo);\n    const uniformData = [\n      { type: \"uint32\", data: [size] },\n      { type: \"uint32\", data: [numChannels] },\n      { type: \"uint32\", data: [...strides] }\n    ];\n    const input2 = backend2.makeTensorInfo([height, width], \"int32\");\n    const info = backend2.tensorMap.get(input2.dataId);\n    info.resourceInfo = textureInfo;\n    const result = backend2.runWebGPUProgram(program, [input2], \"int32\", uniformData);\n    backend2.disposeData(input2.dataId);\n    return result;\n  }\n  const imageData = pixels.data;\n  let pixelArray = imageData;\n  if (numChannels != null && numChannels !== 4) {\n    pixelArray = new Uint8Array(pixels.width * pixels.height * numChannels);\n    const dataLength = imageData.length;\n    let j = 0;\n    for (let i = 0; i < dataLength; i++) {\n      if (i % 4 < numChannels) {\n        pixelArray[j++] = imageData[i];\n      }\n    }\n  }\n  const output = backend2.makeTensorInfo(outputShape, \"int32\", new Int32Array(pixelArray));\n  backend2.uploadToGPU(output.dataId);\n  return output;\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/batchnorm_webgpu.js\nvar BatchNormProgram2 = class {\n  constructor(xShape, meanShape, varianceShape, offsetShape, scaleShape) {\n    this.uniforms = \"varianceEpsilon : f32,\";\n    this.workGroupSize = [128, 1, 1];\n    this.size = true;\n    this.variableNames = [\"x\", \"mean\", \"variance\"];\n    backend_util_exports.assertAndGetBroadcastShape(xShape, meanShape);\n    backend_util_exports.assertAndGetBroadcastShape(xShape, varianceShape);\n    this.outputShape = xShape;\n    this.dispatchLayout = flatDispatchLayout(this.outputShape);\n    this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, this.workGroupSize);\n    if (offsetShape != null) {\n      backend_util_exports.assertAndGetBroadcastShape(xShape, offsetShape);\n      this.variableNames.push(\"offset\");\n    }\n    if (scaleShape != null) {\n      backend_util_exports.assertAndGetBroadcastShape(xShape, scaleShape);\n      this.variableNames.push(\"scale\");\n    }\n    this.offsetShape = offsetShape;\n    this.scaleShape = scaleShape;\n    this.shaderKey = \"batchNorm\";\n  }\n  getUserCode() {\n    let offsetSnippet = \"0.0\";\n    if (this.offsetShape != null) {\n      offsetSnippet = \"getOffsetByOutputIndex(index)\";\n    }\n    let scaleSnippet = \"1.0\";\n    if (this.scaleShape != null) {\n      scaleSnippet = \"getScaleByOutputIndex(index)\";\n    }\n    const userCode = `\n      ${getMainHeaderString(\"index\")} {\n        if (index < uniforms.size)\n        {\n          let xValue = getXByOutputIndex(index);\n          let meanValue = getMeanByOutputIndex(index);\n          let varianValue = getVarianceByOutputIndex(index);\n          let offsetValue = ${offsetSnippet};\n          let scaleValue = ${scaleSnippet};\n          let inv = scaleValue * inverseSqrt(varianValue + f32(uniforms.varianceEpsilon));\n          setOutputAtIndex(index,dot(vec3<f32>(xValue, -meanValue, offsetValue), vec3<f32>(inv, inv, 1.0)));\n        }\n      }\n  `;\n    return userCode;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/FusedBatchNorm.js\nvar fusedBatchNormConfig2 = {\n  kernelName: FusedBatchNorm,\n  backendName: \"webgpu\",\n  kernelFunc: ({ inputs, attrs, backend: backend2 }) => {\n    const { x, scale: scale2, offset, mean: mean5, variance } = inputs;\n    const { varianceEpsilon } = attrs;\n    const webGPUBackend = backend2;\n    const batchNormInputs = [x, mean5, variance];\n    let offsetShape = null;\n    if (offset != null) {\n      offsetShape = offset.shape;\n      batchNormInputs.push(offset);\n    }\n    let scaleShape = null;\n    if (scale2 != null) {\n      scaleShape = scale2.shape;\n      batchNormInputs.push(scale2);\n    }\n    const program = new BatchNormProgram2(x.shape, mean5.shape, variance.shape, offsetShape, scaleShape);\n    const uniformData = [{ type: \"float32\", data: [varianceEpsilon] }];\n    return webGPUBackend.runWebGPUProgram(program, batchNormInputs, x.dtype, uniformData);\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/FusedConv2D.js\nfunction fusedConv2d3(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x, filter, bias, preluActivationWeights } = inputs;\n  const { strides, pad: pad3, dataFormat, dilations, dimRoundingMode, activation: activation2, leakyreluAlpha } = attrs;\n  const $dataFormat = backend_util_exports.convertConv2DDataFormat(dataFormat);\n  const convInfo = backend_util_exports.computeConv2DInfo(x.shape, filter.shape, strides, dilations, pad3, dimRoundingMode, false, $dataFormat);\n  return conv2DImpl({\n    x,\n    filter,\n    convInfo,\n    backend: backend2,\n    bias,\n    preluActivationWeights,\n    leakyreluAlpha,\n    activation: activation2\n  });\n}\nvar fusedConv2DConfig4 = {\n  kernelName: FusedConv2D,\n  backendName: \"webgpu\",\n  kernelFunc: fusedConv2d3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/FusedDepthwiseConv2D.js\nfunction fusedDepthwiseConv2D3(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x, filter, bias, preluActivationWeights } = inputs;\n  const { strides, pad: pad3, dilations, dimRoundingMode, activation: activation2, leakyreluAlpha } = attrs;\n  let $dilations = dilations;\n  if ($dilations == null) {\n    $dilations = [1, 1];\n  }\n  util_exports.assert(backend_util_exports.eitherStridesOrDilationsAreOne(strides, $dilations), () => `Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${strides} and dilations '${$dilations}'`);\n  const convInfo = backend_util_exports.computeConv2DInfo(x.shape, filter.shape, strides, $dilations, pad3, dimRoundingMode, true);\n  const programInputs = [x, filter];\n  const hasBias = bias != null;\n  const hasPreluActivationWeights = preluActivationWeights != null;\n  if (hasBias) {\n    programInputs.push(bias);\n  }\n  if (hasPreluActivationWeights) {\n    programInputs.push(preluActivationWeights);\n  }\n  const dimensions = [\n    { type: \"int32\", data: [convInfo.padInfo.top, convInfo.padInfo.left] },\n    { type: \"int32\", data: [convInfo.inHeight, convInfo.inWidth] }\n  ];\n  let program;\n  if (convInfo.inHeight > 4 && convInfo.inWidth > 4 && convInfo.strideHeight === 1 && convInfo.strideWidth === 1 && convInfo.inChannels === convInfo.outChannels && convInfo.dilationHeight === 1 && convInfo.dilationWidth === 1 && convInfo.inChannels % 4 === 0) {\n    program = new DepthwiseConv2DVec4Program(convInfo, hasBias, activation2, hasPreluActivationWeights);\n  } else {\n    program = new DepthwiseConv2DProgram2(convInfo, hasBias, activation2, hasPreluActivationWeights);\n    dimensions.push({ type: \"int32\", data: [convInfo.filterHeight] }, { type: \"int32\", data: [convInfo.filterWidth] }, { type: \"int32\", data: [convInfo.strideHeight, convInfo.strideWidth] }, {\n      type: \"int32\",\n      data: [convInfo.dilationHeight, convInfo.dilationWidth]\n    });\n  }\n  if (activation2 === \"leakyrelu\") {\n    dimensions.push({ type: \"float32\", data: [leakyreluAlpha] });\n    program.uniforms += \" alpha : f32,\";\n  }\n  const result = backend2.runWebGPUProgram(program, programInputs, \"float32\", dimensions);\n  return result;\n}\nvar fusedDepthwiseConv2DConfig4 = {\n  kernelName: FusedDepthwiseConv2D,\n  backendName: \"webgpu\",\n  kernelFunc: fusedDepthwiseConv2D3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/gather_nd_webgpu.js\nvar GatherNDProgram2 = class {\n  constructor(sliceDim, shape) {\n    this.variableNames = [\"A\", \"indices\"];\n    this.workGroupSize = [64, 1, 1];\n    this.size = true;\n    this.outputShape = shape;\n    this.dispatchLayout = flatDispatchLayout(this.outputShape);\n    this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, this.workGroupSize);\n    this.shaderKey = `gathernd_${sliceDim}`;\n    this.sliceDim = sliceDim;\n    this.uniforms = `sliceDim : i32, strides : ${getCoordsDataType2(sliceDim)},`;\n  }\n  getUserCode() {\n    let strideString;\n    if (this.sliceDim > 1) {\n      strideString = \"uniforms.strides[j]\";\n    } else {\n      strideString = \"uniforms.strides\";\n    }\n    const userCode = `\n      ${getMainHeaderString(\"index\")} {\n        if (index < uniforms.size) {\n          let coords = getCoordsFromIndex(index);\n          var flattenIndex = 0;\n          for (var j = 0; j < uniforms.sliceDim; j = j + 1) {\n            let indexTemp = i32(round(getIndices(coords[0], j)));\n            let strideNum = ${strideString};\n            flattenIndex = flattenIndex + indexTemp * strideNum;\n          }\n\n          setOutputAtIndex(index, getA(flattenIndex, coords[1]));\n        }\n      }\n      `;\n    return userCode;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/GatherNd.js\nfunction gatherNd4(args) {\n  const { inputs, backend: backend2 } = args;\n  const { params, indices } = inputs;\n  const indicesShape = indices.shape;\n  const sliceRank = indicesShape[indicesShape.length - 1];\n  const paramsSize = util_exports.sizeFromShape(params.shape);\n  const [resultShape, numSlices, sliceSize, strides] = backend_util_exports.prepareAndValidate(params, indices);\n  const flattenIndices = reshape6({ inputs: { x: indices }, backend: backend2, attrs: { shape: [numSlices, sliceRank] } });\n  const flattenX = reshape6({\n    inputs: { x: params },\n    backend: backend2,\n    attrs: { shape: [util_exports.sizeFromShape(params.shape) / sliceSize, sliceSize] }\n  });\n  if (backend2.shouldExecuteOnCPU([params, indices]) || params.dtype === \"string\") {\n    const indicesData = backend2.readSync(indices.dataId);\n    const paramsBuf = backend2.bufferSync(params);\n    const outValue = gatherNdImplCPU2(indicesData, paramsBuf, params.dtype, numSlices, sliceRank, sliceSize, strides, params.shape, paramsSize);\n    return backend2.makeTensorInfo(resultShape, params.dtype, outValue.values);\n  }\n  const program = new GatherNDProgram2(sliceRank, [numSlices, sliceSize]);\n  const uniformData = [{ type: \"int32\", data: [sliceRank] }, { type: \"int32\", data: strides }];\n  const res = backend2.runWebGPUProgram(program, [flattenX, flattenIndices], flattenX.dtype, uniformData);\n  const reshaped = reshape6({ inputs: { x: res }, backend: backend2, attrs: { shape: resultShape } });\n  backend2.disposeData(flattenIndices.dataId);\n  backend2.disposeData(flattenX.dataId);\n  backend2.disposeData(res.dataId);\n  return reshaped;\n}\nvar gatherNdConfig4 = {\n  kernelName: GatherNd,\n  backendName: \"webgpu\",\n  kernelFunc: gatherNd4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/gather_webgpu.js\nvar GatherProgram2 = class {\n  constructor(aShape, outputShape) {\n    this.variableNames = [\"A\", \"indices\"];\n    this.workGroupSize = [64, 1, 1];\n    this.size = true;\n    this.outputShape = aShape.slice();\n    this.aShape = aShape;\n    this.outputShape = outputShape;\n    this.dispatchLayout = flatDispatchLayout(this.outputShape);\n    this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, this.workGroupSize);\n    this.shaderKey = `gather`;\n  }\n  getUserCode() {\n    const sourceCoords = getSourceCoords4(this.aShape);\n    const userCode = `\n      ${getMainHeaderString(\"index\")} {\n        if (index < uniforms.size) {\n          let resRC = getCoordsFromIndex(index);\n          let indexZ = i32(getIndices(resRC.x, resRC.z));\n          let inBounds = select(0.0, 1.0, indexZ >= 0 && indexZ < uniforms.aShape[2]);\n          setOutputAtIndex(index, inBounds * getA(${sourceCoords}));\n        }\n      }\n    `;\n    return userCode;\n  }\n};\nfunction getSourceCoords4(aShape) {\n  const currentCoords = [\"resRC.x\", \"resRC.y\", \"resRC.z\", \"resRC.w\"];\n  const sourceCoords = [];\n  for (let i = 0; i < aShape.length; i++) {\n    if (i === 2) {\n      sourceCoords.push(\"indexZ\");\n    } else {\n      sourceCoords.push(`${currentCoords[i]}`);\n    }\n  }\n  return sourceCoords.join();\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/GatherV2.js\nfunction gatherV24(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x, indices } = inputs;\n  const { axis, batchDims } = attrs;\n  const parsedAxis = util_exports.parseAxisParam(axis, x.shape)[0];\n  const shapeInfo = backend_util_exports.segment_util.collectGatherOpShapeInfo(x, indices, parsedAxis, batchDims);\n  const indicesSize = util_exports.sizeFromShape(indices.shape);\n  const toDispose = [];\n  const flattenX = reshape6({\n    inputs: { x },\n    backend: backend2,\n    attrs: {\n      shape: [\n        shapeInfo.batchSize,\n        shapeInfo.outerSize,\n        shapeInfo.dimSize,\n        shapeInfo.sliceSize\n      ]\n    }\n  });\n  const flattenIndex = reshape6({\n    inputs: { x: indices },\n    backend: backend2,\n    attrs: { shape: [shapeInfo.batchSize, indicesSize / shapeInfo.batchSize] }\n  });\n  toDispose.push(flattenX);\n  toDispose.push(flattenIndex);\n  const flattenOutputShape = [\n    shapeInfo.batchSize,\n    shapeInfo.outerSize,\n    indicesSize / shapeInfo.batchSize,\n    shapeInfo.sliceSize\n  ];\n  if (backend2.shouldExecuteOnCPU([x, indices])) {\n    const indicesBufferInfo = backend2.tensorMap.get(flattenIndex.dataId);\n    const indicesValues = indicesBufferInfo.values;\n    const indicesBuf = buffer(flattenIndex.shape, flattenIndex.dtype, indicesValues);\n    const xBufferInfo = backend2.tensorMap.get(flattenX.dataId);\n    const xValues = xBufferInfo.values;\n    const xBuf = buffer(flattenX.shape, flattenX.dtype, xValues);\n    const outBuf = gatherV2ImplCPU2(xBuf, indicesBuf, flattenOutputShape);\n    toDispose.forEach((t) => backend2.disposeData(t.dataId));\n    return backend2.makeTensorInfo(shapeInfo.outputShape, outBuf.dtype, outBuf.values);\n  }\n  const program = new GatherProgram2(flattenX.shape, flattenOutputShape);\n  const res = backend2.runWebGPUProgram(program, [flattenX, flattenIndex], flattenX.dtype);\n  toDispose.push(res);\n  const reshaped = reshape6({ inputs: { x: res }, backend: backend2, attrs: { shape: shapeInfo.outputShape } });\n  toDispose.forEach((t) => backend2.disposeData(t.dataId));\n  return reshaped;\n}\nvar gatherV2Config4 = {\n  kernelName: GatherV2,\n  backendName: \"webgpu\",\n  kernelFunc: gatherV24\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/Greater.js\nvar greater5 = binaryKernelFunc3({\n  opType: BinaryOpType.GREATER,\n  cpuKernelImpl: greaterImplCPU2,\n  dtype: \"bool\"\n});\nvar greaterConfig4 = {\n  kernelName: Greater,\n  backendName: \"webgpu\",\n  kernelFunc: greater5\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/GreaterEqual.js\nvar greaterEqual4 = binaryKernelFunc3({\n  opType: BinaryOpType.GREATER_EQUAL,\n  dtype: \"bool\",\n  cpuKernelImpl: greaterEqualImplCPU2\n});\nvar greaterEqualConfig4 = {\n  kernelName: GreaterEqual,\n  backendName: \"webgpu\",\n  kernelFunc: greaterEqual4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/IsNaN.js\nvar isNaN5 = unaryKernelFunc3({ opType: UnaryOpType.IS_NAN, dtype: \"bool\" });\nvar isNaNConfig3 = {\n  kernelName: IsNan,\n  backendName: \"webgpu\",\n  kernelFunc: isNaN5\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/LeakyRelu.js\nfunction leakyRelu5(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x } = inputs;\n  const { alpha } = attrs;\n  const uniformData = [{ type: \"float32\", data: [alpha] }];\n  const program = new UnaryOpProgram2(x.shape, UnaryOpType.LEAKYRELU);\n  program.uniforms = \"alpha : f32,\";\n  return backend2.runWebGPUProgram(program, [x], \"float32\", uniformData);\n}\nvar leakyReluConfig4 = {\n  kernelName: LeakyRelu,\n  backendName: \"webgpu\",\n  kernelFunc: leakyRelu5\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/Less.js\nvar less5 = binaryKernelFunc3({ opType: BinaryOpType.LESS, dtype: \"bool\", cpuKernelImpl: lessImplCPU2 });\nvar lessConfig4 = {\n  kernelName: Less,\n  backendName: \"webgpu\",\n  kernelFunc: less5\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/LessEqual.js\nvar lessEqual4 = binaryKernelFunc3({\n  opType: BinaryOpType.LESS_EQUAL,\n  dtype: \"bool\",\n  cpuKernelImpl: lessEqualImplCPU2\n});\nvar lessEqualConfig4 = {\n  kernelName: LessEqual,\n  backendName: \"webgpu\",\n  kernelFunc: lessEqual4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/Log.js\nvar log5 = unaryKernelFunc3({ opType: UnaryOpType.LOG, cpuKernelImpl: logImplCPU2 });\nvar logConfig4 = {\n  kernelName: Log,\n  backendName: \"webgpu\",\n  kernelFunc: log5\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/LogicalAnd.js\nvar logicalAnd4 = binaryKernelFunc3({ opType: BinaryOpType.LOGICAL_AND, dtype: \"bool\" });\nvar logicalAndConfig4 = {\n  kernelName: LogicalAnd,\n  backendName: \"webgpu\",\n  kernelFunc: logicalAnd4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/LogicalNot.js\nvar logicalNot4 = unaryKernelFunc3({ opType: UnaryOpType.LOGICAL_NOT });\nvar logicalNotConfig4 = {\n  kernelName: LogicalNot,\n  backendName: \"webgpu\",\n  kernelFunc: logicalNot4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/Maximum.js\nvar maximum5 = binaryKernelFunc3({\n  opType: BinaryOpType.MAX,\n  cpuKernelImpl: maximumImplCPU2\n});\nvar maximumConfig4 = {\n  kernelName: Maximum,\n  backendName: \"webgpu\",\n  kernelFunc: maximum5\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/MaxPool.js\nfunction maxPool5(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x } = inputs;\n  const { filterSize, strides, pad: pad3, dimRoundingMode } = attrs;\n  const dilations = 1;\n  const convInfo = backend_util_exports.computePool2DInfo(x.shape, filterSize, strides, dilations, pad3, dimRoundingMode);\n  return poolImpl(x, convInfo, \"max\", backend2);\n}\nvar maxPoolConfig4 = {\n  kernelName: MaxPool,\n  backendName: \"webgpu\",\n  kernelFunc: maxPool5\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/Min.js\nfunction min6(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x } = inputs;\n  const { axis, keepDims } = attrs;\n  return reduce2(x, axis, keepDims, \"min\", backend2);\n}\nvar minConfig4 = {\n  kernelName: Min,\n  backendName: \"webgpu\",\n  kernelFunc: min6\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/Minimum.js\nvar minimum5 = binaryKernelFunc3({\n  opType: BinaryOpType.MIN,\n  cpuKernelImpl: minimumImplCPU2\n});\nvar minimumConfig4 = {\n  kernelName: Minimum,\n  backendName: \"webgpu\",\n  kernelFunc: minimum5\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/mirror_pad_webgpu.js\nvar MirrorPadProgram2 = class {\n  constructor(xShape, paddings, mode) {\n    this.uniforms = \"\";\n    this.variableNames = [\"x\"];\n    this.workGroupSize = [64, 1, 1];\n    this.size = true;\n    this.outputShape = paddings.map((p2, i) => p2[0] + xShape[i] + p2[1]);\n    this.dispatchLayout = flatDispatchLayout(this.outputShape);\n    this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, this.workGroupSize);\n    this.xShape = xShape;\n    paddings.map((_, i) => {\n      this.uniforms += ` pad${i} : vec2<i32>,`;\n    });\n    this.offset = mode === \"reflect\" ? 0 : 1;\n    this.shaderKey = `mirrorPad_${mode}`;\n  }\n  getUserCode() {\n    const rank = this.xShape.length;\n    const start = this.xShape.map((_, i) => `uniforms.pad${i}[0]`).join(\",\");\n    const end = this.xShape.map((_, i) => `uniforms.pad${i}[0] + uniforms.xShape${rank > 1 ? `[${i}]` : \"\"}`).join(\",\");\n    const shaderStart = rank === 1 ? \"start\" : \"start[i]\";\n    const shaderEnd = rank === 1 ? \"end\" : \"end[i]\";\n    const shaderOutC = rank === 1 ? \"outC\" : \"outC[i]\";\n    const dtype = getCoordsDataType2(rank);\n    const unpackedCoords = rank > 1 ? [\"coords[0]\", \"coords[1]\", \"coords[2]\", \"coords[3]\"].slice(0, rank) : \"coords\";\n    return `\n      ${getMainHeaderString(\"index\")} {\n        if (index < uniforms.size) {\n          let start = ${dtype}(${start});\n          let end = ${dtype}(${end});\n          var outC = getCoordsFromIndex(index);\n          for (var i = 0; i < ${rank}; i = i + 1) {\n            if (${shaderOutC} < ${shaderStart}) {\n              ${shaderOutC} = ${shaderStart} * 2 - ${shaderOutC} - ${this.offset};\n            } else if(${shaderOutC} >= ${shaderEnd}) {\n              ${shaderOutC} = (${shaderEnd} - 1) * 2 - ${shaderOutC} + ${this.offset};\n            }\n          }\n          let coords = outC - start;\n          setOutputAtIndex(index, getX(${unpackedCoords}));\n        }\n      }\n    `;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/MirrorPad.js\nvar mirrorPadConfig4 = {\n  kernelName: MirrorPad,\n  backendName: \"webgpu\",\n  kernelFunc: ({ inputs, attrs, backend: backend2 }) => {\n    const { x } = inputs;\n    const { paddings, mode } = attrs;\n    const webGPUBackend = backend2;\n    const uniformData = paddings.map((p2) => {\n      return { type: \"int32\", data: [p2[0], p2[1]] };\n    });\n    const program = new MirrorPadProgram2(x.shape, paddings, mode);\n    const output = webGPUBackend.runWebGPUProgram(program, [x], x.dtype, uniformData);\n    return output;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/Neg.js\nfunction neg4(args) {\n  const { inputs, backend: backend2 } = args;\n  const { x } = inputs;\n  if (backend2.shouldExecuteOnCPU([x])) {\n    const xData = backend2.tensorMap.get(x.dataId);\n    const [outValues, newShape] = negImplCPU2(xData.values, x.shape, x.dtype);\n    return backend2.makeTensorInfo(newShape, x.dtype, outValues);\n  }\n  const program = new UnaryOpProgram2(x.shape, UnaryOpType.NEG);\n  return backend2.runWebGPUProgram(program, [x], x.dtype);\n}\nvar negConfig4 = {\n  kernelName: Neg,\n  backendName: \"webgpu\",\n  kernelFunc: neg4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/NonMaxSuppressionV3.js\nfunction nonMaxSuppressionV33(args) {\n  console.warn(\"tf.nonMaxSuppression() in webgpu locks the UI thread. Call tf.nonMaxSuppressionAsync() instead\");\n  const { inputs, backend: backend2, attrs } = args;\n  const { boxes, scores } = inputs;\n  const { maxOutputSize, iouThreshold, scoreThreshold } = attrs;\n  const boxesVals = backend2.readSync(boxes.dataId);\n  const scoresVals = backend2.readSync(scores.dataId);\n  const { selectedIndices } = kernel_impls_exports.nonMaxSuppressionV3Impl(boxesVals, scoresVals, maxOutputSize, iouThreshold, scoreThreshold);\n  return backend2.makeTensorInfo([selectedIndices.length], \"int32\", new Int32Array(selectedIndices));\n}\nvar nonMaxSuppressionV3Config4 = {\n  kernelName: NonMaxSuppressionV3,\n  backendName: \"webgpu\",\n  kernelFunc: nonMaxSuppressionV33\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/NonMaxSuppressionV5.js\nfunction nonMaxSuppressionV53(args) {\n  console.warn(\"tf.nonMaxSuppression() in webgpu locks the UI thread. Call tf.nonMaxSuppressionAsync() instead\");\n  const { inputs, backend: backend2, attrs } = args;\n  const { boxes, scores } = inputs;\n  const { maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma } = attrs;\n  const boxesVals = backend2.readSync(boxes.dataId);\n  const scoresVals = backend2.readSync(scores.dataId);\n  const maxOutputSizeVal = maxOutputSize;\n  const iouThresholdVal = iouThreshold;\n  const scoreThresholdVal = scoreThreshold;\n  const softNmsSigmaVal = softNmsSigma;\n  const { selectedIndices, selectedScores } = kernel_impls_exports.nonMaxSuppressionV5Impl(boxesVals, scoresVals, maxOutputSizeVal, iouThresholdVal, scoreThresholdVal, softNmsSigmaVal);\n  return [\n    backend2.makeTensorInfo([selectedIndices.length], \"int32\", new Int32Array(selectedIndices)),\n    backend2.makeTensorInfo([selectedScores.length], \"float32\", new Float32Array(selectedScores))\n  ];\n}\nvar nonMaxSuppressionV5Config4 = {\n  kernelName: NonMaxSuppressionV5,\n  backendName: \"webgpu\",\n  kernelFunc: nonMaxSuppressionV53\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/ZerosLike.js\nfunction zerosLike5(args) {\n  const { inputs, backend: backend2 } = args;\n  const { x } = inputs;\n  if (x.dtype === \"complex64\") {\n    const realPart = real4({ inputs: { input: x }, backend: backend2 });\n    const r = zerosLike5({ inputs: { x: realPart }, backend: backend2 });\n    const imagPart = imag4({ inputs: { input: x }, backend: backend2 });\n    const i = zerosLike5({ inputs: { x: imagPart }, backend: backend2 });\n    const result = complex4({ inputs: { real: r, imag: i }, backend: backend2 });\n    backend2.disposeData(realPart.dataId);\n    backend2.disposeData(r.dataId);\n    backend2.disposeData(imagPart.dataId);\n    backend2.disposeData(i.dataId);\n    return result;\n  } else {\n    return fill5({\n      attrs: {\n        shape: x.shape,\n        dtype: x.dtype,\n        value: x.dtype === \"string\" ? \"\" : 0\n      },\n      backend: backend2\n    });\n  }\n}\nvar zerosLikeConfig4 = {\n  kernelName: ZerosLike,\n  backendName: \"webgpu\",\n  kernelFunc: zerosLike5\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/OnesLike.js\nfunction onesLike5(args) {\n  const { inputs, backend: backend2 } = args;\n  const { x } = inputs;\n  if (x.dtype === \"string\") {\n    throw new Error(\"onesLike is not supported under string dtype\");\n  } else if (x.dtype === \"complex64\") {\n    const realPart = real4({ inputs: { input: x }, backend: backend2 });\n    const r = onesLike5({ inputs: { x: realPart }, backend: backend2 });\n    const imagPart = imag4({ inputs: { input: x }, backend: backend2 });\n    const i = zerosLike5({ inputs: { x: imagPart }, backend: backend2 });\n    const result = complex4({ inputs: { real: r, imag: i }, backend: backend2 });\n    backend2.disposeData(realPart.dataId);\n    backend2.disposeData(r.dataId);\n    backend2.disposeData(imagPart.dataId);\n    backend2.disposeData(i.dataId);\n    return result;\n  } else {\n    return fill5({ attrs: { shape: x.shape, dtype: x.dtype, value: 1 }, backend: backend2 });\n  }\n}\nvar onesLikeConfig4 = {\n  kernelName: OnesLike,\n  backendName: \"webgpu\",\n  kernelFunc: onesLike5\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/Pack.js\nfunction pack4(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { axis } = attrs;\n  if (inputs.length === 1) {\n    return expandDims6({ inputs: { input: inputs[0] }, backend: backend2, attrs: { dim: axis } });\n  }\n  const shape = inputs[0].shape;\n  const dtype = inputs[0].dtype;\n  inputs.forEach((t) => {\n    util_exports.assertShapesMatch(shape, t.shape, \"All tensors passed to stack must have matching shapes\");\n    util_exports.assert(dtype === t.dtype, () => \"All tensors passed to stack must have matching dtypes\");\n  });\n  const intermediateTensorInfos = [];\n  const expandedTensors = inputs.map((t) => {\n    const expandedT = expandDims6({ inputs: { input: t }, backend: backend2, attrs: { dim: axis } });\n    intermediateTensorInfos.push(expandedT);\n    return expandedT;\n  });\n  const result = concat5({ inputs: expandedTensors, backend: backend2, attrs: { axis } });\n  intermediateTensorInfos.forEach((t) => backend2.disposeData(t.dataId));\n  return result;\n}\nvar packConfig4 = {\n  kernelName: Pack,\n  backendName: \"webgpu\",\n  kernelFunc: pack4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/pad_webgpu.js\nvar PadProgram2 = class {\n  constructor(xShape, paddings) {\n    this.variableNames = [\"x\"];\n    this.uniforms = \"constantValue : f32,\";\n    this.workGroupSize = [64, 1, 1];\n    this.size = true;\n    this.outputShape = paddings.map((p2, i) => p2[0] + xShape[i] + p2[1]);\n    this.dispatchLayout = flatDispatchLayout(this.outputShape);\n    this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, this.workGroupSize);\n    paddings.map((_, i) => {\n      this.uniforms += ` pad${i} : vec2<i32>,`;\n    });\n    this.xShape = xShape;\n    this.shaderKey = \"pad\";\n  }\n  getUserCode() {\n    const rank = this.xShape.length;\n    const type = getCoordsDataType2(rank);\n    const start = this.xShape.map((_, i) => `uniforms.pad${i}[0]`).join(\",\");\n    const end = this.xShape.map((_, i) => `uniforms.pad${i}[0] + uniforms.xShape${rank > 1 ? `[${i}]` : \"\"}`).join(\",\");\n    const startValue = rank > 1 ? `${type}(${start})` : `${start}`;\n    const endValue = rank > 1 ? `${type}(${end})` : `${end}`;\n    const leftPadCondition = rank > 1 ? `any(outC < start)` : `outC < start`;\n    const rightPadCondition = rank > 1 ? `any(outC >= end)` : `outC >= end`;\n    const unpackedCoords = rank > 1 ? [\"coords[0]\", \"coords[1]\", \"coords[2]\", \"coords[3]\"].slice(0, rank) : \"coords\";\n    const userCode = `\n      ${getMainHeaderString(\"index\")} {\n        if (index < uniforms.size) {\n          let start = ${startValue};\n          let end = ${endValue};\n          let outC = getCoordsFromIndex(index);\n\n          if (${leftPadCondition} || ${rightPadCondition}) {\n            setOutputAtIndex(index, uniforms.constantValue);\n          } else {\n            let coords = outC - start;\n            setOutputAtIndex(index, getX(${unpackedCoords}));\n          }\n        }\n      }\n    `;\n    return userCode;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/PadV2.js\nvar padV23 = (args) => {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x } = inputs;\n  const { paddings, constantValue } = attrs;\n  if (paddings.every((p2) => util_exports.arraysEqual(p2, [0, 0]))) {\n    return identity5({ inputs: { x }, backend: backend2 });\n  }\n  if (util_exports.sizeFromShape(x.shape) === 0) {\n    const outputShape = paddings.map((p2, i) => p2[0] + x.shape[i] + p2[1]);\n    return fill5({\n      backend: backend2,\n      attrs: { shape: outputShape, value: constantValue, dtype: x.dtype }\n    });\n  }\n  const uniformData = [{ type: \"float32\", data: [constantValue] }];\n  paddings.map((p2) => uniformData.push({ type: \"int32\", data: [p2[0], p2[1]] }));\n  const program = new PadProgram2(x.shape, paddings);\n  return backend2.runWebGPUProgram(program, [x], x.dtype, uniformData);\n};\nvar padV2Config4 = {\n  kernelName: PadV2,\n  backendName: \"webgpu\",\n  kernelFunc: padV23\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/Pow.js\nvar pow4 = binaryKernelFunc3({\n  opType: BinaryOpType.POW\n});\nvar powConfig4 = {\n  kernelName: Pow,\n  backendName: \"webgpu\",\n  kernelFunc: pow4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/Prelu.js\nfunction prelu6(args) {\n  const { inputs, backend: backend2 } = args;\n  const { x, alpha } = inputs;\n  const program = new BinaryOpProgram2(BinaryOpType.PRELU, x.shape, alpha.shape);\n  return backend2.runWebGPUProgram(program, [x, alpha], \"float32\");\n}\nvar preluConfig4 = {\n  kernelName: Prelu,\n  backendName: \"webgpu\",\n  kernelFunc: prelu6\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/Prod.js\nfunction prod5(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x } = inputs;\n  const { axis, keepDims } = attrs;\n  return reduce2(x, axis, keepDims, \"prod\", backend2);\n}\nvar prodConfig4 = {\n  kernelName: Prod,\n  backendName: \"webgpu\",\n  kernelFunc: prod5\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/Range.js\nvar range6 = (args) => {\n  const { backend: backend2, attrs } = args;\n  const { start, stop, step: step5, dtype } = attrs;\n  const values = rangeImplCPU2(start, stop, step5, dtype);\n  return backend2.makeTensorInfo([values.length], dtype, values);\n};\nvar rangeConfig4 = {\n  kernelName: Range,\n  backendName: \"webgpu\",\n  kernelFunc: range6\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/RealDiv.js\nvar realDiv2 = binaryKernelFunc3({ opType: BinaryOpType.DIV });\nvar realDivConfig4 = {\n  kernelName: RealDiv,\n  backendName: \"webgpu\",\n  kernelFunc: realDiv2\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/Reciprocal.js\nvar reciprocal4 = unaryKernelFunc3({ opType: UnaryOpType.RECIPROCAL });\nvar reciprocalConfig3 = {\n  kernelName: Reciprocal,\n  backendName: \"webgpu\",\n  kernelFunc: reciprocal4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/Relu.js\nvar relu4 = unaryKernelFunc3({ opType: UnaryOpType.RELU });\nvar reluConfig4 = {\n  kernelName: Relu,\n  backendName: \"webgpu\",\n  kernelFunc: relu4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/Relu6.js\nvar relu64 = unaryKernelFunc3({ opType: UnaryOpType.RELU6 });\nvar relu6Config4 = {\n  kernelName: Relu6,\n  backendName: \"webgpu\",\n  kernelFunc: relu64\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/resize_bilinear_webgpu.js\nvar ResizeBilinearProgram2 = class {\n  constructor(inputShape, newHeight, newWidth) {\n    this.variableNames = [\"x\"];\n    this.uniforms = \"adjustHeightWidth : vec2<f32>, halfPixelCenters : f32,\";\n    this.workGroupSize = [64, 1, 1];\n    this.size = true;\n    this.outputShape = [inputShape[0], newHeight, newWidth, inputShape[3]];\n    this.dispatchLayout = flatDispatchLayout(this.outputShape);\n    this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, this.workGroupSize);\n    this.shaderKey = `resizeBilinear`;\n  }\n  getUserCode() {\n    const userCode = `\n      ${getMainHeaderString(\"index\")} {\n        if (index < uniforms.size) {\n        let coords = getCoordsFromIndex(index);\n          let b = coords[0];\n          let d = coords[3];\n          let rc = coords.yz;\n\n          let effectiveInSize = vec2<f32>(\n            f32(uniforms.xShape.y) - uniforms.adjustHeightWidth[0],\n            f32(uniforms.xShape.z) - uniforms.adjustHeightWidth[1]);\n\n          let effectiveOutSize = vec2<f32>(\n            f32(uniforms.outShape.y) - uniforms.adjustHeightWidth[0],\n            f32(uniforms.outShape.z) - uniforms.adjustHeightWidth[1]);\n\n          let effectiveInputOverOutputRatioRC =\n              effectiveInSize / effectiveOutSize;\n\n          // Fractional source index\n          let sourceFracIndexRC =\n            (vec2<f32>(rc) + vec2<f32>(uniforms.halfPixelCenters)) *\n            effectiveInputOverOutputRatioRC - vec2<f32>(uniforms.halfPixelCenters);\n\n          // Compute the four integer indices.\n          let sourceFloorRC = vec2<i32>(sourceFracIndexRC);\n          let sourceCeilRC = vec2<i32>(\n            min(vec2<f32>(uniforms.xShape.yz) - vec2<f32>(1.0), ceil(sourceFracIndexRC)));\n\n          let topLeft = getX(b, sourceFloorRC.x, sourceFloorRC.y, d);\n          let bottomLeft = getX(b, sourceCeilRC.x, sourceFloorRC.y, d);\n          let topRight = getX(b, sourceFloorRC.x, sourceCeilRC.y, d);\n          let bottomRight = getX(b, sourceCeilRC.x, sourceCeilRC.y, d);\n\n          let fracRC = sourceFracIndexRC - vec2<f32>(sourceFloorRC);\n\n          let top = topLeft + (topRight - topLeft) * fracRC.y;\n          let bottom = bottomLeft + (bottomRight - bottomLeft) * fracRC.y;\n          let newValue = top + (bottom - top) * fracRC.x;\n\n          setOutputAtIndex(index, newValue);\n        }\n      }\n    `;\n    return userCode;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/ResizeBilinear.js\nfunction resizeBilinear5(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { images } = inputs;\n  const { alignCorners, size, halfPixelCenters } = attrs;\n  const [newHeight, newWidth] = size;\n  const adjustHeight = alignCorners && newHeight > 1 ? 1 : 0;\n  const adjustWidth = alignCorners && newWidth > 1 ? 1 : 0;\n  const halfPixelCentersValue = halfPixelCenters ? 0.5 : 0;\n  const uniformData = [\n    { type: \"float32\", data: [adjustHeight, adjustWidth] },\n    { type: \"float32\", data: [halfPixelCentersValue] }\n  ];\n  const program = new ResizeBilinearProgram2(images.shape, newHeight, newWidth);\n  return backend2.runWebGPUProgram(program, [images], \"float32\", uniformData);\n}\nvar resizeBilinearConfig4 = {\n  kernelName: ResizeBilinear,\n  backendName: \"webgpu\",\n  kernelFunc: resizeBilinear5\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/resize_nearest_neighbor_webgpu.js\nvar ResizeNearestNeighborProgram2 = class {\n  constructor(inputShape, newHeight, newWidth, halfPixelCenters) {\n    this.variableNames = [\"x\"];\n    this.uniforms = \"adjustHeightWidth : vec2<f32>, roundBase : f32,\";\n    this.workGroupSize = [64, 1, 1];\n    this.size = true;\n    this.outputShape = [inputShape[0], newHeight, newWidth, inputShape[3]];\n    this.dispatchLayout = flatDispatchLayout(this.outputShape);\n    this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, this.workGroupSize);\n    this.halfPixelCenters = halfPixelCenters;\n    this.shaderKey = `resizeNearest_${halfPixelCenters}`;\n  }\n  getUserCode() {\n    let sourceFracIndexRC;\n    if (this.halfPixelCenters) {\n      sourceFracIndexRC = `max((vec2<f32>(rc) + vec2<f32>(0.5)) * effectiveInputOverOutputRatioRC, vec2<f32>(0.0))`;\n    } else {\n      sourceFracIndexRC = `vec2<f32>(rc) * effectiveInputOverOutputRatioRC`;\n    }\n    const userCode = `\n      ${getMainHeaderString(\"index\")} {\n        if (index < uniforms.size) {\n          let coords = getCoordsFromIndex(index);\n          let b = coords[0];\n          let d = coords[3];\n          let rc = coords.yz;\n\n          let effectiveInSize = vec2<f32>(\n            f32(uniforms.xShape.y) - uniforms.adjustHeightWidth[0],\n            f32(uniforms.xShape.z) - uniforms.adjustHeightWidth[1]);\n\n          let effectiveOutSize = vec2<f32>(\n            f32(uniforms.outShape.y) - uniforms.adjustHeightWidth[0],\n            f32(uniforms.outShape.z) - uniforms.adjustHeightWidth[1]);\n\n          let effectiveInputOverOutputRatioRC =\n              effectiveInSize / effectiveOutSize;\n\n          // Fractional source index\n          let sourceFracIndexRC = ${sourceFracIndexRC};\n\n          // Compute the coordinators of nearest neighbor point.\n          let inputShapeRC = vec2<f32>(f32(uniforms.xShape.y), f32(uniforms.xShape.z));\n          let sourceNearestRC = vec2<i32>(\n            min(inputShapeRC - 1.0, floor(sourceFracIndexRC + uniforms.roundBase)));\n          let newValue = getX(b, sourceNearestRC.x, sourceNearestRC.y, d);\n\n          setOutputAtIndex(index, newValue);\n        }\n      }\n    `;\n    return userCode;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/ResizeNearestNeighbor.js\nfunction resizeNearestNeighbor5(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { images } = inputs;\n  const { alignCorners, halfPixelCenters, size } = attrs;\n  const [newHeight, newWidth] = size;\n  const adjustHeight = alignCorners && newHeight > 1 ? 1 : 0;\n  const adjustWidth = alignCorners && newWidth > 1 ? 1 : 0;\n  const roundBase = alignCorners ? 0.5 : 0;\n  const uniformData = [\n    { type: \"float32\", data: [adjustHeight, adjustWidth] },\n    { type: \"float32\", data: [roundBase] }\n  ];\n  const program = new ResizeNearestNeighborProgram2(images.shape, newHeight, newWidth, halfPixelCenters);\n  return backend2.runWebGPUProgram(program, [images], images.dtype, uniformData);\n}\nvar resizeNearestNeighborConfig4 = {\n  kernelName: ResizeNearestNeighbor,\n  backendName: \"webgpu\",\n  kernelFunc: resizeNearestNeighbor5\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/rotate_webgpu.js\nvar RotateProgram2 = class {\n  constructor(imageShape, fillValue) {\n    this.outputShape = [];\n    this.variableNames = [\"x\"];\n    this.workGroupSize = [64, 1, 1];\n    this.size = true;\n    this.outputShape = imageShape;\n    this.dispatchLayout = flatDispatchLayout(this.outputShape);\n    this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, this.workGroupSize);\n    this.uniforms = `centerX : f32, centerY : f32, sinRadians : f32,\n          cosRadians : f32,`;\n    this.shaderKey = \"rotate\";\n    this.outputShape = imageShape;\n    if (typeof fillValue === \"number\") {\n      this.uniforms += ` fillValue : f32,`;\n      this.fillSnippet = `var outputValue = uniforms.fillValue;`;\n      this.shaderKey += \"_float\";\n    } else {\n      this.uniforms += ` fillValue : vec3<f32>,`;\n      this.fillSnippet = `var outputValue = uniforms.fillValue[coords[3]];`;\n      this.shaderKey += \"_vec3\";\n    }\n  }\n  getUserCode() {\n    const userCode = `\n        ${getMainHeaderString(\"index\")} {\n          if (index < uniforms.size) {\n            let coords = getCoordsFromIndex(index);\n            let coordXFloat = (f32(coords[2]) - uniforms.centerX) *\n                uniforms.cosRadians - (f32(coords[1]) - uniforms.centerY) *\n                uniforms.sinRadians;\n            let coordYFloat = (f32(coords[2]) - uniforms.centerX) *\n                uniforms.sinRadians + (f32(coords[1]) - uniforms.centerY) *\n                uniforms.cosRadians;\n            let coordX = i32(round(coordXFloat + uniforms.centerX));\n            let coordY = i32(round(coordYFloat + uniforms.centerY));\n            ${this.fillSnippet}\n            if(coordX >= 0 && coordX < uniforms.xShape[2] && coordY >= 0 &&\n                coordY < uniforms.xShape[1]) {\n              outputValue = getX(coords[0], coordY, coordX, coords[3]);\n            }\n            setOutputAtIndex(index, outputValue);\n          }\n        }\n      `;\n    return userCode;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/RotateWithOffset.js\nvar rotateWithOffsetConfig4 = {\n  kernelName: RotateWithOffset,\n  backendName: \"webgpu\",\n  kernelFunc: ({ inputs, attrs, backend: backend2 }) => {\n    const { image: image2 } = inputs;\n    const { radians, fillValue, center } = attrs;\n    const webgpuBackend = backend2;\n    const program = new RotateProgram2(image2.shape, fillValue);\n    const [centerX, centerY] = backend_util_exports.getImageCenter(center, image2.shape[1], image2.shape[2]);\n    const uniformData = [\n      { type: \"float32\", data: [centerX] },\n      { type: \"float32\", data: [centerY] },\n      { type: \"float32\", data: [Math.sin(radians)] },\n      { type: \"float32\", data: [Math.cos(radians)] }\n    ];\n    if (typeof fillValue === \"number\") {\n      uniformData.push({ type: \"float32\", data: [Number.parseFloat(fillValue.toFixed(2))] });\n    } else {\n      uniformData.push({ type: \"float32\", data: fillValue });\n    }\n    const output = webgpuBackend.runWebGPUProgram(program, [image2], image2.dtype, uniformData);\n    return output;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/Rsqrt.js\nvar rsqrt4 = unaryKernelFunc3({ opType: UnaryOpType.RSQRT, cpuKernelImpl: rsqrtImplCPU2 });\nvar rsqrtConfig4 = {\n  kernelName: Rsqrt,\n  backendName: \"webgpu\",\n  kernelFunc: rsqrt4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/scatter_webgpu.js\nvar ScatterProgram2 = class {\n  constructor(flattenXShape, sliceDim, indicesRank, updatesRank, strides, shape, outputDtype, sumDupeIndices = true) {\n    this.variableNames = [\"updates\", \"indices\"];\n    this.workGroupSize = [64, 1, 1];\n    this.atomic = true;\n    this.outputShape = shape;\n    this.type = outputDtype;\n    this.sumDupeIndices = sumDupeIndices;\n    this.dispatchLayout = flatDispatchLayout(flattenXShape);\n    this.dispatch = computeDispatch(this.dispatchLayout, flattenXShape, this.workGroupSize);\n    this.sliceDimGreaterThanOne = sliceDim > 1;\n    this.shaderKey = `scatter_${indicesRank}_${updatesRank}_${this.sliceDimGreaterThanOne}_${outputDtype}_${sumDupeIndices}`;\n    const stridesType = getCoordsDataType2(strides.length);\n    this.uniforms = `sliceDim : i32, strides: ${stridesType}, size: i32,`;\n    this.updatesRank = updatesRank;\n    this.indicesRank = indicesRank;\n  }\n  getUserCode() {\n    let indicesString = \"\";\n    if (this.indicesRank === 1) {\n      indicesString = \"coords[0]\";\n    } else if (this.indicesRank === 2) {\n      indicesString = \"coords[0], j\";\n    }\n    const indicesSnippet = `getIndices(${indicesString})`;\n    const strideString = this.sliceDimGreaterThanOne ? \"uniforms.strides[j]\" : \"uniforms.strides\";\n    let outCoordsString = \"\";\n    let getUpdatesCoordsFromFlatIndex = \"\";\n    if (this.dispatchLayout.x.length === 1) {\n      outCoordsString = \"flattenedIndex\";\n      getUpdatesCoordsFromFlatIndex = `\n      fn getUpdatesCoordsFromFlatIndex(index : i32) -> i32 {\n        return index;\n      }\n      `;\n    } else if (this.dispatchLayout.x.length === 2) {\n      outCoordsString = \"vec2<i32>(flattenedIndex, coords[1])\";\n      getUpdatesCoordsFromFlatIndex = `\n      fn getUpdatesCoordsFromFlatIndex(index : i32) -> vec2<i32> {\n        // N.B. |updates| could be a scalar tensor, conceptually representing a\n        // 2D tensor with all values equal to that. By design, its size must be\n        // the same as |outShape[1]| in one dimension, and |indicesShape[0]|\n        // gives the other.\n        let sliceSize = uniforms.outShape[1];\n        let d0 = index / sliceSize;\n        let d1 = index - d0 * sliceSize;\n        return vec2<i32>(d0, d1);\n      }\n      `;\n    }\n    const updatesString = Array.from({ length: this.updatesRank }, (_, idx) => `coords[${idx}]`);\n    const updatesSnippet = `getUpdates(${updatesString.join(\", \")})`;\n    const atomicRMW = (ptr, val) => {\n      let atomicAddSnippet = `atomicAdd(${ptr}, bitcast<i32>(${val}))`;\n      if (this.type === \"float32\") {\n        atomicAddSnippet = `\n          {\n            var oldBits = 0;\n            var newBits = bitcast<i32>(${val});\n            loop {\n              let info = atomicCompareExchangeWeak(${ptr}, oldBits, newBits);\n              if (info.exchanged) {\n                break;\n              }\n              oldBits = info.old_value;\n              let oldValue = bitcast<f32>(oldBits);\n              let newValue = oldValue + (${val});\n              newBits = bitcast<i32>(newValue);\n            }\n          }\n        `;\n      }\n      const atomicStoreSnippet = `atomicStore(${ptr}, bitcast<i32>(${val}));`;\n      return this.sumDupeIndices ? atomicAddSnippet : atomicStoreSnippet;\n    };\n    const userCode = `\n    ${getUpdatesCoordsFromFlatIndex}\n\n      ${getMainHeaderString(\"index\")} {\n        if (index < uniforms.size) {\n          let coords = getUpdatesCoordsFromFlatIndex(index);\n          var flattenedIndex = 0;\n          for (var j = 0; j < uniforms.sliceDim; j = j + 1) {\n            let indexInside = i32(round(${indicesSnippet}));\n            flattenedIndex = flattenedIndex + indexInside * ${strideString};\n          }\n          let updateValue =\n              ${mapToWgslTypes(this.type, false)}(${updatesSnippet});\n          let flatIndex = getOutputIndexFromCoords(${outCoordsString});\n\n          ${atomicRMW(\"&result[flatIndex]\", \"updateValue\")};\n        }\n      }`;\n    return userCode;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/ScatterNd.js\nfunction scatterNd4(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { indices, updates } = inputs;\n  const { shape } = attrs;\n  const { sliceRank, numUpdates, sliceSize, strides, outputSize } = backend_util_exports.calculateShapes(updates, indices, shape);\n  const flattenShape = [outputSize / sliceSize, sliceSize];\n  if (outputSize === 0) {\n    return backend2.makeTensorInfo(shape, indices.dtype);\n  }\n  const flattenIndices = reshape6({ inputs: { x: indices }, backend: backend2, attrs: { shape: [numUpdates, sliceRank] } });\n  const flattenX = reshape6({ inputs: { x: updates }, backend: backend2, attrs: { shape: [numUpdates, sliceSize] } });\n  const type = flattenX.dtype;\n  const output = fill5({ backend: backend2, attrs: { shape: flattenShape, value: 0, dtype: type } });\n  const size = util_exports.sizeFromShape(flattenX.shape);\n  const uniformData = [\n    { type: \"int32\", data: [sliceRank] },\n    { type: \"int32\", data: strides },\n    { type: \"int32\", data: [size] }\n  ];\n  const program = new ScatterProgram2(flattenX.shape, sliceRank, flattenIndices.shape.length, flattenX.shape.length, strides, flattenShape, type);\n  const res = backend2.runWebGPUProgram(program, [flattenX, flattenIndices], type, uniformData, output);\n  const reshaped = reshape6({ inputs: { x: res }, backend: backend2, attrs: { shape } });\n  backend2.disposeData(flattenIndices.dataId);\n  backend2.disposeData(flattenX.dataId);\n  backend2.disposeData(res.dataId);\n  return reshaped;\n}\nvar scatterNdConfig4 = {\n  kernelName: ScatterNd,\n  backendName: \"webgpu\",\n  kernelFunc: scatterNd4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/select_webgpu.js\nvar SelectProgram2 = class {\n  constructor(cRank, shape, rank) {\n    this.variableNames = [\"c\", \"a\", \"b\"];\n    this.workGroupSize = [64, 1, 1];\n    this.size = true;\n    this.outputShape = shape;\n    this.dispatchLayout = flatDispatchLayout(this.outputShape);\n    this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, this.workGroupSize);\n    this.cRank = cRank;\n    this.rank = rank;\n    this.shaderKey = \"select\";\n  }\n  getUserCode() {\n    let cCoords;\n    let abCoords;\n    if (this.rank > 4) {\n      throw Error(`Where for rank ${this.rank} is not yet supported`);\n    }\n    if (this.rank === 1) {\n      abCoords = `resRC`;\n      cCoords = `resRC`;\n    } else {\n      const currentCoords = [\"resRC.x\", \"resRC.y\", \"resRC.z\", \"resRC.w\"];\n      const cCoordVars = [];\n      const abCoordVars = [];\n      for (let i = 0; i < this.outputShape.length; i++) {\n        abCoordVars.push(`${currentCoords[i]}`);\n        if (i < this.cRank) {\n          cCoordVars.push(`${currentCoords[i]}`);\n        }\n      }\n      cCoords = cCoordVars.join();\n      abCoords = abCoordVars.join();\n    }\n    const userCode = `\n      ${getMainHeaderString(\"index\")} {\n        if (index < uniforms.size) {\n          let resRC = getCoordsFromIndex(index);\n          let cVal = getC(${cCoords});\n          if (cVal >= 1.0) {\n            setOutputAtIndex(index, getA(${abCoords}));\n          } else {\n            setOutputAtIndex(index, getB(${abCoords}));\n          }\n        }\n      }\n    `;\n    return userCode;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/Select.js\nfunction select5(args) {\n  const { inputs, backend: backend2 } = args;\n  const { condition, t, e } = inputs;\n  const program = new SelectProgram2(condition.shape.length, t.shape, t.shape.length);\n  return backend2.runWebGPUProgram(program, [condition, t, e], upcastType(t.dtype, e.dtype));\n}\nvar selectConfig4 = {\n  kernelName: Select,\n  backendName: \"webgpu\",\n  kernelFunc: select5\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/Sigmoid.js\nvar sigmoid5 = unaryKernelFunc3({ opType: UnaryOpType.SIGMOID });\nvar sigmoidConfig4 = {\n  kernelName: Sigmoid,\n  backendName: \"webgpu\",\n  kernelFunc: sigmoid5\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/Sin.js\nvar sin4 = unaryKernelFunc3({ opType: UnaryOpType.SIN });\nvar sinConfig4 = {\n  kernelName: Sin,\n  backendName: \"webgpu\",\n  kernelFunc: sin4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/Sinh.js\nvar sinh4 = unaryKernelFunc3({ opType: UnaryOpType.SINH });\nvar sinhConfig3 = {\n  kernelName: Sinh,\n  backendName: \"webgpu\",\n  kernelFunc: sinh4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/Sub.js\nvar sub4 = binaryKernelFunc3({ opType: BinaryOpType.SUB, cpuKernelImpl: subImplCPU2, supportsComplex: true });\nvar subConfig4 = {\n  kernelName: Sub,\n  backendName: \"webgpu\",\n  kernelFunc: sub4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/Softmax.js\nfunction softmax6(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { logits } = inputs;\n  const { dim } = attrs;\n  const axes = util_exports.parseAxisParam([dim], logits.shape);\n  const maxLogit = max6({\n    inputs: { x: logits },\n    backend: backend2,\n    attrs: { reductionIndices: axes, keepDims: false }\n  });\n  const expandedShape = backend_util_exports.expandShapeToKeepDim(maxLogit.shape, axes);\n  const maxLogitsReshaped = reshape6({ inputs: { x: maxLogit }, backend: backend2, attrs: { shape: expandedShape } });\n  const a = sub4({ inputs: { a: logits, b: maxLogitsReshaped }, backend: backend2 });\n  const b = exp4({ inputs: { x: a }, backend: backend2 });\n  const sumExp = sum6({ inputs: { x: b }, backend: backend2, attrs: { axis: axes, keepDims: false } });\n  const sumExpReshaped = reshape6({ inputs: { x: sumExp }, backend: backend2, attrs: { shape: expandedShape } });\n  const res = realDiv2({ inputs: { a: b, b: sumExpReshaped }, backend: backend2 });\n  backend2.disposeData(maxLogit.dataId);\n  backend2.disposeData(maxLogitsReshaped.dataId);\n  backend2.disposeData(a.dataId);\n  backend2.disposeData(b.dataId);\n  backend2.disposeData(sumExp.dataId);\n  backend2.disposeData(sumExpReshaped.dataId);\n  return res;\n}\nvar softmaxConfig4 = {\n  kernelName: Softmax,\n  backendName: \"webgpu\",\n  kernelFunc: softmax6\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/SpaceToBatchND.js\nvar spaceToBatchND5 = (args) => {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x } = inputs;\n  const { blockShape, paddings } = attrs;\n  util_exports.assert(x.shape.length <= 4, () => \"spaceToBatchND for rank > 4 with a WebGPU backend not implemented yet\");\n  const prod6 = blockShape.reduce((a, b) => a * b);\n  const completePaddings = [[0, 0]];\n  completePaddings.push(...paddings);\n  for (let i = 1 + blockShape.length; i < x.shape.length; ++i) {\n    completePaddings.push([0, 0]);\n  }\n  const toDispose = [];\n  const paddedX = padV23({\n    inputs: { x },\n    backend: backend2,\n    attrs: { paddings: completePaddings, constantValue: 0 }\n  });\n  const reshapedPaddedShape = backend_util_exports.getReshaped(paddedX.shape, blockShape, prod6, false);\n  const permutedReshapedPaddedPermutation = backend_util_exports.getPermuted(reshapedPaddedShape.length, blockShape.length, false);\n  const flattenShape = backend_util_exports.getReshapedPermuted(paddedX.shape, blockShape, prod6, false);\n  const reshapedPaddedX = reshape6({ inputs: { x: paddedX }, backend: backend2, attrs: { shape: reshapedPaddedShape } });\n  const paddedXT = transpose5({\n    inputs: { x: reshapedPaddedX },\n    backend: backend2,\n    attrs: { perm: permutedReshapedPaddedPermutation }\n  });\n  const result = reshape6({ inputs: { x: paddedXT }, backend: backend2, attrs: { shape: flattenShape } });\n  toDispose.push(paddedX);\n  toDispose.push(reshapedPaddedX);\n  toDispose.push(paddedXT);\n  toDispose.forEach((t) => backend2.disposeData(t.dataId));\n  return result;\n};\nvar spaceToBatchNDConfig4 = {\n  kernelName: SpaceToBatchND,\n  backendName: \"webgpu\",\n  kernelFunc: spaceToBatchND5\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/tile_webgpu.js\nvar TileProgram2 = class {\n  constructor(aShape, reps) {\n    this.variableNames = [\"A\"];\n    this.workGroupSize = [64, 1, 1];\n    this.size = true;\n    const outputShape = new Array(aShape.length);\n    for (let i = 0; i < outputShape.length; i++) {\n      outputShape[i] = aShape[i] * reps[i];\n    }\n    this.outputShape = outputShape;\n    this.dispatchLayout = flatDispatchLayout(this.outputShape);\n    this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, this.workGroupSize);\n    this.rank = this.outputShape.length;\n    this.shaderKey = \"tile\";\n  }\n  getUserCode() {\n    const sourceCoords = getSourceCoords5(this.rank, \"uniforms.\");\n    const userCode = `\n      ${getMainHeaderString(\"index\")} {\n        if (index < uniforms.size) {\n          let resRC = getCoordsFromIndex(index);\n          setOutputAtIndex(index, getA(${sourceCoords}));\n        }\n      }\n    `;\n    return userCode;\n  }\n};\nfunction getSourceCoords5(rank, uniformPrefix = \"\") {\n  if (rank >= 5) {\n    throw Error(`Tile for rank ${rank} is not yet supported`);\n  }\n  if (rank === 1) {\n    return `(resRC % ${uniformPrefix}aShape)`;\n  }\n  const currentCoords = [\"resRC.x\", \"resRC.y\", \"resRC.z\", \"resRC.w\"];\n  const sourceCoords = [];\n  for (let i = 0; i < rank; i++) {\n    sourceCoords.push(`(${currentCoords[i]} % ${uniformPrefix}aShape[${i}])`);\n  }\n  return sourceCoords.join();\n}\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/Tile.js\nfunction tile6(params) {\n  const { inputs, backend: backend2, attrs } = params;\n  const { x } = inputs;\n  const { reps } = attrs;\n  if (backend2.shouldExecuteOnCPU([x]) || x.dtype === \"string\" || x.shape.length >= 5) {\n    const data = backend2.readSync(x.dataId);\n    const value = x.dtype === \"string\" ? data.map((d) => util_exports.decodeString(d)) : data;\n    const buf = buffer(x.shape, x.dtype, value);\n    const outBuf = tileImplCPU2(buf, reps);\n    return backend2.makeTensorInfo(outBuf.shape, outBuf.dtype, outBuf.values);\n  }\n  const program = new TileProgram2(x.shape, reps);\n  const output = backend2.runWebGPUProgram(program, [x], x.dtype);\n  return output;\n}\nvar tileConfig4 = {\n  kernelName: Tile,\n  backendName: \"webgpu\",\n  kernelFunc: tile6\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/SparseToDense.js\nfunction sparseToDense4(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { sparseIndices, sparseValues, defaultValue } = inputs;\n  const { outputShape } = attrs;\n  const { sliceRank, numUpdates, sliceSize, strides, outputSize } = backend_util_exports.calculateShapes(sparseValues, sparseIndices, outputShape);\n  const sumDupeIndices = false;\n  if (sparseValues.dtype === \"string\") {\n    const indicesBuf = backend2.bufferSync(sparseIndices);\n    const updatesBuf = backend2.bufferSync(sparseValues);\n    const $defaultValue2 = util_exports.decodeString(backend2.readSync(defaultValue.dataId)[0]);\n    const outBuf = scatterImplCPU2(indicesBuf, updatesBuf, outputShape, outputSize, sliceSize, numUpdates, sliceRank, strides, $defaultValue2, sumDupeIndices);\n    return backend2.makeTensorInfo(outputShape, outBuf.dtype, outBuf.values);\n  }\n  const flattenShape = [outputSize / sliceSize, sliceSize];\n  const $sparseIndices = reshape6({\n    inputs: { x: sparseIndices },\n    backend: backend2,\n    attrs: { shape: [numUpdates, sliceRank] }\n  });\n  const $sparseValues = sparseValues.shape.length ? reshape6({\n    inputs: { x: sparseValues },\n    backend: backend2,\n    attrs: { shape: [numUpdates, sliceSize] }\n  }) : identity5({ inputs: { x: sparseValues }, backend: backend2 });\n  const type = $sparseValues.dtype;\n  const zero = backend2.makeTensorInfo([], type, util_exports.makeZerosTypedArray(1, type));\n  const $defaultValue = reshape6({\n    inputs: { x: defaultValue },\n    backend: backend2,\n    attrs: { shape: Array(flattenShape.length).fill(1) }\n  });\n  const $denseValues = tile6({ inputs: { x: $defaultValue }, backend: backend2, attrs: { reps: flattenShape } });\n  const size = util_exports.sizeFromShape([numUpdates, sliceSize]);\n  const uniformData = [\n    { type: \"int32\", data: [sliceRank] },\n    { type: \"int32\", data: strides },\n    { type: \"int32\", data: [size] }\n  ];\n  switch (numUpdates) {\n    case 0:\n      break;\n    case 1:\n      if (true) {\n        const program = new ScatterProgram2([numUpdates, sliceSize], sliceRank, $sparseIndices.shape.length, $sparseValues.shape.length, strides, flattenShape, type, sumDupeIndices);\n        backend2.runWebGPUProgram(program, [$sparseValues, $sparseIndices], type, uniformData, $denseValues);\n      }\n      break;\n    default:\n      if (true) {\n        const program = new ScatterProgram2([numUpdates, sliceSize], sliceRank, $sparseIndices.shape.length, zero.shape.length, strides, flattenShape, type, sumDupeIndices);\n        backend2.runWebGPUProgram(program, [zero, $sparseIndices], type, uniformData, $denseValues);\n      }\n      {\n        const program = new ScatterProgram2([numUpdates, sliceSize], sliceRank, $sparseIndices.shape.length, $sparseValues.shape.length, strides, flattenShape, type);\n        backend2.runWebGPUProgram(program, [$sparseValues, $sparseIndices], type, uniformData, $denseValues);\n      }\n  }\n  const denseValues = reshape6({ inputs: { x: $denseValues }, backend: backend2, attrs: { shape: outputShape } });\n  backend2.disposeData($sparseIndices.dataId);\n  backend2.disposeData($sparseValues.dataId);\n  backend2.disposeData($defaultValue.dataId);\n  backend2.disposeData(zero.dataId);\n  backend2.disposeData($denseValues.dataId);\n  return denseValues;\n}\nvar sparseToDenseConfig3 = {\n  kernelName: SparseToDense,\n  backendName: \"webgpu\",\n  kernelFunc: sparseToDense4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/SplitV.js\nfunction splitV4(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x } = inputs;\n  const { numOrSizeSplits, axis } = attrs;\n  const $axis = util_exports.parseAxisParam(axis, x.shape)[0];\n  const splitSizes = backend_util_exports.prepareSplitSize(x, numOrSizeSplits, $axis);\n  const xRank = x.shape.length;\n  const begin = new Array(xRank).fill(0);\n  const size = x.shape.slice();\n  return splitSizes.map((s) => {\n    const sliceSize = [...size];\n    sliceSize[$axis] = s;\n    const sliceT = slice5({ inputs: { x }, backend: backend2, attrs: { begin, size: sliceSize } });\n    begin[$axis] += s;\n    return sliceT;\n  });\n}\nvar splitVConfig4 = {\n  kernelName: SplitV,\n  backendName: \"webgpu\",\n  kernelFunc: splitV4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/Sqrt.js\nvar sqrt4 = unaryKernelFunc3({ opType: UnaryOpType.SQRT });\nvar sqrtConfig4 = {\n  kernelName: Sqrt,\n  backendName: \"webgpu\",\n  kernelFunc: sqrt4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/Square.js\nvar squareConfig4 = {\n  kernelName: Square,\n  backendName: \"webgpu\",\n  kernelFunc: ({ inputs, backend: backend2 }) => {\n    const { x } = inputs;\n    const webGPUBackend = backend2;\n    const program = new UnaryOpProgram2(x.shape, UnaryOpType.SQUARE);\n    return webGPUBackend.runWebGPUProgram(program, [x], x.dtype);\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/SquaredDifference.js\nvar squaredDifference4 = binaryKernelFunc3({\n  opType: BinaryOpType.SQUARED_DIFFERENCE\n});\nvar squaredDifferenceConfig4 = {\n  kernelName: SquaredDifference,\n  backendName: \"webgpu\",\n  kernelFunc: squaredDifference4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/strided_slice_webgpu.js\nvar StridedSliceProgram2 = class {\n  constructor(destSize) {\n    this.variableNames = [\"x\"];\n    this.workPerThread = 1;\n    this.workGroupSize = [64, 1, 1];\n    this.size = true;\n    this.outputShape = destSize;\n    this.dispatchLayout = flatDispatchLayout(this.outputShape);\n    this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, this.workGroupSize, [this.workPerThread, 1, 1]);\n    const dtype = getCoordsDataType2(this.outputShape.length);\n    this.uniforms = `begin : ${dtype},  strides : ${dtype}, `;\n    this.shaderKey = \"stridedSlice\";\n  }\n  getUserCode() {\n    const rank = this.outputShape.length;\n    let newCoords = \"\";\n    if (rank === 1) {\n      newCoords = \"coords * uniforms.strides + uniforms.begin\";\n    } else {\n      let outputAxis = 0;\n      newCoords = this.outputShape.map((_, i) => {\n        outputAxis++;\n        return this.outputShape.length === 1 ? `coords * uniforms.strides[${i}] + uniforms.begin[${i}]` : `coords[${outputAxis - 1}] * uniforms.strides[${i}] + uniforms.begin[${i}]`;\n      }).join(\",\");\n    }\n    const userCode = `\n       ${getMainHeaderString(\"index\")} {\n         if (index < uniforms.size) {\n           let coords = getCoordsFromIndex(index);\n           setOutputAtIndex(index, getX(${newCoords}));\n         }\n       }\n     `;\n    return userCode;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/StridedSlice.js\nfunction stridedSlice5(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x } = inputs;\n  const { begin, end, strides, beginMask, endMask, ellipsisMask, newAxisMask, shrinkAxisMask } = attrs;\n  const { finalShapeSparse, finalShape, isIdentity, sliceDim0, isSimpleSlice, begin: $begin, end: $end, strides: $strides } = slice_util_exports.sliceInfo(x.shape, begin, end, strides, beginMask, endMask, ellipsisMask, newAxisMask, shrinkAxisMask);\n  let result;\n  if (isIdentity) {\n    result = reshape6({ inputs: { x }, backend: backend2, attrs: { shape: finalShape } });\n  } else if (sliceDim0 || isSimpleSlice) {\n    util_exports.assert(x.shape.length >= 1, () => `Input must have rank at least 1, got: ${x.shape.length}`);\n    const size = slice_util_exports.computeOutShape($begin, $end, $strides);\n    const sliced = slice5({ inputs: { x }, backend: backend2, attrs: { begin: $begin, size } });\n    result = reshape6({ inputs: { x: sliced }, backend: backend2, attrs: { shape: finalShape } });\n    backend2.disposeData(sliced.dataId);\n  } else {\n    const shouldExecuteOnCPU = backend2.shouldExecuteOnCPU([x]);\n    if (shouldExecuteOnCPU) {\n      const values = backend2.readSync(x.dataId);\n      const xBuf = buffer(x.shape, x.dtype, values);\n      const resultValues = stridedSliceImplCPU2(finalShapeSparse, xBuf, $strides, $begin);\n      result = backend2.makeTensorInfo(finalShape, x.dtype, resultValues.values);\n    } else {\n      const program = new StridedSliceProgram2(finalShapeSparse);\n      const uniformData = [{ type: \"int32\", data: $begin }, { type: \"int32\", data: $strides }];\n      const resultValues = backend2.runWebGPUProgram(program, [x], x.dtype, uniformData);\n      result = reshape6({ inputs: { x: resultValues }, backend: backend2, attrs: { shape: finalShape } });\n      backend2.disposeData(resultValues.dataId);\n    }\n  }\n  return result;\n}\nvar stridedSliceConfig4 = {\n  kernelName: StridedSlice,\n  backendName: \"webgpu\",\n  kernelFunc: stridedSlice5\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/StringNGrams.js\nfunction stringNGrams5(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { separator, nGramWidths, leftPad, rightPad: rightPad2, padWidth, preserveShortSequences } = attrs;\n  const { data, dataSplits } = inputs;\n  const $data = backend2.readSync(data.dataId);\n  const $dataSplits = backend2.readSync(dataSplits.dataId);\n  const [nGrams, nGramsSplits] = stringNGramsImplCPU2($data, $dataSplits, separator, nGramWidths, leftPad, rightPad2, padWidth, preserveShortSequences);\n  return [\n    backend2.makeTensorInfo([nGrams.length], \"string\", nGrams),\n    backend2.makeTensorInfo(dataSplits.shape, \"int32\", nGramsSplits)\n  ];\n}\nvar stringNGramsConfig4 = {\n  kernelName: StringNGrams,\n  backendName: \"webgpu\",\n  kernelFunc: stringNGrams5\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/Tanh.js\nvar tanh5 = unaryKernelFunc3({ opType: UnaryOpType.TANH });\nvar tanhConfig4 = {\n  kernelName: Tanh,\n  backendName: \"webgpu\",\n  kernelFunc: tanh5\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/top_k_webgpu.js\nvar SwapProgram2 = class {\n  constructor(shape) {\n    this.variableNames = [\"x\", \"indices\"];\n    this.workGroupSize = [256, 1, 1];\n    this.size = true;\n    this.outputShape = shape;\n    this.dispatchLayout = flatDispatchLayout(this.outputShape);\n    this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, this.workGroupSize);\n    this.uniforms = `inputSize : i32, firstPass : i32, negativeInf : f32,\n        dir : i32, inc : i32,`;\n    this.shaderKey = \"swap\";\n  }\n  getUserCode() {\n    const userCode = `\n        ${getMainHeaderString(\"index\")} {\n          if (index < uniforms.size) {\n            let outC = getCoordsFromIndex(index);\n            let batch = outC[0];\n            let elemIdx = outC[1];\n            // We compare elements pair-wise within a group of size 2 * inc.\n            // The comparing rule for each group alternates between ascending\n            // and descending. Within each group, we compare each pair at\n            // positions i and i+inc. To decide whether an element at position i\n            // is x0 or x1, we mod it by 2 * inc, if the result is smaller than\n            // inc, it is in the first half of the group, we denote it as x0,\n            // otherwise we denote it as x1.\n            // For example, as shown in the Bitonic top K paper referenced\n            // above, Figure5(a) shows that element[1] is in the second half of\n            // the group when group size is 2, but it is in the first half of\n            // the group when group size is 4.\n            let isFirstInPair = elemIdx % (2 * uniforms.inc) < uniforms.inc;\n            var i = 0;\n            if (isFirstInPair) {\n              i = elemIdx;\n            } else {\n              i = elemIdx - uniforms.inc;\n            }\n\n            var i0 = 0;\n            if (uniforms.firstPass == 1) {\n              i0 = i;\n            } else {\n              i0 = i32(getIndices(batch, i));\n            }\n\n            var i1 = 0;\n            if (uniforms.firstPass == 1) {\n              i1 = i + uniforms.inc;\n            } else {\n              i1 = i32(getIndices(batch, i + uniforms.inc));\n            }\n\n            var x0 = f32(0.0);\n            var x1 = f32(0.0);\n            if (i0 < uniforms.inputSize) {\n              x0 = getX(batch, i0);\n            } else {\n              x0 = uniforms.negativeInf;\n            }\n            if (i1 < uniforms.inputSize) {\n              x1 = getX(batch, i1);\n            } else {\n              x1 = uniforms.negativeInf;\n            }\n\n            let reverse = elemIdx % (2 * uniforms.dir) >= uniforms.dir;\n            let isGreater = x0 > x1 || (x0 == x1 && i1 > i0);\n            if (reverse == isGreater) {\n              // Elements in opposite order of direction\n              let iTemp = i0;\n              i0 = i1;\n              i1 = iTemp;\n            }\n            if (isFirstInPair) {\n              setOutputAtIndex(index, f32(i0));\n            } else {\n              setOutputAtIndex(index, f32(i1));\n            }\n          }\n        }\n      `;\n    return userCode;\n  }\n};\nvar MergeProgram2 = class {\n  constructor(shape) {\n    this.variableNames = [\"x\", \"indices\"];\n    this.workGroupSize = [256, 1, 1];\n    this.size = true;\n    this.outputShape = shape;\n    this.dispatchLayout = flatDispatchLayout(this.outputShape);\n    this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, this.workGroupSize);\n    this.uniforms = `inputSize : i32, firstPass : i32, k : i32,`;\n    this.shaderKey = \"merge\";\n  }\n  getUserCode() {\n    const userCode = `\n        ${getMainHeaderString(\"index\")} {\n          if (index < uniforms.size) {\n            let outC = getCoordsFromIndex(index);\n            let batch = outC[0];\n            let elemIdx = outC[1];\n            // The output size is half of the previous size.\n            // If the previous sequence is | | | | _ _ _ _  | | | |  _ _ _ _\n            // (k=4), we only need to output the indices at positions |, the\n            // indices at positions _ can be thrown away, see Figure5(b) After\n            // Phase 2 (Merge phase) in the Bitonic Top K paper referenced\n            // above.\n            // For example, the paper shows we only need to output the orange\n            // bars. The output sequence should look like this | | | | | | | |.\n            // Because the sequence is halved, to map the output index back to\n            // the previous sequence to find the corresponding value, we need\n            // to double the index. When we double the index, we basically\n            // interpolate a position, so 2i looks like\n            // | _ | _ | _ | _ | _ | _ | _. We move the | to the first k\n            // position of each 2k positions by - elemIdx % k. E.g. for output\n            // at index 4,5,6,7, we want to get the corresponding element at\n            // original index 8,9,10,11, for output at index 8,9,10,11,\n            // we want to get the corresponding element at original index\n            // 16,17,18,19, so on and so forth.\n\n            var i = 0;\n            if (elemIdx < uniforms.k) {\n              i = elemIdx;\n            } else {\n              i = elemIdx * 2 - elemIdx % uniforms.k;\n            }\n            var i0 = 0;\n            if (uniforms.firstPass == 1) {\n              i0 = i;\n            } else {\n              i0 = i32(getIndices(batch, i));\n            }\n            var i1 = 0;\n            if (uniforms.firstPass == 1) {\n              i1 = i + uniforms.k;\n            } else {\n              i1 = i32(getIndices(batch, i + uniforms.k));\n            }\n\n            let x0 = getX(batch, i0);\n            var x1 = f32(0.0);\n            if (i1 < uniforms.inputSize) {\n              x1 = getX(batch, i1);\n            } else {\n              x1 = x0;\n            }\n\n            if (x0 >= x1) {\n              setOutputAtIndex(index, f32(i0));\n            } else {\n              setOutputAtIndex(index, f32(i1));\n            }\n          }\n        }\n      `;\n    return userCode;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/TopK.js\nfunction disposeIntermediateTensorInfoOrNull2(backend2, tensorInfo) {\n  if (tensorInfo !== null) {\n    backend2.disposeData(tensorInfo.dataId);\n  }\n}\nfunction roundUpToPow22(num) {\n  let pow22 = 1;\n  while (pow22 < num) {\n    pow22 *= 2;\n  }\n  return pow22;\n}\nfunction topK3(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { x } = inputs;\n  const { k, sorted } = attrs;\n  const xShape = x.shape;\n  const lastDim = xShape[xShape.length - 1];\n  if (backend2.shouldExecuteOnCPU([x])) {\n    const xVals = backend2.readSync(x.dataId);\n    const [allTopKVals, allTopKIndices] = topKImplCPU2(xVals, xShape, x.dtype, k, sorted);\n    return [\n      backend2.makeTensorInfo(allTopKVals.shape, allTopKVals.dtype, allTopKVals.values),\n      backend2.makeTensorInfo(allTopKIndices.shape, allTopKIndices.dtype, allTopKIndices.values)\n    ];\n  }\n  if (k === 0) {\n    xShape[xShape.length - 1] = 0;\n    return [\n      backend2.makeTensorInfo(xShape, x.dtype, []),\n      backend2.makeTensorInfo(xShape, \"int32\", [])\n    ];\n  }\n  if (lastDim === 1) {\n    return [\n      x,\n      fill5({ attrs: { shape: xShape, dtype: \"int32\", value: 0 }, backend: backend2 })\n    ];\n  }\n  const xSize = util_exports.sizeFromShape(xShape);\n  const batch = xSize / lastDim;\n  const x2D = reshape6({ inputs: { x }, attrs: { shape: [batch, lastDim] }, backend: backend2 });\n  const kPow2 = roundUpToPow22(k);\n  const lastDimPow2 = roundUpToPow22(lastDim);\n  let indices = null;\n  const getInputs = () => indices === null ? [x2D, x2D] : [x2D, indices];\n  const runSwap = (dir, inc, shape) => {\n    const inputs2 = getInputs();\n    const program = new SwapProgram2(shape);\n    const firstPass = indices === null ? 1 : 0;\n    const uniformDataSwap = [\n      { type: \"int32\", data: [lastDim] },\n      { type: \"int32\", data: [firstPass] },\n      { type: \"float32\", data: [Number.NEGATIVE_INFINITY] },\n      { type: \"int32\", data: [dir] },\n      { type: \"int32\", data: [inc] }\n    ];\n    const prevIndices2 = indices;\n    indices = backend2.runWebGPUProgram(program, inputs2, \"int32\", uniformDataSwap);\n    disposeIntermediateTensorInfoOrNull2(backend2, prevIndices2);\n  };\n  for (let len = 1; len < kPow2; len *= 2) {\n    const dir = len * 2;\n    for (let inc = len; inc >= 1; inc /= 2) {\n      runSwap(dir, inc, [batch, lastDimPow2]);\n    }\n  }\n  for (let indicesSize = lastDimPow2; indicesSize > kPow2; indicesSize /= 2) {\n    const inputs2 = getInputs();\n    const mergeProgram = new MergeProgram2([batch, indicesSize / 2]);\n    const firstPass = indices === null ? 1 : 0;\n    const uniformDataMerge = [\n      { type: \"int32\", data: [lastDim] },\n      { type: \"int32\", data: [firstPass] },\n      { type: \"int32\", data: [kPow2] }\n    ];\n    const prevIndices2 = indices;\n    indices = backend2.runWebGPUProgram(mergeProgram, inputs2, \"int32\", uniformDataMerge);\n    disposeIntermediateTensorInfoOrNull2(backend2, prevIndices2);\n    const len = kPow2 / 2;\n    const dir = len * 2;\n    for (let inc = len; inc >= 1; inc /= 2) {\n      runSwap(dir, inc, indices.shape);\n    }\n  }\n  let prevIndices = indices;\n  indices = slice5({ inputs: { x: indices }, backend: backend2, attrs: { begin: 0, size: [batch, k] } });\n  disposeIntermediateTensorInfoOrNull2(backend2, prevIndices);\n  let values = gatherV24({ inputs: { x: x2D, indices }, backend: backend2, attrs: { axis: 1, batchDims: 1 } });\n  disposeIntermediateTensorInfoOrNull2(backend2, x2D);\n  const newShape = xShape.slice(0, -1);\n  newShape.push(k);\n  prevIndices = indices;\n  indices = reshape6({ inputs: { x: indices }, attrs: { shape: newShape }, backend: backend2 });\n  disposeIntermediateTensorInfoOrNull2(backend2, prevIndices);\n  const prevValues = values;\n  values = reshape6({ inputs: { x: values }, attrs: { shape: newShape }, backend: backend2 });\n  disposeIntermediateTensorInfoOrNull2(backend2, prevValues);\n  return [values, indices];\n}\nvar topKConfig4 = {\n  kernelName: TopK,\n  backendName: \"webgpu\",\n  kernelFunc: topK3\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/transform_webgpu.js\nvar TransformProgram2 = class {\n  constructor(outShape) {\n    this.variableNames = [\"Image\", \"Transforms\"];\n    this.uniforms = \"interpolationModeId : i32, fillModeId : i32, fillValue : f32,\";\n    this.workGroupSize = [64, 1, 1];\n    this.size = true;\n    this.outputShape = outShape;\n    this.dispatchLayout = flatDispatchLayout(this.outputShape);\n    this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, this.workGroupSize);\n    this.shaderKey = \"transform\";\n  }\n  getUserCode() {\n    const userCode = `\n          fn mapCoord(outCoord : f32, len : f32) -> f32{\n            var inCoord = outCoord;\n            if(uniforms.fillModeId == 2) {\n              if (inCoord < 0.0) {\n                if (len <= 1.0) {\n                  inCoord = 0.0;\n                } else {\n                  let sz2 = 2.0 * len;\n                  if (inCoord < sz2) {\n                    inCoord = sz2 * f32(i32(f32(-inCoord / sz2))) +\n                    inCoord;\n                  }\n                  if (inCoord < -len) {\n                    inCoord = inCoord + sz2;\n                  } else {\n                    inCoord = -inCoord - 1.0;\n                  }\n                }\n              } else if (inCoord > len - 1.0) {\n                if (len <= 1.0) {\n                  inCoord = 0.0;\n                } else {\n                  let sz2 = 2.0 * len;\n                  inCoord = inCoord - sz2 * f32(i32(f32(inCoord / sz2)));\n                  if (inCoord >= len) {\n                    inCoord = sz2 - inCoord - 1.0;\n                  }\n                }\n              }\n              return clamp(inCoord, 0.0, len - 1.0);\n            } else if (uniforms.fillModeId == 3) {\n              if (inCoord < 0.0) {\n                if (len <= 1.0) {\n                  inCoord = 0.0;\n                } else {\n                  let sz = len - 1.0;\n                  inCoord = inCoord + len * (f32(i32(f32(-inCoord / sz))) + 1.0);\n                }\n              } else if (inCoord > len - 1.0) {\n                if (len <= 1.0) {\n                  inCoord = 0.0;\n                } else {\n                  let sz = len - 1.0;\n                  inCoord = inCoord - len * f32(i32(f32(inCoord / sz)));\n                }\n              }\n              return clamp(inCoord, 0.0, len - 1.0);\n            } else if (uniforms.fillModeId == 4) {\n              return clamp(outCoord, 0.0, len - 1.0);\n            }\n            return outCoord;\n          }\n          fn readWithFillValue(batch : i32, coordY : i32, coordX : i32,\n            channel : i32) -> f32 {\n            var outputValue : f32;\n            if (0 <= coordY && coordY < uniforms.imageShape[1] && 0 <= coordX && coordX < uniforms.imageShape[2]) {\n                outputValue = getImage(batch, coordY, coordX, channel);\n            } else {\n              outputValue = uniforms.fillValue;\n            }\n            return outputValue;\n          }\n\n          ${getMainHeaderString(\"index\")} {\n            if (index < uniforms.size) {\n              let coords = getCoordsFromIndex(index);\n              var outputValue : f32;\n              let batch = coords[0];\n              let x = coords[2];\n              let y = coords[1];\n              let channel = coords[3];\n              let xf = f32(x);\n              let yf = f32(y);\n              let a1 = getTransforms(batch, 0);\n              let a2 = getTransforms(batch, 1);\n              let a3 = getTransforms(batch, 2);\n              let b1 = getTransforms(batch, 3);\n              let b2 = getTransforms(batch, 4);\n              let b3 = getTransforms(batch, 5);\n              let c1 = getTransforms(batch, 6);\n              let c2 = getTransforms(batch, 7);\n              let projection = c1 * xf + c2 * yf + 1.0;\n              if (projection == 0.0) {\n                outputValue = uniforms.fillValue;\n              } else {\n                let inX = (a1 * xf + a2 * yf + a3) / projection;\n                let inY = (b1 * xf + b2 * yf + b3) / projection;\n                let mapX = mapCoord(inX, f32(uniforms.imageShape[2]));\n                let mapY = mapCoord(inY, f32(uniforms.imageShape[1]));\n\n                if (uniforms.interpolationModeId == 1) {\n                  let coordY = i32(round(mapY));\n                  let coordX = i32(round(mapX));\n                  outputValue = readWithFillValue(batch, coordY, coordX,\n                    channel);\n                } else {\n                  let yFloor = floor(mapY);\n                  let xFloor = floor(mapX);\n                  let yCeil = yFloor + 1.0;\n                  let xCeil = xFloor + 1.0;\n                  let valueYFloor = (xCeil - mapX) *\n                  readWithFillValue(batch, i32(yFloor), i32(xFloor), channel) +\n                  (mapX - xFloor) *\n                  readWithFillValue(batch, i32(yFloor), i32(xCeil), channel);\n                  let valueYCeil = (xCeil - mapX) *\n                  readWithFillValue(batch, i32(yCeil), i32(xFloor), channel) +\n                  (mapX - xFloor) *\n                  readWithFillValue(batch, i32(yCeil), i32(xCeil), channel);\n                  outputValue = (yCeil - mapY) * valueYFloor +\n                  (mapY - yFloor) * valueYCeil;\n                }\n              }\n              setOutputAtIndex(index, outputValue);\n            }\n          }\n        `;\n    return userCode;\n  }\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/Transform.js\nfunction transform5(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { image: image2, transforms } = inputs;\n  const { interpolation, fillMode, fillValue, outputShape } = attrs;\n  const [batch, imageHeight, imageWidth, numChannels] = image2.shape;\n  const [outHeight, outWidth] = outputShape != null ? outputShape : [imageHeight, imageWidth];\n  const outShape = [\n    batch,\n    outHeight,\n    outWidth,\n    numChannels\n  ];\n  const program = new TransformProgram2(outShape);\n  const interpolationModeId = interpolation === \"nearest\" ? 1 : 2;\n  let fillModeId;\n  switch (fillMode) {\n    case \"constant\":\n      fillModeId = 1;\n      break;\n    case \"reflect\":\n      fillModeId = 2;\n      break;\n    case \"wrap\":\n      fillModeId = 3;\n      break;\n    case \"nearest\":\n      fillModeId = 4;\n      break;\n    default:\n      fillModeId = 1;\n      break;\n  }\n  const uniformData = [\n    { type: \"int32\", data: [interpolationModeId] },\n    { type: \"int32\", data: [fillModeId] },\n    { type: \"float32\", data: [fillValue] }\n  ];\n  return backend2.runWebGPUProgram(program, [image2, transforms], \"float32\", uniformData);\n}\nvar transformConfig4 = {\n  kernelName: Transform,\n  backendName: \"webgpu\",\n  kernelFunc: transform5\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/Unpack.js\nfunction unpack4(args) {\n  const { inputs, backend: backend2, attrs } = args;\n  const { value } = inputs;\n  let { axis } = attrs;\n  if (axis < 0) {\n    axis += value.shape.length;\n  }\n  const x = value;\n  const xRank = x.shape.length;\n  const num = value.shape[axis];\n  const outShape = new Array(xRank - 1);\n  let outIndex = 0;\n  for (let i = 0; i < xRank; i++) {\n    if (i !== axis) {\n      outShape[outIndex++] = x.shape[i];\n    }\n  }\n  const toDispose = [];\n  const begin = new Array(xRank).fill(0);\n  const size = x.shape.slice();\n  size[axis] = 1;\n  const res = new Array(num);\n  for (let i = 0; i < res.length; i++) {\n    begin[axis] = i;\n    const sliced = slice5({ inputs: { x }, backend: backend2, attrs: { begin, size } });\n    const reshaped = reshape6({ inputs: { x: sliced }, backend: backend2, attrs: { shape: outShape } });\n    res[i] = reshaped;\n    toDispose.push(sliced);\n  }\n  toDispose.forEach((t) => backend2.disposeData(t.dataId));\n  return res;\n}\nvar unpackConfig4 = {\n  kernelName: Unpack,\n  backendName: \"webgpu\",\n  kernelFunc: unpack4\n};\n\n// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/register_all_kernels.js\nvar kernelConfigs4 = [\n  _fusedMatMulConfig4,\n  absConfig4,\n  addConfig4,\n  addNConfig4,\n  argMaxConfig4,\n  argMinConfig3,\n  atan2Config3,\n  avgPoolConfig4,\n  batchMatMulConfig4,\n  batchToSpaceNDConfig4,\n  castConfig4,\n  ceilConfig4,\n  clipByValueConfig4,\n  complexConfig3,\n  concatConfig4,\n  conv2DConfig4,\n  conv2DBackpropInputConfig4,\n  cosConfig4,\n  coshConfig4,\n  cropAndResizeConfig4,\n  cumprodConfig4,\n  cumsumConfig4,\n  depthToSpaceConfig4,\n  depthwiseConv2dNativeConfig4,\n  einsumConfig3,\n  eluConfig4,\n  equalConfig4,\n  expConfig4,\n  expandDimsConfig4,\n  expm1Config3,\n  fillConfig4,\n  flipLeftRightConfig4,\n  fromPixelsConfig2,\n  floorConfig4,\n  floorDivConfig4,\n  fusedBatchNormConfig2,\n  fusedConv2DConfig4,\n  fusedDepthwiseConv2DConfig4,\n  gatherNdConfig4,\n  gatherV2Config4,\n  greaterConfig4,\n  greaterEqualConfig4,\n  identityConfig4,\n  imagConfig3,\n  isNaNConfig3,\n  leakyReluConfig4,\n  lessConfig4,\n  lessEqualConfig4,\n  logConfig4,\n  logicalAndConfig4,\n  logicalNotConfig4,\n  maxConfig4,\n  maximumConfig4,\n  maxPoolConfig4,\n  meanConfig4,\n  minConfig4,\n  minimumConfig4,\n  mirrorPadConfig4,\n  multiplyConfig4,\n  negConfig4,\n  nonMaxSuppressionV3Config4,\n  nonMaxSuppressionV5Config4,\n  notEqualConfig4,\n  onesLikeConfig4,\n  packConfig4,\n  padV2Config4,\n  powConfig4,\n  preluConfig4,\n  prodConfig4,\n  rangeConfig4,\n  realConfig3,\n  realDivConfig4,\n  reciprocalConfig3,\n  reluConfig4,\n  relu6Config4,\n  reshapeConfig4,\n  resizeBilinearConfig4,\n  resizeNearestNeighborConfig4,\n  rotateWithOffsetConfig4,\n  rsqrtConfig4,\n  scatterNdConfig4,\n  selectConfig4,\n  sigmoidConfig4,\n  sinConfig4,\n  sinhConfig3,\n  sliceConfig4,\n  stridedSliceConfig4,\n  stringNGramsConfig4,\n  softmaxConfig4,\n  spaceToBatchNDConfig4,\n  sparseToDenseConfig3,\n  splitVConfig4,\n  sqrtConfig4,\n  squareConfig4,\n  squaredDifferenceConfig4,\n  subConfig4,\n  sumConfig4,\n  tanhConfig4,\n  tileConfig4,\n  topKConfig4,\n  transformConfig4,\n  transposeConfig4,\n  unpackConfig4,\n  zerosLikeConfig4\n];\nfor (const kernelConfig of kernelConfigs4) {\n  registerKernel(kernelConfig);\n}\n\n// dist/tfjs.version.js\nvar version9 = \"3.20.0\";\nvar version22 = \"3.20.0\";\nvar version32 = \"3.20.0\";\nvar version42 = \"3.20.0\";\nvar version52 = \"3.20.0\";\nvar version62 = \"3.20.0\";\nvar version72 = \"3.20.0\";\nvar version82 = {\n  tfjs: version9,\n  \"tfjs-core\": version22,\n  \"tfjs-data\": version32,\n  \"tfjs-layers\": version42,\n  \"tfjs-converter\": version52,\n  \"tfjs-backend-webgl\": version62,\n  \"tfjs-backend-wasm\": version72\n};\nexport {\n  Abs,\n  Acos,\n  Acosh,\n  AdadeltaOptimizer,\n  AdagradOptimizer,\n  AdamOptimizer,\n  AdamaxOptimizer,\n  Add,\n  AddN,\n  All,\n  Any,\n  ArgMax,\n  ArgMin,\n  Asin,\n  Asinh,\n  Atan,\n  Atan2,\n  Atanh,\n  AvgPool,\n  AvgPool3D,\n  AvgPool3DGrad,\n  AvgPoolGrad,\n  BackendWasm,\n  BatchMatMul,\n  BatchToSpaceND,\n  Bincount,\n  BroadcastArgs,\n  BroadcastTo,\n  Callback,\n  CallbackList,\n  Cast,\n  Ceil,\n  ClipByValue,\n  Complex,\n  ComplexAbs,\n  Concat,\n  Conv2D,\n  Conv2DBackpropFilter,\n  Conv2DBackpropInput,\n  Conv3D,\n  Conv3DBackpropFilterV2,\n  Conv3DBackpropInputV2,\n  Cos,\n  Cosh,\n  CropAndResize,\n  Cumprod,\n  Cumsum,\n  CustomCallback,\n  DataStorage,\n  DenseBincount,\n  DepthToSpace,\n  DepthwiseConv2dNative,\n  DepthwiseConv2dNativeBackpropFilter,\n  DepthwiseConv2dNativeBackpropInput,\n  Diag,\n  Dilation2D,\n  Dilation2DBackpropFilter,\n  Dilation2DBackpropInput,\n  ENV,\n  EarlyStopping,\n  Einsum,\n  Elu,\n  EluGrad,\n  Environment,\n  Equal,\n  Erf,\n  Exp,\n  ExpandDims,\n  Expm1,\n  FFT,\n  Fill,\n  FlipLeftRight,\n  Floor,\n  FloorDiv,\n  FromPixels,\n  FusedBatchNorm,\n  FusedConv2D,\n  FusedDepthwiseConv2D,\n  GPGPUContext,\n  GatherNd,\n  GatherV2,\n  GraphModel,\n  Greater,\n  GreaterEqual,\n  History,\n  IFFT,\n  Identity,\n  Imag,\n  InputSpec,\n  IsFinite,\n  IsInf,\n  IsNan,\n  KernelBackend,\n  LRN,\n  LRNGrad,\n  LayerVariable,\n  LayersModel,\n  LeakyRelu,\n  Less,\n  LessEqual,\n  LinSpace,\n  Log,\n  Log1p,\n  LogSoftmax,\n  LogicalAnd,\n  LogicalNot,\n  LogicalOr,\n  LogicalXor,\n  LowerBound,\n  MathBackendWebGL,\n  Max,\n  MaxPool,\n  MaxPool3D,\n  MaxPool3DGrad,\n  MaxPoolGrad,\n  MaxPoolWithArgmax,\n  Maximum,\n  Mean,\n  Min,\n  Minimum,\n  MirrorPad,\n  Mod,\n  MomentumOptimizer,\n  Multinomial,\n  Multiply,\n  Neg,\n  NonMaxSuppressionV3,\n  NonMaxSuppressionV4,\n  NonMaxSuppressionV5,\n  NotEqual,\n  OP_SCOPE_SUFFIX,\n  OneHot,\n  OnesLike,\n  Optimizer,\n  OptimizerConstructors,\n  Pack,\n  PadV2,\n  Pool,\n  Pow,\n  Prelu,\n  Prod,\n  RMSPropOptimizer,\n  RNN,\n  RaggedTensorToTensor,\n  Range,\n  Rank,\n  Real,\n  RealDiv,\n  Reciprocal,\n  Reduction,\n  Relu,\n  Relu6,\n  Reshape,\n  ResizeBilinear,\n  ResizeBilinearGrad,\n  ResizeNearestNeighbor,\n  ResizeNearestNeighborGrad,\n  Reverse,\n  RotateWithOffset,\n  Round,\n  Rsqrt,\n  SGDOptimizer,\n  ScatterNd,\n  SearchSorted,\n  Select,\n  Selu,\n  Sequential,\n  Sigmoid,\n  Sign,\n  Sin,\n  Sinh,\n  Slice,\n  Softmax,\n  Softplus,\n  SpaceToBatchND,\n  SparseFillEmptyRows,\n  SparseReshape,\n  SparseSegmentMean,\n  SparseSegmentSum,\n  SparseToDense,\n  SplitV,\n  Sqrt,\n  Square,\n  SquaredDifference,\n  Step,\n  StridedSlice,\n  StringNGrams,\n  StringSplit,\n  StringToHashBucketFast,\n  Sub,\n  Sum,\n  SymbolicTensor,\n  Tan,\n  Tanh,\n  Tensor,\n  TensorBuffer,\n  Tile,\n  TopK,\n  Transform,\n  Transpose,\n  Unique,\n  Unpack,\n  UnsortedSegmentSum,\n  UpperBound,\n  Variable,\n  WebGPUBackend,\n  ZerosLike,\n  _FusedMatMul,\n  abs,\n  acos,\n  acosh,\n  add2 as add,\n  addN,\n  all,\n  any,\n  argMax,\n  argMin,\n  asin,\n  asinh,\n  atan,\n  atan2,\n  atanh,\n  avgPool,\n  avgPool3d,\n  backend,\n  backend_util_exports as backend_util,\n  basicLSTMCell,\n  batchNorm,\n  batchNorm2d,\n  batchNorm3d,\n  batchNorm4d,\n  batchToSpaceND,\n  bincount,\n  booleanMaskAsync,\n  broadcastArgs,\n  broadcastTo,\n  broadcast_util_exports as broadcast_util,\n  browser_exports as browser,\n  buffer,\n  callbacks,\n  cast,\n  ceil,\n  clipByValue,\n  clone,\n  complex,\n  concat,\n  concat1d,\n  concat2d,\n  concat3d,\n  concat4d,\n  exports_constraints_exports as constraints,\n  conv1d,\n  conv2d,\n  conv2dTranspose,\n  conv3d,\n  conv3dTranspose,\n  copyRegisteredKernels,\n  cos,\n  cosh,\n  cosineWindow,\n  cumprod,\n  cumsum,\n  customGrad,\n  dist_exports2 as data,\n  denseBincount,\n  deprecationWarn,\n  depthToSpace,\n  depthwiseConv2d,\n  deregisterOp,\n  device_util_exports as device_util,\n  diag,\n  dilation2d,\n  disableDeprecationWarnings,\n  dispose,\n  disposeVariables,\n  div,\n  divNoNan,\n  dot,\n  dropout,\n  einsum,\n  elu,\n  enableDebugMode,\n  enableProdMode,\n  enclosingPowerOfTwo,\n  engine,\n  env,\n  equal,\n  erf,\n  euclideanNorm,\n  exp,\n  expandDims,\n  expm1,\n  eye,\n  fft,\n  fill,\n  findBackend,\n  findBackendFactory,\n  floor,\n  floorDiv,\n  forceHalfFloat,\n  fused_ops_exports as fused,\n  gather,\n  gatherND,\n  gather_nd_util_exports as gather_util,\n  getBackend,\n  getGradient,\n  getKernel,\n  getKernelsForBackend,\n  getThreadsCount,\n  gpgpu_util_exports as gpgpu_util,\n  grad,\n  grads,\n  greater,\n  greaterEqual,\n  ifft,\n  imag,\n  image,\n  inTopKAsync,\n  exports_initializers_exports as initializers,\n  input,\n  io_exports as io,\n  irfft,\n  isFinite2 as isFinite,\n  isInf,\n  isNaN2 as isNaN,\n  keep,\n  kernel_impls_exports as kernel_impls,\n  exports_layers_exports as layers,\n  leakyRelu,\n  less,\n  lessEqual,\n  linalg,\n  linspace,\n  loadGraphModel,\n  loadGraphModelSync,\n  loadLayersModel,\n  localResponseNormalization,\n  log2 as log,\n  log1p,\n  logSigmoid,\n  logSoftmax,\n  logSumExp,\n  logicalAnd,\n  logicalNot,\n  logicalOr,\n  logicalXor,\n  losses,\n  lowerBound,\n  matMul,\n  math_exports as math,\n  max,\n  maxPool,\n  maxPool3d,\n  maxPoolWithArgmax,\n  maximum,\n  mean,\n  memory,\n  meshgrid,\n  exports_metrics_exports as metrics,\n  min,\n  minimum,\n  mirrorPad,\n  mod,\n  model,\n  exports_models_exports as models,\n  moments,\n  movingAverage,\n  mul,\n  multiRNNCell,\n  multinomial,\n  neg,\n  nextFrame,\n  norm,\n  notEqual,\n  oneHot,\n  ones2 as ones,\n  onesLike,\n  op,\n  outerProduct,\n  pad,\n  pad1d,\n  pad2d,\n  pad3d,\n  pad4d,\n  pool,\n  pow,\n  prelu,\n  print,\n  prod,\n  profile,\n  raggedTensorToTensor,\n  rand,\n  randomGamma,\n  randomNormal,\n  randomStandardNormal,\n  randomUniform,\n  range,\n  ready,\n  real,\n  reciprocal,\n  registerBackend,\n  registerCallbackConstructor,\n  registerGradient,\n  registerKernel,\n  registerOp,\n  exports_regularizers_exports as regularizers,\n  relu,\n  relu6,\n  removeBackend,\n  reshape,\n  reverse,\n  reverse1d,\n  reverse2d,\n  reverse3d,\n  reverse4d,\n  rfft,\n  round2 as round,\n  rsqrt,\n  scalar,\n  scatterND,\n  scatter_nd_util_exports as scatter_util,\n  searchSorted,\n  selu,\n  separableConv2d,\n  sequential,\n  serialization_exports as serialization,\n  setBackend,\n  setPlatform,\n  setThreadsCount,\n  setWasmPath,\n  setWasmPaths,\n  setWebGLContext,\n  setdiff1dAsync,\n  sigmoid,\n  sign,\n  signal,\n  sin,\n  sinh,\n  slice,\n  slice1d,\n  slice2d,\n  slice3d,\n  slice4d,\n  slice_util_exports as slice_util,\n  softmax,\n  softplus,\n  spaceToBatchND,\n  sparse,\n  sparseToDense,\n  spectral,\n  split,\n  sqrt,\n  square,\n  squaredDifference,\n  squeeze,\n  stack,\n  step,\n  stridedSlice,\n  string,\n  sub,\n  sum2 as sum,\n  sumOutType,\n  tan,\n  tanh2 as tanh,\n  tensor,\n  tensor1d,\n  tensor2d,\n  tensor3d,\n  tensor4d,\n  tensor5d,\n  tensor6d,\n  tensor_util_exports as tensor_util,\n  test_util_exports as test_util,\n  tidy,\n  tile,\n  time,\n  topk,\n  train,\n  transpose,\n  truncatedNormal,\n  unique,\n  unregisterGradient,\n  unregisterKernel,\n  unsortedSegmentSum,\n  unstack,\n  upcastType,\n  upperBound,\n  util_exports as util,\n  valueAndGrad,\n  valueAndGrads,\n  variable,\n  variableGrads,\n  version82 as version,\n  version3 as version_converter,\n  version as version_core,\n  version2 as version_layers,\n  version8 as version_wasm,\n  version6 as version_webgl,\n  webgl,\n  webgl_util_exports as webgl_util,\n  webgpu_util_exports as webgpu_util,\n  where,\n  whereAsync,\n  zeros,\n  zerosLike\n};\n", "export const vertexIdentity = `\n  precision highp float;\n  attribute vec2 pos;\n  attribute vec2 uv;\n  varying vec2 vUv;\n  uniform float flipY;\n  void main(void) {\n    vUv = uv;\n    gl_Position = vec4(pos.x, pos.y*flipY, 0.0, 1.);\n  }\n`;\n\nexport const fragmentIdentity = `\n  precision highp float;\n  varying vec2 vUv;\n  uniform sampler2D texture;\n  void main(void) {\n    gl_FragColor = texture2D(texture, vUv);\n  }\n`;\n\nexport const colorMatrixWithAlpha = `\n  precision highp float;\n  varying vec2 vUv;\n  uniform sampler2D texture;\n  uniform float m[20];\n  void main(void) {\n    vec4 c = texture2D(texture, vUv);\n    gl_FragColor.r = m[0] * c.r + m[1] * c.g + m[2] * c.b + m[3] * c.a + m[4];\n    gl_FragColor.g = m[5] * c.r + m[6] * c.g + m[7] * c.b + m[8] * c.a + m[9];\n    gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[13] * c.a + m[14];\n    gl_FragColor.a = m[15] * c.r + m[16] * c.g + m[17] * c.b + m[18] * c.a + m[19];\n  }\n`;\n\nexport const colorMatrixWithoutAlpha = `\n  precision highp float;\n  varying vec2 vUv;\n  uniform sampler2D texture;\n  uniform float m[20];\n  void main(void) {\n    vec4 c = texture2D(texture, vUv);\n    gl_FragColor.r = m[0] * c.r + m[1] * c.g + m[2] * c.b + m[4];\n    gl_FragColor.g = m[5] * c.r + m[6] * c.g + m[7] * c.b + m[9];\n    gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[14];\n    gl_FragColor.a = c.a;\n  }\n`;\n\nexport const pixelate = `\n  precision highp float;\n  varying vec2 vUv;\n  uniform vec2 size;\n  uniform sampler2D texture;\n  vec2 pixelate(vec2 coord, vec2 size) {\n    return floor( coord / size ) * size;\n  }\n  void main(void) {\n    gl_FragColor = vec4(0.0);\n    vec2 coord = pixelate(vUv, size);\n    gl_FragColor += texture2D(texture, coord);\n  }\n`;\n\nexport const blur = `\n  precision highp float;\n  varying vec2 vUv;\n  uniform sampler2D texture;\n  uniform vec2 px;\n  void main(void) {\n    gl_FragColor = vec4(0.0);\n    gl_FragColor += texture2D(texture, vUv + vec2(-7.0*px.x, -7.0*px.y))*0.0044299121055113265;\n    gl_FragColor += texture2D(texture, vUv + vec2(-6.0*px.x, -6.0*px.y))*0.00895781211794;\n    gl_FragColor += texture2D(texture, vUv + vec2(-5.0*px.x, -5.0*px.y))*0.0215963866053;\n    gl_FragColor += texture2D(texture, vUv + vec2(-4.0*px.x, -4.0*px.y))*0.0443683338718;\n    gl_FragColor += texture2D(texture, vUv + vec2(-3.0*px.x, -3.0*px.y))*0.0776744219933;\n    gl_FragColor += texture2D(texture, vUv + vec2(-2.0*px.x, -2.0*px.y))*0.115876621105;\n    gl_FragColor += texture2D(texture, vUv + vec2(-1.0*px.x, -1.0*px.y))*0.147308056121;\n    gl_FragColor += texture2D(texture, vUv                             )*0.159576912161;\n    gl_FragColor += texture2D(texture, vUv + vec2( 1.0*px.x,  1.0*px.y))*0.147308056121;\n    gl_FragColor += texture2D(texture, vUv + vec2( 2.0*px.x,  2.0*px.y))*0.115876621105;\n    gl_FragColor += texture2D(texture, vUv + vec2( 3.0*px.x,  3.0*px.y))*0.0776744219933;\n    gl_FragColor += texture2D(texture, vUv + vec2( 4.0*px.x,  4.0*px.y))*0.0443683338718;\n    gl_FragColor += texture2D(texture, vUv + vec2( 5.0*px.x,  5.0*px.y))*0.0215963866053;\n    gl_FragColor += texture2D(texture, vUv + vec2( 6.0*px.x,  6.0*px.y))*0.00895781211794;\n    gl_FragColor += texture2D(texture, vUv + vec2( 7.0*px.x,  7.0*px.y))*0.0044299121055113265;\n  }\n`;\n\nexport const convolution = `\n  precision highp float;\n  varying vec2 vUv;\n  uniform sampler2D texture;\n  uniform vec2 px;\n  uniform float m[9];\n  void main(void) {\n    vec4 c11 = texture2D(texture, vUv - px); // top left\n    vec4 c12 = texture2D(texture, vec2(vUv.x, vUv.y - px.y)); // top center\n    vec4 c13 = texture2D(texture, vec2(vUv.x + px.x, vUv.y - px.y)); // top right\n    vec4 c21 = texture2D(texture, vec2(vUv.x - px.x, vUv.y) ); // mid left\n    vec4 c22 = texture2D(texture, vUv); // mid center\n    vec4 c23 = texture2D(texture, vec2(vUv.x + px.x, vUv.y) ); // mid right\n    vec4 c31 = texture2D(texture, vec2(vUv.x - px.x, vUv.y + px.y) ); // bottom left\n    vec4 c32 = texture2D(texture, vec2(vUv.x, vUv.y + px.y) ); // bottom center\n    vec4 c33 = texture2D(texture, vUv + px ); // bottom right\n    gl_FragColor = \n    c11 * m[0] + c12 * m[1] + c22 * m[2] +\n    c21 * m[3] + c22 * m[4] + c23 * m[5] +\n    c31 * m[6] + c32 * m[7] + c33 * m[8];\n    gl_FragColor.a = c22.a;\n  }\n`;\n", "/**\n * Image Filters in WebGL algoritm implementation\n * Based on: [WebGLImageFilter](https://github.com/phoboslab/WebGLImageFilter)\n */\n\n/* eslint-disable func-names */\n\nimport * as shaders from './imagefxshaders';\nimport { canvas } from './image';\nimport { log } from '../util/util';\n\nconst collect = (source, prefix: string, collection) => {\n  const r = new RegExp('\\\\b' + prefix + ' \\\\w+ (\\\\w+)', 'ig');\n  source.replace(r, (match, name) => {\n    collection[name] = 0;\n    return match;\n  });\n};\n\nclass GLProgram {\n  uniform = {};\n  attribute = {};\n  gl: WebGLRenderingContext;\n  id: WebGLProgram;\n\n  constructor(gl, vertexSource, fragmentSource) {\n    this.gl = gl;\n    const vertexShader = this.compile(vertexSource, this.gl.VERTEX_SHADER);\n    const fragmentShader = this.compile(fragmentSource, this.gl.FRAGMENT_SHADER);\n    this.id = this.gl.createProgram() as WebGLProgram;\n    if (!vertexShader || !fragmentShader) return;\n    if (!this.id) {\n      log('filter: could not create webgl program');\n      return;\n    }\n    this.gl.attachShader(this.id, vertexShader);\n    this.gl.attachShader(this.id, fragmentShader);\n    this.gl.linkProgram(this.id);\n    if (!this.gl.getProgramParameter(this.id, this.gl.LINK_STATUS)) {\n      log(`filter: gl link failed: ${this.gl.getProgramInfoLog(this.id) || 'unknown'}`);\n      return;\n    }\n    this.gl.useProgram(this.id);\n    collect(vertexSource, 'attribute', this.attribute); // Collect attributes\n    for (const a in this.attribute) this.attribute[a] = this.gl.getAttribLocation(this.id, a);\n    collect(vertexSource, 'uniform', this.uniform); // Collect uniforms\n    collect(fragmentSource, 'uniform', this.uniform);\n    for (const u in this.uniform) this.uniform[u] = this.gl.getUniformLocation(this.id, u);\n  }\n\n  compile = (source, type): WebGLShader | null => {\n    const shader = this.gl.createShader(type);\n    if (!shader) {\n      log('filter: could not create shader');\n      return null;\n    }\n    this.gl.shaderSource(shader, source);\n    this.gl.compileShader(shader);\n    if (!this.gl.getShaderParameter(shader, this.gl.COMPILE_STATUS)) {\n      log(`filter: gl compile failed: ${this.gl.getShaderInfoLog(shader) || 'unknown'}`);\n      return null;\n    }\n    return shader;\n  };\n}\n\n// function that is instantiated as class so it has private this members\n/**\n * @class GLImageFilter\n * @property {function} reset reset current filter chain\n * @property {function} add add specified filter to filter chain\n * @property {function} apply execute filter chain and draw result\n * @property {function} draw just draw input to result\n */\n\nexport function GLImageFilter() {\n  let drawCount = 0;\n  let sourceTexture: WebGLTexture | null = null;\n  let lastInChain = false;\n  let currentFramebufferIndex = -1;\n  let tempFramebuffers: [null, null] | [{ fbo: WebGLFramebuffer | null, texture: WebGLTexture | null }] = [null, null];\n  let filterChain: Record<string, unknown>[] = [];\n  let vertexBuffer: WebGLBuffer | null = null;\n  let currentProgram: GLProgram | null = null;\n  const fxcanvas = canvas(100, 100);\n  const shaderProgramCache = { }; // key is the shader program source, value is the compiled program\n  const DRAW = { INTERMEDIATE: 1 };\n  const gl = fxcanvas.getContext('webgl') as WebGLRenderingContext;\n  if (!gl) {\n    log('filter: cannot get webgl context');\n    return;\n  }\n  // @ts-ignore used for sanity checks outside of imagefx\n  this.gl = gl;\n\n  function resize(width, height) {\n    if (width === fxcanvas.width && height === fxcanvas.height) return; // Same width/height? Nothing to do here\n    fxcanvas.width = width;\n    fxcanvas.height = height;\n    if (!vertexBuffer) { // Create the context if we don't have it yet\n      const vertices = new Float32Array([-1, -1, 0, 1, 1, -1, 1, 1, -1, 1, 0, 0, -1, 1, 0, 0, 1, -1, 1, 1, 1, 1, 1, 0]); // Create the vertex buffer for the two triangles [x, y, u, v] * 6\n      vertexBuffer = gl.createBuffer();\n      gl.bindBuffer(gl.ARRAY_BUFFER, vertexBuffer);\n      gl.bufferData(gl.ARRAY_BUFFER, vertices, gl.STATIC_DRAW);\n      gl.pixelStorei(gl.UNPACK_PREMULTIPLY_ALPHA_WEBGL, true);\n    }\n    gl.viewport(0, 0, fxcanvas.width, fxcanvas.height);\n    tempFramebuffers = [null, null]; // Delete old temp framebuffers\n  }\n\n  function createFramebufferTexture(width, height) {\n    const fbo = gl.createFramebuffer();\n    gl.bindFramebuffer(gl.FRAMEBUFFER, fbo);\n    const renderbuffer = gl.createRenderbuffer();\n    gl.bindRenderbuffer(gl.RENDERBUFFER, renderbuffer);\n    const texture = gl.createTexture();\n    gl.bindTexture(gl.TEXTURE_2D, texture);\n    gl.texImage2D(gl.TEXTURE_2D, 0, gl.RGBA, width, height, 0, gl.RGBA, gl.UNSIGNED_BYTE, null);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MAG_FILTER, gl.LINEAR);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MIN_FILTER, gl.LINEAR);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_S, gl.CLAMP_TO_EDGE);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_T, gl.CLAMP_TO_EDGE);\n    gl.framebufferTexture2D(gl.FRAMEBUFFER, gl.COLOR_ATTACHMENT0, gl.TEXTURE_2D, texture, 0);\n    gl.bindTexture(gl.TEXTURE_2D, null);\n    gl.bindFramebuffer(gl.FRAMEBUFFER, null);\n    return { fbo, texture };\n  }\n\n  function getTempFramebuffer(index): { fbo: WebGLFramebuffer | null, texture: WebGLTexture | null } {\n    tempFramebuffers[index] = tempFramebuffers[index] || createFramebufferTexture(fxcanvas.width, fxcanvas.height);\n    return tempFramebuffers[index] as { fbo: WebGLFramebuffer, texture: WebGLTexture };\n  }\n\n  function draw(flags = 0) {\n    if (!currentProgram) return;\n    let source: WebGLTexture | null = null;\n    let target: WebGLFramebuffer | null = null;\n    let flipY = false;\n    if (drawCount === 0) source = sourceTexture; // First draw call - use the source texture\n    else source = getTempFramebuffer(currentFramebufferIndex).texture || null; // All following draw calls use the temp buffer last drawn to\n    drawCount++;\n    if (lastInChain && !(flags & DRAW.INTERMEDIATE)) { // Last filter in our chain - draw directly to the WebGL Canvas. We may also have to flip the image vertically now\n      target = null;\n      flipY = drawCount % 2 === 0;\n    } else {\n      currentFramebufferIndex = (currentFramebufferIndex + 1) % 2;\n      target = getTempFramebuffer(currentFramebufferIndex).fbo || null; // Intermediate draw call - get a temp buffer to draw to\n    }\n    gl.bindTexture(gl.TEXTURE_2D, source); // Bind the source and target and draw the two triangles\n    gl.bindFramebuffer(gl.FRAMEBUFFER, target);\n    gl.uniform1f(currentProgram.uniform['flipY'], (flipY ? -1 : 1));\n    gl.drawArrays(gl.TRIANGLES, 0, 6);\n  }\n\n  function compileShader(fragmentSource): GLProgram | null {\n    if (shaderProgramCache[fragmentSource]) {\n      currentProgram = shaderProgramCache[fragmentSource];\n      gl.useProgram((currentProgram ? currentProgram.id : null) || null);\n      return currentProgram;\n    }\n    currentProgram = new GLProgram(gl, shaders.vertexIdentity, fragmentSource);\n    if (!currentProgram) {\n      log('filter: could not get webgl program');\n      return null;\n    }\n    const floatSize = Float32Array.BYTES_PER_ELEMENT;\n    const vertSize = 4 * floatSize;\n    gl.enableVertexAttribArray(currentProgram.attribute['pos']);\n    gl.vertexAttribPointer(currentProgram.attribute['pos'], 2, gl.FLOAT, false, vertSize, 0 * floatSize);\n    gl.enableVertexAttribArray(currentProgram.attribute['uv']);\n    gl.vertexAttribPointer(currentProgram.attribute['uv'], 2, gl.FLOAT, false, vertSize, 2 * floatSize);\n    shaderProgramCache[fragmentSource] = currentProgram;\n    return currentProgram;\n  }\n\n  const filter = {\n    colorMatrix: (matrix: number[]) => { // general color matrix filter\n      const m = new Float32Array(matrix);\n      m[4] /= 255;\n      m[9] /= 255;\n      m[14] /= 255;\n      m[19] /= 255;\n      const shader = (m[18] === 1 && m[3] === 0 && m[8] === 0 && m[13] === 0 && m[15] === 0 && m[16] === 0 && m[17] === 0 && m[19] === 0) // Can we ignore the alpha value? Makes things a bit faster.\n        ? shaders.colorMatrixWithoutAlpha\n        : shaders.colorMatrixWithAlpha;\n      const program = compileShader(shader);\n      if (!program) return;\n      gl.uniform1fv(program.uniform['m'], m);\n      draw();\n    },\n\n    brightness: (brightness: number) => {\n      const b = (brightness || 0) + 1;\n      filter.colorMatrix([\n        b, 0, 0, 0, 0,\n        0, b, 0, 0, 0,\n        0, 0, b, 0, 0,\n        0, 0, 0, 1, 0,\n      ]);\n    },\n\n    saturation: (amount: number) => {\n      const x = (amount || 0) * 2 / 3 + 1;\n      const y = ((x - 1) * -0.5);\n      filter.colorMatrix([\n        x, y, y, 0, 0,\n        y, x, y, 0, 0,\n        y, y, x, 0, 0,\n        0, 0, 0, 1, 0,\n      ]);\n    },\n\n    desaturate: () => {\n      filter.saturation(-1);\n    },\n\n    contrast: (amount: number) => {\n      const v = (amount || 0) + 1;\n      const o = -128 * (v - 1);\n      filter.colorMatrix([\n        v, 0, 0, 0, o,\n        0, v, 0, 0, o,\n        0, 0, v, 0, o,\n        0, 0, 0, 1, 0,\n      ]);\n    },\n\n    negative: () => {\n      filter.contrast(-2);\n    },\n\n    hue: (rotation: number) => {\n      rotation = (rotation || 0) / 180 * Math.PI;\n      const cos = Math.cos(rotation);\n      const sin = Math.sin(rotation);\n      const lumR = 0.213;\n      const lumG = 0.715;\n      const lumB = 0.072;\n      filter.colorMatrix([\n        lumR + cos * (1 - lumR) + sin * (-lumR), lumG + cos * (-lumG) + sin * (-lumG), lumB + cos * (-lumB) + sin * (1 - lumB), 0, 0,\n        lumR + cos * (-lumR) + sin * (0.143), lumG + cos * (1 - lumG) + sin * (0.140), lumB + cos * (-lumB) + sin * (-0.283), 0, 0,\n        lumR + cos * (-lumR) + sin * (-(1 - lumR)), lumG + cos * (-lumG) + sin * (lumG), lumB + cos * (1 - lumB) + sin * (lumB), 0, 0,\n        0, 0, 0, 1, 0,\n      ]);\n    },\n\n    desaturateLuminance: () => {\n      filter.colorMatrix([\n        0.2764723, 0.9297080, 0.0938197, 0, -37.1,\n        0.2764723, 0.9297080, 0.0938197, 0, -37.1,\n        0.2764723, 0.9297080, 0.0938197, 0, -37.1,\n        0, 0, 0, 1, 0,\n      ]);\n    },\n\n    sepia: () => {\n      filter.colorMatrix([\n        0.393, 0.7689999, 0.18899999, 0, 0,\n        0.349, 0.6859999, 0.16799999, 0, 0,\n        0.272, 0.5339999, 0.13099999, 0, 0,\n        0, 0, 0, 1, 0,\n      ]);\n    },\n\n    brownie: () => {\n      filter.colorMatrix([\n        0.5997023498159715, 0.34553243048391263, -0.2708298674538042, 0, 47.43192855600873,\n        -0.037703249837783157, 0.8609577587992641, 0.15059552388459913, 0, -36.96841498319127,\n        0.24113635128153335, -0.07441037908422492, 0.44972182064877153, 0, -7.562075277591283,\n        0, 0, 0, 1, 0,\n      ]);\n    },\n\n    vintagePinhole: () => {\n      filter.colorMatrix([\n        0.6279345635605994, 0.3202183420819367, -0.03965408211312453, 0, 9.651285835294123,\n        0.02578397704808868, 0.6441188644374771, 0.03259127616149294, 0, 7.462829176470591,\n        0.0466055556782719, -0.0851232987247891, 0.5241648018700465, 0, 5.159190588235296,\n        0, 0, 0, 1, 0,\n      ]);\n    },\n\n    kodachrome: () => {\n      filter.colorMatrix([\n        1.1285582396593525, -0.3967382283601348, -0.03992559172921793, 0, 63.72958762196502,\n        -0.16404339962244616, 1.0835251566291304, -0.05498805115633132, 0, 24.732407896706203,\n        -0.16786010706155763, -0.5603416277695248, 1.6014850761964943, 0, 35.62982807460946,\n        0, 0, 0, 1, 0,\n      ]);\n    },\n\n    technicolor: () => {\n      filter.colorMatrix([\n        1.9125277891456083, -0.8545344976951645, -0.09155508482755585, 0, 11.793603434377337,\n        -0.3087833385928097, 1.7658908555458428, -0.10601743074722245, 0, -70.35205161461398,\n        -0.231103377548616, -0.7501899197440212, 1.847597816108189, 0, 30.950940869491138,\n        0, 0, 0, 1, 0,\n      ]);\n    },\n\n    polaroid: () => {\n      filter.colorMatrix([\n        1.438, -0.062, -0.062, 0, 0,\n        -0.122, 1.378, -0.122, 0, 0,\n        -0.016, -0.016, 1.483, 0, 0,\n        0, 0, 0, 1, 0,\n      ]);\n    },\n\n    shiftToBGR: () => {\n      filter.colorMatrix([\n        0, 0, 1, 0, 0,\n        0, 1, 0, 0, 0,\n        1, 0, 0, 0, 0,\n        0, 0, 0, 1, 0,\n      ]);\n    },\n\n    convolution: (matrix: number[]) => { // general convolution Filter\n      const m = new Float32Array(matrix);\n      const pixelSizeX = 1 / fxcanvas.width;\n      const pixelSizeY = 1 / fxcanvas.height;\n      const program = compileShader(shaders.convolution);\n      if (!program) return;\n      gl.uniform1fv(program.uniform['m'], m);\n      gl.uniform2f(program.uniform['px'], pixelSizeX, pixelSizeY);\n      draw();\n    },\n\n    detectEdges: () => {\n      // @ts-ignore this\n      filter.convolution.call(this, [\n        0, 1, 0,\n        1, -4, 1,\n        0, 1, 0,\n      ]);\n    },\n\n    sobelX: () => {\n      // @ts-ignore this\n      filter.convolution.call(this, [\n        -1, 0, 1,\n        -2, 0, 2,\n        -1, 0, 1,\n      ]);\n    },\n\n    sobelY: () => {\n      // @ts-ignore this\n      filter.convolution.call(this, [\n        -1, -2, -1,\n        0, 0, 0,\n        1, 2, 1,\n      ]);\n    },\n\n    sharpen: (amount) => {\n      const a = amount || 1;\n      // @ts-ignore this\n      filter.convolution.call(this, [\n        0, -1 * a, 0,\n        -1 * a, 1 + 4 * a, -1 * a,\n        0, -1 * a, 0,\n      ]);\n    },\n\n    emboss: (size: number) => {\n      const s = size || 1;\n      // @ts-ignore this\n      filter.convolution.call(this, [\n        -2 * s, -1 * s, 0,\n        -1 * s, 1, 1 * s,\n        0, 1 * s, 2 * s,\n      ]);\n    },\n\n    blur: (size: number) => {\n      const blurSizeX = (size / 7) / fxcanvas.width;\n      const blurSizeY = (size / 7) / fxcanvas.height;\n      const program = compileShader(shaders.blur);\n      if (!program) return;\n      // Vertical\n      gl.uniform2f(program.uniform['px'], 0, blurSizeY);\n      draw(DRAW.INTERMEDIATE);\n      // Horizontal\n      gl.uniform2f(program.uniform['px'], blurSizeX, 0);\n      draw();\n    },\n\n    pixelate: (size: number) => {\n      const blurSizeX = (size) / fxcanvas.width;\n      const blurSizeY = (size) / fxcanvas.height;\n      const program = compileShader(shaders.pixelate);\n      if (!program) return;\n      gl.uniform2f(program.uniform['size'], blurSizeX, blurSizeY);\n      draw();\n    },\n  };\n\n  // @ts-ignore this\n  this.add = function (name) {\n    const args = Array.prototype.slice.call(arguments, 1); // eslint-disable-line prefer-rest-params\n    const func = filter[name];\n    filterChain.push({ func, args });\n  };\n\n  // @ts-ignore this\n  this.reset = function () {\n    filterChain = [];\n  };\n\n  // @ts-ignore this\n  this.get = function () {\n    return filterChain;\n  };\n\n  // @ts-ignore this\n  this.apply = function (image) {\n    resize(image.width, image.height);\n    drawCount = 0;\n    if (!sourceTexture) sourceTexture = gl.createTexture(); // Create the texture for the input image if we haven't yet\n    gl.bindTexture(gl.TEXTURE_2D, sourceTexture);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_S, gl.CLAMP_TO_EDGE);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_T, gl.CLAMP_TO_EDGE);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MIN_FILTER, gl.NEAREST);\n    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MAG_FILTER, gl.NEAREST);\n    gl.texImage2D(gl.TEXTURE_2D, 0, gl.RGBA, gl.RGBA, gl.UNSIGNED_BYTE, image);\n    for (let i = 0; i < filterChain.length; i++) {\n      lastInChain = (i === filterChain.length - 1);\n      const f = filterChain[i];\n      // @ts-ignore function assigment\n      f.func.apply(this, f.args || []);\n    }\n    return fxcanvas;\n  };\n\n  // @ts-ignore this\n  this.draw = function (image) {\n    this.add('brightness', 0);\n    return this.apply(image);\n  };\n}\n", "/**\n * Image enhancements\n */\n\nimport * as tf from '../../dist/tfjs.esm.js';\nimport type { Tensor } from '../exports';\n\nexport async function histogramEqualization(inputImage: Tensor): Promise<Tensor> {\n  // const maxValue = 254; // using 255 results in values slightly larger than 1 due to math rounding errors\n  const squeeze = inputImage.shape.length === 4 ? tf.squeeze(inputImage) : inputImage;\n  const channels = tf.split(squeeze, 3, 2);\n  const min: Tensor[] = [tf.min(channels[0]), tf.min(channels[1]), tf.min(channels[2])];\n  const max: Tensor[] = [tf.max(channels[0]), tf.max(channels[1]), tf.max(channels[2])];\n  const absMax = await Promise.all(max.map((channel) => channel.data()));\n  const maxValue = 0.99 * Math.max(absMax[0][0], absMax[1][0], absMax[2][0]);\n  const sub = [tf.sub(channels[0], min[0]), tf.sub(channels[1], min[1]), tf.sub(channels[2], min[2])];\n  const range = [tf.sub(max[0], min[0]), tf.sub(max[1], min[1]), tf.sub(max[2], min[2])];\n  const fact = [tf.div(maxValue, range[0]), tf.div(maxValue, range[1]), tf.div(maxValue, range[2])];\n  const enh = [tf.mul(sub[0], fact[0]), tf.mul(sub[1], fact[1]), tf.mul(sub[2], fact[2])];\n  const rgb = tf.stack([enh[0], enh[1], enh[2]], 2);\n  const reshape = tf.reshape(rgb, [1, squeeze.shape[0], squeeze.shape[1], 3]);\n  tf.dispose([...channels, ...min, ...max, ...sub, ...range, ...fact, ...enh, rgb, squeeze]);\n  return reshape as Tensor; // output shape is [1, height, width, 3]\n}\n", "/**\n * Image Processing algorithm implementation\n */\n\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as fxImage from './imagefx';\nimport type { Input, AnyCanvas, Tensor, Config } from '../exports';\nimport { env } from '../util/env';\nimport { log } from '../util/util';\nimport * as enhance from './enhance';\n\nconst maxSize = 3840;\n// internal temp canvases\nlet inCanvas: AnyCanvas | null = null; // use global variable to avoid recreating canvas on each frame\nlet outCanvas: AnyCanvas | null = null; // use global variable to avoid recreating canvas on each frame\nlet tmpCanvas: AnyCanvas | null = null; // use global variable to avoid recreating canvas on each frame\n// @ts-ignore // imagefx is js module that should be converted to a class\nlet fx: fxImage.GLImageFilter | null; // instance of imagefx\n\nconst last: { inputSum: number, cacheDiff: number, sumMethod: number, inputTensor: undefined | Tensor } = {\n  inputSum: 0,\n  cacheDiff: 1,\n  sumMethod: 0,\n  inputTensor: undefined,\n};\n\nexport function reset() {\n  last.inputSum = 0;\n  last.cacheDiff = 1;\n  last.sumMethod = 0;\n  last.inputTensor = undefined;\n}\n\nexport function canvas(width: number, height: number): AnyCanvas {\n  let c: AnyCanvas;\n  if (env.browser) { // browser defines canvas object\n    if (env.worker) { // if runing in web  worker use OffscreenCanvas\n      if (typeof OffscreenCanvas === 'undefined') throw new Error('canvas error: attempted to run in web worker but OffscreenCanvas is not supported');\n      c = new OffscreenCanvas(width, height);\n    } else { // otherwise use DOM canvas\n      if (typeof document === 'undefined') throw new Error('canvas error: attempted to run in browser but DOM is not defined');\n      c = document.createElement('canvas');\n      c.width = width;\n      c.height = height;\n    }\n  } else { // if not running in browser, there is no \"default\" canvas object, so we need monkey patch or fail\n    // @ts-ignore // env.canvas is an external monkey-patch\n    if (typeof env.Canvas !== 'undefined') c = new env.Canvas(width, height);\n    else if (typeof globalThis.Canvas !== 'undefined') c = new globalThis.Canvas(width, height);\n    // else throw new Error('canvas error: attempted to use canvas in nodejs without canvas support installed');\n  }\n  // @ts-ignore its either defined or we already threw an error\n  return c;\n}\n\n// helper function to copy canvas from input to output\nexport function copy(input: AnyCanvas, output?: AnyCanvas) {\n  const outputCanvas = output || canvas(input.width, input.height);\n  const ctx = outputCanvas.getContext('2d') as CanvasRenderingContext2D;\n  ctx.drawImage(input, 0, 0);\n  return outputCanvas;\n}\n\n// process input image and return tensor\n// input can be tensor, imagedata, htmlimageelement, htmlvideoelement\n// input is resized and run through imagefx filter\nexport async function process(input: Input, config: Config, getTensor: boolean = true): Promise<{ tensor: Tensor | null, canvas: AnyCanvas | null }> {\n  if (!input) {\n    // throw new Error('input is missing');\n    if (config.debug) log('input error: input is missing');\n    return { tensor: null, canvas: null }; // video may become temporarily unavailable due to onresize\n  }\n  // sanity checks since different browsers do not implement all dom elements\n  if (\n    !(input instanceof tf.Tensor)\n    && !(typeof Image !== 'undefined' && input instanceof Image)\n    && !(typeof env.Canvas !== 'undefined' && input instanceof env.Canvas)\n    && !(typeof globalThis.Canvas !== 'undefined' && input instanceof globalThis.Canvas)\n    && !(typeof ImageData !== 'undefined' && input instanceof ImageData)\n    && !(typeof ImageBitmap !== 'undefined' && input instanceof ImageBitmap)\n    && !(typeof HTMLImageElement !== 'undefined' && input instanceof HTMLImageElement)\n    && !(typeof HTMLMediaElement !== 'undefined' && input instanceof HTMLMediaElement)\n    && !(typeof HTMLVideoElement !== 'undefined' && input instanceof HTMLVideoElement)\n    && !(typeof HTMLCanvasElement !== 'undefined' && input instanceof HTMLCanvasElement)\n    && !(typeof OffscreenCanvas !== 'undefined' && input instanceof OffscreenCanvas)\n  ) {\n    throw new Error('input error: type is not recognized');\n  }\n  if (input instanceof tf.Tensor) { // if input is tensor use as-is without filters but correct shape as needed\n    let tensor: Tensor | null = null;\n    if ((input as Tensor)['isDisposedInternal']) throw new Error('input error: attempted to use tensor but it is disposed');\n    if (!(input as Tensor).shape) throw new Error('input error: attempted to use tensor without a shape');\n    if ((input as Tensor).shape.length === 3) { // [height, width, 3 || 4]\n      if ((input as Tensor).shape[2] === 3) { // [height, width, 3] so add batch\n        tensor = tf.expandDims(input, 0);\n      } else if ((input as Tensor).shape[2] === 4) { // [height, width, 4] so strip alpha and add batch\n        const rgb = tf.slice3d(input, [0, 0, 0], [-1, -1, 3]);\n        tensor = tf.expandDims(rgb, 0);\n        tf.dispose(rgb);\n      }\n    } else if ((input as Tensor).shape.length === 4) { // [1, width, height, 3 || 4]\n      if ((input as Tensor).shape[3] === 3) { // [1, width, height, 3] just clone\n        tensor = tf.clone(input);\n      } else if ((input as Tensor).shape[3] === 4) { // [1, width, height, 4] so strip alpha\n        tensor = tf.slice4d(input, [0, 0, 0, 0], [-1, -1, -1, 3]);\n      }\n    }\n    // at the end shape must be [1, height, width, 3]\n    if (tensor == null || tensor.shape.length !== 4 || tensor.shape[0] !== 1 || tensor.shape[3] !== 3) throw new Error(`input error: attempted to use tensor with unrecognized shape: ${((input as Tensor).shape).toString()}`);\n    if ((tensor).dtype === 'int32') {\n      const cast = tf.cast(tensor, 'float32');\n      tf.dispose(tensor);\n      tensor = cast;\n    }\n    return { tensor, canvas: (config.filter.return ? outCanvas : null) };\n  }\n  // check if resizing will be needed\n  if (typeof input['readyState'] !== 'undefined' && (input as HTMLMediaElement).readyState <= 2) {\n    if (config.debug) log('input stream is not ready');\n    return { tensor: null, canvas: inCanvas }; // video may become temporarily unavailable due to onresize\n  }\n  const originalWidth: number = input['naturalWidth'] || input['videoWidth'] || input['width'] || (input['shape'] && (input['shape'][1] > 0));\n  const originalHeight: number = input['naturalHeight'] || input['videoHeight'] || input['height'] || (input['shape'] && (input['shape'][2] > 0));\n  if (!originalWidth || !originalHeight) {\n    if (config.debug) log('cannot determine input dimensions');\n    return { tensor: null, canvas: inCanvas }; // video may become temporarily unavailable due to onresize\n  }\n  let targetWidth: number = originalWidth;\n  let targetHeight: number = originalHeight;\n  if (targetWidth > maxSize) {\n    targetWidth = maxSize;\n    targetHeight = Math.trunc(targetWidth * originalHeight / originalWidth);\n  }\n  if (targetHeight > maxSize) {\n    targetHeight = maxSize;\n    targetWidth = Math.trunc(targetHeight * originalWidth / originalHeight);\n  }\n\n  // create our canvas and resize it if needed\n  if ((config.filter?.width || 0) > 0) targetWidth = config.filter.width as number;\n  else if ((config.filter?.height || 0) > 0) targetWidth = originalWidth * ((config.filter.height || 0) / originalHeight);\n  if ((config.filter.height || 0) > 0) targetHeight = config.filter.height as number;\n  else if ((config.filter.width || 0) > 0) targetHeight = originalHeight * ((config.filter.width || 0) / originalWidth);\n  if (!targetWidth || !targetHeight) throw new Error('input error: cannot determine dimension');\n  if (!inCanvas || (inCanvas.width !== targetWidth) || (inCanvas.height !== targetHeight)) inCanvas = canvas(targetWidth, targetHeight);\n\n  // draw input to our canvas\n  const inCtx = inCanvas.getContext('2d') as CanvasRenderingContext2D;\n  if ((typeof ImageData !== 'undefined') && (input instanceof ImageData)) {\n    inCtx.putImageData(input, 0, 0);\n  } else {\n    if (config.filter.flip && typeof inCtx.translate !== 'undefined') {\n      inCtx.translate(originalWidth, 0);\n      inCtx.scale(-1, 1);\n      inCtx.drawImage(input as AnyCanvas, 0, 0, originalWidth, originalHeight, 0, 0, inCanvas.width, inCanvas.height);\n      inCtx.setTransform(1, 0, 0, 1, 0, 0); // resets transforms to defaults\n    } else {\n      inCtx.drawImage(input as AnyCanvas, 0, 0, originalWidth, originalHeight, 0, 0, inCanvas.width, inCanvas.height);\n    }\n  }\n\n  if (!outCanvas || (inCanvas.width !== outCanvas.width) || (inCanvas.height !== outCanvas.height)) outCanvas = canvas(inCanvas.width, inCanvas.height); // init output canvas\n\n  // imagefx transforms using gl from input canvas to output canvas\n  if (config.filter.enabled && env.webgl.supported) {\n    if (!fx) fx = env.browser ? new fxImage.GLImageFilter() : null; // && (typeof document !== 'undefined')\n    env.filter = !!fx;\n    if (!fx?.add) {\n      if (config.debug) log('input process error: cannot initialize filters');\n      env.webgl.supported = false;\n      config.filter.enabled = false;\n      copy(inCanvas, outCanvas); // filter failed to initialize\n      // return { tensor: null, canvas: inCanvas };\n    } else {\n      fx.reset();\n      if (config.filter.brightness !== 0) fx.add('brightness', config.filter.brightness);\n      if (config.filter.contrast !== 0) fx.add('contrast', config.filter.contrast);\n      if (config.filter.sharpness !== 0) fx.add('sharpen', config.filter.sharpness);\n      if (config.filter.blur !== 0) fx.add('blur', config.filter.blur);\n      if (config.filter.saturation !== 0) fx.add('saturation', config.filter.saturation);\n      if (config.filter.hue !== 0) fx.add('hue', config.filter.hue);\n      if (config.filter.negative) fx.add('negative');\n      if (config.filter.sepia) fx.add('sepia');\n      if (config.filter.vintage) fx.add('brownie');\n      if (config.filter.sepia) fx.add('sepia');\n      if (config.filter.kodachrome) fx.add('kodachrome');\n      if (config.filter.technicolor) fx.add('technicolor');\n      if (config.filter.polaroid) fx.add('polaroid');\n      if (config.filter.pixelate !== 0) fx.add('pixelate', config.filter.pixelate);\n      if (fx.get() > 0) outCanvas = fx.apply(inCanvas);\n      else outCanvas = fx.draw(inCanvas);\n    }\n  } else {\n    copy(inCanvas, outCanvas); // if no filters applied, output canvas is input canvas\n    if (fx) fx = null;\n    env.filter = !!fx;\n  }\n\n  if (!getTensor) return { tensor: null, canvas: outCanvas }; // just canvas was requested\n  if (!outCanvas) throw new Error('canvas error: cannot create output');\n\n  // create tensor from image unless input was a tensor already\n  let pixels;\n  let depth = 3;\n  if ((typeof ImageData !== 'undefined' && input instanceof ImageData) || ((input as ImageData).data && (input as ImageData).width && (input as ImageData).height)) { // if input is imagedata, just use it\n    if (env.browser && tf.browser) {\n      pixels = tf.browser ? tf.browser.fromPixels(input) : null;\n    } else {\n      depth = (input as ImageData).data.length / (input as ImageData).height / (input as ImageData).width;\n      // const arr = Uint8Array.from(input['data']);\n      const arr = new Uint8Array((input as ImageData).data.buffer);\n      pixels = tf.tensor(arr, [(input as ImageData).height, (input as ImageData).width, depth], 'int32');\n    }\n  } else {\n    if (!tmpCanvas || (outCanvas.width !== tmpCanvas.width) || (outCanvas.height !== tmpCanvas.height)) tmpCanvas = canvas(outCanvas.width, outCanvas.height); // init output canvas\n    if (tf.browser && env.browser) {\n      if (config.backend === 'webgl' || config.backend === 'humangl' || config.backend === 'webgpu') {\n        pixels = tf.browser.fromPixels(outCanvas); // safe to reuse since both backend and context are gl based\n      } else {\n        tmpCanvas = copy(outCanvas); // cannot use output canvas as it already has gl context so we do a silly one more canvas\n        pixels = tf.browser.fromPixels(tmpCanvas);\n      }\n    } else {\n      const tempCanvas = copy(outCanvas); // cannot use output canvas as it already has gl context so we do a silly one more canvas\n      const tempCtx = tempCanvas.getContext('2d') as CanvasRenderingContext2D;\n      const tempData = tempCtx.getImageData(0, 0, targetWidth, targetHeight);\n      depth = tempData.data.length / targetWidth / targetHeight;\n      const arr = new Uint8Array(tempData.data.buffer);\n      pixels = tf.tensor(arr, [targetWidth, targetHeight, depth]);\n    }\n  }\n  if (depth === 4) { // rgba to rgb\n    const rgb = tf.slice3d(pixels, [0, 0, 0], [-1, -1, 3]); // strip alpha channel\n    tf.dispose(pixels);\n    pixels = rgb;\n  }\n  if (!pixels) throw new Error('input error: cannot create tensor');\n  const casted: Tensor = tf.cast(pixels, 'float32');\n  const tensor: Tensor = config.filter.equalization ? await enhance.histogramEqualization(casted) : tf.expandDims(casted, 0);\n  tf.dispose([pixels, casted]);\n  return { tensor, canvas: (config.filter.return ? outCanvas : null) };\n}\n\n/*\nconst checksum = async (input: Tensor): Promise<number> => { // use tf sum or js based sum loop depending on which is faster\n  const resizeFact = 48;\n  const reduced: Tensor = tf.image.resizeBilinear(input, [Math.trunc((input.shape[1] || 1) / resizeFact), Math.trunc((input.shape[2] || 1) / resizeFact)]);\n  const tfSum = async (): Promise<number> => {\n    const sumT = tf.sum(reduced);\n    const sum0 = await sumT.data();\n    tf.dispose(sumT);\n    return sum0[0];\n  };\n  const jsSum = async (): Promise<number> => {\n    const reducedData = await reduced.data(); // raw image rgb array\n    let sum0 = 0;\n    for (let i = 0; i < reducedData.length / 3; i++) sum0 += reducedData[3 * i + 2]; // look only at green value of each pixel\n    return sum0;\n  };\n  if (last.sumMethod === 0) {\n    const t0 = now();\n    await jsSum();\n    const t1 = now();\n    await tfSum();\n    const t2 = now();\n    last.sumMethod = t1 - t0 < t2 - t1 ? 1 : 2;\n  }\n  const res = last.sumMethod === 1 ? await jsSum() : await tfSum();\n  tf.dispose(reduced);\n  return res;\n};\n*/\n\nexport async function skip(config: Partial<Config>, input: Tensor) {\n  let skipFrame = false;\n  if (config.cacheSensitivity === 0 || !input.shape || input.shape.length !== 4 || input.shape[1] > 2048 || input.shape[2] > 2048) return skipFrame; // cache disabled or input is invalid or too large for cache analysis\n\n  /*\n  const checkSum = await checksum(input);\n  const diff = 100 * (Math.max(checkSum, last.inputSum) / Math.min(checkSum, last.inputSum) - 1);\n  last.inputSum = checkSum;\n  // if previous frame was skipped, skip this frame if changed more than cacheSensitivity\n  // if previous frame was not skipped, then look for cacheSensitivity or difference larger than one in previous frame to avoid resetting cache in subsequent frames unnecessarily\n  let skipFrame = diff < Math.max(config.cacheSensitivity, last.cacheDiff);\n  // if difference is above 10x threshold, don't use last value to force reset cache for significant change of scenes or images\n  last.cacheDiff = diff > 10 * config.cacheSensitivity ? 0 : diff;\n  skipFrame = skipFrame && (last.cacheDiff > 0); // if no cached diff value then force no skip\n  */\n\n  if (!last.inputTensor) {\n    last.inputTensor = tf.clone(input);\n  } else if (last.inputTensor.shape[1] !== input.shape[1] || last.inputTensor.shape[2] !== input.shape[2]) { // input resolution changed\n    tf.dispose(last.inputTensor);\n    last.inputTensor = tf.clone(input);\n  } else {\n    const t: Record<string, Tensor> = {};\n    t.diff = tf.sub(input, last.inputTensor);\n    t.squared = tf.mul(t.diff, t.diff);\n    t.sum = tf.sum(t.squared);\n    const diffSum = await t.sum.data();\n    const diffRelative = diffSum[0] / (input.shape[1] || 1) / (input.shape[2] || 1) / 255 / 3; // squared difference relative to input resolution and averaged per channel\n    tf.dispose([last.inputTensor, t.diff, t.squared, t.sum]);\n    last.inputTensor = tf.clone(input);\n    skipFrame = diffRelative <= (config.cacheSensitivity || 0);\n  }\n  return skipFrame;\n}\n\nexport async function compare(config: Partial<Config>, input1: Tensor, input2: Tensor): Promise<number> {\n  const t: Record<string, Tensor> = {};\n  if (!input1 || !input2 || input1.shape.length !== 4 || input1.shape.length !== input2.shape.length) {\n    if (!config.debug) log('invalid input tensor or tensor shapes do not match:', input1.shape, input2.shape);\n    return 0;\n  }\n  if (input1.shape[0] !== 1 || input2.shape[0] !== 1 || input1.shape[3] !== 3 || input2.shape[3] !== 3) {\n    if (!config.debug) log('input tensors must be of shape [1, height, width, 3]:', input1.shape, input2.shape);\n    return 0;\n  }\n  t.input1 = tf.clone(input1);\n  t.input2 = (input1.shape[1] !== input2.shape[1] || input1.shape[2] !== input2.shape[2]) ? tf.image.resizeBilinear(input2, [input1.shape[1], input1.shape[2]]) : tf.clone(input2);\n  t.diff = tf.sub(t.input1, t.input2);\n  t.squared = tf.mul(t.diff, t.diff);\n  t.sum = tf.sum(t.squared);\n  const diffSum = await t.sum.data();\n  const diffRelative = diffSum[0] / (input1.shape[1] || 1) / (input1.shape[2] || 1) / 255 / 3;\n  tf.dispose([t.input1, t.input2, t.diff, t.squared, t.sum]);\n  return diffRelative;\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\nimport * as image from '../image/image';\n\n/** Env class that holds detected capabilities */\nexport class Env {\n  /** Running in Browser */\n  browser: boolean;\n  /** Running in NodeJS */\n  node: boolean;\n  /** Running in WebWorker thread */\n  worker: boolean;\n  /** Detected platform */\n  platform: string = '';\n  /** Detected agent */\n  agent: string = '';\n  /** List of supported backends */\n  backends: string[] = [];\n  /** Has any work been performed so far */\n  initial: boolean;\n  /** Are image filters supported? */\n  filter: boolean | undefined;\n  /** TFJS instance details */\n  tfjs: {\n    version: undefined | string,\n  };\n  /** Is offscreenCanvas supported? */\n  offscreen: undefined | boolean;\n  /** Are performance counter instant values or additive */\n  perfadd: boolean = false;\n  /** If using tfjs-node get version of underlying tensorflow shared library and if gpu acceleration is enabled */\n  tensorflow: {\n    version: undefined | string,\n    gpu: undefined | boolean,\n  } = {\n      version: undefined,\n      gpu: undefined,\n    };\n  /** WASM detected capabilities */\n  wasm: {\n    supported: undefined | boolean,\n    backend: undefined | boolean,\n    simd: undefined | boolean,\n    multithread: undefined | boolean,\n  } = {\n      supported: undefined,\n      backend: undefined,\n      simd: undefined,\n      multithread: undefined,\n    };\n  /** WebGL detected capabilities */\n  webgl: {\n    supported: undefined | boolean,\n    backend: undefined | boolean,\n    version: undefined | string,\n    renderer: undefined | string,\n  } = {\n      supported: undefined,\n      backend: undefined,\n      version: undefined,\n      renderer: undefined,\n    };\n  /** WebGPU detected capabilities */\n  webgpu: {\n    supported: undefined | boolean,\n    backend: undefined | boolean,\n    adapter: undefined | string,\n  } = {\n      supported: undefined,\n      backend: undefined,\n      adapter: undefined,\n    };\n  /** CPU info */\n  cpu: {\n    model: undefined | string,\n    flags: string[],\n  } = {\n      model: undefined,\n      flags: [],\n    };\n  /** List of supported kernels for current backend */\n  kernels: string[] = [];\n  /** MonkeyPatch for Canvas */\n  Canvas: undefined;\n  /** MonkeyPatch for Image */\n  Image: undefined;\n  /** MonkeyPatch for ImageData */\n  ImageData: undefined;\n\n  constructor() {\n    this.browser = typeof navigator !== 'undefined';\n    this.node = (typeof process !== 'undefined') && (typeof process.versions !== 'undefined') && (typeof process.versions.node !== 'undefined');\n    this.tfjs = { version: tf.version['tfjs-core'] };\n    this.offscreen = typeof OffscreenCanvas !== 'undefined';\n    this.initial = true;\n\n    // @ts-ignore WorkerGlobalScope evaluated in browser only\n    this.worker = this.browser && this.offscreen ? (typeof WorkerGlobalScope !== 'undefined') : undefined;\n    if (typeof navigator !== 'undefined') {\n      const raw = navigator.userAgent.match(/\\(([^()]+)\\)/g);\n      if (raw?.[0]) {\n        const platformMatch = raw[0].match(/\\(([^()]+)\\)/g);\n        this.platform = (platformMatch?.[0]) ? platformMatch[0].replace(/\\(|\\)/g, '') : '';\n        this.agent = navigator.userAgent.replace(raw[0], '');\n        if (this.platform[1]) this.agent = this.agent.replace(raw[1], '');\n        this.agent = this.agent.replace(/  /g, ' ');\n        // chrome offscreencanvas gpu memory leak\n        /*\n        const isChrome = env.agent.match(/Chrome\\/.[0-9]/g);\n        const verChrome = isChrome && isChrome[0] ? isChrome[0].split('/')[1] : 0;\n        if (verChrome > 92 && verChrome < 96) {\n          log('disabling offscreenCanvas due to browser error:', isChrome ? isChrome[0] : 'unknown');\n          this.offscreen = false;\n        }\n        */\n      }\n    } else if (typeof process !== 'undefined') {\n      this.platform = `${process.platform} ${process.arch}`;\n      this.agent = `NodeJS ${process.version}`;\n    }\n  }\n\n  /** update backend information */\n  async updateBackend() {\n    // analyze backends\n    this.backends = Object.keys(tf.engine().registryFactory);\n    this.tensorflow = {\n      version: (tf.backend().binding ? tf.backend().binding.TF_Version : undefined),\n      gpu: (tf.backend().binding ? tf.backend().binding.isUsingGpuDevice() : undefined),\n    };\n    this.wasm.supported = typeof WebAssembly !== 'undefined';\n    this.wasm.backend = this.backends.includes('wasm');\n    if (this.wasm.supported && this.wasm.backend && tf.getBackend() === 'wasm') {\n      this.wasm.simd = tf.env().get('WASM_HAS_SIMD_SUPPORT');\n      this.wasm.multithread = tf.env().get('WASM_HAS_MULTITHREAD_SUPPORT');\n    }\n    const c = image.canvas(100, 100);\n    const ctx = c ? c.getContext('webgl2') : undefined; // causes too many gl contexts\n    // const ctx = typeof tf.backend().getGPGPUContext !== undefined ? tf.backend().getGPGPUContext : null;\n    this.webgl.supported = typeof ctx !== 'undefined';\n    this.webgl.backend = this.backends.includes('webgl');\n    if (this.webgl.supported && this.webgl.backend && (tf.getBackend() === 'webgl' || tf.getBackend() === 'humangl')) {\n      const gl = tf.backend().gpgpu !== 'undefined' ? await tf.backend().getGPGPUContext().gl : null;\n      if (gl) {\n        this.webgl.version = gl.getParameter(gl.VERSION);\n        this.webgl.renderer = gl.getParameter(gl.RENDERER);\n      }\n    }\n    this.webgpu.supported = this.browser && typeof navigator.gpu !== 'undefined';\n    this.webgpu.backend = this.backends.includes('webgpu');\n    try {\n      if (this.webgpu.supported) {\n        const adapter = await navigator.gpu.requestAdapter();\n        this.webgpu.adapter = adapter ? adapter.name : undefined;\n      }\n    } catch {\n      this.webgpu.supported = false;\n    }\n    try {\n      this.kernels = tf.getKernelsForBackend(tf.getBackend()).map((kernel) => (kernel.kernelName as string).toLowerCase());\n    } catch { /**/ }\n  }\n\n  /** update cpu information */\n  updateCPU() {\n    const cpu = { model: '', flags: [] };\n    if (this.node && this.platform.startsWith('linux')) {\n      /*\n      const fs = require('fs');\n      try {\n        const data = fs.readFileSync('/proc/cpuinfo').toString();\n        for (const line of data.split('\\n')) {\n          if (line.startsWith('model name')) cpu.model = line.match(/:(.*)/g)[0].replace(':', '').trim();\n          if (line.startsWith('flags')) cpu.flags = line.match(/:(.*)/g)[0].replace(':', '').trim().split(' ').sort();\n        }\n      } catch { }\n      */\n    }\n    if (!this.cpu) Object.defineProperty(this, 'cpu', { value: cpu });\n    else this.cpu = cpu;\n  }\n}\n\nexport const env = new Env();\n", "import { log } from './util';\n\n// const log = (...msg) => console.log('webcam', ...msg); // eslint-disable-line no-console\n\n/** WebCam configuration */\nexport interface WebCamConfig {\n  /**\n   * element can be:\n   * - string which indicates dom element id\n   * - actual HTMLVideo dom element\n   * - undefined in which case a new HTMLVideoElement will be created\n   */\n  element: string | HTMLVideoElement | undefined,\n  /** print messages on console */\n  debug: boolean,\n  /** use front or back camera */\n  mode: 'front' | 'back',\n  /** camera crop mode */\n  crop: boolean,\n  /** desired webcam width */\n  width: number,\n  /** desired webcam height */\n  height: number,\n}\n\nexport class WebCam { // eslint-disable-line @typescript-eslint/no-extraneous-class\n  /** current webcam configuration */\n  config: WebCamConfig;\n  /** instance of dom element associated with webcam stream */\n  element: HTMLVideoElement | undefined;\n  /** active webcam stream */\n  stream: MediaStream | undefined;\n\n  constructor() {\n    this.config = {\n      element: undefined,\n      debug: true,\n      mode: 'front',\n      crop: false,\n      width: 0,\n      height: 0,\n    };\n  }\n\n  /** get active webcam stream track */\n  public get track(): MediaStreamTrack | undefined {\n    if (!this.stream) return undefined;\n    return this.stream.getVideoTracks()[0];\n  }\n\n  /** get webcam capabilities */\n  public get capabilities(): MediaTrackCapabilities | undefined {\n    if (!this.track) return undefined;\n    return this.track.getCapabilities ? this.track.getCapabilities() : undefined;\n  }\n\n  /** get webcam constraints */\n  public get constraints(): MediaTrackConstraints | undefined {\n    if (!this.track) return undefined;\n    return this.track.getConstraints ? this.track.getConstraints() : undefined;\n  }\n\n  /** get webcam settings */\n  public get settings(): MediaTrackSettings | undefined {\n    if (!this.stream) return undefined;\n    const track: MediaStreamTrack = this.stream.getVideoTracks()[0];\n    return track.getSettings ? track.getSettings() : undefined;\n  }\n\n  /** get webcam label */\n  public get label(): string {\n    if (!this.track) return '';\n    return this.track.label;\n  }\n\n  /** is webcam paused */\n  public get paused(): boolean {\n    return this.element?.paused || false;\n  }\n\n  /** webcam current width */\n  public get width(): number {\n    return this.element?.videoWidth || 0;\n  }\n\n  /** webcam current height */\n  public get height(): number {\n    return this.element?.videoHeight || 0;\n  }\n\n  /** start method initializizes webcam stream and associates it with a dom video element */\n  public start = async (webcamConfig?: Partial<WebCamConfig>): Promise<void> => {\n    // set config\n    if (webcamConfig?.debug) this.config.debug = webcamConfig?.debug;\n    if (webcamConfig?.crop) this.config.crop = webcamConfig?.crop;\n    if (webcamConfig?.mode) this.config.mode = webcamConfig?.mode;\n    if (webcamConfig?.width) this.config.width = webcamConfig?.width;\n    if (webcamConfig?.height) this.config.height = webcamConfig?.height;\n\n    // use or create dom element\n    if (webcamConfig?.element) {\n      if (typeof webcamConfig.element === 'string') {\n        const el = document.getElementById(webcamConfig.element);\n        if (el && el instanceof HTMLVideoElement) {\n          this.element = el;\n        } else {\n          if (this.config.debug) log('webcam', 'cannot get dom element', webcamConfig.element);\n          return;\n        }\n      } else if (webcamConfig.element instanceof HTMLVideoElement) {\n        this.element = webcamConfig.element;\n      } else {\n        if (this.config.debug) log('webcam', 'unknown dom element', webcamConfig.element);\n        return;\n      }\n    } else {\n      this.element = document.createElement('video');\n    }\n\n    // set constraints to use\n    const requestedConstraints: DisplayMediaStreamConstraints = {\n      audio: false,\n      video: {\n        facingMode: this.config.mode === 'front' ? 'user' : 'environment',\n        // @ts-ignore // resizeMode is still not defined in tslib\n        resizeMode: this.config.crop ? 'crop-and-scale' : 'none',\n        width: { ideal: this.config.width > 0 ? this.config.width : window.innerWidth },\n        height: { ideal: this.config.height > 0 ? this.config.height : window.innerHeight },\n      },\n    };\n\n    // set default event listeners\n    this.element.addEventListener('play', () => { if (this.config.debug) log('webcam', 'play'); });\n    this.element.addEventListener('pause', () => { if (this.config.debug) log('webcam', 'pause'); });\n    this.element.addEventListener('click', async () => { // pause when clicked on screen and resume on next click\n      if (!this.element || !this.stream) return;\n      if (this.element.paused) await this.element.play();\n      else this.element.pause();\n    });\n\n    // get webcam and set it to run in dom element\n    if (!navigator?.mediaDevices) {\n      if (this.config.debug) log('webcam', 'no devices');\n      return;\n    }\n    try {\n      this.stream = await navigator.mediaDevices.getUserMedia(requestedConstraints); // get stream that satisfies constraints\n    } catch (err) {\n      log('webcam', err);\n      return;\n    }\n    if (!this.stream) {\n      if (this.config.debug) log('webcam', 'no stream');\n      return;\n    }\n    this.element.srcObject = this.stream; // assign it to dom element\n    const ready = new Promise((resolve) => { // wait until stream is ready\n      if (!this.element) resolve(false);\n      else this.element.onloadeddata = () => resolve(true);\n    });\n    await ready;\n    await this.element.play(); // start playing\n\n    if (this.config.debug) {\n      log('webcam', {\n        width: this.width,\n        height: this.height,\n        label: this.label,\n        stream: this.stream,\n        track: this.track,\n        settings: this.settings,\n        constraints: this.constraints,\n        capabilities: this.capabilities,\n      });\n    }\n  };\n\n  /** pause webcam video method */\n  public pause = (): void => {\n    if (this.element) this.element.pause();\n  };\n\n  /** play webcam video method */\n  public play = async (): Promise<void> => {\n    if (this.element) await this.element.play();\n  };\n\n  /** stop method stops active webcam stream track and disconnects webcam */\n  public stop = (): void => {\n    if (this.config.debug) log('webcam', 'stop');\n    if (this.track) this.track.stop();\n  };\n}\n", "import { log, join } from '../util/util';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport type { GraphModel } from './types';\nimport type { Config } from '../config';\nimport * as modelsDefs from '../../models/models.json';\n// import { validateModel } from '../models';\n\nconst options = {\n  cacheModels: true,\n  cacheSupported: true,\n  verbose: true,\n  debug: false,\n  modelBasePath: '',\n};\n\nexport interface ModelInfo {\n  name: string,\n  inCache: boolean,\n  sizeDesired: number,\n  sizeFromManifest: number,\n  sizeLoadedWeights: number,\n}\n\nexport const modelStats: Record<string, ModelInfo> = {};\n\nasync function httpHandler(url: string, init?: RequestInit): Promise<Response | null> {\n  if (options.debug) log('load model fetch:', url, init);\n  return fetch(url, init);\n}\n\nexport function setModelLoadOptions(config: Config) {\n  options.cacheModels = config.cacheModels;\n  options.verbose = config.debug;\n  options.modelBasePath = config.modelBasePath;\n}\n\nexport async function loadModel(modelPath: string | undefined): Promise<GraphModel> {\n  let modelUrl = join(options.modelBasePath, modelPath || '');\n  if (!modelUrl.toLowerCase().endsWith('.json')) modelUrl += '.json';\n  const modelPathSegments = modelUrl.includes('/') ? modelUrl.split('/') : modelUrl.split('\\\\');\n  const shortModelName = modelPathSegments[modelPathSegments.length - 1].replace('.json', '');\n  const cachedModelName = 'indexeddb://' + shortModelName; // generate short model name for cache\n  modelStats[shortModelName] = {\n    name: shortModelName,\n    sizeFromManifest: 0,\n    sizeLoadedWeights: 0,\n    sizeDesired: modelsDefs[shortModelName],\n    inCache: false,\n  };\n  options.cacheSupported = (typeof indexedDB !== 'undefined'); // check if localStorage and indexedb are available\n  let cachedModels = {};\n  try {\n    cachedModels = (options.cacheSupported && options.cacheModels) ? await tf.io.listModels() : {}; // list all models already in cache // this fails for webview although localStorage is defined\n  } catch {\n    options.cacheSupported = false;\n  }\n  modelStats[shortModelName].inCache = (options.cacheSupported && options.cacheModels) && Object.keys(cachedModels).includes(cachedModelName); // is model found in cache\n  const tfLoadOptions = typeof fetch === 'undefined' ? {} : { fetchFunc: (url: string, init?: RequestInit) => httpHandler(url, init) };\n  let model: GraphModel = new tf.GraphModel(modelStats[shortModelName].inCache ? cachedModelName : modelUrl, tfLoadOptions) as unknown as GraphModel; // create model prototype and decide if load from cache or from original modelurl\n  let loaded = false;\n  try {\n    // @ts-ignore private function\n    model.findIOHandler(); // decide how to actually load a model\n    if (options.debug) log('model load handler:', model['handler']);\n  } catch (err) {\n    log('error finding model i/o handler:', modelUrl, err);\n  }\n  try {\n    // @ts-ignore private property\n    const artifacts = await model.handler?.load() || null; // load manifest\n    modelStats[shortModelName].sizeFromManifest = artifacts?.weightData?.byteLength || 0;\n    if (artifacts) model.loadSync(artifacts); // load weights\n    else model = await tf.loadGraphModel(modelStats[shortModelName].inCache ? cachedModelName : modelUrl, tfLoadOptions) as unknown as GraphModel;\n    // @ts-ignore private property\n    modelStats[shortModelName].sizeLoadedWeights = model.artifacts?.weightData?.byteLength || 0;\n    if (options.verbose) log('load:', { model: shortModelName, url: model['modelUrl'], bytes: modelStats[shortModelName].sizeLoadedWeights });\n    loaded = true;\n  } catch (err) {\n    log('error loading model:', modelUrl, err);\n  }\n  if (loaded && options.cacheModels && options.cacheSupported && !modelStats[shortModelName].inCache) { // save model to cache\n    try {\n      const saveResult = await model.save(cachedModelName);\n      if (options.debug) log('model saved:', cachedModelName, saveResult);\n    } catch (err) {\n      log('error saving model:', modelUrl, err);\n    }\n  }\n  // validateModel(null, model, `${modelPath || ''}`);\n  return model;\n}\n", "/**\n * Loader and Validator for all models used by Human\n */\n\nimport { env } from './util/env';\nimport { log } from './util/util';\nimport * as antispoof from './face/antispoof';\nimport * as blazeface from './face/blazeface';\nimport * as blazepose from './body/blazepose';\nimport * as centernet from './object/centernet';\nimport * as efficientpose from './body/efficientpose';\nimport * as emotion from './gear/emotion';\nimport * as facemesh from './face/facemesh';\nimport * as faceres from './face/faceres';\nimport * as gear from './gear/gear';\nimport * as handpose from './hand/handpose';\nimport * as handtrack from './hand/handtrack';\nimport * as insightface from './face/insightface';\nimport * as iris from './face/iris';\nimport * as liveness from './face/liveness';\nimport * as meet from './segmentation/meet';\nimport * as mobilefacenet from './face/mobilefacenet';\nimport * as movenet from './body/movenet';\nimport * as nanodet from './object/nanodet';\nimport * as posenet from './body/posenet';\nimport * as rvm from './segmentation/rvm';\nimport * as selfie from './segmentation/selfie';\nimport * as ssrnetAge from './gear/ssrnet-age';\nimport * as ssrnetGender from './gear/ssrnet-gender';\nimport { modelStats, ModelInfo } from './tfjs/load';\nimport type { GraphModel } from './tfjs/types';\nimport type { Human } from './human';\n\n/** Instances of all possible TFJS Graph Models used by Human\n * - loaded as needed based on configuration\n * - initialized explictly with `human.load()` method\n * - initialized implicity on first call to `human.detect()`\n * - each model can be `null` if not loaded, instance of `GraphModel` if loaded or `Promise` if loading\n */\nexport class Models {\n  ssrnetage: null | GraphModel | Promise<GraphModel> = null;\n  gear: null | GraphModel | Promise<GraphModel> = null;\n  blazeposedetect: null | GraphModel | Promise<GraphModel> = null;\n  blazepose: null | GraphModel | Promise<GraphModel> = null;\n  centernet: null | GraphModel | Promise<GraphModel> = null;\n  efficientpose: null | GraphModel | Promise<GraphModel> = null;\n  mobilefacenet: null | GraphModel | Promise<GraphModel> = null;\n  insightface: null | GraphModel | Promise<GraphModel> = null;\n  emotion: null | GraphModel | Promise<GraphModel> = null;\n  facedetect: null | GraphModel | Promise<GraphModel> = null;\n  faceiris: null | GraphModel | Promise<GraphModel> = null;\n  facemesh: null | GraphModel | Promise<GraphModel> = null;\n  faceres: null | GraphModel | Promise<GraphModel> = null;\n  ssrnetgender: null | GraphModel | Promise<GraphModel> = null;\n  handpose: null | GraphModel | Promise<GraphModel> = null;\n  handskeleton: null | GraphModel | Promise<GraphModel> = null;\n  handtrack: null | GraphModel | Promise<GraphModel> = null;\n  liveness: null | GraphModel | Promise<GraphModel> = null;\n  meet: null | GraphModel | Promise<GraphModel> = null;\n  movenet: null | GraphModel | Promise<GraphModel> = null;\n  nanodet: null | GraphModel | Promise<GraphModel> = null;\n  posenet: null | GraphModel | Promise<GraphModel> = null;\n  selfie: null | GraphModel | Promise<GraphModel> = null;\n  rvm: null | GraphModel | Promise<GraphModel> = null;\n  antispoof: null | GraphModel | Promise<GraphModel> = null;\n}\n\n/** structure that holds global stats for currently loaded models */\nexport interface ModelStats {\n  numLoadedModels: number,\n  numDefinedModels: number,\n  percentageLoaded: number,\n  totalSizeFromManifest: number,\n  totalSizeWeights: number,\n  totalSizeLoading: number,\n  totalSizeEnabled: undefined,\n  modelStats: ModelInfo[],\n}\n\nlet instance: Human;\n\nexport const getModelStats = (currentInstance: Human): ModelStats => {\n  if (currentInstance) instance = currentInstance;\n  if (!instance) log('instance not registred');\n  let totalSizeFromManifest = 0;\n  let totalSizeWeights = 0;\n  let totalSizeLoading = 0;\n  for (const m of Object.values(modelStats)) {\n    totalSizeFromManifest += m.sizeFromManifest;\n    totalSizeWeights += m.sizeLoadedWeights;\n    totalSizeLoading += m.sizeDesired;\n  }\n  const percentageLoaded = totalSizeLoading > 0 ? totalSizeWeights / totalSizeLoading : 0;\n  return {\n    numLoadedModels: Object.values(modelStats).length,\n    numDefinedModels: Object.keys(instance.models).length,\n    percentageLoaded,\n    totalSizeFromManifest,\n    totalSizeWeights,\n    totalSizeLoading,\n    totalSizeEnabled: undefined,\n    modelStats: Object.values(modelStats),\n  };\n};\n\nexport function reset(currentInstance: Human): void {\n  if (currentInstance) instance = currentInstance;\n  // if (instance.config.debug) log('resetting loaded models');\n  for (const model of Object.keys(instance.models)) instance.models[model as keyof Models] = null;\n}\n\n/** Load method preloads all instance.configured models on-demand */\nexport async function load(currentInstance: Human): Promise<void> {\n  if (currentInstance) instance = currentInstance;\n  if (!instance) log('instance not registred');\n  if (env.initial) reset(instance);\n  if (instance.config.hand.enabled) { // handpose model is a combo that must be loaded as a whole\n    if (!instance.models.handpose && instance.config.hand.detector?.modelPath?.includes('handdetect')) {\n      [instance.models.handpose, instance.models.handskeleton] = await handpose.load(instance.config);\n    }\n    if (!instance.models.handskeleton && instance.config.hand.landmarks && instance.config.hand.detector?.modelPath?.includes('handdetect')) {\n      [instance.models.handpose, instance.models.handskeleton] = await handpose.load(instance.config);\n    }\n  }\n  if (instance.config.body.enabled && !instance.models.blazepose && instance.config.body.modelPath?.includes('blazepose')) instance.models.blazepose = blazepose.loadPose(instance.config);\n  if (instance.config.body.enabled && !instance.models.blazeposedetect && instance.config.body['detector'] && instance.config.body['detector'].modelPath) instance.models.blazeposedetect = blazepose.loadDetect(instance.config);\n  if (instance.config.body.enabled && !instance.models.efficientpose && instance.config.body.modelPath?.includes('efficientpose')) instance.models.efficientpose = efficientpose.load(instance.config);\n  if (instance.config.body.enabled && !instance.models.movenet && instance.config.body.modelPath?.includes('movenet')) instance.models.movenet = movenet.load(instance.config);\n  if (instance.config.body.enabled && !instance.models.posenet && instance.config.body.modelPath?.includes('posenet')) instance.models.posenet = posenet.load(instance.config);\n  if (instance.config.face.enabled && !instance.models.facedetect) instance.models.facedetect = blazeface.load(instance.config);\n  if (instance.config.face.enabled && instance.config.face.antispoof?.enabled && !instance.models.antispoof) instance.models.antispoof = antispoof.load(instance.config);\n  if (instance.config.face.enabled && instance.config.face.liveness?.enabled && !instance.models.liveness) instance.models.liveness = liveness.load(instance.config);\n  if (instance.config.face.enabled && instance.config.face.description?.enabled && !instance.models.faceres) instance.models.faceres = faceres.load(instance.config);\n  if (instance.config.face.enabled && instance.config.face.emotion?.enabled && !instance.models.emotion) instance.models.emotion = emotion.load(instance.config);\n  if (instance.config.face.enabled && instance.config.face.iris?.enabled && !instance.config.face.attention?.enabled && !instance.models.faceiris) instance.models.faceiris = iris.load(instance.config);\n  if (instance.config.face.enabled && instance.config.face.mesh?.enabled && (!instance.models.facemesh)) instance.models.facemesh = facemesh.load(instance.config);\n  if (instance.config.face.enabled && instance.config.face['gear']?.enabled && !instance.models.gear) instance.models.gear = gear.load(instance.config);\n  if (instance.config.face.enabled && instance.config.face['ssrnet']?.enabled && !instance.models.ssrnetage) instance.models.ssrnetage = ssrnetAge.load(instance.config);\n  if (instance.config.face.enabled && instance.config.face['ssrnet']?.enabled && !instance.models.ssrnetgender) instance.models.ssrnetgender = ssrnetGender.load(instance.config);\n  if (instance.config.face.enabled && instance.config.face['mobilefacenet']?.enabled && !instance.models.mobilefacenet) instance.models.mobilefacenet = mobilefacenet.load(instance.config);\n  if (instance.config.face.enabled && instance.config.face['insightface']?.enabled && !instance.models.insightface) instance.models.insightface = insightface.load(instance.config);\n  if (instance.config.hand.enabled && !instance.models.handtrack && instance.config.hand.detector?.modelPath?.includes('handtrack')) instance.models.handtrack = handtrack.loadDetect(instance.config);\n  if (instance.config.hand.enabled && instance.config.hand.landmarks && !instance.models.handskeleton && instance.config.hand.detector?.modelPath?.includes('handtrack')) instance.models.handskeleton = handtrack.loadSkeleton(instance.config);\n  if (instance.config.object.enabled && !instance.models.centernet && instance.config.object.modelPath?.includes('centernet')) instance.models.centernet = centernet.load(instance.config);\n  if (instance.config.object.enabled && !instance.models.nanodet && instance.config.object.modelPath?.includes('nanodet')) instance.models.nanodet = nanodet.load(instance.config);\n  if (instance.config.segmentation.enabled && !instance.models.selfie && instance.config.segmentation.modelPath?.includes('selfie')) instance.models.selfie = selfie.load(instance.config);\n  if (instance.config.segmentation.enabled && !instance.models.meet && instance.config.segmentation.modelPath?.includes('meet')) instance.models.meet = meet.load(instance.config);\n  if (instance.config.segmentation.enabled && !instance.models.rvm && instance.config.segmentation.modelPath?.includes('rvm')) instance.models.rvm = rvm.load(instance.config);\n\n  // models are loaded in parallel asynchronously so lets wait until they are actually loaded\n  for await (const model of Object.keys(instance.models)) {\n    if (instance.models[model as keyof Models] && typeof instance.models[model as keyof Models] !== 'undefined') {\n      instance.models[model as keyof Models] = await instance.models[model as keyof Models];\n    }\n  }\n}\n\nexport interface KernelOps { name: string, url: string, missing: string[], ops: string[] }\n\nexport function validateModel(currentInstance: Human | null, model: GraphModel | null, name: string): KernelOps | null {\n  if (!model) return null;\n  if (currentInstance) instance = currentInstance;\n  if (!instance) log('instance not registred');\n  if (!instance?.config?.validateModels) return null;\n  const simpleOps = ['const', 'placeholder', 'noop', 'pad', 'squeeze', 'add', 'sub', 'mul', 'div'];\n  const ignoreOps = ['biasadd', 'fusedbatchnormv3', 'matmul', 'switch', 'shape', 'merge', 'split', 'broadcastto'];\n  const ops: string[] = [];\n  const missing: string[] = [];\n  interface Op { name: string, category: string, op: string }\n  const url = model['modelUrl'] as string;\n  const executor = model['executor'];\n  if (executor?.graph?.nodes) {\n    for (const kernel of Object.values(executor.graph.nodes)) {\n      const op = (kernel as Op).op.toLowerCase();\n      if (!ops.includes(op)) ops.push(op);\n    }\n  } else {\n    if (!executor && instance.config.debug) {\n      log('model not loaded', name);\n    }\n  }\n  for (const op of ops) {\n    if (!simpleOps.includes(op) // exclude simple ops\n      && !ignoreOps.includes(op) // exclude specific ops\n      && !instance.env.kernels.includes(op) // check actual kernel ops\n      && !instance.env.kernels.includes(op.replace('_', '')) // check variation without _\n      && !instance.env.kernels.includes(op.replace('native', '')) // check standard variation\n      && !instance.env.kernels.includes(op.replace('v2', ''))) { // check non-versioned variation\n      missing.push(op);\n    }\n  }\n  if (instance.config.debug && missing.length > 0) log('model validation failed:', name, missing);\n  return missing.length > 0 ? { name, missing, ops, url } : null;\n}\n\nexport function validate(currentInstance: Human): { name: string, missing: string[] }[] {\n  if (currentInstance) instance = currentInstance;\n  if (!instance) log('instance not registred');\n  const missing: KernelOps[] = [];\n  for (const defined of Object.keys(currentInstance.models)) {\n    const model: GraphModel | null = currentInstance.models[defined as keyof Models] as GraphModel | null;\n    if (!model) continue;\n    const res = validateModel(currentInstance, model, defined);\n    if (res) missing.push(res);\n  }\n  return missing;\n}\n", "/**\n * Anti-spoofing model implementation\n */\n\nimport { log, now } from '../util/util';\nimport type { Config } from '../config';\nimport type { GraphModel, Tensor } from '../tfjs/types';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { loadModel } from '../tfjs/load';\nimport { env } from '../util/env';\n\nlet model: GraphModel | null;\nconst cached: number[] = [];\nlet skipped = Number.MAX_SAFE_INTEGER;\nlet lastCount = 0;\nlet lastTime = 0;\n\nexport async function load(config: Config): Promise<GraphModel> {\n  if (env.initial) model = null;\n  if (!model) model = await loadModel(config.face.antispoof?.modelPath);\n  else if (config.debug) log('cached model:', model['modelUrl']);\n  return model;\n}\n\nexport async function predict(image: Tensor, config: Config, idx: number, count: number): Promise<number> {\n  if (!model || !model?.['executor']) return 0;\n  const skipTime = (config.face.antispoof?.skipTime || 0) > (now() - lastTime);\n  const skipFrame = skipped < (config.face.antispoof?.skipFrames || 0);\n  if (config.skipAllowed && skipTime && skipFrame && (lastCount === count) && cached[idx]) {\n    skipped++;\n    return cached[idx];\n  }\n  skipped = 0;\n  return new Promise(async (resolve) => {\n    const resize = tf.image.resizeBilinear(image, [model?.inputs[0].shape ? model.inputs[0].shape[2] : 0, model?.inputs[0].shape ? model.inputs[0].shape[1] : 0], false);\n    const res = model?.execute(resize) as Tensor;\n    const num = (await res.data())[0];\n    cached[idx] = Math.round(100 * num) / 100;\n    lastCount = count;\n    lastTime = now();\n    tf.dispose([resize, res]);\n    resolve(cached[idx]);\n  });\n}\n", "/**\n * BlazeFace, FaceMesh & Iris model implementation\n * See `facemesh.ts` for entry point\n */\n\nexport const meshAnnotations: Record<string, number[]> = {\n  silhouette: [\n    10, 338, 297, 332, 284, 251, 389, 356, 454, 323, 361, 288,\n    397, 365, 379, 378, 400, 377, 152, 148, 176, 149, 150, 136,\n    172, 58, 132, 93, 234, 127, 162, 21, 54, 103, 67, 109,\n  ],\n  // lipsUpperOuter: [61, 185, 40, 39, 37, 0, 267, 269, 270, 409, 291], // 11\n  // lipsLowerOuter: [146, 91, 181, 84, 17, 314, 405, 321, 375, 291], // 10\n  // lipsUpperInner: [78, 191, 80, 81, 82, 13, 312, 311, 310, 415, 308], // 11\n  // lipsLowerInner: [78, 95, 88, 178, 87, 14, 317, 402, 318, 324, 308], // 11\n  lipsUpperOuter: [185, 40, 39, 37, 0, 267, 269, 270, 409],\n  lipsLowerOuter: [61, 146, 91, 181, 84, 17, 314, 405, 321, 375, 291],\n  lipsUpperInner: [191, 80, 81, 82, 13, 312, 311, 310, 415],\n  lipsLowerInner: [78, 95, 88, 178, 87, 14, 317, 402, 318, 324, 308],\n  lipsLowerSemiOuter: [76, 77, 90, 180, 85, 16, 315, 404, 320, 307, 306],\n  lipsUpperSemiOuter: [184, 74, 73, 72, 11, 302, 303, 304, 408],\n  lipsLowerSemiInner: [62, 96, 89, 179, 86, 15, 316, 403, 319, 325, 292],\n  lipsUpperSemiInner: [183, 42, 41, 38, 12, 268, 271, 272, 407],\n  rightEyeUpper0: [246, 161, 160, 159, 158, 157, 173], // 7\n  rightEyeLower0: [33, 7, 163, 144, 145, 153, 154, 155, 133], // 9\n  rightEyeUpper1: [247, 30, 29, 27, 28, 56, 190], // 7\n  rightEyeLower1: [130, 25, 110, 24, 23, 22, 26, 112, 243], // 9\n  rightEyeUpper2: [113, 225, 224, 223, 222, 221, 189], // 7\n  rightEyeLower2: [226, 31, 228, 229, 230, 231, 232, 233, 244], // 9\n  rightEyeLower3: [143, 111, 117, 118, 119, 120, 121, 128, 245], // 9\n  rightEyebrowUpper: [156, 70, 63, 105, 66, 107, 55, 193], // 8\n  rightEyebrowLower: [35, 124, 46, 53, 52, 65], // 6\n  rightEyeIris: [473, 474, 475, 476, 477], // 5\n  leftEyeUpper0: [466, 388, 387, 386, 385, 384, 398],\n  leftEyeLower0: [263, 249, 390, 373, 374, 380, 381, 382, 362],\n  leftEyeUpper1: [467, 260, 259, 257, 258, 286, 414],\n  leftEyeLower1: [359, 255, 339, 254, 253, 252, 256, 341, 463],\n  leftEyeUpper2: [342, 445, 444, 443, 442, 441, 413],\n  leftEyeLower2: [446, 261, 448, 449, 450, 451, 452, 453, 464],\n  leftEyeLower3: [372, 340, 346, 347, 348, 349, 350, 357, 465],\n  leftEyebrowUpper: [383, 300, 293, 334, 296, 336, 285, 417],\n  leftEyebrowLower: [265, 353, 276, 283, 282, 295],\n  leftEyeIris: [468, 469, 470, 471, 472],\n  midwayBetweenEyes: [168],\n  noseTip: [1],\n  noseBottom: [2],\n  noseRightCorner: [98],\n  noseLeftCorner: [327],\n  rightCheek: [205],\n  leftCheek: [425],\n};\n\nexport const meshLandmarks: Record<string, number | number[]> = {\n  count: 468,\n  mouth: 13,\n  symmetryLine: [13, meshAnnotations.midwayBetweenEyes[0]],\n};\n\nexport const blazeFaceLandmarks: Record<string, number | number[]> = {\n  leftEye: 0,\n  rightEye: 1,\n  nose: 2,\n  mouth: 3,\n  leftEar: 4,\n  rightEar: 5,\n  symmetryLine: [3, 2],\n};\n\nexport const irisIndices: { key: string, indices: number[] }[] = [ // A mapping from facemesh model keypoints to iris model keypoints.\n  { key: 'EyeUpper0', indices: [9, 10, 11, 12, 13, 14, 15] }, // 7 x 3d\n  { key: 'EyeUpper1', indices: [25, 26, 27, 28, 29, 30, 31] }, // 7 x 3d\n  { key: 'EyeUpper2', indices: [41, 42, 43, 44, 45, 46, 47] }, // 7 x 3d\n  { key: 'EyeLower0', indices: [0, 1, 2, 3, 4, 5, 6, 7, 8] }, // 7 x 3d\n  { key: 'EyeLower1', indices: [16, 17, 18, 19, 20, 21, 22, 23, 24] }, // 9 x 3d\n  { key: 'EyeLower2', indices: [32, 33, 34, 35, 36, 37, 38, 39, 40] }, // 9 x 3d\n  { key: 'EyeLower3', indices: [54, 55, 56, 57, 58, 59, 60, 61, 62] }, // 9 x 3d\n  { key: 'EyebrowUpper', indices: [63, 64, 65, 66, 67, 68, 69, 70] }, // 8 x 3d\n  { key: 'EyebrowLower', indices: [48, 49, 50, 51, 52, 53] }, // 6 x 3d\n];\n\nexport const UV468: [number, number][] = [\n  [0.499976992607117, 0.652534008026123],\n  [0.500025987625122, 0.547487020492554],\n  [0.499974012374878, 0.602371990680695],\n  [0.482113003730774, 0.471979022026062],\n  [0.500150978565216, 0.527155995368958],\n  [0.499909996986389, 0.498252987861633],\n  [0.499523013830185, 0.40106201171875],\n  [0.289712011814117, 0.380764007568359],\n  [0.499954998493195, 0.312398016452789],\n  [0.499987006187439, 0.269918978214264],\n  [0.500023007392883, 0.107050001621246],\n  [0.500023007392883, 0.666234016418457],\n  [0.5000159740448, 0.679224014282227],\n  [0.500023007392883, 0.692348003387451],\n  [0.499976992607117, 0.695277988910675],\n  [0.499976992607117, 0.70593398809433],\n  [0.499976992607117, 0.719385027885437],\n  [0.499976992607117, 0.737019002437592],\n  [0.499967992305756, 0.781370997428894],\n  [0.499816000461578, 0.562981009483337],\n  [0.473773002624512, 0.573909997940063],\n  [0.104906998574734, 0.254140973091125],\n  [0.365929991006851, 0.409575998783112],\n  [0.338757991790771, 0.41302502155304],\n  [0.311120003461838, 0.409460008144379],\n  [0.274657994508743, 0.389131009578705],\n  [0.393361985683441, 0.403706014156342],\n  [0.345234006643295, 0.344011008739471],\n  [0.370094001293182, 0.346076011657715],\n  [0.319321990013123, 0.347265005111694],\n  [0.297903001308441, 0.353591024875641],\n  [0.24779200553894, 0.410809993743896],\n  [0.396889001131058, 0.842755019664764],\n  [0.280097991228104, 0.375599980354309],\n  [0.106310002505779, 0.399955987930298],\n  [0.2099249958992, 0.391353011131287],\n  [0.355807989835739, 0.534406006336212],\n  [0.471751004457474, 0.65040397644043],\n  [0.474155008792877, 0.680191993713379],\n  [0.439785003662109, 0.657229006290436],\n  [0.414617002010345, 0.66654098033905],\n  [0.450374007225037, 0.680860996246338],\n  [0.428770989179611, 0.682690978050232],\n  [0.374971002340317, 0.727805018424988],\n  [0.486716985702515, 0.547628998756409],\n  [0.485300987958908, 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0.359827995300293],\n  [0.583841025829315, 0.368713974952698],\n  [0.586614012718201, 0.692366003990173],\n  [0.771915018558502, 0.683578014373779],\n  [0.531597018241882, 0.352482974529266],\n  [0.588370978832245, 0.804440975189209],\n  [0.52079701423645, 0.442565023899078],\n  [0.567984998226166, 0.493479013442993],\n  [0.543282985687256, 0.819254994392395],\n  [0.655317008495331, 0.745514988899231],\n  [0.621008992195129, 0.574018001556396],\n  [0.625559985637665, 0.78031200170517],\n  [0.680198013782501, 0.570719003677368],\n  [0.64276397228241, 0.604337990283966],\n  [0.704662978649139, 0.621529996395111],\n  [0.552012026309967, 0.862591981887817],\n  [0.589071989059448, 0.508637011051178],\n  [0.685944974422455, 0.775357007980347],\n  [0.645735025405884, 0.812640011310577],\n  [0.675342977046967, 0.703978002071381],\n  [0.810858011245728, 0.646304965019226],\n  [0.72012197971344, 0.714666962623596],\n  [0.866151988506317, 0.682704985141754],\n  [0.663187026977539, 0.644596993923187],\n  [0.570082008838654, 0.466325998306274],\n  [0.544561982154846, 0.548375964164734],\n  [0.562758982181549, 0.558784961700439],\n  [0.531987011432648, 0.530140042304993],\n  [0.585271000862122, 0.335177004337311],\n  [0.622952997684479, 0.32277899980545],\n  [0.655896008014679, 0.320163011550903],\n  [0.687132000923157, 0.322345972061157],\n  [0.716481983661652, 0.333200991153717],\n  [0.758756995201111, 0.382786989212036],\n  [0.897013008594513, 0.468769013881683],\n  [0.732392013072968, 0.424547016620636],\n  [0.70211398601532, 0.433162987232208],\n  [0.66652500629425, 0.433866024017334],\n  [0.633504986763, 0.426087975502014],\n  [0.603875994682312, 0.416586995124817],\n  [0.579657971858978, 0.409945011138916],\n  [0.992439985275269, 0.480777025222778],\n  [0.567192018032074, 0.569419980049133],\n  [0.54136598110199, 0.478899002075195],\n  [0.526564002037048, 0.546118021011353],\n  [0.523913025856018, 0.563830018043518],\n  [0.531529009342194, 0.555056989192963],\n  [0.566035985946655, 0.582329034805298],\n  [0.51631098985672, 0.563053965568542],\n  [0.5174720287323, 0.577877044677734],\n  [0.573594987392426, 0.389806985855103],\n  [0.560697972774506, 0.395331978797913],\n  [0.549755990505219, 0.399751007556915],\n  [0.710287988185883, 0.368252992630005],\n  [0.723330020904541, 0.363372981548309],\n];\n\nexport const TRI468: number[] = [\n  127, 34, 139, 11, 0, 37, 232, 231, 120, 72, 37, 39, 128, 121, 47, 232, 121, 128, 104, 69, 67, 175, 171, 148, 157, 154, 155, 118, 50, 101, 73, 39, 40, 9,\n  151, 108, 48, 115, 131, 194, 204, 211, 74, 40, 185, 80, 42, 183, 40, 92, 186, 230, 229, 118, 202, 212, 214, 83, 18, 17, 76, 61, 146, 160, 29, 30, 56,\n  157, 173, 106, 204, 194, 135, 214, 192, 203, 165, 98, 21, 71, 68, 51, 45, 4, 144, 24, 23, 77, 146, 91, 205, 50, 187, 201, 200, 18, 91, 106, 182, 90, 91,\n  181, 85, 84, 17, 206, 203, 36, 148, 171, 140, 92, 40, 39, 193, 189, 244, 159, 158, 28, 247, 246, 161, 236, 3, 196, 54, 68, 104, 193, 168, 8, 117,\n  228, 31, 189, 193, 55, 98, 97, 99, 126, 47, 100, 166, 79, 218, 155, 154, 26, 209, 49, 131, 135, 136, 150, 47, 126, 217, 223, 52, 53, 45, 51, 134, 211,\n  170, 140, 67, 69, 108, 43, 106, 91, 230, 119, 120, 226, 130, 247, 63, 53, 52, 238, 20, 242, 46, 70, 156, 78, 62, 96, 46, 53, 63, 143, 34, 227, 173,\n  155, 133, 123, 117, 111, 44, 125, 19, 236, 134, 51, 216, 206, 205, 154, 153, 22, 39, 37, 167, 200, 201, 208, 36, 142, 100, 57, 212, 202, 20, 60, 99, 28,\n  158, 157, 35, 226, 113, 160, 159, 27, 204, 202, 210, 113, 225, 46, 43, 202, 204, 62, 76, 77, 137, 123, 116, 41, 38, 72, 203, 129, 142, 64, 98, 240, 49,\n  102, 64, 41, 73, 74, 212, 216, 207, 42, 74, 184, 169, 170, 211, 170, 149, 176, 105, 66, 69, 122, 6, 168, 123, 147, 187, 96, 77, 90, 65, 55, 107, 89,\n  90, 180, 101, 100, 120, 63, 105, 104, 93, 137, 227, 15, 86, 85, 129, 102, 49, 14, 87, 86, 55, 8, 9, 100, 47, 121, 145, 23, 22, 88, 89, 179, 6, 122,\n  196, 88, 95, 96, 138, 172, 136, 215, 58, 172, 115, 48, 219, 42, 80, 81, 195, 3, 51, 43, 146, 61, 171, 175, 199, 81, 82, 38, 53, 46, 225, 144, 163, 110,\n  246, 33, 7, 52, 65, 66, 229, 228, 117, 34, 127, 234, 107, 108, 69, 109, 108, 151, 48, 64, 235, 62, 78, 191, 129, 209, 126, 111, 35, 143, 163, 161, 246,\n  117, 123, 50, 222, 65, 52, 19, 125, 141, 221, 55, 65, 3, 195, 197, 25, 7, 33, 220, 237, 44, 70, 71, 139, 122, 193, 245, 247, 130, 33, 71, 21, 162,\n  153, 158, 159, 170, 169, 150, 188, 174, 196, 216, 186, 92, 144, 160, 161, 2, 97, 167, 141, 125, 241, 164, 167, 37, 72, 38, 12, 145, 159, 160, 38, 82, 13,\n  63, 68, 71, 226, 35, 111, 158, 153, 154, 101, 50, 205, 206, 92, 165, 209, 198, 217, 165, 167, 97, 220, 115, 218, 133, 112, 243, 239, 238, 241, 214,\n  135, 169, 190, 173, 133, 171, 208, 32, 125, 44, 237, 86, 87, 178, 85, 86, 179, 84, 85, 180, 83, 84, 181, 201, 83, 182, 137, 93, 132, 76, 62, 183, 61,\n  76, 184, 57, 61, 185, 212, 57, 186, 214, 207, 187, 34, 143, 156, 79, 239, 237, 123, 137, 177, 44, 1, 4, 201, 194, 32, 64, 102, 129, 213, 215, 138, 59,\n  166, 219, 242, 99, 97, 2, 94, 141, 75, 59, 235, 24, 110, 228, 25, 130, 226, 23, 24, 229, 22, 23, 230, 26, 22, 231, 112, 26, 232, 189, 190, 243, 221, 56,\n  190, 28, 56, 221, 27, 28, 222, 29, 27, 223, 30, 29, 224, 247, 30, 225, 238, 79, 20, 166, 59, 75, 60, 75, 240, 147, 177, 215, 20, 79, 166, 187, 147, 213,\n  112, 233, 244, 233, 128, 245, 128, 114, 188, 114, 217, 174, 131, 115, 220, 217, 198, 236, 198, 131, 134, 177, 132, 58, 143, 35, 124, 110, 163, 7, 228,\n  110, 25, 356, 389, 368, 11, 302, 267, 452, 350, 349, 302, 303, 269, 357, 343, 277, 452, 453, 357, 333, 332, 297, 175, 152, 377, 384, 398, 382, 347,\n  348, 330, 303, 304, 270, 9, 336, 337, 278, 279, 360, 418, 262, 431, 304, 408, 409, 310, 415, 407, 270, 409, 410, 450, 348, 347, 422, 430, 434, 313,\n  314, 17, 306, 307, 375, 387, 388, 260, 286, 414, 398, 335, 406, 418, 364, 367, 416, 423, 358, 327, 251, 284, 298, 281, 5, 4, 373, 374, 253, 307, 320,\n  321, 425, 427, 411, 421, 313, 18, 321, 405, 406, 320, 404, 405, 315, 16, 17, 426, 425, 266, 377, 400, 369, 322, 391, 269, 417, 465, 464, 386, 257, 258,\n  466, 260, 388, 456, 399, 419, 284, 332, 333, 417, 285, 8, 346, 340, 261, 413, 441, 285, 327, 460, 328, 355, 371, 329, 392, 439, 438, 382, 341, 256,\n  429, 420, 360, 364, 394, 379, 277, 343, 437, 443, 444, 283, 275, 440, 363, 431, 262, 369, 297, 338, 337, 273, 375, 321, 450, 451, 349, 446, 342, 467,\n  293, 334, 282, 458, 461, 462, 276, 353, 383, 308, 324, 325, 276, 300, 293, 372, 345, 447, 382, 398, 362, 352, 345, 340, 274, 1, 19, 456, 248, 281, 436,\n  427, 425, 381, 256, 252, 269, 391, 393, 200, 199, 428, 266, 330, 329, 287, 273, 422, 250, 462, 328, 258, 286, 384, 265, 353, 342, 387, 259, 257, 424,\n  431, 430, 342, 353, 276, 273, 335, 424, 292, 325, 307, 366, 447, 345, 271, 303, 302, 423, 266, 371, 294, 455, 460, 279, 278, 294, 271, 272, 304, 432,\n  434, 427, 272, 407, 408, 394, 430, 431, 395, 369, 400, 334, 333, 299, 351, 417, 168, 352, 280, 411, 325, 319, 320, 295, 296, 336, 319, 403, 404, 330,\n  348, 349, 293, 298, 333, 323, 454, 447, 15, 16, 315, 358, 429, 279, 14, 15, 316, 285, 336, 9, 329, 349, 350, 374, 380, 252, 318, 402, 403, 6, 197, 419,\n  318, 319, 325, 367, 364, 365, 435, 367, 397, 344, 438, 439, 272, 271, 311, 195, 5, 281, 273, 287, 291, 396, 428, 199, 311, 271, 268, 283, 444, 445,\n  373, 254, 339, 263, 466, 249, 282, 334, 296, 449, 347, 346, 264, 447, 454, 336, 296, 299, 338, 10, 151, 278, 439, 455, 292, 407, 415, 358, 371, 355,\n  340, 345, 372, 390, 249, 466, 346, 347, 280, 442, 443, 282, 19, 94, 370, 441, 442, 295, 248, 419, 197, 263, 255, 359, 440, 275, 274, 300, 383, 368,\n  351, 412, 465, 263, 467, 466, 301, 368, 389, 380, 374, 386, 395, 378, 379, 412, 351, 419, 436, 426, 322, 373, 390, 388, 2, 164, 393, 370, 462, 461,\n  164, 0, 267, 302, 11, 12, 374, 373, 387, 268, 12, 13, 293, 300, 301, 446, 261, 340, 385, 384, 381, 330, 266, 425, 426, 423, 391, 429, 355, 437, 391,\n  327, 326, 440, 457, 438, 341, 382, 362, 459, 457, 461, 434, 430, 394, 414, 463, 362, 396, 369, 262, 354, 461, 457, 316, 403, 402, 315, 404, 403, 314,\n  405, 404, 313, 406, 405, 421, 418, 406, 366, 401, 361, 306, 408, 407, 291, 409, 408, 287, 410, 409, 432, 436, 410, 434, 416, 411, 264, 368, 383, 309,\n  438, 457, 352, 376, 401, 274, 275, 4, 421, 428, 262, 294, 327, 358, 433, 416, 367, 289, 455, 439, 462, 370, 326, 2, 326, 370, 305, 460, 455, 254,\n  449, 448, 255, 261, 446, 253, 450, 449, 252, 451, 450, 256, 452, 451, 341, 453, 452, 413, 464, 463, 441, 413, 414, 258, 442, 441, 257, 443, 442, 259,\n  444, 443, 260, 445, 444, 467, 342, 445, 459, 458, 250, 289, 392, 290, 290, 328, 460, 376, 433, 435, 250, 290, 392, 411, 416, 433, 341, 463, 464, 453,\n  464, 465, 357, 465, 412, 343, 412, 399, 360, 363, 440, 437, 399, 456, 420, 456, 363, 401, 435, 288, 372, 383, 353, 339, 255, 249, 448, 261, 255, 133,\n  243, 190, 133, 155, 112, 33, 246, 247, 33, 130, 25, 398, 384, 286, 362, 398, 414, 362, 463, 341, 263, 359, 467, 263, 249, 255, 466, 467, 260, 75, 60,\n  166, 238, 239, 79, 162, 127, 139, 72, 11, 37, 121, 232, 120, 73, 72, 39, 114, 128, 47, 233, 232, 128, 103, 104, 67, 152, 175, 148, 173, 157, 155,\n  119, 118, 101, 74, 73, 40, 107, 9, 108, 49, 48, 131, 32, 194, 211, 184, 74, 185, 191, 80, 183, 185, 40, 186, 119, 230, 118, 210, 202, 214, 84, 83, 17,\n  77, 76, 146, 161, 160, 30, 190, 56, 173, 182, 106, 194, 138, 135, 192, 129, 203, 98, 54, 21, 68, 5, 51, 4, 145, 144, 23, 90, 77, 91, 207, 205, 187, 83,\n  201, 18, 181, 91, 182, 180, 90, 181, 16, 85, 17, 205, 206, 36, 176, 148, 140, 165, 92, 39, 245, 193, 244, 27, 159, 28, 30, 247, 161, 174, 236, 196,\n  103, 54, 104, 55, 193, 8, 111, 117, 31, 221, 189, 55, 240, 98, 99, 142, 126, 100, 219, 166, 218, 112, 155, 26, 198, 209, 131, 169, 135, 150, 114, 47,\n  217, 224, 223, 53, 220, 45, 134, 32, 211, 140, 109, 67, 108, 146, 43, 91, 231, 230, 120, 113, 226, 247, 105, 63, 52, 241, 238, 242, 124, 46, 156, 95,\n  78, 96, 70, 46, 63, 116, 143, 227, 116, 123, 111, 1, 44, 19, 3, 236, 51, 207, 216, 205, 26, 154, 22, 165, 39, 167, 199, 200, 208, 101, 36, 100, 43,\n  57, 202, 242, 20, 99, 56, 28, 157, 124, 35, 113, 29, 160, 27, 211, 204, 210, 124, 113, 46, 106, 43, 204, 96, 62, 77, 227, 137, 116, 73, 41, 72, 36, 203,\n  142, 235, 64, 240, 48, 49, 64, 42, 41, 74, 214, 212, 207, 183, 42, 184, 210, 169, 211, 140, 170, 176, 104, 105, 69, 193, 122, 168, 50, 123, 187, 89, 96,\n  90, 66, 65, 107, 179, 89, 180, 119, 101, 120, 68, 63, 104, 234, 93, 227, 16, 15, 85, 209, 129, 49, 15, 14, 86, 107, 55, 9, 120, 100, 121, 153, 145, 22,\n  178, 88, 179, 197, 6, 196, 89, 88, 96, 135, 138, 136, 138, 215, 172, 218, 115, 219, 41, 42, 81, 5, 195, 51, 57, 43, 61, 208, 171, 199, 41, 81, 38,\n  224, 53, 225, 24, 144, 110, 105, 52, 66, 118, 229, 117, 227, 34, 234, 66, 107, 69, 10, 109, 151, 219, 48, 235, 183, 62, 191, 142, 129, 126, 116, 111,\n  143, 7, 163, 246, 118, 117, 50, 223, 222, 52, 94, 19, 141, 222, 221, 65, 196, 3, 197, 45, 220, 44, 156, 70, 139, 188, 122, 245, 139, 71, 162, 145,\n  153, 159, 149, 170, 150, 122, 188, 196, 206, 216, 92, 163, 144, 161, 164, 2, 167, 242, 141, 241, 0, 164, 37, 11, 72, 12, 144, 145, 160, 12, 38, 13, 70,\n  63, 71, 31, 226, 111, 157, 158, 154, 36, 101, 205, 203, 206, 165, 126, 209, 217, 98, 165, 97, 237, 220, 218, 237, 239, 241, 210, 214, 169, 140, 171, 32,\n  241, 125, 237, 179, 86, 178, 180, 85, 179, 181, 84, 180, 182, 83, 181, 194, 201, 182, 177, 137, 132, 184, 76, 183, 185, 61, 184, 186, 57, 185, 216, 212,\n  186, 192, 214, 187, 139, 34, 156, 218, 79, 237, 147, 123, 177, 45, 44, 4, 208, 201, 32, 98, 64, 129, 192, 213, 138, 235, 59, 219, 141, 242, 97, 97, 2,\n  141, 240, 75, 235, 229, 24, 228, 31, 25, 226, 230, 23, 229, 231, 22, 230, 232, 26, 231, 233, 112, 232, 244, 189, 243, 189, 221, 190, 222, 28, 221,\n  223, 27, 222, 224, 29, 223, 225, 30, 224, 113, 247, 225, 99, 60, 240, 213, 147, 215, 60, 20, 166, 192, 187, 213, 243, 112, 244, 244, 233, 245, 245,\n  128, 188, 188, 114, 174, 134, 131, 220, 174, 217, 236, 236, 198, 134, 215, 177, 58, 156, 143, 124, 25, 110, 7, 31, 228, 25, 264, 356, 368, 0, 11, 267,\n  451, 452, 349, 267, 302, 269, 350, 357, 277, 350, 452, 357, 299, 333, 297, 396, 175, 377, 381, 384, 382, 280, 347, 330, 269, 303, 270, 151, 9, 337,\n  344, 278, 360, 424, 418, 431, 270, 304, 409, 272, 310, 407, 322, 270, 410, 449, 450, 347, 432, 422, 434, 18, 313, 17, 291, 306, 375, 259, 387, 260,\n  424, 335, 418, 434, 364, 416, 391, 423, 327, 301, 251, 298, 275, 281, 4, 254, 373, 253, 375, 307, 321, 280, 425, 411, 200, 421, 18, 335, 321, 406,\n  321, 320, 405, 314, 315, 17, 423, 426, 266, 396, 377, 369, 270, 322, 269, 413, 417, 464, 385, 386, 258, 248, 456, 419, 298, 284, 333, 168, 417, 8,\n  448, 346, 261, 417, 413, 285, 326, 327, 328, 277, 355, 329, 309, 392, 438, 381, 382, 256, 279, 429, 360, 365, 364, 379, 355, 277, 437, 282, 443, 283,\n  281, 275, 363, 395, 431, 369, 299, 297, 337, 335, 273, 321, 348, 450, 349, 359, 446, 467, 283, 293, 282, 250, 458, 462, 300, 276, 383, 292, 308, 325,\n  283, 276, 293, 264, 372, 447, 346, 352, 340, 354, 274, 19, 363, 456, 281, 426, 436, 425, 380, 381, 252, 267, 269, 393, 421, 200, 428, 371, 266, 329,\n  432, 287, 422, 290, 250, 328, 385, 258, 384, 446, 265, 342, 386, 387, 257, 422, 424, 430, 445, 342, 276, 422, 273, 424, 306, 292, 307, 352, 366, 345,\n  268, 271, 302, 358, 423, 371, 327, 294, 460, 331, 279, 294, 303, 271, 304, 436, 432, 427, 304, 272, 408, 395, 394, 431, 378, 395, 400, 296, 334, 299,\n  6, 351, 168, 376, 352, 411, 307, 325, 320, 285, 295, 336, 320, 319, 404, 329, 330, 349, 334, 293, 333, 366, 323, 447, 316, 15, 315, 331, 358, 279,\n  317, 14, 316, 8, 285, 9, 277, 329, 350, 253, 374, 252, 319, 318, 403, 351, 6, 419, 324, 318, 325, 397, 367, 365, 288, 435, 397, 278, 344, 439, 310,\n  272, 311, 248, 195, 281, 375, 273, 291, 175, 396, 199, 312, 311, 268, 276, 283, 445, 390, 373, 339, 295, 282, 296, 448, 449, 346, 356, 264, 454, 337,\n  336, 299, 337, 338, 151, 294, 278, 455, 308, 292, 415, 429, 358, 355, 265, 340, 372, 388, 390, 466, 352, 346, 280, 295, 442, 282, 354, 19, 370, 285,\n  441, 295, 195, 248, 197, 457, 440, 274, 301, 300, 368, 417, 351, 465, 251, 301, 389, 385, 380, 386, 394, 395, 379, 399, 412, 419, 410, 436, 322, 387,\n  373, 388, 326, 2, 393, 354, 370, 461, 393, 164, 267, 268, 302, 12, 386, 374, 387, 312, 268, 13, 298, 293, 301, 265, 446, 340, 380, 385, 381, 280, 330,\n  425, 322, 426, 391, 420, 429, 437, 393, 391, 326, 344, 440, 438, 458, 459, 461, 364, 434, 394, 428, 396, 262, 274, 354, 457, 317, 316, 402, 316, 315,\n  403, 315, 314, 404, 314, 313, 405, 313, 421, 406, 323, 366, 361, 292, 306, 407, 306, 291, 408, 291, 287, 409, 287, 432, 410, 427, 434, 411, 372, 264,\n  383, 459, 309, 457, 366, 352, 401, 1, 274, 4, 418, 421, 262, 331, 294, 358, 435, 433, 367, 392, 289, 439, 328, 462, 326, 94, 2, 370, 289, 305, 455, 339,\n  254, 448, 359, 255, 446, 254, 253, 449, 253, 252, 450, 252, 256, 451, 256, 341, 452, 414, 413, 463, 286, 441, 414, 286, 258, 441, 258, 257, 442, 257,\n  259, 443, 259, 260, 444, 260, 467, 445, 309, 459, 250, 305, 289, 290, 305, 290, 460, 401, 376, 435, 309, 250, 392, 376, 411, 433, 453, 341, 464, 357,\n  453, 465, 343, 357, 412, 437, 343, 399, 344, 360, 440, 420, 437, 456, 360, 420, 363, 361, 401, 288, 265, 372, 353, 390, 339, 249, 339, 448, 255];\n\nexport const TRI68: number[] = [0, 1, 36, 0, 36, 17, 1, 2, 41, 1, 41, 36, 2, 3, 31, 2, 31, 41, 3, 4, 48, 3, 48, 31, 4, 5, 48, 5, 6, 48, 6, 7, 59, 6, 59, 48, 7, 8, 58, 7, 58, 59,\n  8, 9, 56, 8, 56, 57, 8, 57, 58, 9, 10, 55, 9, 55, 56, 10, 11, 54, 10, 54, 55, 11, 12, 54, 12, 13, 54, 13, 14, 35, 13, 35, 54, 14, 15, 46, 14, 46, 35, 15, 16,\n  45, 15, 45, 46, 16, 26, 45, 17, 36, 18, 18, 37, 19, 18, 36, 37, 19, 38, 20, 19, 37, 38, 20, 39, 21, 20, 38, 39, 21, 39, 27, 22, 42, 23, 22, 27, 42, 23, 43, 24,\n  23, 42, 43, 24, 44, 25, 24, 43, 44, 25, 45, 26, 25, 44, 45, 27, 39, 28, 27, 28, 42, 28, 39, 29, 28, 29, 42, 29, 31, 30, 29, 30, 35, 29, 40, 31, 29, 35, 47, 29,\n  39, 40, 29, 47, 42, 30, 31, 32, 30, 32, 33, 30, 33, 34, 30, 34, 35, 31, 50, 32, 31, 40, 41, 31, 48, 49, 31, 49, 50, 32, 51, 33, 32, 50, 51, 33, 51, 34, 34, 52,\n  35, 34, 51, 52, 35, 46, 47, 35, 52, 53, 35, 53, 54, 36, 41, 37, 37, 40, 38, 37, 41, 40, 38, 40, 39, 42, 47, 43, 43, 47, 44, 44, 46, 45, 44, 47, 46, 48, 60, 49,\n  48, 59, 60, 49, 61, 50, 49, 60, 61, 50, 62, 51, 50, 61, 62, 51, 62, 52, 52, 63, 53, 52, 62, 63, 53, 64, 54, 53, 63, 64, 54, 64, 55, 55, 65, 56, 55, 64, 65, 56,\n  66, 57, 56, 65, 66, 57, 66, 58, 58, 67, 59, 58, 66, 67, 59, 67, 60, 60, 67, 61, 61, 66, 62, 61, 67, 66, 62, 66, 63, 63, 65, 64, 63, 66, 65, 21, 27, 22];\n\nexport const TRI33: number[] = [\n  /*  eyes  */ 0, 8, 7, 7, 8, 1, 2, 10, 9, 9, 10, 3,\n  /*  brows */ 17, 0, 18, 18, 0, 7, 18, 7, 19, 19, 7, 1, 19, 1, 11, 19, 11, 20, 21, 3, 22, 21, 9, 3, 20, 9, 21, 20, 2, 9, 20, 11, 2,\n  /*  4head */ 23, 17, 18, 25, 21, 22, 24, 19, 20, 24, 18, 19, 24, 20, 21, 24, 23, 18, 24, 21, 25,\n  /*  nose  */ 11, 12, 4, 11, 4, 13, 1, 12, 11, 11, 13, 2, 12, 14, 4, 4, 14, 13,\n  /* up-lip */ 14, 5, 15, 14, 15, 6, 12, 5, 14, 14, 6, 13,\n  /* cheeks */ 8, 12, 1, 2, 13, 10, 8, 26, 12, 10, 13, 27, 26, 5, 12, 13, 6, 27, 0, 26, 8, 10, 27, 3,\n  /*  chin  */ 5, 32, 16, 16, 32, 6, 5, 30, 32, 6, 32, 31,\n  /*  cont  */ 26, 30, 5, 27, 6, 31, 0, 28, 26, 3, 27, 29, 17, 28, 0, 3, 29, 22, 23, 28, 17, 22, 29, 25, 28, 30, 26, 27, 31, 29,\n];\n\nexport const TRI7: number[] = [0, 4, 1, 2, 4, 3, 4, 5, 6];\n\nexport const VTX68: number[] = [\n  /* cont  */ 127, 234, 132, 58, 172, 150, 149, 148, 152, 377, 378, 379, 397, 288, 361, 454, 356,\n  /* brows */ 70, 63, 105, 66, 107, 336, 296, 334, 293, 300,\n  /* nose  */ 168, 6, 195, 4, 98, 97, 2, 326, 327,\n  /* eyes  */ 33, 160, 158, 133, 153, 144, 362, 385, 387, 263, 373, 380,\n  /* lip   */ 57, 40, 37, 0, 267, 270, 287, 321, 314, 17, 84, 91,\n  /* mouth */ 78, 81, 13, 311, 308, 402, 14, 178,\n];\n\nexport const VTX33: number[] = [33, 133, 362, 263, 1, 62, 308, 159, 145, 386, 374, 6, 102, 331, 2, 13, 14, 70, 105, 107, 336, 334, 300, 54, 10, 284, 50, 280, 234, 454, 58, 288, 152];\n\nexport const VTX7: number[] = [33, 133, 362, 263, 1, 78, 308];\n\nexport const UV68 = VTX68.map((x) => UV468[x]);\n\nexport const UV33 = VTX33.map((x) => UV468[x]);\n\nexport const UV7 = VTX7.map((x) => UV468[x]);\n\n// https://github.com/tensorflow/tfjs-models/blob/master/face-landmarks-detection/src/constants.ts\n// https://github.com/google/mediapipe/mediapipe/python/solutions/face_mesh_connections.py\n\ntype PairArray = [number, number][];\n\nfunction connectionsToIndices(connections: PairArray) {\n  const indices = connections.map((connection) => connection[0]);\n  indices.push(connections[connections.length - 1][1]);\n  return indices;\n}\n\nexport const pairsLips: PairArray = [\n  [61, 146], [146, 91], [91, 181], [181, 84], [84, 17], [17, 314], [314, 405], [405, 321], [321, 375], [375, 291], [61, 185], [185, 40], [40, 39], [39, 37], [37, 0], [0, 267], [267, 269], [269, 270], [270, 409], [409, 291],\n  [78, 95], [95, 88], [88, 178], [178, 87], [87, 14], [14, 317], [317, 402], [402, 318], [318, 324], [324, 308], [78, 191], [191, 80], [80, 81], [81, 82], [82, 13], [13, 312], [312, 311], [311, 310], [310, 415], [415, 308],\n];\n\nexport const pairsLeftEye: PairArray = [[263, 249], [249, 390], [390, 373], [373, 374], [374, 380], [380, 381], [381, 382], [382, 362], [263, 466], [466, 388], [388, 387], [387, 386], [386, 385], [385, 384], [384, 398], [398, 362]];\n\nexport const pairsLeftEyebrow: PairArray = [[276, 283], [283, 282], [282, 295], [295, 285], [300, 293], [293, 334], [334, 296], [296, 336]];\n\nexport const pairsLeftIris: PairArray = [[474, 475], [475, 476], [476, 477], [477, 474]];\n\nexport const pairsRightEye: PairArray = [[33, 7], [7, 163], [163, 144], [144, 145], [145, 153], [153, 154], [154, 155], [155, 133], [33, 246], [246, 161], [161, 160], [160, 159], [159, 158], [158, 157], [157, 173], [173, 133]];\n\nexport const pairsRightEyebrow: PairArray = [[46, 53], [53, 52], [52, 65], [65, 55], [70, 63], [63, 105], [105, 66], [66, 107]];\n\nexport const pairsRightIris: PairArray = [[469, 470], [470, 471], [471, 472], [472, 469]];\n\nexport const pairsFaceContour: PairArray = [\n  [10, 338], [338, 297], [297, 332], [332, 284], [284, 251], [251, 389],\n  [389, 356], [356, 454], [454, 323], [323, 361], [361, 288], [288, 397],\n  [397, 365], [365, 379], [379, 378], [378, 400], [400, 377], [377, 152],\n  [152, 148], [148, 176], [176, 149], [149, 150], [150, 136], [136, 172],\n  [172, 58], [58, 132], [132, 93], [93, 234], [234, 127], [127, 162],\n  [162, 21], [21, 54], [54, 103], [103, 67], [67, 109], [109, 10],\n];\n\nexport const contourKeypoints = {\n  lips: connectionsToIndices(pairsLips),\n  leftEye: connectionsToIndices(pairsLeftEye),\n  leftEyebrow: connectionsToIndices(pairsLeftEyebrow),\n  leftIris: connectionsToIndices(pairsLeftIris),\n  rightEye: connectionsToIndices(pairsRightEye),\n  rightEyebrow: connectionsToIndices(pairsRightEyebrow),\n  rightIris: connectionsToIndices(pairsRightIris),\n  faceOval: connectionsToIndices(pairsFaceContour),\n};\n\nexport const pairsFaceMesh: PairArray = [\n  [127, 34], [34, 139], [139, 127], [11, 0], [0, 37], [37, 11],\n  [232, 231], [231, 120], [120, 232], [72, 37], [37, 39], [39, 72],\n  [128, 121], [121, 47], [47, 128], [232, 121], [121, 128], [128, 232],\n  [104, 69], [69, 67], [67, 104], [175, 171], [171, 148], [148, 175],\n  [118, 50], [50, 101], [101, 118], [73, 39], [39, 40], [40, 73],\n  [9, 151], [151, 108], [108, 9], [48, 115], [115, 131], [131, 48],\n  [194, 204], [204, 211], [211, 194], [74, 40], [40, 185], [185, 74],\n  [80, 42], [42, 183], [183, 80], [40, 92], [92, 186], [186, 40],\n  [230, 229], [229, 118], [118, 230], [202, 212], [212, 214], [214, 202],\n  [83, 18], [18, 17], [17, 83], [76, 61], [61, 146], [146, 76],\n  [160, 29], [29, 30], [30, 160], [56, 157], [157, 173], [173, 56],\n  [106, 204], [204, 194], [194, 106], [135, 214], [214, 192], [192, 135],\n  [203, 165], [165, 98], [98, 203], [21, 71], [71, 68], [68, 21],\n  [51, 45], [45, 4], [4, 51], [144, 24], [24, 23], [23, 144],\n  [77, 146], [146, 91], [91, 77], [205, 50], [50, 187], [187, 205],\n  [201, 200], [200, 18], [18, 201], [91, 106], [106, 182], [182, 91],\n  [90, 91], [91, 181], [181, 90], [85, 84], [84, 17], [17, 85],\n  [206, 203], [203, 36], [36, 206], [148, 171], [171, 140], [140, 148],\n  [92, 40], [40, 39], [39, 92], [193, 189], [189, 244], [244, 193],\n  [159, 158], [158, 28], [28, 159], [247, 246], [246, 161], [161, 247],\n  [236, 3], [3, 196], [196, 236], [54, 68], [68, 104], [104, 54],\n  [193, 168], [168, 8], [8, 193], [117, 228], [228, 31], [31, 117],\n  [189, 193], [193, 55], [55, 189], [98, 97], [97, 99], [99, 98],\n  [126, 47], [47, 100], [100, 126], [166, 79], [79, 218], [218, 166],\n  [155, 154], [154, 26], [26, 155], [209, 49], [49, 131], [131, 209],\n  [135, 136], [136, 150], [150, 135], [47, 126], [126, 217], [217, 47],\n  [223, 52], [52, 53], [53, 223], [45, 51], [51, 134], [134, 45],\n  [211, 170], [170, 140], [140, 211], [67, 69], [69, 108], [108, 67],\n  [43, 106], [106, 91], [91, 43], [230, 119], [119, 120], [120, 230],\n  [226, 130], [130, 247], [247, 226], [63, 53], [53, 52], [52, 63],\n  [238, 20], [20, 242], [242, 238], [46, 70], [70, 156], [156, 46],\n  [78, 62], [62, 96], [96, 78], [46, 53], [53, 63], [63, 46],\n  [143, 34], [34, 227], [227, 143], [123, 117], [117, 111], [111, 123],\n  [44, 125], [125, 19], [19, 44], [236, 134], [134, 51], [51, 236],\n  [216, 206], [206, 205], [205, 216], [154, 153], [153, 22], [22, 154],\n  [39, 37], [37, 167], [167, 39], [200, 201], [201, 208], [208, 200],\n  [36, 142], [142, 100], [100, 36], [57, 212], [212, 202], [202, 57],\n  [20, 60], [60, 99], [99, 20], [28, 158], [158, 157], [157, 28],\n  [35, 226], [226, 113], [113, 35], [160, 159], [159, 27], [27, 160],\n  [204, 202], [202, 210], [210, 204], [113, 225], [225, 46], [46, 113],\n  [43, 202], [202, 204], [204, 43], [62, 76], [76, 77], [77, 62],\n  [137, 123], [123, 116], [116, 137], [41, 38], [38, 72], [72, 41],\n  [203, 129], [129, 142], [142, 203], [64, 98], [98, 240], [240, 64],\n  [49, 102], [102, 64], [64, 49], [41, 73], [73, 74], [74, 41],\n  [212, 216], [216, 207], [207, 212], [42, 74], [74, 184], [184, 42],\n  [169, 170], [170, 211], [211, 169], [170, 149], [149, 176], [176, 170],\n  [105, 66], [66, 69], [69, 105], [122, 6], [6, 168], [168, 122],\n  [123, 147], [147, 187], [187, 123], [96, 77], [77, 90], [90, 96],\n  [65, 55], [55, 107], [107, 65], [89, 90], [90, 180], [180, 89],\n  [101, 100], [100, 120], [120, 101], [63, 105], [105, 104], [104, 63],\n  [93, 137], [137, 227], [227, 93], [15, 86], [86, 85], [85, 15],\n  [129, 102], [102, 49], [49, 129], [14, 87], [87, 86], [86, 14],\n  [55, 8], [8, 9], [9, 55], [100, 47], [47, 121], [121, 100],\n  [145, 23], [23, 22], [22, 145], [88, 89], [89, 179], [179, 88],\n  [6, 122], [122, 196], [196, 6], [88, 95], [95, 96], [96, 88],\n  [138, 172], [172, 136], [136, 138], [215, 58], [58, 172], [172, 215],\n  [115, 48], [48, 219], [219, 115], [42, 80], [80, 81], [81, 42],\n  [195, 3], [3, 51], [51, 195], [43, 146], [146, 61], [61, 43],\n  [171, 175], [175, 199], [199, 171], [81, 82], [82, 38], [38, 81],\n  [53, 46], [46, 225], [225, 53], [144, 163], [163, 110], [110, 144],\n  [52, 65], [65, 66], [66, 52], [229, 228], [228, 117], [117, 229],\n  [34, 127], [127, 234], [234, 34], [107, 108], [108, 69], [69, 107],\n  [109, 108], [108, 151], [151, 109], [48, 64], [64, 235], [235, 48],\n  [62, 78], [78, 191], [191, 62], [129, 209], [209, 126], [126, 129],\n  [111, 35], [35, 143], [143, 111], [117, 123], [123, 50], [50, 117],\n  [222, 65], [65, 52], [52, 222], [19, 125], [125, 141], [141, 19],\n  [221, 55], [55, 65], [65, 221], [3, 195], [195, 197], [197, 3],\n  [25, 7], [7, 33], [33, 25], [220, 237], [237, 44], [44, 220],\n  [70, 71], [71, 139], [139, 70], [122, 193], [193, 245], [245, 122],\n  [247, 130], [130, 33], [33, 247], [71, 21], [21, 162], [162, 71],\n  [170, 169], [169, 150], [150, 170], [188, 174], [174, 196], [196, 188],\n  [216, 186], [186, 92], [92, 216], [2, 97], [97, 167], [167, 2],\n  [141, 125], [125, 241], [241, 141], [164, 167], [167, 37], [37, 164],\n  [72, 38], [38, 12], [12, 72], [38, 82], [82, 13], [13, 38],\n  [63, 68], [68, 71], [71, 63], [226, 35], [35, 111], [111, 226],\n  [101, 50], [50, 205], [205, 101], [206, 92], [92, 165], [165, 206],\n  [209, 198], [198, 217], [217, 209], [165, 167], [167, 97], [97, 165],\n  [220, 115], [115, 218], [218, 220], [133, 112], [112, 243], [243, 133],\n  [239, 238], [238, 241], [241, 239], [214, 135], [135, 169], [169, 214],\n  [190, 173], [173, 133], [133, 190], [171, 208], [208, 32], [32, 171],\n  [125, 44], [44, 237], [237, 125], [86, 87], [87, 178], [178, 86],\n  [85, 86], [86, 179], [179, 85], [84, 85], [85, 180], [180, 84],\n  [83, 84], [84, 181], [181, 83], [201, 83], [83, 182], [182, 201],\n  [137, 93], [93, 132], [132, 137], [76, 62], [62, 183], [183, 76],\n  [61, 76], [76, 184], [184, 61], [57, 61], [61, 185], [185, 57],\n  [212, 57], [57, 186], [186, 212], [214, 207], [207, 187], [187, 214],\n  [34, 143], [143, 156], [156, 34], [79, 239], [239, 237], [237, 79],\n  [123, 137], [137, 177], [177, 123], [44, 1], [1, 4], [4, 44],\n  [201, 194], [194, 32], [32, 201], [64, 102], [102, 129], [129, 64],\n  [213, 215], [215, 138], [138, 213], [59, 166], [166, 219], [219, 59],\n  [242, 99], [99, 97], [97, 242], [2, 94], [94, 141], [141, 2],\n  [75, 59], [59, 235], [235, 75], [24, 110], [110, 228], [228, 24],\n  [25, 130], [130, 226], [226, 25], [23, 24], [24, 229], [229, 23],\n  [22, 23], [23, 230], [230, 22], [26, 22], [22, 231], [231, 26],\n  [112, 26], [26, 232], [232, 112], [189, 190], [190, 243], [243, 189],\n  [221, 56], [56, 190], [190, 221], [28, 56], [56, 221], [221, 28],\n  [27, 28], [28, 222], [222, 27], [29, 27], [27, 223], [223, 29],\n  [30, 29], [29, 224], [224, 30], [247, 30], [30, 225], [225, 247],\n  [238, 79], [79, 20], [20, 238], [166, 59], [59, 75], [75, 166],\n  [60, 75], [75, 240], [240, 60], [147, 177], [177, 215], [215, 147],\n  [20, 79], [79, 166], [166, 20], [187, 147], [147, 213], [213, 187],\n  [112, 233], [233, 244], [244, 112], [233, 128], [128, 245], [245, 233],\n  [128, 114], [114, 188], [188, 128], [114, 217], [217, 174], [174, 114],\n  [131, 115], [115, 220], [220, 131], [217, 198], [198, 236], [236, 217],\n  [198, 131], [131, 134], [134, 198], [177, 132], [132, 58], [58, 177],\n  [143, 35], [35, 124], [124, 143], [110, 163], [163, 7], [7, 110],\n  [228, 110], [110, 25], [25, 228], [356, 389], [389, 368], [368, 356],\n  [11, 302], [302, 267], [267, 11], [452, 350], [350, 349], [349, 452],\n  [302, 303], [303, 269], [269, 302], [357, 343], [343, 277], [277, 357],\n  [452, 453], [453, 357], [357, 452], [333, 332], [332, 297], [297, 333],\n  [175, 152], [152, 377], [377, 175], [347, 348], [348, 330], [330, 347],\n  [303, 304], [304, 270], [270, 303], [9, 336], [336, 337], [337, 9],\n  [278, 279], [279, 360], [360, 278], [418, 262], [262, 431], [431, 418],\n  [304, 408], [408, 409], [409, 304], [310, 415], [415, 407], [407, 310],\n  [270, 409], [409, 410], [410, 270], [450, 348], [348, 347], [347, 450],\n  [422, 430], [430, 434], [434, 422], [313, 314], [314, 17], [17, 313],\n  [306, 307], [307, 375], [375, 306], [387, 388], [388, 260], [260, 387],\n  [286, 414], [414, 398], [398, 286], [335, 406], [406, 418], [418, 335],\n  [364, 367], [367, 416], [416, 364], [423, 358], [358, 327], [327, 423],\n  [251, 284], [284, 298], [298, 251], [281, 5], [5, 4], [4, 281],\n  [373, 374], [374, 253], [253, 373], [307, 320], [320, 321], [321, 307],\n  [425, 427], [427, 411], [411, 425], [421, 313], [313, 18], [18, 421],\n  [321, 405], [405, 406], [406, 321], [320, 404], [404, 405], [405, 320],\n  [315, 16], [16, 17], [17, 315], [426, 425], [425, 266], [266, 426],\n  [377, 400], [400, 369], [369, 377], [322, 391], [391, 269], [269, 322],\n  [417, 465], [465, 464], [464, 417], [386, 257], [257, 258], [258, 386],\n  [466, 260], [260, 388], [388, 466], [456, 399], [399, 419], [419, 456],\n  [284, 332], [332, 333], [333, 284], [417, 285], [285, 8], [8, 417],\n  [346, 340], [340, 261], [261, 346], [413, 441], [441, 285], [285, 413],\n  [327, 460], [460, 328], [328, 327], [355, 371], [371, 329], [329, 355],\n  [392, 439], [439, 438], [438, 392], [382, 341], [341, 256], [256, 382],\n  [429, 420], [420, 360], [360, 429], [364, 394], [394, 379], [379, 364],\n  [277, 343], [343, 437], [437, 277], [443, 444], [444, 283], [283, 443],\n  [275, 440], [440, 363], [363, 275], [431, 262], [262, 369], [369, 431],\n  [297, 338], [338, 337], [337, 297], [273, 375], [375, 321], [321, 273],\n  [450, 451], [451, 349], [349, 450], [446, 342], [342, 467], [467, 446],\n  [293, 334], [334, 282], [282, 293], [458, 461], [461, 462], [462, 458],\n  [276, 353], [353, 383], [383, 276], [308, 324], [324, 325], [325, 308],\n  [276, 300], [300, 293], [293, 276], [372, 345], [345, 447], [447, 372],\n  [352, 345], [345, 340], [340, 352], [274, 1], [1, 19], [19, 274],\n  [456, 248], [248, 281], [281, 456], [436, 427], [427, 425], [425, 436],\n  [381, 256], [256, 252], [252, 381], [269, 391], [391, 393], [393, 269],\n  [200, 199], [199, 428], [428, 200], [266, 330], [330, 329], [329, 266],\n  [287, 273], [273, 422], [422, 287], [250, 462], [462, 328], [328, 250],\n  [258, 286], [286, 384], [384, 258], [265, 353], [353, 342], [342, 265],\n  [387, 259], [259, 257], [257, 387], [424, 431], [431, 430], [430, 424],\n  [342, 353], [353, 276], [276, 342], [273, 335], [335, 424], [424, 273],\n  [292, 325], [325, 307], [307, 292], [366, 447], [447, 345], [345, 366],\n  [271, 303], [303, 302], [302, 271], [423, 266], [266, 371], [371, 423],\n  [294, 455], [455, 460], [460, 294], [279, 278], [278, 294], [294, 279],\n  [271, 272], [272, 304], [304, 271], [432, 434], [434, 427], [427, 432],\n  [272, 407], [407, 408], [408, 272], [394, 430], [430, 431], [431, 394],\n  [395, 369], [369, 400], [400, 395], [334, 333], 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[364, 379], [379, 365],\n  [355, 277], [277, 437], [437, 355], [282, 443], [443, 283], [283, 282],\n  [281, 275], [275, 363], [363, 281], [395, 431], [431, 369], [369, 395],\n  [299, 297], [297, 337], [337, 299], [335, 273], [273, 321], [321, 335],\n  [348, 450], [450, 349], [349, 348], [359, 446], [446, 467], [467, 359],\n  [283, 293], [293, 282], [282, 283], [250, 458], [458, 462], [462, 250],\n  [300, 276], [276, 383], [383, 300], [292, 308], [308, 325], [325, 292],\n  [283, 276], [276, 293], [293, 283], [264, 372], [372, 447], [447, 264],\n  [346, 352], [352, 340], [340, 346], [354, 274], [274, 19], [19, 354],\n  [363, 456], [456, 281], [281, 363], [426, 436], [436, 425], [425, 426],\n  [380, 381], [381, 252], [252, 380], [267, 269], [269, 393], [393, 267],\n  [421, 200], [200, 428], [428, 421], [371, 266], [266, 329], [329, 371],\n  [432, 287], [287, 422], [422, 432], [290, 250], [250, 328], [328, 290],\n  [385, 258], [258, 384], [384, 385], [446, 265], [265, 342], [342, 446],\n  [386, 387], [387, 257], [257, 386], [422, 424], [424, 430], [430, 422],\n  [445, 342], [342, 276], [276, 445], [422, 273], [273, 424], [424, 422],\n  [306, 292], [292, 307], [307, 306], [352, 366], [366, 345], [345, 352],\n  [268, 271], [271, 302], [302, 268], [358, 423], [423, 371], [371, 358],\n  [327, 294], [294, 460], [460, 327], [331, 279], [279, 294], [294, 331],\n  [303, 271], [271, 304], [304, 303], [436, 432], [432, 427], [427, 436],\n  [304, 272], [272, 408], [408, 304], [395, 394], [394, 431], [431, 395],\n  [378, 395], [395, 400], [400, 378], [296, 334], [334, 299], [299, 296],\n  [6, 351], [351, 168], [168, 6], [376, 352], [352, 411], [411, 376],\n  [307, 325], [325, 320], [320, 307], [285, 295], [295, 336], [336, 285],\n  [320, 319], [319, 404], [404, 320], [329, 330], [330, 349], [349, 329],\n  [334, 293], [293, 333], [333, 334], [366, 323], [323, 447], [447, 366],\n  [316, 15], [15, 315], [315, 316], [331, 358], [358, 279], [279, 331],\n  [317, 14], [14, 316], [316, 317], [8, 285], [285, 9], [9, 8],\n  [277, 329], [329, 350], [350, 277], [253, 374], [374, 252], [252, 253],\n  [319, 318], [318, 403], [403, 319], [351, 6], [6, 419], [419, 351],\n  [324, 318], [318, 325], [325, 324], [397, 367], [367, 365], [365, 397],\n  [288, 435], [435, 397], [397, 288], [278, 344], [344, 439], [439, 278],\n  [310, 272], [272, 311], [311, 310], [248, 195], [195, 281], [281, 248],\n  [375, 273], [273, 291], [291, 375], [175, 396], [396, 199], [199, 175],\n  [312, 311], [311, 268], [268, 312], [276, 283], [283, 445], [445, 276],\n  [390, 373], [373, 339], [339, 390], [295, 282], [282, 296], [296, 295],\n  [448, 449], [449, 346], [346, 448], [356, 264], [264, 454], [454, 356],\n  [337, 336], [336, 299], [299, 337], [337, 338], [338, 151], [151, 337],\n  [294, 278], [278, 455], [455, 294], [308, 292], [292, 415], [415, 308],\n  [429, 358], [358, 355], [355, 429], [265, 340], [340, 372], [372, 265],\n  [352, 346], [346, 280], [280, 352], [295, 442], [442, 282], [282, 295],\n  [354, 19], [19, 370], [370, 354], [285, 441], [441, 295], [295, 285],\n  [195, 248], [248, 197], [197, 195], [457, 440], [440, 274], [274, 457],\n  [301, 300], [300, 368], [368, 301], [417, 351], [351, 465], [465, 417],\n  [251, 301], [301, 389], [389, 251], [394, 395], [395, 379], [379, 394],\n  [399, 412], [412, 419], [419, 399], [410, 436], [436, 322], [322, 410],\n  [326, 2], [2, 393], [393, 326], [354, 370], [370, 461], [461, 354],\n  [393, 164], [164, 267], [267, 393], [268, 302], [302, 12], [12, 268],\n  [312, 268], [268, 13], [13, 312], [298, 293], [293, 301], [301, 298],\n  [265, 446], [446, 340], [340, 265], [280, 330], [330, 425], [425, 280],\n  [322, 426], [426, 391], [391, 322], [420, 429], [429, 437], [437, 420],\n  [393, 391], [391, 326], [326, 393], [344, 440], [440, 438], [438, 344],\n  [458, 459], [459, 461], [461, 458], [364, 434], [434, 394], [394, 364],\n  [428, 396], [396, 262], [262, 428], [274, 354], [354, 457], [457, 274],\n  [317, 316], [316, 402], [402, 317], [316, 315], [315, 403], [403, 316],\n  [315, 314], [314, 404], [404, 315], [314, 313], [313, 405], [405, 314],\n  [313, 421], [421, 406], [406, 313], [323, 366], [366, 361], [361, 323],\n  [292, 306], [306, 407], [407, 292], [306, 291], [291, 408], [408, 306],\n  [291, 287], [287, 409], [409, 291], [287, 432], [432, 410], [410, 287],\n  [427, 434], [434, 411], [411, 427], [372, 264], [264, 383], [383, 372],\n  [459, 309], [309, 457], [457, 459], [366, 352], [352, 401], [401, 366],\n  [1, 274], [274, 4], [4, 1], [418, 421], [421, 262], [262, 418],\n  [331, 294], [294, 358], [358, 331], [435, 433], [433, 367], [367, 435],\n  [392, 289], [289, 439], [439, 392], [328, 462], [462, 326], [326, 328],\n  [94, 2], [2, 370], [370, 94], [289, 305], [305, 455], [455, 289],\n  [339, 254], [254, 448], [448, 339], [359, 255], [255, 446], [446, 359],\n  [254, 253], [253, 449], [449, 254], [253, 252], [252, 450], [450, 253],\n  [252, 256], [256, 451], [451, 252], [256, 341], [341, 452], [452, 256],\n  [414, 413], [413, 463], [463, 414], [286, 441], [441, 414], [414, 286],\n  [286, 258], [258, 441], [441, 286], [258, 257], [257, 442], [442, 258],\n  [257, 259], [259, 443], [443, 257], [259, 260], [260, 444], [444, 259],\n  [260, 467], [467, 445], [445, 260], [309, 459], [459, 250], [250, 309],\n  [305, 289], [289, 290], [290, 305], [305, 290], [290, 460], [460, 305],\n  [401, 376], [376, 435], [435, 401], [309, 250], [250, 392], [392, 309],\n  [376, 411], [411, 433], [433, 376], [453, 341], [341, 464], [464, 453],\n  [357, 453], [453, 465], [465, 357], [343, 357], [357, 412], [412, 343],\n  [437, 343], [343, 399], [399, 437], [344, 360], [360, 440], [440, 344],\n  [420, 437], [437, 456], [456, 420], [360, 420], [420, 363], [363, 360],\n  [361, 401], [401, 288], [288, 361], [265, 372], [372, 353], [353, 265],\n  [390, 339], [339, 249], [249, 390], [339, 448], [448, 255], [255, 339],\n];\n", "import * as tf from '../../dist/tfjs.esm.js';\nimport type { Tensor } from './types';\n\nexport const constants: Record<string, Tensor | number | number[]> = {\n  tf255: 255.0,\n  tf1: 1.0,\n  tf2: 2.0,\n  tf05: 0.5,\n  tf127: 127.5,\n  rgb: [0.2989, 0.5870, 0.1140],\n};\n\nexport function init() {\n  constants.tf255 = tf.scalar(255.0, 'float32');\n  constants.tf1 = tf.scalar(1.0, 'float32');\n  constants.tf2 = tf.scalar(2.0, 'float32');\n  constants.tf05 = tf.scalar(0.5, 'float32');\n  constants.tf127 = tf.scalar(127.5, 'float32');\n  constants.rgb = tf.tensor1d([0.2989, 0.5870, 0.1140], 'float32'); // factors for red/green/blue colors when converting to grayscale\n}\n", "/**\n * BlazeFace, FaceMesh & Iris model implementation\n * See `facemesh.ts` for entry point\n */\n\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as coords from './facemeshcoords';\nimport { constants } from '../tfjs/constants';\nimport type { Box, Point } from '../result';\nimport { env } from '../util/env';\n\nexport const createBox = (startEndTensor) => ({ startPoint: tf.slice(startEndTensor, [0, 0], [-1, 2]), endPoint: tf.slice(startEndTensor, [0, 2], [-1, 2]) });\n\nexport const disposeBox = (t) => tf.dispose([t.startPoint, t.endPoint]);\n\nexport const getBoxSize = (box): [number, number] => [Math.abs(box.endPoint[0] - box.startPoint[0]), Math.abs(box.endPoint[1] - box.startPoint[1])];\n\nexport const getBoxCenter = (box): [number, number, number] => [box.startPoint[0] + (box.endPoint[0] - box.startPoint[0]) / 2, box.startPoint[1] + (box.endPoint[1] - box.startPoint[1]) / 2, 1];\n\nexport const clampBox = (box, input): Box => (box ? [\n  Math.trunc(Math.max(0, box.startPoint[0])),\n  Math.trunc(Math.max(0, box.startPoint[1])),\n  Math.trunc(Math.min((input.shape[2] || 0), box.endPoint[0]) - Math.max(0, box.startPoint[0])),\n  Math.trunc(Math.min((input.shape[1] || 0), box.endPoint[1]) - Math.max(0, box.startPoint[1])),\n] : [0, 0, 0, 0]);\n\nexport const getRawBox = (box, input): Box => (box ? [\n  box.startPoint[0] / (input.shape[2] || 0),\n  box.startPoint[1] / (input.shape[1] || 0),\n  (box.endPoint[0] - box.startPoint[0]) / (input.shape[2] || 0),\n  (box.endPoint[1] - box.startPoint[1]) / (input.shape[1] || 0),\n] : [0, 0, 0, 0]);\n\nexport const scaleBoxCoordinates = (box, factor) => {\n  const startPoint: Point = [box.startPoint[0] * factor[0], box.startPoint[1] * factor[1]];\n  const endPoint: Point = [box.endPoint[0] * factor[0], box.endPoint[1] * factor[1]];\n  return { startPoint, endPoint, landmarks: box.landmarks, confidence: box.confidence };\n};\n\nexport const cutAndResize = (box, image, cropSize) => {\n  const h = image.shape[1];\n  const w = image.shape[2];\n  const cutBox = [box.startPoint[1] / h, box.startPoint[0] / w, box.endPoint[1] / h, box.endPoint[0] / w];\n  const crop = tf.image.cropAndResize(image, [cutBox], [0], cropSize);\n  const norm = tf.div(crop, constants.tf255);\n  tf.dispose(crop);\n  return norm;\n};\n\nexport const enlargeBox = (box, factor) => {\n  const center = getBoxCenter(box);\n  const size = getBoxSize(box);\n  const halfSize: [number, number] = [factor * size[0] / 2, factor * size[1] / 2];\n  return { startPoint: [center[0] - halfSize[0], center[1] - halfSize[1]] as Point, endPoint: [center[0] + halfSize[0], center[1] + halfSize[1]] as Point, landmarks: box.landmarks, confidence: box.confidence };\n};\n\nexport const squarifyBox = (box) => {\n  const centers = getBoxCenter(box);\n  const size = getBoxSize(box);\n  const halfSize = Math.max(...size) / 2;\n  return { startPoint: [Math.round(centers[0] - halfSize), Math.round(centers[1] - halfSize)] as Point, endPoint: [Math.round(centers[0] + halfSize), Math.round(centers[1] + halfSize)] as Point, landmarks: box.landmarks, confidence: box.confidence };\n};\n\nexport const calculateLandmarksBoundingBox = (landmarks) => {\n  const x = landmarks.map((d) => d[0]);\n  const y = landmarks.map((d) => d[1]);\n  return { startPoint: [Math.min(...x), Math.min(...y)] as Point, endPoint: [Math.max(...x), Math.max(...y)] as Point, landmarks };\n};\n\nexport const fixedRotationMatrix = [[1, 0, 0], [0, 1, 0], [0, 0, 1]];\n\nexport const normalizeRadians = (angle: number) => angle - 2 * Math.PI * Math.floor((angle + Math.PI) / (2 * Math.PI));\n\nexport const computeRotation = (point1, point2) => normalizeRadians(Math.PI / 2 - Math.atan2(-(point2[1] - point1[1]), point2[0] - point1[0]));\n\nexport const radToDegrees = (rad) => rad * 180 / Math.PI;\n\nexport const buildTranslationMatrix = (x, y) => [[1, 0, x], [0, 1, y], [0, 0, 1]];\n\nexport const dot = (v1: number[], v2: number[]) => {\n  let product = 0;\n  for (let i = 0; i < v1.length; i++) product += v1[i] * v2[i];\n  return product;\n};\n\nexport const getColumnFrom2DArr = (arr, columnIndex) => {\n  const column: number[] = [];\n  for (let i = 0; i < arr.length; i++) column.push(arr[i][columnIndex]);\n  return column;\n};\n\nexport const multiplyTransformMatrices = (mat1, mat2) => {\n  const product: number[][] = [];\n  const size = mat1.length;\n  for (let row = 0; row < size; row++) {\n    product.push([]);\n    for (let col = 0; col < size; col++) product[row].push(dot(mat1[row], getColumnFrom2DArr(mat2, col)));\n  }\n  return product;\n};\n\nexport const buildRotationMatrix = (rotation, center) => {\n  const cosA = Math.cos(rotation);\n  const sinA = Math.sin(rotation);\n  const rotationMatrix = [[cosA, -sinA, 0], [sinA, cosA, 0], [0, 0, 1]];\n  const translationMatrix = buildTranslationMatrix(center[0], center[1]);\n  const translationTimesRotation = multiplyTransformMatrices(translationMatrix, rotationMatrix);\n  const negativeTranslationMatrix = buildTranslationMatrix(-center[0], -center[1]);\n  return multiplyTransformMatrices(translationTimesRotation, negativeTranslationMatrix);\n};\n\nexport const invertTransformMatrix = (matrix) => {\n  const rotationComponent = [[matrix[0][0], matrix[1][0]], [matrix[0][1], matrix[1][1]]];\n  const translationComponent = [matrix[0][2], matrix[1][2]];\n  const invertedTranslation = [-dot(rotationComponent[0], translationComponent), -dot(rotationComponent[1], translationComponent)];\n  return [rotationComponent[0].concat(invertedTranslation[0]), rotationComponent[1].concat(invertedTranslation[1]), [0, 0, 1]];\n};\n\nexport const rotatePoint = (homogeneousCoordinate, rotationMatrix) => [dot(homogeneousCoordinate, rotationMatrix[0]), dot(homogeneousCoordinate, rotationMatrix[1])];\n\nexport const xyDistanceBetweenPoints = (a, b) => Math.sqrt(((a[0] - b[0]) ** 2) + ((a[1] - b[1]) ** 2));\n\nexport function generateAnchors(inputSize: number) {\n  const spec = inputSize === 192\n    ? { strides: [4], anchors: [1] } // facemesh-detector\n    : { strides: [inputSize / 16, inputSize / 8], anchors: [2, 6] }; // blazeface\n  const anchors: [number, number][] = [];\n  for (let i = 0; i < spec.strides.length; i++) {\n    const stride = spec.strides[i];\n    const gridRows = Math.floor((inputSize + stride - 1) / stride);\n    const gridCols = Math.floor((inputSize + stride - 1) / stride);\n    const anchorsNum = spec.anchors[i];\n    for (let gridY = 0; gridY < gridRows; gridY++) {\n      const anchorY = stride * (gridY + 0.5);\n      for (let gridX = 0; gridX < gridCols; gridX++) {\n        const anchorX = stride * (gridX + 0.5);\n        for (let n = 0; n < anchorsNum; n++) anchors.push([anchorX, anchorY]);\n      }\n    }\n  }\n  return anchors;\n}\n\nexport function transformRawCoords(coordsRaw, box, angle, rotationMatrix, inputSize) {\n  const boxSize = getBoxSize(box);\n  const coordsScaled = coordsRaw.map((coord) => ([ // scaled around zero-point\n    (boxSize[0] / inputSize) * (coord[0] - (inputSize / 2)),\n    (boxSize[1] / inputSize) * (coord[1] - (inputSize / 2)),\n    (coord[2] || 0),\n  ]));\n  const largeAngle = angle && (angle !== 0) && (Math.abs(angle) > 0.2);\n  const coordsRotationMatrix = largeAngle ? buildRotationMatrix(angle, [0, 0]) : fixedRotationMatrix;\n  const coordsRotated = largeAngle ? coordsScaled.map((coord) => ([...rotatePoint(coord, coordsRotationMatrix), coord[2]])) : coordsScaled;\n  const inverseRotationMatrix = largeAngle ? invertTransformMatrix(rotationMatrix) : fixedRotationMatrix;\n  const boxCenter = getBoxCenter(box);\n  const offsets = [dot(boxCenter, inverseRotationMatrix[0]), dot(boxCenter, inverseRotationMatrix[1])];\n  return coordsRotated.map((coord) => ([\n    Math.trunc(coord[0] + offsets[0]),\n    Math.trunc(coord[1] + offsets[1]),\n    Math.trunc(coord[2] || 0),\n  ]));\n}\n\nexport function correctFaceRotation(rotate, box, input, inputSize) {\n  const symmetryLine = (box.landmarks.length >= coords.meshLandmarks.count)\n    ? coords.meshLandmarks.symmetryLine\n    : coords.blazeFaceLandmarks.symmetryLine;\n  let angle = 0; // default\n  let rotationMatrix = fixedRotationMatrix; // default\n  let face; // default\n\n  if (rotate && env.kernels.includes('rotatewithoffset')) {\n    angle = computeRotation(box.landmarks[symmetryLine[0]], box.landmarks[symmetryLine[1]]);\n    const largeAngle = angle && (angle !== 0) && (Math.abs(angle) > 0.2);\n    if (largeAngle) { // perform rotation only if angle is sufficiently high\n      const center: Point = getBoxCenter(box);\n      const centerRaw: Point = [center[0] / input.shape[2], center[1] / input.shape[1]];\n      const rotated = tf.image.rotateWithOffset(input, angle, 0, centerRaw);\n      rotationMatrix = buildRotationMatrix(-angle, center);\n      face = cutAndResize(box, rotated, [inputSize, inputSize]);\n      tf.dispose(rotated);\n    } else {\n      face = cutAndResize(box, input, [inputSize, inputSize]);\n    }\n  } else {\n    face = cutAndResize(box, input, [inputSize, inputSize]);\n  }\n  return [angle, rotationMatrix, face];\n}\n\nexport const findFaceCenter = (mesh) => {\n  const x = mesh.map((m) => m[0]);\n  const y = mesh.map((m) => m[1]);\n  // weighted center\n  /*\n  const sum = (arr: number[]) => arr.reduce((prev, curr) => prev + curr, 0);\n  return [sum(x) / mesh.length, sum(y) / mesh.length];\n  */\n  // absolute center\n  return [Math.min(...x) + (Math.max(...x) - Math.min(...x)) / 2, Math.min(...y) + (Math.max(...y) - Math.min(...y)) / 2];\n};\n\nexport const calculateFaceBox = (mesh, previousBox) => {\n  const center = findFaceCenter(mesh);\n  const boxSize = getBoxSize(previousBox);\n  const calculatedBox = {\n    startPoint: [center[0] - boxSize[0] / 2, center[1] - boxSize[1] / 2] as Point,\n    endPoint: [center[0] + boxSize[0] / 2, center[1] + boxSize[1] / 2] as Point,\n  };\n  return calculatedBox;\n};\n", "/**\n * BlazeFace, FaceMesh & Iris model implementation\n * See `facemesh.ts` for entry point\n */\n\nimport { log } from '../util/util';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as util from './facemeshutil';\nimport { loadModel } from '../tfjs/load';\nimport { constants } from '../tfjs/constants';\nimport type { Config } from '../config';\nimport type { Tensor, GraphModel } from '../tfjs/types';\nimport { env } from '../util/env';\nimport type { Point } from '../result';\n\nconst keypointsCount = 6;\nconst faceBoxScaleFactor = 1.4;\nlet model: GraphModel | null;\nlet anchors: Tensor | null = null;\nlet inputSize = 0;\nlet inputSizeT: Tensor | null = null;\n\ninterface DetectBox { startPoint: Point, endPoint: Point, landmarks: Point[], confidence: number }\n\nexport const size = () => inputSize;\n\nexport async function load(config: Config): Promise<GraphModel> {\n  if (env.initial) model = null;\n  if (!model) model = await loadModel(config.face.detector?.modelPath);\n  else if (config.debug) log('cached model:', model['modelUrl']);\n  inputSize = (model['executor'] && model.inputs[0].shape) ? model.inputs[0].shape[2] : 256;\n  inputSizeT = tf.scalar(inputSize, 'int32') as Tensor;\n  anchors = tf.tensor2d(util.generateAnchors(inputSize)) as Tensor;\n  return model;\n}\n\nfunction decodeBoxes(boxOutputs: Tensor) {\n  const t: Record<string, Tensor> = {};\n  t.boxStarts = tf.slice(boxOutputs, [0, 1], [-1, 2]);\n  t.centers = tf.add(t.boxStarts, anchors);\n  t.boxSizes = tf.slice(boxOutputs, [0, 3], [-1, 2]);\n  t.boxSizesNormalized = tf.div(t.boxSizes, inputSizeT);\n  t.centersNormalized = tf.div(t.centers, inputSizeT);\n  t.halfBoxSize = tf.div(t.boxSizesNormalized, constants.tf2);\n  t.starts = tf.sub(t.centersNormalized, t.halfBoxSize);\n  t.ends = tf.add(t.centersNormalized, t.halfBoxSize);\n  t.startNormalized = tf.mul(t.starts, inputSizeT);\n  t.endNormalized = tf.mul(t.ends, inputSizeT);\n  const boxes = tf.concat2d([t.startNormalized, t.endNormalized], 1);\n  Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n  return boxes;\n}\n\nexport async function getBoxes(inputImage: Tensor, config: Config) {\n  // sanity check on input\n  if ((!inputImage) || (inputImage['isDisposedInternal']) || (inputImage.shape.length !== 4) || (inputImage.shape[1] < 1) || (inputImage.shape[2] < 1)) return [];\n  const t: Record<string, Tensor> = {};\n  t.resized = tf.image.resizeBilinear(inputImage, [inputSize, inputSize]);\n  t.div = tf.div(t.resized, constants.tf127);\n  t.normalized = tf.sub(t.div, constants.tf05);\n  const res = model?.execute(t.normalized) as Tensor[];\n  if (Array.isArray(res) && res.length > 2) { // pinto converted model?\n    const sorted = res.sort((a, b) => a.size - b.size);\n    t.concat384 = tf.concat([sorted[0], sorted[2]], 2); // dim: 384, 1 + 16\n    t.concat512 = tf.concat([sorted[1], sorted[3]], 2); // dim: 512, 1 + 16\n    t.concat = tf.concat([t.concat512, t.concat384], 1);\n    t.batch = tf.squeeze(t.concat, 0);\n  } else if (Array.isArray(res)) { // new facemesh-detection tfhub model\n    t.batch = tf.squeeze(res[0]);\n  } else { // original blazeface tfhub model\n    t.batch = tf.squeeze(res);\n  }\n  tf.dispose(res);\n  t.boxes = decodeBoxes(t.batch);\n  t.logits = tf.slice(t.batch, [0, 0], [-1, 1]);\n  t.sigmoid = tf.sigmoid(t.logits);\n  t.scores = tf.squeeze(t.sigmoid);\n  t.nms = await tf.image.nonMaxSuppressionAsync(t.boxes, t.scores, (config.face.detector?.maxDetected || 0), (config.face.detector?.iouThreshold || 0), (config.face.detector?.minConfidence || 0));\n  const nms = await t.nms.array() as number[];\n  const boxes: DetectBox[] = [];\n  const scores = await t.scores.data();\n  for (let i = 0; i < nms.length; i++) {\n    const confidence = scores[nms[i]];\n    if (confidence > (config.face.detector?.minConfidence || 0)) {\n      const b: Record<string, Tensor> = {};\n      b.bbox = tf.slice(t.boxes, [nms[i], 0], [1, -1]);\n      b.slice = tf.slice(t.batch, [nms[i], keypointsCount - 1], [1, -1]);\n      b.squeeze = tf.squeeze(b.slice);\n      b.landmarks = tf.reshape(b.squeeze, [keypointsCount, -1]);\n      const points = await b.bbox.data();\n      const rawBox = {\n        startPoint: [points[0], points[1]] as Point,\n        endPoint: [points[2], points[3]] as Point,\n        landmarks: (await b.landmarks.array()) as Point[],\n        confidence,\n      };\n      const scaledBox = util.scaleBoxCoordinates(rawBox, [(inputImage.shape[2] || 0) / inputSize, (inputImage.shape[1] || 0) / inputSize]);\n      const enlargedBox = util.enlargeBox(scaledBox, config.face['scale'] || faceBoxScaleFactor);\n      const squaredBox = util.squarifyBox(enlargedBox);\n      boxes.push(squaredBox);\n      Object.keys(b).forEach((tensor) => tf.dispose(b[tensor]));\n    }\n  }\n  Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n  return boxes;\n}\n", "/* eslint-disable no-multi-spaces */\n\nexport const kpt: string[] = [\n  'nose',            //  0\n  'leftEyeInside',   //  1\n  'leftEye',         //  2\n  'leftEyeOutside',  //  3\n  'rightEyeInside',  //  4\n  'rightEye',        //  5\n  'rightEyeOutside', //  6\n  'leftEar',         //  7\n  'rightEar',        //  8\n  'leftMouth',       //  9\n  'rightMouth',      // 10\n  'leftShoulder',    // 11\n  'rightShoulder',   // 12\n  'leftElbow',       // 13\n  'rightElbow',      // 14\n  'leftWrist',       // 15\n  'rightWrist',      // 16\n  'leftPinky',       // 17\n  'rightPinky',      // 18\n  'leftIndex',       // 19\n  'rightIndex',      // 20\n  'leftThumb',       // 21\n  'rightThumb',      // 22\n  'leftHip',         // 23\n  'rightHip',        // 24\n  'leftKnee',        // 25\n  'rightKnee',       // 26\n  'leftAnkle',       // 27\n  'rightAnkle',      // 28\n  'leftHeel',        // 29\n  'rightHeel',       // 30\n  'leftFoot',        // 31\n  'rightFoot',       // 32\n  'bodyCenter',      // 33\n  'bodyTop',         // 34\n  'leftPalm',        // 35 // z-coord not ok\n  'leftHand',        // 36 // similar to wrist but z-coord not ok\n  'rightPalm',       // 37 // z-coord not ok\n  'rightHand',       // 38 // similar to wrist but z-coord not ok\n];\n\nexport const connected: Record<string, string[]> = {\n  shoulders: ['leftShoulder', 'rightShoulder'],\n  hips: ['rightHip', 'leftHip'],\n  mouth: ['leftMouth', 'rightMouth'],\n  leftLegUpper: ['leftHip', 'leftKnee'],\n  leftLegLower: ['leftKnee', 'leftAnkle'],\n  leftFoot: ['leftAnkle', 'leftHeel', 'leftFoot'],\n  leftTorso: ['leftShoulder', 'leftHip'],\n  leftArmUpper: ['leftShoulder', 'leftElbow'],\n  leftArmLower: ['leftElbow', 'leftWrist'],\n  leftHand: ['leftWrist', 'leftPalm'],\n  leftHandPinky: ['leftPalm', 'leftPinky'],\n  leftHandIndex: ['leftPalm', 'leftIndex'],\n  leftHandThumb: ['leftPalm', 'leftThumb'],\n  leftEyeOutline: ['leftEyeInside', 'leftEyeOutside'],\n  rightLegUpper: ['rightHip', 'rightKnee'],\n  rightLegLower: ['rightKnee', 'rightAnkle'],\n  rightFoot: ['rightAnkle', 'rightHeel', 'rightFoot'],\n  rightTorso: ['rightShoulder', 'rightHip'],\n  rightArmUpper: ['rightShoulder', 'rightElbow'],\n  rightArmLower: ['rightElbow', 'rightWrist'],\n  rightHand: ['rightWrist', 'rightPalm'],\n  rightHandPinky: ['rightPalm', 'rightPinky'],\n  rightHandIndex: ['rightPalm', 'rightIndex'],\n  rightHandThumb: ['rightPalm', 'rightThumb'],\n  rightEyeOutline: ['rightEyeInside', 'rightEyeOutside'],\n};\n", "import * as tf from '../../dist/tfjs.esm.js';\nimport type { Tensor } from '../tfjs/types';\nimport type { Box } from '../result';\nimport type { Config } from '../config';\n\ninterface DetectedBox { box: Box, boxRaw: Box, score: number }\n\nconst inputSize = 224;\nlet anchorTensor: { x, y };\nconst numLayers = 5;\nconst strides = [8, 16, 32, 32, 32];\n\nexport function createAnchors() {\n  const anchors: { x: number, y: number }[] = [];\n  let layerId = 0;\n  while (layerId < numLayers) {\n    let anchorCount = 0;\n    let lastSameStrideLayer = layerId;\n    while (lastSameStrideLayer < strides.length && strides[lastSameStrideLayer] === strides[layerId]) {\n      anchorCount += 2;\n      lastSameStrideLayer++;\n    }\n    const stride = strides[layerId];\n    const featureMapHeight = Math.ceil(inputSize / stride);\n    const featureMapWidth = Math.ceil(inputSize / stride);\n    for (let y = 0; y < featureMapHeight; ++y) {\n      for (let x = 0; x < featureMapWidth; ++x) {\n        for (let anchorId = 0; anchorId < anchorCount; ++anchorId) {\n          anchors.push({ x: (x + 0.5) / featureMapWidth, y: (y + 0.5) / featureMapHeight });\n        }\n      }\n    }\n    layerId = lastSameStrideLayer;\n  }\n  anchorTensor = { x: tf.tensor1d(anchors.map((a) => a.x)), y: tf.tensor1d(anchors.map((a) => a.y)) };\n}\n\nconst cropFactor = [5.0, 5.0];\nfunction decodeBoxes(boxesTensor, anchor): Tensor {\n  return tf.tidy(() => {\n    const split = tf.split(boxesTensor, 12, 1); // first 4 are box data [x,y,w,h] and 4 are keypoints data [x,y] for total of 12\n    let xCenter = tf.squeeze(split[0]);\n    let yCenter = tf.squeeze(split[1]);\n    let width = tf.squeeze(split[2]);\n    let height = tf.squeeze(split[3]);\n    xCenter = tf.add(tf.div(xCenter, inputSize), anchor.x);\n    yCenter = tf.add(tf.div(yCenter, inputSize), anchor.y);\n    width = tf.mul(tf.div(width, inputSize), cropFactor[0]);\n    height = tf.mul(tf.div(height, inputSize), cropFactor[1]);\n    const xMin = tf.sub(xCenter, tf.div(width, 2));\n    const yMin = tf.sub(yCenter, tf.div(height, 2));\n    const boxes = tf.stack([xMin, yMin, width, height], 1);\n    return boxes;\n  });\n}\n\nexport async function decode(boxesTensor: Tensor, logitsTensor: Tensor, config: Config, outputSize: [number, number]): Promise<DetectedBox[]> {\n  const t: Record<string, Tensor> = {};\n  t.boxes = decodeBoxes(boxesTensor, anchorTensor);\n  t.scores = tf.sigmoid(logitsTensor);\n  t.argmax = tf.argMax(t.scores);\n  const i = (await t.argmax.data())[0];\n  const scores = await t.scores.data();\n  const detected: { box: Box, boxRaw: Box, score: number }[] = [];\n  const minScore = config.body?.['detector']?.minConfidence || 0;\n  if (scores[i] >= minScore) {\n    const boxes = await t.boxes.array();\n    const boxRaw: Box = boxes[i];\n    const box: Box = [boxRaw[0] * outputSize[0], boxRaw[1] * outputSize[1], boxRaw[2] * outputSize[0], boxRaw[3] * outputSize[1]];\n    // console.log(box);\n    detected.push({ box, boxRaw, score: scores[i] });\n  }\n  /*\n  t.nms = await tf.image.nonMaxSuppressionAsync(t.boxes, t.scores, 1, config.body.detector?.minConfidence || 0.1, config.body.detector?.iouThreshold || 0.1);\n  const boxes = t.boxes.arraySync();\n  const scores = t.scores.dataSync();\n  const nms = t.nms.dataSync();\n  const detected: Array<DetectedBox> = [];\n  for (const i of Array.from(nms)) {\n    const boxRaw: Box = boxes[i];\n    const box: Box = [boxRaw[0] * outputSize[0], boxRaw[0] * outputSize[1], boxRaw[3] * outputSize[0], boxRaw[2] * outputSize[1]];\n    detected.push({ box, boxRaw, score: scores[i] });\n  }\n  */\n  Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n  return detected;\n}\n", "import type { Point, Box } from '../result';\n\nexport function calc(keypoints: Point[], outputSize: [number, number] = [1, 1]) {\n  const coords = [keypoints.map((pt) => pt[0]), keypoints.map((pt) => pt[1])]; // all x/y coords\n  const min = [Math.min(...coords[0]), Math.min(...coords[1])];\n  const max = [Math.max(...coords[0]), Math.max(...coords[1])];\n  const box: Box = [min[0], min[1], max[0] - min[0], max[1] - min[1]];\n  const boxRaw: Box = [box[0] / outputSize[0], box[1] / outputSize[1], box[2] / outputSize[0], box[3] / outputSize[1]];\n  return { box, boxRaw };\n}\n\nexport function square(keypoints: Point[], outputSize: [number, number] = [1, 1]) {\n  const coords = [keypoints.map((pt) => pt[0]), keypoints.map((pt) => pt[1])]; // all x/y coords\n  const min = [Math.min(...coords[0]), Math.min(...coords[1])];\n  const max = [Math.max(...coords[0]), Math.max(...coords[1])];\n  const center = [(min[0] + max[0]) / 2, (min[1] + max[1]) / 2]; // find center x and y coord of all fingers\n  const dist = Math.max(center[0] - min[0], center[1] - min[1], -center[0] + max[0], -center[1] + max[1]); // largest distance from center in any direction\n  const box: Box = [Math.trunc(center[0] - dist), Math.trunc(center[1] - dist), Math.trunc(2 * dist), Math.trunc(2 * dist)];\n  const boxRaw: Box = [box[0] / outputSize[0], box[1] / outputSize[1], box[2] / outputSize[0], box[3] / outputSize[1]];\n  return { box, boxRaw };\n}\n\nexport function scale(box: Box, scaleFact: number) {\n  const dist = [box[2] * scaleFact, box[3] * scaleFact];\n  const newBox: Box = [\n    box[0] - (dist[0] - box[2]) / 2,\n    box[1] - (dist[1] - box[3]) / 2,\n    dist[0],\n    dist[1],\n  ];\n  return newBox;\n}\n\nexport function crop(box: Box) { // [y1, x1, y2, x2] clamped to 0..1\n  const yxBox: Box = [Math.max(0, box[1]), Math.max(0, box[0]), Math.min(1, box[3] + box[1]), Math.min(1, box[2] + box[0])];\n  return yxBox;\n}\n", "/**\n * BlazePose model implementation\n */\n\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { loadModel } from '../tfjs/load';\nimport { constants } from '../tfjs/constants';\nimport { log, now } from '../util/util';\nimport type { BodyKeypoint, BodyResult, BodyLandmark, Box, Point, BodyAnnotation } from '../result';\nimport type { GraphModel, Tensor } from '../tfjs/types';\nimport type { Config } from '../config';\nimport * as coords from './blazeposecoords';\nimport * as detect from './blazeposedetector';\nimport * as box from '../util/box';\n\nconst env = { initial: true };\n// const models: [GraphModel | null, GraphModel | null] = [null, null];\nconst models: { detector: GraphModel | null, landmarks: GraphModel | null } = { detector: null, landmarks: null };\nconst inputSize: { detector: [number, number], landmarks: [number, number] } = { detector: [224, 224], landmarks: [256, 256] };\nlet skipped = Number.MAX_SAFE_INTEGER;\nconst outputNodes: { detector: string[], landmarks: string[] } = {\n  landmarks: ['ld_3d', 'activation_segmentation', 'activation_heatmap', 'world_3d', 'output_poseflag'],\n  detector: [],\n};\n\nlet cache: BodyResult | null = null;\nlet cropBox: Box | undefined;\nlet padding: [number, number][] = [[0, 0], [0, 0], [0, 0], [0, 0]];\nlet lastTime = 0;\n\nconst sigmoid = (x) => (1 - (1 / (1 + Math.exp(x))));\n\nexport async function loadDetect(config: Config): Promise<GraphModel> {\n  if (env.initial) models.detector = null;\n  if (!models.detector && config.body['detector'] && config.body['detector'].modelPath || '') {\n    models.detector = await loadModel(config.body['detector'].modelPath);\n    const inputs = models.detector?.['executor'] ? Object.values(models.detector.modelSignature['inputs']) : undefined;\n    inputSize.detector[0] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[1].size) : 0;\n    inputSize.detector[1] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 0;\n  } else if (config.debug && models.detector) log('cached model:', models.detector['modelUrl']);\n  detect.createAnchors();\n  return models.detector as GraphModel;\n}\n\nexport async function loadPose(config: Config): Promise<GraphModel> {\n  if (env.initial) models.landmarks = null;\n  if (!models.landmarks) {\n    models.landmarks = await loadModel(config.body.modelPath);\n    const inputs = models.landmarks?.['executor'] ? Object.values(models.landmarks.modelSignature['inputs']) : undefined;\n    inputSize.landmarks[0] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[1].size) : 0;\n    inputSize.landmarks[1] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 0;\n  } else if (config.debug) log('cached model:', models.landmarks['modelUrl']);\n  return models.landmarks;\n}\n\nexport async function load(config: Config): Promise<[GraphModel | null, GraphModel | null]> {\n  if (!models.detector) await loadDetect(config);\n  if (!models.landmarks) await loadPose(config);\n  return [models.detector, models.landmarks];\n}\n\nfunction prepareImage(input: Tensor, size: number): Tensor {\n  const t: Record<string, Tensor> = {};\n  if (!input?.shape?.[1] || !input?.shape?.[2]) return input;\n  let final: Tensor;\n  if (cropBox) {\n    t.cropped = tf.image.cropAndResize(input, [cropBox], [0], [input.shape[1], input.shape[2]]); // if we have cached box use it to crop input\n  }\n  if (input.shape[1] !== input.shape[2]) { // only pad if width different than height\n    const height: [number, number] = [\n      input.shape[2] > input.shape[1] ? Math.trunc((input.shape[2] - input.shape[1]) / 2) : 0,\n      input.shape[2] > input.shape[1] ? Math.trunc((input.shape[2] - input.shape[1]) / 2) : 0,\n    ];\n    const width: [number, number] = [\n      input.shape[1] > input.shape[2] ? Math.trunc((input.shape[1] - input.shape[2]) / 2) : 0,\n      input.shape[1] > input.shape[2] ? Math.trunc((input.shape[1] - input.shape[2]) / 2) : 0,\n    ];\n    padding = [\n      [0, 0], // dont touch batch\n      height, // height before&after\n      width, // width before&after\n      [0, 0], // dont touch rbg\n    ];\n    t.pad = tf.pad(t.cropped || input, padding); // use cropped box if it exists\n    t.resize = tf.image.resizeBilinear(t.pad, [size, size]);\n    final = tf.div(t.resize, constants.tf255);\n  } else if (input.shape[1] !== size) { // if input needs resizing\n    t.resize = tf.image.resizeBilinear(t.cropped || input, [size, size]);\n    final = tf.div(t.resize, constants.tf255);\n  } else { // if input is already in a correct resolution just normalize it\n    final = tf.div(t.cropped || input, constants.tf255);\n  }\n  Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n  return final;\n}\n\nfunction rescaleKeypoints(keypoints: BodyKeypoint[], outputSize: [number, number]): BodyKeypoint[] {\n  for (const kpt of keypoints) { // first rescale due to padding\n    kpt.position = [\n      Math.trunc(kpt.position[0] * (outputSize[0] + padding[2][0] + padding[2][1]) / outputSize[0] - padding[2][0]),\n      Math.trunc(kpt.position[1] * (outputSize[1] + padding[1][0] + padding[1][1]) / outputSize[1] - padding[1][0]),\n      kpt.position[2] as number,\n    ];\n    kpt.positionRaw = [kpt.position[0] / outputSize[0], kpt.position[1] / outputSize[1], 2 * (kpt.position[2] as number) / (outputSize[0] + outputSize[1])];\n  }\n  if (cropBox) { // second rescale due to cropping\n    for (const kpt of keypoints) {\n      kpt.positionRaw = [\n        kpt.positionRaw[0] + cropBox[1], // correct offset due to crop\n        kpt.positionRaw[1] + cropBox[0], // correct offset due to crop\n        kpt.positionRaw[2] as number,\n      ];\n      kpt.position = [\n        Math.trunc(kpt.positionRaw[0] * outputSize[0]),\n        Math.trunc(kpt.positionRaw[1] * outputSize[1]),\n        kpt.positionRaw[2] as number,\n      ];\n    }\n  }\n  return keypoints;\n}\n\nfunction fixKeypoints(keypoints: BodyKeypoint[]) {\n  // palm z-coord is incorrect around near-zero so we approximate it\n  const leftPalm = keypoints.find((k) => k.part === 'leftPalm') as BodyKeypoint;\n  const leftWrist = keypoints.find((k) => k.part === 'leftWrist') as BodyKeypoint;\n  const leftIndex = keypoints.find((k) => k.part === 'leftIndex') as BodyKeypoint;\n  leftPalm.position[2] = ((leftWrist.position[2] || 0) + (leftIndex.position[2] || 0)) / 2;\n  const rightPalm = keypoints.find((k) => k.part === 'rightPalm') as BodyKeypoint;\n  const rightWrist = keypoints.find((k) => k.part === 'rightWrist') as BodyKeypoint;\n  const rightIndex = keypoints.find((k) => k.part === 'rightIndex') as BodyKeypoint;\n  rightPalm.position[2] = ((rightWrist.position[2] || 0) + (rightIndex.position[2] || 0)) / 2;\n}\n\nasync function detectLandmarks(input: Tensor, config: Config, outputSize: [number, number]): Promise<BodyResult | null> {\n  /**\n   * t.ld: 39 keypoints [x,y,z,score,presence] normalized to input size\n   * t.segmentation:\n   * t.heatmap:\n   * t.world: 39 keypoints [x,y,z] normalized to -1..1\n   * t.poseflag: body score\n  */\n  if (!models.landmarks?.['executor']) return null;\n  const t: Record<string, Tensor> = {};\n  [t.ld/* 1,195(39*5) */, t.segmentation/* 1,256,256,1 */, t.heatmap/* 1,64,64,39 */, t.world/* 1,117(39*3) */, t.poseflag/* 1,1 */] = models.landmarks?.execute(input, outputNodes.landmarks) as Tensor[]; // run model\n  const poseScore = (await t.poseflag.data())[0];\n  const points = await t.ld.data();\n  const distances = await t.world.data();\n  Object.keys(t).forEach((tensor) => tf.dispose(t[tensor])); // dont need tensors after this\n  const keypointsRelative: BodyKeypoint[] = [];\n  const depth = 5; // each points has x,y,z,visibility,presence\n  for (let i = 0; i < points.length / depth; i++) {\n    const score = sigmoid(points[depth * i + 3]);\n    const presence = sigmoid(points[depth * i + 4]);\n    const adjScore = Math.trunc(100 * score * presence * poseScore) / 100;\n    const positionRaw: Point = [points[depth * i + 0] / inputSize.landmarks[0], points[depth * i + 1] / inputSize.landmarks[1], points[depth * i + 2] + 0];\n    const position: Point = [Math.trunc(outputSize[0] * positionRaw[0]), Math.trunc(outputSize[1] * positionRaw[1]), positionRaw[2] as number];\n    const distance: Point = [distances[depth * i + 0], distances[depth * i + 1], distances[depth * i + 2] + 0];\n    keypointsRelative.push({ part: coords.kpt[i] as BodyLandmark, positionRaw, position, distance, score: adjScore });\n  }\n  if (poseScore < (config.body.minConfidence || 0)) return null;\n  fixKeypoints(keypointsRelative);\n  const keypoints: BodyKeypoint[] = rescaleKeypoints(keypointsRelative, outputSize); // keypoints were relative to input image which is padded\n  const kpts = keypoints.map((k) => k.position);\n  const boxes = box.calc(kpts, [outputSize[0], outputSize[1]]); // now find boxes based on rescaled keypoints\n  const annotations: Record<BodyAnnotation, Point[][]> = {} as Record<BodyAnnotation, Point[][]>;\n  for (const [name, indexes] of Object.entries(coords.connected)) {\n    const pt: Point[][] = [];\n    for (let i = 0; i < indexes.length - 1; i++) {\n      const pt0 = keypoints.find((kpt) => kpt.part === indexes[i]);\n      const pt1 = keypoints.find((kpt) => kpt.part === indexes[i + 1]);\n      if (pt0 && pt1) pt.push([pt0.position, pt1.position]);\n    }\n    annotations[name] = pt;\n  }\n  const body = { id: 0, score: Math.trunc(100 * poseScore) / 100, box: boxes.box, boxRaw: boxes.boxRaw, keypoints, annotations };\n  return body;\n}\n\n/*\ninterface DetectedBox { box: Box, boxRaw: Box, score: number }\n\nfunction rescaleBoxes(boxes: Array<DetectedBox>, outputSize: [number, number]): Array<DetectedBox> {\n  for (const b of boxes) {\n    b.box = [\n      Math.trunc(b.box[0] * (outputSize[0] + padding[2][0] + padding[2][1]) / outputSize[0]),\n      Math.trunc(b.box[1] * (outputSize[1] + padding[1][0] + padding[1][1]) / outputSize[1]),\n      Math.trunc(b.box[2] * (outputSize[0] + padding[2][0] + padding[2][1]) / outputSize[0]),\n      Math.trunc(b.box[3] * (outputSize[1] + padding[1][0] + padding[1][1]) / outputSize[1]),\n    ];\n    b.boxRaw = [b.box[0] / outputSize[0], b.box[1] / outputSize[1], b.box[2] / outputSize[0], b.box[3] / outputSize[1]];\n  }\n  return boxes;\n}\n\nasync function detectBoxes(input: Tensor, config: Config, outputSize: [number, number]) {\n  const t: Record<string, Tensor> = {};\n  t.res = models.detector?.execute(input, ['Identity']) as Tensor; //\n  t.logitsRaw = tf.slice(t.res, [0, 0, 0], [1, -1, 1]);\n  t.boxesRaw = tf.slice(t.res, [0, 0, 1], [1, -1, -1]);\n  t.logits = tf.squeeze(t.logitsRaw);\n  t.boxes = tf.squeeze(t.boxesRaw);\n  const boxes = await detect.decode(t.boxes, t.logits, config, outputSize);\n  rescaleBoxes(boxes, outputSize);\n  Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n  return boxes;\n}\n*/\n\nexport async function predict(input: Tensor, config: Config): Promise<BodyResult[]> {\n  const outputSize: [number, number] = [input.shape[2] || 0, input.shape[1] || 0];\n  const skipTime = (config.body.skipTime || 0) > (now() - lastTime);\n  const skipFrame = skipped < (config.body.skipFrames || 0);\n  if (config.skipAllowed && skipTime && skipFrame && cache !== null) {\n    skipped++;\n  } else {\n    const t: Record<string, Tensor> = {};\n    /*\n    if (config.body['detector'] && config.body['detector']['enabled']) {\n      t.detector = await prepareImage(input, 224);\n      const boxes = await detectBoxes(t.detector, config, outputSize);\n    }\n    */\n    t.landmarks = prepareImage(input, 256); // padded and resized\n    cache = await detectLandmarks(t.landmarks, config, outputSize);\n    /*\n    cropBox = [0, 0, 1, 1]; // reset crop coordinates\n    if (cache?.boxRaw && config.skipAllowed) {\n      const cx = (2.0 * cache.boxRaw[0] + cache.boxRaw[2]) / 2;\n      const cy = (2.0 * cache.boxRaw[1] + cache.boxRaw[3]) / 2;\n      let size = cache.boxRaw[2] > cache.boxRaw[3] ? cache.boxRaw[2] : cache.boxRaw[3];\n      size = (size * 1.0) / 2; // enlarge and half it\n      if (cx > 0.1 && cx < 0.9 && cy > 0.1 && cy < 0.9 && size > 0.1) { // only update if box is sane\n        const y = 0; // cy - size;\n        const x = cx - size;\n        cropBox = [y, x, y + 1, x + 1]; // [y0,x0,y1,x1] used for cropping but width/height are not yet implemented so we only reposition image to center of body\n      }\n    }\n    */\n    Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n    lastTime = now();\n    skipped = 0;\n  }\n  return cache ? [cache] : [];\n}\n", "/**\n * CoCo Labels used by object detection implementations\n */\nexport const labels = [\n  { class: 1, label: 'person' },\n  { class: 2, label: 'bicycle' },\n  { class: 3, label: 'car' },\n  { class: 4, label: 'motorcycle' },\n  { class: 5, label: 'airplane' },\n  { class: 6, label: 'bus' },\n  { class: 7, label: 'train' },\n  { class: 8, label: 'truck' },\n  { class: 9, label: 'boat' },\n  { class: 10, label: 'traffic light' },\n  { class: 11, label: 'fire hydrant' },\n  { class: 12, label: 'stop sign' },\n  { class: 13, label: 'parking meter' },\n  { class: 14, label: 'bench' },\n  { class: 15, label: 'bird' },\n  { class: 16, label: 'cat' },\n  { class: 17, label: 'dog' },\n  { class: 18, label: 'horse' },\n  { class: 19, label: 'sheep' },\n  { class: 20, label: 'cow' },\n  { class: 21, label: 'elephant' },\n  { class: 22, label: 'bear' },\n  { class: 23, label: 'zebra' },\n  { class: 24, label: 'giraffe' },\n  { class: 25, label: 'backpack' },\n  { class: 26, label: 'umbrella' },\n  { class: 27, label: 'handbag' },\n  { class: 28, label: 'tie' },\n  { class: 29, label: 'suitcase' },\n  { class: 30, label: 'frisbee' },\n  { class: 31, label: 'skis' },\n  { class: 32, label: 'snowboard' },\n  { class: 33, label: 'sports ball' },\n  { class: 34, label: 'kite' },\n  { class: 35, label: 'baseball bat' },\n  { class: 36, label: 'baseball glove' },\n  { class: 37, label: 'skateboard' },\n  { class: 38, label: 'surfboard' },\n  { class: 39, label: 'tennis racket' },\n  { class: 40, label: 'bottle' },\n  { class: 41, label: 'wine glass' },\n  { class: 42, label: 'cup' },\n  { class: 43, label: 'fork' },\n  { class: 44, label: 'knife' },\n  { class: 45, label: 'spoon' },\n  { class: 46, label: 'bowl' },\n  { class: 47, label: 'banana' },\n  { class: 48, label: 'apple' },\n  { class: 49, label: 'sandwich' },\n  { class: 50, label: 'orange' },\n  { class: 51, label: 'broccoli' },\n  { class: 52, label: 'carrot' },\n  { class: 53, label: 'hot dog' },\n  { class: 54, label: 'pizza' },\n  { class: 55, label: 'donut' },\n  { class: 56, label: 'cake' },\n  { class: 57, label: 'chair' },\n  { class: 58, label: 'couch' },\n  { class: 59, label: 'potted plant' },\n  { class: 60, label: 'bed' },\n  { class: 61, label: 'dining table' },\n  { class: 62, label: 'toilet' },\n  { class: 63, label: 'tv' },\n  { class: 64, label: 'laptop' },\n  { class: 65, label: 'mouse' },\n  { class: 66, label: 'remote' },\n  { class: 67, label: 'keyboard' },\n  { class: 68, label: 'cell phone' },\n  { class: 69, label: 'microwave' },\n  { class: 70, label: 'oven' },\n  { class: 71, label: 'toaster' },\n  { class: 72, label: 'sink' },\n  { class: 73, label: 'refrigerator' },\n  { class: 74, label: 'book' },\n  { class: 75, label: 'clock' },\n  { class: 76, label: 'vase' },\n  { class: 77, label: 'scissors' },\n  { class: 78, label: 'teddy bear' },\n  { class: 79, label: 'hair drier' },\n  { class: 80, label: 'toothbrush' },\n];\n", "/**\n * CenterNet object detection model implementation\n *\n * Based on: [**NanoDet**](https://github.com/RangiLyu/nanodet)\n */\n\nimport { log, now } from '../util/util';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { loadModel } from '../tfjs/load';\nimport { labels } from './labels';\nimport type { ObjectResult, ObjectType, Box } from '../result';\nimport type { GraphModel, Tensor } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\n\nlet model: GraphModel | null;\nlet inputSize = 0;\nlet last: ObjectResult[] = [];\nlet lastTime = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nexport async function load(config: Config): Promise<GraphModel> {\n  if (env.initial) model = null;\n  if (!model) {\n    // fakeOps(['floormod'], config);\n    model = await loadModel(config.object.modelPath);\n    const inputs = model?.['executor'] ? Object.values(model.modelSignature['inputs']) : undefined;\n    inputSize = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 0;\n  } else if (config.debug) log('cached model:', model['modelUrl']);\n  return model;\n}\n\nasync function process(res: Tensor | null, outputShape: [number, number], config: Config) {\n  if (!res) return [];\n  const t: Record<string, Tensor> = {};\n  const results: ObjectResult[] = [];\n  const detections = await res.array() as number[][][];\n  t.squeeze = tf.squeeze(res);\n  const arr = tf.split(t.squeeze, 6, 1) as Tensor[]; // x1, y1, x2, y2, score, class\n  t.stack = tf.stack([arr[1], arr[0], arr[3], arr[2]], 1); // reorder dims as tf.nms expects y, x\n  t.boxes = tf.squeeze(t.stack);\n  t.scores = tf.squeeze(arr[4]);\n  t.classes = tf.squeeze(arr[5]);\n  tf.dispose([res, ...arr]);\n  t.nms = await tf.image.nonMaxSuppressionAsync(t.boxes, t.scores, config.object.maxDetected, config.object.iouThreshold, (config.object.minConfidence || 0));\n  const nms = await t.nms.data();\n  let i = 0;\n  for (const id of Array.from(nms)) {\n    const score = Math.trunc(100 * detections[0][id][4]) / 100;\n    const classVal = detections[0][id][5];\n    if (Number.isNaN(classVal)) continue;\n    const label = labels[classVal].label as ObjectType;\n    const [x, y] = [\n      detections[0][id][0] / inputSize,\n      detections[0][id][1] / inputSize,\n    ];\n    const boxRaw: Box = [\n      x,\n      y,\n      detections[0][id][2] / inputSize - x,\n      detections[0][id][3] / inputSize - y,\n    ];\n    const box: Box = [\n      Math.trunc(boxRaw[0] * outputShape[0]),\n      Math.trunc(boxRaw[1] * outputShape[1]),\n      Math.trunc(boxRaw[2] * outputShape[0]),\n      Math.trunc(boxRaw[3] * outputShape[1]),\n    ];\n    results.push({ id: i++, score, class: classVal, label, box, boxRaw });\n  }\n  Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n  return results;\n}\n\nexport async function predict(input: Tensor, config: Config): Promise<ObjectResult[]> {\n  if (!model?.['executor']) return [];\n  const skipTime = (config.object.skipTime || 0) > (now() - lastTime);\n  const skipFrame = skipped < (config.object.skipFrames || 0);\n  if (config.skipAllowed && skipTime && skipFrame && (last.length > 0)) {\n    skipped++;\n    return last;\n  }\n  skipped = 0;\n  return new Promise(async (resolve) => {\n    const outputSize = [input.shape[2] || 0, input.shape[1] || 0] as [number, number];\n    const resize = tf.image.resizeBilinear(input, [inputSize, inputSize]);\n    const objectT = config.object.enabled ? model?.execute(resize, ['tower_0/detections']) as Tensor : null;\n    lastTime = now();\n    tf.dispose(resize);\n\n    const obj = await process(objectT, outputSize, config);\n    last = obj;\n\n    resolve(obj);\n  });\n}\n", "export const kpt: string[] = [\n  'head',\n  'neck',\n  'rightShoulder',\n  'rightElbow',\n  'rightWrist',\n  'chest',\n  'leftShoulder',\n  'leftElbow',\n  'leftWrist',\n  'bodyCenter',\n  'rightHip',\n  'rightKnee',\n  'rightAnkle',\n  'leftHip',\n  'leftKnee',\n  'leftAnkle',\n];\n\nexport const connected: Record<string, string[]> = {\n  leftLeg: ['leftHip', 'leftKnee', 'leftAnkle'],\n  rightLeg: ['rightHip', 'rightKnee', 'rightAnkle'],\n  torso: ['leftShoulder', 'rightShoulder', 'rightHip', 'leftHip', 'leftShoulder'],\n  leftArm: ['leftShoulder', 'leftElbow', 'leftWrist'],\n  rightArm: ['rightShoulder', 'rightElbow', 'rightWrist'],\n  head: [],\n};\n", "/**\n * EfficientPose model implementation\n *\n * Based on: [**EfficientPose**](https://github.com/daniegr/EfficientPose)\n */\n\nimport { log, now } from '../util/util';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { loadModel } from '../tfjs/load';\nimport * as coords from './efficientposecoords';\nimport { constants } from '../tfjs/constants';\nimport type { BodyResult, Point, BodyLandmark, BodyAnnotation } from '../result';\nimport type { GraphModel, Tensor } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\n\nlet model: GraphModel | null;\nlet lastTime = 0;\nconst cache: BodyResult = { id: 0, keypoints: [], box: [0, 0, 0, 0], boxRaw: [0, 0, 0, 0], score: 0, annotations: {} as Record<BodyAnnotation, Point[][]> };\n\n// const keypoints: Array<BodyKeypoint> = [];\n// let box: Box = [0, 0, 0, 0];\n// let boxRaw: Box = [0, 0, 0, 0];\n// let score = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nexport async function load(config: Config): Promise<GraphModel> {\n  if (env.initial) model = null;\n  if (!model) model = await loadModel(config.body.modelPath);\n  else if (config.debug) log('cached model:', model['modelUrl']);\n  return model;\n}\n\n// performs argmax and max functions on a 2d tensor\nasync function max2d(inputs, minScore): Promise<[number, number, number]> {\n  const [width, height] = inputs.shape;\n  const reshaped = tf.reshape(inputs, [height * width]); // combine all data\n  const max = tf.max(reshaped, 0);\n  const newScore: number = (await max.data())[0]; // get highest score\n  if (newScore > minScore) { // skip coordinate calculation is score is too low\n    const coordinates = tf.argMax(reshaped, 0);\n    const mod = tf.mod(coordinates, width);\n    const x = (await mod.data())[0];\n    const div = tf.div(coordinates, width);\n    const y: number = (await div.data())[0];\n    tf.dispose([reshaped, max, coordinates, mod, div]);\n    return [x, y, newScore];\n  }\n  tf.dispose([reshaped, max]);\n  return [0, 0, newScore];\n}\n\nexport async function predict(image: Tensor, config: Config): Promise<BodyResult[]> {\n  if (!model?.['executor']) return [];\n  const skipTime = (config.body.skipTime || 0) > (now() - lastTime);\n  const skipFrame = skipped < (config.body.skipFrames || 0);\n  if (config.skipAllowed && skipTime && skipFrame && Object.keys(cache.keypoints).length > 0) {\n    skipped++;\n    return [cache];\n  }\n  skipped = 0;\n  return new Promise(async (resolve) => {\n    const tensor = tf.tidy(() => {\n      if (!model?.inputs[0].shape) return null;\n      const resize = tf.image.resizeBilinear(image, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false);\n      const enhance = tf.mul(resize, constants.tf2);\n      const norm = tf.sub(enhance, constants.tf1);\n      return norm;\n    });\n    let resT;\n    if (config.body.enabled) resT = model?.execute(tensor);\n    lastTime = now();\n    tf.dispose(tensor);\n\n    if (resT) {\n      cache.keypoints.length = 0;\n      const squeeze = tf.squeeze(resT);\n      tf.dispose(resT);\n      // body parts are basically just a stack of 2d tensors\n      const stack = tf.unstack(squeeze, 2);\n      tf.dispose(squeeze);\n\n      // process each unstacked tensor as a separate body part\n      for (let id = 0; id < stack.length; id++) {\n        // actual processing to get coordinates and score\n        const [x, y, partScore] = await max2d(stack[id], config.body.minConfidence);\n        if (partScore > (config.body.minConfidence || 0)) {\n          cache.keypoints.push({\n            score: Math.round(100 * partScore) / 100,\n            part: coords.kpt[id] as BodyLandmark,\n            positionRaw: [ // normalized to 0..1\n              // @ts-ignore model is not undefined here\n              x / model.inputs[0].shape[2], y / model.inputs[0].shape[1],\n            ],\n            position: [ // normalized to input image size\n              // @ts-ignore model is not undefined here\n              Math.round(image.shape[2] * x / model.inputs[0].shape[2]), Math.round(image.shape[1] * y / model.inputs[0].shape[1]),\n            ],\n          });\n        }\n      }\n      stack.forEach((s) => tf.dispose(s));\n    }\n    cache.score = cache.keypoints.reduce((prev, curr) => (curr.score > prev ? curr.score : prev), 0);\n    const x = cache.keypoints.map((a) => a.position[0]);\n    const y = cache.keypoints.map((a) => a.position[1]);\n    cache.box = [\n      Math.min(...x),\n      Math.min(...y),\n      Math.max(...x) - Math.min(...x),\n      Math.max(...y) - Math.min(...y),\n    ];\n    const xRaw = cache.keypoints.map((a) => a.positionRaw[0]);\n    const yRaw = cache.keypoints.map((a) => a.positionRaw[1]);\n    cache.boxRaw = [\n      Math.min(...xRaw),\n      Math.min(...yRaw),\n      Math.max(...xRaw) - Math.min(...xRaw),\n      Math.max(...yRaw) - Math.min(...yRaw),\n    ];\n    for (const [name, indexes] of Object.entries(coords.connected)) {\n      const pt: Point[][] = [];\n      for (let i = 0; i < indexes.length - 1; i++) {\n        const pt0 = cache.keypoints.find((kpt) => kpt.part === indexes[i]);\n        const pt1 = cache.keypoints.find((kpt) => kpt.part === indexes[i + 1]);\n        if (pt0 && pt1 && pt0.score > (config.body.minConfidence || 0) && pt1.score > (config.body.minConfidence || 0)) pt.push([pt0.position, pt1.position]);\n      }\n      cache.annotations[name] = pt;\n    }\n    resolve([cache]);\n  });\n}\n", "/**\n * Emotion model implementation\n *\n * [**Oarriaga**](https://github.com/oarriaga/face_classification)\n */\n\nimport type { Emotion } from '../result';\nimport { log, now } from '../util/util';\nimport type { Config } from '../config';\nimport type { GraphModel, Tensor } from '../tfjs/types';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { loadModel } from '../tfjs/load';\nimport { env } from '../util/env';\nimport { constants } from '../tfjs/constants';\n\nconst annotations = ['angry', 'disgust', 'fear', 'happy', 'sad', 'surprise', 'neutral'];\nlet model: GraphModel | null;\nconst last: { score: number, emotion: Emotion }[][] = [];\nlet lastCount = 0;\nlet lastTime = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nexport async function load(config: Config): Promise<GraphModel> {\n  if (env.initial) model = null;\n  if (!model) model = await loadModel(config.face.emotion?.modelPath);\n  else if (config.debug) log('cached model:', model['modelUrl']);\n  return model;\n}\n\nexport async function predict(image: Tensor, config: Config, idx: number, count: number): Promise<{ score: number, emotion: Emotion }[]> {\n  if (!model) return [];\n  const skipFrame = skipped < (config.face.emotion?.skipFrames || 0);\n  const skipTime = (config.face.emotion?.skipTime || 0) > (now() - lastTime);\n  if (config.skipAllowed && skipTime && skipFrame && (lastCount === count) && last[idx] && (last[idx].length > 0)) {\n    skipped++;\n    return last[idx];\n  }\n  skipped = 0;\n  return new Promise(async (resolve) => {\n    const obj: { score: number, emotion: Emotion }[] = [];\n    if (config.face.emotion?.enabled) {\n      const t: Record<string, Tensor> = {};\n      const inputSize = model?.inputs[0].shape ? model.inputs[0].shape[2] : 0;\n      t.resize = tf.image.resizeBilinear(image, [inputSize, inputSize], false);\n      // const box = [[0.15, 0.15, 0.85, 0.85]]; // empyrical values for top, left, bottom, right\n      // const resize = tf.image.cropAndResize(image, box, [0], [inputSize, inputSize]);\n      // [t.red, t.green, t.blue] = tf.split(t.resize, 3, 3);\n      // weighted rgb to grayscale: https://www.mathworks.com/help/matlab/ref/rgb2gray.html\n      // t.redNorm = tf.mul(t.red, rgb[0]);\n      // t.greenNorm = tf.mul(t.green, rgb[1]);\n      // t.blueNorm = tf.mul(t.blue, rgb[2]);\n      // t.grayscale = tf.addN([t.redNorm, t.greenNorm, t.blueNorm]);\n      t.channels = tf.mul(t.resize, constants.rgb);\n      t.grayscale = tf.sum(t.channels, 3, true);\n      t.grayscaleSub = tf.sub(t.grayscale, constants.tf05);\n      t.grayscaleMul = tf.mul(t.grayscaleSub, constants.tf2);\n      t.emotion = model?.execute(t.grayscaleMul) as Tensor; // result is already in range 0..1, no need for additional activation\n      lastTime = now();\n      const data = await t.emotion.data();\n      for (let i = 0; i < data.length; i++) {\n        if (data[i] > (config.face.emotion.minConfidence || 0)) obj.push({ score: Math.min(0.99, Math.trunc(100 * data[i]) / 100), emotion: annotations[i] as Emotion });\n      }\n      obj.sort((a, b) => b.score - a.score);\n      Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n    }\n    last[idx] = obj;\n    lastCount = count;\n    resolve(obj);\n  });\n}\n", "import * as coords from './facemeshcoords';\nimport * as util from './facemeshutil';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport type { Tensor, GraphModel } from '../tfjs/types';\nimport { env } from '../util/env';\nimport { log } from '../util/util';\nimport { loadModel } from '../tfjs/load';\nimport type { Config } from '../config';\nimport type { Point } from '../result';\n\nlet model: GraphModel | null;\nlet inputSize = 0;\n\nconst irisEnlarge = 2.3;\n\nconst leftOutline = coords.meshAnnotations.leftEyeLower0;\nconst rightOutline = coords.meshAnnotations.rightEyeLower0;\n\nconst eyeLandmarks = {\n  leftBounds: [leftOutline[0], leftOutline[leftOutline.length - 1]],\n  rightBounds: [rightOutline[0], rightOutline[rightOutline.length - 1]],\n};\n\nconst irisLandmarks = {\n  upperCenter: 3,\n  lowerCenter: 4,\n  index: 71,\n  numCoordinates: 76,\n};\n\nexport async function load(config: Config): Promise<GraphModel> {\n  if (env.initial) model = null;\n  if (!model) model = await loadModel(config.face.iris?.modelPath);\n  else if (config.debug) log('cached model:', model['modelUrl']);\n  inputSize = (model?.['executor'] && model.inputs?.[0].shape) ? model.inputs[0].shape[2] : 0;\n  if (inputSize === -1) inputSize = 64;\n  return model;\n}\n\n// Replace the raw coordinates returned by facemesh with refined iris model coordinates and update the z coordinate to be an average of the original and the new.\nexport function replaceIrisCoords(rawCoords, newCoords, prefix, keys) {\n  for (let i = 0; i < coords.irisIndices.length; i++) {\n    const { key, indices } = coords.irisIndices[i];\n    const originalIndices = coords.meshAnnotations[`${prefix}${key}`];\n    if (!keys || keys.includes(key)) {\n      for (let j = 0; j < indices.length; j++) {\n        const index = indices[j];\n        rawCoords[originalIndices[j]] = [\n          newCoords[index][0],\n          newCoords[index][1],\n          (newCoords[index][2] + rawCoords[originalIndices[j]][2]) / 2,\n        ];\n      }\n    }\n  }\n}\n\nexport const getLeftToRightEyeDepthDifference = (rawCoords) => {\n  const leftEyeZ = rawCoords[eyeLandmarks.leftBounds[0]][2];\n  const rightEyeZ = rawCoords[eyeLandmarks.rightBounds[0]][2];\n  return leftEyeZ - rightEyeZ;\n};\n\n// Returns a box describing a cropped region around the eye fit for passing to the iris model.\nexport const getEyeBox = (rawCoords, face, eyeInnerCornerIndex, eyeOuterCornerIndex, meshSize, flip = false) => {\n  const box = util.squarifyBox(util.enlargeBox(util.calculateLandmarksBoundingBox([rawCoords[eyeInnerCornerIndex], rawCoords[eyeOuterCornerIndex]]), irisEnlarge));\n  const boxSize = util.getBoxSize(box);\n  let crop = tf.image.cropAndResize(face, [[\n    box.startPoint[1] / meshSize,\n    box.startPoint[0] / meshSize, box.endPoint[1] / meshSize,\n    box.endPoint[0] / meshSize,\n  ]], [0], [inputSize, inputSize]);\n  if (flip && env.kernels.includes('flipleftright')) {\n    const flipped = tf.image.flipLeftRight(crop); // flipLeftRight is not defined for tfjs-node\n    tf.dispose(crop);\n    crop = flipped;\n  }\n  return { box, boxSize, crop };\n};\n\n// Given a cropped image of an eye, returns the coordinates of the contours surrounding the eye and the iris.\nexport const getEyeCoords = (eyeData, eyeBox, eyeBoxSize, flip = false) => {\n  const eyeRawCoords: Point[] = [];\n  for (let i = 0; i < irisLandmarks.numCoordinates; i++) {\n    const x = eyeData[i * 3];\n    const y = eyeData[i * 3 + 1];\n    const z = eyeData[i * 3 + 2];\n    eyeRawCoords.push([\n      (flip ? (1 - (x / inputSize)) : (x / inputSize)) * eyeBoxSize[0] + eyeBox.startPoint[0],\n      (y / inputSize) * eyeBoxSize[1] + eyeBox.startPoint[1], z,\n    ]);\n  }\n  return { rawCoords: eyeRawCoords, iris: eyeRawCoords.slice(irisLandmarks.index) };\n};\n\n// The z-coordinates returned for the iris are unreliable, so we take the z values from the surrounding keypoints.\nexport const getAdjustedIrisCoords = (rawCoords, irisCoords, direction) => {\n  const upperCenterZ = rawCoords[coords.meshAnnotations[`${direction}EyeUpper0`][irisLandmarks.upperCenter]][2];\n  const lowerCenterZ = rawCoords[coords.meshAnnotations[`${direction}EyeLower0`][irisLandmarks.lowerCenter]][2];\n  const averageZ = (upperCenterZ + lowerCenterZ) / 2;\n  // Iris indices: 0: center | 1: right | 2: above | 3: left | 4: below\n  return irisCoords.map((coord, i) => {\n    let z = averageZ;\n    if (i === 2) {\n      z = upperCenterZ;\n    } else if (i === 4) {\n      z = lowerCenterZ;\n    }\n    return [coord[0], coord[1], z];\n  });\n};\n\nexport async function augmentIris(rawCoords, face, meshSize) {\n  if (!model?.['executor']) return rawCoords;\n  const { box: leftEyeBox, boxSize: leftEyeBoxSize, crop: leftEyeCrop } = getEyeBox(rawCoords, face, eyeLandmarks.leftBounds[0], eyeLandmarks.leftBounds[1], meshSize, true);\n  const { box: rightEyeBox, boxSize: rightEyeBoxSize, crop: rightEyeCrop } = getEyeBox(rawCoords, face, eyeLandmarks.rightBounds[0], eyeLandmarks.rightBounds[1], meshSize, true);\n  const combined = tf.concat([leftEyeCrop, rightEyeCrop]);\n  tf.dispose(leftEyeCrop);\n  tf.dispose(rightEyeCrop);\n  const eyePredictions = model.execute(combined) as Tensor;\n  tf.dispose(combined);\n  const eyePredictionsData = await eyePredictions.data();\n  tf.dispose(eyePredictions);\n  const leftEyeData = eyePredictionsData.slice(0, irisLandmarks.numCoordinates * 3);\n  const { rawCoords: leftEyeRawCoords, iris: leftIrisRawCoords } = getEyeCoords(leftEyeData, leftEyeBox, leftEyeBoxSize, true);\n  const rightEyeData = eyePredictionsData.slice(irisLandmarks.numCoordinates * 3);\n  const { rawCoords: rightEyeRawCoords, iris: rightIrisRawCoords } = getEyeCoords(rightEyeData, rightEyeBox, rightEyeBoxSize, false);\n  const leftToRightEyeDepthDifference = getLeftToRightEyeDepthDifference(rawCoords);\n  if (Math.abs(leftToRightEyeDepthDifference) < 30) { // User is looking straight ahead.\n    replaceIrisCoords(rawCoords, leftEyeRawCoords, 'left', null);\n    replaceIrisCoords(rawCoords, rightEyeRawCoords, 'right', null);\n    // If the user is looking to the left or to the right, the iris coordinates tend to diverge too much from the mesh coordinates for them to be merged so we only update a single contour line above and below the eye.\n  } else if (leftToRightEyeDepthDifference < 1) { // User is looking towards the right.\n    replaceIrisCoords(rawCoords, leftEyeRawCoords, 'left', ['EyeUpper0', 'EyeLower0']);\n  } else { // User is looking towards the left.\n    replaceIrisCoords(rawCoords, rightEyeRawCoords, 'right', ['EyeUpper0', 'EyeLower0']);\n  }\n  const adjustedLeftIrisCoords = getAdjustedIrisCoords(rawCoords, leftIrisRawCoords, 'left');\n  const adjustedRightIrisCoords = getAdjustedIrisCoords(rawCoords, rightIrisRawCoords, 'right');\n  const newCoords = rawCoords.concat(adjustedLeftIrisCoords).concat(adjustedRightIrisCoords);\n  return newCoords;\n}\n", "// @tensorflow/tfjs-models/face-landmark-detection/src/constants.ts\n// https://github.com/google/mediapipe/mediapipe/python/solutions/face_mesh_connections.py\n\ntype PairArray = [number, number][];\n\nconst LIPS_CONNECTIONS: PairArray = [\n  [61, 146], [146, 91], [91, 181], [181, 84], [84, 17], [17, 314], [314, 405], [405, 321], [321, 375], [375, 291], [61, 185], [185, 40], [40, 39], [39, 37], [37, 0], [0, 267], [267, 269], [269, 270], [270, 409], [409, 291],\n  [78, 95], [95, 88], [88, 178], [178, 87], [87, 14], [14, 317], [317, 402], [402, 318], [318, 324], [324, 308], [78, 191], [191, 80], [80, 81], [81, 82], [82, 13], [13, 312], [312, 311], [311, 310], [310, 415], [415, 308],\n];\n\nconst LEFT_EYE_CONNECTIONS: PairArray = [[263, 249], [249, 390], [390, 373], [373, 374], [374, 380], [380, 381], [381, 382], [382, 362], [263, 466], [466, 388], [388, 387], [387, 386], [386, 385], [385, 384], [384, 398], [398, 362]];\n\nconst LEFT_EYEBROW_CONNECTIONS: PairArray = [[276, 283], [283, 282], [282, 295], [295, 285], [300, 293], [293, 334], [334, 296], [296, 336]];\n\nconst LEFT_IRIS_CONNECTIONS: PairArray = [[474, 475], [475, 476], [476, 477], [477, 474]];\n\nconst RIGHT_EYE_CONNECTIONS: PairArray = [[33, 7], [7, 163], [163, 144], [144, 145], [145, 153], [153, 154], [154, 155], [155, 133], [33, 246], [246, 161], [161, 160], [160, 159], [159, 158], [158, 157], [157, 173], [173, 133]];\n\nconst RIGHT_EYEBROW_CONNECTIONS: PairArray = [[46, 53], [53, 52], [52, 65], [65, 55], [70, 63], [63, 105], [105, 66], [66, 107]];\n\nconst RIGHT_IRIS_CONNECTIONS: PairArray = [[469, 470], [470, 471], [471, 472], [472, 469]];\n\nconst FACE_OVAL_CONNECTIONS: PairArray = [\n  [10, 338], [338, 297], [297, 332], [332, 284], [284, 251], [251, 389], [389, 356], [356, 454], [454, 323], [323, 361], [361, 288], [288, 397], [397, 365], [365, 379], [379, 378], [378, 400], [400, 377], [377, 152],\n  [152, 148], [148, 176], [176, 149], [149, 150], [150, 136], [136, 172], [172, 58], [58, 132], [132, 93], [93, 234], [234, 127], [127, 162], [162, 21], [21, 54], [54, 103], [103, 67], [67, 109], [109, 10],\n];\n\nexport const MEDIAPIPE_FACE_MESH_CONNECTED_KEYPOINTS_PAIRS: PairArray = [\n  [127, 34], [34, 139], [139, 127], [11, 0], [0, 37], [37, 11], [232, 231], [231, 120], [120, 232], [72, 37], [37, 39], [39, 72], [128, 121], [121, 47], [47, 128], [232, 121], [121, 128], [128, 232],\n  [104, 69], [69, 67], [67, 104], [175, 171], [171, 148], [148, 175], [118, 50], [50, 101], [101, 118], [73, 39], [39, 40], [40, 73], [9, 151], [151, 108], [108, 9], [48, 115], [115, 131], [131, 48],\n  [194, 204], [204, 211], [211, 194], [74, 40], [40, 185], [185, 74], [80, 42], [42, 183], [183, 80], [40, 92], [92, 186], [186, 40], [230, 229], [229, 118], [118, 230], [202, 212], [212, 214], [214, 202],\n  [83, 18], [18, 17], [17, 83], [76, 61], [61, 146], [146, 76], [160, 29], [29, 30], [30, 160], [56, 157], [157, 173], [173, 56], [106, 204], [204, 194], [194, 106], [135, 214], [214, 192], [192, 135],\n  [203, 165], [165, 98], [98, 203], [21, 71], [71, 68], [68, 21], [51, 45], [45, 4], [4, 51], [144, 24], [24, 23], [23, 144], [77, 146], [146, 91], [91, 77], [205, 50], [50, 187], [187, 205],\n  [201, 200], [200, 18], [18, 201], [91, 106], [106, 182], [182, 91], [90, 91], [91, 181], [181, 90], [85, 84], [84, 17], [17, 85], [206, 203], [203, 36], [36, 206], [148, 171], [171, 140], [140, 148],\n  [92, 40], [40, 39], [39, 92], [193, 189], [189, 244], [244, 193], [159, 158], [158, 28], [28, 159], [247, 246], [246, 161], [161, 247], [236, 3], [3, 196], [196, 236], [54, 68], [68, 104], [104, 54],\n  [193, 168], [168, 8], [8, 193], [117, 228], [228, 31], [31, 117], [189, 193], [193, 55], [55, 189], [98, 97], [97, 99], [99, 98], [126, 47], [47, 100], [100, 126], [166, 79], [79, 218], [218, 166],\n  [155, 154], [154, 26], [26, 155], [209, 49], [49, 131], [131, 209], [135, 136], [136, 150], [150, 135], [47, 126], [126, 217], [217, 47], [223, 52], [52, 53], [53, 223], [45, 51], [51, 134], [134, 45],\n  [211, 170], [170, 140], [140, 211], [67, 69], [69, 108], [108, 67], [43, 106], [106, 91], [91, 43], [230, 119], [119, 120], [120, 230], [226, 130], [130, 247], [247, 226], [63, 53], [53, 52], [52, 63],\n  [238, 20], [20, 242], [242, 238], [46, 70], [70, 156], [156, 46], [78, 62], [62, 96], [96, 78], [46, 53], [53, 63], [63, 46], [143, 34], [34, 227], [227, 143], [123, 117], [117, 111], [111, 123],\n  [44, 125], [125, 19], [19, 44], [236, 134], [134, 51], [51, 236], [216, 206], [206, 205], [205, 216], [154, 153], [153, 22], [22, 154], [39, 37], [37, 167], [167, 39], [200, 201], [201, 208], [208, 200],\n  [36, 142], [142, 100], [100, 36], [57, 212], [212, 202], [202, 57], [20, 60], [60, 99], [99, 20], [28, 158], [158, 157], [157, 28], [35, 226], [226, 113], [113, 35], [160, 159], [159, 27], [27, 160],\n  [204, 202], [202, 210], [210, 204], [113, 225], [225, 46], [46, 113], [43, 202], [202, 204], [204, 43], [62, 76], [76, 77], [77, 62], [137, 123], [123, 116], [116, 137], [41, 38], [38, 72], [72, 41],\n  [203, 129], [129, 142], [142, 203], [64, 98], [98, 240], [240, 64], [49, 102], [102, 64], [64, 49], [41, 73], [73, 74], [74, 41], [212, 216], [216, 207], [207, 212], [42, 74], [74, 184], [184, 42],\n  [169, 170], [170, 211], [211, 169], [170, 149], [149, 176], [176, 170], [105, 66], [66, 69], [69, 105], [122, 6], [6, 168], [168, 122], [123, 147], [147, 187], [187, 123], [96, 77], [77, 90], [90, 96],\n  [65, 55], [55, 107], [107, 65], [89, 90], [90, 180], [180, 89], [101, 100], [100, 120], [120, 101], [63, 105], [105, 104], [104, 63], [93, 137], [137, 227], [227, 93], [15, 86], [86, 85], [85, 15],\n  [129, 102], [102, 49], [49, 129], [14, 87], [87, 86], [86, 14], [55, 8], [8, 9], [9, 55], [100, 47], [47, 121], [121, 100], [145, 23], [23, 22], [22, 145], [88, 89], [89, 179], [179, 88],\n  [6, 122], [122, 196], [196, 6], [88, 95], [95, 96], [96, 88], [138, 172], [172, 136], [136, 138], [215, 58], [58, 172], [172, 215], [115, 48], [48, 219], [219, 115], [42, 80], [80, 81], [81, 42],\n  [195, 3], [3, 51], [51, 195], [43, 146], [146, 61], [61, 43], [171, 175], [175, 199], [199, 171], [81, 82], [82, 38], [38, 81], [53, 46], [46, 225], [225, 53], [144, 163], [163, 110], [110, 144],\n  [52, 65], [65, 66], [66, 52], [229, 228], [228, 117], [117, 229], [34, 127], [127, 234], [234, 34], [107, 108], [108, 69], [69, 107], [109, 108], [108, 151], [151, 109], [48, 64], [64, 235], [235, 48],\n  [62, 78], [78, 191], [191, 62], [129, 209], [209, 126], [126, 129], [111, 35], [35, 143], [143, 111], [117, 123], [123, 50], [50, 117], [222, 65], [65, 52], [52, 222], [19, 125], [125, 141], [141, 19],\n  [221, 55], [55, 65], [65, 221], [3, 195], [195, 197], [197, 3], [25, 7], [7, 33], [33, 25], [220, 237], [237, 44], [44, 220], [70, 71], [71, 139], [139, 70], [122, 193], [193, 245], [245, 122],\n  [247, 130], [130, 33], [33, 247], [71, 21], [21, 162], [162, 71], [170, 169], [169, 150], [150, 170], [188, 174], [174, 196], [196, 188], [216, 186], [186, 92], [92, 216], [2, 97], [97, 167], [167, 2],\n  [141, 125], [125, 241], [241, 141], [164, 167], [167, 37], [37, 164], [72, 38], [38, 12], [12, 72], [38, 82], [82, 13], [13, 38], [63, 68], [68, 71], [71, 63], [226, 35], [35, 111], [111, 226],\n  [101, 50], [50, 205], [205, 101], [206, 92], [92, 165], [165, 206], [209, 198], [198, 217], [217, 209], [165, 167], [167, 97], [97, 165], [220, 115], [115, 218], [218, 220], [133, 112], [112, 243], [243, 133],\n  [239, 238], [238, 241], [241, 239], [214, 135], [135, 169], [169, 214], [190, 173], [173, 133], [133, 190], [171, 208], [208, 32], [32, 171], [125, 44], [44, 237], [237, 125], [86, 87], [87, 178], [178, 86],\n  [85, 86], [86, 179], [179, 85], [84, 85], [85, 180], [180, 84], [83, 84], [84, 181], [181, 83], [201, 83], [83, 182], [182, 201], [137, 93], [93, 132], [132, 137], [76, 62], [62, 183], [183, 76],\n  [61, 76], [76, 184], [184, 61], [57, 61], [61, 185], [185, 57], [212, 57], [57, 186], [186, 212], [214, 207], [207, 187], [187, 214], [34, 143], [143, 156], [156, 34], [79, 239], [239, 237], [237, 79],\n  [123, 137], [137, 177], [177, 123], [44, 1], [1, 4], [4, 44], [201, 194], [194, 32], [32, 201], [64, 102], [102, 129], [129, 64], [213, 215], [215, 138], [138, 213], [59, 166], [166, 219], [219, 59],\n  [242, 99], [99, 97], [97, 242], [2, 94], [94, 141], [141, 2], [75, 59], [59, 235], [235, 75], [24, 110], [110, 228], [228, 24], [25, 130], [130, 226], [226, 25], [23, 24], [24, 229], [229, 23],\n  [22, 23], [23, 230], [230, 22], [26, 22], [22, 231], [231, 26], [112, 26], [26, 232], [232, 112], [189, 190], [190, 243], [243, 189], [221, 56], [56, 190], [190, 221], [28, 56], [56, 221], [221, 28],\n  [27, 28], [28, 222], [222, 27], [29, 27], [27, 223], [223, 29], [30, 29], [29, 224], [224, 30], [247, 30], [30, 225], [225, 247], [238, 79], [79, 20], [20, 238], [166, 59], [59, 75], [75, 166],\n  [60, 75], [75, 240], [240, 60], [147, 177], [177, 215], [215, 147], [20, 79], [79, 166], [166, 20], [187, 147], [147, 213], [213, 187], [112, 233], [233, 244], [244, 112], [233, 128], [128, 245], [245, 233],\n  [128, 114], [114, 188], [188, 128], [114, 217], [217, 174], [174, 114], [131, 115], [115, 220], [220, 131], [217, 198], [198, 236], [236, 217], [198, 131], [131, 134], [134, 198], [177, 132], [132, 58], [58, 177],\n  [143, 35], [35, 124], [124, 143], [110, 163], [163, 7], [7, 110], [228, 110], [110, 25], [25, 228], [356, 389], [389, 368], [368, 356], [11, 302], [302, 267], [267, 11], [452, 350], [350, 349], [349, 452],\n  [302, 303], [303, 269], [269, 302], [357, 343], [343, 277], [277, 357], [452, 453], [453, 357], [357, 452], [333, 332], [332, 297], [297, 333], [175, 152], [152, 377], [377, 175], [347, 348], [348, 330], [330, 347],\n  [303, 304], [304, 270], [270, 303], [9, 336], [336, 337], [337, 9], [278, 279], [279, 360], [360, 278], [418, 262], [262, 431], [431, 418], [304, 408], [408, 409], [409, 304], [310, 415], [415, 407], [407, 310],\n  [270, 409], [409, 410], [410, 270], [450, 348], [348, 347], [347, 450], [422, 430], [430, 434], [434, 422], [313, 314], [314, 17], [17, 313], [306, 307], [307, 375], [375, 306], [387, 388], [388, 260], [260, 387],\n  [286, 414], [414, 398], [398, 286], [335, 406], [406, 418], [418, 335], [364, 367], [367, 416], [416, 364], [423, 358], [358, 327], [327, 423], [251, 284], [284, 298], [298, 251], [281, 5], [5, 4], [4, 281],\n  [373, 374], [374, 253], [253, 373], [307, 320], [320, 321], [321, 307], [425, 427], [427, 411], [411, 425], [421, 313], [313, 18], [18, 421], [321, 405], [405, 406], [406, 321], [320, 404], [404, 405], [405, 320],\n  [315, 16], [16, 17], [17, 315], [426, 425], [425, 266], [266, 426], [377, 400], [400, 369], [369, 377], [322, 391], [391, 269], [269, 322], [417, 465], [465, 464], [464, 417], [386, 257], [257, 258], [258, 386],\n  [466, 260], [260, 388], [388, 466], [456, 399], [399, 419], [419, 456], [284, 332], [332, 333], [333, 284], [417, 285], [285, 8], [8, 417], [346, 340], [340, 261], [261, 346], [413, 441], [441, 285], [285, 413],\n  [327, 460], [460, 328], [328, 327], [355, 371], [371, 329], [329, 355], [392, 439], [439, 438], [438, 392], [382, 341], [341, 256], [256, 382], [429, 420], [420, 360], [360, 429], [364, 394], [394, 379], [379, 364],\n  [277, 343], [343, 437], [437, 277], [443, 444], [444, 283], [283, 443], [275, 440], [440, 363], [363, 275], [431, 262], [262, 369], [369, 431], [297, 338], [338, 337], [337, 297], [273, 375], [375, 321], [321, 273],\n  [450, 451], [451, 349], [349, 450], [446, 342], [342, 467], [467, 446], [293, 334], [334, 282], [282, 293], [458, 461], [461, 462], [462, 458], [276, 353], [353, 383], [383, 276], [308, 324], [324, 325], [325, 308],\n  [276, 300], [300, 293], [293, 276], [372, 345], [345, 447], [447, 372], [352, 345], [345, 340], [340, 352], [274, 1], [1, 19], [19, 274], [456, 248], [248, 281], [281, 456], [436, 427], [427, 425], [425, 436],\n  [381, 256], [256, 252], [252, 381], [269, 391], [391, 393], [393, 269], [200, 199], [199, 428], [428, 200], [266, 330], [330, 329], [329, 266], [287, 273], [273, 422], [422, 287], [250, 462], [462, 328], [328, 250],\n  [258, 286], [286, 384], [384, 258], [265, 353], [353, 342], [342, 265], [387, 259], [259, 257], [257, 387], [424, 431], [431, 430], [430, 424], [342, 353], [353, 276], [276, 342], [273, 335], [335, 424], [424, 273],\n  [292, 325], [325, 307], [307, 292], [366, 447], [447, 345], [345, 366], [271, 303], [303, 302], [302, 271], [423, 266], [266, 371], [371, 423], [294, 455], [455, 460], [460, 294], [279, 278], [278, 294], [294, 279],\n  [271, 272], [272, 304], [304, 271], [432, 434], [434, 427], [427, 432], [272, 407], [407, 408], [408, 272], [394, 430], [430, 431], [431, 394], [395, 369], [369, 400], [400, 395], [334, 333], [333, 299], [299, 334],\n  [351, 417], [417, 168], [168, 351], [352, 280], [280, 411], [411, 352], [325, 319], [319, 320], [320, 325], [295, 296], [296, 336], [336, 295], [319, 403], [403, 404], [404, 319], [330, 348], [348, 349], [349, 330],\n  [293, 298], [298, 333], [333, 293], [323, 454], [454, 447], [447, 323], [15, 16], [16, 315], [315, 15], [358, 429], [429, 279], [279, 358], [14, 15], [15, 316], [316, 14], [285, 336], [336, 9], [9, 285],\n  [329, 349], [349, 350], [350, 329], [374, 380], [380, 252], [252, 374], [318, 402], [402, 403], [403, 318], [6, 197], [197, 419], [419, 6], [318, 319], [319, 325], [325, 318], [367, 364], [364, 365], [365, 367],\n  [435, 367], [367, 397], [397, 435], [344, 438], [438, 439], [439, 344], [272, 271], [271, 311], [311, 272], [195, 5], [5, 281], [281, 195], [273, 287], [287, 291], [291, 273], [396, 428], [428, 199], [199, 396],\n  [311, 271], [271, 268], [268, 311], [283, 444], [444, 445], [445, 283], [373, 254], [254, 339], [339, 373], [282, 334], [334, 296], [296, 282], [449, 347], [347, 346], [346, 449], [264, 447], [447, 454], [454, 264],\n  [336, 296], [296, 299], [299, 336], [338, 10], [10, 151], [151, 338], [278, 439], [439, 455], [455, 278], [292, 407], [407, 415], [415, 292], [358, 371], [371, 355], [355, 358], [340, 345], [345, 372], [372, 340],\n  [346, 347], [347, 280], [280, 346], [442, 443], [443, 282], [282, 442], [19, 94], [94, 370], [370, 19], [441, 442], [442, 295], [295, 441], [248, 419], [419, 197], [197, 248], [263, 255], [255, 359], [359, 263],\n  [440, 275], [275, 274], [274, 440], [300, 383], [383, 368], [368, 300], [351, 412], [412, 465], [465, 351], [263, 467], [467, 466], [466, 263], [301, 368], [368, 389], [389, 301], [395, 378], [378, 379], [379, 395],\n  [412, 351], [351, 419], [419, 412], [436, 426], [426, 322], [322, 436], [2, 164], [164, 393], [393, 2], [370, 462], [462, 461], [461, 370], [164, 0], [0, 267], [267, 164], [302, 11], [11, 12], [12, 302],\n  [268, 12], [12, 13], [13, 268], [293, 300], [300, 301], [301, 293], [446, 261], [261, 340], [340, 446], [330, 266], [266, 425], [425, 330], [426, 423], [423, 391], [391, 426], [429, 355], [355, 437], [437, 429],\n  [391, 327], [327, 326], [326, 391], [440, 457], [457, 438], [438, 440], [341, 382], [382, 362], [362, 341], [459, 457], [457, 461], [461, 459], [434, 430], [430, 394], [394, 434], [414, 463], [463, 362], [362, 414],\n  [396, 369], [369, 262], [262, 396], [354, 461], [461, 457], [457, 354], [316, 403], [403, 402], [402, 316], [315, 404], [404, 403], [403, 315], [314, 405], [405, 404], [404, 314], [313, 406], [406, 405], [405, 313],\n  [421, 418], [418, 406], [406, 421], [366, 401], [401, 361], [361, 366], [306, 408], [408, 407], [407, 306], [291, 409], [409, 408], [408, 291], [287, 410], [410, 409], [409, 287], [432, 436], [436, 410], [410, 432],\n  [434, 416], [416, 411], [411, 434], [264, 368], [368, 383], [383, 264], [309, 438], [438, 457], [457, 309], [352, 376], [376, 401], [401, 352], [274, 275], [275, 4], [4, 274], [421, 428], [428, 262], [262, 421],\n  [294, 327], [327, 358], [358, 294], [433, 416], [416, 367], [367, 433], [289, 455], [455, 439], [439, 289], [462, 370], [370, 326], [326, 462], [2, 326], [326, 370], [370, 2], [305, 460], [460, 455], [455, 305],\n  [254, 449], [449, 448], [448, 254], [255, 261], [261, 446], [446, 255], [253, 450], [450, 449], [449, 253], [252, 451], [451, 450], [450, 252], [256, 452], [452, 451], [451, 256], [341, 453], [453, 452], [452, 341],\n  [413, 464], [464, 463], [463, 413], [441, 413], [413, 414], [414, 441], [258, 442], [442, 441], [441, 258], [257, 443], [443, 442], [442, 257], [259, 444], [444, 443], [443, 259], [260, 445], [445, 444], [444, 260],\n  [467, 342], [342, 445], [445, 467], [459, 458], [458, 250], [250, 459], [289, 392], [392, 290], [290, 289], [290, 328], [328, 460], [460, 290], [376, 433], [433, 435], [435, 376], [250, 290], [290, 392], [392, 250],\n  [411, 416], [416, 433], [433, 411], [341, 463], [463, 464], [464, 341], [453, 464], [464, 465], [465, 453], [357, 465], [465, 412], [412, 357], [343, 412], [412, 399], [399, 343], [360, 363], [363, 440], [440, 360],\n  [437, 399], [399, 456], [456, 437], [420, 456], [456, 363], [363, 420], [401, 435], [435, 288], [288, 401], [372, 383], [383, 353], [353, 372], [339, 255], [255, 249], [249, 339], [448, 261], [261, 255], [255, 448],\n  [133, 243], [243, 190], [190, 133], [133, 155], [155, 112], [112, 133], [33, 246], [246, 247], [247, 33], [33, 130], [130, 25], [25, 33], [398, 384], [384, 286], [286, 398], [362, 398], [398, 414], [414, 362],\n  [362, 463], [463, 341], [341, 362], [263, 359], [359, 467], [467, 263], [263, 249], [249, 255], [255, 263], [466, 467], [467, 260], [260, 466], [75, 60], [60, 166], [166, 75], [238, 239], [239, 79], [79, 238],\n  [162, 127], [127, 139], [139, 162], [72, 11], [11, 37], [37, 72], [121, 232], [232, 120], [120, 121], [73, 72], [72, 39], [39, 73], [114, 128], [128, 47], [47, 114], [233, 232], [232, 128], [128, 233],\n  [103, 104], [104, 67], [67, 103], [152, 175], [175, 148], [148, 152], [119, 118], [118, 101], [101, 119], [74, 73], [73, 40], [40, 74], [107, 9], [9, 108], [108, 107], [49, 48], [48, 131], [131, 49],\n  [32, 194], [194, 211], [211, 32], [184, 74], [74, 185], [185, 184], [191, 80], [80, 183], [183, 191], [185, 40], [40, 186], [186, 185], [119, 230], [230, 118], [118, 119], [210, 202], [202, 214], [214, 210],\n  [84, 83], [83, 17], [17, 84], [77, 76], [76, 146], [146, 77], [161, 160], [160, 30], [30, 161], [190, 56], [56, 173], [173, 190], [182, 106], [106, 194], [194, 182], [138, 135], [135, 192], [192, 138],\n  [129, 203], [203, 98], [98, 129], [54, 21], [21, 68], [68, 54], [5, 51], [51, 4], [4, 5], [145, 144], [144, 23], [23, 145], [90, 77], [77, 91], [91, 90], [207, 205], [205, 187], [187, 207],\n  [83, 201], [201, 18], [18, 83], [181, 91], [91, 182], [182, 181], [180, 90], [90, 181], [181, 180], [16, 85], [85, 17], [17, 16], [205, 206], [206, 36], [36, 205], [176, 148], [148, 140], [140, 176],\n  [165, 92], [92, 39], [39, 165], [245, 193], [193, 244], [244, 245], [27, 159], [159, 28], [28, 27], [30, 247], [247, 161], [161, 30], [174, 236], [236, 196], [196, 174], [103, 54], [54, 104], [104, 103],\n  [55, 193], [193, 8], [8, 55], [111, 117], [117, 31], [31, 111], [221, 189], [189, 55], [55, 221], [240, 98], [98, 99], [99, 240], [142, 126], [126, 100], [100, 142], [219, 166], [166, 218], [218, 219],\n  [112, 155], [155, 26], [26, 112], [198, 209], [209, 131], [131, 198], [169, 135], [135, 150], [150, 169], [114, 47], [47, 217], [217, 114], [224, 223], [223, 53], [53, 224], [220, 45], [45, 134], [134, 220],\n  [32, 211], [211, 140], [140, 32], [109, 67], [67, 108], [108, 109], [146, 43], [43, 91], [91, 146], [231, 230], [230, 120], [120, 231], [113, 226], [226, 247], [247, 113], [105, 63], [63, 52], [52, 105],\n  [241, 238], [238, 242], [242, 241], [124, 46], [46, 156], [156, 124], [95, 78], [78, 96], [96, 95], [70, 46], [46, 63], [63, 70], [116, 143], [143, 227], [227, 116], [116, 123], [123, 111], [111, 116],\n  [1, 44], [44, 19], [19, 1], [3, 236], [236, 51], [51, 3], [207, 216], [216, 205], [205, 207], [26, 154], [154, 22], [22, 26], [165, 39], [39, 167], [167, 165], [199, 200], [200, 208], [208, 199],\n  [101, 36], [36, 100], [100, 101], [43, 57], [57, 202], [202, 43], [242, 20], [20, 99], [99, 242], [56, 28], [28, 157], [157, 56], [124, 35], [35, 113], [113, 124], [29, 160], [160, 27], [27, 29],\n  [211, 204], [204, 210], [210, 211], [124, 113], [113, 46], [46, 124], [106, 43], [43, 204], [204, 106], [96, 62], [62, 77], [77, 96], [227, 137], [137, 116], [116, 227], [73, 41], [41, 72], [72, 73],\n  [36, 203], [203, 142], [142, 36], [235, 64], [64, 240], [240, 235], [48, 49], [49, 64], [64, 48], [42, 41], [41, 74], [74, 42], [214, 212], [212, 207], [207, 214], [183, 42], [42, 184], [184, 183],\n  [210, 169], [169, 211], [211, 210], [140, 170], [170, 176], [176, 140], [104, 105], [105, 69], [69, 104], [193, 122], [122, 168], [168, 193], [50, 123], [123, 187], [187, 50], [89, 96], [96, 90], [90, 89],\n  [66, 65], [65, 107], [107, 66], [179, 89], [89, 180], [180, 179], [119, 101], [101, 120], [120, 119], [68, 63], [63, 104], [104, 68], [234, 93], [93, 227], [227, 234], [16, 15], [15, 85], [85, 16],\n  [209, 129], [129, 49], [49, 209], [15, 14], [14, 86], [86, 15], [107, 55], [55, 9], [9, 107], [120, 100], [100, 121], [121, 120], [153, 145], [145, 22], [22, 153], [178, 88], [88, 179], [179, 178],\n  [197, 6], [6, 196], [196, 197], [89, 88], [88, 96], [96, 89], [135, 138], [138, 136], [136, 135], [138, 215], [215, 172], [172, 138], [218, 115], [115, 219], [219, 218], [41, 42], [42, 81], [81, 41],\n  [5, 195], [195, 51], [51, 5], [57, 43], [43, 61], [61, 57], [208, 171], [171, 199], [199, 208], [41, 81], [81, 38], [38, 41], [224, 53], [53, 225], [225, 224], [24, 144], [144, 110], [110, 24],\n  [105, 52], [52, 66], [66, 105], [118, 229], [229, 117], [117, 118], [227, 34], [34, 234], [234, 227], [66, 107], [107, 69], [69, 66], [10, 109], [109, 151], [151, 10], [219, 48], [48, 235], [235, 219],\n  [183, 62], [62, 191], [191, 183], [142, 129], [129, 126], [126, 142], [116, 111], [111, 143], [143, 116], [118, 117], [117, 50], [50, 118], [223, 222], [222, 52], [52, 223], [94, 19], [19, 141], [141, 94],\n  [222, 221], [221, 65], [65, 222], [196, 3], [3, 197], [197, 196], [45, 220], [220, 44], [44, 45], [156, 70], [70, 139], [139, 156], [188, 122], [122, 245], [245, 188], [139, 71], [71, 162], [162, 139],\n  [149, 170], [170, 150], [150, 149], [122, 188], [188, 196], [196, 122], [206, 216], [216, 92], [92, 206], [164, 2], [2, 167], [167, 164], [242, 141], [141, 241], [241, 242], [0, 164], [164, 37], [37, 0],\n  [11, 72], [72, 12], [12, 11], [12, 38], [38, 13], [13, 12], [70, 63], [63, 71], [71, 70], [31, 226], [226, 111], [111, 31], [36, 101], [101, 205], [205, 36], [203, 206], [206, 165], [165, 203],\n  [126, 209], [209, 217], [217, 126], [98, 165], [165, 97], [97, 98], [237, 220], [220, 218], [218, 237], [237, 239], [239, 241], [241, 237], [210, 214], [214, 169], [169, 210], [140, 171], [171, 32], [32, 140],\n  [241, 125], [125, 237], [237, 241], [179, 86], [86, 178], [178, 179], [180, 85], [85, 179], [179, 180], [181, 84], [84, 180], [180, 181], [182, 83], [83, 181], [181, 182], [194, 201], [201, 182], [182, 194],\n  [177, 137], [137, 132], [132, 177], [184, 76], [76, 183], [183, 184], [185, 61], [61, 184], [184, 185], [186, 57], [57, 185], [185, 186], [216, 212], [212, 186], [186, 216], [192, 214], [214, 187], [187, 192],\n  [139, 34], [34, 156], [156, 139], [218, 79], [79, 237], [237, 218], [147, 123], [123, 177], [177, 147], [45, 44], [44, 4], [4, 45], [208, 201], [201, 32], [32, 208], [98, 64], [64, 129], [129, 98],\n  [192, 213], [213, 138], [138, 192], [235, 59], [59, 219], [219, 235], [141, 242], [242, 97], [97, 141], [97, 2], [2, 141], [141, 97], [240, 75], [75, 235], [235, 240], [229, 24], [24, 228], [228, 229],\n  [31, 25], [25, 226], [226, 31], [230, 23], [23, 229], [229, 230], [231, 22], [22, 230], [230, 231], [232, 26], [26, 231], [231, 232], [233, 112], [112, 232], [232, 233], [244, 189], [189, 243], [243, 244],\n  [189, 221], [221, 190], [190, 189], [222, 28], [28, 221], [221, 222], [223, 27], [27, 222], [222, 223], [224, 29], [29, 223], [223, 224], [225, 30], [30, 224], [224, 225], [113, 247], [247, 225], [225, 113],\n  [99, 60], [60, 240], [240, 99], [213, 147], [147, 215], [215, 213], [60, 20], [20, 166], [166, 60], [192, 187], [187, 213], [213, 192], [243, 112], [112, 244], [244, 243], [244, 233], [233, 245], [245, 244],\n  [245, 128], [128, 188], [188, 245], [188, 114], [114, 174], [174, 188], [134, 131], [131, 220], [220, 134], [174, 217], [217, 236], [236, 174], [236, 198], [198, 134], [134, 236], [215, 177], [177, 58], [58, 215],\n  [156, 143], [143, 124], [124, 156], [25, 110], [110, 7], [7, 25], [31, 228], [228, 25], [25, 31], [264, 356], [356, 368], [368, 264], [0, 11], [11, 267], [267, 0], [451, 452], [452, 349], [349, 451],\n  [267, 302], [302, 269], [269, 267], [350, 357], [357, 277], [277, 350], [350, 452], [452, 357], [357, 350], [299, 333], [333, 297], [297, 299], [396, 175], [175, 377], [377, 396], [280, 347], [347, 330], [330, 280],\n  [269, 303], [303, 270], [270, 269], [151, 9], [9, 337], [337, 151], [344, 278], [278, 360], [360, 344], [424, 418], [418, 431], [431, 424], [270, 304], [304, 409], [409, 270], [272, 310], [310, 407], [407, 272],\n  [322, 270], [270, 410], [410, 322], [449, 450], [450, 347], [347, 449], [432, 422], [422, 434], [434, 432], [18, 313], [313, 17], [17, 18], [291, 306], [306, 375], [375, 291], [259, 387], [387, 260], [260, 259],\n  [424, 335], [335, 418], [418, 424], [434, 364], [364, 416], [416, 434], [391, 423], [423, 327], [327, 391], [301, 251], [251, 298], [298, 301], [275, 281], [281, 4], [4, 275], [254, 373], [373, 253], [253, 254],\n  [375, 307], [307, 321], [321, 375], [280, 425], [425, 411], [411, 280], [200, 421], [421, 18], [18, 200], [335, 321], [321, 406], [406, 335], [321, 320], [320, 405], [405, 321], [314, 315], [315, 17], [17, 314],\n  [423, 426], [426, 266], [266, 423], [396, 377], [377, 369], [369, 396], [270, 322], [322, 269], [269, 270], [413, 417], [417, 464], [464, 413], [385, 386], [386, 258], [258, 385], [248, 456], [456, 419], [419, 248],\n  [298, 284], [284, 333], [333, 298], [168, 417], [417, 8], [8, 168], [448, 346], [346, 261], [261, 448], [417, 413], [413, 285], [285, 417], [326, 327], [327, 328], [328, 326], [277, 355], [355, 329], [329, 277],\n  [309, 392], [392, 438], [438, 309], [381, 382], [382, 256], [256, 381], [279, 429], [429, 360], [360, 279], [365, 364], [364, 379], [379, 365], [355, 277], [277, 437], [437, 355], [282, 443], [443, 283], [283, 282],\n  [281, 275], [275, 363], [363, 281], [395, 431], [431, 369], [369, 395], [299, 297], [297, 337], [337, 299], [335, 273], [273, 321], [321, 335], [348, 450], [450, 349], [349, 348], [359, 446], [446, 467], [467, 359],\n  [283, 293], [293, 282], [282, 283], [250, 458], [458, 462], [462, 250], [300, 276], [276, 383], [383, 300], [292, 308], [308, 325], [325, 292], [283, 276], [276, 293], [293, 283], [264, 372], [372, 447], [447, 264],\n  [346, 352], [352, 340], [340, 346], [354, 274], [274, 19], [19, 354], [363, 456], [456, 281], [281, 363], [426, 436], [436, 425], [425, 426], [380, 381], [381, 252], [252, 380], [267, 269], [269, 393], [393, 267],\n  [421, 200], [200, 428], [428, 421], [371, 266], [266, 329], [329, 371], [432, 287], [287, 422], [422, 432], [290, 250], [250, 328], [328, 290], [385, 258], [258, 384], [384, 385], [446, 265], [265, 342], [342, 446],\n  [386, 387], [387, 257], [257, 386], [422, 424], [424, 430], [430, 422], [445, 342], [342, 276], [276, 445], [422, 273], [273, 424], [424, 422], [306, 292], [292, 307], [307, 306], [352, 366], [366, 345], [345, 352],\n  [268, 271], [271, 302], [302, 268], [358, 423], [423, 371], [371, 358], [327, 294], [294, 460], [460, 327], [331, 279], [279, 294], [294, 331], [303, 271], [271, 304], [304, 303], [436, 432], [432, 427], [427, 436],\n  [304, 272], [272, 408], [408, 304], [395, 394], [394, 431], [431, 395], [378, 395], [395, 400], [400, 378], [296, 334], [334, 299], [299, 296], [6, 351], [351, 168], [168, 6], [376, 352], [352, 411], [411, 376],\n  [307, 325], [325, 320], [320, 307], [285, 295], [295, 336], [336, 285], [320, 319], [319, 404], [404, 320], [329, 330], [330, 349], [349, 329], [334, 293], [293, 333], [333, 334], [366, 323], [323, 447], [447, 366],\n  [316, 15], [15, 315], [315, 316], [331, 358], [358, 279], [279, 331], [317, 14], [14, 316], [316, 317], [8, 285], [285, 9], [9, 8], [277, 329], [329, 350], [350, 277], [253, 374], [374, 252], [252, 253],\n  [319, 318], [318, 403], [403, 319], [351, 6], [6, 419], [419, 351], [324, 318], [318, 325], [325, 324], [397, 367], [367, 365], [365, 397], [288, 435], [435, 397], [397, 288], [278, 344], [344, 439], [439, 278],\n  [310, 272], [272, 311], [311, 310], [248, 195], [195, 281], [281, 248], [375, 273], [273, 291], [291, 375], [175, 396], [396, 199], [199, 175], [312, 311], [311, 268], [268, 312], [276, 283], [283, 445], [445, 276],\n  [390, 373], [373, 339], [339, 390], [295, 282], [282, 296], [296, 295], [448, 449], [449, 346], [346, 448], [356, 264], [264, 454], [454, 356], [337, 336], [336, 299], [299, 337], [337, 338], [338, 151], [151, 337],\n  [294, 278], [278, 455], [455, 294], [308, 292], [292, 415], [415, 308], [429, 358], [358, 355], [355, 429], [265, 340], [340, 372], [372, 265], [352, 346], [346, 280], [280, 352], [295, 442], [442, 282], [282, 295],\n  [354, 19], [19, 370], [370, 354], [285, 441], [441, 295], [295, 285], [195, 248], [248, 197], [197, 195], [457, 440], [440, 274], [274, 457], [301, 300], [300, 368], [368, 301], [417, 351], [351, 465], [465, 417],\n  [251, 301], [301, 389], [389, 251], [394, 395], [395, 379], [379, 394], [399, 412], [412, 419], [419, 399], [410, 436], [436, 322], [322, 410], [326, 2], [2, 393], [393, 326], [354, 370], [370, 461], [461, 354],\n  [393, 164], [164, 267], [267, 393], [268, 302], [302, 12], [12, 268], [312, 268], [268, 13], [13, 312], [298, 293], [293, 301], [301, 298], [265, 446], [446, 340], [340, 265], [280, 330], [330, 425], [425, 280],\n  [322, 426], [426, 391], [391, 322], [420, 429], [429, 437], [437, 420], [393, 391], [391, 326], [326, 393], [344, 440], [440, 438], [438, 344], [458, 459], [459, 461], [461, 458], [364, 434], [434, 394], [394, 364],\n  [428, 396], [396, 262], [262, 428], [274, 354], [354, 457], [457, 274], [317, 316], [316, 402], [402, 317], [316, 315], [315, 403], [403, 316], [315, 314], [314, 404], [404, 315], [314, 313], [313, 405], [405, 314],\n  [313, 421], [421, 406], [406, 313], [323, 366], [366, 361], [361, 323], [292, 306], [306, 407], [407, 292], [306, 291], [291, 408], [408, 306], [291, 287], [287, 409], [409, 291], [287, 432], [432, 410], [410, 287],\n  [427, 434], [434, 411], [411, 427], [372, 264], [264, 383], [383, 372], [459, 309], [309, 457], [457, 459], [366, 352], [352, 401], [401, 366], [1, 274], [274, 4], [4, 1], [418, 421], [421, 262], [262, 418],\n  [331, 294], [294, 358], [358, 331], [435, 433], [433, 367], [367, 435], [392, 289], [289, 439], [439, 392], [328, 462], [462, 326], [326, 328], [94, 2], [2, 370], [370, 94], [289, 305], [305, 455], [455, 289],\n  [339, 254], [254, 448], [448, 339], [359, 255], [255, 446], [446, 359], [254, 253], [253, 449], [449, 254], [253, 252], [252, 450], [450, 253], [252, 256], [256, 451], [451, 252], [256, 341], [341, 452], [452, 256],\n  [414, 413], [413, 463], [463, 414], [286, 441], [441, 414], [414, 286], [286, 258], [258, 441], [441, 286], [258, 257], [257, 442], [442, 258], [257, 259], [259, 443], [443, 257], [259, 260], [260, 444], [444, 259],\n  [260, 467], [467, 445], [445, 260], [309, 459], [459, 250], [250, 309], [305, 289], [289, 290], [290, 305], [305, 290], [290, 460], [460, 305], [401, 376], [376, 435], [435, 401], [309, 250], [250, 392], [392, 309],\n  [376, 411], [411, 433], [433, 376], [453, 341], [341, 464], [464, 453], [357, 453], [453, 465], [465, 357], [343, 357], [357, 412], [412, 343], [437, 343], [343, 399], [399, 437], [344, 360], [360, 440], [440, 344],\n  [420, 437], [437, 456], [456, 420], [360, 420], [420, 363], [363, 360], [361, 401], [401, 288], [288, 361], [265, 372], [372, 353], [353, 265], [390, 339], [339, 249], [249, 390], [339, 448], [448, 255], [255, 339],\n];\n\nfunction connectionsToIndices(connections: PairArray) {\n  const indices = connections.map((connection) => connection[0]);\n  indices.push(connections[connections.length - 1][1]);\n  return indices;\n}\n\nexport const MEDIAPIPE_FACE_MESH_KEYPOINTS_BY_CONTOUR = {\n  lips: connectionsToIndices(LIPS_CONNECTIONS),\n  leftEye: connectionsToIndices(LEFT_EYE_CONNECTIONS),\n  leftEyebrow: connectionsToIndices(LEFT_EYEBROW_CONNECTIONS),\n  leftIris: connectionsToIndices(LEFT_IRIS_CONNECTIONS),\n  rightEye: connectionsToIndices(RIGHT_EYE_CONNECTIONS),\n  rightEyebrow: connectionsToIndices(RIGHT_EYEBROW_CONNECTIONS),\n  rightIris: connectionsToIndices(RIGHT_IRIS_CONNECTIONS),\n  faceOval: connectionsToIndices(FACE_OVAL_CONNECTIONS),\n};\n\nconst indexLabelPairs: [number, string][] = Object.entries(MEDIAPIPE_FACE_MESH_KEYPOINTS_BY_CONTOUR)\n  .map(([label, indices]) => indices.map((index) => [index, label] as [number, string]))\n  .flat();\n\nexport const MEDIAPIPE_FACE_MESH_KEYPOINTS = new Map(indexLabelPairs);\n\ntype AssignAverage = number[];\nexport interface LandmarksRefinementConfig {\n  indexesMapping: number[]; // Maps indexes of the given set of landmarks to indexes of the resulting set of landmarks. Should be non empty and contain the same amount of indexes as landmarks in the corresponding input\n  zRefinement: 'none'|'copy'|AssignAverage; // Z refinement instructions.\n}\n\nexport const LANDMARKS_REFINEMENT_LIPS_CONFIG = [\n  61, 146, 91, 181, 84, 17, 314, 405, 321, 375, 291, // Lower outer.\n  185, 40, 39, 37, 0, 267, 269, 270, 409, // Upper outer(excluding corners).\n  78, 95, 88, 178, 87, 14, 317, 402, 318, 324, 308, // Lower inner.\n  191, 80, 81, 82, 13, 312, 311, 310, 415, // Upper inner(excluding corners).\n  76, 77, 90, 180, 85, 16, 315, 404, 320, 307, 306, // Lower semi - outer.\n  184, 74, 73, 72, 11, 302, 303, 304, 408, // Upper semi - outer(excluding corners).\n  62, 96, 89, 179, 86, 15, 316, 403, 319, 325, 292, // Lower semi - inner.\n  183, 42, 41, 38, 12, 268, 271, 272, 407, // Upper semi - inner(excluding corners).\n];\n\nexport const LANDMARKS_REFINEMENT_LEFT_EYE_CONFIG = [\n  33, 7, 163, 144, 145, 153, 154, 155, 133, // Lower contour.\n  246, 161, 160, 159, 158, 157, 173, // upper contour (excluding corners).\n  130, 25, 110, 24, 23, 22, 26, 112, 243, // Halo x2 lower contour.\n  247, 30, 29, 27, 28, 56, 190, // Halo x2 upper contour (excluding corners).\n  226, 31, 228, 229, 230, 231, 232, 233, 244, // Halo x3 lower contour.\n  113, 225, 224, 223, 222, 221, 189, // Halo x3 upper contour (excluding corners).\n  35, 124, 46, 53, 52, 65, // Halo x4 upper contour (no lower because of mesh structure) or eyebrow inner contour.\n  143, 111, 117, 118, 119, 120, 121, 128, 245, // Halo x5 lower contour.\n  156, 70, 63, 105, 66, 107, 55, 193, // Halo x5 upper contour (excluding corners) or eyebrow outer contour.\n];\n\nexport const LANDMARKS_REFINEMENT_RIGHT_EYE_CONFIG = [\n  263, 249, 390, 373, 374, 380, 381, 382, 362, // Lower contour.\n  466, 388, 387, 386, 385, 384, 398, // Upper contour (excluding corners).\n  359, 255, 339, 254, 253, 252, 256, 341, 463, // Halo x2 lower contour.\n  467, 260, 259, 257, 258, 286, 414, // Halo x2 upper contour (excluding corners).\n  446, 261, 448, 449, 450, 451, 452, 453, 464, // Halo x3 lower contour.\n  342, 445, 444, 443, 442, 441, 413, // Halo x3 upper contour (excluding corners).\n  265, 353, 276, 283, 282, 295, // Halo x4 upper contour (no lower because of mesh structure) or/ eyebrow inner contour.\n  372, 340, 346, 347, 348, 349, 350, 357, 465, // Halo x5 lower contour.\n  383, 300, 293, 334, 296, 336, 285, 417, // Halo x5 upper contour (excluding corners) or eyebrow outer contour.\n];\n\nexport const LANDMARKS_REFINEMENT_LEFT_IRIS_CONFIG = [\n  468, // Center.\n  469, // Iris right edge.\n  470, // Iris top edge.\n  471, // Iris left edge.\n  472, // Iris bottom edge.\n];\n/*\nzRefinement: [\n  33, 7, 163, 144, 145, 153, 154, 155, 133, // Lower contour.\n  246, 161, 160, 159, 158, 157, 173, // Upper contour (excluding corners).\n];\n*/\n\nexport const LANDMARKS_REFINEMENT_RIGHT_IRIS_CONFIG = [\n  473, // Center.\n  474, // Iris right edge.\n  475, // Iris top edge.\n  476, // Iris left edge.\n  477, // Iris bottom edge.\n];\n/*\nzRefinement: [\n  263, 249, 390, 373, 374, 380, 381, 382, 362, // Lower contour.\n  466, 388, 387, 386, 385, 384, 398, // Upper contour (excluding corners).\n];\n*/\n", "import * as constants from './constants';\nimport type { Tensor } from '../tfjs/types';\n\nexport async function augment(rawCoords, results: Tensor[]) {\n  const t: Record<string, Float32Array> = { // all attention models produce 2d results so it needs to be later augmented with correct z-coords\n    // mesh: results[0], // already have it in rawCoords // output_mesh_identity\n    // flag: results[1], // already processed in parent // conv_faceflag\n    lips: await results.filter((r) => r.size === 160)?.[0]?.data() as Float32Array, // 80 x 2d = 160 // output_lips\n    irisL: await results.filter((r) => r.size === 10)?.[0]?.data() as Float32Array, // 5 x 2d = 10 // output_right_iris\n    eyeL: await results.filter((r) => r.size === 142)?.[0]?.data() as Float32Array, // 71 x 2d = 142 // output_right_eye\n    irisR: await results.filter((r) => r.size === 10)?.[1]?.data() as Float32Array, // 5 x 2d = 10 // output_left_iris\n    eyeR: await results.filter((r) => r.size === 142)?.[1]?.data() as Float32Array, // 71 x 2d = 142// output_left_eye\n  };\n  for (const val of Object.values(t)) {\n    if (!val) return rawCoords; // could not find tensor\n  }\n\n  // augment iris: adds additional 5 keypoints per eye\n  const irisLDepth = constants.LANDMARKS_REFINEMENT_LEFT_EYE_CONFIG.reduce((prev, curr) => prev += rawCoords[curr][2], 0) / constants.LANDMARKS_REFINEMENT_LEFT_EYE_CONFIG.length; // get average z-coord for iris\n  for (let i = 0; i < t.irisL.length / 2; i++) rawCoords.push([t.irisL[2 * i + 0], t.irisL[2 * i + 1], irisLDepth]);\n  const irisRDepth = constants.LANDMARKS_REFINEMENT_RIGHT_EYE_CONFIG.reduce((prev, curr) => prev += rawCoords[curr][2], 0) / constants.LANDMARKS_REFINEMENT_RIGHT_EYE_CONFIG.length; // get average z-coord for iris\n  for (let i = 0; i < t.irisR.length / 2; i++) rawCoords.push([t.irisR[2 * i + 0], t.irisR[2 * i + 1], irisRDepth]);\n\n  // augment eyes: replaces eye keypoints based on heuristic mapping\n  for (let i = 0; i < t.eyeL.length / 2; i++) rawCoords[constants.LANDMARKS_REFINEMENT_LEFT_EYE_CONFIG[i]] = [t.eyeL[2 * i + 0], t.eyeL[2 * i + 1], rawCoords[constants.LANDMARKS_REFINEMENT_LEFT_EYE_CONFIG[i]][2]];\n  for (let i = 0; i < t.eyeR.length / 2; i++) rawCoords[constants.LANDMARKS_REFINEMENT_RIGHT_EYE_CONFIG[i]] = [t.eyeR[2 * i + 0], t.eyeR[2 * i + 1], rawCoords[constants.LANDMARKS_REFINEMENT_RIGHT_EYE_CONFIG[i]][2]];\n\n  // augment lips: replaces eye keypoints based on heuristic mapping\n  for (let i = 0; i < t.lips.length / 2; i++) rawCoords[constants.LANDMARKS_REFINEMENT_LIPS_CONFIG[i]] = [t.lips[2 * i + 0], t.lips[2 * i + 1], rawCoords[constants.LANDMARKS_REFINEMENT_LIPS_CONFIG[i]][2]];\n\n  return rawCoords;\n}\n", "/**\n * BlazeFace, FaceMesh & Iris model implementation\n *\n * Based on:\n * - [**MediaPipe BlazeFace**](https://drive.google.com/file/d/1f39lSzU5Oq-j_OXgS67KfN5wNsoeAZ4V/view)\n * - Facial Spacial Geometry: [**MediaPipe FaceMesh**](https://drive.google.com/file/d/1VFC_wIpw4O7xBOiTgUldl79d9LA-LsnA/view)\n * - Eye Iris Details: [**MediaPipe Iris**](https://drive.google.com/file/d/1bsWbokp9AklH2ANjCfmjqEzzxO1CNbMu/view)\n */\n\nimport { log, now } from '../util/util';\nimport { loadModel } from '../tfjs/load';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as blazeface from './blazeface';\nimport * as util from './facemeshutil';\nimport * as coords from './facemeshcoords';\nimport * as iris from './iris';\nimport * as attention from './attention';\nimport { histogramEqualization } from '../image/enhance';\nimport { env } from '../util/env';\nimport type { GraphModel, Tensor } from '../tfjs/types';\nimport type { FaceResult, FaceLandmark, Point } from '../result';\nimport type { Config } from '../config';\n\ninterface DetectBox { startPoint: Point, endPoint: Point, landmarks: Point[], confidence: number }\n\nconst cache = {\n  boxes: [] as DetectBox[],\n  skipped: Number.MAX_SAFE_INTEGER,\n  timestamp: 0,\n};\n\nlet model: GraphModel | null = null;\nlet inputSize = 0;\n\nexport async function predict(input: Tensor, config: Config): Promise<FaceResult[]> {\n  if (!model?.['executor']) return [];\n  // reset cached boxes\n  const skipTime = (config.face.detector?.skipTime || 0) > (now() - cache.timestamp);\n  const skipFrame = cache.skipped < (config.face.detector?.skipFrames || 0);\n  if (!config.skipAllowed || !skipTime || !skipFrame || cache.boxes.length === 0) {\n    cache.boxes = await blazeface.getBoxes(input, config); // get results from blazeface detector\n    cache.timestamp = now();\n    cache.skipped = 0;\n  } else {\n    cache.skipped++;\n  }\n  const faces: FaceResult[] = [];\n  const newCache: DetectBox[] = [];\n  let id = 0;\n  const size = inputSize;\n  for (let i = 0; i < cache.boxes.length; i++) {\n    const box = cache.boxes[i];\n    let angle = 0;\n    let rotationMatrix;\n    const face: FaceResult = { // init face result\n      id: id++,\n      mesh: [],\n      meshRaw: [],\n      box: [0, 0, 0, 0],\n      boxRaw: [0, 0, 0, 0],\n      score: 0,\n      boxScore: 0,\n      faceScore: 0,\n      // contoursRaw: [],\n      // contours: [],\n      annotations: {} as Record<FaceLandmark, Point[]>,\n    };\n\n    // optional rotation correction based on detector data only if mesh is disabled otherwise perform it later when we have more accurate mesh data. if no rotation correction this function performs crop\n    [angle, rotationMatrix, face.tensor] = util.correctFaceRotation(config.face.detector?.rotation, box, input, config.face.mesh?.enabled ? inputSize : blazeface.size());\n    if (config.filter.equalization) {\n      const equilized = face.tensor ? await histogramEqualization(face.tensor) : undefined;\n      tf.dispose(face.tensor);\n      if (equilized) face.tensor = equilized;\n    }\n    face.boxScore = Math.round(100 * box.confidence) / 100;\n    if (!config.face.mesh?.enabled) { // mesh not enabled, return resuts from detector only\n      face.box = util.clampBox(box, input);\n      face.boxRaw = util.getRawBox(box, input);\n      face.score = face.boxScore;\n      face.mesh = box.landmarks.map((pt) => [\n        ((box.startPoint[0] + box.endPoint[0])) / 2 + ((box.endPoint[0] + box.startPoint[0]) * pt[0] / blazeface.size()),\n        ((box.startPoint[1] + box.endPoint[1])) / 2 + ((box.endPoint[1] + box.startPoint[1]) * pt[1] / blazeface.size()),\n      ]);\n      face.meshRaw = face.mesh.map((pt) => [pt[0] / (input.shape[2] || 0), pt[1] / (input.shape[1] || 0), (pt[2] || 0) / size]);\n      for (const key of Object.keys(coords.blazeFaceLandmarks)) {\n        face.annotations[key] = [face.mesh[coords.blazeFaceLandmarks[key] as number]]; // add annotations\n      }\n    } else if (!model) { // mesh enabled, but not loaded\n      if (config.debug) log('face mesh detection requested, but model is not loaded');\n    } else { // mesh enabled\n      if (config.face.attention?.enabled && !env.kernels.includes('atan2')) {\n        config.face.attention.enabled = false;\n        tf.dispose(face.tensor);\n        return faces;\n      }\n      const results = model.execute(face.tensor as Tensor) as Tensor[];\n      const confidenceT = results.find((t) => t.shape[t.shape.length - 1] === 1) as Tensor;\n      const faceConfidence = await confidenceT.data();\n      face.faceScore = Math.round(100 * faceConfidence[0]) / 100;\n      if (face.faceScore < (config.face.detector?.minConfidence || 1)) { // low confidence in detected mesh\n        box.confidence = face.faceScore; // reset confidence of cached box\n        if (config.face.mesh.keepInvalid) {\n          face.box = util.clampBox(box, input);\n          face.boxRaw = util.getRawBox(box, input);\n          face.score = face.boxScore;\n          face.mesh = box.landmarks.map((pt) => [\n            ((box.startPoint[0] + box.endPoint[0])) / 2 + ((box.endPoint[0] + box.startPoint[0]) * pt[0] / blazeface.size()),\n            ((box.startPoint[1] + box.endPoint[1])) / 2 + ((box.endPoint[1] + box.startPoint[1]) * pt[1] / blazeface.size()),\n          ]);\n          face.meshRaw = face.mesh.map((pt) => [pt[0] / (input.shape[2] || 1), pt[1] / (input.shape[1] || 1), (pt[2] || 0) / size]);\n          for (const key of Object.keys(coords.blazeFaceLandmarks)) {\n            face.annotations[key] = [face.mesh[coords.blazeFaceLandmarks[key] as number]]; // add annotations\n          }\n        }\n      } else {\n        const meshT = results.find((t) => t.shape[t.shape.length - 1] === 1404) as Tensor;\n        const coordsReshaped = tf.reshape(meshT, [-1, 3]);\n        let rawCoords = await coordsReshaped.array();\n        tf.dispose(coordsReshaped);\n        if (config.face.attention?.enabled) {\n          rawCoords = await attention.augment(rawCoords, results); // augment iris results using attention model results\n        } else if (config.face.iris?.enabled) {\n          rawCoords = await iris.augmentIris(rawCoords, face.tensor, inputSize); // run iris model and augment results\n        }\n        face.mesh = util.transformRawCoords(rawCoords, box, angle, rotationMatrix, inputSize); // get processed mesh\n        face.meshRaw = face.mesh.map((pt) => [pt[0] / (input.shape[2] || 0), pt[1] / (input.shape[1] || 0), (pt[2] || 0) / size]);\n        for (const key of Object.keys(coords.meshAnnotations)) face.annotations[key] = coords.meshAnnotations[key].map((index) => face.mesh[index]); // add annotations\n        face.score = face.faceScore;\n        const calculatedBox = { ...util.calculateFaceBox(face.mesh, box), confidence: box.confidence, landmarks: box.landmarks };\n        face.box = util.clampBox(calculatedBox, input);\n        face.boxRaw = util.getRawBox(calculatedBox, input);\n        /*\n        const contoursT = results.find((t) => t.shape[t.shape.length - 1] === 266) as Tensor;\n        const contoursData = contoursT && await contoursT.data(); // 133 x 2d points\n        face.contoursRaw = [];\n        for (let j = 0; j < contoursData.length / 2; j++) face.contoursRaw.push([contoursData[2 * j + 0] / inputSize, contoursData[2 * j + 1] / inputSize]);\n        face.contours = face.contoursRaw.map((c) => [Math.trunc((input.shape[2] || 1) * c[0]), Math.trunc((input.shape[1] || 1) * c[1])]);\n        */\n        newCache.push(calculatedBox);\n      }\n      tf.dispose(results);\n    }\n    if (face.score > (config.face.detector?.minConfidence || 1)) faces.push(face);\n    else tf.dispose(face.tensor);\n  }\n  cache.boxes = newCache; // reset cache\n  return faces;\n}\n\nexport async function load(config: Config): Promise<GraphModel> {\n  if (env.initial) model = null;\n  if (config.face.attention?.enabled && model?.['signature']) {\n    if (Object.keys(model?.['signature']?.outputs || {}).length < 6) model = null;\n  }\n  if (!model) {\n    if (config.face.attention?.enabled) model = await loadModel(config.face.attention.modelPath);\n    else model = await loadModel(config.face.mesh?.modelPath);\n  } else if (config.debug) {\n    log('cached model:', model['modelUrl']);\n  }\n  inputSize = (model['executor'] && model?.inputs?.[0].shape) ? model?.inputs?.[0].shape[2] : 256;\n  return model;\n}\n\nexport const triangulation = coords.TRI468;\nexport const uvmap = coords.UV468;\n", "/**\n * FaceRes model implementation\n *\n * Returns Age, Gender, Descriptor\n * Implements Face simmilarity function\n *\n * Based on: [**HSE-FaceRes**](https://github.com/HSE-asavchenko/HSE_FaceRec_tf)\n */\n\nimport { log, now } from '../util/util';\nimport { env } from '../util/env';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { loadModel } from '../tfjs/load';\nimport { constants } from '../tfjs/constants';\nimport type { Tensor, GraphModel } from '../tfjs/types';\nimport type { Config } from '../config';\nimport type { Gender, Race } from '../result';\n\nexport interface FaceRes { age: number, gender: Gender, genderScore: number, descriptor: number[], race?: { score: number, race: Race }[] }\n\nlet model: GraphModel | null;\nconst last: FaceRes[] = [];\n\nlet lastTime = 0;\nlet lastCount = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nexport async function load(config: Config): Promise<GraphModel> {\n  if (env.initial) model = null;\n  if (!model) model = await loadModel(config.face.description?.modelPath);\n  else if (config.debug) log('cached model:', model['modelUrl']);\n  return model;\n}\n\nexport function enhance(input): Tensor {\n  const tensor = (input.image || input.tensor || input) as Tensor; // input received from detector is already normalized to 0..1, input is also assumed to be straightened\n  if (!model?.inputs[0].shape) return tensor; // model has no shape so no point continuing\n  const crop: Tensor = tf.image.resizeBilinear(tensor, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false);\n  const norm: Tensor = tf.mul(crop, constants.tf255);\n  tf.dispose(crop);\n  return norm;\n  /*\n  // do a tight crop of image and resize it to fit the model\n  const box = [[0.05, 0.15, 0.85, 0.85]]; // empyrical values for top, left, bottom, right\n  const crop = (tensor.shape.length === 3)\n    ? tf.image.cropAndResize(tf.expandDims(tensor, 0), box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]) // add batch dimension if missing\n    : tf.image.cropAndResize(tensor, box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]);\n  */\n  /*\n  // convert to black&white to avoid colorization impact\n  const rgb = [0.2989, 0.5870, 0.1140]; // factors for red/green/blue colors when converting to grayscale: https://www.mathworks.com/help/matlab/ref/rgb2gray.html\n  const [red, green, blue] = tf.split(crop, 3, 3);\n  const redNorm = tf.mul(red, rgb[0]);\n  const greenNorm = tf.mul(green, rgb[1]);\n  const blueNorm = tf.mul(blue, rgb[2]);\n  const grayscale = tf.addN([redNorm, greenNorm, blueNorm]);\n  const merge = tf.stack([grayscale, grayscale, grayscale], 3).squeeze(4);\n  */\n}\n\nexport async function predict(image: Tensor, config: Config, idx: number, count: number): Promise<FaceRes> {\n  const obj: FaceRes = {\n    age: 0 as number,\n    gender: 'unknown' as Gender,\n    genderScore: 0 as number,\n    descriptor: [] as number[],\n  };\n  if (!model?.['executor']) return obj;\n  const skipFrame = skipped < (config.face.description?.skipFrames || 0);\n  const skipTime = (config.face.description?.skipTime || 0) > (now() - lastTime);\n  if (config.skipAllowed && skipFrame && skipTime && (lastCount === count) && (last?.[idx]?.age > 0) && (last?.[idx]?.genderScore > 0)) {\n    skipped++;\n    return last[idx];\n  }\n  skipped = 0;\n  return new Promise(async (resolve) => {\n    if (config.face.description?.enabled) {\n      const enhanced = enhance(image);\n      const resT = model?.execute(enhanced) as Tensor[];\n      lastTime = now();\n      tf.dispose(enhanced);\n      const genderT = resT.find((t) => t.shape[1] === 1) as Tensor;\n      const gender = await genderT.data();\n      const confidence = Math.trunc(200 * Math.abs((gender[0] - 0.5))) / 100;\n      if (confidence > (config.face.description.minConfidence || 0)) {\n        obj.gender = gender[0] <= 0.5 ? 'female' : 'male';\n        obj.genderScore = Math.min(0.99, confidence);\n      }\n      const argmax = tf.argMax(resT.find((t) => t.shape[1] === 100), 1);\n      const ageIdx: number = (await argmax.data())[0];\n      tf.dispose(argmax);\n      const ageT = resT.find((t) => t.shape[1] === 100) as Tensor;\n      const all = await ageT.data();\n      obj.age = Math.round(all[ageIdx - 1] > all[ageIdx + 1] ? 10 * ageIdx - 100 * all[ageIdx - 1] : 10 * ageIdx + 100 * all[ageIdx + 1]) / 10;\n\n      if (Number.isNaN(gender[0]) || Number.isNaN(all[0])) log('faceres error:', { model, result: resT });\n\n      const desc = resT.find((t) => t.shape[1] === 1024);\n      // const reshape = desc.reshape([128, 8]); // reshape large 1024-element descriptor to 128 x 8\n      // const reduce = reshape.logSumExp(1); // reduce 2nd dimension by calculating logSumExp on it which leaves us with 128-element descriptor\n      const descriptor = desc ? await desc.data() : [] as number[];\n      obj.descriptor = Array.from(descriptor);\n      resT.forEach((t) => tf.dispose(t));\n    }\n    last[idx] = obj;\n    lastCount = count;\n    resolve(obj);\n  });\n}\n", "/**\n * GEAR [gender/emotion/age/race] model implementation\n *\n * Based on: [**GEAR Predictor**](https://github.com/Udolf15/GEAR-Predictor)\n */\n\nimport { log, now } from '../util/util';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { loadModel } from '../tfjs/load';\nimport type { Gender, Race } from '../result';\nimport type { Config } from '../config';\nimport type { GraphModel, Tensor } from '../tfjs/types';\nimport { env } from '../util/env';\n\nexport interface GearType { age: number, gender: Gender, genderScore: number, race: { score: number, race: Race }[] }\nlet model: GraphModel | null;\nconst last: GearType[] = [];\nconst raceNames = ['white', 'black', 'asian', 'indian', 'other'];\nconst ageWeights = [15, 23, 28, 35.5, 45.5, 55.5, 65];\nlet lastCount = 0;\nlet lastTime = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nexport async function load(config: Config) {\n  if (env.initial) model = null;\n  if (!model) model = await loadModel(config.face.gear?.modelPath);\n  else if (config.debug) log('cached model:', model['modelUrl']);\n  return model;\n}\n\nexport async function predict(image: Tensor, config: Config, idx: number, count: number): Promise<GearType> {\n  if (!model) return { age: 0, gender: 'unknown', genderScore: 0, race: [] };\n  const skipFrame = skipped < (config.face.gear?.skipFrames || 0);\n  const skipTime = (config.face.gear?.skipTime || 0) > (now() - lastTime);\n  if (config.skipAllowed && skipTime && skipFrame && (lastCount === count) && last[idx]) {\n    skipped++;\n    return last[idx];\n  }\n  skipped = 0;\n  return new Promise(async (resolve) => {\n    if (!model?.inputs[0].shape) return;\n    const t: Record<string, Tensor> = {};\n    // t.resize = tf.image.resizeBilinear(image, [model?.inputs[0].shape[2], model?.inputs[0].shape[1]], false);\n    const box = [[0.0, 0.10, 0.90, 0.90]]; // empyrical values for top, left, bottom, right\n    t.resize = tf.image.cropAndResize(image, box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]);\n    const obj: GearType = { age: 0, gender: 'unknown', genderScore: 0, race: [] };\n    if (config.face.gear?.enabled) [t.age, t.gender, t.race] = model.execute(t.resize, ['age_output', 'gender_output', 'race_output']) as Tensor[];\n    const gender = await t.gender.data();\n    obj.gender = gender[0] > gender[1] ? 'male' : 'female';\n    obj.genderScore = Math.round(100 * (gender[0] > gender[1] ? gender[0] : gender[1])) / 100;\n    const race = await t.race.data();\n    for (let i = 0; i < race.length; i++) {\n      if (race[i] > (config.face.gear?.minConfidence || 0.2)) obj.race.push({ score: Math.round(100 * race[i]) / 100, race: raceNames[i] as Race });\n    }\n    obj.race.sort((a, b) => b.score - a.score);\n    // {0: 'Below20', 1: '21-25', 2: '26-30', 3: '31-40',4: '41-50', 5: '51-60', 6: 'Above60'}\n    const ageDistribution = Array.from(await t.age.data());\n    const ageSorted = ageDistribution.map((a, i) => [ageWeights[i], a]).sort((a, b) => b[1] - a[1]);\n    let age = ageSorted[0][0]; // pick best starting point\n    for (let i = 1; i < ageSorted.length; i++) age += ageSorted[i][1] * (ageSorted[i][0] - age); // adjust with each other choice by weight\n    obj.age = Math.round(10 * age) / 10;\n    Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n    last[idx] = obj;\n    lastCount = count;\n    lastTime = now();\n    resolve(obj);\n  });\n}\n", "import * as tf from '../../dist/tfjs.esm.js';\nimport type { Point } from '../result';\n\nexport function getBoxSize(box) {\n  return [\n    Math.abs(box.endPoint[0] - box.startPoint[0]),\n    Math.abs(box.endPoint[1] - box.startPoint[1]),\n  ];\n}\n\nexport function getBoxCenter(box) {\n  return [\n    box.startPoint[0] + (box.endPoint[0] - box.startPoint[0]) / 2,\n    box.startPoint[1] + (box.endPoint[1] - box.startPoint[1]) / 2,\n  ];\n}\n\nexport function cutBoxFromImageAndResize(box, image, cropSize) {\n  const h = image.shape[1];\n  const w = image.shape[2];\n  const boxes = [[\n    box.startPoint[1] / h,\n    box.startPoint[0] / w,\n    box.endPoint[1] / h,\n    box.endPoint[0] / w,\n  ]];\n  return tf.image.cropAndResize(image, boxes, [0], cropSize);\n}\n\nexport function scaleBoxCoordinates(box, factor) {\n  const startPoint = [box.startPoint[0] * factor[0], box.startPoint[1] * factor[1]] as Point;\n  const endPoint = [box.endPoint[0] * factor[0], box.endPoint[1] * factor[1]] as Point;\n  const palmLandmarks = box.palmLandmarks.map((coord) => {\n    const scaledCoord = [coord[0] * factor[0], coord[1] * factor[1]];\n    return scaledCoord;\n  });\n  return { startPoint, endPoint, palmLandmarks, confidence: box.confidence };\n}\n\nexport function enlargeBox(box, factor = 1.5) {\n  const center = getBoxCenter(box);\n  const size = getBoxSize(box);\n  const newHalfSize = [factor * size[0] / 2, factor * size[1] / 2];\n  const startPoint = [center[0] - newHalfSize[0], center[1] - newHalfSize[1]] as Point;\n  const endPoint = [center[0] + newHalfSize[0], center[1] + newHalfSize[1]] as Point;\n  return { startPoint, endPoint, palmLandmarks: box.palmLandmarks };\n}\n\nexport function squarifyBox(box) {\n  const centers = getBoxCenter(box);\n  const size = getBoxSize(box);\n  const maxEdge = Math.max(...size);\n  const halfSize = maxEdge / 2;\n  const startPoint = [centers[0] - halfSize, centers[1] - halfSize] as Point;\n  const endPoint = [centers[0] + halfSize, centers[1] + halfSize] as Point;\n  return { startPoint, endPoint, palmLandmarks: box.palmLandmarks };\n}\n\nexport function shiftBox(box, shiftFactor) {\n  const boxSize = [\n    box.endPoint[0] - box.startPoint[0],\n    box.endPoint[1] - box.startPoint[1],\n  ];\n  const shiftVector = [boxSize[0] * shiftFactor[0], boxSize[1] * shiftFactor[1]];\n  const startPoint = [box.startPoint[0] + shiftVector[0], box.startPoint[1] + shiftVector[1]] as Point;\n  const endPoint = [box.endPoint[0] + shiftVector[0], box.endPoint[1] + shiftVector[1]] as Point;\n  return { startPoint, endPoint, palmLandmarks: box.palmLandmarks };\n}\n\nexport function normalizeRadians(angle) {\n  return angle - 2 * Math.PI * Math.floor((angle + Math.PI) / (2 * Math.PI));\n}\n\nexport function computeRotation(point1, point2) {\n  const radians = Math.PI / 2 - Math.atan2(-(point2[1] - point1[1]), point2[0] - point1[0]);\n  return normalizeRadians(radians);\n}\n\nexport const buildTranslationMatrix = (x, y) => [[1, 0, x], [0, 1, y], [0, 0, 1]];\n\nexport function dot(v1, v2) {\n  let product = 0;\n  for (let i = 0; i < v1.length; i++) {\n    product += v1[i] * v2[i];\n  }\n  return product;\n}\n\nexport function getColumnFrom2DArr(arr, columnIndex) {\n  const column: number[] = [];\n  for (let i = 0; i < arr.length; i++) {\n    column.push(arr[i][columnIndex]);\n  }\n  return column;\n}\n\nexport function multiplyTransformMatrices(mat1, mat2) {\n  const product: number[][] = [];\n  const size = mat1.length;\n  for (let row = 0; row < size; row++) {\n    product.push([]);\n    for (let col = 0; col < size; col++) {\n      product[row].push(dot(mat1[row], getColumnFrom2DArr(mat2, col)));\n    }\n  }\n  return product;\n}\n\nexport function buildRotationMatrix(rotation, center) {\n  const cosA = Math.cos(rotation);\n  const sinA = Math.sin(rotation);\n  const rotationMatrix = [[cosA, -sinA, 0], [sinA, cosA, 0], [0, 0, 1]];\n  const translationMatrix = buildTranslationMatrix(center[0], center[1]);\n  const translationTimesRotation = multiplyTransformMatrices(translationMatrix, rotationMatrix);\n  const negativeTranslationMatrix = buildTranslationMatrix(-center[0], -center[1]);\n  return multiplyTransformMatrices(translationTimesRotation, negativeTranslationMatrix);\n}\n\nexport function invertTransformMatrix(matrix) {\n  const rotationComponent = [[matrix[0][0], matrix[1][0]], [matrix[0][1], matrix[1][1]]];\n  const translationComponent = [matrix[0][2], matrix[1][2]];\n  const invertedTranslation = [\n    -dot(rotationComponent[0], translationComponent),\n    -dot(rotationComponent[1], translationComponent),\n  ];\n  return [\n    rotationComponent[0].concat(invertedTranslation[0]),\n    rotationComponent[1].concat(invertedTranslation[1]),\n    [0, 0, 1],\n  ];\n}\n\nexport function rotatePoint(homogeneousCoordinate, rotationMatrix) {\n  return [\n    dot(homogeneousCoordinate, rotationMatrix[0]),\n    dot(homogeneousCoordinate, rotationMatrix[1]),\n  ];\n}\n", "/**\n * HandPose model implementation constants\n * See `handpose.ts` for entry point\n */\n\nexport const anchors = [\n  { x: 0.015625, y: 0.015625 },\n  { x: 0.015625, y: 0.015625 },\n  { x: 0.046875, y: 0.015625 },\n  { x: 0.046875, y: 0.015625 },\n  { x: 0.078125, y: 0.015625 },\n  { x: 0.078125, y: 0.015625 },\n  { x: 0.109375, y: 0.015625 },\n  { x: 0.109375, y: 0.015625 },\n  { x: 0.140625, y: 0.015625 },\n  { x: 0.140625, y: 0.015625 },\n  { x: 0.171875, y: 0.015625 },\n  { x: 0.171875, y: 0.015625 },\n  { x: 0.203125, y: 0.015625 },\n  { x: 0.203125, y: 0.015625 },\n  { x: 0.234375, y: 0.015625 },\n  { x: 0.234375, y: 0.015625 },\n  { x: 0.265625, y: 0.015625 },\n  { x: 0.265625, y: 0.015625 },\n  { x: 0.296875, y: 0.015625 },\n  { x: 0.296875, y: 0.015625 },\n  { x: 0.328125, y: 0.015625 },\n  { x: 0.328125, y: 0.015625 },\n  { x: 0.359375, y: 0.015625 },\n  { x: 0.359375, y: 0.015625 },\n  { x: 0.390625, y: 0.015625 },\n  { x: 0.390625, y: 0.015625 },\n  { x: 0.421875, y: 0.015625 },\n  { x: 0.421875, y: 0.015625 },\n  { x: 0.453125, y: 0.015625 },\n  { x: 0.453125, y: 0.015625 },\n  { x: 0.484375, y: 0.015625 },\n  { x: 0.484375, y: 0.015625 },\n  { x: 0.515625, y: 0.015625 },\n  { x: 0.515625, y: 0.015625 },\n  { x: 0.546875, y: 0.015625 },\n  { x: 0.546875, y: 0.015625 },\n  { x: 0.578125, y: 0.015625 },\n  { x: 0.578125, y: 0.015625 },\n  { x: 0.609375, y: 0.015625 },\n  { x: 0.609375, y: 0.015625 },\n  { x: 0.640625, y: 0.015625 },\n  { x: 0.640625, y: 0.015625 },\n  { x: 0.671875, y: 0.015625 },\n  { x: 0.671875, y: 0.015625 },\n  { x: 0.703125, y: 0.015625 },\n  { x: 0.703125, y: 0.015625 },\n  { x: 0.734375, y: 0.015625 },\n  { x: 0.734375, y: 0.015625 },\n  { x: 0.765625, y: 0.015625 },\n  { x: 0.765625, y: 0.015625 },\n  { x: 0.796875, y: 0.015625 },\n  { x: 0.796875, y: 0.015625 },\n  { x: 0.828125, y: 0.015625 },\n  { x: 0.828125, y: 0.015625 },\n  { x: 0.859375, y: 0.015625 },\n  { x: 0.859375, y: 0.015625 },\n  { x: 0.890625, y: 0.015625 },\n  { x: 0.890625, y: 0.015625 },\n  { x: 0.921875, y: 0.015625 },\n  { x: 0.921875, y: 0.015625 },\n  { x: 0.953125, y: 0.015625 },\n  { x: 0.953125, y: 0.015625 },\n  { x: 0.984375, y: 0.015625 },\n  { x: 0.984375, y: 0.015625 },\n  { x: 0.015625, y: 0.046875 },\n  { x: 0.015625, y: 0.046875 },\n  { x: 0.046875, y: 0.046875 },\n  { x: 0.046875, y: 0.046875 },\n  { x: 0.078125, y: 0.046875 },\n  { x: 0.078125, y: 0.046875 },\n  { x: 0.109375, y: 0.046875 },\n  { x: 0.109375, y: 0.046875 },\n  { x: 0.140625, y: 0.046875 },\n  { x: 0.140625, y: 0.046875 },\n  { x: 0.171875, y: 0.046875 },\n  { x: 0.171875, y: 0.046875 },\n  { x: 0.203125, y: 0.046875 },\n  { x: 0.203125, y: 0.046875 },\n  { x: 0.234375, y: 0.046875 },\n  { x: 0.234375, y: 0.046875 },\n  { x: 0.265625, y: 0.046875 },\n  { x: 0.265625, y: 0.046875 },\n  { x: 0.296875, y: 0.046875 },\n  { x: 0.296875, y: 0.046875 },\n  { x: 0.328125, y: 0.046875 },\n  { x: 0.328125, y: 0.046875 },\n  { x: 0.359375, y: 0.046875 },\n  { x: 0.359375, y: 0.046875 },\n  { x: 0.390625, y: 0.046875 },\n  { x: 0.390625, y: 0.046875 },\n  { x: 0.421875, y: 0.046875 },\n  { x: 0.421875, y: 0.046875 },\n  { x: 0.453125, y: 0.046875 },\n  { x: 0.453125, y: 0.046875 },\n  { x: 0.484375, y: 0.046875 },\n  { x: 0.484375, y: 0.046875 },\n  { x: 0.515625, y: 0.046875 },\n  { x: 0.515625, y: 0.046875 },\n  { x: 0.546875, y: 0.046875 },\n  { x: 0.546875, y: 0.046875 },\n  { x: 0.578125, y: 0.046875 },\n  { x: 0.578125, y: 0.046875 },\n  { x: 0.609375, y: 0.046875 },\n  { x: 0.609375, y: 0.046875 },\n  { x: 0.640625, y: 0.046875 },\n  { x: 0.640625, y: 0.046875 },\n  { x: 0.671875, y: 0.046875 },\n  { x: 0.671875, y: 0.046875 },\n  { x: 0.703125, y: 0.046875 },\n  { x: 0.703125, y: 0.046875 },\n  { x: 0.734375, y: 0.046875 },\n  { x: 0.734375, y: 0.046875 },\n  { x: 0.765625, y: 0.046875 },\n  { x: 0.765625, y: 0.046875 },\n  { x: 0.796875, y: 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x: 0.3125, y: 0.3125 },\n  { x: 0.3125, y: 0.3125 },\n  { x: 0.3125, y: 0.3125 },\n  { x: 0.3125, y: 0.3125 },\n  { x: 0.4375, y: 0.3125 },\n  { x: 0.4375, y: 0.3125 },\n  { x: 0.4375, y: 0.3125 },\n  { x: 0.4375, y: 0.3125 },\n  { x: 0.4375, y: 0.3125 },\n  { x: 0.4375, y: 0.3125 },\n  { x: 0.5625, y: 0.3125 },\n  { x: 0.5625, y: 0.3125 },\n  { x: 0.5625, y: 0.3125 },\n  { x: 0.5625, y: 0.3125 },\n  { x: 0.5625, y: 0.3125 },\n  { x: 0.5625, y: 0.3125 },\n  { x: 0.6875, y: 0.3125 },\n  { x: 0.6875, y: 0.3125 },\n  { x: 0.6875, y: 0.3125 },\n  { x: 0.6875, y: 0.3125 },\n  { x: 0.6875, y: 0.3125 },\n  { x: 0.6875, y: 0.3125 },\n  { x: 0.8125, y: 0.3125 },\n  { x: 0.8125, y: 0.3125 },\n  { x: 0.8125, y: 0.3125 },\n  { x: 0.8125, y: 0.3125 },\n  { x: 0.8125, y: 0.3125 },\n  { x: 0.8125, y: 0.3125 },\n  { x: 0.9375, y: 0.3125 },\n  { x: 0.9375, y: 0.3125 },\n  { x: 0.9375, y: 0.3125 },\n  { x: 0.9375, y: 0.3125 },\n  { x: 0.9375, y: 0.3125 },\n  { x: 0.9375, y: 0.3125 },\n  { x: 0.0625, y: 0.4375 },\n  { x: 0.0625, y: 0.4375 },\n  { x: 0.0625, y: 0.4375 },\n  { x: 0.0625, y: 0.4375 },\n  { x: 0.0625, y: 0.4375 },\n  { x: 0.0625, y: 0.4375 },\n  { x: 0.1875, y: 0.4375 },\n  { x: 0.1875, y: 0.4375 },\n  { x: 0.1875, y: 0.4375 },\n  { x: 0.1875, y: 0.4375 },\n  { x: 0.1875, y: 0.4375 },\n  { x: 0.1875, y: 0.4375 },\n  { x: 0.3125, y: 0.4375 },\n  { x: 0.3125, y: 0.4375 },\n  { x: 0.3125, y: 0.4375 },\n  { x: 0.3125, y: 0.4375 },\n  { x: 0.3125, y: 0.4375 },\n  { x: 0.3125, y: 0.4375 },\n  { x: 0.4375, y: 0.4375 },\n  { x: 0.4375, y: 0.4375 },\n  { x: 0.4375, y: 0.4375 },\n  { x: 0.4375, y: 0.4375 },\n  { x: 0.4375, y: 0.4375 },\n  { x: 0.4375, y: 0.4375 },\n  { x: 0.5625, y: 0.4375 },\n  { x: 0.5625, y: 0.4375 },\n  { x: 0.5625, y: 0.4375 },\n  { x: 0.5625, y: 0.4375 },\n  { x: 0.5625, y: 0.4375 },\n  { x: 0.5625, y: 0.4375 },\n  { x: 0.6875, y: 0.4375 },\n  { x: 0.6875, y: 0.4375 },\n  { x: 0.6875, y: 0.4375 },\n  { x: 0.6875, y: 0.4375 },\n  { x: 0.6875, y: 0.4375 },\n  { x: 0.6875, y: 0.4375 },\n  { x: 0.8125, y: 0.4375 },\n  { x: 0.8125, y: 0.4375 },\n  { x: 0.8125, y: 0.4375 },\n  { x: 0.8125, y: 0.4375 },\n  { x: 0.8125, y: 0.4375 },\n  { x: 0.8125, y: 0.4375 },\n  { x: 0.9375, y: 0.4375 },\n  { x: 0.9375, y: 0.4375 },\n  { x: 0.9375, y: 0.4375 },\n  { x: 0.9375, y: 0.4375 },\n  { x: 0.9375, y: 0.4375 },\n  { x: 0.9375, y: 0.4375 },\n  { x: 0.0625, y: 0.5625 },\n  { x: 0.0625, y: 0.5625 },\n  { x: 0.0625, y: 0.5625 },\n  { x: 0.0625, y: 0.5625 },\n  { x: 0.0625, y: 0.5625 },\n  { x: 0.0625, y: 0.5625 },\n  { x: 0.1875, y: 0.5625 },\n  { x: 0.1875, y: 0.5625 },\n  { x: 0.1875, y: 0.5625 },\n  { x: 0.1875, y: 0.5625 },\n  { x: 0.1875, y: 0.5625 },\n  { x: 0.1875, y: 0.5625 },\n  { x: 0.3125, y: 0.5625 },\n  { x: 0.3125, y: 0.5625 },\n  { x: 0.3125, y: 0.5625 },\n  { x: 0.3125, y: 0.5625 },\n  { x: 0.3125, y: 0.5625 },\n  { x: 0.3125, y: 0.5625 },\n  { x: 0.4375, y: 0.5625 },\n  { x: 0.4375, y: 0.5625 },\n  { x: 0.4375, y: 0.5625 },\n  { x: 0.4375, y: 0.5625 },\n  { x: 0.4375, y: 0.5625 },\n  { x: 0.4375, y: 0.5625 },\n  { x: 0.5625, y: 0.5625 },\n  { x: 0.5625, y: 0.5625 },\n  { x: 0.5625, y: 0.5625 },\n  { x: 0.5625, y: 0.5625 },\n  { x: 0.5625, y: 0.5625 },\n  { x: 0.5625, y: 0.5625 },\n  { x: 0.6875, y: 0.5625 },\n  { x: 0.6875, y: 0.5625 },\n  { x: 0.6875, y: 0.5625 },\n  { x: 0.6875, y: 0.5625 },\n  { x: 0.6875, y: 0.5625 },\n  { x: 0.6875, y: 0.5625 },\n  { x: 0.8125, y: 0.5625 },\n  { x: 0.8125, y: 0.5625 },\n  { x: 0.8125, y: 0.5625 },\n  { x: 0.8125, y: 0.5625 },\n  { x: 0.8125, y: 0.5625 },\n  { x: 0.8125, y: 0.5625 },\n  { x: 0.9375, y: 0.5625 },\n  { x: 0.9375, y: 0.5625 },\n  { x: 0.9375, y: 0.5625 },\n  { x: 0.9375, y: 0.5625 },\n  { x: 0.9375, y: 0.5625 },\n  { x: 0.9375, y: 0.5625 },\n  { x: 0.0625, y: 0.6875 },\n  { x: 0.0625, y: 0.6875 },\n  { x: 0.0625, y: 0.6875 },\n  { x: 0.0625, y: 0.6875 },\n  { x: 0.0625, y: 0.6875 },\n  { x: 0.0625, y: 0.6875 },\n  { x: 0.1875, y: 0.6875 },\n  { x: 0.1875, y: 0.6875 },\n  { x: 0.1875, y: 0.6875 },\n  { x: 0.1875, y: 0.6875 },\n  { x: 0.1875, y: 0.6875 },\n  { x: 0.1875, y: 0.6875 },\n  { x: 0.3125, y: 0.6875 },\n  { x: 0.3125, y: 0.6875 },\n  { x: 0.3125, y: 0.6875 },\n  { x: 0.3125, y: 0.6875 },\n  { x: 0.3125, y: 0.6875 },\n  { x: 0.3125, y: 0.6875 },\n  { x: 0.4375, y: 0.6875 },\n  { x: 0.4375, y: 0.6875 },\n  { x: 0.4375, y: 0.6875 },\n  { x: 0.4375, y: 0.6875 },\n  { x: 0.4375, y: 0.6875 },\n  { x: 0.4375, y: 0.6875 },\n  { x: 0.5625, y: 0.6875 },\n  { x: 0.5625, y: 0.6875 },\n  { x: 0.5625, y: 0.6875 },\n  { x: 0.5625, y: 0.6875 },\n  { x: 0.5625, y: 0.6875 },\n  { x: 0.5625, y: 0.6875 },\n  { x: 0.6875, y: 0.6875 },\n  { x: 0.6875, y: 0.6875 },\n  { x: 0.6875, y: 0.6875 },\n  { x: 0.6875, y: 0.6875 },\n  { x: 0.6875, y: 0.6875 },\n  { x: 0.6875, y: 0.6875 },\n  { x: 0.8125, y: 0.6875 },\n  { x: 0.8125, y: 0.6875 },\n  { x: 0.8125, y: 0.6875 },\n  { x: 0.8125, y: 0.6875 },\n  { x: 0.8125, y: 0.6875 },\n  { x: 0.8125, y: 0.6875 },\n  { x: 0.9375, y: 0.6875 },\n  { x: 0.9375, y: 0.6875 },\n  { x: 0.9375, y: 0.6875 },\n  { x: 0.9375, y: 0.6875 },\n  { x: 0.9375, y: 0.6875 },\n  { x: 0.9375, y: 0.6875 },\n  { x: 0.0625, y: 0.8125 },\n  { x: 0.0625, y: 0.8125 },\n  { x: 0.0625, y: 0.8125 },\n  { x: 0.0625, y: 0.8125 },\n  { x: 0.0625, y: 0.8125 },\n  { x: 0.0625, y: 0.8125 },\n  { x: 0.1875, y: 0.8125 },\n  { x: 0.1875, y: 0.8125 },\n  { x: 0.1875, y: 0.8125 },\n  { x: 0.1875, y: 0.8125 },\n  { x: 0.1875, y: 0.8125 },\n  { x: 0.1875, y: 0.8125 },\n  { x: 0.3125, y: 0.8125 },\n  { x: 0.3125, y: 0.8125 },\n  { x: 0.3125, y: 0.8125 },\n  { x: 0.3125, y: 0.8125 },\n  { x: 0.3125, y: 0.8125 },\n  { x: 0.3125, y: 0.8125 },\n  { x: 0.4375, y: 0.8125 },\n  { x: 0.4375, y: 0.8125 },\n  { x: 0.4375, y: 0.8125 },\n  { x: 0.4375, y: 0.8125 },\n  { x: 0.4375, y: 0.8125 },\n  { x: 0.4375, y: 0.8125 },\n  { x: 0.5625, y: 0.8125 },\n  { x: 0.5625, y: 0.8125 },\n  { x: 0.5625, y: 0.8125 },\n  { x: 0.5625, y: 0.8125 },\n  { x: 0.5625, y: 0.8125 },\n  { x: 0.5625, y: 0.8125 },\n  { x: 0.6875, y: 0.8125 },\n  { x: 0.6875, y: 0.8125 },\n  { x: 0.6875, y: 0.8125 },\n  { x: 0.6875, y: 0.8125 },\n  { x: 0.6875, y: 0.8125 },\n  { x: 0.6875, y: 0.8125 },\n  { x: 0.8125, y: 0.8125 },\n  { x: 0.8125, y: 0.8125 },\n  { x: 0.8125, y: 0.8125 },\n  { x: 0.8125, y: 0.8125 },\n  { x: 0.8125, y: 0.8125 },\n  { x: 0.8125, y: 0.8125 },\n  { x: 0.9375, y: 0.8125 },\n  { x: 0.9375, y: 0.8125 },\n  { x: 0.9375, y: 0.8125 },\n  { x: 0.9375, y: 0.8125 },\n  { x: 0.9375, y: 0.8125 },\n  { x: 0.9375, y: 0.8125 },\n  { x: 0.0625, y: 0.9375 },\n  { x: 0.0625, y: 0.9375 },\n  { x: 0.0625, y: 0.9375 },\n  { x: 0.0625, y: 0.9375 },\n  { x: 0.0625, y: 0.9375 },\n  { x: 0.0625, y: 0.9375 },\n  { x: 0.1875, y: 0.9375 },\n  { x: 0.1875, y: 0.9375 },\n  { x: 0.1875, y: 0.9375 },\n  { x: 0.1875, y: 0.9375 },\n  { x: 0.1875, y: 0.9375 },\n  { x: 0.1875, y: 0.9375 },\n  { x: 0.3125, y: 0.9375 },\n  { x: 0.3125, y: 0.9375 },\n  { x: 0.3125, y: 0.9375 },\n  { x: 0.3125, y: 0.9375 },\n  { x: 0.3125, y: 0.9375 },\n  { x: 0.3125, y: 0.9375 },\n  { x: 0.4375, y: 0.9375 },\n  { x: 0.4375, y: 0.9375 },\n  { x: 0.4375, y: 0.9375 },\n  { x: 0.4375, y: 0.9375 },\n  { x: 0.4375, y: 0.9375 },\n  { x: 0.4375, y: 0.9375 },\n  { x: 0.5625, y: 0.9375 },\n  { x: 0.5625, y: 0.9375 },\n  { x: 0.5625, y: 0.9375 },\n  { x: 0.5625, y: 0.9375 },\n  { x: 0.5625, y: 0.9375 },\n  { x: 0.5625, y: 0.9375 },\n  { x: 0.6875, y: 0.9375 },\n  { x: 0.6875, y: 0.9375 },\n  { x: 0.6875, y: 0.9375 },\n  { x: 0.6875, y: 0.9375 },\n  { x: 0.6875, y: 0.9375 },\n  { x: 0.6875, y: 0.9375 },\n  { x: 0.8125, y: 0.9375 },\n  { x: 0.8125, y: 0.9375 },\n  { x: 0.8125, y: 0.9375 },\n  { x: 0.8125, y: 0.9375 },\n  { x: 0.8125, y: 0.9375 },\n  { x: 0.8125, y: 0.9375 },\n  { x: 0.9375, y: 0.9375 },\n  { x: 0.9375, y: 0.9375 },\n  { x: 0.9375, y: 0.9375 },\n  { x: 0.9375, y: 0.9375 },\n  { x: 0.9375, y: 0.9375 },\n  { x: 0.9375, y: 0.9375 },\n];\n", "/**\n * HandPose model implementation\n * See `handpose.ts` for entry point\n */\n\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as util from './handposeutil';\nimport * as anchors from './handposeanchors';\nimport { constants } from '../tfjs/constants';\nimport type { Tensor, GraphModel } from '../tfjs/types';\nimport type { Point } from '../result';\nimport type { Config } from '../config';\n\nexport class HandDetector {\n  model: GraphModel;\n  anchors: number[][];\n  anchorsTensor: Tensor;\n  inputSize: number;\n  inputSizeTensor: Tensor;\n  doubleInputSizeTensor: Tensor;\n\n  constructor(model: GraphModel) {\n    this.model = model;\n    this.anchors = anchors.anchors.map((anchor) => [anchor.x, anchor.y]);\n    this.anchorsTensor = tf.tensor2d(this.anchors);\n    this.inputSize = this?.model?.inputs?.[0]?.shape?.[2] || 0;\n    this.inputSizeTensor = tf.tensor1d([this.inputSize, this.inputSize]);\n    this.doubleInputSizeTensor = tf.tensor1d([this.inputSize * 2, this.inputSize * 2]);\n  }\n\n  normalizeBoxes(boxes) {\n    const t: Record<string, Tensor> = {};\n    t.boxOffsets = tf.slice(boxes, [0, 0], [-1, 2]);\n    t.boxSizes = tf.slice(boxes, [0, 2], [-1, 2]);\n    t.div = tf.div(t.boxOffsets, this.inputSizeTensor);\n    t.boxCenterPoints = tf.add(t.div, this.anchorsTensor);\n    t.halfBoxSizes = tf.div(t.boxSizes, this.doubleInputSizeTensor);\n    t.sub = tf.sub(t.boxCenterPoints, t.halfBoxSizes);\n    t.startPoints = tf.mul(t.sub, this.inputSizeTensor);\n    t.add = tf.add(t.boxCenterPoints, t.halfBoxSizes);\n    t.endPoints = tf.mul(t.add, this.inputSizeTensor);\n    const res = tf.concat2d([t.startPoints, t.endPoints], 1);\n    Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n    return res as Tensor;\n  }\n\n  normalizeLandmarks(rawPalmLandmarks, index: number) {\n    const t: Record<string, Tensor> = {};\n    t.reshape = tf.reshape(rawPalmLandmarks, [-1, 7, 2]);\n    t.div = tf.div(t.reshape, this.inputSizeTensor);\n    t.landmarks = tf.add(t.div, this.anchors[index] ? this.anchors[index] : 0);\n    const res = tf.mul(t.landmarks, this.inputSizeTensor);\n    Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n    return res as Tensor;\n  }\n\n  async predict(input: Tensor, config: Config): Promise<{ startPoint: Point; endPoint: Point, palmLandmarks: Point[]; confidence: number }[]> {\n    const t: Record<string, Tensor> = {};\n    t.resize = tf.image.resizeBilinear(input, [this.inputSize, this.inputSize]);\n    t.div = tf.div(t.resize, constants.tf127);\n    t.image = tf.sub(t.div, constants.tf1);\n    t.batched = this.model.execute(t.image) as Tensor;\n    t.predictions = tf.squeeze(t.batched);\n    t.slice = tf.slice(t.predictions, [0, 0], [-1, 1]);\n    t.sigmoid = tf.sigmoid(t.slice);\n    t.scores = tf.squeeze(t.sigmoid);\n    const scores = await t.scores.data();\n    t.boxes = tf.slice(t.predictions, [0, 1], [-1, 4]);\n    t.norm = this.normalizeBoxes(t.boxes);\n    // box detection is flaky so we look for 3x boxes than we need results\n    t.nms = await tf.image.nonMaxSuppressionAsync(t.norm, t.scores, 3 * (config.hand?.maxDetected || 1), config.hand.iouThreshold, config.hand.minConfidence);\n    const nms = await t.nms.array() as number[];\n    const hands: { startPoint: Point; endPoint: Point; palmLandmarks: Point[]; confidence: number }[] = [];\n    for (const index of nms) {\n      const p: Record<string, Tensor> = {};\n      p.box = tf.slice(t.norm, [index, 0], [1, -1]);\n      p.slice = tf.slice(t.predictions, [index, 5], [1, 14]);\n      p.norm = this.normalizeLandmarks(p.slice, index);\n      p.palmLandmarks = tf.reshape(p.norm, [-1, 2]);\n      const box = await p.box.data();\n      const startPoint = box.slice(0, 2) as unknown as Point;\n      const endPoint = box.slice(2, 4) as unknown as Point;\n      const palmLandmarks = await p.palmLandmarks.array();\n      const hand = { startPoint, endPoint, palmLandmarks, confidence: scores[index] };\n      const scaled = util.scaleBoxCoordinates(hand, [(input.shape[2] || 1) / this.inputSize, (input.shape[1] || 0) / this.inputSize]);\n      hands.push(scaled);\n      Object.keys(p).forEach((tensor) => tf.dispose(p[tensor]));\n    }\n    Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n    return hands;\n  }\n}\n", "/**\n * HandPose model implementation\n * See `handpose.ts` for entry point\n */\n\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as util from './handposeutil';\nimport type * as detector from './handposedetector';\nimport { constants } from '../tfjs/constants';\nimport type { Tensor, GraphModel } from '../tfjs/types';\nimport { env } from '../util/env';\nimport { now } from '../util/util';\nimport type { Point } from '../result';\n\nconst palmBoxEnlargeFactor = 5; // default 3\nconst handBoxEnlargeFactor = 1.65; // default 1.65\nconst palmLandmarkIds = [0, 5, 9, 13, 17, 1, 2];\nconst palmLandmarksPalmBase = 0;\nconst palmLandmarksMiddleFingerBase = 2;\nlet lastTime = 0;\n\nexport class HandPipeline {\n  handDetector: detector.HandDetector;\n  handPoseModel: GraphModel;\n  inputSize: number;\n  storedBoxes: ({ startPoint: Point; endPoint: Point; palmLandmarks: Point[]; confidence: number } | null)[];\n  skipped: number;\n  detectedHands: number;\n\n  constructor(handDetector, handPoseModel) {\n    this.handDetector = handDetector;\n    this.handPoseModel = handPoseModel;\n    this.inputSize = this.handPoseModel?.inputs?.[0].shape?.[2] || 0;\n    this.storedBoxes = [];\n    this.skipped = Number.MAX_SAFE_INTEGER;\n    this.detectedHands = 0;\n  }\n\n  calculateLandmarksBoundingBox(landmarks) { // eslint-disable-line class-methods-use-this\n    const xs = landmarks.map((d) => d[0]);\n    const ys = landmarks.map((d) => d[1]);\n    const startPoint = [Math.min(...xs), Math.min(...ys)];\n    const endPoint = [Math.max(...xs), Math.max(...ys)];\n    return { startPoint, endPoint };\n  }\n\n  getBoxForPalmLandmarks(palmLandmarks, rotationMatrix) {\n    const rotatedPalmLandmarks = palmLandmarks.map((coord) => util.rotatePoint([...coord, 1], rotationMatrix));\n    const boxAroundPalm = this.calculateLandmarksBoundingBox(rotatedPalmLandmarks);\n    return util.enlargeBox(util.squarifyBox(boxAroundPalm), palmBoxEnlargeFactor);\n  }\n\n  getBoxForHandLandmarks(landmarks) {\n    const boundingBox = this.calculateLandmarksBoundingBox(landmarks);\n    const boxAroundHand = util.enlargeBox(util.squarifyBox(boundingBox), handBoxEnlargeFactor);\n    boxAroundHand.palmLandmarks = [];\n    for (let i = 0; i < palmLandmarkIds.length; i++) {\n      boxAroundHand.palmLandmarks.push(landmarks[palmLandmarkIds[i]].slice(0, 2));\n    }\n    return boxAroundHand;\n  }\n\n  transformRawCoords(rawCoords, box2, angle, rotationMatrix) {\n    const boxSize = util.getBoxSize(box2);\n    const scaleFactor = [boxSize[0] / this.inputSize, boxSize[1] / this.inputSize, (boxSize[0] + boxSize[1]) / this.inputSize / 2];\n    const coordsScaled = rawCoords.map((coord) => [\n      scaleFactor[0] * (coord[0] - this.inputSize / 2),\n      scaleFactor[1] * (coord[1] - this.inputSize / 2),\n      scaleFactor[2] * coord[2],\n    ]);\n    const coordsRotationMatrix = util.buildRotationMatrix(angle, [0, 0]);\n    const coordsRotated = coordsScaled.map((coord) => {\n      const rotated = util.rotatePoint(coord, coordsRotationMatrix);\n      return [...rotated, coord[2]];\n    });\n    const inverseRotationMatrix = util.invertTransformMatrix(rotationMatrix);\n    const boxCenter = [...util.getBoxCenter(box2), 1];\n    const originalBoxCenter = [\n      util.dot(boxCenter, inverseRotationMatrix[0]),\n      util.dot(boxCenter, inverseRotationMatrix[1]),\n    ];\n    return coordsRotated.map((coord) => [\n      Math.trunc(coord[0] + originalBoxCenter[0]),\n      Math.trunc(coord[1] + originalBoxCenter[1]),\n      Math.trunc(coord[2]),\n    ]);\n  }\n\n  async estimateHands(image, config) {\n    let useFreshBox = false;\n\n    // run new detector every skipFrames\n    let boxes;\n    const skipTime = (config.hand.skipTime || 0) > (now() - lastTime);\n    const skipFrame = this.skipped < (config.hand.skipFrames || 0);\n    if (config.skipAllowed && skipTime && skipFrame) {\n      boxes = await this.handDetector.predict(image, config);\n      this.skipped = 0;\n    }\n    if (config.skipAllowed) this.skipped++;\n\n    // if detector result count doesn't match current working set, use it to reset current working set\n    if (boxes && (boxes.length > 0) && ((boxes.length !== this.detectedHands) && (this.detectedHands !== config.hand.maxDetected) || !config.hand.landmarks)) {\n      this.detectedHands = 0;\n      this.storedBoxes = [...boxes];\n      // for (const possible of boxes) this.storedBoxes.push(possible);\n      if (this.storedBoxes.length > 0) useFreshBox = true;\n    }\n    const hands: { landmarks: Point[], confidence: number, boxConfidence: number, fingerConfidence: number, box: { topLeft: Point, bottomRight: Point } }[] = [];\n\n    // go through working set of boxes\n    for (let i = 0; i < this.storedBoxes.length; i++) {\n      const currentBox = this.storedBoxes[i];\n      if (!currentBox) continue;\n      if (config.hand.landmarks) {\n        const angle = config.hand.rotation ? util.computeRotation(currentBox.palmLandmarks[palmLandmarksPalmBase], currentBox.palmLandmarks[palmLandmarksMiddleFingerBase]) : 0;\n        const palmCenter = util.getBoxCenter(currentBox);\n        const palmCenterNormalized = [palmCenter[0] / image.shape[2], palmCenter[1] / image.shape[1]];\n        const rotatedImage = config.hand.rotation && env.kernels.includes('rotatewithoffset') ? tf.image.rotateWithOffset(image, angle, 0, palmCenterNormalized) : image.clone();\n        const rotationMatrix = util.buildRotationMatrix(-angle, palmCenter);\n        const newBox = useFreshBox ? this.getBoxForPalmLandmarks(currentBox.palmLandmarks, rotationMatrix) : currentBox;\n        const croppedInput = util.cutBoxFromImageAndResize(newBox, rotatedImage, [this.inputSize, this.inputSize]);\n        const handImage = tf.div(croppedInput, constants.tf255);\n        tf.dispose(croppedInput);\n        tf.dispose(rotatedImage);\n        const [confidenceT, keypoints] = this.handPoseModel.execute(handImage) as Tensor[];\n        lastTime = now();\n        tf.dispose(handImage);\n        const confidence = (await confidenceT.data())[0];\n        tf.dispose(confidenceT);\n        if (confidence >= config.hand.minConfidence / 4) {\n          const keypointsReshaped = tf.reshape(keypoints, [-1, 3]);\n          const rawCoords = await keypointsReshaped.array();\n          tf.dispose(keypoints);\n          tf.dispose(keypointsReshaped);\n          const coords = this.transformRawCoords(rawCoords, newBox, angle, rotationMatrix);\n          const nextBoundingBox = this.getBoxForHandLandmarks(coords);\n          this.storedBoxes[i] = { ...nextBoundingBox, confidence };\n          const result = {\n            landmarks: coords,\n            confidence,\n            boxConfidence: currentBox.confidence,\n            fingerConfidence: confidence,\n            box: { topLeft: nextBoundingBox.startPoint, bottomRight: nextBoundingBox.endPoint },\n          };\n          hands.push(result);\n        } else {\n          this.storedBoxes[i] = null;\n        }\n        tf.dispose(keypoints);\n      } else {\n        // const enlarged = box.enlargeBox(box.squarifyBox(box.shiftBox(currentBox, HAND_BOX_SHIFT_VECTOR)), handBoxEnlargeFactor);\n        const enlarged = util.enlargeBox(util.squarifyBox(currentBox), handBoxEnlargeFactor);\n        const result = {\n          confidence: currentBox.confidence,\n          boxConfidence: currentBox.confidence,\n          fingerConfidence: 0,\n          box: { topLeft: enlarged.startPoint, bottomRight: enlarged.endPoint },\n          landmarks: [],\n        };\n        hands.push(result);\n      }\n    }\n    this.storedBoxes = this.storedBoxes.filter((a) => a !== null);\n    this.detectedHands = hands.length;\n    if (hands.length > config.hand.maxDetected) hands.length = config.hand.maxDetected;\n    return hands;\n  }\n}\n", "/**\n * FingerPose algorithm implementation\n * See `fingerpose.ts` for entry point\n */\n\nexport const Finger = {\n  thumb: 0,\n  index: 1,\n  middle: 2,\n  ring: 3,\n  pinky: 4,\n  all: [0, 1, 2, 3, 4], // just for convenience\n  nameMapping: { 0: 'thumb', 1: 'index', 2: 'middle', 3: 'ring', 4: 'pinky' },\n  // Describes mapping of joints based on the 21 points returned by handpose.\n  // [0]     Palm\n  // [1-4]   Thumb\n  // [5-8]   Index\n  // [9-12]  Middle\n  // [13-16] Ring\n  // [17-20] Pinky\n  pointsMapping: {\n    0: [[0, 1], [1, 2], [2, 3], [3, 4]],\n    1: [[0, 5], [5, 6], [6, 7], [7, 8]],\n    2: [[0, 9], [9, 10], [10, 11], [11, 12]],\n    3: [[0, 13], [13, 14], [14, 15], [15, 16]],\n    4: [[0, 17], [17, 18], [18, 19], [19, 20]],\n  },\n  getName: (value) => Finger.nameMapping[value],\n  getPoints: (value) => Finger.pointsMapping[value],\n};\n\nexport const FingerCurl = {\n  none: 0,\n  half: 1,\n  full: 2,\n  nameMapping: { 0: 'none', 1: 'half', 2: 'full' },\n  getName: (value) => FingerCurl.nameMapping[value],\n};\n\nexport const FingerDirection = {\n  verticalUp: 0,\n  verticalDown: 1,\n  horizontalLeft: 2,\n  horizontalRight: 3,\n  diagonalUpRight: 4,\n  diagonalUpLeft: 5,\n  diagonalDownRight: 6,\n  diagonalDownLeft: 7,\n  nameMapping: { 0: 'verticalUp', 1: 'verticalDown', 2: 'horizontalLeft', 3: 'horizontalRight', 4: 'diagonalUpRight', 5: 'diagonalUpLeft', 6: 'diagonalDownRight', 7: 'diagonalDownLeft' },\n  getName: (value) => FingerDirection.nameMapping[value],\n};\n\nexport class FingerGesture {\n  name;\n  curls;\n  directions;\n  weights;\n  weightsRelative;\n\n  constructor(name) {\n    // name (should be unique)\n    this.name = name;\n    this.curls = {};\n    this.directions = {};\n    this.weights = [1.0, 1.0, 1.0, 1.0, 1.0];\n    this.weightsRelative = [1.0, 1.0, 1.0, 1.0, 1.0];\n  }\n\n  curl(finger, curl, confidence) {\n    if (typeof this.curls[finger] === 'undefined') this.curls[finger] = [];\n    this.curls[finger].push([curl, confidence]);\n  }\n\n  direction(finger, position, confidence) {\n    if (!this.directions[finger]) this.directions[finger] = [];\n    this.directions[finger].push([position, confidence]);\n  }\n\n  weight(finger, weight) {\n    this.weights[finger] = weight;\n    // recalculate relative weights\n    const total = this.weights.reduce((a, b) => a + b, 0);\n    this.weightsRelative = this.weights.map((el) => el * 5 / total);\n  }\n\n  matchAgainst(detectedCurls, detectedDirections) {\n    let confidence = 0.0;\n    // look at the detected curl of each finger and compare with\n    // the expected curl of this finger inside current gesture\n    for (const fingerIdx in detectedCurls) {\n      const detectedCurl = detectedCurls[fingerIdx];\n      const expectedCurls = this.curls[fingerIdx];\n      if (typeof expectedCurls === 'undefined') {\n        // no curl description available for this finger\n        // add default confidence of \"1\"\n        confidence += this.weightsRelative[fingerIdx];\n        continue;\n      }\n      // compare to each possible curl of this specific finger\n      for (const [expectedCurl, score] of expectedCurls) {\n        if (detectedCurl === expectedCurl) {\n          confidence += score * this.weightsRelative[fingerIdx];\n          break;\n        }\n      }\n    }\n    // same for detected direction of each finger\n    for (const fingerIdx in detectedDirections) {\n      const detectedDirection = detectedDirections[fingerIdx];\n      const expectedDirections = this.directions[fingerIdx];\n      if (typeof expectedDirections === 'undefined') {\n        // no direction description available for this finger\n        // add default confidence of \"1\"\n        confidence += this.weightsRelative[fingerIdx];\n        continue;\n      }\n      // compare to each possible direction of this specific finger\n      for (const [expectedDirection, score] of expectedDirections) {\n        if (detectedDirection === expectedDirection) {\n          confidence += score * this.weightsRelative[fingerIdx];\n          break;\n        }\n      }\n    }\n    return confidence / 10;\n  }\n}\n", "/**\n * FingerPose algorithm implementation\n * See `fingerpose.ts` for entry point\n */\n\nimport { Finger, FingerCurl, FingerDirection, FingerGesture } from './fingerdef';\n\nexport const { thumb, index, middle, ring, pinky } = Finger;\nexport const { none, half, full } = FingerCurl;\nexport const { verticalUp, verticalDown, horizontalLeft, horizontalRight, diagonalUpRight, diagonalUpLeft, diagonalDownRight, diagonalDownLeft } = FingerDirection;\n\n// describe thumbs up gesture \uD83D\uDC4D\nconst ThumbsUp = new FingerGesture('thumbs up');\nThumbsUp.curl(thumb, none, 1.0);\nThumbsUp.direction(thumb, verticalUp, 1.0);\nThumbsUp.direction(thumb, diagonalUpLeft, 0.25);\nThumbsUp.direction(thumb, diagonalUpRight, 0.25);\nfor (const finger of [Finger.index, Finger.middle, Finger.ring, Finger.pinky]) {\n  ThumbsUp.curl(finger, full, 1.0);\n  ThumbsUp.direction(finger, horizontalLeft, 1.0);\n  ThumbsUp.direction(finger, horizontalRight, 1.0);\n}\n\n// describe Victory gesture \u270C\uFE0F\nconst Victory = new FingerGesture('victory');\nVictory.curl(thumb, half, 0.5);\nVictory.curl(thumb, none, 0.5);\nVictory.direction(thumb, verticalUp, 1.0);\nVictory.direction(thumb, diagonalUpLeft, 1.0);\nVictory.curl(index, none, 1.0);\nVictory.direction(index, verticalUp, 0.75);\nVictory.direction(index, diagonalUpLeft, 1.0);\nVictory.curl(middle, none, 1.0);\nVictory.direction(middle, verticalUp, 1.0);\nVictory.direction(middle, diagonalUpLeft, 0.75);\nVictory.curl(ring, full, 1.0);\nVictory.direction(ring, verticalUp, 0.2);\nVictory.direction(ring, diagonalUpLeft, 1.0);\nVictory.direction(ring, horizontalLeft, 0.2);\nVictory.curl(pinky, full, 1.0);\nVictory.direction(pinky, verticalUp, 0.2);\nVictory.direction(pinky, diagonalUpLeft, 1.0);\nVictory.direction(pinky, horizontalLeft, 0.2);\nVictory.weight(index, 2);\nVictory.weight(middle, 2);\n\n// describe Point gesture \u270C\uFE0F\nconst Point = new FingerGesture('point');\nPoint.curl(thumb, full, 1.0);\nPoint.curl(index, none, 0.5);\nPoint.curl(middle, full, 0.5);\nPoint.curl(ring, full, 0.5);\nPoint.curl(pinky, full, 0.5);\nPoint.weight(index, 2);\nPoint.weight(middle, 2);\n\n// describe Point gesture \u270C\uFE0F\nconst MiddleFinger = new FingerGesture('middle finger');\nMiddleFinger.curl(thumb, none, 1.0);\nMiddleFinger.curl(index, full, 0.5);\nMiddleFinger.curl(middle, full, 0.5);\nMiddleFinger.curl(ring, full, 0.5);\nMiddleFinger.curl(pinky, full, 0.5);\nMiddleFinger.weight(index, 2);\nMiddleFinger.weight(middle, 2);\n\n// describe Open Palm gesture \u270C\uFE0F\nconst OpenPalm = new FingerGesture('open palm');\nOpenPalm.curl(thumb, none, 0.75);\nOpenPalm.curl(index, none, 0.75);\nOpenPalm.curl(middle, none, 0.75);\nOpenPalm.curl(ring, none, 0.75);\nOpenPalm.curl(pinky, none, 0.75);\n\nexport default [ThumbsUp, Victory, Point, MiddleFinger, OpenPalm];\n", "/**\n * FingerPose algorithm implementation constants\n *\n * Based on: [**FingerPose***](https://github.com/andypotato/fingerpose)\n */\n\n/* eslint-disable camelcase */\n\nimport { Finger, FingerCurl, FingerDirection } from './fingerdef';\nimport Gestures from '../hand/fingergesture';\n\nconst minConfidence = 0.7;\nconst options = {\n  // curl estimation\n  HALF_CURL_START_LIMIT: 60.0,\n  NO_CURL_START_LIMIT: 130.0,\n  // direction estimation\n  DISTANCE_VOTE_POWER: 1.1,\n  SINGLE_ANGLE_VOTE_POWER: 0.9,\n  TOTAL_ANGLE_VOTE_POWER: 1.6,\n};\n\nfunction calculateSlope(point1x, point1y, point2x, point2y) {\n  const value = (point1y - point2y) / (point1x - point2x);\n  let slope = Math.atan(value) * 180 / Math.PI;\n  if (slope <= 0) slope = -slope;\n  else if (slope > 0) slope = 180 - slope;\n  return slope;\n}\n\n// point1, point2 are 2d or 3d point arrays (xy[z])\n// returns either a single scalar (2d) or array of two slopes (3d)\nfunction getSlopes(point1, point2) {\n  if (!point1 || !point2) return [0, 0];\n  const slopeXY = calculateSlope(point1[0], point1[1], point2[0], point2[1]);\n  if (point1.length === 2) return slopeXY;\n  const slopeYZ = calculateSlope(point1[1], point1[2], point2[1], point2[2]);\n  return [slopeXY, slopeYZ];\n}\n\nfunction angleOrientationAt(angle, weightageAt = 1.0) {\n  let isVertical = 0;\n  let isDiagonal = 0;\n  let isHorizontal = 0;\n  if (angle >= 75.0 && angle <= 105.0) isVertical = 1 * weightageAt;\n  else if (angle >= 25.0 && angle <= 155.0) isDiagonal = 1 * weightageAt;\n  else isHorizontal = 1 * weightageAt;\n  return [isVertical, isDiagonal, isHorizontal];\n}\n\nfunction estimateFingerCurl(startPoint, midPoint, endPoint) {\n  const start_mid_x_dist = startPoint[0] - midPoint[0];\n  const start_end_x_dist = startPoint[0] - endPoint[0];\n  const mid_end_x_dist = midPoint[0] - endPoint[0];\n  const start_mid_y_dist = startPoint[1] - midPoint[1];\n  const start_end_y_dist = startPoint[1] - endPoint[1];\n  const mid_end_y_dist = midPoint[1] - endPoint[1];\n  const start_mid_z_dist = startPoint[2] - midPoint[2];\n  const start_end_z_dist = startPoint[2] - endPoint[2];\n  const mid_end_z_dist = midPoint[2] - endPoint[2];\n  const start_mid_dist = Math.sqrt(start_mid_x_dist * start_mid_x_dist + start_mid_y_dist * start_mid_y_dist + start_mid_z_dist * start_mid_z_dist);\n  const start_end_dist = Math.sqrt(start_end_x_dist * start_end_x_dist + start_end_y_dist * start_end_y_dist + start_end_z_dist * start_end_z_dist);\n  const mid_end_dist = Math.sqrt(mid_end_x_dist * mid_end_x_dist + mid_end_y_dist * mid_end_y_dist + mid_end_z_dist * mid_end_z_dist);\n  let cos_in = (mid_end_dist * mid_end_dist + start_mid_dist * start_mid_dist - start_end_dist * start_end_dist) / (2 * mid_end_dist * start_mid_dist);\n  if (cos_in > 1.0) cos_in = 1.0;\n  else if (cos_in < -1.0) cos_in = -1.0;\n  let angleOfCurve = Math.acos(cos_in);\n  angleOfCurve = (57.2958 * angleOfCurve) % 180;\n  let fingerCurl;\n  if (angleOfCurve > options.NO_CURL_START_LIMIT) fingerCurl = FingerCurl.none;\n  else if (angleOfCurve > options.HALF_CURL_START_LIMIT) fingerCurl = FingerCurl.half;\n  else fingerCurl = FingerCurl.full;\n  return fingerCurl;\n}\n\nfunction estimateHorizontalDirection(start_end_x_dist, start_mid_x_dist, mid_end_x_dist, max_dist_x) {\n  let estimatedDirection;\n  if (max_dist_x === Math.abs(start_end_x_dist)) {\n    if (start_end_x_dist > 0) estimatedDirection = FingerDirection.horizontalLeft;\n    else estimatedDirection = FingerDirection.horizontalRight;\n  } else if (max_dist_x === Math.abs(start_mid_x_dist)) {\n    if (start_mid_x_dist > 0) estimatedDirection = FingerDirection.horizontalLeft;\n    else estimatedDirection = FingerDirection.horizontalRight;\n  } else {\n    if (mid_end_x_dist > 0) estimatedDirection = FingerDirection.horizontalLeft;\n    else estimatedDirection = FingerDirection.horizontalRight;\n  }\n  return estimatedDirection;\n}\n\nfunction estimateVerticalDirection(start_end_y_dist, start_mid_y_dist, mid_end_y_dist, max_dist_y) {\n  let estimatedDirection;\n  if (max_dist_y === Math.abs(start_end_y_dist)) {\n    if (start_end_y_dist < 0) estimatedDirection = FingerDirection.verticalDown;\n    else estimatedDirection = FingerDirection.verticalUp;\n  } else if (max_dist_y === Math.abs(start_mid_y_dist)) {\n    if (start_mid_y_dist < 0) estimatedDirection = FingerDirection.verticalDown;\n    else estimatedDirection = FingerDirection.verticalUp;\n  } else {\n    if (mid_end_y_dist < 0) estimatedDirection = FingerDirection.verticalDown;\n    else estimatedDirection = FingerDirection.verticalUp;\n  }\n  return estimatedDirection;\n}\n\nfunction estimateDiagonalDirection(start_end_y_dist, start_mid_y_dist, mid_end_y_dist, max_dist_y, start_end_x_dist, start_mid_x_dist, mid_end_x_dist, max_dist_x) {\n  let estimatedDirection;\n  const reqd_vertical_direction = estimateVerticalDirection(start_end_y_dist, start_mid_y_dist, mid_end_y_dist, max_dist_y);\n  const reqd_horizontal_direction = estimateHorizontalDirection(start_end_x_dist, start_mid_x_dist, mid_end_x_dist, max_dist_x);\n  if (reqd_vertical_direction === FingerDirection.verticalUp) {\n    if (reqd_horizontal_direction === FingerDirection.horizontalLeft) estimatedDirection = FingerDirection.diagonalUpLeft;\n    else estimatedDirection = FingerDirection.diagonalUpRight;\n  } else {\n    if (reqd_horizontal_direction === FingerDirection.horizontalLeft) estimatedDirection = FingerDirection.diagonalDownLeft;\n    else estimatedDirection = FingerDirection.diagonalDownRight;\n  }\n  return estimatedDirection;\n}\n\nfunction calculateFingerDirection(startPoint, midPoint, endPoint, fingerSlopes) {\n  const start_mid_x_dist = startPoint[0] - midPoint[0];\n  const start_end_x_dist = startPoint[0] - endPoint[0];\n  const mid_end_x_dist = midPoint[0] - endPoint[0];\n  const start_mid_y_dist = startPoint[1] - midPoint[1];\n  const start_end_y_dist = startPoint[1] - endPoint[1];\n  const mid_end_y_dist = midPoint[1] - endPoint[1];\n  const max_dist_x = Math.max(Math.abs(start_mid_x_dist), Math.abs(start_end_x_dist), Math.abs(mid_end_x_dist));\n  const max_dist_y = Math.max(Math.abs(start_mid_y_dist), Math.abs(start_end_y_dist), Math.abs(mid_end_y_dist));\n  let voteVertical = 0.0;\n  let voteDiagonal = 0.0;\n  let voteHorizontal = 0.0;\n  const start_end_x_y_dist_ratio = max_dist_y / (max_dist_x + 0.00001);\n  if (start_end_x_y_dist_ratio > 1.5) voteVertical += options.DISTANCE_VOTE_POWER;\n  else if (start_end_x_y_dist_ratio > 0.66) voteDiagonal += options.DISTANCE_VOTE_POWER;\n  else voteHorizontal += options.DISTANCE_VOTE_POWER;\n  const start_mid_dist = Math.sqrt(start_mid_x_dist * start_mid_x_dist + start_mid_y_dist * start_mid_y_dist);\n  const start_end_dist = Math.sqrt(start_end_x_dist * start_end_x_dist + start_end_y_dist * start_end_y_dist);\n  const mid_end_dist = Math.sqrt(mid_end_x_dist * mid_end_x_dist + mid_end_y_dist * mid_end_y_dist);\n  const max_dist = Math.max(start_mid_dist, start_end_dist, mid_end_dist);\n  let calc_start_point_x = startPoint[0];\n  let calc_start_point_y = startPoint[1];\n  let calc_end_point_x = endPoint[0];\n  let calc_end_point_y = endPoint[1];\n  if (max_dist === start_mid_dist) {\n    calc_end_point_x = endPoint[0];\n    calc_end_point_y = endPoint[1];\n  } else if (max_dist === mid_end_dist) {\n    calc_start_point_x = midPoint[0];\n    calc_start_point_y = midPoint[1];\n  }\n  const calcStartPoint = [calc_start_point_x, calc_start_point_y];\n  const calcEndPoint = [calc_end_point_x, calc_end_point_y];\n  const totalAngle = getSlopes(calcStartPoint, calcEndPoint);\n  const votes = angleOrientationAt(totalAngle, options.TOTAL_ANGLE_VOTE_POWER);\n  voteVertical += votes[0];\n  voteDiagonal += votes[1];\n  voteHorizontal += votes[2];\n  for (const fingerSlope of fingerSlopes) {\n    const fingerVotes = angleOrientationAt(fingerSlope, options.SINGLE_ANGLE_VOTE_POWER);\n    voteVertical += fingerVotes[0];\n    voteDiagonal += fingerVotes[1];\n    voteHorizontal += fingerVotes[2];\n  }\n  // in case of tie, highest preference goes to Vertical,\n  // followed by horizontal and then diagonal\n  let estimatedDirection;\n  if (voteVertical === Math.max(voteVertical, voteDiagonal, voteHorizontal)) {\n    estimatedDirection = estimateVerticalDirection(start_end_y_dist, start_mid_y_dist, mid_end_y_dist, max_dist_y);\n  } else if (voteHorizontal === Math.max(voteDiagonal, voteHorizontal)) {\n    estimatedDirection = estimateHorizontalDirection(start_end_x_dist, start_mid_x_dist, mid_end_x_dist, max_dist_x);\n  } else {\n    estimatedDirection = estimateDiagonalDirection(start_end_y_dist, start_mid_y_dist, mid_end_y_dist, max_dist_y, start_end_x_dist, start_mid_x_dist, mid_end_x_dist, max_dist_x);\n  }\n  return estimatedDirection;\n}\n\nfunction estimate(landmarks) {\n  // step 1: calculate slopes\n  const slopesXY: number[][] = [];\n  const slopesYZ: number[][] = [];\n  const fingerCurls: number[] = [];\n  const fingerDirections: number[] = [];\n  if (!landmarks) return { curls: fingerCurls, directions: fingerDirections };\n\n  // step 1: calculate slopes\n  for (const finger of Finger.all) {\n    const points = Finger.getPoints(finger);\n    const slopeAtXY: number[] = [];\n    const slopeAtYZ: number[] = [];\n    for (const point of points) {\n      const point1 = landmarks[point[0]];\n      const point2 = landmarks[point[1]];\n      // calculate single slope\n      const slopes = getSlopes(point1, point2);\n      const slopeXY = slopes[0];\n      const slopeYZ = slopes[1];\n      slopeAtXY.push(slopeXY);\n      slopeAtYZ.push(slopeYZ);\n    }\n    slopesXY.push(slopeAtXY);\n    slopesYZ.push(slopeAtYZ);\n  }\n\n  // step 2: calculate orientations\n  for (const finger of Finger.all) {\n    // start finger predictions from palm - except for thumb\n    const pointIndexAt = (finger === Finger.thumb) ? 1 : 0;\n    const fingerPointsAt = Finger.getPoints(finger);\n    const startPoint = landmarks[fingerPointsAt[pointIndexAt][0]];\n    const midPoint = landmarks[fingerPointsAt[pointIndexAt + 1][1]];\n    const endPoint = landmarks[fingerPointsAt[3][1]];\n    // check if finger is curled\n    const fingerCurled = estimateFingerCurl(startPoint, midPoint, endPoint);\n    const fingerPosition = calculateFingerDirection(startPoint, midPoint, endPoint, slopesXY[finger].slice(pointIndexAt));\n    fingerCurls[finger] = fingerCurled;\n    fingerDirections[finger] = fingerPosition;\n  }\n  return { curls: fingerCurls, directions: fingerDirections };\n}\n\nexport function analyze(keypoints) { // get estimations of curl / direction for each finger\n  if (!keypoints || keypoints.length === 0) return null;\n  const estimatorRes = estimate(keypoints);\n  const landmarks = {};\n  for (const fingerIdx of Finger.all) {\n    landmarks[Finger.getName(fingerIdx)] = {\n      curl: FingerCurl.getName(estimatorRes.curls[fingerIdx]),\n      direction: FingerDirection.getName(estimatorRes.directions[fingerIdx]),\n    };\n  }\n  return landmarks;\n}\n\nexport function match(keypoints) { // compare gesture description to each known gesture\n  const poses: { name: string, confidence: number }[] = [];\n  if (!keypoints || keypoints.length === 0) return poses;\n  const estimatorRes = estimate(keypoints);\n  for (const gesture of Gestures) {\n    const confidence = gesture.matchAgainst(estimatorRes.curls, estimatorRes.directions);\n    if (confidence >= minConfidence) poses.push({ name: gesture.name, confidence });\n  }\n  return poses;\n}\n", "/**\n * HandPose model implementation\n *\n * Based on: [**MediaPipe HandPose**](https://drive.google.com/file/d/1sv4sSb9BSNVZhLzxXJ0jBv9DqD-4jnAz/view)\n */\n\nimport { log } from '../util/util';\nimport * as handdetector from './handposedetector';\nimport * as handpipeline from './handposepipeline';\nimport * as fingerPose from './fingerpose';\nimport { loadModel } from '../tfjs/load';\nimport type { HandResult, Box, Point } from '../result';\nimport type { Tensor, GraphModel } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\n\nconst meshAnnotations = {\n  thumb: [1, 2, 3, 4],\n  index: [5, 6, 7, 8],\n  middle: [9, 10, 11, 12],\n  ring: [13, 14, 15, 16],\n  pinky: [17, 18, 19, 20],\n  palm: [0],\n};\n\nlet handDetectorModel: GraphModel | null;\nlet handPoseModel: GraphModel | null;\nlet handPipeline: handpipeline.HandPipeline;\n\nexport async function predict(input: Tensor, config: Config): Promise<HandResult[]> {\n  const predictions = await handPipeline.estimateHands(input, config);\n  if (!predictions) return [];\n  const hands: HandResult[] = [];\n  for (let i = 0; i < predictions.length; i++) {\n    const annotations = {};\n    if (predictions[i].landmarks) {\n      for (const key of Object.keys(meshAnnotations)) {\n        annotations[key] = meshAnnotations[key].map((index) => predictions[i].landmarks[index]);\n      }\n    }\n    const keypoints = predictions[i].landmarks as unknown as Point[];\n    let box: Box = [Number.MAX_SAFE_INTEGER, Number.MAX_SAFE_INTEGER, 0, 0]; // maximums so conditionals work\n    let boxRaw: Box = [0, 0, 0, 0];\n    if (keypoints && keypoints.length > 0) { // if we have landmarks, calculate box based on landmarks\n      for (const pt of keypoints) {\n        if (pt[0] < box[0]) box[0] = pt[0];\n        if (pt[1] < box[1]) box[1] = pt[1];\n        if (pt[0] > box[2]) box[2] = pt[0];\n        if (pt[1] > box[3]) box[3] = pt[1];\n      }\n      box[2] -= box[0];\n      box[3] -= box[1];\n      boxRaw = [box[0] / (input.shape[2] || 0), box[1] / (input.shape[1] || 0), box[2] / (input.shape[2] || 0), box[3] / (input.shape[1] || 0)];\n    } else { // otherwise use box from prediction\n      box = predictions[i].box ? [\n        Math.trunc(Math.max(0, predictions[i].box.topLeft[0])),\n        Math.trunc(Math.max(0, predictions[i].box.topLeft[1])),\n        Math.trunc(Math.min((input.shape[2] || 0), predictions[i].box.bottomRight[0]) - Math.max(0, predictions[i].box.topLeft[0])),\n        Math.trunc(Math.min((input.shape[1] || 0), predictions[i].box.bottomRight[1]) - Math.max(0, predictions[i].box.topLeft[1])),\n      ] : [0, 0, 0, 0];\n      boxRaw = [\n        (predictions[i].box.topLeft[0]) / (input.shape[2] || 0),\n        (predictions[i].box.topLeft[1]) / (input.shape[1] || 0),\n        (predictions[i].box.bottomRight[0] - predictions[i].box.topLeft[0]) / (input.shape[2] || 0),\n        (predictions[i].box.bottomRight[1] - predictions[i].box.topLeft[1]) / (input.shape[1] || 0),\n      ];\n    }\n    const landmarks = fingerPose.analyze(keypoints);\n    hands.push({\n      id: i,\n      score: Math.round(100 * predictions[i].confidence) / 100,\n      boxScore: Math.round(100 * predictions[i].boxConfidence) / 100,\n      fingerScore: Math.round(100 * predictions[i].fingerConfidence) / 100,\n      label: 'hand',\n      box,\n      boxRaw,\n      keypoints,\n      annotations: annotations as HandResult['annotations'],\n      landmarks: landmarks as HandResult['landmarks'],\n    });\n  }\n  return hands;\n}\n\nexport async function load(config: Config): Promise<[GraphModel | null, GraphModel | null]> {\n  if (env.initial) {\n    handDetectorModel = null;\n    handPoseModel = null;\n  }\n  if (!handDetectorModel || !handPoseModel) {\n    [handDetectorModel, handPoseModel] = await Promise.all([\n      config.hand.enabled ? loadModel(config.hand.detector?.modelPath) : null,\n      config.hand.landmarks ? loadModel(config.hand.skeleton?.modelPath) : null,\n    ]);\n  } else {\n    if (config.debug) log('cached model:', handDetectorModel['modelUrl']);\n    if (config.debug) log('cached model:', handPoseModel['modelUrl']);\n  }\n  const handDetector = handDetectorModel ? new handdetector.HandDetector(handDetectorModel) : undefined;\n  if (handDetector && handPoseModel) handPipeline = new handpipeline.HandPipeline(handDetector, handPoseModel);\n  return [handDetectorModel, handPoseModel];\n}\n", "/**\n * HandTrack model implementation\n *\n * Based on:\n * - Hand Detection & Skeleton: [**MediaPipe HandPose**](https://drive.google.com/file/d/1sv4sSb9BSNVZhLzxXJ0jBv9DqD-4jnAz/view)\n * - Hand Tracking: [**HandTracking**](https://github.com/victordibia/handtracking)\n */\n\nimport { log, now } from '../util/util';\nimport * as box from '../util/box';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { loadModel } from '../tfjs/load';\nimport type { HandResult, HandType, Box, Point } from '../result';\nimport type { GraphModel, Tensor } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\nimport * as fingerPose from './fingerpose';\nimport { fakeOps } from '../tfjs/backend';\nimport { constants } from '../tfjs/constants';\n\nconst models: [GraphModel | null, GraphModel | null] = [null, null];\nconst modelOutputNodes = ['StatefulPartitionedCall/Postprocessor/Slice', 'StatefulPartitionedCall/Postprocessor/ExpandDims_1'];\n\nconst inputSize = [[0, 0], [0, 0]];\n\nconst classes = ['hand', 'fist', 'pinch', 'point', 'face', 'tip', 'pinchtip'];\nconst faceIndex = 4;\n\nconst boxExpandFact = 1.6;\nconst maxDetectorResolution = 512;\nconst detectorExpandFact = 1.4;\n\nlet skipped = Number.MAX_SAFE_INTEGER;\nlet lastTime = 0;\nlet outputSize: [number, number] = [0, 0];\n\ninterface HandDetectResult {\n  id: number,\n  score: number,\n  box: Box,\n  boxRaw: Box,\n  label: HandType,\n}\n\nconst cache: {\n  boxes: HandDetectResult[],\n  hands: HandResult[];\n} = {\n  boxes: [],\n  hands: [],\n};\n\nconst fingerMap = {\n  /*\n  thumb: [0, 1, 2, 3, 4],\n  index: [0, 5, 6, 7, 8],\n  middle: [0, 9, 10, 11, 12],\n  ring: [0, 13, 14, 15, 16],\n  pinky: [0, 17, 18, 19, 20],\n  palm: [0],\n  */\n  thumb: [1, 2, 3, 4],\n  index: [5, 6, 7, 8],\n  middle: [9, 10, 11, 12],\n  ring: [13, 14, 15, 16],\n  pinky: [17, 18, 19, 20],\n  base: [0],\n  palm: [0, 17, 13, 9, 5, 1, 0],\n};\n\nexport async function loadDetect(config: Config): Promise<GraphModel> {\n  // HandTrack Model: Original: <https://github.com/victordibia/handtracking> TFJS Port: <https://github.com/victordibia/handtrack.js/>\n  if (env.initial) models[0] = null;\n  if (!models[0]) {\n    // handtrack model has some kernel ops defined in model but those are never referenced and non-existent in tfjs\n    // ideally need to prune the model itself\n    fakeOps(['tensorlistreserve', 'enter', 'tensorlistfromtensor', 'merge', 'loopcond', 'switch', 'exit', 'tensorliststack', 'nextiteration', 'tensorlistsetitem', 'tensorlistgetitem', 'reciprocal', 'shape', 'split', 'where'], config);\n    models[0] = await loadModel(config.hand.detector?.modelPath);\n    const inputs = models[0]['executor'] ? Object.values(models[0].modelSignature['inputs']) : undefined;\n    inputSize[0][0] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[1].size) : 0;\n    inputSize[0][1] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 0;\n  } else if (config.debug) log('cached model:', models[0]['modelUrl']);\n  return models[0];\n}\n\nexport async function loadSkeleton(config: Config): Promise<GraphModel> {\n  if (env.initial) models[1] = null;\n  if (!models[1]) {\n    models[1] = await loadModel(config.hand.skeleton?.modelPath);\n    const inputs = models[1]['executor'] ? Object.values(models[1].modelSignature['inputs']) : undefined;\n    inputSize[1][0] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[1].size) : 0;\n    inputSize[1][1] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 0;\n  } else if (config.debug) log('cached model:', models[1]['modelUrl']);\n  return models[1];\n}\n\nexport async function load(config: Config): Promise<[GraphModel | null, GraphModel | null]> {\n  if (!models[0]) await loadDetect(config);\n  if (!models[1]) await loadSkeleton(config);\n  return models;\n}\n\nasync function detectHands(input: Tensor, config: Config): Promise<HandDetectResult[]> {\n  const hands: HandDetectResult[] = [];\n  if (!input || !models[0]) return hands;\n  const t: Record<string, Tensor> = {};\n  const ratio = (input.shape[2] || 1) / (input.shape[1] || 1);\n  const height = Math.min(Math.round((input.shape[1] || 0) / 8) * 8, maxDetectorResolution); // use dynamic input size but cap at 512\n  const width = Math.round(height * ratio / 8) * 8;\n  t.resize = tf.image.resizeBilinear(input, [height, width]); // todo: resize with padding\n  t.cast = tf.cast(t.resize, 'int32');\n  [t.rawScores, t.rawBoxes] = await models[0].executeAsync(t.cast, modelOutputNodes) as Tensor[];\n  t.boxes = tf.squeeze(t.rawBoxes, [0, 2]);\n  t.scores = tf.squeeze(t.rawScores, [0]);\n  const classScores: Tensor[] = tf.unstack(t.scores, 1); // unstack scores based on classes\n  tf.dispose(classScores[faceIndex]);\n  classScores.splice(faceIndex, 1); // remove faces\n  t.filtered = tf.stack(classScores, 1); // restack\n  tf.dispose(classScores);\n  // t.filtered = t.scores;\n  t.max = tf.max(t.filtered, 1); // max overall score\n  t.argmax = tf.argMax(t.filtered, 1); // class index of max overall score\n  let id = 0;\n  t.nms = await tf.image.nonMaxSuppressionAsync(t.boxes, t.max, (config.hand.maxDetected || 0) + 1, config.hand.iouThreshold || 0, config.hand.minConfidence || 1);\n  const nms = await t.nms.data();\n  const scores = await t.max.data();\n  const classNum = await t.argmax.data();\n  for (const nmsIndex of Array.from(nms)) { // generates results for each class\n    const boxSlice = tf.slice(t.boxes, nmsIndex, 1);\n    const boxYX = await boxSlice.data();\n    tf.dispose(boxSlice);\n    const boxData: Box = [boxYX[1], boxYX[0], boxYX[3] - boxYX[1], boxYX[2] - boxYX[0]]; // yx box reshaped to standard box\n    const boxRaw: Box = box.scale(boxData, detectorExpandFact);\n    const boxFull: Box = [Math.trunc(boxData[0] * outputSize[0]), Math.trunc(boxData[1] * outputSize[1]), Math.trunc(boxData[2] * outputSize[0]), Math.trunc(boxData[3] * outputSize[1])];\n    const score = scores[nmsIndex];\n    const label = classes[classNum[nmsIndex]] as HandType;\n    const hand: HandDetectResult = { id: id++, score, box: boxFull, boxRaw, label };\n    hands.push(hand);\n  }\n  Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n  hands.sort((a, b) => b.score - a.score);\n  if (hands.length > (config.hand.maxDetected || 1)) hands.length = (config.hand.maxDetected || 1);\n  return hands;\n}\n\nasync function detectFingers(input: Tensor, h: HandDetectResult, config: Config): Promise<HandResult> {\n  const hand: HandResult = { // initial values inherited from hand detect\n    id: h.id,\n    score: Math.round(100 * h.score) / 100,\n    boxScore: Math.round(100 * h.score) / 100,\n    fingerScore: 0,\n    box: h.box,\n    boxRaw: h.boxRaw,\n    label: h.label,\n    keypoints: [],\n    landmarks: {} as HandResult['landmarks'],\n    annotations: {} as HandResult['annotations'],\n  };\n  if (input && models[1] && config.hand.landmarks && h.score > (config.hand.minConfidence || 0)) {\n    const t: Record<string, Tensor> = {};\n    const boxCrop = [h.boxRaw[1], h.boxRaw[0], h.boxRaw[3] + h.boxRaw[1], h.boxRaw[2] + h.boxRaw[0]] as Box;\n    t.crop = tf.image.cropAndResize(input, [boxCrop], [0], [inputSize[1][0], inputSize[1][1]], 'bilinear');\n    t.div = tf.div(t.crop, constants.tf255);\n    [t.score, t.keypoints] = models[1].execute(t.div, ['Identity_1', 'Identity']) as Tensor[];\n    const rawScore = (await t.score.data())[0];\n    const score = (100 - Math.trunc(100 / (1 + Math.exp(rawScore)))) / 100; // reverse sigmoid value\n    if (score >= (config.hand.minConfidence || 0)) {\n      hand.fingerScore = score;\n      t.reshaped = tf.reshape(t.keypoints, [-1, 3]);\n      const coordsData: Point[] = await t.reshaped.array() as Point[];\n      const coordsRaw: Point[] = coordsData.map((kpt) => [kpt[0] / inputSize[1][1], kpt[1] / inputSize[1][0], (kpt[2] || 0)]);\n      const coordsNorm: Point[] = coordsRaw.map((kpt) => [kpt[0] * h.boxRaw[2], kpt[1] * h.boxRaw[3], (kpt[2] || 0)]);\n      hand.keypoints = (coordsNorm).map((kpt) => [outputSize[0] * (kpt[0] + h.boxRaw[0]), outputSize[1] * (kpt[1] + h.boxRaw[1]), (kpt[2] || 0)]);\n      hand.landmarks = fingerPose.analyze(hand.keypoints) as HandResult['landmarks']; // calculate finger gestures\n      for (const key of Object.keys(fingerMap)) { // map keypoints to per-finger annotations\n        hand.annotations[key] = fingerMap[key].map((index: number) => (hand.landmarks && hand.keypoints[index] ? hand.keypoints[index] : null));\n      }\n    }\n    Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n  }\n  return hand;\n}\n\nexport async function predict(input: Tensor, config: Config): Promise<HandResult[]> {\n  if (!models[0]?.['executor'] || !models[1]?.['executor'] || !models[0].inputs[0].shape || !models[1].inputs[0].shape) return []; // something is wrong with the model\n  outputSize = [input.shape[2] || 0, input.shape[1] || 0];\n  skipped++; // increment skip frames\n  const skipTime = (config.hand.skipTime || 0) > (now() - lastTime);\n  const skipFrame = skipped < (config.hand.skipFrames || 0);\n  if (config.skipAllowed && skipTime && skipFrame) {\n    return cache.hands; // return cached results without running anything\n  }\n  return new Promise(async (resolve) => {\n    const skipTimeExtended = 3 * (config.hand.skipTime || 0) > (now() - lastTime);\n    const skipFrameExtended = skipped < 3 * (config.hand.skipFrames || 0);\n    if (config.skipAllowed && cache.hands.length === config.hand.maxDetected) { // we have all detected hands so we're definitely skipping\n      cache.hands = await Promise.all(cache.boxes.map((handBox) => detectFingers(input, handBox, config)));\n    } else if (config.skipAllowed && skipTimeExtended && skipFrameExtended && cache.hands.length > 0) { // we have some cached results: maybe not enough but anyhow continue for bit longer\n      cache.hands = await Promise.all(cache.boxes.map((handBox) => detectFingers(input, handBox, config)));\n    } else { // finally rerun detector\n      cache.boxes = await detectHands(input, config);\n      lastTime = now();\n      cache.hands = await Promise.all(cache.boxes.map((handBox) => detectFingers(input, handBox, config)));\n      skipped = 0;\n    }\n\n    const oldCache = [...cache.boxes];\n    cache.boxes.length = 0; // reset cache\n    if (config.cacheSensitivity > 0) {\n      for (let i = 0; i < cache.hands.length; i++) {\n        const boxKpt = box.square(cache.hands[i].keypoints, outputSize);\n        if (boxKpt.box[2] / (input.shape[2] || 1) > 0.05 && boxKpt.box[3] / (input.shape[1] || 1) > 0.05 && cache.hands[i].fingerScore && cache.hands[i].fingerScore > (config.hand.minConfidence || 0)) {\n          const boxScale = box.scale(boxKpt.box, boxExpandFact);\n          const boxScaleRaw = box.scale(boxKpt.boxRaw, boxExpandFact);\n          // const boxCrop = box.crop(boxScaleRaw);\n          cache.boxes.push({ ...oldCache[i], box: boxScale, boxRaw: boxScaleRaw });\n        }\n      }\n    }\n    for (let i = 0; i < cache.hands.length; i++) { // replace detected boxes with calculated boxes in final output\n      const bbox = box.calc(cache.hands[i].keypoints, outputSize);\n      cache.hands[i].box = bbox.box;\n      cache.hands[i].boxRaw = bbox.boxRaw;\n    }\n    resolve(cache.hands);\n  });\n}\n", "/**\n * InsightFace model implementation\n *\n * Based on: [**DeepInsight InsightFace**](https://github.com/deepinsight/insightface)\n *\n * Alternative face embedding detection\n */\n\nimport { log, now } from '../util/util';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { loadModel } from '../tfjs/load';\nimport type { Tensor, GraphModel } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\n\nlet model: GraphModel | null;\nconst last: number[][] = [];\nlet lastCount = 0;\nlet lastTime = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nexport async function load(config: Config): Promise<GraphModel> {\n  if (env.initial) model = null;\n  if (!model) model = await loadModel(config.face['insightface'].modelPath);\n  else if (config.debug) log('cached model:', model['modelUrl']);\n  return model;\n}\n\nexport async function predict(input: Tensor, config: Config, idx, count): Promise<number[]> {\n  if (!model?.['executor']) return [];\n  const skipFrame = skipped < (config.face['insightface']?.skipFrames || 0);\n  const skipTime = (config.face['insightface']?.skipTime || 0) > (now() - lastTime);\n  if (config.skipAllowed && skipTime && skipFrame && (lastCount === count) && last[idx]) {\n    skipped++;\n    return last[idx];\n  }\n  return new Promise(async (resolve) => {\n    let data: number[] = [];\n    if (config.face['insightface']?.enabled && model?.inputs[0].shape) {\n      const t: Record<string, Tensor> = {};\n      t.crop = tf.image.resizeBilinear(input, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false); // just resize to fit the embedding model\n      // do a tight crop of image and resize it to fit the model\n      // const box = [[0.05, 0.15, 0.85, 0.85]]; // empyrical values for top, left, bottom, right\n      // t.crop = tf.image.cropAndResize(input, box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]);\n      t.data = model.execute(t.crop) as Tensor;\n      const output = await t.data.data();\n      data = Array.from(output); // convert typed array to simple array\n      Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n    }\n    last[idx] = data;\n    lastCount = count;\n    lastTime = now();\n    resolve(data);\n  });\n}\n", "/**\n * Anti-spoofing model implementation\n */\n\nimport { log, now } from '../util/util';\nimport { loadModel } from '../tfjs/load';\nimport type { Config } from '../config';\nimport type { GraphModel, Tensor } from '../tfjs/types';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { env } from '../util/env';\n\nlet model: GraphModel | null;\nconst cached: number[] = [];\nlet skipped = Number.MAX_SAFE_INTEGER;\nlet lastCount = 0;\nlet lastTime = 0;\n\nexport async function load(config: Config): Promise<GraphModel> {\n  if (env.initial) model = null;\n  if (!model) model = await loadModel(config.face.liveness?.modelPath);\n  else if (config.debug) log('cached model:', model['modelUrl']);\n  return model;\n}\n\nexport async function predict(image: Tensor, config: Config, idx: number, count: number): Promise<number> {\n  if (!model?.['executor']) return 0;\n  const skipTime = (config.face.liveness?.skipTime || 0) > (now() - lastTime);\n  const skipFrame = skipped < (config.face.liveness?.skipFrames || 0);\n  if (config.skipAllowed && skipTime && skipFrame && (lastCount === count) && cached[idx]) {\n    skipped++;\n    return cached[idx];\n  }\n  skipped = 0;\n  return new Promise(async (resolve) => {\n    const resize = tf.image.resizeBilinear(image, [model?.inputs[0].shape ? model.inputs[0].shape[2] : 0, model?.inputs[0].shape ? model.inputs[0].shape[1] : 0], false);\n    const res = model?.execute(resize) as Tensor;\n    const num = (await res.data())[0];\n    cached[idx] = Math.round(100 * num) / 100;\n    lastCount = count;\n    lastTime = now();\n    tf.dispose([resize, res]);\n    resolve(cached[idx]);\n  });\n}\n", "/**\n * Image segmentation for body detection model\n *\n * Based on:\n * - [**MediaPipe Meet**](https://drive.google.com/file/d/1lnP1bRi9CSqQQXUHa13159vLELYDgDu0/preview)\n */\n\nimport { log } from '../util/util';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { loadModel } from '../tfjs/load';\nimport { constants } from '../tfjs/constants';\nimport type { GraphModel, Tensor } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\n\nlet model: GraphModel;\n\nexport async function load(config: Config): Promise<GraphModel> {\n  if (!model || env.initial) model = await loadModel(config.segmentation.modelPath);\n  else if (config.debug) log('cached model:', model['modelUrl']);\n  return model;\n}\n\nexport async function predict(input: Tensor, config: Config): Promise<Tensor | null> {\n  if (!model) model = await load(config);\n  if (!model?.['executor'] || !model?.inputs?.[0].shape) return null; // something is wrong with the model\n  const t: Record<string, Tensor> = {};\n  t.resize = tf.image.resizeBilinear(input, [model.inputs[0].shape ? model.inputs[0].shape[1] : 0, model.inputs[0].shape ? model.inputs[0].shape[2] : 0], false);\n  t.norm = tf.div(t.resize, constants.tf255);\n  t.res = model.execute(t.norm) as Tensor;\n  t.squeeze = tf.squeeze(t.res, 0);\n  // t.softmax = tf.softmax(t.squeeze); // model meet has two channels for fg and bg\n  [t.bgRaw, t.fgRaw] = tf.unstack(t.squeeze, 2);\n  // t.bg = tf.softmax(t.bgRaw); // we can ignore bg channel\n  t.fg = tf.softmax(t.fgRaw);\n  t.mul = tf.mul(t.fg, constants.tf255);\n  t.expand = tf.expandDims(t.mul, 2);\n  t.output = tf.image.resizeBilinear(t.expand, [input.shape[1], input.shape[2]]);\n  let rgba: Tensor;\n  switch (config.segmentation.mode || 'default') {\n    case 'default':\n      t.input = tf.squeeze(input);\n      t.concat = tf.concat([t.input, t.output], -1);\n      rgba = tf.cast(t.concat, 'int32'); // combined original with alpha\n      break;\n    case 'alpha':\n      rgba = tf.cast(t.output, 'int32'); // just get alpha value from model\n      break;\n    default:\n      rgba = tf.tensor(0);\n  }\n  Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n  return rgba;\n}\n", "/**\n * MobileFaceNet model implementation\n *\n * Based on: [**BecauseofAI MobileFace**](https://github.com/becauseofAI/MobileFace)\n *\n * Obsolete and replaced by `faceres` that performs age/gender/descriptor analysis\n */\n\nimport { log, now } from '../util/util';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { loadModel } from '../tfjs/load';\nimport type { Tensor, GraphModel } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\n\nlet model: GraphModel | null;\nconst last: number[][] = [];\nlet lastCount = 0;\nlet lastTime = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nexport async function load(config: Config): Promise<GraphModel> {\n  if (env.initial) model = null;\n  if (!model) model = await loadModel(config.face['mobilefacenet']?.modelPath);\n  else if (config.debug) log('cached model:', model['modelUrl']);\n  return model;\n}\n\n/*\n// convert to black&white to avoid colorization impact\nconst rgb = [0.2989, 0.5870, 0.1140]; // factors for red/green/blue colors when converting to grayscale: https://www.mathworks.com/help/matlab/ref/rgb2gray.html\nconst [red, green, blue] = tf.split(crop, 3, 3);\nconst redNorm = tf.mul(red, rgb[0]);\nconst greenNorm = tf.mul(green, rgb[1]);\nconst blueNorm = tf.mul(blue, rgb[2]);\nconst grayscale = tf.addN([redNorm, greenNorm, blueNorm]);\nconst merge = tf.stack([grayscale, grayscale, grayscale], 3).squeeze(4);\n\n// optional increase image contrast\n// or do it per-channel so mean is done on each channel\n// or do it based on histogram\nconst mean = merge.mean();\nconst factor = 5;\nconst contrast = merge.sub(mean).mul(factor).add(mean);\n*/\n\nexport async function predict(input: Tensor, config: Config, idx, count): Promise<number[]> {\n  if (!model?.['executor']) return [];\n  const skipFrame = skipped < (config.face['mobilefacenet']?.skipFrames || 0);\n  const skipTime = (config.face['mobilefacenet']?.skipTime || 0) > (now() - lastTime);\n  if (config.skipAllowed && skipTime && skipFrame && (lastCount === count) && last[idx]) {\n    skipped++;\n    return last[idx];\n  }\n  return new Promise(async (resolve) => {\n    let data: number[] = [];\n    if (config.face['mobilefacenet']?.enabled && model?.inputs[0].shape) {\n      const t: Record<string, Tensor> = {};\n      t.crop = tf.image.resizeBilinear(input, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false); // just resize to fit the embedding model\n      // do a tight crop of image and resize it to fit the model\n      // const box = [[0.05, 0.15, 0.85, 0.85]]; // empyrical values for top, left, bottom, right\n      // t.crop = tf.image.cropAndResize(input, box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]);\n      t.data = model.execute(t.crop) as Tensor;\n      /*\n      // optional normalize outputs with l2 normalization\n      const scaled = tf.tidy(() => {\n        const l2 = res.norm('euclidean');\n        const scale = res.div(l2);\n        return scale;\n      });\n\n      // optional reduce feature vector complexity\n      const reshape = tf.reshape(res, [128, 2]); // split 256 vectors into 128 x 2\n      const reduce = reshape.logSumExp(1); // reduce 2nd dimension by calculating logSumExp on it\n      */\n      const output = await t.data.data();\n      data = Array.from(output); // convert typed array to simple array\n      Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n    }\n    last[idx] = data;\n    lastCount = count;\n    lastTime = now();\n    resolve(data);\n  });\n}\n", "export const kpt: string[] = [ // used to create part labels\n  'nose',\n  'leftEye',\n  'rightEye',\n  'leftEar',\n  'rightEar',\n  'leftShoulder',\n  'rightShoulder',\n  'leftElbow',\n  'rightElbow',\n  'leftWrist',\n  'rightWrist',\n  'leftHip',\n  'rightHip',\n  'leftKnee',\n  'rightKnee',\n  'leftAnkle',\n  'rightAnkle',\n];\n\nexport const horizontal: string[][] = [ // used to fix left vs right\n  ['leftEye', 'rightEye'],\n  ['leftEar', 'rightEar'],\n  ['leftShoulder', 'rightShoulder'],\n  ['leftElbow', 'rightElbow'],\n  ['leftWrist', 'rightWrist'],\n  ['leftHip', 'rightHip'],\n  ['leftKnee', 'rightKnee'],\n  ['leftAnkle', 'rightAnkle'],\n];\n\nexport const vertical: string[][] = [ // used to remove unlikely keypoint positions\n  ['leftKnee', 'leftShoulder'],\n  ['rightKnee', 'rightShoulder'],\n  ['leftAnkle', 'leftKnee'],\n  ['rightAnkle', 'rightKnee'],\n];\n\nexport const relative: string[][][] = [ // used to match relative body parts\n  [['leftHip', 'rightHip'], ['leftShoulder', 'rightShoulder']],\n  [['leftElbow', 'rightElbow'], ['leftShoulder', 'rightShoulder']],\n];\n\nexport const connected: Record<string, string[]> = { // used to create body outline in annotations\n  leftLeg: ['leftHip', 'leftKnee', 'leftAnkle'],\n  rightLeg: ['rightHip', 'rightKnee', 'rightAnkle'],\n  torso: ['leftShoulder', 'rightShoulder', 'rightHip', 'leftHip', 'leftShoulder'],\n  leftArm: ['leftShoulder', 'leftElbow', 'leftWrist'],\n  rightArm: ['rightShoulder', 'rightElbow', 'rightWrist'],\n  head: [],\n};\n", "import type { BodyKeypoint, BodyResult } from '../result';\nimport * as box from '../util/box';\nimport * as coords from './movenetcoords';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport type { Tensor } from '../tfjs/types';\n\nconst maxJitter = 0.005; // default allowed jitter is within 0.5%\n\nconst cache: {\n  keypoints: BodyKeypoint[],\n  padding: [number, number][];\n} = {\n  keypoints: [],\n  padding: [[0, 0], [0, 0], [0, 0], [0, 0]],\n};\n\nexport function bodyParts(body: BodyResult) { // model sometimes mixes up left vs right keypoints so we fix them\n  for (const pair of coords.horizontal) { // fix body parts left vs right\n    const left = body.keypoints.findIndex((kp) => kp.part === pair[0]);\n    const right = body.keypoints.findIndex((kp) => kp.part === pair[1]);\n    if (body.keypoints[left] && body.keypoints[right]) {\n      if (body.keypoints[left].position[0] < body.keypoints[right].position[0]) {\n        const tmp = body.keypoints[left];\n        body.keypoints[left] = body.keypoints[right];\n        body.keypoints[right] = tmp;\n      }\n    }\n  }\n  for (const pair of coords.vertical) { // remove body parts with improbable vertical position\n    const lower = body.keypoints.findIndex((kp) => (kp && kp.part === pair[0]));\n    const higher = body.keypoints.findIndex((kp) => (kp && kp.part === pair[1]));\n    if (body.keypoints[lower] && body.keypoints[higher]) {\n      if (body.keypoints[lower].position[1] < body.keypoints[higher].position[1]) {\n        body.keypoints.splice(lower, 1);\n      }\n    }\n  }\n  for (const [pair, compare] of coords.relative) { // rearrange body parts according to their relative position\n    const left = body.keypoints.findIndex((kp) => (kp && kp.part === pair[0]));\n    const right = body.keypoints.findIndex((kp) => (kp && kp.part === pair[1]));\n    const leftTo = body.keypoints.findIndex((kp) => (kp && kp.part === compare[0]));\n    const rightTo = body.keypoints.findIndex((kp) => (kp && kp.part === compare[1]));\n    if (!body.keypoints[leftTo] || !body.keypoints[rightTo]) continue; // only if we have both compare points\n    const distanceLeft = body.keypoints[left] ? [\n      Math.abs(body.keypoints[leftTo].position[0] - body.keypoints[left].position[0]),\n      Math.abs(body.keypoints[rightTo].position[0] - body.keypoints[left].position[0]),\n    ] : [0, 0];\n    const distanceRight = body.keypoints[right] ? [\n      Math.abs(body.keypoints[rightTo].position[0] - body.keypoints[right].position[0]),\n      Math.abs(body.keypoints[leftTo].position[0] - body.keypoints[right].position[0]),\n    ] : [0, 0];\n    if (distanceLeft[0] > distanceLeft[1] || distanceRight[0] > distanceRight[1]) { // should flip keypoints\n      const tmp = body.keypoints[left];\n      body.keypoints[left] = body.keypoints[right];\n      body.keypoints[right] = tmp;\n    }\n  }\n}\n\nexport function jitter(keypoints: BodyKeypoint[]): BodyKeypoint[] {\n  for (let i = 0; i < keypoints.length; i++) {\n    if (keypoints[i] && cache.keypoints[i]) {\n      const diff = [Math.abs(keypoints[i].positionRaw[0] - cache.keypoints[i].positionRaw[0]), Math.abs(keypoints[i].positionRaw[1] - cache.keypoints[i].positionRaw[1])];\n      if (diff[0] < maxJitter && diff[1] < maxJitter) {\n        keypoints[i] = cache.keypoints[i]; // below jitter so replace keypoint\n      } else {\n        cache.keypoints[i] = keypoints[i]; // above jitter so update cache\n      }\n    } else {\n      cache.keypoints[i] = keypoints[i]; // cache for keypoint doesnt exist so create it here\n    }\n  }\n  return keypoints;\n}\n\nexport function padInput(input: Tensor, inputSize: number): Tensor {\n  const t: Record<string, Tensor> = {};\n  if (!input?.shape?.[1] || !input?.shape?.[2]) return input;\n  cache.padding = [\n    [0, 0], // dont touch batch\n    [input.shape[2] > input.shape[1] ? Math.trunc((input.shape[2] - input.shape[1]) / 2) : 0, input.shape[2] > input.shape[1] ? Math.trunc((input.shape[2] - input.shape[1]) / 2) : 0], // height before&after\n    [input.shape[1] > input.shape[2] ? Math.trunc((input.shape[1] - input.shape[2]) / 2) : 0, input.shape[1] > input.shape[2] ? Math.trunc((input.shape[1] - input.shape[2]) / 2) : 0], // width before&after\n    [0, 0], // dont touch rbg\n  ];\n  t.pad = tf.pad(input, cache.padding);\n  t.resize = tf.image.resizeBilinear(t.pad, [inputSize, inputSize]);\n  const final = tf.cast(t.resize, 'int32');\n  Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n  return final;\n}\n\nexport function rescaleBody(body: BodyResult, outputSize: [number, number]): BodyResult {\n  body.keypoints = body.keypoints.filter((kpt) => kpt?.position); // filter invalid keypoints\n  for (const kpt of body.keypoints) {\n    kpt.position = [\n      kpt.position[0] * (outputSize[0] + cache.padding[2][0] + cache.padding[2][1]) / outputSize[0] - cache.padding[2][0],\n      kpt.position[1] * (outputSize[1] + cache.padding[1][0] + cache.padding[1][1]) / outputSize[1] - cache.padding[1][0],\n    ];\n    kpt.positionRaw = [\n      kpt.position[0] / outputSize[0], kpt.position[1] / outputSize[1],\n    ];\n  }\n  const rescaledBoxes = box.calc(body.keypoints.map((pt) => pt.position), outputSize);\n  body.box = rescaledBoxes.box;\n  body.boxRaw = rescaledBoxes.boxRaw;\n  return body;\n}\n", "/**\n * MoveNet model implementation\n *\n * Based on: [**MoveNet**](https://blog.tensorflow.org/2021/05/next-generation-pose-detection-with-movenet-and-tensorflowjs.html)\n */\n\nimport { log, now } from '../util/util';\nimport * as box from '../util/box';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as coords from './movenetcoords';\nimport * as fix from './movenetfix';\nimport { loadModel } from '../tfjs/load';\nimport type { BodyKeypoint, BodyResult, BodyLandmark, BodyAnnotation, Box, Point } from '../result';\nimport type { GraphModel, Tensor } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { fakeOps } from '../tfjs/backend';\nimport { env } from '../util/env';\n\nlet model: GraphModel | null;\nlet inputSize = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n// const boxExpandFact = 1.5; // increase to 150%\n\nconst cache: {\n  boxes: Box[], // unused\n  bodies: BodyResult[];\n  last: number,\n} = {\n  boxes: [],\n  bodies: [],\n  last: 0,\n};\n\nexport async function load(config: Config): Promise<GraphModel> {\n  if (env.initial) model = null;\n  if (!model) {\n    fakeOps(['size'], config);\n    model = await loadModel(config.body.modelPath);\n  } else if (config.debug) log('cached model:', model['modelUrl']);\n  inputSize = (model?.['executor'] && model?.inputs?.[0].shape) ? model.inputs[0].shape[2] : 0;\n  if (inputSize < 64) inputSize = 256;\n  return model;\n}\n\nfunction parseSinglePose(res, config, image) {\n  const kpt = res[0][0];\n  const keypoints: BodyKeypoint[] = [];\n  let score = 0;\n  for (let id = 0; id < kpt.length; id++) {\n    score = kpt[id][2];\n    if (score > config.body.minConfidence) {\n      const positionRaw: Point = [kpt[id][1], kpt[id][0]];\n      keypoints.push({\n        score: Math.round(100 * score) / 100,\n        part: coords.kpt[id] as BodyLandmark,\n        positionRaw,\n        position: [ // normalized to input image size\n          Math.round((image.shape[2] || 0) * positionRaw[0]),\n          Math.round((image.shape[1] || 0) * positionRaw[1]),\n        ],\n      });\n    }\n  }\n  score = keypoints.reduce((prev, curr) => (curr.score > prev ? curr.score : prev), 0);\n  const bodies: BodyResult[] = [];\n  const newBox = box.calc(keypoints.map((pt) => pt.position), [image.shape[2], image.shape[1]]);\n  const annotations: Record<string, Point[][]> = {};\n  for (const [name, indexes] of Object.entries(coords.connected)) {\n    const pt: Point[][] = [];\n    for (let i = 0; i < indexes.length - 1; i++) {\n      const pt0 = keypoints.find((kp) => kp.part === indexes[i]);\n      const pt1 = keypoints.find((kp) => kp.part === indexes[i + 1]);\n      if (pt0 && pt1 && pt0.score > (config.body.minConfidence || 0) && pt1.score > (config.body.minConfidence || 0)) pt.push([pt0.position, pt1.position]);\n    }\n    annotations[name] = pt;\n  }\n  const body: BodyResult = { id: 0, score, box: newBox.box, boxRaw: newBox.boxRaw, keypoints, annotations };\n  fix.bodyParts(body);\n  bodies.push(body);\n  return bodies;\n}\n\nfunction parseMultiPose(res, config, image) {\n  const bodies: BodyResult[] = [];\n  for (let id = 0; id < res[0].length; id++) {\n    const kpt = res[0][id];\n    const totalScore = Math.round(100 * kpt[51 + 4]) / 100;\n    if (totalScore > config.body.minConfidence) {\n      const keypoints: BodyKeypoint[] = [];\n      for (let i = 0; i < 17; i++) {\n        const score = kpt[3 * i + 2];\n        if (score > config.body.minConfidence) {\n          const positionRaw: Point = [kpt[3 * i + 1], kpt[3 * i + 0]];\n          keypoints.push({\n            part: coords.kpt[i] as BodyLandmark,\n            score: Math.round(100 * score) / 100,\n            positionRaw,\n            position: [Math.round((image.shape[2] || 0) * positionRaw[0]), Math.round((image.shape[1] || 0) * positionRaw[1])],\n          });\n        }\n      }\n      const newBox = box.calc(keypoints.map((pt) => pt.position), [image.shape[2], image.shape[1]]);\n      // movenet-multipose has built-in box details\n      // const boxRaw: Box = [kpt[51 + 1], kpt[51 + 0], kpt[51 + 3] - kpt[51 + 1], kpt[51 + 2] - kpt[51 + 0]];\n      // const box: Box = [Math.trunc(boxRaw[0] * (image.shape[2] || 0)), Math.trunc(boxRaw[1] * (image.shape[1] || 0)), Math.trunc(boxRaw[2] * (image.shape[2] || 0)), Math.trunc(boxRaw[3] * (image.shape[1] || 0))];\n      const annotations: Record<BodyAnnotation, Point[][]> = {} as Record<BodyAnnotation, Point[][]>;\n      for (const [name, indexes] of Object.entries(coords.connected)) {\n        const pt: Point[][] = [];\n        for (let i = 0; i < indexes.length - 1; i++) {\n          const pt0 = keypoints.find((kp) => kp.part === indexes[i]);\n          const pt1 = keypoints.find((kp) => kp.part === indexes[i + 1]);\n          if (pt0 && pt1 && pt0.score > (config.body.minConfidence || 0) && pt1.score > (config.body.minConfidence || 0)) pt.push([pt0.position, pt1.position]);\n        }\n        annotations[name] = pt;\n      }\n      const body: BodyResult = { id, score: totalScore, box: newBox.box, boxRaw: newBox.boxRaw, keypoints: [...keypoints], annotations };\n      fix.bodyParts(body);\n      bodies.push(body);\n    }\n  }\n  bodies.sort((a, b) => b.score - a.score);\n  if (bodies.length > config.body.maxDetected) bodies.length = config.body.maxDetected;\n  return bodies;\n}\n\nexport async function predict(input: Tensor, config: Config): Promise<BodyResult[]> {\n  if (!model?.['executor'] || !model?.inputs?.[0].shape) return []; // something is wrong with the model\n  if (!config.skipAllowed) cache.boxes.length = 0; // allowed to use cache or not\n  skipped++; // increment skip frames\n  const skipTime = (config.body.skipTime || 0) > (now() - cache.last);\n  const skipFrame = skipped < (config.body.skipFrames || 0);\n  if (config.skipAllowed && skipTime && skipFrame) {\n    return cache.bodies; // return cached results without running anything\n  }\n  return new Promise(async (resolve) => {\n    const t: Record<string, Tensor> = {};\n    skipped = 0;\n    // run detection on squared input and cached boxes\n    /*\n    cache.bodies = []; // reset bodies result\n    if (cache.boxes.length >= (config.body.maxDetected || 0)) { // if we have enough cached boxes run detection using cache\n      for (let i = 0; i < cache.boxes.length; i++) { // run detection based on cached boxes\n        t.crop = tf.image.cropAndResize(input, [cache.boxes[i]], [0], [inputSize, inputSize], 'bilinear');\n        t.cast = tf.cast(t.crop, 'int32');\n        // t.input = prepareImage(input);\n        t.res = model?.execute(t.cast) as Tensor;\n        const res = await t.res.array();\n        const newBodies = (t.res.shape[2] === 17) ? await parseSinglePose(res, config, input, cache.boxes[i]) : await parseMultiPose(res, config, input, cache.boxes[i]);\n        cache.bodies = cache.bodies.concat(newBodies);\n        Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n      }\n    }\n    if (cache.bodies.length !== config.body.maxDetected) { // did not find enough bodies based on cached boxes so run detection on full frame\n      t.input = prepareImage(input);\n      t.res = model?.execute(t.input) as Tensor;\n      const res = await t.res.array();\n      cache.bodies = (t.res.shape[2] === 17) ? await parseSinglePose(res, config, input, [0, 0, 1, 1]) : await parseMultiPose(res, config, input, [0, 0, 1, 1]);\n      for (const body of cache.bodies) rescaleBody(body, [input.shape[2] || 1, input.shape[1] || 1]);\n      Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n    }\n    cache.boxes.length = 0; // reset cache\n    for (let i = 0; i < cache.bodies.length; i++) {\n      if (cache.bodies[i].keypoints.length > (coords.kpt.length / 2)) { // only update cache if we detected at least half keypoints\n        const scaledBox = box.scale(cache.bodies[i].boxRaw, boxExpandFact);\n        const cropBox = box.crop(scaledBox);\n        cache.boxes.push(cropBox);\n      }\n    }\n    */\n\n    // run detection on squared input and no cached boxes\n    t.input = fix.padInput(input, inputSize);\n    t.res = model?.execute(t.input) as Tensor;\n    cache.last = now();\n    const res = await t.res.array();\n    cache.bodies = (t.res.shape[2] === 17)\n      ? parseSinglePose(res, config, input)\n      : parseMultiPose(res, config, input);\n    for (const body of cache.bodies) {\n      fix.rescaleBody(body, [input.shape[2] || 1, input.shape[1] || 1]);\n      fix.jitter(body.keypoints);\n    }\n    Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n\n    resolve(cache.bodies);\n  });\n}\n", "/**\n * NanoDet object detection model implementation\n *\n * Based on: [**MB3-CenterNet**](https://github.com/610265158/mobilenetv3_centernet)\n */\n\nimport { log, now } from '../util/util';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { loadModel } from '../tfjs/load';\nimport { constants } from '../tfjs/constants';\nimport { labels } from './labels';\nimport type { ObjectResult, ObjectType, Box } from '../result';\nimport type { GraphModel, Tensor } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\n\nlet model: GraphModel;\nlet last: ObjectResult[] = [];\nlet lastTime = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\nlet inputSize = 0;\n\nconst scaleBox = 2.5; // increase box size\n\nexport async function load(config: Config): Promise<GraphModel> {\n  if (!model || env.initial) {\n    model = await loadModel(config.object.modelPath);\n    const inputs = model?.['executor'] ? Object.values(model.modelSignature['inputs']) : undefined;\n    inputSize = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 416;\n  } else if (config.debug) log('cached model:', model['modelUrl']);\n  return model;\n}\n\nasync function process(res: Tensor[], outputShape: [number, number], config: Config) {\n  let id = 0;\n  let results: ObjectResult[] = [];\n  const size = inputSize;\n  for (const strideSize of [1, 2, 4]) { // try each stride size as it detects large/medium/small objects\n    // find scores, boxes, classes\n    const baseSize = strideSize * 13; // 13x13=169, 26x26=676, 52x52=2704\n    // find boxes and scores output depending on stride\n    const scoresT = tf.squeeze(res.find((a: Tensor) => (a.shape[1] === (baseSize ** 2) && (a.shape[2] || 0) === labels.length)));\n    const scores = await scoresT.array(); // optionally use exponential scores or just as-is\n    const featuresT = tf.squeeze(res.find((a: Tensor) => (a.shape[1] === (baseSize ** 2) && (a.shape[2] || 0) < labels.length)));\n    const boxesMaxT = featuresT.reshape([-1, 4, featuresT.shape[1] / 4]); // reshape [output] to [4, output / 4] where number is number of different features inside each stride\n    const boxIdxT = boxesMaxT.argMax(2); // what we need is indexes of features with highest scores, not values itself\n    const boxIdx = await boxIdxT.array(); // what we need is indexes of features with highest scores, not values itself\n    for (let i = 0; i < scoresT.shape[0]; i++) { // total strides (x * y matrix)\n      for (let j = 0; j < scoresT.shape[1]; j++) { // one score for each class\n        const score = scores[i][j]; // get score for current position\n        if (score > (config.object.minConfidence || 0) && j !== 61) {\n          const cx = (0.5 + Math.trunc(i % baseSize)) / baseSize; // center.x normalized to range 0..1\n          const cy = (0.5 + Math.trunc(i / baseSize)) / baseSize; // center.y normalized to range 0..1\n          const boxOffset = boxIdx[i].map((a: number) => a * (baseSize / strideSize / (size))); // just grab indexes of features with highest scores\n          const [x, y] = [\n            cx - (scaleBox / strideSize * boxOffset[0]),\n            cy - (scaleBox / strideSize * boxOffset[1]),\n          ];\n          const [w, h] = [\n            cx + (scaleBox / strideSize * boxOffset[2]) - x,\n            cy + (scaleBox / strideSize * boxOffset[3]) - y,\n          ];\n          let boxRaw: Box = [x, y, w, h]; // results normalized to range 0..1\n          boxRaw = boxRaw.map((a) => Math.max(0, Math.min(a, 1))) as Box; // fix out-of-bounds coords\n          const box = [ // results normalized to input image pixels\n            boxRaw[0] * outputShape[0],\n            boxRaw[1] * outputShape[1],\n            boxRaw[2] * outputShape[0],\n            boxRaw[3] * outputShape[1],\n          ];\n          const result = {\n            id: id++,\n            // strideSize,\n            score: Math.round(100 * score) / 100,\n            class: j + 1,\n            label: labels[j].label as ObjectType,\n            // center: [Math.trunc(outputShape[0] * cx), Math.trunc(outputShape[1] * cy)],\n            // centerRaw: [cx, cy],\n            box: box.map((a) => Math.trunc(a)) as Box,\n            boxRaw,\n          };\n          results.push(result);\n        }\n      }\n    }\n    tf.dispose([scoresT, featuresT, boxesMaxT, boxIdxT]);\n  }\n\n  // normally nms is run on raw results, but since boxes need to be calculated this way we skip calulcation of\n  // unnecessary boxes and run nms only on good candidates (basically it just does IOU analysis as scores are already filtered)\n  const nmsBoxes = results.map((a) => [a.boxRaw[1], a.boxRaw[0], a.boxRaw[3], a.boxRaw[2]]); // switches coordinates from x,y to y,x as expected by tf.nms\n  const nmsScores = results.map((a) => a.score);\n  let nmsIdx: number[] = [];\n  if (nmsBoxes && nmsBoxes.length > 0) {\n    const nms = await tf.image.nonMaxSuppressionAsync(nmsBoxes, nmsScores, config.object.maxDetected, config.object.iouThreshold, config.object.minConfidence);\n    nmsIdx = await nms.data();\n    tf.dispose(nms);\n  }\n\n  // filter & sort results\n  results = results\n    .filter((_val, idx) => nmsIdx.includes(idx))\n    .sort((a, b) => (b.score - a.score));\n\n  return results;\n}\n\nexport async function predict(image: Tensor, config: Config): Promise<ObjectResult[]> {\n  if (!model?.['executor']) return [];\n  const skipTime = (config.object.skipTime || 0) > (now() - lastTime);\n  const skipFrame = skipped < (config.object.skipFrames || 0);\n  if (config.skipAllowed && skipTime && skipFrame && (last.length > 0)) {\n    skipped++;\n    return last;\n  }\n  skipped = 0;\n  if (!env.kernels.includes('mod') || !env.kernels.includes('sparsetodense')) return last;\n  return new Promise(async (resolve) => {\n    const outputSize = [image.shape[2] || 0, image.shape[1] || 0];\n    const resizeT = tf.image.resizeBilinear(image, [inputSize, inputSize], false);\n    const normT = tf.div(resizeT, constants.tf255);\n    const transposeT = tf.transpose(normT, [0, 3, 1, 2]);\n\n    let objectT;\n    if (config.object.enabled) objectT = model.execute(transposeT);\n    lastTime = now();\n\n    const obj = await process(objectT as Tensor[], outputSize as [number, number], config);\n    last = obj;\n    tf.dispose([resizeT, normT, transposeT, ...objectT]);\n    resolve(obj);\n  });\n}\n", "/**\n * PoseNet body detection model implementation constants\n * See `posenet.ts` for entry point\n */\n\nimport type { Point, BodyResult, BodyAnnotation, BodyLandmark } from '../result';\n\nexport const partNames = [\n  'nose', 'leftEye', 'rightEye', 'leftEar', 'rightEar', 'leftShoulder',\n  'rightShoulder', 'leftElbow', 'rightElbow', 'leftWrist', 'rightWrist',\n  'leftHip', 'rightHip', 'leftKnee', 'rightKnee', 'leftAnkle', 'rightAnkle',\n];\n\nexport const count = partNames.length; // 17 keypoints\n\nexport const partIds = partNames.reduce((result, jointName, i) => {\n  result[jointName] = i;\n  return result;\n}, {});\n\nconst connectedPartNames = [\n  ['leftHip', 'leftShoulder'], ['leftElbow', 'leftShoulder'],\n  ['leftElbow', 'leftWrist'], ['leftHip', 'leftKnee'],\n  ['leftKnee', 'leftAnkle'], ['rightHip', 'rightShoulder'],\n  ['rightElbow', 'rightShoulder'], ['rightElbow', 'rightWrist'],\n  ['rightHip', 'rightKnee'], ['rightKnee', 'rightAnkle'],\n  ['leftShoulder', 'rightShoulder'], ['leftHip', 'rightHip'],\n];\nexport const connectedPartIndices = connectedPartNames.map(([jointNameA, jointNameB]) => ([partIds[jointNameA], partIds[jointNameB]]));\n\nexport const poseChain = [\n  ['nose', 'leftEye'], ['leftEye', 'leftEar'], ['nose', 'rightEye'],\n  ['rightEye', 'rightEar'], ['nose', 'leftShoulder'],\n  ['leftShoulder', 'leftElbow'], ['leftElbow', 'leftWrist'],\n  ['leftShoulder', 'leftHip'], ['leftHip', 'leftKnee'],\n  ['leftKnee', 'leftAnkle'], ['nose', 'rightShoulder'],\n  ['rightShoulder', 'rightElbow'], ['rightElbow', 'rightWrist'],\n  ['rightShoulder', 'rightHip'], ['rightHip', 'rightKnee'],\n  ['rightKnee', 'rightAnkle'],\n];\n\nexport function eitherPointDoesntMeetConfidence(a: number, b: number, minConfidence: number) {\n  return (a < minConfidence || b < minConfidence);\n}\n\nexport function getAdjacentKeyPoints(keypoints, minConfidence: number) {\n  return connectedPartIndices.reduce((result, [leftJoint, rightJoint]) => {\n    if (eitherPointDoesntMeetConfidence(keypoints[leftJoint].score, keypoints[rightJoint].score, minConfidence)) {\n      return result;\n    }\n    result.push([keypoints[leftJoint], keypoints[rightJoint]]);\n    return result;\n  }, []);\n}\n\nexport function getBoundingBox(keypoints): [number, number, number, number] {\n  const coord = keypoints.reduce(({ maxX, maxY, minX, minY }, { position: { x, y } }) => ({\n    maxX: Math.max(maxX, x),\n    maxY: Math.max(maxY, y),\n    minX: Math.min(minX, x),\n    minY: Math.min(minY, y),\n  }), {\n    maxX: Number.NEGATIVE_INFINITY,\n    maxY: Number.NEGATIVE_INFINITY,\n    minX: Number.POSITIVE_INFINITY,\n    minY: Number.POSITIVE_INFINITY,\n  });\n  return [coord.minX, coord.minY, coord.maxX - coord.minX, coord.maxY - coord.minY];\n}\n\nexport function scalePoses(poses, [height, width], [inputResolutionHeight, inputResolutionWidth]): BodyResult[] {\n  const scaleY = height / inputResolutionHeight;\n  const scaleX = width / inputResolutionWidth;\n  const scalePose = (pose, i): BodyResult => ({\n    id: i,\n    score: pose.score,\n    boxRaw: [pose.box[0] / inputResolutionWidth, pose.box[1] / inputResolutionHeight, pose.box[2] / inputResolutionWidth, pose.box[3] / inputResolutionHeight],\n    box: [Math.trunc(pose.box[0] * scaleX), Math.trunc(pose.box[1] * scaleY), Math.trunc(pose.box[2] * scaleX), Math.trunc(pose.box[3] * scaleY)],\n    keypoints: pose.keypoints.map(({ score, part, position }) => ({\n      score: score as number,\n      part: part as BodyLandmark,\n      position: [Math.trunc(position.x * scaleX), Math.trunc(position.y * scaleY)] as Point,\n      positionRaw: [position.x / inputResolutionHeight, position.y / inputResolutionHeight] as Point,\n    })),\n    annotations: {} as Record<BodyAnnotation, Point[][]>,\n  });\n  const scaledPoses = poses.map((pose, i) => scalePose(pose, i));\n  return scaledPoses;\n}\n\n// algorithm based on Coursera Lecture from Algorithms, Part 1: https://www.coursera.org/learn/algorithms-part1/lecture/ZjoSM/heapsort\nexport class MaxHeap {\n  priorityQueue: unknown[]; // don't touch\n  numberOfElements: number;\n  getElementValue: unknown; // function call\n\n  constructor(maxSize, getElementValue) {\n    this.priorityQueue = new Array(maxSize);\n    this.numberOfElements = -1;\n    this.getElementValue = getElementValue;\n  }\n\n  enqueue(x) {\n    this.priorityQueue[++this.numberOfElements] = x;\n    this.swim(this.numberOfElements);\n  }\n\n  dequeue() {\n    const max = this.priorityQueue[0];\n    this.exchange(0, this.numberOfElements--);\n    this.sink(0);\n    this.priorityQueue[this.numberOfElements + 1] = null;\n    return max;\n  }\n\n  empty() { return this.numberOfElements === -1; }\n\n  size() { return this.numberOfElements + 1; }\n\n  all() { return this.priorityQueue.slice(0, this.numberOfElements + 1); }\n\n  max() { return this.priorityQueue[0]; }\n\n  swim(k) {\n    while (k > 0 && this.less(Math.floor(k / 2), k)) {\n      this.exchange(k, Math.floor(k / 2));\n      k = Math.floor(k / 2);\n    }\n  }\n\n  sink(k) {\n    while (2 * k <= this.numberOfElements) {\n      let j = 2 * k;\n      if (j < this.numberOfElements && this.less(j, j + 1)) j++;\n      if (!this.less(k, j)) break;\n      this.exchange(k, j);\n      k = j;\n    }\n  }\n\n  getValueAt(i) {\n    // @ts-ignore getter is of unknown type\n    return this.getElementValue(this.priorityQueue[i]);\n  }\n\n  less(i, j) {\n    return this.getValueAt(i) < this.getValueAt(j);\n  }\n\n  exchange(i, j) {\n    const t = this.priorityQueue[i];\n    this.priorityQueue[i] = this.priorityQueue[j];\n    this.priorityQueue[j] = t;\n  }\n}\n\nexport function getOffsetPoint(y, x, keypoint: number, offsets) {\n  return {\n    y: offsets.get(y, x, keypoint),\n    x: offsets.get(y, x, keypoint + count),\n  };\n}\n\nexport function getImageCoords(part, outputStride: number, offsets) {\n  const { heatmapY, heatmapX, id: keypoint } = part;\n  const { y, x } = getOffsetPoint(heatmapY, heatmapX, keypoint, offsets);\n  return {\n    x: part.heatmapX * outputStride + x,\n    y: part.heatmapY * outputStride + y,\n  };\n}\n\nexport function fillArray(element, size) {\n  const result = new Array(size);\n  for (let i = 0; i < size; i++) {\n    result[i] = element;\n  }\n  return result;\n}\n\nexport function clamp(a, min, max) {\n  if (a < min) return min;\n  if (a > max) return max;\n  return a;\n}\n\nexport function squaredDistance(y1, x1, y2, x2) {\n  const dy = y2 - y1;\n  const dx = x2 - x1;\n  return dy * dy + dx * dx;\n}\n\nexport function addVectors(a: { x: number, y: number }, b: { x: number, y: number }) {\n  return { x: a.x + b.x, y: a.y + b.y };\n}\n\nexport function clampVector(a, min, max) {\n  return { y: clamp(a.y, min, max), x: clamp(a.x, min, max) };\n}\n", "/**\n * PoseNet body detection model implementation\n *\n * Based on: [**PoseNet**](https://medium.com/tensorflow/real-time-human-pose-estimation-in-the-browser-with-tensorflow-js-7dd0bc881cd5)\n */\n\nimport { log } from '../util/util';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { loadModel } from '../tfjs/load';\nimport type { BodyResult, BodyLandmark, Box } from '../result';\nimport type { Tensor, GraphModel } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\nimport * as utils from './posenetutils';\n\nlet model: GraphModel;\nconst poseNetOutputs = ['MobilenetV1/offset_2/BiasAdd'/* offsets */, 'MobilenetV1/heatmap_2/BiasAdd'/* heatmapScores */, 'MobilenetV1/displacement_fwd_2/BiasAdd'/* displacementFwd */, 'MobilenetV1/displacement_bwd_2/BiasAdd'/* displacementBwd */];\nconst localMaximumRadius = 1;\nconst outputStride = 16;\nconst squaredNmsRadius = 50 ** 2;\n\nfunction traverse(edgeId: number, sourceKeypoint, targetId, scores, offsets, displacements, offsetRefineStep = 2) {\n  const getDisplacement = (point) => ({\n    y: displacements.get(point.y, point.x, edgeId),\n    x: displacements.get(point.y, point.x, (displacements.shape[2] / 2) + edgeId),\n  });\n  const getStridedIndexNearPoint = (point, height, width) => ({\n    y: utils.clamp(Math.round(point.y / outputStride), 0, height - 1),\n    x: utils.clamp(Math.round(point.x / outputStride), 0, width - 1),\n  });\n\n  const [height, width] = scores.shape;\n  // Nearest neighbor interpolation for the source->target displacements.\n  const sourceKeypointIndices = getStridedIndexNearPoint(sourceKeypoint.position, height, width);\n  const displacement = getDisplacement(sourceKeypointIndices);\n  const displacedPoint = utils.addVectors(sourceKeypoint.position, displacement);\n  let targetKeypoint = displacedPoint;\n  for (let i = 0; i < offsetRefineStep; i++) {\n    const targetKeypointIndices = getStridedIndexNearPoint(targetKeypoint, height, width);\n    const offsetPoint = utils.getOffsetPoint(targetKeypointIndices.y, targetKeypointIndices.x, targetId, offsets);\n    targetKeypoint = utils.addVectors(\n      { x: targetKeypointIndices.x * outputStride, y: targetKeypointIndices.y * outputStride },\n      { x: offsetPoint.x, y: offsetPoint.y },\n    );\n  }\n  const targetKeyPointIndices = getStridedIndexNearPoint(targetKeypoint, height, width);\n  const score = scores.get(targetKeyPointIndices.y, targetKeyPointIndices.x, targetId);\n  return { position: targetKeypoint, part: utils.partNames[targetId], score };\n}\n\nexport function decodePose(root, scores, offsets, displacementsFwd, displacementsBwd) {\n  const tuples = utils.poseChain.map(([parentJoinName, childJoinName]) => ([utils.partIds[parentJoinName], utils.partIds[childJoinName]]));\n  const edgesFwd = tuples.map(([, childJointId]) => childJointId);\n  const edgesBwd = tuples.map(([parentJointId]) => parentJointId);\n  const numParts = scores.shape[2]; // [21,21,17]\n  const numEdges = edgesFwd.length;\n  const keypoints = new Array(numParts);\n  // Start a new detection instance at the position of the root.\n  const rootPoint = utils.getImageCoords(root.part, outputStride, offsets);\n  keypoints[root.part.id] = {\n    score: root.score,\n    part: utils.partNames[root.part.id] as BodyLandmark,\n    position: rootPoint,\n  };\n  // Decode the part positions upwards in the tree, following the backward displacements.\n  for (let edge = numEdges - 1; edge >= 0; --edge) {\n    const sourceId = edgesFwd[edge];\n    const targetId = edgesBwd[edge];\n    if (keypoints[sourceId] && !keypoints[targetId]) {\n      keypoints[targetId] = traverse(edge, keypoints[sourceId], targetId, scores, offsets, displacementsBwd);\n    }\n  }\n  // Decode the part positions downwards in the tree, following the forward displacements.\n  for (let edge = 0; edge < numEdges; ++edge) {\n    const sourceId = edgesBwd[edge];\n    const targetId = edgesFwd[edge];\n    if (keypoints[sourceId] && !keypoints[targetId]) {\n      keypoints[targetId] = traverse(edge, keypoints[sourceId], targetId, scores, offsets, displacementsFwd);\n    }\n  }\n  return keypoints;\n}\n\nfunction scoreIsMaximumInLocalWindow(keypointId, score: number, heatmapY: number, heatmapX: number, scores) {\n  const [height, width]: [number, number] = scores.shape;\n  let localMaximum = true;\n  const yStart = Math.max(heatmapY - localMaximumRadius, 0);\n  const yEnd = Math.min(heatmapY + localMaximumRadius + 1, height);\n  for (let yCurrent = yStart; yCurrent < yEnd; ++yCurrent) {\n    const xStart = Math.max(heatmapX - localMaximumRadius, 0);\n    const xEnd = Math.min(heatmapX + localMaximumRadius + 1, width);\n    for (let xCurrent = xStart; xCurrent < xEnd; ++xCurrent) {\n      if (scores.get(yCurrent, xCurrent, keypointId) > score) {\n        localMaximum = false;\n        break;\n      }\n    }\n    if (!localMaximum) break;\n  }\n  return localMaximum;\n}\n\nexport function buildPartWithScoreQueue(minConfidence, scores) {\n  const [height, width, numKeypoints] = scores.shape;\n  const queue = new utils.MaxHeap(height * width * numKeypoints, ({ score }) => score);\n  for (let heatmapY = 0; heatmapY < height; ++heatmapY) {\n    for (let heatmapX = 0; heatmapX < width; ++heatmapX) {\n      for (let keypointId = 0; keypointId < numKeypoints; ++keypointId) {\n        const score = scores.get(heatmapY, heatmapX, keypointId);\n        // Only consider parts with score greater or equal to threshold as root candidates.\n        if (score < minConfidence) continue;\n        // Only consider keypoints whose score is maximum in a local window.\n        if (scoreIsMaximumInLocalWindow(keypointId, score, heatmapY, heatmapX, scores)) queue.enqueue({ score, part: { heatmapY, heatmapX, id: keypointId } });\n      }\n    }\n  }\n  return queue;\n}\n\nfunction withinRadius(poses, { x, y }, keypointId) {\n  return poses.some(({ keypoints }) => {\n    const correspondingKeypoint = keypoints[keypointId]?.position;\n    if (!correspondingKeypoint) return false;\n    return utils.squaredDistance(y, x, correspondingKeypoint.y, correspondingKeypoint.x) <= squaredNmsRadius;\n  });\n}\n\nfunction getInstanceScore(existingPoses, keypoints) {\n  const notOverlappedKeypointScores = keypoints.reduce((result, { position, score }, keypointId) => {\n    if (!withinRadius(existingPoses, position, keypointId)) result += score;\n    return result;\n  }, 0.0);\n  return notOverlappedKeypointScores / keypoints.length;\n}\n\nexport function decode(offsets, scores, displacementsFwd, displacementsBwd, maxDetected, minConfidence) {\n  const poses: { keypoints, box: Box, score: number }[] = [];\n  const queue = buildPartWithScoreQueue(minConfidence, scores);\n  // Generate at most maxDetected object instances per image in decreasing root part score order.\n  while (poses.length < maxDetected && !queue.empty()) {\n    // The top element in the queue is the next root candidate.\n    const root = queue.dequeue();\n    // Part-based non-maximum suppression: We reject a root candidate if it is within a disk of `nmsRadius` pixels from the corresponding part of a previously detected instance.\n    // @ts-ignore this one is tree walk\n    const rootImageCoords = utils.getImageCoords(root.part, outputStride, offsets);\n    // @ts-ignore this one is tree walk\n    if (withinRadius(poses, rootImageCoords, root.part.id)) continue;\n    // Else start a new detection instance at the position of the root.\n    let keypoints = decodePose(root, scores, offsets, displacementsFwd, displacementsBwd);\n    keypoints = keypoints.filter((a) => a.score > minConfidence);\n    const score = getInstanceScore(poses, keypoints);\n    const box = utils.getBoundingBox(keypoints);\n    if (score > minConfidence) poses.push({ keypoints, box, score: Math.round(100 * score) / 100 });\n  }\n  return poses;\n}\n\nexport async function predict(input: Tensor, config: Config): Promise<BodyResult[]> {\n  /** posenet is mostly obsolete\n   * caching is not implemented\n   */\n  if (!model?.['executor']) return [];\n  const res = tf.tidy(() => {\n    if (!model.inputs[0].shape) return [];\n    const resized = tf.image.resizeBilinear(input, [model.inputs[0].shape[2], model.inputs[0].shape[1]]);\n    const normalized = tf.sub(tf.div(tf.cast(resized, 'float32'), 127.5), 1.0);\n    const results: Tensor[] = model.execute(normalized, poseNetOutputs) as Tensor[];\n    const results3d = results.map((y) => tf.squeeze(y, [0]));\n    results3d[1] = tf.sigmoid(results3d[1]); // apply sigmoid on scores\n    return results3d;\n  });\n\n  const buffers = await Promise.all(res.map((tensor: Tensor) => tensor.buffer()));\n  for (const t of res) tf.dispose(t);\n\n  const decoded = decode(buffers[0], buffers[1], buffers[2], buffers[3], config.body.maxDetected, config.body.minConfidence);\n  if (!model.inputs[0].shape) return [];\n  const scaled = utils.scalePoses(decoded, [input.shape[1], input.shape[2]], [model.inputs[0].shape[2], model.inputs[0].shape[1]]);\n  return scaled;\n}\n\nexport async function load(config: Config): Promise<GraphModel> {\n  if (!model || env.initial) model = await loadModel(config.body.modelPath);\n  else if (config.debug) log('cached model:', model['modelUrl']);\n  return model;\n}\n", "/**\n * Image segmentation for body detection model\n *\n * Based on:\n * - [**Robust Video Matting**](https://github.com/PeterL1n/RobustVideoMatting)\n */\n\nimport { log } from '../util/util';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { loadModel } from '../tfjs/load';\nimport { constants } from '../tfjs/constants';\nimport type { GraphModel, Tensor } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\n\nlet model: GraphModel;\n\n// internal state varaibles\nconst outputNodes = ['fgr', 'pha', 'r1o', 'r2o', 'r3o', 'r4o'];\nconst t: Record<string, Tensor> = {}; // contains input tensor and recurrent states\nlet ratio = 0;\n\nfunction init(config: Config) {\n  tf.dispose([t.r1i, t.r2i, t.r3i, t.r4i, t.downsample_ratio]);\n  t.r1i = tf.tensor(0.0);\n  t.r2i = tf.tensor(0.0);\n  t.r3i = tf.tensor(0.0);\n  t.r4i = tf.tensor(0.0);\n  ratio = config.segmentation.ratio || 0.5;\n  t.downsample_ratio = tf.tensor(ratio); // initialize downsample ratio\n}\n\nexport async function load(config: Config): Promise<GraphModel> {\n  if (!model || env.initial) model = await loadModel(config.segmentation.modelPath);\n  else if (config.debug) log('cached model:', model['modelUrl']);\n  init(config);\n  return model;\n}\n\nconst normalize = (r: Tensor) => tf.tidy(() => {\n  const squeeze = tf.squeeze(r, ([0]));\n  const mul = tf.mul(squeeze, constants.tf255);\n  const cast = tf.cast(mul, 'int32');\n  return cast as Tensor;\n});\n\nfunction getRGBA(fgr: Tensor | null, pha: Tensor | null): Tensor { // gets rgba // either fgr or pha must be present\n  const rgb = fgr\n    ? normalize(fgr) // normalize and use value\n    : tf.fill([pha!.shape[1] || 0, pha!.shape[2] || 0, 3], 255, 'int32'); // eslint-disable-line @typescript-eslint/no-non-null-assertion\n  const a = pha\n    ? normalize(pha) // normalize and use value\n    : tf.fill([fgr!.shape[1] || 0, fgr!.shape[2] || 0, 1], 255, 'int32'); // eslint-disable-line @typescript-eslint/no-non-null-assertion\n  const rgba = tf.concat([rgb, a], -1);\n  tf.dispose([rgb, a]);\n  return rgba;\n}\n\nfunction getState(state: Tensor): Tensor { // gets internal recurrent states\n  return tf.tidy(() => {\n    const r: Record<string, Tensor | Tensor[]> = {};\n    r.unstack = tf.unstack(state, -1);\n    r.concat = tf.concat(r.unstack, 1);\n    r.split = tf.split(r.concat, 4, 1);\n    r.stack = tf.concat(r.split, 2);\n    r.squeeze = tf.squeeze(r.stack, [0]);\n    r.expand = tf.expandDims(r.squeeze, -1);\n    r.add = tf.add(r.expand, 1);\n    r.mul = tf.mul(r.add, 127.5);\n    r.cast = tf.cast(r.mul, 'int32');\n    r.tile = tf.tile(r.cast, [1, 1, 3]) as Tensor;\n    r.alpha = tf.fill([r.tile.shape[0] || 0, r.tile.shape[1] || 0, 1], 255, 'int32');\n    return tf.concat([r.tile, r.alpha], -1) as Tensor;\n  });\n}\n\nexport async function predict(input: Tensor, config: Config): Promise<Tensor | null> {\n  if (!model) model = await load(config);\n  if (!model?.['executor']) return null;\n  // const expand = tf.expandDims(input, 0);\n  t.src = tf.div(input, 255);\n  if (ratio !== config.segmentation.ratio) init(config); // reinitialize recurrent states if requested downsample ratio changed\n  const [fgr, pha, r1o, r2o, r3o, r4o] = await model.executeAsync(t, outputNodes) as Tensor[]; // execute model\n  let rgba: Tensor;\n  switch (config.segmentation.mode || 'default') {\n    case 'default':\n      rgba = getRGBA(fgr, pha);\n      break;\n    case 'alpha':\n      rgba = getRGBA(null, pha);\n      break;\n    case 'foreground':\n      rgba = getRGBA(fgr, null);\n      break;\n    case 'state':\n      rgba = getState(r1o); // can view any internal recurrent state r10, r20, r3o, r4o\n      break;\n    default:\n      rgba = tf.tensor(0);\n  }\n  tf.dispose([t.src, fgr, pha, t.r1i, t.r2i, t.r3i, t.r4i]);\n  [t.r1i, t.r2i, t.r3i, t.r4i] = [r1o, r2o, r3o, r4o]; // update recurrent states\n  return rgba;\n}\n", "/**\n * Image segmentation for body detection model\n *\n * Based on:\n * - [**MediaPipe Selfie**](https://drive.google.com/file/d/1dCfozqknMa068vVsO2j_1FgZkW_e3VWv/preview)\n */\n\nimport { log } from '../util/util';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { loadModel } from '../tfjs/load';\nimport { constants } from '../tfjs/constants';\nimport type { GraphModel, Tensor } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\n\nlet model: GraphModel;\n\nexport async function load(config: Config): Promise<GraphModel> {\n  if (!model || env.initial) model = await loadModel(config.segmentation.modelPath);\n  else if (config.debug) log('cached model:', model['modelUrl']);\n  return model;\n}\n\nexport async function predict(input: Tensor, config: Config): Promise<Tensor | null> {\n  if (!model) model = await load(config);\n  if (!model?.['executor'] || !model?.inputs?.[0].shape) return null; // something is wrong with the model\n  const t: Record<string, Tensor> = {};\n  t.resize = tf.image.resizeBilinear(input, [model.inputs[0].shape ? model.inputs[0].shape[1] : 0, model.inputs[0].shape ? model.inputs[0].shape[2] : 0], false);\n  t.norm = tf.div(t.resize, constants.tf255);\n  t.res = model.execute(t.norm) as Tensor;\n  t.squeeze = tf.squeeze(t.res, 0); // meet.shape:[1,256,256,1], selfie.shape:[1,144,256,2]\n  t.alpha = tf.image.resizeBilinear(t.squeeze, [input.shape[1], input.shape[2]]); // model selfie has a single channel that we can use directly\n  t.mul = tf.mul(t.alpha, constants.tf255);\n  let rgba: Tensor;\n  switch (config.segmentation.mode || 'default') {\n    case 'default':\n      t.input = tf.squeeze(input);\n      t.concat = tf.concat([t.input, t.mul], -1);\n      rgba = tf.cast(t.concat, 'int32'); // combined original with alpha\n      break;\n    case 'alpha':\n      rgba = tf.cast(t.mul, 'int32'); // just get alpha value from model\n      break;\n    default:\n      rgba = tf.tensor(0);\n  }\n  Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n  return rgba;\n}\n", "/**\n * Age model implementation\n *\n * Based on: [**SSR-Net**](https://github.com/shamangary/SSR-Net)\n */\n\nimport { log, now } from '../util/util';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { loadModel } from '../tfjs/load';\nimport { env } from '../util/env';\nimport { constants } from '../tfjs/constants';\nimport type { Config } from '../config';\nimport type { GraphModel, Tensor } from '../tfjs/types';\n\nlet model: GraphModel | null;\nconst last: { age: number }[] = [];\nlet lastCount = 0;\nlet lastTime = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nexport async function load(config: Config) {\n  if (env.initial) model = null;\n  if (!model) model = await loadModel(config.face['ssrnet'].modelPathAge);\n  else if (config.debug) log('cached model:', model['modelUrl']);\n  return model;\n}\n\nexport async function predict(image: Tensor, config: Config, idx: number, count: number): Promise<{ age: number }> {\n  if (!model) return { age: 0 };\n  const skipFrame = skipped < (config.face['ssrnet']?.skipFrames || 0);\n  const skipTime = (config.face['ssrnet']?.skipTime || 0) > (now() - lastTime);\n  if (config.skipAllowed && skipFrame && skipTime && (lastCount === count) && last[idx]?.age && (last[idx]?.age > 0)) {\n    skipped++;\n    return last[idx];\n  }\n  skipped = 0;\n  return new Promise(async (resolve) => {\n    if (!model?.inputs || !model.inputs[0] || !model.inputs[0].shape) return;\n    const t: Record<string, Tensor> = {};\n    t.resize = tf.image.resizeBilinear(image, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false);\n    t.enhance = tf.mul(t.resize, constants.tf255);\n    const obj = { age: 0 };\n    if (config.face['ssrnet']?.enabled) t.age = model.execute(t.enhance) as Tensor;\n    if (t.age) {\n      const data = await t.age.data();\n      obj.age = Math.trunc(10 * data[0]) / 10;\n    }\n    Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n    last[idx] = obj;\n    lastCount = count;\n    lastTime = now();\n    resolve(obj);\n  });\n}\n", "/**\n * Gender model implementation\n *\n * Based on: [**SSR-Net**](https://github.com/shamangary/SSR-Net)\n */\n\nimport { log, now } from '../util/util';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { loadModel } from '../tfjs/load';\nimport { constants } from '../tfjs/constants';\nimport type { Gender } from '../result';\nimport type { Config } from '../config';\nimport type { GraphModel, Tensor } from '../tfjs/types';\nimport { env } from '../util/env';\n\nlet model: GraphModel | null;\nconst last: { gender: Gender, genderScore: number }[] = [];\nlet lastCount = 0;\nlet lastTime = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n\n// tuning values\nconst rgb = [0.2989, 0.5870, 0.1140]; // factors for red/green/blue colors when converting to grayscale\n\nexport async function load(config: Config) {\n  if (env.initial) model = null;\n  if (!model) model = await loadModel(config.face['ssrnet']?.modelPathGender);\n  else if (config.debug) log('cached model:', model['modelUrl']);\n  return model;\n}\n\nexport async function predict(image: Tensor, config: Config, idx, count): Promise<{ gender: Gender, genderScore: number }> {\n  if (!model) return { gender: 'unknown', genderScore: 0 };\n  const skipFrame = skipped < (config.face['ssrnet']?.skipFrames || 0);\n  const skipTime = (config.face['ssrnet']?.skipTime || 0) > (now() - lastTime);\n  if (config.skipAllowed && skipFrame && skipTime && (lastCount === count) && last[idx]?.gender && (last[idx]?.genderScore > 0)) {\n    skipped++;\n    return last[idx];\n  }\n  skipped = 0;\n  return new Promise(async (resolve) => {\n    if (!model?.inputs[0].shape) return;\n    const t: Record<string, Tensor> = {};\n    t.resize = tf.image.resizeBilinear(image, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false);\n    t.enhance = tf.tidy(() => {\n      const [red, green, blue] = tf.split(t.resize, 3, 3);\n      const redNorm = tf.mul(red, rgb[0]);\n      const greenNorm = tf.mul(green, rgb[1]);\n      const blueNorm = tf.mul(blue, rgb[2]);\n      const grayscale = tf.addN([redNorm, greenNorm, blueNorm]);\n      const normalize = tf.mul(tf.sub(grayscale, constants.tf05), 2); // range grayscale:-1..1\n      return normalize;\n    });\n    const obj: { gender: Gender, genderScore: number } = { gender: 'unknown', genderScore: 0 };\n    if (config.face['ssrnet']?.enabled) t.gender = model.execute(t.enhance) as Tensor;\n    const data = await t.gender.data();\n    obj.gender = data[0] > data[1] ? 'female' : 'male'; // returns two values 0..1, bigger one is prediction\n    obj.genderScore = data[0] > data[1] ? (Math.trunc(100 * data[0]) / 100) : (Math.trunc(100 * data[1]) / 100);\n    Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n    last[idx] = obj;\n    lastCount = count;\n    lastTime = now();\n    resolve(obj);\n  });\n}\n", "/** TFJS custom backend registration */\n\nimport type { Human } from '../human';\nimport { log } from '../util/util';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as image from '../image/image';\nimport * as models from '../models';\nimport type { AnyCanvas } from '../exports';\n// import { env } from '../env';\n\nexport const config = {\n  name: 'humangl',\n  priority: 999,\n  canvas: null as null | AnyCanvas,\n  gl: null as null | WebGL2RenderingContext,\n  extensions: [] as string[] | null,\n  webGLattr: { // https://www.khronos.org/registry/webgl/specs/latest/1.0/#5.2\n    alpha: false,\n    antialias: false,\n    premultipliedAlpha: false,\n    preserveDrawingBuffer: false,\n    depth: false,\n    stencil: false,\n    failIfMajorPerformanceCaveat: false, // default=true\n    desynchronized: true, // default=undefined\n  },\n};\n\nfunction extensions(): void {\n  /*\n  https://www.khronos.org/registry/webgl/extensions/\n  https://webglreport.com/?v=2\n  */\n  const gl = config.gl;\n  if (!gl) return;\n  config.extensions = gl.getSupportedExtensions();\n  // gl.getExtension('KHR_parallel_shader_compile');\n}\n\n/**\n * Registers custom WebGL2 backend to be used by Human library\n *\n * @returns void\n */\nexport function register(instance: Human): void {\n  // force backend reload if gl context is not valid\n  if (instance.config.backend !== 'humangl') return;\n  if ((config.name in tf.engine().registry) && !config?.gl?.getParameter(config.gl.VERSION)) {\n    log('humangl error: backend invalid context');\n    models.reset(instance);\n    /*\n    log('resetting humangl backend');\n    await tf.removeBackend(config.name);\n    await register(instance); // re-register\n    */\n  }\n  if (!tf.findBackend(config.name)) {\n    try {\n      config.canvas = image.canvas(100, 100);\n    } catch (err) {\n      log('humangl error: cannot create canvas:', err);\n      return;\n    }\n    try {\n      config.gl = config.canvas.getContext('webgl2', config.webGLattr);\n      if (!config.gl) {\n        log('humangl error: cannot get webgl context');\n        return;\n      }\n      const glv2 = config.gl.getParameter(config.gl.VERSION).includes('2.0');\n      if (!glv2) {\n        log('backend override: using fallback webgl backend as webgl 2.0 is not detected');\n        instance.config.backend = 'webgl';\n        return;\n      }\n      if (config.canvas) {\n        config.canvas.addEventListener('webglcontextlost', (e) => {\n          log('humangl error:', e.type);\n          log('possible browser memory leak using webgl or conflict with multiple backend registrations');\n          instance.emit('error');\n          throw new Error('backend error: webgl context lost');\n          // log('resetting humangl backend');\n          // env.initial = true;\n          // models.reset(instance);\n          // await tf.removeBackend(config.name);\n          // await register(instance); // re-register\n        });\n        config.canvas.addEventListener('webglcontextrestored', (e) => {\n          log('humangl error: context restored:', e);\n        });\n        config.canvas.addEventListener('webglcontextcreationerror', (e) => {\n          log('humangl error: context create:', e);\n        });\n      }\n    } catch (err) {\n      log('humangl error: cannot get webgl context:', err);\n      return;\n    }\n    try {\n      tf.setWebGLContext(2, config.gl);\n    } catch (err) {\n      log('humangl error: cannot set webgl context:', err);\n      return;\n    }\n    try {\n      const ctx = new tf.GPGPUContext(config.gl);\n      tf.registerBackend(config.name, () => new tf.MathBackendWebGL(ctx), config.priority);\n    } catch (err) {\n      log('humangl error: cannot register webgl backend:', err);\n      return;\n    }\n    try {\n      const kernels = tf.getKernelsForBackend('webgl');\n      kernels.forEach((kernelConfig) => {\n        const newKernelConfig = { ...kernelConfig, backendName: config.name };\n        tf.registerKernel(newKernelConfig);\n      });\n    } catch (err) {\n      log('humangl error: cannot update webgl backend registration:', err);\n      return;\n    }\n    try {\n      if (tf.env().flagRegistry.WEBGL_VERSION) tf.env().set('WEBGL_VERSION', 2);\n    } catch (err) {\n      log('humangl error: cannot set WebGL backend flags:', err);\n      return;\n    }\n    extensions();\n    const current = tf.backend().getGPGPUContext ? tf.backend().getGPGPUContext().gl : null;\n    if (current) {\n      if (instance.config.debug) log('humangl backend registered:', { webgl: current.getParameter(current.VERSION) as string, renderer: current.getParameter(current.RENDERER) as string });\n    } else {\n      log('humangl error: no current gl context:', current, config.gl);\n    }\n  }\n}\n", "/** TFJS backend initialization and customization */\n\nimport type { Human, Config } from '../human';\nimport { log, now } from '../util/util';\nimport { env } from '../util/env';\nimport * as humangl from './humangl';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as constants from './constants';\n\nfunction registerCustomOps(config: Config) {\n  const newKernels: string[] = [];\n  if (!env.kernels.includes('mod')) {\n    const kernelMod = {\n      kernelName: 'Mod',\n      backendName: tf.getBackend(),\n      kernelFunc: (op) => tf.tidy(() => tf.sub(op.inputs.a, tf.mul(tf.div(op.inputs.a, op.inputs.b), op.inputs.b))),\n    };\n    tf.registerKernel(kernelMod);\n    env.kernels.push('mod');\n    newKernels.push('mod');\n  }\n  if (!env.kernels.includes('floormod')) {\n    const kernelFloorMod = {\n      kernelName: 'FloorMod',\n      backendName: tf.getBackend(),\n      kernelFunc: (op) => tf.tidy(() => tf.add(tf.mul(tf.floorDiv(op.inputs.a / op.inputs.b), op.inputs.b), tf.mod(op.inputs.a, op.inputs.b))),\n    };\n    tf.registerKernel(kernelFloorMod);\n    env.kernels.push('floormod');\n    newKernels.push('floormod');\n  }\n  /*\n  if (!env.kernels.includes('atan2') && config.softwareKernels) {\n    const kernelAtan2 = {\n      kernelName: 'Atan2',\n      backendName: tf.getBackend(),\n      kernelFunc: (op) => tf.tidy(() => {\n        const backend = tf.getBackend();\n        tf.setBackend('cpu');\n        const t = tf.atan2(op.inputs.a, op.inputs.b);\n        tf.setBackend(backend);\n        return t;\n      }),\n    };\n    if (config.debug) log('registered kernel:', 'atan2');\n    log('registered kernel:', 'atan2');\n    tf.registerKernel(kernelAtan2);\n    env.kernels.push('atan2');\n    newKernels.push('atan2');\n  }\n  */\n  if (!env.kernels.includes('rotatewithoffset') && config.softwareKernels) {\n    const kernelRotateWithOffset = {\n      kernelName: 'RotateWithOffset',\n      backendName: tf.getBackend(),\n      kernelFunc: (op) => tf.tidy(() => {\n        const backend = tf.getBackend();\n        tf.setBackend('cpu');\n        const t = tf.image.rotateWithOffset(op.inputs.image, op.attrs.radians, op.attrs.fillValue, op.attrs.center);\n        tf.setBackend(backend);\n        return t;\n      }),\n    };\n    tf.registerKernel(kernelRotateWithOffset);\n    env.kernels.push('rotatewithoffset');\n    newKernels.push('rotatewithoffset');\n  }\n  if ((newKernels.length > 0) && config.debug) log('registered kernels:', newKernels);\n}\n\nlet defaultFlags: Record<string, unknown> = {};\n\nexport async function check(instance: Human, force = false) {\n  instance.state = 'backend';\n  if (force || env.initial || (instance.config.backend && (instance.config.backend.length > 0) && (tf.getBackend() !== instance.config.backend))) {\n    const timeStamp = now();\n\n    if (instance.config.backend && instance.config.backend.length > 0) {\n      // detect web worker\n      // @ts-ignore ignore missing type for WorkerGlobalScope as that is the point\n      if (typeof window === 'undefined' && typeof WorkerGlobalScope !== 'undefined' && instance.config.debug) {\n        if (instance.config.debug) log('running inside web worker');\n      }\n\n      // force browser vs node backend\n      if (env.browser && instance.config.backend === 'tensorflow') {\n        if (instance.config.debug) log('override: backend set to tensorflow while running in browser');\n        instance.config.backend = 'webgl';\n      }\n      if (env.node && (instance.config.backend === 'webgl' || instance.config.backend === 'humangl')) {\n        if (instance.config.debug) log(`override: backend set to ${instance.config.backend} while running in nodejs`);\n        instance.config.backend = 'tensorflow';\n      }\n\n      // handle webgpu\n      if (env.browser && instance.config.backend === 'webgpu') {\n        if (typeof navigator === 'undefined' || typeof navigator.gpu === 'undefined') {\n          log('override: backend set to webgpu but browser does not support webgpu');\n          instance.config.backend = 'webgl';\n        } else {\n          const adapter = await navigator.gpu.requestAdapter();\n          if (instance.config.debug) log('enumerated webgpu adapter:', adapter);\n          if (!adapter) {\n            log('override: backend set to webgpu but browser reports no available gpu');\n            instance.config.backend = 'webgl';\n          } else {\n            // @ts-ignore requestAdapterInfo is not in tslib\n            const adapterInfo = 'requestAdapterInfo' in adapter ? await (adapter as GPUAdapter).requestAdapterInfo() : undefined;\n            // if (adapter.features) adapter.features.forEach((feature) => log('webgpu features:', feature));\n            log('webgpu adapter info:', adapterInfo);\n          }\n        }\n      }\n\n      // check available backends\n      let available = Object.keys(tf.engine().registryFactory as Record<string, unknown>);\n      if (instance.config.backend === 'humangl' && !available.includes('humangl')) {\n        humangl.register(instance);\n        available = Object.keys(tf.engine().registryFactory as Record<string, unknown>);\n      }\n      if (instance.config.debug) log('available backends:', available);\n\n      if (!available.includes(instance.config.backend)) {\n        log(`error: backend ${instance.config.backend} not found in registry`);\n        instance.config.backend = env.node ? 'tensorflow' : 'webgl';\n        if (instance.config.debug) log(`override: setting backend ${instance.config.backend}`);\n      }\n\n      if (instance.config.debug) log('setting backend:', [instance.config.backend]);\n\n      // customize wasm\n      if (instance.config.backend === 'wasm') {\n        if (tf.env().flagRegistry.CANVAS2D_WILL_READ_FREQUENTLY) tf.env().set('CANVAS2D_WILL_READ_FREQUENTLY', true);\n        if (instance.config.debug) log('wasm path:', instance.config.wasmPath);\n        if (typeof tf.setWasmPaths !== 'undefined') tf.setWasmPaths(instance.config.wasmPath, instance.config.wasmPlatformFetch);\n        else throw new Error('backend error: attempting to use wasm backend but wasm path is not set');\n        let mt = false;\n        let simd = false;\n        try {\n          mt = await tf.env().getAsync('WASM_HAS_MULTITHREAD_SUPPORT');\n          simd = await tf.env().getAsync('WASM_HAS_SIMD_SUPPORT');\n          if (instance.config.debug) log(`wasm execution: ${simd ? 'simd' : 'no simd'} ${mt ? 'multithreaded' : 'singlethreaded'}`);\n          if (instance.config.debug && !simd) log('warning: wasm simd support is not enabled');\n        } catch {\n          log('wasm detection failed');\n        }\n      }\n\n      try {\n        await tf.setBackend(instance.config.backend);\n        await tf.ready();\n      } catch (err) {\n        log('error: cannot set backend:', instance.config.backend, err);\n        return false;\n      }\n      if (instance.config.debug) defaultFlags = JSON.parse(JSON.stringify(tf.env().flags));\n    }\n\n    // customize humangl\n    if (tf.getBackend() === 'humangl' || tf.getBackend() === 'webgl') {\n      if (tf.env().flagRegistry.WEBGL_USE_SHAPES_UNIFORMS) tf.env().set('WEBGL_USE_SHAPES_UNIFORMS', true); // default=false <https://github.com/tensorflow/tfjs/issues/5205>\n      if (tf.env().flagRegistry.WEBGL_EXP_CONV) tf.env().set('WEBGL_EXP_CONV', true); // default=false <https://github.com/tensorflow/tfjs/issues/6678>\n      // if (tf.env().flagRegistry['WEBGL_PACK_DEPTHWISECONV'])  tf.env().set('WEBGL_PACK_DEPTHWISECONV', false); // default=true <https://github.com/tensorflow/tfjs/pull/4909>\n      // if (tf.env().flagRegistry.USE_SETTIMEOUTCUSTOM) tf.env().set('USE_SETTIMEOUTCUSTOM', true); // default=false <https://github.com/tensorflow/tfjs/issues/6687>\n      // if (tf.env().flagRegistry.CPU_HANDOFF_SIZE_THRESHOLD) tf.env().set('CPU_HANDOFF_SIZE_THRESHOLD', 1024); // default=1000\n      // if (tf.env().flagRegistry['WEBGL_FORCE_F16_TEXTURES'] && !instance.config.object.enabled) tf.env().set('WEBGL_FORCE_F16_TEXTURES', true); // safe to use 16bit precision\n      if (instance.config.debug && typeof instance.config.deallocate !== 'undefined' && instance.config.deallocate) { // hidden param\n        log('changing webgl: WEBGL_DELETE_TEXTURE_THRESHOLD:', true);\n        tf.env().set('WEBGL_DELETE_TEXTURE_THRESHOLD', 0);\n      }\n    }\n\n    // customize webgpu\n    if (tf.getBackend() === 'webgpu') {\n      // if (tf.env().flagRegistry['WEBGPU_CPU_HANDOFF_SIZE_THRESHOLD']) tf.env().set('WEBGPU_CPU_HANDOFF_SIZE_THRESHOLD', 512);\n      // if (tf.env().flagRegistry['WEBGPU_DEFERRED_SUBMIT_BATCH_SIZE']) tf.env().set('WEBGPU_DEFERRED_SUBMIT_BATCH_SIZE', 0);\n      // if (tf.env().flagRegistry['WEBGPU_CPU_FORWARD']) tf.env().set('WEBGPU_CPU_FORWARD', true);\n    }\n\n    if (instance.config.debug) {\n      const newFlags = tf.env().flags;\n      const updatedFlags = {};\n      for (const key of Object.keys(newFlags)) {\n        if (defaultFlags[key] === newFlags[key]) continue;\n        updatedFlags[key] = newFlags[key];\n      }\n      if (instance.config.debug && Object.keys(updatedFlags).length > 0) log('backend:', tf.getBackend(), 'flags:', updatedFlags);\n    }\n\n    if (instance.config.flags && Object.keys(instance.config.flags).length > 0) {\n      if (instance.config.debug) log('flags:', instance.config['flags']);\n      for (const [key, val] of Object.entries(instance.config.flags)) {\n        tf.env().set(key, val);\n      }\n    }\n\n    tf.enableProdMode();\n    constants.init();\n    instance.performance.initBackend = Math.trunc(now() - timeStamp);\n    instance.config.backend = tf.getBackend();\n    await env.updateBackend(); // update env on backend init\n    registerCustomOps(instance.config);\n    // await env.updateBackend(); // update env on backend init\n    env.initial = false;\n  }\n  return true;\n}\n\n// register fake missing tfjs ops\nexport function fakeOps(kernelNames: string[], config) {\n  // if (config.debug) log('registerKernel:', kernelNames);\n  for (const kernelName of kernelNames) {\n    const kernelConfig = {\n      kernelName,\n      backendName: config.backend,\n      kernelFunc: () => { if (config.debug) log('kernelFunc', kernelName, config.backend); },\n      // setupFunc: () => { if (config.debug) log('kernelFunc', kernelName, config.backend); },\n      // disposeFunc: () => { if (config.debug) log('kernelFunc', kernelName, config.backend); },\n    };\n    tf.registerKernel(kernelConfig);\n  }\n  env.kernels = tf.getKernelsForBackend(tf.getBackend()).map((kernel) => (kernel.kernelName as string).toLowerCase()); // re-scan registered ops\n}\n", "/**\n * Module that implements helper draw functions, exposed as human.draw\n */\n\nimport { mergeDeep, now } from '../util/util';\nimport { env } from '../util/env';\nimport { getCanvasContext, rect } from './primitives';\nimport { options } from './options';\nimport { face } from './face';\nimport { body } from './body';\nimport { hand } from './hand';\nimport { object } from './object';\nimport { gesture } from './gesture';\nimport type { Result, PersonResult } from '../result';\nimport type { AnyCanvas, DrawOptions } from '../exports';\n\nlet drawTime = 0;\n\nexport { options } from './options';\nexport { face } from './face';\nexport { body } from './body';\nexport { hand } from './hand';\nexport { object } from './object';\nexport { gesture } from './gesture';\n\n/** draw combined person results instead of individual detection result objects */\nexport function person(inCanvas: AnyCanvas, result: PersonResult[], drawOptions?: Partial<DrawOptions>) {\n  const localOptions: DrawOptions = mergeDeep(options, drawOptions);\n  if (!result || !inCanvas) return;\n  const ctx = getCanvasContext(inCanvas);\n  if (!ctx) return;\n  ctx.lineJoin = 'round';\n  ctx.font = localOptions.font;\n\n  for (let i = 0; i < result.length; i++) {\n    if (localOptions.drawBoxes) {\n      ctx.strokeStyle = localOptions.color;\n      ctx.fillStyle = localOptions.color;\n      rect(ctx, result[i].box[0], result[i].box[1], result[i].box[2], result[i].box[3], localOptions);\n      if (localOptions.drawLabels) {\n        const label = `person #${i}`;\n        if (localOptions.shadowColor && localOptions.shadowColor !== '') {\n          ctx.fillStyle = localOptions.shadowColor;\n          ctx.fillText(label, result[i].box[0] + 3, 1 + result[i].box[1] + localOptions.lineHeight, result[i].box[2]);\n        }\n        ctx.fillStyle = localOptions.labelColor;\n        ctx.fillText(label, result[i].box[0] + 2, 0 + result[i].box[1] + localOptions.lineHeight, result[i].box[2]);\n      }\n      ctx.stroke();\n    }\n  }\n}\n\n/** draw processed canvas */\nexport function canvas(input: AnyCanvas | HTMLImageElement | HTMLVideoElement, output: AnyCanvas) {\n  if (!input || !output) return;\n  const ctx = getCanvasContext(output);\n  if (!ctx) return;\n  ctx.drawImage(input, 0, 0);\n}\n\n/** meta-function that performs draw for: canvas, face, body, hand */\nexport async function all(inCanvas: AnyCanvas, result: Result, drawOptions?: Partial<DrawOptions>) {\n  if (!result?.performance || !inCanvas) return null;\n  const timeStamp = now();\n  const localOptions = mergeDeep(options, drawOptions);\n  const promise = Promise.all([\n    face(inCanvas, result.face, localOptions),\n    body(inCanvas, result.body, localOptions),\n    hand(inCanvas, result.hand, localOptions),\n    object(inCanvas, result.object, localOptions),\n    gesture(inCanvas, result.gesture, localOptions), // gestures do not have buffering\n    // person(inCanvas, result.persons, localOptions); // already included above\n  ]);\n  drawTime = env.perfadd ? drawTime + Math.round(now() - timeStamp) : Math.round(now() - timeStamp);\n  result.performance.draw = drawTime;\n  return promise;\n}\n", "import { log } from '../util/util';\nimport type { AnyCanvas } from '../exports';\nimport type { Point } from '../result';\nimport type { DrawOptions } from './options';\n\nexport const getCanvasContext = (input: AnyCanvas) => {\n  if (!input) log('draw error: invalid canvas');\n  else if (!input.getContext) log('draw error: canvas context not defined');\n  else {\n    const ctx = input.getContext('2d');\n    if (!ctx) log('draw error: cannot get canvas context');\n    else return ctx;\n  }\n  return null;\n};\n\nexport const rad2deg = (theta: number) => Math.round((theta * 180) / Math.PI);\n\nexport const colorDepth = (z: number | undefined, opt: DrawOptions): string => { // performance optimization needed\n  if (!opt.useDepth || typeof z === 'undefined') return opt.color;\n  const rgb = Uint8ClampedArray.from([127 + (2 * z), 127 - (2 * z), 255]);\n  return `rgba(${rgb[0]}, ${rgb[1]}, ${rgb[2]}, ${opt.alpha})`;\n};\n\nexport function point(ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D, x: number, y: number, z: number | undefined, localOptions: DrawOptions) {\n  ctx.fillStyle = colorDepth(z, localOptions);\n  ctx.beginPath();\n  ctx.arc(x, y, localOptions.pointSize, 0, 2 * Math.PI);\n  ctx.fill();\n}\n\nexport function rect(ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D, x: number, y: number, width: number, height: number, localOptions: DrawOptions) {\n  ctx.beginPath();\n  ctx.lineWidth = localOptions.lineWidth;\n  if (localOptions.useCurves) {\n    const cx = (x + x + width) / 2;\n    const cy = (y + y + height) / 2;\n    ctx.ellipse(cx, cy, width / 2, height / 2, 0, 0, 2 * Math.PI);\n  } else {\n    ctx.moveTo(x + localOptions.roundRect, y);\n    ctx.lineTo(x + width - localOptions.roundRect, y);\n    ctx.quadraticCurveTo(x + width, y, x + width, y + localOptions.roundRect);\n    ctx.lineTo(x + width, y + height - localOptions.roundRect);\n    ctx.quadraticCurveTo(x + width, y + height, x + width - localOptions.roundRect, y + height);\n    ctx.lineTo(x + localOptions.roundRect, y + height);\n    ctx.quadraticCurveTo(x, y + height, x, y + height - localOptions.roundRect);\n    ctx.lineTo(x, y + localOptions.roundRect);\n    ctx.quadraticCurveTo(x, y, x + localOptions.roundRect, y);\n    ctx.closePath();\n  }\n  ctx.stroke();\n}\n\nexport function lines(ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D, points: Point[], localOptions: DrawOptions) {\n  if (points.length < 2) return;\n  ctx.beginPath();\n  ctx.moveTo(points[0][0], points[0][1]);\n  for (const pt of points) {\n    ctx.strokeStyle = colorDepth(pt[2] || 0, localOptions);\n    ctx.lineTo(Math.trunc(pt[0]), Math.trunc(pt[1]));\n  }\n  ctx.stroke();\n  if (localOptions.fillPolygons) {\n    ctx.closePath();\n    ctx.fill();\n  }\n}\n\nexport function curves(ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D, points: Point[], localOptions: DrawOptions) {\n  if (points.length < 2) return;\n  ctx.lineWidth = localOptions.lineWidth;\n  if (!localOptions.useCurves || points.length <= 2) {\n    lines(ctx, points, localOptions);\n    return;\n  }\n  ctx.moveTo(points[0][0], points[0][1]);\n  for (let i = 0; i < points.length - 2; i++) {\n    const xc = (points[i][0] + points[i + 1][0]) / 2;\n    const yc = (points[i][1] + points[i + 1][1]) / 2;\n    ctx.quadraticCurveTo(points[i][0], points[i][1], xc, yc);\n  }\n  ctx.quadraticCurveTo(points[points.length - 2][0], points[points.length - 2][1], points[points.length - 1][0], points[points.length - 1][1]);\n  ctx.stroke();\n  if (localOptions.fillPolygons) {\n    ctx.closePath();\n    ctx.fill();\n  }\n}\n\nexport function arrow(ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D, from: Point, to: Point, radius = 5) {\n  let angle;\n  let x;\n  let y;\n  ctx.beginPath();\n  ctx.moveTo(from[0], from[1]);\n  ctx.lineTo(to[0], to[1]);\n  angle = Math.atan2(to[1] - from[1], to[0] - from[0]);\n  x = radius * Math.cos(angle) + to[0];\n  y = radius * Math.sin(angle) + to[1];\n  ctx.moveTo(x, y);\n  angle += (1.0 / 3.0) * (2 * Math.PI);\n  x = radius * Math.cos(angle) + to[0];\n  y = radius * Math.sin(angle) + to[1];\n  ctx.lineTo(x, y);\n  angle += (1.0 / 3.0) * (2 * Math.PI);\n  x = radius * Math.cos(angle) + to[0];\n  y = radius * Math.sin(angle) + to[1];\n  ctx.lineTo(x, y);\n  ctx.closePath();\n  ctx.stroke();\n  ctx.fill();\n}\n", "/** Draw Options\n * - Accessed via `human.draw.options` or provided per each draw method as the drawOptions optional parameter\n */\nexport interface DrawOptions {\n  /** draw line color */\n  color: string,\n  /** alpha value used for lines */\n  alpha: number,\n  /** label color */\n  labelColor: string,\n  /** label shadow color */\n  shadowColor: string,\n  /** label font */\n  font: string,\n  /** line spacing between labels */\n  lineHeight: number,\n  /** line width for drawn lines */\n  lineWidth: number,\n  /** size of drawn points */\n  pointSize: number,\n  /** draw rounded boxes by n pixels */\n  roundRect: number,\n  /** should points be drawn? */\n  drawPoints: boolean,\n  /** should labels be drawn? */\n  drawLabels: boolean,\n  /** should face attention keypoints be highlighted */\n  drawAttention: boolean;\n  /** should detected gestures be drawn? */\n  drawGestures: boolean,\n  /** should draw boxes around detection results? */\n  drawBoxes: boolean,\n  /** should draw polygons from detection points? */\n  drawPolygons: boolean,\n  /** should draw gaze arrows? */\n  drawGaze: boolean,\n  /** should fill polygons? */\n  fillPolygons: boolean,\n  /** use z-coordinate when available */\n  useDepth: boolean,\n  /** should lines be curved? */\n  useCurves: boolean,\n}\n\n/** currently set draw options {@link DrawOptions} */\nexport const options: DrawOptions = {\n  color: 'rgba(173, 216, 230, 0.6)' as string, // 'lightblue' with light alpha channel\n  labelColor: 'rgba(173, 216, 230, 1)' as string, // 'lightblue' with dark alpha channel\n  shadowColor: 'black' as string,\n  alpha: 0.5 as number,\n  font: 'small-caps 16px \"Segoe UI\"' as string,\n  lineHeight: 18 as number,\n  lineWidth: 4 as number,\n  pointSize: 2 as number,\n  roundRect: 8 as number,\n  drawPoints: false as boolean,\n  drawLabels: true as boolean,\n  drawBoxes: true as boolean,\n  drawAttention: true as boolean,\n  drawGestures: true as boolean,\n  drawPolygons: true as boolean,\n  drawGaze: true as boolean,\n  fillPolygons: false as boolean,\n  useDepth: true as boolean,\n  useCurves: false as boolean,\n};\n", "import { TRI468 as triangulation } from '../face/facemeshcoords';\nimport { mergeDeep } from '../util/util';\nimport { getCanvasContext, rad2deg, rect, point, lines, arrow } from './primitives';\nimport { options } from './options';\nimport * as facemeshConstants from '../face/constants';\nimport type { FaceResult } from '../result';\nimport type { AnyCanvas, DrawOptions } from '../exports';\n\nlet opt: DrawOptions;\n\nfunction drawLabels(f: FaceResult, ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D) {\n  if (opt.drawLabels) {\n    // silly hack since fillText does not suport new line\n    const labels:string[] = [];\n    labels.push(`face: ${Math.trunc(100 * f.score)}%`);\n    if (f.genderScore) labels.push(`${f.gender || ''} ${Math.trunc(100 * f.genderScore)}%`);\n    if (f.age) labels.push(`age: ${f.age || ''}`);\n    if (f.iris) labels.push(`distance: ${f.iris}`);\n    if (f.real) labels.push(`real: ${Math.trunc(100 * f.real)}%`);\n    if (f.live) labels.push(`live: ${Math.trunc(100 * f.live)}%`);\n    if (f.emotion && f.emotion.length > 0) {\n      const emotion = f.emotion.map((a) => `${Math.trunc(100 * a.score)}% ${a.emotion}`);\n      if (emotion.length > 3) emotion.length = 3;\n      labels.push(emotion.join(' '));\n    }\n    if (f.rotation?.angle && f.rotation?.gaze) {\n      if (f.rotation.angle.roll) labels.push(`roll: ${rad2deg(f.rotation.angle.roll)}\u00B0 yaw:${rad2deg(f.rotation.angle.yaw)}\u00B0 pitch:${rad2deg(f.rotation.angle.pitch)}\u00B0`);\n      if (f.rotation.gaze.bearing) labels.push(`gaze: ${rad2deg(f.rotation.gaze.bearing)}\u00B0`);\n    }\n    if (labels.length === 0) labels.push('face');\n    ctx.fillStyle = opt.color;\n    for (let i = labels.length - 1; i >= 0; i--) {\n      const x = Math.max(f.box[0], 0);\n      const y = i * opt.lineHeight + f.box[1];\n      if (opt.shadowColor && opt.shadowColor !== '') {\n        ctx.fillStyle = opt.shadowColor;\n        ctx.fillText(labels[i], x + 5, y + 16);\n      }\n      ctx.fillStyle = opt.labelColor;\n      ctx.fillText(labels[i], x + 4, y + 15);\n    }\n  }\n}\n\nfunction drawIrisElipse(f: FaceResult, ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D) {\n  // iris: array[center, left, top, right, bottom]\n  if (f.annotations?.leftEyeIris && f.annotations?.leftEyeIris[0]) {\n    ctx.strokeStyle = opt.useDepth ? 'rgba(255, 200, 255, 0.3)' : opt.color;\n    ctx.beginPath();\n    const sizeX = Math.abs(f.annotations.leftEyeIris[3][0] - f.annotations.leftEyeIris[1][0]) / 2;\n    const sizeY = Math.abs(f.annotations.leftEyeIris[4][1] - f.annotations.leftEyeIris[2][1]) / 2;\n    ctx.ellipse(f.annotations.leftEyeIris[0][0], f.annotations.leftEyeIris[0][1], sizeX, sizeY, 0, 0, 2 * Math.PI);\n    ctx.stroke();\n    if (opt.fillPolygons) {\n      ctx.fillStyle = opt.useDepth ? 'rgba(255, 255, 200, 0.3)' : opt.color;\n      ctx.fill();\n    }\n  }\n  if (f.annotations?.rightEyeIris && f.annotations?.rightEyeIris[0]) {\n    ctx.strokeStyle = opt.useDepth ? 'rgba(255, 200, 255, 0.3)' : opt.color;\n    ctx.beginPath();\n    const sizeX = Math.abs(f.annotations.rightEyeIris[3][0] - f.annotations.rightEyeIris[1][0]) / 2;\n    const sizeY = Math.abs(f.annotations.rightEyeIris[4][1] - f.annotations.rightEyeIris[2][1]) / 2;\n    ctx.ellipse(f.annotations.rightEyeIris[0][0], f.annotations.rightEyeIris[0][1], sizeX, sizeY, 0, 0, 2 * Math.PI);\n    ctx.stroke();\n    if (opt.fillPolygons) {\n      ctx.fillStyle = opt.useDepth ? 'rgba(255, 255, 200, 0.3)' : opt.color;\n      ctx.fill();\n    }\n  }\n}\n\nfunction drawGazeSpheres(f: FaceResult, ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D) {\n  if (opt.drawGaze && f.rotation?.angle && typeof Path2D !== 'undefined') {\n    ctx.strokeStyle = 'pink';\n    const valX = (f.box[0] + f.box[2] / 2) - (f.box[3] * rad2deg(f.rotation.angle.yaw) / 90);\n    const valY = (f.box[1] + f.box[3] / 2) + (f.box[2] * rad2deg(f.rotation.angle.pitch) / 90);\n    const pathV = new Path2D(`\n      M ${f.box[0] + f.box[2] / 2} ${f.box[1]}\n      C\n        ${valX} ${f.box[1]},\n        ${valX} ${f.box[1] + f.box[3]},\n        ${f.box[0] + f.box[2] / 2} ${f.box[1] + f.box[3]}\n    `);\n    const pathH = new Path2D(`\n      M ${f.box[0]} ${f.box[1] + f.box[3] / 2}\n      C \n        ${f.box[0]} ${valY},\n        ${f.box[0] + f.box[2]} ${valY},\n        ${f.box[0] + f.box[2]} ${f.box[1] + f.box[3] / 2}\n    `);\n    ctx.stroke(pathH);\n    ctx.stroke(pathV);\n  }\n}\n\nfunction drawGazeArrows(f: FaceResult, ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D) {\n  if (opt.drawGaze && f.rotation?.gaze.strength && f.rotation.gaze.bearing && f.annotations.leftEyeIris && f.annotations.rightEyeIris && f.annotations.leftEyeIris[0] && f.annotations.rightEyeIris[0]) {\n    ctx.strokeStyle = 'pink';\n    ctx.fillStyle = 'pink';\n    const leftGaze = [\n      f.annotations.leftEyeIris[0][0] + (Math.sin(f.rotation.gaze.bearing) * f.rotation.gaze.strength * f.box[3]),\n      f.annotations.leftEyeIris[0][1] + (Math.cos(f.rotation.gaze.bearing) * f.rotation.gaze.strength * f.box[2]),\n    ];\n    arrow(ctx, [f.annotations.leftEyeIris[0][0], f.annotations.leftEyeIris[0][1]], [leftGaze[0], leftGaze[1]], 4);\n    const rightGaze = [\n      f.annotations.rightEyeIris[0][0] + (Math.sin(f.rotation.gaze.bearing) * f.rotation.gaze.strength * f.box[3]),\n      f.annotations.rightEyeIris[0][1] + (Math.cos(f.rotation.gaze.bearing) * f.rotation.gaze.strength * f.box[2]),\n    ];\n    arrow(ctx, [f.annotations.rightEyeIris[0][0], f.annotations.rightEyeIris[0][1]], [rightGaze[0], rightGaze[1]], 4);\n  }\n}\n\nfunction drawFacePolygons(f: FaceResult, ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D) {\n  if (opt.drawPolygons && f.mesh.length >= 468) {\n    ctx.lineWidth = 1;\n    for (let i = 0; i < triangulation.length / 3; i++) {\n      const points = [triangulation[i * 3 + 0], triangulation[i * 3 + 1], triangulation[i * 3 + 2]].map((index) => f.mesh[index]);\n      lines(ctx, points, opt);\n    }\n    drawIrisElipse(f, ctx);\n  }\n  /*\n  if (opt.drawPolygons && f.contours.length > 1) {\n    ctx.lineWidth = 5;\n    lines(ctx, f.contours, opt);\n  }\n  ctx.lineWidth = 1;\n  */\n}\n\nfunction drawFacePoints(f: FaceResult, ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D) {\n  if (opt.drawPoints && f.mesh.length >= 468) {\n    for (let i = 0; i < f.mesh.length; i++) {\n      point(ctx, f.mesh[i][0], f.mesh[i][1], f.mesh[i][2], opt);\n      if (opt.drawAttention) {\n        if (facemeshConstants.LANDMARKS_REFINEMENT_LIPS_CONFIG.includes(i)) point(ctx, f.mesh[i][0], f.mesh[i][1], (f.mesh[i][2] as number) + 127, opt);\n        if (facemeshConstants.LANDMARKS_REFINEMENT_LEFT_EYE_CONFIG.includes(i)) point(ctx, f.mesh[i][0], f.mesh[i][1], (f.mesh[i][2] as number) - 127, opt);\n        if (facemeshConstants.LANDMARKS_REFINEMENT_RIGHT_EYE_CONFIG.includes(i)) point(ctx, f.mesh[i][0], f.mesh[i][1], (f.mesh[i][2] as number) - 127, opt);\n      }\n    }\n  }\n}\n\nfunction drawFaceBoxes(f: FaceResult, ctx) {\n  if (opt.drawBoxes) {\n    rect(ctx, f.box[0], f.box[1], f.box[2], f.box[3], opt);\n  }\n}\n\n/** draw detected faces */\nexport function face(inCanvas: AnyCanvas, result: FaceResult[], drawOptions?: Partial<DrawOptions>) {\n  opt = mergeDeep(options, drawOptions);\n  if (!result || !inCanvas) return;\n  const ctx = getCanvasContext(inCanvas);\n  if (!ctx) return;\n  ctx.font = opt.font;\n  ctx.strokeStyle = opt.color;\n  ctx.fillStyle = opt.color;\n  for (const f of result) {\n    drawFaceBoxes(f, ctx);\n    drawLabels(f, ctx);\n    if (f.mesh && f.mesh.length > 0) {\n      drawFacePoints(f, ctx);\n      drawFacePolygons(f, ctx);\n      drawGazeSpheres(f, ctx);\n      drawGazeArrows(f, ctx);\n    }\n  }\n}\n", "import { mergeDeep } from '../util/util';\nimport { getCanvasContext, rect, point, curves, colorDepth } from './primitives';\nimport { options } from './options';\nimport type { BodyResult } from '../result';\nimport type { AnyCanvas, DrawOptions } from '../exports';\n\n/** draw detected bodies */\nexport function body(inCanvas: AnyCanvas, result: BodyResult[], drawOptions?: Partial<DrawOptions>) {\n  const localOptions: DrawOptions = mergeDeep(options, drawOptions);\n  if (!result || !inCanvas) return;\n  const ctx = getCanvasContext(inCanvas);\n  if (!ctx) return;\n  ctx.lineJoin = 'round';\n  for (let i = 0; i < result.length; i++) {\n    ctx.strokeStyle = localOptions.color;\n    ctx.fillStyle = localOptions.color;\n    ctx.lineWidth = localOptions.lineWidth;\n    ctx.font = localOptions.font;\n    if (localOptions.drawBoxes && result[i].box && result[i].box.length === 4) {\n      rect(ctx, result[i].box[0], result[i].box[1], result[i].box[2], result[i].box[3], localOptions);\n      if (localOptions.drawLabels) {\n        if (localOptions.shadowColor && localOptions.shadowColor !== '') {\n          ctx.fillStyle = localOptions.shadowColor;\n          ctx.fillText(`body ${100 * result[i].score}%`, result[i].box[0] + 3, 1 + result[i].box[1] + localOptions.lineHeight, result[i].box[2]);\n        }\n        ctx.fillStyle = localOptions.labelColor;\n        ctx.fillText(`body ${100 * result[i].score}%`, result[i].box[0] + 2, 0 + result[i].box[1] + localOptions.lineHeight, result[i].box[2]);\n      }\n    }\n    if (localOptions.drawPoints && result[i].keypoints) {\n      for (let pt = 0; pt < result[i].keypoints.length; pt++) {\n        if (!result[i].keypoints[pt].score || (result[i].keypoints[pt].score === 0)) continue;\n        ctx.fillStyle = colorDepth(result[i].keypoints[pt].position[2], localOptions);\n        point(ctx, result[i].keypoints[pt].position[0], result[i].keypoints[pt].position[1], 0, localOptions);\n      }\n    }\n    if (localOptions.drawLabels && result[i].keypoints) {\n      ctx.font = localOptions.font;\n      for (const pt of result[i].keypoints) {\n        if (!pt.score || (pt.score === 0)) continue;\n        ctx.fillStyle = colorDepth(pt.position[2], localOptions);\n        ctx.fillText(`${pt.part} ${Math.trunc(100 * pt.score)}%`, pt.position[0] + 4, pt.position[1] + 4);\n      }\n    }\n    if (localOptions.drawPolygons && result[i].keypoints && result[i].annotations) {\n      for (const part of Object.values(result[i].annotations)) {\n        for (const connected of part) curves(ctx, connected, localOptions);\n      }\n    }\n  }\n}\n", "import { mergeDeep } from '../util/util';\nimport { getCanvasContext, rect, point, colorDepth } from './primitives';\nimport { options } from './options';\nimport type { HandResult } from '../result';\nimport type { AnyCanvas, DrawOptions, Point } from '../exports';\n\n/** draw detected hands */\nexport function hand(inCanvas: AnyCanvas, result: HandResult[], drawOptions?: Partial<DrawOptions>) {\n  const localOptions: DrawOptions = mergeDeep(options, drawOptions);\n  if (!result || !inCanvas) return;\n  const ctx = getCanvasContext(inCanvas);\n  if (!ctx) return;\n  ctx.lineJoin = 'round';\n  ctx.font = localOptions.font;\n  for (const h of result) {\n    if (localOptions.drawBoxes) {\n      ctx.strokeStyle = localOptions.color;\n      ctx.fillStyle = localOptions.color;\n      rect(ctx, h.box[0], h.box[1], h.box[2], h.box[3], localOptions);\n      if (localOptions.drawLabels) {\n        if (localOptions.shadowColor && localOptions.shadowColor !== '') {\n          ctx.fillStyle = localOptions.shadowColor;\n          ctx.fillText(`hand:${Math.trunc(100 * h.score)}%`, h.box[0] + 3, 1 + h.box[1] + localOptions.lineHeight, h.box[2]); // can use h.label\n        }\n        ctx.fillStyle = localOptions.labelColor;\n        ctx.fillText(`hand:${Math.trunc(100 * h.score)}%`, h.box[0] + 2, 0 + h.box[1] + localOptions.lineHeight, h.box[2]); // can use h.label\n      }\n      ctx.stroke();\n    }\n    if (localOptions.drawPoints) {\n      if (h.keypoints && h.keypoints.length > 0) {\n        for (const pt of h.keypoints) {\n          ctx.fillStyle = colorDepth(pt[2], localOptions);\n          point(ctx, pt[0], pt[1], 0, localOptions);\n        }\n      }\n    }\n    if (localOptions.drawLabels && h.annotations) {\n      const addHandLabel = (part: Point[], title: string) => {\n        if (!part || part.length === 0 || !part[0]) return;\n        const z = part[part.length - 1][2] || -256;\n        ctx.fillStyle = colorDepth(z, localOptions);\n        ctx.fillText(title, part[part.length - 1][0] + 4, part[part.length - 1][1] + 4);\n      };\n      ctx.font = localOptions.font;\n      addHandLabel(h.annotations.index, 'index');\n      addHandLabel(h.annotations.middle, 'middle');\n      addHandLabel(h.annotations.ring, 'ring');\n      addHandLabel(h.annotations.pinky, 'pinky');\n      addHandLabel(h.annotations.thumb, 'thumb');\n      addHandLabel(h.annotations.palm, 'palm');\n    }\n    if (localOptions.drawPolygons && h.annotations) {\n      const addHandLine = (part: Point[]) => {\n        if (!part || part.length === 0 || !part[0]) return;\n        for (let i = 0; i < part.length; i++) {\n          ctx.beginPath();\n          const z = part[i][2] || 0;\n          ctx.strokeStyle = colorDepth(i * z, localOptions);\n          ctx.moveTo(part[i > 0 ? i - 1 : 0][0], part[i > 0 ? i - 1 : 0][1]);\n          ctx.lineTo(part[i][0], part[i][1]);\n          ctx.stroke();\n        }\n      };\n      ctx.lineWidth = localOptions.lineWidth;\n      addHandLine(h.annotations.index);\n      addHandLine(h.annotations.middle);\n      addHandLine(h.annotations.ring);\n      addHandLine(h.annotations.pinky);\n      addHandLine(h.annotations.thumb);\n      // addPart(h.annotations.palm);\n    }\n  }\n}\n", "import { mergeDeep } from '../util/util';\nimport { getCanvasContext, rect } from './primitives';\nimport { options } from './options';\nimport type { ObjectResult } from '../result';\nimport type { AnyCanvas, DrawOptions } from '../exports';\n\n/** draw detected objects */\nexport function object(inCanvas: AnyCanvas, result: ObjectResult[], drawOptions?: Partial<DrawOptions>) {\n  const localOptions: DrawOptions = mergeDeep(options, drawOptions);\n  if (!result || !inCanvas) return;\n  const ctx = getCanvasContext(inCanvas);\n  if (!ctx) return;\n  ctx.lineJoin = 'round';\n  ctx.font = localOptions.font;\n  for (const h of result) {\n    if (localOptions.drawBoxes) {\n      ctx.strokeStyle = localOptions.color;\n      ctx.fillStyle = localOptions.color;\n      rect(ctx, h.box[0], h.box[1], h.box[2], h.box[3], localOptions);\n      if (localOptions.drawLabels) {\n        const label = `${h.label} ${Math.round(100 * h.score)}%`;\n        if (localOptions.shadowColor && localOptions.shadowColor !== '') {\n          ctx.fillStyle = localOptions.shadowColor;\n          ctx.fillText(label, h.box[0] + 3, 1 + h.box[1] + localOptions.lineHeight, h.box[2]);\n        }\n        ctx.fillStyle = localOptions.labelColor;\n        ctx.fillText(label, h.box[0] + 2, 0 + h.box[1] + localOptions.lineHeight, h.box[2]);\n      }\n      ctx.stroke();\n    }\n  }\n}\n", "import { mergeDeep } from '../util/util';\nimport { getCanvasContext } from './primitives';\nimport { options } from './options';\nimport type { GestureResult } from '../result';\nimport type { AnyCanvas, DrawOptions } from '../exports';\n\n/** draw detected gestures */\nexport function gesture(inCanvas: AnyCanvas, result: GestureResult[], drawOptions?: Partial<DrawOptions>) {\n  const localOptions: DrawOptions = mergeDeep(options, drawOptions);\n  if (!result || !inCanvas) return;\n  if (localOptions.drawGestures) {\n    const ctx = getCanvasContext(inCanvas);\n    if (!ctx) return;\n    ctx.font = localOptions.font;\n    ctx.fillStyle = localOptions.color;\n    let i = 1;\n    for (let j = 0; j < result.length; j++) {\n      let where: unknown[] = []; // what&where is a record\n      let what: unknown[] = []; // what&where is a record\n      [where, what] = Object.entries(result[j]);\n      if ((what.length > 1) && ((what[1] as string).length > 0)) {\n        const who = where[1] as number > 0 ? `#${where[1]}` : '';\n        const label = `${where[0]} ${who}: ${what[1]}`;\n        if (localOptions.shadowColor && localOptions.shadowColor !== '') {\n          ctx.fillStyle = localOptions.shadowColor;\n          ctx.fillText(label, 8, 2 + (i * localOptions.lineHeight));\n        }\n        ctx.fillStyle = localOptions.labelColor;\n        ctx.fillText(label, 6, 0 + (i * localOptions.lineHeight));\n        i += 1;\n      }\n    }\n  }\n}\n", "import type { Tensor } from '../tfjs/types';\nimport type { FaceResult } from '../result';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport { meshAnnotations } from './facemeshcoords';\n\nconst expandFact = 0.1;\nconst alpha = 0.5;\n\n// point inclusion in polygon based on https://wrf.ecse.rpi.edu/Research/Short_Notes/pnpoly.html\nfunction insidePoly(x: number, y: number, polygon: { x: number, y: number }[]): boolean {\n  let inside = false;\n  let j = polygon.length - 1;\n  for (let i = 0; i < polygon.length; j = i++) {\n    if (((polygon[i].y > y) !== (polygon[j].y > y)) && (x < (polygon[j].x - polygon[i].x) * (y - polygon[i].y) / (polygon[j].y - polygon[i].y) + polygon[i].x)) inside = !inside;\n  }\n  return inside;\n}\n\nexport async function mask(face: FaceResult): Promise<Tensor | undefined> {\n  if (!face.tensor) return face.tensor;\n  if (!face.mesh || face.mesh.length < 100) return face.tensor;\n  const width = face.tensor.shape[2] || 0;\n  const height = face.tensor.shape[1] || 0;\n  const buffer = await face.tensor.buffer();\n  let silhouette: { x: number, y: number }[] = [];\n  for (const pt of meshAnnotations.silhouette) silhouette.push({ x: (face.mesh[pt][0] - face.box[0]) / face.box[2], y: (face.mesh[pt][1] - face.box[1]) / face.box[3] }); // add all silhouette points scaled to local box\n  if (expandFact && expandFact > 0) silhouette = silhouette.map((pt) => ({ x: pt.x > 0.5 ? pt.x + expandFact : pt.x - expandFact, y: pt.y > 0.5 ? pt.y + expandFact : pt.y - expandFact })); // expand silhouette\n  for (let x = 0; x < width; x++) {\n    for (let y = 0; y < height; y++) {\n      const inside = insidePoly(x / width, y / width, silhouette);\n      if (!inside) {\n        buffer.set(alpha * buffer.get(0, y, x, 0), 0, y, x, 0);\n        buffer.set(alpha * buffer.get(0, y, x, 1), 0, y, x, 1);\n        buffer.set(alpha * buffer.get(0, y, x, 2), 0, y, x, 2);\n      }\n    }\n  }\n  const output = buffer.toTensor();\n  tf.dispose(buffer);\n  return output;\n}\n", "import type { Point, FaceResult } from '../result';\n\ntype Vector = [number, number, number];\n\nconst calculateGaze = (face: FaceResult): { bearing: number, strength: number } => {\n  const radians = (pt1: Point, pt2: Point) => Math.atan2(pt1[1] - pt2[1], pt1[0] - pt2[0]); // function to calculate angle between any two points\n  if (!face.annotations.rightEyeIris || !face.annotations.leftEyeIris) return { bearing: 0, strength: 0 };\n\n  const offsetIris = [0, -0.1]; // iris center may not align with average of eye extremes\n  const eyeRatio = 1; // factor to normalize changes x vs y\n\n  const left = (face.mesh[33][2] || 0) > (face.mesh[263][2] || 0); // pick left or right eye depending which one is closer bazed on outsize point z axis\n  const irisCenter = left ? face.mesh[473] : face.mesh[468];\n  const eyeCenter = left // eye center is average of extreme points on x axis for both x and y, ignoring y extreme points as eyelids naturally open/close more when gazing up/down so relative point is less precise\n    ? [(face.mesh[133][0] + face.mesh[33][0]) / 2, (face.mesh[133][1] + face.mesh[33][1]) / 2]\n    : [(face.mesh[263][0] + face.mesh[362][0]) / 2, (face.mesh[263][1] + face.mesh[362][1]) / 2];\n  const eyeSize = left // eye size is difference between extreme points for both x and y, used to normalize & squarify eye dimensions\n    ? [face.mesh[133][0] - face.mesh[33][0], face.mesh[23][1] - face.mesh[27][1]]\n    : [face.mesh[263][0] - face.mesh[362][0], face.mesh[253][1] - face.mesh[257][1]];\n  const eyeDiff: Point = [ // x distance between extreme point and center point normalized with eye size\n    (eyeCenter[0] - irisCenter[0]) / eyeSize[0] - offsetIris[0],\n    eyeRatio * (irisCenter[1] - eyeCenter[1]) / eyeSize[1] - offsetIris[1],\n  ];\n  let strength = Math.sqrt((eyeDiff[0] * eyeDiff[0]) + (eyeDiff[1] * eyeDiff[1])); // vector length is a diagonal between two differences\n  strength = Math.min(strength, face.boxRaw[2] / 2, face.boxRaw[3] / 2); // limit strength to half of box size to avoid clipping due to low precision\n  const bearing = (radians([0, 0], eyeDiff) + (Math.PI / 2)) % Math.PI; // using eyeDiff instead eyeCenter/irisCenter combo due to manual adjustments and rotate clockwise 90degrees\n  return { bearing, strength };\n};\n\nexport const calculateFaceAngle = (face: FaceResult, imageSize: [number, number]): {\n  angle: { pitch: number, yaw: number, roll: number },\n  matrix: [number, number, number, number, number, number, number, number, number],\n  gaze: { bearing: number, strength: number },\n} => {\n  // const degrees = (theta) => Math.abs(((theta * 180) / Math.PI) % 360);\n  const normalize = (v: Vector): Vector => { // normalize vector\n    const length = Math.sqrt(v[0] * v[0] + v[1] * v[1] + v[2] * v[2]);\n    v[0] /= length;\n    v[1] /= length;\n    v[2] /= length;\n    return v;\n  };\n  const subVectors = (a: Vector, b: Vector): Vector => { // vector subtraction (a - b)\n    const x = a[0] - b[0];\n    const y = a[1] - b[1];\n    const z = a[2] - b[2];\n    return [x, y, z];\n  };\n  const crossVectors = (a: Vector, b: Vector): Vector => { // vector cross product (a x b)\n    const x = a[1] * b[2] - a[2] * b[1];\n    const y = a[2] * b[0] - a[0] * b[2];\n    const z = a[0] * b[1] - a[1] * b[0];\n    return [x, y, z];\n  };\n  // 3x3 rotation matrix to Euler angles based on https://www.geometrictools.com/Documentation/EulerAngles.pdf\n  const rotationMatrixToEulerAngle = (r: number[]): { pitch: number, yaw: number, roll: number } => {\n    const [r00, _r01, _r02, r10, r11, r12, r20, r21, r22] = r; // eslint-disable-line @typescript-eslint/no-unused-vars\n    let thetaX: number;\n    let thetaY: number;\n    let thetaZ: number;\n    if (r10 < 1) { // YZX calculation\n      if (r10 > -1) {\n        thetaZ = Math.asin(r10);\n        thetaY = Math.atan2(-r20, r00);\n        thetaX = Math.atan2(-r12, r11);\n      } else {\n        thetaZ = -Math.PI / 2;\n        thetaY = -Math.atan2(r21, r22);\n        thetaX = 0;\n      }\n    } else {\n      thetaZ = Math.PI / 2;\n      thetaY = Math.atan2(r21, r22);\n      thetaX = 0;\n    }\n    if (Number.isNaN(thetaX)) thetaX = 0;\n    if (Number.isNaN(thetaY)) thetaY = 0;\n    if (Number.isNaN(thetaZ)) thetaZ = 0;\n    return { pitch: 2 * -thetaX, yaw: 2 * -thetaY, roll: 2 * -thetaZ };\n  };\n\n  /*\n  const meshToEulerAngle = (mesh) => { // simple Euler angle calculation based existing 3D mesh\n    const radians = (a1, a2, b1, b2) => Math.atan2(b2 - a2, b1 - a1);\n    return { // values are in radians in range of -pi/2 to pi/2 which is -90 to +90 degrees, value of 0 means center\n      pitch: radians(mesh[10][1], mesh[10][2], mesh[152][1], mesh[152][2]), // looking at y,z of top and bottom points of the face // pitch is face move up/down\n      yaw: radians(mesh[33][0], mesh[33][2], mesh[263][0], mesh[263][2]), // looking at x,z of outside corners of leftEye and rightEye // yaw is face turn left/right\n      roll: radians(mesh[33][0], mesh[33][1], mesh[263][0], mesh[263][1]), // looking at x,y of outside corners of leftEye and rightEye // roll is face lean left/right\n    };\n  };\n  */\n\n  // initialize gaze and mesh\n  const mesh = face.meshRaw;\n  if (!mesh || mesh.length < 300) return { angle: { pitch: 0, yaw: 0, roll: 0 }, matrix: [1, 0, 0, 0, 1, 0, 0, 0, 1], gaze: { bearing: 0, strength: 0 } };\n\n  const size = Math.max(face.boxRaw[2] * imageSize[0], face.boxRaw[3] * imageSize[1]) / 1.5;\n  // top, bottom, left, right\n  const pts: Point[] = [mesh[10], mesh[152], mesh[234], mesh[454]].map((pt) => [pt[0] * imageSize[0] / size, pt[1] * imageSize[1] / size, pt[2]] as Point); // make the xyz coordinates proportional, independent of the image/box size\n\n  const yAxis = normalize(subVectors(pts[1] as Vector, pts[0] as Vector));\n  let xAxis = normalize(subVectors(pts[3] as Vector, pts[2] as Vector));\n  const zAxis = normalize(crossVectors(xAxis, yAxis));\n  // adjust xAxis to make sure that all axes are perpendicular to each other\n  xAxis = crossVectors(yAxis, zAxis);\n\n  // Rotation Matrix from Axis Vectors - http://renderdan.blogspot.com/2006/05/rotation-matrix-from-axis-vectors.html\n  // 3x3 rotation matrix is flatten to array in row-major order. Note that the rotation represented by this matrix is inverted.\n  const matrix: [number, number, number, number, number, number, number, number, number] = [\n    xAxis[0], xAxis[1], xAxis[2],\n    yAxis[0], yAxis[1], yAxis[2],\n    zAxis[0], zAxis[1], zAxis[2],\n  ];\n  const angle = rotationMatrixToEulerAngle(matrix);\n  // const angle = meshToEulerAngle(mesh);\n\n  // we have iris keypoints so we can calculate gaze direction\n  const gaze = mesh.length === 478 ? calculateGaze(face) : { bearing: 0, strength: 0 };\n\n  return { angle, matrix, gaze };\n};\n", "/**\n * Face algorithm implementation\n * Uses FaceMesh, Emotion and FaceRes models to create a unified pipeline\n */\n\nimport { log, now } from '../util/util';\nimport { env } from '../util/env';\nimport * as tf from '../../dist/tfjs.esm.js';\nimport * as facemesh from './facemesh';\nimport * as emotion from '../gear/emotion';\nimport * as faceres from './faceres';\nimport * as mask from './mask';\nimport * as antispoof from './antispoof';\nimport * as liveness from './liveness';\nimport * as gear from '../gear/gear';\nimport * as ssrnetAge from '../gear/ssrnet-age';\nimport * as ssrnetGender from '../gear/ssrnet-gender';\nimport * as mobilefacenet from './mobilefacenet';\nimport * as insightface from './insightface';\nimport type { FaceResult, Emotion, Gender, Race } from '../result';\nimport type { Tensor } from '../tfjs/types';\nimport type { Human } from '../human';\nimport { calculateFaceAngle } from './angles';\n\ninterface DescRes { age: number, gender: Gender, genderScore: number, descriptor: number[], race?: { score: number, race: Race }[] }\n\nexport const detectFace = async (instance: Human /* instance of human */, input: Tensor): Promise<FaceResult[]> => {\n  // run facemesh, includes blazeface and iris\n  let timeStamp: number = now();\n  let ageRes: { age: number } | Promise<{ age: number }> | null;\n  let gearRes: gear.GearType | Promise<gear.GearType> | null;\n  let genderRes: { gender: string, genderScore: number } | Promise<{ gender: string, genderScore: number }> | null;\n  let emotionRes: { score: number, emotion: Emotion }[] | Promise<{ score: number, emotion: Emotion }[]>;\n  let mobilefacenetRes: number[] | Promise<number[]> | null;\n  let insightfaceRes: number[] | Promise<number[]> | null;\n  let antispoofRes: number | Promise<number> | null;\n  let livenessRes: number | Promise<number> | null;\n  let descRes: DescRes | Promise<DescRes> | null;\n\n  const faceRes: FaceResult[] = [];\n  instance.state = 'run:face';\n\n  const faces = await facemesh.predict(input, instance.config);\n  instance.performance.face = env.perfadd ? (instance.performance.face || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n  if (!input.shape || input.shape.length !== 4) return [];\n  if (!faces) return [];\n  // for (const face of faces) {\n  for (let i = 0; i < faces.length; i++) {\n    instance.analyze('Get Face');\n\n    // is something went wrong, skip the face\n    // @ts-ignore possibly undefied\n    if (!faces[i].tensor || faces[i].tensor.isDisposedInternal) {\n      log('Face object is disposed:', faces[i].tensor);\n      continue;\n    }\n\n    // optional face mask\n    if (instance.config.face.detector?.mask) {\n      const masked = await mask.mask(faces[i]);\n      tf.dispose(faces[i].tensor);\n      if (masked) faces[i].tensor = masked;\n    }\n\n    // calculate face angles\n    const rotation = faces[i].mesh && (faces[i].mesh.length > 200) ? calculateFaceAngle(faces[i], [input.shape[2], input.shape[1]]) : null;\n\n    // run emotion, inherits face from blazeface\n    instance.analyze('Start Emotion:');\n    if (instance.config.async) {\n      emotionRes = instance.config.face.emotion?.enabled ? emotion.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : [];\n    } else {\n      instance.state = 'run:emotion';\n      timeStamp = now();\n      emotionRes = instance.config.face.emotion?.enabled ? await emotion.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : [];\n      instance.performance.emotion = env.perfadd ? (instance.performance.emotion || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n    }\n    instance.analyze('End Emotion:');\n\n    // run antispoof, inherits face from blazeface\n    instance.analyze('Start AntiSpoof:');\n    if (instance.config.async) {\n      antispoofRes = instance.config.face.antispoof?.enabled ? antispoof.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : 0;\n    } else {\n      instance.state = 'run:antispoof';\n      timeStamp = now();\n      antispoofRes = instance.config.face.antispoof?.enabled ? await antispoof.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : 0;\n      instance.performance.antispoof = env.perfadd ? (instance.performance.antispoof || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n    }\n    instance.analyze('End AntiSpoof:');\n\n    // run liveness, inherits face from blazeface\n    instance.analyze('Start Liveness:');\n    if (instance.config.async) {\n      livenessRes = instance.config.face.liveness?.enabled ? liveness.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : 0;\n    } else {\n      instance.state = 'run:liveness';\n      timeStamp = now();\n      livenessRes = instance.config.face.liveness?.enabled ? await liveness.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : 0;\n      instance.performance.liveness = env.perfadd ? (instance.performance.antispoof || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n    }\n    instance.analyze('End Liveness:');\n\n    // run gear, inherits face from blazeface\n    instance.analyze('Start GEAR:');\n    if (instance.config.async) {\n      gearRes = instance.config.face.gear?.enabled ? gear.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : null;\n    } else {\n      instance.state = 'run:gear';\n      timeStamp = now();\n      gearRes = instance.config.face.gear?.enabled ? await gear.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : null;\n      instance.performance.gear = Math.trunc(now() - timeStamp);\n    }\n    instance.analyze('End GEAR:');\n\n    // run gear, inherits face from blazeface\n    instance.analyze('Start SSRNet:');\n    if (instance.config.async) {\n      ageRes = instance.config.face['ssrnet']?.enabled ? ssrnetAge.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : null;\n      genderRes = instance.config.face['ssrnet']?.enabled ? ssrnetGender.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : null;\n    } else {\n      instance.state = 'run:ssrnet';\n      timeStamp = now();\n      ageRes = instance.config.face['ssrnet']?.enabled ? await ssrnetAge.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : null;\n      genderRes = instance.config.face['ssrnet']?.enabled ? await ssrnetGender.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : null;\n      instance.performance.ssrnet = Math.trunc(now() - timeStamp);\n    }\n    instance.analyze('End SSRNet:');\n\n    // run mobilefacenet alternative, inherits face from blazeface\n    instance.analyze('Start MobileFaceNet:');\n    if (instance.config.async) {\n      mobilefacenetRes = instance.config.face['mobilefacenet']?.enabled ? mobilefacenet.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : null;\n    } else {\n      instance.state = 'run:mobilefacenet';\n      timeStamp = now();\n      mobilefacenetRes = instance.config.face['mobilefacenet']?.enabled ? await mobilefacenet.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : null;\n      instance.performance.mobilefacenet = Math.trunc(now() - timeStamp);\n    }\n    instance.analyze('End MobileFaceNet:');\n\n    // run insightface alternative, inherits face from blazeface\n    instance.analyze('Start InsightFace:');\n    if (instance.config.async) {\n      insightfaceRes = instance.config.face['insightface']?.enabled ? insightface.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : null;\n    } else {\n      instance.state = 'run:mobilefacenet';\n      timeStamp = now();\n      insightfaceRes = instance.config.face['insightface']?.enabled ? await insightface.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length) : null;\n      instance.performance.mobilefacenet = Math.trunc(now() - timeStamp);\n    }\n    instance.analyze('End InsightFace:');\n\n    // run faceres, inherits face from blazeface\n    instance.analyze('Start Description:');\n    if (instance.config.async) {\n      descRes = faceres.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length);\n    } else {\n      instance.state = 'run:description';\n      timeStamp = now();\n      descRes = await faceres.predict(faces[i].tensor || tf.tensor([]), instance.config, i, faces.length);\n      instance.performance.description = env.perfadd ? (instance.performance.description || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n    }\n    instance.analyze('End Description:');\n\n    // if async wait for results\n    if (instance.config.async) {\n      [ageRes, genderRes, emotionRes, mobilefacenetRes, insightfaceRes, descRes, gearRes, antispoofRes, livenessRes] = await Promise.all([ageRes, genderRes, emotionRes, mobilefacenetRes, insightfaceRes, descRes, gearRes, antispoofRes, livenessRes]);\n    }\n    instance.analyze('Finish Face:');\n\n    if (instance.config.face['ssrnet']?.enabled && ageRes && genderRes) { // override age/gender if ssrnet model is used\n      descRes = {\n        ...(descRes as DescRes),\n        age: (ageRes as { age: number}).age,\n        gender: (genderRes as { gender: Gender, genderScore: number }).gender,\n        genderScore: (genderRes as { gender: Gender, genderScore: number }).genderScore,\n      };\n    }\n    if (instance.config.face.gear?.enabled && gearRes) { // override age/gender/race if gear model is used\n      descRes = {\n        ...(descRes as DescRes),\n        age: (gearRes as gear.GearType).age,\n        gender: (gearRes as gear.GearType).gender,\n        genderScore: (gearRes as gear.GearType).genderScore,\n        race: (gearRes as gear.GearType).race,\n      };\n    }\n    if (instance.config.face['mobilefacenet']?.enabled && mobilefacenetRes) { // override descriptor if mobilefacenet model is used\n      (descRes as DescRes).descriptor = mobilefacenetRes as number[];\n    }\n\n    if (instance.config.face['insightface']?.enabled && insightfaceRes) { // override descriptor if insightface model is used\n      (descRes as DescRes).descriptor = insightfaceRes as number[];\n    }\n\n    // calculate iris distance\n    // iris: array[ center, left, top, right, bottom]\n    if (!instance.config.face.iris?.enabled) {\n      // if (faces[i]?.annotations?.leftEyeIris) delete faces[i].annotations.leftEyeIris;\n      // if (faces[i]?.annotations?.rightEyeIris) delete faces[i].annotations.rightEyeIris;\n    }\n    const irisSize = (faces[i]?.annotations?.leftEyeIris?.[0] && faces[i]?.annotations?.rightEyeIris?.[0]\n      && (faces[i].annotations.leftEyeIris.length > 0) && (faces[i].annotations.rightEyeIris.length > 0)\n      && (faces[i].annotations.leftEyeIris[0] !== null) && (faces[i].annotations.rightEyeIris[0] !== null))\n      ? Math.max(Math.abs(faces[i].annotations.leftEyeIris[3][0] - faces[i].annotations.leftEyeIris[1][0]), Math.abs(faces[i].annotations.rightEyeIris[4][1] - faces[i].annotations.rightEyeIris[2][1])) / input.shape[2]\n      : 0; // note: average human iris size is 11.7mm\n\n    // optionally return tensor\n    const tensor = instance.config.face.detector?.return ? tf.squeeze(faces[i].tensor) : null;\n    // dispose original face tensor\n    tf.dispose(faces[i].tensor);\n    // delete temp face image\n    if (faces[i].tensor) delete faces[i].tensor;\n    // combine results\n    const res: FaceResult = {\n      ...faces[i],\n      id: i,\n    };\n    if ((descRes as DescRes).age) res.age = (descRes as DescRes).age;\n    if ((descRes as DescRes).gender) res.gender = (descRes as DescRes).gender;\n    if ((descRes as DescRes).genderScore) res.genderScore = (descRes as DescRes).genderScore;\n    if ((descRes as DescRes).descriptor) res.embedding = (descRes as DescRes).descriptor;\n    if ((descRes as DescRes).race) res.race = (descRes as DescRes).race as { score: number, race: Race }[];\n    if (emotionRes) res.emotion = emotionRes as { score: number, emotion: Emotion }[];\n    if (antispoofRes) res.real = antispoofRes as number;\n    if (livenessRes) res.live = livenessRes as number;\n    if (irisSize && irisSize !== 0) res.iris = Math.trunc(500 / irisSize / 11.7) / 100;\n    if (rotation) res.rotation = rotation;\n    if (tensor) res.tensor = tensor;\n    faceRes.push(res);\n    instance.analyze('End Face');\n  }\n  instance.analyze('End FaceMesh:');\n  if (instance.config.async) {\n    if (instance.performance.face) delete instance.performance.face;\n    if (instance.performance.age) delete instance.performance.age;\n    if (instance.performance.gender) delete instance.performance.gender;\n    if (instance.performance.emotion) delete instance.performance.emotion;\n  }\n  return faceRes;\n};\n", "/**\n * Gesture detection algorithm\n */\n\nimport type { GestureResult, BodyResult, FaceResult, HandResult, Point } from '../result';\nimport * as fingerPose from '../hand/fingerpose';\n\n/** face gesture type */\nexport type FaceGesture =\n  `facing ${'left' | 'center' | 'right'}`\n  | `blink ${'left' | 'right'} eye`\n  | `mouth ${number}% open`\n  | `head ${'up' | 'down'}`;\n\n/** iris gesture type */\nexport type IrisGesture =\n  'facing center'\n  | `looking ${'left' | 'right' | 'up' | 'down'}`\n  | 'looking center';\n\n/** body gesture type */\nexport type BodyGesture =\n  `leaning ${'left' | 'right'}`\n  | `raise ${'left' | 'right'} hand`\n  | 'i give up';\n\n/** hand gesture type */\nexport type HandGesture =\n  `${'thumb' | 'index' | 'middle' | 'ring' | 'pinky'} forward`\n  | `${'thumb' | 'index' | 'middle' | 'ring' | 'pinky'} up`\n  | 'victory'\n  | 'thumbs up';\n\nexport const body = (res: BodyResult[]): GestureResult[] => {\n  if (!res) return [];\n  const gestures: { body: number, gesture: BodyGesture }[] = [];\n  for (let i = 0; i < res.length; i++) {\n    // raising hands\n    const leftWrist = res[i].keypoints.find((a) => (a.part === 'leftWrist'));\n    const rightWrist = res[i].keypoints.find((a) => (a.part === 'rightWrist'));\n    const nose = res[i].keypoints.find((a) => (a.part === 'nose'));\n    if (nose && leftWrist && rightWrist && (leftWrist.position[1] < nose.position[1]) && (rightWrist.position[1] < nose.position[1])) gestures.push({ body: i, gesture: 'i give up' });\n    else if (nose && leftWrist && (leftWrist.position[1] < nose.position[1])) gestures.push({ body: i, gesture: 'raise left hand' });\n    else if (nose && rightWrist && (rightWrist.position[1] < nose.position[1])) gestures.push({ body: i, gesture: 'raise right hand' });\n\n    // leaning\n    const leftShoulder = res[i].keypoints.find((a) => (a.part === 'leftShoulder'));\n    const rightShoulder = res[i].keypoints.find((a) => (a.part === 'rightShoulder'));\n    if (leftShoulder && rightShoulder && Math.abs(leftShoulder.positionRaw[1] - rightShoulder.positionRaw[1]) > 0.1) {\n      gestures.push({ body: i, gesture: `leaning ${(leftShoulder.position[1] > rightShoulder.position[1]) ? 'left' : 'right'}` });\n    }\n  }\n  return gestures;\n};\n\nexport const face = (res: FaceResult[]): GestureResult[] => {\n  if (!res) return [];\n  const gestures: { face: number, gesture: FaceGesture }[] = [];\n  for (let i = 0; i < res.length; i++) {\n    if (res[i].mesh && res[i].mesh.length > 450) {\n      const zDiff = (res[i].mesh[33][2] || 0) - (res[i].mesh[263][2] || 0);\n      const xDiff = res[i].mesh[33][0] - res[i].mesh[263][0];\n      if (Math.abs(zDiff / xDiff) <= 0.15) gestures.push({ face: i, gesture: 'facing center' });\n      else gestures.push({ face: i, gesture: `facing ${zDiff < 0 ? 'left' : 'right'}` });\n      const openLeft = Math.abs(res[i].mesh[374][1] - res[i].mesh[386][1]) / Math.abs(res[i].mesh[443][1] - res[i].mesh[450][1]); // center of eye inner lid y coord div center of wider eye border y coord\n      if (openLeft < 0.2) gestures.push({ face: i, gesture: 'blink left eye' });\n      const openRight = Math.abs(res[i].mesh[145][1] - res[i].mesh[159][1]) / Math.abs(res[i].mesh[223][1] - res[i].mesh[230][1]); // center of eye inner lid y coord div center of wider eye border y coord\n      if (openRight < 0.2) gestures.push({ face: i, gesture: 'blink right eye' });\n      const mouthOpen = Math.min(100, 500 * Math.abs(res[i].mesh[13][1] - res[i].mesh[14][1]) / Math.abs(res[i].mesh[10][1] - res[i].mesh[152][1]));\n      if (mouthOpen > 10) gestures.push({ face: i, gesture: `mouth ${Math.trunc(mouthOpen)}% open` });\n      const chinDepth = res[i].mesh[152][2] || 0;\n      if (Math.abs(chinDepth) > 10) gestures.push({ face: i, gesture: `head ${chinDepth < 0 ? 'up' : 'down'}` });\n    }\n  }\n  return gestures;\n};\n\nexport const iris = (res: FaceResult[]): GestureResult[] => {\n  if (!res) return [];\n  const gestures: { iris: number, gesture: IrisGesture }[] = [];\n  for (let i = 0; i < res.length; i++) {\n    if (!res[i].annotations?.leftEyeIris?.[0] || !res[i].annotations?.rightEyeIris?.[0]) continue;\n    const sizeXLeft = res[i].annotations.leftEyeIris[3][0] - res[i].annotations.leftEyeIris[1][0];\n    const sizeYLeft = res[i].annotations.leftEyeIris[4][1] - res[i].annotations.leftEyeIris[2][1];\n    const areaLeft = Math.abs(sizeXLeft * sizeYLeft);\n\n    const sizeXRight = res[i].annotations.rightEyeIris[3][0] - res[i].annotations.rightEyeIris[1][0];\n    const sizeYRight = res[i].annotations.rightEyeIris[4][1] - res[i].annotations.rightEyeIris[2][1];\n    const areaRight = Math.abs(sizeXRight * sizeYRight);\n\n    let center = false;\n    const difference = Math.abs(areaLeft - areaRight) / Math.max(areaLeft, areaRight);\n    if (difference < 0.25) {\n      center = true;\n      gestures.push({ iris: i, gesture: 'facing center' });\n    }\n\n    const leftIrisCenterX = Math.abs(res[i].mesh[263][0] - res[i].annotations.leftEyeIris[0][0]) / res[i].box[2];\n    const rightIrisCenterX = Math.abs(res[i].mesh[33][0] - res[i].annotations.rightEyeIris[0][0]) / res[i].box[2];\n    if (leftIrisCenterX > 0.06 || rightIrisCenterX > 0.06) center = false;\n    if (leftIrisCenterX > rightIrisCenterX) { // check eye with bigger offset\n      if (leftIrisCenterX > 0.05) gestures.push({ iris: i, gesture: 'looking right' });\n    } else {\n      if (rightIrisCenterX > 0.05) gestures.push({ iris: i, gesture: 'looking left' });\n    }\n\n    const rightIrisCenterY = Math.abs(res[i].mesh[145][1] - res[i].annotations.rightEyeIris[0][1]) / res[i].box[3];\n    const leftIrisCenterY = Math.abs(res[i].mesh[374][1] - res[i].annotations.leftEyeIris[0][1]) / res[i].box[3];\n    if (leftIrisCenterY < 0.01 || rightIrisCenterY < 0.01 || leftIrisCenterY > 0.022 || rightIrisCenterY > 0.022) center = false;\n    if (leftIrisCenterY < 0.01 || rightIrisCenterY < 0.01) gestures.push({ iris: i, gesture: 'looking down' });\n    if (leftIrisCenterY > 0.022 || rightIrisCenterY > 0.022) gestures.push({ iris: i, gesture: 'looking up' });\n\n    // still center;\n    if (center) gestures.push({ iris: i, gesture: 'looking center' });\n  }\n  return gestures;\n};\n\nexport const hand = (res: HandResult[]): GestureResult[] => {\n  if (!res) return [];\n  const gestures: { hand: number, gesture: HandGesture }[] = [];\n  for (let i = 0; i < res.length; i++) {\n    const fingers: { name: string, position: Point }[] = [];\n    if (res[i].annotations) {\n      for (const [finger, pos] of Object.entries(res[i].annotations)) {\n        if (finger !== 'palmBase' && Array.isArray(pos) && pos[0]) fingers.push({ name: finger.toLowerCase(), position: pos[0] }); // get tip of each finger\n      }\n    }\n    if (fingers && fingers.length > 0) {\n      const closest = fingers.reduce((best, a) => ((best.position[2] || 0) < (a.position[2] || 0) ? best : a));\n      gestures.push({ hand: i, gesture: `${closest.name} forward` as HandGesture });\n      const highest = fingers.reduce((best, a) => (best.position[1] < a.position[1] ? best : a));\n      gestures.push({ hand: i, gesture: `${highest.name} up` as HandGesture });\n    }\n    if (res[i].keypoints) {\n      const poses = fingerPose.match(res[i].keypoints);\n      for (const pose of poses) gestures.push({ hand: i, gesture: pose.name as HandGesture });\n    }\n  }\n  return gestures;\n};\n", "/**\n * Results interpolation for smoothening of video detection results inbetween detected frames\n */\n\nimport type { Result, FaceResult, BodyResult, HandResult, ObjectResult, PersonResult, Box, Point, BodyLandmark, BodyAnnotation } from '../result';\nimport type { Config } from '../config';\n\nimport * as moveNetCoords from '../body/movenetcoords';\nimport * as blazePoseCoords from '../body/blazeposecoords';\nimport * as efficientPoseCoords from '../body/efficientposecoords';\nimport { now } from './util';\nimport { env } from './env';\n\nconst bufferedResult: Result = { face: [], body: [], hand: [], gesture: [], object: [], persons: [], performance: {}, timestamp: 0, error: null };\nlet interpolateTime = 0;\n\nexport function calc(newResult: Result, config: Config): Result {\n  const t0 = now();\n  if (!newResult) return { face: [], body: [], hand: [], gesture: [], object: [], persons: [], performance: {}, timestamp: 0, error: null };\n  // each record is only updated using deep clone when number of detected record changes, otherwise it will converge by itself\n  // otherwise bufferedResult is a shallow clone of result plus updated local calculated values\n  // thus mixing by-reference and by-value assignments to minimize memory operations\n\n  const elapsed = Date.now() - newResult.timestamp;\n\n  /* curve fitted: buffer = 8 - ln(delay)\n     interpolation formula: current = ((buffer - 1) * previous + live) / buffer\n    - at  50ms delay buffer = ~4.1 => 28% towards live data\n    - at 250ms delay buffer = ~2.5 => 40% towards live data\n    - at 500ms delay buffer = ~1.8 => 55% towards live data\n    - at 750ms delay buffer = ~1.4 => 71% towards live data\n    - at  1sec delay buffer = 1 which means live data is used\n  */\n  const bufferedFactor = elapsed < 1000 ? 8 - Math.log(elapsed + 1) : 1;\n\n  if (newResult.canvas) bufferedResult.canvas = newResult.canvas;\n  if (newResult.error) bufferedResult.error = newResult.error;\n\n  // interpolate body results\n  if (!bufferedResult.body || (newResult.body.length !== bufferedResult.body.length)) {\n    bufferedResult.body = JSON.parse(JSON.stringify(newResult.body)) as BodyResult[]; // deep clone once\n  } else {\n    for (let i = 0; i < newResult.body.length; i++) {\n      const box = newResult.body[i].box // update box\n        .map((newBoxCoord, j) => ((bufferedFactor - 1) * bufferedResult.body[i].box[j] + newBoxCoord) / bufferedFactor) as Box;\n      const boxRaw = newResult.body[i].boxRaw // update boxRaw\n        .map((newBoxCoord, j) => ((bufferedFactor - 1) * bufferedResult.body[i].boxRaw[j] + newBoxCoord) / bufferedFactor) as Box;\n      const keypoints = (newResult.body[i].keypoints // update keypoints\n        .map((newKpt, j) => ({\n          score: newKpt.score,\n          part: newKpt.part,\n          position: [\n            bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].position[0] || 0) + (newKpt.position[0] || 0)) / bufferedFactor : newKpt.position[0],\n            bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].position[1] || 0) + (newKpt.position[1] || 0)) / bufferedFactor : newKpt.position[1],\n            bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].position[2] || 0) + (newKpt.position[2] || 0)) / bufferedFactor : newKpt.position[2],\n          ],\n          positionRaw: [\n            bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].positionRaw[0] || 0) + (newKpt.positionRaw[0] || 0)) / bufferedFactor : newKpt.positionRaw[0],\n            bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].positionRaw[1] || 0) + (newKpt.positionRaw[1] || 0)) / bufferedFactor : newKpt.positionRaw[1],\n            bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].positionRaw[2] || 0) + (newKpt.positionRaw[2] || 0)) / bufferedFactor : newKpt.positionRaw[2],\n          ],\n          distance: [\n            bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].distance?.[0] || 0) + (newKpt.distance?.[0] || 0)) / bufferedFactor : newKpt.distance?.[0],\n            bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].distance?.[1] || 0) + (newKpt.distance?.[1] || 0)) / bufferedFactor : newKpt.distance?.[1],\n            bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].distance?.[2] || 0) + (newKpt.distance?.[2] || 0)) / bufferedFactor : newKpt.distance?.[2],\n          ],\n        }))) as { score: number, part: BodyLandmark, position: [number, number, number?], positionRaw: [number, number, number?] }[];\n\n      const annotations: Record<BodyAnnotation, Point[][]> = {} as Record<BodyAnnotation, Point[][]>; // recreate annotations\n      let coords = { connected: {} };\n      if (config.body.modelPath?.includes('efficientpose')) coords = efficientPoseCoords;\n      else if (config.body.modelPath?.includes('blazepose')) coords = blazePoseCoords;\n      else if (config.body.modelPath?.includes('movenet')) coords = moveNetCoords;\n      for (const [name, indexes] of Object.entries(coords.connected as Record<string, string[]>)) {\n        const pt: Point[][] = [];\n        for (let j = 0; j < indexes.length - 1; j++) {\n          const pt0 = keypoints.find((kp) => kp.part === indexes[j]);\n          const pt1 = keypoints.find((kp) => kp.part === indexes[j + 1]);\n          // if (pt0 && pt1 && pt0.score > (config.body.minConfidence || 0) && pt1.score > (config.body.minConfidence || 0)) pt.push([pt0.position, pt1.position]);\n          if (pt0 && pt1) pt.push([pt0.position, pt1.position]);\n        }\n        annotations[name] = pt;\n      }\n      bufferedResult.body[i] = { ...newResult.body[i], box, boxRaw, keypoints, annotations }; // shallow clone plus updated values\n    }\n  }\n\n  // interpolate hand results\n  if (!bufferedResult.hand || (newResult.hand.length !== bufferedResult.hand.length)) {\n    bufferedResult.hand = JSON.parse(JSON.stringify(newResult.hand)); // deep clone once\n  } else {\n    for (let i = 0; i < newResult.hand.length; i++) {\n      const box = (newResult.hand[i].box// update box\n        .map((b, j) => ((bufferedFactor - 1) * bufferedResult.hand[i].box[j] + b) / bufferedFactor)) as Box;\n      const boxRaw = (newResult.hand[i].boxRaw // update boxRaw\n        .map((b, j) => ((bufferedFactor - 1) * bufferedResult.hand[i].boxRaw[j] + b) / bufferedFactor)) as Box;\n      if (bufferedResult.hand[i].keypoints.length !== newResult.hand[i].keypoints.length) bufferedResult.hand[i].keypoints = newResult.hand[i].keypoints; // reset keypoints as previous frame did not have them\n      const keypoints = newResult.hand[i].keypoints && newResult.hand[i].keypoints.length > 0 ? newResult.hand[i].keypoints // update landmarks\n        .map((landmark, j) => landmark\n          .map((coord, k) => (((bufferedFactor - 1) * (bufferedResult.hand[i].keypoints[j][k] || 1) + (coord || 0)) / bufferedFactor)) as Point)\n        : [];\n      let annotations = {};\n      if (Object.keys(bufferedResult.hand[i].annotations).length !== Object.keys(newResult.hand[i].annotations).length) {\n        bufferedResult.hand[i].annotations = newResult.hand[i].annotations; // reset annotations as previous frame did not have them\n        annotations = bufferedResult.hand[i].annotations;\n      } else if (newResult.hand[i].annotations) {\n        for (const key of Object.keys(newResult.hand[i].annotations)) { // update annotations\n          annotations[key] = newResult.hand[i]?.annotations?.[key]?.[0]\n            ? newResult.hand[i].annotations[key]\n              .map((val, j: number) => val\n                .map((coord: number, k: number) => ((bufferedFactor - 1) * bufferedResult.hand[i].annotations[key][j][k] + coord) / bufferedFactor))\n            : null;\n        }\n      }\n      bufferedResult.hand[i] = { ...newResult.hand[i], box, boxRaw, keypoints, annotations: annotations as HandResult['annotations'] }; // shallow clone plus updated values\n    }\n  }\n\n  // interpolate face results\n  if (!bufferedResult.face || (newResult.face.length !== bufferedResult.face.length)) {\n    bufferedResult.face = JSON.parse(JSON.stringify(newResult.face)) as FaceResult[]; // deep clone once\n  } else {\n    for (let i = 0; i < newResult.face.length; i++) {\n      const box = (newResult.face[i].box // update box\n        .map((b, j) => ((bufferedFactor - 1) * bufferedResult.face[i].box[j] + b) / bufferedFactor)) as Box;\n      const boxRaw = (newResult.face[i].boxRaw // update boxRaw\n        .map((b, j) => ((bufferedFactor - 1) * bufferedResult.face[i].boxRaw[j] + b) / bufferedFactor)) as Box;\n      if (newResult.face[i].rotation) {\n        const rotation: {\n          matrix: [number, number, number, number, number, number, number, number, number],\n          angle: { roll: number, yaw: number, pitch: number },\n          gaze: { bearing: number, strength: number }\n        } = { matrix: [0, 0, 0, 0, 0, 0, 0, 0, 0], angle: { roll: 0, yaw: 0, pitch: 0 }, gaze: { bearing: 0, strength: 0 } };\n        rotation.matrix = newResult.face[i].rotation?.matrix as [number, number, number, number, number, number, number, number, number];\n        rotation.angle = {\n          roll: ((bufferedFactor - 1) * (bufferedResult.face[i].rotation?.angle?.roll || 0) + (newResult.face[i].rotation?.angle?.roll || 0)) / bufferedFactor,\n          yaw: ((bufferedFactor - 1) * (bufferedResult.face[i].rotation?.angle?.yaw || 0) + (newResult.face[i].rotation?.angle?.yaw || 0)) / bufferedFactor,\n          pitch: ((bufferedFactor - 1) * (bufferedResult.face[i].rotation?.angle?.pitch || 0) + (newResult.face[i].rotation?.angle?.pitch || 0)) / bufferedFactor,\n        };\n        rotation.gaze = {\n          // not fully correct due projection on circle, also causes wrap-around draw on jump from negative to positive\n          bearing: ((bufferedFactor - 1) * (bufferedResult.face[i].rotation?.gaze.bearing || 0) + (newResult.face[i].rotation?.gaze.bearing || 0)) / bufferedFactor,\n          strength: ((bufferedFactor - 1) * (bufferedResult.face[i].rotation?.gaze.strength || 0) + (newResult.face[i].rotation?.gaze.strength || 0)) / bufferedFactor,\n        };\n        bufferedResult.face[i] = { ...newResult.face[i], rotation, box, boxRaw }; // shallow clone plus updated values\n      } else {\n        bufferedResult.face[i] = { ...newResult.face[i], box, boxRaw }; // shallow clone plus updated values\n      }\n    }\n  }\n\n  // interpolate object detection results\n  if (!bufferedResult.object || (newResult.object.length !== bufferedResult.object.length)) {\n    bufferedResult.object = JSON.parse(JSON.stringify(newResult.object)) as ObjectResult[]; // deep clone once\n  } else {\n    for (let i = 0; i < newResult.object.length; i++) {\n      const box = (newResult.object[i].box // update box\n        .map((b, j) => ((bufferedFactor - 1) * bufferedResult.object[i].box[j] + b) / bufferedFactor)) as Box;\n      const boxRaw = (newResult.object[i].boxRaw // update boxRaw\n        .map((b, j) => ((bufferedFactor - 1) * bufferedResult.object[i].boxRaw[j] + b) / bufferedFactor)) as Box;\n      bufferedResult.object[i] = { ...newResult.object[i], box, boxRaw }; // shallow clone plus updated values\n    }\n  }\n\n  // interpolate person results\n  if (newResult.persons) {\n    const newPersons = newResult.persons; // trigger getter function\n    if (!bufferedResult.persons || (newPersons.length !== bufferedResult.persons.length)) {\n      bufferedResult.persons = JSON.parse(JSON.stringify(newPersons)) as PersonResult[];\n    } else {\n      for (let i = 0; i < newPersons.length; i++) { // update person box, we don't update the rest as it's updated as reference anyhow\n        bufferedResult.persons[i].box = (newPersons[i].box\n          .map((box, j) => ((bufferedFactor - 1) * bufferedResult.persons[i].box[j] + box) / bufferedFactor)) as Box;\n      }\n    }\n  }\n\n  // just copy latest gestures without interpolation\n  if (newResult.gesture) bufferedResult.gesture = newResult.gesture;\n\n  // append interpolation performance data\n  const t1 = now();\n  interpolateTime = env.perfadd ? interpolateTime + Math.round(t1 - t0) : Math.round(t1 - t0);\n  if (newResult.performance) bufferedResult.performance = { ...newResult.performance, interpolate: interpolateTime };\n\n  return bufferedResult;\n}\n", "/** Face descriptor type as number array */\nexport type Descriptor = number[]\nexport type MatchOptions = { order?: number, threshold?: number, multiplier?: number, min?: number, max?: number } | undefined;\n\n/** Calculates distance between two descriptors\n * @param options - calculation options\n * - order - algorithm to use\n *   Euclidean distance if `order` is 2 (default), Minkowski distance algorithm of nth order if `order` is higher than 2\n * - multiplier - by how much to enhance difference analysis in range of 1..100\n *   default is 20 which normalizes results to similarity above 0.5 can be considered a match\n */\nexport function distance(descriptor1: Descriptor, descriptor2: Descriptor, options: MatchOptions = { order: 2, multiplier: 25 }) {\n  // general minkowski distance, euclidean distance is limited case where order is 2\n  if (!descriptor1 || !descriptor1) return Number.MAX_SAFE_INTEGER;\n  let sum = 0;\n  for (let i = 0; i < descriptor1.length; i++) {\n    const diff = (!options.order || options.order === 2) ? (descriptor1[i] - descriptor2[i]) : (Math.abs(descriptor1[i] - descriptor2[i]));\n    sum += (!options.order || options.order === 2) ? (diff * diff) : (diff ** options.order);\n  }\n  return (options.multiplier || 20) * sum;\n}\n\n// invert distance to similarity, normalize to given range and clamp\nconst normalizeDistance = (dist, order, min, max) => {\n  if (dist === 0) return 1; // short circuit for identical inputs\n  const root = order === 2 ? Math.sqrt(dist) : dist ** (1 / order); // take root of distance\n  const norm = (1 - (root / 100) - min) / (max - min); // normalize to range\n  const clamp = Math.max(Math.min(norm, 1), 0); // clamp to 0..1\n  return clamp;\n};\n\n/** Calculates normalized similarity between two face descriptors based on their `distance`\n * @param options - calculation options\n * - order - algorithm to use\n *   Euclidean distance if `order` is 2 (default), Minkowski distance algorithm of nth order if `order` is higher than 2\n * - multiplier - by how much to enhance difference analysis in range of 1..100\n *   default is 20 which normalizes results to similarity above 0.5 can be considered a match\n * - min - normalize similarity result to a given range\n * - max - normalzie similarity resutl to a given range\n *   default is 0.2...0.8\n * Returns similarity between two face descriptors normalized to 0..1 range where 0 is no similarity and 1 is perfect similarity\n */\nexport function similarity(descriptor1: Descriptor, descriptor2: Descriptor, options: MatchOptions = { order: 2, multiplier: 25, min: 0.2, max: 0.8 }) {\n  const dist = distance(descriptor1, descriptor2, options);\n  return normalizeDistance(dist, options.order || 2, options.min || 0, options.max || 1);\n}\n\n/** Matches given descriptor to a closest entry in array of descriptors\n * @param descriptor - face descriptor\n * @param descriptors - array of face descriptors to commpare given descriptor to\n * @param options - see `similarity` method for options description\n * Returns\n * - `index` index array index where best match was found or -1 if no matches\n * - `distance` calculated `distance` of given descriptor to the best match\n * - `similarity` calculated normalized `similarity` of given descriptor to the best match\n*/\nexport function match(descriptor: Descriptor, descriptors: Descriptor[], options: MatchOptions = { order: 2, multiplier: 25, threshold: 0, min: 0.2, max: 0.8 }) {\n  if (!Array.isArray(descriptor) || !Array.isArray(descriptors) || descriptor.length < 64 || descriptors.length === 0) { // validate input\n    return { index: -1, distance: Number.POSITIVE_INFINITY, similarity: 0 };\n  }\n  let lowestDistance = Number.MAX_SAFE_INTEGER;\n  let index = -1;\n  for (let i = 0; i < descriptors.length; i++) {\n    const res = descriptors[i].length === descriptor.length ? distance(descriptor, descriptors[i], options) : Number.MAX_SAFE_INTEGER;\n    if (res < lowestDistance) {\n      lowestDistance = res;\n      index = i;\n    }\n    if (lowestDistance < (options.threshold || 0)) break;\n  }\n  const normalizedSimilarity = normalizeDistance(lowestDistance, options.order || 2, options.min || 0, options.max || 1);\n  return { index, distance: lowestDistance, similarity: normalizedSimilarity };\n}\n", "/**\n * Analyze detection Results and sort&combine them into per-person view\n */\n\nimport type { FaceResult, BodyResult, HandResult, GestureResult, PersonResult, Box } from '../result';\n\nexport function join(faces: FaceResult[], bodies: BodyResult[], hands: HandResult[], gestures: GestureResult[], shape: number[] | undefined): PersonResult[] {\n  let id = 0;\n  const persons: PersonResult[] = [];\n  for (const face of faces) { // person is defined primarily by face and then we append other objects as found\n    const person: PersonResult = { id: id++, face, body: null, hands: { left: null, right: null }, gestures: [], box: [0, 0, 0, 0] };\n    for (const body of bodies) {\n      if (face.box[0] > body.box[0] // x within body\n        && face.box[0] < body.box[0] + body.box[2]\n        && face.box[1] + face.box[3] > body.box[1] // y within body\n        && face.box[1] + face.box[3] < body.box[1] + body.box[3]) {\n        person.body = body;\n      }\n    }\n    if (person.body) { // only try to join hands if body is found\n      for (const hand of hands) {\n        if (hand.box[0] + hand.box[2] > person.body.box[0] // x within body for left hand\n          && hand.box[0] + hand.box[2] < person.body.box[0] + person.body.box[2]\n          && hand.box[1] + hand.box[3] > person.body.box[1] // x within body for left hand\n          && hand.box[1] + hand.box[3] < person.body.box[1] + person.body.box[3]) {\n          if (person.hands) person.hands.left = hand;\n        }\n        if (hand.box[0] < person.body.box[0] + person.body.box[2] // x within body for right hand\n          && hand.box[0] > person.body.box[0]\n          && hand.box[1] + hand.box[3] > person.body.box[1] // x within body for right hand\n          && hand.box[1] + hand.box[3] < person.body.box[1] + person.body.box[3]) {\n          if (person.hands) person.hands.right = hand;\n        }\n      }\n    }\n    for (const gesture of gestures) { // append all gestures according to ids\n      if (gesture['face'] !== undefined && gesture['face'] === face.id) person.gestures.push(gesture);\n      else if (gesture['iris'] !== undefined && gesture['iris'] === face.id) person.gestures.push(gesture);\n      else if (gesture['body'] !== undefined && gesture['body'] === person.body?.id) person.gestures.push(gesture);\n      else if (gesture['hand'] !== undefined && gesture['hand'] === person.hands.left?.id) person.gestures.push(gesture);\n      else if (gesture['hand'] !== undefined && gesture['hand'] === person.hands.right?.id) person.gestures.push(gesture);\n    }\n\n    // create new overarching box from all boxes belonging to person\n    const x: number[] = [];\n    const y: number[] = [];\n    const extractXY = (box: Box | undefined) => { // extract all [x, y] coordinates from boxes [x, y, width, height]\n      if (box && box.length === 4) {\n        x.push(box[0], box[0] + box[2]);\n        y.push(box[1], box[1] + box[3]);\n      }\n    };\n    extractXY(person.face.box);\n    extractXY(person.body?.box);\n    extractXY(person.hands.left?.box);\n    extractXY(person.hands.right?.box);\n    const minX = Math.min(...x);\n    const minY = Math.min(...y);\n    person.box = [minX, minY, Math.max(...x) - minX, Math.max(...y) - minY]; // create new overarching box\n\n    // shape is known so we calculate boxRaw as well\n    if (shape?.[1] && shape?.[2]) person.boxRaw = [person.box[0] / shape[2], person.box[1] / shape[1], person.box[2] / shape[2], person.box[3] / shape[1]];\n\n    persons.push(person);\n  }\n  return persons;\n}\n", "/**\n * Embedded sample images used during warmup in dataURL format\n */\n\n// data:image/jpeg;base64,\nexport const face = 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"/**\n * Warmup algorithm that uses embedded images to exercise loaded models for faster future inference\n */\n\nimport { log, now, mergeDeep } from './util/util';\nimport * as sample from './sample';\nimport * as tf from '../dist/tfjs.esm.js';\nimport * as image from './image/image';\nimport * as backend from './tfjs/backend';\nimport { env } from './util/env';\nimport type { Config } from './config';\nimport type { Result } from './result';\nimport { Human, models } from './human';\nimport type { Tensor } from './exports';\n\nasync function warmupBitmap(instance: Human): Promise<Result | undefined> {\n  const b64toBlob = (base64: string, type = 'application/octet-stream') => fetch(`data:${type};base64,${base64}`).then((res) => res.blob());\n  let blob: Blob | null;\n  let res: Result | undefined;\n  switch (instance.config.warmup) {\n    case 'face': blob = await b64toBlob(sample.face); break;\n    case 'body':\n    case 'full': blob = await b64toBlob(sample.body); break;\n    default: blob = null;\n  }\n  if (blob) {\n    const bitmap = await createImageBitmap(blob);\n    res = await instance.detect(bitmap, instance.config);\n    bitmap.close();\n  }\n  return res;\n}\n\nasync function warmupCanvas(instance: Human): Promise<Result | undefined> {\n  return new Promise((resolve) => {\n    let src: string;\n    // let size = 0;\n    switch (instance.config.warmup) {\n      case 'face':\n        // size = 256;\n        src = 'data:image/jpeg;base64,' + sample.face;\n        break;\n      case 'full':\n      case 'body':\n        // size = 1200;\n        src = 'data:image/jpeg;base64,' + sample.body;\n        break;\n      default:\n        src = '';\n    }\n    // src = encodeURI('../assets/human-sample-upper.jpg');\n    let img: HTMLImageElement;\n    if (typeof Image !== 'undefined') img = new Image();\n    // @ts-ignore env.image is an external monkey-patch\n    else if (env.Image) img = new env.Image();\n    else return;\n    img.onload = async () => {\n      const canvas = image.canvas(img.naturalWidth, img.naturalHeight);\n      if (!canvas) {\n        log('Warmup: Canvas not found');\n        resolve(undefined);\n      } else {\n        const ctx = canvas.getContext('2d');\n        if (ctx) ctx.drawImage(img, 0, 0);\n        // const data = ctx?.getImageData(0, 0, canvas.height, canvas.width);\n        const tensor = await instance.image(canvas);\n        const res = tensor.tensor ? await instance.detect(tensor.tensor, instance.config) : undefined;\n        resolve(res);\n      }\n    };\n    if (src) img.src = src;\n    else resolve(undefined);\n  });\n}\n\nasync function warmupNode(instance: Human): Promise<Result | undefined> {\n  const atob = (str: string) => Buffer.from(str, 'base64');\n  let img;\n  if (instance.config.warmup === 'face') img = atob(sample.face);\n  else img = atob(sample.body);\n  let res: Result;\n  if (('node' in tf) && (tf.getBackend() === 'tensorflow')) {\n    const data: Tensor = tf['node'].decodeJpeg(img); // eslint-disable-line import/namespace\n    const expanded: Tensor = tf.expandDims(data, 0);\n    instance.tf.dispose(data);\n    // log('Input:', expanded);\n    res = await instance.detect(expanded, instance.config);\n    instance.tf.dispose(expanded);\n  } else {\n    if (instance.config.debug) log('Warmup tfjs-node not loaded');\n    /*\n    const input = await canvasJS.loadImage(img);\n    const canvas = canvasJS.createCanvas(input.width, input.height);\n    const ctx = canvas.getContext('2d');\n    ctx.drawImage(img, 0, 0, input.width, input.height);\n    res = await instance.detect(input, instance.config);\n    */\n  }\n  // @ts-ignore\n  return res;\n}\n\nasync function runInference(instance: Human) {\n  let res: Result | undefined;\n  if (typeof createImageBitmap === 'function') res = await warmupBitmap(instance);\n  else if (typeof Image !== 'undefined' || env.Canvas !== undefined) res = await warmupCanvas(instance);\n  else res = await warmupNode(instance);\n  return res;\n}\n\n/** Runs pre-compile on all loaded models */\nexport async function runCompile(instance: Human) {\n  if (!tf.env().flagRegistry.ENGINE_COMPILE_ONLY) return; // tfjs does not support compile-only inference\n  const backendType = tf.getBackend();\n  const webGLBackend = tf.backend();\n  if ((backendType !== 'webgl' && backendType !== 'humangl') || !webGLBackend?.checkCompileCompletion) {\n    // log('compile pass: skip');\n    return;\n  }\n  tf.env().set('ENGINE_COMPILE_ONLY', true);\n  const numTensorsStart = tf.engine().state.numTensors;\n  const compiledModels: string[] = [];\n  for (const [modelName, model] of Object.entries(instance.models).filter(([key, val]) => (key !== null && val !== null))) {\n    const shape = (model.inputs?.[0]?.shape) ? [...model.inputs[0].shape] : [1, 64, 64, 3];\n    const dtype: string = (model.inputs?.[0]?.dtype) ? model.inputs[0].dtype : 'float32';\n    for (let dim = 0; dim < shape.length; dim++) {\n      if (shape[dim] === -1) shape[dim] = dim === 0 ? 1 : 64; // override batch number and any dynamic dimensions\n    }\n    const tensor = tf.zeros(shape, dtype);\n    try {\n      const res = model.execute(tensor);\n      compiledModels.push(modelName);\n      if (Array.isArray(res)) res.forEach((t) => tf.dispose(t));\n      else tf.dispose(res);\n    } catch {\n      if (instance.config.debug) log('compile fail model:', modelName);\n    }\n    tf.dispose(tensor);\n  }\n  const kernels = await webGLBackend.checkCompileCompletionAsync();\n  webGLBackend.getUniformLocations();\n  if (instance.config.debug) log('compile pass:', { models: compiledModels, kernels: kernels.length });\n  tf.env().set('ENGINE_COMPILE_ONLY', false);\n  const numTensorsEnd = tf.engine().state.numTensors;\n  if ((numTensorsEnd - numTensorsStart) > 0) log('tensor leak:', numTensorsEnd - numTensorsStart);\n}\n\n/** Warmup method pre-initializes all configured models for faster inference\n * - can take significant time on startup\n * - only used in browser environments for `webgl` and `humangl` backends\n * @param userConfig?: Config\n*/\nexport async function warmup(instance: Human, userConfig?: Partial<Config>): Promise<Result | undefined> {\n  await backend.check(instance, false);\n  const t0 = now();\n  instance.state = 'warmup';\n  if (userConfig) instance.config = mergeDeep(instance.config, userConfig) as Config;\n  if (!instance.config.warmup || instance.config.warmup.length === 0 || instance.config.warmup === 'none') {\n    return { face: [], body: [], hand: [], gesture: [], object: [], performance: instance.performance, timestamp: now(), persons: [], error: null };\n  }\n  return new Promise(async (resolve) => {\n    await models.load(instance);\n    await runCompile(instance);\n    const res = await runInference(instance);\n    const t1 = now();\n    if (instance.config.debug) log('warmup', instance.config.warmup, Math.round(t1 - t0), 'ms');\n    instance.emit('warmup');\n    resolve(res);\n  });\n}\n", "/**\n * Human main module\n * @default Human Library\n * @summary <https://github.com/vladmandic/human>\n * @author <https://github.com/vladmandic>\n * @copyright <https://github.com/vladmandic>\n * @license MIT\n */\n\n// module imports\nimport { log, now, mergeDeep, validate } from './util/util';\nimport { defaults } from './config';\nimport { env, Env } from './util/env';\nimport { WebCam } from './util/webcam';\nimport { setModelLoadOptions } from './tfjs/load';\nimport * as tf from '../dist/tfjs.esm.js';\nimport * as app from '../package.json';\nimport * as backend from './tfjs/backend';\nimport * as draw from './draw/draw';\nimport * as blazepose from './body/blazepose';\nimport * as centernet from './object/centernet';\nimport * as efficientpose from './body/efficientpose';\nimport * as face from './face/face';\nimport * as facemesh from './face/facemesh';\nimport * as faceres from './face/faceres';\nimport * as gesture from './gesture/gesture';\nimport * as handpose from './hand/handpose';\nimport * as handtrack from './hand/handtrack';\nimport * as humangl from './tfjs/humangl';\nimport * as image from './image/image';\nimport * as interpolate from './util/interpolate';\nimport * as meet from './segmentation/meet';\nimport * as match from './face/match';\nimport * as models from './models';\nimport * as movenet from './body/movenet';\nimport * as nanodet from './object/nanodet';\nimport * as persons from './util/persons';\nimport * as posenet from './body/posenet';\nimport * as rvm from './segmentation/rvm';\nimport * as selfie from './segmentation/selfie';\nimport * as warmups from './warmup';\n\n// type definitions\nimport type { Input, Tensor, DrawOptions, Config, Result, FaceResult, HandResult, BodyResult, ObjectResult, GestureResult, PersonResult, AnyCanvas } from './exports';\n// type exports\nexport * from './exports';\n\n/** **Human** library main class\n *\n * All methods and properties are available only as members of Human class\n *\n * - Configuration object definition: {@link Config}\n * - Results object definition: {@link Result}\n * - Possible inputs: {@link Input}\n *\n * @param userConfig - {@link Config}\n * @returns instance of {@link Human}\n */\nexport class Human {\n  /** Current version of Human library in *semver* format */\n  version: string;\n\n  /** Current configuration\n   * - Defaults: [config](https://github.com/vladmandic/human/blob/main/src/config.ts#L262)\n   */\n  config: Config;\n\n  /** Last known result of detect run\n   * - Can be accessed anytime after initial detection\n  */\n  result: Result;\n\n  /** Current state of Human library\n   * - Can be polled to determine operations that are currently executed\n   * - Progresses through: 'config', 'check', 'backend', 'load', 'run:<model>', 'idle'\n   */\n  state: string;\n\n  /** currenty processed image tensor and canvas */\n  process: { tensor: Tensor | null, canvas: AnyCanvas | null };\n\n  /** Instance of TensorFlow/JS used by Human\n   *  - Can be embedded or externally provided\n   * [TFJS API](https://js.tensorflow.org/api/latest/)\n   */\n  tf;\n\n  /** Object containing environment information used for diagnostics */\n  env: Env;\n\n  /** Draw helper classes that can draw detected objects on canvas using specified draw\n   * - canvas: draws input to canvas\n   * - options: are global settings for all draw operations, can be overriden for each draw method {@link DrawOptions}\n   * - face, body, hand, gesture, object, person: draws detected results as overlays on canvas\n   */\n  draw: { canvas: typeof draw.canvas, face: typeof draw.face, body: typeof draw.body, hand: typeof draw.hand, gesture: typeof draw.gesture, object: typeof draw.object, person: typeof draw.person, all: typeof draw.all, options: DrawOptions };\n\n  /** Currently loaded models\n   * @internal\n   * {@link models#Models}\n  */\n  models: models.Models;\n\n  /** Container for events dispatched by Human\n   * Possible events:\n   * - `create`: triggered when Human object is instantiated\n   * - `load`: triggered when models are loaded (explicitly or on-demand)\n   * - `image`: triggered when input image is processed\n   * - `result`: triggered when detection is complete\n   * - `warmup`: triggered when warmup is complete\n   * - `error`: triggered on some errors\n   */\n  events: EventTarget | undefined;\n  /** Reference face triangualtion array of 468 points, used for triangle references between points */\n  faceTriangulation: number[];\n  /** Refernce UV map of 468 values, used for 3D mapping of the face mesh */\n  faceUVMap: [number, number][];\n  /** Performance object that contains values for all recently performed operations */\n  performance: Record<string, number>; // perf members are dynamically defined as needed\n  #numTensors: number;\n  #analyzeMemoryLeaks: boolean;\n  #checkSanity: boolean;\n  /** WebGL debug info */\n  gl: Record<string, unknown>;\n  // definition end\n\n  /** Constructor for **Human** library that is futher used for all operations\n   * @param userConfig - user configuration object {@link Config}\n   */\n  constructor(userConfig?: Partial<Config>) {\n    this.env = env;\n    /*\n    defaults.wasmPath = tf.version['tfjs-core'].includes('-') // custom build or official build\n      ? 'https://vladmandic.github.io/tfjs/dist/'\n      : `https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-backend-wasm@${tf.version_core}/dist/`;\n    */\n    const tfVersion = (tf.version.tfjs || tf.version_core).replace(/-(.*)/, '');\n    defaults.wasmPath = `https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-backend-wasm@${tfVersion}/dist/`;\n    defaults.modelBasePath = env.browser ? '../models/' : 'file://models/';\n    defaults.backend = env.browser ? 'webgl' : 'tensorflow';\n    this.version = app.version; // expose version property on instance of class\n    Object.defineProperty(this, 'version', { value: app.version }); // expose version property directly on class itself\n    this.config = JSON.parse(JSON.stringify(defaults));\n    Object.seal(this.config);\n    this.config.cacheModels = typeof indexedDB !== 'undefined';\n    if (userConfig) this.config = mergeDeep(this.config, userConfig);\n    setModelLoadOptions(this.config);\n    this.tf = tf;\n    this.state = 'idle';\n    this.#numTensors = 0;\n    this.#analyzeMemoryLeaks = false;\n    this.#checkSanity = false;\n    this.performance = {};\n    this.events = (typeof EventTarget !== 'undefined') ? new EventTarget() : undefined;\n    // object that contains all initialized models\n    this.models = new models.Models();\n    // reexport draw methods\n    this.draw = {\n      options: draw.options,\n      canvas: (input: AnyCanvas | HTMLImageElement | HTMLVideoElement, output: AnyCanvas) => draw.canvas(input, output),\n      face: (output: AnyCanvas, result: FaceResult[], options?: Partial<DrawOptions>) => draw.face(output, result, options),\n      body: (output: AnyCanvas, result: BodyResult[], options?: Partial<DrawOptions>) => draw.body(output, result, options),\n      hand: (output: AnyCanvas, result: HandResult[], options?: Partial<DrawOptions>) => draw.hand(output, result, options),\n      gesture: (output: AnyCanvas, result: GestureResult[], options?: Partial<DrawOptions>) => draw.gesture(output, result, options),\n      object: (output: AnyCanvas, result: ObjectResult[], options?: Partial<DrawOptions>) => draw.object(output, result, options),\n      person: (output: AnyCanvas, result: PersonResult[], options?: Partial<DrawOptions>) => draw.person(output, result, options),\n      all: (output: AnyCanvas, result: Result, options?: Partial<DrawOptions>) => draw.all(output, result, options),\n    };\n    this.result = { face: [], body: [], hand: [], gesture: [], object: [], performance: {}, timestamp: 0, persons: [], error: null };\n    // export access to image processing\n    this.process = { tensor: null, canvas: null };\n    // export raw access to underlying models\n    this.faceTriangulation = facemesh.triangulation;\n    this.faceUVMap = facemesh.uvmap;\n    // set gl info\n    this.gl = humangl.config;\n    // init model validation\n    models.validateModel(this, null, '');\n    // include platform info\n    this.emit('create');\n    if (this.config.debug || this.env.browser) log(`version: ${this.version}`);\n    if (this.config.debug) log(`tfjs version: ${this.tf.version['tfjs-core']}`);\n    const envTemp = JSON.parse(JSON.stringify(this.env));\n    delete envTemp.kernels;\n    delete envTemp.initial;\n    delete envTemp.perfadd;\n    if (this.config.debug) log('environment:', envTemp);\n  }\n\n  /** internal function to measure tensor leaks */\n  analyze = (...msg: string[]) => {\n    if (!this.#analyzeMemoryLeaks) return;\n    const currentTensors = this.tf.engine().state.numTensors;\n    const previousTensors = this.#numTensors;\n    this.#numTensors = currentTensors;\n    const leaked = currentTensors - previousTensors;\n    if (leaked !== 0) log(...msg, leaked);\n  };\n\n  /** internal function for quick sanity check on inputs @hidden */\n  #sanity = (input: Input): null | string => {\n    if (!this.#checkSanity) return null;\n    if (!input) return 'input is not defined';\n    if (this.env.node && !(input instanceof tf.Tensor)) return 'input must be a tensor';\n    try {\n      this.tf.getBackend();\n    } catch {\n      return 'backend not loaded';\n    }\n    return null;\n  };\n\n  /** Reset configuration to default values */\n  reset(): void {\n    const currentBackend = this.config.backend; // save backend;\n    this.config = JSON.parse(JSON.stringify(defaults));\n    this.config.backend = currentBackend;\n    image.reset();\n    env.initial = true;\n  }\n\n  /** Validate current configuration schema */\n  validate(userConfig?: Partial<Config>) {\n    const msgs = validate(defaults, userConfig || this.config);\n    if (msgs.length === 0) this.config = mergeDeep(this.config, userConfig) as Config;\n    return msgs;\n  }\n\n  /** Check model for invalid kernel ops for current backend */\n  check() {\n    return models.validate(this);\n  }\n\n  /** Exports face matching methods {@link match#similarity} */\n  public similarity = match.similarity;\n  /** Exports face matching methods {@link match#distance} */\n  public distance = match.distance;\n  /** Exports face matching methods {@link match#match} */\n  public match = match.match;\n\n  /** Utility wrapper for performance.now() */\n  now(): number { // eslint-disable-line class-methods-use-this\n    return now();\n  }\n\n  /** Process input as return canvas and tensor\n   *\n   * @param input - any input {@link Input}\n   * @param getTensor - should image processing also return tensor or just canvas\n   * Returns object with `tensor` and `canvas`\n   */\n  image(input: Input, getTensor: boolean = true) {\n    return image.process(input, this.config, getTensor);\n  }\n\n  /** Segmentation method takes any input and returns RGBA tensor\n   * Note: Segmentation is not triggered as part of detect process\n   *\n   * @param input - {@link Input}\n   * Returns tensor which contains image data in RGBA format\n   */\n  async segmentation(input: Input, userConfig?: Partial<Config>): Promise<Tensor | null> {\n    if (userConfig) this.config = mergeDeep(this.config, userConfig) as Config;\n    if (!this.config.segmentation.enabled) return null;\n    const processed = await image.process(input, this.config);\n    if (!processed.tensor) return null;\n    let tensor: Tensor | null = null;\n    if (this.config.segmentation.modelPath?.includes('rvm')) tensor = await rvm.predict(processed.tensor, this.config);\n    if (this.config.segmentation.modelPath?.includes('meet')) tensor = await meet.predict(processed.tensor, this.config);\n    if (this.config.segmentation.modelPath?.includes('selfie')) tensor = await selfie.predict(processed.tensor, this.config);\n    tf.dispose(processed.tensor);\n    return tensor;\n  }\n\n  /** Enhance method performs additional enhacements to face image previously detected for futher processing\n   *\n   * @param input - Tensor as provided in human.result.face[n].tensor\n   * @returns Tensor\n   */\n  enhance(input: Tensor): Tensor | null { // eslint-disable-line class-methods-use-this\n    return faceres.enhance(input);\n  }\n\n  /** Compare two input tensors for pixel simmilarity\n   * - use `human.image` to process any valid input and get a tensor that can be used for compare\n   * - when passing manually generated tensors:\n   *  - both input tensors must be in format [1, height, width, 3]\n   *  - if resolution of tensors does not match, second tensor will be resized to match resolution of the first tensor\n   * - return value is pixel similarity score normalized by input resolution and rgb channels\n  */\n  compare(firstImageTensor: Tensor, secondImageTensor: Tensor): Promise<number> {\n    return image.compare(this.config, firstImageTensor, secondImageTensor);\n  }\n\n  /** Explicit backend initialization\n   *  - Normally done implicitly during initial load phase\n   *  - Call to explictly register and initialize TFJS backend without any other operations\n   *  - Use when changing backend during runtime\n   */\n  async init(): Promise<void> {\n    await backend.check(this, true);\n    await this.tf.ready();\n    image.reset();\n  }\n\n  /** WebCam helper methods\n   *\n   */\n  public webcam = new WebCam();\n\n  /** Load method preloads all configured models on-demand\n   * - Not explicitly required as any required model is load implicitly on it's first run\n   *\n   * @param userConfig - {@link Config}\n  */\n  async load(userConfig?: Partial<Config>): Promise<void> {\n    this.state = 'load';\n    const timeStamp = now();\n    const count = Object.values(this.models).filter((model) => model).length;\n    if (userConfig) this.config = mergeDeep(this.config, userConfig) as Config;\n\n    if (this.env.initial) { // print version info on first run and check for correct backend setup\n      if (!await backend.check(this, false)) log('error: backend check failed');\n      await tf.ready();\n      if (this.env.browser) {\n        if (this.config.debug) log('configuration:', this.config);\n        if (this.config.debug) log('tf flags:', this.tf.ENV.flags);\n      }\n    }\n\n    await models.load(this); // actually loads models\n    if (this.env.initial && this.config.debug) log('tf engine state:', this.tf.engine().state.numBytes, 'bytes', this.tf.engine().state.numTensors, 'tensors'); // print memory stats on first run\n    this.env.initial = false;\n\n    const loaded = Object.values(this.models).filter((model) => model).length;\n    if (loaded !== count) { // number of loaded models changed\n      models.validate(this); // validate kernel ops used by model against current backend\n      this.emit('load');\n    }\n\n    const current = Math.trunc(now() - timeStamp);\n    if (current > (this.performance.loadModels || 0)) this.performance.loadModels = this.env.perfadd ? (this.performance.loadModels || 0) + current : current;\n  }\n\n  /** emit event */\n  emit = (event: string) => {\n    if (this.events?.dispatchEvent) this.events.dispatchEvent(new Event(event));\n  };\n\n  /** Runs interpolation using last known result and returns smoothened result\n   * Interpolation is based on time since last known result so can be called independently\n   *\n   * @param result - {@link Result} optional use specific result set to run interpolation on\n   * @returns result - {@link Result}\n   */\n  next(result: Result = this.result): Result {\n    return interpolate.calc(result, this.config);\n  }\n\n  /** get model loading/loaded stats */\n  getModelStats(): models.ModelStats { return models.getModelStats(this); }\n\n  /** Warmup method pre-initializes all configured models for faster inference\n   * - can take significant time on startup\n   * - only used for `webgl` and `humangl` backends\n   * @param userConfig - {@link Config}\n   * @returns result - {@link Result}\n  */\n  async warmup(userConfig?: Partial<Config>) {\n    const t0 = now();\n    const res = await warmups.warmup(this, userConfig);\n    const t1 = now();\n    this.performance.warmup = Math.trunc(t1 - t0);\n    return res;\n  }\n\n  /** Run detect with tensorflow profiling\n   * - result object will contain total exeuction time information for top-20 kernels\n   * - actual detection object can be accessed via `human.result`\n  */\n  async profile(input: Input, userConfig?: Partial<Config>): Promise<{ kernel: string, time: number, perc: number }[]> {\n    const profile = await this.tf.profile(() => this.detect(input, userConfig));\n    const kernels: Record<string, number> = {};\n    let total = 0;\n    for (const kernel of profile.kernels) { // sum kernel time values per kernel\n      if (kernels[kernel.name]) kernels[kernel.name] += kernel.kernelTimeMs;\n      else kernels[kernel.name] = kernel.kernelTimeMs;\n      total += kernel.kernelTimeMs;\n    }\n    const kernelArr: { kernel: string, time: number, perc: number }[] = [];\n    Object.entries(kernels).forEach((key) => kernelArr.push({ kernel: key[0], time: key[1] as unknown as number, perc: 0 })); // convert to array\n    for (const kernel of kernelArr) {\n      kernel.perc = Math.round(1000 * kernel.time / total) / 1000;\n      kernel.time = Math.round(1000 * kernel.time) / 1000;\n    }\n    kernelArr.sort((a, b) => b.time - a.time); // sort\n    kernelArr.length = 20; // crop\n    return kernelArr;\n  }\n\n  /** Main detection method\n   * - Analyze configuration: {@link Config}\n   * - Pre-process input: {@link Input}\n   * - Run inference for all configured models\n   * - Process and return result: {@link Result}\n   *\n   * @param input - {@link Input}\n   * @param userConfig - {@link Config}\n   * @returns result - {@link Result}\n  */\n  async detect(input: Input, userConfig?: Partial<Config>): Promise<Result> {\n    // detection happens inside a promise\n    this.state = 'detect';\n    return new Promise(async (resolve) => {\n      this.state = 'config';\n      let timeStamp;\n\n      // update configuration\n      this.config = mergeDeep(this.config, userConfig) as Config;\n\n      // sanity checks\n      this.state = 'check';\n      const error = this.#sanity(input);\n      if (error) {\n        log(error, input);\n        this.emit('error');\n        resolve({ face: [], body: [], hand: [], gesture: [], object: [], performance: this.performance, timestamp: now(), persons: [], error });\n      }\n\n      const timeStart = now();\n\n      // load models if enabled\n      await this.load();\n\n      timeStamp = now();\n      this.state = 'image';\n      const img = await image.process(input, this.config) as { canvas: AnyCanvas, tensor: Tensor };\n      this.process = img;\n      this.performance.inputProcess = this.env.perfadd ? (this.performance.inputProcess || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n      this.analyze('Get Image:');\n\n      if (!img.tensor) {\n        if (this.config.debug) log('could not convert input to tensor');\n        this.emit('error');\n        resolve({ face: [], body: [], hand: [], gesture: [], object: [], performance: this.performance, timestamp: now(), persons: [], error: 'could not convert input to tensor' });\n        return;\n      }\n      this.emit('image');\n\n      timeStamp = now();\n      this.config.skipAllowed = await image.skip(this.config, img.tensor);\n      if (!this.performance.totalFrames) this.performance.totalFrames = 0;\n      if (!this.performance.cachedFrames) this.performance.cachedFrames = 0;\n      (this.performance.totalFrames)++;\n      if (this.config.skipAllowed) this.performance.cachedFrames++;\n      this.performance.cacheCheck = this.env.perfadd ? (this.performance.cacheCheck || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n      this.analyze('Check Changed:');\n\n      // prepare where to store model results\n      // keep them with weak typing as it can be promise or not\n      let faceRes: FaceResult[] | Promise<FaceResult[]> | never[] = [];\n      let bodyRes: BodyResult[] | Promise<BodyResult[]> | never[] = [];\n      let handRes: HandResult[] | Promise<HandResult[]> | never[] = [];\n      let objectRes: ObjectResult[] | Promise<ObjectResult[]> | never[] = [];\n\n      // run face detection followed by all models that rely on face bounding box: face mesh, age, gender, emotion\n      this.state = 'detect:face';\n      if (this.config.async) {\n        faceRes = this.config.face.enabled ? face.detectFace(this, img.tensor) : [];\n        if (this.performance.face) delete this.performance.face;\n      } else {\n        timeStamp = now();\n        faceRes = this.config.face.enabled ? await face.detectFace(this, img.tensor) : [];\n        this.performance.face = this.env.perfadd ? (this.performance.face || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n      }\n\n      if (this.config.async && (this.config.body.maxDetected === -1 || this.config.hand.maxDetected === -1)) faceRes = await faceRes; // need face result for auto-detect number of hands or bodies\n\n      // run body: can be posenet, blazepose, efficientpose, movenet\n      this.analyze('Start Body:');\n      this.state = 'detect:body';\n      const bodyConfig = this.config.body.maxDetected === -1 ? mergeDeep(this.config, { body: { maxDetected: this.config.face.enabled ? 1 * (faceRes as FaceResult[]).length : 1 } }) : this.config; // autodetect number of bodies\n      if (this.config.async) {\n        if (this.config.body.modelPath?.includes('posenet')) bodyRes = this.config.body.enabled ? posenet.predict(img.tensor, bodyConfig) : [];\n        else if (this.config.body.modelPath?.includes('blazepose')) bodyRes = this.config.body.enabled ? blazepose.predict(img.tensor, bodyConfig) : [];\n        else if (this.config.body.modelPath?.includes('efficientpose')) bodyRes = this.config.body.enabled ? efficientpose.predict(img.tensor, bodyConfig) : [];\n        else if (this.config.body.modelPath?.includes('movenet')) bodyRes = this.config.body.enabled ? movenet.predict(img.tensor, bodyConfig) : [];\n        if (this.performance.body) delete this.performance.body;\n      } else {\n        timeStamp = now();\n        if (this.config.body.modelPath?.includes('posenet')) bodyRes = this.config.body.enabled ? await posenet.predict(img.tensor, bodyConfig) : [];\n        else if (this.config.body.modelPath?.includes('blazepose')) bodyRes = this.config.body.enabled ? await blazepose.predict(img.tensor, bodyConfig) : [];\n        else if (this.config.body.modelPath?.includes('efficientpose')) bodyRes = this.config.body.enabled ? await efficientpose.predict(img.tensor, bodyConfig) : [];\n        else if (this.config.body.modelPath?.includes('movenet')) bodyRes = this.config.body.enabled ? await movenet.predict(img.tensor, bodyConfig) : [];\n        this.performance.body = this.env.perfadd ? (this.performance.body || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n      }\n      this.analyze('End Body:');\n\n      // run handpose\n      this.analyze('Start Hand:');\n      this.state = 'detect:hand';\n      const handConfig = this.config.hand.maxDetected === -1 ? mergeDeep(this.config, { hand: { maxDetected: this.config.face.enabled ? 2 * (faceRes as FaceResult[]).length : 1 } }) : this.config; // autodetect number of hands\n      if (this.config.async) {\n        if (this.config.hand.detector?.modelPath?.includes('handdetect')) handRes = this.config.hand.enabled ? handpose.predict(img.tensor, handConfig) : [];\n        else if (this.config.hand.detector?.modelPath?.includes('handtrack')) handRes = this.config.hand.enabled ? handtrack.predict(img.tensor, handConfig) : [];\n        if (this.performance.hand) delete this.performance.hand;\n      } else {\n        timeStamp = now();\n        if (this.config.hand.detector?.modelPath?.includes('handdetect')) handRes = this.config.hand.enabled ? await handpose.predict(img.tensor, handConfig) : [];\n        else if (this.config.hand.detector?.modelPath?.includes('handtrack')) handRes = this.config.hand.enabled ? await handtrack.predict(img.tensor, handConfig) : [];\n        this.performance.hand = this.env.perfadd ? (this.performance.hand || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n      }\n      this.analyze('End Hand:');\n\n      // run object detection\n      this.analyze('Start Object:');\n      this.state = 'detect:object';\n      if (this.config.async) {\n        if (this.config.object.modelPath?.includes('nanodet')) objectRes = this.config.object.enabled ? nanodet.predict(img.tensor, this.config) : [];\n        else if (this.config.object.modelPath?.includes('centernet')) objectRes = this.config.object.enabled ? centernet.predict(img.tensor, this.config) : [];\n        if (this.performance.object) delete this.performance.object;\n      } else {\n        timeStamp = now();\n        if (this.config.object.modelPath?.includes('nanodet')) objectRes = this.config.object.enabled ? await nanodet.predict(img.tensor, this.config) : [];\n        else if (this.config.object.modelPath?.includes('centernet')) objectRes = this.config.object.enabled ? await centernet.predict(img.tensor, this.config) : [];\n        this.performance.object = this.env.perfadd ? (this.performance.object || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n      }\n      this.analyze('End Object:');\n\n      // if async wait for results\n      this.state = 'detect:await';\n      if (this.config.async) [faceRes, bodyRes, handRes, objectRes] = await Promise.all([faceRes, bodyRes, handRes, objectRes]);\n\n      // run gesture analysis last\n      this.state = 'detect:gesture';\n      let gestureRes: GestureResult[] = [];\n      if (this.config.gesture.enabled) {\n        timeStamp = now();\n        gestureRes = [...gesture.face(faceRes as FaceResult[]), ...gesture.body(bodyRes as BodyResult[]), ...gesture.hand(handRes as HandResult[]), ...gesture.iris(faceRes as FaceResult[])];\n        if (!this.config.async) this.performance.gesture = this.env.perfadd ? 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 }
diff --git a/dist/human.js b/dist/human.js
index 4846a59d..fd8983f6 100644
--- a/dist/human.js
+++ b/dist/human.js
@@ -4,64 +4,64 @@
   author: <https://github.com/vladmandic>'
 */
 
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Buffer shape=${this.shape}`;throw new Error(r)}t++}let n=e[e.length-1];for(let s=0;s<e.length-1;++s)n+=this.strides[s]*e[s];return this.values[n]}locToIndex(e){if(this.rank===0)return 0;if(this.rank===1)return e[0];let t=e[e.length-1];for(let n=0;n<e.length-1;++n)t+=this.strides[n]*e[n];return t}indexToLoc(e){if(this.rank===0)return[];if(this.rank===1)return[e];let t=new Array(this.shape.length);for(let n=0;n<t.length-1;++n)t[n]=Math.floor(e/this.strides[n]),e-=t[n]*this.strides[n];return t[t.length-1]=e,t}get rank(){return this.shape.length}toTensor(){return Dr().makeTensor(this.values,this.shape,this.dtype)}},Dr=null,Yu=null,OD=null;function MD(e){Dr=e}function zD(e){Yu=e}function LD(e){OD=e}var rt=class{constructor(e,t,n,s){this.kept=!1,this.isDisposedInternal=!1,this.shape=e.slice(),this.dtype=t||"float32",this.size=Et(e),this.strides=kc(e),this.dataId=n,this.id=s,this.rankType=this.rank<5?this.rank.toString():"higher"}get rank(){return this.shape.length}async buffer(){let e=await this.data();return Yu.buffer(this.shape,this.dtype,e)}bufferSync(){return Yu.buffer(this.shape,this.dtype,this.dataSync())}async array(){let e=await this.data();return ec(this.shape,e,this.dtype==="complex64")}arraySync(){return ec(this.shape,this.dataSync(),this.dtype==="complex64")}async data(){this.throwIfDisposed();let e=Dr().read(this.dataId);if(this.dtype==="string"){let t=await e;try{return t.map(n=>Tm(n))}catch(n){throw new Error("Failed to decode the string bytes into utf-8. To get the original bytes, call tensor.bytes().")}}return e}dataToGPU(e){return this.throwIfDisposed(),Dr().readToGPU(this.dataId,e)}dataSync(){this.throwIfDisposed();let e=Dr().readSync(this.dataId);if(this.dtype==="string")try{return e.map(t=>Tm(t))}catch(t){throw new Error("Failed to decode the string bytes into utf-8. To get the original bytes, call tensor.bytes().")}return e}async bytes(){this.throwIfDisposed();let e=await Dr().read(this.dataId);return this.dtype==="string"?e:new Uint8Array(e.buffer)}dispose(){this.isDisposed||(Dr().disposeTensor(this),this.isDisposedInternal=!0)}get isDisposed(){return this.isDisposedInternal}throwIfDisposed(){if(this.isDisposed)throw new Error("Tensor is disposed.")}print(e=!1){return Yu.print(this,e)}clone(){return this.throwIfDisposed(),Yu.clone(this)}toString(e=!1){let t=this.dataSync();return PD(t,this.shape,this.dtype,e)}cast(e){return this.throwIfDisposed(),Yu.cast(this,e)}variable(e=!0,t,n){return this.throwIfDisposed(),Dr().makeVariable(this,e,t,n)}};Object.defineProperty(rt,Symbol.hasInstance,{value:e=>!!e&&e.data!=null&&e.dataSync!=null&&e.throwIfDisposed!=null});function oe(){return Ly("Tensor",()=>rt)}oe();var Tp=class extends rt{constructor(e,t,n,s){super(e.shape,e.dtype,e.dataId,s),this.trainable=t,this.name=n}assign(e){if(e.dtype!==this.dtype)throw new Error(`dtype of the new value (${e.dtype}) and previous value (${this.dtype}) must match`);if(!fo(e.shape,this.shape))throw new Error(`shape of the new value (${e.shape}) and previous value (${this.shape}) must match`);Dr().disposeTensor(this),this.dataId=e.dataId,Dr().incRef(this,null)}dispose(){Dr().disposeVariable(this),this.isDisposedInternal=!0}};Object.defineProperty(Tp,Symbol.hasInstance,{value:e=>e instanceof rt&&e.assign!=null&&e.assign instanceof Function});var Or={};je(Or,{assertTypesMatch:()=>F6,getTensorsInContainer:()=>Wy,isTensorInList:()=>WD,makeTypesMatch:()=>jt});var D3;(function(e){e.R0="R0",e.R1="R1",e.R2="R2",e.R3="R3",e.R4="R4",e.R5="R5",e.R6="R6"})(D3||(D3={}));var $3;(function(e){e.float32="float32",e.int32="int32",e.bool="int32",e.complex64="complex64"})($3||($3={}));var P3;(function(e){e.float32="float32",e.int32="int32",e.bool="bool",e.complex64="complex64"})(P3||(P3={}));var F3;(function(e){e.float32="float32",e.int32="float32",e.bool="float32",e.complex64="complex64"})(F3||(F3={}));var O3;(function(e){e.float32="complex64",e.int32="complex64",e.bool="complex64",e.complex64="complex64"})(O3||(O3={}));var BD={float32:F3,int32:$3,bool:P3,complex64:O3};function Gn(e,t){if(e==="string"||t==="string"){if(e==="string"&&t==="string")return"string";throw new Error(`Can not upcast ${e} with ${t}`)}return BD[e][t]}function uh(e){return Gn(e,"int32")}function jt(e,t){if(e.dtype===t.dtype)return[e,t];let n=Gn(e.dtype,t.dtype);return[e.cast(n),t.cast(n)]}function F6(e,t){O(e.dtype===t.dtype,()=>`The dtypes of the first(${e.dtype}) and second(${t.dtype}) input must match`)}function WD(e,t){return t.some(n=>n.id===e.id)}function Wy(e){let t=[];return O6(e,t,new Set),t}function O6(e,t,n){if(e==null)return;if(e instanceof rt){t.push(e);return}if(!VD(e))return;let s=e;for(let r in s){let a=s[r];n.has(a)||(n.add(a),O6(a,t,n))}}function VD(e){return Array.isArray(e)||typeof e=="object"}function g3(e){return e.kernelName!=null}var Rv=class{constructor(){this.registeredVariables={},this.nextTapeNodeId=0,this.numBytes=0,this.numTensors=0,this.numStringTensors=0,this.numDataBuffers=0,this.gradientDepth=0,this.kernelDepth=0,this.scopeStack=[],this.numDataMovesStack=[],this.nextScopeId=0,this.tensorInfo=new WeakMap,this.profiling=!1,this.activeProfile={newBytes:0,newTensors:0,peakBytes:0,kernels:[],result:null,get kernelNames(){return Array.from(new Set(this.kernels.map(e=>e.name)))}}}dispose(){for(let e in this.registeredVariables)this.registeredVariables[e].dispose()}},Np=class{constructor(e){this.ENV=e,this.registry={},this.registryFactory={},this.pendingBackendInitId=0,this.state=new Rv}async ready(){if(this.pendingBackendInit!=null)return this.pendingBackendInit.then(()=>{});if(this.backendInstance!=null)return;let e=this.getSortedBackends();for(let t=0;t<e.length;t++){let n=e[t];if(await this.initializeBackend(n).success){await this.setBackend(n);return}}throw new Error("Could not initialize any backends, all backend initializations failed.")}get backend(){if(this.pendingBackendInit!=null)throw new Error(`Backend '${this.backendName}' has not yet been initialized. Make sure to await tf.ready() or await tf.setBackend() before calling other methods`);if(this.backendInstance==null){let{name:e,asyncInit:t}=this.initializeBackendsAndReturnBest();if(t)throw new Error(`The highest priority backend '${e}' has not yet been initialized. Make sure to await tf.ready() or await tf.setBackend() before calling other methods`);this.setBackend(e)}return this.backendInstance}backendNames(){return Object.keys(this.registryFactory)}findBackend(e){if(!(e in this.registry))if(e in this.registryFactory){let{asyncInit:t}=this.initializeBackend(e);if(t)return null}else return null;return this.registry[e]}findBackendFactory(e){return e in this.registryFactory?this.registryFactory[e].factory:null}registerBackend(e,t,n=1){return e in this.registryFactory?(qa(`${e} backend was already registered. Reusing existing backend factory.`),!1):(this.registryFactory[e]={factory:t,priority:n},!0)}async setBackend(e){if(this.registryFactory[e]==null)throw new Error(`Backend name '${e}' not found in registry`);if(this.backendName=e,this.registry[e]==null){this.backendInstance=null;let{success:t,asyncInit:n}=this.initializeBackend(e);if(!(n?await t:t))return!1}return this.backendInstance=this.registry[e],this.setupRegisteredKernels(),this.profiler=new ED(this.backendInstance),!0}setupRegisteredKernels(){na(this.backendName).forEach(t=>{t.setupFunc!=null&&t.setupFunc(this.backendInstance)})}disposeRegisteredKernels(e){na(e).forEach(n=>{n.disposeFunc!=null&&n.disposeFunc(this.registry[e])})}initializeBackend(e){let t=this.registryFactory[e];if(t==null)throw new Error(`Cannot initialize backend ${e}, no registration found.`);try{let n=t.factory();if(n&&!(n instanceof wc)&&typeof n.then=="function"){let s=++this.pendingBackendInitId,r=n.then(a=>s<this.pendingBackendInitId?!1:(this.registry[e]=a,this.pendingBackendInit=null,!0)).catch(a=>(s<this.pendingBackendInitId||(this.pendingBackendInit=null,qa(`Initialization of backend ${e} failed`),qa(a.stack||a.message)),!1));return this.pendingBackendInit=r,{success:r,asyncInit:!0}}else return this.registry[e]=n,{success:!0,asyncInit:!1}}catch(n){return qa(`Initialization of backend ${e} failed`),qa(n.stack||n.message),{success:!1,asyncInit:!1}}}removeBackend(e){if(!(e in this.registryFactory))throw new Error(`${e} backend not found in registry`);this.backendName===e&&this.pendingBackendInit!=null&&this.pendingBackendInitId++,e in this.registry&&(this.disposeRegisteredKernels(e),this.registry[e].dispose(),delete this.registry[e]),delete this.registryFactory[e],this.backendName===e&&(this.pendingBackendInit=null,this.backendName=null,this.backendInstance=null)}getSortedBackends(){if(Object.keys(this.registryFactory).length===0)throw new Error("No backend found in registry.");return Object.keys(this.registryFactory).sort((e,t)=>this.registryFactory[t].priority-this.registryFactory[e].priority)}initializeBackendsAndReturnBest(){let e=this.getSortedBackends();for(let t=0;t<e.length;t++){let n=e[t],{success:s,asyncInit:r}=this.initializeBackend(n);if(r||s)return{name:n,asyncInit:r}}throw new Error("Could not initialize any backends, all backend initializations failed.")}moveData(e,t){let n=this.state.tensorInfo.get(t),s=n.backend,r=this.readSync(t),a=s.refCount(t);s.disposeData(t,!0),n.backend=e,e.move(t,r,n.shape,n.dtype,a),this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack[this.state.numDataMovesStack.length-1]++}tidy(e,t){let n=null;if(t==null){if(typeof e!="function")throw new Error("Please provide a function to tidy()");t=e}else{if(typeof e!="string"&&!(e instanceof String))throw new Error("When calling with two arguments, the first argument to tidy() must be a string");if(typeof t!="function")throw new Error("When calling with two arguments, the 2nd argument to tidy() must be a function");n=e}let s;return this.scopedRun(()=>this.startScope(n),()=>this.endScope(s),()=>(s=t(),s instanceof Promise&&console.error("Cannot return a Promise inside of tidy."),s))}scopedRun(e,t,n){e();try{let s=n();return t(),s}catch(s){throw t(),s}}nextTensorId(){return Np.nextTensorId++}nextVariableId(){return Np.nextVariableId++}clone(e){let t=B.runKernel(Po,{x:e}),n={x:e},s=a=>({x:()=>{let o="float32",i={x:a},l={dtype:o};return B.runKernel(xo,i,l)}}),r=[];return this.addTapeNode(this.state.activeScope.name,n,[t],s,r,{}),t}runKernel(e,t,n){if(this.backendName==null&&this.backend,!(Cm(e,this.backendName)!=null))throw new Error(`Kernel '${e}' not registered for backend '${this.backendName}'`);return this.runKernelFunc({kernelName:e,inputs:t,attrs:n})}shouldCheckForMemLeaks(){return this.ENV.getBool("IS_TEST")}checkKernelForMemLeak(e,t,n){let s=this.backend.numDataIds(),r=0;n.forEach(i=>{r+=i.dtype==="complex64"?3:1});let a=this.state.numDataMovesStack[this.state.numDataMovesStack.length-1],o=s-t-r-a;if(o>0)throw new Error(`Backend '${this.backendName}' has an internal memory leak (${o} data ids) after running '${e}'`)}runKernelFunc(e){let t,n=[],s=this.isTapeOn(),r=this.state.numBytes,a=this.state.numTensors;this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack.push(0);let o;this.backendName==null&&this.backend;let i,l=g3(e)?e.kernelName:this.state.activeScope!=null?this.state.activeScope.name:"";if(g3(e)){let{kernelName:h,inputs:f,attrs:m}=e;this.backendName==null&&this.backend;let g=Cm(h,this.backendName);O(g!=null,()=>`Cannot find registered kernel '${h}' for backend '${this.backendName}'`),o=()=>{let y=this.backend.numDataIds();i=g.kernelFunc({inputs:f,attrs:m,backend:this.backend});let x=Array.isArray(i)?i:[i];this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(h,y,x);let A=x.map(b=>b.rank!=null?b:this.makeTensorFromTensorInfo(b));if(s){let b=this.getTensorsForGradient(h,f,A);n=this.saveTensorsForBackwardMode(b)}return A}}else{let{forwardFunc:h}=e,f=m=>{!s||(n=m.map(g=>this.keep(this.clone(g))))};o=()=>{let m=this.backend.numDataIds();i=this.tidy(()=>h(this.backend,f));let g=Array.isArray(i)?i:[i];return this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(l,m,g),g}}let{inputs:u,attrs:c}=e,p=g3(e)?null:e.backwardsFunc,d;return this.scopedRun(()=>this.state.kernelDepth++,()=>this.state.kernelDepth--,()=>{!this.ENV.getBool("DEBUG")&&!this.state.profiling?t=o():(d=this.profiler.profileKernel(l,u,()=>o()),this.ENV.getBool("DEBUG")&&this.profiler.logKernelProfile(d),t=d.outputs)}),s&&this.addTapeNode(l,u,t,p,n,c),this.state.profiling&&this.state.activeProfile.kernels.push({name:l,bytesAdded:this.state.numBytes-r,totalBytesSnapshot:this.state.numBytes,tensorsAdded:this.state.numTensors-a,totalTensorsSnapshot:this.state.numTensors,inputShapes:Object.keys(u).map(h=>u[h]!=null?u[h].shape:null),outputShapes:t.map(h=>h.shape),kernelTimeMs:d.timeMs,extraInfo:d.extraInfo}),Array.isArray(i)?t:t[0]}saveTensorsForBackwardMode(e){return e.map(n=>this.keep(this.clone(n)))}getTensorsForGradient(e,t,n){let s=R3(e);if(s!=null){let r=s.inputsToSave||[],a=s.outputsToSave||[],o;s.saveAllInputs?(O(Array.isArray(t),()=>"saveAllInputs is true, expected inputs to be an array."),o=Object.keys(t).map(l=>t[l])):o=r.map(l=>t[l]);let i=n.filter((l,u)=>a[u]);return o.concat(i)}return[]}makeTensor(e,t,n,s){if(e==null)throw new Error("Values passed to engine.makeTensor() are null");n=n||"float32",s=s||this.backend;let r=e;n==="string"&&Xa(e[0])&&(r=e.map(i=>lh(i)));let a=s.write(r,t,n),o=new rt(t,n,a,this.nextTensorId());if(this.trackTensor(o,s),n==="string"){let i=this.state.tensorInfo.get(a),l=w6(r);this.state.numBytes+=l-i.bytes,i.bytes=l}return o}makeTensorFromDataId(e,t,n,s){n=n||"float32";let r={dataId:e,shape:t,dtype:n};return this.makeTensorFromTensorInfo(r,s)}makeTensorFromTensorInfo(e,t){let{dataId:n,shape:s,dtype:r}=e,a=new rt(s,r,n,this.nextTensorId());return this.trackTensor(a,t),a}makeVariable(e,t=!0,n,s){n=n||this.nextVariableId().toString(),s!=null&&s!==e.dtype&&(e=e.cast(s));let r=new Tp(e,t,n,this.nextTensorId());if(this.state.registeredVariables[r.name]!=null)throw new Error(`Variable with name ${r.name} was already registered`);return this.state.registeredVariables[r.name]=r,this.incRef(r,this.backend),r}trackTensor(e,t){this.state.numTensors++,e.dtype==="string"&&this.state.numStringTensors++;let n=0;e.dtype!=="complex64"&&e.dtype!=="string"&&(n=e.size*E3(e.dtype)),this.state.numBytes+=n,this.state.tensorInfo.has(e.dataId)||(this.state.numDataBuffers++,this.state.tensorInfo.set(e.dataId,{backend:t||this.backend,dtype:e.dtype,shape:e.shape,bytes:n})),e instanceof Tp||this.track(e)}incRef(e,t){this.trackTensor(e,t),this.backend.incRef(e.dataId)}removeDataId(e,t){this.state.tensorInfo.has(e)&&this.state.tensorInfo.get(e).backend===t&&(this.state.tensorInfo.delete(e),this.state.numDataBuffers--)}disposeTensor(e){if(!this.state.tensorInfo.has(e.dataId))return;let t=this.state.tensorInfo.get(e.dataId);if(this.state.numTensors--,e.dtype==="string"&&(this.state.numStringTensors--,this.state.numBytes-=t.bytes),e.dtype!=="complex64"&&e.dtype!=="string"){let n=e.size*E3(e.dtype);this.state.numBytes-=n}t.backend.disposeData(e.dataId)&&this.removeDataId(e.dataId,t.backend)}disposeVariables(){for(let e in this.state.registeredVariables){let t=this.state.registeredVariables[e];this.disposeVariable(t)}}disposeVariable(e){this.disposeTensor(e),this.state.registeredVariables[e.name]!=null&&delete this.state.registeredVariables[e.name]}memory(){let e=this.backend.memory();return e.numTensors=this.state.numTensors,e.numDataBuffers=this.state.numDataBuffers,e.numBytes=this.state.numBytes,this.state.numStringTensors>0&&(e.unreliable=!0,e.reasons==null&&(e.reasons=[]),e.reasons.push("Memory usage by string tensors is approximate (2 bytes per character)")),e}async profile(e){this.state.profiling=!0;let t=this.state.numBytes,n=this.state.numTensors;this.state.activeProfile.kernels=[],this.state.activeProfile.result=await e(),this.state.profiling=!1,this.state.activeProfile.peakBytes=Math.max(...this.state.activeProfile.kernels.map(s=>s.totalBytesSnapshot)),this.state.activeProfile.newBytes=this.state.numBytes-t,this.state.activeProfile.newTensors=this.state.numTensors-n;for(let s of this.state.activeProfile.kernels)s.kernelTimeMs=await s.kernelTimeMs,s.extraInfo=await s.extraInfo;return this.state.activeProfile}isTapeOn(){return this.state.gradientDepth>0&&this.state.kernelDepth===0}addTapeNode(e,t,n,s,r,a){let o={id:this.state.nextTapeNodeId++,kernelName:e,inputs:t,outputs:n,saved:r},i=R3(e);i!=null&&(s=i.gradFunc),s!=null&&(o.gradient=l=>(l=l.map((u,c)=>{if(u==null){let p=n[c],d=t0(p.size,p.dtype);return this.makeTensor(d,p.shape,p.dtype)}return u}),s(l.length>1?l:l[0],r,a))),this.state.activeTape.push(o)}keep(e){return e.kept=!0,e}startTape(){this.state.gradientDepth===0&&(this.state.activeTape=[]),this.state.gradientDepth++}endTape(){this.state.gradientDepth--}startScope(e){let t={track:[],name:"unnamed scope",id:this.state.nextScopeId++};e&&(t.name=e),this.state.scopeStack.push(t),this.state.activeScope=t}endScope(e){let t=Wy(e),n=new Set(t.map(r=>r.id));for(let r=0;r<this.state.activeScope.track.length;r++){let a=this.state.activeScope.track[r];!a.kept&&!n.has(a.id)&&a.dispose()}let s=this.state.scopeStack.pop();this.state.activeScope=this.state.scopeStack.length===0?null:this.state.scopeStack[this.state.scopeStack.length-1],t.forEach(r=>{!r.kept&&r.scopeId===s.id&&this.track(r)})}gradients(e,t,n,s=!1){if(O(t.length>0,()=>"gradients() received an empty list of xs."),n!=null&&n.dtype!=="float32")throw new Error(`dy must have 'float32' dtype, but has '${n.dtype}'`);let r=this.scopedRun(()=>this.startTape(),()=>this.endTape(),()=>this.tidy("forward",e));O(r instanceof rt,()=>"The result y returned by f() must be a tensor.");let a=DD(this.state.activeTape,t,r);if(!s&&a.length===0&&t.length>0)throw new Error("Cannot compute gradient of y=f(x) with respect to x. 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s=++this.pendingBackendInitId,r=n.then(a=>s<this.pendingBackendInitId?!1:(this.registry[e]=a,this.pendingBackendInit=null,!0)).catch(a=>(s<this.pendingBackendInitId||(this.pendingBackendInit=null,Ya(`Initialization of backend ${e} failed`),Ya(a.stack||a.message)),!1));return this.pendingBackendInit=r,{success:r,asyncInit:!0}}else return this.registry[e]=n,{success:!0,asyncInit:!1}}catch(n){return Ya(`Initialization of backend ${e} failed`),Ya(n.stack||n.message),{success:!1,asyncInit:!1}}}removeBackend(e){if(!(e in this.registryFactory))throw new Error(`${e} backend not found in registry`);this.backendName===e&&this.pendingBackendInit!=null&&this.pendingBackendInitId++,e in this.registry&&(this.disposeRegisteredKernels(e),this.registry[e].dispose(),delete this.registry[e]),delete this.registryFactory[e],this.backendName===e&&(this.pendingBackendInit=null,this.backendName=null,this.backendInstance=null)}getSortedBackends(){if(Object.keys(this.registryFactory).length===0)throw new Error("No backend found in registry.");return Object.keys(this.registryFactory).sort((e,t)=>this.registryFactory[t].priority-this.registryFactory[e].priority)}initializeBackendsAndReturnBest(){let e=this.getSortedBackends();for(let t=0;t<e.length;t++){let n=e[t],{success:s,asyncInit:r}=this.initializeBackend(n);if(r||s)return{name:n,asyncInit:r}}throw new Error("Could not initialize any backends, all backend initializations failed.")}moveData(e,t){let n=this.state.tensorInfo.get(t),s=n.backend,r=this.readSync(t),a=s.refCount(t);s.disposeData(t,!0),n.backend=e,e.move(t,r,n.shape,n.dtype,a),this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack[this.state.numDataMovesStack.length-1]++}tidy(e,t){let n=null;if(t==null){if(typeof e!="function")throw new Error("Please provide a function to tidy()");t=e}else{if(typeof e!="string"&&!(e instanceof String))throw new Error("When calling with two arguments, the first argument to tidy() must be a string");if(typeof t!="function")throw new 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s=this.backend.numDataIds(),r=0;n.forEach(i=>{r+=i.dtype==="complex64"?3:1});let a=this.state.numDataMovesStack[this.state.numDataMovesStack.length-1],o=s-t-r-a;if(o>0)throw new Error(`Backend '${this.backendName}' has an internal memory leak (${o} data ids) after running '${e}'`)}runKernelFunc(e){let t,n=[],s=this.isTapeOn(),r=this.state.numBytes,a=this.state.numTensors;this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack.push(0);let o;this.backendName==null&&this.backend;let i,l=x3(e)?e.kernelName:this.state.activeScope!=null?this.state.activeScope.name:"";if(x3(e)){let{kernelName:h,inputs:f,attrs:m}=e;this.backendName==null&&this.backend;let g=Cm(h,this.backendName);O(g!=null,()=>`Cannot find registered kernel '${h}' for backend '${this.backendName}'`),o=()=>{let y=this.backend.numDataIds();i=g.kernelFunc({inputs:f,attrs:m,backend:this.backend});let x=Array.isArray(i)?i:[i];this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(h,y,x);let A=x.map(b=>b.rank!=null?b:this.makeTensorFromTensorInfo(b));if(s){let b=this.getTensorsForGradient(h,f,A);n=this.saveTensorsForBackwardMode(b)}return A}}else{let{forwardFunc:h}=e,f=m=>{!s||(n=m.map(g=>this.keep(this.clone(g))))};o=()=>{let m=this.backend.numDataIds();i=this.tidy(()=>h(this.backend,f));let g=Array.isArray(i)?i:[i];return this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(l,m,g),g}}let{inputs:u,attrs:c}=e,p=x3(e)?null:e.backwardsFunc,d;return this.scopedRun(()=>this.state.kernelDepth++,()=>this.state.kernelDepth--,()=>{!this.ENV.getBool("DEBUG")&&!this.state.profiling?t=o():(d=this.profiler.profileKernel(l,u,()=>o()),this.ENV.getBool("DEBUG")&&this.profiler.logKernelProfile(d),t=d.outputs)}),s&&this.addTapeNode(l,u,t,p,n,c),this.state.profiling&&this.state.activeProfile.kernels.push({name:l,bytesAdded:this.state.numBytes-r,totalBytesSnapshot:this.state.numBytes,tensorsAdded:this.state.numTensors-a,totalTensorsSnapshot:this.state.numTensors,inputShapes:Object.keys(u).map(h=>u[h]!=null?u[h].shape:null),outputShapes:t.map(h=>h.shape),kernelTimeMs:d.timeMs,extraInfo:d.extraInfo}),Array.isArray(i)?t:t[0]}saveTensorsForBackwardMode(e){return e.map(n=>this.keep(this.clone(n)))}getTensorsForGradient(e,t,n){let s=$3(e);if(s!=null){let r=s.inputsToSave||[],a=s.outputsToSave||[],o;s.saveAllInputs?(O(Array.isArray(t),()=>"saveAllInputs is true, expected inputs to be an array."),o=Object.keys(t).map(l=>t[l])):o=r.map(l=>t[l]);let 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Tw=W({basicLSTMCell_:xF});function bF(e,t,n){let s=D(e,"x","batchToSpaceND"),r=t.reduce((i,l)=>i*l);O(s.rank>=1+t.length,()=>`input rank is ${s.rank} but should be > than blockShape.length ${t.length}`),O(n.length===t.length,()=>`crops.length is ${n.length} but should be equal to blockShape.length  ${t.length}`),O(s.shape[0]%r===0,()=>`input tensor batch is ${s.shape[0]} but is not divisible by the product of the elements of blockShape ${t.join(" * ")} === ${r}`);let a={x:s},o={blockShape:t,crops:n};return B.runKernel(vl,a,o)}var Ah=W({batchToSpaceND_:bF});function vF(e){let t;return e.rank===0||e.rank===1?t=V(e,[1,1,1,e.size]):e.rank===2?t=V(e,[1,1,e.shape[0],e.shape[1]]):e.rank===3?t=V(e,[1,e.shape[0],e.shape[1],e.shape[2]]):t=e,t}function wF(e,t,n,s,r,a){a==null&&(a=.001);let o=D(e,"x","batchNorm"),i=D(t,"mean","batchNorm"),l=D(n,"variance","batchNorm"),u;r!=null&&(u=D(r,"scale","batchNorm"));let c;s!=null&&(c=D(s,"offset","batchNorm")),O(i.rank===l.rank,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),O(c==null||i.rank===c.rank,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),O(u==null||i.rank===u.rank,()=>"Batch normalization gradient requires mean and scale to have equal ranks.");let d={x:vF(o),scale:u,offset:c,mean:i,variance:l},h={varianceEpsilon:a},f=B.runKernel(Oo,d,h);return V(f,o.shape)}var Qc=W({batchNorm_:wF});function kF(e,t,n,s,r,a){let o=D(e,"x","batchNorm"),i=D(t,"mean","batchNorm"),l=D(n,"variance","batchNorm"),u;r!=null&&(u=D(r,"scale","batchNorm"));let c;return s!=null&&(c=D(s,"offset","batchNorm")),O(o.rank===2,()=>`Error in batchNorm2D: x must be rank 2 but got rank ${o.rank}.`),O(i.rank===2||i.rank===1,()=>`Error in batchNorm2D: mean must be rank 2 or rank 1 but got rank ${i.rank}.`),O(l.rank===2||l.rank===1,()=>`Error in batchNorm2D: variance must be rank 2 or rank 1 but got rank ${l.rank}.`),u!=null&&O(u.rank===2||u.rank===1,()=>`Error in batchNorm2D: scale must be rank 2 or rank 1 but got rank ${u.rank}.`),c!=null&&O(c.rank===2||c.rank===1,()=>`Error in batchNorm2D: offset must be rank 2 or rank 1 but got rank ${c.rank}.`),Qc(o,i,l,c,u,a)}var xA=W({batchNorm2d_:kF});function SF(e,t,n,s,r,a){let o=D(e,"x","batchNorm"),i=D(t,"mean","batchNorm"),l=D(n,"variance","batchNorm"),u;r!=null&&(u=D(r,"scale","batchNorm"));let c;return s!=null&&(c=D(s,"offset","batchNorm")),O(o.rank===3,()=>`Error in batchNorm3D: x must be rank 3 but got rank ${o.rank}.`),O(i.rank===3||i.rank===1,()=>`Error in batchNorm3D: mean must be rank 3 or rank 1 but got rank ${i.rank}.`),O(l.rank===3||l.rank===1,()=>`Error in batchNorm3D: variance must be rank 3 or rank 1 but got rank ${l.rank}.`),u!=null&&O(u.rank===3||u.rank===1,()=>`Error in batchNorm3D: scale must be rank 3 or rank 1 but got rank ${u.rank}.`),c!=null&&O(c.rank===3||c.rank===1,()=>`Error in batchNorm3D: offset must be rank 3 or rank 1 but got rank ${c.rank}.`),Qc(o,i,l,c,u,a)}var bA=W({batchNorm3d_:SF});function IF(e,t,n,s,r,a){let o=D(e,"x","batchNorm"),i=D(t,"mean","batchNorm"),l=D(n,"variance","batchNorm"),u;r!=null&&(u=D(r,"scale","batchNorm"));let c;return s!=null&&(c=D(s,"offset","batchNorm")),O(o.rank===4,()=>`Error in batchNorm4D: x must be rank 4 but got rank ${o.rank}.`),O(i.rank===4||i.rank===1,()=>`Error in batchNorm4D: mean must be rank 4 or rank 1 but got rank ${i.rank}.`),O(l.rank===4||l.rank===1,()=>`Error in batchNorm4D: variance must be rank 4 or rank 1 but got rank ${l.rank}.`),u!=null&&O(u.rank===4||u.rank===1,()=>`Error in batchNorm4D: scale must be rank 4 or rank 1 but got rank ${u.rank}.`),c!=null&&O(c.rank===4||c.rank===1,()=>`Error in batchNorm4D: offset must be rank 4 or rank 1 but got rank ${c.rank}.`),Qc(o,i,l,c,u,a)}var vA=W({batchNorm4d_:IF});function CF(e,t,n){let s=D(e,"x","bincount"),r=D(t,"weights","bincount");O(s.dtype==="int32",()=>`Error in bincount: input dtype must be int32, but got ${s.dtype}`),O(n>=0,()=>`size must be non-negative, but got ${n}.`),O(r.size===s.size||r.size===0,()=>`Error in bincount: weights must have the same size as input or0-length, but got input shape: ${s.shape}, weights shape: ${r.shape}.`);let a={x:s,weights:r},o={size:n};return B.runKernel(r0,a,o)}var wA=W({bincount_:CF});function TF(e,t){let n=D(e,"s0","broadcastArgs","int32"),s=D(t,"s1","broadcastArgs","int32");if(n.rank!==1)throw new Error(`broadcastArgs(): first input must be a vector (rank=1). Has rank ${n.rank}`);if(s.rank!==1)throw new Error(`broadcastArgs(): second input must be a vector (rank=1). Has rank ${s.rank}`);let r={s0:n,s1:s};return B.runKernel(a0,r)}var Nw=W({broadcastArgs_:TF});function NF(e,t){let n=D(e,"broadcastTo","x"),s=n.shape;if(t.some(u=>!(u>0)||u%1!==0))throw new Error(`broadcastTo(): Invalid broadcast shape [${t}].`);if(t.length<n.rank)throw new Error(`broadcastTo(): shape.length=${t.length} < input.rank=${n.rank}.`);if(t.length>n.rank){let u=n.shape.slice();for(;u.length<t.length;)u.unshift(1);n=V(n,u)}let r=n.shape,a=Array.from(t);for(let u=t.length-1;u>=0;u--)if(r[u]===t[u])a[u]=1;else if(n.shape[u]!==1)throw new Error(`broadcastTo(): [${s}] cannot be broadcast to [${t}].`);if(a.map((u,c)=>u>1?c:-1).filter(u=>u>=0).length===0)return Gn(n);let i={x:n},l={reps:a};return B.runKernel(Pa,i,l)}var nl=W({broadcastTo_:NF});function EF(e){let n={x:D(e,"x","ceil","float32")};return B.runKernel(So,n)}var kA=W({ceil_:EF});function RF(e,t,n){let s=D(e,"x","clipByValue");O(t<=n,()=>`Error in clip: min (${t}) must be less than or equal to max (${n}).`);let r={x:s},a={clipValueMin:t,clipValueMax:n};return B.runKernel($a,r,a)}var ws=W({clipByValue_:RF});function _F(e){return ct(e,0)}var SA=W({concat1d_:_F});function DF(e,t){return ct(e,t)}var cu=W({concat2d_:DF});function $F(e,t){return ct(e,t)}var IA=W({concat3d_:$F});function PF(e,t){return ct(e,t)}var CA=W({concat4d_:PF});function FF(e,t,n,s,r="NHWC",a=[1,1],o){let i=D(e,"x","conv2d","float32"),l=D(t,"filter","conv2d","float32"),u=i,c=!1;i.rank===3&&(c=!0,u=V(i,[1,i.shape[0],i.shape[1],i.shape[2]])),O(u.rank===4,()=>`Error in conv2d: input must be rank 4, but got rank ${u.rank}.`),O(l.rank===4,()=>`Error in conv2d: filter must be rank 4, but got rank ${l.rank}.`),ps("conv2d",s,o);let p=r==="NHWC"?u.shape[3]:u.shape[1];O(p===l.shape[2],()=>`Error in conv2d: depth of input (${p}) must match input depth for filter ${l.shape[2]}.`),O(ua(n,a),()=>`Error in conv2D: Either strides or dilations must be 1. Got strides ${n} and dilations '${a}'`);let d={x:u,filter:l},h={strides:n,pad:s,dataFormat:r,dilations:a,dimRoundingMode:o},f=B.runKernel(Io,d,h);return c?V(f,[f.shape[1],f.shape[2],f.shape[3]]):f}var Na=W({conv2d_:FF});function OF(e,t,n,s,r="NWC",a=1,o){let i=D(e,"x","conv1d"),l=D(t,"filter","conv1d"),u=i,c=!1;i.rank===2&&(c=!0,u=V(i,[1,i.shape[0],i.shape[1]])),O(u.rank===3,()=>`Error in conv1d: input must be rank 3, but got rank ${u.rank}.`),O(l.rank===3,()=>`Error in conv1d: filter must be rank 3, but got rank ${l.rank}.`),ps("conv1d",s,o),O(u.shape[2]===l.shape[1],()=>`Error in conv1d: depth of input (${u.shape[2]}) must match input depth for filter ${l.shape[1]}.`),O(ua(n,a),()=>`Error in conv1D: Either stride or dilation must be 1. Got stride ${n} and dilation '${a}'`),O(r==="NWC",()=>`Error in conv1d: got dataFormat of ${r} but only NWC is currently supported.`);let p=V(l,[1,l.shape[0],l.shape[1],l.shape[2]]),d=V(u,[u.shape[0],1,u.shape[1],u.shape[2]]),g=Na(d,p,[1,n],s,"NHWC",[1,a],o);return c?V(g,[g.shape[2],g.shape[3]]):V(g,[g.shape[0],g.shape[2],g.shape[3]])}var R0=W({conv1d_:OF});function MF(e,t,n,s,r,a="NHWC",o){O(e.length===t.rank,()=>`Length of inShape (${e.length}) and rank of dy (${t.rank}) must match`);let i=e,l=t,u=!1;t.rank===3&&(u=!0,l=V(t,[1,t.shape[0],t.shape[1],t.shape[2]]),i=[1,e[0],e[1],e[2]]),O(i.length===4,()=>`Error in conv2dDerInput: inShape must be length 4, but got length ${i.length}.`),O(l.rank===4,()=>`Error in conv2dDerInput: dy must be rank 4, but got rank ${l.rank}`),O(n.rank===4,()=>`Error in conv2dDerInput: filter must be rank 4, but got rank ${n.rank}`);let c=a==="NHWC"?i[3]:i[1],p=a==="NHWC"?l.shape[3]:l.shape[1];O(c===n.shape[2],()=>`Error in conv2dDerInput: depth of input (${c}) must match input depth for filter ${n.shape[2]}.`),O(p===n.shape[3],()=>`Error in conv2dDerInput: depth of output (${p}) must match output depth for filter ${n.shape[3]}.`),ps("conv2dDerInput",r,o);let d={dy:l,filter:n},h={strides:s,pad:r,dataFormat:a,dimRoundingMode:o,inputShape:i},f=B.runKernel(Co,d,h);return u?V(f,[f.shape[1],f.shape[2],f.shape[3]]):f}var TA=W({conv2DBackpropInput_:MF});function zF(e,t,n,s,r,a){let o=D(e,"x","conv2dTranspose"),i=D(t,"filter","conv2dTranspose");return TA(n,o,i,s,r,"NHWC",a)}var _0=W({conv2dTranspose_:zF});function LF(e,t,n,s,r="NDHWC",a=[1,1,1]){let o=D(e,"x","conv3d"),i=D(t,"filter","conv3d"),l=o,u=!1;o.rank===4&&(u=!0,l=V(o,[1,o.shape[0],o.shape[1],o.shape[2],o.shape[3]])),O(l.rank===5,()=>`Error in conv3d: input must be rank 5, but got rank ${l.rank}.`),O(i.rank===5,()=>`Error in conv3d: filter must be rank 5, but got rank ${i.rank}.`),O(l.shape[4]===i.shape[3],()=>`Error in conv3d: depth of input (${l.shape[4]}) must match input depth for filter ${i.shape[3]}.`),O(ua(n,a),()=>`Error in conv3D: Either strides or dilations must be 1. Got strides ${n} and dilations '${a}'`),O(r==="NDHWC",()=>`Error in conv3d: got dataFormat of ${r} but only NDHWC is currently supported.`);let c={x:l,filter:i},p={strides:n,pad:s,dataFormat:r,dilations:a},d=B.runKernel(Xp,c,p);return u?V(d,[d.shape[1],d.shape[2],d.shape[3],d.shape[4]]):d}var NA=W({conv3d_:LF});function BF(e,t,n,s,r){O(e.length===t.rank,()=>`Length of inShape (${e.length}) and rank of dy (${t.rank}) must match`);let a=e,o=t,i=!1;t.rank===4&&(i=!0,o=V(t,[1,t.shape[0],t.shape[1],t.shape[2],t.shape[3]]),a=[1,e[0],e[1],e[2],e[3]]);let l=a[4],u=o.shape[4];O(a.length===5,()=>`Error in conv3dDerInput: inShape must be length 5, but got length ${a.length}.`),O(o.rank===5,()=>`Error in conv3dDerInput: dy must be rank 5, but got rank ${o.rank}`),O(n.rank===5,()=>`Error in conv3dDerInput: filter must be rank 5, but got rank ${n.rank}`),O(l===n.shape[3],()=>`Error in conv3dDerInput: depth of input (${l}) must match input depth for filter ${n.shape[3]}.`),O(u===n.shape[4],()=>`Error in conv3dDerInput: depth of output (${u}) must match output depth for filter ${n.shape[4]}.`);let c={dy:o,filter:n},p={pad:r,strides:s,inputShape:a},d=B.runKernel(l0,c,p);return i?V(d,[d.shape[1],d.shape[2],d.shape[3],d.shape[4]]):d}var Ew=W({conv3DBackpropInput_:BF});function WF(e,t,n,s,r){let a=D(e,"x","conv3dTranspose"),o=D(t,"filter","conv3dTranspose");return Ew(n,a,o,s,r)}var EA=W({conv3dTranspose_:WF});function VF(e){let n={x:D(e,"x","cos","float32")};return B.runKernel(To,n)}var xh=W({cos_:VF});function UF(e){let n={x:D(e,"x","cosh","float32")};return B.runKernel(No,n)}var D0=W({cosh_:UF});function GF(e,t=0,n=!1,s=!1){let a={x:D(e,"x","cumprod")},o={axis:t,exclusive:n,reverse:s};return B.runKernel(kl,a,o)}var Dp=W({cumprod_:GF});function HF(e,t=0,n=!1,s=!1){let a={x:D(e,"x","cumsum")},o={axis:t,exclusive:n,reverse:s};return B.runKernel(Eo,a,o)}var $0=W({cumsum_:HF});function jF(e,t,n,s=!1){let r=D(e,"x","denseBincount"),a=D(t,"weights","denseBincount");O(r.dtype==="int32",()=>`Error in denseBincount: input dtype must be int32, but got ${r.dtype}`),O(r.rank<=2,()=>`Error in denseBincount: input must be at most rank 2, but got rank ${r.rank}.`),O(n>=0,()=>`size must be non-negative, but got ${n}.`),O(a.size===r.size||a.size===0,()=>`Error in denseBincount: weights must have the same shape as x or 0-length, but got x shape: ${r.shape}, weights shape: ${a.shape}.`);let o={x:r,weights:a},i={size:n,binaryOutput:s};return B.runKernel(u0,o,i)}var Rw=W({denseBincount_:jF});function qF(e,t,n="NHWC"){let s=D(e,"x","depthToSpace","float32"),r=n==="NHWC"?s.shape[1]:s.shape[2],a=n==="NHWC"?s.shape[2]:s.shape[3],o=n==="NHWC"?s.shape[3]:s.shape[1];O(t>1,()=>`blockSize should be > 1 for depthToSpace, but was: ${t}`),O(r*t>=0,()=>`Negative dimension size caused by overflow when multiplying
     ${r} and ${t}  for depthToSpace with input shape
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Error("getWeights() is not implemented for Adamax yet.")}async setWeights(e){throw new Error("setWeights() is not implemented for Adamax yet.")}getConfig(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon,decay:this.decay}}static fromConfig(e,t){return new e(t.learningRate,t.beta1,t.beta2,t.epsilon,t.decay)}};l2.className="Adamax";ci(l2);var Eh=class extends Da{constructor(e){super(),this.learningRate=e,this.setLearningRate(e)}applyGradients(e){(Array.isArray(e)?e.map(n=>n.name):Object.keys(e)).forEach((n,s)=>{let r=Array.isArray(e)?e[s].tensor:e[n];if(r==null)return;let a=B.registeredVariables[n];Y(()=>{let o=de(z(this.c,r),a);a.assign(o)})}),this.incrementIterations()}setLearningRate(e){this.learningRate=e,this.c!=null&&this.c.dispose(),this.c=In(Te(-e))}dispose(){this.c.dispose()}async getWeights(){return[await this.saveIterations()]}async setWeights(e){if(e=await this.extractIterations(e),e.length!==0)throw new Error("SGD optimizer does 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e={theta:this.theta},t=super.getConfig();return Object.assign(e,t),e}};M5.className="ThresholdedReLU";he.registerClass(M5);var z5=class extends ct{constructor(e){super(e==null?{}:e),this.DEFAULT_AXIS=1,e==null&&(e={}),this.softmax=new _5().apply,this.axis=e.axis==null?this.DEFAULT_AXIS:e.axis}call(e,t){let n=Ze(e);return this.softmax(n,this.axis)}computeOutputShape(e){return e}getConfig(){let e={axis:this.axis},t=super.getConfig();return Object.assign(e,t),e}};z5.className="Softmax";he.registerClass(z5);function sc(e,t,n){if(typeof e=="number")return ll(e,t);if(e.length!==t)throw new j(`The ${n} argument must be an integer or tuple of ${t} integers. 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Received: ${JSON.stringify(e)} including a non-integer number ${r}`)}return e}function zr(e,t,n,s,r=1){if(e==null)return e;let a=t+(t-1)*(r-1),o;return n==="same"?o=e:o=e-a+1,Math.floor((o+s-1)/s)}function Jr(e,t,n,s){if(e==null)return null;if(s==="valid")e=e*t+oo([n-t,0]);else if(s==="same")e=e*t;else throw new j(`Unsupport padding mode: ${s}.`);return e}function L5(e,t){return Y(()=>(en(t),t==="channelsFirst"?nt(e,[0,2,3,1]):e))}function k8(e,t){return Y(()=>(en(t),t==="channelsFirst"?nt(e,[0,2,3,4,1]):e))}function WG(e,t,n,s=1,r="valid",a,o=1){return Y(()=>{if(a==null&&(a=Br()),en(a),e.shape.length!==3)throw new j(`The input of a conv1dWithBias operation should be 3, but is ${e.shape.length} instead.`);if(t.shape.length!==3)throw new j(`The kernel for a conv1dWithBias operation should be 3, but is ${t.shape.length} instead`);if(n!=null&&n.shape.length!==1)throw new j(`The bias for a conv1dWithBias operation should be 1, but is ${t.shape.length} instead`);if(a==="channelsFirst"&&(e=nt(e,[0,2,1])),r==="causal")throw new Ke("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");let i=R0(e,t,s,r==="same"?"same":"valid","NWC",o);return n!=null&&(i=Ur(i,n)),i})}function p7(e,t,n,s=[1,1],r="valid",a,o,i=null){return Y(()=>{if(a==null&&(a=Br()),en(a),e.rank!==3&&e.rank!==4)throw new j(`conv2dWithBiasActivation expects input to be of rank 3 or 4, but received ${e.rank}.`);if(t.rank!==3&&t.rank!==4)throw new j(`conv2dWithBiasActivation expects kernel to be of rank 3 or 4, but received ${e.rank}.`);let l=L5(e,a);if(r==="causal")throw new Ke("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");return l=pc.conv2d({x:l,filter:t,strides:s,pad:r==="same"?"same":"valid",dilations:o,dataFormat:"NHWC",bias:n,activation:i}),a==="channelsFirst"&&(l=nt(l,[0,3,1,2])),l})}function VG(e,t,n,s=[1,1,1],r="valid",a,o){return Y(()=>{if(a==null&&(a=Br()),en(a),e.rank!==4&&e.rank!==5)throw new j(`conv3dWithBias expects input to be of rank 4 or 5, but received ${e.rank}.`);if(t.rank!==4&&t.rank!==5)throw new j(`conv3dWithBias expects kernel to be of rank 4 or 5, but received ${e.rank}.`);let i=k8(e,a);if(r==="causal")throw new Ke("The support for CAUSAL padding mode in conv3dWithBias is not implemented yet.");return i=IA(i,t,s,r==="same"?"same":"valid","NDHWC",o),n!=null&&(i=Ur(i,n)),a==="channelsFirst"&&(i=nt(i,[0,4,1,2,3])),i})}var B5=class extends ct{constructor(e,t){if(super(t),this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",B5.verifyArgs(t),this.rank=e,Cn(this.rank,"rank"),this.rank!==1&&this.rank!==2&&this.rank!==3)throw new Ke(`Convolution layer for rank other than 1, 2, or 3 (${this.rank}) is not implemented yet.`);if(this.kernelSize=sc(t.kernelSize,e,"kernelSize"),this.strides=sc(t.strides==null?1:t.strides,e,"strides"),this.padding=t.padding==null?"valid":t.padding,ar(this.padding),this.dataFormat=t.dataFormat==null?"channelsLast":t.dataFormat,en(this.dataFormat),this.activation=lo(t.activation),this.useBias=t.useBias==null?!0:t.useBias,this.biasInitializer=Ot(t.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.biasConstraint=bn(t.biasConstraint),this.biasRegularizer=Mt(t.biasRegularizer),this.activityRegularizer=Mt(t.activityRegularizer),this.dilationRate=sc(t.dilationRate==null?1:t.dilationRate,e,"dilationRate"),this.rank===1&&Array.isArray(this.dilationRate)&&this.dilationRate.length!==1)throw new j(`dilationRate must be a number or an array of a single number for 1D convolution, but received ${JSON.stringify(this.dilationRate)}`);if(this.rank===2){if(typeof this.dilationRate=="number")this.dilationRate=[this.dilationRate,this.dilationRate];else if(this.dilationRate.length!==2)throw new j(`dilationRate must be a number or array of two numbers for 2D convolution, but received ${JSON.stringify(this.dilationRate)}`)}else if(this.rank===3){if(typeof this.dilationRate=="number")this.dilationRate=[this.dilationRate,this.dilationRate,this.dilationRate];else if(this.dilationRate.length!==3)throw new j(`dilationRate must be a number or array of three numbers for 3D convolution, but received ${JSON.stringify(this.dilationRate)}`)}}static verifyArgs(e){if(Yr("kernelSize"in e,"required key 'kernelSize' not in config"),typeof e.kernelSize!="number"&&!c5(e.kernelSize,"number",1,3))throw new j(`BaseConv expects config.kernelSize to be number or number[] with length 1, 2, or 3, but received ${JSON.stringify(e.kernelSize)}.`)}getConfig(){let e={kernelSize:this.kernelSize,strides:this.strides,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,activation:io(this.activation),useBias:this.useBias,biasInitializer:Gt(this.biasInitializer),biasRegularizer:It(this.biasRegularizer),activityRegularizer:It(this.activityRegularizer),biasConstraint:xn(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}},Oh=class extends B5{constructor(e,t){super(e,t),this.kernel=null,Oh.verifyArgs(t),this.filters=t.filters,Cn(this.filters,"filters"),this.kernelInitializer=Ot(t.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.kernelConstraint=bn(t.kernelConstraint),this.kernelRegularizer=Mt(t.kernelRegularizer)}build(e){e=xt(e);let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new j(`The channel dimension of the input should be defined. Found ${e[t]}`);let n=e[t],s=this.kernelSize.concat([n,this.filters]);this.kernel=this.addWeight("kernel",s,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[{ndim:this.rank+2,axes:{[t]:n}}],this.built=!0}call(e,t){return Y(()=>{e=Ze(e);let n,s=this.bias==null?null:this.bias.read(),r=_k(this.activation.getClassName());if(r!=null&&this.rank===2)n=p7(e,this.kernel.read(),s,this.strides,this.padding,this.dataFormat,this.dilationRate,r);else{if(this.rank===1)n=WG(e,this.kernel.read(),s,this.strides[0],this.padding,this.dataFormat,this.dilationRate[0]);else if(this.rank===2)n=p7(e,this.kernel.read(),s,this.strides,this.padding,this.dataFormat,this.dilationRate);else if(this.rank===3)n=VG(e,this.kernel.read(),s,this.strides,this.padding,this.dataFormat,this.dilationRate);else throw new Ke("convolutions greater than 3D are not implemented yet.");this.activation!=null&&(n=this.activation.apply(n))}return n})}computeOutputShape(e){e=xt(e);let t=[],n=this.dataFormat==="channelsLast"?e.slice(1,e.length-1):e.slice(2);for(let r=0;r<n.length;++r){let a=zr(n[r],this.kernelSize[r],this.padding,this.strides[r],typeof this.dilationRate=="number"?this.dilationRate:this.dilationRate[r]);t.push(a)}let s=[e[0]];return this.dataFormat==="channelsLast"?(s=s.concat(t),s.push(this.filters)):(s.push(this.filters),s=s.concat(t)),s}getConfig(){let e={filters:this.filters,kernelInitializer:Gt(this.kernelInitializer),kernelRegularizer:It(this.kernelRegularizer),kernelConstraint:xn(this.kernelConstraint)},t=super.getConfig();return Object.assign(e,t),e}static verifyArgs(e){if(!("filters"in e)||typeof e.filters!="number"||e.filters<1)throw new j(`Convolution layer expected config.filters to be a 'number' > 0 but got ${JSON.stringify(e.filters)}`)}},Mh=class extends Oh{constructor(e){super(2,e),Mh.verifyArgs(e)}getConfig(){let e=super.getConfig();return delete e.rank,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!c5(e.kernelSize,"number",1,2))throw new j(`Conv2D expects config.kernelSize to be number or number[] with length 1 or 2, but received ${JSON.stringify(e.kernelSize)}.`)}};Mh.className="Conv2D";he.registerClass(Mh);var zh=class extends Oh{constructor(e){super(3,e),zh.verifyArgs(e)}getConfig(){let e=super.getConfig();return delete e.rank,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!(Array.isArray(e.kernelSize)&&(e.kernelSize.length===1||e.kernelSize.length===3)))throw new j(`Conv3D expects config.kernelSize to be number or [number, number, number], but received ${JSON.stringify(e.kernelSize)}.`)}};zh.className="Conv3D";he.registerClass(zh);var W5=class extends Mh{constructor(e){if(super(e),this.inputSpec=[new on({ndim:4})],this.padding!=="same"&&this.padding!=="valid")throw new j(`Conv2DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(e){if(e=xt(e),e.length!==4)throw new j("Input should have rank 4; Received input shape: "+JSON.stringify(e));let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new j("The channel dimension of the inputs should be defined. Found `None`.");let n=e[t],s=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight("kernel",s,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new on({ndim:4,axes:{[t]:n}})],this.built=!0}call(e,t){return Y(()=>{let n=Ze(e);if(n.shape.length!==4)throw new j(`Conv2DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${n.shape.length}`);let s=n.shape,r=s[0],a,o;this.dataFormat==="channelsFirst"?(a=2,o=3):(a=1,o=2);let i=s[a],l=s[o],u=this.kernelSize[0],c=this.kernelSize[1],p=this.strides[0],d=this.strides[1],h=Jr(i,p,u,this.padding),f=Jr(l,d,c,this.padding),m=[r,h,f,this.filters];this.dataFormat!=="channelsLast"&&(n=nt(n,[0,2,3,1]));let g=_0(n,this.kernel.read(),m,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(g=nt(g,[0,3,1,2])),this.bias!=null&&(g=Ur(g,this.bias.read(),this.dataFormat)),this.activation!=null&&(g=this.activation.apply(g)),g})}computeOutputShape(e){e=xt(e);let t=e.slice(),n,s,r;this.dataFormat==="channelsFirst"?(n=1,s=2,r=3):(n=3,s=1,r=2);let a=this.kernelSize[0],o=this.kernelSize[1],i=this.strides[0],l=this.strides[1];return t[n]=this.filters,t[s]=Jr(t[s],i,a,this.padding),t[r]=Jr(t[r],l,o,this.padding),t}getConfig(){let e=super.getConfig();return delete e.dilationRate,e}};W5.className="Conv2DTranspose";he.registerClass(W5);var V5=class extends zh{constructor(e){if(super(e),this.inputSpec=[new on({ndim:5})],this.padding!=="same"&&this.padding!=="valid")throw new j(`Conv3DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(e){if(e=xt(e),e.length!==5)throw new j("Input should have rank 5; Received input shape: "+JSON.stringify(e));let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new j("The channel dimension of the inputs should be defined. Found `None`.");let n=e[t],s=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight("kernel",s,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new on({ndim:5,axes:{[t]:n}})],this.built=!0}call(e,t){return Y(()=>{let n=Ze(e);if(n.shape.length!==5)throw new j(`Conv3DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${n.shape.length}`);let s=n.shape,r=s[0],a,o,i;this.dataFormat==="channelsFirst"?(i=2,a=3,o=4):(i=1,a=2,o=3);let l=s[i],u=s[a],c=s[o],p=this.kernelSize[0],d=this.kernelSize[1],h=this.kernelSize[2],f=this.strides[0],m=this.strides[1],g=this.strides[2],y=Jr(l,f,p,this.padding),x=Jr(u,m,d,this.padding),A=Jr(c,g,h,this.padding),b=[r,y,x,A,this.filters];this.dataFormat!=="channelsLast"&&(n=nt(n,[0,2,3,4,1]));let w=CA(n,this.kernel.read(),b,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(w=nt(w,[0,4,1,2,3])),this.bias!==null&&(w=Ur(w,this.bias.read(),this.dataFormat)),this.activation!==null&&(w=this.activation.apply(w)),w})}computeOutputShape(e){e=xt(e);let t=e.slice(),n,s,r,a;this.dataFormat==="channelsFirst"?(n=1,s=2,r=3,a=4):(n=4,s=1,r=2,a=3);let o=this.kernelSize[0],i=this.kernelSize[1],l=this.kernelSize[2],u=this.strides[0],c=this.strides[1],p=this.strides[2];return t[n]=this.filters,t[s]=Jr(t[s],u,o,this.padding),t[r]=Jr(t[r],c,i,this.padding),t[a]=Jr(t[a],p,l,this.padding),t}getConfig(){let e=super.getConfig();return delete e.dilationRate,e}};V5.className="Conv3DTranspose";he.registerClass(V5);var S8=class extends Oh{constructor(e,t){if(super(e,t),this.DEFAULT_DEPTHWISE_INITIALIZER="glorotUniform",this.DEFAULT_POINTWISE_INITIALIZER="glorotUniform",this.depthwiseKernel=null,this.pointwiseKernel=null,t.filters==null)throw new j("The `filters` configuration field is required by SeparableConv, but is unspecified.");if(t.kernelInitializer!=null||t.kernelRegularizer!=null||t.kernelConstraint!=null)throw new j("Fields kernelInitializer, kernelRegularizer and kernelConstraint are invalid for SeparableConv2D. Use depthwiseInitializer, depthwiseRegularizer, depthwiseConstraint, pointwiseInitializer, pointwiseRegularizer and pointwiseConstraint instead.");if(t.padding!=null&&t.padding!=="same"&&t.padding!=="valid")throw new j(`SeparableConv${this.rank}D supports only padding modes: 'same' and 'valid', but received ${JSON.stringify(t.padding)}`);this.depthMultiplier=t.depthMultiplier==null?1:t.depthMultiplier,this.depthwiseInitializer=Ot(t.depthwiseInitializer||this.DEFAULT_DEPTHWISE_INITIALIZER),this.depthwiseRegularizer=Mt(t.depthwiseRegularizer),this.depthwiseConstraint=bn(t.depthwiseConstraint),this.pointwiseInitializer=Ot(t.depthwiseInitializer||this.DEFAULT_POINTWISE_INITIALIZER),this.pointwiseRegularizer=Mt(t.pointwiseRegularizer),this.pointwiseConstraint=bn(t.pointwiseConstraint)}build(e){if(e=xt(e),e.length<this.rank+2)throw new j(`Inputs to SeparableConv${this.rank}D should have rank ${this.rank+2}, but received input shape: ${JSON.stringify(e)}`);let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null||e[t]<0)throw new j(`The channel dimension of the inputs should be defined, but found ${JSON.stringify(e[t])}`);let n=e[t],s=this.kernelSize.concat([n,this.depthMultiplier]),r=[];for(let o=0;o<this.rank;++o)r.push(1);r.push(n*this.depthMultiplier,this.filters);let a=!0;this.depthwiseKernel=this.addWeight("depthwise_kernel",s,"float32",this.depthwiseInitializer,this.depthwiseRegularizer,a,this.depthwiseConstraint),this.pointwiseKernel=this.addWeight("pointwise_kernel",r,"float32",this.pointwiseInitializer,this.pointwiseRegularizer,a,this.pointwiseConstraint),this.useBias?this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,a,this.biasConstraint):this.bias=null,this.inputSpec=[new on({ndim:this.rank+2,axes:{[t]:n}})],this.built=!0}call(e,t){return Y(()=>{e=Ze(e);let n;if(this.rank===1)throw new Ke("1D separable convolution is not implemented yet.");return this.rank===2&&(this.dataFormat==="channelsFirst"&&(e=nt(e,[0,2,3,1])),n=j0(e,this.depthwiseKernel.read(),this.pointwiseKernel.read(),this.strides,this.padding,this.dilationRate,"NHWC")),this.useBias&&(n=Ur(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),this.dataFormat==="channelsFirst"&&(n=nt(n,[0,3,1,2])),n})}getConfig(){let e=super.getConfig();return delete e.rank,delete e.kernelInitializer,delete e.kernelRegularizer,delete e.kernelConstraint,e.depthwiseInitializer=Gt(this.depthwiseInitializer),e.pointwiseInitializer=Gt(this.pointwiseInitializer),e.depthwiseRegularizer=It(this.depthwiseRegularizer),e.pointwiseRegularizer=It(this.pointwiseRegularizer),e.depthwiseConstraint=xn(this.depthwiseConstraint),e.pointwiseConstraint=xn(this.pointwiseConstraint),e}};S8.className="SeparableConv";var U5=class extends S8{constructor(e){super(2,e)}};U5.className="SeparableConv2D";he.registerClass(U5);var S2=class extends Oh{constructor(e){super(1,e),S2.verifyArgs(e),this.inputSpec=[{ndim:3}]}getConfig(){let e=super.getConfig();return delete e.rank,delete e.dataFormat,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!c5(e.kernelSize,"number",1,1))throw new j(`Conv1D expects config.kernelSize to be number or number[] with length 1, but received ${JSON.stringify(e.kernelSize)}.`)}};S2.className="Conv1D";he.registerClass(S2);var G5=class extends ct{constructor(e){super(e),typeof e.cropping=="number"?this.cropping=[[e.cropping,e.cropping],[e.cropping,e.cropping]]:typeof e.cropping[0]=="number"?this.cropping=[[e.cropping[0],e.cropping[0]],[e.cropping[1],e.cropping[1]]]:this.cropping=e.cropping,this.dataFormat=e.dataFormat===void 0?"channelsLast":e.dataFormat,this.inputSpec=[{ndim:4}]}computeOutputShape(e){return this.dataFormat==="channelsFirst"?[e[0],e[1],e[2]-this.cropping[0][0]-this.cropping[0][1],e[3]-this.cropping[1][0]-this.cropping[1][1]]:[e[0],e[1]-this.cropping[0][0]-this.cropping[0][1],e[2]-this.cropping[1][0]-this.cropping[1][1],e[3]]}call(e,t){return Y(()=>{if(e=Ze(e),this.dataFormat==="channelsLast"){let n=em(e,this.cropping[0][0],e.shape[1]-this.cropping[0][0]-this.cropping[0][1],2);return em(n,this.cropping[1][0],e.shape[2]-this.cropping[1][1]-this.cropping[1][0],3)}else{let n=em(e,this.cropping[0][0],e.shape[2]-this.cropping[0][0]-this.cropping[0][1],3);return em(n,this.cropping[1][0],e.shape[3]-this.cropping[1][1]-this.cropping[1][0],4)}})}getConfig(){let e={cropping:this.cropping,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};G5.className="Cropping2D";he.registerClass(G5);var H5=class extends ct{constructor(e){super(e),this.DEFAULT_SIZE=[2,2],this.inputSpec=[{ndim:4}],this.size=e.size==null?this.DEFAULT_SIZE:e.size,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,en(this.dataFormat),this.interpolation=e.interpolation==null?"nearest":e.interpolation,JV(this.interpolation)}computeOutputShape(e){if(this.dataFormat==="channelsFirst"){let t=e[2]==null?null:this.size[0]*e[2],n=e[3]==null?null:this.size[1]*e[3];return[e[0],e[1],t,n]}else{let t=e[1]==null?null:this.size[0]*e[1],n=e[2]==null?null:this.size[1]*e[2];return[e[0],t,n,e[3]]}}call(e,t){return Y(()=>{let n=Ze(e),s=n.shape;if(this.dataFormat==="channelsFirst"){n=nt(n,[0,2,3,1]);let r=this.size[0]*s[2],a=this.size[1]*s[3],o=this.interpolation==="nearest"?Ce.resizeNearestNeighbor(n,[r,a]):Ce.resizeBilinear(n,[r,a]);return nt(o,[0,3,1,2])}else{let r=this.size[0]*s[1],a=this.size[1]*s[2];return this.interpolation==="nearest"?Ce.resizeNearestNeighbor(n,[r,a]):Ce.resizeBilinear(n,[r,a])}})}getConfig(){let e={size:this.size,dataFormat:this.dataFormat,interpolation:this.interpolation},t=super.getConfig();return Object.assign(e,t),e}};H5.className="UpSampling2D";he.registerClass(H5);function UG(e,t,n=[1,1],s="valid",r,a){return Y(()=>{r==null&&(r=Br()),en(r);let o=L5(e,r);if(e.rank!==4)throw new j(`Input for depthwiseConv2d is required to be 4-D, but is instead ${e.rank}-D`);if(t.rank!==4)throw new j(`depthwiseKernel is required to be 4-D, but is instead ${t.rank}-D`);return o=Zc(o,t,n,s==="same"?"same":"valid","NHWC",a),r==="channelsFirst"&&(o=nt(o,[0,3,1,2])),o})}var j5=class extends B5{constructor(e){super(2,e),this.depthwiseKernel=null,this.depthMultiplier=e.depthMultiplier==null?1:e.depthMultiplier,this.depthwiseInitializer=Ot(e.depthwiseInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.depthwiseConstraint=bn(e.depthwiseConstraint),this.depthwiseRegularizer=Mt(e.depthwiseRegularizer)}build(e){if(e=xt(e),e.length<4)throw new j(`Inputs to DepthwiseConv2D should have rank 4. Received input shape: ${JSON.stringify(e)}.`);let t=this.dataFormat==="channelsFirst"?1:3;if(e[t]==null||e[t]<0)throw new j(`The channel dimension of the inputs to DepthwiseConv2D should be defined, but is not (${e[t]}).`);let n=e[t],s=[this.kernelSize[0],this.kernelSize[1],n,this.depthMultiplier];this.depthwiseKernel=this.addWeight("depthwise_kernel",s,null,this.depthwiseInitializer,this.depthwiseRegularizer,!0,this.depthwiseConstraint),this.useBias?this.bias=this.addWeight("bias",[n*this.depthMultiplier],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return Y(()=>{e=Ze(e);let n=UG(e,this.depthwiseKernel.read(),this.strides,this.padding,this.dataFormat,null);return this.useBias&&(n=Ur(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),n})}computeOutputShape(e){e=xt(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2],s=this.dataFormat==="channelsFirst"?e[1]*this.depthMultiplier:e[3]*this.depthMultiplier,r=zr(t,this.kernelSize[0],this.padding,this.strides[0]),a=zr(n,this.kernelSize[1],this.padding,this.strides[1]);return this.dataFormat==="channelsFirst"?[e[0],s,r,a]:[e[0],r,a,s]}getConfig(){let e=super.getConfig();return e.depthMultiplier=this.depthMultiplier,e.depthwiseInitializer=Gt(this.depthwiseInitializer),e.depthwiseRegularizer=It(this.depthwiseRegularizer),e.depthwiseConstraint=xn(this.depthwiseRegularizer),e}};j5.className="DepthwiseConv2D";he.registerClass(j5);function I8(e,t,n,s){if(Array.isArray(e)){if(t!=null||n!=null)throw new j("When inputs is an array, neither initialState or constants should be provided");s!=null&&(n=e.slice(e.length-s,e.length),e=e.slice(0,e.length-s)),e.length>1&&(t=e.slice(1,e.length)),e=e[0]}function r(a){return a==null||Array.isArray(a)?a:[a]}return t=r(t),n=r(n),{inputs:e,initialState:t,constants:n}}function C8(e,t,n,s=!1,r,a,o=!1,i=!1){return Y(()=>{let l=t.shape.length;if(l<3)throw new j(`Input should be at least 3D, but is ${l}D.`);let u=[1,0].concat(Lr(2,l));if(t=nt(t,u),a!=null)throw new Ke("The rnn() functoin of the deeplearn.js backend does not support constants yet.");o&&console.warn("Backend rnn(): the unroll = true option is not applicable to the imperative deeplearn.js backend."),r!=null&&(r=Ae(Ae(r,"bool"),"float32"),r.rank===l-1&&(r=Wt(r,-1)),r=nt(r,u)),s&&(t=er(t,0),r!=null&&(r=er(r,0)));let c=[],p,d=n,h=t.shape[0],f=Mn(t),m;r!=null&&(m=Mn(r));for(let y=0;y<h;++y){let x=f[y],A=Y(()=>e(x,d));if(r==null)p=A[0],d=A[1];else{let b=Y(()=>{let w=m[y],k=ye(zs(w),w),C=de(z(A[0],w),z(d[0],k)),E=d.map((_,$)=>de(z(A[1][$],w),z(_,k)));return{output:C,newStates:E}});p=b.output,d=b.newStates}i&&c.push(p)}let g;return i&&(g=un(c,1)),[p,g,d]})}var la=class extends ct{constructor(e){super(e);let t;if(e.cell==null)throw new j("cell property is missing for the constructor of RNN.");if(Array.isArray(e.cell)?t=new T2({cells:e.cell}):t=e.cell,t.stateSize==null)throw new j("The RNN cell should have an attribute `stateSize` (tuple of integers, one integer per RNN state).");this.cell=t,this.returnSequences=e.returnSequences==null?!1:e.returnSequences,this.returnState=e.returnState==null?!1:e.returnState,this.goBackwards=e.goBackwards==null?!1:e.goBackwards,this._stateful=e.stateful==null?!1:e.stateful,this.unroll=e.unroll==null?!1:e.unroll,this.supportsMasking=!0,this.inputSpec=[new on({ndim:3})],this.stateSpec=null,this.states_=null,this.numConstants=null,this.keptStates=[]}getStates(){if(this.states_==null){let e=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;return Lr(0,e).map(t=>null)}else return this.states_}setStates(e){this.states_=e}computeOutputShape(e){K3(e)&&(e=e[0]),e=e;let t=this.cell.stateSize;Array.isArray(t)||(t=[t]);let n=t[0],s;if(this.returnSequences?s=[e[0],e[1],n]:s=[e[0],n],this.returnState){let r=[];for(let a of t)r.push([e[0],a]);return[s].concat(r)}else return s}computeMask(e,t){return Y(()=>{Array.isArray(t)&&(t=t[0]);let n=this.returnSequences?t:null;if(this.returnState){let s=this.states.map(r=>null);return[n].concat(s)}else return n})}get states(){if(this.states_==null){let e=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1,t=[];for(let n=0;n<e;++n)t.push(null);return t}else return this.states_}set states(e){this.states_=e}build(e){if(this.numConstants!=null)throw new Ke("Constants support is not implemented in RNN yet.");K3(e)&&(e=e[0]),e=e;let n=this.stateful?e[0]:null,s=e.slice(2);this.inputSpec[0]=new on({shape:[n,null,...s]});let r=[e[0]].concat(e.slice(2));this.cell.build(r);let a;if(Array.isArray(this.cell.stateSize)?a=this.cell.stateSize:a=[this.cell.stateSize],this.stateSpec!=null){if(!v.arraysEqual(this.stateSpec.map(o=>o.shape[o.shape.length-1]),a))throw new j(`An initialState was passed that is not compatible with cell.stateSize. Received stateSpec=${this.stateSpec}; However cell.stateSize is ${this.cell.stateSize}`)}else this.stateSpec=a.map(o=>new on({shape:[null,o]}));this.stateful&&this.resetStates()}resetStates(e,t=!1){Y(()=>{if(!this.stateful)throw new ga("Cannot call resetStates() on an RNN Layer that is not stateful.");let n=this.inputSpec[0].shape[0];if(n==null)throw new j("If an RNN is stateful, it needs to know its batch size. Specify the batch size of your input tensors: \n- If using a Sequential model, specify the batch size by passing a `batchInputShape` option to your first layer.\n- If using the functional API, specify the batch size by passing a `batchShape` option to your Input layer.");if(this.states_==null)Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(s=>Ut([n,s])):this.states_=[Ut([n,this.cell.stateSize])];else if(e==null)ee(this.states_),this.keptStates!=null&&(ee(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(s=>Ut([n,s])):this.states_[0]=Ut([n,this.cell.stateSize]);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new j(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${e.length} state value(s). Input received: ${e}`);t===!0?this.keptStates.push(this.states_.slice()):ee(this.states_);for(let s=0;s<this.states_.length;++s){let r=e[s],a=Array.isArray(this.cell.stateSize)?this.cell.stateSize[s]:this.cell.stateSize,o=[n,a];if(!v.arraysEqual(r.shape,o))throw new j(`State ${s} is incompatible with layer ${this.name}: expected shape=${o}, received shape=${r.shape}`);this.states_[s]=r}}this.states_=this.states_.map(s=>In(s.clone()))})}apply(e,t){let n=t==null?null:t.initialState,s=t==null?null:t.constants;t==null&&(t={});let r=I8(e,n,s,this.numConstants);e=r.inputs,n=r.initialState,s=r.constants;let a=[],o=[];if(n!=null){t.initialState=n,a=a.concat(n),this.stateSpec=[];for(let l of n)this.stateSpec.push(new on({shape:l.shape}));o=o.concat(this.stateSpec)}if(s!=null&&(t.constants=s,a=a.concat(s),this.numConstants=s.length),a[0]instanceof Fr){let l=[e].concat(a),u=this.inputSpec.concat(o),c=this.inputSpec;this.inputSpec=u;let p=super.apply(l,t);return this.inputSpec=c,p}else return super.apply(e,t)}call(e,t){return Y(()=>{let n=t==null?null:t.mask,s=t==null?null:t.training,r=t==null?null:t.initialState;e=Ze(e),r==null&&(this.stateful?r=this.states_:r=this.getInitialState(e));let a=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;if(r.length!==a)throw new j(`RNN Layer has ${a} state(s) but was passed ${r.length} initial state(s).`);this.unroll&&console.warn("Ignoring unroll = true for RNN layer, due to imperative backend.");let o={training:s},l=C8((h,f)=>{let m=this.cell.call([h].concat(f),o);return[m[0],m.slice(1)]},e,r,this.goBackwards,n,null,this.unroll,this.returnSequences),u=l[0],c=l[1],p=l[2];this.stateful&&this.resetStates(p,s);let d=this.returnSequences?c:u;return this.returnState?[d].concat(p):d})}getInitialState(e){return Y(()=>{let t=Ut(e.shape);return t=Se(t,[1,2]),t=_h(t),Array.isArray(this.cell.stateSize)?this.cell.stateSize.map(n=>n>1?q3(t,[1,n]):t):this.cell.stateSize>1?[q3(t,[1,this.cell.stateSize])]:[t]})}get trainableWeights(){return this.trainable?this.cell.trainableWeights:[]}get nonTrainableWeights(){return this.trainable?this.cell.nonTrainableWeights:this.cell.weights}setFastWeightInitDuringBuild(e){super.setFastWeightInitDuringBuild(e),this.cell!=null&&this.cell.setFastWeightInitDuringBuild(e)}getConfig(){let e=super.getConfig(),t={returnSequences:this.returnSequences,returnState:this.returnState,goBackwards:this.goBackwards,stateful:this.stateful,unroll:this.unroll};this.numConstants!=null&&(t.numConstants=this.numConstants);let n=this.cell.getConfig();return this.getClassName()===la.className&&(t.cell={className:this.cell.getClassName(),config:n}),Object.assign({},n,e,t)}static fromConfig(e,t,n={}){let s=t.cell,r=Mr(s,n);return new e(Object.assign(t,{cell:r}))}};la.className="RNN";he.registerClass(la);var Lh=class extends ct{},I2=class extends Lh{constructor(e){super(e),this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",this.units=e.units,Cn(this.units,"units"),this.activation=lo(e.activation==null?this.DEFAULT_ACTIVATION:e.activation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=Ot(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=Ot(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=Ot(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=Mt(e.kernelRegularizer),this.recurrentRegularizer=Mt(e.recurrentRegularizer),this.biasRegularizer=Mt(e.biasRegularizer),this.kernelConstraint=bn(e.kernelConstraint),this.recurrentConstraint=bn(e.recurrentConstraint),this.biasConstraint=bn(e.biasConstraint),this.dropout=hc([1,oo([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=hc([1,oo([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.dropoutFunc=e.dropoutFunc,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=xt(e),this.kernel=this.addWeight("kernel",[e[e.length-1],this.units],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias?this.bias=this.addWeight("bias",[this.units],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return Y(()=>{if(e=e,e.length!==2)throw new j(`SimpleRNNCell expects 2 input Tensors, got ${e.length}.`);let n=e[1];e=e[0];let s=t.training==null?!1:t.training;0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=uo({ones:()=>zs(e),rate:this.dropout,training:s,dropoutFunc:this.dropoutFunc})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=uo({ones:()=>zs(n),rate:this.recurrentDropout,training:s,dropoutFunc:this.dropoutFunc}));let r,a=this.dropoutMask,o=this.recurrentDropoutMask;a!=null?r=ta(z(e,a),this.kernel.read()):r=ta(e,this.kernel.read()),this.bias!=null&&(r=Ur(r,this.bias.read())),o!=null&&(n=z(n,o));let i=de(r,ta(n,this.recurrentKernel.read()));return this.activation!=null&&(i=this.activation.apply(i)),[i,i]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:io(this.activation),useBias:this.useBias,kernelInitializer:Gt(this.kernelInitializer),recurrentInitializer:Gt(this.recurrentInitializer),biasInitializer:Gt(this.biasInitializer),kernelRegularizer:It(this.kernelRegularizer),recurrentRegularizer:It(this.recurrentRegularizer),biasRegularizer:It(this.biasRegularizer),activityRegularizer:It(this.activityRegularizer),kernelConstraint:xn(this.kernelConstraint),recurrentConstraint:xn(this.recurrentConstraint),biasConstraint:xn(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout};return Object.assign({},e,t)}};I2.className="SimpleRNNCell";he.registerClass(I2);var q5=class extends la{constructor(e){e.cell=new I2(e),super(e)}call(e,t){return Y(()=>{this.cell.dropoutMask!=null&&(ee(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(ee(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,s=t==null?null:t.training,r=t==null?null:t.initialState;return super.call(e,{mask:n,training:s,initialState:r})})}static fromConfig(e,t){return new e(t)}};q5.className="SimpleRNN";he.registerClass(q5);var C2=class extends Lh{constructor(e){if(super(e),this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_RECURRENT_ACTIVATION="hardSigmoid",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",e.resetAfter)throw new j("GRUCell does not support reset_after parameter set to true.");this.units=e.units,Cn(this.units,"units"),this.activation=lo(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=lo(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=Ot(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=Ot(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=Ot(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=Mt(e.kernelRegularizer),this.recurrentRegularizer=Mt(e.recurrentRegularizer),this.biasRegularizer=Mt(e.biasRegularizer),this.kernelConstraint=bn(e.kernelConstraint),this.recurrentConstraint=bn(e.recurrentConstraint),this.biasConstraint=bn(e.biasConstraint),this.dropout=hc([1,oo([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=hc([1,oo([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.dropoutFunc=e.dropoutFunc,this.implementation=e.implementation,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=xt(e);let t=e[e.length-1];this.kernel=this.addWeight("kernel",[t,this.units*3],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units*3],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias?this.bias=this.addWeight("bias",[this.units*3],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return Y(()=>{if(e=e,e.length!==2)throw new j(`GRUCell expects 2 input Tensors (inputs, h, c), got ${e.length}.`);let n=t.training==null?!1:t.training,s=e[1];e=e[0],0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=uo({ones:()=>zs(e),rate:this.dropout,training:n,count:3,dropoutFunc:this.dropoutFunc})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=uo({ones:()=>zs(s),rate:this.recurrentDropout,training:n,count:3,dropoutFunc:this.dropoutFunc}));let r=this.dropoutMask,a=this.recurrentDropoutMask,o,i,l;0<this.dropout&&this.dropout<1&&(e=z(e,r[0]));let u=ta(e,this.kernel.read());this.useBias&&(u=Ur(u,this.bias.read())),0<this.recurrentDropout&&this.recurrentDropout<1&&(s=z(s,a[0]));let c=this.recurrentKernel.read(),[p,d]=Qt(c,[2*this.units,this.units],c.rank-1),h=ta(s,p),[f,m,g]=Qt(u,3,u.rank-1),[y,x]=Qt(h,2,h.rank-1);o=this.recurrentActivation.apply(de(f,y)),i=this.recurrentActivation.apply(de(m,x));let A=ta(z(i,s),d);l=this.activation.apply(de(g,A));let b=de(z(o,s),z(de(1,Pt(o)),l));return[b,b]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:io(this.activation),recurrentActivation:io(this.recurrentActivation),useBias:this.useBias,kernelInitializer:Gt(this.kernelInitializer),recurrentInitializer:Gt(this.recurrentInitializer),biasInitializer:Gt(this.biasInitializer),kernelRegularizer:It(this.kernelRegularizer),recurrentRegularizer:It(this.recurrentRegularizer),biasRegularizer:It(this.biasRegularizer),activityRegularizer:It(this.activityRegularizer),kernelConstraint:xn(this.kernelConstraint),recurrentConstraint:xn(this.recurrentConstraint),biasConstraint:xn(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation,resetAfter:!1};return Object.assign({},e,t)}};C2.className="GRUCell";he.registerClass(C2);var X5=class extends la{constructor(e){e.implementation===0&&console.warn("`implementation=0` has been deprecated, and now defaults to `implementation=1`. 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Please update your layer call."),e.cell=new Bh(e),super(e)}call(e,t){return Y(()=>{this.cell.dropoutMask!=null&&(ee(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(ee(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,s=t==null?null:t.training,r=t==null?null:t.initialState;return super.call(e,{mask:n,training:s,initialState:r})})}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}};K5.className="LSTM";he.registerClass(K5);var T2=class extends Lh{constructor(e){super(e),this.cells=e.cells}get stateSize(){let e=[];for(let t of this.cells.slice().reverse())Array.isArray(t.stateSize)?e.push(...t.stateSize):e.push(t.stateSize);return e}call(e,t){return Y(()=>{e=e;let n=e.slice(1),s=[];for(let o of this.cells.slice().reverse())Array.isArray(o.stateSize)?s.push(n.splice(0,o.stateSize.length)):s.push(n.splice(0,1));s.reverse();let r=[],a;for(let o=0;o<this.cells.length;++o){let i=this.cells[o];n=s[o],o===0?a=[e[0]].concat(n):a=[a[0]].concat(n),a=i.call(a,t),r.push(a.slice(1))}n=[];for(let o of r.slice().reverse())n.push(...o);return[a[0]].concat(n)})}build(e){K3(e)&&(e=e[0]),e=e;let t;this.cells.forEach((n,s)=>{Qi(`RNNCell_${s}`,()=>{n.build(e),Array.isArray(n.stateSize)?t=n.stateSize[0]:t=n.stateSize,e=[e[0],t]})}),this.built=!0}getConfig(){let e=super.getConfig(),t=r=>({className:r.getClassName(),config:r.getConfig()}),s={cells:this.cells.map(t)};return Object.assign({},e,s)}static fromConfig(e,t,n={}){let s=[];for(let r of t.cells)s.push(Mr(r,n));return new e({cells:s})}get trainableWeights(){if(!this.trainable)return[];let e=[];for(let t of this.cells)e.push(...t.trainableWeights);return e}get nonTrainableWeights(){let e=[];for(let t of this.cells)e.push(...t.nonTrainableWeights);if(!this.trainable){let t=[];for(let n of this.cells)t.push(...n.trainableWeights);return t.concat(e)}return e}getWeights(){let e=[];for(let t of this.cells)e.push(...t.weights);return Z3(e)}setWeights(e){let t=[];for(let n of this.cells){let s=n.weights.length,r=e.splice(s);for(let a=0;a<n.weights.length;++a)t.push([n.weights[a],r[a]])}x5(t)}};T2.className="StackedRNNCells";he.registerClass(T2);function uo(e){let{ones:t,rate:n,training:s=!1,count:r=1,dropoutFunc:a}=e,o=()=>a!=null?a(t(),n):zk(t(),n),i=()=>$h(o,t,s);return!r||r<=1?In(i().clone()):Array(r).fill(void 0).map(i).map(u=>In(u.clone()))}var GG=function(e,t){var n={};for(var s in e)Object.prototype.hasOwnProperty.call(e,s)&&t.indexOf(s)<0&&(n[s]=e[s]);if(e!=null&&typeof Object.getOwnPropertySymbols=="function")for(var r=0,s=Object.getOwnPropertySymbols(e);r<s.length;r++)t.indexOf(s[r])<0&&Object.prototype.propertyIsEnumerable.call(e,s[r])&&(n[s[r]]=e[s[r]]);return n},T8=class extends la{constructor(e){if(e.unroll)throw new Ke("Unrolling is not possible with convolutional RNNs.");if(Array.isArray(e.cell))throw new Ke("It is not possible at the moment to stack convolutional cells.");super(e),this.inputSpec=[new on({ndim:5})]}call(e,t){return Y(()=>{if(this.cell.dropoutMask!=null&&(ee(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(ee(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null),t&&t.constants)throw new j("ConvRNN2D cell does not support constants");let n=t==null?null:t.mask,s=t==null?null:t.training,r=t==null?null:t.initialState;return super.call(e,{mask:n,training:s,initialState:r})})}computeOutputShape(e){let t=this.computeSingleOutputShape(e);return this.returnSequences||(t=[t[0],...t.slice(2)]),this.returnState&&(t=[t,...Array(2).fill([e[0],...t.slice(-3)])]),t}getInitialState(e){return Y(()=>{let{stateSize:t}=this.cell,n=e.shape,s=this.computeSingleOutputShape(n),r=[s[0],...s.slice(2)],a=Ut(r);return Array.isArray(t)?Array(t.length).fill(a):[a]})}resetStates(e,t=!1){Y(()=>{if(!this.stateful)throw new ga("Cannot call resetStates() on an RNN Layer that is not stateful.");let n=this.inputSpec[0].shape,s=this.computeSingleOutputShape(n),r=[s[0],...s.slice(2)];if(n[0]==null)throw new j("If an RNN is stateful, it needs to know its batch size. Specify the batch size of your input tensors: \n- If using a Sequential model, specify the batch size by passing a `batchInputShape` option to your first layer.\n- If using the functional API, specify the batch size by passing a `batchShape` option to your Input layer.");if(this.getStates()==null)Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>Ut(r)):this.states_=[Ut(r)];else if(e==null)ee(this.states_),this.keptStates!=null&&(ee(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>Ut(r)):this.states_[0]=Ut(r);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new j(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${e.length} state value(s). Input received: ${e}`);t?this.keptStates.push(this.states_.slice()):ee(this.states_);for(let o=0;o<this.states_.length;++o){let i=e[o],l=r;if(!v.arraysEqual(i.shape,l))throw new j(`State ${o} is incompatible with layer ${this.name}: expected shape=${l}, received shape=${i.shape}`);this.states_[o]=i}}this.states_=this.states_.map(o=>In(o.clone()))})}computeSingleOutputShape(e){let{dataFormat:t,filters:n,kernelSize:s,padding:r,strides:a,dilationRate:o}=this.cell,i=t==="channelsFirst",l=e[i?3:2],u=e[i?4:3],c=zr(l,s[0],r,a[0],o[0]),p=zr(u,s[1],r,a[1],o[1]);return[...e.slice(0,2),...i?[n,c,p]:[c,p,n]]}};T8.className="ConvRNN2D";var N2=class extends Bh{constructor(e){let{filters:t,kernelSize:n,strides:s,padding:r,dataFormat:a,dilationRate:o}=e;super(Object.assign({},e,{units:t})),this.filters=t,Cn(this.filters,"filters"),this.kernelSize=sc(n,2,"kernelSize"),this.kernelSize.forEach(i=>Cn(i,"kernelSize")),this.strides=sc(s||1,2,"strides"),this.strides.forEach(i=>Cn(i,"strides")),this.padding=r||"valid",ar(this.padding),this.dataFormat=a||"channelsLast",en(this.dataFormat),this.dilationRate=sc(o||1,2,"dilationRate"),this.dilationRate.forEach(i=>Cn(i,"dilationRate"))}build(e){var t;e=xt(e);let n=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[n]==null)throw new j(`The channel dimension of the input should be defined. Found ${e[n]}`);let s=e[n],r=4,a=this.kernelSize.concat([s,this.filters*r]);this.kernel=this.addWeight("kernel",a,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint);let o=this.kernelSize.concat([this.filters,this.filters*r]);if(this.recurrentKernel=this.addWeight("recurrent_kernel",o,null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias){let i;if(this.unitForgetBias){let l=this.biasInitializer,u=this.filters;i=new(t=class extends Ar{apply(p,d){let h=l.apply([u]),f=$s([u]),m=l.apply([u*2]);return d5([h,f,m])}},t.className="CustomInit",t)}else i=this.biasInitializer;this.bias=this.addWeight("bias",[this.filters*r],null,i,this.biasRegularizer,!0,this.biasConstraint)}this.built=!0}call(e,t){return Y(()=>{if(e.length!==3)throw new j(`ConvLSTM2DCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let n=t.training||!1,s=e[0],r=e[1],a=e[2],o=4;0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=uo({ones:()=>zs(s),rate:this.dropout,training:n,count:o,dropoutFunc:this.dropoutFunc}));let i=this.dropoutMask,l=(Z,J,Q)=>!J||!J[Q]?Z:z(J[Q],Z),u=l(s,i,0),c=l(s,i,1),p=l(s,i,2),d=l(s,i,3);0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=uo({ones:()=>zs(r),rate:this.recurrentDropout,training:n,count:o,dropoutFunc:this.dropoutFunc}));let h=this.recurrentDropoutMask,f=l(r,h,0),m=l(r,h,1),g=l(r,h,2),y=l(r,h,3),x=3,[A,b,w,k]=Qt(this.kernel.read(),o,x),[C,E,_,$]=this.useBias?Qt(this.bias.read(),o):[null,null,null,null];u=this.inputConv(u,A,C,this.padding),c=this.inputConv(c,b,E,this.padding),p=this.inputConv(p,w,_,this.padding),d=this.inputConv(d,k,$,this.padding);let[R,P,S,M]=Qt(this.recurrentKernel.read(),o,x);f=this.recurrentConv(f,R),m=this.recurrentConv(m,P),g=this.recurrentConv(g,S),y=this.recurrentConv(y,M);let L=this.recurrentActivation.apply(de(u,f)),U=this.recurrentActivation.apply(de(c,m)),K=de(z(U,a),z(L,this.activation.apply(de(p,g)))),q=z(this.recurrentActivation.apply(de(d,y)),this.activation.apply(K));return[q,q,K]})}getConfig(){let e=super.getConfig(),{units:t}=e,n=GG(e,["units"]),s={filters:this.filters,kernelSize:this.kernelSize,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,strides:this.strides};return Object.assign({},n,s)}inputConv(e,t,n,s){let r=Sa(e,t,this.strides,s||"valid",this.dataFormat==="channelsFirst"?"NCHW":"NHWC",this.dilationRate);return n?Ur(r,n,this.dataFormat):r}recurrentConv(e,t){return Sa(e,t,1,"same",this.dataFormat==="channelsFirst"?"NCHW":"NHWC")}};N2.className="ConvLSTM2DCell";he.registerClass(N2);var Z5=class extends T8{constructor(e){let t=new N2(e);super(Object.assign({},e,{cell:t}))}static fromConfig(e,t){return new e(t)}};Z5.className="ConvLSTM2D";he.registerClass(Z5);var E2=class extends ct{constructor(e){super(e),this.rate=Math.max(Math.min(e.rate,1),0),this.noiseShape=e.noiseShape,this.seed=e.seed,this.supportsMasking=!0}getNoiseShape(e){if(this.noiseShape==null)return this.noiseShape;let t=e.shape,n=[];for(let s=0;s<this.noiseShape.length;++s)n.push(this.noiseShape[s]==null?t[s]:this.noiseShape[s]);return n}call(e,t){return Y(()=>{this.invokeCallHook(e,t);let n=Ze(e);if(0<this.rate&&this.rate<1){let s=t.training==null?!1:t.training,r=this.getNoiseShape(n);return $h(()=>zk(n,this.rate,r,this.seed),()=>n,s)}return e})}getConfig(){let e={rate:this.rate,noiseShape:this.noiseShape,seed:this.seed},t=super.getConfig();return Object.assign(e,t),e}dispose(){return super.dispose()}};E2.className="Dropout";he.registerClass(E2);var Y5=class extends E2{constructor(e){super(e),this.inputSpec=[{ndim:3}]}getNoiseShape(e){let t=e.shape;return[t[0],1,t[2]]}};Y5.className="SpatialDropout1D";he.registerClass(Y5);var J5=class extends ct{constructor(e){if(super(e),this.activation=null,this.useBias=!0,this.kernel=null,this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",e.batchInputShape==null&&e.inputShape==null&&e.inputDim!=null){let t=null;e.batchSize!=null&&(t=e.batchSize),this.batchInputShape=[t,e.inputDim]}this.units=e.units,Cn(this.units,"units"),this.activation=lo(e.activation),e.useBias!=null&&(this.useBias=e.useBias),this.kernelInitializer=Ot(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.biasInitializer=Ot(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelConstraint=bn(e.kernelConstraint),this.biasConstraint=bn(e.biasConstraint),this.kernelRegularizer=Mt(e.kernelRegularizer),this.biasRegularizer=Mt(e.biasRegularizer),this.activityRegularizer=Mt(e.activityRegularizer),this.supportsMasking=!0,this.inputSpec=[{minNDim:2}]}build(e){e=xt(e);let t=e[e.length-1];this.kernel==null&&(this.kernel=this.addWeight("kernel",[t,this.units],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.units],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint))),this.inputSpec=[{minNDim:2,axes:{[-1]:t}}],this.built=!0}computeOutputShape(e){e=xt(e);let t=e.slice();return t[t.length-1]=this.units,t}call(e,t){return Y(()=>{this.invokeCallHook(e,t);let n=Ze(e),s=_k(this.activation.getClassName()),r;return s!=null?r=ta(n,this.kernel.read(),s,this.bias?this.bias.read():null):(r=ta(n,this.kernel.read()),this.bias!=null&&(r=Ur(r,this.bias.read())),this.activation!=null&&(r=this.activation.apply(r))),r})}getConfig(){let e={units:this.units,activation:io(this.activation),useBias:this.useBias,kernelInitializer:Gt(this.kernelInitializer),biasInitializer:Gt(this.biasInitializer),kernelRegularizer:It(this.kernelRegularizer),biasRegularizer:It(this.biasRegularizer),activityRegularizer:It(this.activityRegularizer),kernelConstraint:xn(this.kernelConstraint),biasConstraint:xn(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}};J5.className="Dense";he.registerClass(J5);var Q5=class extends ct{constructor(e){e=e||{},super(e),this.inputSpec=[{minNDim:3}],this.dataFormat=e.dataFormat}computeOutputShape(e){e=xt(e);for(let t of e.slice(1))if(t==null)throw new j(`The shape of the input to "Flatten" is not fully defined (got ${e.slice(1)}). 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e={axis:this.axis,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:Gt(this.betaInitializer),gammaInitializer:Gt(this.gammaInitializer),betaRegularizer:It(this.betaRegularizer),gammaRegularizer:It(this.gammaRegularizer)},t=super.getConfig();return Object.assign(e,t),e}};yx.className="LayerNormalization";he.registerClass(yx);function KG(e,t,n){return Y(()=>{if(e.rank!==4)throw new j(`temporalPadding expects input tensor to be 4-D, but received a ${e.rank}-D tensor.`);if(t==null&&(t=[[1,1],[1,1]]),t.length!==2||t[0].length!==2||t[1].length!==2)throw new j("spatial2dPadding expects `padding` to be an Array of two Arrays, each of which is an Array of two integers.");if(n==null&&(n=Br()),n!=="channelsLast"&&n!=="channelsFirst")throw new j(`Unknown data format: ${n}. 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a==="max"?o=wh(e,t,n,i):o=gh(e,t,n,i),r==="channelsFirst"&&(o=nt(o,[0,3,1,2])),o})}function N8(e,t,n,s,r,a){return Y(()=>{en(r),$k(a),ar(s),n==null&&(n=[1,1,1]),s==null&&(s="valid"),r==null&&(r=Br()),a==null&&(a="max"),e=k8(e,r);let o,i=s==="same"?"same":"valid";return a==="max"?o=VA(e,t,n,i):o=mA(e,t,n,i),r==="channelsFirst"&&(o=nt(o,[0,4,1,2,3])),o})}var E8=class extends ct{constructor(e){if(e.poolSize==null&&(e.poolSize=2),super(e),typeof e.poolSize=="number")this.poolSize=[e.poolSize];else if(Array.isArray(e.poolSize)&&e.poolSize.length===1&&typeof e.poolSize[0]=="number")this.poolSize=e.poolSize;else throw new j(`poolSize for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.poolSize)}`);if(Cn(this.poolSize,"poolSize"),e.strides==null)this.strides=this.poolSize;else if(typeof e.strides=="number")this.strides=[e.strides];else if(Array.isArray(e.strides)&&e.strides.length===1&&typeof 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t=zr(t,this.poolSize[0],this.padding,this.strides[0]),n=zr(n,this.poolSize[1],this.padding,this.strides[1]),this.dataFormat==="channelsFirst"?[e[0],e[1],t,n]:[e[0],t,n,e[3]]}call(e,t){return Y(()=>(this.invokeCallHook(e,t),this.poolingFunction(Ze(e),this.poolSize,this.strides,this.padding,this.dataFormat)))}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}},vx=class extends R8{constructor(e){super(e)}poolingFunction(e,t,n,s,r){return en(r),ar(s),R2(e,t,n,s,r,"max")}};vx.className="MaxPooling2D";he.registerClass(vx);var wx=class extends R8{constructor(e){super(e)}poolingFunction(e,t,n,s,r){return en(r),ar(s),R2(e,t,n,s,r,"avg")}};wx.className="AveragePooling2D";he.registerClass(wx);var _8=class extends ct{constructor(e){if(e.poolSize==null&&(e.poolSize=[2,2,2]),super(e),this.poolSize=Array.isArray(e.poolSize)?e.poolSize:[e.poolSize,e.poolSize,e.poolSize],e.strides==null)this.strides=this.poolSize;else if(Array.isArray(e.strides)){if(e.strides.length!==3)throw new j(`If the strides property of a 3D pooling layer is an Array, it is expected to have a length of 3, but received length ${e.strides.length}.`);this.strides=e.strides}else this.strides=[e.strides,e.strides,e.strides];Cn(this.poolSize,"poolSize"),Cn(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,en(this.dataFormat),ar(this.padding),this.inputSpec=[new on({ndim:5})]}computeOutputShape(e){e=xt(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2],s=this.dataFormat==="channelsFirst"?e[4]:e[3];return t=zr(t,this.poolSize[0],this.padding,this.strides[0]),n=zr(n,this.poolSize[1],this.padding,this.strides[1]),s=zr(s,this.poolSize[2],this.padding,this.strides[2]),this.dataFormat==="channelsFirst"?[e[0],e[1],t,n,s]:[e[0],t,n,s,e[4]]}call(e,t){return Y(()=>(this.invokeCallHook(e,t),this.poolingFunction(Ze(e),this.poolSize,this.strides,this.padding,this.dataFormat)))}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}},kx=class extends _8{constructor(e){super(e)}poolingFunction(e,t,n,s,r){return en(r),ar(s),N8(e,t,n,s,r,"max")}};kx.className="MaxPooling3D";he.registerClass(kx);var Sx=class extends _8{constructor(e){super(e)}poolingFunction(e,t,n,s,r){return en(r),ar(s),N8(e,t,n,s,r,"avg")}};Sx.className="AveragePooling3D";he.registerClass(Sx);var D8=class extends ct{constructor(e){super(e),this.inputSpec=[new on({ndim:3})]}computeOutputShape(e){return[e[0],e[2]]}call(e,t){throw new Ke}},Ix=class extends D8{constructor(e){super(e||{})}call(e,t){return Y(()=>{let n=Ze(e);return Vt(n,1)})}};Ix.className="GlobalAveragePooling1D";he.registerClass(Ix);var Cx=class extends D8{constructor(e){super(e||{})}call(e,t){return Y(()=>{let n=Ze(e);return An(n,1)})}};Cx.className="GlobalMaxPooling1D";he.registerClass(Cx);var $8=class extends ct{constructor(e){super(e),this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,en(this.dataFormat),this.inputSpec=[new on({ndim:4})]}computeOutputShape(e){return e=e,this.dataFormat==="channelsLast"?[e[0],e[3]]:[e[0],e[1]]}call(e,t){throw new Ke}getConfig(){let e={dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}},Tx=class extends $8{call(e,t){return Y(()=>{let n=Ze(e);return this.dataFormat==="channelsLast"?Vt(n,[1,2]):Vt(n,[2,3])})}};Tx.className="GlobalAveragePooling2D";he.registerClass(Tx);var Nx=class extends $8{call(e,t){return Y(()=>{let n=Ze(e);return 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e(a)}},Ex=class extends P8{constructor(e){super(e),this.supportsMasking=!0}build(e){if(e=xt(e),e.length<3)throw new j(`TimeDistributed layer expects an input shape >= 3D, but received input shape ${JSON.stringify(e)}`);this.inputSpec=[{shape:e}];let t=[e[0]].concat(e.slice(2));this.layer.built||(this.layer.build(t),this.layer.built=!0),super.build(e)}computeOutputShape(e){e=xt(e);let t=[e[0]].concat(e.slice(2)),n=this.layer.computeOutputShape(t),s=e[1];return[n[0],s].concat(n.slice(1))}call(e,t){return Y(()=>(e=Ze(e),C8((a,o)=>[Ze(this.layer.call(a,t)),[]],e,[],!1,null,null,!1,!0)[1]))}};Ex.className="TimeDistributed";he.registerClass(Ex);function ZG(e){du(YV,"BidirectionalMergeMode",e)}var YG="concat",Rx=class extends P8{constructor(e){super(e);let t=e.layer.getConfig(),n={};n.className=e.layer.getClassName(),n.config=t,this.forwardLayer=Mr(n),t.goBackwards=t.goBackwards!==!0;let s={};if(s.className=e.layer.getClassName(),s.config=t,this.backwardLayer=Mr(s),this.forwardLayer.name="forward_"+this.forwardLayer.name,this.backwardLayer.name="backward_"+this.backwardLayer.name,this.mergeMode=e.mergeMode===void 0?YG:e.mergeMode,ZG(this.mergeMode),e.weights)throw new Ke("weights support is not implemented for Bidirectional layer yet.");this._stateful=e.layer.stateful,this.returnSequences=e.layer.returnSequences,this.returnState=e.layer.returnState,this.supportsMasking=!0,this._trainable=!0,this.inputSpec=e.layer.inputSpec,this.numConstants=null}get trainable(){return this._trainable}set trainable(e){this._trainable=e,this.forwardLayer!=null&&(this.forwardLayer.trainable=e),this.backwardLayer!=null&&(this.backwardLayer.trainable=e)}getWeights(){return this.forwardLayer.getWeights().concat(this.backwardLayer.getWeights())}setWeights(e){let t=e.length,n=Math.floor(t/2);this.forwardLayer.setWeights(e.slice(0,n)),this.backwardLayer.setWeights(e.slice(n))}computeOutputShape(e){let t=this.forwardLayer.computeOutputShape(e);Array.isArray(t)&&Array.isArray(t[0])||(t=[t]),t=t;let n,s,r;return this.returnState&&(r=t.slice(1)),n=t[0],n=n,this.mergeMode==="concat"?(n[n.length-1]*=2,s=[n]):this.mergeMode==null?s=[n,n.slice()]:s=[n],this.returnState?this.mergeMode==null?s.concat(r).concat(r.slice()):[n].concat(r).concat(r.slice()):ys(s)}apply(e,t){let n=t==null?null:t.initialState,s=t==null?null:t.constants;t==null&&(t={});let r=I8(e,n,s,this.numConstants);if(e=r.inputs,n=r.initialState,s=r.constants,Array.isArray(e)&&(n=e.slice(1),e=e[0]),(n==null||n.length===0)&&s==null)return super.apply(e,t);let a=[],o=[];if(n!=null){let l=n.length;if(l%2>0)throw new j("When passing `initialState` to a Bidrectional RNN, the state should be an Array containing the states of the underlying RNNs.");t.initialState=n,a.push(...n);let u=n.map(c=>new on({shape:c.shape}));this.forwardLayer.stateSpec=u.slice(0,l/2),this.backwardLayer.stateSpec=u.slice(l/2),o.push(...u)}if(s!=null)throw new Ke("Support for constants in Bidirectional layers is not implemented yet.");let i=a[0]instanceof Fr;for(let l of a)if(l instanceof Fr!==i)throw new j("The initial state of a Bidirectional layer cannot be specified as a mix of symbolic and non-symbolic tensors");if(i){let l=[e].concat(a),u=this.inputSpec.concat(o),c=this.inputSpec;this.inputSpec=u;let p=super.apply(l,t);return this.inputSpec=c,p}else return super.apply(e,t)}call(e,t){return Y(()=>{let n=t.initialState,s,r;if(n==null)s=this.forwardLayer.call(e,t),r=this.backwardLayer.call(e,t);else{let i=n.slice(0,n.length/2),l=n.slice(n.length/2);s=this.forwardLayer.call(e,Object.assign(t,{initialState:i})),r=this.backwardLayer.call(e,Object.assign(t,{initialState:l}))}let a;this.returnState&&(Array.isArray(s)&&(a=s.slice(1).concat(r.slice(1))),s=s[0],r=r[0]),this.returnSequences&&(r=er(r,1));let 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Found `None`.");let n=e[t],s=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight("kernel",s,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new on({ndim:5,axes:{[t]:n}})],this.built=!0}call(e,t){return Y(()=>{let n=et(e);if(n.shape.length!==5)throw new j(`Conv3DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${n.shape.length}`);let s=n.shape,r=s[0],a,o,i;this.dataFormat==="channelsFirst"?(i=2,a=3,o=4):(i=1,a=2,o=3);let l=s[i],u=s[a],c=s[o],p=this.kernelSize[0],d=this.kernelSize[1],h=this.kernelSize[2],f=this.strides[0],m=this.strides[1],g=this.strides[2],y=ea(l,f,p,this.padding),x=ea(u,m,d,this.padding),A=ea(c,g,h,this.padding),b=[r,y,x,A,this.filters];this.dataFormat!=="channelsLast"&&(n=at(n,[0,2,3,4,1]));let w=EA(n,this.kernel.read(),b,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(w=at(w,[0,4,1,2,3])),this.bias!==null&&(w=Hr(w,this.bias.read(),this.dataFormat)),this.activation!==null&&(w=this.activation.apply(w)),w})}computeOutputShape(e){e=bt(e);let t=e.slice(),n,s,r,a;this.dataFormat==="channelsFirst"?(n=1,s=2,r=3,a=4):(n=4,s=1,r=2,a=3);let o=this.kernelSize[0],i=this.kernelSize[1],l=this.kernelSize[2],u=this.strides[0],c=this.strides[1],p=this.strides[2];return t[n]=this.filters,t[s]=ea(t[s],u,o,this.padding),t[r]=ea(t[r],c,i,this.padding),t[a]=ea(t[a],p,l,this.padding),t}getConfig(){let e=super.getConfig();return delete e.dilationRate,e}};H5.className="Conv3DTranspose";he.registerClass(H5);var R8=class extends Mh{constructor(e,t){if(super(e,t),this.DEFAULT_DEPTHWISE_INITIALIZER="glorotUniform",this.DEFAULT_POINTWISE_INITIALIZER="glorotUniform",this.depthwiseKernel=null,this.pointwiseKernel=null,t.filters==null)throw new j("The `filters` configuration field is required by SeparableConv, but is unspecified.");if(t.kernelInitializer!=null||t.kernelRegularizer!=null||t.kernelConstraint!=null)throw new j("Fields kernelInitializer, kernelRegularizer and kernelConstraint are invalid for SeparableConv2D. Use depthwiseInitializer, depthwiseRegularizer, depthwiseConstraint, pointwiseInitializer, pointwiseRegularizer and pointwiseConstraint instead.");if(t.padding!=null&&t.padding!=="same"&&t.padding!=="valid")throw new j(`SeparableConv${this.rank}D supports only padding modes: 'same' and 'valid', but received ${JSON.stringify(t.padding)}`);this.depthMultiplier=t.depthMultiplier==null?1:t.depthMultiplier,this.depthwiseInitializer=Mt(t.depthwiseInitializer||this.DEFAULT_DEPTHWISE_INITIALIZER),this.depthwiseRegularizer=zt(t.depthwiseRegularizer),this.depthwiseConstraint=vn(t.depthwiseConstraint),this.pointwiseInitializer=Mt(t.depthwiseInitializer||this.DEFAULT_POINTWISE_INITIALIZER),this.pointwiseRegularizer=zt(t.pointwiseRegularizer),this.pointwiseConstraint=vn(t.pointwiseConstraint)}build(e){if(e=bt(e),e.length<this.rank+2)throw new j(`Inputs to SeparableConv${this.rank}D should have rank ${this.rank+2}, but received input shape: ${JSON.stringify(e)}`);let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null||e[t]<0)throw new j(`The channel dimension of the inputs should be defined, but found ${JSON.stringify(e[t])}`);let n=e[t],s=this.kernelSize.concat([n,this.depthMultiplier]),r=[];for(let o=0;o<this.rank;++o)r.push(1);r.push(n*this.depthMultiplier,this.filters);let a=!0;this.depthwiseKernel=this.addWeight("depthwise_kernel",s,"float32",this.depthwiseInitializer,this.depthwiseRegularizer,a,this.depthwiseConstraint),this.pointwiseKernel=this.addWeight("pointwise_kernel",r,"float32",this.pointwiseInitializer,this.pointwiseRegularizer,a,this.pointwiseConstraint),this.useBias?this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,a,this.biasConstraint):this.bias=null,this.inputSpec=[new on({ndim:this.rank+2,axes:{[t]:n}})],this.built=!0}call(e,t){return Y(()=>{e=et(e);let n;if(this.rank===1)throw new Je("1D separable convolution is not implemented yet.");return this.rank===2&&(this.dataFormat==="channelsFirst"&&(e=at(e,[0,2,3,1])),n=j0(e,this.depthwiseKernel.read(),this.pointwiseKernel.read(),this.strides,this.padding,this.dilationRate,"NHWC")),this.useBias&&(n=Hr(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),this.dataFormat==="channelsFirst"&&(n=at(n,[0,3,1,2])),n})}getConfig(){let e=super.getConfig();return delete e.rank,delete e.kernelInitializer,delete e.kernelRegularizer,delete e.kernelConstraint,e.depthwiseInitializer=Ht(this.depthwiseInitializer),e.pointwiseInitializer=Ht(this.pointwiseInitializer),e.depthwiseRegularizer=Ct(this.depthwiseRegularizer),e.pointwiseRegularizer=Ct(this.pointwiseRegularizer),e.depthwiseConstraint=bn(this.depthwiseConstraint),e.pointwiseConstraint=bn(this.pointwiseConstraint),e}};R8.className="SeparableConv";var j5=class extends R8{constructor(e){super(2,e)}};j5.className="SeparableConv2D";he.registerClass(j5);var S2=class extends Mh{constructor(e){super(1,e),S2.verifyArgs(e),this.inputSpec=[{ndim:3}]}getConfig(){let e=super.getConfig();return delete e.rank,delete e.dataFormat,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!h5(e.kernelSize,"number",1,1))throw new j(`Conv1D expects config.kernelSize to be number or number[] with length 1, but received ${JSON.stringify(e.kernelSize)}.`)}};S2.className="Conv1D";he.registerClass(S2);var q5=class extends pt{constructor(e){super(e),typeof e.cropping=="number"?this.cropping=[[e.cropping,e.cropping],[e.cropping,e.cropping]]:typeof e.cropping[0]=="number"?this.cropping=[[e.cropping[0],e.cropping[0]],[e.cropping[1],e.cropping[1]]]:this.cropping=e.cropping,this.dataFormat=e.dataFormat===void 0?"channelsLast":e.dataFormat,this.inputSpec=[{ndim:4}]}computeOutputShape(e){return this.dataFormat==="channelsFirst"?[e[0],e[1],e[2]-this.cropping[0][0]-this.cropping[0][1],e[3]-this.cropping[1][0]-this.cropping[1][1]]:[e[0],e[1]-this.cropping[0][0]-this.cropping[0][1],e[2]-this.cropping[1][0]-this.cropping[1][1],e[3]]}call(e,t){return Y(()=>{if(e=et(e),this.dataFormat==="channelsLast"){let n=em(e,this.cropping[0][0],e.shape[1]-this.cropping[0][0]-this.cropping[0][1],2);return em(n,this.cropping[1][0],e.shape[2]-this.cropping[1][1]-this.cropping[1][0],3)}else{let n=em(e,this.cropping[0][0],e.shape[2]-this.cropping[0][0]-this.cropping[0][1],3);return em(n,this.cropping[1][0],e.shape[3]-this.cropping[1][1]-this.cropping[1][0],4)}})}getConfig(){let e={cropping:this.cropping,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};q5.className="Cropping2D";he.registerClass(q5);var X5=class extends pt{constructor(e){super(e),this.DEFAULT_SIZE=[2,2],this.inputSpec=[{ndim:4}],this.size=e.size==null?this.DEFAULT_SIZE:e.size,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,en(this.dataFormat),this.interpolation=e.interpolation==null?"nearest":e.interpolation,cU(this.interpolation)}computeOutputShape(e){if(this.dataFormat==="channelsFirst"){let t=e[2]==null?null:this.size[0]*e[2],n=e[3]==null?null:this.size[1]*e[3];return[e[0],e[1],t,n]}else{let t=e[1]==null?null:this.size[0]*e[1],n=e[2]==null?null:this.size[1]*e[2];return[e[0],t,n,e[3]]}}call(e,t){return Y(()=>{let n=et(e),s=n.shape;if(this.dataFormat==="channelsFirst"){n=at(n,[0,2,3,1]);let r=this.size[0]*s[2],a=this.size[1]*s[3],o=this.interpolation==="nearest"?Ce.resizeNearestNeighbor(n,[r,a]):Ce.resizeBilinear(n,[r,a]);return at(o,[0,3,1,2])}else{let r=this.size[0]*s[1],a=this.size[1]*s[2];return this.interpolation==="nearest"?Ce.resizeNearestNeighbor(n,[r,a]):Ce.resizeBilinear(n,[r,a])}})}getConfig(){let e={size:this.size,dataFormat:this.dataFormat,interpolation:this.interpolation},t=super.getConfig();return Object.assign(e,t),e}};X5.className="UpSampling2D";he.registerClass(X5);function tH(e,t,n=[1,1],s="valid",r,a){return Y(()=>{r==null&&(r=Vr()),en(r);let o=V5(e,r);if(e.rank!==4)throw new j(`Input for depthwiseConv2d is required to be 4-D, but is instead ${e.rank}-D`);if(t.rank!==4)throw new j(`depthwiseKernel is required to be 4-D, but is instead ${t.rank}-D`);return o=ed(o,t,n,s==="same"?"same":"valid","NHWC",a),r==="channelsFirst"&&(o=at(o,[0,3,1,2])),o})}var K5=class extends U5{constructor(e){super(2,e),this.depthwiseKernel=null,this.depthMultiplier=e.depthMultiplier==null?1:e.depthMultiplier,this.depthwiseInitializer=Mt(e.depthwiseInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.depthwiseConstraint=vn(e.depthwiseConstraint),this.depthwiseRegularizer=zt(e.depthwiseRegularizer)}build(e){if(e=bt(e),e.length<4)throw new j(`Inputs to DepthwiseConv2D should have rank 4. Received input shape: ${JSON.stringify(e)}.`);let t=this.dataFormat==="channelsFirst"?1:3;if(e[t]==null||e[t]<0)throw new j(`The channel dimension of the inputs to DepthwiseConv2D should be defined, but is not (${e[t]}).`);let n=e[t],s=[this.kernelSize[0],this.kernelSize[1],n,this.depthMultiplier];this.depthwiseKernel=this.addWeight("depthwise_kernel",s,null,this.depthwiseInitializer,this.depthwiseRegularizer,!0,this.depthwiseConstraint),this.useBias?this.bias=this.addWeight("bias",[n*this.depthMultiplier],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return Y(()=>{e=et(e);let n=tH(e,this.depthwiseKernel.read(),this.strides,this.padding,this.dataFormat,null);return this.useBias&&(n=Hr(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),n})}computeOutputShape(e){e=bt(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2],s=this.dataFormat==="channelsFirst"?e[1]*this.depthMultiplier:e[3]*this.depthMultiplier,r=Br(t,this.kernelSize[0],this.padding,this.strides[0]),a=Br(n,this.kernelSize[1],this.padding,this.strides[1]);return this.dataFormat==="channelsFirst"?[e[0],s,r,a]:[e[0],r,a,s]}getConfig(){let e=super.getConfig();return e.depthMultiplier=this.depthMultiplier,e.depthwiseInitializer=Ht(this.depthwiseInitializer),e.depthwiseRegularizer=Ct(this.depthwiseRegularizer),e.depthwiseConstraint=bn(this.depthwiseRegularizer),e}};K5.className="DepthwiseConv2D";he.registerClass(K5);function _8(e,t,n,s){if(Array.isArray(e)){if(t!=null||n!=null)throw new j("When inputs is an array, neither initialState or constants should be provided");s!=null&&(n=e.slice(e.length-s,e.length),e=e.slice(0,e.length-s)),e.length>1&&(t=e.slice(1,e.length)),e=e[0]}function r(a){return a==null||Array.isArray(a)?a:[a]}return t=r(t),n=r(n),{inputs:e,initialState:t,constants:n}}function D8(e,t,n,s=!1,r,a,o=!1,i=!1){return Y(()=>{let l=t.shape.length;if(l<3)throw new j(`Input should be at least 3D, but is ${l}D.`);let u=[1,0].concat(Wr(2,l));if(t=at(t,u),a!=null)throw new Je("The rnn() functoin of the deeplearn.js backend does not support constants yet.");o&&console.warn("Backend rnn(): the unroll = true option is not applicable to the imperative deeplearn.js backend."),r!=null&&(r=ge(ge(r,"bool"),"float32"),r.rank===l-1&&(r=Ft(r,-1)),r=at(r,u)),s&&(t=nr(t,0),r!=null&&(r=nr(r,0)));let c=[],p,d=n,h=t.shape[0],f=wn(t),m;r!=null&&(m=wn(r));for(let y=0;y<h;++y){let x=f[y],A=Y(()=>e(x,d));if(r==null)p=A[0],d=A[1];else{let b=Y(()=>{let w=m[y],k=Ae(Ws(w),w),C=de(z(A[0],w),z(d[0],k)),E=d.map((_,$)=>de(z(A[1][$],w),z(_,k)));return{output:C,newStates:E}});p=b.output,d=b.newStates}i&&c.push(p)}let g;return i&&(g=un(c,1)),[p,g,d]})}var pa=class extends pt{constructor(e){super(e);let t;if(e.cell==null)throw new j("cell property is missing for the constructor of RNN.");if(Array.isArray(e.cell)?t=new T2({cells:e.cell}):t=e.cell,t.stateSize==null)throw new j("The RNN cell should have an attribute `stateSize` (tuple of integers, one integer per RNN state).");this.cell=t,this.returnSequences=e.returnSequences==null?!1:e.returnSequences,this.returnState=e.returnState==null?!1:e.returnState,this.goBackwards=e.goBackwards==null?!1:e.goBackwards,this._stateful=e.stateful==null?!1:e.stateful,this.unroll=e.unroll==null?!1:e.unroll,this.supportsMasking=!0,this.inputSpec=[new on({ndim:3})],this.stateSpec=null,this.states_=null,this.numConstants=null,this.keptStates=[]}getStates(){if(this.states_==null){let e=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;return Wr(0,e).map(t=>null)}else return this.states_}setStates(e){this.states_=e}computeOutputShape(e){J3(e)&&(e=e[0]),e=e;let t=this.cell.stateSize;Array.isArray(t)||(t=[t]);let n=t[0],s;if(this.returnSequences?s=[e[0],e[1],n]:s=[e[0],n],this.returnState){let r=[];for(let a of t)r.push([e[0],a]);return[s].concat(r)}else return s}computeMask(e,t){return Y(()=>{Array.isArray(t)&&(t=t[0]);let n=this.returnSequences?t:null;if(this.returnState){let s=this.states.map(r=>null);return[n].concat(s)}else return n})}get states(){if(this.states_==null){let e=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1,t=[];for(let n=0;n<e;++n)t.push(null);return t}else return this.states_}set states(e){this.states_=e}build(e){if(this.numConstants!=null)throw new Je("Constants support is not implemented in RNN yet.");J3(e)&&(e=e[0]),e=e;let n=this.stateful?e[0]:null,s=e.slice(2);this.inputSpec[0]=new on({shape:[n,null,...s]});let r=[e[0]].concat(e.slice(2));this.cell.build(r);let a;if(Array.isArray(this.cell.stateSize)?a=this.cell.stateSize:a=[this.cell.stateSize],this.stateSpec!=null){if(!v.arraysEqual(this.stateSpec.map(o=>o.shape[o.shape.length-1]),a))throw new j(`An initialState was passed that is not compatible with cell.stateSize. Received stateSpec=${this.stateSpec}; However cell.stateSize is ${this.cell.stateSize}`)}else this.stateSpec=a.map(o=>new on({shape:[null,o]}));this.stateful&&this.resetStates()}resetStates(e,t=!1){Y(()=>{if(!this.stateful)throw new ba("Cannot call resetStates() on an RNN Layer that is not stateful.");let n=this.inputSpec[0].shape[0];if(n==null)throw new j("If an RNN is stateful, it needs to know its batch size. Specify the batch size of your input tensors: \n- If using a Sequential model, specify the batch size by passing a `batchInputShape` option to your first layer.\n- If using the functional API, specify the batch size by passing a `batchShape` option to your Input layer.");if(this.states_==null)Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(s=>Gt([n,s])):this.states_=[Gt([n,this.cell.stateSize])];else if(e==null)Q(this.states_),this.keptStates!=null&&(Q(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(s=>Gt([n,s])):this.states_[0]=Gt([n,this.cell.stateSize]);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new j(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${e.length} state value(s). Input received: ${e}`);t===!0?this.keptStates.push(this.states_.slice()):Q(this.states_);for(let s=0;s<this.states_.length;++s){let r=e[s],a=Array.isArray(this.cell.stateSize)?this.cell.stateSize[s]:this.cell.stateSize,o=[n,a];if(!v.arraysEqual(r.shape,o))throw new j(`State ${s} is incompatible with layer ${this.name}: expected shape=${o}, received shape=${r.shape}`);this.states_[s]=r}}this.states_=this.states_.map(s=>Tn(s.clone()))})}apply(e,t){let n=t==null?null:t.initialState,s=t==null?null:t.constants;t==null&&(t={});let r=_8(e,n,s,this.numConstants);e=r.inputs,n=r.initialState,s=r.constants;let a=[],o=[];if(n!=null){t.initialState=n,a=a.concat(n),this.stateSpec=[];for(let l of n)this.stateSpec.push(new on({shape:l.shape}));o=o.concat(this.stateSpec)}if(s!=null&&(t.constants=s,a=a.concat(s),this.numConstants=s.length),a[0]instanceof Mr){let l=[e].concat(a),u=this.inputSpec.concat(o),c=this.inputSpec;this.inputSpec=u;let p=super.apply(l,t);return this.inputSpec=c,p}else return super.apply(e,t)}call(e,t){return Y(()=>{let n=t==null?null:t.mask,s=t==null?null:t.training,r=t==null?null:t.initialState;e=et(e),r==null&&(this.stateful?r=this.states_:r=this.getInitialState(e));let a=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;if(r.length!==a)throw new j(`RNN Layer has ${a} state(s) but was passed ${r.length} initial state(s).`);this.unroll&&console.warn("Ignoring unroll = true for RNN layer, due to imperative backend.");let o={training:s},l=D8((h,f)=>{let m=this.cell.call([h].concat(f),o);return[m[0],m.slice(1)]},e,r,this.goBackwards,n,null,this.unroll,this.returnSequences),u=l[0],c=l[1],p=l[2];this.stateful&&this.resetStates(p,s);let d=this.returnSequences?c:u;return this.returnState?[d].concat(p):d})}getInitialState(e){return Y(()=>{let t=Gt(e.shape);return t=Se(t,[1,2]),t=Dh(t),Array.isArray(this.cell.stateSize)?this.cell.stateSize.map(n=>n>1?Z3(t,[1,n]):t):this.cell.stateSize>1?[Z3(t,[1,this.cell.stateSize])]:[t]})}get trainableWeights(){return this.trainable?this.cell.trainableWeights:[]}get nonTrainableWeights(){return this.trainable?this.cell.nonTrainableWeights:this.cell.weights}setFastWeightInitDuringBuild(e){super.setFastWeightInitDuringBuild(e),this.cell!=null&&this.cell.setFastWeightInitDuringBuild(e)}getConfig(){let e=super.getConfig(),t={returnSequences:this.returnSequences,returnState:this.returnState,goBackwards:this.goBackwards,stateful:this.stateful,unroll:this.unroll};this.numConstants!=null&&(t.numConstants=this.numConstants);let n=this.cell.getConfig();return this.getClassName()===pa.className&&(t.cell={className:this.cell.getClassName(),config:n}),Object.assign({},n,e,t)}static fromConfig(e,t,n={}){let s=t.cell,r=Lr(s,n);return new e(Object.assign(t,{cell:r}))}};pa.className="RNN";he.registerClass(pa);var Bh=class extends pt{},I2=class extends Bh{constructor(e){super(e),this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",this.units=e.units,Nn(this.units,"units"),this.activation=ho(e.activation==null?this.DEFAULT_ACTIVATION:e.activation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=Mt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=Mt(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=Mt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=zt(e.kernelRegularizer),this.recurrentRegularizer=zt(e.recurrentRegularizer),this.biasRegularizer=zt(e.biasRegularizer),this.kernelConstraint=vn(e.kernelConstraint),this.recurrentConstraint=vn(e.recurrentConstraint),this.biasConstraint=vn(e.biasConstraint),this.dropout=yc([1,co([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=yc([1,co([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.dropoutFunc=e.dropoutFunc,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=bt(e),this.kernel=this.addWeight("kernel",[e[e.length-1],this.units],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias?this.bias=this.addWeight("bias",[this.units],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return Y(()=>{if(e=e,e.length!==2)throw new j(`SimpleRNNCell expects 2 input Tensors, got ${e.length}.`);let n=e[1];e=e[0];let s=t.training==null?!1:t.training;0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=fo({ones:()=>Ws(e),rate:this.dropout,training:s,dropoutFunc:this.dropoutFunc})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=fo({ones:()=>Ws(n),rate:this.recurrentDropout,training:s,dropoutFunc:this.dropoutFunc}));let r,a=this.dropoutMask,o=this.recurrentDropoutMask;a!=null?r=sa(z(e,a),this.kernel.read()):r=sa(e,this.kernel.read()),this.bias!=null&&(r=Hr(r,this.bias.read())),o!=null&&(n=z(n,o));let i=de(r,sa(n,this.recurrentKernel.read()));return this.activation!=null&&(i=this.activation.apply(i)),[i,i]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:po(this.activation),useBias:this.useBias,kernelInitializer:Ht(this.kernelInitializer),recurrentInitializer:Ht(this.recurrentInitializer),biasInitializer:Ht(this.biasInitializer),kernelRegularizer:Ct(this.kernelRegularizer),recurrentRegularizer:Ct(this.recurrentRegularizer),biasRegularizer:Ct(this.biasRegularizer),activityRegularizer:Ct(this.activityRegularizer),kernelConstraint:bn(this.kernelConstraint),recurrentConstraint:bn(this.recurrentConstraint),biasConstraint:bn(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout};return Object.assign({},e,t)}};I2.className="SimpleRNNCell";he.registerClass(I2);var Z5=class extends pa{constructor(e){e.cell=new I2(e),super(e)}call(e,t){return Y(()=>{this.cell.dropoutMask!=null&&(Q(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Q(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,s=t==null?null:t.training,r=t==null?null:t.initialState;return super.call(e,{mask:n,training:s,initialState:r})})}static fromConfig(e,t){return new e(t)}};Z5.className="SimpleRNN";he.registerClass(Z5);var C2=class extends Bh{constructor(e){if(super(e),this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_RECURRENT_ACTIVATION="hardSigmoid",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",e.resetAfter)throw new j("GRUCell does not support reset_after parameter set to true.");this.units=e.units,Nn(this.units,"units"),this.activation=ho(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=ho(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=Mt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=Mt(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=Mt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=zt(e.kernelRegularizer),this.recurrentRegularizer=zt(e.recurrentRegularizer),this.biasRegularizer=zt(e.biasRegularizer),this.kernelConstraint=vn(e.kernelConstraint),this.recurrentConstraint=vn(e.recurrentConstraint),this.biasConstraint=vn(e.biasConstraint),this.dropout=yc([1,co([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=yc([1,co([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.dropoutFunc=e.dropoutFunc,this.implementation=e.implementation,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=bt(e);let t=e[e.length-1];this.kernel=this.addWeight("kernel",[t,this.units*3],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units*3],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias?this.bias=this.addWeight("bias",[this.units*3],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return Y(()=>{if(e=e,e.length!==2)throw new j(`GRUCell expects 2 input Tensors (inputs, h, c), got ${e.length}.`);let n=t.training==null?!1:t.training,s=e[1];e=e[0],0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=fo({ones:()=>Ws(e),rate:this.dropout,training:n,count:3,dropoutFunc:this.dropoutFunc})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=fo({ones:()=>Ws(s),rate:this.recurrentDropout,training:n,count:3,dropoutFunc:this.dropoutFunc}));let r=this.dropoutMask,a=this.recurrentDropoutMask,o,i,l;0<this.dropout&&this.dropout<1&&(e=z(e,r[0]));let u=sa(e,this.kernel.read());this.useBias&&(u=Hr(u,this.bias.read())),0<this.recurrentDropout&&this.recurrentDropout<1&&(s=z(s,a[0]));let c=this.recurrentKernel.read(),[p,d]=qt(c,[2*this.units,this.units],c.rank-1),h=sa(s,p),[f,m,g]=qt(u,3,u.rank-1),[y,x]=qt(h,2,h.rank-1);o=this.recurrentActivation.apply(de(f,y)),i=this.recurrentActivation.apply(de(m,x));let A=sa(z(i,s),d);l=this.activation.apply(de(g,A));let b=de(z(o,s),z(de(1,Pt(o)),l));return[b,b]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:po(this.activation),recurrentActivation:po(this.recurrentActivation),useBias:this.useBias,kernelInitializer:Ht(this.kernelInitializer),recurrentInitializer:Ht(this.recurrentInitializer),biasInitializer:Ht(this.biasInitializer),kernelRegularizer:Ct(this.kernelRegularizer),recurrentRegularizer:Ct(this.recurrentRegularizer),biasRegularizer:Ct(this.biasRegularizer),activityRegularizer:Ct(this.activityRegularizer),kernelConstraint:bn(this.kernelConstraint),recurrentConstraint:bn(this.recurrentConstraint),biasConstraint:bn(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation,resetAfter:!1};return Object.assign({},e,t)}};C2.className="GRUCell";he.registerClass(C2);var Y5=class extends pa{constructor(e){e.implementation===0&&console.warn("`implementation=0` has been deprecated, and now defaults to `implementation=1`. 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Please update your layer call."),e.cell=new Wh(e),super(e)}call(e,t){return Y(()=>{this.cell.dropoutMask!=null&&(Q(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Q(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,s=t==null?null:t.training,r=t==null?null:t.initialState;return super.call(e,{mask:n,training:s,initialState:r})})}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}};J5.className="LSTM";he.registerClass(J5);var T2=class extends Bh{constructor(e){super(e),this.cells=e.cells}get stateSize(){let e=[];for(let t of this.cells.slice().reverse())Array.isArray(t.stateSize)?e.push(...t.stateSize):e.push(t.stateSize);return e}call(e,t){return Y(()=>{e=e;let n=e.slice(1),s=[];for(let o of this.cells.slice().reverse())Array.isArray(o.stateSize)?s.push(n.splice(0,o.stateSize.length)):s.push(n.splice(0,1));s.reverse();let r=[],a;for(let o=0;o<this.cells.length;++o){let i=this.cells[o];n=s[o],o===0?a=[e[0]].concat(n):a=[a[0]].concat(n),a=i.call(a,t),r.push(a.slice(1))}n=[];for(let o of r.slice().reverse())n.push(...o);return[a[0]].concat(n)})}build(e){J3(e)&&(e=e[0]),e=e;let t;this.cells.forEach((n,s)=>{sl(`RNNCell_${s}`,()=>{n.build(e),Array.isArray(n.stateSize)?t=n.stateSize[0]:t=n.stateSize,e=[e[0],t]})}),this.built=!0}getConfig(){let e=super.getConfig(),t=r=>({className:r.getClassName(),config:r.getConfig()}),s={cells:this.cells.map(t)};return Object.assign({},e,s)}static fromConfig(e,t,n={}){let s=[];for(let r of t.cells)s.push(Lr(r,n));return new e({cells:s})}get trainableWeights(){if(!this.trainable)return[];let e=[];for(let t of this.cells)e.push(...t.trainableWeights);return e}get nonTrainableWeights(){let e=[];for(let t of this.cells)e.push(...t.nonTrainableWeights);if(!this.trainable){let t=[];for(let n of this.cells)t.push(...n.trainableWeights);return t.concat(e)}return e}getWeights(){let e=[];for(let t of this.cells)e.push(...t.weights);return Q3(e)}setWeights(e){let t=[];for(let n of this.cells){let s=n.weights.length,r=e.splice(s);for(let a=0;a<n.weights.length;++a)t.push([n.weights[a],r[a]])}w5(t)}};T2.className="StackedRNNCells";he.registerClass(T2);function fo(e){let{ones:t,rate:n,training:s=!1,count:r=1,dropoutFunc:a}=e,o=()=>a!=null?a(t(),n):Gk(t(),n),i=()=>Ph(o,t,s);return!r||r<=1?Tn(i().clone()):Array(r).fill(void 0).map(i).map(u=>Tn(u.clone()))}var nH=function(e,t){var n={};for(var s in e)Object.prototype.hasOwnProperty.call(e,s)&&t.indexOf(s)<0&&(n[s]=e[s]);if(e!=null&&typeof Object.getOwnPropertySymbols=="function")for(var r=0,s=Object.getOwnPropertySymbols(e);r<s.length;r++)t.indexOf(s[r])<0&&Object.prototype.propertyIsEnumerable.call(e,s[r])&&(n[s[r]]=e[s[r]]);return n},$8=class extends pa{constructor(e){if(e.unroll)throw new Je("Unrolling is not possible with convolutional RNNs.");if(Array.isArray(e.cell))throw new Je("It is not possible at the moment to stack convolutional cells.");super(e),this.inputSpec=[new on({ndim:5})]}call(e,t){return Y(()=>{if(this.cell.dropoutMask!=null&&(Q(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Q(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null),t&&t.constants)throw new j("ConvRNN2D cell does not support constants");let n=t==null?null:t.mask,s=t==null?null:t.training,r=t==null?null:t.initialState;return super.call(e,{mask:n,training:s,initialState:r})})}computeOutputShape(e){let t=this.computeSingleOutputShape(e);return this.returnSequences||(t=[t[0],...t.slice(2)]),this.returnState&&(t=[t,...Array(2).fill([e[0],...t.slice(-3)])]),t}getInitialState(e){return Y(()=>{let{stateSize:t}=this.cell,n=e.shape,s=this.computeSingleOutputShape(n),r=[s[0],...s.slice(2)],a=Gt(r);return Array.isArray(t)?Array(t.length).fill(a):[a]})}resetStates(e,t=!1){Y(()=>{if(!this.stateful)throw new ba("Cannot call resetStates() on an RNN Layer that is not stateful.");let n=this.inputSpec[0].shape,s=this.computeSingleOutputShape(n),r=[s[0],...s.slice(2)];if(n[0]==null)throw new j("If an RNN is stateful, it needs to know its batch size. Specify the batch size of your input tensors: \n- If using a Sequential model, specify the batch size by passing a `batchInputShape` option to your first layer.\n- If using the functional API, specify the batch size by passing a `batchShape` option to your Input layer.");if(this.getStates()==null)Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>Gt(r)):this.states_=[Gt(r)];else if(e==null)Q(this.states_),this.keptStates!=null&&(Q(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>Gt(r)):this.states_[0]=Gt(r);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new j(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${e.length} state value(s). Input received: ${e}`);t?this.keptStates.push(this.states_.slice()):Q(this.states_);for(let o=0;o<this.states_.length;++o){let i=e[o],l=r;if(!v.arraysEqual(i.shape,l))throw new j(`State ${o} is incompatible with layer ${this.name}: expected shape=${l}, received shape=${i.shape}`);this.states_[o]=i}}this.states_=this.states_.map(o=>Tn(o.clone()))})}computeSingleOutputShape(e){let{dataFormat:t,filters:n,kernelSize:s,padding:r,strides:a,dilationRate:o}=this.cell,i=t==="channelsFirst",l=e[i?3:2],u=e[i?4:3],c=Br(l,s[0],r,a[0],o[0]),p=Br(u,s[1],r,a[1],o[1]);return[...e.slice(0,2),...i?[n,c,p]:[c,p,n]]}};$8.className="ConvRNN2D";var N2=class extends Wh{constructor(e){let{filters:t,kernelSize:n,strides:s,padding:r,dataFormat:a,dilationRate:o}=e;super(Object.assign({},e,{units:t})),this.filters=t,Nn(this.filters,"filters"),this.kernelSize=ic(n,2,"kernelSize"),this.kernelSize.forEach(i=>Nn(i,"kernelSize")),this.strides=ic(s||1,2,"strides"),this.strides.forEach(i=>Nn(i,"strides")),this.padding=r||"valid",or(this.padding),this.dataFormat=a||"channelsLast",en(this.dataFormat),this.dilationRate=ic(o||1,2,"dilationRate"),this.dilationRate.forEach(i=>Nn(i,"dilationRate"))}build(e){var t;e=bt(e);let n=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[n]==null)throw new j(`The channel dimension of the input should be defined. Found ${e[n]}`);let s=e[n],r=4,a=this.kernelSize.concat([s,this.filters*r]);this.kernel=this.addWeight("kernel",a,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint);let o=this.kernelSize.concat([this.filters,this.filters*r]);if(this.recurrentKernel=this.addWeight("recurrent_kernel",o,null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias){let i;if(this.unitForgetBias){let l=this.biasInitializer,u=this.filters;i=new(t=class extends xr{apply(p,d){let h=l.apply([u]),f=Os([u]),m=l.apply([u*2]);return f5([h,f,m])}},t.className="CustomInit",t)}else i=this.biasInitializer;this.bias=this.addWeight("bias",[this.filters*r],null,i,this.biasRegularizer,!0,this.biasConstraint)}this.built=!0}call(e,t){return Y(()=>{if(e.length!==3)throw new j(`ConvLSTM2DCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let n=t.training||!1,s=e[0],r=e[1],a=e[2],o=4;0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=fo({ones:()=>Ws(s),rate:this.dropout,training:n,count:o,dropoutFunc:this.dropoutFunc}));let i=this.dropoutMask,l=(Z,J,te)=>!J||!J[te]?Z:z(J[te],Z),u=l(s,i,0),c=l(s,i,1),p=l(s,i,2),d=l(s,i,3);0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=fo({ones:()=>Ws(r),rate:this.recurrentDropout,training:n,count:o,dropoutFunc:this.dropoutFunc}));let h=this.recurrentDropoutMask,f=l(r,h,0),m=l(r,h,1),g=l(r,h,2),y=l(r,h,3),x=3,[A,b,w,k]=qt(this.kernel.read(),o,x),[C,E,_,$]=this.useBias?qt(this.bias.read(),o):[null,null,null,null];u=this.inputConv(u,A,C,this.padding),c=this.inputConv(c,b,E,this.padding),p=this.inputConv(p,w,_,this.padding),d=this.inputConv(d,k,$,this.padding);let[R,P,S,M]=qt(this.recurrentKernel.read(),o,x);f=this.recurrentConv(f,R),m=this.recurrentConv(m,P),g=this.recurrentConv(g,S),y=this.recurrentConv(y,M);let L=this.recurrentActivation.apply(de(u,f)),U=this.recurrentActivation.apply(de(c,m)),K=de(z(U,a),z(L,this.activation.apply(de(p,g)))),q=z(this.recurrentActivation.apply(de(d,y)),this.activation.apply(K));return[q,q,K]})}getConfig(){let e=super.getConfig(),{units:t}=e,n=nH(e,["units"]),s={filters:this.filters,kernelSize:this.kernelSize,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,strides:this.strides};return Object.assign({},n,s)}inputConv(e,t,n,s){let 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e={axis:this.axis,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:Ht(this.betaInitializer),gammaInitializer:Ht(this.gammaInitializer),betaRegularizer:Ct(this.betaRegularizer),gammaRegularizer:Ct(this.gammaRegularizer)},t=super.getConfig();return Object.assign(e,t),e}};bx.className="LayerNormalization";he.registerClass(bx);function iH(e,t,n){return Y(()=>{if(e.rank!==4)throw new j(`temporalPadding expects input tensor to be 4-D, but received a ${e.rank}-D tensor.`);if(t==null&&(t=[[1,1],[1,1]]),t.length!==2||t[0].length!==2||t[1].length!==2)throw new j("spatial2dPadding expects `padding` to be an Array of two Arrays, each of which is an Array of two integers.");if(n==null&&(n=Vr()),n!=="channelsLast"&&n!=="channelsFirst")throw new j(`Unknown data format: ${n}. 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a==="max"?o=kh(e,t,n,i):o=yh(e,t,n,i),r==="channelsFirst"&&(o=at(o,[0,3,1,2])),o})}function P8(e,t,n,s,r,a){return Y(()=>{en(r),Lk(a),or(s),n==null&&(n=[1,1,1]),s==null&&(s="valid"),r==null&&(r=Vr()),a==null&&(a="max"),e=E8(e,r);let o,i=s==="same"?"same":"valid";return a==="max"?o=HA(e,t,n,i):o=AA(e,t,n,i),r==="channelsFirst"&&(o=at(o,[0,4,1,2,3])),o})}var F8=class extends pt{constructor(e){if(e.poolSize==null&&(e.poolSize=2),super(e),typeof e.poolSize=="number")this.poolSize=[e.poolSize];else if(Array.isArray(e.poolSize)&&e.poolSize.length===1&&typeof e.poolSize[0]=="number")this.poolSize=e.poolSize;else throw new j(`poolSize for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.poolSize)}`);if(Nn(this.poolSize,"poolSize"),e.strides==null)this.strides=this.poolSize;else if(typeof e.strides=="number")this.strides=[e.strides];else if(Array.isArray(e.strides)&&e.strides.length===1&&typeof 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en(r),or(s),R2(e,t,n,s,r,"avg")}};kx.className="AveragePooling1D";he.registerClass(kx);var O8=class extends pt{constructor(e){if(e.poolSize==null&&(e.poolSize=[2,2]),super(e),this.poolSize=Array.isArray(e.poolSize)?e.poolSize:[e.poolSize,e.poolSize],e.strides==null)this.strides=this.poolSize;else if(Array.isArray(e.strides)){if(e.strides.length!==2)throw new j(`If the strides property of a 2D pooling layer is an Array, it is expected to have a length of 2, but received length ${e.strides.length}.`);this.strides=e.strides}else this.strides=[e.strides,e.strides];Nn(this.poolSize,"poolSize"),Nn(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,en(this.dataFormat),or(this.padding),this.inputSpec=[new on({ndim:4})]}computeOutputShape(e){e=bt(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2];return t=Br(t,this.poolSize[0],this.padding,this.strides[0]),n=Br(n,this.poolSize[1],this.padding,this.strides[1]),this.dataFormat==="channelsFirst"?[e[0],e[1],t,n]:[e[0],t,n,e[3]]}call(e,t){return Y(()=>(this.invokeCallHook(e,t),this.poolingFunction(et(e),this.poolSize,this.strides,this.padding,this.dataFormat)))}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}},Sx=class extends O8{constructor(e){super(e)}poolingFunction(e,t,n,s,r){return en(r),or(s),R2(e,t,n,s,r,"max")}};Sx.className="MaxPooling2D";he.registerClass(Sx);var Ix=class extends O8{constructor(e){super(e)}poolingFunction(e,t,n,s,r){return en(r),or(s),R2(e,t,n,s,r,"avg")}};Ix.className="AveragePooling2D";he.registerClass(Ix);var M8=class extends pt{constructor(e){if(e.poolSize==null&&(e.poolSize=[2,2,2]),super(e),this.poolSize=Array.isArray(e.poolSize)?e.poolSize:[e.poolSize,e.poolSize,e.poolSize],e.strides==null)this.strides=this.poolSize;else if(Array.isArray(e.strides)){if(e.strides.length!==3)throw new j(`If the strides property of a 3D pooling layer is an Array, it is expected to have a length of 3, but received length ${e.strides.length}.`);this.strides=e.strides}else this.strides=[e.strides,e.strides,e.strides];Nn(this.poolSize,"poolSize"),Nn(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,en(this.dataFormat),or(this.padding),this.inputSpec=[new on({ndim:5})]}computeOutputShape(e){e=bt(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2],s=this.dataFormat==="channelsFirst"?e[4]:e[3];return t=Br(t,this.poolSize[0],this.padding,this.strides[0]),n=Br(n,this.poolSize[1],this.padding,this.strides[1]),s=Br(s,this.poolSize[2],this.padding,this.strides[2]),this.dataFormat==="channelsFirst"?[e[0],e[1],t,n,s]:[e[0],t,n,s,e[4]]}call(e,t){return Y(()=>(this.invokeCallHook(e,t),this.poolingFunction(et(e),this.poolSize,this.strides,this.padding,this.dataFormat)))}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}},Cx=class extends M8{constructor(e){super(e)}poolingFunction(e,t,n,s,r){return en(r),or(s),P8(e,t,n,s,r,"max")}};Cx.className="MaxPooling3D";he.registerClass(Cx);var Tx=class extends M8{constructor(e){super(e)}poolingFunction(e,t,n,s,r){return en(r),or(s),P8(e,t,n,s,r,"avg")}};Tx.className="AveragePooling3D";he.registerClass(Tx);var z8=class extends pt{constructor(e){super(e),this.inputSpec=[new on({ndim:3})]}computeOutputShape(e){return[e[0],e[2]]}call(e,t){throw new Je}},Nx=class extends z8{constructor(e){super(e||{})}call(e,t){return Y(()=>{let n=et(e);return Ut(n,1)})}};Nx.className="GlobalAveragePooling1D";he.registerClass(Nx);var Ex=class extends z8{constructor(e){super(e||{})}call(e,t){return Y(()=>{let n=et(e);return xn(n,1)})}};Ex.className="GlobalMaxPooling1D";he.registerClass(Ex);var L8=class extends pt{constructor(e){super(e),this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,en(this.dataFormat),this.inputSpec=[new on({ndim:4})]}computeOutputShape(e){return e=e,this.dataFormat==="channelsLast"?[e[0],e[3]]:[e[0],e[1]]}call(e,t){throw new Je}getConfig(){let e={dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}},Rx=class extends L8{call(e,t){return Y(()=>{let n=et(e);return this.dataFormat==="channelsLast"?Ut(n,[1,2]):Ut(n,[2,3])})}};Rx.className="GlobalAveragePooling2D";he.registerClass(Rx);var _x=class extends L8{call(e,t){return Y(()=>{let n=et(e);return 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e(a)}},Dx=class extends B8{constructor(e){super(e),this.supportsMasking=!0}build(e){if(e=bt(e),e.length<3)throw new j(`TimeDistributed layer expects an input shape >= 3D, but received input shape ${JSON.stringify(e)}`);this.inputSpec=[{shape:e}];let t=[e[0]].concat(e.slice(2));this.layer.built||(this.layer.build(t),this.layer.built=!0),super.build(e)}computeOutputShape(e){e=bt(e);let t=[e[0]].concat(e.slice(2)),n=this.layer.computeOutputShape(t),s=e[1];return[n[0],s].concat(n.slice(1))}call(e,t){return Y(()=>(e=et(e),D8((a,o)=>[et(this.layer.call(a,t)),[]],e,[],!1,null,null,!1,!0)[1]))}};Dx.className="TimeDistributed";he.registerClass(Dx);function lH(e){mu(uU,"BidirectionalMergeMode",e)}var uH="concat",$x=class extends B8{constructor(e){super(e);let t=e.layer.getConfig(),n={};n.className=e.layer.getClassName(),n.config=t,this.forwardLayer=Lr(n),t.goBackwards=t.goBackwards!==!0;let s={};if(s.className=e.layer.getClassName(),s.config=t,this.backwardLayer=Lr(s),this.forwardLayer.name="forward_"+this.forwardLayer.name,this.backwardLayer.name="backward_"+this.backwardLayer.name,this.mergeMode=e.mergeMode===void 0?uH:e.mergeMode,lH(this.mergeMode),e.weights)throw new Je("weights support is not implemented for Bidirectional layer yet.");this._stateful=e.layer.stateful,this.returnSequences=e.layer.returnSequences,this.returnState=e.layer.returnState,this.supportsMasking=!0,this._trainable=!0,this.inputSpec=e.layer.inputSpec,this.numConstants=null}get trainable(){return this._trainable}set trainable(e){this._trainable=e,this.forwardLayer!=null&&(this.forwardLayer.trainable=e),this.backwardLayer!=null&&(this.backwardLayer.trainable=e)}getWeights(){return this.forwardLayer.getWeights().concat(this.backwardLayer.getWeights())}setWeights(e){let 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TypeError(`Node type ${e.op} is not implemented`)}};function fr(e,t,n=""){if(!(typeof e=="number"||typeof t=="number")){v.assert(e.length===t.length,()=>n+` Shapes ${e} and ${t} must match`);for(let s=0;s<e.length;s++){let r=e[s],a=t[s];v.assert(r<0||a<0||r===a,()=>n+` Shapes ${e} and ${t} must match`)}}}function k7(e){return!(typeof e=="number"||e.some(t=>t<0))}function op(e,t,n){let s=my(e,n),r=!k7(s);if(r&&t.length===0)throw new Error(`Tried to calculate elements of an empty list with non-fully-defined elementShape: ${s}`);if(r&&t.forEach(a=>{s=my(a.shape,s)}),!k7(s))throw new Error(`Non-fully-defined elementShape: ${s}`);return s}function my(e,t){if(typeof e=="number")return t;if(typeof t=="number")return e;if(e.length!==t.length)throw new Error(`Incompatible ranks during merge: ${e} vs. ${t}`);let n=[];for(let s=0;s<e.length;++s){let r=e[s],a=t[s];if(r>=0&&a>=0&&r!==a)throw new Error(`Incompatible shape during merge: ${e} vs. ${t}`);n[s]=r>=0?r:a}return n}var 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TypeError(`Node type ${e.op} is not implemented`)}},vq=(e,t,n,s=zn)=>{switch(e.op){case"FFT":return[s.fft(I("x",e,t,n))];case"IFFT":return[s.ifft(I("x",e,t,n))];case"RFFT":return[s.rfft(I("x",e,t,n))];case"IRFFT":return[s.irfft(I("x",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},wq=(e,t,n,s=zn)=>{switch(e.op){case"StringNGrams":{let{nGrams:r,nGramsSplits:a}=s.string.stringNGrams(I("data",e,t,n),I("dataSplits",e,t,n),I("separator",e,t,n),I("nGramWidths",e,t,n),I("leftPad",e,t,n),I("rightPad",e,t,n),I("padWidth",e,t,n),I("preserveShortSequences",e,t,n));return[r,a]}case"StringSplit":{let{indices:r,values:a,shape:o}=s.string.stringSplit(I("input",e,t,n),I("delimiter",e,t,n),I("skipEmpty",e,t,n));return[r,a,o]}case"StringToHashBucketFast":return[s.string.stringToHashBucketFast(I("input",e,t,n),I("numBuckets",e,t,n))];default:throw TypeError(`Node type ${e.op} is not 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r(()=>vq(o,i,l));case"string":return r(()=>wq(o,i,l));case"transformation":return r(()=>kq(o,i,l));case"hash_table":return hq(o,i,l,s);case"custom":let u=G8(o.op);if(u&&u.customExecutor)return u.customExecutor(new Jj(o,i,l));throw TypeError(`Custom op ${o.op} is not registered.`);default:throw TypeError(`Unknown op '${o.op}'. File an issue at https://github.com/tensorflow/tfjs/issues so we can add it, or register a custom execution with tf.registerOp()`)}})(e,t,n);return v.isPromise(a)?a.then(o=>[].concat(o)):[].concat(a)}var b7=class{constructor(e={},t={},n={},s={}){this.weightMap=e,this.tensorArrayMap=t,this.tensorListMap=n,this.functionMap=s,this.rootContext={id:0,frameName:"",iterationId:0},this.contexts=[this.rootContext],this.lastId=0,this.generateCurrentContextIds()}newFrame(e,t){return{id:e,frameName:t,iterationId:0}}set currentContext(e){this.contexts!==e&&(this.contexts=e,this.generateCurrentContextIds())}get currentContext(){return this.contexts}get currentContextId(){return this._currentContextIds[0]}get currentContextIds(){return this._currentContextIds}generateCurrentContextIds(){let e=[];for(let t=0;t<this.contexts.length-1;t++){let n=this.contexts.slice(0,this.contexts.length-t);e.push(this.contextIdforContexts(n))}e.push(""),this._currentContextIds=e}contextIdforContexts(e){return e?e.map(t=>t.id===0&&t.iterationId===0?"":`${t.frameName}-${t.iterationId}`).join("/"):""}enterFrame(e){this.contexts&&(this.lastId++,this.contexts=this.contexts.slice(),this.contexts.push(this.newFrame(this.lastId,e)),this._currentContextIds.unshift(this.contextIdforContexts(this.contexts)))}exitFrame(){if(this.contexts&&this.contexts.length>1)this.contexts=this.contexts.slice(),this.contexts.splice(-1),this.currentContextIds.shift();else throw new Error("Cannot exit frame, the context is empty")}nextIteration(){if(this.contexts&&this.contexts.length>0){this.contexts=this.contexts.slice(),this.lastId++;let e=Object.assign({},this.contexts[this.contexts.length-1]);e.iterationId+=1,e.id=this.lastId,this.contexts.splice(-1,1,e),this._currentContextIds.splice(0,1,this.contextIdforContexts(this.contexts))}else throw new Error("Cannot increase frame iteration, the context is empty")}getWeight(e){return this.weightMap[e]}addTensorArray(e){this.tensorArrayMap[e.id]=e}getTensorArray(e){return this.tensorArrayMap[e]}addTensorList(e){this.tensorListMap[e.id]=e}getTensorList(e){return this.tensorListMap[e]}dispose(e){for(let t in this.tensorArrayMap)this.tensorArrayMap[t].clearAndClose(e);for(let t in this.tensorListMap)this.tensorListMap[t].clearAndClose(e)}};function v7(e,t,n,s){let r=new Set,a=[],o=null,i=null,l=new Set,u=Object.keys(e).map(d=>Rs(d)[0]),c=[];s!=null&&(c=s.map(d=>Rs(d.name)[0]));let p=[...t];for(;p.length>0;){let d=p.pop();if((pS(d)||Nq(d)||Eq(d))&&o==null&&(o=d,i=o.children.map(h=>h.name).filter(h=>r.has(h))),r.add(d.name),n[d.name]==null&&u.indexOf(d.name)===-1&&c.indexOf(d.name)===-1){if(d.inputs.length===0){a.push(d.name);continue}d.inputs.forEach(h=>{l.has(h.name)||(l.add(h.name),p.push(h))})}}return{inputs:e,outputs:t,usedNodes:r,missingInputs:a,dynamicNode:o,syncInputs:i}}function Sq(e,t,n){let{usedNodes:s,inputs:r}=n,a=[],o=Object.keys(r).map(c=>Rs(c)[0]).map(c=>e.nodes[c]),i=e.initNodes;o.forEach(c=>{s.has(c.name)&&a.push(c)}),e.weights.forEach(c=>{s.has(c.name)&&a.push(c)}),i!=null&&i.forEach(c=>{s.has(c.name)&&a.push(c)});let l=new Set,u=[];for(;a.length>0;){let c=a.pop();l.add(c.name),t[c.name]||u.push(c),c.children.forEach(p=>{!l.has(p.name)&&s.has(p.name)&&p.inputs.every(d=>l.has(d.name))&&a.push(p)})}return u}var Iq=["Switch","Merge","Enter","Exit","NextIteration","StatelessIf","StatelessWhile","if","While"],Cq=["NonMaxSuppressionV2","NonMaxSuppressionV3","NonMaxSuppressionV5","Where"],Tq=["HashTable","HashTableV2","LookupTableImport","LookupTableImportV2","LookupTableFind","LookupTableFindV2","LookupTableSize","LookupTableSizeV2"];function pS(e){return Iq.indexOf(e.op)>=0}function Nq(e){return Cq.indexOf(e.op)>=0}function Eq(e){return Tq.indexOf(e.op)>=0}var hy=class{constructor(e,t){this.graph=e,this.parent=t,this.compiledMap=new Map,this._weightMap={},this.SEPERATOR=",",this._functions={},this._functionExecutorMap={},this.intermediateTensors={},this.keepTensorForDebug=!1,this._outputs=e.outputs,this._inputs=e.inputs,this._initNodes=e.initNodes,this._signature=e.signature,this._functions=e.functions,e.functions!=null&&Object.keys(e.functions).forEach(n=>{this._functionExecutorMap[n]=new hy(e.functions[n],this)})}get weightIds(){return this.parent?this.parent.weightIds:this._weightIds}get functionExecutorMap(){return this.parent?this.parent.functionExecutorMap:this._functionExecutorMap}get weightMap(){return this.parent?this.parent.weightMap:this._weightMap}set weightMap(e){let t=Object.keys(e).map(n=>e[n].map(s=>s.id));this._weightIds=[].concat(...t),this._weightMap=e}set resourceManager(e){this._resourceManager=e}get inputs(){return this._inputs.map(e=>({name:e.name,shape:e.attrParams.shape?e.attrParams.shape.value:void 0,dtype:e.attrParams.dtype?e.attrParams.dtype.value:void 0}))}get outputs(){return this._outputs.map(e=>({name:e.name,shape:e.attrParams.shape?e.attrParams.shape.value:void 0,dtype:e.attrParams.dtype?e.attrParams.dtype.value:void 0}))}get inputNodes(){return this._inputs.map(e=>e.signatureKey||e.name)}get outputNodes(){return this._outputs.map(e=>{let t=e.signatureKey||e.name;return e.defaultOutput?`${t}:${e.defaultOutput}`:t})}get functions(){return Object.keys(this._functions).reduce((e,t)=>(e[t]=this._functions[t].signature,e),{})}getCompilationKey(e,t){let n=e.map(r=>r.name).sort(),s=t.map(r=>r.name).sort();return n.join(this.SEPERATOR)+"--"+s.join(this.SEPERATOR)}compile(e,t){let n=v7(e,t,this.weightMap,this._initNodes),{missingInputs:s,dynamicNode:r,syncInputs:a}=n;if(r!=null)throw new Error(`This execution contains the node '${r.name}', which has the dynamic op '${r.op}'. Please use model.executeAsync() instead. Alternatively, to avoid the dynamic ops, specify the inputs [${a}]`);if(s.length>0){let o=t.map(l=>l.name),i=Object.keys(e);throw new Error(`Cannot compute the outputs [${o}] from the provided inputs [${i}]. Missing the following inputs: [${s}]`)}return Sq(this.graph,this.weightMap,n)}execute(e,t){e=this.mapInputs(e);let n=Object.keys(e).sort();this.checkInputs(e),this.checkInputShapeAndType(e),t=this.mapOutputs(t),this.checkOutputs(t);let s=n.map(c=>this.graph.nodes[Rs(c)[0]]),r=t.map(c=>Rs(c)[0]),a=r.map(c=>this.graph.nodes[c]);this.resetIntermediateTensors(),a.length===0&&(a=this._outputs);let o=this.getCompilationKey(s,a),i=this.compiledMap.get(o);i==null&&(i=this.compile(e,a),this.compiledMap.set(o,i));let l={},u={};return Y(()=>{let c=new b7(this.weightMap,l,u,this.functionExecutorMap),p=Object.assign({},this.weightMap);Object.keys(e).forEach(f=>{let[m,g]=Rs(f),y=[];y[g]=e[f],p[m]=y});let d=this.getFrozenTensorIds(p),h={};for(let f=0;f<i.length;f++){let m=i[f];if(!p[m.name]){let g=x7(m,p,c,this._resourceManager);if(v.isPromise(g))throw new Error(`The execution of the op '${m.op}' returned a promise. Please use model.executeAsync() instead.`);p[m.name]=g,this.checkTensorForDisposal(m.name,m,p,c,d,r,h)}}return this.parent==null&&c.dispose(d),t.map(f=>as(f,p,c))})}getFrozenTensorIds(e){let t=[].concat.apply([],Object.keys(e).map(n=>e[n]).map(n=>n.map(s=>s.id)));return new Set(t)}checkTensorForDisposal(e,t,n,s,r,a,o){t.category==="control"||a.indexOf(e)!==-1||(n[e].forEach(i=>{i!=null&&(o[i.id]=(o[i.id]||0)+t.children.length)}),t.inputs.forEach(i=>{if(i.category!=="control"){let l=_j(i.name,n,s);l!=null&&l.forEach(u=>{if(u&&!u.kept&&!r.has(u.id)){let c=o[u.id];if(c===1){if(!this.keepTensorForDebug)u.dispose();else{let[p,d]=Qr(t.name,s);this.intermediateTensors[p]?this.intermediateTensors[p][d]=u:(this.intermediateTensors[p]=[],this.intermediateTensors[p][d]=u)}delete o[u.id]}else c!=null&&o[u.id]--}})}}))}async executeAsync(e,t){return this._executeAsync(e,t)}disposeIntermediateTensors(){!this.intermediateTensors||(Object.keys(this.intermediateTensors).forEach(e=>this.intermediateTensors[e].forEach(t=>t.dispose())),this.disposeTensorsMap())}disposeTensorsMap(){!this.tensorsMap||Object.keys(this.tensorsMap).forEach(e=>{this.tensorsMap[e].forEach(n=>{n&&!n.kept&&!n.isDisposed&&!this.keepIds.has(n.id)&&n.dispose()})})}getIntermediateTensors(){return this.tensorsMap}resetIntermediateTensors(){for(let e in this.intermediateTensors)this.intermediateTensors[e].forEach(t=>t.dispose()),delete this.intermediateTensors[e]}async _executeAsync(e,t,n=!1,s={},r={}){n||(e=this.mapInputs(e),this.checkInputs(e),this.checkInputShapeAndType(e),t=this.mapOutputs(t),this.checkOutputs(t));try{this.keepTensorForDebug=H().getBool("KEEP_INTERMEDIATE_TENSORS")}catch(u){console.warn(u.message)}this.resetIntermediateTensors();let a=new b7(this.weightMap,s,r,this.functionExecutorMap);this.tensorsMap=await this.executeWithControlFlow(e,a,t,n);let o=t.map(u=>as(u,this.tensorsMap,a)),i=o.map(u=>u.id),l=Object.keys(e).map(u=>e[u].id);return this.keepIds=new Set([...i,...l,...this.weightIds]),this.keepTensorForDebug||this.disposeTensorsMap(),this.parent==null&&a.dispose(this.keepIds),o}async executeFunctionAsync(e,t,n){let s=e.reduce((r,a,o)=>(r[this.inputs[o].name]=a,r),{});return this._executeAsync(s,this.outputNodes,!0,t,n)}async executeWithControlFlow(e,t,n,s){let r=Object.keys(e),a=r.map(x=>this.graph.nodes[Rs(x)[0]]),o=n.map(x=>Rs(x)[0]),i=o.map(x=>this.graph.nodes[x]);i.length===0&&(i=this._outputs);let{usedNodes:l,missingInputs:u,dynamicNode:c,syncInputs:p}=v7(e,i,this.weightMap,this._initNodes),d=[...a,...this.graph.weights,...this._initNodes||[]].map(x=>({node:x,contexts:t.currentContext})),h=Object.assign({},this.weightMap);Object.keys(e).forEach(x=>{let[A,b]=Rs(x),w=[];w[b]=e[x],h[A]=w});let f={},m=this.getFrozenTensorIds(h),g={};for(;d.length>0;){let x=this.processStack(a,d,t,h,g,m,o,f,l);await Promise.all(x)}c==null&&!s&&console.warn("This model execution did not contain any nodes with control flow or dynamic output shapes. You can use model.execute() instead.");let y=i.filter(x=>!pS(x)&&!as(x.name,h,t)).map(x=>x.name);if(y.length>0){let x="";throw c!=null&&(x=`Alternatively, to avoid the dynamic ops, use model.execute() and specify the inputs [${p}]`),new Error(`Cannot compute the outputs [${y}] from the provided inputs [${r}]. Consider providing the following inputs: [${u}]. ${x}`)}return h}processStack(e,t,n,s,r,a,o,i,l){let u=[];for(;t.length>0;){let c=t.pop();n.currentContext=c.contexts;let p="";if(c.node.op==="Enter"&&I("isConstant",c.node,s,n)&&([p]=Qr(c.node.name,n)),s[c.node.name]==null){let d=x7(c.node,s,n,this._resourceManager);p||([p]=Qr(c.node.name,n));let h=n.currentContext;v.isPromise(d)?u.push(d.then(f=>(s[p]=f,n.currentContext=h,this.checkTensorForDisposal(p,c.node,s,n,a,o,i),this.processChildNodes(c.node,t,n,s,r,l),f))):(s[p]=d,this.checkTensorForDisposal(p,c.node,s,n,a,o,i),this.processChildNodes(c.node,t,n,s,r,l))}else this.processChildNodes(c.node,t,n,s,r,l)}return u}processChildNodes(e,t,n,s,r,a){e.children.forEach(o=>{let[i]=Qr(o.name,n);r[i]||!a.has(o.name)||(o.op==="Merge"?o.inputNames.some(l=>!!as(l,s,n))&&(r[i]=!0,t.push({contexts:n.currentContext,node:o})):o.inputNames.every(l=>!!as(l,s,n))&&(r[i]=!0,t.push({contexts:n.currentContext,node:o})))})}dispose(){Object.keys(this.weightMap).forEach(e=>this.weightMap[e].forEach(t=>t.dispose()))}checkInputShapeAndType(e){Object.keys(e).forEach(t=>{let n=e[t],[s]=Rs(t),r=this.graph.nodes[s];if(r.attrParams.shape&&r.attrParams.shape.value){let a=r.attrParams.shape.value,o=a.length===n.shape.length&&n.shape.every((i,l)=>a[l]===-1||a[l]===i);v.assert(o,()=>`The shape of dict['${r.name}'] provided in model.execute(dict) must be [${a}], but was [${n.shape}]`)}r.attrParams.dtype&&r.attrParams.dtype.value&&v.assert(n.dtype===r.attrParams.dtype.value,()=>`The dtype of dict['${r.name}'] provided in model.execute(dict) must be ${r.attrParams.dtype.value}, but was ${n.dtype}`)})}mapInputs(e){let t={};for(let n in e)if(this._signature!=null&&this._signature.inputs!=null&&this._signature.inputs[n]!=null){let s=this._signature.inputs[n];t[s.name]=e[n]}else t[n]=e[n];return t}checkInputs(e){let t=Object.keys(e).filter(n=>{let[s]=Rs(n);return this.graph.nodes[s]==null});if(t.length>0)throw new Error(`The dict provided in model.execute(dict) has keys: [${t}] that are not part of graph`)}mapOutputs(e){return e.map(t=>this._signature!=null&&this._signature.outputs!=null&&this._signature.outputs[t]!=null?this._signature.outputs[t].name:t,{})}checkOutputs(e){e.forEach(t=>{let[n]=Rs(t);if(!this.graph.nodes[n])throw new Error(`The output '${t}' is not found in the graph`)})}},Rq=class{constructor(e={},t={}){this.hashTableNameToHandle=e,this.hashTableMap=t}addHashTable(e,t){this.hashTableNameToHandle[e]=t.handle,this.hashTableMap[t.id]=t}getHashTableHandleByName(e){return this.hashTableNameToHandle[e]}getHashTableById(e){return this.hashTableMap[e]}dispose(){for(let e in this.hashTableMap)this.hashTableMap[e].clearAndClose(),delete this.hashTableMap[e];for(let e in this.hashTableNameToHandle)this.hashTableNameToHandle[e].dispose(),delete this.hashTableNameToHandle[e]}},_q="?tfjs-format=file",Dq="model.json",Wh=class{constructor(e,t={},n=Ds){this.modelUrl=e,this.loadOptions=t,this.version="n/a",this.io=n,t==null&&(this.loadOptions={}),this.resourceManager=new Rq}get modelVersion(){return this.version}get inputNodes(){return this.executor.inputNodes}get outputNodes(){return this.executor.outputNodes}get inputs(){return this.executor.inputs}get outputs(){return this.executor.outputs}get weights(){return this.executor.weightMap}get metadata(){return this.artifacts.userDefinedMetadata}get modelSignature(){return this.signature}get modelStructuredOutputKeys(){return this.structuredOutputKeys}findIOHandler(){let e=this.modelUrl;if(e.load!=null)this.handler=e;else if(this.loadOptions.requestInit!=null)this.handler=this.io.browserHTTPRequest(e,this.loadOptions);else{let t=this.io.getLoadHandlers(e,this.loadOptions);if(t.length===0)t.push(this.io.browserHTTPRequest(e,this.loadOptions));else if(t.length>1)throw new Error(`Found more than one (${t.length}) load handlers for URL '${[e]}'`);this.handler=t[0]}}load(){if(this.findIOHandler(),this.handler.load==null)throw new Error("Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.");let e=this.handler.load();return v.isPromise(e)?e.then(t=>this.loadSync(t)):this.loadSync(e)}loadSync(e){this.artifacts=e;let t=this.artifacts.modelTopology,n=this.artifacts.signature;if(this.artifacts.userDefinedMetadata!=null){let r=this.artifacts.userDefinedMetadata;r.signature!=null&&(n=r.signature),r.structuredOutputKeys!=null&&(this.structuredOutputKeys=r.structuredOutputKeys)}this.signature=n,this.version=`${t.versions.producer}.${t.versions.minConsumer}`;let 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Please provide 'user' or 'environment'`);try{this.stream=await navigator.mediaDevices.getUserMedia({video:{deviceId:this.webcamConfig.deviceId,facingMode:this.webcamConfig.facingMode?this.webcamConfig.facingMode:"user",width:this.webcamVideoElement.width,height:this.webcamVideoElement.height}})}catch(e){throw e.message=`Error thrown while initializing video stream: ${e.message}`,e}if(!this.stream)throw new Error("Could not obtain video from webcam.");try{this.webcamVideoElement.srcObject=this.stream}catch(e){console.log(e),this.webcamVideoElement.src=window.URL.createObjectURL(this.stream)}return this.webcamVideoElement.play(),this.isClosed=!1,new Promise(e=>{this.webcamVideoElement.onloadedmetadata=()=>{e()}})}async next(){if(this.isClosed)return{value:null,done:!0};let e;try{e=sr.fromPixels(this.webcamVideoElement)}catch(t){throw new Error(`Error thrown converting video to pixels: ${JSON.stringify(t)}`)}if(this.resize)try{return{value:this.cropAndResizeFrame(e),done:!1}}catch(t){throw new Error(`Error thrown cropping the video: ${t.message}`)}finally{e.dispose()}else return{value:e,done:!1}}needToResize(){return!!(this.webcamConfig.resizeWidth&&this.webcamConfig.resizeHeight&&(this.webcamVideoElement.width!==this.webcamConfig.resizeWidth||this.webcamVideoElement.height!==this.webcamConfig.resizeHeight))}cropAndResizeFrame(e){return Y(()=>{let t=Wt(Ae(e,"float32"),0),n;n=Ce.cropAndResize(t,this.cropBox,this.cropBoxInd,this.cropSize,"bilinear");let s=n.shape;return V(n,s.slice(1))})}async capture(){return(await this.next()).value}stop(){this.stream.getTracks().forEach(t=>t.stop());try{this.webcamVideoElement.srcObject=null}catch(t){console.log(t),this.webcamVideoElement.src=null}this.isClosed=!0}toArray(){throw new Error("Can not convert infinite video stream to array.")}},IS=class{},CS=class extends Tn{split(e){return new lX(this,e)}},lX=class extends CS{constructor(e,t){super(),this.upstream=e,this.impl=new uX(e,t)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},uX=class extends Lx{constructor(e,t){super(),this.upstream=e,this.separator=t,this.carryover=""}summary(){return`${this.upstream.summary()} -> Split('${this.separator}')`}async pump(){let e=await this.upstream.next();if(e.done)return this.carryover===""?!1:(this.outputQueue.push(this.carryover),this.carryover="",!0);let t=e.value.split(this.separator);t[0]=this.carryover+t[0];for(let n of t.slice(0,-1))this.outputQueue.push(n);return this.carryover=t[t.length-1],!0}},cX=class extends Tn{decodeUTF8(){return new dX(this)}},dX=class extends CS{constructor(e){super(),this.upstream=e,this.impl=new pX(e)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},pX=class extends Lx{constructor(e){if(super(),this.upstream=e,H().get("IS_BROWSER"))this.decoder=new TextDecoder("utf-8");else{let{StringDecoder:t}=m6();this.decoder=new t("utf8")}}summary(){return`${this.upstream.summary()} -> Utf8`}async pump(){let e=await this.upstream.next(),t;if(e.done)return!1;t=e.value;let n;return H().get("IS_BROWSER")?n=this.decoder.decode(t,{stream:!0}):n=this.decoder.write(Buffer.from(t.buffer)),this.outputQueue.push(n),!0}},TS=class extends cX{constructor(e,t={}){super(),this.file=e,this.options=t,v.assert(e instanceof Uint8Array||(H().get("IS_BROWSER")?e instanceof File||e instanceof Blob:!1),()=>"FileChunkIterator only supports File, Blob and Uint8Array right now."),this.offset=t.offset||0,this.chunkSize=t.chunkSize||1024*1024}summary(){return`FileChunks ${this.file}`}async next(){return this.offset>=(this.file instanceof Uint8Array?this.file.byteLength:this.file.size)?{value:null,done:!0}:{value:await new Promise((t,n)=>{let s=this.offset+this.chunkSize;if(this.file instanceof Uint8Array)t(new Uint8Array(this.file.slice(this.offset,s)));else{let r=new FileReader;r.onload=o=>{let i=r.result;if(i instanceof 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implemented`)}},Tq=(e,t,n,s=Ln)=>{switch(e.op){case"Equal":return[s.equal(I("a",e,t,n),I("b",e,t,n))];case"NotEqual":return[s.notEqual(I("a",e,t,n),I("b",e,t,n))];case"Greater":return[s.greater(I("a",e,t,n),I("b",e,t,n))];case"GreaterEqual":return[s.greaterEqual(I("a",e,t,n),I("b",e,t,n))];case"Less":return[s.less(I("a",e,t,n),I("b",e,t,n))];case"LessEqual":return[s.lessEqual(I("a",e,t,n),I("b",e,t,n))];case"LogicalAnd":return[s.logicalAnd(I("a",e,t,n),I("b",e,t,n))];case"LogicalNot":return[s.logicalNot(I("a",e,t,n))];case"LogicalOr":return[s.logicalOr(I("a",e,t,n),I("b",e,t,n))];case"Select":case"SelectV2":return[s.where(I("condition",e,t,n),I("a",e,t,n),I("b",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},Nq=(e,t,n,s=Ln)=>{switch(e.op){case"BatchMatMul":case"BatchMatMulV2":case"MatMul":return[s.matMul(I("a",e,t,n),I("b",e,t,n),I("transposeA",e,t,n),I("transposeB",e,t,n))];case"Einsum":return[s.einsum(I("equation",e,t,n),...I("tensors",e,t,n))];case"Transpose":return[s.transpose(I("x",e,t,n),I("perm",e,t,n))];case"_FusedMatMul":let[r,a]=I("fusedOps",e,t,n),o=r==="biasadd",i=a==="prelu",l=I("numArgs",e,t,n),u=I("leakyreluAlpha",e,t,n);if(o){if(i&&l!==2)throw new Error("Fused MatMul with BiasAdd and Prelu must have two extra arguments: bias and alpha.");if(!i&&l!==1)throw new Error("Fused MatMul with BiasAdd must have one extra argument: bias.")}let[c,p]=I("args",e,t,n);return[s.fused.matMul({a:I("a",e,t,n),b:I("b",e,t,n),transposeA:I("transposeA",e,t,n),transposeB:I("transposeB",e,t,n),bias:c,activation:a,preluActivationWeights:p,leakyreluAlpha:u})];default:throw TypeError(`Node type ${e.op} is not implemented`)}},Eq=(e,t,n,s=Ln)=>{switch(e.op){case"EuclideanNorm":return[s.euclideanNorm(I("x",e,t,n),I("axis",e,t,n),I("keepDims",e,t,n))];case"FusedBatchNorm":case"FusedBatchNormV2":return[s.batchNorm(I("x",e,t,n),I("mean",e,t,n),I("variance",e,t,n),I("offset",e,t,n),I("scale",e,t,n),I("epsilon",e,t,n))];case"FusedBatchNormV3":return[s.batchNorm(I("x",e,t,n),I("mean",e,t,n),I("variance",e,t,n),I("offset",e,t,n),I("scale",e,t,n),I("epsilon",e,t,n))];case"LRN":return[s.localResponseNormalization(I("x",e,t,n),I("radius",e,t,n),I("bias",e,t,n),I("alpha",e,t,n),I("beta",e,t,n))];case"Softmax":return[s.softmax(I("x",e,t,n))];case"LogSoftmax":return[s.logSoftmax(I("x",e,t,n))];case"SparseToDense":return[s.sparseToDense(I("sparseIndices",e,t,n),I("outputShape",e,t,n),I("sparseValues",e,t,n),I("defaultValue",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},Rq=(e,t,n,s=Ln)=>{switch(e.op){case"Max":{let i=I("axis",e,t,n),l=I("keepDims",e,t,n);return[s.max(I("x",e,t,n),i,l)]}case"Mean":{let i=I("axis",e,t,n),l=I("keepDims",e,t,n);return[s.mean(I("x",e,t,n),i,l)]}case"Min":{let i=I("axis",e,t,n),l=I("keepDims",e,t,n);return[s.min(I("x",e,t,n),i,l)]}case"Sum":{let i=I("axis",e,t,n),l=I("keepDims",e,t,n);return[s.sum(I("x",e,t,n),i,l)]}case"All":{let i=I("axis",e,t,n),l=I("keepDims",e,t,n);return[s.all(I("x",e,t,n),i,l)]}case"Any":{let i=I("axis",e,t,n),l=I("keepDims",e,t,n);return[s.any(I("x",e,t,n),i,l)]}case"ArgMax":{let i=I("axis",e,t,n);return[s.argMax(I("x",e,t,n),i)]}case"ArgMin":{let i=I("axis",e,t,n);return[s.argMin(I("x",e,t,n),i)]}case"Prod":{let i=I("axis",e,t,n),l=I("keepDims",e,t,n);return[s.prod(I("x",e,t,n),i,l)]}case"Cumprod":{let i=I("axis",e,t,n),l=I("exclusive",e,t,n),u=I("reverse",e,t,n);return[s.cumprod(I("x",e,t,n),i,l,u)]}case"Cumsum":{let i=I("axis",e,t,n),l=I("exclusive",e,t,n),u=I("reverse",e,t,n);return[s.cumsum(I("x",e,t,n),i,l,u)]}case"Bincount":let r=I("x",e,t,n),a=I("weights",e,t,n),o=I("size",e,t,n);return[s.bincount(r,a,o)];case"DenseBincount":{let i=I("x",e,t,n),l=I("weights",e,t,n),u=I("size",e,t,n),c=I("binaryOutput",e,t,n);return[s.denseBincount(i,l,u,c)]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},_q=(e,t,n,s=Ln)=>{switch(e.op){case"ConcatV2":case"Concat":{let r=I("n",e,t,n),a=I("axis",e,t,n),o=I("tensors",e,t,n);return o=o.slice(0,r),[s.concat(o,a)]}case"Gather":{let r=I("x",e,t,n),a=I("indices",e,t,n);return[s.gather(r,s.cast(a,"int32"),0)]}case"GatherV2":{let r=I("axis",e,t,n),a=I("batchDims",e,t,n),o=I("x",e,t,n),i=I("indices",e,t,n);return[s.gather(o,s.cast(i,"int32"),r,a)]}case"Reverse":{let r=I("dims",e,t,n),a=[];for(let i=0;i<r.length;i++)r[i]&&a.push(i);let o=I("x",e,t,n);return[s.reverse(o,a)]}case"ReverseV2":{let r=I("axis",e,t,n),a=I("x",e,t,n);return[s.reverse(a,r)]}case"Slice":{let r=I("begin",e,t,n),a=I("size",e,t,n);return[s.slice(I("x",e,t,n),r,a)]}case"StridedSlice":{let r=I("begin",e,t,n),a=I("end",e,t,n),o=I("strides",e,t,n),i=I("beginMask",e,t,n),l=I("endMask",e,t,n),u=I("ellipsisMask",e,t,n),c=I("newAxisMask",e,t,n),p=I("shrinkAxisMask",e,t,n),d=I("x",e,t,n);return[s.stridedSlice(d,r,a,o,i,l,u,c,p)]}case"Pack":return Y(()=>{let r=I("axis",e,t,n),a=I("tensors",e,t,n),o=a[0].shape,i=s.squeeze(a[0]).shape,l=a.map(u=>{let c=v.arraysEqual(u.shape,o);if(!c&&!v.arraysEqual(s.squeeze(u).shape,i))throw new Error("the input tensors shape does not match");return c?u:s.reshape(u,o)});return[s.stack(l,r)]});case"Unpack":{let r=I("axis",e,t,n),a=I("tensor",e,t,n);return s.unstack(a,r)}case"Tile":{let r=I("reps",e,t,n);return[s.tile(I("x",e,t,n),r)]}case"Split":case"SplitV":{let r=I("axis",e,t,n),a=I("numOrSizeSplits",e,t,n),o=I("x",e,t,n);return s.split(o,a,r)}case"ScatterNd":{let r=I("indices",e,t,n),a=I("values",e,t,n),o=I("shape",e,t,n);return[s.scatterND(r,a,o)]}case"GatherNd":{let r=I("x",e,t,n),a=I("indices",e,t,n);return[s.gatherND(r,a)]}case"SparseToDense":{let r=I("sparseIndices",e,t,n),a=I("outputShape",e,t,n),o=I("sparseValues",e,t,n),i=I("defaultValue",e,t,n);return[s.sparseToDense(r,o,a,o.dtype===i.dtype?i:s.cast(i,o.dtype))]}default:throw TypeError(`Node type ${e.op} is not implemented`)}},Dq=(e,t,n,s=Ln)=>{switch(e.op){case"SparseFillEmptyRows":{let{outputIndices:r,outputValues:a,emptyRowIndicator:o,reverseIndexMap:i}=s.sparse.sparseFillEmptyRows(I("indices",e,t,n),I("values",e,t,n),I("denseShape",e,t,n),I("defaultValue",e,t,n));return[r,a,o,i]}case"SparseReshape":{let{outputIndices:r,outputShape:a}=s.sparse.sparseReshape(I("inputIndices",e,t,n),I("inputShape",e,t,n),I("newShape",e,t,n));return[r,a]}case"SparseSegmentMean":return[s.sparse.sparseSegmentMean(I("data",e,t,n),I("indices",e,t,n),I("segmentIds",e,t,n))];case"SparseSegmentSum":return[s.sparse.sparseSegmentSum(I("data",e,t,n),I("indices",e,t,n),I("segmentIds",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},$q=(e,t,n,s=Ln)=>{switch(e.op){case"FFT":return[s.fft(I("x",e,t,n))];case"IFFT":return[s.ifft(I("x",e,t,n))];case"RFFT":return[s.rfft(I("x",e,t,n))];case"IRFFT":return[s.irfft(I("x",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},Pq=(e,t,n,s=Ln)=>{switch(e.op){case"StringNGrams":{let{nGrams:r,nGramsSplits:a}=s.string.stringNGrams(I("data",e,t,n),I("dataSplits",e,t,n),I("separator",e,t,n),I("nGramWidths",e,t,n),I("leftPad",e,t,n),I("rightPad",e,t,n),I("padWidth",e,t,n),I("preserveShortSequences",e,t,n));return[r,a]}case"StringSplit":{let{indices:r,values:a,shape:o}=s.string.stringSplit(I("input",e,t,n),I("delimiter",e,t,n),I("skipEmpty",e,t,n));return[r,a,o]}case"StringToHashBucketFast":return[s.string.stringToHashBucketFast(I("input",e,t,n),I("numBuckets",e,t,n))];default:throw TypeError(`Node type ${e.op} is not 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r=I("blockSize",e,t,n),a=I("dataFormat",e,t,n).toUpperCase();return[s.depthToSpace(I("x",e,t,n),r,a)]}case"BroadcastTo":return[s.broadcastTo(I("x",e,t,n),I("shape",e,t,n))];case"BroadcastArgs":return[s.broadcastArgs(I("s0",e,t,n),I("s1",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}};function I7(e,t,n,s,r=Y){let a=((o,i,l)=>{switch(o.category){case"arithmetic":return r(()=>dq(o,i,l));case"basic_math":return r(()=>pq(o,i,l));case"control":return Aq(o,i,l);case"convolution":return r(()=>xq(o,i,l));case"creation":return r(()=>bq(o,i,l));case"dynamic":return vq(o,i,l);case"evaluation":return r(()=>wq(o,i,l));case"image":return r(()=>Cq(o,i,l));case"graph":return r(()=>kq(o,i,l));case"logical":return r(()=>Tq(o,i,l));case"matrices":return r(()=>Nq(o,i,l));case"normalization":return r(()=>Eq(o,i,l));case"reduction":return r(()=>Rq(o,i,l));case"slice_join":return r(()=>_q(o,i,l));case"sparse":return r(()=>Dq(o,i,l));case"spectral":return r(()=>$q(o,i,l));case"string":return r(()=>Pq(o,i,l));case"transformation":return r(()=>Fq(o,i,l));case"hash_table":return Iq(o,i,l,s);case"custom":let u=Z8(o.op);if(u&&u.customExecutor)return u.customExecutor(new cq(o,i,l));throw TypeError(`Custom op ${o.op} is not registered.`);default:throw TypeError(`Unknown op '${o.op}'. File an issue at https://github.com/tensorflow/tfjs/issues so we can add it, or register a custom execution with tf.registerOp()`)}})(e,t,n);return v.isPromise(a)?a.then(o=>[].concat(o)):[].concat(a)}var C7=class{constructor(e={},t={},n={},s={}){this.weightMap=e,this.tensorArrayMap=t,this.tensorListMap=n,this.functionMap=s,this.rootContext={id:0,frameName:"",iterationId:0},this.contexts=[this.rootContext],this.lastId=0,this.generateCurrentContextIds()}newFrame(e,t){return{id:e,frameName:t,iterationId:0}}set currentContext(e){this.contexts!==e&&(this.contexts=e,this.generateCurrentContextIds())}get currentContext(){return this.contexts}get currentContextId(){return this._currentContextIds[0]}get currentContextIds(){return this._currentContextIds}generateCurrentContextIds(){let e=[];for(let t=0;t<this.contexts.length-1;t++){let n=this.contexts.slice(0,this.contexts.length-t);e.push(this.contextIdforContexts(n))}e.push(""),this._currentContextIds=e}contextIdforContexts(e){return e?e.map(t=>t.id===0&&t.iterationId===0?"":`${t.frameName}-${t.iterationId}`).join("/"):""}enterFrame(e){this.contexts&&(this.lastId++,this.contexts=this.contexts.slice(),this.contexts.push(this.newFrame(this.lastId,e)),this._currentContextIds.unshift(this.contextIdforContexts(this.contexts)))}exitFrame(){if(this.contexts&&this.contexts.length>1)this.contexts=this.contexts.slice(),this.contexts.splice(-1),this.currentContextIds.shift();else throw new Error("Cannot exit frame, the context is empty")}nextIteration(){if(this.contexts&&this.contexts.length>0){this.contexts=this.contexts.slice(),this.lastId++;let e=Object.assign({},this.contexts[this.contexts.length-1]);e.iterationId+=1,e.id=this.lastId,this.contexts.splice(-1,1,e),this._currentContextIds.splice(0,1,this.contextIdforContexts(this.contexts))}else throw new Error("Cannot increase frame iteration, the context is empty")}getWeight(e){return this.weightMap[e]}addTensorArray(e){this.tensorArrayMap[e.id]=e}getTensorArray(e){return this.tensorArrayMap[e]}addTensorList(e){this.tensorListMap[e.id]=e}getTensorList(e){return this.tensorListMap[e]}dispose(e){for(let t in this.tensorArrayMap)this.tensorArrayMap[t].clearAndClose(e);for(let t in this.tensorListMap)this.tensorListMap[t].clearAndClose(e)}};function T7(e,t,n,s){let r=new Set,a=[],o=null,i=null,l=new Set,u=Object.keys(e).map(d=>$s(d)[0]),c=[];s!=null&&(c=s.map(d=>$s(d.name)[0]));let p=[...t];for(;p.length>0;){let d=p.pop();if((AS(d)||Bq(d)||Wq(d))&&o==null&&(o=d,i=o.children.map(h=>h.name).filter(h=>r.has(h))),r.add(d.name),n[d.name]==null&&u.indexOf(d.name)===-1&&c.indexOf(d.name)===-1){if(d.inputs.length===0){a.push(d.name);continue}d.inputs.forEach(h=>{l.has(h.name)||(l.add(h.name),p.push(h))})}}return{inputs:e,outputs:t,usedNodes:r,missingInputs:a,dynamicNode:o,syncInputs:i}}function Oq(e,t,n){let{usedNodes:s,inputs:r}=n,a=[],o=Object.keys(r).map(c=>$s(c)[0]).map(c=>e.nodes[c]),i=e.initNodes;o.forEach(c=>{s.has(c.name)&&a.push(c)}),e.weights.forEach(c=>{s.has(c.name)&&a.push(c)}),i!=null&&i.forEach(c=>{s.has(c.name)&&a.push(c)});let l=new Set,u=[];for(;a.length>0;){let c=a.pop();l.add(c.name),t[c.name]||u.push(c),c.children.forEach(p=>{!l.has(p.name)&&s.has(p.name)&&p.inputs.every(d=>l.has(d.name))&&a.push(p)})}return u}var Mq=["Switch","Merge","Enter","Exit","NextIteration","StatelessIf","StatelessWhile","if","While"],zq=["NonMaxSuppressionV2","NonMaxSuppressionV3","NonMaxSuppressionV5","Where"],Lq=["HashTable","HashTableV2","LookupTableImport","LookupTableImportV2","LookupTableFind","LookupTableFindV2","LookupTableSize","LookupTableSizeV2"];function AS(e){return Mq.indexOf(e.op)>=0}function Bq(e){return zq.indexOf(e.op)>=0}function Wq(e){return Lq.indexOf(e.op)>=0}var gy=class{constructor(e,t){this.graph=e,this.parent=t,this.compiledMap=new Map,this._weightMap={},this.SEPERATOR=",",this._functions={},this._functionExecutorMap={},this.intermediateTensors={},this.keepTensorForDebug=!1,this._outputs=e.outputs,this._inputs=e.inputs,this._initNodes=e.initNodes,this._signature=e.signature,this._functions=e.functions,e.functions!=null&&Object.keys(e.functions).forEach(n=>{this._functionExecutorMap[n]=new gy(e.functions[n],this)})}get weightIds(){return this.parent?this.parent.weightIds:this._weightIds}get functionExecutorMap(){return this.parent?this.parent.functionExecutorMap:this._functionExecutorMap}get weightMap(){return this.parent?this.parent.weightMap:this._weightMap}set weightMap(e){let t=Object.keys(e).map(n=>e[n].map(s=>s.id));this._weightIds=[].concat(...t),this._weightMap=e}set resourceManager(e){this._resourceManager=e}get inputs(){return this._inputs.map(e=>({name:e.name,shape:e.attrParams.shape?e.attrParams.shape.value:void 0,dtype:e.attrParams.dtype?e.attrParams.dtype.value:void 0}))}get outputs(){return this._outputs.map(e=>({name:e.name,shape:e.attrParams.shape?e.attrParams.shape.value:void 0,dtype:e.attrParams.dtype?e.attrParams.dtype.value:void 0}))}get inputNodes(){return this._inputs.map(e=>e.signatureKey||e.name)}get outputNodes(){return this._outputs.map(e=>{let t=e.signatureKey||e.name;return e.defaultOutput?`${t}:${e.defaultOutput}`:t})}get functions(){return Object.keys(this._functions).reduce((e,t)=>(e[t]=this._functions[t].signature,e),{})}getCompilationKey(e,t){let n=e.map(r=>r.name).sort(),s=t.map(r=>r.name).sort();return n.join(this.SEPERATOR)+"--"+s.join(this.SEPERATOR)}compile(e,t){let n=T7(e,t,this.weightMap,this._initNodes),{missingInputs:s,dynamicNode:r,syncInputs:a}=n;if(r!=null)throw new Error(`This execution contains the node '${r.name}', which has the dynamic op '${r.op}'. Please use model.executeAsync() instead. Alternatively, to avoid the dynamic ops, specify the inputs [${a}]`);if(s.length>0){let o=t.map(l=>l.name),i=Object.keys(e);throw new Error(`Cannot compute the outputs [${o}] from the provided inputs [${i}]. Missing the following inputs: [${s}]`)}return Oq(this.graph,this.weightMap,n)}execute(e,t){e=this.mapInputs(e);let n=Object.keys(e).sort();this.checkInputs(e),this.checkInputShapeAndType(e),t=this.mapOutputs(t),this.checkOutputs(t);let s=n.map(c=>this.graph.nodes[$s(c)[0]]),r=t.map(c=>$s(c)[0]),a=r.map(c=>this.graph.nodes[c]);this.resetIntermediateTensors(),a.length===0&&(a=this._outputs);let o=this.getCompilationKey(s,a),i=this.compiledMap.get(o);i==null&&(i=this.compile(e,a),this.compiledMap.set(o,i));let l={},u={};return Y(()=>{let c=new C7(this.weightMap,l,u,this.functionExecutorMap),p=Object.assign({},this.weightMap);Object.keys(e).forEach(f=>{let[m,g]=$s(f),y=[];y[g]=e[f],p[m]=y});let d=this.getFrozenTensorIds(p),h={};for(let f=0;f<i.length;f++){let m=i[f];if(!p[m.name]){let g=I7(m,p,c,this._resourceManager);if(v.isPromise(g))throw new Error(`The execution of the op '${m.op}' returned a promise. Please use model.executeAsync() instead.`);p[m.name]=g,this.checkTensorForDisposal(m.name,m,p,c,d,r,h)}}return this.parent==null&&c.dispose(d),t.map(f=>ls(f,p,c))})}getFrozenTensorIds(e){let t=[].concat.apply([],Object.keys(e).map(n=>e[n]).map(n=>n.map(s=>s.id)));return new Set(t)}checkTensorForDisposal(e,t,n,s,r,a,o){t.category==="control"||a.indexOf(e)!==-1||(n[e].forEach(i=>{i!=null&&(o[i.id]=(o[i.id]||0)+t.children.length)}),t.inputs.forEach(i=>{if(i.category!=="control"){let l=Uj(i.name,n,s);l!=null&&l.forEach(u=>{if(u&&!u.kept&&!r.has(u.id)){let c=o[u.id];if(c===1){if(!this.keepTensorForDebug)u.dispose();else{let[p,d]=ta(t.name,s);this.intermediateTensors[p]?this.intermediateTensors[p][d]=u:(this.intermediateTensors[p]=[],this.intermediateTensors[p][d]=u)}delete o[u.id]}else c!=null&&o[u.id]--}})}}))}async executeAsync(e,t){return this._executeAsync(e,t)}disposeIntermediateTensors(){!this.intermediateTensors||(Object.keys(this.intermediateTensors).forEach(e=>this.intermediateTensors[e].forEach(t=>t.dispose())),this.disposeTensorsMap())}disposeTensorsMap(){!this.tensorsMap||Object.keys(this.tensorsMap).forEach(e=>{this.tensorsMap[e].forEach(n=>{n&&!n.kept&&!n.isDisposed&&!this.keepIds.has(n.id)&&n.dispose()})})}getIntermediateTensors(){return this.tensorsMap}resetIntermediateTensors(){for(let e in this.intermediateTensors)this.intermediateTensors[e].forEach(t=>t.dispose()),delete this.intermediateTensors[e]}async _executeAsync(e,t,n=!1,s={},r={}){n||(e=this.mapInputs(e),this.checkInputs(e),this.checkInputShapeAndType(e),t=this.mapOutputs(t),this.checkOutputs(t));try{this.keepTensorForDebug=H().getBool("KEEP_INTERMEDIATE_TENSORS")}catch(u){console.warn(u.message)}this.resetIntermediateTensors();let a=new C7(this.weightMap,s,r,this.functionExecutorMap);this.tensorsMap=await this.executeWithControlFlow(e,a,t,n);let o=t.map(u=>ls(u,this.tensorsMap,a)),i=o.map(u=>u.id),l=Object.keys(e).map(u=>e[u].id);return this.keepIds=new Set([...i,...l,...this.weightIds]),this.keepTensorForDebug||this.disposeTensorsMap(),this.parent==null&&a.dispose(this.keepIds),o}async executeFunctionAsync(e,t,n){let s=e.reduce((r,a,o)=>(r[this.inputs[o].name]=a,r),{});return this._executeAsync(s,this.outputNodes,!0,t,n)}async executeWithControlFlow(e,t,n,s){let r=Object.keys(e),a=r.map(x=>this.graph.nodes[$s(x)[0]]),o=n.map(x=>$s(x)[0]),i=o.map(x=>this.graph.nodes[x]);i.length===0&&(i=this._outputs);let{usedNodes:l,missingInputs:u,dynamicNode:c,syncInputs:p}=T7(e,i,this.weightMap,this._initNodes),d=[...a,...this.graph.weights,...this._initNodes||[]].map(x=>({node:x,contexts:t.currentContext})),h=Object.assign({},this.weightMap);Object.keys(e).forEach(x=>{let[A,b]=$s(x),w=[];w[b]=e[x],h[A]=w});let f={},m=this.getFrozenTensorIds(h),g={};for(;d.length>0;){let x=this.processStack(a,d,t,h,g,m,o,f,l);await Promise.all(x)}c==null&&!s&&console.warn("This model execution did not contain any nodes with control flow or dynamic output shapes. You can use model.execute() instead.");let y=i.filter(x=>!AS(x)&&!ls(x.name,h,t)).map(x=>x.name);if(y.length>0){let x="";throw c!=null&&(x=`Alternatively, to avoid the dynamic ops, use model.execute() and specify the inputs [${p}]`),new Error(`Cannot compute the outputs [${y}] from the provided inputs [${r}]. Consider providing the following inputs: [${u}]. ${x}`)}return h}processStack(e,t,n,s,r,a,o,i,l){let u=[];for(;t.length>0;){let c=t.pop();n.currentContext=c.contexts;let p="";if(c.node.op==="Enter"&&I("isConstant",c.node,s,n)&&([p]=ta(c.node.name,n)),s[c.node.name]==null){let d=I7(c.node,s,n,this._resourceManager);p||([p]=ta(c.node.name,n));let h=n.currentContext;v.isPromise(d)?u.push(d.then(f=>(s[p]=f,n.currentContext=h,this.checkTensorForDisposal(p,c.node,s,n,a,o,i),this.processChildNodes(c.node,t,n,s,r,l),f))):(s[p]=d,this.checkTensorForDisposal(p,c.node,s,n,a,o,i),this.processChildNodes(c.node,t,n,s,r,l))}else this.processChildNodes(c.node,t,n,s,r,l)}return u}processChildNodes(e,t,n,s,r,a){e.children.forEach(o=>{let[i]=ta(o.name,n);r[i]||!a.has(o.name)||(o.op==="Merge"?o.inputNames.some(l=>!!ls(l,s,n))&&(r[i]=!0,t.push({contexts:n.currentContext,node:o})):o.inputNames.every(l=>!!ls(l,s,n))&&(r[i]=!0,t.push({contexts:n.currentContext,node:o})))})}dispose(){Object.keys(this.weightMap).forEach(e=>this.weightMap[e].forEach(t=>t.dispose()))}checkInputShapeAndType(e){Object.keys(e).forEach(t=>{let n=e[t],[s]=$s(t),r=this.graph.nodes[s];if(r.attrParams.shape&&r.attrParams.shape.value){let a=r.attrParams.shape.value,o=a.length===n.shape.length&&n.shape.every((i,l)=>a[l]===-1||a[l]===i);v.assert(o,()=>`The shape of dict['${r.name}'] provided in model.execute(dict) must be [${a}], but was [${n.shape}]`)}r.attrParams.dtype&&r.attrParams.dtype.value&&v.assert(n.dtype===r.attrParams.dtype.value,()=>`The dtype of dict['${r.name}'] provided in model.execute(dict) must be ${r.attrParams.dtype.value}, but was ${n.dtype}`)})}mapInputs(e){let t={};for(let n in e)if(this._signature!=null&&this._signature.inputs!=null&&this._signature.inputs[n]!=null){let s=this._signature.inputs[n];t[s.name]=e[n]}else t[n]=e[n];return t}checkInputs(e){let t=Object.keys(e).filter(n=>{let[s]=$s(n);return this.graph.nodes[s]==null});if(t.length>0)throw new Error(`The dict provided in model.execute(dict) has keys: [${t}] that are not part of graph`)}mapOutputs(e){return e.map(t=>this._signature!=null&&this._signature.outputs!=null&&this._signature.outputs[t]!=null?this._signature.outputs[t].name:t,{})}checkOutputs(e){e.forEach(t=>{let[n]=$s(t);if(!this.graph.nodes[n])throw new Error(`The output '${t}' is not found in the graph`)})}},Vq=class{constructor(e={},t={}){this.hashTableNameToHandle=e,this.hashTableMap=t}addHashTable(e,t){this.hashTableNameToHandle[e]=t.handle,this.hashTableMap[t.id]=t}getHashTableHandleByName(e){return this.hashTableNameToHandle[e]}getHashTableById(e){return this.hashTableMap[e]}dispose(){for(let e in this.hashTableMap)this.hashTableMap[e].clearAndClose(),delete this.hashTableMap[e];for(let e in this.hashTableNameToHandle)this.hashTableNameToHandle[e].dispose(),delete this.hashTableNameToHandle[e]}},Uq="?tfjs-format=file",Gq="model.json",Vh=class{constructor(e,t={},n=Fs){this.modelUrl=e,this.loadOptions=t,this.version="n/a",this.io=n,t==null&&(this.loadOptions={}),this.resourceManager=new Vq}get modelVersion(){return this.version}get inputNodes(){return this.executor.inputNodes}get outputNodes(){return this.executor.outputNodes}get inputs(){return this.executor.inputs}get outputs(){return this.executor.outputs}get weights(){return this.executor.weightMap}get metadata(){return this.artifacts.userDefinedMetadata}get modelSignature(){return this.signature}get modelStructuredOutputKeys(){return this.structuredOutputKeys}findIOHandler(){let e=this.modelUrl;if(e.load!=null)this.handler=e;else if(this.loadOptions.requestInit!=null)this.handler=this.io.browserHTTPRequest(e,this.loadOptions);else{let t=this.io.getLoadHandlers(e,this.loadOptions);if(t.length===0)t.push(this.io.browserHTTPRequest(e,this.loadOptions));else if(t.length>1)throw new Error(`Found more than one (${t.length}) load handlers for URL '${[e]}'`);this.handler=t[0]}}load(){if(this.findIOHandler(),this.handler.load==null)throw new Error("Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.");let e=this.handler.load();return v.isPromise(e)?e.then(t=>this.loadSync(t)):this.loadSync(e)}loadSync(e){this.artifacts=e;let t=this.artifacts.modelTopology,n=this.artifacts.signature;if(this.artifacts.userDefinedMetadata!=null){let r=this.artifacts.userDefinedMetadata;r.signature!=null&&(n=r.signature),r.structuredOutputKeys!=null&&(this.structuredOutputKeys=r.structuredOutputKeys)}this.signature=n,this.version=`${t.versions.producer}.${t.versions.minConsumer}`;let s=this.io.decodeWeights(this.artifacts.weightData,this.artifacts.weightSpecs);if(this.executor=new gy(v7.Instance.transformGraph(t,this.signature)),this.executor.weightMap=this.convertTensorMapToTensorsMap(s),this.executor.resourceManager=this.resourceManager,e.modelInitializer!=null&&e.modelInitializer.node!=null){let r=v7.Instance.transformGraph(e.modelInitializer);this.initializer=new gy(r),this.initializer.weightMap=this.executor.weightMap,this.initializer.resourceManager=this.resourceManager,this.initializer.executeAsync({},[])}return!0}async save(e,t){if(typeof e=="string"){let n=this.io.getSaveHandlers(e);if(n.length===0)throw new Error(`Cannot find any save handlers for URL '${e}'`);if(n.length>1)throw new Error(`Found more than one (${n.length}) save handlers for URL '${e}'`);e=n[0]}if(e.save==null)throw new Error("GraphModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.");return e.save(this.artifacts)}predict(e,t){let n=this.execute(e,this.outputNodes);if(this.structuredOutputKeys){let s=n instanceof it?[n]:n,r={};return s.forEach((a,o)=>r[this.structuredOutputKeys[o]]=a),r}return n}normalizeInputs(e){if(!(e instanceof it)&&!Array.isArray(e))return e;if(e=Array.isArray(e)?e:[e],e.length!==this.inputNodes.length)throw new Error(`Input tensor count mismatch,the graph model has ${this.inputNodes.length} placeholders, while there are ${e.length} input tensors.`);return this.inputNodes.reduce((t,n,s)=>(t[n]=e[s],t),{})}normalizeOutputs(e){return e=e||this.outputNodes,Array.isArray(e)?e:[e]}execute(e,t){e=this.normalizeInputs(e),t=this.normalizeOutputs(t);let n=this.executor.execute(e,t);return n.length>1?n:n[0]}async executeAsync(e,t){e=this.normalizeInputs(e),t=this.normalizeOutputs(t);let n=await this.executor.executeAsync(e,t);return n.length>1?n:n[0]}getIntermediateTensors(){return 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extends En{constructor(e,t){super(),this.upstream=e,this.maxCount=t,this.count=0}summary(){return`${this.upstream.summary()} -> Take`}async next(){return this.count++>=this.maxCount?{value:null,done:!0}:this.upstream.next()}},uX=class extends En{constructor(e,t,n=!0){super(),this.upstream=e,this.batchSize=t,this.enableSmallLastBatch=n,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> RowMajorBatch`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){let e=[];for(;e.length<this.batchSize;){let t=await this.upstream.next();if(t.done)return this.enableSmallLastBatch&&e.length>0?{value:e,done:!1}:{value:null,done:!0};e.push(t.value)}return{value:e,done:!1}}},cX=class extends En{constructor(e,t){super(),this.upstream=e,this.predicate=t,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Filter`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;;){let e=await this.upstream.next();if(e.done||this.predicate(e.value))return e;Q(e.value)}}},dX=class extends En{constructor(e,t){super(),this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> Map`}async next(){let e=await this.upstream.next();if(e.done)return{value:null,done:!0};let t=zr.getTensorsInContainer(e.value),n=this.transform(e.value),s=zr.getTensorsInContainer(n);for(let r of t)zr.isTensorInList(r,s)||r.dispose();return{value:n,done:!1}}},pX=class extends En{constructor(e,t){super(),this.upstream=e,this.handler=t,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> handleErrors`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;;)try{return await this.upstream.next()}catch(e){if(!this.handler(e))return{value:null,done:!0}}}},N7=class extends En{constructor(e,t){super(),this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> AsyncMap`}async next(){let e=await this.upstream.next();if(e.done)return{value:null,done:!0};let t=zr.getTensorsInContainer(e.value),n=await this.transform(e.value),s=zr.getTensorsInContainer(n);for(let r of t)zr.isTensorInList(r,s)||r.dispose();return{value:n,done:!1}}},Vx=class extends En{constructor(){super(),this.outputQueue=new Bx,this.lastRead=Promise.resolve({value:null,done:!1})}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;this.outputQueue.length()===0;)if(!await this.pump())return{value:null,done:!0};return{value:this.outputQueue.shift(),done:!1}}},hX=class extends Vx{constructor(e,t){super(),this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> Flatmap`}async pump(){let e=await this.upstream.next();if(e.done)return!1;let t=zr.getTensorsInContainer(e.value),n=this.transform(e.value),s=zr.getTensorsInContainer(n);this.outputQueue.pushAll(n);for(let r of t)zr.isTensorInList(r,s)||r.dispose();return!0}},IS=class extends En{constructor(e,t){super(),this.baseErrorHandler=t,this.lastRead=null,this.iterator=null,this.moreIterators=e}summary(){return"TODO: fill in upstream of chained summaries -> Chained"}async next(){return this.lastRead=this.readFromChain(this.lastRead),this.lastRead}async readFromChain(e){if(await e,this.iterator==null){let n=await this.moreIterators.next();if(n.done)return{value:null,done:!0};this.iterator=n.value,this.baseErrorHandler!=null&&(this.iterator=this.iterator.handleErrors(this.baseErrorHandler))}let t=await this.iterator.next();return t.done?(this.iterator=null,this.readFromChain(e)):t}},eo;(function(e){e[e.FAIL=0]="FAIL",e[e.SHORTEST=1]="SHORTEST",e[e.LONGEST=2]="LONGEST"})(eo||(eo={}));var fX=class extends En{constructor(e,t=eo.FAIL){super(),this.iterators=e,this.mismatchMode=t,this.count=0,this.currentPromise=null}summary(){return"{TODO: fill in upstream of zip summaries} -> Zip"}async nextState(e){await e;let t=0,n=0;function s(a){return a instanceof En?{value:a.next().then(i=>(t++,i.done&&n++,i.value)),recurse:!1}:{value:null,recurse:!0}}let r=await wS(this.iterators,s);if(t===n)return{value:null,done:!0};if(n>0)switch(this.mismatchMode){case eo.FAIL:throw new Error(`Zipped streams should have the same length. Mismatched at element ${this.count}.`);case eo.SHORTEST:return{value:null,done:!0};case eo.LONGEST:default:}return this.count++,{value:r,done:!1}}async next(){return this.currentPromise=this.nextState(this.currentPromise),this.currentPromise}},CS=class extends En{constructor(e,t){super(),this.upstream=e,this.bufferSize=t,this.buffer=new kS(t)}summary(){return`${this.upstream.summary()} -> Prefetch`}refill(){for(;!this.buffer.isFull();){let e=this.upstream.next();this.buffer.push(e)}}next(){return this.refill(),this.buffer.shift()}},mX=class extends CS{constructor(e,t,n){super(e,t),this.upstream=e,this.windowSize=t,this.upstreamExhausted=!1,this.random=Kq.alea(n||v.now().toString()),this.lastRead=Promise.resolve({value:null,done:!1})}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}randomInt(e){return Math.floor(this.random()*e)}chooseIndex(){return this.randomInt(this.buffer.length())}async serialNext(){for(this.upstreamExhausted||this.refill();!this.buffer.isEmpty();){let e=this.chooseIndex(),t=await this.buffer.shuffleExcise(e);if(t.done)this.upstreamExhausted=!0;else return this.refill(),t}return{value:null,done:!0}}},cd=class{constructor(){this.size=null}batch(e,t=!0){let n=this;v.assert(e>0,()=>`batchSize needs to be positive, but it is
+      ${e}`);let s;return this.size===1/0||this.size==null?s=this.size:t?s=Math.ceil(this.size/e):s=Math.floor(this.size/e),Ds(async()=>(await n.iterator()).columnMajorBatch(e,t,AX),s)}concatenate(e){let t=this,n;return this.size===1/0||e.size===1/0?n=1/0:this.size!=null&&e.size!=null?n=this.size+e.size:n=null,Ds(async()=>(await t.iterator()).concatenate(await e.iterator()),n)}filter(e){let t=this,n;return this.size===1/0?n=1/0:n=null,Ds(async()=>(await t.iterator()).filter(s=>Y(()=>e(s))),n)}async forEachAsync(e){return(await this.iterator()).forEachAsync(e)}map(e){let t=this;return Ds(async()=>(await t.iterator()).map(n=>Y(()=>e(n))),this.size)}mapAsync(e){let t=this;return Ds(async()=>(await t.iterator()).mapAsync(e),this.size)}prefetch(e){if(e==null)throw new RangeError("`Dataset.prefetch()` requires bufferSize to be specified.");let t=this;return Ds(async()=>(await t.iterator()).prefetch(e),this.size)}repeat(e){let t=this,n;return this.size!=null&&e>0?n=this.size*e:e===0?n=0:this.size!=null&&(e===void 0||e<0)?n=1/0:n=null,Ds(async()=>{let s=Wx(async()=>({value:await t.iterator(),done:!1}));return nX(s.take(e))},n)}skip(e){let t=this,n;return this.size!=null&&e>=0&&this.size>=e?n=this.size-e:this.size!=null&&(this.size<e||e===void 0||e<0)?n=0:n=null,Ds(async()=>(await t.iterator()).skip(e),n)}shuffle(e,t,n=!0){if(e==null||e<0)throw this.size==null?new RangeError("`Dataset.shuffle()` requires bufferSize to be specified."):new RangeError(`\`Dataset.shuffle()\` requires bufferSize to be specified.  If your data fits in main memory (for regular JS objects), and/or GPU memory (for \`tf.Tensor\`s), consider setting bufferSize to the dataset size (${this.size} elements)`);let s=this,r=Xq.alea(t||v.now().toString());return Ds(async()=>{let a=r.int32();return n&&(a+=r.int32()),(await s.iterator()).shuffle(e,a.toString())},this.size)}take(e){let t=this,n;return this.size!=null&&this.size>e?n=e:this.size!=null&&this.size<=e?n=this.size:n=null,Ds(async()=>(await t.iterator()).take(e),n)}async toArray(){if(this.size===1/0)throw new Error("Can not convert infinite data stream to array.");return(await this.iterator()).toArray()}async toArrayForTest(){if(this.size===1/0)throw new Error("Can not convert infinite data stream to array.");return(await this.iterator()).toArrayForTest()}};cd.MAX_BUFFER_SIZE=1e4;function Ds(e,t=null){return new class extends cd{constructor(){super(...arguments),this.size=t}async iterator(){return e()}}}function gX(e){return Ds(async()=>SS(e),e.length)}function yX(e){if(!vc(e))throw new Error("The argument to zip() must be an object or array.");let t;if(Array.isArray(e))for(let n=0;n<e.length;n++)t=t==null?e[n].size:Math.min(t,e[n].size);else if(e instanceof Object)for(let n in e)t=t==null?e[n].size:Math.min(t,e[n].size);return Ds(async()=>{let n=await wS(e,s=>{if(s instanceof cd)return{value:s.iterator(),recurse:!1};if(vc(s))return{value:null,recurse:!0};throw new Error("Leaves of the structure passed to zip() must be Datasets, not primitives.")});return sX(n,eo.SHORTEST)},t)}function AX(e){if(e===null)return null;let t=e[0];return Jq(t)?{value:xX(e),recurse:!1}:{value:null,recurse:!0}}function xX(e){if(e.length===0)throw new Error("Can't make a batch of zero elements.");return e[0]instanceof it?un(e):Xe(e)}var TS=class extends cd{constructor(e){super(),this.input=e}async iterator(){return(await this.input.iterator()).decodeUTF8().split(`
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Should have ${this.fullColumnNames.length} elements in a row, but got ${n}`);return n}},ES=class extends En{constructor(e){super(),this.microphoneConfig=e,this.isClosed=!1,this.fftSize=e.fftSize||1024;let t=Math.log2(this.fftSize);if(this.fftSize<0||t<4||t>14||!Number.isInteger(t))throw new Error(`Invalid fftSize: it must be a power of 2 between 2 to 4 and 2 to 14, but got ${this.fftSize}`);if(this.numFrames=e.numFramesPerSpectrogram||43,this.sampleRateHz=e.sampleRateHz,this.columnTruncateLength=e.columnTruncateLength||this.fftSize,this.audioTrackConstraints=e.audioTrackConstraints,this.smoothingTimeConstant=e.smoothingTimeConstant||0,this.includeSpectrogram=e.includeSpectrogram!==!1,this.includeWaveform=e.includeWaveform===!0,!this.includeSpectrogram&&!this.includeWaveform)throw new Error("Both includeSpectrogram and includeWaveform are false. At least one type of data should be returned.")}summary(){return"microphone"}static async create(e={}){if(!H().get("IS_BROWSER"))throw new Error("microphone API is only supported in browser environment.");let t=new ES(e);return await t.start(),t}async start(){try{this.stream=await navigator.mediaDevices.getUserMedia({audio:this.audioTrackConstraints==null?!0:this.audioTrackConstraints,video:!1})}catch(n){throw new Error(`Error thrown while initializing video stream: ${n.message}`)}if(!this.stream)throw new Error("Could not obtain audio from microphone.");let e=window.AudioContext||window.webkitAudioContext;if(this.audioContext=new e,!this.sampleRateHz)this.sampleRateHz=this.audioContext.sampleRate;else if(this.audioContext.sampleRate!==this.sampleRateHz)throw new Error(`Mismatch in sampling rate: Expected: ${this.sampleRateHz}; Actual: ${this.audioContext.sampleRate}`);let t=this.audioContext.createMediaStreamSource(this.stream);this.analyser=this.audioContext.createAnalyser(),this.analyser.fftSize=this.fftSize*2,this.analyser.smoothingTimeConstant=this.smoothingTimeConstant,t.connect(this.analyser),this.freqData=new Float32Array(this.fftSize),this.timeData=new Float32Array(this.fftSize)}async next(){if(this.isClosed)return{value:null,done:!0};let e,t,n=await this.getAudioData();if(this.includeSpectrogram){let s=this.flattenQueue(n.freqDataQueue);e=this.getTensorFromAudioDataArray(s,[this.numFrames,this.columnTruncateLength,1])}if(this.includeWaveform){let s=this.flattenQueue(n.timeDataQueue);t=this.getTensorFromAudioDataArray(s,[this.numFrames*this.fftSize,1])}return{value:{spectrogram:e,waveform:t},done:!1}}async capture(){return(await this.next()).value}async getAudioData(){let e=[],t=[],n=0;return new Promise(s=>{let r=setInterval(()=>{this.includeSpectrogram&&(this.analyser.getFloatFrequencyData(this.freqData),this.freqData[0]===-1/0&&s({freqDataQueue:e,timeDataQueue:t}),e.push(this.freqData.slice(0,this.columnTruncateLength))),this.includeWaveform&&(this.analyser.getFloatTimeDomainData(this.timeData),t.push(this.timeData.slice())),++n===this.numFrames&&(clearInterval(r),s({freqDataQueue:e,timeDataQueue:t}))},this.fftSize/this.sampleRateHz*1e3)})}stop(){this.isClosed||(this.isClosed=!0,this.analyser.disconnect(),this.audioContext.close(),this.stream!=null&&this.stream.getTracks().length>0&&this.stream.getTracks()[0].stop())}toArray(){throw new Error("Can not convert infinite audio stream to array.")}getSampleRate(){return this.sampleRateHz}flattenQueue(e){let t=e[0].length,n=new Float32Array(e.length*t);return e.forEach((s,r)=>n.set(s,r*t)),n}getTensorFromAudioDataArray(e,t){let n=new Float32Array(v.sizeFromShape(t));return n.set(e,n.length-e.length),Xe(n,t)}},RS=class extends En{constructor(e,t){if(super(),this.webcamVideoElement=e,this.webcamConfig=t,this.isClosed=!0,this.resize=!1,this.needToResize())if(this.resize=!0,this.cropSize=[this.webcamConfig.resizeHeight,this.webcamConfig.resizeWidth],this.cropBoxInd=Ot([0],"int32"),this.webcamConfig.centerCrop){let n=this.webcamConfig.resizeWidth*1/this.webcamVideoElement.width,s=this.webcamConfig.resizeHeight*1/this.webcamVideoElement.height,r=(1-n)/2,a=(1-s)/2,o=r+n,i=s+a;this.cropBox=mr([a,r,i,o],[1,4])}else this.cropBox=mr([0,0,1,1],[1,4])}summary(){return"webcam"}static async create(e,t={}){if(!H().get("IS_BROWSER"))throw new Error("tf.data.webcam is only supported in browser environment.");if(!e){if(e=document.createElement("video"),!t.resizeWidth||!t.resizeHeight)throw new Error("Please provide webcam video element, or resizeWidth and resizeHeight to create a hidden video element.");e.width=t.resizeWidth,e.height=t.resizeHeight}let n=new RS(e,t);return await n.start(),n}async start(){this.webcamConfig.facingMode&&v.assert(this.webcamConfig.facingMode==="user"||this.webcamConfig.facingMode==="environment",()=>`Invalid webcam facing mode: ${this.webcamConfig.facingMode}. Please provide 'user' or 'environment'`);try{this.stream=await navigator.mediaDevices.getUserMedia({video:{deviceId:this.webcamConfig.deviceId,facingMode:this.webcamConfig.facingMode?this.webcamConfig.facingMode:"user",width:this.webcamVideoElement.width,height:this.webcamVideoElement.height}})}catch(e){throw e.message=`Error thrown while initializing video stream: ${e.message}`,e}if(!this.stream)throw new Error("Could not obtain video from webcam.");try{this.webcamVideoElement.srcObject=this.stream}catch(e){console.log(e),this.webcamVideoElement.src=window.URL.createObjectURL(this.stream)}return this.webcamVideoElement.play(),this.isClosed=!1,new Promise(e=>{this.webcamVideoElement.onloadedmetadata=()=>{e()}})}async next(){if(this.isClosed)return{value:null,done:!0};let e;try{e=la.fromPixels(this.webcamVideoElement)}catch(t){throw new Error(`Error thrown converting video to pixels: ${JSON.stringify(t)}`)}if(this.resize)try{return{value:this.cropAndResizeFrame(e),done:!1}}catch(t){throw new Error(`Error thrown cropping the video: ${t.message}`)}finally{e.dispose()}else return{value:e,done:!1}}needToResize(){return!!(this.webcamConfig.resizeWidth&&this.webcamConfig.resizeHeight&&(this.webcamVideoElement.width!==this.webcamConfig.resizeWidth||this.webcamVideoElement.height!==this.webcamConfig.resizeHeight))}cropAndResizeFrame(e){return Y(()=>{let t=Ft(ge(e,"float32"),0),n;n=Ce.cropAndResize(t,this.cropBox,this.cropBoxInd,this.cropSize,"bilinear");let s=n.shape;return V(n,s.slice(1))})}async capture(){return(await this.next()).value}stop(){this.stream.getTracks().forEach(t=>t.stop());try{this.webcamVideoElement.srcObject=null}catch(t){console.log(t),this.webcamVideoElement.src=null}this.isClosed=!0}toArray(){throw new Error("Can not convert infinite video stream to array.")}},_S=class{},DS=class extends En{split(e){return new bX(this,e)}},bX=class extends 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h=n.data.get(p.dataId).values,{outVals:f,outShape:m,outDtype:g}=tI(p.shape,p.dtype,h,c),y=m;return o&&(y=T.expandShapeToKeepDim(m,l)),d.forEach(x=>n.disposeIntermediateTensorInfo(x)),n.makeTensorInfo(y,g,f)}var oK={kernelName:Xo,backendName:"cpu",kernelFunc:aK},cr=T.RowPartitionType,fy=class{constructor(e,t,n,s,r,a,o,i,l,u){this.shape=e,this.shapeShape=t,this.values=n,this.valuesShape=s,this.valuesDType=r,this.defaultValue=a,this.defaultValueShape=o,this.rowPartitionValues=i,this.rowPartitionValuesShapes=l,this.rowPartitionTypes=T.getRowPartitionTypesHelper(u),this.raggedRank=T.getRaggedRank(this.rowPartitionTypes)}getRowPartitionTypeByDimension(e){return this.rowPartitionTypes[0]===cr.FIRST_DIM_SIZE?this.rowPartitionTypes[e+1]:this.rowPartitionTypes[e]}getRowPartitionTensor(e){return this.rowPartitionTypes[0]===cr.FIRST_DIM_SIZE?this.rowPartitionValues[e+1]:this.rowPartitionValues[e]}getMaxWidth(e){let t=this.getRowPartitionTensor(e-1);switch(this.getRowPartitionTypeByDimension(e-1)){case cr.VALUE_ROWIDS:return fy.getMaxWidthValueRowID(t);case cr.ROW_SPLITS:return fy.getMaxWidthRowSplit(t);default:throw new Error(`Cannot handle partition type ${cr[this.getRowPartitionTypeByDimension(e-1)]}`)}}static getMaxWidthRowSplit(e){let t=e.length;if(t===0||t===1)return 0;let n=0;for(let s=0;s<t-1;++s){let r=e[s+1]-e[s];r>n&&(n=r)}return n}static getMaxWidthValueRowID(e){let t=e.length;if(t===0)return 0;let n=0,s=e[0],r=0;for(let a=1;a<t;++a){let o=e[a];o!==s&&(s=o,r=Math.max(a-n,r),n=a)}return Math.max(t-n,r)}tensorShapeFromTensor(e,t,n=!0){if(t.length===0){if(e[0]===-1)return[];throw new Error("The only valid scalar shape tensor is the fully unknown shape specified as -1.")}return C7(e,n)}calculateOutputSize(e){let t=this.valuesShape,n=this.defaultValueShape;T.validateDefaultValueShape(n,t);let s=this.tensorShapeFromTensor(this.shape,this.shapeShape),a=T.combineRaggedTensorToTensorShapes(this.raggedRank,s,t);a[0]<0&&(a[0]=e);for(let o=1;o<=this.raggedRank;++o)a[o]<0&&(a[o]=this.getMaxWidth(o));return a}calculateFirstParentOutputIndex(e,t,n){let s=Math.min(e,n),r=[],a=0;for(let o=0;o<s;++o,a+=t)r.push(a);for(let o=s;o<e;++o)r.push(-1);return v.assert(r.length===e,()=>"Final length of result must be equal to firstDimension."),r}calculateOutputIndexRowSplit(e,t,n,s){let r=e.length,a=[];for(let o=0;o<r-1;++o){let i=e[o+1]-e[o],l=Math.min(s,i),u=t[o];u===-1&&(l=0);for(let c=0;c<l;++c)a.push(u),u+=n;for(let c=0;c<i-l;++c)a.push(-1)}if(r>0&&a.length!==e[r-1])throw new Error("Invalid row split size.");return a}calculateOutputIndexValueRowID(e,t,n,s){let r=e.length,a=[];if(r===0)return[];let o=0,i=e[0];if(i>=t.length)throw new Error(`Got currentValueRowId=${i}, which is not less than ${t.length}`);let l=t[i];a.push(l);for(let u=1;u<r;++u){let c=e[u];if(c===i)l>=0&&(++o,o<s?l+=n:l=-1);else{if(o=0,i=c,c>=t.length)throw new Error(`Got nextValueRowId=${c} which is not less than ${t.length}`);l=t[c]}a.push(l)}if(a.length!==e.length)throw new Error("Invalid row ids.");return a}calculateOutputIndex(e,t,n,s){let r=this.getRowPartitionTensor(e),a=this.getRowPartitionTypeByDimension(e);switch(a){case cr.VALUE_ROWIDS:return this.calculateOutputIndexValueRowID(r,t,n,s);case cr.ROW_SPLITS:if(r.length-1>t.length)throw new Error(`Row partition size is greater than output size: ${r.length-1} > ${t.length}`);return this.calculateOutputIndexRowSplit(r,t,n,s);default:throw new Error(`Unsupported partition type: ${cr[a]}`)}}getFirstDimensionSize(){let e=this.rowPartitionValues[0];if(this.rowPartitionTypes.length===0)throw new Error("No row_partition_types given.");let t=this.rowPartitionTypes[0];switch(t){case cr.FIRST_DIM_SIZE:return e[0];case cr.VALUE_ROWIDS:throw new Error("Cannot handle VALUE_ROWIDS in first dimension.");case cr.ROW_SPLITS:return this.rowPartitionValuesShapes[0][0]-1;default:throw new Error(`Cannot handle type ${cr[t]}`)}}compute(){if(this.rowPartitionValues[0].length<=0)throw new Error("Invalid first partition input. 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l=="number"?h.values.fill(l):typeof l=="boolean"&&h.values.fill(+l);for(let f=0;f<a;f++){let m=[],g=0;for(let y=0;y<o;y++){let x=p[f*o+y];m.push(x),g+=x*i[y]}if(g<0||g>=s/r)throw new Error(`Invalid indices: ${m} does not index into ${n}`);for(let y=0;y<r;y++)u?h.values[g*r+y]+=d[f*r+y]:h.values[g*r+y]=t.rank===0?d[0]:d[f*r+y]}return h}var uK=fi(e=>1/(1+Math.exp(-e))),rI=bt(ti,e=>1/(1+Math.exp(-e))),cK={kernelName:ti,backendName:"cpu",kernelFunc:rI};function Um(e,t,n,s,r){let a=Ht.isSliceContinous(s,t,n),o=v.sizeFromShape(n),i=v.computeStrides(s);if(a){let p=Ht.computeFlatOffset(t,i);return r==="string"?e.slice(p,p+o):e.subarray(p,p+o)}let l=r==="string"?T.fromUint8ToStringArray(e):e,u=Ve(s,r,l),c=Ve(n,r);for(let p=0;p<c.size;++p){let d=c.indexToLoc(p),h=d.map((f,m)=>f+t[m]);c.set(u.get(...h),...d)}return r==="string"?T.fromStringArrayToUint8(c.values):c.values}function cl(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{begin:a,size:o}=s;Ne(r,"slice");let[i,l]=Ht.parseSliceParams(r,a,o);Ht.assertParamsValid(r,i,l);let u=n.data.get(r.dataId).values,c=Um(u,i,l,r.shape,r.dtype);return n.makeTensorInfo(l,r.dtype,c)}var dK={kernelName:ql,backendName:"cpu",kernelFunc:cl};function aI(e,t,n,s,r,a,o){let i=t[0],l=a[0],u=new Array(l),c=new Array(i),p=t[1];if(l===0){if(i!==0)throw new Error(T.getSparseFillEmptyRowsIndicesDenseShapeMismatch(i));let g=v.getArrayFromDType(n,0),y=v.getArrayFromDType(r,0);return[g,[0,p],y,u,c]}let d=!0,h=0,f=new Array(l).fill(0);for(let g=0;g<i;++g){let y=e[g*p];if(y<0)throw new Error(T.getSparseFillEmptyRowsNegativeIndexErrorMessage(g,y));if(y>=l)throw new Error(T.getSparseFillEmptyRowsOutOfRangeIndexErrorMessage(g,y,l));++f[y],d=d&&y>=h,h=y}let m=!0;for(let g=0;g<l;++g){let y=f[g]===0;u[g]=y,m=m&&!y,f[g]=Math.max(f[g],1),g>0&&(f[g]+=f[g-1])}if(m&&d){let g=e,y=s;for(let x=0;x<i;++x)c[x]=x;return[g,[i,p],y,u,c]}else{let g=f[l-1],y=v.getArrayFromDType(n,g*p),x=v.getArrayFromDType(r,g),A=new Array(l).fill(0);for(let b=0;b<i;++b){let w=e[b*p],k=A[w],C=(w===0?0:f[w-1])+k;A[w]++;for(let E=0;E<p;++E)y[C*p+E]=e[b*p+E];x[C]=s[b],c[b]=C}for(let b=0;b<l;++b)if(A[b]===0){let k=b===0?0:f[b-1];y[k*p+0]=b;for(let C=1;C<p;++C)y[k*p+C]=0;x[k]=o}return[y,[g,p],x,u,c]}}function oI(e,t,n,s,r){let a=v.sizeFromShape(s),o=t[0],i=r.length,l=[],u=1,c=-1;for(let g=0;g<i;++g){let y=r[g];if(y===-1){if(c!==-1)throw new Error(T.getSparseReshapeMultipleNegativeOneOutputDimErrorMessage(c,g));c=g,l.push(1)}else{if(y<0)throw new Error(T.getSparseReshapeNegativeOutputDimErrorMessage(g,y));u*=y,l.push(y)}}if(c!==-1){if(u<=0)throw new Error(T.getSparseReshapeEmptyTensorZeroOutputDimErrorMessage());let g=Math.trunc(a/u);if(u*g!==a)throw new Error(T.getSparseReshapeInputOutputMultipleErrorMessage(s,l));l[c]=g}if(v.sizeFromShape(l)!==a)throw new Error(T.getSparseReshapeInputOutputMismatchErrorMessage(s,l));let d=s.length,h=[];if(d>0){h[d-1]=1;for(let g=d-2;g>=0;--g)h[g]=h[g+1]*s[g+1]}let f=[];if(i>0){f[i-1]=1;for(let g=i-2;g>=0;--g)f[g]=f[g+1]*l[g+1]}let m=v.getArrayFromDType(n,o*i);for(let g=0;g<o;++g){let y=0;for(let x=0;x<d;++x)y+=e[g*d+x]*h[x];for(let x=0;x<i;++x)m[g*i+x]=Math.trunc(y/f[x]),y%=f[x]}return[m,[o,i],l]}function Xx(e,t,n,s,r,a=!1,o=0){let i=s.length,l=[t[0],e.length/t[0]],u=l[1],p=i>0?r[i-1]+1:0;if(p<0)throw new Error(T.getSparseSegmentReductionNegativeSegmentIdsErrorMessage());let d=t.slice();d[0]=p;let h=d.reduce((A,b)=>A*b,1),f=v.getArrayFromDType(n,h);if(i===0)return p>0&&f.fill(o),[f,d];if(p<=0)throw new Error(T.getSparseSegmentReductionNegativeSegmentIdsErrorMessage());let m=0,g=1,y=0,x=r[m];for(;;){let A=0;if(g<i){if(A=r[g],x===A){++g;continue}if(x>=A)throw new Error(T.getSparseSegmentReductionNonIncreasingSegmentIdsErrorMessage())}if(x<0||x>=p)throw new Error(T.getSparseSegmentReductionSegmentIdOutOfRangeErrorMessage(x,p));x>y&&f.fill(o,y*u,x*u);for(let b=m;b<g;++b){let w=s[b];if(w<0||w>=l[0])throw new Error(T.getSparseSegmentReductionIndicesOutOfRangeErrorMessage(b,s[b],l[0]));for(let k=0;k<u;k++)f[x*u+k]+=e[w*u+k]}if(a)for(let b=0;b<u;b++)f[x*u+b]/=g-m;if(m=g,++g,y=x+1,x=A,g>i)break}return y<p&&f.fill(o,y*u,p*u),[f,d]}var pK=fi(e=>Math.sqrt(e)),hK=bt(ni,e=>Math.sqrt(e)),fK={kernelName:ni,backendName:"cpu",kernelFunc:hK},iI=pn((e,t)=>{let n=e-t;return n*n}),mK=Nn(ai,iI),gK={kernelName:ai,backendName:"cpu",kernelFunc:mK};function lI(e,t,n,s){let r=Ve(e,t.dtype);for(let a=0;a<r.size;a++){let o=r.indexToLoc(a),i=new Array(o.length);for(let l=0;l<i.length;l++)i[l]=o[l]*n[l]+s[l];r.set(t.get(...i),...o)}return r}var yK=class{constructor(e,t,n,s,r,a){this.separator=v.encodeString(e),this.nGramWidths=t,this.leftPad=v.encodeString(n),this.rightPad=v.encodeString(s),this.padWidth=r,this.preserveShort=a}getPadWidth(e){return Math.min(this.padWidth<0?e-1:this.padWidth,e-1)}getNumNGrams(e,t){let n=this.getPadWidth(t);return Math.max(0,e+2*n-t+1)}createNGrams(e,t,n,s,r,a){for(let o=0;o<r;++o){let i=this.getPadWidth(a),l=Math.max(0,i-o),u=Math.max(0,i-(r-(o+1))),c=a-(l+u),p=t+(l>0?0:o-i),d=0;d+=l*this.leftPad.length;for(let y=0;y<c;++y)d+=e[p+y].length;d+=u*this.rightPad.length,d+=(l+u+c-1)*this.separator.length,n[s+o]=new Uint8Array(d);let f=n[s+o],m=0,g=y=>y.forEach(x=>f[m++]=x);for(let y=0;y<l;++y)g(this.leftPad),g(this.separator);for(let y=0;y<c-1;++y)g(e[p+y]),g(this.separator);if(c>0){g(e[p+c-1]);for(let y=0;y<u;++y)g(this.separator),g(this.rightPad)}else{for(let y=0;y<u-1;++y)g(this.rightPad),g(this.separator);g(this.rightPad)}}}compute(e,t){let n=e.length,s=t.length;if(s>0){let i=t[0];if(i!==0)throw new Error(`First split value must be 0, got ${i}`);for(let l=1;l<s;++l){let u=t[l]>=i;if(u=u&&t[l]<=n,!u)throw new Error(`Invalid split value ${t[l]}, must be in [${i}, ${n}]`);i=t[l]}if(i!==n)throw new Error(`Last split value must be data size. 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JZ={kernelName:c0,backendName:"cpu",kernelFunc:YZ};function QZ(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,filter:a}=t,{strides:o,dilations:i,pad:l,dimRoundingMode:u,inputShape:c}=s;Ne([r,a],"depthwiseConv2DNativeBackpropInput");let p=v.computeStrides(r.shape),d=v.computeStrides(a.shape),h=T.computeConv2DInfo(c,a.shape,o,i,l,u,!0),f=new yn(h.inShape,"float32"),m=f.values,[g,y,x]=f.strides,A=n.data.get(r.dataId).values,[b,w,k]=p,C=n.data.get(a.dataId).values,[E,_,$]=d,{batchSize:R,filterHeight:P,filterWidth:S,inChannels:M,inHeight:L,inWidth:U,outChannels:K,outHeight:q,outWidth:Z,strideHeight:J,strideWidth:Q}=h,le=P-1-h.padInfo.top,ae=S-1-h.padInfo.left,pe=K/M;for(let ce=0;ce<R;++ce)for(let xe=0;xe<M;++xe)for(let ie=0;ie<L;++ie){let _e=ie-le,De=Math.max(0,Math.ceil(_e/J)),Ue=Math.min(q,(P+_e)/J);for(let ze=0;ze<U;++ze){let lt=ze-ae,yt=Math.max(0,Math.ceil(lt/Q)),ht=Math.min(Z,(S+lt)/Q),At=0;for(let Me=De;Me<Ue;++Me){let Tt=Me*J-_e;for(let St=yt;St<ht;++St){let 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ZS=pn((e,t)=>e>t?1:0),ZX=Rn(Dl,ZS,null,"bool"),YX={kernelName:Dl,backendName:"cpu",kernelFunc:ZX},YS=pn((e,t)=>e>=t?1:0),JX=Rn(Mo,YS,null,"bool"),QX={kernelName:Mo,backendName:"cpu",kernelFunc:JX},JS=pn((e,t)=>e<t?1:0),eK=Rn(Pl,JS,null,"bool"),tK={kernelName:Pl,backendName:"cpu",kernelFunc:eK},QS=pn((e,t)=>e<=t?1:0),nK=Rn(Fl,QS,null,"bool"),sK={kernelName:Fl,backendName:"cpu",kernelFunc:nK};function eI(e,t,n){let s=(t-e)/(n-1),r=v.makeZerosTypedArray(n,"float32");r[0]=e;for(let a=1;a<r.length;a++)r[a]=r[a-1]+s;return r}var tI=Ai(e=>Math.log(e)),rK=dd(Bo,tI),aK={kernelName:Bo,backendName:"cpu",kernelFunc:rK};function nI(e,t,n,s){let r=v.getTypedArrayFromDType(s,v.sizeFromShape(n));for(let a=0;a<r.length;++a){let o=a*t,i=e[o];for(let l=0;l<t;++l){let u=e[o+l];(Number.isNaN(u)||u>i)&&(i=u)}r[a]=i}return r}var sI=pn((e,t)=>Math.max(e,t)),oK=Rn(Vo,sI),iK={kernelName:Vo,backendName:"cpu",kernelFunc:oK},rI=pn((e,t)=>Math.min(e,t)),lK=Rn(jo,rI),uK={kernelName:jo,backendName:"cpu",kernelFunc:lK},Xx=pn((e,t)=>e*t),cK=Hx((e,t,n,s)=>({real:e*n-t*s,imag:e*s+t*n})),_2=Rn(Xo,Xx,cK),dK={kernelName:Xo,backendName:"cpu",kernelFunc:_2};function aI(e,t,n){let s=v.createScalarValue(-1,n);return Xx([],t,s,e,n)}function pK(e){let{inputs:t,backend:n}=e,{x:s}=t;Ne(s,"neg");let r=n.data.get(s.dataId).values,[a,o]=aI(r,s.shape,s.dtype);return n.makeTensorInfo(o,s.dtype,a)}var hK={kernelName:zl,backendName:"cpu",kernelFunc:pK},oI=pn((e,t)=>e!==t?1:0),fK=Rn(Ll,oI,null,"bool"),mK={kernelName:Ll,backendName:"cpu",kernelFunc:fK};function Kx(e,t,n,s,r){let a=t.length,o=v.sizeFromShape(t),i=v.computeStrides(t),l=v.computeStrides(r),u=v.getTypedArrayFromDType(n,v.sizeFromShape(r));for(let c=0;c<o;++c){let p=v.indexToLoc(c,a,i),d=new Array(p.length);for(let f=0;f<d.length;f++)d[f]=p[s[f]];let h=v.locToIndex(d,a,l);u[h]=e[c]}return u}function Ss(e){let{inputs:t,attrs:n,backend:s}=e,{x:r}=t,{perm:a}=n;Ne(r,"transpose");let o=r.shape.length,i=new Array(o);for(let p=0;p<i.length;p++)i[p]=r.shape[a[p]];let l=s.data.get(r.dataId).values,u=Kx(l,r.shape,r.dtype,a,i);return{dataId:s.write(u,i,r.dtype),shape:i,dtype:r.dtype}}var gK={kernelName:na,backendName:"cpu",kernelFunc:Ss};function iI(e,t,n,s){let[r,a]=T.computeOutAndReduceShapes(e,s),o=Hn(t,"int32"),i=v.makeZerosTypedArray(v.sizeFromShape(r),o),l=v.sizeFromShape(a);for(let u=0;u<i.length;++u){let c=u*l,p=1;for(let d=0;d<l;++d)p*=n[c+d];i[u]=p}return{outVals:i,outShape:r,outDtype:o}}function yK(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:o}=s;Ne(r,"prod");let i=r.shape.length,l=v.parseAxisParam(a,r.shape),u=T.getAxesPermutation(l,i),c=l,p=r,d=[];u!=null&&(p=Ss({inputs:{x:r},backend:n,attrs:{perm:u}}),d.push(p),c=T.getInnerMostAxes(c.length,i));let h=n.data.get(p.dataId).values,{outVals:f,outShape:m,outDtype:g}=iI(p.shape,p.dtype,h,c),y=m;return o&&(y=T.expandShapeToKeepDim(m,l)),d.forEach(x=>n.disposeIntermediateTensorInfo(x)),n.makeTensorInfo(y,g,f)}var AK={kernelName:Jo,backendName:"cpu",kernelFunc:yK},dr=T.RowPartitionType,yy=class{constructor(e,t,n,s,r,a,o,i,l,u){this.shape=e,this.shapeShape=t,this.values=n,this.valuesShape=s,this.valuesDType=r,this.defaultValue=a,this.defaultValueShape=o,this.rowPartitionValues=i,this.rowPartitionValuesShapes=l,this.rowPartitionTypes=T.getRowPartitionTypesHelper(u),this.raggedRank=T.getRaggedRank(this.rowPartitionTypes)}getRowPartitionTypeByDimension(e){return this.rowPartitionTypes[0]===dr.FIRST_DIM_SIZE?this.rowPartitionTypes[e+1]:this.rowPartitionTypes[e]}getRowPartitionTensor(e){return this.rowPartitionTypes[0]===dr.FIRST_DIM_SIZE?this.rowPartitionValues[e+1]:this.rowPartitionValues[e]}getMaxWidth(e){let t=this.getRowPartitionTensor(e-1);switch(this.getRowPartitionTypeByDimension(e-1)){case dr.VALUE_ROWIDS:return yy.getMaxWidthValueRowID(t);case dr.ROW_SPLITS:return yy.getMaxWidthRowSplit(t);default:throw new Error(`Cannot handle partition type ${dr[this.getRowPartitionTypeByDimension(e-1)]}`)}}static getMaxWidthRowSplit(e){let t=e.length;if(t===0||t===1)return 0;let n=0;for(let s=0;s<t-1;++s){let r=e[s+1]-e[s];r>n&&(n=r)}return n}static getMaxWidthValueRowID(e){let t=e.length;if(t===0)return 0;let n=0,s=e[0],r=0;for(let a=1;a<t;++a){let o=e[a];o!==s&&(s=o,r=Math.max(a-n,r),n=a)}return Math.max(t-n,r)}tensorShapeFromTensor(e,t,n=!0){if(t.length===0){if(e[0]===-1)return[];throw new Error("The only valid scalar shape tensor is the fully unknown shape specified as -1.")}return D7(e,n)}calculateOutputSize(e){let t=this.valuesShape,n=this.defaultValueShape;T.validateDefaultValueShape(n,t);let s=this.tensorShapeFromTensor(this.shape,this.shapeShape),a=T.combineRaggedTensorToTensorShapes(this.raggedRank,s,t);a[0]<0&&(a[0]=e);for(let o=1;o<=this.raggedRank;++o)a[o]<0&&(a[o]=this.getMaxWidth(o));return a}calculateFirstParentOutputIndex(e,t,n){let s=Math.min(e,n),r=[],a=0;for(let o=0;o<s;++o,a+=t)r.push(a);for(let o=s;o<e;++o)r.push(-1);return v.assert(r.length===e,()=>"Final length of result must be equal to firstDimension."),r}calculateOutputIndexRowSplit(e,t,n,s){let r=e.length,a=[];for(let o=0;o<r-1;++o){let i=e[o+1]-e[o],l=Math.min(s,i),u=t[o];u===-1&&(l=0);for(let c=0;c<l;++c)a.push(u),u+=n;for(let c=0;c<i-l;++c)a.push(-1)}if(r>0&&a.length!==e[r-1])throw new Error("Invalid row split size.");return a}calculateOutputIndexValueRowID(e,t,n,s){let r=e.length,a=[];if(r===0)return[];let o=0,i=e[0];if(i>=t.length)throw new Error(`Got currentValueRowId=${i}, which is not less than ${t.length}`);let l=t[i];a.push(l);for(let u=1;u<r;++u){let c=e[u];if(c===i)l>=0&&(++o,o<s?l+=n:l=-1);else{if(o=0,i=c,c>=t.length)throw new Error(`Got nextValueRowId=${c} which is not less than ${t.length}`);l=t[c]}a.push(l)}if(a.length!==e.length)throw new Error("Invalid row ids.");return a}calculateOutputIndex(e,t,n,s){let r=this.getRowPartitionTensor(e),a=this.getRowPartitionTypeByDimension(e);switch(a){case dr.VALUE_ROWIDS:return this.calculateOutputIndexValueRowID(r,t,n,s);case dr.ROW_SPLITS:if(r.length-1>t.length)throw new Error(`Row partition size is greater than output size: ${r.length-1} > ${t.length}`);return this.calculateOutputIndexRowSplit(r,t,n,s);default:throw new Error(`Unsupported partition type: ${dr[a]}`)}}getFirstDimensionSize(){let e=this.rowPartitionValues[0];if(this.rowPartitionTypes.length===0)throw new Error("No row_partition_types given.");let t=this.rowPartitionTypes[0];switch(t){case dr.FIRST_DIM_SIZE:return e[0];case dr.VALUE_ROWIDS:throw new Error("Cannot handle VALUE_ROWIDS in first dimension.");case dr.ROW_SPLITS:return this.rowPartitionValuesShapes[0][0]-1;default:throw new Error(`Cannot handle type ${dr[t]}`)}}compute(){if(this.rowPartitionValues[0].length<=0)throw new Error("Invalid first partition input. 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l=="number"?h.values.fill(l):typeof l=="boolean"&&h.values.fill(+l);for(let f=0;f<a;f++){let m=[],g=0;for(let y=0;y<o;y++){let x=p[f*o+y];m.push(x),g+=x*i[y]}if(g<0||g>=s/r)throw new Error(`Invalid indices: ${m} does not index into ${n}`);for(let y=0;y<r;y++)u?h.values[g*r+y]+=d[f*r+y]:h.values[g*r+y]=t.rank===0?d[0]:d[f*r+y]}return h}var vK=Ai(e=>1/(1+Math.exp(-e))),cI=vt(ai,e=>1/(1+Math.exp(-e))),wK={kernelName:ai,backendName:"cpu",kernelFunc:cI};function Um(e,t,n,s,r){let a=jt.isSliceContinous(s,t,n),o=v.sizeFromShape(n),i=v.computeStrides(s);if(a){let p=jt.computeFlatOffset(t,i);return r==="string"?e.slice(p,p+o):e.subarray(p,p+o)}let l=r==="string"?T.fromUint8ToStringArray(e):e,u=Ue(s,r,l),c=Ue(n,r);for(let p=0;p<c.size;++p){let d=c.indexToLoc(p),h=d.map((f,m)=>f+t[m]);c.set(u.get(...h),...d)}return r==="string"?T.fromStringArrayToUint8(c.values):c.values}function fl(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{begin:a,size:o}=s;Ne(r,"slice");let[i,l]=jt.parseSliceParams(r,a,o);jt.assertParamsValid(r,i,l);let u=n.data.get(r.dataId).values,c=Um(u,i,l,r.shape,r.dtype);return n.makeTensorInfo(l,r.dtype,c)}var kK={kernelName:Yl,backendName:"cpu",kernelFunc:fl};function dI(e,t,n,s,r,a,o){let i=t[0],l=a[0],u=new Array(l),c=new Array(i),p=t[1];if(l===0){if(i!==0)throw new Error(T.getSparseFillEmptyRowsIndicesDenseShapeMismatch(i));let g=v.getArrayFromDType(n,0),y=v.getArrayFromDType(r,0);return[g,[0,p],y,u,c]}let d=!0,h=0,f=new Array(l).fill(0);for(let g=0;g<i;++g){let y=e[g*p];if(y<0)throw new Error(T.getSparseFillEmptyRowsNegativeIndexErrorMessage(g,y));if(y>=l)throw new Error(T.getSparseFillEmptyRowsOutOfRangeIndexErrorMessage(g,y,l));++f[y],d=d&&y>=h,h=y}let m=!0;for(let g=0;g<l;++g){let y=f[g]===0;u[g]=y,m=m&&!y,f[g]=Math.max(f[g],1),g>0&&(f[g]+=f[g-1])}if(m&&d){let g=e,y=s;for(let x=0;x<i;++x)c[x]=x;return[g,[i,p],y,u,c]}else{let g=f[l-1],y=v.getArrayFromDType(n,g*p),x=v.getArrayFromDType(r,g),A=new Array(l).fill(0);for(let b=0;b<i;++b){let w=e[b*p],k=A[w],C=(w===0?0:f[w-1])+k;A[w]++;for(let E=0;E<p;++E)y[C*p+E]=e[b*p+E];x[C]=s[b],c[b]=C}for(let b=0;b<l;++b)if(A[b]===0){let k=b===0?0:f[b-1];y[k*p+0]=b;for(let C=1;C<p;++C)y[k*p+C]=0;x[k]=o}return[y,[g,p],x,u,c]}}function pI(e,t,n,s,r){let a=v.sizeFromShape(s),o=t[0],i=r.length,l=[],u=1,c=-1;for(let g=0;g<i;++g){let y=r[g];if(y===-1){if(c!==-1)throw new Error(T.getSparseReshapeMultipleNegativeOneOutputDimErrorMessage(c,g));c=g,l.push(1)}else{if(y<0)throw new Error(T.getSparseReshapeNegativeOutputDimErrorMessage(g,y));u*=y,l.push(y)}}if(c!==-1){if(u<=0)throw new Error(T.getSparseReshapeEmptyTensorZeroOutputDimErrorMessage());let g=Math.trunc(a/u);if(u*g!==a)throw new Error(T.getSparseReshapeInputOutputMultipleErrorMessage(s,l));l[c]=g}if(v.sizeFromShape(l)!==a)throw new Error(T.getSparseReshapeInputOutputMismatchErrorMessage(s,l));let d=s.length,h=[];if(d>0){h[d-1]=1;for(let g=d-2;g>=0;--g)h[g]=h[g+1]*s[g+1]}let f=[];if(i>0){f[i-1]=1;for(let g=i-2;g>=0;--g)f[g]=f[g+1]*l[g+1]}let m=v.getArrayFromDType(n,o*i);for(let g=0;g<o;++g){let y=0;for(let x=0;x<d;++x)y+=e[g*d+x]*h[x];for(let x=0;x<i;++x)m[g*i+x]=Math.trunc(y/f[x]),y%=f[x]}return[m,[o,i],l]}function Yx(e,t,n,s,r,a=!1,o=0){let i=s.length,l=[t[0],e.length/t[0]],u=l[1],p=i>0?r[i-1]+1:0;if(p<0)throw new Error(T.getSparseSegmentReductionNegativeSegmentIdsErrorMessage());let d=t.slice();d[0]=p;let h=d.reduce((A,b)=>A*b,1),f=v.getArrayFromDType(n,h);if(i===0)return p>0&&f.fill(o),[f,d];if(p<=0)throw new Error(T.getSparseSegmentReductionNegativeSegmentIdsErrorMessage());let m=0,g=1,y=0,x=r[m];for(;;){let A=0;if(g<i){if(A=r[g],x===A){++g;continue}if(x>=A)throw new Error(T.getSparseSegmentReductionNonIncreasingSegmentIdsErrorMessage())}if(x<0||x>=p)throw new Error(T.getSparseSegmentReductionSegmentIdOutOfRangeErrorMessage(x,p));x>y&&f.fill(o,y*u,x*u);for(let 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cY={kernelName:c0,backendName:"cpu",kernelFunc:uY};function dY(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,filter:a}=t,{strides:o,dilations:i,pad:l,dimRoundingMode:u,inputShape:c}=s;Ne([r,a],"depthwiseConv2DNativeBackpropInput");let p=v.computeStrides(r.shape),d=v.computeStrides(a.shape),h=T.computeConv2DInfo(c,a.shape,o,i,l,u,!0),f=new An(h.inShape,"float32"),m=f.values,[g,y,x]=f.strides,A=n.data.get(r.dataId).values,[b,w,k]=p,C=n.data.get(a.dataId).values,[E,_,$]=d,{batchSize:R,filterHeight:P,filterWidth:S,inChannels:M,inHeight:L,inWidth:U,outChannels:K,outHeight:q,outWidth:Z,strideHeight:J,strideWidth:te}=h,le=P-1-h.padInfo.top,ae=S-1-h.padInfo.left,pe=K/M;for(let ce=0;ce<R;++ce)for(let xe=0;xe<M;++xe)for(let ie=0;ie<L;++ie){let _e=ie-le,De=Math.max(0,Math.ceil(_e/J)),Ge=Math.min(q,(P+_e)/J);for(let ze=0;ze<U;++ze){let ut=ze-ae,At=Math.max(0,Math.ceil(ut/te)),ft=Math.min(Z,(S+ut)/te),xt=0;for(let Me=De;Me<Ge;++Me){let Tt=Me*J-_e;for(let It=At;It<ft;++It){let 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       bool isnan_custom(float val) {
         uint floatToUint = floatBitsToUint(val);
         return (floatToUint & 0x7fffffffu) > 0x7f800000u;
@@ -107,15 +107,15 @@ Hi, looks like you are running TensorFlow.js in Node.js. To speed things up dram
       ivec4 round(vec4 value) {
         return ivec4(floor(value + vec4(0.5)));
       }
-    `),{version:e,attribute:t,varyingVs:n,varyingFs:s,texture2D:r,output:a,defineOutput:o,defineSpecialNaN:i,defineSpecialInf:l,defineRound:u}}function fu(e,t,n="index"){let s=v.computeStrides(t);return s.map((r,a)=>{let o=`int ${e[a]} = ${n} / ${r}`,i=a===s.length-1?`int ${e[a+1]} = ${n} - ${e[a]} * ${r}`:`index -= ${e[a]} * ${r}`;return`${o}; ${i};`}).join("")}function $2(e,t,n="index"){let s=v.computeStrides(t);return s.map((r,a)=>{let o=`int ${e[a]} = ${n} / outShapeStrides[${a}]`,i=a===s.length-1?`int ${e[a+1]} = ${n} - ${e[a]} * outShapeStrides[${a}]`:`index -= ${e[a]} * outShapeStrides[${a}]`;return`${o}; ${i};`}).join("")}function qee(e,t){let n=e.length,s=e.map(a=>`${t}[${a}]`),r=new Array(n-1);r[n-2]=s[n-1];for(let a=n-3;a>=0;--a)r[a]=`(${r[a+1]} * ${s[a+1]})`;return r}function Xee(e,t,n="index"){let s=e.map((a,o)=>o),r=qee(s,t);return r.map((a,o)=>{let i=`int ${e[o]} = ${n} / ${r[o]}`,l=o===r.length-1?`int ${e[o+1]} = ${n} - ${e[o]} * ${r[o]}`:`index -= ${e[o]} * ${r[o]}`;return`${i}; ${l};`}).join("")}function rb(e){let t=v.computeStrides(e).map(n=>n.toString());return`
+    `),{version:e,attribute:t,varyingVs:n,varyingFs:s,texture2D:r,output:a,defineOutput:o,defineSpecialNaN:i,defineSpecialInf:l,defineRound:u}}function Au(e,t,n="index"){let s=v.computeStrides(t);return s.map((r,a)=>{let o=`int ${e[a]} = ${n} / ${r}`,i=a===s.length-1?`int ${e[a+1]} = ${n} - ${e[a]} * ${r}`:`index -= ${e[a]} * ${r}`;return`${o}; ${i};`}).join("")}function $2(e,t,n="index"){let s=v.computeStrides(t);return s.map((r,a)=>{let o=`int ${e[a]} = ${n} / outShapeStrides[${a}]`,i=a===s.length-1?`int ${e[a+1]} = ${n} - ${e[a]} * outShapeStrides[${a}]`:`index -= ${e[a]} * outShapeStrides[${a}]`;return`${o}; ${i};`}).join("")}function ate(e,t){let n=e.length,s=e.map(a=>`${t}[${a}]`),r=new Array(n-1);r[n-2]=s[n-1];for(let a=n-3;a>=0;--a)r[a]=`(${r[a+1]} * ${s[a+1]})`;return r}function ote(e,t,n="index"){let s=e.map((a,o)=>o),r=ate(s,t);return r.map((a,o)=>{let i=`int ${e[o]} = ${n} / ${r[o]}`,l=o===r.length-1?`int ${e[o+1]} = ${n} - ${e[o]} * ${r[o]}`:`index -= ${e[o]} * ${r[o]}`;return`${i}; ${l};`}).join("")}function ib(e){let t=v.computeStrides(e).map(n=>n.toString());return`
   int getFlatIndex(ivec3 coords) {
     return coords.x * ${t[0]} + coords.y * ${t[1]} + coords.z;
   }
-`}function ab(){return`
+`}function lb(){return`
   int getFlatIndex(ivec3 coords) {
     return coords.x * outShapeStrides[0] + coords.y * outShapeStrides[1] + coords.z;
   }
-`}var t9=`
+`}var i9=`
   const float FLOAT_MAX = 1.70141184e38;
   const float FLOAT_MIN = 1.17549435e-38;
 
@@ -154,22 +154,22 @@ Hi, looks like you are running TensorFlow.js in Node.js. To speed things up dram
 
     return c / 255.0;
   }
-`,{getBroadcastDims:n9}=T;function Kee(e,t,n){let s=[];if(e.forEach(h=>{let f=v.sizeFromShape(h.shapeInfo.logicalShape);if(h.shapeInfo.isUniform?s.push(`uniform float ${h.name}${f>1?`[${f}]`:""};`):(s.push(`uniform sampler2D ${h.name};`),s.push(`uniform int offset${h.name};`)),n.enableShapeUniforms){let{uniformShape:m}=ob(n.packedInputs,h.shapeInfo.logicalShape,h.shapeInfo.texShape);switch(m.length){case 1:s.push(`uniform int ${h.name}Shape;`);break;case 2:s.push(`uniform ivec2 ${h.name}Shape;`);break;case 3:s.push(`uniform ivec3 ${h.name}Shape;`);break;case 4:s.push(`uniform ivec4 ${h.name}Shape;`);break;default:break}s.push(`uniform ivec2 ${h.name}TexShape;`)}}),n.enableShapeUniforms){switch(t.logicalShape.length){case 1:s.push("uniform int outShape;");break;case 2:s.push("uniform ivec2 outShape;"),s.push("uniform int outShapeStrides;");break;case 3:s.push("uniform ivec3 outShape;"),s.push("uniform ivec2 outShapeStrides;");break;case 4:s.push("uniform ivec4 outShape;"),s.push("uniform ivec3 outShapeStrides;");break;default:break}s.push("uniform ivec2 outTexShape;")}n.customUniforms&&n.customUniforms.forEach(h=>{s.push(`uniform ${h.type} ${h.name}${h.arrayIndex?`[${h.arrayIndex}]`:""};`)});let r=s.join(`
-`),a=e.map(h=>Zee(h,t,n.packedInputs,n.enableShapeUniforms)).join(`
-`),o=t.texShape,i=cs(),l=Qee(i),u,c,p=nte(i);return t.isPacked?(u=Yee(t.logicalShape,o,n.enableShapeUniforms),c=tte(i)):(u=Jee(t.logicalShape,o,n.enableShapeUniforms),c=ete(i)),n.packedInputs&&(p+=ote),[p,l,c,r,u,a,n.userCode].join(`
-`)}function dd(e,t=!1){let n=e.shapeInfo.logicalShape;switch(n.length){case 0:return Ate(e,t);case 1:return bte(e,t);case 2:return wte(e,t);case 3:return Ste(e,t);case 4:return Cte(e,t);case 5:return Tte(e);case 6:return Nte(e);default:throw new Error(`${n.length}-D input sampling is not yet supported`)}}function s9(e,t){switch(e.shapeInfo.logicalShape.length){case 0:return yte(e);case 1:return xte(e,t);case 2:return vte(e,t);case 3:return kte(e,t);default:return Ite(e,t)}}function Zee(e,t,n=!1,s){let r="";n?r+=s9(e,s):r+=dd(e,s);let a=e.shapeInfo.logicalShape,o=t.logicalShape;return a.length<=o.length&&(n?r+=Ete(e,t):r+=Rte(e,t)),r}function Yee(e,t,n){switch(e.length){case 0:return r9();case 1:return ite(e,t,n);case 2:return mte(e,t,n);case 3:return ute(e,t,n);default:return dte(e,t,n)}}function Jee(e,t,n){switch(e.length){case 0:return r9();case 1:return lte(e,t,n);case 2:return gte(e,t,n);case 3:return cte(e,t,n);case 4:return pte(e,t,n);case 5:return hte(e,t);case 6:return fte(e,t);default:throw new Error(`${e.length}-D output sampling is not yet supported`)}}function Qee(e){return`
+`,{getBroadcastDims:l9}=T;function ite(e,t,n){let s=[];if(e.forEach(h=>{let f=v.sizeFromShape(h.shapeInfo.logicalShape);if(h.shapeInfo.isUniform?s.push(`uniform float ${h.name}${f>1?`[${f}]`:""};`):(s.push(`uniform sampler2D ${h.name};`),s.push(`uniform int offset${h.name};`)),n.enableShapeUniforms){let{uniformShape:m}=ub(n.packedInputs,h.shapeInfo.logicalShape,h.shapeInfo.texShape);switch(m.length){case 1:s.push(`uniform int ${h.name}Shape;`);break;case 2:s.push(`uniform ivec2 ${h.name}Shape;`);break;case 3:s.push(`uniform ivec3 ${h.name}Shape;`);break;case 4:s.push(`uniform ivec4 ${h.name}Shape;`);break;default:break}s.push(`uniform ivec2 ${h.name}TexShape;`)}}),n.enableShapeUniforms){switch(t.logicalShape.length){case 1:s.push("uniform int outShape;");break;case 2:s.push("uniform ivec2 outShape;"),s.push("uniform int outShapeStrides;");break;case 3:s.push("uniform ivec3 outShape;"),s.push("uniform ivec2 outShapeStrides;");break;case 4:s.push("uniform ivec4 outShape;"),s.push("uniform ivec3 outShapeStrides;");break;default:break}s.push("uniform ivec2 outTexShape;")}n.customUniforms&&n.customUniforms.forEach(h=>{s.push(`uniform ${h.type} ${h.name}${h.arrayIndex?`[${h.arrayIndex}]`:""};`)});let r=s.join(`
+`),a=e.map(h=>lte(h,t,n.packedInputs,n.enableShapeUniforms)).join(`
+`),o=t.texShape,i=hs(),l=dte(i),u,c,p=fte(i);return t.isPacked?(u=ute(t.logicalShape,o,n.enableShapeUniforms),c=hte(i)):(u=cte(t.logicalShape,o,n.enableShapeUniforms),c=pte(i)),n.packedInputs&&(p+=Ate),[p,l,c,r,u,a,n.userCode].join(`
+`)}function fd(e,t=!1){let n=e.shapeInfo.logicalShape;switch(n.length){case 0:return Rte(e,t);case 1:return Dte(e,t);case 2:return Pte(e,t);case 3:return Ote(e,t);case 4:return zte(e,t);case 5:return Lte(e);case 6:return Bte(e);default:throw new Error(`${n.length}-D input sampling is not yet supported`)}}function u9(e,t){switch(e.shapeInfo.logicalShape.length){case 0:return Ete(e);case 1:return _te(e,t);case 2:return $te(e,t);case 3:return Fte(e,t);default:return Mte(e,t)}}function lte(e,t,n=!1,s){let r="";n?r+=u9(e,s):r+=fd(e,s);let a=e.shapeInfo.logicalShape,o=t.logicalShape;return a.length<=o.length&&(n?r+=Wte(e,t):r+=Vte(e,t)),r}function ute(e,t,n){switch(e.length){case 0:return c9();case 1:return xte(e,t,n);case 2:return Tte(e,t,n);case 3:return vte(e,t,n);default:return kte(e,t,n)}}function cte(e,t,n){switch(e.length){case 0:return c9();case 1:return bte(e,t,n);case 2:return Nte(e,t,n);case 3:return wte(e,t,n);case 4:return Ste(e,t,n);case 5:return Ite(e,t);case 6:return Cte(e,t);default:throw new Error(`${e.length}-D output sampling is not yet supported`)}}function dte(e){return`
     float sampleTexture(sampler2D textureSampler, vec2 uv) {
       return ${e.texture2D}(textureSampler, uv).r;
     }
-  `}function ete(e){return`
+  `}function pte(e){return`
     void setOutput(float val) {
       ${e.output} = vec4(val, 0, 0, 0);
     }
-  `}function tte(e){return`
+  `}function hte(e){return`
     void setOutput(vec4 val) {
       ${e.output} = val;
     }
-  `}function nte(e){return`${e.version}
+  `}function fte(e){return`${e.version}
     precision highp float;
     precision highp int;
     precision highp sampler2D;
@@ -224,10 +224,10 @@ Hi, looks like you are running TensorFlow.js in Node.js. To speed things up dram
       return fract((p3.x + p3.y) * p3.z);
     }
 
-    ${ste}
-    ${rte}
-    ${ate}
-  `}var ste=`
+    ${mte}
+    ${gte}
+    ${yte}
+  `}var mte=`
 vec2 uvFromFlat(int texNumR, int texNumC, int index) {
   int texR = index / texNumC;
   int texC = index - texR * texNumC;
@@ -239,7 +239,7 @@ vec2 packedUVfrom1D(int texNumR, int texNumC, int index) {
   int texC = texelIndex - texR * texNumC;
   return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);
 }
-`,rte=`
+`,gte=`
 vec2 packedUVfrom2D(int texelsInLogicalRow, int texNumR,
   int texNumC, int row, int col) {
   int texelIndex = (row / 2) * texelsInLogicalRow + (col / 2);
@@ -247,7 +247,7 @@ vec2 packedUVfrom2D(int texelsInLogicalRow, int texNumR,
   int texC = texelIndex - texR * texNumC;
   return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);
 }
-`,ate=`
+`,yte=`
 vec2 packedUVfrom3D(int texNumR, int texNumC,
     int texelsInBatch, int texelsInLogicalRow, int b,
     int row, int col) {
@@ -256,7 +256,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
   int texC = index - texR * texNumC;
   return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);
 }
-`,ote=`
+`,Ate=`
   float getChannel(vec4 frag, vec2 innerDims) {
     vec2 modCoord = mod(innerDims, 2.);
     return modCoord.x == 0. ?
@@ -267,11 +267,11 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
     float modCoord = mod(float(dim), 2.);
     return modCoord == 0. ? frag.r : frag.g;
   }
-`;function r9(){return`
+`;function c9(){return`
     int getOutputCoords() {
       return 0;
     }
-  `}function ite(e,t,n){let s=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)];return s[0]===1?n?`
+  `}function xte(e,t,n){let s=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)];return s[0]===1?n?`
       int getOutputCoords() {
         return 2 * int(resultUV.x * ceil(float(outTexShape[1]) / 2.0));
       }
@@ -300,7 +300,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
                              vec2(${s[0]}, ${s[1]}));
       return 2 * (resTexRC.x * ${s[1]} + resTexRC.y);
     }
-  `}function lte(e,t,n){return t[0]===1?n?`
+  `}function bte(e,t,n){return t[0]===1?n?`
       int getOutputCoords() {
         return int(resultUV.x * float(outTexShape[1]));
       }
@@ -328,7 +328,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
                              vec2(${t[0]}, ${t[1]}));
       return resTexRC.x * ${t[1]} + resTexRC.y;
     }
-  `}function ute(e,t,n){if(n)return`
+  `}function vte(e,t,n){if(n)return`
     ivec3 getOutputCoords() {
       ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));
       int texelsInLogicalRow = int(ceil(float(outShape[2]) / 2.0));
@@ -359,7 +359,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
 
       return ivec3(b, r, c);
     }
-  `}function cte(e,t,n){if(n)return`
+  `}function wte(e,t,n){if(n)return`
   ivec3 getOutputCoords() {
     ivec2 resTexRC = ivec2(resultUV.yx *
                            vec2(outTexShape[0], outTexShape[1]));
@@ -367,7 +367,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
     ${$2(["r","c","d"],e)}
     return ivec3(r, c, d);
   }
-`;let s=fu(["r","c","d"],e);return`
+`;let s=Au(["r","c","d"],e);return`
     ivec3 getOutputCoords() {
       ivec2 resTexRC = ivec2(resultUV.yx *
                              vec2(${t[0]}, ${t[1]}));
@@ -375,7 +375,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
       ${s}
       return ivec3(r, c, d);
     }
-  `}function dte(e,t,n){if(n)return`
+  `}function kte(e,t,n){if(n)return`
     ivec4 getOutputCoords() {
       ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));
       ivec2 resTexRC = ivec2(resultUV.yx *
@@ -416,7 +416,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
 
       return ivec${e.length}(${l});
     }
-  `}function pte(e,t,n){if(n)return`
+  `}function Ste(e,t,n){if(n)return`
     ivec4 getOutputCoords() {
       ivec2 resTexRC = ivec2(resultUV.yx *
         vec2(outTexShape[0], outTexShape[1]));
@@ -424,7 +424,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
       ${$2(["r","c","d","d2"],e)}
       return ivec4(r, c, d, d2);
     }
-  `;let s=fu(["r","c","d","d2"],e);return`
+  `;let s=Au(["r","c","d","d2"],e);return`
     ivec4 getOutputCoords() {
       ivec2 resTexRC = ivec2(resultUV.yx *
         vec2(${t[0]}, ${t[1]}));
@@ -432,7 +432,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
       ${s}
       return ivec4(r, c, d, d2);
     }
-  `}function hte(e,t){let n=fu(["r","c","d","d2","d3"],e);return`
+  `}function Ite(e,t){let n=Au(["r","c","d","d2","d3"],e);return`
     ivec5 getOutputCoords() {
       ivec2 resTexRC = ivec2(resultUV.yx * vec2(${t[0]},
                              ${t[1]}));
@@ -444,7 +444,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
       ivec5 outShape = ivec5(r, c, d, d2, d3);
       return outShape;
     }
-  `}function fte(e,t){let n=fu(["r","c","d","d2","d3","d4"],e);return`
+  `}function Cte(e,t){let n=Au(["r","c","d","d2","d3","d4"],e);return`
     ivec6 getOutputCoords() {
       ivec2 resTexRC = ivec2(resultUV.yx *
         vec2(${t[0]}, ${t[1]}));
@@ -455,7 +455,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
       ivec6 result = ivec6(r, c, d, d2, d3, d4);
       return result;
     }
-  `}function mte(e,t,n){let s=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)];if(v.arraysEqual(e,t))return n?`
+  `}function Tte(e,t,n){let s=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)];if(v.arraysEqual(e,t))return n?`
       ivec2 getOutputCoords() {
         ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));
         return 2 * ivec2(resultUV.yx * vec2(packedTexShape[0], packedTexShape[1]));
@@ -488,7 +488,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
 
       return ivec2(r, c);
     }
-  `}function gte(e,t,n){return v.arraysEqual(e,t)?n?`
+  `}function Nte(e,t,n){return v.arraysEqual(e,t)?n?`
       ivec2 getOutputCoords() {
         return ivec2(resultUV.yx * vec2(outTexShape[0], outTexShape[1]));
       }
@@ -542,15 +542,15 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
       int c = index - r * ${e[1]};
       return ivec2(r, c);
     }
-  `}function mu(e){return`offset${e}`}function yte(e){let t=e.name,n="get"+t.charAt(0).toUpperCase()+t.slice(1),s=cs();return`
+  `}function xu(e){return`offset${e}`}function Ete(e){let t=e.name,n="get"+t.charAt(0).toUpperCase()+t.slice(1),s=hs();return`
     vec4 ${n}() {
       return ${s.texture2D}(${t}, halfCR);
     }
-  `}function Ate(e,t){let n=e.name,s="get"+n.charAt(0).toUpperCase()+n.slice(1);if(e.shapeInfo.isUniform)return`float ${s}() {return ${n};}`;let[r,a]=e.shapeInfo.texShape;if(r===1&&a===1)return`
+  `}function Rte(e,t){let n=e.name,s="get"+n.charAt(0).toUpperCase()+n.slice(1);if(e.shapeInfo.isUniform)return`float ${s}() {return ${n};}`;let[r,a]=e.shapeInfo.texShape;if(r===1&&a===1)return`
       float ${s}() {
         return sampleTexture(${n}, halfCR);
       }
-    `;let o=mu(n);if(t)return`
+    `;let o=xu(n);if(t)return`
     float ${s}() {
       vec2 uv = uvFromFlat(${n}TexShape[0], ${n}TexShape[1], ${o});
       return sampleTexture(${n}, uv);
@@ -560,7 +560,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
       vec2 uv = uvFromFlat(${i}, ${l}, ${o});
       return sampleTexture(${n}, uv);
     }
-  `}function xte(e,t){let n=e.name,s="get"+n.charAt(0).toUpperCase()+n.slice(1),r=e.shapeInfo.texShape,a=cs();if(t)return`
+  `}function _te(e,t){let n=e.name,s="get"+n.charAt(0).toUpperCase()+n.slice(1),r=e.shapeInfo.texShape,a=hs();if(t)return`
     vec4 ${s}(int index) {
       ivec2 packedTexShape = ivec2(ceil(float(${n}TexShape[0]) / 2.0), ceil(float(${n}TexShape[1]) / 2.0));
       vec2 uv = packedUVfrom1D(
@@ -573,15 +573,15 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
         ${o[0]}, ${o[1]}, index);
       return ${a.texture2D}(${n}, uv);
     }
-  `}function bte(e,t){let n=e.name,s="get"+n.charAt(0).toUpperCase()+n.slice(1);if(e.shapeInfo.isUniform)return`
+  `}function Dte(e,t){let n=e.name,s="get"+n.charAt(0).toUpperCase()+n.slice(1);if(e.shapeInfo.isUniform)return`
       float ${s}(int index) {
-        ${pd(e)}
+        ${md(e)}
       }
     `;let r=e.shapeInfo.texShape,a=r[0],o=r[1];if(o===1&&a===1)return`
       float ${s}(int index) {
         return sampleTexture(${n}, halfCR);
       }
-    `;let i=mu(n);return o===1?t?`
+    `;let i=xu(n);return o===1?t?`
       float ${s}(int index) {
         vec2 uv = vec2(0.5, (float(index + ${i}) + 0.5) / float(${n}TexShape[0]));
         return sampleTexture(${n}, uv);
@@ -611,7 +611,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
       vec2 uv = uvFromFlat(${a}, ${o}, index + ${i});
       return sampleTexture(${n}, uv);
     }
-  `}function vte(e,t){let n=e.shapeInfo.logicalShape,s=e.name,r="get"+s.charAt(0).toUpperCase()+s.slice(1),a=e.shapeInfo.texShape,o=a[0],i=a[1],l=cs();if(a!=null&&v.arraysEqual(n,a))return t?`
+  `}function $te(e,t){let n=e.shapeInfo.logicalShape,s=e.name,r="get"+s.charAt(0).toUpperCase()+s.slice(1),a=e.shapeInfo.texShape,o=a[0],i=a[1],l=hs();if(a!=null&&v.arraysEqual(n,a))return t?`
       vec4 ${r}(int row, int col) {
         vec2 uv = (vec2(col, row) + halfCR) / vec2(${s}TexShape[1], ${s}TexShape[0]);
 
@@ -635,7 +635,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
       vec2 uv = packedUVfrom2D(${c}, ${u[0]}, ${u[1]}, row, col);
       return ${l.texture2D}(${s}, uv);
     }
-  `}function wte(e,t){let n=e.shapeInfo.logicalShape,s=e.name,r="get"+s.charAt(0).toUpperCase()+s.slice(1),a=e.shapeInfo.texShape;if(a!=null&&v.arraysEqual(n,a)){if(t)return`
+  `}function Pte(e,t){let n=e.shapeInfo.logicalShape,s=e.name,r="get"+s.charAt(0).toUpperCase()+s.slice(1),a=e.shapeInfo.texShape;if(a!=null&&v.arraysEqual(n,a)){if(t)return`
       float ${r}(int row, int col) {
         vec2 uv = (vec2(col, row) + halfCR) / vec2(${s}TexShape[1], ${s}TexShape[0]);
         return sampleTexture(${s}, uv);
@@ -645,17 +645,17 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
       vec2 uv = (vec2(col, row) + halfCR) / vec2(${h}.0, ${d}.0);
       return sampleTexture(${s}, uv);
     }
-  `}let{newShape:o,keptDims:i}=v.squeezeShape(n),l=o;if(l.length<n.length){let d=hd(e,l),h=["row","col"];return`
-      ${dd(d,t)}
+  `}let{newShape:o,keptDims:i}=v.squeezeShape(n),l=o;if(l.length<n.length){let d=gd(e,l),h=["row","col"];return`
+      ${fd(d,t)}
       float ${r}(int row, int col) {
-        return ${r}(${fd(h,i)});
+        return ${r}(${yd(h,i)});
       }
     `}if(e.shapeInfo.isUniform)return`
       float ${r}(int row, int col) {
         int index = round(dot(vec2(row, col), vec2(${n[1]}, 1)));
-        ${pd(e)}
+        ${md(e)}
       }
-    `;let u=a[0],c=a[1],p=mu(s);return c===1?t?`
+    `;let u=a[0],c=a[1],p=xu(s);return c===1?t?`
       float ${r}(int row, int col) {
         float index = dot(vec3(row, col, ${p}), vec3(${s}Shape[1], 1, 1));
         vec2 uv = vec2(0.5, (index + 0.5) / float(${s}TexShape[0]));
@@ -693,12 +693,12 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
     vec2 uv = uvFromFlat(${u}, ${c}, index);
     return sampleTexture(${s}, uv);
   }
-`}function kte(e,t){let n=e.shapeInfo.logicalShape,s=e.name,r="get"+s.charAt(0).toUpperCase()+s.slice(1),a=e.shapeInfo.texShape,o=[Math.ceil(a[0]/2),Math.ceil(a[1]/2)];if(n[0]===1){let d=n.slice(1),h=[1,2],f=hd(e,d),m=["b","row","col"];return`
-        ${s9(f,t)}
+`}function Fte(e,t){let n=e.shapeInfo.logicalShape,s=e.name,r="get"+s.charAt(0).toUpperCase()+s.slice(1),a=e.shapeInfo.texShape,o=[Math.ceil(a[0]/2),Math.ceil(a[1]/2)];if(n[0]===1){let d=n.slice(1),h=[1,2],f=gd(e,d),m=["b","row","col"];return`
+        ${u9(f,t)}
         vec4 ${r}(int b, int row, int col) {
-          return ${r}(${fd(m,h)});
+          return ${r}(${yd(m,h)});
         }
-      `}let i=cs();if(t)return`
+      `}let i=hs();if(t)return`
     vec4 ${r}(int b, int row, int col) {
       ivec2 packedTexShape = ivec2(ceil(float(${s}TexShape[0]) / 2.0), ceil(float(${s}TexShape[1]) / 2.0));
       int valuesPerRow = int(ceil(float(${s}Shape[2]) / 2.0));
@@ -713,16 +713,16 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
         ${l}, ${u}, ${p}, ${c}, b, row, col);
       return ${i.texture2D}(${s}, uv);
     }
-  `}function Ste(e,t){let n=e.shapeInfo.logicalShape,s=e.name,r="get"+s.charAt(0).toUpperCase()+s.slice(1),a=n[1]*n[2],o=n[2],{newShape:i,keptDims:l}=v.squeezeShape(n),u=i;if(u.length<n.length){let m=hd(e,u),g=["row","col","depth"];return`
-        ${dd(m,t)}
+  `}function Ote(e,t){let n=e.shapeInfo.logicalShape,s=e.name,r="get"+s.charAt(0).toUpperCase()+s.slice(1),a=n[1]*n[2],o=n[2],{newShape:i,keptDims:l}=v.squeezeShape(n),u=i;if(u.length<n.length){let m=gd(e,u),g=["row","col","depth"];return`
+        ${fd(m,t)}
         float ${r}(int row, int col, int depth) {
-          return ${r}(${fd(g,l)});
+          return ${r}(${yd(g,l)});
         }
       `}if(e.shapeInfo.isUniform)return`
       float ${r}(int row, int col, int depth) {
         int index = round(dot(vec3(row, col, depth),
                           vec3(${a}, ${o}, 1)));
-        ${pd(e)}
+        ${md(e)}
       }
     `;let c=e.shapeInfo.texShape,p=c[0],d=c[1],h=e.shapeInfo.flatOffset;if(d===a&&h==null)return t?`
       float ${r}(int row, int col, int depth) {
@@ -755,7 +755,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
       vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${d}.0, ${p}.0);
       return sampleTexture(${s}, uv);
     }
-  `;let f=mu(s);return t?`
+  `;let f=xu(s);return t?`
     float ${r}(int row, int col, int depth) {
       // Explicitly use integer operations as dot() only works on floats.
       int stride0 = ${s}Shape[1] * ${s}Shape[2];
@@ -771,7 +771,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
         vec2 uv = uvFromFlat(${p}, ${d}, index);
         return sampleTexture(${s}, uv);
       }
-  `}function Ite(e,t){let n=e.name,s="get"+n.charAt(0).toUpperCase()+n.slice(1),r=cs();if(t)return`
+  `}function Mte(e,t){let n=e.name,s="get"+n.charAt(0).toUpperCase()+n.slice(1),r=hs();if(t)return`
     vec4 ${s}(int b2, int b, int row, int col) {
       int valuesPerRow = int(ceil(float(${n}Shape[3]) / 2.0));
       int texelsInBatch = valuesPerRow * int(ceil(float(${n}Shape[2]) / 2.0));
@@ -791,16 +791,16 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
       vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${c}, ${u});
       return ${r.texture2D}(${n}, uv);
     }
-  `}function Cte(e,t){let n=e.shapeInfo.logicalShape,s=e.name,r="get"+s.charAt(0).toUpperCase()+s.slice(1),a=n[3],o=n[2]*a,i=n[1]*o,{newShape:l,keptDims:u}=v.squeezeShape(n);if(l.length<n.length){let x=hd(e,l),A=["row","col","depth","depth2"];return`
-      ${dd(x,t)}
+  `}function zte(e,t){let n=e.shapeInfo.logicalShape,s=e.name,r="get"+s.charAt(0).toUpperCase()+s.slice(1),a=n[3],o=n[2]*a,i=n[1]*o,{newShape:l,keptDims:u}=v.squeezeShape(n);if(l.length<n.length){let x=gd(e,l),A=["row","col","depth","depth2"];return`
+      ${fd(x,t)}
       float ${r}(int row, int col, int depth, int depth2) {
-        return ${r}(${fd(A,u)});
+        return ${r}(${yd(A,u)});
       }
     `}if(e.shapeInfo.isUniform)return`
       float ${r}(int row, int col, int depth, int depth2) {
         int index = round(dot(vec4(row, col, depth, depth2),
                           vec4(${i}, ${o}, ${a}, 1)));
-        ${pd(e)}
+        ${md(e)}
       }
     `;let c=e.shapeInfo.flatOffset,p=e.shapeInfo.texShape,d=p[0],h=p[1],f=`int stride2 = ${s}Shape[3];`,m=`int stride1 = ${s}Shape[2] * stride2;`,g=`int stride0 = ${s}Shape[1] * stride1;`;if(h===i&&c==null)return t?`
       float ${r}(int row, int col, int depth, int depth2) {
@@ -842,7 +842,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
                   vec2(${h}.0, ${d}.0);
         return sampleTexture(${s}, uv);
       }
-    `;let y=mu(s);return t?`
+    `;let y=xu(s);return t?`
     float ${r}(int row, int col, int depth, int depth2) {
       // Explicitly use integer operations as dot() only works on floats.
       ${f}
@@ -861,10 +861,10 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
       vec2 uv = uvFromFlat(${d}, ${h}, index + ${y});
       return sampleTexture(${s}, uv);
     }
-  `}function Tte(e){let t=e.shapeInfo.logicalShape,n=e.name,s="get"+n.charAt(0).toUpperCase()+n.slice(1),r=t[4],a=t[3]*r,o=t[2]*a,i=t[1]*o,{newShape:l,keptDims:u}=v.squeezeShape(t);if(l.length<t.length){let m=hd(e,l),g=["row","col","depth","depth2","depth3"];return`
-      ${dd(m)}
+  `}function Lte(e){let t=e.shapeInfo.logicalShape,n=e.name,s="get"+n.charAt(0).toUpperCase()+n.slice(1),r=t[4],a=t[3]*r,o=t[2]*a,i=t[1]*o,{newShape:l,keptDims:u}=v.squeezeShape(t);if(l.length<t.length){let m=gd(e,l),g=["row","col","depth","depth2","depth3"];return`
+      ${fd(m)}
       float ${s}(int row, int col, int depth, int depth2, int depth3) {
-        return ${s}(${fd(g,u)});
+        return ${s}(${yd(g,u)});
       }
     `}if(e.shapeInfo.isUniform)return`
       float ${s}(int row, int col, int depth, int depth2, int depth3) {
@@ -872,7 +872,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
           vec4(row, col, depth, depth2),
           vec4(${i}, ${o}, ${a}, ${r})) +
           depth3;
-        ${pd(e)}
+        ${md(e)}
       }
     `;let c=e.shapeInfo.flatOffset,p=e.shapeInfo.texShape,d=p[0],h=p[1];if(h===i&&c==null)return`
       float ${s}(int row, int col, int depth, int depth2, int depth3) {
@@ -894,7 +894,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
                   vec2(${h}.0, ${d}.0);
         return sampleTexture(${n}, uv);
       }
-    `;let f=mu(n);return`
+    `;let f=xu(n);return`
     float ${s}(int row, int col, int depth, int depth2, int depth3) {
       // Explicitly use integer operations as dot() only works on floats.
       int index = row * ${i} + col * ${o} + depth * ${a} +
@@ -902,11 +902,11 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
       vec2 uv = uvFromFlat(${d}, ${h}, index);
       return sampleTexture(${n}, uv);
     }
-  `}function Nte(e){let t=e.shapeInfo.logicalShape,n=e.name,s="get"+n.charAt(0).toUpperCase()+n.slice(1),{newShape:r,keptDims:a}=v.squeezeShape(t);if(r.length<t.length){let g=hd(e,r),y=["row","col","depth","depth2","depth3","depth4"];return`
-      ${dd(g)}
+  `}function Bte(e){let t=e.shapeInfo.logicalShape,n=e.name,s="get"+n.charAt(0).toUpperCase()+n.slice(1),{newShape:r,keptDims:a}=v.squeezeShape(t);if(r.length<t.length){let g=gd(e,r),y=["row","col","depth","depth2","depth3","depth4"];return`
+      ${fd(g)}
       float ${s}(int row, int col, int depth,
                     int depth2, int depth3, int depth4) {
-        return ${s}(${fd(y,a)});
+        return ${s}(${yd(y,a)});
       }
     `}let o=t[5],i=t[4]*o,l=t[3]*i,u=t[2]*l,c=t[1]*u;if(e.shapeInfo.isUniform)return`
       float ${s}(int row, int col, int depth,
@@ -917,7 +917,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
           dot(
             vec2(depth3, depth4),
             vec2(${o}, 1)));
-        ${pd(e)}
+        ${md(e)}
       }
     `;let p=e.shapeInfo.flatOffset,d=e.shapeInfo.texShape,h=d[0],f=d[1];if(f===c&&p==null)return`
       float ${s}(int row, int col, int depth,
@@ -943,7 +943,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
                   vec2(${f}.0, ${h}.0);
         return sampleTexture(${n}, uv);
       }
-    `;let m=mu(n);return`
+    `;let m=xu(n);return`
     float ${s}(int row, int col, int depth,
                   int depth2, int depth3, int depth4) {
       // Explicitly use integer operations as dot() only works on floats.
@@ -952,13 +952,13 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
       vec2 uv = uvFromFlat(${h}, ${f}, index);
       return sampleTexture(${n}, uv);
     }
-  `}function pd(e){let t=e.name,n=v.sizeFromShape(e.shapeInfo.logicalShape);return n<2?`return ${t};`:`
+  `}function md(e){let t=e.name,n=v.sizeFromShape(e.shapeInfo.logicalShape);return n<2?`return ${t};`:`
     for (int i = 0; i < ${n}; i++) {
       if (i == index) {
         return ${t}[i];
       }
     }
-  `}function Ete(e,t){let n=e.name,s=n.charAt(0).toUpperCase()+n.slice(1),r="get"+s+"AtOutCoords",a=e.shapeInfo.logicalShape.length,o=t.logicalShape.length,i=n9(e.shapeInfo.logicalShape,t.logicalShape),l=wt(o),u=o-a,c,p=["x","y","z","w","u","v"];a===0?c="":o<2&&i.length>=1?c="coords = 0;":c=i.map(x=>`coords.${p[x+u]} = 0;`).join(`
+  `}function Wte(e,t){let n=e.name,s=n.charAt(0).toUpperCase()+n.slice(1),r="get"+s+"AtOutCoords",a=e.shapeInfo.logicalShape.length,o=t.logicalShape.length,i=l9(e.shapeInfo.logicalShape,t.logicalShape),l=kt(o),u=o-a,c,p=["x","y","z","w","u","v"];a===0?c="":o<2&&i.length>=1?c="coords = 0;":c=i.map(x=>`coords.${p[x+u]} = 0;`).join(`
 `);let d="";o<2&&a>0?d="coords":d=e.shapeInfo.logicalShape.map((x,A)=>`coords.${p[A+u]}`).join(", ");let h="return outputValue;",m=v.sizeFromShape(e.shapeInfo.logicalShape)===1,y=v.sizeFromShape(t.logicalShape)===1;if(a===1&&!m&&!y)h=`
       return vec4(outputValue.xy, outputValue.xy);
     `;else if(m&&!y)o===1?h=`
@@ -972,20 +972,20 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
       vec4 outputValue = get${s}(${d});
       ${h}
     }
-  `}function Rte(e,t){let n=e.name,s=n.charAt(0).toUpperCase()+n.slice(1),r="get"+s+"AtOutCoords",a=t.texShape,o=e.shapeInfo.texShape,i=e.shapeInfo.logicalShape.length,l=t.logicalShape.length;if(!e.shapeInfo.isUniform&&i===l&&e.shapeInfo.flatOffset==null&&v.arraysEqual(o,a))return`
+  `}function Vte(e,t){let n=e.name,s=n.charAt(0).toUpperCase()+n.slice(1),r="get"+s+"AtOutCoords",a=t.texShape,o=e.shapeInfo.texShape,i=e.shapeInfo.logicalShape.length,l=t.logicalShape.length;if(!e.shapeInfo.isUniform&&i===l&&e.shapeInfo.flatOffset==null&&v.arraysEqual(o,a))return`
       float ${r}() {
         return sampleTexture(${n}, resultUV);
       }
-    `;let u=wt(l),c=n9(e.shapeInfo.logicalShape,t.logicalShape),p=l-i,d,h=["x","y","z","w","u","v"];i===0?d="":l<2&&c.length>=1?d="coords = 0;":d=c.map(m=>`coords.${h[m+p]} = 0;`).join(`
+    `;let u=kt(l),c=l9(e.shapeInfo.logicalShape,t.logicalShape),p=l-i,d,h=["x","y","z","w","u","v"];i===0?d="":l<2&&c.length>=1?d="coords = 0;":d=c.map(m=>`coords.${h[m+p]} = 0;`).join(`
 `);let f="";return l<2&&i>0?f="coords":f=e.shapeInfo.logicalShape.map((m,g)=>`coords.${h[g+p]}`).join(", "),`
     float ${r}() {
       ${u} coords = getOutputCoords();
       ${d}
       return get${s}(${f});
     }
-  `}function wt(e){if(e<=1)return"int";if(e===2)return"ivec2";if(e===3)return"ivec3";if(e===4)return"ivec4";if(e===5)return"ivec5";if(e===6)return"ivec6";throw Error(`GPU for rank ${e} is not yet supported`)}function ob(e,t,n){let{newShape:s,keptDims:r}=v.squeezeShape(t),a=t.length,o=e&&a===3&&t[0]===1,i=o?t.slice(1):s,l=!e&&a>1&&!v.arraysEqual(t,n)&&s.length<a||o;return{useSqueezeShape:l,uniformShape:l?i:t,keptDims:r}}function hd(e,t){let n=JSON.parse(JSON.stringify(e));return n.shapeInfo.logicalShape=t,n}function fd(e,t){return t.map(n=>e[n]).join(", ")}function _te(e,t,n,s){let r=n.map((c,p)=>{let d={logicalShape:c.shape,texShape:c.isUniform?null:c.texData.texShape,isUniform:c.isUniform,isPacked:c.isUniform?!1:c.texData.isPacked,flatOffset:null};return c.texData!=null&&c.texData.slice!=null&&c.texData.slice.flatOffset>0&&(d.flatOffset=c.texData.slice.flatOffset),{name:t.variableNames[p],shapeInfo:d}}),a=r.map(c=>c.shapeInfo),o={logicalShape:s.shape,texShape:s.texData.texShape,isUniform:!1,isPacked:s.texData.isPacked,flatOffset:null},i=Kee(r,o,t),l=PI(e.gl,i),u=e.createProgram(l);return H().get("ENGINE_COMPILE_ONLY")?{program:t,fragmentShader:l,source:i,webGLProgram:u,inShapeInfos:a,outShapeInfo:o,uniformLocations:null,customUniformLocations:null,infLoc:null,nanLoc:null,inShapesLocations:null,inTexShapesLocations:null,outShapeLocation:null,outShapeStridesLocation:null,outTexShapeLocation:null}:Object.assign({program:t,fragmentShader:l,source:i,webGLProgram:u,inShapeInfos:a,outShapeInfo:o},a9(e,t,u))}function a9(e,t,n){let s={},r={},a={},o=[],i,l,u,c=null,p=null;p=e.getUniformLocation(n,"NAN",!1),H().getNumber("WEBGL_VERSION")===1&&(c=e.getUniformLocation(n,"INFINITY",!1));let d=!1;for(let h=0;h<t.variableNames.length;h++){let f=t.variableNames[h];s[f]=e.getUniformLocation(n,f,d),s[`offset${f}`]=e.getUniformLocation(n,`offset${f}`,d),t.enableShapeUniforms&&(r[`${f}Shape`]=e.getUniformLocation(n,`${f}Shape`,d),a[`${f}TexShape`]=e.getUniformLocation(n,`${f}TexShape`,d))}return t.enableShapeUniforms&&(i=e.getUniformLocation(n,"outShape",d),u=e.getUniformLocation(n,"outShapeStrides",d),l=e.getUniformLocation(n,"outTexShape",d)),t.customUniforms&&t.customUniforms.forEach((h,f)=>{o[f]=e.getUniformLocation(n,h.name,d)}),{uniformLocations:s,customUniformLocations:o,infLoc:c,nanLoc:p,inShapesLocations:r,inTexShapesLocations:a,outShapeLocation:i,outShapeStridesLocation:u,outTexShapeLocation:l}}function E7(e,t){if(e.length!==t.length)throw Error(`Binary was compiled with ${e.length} inputs, but was executed with ${t.length} inputs`);e.forEach((n,s)=>{let r=n.logicalShape,a=t[s],o=a.shape;if(!v.arraysEqual(r,o))throw Error(`Binary was compiled with different shapes than the current args. Shapes ${r} and ${o} must match`);if(n.isUniform&&a.isUniform)return;let i=n.texShape,l=a.isUniform?null:a.texData.texShape;if(!v.arraysEqual(i,l))throw Error(`Binary was compiled with different texture shapes than the current args. Shape ${i} and ${l} must match`)})}function Dte(e,t,n,s,r){t.program.enableShapeUniforms||(E7(t.inShapeInfos,n),E7([t.outShapeInfo],[s]));let a=s.texData.texture,o=s.texData.texShape;s.texData.isPacked?e.setOutputPackedMatrixTexture(a.texture,o[0],o[1]):e.setOutputMatrixTexture(a.texture,o[0],o[1]),e.setProgram(t.webGLProgram),H().getNumber("WEBGL_VERSION")===1&&t.infLoc!==null&&e.gl.uniform1f(t.infLoc,1/0),t.nanLoc!==null&&e.gl.uniform1f(t.nanLoc,NaN),n.forEach((l,u)=>{let c=t.program.variableNames[u],p=t.uniformLocations[c],d=t.uniformLocations[`offset${c}`],h=t.inShapesLocations[`${c}Shape`],f=t.inTexShapesLocations[`${c}TexShape`];if(h){let{uniformShape:m}=ob(t.program.packedInputs,l.shape,l.texData.texShape);switch(m.length){case 1:e.gl.uniform1iv(h,new Int32Array(m));break;case 2:e.gl.uniform2iv(h,new Int32Array(m));break;case 3:e.gl.uniform3iv(h,new Int32Array(m));break;case 4:e.gl.uniform4iv(h,new Int32Array(m));break;default:break}}if(f&&e.gl.uniform2i(f,l.texData.texShape[0],l.texData.texShape[1]),p!=null){if(l.isUniform){if(v.sizeFromShape(l.shape)<2)e.gl.uniform1f(p,l.uniformValues[0]);else{let m=l.uniformValues;m instanceof Float32Array||(m=new Float32Array(m)),e.gl.uniform1fv(p,m)}return}l.texData.slice!=null&&d!=null&&e.gl.uniform1i(d,l.texData.slice.flatOffset),e.setInputMatrixTexture(l.texData.texture.texture,p,u)}});let i=t.outShapeLocation;if(i)switch(s.shape.length){case 1:e.gl.uniform1iv(i,new Int32Array(s.shape));break;case 2:e.gl.uniform2iv(i,new Int32Array(s.shape));break;case 3:e.gl.uniform3iv(i,new Int32Array(s.shape));break;case 4:e.gl.uniform4iv(i,new Int32Array(s.shape));break;default:break}if(t.outShapeStridesLocation){let l=v.computeStrides(s.shape);switch(s.shape.length){case 2:e.gl.uniform1iv(t.outShapeStridesLocation,new Int32Array(l));break;case 3:e.gl.uniform2iv(t.outShapeStridesLocation,new Int32Array(l));break;case 4:e.gl.uniform3iv(t.outShapeStridesLocation,new Int32Array(l));break;default:break}}t.outTexShapeLocation&&e.gl.uniform2i(t.outTexShapeLocation,s.texData.texShape[0],s.texData.texShape[1]),t.program.customUniforms&&r&&t.program.customUniforms.forEach((l,u)=>{let c=t.customUniformLocations[u],p=r[u];if(l.type==="float")e.gl.uniform1fv(c,p);else if(l.type==="vec2")e.gl.uniform2fv(c,p);else if(l.type==="vec3")e.gl.uniform3fv(c,p);else if(l.type==="vec4")e.gl.uniform4fv(c,p);else if(l.type==="int")e.gl.uniform1iv(c,p);else if(l.type==="ivec2")e.gl.uniform2iv(c,p);else if(l.type==="ivec3")e.gl.uniform3iv(c,p);else if(l.type==="ivec4")e.gl.uniform4iv(c,p);else throw Error(`uniform type ${l.type} is not supported yet.`)}),e.executeProgram()}function $te(e,t,n){let s="";t.concat(n).forEach(o=>{let i=o.texData!=null&&o.texData.slice!=null&&o.texData.slice.flatOffset>0;if(e.enableShapeUniforms&&!o.isUniform){let l=o.texData.texShape,{useSqueezeShape:u,uniformShape:c,keptDims:p}=ob(e.packedInputs,o.shape,l),d="",h="",f="";if(c.length===1&&e.packedInputs){let w=[Math.ceil(l[0]/2),Math.ceil(l[1]/2)];d=`${w[0]>1}_${w[1]>1}`}else if(c.length===2&&!e.packedInputs)h=`${c[0]>1}_${c[1]>1}`;else if(c.length>2&&!e.packedInputs){let w=v.computeStrides(c);f=`${w[0]===l[1]}_${w[w.length-1]===l[1]}`}let m=o.shape.length,g=c.length===2&&v.arraysEqual(o.shape,l),y=v.sizeFromShape(o.shape)===1,x=T.getBroadcastDims(o.shape,n.shape),A=!e.packedInputs&&m===n.shape.length&&v.arraysEqual(l,n.texData.texShape),b=e.packedInputs||c.length>2?"":`${l[0]>1}_${l[1]>1}`;s+=`${m}_${A}_${u?p:""}_${c.length}_${y}_${x}_${g}_${d}_${h}_${f}_${b}_${i}`}else{let l=o.isUniform?"uniform":o.texData.texShape;s+=`${o.shape}_${l}_${i}`}});let r=e.userCode,a=e.constructor.name;return a+="_"+s+"_"+r+`${H().getNumber("WEBGL_VERSION")}`,a}function ds(e){return H().getBool("WEBGL_USE_SHAPES_UNIFORMS")&&e<=4}var Pte=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.outPackingScheme=Fp.DENSE,this.customUniforms=[{name:"texShape",type:"ivec2"}];let t=cs();this.outputShape=e,this.enableShapeUniforms=ds(this.outputShape.length),this.userCode=`
+  `}function kt(e){if(e<=1)return"int";if(e===2)return"ivec2";if(e===3)return"ivec3";if(e===4)return"ivec4";if(e===5)return"ivec5";if(e===6)return"ivec6";throw Error(`GPU for rank ${e} is not yet supported`)}function ub(e,t,n){let{newShape:s,keptDims:r}=v.squeezeShape(t),a=t.length,o=e&&a===3&&t[0]===1,i=o?t.slice(1):s,l=!e&&a>1&&!v.arraysEqual(t,n)&&s.length<a||o;return{useSqueezeShape:l,uniformShape:l?i:t,keptDims:r}}function gd(e,t){let n=JSON.parse(JSON.stringify(e));return n.shapeInfo.logicalShape=t,n}function yd(e,t){return t.map(n=>e[n]).join(", ")}function Ute(e,t,n,s){let r=n.map((c,p)=>{let d={logicalShape:c.shape,texShape:c.isUniform?null:c.texData.texShape,isUniform:c.isUniform,isPacked:c.isUniform?!1:c.texData.isPacked,flatOffset:null};return c.texData!=null&&c.texData.slice!=null&&c.texData.slice.flatOffset>0&&(d.flatOffset=c.texData.slice.flatOffset),{name:t.variableNames[p],shapeInfo:d}}),a=r.map(c=>c.shapeInfo),o={logicalShape:s.shape,texShape:s.texData.texShape,isUniform:!1,isPacked:s.texData.isPacked,flatOffset:null},i=ite(r,o,t),l=BI(e.gl,i),u=e.createProgram(l);return H().get("ENGINE_COMPILE_ONLY")?{program:t,fragmentShader:l,source:i,webGLProgram:u,inShapeInfos:a,outShapeInfo:o,uniformLocations:null,customUniformLocations:null,infLoc:null,nanLoc:null,inShapesLocations:null,inTexShapesLocations:null,outShapeLocation:null,outShapeStridesLocation:null,outTexShapeLocation:null}:Object.assign({program:t,fragmentShader:l,source:i,webGLProgram:u,inShapeInfos:a,outShapeInfo:o},d9(e,t,u))}function d9(e,t,n){let s={},r={},a={},o=[],i,l,u,c=null,p=null;p=e.getUniformLocation(n,"NAN",!1),H().getNumber("WEBGL_VERSION")===1&&(c=e.getUniformLocation(n,"INFINITY",!1));let d=!1;for(let h=0;h<t.variableNames.length;h++){let f=t.variableNames[h];s[f]=e.getUniformLocation(n,f,d),s[`offset${f}`]=e.getUniformLocation(n,`offset${f}`,d),t.enableShapeUniforms&&(r[`${f}Shape`]=e.getUniformLocation(n,`${f}Shape`,d),a[`${f}TexShape`]=e.getUniformLocation(n,`${f}TexShape`,d))}return t.enableShapeUniforms&&(i=e.getUniformLocation(n,"outShape",d),u=e.getUniformLocation(n,"outShapeStrides",d),l=e.getUniformLocation(n,"outTexShape",d)),t.customUniforms&&t.customUniforms.forEach((h,f)=>{o[f]=e.getUniformLocation(n,h.name,d)}),{uniformLocations:s,customUniformLocations:o,infLoc:c,nanLoc:p,inShapesLocations:r,inTexShapesLocations:a,outShapeLocation:i,outShapeStridesLocation:u,outTexShapeLocation:l}}function F7(e,t){if(e.length!==t.length)throw Error(`Binary was compiled with ${e.length} inputs, but was executed with ${t.length} inputs`);e.forEach((n,s)=>{let r=n.logicalShape,a=t[s],o=a.shape;if(!v.arraysEqual(r,o))throw Error(`Binary was compiled with different shapes than the current args. Shapes ${r} and ${o} must match`);if(n.isUniform&&a.isUniform)return;let i=n.texShape,l=a.isUniform?null:a.texData.texShape;if(!v.arraysEqual(i,l))throw Error(`Binary was compiled with different texture shapes than the current args. Shape ${i} and ${l} must match`)})}function Gte(e,t,n,s,r){t.program.enableShapeUniforms||(F7(t.inShapeInfos,n),F7([t.outShapeInfo],[s]));let a=s.texData.texture,o=s.texData.texShape;s.texData.isPacked?e.setOutputPackedMatrixTexture(a.texture,o[0],o[1]):e.setOutputMatrixTexture(a.texture,o[0],o[1]),e.setProgram(t.webGLProgram),H().getNumber("WEBGL_VERSION")===1&&t.infLoc!==null&&e.gl.uniform1f(t.infLoc,1/0),t.nanLoc!==null&&e.gl.uniform1f(t.nanLoc,NaN),n.forEach((l,u)=>{let c=t.program.variableNames[u],p=t.uniformLocations[c],d=t.uniformLocations[`offset${c}`],h=t.inShapesLocations[`${c}Shape`],f=t.inTexShapesLocations[`${c}TexShape`];if(h){let{uniformShape:m}=ub(t.program.packedInputs,l.shape,l.texData.texShape);switch(m.length){case 1:e.gl.uniform1iv(h,new Int32Array(m));break;case 2:e.gl.uniform2iv(h,new Int32Array(m));break;case 3:e.gl.uniform3iv(h,new Int32Array(m));break;case 4:e.gl.uniform4iv(h,new Int32Array(m));break;default:break}}if(f&&e.gl.uniform2i(f,l.texData.texShape[0],l.texData.texShape[1]),p!=null){if(l.isUniform){if(v.sizeFromShape(l.shape)<2)e.gl.uniform1f(p,l.uniformValues[0]);else{let m=l.uniformValues;m instanceof Float32Array||(m=new Float32Array(m)),e.gl.uniform1fv(p,m)}return}l.texData.slice!=null&&d!=null&&e.gl.uniform1i(d,l.texData.slice.flatOffset),e.setInputMatrixTexture(l.texData.texture.texture,p,u)}});let i=t.outShapeLocation;if(i)switch(s.shape.length){case 1:e.gl.uniform1iv(i,new Int32Array(s.shape));break;case 2:e.gl.uniform2iv(i,new Int32Array(s.shape));break;case 3:e.gl.uniform3iv(i,new Int32Array(s.shape));break;case 4:e.gl.uniform4iv(i,new Int32Array(s.shape));break;default:break}if(t.outShapeStridesLocation){let l=v.computeStrides(s.shape);switch(s.shape.length){case 2:e.gl.uniform1iv(t.outShapeStridesLocation,new Int32Array(l));break;case 3:e.gl.uniform2iv(t.outShapeStridesLocation,new Int32Array(l));break;case 4:e.gl.uniform3iv(t.outShapeStridesLocation,new Int32Array(l));break;default:break}}t.outTexShapeLocation&&e.gl.uniform2i(t.outTexShapeLocation,s.texData.texShape[0],s.texData.texShape[1]),t.program.customUniforms&&r&&t.program.customUniforms.forEach((l,u)=>{let c=t.customUniformLocations[u],p=r[u];if(l.type==="float")e.gl.uniform1fv(c,p);else if(l.type==="vec2")e.gl.uniform2fv(c,p);else if(l.type==="vec3")e.gl.uniform3fv(c,p);else if(l.type==="vec4")e.gl.uniform4fv(c,p);else if(l.type==="int")e.gl.uniform1iv(c,p);else if(l.type==="ivec2")e.gl.uniform2iv(c,p);else if(l.type==="ivec3")e.gl.uniform3iv(c,p);else if(l.type==="ivec4")e.gl.uniform4iv(c,p);else throw Error(`uniform type ${l.type} is not supported yet.`)}),e.executeProgram()}function Hte(e,t,n){let s="";t.concat(n).forEach(o=>{let i=o.texData!=null&&o.texData.slice!=null&&o.texData.slice.flatOffset>0;if(e.enableShapeUniforms&&!o.isUniform){let l=o.texData.texShape,{useSqueezeShape:u,uniformShape:c,keptDims:p}=ub(e.packedInputs,o.shape,l),d="",h="",f="";if(c.length===1&&e.packedInputs){let w=[Math.ceil(l[0]/2),Math.ceil(l[1]/2)];d=`${w[0]>1}_${w[1]>1}`}else if(c.length===2&&!e.packedInputs)h=`${c[0]>1}_${c[1]>1}`;else if(c.length>2&&!e.packedInputs){let w=v.computeStrides(c);f=`${w[0]===l[1]}_${w[w.length-1]===l[1]}`}let m=o.shape.length,g=c.length===2&&v.arraysEqual(o.shape,l),y=v.sizeFromShape(o.shape)===1,x=T.getBroadcastDims(o.shape,n.shape),A=!e.packedInputs&&m===n.shape.length&&v.arraysEqual(l,n.texData.texShape),b=e.packedInputs||c.length>2?"":`${l[0]>1}_${l[1]>1}`;s+=`${m}_${A}_${u?p:""}_${c.length}_${y}_${x}_${g}_${d}_${h}_${f}_${b}_${i}`}else{let l=o.isUniform?"uniform":o.texData.texShape;s+=`${o.shape}_${l}_${i}`}});let r=e.userCode,a=e.constructor.name;return a+="_"+s+"_"+r+`${H().getNumber("WEBGL_VERSION")}`,a}function fs(e){return H().getBool("WEBGL_USE_SHAPES_UNIFORMS")&&e<=4}var jte=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.outPackingScheme=Op.DENSE,this.customUniforms=[{name:"texShape",type:"ivec2"}];let t=hs();this.outputShape=e,this.enableShapeUniforms=fs(this.outputShape.length),this.userCode=`
       ivec3 outCoordsFromFlatIndex(int index) {
-        ${this.enableShapeUniforms?$2(["r","c","d"],e):fu(["r","c","d"],e)}
+        ${this.enableShapeUniforms?$2(["r","c","d"],e):Au(["r","c","d"],e)}
         return ivec3(r, c, d);
       }
 
@@ -1003,9 +1003,9 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
 
         ${t.output} = result;
       }
-    `}},Fte=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outPackingScheme=Fp.DENSE,this.customUniforms=[{name:"texShape",type:"ivec2"}];let t=cs();this.outputShape=e,this.enableShapeUniforms=ds(this.outputShape.length),this.userCode=`
+    `}},qte=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outPackingScheme=Op.DENSE,this.customUniforms=[{name:"texShape",type:"ivec2"}];let t=hs();this.outputShape=e,this.enableShapeUniforms=fs(this.outputShape.length),this.userCode=`
       ivec3 outCoordsFromFlatIndex(int index) {
-        ${this.enableShapeUniforms?$2(["r","c","d"],e):fu(["r","c","d"],e)}
+        ${this.enableShapeUniforms?$2(["r","c","d"],e):Au(["r","c","d"],e)}
         return ivec3(r, c, d);
       }
 
@@ -1023,23 +1023,23 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
 
         ${t.output} = result;
       }
-    `}},Ote=class{constructor(e){this.variableNames=["A"],this.outTexUsage=Ys.DOWNLOAD;let t=cs();this.outputShape=e,this.userCode=`
-      ${t9}
+    `}},Xte=class{constructor(e){this.variableNames=["A"],this.outTexUsage=Qs.DOWNLOAD;let t=hs();this.outputShape=e,this.userCode=`
+      ${i9}
 
       void main() {
         float x = getAAtOutCoords();
         ${t.output} = encode_float(x);
       }
-    `}},Mte=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outTexUsage=Ys.DOWNLOAD;let t=cs();this.outputShape=e,this.userCode=`
-      ${t9}
+    `}},Kte=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outTexUsage=Qs.DOWNLOAD;let t=hs();this.outputShape=e,this.userCode=`
+      ${i9}
 
       void main() {
         ivec3 coords = getOutputCoords();
         float x = getChannel(getAAtOutCoords(), vec2(coords.y, coords.z));
         ${t.output} = encode_float(x);
       }
-    `}},zte=class{constructor(e,t=!1){this.variableNames=["A"],this.customUniforms=[{name:"texShape",type:"ivec2"}];let n=cs();this.outputShape=e,this.enableShapeUniforms=ds(this.outputShape.length);let s="result";t&&(s="floor(result * 255. + 0.5)"),this.userCode=`
-      ${this.enableShapeUniforms?ab():rb(e)}
+    `}},Zte=class{constructor(e,t=!1){this.variableNames=["A"],this.customUniforms=[{name:"texShape",type:"ivec2"}];let n=hs();this.outputShape=e,this.enableShapeUniforms=fs(this.outputShape.length);let s="result";t&&(s="floor(result * 255. + 0.5)"),this.userCode=`
+      ${this.enableShapeUniforms?lb():ib(e)}
 
       void main() {
         ivec3 coords = getOutputCoords();
@@ -1068,7 +1068,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
 
         ${n.output} = vec4(${s}, 0., 0., 0.);
       }
-    `}},Lte=class{constructor(e,t=!1){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.customUniforms=[{name:"texShape",type:"ivec2"}];let n=cs();this.outputShape=e,this.enableShapeUniforms=ds(this.outputShape.length);let s="",r="result";t&&(r="floor(result * 255. + 0.5)");for(let a=0;a<=1;a++)for(let o=0;o<=1;o++){let i=a*2+o;s+=`
+    `}},Yte=class{constructor(e,t=!1){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.customUniforms=[{name:"texShape",type:"ivec2"}];let n=hs();this.outputShape=e,this.enableShapeUniforms=fs(this.outputShape.length);let s="",r="result";t&&(r="floor(result * 255. + 0.5)");for(let a=0;a<=1;a++)for(let o=0;o<=1;o++){let i=a*2+o;s+=`
           localCoords = coords;
           if(localCoords[2] + ${o} < ${this.enableShapeUniforms?"outShape[2]":`${e[2]}`}) {
           localCoords[2] += ${o};
@@ -1097,7 +1097,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
           }
         }
         `}this.userCode=`
-        ${this.enableShapeUniforms?ab():rb(e)}
+        ${this.enableShapeUniforms?lb():ib(e)}
 
         void main() {
           ivec3 coords = getOutputCoords();
@@ -1112,7 +1112,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
 
           ${n.output} = ${r};
         }
-    `}},o9={};je(o9,{bindVertexProgramAttributeStreams:()=>m9,createBufferFromOutputTexture:()=>A9,createFloat16MatrixTexture:()=>d9,createFloat16PackedMatrixTexture:()=>f9,createFloat32MatrixTexture:()=>c9,createIndexBuffer:()=>u9,createPackedMatrixTexture:()=>h9,createUnsignedBytesMatrixTexture:()=>p9,createVertexBuffer:()=>l9,createVertexShader:()=>i9,downloadByteEncodedFloatMatrixFromOutputTexture:()=>b9,downloadFloat32MatrixFromBuffer:()=>x9,downloadMatrixFromPackedOutputTexture:()=>w9,downloadPackedMatrixFromBuffer:()=>v9,getInternalFormatForFloat16MatrixTexture:()=>lb,getInternalFormatForFloat16PackedMatrixTexture:()=>db,getInternalFormatForFloat32MatrixTexture:()=>ib,getInternalFormatForPackedMatrixTexture:()=>cb,getInternalFormatForUnsignedBytesMatrixTexture:()=>ub,uploadDenseMatrixToTexture:()=>g9,uploadPixelDataToTexture:()=>y9});function i9(e){let t=cs(),n=`${t.version}
+    `}},p9={};qe(p9,{bindVertexProgramAttributeStreams:()=>v9,createBufferFromOutputTexture:()=>S9,createFloat16MatrixTexture:()=>y9,createFloat16PackedMatrixTexture:()=>b9,createFloat32MatrixTexture:()=>g9,createIndexBuffer:()=>m9,createPackedMatrixTexture:()=>x9,createUnsignedBytesMatrixTexture:()=>A9,createVertexBuffer:()=>f9,createVertexShader:()=>h9,downloadByteEncodedFloatMatrixFromOutputTexture:()=>C9,downloadFloat32MatrixFromBuffer:()=>I9,downloadMatrixFromPackedOutputTexture:()=>N9,downloadPackedMatrixFromBuffer:()=>T9,getInternalFormatForFloat16MatrixTexture:()=>db,getInternalFormatForFloat16PackedMatrixTexture:()=>fb,getInternalFormatForFloat32MatrixTexture:()=>cb,getInternalFormatForPackedMatrixTexture:()=>hb,getInternalFormatForUnsignedBytesMatrixTexture:()=>pb,uploadDenseMatrixToTexture:()=>w9,uploadPixelDataToTexture:()=>k9});function h9(e){let t=hs(),n=`${t.version}
     precision highp float;
     ${t.attribute} vec3 clipSpacePos;
     ${t.attribute} vec2 uv;
@@ -1121,11 +1121,11 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
     void main() {
       gl_Position = vec4(clipSpacePos, 1);
       resultUV = uv;
-    }`;return $I(e,n)}function l9(e){let t=new Float32Array([-1,1,0,0,1,-1,-1,0,0,0,1,1,0,1,1,1,-1,0,1,0]);return MI(e,t)}function u9(e){let t=new Uint16Array([0,1,2,2,1,3]);return zI(e,t)}function Gh(e,t,n,s,r,a){BI(t,n);let o=LI(e),i=e.TEXTURE_2D;return Ie(e,()=>e.bindTexture(i,o)),Ie(e,()=>e.texParameteri(i,e.TEXTURE_WRAP_S,e.CLAMP_TO_EDGE)),Ie(e,()=>e.texParameteri(i,e.TEXTURE_WRAP_T,e.CLAMP_TO_EDGE)),Ie(e,()=>e.texParameteri(i,e.TEXTURE_MIN_FILTER,e.NEAREST)),Ie(e,()=>e.texParameteri(i,e.TEXTURE_MAG_FILTER,e.NEAREST)),H().getNumber("WEBGL_VERSION")===1?Ie(e,()=>e.texImage2D(i,0,s,t,n,0,r,a,null)):Ie(e,()=>e.texStorage2D(i,1,s,t,n)),Ie(e,()=>e.bindTexture(e.TEXTURE_2D,null)),{texture:o,texShape:[n,t]}}function ib(e){return e.internalFormatFloat}function c9(e,t,n,s){let[r,a]=Uh(t,n);return Gh(e,r,a,ib(s),s.textureFormatFloat,e.FLOAT)}function lb(e){return e.internalFormatHalfFloat}function d9(e,t,n,s){let[r,a]=Uh(t,n);return 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Ie(e,()=>e.bufferData(e.PIXEL_PACK_BUFFER,i,e.STREAM_READ)),Ie(e,()=>e.readPixels(0,0,n,t,e.RGBA,e.FLOAT,0)),Ie(e,()=>e.bindBuffer(e.PIXEL_PACK_BUFFER,null)),r}function x9(e,t,n){let s=e,r=new Float32Array(n);return s.bindBuffer(s.PIXEL_PACK_BUFFER,t),s.getBufferSubData(s.PIXEL_PACK_BUFFER,0,r),s.bindBuffer(s.PIXEL_PACK_BUFFER,null),r}function b9(e,t,n,s){let[r,a]=Uh(t,n),o=4,i=new Uint8Array(Fee(t*n,o));return Ie(e,()=>e.readPixels(0,0,r,a,s.downloadTextureFormat,e.UNSIGNED_BYTE,i)),new Float32Array(i.buffer)}function v9(e,t,n,s,r,a,o,i){let l=e,u=new Float32Array(Oee(a,o));return l.bindBuffer(l.PIXEL_PACK_BUFFER,t),l.getBufferSubData(l.PIXEL_PACK_BUFFER,0,u),l.bindBuffer(l.PIXEL_PACK_BUFFER,null),u}function w9(e,t,n){let s=new Float32Array(t*n*4);return Ie(e,()=>e.readPixels(0,0,n,t,e.RGBA,e.FLOAT,s)),s}var rc=class{constructor(e){this.outputTexture=null,this.program=null,this.disposed=!1,this.vertexAttrsAreBound=!1,this.itemsToPoll=[];let t=H().getNumber("WEBGL_VERSION");e!=null?(this.gl=e,D2(t,e)):this.gl=Wr(t);let n="WEBGL_color_buffer_float",s="EXT_color_buffer_half_float";if(this.parallelCompilationExtension=this.gl.getExtension("KHR_parallel_shader_compile"),H().getNumber("WEBGL_VERSION")===1){let r="OES_texture_float",a="OES_texture_half_float";if(this.textureFloatExtension=hp(this.gl,r),Js(this.gl,a))this.textureHalfFloatExtension=hp(this.gl,a);else if(H().get("WEBGL_FORCE_F16_TEXTURES"))throw new Error("GL context does not support half float textures, yet the environment flag WEBGL_FORCE_F16_TEXTURES is set to true.");if(this.colorBufferFloatExtension=this.gl.getExtension(n),Js(this.gl,s))this.colorBufferHalfFloatExtension=hp(this.gl,s);else if(H().get("WEBGL_FORCE_F16_TEXTURES"))throw new Error("GL context does not support color renderable half floats, yet the environment flag WEBGL_FORCE_F16_TEXTURES is set to true.")}else 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This is probably a resource leak, delete the output matrix texture with GPGPUContext.deleteMatrixTexture before disposing.");let e=this.gl;Ie(e,()=>e.finish()),Ie(e,()=>e.bindFramebuffer(e.FRAMEBUFFER,null)),Ie(e,()=>e.deleteFramebuffer(this.framebuffer)),Ie(e,()=>e.bindBuffer(e.ARRAY_BUFFER,null)),Ie(e,()=>e.bindBuffer(e.ELEMENT_ARRAY_BUFFER,null)),Ie(e,()=>e.deleteBuffer(this.indexBuffer)),this.disposed=!0}createFloat32MatrixTexture(e,t){return this.throwIfDisposed(),c9(this.gl,e,t,this.textureConfig)}createFloat16MatrixTexture(e,t){return this.throwIfDisposed(),d9(this.gl,e,t,this.textureConfig)}createUnsignedBytesMatrixTexture(e,t){return this.throwIfDisposed(),p9(this.gl,e,t,this.textureConfig)}uploadPixelDataToTexture(e,t){this.throwIfDisposed(),y9(this.gl,e,t)}uploadDenseMatrixToTexture(e,t,n,s){this.throwIfDisposed(),g9(this.gl,e,t,n,s,this.textureConfig)}createFloat16PackedMatrixTexture(e,t){return 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this.throwIfDisposed(),this.gl.getUniformLocation(e,t)}setInputMatrixTexture(e,t,n){this.throwIfDisposed(),this.throwIfNoProgram(),HI(this.gl,e,t,n)}setOutputMatrixTexture(e,t,n){this.setOutputMatrixTextureDriver(e,n,t)}setOutputPackedMatrixTexture(e,t,n){this.throwIfDisposed();let[s,r]=ud(t,n);this.setOutputMatrixTextureDriver(e,s,r)}setOutputMatrixWriteRegion(e,t,n,s){this.setOutputMatrixWriteRegionDriver(n,e,s,t)}setOutputPackedMatrixWriteRegion(e,t,n,s){throw new Error("setOutputPackedMatrixWriteRegion not implemented.")}debugValidate(){this.program!=null&&fm(this.gl,this.program),fp(this.gl)}executeProgram(){this.throwIfDisposed(),this.throwIfNoProgram();let e=this.gl;this.debug&&this.debugValidate(),Ie(e,()=>e.drawElements(e.TRIANGLES,6,e.UNSIGNED_SHORT,0))}blockUntilAllProgramsCompleted(){this.throwIfDisposed(),Ie(this.gl,()=>this.gl.finish())}getQueryTimerExtension(){return 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Hh(e,r,a,db(s),s.textureFormatFloat,s.textureTypeHalfFloat)}function pb(e){return e.downloadTextureFormat}function A9(e,t,n,s){let[r,a]=Gh(t,n);return Hh(e,r,a,pb(s),e.RGBA,e.UNSIGNED_BYTE)}function hb(e){return e.internalFormatPackedFloat}function x9(e,t,n,s){let[r,a]=pd(t,n);return Hh(e,r,a,hb(s),e.RGBA,e.FLOAT)}function fb(e){return e.internalFormatPackedHalfFloat}function b9(e,t,n,s){let[r,a]=pd(t,n);return Hh(e,r,a,fb(s),e.RGBA,s.textureTypeHalfFloat)}function v9(e,t,n){return Ie(e,()=>e.bindBuffer(e.ARRAY_BUFFER,n)),by(e,t,"clipSpacePos",n,3,20,0)&&by(e,t,"uv",n,2,20,12)}function w9(e,t,n,s,r,a){Ie(e,()=>e.bindTexture(e.TEXTURE_2D,t));let o,i,l;r instanceof Uint8Array?(o=new Uint8Array(n*s*4),i=e.UNSIGNED_BYTE,l=e.RGBA):(o=new Float32Array(n*s*4),i=e.FLOAT,l=a.internalFormatPackedFloat),o.set(r),H().getNumber("WEBGL_VERSION")===2?Ie(e,()=>e.texSubImage2D(e.TEXTURE_2D,0,0,0,n,s,e.RGBA,i,o)):Ie(e,()=>e.texImage2D(e.TEXTURE_2D,0,l,n,s,0,e.RGBA,i,o)),Ie(e,()=>e.bindTexture(e.TEXTURE_2D,null))}function k9(e,t,n){Ie(e,()=>e.bindTexture(e.TEXTURE_2D,t)),n.data instanceof Uint8Array?H().getNumber("WEBGL_VERSION")===2?Ie(e,()=>e.texSubImage2D(e.TEXTURE_2D,0,0,0,n.width,n.height,e.RGBA,e.UNSIGNED_BYTE,n.data)):Ie(e,()=>e.texImage2D(e.TEXTURE_2D,0,e.RGBA,n.width,n.height,0,e.RGBA,e.UNSIGNED_BYTE,n.data)):H().getNumber("WEBGL_VERSION")===2?Ie(e,()=>e.texSubImage2D(e.TEXTURE_2D,0,0,0,e.RGBA,e.UNSIGNED_BYTE,n)):Ie(e,()=>e.texImage2D(e.TEXTURE_2D,0,e.RGBA,e.RGBA,e.UNSIGNED_BYTE,n)),Ie(e,()=>e.bindTexture(e.TEXTURE_2D,null))}function S9(e,t,n,s){let r=e.createBuffer();Ie(e,()=>e.bindBuffer(e.PIXEL_PACK_BUFFER,r));let i=4*4*t*n;return Ie(e,()=>e.bufferData(e.PIXEL_PACK_BUFFER,i,e.STREAM_READ)),Ie(e,()=>e.readPixels(0,0,n,t,e.RGBA,e.FLOAT,0)),Ie(e,()=>e.bindBuffer(e.PIXEL_PACK_BUFFER,null)),r}function I9(e,t,n){let s=e,r=new Float32Array(n);return s.bindBuffer(s.PIXEL_PACK_BUFFER,t),s.getBufferSubData(s.PIXEL_PACK_BUFFER,0,r),s.bindBuffer(s.PIXEL_PACK_BUFFER,null),r}function C9(e,t,n,s){let[r,a]=Gh(t,n),o=4,i=new Uint8Array(qee(t*n,o));return Ie(e,()=>e.readPixels(0,0,r,a,s.downloadTextureFormat,e.UNSIGNED_BYTE,i)),new Float32Array(i.buffer)}function T9(e,t,n,s,r,a,o,i){let l=e,u=new Float32Array(Xee(a,o));return l.bindBuffer(l.PIXEL_PACK_BUFFER,t),l.getBufferSubData(l.PIXEL_PACK_BUFFER,0,u),l.bindBuffer(l.PIXEL_PACK_BUFFER,null),u}function N9(e,t,n){let s=new Float32Array(t*n*4);return Ie(e,()=>e.readPixels(0,0,n,t,e.RGBA,e.FLOAT,s)),s}var lc=class{constructor(e){this.outputTexture=null,this.program=null,this.disposed=!1,this.vertexAttrsAreBound=!1,this.itemsToPoll=[];let t=H().getNumber("WEBGL_VERSION");e!=null?(this.gl=e,D2(t,e)):this.gl=Ur(t);let n="WEBGL_color_buffer_float",s="EXT_color_buffer_half_float";if(this.parallelCompilationExtension=this.gl.getExtension("KHR_parallel_shader_compile"),H().getNumber("WEBGL_VERSION")===1){let r="OES_texture_float",a="OES_texture_half_float";if(this.textureFloatExtension=fp(this.gl,r),er(this.gl,a))this.textureHalfFloatExtension=fp(this.gl,a);else if(H().get("WEBGL_FORCE_F16_TEXTURES"))throw new Error("GL context does not support half float textures, yet the environment flag WEBGL_FORCE_F16_TEXTURES is set to true.");if(this.colorBufferFloatExtension=this.gl.getExtension(n),er(this.gl,s))this.colorBufferHalfFloatExtension=fp(this.gl,s);else if(H().get("WEBGL_FORCE_F16_TEXTURES"))throw new Error("GL context does not support color renderable half floats, yet the environment flag WEBGL_FORCE_F16_TEXTURES is set to true.")}else if(n="EXT_color_buffer_float",er(this.gl,n))this.colorBufferFloatExtension=this.gl.getExtension(n);else if(er(this.gl,s))this.colorBufferHalfFloatExtension=this.gl.getExtension(s);else throw new Error("GL context does not support color renderable floats");this.vertexBuffer=f9(this.gl),this.indexBuffer=m9(this.gl),this.framebuffer=qI(this.gl),this.textureConfig=ab(this.gl,this.textureHalfFloatExtension)}get debug(){return H().getBool("DEBUG")}dispose(){if(this.disposed)return;this.program!=null&&console.warn("Disposing a GPGPUContext that still has a bound WebGLProgram. This is probably a resource leak, delete the program with GPGPUContext.deleteProgram before disposing."),this.outputTexture!=null&&console.warn("Disposing a GPGPUContext that still has a bound output matrix texture.  This is probably a resource leak, delete the output matrix texture with GPGPUContext.deleteMatrixTexture before disposing.");let e=this.gl;Ie(e,()=>e.finish()),Ie(e,()=>e.bindFramebuffer(e.FRAMEBUFFER,null)),Ie(e,()=>e.deleteFramebuffer(this.framebuffer)),Ie(e,()=>e.bindBuffer(e.ARRAY_BUFFER,null)),Ie(e,()=>e.bindBuffer(e.ELEMENT_ARRAY_BUFFER,null)),Ie(e,()=>e.deleteBuffer(this.indexBuffer)),this.disposed=!0}createFloat32MatrixTexture(e,t){return this.throwIfDisposed(),g9(this.gl,e,t,this.textureConfig)}createFloat16MatrixTexture(e,t){return this.throwIfDisposed(),y9(this.gl,e,t,this.textureConfig)}createUnsignedBytesMatrixTexture(e,t){return this.throwIfDisposed(),A9(this.gl,e,t,this.textureConfig)}uploadPixelDataToTexture(e,t){this.throwIfDisposed(),k9(this.gl,e,t)}uploadDenseMatrixToTexture(e,t,n,s){this.throwIfDisposed(),w9(this.gl,e,t,n,s,this.textureConfig)}createFloat16PackedMatrixTexture(e,t){return this.throwIfDisposed(),b9(this.gl,e,t,this.textureConfig)}createPackedMatrixTexture(e,t){return this.throwIfDisposed(),x9(this.gl,e,t,this.textureConfig)}deleteMatrixTexture(e){this.throwIfDisposed(),this.outputTexture===e&&(vy(this.gl,this.framebuffer),this.outputTexture=null),Ie(this.gl,()=>this.gl.deleteTexture(e))}downloadByteEncodedFloatMatrixFromOutputTexture(e,t,n){return this.downloadMatrixDriver(e,()=>C9(this.gl,t,n,this.textureConfig))}downloadPackedMatrixFromBuffer(e,t,n,s,r,a){return T9(this.gl,e,t,n,s,r,a,this.textureConfig)}downloadFloat32MatrixFromBuffer(e,t){return I9(this.gl,e,t)}createBufferFromTexture(e,t,n){this.bindTextureToFrameBuffer(e);let s=S9(this.gl,t,n,this.textureConfig);return this.unbindTextureToFrameBuffer(),s}createAndWaitForFence(){let e=this.createFence(this.gl);return this.pollFence(e)}createFence(e){let t,n;if(H().getBool("WEBGL_FENCE_API_ENABLED")){let s=e,r=s.fenceSync(s.SYNC_GPU_COMMANDS_COMPLETE,0);e.flush(),n=()=>{let a=s.clientWaitSync(r,0,0);return a===s.ALREADY_SIGNALED||a===s.CONDITION_SATISFIED},t=r}else H().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")>0?(t=this.beginQuery(),this.endQuery(),n=()=>this.isQueryAvailable(t,H().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))):n=()=>!0;return{query:t,isFencePassed:n}}downloadMatrixFromPackedTexture(e,t,n){return this.downloadMatrixDriver(e,()=>N9(this.gl,t,n))}createProgram(e){this.throwIfDisposed();let t=this.gl;this.vertexShader==null&&(this.vertexShader=h9(t));let n=WI(t);return Ie(t,()=>t.attachShader(n,this.vertexShader)),Ie(t,()=>t.attachShader(n,e)),VI(t,n),this.debug&&fm(t,n),this.vertexAttrsAreBound||(this.setProgram(n),this.vertexAttrsAreBound=v9(t,this.program,this.vertexBuffer)),n}deleteProgram(e){this.throwIfDisposed(),e===this.program&&(this.program=null),e!=null&&Ie(this.gl,()=>this.gl.deleteProgram(e))}setProgram(e){this.throwIfDisposed(),this.program=e,this.program!=null&&this.debug&&fm(this.gl,this.program),Ie(this.gl,()=>this.gl.useProgram(e))}getUniformLocation(e,t,n=!0){return this.throwIfDisposed(),n?KI(this.gl,e,t):ZI(this.gl,e,t)}getAttributeLocation(e,t){return this.throwIfDisposed(),Ie(this.gl,()=>this.gl.getAttribLocation(e,t))}getUniformLocationNoThrow(e,t){return this.throwIfDisposed(),this.gl.getUniformLocation(e,t)}setInputMatrixTexture(e,t,n){this.throwIfDisposed(),this.throwIfNoProgram(),YI(this.gl,e,t,n)}setOutputMatrixTexture(e,t,n){this.setOutputMatrixTextureDriver(e,n,t)}setOutputPackedMatrixTexture(e,t,n){this.throwIfDisposed();let[s,r]=pd(t,n);this.setOutputMatrixTextureDriver(e,s,r)}setOutputMatrixWriteRegion(e,t,n,s){this.setOutputMatrixWriteRegionDriver(n,e,s,t)}setOutputPackedMatrixWriteRegion(e,t,n,s){throw new Error("setOutputPackedMatrixWriteRegion not implemented.")}debugValidate(){this.program!=null&&fm(this.gl,this.program),mp(this.gl)}executeProgram(){this.throwIfDisposed(),this.throwIfNoProgram();let e=this.gl;this.debug&&this.debugValidate(),Ie(e,()=>e.drawElements(e.TRIANGLES,6,e.UNSIGNED_SHORT,0))}blockUntilAllProgramsCompleted(){this.throwIfDisposed(),Ie(this.gl,()=>this.gl.finish())}getQueryTimerExtension(){return this.disjointQueryTimerExtension==null&&(this.disjointQueryTimerExtension=fp(this.gl,H().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")===2?"EXT_disjoint_timer_query_webgl2":"EXT_disjoint_timer_query")),this.disjointQueryTimerExtension}getQueryTimerExtensionWebGL2(){return this.getQueryTimerExtension()}getQueryTimerExtensionWebGL1(){return this.getQueryTimerExtension()}beginQuery(){if(H().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")===2){let n=this.gl,s=this.getQueryTimerExtensionWebGL2(),r=n.createQuery();return n.beginQuery(s.TIME_ELAPSED_EXT,r),r}let e=this.getQueryTimerExtensionWebGL1(),t=e.createQueryEXT();return e.beginQueryEXT(e.TIME_ELAPSED_EXT,t),t}endQuery(){if(H().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")===2){let t=this.gl,n=this.getQueryTimerExtensionWebGL2();t.endQuery(n.TIME_ELAPSED_EXT);return}let e=this.getQueryTimerExtensionWebGL1();e.endQueryEXT(e.TIME_ELAPSED_EXT)}async waitForQueryAndGetTime(e){return await v.repeatedTry(()=>this.disposed||this.isQueryAvailable(e,H().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))),this.getQueryTime(e,H().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))}getQueryTime(e,t){if(t===0)return null;if(t===2){let n=this.gl;return n.getQueryParameter(e,n.QUERY_RESULT)/1e6}else{let n=this.getQueryTimerExtensionWebGL1();return n.getQueryObjectEXT(e,n.QUERY_RESULT_EXT)/1e6}}isQueryAvailable(e,t){if(t===0)return!0;if(t===2){let n=this.gl,s=this.getQueryTimerExtensionWebGL2(),r=n.getQueryParameter(e,n.QUERY_RESULT_AVAILABLE);return this.disjoint==null&&(this.disjoint=this.gl.getParameter(s.GPU_DISJOINT_EXT)),r&&!this.disjoint}else{let n=this.getQueryTimerExtensionWebGL1(),s=n.getQueryObjectEXT(e,n.QUERY_RESULT_AVAILABLE_EXT);return this.disjoint==null&&(this.disjoint=this.gl.getParameter(n.GPU_DISJOINT_EXT)),s&&!this.disjoint}}pollFence(e){return new Promise(t=>{this.addItemToPoll(()=>e.isFencePassed(),()=>t())})}pollItems(){let e=Jte(this.itemsToPoll.map(t=>t.isDoneFn));for(let t=0;t<=e;++t){let{resolveFn:n}=this.itemsToPoll[t];n()}this.itemsToPoll=this.itemsToPoll.slice(e+1)}addItemToPoll(e,t){this.itemsToPoll.push({isDoneFn:e,resolveFn:t}),!(this.itemsToPoll.length>1)&&v.repeatedTry(()=>(this.pollItems(),this.itemsToPoll.length===0))}bindTextureToFrameBuffer(e){this.throwIfDisposed(),mm(this.gl,e,this.framebuffer),this.debug&&mp(this.gl)}unbindTextureToFrameBuffer(){this.outputTexture!=null?(mm(this.gl,this.outputTexture,this.framebuffer),this.debug&&mp(this.gl)):vy(this.gl,this.framebuffer)}downloadMatrixDriver(e,t){this.bindTextureToFrameBuffer(e);let n=t();return this.unbindTextureToFrameBuffer(),n}setOutputMatrixTextureDriver(e,t,n){this.throwIfDisposed();let s=this.gl;mm(s,e,this.framebuffer),this.debug&&mp(s),this.outputTexture=e,Ie(s,()=>s.viewport(0,0,t,n)),Ie(s,()=>s.scissor(0,0,t,n))}setOutputMatrixWriteRegionDriver(e,t,n,s){this.throwIfDisposed(),Ie(this.gl,()=>this.gl.scissor(e,t,n,s))}throwIfDisposed(){if(this.disposed)throw new Error("Attempted to use disposed GPGPUContext.")}throwIfNoProgram(){if(this.program==null)throw new Error("No GPU program is currently set.")}};function Jte(e){let t=0;for(;t<e.length&&e[t]();++t);return t-1}var{addImpl:Qte,bincountImpl:E9,bincountReduceImpl:ene,castImpl:tne,ceilImpl:nne,concatImpl:sne,equalImpl:rne,expImpl:ane,expm1Impl:one,floorImpl:ine,gatherNdImpl:lne,gatherV2Impl:une,greaterImpl:cne,greaterEqualImpl:dne,lessImpl:pne,lessEqualImpl:hne,linSpaceImpl:fne,logImpl:mne,maxImpl:gne,maximumImpl:yne,minimumImpl:Ane,multiplyImpl:xne,negImpl:bne,notEqualImpl:vne,prodImpl:wne,raggedTensorToTensorImpl:kne,rangeImpl:Sne,rsqrtImpl:Ine,scatterImpl:Cne,sigmoidImpl:Tne,simpleAbsImpl:R9,sliceImpl:Nne,sparseFillEmptyRowsImpl:Ene,sparseReshapeImpl:Rne,sparseSegmentReductionImpl:_9,sqrtImpl:_ne,stridedSliceImpl:Dne,stringNGramsImpl:$ne,stringSplitImpl:Pne,stringToHashBucketFastImpl:Fne,subImpl:One,tileImpl:Mne,topKImpl:zne,transposeImpl:mb,uniqueImpl:Lne}=Gx;function D9(e,t){return["x","y","z","w","u","v"].slice(0,t).map(n=>`${e}.${n}`)}function us(e,t){return t===1?[e]:D9(e,t)}function Bne(e,t){if(e===1)return"rc";let n="";for(let s=0;s<e;s++)n+=t[s],s<e-1&&(n+=",");return n}var Wne=class{constructor(e){if(this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.outputShape=e,this.rank=e.length,this.enableShapeUniforms=fs(this.outputShape.length),this.rank===0)this.userCode=`
         void main() {
           setOutput(vec4(getA(), 0., 0., 0.));
         }
-      `;else{let t=os("rc",this.rank),n=wt(this.rank),s=this.getOutOfBoundsCondition(t),r=this.getSetup(t),a=this.getOutput(t);this.userCode=`
+      `;else{let t=us("rc",this.rank),n=kt(this.rank),s=this.getOutOfBoundsCondition(t),r=this.getSetup(t),a=this.getOutput(t);this.userCode=`
         void main() {
           ${n} rc = getOutputCoords();
 
@@ -1148,7 +1148,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
     `}getOutput(e){let t=this.getSourceCoordsArr(e);return this.rank===1?`getA(rc), (rc + 1 >= ${this.enableShapeUniforms?"outShape":this.outputShape[0]} ? 0. : getA(rc + 1)), 0, 0`:`getA(${t[0]}),
             cEdge ? 0. : getA(${t[1]}),
             rEdge ? 0. : getA(${t[2]}),
-            rEdge || cEdge ? 0. : getA(${t[3]})`}},T9=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"inputShape",type:"ivec3"}],this.outputShape=e,this.enableShapeUniforms=ds(this.outputShape.length);let n="";for(let s=0;s<4;s++){let r="thisRC = rc;";s%2===1&&(r+="thisRC.z += 1;"),s>1&&(r+="thisRC.y += 1;"),n+=`
+            rEdge || cEdge ? 0. : getA(${t[3]})`}},$9=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"inputShape",type:"ivec3"}],this.outputShape=e,this.enableShapeUniforms=fs(this.outputShape.length);let n="";for(let s=0;s<4;s++){let r="thisRC = rc;";s%2===1&&(r+="thisRC.z += 1;"),s>1&&(r+="thisRC.y += 1;"),n+=`
         ${r}
         ${s>0?"if(thisRC.y < rows && thisRC.z < cols){":""}
           int flatIndex = getFlatIndex(thisRC);
@@ -1160,8 +1160,8 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
             getChannel(getA(inputRC.x, inputRC.y, inputRC.z), inputRCInnerDims);
         ${s>0?"}":""}
       `}this.userCode=`
-      ${Rne(t,this.enableShapeUniforms)}
-      ${this.enableShapeUniforms?ab():rb(e)}
+      ${Vne(t,this.enableShapeUniforms)}
+      ${this.enableShapeUniforms?lb():ib(e)}
 
       void main() {
         ivec3 rc = getOutputCoords();
@@ -1176,12 +1176,12 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
 
         setOutput(result);
       }
-    `}};function Rne(e,t){return`
+    `}};function Vne(e,t){return`
     ivec3 inputCoordsFromReshapedOutCoords(int index) {
-      ${t?Xee(["r","c","d"],"inputShape"):fu(["r","c","d"],e)}
+      ${t?ote(["r","c","d"],"inputShape"):Au(["r","c","d"],e)}
       return ivec3(r, c, d);
     }
-  `}var _ne=class{constructor(e){this.gpgpu=e,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0,this.freeTextures={},this.logEnabled=!1,this.usedTextures={}}acquireTexture(e,t,n){let s=_7(t,n),r=D7(e,s,n);r in this.freeTextures||(this.freeTextures[r]=[]),r in this.usedTextures||(this.usedTextures[r]=[]);let a=R7(e,s,this.gpgpu.gl,this.gpgpu.textureConfig,n);if(this.freeTextures[r].length>0){this.numFreeTextures--,this.numUsedTextures++,this._numBytesFree-=a,this.log();let i=this.freeTextures[r].shift();return this.usedTextures[r].push(i),i}let o;return s===$n.PACKED_2X2_FLOAT32?o=this.gpgpu.createPackedMatrixTexture(e[0],e[1]):s===$n.PACKED_2X2_FLOAT16?o=this.gpgpu.createFloat16PackedMatrixTexture(e[0],e[1]):s===$n.UNPACKED_FLOAT32?o=this.gpgpu.createFloat32MatrixTexture(e[0],e[1]):s===$n.UNPACKED_FLOAT16?o=this.gpgpu.createFloat16MatrixTexture(e[0],e[1]):s===$n.PACKED_4X1_UNSIGNED_BYTE&&(o=this.gpgpu.createUnsignedBytesMatrixTexture(e[0],e[1])),this.usedTextures[r].push(o),this.numUsedTextures++,this._numBytesAllocated+=a,this.log(),o}releaseTexture(e,t,n,s){if(this.freeTextures==null)return;let r=_7(n,s),a=D7(t,r,s);a in this.freeTextures||(this.freeTextures[a]=[]);let o=R7(t,r,this.gpgpu.gl,this.gpgpu.textureConfig,s),i=H().get("WEBGL_DELETE_TEXTURE_THRESHOLD");i!==-1&&this._numBytesAllocated>i?(this.gpgpu.deleteMatrixTexture(e.texture),this._numBytesAllocated-=o):(this.freeTextures[a].push(e),this.numFreeTextures++,this._numBytesFree+=o),this.numUsedTextures--;let l=this.usedTextures[a],u=l.indexOf(e);if(u<0)throw new Error("Cannot release a texture that was never provided by this texture manager");l.splice(u,1),this.log()}log(){if(!this.logEnabled)return;let e=this.numFreeTextures+this.numUsedTextures;console.log("Free/Used",`${this.numFreeTextures} / ${this.numUsedTextures}`,`(${e})`);let t=this._numBytesFree/this._numBytesAllocated;console.log(`Bytes allocated: ${this._numBytesAllocated}`),console.log(`Bytes unused: ${this._numBytesFree} (${Math.round(100*t)}%)`)}get numBytesAllocated(){return this._numBytesAllocated}get numBytesFree(){return this._numBytesFree}getNumUsedTextures(){return this.numUsedTextures}getNumFreeTextures(){return this.numFreeTextures}dispose(){if(this.freeTextures!=null){for(let e in this.freeTextures)this.freeTextures[e].forEach(t=>{this.gpgpu.deleteMatrixTexture(t.texture)});for(let e in this.usedTextures)this.usedTextures[e].forEach(t=>{this.gpgpu.deleteMatrixTexture(t.texture)});this.freeTextures=null,this.usedTextures=null,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0}}};function Dne(e,t){let n=e;if(t===n.R32F)return 4;if(t===n.R16F)return 2;if(t===n.RGBA32F)return 16;if(t===e.RGBA)return 16;if(t===n.RGBA16F)return 8;if(t===n.RGBA8)return 4;throw new Error(`Unknown internal format ${t}`)}function R7(e,t,n,s,r){let a=$ne(t,s),o;if(r){let[l,u]=ud(e[0],e[1]);o=l*u}else{let[l,u]=Uh(e[0],e[1]);o=l*u}let i=Dne(n,a);return o*i}function $ne(e,t){switch(e){case $n.PACKED_2X2_FLOAT32:return cb(t);case $n.PACKED_2X2_FLOAT16:return db(t);case $n.UNPACKED_FLOAT32:return ib(t);case $n.UNPACKED_FLOAT16:return lb(t);case $n.PACKED_4X1_UNSIGNED_BYTE:return ub(t);default:throw new Error(`Unknown physical texture type ${e}`)}}function Pne(e){return H().getBool("WEBGL_RENDER_FLOAT32_ENABLED")?e?$n.PACKED_2X2_FLOAT32:$n.UNPACKED_FLOAT32:e?$n.PACKED_2X2_FLOAT16:$n.UNPACKED_FLOAT16}function _7(e,t){if(e===Ys.UPLOAD)return $n.PACKED_2X2_FLOAT32;if(e===Ys.RENDER||e==null)return Pne(t);if(e===Ys.DOWNLOAD||e===Ys.PIXELS)return $n.PACKED_4X1_UNSIGNED_BYTE;throw new Error(`Unknown logical texture type ${e}`)}function D7(e,t,n){return`${e[0]}_${e[1]}_${t}_${n}`}var ba=class{constructor(e,t){this.variableNames=["A"],this.outputShape=e,this.enableShapeUniforms=ds(this.outputShape.length),this.userCode=`
+  `}var Une=class{constructor(e){this.gpgpu=e,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0,this.freeTextures={},this.logEnabled=!1,this.usedTextures={}}acquireTexture(e,t,n){let s=M7(t,n),r=z7(e,s,n);r in this.freeTextures||(this.freeTextures[r]=[]),r in this.usedTextures||(this.usedTextures[r]=[]);let a=O7(e,s,this.gpgpu.gl,this.gpgpu.textureConfig,n);if(this.freeTextures[r].length>0){this.numFreeTextures--,this.numUsedTextures++,this._numBytesFree-=a,this.log();let i=this.freeTextures[r].shift();return this.usedTextures[r].push(i),i}let o;return s===Fn.PACKED_2X2_FLOAT32?o=this.gpgpu.createPackedMatrixTexture(e[0],e[1]):s===Fn.PACKED_2X2_FLOAT16?o=this.gpgpu.createFloat16PackedMatrixTexture(e[0],e[1]):s===Fn.UNPACKED_FLOAT32?o=this.gpgpu.createFloat32MatrixTexture(e[0],e[1]):s===Fn.UNPACKED_FLOAT16?o=this.gpgpu.createFloat16MatrixTexture(e[0],e[1]):s===Fn.PACKED_4X1_UNSIGNED_BYTE&&(o=this.gpgpu.createUnsignedBytesMatrixTexture(e[0],e[1])),this.usedTextures[r].push(o),this.numUsedTextures++,this._numBytesAllocated+=a,this.log(),o}releaseTexture(e,t,n,s){if(this.freeTextures==null)return;let r=M7(n,s),a=z7(t,r,s);a in this.freeTextures||(this.freeTextures[a]=[]);let o=O7(t,r,this.gpgpu.gl,this.gpgpu.textureConfig,s),i=H().get("WEBGL_DELETE_TEXTURE_THRESHOLD");i!==-1&&this._numBytesAllocated>i?(this.gpgpu.deleteMatrixTexture(e.texture),this._numBytesAllocated-=o):(this.freeTextures[a].push(e),this.numFreeTextures++,this._numBytesFree+=o),this.numUsedTextures--;let l=this.usedTextures[a],u=l.indexOf(e);if(u<0)throw new Error("Cannot release a texture that was never provided by this texture manager");l.splice(u,1),this.log()}log(){if(!this.logEnabled)return;let e=this.numFreeTextures+this.numUsedTextures;console.log("Free/Used",`${this.numFreeTextures} / ${this.numUsedTextures}`,`(${e})`);let t=this._numBytesFree/this._numBytesAllocated;console.log(`Bytes allocated: ${this._numBytesAllocated}`),console.log(`Bytes unused: ${this._numBytesFree} (${Math.round(100*t)}%)`)}get numBytesAllocated(){return this._numBytesAllocated}get numBytesFree(){return this._numBytesFree}getNumUsedTextures(){return this.numUsedTextures}getNumFreeTextures(){return this.numFreeTextures}dispose(){if(this.freeTextures!=null){for(let e in this.freeTextures)this.freeTextures[e].forEach(t=>{this.gpgpu.deleteMatrixTexture(t.texture)});for(let e in this.usedTextures)this.usedTextures[e].forEach(t=>{this.gpgpu.deleteMatrixTexture(t.texture)});this.freeTextures=null,this.usedTextures=null,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0}}};function Gne(e,t){let n=e;if(t===n.R32F)return 4;if(t===n.R16F)return 2;if(t===n.RGBA32F)return 16;if(t===e.RGBA)return 16;if(t===n.RGBA16F)return 8;if(t===n.RGBA8)return 4;throw new Error(`Unknown internal format ${t}`)}function O7(e,t,n,s,r){let a=Hne(t,s),o;if(r){let[l,u]=pd(e[0],e[1]);o=l*u}else{let[l,u]=Gh(e[0],e[1]);o=l*u}let i=Gne(n,a);return o*i}function Hne(e,t){switch(e){case Fn.PACKED_2X2_FLOAT32:return hb(t);case Fn.PACKED_2X2_FLOAT16:return fb(t);case Fn.UNPACKED_FLOAT32:return cb(t);case Fn.UNPACKED_FLOAT16:return db(t);case Fn.PACKED_4X1_UNSIGNED_BYTE:return pb(t);default:throw new Error(`Unknown physical texture type ${e}`)}}function jne(e){return H().getBool("WEBGL_RENDER_FLOAT32_ENABLED")?e?Fn.PACKED_2X2_FLOAT32:Fn.UNPACKED_FLOAT32:e?Fn.PACKED_2X2_FLOAT16:Fn.UNPACKED_FLOAT16}function M7(e,t){if(e===Qs.UPLOAD)return Fn.PACKED_2X2_FLOAT32;if(e===Qs.RENDER||e==null)return jne(t);if(e===Qs.DOWNLOAD||e===Qs.PIXELS)return Fn.PACKED_4X1_UNSIGNED_BYTE;throw new Error(`Unknown logical texture type ${e}`)}function z7(e,t,n){return`${e[0]}_${e[1]}_${t}_${n}`}var Sa=class{constructor(e,t){this.variableNames=["A"],this.outputShape=e,this.enableShapeUniforms=fs(this.outputShape.length),this.userCode=`
       float unaryOperation(float x) {
         ${t}
       }
@@ -1192,11 +1192,11 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
 
         setOutput(y);
       }
-    `}},xr="if (isnan(x)) return x;",Fne="return x;",$7="return abs(x);",One="return (x >= 0.0) ? x : (exp(x) - 1.0);",Mne=xr+`
+    `}},br="if (isnan(x)) return x;",qne="return x;",L7="return abs(x);",Xne="return (x >= 0.0) ? x : (exp(x) - 1.0);",Kne=br+`
   return (x < 0.0) ? 0.0 : x;
-`,zne=xr+`
+`,Zne=br+`
   return (x < 0.0) ? 0.0 : min(6.0, x);
-`,qu="return x;",Lne="return 1.0 / (1.0 + exp(-1.0 * x));",Bne="return x;",Wne=`
+`,Yu="return x;",Yne="return 1.0 / (1.0 + exp(-1.0 * x));",Jne="return x;",Qne=`
   vec4 result;
 
   result.r = (x.r >= 0.0) ? x.r : (exp(x.r) - 1.0);
@@ -1205,7 +1205,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
   result.a = (x.a >= 0.0) ? x.a : (exp(x.a) - 1.0);
 
   return result;
-`,Vne=`
+`,ese=`
   vec4 result = x * vec4(greaterThanEqual(x, vec4(0.0)));
   bvec4 isNaN = isnan(x);
 
@@ -1215,7 +1215,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
   result.a = isNaN.a ? x.a : result.a;
 
   return result;
-`,Une=`
+`,tse=`
   vec4 result = min(x, vec4(6.)) * vec4(greaterThanEqual(x, vec4(0.0)));
   bvec4 isNaN = isnan(x);
 
@@ -1225,7 +1225,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
   result.a = isNaN.a ? x.a : result.a;
 
   return result;
-`,Gne="return 1.0 / (1.0 + exp(-1.0 * x));",Zi=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.enableShapeUniforms=ds(this.outputShape.length),this.userCode=`
+`,nse="return 1.0 / (1.0 + exp(-1.0 * x));",el=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.enableShapeUniforms=fs(this.outputShape.length),this.userCode=`
       vec4 unaryOperation(vec4 x) {
         ${t}
       }
@@ -1236,17 +1236,17 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
 
         setOutput(y);
       }
-    `}},Hne=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outputShape=e,this.enableShapeUniforms=ds(this.outputShape.length);let t=e.length,n=os("rc",t),s=wt(t),r=Nne(t,n),a=n.slice(-2),o=t<=1?"rc":`vec2(${a.join(",")})`;this.userCode=`
+    `}},sse=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outputShape=e,this.enableShapeUniforms=fs(this.outputShape.length);let t=e.length,n=us("rc",t),s=kt(t),r=Bne(t,n),a=n.slice(-2),o=t<=1?"rc":`vec2(${a.join(",")})`;this.userCode=`
       void main() {
         ${s} rc = getOutputCoords();
         vec4 packedInput = getA(${r});
 
         setOutput(getChannel(packedInput, ${o}));
       }
-    `}},jne=yr.whereImpl,qne=1e-7,Xne=1e-4,lm={};function Kne(e){return e in lm||(lm[e]={}),lm[e]}var Zne=H().getNumber("CPU_HANDOFF_SIZE_THRESHOLD"),Yne=600;function Jne(){return H().global.screen==null?1024:H().global.screen.height*H().global.screen.width*window.devicePixelRatio*Yne/1024/1024}var md=class extends wc{constructor(e){if(super(),this.pendingRead=new WeakMap,this.pendingDisposal=new WeakSet,this.dataRefCount=new WeakMap,this.numBytesInGPU=0,this.uploadWaitMs=0,this.downloadWaitMs=0,this.lastGlFlushTime=0,this.warnedAboutMemory=!1,this.pendingDeletes=0,this.disposed=!1,!H().getBool("HAS_WEBGL"))throw new Error("WebGL is not supported on this device");let t;if(e!=null){if(e instanceof rc)t=e;else{let n=Wr(H().getNumber("WEBGL_VERSION"),e);t=new rc(n)}this.binaryCache={},this.gpgpuCreatedLocally=!1}else{let n=Wr(H().getNumber("WEBGL_VERSION"));t=new rc(n),this.binaryCache=Kne(H().getNumber("WEBGL_VERSION")),this.gpgpuCreatedLocally=!0}this.gpgpu=t,this.canvas=this.gpgpu.gl.canvas,this.textureManager=new _ne(this.gpgpu),this.numMBBeforeWarning=Jne(),this.texData=new Up(this,Jt())}nextDataId(){return md.nextDataId++}numDataIds(){return this.texData.numDataIds()-this.pendingDeletes}write(e,t,n){if((H().getBool("WEBGL_CHECK_NUMERICAL_PROBLEMS")||H().getBool("DEBUG"))&&this.checkNumericalProblems(e),n==="complex64"&&e!=null)throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");let s={id:this.nextDataId()};return this.texData.set(s,{shape:t,dtype:n,values:e,usage:Ys.UPLOAD,refCount:1}),s}refCount(e){return this.texData.has(e)?this.texData.get(e).refCount:0}incRef(e){let t=this.texData.get(e);t.refCount++}decRef(e){if(this.texData.has(e)){let t=this.texData.get(e);t.refCount--}}move(e,t,n,s,r){if(H().getBool("DEBUG")&&this.checkNumericalProblems(t),s==="complex64")throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");this.texData.set(e,{shape:n,dtype:s,values:t,usage:Ys.UPLOAD,refCount:r})}disposeIntermediateTensorInfo(e){this.disposeData(e.dataId)}readSync(e){let t=this.texData.get(e),{values:n,dtype:s,complexTensorInfos:r,slice:a,shape:o,isPacked:i}=t;if(a!=null){let p;i?p=new Zi(o,qu):p=new ba(o,qu);let d=this.runWebGLProgram(p,[{dataId:e,shape:o,dtype:s}],s),h=this.readSync(d.dataId);return this.disposeIntermediateTensorInfo(d),h}if(n!=null)return this.convertAndCacheOnCPU(e);if(s==="string")return n;let l=this.activeTimers!=null,u;l&&(u=v.now());let c;if(s==="complex64"){let p=this.readSync(r.real.dataId),d=this.readSync(r.imag.dataId);c=T.mergeRealAndImagArrays(p,d)}else c=this.getValuesFromTexture(e);return l&&(this.downloadWaitMs+=v.now()-u),this.convertAndCacheOnCPU(e,c)}async read(e){if(this.pendingRead.has(e)){let h=this.pendingRead.get(e);return new Promise(f=>h.push(f))}let t=this.texData.get(e),{values:n,shape:s,slice:r,dtype:a,complexTensorInfos:o,isPacked:i}=t;if(r!=null){let h;i?h=new Zi(s,qu):h=new ba(s,qu);let f=this.runWebGLProgram(h,[{dataId:e,shape:s,dtype:a}],a),m=this.read(f.dataId);return this.disposeIntermediateTensorInfo(f),m}if(n!=null)return this.convertAndCacheOnCPU(e);if(H().getBool("DEBUG")&&!H().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")&&H().getNumber("WEBGL_VERSION")===2)throw new Error("tensor.data() with WEBGL_DOWNLOAD_FLOAT_ENABLED=false and WEBGL_VERSION=2 not yet supported.");let l=null,u;if(a!=="complex64"&&H().get("WEBGL_BUFFER_SUPPORTED")){u=this.decode(e);let h=this.texData.get(u.dataId);l=this.gpgpu.createBufferFromTexture(h.texture.texture,...om(s))}this.pendingRead.set(e,[]),a!=="complex64"&&await this.gpgpu.createAndWaitForFence();let c;if(a==="complex64"){let h=await Promise.all([this.read(o.real.dataId),this.read(o.imag.dataId)]),f=h[0],m=h[1];c=T.mergeRealAndImagArrays(f,m)}else if(l==null)c=this.getValuesFromTexture(e);else{let h=v.sizeFromShape(s);c=this.gpgpu.downloadFloat32MatrixFromBuffer(l,h)}if(u!=null&&this.disposeIntermediateTensorInfo(u),l!=null){let h=this.gpgpu.gl;Ie(h,()=>h.deleteBuffer(l))}let p=this.convertAndCacheOnCPU(e,c),d=this.pendingRead.get(e);return this.pendingRead.delete(e),d.forEach(h=>h(p)),this.pendingDisposal.has(e)&&(this.pendingDisposal.delete(e),this.disposeData(e)&&Jt().removeDataId(e,this),this.pendingDeletes--),p}readToGPU(e,t={}){let n=this.texData.get(e),{values:s,shape:r,slice:a,dtype:o,isPacked:i,texture:l}=n;if(o==="complex64")throw new Error("Does not support reading texture for complex64 dtype.");if(a!=null){let d;i?d=new Zi(r,qu):d=new ba(r,qu);let h=this.runWebGLProgram(d,[{dataId:e,shape:r,dtype:o}],o),f=this.readToGPU(h,t);return this.disposeIntermediateTensorInfo(h),f}if(l==null)throw s!=null?new Error("Data is not on GPU but on CPU."):new Error("There is no data on GPU or CPU.");let u=this.decode(e,t.customTexShape),c=Jt().makeTensorFromTensorInfo(u),p=this.texData.get(u.dataId);return Object.assign({tensorRef:c},p.texture)}bufferSync(e){let t=this.readSync(e.dataId);if(e.dtype==="string")try{let n=t.map(s=>v.decodeString(s));return Ve(e.shape,e.dtype,n)}catch(n){throw new Error("Failed to decode encoded string bytes into utf-8")}return Ve(e.shape,e.dtype,t)}checkNumericalProblems(e){if(e!=null)for(let t=0;t<e.length;t++){let n=e[t];if(!_I(n))throw H().getBool("WEBGL_RENDER_FLOAT32_CAPABLE")?Error(`The value ${n} cannot be represented with your current settings. Consider enabling float32 rendering: 'tf.env().set('WEBGL_RENDER_FLOAT32_ENABLED', true);'`):Error(`The value ${n} cannot be represented on this device.`)}}getValuesFromTexture(e){let{shape:t,dtype:n,isPacked:s}=this.texData.get(e),r=v.sizeFromShape(t);if(H().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")){let p=this.decode(e),d=this.texData.get(p.dataId),h=this.gpgpu.downloadMatrixFromPackedTexture(d.texture.texture,...om(t)).subarray(0,r);return this.disposeIntermediateTensorInfo(p),h}let a=H().getBool("WEBGL_PACK")&&s===!0,o=a?gm(t):t,i=a?new Mte(o):new Ote(o),l=this.runWebGLProgram(i,[{shape:o,dtype:n,dataId:e}],"float32"),u=this.texData.get(l.dataId),c=this.gpgpu.downloadByteEncodedFloatMatrixFromOutputTexture(u.texture.texture,u.texShape[0],u.texShape[1]).subarray(0,r);return this.disposeIntermediateTensorInfo(l),c}timerAvailable(){return H().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0}time(e){let t=this.activeTimers,n=[],s=!1;this.programTimersStack==null?(this.programTimersStack=n,s=!0):this.activeTimers.push(n),this.activeTimers=n,e();let r=v.flatten(this.activeTimers.map(i=>i.query)).filter(i=>i!=null),a=v.flatten(this.activeTimers.map(i=>i.name)).filter(i=>i!=null);this.activeTimers=t,s&&(this.programTimersStack=null);let o={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null};return(async()=>{if(H().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0){let i=await Promise.all(r);o.kernelMs=v.sum(i),o.getExtraProfileInfo=()=>i.map((l,u)=>({name:a[u],ms:l})).map(l=>`${l.name}: ${l.ms}`).join(", ")}else o.kernelMs={error:"WebGL query timers are not supported in this environment."};return this.uploadWaitMs=0,this.downloadWaitMs=0,o})()}memory(){return{unreliable:!1,numBytesInGPU:this.numBytesInGPU,numBytesInGPUAllocated:this.textureManager.numBytesAllocated,numBytesInGPUFree:this.textureManager.numBytesFree}}startTimer(){return H().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?this.gpgpu.beginQuery():{startMs:v.now(),endMs:null}}endTimer(e){return H().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?(this.gpgpu.endQuery(),e):(e.endMs=v.now(),e)}async getQueryTime(e){if(H().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0)return this.gpgpu.waitForQueryAndGetTime(e);let t=e;return t.endMs-t.startMs}disposeData(e,t=!1){if(this.pendingDisposal.has(e))return!1;if(!this.texData.has(e))return!0;if(t?this.texData.get(e).refCount=0:this.texData.get(e).refCount--,!t&&this.texData.get(e).refCount>0)return!1;if(this.pendingRead.has(e))return this.pendingDisposal.add(e),this.pendingDeletes++,!1;this.releaseGPUData(e);let{complexTensorInfos:n}=this.texData.get(e);return n!=null&&(this.disposeData(n.real.dataId,t),this.disposeData(n.imag.dataId,t)),this.texData.delete(e),!0}releaseGPUData(e){let{texture:t,dtype:n,texShape:s,usage:r,isPacked:a,slice:o}=this.texData.get(e),i=o&&o.origDataId||e,l=this.dataRefCount.get(i);l>1?this.dataRefCount.set(i,l-1):(this.dataRefCount.delete(i),t!=null&&(this.numBytesInGPU-=this.computeBytes(s,n),this.textureManager.releaseTexture(t,s,r,a)));let u=this.texData.get(e);u.texture=null,u.texShape=null,u.isPacked=!1,u.slice=null}getTexture(e){return this.uploadToGPU(e),this.texData.get(e).texture.texture}getDataInfo(e){return this.texData.get(e)}shouldExecuteOnCPU(e,t=Zne){return H().getBool("WEBGL_CPU_FORWARD")&&e.every(n=>this.texData.get(n.dataId).texture==null&&v.sizeFromShape(n.shape)<t)}getGPGPUContext(){return this.gpgpu}where(e){T.warn("tf.where() in webgl locks the UI thread. Call tf.whereAsync() instead");let t=e.dataSync();return jne(e.shape,t)}packedUnaryOp(e,t,n){let s=new Zi(e.shape,t),r=this.compileAndRun(s,[e],n);return Jt().makeTensorFromTensorInfo(r)}abs(e){if(this.shouldExecuteOnCPU([e])&&e.dtype!=="complex64"){let s=S9(this.texData.get(e.dataId).values);return this.makeOutput(e.shape,e.dtype,s)}if(H().getBool("WEBGL_PACK_UNARY_OPERATIONS"))return this.packedUnaryOp(e,$7,e.dtype);let t=new ba(e.shape,$7),n=this.compileAndRun(t,[e]);return Jt().makeTensorFromTensorInfo(n)}makeTensorInfo(e,t,n){let s;if(t==="string"&&n!=null&&n.length>0&&v.isString(n[0])){let r=n.map(a=>v.encodeString(a));s=this.write(r,e,t)}else s=this.write(n,e,t);return this.texData.get(s).usage=null,{dataId:s,shape:e,dtype:t}}makeOutput(e,t,n){return Jt().makeTensorFromTensorInfo(this.makeTensorInfo(e,t,n),this)}unpackTensor(e){let t=new Hne(e.shape);return this.runWebGLProgram(t,[e],e.dtype)}packTensor(e){let t=new Ene(e.shape),n=!0;return this.runWebGLProgram(t,[e],e.dtype,null,n)}packedReshape(e,t){let n=[dl(e.shape),...pl(e.shape)],s={dtype:e.dtype,shape:n,dataId:e.dataId},r=[dl(t),...pl(t)],a=new T9(r,n),o=!0,i=[n],l=this.runWebGLProgram(a,[s],e.dtype,i,o);return{dataId:l.dataId,shape:t,dtype:l.dtype}}decode(e,t){let n=this.texData.get(e),{isPacked:s,shape:r,dtype:a}=n;if(t!=null){let p=v.sizeFromShape(r),d=t[0]*t[1]*4;v.assert(p<=d,()=>"customTexShape is too small. Row * Column * 4 should be equal or larger than the size of the tensor data.")}let o=gm(r),i;s?i=new Fte(o):i=new Pte(o);let l=!0,u=[t!=null?t:om(o)],c=this.runWebGLProgram(i,[{shape:o,dtype:a,dataId:e}],a,u,l,t);return{dtype:a,shape:r,dataId:c.dataId}}runWebGLProgram(e,t,n,s,r=!1,a){let o=this.makeTensorInfo(e.outputShape,n),i=this.texData.get(o.dataId);if(e.packedOutput&&(i.isPacked=!0),e.outPackingScheme===Fp.DENSE){let g=a!=null?a:om(e.outputShape);i.texShape=g.map(y=>y*2)}if(e.outTexUsage!=null&&(i.usage=e.outTexUsage),v.sizeFromShape(o.shape)===0)return i.values=v.getTypedArrayFromDType(o.dtype,0),o;let l=[],u=t.map(g=>{if(g.dtype==="complex64")throw new Error("GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.");let y=this.texData.get(g.dataId);if(y.texture==null){if(!e.packedInputs&&v.sizeFromShape(g.shape)<=H().getNumber("WEBGL_SIZE_UPLOAD_UNIFORM"))return{shape:g.shape,texData:null,isUniform:!0,uniformValues:y.values};e.packedInputs&&(y.isPacked=!0,y.shape=g.shape)}if(this.uploadToGPU(g.dataId),!!y.isPacked!=!!e.packedInputs)g=y.isPacked?this.unpackTensor(g):this.packTensor(g),l.push(g),y=this.texData.get(g.dataId);else if(y.isPacked&&!Op(y.shape,g.shape)){let x=g,A=g.shape;g.shape=y.shape,g=this.packedReshape(g,A),l.push(g),y=this.texData.get(g.dataId),x.shape=A}return{shape:g.shape,texData:y,isUniform:!1}});this.uploadToGPU(o.dataId);let c={shape:o.shape,texData:i,isUniform:!1},p=$te(e,u,c),d=this.getAndSaveBinary(p,()=>_te(this.gpgpu,e,u,c)),h=this.activeTimers!=null,f;h&&(f=this.startTimer()),H().get("ENGINE_COMPILE_ONLY")||Dte(this.gpgpu,d,u,c,s),l.forEach(g=>this.disposeIntermediateTensorInfo(g)),h&&(f=this.endTimer(f),this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime(f)}));let m=H().get("WEBGL_FLUSH_THRESHOLD");if(m>0){let g=v.now();g-this.lastGlFlushTime>m&&(this.gpgpu.gl.flush(),this.lastGlFlushTime=g)}if(!H().getBool("WEBGL_LAZILY_UNPACK")&&i.isPacked&&r===!1){let g=this.unpackTensor(o);return this.disposeIntermediateTensorInfo(o),g}return o}compileAndRun(e,t,n,s,r=!1){return n=n||t[0].dtype,this.runWebGLProgram(e,t,n,s,r)}getAndSaveBinary(e,t){return e in this.binaryCache||(this.binaryCache[e]=t()),this.binaryCache[e]}getTextureManager(){return this.textureManager}dispose(){this.disposed||(H().getBool("IS_TEST")||Object.keys(this.binaryCache).forEach(t=>{this.gpgpu.deleteProgram(this.binaryCache[t].webGLProgram),delete this.binaryCache[t]}),this.textureManager.dispose(),this.canvas!=null&&typeof HTMLCanvasElement!="undefined"&&this.canvas instanceof HTMLCanvasElement?this.canvas.remove():this.canvas=null,this.gpgpuCreatedLocally&&(this.gpgpu.program=null,this.gpgpu.dispose()),this.disposed=!0)}floatPrecision(){return this.floatPrecisionValue==null&&(this.floatPrecisionValue=Y(()=>{if(!H().get("WEBGL_RENDER_FLOAT32_ENABLED")){let e=H().getBool("DEBUG");H().set("DEBUG",!1);let t=this.abs(Te(1e-8)).dataSync()[0];if(H().set("DEBUG",e),t>0)return 32}return 16})),this.floatPrecisionValue}epsilon(){return this.floatPrecision()===32?qne:Xne}uploadToGPU(e){let t=this.texData.get(e),{shape:n,dtype:s,values:r,texture:a,usage:o,isPacked:i}=t;if(a!=null)return;let l=this.activeTimers!=null,u;l&&(u=v.now());let c=t.texShape;if(c==null&&(c=XI(n,i),t.texShape=c),r!=null){let p=gm(n),d,h=c[1],f=c[0],m=r instanceof Uint8Array||r instanceof Uint8ClampedArray;(i||!m)&&([h,f]=ud(c[0],c[1])),i?d=new Lte(p,m):d=new zte(p,m);let g=m?[f,h]:c,y=this.makeTensorInfo(g,s),x=this.texData.get(y.dataId);m?x.usage=Ys.PIXELS:x.usage=Ys.UPLOAD,x.texShape=g,this.gpgpu.uploadDenseMatrixToTexture(this.getTexture(y.dataId),h,f,r);let A=[[f,h]],b=!0,w=this.runWebGLProgram(d,[y],s,A,b),k=this.texData.get(w.dataId);t.texShape=k.texShape,t.isPacked=k.isPacked,t.usage=k.usage,H().get("ENGINE_COMPILE_ONLY")?this.disposeData(w.dataId):(t.texture=k.texture,t.values=null,this.texData.delete(w.dataId)),this.disposeIntermediateTensorInfo(y),l&&(this.uploadWaitMs+=v.now()-u)}else{let p=this.acquireTexture(c,o,s,i);t.texture=p}}convertAndCacheOnCPU(e,t){let n=this.texData.get(e),{dtype:s}=n;return this.releaseGPUData(e),t!=null&&(n.values=Qne(t,s)),n.values}acquireTexture(e,t,n,s){if(this.numBytesInGPU+=this.computeBytes(e,n),!this.warnedAboutMemory&&this.numBytesInGPU>this.numMBBeforeWarning*1024*1024){let r=(this.numBytesInGPU/1024/1024).toFixed(2);this.warnedAboutMemory=!0,console.warn(`High memory usage in GPU: ${r} MB, most likely due to a memory leak`)}return this.textureManager.acquireTexture(e,t,s)}computeBytes(e,t){return e[0]*e[1]*v.bytesPerElement(t)}checkCompileCompletion(){for(let[,e]of Object.entries(this.binaryCache))this.checkCompletion_(e)}async checkCompileCompletionAsync(){let e=[];if(this.gpgpu.parallelCompilationExtension){for(let[,t]of Object.entries(this.binaryCache))e.push(this.checkCompletionAsync_(t));return Promise.all(e)}else{for(let[,t]of Object.entries(this.binaryCache)){let n=new Promise(s=>{try{this.checkCompletion_(t),s(!0)}catch(r){throw r}});e.push(n)}return Promise.all(e)}}async checkCompletionAsync_(e){return this.gpgpu.gl.getProgramParameter(e.webGLProgram,this.gpgpu.parallelCompilationExtension.COMPLETION_STATUS_KHR)?this.checkCompletion_(e):(await i5(),this.checkCompletionAsync_(e))}checkCompletion_(e){if(this.gpgpu.gl.getProgramParameter(e.webGLProgram,this.gpgpu.gl.LINK_STATUS)===!1)throw console.log(this.gpgpu.gl.getProgramInfoLog(e.webGLProgram)),this.gpgpu.gl.getShaderParameter(e.fragmentShader,this.gpgpu.gl.COMPILE_STATUS)===!1?(sb(e.source,this.gpgpu.gl.getShaderInfoLog(e.fragmentShader)),new Error("Failed to compile fragment shader.")):new Error("Failed to link vertex and fragment shaders.");return!0}getUniformLocations(){for(let[,e]of Object.entries(this.binaryCache)){let{uniformLocations:t,customUniformLocations:n,infLoc:s,nanLoc:r,inShapesLocations:a,inTexShapesLocations:o,outShapeLocation:i,outShapeStridesLocation:l,outTexShapeLocation:u}=a9(this.gpgpu,e.program,e.webGLProgram);e.uniformLocations=t,e.customUniformLocations=n,e.infLoc=s,e.nanLoc=r,e.inShapesLocations=a,e.inTexShapesLocations=o,e.outShapeLocation=i,e.outShapeStridesLocation=l,e.outTexShapeLocation=u}}};md.nextDataId=0;function Qne(e,t){if(t==="float32"||t==="complex64")return e;if(t==="int32"||t==="bool"){let n=t==="int32"?new Int32Array(e.length):new Uint8Array(e.length);for(let s=0;s<n.length;++s)n[s]=Math.round(e[s]);return n}else throw new Error(`Unknown dtype ${t}`)}var ese="3.20.0";function N9(){H().set("WEBGL_FORCE_F16_TEXTURES",!0)}ch.isBrowser()&&ru("webgl",()=>new md,2);var tse={forceHalfFloat:N9},E9=`
+    `}},rse=Ar.whereImpl,ase=1e-7,ose=1e-4,lm={};function ise(e){return e in lm||(lm[e]={}),lm[e]}var lse=H().getNumber("CPU_HANDOFF_SIZE_THRESHOLD"),use=600;function cse(){return H().global.screen==null?1024:H().global.screen.height*H().global.screen.width*window.devicePixelRatio*use/1024/1024}var Ad=class extends Cc{constructor(e){if(super(),this.pendingRead=new WeakMap,this.pendingDisposal=new WeakSet,this.dataRefCount=new WeakMap,this.numBytesInGPU=0,this.uploadWaitMs=0,this.downloadWaitMs=0,this.lastGlFlushTime=0,this.warnedAboutMemory=!1,this.pendingDeletes=0,this.disposed=!1,!H().getBool("HAS_WEBGL"))throw new Error("WebGL is not supported on this device");let t;if(e!=null){if(e instanceof lc)t=e;else{let n=Ur(H().getNumber("WEBGL_VERSION"),e);t=new lc(n)}this.binaryCache={},this.gpgpuCreatedLocally=!1}else{let n=Ur(H().getNumber("WEBGL_VERSION"));t=new lc(n),this.binaryCache=ise(H().getNumber("WEBGL_VERSION")),this.gpgpuCreatedLocally=!0}this.gpgpu=t,this.canvas=this.gpgpu.gl.canvas,this.textureManager=new Une(this.gpgpu),this.numMBBeforeWarning=cse(),this.texData=new Gp(this,Qt())}nextDataId(){return Ad.nextDataId++}numDataIds(){return this.texData.numDataIds()-this.pendingDeletes}write(e,t,n){if((H().getBool("WEBGL_CHECK_NUMERICAL_PROBLEMS")||H().getBool("DEBUG"))&&this.checkNumericalProblems(e),n==="complex64"&&e!=null)throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");let s={id:this.nextDataId()};return this.texData.set(s,{shape:t,dtype:n,values:e,usage:Qs.UPLOAD,refCount:1}),s}refCount(e){return this.texData.has(e)?this.texData.get(e).refCount:0}incRef(e){let t=this.texData.get(e);t.refCount++}decRef(e){if(this.texData.has(e)){let t=this.texData.get(e);t.refCount--}}move(e,t,n,s,r){if(H().getBool("DEBUG")&&this.checkNumericalProblems(t),s==="complex64")throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");this.texData.set(e,{shape:n,dtype:s,values:t,usage:Qs.UPLOAD,refCount:r})}disposeIntermediateTensorInfo(e){this.disposeData(e.dataId)}readSync(e){let t=this.texData.get(e),{values:n,dtype:s,complexTensorInfos:r,slice:a,shape:o,isPacked:i}=t;if(a!=null){let p;i?p=new el(o,Yu):p=new Sa(o,Yu);let d=this.runWebGLProgram(p,[{dataId:e,shape:o,dtype:s}],s),h=this.readSync(d.dataId);return this.disposeIntermediateTensorInfo(d),h}if(n!=null)return this.convertAndCacheOnCPU(e);if(s==="string")return n;let l=this.activeTimers!=null,u;l&&(u=v.now());let c;if(s==="complex64"){let p=this.readSync(r.real.dataId),d=this.readSync(r.imag.dataId);c=T.mergeRealAndImagArrays(p,d)}else c=this.getValuesFromTexture(e);return l&&(this.downloadWaitMs+=v.now()-u),this.convertAndCacheOnCPU(e,c)}async read(e){if(this.pendingRead.has(e)){let h=this.pendingRead.get(e);return new Promise(f=>h.push(f))}let t=this.texData.get(e),{values:n,shape:s,slice:r,dtype:a,complexTensorInfos:o,isPacked:i}=t;if(r!=null){let h;i?h=new el(s,Yu):h=new Sa(s,Yu);let f=this.runWebGLProgram(h,[{dataId:e,shape:s,dtype:a}],a),m=this.read(f.dataId);return this.disposeIntermediateTensorInfo(f),m}if(n!=null)return this.convertAndCacheOnCPU(e);if(H().getBool("DEBUG")&&!H().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")&&H().getNumber("WEBGL_VERSION")===2)throw new Error("tensor.data() with WEBGL_DOWNLOAD_FLOAT_ENABLED=false and WEBGL_VERSION=2 not yet supported.");let l=null,u;if(a!=="complex64"&&H().get("WEBGL_BUFFER_SUPPORTED")){u=this.decode(e);let h=this.texData.get(u.dataId);l=this.gpgpu.createBufferFromTexture(h.texture.texture,...om(s))}this.pendingRead.set(e,[]),a!=="complex64"&&await this.gpgpu.createAndWaitForFence();let c;if(a==="complex64"){let h=await Promise.all([this.read(o.real.dataId),this.read(o.imag.dataId)]),f=h[0],m=h[1];c=T.mergeRealAndImagArrays(f,m)}else if(l==null)c=this.getValuesFromTexture(e);else{let h=v.sizeFromShape(s);c=this.gpgpu.downloadFloat32MatrixFromBuffer(l,h)}if(u!=null&&this.disposeIntermediateTensorInfo(u),l!=null){let h=this.gpgpu.gl;Ie(h,()=>h.deleteBuffer(l))}let p=this.convertAndCacheOnCPU(e,c),d=this.pendingRead.get(e);return this.pendingRead.delete(e),d.forEach(h=>h(p)),this.pendingDisposal.has(e)&&(this.pendingDisposal.delete(e),this.disposeData(e)&&Qt().removeDataId(e,this),this.pendingDeletes--),p}readToGPU(e,t={}){let n=this.texData.get(e),{values:s,shape:r,slice:a,dtype:o,isPacked:i,texture:l}=n;if(o==="complex64")throw new Error("Does not support reading texture for complex64 dtype.");if(a!=null){let d;i?d=new el(r,Yu):d=new Sa(r,Yu);let h=this.runWebGLProgram(d,[{dataId:e,shape:r,dtype:o}],o),f=this.readToGPU(h,t);return this.disposeIntermediateTensorInfo(h),f}if(l==null)throw s!=null?new Error("Data is not on GPU but on CPU."):new Error("There is no data on GPU or CPU.");let u=this.decode(e,t.customTexShape),c=Qt().makeTensorFromTensorInfo(u),p=this.texData.get(u.dataId);return Object.assign({tensorRef:c},p.texture)}bufferSync(e){let t=this.readSync(e.dataId);if(e.dtype==="string")try{let n=t.map(s=>v.decodeString(s));return Ue(e.shape,e.dtype,n)}catch(n){throw new Error("Failed to decode encoded string bytes into utf-8")}return Ue(e.shape,e.dtype,t)}checkNumericalProblems(e){if(e!=null)for(let t=0;t<e.length;t++){let n=e[t];if(!MI(n))throw H().getBool("WEBGL_RENDER_FLOAT32_CAPABLE")?Error(`The value ${n} cannot be represented with your current settings. Consider enabling float32 rendering: 'tf.env().set('WEBGL_RENDER_FLOAT32_ENABLED', true);'`):Error(`The value ${n} cannot be represented on this device.`)}}getValuesFromTexture(e){let{shape:t,dtype:n,isPacked:s}=this.texData.get(e),r=v.sizeFromShape(t);if(H().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")){let p=this.decode(e),d=this.texData.get(p.dataId),h=this.gpgpu.downloadMatrixFromPackedTexture(d.texture.texture,...om(t)).subarray(0,r);return this.disposeIntermediateTensorInfo(p),h}let a=H().getBool("WEBGL_PACK")&&s===!0,o=a?gm(t):t,i=a?new Kte(o):new Xte(o),l=this.runWebGLProgram(i,[{shape:o,dtype:n,dataId:e}],"float32"),u=this.texData.get(l.dataId),c=this.gpgpu.downloadByteEncodedFloatMatrixFromOutputTexture(u.texture.texture,u.texShape[0],u.texShape[1]).subarray(0,r);return this.disposeIntermediateTensorInfo(l),c}timerAvailable(){return H().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0}time(e){let t=this.activeTimers,n=[],s=!1;this.programTimersStack==null?(this.programTimersStack=n,s=!0):this.activeTimers.push(n),this.activeTimers=n,e();let r=v.flatten(this.activeTimers.map(i=>i.query)).filter(i=>i!=null),a=v.flatten(this.activeTimers.map(i=>i.name)).filter(i=>i!=null);this.activeTimers=t,s&&(this.programTimersStack=null);let o={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null};return(async()=>{if(H().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0){let i=await Promise.all(r);o.kernelMs=v.sum(i),o.getExtraProfileInfo=()=>i.map((l,u)=>({name:a[u],ms:l})).map(l=>`${l.name}: ${l.ms}`).join(", ")}else o.kernelMs={error:"WebGL query timers are not supported in this environment."};return this.uploadWaitMs=0,this.downloadWaitMs=0,o})()}memory(){return{unreliable:!1,numBytesInGPU:this.numBytesInGPU,numBytesInGPUAllocated:this.textureManager.numBytesAllocated,numBytesInGPUFree:this.textureManager.numBytesFree}}startTimer(){return H().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?this.gpgpu.beginQuery():{startMs:v.now(),endMs:null}}endTimer(e){return H().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?(this.gpgpu.endQuery(),e):(e.endMs=v.now(),e)}async getQueryTime(e){if(H().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0)return this.gpgpu.waitForQueryAndGetTime(e);let t=e;return t.endMs-t.startMs}disposeData(e,t=!1){if(this.pendingDisposal.has(e))return!1;if(!this.texData.has(e))return!0;if(t?this.texData.get(e).refCount=0:this.texData.get(e).refCount--,!t&&this.texData.get(e).refCount>0)return!1;if(this.pendingRead.has(e))return this.pendingDisposal.add(e),this.pendingDeletes++,!1;this.releaseGPUData(e);let{complexTensorInfos:n}=this.texData.get(e);return n!=null&&(this.disposeData(n.real.dataId,t),this.disposeData(n.imag.dataId,t)),this.texData.delete(e),!0}releaseGPUData(e){let{texture:t,dtype:n,texShape:s,usage:r,isPacked:a,slice:o}=this.texData.get(e),i=o&&o.origDataId||e,l=this.dataRefCount.get(i);l>1?this.dataRefCount.set(i,l-1):(this.dataRefCount.delete(i),t!=null&&(this.numBytesInGPU-=this.computeBytes(s,n),this.textureManager.releaseTexture(t,s,r,a)));let u=this.texData.get(e);u.texture=null,u.texShape=null,u.isPacked=!1,u.slice=null}getTexture(e){return this.uploadToGPU(e),this.texData.get(e).texture.texture}getDataInfo(e){return this.texData.get(e)}shouldExecuteOnCPU(e,t=lse){return H().getBool("WEBGL_CPU_FORWARD")&&e.every(n=>this.texData.get(n.dataId).texture==null&&v.sizeFromShape(n.shape)<t)}getGPGPUContext(){return this.gpgpu}where(e){T.warn("tf.where() in webgl locks the UI thread. Call tf.whereAsync() instead");let t=e.dataSync();return rse(e.shape,t)}packedUnaryOp(e,t,n){let s=new el(e.shape,t),r=this.compileAndRun(s,[e],n);return Qt().makeTensorFromTensorInfo(r)}abs(e){if(this.shouldExecuteOnCPU([e])&&e.dtype!=="complex64"){let s=R9(this.texData.get(e.dataId).values);return this.makeOutput(e.shape,e.dtype,s)}if(H().getBool("WEBGL_PACK_UNARY_OPERATIONS"))return this.packedUnaryOp(e,L7,e.dtype);let t=new Sa(e.shape,L7),n=this.compileAndRun(t,[e]);return Qt().makeTensorFromTensorInfo(n)}makeTensorInfo(e,t,n){let s;if(t==="string"&&n!=null&&n.length>0&&v.isString(n[0])){let r=n.map(a=>v.encodeString(a));s=this.write(r,e,t)}else s=this.write(n,e,t);return this.texData.get(s).usage=null,{dataId:s,shape:e,dtype:t}}makeOutput(e,t,n){return Qt().makeTensorFromTensorInfo(this.makeTensorInfo(e,t,n),this)}unpackTensor(e){let t=new sse(e.shape);return this.runWebGLProgram(t,[e],e.dtype)}packTensor(e){let t=new Wne(e.shape),n=!0;return this.runWebGLProgram(t,[e],e.dtype,null,n)}packedReshape(e,t){let n=[ml(e.shape),...gl(e.shape)],s={dtype:e.dtype,shape:n,dataId:e.dataId},r=[ml(t),...gl(t)],a=new $9(r,n),o=!0,i=[n],l=this.runWebGLProgram(a,[s],e.dtype,i,o);return{dataId:l.dataId,shape:t,dtype:l.dtype}}decode(e,t){let n=this.texData.get(e),{isPacked:s,shape:r,dtype:a}=n;if(t!=null){let p=v.sizeFromShape(r),d=t[0]*t[1]*4;v.assert(p<=d,()=>"customTexShape is too small. Row * Column * 4 should be equal or larger than the size of the tensor data.")}let o=gm(r),i;s?i=new qte(o):i=new jte(o);let l=!0,u=[t!=null?t:om(o)],c=this.runWebGLProgram(i,[{shape:o,dtype:a,dataId:e}],a,u,l,t);return{dtype:a,shape:r,dataId:c.dataId}}runWebGLProgram(e,t,n,s,r=!1,a){let o=this.makeTensorInfo(e.outputShape,n),i=this.texData.get(o.dataId);if(e.packedOutput&&(i.isPacked=!0),e.outPackingScheme===Op.DENSE){let g=a!=null?a:om(e.outputShape);i.texShape=g.map(y=>y*2)}if(e.outTexUsage!=null&&(i.usage=e.outTexUsage),v.sizeFromShape(o.shape)===0)return i.values=v.getTypedArrayFromDType(o.dtype,0),o;let l=[],u=t.map(g=>{if(g.dtype==="complex64")throw new Error("GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.");let y=this.texData.get(g.dataId);if(y.texture==null){if(!e.packedInputs&&v.sizeFromShape(g.shape)<=H().getNumber("WEBGL_SIZE_UPLOAD_UNIFORM"))return{shape:g.shape,texData:null,isUniform:!0,uniformValues:y.values};e.packedInputs&&(y.isPacked=!0,y.shape=g.shape)}if(this.uploadToGPU(g.dataId),!!y.isPacked!=!!e.packedInputs)g=y.isPacked?this.unpackTensor(g):this.packTensor(g),l.push(g),y=this.texData.get(g.dataId);else if(y.isPacked&&!Mp(y.shape,g.shape)){let x=g,A=g.shape;g.shape=y.shape,g=this.packedReshape(g,A),l.push(g),y=this.texData.get(g.dataId),x.shape=A}return{shape:g.shape,texData:y,isUniform:!1}});this.uploadToGPU(o.dataId);let c={shape:o.shape,texData:i,isUniform:!1},p=Hte(e,u,c),d=this.getAndSaveBinary(p,()=>Ute(this.gpgpu,e,u,c)),h=this.activeTimers!=null,f;h&&(f=this.startTimer()),H().get("ENGINE_COMPILE_ONLY")||Gte(this.gpgpu,d,u,c,s),l.forEach(g=>this.disposeIntermediateTensorInfo(g)),h&&(f=this.endTimer(f),this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime(f)}));let m=H().get("WEBGL_FLUSH_THRESHOLD");if(m>0){let g=v.now();g-this.lastGlFlushTime>m&&(this.gpgpu.gl.flush(),this.lastGlFlushTime=g)}if(!H().getBool("WEBGL_LAZILY_UNPACK")&&i.isPacked&&r===!1){let g=this.unpackTensor(o);return this.disposeIntermediateTensorInfo(o),g}return o}compileAndRun(e,t,n,s,r=!1){return n=n||t[0].dtype,this.runWebGLProgram(e,t,n,s,r)}getAndSaveBinary(e,t){return e in this.binaryCache||(this.binaryCache[e]=t()),this.binaryCache[e]}getTextureManager(){return this.textureManager}dispose(){this.disposed||(H().getBool("IS_TEST")||Object.keys(this.binaryCache).forEach(t=>{this.gpgpu.deleteProgram(this.binaryCache[t].webGLProgram),delete this.binaryCache[t]}),this.textureManager.dispose(),this.canvas!=null&&typeof HTMLCanvasElement!="undefined"&&this.canvas instanceof HTMLCanvasElement?this.canvas.remove():this.canvas=null,this.gpgpuCreatedLocally&&(this.gpgpu.program=null,this.gpgpu.dispose()),this.disposed=!0)}floatPrecision(){return this.floatPrecisionValue==null&&(this.floatPrecisionValue=Y(()=>{if(!H().get("WEBGL_RENDER_FLOAT32_ENABLED")){let e=H().getBool("DEBUG");H().set("DEBUG",!1);let t=this.abs(Te(1e-8)).dataSync()[0];if(H().set("DEBUG",e),t>0)return 32}return 16})),this.floatPrecisionValue}epsilon(){return this.floatPrecision()===32?ase:ose}uploadToGPU(e){let t=this.texData.get(e),{shape:n,dtype:s,values:r,texture:a,usage:o,isPacked:i}=t;if(a!=null)return;let l=this.activeTimers!=null,u;l&&(u=v.now());let c=t.texShape;if(c==null&&(c=e9(n,i),t.texShape=c),r!=null){let p=gm(n),d,h=c[1],f=c[0],m=r instanceof Uint8Array||r instanceof Uint8ClampedArray;(i||!m)&&([h,f]=pd(c[0],c[1])),i?d=new Yte(p,m):d=new Zte(p,m);let g=m?[f,h]:c,y=this.makeTensorInfo(g,s),x=this.texData.get(y.dataId);m?x.usage=Qs.PIXELS:x.usage=Qs.UPLOAD,x.texShape=g,this.gpgpu.uploadDenseMatrixToTexture(this.getTexture(y.dataId),h,f,r);let A=[[f,h]],b=!0,w=this.runWebGLProgram(d,[y],s,A,b),k=this.texData.get(w.dataId);t.texShape=k.texShape,t.isPacked=k.isPacked,t.usage=k.usage,H().get("ENGINE_COMPILE_ONLY")?this.disposeData(w.dataId):(t.texture=k.texture,t.values=null,this.texData.delete(w.dataId)),this.disposeIntermediateTensorInfo(y),l&&(this.uploadWaitMs+=v.now()-u)}else{let p=this.acquireTexture(c,o,s,i);t.texture=p}}convertAndCacheOnCPU(e,t){let n=this.texData.get(e),{dtype:s}=n;return this.releaseGPUData(e),t!=null&&(n.values=dse(t,s)),n.values}acquireTexture(e,t,n,s){if(this.numBytesInGPU+=this.computeBytes(e,n),!this.warnedAboutMemory&&this.numBytesInGPU>this.numMBBeforeWarning*1024*1024){let r=(this.numBytesInGPU/1024/1024).toFixed(2);this.warnedAboutMemory=!0,console.warn(`High memory usage in GPU: ${r} MB, most likely due to a memory leak`)}return this.textureManager.acquireTexture(e,t,s)}computeBytes(e,t){return e[0]*e[1]*v.bytesPerElement(t)}checkCompileCompletion(){for(let[,e]of Object.entries(this.binaryCache))this.checkCompletion_(e)}async checkCompileCompletionAsync(){let e=[];if(this.gpgpu.parallelCompilationExtension){for(let[,t]of Object.entries(this.binaryCache))e.push(this.checkCompletionAsync_(t));return Promise.all(e)}else{for(let[,t]of Object.entries(this.binaryCache)){let n=new Promise(s=>{try{this.checkCompletion_(t),s(!0)}catch(r){throw r}});e.push(n)}return Promise.all(e)}}async checkCompletionAsync_(e){return this.gpgpu.gl.getProgramParameter(e.webGLProgram,this.gpgpu.parallelCompilationExtension.COMPLETION_STATUS_KHR)?this.checkCompletion_(e):(await c5(),this.checkCompletionAsync_(e))}checkCompletion_(e){if(this.gpgpu.gl.getProgramParameter(e.webGLProgram,this.gpgpu.gl.LINK_STATUS)===!1)throw console.log(this.gpgpu.gl.getProgramInfoLog(e.webGLProgram)),this.gpgpu.gl.getShaderParameter(e.fragmentShader,this.gpgpu.gl.COMPILE_STATUS)===!1?(ob(e.source,this.gpgpu.gl.getShaderInfoLog(e.fragmentShader)),new Error("Failed to compile fragment shader.")):new Error("Failed to link vertex and fragment shaders.");return!0}getUniformLocations(){for(let[,e]of Object.entries(this.binaryCache)){let{uniformLocations:t,customUniformLocations:n,infLoc:s,nanLoc:r,inShapesLocations:a,inTexShapesLocations:o,outShapeLocation:i,outShapeStridesLocation:l,outTexShapeLocation:u}=d9(this.gpgpu,e.program,e.webGLProgram);e.uniformLocations=t,e.customUniformLocations=n,e.infLoc=s,e.nanLoc=r,e.inShapesLocations=a,e.inTexShapesLocations=o,e.outShapeLocation=i,e.outShapeStridesLocation=l,e.outTexShapeLocation=u}}};Ad.nextDataId=0;function dse(e,t){if(t==="float32"||t==="complex64")return e;if(t==="int32"||t==="bool"){let n=t==="int32"?new Int32Array(e.length):new Uint8Array(e.length);for(let s=0;s<n.length;++s)n[s]=Math.round(e[s]);return n}else throw new Error(`Unknown dtype ${t}`)}var pse="3.20.0";function P9(){H().set("WEBGL_FORCE_F16_TEXTURES",!0)}dh.isBrowser()&&lu("webgl",()=>new Ad,2);var hse={forceHalfFloat:P9},F9=`
   if (isnan(a)) return a;
   if (isnan(b)) return b;
-`,vc=class{constructor(e,t,n){this.variableNames=["A","B"],this.outputShape=T.assertAndGetBroadcastShape(t,n),this.enableShapeUniforms=ds(this.outputShape.length),this.userCode=`
+`,Ic=class{constructor(e,t,n){this.variableNames=["A","B"],this.outputShape=T.assertAndGetBroadcastShape(t,n),this.enableShapeUniforms=fs(this.outputShape.length),this.userCode=`
       float binaryOperation(float a, float b) {
         ${e}
       }
@@ -1261,12 +1261,12 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
   result.g = isNaN.g > 0. ? NAN : result.g;
   result.b = isNaN.b > 0. ? NAN : result.b;
   result.a = isNaN.a > 0. ? NAN : result.a;
-`,Hh=class{constructor(e,t,n,s=!1){this.variableNames=["A","B"],this.supportsBroadcasting=!0,this.packedInputs=!0,this.packedOutput=!0,this.outputShape=T.assertAndGetBroadcastShape(t,n);let r=this.outputShape.length;this.enableShapeUniforms=ds(r);let a="";if(s)if(r===0||v.sizeFromShape(this.outputShape)===1)a=`
+`,jh=class{constructor(e,t,n,s=!1){this.variableNames=["A","B"],this.supportsBroadcasting=!0,this.packedInputs=!0,this.packedOutput=!0,this.outputShape=T.assertAndGetBroadcastShape(t,n);let r=this.outputShape.length;this.enableShapeUniforms=fs(r);let a="";if(s)if(r===0||v.sizeFromShape(this.outputShape)===1)a=`
           result.y = 0.;
           result.z = 0.;
           result.w = 0.;
         `;else if(a=`
-          ${wt(r)} coords = getOutputCoords();
+          ${kt(r)} coords = getOutputCoords();
         `,r===1)this.enableShapeUniforms?a+=`
             result.y = (coords + 1) >= outShape ? 0. : result.y;
             result.z = 0.;
@@ -1275,7 +1275,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
             result.y = (coords + 1) >= ${this.outputShape[0]} ? 0. : result.y;
             result.z = 0.;
             result.w = 0.;
-          `;else{let i=os("coords",r);this.enableShapeUniforms?a+=`
+          `;else{let i=us("coords",r);this.enableShapeUniforms?a+=`
             bool nextRowOutOfBounds =
               (${i[r-2]} + 1) >= outShape[${r} - 2];
             bool nextColOutOfBounds =
@@ -1305,21 +1305,21 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
 
         setOutput(result);
       }
-    `}};function Ls(e){let{inputs:t,backend:n}=e,{x:s}=t;return n.incRef(s.dataId),{dataId:s.dataId,shape:s.shape,dtype:s.dtype}}var nse={kernelName:Po,backendName:"webgl",kernelFunc:Ls};function mi(e){let{inputs:t,backend:n}=e,{real:s,imag:r}=t,a=n.makeTensorInfo(s.shape,"complex64"),o=n.texData.get(a.dataId),i=Ls({inputs:{x:s},backend:n}),l=Ls({inputs:{x:r},backend:n});return o.complexTensorInfos={real:i,imag:l},a}var sse={kernelName:Hp,backendName:"webgl",kernelFunc:mi},R9="return (a < 0.) ? b * a : a;",_9=`
+    `}};function Vs(e){let{inputs:t,backend:n}=e,{x:s}=t;return n.incRef(s.dataId),{dataId:s.dataId,shape:s.shape,dtype:s.dtype}}var fse={kernelName:zo,backendName:"webgl",kernelFunc:Vs};function xi(e){let{inputs:t,backend:n}=e,{real:s,imag:r}=t,a=n.makeTensorInfo(s.shape,"complex64"),o=n.texData.get(a.dataId),i=Vs({inputs:{x:s},backend:n}),l=Vs({inputs:{x:r},backend:n});return o.complexTensorInfos={real:i,imag:l},a}var mse={kernelName:jp,backendName:"webgl",kernelFunc:xi},O9="return (a < 0.) ? b * a : a;",M9=`
   vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));
   return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);
-`;function rse(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{alpha:a}=s,o=n.makeTensorInfo([],"float32",v.createScalarValue(a,"float32")),i=H().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Hh(_9,r.shape,o.shape):new vc(R9,r.shape,o.shape),l=n.runWebGLProgram(i,[r,o],"float32");return n.disposeIntermediateTensorInfo(o),l}var ase={kernelName:Fo,backendName:"webgl",kernelFunc:rse},D9="return (a < 0.) ? b * a : a;",$9=`
+`;function gse(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{alpha:a}=s,o=n.makeTensorInfo([],"float32",v.createScalarValue(a,"float32")),i=H().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new jh(M9,r.shape,o.shape):new Ic(O9,r.shape,o.shape),l=n.runWebGLProgram(i,[r,o],"float32");return n.disposeIntermediateTensorInfo(o),l}var yse={kernelName:Lo,backendName:"webgl",kernelFunc:gse},z9="return (a < 0.) ? b * a : a;",L9=`
   vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));
   return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);
-`;function ose(e){let{inputs:t,backend:n}=e,{x:s,alpha:r}=t,a=H().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Hh($9,s.shape,r.shape):new vc(D9,s.shape,r.shape);return n.runWebGLProgram(a,[s,r],"float32")}var ise={kernelName:qo,backendName:"webgl",kernelFunc:ose},gd="if (isnan(x)) return x;",lse=`
+`;function Ase(e){let{inputs:t,backend:n}=e,{x:s,alpha:r}=t,a=H().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new jh(L9,s.shape,r.shape):new Ic(z9,s.shape,r.shape);return n.runWebGLProgram(a,[s,r],"float32")}var xse={kernelName:Yo,backendName:"webgl",kernelFunc:Ase},xd="if (isnan(x)) return x;",bse=`
   if (isnan(a)) return a;
   if (isnan(b)) return b;
-`,use=`
+`,vse=`
   result.r = isNaN.r > 0. ? NAN : result.r;
   result.g = isNaN.g > 0. ? NAN : result.g;
   result.b = isNaN.b > 0. ? NAN : result.b;
   result.a = isNaN.a > 0. ? NAN : result.a;
-`;function pt({opSnippet:e,packedOpSnippet:t,cpuKernelImpl:n,dtype:s}){return({inputs:r,backend:a})=>{let{x:o}=r,i=a,l=s||o.dtype;if(i.shouldExecuteOnCPU([o])&&n!=null){let p=i.texData.get(o.dataId),d=n(p.values,l);return i.makeTensorInfo(o.shape,l,d)}let u=H().getBool("WEBGL_PACK_UNARY_OPERATIONS")&&t!=null,c;return u?c=new Zi(o.shape,t):c=new ba(o.shape,e),i.runWebGLProgram(c,[o],l)}}function Ln({opSnippet:e,packedOpSnippet:t,checkOutOfBounds:n=!1,supportsComplex:s=!1,cpuKernelImpl:r,dtype:a}){return({inputs:o,backend:i})=>{let{a:l,b:u}=o,c=i;if(s&&l.dtype==="complex64"){let f=c.texData.get(l.dataId),m=c.texData.get(u.dataId),[g,y]=[[f.complexTensorInfos.real,m.complexTensorInfos.real],[f.complexTensorInfos.imag,m.complexTensorInfos.imag]].map(A=>{let[b,w]=A,k={dataId:b.dataId,dtype:b.dtype,shape:l.shape},C={dataId:w.dataId,dtype:w.dtype,shape:u.shape},E=new vc(e,l.shape,u.shape);return c.runWebGLProgram(E,[k,C],Gn(b.dtype,w.dtype))}),x=mi({inputs:{real:g,imag:y},backend:c});return c.disposeIntermediateTensorInfo(g),c.disposeIntermediateTensorInfo(y),x}let p=a||Gn(l.dtype,u.dtype);if((l.dtype==="string"||u.dtype==="string"||c.shouldExecuteOnCPU([l,u]))&&r!=null){let f=c.texData.get(l.dataId).values,m=c.texData.get(u.dataId).values,g=l.dtype==="string"?T.fromUint8ToStringArray(f):f,y=l.dtype==="string"?T.fromUint8ToStringArray(m):m,[x,A]=r(l.shape,u.shape,g,y,p),b=c.makeTensorInfo(A,p),w=c.texData.get(b.dataId);return w.values=x,b}let d=H().getBool("WEBGL_PACK_BINARY_OPERATIONS")&&t!=null,h;return d?h=new Hh(t,l.shape,u.shape,n):h=new vc(e,l.shape,u.shape),c.runWebGLProgram(h,[l,u],p)}}function Mp(e,t=!1){if(e==="linear")return t?Bne:Fne;if(e==="relu")return t?Vne:Mne;if(e==="elu")return t?Wne:One;if(e==="relu6")return t?Une:zne;if(e==="prelu")return t?$9:D9;if(e==="leakyrelu")return t?_9:R9;if(e==="sigmoid")return t?Gne:Lne;throw new Error(`Activation ${e} has not been implemented for the WebGL backend.`)}var P9=class{constructor(e,t,n,s=!1,r=!1,a=!1,o=null,i=!1,l=!1){this.variableNames=["matrixA","matrixB"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=n,this.enableShapeUniforms=ds(this.outputShape.length);let u=s?e[1]:e[2],c=Math.ceil(u/2),p=s?"i * 2, rc.y":"rc.y, i * 2",d=r?"rc.z, i * 2":"i * 2, rc.z",h=s?["a.xxyy","a.zzww"]:["a.xxzz","a.yyww"],f=r?["b.xzxz","b.ywyw"]:["b.xyxy","b.zwzw"],m="",g="";o&&(i?m=`vec4 activation(vec4 a) {
+`;function ht({opSnippet:e,packedOpSnippet:t,cpuKernelImpl:n,dtype:s}){return({inputs:r,backend:a})=>{let{x:o}=r,i=a,l=s||o.dtype;if(i.shouldExecuteOnCPU([o])&&n!=null){let p=i.texData.get(o.dataId),d=n(p.values,l);return i.makeTensorInfo(o.shape,l,d)}let u=H().getBool("WEBGL_PACK_UNARY_OPERATIONS")&&t!=null,c;return u?c=new el(o.shape,t):c=new Sa(o.shape,e),i.runWebGLProgram(c,[o],l)}}function Bn({opSnippet:e,packedOpSnippet:t,checkOutOfBounds:n=!1,supportsComplex:s=!1,cpuKernelImpl:r,dtype:a}){return({inputs:o,backend:i})=>{let{a:l,b:u}=o,c=i;if(s&&l.dtype==="complex64"){let f=c.texData.get(l.dataId),m=c.texData.get(u.dataId),[g,y]=[[f.complexTensorInfos.real,m.complexTensorInfos.real],[f.complexTensorInfos.imag,m.complexTensorInfos.imag]].map(A=>{let[b,w]=A,k={dataId:b.dataId,dtype:b.dtype,shape:l.shape},C={dataId:w.dataId,dtype:w.dtype,shape:u.shape},E=new Ic(e,l.shape,u.shape);return c.runWebGLProgram(E,[k,C],Hn(b.dtype,w.dtype))}),x=xi({inputs:{real:g,imag:y},backend:c});return c.disposeIntermediateTensorInfo(g),c.disposeIntermediateTensorInfo(y),x}let p=a||Hn(l.dtype,u.dtype);if((l.dtype==="string"||u.dtype==="string"||c.shouldExecuteOnCPU([l,u]))&&r!=null){let f=c.texData.get(l.dataId).values,m=c.texData.get(u.dataId).values,g=l.dtype==="string"?T.fromUint8ToStringArray(f):f,y=l.dtype==="string"?T.fromUint8ToStringArray(m):m,[x,A]=r(l.shape,u.shape,g,y,p),b=c.makeTensorInfo(A,p),w=c.texData.get(b.dataId);return w.values=x,b}let d=H().getBool("WEBGL_PACK_BINARY_OPERATIONS")&&t!=null,h;return d?h=new jh(t,l.shape,u.shape,n):h=new Ic(e,l.shape,u.shape),c.runWebGLProgram(h,[l,u],p)}}function zp(e,t=!1){if(e==="linear")return t?Jne:qne;if(e==="relu")return t?ese:Kne;if(e==="elu")return t?Qne:Xne;if(e==="relu6")return t?tse:Zne;if(e==="prelu")return t?L9:z9;if(e==="leakyrelu")return t?M9:O9;if(e==="sigmoid")return t?nse:Yne;throw new Error(`Activation ${e} has not been implemented for the WebGL backend.`)}var B9=class{constructor(e,t,n,s=!1,r=!1,a=!1,o=null,i=!1,l=!1){this.variableNames=["matrixA","matrixB"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=n,this.enableShapeUniforms=fs(this.outputShape.length);let u=s?e[1]:e[2],c=Math.ceil(u/2),p=s?"i * 2, rc.y":"rc.y, i * 2",d=r?"rc.z, i * 2":"i * 2, rc.z",h=s?["a.xxyy","a.zzww"]:["a.xxzz","a.yyww"],f=r?["b.xzxz","b.ywyw"]:["b.xyxy","b.zwzw"],m="",g="";o&&(i?m=`vec4 activation(vec4 a) {
           vec4 b = getPreluActivationWeightsAtOutCoords();
           ${o}
         }`:l?m=`vec4 activation(vec4 a) {
@@ -1358,7 +1358,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
 
         setOutput(result);
       }
-    `}},P7={REAL:"return areal * breal - aimag * bimag;",IMAG:"return areal * bimag + aimag * breal;"},F7=class{constructor(e,t,n){this.variableNames=["AReal","AImag","BReal","BImag"],this.outputShape=T.assertAndGetBroadcastShape(t,n),this.userCode=`
+    `}},B7={REAL:"return areal * breal - aimag * bimag;",IMAG:"return areal * bimag + aimag * breal;"},W7=class{constructor(e,t,n){this.variableNames=["AReal","AImag","BReal","BImag"],this.outputShape=T.assertAndGetBroadcastShape(t,n),this.userCode=`
       float binaryOpComplex(
           float areal, float aimag, float breal, float bimag) {
         ${e}
@@ -1371,7 +1371,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
         float bimag = getBImagAtOutCoords();
         setOutput(binaryOpComplex(areal, aimag, breal, bimag));
       }
-    `}},O7="return a * b;";function hb(e){let{inputs:t,backend:n}=e,{a:s,b:r}=t,a=T.upcastType(s.dtype,r.dtype);if(s.dtype==="complex64"){let i=n.texData.get(s.dataId),l=n.texData.get(r.dataId),u=new F7(P7.REAL,s.shape,r.shape),c=new F7(P7.IMAG,s.shape,r.shape),p=[{dataId:i.complexTensorInfos.real.dataId,dtype:i.complexTensorInfos.real.dtype,shape:s.shape},{dataId:i.complexTensorInfos.imag.dataId,dtype:i.complexTensorInfos.imag.dtype,shape:s.shape},{dataId:l.complexTensorInfos.real.dataId,dtype:l.complexTensorInfos.real.dtype,shape:r.shape},{dataId:l.complexTensorInfos.imag.dataId,dtype:l.complexTensorInfos.imag.dtype,shape:r.shape}],d=n.runWebGLProgram(u,p,"float32"),h=n.runWebGLProgram(c,p,"float32"),f=mi({inputs:{real:d,imag:h},backend:n});return n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(h),f}if(n.shouldExecuteOnCPU([s,r])){let i=n.texData.get(s.dataId),l=n.texData.get(r.dataId),[u,c]=ine(s.shape,r.shape,i.values,l.values,a),p=n.makeTensorInfo(c,a),d=n.texData.get(p.dataId);return d.values=u,p}let o;return H().getBool("WEBGL_PACK_BINARY_OPERATIONS")?o=new Hh(O7,s.shape,r.shape):o=new vc(O7,s.shape,r.shape),n.runWebGLProgram(o,[s,r],a)}var cse={kernelName:Go,backendName:"webgl",kernelFunc:hb};function dse(e,t,n){let s=[dl(e.shape),...pl(e.shape)],r={dtype:e.dtype,shape:s,dataId:e.dataId},a=[dl(t),...pl(t)],o=new T9(a,s),i=!0,l=[s],u=n.runWebGLProgram(o,[r],e.dtype,l,i);return{dataId:u.dataId,shape:t,dtype:u.dtype}}function we(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{shape:a}=s,o=n,i=v.sizeFromShape(r.shape),l=v.inferFromImplicitShape(a,i),u=v.sizeFromShape(l);v.assert(i===u,()=>`The new shape (${l}) has ${u} elements and the old shape (${r.shape}) has ${i} elements. The new shape and old shape must have the same number of elements.`);let c=o.texData.get(r.dataId);return c.isPacked&&!Op(r.shape,l)&&!(c.texture!==null&&Op(c.shape,l))?dse(r,l,o):(o.incRef(r.dataId),{dataId:r.dataId,shape:l,dtype:r.dtype})}var pse={kernelName:Vl,backendName:"webgl",kernelFunc:we},M7=class{constructor(e,t){this.variableNames=["x"];let{windowSize:n,batchSize:s,inSize:r,outSize:a}=e;this.outputShape=[s,a];let o=Math.floor(n/4)*4,i=n%4,l="sumValue += dot(values, ones);";if(t!=null){let c=1/t;l=`sumValue += dot(values * ${v.isInt(c)?c.toPrecision(2):c}, ones);`}let u="";r%n>0&&(u=`
+    `}},V7="return a * b;";function gb(e){let{inputs:t,backend:n}=e,{a:s,b:r}=t,a=T.upcastType(s.dtype,r.dtype);if(s.dtype==="complex64"){let i=n.texData.get(s.dataId),l=n.texData.get(r.dataId),u=new W7(B7.REAL,s.shape,r.shape),c=new W7(B7.IMAG,s.shape,r.shape),p=[{dataId:i.complexTensorInfos.real.dataId,dtype:i.complexTensorInfos.real.dtype,shape:s.shape},{dataId:i.complexTensorInfos.imag.dataId,dtype:i.complexTensorInfos.imag.dtype,shape:s.shape},{dataId:l.complexTensorInfos.real.dataId,dtype:l.complexTensorInfos.real.dtype,shape:r.shape},{dataId:l.complexTensorInfos.imag.dataId,dtype:l.complexTensorInfos.imag.dtype,shape:r.shape}],d=n.runWebGLProgram(u,p,"float32"),h=n.runWebGLProgram(c,p,"float32"),f=xi({inputs:{real:d,imag:h},backend:n});return n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(h),f}if(n.shouldExecuteOnCPU([s,r])){let i=n.texData.get(s.dataId),l=n.texData.get(r.dataId),[u,c]=xne(s.shape,r.shape,i.values,l.values,a),p=n.makeTensorInfo(c,a),d=n.texData.get(p.dataId);return d.values=u,p}let o;return H().getBool("WEBGL_PACK_BINARY_OPERATIONS")?o=new jh(V7,s.shape,r.shape):o=new Ic(V7,s.shape,r.shape),n.runWebGLProgram(o,[s,r],a)}var wse={kernelName:Xo,backendName:"webgl",kernelFunc:gb};function kse(e,t,n){let s=[ml(e.shape),...gl(e.shape)],r={dtype:e.dtype,shape:s,dataId:e.dataId},a=[ml(t),...gl(t)],o=new $9(a,s),i=!0,l=[s],u=n.runWebGLProgram(o,[r],e.dtype,l,i);return{dataId:u.dataId,shape:t,dtype:u.dtype}}function we(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{shape:a}=s,o=n,i=v.sizeFromShape(r.shape),l=v.inferFromImplicitShape(a,i),u=v.sizeFromShape(l);v.assert(i===u,()=>`The new shape (${l}) has ${u} elements and the old shape (${r.shape}) has ${i} elements. The new shape and old shape must have the same number of elements.`);let c=o.texData.get(r.dataId);return c.isPacked&&!Mp(r.shape,l)&&!(c.texture!==null&&Mp(c.shape,l))?kse(r,l,o):(o.incRef(r.dataId),{dataId:r.dataId,shape:l,dtype:r.dtype})}var Sse={kernelName:jl,backendName:"webgl",kernelFunc:we},U7=class{constructor(e,t){this.variableNames=["x"];let{windowSize:n,batchSize:s,inSize:r,outSize:a}=e;this.outputShape=[s,a];let o=Math.floor(n/4)*4,i=n%4,l="sumValue += dot(values, ones);";if(t!=null){let c=1/t;l=`sumValue += dot(values * ${v.isInt(c)?c.toPrecision(2):c}, ones);`}let u="";r%n>0&&(u=`
         if (inIdx < 0 || inIdx >= ${r}) {
           return 0.0;
         }
@@ -1424,7 +1424,7 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
         }
         setOutput(sumValue);
       }
-    `}},hse=class{constructor(e,t){this.variableNames=["x"];let{windowSize:n,batchSize:s,inSize:r,outSize:a}=e;this.outputShape=[s,a];let o="0.0",i="";t==="prod"?o="1.0":t==="min"?(o="1.0 / 1e-20",i="min"):t==="max"&&(o="-1.0 / 1e-20",i="max");let l=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="sum"?l="sumValue":t==="prod"?l="prodValue":t==="all"?l="allValue":t==="any"&&(l="anyValue");let u=Math.floor(n/4)*4,c=n%4,p=`
+    `}},Ise=class{constructor(e,t){this.variableNames=["x"];let{windowSize:n,batchSize:s,inSize:r,outSize:a}=e;this.outputShape=[s,a];let o="0.0",i="";t==="prod"?o="1.0":t==="min"?(o="1.0 / 1e-20",i="min"):t==="max"&&(o="-1.0 / 1e-20",i="max");let l=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="sum"?l="sumValue":t==="prod"?l="prodValue":t==="all"?l="allValue":t==="any"&&(l="anyValue");let u=Math.floor(n/4)*4,c=n%4,p=`
       if (${t==="sum"}) {
         sumValue += dot(values, ones);
       } else if (${t==="prod"}) {
@@ -1516,12 +1516,12 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
         }
         setOutput(${l});
       }
-    `}};function fse(e){let t=[];for(;t.length===0||t[t.length-1].outSize!==1;){let n=t.length?t[t.length-1].outSize:e[1],s=T.computeOptimalWindowSize(n);t.push({inSize:n,windowSize:s,outSize:Math.ceil(n/s)})}return t}function gu(e,t,n,s){let r=fse(e.shape),a=e;for(let o=0;o<r.length;o++){let{inSize:i,windowSize:l,outSize:u}=r[o],c,p;n==="mean"?c=o===0?new M7({windowSize:l,inSize:i,batchSize:e.shape[0],outSize:u},i):new M7({windowSize:l,inSize:i,batchSize:e.shape[0],outSize:u}):c=new hse({windowSize:l,inSize:i,batchSize:e.shape[0],outSize:u},n),p=a,a=s.runWebGLProgram(c,[a],t),p.dataId!==e.dataId&&s.disposeIntermediateTensorInfo(p)}return a}var mse=class{constructor(e,t){this.variableNames=["A"];let n=new Array(e.length);for(let a=0;a<n.length;a++)n[a]=e[t[a]];this.outputShape=n,this.rank=n.length;let s=wt(this.rank),r=gse(t);this.userCode=`
+    `}};function Cse(e){let t=[];for(;t.length===0||t[t.length-1].outSize!==1;){let n=t.length?t[t.length-1].outSize:e[1],s=T.computeOptimalWindowSize(n);t.push({inSize:n,windowSize:s,outSize:Math.ceil(n/s)})}return t}function bu(e,t,n,s){let r=Cse(e.shape),a=e;for(let o=0;o<r.length;o++){let{inSize:i,windowSize:l,outSize:u}=r[o],c,p;n==="mean"?c=o===0?new U7({windowSize:l,inSize:i,batchSize:e.shape[0],outSize:u},i):new U7({windowSize:l,inSize:i,batchSize:e.shape[0],outSize:u}):c=new Ise({windowSize:l,inSize:i,batchSize:e.shape[0],outSize:u},n),p=a,a=s.runWebGLProgram(c,[a],t),p.dataId!==e.dataId&&s.disposeIntermediateTensorInfo(p)}return a}var Tse=class{constructor(e,t){this.variableNames=["A"];let n=new Array(e.length);for(let a=0;a<n.length;a++)n[a]=e[t[a]];this.outputShape=n,this.rank=n.length;let s=kt(this.rank),r=Nse(t);this.userCode=`
     void main() {
       ${s} resRC = getOutputCoords();
       setOutput(getA(${r}));
     }
-    `}};function gse(e){let t=e.length;if(t>6)throw Error(`Transpose for rank ${t} is not yet supported`);let n=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u","resRC.v"],s=new Array(t);for(let r=0;r<e.length;r++)s[e[r]]=n[r];return s.join()}var yse=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0;let n=new Array(e.length);for(let u=0;u<n.length;u++)n[u]=e[t[u]];if(this.outputShape=n,this.rank=n.length,this.rank>6)throw Error(`Packed transpose for rank ${this.rank} is not yet supported.`);let s=wt(this.rank),r=C9("rc",this.rank),a=new Array(this.rank);for(let u=0;u<t.length;u++)a[t[u]]=r[u];let o=`vec2(${a.slice(-2).join()})`,i=`++${r[this.rank-1]} < ${n[this.rank-1]}`,l=`getChannel(getA(${a.join()}), ${o})`;this.userCode=`
+    `}};function Nse(e){let t=e.length;if(t>6)throw Error(`Transpose for rank ${t} is not yet supported`);let n=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u","resRC.v"],s=new Array(t);for(let r=0;r<e.length;r++)s[e[r]]=n[r];return s.join()}var Ese=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0;let n=new Array(e.length);for(let u=0;u<n.length;u++)n[u]=e[t[u]];if(this.outputShape=n,this.rank=n.length,this.rank>6)throw Error(`Packed transpose for rank ${this.rank} is not yet supported.`);let s=kt(this.rank),r=D9("rc",this.rank),a=new Array(this.rank);for(let u=0;u<t.length;u++)a[t[u]]=r[u];let o=`vec2(${a.slice(-2).join()})`,i=`++${r[this.rank-1]} < ${n[this.rank-1]}`,l=`getChannel(getA(${a.join()}), ${o})`;this.userCode=`
     void main() {
       ${s} rc = getOutputCoords();
       vec4 result = vec4(0.);
@@ -1538,14 +1538,14 @@ vec2 packedUVfrom3D(int texNumR, int texNumC,
       }
       setOutput(result);
     }
-    `}};function F2(e,t,n){let s=H().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new yse(e.shape,t):new mse(e.shape,t);return n.runWebGLProgram(s,[e],e.dtype)}function Ase(e,t,n,s){let r=t,a=e.shape.length,o=v.parseAxisParam(r,e.shape),i=o,l=T.getAxesPermutation(i,a),u=l!=null,c=e;u&&(c=F2(e,l,s),i=T.getInnerMostAxes(i.length,a)),T.assertAxesAreInnerMostDims("sum",i,a);let[p,d]=T.computeOutAndReduceShapes(c.shape,i),h=p;n&&(h=T.expandShapeToKeepDim(p,o));let f=v.sizeFromShape(d),g=v.sizeFromShape(e.shape)/f,y=we({inputs:{x:c},attrs:{shape:[g,f]},backend:s}),x=uh(e.dtype),A=gu(y,x,"sum",s),b=we({inputs:{x:A},attrs:{shape:h},backend:s});return s.disposeIntermediateTensorInfo(y),s.disposeIntermediateTensorInfo(A),u&&s.disposeIntermediateTensorInfo(c),b}function O2(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:o}=s;return Ase(r,a,o,n)}var xse={kernelName:si,backendName:"webgl",kernelFunc:O2};function is(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{perm:a}=s,o=n,i=r.shape.length,l=new Array(i);for(let c=0;c<l.length;c++)l[c]=r.shape[a[c]];let u;if(o.shouldExecuteOnCPU([r])){let p=o.texData.get(r.dataId).values,d=pb(p,r.shape,r.dtype,a,l);u=o.makeTensorInfo(l,r.dtype);let h=o.texData.get(u.dataId);h.values=d}else u=F2(r,a,o);return u}var bse={kernelName:ea,backendName:"webgl",kernelFunc:is},F9=1e3;function qm({a:e,b:t,transposeA:n,transposeB:s,backend:r,bias:a=null,preluActivationWeights:o=null,leakyreluAlpha:i=0,activation:l=null}){let u=e.shape.length,c=t.shape.length,p=n?e.shape[u-2]:e.shape[u-1],d=s?t.shape[c-1]:t.shape[c-2],h=n?e.shape[u-1]:e.shape[u-2],f=s?t.shape[c-2]:t.shape[c-1],m=e.shape.slice(0,-2),g=t.shape.slice(0,-2),y=v.sizeFromShape(m),x=v.sizeFromShape(g),b=au.assertAndGetBroadcastShape(e.shape.slice(0,-2),t.shape.slice(0,-2)).concat([h,f]);v.assert(p===d,()=>`Error in matMul: inner shapes (${p}) and (${d}) of Tensors with shapes ${e.shape} and ${t.shape} and transposeA=${n} and transposeB=${s} must match.`);let w=n?[y,p,h]:[y,h,p],k=s?[x,f,d]:[x,d,f],C=we({inputs:{x:e},backend:r,attrs:{shape:w}}),E=we({inputs:{x:t},backend:r,attrs:{shape:k}}),_=[C,E],$=Math.max(y,x),R=n?C.shape[1]:C.shape[2],P=a!=null,S=o!=null,M=l==="leakyrelu",L=l!=null?Mp(l,!0):null,U=P||S||M||L!=null,K;if((h===1||f===1)&&R>F9&&U===!1){let Z=C,J=E;n&&(Z=is({inputs:{x:C},backend:r,attrs:{perm:[0,2,1]}}),_.push(Z)),s&&(J=is({inputs:{x:E},backend:r,attrs:{perm:[0,2,1]}}),_.push(J));let Q=f!==1,le=f===1,ae=Z;Q&&(ae=we({inputs:{x:Z},backend:r,attrs:{shape:[$,R,1]}}),_.push(ae));let pe=f===1?2:1,ce=J;le&&(ce=we({inputs:{x:J},backend:r,attrs:{shape:[$,1,R]}}),_.push(ce));let xe=hb({inputs:{a:ae,b:ce},backend:r});K=O2({inputs:{x:xe},backend:r,attrs:{axis:pe,keepDims:!0}}),_.push(xe)}else{let Z=Gn(e.dtype,t.dtype),J=new P9(w,k,[$,h,f],n,s,P,L,S,M),Q=[C,E];if(a!=null&&Q.push(a),S&&Q.push(o),M){let le=r.makeTensorInfo([],"float32",v.createScalarValue(i,"float32"));Q.push(le),_.push(le)}K=r.runWebGLProgram(J,Q,Z)}let q=we({inputs:{x:K},backend:r,attrs:{shape:b}});_.push(K);for(let Z of _)r.disposeIntermediateTensorInfo(Z);return q}function vse(e){let{inputs:t,backend:n,attrs:s}=e,{a:r,b:a,bias:o,preluActivationWeights:i}=t,{transposeA:l,transposeB:u,activation:c,leakyreluAlpha:p}=s;return qm({a:r,b:a,transposeA:l,transposeB:u,backend:n,bias:o,preluActivationWeights:i,leakyreluAlpha:p,activation:c})}var wse={kernelName:to,backendName:"webgl",kernelFunc:vse},z7="return abs(x);";function kse(e){let{inputs:t,backend:n}=e,{x:s}=t;if(n.shouldExecuteOnCPU([s])&&s.dtype!=="complex64"){let a=n.texData.get(s.dataId),o=S9(a.values);return n.makeTensorInfo(s.shape,s.dtype,o)}let r;return H().getBool("WEBGL_PACK_UNARY_OPERATIONS")?r=new Zi(s.shape,z7):r=new ba(s.shape,z7),n.runWebGLProgram(r,[s],s.dtype)}var Sse={kernelName:ml,backendName:"webgl",kernelFunc:kse},Ise=xr+`
+    `}};function F2(e,t,n){let s=H().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new Ese(e.shape,t):new Tse(e.shape,t);return n.runWebGLProgram(s,[e],e.dtype)}function Rse(e,t,n,s){let r=t,a=e.shape.length,o=v.parseAxisParam(r,e.shape),i=o,l=T.getAxesPermutation(i,a),u=l!=null,c=e;u&&(c=F2(e,l,s),i=T.getInnerMostAxes(i.length,a)),T.assertAxesAreInnerMostDims("sum",i,a);let[p,d]=T.computeOutAndReduceShapes(c.shape,i),h=p;n&&(h=T.expandShapeToKeepDim(p,o));let f=v.sizeFromShape(d),g=v.sizeFromShape(e.shape)/f,y=we({inputs:{x:c},attrs:{shape:[g,f]},backend:s}),x=ch(e.dtype),A=bu(y,x,"sum",s),b=we({inputs:{x:A},attrs:{shape:h},backend:s});return s.disposeIntermediateTensorInfo(y),s.disposeIntermediateTensorInfo(A),u&&s.disposeIntermediateTensorInfo(c),b}function O2(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:o}=s;return Rse(r,a,o,n)}var _se={kernelName:ii,backendName:"webgl",kernelFunc:O2};function cs(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{perm:a}=s,o=n,i=r.shape.length,l=new Array(i);for(let c=0;c<l.length;c++)l[c]=r.shape[a[c]];let u;if(o.shouldExecuteOnCPU([r])){let p=o.texData.get(r.dataId).values,d=mb(p,r.shape,r.dtype,a,l);u=o.makeTensorInfo(l,r.dtype);let h=o.texData.get(u.dataId);h.values=d}else u=F2(r,a,o);return u}var Dse={kernelName:na,backendName:"webgl",kernelFunc:cs},W9=1e3;function qm({a:e,b:t,transposeA:n,transposeB:s,backend:r,bias:a=null,preluActivationWeights:o=null,leakyreluAlpha:i=0,activation:l=null}){let u=e.shape.length,c=t.shape.length,p=n?e.shape[u-2]:e.shape[u-1],d=s?t.shape[c-1]:t.shape[c-2],h=n?e.shape[u-1]:e.shape[u-2],f=s?t.shape[c-2]:t.shape[c-1],m=e.shape.slice(0,-2),g=t.shape.slice(0,-2),y=v.sizeFromShape(m),x=v.sizeFromShape(g),b=uu.assertAndGetBroadcastShape(e.shape.slice(0,-2),t.shape.slice(0,-2)).concat([h,f]);v.assert(p===d,()=>`Error in matMul: inner shapes (${p}) and (${d}) of Tensors with shapes ${e.shape} and ${t.shape} and transposeA=${n} and transposeB=${s} must match.`);let w=n?[y,p,h]:[y,h,p],k=s?[x,f,d]:[x,d,f],C=we({inputs:{x:e},backend:r,attrs:{shape:w}}),E=we({inputs:{x:t},backend:r,attrs:{shape:k}}),_=[C,E],$=Math.max(y,x),R=n?C.shape[1]:C.shape[2],P=a!=null,S=o!=null,M=l==="leakyrelu",L=l!=null?zp(l,!0):null,U=P||S||M||L!=null,K;if((h===1||f===1)&&R>W9&&U===!1){let Z=C,J=E;n&&(Z=cs({inputs:{x:C},backend:r,attrs:{perm:[0,2,1]}}),_.push(Z)),s&&(J=cs({inputs:{x:E},backend:r,attrs:{perm:[0,2,1]}}),_.push(J));let te=f!==1,le=f===1,ae=Z;te&&(ae=we({inputs:{x:Z},backend:r,attrs:{shape:[$,R,1]}}),_.push(ae));let pe=f===1?2:1,ce=J;le&&(ce=we({inputs:{x:J},backend:r,attrs:{shape:[$,1,R]}}),_.push(ce));let xe=gb({inputs:{a:ae,b:ce},backend:r});K=O2({inputs:{x:xe},backend:r,attrs:{axis:pe,keepDims:!0}}),_.push(xe)}else{let Z=Hn(e.dtype,t.dtype),J=new B9(w,k,[$,h,f],n,s,P,L,S,M),te=[C,E];if(a!=null&&te.push(a),S&&te.push(o),M){let le=r.makeTensorInfo([],"float32",v.createScalarValue(i,"float32"));te.push(le),_.push(le)}K=r.runWebGLProgram(J,te,Z)}let q=we({inputs:{x:K},backend:r,attrs:{shape:b}});_.push(K);for(let Z of _)r.disposeIntermediateTensorInfo(Z);return q}function $se(e){let{inputs:t,backend:n,attrs:s}=e,{a:r,b:a,bias:o,preluActivationWeights:i}=t,{transposeA:l,transposeB:u,activation:c,leakyreluAlpha:p}=s;return qm({a:r,b:a,transposeA:l,transposeB:u,backend:n,bias:o,preluActivationWeights:i,leakyreluAlpha:p,activation:c})}var Pse={kernelName:ao,backendName:"webgl",kernelFunc:$se},G7="return abs(x);";function Fse(e){let{inputs:t,backend:n}=e,{x:s}=t;if(n.shouldExecuteOnCPU([s])&&s.dtype!=="complex64"){let a=n.texData.get(s.dataId),o=R9(a.values);return n.makeTensorInfo(s.shape,s.dtype,o)}let r;return H().getBool("WEBGL_PACK_UNARY_OPERATIONS")?r=new el(s.shape,G7):r=new Sa(s.shape,G7),n.runWebGLProgram(r,[s],s.dtype)}var Ose={kernelName:xl,backendName:"webgl",kernelFunc:Fse},Mse=br+`
   if (abs(x) > 1.) {
     return NAN;
   }
   return acos(x);
-`,Cse=pt({opSnippet:Ise}),Tse={kernelName:Sc,backendName:"webgl",kernelFunc:Cse},Nse=xr+`
+`,zse=ht({opSnippet:Mse}),Lse={kernelName:Nc,backendName:"webgl",kernelFunc:zse},Bse=br+`
   if (x < 1.0) return NAN;
-return log(x + sqrt(x * x - 1.0));`,Ese=pt({opSnippet:Nse}),Rse={kernelName:Ic,backendName:"webgl",kernelFunc:Ese},L7="return a + b;",_se=Ln({opSnippet:L7,packedOpSnippet:L7,supportsComplex:!0,cpuKernelImpl:Wte}),Dse={kernelName:Na,backendName:"webgl",kernelFunc:_se},$se=class{constructor(e,t){this.outputShape=[],this.outputShape=e,this.variableNames=t.map((r,a)=>`T${a}`);let n=[];this.variableNames.forEach(r=>{n.push(`float v${r} = get${r}AtOutCoords();`)});let s=this.variableNames.map(r=>`v${r}`).join(" + ");this.userCode=`
+return log(x + sqrt(x * x - 1.0));`,Wse=ht({opSnippet:Bse}),Vse={kernelName:Ec,backendName:"webgl",kernelFunc:Wse},H7="return a + b;",Use=Bn({opSnippet:H7,packedOpSnippet:H7,supportsComplex:!0,cpuKernelImpl:Qte}),Gse={kernelName:Da,backendName:"webgl",kernelFunc:Use},Hse=class{constructor(e,t){this.outputShape=[],this.outputShape=e,this.variableNames=t.map((r,a)=>`T${a}`);let n=[];this.variableNames.forEach(r=>{n.push(`float v${r} = get${r}AtOutCoords();`)});let s=this.variableNames.map(r=>`v${r}`).join(" + ");this.userCode=`
       void main() {
         ${n.join(`
         `)}
@@ -1553,7 +1553,7 @@ return log(x + sqrt(x * x - 1.0));`,Ese=pt({opSnippet:Nse}),Rse={kernelName:Ic,b
         float result = ${s};
         setOutput(result);
       }
-    `}},Pse=class{constructor(e,t){this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.variableNames=t.map((r,a)=>`T${a}`);let n=[];this.variableNames.forEach(r=>{n.push(`vec4 v${r} = get${r}AtOutCoords();`)});let s=this.variableNames.map(r=>`v${r}`).join(" + ");this.userCode=`
+    `}},jse=class{constructor(e,t){this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.variableNames=t.map((r,a)=>`T${a}`);let n=[];this.variableNames.forEach(r=>{n.push(`vec4 v${r} = get${r}AtOutCoords();`)});let s=this.variableNames.map(r=>`v${r}`).join(" + ");this.userCode=`
       void main() {
         ${n.join(`
         `)}
@@ -1561,7 +1561,7 @@ return log(x + sqrt(x * x - 1.0));`,Ese=pt({opSnippet:Nse}),Rse={kernelName:Ic,b
         vec4 result = ${s};
         setOutput(result);
       }
-    `}};function xm(e){let{inputs:t,backend:n}=e,s=t;if(s.length===1)return Ls({inputs:{x:s[0]},backend:n});if(s.length>H().get("WEBGL_MAX_TEXTURES_IN_SHADER")){let l=Math.floor(s.length/2),u=xm({inputs:s.slice(0,l),backend:n}),c=xm({inputs:s.slice(l),backend:n});return xm({inputs:[u,c],backend:n})}let r=s.map(l=>l.dtype).reduce((l,u)=>Gn(l,u)),a=s.map(l=>l.shape),i=H().getBool("WEBGL_PACK")?new Pse(s[0].shape,a):new $se(s[0].shape,a);return n.runWebGLProgram(i,s,r)}var Fse={kernelName:mo,backendName:"webgl",kernelFunc:xm};function Ose(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:o}=s,i=r.shape.length,l=v.parseAxisParam(a,r.shape),u=l,c=T.getAxesPermutation(u,i),p=r;c!=null&&(p=is({inputs:{x:r},backend:n,attrs:{perm:c}}),u=T.getInnerMostAxes(u.length,i)),T.assertAxesAreInnerMostDims("all",u,i);let[d,h]=T.computeOutAndReduceShapes(p.shape,u),f=v.sizeFromShape(h),m=we({inputs:{x:p},backend:n,attrs:{shape:[-1,f]}}),g=gu(m,m.dtype,"all",n),y;if(o){let x=T.expandShapeToKeepDim(d,l);y=we({inputs:{x:g},backend:n,attrs:{shape:x}})}else y=we({inputs:{x:g},backend:n,attrs:{shape:d}});return n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(g),c!=null&&n.disposeIntermediateTensorInfo(p),y}var Mse={kernelName:Cc,backendName:"webgl",kernelFunc:Ose};function zse(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:o}=s,i=r.shape.length,l=v.parseAxisParam(a,r.shape),u=l,c=T.getAxesPermutation(u,i),p=r;c!=null&&(p=is({inputs:{x:r},backend:n,attrs:{perm:c}}),u=T.getInnerMostAxes(u.length,i)),T.assertAxesAreInnerMostDims("any",u,i);let[d,h]=T.computeOutAndReduceShapes(p.shape,u),f=v.sizeFromShape(h),m=we({inputs:{x:p},backend:n,attrs:{shape:[-1,f]}}),g=gu(m,m.dtype,"any",n),y;if(o){let x=T.expandShapeToKeepDim(d,l);y=we({inputs:{x:g},backend:n,attrs:{shape:x}})}else y=we({inputs:{x:g},backend:n,attrs:{shape:d}});return n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(g),c!=null&&n.disposeIntermediateTensorInfo(p),y}var Lse={kernelName:Tc,backendName:"webgl",kernelFunc:zse},Bse=class{constructor(e,t,n){this.variableNames=["A"];let{windowSize:s,batchSize:r,outSize:a}=e;n||this.variableNames.push("bestIndicesA"),this.outputShape=[r,a];let o=t==="max"?">":"<",i=n?"inOffset + i;":"round(getBestIndicesA(batch, inOffset + i));";this.userCode=`
+    `}};function xm(e){let{inputs:t,backend:n}=e,s=t;if(s.length===1)return Vs({inputs:{x:s[0]},backend:n});if(s.length>H().get("WEBGL_MAX_TEXTURES_IN_SHADER")){let l=Math.floor(s.length/2),u=xm({inputs:s.slice(0,l),backend:n}),c=xm({inputs:s.slice(l),backend:n});return xm({inputs:[u,c],backend:n})}let r=s.map(l=>l.dtype).reduce((l,u)=>Hn(l,u)),a=s.map(l=>l.shape),i=H().getBool("WEBGL_PACK")?new jse(s[0].shape,a):new Hse(s[0].shape,a);return n.runWebGLProgram(i,s,r)}var qse={kernelName:xo,backendName:"webgl",kernelFunc:xm};function Xse(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:o}=s,i=r.shape.length,l=v.parseAxisParam(a,r.shape),u=l,c=T.getAxesPermutation(u,i),p=r;c!=null&&(p=cs({inputs:{x:r},backend:n,attrs:{perm:c}}),u=T.getInnerMostAxes(u.length,i)),T.assertAxesAreInnerMostDims("all",u,i);let[d,h]=T.computeOutAndReduceShapes(p.shape,u),f=v.sizeFromShape(h),m=we({inputs:{x:p},backend:n,attrs:{shape:[-1,f]}}),g=bu(m,m.dtype,"all",n),y;if(o){let x=T.expandShapeToKeepDim(d,l);y=we({inputs:{x:g},backend:n,attrs:{shape:x}})}else y=we({inputs:{x:g},backend:n,attrs:{shape:d}});return n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(g),c!=null&&n.disposeIntermediateTensorInfo(p),y}var Kse={kernelName:Rc,backendName:"webgl",kernelFunc:Xse};function Zse(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:o}=s,i=r.shape.length,l=v.parseAxisParam(a,r.shape),u=l,c=T.getAxesPermutation(u,i),p=r;c!=null&&(p=cs({inputs:{x:r},backend:n,attrs:{perm:c}}),u=T.getInnerMostAxes(u.length,i)),T.assertAxesAreInnerMostDims("any",u,i);let[d,h]=T.computeOutAndReduceShapes(p.shape,u),f=v.sizeFromShape(h),m=we({inputs:{x:p},backend:n,attrs:{shape:[-1,f]}}),g=bu(m,m.dtype,"any",n),y;if(o){let x=T.expandShapeToKeepDim(d,l);y=we({inputs:{x:g},backend:n,attrs:{shape:x}})}else y=we({inputs:{x:g},backend:n,attrs:{shape:d}});return n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(g),c!=null&&n.disposeIntermediateTensorInfo(p),y}var Yse={kernelName:_c,backendName:"webgl",kernelFunc:Zse},Jse=class{constructor(e,t,n){this.variableNames=["A"];let{windowSize:s,batchSize:r,outSize:a}=e;n||this.variableNames.push("bestIndicesA"),this.outputShape=[r,a];let o=t==="max"?">":"<",i=n?"inOffset + i;":"round(getBestIndicesA(batch, inOffset + i));";this.userCode=`
       void main() {
         ivec2 coords = getOutputCoords();
         int batch = coords[0];
@@ -1581,7 +1581,7 @@ return log(x + sqrt(x * x - 1.0));`,Ese=pt({opSnippet:Nse}),Rse={kernelName:Ic,b
         }
         setOutput(float(bestIndex));
       }
-    `}},Wse=class{constructor(e,t,n,s){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,v.assert(e.length>2,()=>`Packed arg${n.charAt(0).toUpperCase()+n.slice(1)} supports only inputs with rank above 2.`);let r=e[e.length-1],a=Math.ceil(r/t);this.outputShape=e.slice(0,-1),a>1&&this.outputShape.push(a),s||this.variableNames.push("bestIndicesA");let o=this.outputShape,i=o.length,l=wt(i),u=os("coords",i),c,p;if(a===1){p=i+1;let C=wt(p);c=`
+    `}},Qse=class{constructor(e,t,n,s){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,v.assert(e.length>2,()=>`Packed arg${n.charAt(0).toUpperCase()+n.slice(1)} supports only inputs with rank above 2.`);let r=e[e.length-1],a=Math.ceil(r/t);this.outputShape=e.slice(0,-1),a>1&&this.outputShape.push(a),s||this.variableNames.push("bestIndicesA");let o=this.outputShape,i=o.length,l=kt(i),u=us("coords",i),c,p;if(a===1){p=i+1;let C=kt(p);c=`
         ${C} sourceLocR = ${C}(${u.join()}, 0);
         ++${u[i-1]};
         ${C} sourceLocG = ${C}(${u.join()}, 0);
@@ -1597,7 +1597,7 @@ return log(x + sqrt(x * x - 1.0));`,Ese=pt({opSnippet:Nse}),Rse={kernelName:Ic,b
         ${l} sourceLocA = coords;
         --${u[i-1]};
         ${l} sourceLocB = coords;
-        --${u[i-2]};`;let d=["x","y","z","w","u","v"].slice(0,p),h="."+d[p-1],f=d.map(C=>"int "+C),m=os("sourceLocR",p-1).concat("inIdx.r"),g=os("sourceLocG",p-1).concat("inIdx.g"),y=os("sourceLocB",p-1).concat("inIdx.b"),x=os("sourceLocA",p-1).concat("inIdx.a"),A=n==="max"?"greaterThan":"lessThan",b=s?"":`
+        --${u[i-2]};`;let d=["x","y","z","w","u","v"].slice(0,p),h="."+d[p-1],f=d.map(C=>"int "+C),m=us("sourceLocR",p-1).concat("inIdx.r"),g=us("sourceLocG",p-1).concat("inIdx.g"),y=us("sourceLocB",p-1).concat("inIdx.b"),x=us("sourceLocA",p-1).concat("inIdx.a"),A=n==="max"?"greaterThan":"lessThan",b=s?"":`
           inIdx = round(vec4(getBestIndicesAChannel(${m.join()}),
                              getBestIndicesAChannel(${g.join()}),
                              getBestIndicesAChannel(${y.join()}),
@@ -1643,23 +1643,23 @@ return log(x + sqrt(x * x - 1.0));`,Ese=pt({opSnippet:Nse}),Rse={kernelName:Ic,b
         }
         setOutput(bestIndex);
       }
-    `}};function O9(e,t,n,s=null){let r=t.shape[0],a=t.shape[1];s!=null&&(r=s.shape[0],a=s.shape[1]);let o=T.computeOptimalWindowSize(a),i={windowSize:o,inSize:a,batchSize:r,outSize:Math.ceil(a/o)},l=new Bse(i,n,s==null),u=[t];s!=null&&u.push(s);let c=e.runWebGLProgram(l,u,"int32");if(c.shape[1]===1)return c;let p=O9(e,t,n,c);return e.disposeIntermediateTensorInfo(c),p}function M9(e,t,n,s=null){let r=s!=null?s.shape:t.shape,a=r[r.length-1],o=T.computeOptimalWindowSize(a),i=new Wse(r,o,n,s==null),l=s==null?[t]:[t,s],u=e.runWebGLProgram(i,l,"int32");if(u.shape.length===t.shape.length){let c=M9(e,t,n,u);return e.disposeIntermediateTensorInfo(u),c}return u}function z9(e,t,n,s){let r=[n];if(T.assertAxesAreInnerMostDims("arg"+s.charAt(0).toUpperCase()+s.slice(1),r,t.shape.length),!H().getBool("WEBGL_PACK_REDUCE")||t.shape.length<=2){let a=[],o=e.texData.get(t.dataId),i=o!==null&&o.isPacked,l=t;i&&(l=e.unpackTensor(t),a.push(l));let[u,c]=T.computeOutAndReduceShapes(l.shape,r),p=v.sizeFromShape(c),d=we({inputs:{x:l},backend:e,attrs:{shape:[-1,p]}});a.push(d);let h=O9(e,d,s);a.push(h);let f=we({inputs:{x:h},backend:e,attrs:{shape:u}});return a.forEach(m=>e.disposeIntermediateTensorInfo(m)),f}return M9(e,t,s)}function Vse(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a}=s,o=v.parseAxisParam(a,r.shape),i=T.getAxesPermutation(o,r.shape.length),l=r,u=[];i!=null&&(l=is({inputs:{x:r},backend:n,attrs:{perm:i}}),u.push(l),o=T.getInnerMostAxes(o.length,l.shape.length)),T.assertAxesAreInnerMostDims("argMax",[o[0]],l.shape.length);let c=z9(n,l,o[0],"max");return u.forEach(p=>n.disposeIntermediateTensorInfo(p)),c}var Use={kernelName:go,backendName:"webgl",kernelFunc:Vse};function Gse(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a}=s,o=v.parseAxisParam(a,r.shape),i=T.getAxesPermutation(o,r.shape.length),l=r,u=[];i!=null&&(l=is({inputs:{x:r},backend:n,attrs:{perm:i}}),u.push(l),o=T.getInnerMostAxes(o.length,l.shape.length)),T.assertAxesAreInnerMostDims("argMin",[o[0]],l.shape.length);let c=z9(n,l,o[0],"min");return u.forEach(p=>n.disposeIntermediateTensorInfo(p)),c}var Hse={kernelName:Nc,backendName:"webgl",kernelFunc:Gse},jse=xr+`
+    `}};function V9(e,t,n,s=null){let r=t.shape[0],a=t.shape[1];s!=null&&(r=s.shape[0],a=s.shape[1]);let o=T.computeOptimalWindowSize(a),i={windowSize:o,inSize:a,batchSize:r,outSize:Math.ceil(a/o)},l=new Jse(i,n,s==null),u=[t];s!=null&&u.push(s);let c=e.runWebGLProgram(l,u,"int32");if(c.shape[1]===1)return c;let p=V9(e,t,n,c);return e.disposeIntermediateTensorInfo(c),p}function U9(e,t,n,s=null){let r=s!=null?s.shape:t.shape,a=r[r.length-1],o=T.computeOptimalWindowSize(a),i=new Qse(r,o,n,s==null),l=s==null?[t]:[t,s],u=e.runWebGLProgram(i,l,"int32");if(u.shape.length===t.shape.length){let c=U9(e,t,n,u);return e.disposeIntermediateTensorInfo(u),c}return u}function G9(e,t,n,s){let r=[n];if(T.assertAxesAreInnerMostDims("arg"+s.charAt(0).toUpperCase()+s.slice(1),r,t.shape.length),!H().getBool("WEBGL_PACK_REDUCE")||t.shape.length<=2){let a=[],o=e.texData.get(t.dataId),i=o!==null&&o.isPacked,l=t;i&&(l=e.unpackTensor(t),a.push(l));let[u,c]=T.computeOutAndReduceShapes(l.shape,r),p=v.sizeFromShape(c),d=we({inputs:{x:l},backend:e,attrs:{shape:[-1,p]}});a.push(d);let h=V9(e,d,s);a.push(h);let f=we({inputs:{x:h},backend:e,attrs:{shape:u}});return a.forEach(m=>e.disposeIntermediateTensorInfo(m)),f}return U9(e,t,s)}function ere(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a}=s,o=v.parseAxisParam(a,r.shape),i=T.getAxesPermutation(o,r.shape.length),l=r,u=[];i!=null&&(l=cs({inputs:{x:r},backend:n,attrs:{perm:i}}),u.push(l),o=T.getInnerMostAxes(o.length,l.shape.length)),T.assertAxesAreInnerMostDims("argMax",[o[0]],l.shape.length);let c=G9(n,l,o[0],"max");return u.forEach(p=>n.disposeIntermediateTensorInfo(p)),c}var tre={kernelName:bo,backendName:"webgl",kernelFunc:ere};function nre(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a}=s,o=v.parseAxisParam(a,r.shape),i=T.getAxesPermutation(o,r.shape.length),l=r,u=[];i!=null&&(l=cs({inputs:{x:r},backend:n,attrs:{perm:i}}),u.push(l),o=T.getInnerMostAxes(o.length,l.shape.length)),T.assertAxesAreInnerMostDims("argMin",[o[0]],l.shape.length);let c=G9(n,l,o[0],"min");return u.forEach(p=>n.disposeIntermediateTensorInfo(p)),c}var sre={kernelName:Dc,backendName:"webgl",kernelFunc:nre},rre=br+`
   if (abs(x) > 1.) {
     return NAN;
   }
   return asin(x);
-`,qse=pt({opSnippet:jse}),Xse={kernelName:Ec,backendName:"webgl",kernelFunc:qse},Kse=xr+"return log(x + sqrt(x * x + 1.0));",Zse=pt({opSnippet:Kse}),Yse={kernelName:Rc,backendName:"webgl",kernelFunc:Zse},Jse=xr+`
+`,are=ht({opSnippet:rre}),ore={kernelName:$c,backendName:"webgl",kernelFunc:are},ire=br+"return log(x + sqrt(x * x + 1.0));",lre=ht({opSnippet:ire}),ure={kernelName:Pc,backendName:"webgl",kernelFunc:lre},cre=br+`
   return atan(x);
-`,Qse=pt({opSnippet:Jse}),ere={kernelName:_c,backendName:"webgl",kernelFunc:Qse},tre=lse+`
+`,dre=ht({opSnippet:cre}),pre={kernelName:Fc,backendName:"webgl",kernelFunc:dre},hre=bse+`
   return atan(a, b);
-`,nre=`
+`,fre=`
   vec4 result = atan(a, b);
   vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
-  `+use+`
+  `+vse+`
   return result;
-`,sre=Ln({opSnippet:tre,packedOpSnippet:nre}),rre={kernelName:gl,backendName:"webgl",kernelFunc:sre},are=xr+`
+`,mre=Bn({opSnippet:hre,packedOpSnippet:fre}),gre={kernelName:bl,backendName:"webgl",kernelFunc:mre},yre=br+`
   if ((x < -1.0) || (x > 1.0)) return NAN;
-return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,ore=pt({opSnippet:are}),ire={kernelName:Dc,backendName:"webgl",kernelFunc:ore},zp=class{constructor(e,t,n,s=!1,r=!1){if(this.variableNames=["x"],t==="avg"&&n)throw new Error("Cannot compute positions for average pool.");let a=e.filterWidth,o=e.strideHeight,i=e.strideWidth,l=e.dilationHeight,u=e.dilationWidth,c=e.effectiveFilterHeight,p=e.effectiveFilterWidth,d=e.padInfo.top,h=e.padInfo.left;this.outputShape=e.outShape;let f=t==="avg",m=`((batch  * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + d`,g=`(xR * ${e.inWidth} + xC) * ${e.inChannels} + d`,y="0.0";if(f||(y="-1.0 / 1e-20"),n){let C=">=";this.userCode=`
+return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,Are=ht({opSnippet:yre}),xre={kernelName:Oc,backendName:"webgl",kernelFunc:Are},Lp=class{constructor(e,t,n,s=!1,r=!1){if(this.variableNames=["x"],t==="avg"&&n)throw new Error("Cannot compute positions for average pool.");let a=e.filterWidth,o=e.strideHeight,i=e.strideWidth,l=e.dilationHeight,u=e.dilationWidth,c=e.effectiveFilterHeight,p=e.effectiveFilterWidth,d=e.padInfo.top,h=e.padInfo.left;this.outputShape=e.outShape;let f=t==="avg",m=`((batch  * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + d`,g=`(xR * ${e.inWidth} + xC) * ${e.inChannels} + d`,y="0.0";if(f||(y="-1.0 / 1e-20"),n){let C=">=";this.userCode=`
         const ivec2 strides = ivec2(${o}, ${i});
         const ivec2 pads = ivec2(${d}, ${h});
 
@@ -1800,7 +1800,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,ore=pt({opSnippet:are}),ire={kernel
         }
         setOutput(${A});
       }
-    `}},fb=class{constructor(e,t,n,s=!1,r=!1){if(this.variableNames=["x"],t==="avg"&&n)throw new Error("Cannot compute positions for average pool.");let a=e.filterWidth,o=e.strideDepth,i=e.strideHeight,l=e.strideWidth,u=e.dilationDepth,c=e.dilationHeight,p=e.dilationWidth,d=e.effectiveFilterDepth,h=e.effectiveFilterHeight,f=e.effectiveFilterWidth,m=e.padInfo.front,g=e.padInfo.top,y=e.padInfo.left;this.outputShape=e.outShape;let x=t==="avg",A="0.0";if(x||(A="-1.0 / 1e-20"),n){let _=">=";this.userCode=`
+    `}},yb=class{constructor(e,t,n,s=!1,r=!1){if(this.variableNames=["x"],t==="avg"&&n)throw new Error("Cannot compute positions for average pool.");let a=e.filterWidth,o=e.strideDepth,i=e.strideHeight,l=e.strideWidth,u=e.dilationDepth,c=e.dilationHeight,p=e.dilationWidth,d=e.effectiveFilterDepth,h=e.effectiveFilterHeight,f=e.effectiveFilterWidth,m=e.padInfo.front,g=e.padInfo.top,y=e.padInfo.left;this.outputShape=e.outShape;let x=t==="avg",A="0.0";if(x||(A="-1.0 / 1e-20"),n){let _=">=";this.userCode=`
         const ivec3 strides =
             ivec3(${o}, ${i}, ${l});
         const ivec3 pads = ivec3(${m}, ${g}, ${y});
@@ -1963,7 +1963,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,ore=pt({opSnippet:are}),ire={kernel
           setOutput(${w});
         }
       }
-    `}};function lre(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t;cd(r,"avgPool");let{filterSize:a,strides:o,pad:i,dimRoundingMode:l}=s,u=1;v.assert(T.eitherStridesOrDilationsAreOne(o,u),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${o} and dilations '${u}'`);let c=T.computePool2DInfo(r.shape,a,o,u,i,l);if(c.filterWidth===1&&c.filterHeight===1&&v.arraysEqual(c.inShape,c.outShape))return Ls({inputs:{x:r},backend:n});let p=new zp(c,"avg",!1);return n.runWebGLProgram(p,[r],"float32")}var ure={kernelName:yo,backendName:"webgl",kernelFunc:lre};function cre(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{filterSize:a,strides:o,pad:i,dimRoundingMode:l,dataFormat:u}=s,c=[1,1,1],p=T.computePool3DInfo(r.shape,a,o,c,i,l,u),d=new fb(p,"avg",!1);return n.runWebGLProgram(d,[r],"float32")}var dre={kernelName:Gp,backendName:"webgl",kernelFunc:cre},pre=class{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,s=e.strideHeight,r=e.strideWidth,a=e.dilationHeight,o=e.dilationWidth,i=e.effectiveFilterHeight,l=e.effectiveFilterWidth,u=i-1-e.padInfo.top,c=l-1-e.padInfo.left,p=1/(t*n);this.userCode=`
+    `}};function bre(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t;hd(r,"avgPool");let{filterSize:a,strides:o,pad:i,dimRoundingMode:l}=s,u=1;v.assert(T.eitherStridesOrDilationsAreOne(o,u),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${o} and dilations '${u}'`);let c=T.computePool2DInfo(r.shape,a,o,u,i,l);if(c.filterWidth===1&&c.filterHeight===1&&v.arraysEqual(c.inShape,c.outShape))return Vs({inputs:{x:r},backend:n});let p=new Lp(c,"avg",!1);return n.runWebGLProgram(p,[r],"float32")}var vre={kernelName:vo,backendName:"webgl",kernelFunc:bre};function wre(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{filterSize:a,strides:o,pad:i,dimRoundingMode:l,dataFormat:u}=s,c=[1,1,1],p=T.computePool3DInfo(r.shape,a,o,c,i,l,u),d=new yb(p,"avg",!1);return n.runWebGLProgram(d,[r],"float32")}var kre={kernelName:Hp,backendName:"webgl",kernelFunc:wre},Sre=class{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,s=e.strideHeight,r=e.strideWidth,a=e.dilationHeight,o=e.dilationWidth,i=e.effectiveFilterHeight,l=e.effectiveFilterWidth,u=i-1-e.padInfo.top,c=l-1-e.padInfo.left,p=1/(t*n);this.userCode=`
       const ivec2 pads = ivec2(${u}, ${c});
       const float avgMultiplier = float(${p});
 
@@ -2005,7 +2005,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,ore=pt({opSnippet:are}),ire={kernel
         }
         setOutput(dotProd);
       }
-    `}},hre=class{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;let t=e.filterDepth,n=e.filterHeight,s=e.filterWidth,r=e.strideDepth,a=e.strideHeight,o=e.strideWidth,i=e.dilationDepth,l=e.dilationHeight,u=e.dilationWidth,c=e.effectiveFilterDepth,p=e.effectiveFilterHeight,d=e.effectiveFilterWidth,h=c-1-e.padInfo.front,f=p-1-e.padInfo.top,m=d-1-e.padInfo.left,g=1/(t*n*s);this.userCode=`
+    `}},Ire=class{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;let t=e.filterDepth,n=e.filterHeight,s=e.filterWidth,r=e.strideDepth,a=e.strideHeight,o=e.strideWidth,i=e.dilationDepth,l=e.dilationHeight,u=e.dilationWidth,c=e.effectiveFilterDepth,p=e.effectiveFilterHeight,d=e.effectiveFilterWidth,h=c-1-e.padInfo.front,f=p-1-e.padInfo.top,m=d-1-e.padInfo.left,g=1/(t*n*s);this.userCode=`
       const ivec3 pads = ivec3(${h}, ${f}, ${m});
       const float avgMultiplier = float(${g});
 
@@ -2061,7 +2061,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,ore=pt({opSnippet:are}),ire={kernel
         }
         setOutput(dotProd);
       }
-    `}};function fre(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,input:a}=t,o=a,{filterSize:i,strides:l,pad:u,dimRoundingMode:c}=s,p=[1,1,1],d=T.computePool3DInfo(o.shape,i,l,p,u,c),h=new hre(d);return n.runWebGLProgram(h,[r],o.dtype)}var mre={kernelName:s0,backendName:"webgl",kernelFunc:fre};function gre(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,input:a}=t,o=a;cd([r,a],"avgPoolGrad");let{filterSize:i,strides:l,pad:u}=s,c=T.computePool2DInfo(o.shape,i,l,1,u),p=new pre(c);return n.runWebGLProgram(p,[r],o.dtype)}var yre={kernelName:n0,backendName:"webgl",kernelFunc:gre};function Are(e){let{inputs:t,backend:n,attrs:s}=e,{a:r,b:a}=t,{transposeA:o,transposeB:i}=s;return qm({a:r,b:a,transposeA:o,transposeB:i,backend:n})}var xre={kernelName:Ao,backendName:"webgl",kernelFunc:Are},bre=class{constructor(e,t,n,s,r,a){this.outputShape=[],this.variableNames=["x","mean","variance"],T.assertAndGetBroadcastShape(e,t),T.assertAndGetBroadcastShape(e,n);let o="0.0";s!=null&&(T.assertAndGetBroadcastShape(e,s),this.variableNames.push("offset"),o="getOffsetAtOutCoords()");let i="1.0";r!=null&&(T.assertAndGetBroadcastShape(e,r),this.variableNames.push("scale"),i="getScaleAtOutCoords()"),this.outputShape=e,this.userCode=`
+    `}};function Cre(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,input:a}=t,o=a,{filterSize:i,strides:l,pad:u,dimRoundingMode:c}=s,p=[1,1,1],d=T.computePool3DInfo(o.shape,i,l,p,u,c),h=new Ire(d);return n.runWebGLProgram(h,[r],o.dtype)}var Tre={kernelName:s0,backendName:"webgl",kernelFunc:Cre};function Nre(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,input:a}=t,o=a;hd([r,a],"avgPoolGrad");let{filterSize:i,strides:l,pad:u}=s,c=T.computePool2DInfo(o.shape,i,l,1,u),p=new Sre(c);return n.runWebGLProgram(p,[r],o.dtype)}var Ere={kernelName:n0,backendName:"webgl",kernelFunc:Nre};function Rre(e){let{inputs:t,backend:n,attrs:s}=e,{a:r,b:a}=t,{transposeA:o,transposeB:i}=s;return qm({a:r,b:a,transposeA:o,transposeB:i,backend:n})}var _re={kernelName:wo,backendName:"webgl",kernelFunc:Rre},Dre=class{constructor(e,t,n,s,r,a){this.outputShape=[],this.variableNames=["x","mean","variance"],T.assertAndGetBroadcastShape(e,t),T.assertAndGetBroadcastShape(e,n);let o="0.0";s!=null&&(T.assertAndGetBroadcastShape(e,s),this.variableNames.push("offset"),o="getOffsetAtOutCoords()");let i="1.0";r!=null&&(T.assertAndGetBroadcastShape(e,r),this.variableNames.push("scale"),i="getScaleAtOutCoords()"),this.outputShape=e,this.userCode=`
       void main() {
         float x = getXAtOutCoords();
         float mean = getMeanAtOutCoords();
@@ -2071,7 +2071,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,ore=pt({opSnippet:are}),ire={kernel
         float inv = scale * inversesqrt(variance + float(${a}));
         setOutput(dot(vec3(x, -mean, offset), vec3(inv, inv, 1)));
       }
-    `}},vre=class{constructor(e,t,n,s,r,a){this.packedInputs=!0,this.packedOutput=!0,this.variableNames=["x","mean","variance"],T.assertAndGetBroadcastShape(e,t),T.assertAndGetBroadcastShape(e,n);let o="vec4(0.0)";s!=null&&(T.assertAndGetBroadcastShape(e,s),this.variableNames.push("offset"),o="getOffsetAtOutCoords()");let i="vec4(1.0)";r!=null&&(T.assertAndGetBroadcastShape(e,r),this.variableNames.push("scale"),i="getScaleAtOutCoords()"),this.outputShape=e,this.userCode=`
+    `}},$re=class{constructor(e,t,n,s,r,a){this.packedInputs=!0,this.packedOutput=!0,this.variableNames=["x","mean","variance"],T.assertAndGetBroadcastShape(e,t),T.assertAndGetBroadcastShape(e,n);let o="vec4(0.0)";s!=null&&(T.assertAndGetBroadcastShape(e,s),this.variableNames.push("offset"),o="getOffsetAtOutCoords()");let i="vec4(1.0)";r!=null&&(T.assertAndGetBroadcastShape(e,r),this.variableNames.push("scale"),i="getScaleAtOutCoords()"),this.outputShape=e,this.userCode=`
       void main() {
         vec4 offset = ${o};
         vec4 scale = ${i};
@@ -2084,7 +2084,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,ore=pt({opSnippet:are}),ire={kernel
 
         setOutput((x - mean) * inv + offset);
       }
-    `}},wre=({inputs:e,backend:t,attrs:n})=>{let{x:s,mean:r,variance:a,offset:o,scale:i}=e;v.assert(r.shape.length===a.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),v.assert(o==null||r.shape.length===o.shape.length,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),v.assert(i==null||r.shape.length===i.shape.length,()=>"Batch normalization gradient requires mean and scale to have equal ranks.");let{varianceEpsilon:l}=n;l==null&&(l=.001);let u=[s,r,a],c=null;o!=null&&(c=o.shape,u.push(o));let p=null;i!=null&&(p=i.shape,u.push(i));let d=H().getBool("WEBGL_PACK_NORMALIZATION")?new vre(s.shape,r.shape,a.shape,c,p,l):new bre(s.shape,r.shape,a.shape,c,p,l);return t.runWebGLProgram(d,u,u[0].dtype)},kre={kernelName:Do,backendName:"webgl",kernelFunc:wre},Sre=class{constructor(e){this.variableNames=["source"],this.outputShape=e,this.rank=e.length;let t=wt(this.rank);this.customUniforms=[{name:"start",arrayIndex:this.rank,type:"int"}];let n=Ire(this.rank),s,r=e.map((a,o)=>`sourceLoc.${vy[o]} = start[${o}] + coords.${vy[o]};`);s=`
+    `}},Pre=({inputs:e,backend:t,attrs:n})=>{let{x:s,mean:r,variance:a,offset:o,scale:i}=e;v.assert(r.shape.length===a.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),v.assert(o==null||r.shape.length===o.shape.length,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),v.assert(i==null||r.shape.length===i.shape.length,()=>"Batch normalization gradient requires mean and scale to have equal ranks.");let{varianceEpsilon:l}=n;l==null&&(l=.001);let u=[s,r,a],c=null;o!=null&&(c=o.shape,u.push(o));let p=null;i!=null&&(p=i.shape,u.push(i));let d=H().getBool("WEBGL_PACK_NORMALIZATION")?new $re(s.shape,r.shape,a.shape,c,p,l):new Dre(s.shape,r.shape,a.shape,c,p,l);return t.runWebGLProgram(d,u,u[0].dtype)},Fre={kernelName:Oo,backendName:"webgl",kernelFunc:Pre},Ore=class{constructor(e){this.variableNames=["source"],this.outputShape=e,this.rank=e.length;let t=kt(this.rank);this.customUniforms=[{name:"start",arrayIndex:this.rank,type:"int"}];let n=Mre(this.rank),s,r=e.map((a,o)=>`sourceLoc.${Sy[o]} = start[${o}] + coords.${Sy[o]};`);s=`
         ${t} sourceLoc;
         ${t} coords = getOutputCoords();
         ${r.join(`
@@ -2094,7 +2094,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,ore=pt({opSnippet:are}),ire={kernel
         ${s}
         setOutput(getSource(${n}));
       }
-    `}},vy=["x","y","z","w","u","v"];function Ire(e){if(e===1)return"sourceLoc";if(e<=6)return vy.slice(0,e).map(t=>"sourceLoc."+t).join(",");throw Error(`Slicing for rank ${e} is not yet supported`)}var Cre=class{constructor(e){this.variableNames=["source"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.rank=e.length,this.customUniforms=[{name:"start",arrayIndex:this.rank,type:"int"}];let t=wt(this.rank),n=os("coords",this.rank),s=os("sourceLoc",this.rank),r=this.rank===1?"sourceLoc":`vec2(${s.slice(-2).join()})`,a=`getChannel(getSource(${s.join()}), ${r})`,o=`
+    `}},Sy=["x","y","z","w","u","v"];function Mre(e){if(e===1)return"sourceLoc";if(e<=6)return Sy.slice(0,e).map(t=>"sourceLoc."+t).join(",");throw Error(`Slicing for rank ${e} is not yet supported`)}var zre=class{constructor(e){this.variableNames=["source"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.rank=e.length,this.customUniforms=[{name:"start",arrayIndex:this.rank,type:"int"}];let t=kt(this.rank),n=us("coords",this.rank),s=us("sourceLoc",this.rank),r=this.rank===1?"sourceLoc":`vec2(${s.slice(-2).join()})`,a=`getChannel(getSource(${s.join()}), ${r})`,o=`
       result.x = ${a};
       if (++${n[this.rank-1]} < ${e[this.rank-1]}) {
         ++${s[this.rank-1]};
@@ -2123,7 +2123,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,ore=pt({opSnippet:are}),ire={kernel
         ${i}
         setOutput(result);
       }
-    `}};function Tre(e,t,n,s){let r=s.texData.get(e.dataId),a=s.makeTensorInfo(n,e.dtype),o=s.texData.get(a.dataId);Object.assign(o,r),o.refCount=1,o.shape=n,o.dtype=e.dtype;let i=Ht.computeFlatOffset(t,v.computeStrides(e.shape));r.slice&&(i+=r.slice.flatOffset),o.slice={flatOffset:i,origDataId:r.slice&&r.slice.origDataId||e.dataId};let l=s.dataRefCount.get(o.slice.origDataId)||1;return s.dataRefCount.set(o.slice.origDataId,l+1),a}function yd(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{begin:a,size:o}=s,[i,l]=Ht.parseSliceParams(r,a,o);if(Ht.assertParamsValid(r,i,l),v.sizeFromShape(l)===0)return n.makeTensorInfo(l,r.dtype,[]);if(n.shouldExecuteOnCPU([r])||r.dtype==="string"){let p=n.texData.get(r.dataId),d=gne(p.values,i,l,r.shape,r.dtype);return n.makeTensorInfo(l,r.dtype,d)}let{isPacked:u}=n.texData.get(r.dataId),c=Ht.isSliceContinous(r.shape,i,l);if(u||!c){let p=H().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new Cre(l):new Sre(l),d=[i];return n.runWebGLProgram(p,[r],r.dtype,d)}return n.uploadToGPU(r.dataId),Tre(r,i,l,n)}var Nre={kernelName:ql,backendName:"webgl",kernelFunc:yd},Ere=e=>{let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockShape:a,crops:o}=s;v.assert(r.shape.length<=4,()=>"batchToSpaceND for rank > 4 with a WebGL backend not implemented yet");let i=a.reduce((x,A)=>x*A),l=T.getReshaped(r.shape,a,i),u=T.getPermuted(l.length,a.length),c=T.getReshapedPermuted(r.shape,a,i),p=T.getSliceBeginCoords(o,a.length),d=T.getSliceSize(c,o,a.length),h=[],f=we({inputs:{x:r},backend:n,attrs:{shape:l}}),m=is({inputs:{x:f},backend:n,attrs:{perm:u}}),g=we({inputs:{x:m},backend:n,attrs:{shape:c}}),y=yd({inputs:{x:g},backend:n,attrs:{begin:p,size:d}});return h.push(f),h.push(m),h.push(g),h.forEach(x=>n.disposeIntermediateTensorInfo(x)),y},Rre={kernelName:yl,backendName:"webgl",kernelFunc:Ere};function _re(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,weights:a}=t,{size:o}=s,i=n.readSync(r.dataId),l=n.readSync(a.dataId),u=k9(i,l,a.dtype,a.shape,o);return n.makeTensorInfo([o],a.dtype,u)}var Dre={kernelName:r0,backendName:"webgl",kernelFunc:_re};function $re(e){let{inputs:t,backend:n}=e,{s0:s,s1:r}=t,a=n.readSync(s.dataId),o=n.readSync(r.dataId),i=T.assertAndGetBroadcastShape(Array.from(a),Array.from(o));return n.makeTensorInfo([i.length],"int32",Int32Array.from(i))}var Pre={kernelName:a0,backendName:"webgl",kernelFunc:$re},Fre="return float(a != b);",L9=Ln({opSnippet:Fre,cpuKernelImpl:une,dtype:"bool"}),Ore={kernelName:Fl,backendName:"webgl",kernelFunc:L9};function jh(e){let{inputs:t,backend:n}=e,{input:s}=t,r=n.texData.get(s.dataId);return Ls({inputs:{x:r.complexTensorInfos.real},backend:n})}var Mre={kernelName:Qp,backendName:"webgl",kernelFunc:jh},zre="return float(int(x));";function Lre(e,t){let n=new ba(e.shape,zre),s=t.runWebGLProgram(n,[e],"int32");return{dataId:s.dataId,shape:s.shape,dtype:s.dtype}}function wy(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{dtype:a}=s;if(a==="complex64"){if(r.dtype==="complex64")return Ls({inputs:{x:r},backend:n});let o=Ut(r.shape),i=wy({inputs:{x:r},backend:n,attrs:{dtype:"float32"}}),l=mi({inputs:{real:i,imag:o},backend:n});return o.dispose(),n.disposeIntermediateTensorInfo(i),l}if(r.dtype==="complex64"){let o=jh({inputs:{input:r},backend:n}),i=wy({inputs:{x:o},backend:n,attrs:{dtype:a}});return n.disposeIntermediateTensorInfo(o),i}if(!v.hasEncodingLoss(r.dtype,a)){let o=Ls({inputs:{x:r},backend:n});return{dataId:o.dataId,shape:o.shape,dtype:a}}if(n.shouldExecuteOnCPU([r])){let o=n.texData.get(r.dataId).values,[i,l,u]=Ute(o,r.shape,r.dtype,a);return n.makeTensorInfo(i,l,u)}if(a==="int32")return Lre(r,n);if(a==="bool"){let o=n.makeTensorInfo([],"bool",v.getTypedArrayFromDType("bool",1)),l=L9({inputs:{a:r,b:o},backend:n});return n.disposeIntermediateTensorInfo(o),l}throw new Error(`Error in Cast: failed to cast ${r.dtype} to ${a}`)}var Bre={kernelName:xo,backendName:"webgl",kernelFunc:wy},B7="return ceil(x);",Wre=pt({opSnippet:B7,packedOpSnippet:B7,cpuKernelImpl:Gte}),Vre={kernelName:bo,backendName:"webgl",kernelFunc:Wre},Ure=class{constructor(e){this.variableNames=["A"],this.customUniforms=[{name:"minVal",type:"float"},{name:"maxVal",type:"float"}],this.outputShape=e,this.userCode=`
+    `}};function Lre(e,t,n,s){let r=s.texData.get(e.dataId),a=s.makeTensorInfo(n,e.dtype),o=s.texData.get(a.dataId);Object.assign(o,r),o.refCount=1,o.shape=n,o.dtype=e.dtype;let i=jt.computeFlatOffset(t,v.computeStrides(e.shape));r.slice&&(i+=r.slice.flatOffset),o.slice={flatOffset:i,origDataId:r.slice&&r.slice.origDataId||e.dataId};let l=s.dataRefCount.get(o.slice.origDataId)||1;return s.dataRefCount.set(o.slice.origDataId,l+1),a}function bd(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{begin:a,size:o}=s,[i,l]=jt.parseSliceParams(r,a,o);if(jt.assertParamsValid(r,i,l),v.sizeFromShape(l)===0)return n.makeTensorInfo(l,r.dtype,[]);if(n.shouldExecuteOnCPU([r])||r.dtype==="string"){let p=n.texData.get(r.dataId),d=Nne(p.values,i,l,r.shape,r.dtype);return n.makeTensorInfo(l,r.dtype,d)}let{isPacked:u}=n.texData.get(r.dataId),c=jt.isSliceContinous(r.shape,i,l);if(u||!c){let p=H().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new zre(l):new Ore(l),d=[i];return n.runWebGLProgram(p,[r],r.dtype,d)}return n.uploadToGPU(r.dataId),Lre(r,i,l,n)}var Bre={kernelName:Yl,backendName:"webgl",kernelFunc:bd},Wre=e=>{let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockShape:a,crops:o}=s;v.assert(r.shape.length<=4,()=>"batchToSpaceND for rank > 4 with a WebGL backend not implemented yet");let i=a.reduce((x,A)=>x*A),l=T.getReshaped(r.shape,a,i),u=T.getPermuted(l.length,a.length),c=T.getReshapedPermuted(r.shape,a,i),p=T.getSliceBeginCoords(o,a.length),d=T.getSliceSize(c,o,a.length),h=[],f=we({inputs:{x:r},backend:n,attrs:{shape:l}}),m=cs({inputs:{x:f},backend:n,attrs:{perm:u}}),g=we({inputs:{x:m},backend:n,attrs:{shape:c}}),y=bd({inputs:{x:g},backend:n,attrs:{begin:p,size:d}});return h.push(f),h.push(m),h.push(g),h.forEach(x=>n.disposeIntermediateTensorInfo(x)),y},Vre={kernelName:vl,backendName:"webgl",kernelFunc:Wre};function Ure(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,weights:a}=t,{size:o}=s,i=n.readSync(r.dataId),l=n.readSync(a.dataId),u=E9(i,l,a.dtype,a.shape,o);return n.makeTensorInfo([o],a.dtype,u)}var Gre={kernelName:r0,backendName:"webgl",kernelFunc:Ure};function Hre(e){let{inputs:t,backend:n}=e,{s0:s,s1:r}=t,a=n.readSync(s.dataId),o=n.readSync(r.dataId),i=T.assertAndGetBroadcastShape(Array.from(a),Array.from(o));return n.makeTensorInfo([i.length],"int32",Int32Array.from(i))}var jre={kernelName:a0,backendName:"webgl",kernelFunc:Hre},qre="return float(a != b);",H9=Bn({opSnippet:qre,cpuKernelImpl:vne,dtype:"bool"}),Xre={kernelName:Ll,backendName:"webgl",kernelFunc:H9};function qh(e){let{inputs:t,backend:n}=e,{input:s}=t,r=n.texData.get(s.dataId);return Vs({inputs:{x:r.complexTensorInfos.real},backend:n})}var Kre={kernelName:eh,backendName:"webgl",kernelFunc:qh},Zre="return float(int(x));";function Yre(e,t){let n=new Sa(e.shape,Zre),s=t.runWebGLProgram(n,[e],"int32");return{dataId:s.dataId,shape:s.shape,dtype:s.dtype}}function Iy(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{dtype:a}=s;if(a==="complex64"){if(r.dtype==="complex64")return Vs({inputs:{x:r},backend:n});let o=Gt(r.shape),i=Iy({inputs:{x:r},backend:n,attrs:{dtype:"float32"}}),l=xi({inputs:{real:i,imag:o},backend:n});return o.dispose(),n.disposeIntermediateTensorInfo(i),l}if(r.dtype==="complex64"){let o=qh({inputs:{input:r},backend:n}),i=Iy({inputs:{x:o},backend:n,attrs:{dtype:a}});return n.disposeIntermediateTensorInfo(o),i}if(!v.hasEncodingLoss(r.dtype,a)){let o=Vs({inputs:{x:r},backend:n});return{dataId:o.dataId,shape:o.shape,dtype:a}}if(n.shouldExecuteOnCPU([r])){let o=n.texData.get(r.dataId).values,[i,l,u]=tne(o,r.shape,r.dtype,a);return n.makeTensorInfo(i,l,u)}if(a==="int32")return Yre(r,n);if(a==="bool"){let o=n.makeTensorInfo([],"bool",v.getTypedArrayFromDType("bool",1)),l=H9({inputs:{a:r,b:o},backend:n});return n.disposeIntermediateTensorInfo(o),l}throw new Error(`Error in Cast: failed to cast ${r.dtype} to ${a}`)}var Jre={kernelName:ko,backendName:"webgl",kernelFunc:Iy},j7="return ceil(x);",Qre=ht({opSnippet:j7,packedOpSnippet:j7,cpuKernelImpl:nne}),eae={kernelName:So,backendName:"webgl",kernelFunc:Qre},tae=class{constructor(e){this.variableNames=["A"],this.customUniforms=[{name:"minVal",type:"float"},{name:"maxVal",type:"float"}],this.outputShape=e,this.userCode=`
 
       void main() {
         float value = getAAtOutCoords();
@@ -2134,7 +2134,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,ore=pt({opSnippet:are}),ire={kernel
 
         setOutput(clamp(value, minVal, maxVal));
       }
-    `}},Gre=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"minVal",type:"float"},{name:"maxVal",type:"float"}],this.outputShape=e,this.userCode=`
+    `}},nae=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"minVal",type:"float"},{name:"maxVal",type:"float"}],this.outputShape=e,this.userCode=`
       void main() {
         vec4 value = getAAtOutCoords();
 
@@ -2145,7 +2145,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,ore=pt({opSnippet:are}),ire={kernel
 
         setOutput(clamp(value, vec4(minVal), vec4(maxVal)));
       }
-    `}};function Hre(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{clipValueMin:a,clipValueMax:o}=s,i;H().getBool("WEBGL_PACK_CLIP")?i=new Gre(r.shape):i=new Ure(r.shape);let l=[[a],[o]];return n.runWebGLProgram(i,[r],r.dtype,l)}var jre={kernelName:Ea,backendName:"webgl",kernelFunc:Hre},qre=class{constructor(e){this.variableNames=["real","imag"],this.outputShape=e,this.userCode=`
+    `}};function sae(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{clipValueMin:a,clipValueMax:o}=s,i;H().getBool("WEBGL_PACK_CLIP")?i=new nae(r.shape):i=new tae(r.shape);let l=[[a],[o]];return n.runWebGLProgram(i,[r],r.dtype,l)}var rae={kernelName:$a,backendName:"webgl",kernelFunc:sae},aae=class{constructor(e){this.variableNames=["real","imag"],this.outputShape=e,this.userCode=`
       void main() {
         float re = abs(getRealAtOutCoords());
         float im = abs(getImagAtOutCoords());
@@ -2158,7 +2158,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,ore=pt({opSnippet:are}),ire={kernel
           mx == 0.0 ? 0.0 : mx * length(vec2(1, min(re, im)/mx))
         );
       }
-    `}};function W7(e,t){return{dataId:t.dataId,dtype:t.dtype,shape:e.shape}}function Xre(e){let{inputs:t,backend:n}=e,{x:s}=t,r=n.texData.get(s.dataId),a=new qre(s.shape),o=[W7(s,r.complexTensorInfos.real),W7(s,r.complexTensorInfos.imag)];return n.runWebGLProgram(a,o,o[0].dtype)}var Kre={kernelName:jp,backendName:"webgl",kernelFunc:Xre},Zre=class{constructor(e){this.outputShape=[],this.outputShape=T.computeOutShape(e,1),this.variableNames=e.map((a,o)=>`T${o}`);let t=new Array(e.length-1);t[0]=e[0][1];for(let a=1;a<t.length;a++)t[a]=t[a-1]+e[a][1];let n=[`if (yC < ${t[0]}) setOutput(getT0(yR, yC));`];for(let a=1;a<t.length;a++){let o=t[a-1];n.push(`else if (yC < ${t[a]}) setOutput(getT${a}(yR, yC-${o}));`)}let s=t.length,r=t[t.length-1];n.push(`else setOutput(getT${s}(yR, yC-${r}));`),this.userCode=`
+    `}};function q7(e,t){return{dataId:t.dataId,dtype:t.dtype,shape:e.shape}}function oae(e){let{inputs:t,backend:n}=e,{x:s}=t,r=n.texData.get(s.dataId),a=new aae(s.shape),o=[q7(s,r.complexTensorInfos.real),q7(s,r.complexTensorInfos.imag)];return n.runWebGLProgram(a,o,o[0].dtype)}var iae={kernelName:qp,backendName:"webgl",kernelFunc:oae},lae=class{constructor(e){this.outputShape=[],this.outputShape=T.computeOutShape(e,1),this.variableNames=e.map((a,o)=>`T${o}`);let t=new Array(e.length-1);t[0]=e[0][1];for(let a=1;a<t.length;a++)t[a]=t[a-1]+e[a][1];let n=[`if (yC < ${t[0]}) setOutput(getT0(yR, yC));`];for(let a=1;a<t.length;a++){let o=t[a-1];n.push(`else if (yC < ${t[a]}) setOutput(getT${a}(yR, yC-${o}));`)}let s=t.length,r=t[t.length-1];n.push(`else setOutput(getT${s}(yR, yC-${r}));`),this.userCode=`
       void main() {
         ivec2 coords = getOutputCoords();
         int yR = coords.x;
@@ -2167,7 +2167,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,ore=pt({opSnippet:are}),ire={kernel
         ${n.join(`
         `)}
       }
-    `}},Yre=class{constructor(e,t){this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[],this.outputShape=T.computeOutShape(e,t);let n=this.outputShape,s=n.length,r=wt(s),a=os("coords",s),o=["x","y","z","w","u","v"].slice(0,s);this.variableNames=e.map((f,m)=>`T${m}`);let i=new Array(e.length-1);i[0]=e[0][t];for(let f=1;f<i.length;f++)i[f]=i[f-1]+e[f][t];let l=o[t],u=o.slice(-2),c=o.join(),p=`if (${l} < ${i[0]}) {
+    `}},uae=class{constructor(e,t){this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[],this.outputShape=T.computeOutShape(e,t);let n=this.outputShape,s=n.length,r=kt(s),a=us("coords",s),o=["x","y","z","w","u","v"].slice(0,s);this.variableNames=e.map((f,m)=>`T${m}`);let i=new Array(e.length-1);i[0]=e[0][t];for(let f=1;f<i.length;f++)i[f]=i[f-1]+e[f][t];let l=o[t],u=o.slice(-2),c=o.join(),p=`if (${l} < ${i[0]}) {
         return getChannel(
             getT0(${c}), vec2(${u.join()}));
         }`;for(let f=1;f<i.length;f++){let m=i[f-1];p+=`
@@ -2204,7 +2204,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,ore=pt({opSnippet:are}),ire={kernel
         }
         setOutput(result);
       }
-    `}};function um(e,t,n){let s=e.indexOf(t);return e.map((a,o)=>o===s?`${a} - ${n}`:a).join()}function M2(e){let{inputs:t,backend:n}=e,{input:s}=t,r=n.texData.get(s.dataId);return Ls({inputs:{x:r.complexTensorInfos.imag},backend:n})}var Jre={kernelName:Zp,backendName:"webgl",kernelFunc:M2};function mp(e,t,n){let s=e[0].dtype;if(s==="complex64"){let p=e.map(g=>jh({inputs:{input:g},backend:n})),d=e.map(g=>M2({inputs:{input:g},backend:n})),h=mp(p,t,n),f=mp(d,t,n),m=mi({inputs:{real:h,imag:f},backend:n});return p.forEach(g=>n.disposeIntermediateTensorInfo(g)),d.forEach(g=>n.disposeIntermediateTensorInfo(g)),n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(f),m}let r=n.shouldExecuteOnCPU(e);if(s==="string"&&(r=!0),r){let p=e.map(x=>{let A=v.sizeFromShape(x.shape.slice(t));return we({inputs:{x},backend:n,attrs:{shape:[-1,A]}})}),d=p.map(x=>({vals:n.readSync(x.dataId),shape:x.shape})),h=T.computeOutShape(p.map(x=>x.shape),1),f=p[0].shape[0]===1,m=Hte(d,h,s,f),g=T.computeOutShape(e.map(x=>x.shape),t),y=n.makeTensorInfo(g,s,m);return p.forEach(x=>n.disposeIntermediateTensorInfo(x)),y}let a=H().getNumber("WEBGL_MAX_TEXTURES_IN_SHADER");if(e.length>a){let p=[];for(let h=0;h<e.length;h+=a){let f=e.slice(h,h+a);p.push(mp(f,t,n))}let d=mp(p,t,n);for(let h of p)n.disposeIntermediateTensorInfo(h);return d}if(H().getBool("WEBGL_PACK_ARRAY_OPERATIONS")&&e[0].shape.length>1){let p=new Yre(e.map(d=>d.shape),t);return n.runWebGLProgram(p,e,s)}let{tensors2D:o,outShape:i}=Qre(e,t,n),l=new Zre(o.map(p=>p.shape)),u=n.runWebGLProgram(l,o,s);o.forEach(p=>n.disposeIntermediateTensorInfo(p));let c=we({inputs:{x:u},attrs:{shape:i},backend:n});return n.disposeIntermediateTensorInfo(u),c}function Qre(e,t,n){let s=T.computeOutShape(e.map(a=>a.shape),t);return{tensors2D:e.map(a=>we({inputs:{x:a},attrs:{shape:[-1,v.sizeFromShape(a.shape.slice(t))]},backend:n})),outShape:s}}function B9(e){let{inputs:t,backend:n,attrs:s}=e,{axis:r}=s,a=v.parseAxisParam(r,t[0].shape)[0],o=T.computeOutShape(t.map(u=>u.shape),a);if(v.sizeFromShape(o)===0)return n.makeTensorInfo(o,t[0].dtype,[]);let i=t.filter(u=>v.sizeFromShape(u.shape)>0);if(i.length===1)return Ls({inputs:{x:i[0]},backend:n});let l=i.map(u=>u.shape);return T.assertParamsConsistent(l,a),mp(i,a,n)}var eae={kernelName:Al,backendName:"webgl",kernelFunc:B9},W9=class{constructor(e,t=!1,n=null,s=!1,r=!1){this.variableNames=["x","W"],this.outputShape=e.outShape;let a=e.padInfo.top,o=e.padInfo.left,i=e.strideHeight,l=e.strideWidth,u=e.dilationHeight,c=e.dilationWidth,p=e.filterHeight,d=e.filterWidth,h=Math.floor(e.inChannels/4)*4,f=e.inChannels%4,m=e.dataFormat==="channelsLast",g=m?1:2,y=m?2:3,x=m?3:1,A="",b="";n&&(s?A=`float activation(float a) {
+    `}};function um(e,t,n){let s=e.indexOf(t);return e.map((a,o)=>o===s?`${a} - ${n}`:a).join()}function M2(e){let{inputs:t,backend:n}=e,{input:s}=t,r=n.texData.get(s.dataId);return Vs({inputs:{x:r.complexTensorInfos.imag},backend:n})}var cae={kernelName:Yp,backendName:"webgl",kernelFunc:M2};function gp(e,t,n){let s=e[0].dtype;if(s==="complex64"){let p=e.map(g=>qh({inputs:{input:g},backend:n})),d=e.map(g=>M2({inputs:{input:g},backend:n})),h=gp(p,t,n),f=gp(d,t,n),m=xi({inputs:{real:h,imag:f},backend:n});return p.forEach(g=>n.disposeIntermediateTensorInfo(g)),d.forEach(g=>n.disposeIntermediateTensorInfo(g)),n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(f),m}let r=n.shouldExecuteOnCPU(e);if(s==="string"&&(r=!0),r){let p=e.map(x=>{let A=v.sizeFromShape(x.shape.slice(t));return we({inputs:{x},backend:n,attrs:{shape:[-1,A]}})}),d=p.map(x=>({vals:n.readSync(x.dataId),shape:x.shape})),h=T.computeOutShape(p.map(x=>x.shape),1),f=p[0].shape[0]===1,m=sne(d,h,s,f),g=T.computeOutShape(e.map(x=>x.shape),t),y=n.makeTensorInfo(g,s,m);return p.forEach(x=>n.disposeIntermediateTensorInfo(x)),y}let a=H().getNumber("WEBGL_MAX_TEXTURES_IN_SHADER");if(e.length>a){let p=[];for(let h=0;h<e.length;h+=a){let f=e.slice(h,h+a);p.push(gp(f,t,n))}let d=gp(p,t,n);for(let h of p)n.disposeIntermediateTensorInfo(h);return d}if(H().getBool("WEBGL_PACK_ARRAY_OPERATIONS")&&e[0].shape.length>1){let p=new uae(e.map(d=>d.shape),t);return n.runWebGLProgram(p,e,s)}let{tensors2D:o,outShape:i}=dae(e,t,n),l=new lae(o.map(p=>p.shape)),u=n.runWebGLProgram(l,o,s);o.forEach(p=>n.disposeIntermediateTensorInfo(p));let c=we({inputs:{x:u},attrs:{shape:i},backend:n});return n.disposeIntermediateTensorInfo(u),c}function dae(e,t,n){let s=T.computeOutShape(e.map(a=>a.shape),t);return{tensors2D:e.map(a=>we({inputs:{x:a},attrs:{shape:[-1,v.sizeFromShape(a.shape.slice(t))]},backend:n})),outShape:s}}function j9(e){let{inputs:t,backend:n,attrs:s}=e,{axis:r}=s,a=v.parseAxisParam(r,t[0].shape)[0],o=T.computeOutShape(t.map(u=>u.shape),a);if(v.sizeFromShape(o)===0)return n.makeTensorInfo(o,t[0].dtype,[]);let i=t.filter(u=>v.sizeFromShape(u.shape)>0);if(i.length===1)return Vs({inputs:{x:i[0]},backend:n});let l=i.map(u=>u.shape);return T.assertParamsConsistent(l,a),gp(i,a,n)}var pae={kernelName:wl,backendName:"webgl",kernelFunc:j9},q9=class{constructor(e,t=!1,n=null,s=!1,r=!1){this.variableNames=["x","W"],this.outputShape=e.outShape;let a=e.padInfo.top,o=e.padInfo.left,i=e.strideHeight,l=e.strideWidth,u=e.dilationHeight,c=e.dilationWidth,p=e.filterHeight,d=e.filterWidth,h=Math.floor(e.inChannels/4)*4,f=e.inChannels%4,m=e.dataFormat==="channelsLast",g=m?1:2,y=m?2:3,x=m?3:1,A="",b="";n&&(s?A=`float activation(float a) {
           float b = getPreluActivationWeightsAtOutCoords();
           ${n}
         }`:r?A=`float activation(float a) {
@@ -2338,7 +2338,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,ore=pt({opSnippet:are}),ire={kernel
         ${b}
         setOutput(result);
       }
-    `}},tae=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let t=e.padInfo.front,n=e.padInfo.top,s=e.padInfo.left,r=e.strideDepth,a=e.strideHeight,o=e.strideWidth,i=e.dilationDepth,l=e.dilationHeight,u=e.dilationWidth,c=e.filterDepth,p=e.filterHeight,d=e.filterWidth,h=Math.floor(e.inChannels/4)*4,f=e.inChannels%4;this.userCode=`
+    `}},hae=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let t=e.padInfo.front,n=e.padInfo.top,s=e.padInfo.left,r=e.strideDepth,a=e.strideHeight,o=e.strideWidth,i=e.dilationDepth,l=e.dilationHeight,u=e.dilationWidth,c=e.filterDepth,p=e.filterHeight,d=e.filterWidth,h=Math.floor(e.inChannels/4)*4,f=e.inChannels%4;this.userCode=`
       const ivec3 strides = ivec3(${r}, ${a}, ${o});
       const ivec3 pads = ivec3(${t}, ${n}, ${s});
 
@@ -2426,7 +2426,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,ore=pt({opSnippet:are}),ire={kernel
         }
         setOutput(dotProd);
       }
-    `}},V9=class{constructor(e,t=!1,n=null,s=!1,r=!1){this.variableNames=["x","W"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"pads",type:"ivec2"},{name:"strides",type:"ivec2"},{name:"dilations",type:"ivec2"},{name:"inDims",type:"ivec2"}],this.outputShape=e.outShape,this.enableShapeUniforms=ds(this.outputShape.length);let a=e.padInfo.left,o=e.strideWidth,i=e.dilationWidth,l=e.filterHeight,u=e.filterWidth,c=u,p=`
+    `}},X9=class{constructor(e,t=!1,n=null,s=!1,r=!1){this.variableNames=["x","W"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"pads",type:"ivec2"},{name:"strides",type:"ivec2"},{name:"dilations",type:"ivec2"},{name:"inDims",type:"ivec2"}],this.outputShape=e.outShape,this.enableShapeUniforms=fs(this.outputShape.length);let a=e.padInfo.left,o=e.strideWidth,i=e.dilationWidth,l=e.filterHeight,u=e.filterWidth,c=u,p=`
        int xR; int xC; int xCOffset;
        vec4 wTexel; vec4 previous; vec4 final;`;for(let m=0;m<u;m++)p+=`
            vec4 xTexelC${m*2};
@@ -2622,7 +2622,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,ore=pt({opSnippet:are}),ire={kernel
          ${h}
          setOutput(result);
        }
-     `}},nae=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"inputShape",type:"ivec4"},{name:"pad",type:"ivec2"},{name:"stride",type:"ivec2"},{name:"dilation",type:"ivec2"},{name:"inChannels",type:"int"},{name:"itemsPerBlockRow",type:"int"},{name:"outWidth",type:"int"}],this.outputShape=e,this.enableShapeUniforms=ds(this.outputShape.length);let{dataFormat:n}=t,s=cs(),r=n==="channelsLast",a=r?1:2,o=r?2:3,i=this.enableShapeUniforms?"if(blockIndex < outShape[2] && pos < outShape[1]) {":`if(blockIndex < ${e[2]} && pos < ${e[1]}) {`,l="";for(let u=0;u<=1;u++)for(let c=0;c<=1;c++)l+=`
+     `}},fae=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"inputShape",type:"ivec4"},{name:"pad",type:"ivec2"},{name:"stride",type:"ivec2"},{name:"dilation",type:"ivec2"},{name:"inChannels",type:"int"},{name:"itemsPerBlockRow",type:"int"},{name:"outWidth",type:"int"}],this.outputShape=e,this.enableShapeUniforms=fs(this.outputShape.length);let{dataFormat:n}=t,s=hs(),r=n==="channelsLast",a=r?1:2,o=r?2:3,i=this.enableShapeUniforms?"if(blockIndex < outShape[2] && pos < outShape[1]) {":`if(blockIndex < ${e[2]} && pos < ${e[1]}) {`,l="";for(let u=0;u<=1;u++)for(let c=0;c<=1;c++)l+=`
           blockIndex = rc.z + ${c};
           pos = rc.y + ${u};
 
@@ -2669,7 +2669,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,ore=pt({opSnippet:are}),ire={kernel
 
         ${s.output} = result;
       }
-    `}};function Xm(e,t){let n=e.length;return n>=3?t?[...e.slice(0,-3),e[n-3]*e[n-2],e[n-1]]:[...e.slice(0,-3),e[n-3],e[n-2]*e[n-1]]:!t&&n===1&&e[0]>1?[e[0],1]:null}function U9({x:e,filter:t,convInfo:n,backend:s,bias:r=null,preluActivationWeights:a=null,leakyreluAlpha:o=0,activation:i=null}){let l=e.shape,u=s.texData.get(e.dataId),c=n.inChannels,p=l[0]*l[1]*l[2],d=n.outChannels,h=n.dataFormat==="channelsLast",f=!1,m=!1,g,y=[];if(a!=null){let b=Xm(a.shape,h);b!=null&&(a=we({inputs:{x:a},backend:s,attrs:{shape:b}}),y.push(a))}if(r!=null){let b=Xm(r.shape,h);b!=null&&(r=we({inputs:{x:r},backend:s,attrs:{shape:b}}),y.push(r))}if(!((p===1||d===1)&&c>F9)&&u.isPacked&&h&&u.texture!=null&&l[2]%2!==0&&v.arraysEqual(u.shape.slice(-3),l.slice(-3))){let b=l[0]*l[1]*(l[2]+1),w={dataId:e.dataId,shape:[1,b,n.inChannels],dtype:e.dtype},k=u.shape;u.shape=u.shape.slice(),u.shape[u.shape.length-2]++,v.assert(Op(u.shape,w.shape),()=>`packed reshape ${u.shape} to ${w.shape} isn't free`);let C=we({inputs:{x:t},backend:s,attrs:{shape:[1,n.inChannels,n.outChannels]}});y.push(C);let E=qm({a:w,b:C,backend:s,transposeA:f,transposeB:m,bias:r,activation:i,preluActivationWeights:a,leakyreluAlpha:o}),_=s.texData.get(E.dataId);v.assert(_.isPacked,()=>"batchMatMul result is expected to be packed"),u.shape=k,_.shape=n.outShape,g=Ls({inputs:{x:E},backend:s}),g.shape=n.outShape,y.push(E)}else{let b=n.outHeight*n.outWidth,w=we({inputs:{x:e},backend:s,attrs:{shape:h?[n.batchSize,b,n.inChannels]:[n.batchSize,n.inChannels,b]}}),k=we({inputs:{x:t},backend:s,attrs:{shape:[1,n.inChannels,n.outChannels]}}),C=qm({a:h?w:k,b:h?k:w,transposeA:!h,transposeB:m,backend:s,bias:r,activation:i,preluActivationWeights:a,leakyreluAlpha:o});g=we({inputs:{x:C},backend:s,attrs:{shape:n.outShape}}),y.push(w),y.push(k),y.push(C)}for(let b of y)s.disposeIntermediateTensorInfo(b);return g}function G9({x:e,filter:t,convInfo:n,backend:s,bias:r=null,preluActivationWeights:a=null,leakyreluAlpha:o=0,activation:i=null}){let{filterWidth:l,filterHeight:u,inChannels:c,outWidth:p,outHeight:d,dataFormat:h}=n,f=h==="channelsLast",m=l*u*c,g=d*p,y=[n.batchSize,m,g],x=!0,A=!1,b=[];if(a!=null){let q=Xm(a.shape,f);q!=null&&(a=we({inputs:{x:a},backend:s,attrs:{shape:q}}),b.push(a))}if(r!=null){let q=Xm(r.shape,f);q!=null&&(r=we({inputs:{x:r},backend:s,attrs:{shape:q}}),b.push(r))}let w=we({inputs:{x:t},backend:s,attrs:{shape:[1,m,v.sizeFromShape(t.shape)/m]}});b.push(w);let k=new nae(y,n),C=[e.shape,[n.padInfo.top,n.padInfo.left],[n.strideHeight,n.strideWidth],[n.dilationHeight,n.dilationWidth],[n.inChannels],[n.filterWidth*n.inChannels],[n.outWidth]],E=s.runWebGLProgram(k,[e],"float32",C),_=we({inputs:{x:E},backend:s,attrs:{shape:y}});b.push(E),b.push(_);let $=r!=null,R=a!=null,P=i==="leakyrelu",S=i?Mp(i,!0):null,M=new P9(f?_.shape:w.shape,f?w.shape:_.shape,f?[n.batchSize,g,n.outChannels]:[n.batchSize,n.outChannels,g],x,A,$,S,R,P),L=f?[_,w]:[w,_];if(r&&L.push(r),R&&L.push(a),P){let q=s.makeTensorInfo([],"float32",v.createScalarValue(o,"float32"));L.push(q),b.push(q)}let U=s.runWebGLProgram(M,L,"float32"),K=we({inputs:{x:U},backend:s,attrs:{shape:n.outShape}});b.push(U);for(let q of b)s.disposeIntermediateTensorInfo(q);return K}function sae(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a}=t,{strides:o,pad:i,dataFormat:l,dilations:u,dimRoundingMode:c}=s,p=T.convertConv2DDataFormat(l),d=T.computeConv2DInfo(r.shape,a.shape,o,u,i,c,!1,p),h;if(d.filterHeight===1&&d.filterWidth===1&&d.dilationHeight===1&&d.dilationWidth===1&&d.strideHeight===1&&d.strideWidth===1&&(d.padInfo.type==="SAME"||d.padInfo.type==="VALID"))h=U9({x:r,filter:a,convInfo:d,backend:n});else if(d.strideWidth<=2&&p==="channelsLast"&&H().getBool("WEBGL_EXP_CONV")){let m=new V9(d),g=[[d.padInfo.top,d.padInfo.left],[d.strideHeight,d.strideWidth],[d.dilationHeight,d.dilationWidth],[d.inHeight,d.inWidth]];h=n.runWebGLProgram(m,[r,a],"float32",g)}else if(H().getBool("WEBGL_CONV_IM2COL"))h=G9({x:r,filter:a,convInfo:d,backend:n});else{let m=new W9(d);h=n.runWebGLProgram(m,[r,a],"float32")}let f=we({inputs:{x:h},backend:n,attrs:{shape:d.outShape}});return n.disposeIntermediateTensorInfo(h),f}var rae={kernelName:vo,backendName:"webgl",kernelFunc:sae},aae=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideHeight,n=e.strideWidth,s=e.padInfo.top,r=e.padInfo.left,a=e.dataFormat==="channelsLast";this.userCode=`
+    `}};function Xm(e,t){let n=e.length;return n>=3?t?[...e.slice(0,-3),e[n-3]*e[n-2],e[n-1]]:[...e.slice(0,-3),e[n-3],e[n-2]*e[n-1]]:!t&&n===1&&e[0]>1?[e[0],1]:null}function K9({x:e,filter:t,convInfo:n,backend:s,bias:r=null,preluActivationWeights:a=null,leakyreluAlpha:o=0,activation:i=null}){let l=e.shape,u=s.texData.get(e.dataId),c=n.inChannels,p=l[0]*l[1]*l[2],d=n.outChannels,h=n.dataFormat==="channelsLast",f=!1,m=!1,g,y=[];if(a!=null){let b=Xm(a.shape,h);b!=null&&(a=we({inputs:{x:a},backend:s,attrs:{shape:b}}),y.push(a))}if(r!=null){let b=Xm(r.shape,h);b!=null&&(r=we({inputs:{x:r},backend:s,attrs:{shape:b}}),y.push(r))}if(!((p===1||d===1)&&c>W9)&&u.isPacked&&h&&u.texture!=null&&l[2]%2!==0&&v.arraysEqual(u.shape.slice(-3),l.slice(-3))){let b=l[0]*l[1]*(l[2]+1),w={dataId:e.dataId,shape:[1,b,n.inChannels],dtype:e.dtype},k=u.shape;u.shape=u.shape.slice(),u.shape[u.shape.length-2]++,v.assert(Mp(u.shape,w.shape),()=>`packed reshape ${u.shape} to ${w.shape} isn't free`);let C=we({inputs:{x:t},backend:s,attrs:{shape:[1,n.inChannels,n.outChannels]}});y.push(C);let E=qm({a:w,b:C,backend:s,transposeA:f,transposeB:m,bias:r,activation:i,preluActivationWeights:a,leakyreluAlpha:o}),_=s.texData.get(E.dataId);v.assert(_.isPacked,()=>"batchMatMul result is expected to be packed"),u.shape=k,_.shape=n.outShape,g=Vs({inputs:{x:E},backend:s}),g.shape=n.outShape,y.push(E)}else{let b=n.outHeight*n.outWidth,w=we({inputs:{x:e},backend:s,attrs:{shape:h?[n.batchSize,b,n.inChannels]:[n.batchSize,n.inChannels,b]}}),k=we({inputs:{x:t},backend:s,attrs:{shape:[1,n.inChannels,n.outChannels]}}),C=qm({a:h?w:k,b:h?k:w,transposeA:!h,transposeB:m,backend:s,bias:r,activation:i,preluActivationWeights:a,leakyreluAlpha:o});g=we({inputs:{x:C},backend:s,attrs:{shape:n.outShape}}),y.push(w),y.push(k),y.push(C)}for(let b of y)s.disposeIntermediateTensorInfo(b);return g}function Z9({x:e,filter:t,convInfo:n,backend:s,bias:r=null,preluActivationWeights:a=null,leakyreluAlpha:o=0,activation:i=null}){let{filterWidth:l,filterHeight:u,inChannels:c,outWidth:p,outHeight:d,dataFormat:h}=n,f=h==="channelsLast",m=l*u*c,g=d*p,y=[n.batchSize,m,g],x=!0,A=!1,b=[];if(a!=null){let q=Xm(a.shape,f);q!=null&&(a=we({inputs:{x:a},backend:s,attrs:{shape:q}}),b.push(a))}if(r!=null){let q=Xm(r.shape,f);q!=null&&(r=we({inputs:{x:r},backend:s,attrs:{shape:q}}),b.push(r))}let w=we({inputs:{x:t},backend:s,attrs:{shape:[1,m,v.sizeFromShape(t.shape)/m]}});b.push(w);let k=new fae(y,n),C=[e.shape,[n.padInfo.top,n.padInfo.left],[n.strideHeight,n.strideWidth],[n.dilationHeight,n.dilationWidth],[n.inChannels],[n.filterWidth*n.inChannels],[n.outWidth]],E=s.runWebGLProgram(k,[e],"float32",C),_=we({inputs:{x:E},backend:s,attrs:{shape:y}});b.push(E),b.push(_);let $=r!=null,R=a!=null,P=i==="leakyrelu",S=i?zp(i,!0):null,M=new B9(f?_.shape:w.shape,f?w.shape:_.shape,f?[n.batchSize,g,n.outChannels]:[n.batchSize,n.outChannels,g],x,A,$,S,R,P),L=f?[_,w]:[w,_];if(r&&L.push(r),R&&L.push(a),P){let q=s.makeTensorInfo([],"float32",v.createScalarValue(o,"float32"));L.push(q),b.push(q)}let U=s.runWebGLProgram(M,L,"float32"),K=we({inputs:{x:U},backend:s,attrs:{shape:n.outShape}});b.push(U);for(let q of b)s.disposeIntermediateTensorInfo(q);return K}function mae(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a}=t,{strides:o,pad:i,dataFormat:l,dilations:u,dimRoundingMode:c}=s,p=T.convertConv2DDataFormat(l),d=T.computeConv2DInfo(r.shape,a.shape,o,u,i,c,!1,p),h;if(d.filterHeight===1&&d.filterWidth===1&&d.dilationHeight===1&&d.dilationWidth===1&&d.strideHeight===1&&d.strideWidth===1&&(d.padInfo.type==="SAME"||d.padInfo.type==="VALID"))h=K9({x:r,filter:a,convInfo:d,backend:n});else if(d.strideWidth<=2&&p==="channelsLast"&&H().getBool("WEBGL_EXP_CONV")){let m=new X9(d),g=[[d.padInfo.top,d.padInfo.left],[d.strideHeight,d.strideWidth],[d.dilationHeight,d.dilationWidth],[d.inHeight,d.inWidth]];h=n.runWebGLProgram(m,[r,a],"float32",g)}else if(H().getBool("WEBGL_CONV_IM2COL"))h=Z9({x:r,filter:a,convInfo:d,backend:n});else{let m=new q9(d);h=n.runWebGLProgram(m,[r,a],"float32")}let f=we({inputs:{x:h},backend:n,attrs:{shape:d.outShape}});return n.disposeIntermediateTensorInfo(h),f}var gae={kernelName:Io,backendName:"webgl",kernelFunc:mae},yae=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideHeight,n=e.strideWidth,s=e.padInfo.top,r=e.padInfo.left,a=e.dataFormat==="channelsLast";this.userCode=`
       void main() {
         ivec4 coords = getOutputCoords();
         int wR = coords.x;
@@ -2711,7 +2711,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,ore=pt({opSnippet:are}),ire={kernel
         }
         setOutput(dotProd);
       }
-    `}},oae=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,s=e.strideHeight,r=e.strideWidth,a=e.dataFormat==="channelsLast",o=t-1-e.padInfo.top,i=n-1-e.padInfo.left,l=a?1:2,u=a?2:3,c=a?3:1;this.userCode=`
+    `}},Aae=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,s=e.strideHeight,r=e.strideWidth,a=e.dataFormat==="channelsLast",o=t-1-e.padInfo.top,i=n-1-e.padInfo.left,l=a?1:2,u=a?2:3,c=a?3:1;this.userCode=`
       const ivec2 pads = ivec2(${o}, ${i});
 
       void main() {
@@ -2764,7 +2764,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,ore=pt({opSnippet:are}),ire={kernel
         }
         setOutput(dotProd);
       }
-    `}},iae=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideDepth,n=e.strideHeight,s=e.strideWidth,r=e.padInfo.front,a=e.padInfo.top,o=e.padInfo.left;this.userCode=`
+    `}},xae=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideDepth,n=e.strideHeight,s=e.strideWidth,r=e.padInfo.front,a=e.padInfo.top,o=e.padInfo.left;this.userCode=`
       void main() {
         ivec5 coords = getOutputCoords();
         int wF = coords.x;
@@ -2806,7 +2806,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,ore=pt({opSnippet:are}),ire={kernel
         }
         setOutput(dotProd);
       }
-    `}},lae=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterDepth,n=e.filterHeight,s=e.filterWidth,r=e.strideDepth,a=e.strideHeight,o=e.strideWidth,i=t-1-e.padInfo.front,l=n-1-e.padInfo.top,u=s-1-e.padInfo.left;this.userCode=`
+    `}},bae=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterDepth,n=e.filterHeight,s=e.filterWidth,r=e.strideDepth,a=e.strideHeight,o=e.strideWidth,i=t-1-e.padInfo.front,l=n-1-e.padInfo.top,u=s-1-e.padInfo.left;this.userCode=`
       const ivec3 pads = ivec3(${i}, ${l}, ${u});
 
       void main() {
@@ -2863,12 +2863,12 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,ore=pt({opSnippet:are}),ire={kernel
         }
         setOutput(dotProd);
       }
-    `}};function uae(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,dy:a}=t,{strides:o,pad:i,dataFormat:l,dimRoundingMode:u,filterShape:c}=s,p=T.convertConv2DDataFormat(l),d=T.computeConv2DInfo(r.shape,c,o,1,i,u,!1,p),h=new aae(d);return n.runWebGLProgram(h,[r,a],"float32")}var cae={kernelName:o0,backendName:"webgl",kernelFunc:uae};function dae(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,filter:a}=t,{inputShape:o,strides:i,pad:l,dataFormat:u,dimRoundingMode:c}=s,p=T.convertConv2DDataFormat(u),d=T.computeConv2DInfo(o,a.shape,i,1,l,c,!1,p),h=new oae(d);return n.runWebGLProgram(h,[r,a],"float32")}var pae={kernelName:wo,backendName:"webgl",kernelFunc:dae};function hae(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a}=t,{strides:o,pad:i,dilations:l}=s,u=T.computeConv3DInfo(r.shape,a.shape,o,l,i),c=new tae(u);return n.runWebGLProgram(c,[r,a],"float32")}var fae={kernelName:qp,backendName:"webgl",kernelFunc:hae};function mae(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,dy:a}=t,{strides:o,pad:i,filterShape:l}=s,u=T.computeConv3DInfo(r.shape,l,o,1,i),c=new iae(u);return n.runWebGLProgram(c,[r,a],"float32")}var gae={kernelName:i0,backendName:"webgl",kernelFunc:mae};function yae(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,filter:a}=t,{pad:o,strides:i,inputShape:l}=s,u=T.computeConv3DInfo(l,a.shape,i,1,o),c=new lae(u);return n.runWebGLProgram(c,[r,a],"float32")}var Aae={kernelName:l0,backendName:"webgl",kernelFunc:yae},xae=gd+`
+    `}};function vae(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,dy:a}=t,{strides:o,pad:i,dataFormat:l,dimRoundingMode:u,filterShape:c}=s,p=T.convertConv2DDataFormat(l),d=T.computeConv2DInfo(r.shape,c,o,1,i,u,!1,p),h=new yae(d);return n.runWebGLProgram(h,[r,a],"float32")}var wae={kernelName:o0,backendName:"webgl",kernelFunc:vae};function kae(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,filter:a}=t,{inputShape:o,strides:i,pad:l,dataFormat:u,dimRoundingMode:c}=s,p=T.convertConv2DDataFormat(u),d=T.computeConv2DInfo(o,a.shape,i,1,l,c,!1,p),h=new Aae(d);return n.runWebGLProgram(h,[r,a],"float32")}var Sae={kernelName:Co,backendName:"webgl",kernelFunc:kae};function Iae(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a}=t,{strides:o,pad:i,dilations:l}=s,u=T.computeConv3DInfo(r.shape,a.shape,o,l,i),c=new hae(u);return n.runWebGLProgram(c,[r,a],"float32")}var Cae={kernelName:Xp,backendName:"webgl",kernelFunc:Iae};function Tae(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,dy:a}=t,{strides:o,pad:i,filterShape:l}=s,u=T.computeConv3DInfo(r.shape,l,o,1,i),c=new xae(u);return n.runWebGLProgram(c,[r,a],"float32")}var Nae={kernelName:i0,backendName:"webgl",kernelFunc:Tae};function Eae(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,filter:a}=t,{pad:o,strides:i,inputShape:l}=s,u=T.computeConv3DInfo(l,a.shape,i,1,o),c=new bae(u);return n.runWebGLProgram(c,[r,a],"float32")}var Rae={kernelName:l0,backendName:"webgl",kernelFunc:Eae},_ae=xd+`
   return cos(x);
-`,bae=pt({opSnippet:xae}),vae={kernelName:ko,backendName:"webgl",kernelFunc:bae},wae=`
+`,Dae=ht({opSnippet:_ae}),$ae={kernelName:To,backendName:"webgl",kernelFunc:Dae},Pae=`
   float e2x = exp(-x);
   return (e2x + 1.0 / e2x) / 2.0;
-`,kae=pt({opSnippet:wae}),Sae={kernelName:So,backendName:"webgl",kernelFunc:kae},Iae=class{constructor(e,t,n,s,r){this.variableNames=["Image","Boxes","BoxInd"],this.outputShape=[];let[a,o,i,l]=e,[u]=t,[c,p]=n;this.outputShape=[u,c,p,l];let d=s==="bilinear"?1:0,[h,f]=[`${o-1}.0`,`${i-1}.0`],[m,g,y]=c>1?[`${(o-1)/(c-1)}`,"(y2-y1) * height_ratio",`y1*${h} + float(y)*(height_scale)`]:["0.0","0.0",`0.5 * (y1+y2) * ${h}`],[x,A,b]=p>1?[`${(i-1)/(p-1)}`,"(x2-x1) * width_ratio",`x1*${f} + float(x)*(width_scale)`]:["0.0","0.0",`0.5 * (x1+x2) * ${f}`];this.userCode=`
+`,Fae=ht({opSnippet:Pae}),Oae={kernelName:No,backendName:"webgl",kernelFunc:Fae},Mae=class{constructor(e,t,n,s,r){this.variableNames=["Image","Boxes","BoxInd"],this.outputShape=[];let[a,o,i,l]=e,[u]=t,[c,p]=n;this.outputShape=[u,c,p,l];let d=s==="bilinear"?1:0,[h,f]=[`${o-1}.0`,`${i-1}.0`],[m,g,y]=c>1?[`${(o-1)/(c-1)}`,"(y2-y1) * height_ratio",`y1*${h} + float(y)*(height_scale)`]:["0.0","0.0",`0.5 * (y1+y2) * ${h}`],[x,A,b]=p>1?[`${(i-1)/(p-1)}`,"(x2-x1) * width_ratio",`x1*${f} + float(x)*(width_scale)`]:["0.0","0.0",`0.5 * (x1+x2) * ${f}`];this.userCode=`
       const float height_ratio = float(${m});
       const float width_ratio = float(${x});
       void main() {
@@ -2929,20 +2929,20 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,ore=pt({opSnippet:are}),ire={kernel
           setOutput(newValue);
         }
       }
-    `}},Cae=e=>{let{inputs:t,backend:n,attrs:s}=e,{image:r,boxes:a,boxInd:o}=t,{cropSize:i,method:l,extrapolationValue:u}=s,c=new Iae(r.shape,a.shape,i,l,u);return n.runWebGLProgram(c,[r,a,o],"float32")},Tae={kernelName:bl,backendName:"webgl",kernelFunc:Cae},Lp;(function(e){e.Prod="*",e.Sum="+"})(Lp||(Lp={}));var V7=class{constructor(e,t,n,s){this.op=e,this.outputShape=t,this.variableNames=["x"],this.customUniforms=[{name:"index",type:"float"}];let r=this.outputShape.length,a=this.op===Lp.Prod?"1.0":"0.0",o=n?a:`getX(${U7(r,"coords",this.op)})`,i=this.outputShape[this.outputShape.length-1],l="",u="";n?(l=s?`end != ${i-1}`:"end != 0",u=s?"end + 1":"end - 1"):(l=s?`end + pow2 < ${i}`:"end >= pow2",u=s?"end + pow2":"end - pow2"),this.userCode=`
+    `}},zae=e=>{let{inputs:t,backend:n,attrs:s}=e,{image:r,boxes:a,boxInd:o}=t,{cropSize:i,method:l,extrapolationValue:u}=s,c=new Mae(r.shape,a.shape,i,l,u);return n.runWebGLProgram(c,[r,a,o],"float32")},Lae={kernelName:Sl,backendName:"webgl",kernelFunc:zae},Bp;(function(e){e.Prod="*",e.Sum="+"})(Bp||(Bp={}));var X7=class{constructor(e,t,n,s){this.op=e,this.outputShape=t,this.variableNames=["x"],this.customUniforms=[{name:"index",type:"float"}];let r=this.outputShape.length,a=this.op===Bp.Prod?"1.0":"0.0",o=n?a:`getX(${K7(r,"coords",this.op)})`,i=this.outputShape[this.outputShape.length-1],l="",u="";n?(l=s?`end != ${i-1}`:"end != 0",u=s?"end + 1":"end - 1"):(l=s?`end + pow2 < ${i}`:"end >= pow2",u=s?"end + pow2":"end - pow2"),this.userCode=`
       void main() {
-        ${wt(r)} coords = getOutputCoords();
-        int end = ${G7(r,"coords",this.op)};
+        ${kt(r)} coords = getOutputCoords();
+        int end = ${Z7(r,"coords",this.op)};
         float val = ${o};
         int pow2 = int(pow(2.0, index));
         if (${l}) {
           int idx = ${u};
-          ${G7(r,"coords",this.op)} = idx;
-          val ${this.op}= getX(${U7(r,"coords",this.op)});
+          ${Z7(r,"coords",this.op)} = idx;
+          val ${this.op}= getX(${K7(r,"coords",this.op)});
         }
         setOutput(val);
       }
-    `}};function U7(e,t,n){if(e===1)return`${t}`;if(e===2)return`${t}.x, ${t}.y`;if(e===3)return`${t}.x, ${t}.y, ${t}.z`;if(e===4)return`${t}.x, ${t}.y, ${t}.z, ${t}.w`;throw new Error(`Cumulative ${n} for rank ${e} is not yet supported`)}function G7(e,t,n){if(e===1)return`${t}`;if(e===2)return`${t}.y`;if(e===3)return`${t}.z`;if(e===4)return`${t}.w`;throw new Error(`Cumulative ${n} for rank ${e} is not yet supported`)}function H9(e,t,n,s,r,a){let o=t.shape.length,i=T.getAxesPermutation([s],o),l=t;i!=null&&(l=is({inputs:{x:t},backend:n,attrs:{perm:i}}));let u=T.getInnerMostAxes(1,o)[0];if(u!==o-1)throw new Error(`WebGL cumprod shader expects an inner-most axis=${t.shape.length-1} but got axis=${s}`);let c=l.shape[u],p=Ls({inputs:{x:l},backend:n});for(let d=0;d<=Math.ceil(Math.log2(c))-1;d++){let h=new V7(e,l.shape,!1,a),f=[[d]],m=p;p=n.runWebGLProgram(h,[p],p.dtype,f),n.disposeIntermediateTensorInfo(m)}if(r){let d=new V7(e,l.shape,r,a),h=p;p=n.runWebGLProgram(d,[p],p.dtype),n.disposeIntermediateTensorInfo(h)}if(i!=null){let d=T.getUndoAxesPermutation(i),h=is({inputs:{x:p},backend:n,attrs:{perm:d}});return n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(l),h}return p}function Nae(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,exclusive:o,reverse:i}=s;return H9(Lp.Prod,r,n,a,o,i)}var Eae={kernelName:xl,backendName:"webgl",kernelFunc:Nae};function Rae(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,exclusive:o,reverse:i}=s;return H9(Lp.Sum,r,n,a,o,i)}var _ae={kernelName:Io,backendName:"webgl",kernelFunc:Rae};function Dae(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,weights:a}=t,{size:o,binaryOutput:i}=s;if(r.shape.length===1){let l=n.readSync(r.dataId),u=n.readSync(a.dataId),c=k9(l,u,a.dtype,a.shape,o);return n.makeTensorInfo([o],a.dtype,c)}else if(r.shape.length===2){let l=n.bufferSync(r),u=n.bufferSync(a),c=Vte(l,u,o,i);return n.makeTensorInfo(c.shape,a.dtype,c.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${r.shape.length}.`)}var $ae={kernelName:u0,backendName:"webgl",kernelFunc:Dae},Pae=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=[],this.outputShape=e,this.blockSize=t,this.dataFormat=n,this.userCode=`
+    `}};function K7(e,t,n){if(e===1)return`${t}`;if(e===2)return`${t}.x, ${t}.y`;if(e===3)return`${t}.x, ${t}.y, ${t}.z`;if(e===4)return`${t}.x, ${t}.y, ${t}.z, ${t}.w`;throw new Error(`Cumulative ${n} for rank ${e} is not yet supported`)}function Z7(e,t,n){if(e===1)return`${t}`;if(e===2)return`${t}.y`;if(e===3)return`${t}.z`;if(e===4)return`${t}.w`;throw new Error(`Cumulative ${n} for rank ${e} is not yet supported`)}function Y9(e,t,n,s,r,a){let o=t.shape.length,i=T.getAxesPermutation([s],o),l=t;i!=null&&(l=cs({inputs:{x:t},backend:n,attrs:{perm:i}}));let u=T.getInnerMostAxes(1,o)[0];if(u!==o-1)throw new Error(`WebGL cumprod shader expects an inner-most axis=${t.shape.length-1} but got axis=${s}`);let c=l.shape[u],p=Vs({inputs:{x:l},backend:n});for(let d=0;d<=Math.ceil(Math.log2(c))-1;d++){let h=new X7(e,l.shape,!1,a),f=[[d]],m=p;p=n.runWebGLProgram(h,[p],p.dtype,f),n.disposeIntermediateTensorInfo(m)}if(r){let d=new X7(e,l.shape,r,a),h=p;p=n.runWebGLProgram(d,[p],p.dtype),n.disposeIntermediateTensorInfo(h)}if(i!=null){let d=T.getUndoAxesPermutation(i),h=cs({inputs:{x:p},backend:n,attrs:{perm:d}});return n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(l),h}return p}function Bae(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,exclusive:o,reverse:i}=s;return Y9(Bp.Prod,r,n,a,o,i)}var Wae={kernelName:kl,backendName:"webgl",kernelFunc:Bae};function Vae(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,exclusive:o,reverse:i}=s;return Y9(Bp.Sum,r,n,a,o,i)}var Uae={kernelName:Eo,backendName:"webgl",kernelFunc:Vae};function Gae(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,weights:a}=t,{size:o,binaryOutput:i}=s;if(r.shape.length===1){let l=n.readSync(r.dataId),u=n.readSync(a.dataId),c=E9(l,u,a.dtype,a.shape,o);return n.makeTensorInfo([o],a.dtype,c)}else if(r.shape.length===2){let l=n.bufferSync(r),u=n.bufferSync(a),c=ene(l,u,o,i);return n.makeTensorInfo(c.shape,a.dtype,c.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${r.shape.length}.`)}var Hae={kernelName:u0,backendName:"webgl",kernelFunc:Gae},jae=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=[],this.outputShape=e,this.blockSize=t,this.dataFormat=n,this.userCode=`
     void main() {
       ivec4 coords = getOutputCoords();
       int b = coords[0];
@@ -2961,7 +2961,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,ore=pt({opSnippet:are}),ire={kernel
       float result = ${this.getInputSamplingString()};
       setOutput(result);
     }
-  `}getHeightCoordString(){return this.dataFormat==="NHWC"?"coords[1]":"coords[2]"}getWidthCoordString(){return this.dataFormat==="NHWC"?"coords[2]":"coords[3]"}getDepthCoordString(){return this.dataFormat==="NHWC"?"coords[3]":"coords[1]"}getOutputDepthSize(){return this.dataFormat==="NHWC"?this.outputShape[3]:this.outputShape[1]}getInputSamplingString(){return this.dataFormat==="NHWC"?"getX(b, in_h, in_w, in_d)":"getX(b, in_d, in_h, in_w)"}};function Fae(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockSize:a,dataFormat:o}=s,i=r.shape[0],l=o==="NHWC"?r.shape[1]:r.shape[2],u=o==="NHWC"?r.shape[2]:r.shape[3],c=o==="NHWC"?r.shape[3]:r.shape[1],p=l*a,d=u*a,h=c/(a*a),f=o==="NHWC"?[i,p,d,h]:[i,h,p,d],m=new Pae(f,a,o);return n.runWebGLProgram(m,[r],r.dtype)}var Oae={kernelName:vl,backendName:"webgl",kernelFunc:Fae},j9=class{constructor(e,t=!1,n=null,s=!1,r=!1){this.variableNames=["x","W"],this.customUniforms=[{name:"pads",type:"ivec2"},{name:"strides",type:"ivec2"},{name:"dilations",type:"ivec2"},{name:"inDims",type:"ivec2"}],this.outputShape=e.outShape,this.enableShapeUniforms=ds(this.outputShape.length);let a=e.filterHeight,o=e.filterWidth,i=e.outChannels/e.inChannels,l="",u="";n&&(s?l=`float activation(float a) {
+  `}getHeightCoordString(){return this.dataFormat==="NHWC"?"coords[1]":"coords[2]"}getWidthCoordString(){return this.dataFormat==="NHWC"?"coords[2]":"coords[3]"}getDepthCoordString(){return this.dataFormat==="NHWC"?"coords[3]":"coords[1]"}getOutputDepthSize(){return this.dataFormat==="NHWC"?this.outputShape[3]:this.outputShape[1]}getInputSamplingString(){return this.dataFormat==="NHWC"?"getX(b, in_h, in_w, in_d)":"getX(b, in_d, in_h, in_w)"}};function qae(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockSize:a,dataFormat:o}=s,i=r.shape[0],l=o==="NHWC"?r.shape[1]:r.shape[2],u=o==="NHWC"?r.shape[2]:r.shape[3],c=o==="NHWC"?r.shape[3]:r.shape[1],p=l*a,d=u*a,h=c/(a*a),f=o==="NHWC"?[i,p,d,h]:[i,h,p,d],m=new jae(f,a,o);return n.runWebGLProgram(m,[r],r.dtype)}var Xae={kernelName:Il,backendName:"webgl",kernelFunc:qae},J9=class{constructor(e,t=!1,n=null,s=!1,r=!1){this.variableNames=["x","W"],this.customUniforms=[{name:"pads",type:"ivec2"},{name:"strides",type:"ivec2"},{name:"dilations",type:"ivec2"},{name:"inDims",type:"ivec2"}],this.outputShape=e.outShape,this.enableShapeUniforms=fs(this.outputShape.length);let a=e.filterHeight,o=e.filterWidth,i=e.outChannels/e.inChannels,l="",u="";n&&(s?l=`float activation(float a) {
           float b = getPreluActivationWeightsAtOutCoords();
           ${n}
         }`:r?l=`float activation(float a) {
@@ -3014,7 +3014,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,ore=pt({opSnippet:are}),ire={kernel
         ${u}
         setOutput(result);
       }
-    `}},q9=class{constructor(e,t=!1,n=null,s=!1,r=!1){this.variableNames=["x","W"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"pads",type:"ivec2"},{name:"strides",type:"ivec2"},{name:"dilations",type:"ivec2"},{name:"inDims",type:"ivec2"}],this.outputShape=e.outShape,this.enableShapeUniforms=ds(this.outputShape.length);let a=e.outChannels/e.inChannels,o=e.padInfo.left,i=e.strideWidth,l=e.dilationWidth,u=e.filterHeight,c=e.filterWidth,p=c,d=`
+    `}},Q9=class{constructor(e,t=!1,n=null,s=!1,r=!1){this.variableNames=["x","W"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"pads",type:"ivec2"},{name:"strides",type:"ivec2"},{name:"dilations",type:"ivec2"},{name:"inDims",type:"ivec2"}],this.outputShape=e.outShape,this.enableShapeUniforms=fs(this.outputShape.length);let a=e.outChannels/e.inChannels,o=e.padInfo.left,i=e.strideWidth,l=e.dilationWidth,u=e.filterHeight,c=e.filterWidth,p=c,d=`
       int xR; int xC; int xCOffset;
       vec4 wTexel; vec4 previous; vec4 final;`;for(let g=0;g<c;g++)d+=`
           vec4 xTexelC${g*2};
@@ -3203,7 +3203,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,ore=pt({opSnippet:are}),ire={kernel
         ${f}
         setOutput(result);
       }
-    `}};function Mae(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a}=t,{strides:o,pad:i,dilations:l,dimRoundingMode:u}=s,c=l;c==null&&(c=[1,1]),v.assert(T.eitherStridesOrDilationsAreOne(o,c),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${o} and dilations '${c}'`);let p=T.computeConv2DInfo(r.shape,a.shape,o,c,i,u,!0),d;H().getBool("WEBGL_PACK_DEPTHWISECONV")&&p.strideWidth<=2&&p.outChannels/p.inChannels===1?d=new q9(p):d=new j9(p);let h=[[p.padInfo.top,p.padInfo.left],[p.strideHeight,p.strideWidth],[p.dilationHeight,p.dilationWidth],[p.inHeight,p.inWidth]];return n.runWebGLProgram(d,[r,a],"float32",h)}var zae={kernelName:Co,backendName:"webgl",kernelFunc:Mae},Lae=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideHeight,n=e.strideWidth,s=e.padInfo.top,r=e.padInfo.left,a=e.outChannels/e.inChannels;this.userCode=`
+    `}};function Kae(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a}=t,{strides:o,pad:i,dilations:l,dimRoundingMode:u}=s,c=l;c==null&&(c=[1,1]),v.assert(T.eitherStridesOrDilationsAreOne(o,c),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${o} and dilations '${c}'`);let p=T.computeConv2DInfo(r.shape,a.shape,o,c,i,u,!0),d;H().getBool("WEBGL_PACK_DEPTHWISECONV")&&p.strideWidth<=2&&p.outChannels/p.inChannels===1?d=new Q9(p):d=new J9(p);let h=[[p.padInfo.top,p.padInfo.left],[p.strideHeight,p.strideWidth],[p.dilationHeight,p.dilationWidth],[p.inHeight,p.inWidth]];return n.runWebGLProgram(d,[r,a],"float32",h)}var Zae={kernelName:Ro,backendName:"webgl",kernelFunc:Kae},Yae=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideHeight,n=e.strideWidth,s=e.padInfo.top,r=e.padInfo.left,a=e.outChannels/e.inChannels;this.userCode=`
       void main() {
         ivec4 coords = getOutputCoords();
         int wR = coords.x;
@@ -3238,7 +3238,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,ore=pt({opSnippet:are}),ire={kernel
         }
         setOutput(dotProd);
       }
-    `}},Bae=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,s=e.strideHeight,r=e.strideWidth,a=t-1-e.padInfo.top,o=n-1-e.padInfo.left,i=e.outChannels/e.inChannels;this.userCode=`
+    `}},Jae=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,s=e.strideHeight,r=e.strideWidth,a=t-1-e.padInfo.top,o=n-1-e.padInfo.left,i=e.outChannels/e.inChannels;this.userCode=`
       const ivec2 pads = ivec2(${a}, ${o});
 
       void main() {
@@ -3283,13 +3283,13 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,ore=pt({opSnippet:are}),ire={kernel
         }
         setOutput(dotProd);
       }
-    `}};function Wae(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,dy:a}=t,{strides:o,dilations:i,pad:l,dimRoundingMode:u,filterShape:c}=s,p=T.computeConv2DInfo(r.shape,c,o,i,l,u,!0),d=new Lae(p);return n.runWebGLProgram(d,[r,a],"float32")}var Vae={kernelName:c0,backendName:"webgl",kernelFunc:Wae};function Uae(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,filter:a}=t,{strides:o,dilations:i,pad:l,dimRoundingMode:u,inputShape:c}=s,p=T.computeConv2DInfo(c,a.shape,o,i,l,u,!0),d=new Bae(p);return n.runWebGLProgram(d,[r,a],"float32")}var Gae={kernelName:d0,backendName:"webgl",kernelFunc:Uae},Hae=class{constructor(e){this.variableNames=["X"],this.outputShape=[e,e],this.userCode=`
+    `}};function Qae(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,dy:a}=t,{strides:o,dilations:i,pad:l,dimRoundingMode:u,filterShape:c}=s,p=T.computeConv2DInfo(r.shape,c,o,i,l,u,!0),d=new Yae(p);return n.runWebGLProgram(d,[r,a],"float32")}var eoe={kernelName:c0,backendName:"webgl",kernelFunc:Qae};function toe(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,filter:a}=t,{strides:o,dilations:i,pad:l,dimRoundingMode:u,inputShape:c}=s,p=T.computeConv2DInfo(c,a.shape,o,i,l,u,!0),d=new Jae(p);return n.runWebGLProgram(d,[r,a],"float32")}var noe={kernelName:d0,backendName:"webgl",kernelFunc:toe},soe=class{constructor(e){this.variableNames=["X"],this.outputShape=[e,e],this.userCode=`
       void main() {
           ivec2 coords = getOutputCoords();
           float val = coords[0] == coords[1] ? getX(coords[0]) : 0.0;
           setOutput(val);
       }
-    `}};function jae(e){let{inputs:t,backend:n}=e,{x:s}=t,r=[...s.shape,...s.shape],a=v.sizeFromShape(s.shape),o=we({inputs:{x:s},backend:n,attrs:{shape:[a]}}),i=new Hae(a),l=n.runWebGLProgram(i,[o],o.dtype),u=we({inputs:{x:l},backend:n,attrs:{shape:r}});return n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(l),u}var qae={kernelName:p0,backendName:"webgl",kernelFunc:jae},Xae=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let{inHeight:t,inWidth:n,padInfo:s,strideHeight:r,strideWidth:a,filterHeight:o,filterWidth:i,dilationHeight:l,dilationWidth:u}=e,{top:c,left:p}=s;this.userCode=`
+    `}};function roe(e){let{inputs:t,backend:n}=e,{x:s}=t,r=[...s.shape,...s.shape],a=v.sizeFromShape(s.shape),o=we({inputs:{x:s},backend:n,attrs:{shape:[a]}}),i=new soe(a),l=n.runWebGLProgram(i,[o],o.dtype),u=we({inputs:{x:l},backend:n,attrs:{shape:r}});return n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(l),u}var aoe={kernelName:p0,backendName:"webgl",kernelFunc:roe},ooe=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let{inHeight:t,inWidth:n,padInfo:s,strideHeight:r,strideWidth:a,filterHeight:o,filterWidth:i,dilationHeight:l,dilationWidth:u}=e,{top:c,left:p}=s;this.userCode=`
       const ivec2 strides = ivec2(${r}, ${a});
       const ivec2 pads = ivec2(${c}, ${p});
       const float neg_infinity = -3.4e38;
@@ -3327,7 +3327,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,ore=pt({opSnippet:are}),ire={kernel
         float result = curVal;
         setOutput(result);
       }
-    `}};function Kae(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a}=t,{strides:o,pad:i,dilations:l}=s,u=T.computeDilation2DInfo(r.shape,a.shape,o,i,"NHWC",l),c,p=new Xae(u);c=n.runWebGLProgram(p,[r,a],"float32");let d=we({inputs:{x:c},backend:n,attrs:{shape:u.outShape}});return n.disposeIntermediateTensorInfo(c),d}var Zae={kernelName:Xp,backendName:"webgl",kernelFunc:Kae};function Yae(e){let{inputs:t,backend:n,attrs:s}=e,{equation:r}=s,a=t,{allDims:o,summedDims:i,idDims:l}=T.decodeEinsumEquation(r,a.length);T.checkEinsumDimSizes(o.length,l,a);let{path:u,steps:c}=T.getEinsumComputePath(i,l),p=c.length,d=null,h=o.length,f=[];for(let m=0;m<p;++m){for(let g of c[m]){let{permutationIndices:y,expandDims:x}=T.getEinsumPermutation(h,l[g]),A;T.isIdentityPermutation(y)?A=a[g]:(A=is({inputs:{x:a[g]},backend:n,attrs:{perm:y}}),f.push(A));let b=A.shape.slice();for(let w=0;w<x.length;++w)b.splice(x[w],0,1);v.arraysEqual(A.shape,b)||(A=we({inputs:{x:A},backend:n,attrs:{shape:b}}),f.push(A)),d===null?d=A:(d=hb({inputs:{a:A,b:d},backend:n}),f.push(d))}m<p-1&&(u[m]>=0&&(d=O2({inputs:{x:d},backend:n,attrs:{axis:u[m]-(o.length-h),keepDims:!1}}),f.push(d)),h--)}for(let m of f)m!==d&&n.disposeIntermediateTensorInfo(m);return d}var Jae={kernelName:Kp,backendName:"webgl",kernelFunc:Yae},Qae="return (x >= 0.0) ? x : (exp(x) - 1.0);",eoe=`
+    `}};function ioe(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a}=t,{strides:o,pad:i,dilations:l}=s,u=T.computeDilation2DInfo(r.shape,a.shape,o,i,"NHWC",l),c,p=new ooe(u);c=n.runWebGLProgram(p,[r,a],"float32");let d=we({inputs:{x:c},backend:n,attrs:{shape:u.outShape}});return n.disposeIntermediateTensorInfo(c),d}var loe={kernelName:Kp,backendName:"webgl",kernelFunc:ioe};function uoe(e){let{inputs:t,backend:n,attrs:s}=e,{equation:r}=s,a=t,{allDims:o,summedDims:i,idDims:l}=T.decodeEinsumEquation(r,a.length);T.checkEinsumDimSizes(o.length,l,a);let{path:u,steps:c}=T.getEinsumComputePath(i,l),p=c.length,d=null,h=o.length,f=[];for(let m=0;m<p;++m){for(let g of c[m]){let{permutationIndices:y,expandDims:x}=T.getEinsumPermutation(h,l[g]),A;T.isIdentityPermutation(y)?A=a[g]:(A=cs({inputs:{x:a[g]},backend:n,attrs:{perm:y}}),f.push(A));let b=A.shape.slice();for(let w=0;w<x.length;++w)b.splice(x[w],0,1);v.arraysEqual(A.shape,b)||(A=we({inputs:{x:A},backend:n,attrs:{shape:b}}),f.push(A)),d===null?d=A:(d=gb({inputs:{a:A,b:d},backend:n}),f.push(d))}m<p-1&&(u[m]>=0&&(d=O2({inputs:{x:d},backend:n,attrs:{axis:u[m]-(o.length-h),keepDims:!1}}),f.push(d)),h--)}for(let m of f)m!==d&&n.disposeIntermediateTensorInfo(m);return d}var coe={kernelName:Zp,backendName:"webgl",kernelFunc:uoe},doe="return (x >= 0.0) ? x : (exp(x) - 1.0);",poe=`
   vec4 result;
 
   result.r = (x.r >= 0.0) ? x.r : (exp(x.r) - 1.0);
@@ -3336,12 +3336,12 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,ore=pt({opSnippet:are}),ire={kernel
   result.a = (x.a >= 0.0) ? x.a : (exp(x.a) - 1.0);
 
   return result;
-`,toe=pt({opSnippet:Qae,packedOpSnippet:eoe}),noe={kernelName:No,backendName:"webgl",kernelFunc:toe},soe="return (b >= 1.0) ? a : a * (b + 1.0);",roe=`
+`,hoe=ht({opSnippet:doe,packedOpSnippet:poe}),foe={kernelName:Do,backendName:"webgl",kernelFunc:hoe},moe="return (b >= 1.0) ? a : a * (b + 1.0);",goe=`
   vec4 bGTEZero = vec4(greaterThanEqual(b, vec4(0.)));
   return (bGTEZero * a) + ((vec4(1.0) - bGTEZero) * (a * (b + vec4(1.0))));
-`,aoe=e=>{let{inputs:t,backend:n}=e,{dy:s,y:r}=t,a=H().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Hh(roe,s.shape,r.shape):new vc(soe,s.shape,r.shape);return n.runWebGLProgram(a,[s,r],s.dtype)},ooe={kernelName:h0,backendName:"webgl",kernelFunc:aoe},ioe=`
+`,yoe=e=>{let{inputs:t,backend:n}=e,{dy:s,y:r}=t,a=H().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new jh(goe,s.shape,r.shape):new Ic(moe,s.shape,r.shape);return n.runWebGLProgram(a,[s,r],s.dtype)},Aoe={kernelName:h0,backendName:"webgl",kernelFunc:yoe},xoe=`
   return vec4(equal(a, b));
-`,loe="return float(a == b);",uoe=Ln({opSnippet:loe,packedOpSnippet:ioe,dtype:"bool",cpuKernelImpl:jte}),coe={kernelName:wl,backendName:"webgl",kernelFunc:uoe},doe=`
+`,boe="return float(a == b);",voe=Bn({opSnippet:boe,packedOpSnippet:xoe,dtype:"bool",cpuKernelImpl:rne}),woe={kernelName:Cl,backendName:"webgl",kernelFunc:voe},koe=`
   // Error function is calculated approximately with elementary function.
   // See "Handbook of Mathematical Functions with Formulas,
   // Graphs, and Mathematical Tables", Abramowitz and Stegun.
@@ -3356,9 +3356,9 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,ore=pt({opSnippet:are}),ire={kernel
   x = abs(x);
   float t = 1.0 / (1.0 + p * x);
   return sign * (1.0 - (((((a5*t + a4)*t) + a3)*t + a2)*t + a1)*t*exp(-x*x));
-`,poe=pt({opSnippet:doe}),hoe={kernelName:$c,backendName:"webgl",kernelFunc:poe},foe=gd+`
+`,Soe=ht({opSnippet:koe}),Ioe={kernelName:Mc,backendName:"webgl",kernelFunc:Soe},Coe=xd+`
   return exp(x);
-`,moe=`
+`,Toe=`
   vec4 result = exp(x);
   bvec4 isNaN = isnan(x);
   result.r = isNaN.r ? x.r : result.r;
@@ -3367,7 +3367,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,ore=pt({opSnippet:are}),ire={kernel
   result.a = isNaN.a ? x.a : result.a;
 
   return result;
-`,X9=pt({opSnippet:foe,packedOpSnippet:moe,cpuKernelImpl:qte,dtype:"float32"}),goe={kernelName:Eo,backendName:"webgl",kernelFunc:X9};function ky(e){let{inputs:t,attrs:n,backend:s}=e,{dim:r}=n,{input:a}=t,o=a.shape.length,i=a.shape.slice(),l=r;return r<0&&(v.assert(-(o+1)<=r,()=>`Axis must be in the interval [${-(o+1)}, ${o}]`),l=o+r+1),i.splice(l,0,1),we({inputs:{x:a},backend:s,attrs:{shape:i}})}var yoe={kernelName:kl,backendName:"webgl",kernelFunc:ky},H7="return exp(x) - 1.0;",Aoe=pt({opSnippet:H7,packedOpSnippet:H7,cpuKernelImpl:Xte}),xoe={kernelName:Sl,backendName:"webgl",kernelFunc:Aoe},j7=class{constructor(e,t,n){this.variableNames=["real","imag"];let s=t[1];this.outputShape=t;let r=n?`2.0 * ${Math.PI}`:`-2.0 * ${Math.PI}`,a=n?`${s}.0`:"1.0",o;if(e==="real")o="return real * expR - imag * expI;";else if(e==="imag")o="return real * expI + imag * expR;";else throw new Error(`FFT component must be either "real" or "imag", got ${e}.`);this.userCode=`
+`,eC=ht({opSnippet:Coe,packedOpSnippet:Toe,cpuKernelImpl:ane,dtype:"float32"}),Noe={kernelName:$o,backendName:"webgl",kernelFunc:eC};function Cy(e){let{inputs:t,attrs:n,backend:s}=e,{dim:r}=n,{input:a}=t,o=a.shape.length,i=a.shape.slice(),l=r;return r<0&&(v.assert(-(o+1)<=r,()=>`Axis must be in the interval [${-(o+1)}, ${o}]`),l=o+r+1),i.splice(l,0,1),we({inputs:{x:a},backend:s,attrs:{shape:i}})}var Eoe={kernelName:Tl,backendName:"webgl",kernelFunc:Cy},Y7="return exp(x) - 1.0;",Roe=ht({opSnippet:Y7,packedOpSnippet:Y7,cpuKernelImpl:one}),_oe={kernelName:Nl,backendName:"webgl",kernelFunc:Roe},J7=class{constructor(e,t,n){this.variableNames=["real","imag"];let s=t[1];this.outputShape=t;let r=n?`2.0 * ${Math.PI}`:`-2.0 * ${Math.PI}`,a=n?`${s}.0`:"1.0",o;if(e==="real")o="return real * expR - imag * expI;";else if(e==="imag")o="return real * expI + imag * expR;";else throw new Error(`FFT component must be either "real" or "imag", got ${e}.`);this.userCode=`
       const float exponentMultiplier = ${r};
 
       float unaryOpComplex(float real, float expR, float imag, float expI) {
@@ -3400,12 +3400,12 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,ore=pt({opSnippet:are}),ire={kernel
         ivec2 coords = getOutputCoords();
         setOutput(mulMatDFT(coords[0], coords[1]));
       }
-    `}};function K9(e,t,n){let s=n.texData.get(e.dataId),r=v.sizeFromShape(e.shape),a=e.shape[e.shape.length-1],o=r/a,i=we({inputs:{x:e},backend:n,attrs:{shape:[o,a]}}),l=i.shape,u=new j7("real",l,t),c=new j7("imag",l,t),p=[{dataId:s.complexTensorInfos.real.dataId,dtype:s.complexTensorInfos.real.dtype,shape:l},{dataId:s.complexTensorInfos.imag.dataId,dtype:s.complexTensorInfos.imag.dtype,shape:l}],d=n.runWebGLProgram(u,p,"float32"),h=n.runWebGLProgram(c,p,"float32"),f=mi({inputs:{real:d,imag:h},backend:n});n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(h);let m=we({inputs:{x:f},backend:n,attrs:{shape:e.shape}});return n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(f),m}function boe(e){let{inputs:t,backend:n}=e,{input:s}=t;return K9(s,!1,n)}var voe={kernelName:f0,backendName:"webgl",kernelFunc:boe},woe=class{constructor(e,t){this.outputShape=[],this.customUniforms=[{name:"value",type:"float"}],this.variableNames=["x"],this.outputShape=e,this.userCode=`
+    `}};function tC(e,t,n){let s=n.texData.get(e.dataId),r=v.sizeFromShape(e.shape),a=e.shape[e.shape.length-1],o=r/a,i=we({inputs:{x:e},backend:n,attrs:{shape:[o,a]}}),l=i.shape,u=new J7("real",l,t),c=new J7("imag",l,t),p=[{dataId:s.complexTensorInfos.real.dataId,dtype:s.complexTensorInfos.real.dtype,shape:l},{dataId:s.complexTensorInfos.imag.dataId,dtype:s.complexTensorInfos.imag.dtype,shape:l}],d=n.runWebGLProgram(u,p,"float32"),h=n.runWebGLProgram(c,p,"float32"),f=xi({inputs:{real:d,imag:h},backend:n});n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(h);let m=we({inputs:{x:f},backend:n,attrs:{shape:e.shape}});return n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(f),m}function Doe(e){let{inputs:t,backend:n}=e,{input:s}=t;return tC(s,!1,n)}var $oe={kernelName:f0,backendName:"webgl",kernelFunc:Doe},Poe=class{constructor(e,t){this.outputShape=[],this.customUniforms=[{name:"value",type:"float"}],this.variableNames=["x"],this.outputShape=e,this.userCode=`
       void main() {
         // Input can be obtained from uniform value.
         setOutput(value);
       }
-    `}};function qh(e){let{backend:t,attrs:n}=e,{shape:s,value:r}=n,{dtype:a}=n;if(a=a||v.inferDtype(r),a==="string"){let o=v.getArrayFromDType(a,v.sizeFromShape(s));return o.fill(r),t.makeTensorInfo(s,a,o)}else{let o=new woe(s,r),i=[[r]];return t.runWebGLProgram(o,[],a,i)}}var koe={kernelName:Pc,backendName:"webgl",kernelFunc:qh},Soe=class{constructor(e){this.variableNames=["Image"],this.outputShape=[];let t=e[2];this.outputShape=e,this.userCode=`
+    `}};function Xh(e){let{backend:t,attrs:n}=e,{shape:s,value:r}=n,{dtype:a}=n;if(a=a||v.inferDtype(r),a==="string"){let o=v.getArrayFromDType(a,v.sizeFromShape(s));return o.fill(r),t.makeTensorInfo(s,a,o)}else{let o=new Poe(s,r),i=[[r]];return t.runWebGLProgram(o,[],a,i)}}var Foe={kernelName:zc,backendName:"webgl",kernelFunc:Xh},Ooe=class{constructor(e){this.variableNames=["Image"],this.outputShape=[];let t=e[2];this.outputShape=e,this.userCode=`
         void main() {
           ivec4 coords = getOutputCoords();
           int x = coords[2];
@@ -3419,7 +3419,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,ore=pt({opSnippet:are}),ire={kernel
           }
           setOutput(outputValue);
         }
-    `}},Ioe={kernelName:Il,backendName:"webgl",kernelFunc:({inputs:e,backend:t})=>{let{image:n}=e,s=t,r=new Soe(n.shape);return s.runWebGLProgram(r,[n],n.dtype)}},q7="return floor(x);",Coe=pt({opSnippet:q7,packedOpSnippet:q7,cpuKernelImpl:Kte}),Toe={kernelName:Ro,backendName:"webgl",kernelFunc:Coe},Noe=`
+    `}},Moe={kernelName:El,backendName:"webgl",kernelFunc:({inputs:e,backend:t})=>{let{image:n}=e,s=t,r=new Ooe(n.shape);return s.runWebGLProgram(r,[n],n.dtype)}},Q7="return floor(x);",zoe=ht({opSnippet:Q7,packedOpSnippet:Q7,cpuKernelImpl:ine}),Loe={kernelName:Po,backendName:"webgl",kernelFunc:zoe},Boe=`
   float s = sign(a) * sign(b);
   int ia = round(a);
   int ib = round(b);
@@ -3429,7 +3429,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,ore=pt({opSnippet:are}),ire={kernel
   } else {
     return NAN;
   }
-`,Eoe=`
+`,Woe=`
   ivec4 ia = round(a);
   ivec4 ib = round(b);
   bvec4 cond = notEqual(ib, ivec4(0));
@@ -3450,7 +3450,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,ore=pt({opSnippet:are}),ire={kernel
     result[3] = idiv(ia[3], ib[3], s[3]);
   }
   return vec4(result);
-`,Roe=Ln({opSnippet:Noe,packedOpSnippet:Eoe,dtype:"int32"}),_oe={kernelName:_o,backendName:"webgl",kernelFunc:Roe},Doe=class{constructor(e){this.variableNames=["A"];let t=cs(),[n,s]=e;this.outputShape=e,this.userCode=`
+`,Voe=Bn({opSnippet:Boe,packedOpSnippet:Woe,dtype:"int32"}),Uoe={kernelName:Fo,backendName:"webgl",kernelFunc:Voe},Goe=class{constructor(e){this.variableNames=["A"];let t=hs(),[n,s]=e;this.outputShape=e,this.userCode=`
       void main() {
         ivec3 coords = getOutputCoords();
         int texR = coords[0];
@@ -3472,7 +3472,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,ore=pt({opSnippet:are}),ire={kernel
 
         setOutput(floor(value * 255.0 + 0.5));
       }
-    `}},$oe=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0;let t=cs(),[n,s]=e;this.outputShape=e,this.userCode=`
+    `}},Hoe=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0;let t=hs(),[n,s]=e;this.outputShape=e,this.userCode=`
       void main() {
         ivec3 coords = getOutputCoords();
         int texR = coords[0];
@@ -3506,7 +3506,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,ore=pt({opSnippet:are}),ire={kernel
 
         ${t.output} = result;
       }
-    `}},Poe={kernelName:Sp,backendName:"webgl",kernelFunc:Foe},Xu,C3=H().getBool("CANVAS2D_WILL_READ_FREQUENTLY_FOR_GPU");function Foe(e){let{inputs:t,backend:n,attrs:s}=e,{pixels:r}=t,{numChannels:a}=s,o=typeof HTMLVideoElement!="undefined"&&r instanceof HTMLVideoElement,i=typeof HTMLImageElement!="undefined"&&r instanceof HTMLImageElement,[l,u]=o?[r.videoWidth,r.videoHeight]:[r.width,r.height],c=[u,l],p=[u,l,a];if(i||o){let m=H().getBool("CANVAS2D_WILL_READ_FREQUENTLY_FOR_GPU");(Xu==null||m!==C3)&&(C3=m,Xu=document.createElement("canvas").getContext("2d",{willReadFrequently:C3})),Xu.canvas.width=l,Xu.canvas.height=u,Xu.drawImage(r,0,0,l,u),r=Xu.canvas}let d=n.makeTensorInfo(c,"int32");n.texData.get(d.dataId).usage=Ys.PIXELS,n.gpgpu.uploadPixelDataToTexture(n.getTexture(d.dataId),r);let h=H().getBool("WEBGL_PACK")?new $oe(p):new Doe(p),f=n.runWebGLProgram(h,[d],"int32");return n.disposeData(d.dataId),f}function Ooe(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a,bias:o,preluActivationWeights:i}=t,{strides:l,pad:u,dataFormat:c,dilations:p,dimRoundingMode:d,activation:h,leakyreluAlpha:f}=s,m=T.convertConv2DDataFormat(c),g=T.computeConv2DInfo(r.shape,a.shape,l,p,u,d,!1,m),y,x=[],A=o!=null,b=i!=null,w=h==="leakyrelu",k=()=>{let E=[r,a],_=($,R)=>{if(R==="NCHW"&&$.shape.length===1&&$.shape[0]!==1){let P=we({inputs:{x:$},backend:n,attrs:{shape:[$.shape[0],1,1]}});return x.push(P),P}return $};if(A&&E.push(_(o,c)),b&&E.push(_(i,c)),w){let $=n.makeTensorInfo([],"float32",v.createScalarValue(f,"float32"));E.push($),x.push($)}return E};if(g.filterHeight===1&&g.filterWidth===1&&g.dilationHeight===1&&g.dilationWidth===1&&g.strideHeight===1&&g.strideWidth===1&&(g.padInfo.type==="SAME"||g.padInfo.type==="VALID"))y=U9({x:r,filter:a,convInfo:g,backend:n,bias:o,activation:h,preluActivationWeights:i,leakyreluAlpha:f});else if(g.strideWidth<=2&&m==="channelsLast"&&H().getBool("WEBGL_EXP_CONV")){let E=h?Mp(h,!0):null,_=new V9(g,A,E,b,w),$=[[g.padInfo.top,g.padInfo.left],[g.strideHeight,g.strideWidth],[g.dilationHeight,g.dilationWidth],[g.inHeight,g.inWidth]],R=k();y=n.runWebGLProgram(_,R,"float32",$)}else if(H().getBool("WEBGL_CONV_IM2COL"))y=G9({x:r,filter:a,convInfo:g,backend:n,bias:o,activation:h,preluActivationWeights:i,leakyreluAlpha:f});else{let E=h?Mp(h,!1):null,_=new W9(g,A,E,b,w),$=k();y=n.runWebGLProgram(_,$,"float32")}let C=we({inputs:{x:y},backend:n,attrs:{shape:g.outShape}});return x.push(y),x.forEach(E=>n.disposeIntermediateTensorInfo(E)),C}var Moe={kernelName:no,backendName:"webgl",kernelFunc:Ooe};function zoe(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a,bias:o,preluActivationWeights:i}=t,{strides:l,pad:u,dilations:c,dimRoundingMode:p,activation:d,leakyreluAlpha:h}=s,f=[],m=c;m==null&&(m=[1,1]),v.assert(T.eitherStridesOrDilationsAreOne(l,m),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${l} and dilations '${m}'`);let g=T.computeConv2DInfo(r.shape,a.shape,l,m,u,p,!0),y=H().getBool("WEBGL_PACK_DEPTHWISECONV")&&g.strideWidth<=2&&g.outChannels/g.inChannels===1,x=d?Mp(d,y):null,A=[r,a],b=o!=null,w=i!=null,k=d==="leakyrelu";if(b&&A.push(o),w&&A.push(i),k){let $=n.makeTensorInfo([],"float32",v.createScalarValue(h,"float32"));A.push($),f.push($)}let C;y?C=new q9(g,b,x,w,k):C=new j9(g,b,x,w,k);let E=[[g.padInfo.top,g.padInfo.left],[g.strideHeight,g.strideWidth],[g.dilationHeight,g.dilationWidth],[g.inHeight,g.inWidth]],_=n.runWebGLProgram(C,A,"float32",E);return f.forEach($=>n.disposeIntermediateTensorInfo($)),_}var Loe={kernelName:so,backendName:"webgl",kernelFunc:zoe},Boe=class{constructor(e,t,n,s){this.sliceDim=e,this.strides=t,this.paramsShape=s,this.variableNames=["x","indices"],this.outputShape=n;let r=wt(t.length),a=wt(n.length),o=this.sliceDim>1?"strides[j]":"strides",i=wt(s.length),l=s.length>1?"paramsShape[j]":"paramsShape";this.userCode=`
+    `}},joe={kernelName:Ip,backendName:"webgl",kernelFunc:qoe},Ju,E3=H().getBool("CANVAS2D_WILL_READ_FREQUENTLY_FOR_GPU");function qoe(e){let{inputs:t,backend:n,attrs:s}=e,{pixels:r}=t,{numChannels:a}=s,o=typeof HTMLVideoElement!="undefined"&&r instanceof HTMLVideoElement,i=typeof HTMLImageElement!="undefined"&&r instanceof HTMLImageElement,[l,u]=o?[r.videoWidth,r.videoHeight]:[r.width,r.height],c=[u,l],p=[u,l,a];if(i||o){let m=H().getBool("CANVAS2D_WILL_READ_FREQUENTLY_FOR_GPU");(Ju==null||m!==E3)&&(E3=m,Ju=document.createElement("canvas").getContext("2d",{willReadFrequently:E3})),Ju.canvas.width=l,Ju.canvas.height=u,Ju.drawImage(r,0,0,l,u),r=Ju.canvas}let d=n.makeTensorInfo(c,"int32");n.texData.get(d.dataId).usage=Qs.PIXELS,n.gpgpu.uploadPixelDataToTexture(n.getTexture(d.dataId),r);let h=H().getBool("WEBGL_PACK")?new Hoe(p):new Goe(p),f=n.runWebGLProgram(h,[d],"int32");return n.disposeData(d.dataId),f}function Xoe(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a,bias:o,preluActivationWeights:i}=t,{strides:l,pad:u,dataFormat:c,dilations:p,dimRoundingMode:d,activation:h,leakyreluAlpha:f}=s,m=T.convertConv2DDataFormat(c),g=T.computeConv2DInfo(r.shape,a.shape,l,p,u,d,!1,m),y,x=[],A=o!=null,b=i!=null,w=h==="leakyrelu",k=()=>{let E=[r,a],_=($,R)=>{if(R==="NCHW"&&$.shape.length===1&&$.shape[0]!==1){let P=we({inputs:{x:$},backend:n,attrs:{shape:[$.shape[0],1,1]}});return x.push(P),P}return $};if(A&&E.push(_(o,c)),b&&E.push(_(i,c)),w){let $=n.makeTensorInfo([],"float32",v.createScalarValue(f,"float32"));E.push($),x.push($)}return E};if(g.filterHeight===1&&g.filterWidth===1&&g.dilationHeight===1&&g.dilationWidth===1&&g.strideHeight===1&&g.strideWidth===1&&(g.padInfo.type==="SAME"||g.padInfo.type==="VALID"))y=K9({x:r,filter:a,convInfo:g,backend:n,bias:o,activation:h,preluActivationWeights:i,leakyreluAlpha:f});else if(g.strideWidth<=2&&m==="channelsLast"&&H().getBool("WEBGL_EXP_CONV")){let E=h?zp(h,!0):null,_=new X9(g,A,E,b,w),$=[[g.padInfo.top,g.padInfo.left],[g.strideHeight,g.strideWidth],[g.dilationHeight,g.dilationWidth],[g.inHeight,g.inWidth]],R=k();y=n.runWebGLProgram(_,R,"float32",$)}else if(H().getBool("WEBGL_CONV_IM2COL"))y=Z9({x:r,filter:a,convInfo:g,backend:n,bias:o,activation:h,preluActivationWeights:i,leakyreluAlpha:f});else{let E=h?zp(h,!1):null,_=new q9(g,A,E,b,w),$=k();y=n.runWebGLProgram(_,$,"float32")}let C=we({inputs:{x:y},backend:n,attrs:{shape:g.outShape}});return x.push(y),x.forEach(E=>n.disposeIntermediateTensorInfo(E)),C}var Koe={kernelName:oo,backendName:"webgl",kernelFunc:Xoe};function Zoe(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a,bias:o,preluActivationWeights:i}=t,{strides:l,pad:u,dilations:c,dimRoundingMode:p,activation:d,leakyreluAlpha:h}=s,f=[],m=c;m==null&&(m=[1,1]),v.assert(T.eitherStridesOrDilationsAreOne(l,m),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${l} and dilations '${m}'`);let g=T.computeConv2DInfo(r.shape,a.shape,l,m,u,p,!0),y=H().getBool("WEBGL_PACK_DEPTHWISECONV")&&g.strideWidth<=2&&g.outChannels/g.inChannels===1,x=d?zp(d,y):null,A=[r,a],b=o!=null,w=i!=null,k=d==="leakyrelu";if(b&&A.push(o),w&&A.push(i),k){let $=n.makeTensorInfo([],"float32",v.createScalarValue(h,"float32"));A.push($),f.push($)}let C;y?C=new Q9(g,b,x,w,k):C=new J9(g,b,x,w,k);let E=[[g.padInfo.top,g.padInfo.left],[g.strideHeight,g.strideWidth],[g.dilationHeight,g.dilationWidth],[g.inHeight,g.inWidth]],_=n.runWebGLProgram(C,A,"float32",E);return f.forEach($=>n.disposeIntermediateTensorInfo($)),_}var Yoe={kernelName:io,backendName:"webgl",kernelFunc:Zoe},Joe=class{constructor(e,t,n,s){this.sliceDim=e,this.strides=t,this.paramsShape=s,this.variableNames=["x","indices"],this.outputShape=n;let r=kt(t.length),a=kt(n.length),o=this.sliceDim>1?"strides[j]":"strides",i=kt(s.length),l=s.length>1?"paramsShape[j]":"paramsShape";this.userCode=`
         ${r} strides = ${r}(${this.strides});
         ${i} paramsShape = ${i}(${this.paramsShape});
          void main() {
@@ -3521,24 +3521,24 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,ore=pt({opSnippet:are}),ire={kernel
           }
           setOutput(out_of_bounds ? 0.0 : getX(flattenIndex, coords[1]));
         }
-      `}};function Woe(e){let{inputs:t,backend:n}=e,{params:s,indices:r}=t,a=r.shape,o=a[a.length-1],i=v.sizeFromShape(s.shape),[l,u,c,p]=T.prepareAndValidate(s,r),d=we({inputs:{x:r},backend:n,attrs:{shape:[u,o]}}),h=we({inputs:{x:s},backend:n,attrs:{shape:[v.sizeFromShape(s.shape)/c,c]}});if(n.shouldExecuteOnCPU([s,r])||s.dtype==="string"){let y=n.readSync(r.dataId),x=n.bufferSync(s),A=Zte(y,x,s.dtype,u,o,c,p,s.shape,i);return n.makeTensorInfo(l,s.dtype,A.values)}let f=new Boe(o,p,[u,c],s.shape),m=n.runWebGLProgram(f,[h,d],h.dtype),g=we({inputs:{x:m},backend:n,attrs:{shape:l}});return n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(m),g}var Voe={kernelName:Tl,backendName:"webgl",kernelFunc:Woe},Uoe=class{constructor(e,t){this.variableNames=["A","indices"],this.outputShape=t,this.rank=t.length;let n=wt(this.rank),s=Goe(e,2);this.userCode=`
+      `}};function Qoe(e){let{inputs:t,backend:n}=e,{params:s,indices:r}=t,a=r.shape,o=a[a.length-1],i=v.sizeFromShape(s.shape),[l,u,c,p]=T.prepareAndValidate(s,r),d=we({inputs:{x:r},backend:n,attrs:{shape:[u,o]}}),h=we({inputs:{x:s},backend:n,attrs:{shape:[v.sizeFromShape(s.shape)/c,c]}});if(n.shouldExecuteOnCPU([s,r])||s.dtype==="string"){let y=n.readSync(r.dataId),x=n.bufferSync(s),A=lne(y,x,s.dtype,u,o,c,p,s.shape,i);return n.makeTensorInfo(l,s.dtype,A.values)}let f=new Joe(o,p,[u,c],s.shape),m=n.runWebGLProgram(f,[h,d],h.dtype),g=we({inputs:{x:m},backend:n,attrs:{shape:l}});return n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(m),g}var eie={kernelName:_l,backendName:"webgl",kernelFunc:Qoe},tie=class{constructor(e,t){this.variableNames=["A","indices"],this.outputShape=t,this.rank=t.length;let n=kt(this.rank),s=nie(e,2);this.userCode=`
       void main() {
         ${n} resRC = getOutputCoords();
         int index = int(getIndices(resRC.x, resRC.z));
         float inBounds = (index >= 0) && (index < ${e[2]}) ? 1.0 : 0.0;
         setOutput(inBounds * getA(${s}));
       }
-    `}};function Goe(e,t){let n=["resRC.x","resRC.y","resRC.z","resRC.w"],s=[];for(let r=0;r<e.length;r++)r===2?s.push("index"):s.push(`${n[r]}`);return s.join()}function Z9(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,indices:a}=t,{axis:o,batchDims:i}=s,l=v.parseAxisParam(o,r.shape)[0];if(H().get("DEBUG")){let x=n.readSync(a.dataId),A=r.shape[l];for(let b=0;b<x.length;++b){let w=x[b];v.assert(w<=A-1&&w>=0,()=>`GatherV2: the index value ${w} is not in [0, ${A-1}]`)}}let u=T.segment_util.collectGatherOpShapeInfo(r,a,l,i),c=v.sizeFromShape(a.shape),p=[],d=we({inputs:{x:r},backend:n,attrs:{shape:[u.batchSize,u.outerSize,u.dimSize,u.sliceSize]}}),h=we({inputs:{x:a},backend:n,attrs:{shape:[u.batchSize,c/u.batchSize]}});p.push(d),p.push(h);let f=[u.batchSize,u.outerSize,c/u.batchSize,u.sliceSize];if(n.shouldExecuteOnCPU([r,a])||r.dtype==="string"){let x=n.bufferSync(h),A=n.bufferSync(d),b=Yte(A,x,f);return p.forEach(w=>n.disposeIntermediateTensorInfo(w)),n.makeTensorInfo(u.outputShape,b.dtype,b.values)}let m=new Uoe(d.shape,f),g=n.runWebGLProgram(m,[d,h],d.dtype);p.push(g);let y=we({inputs:{x:g},backend:n,attrs:{shape:u.outputShape}});return p.forEach(x=>n.disposeIntermediateTensorInfo(x)),y}var Hoe={kernelName:Cl,backendName:"webgl",kernelFunc:Z9},joe="return float(a > b);",qoe=`
+    `}};function nie(e,t){let n=["resRC.x","resRC.y","resRC.z","resRC.w"],s=[];for(let r=0;r<e.length;r++)r===2?s.push("index"):s.push(`${n[r]}`);return s.join()}function nC(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,indices:a}=t,{axis:o,batchDims:i}=s,l=v.parseAxisParam(o,r.shape)[0];if(H().get("DEBUG")){let x=n.readSync(a.dataId),A=r.shape[l];for(let b=0;b<x.length;++b){let w=x[b];v.assert(w<=A-1&&w>=0,()=>`GatherV2: the index value ${w} is not in [0, ${A-1}]`)}}let u=T.segment_util.collectGatherOpShapeInfo(r,a,l,i),c=v.sizeFromShape(a.shape),p=[],d=we({inputs:{x:r},backend:n,attrs:{shape:[u.batchSize,u.outerSize,u.dimSize,u.sliceSize]}}),h=we({inputs:{x:a},backend:n,attrs:{shape:[u.batchSize,c/u.batchSize]}});p.push(d),p.push(h);let f=[u.batchSize,u.outerSize,c/u.batchSize,u.sliceSize];if(n.shouldExecuteOnCPU([r,a])||r.dtype==="string"){let x=n.bufferSync(h),A=n.bufferSync(d),b=une(A,x,f);return p.forEach(w=>n.disposeIntermediateTensorInfo(w)),n.makeTensorInfo(u.outputShape,b.dtype,b.values)}let m=new tie(d.shape,f),g=n.runWebGLProgram(m,[d,h],d.dtype);p.push(g);let y=we({inputs:{x:g},backend:n,attrs:{shape:u.outputShape}});return p.forEach(x=>n.disposeIntermediateTensorInfo(x)),y}var sie={kernelName:Rl,backendName:"webgl",kernelFunc:nC},rie="return float(a > b);",aie=`
   return vec4(greaterThan(a, b));
-`,Xoe=Ln({opSnippet:joe,packedOpSnippet:qoe,cpuKernelImpl:Jte,dtype:"bool"}),Koe={kernelName:Nl,backendName:"webgl",kernelFunc:Xoe},Zoe="return float(a >= b);",Yoe=`
+`,oie=Bn({opSnippet:rie,packedOpSnippet:aie,cpuKernelImpl:cne,dtype:"bool"}),iie={kernelName:Dl,backendName:"webgl",kernelFunc:oie},lie="return float(a >= b);",uie=`
   return vec4(greaterThanEqual(a, b));
-`,Joe=Ln({opSnippet:Zoe,packedOpSnippet:Yoe,dtype:"bool",cpuKernelImpl:Qte}),Qoe={kernelName:$o,backendName:"webgl",kernelFunc:Joe};function eie(e){let{inputs:t,backend:n}=e,{input:s}=t;return K9(s,!0,n)}var tie={kernelName:m0,backendName:"webgl",kernelFunc:eie},nie="return float(!isnan(x) && !isinf(x));",sie=pt({opSnippet:nie,dtype:"bool"}),rie={kernelName:Fc,backendName:"webgl",kernelFunc:sie},aie="return float(isinf(x));",oie=pt({opSnippet:aie,dtype:"bool"}),iie={kernelName:Oc,backendName:"webgl",kernelFunc:oie},lie="return float(isnan(x));",uie=pt({opSnippet:lie,dtype:"bool"}),cie={kernelName:El,backendName:"webgl",kernelFunc:uie},die="return float(a < b);",pie=`
+`,cie=Bn({opSnippet:lie,packedOpSnippet:uie,dtype:"bool",cpuKernelImpl:dne}),die={kernelName:Mo,backendName:"webgl",kernelFunc:cie};function pie(e){let{inputs:t,backend:n}=e,{input:s}=t;return tC(s,!0,n)}var hie={kernelName:m0,backendName:"webgl",kernelFunc:pie},fie="return float(!isnan(x) && !isinf(x));",mie=ht({opSnippet:fie,dtype:"bool"}),gie={kernelName:Lc,backendName:"webgl",kernelFunc:mie},yie="return float(isinf(x));",Aie=ht({opSnippet:yie,dtype:"bool"}),xie={kernelName:Bc,backendName:"webgl",kernelFunc:Aie},bie="return float(isnan(x));",vie=ht({opSnippet:bie,dtype:"bool"}),wie={kernelName:$l,backendName:"webgl",kernelFunc:vie},kie="return float(a < b);",Sie=`
   return vec4(lessThan(a, b));
-`,hie=Ln({opSnippet:die,packedOpSnippet:pie,cpuKernelImpl:ene,dtype:"bool"}),fie={kernelName:Rl,backendName:"webgl",kernelFunc:hie},mie="return float(a <= b);",gie=`
+`,Iie=Bn({opSnippet:kie,packedOpSnippet:Sie,cpuKernelImpl:pne,dtype:"bool"}),Cie={kernelName:Pl,backendName:"webgl",kernelFunc:Iie},Tie="return float(a <= b);",Nie=`
   return vec4(lessThanEqual(a, b));
-`,yie=Ln({opSnippet:mie,packedOpSnippet:gie,cpuKernelImpl:tne,dtype:"bool"}),Aie={kernelName:_l,backendName:"webgl",kernelFunc:yie};function xie(e){let{backend:t,attrs:n}=e,{start:s,stop:r,num:a}=n,o=nne(s,r,a);return t.makeTensorInfo([o.length],"float32",o)}var bie={kernelName:g0,backendName:"webgl",kernelFunc:xie},vie=gd+`
+`,Eie=Bn({opSnippet:Tie,packedOpSnippet:Nie,cpuKernelImpl:hne,dtype:"bool"}),Rie={kernelName:Fl,backendName:"webgl",kernelFunc:Eie};function _ie(e){let{backend:t,attrs:n}=e,{start:s,stop:r,num:a}=n,o=fne(s,r,a);return t.makeTensorInfo([o.length],"float32",o)}var Die={kernelName:g0,backendName:"webgl",kernelFunc:_ie},$ie=xd+`
   return x < 0.0 ? 0./0. : log(x);
-`,wie=`
+`,Pie=`
   vec4 result = log(x);
   bvec4 isNaN = isnan(x);
   result.r = isNaN.r ? x.r : (x.r < 0.0 ? 0./0. : result.r);
@@ -3546,18 +3546,18 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,ore=pt({opSnippet:are}),ire={kernel
   result.b = isNaN.b ? x.b : (x.b < 0.0 ? 0./0. : result.b);
   result.a = isNaN.a ? x.a : (x.a < 0.0 ? 0./0. : result.a);
   return result;
-`,kie=pt({opSnippet:vie,packedOpSnippet:wie,cpuKernelImpl:sne}),Sie={kernelName:Oo,backendName:"webgl",kernelFunc:kie},Iie=gd+`
+`,Fie=ht({opSnippet:$ie,packedOpSnippet:Pie,cpuKernelImpl:mne}),Oie={kernelName:Bo,backendName:"webgl",kernelFunc:Fie},Mie=xd+`
   return log(1.0 + x);
-`,Cie=pt({opSnippet:Iie}),Tie={kernelName:Mc,backendName:"webgl",kernelFunc:Cie},Nie="return float(a >= 1.0 && b >= 1.0);",Eie=`
+`,zie=ht({opSnippet:Mie}),Lie={kernelName:Wc,backendName:"webgl",kernelFunc:zie},Bie="return float(a >= 1.0 && b >= 1.0);",Wie=`
   return vec4(
     vec4(greaterThanEqual(a, vec4(1.0))) *
     vec4(greaterThanEqual(b, vec4(1.0))));
-`,Rie=Ln({opSnippet:Nie,packedOpSnippet:Eie,dtype:"bool"}),_ie={kernelName:Dl,backendName:"webgl",kernelFunc:Rie},Die="return float(!(x >= 1.0));",$ie=pt({opSnippet:Die}),Pie={kernelName:$l,backendName:"webgl",kernelFunc:$ie},Fie="return float(a >= 1.0 || b >= 1.0);",Oie=`
+`,Vie=Bn({opSnippet:Bie,packedOpSnippet:Wie,dtype:"bool"}),Uie={kernelName:Ol,backendName:"webgl",kernelFunc:Vie},Gie="return float(!(x >= 1.0));",Hie=ht({opSnippet:Gie}),jie={kernelName:Ml,backendName:"webgl",kernelFunc:Hie},qie="return float(a >= 1.0 || b >= 1.0);",Xie=`
   return min(
     vec4(greaterThanEqual(a, vec4(1.0))) +
     vec4(greaterThanEqual(b, vec4(1.0))),
     vec4(1.0));
-`,Mie=Ln({opSnippet:Fie,packedOpSnippet:Oie,dtype:"bool"}),zie={kernelName:zc,backendName:"webgl",kernelFunc:Mie},Lie=class{constructor(e,t,n,s,r){this.variableNames=["x"],this.outputShape=[];let a=t,o=e[3]-1;this.outputShape=e;let i,l=`float(${n}) + float(${s}) * sum`;r===.5?i=`inversesqrt(${l})`:r===1?i=`1.0/(${l})`:i=`exp(log(${l}) * float(-${r}));`,this.userCode=`
+`,Kie=Bn({opSnippet:qie,packedOpSnippet:Xie,dtype:"bool"}),Zie={kernelName:Vc,backendName:"webgl",kernelFunc:Kie},Yie=class{constructor(e,t,n,s,r){this.variableNames=["x"],this.outputShape=[];let a=t,o=e[3]-1;this.outputShape=e;let i,l=`float(${n}) + float(${s}) * sum`;r===.5?i=`inversesqrt(${l})`:r===1?i=`1.0/(${l})`:i=`exp(log(${l}) * float(-${r}));`,this.userCode=`
       void main() {
         ivec4 coords = getOutputCoords();
         int b = coords[0];
@@ -3576,7 +3576,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,ore=pt({opSnippet:are}),ire={kernel
         float val = x * ${i};
         setOutput(val);
       }
-    `}},Bie=class{constructor(e,t,n,s,r){this.variableNames=["x"],this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0;let a=t,o=e[3]-1;this.outputShape=e;let i,l=`float(${n}) + float(${s}) * sum`;r===.5?i=`inversesqrt(${l})`:r===1?i=`1.0/(${l})`:i=`exp(log(${l}) * float(-${r}));`,this.userCode=`
+    `}},Jie=class{constructor(e,t,n,s,r){this.variableNames=["x"],this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0;let a=t,o=e[3]-1;this.outputShape=e;let i,l=`float(${n}) + float(${s}) * sum`;r===.5?i=`inversesqrt(${l})`:r===1?i=`1.0/(${l})`:i=`exp(log(${l}) * float(-${r}));`,this.userCode=`
       void main() {
         ivec4 coords = getOutputCoords();
         int b = coords.x;
@@ -3638,7 +3638,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,ore=pt({opSnippet:are}),ire={kernel
         vec4 result = xAtOutputCoords * ${i};
         setOutput(result);
       }
-    `}},Wie=e=>{let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{depthRadius:a,bias:o,alpha:i,beta:l}=s,u=H().getBool("WEBGL_PACK_NORMALIZATION")?new Bie(r.shape,a,o,i,l):new Lie(r.shape,a,o,i,l);return n.runWebGLProgram(u,[r],r.dtype)},Vie={kernelName:Yp,backendName:"webgl",kernelFunc:Wie},Uie=class{constructor(e,t,n,s,r){this.variableNames=["inputImage","outputImage","dy"],this.outputShape=[],this.outputShape=e,this.depth=e[3],this.depthRadius=t,this.bias=n,this.alpha=s,this.beta=r,this.userCode=`
+    `}},Qie=e=>{let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{depthRadius:a,bias:o,alpha:i,beta:l}=s,u=H().getBool("WEBGL_PACK_NORMALIZATION")?new Jie(r.shape,a,o,i,l):new Yie(r.shape,a,o,i,l);return n.runWebGLProgram(u,[r],r.dtype)},ele={kernelName:Jp,backendName:"webgl",kernelFunc:Qie},tle=class{constructor(e,t,n,s,r){this.variableNames=["inputImage","outputImage","dy"],this.outputShape=[],this.outputShape=e,this.depth=e[3],this.depthRadius=t,this.bias=n,this.alpha=s,this.beta=r,this.userCode=`
       void main() {
         ivec4 coords = getOutputCoords();
         int b = coords[0];
@@ -3693,14 +3693,14 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,ore=pt({opSnippet:are}),ire={kernel
       }
       setOutput(result);
       }
-    `}},Gie=e=>{let{inputs:t,backend:n,attrs:s}=e,{x:r,y:a,dy:o}=t,{depthRadius:i,bias:l,alpha:u,beta:c}=s,p=new Uie(r.shape,i,l,u,c);return n.runWebGLProgram(p,[r,a,o],r.dtype)},Hie={kernelName:y0,backendName:"webgl",kernelFunc:Gie};function jie(e,t,n,s){let r=v.sizeFromShape(t),o=v.sizeFromShape(e.shape)/r,i=we({inputs:{x:e},attrs:{shape:[o,r]},backend:s}),l=gu(i,e.dtype,"max",s),u=we({inputs:{x:l},attrs:{shape:n},backend:s});return s.disposeIntermediateTensorInfo(i),s.disposeIntermediateTensorInfo(l),u}function Y9(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{reductionIndices:a,keepDims:o}=s,i=r.shape.length,l=v.parseAxisParam(a,r.shape),u=l,c=T.getAxesPermutation(u,i),p=c!=null,d=n.shouldExecuteOnCPU([r]),h=r;if(p){if(d){let A=n.texData.get(h.dataId).values,b=new Array(i);for(let C=0;C<b.length;C++)b[C]=r.shape[c[C]];let w=pb(A,r.shape,r.dtype,c,b);h=n.makeTensorInfo(b,r.dtype);let k=n.texData.get(h.dataId);k.values=w}else h=F2(r,c,n);u=T.getInnerMostAxes(u.length,i)}T.assertAxesAreInnerMostDims("max",u,i);let[f,m]=T.computeOutAndReduceShapes(h.shape,u),g=f;o&&(g=T.expandShapeToKeepDim(f,l));let y;if(d){let A=n.texData.get(h.dataId).values,b=rne(A,v.sizeFromShape(m),g,r.dtype);y=n.makeTensorInfo(g,r.dtype);let w=n.texData.get(y.dataId);w.values=b}else y=jie(h,m,g,n);return p&&n.disposeIntermediateTensorInfo(h),y}var qie={kernelName:Mo,backendName:"webgl",kernelFunc:Y9},Xie=E9+`
+    `}},nle=e=>{let{inputs:t,backend:n,attrs:s}=e,{x:r,y:a,dy:o}=t,{depthRadius:i,bias:l,alpha:u,beta:c}=s,p=new tle(r.shape,i,l,u,c);return n.runWebGLProgram(p,[r,a,o],r.dtype)},sle={kernelName:y0,backendName:"webgl",kernelFunc:nle};function rle(e,t,n,s){let r=v.sizeFromShape(t),o=v.sizeFromShape(e.shape)/r,i=we({inputs:{x:e},attrs:{shape:[o,r]},backend:s}),l=bu(i,e.dtype,"max",s),u=we({inputs:{x:l},attrs:{shape:n},backend:s});return s.disposeIntermediateTensorInfo(i),s.disposeIntermediateTensorInfo(l),u}function sC(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{reductionIndices:a,keepDims:o}=s,i=r.shape.length,l=v.parseAxisParam(a,r.shape),u=l,c=T.getAxesPermutation(u,i),p=c!=null,d=n.shouldExecuteOnCPU([r]),h=r;if(p){if(d){let A=n.texData.get(h.dataId).values,b=new Array(i);for(let C=0;C<b.length;C++)b[C]=r.shape[c[C]];let w=mb(A,r.shape,r.dtype,c,b);h=n.makeTensorInfo(b,r.dtype);let k=n.texData.get(h.dataId);k.values=w}else h=F2(r,c,n);u=T.getInnerMostAxes(u.length,i)}T.assertAxesAreInnerMostDims("max",u,i);let[f,m]=T.computeOutAndReduceShapes(h.shape,u),g=f;o&&(g=T.expandShapeToKeepDim(f,l));let y;if(d){let A=n.texData.get(h.dataId).values,b=gne(A,v.sizeFromShape(m),g,r.dtype);y=n.makeTensorInfo(g,r.dtype);let w=n.texData.get(y.dataId);w.values=b}else y=rle(h,m,g,n);return p&&n.disposeIntermediateTensorInfo(h),y}var ale={kernelName:Wo,backendName:"webgl",kernelFunc:sC},ole=F9+`
   return max(a, b);
-`,Kie=`
+`,ile=`
   vec4 result = vec4(max(a, b));
   vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
   `+P2+`
   return result;
-`,Zie=Ln({opSnippet:Xie,packedOpSnippet:Kie,cpuKernelImpl:ane}),Yie={kernelName:zo,backendName:"webgl",kernelFunc:Zie};function Jie(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t;cd(r,"maxPool");let{filterSize:a,strides:o,pad:i,dimRoundingMode:l}=s,u=1;v.assert(T.eitherStridesOrDilationsAreOne(o,u),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${o} and dilations '${u}'`);let c=T.computePool2DInfo(r.shape,a,o,u,i,l);if(c.filterWidth===1&&c.filterHeight===1&&v.arraysEqual(c.inShape,c.outShape))return Ls({inputs:{x:r},backend:n});let p=new zp(c,"max",!1);return n.runWebGLProgram(p,[r],r.dtype)}var Qie={kernelName:Lo,backendName:"webgl",kernelFunc:Jie};function ele(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{filterSize:a,strides:o,pad:i,dataFormat:l,dimRoundingMode:u}=s,c=[1,1,1],p=T.computePool3DInfo(r.shape,a,o,c,i,u,l),d=new fb(p,"max",!1);return n.runWebGLProgram(d,[r],r.dtype)}var tle={kernelName:Jp,backendName:"webgl",kernelFunc:ele},nle=class{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;let t=e.strideHeight,n=e.strideWidth,s=e.dilationHeight,r=e.effectiveFilterHeight,a=e.effectiveFilterWidth,o=r-1-e.padInfo.top,i=a-1-e.padInfo.left,l=r*a-1;this.userCode=`
+`,lle=Bn({opSnippet:ole,packedOpSnippet:ile,cpuKernelImpl:yne}),ule={kernelName:Vo,backendName:"webgl",kernelFunc:lle};function cle(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t;hd(r,"maxPool");let{filterSize:a,strides:o,pad:i,dimRoundingMode:l}=s,u=1;v.assert(T.eitherStridesOrDilationsAreOne(o,u),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${o} and dilations '${u}'`);let c=T.computePool2DInfo(r.shape,a,o,u,i,l);if(c.filterWidth===1&&c.filterHeight===1&&v.arraysEqual(c.inShape,c.outShape))return Vs({inputs:{x:r},backend:n});let p=new Lp(c,"max",!1);return n.runWebGLProgram(p,[r],r.dtype)}var dle={kernelName:Uo,backendName:"webgl",kernelFunc:cle};function ple(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{filterSize:a,strides:o,pad:i,dataFormat:l,dimRoundingMode:u}=s,c=[1,1,1],p=T.computePool3DInfo(r.shape,a,o,c,i,u,l),d=new yb(p,"max",!1);return n.runWebGLProgram(d,[r],r.dtype)}var hle={kernelName:Qp,backendName:"webgl",kernelFunc:ple},fle=class{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;let t=e.strideHeight,n=e.strideWidth,s=e.dilationHeight,r=e.effectiveFilterHeight,a=e.effectiveFilterWidth,o=r-1-e.padInfo.top,i=a-1-e.padInfo.left,l=r*a-1;this.userCode=`
       const ivec2 pads = ivec2(${o}, ${i});
 
       void main() {
@@ -3746,7 +3746,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,ore=pt({opSnippet:are}),ire={kernel
         }
         setOutput(dotProd);
       }
-    `}},sle=class{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;let t=e.strideDepth,n=e.strideHeight,s=e.strideWidth,r=e.dilationDepth,a=e.dilationHeight,o=e.dilationWidth,i=e.effectiveFilterDepth,l=e.effectiveFilterHeight,u=e.effectiveFilterWidth,c=i-1-e.padInfo.front,p=l-1-e.padInfo.top,d=u-1-e.padInfo.left,h=i*l*u-1;this.userCode=`
+    `}},mle=class{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;let t=e.strideDepth,n=e.strideHeight,s=e.strideWidth,r=e.dilationDepth,a=e.dilationHeight,o=e.dilationWidth,i=e.effectiveFilterDepth,l=e.effectiveFilterHeight,u=e.effectiveFilterWidth,c=i-1-e.padInfo.front,p=l-1-e.padInfo.top,d=u-1-e.padInfo.left,h=i*l*u-1;this.userCode=`
       const ivec3 pads = ivec3(${c}, ${p}, ${d});
 
       void main() {
@@ -3810,14 +3810,14 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,ore=pt({opSnippet:are}),ire={kernel
         }
         setOutput(dotProd);
       }
-    `}};function rle(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,input:a}=t,o=a,{filterSize:i,strides:l,pad:u,dimRoundingMode:c}=s,p=[1,1,1],d=T.computePool3DInfo(o.shape,i,l,p,u,c),h=new fb(d,"max",!0),f=n.runWebGLProgram(h,[o],o.dtype),m=new sle(d),g=n.runWebGLProgram(m,[r,f],o.dtype);return n.disposeIntermediateTensorInfo(f),g}var ale={kernelName:x0,backendName:"webgl",kernelFunc:rle};function ole(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,input:a,output:o}=t,i=a;cd([a,o],"maxPoolGrad");let{filterSize:l,strides:u,pad:c,dimRoundingMode:p}=s,d=T.computePool2DInfo(i.shape,l,u,1,c,p),h=!0,f=new zp(d,"max",h),m=n.runWebGLProgram(f,[i],i.dtype),g=new nle(d),y=n.runWebGLProgram(g,[r,m],i.dtype);return n.disposeIntermediateTensorInfo(m),y}var ile={kernelName:A0,backendName:"webgl",kernelFunc:ole};function lle(e,t,n,s){let r=new zp(n,"max",!1),a=s.runWebGLProgram(r,[e],"float32");r=new zp(n,"max",!0,!0,t);let o=s.runWebGLProgram(r,[e],"float32");return[a,o]}var ule={kernelName:b0,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{x:s}=e,{filterSize:r,strides:a,pad:o,includeBatchInIndex:i}=t,l=n;v.assert(s.shape.length===4,()=>`Error in maxPool: input must be rank 4 but got rank ${s.shape.length}.`);let u=[1,1];v.assert(T.eitherStridesOrDilationsAreOne(a,u),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${a} and dilations '${u}'`);let c=T.computePool2DInfo(s.shape,r,a,u,o),[p,d]=lle(s,i,c,l);return[p,d]}};function cle(e,t,n,s){let r=v.sizeFromShape(t),o=v.sizeFromShape(e.shape)/r,i=we({inputs:{x:e},attrs:{shape:[o,r]},backend:s}),l=gu(i,"float32","mean",s),u=we({inputs:{x:l},attrs:{shape:n},backend:s});return s.disposeIntermediateTensorInfo(i),s.disposeIntermediateTensorInfo(l),u}var dle={kernelName:Bo,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{x:s}=e,{keepDims:r,axis:a}=t,o=n,i=s.shape.length,l=v.parseAxisParam(a,s.shape),u=l,c=T.getAxesPermutation(u,i),p=c!=null,d=o.shouldExecuteOnCPU([s]),h=[],f=s;if(p){if(d){let b=o.texData.get(f.dataId).values,w=new Array(i);for(let E=0;E<w.length;E++)w[E]=s.shape[c[E]];let k=pb(b,s.shape,s.dtype,c,w);f=o.makeTensorInfo(w,s.dtype);let C=o.texData.get(f.dataId);C.values=k}else f=F2(s,c,o);h.push(f),u=T.getInnerMostAxes(u.length,i)}T.assertAxesAreInnerMostDims("sum",u,i);let[m,g]=T.computeOutAndReduceShapes(f.shape,u),y=m;r&&(y=T.expandShapeToKeepDim(m,l));let x=cle(f,g,y,o);for(let A of h)o.disposeIntermediateTensorInfo(A);return x}};function ple(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:o}=s,i=r.shape.length,l=v.parseAxisParam(a,r.shape),u=l,c=T.getAxesPermutation(u,i),p=r;c!=null&&(p=is({inputs:{x:r},backend:n,attrs:{perm:c}}),u=T.getInnerMostAxes(u.length,r.shape.length)),T.assertAxesAreInnerMostDims("min",u,i);let[d,h]=T.computeOutAndReduceShapes(p.shape,u),f=v.sizeFromShape(h),m=we({inputs:{x:p},backend:n,attrs:{shape:[-1,f]}}),g=gu(m,m.dtype,"min",n),y;if(o){let x=T.expandShapeToKeepDim(d,l);y=we({inputs:{x:g},backend:n,attrs:{shape:x}})}else y=we({inputs:{x:g},backend:n,attrs:{shape:d}});return n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(g),c!=null&&n.disposeIntermediateTensorInfo(p),y}var hle={kernelName:Wo,backendName:"webgl",kernelFunc:ple},fle=E9+`
+    `}};function gle(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,input:a}=t,o=a,{filterSize:i,strides:l,pad:u,dimRoundingMode:c}=s,p=[1,1,1],d=T.computePool3DInfo(o.shape,i,l,p,u,c),h=new yb(d,"max",!0),f=n.runWebGLProgram(h,[o],o.dtype),m=new mle(d),g=n.runWebGLProgram(m,[r,f],o.dtype);return n.disposeIntermediateTensorInfo(f),g}var yle={kernelName:x0,backendName:"webgl",kernelFunc:gle};function Ale(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,input:a,output:o}=t,i=a;hd([a,o],"maxPoolGrad");let{filterSize:l,strides:u,pad:c,dimRoundingMode:p}=s,d=T.computePool2DInfo(i.shape,l,u,1,c,p),h=!0,f=new Lp(d,"max",h),m=n.runWebGLProgram(f,[i],i.dtype),g=new fle(d),y=n.runWebGLProgram(g,[r,m],i.dtype);return n.disposeIntermediateTensorInfo(m),y}var xle={kernelName:A0,backendName:"webgl",kernelFunc:Ale};function ble(e,t,n,s){let r=new Lp(n,"max",!1),a=s.runWebGLProgram(r,[e],"float32");r=new Lp(n,"max",!0,!0,t);let o=s.runWebGLProgram(r,[e],"float32");return[a,o]}var vle={kernelName:b0,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{x:s}=e,{filterSize:r,strides:a,pad:o,includeBatchInIndex:i}=t,l=n;v.assert(s.shape.length===4,()=>`Error in maxPool: input must be rank 4 but got rank ${s.shape.length}.`);let u=[1,1];v.assert(T.eitherStridesOrDilationsAreOne(a,u),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${a} and dilations '${u}'`);let c=T.computePool2DInfo(s.shape,r,a,u,o),[p,d]=ble(s,i,c,l);return[p,d]}};function wle(e,t,n,s){let r=v.sizeFromShape(t),o=v.sizeFromShape(e.shape)/r,i=we({inputs:{x:e},attrs:{shape:[o,r]},backend:s}),l=bu(i,"float32","mean",s),u=we({inputs:{x:l},attrs:{shape:n},backend:s});return s.disposeIntermediateTensorInfo(i),s.disposeIntermediateTensorInfo(l),u}var kle={kernelName:Go,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{x:s}=e,{keepDims:r,axis:a}=t,o=n,i=s.shape.length,l=v.parseAxisParam(a,s.shape),u=l,c=T.getAxesPermutation(u,i),p=c!=null,d=o.shouldExecuteOnCPU([s]),h=[],f=s;if(p){if(d){let b=o.texData.get(f.dataId).values,w=new Array(i);for(let E=0;E<w.length;E++)w[E]=s.shape[c[E]];let k=mb(b,s.shape,s.dtype,c,w);f=o.makeTensorInfo(w,s.dtype);let C=o.texData.get(f.dataId);C.values=k}else f=F2(s,c,o);h.push(f),u=T.getInnerMostAxes(u.length,i)}T.assertAxesAreInnerMostDims("sum",u,i);let[m,g]=T.computeOutAndReduceShapes(f.shape,u),y=m;r&&(y=T.expandShapeToKeepDim(m,l));let x=wle(f,g,y,o);for(let A of h)o.disposeIntermediateTensorInfo(A);return x}};function Sle(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:o}=s,i=r.shape.length,l=v.parseAxisParam(a,r.shape),u=l,c=T.getAxesPermutation(u,i),p=r;c!=null&&(p=cs({inputs:{x:r},backend:n,attrs:{perm:c}}),u=T.getInnerMostAxes(u.length,r.shape.length)),T.assertAxesAreInnerMostDims("min",u,i);let[d,h]=T.computeOutAndReduceShapes(p.shape,u),f=v.sizeFromShape(h),m=we({inputs:{x:p},backend:n,attrs:{shape:[-1,f]}}),g=bu(m,m.dtype,"min",n),y;if(o){let x=T.expandShapeToKeepDim(d,l);y=we({inputs:{x:g},backend:n,attrs:{shape:x}})}else y=we({inputs:{x:g},backend:n,attrs:{shape:d}});return n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(g),c!=null&&n.disposeIntermediateTensorInfo(p),y}var Ile={kernelName:Ho,backendName:"webgl",kernelFunc:Sle},Cle=F9+`
   return min(a, b);
-`,mle=`
+`,Tle=`
   vec4 result = vec4(min(a, b));
   vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
   `+P2+`
   return result;
-`,gle=Ln({opSnippet:fle,packedOpSnippet:mle,cpuKernelImpl:one}),yle={kernelName:Vo,backendName:"webgl",kernelFunc:gle},Ale=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=t.map((u,c)=>u[0]+e[c]+u[1]);let s=e.length,r=wt(s),a=t.map(u=>u[0]).join(","),o=t.map((u,c)=>u[0]+e[c]).join(","),i=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,s),l=n==="reflect"?0:1;if(s===1){this.userCode=`
+`,Nle=Bn({opSnippet:Cle,packedOpSnippet:Tle,cpuKernelImpl:Ane}),Ele={kernelName:jo,backendName:"webgl",kernelFunc:Nle},Rle=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=t.map((u,c)=>u[0]+e[c]+u[1]);let s=e.length,r=kt(s),a=t.map(u=>u[0]).join(","),o=t.map((u,c)=>u[0]+e[c]).join(","),i=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,s),l=n==="reflect"?0:1;if(s===1){this.userCode=`
         int start = ${a};
         int end = ${o};
 
@@ -3846,7 +3846,7 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,ore=pt({opSnippet:are}),ire={kernel
         ${r} coords = outC - start;
         setOutput(getX(${i}));
       }
-    `}},xle=class{constructor(e,t,n){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t.map((h,f)=>h[0]+e[f]+h[1]);let s=e.length,r=wt(s),a=t.map(h=>h[0]).join(","),o=t.map((h,f)=>h[0]+e[f]).join(","),i=os("rc",s),l=os("source",s),u=`${i[s-1]} < ${this.outputShape[s-1]}`,c=s===1?"source":`vec2(${l.slice(-2).join()})`,p=n==="reflect"?0:1,d="";if(s===1){let h=`
+    `}},_le=class{constructor(e,t,n){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t.map((h,f)=>h[0]+e[f]+h[1]);let s=e.length,r=kt(s),a=t.map(h=>h[0]).join(","),o=t.map((h,f)=>h[0]+e[f]).join(","),i=us("rc",s),l=us("source",s),u=`${i[s-1]} < ${this.outputShape[s-1]}`,c=s===1?"source":`vec2(${l.slice(-2).join()})`,p=n==="reflect"?0:1,d="";if(s===1){let h=`
         ${r} source = rc;
         if (source < start) {
           source = start * 2 - source - ${p};
@@ -3902,13 +3902,13 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,ore=pt({opSnippet:are}),ire={kernel
         ${d}
         setOutput(result);
       }
-    `}},ble=({inputs:e,backend:t,attrs:n})=>{let{x:s}=e,{paddings:r,mode:a}=n,o=H().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new xle(s.shape,r,a):new Ale(s.shape,r,a);return t.runWebGLProgram(o,[s],s.dtype)},vle={kernelName:Uo,backendName:"webgl",kernelFunc:ble},wle=`if (b == 0.0) return NAN;
-  return mod(a, b);`,kle=`
+    `}},Dle=({inputs:e,backend:t,attrs:n})=>{let{x:s}=e,{paddings:r,mode:a}=n,o=H().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new _le(s.shape,r,a):new Rle(s.shape,r,a);return t.runWebGLProgram(o,[s],s.dtype)},$le={kernelName:qo,backendName:"webgl",kernelFunc:Dle},Ple=`if (b == 0.0) return NAN;
+  return mod(a, b);`,Fle=`
   vec4 result = mod(a, b);
   vec4 isNaN = vec4(equal(b, vec4(0.0)));
   `+P2+`
   return result;
-`,Sle=Ln({opSnippet:wle,packedOpSnippet:kle}),Ile={kernelName:Lc,backendName:"webgl",kernelFunc:Sle},Cle=class{constructor(e,t,n){this.variableNames=["probs"],this.customUniforms=[{name:"seed",type:"float"}],this.outputShape=[e,n],this.userCode=`
+`,Ole=Bn({opSnippet:Ple,packedOpSnippet:Fle}),Mle={kernelName:Uc,backendName:"webgl",kernelFunc:Ole},zle=class{constructor(e,t,n){this.variableNames=["probs"],this.customUniforms=[{name:"seed",type:"float"}],this.outputShape=[e,n],this.userCode=`
       void main() {
         ivec2 coords = getOutputCoords();
         int batch = coords[0];
@@ -3928,11 +3928,11 @@ return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,ore=pt({opSnippet:are}),ire={kernel
         // If no other event happened, last event happened.
         setOutput(float(${t-1}));
       }
-    `}},Tle=`
+    `}},Lle=`
 if (a == b) {
   return 1.0;
 };
-return a / b;`,Nle=`
+return a / b;`,Ble=`
   // vec4 one = vec4(equal(a, b));
   // return one + (vec4(1.0) - one) * a / b;
   vec4 result = a / b;
@@ -3950,9 +3950,9 @@ return a / b;`,Nle=`
   }
 
   return result;
-`,J9=Ln({opSnippet:Tle,packedOpSnippet:Nle,checkOutOfBounds:!0}),Ele={kernelName:To,backendName:"webgl",kernelFunc:J9},X7="return a - b;",Q9=Ln({opSnippet:X7,packedOpSnippet:X7,supportsComplex:!0,cpuKernelImpl:Sne}),Rle={kernelName:oi,backendName:"webgl",kernelFunc:Q9};function eC(e){let{inputs:t,backend:n,attrs:s}=e,{logits:r}=t,{dim:a}=s,o=v.parseAxisParam([a],r.shape),i=Y9({inputs:{x:r},backend:n,attrs:{reductionIndices:o,keepDims:!1}}),l=T.expandShapeToKeepDim(i.shape,o),u=we({inputs:{x:i},backend:n,attrs:{shape:l}}),c=Q9({inputs:{a:r,b:u},backend:n}),p=X9({inputs:{x:c},backend:n}),d=O2({inputs:{x:p},backend:n,attrs:{axis:o,keepDims:!1}}),h=we({inputs:{x:d},backend:n,attrs:{shape:l}}),f=J9({inputs:{a:p,b:h},backend:n});return n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(u),n.disposeIntermediateTensorInfo(c),n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(h),f}var _le={kernelName:ri,backendName:"webgl",kernelFunc:eC};function Dle(e){let{inputs:t,backend:n,attrs:s}=e,{logits:r}=t,{numSamples:a,seed:o,normalized:i}=s,l=i?r:eC({inputs:{logits:r},backend:n,attrs:{dim:r.shape.length-1}}),u=l.shape[0],c=l.shape[1],p=new Cle(u,c,a),d=[[o]],h=n.runWebGLProgram(p,[l],"int32",d);return i||n.disposeIntermediateTensorInfo(l),h}var $le={kernelName:v0,backendName:"webgl",kernelFunc:Dle},Ple=xr+`
+`,rC=Bn({opSnippet:Lle,packedOpSnippet:Ble,checkOutOfBounds:!0}),Wle={kernelName:_o,backendName:"webgl",kernelFunc:rC},e6="return a - b;",aC=Bn({opSnippet:e6,packedOpSnippet:e6,supportsComplex:!0,cpuKernelImpl:One}),Vle={kernelName:ci,backendName:"webgl",kernelFunc:aC};function oC(e){let{inputs:t,backend:n,attrs:s}=e,{logits:r}=t,{dim:a}=s,o=v.parseAxisParam([a],r.shape),i=sC({inputs:{x:r},backend:n,attrs:{reductionIndices:o,keepDims:!1}}),l=T.expandShapeToKeepDim(i.shape,o),u=we({inputs:{x:i},backend:n,attrs:{shape:l}}),c=aC({inputs:{a:r,b:u},backend:n}),p=eC({inputs:{x:c},backend:n}),d=O2({inputs:{x:p},backend:n,attrs:{axis:o,keepDims:!1}}),h=we({inputs:{x:d},backend:n,attrs:{shape:l}}),f=rC({inputs:{a:p,b:h},backend:n});return n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(u),n.disposeIntermediateTensorInfo(c),n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(h),f}var Ule={kernelName:li,backendName:"webgl",kernelFunc:oC};function Gle(e){let{inputs:t,backend:n,attrs:s}=e,{logits:r}=t,{numSamples:a,seed:o,normalized:i}=s,l=i?r:oC({inputs:{logits:r},backend:n,attrs:{dim:r.shape.length-1}}),u=l.shape[0],c=l.shape[1],p=new zle(u,c,a),d=[[o]],h=n.runWebGLProgram(p,[l],"int32",d);return i||n.disposeIntermediateTensorInfo(l),h}var Hle={kernelName:v0,backendName:"webgl",kernelFunc:Gle},jle=br+`
   return -x;
-`,Fle=`
+`,qle=`
   vec4 result = -x;
   bvec4 isNaN = isnan(x);
 
@@ -3962,14 +3962,14 @@ return a / b;`,Nle=`
   result.a = isNaN.a ? x.a : result.a;
 
   return result;
-`;function Ole(e){let{inputs:t,backend:n}=e,{x:s}=t;if(n.shouldExecuteOnCPU([s])){let a=n.texData.get(s.dataId),[o,i]=lne(a.values,s.shape,s.dtype);return n.makeTensorInfo(i,s.dtype,o)}let r;return H().getBool("WEBGL_PACK_UNARY_OPERATIONS")?r=new Zi(s.shape,Fle):r=new ba(s.shape,Ple),n.runWebGLProgram(r,[s],s.dtype)}var Mle={kernelName:Pl,backendName:"webgl",kernelFunc:Ole},zle=yr.nonMaxSuppressionV3Impl;function Lle(e){T.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:s}=e,{boxes:r,scores:a}=t,{maxOutputSize:o,iouThreshold:i,scoreThreshold:l}=s,u=n.readSync(r.dataId),c=n.readSync(a.dataId),{selectedIndices:p}=zle(u,c,o,i,l);return n.makeTensorInfo([p.length],"int32",new Int32Array(p))}var Ble={kernelName:Ol,backendName:"webgl",kernelFunc:Lle},Wle=yr.nonMaxSuppressionV4Impl;function Vle(e){T.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:s}=e,{boxes:r,scores:a}=t,{maxOutputSize:o,iouThreshold:i,scoreThreshold:l,padToMaxOutputSize:u}=s,c=n.readSync(r.dataId),p=n.readSync(a.dataId),{selectedIndices:d,validOutputs:h}=Wle(c,p,o,i,l,u);return[n.makeTensorInfo([d.length],"int32",new Int32Array(d)),n.makeTensorInfo([],"int32",new Int32Array([h]))]}var Ule={kernelName:Bc,backendName:"webgl",kernelFunc:Vle},Gle=yr.nonMaxSuppressionV5Impl;function Hle(e){T.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:s}=e,{boxes:r,scores:a}=t,{maxOutputSize:o,iouThreshold:i,scoreThreshold:l,softNmsSigma:u}=s,c=n.readSync(r.dataId),p=n.readSync(a.dataId),d=o,h=i,f=l,m=u,{selectedIndices:g,selectedScores:y}=Gle(c,p,d,h,f,m);return[n.makeTensorInfo([g.length],"int32",new Int32Array(g)),n.makeTensorInfo([y.length],"float32",new Float32Array(y))]}var jle={kernelName:Ml,backendName:"webgl",kernelFunc:Hle},qle=class{constructor(e,t,n,s){this.variableNames=["indices"],this.outputShape=[e,t],this.userCode=`
+`;function Xle(e){let{inputs:t,backend:n}=e,{x:s}=t;if(n.shouldExecuteOnCPU([s])){let a=n.texData.get(s.dataId),[o,i]=bne(a.values,s.shape,s.dtype);return n.makeTensorInfo(i,s.dtype,o)}let r;return H().getBool("WEBGL_PACK_UNARY_OPERATIONS")?r=new el(s.shape,qle):r=new Sa(s.shape,jle),n.runWebGLProgram(r,[s],s.dtype)}var Kle={kernelName:zl,backendName:"webgl",kernelFunc:Xle},Zle=Ar.nonMaxSuppressionV3Impl;function Yle(e){T.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:s}=e,{boxes:r,scores:a}=t,{maxOutputSize:o,iouThreshold:i,scoreThreshold:l}=s,u=n.readSync(r.dataId),c=n.readSync(a.dataId),{selectedIndices:p}=Zle(u,c,o,i,l);return n.makeTensorInfo([p.length],"int32",new Int32Array(p))}var Jle={kernelName:Bl,backendName:"webgl",kernelFunc:Yle},Qle=Ar.nonMaxSuppressionV4Impl;function eue(e){T.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:s}=e,{boxes:r,scores:a}=t,{maxOutputSize:o,iouThreshold:i,scoreThreshold:l,padToMaxOutputSize:u}=s,c=n.readSync(r.dataId),p=n.readSync(a.dataId),{selectedIndices:d,validOutputs:h}=Qle(c,p,o,i,l,u);return[n.makeTensorInfo([d.length],"int32",new Int32Array(d)),n.makeTensorInfo([],"int32",new Int32Array([h]))]}var tue={kernelName:Gc,backendName:"webgl",kernelFunc:eue},nue=Ar.nonMaxSuppressionV5Impl;function sue(e){T.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:s}=e,{boxes:r,scores:a}=t,{maxOutputSize:o,iouThreshold:i,scoreThreshold:l,softNmsSigma:u}=s,c=n.readSync(r.dataId),p=n.readSync(a.dataId),d=o,h=i,f=l,m=u,{selectedIndices:g,selectedScores:y}=nue(c,p,d,h,f,m);return[n.makeTensorInfo([g.length],"int32",new Int32Array(g)),n.makeTensorInfo([y.length],"float32",new Float32Array(y))]}var rue={kernelName:Wl,backendName:"webgl",kernelFunc:sue},aue=class{constructor(e,t,n,s){this.variableNames=["indices"],this.outputShape=[e,t],this.userCode=`
       void main() {
         ivec2 coords = getOutputCoords();
         int index = round(getIndices(coords.x));
         setOutput(mix(float(${s}), float(${n}),
                       float(index == coords.y)));
       }
-    `}},Xle=e=>{let{inputs:t,backend:n,attrs:s}=e,{indices:r}=t,{dtype:a,depth:o,onValue:i,offValue:l}=s,u=v.sizeFromShape(r.shape),c=new qle(u,o,i,l),p=we({inputs:{x:r},backend:n,attrs:{shape:[u]}}),d=n.runWebGLProgram(c,[p],a);n.disposeIntermediateTensorInfo(p);let h=[...r.shape,o],f=we({inputs:{x:d},backend:n,attrs:{shape:h}});return n.disposeIntermediateTensorInfo(d),f},Kle={kernelName:Ll,backendName:"webgl",kernelFunc:Xle};function Km(e){let{inputs:t,backend:n}=e,{x:s}=t;if(s.dtype==="complex64"){let r=jh({inputs:{input:s},backend:n}),a=Km({inputs:{x:r},backend:n}),o=M2({inputs:{input:s},backend:n}),i=Km({inputs:{x:o},backend:n}),l=mi({inputs:{real:a,imag:i},backend:n});return n.disposeIntermediateTensorInfo(r),n.disposeIntermediateTensorInfo(a),n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(i),l}else return qh({attrs:{shape:s.shape,dtype:s.dtype,value:s.dtype==="string"?"":0},backend:n})}var Zle={kernelName:nu,backendName:"webgl",kernelFunc:Km};function tC(e){let{inputs:t,backend:n}=e,{x:s}=t;if(s.dtype==="string")throw new Error("onesLike is not supported under string dtype");if(s.dtype==="complex64"){let r=jh({inputs:{input:s},backend:n}),a=tC({inputs:{x:r},backend:n}),o=M2({inputs:{input:s},backend:n}),i=Km({inputs:{x:o},backend:n}),l=mi({inputs:{real:a,imag:i},backend:n});return n.disposeIntermediateTensorInfo(r),n.disposeIntermediateTensorInfo(a),n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(i),l}else return qh({attrs:{shape:s.shape,dtype:s.dtype,value:1},backend:n})}var Yle={kernelName:zl,backendName:"webgl",kernelFunc:tC};function Jle(e){let{inputs:t,backend:n,attrs:s}=e,{axis:r}=s;if(t.length===1)return ky({inputs:{input:t[0]},backend:n,attrs:{dim:r}});let a=t[0].shape,o=t[0].dtype;t.forEach(c=>{v.assertShapesMatch(a,c.shape,"All tensors passed to stack must have matching shapes"),v.assert(o===c.dtype,()=>"All tensors passed to stack must have matching dtypes")});let i=[],l=t.map(c=>{let p=ky({inputs:{input:c},backend:n,attrs:{dim:r}});return i.push(p),p}),u=B9({inputs:l,backend:n,attrs:{axis:r}});return i.forEach(c=>n.disposeIntermediateTensorInfo(c)),u}var Qle={kernelName:Bl,backendName:"webgl",kernelFunc:Jle},eue=class{constructor(e,t,n){this.variableNames=["x"],this.customUniforms=[{name:"value",type:"float"}],this.outputShape=t.map((l,u)=>l[0]+e[u]+l[1]);let s=e.length,r=wt(s),a=t.map(l=>l[0]).join(","),o=t.map((l,u)=>l[0]+e[u]).join(","),i=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,s);if(s===1){this.userCode=`
+    `}},oue=e=>{let{inputs:t,backend:n,attrs:s}=e,{indices:r}=t,{dtype:a,depth:o,onValue:i,offValue:l}=s,u=v.sizeFromShape(r.shape),c=new aue(u,o,i,l),p=we({inputs:{x:r},backend:n,attrs:{shape:[u]}}),d=n.runWebGLProgram(c,[p],a);n.disposeIntermediateTensorInfo(p);let h=[...r.shape,o],f=we({inputs:{x:d},backend:n,attrs:{shape:h}});return n.disposeIntermediateTensorInfo(d),f},iue={kernelName:Ul,backendName:"webgl",kernelFunc:oue};function Km(e){let{inputs:t,backend:n}=e,{x:s}=t;if(s.dtype==="complex64"){let r=qh({inputs:{input:s},backend:n}),a=Km({inputs:{x:r},backend:n}),o=M2({inputs:{input:s},backend:n}),i=Km({inputs:{x:o},backend:n}),l=xi({inputs:{real:a,imag:i},backend:n});return n.disposeIntermediateTensorInfo(r),n.disposeIntermediateTensorInfo(a),n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(i),l}else return Xh({attrs:{shape:s.shape,dtype:s.dtype,value:s.dtype==="string"?"":0},backend:n})}var lue={kernelName:ou,backendName:"webgl",kernelFunc:Km};function iC(e){let{inputs:t,backend:n}=e,{x:s}=t;if(s.dtype==="string")throw new Error("onesLike is not supported under string dtype");if(s.dtype==="complex64"){let r=qh({inputs:{input:s},backend:n}),a=iC({inputs:{x:r},backend:n}),o=M2({inputs:{input:s},backend:n}),i=Km({inputs:{x:o},backend:n}),l=xi({inputs:{real:a,imag:i},backend:n});return n.disposeIntermediateTensorInfo(r),n.disposeIntermediateTensorInfo(a),n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(i),l}else return Xh({attrs:{shape:s.shape,dtype:s.dtype,value:1},backend:n})}var uue={kernelName:Vl,backendName:"webgl",kernelFunc:iC};function cue(e){let{inputs:t,backend:n,attrs:s}=e,{axis:r}=s;if(t.length===1)return Cy({inputs:{input:t[0]},backend:n,attrs:{dim:r}});let a=t[0].shape,o=t[0].dtype;t.forEach(c=>{v.assertShapesMatch(a,c.shape,"All tensors passed to stack must have matching shapes"),v.assert(o===c.dtype,()=>"All tensors passed to stack must have matching dtypes")});let i=[],l=t.map(c=>{let p=Cy({inputs:{input:c},backend:n,attrs:{dim:r}});return i.push(p),p}),u=j9({inputs:l,backend:n,attrs:{axis:r}});return i.forEach(c=>n.disposeIntermediateTensorInfo(c)),u}var due={kernelName:Gl,backendName:"webgl",kernelFunc:cue},pue=class{constructor(e,t,n){this.variableNames=["x"],this.customUniforms=[{name:"value",type:"float"}],this.outputShape=t.map((l,u)=>l[0]+e[u]+l[1]);let s=e.length,r=kt(s),a=t.map(l=>l[0]).join(","),o=t.map((l,u)=>l[0]+e[u]).join(","),i=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,s);if(s===1){this.userCode=`
         int start = ${a};
         int end = ${o};
 
@@ -3994,7 +3994,7 @@ return a / b;`,Nle=`
           setOutput(getX(${i}));
         }
       }
-    `}},tue=class{constructor(e,t,n){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"value",type:"float"}],this.outputShape=t.map((f,m)=>f[0]+e[m]+f[1]);let s=e.length,r=wt(s),a=t.map(f=>f[0]).join(","),o=t.map((f,m)=>f[0]+e[m]).join(","),i=os("rc",s),l=os("source",s),u=`${i[s-1]} < ${this.outputShape[s-1]}`,c=s===1?"source":`vec2(${l.slice(-2).join()})`,p=[`${r} rc = outputLoc;`,`${i[s-1]} += 1;
+    `}},hue=class{constructor(e,t,n){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"value",type:"float"}],this.outputShape=t.map((f,m)=>f[0]+e[m]+f[1]);let s=e.length,r=kt(s),a=t.map(f=>f[0]).join(","),o=t.map((f,m)=>f[0]+e[m]).join(","),i=us("rc",s),l=us("source",s),u=`${i[s-1]} < ${this.outputShape[s-1]}`,c=s===1?"source":`vec2(${l.slice(-2).join()})`,p=[`${r} rc = outputLoc;`,`${i[s-1]} += 1;
        if(${u}) {
       `,s===1?"":`}
        rc = outputLoc;
@@ -4018,7 +4018,7 @@ return a / b;`,Nle=`
         ${h}
         setOutput(result);
       }
-    `}},nC=e=>{let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{paddings:a,constantValue:o}=s;if(v.sizeFromShape(r.shape)===0){let u=a.map((c,p)=>c[0]+r.shape[p]+c[1]);return qh({backend:n,attrs:{shape:u,value:o,dtype:r.dtype}})}let i=H().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new tue(r.shape,a,o):new eue(r.shape,a,o),l=[[o]];return n.runWebGLProgram(i,[r],r.dtype,l)},nue={kernelName:Ho,backendName:"webgl",kernelFunc:nC},sue=`
+    `}},lC=e=>{let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{paddings:a,constantValue:o}=s;if(v.sizeFromShape(r.shape)===0){let u=a.map((c,p)=>c[0]+r.shape[p]+c[1]);return Xh({backend:n,attrs:{shape:u,value:o,dtype:r.dtype}})}let i=H().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new hue(r.shape,a,o):new pue(r.shape,a,o),l=[[o]];return n.runWebGLProgram(i,[r],r.dtype,l)},fue={kernelName:Ko,backendName:"webgl",kernelFunc:lC},mue=`
   if(a < 0.0 && floor(b) < b){
     return NAN;
   }
@@ -4027,7 +4027,7 @@ return a / b;`,Nle=`
   }
   return (round(mod(b, 2.0)) != 1) ?
       pow(abs(a), b) : sign(a) * pow(abs(a), b);
-`,rue=`
+`,gue=`
   // isModRound1 has 1 for components with round(mod(b, 2.0)) == 1, 0 otherwise.
   vec4 isModRound1 = vec4(equal(round(mod(b, 2.0)), ivec4(1)));
   vec4 multiplier = sign(a) * isModRound1 + (vec4(1.0) - isModRound1);
@@ -4043,9 +4043,9 @@ return a / b;`,Nle=`
   vec4 isNaN = vec4(lessThan(a, vec4(0.0))) * vec4(lessThan(floor(b), b));
   `+P2+`
   return result;
-`,aue=Ln({opSnippet:sue,packedOpSnippet:rue}),oue={kernelName:jo,backendName:"webgl",kernelFunc:aue};function iue(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:o}=s,i=r.shape.length,l=[],u=v.parseAxisParam(a,r.shape),c=u,p=T.getAxesPermutation(c,i),d=r;p!=null&&(d=is({inputs:{x:r},backend:n,attrs:{perm:p}}),c=T.getInnerMostAxes(c.length,i),l.push(d)),T.assertAxesAreInnerMostDims("prod",c,i);let h;if(n.shouldExecuteOnCPU([d])){let f=n.texData.get(d.dataId).values,{outVals:m,outShape:g,outDtype:y}=cne(d.shape,d.dtype,f,c);h=n.makeTensorInfo(g,y,m)}else{let[f,m]=T.computeOutAndReduceShapes(d.shape,c),g=v.sizeFromShape(m),y=we({inputs:{x:d},backend:n,attrs:{shape:[-1,g]}}),x=uh(r.dtype),A=gu(y,x,"prod",n);h=we({inputs:{x:A},backend:n,attrs:{shape:f}}),l.push(y),l.push(A)}if(o){l.push(h);let f=T.expandShapeToKeepDim(h.shape,u);h=we({inputs:{x:h},backend:n,attrs:{shape:f}})}return l.forEach(f=>n.disposeIntermediateTensorInfo(f)),h}var lue={kernelName:Xo,backendName:"webgl",kernelFunc:iue};function uue(e){let{inputs:t,backend:n,attrs:s}=e,{shape:r,values:a,defaultValue:o,rowPartitionTensors:i}=t,{rowPartitionTypes:l}=s,u=n.readSync(r.dataId),c=n.readSync(a.dataId),p=n.readSync(o.dataId),d=i.map(g=>n.readSync(g.dataId)),h=i.map(g=>g.shape),[f,m]=dne(u,r.shape,c,a.shape,a.dtype,p,o.shape,d,h,l);return n.makeTensorInfo(f,a.dtype,m)}var cue={kernelName:w0,backendName:"webgl",kernelFunc:uue},sC=e=>{let{backend:t,attrs:n}=e,{start:s,stop:r,step:a,dtype:o}=n,i=pne(s,r,a,o);return t.makeTensorInfo([i.length],o,i)},due={kernelName:Wc,backendName:"webgl",kernelFunc:sC},pue="return 1.0 / x;",hue=pt({opSnippet:pue}),fue={kernelName:Wl,backendName:"webgl",kernelFunc:hue},mue=xr+`
+`,yue=Bn({opSnippet:mue,packedOpSnippet:gue}),Aue={kernelName:Zo,backendName:"webgl",kernelFunc:yue};function xue(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:o}=s,i=r.shape.length,l=[],u=v.parseAxisParam(a,r.shape),c=u,p=T.getAxesPermutation(c,i),d=r;p!=null&&(d=cs({inputs:{x:r},backend:n,attrs:{perm:p}}),c=T.getInnerMostAxes(c.length,i),l.push(d)),T.assertAxesAreInnerMostDims("prod",c,i);let h;if(n.shouldExecuteOnCPU([d])){let f=n.texData.get(d.dataId).values,{outVals:m,outShape:g,outDtype:y}=wne(d.shape,d.dtype,f,c);h=n.makeTensorInfo(g,y,m)}else{let[f,m]=T.computeOutAndReduceShapes(d.shape,c),g=v.sizeFromShape(m),y=we({inputs:{x:d},backend:n,attrs:{shape:[-1,g]}}),x=ch(r.dtype),A=bu(y,x,"prod",n);h=we({inputs:{x:A},backend:n,attrs:{shape:f}}),l.push(y),l.push(A)}if(o){l.push(h);let f=T.expandShapeToKeepDim(h.shape,u);h=we({inputs:{x:h},backend:n,attrs:{shape:f}})}return l.forEach(f=>n.disposeIntermediateTensorInfo(f)),h}var bue={kernelName:Jo,backendName:"webgl",kernelFunc:xue};function vue(e){let{inputs:t,backend:n,attrs:s}=e,{shape:r,values:a,defaultValue:o,rowPartitionTensors:i}=t,{rowPartitionTypes:l}=s,u=n.readSync(r.dataId),c=n.readSync(a.dataId),p=n.readSync(o.dataId),d=i.map(g=>n.readSync(g.dataId)),h=i.map(g=>g.shape),[f,m]=kne(u,r.shape,c,a.shape,a.dtype,p,o.shape,d,h,l);return n.makeTensorInfo(f,a.dtype,m)}var wue={kernelName:w0,backendName:"webgl",kernelFunc:vue},uC=e=>{let{backend:t,attrs:n}=e,{start:s,stop:r,step:a,dtype:o}=n,i=Sne(s,r,a,o);return t.makeTensorInfo([i.length],o,i)},kue={kernelName:Hc,backendName:"webgl",kernelFunc:uC},Sue="return 1.0 / x;",Iue=ht({opSnippet:Sue}),Cue={kernelName:Hl,backendName:"webgl",kernelFunc:Iue},Tue=br+`
   return (x < 0.0) ? 0.0 : x;
-`,gue=`
+`,Nue=`
   vec4 result = x * vec4(greaterThanEqual(x, vec4(0.0)));
   bvec4 isNaN = isnan(x);
 
@@ -4055,9 +4055,9 @@ return a / b;`,Nle=`
   result.a = isNaN.a ? x.a : result.a;
 
   return result;
-`,yue=pt({opSnippet:mue,packedOpSnippet:gue}),Aue={kernelName:Ko,backendName:"webgl",kernelFunc:yue},xue=xr+`
+`,Eue=ht({opSnippet:Tue,packedOpSnippet:Nue}),Rue={kernelName:Qo,backendName:"webgl",kernelFunc:Eue},_ue=br+`
   return (x < 0.0) ? 0.0 : min(6.0, x);
-`,bue=`
+`,Due=`
   vec4 result = min(x, vec4(6.)) * vec4(greaterThanEqual(x, vec4(0.0)));
   bvec4 isNaN = isnan(x);
 
@@ -4067,7 +4067,7 @@ return a / b;`,Nle=`
   result.a = isNaN.a ? x.a : result.a;
 
   return result;
-`,vue=pt({opSnippet:xue,packedOpSnippet:bue}),wue={kernelName:Jo,backendName:"webgl",kernelFunc:vue},kue=class{constructor(e,t,n,s,r){this.variableNames=["A"],this.outputShape=[];let[a,o,i,l]=e;this.outputShape=[a,t,n,l];let u=[s&&t>1?o-1:o,s&&n>1?i-1:i],c=[s&&t>1?t-1:t,s&&n>1?n-1:n],p;r?p="(vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC - vec2(0.5)":p="vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
+`,$ue=ht({opSnippet:_ue,packedOpSnippet:Due}),Pue={kernelName:ni,backendName:"webgl",kernelFunc:$ue},Fue=class{constructor(e,t,n,s,r){this.variableNames=["A"],this.outputShape=[];let[a,o,i,l]=e;this.outputShape=[a,t,n,l];let u=[s&&t>1?o-1:o,s&&n>1?i-1:i],c=[s&&t>1?t-1:t,s&&n>1?n-1:n],p;r?p="(vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC - vec2(0.5)":p="vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
       const vec2 effectiveInputOverOutputRatioRC = vec2(
           ${u[0]/c[0]},
           ${u[1]/c[1]});
@@ -4100,7 +4100,7 @@ return a / b;`,Nle=`
 
         setOutput(newValue);
       }
-    `}},Sue=class{constructor(e,t,n,s,r){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];let[a,o,i,l]=e;this.outputShape=[a,t,n,l];let u=[s&&t>1?o-1:o,s&&n>1?i-1:i],c=[s&&t>1?t-1:t,s&&n>1?n-1:n],p;r?p="(vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC - vec3(0.5)":p="vec3(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
+    `}},Oue=class{constructor(e,t,n,s,r){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];let[a,o,i,l]=e;this.outputShape=[a,t,n,l];let u=[s&&t>1?o-1:o,s&&n>1?i-1:i],c=[s&&t>1?t-1:t,s&&n>1?n-1:n],p;r?p="(vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC - vec3(0.5)":p="vec3(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
       const vec3 effectiveInputOverOutputRatioRC = vec3(
           ${u[0]/c[0]},
           ${u[1]/c[1]},
@@ -4177,7 +4177,7 @@ return a / b;`,Nle=`
 
         setOutput(newValue);
       }
-    `}};function Iue(e){let{inputs:t,backend:n,attrs:s}=e,{images:r}=t,{alignCorners:a,halfPixelCenters:o,size:i}=s,[l,u]=i,c=H().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new Sue(r.shape,l,u,a,o):new kue(r.shape,l,u,a,o);return n.runWebGLProgram(c,[r],"float32")}var Cue={kernelName:Yo,backendName:"webgl",kernelFunc:Iue},Tue=class{constructor(e,t,n){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;let[,s,r]=t,[,a,o]=e,i=[n&&a>1?s-1:s,n&&o>1?r-1:r],l=[n&&a>1?a-1:a,n&&o>1?o-1:o],u=i[0]/l[0],c=i[1]/l[1],p=1/u,d=1/c,h=Math.ceil(p)*2+2,f=Math.ceil(d)*2+2;this.userCode=`
+    `}};function Mue(e){let{inputs:t,backend:n,attrs:s}=e,{images:r}=t,{alignCorners:a,halfPixelCenters:o,size:i}=s,[l,u]=i,c=H().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new Oue(r.shape,l,u,a,o):new Fue(r.shape,l,u,a,o);return n.runWebGLProgram(c,[r],"float32")}var zue={kernelName:ti,backendName:"webgl",kernelFunc:Mue},Lue=class{constructor(e,t,n){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;let[,s,r]=t,[,a,o]=e,i=[n&&a>1?s-1:s,n&&o>1?r-1:r],l=[n&&a>1?a-1:a,n&&o>1?o-1:o],u=i[0]/l[0],c=i[1]/l[1],p=1/u,d=1/c,h=Math.ceil(p)*2+2,f=Math.ceil(d)*2+2;this.userCode=`
       void main() {
         ivec4 coords = getOutputCoords();
         int b = coords[0];
@@ -4258,7 +4258,7 @@ return a / b;`,Nle=`
 
         setOutput(accumulator);
       }
-    `}};function Nue(e){let{inputs:t,backend:n,attrs:s}=e,{images:r,dy:a}=t,{alignCorners:o}=s,i=new Tue(a.shape,r.shape,o);return n.runWebGLProgram(i,[a],a.dtype)}var Eue={kernelName:S0,backendName:"webgl",kernelFunc:Nue},Rue=class{constructor(e,t,n,s,r){this.variableNames=["A"],this.outputShape=[];let[a,o,i,l]=e;this.outputShape=[a,t,n,l];let u=[s&&t>1?o-1:o,s&&n>1?i-1:i],c=[s&&t>1?t-1:t,s&&n>1?n-1:n],p=s?"0.5":"0.0",d;r?d="max((vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC, vec2(0.0))":d="vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
+    `}};function Bue(e){let{inputs:t,backend:n,attrs:s}=e,{images:r,dy:a}=t,{alignCorners:o}=s,i=new Lue(a.shape,r.shape,o);return n.runWebGLProgram(i,[a],a.dtype)}var Wue={kernelName:S0,backendName:"webgl",kernelFunc:Bue},Vue=class{constructor(e,t,n,s,r){this.variableNames=["A"],this.outputShape=[];let[a,o,i,l]=e;this.outputShape=[a,t,n,l];let u=[s&&t>1?o-1:o,s&&n>1?i-1:i],c=[s&&t>1?t-1:t,s&&n>1?n-1:n],p=s?"0.5":"0.0",d;r?d="max((vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC, vec2(0.0))":d="vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
       const vec2 effectiveInputOverOutputRatioRC = vec2(
           ${u[0]/c[0]},
           ${u[1]/c[1]});
@@ -4280,7 +4280,7 @@ return a / b;`,Nle=`
 
         setOutput(newValue);
       }
-    `}},_ue=class{constructor(e,t,n,s,r){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];let[a,o,i,l]=e;this.outputShape=[a,t,n,l];let u=[s&&t>1?o-1:o,s&&n>1?i-1:i],c=[s&&t>1?t-1:t,s&&n>1?n-1:n],p=s?"0.5":"0.0",d;r?d="max((vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC, vec3(0.0))":d="vec3(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
+    `}},Uue=class{constructor(e,t,n,s,r){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];let[a,o,i,l]=e;this.outputShape=[a,t,n,l];let u=[s&&t>1?o-1:o,s&&n>1?i-1:i],c=[s&&t>1?t-1:t,s&&n>1?n-1:n],p=s?"0.5":"0.0",d;r?d="max((vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC, vec3(0.0))":d="vec3(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
       const vec3 effectiveInputOverOutputRatioRC = vec3(
           ${u[0]/c[0]},
           ${u[1]/c[1]},
@@ -4321,7 +4321,7 @@ return a / b;`,Nle=`
 
         setOutput(newValue);
       }
-    `}};function Due(e){let{inputs:t,backend:n,attrs:s}=e,{images:r}=t,{alignCorners:a,halfPixelCenters:o,size:i}=s,[l,u]=i,c=H().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new _ue(r.shape,l,u,a,o):new Rue(r.shape,l,u,a,o);return n.runWebGLProgram(c,[r],r.dtype)}var $ue={kernelName:Zo,backendName:"webgl",kernelFunc:Due},Pue=class{constructor(e,t,n){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;let[,s,r]=t,[,a,o]=e,i=[n&&a>1?s-1:s,n&&o>1?r-1:r],l=[n&&a>1?a-1:a,n&&o>1?o-1:o],u=i[0]/l[0],c=i[1]/l[1],p=1/u,d=1/c,h=Math.ceil(p)*2+2,f=Math.ceil(d)*2+2;this.userCode=`
+    `}};function Gue(e){let{inputs:t,backend:n,attrs:s}=e,{images:r}=t,{alignCorners:a,halfPixelCenters:o,size:i}=s,[l,u]=i,c=H().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new Uue(r.shape,l,u,a,o):new Vue(r.shape,l,u,a,o);return n.runWebGLProgram(c,[r],r.dtype)}var Hue={kernelName:ei,backendName:"webgl",kernelFunc:Gue},jue=class{constructor(e,t,n){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;let[,s,r]=t,[,a,o]=e,i=[n&&a>1?s-1:s,n&&o>1?r-1:r],l=[n&&a>1?a-1:a,n&&o>1?o-1:o],u=i[0]/l[0],c=i[1]/l[1],p=1/u,d=1/c,h=Math.ceil(p)*2+2,f=Math.ceil(d)*2+2;this.userCode=`
       void main() {
         ivec4 coords = getOutputCoords();
         int b = coords[0];
@@ -4391,17 +4391,17 @@ return a / b;`,Nle=`
 
         setOutput(accumulator);
       }
-    `}};function Fue(e){let{inputs:t,backend:n,attrs:s}=e,{images:r,dy:a}=t,{alignCorners:o}=s,i=new Pue(a.shape,r.shape,o);return n.runWebGLProgram(i,[a],a.dtype)}var Oue={kernelName:k0,backendName:"webgl",kernelFunc:Fue},Mue=class{constructor(e,t){this.variableNames=["x"];let n=e.length;if(n>4)throw new Error(`WebGL backend: Reverse of rank-${n} tensor is not yet supported`);if(this.outputShape=e,n===1){this.userCode=`
+    `}};function que(e){let{inputs:t,backend:n,attrs:s}=e,{images:r,dy:a}=t,{alignCorners:o}=s,i=new jue(a.shape,r.shape,o);return n.runWebGLProgram(i,[a],a.dtype)}var Xue={kernelName:k0,backendName:"webgl",kernelFunc:que},Kue=class{constructor(e,t){this.variableNames=["x"];let n=e.length;if(n>4)throw new Error(`WebGL backend: Reverse of rank-${n} tensor is not yet supported`);if(this.outputShape=e,n===1){this.userCode=`
         void main() {
           int coord = getOutputCoords();
           setOutput(getX(${e[0]} - coord - 1));
         }
-      `;return}let s=o=>t.indexOf(o)!==-1&&e[o]!==1?`${e[o]} - coords[${o}] - 1`:`coords[${o}]`,r=e.map((o,i)=>s(i)).join(","),a=wt(n);this.userCode=`
+      `;return}let s=o=>t.indexOf(o)!==-1&&e[o]!==1?`${e[o]} - coords[${o}] - 1`:`coords[${o}]`,r=e.map((o,i)=>s(i)).join(","),a=kt(n);this.userCode=`
       void main() {
         ${a} coords = getOutputCoords();
         setOutput(getX(${r}));
       }
-    `}},zue=class{constructor(e,t){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0;let n=e.length;if(n>4)throw new Error(`WebGL backend: Reverse of rank-${n} tensor is not yet supported`);this.outputShape=e;let s=os("rc",n),r=`${s[n-1]} + 1 < ${this.outputShape[n-1]}`,a=`${s[n-2]} + 1 < ${this.outputShape[n-2]}`,o=wt(n);n===1?this.userCode=`
+    `}},Zue=class{constructor(e,t){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0;let n=e.length;if(n>4)throw new Error(`WebGL backend: Reverse of rank-${n} tensor is not yet supported`);this.outputShape=e;let s=us("rc",n),r=`${s[n-1]} + 1 < ${this.outputShape[n-1]}`,a=`${s[n-2]} + 1 < ${this.outputShape[n-2]}`,o=kt(n);n===1?this.userCode=`
         void main(){
           int rc = getOutputCoords();
           vec4 result = vec4(0.);
@@ -4429,7 +4429,7 @@ return a / b;`,Nle=`
           }
           setOutput(result);
         }
-    `;function i(h){return p(h)}function l(h){return h[n-1]="("+h[n-1]+" + 1)",p(h)}function u(h){return h[n-2]="("+h[n-2]+" + 1)",p(h)}function c(h){return h[n-1]="("+h[n-1]+" + 1)",h[n-2]="("+h[n-2]+" + 1)",p(h)}function p(h){let f=e.map((y,x)=>d(x,h)),m=f.join(","),g=f.slice(-2).join(",");return`getChannel(getX(${m}), vec2(${g}))`}function d(h,f){return t.indexOf(h)!==-1&&e[h]!==1?`${e[h]} - ${f[h]} - 1`:`${f[h]}`}}};function Lue(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{dims:a}=s,o=r.shape.length,i=v.parseAxisParam(a,r.shape);if(o===0)return Ls({inputs:{x:r},backend:n});let l=H().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new zue(r.shape,i):new Mue(r.shape,i);return n.runWebGLProgram(l,[r],r.dtype)}var Bue={kernelName:Ul,backendName:"webgl",kernelFunc:Lue},Wue=class{constructor(e,t){this.variableNames=["Image"],this.outputShape=[],this.customUniforms=[{name:"params",type:"vec4"}];let n=e[1],s=e[2];this.outputShape=e;let r="";typeof t=="number"?r=`float outputValue = ${t.toFixed(2)};`:r=`
+    `;function i(h){return p(h)}function l(h){return h[n-1]="("+h[n-1]+" + 1)",p(h)}function u(h){return h[n-2]="("+h[n-2]+" + 1)",p(h)}function c(h){return h[n-1]="("+h[n-1]+" + 1)",h[n-2]="("+h[n-2]+" + 1)",p(h)}function p(h){let f=e.map((y,x)=>d(x,h)),m=f.join(","),g=f.slice(-2).join(",");return`getChannel(getX(${m}), vec2(${g}))`}function d(h,f){return t.indexOf(h)!==-1&&e[h]!==1?`${e[h]} - ${f[h]} - 1`:`${f[h]}`}}};function Yue(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{dims:a}=s,o=r.shape.length,i=v.parseAxisParam(a,r.shape);if(o===0)return Vs({inputs:{x:r},backend:n});let l=H().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new Zue(r.shape,i):new Kue(r.shape,i);return n.runWebGLProgram(l,[r],r.dtype)}var Jue={kernelName:ql,backendName:"webgl",kernelFunc:Yue},Que=class{constructor(e,t){this.variableNames=["Image"],this.outputShape=[],this.customUniforms=[{name:"params",type:"vec4"}];let n=e[1],s=e[2];this.outputShape=e;let r="";typeof t=="number"?r=`float outputValue = ${t.toFixed(2)};`:r=`
         vec3 fill = vec3(${t.join(",")});
         float outputValue = fill[coords[3]];`,this.userCode=`
         void main() {
@@ -4448,7 +4448,7 @@ return a / b;`,Nle=`
           }
           setOutput(outputValue);
         }
-    `}},Vue={kernelName:su,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{image:s}=e,{radians:r,fillValue:a,center:o}=t,i=n,l=new Wue(s.shape,a),[u,c]=T.getImageCenter(o,s.shape[1],s.shape[2]),p=[[u,c,Math.sin(r),Math.cos(r)]];return i.runWebGLProgram(l,[s],s.dtype,p)}},Uue=`
+    `}},ece={kernelName:iu,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{image:s}=e,{radians:r,fillValue:a,center:o}=t,i=n,l=new Que(s.shape,a),[u,c]=T.getImageCenter(o,s.shape[1],s.shape[2]),p=[[u,c,Math.sin(r),Math.cos(r)]];return i.runWebGLProgram(l,[s],s.dtype,p)}},tce=`
   // OpenGL ES does not support round function.
   // The algorithm is based on banker's rounding.
   float base = floor(x);
@@ -4463,7 +4463,7 @@ return a / b;`,Nle=`
       return base + 1.0;
     }
   }
-`,Gue=pt({opSnippet:Uue}),Hue={kernelName:Gl,backendName:"webgl",kernelFunc:Gue},jue="return inversesqrt(x);",que=pt({opSnippet:jue,cpuKernelImpl:hne}),Xue={kernelName:Qo,backendName:"webgl",kernelFunc:que},rC=class{constructor(e,t,n,s,r,a,o=!0){this.variableNames=["updates","indices","defaultValue"],this.outputShape=a;let i=wt(r.length),l=wt(a.length),u="";n===1?u="i":n===2&&(u="i, j");let c=`getIndices(${u})`,p="";s===1?p="i":s===2&&(p="i, coords[1]");let d=`getUpdates(${p})`,h=t>1?"strides[j]":"strides";this.userCode=`
+`,nce=ht({opSnippet:tce}),sce={kernelName:Xl,backendName:"webgl",kernelFunc:nce},rce="return inversesqrt(x);",ace=ht({opSnippet:rce,cpuKernelImpl:Ine}),oce={kernelName:si,backendName:"webgl",kernelFunc:ace},cC=class{constructor(e,t,n,s,r,a,o=!0){this.variableNames=["updates","indices","defaultValue"],this.outputShape=a;let i=kt(r.length),l=kt(a.length),u="";n===1?u="i":n===2&&(u="i, j");let c=`getIndices(${u})`,p="";s===1?p="i":s===2&&(p="i, coords[1]");let d=`getUpdates(${p})`,h=t>1?"strides[j]":"strides";this.userCode=`
         ${i} strides = ${i}(${r});
 
         void main() {
@@ -4483,7 +4483,7 @@ return a / b;`,Nle=`
           }
           setOutput(mix(getDefaultValue(), sum, float(found)));
         }
-      `}};function Kue(e){let{inputs:t,backend:n,attrs:s}=e,{indices:r,updates:a}=t,{shape:o}=s,{sliceRank:i,numUpdates:l,sliceSize:u,strides:c,outputSize:p}=T.calculateShapes(a,r,o),d=[p/u,u];if(p===0)return n.makeTensorInfo(o,r.dtype);let h=we({inputs:{x:r},backend:n,attrs:{shape:[l,i]}}),f=we({inputs:{x:a},backend:n,attrs:{shape:[l,u]}}),m=n.makeTensorInfo([],"float32",new Float32Array([0])),g=new rC(l,i,h.shape.length,f.shape.length,c,d),y=n.runWebGLProgram(g,[f,h,m],f.dtype),x=we({inputs:{x:y},backend:n,attrs:{shape:o}});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(y),n.disposeIntermediateTensorInfo(m),x}var Zue={kernelName:Hl,backendName:"webgl",kernelFunc:Kue},Yue=class{constructor(e,t,n,s){this.variableNames=["sortedSequence","values"],this.customUniforms=[{name:"numInputs",type:"int"}],this.outputShape=[e,n];let r="while (left < right) {",a=`for (int i = 0; i < ${Math.ceil(Math.log2(t+1))}; ++i) { if (left >= right) break;`,o=H().getNumber("WEBGL_VERSION")===2?r:a,i=s==="left"?"<":"<=";this.userCode=`
+      `}};function ice(e){let{inputs:t,backend:n,attrs:s}=e,{indices:r,updates:a}=t,{shape:o}=s,{sliceRank:i,numUpdates:l,sliceSize:u,strides:c,outputSize:p}=T.calculateShapes(a,r,o),d=[p/u,u];if(p===0)return n.makeTensorInfo(o,r.dtype);let h=we({inputs:{x:r},backend:n,attrs:{shape:[l,i]}}),f=we({inputs:{x:a},backend:n,attrs:{shape:[l,u]}}),m=n.makeTensorInfo([],"float32",new Float32Array([0])),g=new cC(l,i,h.shape.length,f.shape.length,c,d),y=n.runWebGLProgram(g,[f,h,m],f.dtype),x=we({inputs:{x:y},backend:n,attrs:{shape:o}});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(y),n.disposeIntermediateTensorInfo(m),x}var lce={kernelName:Kl,backendName:"webgl",kernelFunc:ice},uce=class{constructor(e,t,n,s){this.variableNames=["sortedSequence","values"],this.customUniforms=[{name:"numInputs",type:"int"}],this.outputShape=[e,n];let r="while (left < right) {",a=`for (int i = 0; i < ${Math.ceil(Math.log2(t+1))}; ++i) { if (left >= right) break;`,o=H().getNumber("WEBGL_VERSION")===2?r:a,i=s==="left"?"<":"<=";this.userCode=`
        int findBound(int batch, float value) {
          int left = 0;
          int right = numInputs;
@@ -4508,7 +4508,7 @@ return a / b;`,Nle=`
 
          setOutput(float(findBound(batch, value)));
        }
-     `}};function Jue(e){let{inputs:t,backend:n,attrs:s}=e,{sortedSequence:r,values:a}=t,{side:o}=s,i=new Yue(r.shape[0],r.shape[1],a.shape[1],o),l=[[r.shape[1]]];return n.runWebGLProgram(i,[r,a],"int32",l)}var Que={kernelName:I0,backendName:"webgl",kernelFunc:Jue},ece=class{constructor(e,t,n){this.variableNames=["c","a","b"],this.outputShape=t;let s,r;if(n>4)throw Error(`Where for rank ${n} is not yet supported`);if(n===1)r="resRC",s="resRC";else{let o=["resRC.x","resRC.y","resRC.z","resRC.w"],i=[],l=[];for(let u=0;u<t.length;u++)l.push(`${o[u]}`),u<e&&i.push(`${o[u]}`);s=i.join(),r=l.join()}let a=wt(n);this.userCode=`
+     `}};function cce(e){let{inputs:t,backend:n,attrs:s}=e,{sortedSequence:r,values:a}=t,{side:o}=s,i=new uce(r.shape[0],r.shape[1],a.shape[1],o),l=[[r.shape[1]]];return n.runWebGLProgram(i,[r,a],"int32",l)}var dce={kernelName:I0,backendName:"webgl",kernelFunc:cce},pce=class{constructor(e,t,n){this.variableNames=["c","a","b"],this.outputShape=t;let s,r;if(n>4)throw Error(`Where for rank ${n} is not yet supported`);if(n===1)r="resRC",s="resRC";else{let o=["resRC.x","resRC.y","resRC.z","resRC.w"],i=[],l=[];for(let u=0;u<t.length;u++)l.push(`${o[u]}`),u<e&&i.push(`${o[u]}`);s=i.join(),r=l.join()}let a=kt(n);this.userCode=`
       void main() {
         ${a} resRC = getOutputCoords();
         float cVal = getC(${s});
@@ -4518,15 +4518,15 @@ return a / b;`,Nle=`
           setOutput(getB(${r}));
         }
       }
-    `}};function tce(e){let{inputs:t,backend:n}=e,{condition:s,t:r,e:a}=t,o=new ece(s.shape.length,r.shape,r.shape.length);return n.runWebGLProgram(o,[s,r,a],Gn(r.dtype,a.dtype))}var nce={kernelName:jl,backendName:"webgl",kernelFunc:tce},sce=`
+    `}};function hce(e){let{inputs:t,backend:n}=e,{condition:s,t:r,e:a}=t,o=new pce(s.shape.length,r.shape,r.shape.length);return n.runWebGLProgram(o,[s,r,a],Hn(r.dtype,a.dtype))}var fce={kernelName:Zl,backendName:"webgl",kernelFunc:hce},mce=`
   // Stable and Attracting Fixed Point (0, 1) for Normalized Weights.
   // see: https://arxiv.org/abs/1706.02515
   float scaleAlpha = ${T.SELU_SCALEALPHA};
   float scale = ${T.SELU_SCALE};
   return (x >= 0.0) ? scale * x : scaleAlpha * (exp(x) - 1.0);
-`,rce=pt({opSnippet:sce}),ace={kernelName:Vc,backendName:"webgl",kernelFunc:rce},oce=gd+`
+`,gce=ht({opSnippet:mce}),yce={kernelName:jc,backendName:"webgl",kernelFunc:gce},Ace=xd+`
   return 1.0 / (1.0 + exp(-1.0 * x));
-`,ice=`
+`,xce=`
   vec4 result = 1.0 / (1.0 + exp(-1.0 * x));
   bvec4 isNaN = isnan(x);
 
@@ -4536,15 +4536,15 @@ return a / b;`,Nle=`
   result.a = isNaN.a ? x.a : result.a;
 
   return result;
-`,lce=pt({opSnippet:oce,packedOpSnippet:ice,cpuKernelImpl:mne}),uce={kernelName:ti,backendName:"webgl",kernelFunc:lce},cce=`
+`,bce=ht({opSnippet:Ace,packedOpSnippet:xce,cpuKernelImpl:Tne}),vce={kernelName:ai,backendName:"webgl",kernelFunc:bce},wce=`
   if (isnan(x)) { return 0.0; }
   return sign(x);
-`,dce=pt({opSnippet:cce}),pce={kernelName:Uc,backendName:"webgl",kernelFunc:dce},hce=gd+`
+`,kce=ht({opSnippet:wce}),Sce={kernelName:qc,backendName:"webgl",kernelFunc:kce},Ice=xd+`
   return sin(x);
-`,fce=pt({opSnippet:hce}),mce={kernelName:ei,backendName:"webgl",kernelFunc:fce},gce=`
+`,Cce=ht({opSnippet:Ice}),Tce={kernelName:ri,backendName:"webgl",kernelFunc:Cce},Nce=`
   float e2x = exp(x);
   return (e2x - 1.0 / e2x) / 2.0;
-`,yce=pt({opSnippet:gce}),Ace={kernelName:Xl,backendName:"webgl",kernelFunc:yce},xce=`
+`,Ece=ht({opSnippet:Nce}),Rce={kernelName:Jl,backendName:"webgl",kernelFunc:Ece},_ce=`
   float epsilon = 1.1920928955078125e-7;
   float threshold = log(epsilon) + 2.0;
 
@@ -4564,17 +4564,17 @@ return a / b;`,Nle=`
     result = log(exp_x + 1.0);
   }
   return result;
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+        ${o.shape}`);let i=n.readSync(s.dataId),l=n.readSync(r.dataId),u=n.readSync(a.dataId),c=n.readSync(o.dataId)[0],[p,d,h,f,m]=Ene(i,s.shape,s.dtype,l,r.dtype,u,c);return[n.makeTensorInfo(d,s.dtype,p),n.makeTensorInfo([d[0]],r.dtype,h),n.makeTensorInfo([f.length],"bool",new Uint8Array(f.map(g=>Number(g)))),n.makeTensorInfo([m.length],s.dtype,new Int32Array(m))]}var Mce={kernelName:th,backendName:"webgl",kernelFunc:Oce};function zce(e){let{inputs:t,backend:n}=e,{inputIndices:s,inputShape:r,newShape:a}=t;if(s.shape.length!==2)throw new Error(`Input indices should be a matrix but received shape ${s.shape}`);if(r.shape.length!==1)throw new Error(`Input shape should be a vector but received shape ${r.shape}`);if(a.shape.length!==1)throw new Error(`Target shape should be a vector but received shape ${a.shape}`);let o=Array.from(n.readSync(r.dataId)),i=n.readSync(s.dataId),l=Array.from(n.readSync(a.dataId)),[u,c,p]=Rne(i,s.shape,s.dtype,o,l);return[n.makeTensorInfo(c,s.dtype,u),n.makeTensorInfo([p.length],a.dtype,new Int32Array(p))]}var Lce={kernelName:Kc,backendName:"webgl",kernelFunc:zce};function Bce(e){let{inputs:t,backend:n}=e,{data:s,indices:r,segmentIds:a}=t;if(s.shape.length<1)throw new Error("Data should be at least 1 dimensional but received scalar");if(r.shape.length!==1)throw new Error(`Indices should be a vector but received shape
               ${r.shape}`);if(a.shape.length!==1)throw new Error(`Segment ids should be a vector but received shape
-              ${a.shape}`);let o=n.readSync(s.dataId),i=n.readSync(r.dataId),l=n.readSync(a.dataId),[u,c]=I9(o,s.shape,s.dtype,i,l,!0);return n.makeTensorInfo(c,s.dtype,u)}var Ece={kernelName:th,backendName:"webgl",kernelFunc:Nce};function Rce(e){let{inputs:t,backend:n}=e,{data:s,indices:r,segmentIds:a}=t;if(s.shape.length<1)throw new Error("Data should be at least 1 dimensional but received scalar");if(r.shape.length!==1)throw new Error(`Indices should be a vector but received shape
+              ${a.shape}`);let o=n.readSync(s.dataId),i=n.readSync(r.dataId),l=n.readSync(a.dataId),[u,c]=_9(o,s.shape,s.dtype,i,l,!0);return n.makeTensorInfo(c,s.dtype,u)}var Wce={kernelName:nh,backendName:"webgl",kernelFunc:Bce};function Vce(e){let{inputs:t,backend:n}=e,{data:s,indices:r,segmentIds:a}=t;if(s.shape.length<1)throw new Error("Data should be at least 1 dimensional but received scalar");if(r.shape.length!==1)throw new Error(`Indices should be a vector but received shape
              ${r.shape}`);if(a.shape.length!==1)throw new Error(`Segment ids should be a vector but received shape
-             ${a.shape}`);let o=n.readSync(s.dataId),i=n.readSync(r.dataId),l=n.readSync(a.dataId),[u,c]=I9(o,s.shape,s.dtype,i,l);return n.makeTensorInfo(c,s.dtype,u)}var _ce={kernelName:nh,backendName:"webgl",kernelFunc:Rce};function Dce(e){let{inputs:t,backend:n,attrs:s}=e,{sparseIndices:r,sparseValues:a,defaultValue:o}=t,{outputShape:i}=s,{sliceRank:l,numUpdates:u,sliceSize:c,strides:p,outputSize:d}=T.calculateShapes(a,r,i),h=!1;if(a.dtype==="string"){let y=n.bufferSync(r),x=n.bufferSync(a),A=v.decodeString(n.readSync(o.dataId)[0]),b=fne(y,x,i,d,c,u,l,p,A,h);return n.makeTensorInfo(i,b.dtype,b.values)}let f=new rC(u,l,r.shape.length,a.shape.length,p,[d,1],h),m=n.runWebGLProgram(f,[a,r,o],a.dtype),g=we({inputs:{x:m},backend:n,attrs:{shape:i}});return n.disposeIntermediateTensorInfo(m),g}var $ce={kernelName:sh,backendName:"webgl",kernelFunc:Dce};function Pce(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{numOrSizeSplits:a,axis:o}=s,i=v.parseAxisParam(o,r.shape)[0],l=T.prepareSplitSize(r,a,i),u=r.shape.length,c=new Array(u).fill(0),p=r.shape.slice();return l.map(d=>{let h=[...p];h[i]=d;let f=yd({inputs:{x:r},backend:n,attrs:{begin:c,size:h}});return c[i]+=d,f})}var Fce={kernelName:Zl,backendName:"webgl",kernelFunc:Pce},K7="return sqrt(x);",Oce=pt({opSnippet:K7,packedOpSnippet:K7,cpuKernelImpl:xne}),Mce={kernelName:ni,backendName:"webgl",kernelFunc:Oce},zce="return x * x;",Lce=pt({opSnippet:zce}),Bce={kernelName:jc,backendName:"webgl",kernelFunc:Lce},Z7="return (a - b) * (a - b);",Wce=Ln({opSnippet:Z7,packedOpSnippet:Z7}),Vce={kernelName:ai,backendName:"webgl",kernelFunc:Wce};function Uce({inputs:e,attrs:t,backend:n}){let{x:s}=e,r=xr+`
+             ${a.shape}`);let o=n.readSync(s.dataId),i=n.readSync(r.dataId),l=n.readSync(a.dataId),[u,c]=_9(o,s.shape,s.dtype,i,l);return n.makeTensorInfo(c,s.dtype,u)}var Uce={kernelName:sh,backendName:"webgl",kernelFunc:Vce};function Gce(e){let{inputs:t,backend:n,attrs:s}=e,{sparseIndices:r,sparseValues:a,defaultValue:o}=t,{outputShape:i}=s,{sliceRank:l,numUpdates:u,sliceSize:c,strides:p,outputSize:d}=T.calculateShapes(a,r,i),h=!1;if(a.dtype==="string"){let y=n.bufferSync(r),x=n.bufferSync(a),A=v.decodeString(n.readSync(o.dataId)[0]),b=Cne(y,x,i,d,c,u,l,p,A,h);return n.makeTensorInfo(i,b.dtype,b.values)}let f=new cC(u,l,r.shape.length,a.shape.length,p,[d,1],h),m=n.runWebGLProgram(f,[a,r,o],a.dtype),g=we({inputs:{x:m},backend:n,attrs:{shape:i}});return n.disposeIntermediateTensorInfo(m),g}var Hce={kernelName:rh,backendName:"webgl",kernelFunc:Gce};function jce(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{numOrSizeSplits:a,axis:o}=s,i=v.parseAxisParam(o,r.shape)[0],l=T.prepareSplitSize(r,a,i),u=r.shape.length,c=new Array(u).fill(0),p=r.shape.slice();return l.map(d=>{let h=[...p];h[i]=d;let f=bd({inputs:{x:r},backend:n,attrs:{begin:c,size:h}});return c[i]+=d,f})}var qce={kernelName:eu,backendName:"webgl",kernelFunc:jce},t6="return sqrt(x);",Xce=ht({opSnippet:t6,packedOpSnippet:t6,cpuKernelImpl:_ne}),Kce={kernelName:oi,backendName:"webgl",kernelFunc:Xce},Zce="return x * x;",Yce=ht({opSnippet:Zce}),Jce={kernelName:Zc,backendName:"webgl",kernelFunc:Yce},n6="return (a - b) * (a - b);",Qce=Bn({opSnippet:n6,packedOpSnippet:n6}),ede={kernelName:ui,backendName:"webgl",kernelFunc:Qce};function tde({inputs:e,attrs:t,backend:n}){let{x:s}=e,r=br+`
     return x > 0.0 ? 1.0 : float(${t.alpha});
-  `,a=new ba(s.shape,r);return n.runWebGLProgram(a,[s],s.dtype)}var Gce={kernelName:li,backendName:"webgl",kernelFunc:Uce},Hce=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=n;let s=n.length,r=wt(n.length),a=wt(n.length),o="";if(s===1)o="coords * strides + begin";else{let i=0;o=n.map((l,u)=>(i++,n.length===1?`coords * strides[${u}] + begin[${u}]`:`coords[${i-1}] * strides[${u}] + begin[${u}]`)).join(",")}this.userCode=`
+  `,a=new Sa(s.shape,r);return n.runWebGLProgram(a,[s],s.dtype)}var nde={kernelName:pi,backendName:"webgl",kernelFunc:tde},sde=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=n;let s=n.length,r=kt(n.length),a=kt(n.length),o="";if(s===1)o="coords * strides + begin";else{let i=0;o=n.map((l,u)=>(i++,n.length===1?`coords * strides[${u}] + begin[${u}]`:`coords[${i-1}] * strides[${u}] + begin[${u}]`)).join(",")}this.userCode=`
       ${r} begin = ${r}(${e});
       ${r} strides = ${r}(${t});
 
@@ -4582,15 +4582,15 @@ return a / b;`,Nle=`
         ${a} coords = getOutputCoords();
         setOutput(getX(${o}));
       }
-    `}};function jce(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{begin:a,end:o,strides:i,beginMask:l,endMask:u,ellipsisMask:c,newAxisMask:p,shrinkAxisMask:d}=s,{finalShapeSparse:h,finalShape:f,isIdentity:m,sliceDim0:g,isSimpleSlice:y,begin:x,end:A,strides:b}=Ht.sliceInfo(r.shape,a,o,i,l,u,c,p,d),w;if(m)w=we({inputs:{x:r},backend:n,attrs:{shape:f}});else if(g||y){v.assert(r.shape.length>=1,()=>`Input must have rank at least 1, got: ${r.shape.length}`);let C=Ht.computeOutShape(x,A,b),E=yd({inputs:{x:r},backend:n,attrs:{begin:x,size:C}});w=we({inputs:{x:E},backend:n,attrs:{shape:f}}),n.disposeIntermediateTensorInfo(E)}else if(n.shouldExecuteOnCPU([r])){let E=n.readSync(r.dataId),_=Ve(r.shape,r.dtype,E),$=bne(h,_,b,x);w=n.makeTensorInfo(f,r.dtype,$.values)}else{let E=new Hce(x,b,h);w=n.runWebGLProgram(E,[r],r.dtype)}let k=we({inputs:{x:w},backend:n,attrs:{shape:f}});return n.disposeIntermediateTensorInfo(w),k}var qce={kernelName:Yl,backendName:"webgl",kernelFunc:jce};function Xce(e){let{inputs:t,backend:n,attrs:s}=e,{separator:r,nGramWidths:a,leftPad:o,rightPad:i,padWidth:l,preserveShortSequences:u}=s,{data:c,dataSplits:p}=t,d=n.readSync(c.dataId),h=n.readSync(p.dataId),[f,m]=vne(d,h,r,a,o,i,l,u);return[n.makeTensorInfo([f.length],"string",f),n.makeTensorInfo(p.shape,"int32",m)]}var Kce={kernelName:qc,backendName:"webgl",kernelFunc:Xce};function Zce(e){let{inputs:t,backend:n,attrs:s}=e,{skipEmpty:r}=s,{input:a,delimiter:o}=t;if(a.dtype!=="string")throw new Error("Input must be of datatype string");if(a.shape.length!==1)throw new Error(`Input must be a vector, got shape: ${a.shape}`);if(o.shape.length!==0)throw new Error(`Delimiter must be a scalar, got shape: ${o.shape}`);let i=n.readSync(a.dataId),l=n.readSync(o.dataId)[0],[u,c,p]=wne(i,l,r),d=c.length;return[n.makeTensorInfo([d,2],"int32",u),n.makeTensorInfo([d],"string",c),n.makeTensorInfo([2],"int32",new Int32Array(p))]}var Yce={kernelName:rh,backendName:"webgl",kernelFunc:Zce};function Jce(e){let{inputs:t,backend:n,attrs:s}=e,{numBuckets:r}=s,{input:a}=t;if(a.dtype!=="string")throw new Error("Input must be of datatype string");if(r<=0)throw new Error("Number of buckets must be at least 1");let o=n.readSync(a.dataId),i=kne(o,r);return n.makeTensorInfo(a.shape,"int32",i)}var Qce={kernelName:ah,backendName:"webgl",kernelFunc:Jce},ede="return tan(x);",tde=pt({opSnippet:ede}),nde={kernelName:Jl,backendName:"webgl",kernelFunc:tde},sde=`
+    `}};function rde(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{begin:a,end:o,strides:i,beginMask:l,endMask:u,ellipsisMask:c,newAxisMask:p,shrinkAxisMask:d}=s,{finalShapeSparse:h,finalShape:f,isIdentity:m,sliceDim0:g,isSimpleSlice:y,begin:x,end:A,strides:b}=jt.sliceInfo(r.shape,a,o,i,l,u,c,p,d),w;if(m)w=we({inputs:{x:r},backend:n,attrs:{shape:f}});else if(g||y){v.assert(r.shape.length>=1,()=>`Input must have rank at least 1, got: ${r.shape.length}`);let C=jt.computeOutShape(x,A,b),E=bd({inputs:{x:r},backend:n,attrs:{begin:x,size:C}});w=we({inputs:{x:E},backend:n,attrs:{shape:f}}),n.disposeIntermediateTensorInfo(E)}else if(n.shouldExecuteOnCPU([r])){let E=n.readSync(r.dataId),_=Ue(r.shape,r.dtype,E),$=Dne(h,_,b,x);w=n.makeTensorInfo(f,r.dtype,$.values)}else{let E=new sde(x,b,h);w=n.runWebGLProgram(E,[r],r.dtype)}let k=we({inputs:{x:w},backend:n,attrs:{shape:f}});return n.disposeIntermediateTensorInfo(w),k}var ade={kernelName:tu,backendName:"webgl",kernelFunc:rde};function ode(e){let{inputs:t,backend:n,attrs:s}=e,{separator:r,nGramWidths:a,leftPad:o,rightPad:i,padWidth:l,preserveShortSequences:u}=s,{data:c,dataSplits:p}=t,d=n.readSync(c.dataId),h=n.readSync(p.dataId),[f,m]=$ne(d,h,r,a,o,i,l,u);return[n.makeTensorInfo([f.length],"string",f),n.makeTensorInfo(p.shape,"int32",m)]}var ide={kernelName:Yc,backendName:"webgl",kernelFunc:ode};function lde(e){let{inputs:t,backend:n,attrs:s}=e,{skipEmpty:r}=s,{input:a,delimiter:o}=t;if(a.dtype!=="string")throw new Error("Input must be of datatype string");if(a.shape.length!==1)throw new Error(`Input must be a vector, got shape: ${a.shape}`);if(o.shape.length!==0)throw new Error(`Delimiter must be a scalar, got shape: ${o.shape}`);let i=n.readSync(a.dataId),l=n.readSync(o.dataId)[0],[u,c,p]=Pne(i,l,r),d=c.length;return[n.makeTensorInfo([d,2],"int32",u),n.makeTensorInfo([d],"string",c),n.makeTensorInfo([2],"int32",new Int32Array(p))]}var ude={kernelName:ah,backendName:"webgl",kernelFunc:lde};function cde(e){let{inputs:t,backend:n,attrs:s}=e,{numBuckets:r}=s,{input:a}=t;if(a.dtype!=="string")throw new Error("Input must be of datatype string");if(r<=0)throw new Error("Number of buckets must be at least 1");let o=n.readSync(a.dataId),i=Fne(o,r);return n.makeTensorInfo(a.shape,"int32",i)}var dde={kernelName:oh,backendName:"webgl",kernelFunc:cde},pde="return tan(x);",hde=ht({opSnippet:pde}),fde={kernelName:nu,backendName:"webgl",kernelFunc:hde},mde=`
   float e2x = exp(-2.0 * abs(x));
   return sign(x) * (1.0 - e2x) / (1.0 + e2x);
-`,rde=pt({opSnippet:sde}),ade={kernelName:ii,backendName:"webgl",kernelFunc:rde},ode=class{constructor(e,t){this.variableNames=["A"];let n=new Array(e.length);for(let a=0;a<n.length;a++)n[a]=e[a]*t[a];this.outputShape=n,this.rank=n.length;let s=wt(this.rank),r=ide(e);this.userCode=`
+`,gde=ht({opSnippet:mde}),yde={kernelName:di,backendName:"webgl",kernelFunc:gde},Ade=class{constructor(e,t){this.variableNames=["A"];let n=new Array(e.length);for(let a=0;a<n.length;a++)n[a]=e[a]*t[a];this.outputShape=n,this.rank=n.length;let s=kt(this.rank),r=xde(e);this.userCode=`
       void main() {
         ${s} resRC = getOutputCoords();
         setOutput(getA(${r}));
       }
-    `}};function ide(e){let t=e.length;if(t>5)throw Error(`Tile for rank ${t} is not yet supported`);if(t===1)return`imod(resRC, ${e[0]})`;let n=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u"],s=[];for(let r=0;r<e.length;r++)s.push(`imod(${n[r]}, ${e[r]})`);return s.join()}function aC(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{reps:a}=s;if(r.dtype==="string"||r.shape.length>5){let l=n.readSync(r.dataId),u=r.dtype==="string"?l.map(d=>v.decodeString(d)):l,c=Ve(r.shape,r.dtype,u),p=Ine(c,a);return n.makeTensorInfo(p.shape,p.dtype,p.values)}let o=new ode(r.shape,a);return n.runWebGLProgram(o,[r],r.dtype)}var lde={kernelName:Ra,backendName:"webgl",kernelFunc:aC},ude=class{constructor(e){this.variableNames=["x","indices"],this.customUniforms=[{name:"n",type:"int"},{name:"firstPass",type:"int"},{name:"negativeInf",type:"float"},{name:"dir",type:"int"},{name:"inc",type:"int"}],this.outputShape=e,this.userCode=`
+    `}};function xde(e){let t=e.length;if(t>5)throw Error(`Tile for rank ${t} is not yet supported`);if(t===1)return`imod(resRC, ${e[0]})`;let n=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u"],s=[];for(let r=0;r<e.length;r++)s.push(`imod(${n[r]}, ${e[r]})`);return s.join()}function dC(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{reps:a}=s;if(r.dtype==="string"||r.shape.length>5){let l=n.readSync(r.dataId),u=r.dtype==="string"?l.map(d=>v.decodeString(d)):l,c=Ue(r.shape,r.dtype,u),p=Mne(c,a);return n.makeTensorInfo(p.shape,p.dtype,p.values)}let o=new Ade(r.shape,a);return n.runWebGLProgram(o,[r],r.dtype)}var bde={kernelName:Pa,backendName:"webgl",kernelFunc:dC},vde=class{constructor(e){this.variableNames=["x","indices"],this.customUniforms=[{name:"n",type:"int"},{name:"firstPass",type:"int"},{name:"negativeInf",type:"float"},{name:"dir",type:"int"},{name:"inc",type:"int"}],this.outputShape=e,this.userCode=`
        void main() {
          ivec2 coords = getOutputCoords();
          int batch = coords[0];
@@ -4630,7 +4630,7 @@ return a / b;`,Nle=`
             setOutput(float(i1));
          }
        }
-     `}},cde=class{constructor(e){this.variableNames=["x","indices"],this.customUniforms=[{name:"n",type:"int"},{name:"firstPass",type:"int"},{name:"k",type:"int"}],this.outputShape=e,this.userCode=`
+     `}},wde=class{constructor(e){this.variableNames=["x","indices"],this.customUniforms=[{name:"n",type:"int"},{name:"firstPass",type:"int"},{name:"k",type:"int"}],this.outputShape=e,this.userCode=`
     void main() {
          // Takes max of indices (0, k), (1, k + 1), (2, k + 2) ...
          ivec2 coords = getOutputCoords();
@@ -4664,7 +4664,7 @@ return a / b;`,Nle=`
 
          setOutput(x0 >= x1 ? float(i0) : float(i1));
        }
-     `}};function Bi(e,t){t!==null&&e.disposeIntermediateTensorInfo(t)}function Y7(e){let t=1;for(;t<e;)t*=2;return t}function dde(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{k:a,sorted:o}=s,i=H().getNumber("TOPK_LAST_DIM_CPU_HANDOFF_SIZE_THRESHOLD"),l=H().getNumber("TOPK_K_CPU_HANDOFF_THRESHOLD"),u=r.shape,c=u[u.length-1];if(n.shouldExecuteOnCPU([r])||c<i||a>l){let $=n.readSync(r.dataId),[R,P]=Cne($,u,r.dtype,a,o);return[n.makeTensorInfo(R.shape,R.dtype,R.values),n.makeTensorInfo(P.shape,P.dtype,P.values)]}if(a===0)return u[u.length-1]=0,[n.makeTensorInfo(u,r.dtype,[]),n.makeTensorInfo(u,"int32",[])];if(c===1)return[r,qh({attrs:{shape:u,dtype:"int32",value:0},backend:n})];let p=n.texData.get(r.dataId),d=p!==null&&p.isPacked,h=d?n.unpackTensor(r):r,m=v.sizeFromShape(u)/c,g=we({inputs:{x:h},attrs:{shape:[m,c]},backend:n});d&&Bi(n,h);let y=Y7(a),x=Y7(c),A=null,b=()=>A===null?[g,g]:[g,A],w=($,R,P)=>{let S=b(),M=new ude(P),U=[[c],[A===null?1:0],[Number.NEGATIVE_INFINITY],[$],[R]],K=A;A=n.runWebGLProgram(M,S,"int32",U),Bi(n,K)};for(let $=1;$<y;$*=2){let R=$*2;for(let P=$;P>=1;P/=2)w(R,P,[m,x])}for(let $=x;$>y;$/=2){let R=b(),P=new cde([m,$/2]),M=[[c],[A===null?1:0],[y]],L=A;A=n.runWebGLProgram(P,R,"int32",M),Bi(n,L);let U=y/2,K=U*2;for(let q=U;q>=1;q/=2)w(K,q,A.shape)}let k=A;A=yd({inputs:{x:A},backend:n,attrs:{begin:0,size:[m,a]}}),Bi(n,k);let C=Z9({inputs:{x:g,indices:A},backend:n,attrs:{axis:1,batchDims:1}});Bi(n,g);let E=u.slice(0,-1);E.push(a),k=A,A=we({inputs:{x:A},attrs:{shape:E},backend:n}),Bi(n,k);let _=C;return C=we({inputs:{x:C},attrs:{shape:E},backend:n}),Bi(n,_),[C,A]}var pde={kernelName:Ql,backendName:"webgl",kernelFunc:dde},hde=class{constructor(e,t,n,s,r,a){this.variableNames=["Image","Transforms"],this.outputShape=a;let o=n==="nearest"?1:2,i;switch(s){case"constant":i=1;break;case"reflect":i=2;break;case"wrap":i=3;break;case"nearest":i=4;break;default:i=1;break}this.userCode=`
+     `}};function Gi(e,t){t!==null&&e.disposeIntermediateTensorInfo(t)}function s6(e){let t=1;for(;t<e;)t*=2;return t}function kde(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{k:a,sorted:o}=s,i=H().getNumber("TOPK_LAST_DIM_CPU_HANDOFF_SIZE_THRESHOLD"),l=H().getNumber("TOPK_K_CPU_HANDOFF_THRESHOLD"),u=r.shape,c=u[u.length-1];if(n.shouldExecuteOnCPU([r])||c<i||a>l){let $=n.readSync(r.dataId),[R,P]=zne($,u,r.dtype,a,o);return[n.makeTensorInfo(R.shape,R.dtype,R.values),n.makeTensorInfo(P.shape,P.dtype,P.values)]}if(a===0)return u[u.length-1]=0,[n.makeTensorInfo(u,r.dtype,[]),n.makeTensorInfo(u,"int32",[])];if(c===1)return[r,Xh({attrs:{shape:u,dtype:"int32",value:0},backend:n})];let p=n.texData.get(r.dataId),d=p!==null&&p.isPacked,h=d?n.unpackTensor(r):r,m=v.sizeFromShape(u)/c,g=we({inputs:{x:h},attrs:{shape:[m,c]},backend:n});d&&Gi(n,h);let y=s6(a),x=s6(c),A=null,b=()=>A===null?[g,g]:[g,A],w=($,R,P)=>{let S=b(),M=new vde(P),U=[[c],[A===null?1:0],[Number.NEGATIVE_INFINITY],[$],[R]],K=A;A=n.runWebGLProgram(M,S,"int32",U),Gi(n,K)};for(let $=1;$<y;$*=2){let R=$*2;for(let P=$;P>=1;P/=2)w(R,P,[m,x])}for(let $=x;$>y;$/=2){let R=b(),P=new wde([m,$/2]),M=[[c],[A===null?1:0],[y]],L=A;A=n.runWebGLProgram(P,R,"int32",M),Gi(n,L);let U=y/2,K=U*2;for(let q=U;q>=1;q/=2)w(K,q,A.shape)}let k=A;A=bd({inputs:{x:A},backend:n,attrs:{begin:0,size:[m,a]}}),Gi(n,k);let C=nC({inputs:{x:g,indices:A},backend:n,attrs:{axis:1,batchDims:1}});Gi(n,g);let E=u.slice(0,-1);E.push(a),k=A,A=we({inputs:{x:A},attrs:{shape:E},backend:n}),Gi(n,k);let _=C;return C=we({inputs:{x:C},attrs:{shape:E},backend:n}),Gi(n,_),[C,A]}var Sde={kernelName:su,backendName:"webgl",kernelFunc:kde},Ide=class{constructor(e,t,n,s,r,a){this.variableNames=["Image","Transforms"],this.outputShape=a;let o=n==="nearest"?1:2,i;switch(s){case"constant":i=1;break;case"reflect":i=2;break;case"wrap":i=3;break;case"nearest":i=4;break;default:i=1;break}this.userCode=`
             float mapCoord(float outCoord, float len) {
               float inCoord = outCoord;
               if(${i} == 2) {
@@ -4776,7 +4776,7 @@ return a / b;`,Nle=`
               }
               setOutput(outputValue);
             }
-        `}};function fde(e){let{inputs:t,backend:n,attrs:s}=e,{image:r,transforms:a}=t,{interpolation:o,fillMode:i,fillValue:l,outputShape:u}=s,[c,p,d,h]=r.shape,[f,m]=u!=null?u:[p,d],g=[c,f,m,h],y=new hde(p,d,o,i,l,g);return n.runWebGLProgram(y,[r,a],"float32")}var mde={kernelName:eu,backendName:"webgl",kernelFunc:fde};function gde(e){let{inputs:t,attrs:n,backend:s}=e,{axis:r}=n,{x:a}=t;cd(a,"unique"),console.warn("WARNING: ","UI might be locked temporarily as data is being downloaded");let o=s.readSync(a.dataId),{outputValues:i,outputShape:l,indices:u}=Tne(o,r,a.shape,a.dtype);return[s.makeTensorInfo(l,a.dtype,i),s.makeTensorInfo([u.length],"int32",u)]}var yde={kernelName:C0,backendName:"webgl",kernelFunc:gde};function Ade(e){let{inputs:t,backend:n,attrs:s}=e,{value:r}=t,{axis:a}=s;a<0&&(a+=r.shape.length);let o=r,i=o.shape.length,l=r.shape[a],u=new Array(i-1),c=0;for(let m=0;m<i;m++)m!==a&&(u[c++]=o.shape[m]);let p=[],d=new Array(i).fill(0),h=o.shape.slice();h[a]=1;let f=new Array(l);for(let m=0;m<f.length;m++){d[a]=m;let g=yd({inputs:{x:o},backend:n,attrs:{begin:d,size:h}}),y=we({inputs:{x:g},backend:n,attrs:{shape:u}});f[m]=y,p.push(g)}return p.forEach(m=>n.disposeIntermediateTensorInfo(m)),f}var xde={kernelName:tu,backendName:"webgl",kernelFunc:Ade},bde=class{constructor(e,t){this.variableNames=["x","segmentIds"];let n=e.windowSize,s=e.batchSize,r=e.inSize,a=e.numSegments,o=a*Math.ceil(r/n);this.outputShape=[s,o];let i="0.0",l="sumValue",u=Math.floor(n/4)*4,c=n%4,p=`
+        `}};function Cde(e){let{inputs:t,backend:n,attrs:s}=e,{image:r,transforms:a}=t,{interpolation:o,fillMode:i,fillValue:l,outputShape:u}=s,[c,p,d,h]=r.shape,[f,m]=u!=null?u:[p,d],g=[c,f,m,h],y=new Ide(p,d,o,i,l,g);return n.runWebGLProgram(y,[r,a],"float32")}var Tde={kernelName:ru,backendName:"webgl",kernelFunc:Cde};function Nde(e){let{inputs:t,attrs:n,backend:s}=e,{axis:r}=n,{x:a}=t;hd(a,"unique"),console.warn("WARNING: ","UI might be locked temporarily as data is being downloaded");let o=s.readSync(a.dataId),{outputValues:i,outputShape:l,indices:u}=Lne(o,r,a.shape,a.dtype);return[s.makeTensorInfo(l,a.dtype,i),s.makeTensorInfo([u.length],"int32",u)]}var Ede={kernelName:C0,backendName:"webgl",kernelFunc:Nde};function Rde(e){let{inputs:t,backend:n,attrs:s}=e,{value:r}=t,{axis:a}=s;a<0&&(a+=r.shape.length);let o=r,i=o.shape.length,l=r.shape[a],u=new Array(i-1),c=0;for(let m=0;m<i;m++)m!==a&&(u[c++]=o.shape[m]);let p=[],d=new Array(i).fill(0),h=o.shape.slice();h[a]=1;let f=new Array(l);for(let m=0;m<f.length;m++){d[a]=m;let g=bd({inputs:{x:o},backend:n,attrs:{begin:d,size:h}}),y=we({inputs:{x:g},backend:n,attrs:{shape:u}});f[m]=y,p.push(g)}return p.forEach(m=>n.disposeIntermediateTensorInfo(m)),f}var _de={kernelName:au,backendName:"webgl",kernelFunc:Rde},Dde=class{constructor(e,t){this.variableNames=["x","segmentIds"];let n=e.windowSize,s=e.batchSize,r=e.inSize,a=e.numSegments,o=a*Math.ceil(r/n);this.outputShape=[s,o];let i="0.0",l="sumValue",u=Math.floor(n/4)*4,c=n%4,p=`
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     `,d="";r%n>0&&(d=`
         if (inIdx < 0 || inIdx >= ${r}) {
@@ -4882,10 +4882,10 @@ return a / b;`,Nle=`
         }
         setOutput(${l});
       }
-    `}};function vde(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,segmentIds:a}=t,{numSegments:o}=s,i=r.shape.length,l=[],u=0,c=T.getAxesPermutation([u],i),p=r;c!=null&&(p=is({inputs:{x:r},backend:n,attrs:{perm:c}}),l.push(p),u=T.getInnerMostAxes(1,i)[0]);let d=T.segment_util.computeOutShape(p.shape,u,o),h=v.sizeFromShape([p.shape[u]]),f=we({inputs:{x:p},backend:n,attrs:{shape:[-1,h]}});l.push(f);let m=uh(r.dtype),g=(b,w,k,C,E)=>{let _=b.shape[0],$=b.shape[1],R=T.segment_util.segOpComputeOptimalWindowSize($,E),P={windowSize:R,inSize:$,batchSize:_,numSegments:E},S=new bde(P,w),M=n.compileAndRun(S,[b,k],C);if(l.push(M),M.shape[1]===E)return M;let L=sC({backend:n,attrs:{start:0,stop:E,step:1,dtype:"float32"}}),U=aC({inputs:{x:L},backend:n,attrs:{reps:[$/R]}});return l.push(L),l.push(U),g(M,w,U,C,E)},y=g(f,"unsortedSegmentSum",a,m,o),x=we({inputs:{x:y},backend:n,attrs:{shape:d}}),A=x;if(c!=null){l.push(x);let b=T.getUndoAxesPermutation(c);A=is({inputs:{x:A},backend:n,attrs:{perm:b}})}return 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A=T.expandShapeToKeepDim(x.shape,d);x.shape=A}return x}var Ude={kernelName:Tc,backendName:"wasm",setupFunc:Wde,kernelFunc:Vde},dC;function Gde(e){dC=e.wasm.cwrap(go,null,["number","number","number","number","number"])}function Hde(e){let{backend:t,inputs:n,attrs:s}=e,{axis:r}=s,{x:a}=n,o=t.dataIdMap.get(a.dataId).id,i=o,l=a,{transposed:u,axes:c,inputWasTransposed:p}=gi(a,r,t);if(p){let y=t.dataIdMap.get(u.dataId).id;y!==o&&(l=u,i=y)}let d=l.shape.slice(0,-1),h=t.makeOutput(d,"int32"),f=t.dataIdMap.get(h.dataId).id,m=v.sizeFromShape(h.shape),g=l.shape[c[0]];return dC(i,qt[l.dtype],m,g,f),p&&t.disposeData(u.dataId),h}var jde={kernelName:go,backendName:"wasm",kernelFunc:Hde,setupFunc:Gde},pC;function qde(e){pC=e.wasm.cwrap(yo,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function 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Please use 'channelsLast'.`);if(c.dilationWidth!==1||c.dilationHeight!==1)throw new Error(`was backend only supports average pooling with dilation = [1, 1], got [${c.dilationHeight}, ${c.dilationWidth}].`);let b=s.makeOutput(c.outShape,"float32"),w=s.dataIdMap.get(b.dataId).id;return pC(a,r.shape[0],r.shape[1],r.shape[2],p,d,h,f,m,g,y,x,A,w),b}var Kde={kernelName:yo,backendName:"wasm",setupFunc:qde,kernelFunc:Xde};function As(e){let{inputs:t,attrs:n}=e,{x:s}=t,{shape:r}=n,a=v.sizeFromShape(s.shape),o=v.inferFromImplicitShape(r,a);return v.assert(a===v.sizeFromShape(o),()=>`new shape: ${o}, old shape: ${s.shape}. New shape and old shape must have the same number of elements.`),e.backend.incRef(s.dataId),{dataId:s.dataId,shape:o,dtype:s.dtype}}var Zde={kernelName:Vl,backendName:"wasm",kernelFunc:As},hC;function Yde(e){hC=e.wasm.cwrap(Ao,null,["number","array","number","number","array","number","number","number","number"])}function Jde(e){let{inputs:t,backend:n,attrs:s}=e,{a:r,b:a}=t,{transposeA:o,transposeB:i}=s;if(r.dtype!=="float32"||a.dtype!=="float32")throw new Error("BatchMatMul for non non-float32 tensors not yet supported.");let l=r.shape.length,u=a.shape.length,c=o?r.shape[l-2]:r.shape[l-1],p=i?a.shape[u-1]:a.shape[u-2],d=o?r.shape[l-1]:r.shape[l-2],h=i?a.shape[u-2]:a.shape[u-1],f=r.shape.slice(0,-2),m=a.shape.slice(0,-2),g=v.sizeFromShape(f),y=v.sizeFromShape(m),A=au.assertAndGetBroadcastShape(r.shape.slice(0,-2),a.shape.slice(0,-2)).concat([d,h]);v.assert(c===p,()=>`Error in matMul: inner shapes (${c}) and (${p}) of Tensors with shapes ${r.shape} and ${a.shape} and transposeA=${o} and transposeB=${i} must match.`);let b=o?[g,c,d]:[g,d,c],w=i?[y,h,p]:[y,p,h],k=As({inputs:{x:r},backend:n,attrs:{shape:b}}),C=As({inputs:{x:a},backend:n,attrs:{shape:w}}),E=n.dataIdMap.get(k.dataId).id,_=n.dataIdMap.get(C.dataId).id,$=o?k.shape[2]:k.shape[1],R=i?C.shape[1]:C.shape[2],P=Math.max(g,y),S=n.makeOutput([P,$,R],k.dtype),M=n.dataIdMap.get(S.dataId).id,L=new Uint8Array(new Int32Array(k.shape).buffer),U=new Uint8Array(new Int32Array(C.shape).buffer);return hC(E,L,k.shape.length,_,U,C.shape.length,o,i,M),n.disposeData(k.dataId),n.disposeData(C.dataId),S.shape=A,S}var Qde={kernelName:Ao,backendName:"wasm",setupFunc:Yde,kernelFunc:Jde};function hl(e){let{inputs:{x:t},attrs:{begin:n,size:s},backend:r}=e,[a,o]=Ht.parseSliceParams(t,n,s),i=Ht.isSliceContinous(t.shape,a,o),l=r.readSync(t.dataId),u=r.makeOutput(o,t.dtype),c=v.computeStrides(t.shape),p=r.dataIdMap.get(u.dataId);if(i){let f=Ht.computeFlatOffset(a,c);return t.dtype==="string"?p.stringBytes=l.slice(f,f+v.sizeFromShape(o)):r.typedArrayFromHeap(u).set(l.subarray(f,f+v.sizeFromShape(o))),u}if(t.dtype==="string"){let f=Um(l,a,o,t.shape,t.dtype);return p.stringBytes=f,u}let d=r.typedArrayFromHeap(u),h=t.shape.length;if(h===2)epe(l,c[0],d,a,o);else if(h===3)tpe(l,c[0],c[1],d,a,o);else if(h===4)npe(l,c[0],c[1],c[2],d,a,o);else{let f=Um(l,a,o,t.shape,t.dtype);d.set(f)}return u}function epe(e,t,n,s,r){let a=0,o=s[0],i=s[1],l=o+r[0];for(let u=o;u<l;u++){let c=u*t+i;n.set(e.subarray(c,c+r[1]),a),a+=r[1]}}function tpe(e,t,n,s,r,a){let o=0,i=r[0],l=r[1],u=r[2],c=i+a[0],p=l+a[1];for(let d=i;d<c;d++)for(let h=l;h<p;h++){let f=d*t+h*n+u;s.set(e.subarray(f,f+a[2]),o),o+=a[2]}}function npe(e,t,n,s,r,a,o){let i=0,l=a[0],u=a[1],c=a[2],p=l+o[0],d=u+o[1],h=c+o[2],f=a[3];for(let m=l;m<p;m++)for(let g=u;g<d;g++)for(let y=c;y<h;y++){let x=m*t+g*n+y*s+f;r.set(e.subarray(x,x+o[3]),i),i+=o[3]}}var spe={kernelName:ql,backendName:"wasm",kernelFunc:hl};function rpe(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockShape:a,crops:o}=s,i=a.reduce((y,x)=>y*x),l=T.getReshaped(r.shape,a,i),u=T.getPermuted(l.length,a.length),c=T.getReshapedPermuted(r.shape,a,i),p=T.getSliceBeginCoords(o,a.length),d=T.getSliceSize(c,o,a.length),h=As({inputs:{x:r},backend:n,attrs:{shape:l}}),f=po({inputs:{x:h},backend:n,attrs:{perm:u}}),m=As({inputs:{x:f},backend:n,attrs:{shape:c}}),g=hl({inputs:{x:m},backend:n,attrs:{begin:p,size:d}});return n.disposeData(h.dataId),n.disposeData(f.dataId),n.disposeData(h.dataId),g}var ape={kernelName:yl,backendName:"wasm",kernelFunc:rpe};function Ad(e){let{inputs:{x:t},attrs:{dtype:n},backend:s}=e,r=s.makeOutput(t.shape,n),a=s.typedArrayFromHeap(t);return s.typedArrayFromHeap(r).set(a),r}var ope={kernelName:xo,backendName:"wasm",kernelFunc:Ad},ipe=En(bo),fC;function lpe(e){fC=e.wasm.cwrap(Ea,null,["number","number","number","number"])}function upe(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{clipValueMin:a,clipValueMax:o}=s,i=n.dataIdMap.get(r.dataId).id,l=n.makeOutput(r.shape,r.dtype),u=n.dataIdMap.get(l.dataId).id;return fC(i,a,o,u),l}var cpe={kernelName:Ea,backendName:"wasm",setupFunc:lpe,kernelFunc:upe};function mC(e){let{inputs:t,backend:n}=e,s=v.parseAxisParam(e.attrs.axis,t[0].shape)[0],r=T.computeOutShape(t.map(h=>h.shape),s),a=t.filter(h=>v.sizeFromShape(h.shape)>0);if(a.length===1)return z2({inputs:{x:a[0]},backend:n});let o=n.makeOutput(r,t[0].dtype);if(v.sizeFromShape(r)===0)return o;let i=a.map(h=>h.shape);if(T.assertParamsConsistent(i,s),a[0].dtype==="string"){let h=a.map(A=>{let b=v.sizeFromShape(A.shape.slice(s));return As({inputs:{x:A},backend:n,attrs:{shape:[-1,b]}})}),f=h.map(A=>({vals:n.readSync(A.dataId),shape:A.shape}));r=T.computeOutShape(h.map(A=>A.shape),1);let m=h[0].shape[0]===1,g=Gx(f,r,t[0].dtype,m),y=T.computeOutShape(a.map(A=>A.shape),s);o.shape=y;let x=n.dataIdMap.get(o.dataId);return 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hpe(e){let{inputs:t,attrs:n,backend:s}=e,{x:r,filter:a}=t,o=s.dataIdMap.get(r.dataId).id,i=s.dataIdMap.get(a.dataId).id,{strides:l,dilations:u,pad:c,dimRoundingMode:p,dataFormat:d}=n,h=T.convertConv2DDataFormat(d),f=T.computeConv2DInfo(r.shape,a.shape,l,u,c,p,!1,h),m=f.filterHeight,g=f.filterWidth,y=f.padInfo.top,x=f.padInfo.right,A=f.padInfo.bottom,b=f.padInfo.left,w=f.dilationHeight,k=f.dilationWidth,C=f.strideHeight,E=f.strideWidth,_=f.inChannels,$=f.outChannels,R=f.padInfo.type==="SAME"?1:0;if(f.dataFormat!=="channelsLast")throw new Error(`wasm backend Conv2D does not support dataFormat:'${f.dataFormat}'. 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gpe(e){let{backend:t,inputs:n,attrs:s}=e,{dy:r,filter:a}=n,{strides:o,pad:i,dataFormat:l,dimRoundingMode:u,inputShape:c}=s,p=1,d=T.convertConv2DDataFormat(l),h=T.computeConv2DInfo(c,a.shape,o,p,i,u,!1,d),{batchSize:f,filterHeight:m,filterWidth:g,inChannels:y,inHeight:x,inWidth:A,outChannels:b,outHeight:w,outWidth:k,strideHeight:C,strideWidth:E}=h,_=m-1-h.padInfo.top,$=g-1-h.padInfo.left,R=h.dataFormat==="channelsLast",P=v.computeStrides(h.inShape),S=v.computeStrides(r.shape),[M,L,U]=v.computeStrides(a.shape),K=P[0],q=R?P[1]:P[2],Z=R?P[2]:1,J=R?1:P[1],Q=S[0],le=R?S[1]:S[2],ae=R?S[2]:1,pe=R?1:S[1],ce=t.makeOutput(h.inShape,"float32"),xe=t.dataIdMap.get(ce.dataId).id,ie=t.dataIdMap.get(r.dataId).id,_e=t.dataIdMap.get(a.dataId).id;return yC(ie,_e,f,m,g,x,A,y,w,k,b,C,E,_,$,M,L,U,K,q,Z,J,Q,le,ae,pe,xe),ce}var ype={kernelName:wo,backendName:"wasm",setupFunc:mpe,kernelFunc:gpe},Ape=En(ko),xpe=En(So),Sy;(function(e){e[e.bilinear=0]="bilinear",e[e.nearest=1]="nearest"})(Sy||(Sy={}));var AC;function bpe(e){AC=e.wasm.cwrap(bl,null,["number","number","number","number","array","number","number","number","number","number"])}function vpe(e){let{backend:t,inputs:n,attrs:s}=e,{method:r,extrapolationValue:a,cropSize:o}=s,{image:i,boxes:l,boxInd:u}=n,c=l.shape[0],[p,d]=o,h=[c,p,d,i.shape[3]],f=t.dataIdMap.get(i.dataId),m;i.dtype!=="float32"&&(m=Ad({backend:t,inputs:{x:i},attrs:{dtype:"float32"}}),f=t.dataIdMap.get(m.dataId));let g=f.id,y=t.dataIdMap.get(l.dataId).id,x=t.dataIdMap.get(u.dataId).id,A=t.makeOutput(h,"float32"),b=t.dataIdMap.get(A.dataId).id,w=new Uint8Array(new Int32Array(i.shape).buffer);return AC(g,y,x,c,w,p,d,Sy[r],a,b),m!=null&&t.disposeData(m.dataId),A}var wpe={kernelName:bl,backendName:"wasm",setupFunc:bpe,kernelFunc:vpe},xC;function kpe(e){xC=e.wasm.cwrap(xl,null,["number","number","number","number","number","number"])}function Spe(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,exclusive:o,reverse:i}=s,l=r.shape.length;v.assert(r.dtype==="float32"||r.dtype==="int32",()=>`cumprod does not support ${r.dtype} tensors in the WASM backend`);let u=T.getAxesPermutation([a],l),c=r;u!==null&&(c=po({inputs:{x:r},attrs:{perm:u},backend:n}));let p=T.getInnerMostAxes(1,l)[0];T.assertAxesAreInnerMostDims("cumprod",[p],l);let d=n.makeOutput(c.shape,c.dtype),h=c.shape[p],f=n.dataIdMap.get(c.dataId).id,m=n.dataIdMap.get(d.dataId).id;xC(f,o?1:0,i?1:0,h,m,qt[r.dtype]);let g=d;if(u!==null){let y=T.getUndoAxesPermutation(u);g=po({inputs:{x:d},attrs:{perm:y},backend:n}),n.disposeData(c.dataId),n.disposeData(d.dataId)}return g}var Ipe={kernelName:xl,backendName:"wasm",setupFunc:kpe,kernelFunc:Spe},bC;function Cpe(e){bC=e.wasm.cwrap(Io,null,["number","number","number","number","number","number"])}function Tpe(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,exclusive:o,reverse:i}=s,l=r.shape.length;v.assert(r.dtype==="float32"||r.dtype==="int32",()=>`cumsum does not support ${r.dtype} tensors in the WASM backend`);let u=T.getAxesPermutation([a],l),c=r;u!==null&&(c=po({inputs:{x:r},attrs:{perm:u},backend:n}));let p=T.getInnerMostAxes(1,l)[0];T.assertAxesAreInnerMostDims("cumsum",[p],l);let d=n.makeOutput(c.shape,c.dtype),h=c.shape[p],f=n.dataIdMap.get(c.dataId).id,m=n.dataIdMap.get(d.dataId).id;bC(f,o?1:0,i?1:0,h,m,qt[r.dtype]);let g=d;if(u!==null){let y=T.getUndoAxesPermutation(u);g=po({inputs:{x:d},attrs:{perm:y},backend:n}),n.disposeData(c.dataId),n.disposeData(d.dataId)}return g}var Npe={kernelName:Io,backendName:"wasm",setupFunc:Cpe,kernelFunc:Tpe},vC;function Epe(e){vC=e.wasm.cwrap(vl,null,["number","number","number","array","number","array","array","number","number"])}function Rpe(e){let{backend:t,inputs:n,attrs:s}=e,{x:r}=n,{blockSize:a,dataFormat:o}=s,i=r.shape[0],l=o==="NHWC"?r.shape[1]:r.shape[2],u=o==="NHWC"?r.shape[2]:r.shape[3],c=o==="NHWC"?r.shape[3]:r.shape[1],p=l*a,d=u*a,h=c/(a*a),f=o==="NHWC"?[i,p,d,h]:[i,h,p,d],m=t.makeOutput(f,"float32"),y=t.dataIdMap.get(r.dataId).id,x=new Uint8Array(new Int32Array(v.computeStrides(r.shape)).buffer),A=new Uint8Array(new Int32Array(f).buffer),b=new Uint8Array(new Int32Array(v.computeStrides(f)).buffer),w=t.dataIdMap.get(m.dataId).id;return vC(y,a,o==="NHWC"?1:0,x,r.shape.length-1,A,b,f.length,w),m}var _pe={kernelName:vl,backendName:"wasm",setupFunc:Epe,kernelFunc:Rpe},wC;function Dpe(e){wC=e.wasm.cwrap(Co,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function $pe(e){let{inputs:t,attrs:n,backend:s}=e,{x:r,filter:a}=t,o=s.dataIdMap.get(r.dataId).id,i=s.dataIdMap.get(a.dataId).id,{strides:l,dilations:u,pad:c,dimRoundingMode:p}=n,d=u==null?[1,1]:u,h=T.computeConv2DInfo(r.shape,a.shape,l,d,c,p,!0),f=h.filterHeight,m=h.filterWidth,g=h.padInfo.top,y=h.padInfo.right,x=h.padInfo.bottom,A=h.padInfo.left,b=h.dilationHeight,w=h.dilationWidth,k=h.strideHeight,C=h.strideWidth,E=h.inChannels,_=h.outChannels,$=h.padInfo.type==="SAME"?1:0;if(h.dataFormat!=="channelsLast")throw new Error(`wasm backend DepthwiseConv2dNative does not support dataFormat:'${h.dataFormat}'. 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Please use 'NHWC'.`);let J=s.makeOutput(m.outShape,"float32"),Q=s.dataIdMap.get(J.dataId).id,le=i==null?0:s.dataIdMap.get(i.dataId).id;return CC(y,K,q,Z,x,w,k,b,C,E,_,$,U,R,P,S,M,L,A,g,le,f||0,Q),J}var Jpe={kernelName:no,backendName:"wasm",setupFunc:Zpe,kernelFunc:Ype},TC;function Qpe(e){TC=e.wasm.cwrap(so,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function ehe(e){let{inputs:t,attrs:n,backend:s}=e,{x:r,filter:a,bias:o,preluActivationWeights:i}=t,{strides:l,pad:u,dilations:c,dataFormat:p,dimRoundingMode:d,activation:h,leakyreluAlpha:f}=n,m=T.computeConv2DInfo(r.shape,a.shape,l,c,u,d,!0),g=Bp[h];if(g==null)throw new Error(`${h} activation not yet supported for FusedDepthwiseConv2D in the wasm backend.`);let y=s.dataIdMap.get(r.dataId).id,x=s.dataIdMap.get(a.dataId).id,A=m.outChannels,b=0;if(o!=null){let ae=s.dataIdMap.get(o.dataId);if(ae.shape.length!==1)throw new Error(`FusedDepthwiseConv2D only supports rank-1 bias but got rank ${ae.shape.length}.`);if(ae.shape[0]!==A)throw new Error(`FusedDepthwiseConv2D bias shape (${ae.shape}) does not match the number of output channels (${A})`);b=ae.id}let w=m.filterHeight,k=m.filterWidth,C=m.padInfo.top,E=m.padInfo.right,_=m.padInfo.bottom,$=m.padInfo.left,R=m.dilationHeight,P=m.dilationWidth,S=m.strideHeight,M=m.strideWidth,L=m.inChannels,U=m.padInfo.type==="SAME"?1:0,K=m.batchSize,q=m.inHeight,Z=m.inWidth;if(p!=="NHWC")throw new Error(`wasm backend FusedDepthwiseConv2D does not support dataFormat:'${p}'. Please use 'NHWC'.`);let J=s.makeOutput(m.outShape,"float32"),Q=s.dataIdMap.get(J.dataId).id,le=i==null?0:s.dataIdMap.get(i.dataId).id;return TC(y,K,q,Z,x,w,k,b,C,E,_,$,U,R,P,S,M,L,A,g,le,f||0,Q),J}var the={kernelName:so,backendName:"wasm",setupFunc:Qpe,kernelFunc:ehe},NC;function nhe(e){NC=e.wasm.cwrap(Tl,null,["number","number","number","number","number","number","array","number"])}function she(e){let{backend:t,inputs:n}=e,{params:s,indices:r}=n,[a,o,i,l]=Qy.prepareAndValidate(s,r),u=t.makeOutput(a,s.dtype);if(o===0)return u;let c=r.shape,p=c[c.length-1],h=t.dataIdMap.get(s.dataId).id,m=t.dataIdMap.get(r.dataId).id,g=new Uint8Array(new Int32Array(l).buffer),y=t.dataIdMap.get(u.dataId).id;return NC(h,qt[s.dtype],m,o,p,i,g,y),u}var rhe={kernelName:Tl,backendName:"wasm",setupFunc:nhe,kernelFunc:she},EC;function ahe(e){EC=e.wasm.cwrap("Gather",null,["number","number","array","number","number","number","array","number"])}function ohe(e){let{backend:t,inputs:n,attrs:s}=e,{x:r,indices:a}=n,{axis:o,batchDims:i}=s,l=v.parseAxisParam(o,r.shape)[0],u=t.readSync(a.dataId),c=r.shape[l];for(let _=0;_<u.length;++_){let $=u[_];v.assert($<=c-1&&$>=0,()=>`GatherV2: the index value ${$} is not in [0, ${c-1}]`)}let p=T.segment_util.collectGatherOpShapeInfo(r,a,l,i),d=As({inputs:{x:r},attrs:{shape:[p.batchSize,p.outerSize,p.dimSize,p.sliceSize]},backend:t}),h=v.sizeFromShape(a.shape),f=As({inputs:{x:a},attrs:{shape:[p.batchSize,h/p.batchSize]},backend:t}),m=[p.batchSize,p.outerSize,h/p.batchSize,p.sliceSize],g=t.makeOutput(m,r.dtype);if(v.sizeFromShape(r.shape)===0)return g;let y=d.shape.length-1,A=t.dataIdMap.get(d.dataId).id,w=t.dataIdMap.get(f.dataId).id,k=t.dataIdMap.get(g.dataId).id,C=new Uint8Array(new Int32Array(v.computeStrides(d.shape)).buffer),E=new Uint8Array(new Int32Array(v.computeStrides(m)).buffer);return EC(A,qt[r.dtype],C,y,w,p.batchSize,E,k),t.disposeData(d.dataId),t.disposeData(f.dataId),g.shape=p.outputShape,g}var ihe={kernelName:Cl,backendName:"wasm",setupFunc:ahe,kernelFunc:ohe},lhe=!1,uhe=Bn(Nl,lhe,"bool"),che=!1,dhe=Bn($o,che,"bool"),RC;function phe(e){RC=e.wasm.cwrap(Fo,null,["number","number","number","number"])}function hhe(e){let{inputs:{x:t},attrs:{alpha:n},backend:s}=e,r=s.dataIdMap.get(t.dataId).id,a=s.makeOutput(t.shape,"float32");if(v.sizeFromShape(t.shape)!==0){let o=s.dataIdMap.get(a.dataId).id;RC(r,qt[t.dtype],n,o)}return a}var fhe={kernelName:Fo,backendName:"wasm",setupFunc:phe,kernelFunc:hhe},mhe=!1,ghe=Bn(Rl,mhe,"bool"),yhe=!1,Ahe=Bn(_l,yhe,"bool"),xhe=En(Oo),bhe=!1,vhe=Bn(Dl,bhe,"bool"),whe=En($l),khe=!1,She=Bn(zc,khe,"bool"),Ihe=!1,Che=Bn(E6,Ihe,"bool"),_C;function The(e){_C=e.wasm.cwrap(Mo,null,["number","number","number","number"])}function Nhe(e){let{backend:t,inputs:n,attrs:s}=e,{reductionIndices:r,keepDims:a}=s,{x:o}=n,l=t.dataIdMap.get(o.dataId).id,u=o,{transposed:c,axes:p,originalAxes:d,inputWasTransposed:h}=gi(o,r,t);if(h){let A=t.dataIdMap.get(c.dataId).id;u=c,l=A}let f=u.shape.length;T.assertAxesAreInnerMostDims("max",p,f);let[m,g]=T.computeOutAndReduceShapes(u.shape,p),y=v.sizeFromShape(g),x=t.makeOutput(m,o.dtype);if(v.sizeFromShape(u.shape)!==0){let A=t.dataIdMap.get(x.dataId).id;_C(l,qt[o.dtype],y,A)}if(h&&t.disposeData(c.dataId),a){let A=T.expandShapeToKeepDim(x.shape,d);x.shape=A}return x}var Ehe={kernelName:Mo,backendName:"wasm",setupFunc:The,kernelFunc:Nhe},Rhe=!1,_he=Bn(zo,Rhe),DC;function Dhe(e){DC=e.wasm.cwrap(Lo,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function $he(e){let{inputs:t,attrs:n,backend:s}=e,r=t.x,a=s.dataIdMap.get(r.dataId).id;v.assert(r.dtype==="float32",()=>`Error in MaxPool: only float32 input is supported. 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b=T.expandShapeToKeepDim(A.shape,d);A.shape=b}return u.dtype!=="float32"&&t.disposeData(x.dataId),A}var Mhe={kernelName:Bo,backendName:"wasm",setupFunc:Fhe,kernelFunc:Ohe},PC;function zhe(e){PC=e.wasm.cwrap(Wo,null,["number","number","number","number"])}function Lhe(e){let{backend:t,inputs:n,attrs:s}=e,{axis:r,keepDims:a}=s,{x:o}=n,i=t.dataIdMap.get(o.dataId).id,l=i,u=o,{transposed:c,axes:p,originalAxes:d,inputWasTransposed:h}=gi(o,r,t);if(h){let A=t.dataIdMap.get(c.dataId).id;A!==i&&(u=c,l=A)}let f=u.shape.length;T.assertAxesAreInnerMostDims("min",p,f);let[m,g]=T.computeOutAndReduceShapes(u.shape,p),y=v.sizeFromShape(g),x=t.makeOutput(m,u.dtype);if(v.sizeFromShape(u.shape)!==0){let A=t.dataIdMap.get(x.dataId).id;PC(l,qt[o.dtype],y,A)}if(h&&t.disposeData(c.dataId),a){let A=T.expandShapeToKeepDim(x.shape,d);x.shape=A}return x}var Bhe={kernelName:Wo,backendName:"wasm",setupFunc:zhe,kernelFunc:Lhe},Whe=!1,Vhe=Bn(Vo,Whe),Cy;(function(e){e[e.reflect=0]="reflect",e[e.symmetric=1]="symmetric"})(Cy||(Cy={}));var FC;function Uhe(e){FC=e.wasm.cwrap(Uo,null,["number","array","number","number","array","array","number","number"])}function Ghe(e){let{inputs:{x:t},backend:n,attrs:{paddings:s,mode:r}}=e,a=s.map((f,m)=>f[0]+t.shape[m]+f[1]),o=n.dataIdMap.get(t.dataId).id,i=n.makeOutput(a,t.dtype),l=n.dataIdMap.get(i.dataId).id,u=new Uint8Array(new Int32Array(t.shape).buffer),c=s.map(f=>f[0]),p=s.map(f=>f[1]),d=new Uint8Array(new Int32Array(c).buffer),h=new Uint8Array(new Int32Array(p).buffer);return FC(o,u,t.shape.length,qt[t.dtype],d,h,Cy[r],l),i}var Hhe={kernelName:Uo,backendName:"wasm",kernelFunc:Ghe,setupFunc:Uhe},jhe=!0,qhe=Bn(Go,jhe),Xhe=En(Pl);function mb(e,t){let n=new Int32Array(e.wasm.HEAPU8.buffer,t,4),s=n[0],r=n[1],a=n[2],o=n[3];return e.wasm._free(t),{pSelectedIndices:s,selectedSize:r,pSelectedScores:a,pValidOutputs:o}}var OC;function Khe(e){OC=e.wasm.cwrap(Ol,"number",["number","number","number","number","number"])}function Zhe(e){let{backend:t,inputs:n,attrs:s}=e,{iouThreshold:r,maxOutputSize:a,scoreThreshold:o}=s,{boxes:i,scores:l}=n,u=t.dataIdMap.get(i.dataId).id,c=t.dataIdMap.get(l.dataId).id,p=OC(u,c,a,r,o),{pSelectedIndices:d,selectedSize:h,pSelectedScores:f,pValidOutputs:m}=mb(t,p);return t.wasm._free(f),t.wasm._free(m),t.makeOutput([h],"int32",d)}var Yhe={kernelName:Ol,backendName:"wasm",setupFunc:Khe,kernelFunc:Zhe},MC;function Jhe(e){MC=e.wasm.cwrap(Bc,"number",["number","number","number","number","number","bool"])}function Qhe(e){let{backend:t,inputs:n,attrs:s}=e,{iouThreshold:r,maxOutputSize:a,scoreThreshold:o,padToMaxOutputSize:i}=s,{boxes:l,scores:u}=n,c=t.dataIdMap.get(l.dataId).id,p=t.dataIdMap.get(u.dataId).id,d=MC(c,p,a,r,o,i),{pSelectedIndices:h,selectedSize:f,pSelectedScores:m,pValidOutputs:g}=mb(t,d);t.wasm._free(m);let y=t.makeOutput([f],"int32",h),x=t.makeOutput([],"int32",g);return[y,x]}var efe={kernelName:Bc,backendName:"wasm",setupFunc:Jhe,kernelFunc:Qhe},zC;function tfe(e){zC=e.wasm.cwrap(Ml,"number",["number","number","number","number","number","number"])}function nfe(e){let{backend:t,inputs:n,attrs:s}=e,{iouThreshold:r,maxOutputSize:a,scoreThreshold:o,softNmsSigma:i}=s,{boxes:l,scores:u}=n,c=t.dataIdMap.get(l.dataId).id,p=t.dataIdMap.get(u.dataId).id,d=zC(c,p,a,r,o,i),{pSelectedIndices:h,selectedSize:f,pSelectedScores:m,pValidOutputs:g}=mb(t,d);t.wasm._free(g);let y=t.makeOutput([f],"int32",h),x=t.makeOutput([f],"float32",m);return[y,x]}var sfe={kernelName:Ml,backendName:"wasm",setupFunc:tfe,kernelFunc:nfe},rfe=!1,afe=Bn(Fl,rfe,"bool"),LC;function ofe(e){LC=e.wasm.cwrap(Ll,null,["number","number","number","number","number"])}function ife(e){let{inputs:t,backend:n,attrs:s}=e,{indices:r}=t,{dtype:a,depth:o,onValue:i,offValue:l}=s,u=n.makeOutput([...r.shape,o],a),c=n.dataIdMap.get(u.dataId).id,d=n.dataIdMap.get(r.dataId).id;return LC(d,o,i,l,c),u}var lfe={kernelName:Ll,backendName:"wasm",setupFunc:ofe,kernelFunc:ife};function ufe(e){let{inputs:{x:t},backend:n}=e,s=n.makeOutput(t.shape,t.dtype);return n.typedArrayFromHeap(s).fill(1),s}var cfe={kernelName:zl,backendName:"wasm",kernelFunc:ufe};function dfe(e){let{inputs:t,backend:n,attrs:s}=e,{axis:r}=s;if(t.length===1)return Iy({inputs:{input:t[0]},backend:n,attrs:{dim:r}});let a=t[0].shape,o=t[0].dtype;t.forEach(c=>{v.assertShapesMatch(a,c.shape,"All tensors passed to stack must have matching shapes"),v.assert(o===c.dtype,()=>"All tensors passed to stack must have matching 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Wp;(function(e){e[e.linear=0]="linear",e[e.relu=1]="relu",e[e.relu6=2]="relu6",e[e.prelu=3]="prelu",e[e.leakyrelu=4]="leakyrelu",e[e.sigmoid=5]="sigmoid",e[e.elu=6]="elu"})(Wp||(Wp={}));var pC;function Ode(e){pC=e.wasm.cwrap(ao,null,["number","array","number","number","array","number","number","number","number","number","number","number","number"])}function Mde(e){let{inputs:t,backend:n,attrs:s}=e,{a:r,b:a,bias:o,preluActivationWeights:i}=t;if(r.dtype!=="float32"||a.dtype!=="float32")throw new Error("_FusedMatMul for non non-float32 tensors not yet supported.");let{transposeA:l,transposeB:u,activation:c,leakyreluAlpha:p}=s,d=n.dataIdMap.get(r.dataId).id,h=n.dataIdMap.get(a.dataId).id,f=0;if(o!=null){let E=n.dataIdMap.get(o.dataId);if(E.shape.length!==1)throw new Error(`_FusedMatMul only supports rank-1 bias but got rank ${E.shape.length}.`);f=E.id}let m=i==null?0:n.dataIdMap.get(i.dataId).id,g=Wp[c];if(g==null)throw new Error(`${c} activation not yet supported for FusedConv2D in the 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ope(e){let{inputs:t,attrs:n,backend:s}=e,r=t.x,a=s.dataIdMap.get(r.dataId).id,{filterSize:o,strides:i,pad:l,dimRoundingMode:u}=n,c=T.computePool2DInfo(r.shape,o,i,1,l,u),p=c.filterHeight,d=c.filterWidth,h=c.padInfo.top,f=c.padInfo.right,m=c.padInfo.bottom,g=c.padInfo.left,y=c.strideHeight,x=c.strideWidth,A=c.inChannels;if(c.dataFormat!=="channelsLast")throw new Error(`wasm backend does not support dataFormat:'${c.dataFormat}'. 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gpe(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockShape:a,crops:o}=s,i=a.reduce((y,x)=>y*x),l=T.getReshaped(r.shape,a,i),u=T.getPermuted(l.length,a.length),c=T.getReshapedPermuted(r.shape,a,i),p=T.getSliceBeginCoords(o,a.length),d=T.getSliceSize(c,o,a.length),h=vs({inputs:{x:r},backend:n,attrs:{shape:l}}),f=go({inputs:{x:h},backend:n,attrs:{perm:u}}),m=vs({inputs:{x:f},backend:n,attrs:{shape:c}}),g=yl({inputs:{x:m},backend:n,attrs:{begin:p,size:d}});return n.disposeData(h.dataId),n.disposeData(f.dataId),n.disposeData(h.dataId),g}var ype={kernelName:vl,backendName:"wasm",kernelFunc:gpe};function vd(e){let{inputs:{x:t},attrs:{dtype:n},backend:s}=e,r=s.makeOutput(t.shape,n),a=s.typedArrayFromHeap(t);return s.typedArrayFromHeap(r).set(a),r}var Ape={kernelName:ko,backendName:"wasm",kernelFunc:vd},xpe=_n(So),bC;function bpe(e){bC=e.wasm.cwrap($a,null,["number","number","number","number"])}function vpe(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{clipValueMin:a,clipValueMax:o}=s,i=n.dataIdMap.get(r.dataId).id,l=n.makeOutput(r.shape,r.dtype),u=n.dataIdMap.get(l.dataId).id;return bC(i,a,o,u),l}var wpe={kernelName:$a,backendName:"wasm",setupFunc:bpe,kernelFunc:vpe};function vC(e){let{inputs:t,backend:n}=e,s=v.parseAxisParam(e.attrs.axis,t[0].shape)[0],r=T.computeOutShape(t.map(h=>h.shape),s),a=t.filter(h=>v.sizeFromShape(h.shape)>0);if(a.length===1)return z2({inputs:{x:a[0]},backend:n});let o=n.makeOutput(r,t[0].dtype);if(v.sizeFromShape(r)===0)return o;let i=a.map(h=>h.shape);if(T.assertParamsConsistent(i,s),a[0].dtype==="string"){let h=a.map(A=>{let b=v.sizeFromShape(A.shape.slice(s));return vs({inputs:{x:A},backend:n,attrs:{shape:[-1,b]}})}),f=h.map(A=>({vals:n.readSync(A.dataId),shape:A.shape}));r=T.computeOutShape(h.map(A=>A.shape),1);let m=h[0].shape[0]===1,g=qx(f,r,t[0].dtype,m),y=T.computeOutShape(a.map(A=>A.shape),s);o.shape=y;let x=n.dataIdMap.get(o.dataId);return x.stringBytes=T.fromStringArrayToUint8(g),h.forEach(A=>n.disposeData(A.dataId)),o}let l=v.sizeFromShape(a[0].shape.slice(0,s)),u=0,c=a.map(h=>{let f=v.sizeFromShape(h.shape.slice(s));return u+=f,f}),p=a.map(h=>n.typedArrayFromHeap(h)),d=n.typedArrayFromHeap(o);for(let h=0;h<l;h++){let f=h*u;for(let m=0;m<p.length;m++){let g=c[m],y=h*g,x=p[m].subarray(y,y+g);d.set(x,f),f+=g}}return o}var kpe={kernelName:wl,backendName:"wasm",kernelFunc:vC},wC;function Spe(e){wC=e.wasm.cwrap(Io,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Ipe(e){let{inputs:t,attrs:n,backend:s}=e,{x:r,filter:a}=t,o=s.dataIdMap.get(r.dataId).id,i=s.dataIdMap.get(a.dataId).id,{strides:l,dilations:u,pad:c,dimRoundingMode:p,dataFormat:d}=n,h=T.convertConv2DDataFormat(d),f=T.computeConv2DInfo(r.shape,a.shape,l,u,c,p,!1,h),m=f.filterHeight,g=f.filterWidth,y=f.padInfo.top,x=f.padInfo.right,A=f.padInfo.bottom,b=f.padInfo.left,w=f.dilationHeight,k=f.dilationWidth,C=f.strideHeight,E=f.strideWidth,_=f.inChannels,$=f.outChannels,R=f.padInfo.type==="SAME"?1:0;if(f.dataFormat!=="channelsLast")throw new Error(`wasm backend Conv2D does not support dataFormat:'${f.dataFormat}'. Please use 'channelsLast'.`);let P=s.makeOutput(f.outShape,"float32"),S=s.dataIdMap.get(P.dataId).id;return wC(o,r.shape[0],r.shape[1],r.shape[2],i,m,g,y,x,A,b,R,w,k,C,E,_,$,S),P}var Cpe={kernelName:Io,backendName:"wasm",setupFunc:Spe,kernelFunc:Ipe},kC;function Tpe(e){kC=e.wasm.cwrap(Co,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Npe(e){let{backend:t,inputs:n,attrs:s}=e,{dy:r,filter:a}=n,{strides:o,pad:i,dataFormat:l,dimRoundingMode:u,inputShape:c}=s,p=1,d=T.convertConv2DDataFormat(l),h=T.computeConv2DInfo(c,a.shape,o,p,i,u,!1,d),{batchSize:f,filterHeight:m,filterWidth:g,inChannels:y,inHeight:x,inWidth:A,outChannels:b,outHeight:w,outWidth:k,strideHeight:C,strideWidth:E}=h,_=m-1-h.padInfo.top,$=g-1-h.padInfo.left,R=h.dataFormat==="channelsLast",P=v.computeStrides(h.inShape),S=v.computeStrides(r.shape),[M,L,U]=v.computeStrides(a.shape),K=P[0],q=R?P[1]:P[2],Z=R?P[2]:1,J=R?1:P[1],te=S[0],le=R?S[1]:S[2],ae=R?S[2]:1,pe=R?1:S[1],ce=t.makeOutput(h.inShape,"float32"),xe=t.dataIdMap.get(ce.dataId).id,ie=t.dataIdMap.get(r.dataId).id,_e=t.dataIdMap.get(a.dataId).id;return kC(ie,_e,f,m,g,x,A,y,w,k,b,C,E,_,$,M,L,U,K,q,Z,J,te,le,ae,pe,xe),ce}var Epe={kernelName:Co,backendName:"wasm",setupFunc:Tpe,kernelFunc:Npe},Rpe=_n(To),_pe=_n(No),Ty;(function(e){e[e.bilinear=0]="bilinear",e[e.nearest=1]="nearest"})(Ty||(Ty={}));var SC;function Dpe(e){SC=e.wasm.cwrap(Sl,null,["number","number","number","number","array","number","number","number","number","number"])}function $pe(e){let{backend:t,inputs:n,attrs:s}=e,{method:r,extrapolationValue:a,cropSize:o}=s,{image:i,boxes:l,boxInd:u}=n,c=l.shape[0],[p,d]=o,h=[c,p,d,i.shape[3]],f=t.dataIdMap.get(i.dataId),m;i.dtype!=="float32"&&(m=vd({backend:t,inputs:{x:i},attrs:{dtype:"float32"}}),f=t.dataIdMap.get(m.dataId));let g=f.id,y=t.dataIdMap.get(l.dataId).id,x=t.dataIdMap.get(u.dataId).id,A=t.makeOutput(h,"float32"),b=t.dataIdMap.get(A.dataId).id,w=new Uint8Array(new Int32Array(i.shape).buffer);return SC(g,y,x,c,w,p,d,Ty[r],a,b),m!=null&&t.disposeData(m.dataId),A}var Ppe={kernelName:Sl,backendName:"wasm",setupFunc:Dpe,kernelFunc:$pe},IC;function Fpe(e){IC=e.wasm.cwrap(kl,null,["number","number","number","number","number","number"])}function Ope(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,exclusive:o,reverse:i}=s,l=r.shape.length;v.assert(r.dtype==="float32"||r.dtype==="int32",()=>`cumprod does not support ${r.dtype} tensors in the WASM backend`);let u=T.getAxesPermutation([a],l),c=r;u!==null&&(c=go({inputs:{x:r},attrs:{perm:u},backend:n}));let p=T.getInnerMostAxes(1,l)[0];T.assertAxesAreInnerMostDims("cumprod",[p],l);let d=n.makeOutput(c.shape,c.dtype),h=c.shape[p],f=n.dataIdMap.get(c.dataId).id,m=n.dataIdMap.get(d.dataId).id;IC(f,o?1:0,i?1:0,h,m,Kt[r.dtype]);let g=d;if(u!==null){let y=T.getUndoAxesPermutation(u);g=go({inputs:{x:d},attrs:{perm:y},backend:n}),n.disposeData(c.dataId),n.disposeData(d.dataId)}return g}var Mpe={kernelName:kl,backendName:"wasm",setupFunc:Fpe,kernelFunc:Ope},CC;function zpe(e){CC=e.wasm.cwrap(Eo,null,["number","number","number","number","number","number"])}function Lpe(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,exclusive:o,reverse:i}=s,l=r.shape.length;v.assert(r.dtype==="float32"||r.dtype==="int32",()=>`cumsum does not support ${r.dtype} tensors in the WASM backend`);let u=T.getAxesPermutation([a],l),c=r;u!==null&&(c=go({inputs:{x:r},attrs:{perm:u},backend:n}));let p=T.getInnerMostAxes(1,l)[0];T.assertAxesAreInnerMostDims("cumsum",[p],l);let d=n.makeOutput(c.shape,c.dtype),h=c.shape[p],f=n.dataIdMap.get(c.dataId).id,m=n.dataIdMap.get(d.dataId).id;CC(f,o?1:0,i?1:0,h,m,Kt[r.dtype]);let g=d;if(u!==null){let y=T.getUndoAxesPermutation(u);g=go({inputs:{x:d},attrs:{perm:y},backend:n}),n.disposeData(c.dataId),n.disposeData(d.dataId)}return g}var Bpe={kernelName:Eo,backendName:"wasm",setupFunc:zpe,kernelFunc:Lpe},TC;function Wpe(e){TC=e.wasm.cwrap(Il,null,["number","number","number","array","number","array","array","number","number"])}function Vpe(e){let{backend:t,inputs:n,attrs:s}=e,{x:r}=n,{blockSize:a,dataFormat:o}=s,i=r.shape[0],l=o==="NHWC"?r.shape[1]:r.shape[2],u=o==="NHWC"?r.shape[2]:r.shape[3],c=o==="NHWC"?r.shape[3]:r.shape[1],p=l*a,d=u*a,h=c/(a*a),f=o==="NHWC"?[i,p,d,h]:[i,h,p,d],m=t.makeOutput(f,"float32"),y=t.dataIdMap.get(r.dataId).id,x=new Uint8Array(new Int32Array(v.computeStrides(r.shape)).buffer),A=new Uint8Array(new Int32Array(f).buffer),b=new Uint8Array(new Int32Array(v.computeStrides(f)).buffer),w=t.dataIdMap.get(m.dataId).id;return TC(y,a,o==="NHWC"?1:0,x,r.shape.length-1,A,b,f.length,w),m}var Upe={kernelName:Il,backendName:"wasm",setupFunc:Wpe,kernelFunc:Vpe},NC;function Gpe(e){NC=e.wasm.cwrap(Ro,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Hpe(e){let{inputs:t,attrs:n,backend:s}=e,{x:r,filter:a}=t,o=s.dataIdMap.get(r.dataId).id,i=s.dataIdMap.get(a.dataId).id,{strides:l,dilations:u,pad:c,dimRoundingMode:p}=n,d=u==null?[1,1]:u,h=T.computeConv2DInfo(r.shape,a.shape,l,d,c,p,!0),f=h.filterHeight,m=h.filterWidth,g=h.padInfo.top,y=h.padInfo.right,x=h.padInfo.bottom,A=h.padInfo.left,b=h.dilationHeight,w=h.dilationWidth,k=h.strideHeight,C=h.strideWidth,E=h.inChannels,_=h.outChannels,$=h.padInfo.type==="SAME"?1:0;if(h.dataFormat!=="channelsLast")throw new Error(`wasm backend DepthwiseConv2dNative does not support dataFormat:'${h.dataFormat}'. 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b=T.expandShapeToKeepDim(A.shape,d);A.shape=b}return u.dtype!=="float32"&&t.disposeData(x.dataId),A}var Khe={kernelName:Go,backendName:"wasm",setupFunc:qhe,kernelFunc:Xhe},BC;function Zhe(e){BC=e.wasm.cwrap(Ho,null,["number","number","number","number"])}function Yhe(e){let{backend:t,inputs:n,attrs:s}=e,{axis:r,keepDims:a}=s,{x:o}=n,i=t.dataIdMap.get(o.dataId).id,l=i,u=o,{transposed:c,axes:p,originalAxes:d,inputWasTransposed:h}=bi(o,r,t);if(h){let A=t.dataIdMap.get(c.dataId).id;A!==i&&(u=c,l=A)}let f=u.shape.length;T.assertAxesAreInnerMostDims("min",p,f);let[m,g]=T.computeOutAndReduceShapes(u.shape,p),y=v.sizeFromShape(g),x=t.makeOutput(m,u.dtype);if(v.sizeFromShape(u.shape)!==0){let A=t.dataIdMap.get(x.dataId).id;BC(l,Kt[o.dtype],y,A)}if(h&&t.disposeData(c.dataId),a){let A=T.expandShapeToKeepDim(x.shape,d);x.shape=A}return x}var Jhe={kernelName:Ho,backendName:"wasm",setupFunc:Zhe,kernelFunc:Yhe},Qhe=!1,efe=Wn(jo,Qhe),Ey;(function(e){e[e.reflect=0]="reflect",e[e.symmetric=1]="symmetric"})(Ey||(Ey={}));var WC;function tfe(e){WC=e.wasm.cwrap(qo,null,["number","array","number","number","array","array","number","number"])}function nfe(e){let{inputs:{x:t},backend:n,attrs:{paddings:s,mode:r}}=e,a=s.map((f,m)=>f[0]+t.shape[m]+f[1]),o=n.dataIdMap.get(t.dataId).id,i=n.makeOutput(a,t.dtype),l=n.dataIdMap.get(i.dataId).id,u=new Uint8Array(new Int32Array(t.shape).buffer),c=s.map(f=>f[0]),p=s.map(f=>f[1]),d=new Uint8Array(new Int32Array(c).buffer),h=new Uint8Array(new Int32Array(p).buffer);return WC(o,u,t.shape.length,Kt[t.dtype],d,h,Ey[r],l),i}var sfe={kernelName:qo,backendName:"wasm",kernelFunc:nfe,setupFunc:tfe},rfe=!0,afe=Wn(Xo,rfe),ofe=_n(zl);function Ab(e,t){let n=new Int32Array(e.wasm.HEAPU8.buffer,t,4),s=n[0],r=n[1],a=n[2],o=n[3];return e.wasm._free(t),{pSelectedIndices:s,selectedSize:r,pSelectedScores:a,pValidOutputs:o}}var VC;function ife(e){VC=e.wasm.cwrap(Bl,"number",["number","number","number","number","number"])}function lfe(e){let{backend:t,inputs:n,attrs:s}=e,{iouThreshold:r,maxOutputSize:a,scoreThreshold:o}=s,{boxes:i,scores:l}=n,u=t.dataIdMap.get(i.dataId).id,c=t.dataIdMap.get(l.dataId).id,p=VC(u,c,a,r,o),{pSelectedIndices:d,selectedSize:h,pSelectedScores:f,pValidOutputs:m}=Ab(t,p);return t.wasm._free(f),t.wasm._free(m),t.makeOutput([h],"int32",d)}var ufe={kernelName:Bl,backendName:"wasm",setupFunc:ife,kernelFunc:lfe},UC;function cfe(e){UC=e.wasm.cwrap(Gc,"number",["number","number","number","number","number","bool"])}function dfe(e){let{backend:t,inputs:n,attrs:s}=e,{iouThreshold:r,maxOutputSize:a,scoreThreshold:o,padToMaxOutputSize:i}=s,{boxes:l,scores:u}=n,c=t.dataIdMap.get(l.dataId).id,p=t.dataIdMap.get(u.dataId).id,d=UC(c,p,a,r,o,i),{pSelectedIndices:h,selectedSize:f,pSelectedScores:m,pValidOutputs:g}=Ab(t,d);t.wasm._free(m);let y=t.makeOutput([f],"int32",h),x=t.makeOutput([],"int32",g);return[y,x]}var pfe={kernelName:Gc,backendName:"wasm",setupFunc:cfe,kernelFunc:dfe},GC;function hfe(e){GC=e.wasm.cwrap(Wl,"number",["number","number","number","number","number","number"])}function ffe(e){let{backend:t,inputs:n,attrs:s}=e,{iouThreshold:r,maxOutputSize:a,scoreThreshold:o,softNmsSigma:i}=s,{boxes:l,scores:u}=n,c=t.dataIdMap.get(l.dataId).id,p=t.dataIdMap.get(u.dataId).id,d=GC(c,p,a,r,o,i),{pSelectedIndices:h,selectedSize:f,pSelectedScores:m,pValidOutputs:g}=Ab(t,d);t.wasm._free(g);let y=t.makeOutput([f],"int32",h),x=t.makeOutput([f],"float32",m);return[y,x]}var mfe={kernelName:Wl,backendName:"wasm",setupFunc:hfe,kernelFunc:ffe},gfe=!1,yfe=Wn(Ll,gfe,"bool"),HC;function Afe(e){HC=e.wasm.cwrap(Ul,null,["number","number","number","number","number"])}function xfe(e){let{inputs:t,backend:n,attrs:s}=e,{indices:r}=t,{dtype:a,depth:o,onValue:i,offValue:l}=s,u=n.makeOutput([...r.shape,o],a),c=n.dataIdMap.get(u.dataId).id,d=n.dataIdMap.get(r.dataId).id;return HC(d,o,i,l,c),u}var bfe={kernelName:Ul,backendName:"wasm",setupFunc:Afe,kernelFunc:xfe};function vfe(e){let{inputs:{x:t},backend:n}=e,s=n.makeOutput(t.shape,t.dtype);return n.typedArrayFromHeap(s).fill(1),s}var wfe={kernelName:Vl,backendName:"wasm",kernelFunc:vfe};function kfe(e){let{inputs:t,backend:n,attrs:s}=e,{axis:r}=s;if(t.length===1)return Ny({inputs:{input:t[0]},backend:n,attrs:{dim:r}});let a=t[0].shape,o=t[0].dtype;t.forEach(c=>{v.assertShapesMatch(a,c.shape,"All tensors passed to stack must have matching shapes"),v.assert(o===c.dtype,()=>"All tensors passed to stack must have matching 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Int32Array(v.computeStrides(g)).buffer),A=t.makeOutput(g,r.dtype),b=t.dataIdMap.get(A.dataId).id,k=t.dataIdMap.get(r.dataId).id,E=t.dataIdMap.get(a.dataId).id,_=o==="nearest"?1:2,$;switch(i){case"constant":$=1;break;case"reflect":$=2;break;case"wrap":$=3;break;case"nearest":$=4;break;default:$=1;break}return lT(k,E,a.shape[0]>1,c,f,m,h,d,p,y,r.shape.length-1,x,g.length-1,_,$,l,b),A}var qme={kernelName:eu,backendName:"wasm",setupFunc:Hme,kernelFunc:jme};function Xme(e){let{inputs:t,backend:n,attrs:s}=e,{value:r}=t,{axis:a}=s;a<0&&(a+=r.shape.length);let o=r.shape[a],i=r.shape.length,l=new Array(i-1),u=0;for(let h=0;h<i;h++)h!==a&&(l[u++]=r.shape[h]);let c=new Array(o),p=new Array(i).fill(0),d=r.shape.slice();d[a]=1;for(let h=0;h<c.length;h++)p[a]=h,c[h]=hl({inputs:{x:r},attrs:{begin:p,size:d},backend:n});return c.map(({dataId:h,dtype:f})=>({dataId:h,dtype:f,shape:l}))}var Kme={kernelName:tu,backendName:"wasm",kernelFunc:Xme};function Zme(e){let{inputs:{x:t},backend:n}=e,s=n.makeOutput(t.shape,t.dtype);return n.typedArrayFromHeap(s).fill(0),s}var Yme={kernelName:nu,backendName:"wasm",kernelFunc:Zme},Jme=[Cde,Tde,Ede,Dde,Bde,Ude,jde,Kde,Qde,ape,ope,ipe,cpe,dpe,fpe,ype,Ape,xpe,wpe,Ipe,Npe,_pe,Ppe,Fpe,Mpe,zpe,Lpe,Bpe,Upe,Gpe,jpe,Kpe,Jpe,the,rhe,ihe,uhe,dhe,$de,fhe,ghe,Ahe,xhe,vhe,whe,She,Che,Ehe,_he,Phe,Mhe,Bhe,Vhe,Hhe,qhe,Xhe,Yhe,efe,sfe,afe,lfe,cfe,pfe,WC,gfe,xfe,wfe,Sfe,Cfe,Tfe,Nfe,Zde,_fe,Pfe,Mfe,Bfe,Wfe,Vfe,Hfe,Xfe,Yfe,Jfe,spe,tme,sme,ome,ume,dme,hme,mme,gme,yme,xme,wme,Ime,Tme,Eme,_me,$me,Ome,Mme,zme,Wme,Gme,qme,Mde,Kme,Yme];for(let e of Jme)nr(e);var Ty=H();Ty.registerFlag("WASM_HAS_SIMD_SUPPORT",async()=>WebAssembly.validate(new Uint8Array([0,97,115,109,1,0,0,0,1,4,1,96,0,0,3,2,1,0,10,9,1,7,0,65,0,253,15,26,11])));Ty.registerFlag("WASM_HAS_MULTITHREAD_SUPPORT",async()=>{if(Ty.get("IS_NODE"))return!1;try{return new MessageChannel().port1.postMessage(new SharedArrayBuffer(1)),WebAssembly.validate(new Uint8Array([0,97,115,109,1,0,0,0,1,4,1,96,0,0,3,2,1,0,5,4,1,3,1,1,10,11,1,9,0,65,0,254,16,2,0,26,11]))}catch(e){return!1}});var J7=ho(U_()),Qme=ho(G_()),Q7=ho(H_()),e6=J7.default||J7,e0e=Q7.default||Q7,uT=class extends wc{constructor(e){super(),this.wasm=e,this.dataIdNextNumber=1,this.wasm.tfjs.initWithThreadsCount(cT),Ny=this.wasm.tfjs.getThreadsCount(),this.dataIdMap=new Up(this,Jt())}write(e,t,n){let s={id:this.dataIdNextNumber++};return this.move(s,e,t,n,1),s}numDataIds(){return this.dataIdMap.numDataIds()}async time(e){let t=v.now();return e(),{kernelMs:v.now()-t}}move(e,t,n,s,r){let a=this.dataIdNextNumber++;if(s==="string"){let u=t;this.dataIdMap.set(e,{id:a,stringBytes:u,shape:n,dtype:s,memoryOffset:null,refCount:r});return}let o=v.sizeFromShape(n),i=o*v.bytesPerElement(s),l=this.wasm._malloc(i);this.dataIdMap.set(e,{id:a,memoryOffset:l,shape:n,dtype:s,refCount:r}),this.wasm.tfjs.registerTensor(a,o,l),t!=null&&this.wasm.HEAPU8.set(new Uint8Array(t.buffer,t.byteOffset,i),l)}async read(e){return this.readSync(e)}readSync(e,t,n){let{memoryOffset:s,dtype:r,shape:a,stringBytes:o}=this.dataIdMap.get(e);if(r==="string")return(t==null||t===0)&&(n==null||n>=o.length)?o:o.slice(t,n);t=t||0,n=n||v.sizeFromShape(a);let i=v.bytesPerElement(r),l=this.wasm.HEAPU8.slice(s+t*i,s+n*i);return s0e(l.buffer,r)}disposeData(e,t=!1){if(this.dataIdMap.has(e)){let n=this.dataIdMap.get(e);if(n.refCount--,!t&&n.refCount>0)return!1;this.wasm._free(n.memoryOffset),this.wasm.tfjs.disposeData(n.id),this.dataIdMap.delete(e)}return!0}refCount(e){return this.dataIdMap.has(e)?this.dataIdMap.get(e).refCount:0}incRef(e){let t=this.dataIdMap.get(e);t!=null&&t.refCount++}floatPrecision(){return 32}getMemoryOffset(e){return this.dataIdMap.get(e).memoryOffset}dispose(){this.wasm.tfjs.dispose(),"PThread"in this.wasm&&this.wasm.PThread.terminateAllThreads(),this.wasm=null}memory(){return{unreliable:!1}}makeOutput(e,t,n){let s;if(n==null)s=this.write(null,e,t);else{let r=this.dataIdNextNumber++;s={id:r},this.dataIdMap.set(s,{id:r,memoryOffset:n,shape:e,dtype:t,refCount:1});let a=v.sizeFromShape(e);this.wasm.tfjs.registerTensor(r,a,n)}return{dataId:s,shape:e,dtype:t}}typedArrayFromHeap({shape:e,dtype:t,dataId:n}){let s=this.wasm.HEAPU8.buffer,{memoryOffset:r}=this.dataIdMap.get(n),a=v.sizeFromShape(e);switch(t){case"float32":return new Float32Array(s,r,a);case"int32":return new Int32Array(s,r,a);case"bool":return new Uint8Array(s,r,a);default:throw new Error(`Unknown dtype ${t}`)}}};function t0e(e){return(t,n)=>(v.fetch(e,{credentials:"same-origin"}).then(s=>{s.ok||t.env.a(`failed to load wasm binary file at '${e}'`),s.arrayBuffer().then(r=>{WebAssembly.instantiate(r,t).then(a=>{n(a.instance,a.module)})})}),{})}function t6(e,t,n){if(Zm!=null)return Zm;let s="tfjs-backend-wasm.wasm";return e&&t?s="tfjs-backend-wasm-threaded-simd.wasm":e&&(s="tfjs-backend-wasm-simd.wasm"),bp!=null&&bp[s]!=null?bp[s]:n+s}async function n0e(){let[e,t]=await Promise.all([H().getAsync("WASM_HAS_SIMD_SUPPORT"),H().getAsync("WASM_HAS_MULTITHREAD_SUPPORT")]);return new Promise((n,s)=>{let r={};r.locateFile=(i,l)=>{if(i.endsWith(".worker.js")){let u=Qme.wasmWorkerContents.replace(/\n/g,"\\n"),c=new Blob([u],{type:"application/javascript"});return URL.createObjectURL(c)}return i.endsWith(".wasm")?t6(e,t,gp!=null?gp:l):l+i},gb&&(r.instantiateWasm=t0e(t6(e,t,gp!=null?gp:"")));let a=!1;r.onAbort=()=>{if(a||vp)return;vp=!0,s({message:"Make sure the server can serve the `.wasm` file relative to the bundled js file. 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u=t.makeOutput(l,s.dtype),c=t.dataIdMap.get(u.dataId).id,p=t.makeOutput(l,"int32"),d=t.dataIdMap.get(p.dataId).id;return hT(o,i,s.shape.length,Kt[s.dtype],r,a,c,d),[u,p]},n0e={kernelName:su,backendName:"wasm",setupFunc:e0e,kernelFunc:t0e},fT;function s0e(e){fT=e.wasm.cwrap(ru,null,["number","number","bool","number","number","number","number","number","number","array","number","array","number","number","number","number","number"])}function r0e(e){let{backend:t,inputs:n,attrs:s}=e,{image:r,transforms:a}=n,{interpolation:o,fillMode:i,fillValue:l,outputShape:u}=s,[c,p,d,h]=r.shape,[f,m]=u!=null?u:[p,d],g=[c,f,m,h],y=new Uint8Array(new Int32Array(v.computeStrides(r.shape)).buffer),x=new Uint8Array(new Int32Array(v.computeStrides(g)).buffer),A=t.makeOutput(g,r.dtype),b=t.dataIdMap.get(A.dataId).id,k=t.dataIdMap.get(r.dataId).id,E=t.dataIdMap.get(a.dataId).id,_=o==="nearest"?1:2,$;switch(i){case"constant":$=1;break;case"reflect":$=2;break;case"wrap":$=3;break;case"nearest":$=4;break;default:$=1;break}return fT(k,E,a.shape[0]>1,c,f,m,h,d,p,y,r.shape.length-1,x,g.length-1,_,$,l,b),A}var a0e={kernelName:ru,backendName:"wasm",setupFunc:s0e,kernelFunc:r0e};function o0e(e){let{inputs:t,backend:n,attrs:s}=e,{value:r}=t,{axis:a}=s;a<0&&(a+=r.shape.length);let o=r.shape[a],i=r.shape.length,l=new Array(i-1),u=0;for(let h=0;h<i;h++)h!==a&&(l[u++]=r.shape[h]);let c=new Array(o),p=new Array(i).fill(0),d=r.shape.slice();d[a]=1;for(let h=0;h<c.length;h++)p[a]=h,c[h]=yl({inputs:{x:r},attrs:{begin:p,size:d},backend:n});return c.map(({dataId:h,dtype:f})=>({dataId:h,dtype:f,shape:l}))}var i0e={kernelName:au,backendName:"wasm",kernelFunc:o0e};function l0e(e){let{inputs:{x:t},backend:n}=e,s=n.makeOutput(t.shape,t.dtype);return n.typedArrayFromHeap(s).fill(0),s}var u0e={kernelName:ou,backendName:"wasm",kernelFunc:l0e},c0e=[zde,Lde,Wde,Gde,Jde,tpe,rpe,ipe,dpe,ype,Ape,xpe,wpe,kpe,Cpe,Epe,Rpe,_pe,Ppe,Mpe,Bpe,Upe,jpe,qpe,Kpe,Zpe,Ype,Jpe,the,nhe,rhe,ihe,che,hhe,ghe,xhe,vhe,khe,Hde,Che,Nhe,Rhe,_he,$he,Phe,Ohe,zhe,Whe,Uhe,jhe,Khe,Jhe,efe,sfe,afe,ofe,ufe,pfe,mfe,yfe,bfe,wfe,Sfe,qC,Nfe,_fe,Pfe,Ofe,zfe,Lfe,Bfe,lpe,Ufe,jfe,Kfe,Jfe,Qfe,eme,sme,ome,ume,cme,mpe,hme,mme,Ame,vme,kme,Ime,Tme,Nme,Eme,_me,Pme,Mme,Lme,Wme,Ume,Hme,Xme,Kme,Zme,Qme,n0e,a0e,Kde,i0e,u0e];for(let e of c0e)rr(e);var Ry=H();Ry.registerFlag("WASM_HAS_SIMD_SUPPORT",async()=>WebAssembly.validate(new Uint8Array([0,97,115,109,1,0,0,0,1,4,1,96,0,0,3,2,1,0,10,9,1,7,0,65,0,253,15,26,11])));Ry.registerFlag("WASM_HAS_MULTITHREAD_SUPPORT",async()=>{if(Ry.get("IS_NODE"))return!1;try{return new MessageChannel().port1.postMessage(new SharedArrayBuffer(1)),WebAssembly.validate(new Uint8Array([0,97,115,109,1,0,0,0,1,4,1,96,0,0,3,2,1,0,5,4,1,3,1,1,10,11,1,9,0,65,0,254,16,2,0,26,11]))}catch(e){return!1}});var r6=yo(tD()),d0e=yo(nD()),a6=yo(sD()),o6=r6.default||r6,p0e=a6.default||a6,mT=class extends Cc{constructor(e){super(),this.wasm=e,this.dataIdNextNumber=1,this.wasm.tfjs.initWithThreadsCount(gT),_y=this.wasm.tfjs.getThreadsCount(),this.dataIdMap=new Gp(this,Qt())}write(e,t,n){let s={id:this.dataIdNextNumber++};return this.move(s,e,t,n,1),s}numDataIds(){return this.dataIdMap.numDataIds()}async time(e){let t=v.now();return e(),{kernelMs:v.now()-t}}move(e,t,n,s,r){let a=this.dataIdNextNumber++;if(s==="string"){let u=t;this.dataIdMap.set(e,{id:a,stringBytes:u,shape:n,dtype:s,memoryOffset:null,refCount:r});return}let o=v.sizeFromShape(n),i=o*v.bytesPerElement(s),l=this.wasm._malloc(i);this.dataIdMap.set(e,{id:a,memoryOffset:l,shape:n,dtype:s,refCount:r}),this.wasm.tfjs.registerTensor(a,o,l),t!=null&&this.wasm.HEAPU8.set(new Uint8Array(t.buffer,t.byteOffset,i),l)}async read(e){return this.readSync(e)}readSync(e,t,n){let{memoryOffset:s,dtype:r,shape:a,stringBytes:o}=this.dataIdMap.get(e);if(r==="string")return(t==null||t===0)&&(n==null||n>=o.length)?o:o.slice(t,n);t=t||0,n=n||v.sizeFromShape(a);let i=v.bytesPerElement(r),l=this.wasm.HEAPU8.slice(s+t*i,s+n*i);return m0e(l.buffer,r)}disposeData(e,t=!1){if(this.dataIdMap.has(e)){let n=this.dataIdMap.get(e);if(n.refCount--,!t&&n.refCount>0)return!1;this.wasm._free(n.memoryOffset),this.wasm.tfjs.disposeData(n.id),this.dataIdMap.delete(e)}return!0}refCount(e){return this.dataIdMap.has(e)?this.dataIdMap.get(e).refCount:0}incRef(e){let t=this.dataIdMap.get(e);t!=null&&t.refCount++}floatPrecision(){return 32}getMemoryOffset(e){return this.dataIdMap.get(e).memoryOffset}dispose(){this.wasm.tfjs.dispose(),"PThread"in this.wasm&&this.wasm.PThread.terminateAllThreads(),this.wasm=null}memory(){return{unreliable:!1}}makeOutput(e,t,n){let s;if(n==null)s=this.write(null,e,t);else{let r=this.dataIdNextNumber++;s={id:r},this.dataIdMap.set(s,{id:r,memoryOffset:n,shape:e,dtype:t,refCount:1});let a=v.sizeFromShape(e);this.wasm.tfjs.registerTensor(r,a,n)}return{dataId:s,shape:e,dtype:t}}typedArrayFromHeap({shape:e,dtype:t,dataId:n}){let s=this.wasm.HEAPU8.buffer,{memoryOffset:r}=this.dataIdMap.get(n),a=v.sizeFromShape(e);switch(t){case"float32":return new Float32Array(s,r,a);case"int32":return new Int32Array(s,r,a);case"bool":return new Uint8Array(s,r,a);default:throw new Error(`Unknown dtype ${t}`)}}};function h0e(e){return(t,n)=>(v.fetch(e,{credentials:"same-origin"}).then(s=>{s.ok||t.env.a(`failed to load wasm binary file at '${e}'`),s.arrayBuffer().then(r=>{WebAssembly.instantiate(r,t).then(a=>{n(a.instance,a.module)})})}),{})}function i6(e,t,n){if(Zm!=null)return Zm;let s="tfjs-backend-wasm.wasm";return e&&t?s="tfjs-backend-wasm-threaded-simd.wasm":e&&(s="tfjs-backend-wasm-simd.wasm"),vp!=null&&vp[s]!=null?vp[s]:n+s}async function f0e(){let[e,t]=await Promise.all([H().getAsync("WASM_HAS_SIMD_SUPPORT"),H().getAsync("WASM_HAS_MULTITHREAD_SUPPORT")]);return new Promise((n,s)=>{let r={};r.locateFile=(i,l)=>{if(i.endsWith(".worker.js")){let u=d0e.wasmWorkerContents.replace(/\n/g,"\\n"),c=new Blob([u],{type:"application/javascript"});return URL.createObjectURL(c)}return i.endsWith(".wasm")?i6(e,t,yp!=null?yp:l):l+i},xb&&(r.instantiateWasm=h0e(i6(e,t,yp!=null?yp:"")));let a=!1;r.onAbort=()=>{if(a||wp)return;wp=!0,s({message:"Make sure the server can serve the `.wasm` file relative to the bundled js file. For more details see https://github.com/tensorflow/tfjs/blob/master/tfjs-backend-wasm/README.md#using-bundlers"})};let o;t&&e&&Zm==null?(r.mainScriptUrlOrBlob=new Blob(["var WasmBackendModuleThreadedSimd = "+o6.toString()],{type:"text/javascript"}),o=o6(r)):o=p0e(r),o.then(i=>{a=!0,wp=!1;let l=null;i.tfjs={init:i.cwrap("init",null,[]),initWithThreadsCount:i.cwrap("init_with_threads_count",null,["number"]),getThreadsCount:i.cwrap("get_threads_count","number",[]),registerTensor:i.cwrap("register_tensor",null,["number","number","number"]),disposeData:i.cwrap("dispose_data",l,["number"]),dispose:i.cwrap("dispose",l,[])},n({wasm:i})}).catch(s)})}function m0e(e,t){switch(t){case"float32":return new Float32Array(e);case"int32":return new Int32Array(e);case"bool":return new Uint8Array(e);default:throw new Error(`Unknown dtype ${t}`)}}var g0e=["tfjs-backend-wasm.wasm","tfjs-backend-wasm-simd.wasm","tfjs-backend-wasm-threaded-simd.wasm"],Zm=null,yp=null,vp={},wp=!1,xb=!1;function y0e(e,t=!1){if(Qy("setWasmPath has been deprecated in favor of setWasmPaths and will be removed in a future release."),wp)throw new Error("The WASM backend was already initialized. Make sure you call `setWasmPath()` before you call `tf.setBackend()` or `tf.ready()`");Zm=e,xb=t}function L2(e,t=!1){if(wp)throw new Error("The WASM backend was already initialized. Make sure you call `setWasmPaths()` before you call `tf.setBackend()` or `tf.ready()`");if(typeof e=="string")yp=e;else{vp=e;let n=g0e.filter(s=>vp[s]==null);if(n.length>0)throw new Error(`There were no entries found for the following binaries: ${n.join(",")}. Please either call setWasmPaths with a map providing a path for each binary, or with a string indicating the directory where all the binaries can be found.`)}xb=t}var gT=-1,_y=-1;function A0e(e){gT=e}function x0e(){if(_y===-1)throw new Error("WASM backend not initialized.");return _y}var b0e="3.20.0",v0e=2;lu("wasm",async()=>{let{wasm:e}=await f0e();return new mT(e)},v0e);var vi=H();vi.registerFlag("WEBGPU_DEFERRED_SUBMIT_BATCH_SIZE",()=>15);vi.registerFlag("WEBGPU_CPU_FORWARD",()=>!0);vi.registerFlag("WEBGPU_MATMUL_PROGRAM_TYPE",()=>-1);vi.registerFlag("WEBGPU_USE_NAIVE_CONV2D_TRANSPOSE",()=>!1);vi.registerFlag("WEBGPU_USE_LOW_POWER_GPU",()=>!1);vi.registerFlag("WEBGPU_CPU_HANDOFF_SIZE_THRESHOLD",()=>1e3);vi.registerFlag("WEBGPU_USE_PROFILE_TOOL",()=>!1);vi.registerFlag("WEBGPU_IMPORT_EXTERNAL_TEXTURE",()=>!0);var w0e=class{constructor(e){this.device=e,this.numUsedBuffers=0,this.numFreeBuffers=0,this.freeBuffers=new Map,this.usedBuffers=new Map,this.numBytesUsed=0,this.numBytesAllocated=0}acquireUploadBuffer(e,t){return this.acquireBuffer(e,t,!0)}acquireBuffer(e,t,n=!1){let s=l6(e,t);if(this.freeBuffers.has(s)||this.freeBuffers.set(s,[]),this.usedBuffers.has(s)||this.usedBuffers.set(s,[]),this.numBytesUsed+=e,this.numUsedBuffers++,this.freeBuffers.get(s).length>0){this.numFreeBuffers--;let a=this.freeBuffers.get(s).shift();return this.usedBuffers.get(s).push(a),a}this.numBytesAllocated+=e;let r=this.device.createBuffer({size:e,usage:t,mappedAtCreation:n});return this.usedBuffers.get(s).push(r),r}releaseBuffer(e,t,n){if(this.freeBuffers.size===0)return;let s=l6(t,n);this.freeBuffers.has(s)||this.freeBuffers.set(s,[]),this.freeBuffers.get(s).push(e),this.numFreeBuffers++,this.numUsedBuffers--;let r=this.usedBuffers.get(s),a=r.indexOf(e);if(a<0)throw new Error("Cannot release a buffer that was never provided by this buffer manager");r.splice(a,1),this.numBytesUsed-=t}releaseUploadBuffer(e,t,n){e.mapAsync(GPUMapMode.WRITE).then(()=>{this.releaseBuffer(e,t,n)},s=>{})}getNumUsedBuffers(){return this.numUsedBuffers}getNumFreeBuffers(){return this.numFreeBuffers}dispose(){this.freeBuffers.forEach((e,t)=>{e.forEach(n=>{n.destroy()})}),this.usedBuffers.forEach((e,t)=>{e.forEach(n=>{n.destroy()})}),this.freeBuffers=new Map,this.usedBuffers=new Map,this.numUsedBuffers=0,this.numFreeBuffers=0,this.numBytesUsed=0,this.numBytesAllocated=0}};function l6(e,t){return`${e}_${t}`}var k0e=class{constructor(e){this.device=e,this.numUsedTextures=0,this.numFreeTextures=0,this.freeTextures=new Map,this.usedTextures=new Map,this.numBytesUsed=0,this.numBytesAllocated=0}acquireTexture(e,t,n,s){let r=c6(n),a=e*t*r,o=u6(e,t,n,s);if(this.freeTextures.has(o)||this.freeTextures.set(o,[]),this.usedTextures.has(o)||this.usedTextures.set(o,[]),this.numBytesUsed+=a,this.numUsedTextures++,this.freeTextures.get(o).length>0){this.numFreeTextures--;let l=this.freeTextures.get(o).shift();return this.usedTextures.get(o).push(l),l}this.numBytesAllocated+=a;let i=this.device.createTexture({size:[e,t],format:n,usage:s});return this.usedTextures.get(o).push(i),i}releaseTexture(e,t,n,s,r){if(this.freeTextures.size===0)return;let a=u6(t,n,s,r);this.freeTextures.has(a)||this.freeTextures.set(a,[]),this.freeTextures.get(a).push(e),this.numFreeTextures++,this.numUsedTextures--;let o=this.usedTextures.get(a),i=o.indexOf(e);if(i<0)throw new Error("Cannot release a texture that was never provided by this texture manager");o.splice(i,1);let l=c6(s),u=t*n*l;this.numBytesUsed-=u}getNumUsedTextures(){return this.numUsedTextures}getNumFreeTextures(){return this.numFreeTextures}dispose(){this.freeTextures.forEach((e,t)=>{e.forEach(n=>{n.destroy()})}),this.usedTextures.forEach((e,t)=>{e.forEach(n=>{n.destroy()})}),this.freeTextures=new Map,this.usedTextures=new Map,this.numUsedTextures=0,this.numFreeTextures=0,this.numBytesUsed=0,this.numBytesAllocated=0}};function u6(e,t,n,s){return`${e}_${t}_${n}_${s}`}function c6(e){if(e==="rgba8unorm")return 16;throw new Error(`${e} is not supported!`)}function S0e(e,t){if(Math.max(...e)>3)throw new Error("Cannot symbolically compute strides for rank > 4 tensor.");let n=e.length,s=e.map(a=>`${t}[${a}]`),r=new Array(n-1);r[n-2]=s[n-1];for(let a=n-3;a>=0;--a)r[a]=`(${r[a+1]} * ${s[a+1]})`;return r}var I0e=(e,t,n,s)=>{let r={dtype:s.dtype,shape:s.shape},a=C0e(n,r,t),o=e.createShaderModule({code:a,label:t.constructor.name});return e.createComputePipeline({compute:{module:o,entryPoint:"_start"},label:t.constructor.name,layout:"auto"})};function Mn(e){if(e<=1)return"i32";if(e===2)return"vec2<i32>";if(e===3)return"vec3<i32>";if(e===4)return"vec4<i32>";if(e===5)return"vec5";if(e===6)return"vec6";throw Error(`GPU for rank ${e} is not yet supported`)}function Ca(e){if(e===0)return"x";if(e===1)return"y";if(e===2)return"z";if(e===3)return"w";if(e===4)return"u";if(e===5)return"v";throw Error(`Index ${e} is not yet supported`)}function nt(...e){let t;switch(e.length){case 0:t=`
+        ${Vp()}
         fn _start(@builtin(local_invocation_id) LocalId : vec3<u32>,
                   @builtin(global_invocation_id) GlobalId : vec3<u32>,
                   @builtin(num_workgroups) NumWorkgroups : vec3<u32>) {
@@ -4897,7 +4897,7 @@ return a / b;`,Nle=`
 
         fn main()
       `;break;case 1:t=`
-        ${Wp()}
+        ${Vp()}
         fn _start(@builtin(local_invocation_id) LocalId : vec3<u32>,
                   @builtin(global_invocation_id) GlobalId : vec3<u32>,
                   @builtin(num_workgroups) NumWorkgroups : vec3<u32>) {
@@ -4908,9 +4908,9 @@ return a / b;`,Nle=`
         }
 
         fn main(${e[0]} : i32)
-      `;break;default:throw Error("Unreachable")}return t}function Wp(){return`
+      `;break;default:throw Error("Unreachable")}return t}function Vp(){return`
   @compute @workgroup_size(workGroupSizeX, workGroupSizeY, workGroupSizeZ)
-`}function f0e(e,t,n){let s=[];if(s.push(`
+`}function C0e(e,t,n){let s=[];if(s.push(`
       const workGroupSizeX = ${n.workGroupSize[0]}u;
       const workGroupSizeY = ${n.workGroupSize[1]}u;
       const workGroupSizeZ = ${n.workGroupSize[2]}u;
@@ -4921,7 +4921,7 @@ return a / b;`,Nle=`
 
       // Only used when the y/z dimension of workgroup size is 1.
       fn getGlobalIndex() -> i32 {
-        ${dT(n)?"  return i32(globalId.x);":`  let localInvocationIndex = localId.z * workGroupSizeX * workGroupSizeY +
+        ${yT(n)?"  return i32(globalId.x);":`  let localInvocationIndex = localId.z * workGroupSizeX * workGroupSizeY +
                    localId.y * workGroupSizeX + localId.x;
                let workGroupID = (globalId - localId)/vec3<u32>(
                    workGroupSizeX, workGroupSizeY, workGroupSizeZ);
@@ -4939,23 +4939,23 @@ return a / b;`,Nle=`
           outShapeStrides : vec2<i32>,
         };
 
-        @group(0) @binding(0) var<storage, read_write> result: array<${wp(t.dtype,n.isVec4)}>;
+        @group(0) @binding(0) var<storage, read_write> result: array<${kp(t.dtype,n.isVec4)}>;
         @group(0) @binding(2) var<uniform> uniforms: Uniform;
-      `),[a6,s.join(`
-`),o6(t.shape),n.getUserCode()].join(`
-`);let r="struct Uniforms { NAN : f32, ";n.variableNames.forEach((d,h)=>{let f=Fn(e[h].shape.length);r+=`${d.charAt(0).toLowerCase()+d.slice(1)}Shape : ${f}, `}),r+=`outShape : ${Fn(t.shape.length)}, `;let o=t.shape.length-1;r+=`
-         outShapeStrides: ${Fn(o)}, `,n.size&&(r+="size : i32, "),n.uniforms&&(r+=n.uniforms),r+="};",r=w0e(r),s.push(r),n.atomic?s.push(`
+      `),[d6,s.join(`
+`),p6(t.shape),n.getUserCode()].join(`
+`);let r="struct Uniforms { NAN : f32, ";n.variableNames.forEach((d,h)=>{let f=Mn(e[h].shape.length);r+=`${d.charAt(0).toLowerCase()+d.slice(1)}Shape : ${f}, `}),r+=`outShape : ${Mn(t.shape.length)}, `;let o=t.shape.length-1;r+=`
+         outShapeStrides: ${Mn(o)}, `,n.size&&(r+="size : i32, "),n.uniforms&&(r+=n.uniforms),r+="};",r=P0e(r),s.push(r),n.atomic?s.push(`
       @group(0) @binding(0) var<storage, read_write> result: array<atomic<i32>>;
     `):s.push(`
-      @group(0) @binding(0) var<storage, read_write> result: array<${wp(t.dtype,n.isVec4)}>;
+      @group(0) @binding(0) var<storage, read_write> result: array<${kp(t.dtype,n.isVec4)}>;
     `),n.variableNames.forEach((d,h)=>{s.push(`
-      @group(0) @binding(${1+h}) var<storage, read> ${d}: array<${n.variableTypes?n.variableTypes[h]:wp(e[h].dtype,n.isVec4)}>;
+      @group(0) @binding(${1+h}) var<storage, read> ${d}: array<${n.variableTypes?n.variableTypes[h]:kp(e[h].dtype,n.isVec4)}>;
         `)}),r!==""&&s.push(`
       @group(0) @binding(${1+n.variableNames.length}) var<uniform> uniforms: Uniforms;
-      `);let l=x0e(t.shape,n.dispatchLayout),u=[a6,s.join(`
-`),o6(t.shape),l,b0e(t.shape.length)];n.atomic||u.push(v0e(t.shape,t.dtype,n.isVec4));let c=e.map((d,h)=>A0e(d,t.shape,n.variableTypes?n.variableTypes[h]==="vec4<f32>":n.isVec4,n.dispatchLayout.x.length===t.shape.length)).join(`
+      `);let l=_0e(t.shape,n.dispatchLayout),u=[d6,s.join(`
+`),p6(t.shape),l,D0e(t.shape.length)];n.atomic||u.push($0e(t.shape,t.dtype,n.isVec4));let c=e.map((d,h)=>R0e(d,t.shape,n.variableTypes?n.variableTypes[h]==="vec4<f32>":n.isVec4,n.dispatchLayout.x.length===t.shape.length)).join(`
 `);return u.push(c),u.push(n.getUserCode()),u.join(`
-`)}function m0e(e,t,n,s){let r=e.shaderKey;if(e.isFromPixels)return r;let a=n.map(c=>c.dtype).concat(s.dtype),o=n.map(c=>T.getBroadcastDims(c.shape,s.shape)),i=n.map(c=>v.arraysEqual(c.shape,s.shape)).join("_"),l=o.map(c=>c.join("_")).join(";"),u=dT(e)?"flatDispatch":"";return r+="_"+(e.workGroupSize?e.workGroupSize.join(","):"")+t.map(c=>c.length).join(",")+a.join(",")+e.variableNames.join(",")+l+i+u,r}var a6=`
+`)}function T0e(e,t,n,s){let r=e.shaderKey;if(e.isFromPixels)return r;let a=n.map(c=>c.dtype).concat(s.dtype),o=n.map(c=>T.getBroadcastDims(c.shape,s.shape)),i=n.map(c=>v.arraysEqual(c.shape,s.shape)).join("_"),l=o.map(c=>c.join("_")).join(";"),u=yT(e)?"flatDispatch":"";return r+="_"+(e.workGroupSize?e.workGroupSize.join(","):"")+t.map(c=>c.length).join(",")+a.join(",")+e.variableNames.join(",")+l+i+u,r}var d6=`
   struct vec5 {x: i32, y: i32, z: i32, w: i32, u: i32};
   struct vec6 {x: i32, y: i32, z: i32, w: i32, u: i32, v: i32};
 
@@ -5013,15 +5013,15 @@ return a / b;`,Nle=`
   fn isnanVec4(val : vec4<f32>) -> vec4<bool> {
     return vec4<bool>(isnan(val[0]), isnan(val[1]), isnan(val[2]), isnan(val[3]));
   }
-`;function o6(e){let t=e.length;if(t<=1)return"fn getCoordsFromIndex(index : i32) -> i32 { return index; }";let n=v.computeStrides(e),s=Fn(t),r=[];for(let o=0;o<t;o++)r.push(`d${o}`);if(n.length===1)return`    fn getCoordsFromIndex(index : i32) -> vec2<i32> {
+`;function p6(e){let t=e.length;if(t<=1)return"fn getCoordsFromIndex(index : i32) -> i32 { return index; }";let n=v.computeStrides(e),s=Mn(t),r=[];for(let o=0;o<t;o++)r.push(`d${o}`);if(n.length===1)return`    fn getCoordsFromIndex(index : i32) -> vec2<i32> {
       let d0 = index / uniforms.outShapeStrides; let d1 = index - d0 * uniforms.outShapeStrides;
       return vec2<i32>(d0, d1);
-    }`;let a;return a="var index2 = index;"+n.map((o,i)=>{let l=`let ${r[i]} = index2 / uniforms.outShapeStrides.${wa(i)}`,u=i===n.length-1?`let ${r[i+1]} = index2 - ${r[i]} * uniforms.outShapeStrides.${wa(i)}`:`index2 = index2 - ${r[i]} * uniforms.outShapeStrides.${wa(i)}`;return`${l}; ${u};`}).join(""),`
+    }`;let a;return a="var index2 = index;"+n.map((o,i)=>{let l=`let ${r[i]} = index2 / uniforms.outShapeStrides.${Ca(i)}`,u=i===n.length-1?`let ${r[i+1]} = index2 - ${r[i]} * uniforms.outShapeStrides.${Ca(i)}`:`index2 = index2 - ${r[i]} * uniforms.outShapeStrides.${Ca(i)}`;return`${l}; ${u};`}).join(""),`
     fn getCoordsFromIndex(index : i32) -> ${s} {
       ${a}
       return ${s}(${r.join(",")});
     }
-  `}function g0e(e,t){let n=e.name,s=e.shape.length,r=Fn(s),a="get"+n.charAt(0).toUpperCase()+n.slice(1),o=["d0","d1","d2","d3","d4","d5"].slice(0,s),i=o.map(c=>`${c} : i32`).join(", ");if(s<1)return t?`
+  `}function N0e(e,t){let n=e.name,s=e.shape.length,r=Mn(s),a="get"+n.charAt(0).toUpperCase()+n.slice(1),o=["d0","d1","d2","d3","d4","d5"].slice(0,s),i=o.map(c=>`${c} : i32`).join(", ");if(s<1)return t?`
         fn ${a}() -> vec4<f32> {
           return vec4<f32>(${n}[0]);
         }
@@ -5039,7 +5039,7 @@ return a / b;`,Nle=`
       return f32(${n}[getIndexFromCoords${u}(${r}(${o.join(",")}),
         ${l})]);
     }
-   `}function y0e(e,t,n,s){let r=e.name,a=r.charAt(0).toUpperCase()+r.slice(1),o="get"+a+"ByOutput",i=e.shape.length,l=t.length,u=Fn(l);if(v.arraysEqual(e.shape,t)&&s)return n?`
+   `}function E0e(e,t,n,s){let r=e.name,a=r.charAt(0).toUpperCase()+r.slice(1),o="get"+a+"ByOutput",i=e.shape.length,l=t.length,u=Mn(l);if(v.arraysEqual(e.shape,t)&&s)return n?`
       fn ${o}Index(globalIndex : i32) -> vec4<f32> {
         return vec4<f32>(${r}[globalIndex]);
       }
@@ -5071,8 +5071,8 @@ return a / b;`,Nle=`
     fn ${o}Coords(coords : ${u}) -> f32{
       return get${a}();
     }
-  `;l<2&&c.length>=1?d="coords = 0;":d=c.map(g=>`coords.${wa(g+p)} = 0;`).join(`
-`);let h="";if(l<2&&i>0)h="coords";else if(l>1){let g=Fn(i),y=e.shape.map((x,A)=>`coords.${wa(A+p)}`).join(", ");h=`${g}(${y})`}else h="coords";let f=`uniforms.${r.charAt(0).toLowerCase()+r.slice(1)}Shape`,m=`${i}D`;return n?`
+  `;l<2&&c.length>=1?d="coords = 0;":d=c.map(g=>`coords.${Ca(g+p)} = 0;`).join(`
+`);let h="";if(l<2&&i>0)h="coords";else if(l>1){let g=Mn(i),y=e.shape.map((x,A)=>`coords.${Ca(A+p)}`).join(", ");h=`${g}(${y})`}else h="coords";let f=`uniforms.${r.charAt(0).toLowerCase()+r.slice(1)}Shape`,m=`${i}D`;return n?`
     fn ${o}Index(globalIndex : i32) -> vec4<f32> {
       var coords = getCoordsFromIndex(globalIndex);
       ${d}
@@ -5096,13 +5096,13 @@ return a / b;`,Nle=`
     ${d}
     return f32(${r}[getIndexFromCoords${m}(${h}, ${f})]);
   }
-`}function A0e(e,t,n,s){let r=g0e(e,n);return e.shape.length<=t.length&&(r+=y0e(e,t,n,s)),r}function x0e(e,t){let{x:n,y:s=[],z:r=[]}=t,a=e.length;if(n.length===a)return`fn getOutputCoords() -> ${Fn(a)}{
+`}function R0e(e,t,n,s){let r=N0e(e,n);return e.shape.length<=t.length&&(r+=E0e(e,t,n,s)),r}function _0e(e,t){let{x:n,y:s=[],z:r=[]}=t,a=e.length;if(n.length===a)return`fn getOutputCoords() -> ${Mn(a)}{
     let globalIndex = getGlobalIndex();
     return getCoordsFromIndex(globalIndex);
   }
-  `;let o="",i=[n,s,r],l=0;for(let d=0;d<i.length;d++){let h=i[d];if(h.length!==0)if(l+=h.length,h.length===1)o+=`let d${h[0]} = i32(globalId[${d}]);`;else{let f=p0e(h,"uniforms.outShape");o+=`var index${d} = i32(globalId[${d}]);`;for(let m=0;m<f.length;m++)o+=`let d${h[m]} = index${d} / ${f[m]};`,m===f.length-1?o+=`let d${h[m+1]} = index${d} - d${h[m]} * ${f[m]};`:o+=`index${d} = index${d} - d${h[m]} * ${f[m]};`}}let u=[];for(let d=0;d<l;d++)u.push(`d${d}`);let c=Fn(l),p=`fn getOutputCoords() -> ${c} {
+  `;let o="",i=[n,s,r],l=0;for(let d=0;d<i.length;d++){let h=i[d];if(h.length!==0)if(l+=h.length,h.length===1)o+=`let d${h[0]} = i32(globalId[${d}]);`;else{let f=S0e(h,"uniforms.outShape");o+=`var index${d} = i32(globalId[${d}]);`;for(let m=0;m<f.length;m++)o+=`let d${h[m]} = index${d} / ${f[m]};`,m===f.length-1?o+=`let d${h[m+1]} = index${d} - d${h[m]} * ${f[m]};`:o+=`index${d} = index${d} - d${h[m]} * ${f[m]};`}}let u=[];for(let d=0;d<l;d++)u.push(`d${d}`);let c=Mn(l),p=`fn getOutputCoords() -> ${c} {
   ${o}
-`;return u.length===0?p+=`return ${c}(0); }`:p+=`return ${c}(${u.join(",")}); }`,p}function b0e(e){let t="";switch(e){case 0:case 1:t+=`
+`;return u.length===0?p+=`return ${c}(0); }`:p+=`return ${c}(${u.join(",")}); }`,p}function D0e(e){let t="";switch(e){case 0:case 1:t+=`
         fn getOutputIndexFromCoords(coords : i32) -> i32 {
           return coords;
         }
@@ -5136,7 +5136,7 @@ return a / b;`,Nle=`
               coords.u * uniforms.outShapeStrides.u +
               coords.v;
         }
-        `;break;default:v.assert(!1,()=>`Unsupported ${e}D shape`);break}return t}function dT(e){return e.dispatch[1]===1&&e.dispatch[2]===1}function wp(e,t){return e==="float32"?t?"vec4<f32>":"f32":e==="int32"||e==="bool"?t?"vec4<i32>":"i32":e}function v0e(e,t,n){let s=e.length,r=wp(t,n),a;if(n?a=`fn setOutputAtIndex(flatIndex : i32, value : vec4<f32>) {
+        `;break;default:v.assert(!1,()=>`Unsupported ${e}D shape`);break}return t}function yT(e){return e.dispatch[1]===1&&e.dispatch[2]===1}function kp(e,t){return e==="float32"?t?"vec4<f32>":"f32":e==="int32"||e==="bool"?t?"vec4<i32>":"i32":e}function $0e(e,t,n){let s=e.length,r=kp(t,n),a;if(n?a=`fn setOutputAtIndex(flatIndex : i32, value : vec4<f32>) {
       result[flatIndex] = ${r}(value);
     }
     fn setOutputAtIndexI32(flatIndex : i32, value : vec4<i32>) {
@@ -5146,7 +5146,7 @@ return a / b;`,Nle=`
     }
     fn setOutputAtIndexI32(flatIndex : i32, value : i32) {
       result[flatIndex] = ${r}(value);
-    }`,s>=2){let o=["d0","d1","d2","d3","d4","d5"].slice(0,s),i=Fn(s);n?a+=`
+    }`,s>=2){let o=["d0","d1","d2","d3","d4","d5"].slice(0,s),i=Mn(s);n?a+=`
       fn setOutputAtCoords(${o.map(l=>`${l} : i32`).join(", ")}, value : vec4<f32>) {
         let flatIndex = getOutputIndexFromCoords(${i}(${o.join(", ")}));
         setOutputAtIndex(flatIndex / 4, value);
@@ -5164,10 +5164,10 @@ return a / b;`,Nle=`
         let flatIndex = getOutputIndexFromCoords(${i}(${o.join(", ")}));
         setOutputAtIndexI32(flatIndex, value);
       }
-    `}return a}function w0e(e){let t=/(\w+)\s*:\s*vec(5|6)/g;e=e.replace(t,s=>"@align(16) "+s);let n=/vec(5|6)\s*,\s*(\w+)/g;return e=e.replace(n,(s,r,a)=>`vec${r}, @align(16) ${a}`),e}var pT={};je(pT,{ArrayBufferToTypedArray:()=>mT,GPUBytesPerElement:()=>fT,MatMulProgramType:()=>_r,computeDispatch:()=>He,computeWorkGroupInfoForMatMul:()=>hT,computeWorkGroupSizeForConv2d:()=>yb,computeWorkPerThreadForConv2d:()=>Ab,flatDispatchLayout:()=>it,isWebGPUSupported:()=>xb,tilesFitEvenlyIntoShape:()=>k0e});var tl=e=>{let t=1;for(let n=0;n<e.length;n++)t*=e[n];return t};function k0e(e,t){if(e.length!==t.length)throw new Error(`Cannot compute whether rank ${e.length} tiles fit evenly into rank ${t.length} shape - ranks must match.`);return t.every((n,s)=>n%e[s]===0)}function He(e,t,n=[1,1,1],s=[1,1,1]){let[r,a,o]=[Math.ceil(tl(e.x.map(i=>t[i]))/(n[0]*s[0])),e.y?Math.ceil(tl(e.y.map(i=>t[i]))/(n[1]*s[1])):1,e.z?Math.ceil(tl(e.z.map(i=>t[i]))/(n[2]*s[2])):1];return[r,a,o]}function hT(e,t,n,s=!1){let r=[8,8,1],a=[4,4,1];return s||(e<=8&&(a[1]=1),t<=16&&n<=16&&(r[0]=4)),{workGroupSize:r,elementsPerThread:a}}function yb(e,t,n=!1){if(n)return[8,8,1];let s=tl(e.x.map(a=>t[a])),r=tl(e.y.map(a=>t[a]));return s<=4?[4,16,1]:r<=4?[16,4,1]:[16,16,1]}function Ab(e,t,n=!1){if(n)return[4,4,1];let s=tl(e.x.map(a=>t[a])),r=tl(e.y.map(a=>t[a]));return s<=4?[1,2,1]:r<=4?[2,1,1]:[2,2,1]}function it(e){return{x:e.map((t,n)=>n)}}function fT(e){if(e==="float32"||e==="int32"||e==="bool"||e==="string")return 4;if(e==="complex64")return 8;throw new Error(`Unknown dtype ${e}`)}function mT(e,t){if(t==="float32")return new Float32Array(e);if(t==="int32")return new Int32Array(e);if(t==="bool"||t==="string")return Uint8Array.from(new Int32Array(e));throw new Error(`Unknown dtype ${t}`)}function xb(){return(typeof window!="undefined"||typeof WorkerGlobalScope!="undefined")&&!!navigator.gpu}var _r;(function(e){e[e.MatMulReduceProgram=0]="MatMulReduceProgram",e[e.MatMulSplitKProgram=1]="MatMulSplitKProgram",e[e.MatMulSmallOutputSizeProgram=2]="MatMulSmallOutputSizeProgram",e[e.MatMulPackedProgram=3]="MatMulPackedProgram",e[e.MatMulMax=4]="MatMulMax"})(_r||(_r={}));var S0e=H().getNumber("WEBGPU_CPU_HANDOFF_SIZE_THRESHOLD"),I0e=(e,t)=>{let n=e.limits.maxComputeWorkgroupsPerDimension,s=t.dispatchLayout,r=t.dispatch;if(r.every(o=>o<=n))return r;v.assert(r[0]>n&&s.y===void 0&&s.z===void 0,()=>"Dispatch size exceeds WebGPU limits in Y or Z dimension.");let a=Math.ceil(Math.sqrt(r[0]));return a>n?(a=Math.ceil(Math.cbrt(r[0])),v.assert(a<=n,()=>"Total dispatch size exceeds WebGPU maximum."),[a,a,a]):[a,a,1]},B2=class extends wc{constructor(e){if(super(),this.commandQueueOwnedIds=new WeakSet,this.dispatchNumberInEncoder=0,this.disposed=!1,this.downloadWaitMs=0,this.tensorDataPendingDisposal=[],this.stagingPendingDisposal=[],this.uniformPendingDisposal=[],this.uploadWaitMs=0,!xb())throw new Error("WebGPU is not supported on this device");this.pipelineCache={},this.device=e,this.queue=e.queue,this.currentCommandEncoder=null,this.currentComputePass=null,this.supportTimeQuery=e.features.has("timestamp-query"),this.bufferManager=new c0e(this.device),this.textureManager=new d0e(this.device),this.tensorMap=new Up(this,Jt()),this.supportTimeQuery&&(this.querySet=this.device.createQuerySet({type:"timestamp",count:2})),H().getBool("WEBGPU_USE_PROFILE_TOOL")&&(this.dummyCanvas=document.createElement("canvas"),this.dummyCanvas.width=1,this.dummyCanvas.height=1,this.dummyContext=this.dummyCanvas.getContext("webgpu"),this.dummyContext.configure({device:e,format:"bgra8unorm"}),document.body.appendChild(this.dummyCanvas))}nextDataId(){return B2.nextDataId++}floatPrecision(){return 32}defaultGpuBufferUsage(){return GPUBufferUsage.STORAGE|GPUBufferUsage.COPY_SRC|GPUBufferUsage.COPY_DST}disposeData(e,t=!1){if(this.tensorDataPendingDisposal.indexOf(e)>=0)return!1;if(!this.tensorMap.has(e))return!0;let n=this.tensorMap.get(e);if(this.decRef(e),!t&&n.refCount>0)return!1;if(this.commandQueueOwnedIds.has(e))return this.tensorDataPendingDisposal.push(e),!1;let{complexTensorInfos:s}=this.tensorMap.get(e);return s!=null&&(this.disposeData(s.real.dataId,t),this.disposeData(s.imag.dataId,t)),this.releaseResource(e),this.tensorMap.delete(e),!0}memory(){return{numBytesInGPU:this.bufferManager.numBytesUsed,numBytesAllocatedInGPU:this.bufferManager.numBytesAllocated,unreliable:!1}}releaseResource(e){let t=this.tensorMap.get(e);if(!(!t||!t.resourceInfo)){if("texture"in t.resourceInfo){let n=t.resourceInfo;n.texture instanceof GPUTexture&&this.textureManager.releaseTexture(n.texture,n.width,n.height,n.format,n.usage),n.texture=null}else{let n=t.resourceInfo;this.bufferManager.releaseBuffer(n.buffer,n.size,n.usage),n.buffer=null}t.resourceInfo=null}}refCount(e){return this.tensorMap.has(e)?this.tensorMap.get(e).refCount:0}incRef(e){let t=this.tensorMap.get(e);t.refCount++}decRef(e){if(this.tensorMap.has(e)){let t=this.tensorMap.get(e);t.refCount--}}write(e,t,n){if(n==="complex64"&&e!=null)throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");let s={id:this.nextDataId()};return this.tensorMap.set(s,{dtype:n,shape:t,values:e,refCount:1}),s}move(e,t,n,s,r){if(s==="complex64")throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");this.tensorMap.set(e,{dtype:s,shape:n,values:t,refCount:r})}submitQueue(){this.ensureComputePassEnded(),this.queue.submit([this.currentCommandEncoder.finish()]),this.currentCommandEncoder=null,this.dispatchNumberInEncoder=0,this.commandQueueOwnedIds=new WeakSet,this.tensorDataPendingDisposal.forEach(e=>{this.releaseResource(e),this.tensorMap.delete(e)}),this.uniformPendingDisposal.forEach(e=>this.bufferManager.releaseBuffer(e.buffer,e.size,e.usage)),this.stagingPendingDisposal.forEach(e=>this.bufferManager.releaseUploadBuffer(e.buffer,e.size,e.usage)),this.tensorDataPendingDisposal=[],this.uniformPendingDisposal=[],this.stagingPendingDisposal=[]}ensureCommandEncoderReady(){this.currentCommandEncoder||(this.currentCommandEncoder=this.device.createCommandEncoder())}ensureComputePassEnded(){this.currentComputePass&&(this.currentComputePass.end(),this.currentComputePass=null)}getComputePass(){return this.currentComputePass||(this.currentComputePass=this.currentCommandEncoder.beginComputePass()),this.currentComputePass}async getBufferData(e,t){let n=this.bufferManager.acquireBuffer(t,GPUBufferUsage.COPY_DST|GPUBufferUsage.MAP_READ);this.ensureCommandEncoderReady(),this.ensureComputePassEnded(),this.currentCommandEncoder.copyBufferToBuffer(e,0,n,0,t),this.submitQueue(),await n.mapAsync(GPUMapMode.READ);let s=n.getMappedRange().slice(0);return n.unmap(),n!=null&&this.bufferManager.releaseBuffer(n,t,GPUBufferUsage.COPY_DST|GPUBufferUsage.MAP_READ),H().getBool("WEBGPU_USE_PROFILE_TOOL")&&(v.assert(this.dummyContext!==void 0,()=>"Fail to get context for profiling tool"),this.dummyContext.getCurrentTexture()),s}convertAndCacheOnCPU(e,t){let n=this.tensorMap.get(e);return this.releaseResource(e),n.values=t,n.values}readSync(e){let t=this.tensorMap.get(e),{values:n}=t;if(n==null)throw new Error("WebGPU readSync is only available for CPU-resident tensors.");return n}async read(e){if(!this.tensorMap.has(e))throw new Error(`Tensor ${e} was not registered!`);let t=this.tensorMap.get(e),{values:n}=t;if(n!=null)return this.convertAndCacheOnCPU(e,n);let s;if(t.dtype==="complex64"){let r=await Promise.all([this.read(t.complexTensorInfos.real.dataId),this.read(t.complexTensorInfos.imag.dataId)]),a=r[0],o=r[1];s=T.mergeRealAndImagArrays(a,o)}else{let r=t.resourceInfo,a=await this.getBufferData(r.buffer,r.size);s=mT(a,t.dtype)}return this.convertAndCacheOnCPU(e,s),s}readToGPU(e){let t=this.tensorMap.get(e),{values:n,dtype:s,shape:r,resourceInfo:a}=t;if(s==="complex64")throw new Error("Does not support reading buffer for complex64 dtype.");if(a==null)throw n!=null?new Error("Data is not on GPU but on CPU."):new Error("There is no data on GPU or CPU.");let o=a.size,i=this.bufferManager.acquireBuffer(o,a.usage);this.ensureCommandEncoderReady(),this.ensureComputePassEnded(),this.currentCommandEncoder.copyBufferToBuffer(a.buffer,0,i,0,o),this.submitQueue();let l=this.makeTensorInfo(r,s),u=Jt().makeTensorFromTensorInfo(l),c=this.tensorMap.get(l.dataId);return c.resourceInfo={size:o,usage:this.defaultGpuBufferUsage(),buffer:i},{tensorRef:u,buffer:i,bufSize:o}}bufferSync(e){let t=this.readSync(e.dataId);if(e.dtype==="string")try{let n=t.map(s=>v.decodeString(s));return Ve(e.shape,e.dtype,n)}catch(n){throw new Error("Failed to decode encoded string bytes into utf-8")}return Ve(e.shape,e.dtype,t)}async time(e){this.supportTimeQuery||console.warn("This device doesn't support timestamp-query extension. Start Chrome browser with flag --disable-dawn-features=disallow_unsafe_apis then try again. Otherwise, zero will be shown for the kernel time when profiling mode is enabled. Using performance.now is not workable for webgpu since it doesn't support synchronous data read from GPU.");let t=this.activeTimers,n=[],s=!1;this.programTimersStack==null?(this.programTimersStack=n,s=!0):this.activeTimers.push(n),this.activeTimers=n,e();let r=v.flatten(this.activeTimers.map(l=>l.query)).filter(l=>l!=null),a=v.flatten(this.activeTimers.map(l=>l.name)).filter(l=>l!=null);this.activeTimers=t,s&&(this.programTimersStack=null);let o={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null},i=await Promise.all(r);return o.kernelMs=v.sum(i),o.getExtraProfileInfo=()=>i.map((l,u)=>({name:a[u],ms:l})).map(l=>`${l.name}: ${l.ms}`).join(", "),this.uploadWaitMs=0,this.downloadWaitMs=0,o}makeTensorInfo(e,t,n){return t==="string"&&n!=null&&n.length>0&&v.isString(n[0])&&(n=n.map(r=>v.encodeString(r))),{dataId:this.write(n,e,t),shape:e,dtype:t}}tensorToBinding(e){if(!e)return null;let t=this.tensorMap.get(e.dataId);if("texture"in t.resourceInfo){let s=t.resourceInfo;return s.texture instanceof GPUExternalTexture?s.texture:s.texture.createView()}let n=t.resourceInfo;return{offset:0,size:n.size,buffer:n.buffer}}async getQueryTime(e){return this.supportTimeQuery?this.getTimeFromQuerySet(e):0}uploadToGPU(e){let t=this.tensorMap.get(e);if(t.resourceInfo)return;let n=fT(t.dtype)*v.sizeFromShape(t.shape),s=this.bufferManager.acquireBuffer(n,this.defaultGpuBufferUsage());if(t.resourceInfo={size:n,usage:this.defaultGpuBufferUsage(),buffer:s},t.values){let r=this.bufferManager.acquireUploadBuffer(n,GPUBufferUsage.MAP_WRITE|GPUBufferUsage.COPY_SRC),a=r.getMappedRange();t.dtype==="int32"||t.dtype==="bool"?new Int32Array(a).set(t.values):new Float32Array(a).set(t.values),r.unmap(),this.ensureCommandEncoderReady(),this.ensureComputePassEnded(),this.currentCommandEncoder.copyBufferToBuffer(r,0,s,0,n);let o={size:n,usage:GPUBufferUsage.MAP_WRITE|GPUBufferUsage.COPY_SRC,buffer:r};this.stagingPendingDisposal.push(o)}}makeUniforms(e){let t=0,n=0,s=[];e.forEach(i=>{i.data.length===0&&(i.data=[1]);let l;switch(i.data.length){case 1:l=4;break;case 2:l=8;break;case 3:l=16;break;case 4:l=16;break;case 5:l=16;break;case 6:l=16;break;default:v.assert(!1,()=>`Unsupported ${i.data.length}D shape`)}(n===5||n===6)&&(l=16),t=Math.ceil(t/l)*l,n=i.data.length,s.push(t),t+=i.data.length*4});let r=new ArrayBuffer(t);e.forEach((i,l)=>{let u=s[l];i.type==="int32"?new Int32Array(r,u,i.data.length).set(i.data):i.type==="uint32"?new Uint32Array(r,u,i.data.length).set(i.data):new Float32Array(r,u,i.data.length).set(i.data)});let a=this.bufferManager.acquireBuffer(t,GPUBufferUsage.COPY_DST|GPUBufferUsage.UNIFORM);this.queue.writeBuffer(a,0,r,0,t);let o={size:t,usage:GPUBufferUsage.COPY_DST|GPUBufferUsage.UNIFORM,buffer:a};return this.uniformPendingDisposal.push(o),{offset:0,size:t,buffer:a}}runWebGPUProgram(e,t,n,s,r){if(r||(r=this.makeTensorInfo(e.outputShape,n)),v.sizeFromShape(r.shape)===0)return this.tensorMap.get(r.dataId).values=v.getTypedArrayFromDType(r.dtype,0),r;this.uploadToGPU(r.dataId),e.dispatch=I0e(this.device,e);let a=[],o=[];if(!e.isFromPixels){a.push({type:"float32",data:[NaN]}),o=t.concat(r).map(g=>g.shape);let f="int32";o.map(g=>{a.push({type:f,data:g})});let m=v.computeStrides(r.shape);if(a.push({type:f,data:m}),e.size){let g=v.sizeFromShape(e.outputShape);a.push({type:f,data:[e.isVec4?g/4:g]})}}let i=t.map((f,m)=>{if(f.dtype==="complex64")throw new Error("GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.");return this.uploadToGPU(f.dataId),{dtype:this.tensorMap.get(f.dataId).dtype,shape:f.shape,name:e.variableNames[m]}}),l=m0e(e,o,i,r),u;l in this.pipelineCache?u=this.pipelineCache[l]:(u=h0e(this.device,e,i,r),this.pipelineCache[l]=u),s&&(a=[...a,...s]);let c=[this.tensorToBinding(r),...t.map(f=>this.tensorToBinding(f)),this.makeUniforms(a)],p=this.device.createBindGroup({layout:u.getBindGroupLayout(0),entries:c.map((f,m)=>({binding:m,resource:f}))});this.ensureCommandEncoderReady();let d=this.getComputePass(),h=this.activeTimers!=null;return h&&this.supportTimeQuery&&d.writeTimestamp(this.querySet,0),d.setPipeline(u),d.setBindGroup(0,p),d.dispatchWorkgroups(e.dispatch[0],e.dispatch[1],e.dispatch[2]),h&&this.supportTimeQuery&&d.writeTimestamp(this.querySet,1),this.dispatchNumberInEncoder++,t.forEach(f=>{this.commandQueueOwnedIds.add(f.dataId)}),this.commandQueueOwnedIds.add(r.dataId),H().get("WEBGPU_DEFERRED_SUBMIT_BATCH_SIZE")<=this.dispatchNumberInEncoder&&this.submitQueue(),h&&this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime(this.querySet)}),r}async getTimeFromQuerySet(e){let t=this.bufferManager.acquireBuffer(16,GPUBufferUsage.COPY_SRC|GPUBufferUsage.QUERY_RESOLVE),n=this.bufferManager.acquireBuffer(16,GPUBufferUsage.MAP_READ|GPUBufferUsage.COPY_DST);this.ensureCommandEncoderReady(),this.ensureComputePassEnded(),this.currentCommandEncoder.resolveQuerySet(e,0,2,t,0),this.currentCommandEncoder.copyBufferToBuffer(t,0,n,0,16),this.submitQueue(),await n.mapAsync(GPUMapMode.READ);let s=new BigUint64Array(n.getMappedRange()),r=Number(s[1]-s[0]);return n.unmap(),this.bufferManager.releaseBuffer(n,16,GPUBufferUsage.MAP_READ|GPUBufferUsage.COPY_DST),this.bufferManager.releaseBuffer(t,16,GPUBufferUsage.COPY_SRC|GPUBufferUsage.QUERY_RESOLVE),r/1e6}shouldExecuteOnCPU(e,t=S0e){return H().getBool("WEBGPU_CPU_FORWARD")&&e.every(n=>this.tensorMap.get(n.dataId).resourceInfo==null&&v.sizeFromShape(n.shape)<t)}numDataIds(){return this.tensorMap.numDataIds()-this.tensorDataPendingDisposal.length}dispose(){this.disposed||(this.bufferManager.dispose(),this.textureManager.dispose(),this.disposed=!0)}};B2.nextDataId=0;xb()&&ru("webgpu",async()=>{H().set("CHECK_COMPUTATION_FOR_ERRORS",!1);let e={powerPreference:H().get("WEBGPU_USE_LOW_POWER_GPU")?"low-power":"high-performance"},t=await navigator.gpu.requestAdapter(e),n=t.limits,s={},r=t.features.has("timestamp-query");s.requiredLimits={maxComputeWorkgroupStorageSize:n.maxComputeWorkgroupStorageSize,maxComputeWorkgroupsPerDimension:n.maxComputeWorkgroupsPerDimension,maxStorageBufferBindingSize:n.maxStorageBufferBindingSize},r&&(s.requiredFeatures=["timestamp-query"]);let a=await t.requestDevice(s);return new B2(a)},3);var Xe;(function(e){e[e.MUL=0]="MUL",e[e.ADD=1]="ADD",e[e.ATAN2=2]="ATAN2",e[e.SUB=3]="SUB",e[e.DIV=4]="DIV",e[e.EQUAL=5]="EQUAL",e[e.GREATER=6]="GREATER",e[e.GREATER_EQUAL=7]="GREATER_EQUAL",e[e.LESS=8]="LESS",e[e.LESS_EQUAL=9]="LESS_EQUAL",e[e.LOGICAL_AND=10]="LOGICAL_AND",e[e.NOT_EQUAL=11]="NOT_EQUAL",e[e.SQUARED_DIFFERENCE=12]="SQUARED_DIFFERENCE",e[e.INT_DIV=13]="INT_DIV",e[e.POW=14]="POW",e[e.PRELU=15]="PRELU",e[e.MAX=16]="MAX",e[e.MIN=17]="MIN",e[e.COMPLEX_MULTIPLY_REAL=18]="COMPLEX_MULTIPLY_REAL",e[e.COMPLEX_MULTIPLY_IMAG=19]="COMPLEX_MULTIPLY_IMAG"})(Xe||(Xe={}));var C0e=`
+    `}return a}function P0e(e){let t=/(\w+)\s*:\s*vec(5|6)/g;e=e.replace(t,s=>"@align(16) "+s);let n=/vec(5|6)\s*,\s*(\w+)/g;return e=e.replace(n,(s,r,a)=>`vec${r}, @align(16) ${a}`),e}var AT={};qe(AT,{ArrayBufferToTypedArray:()=>vT,GPUBytesPerElement:()=>bT,MatMulProgramType:()=>$r,computeDispatch:()=>je,computeWorkGroupInfoForMatMul:()=>xT,computeWorkGroupSizeForConv2d:()=>bb,computeWorkPerThreadForConv2d:()=>vb,flatDispatchLayout:()=>lt,isWebGPUSupported:()=>wb,tilesFitEvenlyIntoShape:()=>F0e});var al=e=>{let t=1;for(let n=0;n<e.length;n++)t*=e[n];return t};function F0e(e,t){if(e.length!==t.length)throw new Error(`Cannot compute whether rank ${e.length} tiles fit evenly into rank ${t.length} shape - ranks must match.`);return t.every((n,s)=>n%e[s]===0)}function je(e,t,n=[1,1,1],s=[1,1,1]){let[r,a,o]=[Math.ceil(al(e.x.map(i=>t[i]))/(n[0]*s[0])),e.y?Math.ceil(al(e.y.map(i=>t[i]))/(n[1]*s[1])):1,e.z?Math.ceil(al(e.z.map(i=>t[i]))/(n[2]*s[2])):1];return[r,a,o]}function xT(e,t,n,s=!1){let r=[8,8,1],a=[4,4,1];return s||(e<=8&&(a[1]=1),t<=16&&n<=16&&(r[0]=4)),{workGroupSize:r,elementsPerThread:a}}function bb(e,t,n=!1){if(n)return[8,8,1];let s=al(e.x.map(a=>t[a])),r=al(e.y.map(a=>t[a]));return s<=4?[4,16,1]:r<=4?[16,4,1]:[16,16,1]}function vb(e,t,n=!1){if(n)return[4,4,1];let s=al(e.x.map(a=>t[a])),r=al(e.y.map(a=>t[a]));return s<=4?[1,2,1]:r<=4?[2,1,1]:[2,2,1]}function lt(e){return{x:e.map((t,n)=>n)}}function bT(e){if(e==="float32"||e==="int32"||e==="bool"||e==="string")return 4;if(e==="complex64")return 8;throw new Error(`Unknown dtype ${e}`)}function vT(e,t){if(t==="float32")return new Float32Array(e);if(t==="int32")return new Int32Array(e);if(t==="bool"||t==="string")return Uint8Array.from(new Int32Array(e));throw new Error(`Unknown dtype ${t}`)}function wb(){return(typeof window!="undefined"||typeof WorkerGlobalScope!="undefined")&&!!navigator.gpu}var $r;(function(e){e[e.MatMulReduceProgram=0]="MatMulReduceProgram",e[e.MatMulSplitKProgram=1]="MatMulSplitKProgram",e[e.MatMulSmallOutputSizeProgram=2]="MatMulSmallOutputSizeProgram",e[e.MatMulPackedProgram=3]="MatMulPackedProgram",e[e.MatMulMax=4]="MatMulMax"})($r||($r={}));var O0e=H().getNumber("WEBGPU_CPU_HANDOFF_SIZE_THRESHOLD"),M0e=(e,t)=>{let n=e.limits.maxComputeWorkgroupsPerDimension,s=t.dispatchLayout,r=t.dispatch;if(r.every(o=>o<=n))return r;v.assert(r[0]>n&&s.y===void 0&&s.z===void 0,()=>"Dispatch size exceeds WebGPU limits in Y or Z dimension.");let a=Math.ceil(Math.sqrt(r[0]));return a>n?(a=Math.ceil(Math.cbrt(r[0])),v.assert(a<=n,()=>"Total dispatch size exceeds WebGPU maximum."),[a,a,a]):[a,a,1]},B2=class extends Cc{constructor(e){if(super(),this.commandQueueOwnedIds=new WeakSet,this.dispatchNumberInEncoder=0,this.disposed=!1,this.downloadWaitMs=0,this.tensorDataPendingDisposal=[],this.stagingPendingDisposal=[],this.uniformPendingDisposal=[],this.uploadWaitMs=0,!wb())throw new Error("WebGPU is not supported on this device");this.pipelineCache={},this.device=e,this.queue=e.queue,this.currentCommandEncoder=null,this.currentComputePass=null,this.supportTimeQuery=e.features.has("timestamp-query"),this.bufferManager=new w0e(this.device),this.textureManager=new k0e(this.device),this.tensorMap=new Gp(this,Qt()),this.supportTimeQuery&&(this.querySet=this.device.createQuerySet({type:"timestamp",count:2})),H().getBool("WEBGPU_USE_PROFILE_TOOL")&&(this.dummyCanvas=document.createElement("canvas"),this.dummyCanvas.width=1,this.dummyCanvas.height=1,this.dummyContext=this.dummyCanvas.getContext("webgpu"),this.dummyContext.configure({device:e,format:"bgra8unorm"}),document.body.appendChild(this.dummyCanvas))}nextDataId(){return B2.nextDataId++}floatPrecision(){return 32}defaultGpuBufferUsage(){return GPUBufferUsage.STORAGE|GPUBufferUsage.COPY_SRC|GPUBufferUsage.COPY_DST}disposeData(e,t=!1){if(this.tensorDataPendingDisposal.indexOf(e)>=0)return!1;if(!this.tensorMap.has(e))return!0;let n=this.tensorMap.get(e);if(this.decRef(e),!t&&n.refCount>0)return!1;if(this.commandQueueOwnedIds.has(e))return this.tensorDataPendingDisposal.push(e),!1;let{complexTensorInfos:s}=this.tensorMap.get(e);return s!=null&&(this.disposeData(s.real.dataId,t),this.disposeData(s.imag.dataId,t)),this.releaseResource(e),this.tensorMap.delete(e),!0}memory(){return{numBytesInGPU:this.bufferManager.numBytesUsed,numBytesAllocatedInGPU:this.bufferManager.numBytesAllocated,unreliable:!1}}releaseResource(e){let t=this.tensorMap.get(e);if(!(!t||!t.resourceInfo)){if("texture"in t.resourceInfo){let n=t.resourceInfo;n.texture instanceof GPUTexture&&this.textureManager.releaseTexture(n.texture,n.width,n.height,n.format,n.usage),n.texture=null}else{let n=t.resourceInfo;this.bufferManager.releaseBuffer(n.buffer,n.size,n.usage),n.buffer=null}t.resourceInfo=null}}refCount(e){return this.tensorMap.has(e)?this.tensorMap.get(e).refCount:0}incRef(e){let t=this.tensorMap.get(e);t.refCount++}decRef(e){if(this.tensorMap.has(e)){let t=this.tensorMap.get(e);t.refCount--}}write(e,t,n){if(n==="complex64"&&e!=null)throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");let s={id:this.nextDataId()};return this.tensorMap.set(s,{dtype:n,shape:t,values:e,refCount:1}),s}move(e,t,n,s,r){if(s==="complex64")throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");this.tensorMap.set(e,{dtype:s,shape:n,values:t,refCount:r})}submitQueue(){this.ensureComputePassEnded(),this.queue.submit([this.currentCommandEncoder.finish()]),this.currentCommandEncoder=null,this.dispatchNumberInEncoder=0,this.commandQueueOwnedIds=new WeakSet,this.tensorDataPendingDisposal.forEach(e=>{this.releaseResource(e),this.tensorMap.delete(e)}),this.uniformPendingDisposal.forEach(e=>this.bufferManager.releaseBuffer(e.buffer,e.size,e.usage)),this.stagingPendingDisposal.forEach(e=>this.bufferManager.releaseUploadBuffer(e.buffer,e.size,e.usage)),this.tensorDataPendingDisposal=[],this.uniformPendingDisposal=[],this.stagingPendingDisposal=[]}ensureCommandEncoderReady(){this.currentCommandEncoder||(this.currentCommandEncoder=this.device.createCommandEncoder())}ensureComputePassEnded(){this.currentComputePass&&(this.currentComputePass.end(),this.currentComputePass=null)}getComputePass(){return this.currentComputePass||(this.currentComputePass=this.currentCommandEncoder.beginComputePass()),this.currentComputePass}async getBufferData(e,t){let n=this.bufferManager.acquireBuffer(t,GPUBufferUsage.COPY_DST|GPUBufferUsage.MAP_READ);this.ensureCommandEncoderReady(),this.ensureComputePassEnded(),this.currentCommandEncoder.copyBufferToBuffer(e,0,n,0,t),this.submitQueue(),await n.mapAsync(GPUMapMode.READ);let s=n.getMappedRange().slice(0);return n.unmap(),n!=null&&this.bufferManager.releaseBuffer(n,t,GPUBufferUsage.COPY_DST|GPUBufferUsage.MAP_READ),H().getBool("WEBGPU_USE_PROFILE_TOOL")&&(v.assert(this.dummyContext!==void 0,()=>"Fail to get context for profiling tool"),this.dummyContext.getCurrentTexture()),s}convertAndCacheOnCPU(e,t){let n=this.tensorMap.get(e);return this.releaseResource(e),n.values=t,n.values}readSync(e){let t=this.tensorMap.get(e),{values:n}=t;if(n==null)throw new Error("WebGPU readSync is only available for CPU-resident tensors.");return n}async read(e){if(!this.tensorMap.has(e))throw new Error(`Tensor ${e} was not registered!`);let t=this.tensorMap.get(e),{values:n}=t;if(n!=null)return this.convertAndCacheOnCPU(e,n);let s;if(t.dtype==="complex64"){let r=await Promise.all([this.read(t.complexTensorInfos.real.dataId),this.read(t.complexTensorInfos.imag.dataId)]),a=r[0],o=r[1];s=T.mergeRealAndImagArrays(a,o)}else{let r=t.resourceInfo,a=await this.getBufferData(r.buffer,r.size);s=vT(a,t.dtype)}return this.convertAndCacheOnCPU(e,s),s}readToGPU(e){let t=this.tensorMap.get(e),{values:n,dtype:s,shape:r,resourceInfo:a}=t;if(s==="complex64")throw new Error("Does not support reading buffer for complex64 dtype.");if(a==null)throw n!=null?new Error("Data is not on GPU but on CPU."):new Error("There is no data on GPU or CPU.");let o=a.size,i=this.bufferManager.acquireBuffer(o,a.usage);this.ensureCommandEncoderReady(),this.ensureComputePassEnded(),this.currentCommandEncoder.copyBufferToBuffer(a.buffer,0,i,0,o),this.submitQueue();let l=this.makeTensorInfo(r,s),u=Qt().makeTensorFromTensorInfo(l),c=this.tensorMap.get(l.dataId);return c.resourceInfo={size:o,usage:this.defaultGpuBufferUsage(),buffer:i},{tensorRef:u,buffer:i,bufSize:o}}bufferSync(e){let t=this.readSync(e.dataId);if(e.dtype==="string")try{let n=t.map(s=>v.decodeString(s));return Ue(e.shape,e.dtype,n)}catch(n){throw new Error("Failed to decode encoded string bytes into utf-8")}return Ue(e.shape,e.dtype,t)}async time(e){this.supportTimeQuery||console.warn("This device doesn't support timestamp-query extension. Start Chrome browser with flag --disable-dawn-features=disallow_unsafe_apis then try again. Otherwise, zero will be shown for the kernel time when profiling mode is enabled. Using performance.now is not workable for webgpu since it doesn't support synchronous data read from GPU.");let t=this.activeTimers,n=[],s=!1;this.programTimersStack==null?(this.programTimersStack=n,s=!0):this.activeTimers.push(n),this.activeTimers=n,e();let r=v.flatten(this.activeTimers.map(l=>l.query)).filter(l=>l!=null),a=v.flatten(this.activeTimers.map(l=>l.name)).filter(l=>l!=null);this.activeTimers=t,s&&(this.programTimersStack=null);let o={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null},i=await Promise.all(r);return o.kernelMs=v.sum(i),o.getExtraProfileInfo=()=>i.map((l,u)=>({name:a[u],ms:l})).map(l=>`${l.name}: ${l.ms}`).join(", "),this.uploadWaitMs=0,this.downloadWaitMs=0,o}makeTensorInfo(e,t,n){return t==="string"&&n!=null&&n.length>0&&v.isString(n[0])&&(n=n.map(r=>v.encodeString(r))),{dataId:this.write(n,e,t),shape:e,dtype:t}}tensorToBinding(e){if(!e)return null;let t=this.tensorMap.get(e.dataId);if("texture"in t.resourceInfo){let s=t.resourceInfo;return s.texture instanceof GPUExternalTexture?s.texture:s.texture.createView()}let n=t.resourceInfo;return{offset:0,size:n.size,buffer:n.buffer}}async getQueryTime(e){return this.supportTimeQuery?this.getTimeFromQuerySet(e):0}uploadToGPU(e){let t=this.tensorMap.get(e);if(t.resourceInfo)return;let n=bT(t.dtype)*v.sizeFromShape(t.shape),s=this.bufferManager.acquireBuffer(n,this.defaultGpuBufferUsage());if(t.resourceInfo={size:n,usage:this.defaultGpuBufferUsage(),buffer:s},t.values){let r=this.bufferManager.acquireUploadBuffer(n,GPUBufferUsage.MAP_WRITE|GPUBufferUsage.COPY_SRC),a=r.getMappedRange();t.dtype==="int32"||t.dtype==="bool"?new Int32Array(a).set(t.values):new Float32Array(a).set(t.values),r.unmap(),this.ensureCommandEncoderReady(),this.ensureComputePassEnded(),this.currentCommandEncoder.copyBufferToBuffer(r,0,s,0,n);let o={size:n,usage:GPUBufferUsage.MAP_WRITE|GPUBufferUsage.COPY_SRC,buffer:r};this.stagingPendingDisposal.push(o)}}makeUniforms(e){let t=0,n=0,s=[];e.forEach(i=>{i.data.length===0&&(i.data=[1]);let l;switch(i.data.length){case 1:l=4;break;case 2:l=8;break;case 3:l=16;break;case 4:l=16;break;case 5:l=16;break;case 6:l=16;break;default:v.assert(!1,()=>`Unsupported ${i.data.length}D shape`)}(n===5||n===6)&&(l=16),t=Math.ceil(t/l)*l,n=i.data.length,s.push(t),t+=i.data.length*4});let r=new ArrayBuffer(t);e.forEach((i,l)=>{let u=s[l];i.type==="int32"?new Int32Array(r,u,i.data.length).set(i.data):i.type==="uint32"?new Uint32Array(r,u,i.data.length).set(i.data):new Float32Array(r,u,i.data.length).set(i.data)});let a=this.bufferManager.acquireBuffer(t,GPUBufferUsage.COPY_DST|GPUBufferUsage.UNIFORM);this.queue.writeBuffer(a,0,r,0,t);let o={size:t,usage:GPUBufferUsage.COPY_DST|GPUBufferUsage.UNIFORM,buffer:a};return this.uniformPendingDisposal.push(o),{offset:0,size:t,buffer:a}}runWebGPUProgram(e,t,n,s,r){if(r||(r=this.makeTensorInfo(e.outputShape,n)),v.sizeFromShape(r.shape)===0)return this.tensorMap.get(r.dataId).values=v.getTypedArrayFromDType(r.dtype,0),r;this.uploadToGPU(r.dataId),e.dispatch=M0e(this.device,e);let a=[],o=[];if(!e.isFromPixels){a.push({type:"float32",data:[NaN]}),o=t.concat(r).map(g=>g.shape);let f="int32";o.map(g=>{a.push({type:f,data:g})});let m=v.computeStrides(r.shape);if(a.push({type:f,data:m}),e.size){let g=v.sizeFromShape(e.outputShape);a.push({type:f,data:[e.isVec4?g/4:g]})}}let i=t.map((f,m)=>{if(f.dtype==="complex64")throw new Error("GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.");return this.uploadToGPU(f.dataId),{dtype:this.tensorMap.get(f.dataId).dtype,shape:f.shape,name:e.variableNames[m]}}),l=T0e(e,o,i,r),u;l in this.pipelineCache?u=this.pipelineCache[l]:(u=I0e(this.device,e,i,r),this.pipelineCache[l]=u),s&&(a=[...a,...s]);let c=[this.tensorToBinding(r),...t.map(f=>this.tensorToBinding(f)),this.makeUniforms(a)],p=this.device.createBindGroup({layout:u.getBindGroupLayout(0),entries:c.map((f,m)=>({binding:m,resource:f}))});this.ensureCommandEncoderReady();let d=this.getComputePass(),h=this.activeTimers!=null;return h&&this.supportTimeQuery&&d.writeTimestamp(this.querySet,0),d.setPipeline(u),d.setBindGroup(0,p),d.dispatchWorkgroups(e.dispatch[0],e.dispatch[1],e.dispatch[2]),h&&this.supportTimeQuery&&d.writeTimestamp(this.querySet,1),this.dispatchNumberInEncoder++,t.forEach(f=>{this.commandQueueOwnedIds.add(f.dataId)}),this.commandQueueOwnedIds.add(r.dataId),H().get("WEBGPU_DEFERRED_SUBMIT_BATCH_SIZE")<=this.dispatchNumberInEncoder&&this.submitQueue(),h&&this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime(this.querySet)}),r}async getTimeFromQuerySet(e){let t=this.bufferManager.acquireBuffer(16,GPUBufferUsage.COPY_SRC|GPUBufferUsage.QUERY_RESOLVE),n=this.bufferManager.acquireBuffer(16,GPUBufferUsage.MAP_READ|GPUBufferUsage.COPY_DST);this.ensureCommandEncoderReady(),this.ensureComputePassEnded(),this.currentCommandEncoder.resolveQuerySet(e,0,2,t,0),this.currentCommandEncoder.copyBufferToBuffer(t,0,n,0,16),this.submitQueue(),await n.mapAsync(GPUMapMode.READ);let s=new BigUint64Array(n.getMappedRange()),r=Number(s[1]-s[0]);return n.unmap(),this.bufferManager.releaseBuffer(n,16,GPUBufferUsage.MAP_READ|GPUBufferUsage.COPY_DST),this.bufferManager.releaseBuffer(t,16,GPUBufferUsage.COPY_SRC|GPUBufferUsage.QUERY_RESOLVE),r/1e6}shouldExecuteOnCPU(e,t=O0e){return H().getBool("WEBGPU_CPU_FORWARD")&&e.every(n=>this.tensorMap.get(n.dataId).resourceInfo==null&&v.sizeFromShape(n.shape)<t)}numDataIds(){return this.tensorMap.numDataIds()-this.tensorDataPendingDisposal.length}dispose(){this.disposed||(this.bufferManager.dispose(),this.textureManager.dispose(),this.disposed=!0)}};B2.nextDataId=0;wb()&&lu("webgpu",async()=>{H().set("CHECK_COMPUTATION_FOR_ERRORS",!1);let e={powerPreference:H().get("WEBGPU_USE_LOW_POWER_GPU")?"low-power":"high-performance"},t=await navigator.gpu.requestAdapter(e),n=t.limits,s={},r=t.features.has("timestamp-query");s.requiredLimits={maxComputeWorkgroupStorageSize:n.maxComputeWorkgroupStorageSize,maxComputeWorkgroupsPerDimension:n.maxComputeWorkgroupsPerDimension,maxStorageBufferBindingSize:n.maxStorageBufferBindingSize},r&&(s.requiredFeatures=["timestamp-query"]);let a=await t.requestDevice(s);return new B2(a)},3);var Ye;(function(e){e[e.MUL=0]="MUL",e[e.ADD=1]="ADD",e[e.ATAN2=2]="ATAN2",e[e.SUB=3]="SUB",e[e.DIV=4]="DIV",e[e.EQUAL=5]="EQUAL",e[e.GREATER=6]="GREATER",e[e.GREATER_EQUAL=7]="GREATER_EQUAL",e[e.LESS=8]="LESS",e[e.LESS_EQUAL=9]="LESS_EQUAL",e[e.LOGICAL_AND=10]="LOGICAL_AND",e[e.NOT_EQUAL=11]="NOT_EQUAL",e[e.SQUARED_DIFFERENCE=12]="SQUARED_DIFFERENCE",e[e.INT_DIV=13]="INT_DIV",e[e.POW=14]="POW",e[e.PRELU=15]="PRELU",e[e.MAX=16]="MAX",e[e.MIN=17]="MIN",e[e.COMPLEX_MULTIPLY_REAL=18]="COMPLEX_MULTIPLY_REAL",e[e.COMPLEX_MULTIPLY_IMAG=19]="COMPLEX_MULTIPLY_IMAG"})(Ye||(Ye={}));var z0e=`
   if (isnan(a)) { return a; }
   if (isnan(b)) { return b; }
-  `,gT=`
+  `,wT=`
   if (isNaN.r) {
     resultTemp.r = valueForNaN;
   }
@@ -5180,16 +5180,16 @@ return a / b;`,Nle=`
   if (isNaN.a) {
     resultTemp.a = valueForNaN;
   }
-  `,yT=`
+  `,kT=`
   let isNaN = isnanVec4(a) | isnanVec4(b);
-  ${gT}
-  `,T0e="return a + b;",N0e="return areal * breal - aimag * bimag;",E0e="return areal * bimag + aimag * breal;",R0e="return a / b;",_0e="return a * b;",D0e="return (a - b) * (a - b);",$0e="return a - b;",P0e="return f32(a == b);",F0e="return vec4<f32>(a == b);",O0e="return f32(a > b);",M0e="return vec4<f32>(a > b);",z0e="return f32(a >= b);",L0e="return vec4<f32>(a >= b);",B0e="return f32(a < b);",W0e="return vec4<f32>(a < b);",V0e="return f32(a <= b);",U0e="return vec4<f32>(a <= b);",G0e="return f32(f32(a) >= 1.0 && f32(b) >= 1.0);",H0e=`return (vec4<f32>(a >= vec4<f32>(1.0)) *
-  vec4<f32>(b >= vec4<f32>(1.0)));`,j0e=`
+  ${wT}
+  `,L0e="return a + b;",B0e="return areal * breal - aimag * bimag;",W0e="return areal * bimag + aimag * breal;",V0e="return a / b;",U0e="return a * b;",G0e="return (a - b) * (a - b);",H0e="return a - b;",j0e="return f32(a == b);",q0e="return vec4<f32>(a == b);",X0e="return f32(a > b);",K0e="return vec4<f32>(a > b);",Z0e="return f32(a >= b);",Y0e="return vec4<f32>(a >= b);",J0e="return f32(a < b);",Q0e="return vec4<f32>(a < b);",e2e="return f32(a <= b);",t2e="return vec4<f32>(a <= b);",n2e="return f32(f32(a) >= 1.0 && f32(b) >= 1.0);",s2e=`return (vec4<f32>(a >= vec4<f32>(1.0)) *
+  vec4<f32>(b >= vec4<f32>(1.0)));`,r2e=`
   let s = sign(a) * sign(b);
   let ia = i32(round(a));
   let ib = i32(round(b));
   return f32(idiv(ia, ib, s));
-  `,q0e=`
+  `,a2e=`
   let ia = vec4<i32>(round(a));
   let ib = vec4<i32>(round(b));
   let cond = ib != vec4<i32>(0);
@@ -5210,18 +5210,18 @@ return a / b;`,Nle=`
     resultTemp[3] = idiv(ia[3], ib[3], s[3]);
   }
   return vec4<f32>(resultTemp);
-  `,X0e=`
+  `,o2e=`
   if (isnan(a) || isnan(b)) {
     return 1.0;
   }
   return f32(a != b);
-`,K0e=`
+`,i2e=`
   var resultTemp = vec4<f32>(a != b);
   let valueForNaN = 1.0;
-  ${yT}
+  ${kT}
 
   return resultTemp;
-`,Z0e=`
+`,l2e=`
   if(a < 0.0 && floor(b) < b) {
     return uniforms.NAN;
   }
@@ -5232,7 +5232,7 @@ return a / b;`,Nle=`
     return pow(abs(a), b);
   }
   return sign(a) * pow(abs(a), b);
-  `,Y0e=`
+  `,u2e=`
   let isModRound1Bool = vec4<i32>(round(abs(b) % vec4<f32>(2.0))) == vec4<i32>(1);
   let isModRound1 = vec4<f32>(isModRound1Bool);
   let multiplier = sign(a) * isModRound1 + (vec4<f32>(1.0) - isModRound1);
@@ -5254,22 +5254,22 @@ return a / b;`,Nle=`
   }
   let isNaN = a < vec4<f32>(0.0) & floor(b) < b;
   let valueForNaN = uniforms.NAN;
-  ${gT}
+  ${wT}
   return resultTemp;
-  `,J0e="if (a < 0.0) { return b * a; }  return a;",Q0e=`
+  `,c2e="if (a < 0.0) { return b * a; }  return a;",d2e=`
   let aLessThanZero = vec4<f32>(a < vec4<f32>(0.0));
   return (aLessThanZero * (b * a)) + ((vec4<f32>(1.0) - aLessThanZero) * a);
-  `;function T3(e,t,n="uniforms.NAN"){let s=t?yT:C0e;return t?`
+  `;function R3(e,t,n="uniforms.NAN"){let s=t?kT:z0e;return t?`
     let valueForNaN = ${n};
     var resultTemp = vec4<f32>(${e}(a, b));
     `+s+`
     return resultTemp;
   `:s+`
     return ${e}(a, b);
-  `}function Ym(e,t){switch(e){case Xe.MUL:return _0e;case Xe.ADD:return T0e;case Xe.ATAN2:return T3("atan2",t);case Xe.SUB:return $0e;case Xe.DIV:return R0e;case Xe.EQUAL:return t?F0e:P0e;case Xe.GREATER:return t?M0e:O0e;case Xe.GREATER_EQUAL:return t?L0e:z0e;case Xe.LESS:return t?W0e:B0e;case Xe.LESS_EQUAL:return t?U0e:V0e;case Xe.LOGICAL_AND:return t?H0e:G0e;case Xe.NOT_EQUAL:return t?K0e:X0e;case Xe.SQUARED_DIFFERENCE:return D0e;case Xe.INT_DIV:return t?q0e:j0e;case Xe.PRELU:return t?Q0e:J0e;case Xe.MAX:return T3("max",t);case Xe.MIN:return T3("min",t);case Xe.POW:return t?Y0e:Z0e;case Xe.COMPLEX_MULTIPLY_REAL:return N0e;case Xe.COMPLEX_MULTIPLY_IMAG:return E0e;default:throw new Error(`BinaryType ${e} is not implemented!`)}}var Oe;(function(e){e[e.ABS=0]="ABS",e[e.CEIL=1]="CEIL",e[e.COS=2]="COS",e[e.COSH=3]="COSH",e[e.ELU=4]="ELU",e[e.EXP=5]="EXP",e[e.EXPM1=6]="EXPM1",e[e.FLOOR=7]="FLOOR",e[e.IS_NAN=8]="IS_NAN",e[e.LINEAR=9]="LINEAR",e[e.LOG=10]="LOG",e[e.LOGICAL_NOT=11]="LOGICAL_NOT",e[e.NEG=12]="NEG",e[e.RELU=13]="RELU",e[e.RELU6=14]="RELU6",e[e.LEAKYRELU=15]="LEAKYRELU",e[e.RECIPROCAL=16]="RECIPROCAL",e[e.RSQRT=17]="RSQRT",e[e.SIN=18]="SIN",e[e.SINH=19]="SINH",e[e.SIGMOID=20]="SIGMOID",e[e.SQRT=21]="SQRT",e[e.SQUARE=22]="SQUARE",e[e.TANH=23]="TANH",e[e.TO_INT=24]="TO_INT"})(Oe||(Oe={}));var e2e="return abs(a);",t2e="return ceil(a);",n2e="return cos(a);",s2e=`
+  `}function Ym(e,t){switch(e){case Ye.MUL:return U0e;case Ye.ADD:return L0e;case Ye.ATAN2:return R3("atan2",t);case Ye.SUB:return H0e;case Ye.DIV:return V0e;case Ye.EQUAL:return t?q0e:j0e;case Ye.GREATER:return t?K0e:X0e;case Ye.GREATER_EQUAL:return t?Y0e:Z0e;case Ye.LESS:return t?Q0e:J0e;case Ye.LESS_EQUAL:return t?t2e:e2e;case Ye.LOGICAL_AND:return t?s2e:n2e;case Ye.NOT_EQUAL:return t?i2e:o2e;case Ye.SQUARED_DIFFERENCE:return G0e;case Ye.INT_DIV:return t?a2e:r2e;case Ye.PRELU:return t?d2e:c2e;case Ye.MAX:return R3("max",t);case Ye.MIN:return R3("min",t);case Ye.POW:return t?u2e:l2e;case Ye.COMPLEX_MULTIPLY_REAL:return B0e;case Ye.COMPLEX_MULTIPLY_IMAG:return W0e;default:throw new Error(`BinaryType ${e} is not implemented!`)}}var Oe;(function(e){e[e.ABS=0]="ABS",e[e.CEIL=1]="CEIL",e[e.COS=2]="COS",e[e.COSH=3]="COSH",e[e.ELU=4]="ELU",e[e.EXP=5]="EXP",e[e.EXPM1=6]="EXPM1",e[e.FLOOR=7]="FLOOR",e[e.IS_NAN=8]="IS_NAN",e[e.LINEAR=9]="LINEAR",e[e.LOG=10]="LOG",e[e.LOGICAL_NOT=11]="LOGICAL_NOT",e[e.NEG=12]="NEG",e[e.RELU=13]="RELU",e[e.RELU6=14]="RELU6",e[e.LEAKYRELU=15]="LEAKYRELU",e[e.RECIPROCAL=16]="RECIPROCAL",e[e.RSQRT=17]="RSQRT",e[e.SIN=18]="SIN",e[e.SINH=19]="SINH",e[e.SIGMOID=20]="SIGMOID",e[e.SQRT=21]="SQRT",e[e.SQUARE=22]="SQUARE",e[e.TANH=23]="TANH",e[e.TO_INT=24]="TO_INT"})(Oe||(Oe={}));var p2e="return abs(a);",h2e="return ceil(a);",f2e="return cos(a);",m2e=`
   let e2x = exp(-a);
   return (e2x + 1.0 / e2x) / 2.0;
-`,r2e="return exp(a) - 1.0;",a2e="if (a >= 0.0) { return a; }  return (exp(a) - 1.0);",o2e=`
+`,g2e="return exp(a) - 1.0;",y2e="if (a >= 0.0) { return a; }  return (exp(a) - 1.0);",A2e=`
   var resFloat = exp(a) - vec4<f32>(1.0);
   if (a.r >= 0.0) {
     resFloat.r = a.r;
@@ -5284,36 +5284,36 @@ return a / b;`,Nle=`
     resFloat.a = a.a;
   }
   return resFloat;
-`,i2e="return exp(a);",l2e="return floor(a);",u2e="return f32(isnan(a));",c2e="return a;",d2e=`if (a < 0.0) { return 1.0/0.0; }
-  return log(a);`,p2e="return f32(!(a >= 1.0));",h2e="return -a;",f2e="if (a < 0.0) { return uniforms.alpha * a; } return a;",m2e=`
+`,x2e="return exp(a);",b2e="return floor(a);",v2e="return f32(isnan(a));",w2e="return a;",k2e=`if (a < 0.0) { return 1.0/0.0; }
+  return log(a);`,S2e="return f32(!(a >= 1.0));",I2e="return -a;",C2e="if (a < 0.0) { return uniforms.alpha * a; } return a;",T2e=`
   let aLessThanZero = vec4<f32>(a < vec4<f32>(0.0));
   return (aLessThanZero * (uniforms.alpha * a)) + ((vec4<f32>(1.0) - aLessThanZero) * a);
-`,g2e="return 1.0 / a;",y2e="return select(a, 0.0, a < 0.0);",A2e="return clamp(a, 0.0, 6.0);",x2e="return clamp(a, vec4<f32>(0.0, 0.0, 0.0, 0.0), vec4<f32>(6.0, 6.0, 6.0, 6.0));",b2e=`
+`,N2e="return 1.0 / a;",E2e="return select(a, 0.0, a < 0.0);",R2e="return clamp(a, 0.0, 6.0);",_2e="return clamp(a, vec4<f32>(0.0, 0.0, 0.0, 0.0), vec4<f32>(6.0, 6.0, 6.0, 6.0));",D2e=`
   return select(a, vec4<f32>(0.0), a < vec4<f32>(0.0));
-`,v2e="return 1.0/sqrt(a);",w2e="return 1.0 / (1.0 + exp(-1.0 * a));",k2e="return sin(a);",S2e=`
+`,$2e="return 1.0/sqrt(a);",P2e="return 1.0 / (1.0 + exp(-1.0 * a));",F2e="return sin(a);",O2e=`
   let e2x = exp(a);
   return (e2x - 1.0 / e2x) / 2.0;
-`,I2e="return sqrt(a);",C2e="return a * a;",T2e=`
+`,M2e="return sqrt(a);",z2e="return a * a;",L2e=`
   let e2x = exp(-2.0 * abs(a));
   return sign(a) * (1.0 - e2x) / (1.0 + e2x);
-`,N2e="return f32(i32((a)));";function Ui(e,t){switch(e){case Oe.ABS:return e2e;case Oe.COS:return n2e;case Oe.COSH:return s2e;case Oe.CEIL:return t2e;case Oe.ELU:return t?o2e:a2e;case Oe.EXP:return i2e;case Oe.EXPM1:return r2e;case Oe.FLOOR:return l2e;case Oe.IS_NAN:return u2e;case Oe.LINEAR:return c2e;case Oe.LOG:return d2e;case Oe.LOGICAL_NOT:return p2e;case Oe.NEG:return h2e;case Oe.LEAKYRELU:return t?m2e:f2e;case Oe.RECIPROCAL:return g2e;case Oe.RELU:return t?b2e:y2e;case Oe.RELU6:return t?x2e:A2e;case Oe.RSQRT:return v2e;case Oe.SIGMOID:return w2e;case Oe.SIN:return k2e;case Oe.SINH:return S2e;case Oe.SQRT:return I2e;case Oe.SQUARE:return C2e;case Oe.TANH:return T2e;case Oe.TO_INT:return N2e;default:throw new Error(`BinaryType ${e} is not implemented!`)}}var Yt=e=>{switch(e){case 1:return"f32";case 2:return"vec2<f32>";case 3:return"vec3<f32>";case 4:return"vec4<f32>";default:throw new Error(`${e}-component is not supported.`)}};function Ai(e,t=!1,n=!1,s=3){if(e===null)return"";let r="";if(e==="linear")r=Ui(Oe.LINEAR);else if(e==="relu")r=Ui(Oe.RELU,n);else if(e==="elu")r=Ui(Oe.ELU,n);else if(e==="relu6")r=Ui(Oe.RELU6,n);else if(e==="prelu")r=Ym(Xe.PRELU,n);else if(e==="sigmoid")r=Ui(Oe.SIGMOID,n);else if(e==="leakyrelu")r=Ui(Oe.LEAKYRELU,n);else throw new Error(`Activation ${e} has not been implemented for the WebGPU backend.`);let o=Yt(n?4:1),i="";return t?i=`
+`,B2e="return f32(i32((a)));";function qi(e,t){switch(e){case Oe.ABS:return p2e;case Oe.COS:return f2e;case Oe.COSH:return m2e;case Oe.CEIL:return h2e;case Oe.ELU:return t?A2e:y2e;case Oe.EXP:return x2e;case Oe.EXPM1:return g2e;case Oe.FLOOR:return b2e;case Oe.IS_NAN:return v2e;case Oe.LINEAR:return w2e;case Oe.LOG:return k2e;case Oe.LOGICAL_NOT:return S2e;case Oe.NEG:return I2e;case Oe.LEAKYRELU:return t?T2e:C2e;case Oe.RECIPROCAL:return N2e;case Oe.RELU:return t?D2e:E2e;case Oe.RELU6:return t?_2e:R2e;case Oe.RSQRT:return $2e;case Oe.SIGMOID:return P2e;case Oe.SIN:return F2e;case Oe.SINH:return O2e;case Oe.SQRT:return M2e;case Oe.SQUARE:return z2e;case Oe.TANH:return L2e;case Oe.TO_INT:return B2e;default:throw new Error(`BinaryType ${e} is not implemented!`)}}var Jt=e=>{switch(e){case 1:return"f32";case 2:return"vec2<f32>";case 3:return"vec3<f32>";case 4:return"vec4<f32>";default:throw new Error(`${e}-component is not supported.`)}};function wi(e,t=!1,n=!1,s=3){if(e===null)return"";let r="";if(e==="linear")r=qi(Oe.LINEAR);else if(e==="relu")r=qi(Oe.RELU,n);else if(e==="elu")r=qi(Oe.ELU,n);else if(e==="relu6")r=qi(Oe.RELU6,n);else if(e==="prelu")r=Ym(Ye.PRELU,n);else if(e==="sigmoid")r=qi(Oe.SIGMOID,n);else if(e==="leakyrelu")r=qi(Oe.LEAKYRELU,n);else throw new Error(`Activation ${e} has not been implemented for the WebGPU backend.`);let o=Jt(n?4:1),i="";return t?i=`
       fn activation(a : ${o}, coords : vec${s}<i32>) -> ${o} {
         let b = getPreluActivationWeightsByOutputCoords(coords);
         ${r}
       }`:i=`
       fn activation(a : ${o}, coords : vec${s}<i32>) -> ${o} {
         ${r}
-      }`,i}function xd(e,t){return`
+      }`,i}function wd(e,t){return`
       ${e?"value = value + getBiasByOutputCoords(coords);":""}
       ${t?"value = activation(value, coords);":""}
-      `}function AT(e,t,n,s,r=!1,a=!1,o=!1,i=1){v.assert(n&&i===1||!n,()=>`transposeA ${n} is not compatible with component size ${i}`);let l=`
+      `}function ST(e,t,n,s,r=!1,a=!1,o=!1,i=1){v.assert(n&&i===1||!n,()=>`transposeA ${n} is not compatible with component size ${i}`);let l=`
       let batch = ${e?"0":"batchIn"};
       let batchASize = uniforms.aShape[1] * uniforms.aShape[2];
       ${n?`value = A[(batch * batchASize + col * uniforms.aShape[2] + row) / ${i}];`:`value = A[(batch * batchASize + row * uniforms.aShape[2] + col) / ${i}];`}
 
     `,u;return s===!1?u=`value = B[(batch * batchBSize + row * uniforms.bShape[2] + col) / ${i}];`:u=`value = B[(batch * batchBSize + col * uniforms.bShape[2] + row) / ${i}];`,`
-  fn mm_readA(batchIn: i32, row: i32, colIn: i32) -> ${Yt(i)} {
-    var value = ${Yt(i)}(0.0);
+  fn mm_readA(batchIn: i32, row: i32, colIn: i32) -> ${Jt(i)} {
+    var value = ${Jt(i)}(0.0);
     let col = colIn * ${i};
     ${r&&o?l:`
     ${n?"if(row < uniforms.dimAOuter && col < uniforms.dimInner)":"if(row < uniforms.aShape[1] && col < uniforms.aShape[2])"}
@@ -5324,27 +5324,27 @@ return a / b;`,Nle=`
     return value;
   }
 
-  fn mm_readB(batchIn: i32, row: i32, colIn: i32) -> ${Yt(i)} {
+  fn mm_readB(batchIn: i32, row: i32, colIn: i32) -> ${Jt(i)} {
     let col = colIn * ${i};
     let batch = ${t?"0":"batchIn"};
     let batchBSize = uniforms.bShape[1] * uniforms.bShape[2];
-    var value = ${Yt(i)}(0.0);
+    var value = ${Jt(i)}(0.0);
     ${u}
     return value;
   }
-  `}function bb(e,t,n,s,r,a,o=!1,i=!1,l=!1,u=1){return`
-  ${AT(n,s,r,a,o,i,l,u)}
-  fn mm_write(batch: i32, row: i32, colIn: i32, valueIn: ${Yt(u)}) {
+  `}function kb(e,t,n,s,r,a,o=!1,i=!1,l=!1,u=1){return`
+  ${ST(n,s,r,a,o,i,l,u)}
+  fn mm_write(batch: i32, row: i32, colIn: i32, valueIn: ${Jt(u)}) {
     let col = colIn * ${u};
     ${o&&i?"":"if (row < uniforms.dimAOuter && col < uniforms.dimBOuter)"}
     {
       var value = valueIn;
       let coords = vec3<i32>(batch, row, col);
-      ${xd(e,t)}
+      ${wd(e,t)}
       setOutputAtCoords(coords[0], coords[1], coords[2], value);
     }
   }
-  `}var E2e=e=>e?`
+  `}var W2e=e=>e?`
         mm_Asub[inputRow][inputCol] = mm_readA(batch,
           kStart + inputRow,
           globalRowStart / InnerElementSize + inputCol);
@@ -5352,7 +5352,7 @@ return a / b;`,Nle=`
         mm_Asub[inputRow][inputCol] = mm_readA(batch,
           globalRow + innerRow,
           kStart / InnerElementSize + inputCol);
-        `,R2e=(e,t)=>e?`
+        `,V2e=(e,t)=>e?`
         let ACached0 = mm_Asub[k * InnerElementSize][localRow];
         let ACached1 = mm_Asub[k * InnerElementSize + 1][localRow];
         let ACached2 = mm_Asub[k * InnerElementSize + 2][localRow];
@@ -5410,7 +5410,7 @@ return a / b;`,Nle=`
         for (var innerRow = 0; innerRow < RowPerThread; innerRow = innerRow + 1) {
             let inputRow = tileRow + innerRow;
             let inputCol = tileCol;
-            ${E2e(n)}
+            ${W2e(n)}
         }
 
         // Load one tile of B into local memory.
@@ -5429,7 +5429,7 @@ return a / b;`,Nle=`
             let BCached2 = mm_Bsub[k * InnerElementSize + 2][tileCol];
             ${p===3?"":"let BCached3 = mm_Bsub[k * InnerElementSize + 3][tileCol];"}
 
-            ${R2e(n,p)}
+            ${V2e(n,p)}
         }
 
         workgroupBarrier();
@@ -5438,7 +5438,7 @@ return a / b;`,Nle=`
     for (var innerRow = 0; innerRow < RowPerThread; innerRow = innerRow + 1) {
         mm_write(batch, globalRow + innerRow, globalCol, acc[innerRow]);
     }
-  }`}var _2e=e=>e?`
+  }`}var U2e=e=>e?`
         mm_Asub[inputRow][inputCol] = mm_readA(batch,
           kStart + inputRow,
           globalRowStart + inputCol);
@@ -5446,7 +5446,7 @@ return a / b;`,Nle=`
         mm_Asub[inputRow][inputCol] = mm_readA(batch,
           globalRowStart + inputRow,
           kStart + inputCol);
-        `,D2e=e=>e?"let ACached = mm_Asub[k][tileRow + innerRow];":"let ACached = mm_Asub[tileRow + innerRow][k];";function V2(e,t,n=!1,s=32,r=!1,a=32){let o=e[1]*t[1],i=e[0]*t[0],l=n?o:s,u=n?s:o;v.assert(u%t[1]===0&&l%t[0]===0&&s%t[1]===0,()=>`tileAHight ${u} must be divisible by workGroupSize[1]${t[1]}, tileAWidth ${l} must be divisible by workGroupSize[0]${t[0]}, tileInner ${s} must be divisible by workGroupSize[1]${t[1]}`);let c=u/t[1],p=l/t[0],d=s/t[1];return`
+        `,G2e=e=>e?"let ACached = mm_Asub[k][tileRow + innerRow];":"let ACached = mm_Asub[tileRow + innerRow][k];";function V2(e,t,n=!1,s=32,r=!1,a=32){let o=e[1]*t[1],i=e[0]*t[0],l=n?o:s,u=n?s:o;v.assert(u%t[1]===0&&l%t[0]===0&&s%t[1]===0,()=>`tileAHight ${u} must be divisible by workGroupSize[1]${t[1]}, tileAWidth ${l} must be divisible by workGroupSize[0]${t[0]}, tileInner ${s} must be divisible by workGroupSize[1]${t[1]}`);let c=u/t[1],p=l/t[0],d=s/t[1];return`
     var<workgroup> mm_Asub : array<array<f32, ${l}>, ${u}>;
     var<workgroup> mm_Bsub : array<array<f32, ${i}>, ${s}>;
     const RowPerThread = ${e[1]};
@@ -5492,7 +5492,7 @@ return a / b;`,Nle=`
           for (var innerCol = 0; innerCol < ${p}; innerCol = innerCol + 1) {
             let inputRow = tileRowA + innerRow;
             let inputCol = tileColA + innerCol;
-            ${_2e(n)}
+            ${U2e(n)}
           }
         }
 
@@ -5517,7 +5517,7 @@ return a / b;`,Nle=`
           }
 
           for (var innerRow = 0; innerRow < RowPerThread; innerRow = innerRow + 1) {
-            ${D2e(n)}
+            ${G2e(n)}
             for (var innerCol = 0; innerCol < ColPerThread; innerCol = innerCol + 1) {
               acc[innerRow][innerCol] = acc[innerRow][innerCol] + ACached * BCached[innerCol];
             }
@@ -5534,7 +5534,7 @@ return a / b;`,Nle=`
         }
       }
     }
-  `}var $2e=e=>e?`
+  `}var H2e=e=>e?`
       mm_readA(batch, colA, globalRow),
       mm_readA(batch, colA + 1, globalRow),
       mm_readA(batch, colA + 2, globalRow),
@@ -5544,11 +5544,11 @@ return a / b;`,Nle=`
       mm_readA(batch, globalRow, colA + 1),
       mm_readA(batch, globalRow, colA + 2),
       mm_readA(batch, globalRow, colA + 3)
-  `;function P2e(e,t=!1){return v.assert(e[1]===1&&e[2]===1,()=>`A linear work group size is required. But got ${e}.`),`
+  `;function j2e(e,t=!1){return v.assert(e[1]===1&&e[2]===1,()=>`A linear work group size is required. But got ${e}.`),`
     const TileSize = ${e[0]*4};
     var<workgroup> mm_Asub : array<vec4<f32>, ${e[0]}>;
 
-    ${Je()} {
+    ${nt()} {
       let tileCol = i32(localId.x);
       let globalCol = i32(globalId.x);
       let globalRow = i32(globalId.y);
@@ -5562,7 +5562,7 @@ return a / b;`,Nle=`
       for (var t = 0; t < numTiles; t = t + 1) {
         // Load one tile of A into local memory.
         let colA = t * TileSize + tileCol * 4;
-        mm_Asub[tileCol] = vec4<f32>(${$2e(t)});
+        mm_Asub[tileCol] = vec4<f32>(${H2e(t)});
         workgroupBarrier();
 
         // Compute acc values for a single thread.
@@ -5582,13 +5582,13 @@ return a / b;`,Nle=`
 
       mm_write(batch, globalRow, globalCol, acc);
     }
-  `}var F2e=class{constructor(e,t,n,s,r=!1,a=!1,o=null,i=null,l=null){this.variableNames=["A","B"],this.uniforms="dimAOuter : i32, dimBOuter : i32, dimInner : i32,",this.outputShape=t,this.dispatchLayout={x:[2],y:[1],z:[0]};let u=r?e[1]:e[2];if(this.isVec4=(u%4===0&&!r||t[1]%4===0&&r)&&t[2]%4===0&&!a,this.isVectorA=t[1]===1&&!r,!this.isVec4&&this.isVectorA)this.elementsPerThread=[1,1,1],this.workGroupSize=[32,1,1];else{let d=hT(t[1],u,t[2],r);this.workGroupSize=d.workGroupSize,this.elementsPerThread=d.elementsPerThread}this.dispatch=He(this.dispatchLayout,this.outputShape,this.workGroupSize,this.elementsPerThread);let c=o!=null,p=l!=null;c&&this.variableNames.push("bias"),p&&this.variableNames.push("preluActivationWeights"),this.transposeA=r,this.transposeB=a,this.addBias=c,this.activation=i,this.hasPreluActivationWeights=p,this.batchAEqualOne=n,this.batchBEqualOne=s,[this.fitAOuter,this.fitBOuter,this.fitInner]=this.getShapeFit(t[1],t[2],u),this.shaderKey=`matMulPacked_${this.elementsPerThread}_${r}_${a}_${this.activation}_${this.fitAOuter}_${this.fitBOuter}_${this.fitInner}_${this.isVec4}_${this.isVectorA}_${this.batchAEqualOne}_${this.batchBEqualOne}`}getShapeFit(e,t,n){let s=this.workGroupSize[1]*this.elementsPerThread[1],r=this.workGroupSize[0]*this.elementsPerThread[0];!this.isVec4&&this.isVectorA?this.tileInner=this.workGroupSize[0]*4:this.tileInner=r;let a=e%s===0,o=t%r===0,i=n%this.tileInner===0;return[a,o,i]}getUserCode(){return`
-      ${Ai(this.activation,this.hasPreluActivationWeights,this.isVec4)}
-      ${bb(this.addBias,this.activation,this.batchAEqualOne,this.batchBEqualOne,!1,this.transposeB,this.fitAOuter,this.fitBOuter,this.fitInner,this.isVec4?4:1)}
-      ${this.isVec4?W2(this.elementsPerThread,this.workGroupSize,this.transposeA,this.tileInner,!1,null,this.isVectorA):this.isVectorA?P2e(this.workGroupSize,this.transposeA):V2(this.elementsPerThread,this.workGroupSize,this.transposeA,this.tileInner)}
-    `}};function O2e(){return`
+  `}var q2e=class{constructor(e,t,n,s,r=!1,a=!1,o=null,i=null,l=null){this.variableNames=["A","B"],this.uniforms="dimAOuter : i32, dimBOuter : i32, dimInner : i32,",this.outputShape=t,this.dispatchLayout={x:[2],y:[1],z:[0]};let u=r?e[1]:e[2];if(this.isVec4=(u%4===0&&!r||t[1]%4===0&&r)&&t[2]%4===0&&!a,this.isVectorA=t[1]===1&&!r,!this.isVec4&&this.isVectorA)this.elementsPerThread=[1,1,1],this.workGroupSize=[32,1,1];else{let d=xT(t[1],u,t[2],r);this.workGroupSize=d.workGroupSize,this.elementsPerThread=d.elementsPerThread}this.dispatch=je(this.dispatchLayout,this.outputShape,this.workGroupSize,this.elementsPerThread);let c=o!=null,p=l!=null;c&&this.variableNames.push("bias"),p&&this.variableNames.push("preluActivationWeights"),this.transposeA=r,this.transposeB=a,this.addBias=c,this.activation=i,this.hasPreluActivationWeights=p,this.batchAEqualOne=n,this.batchBEqualOne=s,[this.fitAOuter,this.fitBOuter,this.fitInner]=this.getShapeFit(t[1],t[2],u),this.shaderKey=`matMulPacked_${this.elementsPerThread}_${r}_${a}_${this.activation}_${this.fitAOuter}_${this.fitBOuter}_${this.fitInner}_${this.isVec4}_${this.isVectorA}_${this.batchAEqualOne}_${this.batchBEqualOne}`}getShapeFit(e,t,n){let s=this.workGroupSize[1]*this.elementsPerThread[1],r=this.workGroupSize[0]*this.elementsPerThread[0];!this.isVec4&&this.isVectorA?this.tileInner=this.workGroupSize[0]*4:this.tileInner=r;let a=e%s===0,o=t%r===0,i=n%this.tileInner===0;return[a,o,i]}getUserCode(){return`
+      ${wi(this.activation,this.hasPreluActivationWeights,this.isVec4)}
+      ${kb(this.addBias,this.activation,this.batchAEqualOne,this.batchBEqualOne,!1,this.transposeB,this.fitAOuter,this.fitBOuter,this.fitInner,this.isVec4?4:1)}
+      ${this.isVec4?W2(this.elementsPerThread,this.workGroupSize,this.transposeA,this.tileInner,!1,null,this.isVectorA):this.isVectorA?j2e(this.workGroupSize,this.transposeA):V2(this.elementsPerThread,this.workGroupSize,this.transposeA,this.tileInner)}
+    `}};function X2e(){return`
     var<workgroup> sumValues : array<f32, workGroupSizeX>;
-    ${Je()} {
+    ${nt()} {
       let coords = getOutputCoords();
       let batch = coords[0];
       let row = coords[1];
@@ -5617,11 +5617,11 @@ return a / b;`,Nle=`
         mm_write(batch, row, col, sum);
       }
     }
-  `}var M2e=class{constructor(e,t,n,s=!1,r=!1,a=null,o=null,i=null){this.variableNames=["A","B"],this.uniforms="dimAOuter : i32, dimBOuter : i32, dimInner : i32,",this.workGroupSize=[256,1,1],this.outputShape=e,this.dispatchLayout={x:[],y:[1,2],z:[0]},this.dispatch=He(this.dispatchLayout,this.outputShape,this.workGroupSize);let l=a!=null,u=i!=null;l&&this.variableNames.push("bias"),u&&this.variableNames.push("preluActivationWeights"),this.transposeA=s,this.transposeB=r,this.addBias=l,this.activation=o,this.hasPreluActivationWeights=u,this.batchAEqualOne=t,this.batchBEqualOne=n,this.shaderKey=`matMulReduce_${this.activation}_${s}_${r}_${this.batchAEqualOne}_${this.batchBEqualOne}`}getUserCode(){return`
-      ${Ai(this.activation,this.hasPreluActivationWeights)}
-      ${bb(this.addBias,this.activation,this.batchAEqualOne,this.batchBEqualOne,this.transposeA,this.transposeB)}
-      ${O2e()}
-    `}};function z2e(e){let t=e[1],n=e[0],s=t>n?t:n;return`
+  `}var K2e=class{constructor(e,t,n,s=!1,r=!1,a=null,o=null,i=null){this.variableNames=["A","B"],this.uniforms="dimAOuter : i32, dimBOuter : i32, dimInner : i32,",this.workGroupSize=[256,1,1],this.outputShape=e,this.dispatchLayout={x:[],y:[1,2],z:[0]},this.dispatch=je(this.dispatchLayout,this.outputShape,this.workGroupSize);let l=a!=null,u=i!=null;l&&this.variableNames.push("bias"),u&&this.variableNames.push("preluActivationWeights"),this.transposeA=s,this.transposeB=r,this.addBias=l,this.activation=o,this.hasPreluActivationWeights=u,this.batchAEqualOne=t,this.batchBEqualOne=n,this.shaderKey=`matMulReduce_${this.activation}_${s}_${r}_${this.batchAEqualOne}_${this.batchBEqualOne}`}getUserCode(){return`
+      ${wi(this.activation,this.hasPreluActivationWeights)}
+      ${kb(this.addBias,this.activation,this.batchAEqualOne,this.batchBEqualOne,this.transposeA,this.transposeB)}
+      ${X2e()}
+    `}};function Z2e(e){let t=e[1],n=e[0],s=t>n?t:n;return`
   var<workgroup> mm_Asub : array<array<f32, ${s}>, ${t}>;
   var<workgroup> mm_Bsub : array<array<f32, ${n}>, ${s}>;
 
@@ -5631,7 +5631,7 @@ return a / b;`,Nle=`
   // shared memory, so it is instruction-Level parallelism for arithmetic
   // operations and others handle IO operations between barrier api, makes ALU
   // and load/store units work simultaneously, could improves the performance.
-  ${Je()} {
+  ${nt()} {
     let tileRow = i32(localId.y);
     let tileCol = i32(localId.x);
     let globalRow = i32(globalId.y);
@@ -5671,11 +5671,11 @@ return a / b;`,Nle=`
 
     mm_write(batch, globalRow, globalCol, acc);
   }
-  `}var L2e=class{constructor(e,t,n,s=!1,r=!1,a=null,o=null,i=null){this.variableNames=["A","B"],this.uniforms="dimAOuter : i32, dimBOuter : i32, dimInner : i32,",this.workGroupSize=[16,8,1],this.outputShape=n,this.dispatchLayout={x:[2],y:[1],z:[0]},this.dispatch=[Math.ceil(n[2]/this.workGroupSize[0]),Math.ceil(n[1]/this.workGroupSize[1]),n[0]];let l=a!=null;l&&this.variableNames.push("bias");let u=i!=null;u&&this.variableNames.push("preluActivationWeights"),this.transposeA=s,this.transposeB=r,this.addBias=l,this.activation=o,this.hasPreluActivationWeights=u,this.batchAEqualOne=e[0]===1,this.batchBEqualOne=t[0]===1,this.shaderKey=`matMulSmallOutputSize_${this.activation}_${s}_${r}_${this.batchAEqualOne}_${this.batchBEqualOne}`}getUserCode(){return`
-      ${Ai(this.activation,this.hasPreluActivationWeights)}
-      ${bb(this.addBias,this.activation,this.batchAEqualOne,this.batchBEqualOne,this.transposeA,this.transposeB)}
-      ${z2e(this.workGroupSize)}
-    `}},B2e=class{constructor(e,t,n,s,r=!1,a=!1){this.variableNames=["A","B"],this.uniforms="dimAOuter : i32, dimBOuter : i32, dimInner : i32,",this.workGroupSize=[8,8,1],this.atomic=!0,this.isVec4=!1,this.splitedDimInner=128,v.assert(e[0]===1,()=>"MatMulSplitKProgram only supports batch = 1."),this.outputShape=e,this.dispatchLayout={x:[2],y:[1],z:[0,3]},this.isVec4=(r&&this.outputShape[1]%4===0||!r&&t%4===0)&&this.outputShape[2]%4===0,this.elementsPerThread=[4,4,this.splitedDimInner],this.isVec4||(this.outputShape[1]<16&&(this.elementsPerThread[1]=1),this.outputShape[2]<16&&(this.elementsPerThread[0]=1)),this.dispatch=He(this.dispatchLayout,[this.outputShape[0],this.outputShape[1],this.outputShape[2],t],this.workGroupSize,this.elementsPerThread),this.transposeA=r,this.transposeB=a,this.batchAEqualOne=n,this.batchBEqualOne=s,this.shaderKey=`matMulSplitK_${r}_${a}_${n}_${s}_${this.elementsPerThread}_${this.isVec4}`}getUserCode(){let e=s=>`
+  `}var Y2e=class{constructor(e,t,n,s=!1,r=!1,a=null,o=null,i=null){this.variableNames=["A","B"],this.uniforms="dimAOuter : i32, dimBOuter : i32, dimInner : i32,",this.workGroupSize=[16,8,1],this.outputShape=n,this.dispatchLayout={x:[2],y:[1],z:[0]},this.dispatch=[Math.ceil(n[2]/this.workGroupSize[0]),Math.ceil(n[1]/this.workGroupSize[1]),n[0]];let l=a!=null;l&&this.variableNames.push("bias");let u=i!=null;u&&this.variableNames.push("preluActivationWeights"),this.transposeA=s,this.transposeB=r,this.addBias=l,this.activation=o,this.hasPreluActivationWeights=u,this.batchAEqualOne=e[0]===1,this.batchBEqualOne=t[0]===1,this.shaderKey=`matMulSmallOutputSize_${this.activation}_${s}_${r}_${this.batchAEqualOne}_${this.batchBEqualOne}`}getUserCode(){return`
+      ${wi(this.activation,this.hasPreluActivationWeights)}
+      ${kb(this.addBias,this.activation,this.batchAEqualOne,this.batchBEqualOne,this.transposeA,this.transposeB)}
+      ${Z2e(this.workGroupSize)}
+    `}},J2e=class{constructor(e,t,n,s,r=!1,a=!1){this.variableNames=["A","B"],this.uniforms="dimAOuter : i32, dimBOuter : i32, dimInner : i32,",this.workGroupSize=[8,8,1],this.atomic=!0,this.isVec4=!1,this.splitedDimInner=128,v.assert(e[0]===1,()=>"MatMulSplitKProgram only supports batch = 1."),this.outputShape=e,this.dispatchLayout={x:[2],y:[1],z:[0,3]},this.isVec4=(r&&this.outputShape[1]%4===0||!r&&t%4===0)&&this.outputShape[2]%4===0,this.elementsPerThread=[4,4,this.splitedDimInner],this.isVec4||(this.outputShape[1]<16&&(this.elementsPerThread[1]=1),this.outputShape[2]<16&&(this.elementsPerThread[0]=1)),this.dispatch=je(this.dispatchLayout,[this.outputShape[0],this.outputShape[1],this.outputShape[2],t],this.workGroupSize,this.elementsPerThread),this.transposeA=r,this.transposeB=a,this.batchAEqualOne=n,this.batchBEqualOne=s,this.shaderKey=`matMulSplitK_${r}_${a}_${n}_${s}_${this.elementsPerThread}_${this.isVec4}`}getUserCode(){let e=s=>`
       for (var i = 0; i < ${s}; i = i + 1)
       {
         var oldValue = atomicLoad(&(result[flatIndex + i]));
@@ -5689,8 +5689,8 @@ return a / b;`,Nle=`
         }
       }
       `,t=this.isVec4?4:1;return`
-      ${AT(this.batchAEqualOne,this.batchBEqualOne,!1,this.transposeB,!1,!1,!1,t)}
-      fn mm_write(batch: i32, row : i32, colIn : i32, value : ${Yt(t)}) {
+      ${ST(this.batchAEqualOne,this.batchBEqualOne,!1,this.transposeB,!1,!1,!1,t)}
+      fn mm_write(batch: i32, row : i32, colIn : i32, value : ${Jt(t)}) {
         let col = colIn * ${t};
         if (row < uniforms.dimAOuter && col < uniforms.dimBOuter) {
           let coords = vec3<i32>(batch, row, col);
@@ -5701,29 +5701,29 @@ return a / b;`,Nle=`
         }
       }
       ${this.isVec4?W2(this.elementsPerThread,this.workGroupSize,this.transposeA,32,!0,this.splitedDimInner):V2(this.elementsPerThread,this.workGroupSize,this.transposeA,32,!0,this.splitedDimInner)}
-    `}},W2e=class{constructor(e,t=null,n=null,s=null){this.uniforms="",this.variableNames=["x"],this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=it(this.outputShape),this.dispatch=He(this.dispatchLayout,this.outputShape,this.workGroupSize),this.addBias=t!=null,this.hasPreluActivationWeights=s!=null,this.activation=n,this.addBias&&this.variableNames.push("bias"),this.hasPreluActivationWeights&&this.variableNames.push("preluActivationWeights"),this.shaderKey=`biasActivation_${n}`}getUserCode(){return`
-    ${Ai(this.activation,this.hasPreluActivationWeights)}
-    ${Je("index")} {
+    `}},Q2e=class{constructor(e,t=null,n=null,s=null){this.uniforms="",this.variableNames=["x"],this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=lt(this.outputShape),this.dispatch=je(this.dispatchLayout,this.outputShape,this.workGroupSize),this.addBias=t!=null,this.hasPreluActivationWeights=s!=null,this.activation=n,this.addBias&&this.variableNames.push("bias"),this.hasPreluActivationWeights&&this.variableNames.push("preluActivationWeights"),this.shaderKey=`biasActivation_${n}`}getUserCode(){return`
+    ${wi(this.activation,this.hasPreluActivationWeights)}
+    ${nt("index")} {
       if (index < uniforms.size) {
         let coords = getCoordsFromIndex(index);
         var value = getXByOutputIndex(index);
-        ${xd(this.addBias,this.activation)}
+        ${wd(this.addBias,this.activation)}
         setOutputAtIndex(index, value);
       }
     }
-    `}},V2e=class{constructor(e){this.variableNames=[],this.outputShape=[],this.uniforms="value : f32,",this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=it(this.outputShape),this.dispatch=He(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey="fill"}getUserCode(){return`
-    ${Je("index")} {
+    `}},e1e=class{constructor(e){this.variableNames=[],this.outputShape=[],this.uniforms="value : f32,",this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=lt(this.outputShape),this.dispatch=je(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey="fill"}getUserCode(){return`
+    ${nt("index")} {
       if (index < uniforms.size) {
         setOutputAtIndex(index, uniforms.value);
       }
     }
-  `}};function yu(e){let{backend:t,attrs:n}=e,{shape:s,value:r}=n,{dtype:a}=n;if(a=a||v.inferDtype(r),a==="string"){let o=v.getArrayFromDType(a,v.sizeFromShape(s));return o.fill(r),t.makeTensorInfo(s,a,o)}else{let o=new V2e(s),i=[{type:"float32",data:[r]}];return t.runWebGPUProgram(o,[],a,i)}}var U2e={kernelName:Pc,backendName:"webgpu",kernelFunc:yu};function Ge(e){let{inputs:t,attrs:n}=e,{x:s}=t,{shape:r}=n,a=v.sizeFromShape(s.shape),o=v.inferFromImplicitShape(r,a),i=v.sizeFromShape(o);return v.assert(a===i,()=>`The new shape (${o}) has ${i} elements and the old shape (${s.shape}) has ${a} elements. The new shape and old shape must have the same number of elements.`),e.backend.incRef(s.dataId),{dataId:s.dataId,shape:o,dtype:s.dtype}}var G2e={kernelName:Vl,backendName:"webgpu",kernelFunc:Ge};function vb({a:e,b:t,transposeA:n,transposeB:s,backend:r,bias:a=null,preluActivationWeights:o=null,leakyreluAlpha:i=0,activation:l=null}){let u=e.shape.length,c=t.shape.length,p=n?e.shape[u-2]:e.shape[u-1],d=s?t.shape[c-1]:t.shape[c-2],h=n?e.shape[u-1]:e.shape[u-2],f=s?t.shape[c-2]:t.shape[c-1],m=e.shape.slice(0,-2),g=t.shape.slice(0,-2),y=v.sizeFromShape(m),x=v.sizeFromShape(g),b=au.assertAndGetBroadcastShape(e.shape.slice(0,-2),t.shape.slice(0,-2)).concat([h,f]);v.assert(p===d,()=>`Error in matMul: inner shapes (${p}) and (${d}) of Tensors with shapes ${e.shape} and ${t.shape} and transposeA=${n} and transposeB=${s} must match.`);let w=n?[y,p,h]:[y,h,p],k=s?[x,f,d]:[x,d,f],C=Ge({inputs:{x:e},backend:r,attrs:{shape:w}}),E=Ge({inputs:{x:t},backend:r,attrs:{shape:k}}),_=[C,E],$=Math.max(y,x),R=y===1,P=x===1,S=[C,E],M=[{type:"int32",data:[h]},{type:"int32",data:[f]},{type:"int32",data:[p]}],L,U,K=[$,h,f],q=H().get("WEBGPU_MATMUL_PROGRAM_TYPE");switch(q<0&&(h*f<=128?q=_r.MatMulReduceProgram:$===1&&h<=128&&f<=48&&d>=2e3?q=_r.MatMulSplitKProgram:h<=16&&(f<=512||d>=2*f)||f<=16&&(h<=512||p>=2*h)?q=_r.MatMulSmallOutputSizeProgram:q=_r.MatMulPackedProgram),q){case _r.MatMulReduceProgram:L=new M2e(K,R,P,n,s,a,l,o);break;case _r.MatMulSplitKProgram:{if(U=yu({backend:r,attrs:{shape:K,value:0,dtype:e.dtype}}),L=new B2e(K,d,R,P,n,s),a||l){U=r.runWebGPUProgram(L,S,e.dtype,M,U);let J=new W2e(U.shape,a,l,o),Q=null,le=[U];a&&le.push(a),o&&le.push(o),l==="leakyrelu"&&(Q=[{type:"float32",data:[i]}],J.uniforms+=" alpha : f32,");let ae=r.runWebGPUProgram(J,le,U.dtype,Q);_.push(U);let pe=Ge({inputs:{x:ae},backend:r,attrs:{shape:b}});_.push(ae);for(let ce of _)r.disposeData(ce.dataId);return pe}break}case _r.MatMulSmallOutputSizeProgram:L=new L2e(w,k,K,n,s,a,l,o);break;case _r.MatMulPackedProgram:L=new F2e(w,K,R,P,n,s,a,l,o);break;default:throw new Error(`Unsupported MatMulProgramType ${q}.`)}a&&S.push(a),o&&S.push(o),l==="leakyrelu"&&(M.push({type:"float32",data:[i]}),L.uniforms+=" alpha : f32,"),U=r.runWebGPUProgram(L,S,e.dtype,M,U);let Z=Ge({inputs:{x:U},backend:r,attrs:{shape:b}});_.push(U);for(let J of _)r.disposeData(J.dataId);return Z}function H2e(e){let{inputs:t,backend:n,attrs:s}=e,{a:r,b:a,bias:o,preluActivationWeights:i}=t,{transposeA:l,transposeB:u,activation:c,leakyreluAlpha:p}=s;return vb({a:r,b:a,transposeA:l,transposeB:u,backend:n,bias:o,preluActivationWeights:i,leakyreluAlpha:p,activation:c})}var j2e={kernelName:to,backendName:"webgpu",kernelFunc:H2e},i6=class{constructor(e,t,n){this.variableNames=["AReal","AImag","BReal","BImag"],this.workGroupSize=[128,1,1],this.size=!0,this.outputShape=T.assertAndGetBroadcastShape(t,n),this.dispatchLayout=it(this.outputShape),this.dispatch=He(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey=`binaryOpComplex_${e}`,this.op=e}getUserCode(){return`
+  `}};function vu(e){let{backend:t,attrs:n}=e,{shape:s,value:r}=n,{dtype:a}=n;if(a=a||v.inferDtype(r),a==="string"){let o=v.getArrayFromDType(a,v.sizeFromShape(s));return o.fill(r),t.makeTensorInfo(s,a,o)}else{let o=new e1e(s),i=[{type:"float32",data:[r]}];return t.runWebGPUProgram(o,[],a,i)}}var t1e={kernelName:zc,backendName:"webgpu",kernelFunc:vu};function He(e){let{inputs:t,attrs:n}=e,{x:s}=t,{shape:r}=n,a=v.sizeFromShape(s.shape),o=v.inferFromImplicitShape(r,a),i=v.sizeFromShape(o);return v.assert(a===i,()=>`The new shape (${o}) has ${i} elements and the old shape (${s.shape}) has ${a} elements. The new shape and old shape must have the same number of elements.`),e.backend.incRef(s.dataId),{dataId:s.dataId,shape:o,dtype:s.dtype}}var n1e={kernelName:jl,backendName:"webgpu",kernelFunc:He};function Sb({a:e,b:t,transposeA:n,transposeB:s,backend:r,bias:a=null,preluActivationWeights:o=null,leakyreluAlpha:i=0,activation:l=null}){let u=e.shape.length,c=t.shape.length,p=n?e.shape[u-2]:e.shape[u-1],d=s?t.shape[c-1]:t.shape[c-2],h=n?e.shape[u-1]:e.shape[u-2],f=s?t.shape[c-2]:t.shape[c-1],m=e.shape.slice(0,-2),g=t.shape.slice(0,-2),y=v.sizeFromShape(m),x=v.sizeFromShape(g),b=uu.assertAndGetBroadcastShape(e.shape.slice(0,-2),t.shape.slice(0,-2)).concat([h,f]);v.assert(p===d,()=>`Error in matMul: inner shapes (${p}) and (${d}) of Tensors with shapes ${e.shape} and ${t.shape} and transposeA=${n} and transposeB=${s} must match.`);let w=n?[y,p,h]:[y,h,p],k=s?[x,f,d]:[x,d,f],C=He({inputs:{x:e},backend:r,attrs:{shape:w}}),E=He({inputs:{x:t},backend:r,attrs:{shape:k}}),_=[C,E],$=Math.max(y,x),R=y===1,P=x===1,S=[C,E],M=[{type:"int32",data:[h]},{type:"int32",data:[f]},{type:"int32",data:[p]}],L,U,K=[$,h,f],q=H().get("WEBGPU_MATMUL_PROGRAM_TYPE");switch(q<0&&(h*f<=128?q=$r.MatMulReduceProgram:$===1&&h<=128&&f<=48&&d>=2e3?q=$r.MatMulSplitKProgram:h<=16&&(f<=512||d>=2*f)||f<=16&&(h<=512||p>=2*h)?q=$r.MatMulSmallOutputSizeProgram:q=$r.MatMulPackedProgram),q){case $r.MatMulReduceProgram:L=new K2e(K,R,P,n,s,a,l,o);break;case $r.MatMulSplitKProgram:{if(U=vu({backend:r,attrs:{shape:K,value:0,dtype:e.dtype}}),L=new J2e(K,d,R,P,n,s),a||l){U=r.runWebGPUProgram(L,S,e.dtype,M,U);let J=new Q2e(U.shape,a,l,o),te=null,le=[U];a&&le.push(a),o&&le.push(o),l==="leakyrelu"&&(te=[{type:"float32",data:[i]}],J.uniforms+=" alpha : f32,");let ae=r.runWebGPUProgram(J,le,U.dtype,te);_.push(U);let pe=He({inputs:{x:ae},backend:r,attrs:{shape:b}});_.push(ae);for(let ce of _)r.disposeData(ce.dataId);return pe}break}case $r.MatMulSmallOutputSizeProgram:L=new Y2e(w,k,K,n,s,a,l,o);break;case $r.MatMulPackedProgram:L=new q2e(w,K,R,P,n,s,a,l,o);break;default:throw new Error(`Unsupported MatMulProgramType ${q}.`)}a&&S.push(a),o&&S.push(o),l==="leakyrelu"&&(M.push({type:"float32",data:[i]}),L.uniforms+=" alpha : f32,"),U=r.runWebGPUProgram(L,S,e.dtype,M,U);let Z=He({inputs:{x:U},backend:r,attrs:{shape:b}});_.push(U);for(let J of _)r.disposeData(J.dataId);return Z}function s1e(e){let{inputs:t,backend:n,attrs:s}=e,{a:r,b:a,bias:o,preluActivationWeights:i}=t,{transposeA:l,transposeB:u,activation:c,leakyreluAlpha:p}=s;return Sb({a:r,b:a,transposeA:l,transposeB:u,backend:n,bias:o,preluActivationWeights:i,leakyreluAlpha:p,activation:c})}var r1e={kernelName:ao,backendName:"webgpu",kernelFunc:s1e},h6=class{constructor(e,t,n){this.variableNames=["AReal","AImag","BReal","BImag"],this.workGroupSize=[128,1,1],this.size=!0,this.outputShape=T.assertAndGetBroadcastShape(t,n),this.dispatchLayout=lt(this.outputShape),this.dispatch=je(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey=`binaryOpComplex_${e}`,this.op=e}getUserCode(){return`
       fn binaryOpComplex(
           areal : f32, aimag : f32, breal : f32, bimag : f32) -> f32 {
         ${Ym(this.op,!1)}
       }
 
-      ${Je("index")} {
+      ${nt("index")} {
         if(index < uniforms.size) {
           let areal = getARealByOutputIndex(index);
           let aimag = getAImagByOutputIndex(index);
@@ -5732,14 +5732,14 @@ return a / b;`,Nle=`
           setOutputAtIndex(index, binaryOpComplex(areal, aimag, breal, bimag));
         }
       }
-    `}},Ey=class{constructor(e,t,n){this.size=!0,this.variableNames=["A","B"],this.outputShape=T.assertAndGetBroadcastShape(t,n),this.dispatchLayout=it(this.outputShape),this.op=e,this.useSharedMemoryWithA=t.length===1&&n.length>1&&t[0]<1024,this.useSharedMemoryWithB=n.length===1&&t.length>1&&n[0]<1024,this.useSharedMemoryWithA||this.useSharedMemoryWithB?(this.isVec4=!1,this.lastDimensionSize=this.useSharedMemoryWithB?n[0]:t[0],this.shaderKey=`binary_${this.type}_${e}_${this.lastDimensionSize}_${this.useSharedMemoryWithB}`,this.type="shared",this.workGroupSize=[256,1,1],this.lastDimensionSize<256?this.workPerThread=1:this.lastDimensionSize<512?this.workPerThread=2:this.workPerThread=4):(v.arraysEqual(t,n)&&v.sizeFromShape(t)%4===0?(this.isVec4=!0,this.type="vec4",this.workPerThread=4):(this.isVec4=!1,this.type="plain",this.workPerThread=1),this.shaderKey=`binary_${this.type}_${e}`,this.workGroupSize=[128,1,1]),this.dispatch=He(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1])}getUserCode(){let e;if(this.type==="shared"){let t=this.lastDimensionSize>1?`coords[${this.outputShape.length-1}]`:"0",n=this.useSharedMemoryWithB?`let a = getAByOutputCoords(coords);
+    `}},Dy=class{constructor(e,t,n){this.size=!0,this.variableNames=["A","B"],this.outputShape=T.assertAndGetBroadcastShape(t,n),this.dispatchLayout=lt(this.outputShape),this.op=e,this.useSharedMemoryWithA=t.length===1&&n.length>1&&t[0]<1024,this.useSharedMemoryWithB=n.length===1&&t.length>1&&n[0]<1024,this.useSharedMemoryWithA||this.useSharedMemoryWithB?(this.isVec4=!1,this.lastDimensionSize=this.useSharedMemoryWithB?n[0]:t[0],this.shaderKey=`binary_${this.type}_${e}_${this.lastDimensionSize}_${this.useSharedMemoryWithB}`,this.type="shared",this.workGroupSize=[256,1,1],this.lastDimensionSize<256?this.workPerThread=1:this.lastDimensionSize<512?this.workPerThread=2:this.workPerThread=4):(v.arraysEqual(t,n)&&v.sizeFromShape(t)%4===0?(this.isVec4=!0,this.type="vec4",this.workPerThread=4):(this.isVec4=!1,this.type="plain",this.workPerThread=1),this.shaderKey=`binary_${this.type}_${e}`,this.workGroupSize=[128,1,1]),this.dispatch=je(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1])}getUserCode(){let e;if(this.type==="shared"){let t=this.lastDimensionSize>1?`coords[${this.outputShape.length-1}]`:"0",n=this.useSharedMemoryWithB?`let a = getAByOutputCoords(coords);
           let b = sharedBuf[${t}];`:`let a = sharedBuf[${t}];
           let b = getBByOutputCoords(coords);`;e=`
         fn binaryOperation(a : f32, b : f32) -> f32 {
           ${Ym(this.op,this.isVec4)}
         }
         var<workgroup> sharedBuf : array<f32, ${this.lastDimensionSize}>;
-        ${Je("index")} {
+        ${nt("index")} {
           // Fill in the shared memory buffer. Here we need a loop to make sure
           // that all data in A|B are uploaded when |sharedMemorySize| is larger
           // than work group size.
@@ -5762,25 +5762,25 @@ return a / b;`,Nle=`
        fn binaryOperation(a : ${t}, b : ${t}) -> ${t} {
          ${n}
        }
-       ${Je("index")} {
+       ${nt("index")} {
          if (index < uniforms.size) {
            let a = getAByOutputIndex(index);
            let b = getBByOutputIndex(index);
            setOutputAtIndex(index, binaryOperation(a, b));
          }
        }
-       `}return e}};function tr(e){let{inputs:t}=e,{x:n}=t;return e.backend.incRef(n.dataId),{dataId:n.dataId,shape:n.shape,dtype:n.dtype}}var q2e={kernelName:Po,backendName:"webgpu",kernelFunc:tr};function bd(e){let{inputs:t,backend:n}=e,{real:s,imag:r}=t,a=n.makeTensorInfo(s.shape,"complex64"),o=n.tensorMap.get(a.dataId),i=tr({inputs:{x:s},backend:n}),l=tr({inputs:{x:r},backend:n});return o.complexTensorInfos={real:i,imag:l},a}var X2e={kernelName:Hp,backendName:"webgpu",kernelFunc:bd},Xh=class{constructor(e,t){this.variableNames=["A"],this.size=!0;let n=128;this.workGroupSize=[n,1,1],this.outputShape=e,this.dispatchLayout=it(this.outputShape),this.dispatch=He(this.dispatchLayout,this.outputShape,this.workGroupSize),this.op=t,this.shaderKey=`unary_${t}`}getUserCode(){return`
+       `}return e}};function sr(e){let{inputs:t}=e,{x:n}=t;return e.backend.incRef(n.dataId),{dataId:n.dataId,shape:n.shape,dtype:n.dtype}}var a1e={kernelName:zo,backendName:"webgpu",kernelFunc:sr};function kd(e){let{inputs:t,backend:n}=e,{real:s,imag:r}=t,a=n.makeTensorInfo(s.shape,"complex64"),o=n.tensorMap.get(a.dataId),i=sr({inputs:{x:s},backend:n}),l=sr({inputs:{x:r},backend:n});return o.complexTensorInfos={real:i,imag:l},a}var o1e={kernelName:jp,backendName:"webgpu",kernelFunc:kd},Kh=class{constructor(e,t){this.variableNames=["A"],this.size=!0;let n=128;this.workGroupSize=[n,1,1],this.outputShape=e,this.dispatchLayout=lt(this.outputShape),this.dispatch=je(this.dispatchLayout,this.outputShape,this.workGroupSize),this.op=t,this.shaderKey=`unary_${t}`}getUserCode(){return`
       fn unaryOperation(a : f32) -> f32 {
-        ${Ui(this.op,!1)}
+        ${qi(this.op,!1)}
       }
-      ${Je("index")} {
+      ${nt("index")} {
         if (index < uniforms.size) {
           let a = getAByOutputIndex(index);
           setOutputAtIndex(index, unaryOperation(a));
         }
       }
-      `}};function vn({opType:e,cpuKernelImpl:t,dtype:n}){return({inputs:s,backend:r})=>{let{x:a}=s,o=r,i=n||a.dtype;if(o.shouldExecuteOnCPU([a])&&t!=null){let u=o.tensorMap.get(a.dataId),c=t(u.values,i);return o.makeTensorInfo(a.shape,i,c)}let l=new Xh(a.shape,e);return o.runWebGPUProgram(l,[a],i)}}function jn({opType:e,cpuKernelImpl:t,supportsComplex:n=!1,dtype:s}){return({inputs:r,backend:a})=>{let{a:o,b:i}=r,l=a;if(n&&o.dtype==="complex64"){let p=l.tensorMap.get(o.dataId),d=l.tensorMap.get(i.dataId),h,f;if(e!==Xe.MUL)[h,f]=[[p.complexTensorInfos.real,d.complexTensorInfos.real],[p.complexTensorInfos.imag,d.complexTensorInfos.imag]].map(g=>{let[y,x]=g,A={dataId:y.dataId,dtype:y.dtype,shape:o.shape},b={dataId:x.dataId,dtype:x.dtype,shape:i.shape},w=new Ey(e,o.shape,i.shape);return l.runWebGPUProgram(w,[A,b],Gn(y.dtype,x.dtype))});else{let g=new i6(Xe.COMPLEX_MULTIPLY_REAL,o.shape,i.shape),y=new i6(Xe.COMPLEX_MULTIPLY_IMAG,o.shape,i.shape),x=[{dataId:p.complexTensorInfos.real.dataId,dtype:p.complexTensorInfos.real.dtype,shape:o.shape},{dataId:p.complexTensorInfos.imag.dataId,dtype:p.complexTensorInfos.imag.dtype,shape:o.shape},{dataId:d.complexTensorInfos.real.dataId,dtype:d.complexTensorInfos.real.dtype,shape:i.shape},{dataId:d.complexTensorInfos.imag.dataId,dtype:d.complexTensorInfos.imag.dtype,shape:i.shape}];h=l.runWebGPUProgram(g,x,"float32"),f=l.runWebGPUProgram(y,x,"float32")}let m=bd({inputs:{real:h,imag:f},backend:l});return l.disposeData(h.dataId),l.disposeData(f.dataId),m}let u=s||Gn(o.dtype,i.dtype);if((o.dtype==="string"||i.dtype==="string"||l.shouldExecuteOnCPU([o,i]))&&t!=null){let p=l.tensorMap.get(o.dataId).values,d=l.tensorMap.get(i.dataId).values,h=o.dtype==="string"?T.fromUint8ToStringArray(p):p,f=o.dtype==="string"?T.fromUint8ToStringArray(d):d,[m,g]=t(o.shape,i.shape,h,f,u);return l.makeTensorInfo(g,u,m)}let c=new Ey(e,o.shape,i.shape);return l.runWebGPUProgram(c,[o,i],u)}}var{addImpl:K2e,castImpl:Z2e,ceilImpl:Y2e,concatImpl:J2e,equalImpl:Q2e,expImpl:e1e,expm1Impl:t1e,floorImpl:n1e,gatherNdImpl:s1e,gatherV2Impl:r1e,greaterEqualImpl:a1e,greaterImpl:o1e,lessEqualImpl:i1e,lessImpl:l1e,logImpl:u1e,maxImpl:c1e,maximumImpl:d1e,minimumImpl:p1e,multiplyImpl:h1e,negImpl:f1e,notEqualImpl:m1e,prodImpl:g1e,rangeImpl:y1e,rsqrtImpl:A1e,scatterImpl:x1e,simpleAbsImpl:b1e,sliceImpl:v1e,stridedSliceImpl:w1e,stringNGramsImpl:k1e,subImpl:S1e,tileImpl:I1e,topKImpl:C1e,transposeImpl:T1e,uniqueImpl:Gbe}=Wx,N1e=vn({opType:Oe.ABS,cpuKernelImpl:b1e}),E1e={kernelName:ml,backendName:"webgpu",kernelFunc:N1e},R1e=jn({opType:Xe.ADD,cpuKernelImpl:K2e,supportsComplex:!0}),_1e={kernelName:Na,backendName:"webgpu",kernelFunc:R1e},D1e=class{constructor(e){this.workPerThread=4,this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e[0],this.variableNames=e.map((t,n)=>`T${n}`),this.dispatchLayout=it(this.outputShape),this.dispatch=He(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.shaderKey="addN"}getUserCode(){let e=[];this.variableNames.forEach(s=>{e.push(`let v${s} = get${s}ByOutputCoords(coords);`)});let t=this.variableNames.map(s=>`v${s}`).join(" + ");return`
-      ${Je("index")} {
+      `}};function kn({opType:e,cpuKernelImpl:t,dtype:n}){return({inputs:s,backend:r})=>{let{x:a}=s,o=r,i=n||a.dtype;if(o.shouldExecuteOnCPU([a])&&t!=null){let u=o.tensorMap.get(a.dataId),c=t(u.values,i);return o.makeTensorInfo(a.shape,i,c)}let l=new Kh(a.shape,e);return o.runWebGPUProgram(l,[a],i)}}function qn({opType:e,cpuKernelImpl:t,supportsComplex:n=!1,dtype:s}){return({inputs:r,backend:a})=>{let{a:o,b:i}=r,l=a;if(n&&o.dtype==="complex64"){let p=l.tensorMap.get(o.dataId),d=l.tensorMap.get(i.dataId),h,f;if(e!==Ye.MUL)[h,f]=[[p.complexTensorInfos.real,d.complexTensorInfos.real],[p.complexTensorInfos.imag,d.complexTensorInfos.imag]].map(g=>{let[y,x]=g,A={dataId:y.dataId,dtype:y.dtype,shape:o.shape},b={dataId:x.dataId,dtype:x.dtype,shape:i.shape},w=new Dy(e,o.shape,i.shape);return l.runWebGPUProgram(w,[A,b],Hn(y.dtype,x.dtype))});else{let g=new h6(Ye.COMPLEX_MULTIPLY_REAL,o.shape,i.shape),y=new h6(Ye.COMPLEX_MULTIPLY_IMAG,o.shape,i.shape),x=[{dataId:p.complexTensorInfos.real.dataId,dtype:p.complexTensorInfos.real.dtype,shape:o.shape},{dataId:p.complexTensorInfos.imag.dataId,dtype:p.complexTensorInfos.imag.dtype,shape:o.shape},{dataId:d.complexTensorInfos.real.dataId,dtype:d.complexTensorInfos.real.dtype,shape:i.shape},{dataId:d.complexTensorInfos.imag.dataId,dtype:d.complexTensorInfos.imag.dtype,shape:i.shape}];h=l.runWebGPUProgram(g,x,"float32"),f=l.runWebGPUProgram(y,x,"float32")}let m=kd({inputs:{real:h,imag:f},backend:l});return l.disposeData(h.dataId),l.disposeData(f.dataId),m}let u=s||Hn(o.dtype,i.dtype);if((o.dtype==="string"||i.dtype==="string"||l.shouldExecuteOnCPU([o,i]))&&t!=null){let p=l.tensorMap.get(o.dataId).values,d=l.tensorMap.get(i.dataId).values,h=o.dtype==="string"?T.fromUint8ToStringArray(p):p,f=o.dtype==="string"?T.fromUint8ToStringArray(d):d,[m,g]=t(o.shape,i.shape,h,f,u);return l.makeTensorInfo(g,u,m)}let c=new Dy(e,o.shape,i.shape);return l.runWebGPUProgram(c,[o,i],u)}}var{addImpl:i1e,castImpl:l1e,ceilImpl:u1e,concatImpl:c1e,equalImpl:d1e,expImpl:p1e,expm1Impl:h1e,floorImpl:f1e,gatherNdImpl:m1e,gatherV2Impl:g1e,greaterEqualImpl:y1e,greaterImpl:A1e,lessEqualImpl:x1e,lessImpl:b1e,logImpl:v1e,maxImpl:w1e,maximumImpl:k1e,minimumImpl:S1e,multiplyImpl:I1e,negImpl:C1e,notEqualImpl:T1e,prodImpl:N1e,rangeImpl:E1e,rsqrtImpl:R1e,scatterImpl:_1e,simpleAbsImpl:D1e,sliceImpl:$1e,stridedSliceImpl:P1e,stringNGramsImpl:F1e,subImpl:O1e,tileImpl:M1e,topKImpl:z1e,transposeImpl:L1e,uniqueImpl:r4e}=Gx,B1e=kn({opType:Oe.ABS,cpuKernelImpl:D1e}),W1e={kernelName:xl,backendName:"webgpu",kernelFunc:B1e},V1e=qn({opType:Ye.ADD,cpuKernelImpl:i1e,supportsComplex:!0}),U1e={kernelName:Da,backendName:"webgpu",kernelFunc:V1e},G1e=class{constructor(e){this.workPerThread=4,this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e[0],this.variableNames=e.map((t,n)=>`T${n}`),this.dispatchLayout=lt(this.outputShape),this.dispatch=je(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.shaderKey="addN"}getUserCode(){let e=[];this.variableNames.forEach(s=>{e.push(`let v${s} = get${s}ByOutputCoords(coords);`)});let t=this.variableNames.map(s=>`v${s}`).join(" + ");return`
+      ${nt("index")} {
         for (var i = 0; i < ${this.workPerThread}; i = i + 1) {
           let flatIndex = index * ${this.workPerThread} + i;
           if (flatIndex < uniforms.size) {
@@ -5791,7 +5791,7 @@ return a / b;`,Nle=`
           }
         }
       }
-    `}};function $1e(e){let{inputs:t,backend:n}=e,s=t;if(s.length===1)return tr({inputs:{x:s[0]},backend:n});let r=s.map(i=>i.dtype).reduce((i,l)=>Gn(i,l)),a=s.map(i=>i.shape),o=new D1e(a);return n.runWebGPUProgram(o,s,r)}var P1e={kernelName:mo,backendName:"webgpu",kernelFunc:$1e},xT=class{constructor(e,t,n){this.workGroupSize=[64,1,1],this.variableNames=["x"],this.uniforms="infinityValue : f32,",this.size=!0;let s=[t];this.op=n==="min"?"<":">";let[r,a]=T.computeOutAndReduceShapes(e,s);this.outputShape=r.length===0?[1]:r,this.dispatchLayout=it(this.outputShape),v.sizeFromShape(a)<32||v.sizeFromShape(r)>1e3?(this.type="plain",this.dispatch=He(this.dispatchLayout,this.outputShape,this.workGroupSize)):(this.type="shared",this.dispatch=He(this.dispatchLayout,this.outputShape,[1,1,1])),this.inputShape=e,this.shaderKey=`argMinMax_${this.op}_${this.type}`}getUserCode(){let e=()=>this.inputShape.length===1?"uniforms.xShape":`uniforms.xShape.${wa(this.inputShape.length-1)}`,t=()=>{let n="";if(this.outputShape.length===1)this.inputShape.length!==1&&(n+="outputCoords,");else for(let s=0;s<this.outputShape.length;s++)n+=`outputCoords.${wa(s)},`;return n};return this.type==="shared"?`
+    `}};function H1e(e){let{inputs:t,backend:n}=e,s=t;if(s.length===1)return sr({inputs:{x:s[0]},backend:n});let r=s.map(i=>i.dtype).reduce((i,l)=>Hn(i,l)),a=s.map(i=>i.shape),o=new G1e(a);return n.runWebGPUProgram(o,s,r)}var j1e={kernelName:xo,backendName:"webgpu",kernelFunc:H1e},IT=class{constructor(e,t,n){this.workGroupSize=[64,1,1],this.variableNames=["x"],this.uniforms="infinityValue : f32,",this.size=!0;let s=[t];this.op=n==="min"?"<":">";let[r,a]=T.computeOutAndReduceShapes(e,s);this.outputShape=r.length===0?[1]:r,this.dispatchLayout=lt(this.outputShape),v.sizeFromShape(a)<32||v.sizeFromShape(r)>1e3?(this.type="plain",this.dispatch=je(this.dispatchLayout,this.outputShape,this.workGroupSize)):(this.type="shared",this.dispatch=je(this.dispatchLayout,this.outputShape,[1,1,1])),this.inputShape=e,this.shaderKey=`argMinMax_${this.op}_${this.type}`}getUserCode(){let e=()=>this.inputShape.length===1?"uniforms.xShape":`uniforms.xShape.${Ca(this.inputShape.length-1)}`,t=()=>{let n="";if(this.outputShape.length===1)this.inputShape.length!==1&&(n+="outputCoords,");else for(let s=0;s<this.outputShape.length;s++)n+=`outputCoords.${Ca(s)},`;return n};return this.type==="shared"?`
       fn DIV_CEIL(a : u32, b : u32) -> u32 {
         return ((a - 1u) / b + 1u);
       }
@@ -5801,7 +5801,7 @@ return a / b;`,Nle=`
       var<workgroup> xBestValues : array<f32, ${this.workGroupSize[0]}>;
     `}
 
-      ${Je("index")} {
+      ${nt("index")} {
         let outputIndex = index / i32(workGroupSizeX);
         let reduceLength = ${e()};
 
@@ -5841,7 +5841,7 @@ return a / b;`,Nle=`
         }
       }
     `:`
-      ${Je("index")} {
+      ${nt("index")} {
         if (index < uniforms.size) {
           let outputCoords = getCoordsFromIndex(index);
           var bestIndex = 0;
@@ -5857,10 +5857,10 @@ return a / b;`,Nle=`
           setOutputAtIndexI32(index, bestIndex);
         }
       }
-      `}},F1e=class{constructor(e,t){this.variableNames=["A"],this.workGroupSize=[16,16,1];let n=new Array(e.length);for(let s=0;s<n.length;s++)n[s]=e[t[s]];this.outputShape=n,this.dispatchLayout={x:[0],y:[1]},this.dispatch=He(this.dispatchLayout,this.outputShape,this.workGroupSize,[1,1,1]),this.shaderKey="transposeShared"}getUserCode(){return`
+      `}},q1e=class{constructor(e,t){this.variableNames=["A"],this.workGroupSize=[16,16,1];let n=new Array(e.length);for(let s=0;s<n.length;s++)n[s]=e[t[s]];this.outputShape=n,this.dispatchLayout={x:[0],y:[1]},this.dispatch=je(this.dispatchLayout,this.outputShape,this.workGroupSize,[1,1,1]),this.shaderKey="transposeShared"}getUserCode(){return`
       const TILE_DIM = ${this.workGroupSize[0]};
       var<workgroup> tile : array<array<f32, ${this.workGroupSize[0]+1}>, ${this.workGroupSize[0]}>;
-      ${Wp()}
+      ${Vp()}
       fn _start(@builtin(local_invocation_id) localId : vec3<u32>,
                 @builtin(workgroup_id) workgroupId : vec3<u32>) {
         var x = i32(workgroupId.x) * TILE_DIM + i32(localId.x);
@@ -5879,8 +5879,8 @@ return a / b;`,Nle=`
             [localId.y]);
         }
       }
-    `}},O1e=class{constructor(e,t){this.variableNames=["A"],this.workPerThread=4,this.workGroupSize=[64,1,1],this.size=!0;let n=new Array(e.length);for(let s=0;s<n.length;s++)n[s]=e[t[s]];this.outputShape=n,this.dispatchLayout=it(this.outputShape),this.dispatch=He(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.newDim=t,this.shaderKey=`transpose_${t}`}getUserCode(){let e=Fn(this.outputShape.length),t=M1e(this.newDim);return`
-      ${Je("index")} {
+    `}},X1e=class{constructor(e,t){this.variableNames=["A"],this.workPerThread=4,this.workGroupSize=[64,1,1],this.size=!0;let n=new Array(e.length);for(let s=0;s<n.length;s++)n[s]=e[t[s]];this.outputShape=n,this.dispatchLayout=lt(this.outputShape),this.dispatch=je(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.newDim=t,this.shaderKey=`transpose_${t}`}getUserCode(){let e=Mn(this.outputShape.length),t=K1e(this.newDim);return`
+      ${nt("index")} {
         for(var i = 0; i < ${this.workPerThread}; i = i + 1) {
           let flatIndex = index * ${this.workPerThread} + i;
           if(flatIndex < uniforms.size) {
@@ -5890,8 +5890,8 @@ return a / b;`,Nle=`
           }
         }
       }
-    `}};function M1e(e){let t=e.length;if(t>6)throw Error(`Transpose for rank ${t} is not yet supported`);let n=new Array(t);for(let s=0;s<e.length;s++)n[e[s]]=`resRC.${wa(s)}`;return n.join()}function Ta(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{perm:a}=s,o=n,i=r.shape.length,l=new Array(i);for(let c=0;c<l.length;c++)l[c]=r.shape[a[c]];if(n.shouldExecuteOnCPU([r])){let p=o.tensorMap.get(r.dataId).values,d=T1e(p,r.shape,r.dtype,a,l);return n.makeTensorInfo(l,r.dtype,d)}if(r.shape.length===2&&v.arraysEqual(a,[1,0])){let c=new F1e(r.shape,a);return o.runWebGPUProgram(c,[r],r.dtype)}let u=new O1e(r.shape,a);return o.runWebGPUProgram(u,[r],r.dtype)}var z1e={kernelName:ea,backendName:"webgpu",kernelFunc:Ta};function L1e(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a}=s,o=v.parseAxisParam(a,r.shape),i=T.getAxesPermutation(o,r.shape.length),l=r,u=[];i!=null&&(l=Ta({inputs:{x:r},backend:n,attrs:{perm:i}}),u.push(l),o=T.getInnerMostAxes(o.length,l.shape.length)),T.assertAxesAreInnerMostDims("argMax",[o[0]],l.shape.length);let c=new xT(l.shape,o[0],"max"),p=[{type:"float32",data:[Number.NEGATIVE_INFINITY]}],d=n.runWebGPUProgram(c,[l],"int32",p);return u.forEach(h=>n.disposeData(h.dataId)),d}var B1e={kernelName:go,backendName:"webgpu",kernelFunc:L1e};function W1e(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a}=s,o=v.parseAxisParam(a,r.shape),i=T.getAxesPermutation(o,r.shape.length),l=r,u=[];i!=null&&(l=Ta({inputs:{x:r},backend:n,attrs:{perm:i}}),u.push(l),o=T.getInnerMostAxes(o.length,l.shape.length)),T.assertAxesAreInnerMostDims("argMin",[o[0]],l.shape.length);let c=new xT(l.shape,o[0],"min"),p=[{type:"float32",data:[Number.POSITIVE_INFINITY]}],d=n.runWebGPUProgram(c,[l],"int32",p);return u.forEach(h=>n.disposeData(h.dataId)),d}var V1e={kernelName:Nc,backendName:"webgpu",kernelFunc:W1e},U1e=jn({opType:Xe.ATAN2}),G1e={kernelName:gl,backendName:"webgpu",kernelFunc:U1e},l6=class{constructor(e,t){this.variableNames=["x"],this.uniforms="stride : vec2<i32>, pad : vec2<i32>, dilation : vec2<i32>, convDims : vec2<i32>, filterDims : vec2<i32>,",this.workGroupSize=[128,1,1],this.size=!0,this.outputShape=e.outShape,this.dispatchLayout=it(this.outputShape),this.dispatch=He(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey=`pool2D_${t}`,this.poolType=t}getUserCode(){let e="resultValue = max(value, resultValue);";this.poolType==="avg"&&(e="resultValue = resultValue + value; count = count + 1.0;");let t="resultValue";return this.poolType==="avg"&&(t="resultValue / count"),`
-      ${Je("index")} {
+    `}};function K1e(e){let t=e.length;if(t>6)throw Error(`Transpose for rank ${t} is not yet supported`);let n=new Array(t);for(let s=0;s<e.length;s++)n[e[s]]=`resRC.${Ca(s)}`;return n.join()}function _a(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{perm:a}=s,o=n,i=r.shape.length,l=new Array(i);for(let c=0;c<l.length;c++)l[c]=r.shape[a[c]];if(n.shouldExecuteOnCPU([r])){let p=o.tensorMap.get(r.dataId).values,d=L1e(p,r.shape,r.dtype,a,l);return n.makeTensorInfo(l,r.dtype,d)}if(r.shape.length===2&&v.arraysEqual(a,[1,0])){let c=new q1e(r.shape,a);return o.runWebGPUProgram(c,[r],r.dtype)}let u=new X1e(r.shape,a);return o.runWebGPUProgram(u,[r],r.dtype)}var Z1e={kernelName:na,backendName:"webgpu",kernelFunc:_a};function Y1e(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a}=s,o=v.parseAxisParam(a,r.shape),i=T.getAxesPermutation(o,r.shape.length),l=r,u=[];i!=null&&(l=_a({inputs:{x:r},backend:n,attrs:{perm:i}}),u.push(l),o=T.getInnerMostAxes(o.length,l.shape.length)),T.assertAxesAreInnerMostDims("argMax",[o[0]],l.shape.length);let c=new IT(l.shape,o[0],"max"),p=[{type:"float32",data:[Number.NEGATIVE_INFINITY]}],d=n.runWebGPUProgram(c,[l],"int32",p);return u.forEach(h=>n.disposeData(h.dataId)),d}var J1e={kernelName:bo,backendName:"webgpu",kernelFunc:Y1e};function Q1e(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a}=s,o=v.parseAxisParam(a,r.shape),i=T.getAxesPermutation(o,r.shape.length),l=r,u=[];i!=null&&(l=_a({inputs:{x:r},backend:n,attrs:{perm:i}}),u.push(l),o=T.getInnerMostAxes(o.length,l.shape.length)),T.assertAxesAreInnerMostDims("argMin",[o[0]],l.shape.length);let c=new IT(l.shape,o[0],"min"),p=[{type:"float32",data:[Number.POSITIVE_INFINITY]}],d=n.runWebGPUProgram(c,[l],"int32",p);return u.forEach(h=>n.disposeData(h.dataId)),d}var ege={kernelName:Dc,backendName:"webgpu",kernelFunc:Q1e},tge=qn({opType:Ye.ATAN2}),nge={kernelName:bl,backendName:"webgpu",kernelFunc:tge},f6=class{constructor(e,t){this.variableNames=["x"],this.uniforms="stride : vec2<i32>, pad : vec2<i32>, dilation : vec2<i32>, convDims : vec2<i32>, filterDims : vec2<i32>,",this.workGroupSize=[128,1,1],this.size=!0,this.outputShape=e.outShape,this.dispatchLayout=lt(this.outputShape),this.dispatch=je(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey=`pool2D_${t}`,this.poolType=t}getUserCode(){let e="resultValue = max(value, resultValue);";this.poolType==="avg"&&(e="resultValue = resultValue + value; count = count + 1.0;");let t="resultValue";return this.poolType==="avg"&&(t="resultValue / count"),`
+      ${nt("index")} {
       if (index < uniforms.size) {
         let coords = getCoordsFromIndex(index);
           let batch = coords[0];
@@ -5923,8 +5923,8 @@ return a / b;`,Nle=`
           setOutputAtIndex(index, ${t});
         }
       }
-    `}},H1e=class{constructor(e){this.variableNames=["x"],this.uniforms="stride : vec2<i32>,",this.workGroupSize=[256,1,1],this.size=!0,this.outputShape=e.outShape,this.dispatchLayout=it(this.outputShape),this.dispatch=He(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey="poolWithFilterSizeEqualsOne"}getUserCode(){return`
-      ${Je("index")} {
+    `}},sge=class{constructor(e){this.variableNames=["x"],this.uniforms="stride : vec2<i32>,",this.workGroupSize=[256,1,1],this.size=!0,this.outputShape=e.outShape,this.dispatchLayout=lt(this.outputShape),this.dispatch=je(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey="poolWithFilterSizeEqualsOne"}getUserCode(){return`
+      ${nt("index")} {
         if (index < uniforms.size) {
           let coords = getCoordsFromIndex(index);
           let batch = coords[0];
@@ -5938,7 +5938,7 @@ return a / b;`,Nle=`
           setOutputAtIndex(index, value);
         }
       }
-    `}},j1e=class{constructor(e,t){this.workGroupSize=[64,1,1],this.variableNames=["x"],this.uniforms="reduceSize : i32,",this.size=!0,this.inputShape=[e.batchSize,e.inSize];let[n]=T.computeOutAndReduceShapes(this.inputShape,[1]);this.outputShape=n.length===0?[1]:n,this.dispatchLayout=it(this.outputShape),this.dispatch=He(this.dispatchLayout,this.outputShape,[1,1,1]),this.reduceType=t,this.shaderKey=`reduce_${t}`}getUserCode(){let e="",t="0.0";this.reduceType==="min"||this.reduceType==="max"?(e=`
+    `}},rge=class{constructor(e,t){this.workGroupSize=[64,1,1],this.variableNames=["x"],this.uniforms="reduceSize : i32,",this.size=!0,this.inputShape=[e.batchSize,e.inSize];let[n]=T.computeOutAndReduceShapes(this.inputShape,[1]);this.outputShape=n.length===0?[1]:n,this.dispatchLayout=lt(this.outputShape),this.dispatch=je(this.dispatchLayout,this.outputShape,[1,1,1]),this.reduceType=t,this.shaderKey=`reduce_${t}`}getUserCode(){let e="",t="0.0";this.reduceType==="min"||this.reduceType==="max"?(e=`
          if (isnan(candidate)) {
           bestValue = uniforms.NAN;
          } else if (!isnan(bestValue) && candidate ${this.reduceType==="min"?"<":">"} bestValue)
@@ -5955,7 +5955,7 @@ return a / b;`,Nle=`
          let offset = ${this.outputShape.length===1?"outputCoords":"outputCoords[0]"} * uniforms.reduceSize;
           return offset;
        }
-       ${Je("index")} {
+       ${nt("index")} {
          let outputIndex = index / i32(workGroupSizeX);
          let offset = getOffset(outputIndex);
          var bestValue = ${t};
@@ -5986,8 +5986,8 @@ return a / b;`,Nle=`
           ${n}
         }
        }
-     `}};function Kh(e,t,n,s,r){let a=e.shape.length,o=[],i=v.parseAxisParam(t,e.shape),l=i,u=T.getAxesPermutation(l,a),c=e;u!=null&&(c=Ta({inputs:{x:e},attrs:{perm:u},backend:r}),l=T.getInnerMostAxes(l.length,a),o.push(c)),T.assertAxesAreInnerMostDims(s,l,a);let[p,d]=T.computeOutAndReduceShapes(c.shape,l),h=p;n&&(h=T.expandShapeToKeepDim(p,i));let f;if((s==="max"||s==="prod")&&r.shouldExecuteOnCPU([c])){let m=r.tensorMap.get(c.dataId).values;switch(s){case"max":let g=c1e(m,v.sizeFromShape(d),h,e.dtype);f=r.makeTensorInfo(h,e.dtype,g);break;case"prod":let{outVals:y,outShape:x,outDtype:A}=g1e(c.shape,c.dtype,m,l);f=r.makeTensorInfo(x,A,y);break;default:throw new Error(`${s} CPU implementation is not yet supported.`)}}else{let m=v.sizeFromShape(d),y=v.sizeFromShape(c.shape)/m,x={windowSize:m,inSize:m,batchSize:y,outSize:1},A=s==="mean"?"float32":uh(e.dtype),b=[{type:"int32",data:[m]}],w=new j1e(x,s),k=r.runWebGPUProgram(w,[c],A,b);o.push(k),f=Ge({inputs:{x:k},attrs:{shape:h},backend:r})}return o.forEach(m=>r.disposeData(m.dataId)),f}function wb(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{reductionIndices:a,keepDims:o}=s;return Kh(r,a,o,"max",n)}var q1e={kernelName:Mo,backendName:"webgpu",kernelFunc:wb};function bT(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{keepDims:a,axis:o}=s;return Kh(r,o,a,"mean",n)}var X1e={kernelName:Bo,backendName:"webgpu",kernelFunc:bT};function vT(e,t,n,s){if(t.filterWidth===1&&t.filterHeight===1&&v.arraysEqual(t.inShape,t.outShape))return tr({inputs:{x:e},backend:s});if(t.filterWidth===t.inWidth&&t.filterHeight===t.inHeight&&t.batchSize===1&&t.padInfo.type==="VALID"){let o=e.shape.length,i=Ge({inputs:{x:e},backend:s,attrs:{shape:[e.shape[o-3]*e.shape[o-2],e.shape[o-1]]}}),l;n==="avg"?l=bT({inputs:{x:i},backend:s,attrs:{axis:0,keepDims:!1}}):(v.assert(n==="max",()=>`Invalid pool type ${n}`),l=wb({inputs:{x:i},backend:s,attrs:{reductionIndices:0,keepDims:!1}}));let u=Ge({inputs:{x:l},backend:s,attrs:{shape:t.outShape}});return s.disposeData(i.dataId),s.disposeData(l.dataId),u}let r,a=[{type:"int32",data:[t.strideHeight,t.strideWidth]}];return t.filterHeight===1&&t.filterWidth===1?r=new H1e(t):(n==="avg"?r=new l6(t,"avg"):(v.assert(n==="max",()=>`Invalid pool type ${n}`),r=new l6(t,"max")),a.push({type:"int32",data:[t.padInfo.top,t.padInfo.left]},{type:"int32",data:[t.dilationHeight,t.dilationWidth]},{type:"int32",data:[t.inHeight,t.inWidth]},{type:"int32",data:[t.effectiveFilterHeight,t.effectiveFilterWidth]})),s.runWebGPUProgram(r,[e],e.dtype,a)}function K1e(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{filterSize:a,strides:o,pad:i,dimRoundingMode:l}=s,u=1,c=T.computePool2DInfo(r.shape,a,o,u,i,l);return vT(r,c,"avg",n)}var Z1e={kernelName:yo,backendName:"webgpu",kernelFunc:K1e};function Y1e(e){let{inputs:t,backend:n,attrs:s}=e,{a:r,b:a}=t,{transposeA:o,transposeB:i}=s;return vb({a:r,b:a,transposeA:o,transposeB:i,backend:n})}var J1e={kernelName:Ao,backendName:"webgpu",kernelFunc:Y1e},Q1e=class{constructor(e,t){this.variableNames=["source"],this.workPerThread=1,this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.rank=t.length,this.dispatchLayout=it(this.outputShape),this.dispatch=He(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.start=e,this.uniforms=`start : ${Fn(e.length)}, `,this.shaderKey="slice"}getUserCode(){let e=Fn(this.rank),t=ege(this.rank),n;return this.start.length===1?n=this.outputShape.map((r,a)=>"sourceLoc = uniforms.start + coords;"):n=this.outputShape.map((r,a)=>`sourceLoc.${Ry[a]} = uniforms.start.${wa(a)} + coords.${Ry[a]};`),`
-      ${Je("index")} {
+     `}};function Zh(e,t,n,s,r){let a=e.shape.length,o=[],i=v.parseAxisParam(t,e.shape),l=i,u=T.getAxesPermutation(l,a),c=e;u!=null&&(c=_a({inputs:{x:e},attrs:{perm:u},backend:r}),l=T.getInnerMostAxes(l.length,a),o.push(c)),T.assertAxesAreInnerMostDims(s,l,a);let[p,d]=T.computeOutAndReduceShapes(c.shape,l),h=p;n&&(h=T.expandShapeToKeepDim(p,i));let f;if((s==="max"||s==="prod")&&r.shouldExecuteOnCPU([c])){let m=r.tensorMap.get(c.dataId).values;switch(s){case"max":let g=w1e(m,v.sizeFromShape(d),h,e.dtype);f=r.makeTensorInfo(h,e.dtype,g);break;case"prod":let{outVals:y,outShape:x,outDtype:A}=N1e(c.shape,c.dtype,m,l);f=r.makeTensorInfo(x,A,y);break;default:throw new Error(`${s} CPU implementation is not yet supported.`)}}else{let m=v.sizeFromShape(d),y=v.sizeFromShape(c.shape)/m,x={windowSize:m,inSize:m,batchSize:y,outSize:1},A=s==="mean"?"float32":ch(e.dtype),b=[{type:"int32",data:[m]}],w=new rge(x,s),k=r.runWebGPUProgram(w,[c],A,b);o.push(k),f=He({inputs:{x:k},attrs:{shape:h},backend:r})}return o.forEach(m=>r.disposeData(m.dataId)),f}function Ib(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{reductionIndices:a,keepDims:o}=s;return Zh(r,a,o,"max",n)}var age={kernelName:Wo,backendName:"webgpu",kernelFunc:Ib};function CT(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{keepDims:a,axis:o}=s;return Zh(r,o,a,"mean",n)}var oge={kernelName:Go,backendName:"webgpu",kernelFunc:CT};function TT(e,t,n,s){if(t.filterWidth===1&&t.filterHeight===1&&v.arraysEqual(t.inShape,t.outShape))return sr({inputs:{x:e},backend:s});if(t.filterWidth===t.inWidth&&t.filterHeight===t.inHeight&&t.batchSize===1&&t.padInfo.type==="VALID"){let o=e.shape.length,i=He({inputs:{x:e},backend:s,attrs:{shape:[e.shape[o-3]*e.shape[o-2],e.shape[o-1]]}}),l;n==="avg"?l=CT({inputs:{x:i},backend:s,attrs:{axis:0,keepDims:!1}}):(v.assert(n==="max",()=>`Invalid pool type ${n}`),l=Ib({inputs:{x:i},backend:s,attrs:{reductionIndices:0,keepDims:!1}}));let u=He({inputs:{x:l},backend:s,attrs:{shape:t.outShape}});return s.disposeData(i.dataId),s.disposeData(l.dataId),u}let r,a=[{type:"int32",data:[t.strideHeight,t.strideWidth]}];return t.filterHeight===1&&t.filterWidth===1?r=new sge(t):(n==="avg"?r=new f6(t,"avg"):(v.assert(n==="max",()=>`Invalid pool type ${n}`),r=new f6(t,"max")),a.push({type:"int32",data:[t.padInfo.top,t.padInfo.left]},{type:"int32",data:[t.dilationHeight,t.dilationWidth]},{type:"int32",data:[t.inHeight,t.inWidth]},{type:"int32",data:[t.effectiveFilterHeight,t.effectiveFilterWidth]})),s.runWebGPUProgram(r,[e],e.dtype,a)}function ige(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{filterSize:a,strides:o,pad:i,dimRoundingMode:l}=s,u=1,c=T.computePool2DInfo(r.shape,a,o,u,i,l);return TT(r,c,"avg",n)}var lge={kernelName:vo,backendName:"webgpu",kernelFunc:ige};function uge(e){let{inputs:t,backend:n,attrs:s}=e,{a:r,b:a}=t,{transposeA:o,transposeB:i}=s;return Sb({a:r,b:a,transposeA:o,transposeB:i,backend:n})}var cge={kernelName:wo,backendName:"webgpu",kernelFunc:uge},dge=class{constructor(e,t){this.variableNames=["source"],this.workPerThread=1,this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.rank=t.length,this.dispatchLayout=lt(this.outputShape),this.dispatch=je(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.start=e,this.uniforms=`start : ${Mn(e.length)}, `,this.shaderKey="slice"}getUserCode(){let e=Mn(this.rank),t=pge(this.rank),n;return this.start.length===1?n=this.outputShape.map((r,a)=>"sourceLoc = uniforms.start + coords;"):n=this.outputShape.map((r,a)=>`sourceLoc.${$y[a]} = uniforms.start.${Ca(a)} + coords.${$y[a]};`),`
+      ${nt("index")} {
         if (index < uniforms.size) {
           var sourceLoc : ${e};
           let coords = getCoordsFromIndex(index);
@@ -5996,8 +5996,8 @@ return a / b;`,Nle=`
           setOutputAtIndex(index, getSource(${t}));
         }
       }
-    `}},Ry=["x","y","z","w","u","v"];function ege(e){if(e===1)return"sourceLoc";if(e<=6)return Ry.slice(0,e).map(t=>`sourceLoc.${t}`).join(",");throw Error(`Slicing for rank ${e} is not yet supported`)}function vd(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{begin:a,size:o}=s,[i,l]=Ht.parseSliceParams(r,a,o);if(Ht.assertParamsValid(r,i,l),n.shouldExecuteOnCPU([r])||r.dtype==="string"){let p=n.tensorMap.get(r.dataId),d=v1e(p.values,i,l,r.shape,r.dtype);return n.makeTensorInfo(l,r.dtype,d)}if(v.sizeFromShape(l)===0)return n.makeTensorInfo(l,r.dtype,[]);let u=new Q1e(i,l),c=[{type:"int32",data:i}];return n.runWebGPUProgram(u,[r],r.dtype,c)}var tge={kernelName:ql,backendName:"webgpu",kernelFunc:vd},nge=e=>{let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockShape:a,crops:o}=s;v.assert(r.shape.length<=4,()=>"batchToSpaceND for rank > 4 with a WebGPU backend not implemented yet");let i=a.reduce((x,A)=>x*A),l=T.getReshaped(r.shape,a,i),u=T.getPermuted(l.length,a.length),c=T.getReshapedPermuted(r.shape,a,i),p=T.getSliceBeginCoords(o,a.length),d=T.getSliceSize(c,o,a.length),h=[],f=Ge({inputs:{x:r},backend:n,attrs:{shape:l}}),m=Ta({inputs:{x:f},backend:n,attrs:{perm:u}}),g=Ge({inputs:{x:m},backend:n,attrs:{shape:c}}),y=vd({inputs:{x:g},backend:n,attrs:{begin:p,size:d}});return h.push(f),h.push(m),h.push(g),h.forEach(x=>n.disposeData(x.dataId)),y},sge={kernelName:yl,backendName:"webgpu",kernelFunc:nge},wT=jn({opType:Xe.NOT_EQUAL,dtype:"bool",cpuKernelImpl:m1e}),rge={kernelName:Fl,backendName:"webgpu",kernelFunc:wT};function Zh(e){let{inputs:t,backend:n}=e,{input:s}=t,r=n.tensorMap.get(s.dataId);return tr({inputs:{x:r.complexTensorInfos.real},backend:n})}var age={kernelName:Qp,backendName:"webgpu",kernelFunc:Zh};function oge(e,t){let n=new Xh(e.shape,Oe.TO_INT),s=t.runWebGPUProgram(n,[e],"int32");return{dataId:s.dataId,shape:s.shape,dtype:s.dtype}}function _y(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{dtype:a}=s;if(a==="complex64"){if(r.dtype==="complex64")return tr({inputs:{x:r},backend:n});let o=Ut(r.shape),i=_y({inputs:{x:r},backend:n,attrs:{dtype:"float32"}}),l=bd({inputs:{real:i,imag:o},backend:n});return o.dispose(),n.disposeData(i.dataId),l}if(r.dtype==="complex64"){let o=Zh({inputs:{input:r},backend:n}),i=_y({inputs:{x:o},backend:n,attrs:{dtype:a}});return n.disposeData(o.dataId),i}if(!v.hasEncodingLoss(r.dtype,a)){let o=tr({inputs:{x:r},backend:n});return{dataId:o.dataId,shape:o.shape,dtype:a}}if(n.shouldExecuteOnCPU([r])){let o=n.tensorMap.get(r.dataId).values,[i,l,u]=Z2e(o,r.shape,r.dtype,a);return n.makeTensorInfo(i,l,u)}if(a==="int32")return oge(r,n);if(a==="bool"){let o=n.makeTensorInfo([],"bool",v.getTypedArrayFromDType("bool",1)),l=wT({inputs:{a:r,b:o},backend:n});return n.disposeData(o.dataId),l}throw new Error(`Error in Cast: failed to cast ${r.dtype} to ${a}`)}var ige={kernelName:xo,backendName:"webgpu",kernelFunc:_y},lge=vn({opType:Oe.CEIL,cpuKernelImpl:Y2e}),uge={kernelName:bo,backendName:"webgpu",kernelFunc:lge},cge=class{constructor(e){this.variableNames=["A"],this.uniforms="minVal : f32, maxVal : f32,",this.workPerThread=4,this.workGroupSize=[64,1,1],this.isVec4=!0,this.size=!0,this.outputShape=e,this.dispatchLayout=it(this.outputShape),this.dispatch=He(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.shaderKey="clipVec4"}getUserCode(){return`
-      ${Je("index")} {
+    `}},$y=["x","y","z","w","u","v"];function pge(e){if(e===1)return"sourceLoc";if(e<=6)return $y.slice(0,e).map(t=>`sourceLoc.${t}`).join(",");throw Error(`Slicing for rank ${e} is not yet supported`)}function Sd(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{begin:a,size:o}=s,[i,l]=jt.parseSliceParams(r,a,o);if(jt.assertParamsValid(r,i,l),n.shouldExecuteOnCPU([r])||r.dtype==="string"){let p=n.tensorMap.get(r.dataId),d=$1e(p.values,i,l,r.shape,r.dtype);return n.makeTensorInfo(l,r.dtype,d)}if(v.sizeFromShape(l)===0)return n.makeTensorInfo(l,r.dtype,[]);let u=new dge(i,l),c=[{type:"int32",data:i}];return n.runWebGPUProgram(u,[r],r.dtype,c)}var hge={kernelName:Yl,backendName:"webgpu",kernelFunc:Sd},fge=e=>{let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockShape:a,crops:o}=s;v.assert(r.shape.length<=4,()=>"batchToSpaceND for rank > 4 with a WebGPU backend not implemented yet");let i=a.reduce((x,A)=>x*A),l=T.getReshaped(r.shape,a,i),u=T.getPermuted(l.length,a.length),c=T.getReshapedPermuted(r.shape,a,i),p=T.getSliceBeginCoords(o,a.length),d=T.getSliceSize(c,o,a.length),h=[],f=He({inputs:{x:r},backend:n,attrs:{shape:l}}),m=_a({inputs:{x:f},backend:n,attrs:{perm:u}}),g=He({inputs:{x:m},backend:n,attrs:{shape:c}}),y=Sd({inputs:{x:g},backend:n,attrs:{begin:p,size:d}});return h.push(f),h.push(m),h.push(g),h.forEach(x=>n.disposeData(x.dataId)),y},mge={kernelName:vl,backendName:"webgpu",kernelFunc:fge},NT=qn({opType:Ye.NOT_EQUAL,dtype:"bool",cpuKernelImpl:T1e}),gge={kernelName:Ll,backendName:"webgpu",kernelFunc:NT};function Yh(e){let{inputs:t,backend:n}=e,{input:s}=t,r=n.tensorMap.get(s.dataId);return sr({inputs:{x:r.complexTensorInfos.real},backend:n})}var yge={kernelName:eh,backendName:"webgpu",kernelFunc:Yh};function Age(e,t){let n=new Kh(e.shape,Oe.TO_INT),s=t.runWebGPUProgram(n,[e],"int32");return{dataId:s.dataId,shape:s.shape,dtype:s.dtype}}function Py(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{dtype:a}=s;if(a==="complex64"){if(r.dtype==="complex64")return sr({inputs:{x:r},backend:n});let o=Gt(r.shape),i=Py({inputs:{x:r},backend:n,attrs:{dtype:"float32"}}),l=kd({inputs:{real:i,imag:o},backend:n});return o.dispose(),n.disposeData(i.dataId),l}if(r.dtype==="complex64"){let o=Yh({inputs:{input:r},backend:n}),i=Py({inputs:{x:o},backend:n,attrs:{dtype:a}});return n.disposeData(o.dataId),i}if(!v.hasEncodingLoss(r.dtype,a)){let o=sr({inputs:{x:r},backend:n});return{dataId:o.dataId,shape:o.shape,dtype:a}}if(n.shouldExecuteOnCPU([r])){let o=n.tensorMap.get(r.dataId).values,[i,l,u]=l1e(o,r.shape,r.dtype,a);return n.makeTensorInfo(i,l,u)}if(a==="int32")return Age(r,n);if(a==="bool"){let o=n.makeTensorInfo([],"bool",v.getTypedArrayFromDType("bool",1)),l=NT({inputs:{a:r,b:o},backend:n});return n.disposeData(o.dataId),l}throw new Error(`Error in Cast: failed to cast ${r.dtype} to ${a}`)}var xge={kernelName:ko,backendName:"webgpu",kernelFunc:Py},bge=kn({opType:Oe.CEIL,cpuKernelImpl:u1e}),vge={kernelName:So,backendName:"webgpu",kernelFunc:bge},wge=class{constructor(e){this.variableNames=["A"],this.uniforms="minVal : f32, maxVal : f32,",this.workPerThread=4,this.workGroupSize=[64,1,1],this.isVec4=!0,this.size=!0,this.outputShape=e,this.dispatchLayout=lt(this.outputShape),this.dispatch=je(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.shaderKey="clipVec4"}getUserCode(){return`
+      ${nt("index")} {
         if(index < uniforms.size) {
           let value = getAByOutputIndex(index);
           var clampedValue : vec4<f32>;
@@ -6012,8 +6012,8 @@ return a / b;`,Nle=`
           setOutputAtIndex(index, clampedValue);
         }
       }
-    `}},dge=class{constructor(e){this.variableNames=["A"],this.uniforms="minVal : f32, maxVal : f32,",this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=it(this.outputShape),this.dispatch=He(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey="clip"}getUserCode(){return`
-      ${Je("index")} {
+    `}},kge=class{constructor(e){this.variableNames=["A"],this.uniforms="minVal : f32, maxVal : f32,",this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=lt(this.outputShape),this.dispatch=je(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey="clip"}getUserCode(){return`
+      ${nt("index")} {
         if(index < uniforms.size) {
           let value = getAByOutputIndex(index);
           if (isnan(value)) {
@@ -6023,8 +6023,8 @@ return a / b;`,Nle=`
           setOutputAtIndex(index, clamp(value, uniforms.minVal, uniforms.maxVal));
         }
       }
-    `}};function pge(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{clipValueMin:a,clipValueMax:o}=s,i,l=[{type:"float32",data:[a]},{type:"float32",data:[o]}];return v.sizeFromShape(r.shape)%4===0?i=new cge(r.shape):i=new dge(r.shape),n.runWebGPUProgram(i,[r],r.dtype,l)}var hge={kernelName:Ea,backendName:"webgpu",kernelFunc:pge},fge=class{constructor(e){this.uniforms="",this.workPerThread=4,this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=T.computeOutShape(e,1),this.variableNames=e.map((t,n)=>`T${n}`),this.dispatchLayout=it(this.outputShape),this.dispatch=He(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.offsetLength=e.length-1;for(let t=0;t<this.offsetLength;t++)this.uniforms+=`offset${t} : i32,`;this.shaderKey="concat"}getUserCode(){let e=[];if(this.offsetLength>0){e.push("if (yC < uniforms.offset0){ setOutputAtCoords(coords.x, coords.y, getT0(yR, yC)); }");for(let r=1;r<this.offsetLength;r++)e.push(`else if (yC < uniforms.offset${[r]}){ setOutputAtCoords(coords.x, coords.y, getT${r}(yR, yC - uniforms.offset${r-1})); }`);let n=this.offsetLength,s=this.offsetLength-1;e.push(`else { setOutputAtCoords(coords.x, coords.y, getT${n}(yR, yC - uniforms.offset${s})); }`)}else e.push("setOutputAtCoords(coords.x, coords.y, getT0(yR, yC));");return`
-      ${Je("index")} {
+    `}};function Sge(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{clipValueMin:a,clipValueMax:o}=s,i,l=[{type:"float32",data:[a]},{type:"float32",data:[o]}];return v.sizeFromShape(r.shape)%4===0?i=new wge(r.shape):i=new kge(r.shape),n.runWebGPUProgram(i,[r],r.dtype,l)}var Ige={kernelName:$a,backendName:"webgpu",kernelFunc:Sge},Cge=class{constructor(e){this.uniforms="",this.workPerThread=4,this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=T.computeOutShape(e,1),this.variableNames=e.map((t,n)=>`T${n}`),this.dispatchLayout=lt(this.outputShape),this.dispatch=je(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.offsetLength=e.length-1;for(let t=0;t<this.offsetLength;t++)this.uniforms+=`offset${t} : i32,`;this.shaderKey="concat"}getUserCode(){let e=[];if(this.offsetLength>0){e.push("if (yC < uniforms.offset0){ setOutputAtCoords(coords.x, coords.y, getT0(yR, yC)); }");for(let r=1;r<this.offsetLength;r++)e.push(`else if (yC < uniforms.offset${[r]}){ setOutputAtCoords(coords.x, coords.y, getT${r}(yR, yC - uniforms.offset${r-1})); }`);let n=this.offsetLength,s=this.offsetLength-1;e.push(`else { setOutputAtCoords(coords.x, coords.y, getT${n}(yR, yC - uniforms.offset${s})); }`)}else e.push("setOutputAtCoords(coords.x, coords.y, getT0(yR, yC));");return`
+      ${nt("index")} {
         for(var i = 0; i < ${this.workPerThread}; i = i + 1) {
           let flatIndex = index * ${this.workPerThread} + i;
           if(flatIndex < uniforms.size) {
@@ -6037,7 +6037,7 @@ return a / b;`,Nle=`
           }
         }
       }
-    `}};function U2(e){let{inputs:t,backend:n}=e,{input:s}=t,r=n.tensorMap.get(s.dataId);return tr({inputs:{x:r.complexTensorInfos.imag},backend:n})}var mge={kernelName:Zp,backendName:"webgpu",kernelFunc:U2};function yp(e,t,n){let s=e[0].dtype;if(s==="complex64"){let f=e.map(A=>Zh({inputs:{input:A},backend:n})),m=e.map(A=>U2({inputs:{input:A},backend:n})),g=yp(f,t,n),y=yp(m,t,n),x=bd({inputs:{real:g,imag:y},backend:n});return f.forEach(A=>n.disposeData(A.dataId)),m.forEach(A=>n.disposeData(A.dataId)),n.disposeData(g.dataId),n.disposeData(y.dataId),x}let r=n.shouldExecuteOnCPU(e);if(s==="string"&&(r=!0),r){let f=e.map(w=>{let k=v.sizeFromShape(w.shape.slice(t));return Ge({inputs:{x:w},backend:n,attrs:{shape:[-1,k]}})}),m=f.map(w=>({vals:n.readSync(w.dataId),shape:w.shape})),g=T.computeOutShape(f.map(w=>w.shape),1),y=f[0].shape[0]===1,x=J2e(m,g,s,y),A=T.computeOutShape(e.map(w=>w.shape),t),b=n.makeTensorInfo(A,s,x);return f.forEach(w=>n.disposeData(w.dataId)),b}let a=n.device.limits.maxStorageBuffersPerShaderStage-1;if(e.length>a){let f=[];for(let g=0;g<e.length;g+=a){let y=e.slice(g,g+a);f.push(yp(y,t,n))}let m=yp(f,t,n);for(let g of f)n.disposeData(g.dataId);return m}let{tensors2D:o,outShape:i}=gge(e,t,n),l=o.map(f=>f.shape),u=new fge(l),c=[],p=new Array(l.length-1);if(p.length>0){p[0]=l[0][1],c.push({type:"int32",data:[p[0]]});for(let f=1;f<p.length;f++)p[f]=p[f-1]+l[f][1],c.push({type:"int32",data:[p[f]]})}let d=n.runWebGPUProgram(u,o,o[0].dtype,c);o.forEach(f=>n.disposeData(f.dataId));let h=Ge({inputs:{x:d},backend:n,attrs:{shape:i}});return n.disposeData(d.dataId),h}function gge(e,t,n){let s=T.computeOutShape(e.map(a=>a.shape),t);return{tensors2D:e.map(a=>Ge({inputs:{x:a},backend:n,attrs:{shape:[v.sizeFromShape(a.shape.slice(0,t)),v.sizeFromShape(a.shape.slice(t))]}})),outShape:s}}function kT(e){let{inputs:t,backend:n,attrs:s}=e,{axis:r}=s,a=v.parseAxisParam(r,t[0].shape)[0],o=T.computeOutShape(t.map(u=>u.shape),a);if(v.sizeFromShape(o)===0)return n.makeTensorInfo(o,t[0].dtype,[]);let i=t.filter(u=>v.sizeFromShape(u.shape)>0);if(i.length===1)return tr({inputs:{x:i[0]},backend:n});let l=i.map(u=>u.shape);return T.assertParamsConsistent(l,a),yp(i,a,n)}var yge={kernelName:Al,backendName:"webgpu",kernelFunc:kT};function Age(e,t,n,s,r=!1,a=null,o=!1,i=4,l=4,u=4){let c=_=>{switch(_){case 1:return"resData = x[xIndex];";case 3:return"resData = vec3<f32>(x[xIndex], x[xIndex + 1], x[xIndex + 2]);";case 4:return"resData = x[xIndex / 4];";default:throw new Error(`innerElementSize ${_} is not supported.`)}},p=_=>{switch(_){case 1:return"return W[row * uniforms.wShape[3] + colIn];";case 4:return"return W[row * uniforms.wShape[3] / 4 + colIn];";default:throw new Error(`innerElementSize ${_} is not supported.`)}},d=e?`
+    `}};function U2(e){let{inputs:t,backend:n}=e,{input:s}=t,r=n.tensorMap.get(s.dataId);return sr({inputs:{x:r.complexTensorInfos.imag},backend:n})}var Tge={kernelName:Yp,backendName:"webgpu",kernelFunc:U2};function Ap(e,t,n){let s=e[0].dtype;if(s==="complex64"){let f=e.map(A=>Yh({inputs:{input:A},backend:n})),m=e.map(A=>U2({inputs:{input:A},backend:n})),g=Ap(f,t,n),y=Ap(m,t,n),x=kd({inputs:{real:g,imag:y},backend:n});return f.forEach(A=>n.disposeData(A.dataId)),m.forEach(A=>n.disposeData(A.dataId)),n.disposeData(g.dataId),n.disposeData(y.dataId),x}let r=n.shouldExecuteOnCPU(e);if(s==="string"&&(r=!0),r){let f=e.map(w=>{let k=v.sizeFromShape(w.shape.slice(t));return He({inputs:{x:w},backend:n,attrs:{shape:[-1,k]}})}),m=f.map(w=>({vals:n.readSync(w.dataId),shape:w.shape})),g=T.computeOutShape(f.map(w=>w.shape),1),y=f[0].shape[0]===1,x=c1e(m,g,s,y),A=T.computeOutShape(e.map(w=>w.shape),t),b=n.makeTensorInfo(A,s,x);return f.forEach(w=>n.disposeData(w.dataId)),b}let a=n.device.limits.maxStorageBuffersPerShaderStage-1;if(e.length>a){let f=[];for(let g=0;g<e.length;g+=a){let y=e.slice(g,g+a);f.push(Ap(y,t,n))}let m=Ap(f,t,n);for(let g of f)n.disposeData(g.dataId);return m}let{tensors2D:o,outShape:i}=Nge(e,t,n),l=o.map(f=>f.shape),u=new Cge(l),c=[],p=new Array(l.length-1);if(p.length>0){p[0]=l[0][1],c.push({type:"int32",data:[p[0]]});for(let f=1;f<p.length;f++)p[f]=p[f-1]+l[f][1],c.push({type:"int32",data:[p[f]]})}let d=n.runWebGPUProgram(u,o,o[0].dtype,c);o.forEach(f=>n.disposeData(f.dataId));let h=He({inputs:{x:d},backend:n,attrs:{shape:i}});return n.disposeData(d.dataId),h}function Nge(e,t,n){let s=T.computeOutShape(e.map(a=>a.shape),t);return{tensors2D:e.map(a=>He({inputs:{x:a},backend:n,attrs:{shape:[v.sizeFromShape(a.shape.slice(0,t)),v.sizeFromShape(a.shape.slice(t))]}})),outShape:s}}function ET(e){let{inputs:t,backend:n,attrs:s}=e,{axis:r}=s,a=v.parseAxisParam(r,t[0].shape)[0],o=T.computeOutShape(t.map(u=>u.shape),a);if(v.sizeFromShape(o)===0)return n.makeTensorInfo(o,t[0].dtype,[]);let i=t.filter(u=>v.sizeFromShape(u.shape)>0);if(i.length===1)return sr({inputs:{x:i[0]},backend:n});let l=i.map(u=>u.shape);return T.assertParamsConsistent(l,a),Ap(i,a,n)}var Ege={kernelName:wl,backendName:"webgpu",kernelFunc:ET};function Rge(e,t,n,s,r=!1,a=null,o=!1,i=4,l=4,u=4){let c=_=>{switch(_){case 1:return"resData = x[xIndex];";case 3:return"resData = vec3<f32>(x[xIndex], x[xIndex + 1], x[xIndex + 2]);";case 4:return"resData = x[xIndex / 4];";default:throw new Error(`innerElementSize ${_} is not supported.`)}},p=_=>{switch(_){case 1:return"return W[row * uniforms.wShape[3] + colIn];";case 4:return"return W[row * uniforms.wShape[3] / 4 + colIn];";default:throw new Error(`innerElementSize ${_} is not supported.`)}},d=e?`
       let coord = vec4<i32>(batch, xRow, xCol, xCh);
       `:`
       let coord = vec4<i32>(batch, xCh, xRow, xCol);
@@ -6064,7 +6064,7 @@ return a / b;`,Nle=`
       let xRow = outRow * uniforms.stride[0] + uniforms.dilation[0] * WRow - uniforms.pad[0];
       let xCol = outCol * uniforms.stride[1] + uniforms.dilation[1] * WCol - uniforms.pad[1];
       let xCh = ${y} % inChannels;
-      var resData = ${Yt(i)}(0.0);
+      var resData = ${Jt(i)}(0.0);
       // The bounds checking is always needed since we use it to pad zero for
       // the 'same' padding type.
       if (xRow >= 0 && xRow < ${f} && xCol >= 0 && xCol < ${m}) {
@@ -6079,15 +6079,15 @@ return a / b;`,Nle=`
       if (row < uniforms.dimAOuter && col < uniforms.dimInner) {
         ${x}
       }
-      return ${Yt(i)}(0.0);`:s&&n?`
+      return ${Jt(i)}(0.0);`:s&&n?`
       let col = colIn * ${i};
       ${x}`:`
       let col = colIn * ${i};
       if (row < uniforms.dimInner && col < uniforms.dimBOuter) {
         ${x}
       }
-      return ${Yt(i)}(0.0);`,b=`${p(l)}`,w=Yt(u),k=Yt(e?i:l),C=Yt(e?l:i);return`
-      ${Ai(a,o,u===4,4)}
+      return ${Jt(i)}(0.0);`,b=`${p(l)}`,w=Jt(u),k=Jt(e?i:l),C=Jt(e?l:i);return`
+      ${wi(a,o,u===4,4)}
       fn mm_readA(batch: i32, row : i32, colIn : i32) -> ${k} {
         ${e?A:b}
       }
@@ -6103,13 +6103,13 @@ return a / b;`,Nle=`
         var value = valueIn;
         let outWidth = ${e?"uniforms.outShape[2]":"uniforms.outShape[3]"};
         ${h}
-        ${xd(r,a)}
+        ${wd(r,a)}
         setOutputAtCoords(coords[0], coords[1], coords[2], coords[3], value);
         }
-      }`}var xge=class{constructor(e,t,n,s,r=!1,a=null,o=!1){this.variableNames=["x","W"],this.uniforms="filterDims : vec2<i32>, pad : vec2<i32>, stride : vec2<i32>, dilation : vec2<i32>, dimAOuter : i32, dimBOuter : i32, dimInner : i32,",this.outputShape=e.outShape,this.isChannelsLast=e.dataFormat==="channelsLast",this.isVec4=((e.inChannels%4===0||e.inChannels%3===0)&&this.isChannelsLast||e.outWidth%4===0&&!this.isChannelsLast)&&e.outChannels%4===0,this.dispatchLayout=this.isChannelsLast?{x:[3],y:[1,2],z:[0]}:{x:[2,3],y:[1],z:[0]},this.workGroupSize=yb(this.dispatchLayout,this.outputShape,this.isVec4),this.elementsPerThread=Ab(this.dispatchLayout,this.outputShape,this.isVec4),this.dispatch=He(this.dispatchLayout,this.outputShape,this.workGroupSize,this.elementsPerThread),this.isVec4?(this.isChannelsLast&&e.inChannels%4!==0?(this.innerElementSize=3,this.variableTypes=["f32","vec4<f32>"]):(this.innerElementSize=4,this.variableTypes=["vec4<f32>","vec4<f32>"]),r&&(this.variableNames.push("bias"),this.variableTypes.push("vec4<f32>")),o&&(this.variableNames.push("preluActivationWeights"),this.variableTypes.push("vec4<f32>"))):(this.innerElementSize=this.elementsPerThread[0],r&&this.variableNames.push("bias"),o&&this.variableNames.push("preluActivationWeights")),this.addBias=r,this.activation=a,this.hasPreluActivationWeights=o,this.tileAOuter=this.workGroupSize[1]*this.elementsPerThread[1],this.tileBOuter=this.workGroupSize[0]*this.elementsPerThread[0],this.tileInner=Math.max(this.workGroupSize[0]*this.innerElementSize,this.workGroupSize[1]),this.fitAOuter=t%this.tileAOuter===0,this.fitBOuter=n%this.tileBOuter===0,this.fitInner=s%this.tileInner===0,this.shaderKey=`conv2DMM_${this.elementsPerThread}_${this.activation}}_${this.fitAOuter}_${this.fitBOuter}_${this.fitInner}_${this.isVec4}_${this.innerElementSize}_${this.isChannelsLast}`}getUserCode(){let e=this.isVec4?W2(this.elementsPerThread,this.workGroupSize,!this.isChannelsLast,this.tileInner):V2(this.elementsPerThread,this.workGroupSize,!this.isChannelsLast,this.tileInner),t=this.isVec4?[this.innerElementSize,4,4]:[1,1,1];return`
-    ${Age(this.isChannelsLast,this.fitAOuter,this.fitBOuter,this.fitInner,this.addBias,this.activation,this.hasPreluActivationWeights,t[0],t[1],t[2])}
+      }`}var _ge=class{constructor(e,t,n,s,r=!1,a=null,o=!1){this.variableNames=["x","W"],this.uniforms="filterDims : vec2<i32>, pad : vec2<i32>, stride : vec2<i32>, dilation : vec2<i32>, dimAOuter : i32, dimBOuter : i32, dimInner : i32,",this.outputShape=e.outShape,this.isChannelsLast=e.dataFormat==="channelsLast",this.isVec4=((e.inChannels%4===0||e.inChannels%3===0)&&this.isChannelsLast||e.outWidth%4===0&&!this.isChannelsLast)&&e.outChannels%4===0,this.dispatchLayout=this.isChannelsLast?{x:[3],y:[1,2],z:[0]}:{x:[2,3],y:[1],z:[0]},this.workGroupSize=bb(this.dispatchLayout,this.outputShape,this.isVec4),this.elementsPerThread=vb(this.dispatchLayout,this.outputShape,this.isVec4),this.dispatch=je(this.dispatchLayout,this.outputShape,this.workGroupSize,this.elementsPerThread),this.isVec4?(this.isChannelsLast&&e.inChannels%4!==0?(this.innerElementSize=3,this.variableTypes=["f32","vec4<f32>"]):(this.innerElementSize=4,this.variableTypes=["vec4<f32>","vec4<f32>"]),r&&(this.variableNames.push("bias"),this.variableTypes.push("vec4<f32>")),o&&(this.variableNames.push("preluActivationWeights"),this.variableTypes.push("vec4<f32>"))):(this.innerElementSize=this.elementsPerThread[0],r&&this.variableNames.push("bias"),o&&this.variableNames.push("preluActivationWeights")),this.addBias=r,this.activation=a,this.hasPreluActivationWeights=o,this.tileAOuter=this.workGroupSize[1]*this.elementsPerThread[1],this.tileBOuter=this.workGroupSize[0]*this.elementsPerThread[0],this.tileInner=Math.max(this.workGroupSize[0]*this.innerElementSize,this.workGroupSize[1]),this.fitAOuter=t%this.tileAOuter===0,this.fitBOuter=n%this.tileBOuter===0,this.fitInner=s%this.tileInner===0,this.shaderKey=`conv2DMM_${this.elementsPerThread}_${this.activation}}_${this.fitAOuter}_${this.fitBOuter}_${this.fitInner}_${this.isVec4}_${this.innerElementSize}_${this.isChannelsLast}`}getUserCode(){let e=this.isVec4?W2(this.elementsPerThread,this.workGroupSize,!this.isChannelsLast,this.tileInner):V2(this.elementsPerThread,this.workGroupSize,!this.isChannelsLast,this.tileInner),t=this.isVec4?[this.innerElementSize,4,4]:[1,1,1];return`
+    ${Rge(this.isChannelsLast,this.fitAOuter,this.fitBOuter,this.fitInner,this.addBias,this.activation,this.hasPreluActivationWeights,t[0],t[1],t[2])}
     ${e}
-  `}};function u6(e,t){let n=e.length;return n>=3?t?[...e.slice(0,-3),e[n-3]*e[n-2],e[n-1]]:[...e.slice(0,-3),e[n-3],e[n-2]*e[n-1]]:!t&&n===1&&e[0]>1?[e[0],1]:null}function bge({x:e,filter:t,convInfo:n,backend:s,bias:r=null,preluActivationWeights:a=null,leakyreluAlpha:o=0,activation:i=null}){let l=n.dataFormat==="channelsLast",u=!l,c=!1,p=l&&n.filterHeight===n.inHeight&&n.filterWidth===n.inWidth&&n.padInfo.type==="VALID",d=[],h,f;if(p){let y=n.inHeight*n.inWidth*n.inChannels;h=Ge({inputs:{x:e},backend:s,attrs:{shape:[1,n.batchSize,y]}}),f=Ge({inputs:{x:t},backend:s,attrs:{shape:[1,y,n.outChannels]}})}else h=Ge({inputs:{x:e},backend:s,attrs:{shape:l?[n.batchSize,n.inHeight*n.inWidth,n.inChannels]:[n.batchSize,n.inChannels,n.inHeight*n.inWidth]}}),f=Ge({inputs:{x:t},backend:s,attrs:{shape:[1,n.inChannels,n.outChannels]}});if(d.push(h),d.push(f),a!=null){let y=u6(a.shape,l);y!=null&&(a=Ge({inputs:{x:a},backend:s,attrs:{shape:y}}),d.push(a))}if(r!=null){let y=u6(r.shape,l);y!=null&&(r=Ge({inputs:{x:r},backend:s,attrs:{shape:y}}),d.push(r))}let m=vb({a:l?h:f,b:l?f:h,transposeA:u,transposeB:c,backend:s,bias:r,activation:i,preluActivationWeights:a,leakyreluAlpha:o}),g=Ge({inputs:{x:m},backend:s,attrs:{shape:n.outShape}});d.push(m);for(let y of d)s.disposeData(y.dataId);return g}function ST({x:e,filter:t,convInfo:n,backend:s,bias:r=null,preluActivationWeights:a=null,leakyreluAlpha:o=0,activation:i=null}){let l=r!=null,u=a!=null,c=n.dataFormat==="channelsLast";if(c&&n.filterHeight===n.inHeight&&n.filterWidth===n.inWidth&&n.padInfo.type==="VALID"||n.filterHeight===1&&n.filterWidth===1&&n.dilationHeight===1&&n.dilationWidth===1&&n.strideHeight===1&&n.strideWidth===1&&(n.padInfo.type==="SAME"||n.padInfo.type==="VALID"))return bge({x:e,filter:t,convInfo:n,backend:s,bias:r,activation:i,preluActivationWeights:a,leakyreluAlpha:o});let d=c?n.outHeight*n.outWidth:n.outChannels,h=c?n.outChannels:n.outHeight*n.outWidth,f=n.filterHeight*n.filterWidth*n.inChannels,m=[n.padInfo.top,n.padInfo.left],g=[{type:"int32",data:[n.filterHeight,n.filterWidth]},{type:"int32",data:[...m]},{type:"int32",data:[n.strideHeight,n.strideWidth]},{type:"int32",data:[n.dilationHeight,n.dilationWidth]},{type:"int32",data:[d]},{type:"int32",data:[h]},{type:"int32",data:[f]}],y=new xge(n,d,h,f,l,i,u),x=[],A=[e,t];l&&(!c&&r.shape.length===1&&(r=Ge({inputs:{x:r},backend:s,attrs:{shape:[r.shape[0],1,1]}}),x.push(r)),A.push(r)),u&&(!c&&a.shape.length===1&&(a=Ge({inputs:{x:a},backend:s,attrs:{shape:[a.shape[0],1,1]}}),x.push(a)),A.push(a)),i==="leakyrelu"&&(g.push({type:"float32",data:[o]}),y.uniforms+=" alpha : f32,");let b=s.runWebGPUProgram(y,A,e.dtype,g);for(let w of x)s.disposeData(w.dataId);return b}function vge(e){let{inputs:t,attrs:n,backend:s}=e,{x:r,filter:a}=t,{strides:o,pad:i,dataFormat:l,dilations:u,dimRoundingMode:c}=n,p=T.convertConv2DDataFormat(l),d=T.computeConv2DInfo(r.shape,a.shape,o,u,i,c,!1,p);return ST({x:r,filter:a,convInfo:d,backend:s})}var wge={kernelName:vo,backendName:"webgpu",kernelFunc:vge};function kge(e=4){let t=a=>{switch(a){case 1:return"return W[getIndexFromCoords4D(coord, uniforms.wShape)];";case 4:return`
+  `}};function m6(e,t){let n=e.length;return n>=3?t?[...e.slice(0,-3),e[n-3]*e[n-2],e[n-1]]:[...e.slice(0,-3),e[n-3],e[n-2]*e[n-1]]:!t&&n===1&&e[0]>1?[e[0],1]:null}function Dge({x:e,filter:t,convInfo:n,backend:s,bias:r=null,preluActivationWeights:a=null,leakyreluAlpha:o=0,activation:i=null}){let l=n.dataFormat==="channelsLast",u=!l,c=!1,p=l&&n.filterHeight===n.inHeight&&n.filterWidth===n.inWidth&&n.padInfo.type==="VALID",d=[],h,f;if(p){let y=n.inHeight*n.inWidth*n.inChannels;h=He({inputs:{x:e},backend:s,attrs:{shape:[1,n.batchSize,y]}}),f=He({inputs:{x:t},backend:s,attrs:{shape:[1,y,n.outChannels]}})}else h=He({inputs:{x:e},backend:s,attrs:{shape:l?[n.batchSize,n.inHeight*n.inWidth,n.inChannels]:[n.batchSize,n.inChannels,n.inHeight*n.inWidth]}}),f=He({inputs:{x:t},backend:s,attrs:{shape:[1,n.inChannels,n.outChannels]}});if(d.push(h),d.push(f),a!=null){let y=m6(a.shape,l);y!=null&&(a=He({inputs:{x:a},backend:s,attrs:{shape:y}}),d.push(a))}if(r!=null){let y=m6(r.shape,l);y!=null&&(r=He({inputs:{x:r},backend:s,attrs:{shape:y}}),d.push(r))}let m=Sb({a:l?h:f,b:l?f:h,transposeA:u,transposeB:c,backend:s,bias:r,activation:i,preluActivationWeights:a,leakyreluAlpha:o}),g=He({inputs:{x:m},backend:s,attrs:{shape:n.outShape}});d.push(m);for(let y of d)s.disposeData(y.dataId);return g}function RT({x:e,filter:t,convInfo:n,backend:s,bias:r=null,preluActivationWeights:a=null,leakyreluAlpha:o=0,activation:i=null}){let l=r!=null,u=a!=null,c=n.dataFormat==="channelsLast";if(c&&n.filterHeight===n.inHeight&&n.filterWidth===n.inWidth&&n.padInfo.type==="VALID"||n.filterHeight===1&&n.filterWidth===1&&n.dilationHeight===1&&n.dilationWidth===1&&n.strideHeight===1&&n.strideWidth===1&&(n.padInfo.type==="SAME"||n.padInfo.type==="VALID"))return Dge({x:e,filter:t,convInfo:n,backend:s,bias:r,activation:i,preluActivationWeights:a,leakyreluAlpha:o});let d=c?n.outHeight*n.outWidth:n.outChannels,h=c?n.outChannels:n.outHeight*n.outWidth,f=n.filterHeight*n.filterWidth*n.inChannels,m=[n.padInfo.top,n.padInfo.left],g=[{type:"int32",data:[n.filterHeight,n.filterWidth]},{type:"int32",data:[...m]},{type:"int32",data:[n.strideHeight,n.strideWidth]},{type:"int32",data:[n.dilationHeight,n.dilationWidth]},{type:"int32",data:[d]},{type:"int32",data:[h]},{type:"int32",data:[f]}],y=new _ge(n,d,h,f,l,i,u),x=[],A=[e,t];l&&(!c&&r.shape.length===1&&(r=He({inputs:{x:r},backend:s,attrs:{shape:[r.shape[0],1,1]}}),x.push(r)),A.push(r)),u&&(!c&&a.shape.length===1&&(a=He({inputs:{x:a},backend:s,attrs:{shape:[a.shape[0],1,1]}}),x.push(a)),A.push(a)),i==="leakyrelu"&&(g.push({type:"float32",data:[o]}),y.uniforms+=" alpha : f32,");let b=s.runWebGPUProgram(y,A,e.dtype,g);for(let w of x)s.disposeData(w.dataId);return b}function $ge(e){let{inputs:t,attrs:n,backend:s}=e,{x:r,filter:a}=t,{strides:o,pad:i,dataFormat:l,dilations:u,dimRoundingMode:c}=n,p=T.convertConv2DDataFormat(l),d=T.computeConv2DInfo(r.shape,a.shape,o,u,i,c,!1,p);return RT({x:r,filter:a,convInfo:d,backend:s})}var Pge={kernelName:Io,backendName:"webgpu",kernelFunc:$ge};function Fge(e=4){let t=a=>{switch(a){case 1:return"return W[getIndexFromCoords4D(coord, uniforms.wShape)];";case 4:return`
             let coord1 = vec4<i32>(coordX, coordY, col + 1, rowInner);
             let coord2 = vec4<i32>(coordX, coordY, col + 2, rowInner);
             let coord3 = vec4<i32>(coordX, coordY, col + 3, rowInner);
@@ -6128,10 +6128,10 @@ return a / b;`,Nle=`
       let xR = f32(outRow - uniforms.pads[0] + WRow) / f32(uniforms.stride[0]);
       let xC = f32(outCol - uniforms.pads[1] + WCol) / f32(uniforms.stride[1]);
       if (xR < 0.0 || xR >= f32(uniforms.outBackprop[1]) || fract(xR) > 0.0) {
-        return ${Yt(e)}(0.0);
+        return ${Jt(e)}(0.0);
       }
       if (xC < 0.0 || xC >= f32(uniforms.outBackprop[2]) || fract(xC) > 0.0) {
-        return ${Yt(e)}(0.0);
+        return ${Jt(e)}(0.0);
       }
       let coord = vec4<i32>(
           batch,
@@ -6140,13 +6140,13 @@ return a / b;`,Nle=`
           col % uniforms.outBackprop[3]);
       return x[getIndexFromCoords4D(coord, uniforms.xShape)/${e}];`}
       }
-      return ${Yt(e)}(0.0);`;return`
-  fn mm_readA(batch: i32, row : i32, colIn : i32) -> ${Yt(e)} {
+      return ${Jt(e)}(0.0);`;return`
+  fn mm_readA(batch: i32, row : i32, colIn : i32) -> ${Jt(e)} {
     let col = colIn * ${e};
     ${s}
   }
 
-  fn mm_readB(batch: i32, row : i32, colIn : i32) -> ${Yt(e)} {
+  fn mm_readB(batch: i32, row : i32, colIn : i32) -> ${Jt(e)} {
     let col = colIn * ${e};
     let coordX = uniforms.filterDims.x - 1 -
         row / (uniforms.filterDims[1] * uniforms.outBackprop[3]);
@@ -6158,10 +6158,10 @@ return a / b;`,Nle=`
       let coord = vec4<i32>(coordX, coordY, col, rowInner);
       ${t(e)}
     }
-    return ${Yt(e)}(0.0);
+    return ${Jt(e)}(0.0);
   }
 
-  fn mm_write(batch: i32, row : i32, colIn : i32, valueInput : ${Yt(e)}) {
+  fn mm_write(batch: i32, row : i32, colIn : i32, valueInput : ${Jt(e)}) {
     let col = colIn * ${e};
     if (row < uniforms.dimAOuter && (col + ${e-1}) < uniforms.dimBOuter) {
       var value = valueInput;
@@ -6172,11 +6172,11 @@ return a / b;`,Nle=`
           col);
       result[getIndexFromCoords4D(outCoord, uniforms.outShape)/${e}] = value;
     }
-  }`}var Sge=class{constructor(e){this.variableNames=["x","W"],this.uniforms="filterDims : vec2<i32>, pads : vec2<i32>, stride : vec2<i32>, outBackprop : vec4<i32>, dimAOuter : i32, dimBOuter : i32, dimInner : i32,",this.outputShape=e.inShape,v.assert(e.dataFormat==="channelsLast",()=>"TODO: NCHW is unimplemented"),this.isVec4=e.inChannels%4===0&&e.outChannels%4===0,this.dispatchLayout={x:[3],y:[1,2],z:[0]},this.workGroupSize=yb(this.dispatchLayout,this.outputShape,this.isVec4),this.elementsPerThread=Ab(this.dispatchLayout,this.outputShape,this.isVec4),this.dispatch=He(this.dispatchLayout,this.outputShape,this.workGroupSize,this.elementsPerThread),this.isVec4&&(this.variableTypes=["vec4<f32>","f32"]),this.shaderKey=`conv2DDerInputMM_${this.isVec4}_${this.elementsPerThread}`}getUserCode(){let e=this.isVec4?W2(this.elementsPerThread,this.workGroupSize):V2(this.elementsPerThread,this.workGroupSize);return`
-    ${kge(this.isVec4?4:1)}
+  }`}var Oge=class{constructor(e){this.variableNames=["x","W"],this.uniforms="filterDims : vec2<i32>, pads : vec2<i32>, stride : vec2<i32>, outBackprop : vec4<i32>, dimAOuter : i32, dimBOuter : i32, dimInner : i32,",this.outputShape=e.inShape,v.assert(e.dataFormat==="channelsLast",()=>"TODO: NCHW is unimplemented"),this.isVec4=e.inChannels%4===0&&e.outChannels%4===0,this.dispatchLayout={x:[3],y:[1,2],z:[0]},this.workGroupSize=bb(this.dispatchLayout,this.outputShape,this.isVec4),this.elementsPerThread=vb(this.dispatchLayout,this.outputShape,this.isVec4),this.dispatch=je(this.dispatchLayout,this.outputShape,this.workGroupSize,this.elementsPerThread),this.isVec4&&(this.variableTypes=["vec4<f32>","f32"]),this.shaderKey=`conv2DDerInputMM_${this.isVec4}_${this.elementsPerThread}`}getUserCode(){let e=this.isVec4?W2(this.elementsPerThread,this.workGroupSize):V2(this.elementsPerThread,this.workGroupSize);return`
+    ${Fge(this.isVec4?4:1)}
     ${e}
-    `}},Ige=class{constructor(e){this.variableNames=["dy","W"],this.uniforms="filterDims : vec2<i32>, pads : vec2<i32>, stride : vec2<i32>, outBackprop : vec4<i32>,",this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e.inShape,this.dispatchLayout=it(this.outputShape),this.dispatch=He(this.dispatchLayout,this.outputShape,this.workGroupSize),this.isChannelsLast=e.dataFormat==="channelsLast",this.shaderKey=`conv2DDerInput_${this.isChannelsLast}`}getUserCode(){let e=this.isChannelsLast?1:2,t=this.isChannelsLast?2:3,n=this.isChannelsLast?3:1;return`
-    ${Je("index")} {
+    `}},Mge=class{constructor(e){this.variableNames=["dy","W"],this.uniforms="filterDims : vec2<i32>, pads : vec2<i32>, stride : vec2<i32>, outBackprop : vec4<i32>,",this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e.inShape,this.dispatchLayout=lt(this.outputShape),this.dispatch=je(this.dispatchLayout,this.outputShape,this.workGroupSize),this.isChannelsLast=e.dataFormat==="channelsLast",this.shaderKey=`conv2DDerInput_${this.isChannelsLast}`}getUserCode(){let e=this.isChannelsLast?1:2,t=this.isChannelsLast?2:3,n=this.isChannelsLast?3:1;return`
+    ${nt("index")} {
       if(index < uniforms.size) {
         let coords = getCoordsFromIndex(index);
         let batch = coords[0];
@@ -6224,8 +6224,8 @@ return a / b;`,Nle=`
         setOutputAtIndex(index, dotProd);
       }
     }
-  `}};function Cge(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,filter:a}=t,{inputShape:o,strides:i,pad:l,dataFormat:u,dimRoundingMode:c}=s,p=T.convertConv2DDataFormat(u),d=T.computeConv2DInfo(o,a.shape,i,1,l,c,!1,p),h=[{type:"int32",data:[d.filterHeight,d.filterWidth]},{type:"int32",data:[d.filterHeight-1-d.padInfo.top,d.filterWidth-1-d.padInfo.left]},{type:"int32",data:[d.strideHeight,d.strideWidth]},{type:"int32",data:[d.batchSize,d.outHeight,d.outWidth,d.outChannels]}],f;if(H().getBool("WEBGPU_USE_NAIVE_CONV2D_TRANSPOSE"))f=new Ige(d);else{f=new Sge(d);let m=d.inShape[1]*d.inShape[2],g=d.inShape[3],y=d.filterHeight*d.filterWidth*d.outChannels;h.push({type:"uint32",data:[m]},{type:"uint32",data:[g]},{type:"uint32",data:[y]})}return n.runWebGPUProgram(f,[r,a],"float32",h)}var Tge={kernelName:wo,backendName:"webgpu",kernelFunc:Cge},Nge=vn({opType:Oe.COS}),Ege={kernelName:ko,backendName:"webgpu",kernelFunc:Nge},Rge=vn({opType:Oe.COSH}),_ge={kernelName:So,backendName:"webgpu",kernelFunc:Rge},Dge=class{constructor(e,t,n,s){this.variableNames=["Image","Boxes","BoxInd"],this.uniforms="extrapolationValue : f32,",this.workGroupSize=[64,1,1],this.size=!0;let[r]=t;this.outputShape=[r,n[0],n[1],e],this.dispatchLayout=it(this.outputShape),this.dispatch=He(this.dispatchLayout,this.outputShape,this.workGroupSize),this.methodId=s==="bilinear"?1:0,this.cropHeightBiggerThan1=this.outputShape[1]>1,this.cropWidthBiggerThan1=this.outputShape[2]>1,this.shaderKey=`cropAndResize_${this.methodId}_${this.cropHeightBiggerThan1}_${this.cropWidthBiggerThan1}`}getUserCode(){let[e,t]=["f32(uniforms.imageShape[1] - 1)","f32(uniforms.imageShape[2] - 1)"],[n,s,r]=this.cropHeightBiggerThan1?[`(${e} / f32(uniforms.outShape[1] - 1))`,"(y2-y1) * height_ratio",`y1*${e} + f32(y)*(height_scale)`]:["0.0","0.0",`0.5 * (y1+y2) * ${e}`],[a,o,i]=this.cropWidthBiggerThan1?[`(${t} / f32(uniforms.outShape[2] - 1))`,"(x2-x1) * width_ratio",`x1*${t} + f32(x)*(width_scale)`]:["0.0","0.0",`0.5 * (x1+x2) * ${t}`];return`
-    ${Je("index")} {
+  `}};function zge(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,filter:a}=t,{inputShape:o,strides:i,pad:l,dataFormat:u,dimRoundingMode:c}=s,p=T.convertConv2DDataFormat(u),d=T.computeConv2DInfo(o,a.shape,i,1,l,c,!1,p),h=[{type:"int32",data:[d.filterHeight,d.filterWidth]},{type:"int32",data:[d.filterHeight-1-d.padInfo.top,d.filterWidth-1-d.padInfo.left]},{type:"int32",data:[d.strideHeight,d.strideWidth]},{type:"int32",data:[d.batchSize,d.outHeight,d.outWidth,d.outChannels]}],f;if(H().getBool("WEBGPU_USE_NAIVE_CONV2D_TRANSPOSE"))f=new Mge(d);else{f=new Oge(d);let m=d.inShape[1]*d.inShape[2],g=d.inShape[3],y=d.filterHeight*d.filterWidth*d.outChannels;h.push({type:"uint32",data:[m]},{type:"uint32",data:[g]},{type:"uint32",data:[y]})}return n.runWebGPUProgram(f,[r,a],"float32",h)}var Lge={kernelName:Co,backendName:"webgpu",kernelFunc:zge},Bge=kn({opType:Oe.COS}),Wge={kernelName:To,backendName:"webgpu",kernelFunc:Bge},Vge=kn({opType:Oe.COSH}),Uge={kernelName:No,backendName:"webgpu",kernelFunc:Vge},Gge=class{constructor(e,t,n,s){this.variableNames=["Image","Boxes","BoxInd"],this.uniforms="extrapolationValue : f32,",this.workGroupSize=[64,1,1],this.size=!0;let[r]=t;this.outputShape=[r,n[0],n[1],e],this.dispatchLayout=lt(this.outputShape),this.dispatch=je(this.dispatchLayout,this.outputShape,this.workGroupSize),this.methodId=s==="bilinear"?1:0,this.cropHeightBiggerThan1=this.outputShape[1]>1,this.cropWidthBiggerThan1=this.outputShape[2]>1,this.shaderKey=`cropAndResize_${this.methodId}_${this.cropHeightBiggerThan1}_${this.cropWidthBiggerThan1}`}getUserCode(){let[e,t]=["f32(uniforms.imageShape[1] - 1)","f32(uniforms.imageShape[2] - 1)"],[n,s,r]=this.cropHeightBiggerThan1?[`(${e} / f32(uniforms.outShape[1] - 1))`,"(y2-y1) * height_ratio",`y1*${e} + f32(y)*(height_scale)`]:["0.0","0.0",`0.5 * (y1+y2) * ${e}`],[a,o,i]=this.cropWidthBiggerThan1?[`(${t} / f32(uniforms.outShape[2] - 1))`,"(x2-x1) * width_ratio",`x1*${t} + f32(x)*(width_scale)`]:["0.0","0.0",`0.5 * (x1+x2) * ${t}`];return`
+    ${nt("index")} {
       if (index < uniforms.size) {
         let coords = getCoordsFromIndex(index);
         let height_ratio = f32(${n});
@@ -6280,24 +6280,24 @@ return a / b;`,Nle=`
         }
       }
     }
-    `}},$ge=e=>{let{inputs:t,backend:n,attrs:s}=e,{image:r,boxes:a,boxInd:o}=t,{cropSize:i,method:l,extrapolationValue:u}=s,c=new Dge(r.shape[3],a.shape,i,l),p=[{type:"float32",data:[u]}];return n.runWebGPUProgram(c,[r,a,o],"float32",p)},Pge={kernelName:bl,backendName:"webgpu",kernelFunc:$ge},Vp;(function(e){e.Prod="*",e.Sum="+"})(Vp||(Vp={}));var c6=class{constructor(e,t,n,s){this.variableNames=["x"],this.uniforms="index : f32,",this.size=!0;let r=128;this.workGroupSize=[r,1,1],this.outputShape=t,this.dispatchLayout=it(this.outputShape),this.dispatch=He(this.dispatchLayout,this.outputShape,this.workGroupSize),this.exclusive=n,this.reverse=s,this.op=e,this.shaderKey=`cum_${this.op}_${this.exclusive}_${this.reverse}`}getUserCode(){let e=this.outputShape.length,t=this.op===Vp.Prod?"1.0":"0.0",n=this.exclusive?t:`getX(${d6(e,"coords",this.op)})`,s=this.outputShape[this.outputShape.length-1],r="",a="";return this.exclusive?(r=this.reverse?`end != ${s-1}`:"end != 0",a=this.reverse?"end + 1":"end - 1"):(r=this.reverse?`end + pow2 < ${s}`:"end >= pow2",a=this.reverse?"end + pow2":"end - pow2"),`
-      ${Je("index")} {
+    `}},Hge=e=>{let{inputs:t,backend:n,attrs:s}=e,{image:r,boxes:a,boxInd:o}=t,{cropSize:i,method:l,extrapolationValue:u}=s,c=new Gge(r.shape[3],a.shape,i,l),p=[{type:"float32",data:[u]}];return n.runWebGPUProgram(c,[r,a,o],"float32",p)},jge={kernelName:Sl,backendName:"webgpu",kernelFunc:Hge},Up;(function(e){e.Prod="*",e.Sum="+"})(Up||(Up={}));var g6=class{constructor(e,t,n,s){this.variableNames=["x"],this.uniforms="index : f32,",this.size=!0;let r=128;this.workGroupSize=[r,1,1],this.outputShape=t,this.dispatchLayout=lt(this.outputShape),this.dispatch=je(this.dispatchLayout,this.outputShape,this.workGroupSize),this.exclusive=n,this.reverse=s,this.op=e,this.shaderKey=`cum_${this.op}_${this.exclusive}_${this.reverse}`}getUserCode(){let e=this.outputShape.length,t=this.op===Up.Prod?"1.0":"0.0",n=this.exclusive?t:`getX(${y6(e,"coords",this.op)})`,s=this.outputShape[this.outputShape.length-1],r="",a="";return this.exclusive?(r=this.reverse?`end != ${s-1}`:"end != 0",a=this.reverse?"end + 1":"end - 1"):(r=this.reverse?`end + pow2 < ${s}`:"end >= pow2",a=this.reverse?"end + pow2":"end - pow2"),`
+      ${nt("index")} {
        if (index < uniforms.size) {
          var coords = getCoordsFromIndex(index);
 
-         let end = ${p6(e,"coords",this.op)};
+         let end = ${A6(e,"coords",this.op)};
          var val = ${n};
          let pow2 = i32(pow(2.0, uniforms.index));
          if (${r}) {
            let idx = ${a};
-           ${p6(e,"coords",this.op)} = idx;
-           val ${this.op}= getX(${d6(e,"coords",this.op)});
+           ${A6(e,"coords",this.op)} = idx;
+           val ${this.op}= getX(${y6(e,"coords",this.op)});
          }
          setOutputAtIndex(index, val);
        }
       }
-    `}};function d6(e,t,n){if(e===1)return`${t}`;if(e===2)return`${t}.x, ${t}.y`;if(e===3)return`${t}.x, ${t}.y, ${t}.z`;if(e===4)return`${t}.x, ${t}.y, ${t}.z, ${t}.w`;throw Error(`Cumulative ${n} for rank ${e} is not yet supported`)}function p6(e,t,n){if(e===1)return`${t}`;if(e===2)return`${t}.y`;if(e===3)return`${t}.z`;if(e===4)return`${t}.w`;throw Error(`Cumulative ${n} for rank ${e} is not yet supported`)}function IT(e,t,n,s,r,a){let o=t.shape.length,i=T.getAxesPermutation([s],o),l=t;i!=null&&(l=Ta({inputs:{x:t},backend:n,attrs:{perm:i}}));let u=T.getInnerMostAxes(1,o)[0];if(u!==o-1)throw new Error(`WebGPU cumprod shader expects an inner-most axis=${t.shape.length-1} but got axis=${s}`);let c=l.shape[u],p=tr({inputs:{x:l},backend:n});for(let d=0;d<=Math.ceil(Math.log2(c))-1;d++){let h=new c6(e,l.shape,!1,a),f=p,m=[{type:"float32",data:[d]}];p=n.runWebGPUProgram(h,[p],p.dtype,m),n.disposeData(f.dataId)}if(r){let d=new c6(e,l.shape,r,a),h=p,f=[{type:"float32",data:[0]}];p=n.runWebGPUProgram(d,[p],p.dtype,f),n.disposeData(h.dataId)}if(i!=null){let d=T.getUndoAxesPermutation(i),h=Ta({inputs:{x:p},backend:n,attrs:{perm:d}});return n.disposeData(p.dataId),n.disposeData(l.dataId),h}return p}function Fge(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,exclusive:o,reverse:i}=s;return IT(Vp.Prod,r,n,a,o,i)}var Oge={kernelName:xl,backendName:"webgpu",kernelFunc:Fge};function Mge(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,exclusive:o,reverse:i}=s;return IT(Vp.Sum,r,n,a,o,i)}var zge={kernelName:Io,backendName:"webgpu",kernelFunc:Mge},Lge=class{constructor(e,t){this.variableNames=["x"],this.workGroupSize=[64,1,1],this.size=!0,this.uniforms="blockSize : i32,",this.outputShape=e,this.dispatchLayout=it(this.outputShape),this.dispatch=He(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey=`depthToSpace_${t}`,this.dataFormat=t}getUserCode(){return`
-      ${Je("index")} {
+    `}};function y6(e,t,n){if(e===1)return`${t}`;if(e===2)return`${t}.x, ${t}.y`;if(e===3)return`${t}.x, ${t}.y, ${t}.z`;if(e===4)return`${t}.x, ${t}.y, ${t}.z, ${t}.w`;throw Error(`Cumulative ${n} for rank ${e} is not yet supported`)}function A6(e,t,n){if(e===1)return`${t}`;if(e===2)return`${t}.y`;if(e===3)return`${t}.z`;if(e===4)return`${t}.w`;throw Error(`Cumulative ${n} for rank ${e} is not yet supported`)}function _T(e,t,n,s,r,a){let o=t.shape.length,i=T.getAxesPermutation([s],o),l=t;i!=null&&(l=_a({inputs:{x:t},backend:n,attrs:{perm:i}}));let u=T.getInnerMostAxes(1,o)[0];if(u!==o-1)throw new Error(`WebGPU cumprod shader expects an inner-most axis=${t.shape.length-1} but got axis=${s}`);let c=l.shape[u],p=sr({inputs:{x:l},backend:n});for(let d=0;d<=Math.ceil(Math.log2(c))-1;d++){let h=new g6(e,l.shape,!1,a),f=p,m=[{type:"float32",data:[d]}];p=n.runWebGPUProgram(h,[p],p.dtype,m),n.disposeData(f.dataId)}if(r){let d=new g6(e,l.shape,r,a),h=p,f=[{type:"float32",data:[0]}];p=n.runWebGPUProgram(d,[p],p.dtype,f),n.disposeData(h.dataId)}if(i!=null){let d=T.getUndoAxesPermutation(i),h=_a({inputs:{x:p},backend:n,attrs:{perm:d}});return n.disposeData(p.dataId),n.disposeData(l.dataId),h}return p}function qge(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,exclusive:o,reverse:i}=s;return _T(Up.Prod,r,n,a,o,i)}var Xge={kernelName:kl,backendName:"webgpu",kernelFunc:qge};function Kge(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,exclusive:o,reverse:i}=s;return _T(Up.Sum,r,n,a,o,i)}var Zge={kernelName:Eo,backendName:"webgpu",kernelFunc:Kge},Yge=class{constructor(e,t){this.variableNames=["x"],this.workGroupSize=[64,1,1],this.size=!0,this.uniforms="blockSize : i32,",this.outputShape=e,this.dispatchLayout=lt(this.outputShape),this.dispatch=je(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey=`depthToSpace_${t}`,this.dataFormat=t}getUserCode(){return`
+      ${nt("index")} {
         if (index < uniforms.size) {
           let coords = getCoordsFromIndex(index);
           let b = coords[0];
@@ -6316,8 +6316,8 @@ return a / b;`,Nle=`
           let rlt = ${this.getInputSamplingString()};
           setOutputAtIndex(index, rlt);
         }
-      }`}getHeightCoordString(){return this.dataFormat==="NHWC"?"coords[1]":"coords[2]"}getWidthCoordString(){return this.dataFormat==="NHWC"?"coords[2]":"coords[3]"}getDepthCoordString(){return this.dataFormat==="NHWC"?"coords[3]":"coords[1]"}getOutputDepthSize(){return this.dataFormat==="NHWC"?"uniforms.outShape[3]":"uniforms.outShape[1]"}getInputSamplingString(){return this.dataFormat==="NHWC"?"getX(b, in_h, in_w, in_d)":"getX(b, in_d, in_h, in_w)"}};function Bge(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockSize:a,dataFormat:o}=s,i=r.shape[0],l=o==="NHWC"?r.shape[1]:r.shape[2],u=o==="NHWC"?r.shape[2]:r.shape[3],c=o==="NHWC"?r.shape[3]:r.shape[1],p=l*a,d=u*a,h=c/(a*a),f=o==="NHWC"?[i,p,d,h]:[i,h,p,d],m=[{type:"int32",data:[a]}],g=new Lge(f,o);return n.runWebGPUProgram(g,[r],r.dtype,m)}var Wge={kernelName:vl,backendName:"webgpu",kernelFunc:Bge},Vge=class{constructor(e,t,n,s=!1,r=null,a=!1){this.variableNames=["x","W"],this.uniforms="pad : vec2<i32>, inDims : vec2<i32>,",this.workGroupSize=[16,16,1],this.outputShape=e,this.dispatchLayout={x:[3],y:[2],z:[0,1]},this.dispatch=He(this.dispatchLayout,this.outputShape,this.workGroupSize),s&&this.variableNames.push("bias"),a&&this.variableNames.push("preluActivationWeights"),this.addBias=s,this.activation=r,this.hasPreluActivation=a,this.filterHeight=t,this.filterWidth=n,this.shaderKey=`depthwiseNCHW_${this.activation}_${this.filterHeight}_${this.filterWidth}`}getUserCode(){let e=this.filterWidth*this.filterHeight,t=this.workGroupSize[0]*this.workGroupSize[1]*this.workGroupSize[2],n=this.workGroupSize[1]+this.filterHeight-1,s=this.workGroupSize[0]+this.filterWidth-1;return`
-      ${Ai(this.activation,this.hasPreluActivation,!1,4)}
+      }`}getHeightCoordString(){return this.dataFormat==="NHWC"?"coords[1]":"coords[2]"}getWidthCoordString(){return this.dataFormat==="NHWC"?"coords[2]":"coords[3]"}getDepthCoordString(){return this.dataFormat==="NHWC"?"coords[3]":"coords[1]"}getOutputDepthSize(){return this.dataFormat==="NHWC"?"uniforms.outShape[3]":"uniforms.outShape[1]"}getInputSamplingString(){return this.dataFormat==="NHWC"?"getX(b, in_h, in_w, in_d)":"getX(b, in_d, in_h, in_w)"}};function Jge(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockSize:a,dataFormat:o}=s,i=r.shape[0],l=o==="NHWC"?r.shape[1]:r.shape[2],u=o==="NHWC"?r.shape[2]:r.shape[3],c=o==="NHWC"?r.shape[3]:r.shape[1],p=l*a,d=u*a,h=c/(a*a),f=o==="NHWC"?[i,p,d,h]:[i,h,p,d],m=[{type:"int32",data:[a]}],g=new Yge(f,o);return n.runWebGPUProgram(g,[r],r.dtype,m)}var Qge={kernelName:Il,backendName:"webgpu",kernelFunc:Jge},e3e=class{constructor(e,t,n,s=!1,r=null,a=!1){this.variableNames=["x","W"],this.uniforms="pad : vec2<i32>, inDims : vec2<i32>,",this.workGroupSize=[16,16,1],this.outputShape=e,this.dispatchLayout={x:[3],y:[2],z:[0,1]},this.dispatch=je(this.dispatchLayout,this.outputShape,this.workGroupSize),s&&this.variableNames.push("bias"),a&&this.variableNames.push("preluActivationWeights"),this.addBias=s,this.activation=r,this.hasPreluActivation=a,this.filterHeight=t,this.filterWidth=n,this.shaderKey=`depthwiseNCHW_${this.activation}_${this.filterHeight}_${this.filterWidth}`}getUserCode(){let e=this.filterWidth*this.filterHeight,t=this.workGroupSize[0]*this.workGroupSize[1]*this.workGroupSize[2],n=this.workGroupSize[1]+this.filterHeight-1,s=this.workGroupSize[0]+this.filterWidth-1;return`
+      ${wi(this.activation,this.hasPreluActivation,!1,4)}
 
       var<workgroup> mm_Asub : array<array<f32, ${s}>, ${n}>;
       var<workgroup> mm_Bsub : array<array<f32, ${this.filterWidth}>, ${this.filterHeight}>;
@@ -6330,7 +6330,7 @@ return a / b;`,Nle=`
         return value;
       }
 
-      ${Wp()}
+      ${Vp()}
       fn _start(@builtin(local_invocation_id) LocalId : vec3<u32>,
                 @builtin(global_invocation_id) GlobalId : vec3<u32>,
                 @builtin(local_invocation_index) LocalIndex: u32,
@@ -6381,13 +6381,13 @@ return a / b;`,Nle=`
             value = fma(xVal, wVal, value);
           }
         }
-        ${xd(this.addBias,this.activation)}
+        ${wd(this.addBias,this.activation)}
         if (coordsInBounds4D(coords, uniforms.outShape)) {
           setOutputAtCoords(coords[0], coords[1], coords[2], coords[3], value);
         }
       }
-    `}},CT=class{constructor(e,t=!1,n=null,s=!1){this.variableNames=["x","W"],this.uniforms="pad : vec2<i32>, inDims : vec2<i32>,",this.workGroupSize=[4,4,4],this.isVec4=!0,this.outputShape=e.outShape,this.dispatchLayout={x:[3],y:[2],z:[0,1]},this.dispatch=He(this.dispatchLayout,this.outputShape,this.workGroupSize,[4,4,1]),v.assert(e.dataFormat==="channelsLast",()=>"TODO: NCHW is unimplemented"),t&&this.variableNames.push("bias"),s&&this.variableNames.push("preluActivationWeights"),this.convInfo=e,this.addBias=t,this.activation=n,this.hasPreluActivation=s,this.shaderKey=`depthwiseVec4_${n}_${this.convInfo.filterHeight}_${this.convInfo.filterWidth}`}getUserCode(){let e=4+this.convInfo.filterWidth-1;return`
-      ${Ai(this.activation,this.hasPreluActivation,!0,4)}
+    `}},DT=class{constructor(e,t=!1,n=null,s=!1){this.variableNames=["x","W"],this.uniforms="pad : vec2<i32>, inDims : vec2<i32>,",this.workGroupSize=[4,4,4],this.isVec4=!0,this.outputShape=e.outShape,this.dispatchLayout={x:[3],y:[2],z:[0,1]},this.dispatch=je(this.dispatchLayout,this.outputShape,this.workGroupSize,[4,4,1]),v.assert(e.dataFormat==="channelsLast",()=>"TODO: NCHW is unimplemented"),t&&this.variableNames.push("bias"),s&&this.variableNames.push("preluActivationWeights"),this.convInfo=e,this.addBias=t,this.activation=n,this.hasPreluActivation=s,this.shaderKey=`depthwiseVec4_${n}_${this.convInfo.filterHeight}_${this.convInfo.filterWidth}`}getUserCode(){let e=4+this.convInfo.filterWidth-1;return`
+      ${wi(this.activation,this.hasPreluActivation,!0,4)}
       fn readX(batch : i32, row : i32, col : i32, channel : i32) -> vec4<f32> {
         var value = vec4<f32>(0.0);
         if (row >=0 && row < uniforms.inDims[0] && col >=0 && col < uniforms.inDims[1])
@@ -6396,7 +6396,7 @@ return a / b;`,Nle=`
         }
         return value;
       }
-      ${Wp()}
+      ${Vp()}
       fn _start(@builtin(global_invocation_id) globalId: vec3<u32>) {
         let batch = i32(globalId.z) / uniforms.outShape[1];
         let r = i32(globalId.z) % uniforms.outShape[1];
@@ -6433,16 +6433,16 @@ return a / b;`,Nle=`
           let coords = vec4<i32>(batch, r, c + i, d1);
           if (coordsInBounds4D(coords, uniforms.outShape)) {
             var value = dotProd[i];
-            ${xd(this.addBias,this.activation)}
+            ${wd(this.addBias,this.activation)}
             setOutputAtCoords(coords[0], coords[1], coords[2], coords[3], value);
           }
         }
       }
-    `}},TT=class{constructor(e,t=!1,n=null,s=!1){this.variableNames=["x","W"],this.uniforms=`pad : vec2<i32>, inDims : vec2<i32>, filterHeight : i32,
-      filterWidth : i32, stride : vec2<i32>, dilation : vec2<i32>,`,this.workGroupSize=[256,1,1],this.outputShape=e.outShape,this.dispatchLayout=it(this.outputShape),this.dispatch=He(this.dispatchLayout,this.outputShape,this.workGroupSize),this.isChannelsLast=e.dataFormat==="channelsLast",t&&this.variableNames.push("bias"),s&&this.variableNames.push("preluActivationWeights"),this.convInfo=e,this.addBias=t,this.activation=n,this.hasPreluActivation=s,this.shaderKey=`depthwise_${this.activation}_${this.isChannelsLast}`}getUserCode(){let e=this.isChannelsLast?"getX(batch, xR, xC, d1);":"getX(batch, d1, xR, xC);";return`
-      ${Ai(this.activation,this.hasPreluActivation,!1,4)}
+    `}},$T=class{constructor(e,t=!1,n=null,s=!1){this.variableNames=["x","W"],this.uniforms=`pad : vec2<i32>, inDims : vec2<i32>, filterHeight : i32,
+      filterWidth : i32, stride : vec2<i32>, dilation : vec2<i32>,`,this.workGroupSize=[256,1,1],this.outputShape=e.outShape,this.dispatchLayout=lt(this.outputShape),this.dispatch=je(this.dispatchLayout,this.outputShape,this.workGroupSize),this.isChannelsLast=e.dataFormat==="channelsLast",t&&this.variableNames.push("bias"),s&&this.variableNames.push("preluActivationWeights"),this.convInfo=e,this.addBias=t,this.activation=n,this.hasPreluActivation=s,this.shaderKey=`depthwise_${this.activation}_${this.isChannelsLast}`}getUserCode(){let e=this.isChannelsLast?"getX(batch, xR, xC, d1);":"getX(batch, d1, xR, xC);";return`
+      ${wi(this.activation,this.hasPreluActivation,!1,4)}
 
-      ${Je()} {
+      ${nt()} {
         let coords = getOutputCoords();
         let batch = coords[0];
         let xRCCorner = vec2<i32>(coords.${this.isChannelsLast?"yz":"zw"}) * uniforms.stride - uniforms.pad;
@@ -6500,13 +6500,13 @@ return a / b;`,Nle=`
               }
             }
           }
-          ${xd(this.addBias,this.activation)}
+          ${wd(this.addBias,this.activation)}
         if (coordsInBounds4D(coords, uniforms.outShape)) {
           setOutputAtCoords(coords[0], coords[1], coords[2], coords[3], value);
         }
       }
-    `}};function Uge(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a}=t,{strides:o,pad:i,dataFormat:l,dilations:u,dimRoundingMode:c}=s,p=T.convertConv2DDataFormat(l),d=u;d==null&&(d=[1,1]);let h=T.computeConv2DInfo(r.shape,a.shape,o,d,i,c,!0,p),f=[{type:"int32",data:[h.padInfo.top,h.padInfo.left]},{type:"int32",data:[h.inHeight,h.inWidth]}],m=h.dataFormat==="channelsLast",g;return!m&&h.inHeight>16&&h.inWidth>16&&h.strideHeight===1&&h.strideWidth===1&&h.dilationWidth===1&&h.dilationHeight===1&&h.inChannels===h.outChannels?g=new Vge(h.outShape,h.filterHeight,h.filterWidth):m&&h.inHeight>4&&h.inWidth>4&&h.strideHeight===1&&h.strideWidth===1&&h.inChannels===h.outChannels&&h.dilationHeight===1&&h.dilationWidth===1&&h.inChannels%4===0?g=new CT(h):(g=new TT(h),f.push({type:"int32",data:[h.filterHeight]},{type:"int32",data:[h.filterWidth]},{type:"int32",data:[h.strideHeight,h.strideWidth]},{type:"int32",data:[h.dilationHeight,h.dilationWidth]})),n.runWebGPUProgram(g,[r,a],r.dtype,f)}var Gge={kernelName:Co,backendName:"webgpu",kernelFunc:Uge},NT=jn({opType:Xe.MUL,cpuKernelImpl:h1e,supportsComplex:!0}),Hge={kernelName:Go,backendName:"webgpu",kernelFunc:NT};function kb(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:o}=s;return Kh(r,a,o,"sum",n)}var jge={kernelName:si,backendName:"webgpu",kernelFunc:kb};function qge(e){let{inputs:t,backend:n,attrs:s}=e,{equation:r}=s,a=t,{allDims:o,summedDims:i,idDims:l}=T.decodeEinsumEquation(r,a.length);T.checkEinsumDimSizes(o.length,l,a);let{path:u,steps:c}=T.getEinsumComputePath(i,l),p=c.length,d=null,h=o.length,f=[];for(let m=0;m<p;++m){for(let g of c[m]){let{permutationIndices:y,expandDims:x}=T.getEinsumPermutation(h,l[g]),A;T.isIdentityPermutation(y)?A=a[g]:(A=Ta({inputs:{x:a[g]},backend:n,attrs:{perm:y}}),f.push(A));let b=A.shape.slice();for(let w=0;w<x.length;++w)b.splice(x[w],0,1);v.arraysEqual(A.shape,b)||(A=Ge({inputs:{x:A},backend:n,attrs:{shape:b}}),f.push(A)),d===null?d=A:(d=NT({inputs:{a:A,b:d},backend:n}),f.push(d))}m<p-1&&(u[m]>=0&&(d=kb({inputs:{x:d},backend:n,attrs:{axis:u[m]-(o.length-h),keepDims:!1}}),f.push(d)),h--)}for(let m of f)m!==d&&n.disposeData(m.dataId);return d}var Xge={kernelName:Kp,backendName:"webgpu",kernelFunc:qge},Kge=vn({opType:Oe.ELU}),Zge={kernelName:No,backendName:"webgpu",kernelFunc:Kge},Yge=jn({opType:Xe.EQUAL,dtype:"bool",cpuKernelImpl:Q2e}),Jge={kernelName:wl,backendName:"webgpu",kernelFunc:Yge},ET=vn({opType:Oe.EXP,cpuKernelImpl:e1e,dtype:"float32"}),Qge={kernelName:Eo,backendName:"webgpu",kernelFunc:ET};function Dy(e){let{inputs:t,attrs:n,backend:s}=e,{dim:r}=n,{input:a}=t,o=a.shape.length,i=a.shape.slice(),l=r;return r<0&&(v.assert(-(o+1)<=r,()=>`Axis must be in the interval [${-(o+1)}, ${o}]`),l=o+r+1),i.splice(l,0,1),Ge({inputs:{x:a},backend:s,attrs:{shape:i}})}var e3e={kernelName:kl,backendName:"webgpu",kernelFunc:Dy},t3e=vn({opType:Oe.EXPM1,cpuKernelImpl:t1e}),n3e={kernelName:Sl,backendName:"webgpu",kernelFunc:t3e},s3e=class{constructor(e){this.outputShape=[],this.variableNames=["x"],this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=it(this.outputShape),this.dispatch=He(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey="flipLeftRight"}getUserCode(){return`
-      ${Je("index")} {
+    `}};function t3e(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a}=t,{strides:o,pad:i,dataFormat:l,dilations:u,dimRoundingMode:c}=s,p=T.convertConv2DDataFormat(l),d=u;d==null&&(d=[1,1]);let h=T.computeConv2DInfo(r.shape,a.shape,o,d,i,c,!0,p),f=[{type:"int32",data:[h.padInfo.top,h.padInfo.left]},{type:"int32",data:[h.inHeight,h.inWidth]}],m=h.dataFormat==="channelsLast",g;return!m&&h.inHeight>16&&h.inWidth>16&&h.strideHeight===1&&h.strideWidth===1&&h.dilationWidth===1&&h.dilationHeight===1&&h.inChannels===h.outChannels?g=new e3e(h.outShape,h.filterHeight,h.filterWidth):m&&h.inHeight>4&&h.inWidth>4&&h.strideHeight===1&&h.strideWidth===1&&h.inChannels===h.outChannels&&h.dilationHeight===1&&h.dilationWidth===1&&h.inChannels%4===0?g=new DT(h):(g=new $T(h),f.push({type:"int32",data:[h.filterHeight]},{type:"int32",data:[h.filterWidth]},{type:"int32",data:[h.strideHeight,h.strideWidth]},{type:"int32",data:[h.dilationHeight,h.dilationWidth]})),n.runWebGPUProgram(g,[r,a],r.dtype,f)}var n3e={kernelName:Ro,backendName:"webgpu",kernelFunc:t3e},PT=qn({opType:Ye.MUL,cpuKernelImpl:I1e,supportsComplex:!0}),s3e={kernelName:Xo,backendName:"webgpu",kernelFunc:PT};function Cb(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:o}=s;return Zh(r,a,o,"sum",n)}var r3e={kernelName:ii,backendName:"webgpu",kernelFunc:Cb};function a3e(e){let{inputs:t,backend:n,attrs:s}=e,{equation:r}=s,a=t,{allDims:o,summedDims:i,idDims:l}=T.decodeEinsumEquation(r,a.length);T.checkEinsumDimSizes(o.length,l,a);let{path:u,steps:c}=T.getEinsumComputePath(i,l),p=c.length,d=null,h=o.length,f=[];for(let m=0;m<p;++m){for(let g of c[m]){let{permutationIndices:y,expandDims:x}=T.getEinsumPermutation(h,l[g]),A;T.isIdentityPermutation(y)?A=a[g]:(A=_a({inputs:{x:a[g]},backend:n,attrs:{perm:y}}),f.push(A));let b=A.shape.slice();for(let w=0;w<x.length;++w)b.splice(x[w],0,1);v.arraysEqual(A.shape,b)||(A=He({inputs:{x:A},backend:n,attrs:{shape:b}}),f.push(A)),d===null?d=A:(d=PT({inputs:{a:A,b:d},backend:n}),f.push(d))}m<p-1&&(u[m]>=0&&(d=Cb({inputs:{x:d},backend:n,attrs:{axis:u[m]-(o.length-h),keepDims:!1}}),f.push(d)),h--)}for(let m of f)m!==d&&n.disposeData(m.dataId);return d}var o3e={kernelName:Zp,backendName:"webgpu",kernelFunc:a3e},i3e=kn({opType:Oe.ELU}),l3e={kernelName:Do,backendName:"webgpu",kernelFunc:i3e},u3e=qn({opType:Ye.EQUAL,dtype:"bool",cpuKernelImpl:d1e}),c3e={kernelName:Cl,backendName:"webgpu",kernelFunc:u3e},FT=kn({opType:Oe.EXP,cpuKernelImpl:p1e,dtype:"float32"}),d3e={kernelName:$o,backendName:"webgpu",kernelFunc:FT};function Fy(e){let{inputs:t,attrs:n,backend:s}=e,{dim:r}=n,{input:a}=t,o=a.shape.length,i=a.shape.slice(),l=r;return r<0&&(v.assert(-(o+1)<=r,()=>`Axis must be in the interval [${-(o+1)}, ${o}]`),l=o+r+1),i.splice(l,0,1),He({inputs:{x:a},backend:s,attrs:{shape:i}})}var p3e={kernelName:Tl,backendName:"webgpu",kernelFunc:Fy},h3e=kn({opType:Oe.EXPM1,cpuKernelImpl:h1e}),f3e={kernelName:Nl,backendName:"webgpu",kernelFunc:h3e},m3e=class{constructor(e){this.outputShape=[],this.variableNames=["x"],this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=lt(this.outputShape),this.dispatch=je(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey="flipLeftRight"}getUserCode(){return`
+      ${nt("index")} {
         if (index < uniforms.size) {
           let coords = getCoordsFromIndex(index);
           let coordX = uniforms.xShape[2] - coords[2] - 1;
@@ -6514,9 +6514,9 @@ return a / b;`,Nle=`
           setOutputAtIndex(index, outputValue);
         }
       }
-    `}},r3e={kernelName:Il,backendName:"webgpu",kernelFunc:({inputs:e,backend:t})=>{let{image:n}=e,s=t,r=new s3e(n.shape);return s.runWebGPUProgram(r,[n],n.dtype)}},a3e=vn({opType:Oe.FLOOR,cpuKernelImpl:n1e}),o3e={kernelName:Ro,backendName:"webgpu",kernelFunc:a3e},i3e=jn({opType:Xe.INT_DIV,dtype:"int32"}),l3e={kernelName:_o,backendName:"webgpu",kernelFunc:i3e},u3e=class{constructor(e,t,n=!1){this.isFromPixels=!0,this.outputShape=[0],this.variableNames=[],this.workGroupSize=[256,1,1],this.outputShape=e,this.dispatchLayout=it(this.outputShape),this.dispatch=He(this.dispatchLayout,this.outputShape,this.workGroupSize,[t,1,1]),this.importVideo=n,this.shaderKey=`fromPixels_${this.importVideo}`}getUserCode(){let e=this.importVideo?"textureLoad(src, vec2<i32>(coords.yx));":"textureLoad(src, vec2<i32>(coords.yx), 0)";return`
+    `}},g3e={kernelName:El,backendName:"webgpu",kernelFunc:({inputs:e,backend:t})=>{let{image:n}=e,s=t,r=new m3e(n.shape);return s.runWebGPUProgram(r,[n],n.dtype)}},y3e=kn({opType:Oe.FLOOR,cpuKernelImpl:f1e}),A3e={kernelName:Po,backendName:"webgpu",kernelFunc:y3e},x3e=qn({opType:Ye.INT_DIV,dtype:"int32"}),b3e={kernelName:Fo,backendName:"webgpu",kernelFunc:x3e},v3e=class{constructor(e,t,n=!1){this.isFromPixels=!0,this.outputShape=[0],this.variableNames=[],this.workGroupSize=[256,1,1],this.outputShape=e,this.dispatchLayout=lt(this.outputShape),this.dispatch=je(this.dispatchLayout,this.outputShape,this.workGroupSize,[t,1,1]),this.importVideo=n,this.shaderKey=`fromPixels_${this.importVideo}`}getUserCode(){let e=this.importVideo?"textureLoad(src, vec2<i32>(coords.yx));":"textureLoad(src, vec2<i32>(coords.yx), 0)";return`
       @binding(1) @group(0) var src: ${this.importVideo?"texture_external":"texture_2d<f32>"};
-      ${Je("index")} {
+      ${nt("index")} {
         let flatIndex = index * uniforms.numChannels;
         if (flatIndex < uniforms.size) {
           let coords = getCoordsFromIndex(flatIndex);
@@ -6526,8 +6526,8 @@ return a / b;`,Nle=`
           }
         }
       }
-  `}},c3e={kernelName:Sp,backendName:"webgpu",kernelFunc:d3e},Ku,N3=H().getBool("CANVAS2D_WILL_READ_FREQUENTLY_FOR_GPU"),cm=new Map;function d3e(e){let{inputs:t,backend:n,attrs:s}=e,{pixels:r}=t,{numChannels:a}=s;if(r==null)throw new Error("pixels passed to tf.browser.fromPixels() can not be null");let o=typeof HTMLVideoElement!="undefined"&&r instanceof HTMLVideoElement,i=typeof HTMLImageElement!="undefined"&&r instanceof HTMLImageElement,l=typeof HTMLCanvasElement!="undefined"&&r instanceof HTMLCanvasElement||typeof OffscreenCanvas!="undefined"&&r instanceof OffscreenCanvas,u=typeof ImageBitmap!="undefined"&&r instanceof ImageBitmap,[c,p]=o?[r.videoWidth,r.videoHeight]:[r.width,r.height],d=[p,c,a],h=H().getBool("WEBGPU_IMPORT_EXTERNAL_TEXTURE")&&o,f=o||i;if(u||l||f){let x;if(h){let $=r;if(!cm.has($)||cm.get($).expired){let R={source:$};cm.set($,n.device.importExternalTexture(R))}x={width:c,height:p,format:null,usage:null,texture:cm.get($)}}else{if(f){let S=H().getBool("CANVAS2D_WILL_READ_FREQUENTLY_FOR_GPU");(Ku==null||S!==N3)&&(N3=S,Ku=document.createElement("canvas").getContext("2d",{willReadFrequently:N3})),Ku.canvas.width=c,Ku.canvas.height=p,Ku.drawImage(r,0,0,c,p),r=Ku.canvas}let $=GPUTextureUsage.COPY_DST|GPUTextureUsage.RENDER_ATTACHMENT|GPUTextureUsage.TEXTURE_BINDING,R="rgba8unorm",P=n.textureManager.acquireTexture(d[1],d[0],R,$);n.queue.copyExternalImageToTexture({source:r},{texture:P},[d[1],d[0]]),x={width:c,height:p,format:R,usage:$,texture:P}}let A=v.sizeFromShape(d),b=v.computeStrides(d),w=new u3e(d,a,h),k=[{type:"uint32",data:[A]},{type:"uint32",data:[a]},{type:"uint32",data:[...b]}],C=n.makeTensorInfo([p,c],"int32"),E=n.tensorMap.get(C.dataId);E.resourceInfo=x;let _=n.runWebGPUProgram(w,[C],"int32",k);return n.disposeData(C.dataId),_}let m=r.data,g=m;if(a!=null&&a!==4){g=new Uint8Array(r.width*r.height*a);let x=m.length,A=0;for(let b=0;b<x;b++)b%4<a&&(g[A++]=m[b])}let y=n.makeTensorInfo(d,"int32",new Int32Array(g));return n.uploadToGPU(y.dataId),y}var p3e=class{constructor(e,t,n,s,r){this.uniforms="varianceEpsilon : f32,",this.workGroupSize=[128,1,1],this.size=!0,this.variableNames=["x","mean","variance"],T.assertAndGetBroadcastShape(e,t),T.assertAndGetBroadcastShape(e,n),this.outputShape=e,this.dispatchLayout=it(this.outputShape),this.dispatch=He(this.dispatchLayout,this.outputShape,this.workGroupSize),s!=null&&(T.assertAndGetBroadcastShape(e,s),this.variableNames.push("offset")),r!=null&&(T.assertAndGetBroadcastShape(e,r),this.variableNames.push("scale")),this.offsetShape=s,this.scaleShape=r,this.shaderKey="batchNorm"}getUserCode(){let e="0.0";this.offsetShape!=null&&(e="getOffsetByOutputIndex(index)");let t="1.0";return this.scaleShape!=null&&(t="getScaleByOutputIndex(index)"),`
-      ${Je("index")} {
+  `}},w3e={kernelName:Ip,backendName:"webgpu",kernelFunc:k3e},Qu,_3=H().getBool("CANVAS2D_WILL_READ_FREQUENTLY_FOR_GPU"),cm=new Map;function k3e(e){let{inputs:t,backend:n,attrs:s}=e,{pixels:r}=t,{numChannels:a}=s;if(r==null)throw new Error("pixels passed to tf.browser.fromPixels() can not be null");let o=typeof HTMLVideoElement!="undefined"&&r instanceof HTMLVideoElement,i=typeof HTMLImageElement!="undefined"&&r instanceof HTMLImageElement,l=typeof HTMLCanvasElement!="undefined"&&r instanceof HTMLCanvasElement||typeof OffscreenCanvas!="undefined"&&r instanceof OffscreenCanvas,u=typeof ImageBitmap!="undefined"&&r instanceof ImageBitmap,[c,p]=o?[r.videoWidth,r.videoHeight]:[r.width,r.height],d=[p,c,a],h=H().getBool("WEBGPU_IMPORT_EXTERNAL_TEXTURE")&&o,f=o||i;if(u||l||f){let x;if(h){let $=r;if(!cm.has($)||cm.get($).expired){let R={source:$};cm.set($,n.device.importExternalTexture(R))}x={width:c,height:p,format:null,usage:null,texture:cm.get($)}}else{if(f){let S=H().getBool("CANVAS2D_WILL_READ_FREQUENTLY_FOR_GPU");(Qu==null||S!==_3)&&(_3=S,Qu=document.createElement("canvas").getContext("2d",{willReadFrequently:_3})),Qu.canvas.width=c,Qu.canvas.height=p,Qu.drawImage(r,0,0,c,p),r=Qu.canvas}let $=GPUTextureUsage.COPY_DST|GPUTextureUsage.RENDER_ATTACHMENT|GPUTextureUsage.TEXTURE_BINDING,R="rgba8unorm",P=n.textureManager.acquireTexture(d[1],d[0],R,$);n.queue.copyExternalImageToTexture({source:r},{texture:P},[d[1],d[0]]),x={width:c,height:p,format:R,usage:$,texture:P}}let A=v.sizeFromShape(d),b=v.computeStrides(d),w=new v3e(d,a,h),k=[{type:"uint32",data:[A]},{type:"uint32",data:[a]},{type:"uint32",data:[...b]}],C=n.makeTensorInfo([p,c],"int32"),E=n.tensorMap.get(C.dataId);E.resourceInfo=x;let _=n.runWebGPUProgram(w,[C],"int32",k);return n.disposeData(C.dataId),_}let m=r.data,g=m;if(a!=null&&a!==4){g=new Uint8Array(r.width*r.height*a);let x=m.length,A=0;for(let b=0;b<x;b++)b%4<a&&(g[A++]=m[b])}let y=n.makeTensorInfo(d,"int32",new Int32Array(g));return n.uploadToGPU(y.dataId),y}var S3e=class{constructor(e,t,n,s,r){this.uniforms="varianceEpsilon : f32,",this.workGroupSize=[128,1,1],this.size=!0,this.variableNames=["x","mean","variance"],T.assertAndGetBroadcastShape(e,t),T.assertAndGetBroadcastShape(e,n),this.outputShape=e,this.dispatchLayout=lt(this.outputShape),this.dispatch=je(this.dispatchLayout,this.outputShape,this.workGroupSize),s!=null&&(T.assertAndGetBroadcastShape(e,s),this.variableNames.push("offset")),r!=null&&(T.assertAndGetBroadcastShape(e,r),this.variableNames.push("scale")),this.offsetShape=s,this.scaleShape=r,this.shaderKey="batchNorm"}getUserCode(){let e="0.0";this.offsetShape!=null&&(e="getOffsetByOutputIndex(index)");let t="1.0";return this.scaleShape!=null&&(t="getScaleByOutputIndex(index)"),`
+      ${nt("index")} {
         if (index < uniforms.size)
         {
           let xValue = getXByOutputIndex(index);
@@ -6539,8 +6539,8 @@ return a / b;`,Nle=`
           setOutputAtIndex(index,dot(vec3<f32>(xValue, -meanValue, offsetValue), vec3<f32>(inv, inv, 1.0)));
         }
       }
-  `}},h3e={kernelName:Do,backendName:"webgpu",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{x:s,scale:r,offset:a,mean:o,variance:i}=e,{varianceEpsilon:l}=t,u=n,c=[s,o,i],p=null;a!=null&&(p=a.shape,c.push(a));let d=null;r!=null&&(d=r.shape,c.push(r));let h=new p3e(s.shape,o.shape,i.shape,p,d),f=[{type:"float32",data:[l]}];return u.runWebGPUProgram(h,c,s.dtype,f)}};function f3e(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a,bias:o,preluActivationWeights:i}=t,{strides:l,pad:u,dataFormat:c,dilations:p,dimRoundingMode:d,activation:h,leakyreluAlpha:f}=s,m=T.convertConv2DDataFormat(c),g=T.computeConv2DInfo(r.shape,a.shape,l,p,u,d,!1,m);return ST({x:r,filter:a,convInfo:g,backend:n,bias:o,preluActivationWeights:i,leakyreluAlpha:f,activation:h})}var m3e={kernelName:no,backendName:"webgpu",kernelFunc:f3e};function g3e(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a,bias:o,preluActivationWeights:i}=t,{strides:l,pad:u,dilations:c,dimRoundingMode:p,activation:d,leakyreluAlpha:h}=s,f=c;f==null&&(f=[1,1]),v.assert(T.eitherStridesOrDilationsAreOne(l,f),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${l} and dilations '${f}'`);let m=T.computeConv2DInfo(r.shape,a.shape,l,f,u,p,!0),g=[r,a],y=o!=null,x=i!=null;y&&g.push(o),x&&g.push(i);let A=[{type:"int32",data:[m.padInfo.top,m.padInfo.left]},{type:"int32",data:[m.inHeight,m.inWidth]}],b;return m.inHeight>4&&m.inWidth>4&&m.strideHeight===1&&m.strideWidth===1&&m.inChannels===m.outChannels&&m.dilationHeight===1&&m.dilationWidth===1&&m.inChannels%4===0?b=new CT(m,y,d,x):(b=new TT(m,y,d,x),A.push({type:"int32",data:[m.filterHeight]},{type:"int32",data:[m.filterWidth]},{type:"int32",data:[m.strideHeight,m.strideWidth]},{type:"int32",data:[m.dilationHeight,m.dilationWidth]})),d==="leakyrelu"&&(A.push({type:"float32",data:[h]}),b.uniforms+=" alpha : f32,"),n.runWebGPUProgram(b,g,"float32",A)}var y3e={kernelName:so,backendName:"webgpu",kernelFunc:g3e},A3e=class{constructor(e,t){this.variableNames=["A","indices"],this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.dispatchLayout=it(this.outputShape),this.dispatch=He(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey=`gathernd_${e}`,this.sliceDim=e,this.uniforms=`sliceDim : i32, strides : ${Fn(e)},`}getUserCode(){let e;return this.sliceDim>1?e="uniforms.strides[j]":e="uniforms.strides",`
-      ${Je("index")} {
+  `}},I3e={kernelName:Oo,backendName:"webgpu",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{x:s,scale:r,offset:a,mean:o,variance:i}=e,{varianceEpsilon:l}=t,u=n,c=[s,o,i],p=null;a!=null&&(p=a.shape,c.push(a));let d=null;r!=null&&(d=r.shape,c.push(r));let h=new S3e(s.shape,o.shape,i.shape,p,d),f=[{type:"float32",data:[l]}];return u.runWebGPUProgram(h,c,s.dtype,f)}};function C3e(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a,bias:o,preluActivationWeights:i}=t,{strides:l,pad:u,dataFormat:c,dilations:p,dimRoundingMode:d,activation:h,leakyreluAlpha:f}=s,m=T.convertConv2DDataFormat(c),g=T.computeConv2DInfo(r.shape,a.shape,l,p,u,d,!1,m);return RT({x:r,filter:a,convInfo:g,backend:n,bias:o,preluActivationWeights:i,leakyreluAlpha:f,activation:h})}var T3e={kernelName:oo,backendName:"webgpu",kernelFunc:C3e};function N3e(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a,bias:o,preluActivationWeights:i}=t,{strides:l,pad:u,dilations:c,dimRoundingMode:p,activation:d,leakyreluAlpha:h}=s,f=c;f==null&&(f=[1,1]),v.assert(T.eitherStridesOrDilationsAreOne(l,f),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${l} and dilations '${f}'`);let m=T.computeConv2DInfo(r.shape,a.shape,l,f,u,p,!0),g=[r,a],y=o!=null,x=i!=null;y&&g.push(o),x&&g.push(i);let A=[{type:"int32",data:[m.padInfo.top,m.padInfo.left]},{type:"int32",data:[m.inHeight,m.inWidth]}],b;return m.inHeight>4&&m.inWidth>4&&m.strideHeight===1&&m.strideWidth===1&&m.inChannels===m.outChannels&&m.dilationHeight===1&&m.dilationWidth===1&&m.inChannels%4===0?b=new DT(m,y,d,x):(b=new $T(m,y,d,x),A.push({type:"int32",data:[m.filterHeight]},{type:"int32",data:[m.filterWidth]},{type:"int32",data:[m.strideHeight,m.strideWidth]},{type:"int32",data:[m.dilationHeight,m.dilationWidth]})),d==="leakyrelu"&&(A.push({type:"float32",data:[h]}),b.uniforms+=" alpha : f32,"),n.runWebGPUProgram(b,g,"float32",A)}var E3e={kernelName:io,backendName:"webgpu",kernelFunc:N3e},R3e=class{constructor(e,t){this.variableNames=["A","indices"],this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.dispatchLayout=lt(this.outputShape),this.dispatch=je(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey=`gathernd_${e}`,this.sliceDim=e,this.uniforms=`sliceDim : i32, strides : ${Mn(e)},`}getUserCode(){let e;return this.sliceDim>1?e="uniforms.strides[j]":e="uniforms.strides",`
+      ${nt("index")} {
         if (index < uniforms.size) {
           let coords = getCoordsFromIndex(index);
           var flattenIndex = 0;
@@ -6553,8 +6553,8 @@ return a / b;`,Nle=`
           setOutputAtIndex(index, getA(flattenIndex, coords[1]));
         }
       }
-      `}};function x3e(e){let{inputs:t,backend:n}=e,{params:s,indices:r}=t,a=r.shape,o=a[a.length-1],i=v.sizeFromShape(s.shape),[l,u,c,p]=T.prepareAndValidate(s,r),d=Ge({inputs:{x:r},backend:n,attrs:{shape:[u,o]}}),h=Ge({inputs:{x:s},backend:n,attrs:{shape:[v.sizeFromShape(s.shape)/c,c]}});if(n.shouldExecuteOnCPU([s,r])||s.dtype==="string"){let x=n.readSync(r.dataId),A=n.bufferSync(s),b=s1e(x,A,s.dtype,u,o,c,p,s.shape,i);return n.makeTensorInfo(l,s.dtype,b.values)}let f=new A3e(o,[u,c]),m=[{type:"int32",data:[o]},{type:"int32",data:p}],g=n.runWebGPUProgram(f,[h,d],h.dtype,m),y=Ge({inputs:{x:g},backend:n,attrs:{shape:l}});return n.disposeData(d.dataId),n.disposeData(h.dataId),n.disposeData(g.dataId),y}var b3e={kernelName:Tl,backendName:"webgpu",kernelFunc:x3e},v3e=class{constructor(e,t){this.variableNames=["A","indices"],this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e.slice(),this.aShape=e,this.outputShape=t,this.dispatchLayout=it(this.outputShape),this.dispatch=He(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey="gather"}getUserCode(){let e=w3e(this.aShape);return`
-      ${Je("index")} {
+      `}};function _3e(e){let{inputs:t,backend:n}=e,{params:s,indices:r}=t,a=r.shape,o=a[a.length-1],i=v.sizeFromShape(s.shape),[l,u,c,p]=T.prepareAndValidate(s,r),d=He({inputs:{x:r},backend:n,attrs:{shape:[u,o]}}),h=He({inputs:{x:s},backend:n,attrs:{shape:[v.sizeFromShape(s.shape)/c,c]}});if(n.shouldExecuteOnCPU([s,r])||s.dtype==="string"){let x=n.readSync(r.dataId),A=n.bufferSync(s),b=m1e(x,A,s.dtype,u,o,c,p,s.shape,i);return n.makeTensorInfo(l,s.dtype,b.values)}let f=new R3e(o,[u,c]),m=[{type:"int32",data:[o]},{type:"int32",data:p}],g=n.runWebGPUProgram(f,[h,d],h.dtype,m),y=He({inputs:{x:g},backend:n,attrs:{shape:l}});return n.disposeData(d.dataId),n.disposeData(h.dataId),n.disposeData(g.dataId),y}var D3e={kernelName:_l,backendName:"webgpu",kernelFunc:_3e},$3e=class{constructor(e,t){this.variableNames=["A","indices"],this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e.slice(),this.aShape=e,this.outputShape=t,this.dispatchLayout=lt(this.outputShape),this.dispatch=je(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey="gather"}getUserCode(){let e=P3e(this.aShape);return`
+      ${nt("index")} {
         if (index < uniforms.size) {
           let resRC = getCoordsFromIndex(index);
           let indexZ = i32(getIndices(resRC.x, resRC.z));
@@ -6562,8 +6562,8 @@ return a / b;`,Nle=`
           setOutputAtIndex(index, inBounds * getA(${e}));
         }
       }
-    `}};function w3e(e){let t=["resRC.x","resRC.y","resRC.z","resRC.w"],n=[];for(let s=0;s<e.length;s++)s===2?n.push("indexZ"):n.push(`${t[s]}`);return n.join()}function RT(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,indices:a}=t,{axis:o,batchDims:i}=s,l=v.parseAxisParam(o,r.shape)[0],u=T.segment_util.collectGatherOpShapeInfo(r,a,l,i),c=v.sizeFromShape(a.shape),p=[],d=Ge({inputs:{x:r},backend:n,attrs:{shape:[u.batchSize,u.outerSize,u.dimSize,u.sliceSize]}}),h=Ge({inputs:{x:a},backend:n,attrs:{shape:[u.batchSize,c/u.batchSize]}});p.push(d),p.push(h);let f=[u.batchSize,u.outerSize,c/u.batchSize,u.sliceSize];if(n.shouldExecuteOnCPU([r,a])){let A=n.tensorMap.get(h.dataId).values,b=Ve(h.shape,h.dtype,A),k=n.tensorMap.get(d.dataId).values,C=Ve(d.shape,d.dtype,k),E=r1e(C,b,f);return p.forEach(_=>n.disposeData(_.dataId)),n.makeTensorInfo(u.outputShape,E.dtype,E.values)}let m=new v3e(d.shape,f),g=n.runWebGPUProgram(m,[d,h],d.dtype);p.push(g);let y=Ge({inputs:{x:g},backend:n,attrs:{shape:u.outputShape}});return p.forEach(x=>n.disposeData(x.dataId)),y}var k3e={kernelName:Cl,backendName:"webgpu",kernelFunc:RT},S3e=jn({opType:Xe.GREATER,cpuKernelImpl:o1e,dtype:"bool"}),I3e={kernelName:Nl,backendName:"webgpu",kernelFunc:S3e},C3e=jn({opType:Xe.GREATER_EQUAL,dtype:"bool",cpuKernelImpl:a1e}),T3e={kernelName:$o,backendName:"webgpu",kernelFunc:C3e},N3e=vn({opType:Oe.IS_NAN,dtype:"bool"}),E3e={kernelName:El,backendName:"webgpu",kernelFunc:N3e};function R3e(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{alpha:a}=s,o=[{type:"float32",data:[a]}],i=new Xh(r.shape,Oe.LEAKYRELU);return i.uniforms="alpha : f32,",n.runWebGPUProgram(i,[r],"float32",o)}var _3e={kernelName:Fo,backendName:"webgpu",kernelFunc:R3e},D3e=jn({opType:Xe.LESS,dtype:"bool",cpuKernelImpl:l1e}),$3e={kernelName:Rl,backendName:"webgpu",kernelFunc:D3e},P3e=jn({opType:Xe.LESS_EQUAL,dtype:"bool",cpuKernelImpl:i1e}),F3e={kernelName:_l,backendName:"webgpu",kernelFunc:P3e},O3e=vn({opType:Oe.LOG,cpuKernelImpl:u1e}),M3e={kernelName:Oo,backendName:"webgpu",kernelFunc:O3e},z3e=jn({opType:Xe.LOGICAL_AND,dtype:"bool"}),L3e={kernelName:Dl,backendName:"webgpu",kernelFunc:z3e},B3e=vn({opType:Oe.LOGICAL_NOT}),W3e={kernelName:$l,backendName:"webgpu",kernelFunc:B3e},V3e=jn({opType:Xe.MAX,cpuKernelImpl:d1e}),U3e={kernelName:zo,backendName:"webgpu",kernelFunc:V3e};function G3e(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{filterSize:a,strides:o,pad:i,dimRoundingMode:l}=s,u=1,c=T.computePool2DInfo(r.shape,a,o,u,i,l);return vT(r,c,"max",n)}var H3e={kernelName:Lo,backendName:"webgpu",kernelFunc:G3e};function j3e(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:o}=s;return Kh(r,a,o,"min",n)}var q3e={kernelName:Wo,backendName:"webgpu",kernelFunc:j3e},X3e=jn({opType:Xe.MIN,cpuKernelImpl:p1e}),K3e={kernelName:Vo,backendName:"webgpu",kernelFunc:X3e},Z3e=class{constructor(e,t,n){this.uniforms="",this.variableNames=["x"],this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=t.map((s,r)=>s[0]+e[r]+s[1]),this.dispatchLayout=it(this.outputShape),this.dispatch=He(this.dispatchLayout,this.outputShape,this.workGroupSize),this.xShape=e,t.map((s,r)=>{this.uniforms+=` pad${r} : vec2<i32>,`}),this.offset=n==="reflect"?0:1,this.shaderKey=`mirrorPad_${n}`}getUserCode(){let e=this.xShape.length,t=this.xShape.map((l,u)=>`uniforms.pad${u}[0]`).join(","),n=this.xShape.map((l,u)=>`uniforms.pad${u}[0] + uniforms.xShape${e>1?`[${u}]`:""}`).join(","),s=e===1?"start":"start[i]",r=e===1?"end":"end[i]",a=e===1?"outC":"outC[i]",o=Fn(e),i=e>1?["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,e):"coords";return`
-      ${Je("index")} {
+    `}};function P3e(e){let t=["resRC.x","resRC.y","resRC.z","resRC.w"],n=[];for(let s=0;s<e.length;s++)s===2?n.push("indexZ"):n.push(`${t[s]}`);return n.join()}function OT(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,indices:a}=t,{axis:o,batchDims:i}=s,l=v.parseAxisParam(o,r.shape)[0],u=T.segment_util.collectGatherOpShapeInfo(r,a,l,i),c=v.sizeFromShape(a.shape),p=[],d=He({inputs:{x:r},backend:n,attrs:{shape:[u.batchSize,u.outerSize,u.dimSize,u.sliceSize]}}),h=He({inputs:{x:a},backend:n,attrs:{shape:[u.batchSize,c/u.batchSize]}});p.push(d),p.push(h);let f=[u.batchSize,u.outerSize,c/u.batchSize,u.sliceSize];if(n.shouldExecuteOnCPU([r,a])){let A=n.tensorMap.get(h.dataId).values,b=Ue(h.shape,h.dtype,A),k=n.tensorMap.get(d.dataId).values,C=Ue(d.shape,d.dtype,k),E=g1e(C,b,f);return p.forEach(_=>n.disposeData(_.dataId)),n.makeTensorInfo(u.outputShape,E.dtype,E.values)}let m=new $3e(d.shape,f),g=n.runWebGPUProgram(m,[d,h],d.dtype);p.push(g);let y=He({inputs:{x:g},backend:n,attrs:{shape:u.outputShape}});return p.forEach(x=>n.disposeData(x.dataId)),y}var F3e={kernelName:Rl,backendName:"webgpu",kernelFunc:OT},O3e=qn({opType:Ye.GREATER,cpuKernelImpl:A1e,dtype:"bool"}),M3e={kernelName:Dl,backendName:"webgpu",kernelFunc:O3e},z3e=qn({opType:Ye.GREATER_EQUAL,dtype:"bool",cpuKernelImpl:y1e}),L3e={kernelName:Mo,backendName:"webgpu",kernelFunc:z3e},B3e=kn({opType:Oe.IS_NAN,dtype:"bool"}),W3e={kernelName:$l,backendName:"webgpu",kernelFunc:B3e};function V3e(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{alpha:a}=s,o=[{type:"float32",data:[a]}],i=new Kh(r.shape,Oe.LEAKYRELU);return i.uniforms="alpha : f32,",n.runWebGPUProgram(i,[r],"float32",o)}var U3e={kernelName:Lo,backendName:"webgpu",kernelFunc:V3e},G3e=qn({opType:Ye.LESS,dtype:"bool",cpuKernelImpl:b1e}),H3e={kernelName:Pl,backendName:"webgpu",kernelFunc:G3e},j3e=qn({opType:Ye.LESS_EQUAL,dtype:"bool",cpuKernelImpl:x1e}),q3e={kernelName:Fl,backendName:"webgpu",kernelFunc:j3e},X3e=kn({opType:Oe.LOG,cpuKernelImpl:v1e}),K3e={kernelName:Bo,backendName:"webgpu",kernelFunc:X3e},Z3e=qn({opType:Ye.LOGICAL_AND,dtype:"bool"}),Y3e={kernelName:Ol,backendName:"webgpu",kernelFunc:Z3e},J3e=kn({opType:Oe.LOGICAL_NOT}),Q3e={kernelName:Ml,backendName:"webgpu",kernelFunc:J3e},eye=qn({opType:Ye.MAX,cpuKernelImpl:k1e}),tye={kernelName:Vo,backendName:"webgpu",kernelFunc:eye};function nye(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{filterSize:a,strides:o,pad:i,dimRoundingMode:l}=s,u=1,c=T.computePool2DInfo(r.shape,a,o,u,i,l);return TT(r,c,"max",n)}var sye={kernelName:Uo,backendName:"webgpu",kernelFunc:nye};function rye(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:o}=s;return Zh(r,a,o,"min",n)}var aye={kernelName:Ho,backendName:"webgpu",kernelFunc:rye},oye=qn({opType:Ye.MIN,cpuKernelImpl:S1e}),iye={kernelName:jo,backendName:"webgpu",kernelFunc:oye},lye=class{constructor(e,t,n){this.uniforms="",this.variableNames=["x"],this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=t.map((s,r)=>s[0]+e[r]+s[1]),this.dispatchLayout=lt(this.outputShape),this.dispatch=je(this.dispatchLayout,this.outputShape,this.workGroupSize),this.xShape=e,t.map((s,r)=>{this.uniforms+=` pad${r} : vec2<i32>,`}),this.offset=n==="reflect"?0:1,this.shaderKey=`mirrorPad_${n}`}getUserCode(){let e=this.xShape.length,t=this.xShape.map((l,u)=>`uniforms.pad${u}[0]`).join(","),n=this.xShape.map((l,u)=>`uniforms.pad${u}[0] + uniforms.xShape${e>1?`[${u}]`:""}`).join(","),s=e===1?"start":"start[i]",r=e===1?"end":"end[i]",a=e===1?"outC":"outC[i]",o=Mn(e),i=e>1?["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,e):"coords";return`
+      ${nt("index")} {
         if (index < uniforms.size) {
           let start = ${o}(${t});
           let end = ${o}(${n});
@@ -6579,8 +6579,8 @@ return a / b;`,Nle=`
           setOutputAtIndex(index, getX(${i}));
         }
       }
-    `}},Y3e={kernelName:Uo,backendName:"webgpu",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{x:s}=e,{paddings:r,mode:a}=t,o=n,i=r.map(c=>({type:"int32",data:[c[0],c[1]]})),l=new Z3e(s.shape,r,a);return o.runWebGPUProgram(l,[s],s.dtype,i)}};function J3e(e){let{inputs:t,backend:n}=e,{x:s}=t;if(n.shouldExecuteOnCPU([s])){let a=n.tensorMap.get(s.dataId),[o,i]=f1e(a.values,s.shape,s.dtype);return n.makeTensorInfo(i,s.dtype,o)}let r=new Xh(s.shape,Oe.NEG);return n.runWebGPUProgram(r,[s],s.dtype)}var Q3e={kernelName:Pl,backendName:"webgpu",kernelFunc:J3e};function eye(e){console.warn("tf.nonMaxSuppression() in webgpu locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:s}=e,{boxes:r,scores:a}=t,{maxOutputSize:o,iouThreshold:i,scoreThreshold:l}=s,u=n.readSync(r.dataId),c=n.readSync(a.dataId),{selectedIndices:p}=yr.nonMaxSuppressionV3Impl(u,c,o,i,l);return n.makeTensorInfo([p.length],"int32",new Int32Array(p))}var tye={kernelName:Ol,backendName:"webgpu",kernelFunc:eye};function nye(e){console.warn("tf.nonMaxSuppression() in webgpu locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:s}=e,{boxes:r,scores:a}=t,{maxOutputSize:o,iouThreshold:i,scoreThreshold:l,softNmsSigma:u}=s,c=n.readSync(r.dataId),p=n.readSync(a.dataId),d=o,h=i,f=l,m=u,{selectedIndices:g,selectedScores:y}=yr.nonMaxSuppressionV5Impl(c,p,d,h,f,m);return[n.makeTensorInfo([g.length],"int32",new Int32Array(g)),n.makeTensorInfo([y.length],"float32",new Float32Array(y))]}var sye={kernelName:Ml,backendName:"webgpu",kernelFunc:nye};function Jm(e){let{inputs:t,backend:n}=e,{x:s}=t;if(s.dtype==="complex64"){let r=Zh({inputs:{input:s},backend:n}),a=Jm({inputs:{x:r},backend:n}),o=U2({inputs:{input:s},backend:n}),i=Jm({inputs:{x:o},backend:n}),l=bd({inputs:{real:a,imag:i},backend:n});return n.disposeData(r.dataId),n.disposeData(a.dataId),n.disposeData(o.dataId),n.disposeData(i.dataId),l}else return yu({attrs:{shape:s.shape,dtype:s.dtype,value:s.dtype==="string"?"":0},backend:n})}var rye={kernelName:nu,backendName:"webgpu",kernelFunc:Jm};function _T(e){let{inputs:t,backend:n}=e,{x:s}=t;if(s.dtype==="string")throw new Error("onesLike is not supported under string dtype");if(s.dtype==="complex64"){let r=Zh({inputs:{input:s},backend:n}),a=_T({inputs:{x:r},backend:n}),o=U2({inputs:{input:s},backend:n}),i=Jm({inputs:{x:o},backend:n}),l=bd({inputs:{real:a,imag:i},backend:n});return n.disposeData(r.dataId),n.disposeData(a.dataId),n.disposeData(o.dataId),n.disposeData(i.dataId),l}else return yu({attrs:{shape:s.shape,dtype:s.dtype,value:1},backend:n})}var aye={kernelName:zl,backendName:"webgpu",kernelFunc:_T};function oye(e){let{inputs:t,backend:n,attrs:s}=e,{axis:r}=s;if(t.length===1)return Dy({inputs:{input:t[0]},backend:n,attrs:{dim:r}});let a=t[0].shape,o=t[0].dtype;t.forEach(c=>{v.assertShapesMatch(a,c.shape,"All tensors passed to stack must have matching shapes"),v.assert(o===c.dtype,()=>"All tensors passed to stack must have matching dtypes")});let i=[],l=t.map(c=>{let p=Dy({inputs:{input:c},backend:n,attrs:{dim:r}});return i.push(p),p}),u=kT({inputs:l,backend:n,attrs:{axis:r}});return i.forEach(c=>n.disposeData(c.dataId)),u}var iye={kernelName:Bl,backendName:"webgpu",kernelFunc:oye},lye=class{constructor(e,t){this.variableNames=["x"],this.uniforms="constantValue : f32,",this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=t.map((n,s)=>n[0]+e[s]+n[1]),this.dispatchLayout=it(this.outputShape),this.dispatch=He(this.dispatchLayout,this.outputShape,this.workGroupSize),t.map((n,s)=>{this.uniforms+=` pad${s} : vec2<i32>,`}),this.xShape=e,this.shaderKey="pad"}getUserCode(){let e=this.xShape.length,t=Fn(e),n=this.xShape.map((c,p)=>`uniforms.pad${p}[0]`).join(","),s=this.xShape.map((c,p)=>`uniforms.pad${p}[0] + uniforms.xShape${e>1?`[${p}]`:""}`).join(","),r=e>1?`${t}(${n})`:`${n}`,a=e>1?`${t}(${s})`:`${s}`,o=e>1?"any(outC < start)":"outC < start",i=e>1?"any(outC >= end)":"outC >= end",l=e>1?["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,e):"coords";return`
-      ${Je("index")} {
+    `}},uye={kernelName:qo,backendName:"webgpu",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{x:s}=e,{paddings:r,mode:a}=t,o=n,i=r.map(c=>({type:"int32",data:[c[0],c[1]]})),l=new lye(s.shape,r,a);return o.runWebGPUProgram(l,[s],s.dtype,i)}};function cye(e){let{inputs:t,backend:n}=e,{x:s}=t;if(n.shouldExecuteOnCPU([s])){let a=n.tensorMap.get(s.dataId),[o,i]=C1e(a.values,s.shape,s.dtype);return n.makeTensorInfo(i,s.dtype,o)}let r=new Kh(s.shape,Oe.NEG);return n.runWebGPUProgram(r,[s],s.dtype)}var dye={kernelName:zl,backendName:"webgpu",kernelFunc:cye};function pye(e){console.warn("tf.nonMaxSuppression() in webgpu locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:s}=e,{boxes:r,scores:a}=t,{maxOutputSize:o,iouThreshold:i,scoreThreshold:l}=s,u=n.readSync(r.dataId),c=n.readSync(a.dataId),{selectedIndices:p}=Ar.nonMaxSuppressionV3Impl(u,c,o,i,l);return n.makeTensorInfo([p.length],"int32",new Int32Array(p))}var hye={kernelName:Bl,backendName:"webgpu",kernelFunc:pye};function fye(e){console.warn("tf.nonMaxSuppression() in webgpu locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:s}=e,{boxes:r,scores:a}=t,{maxOutputSize:o,iouThreshold:i,scoreThreshold:l,softNmsSigma:u}=s,c=n.readSync(r.dataId),p=n.readSync(a.dataId),d=o,h=i,f=l,m=u,{selectedIndices:g,selectedScores:y}=Ar.nonMaxSuppressionV5Impl(c,p,d,h,f,m);return[n.makeTensorInfo([g.length],"int32",new Int32Array(g)),n.makeTensorInfo([y.length],"float32",new Float32Array(y))]}var mye={kernelName:Wl,backendName:"webgpu",kernelFunc:fye};function Jm(e){let{inputs:t,backend:n}=e,{x:s}=t;if(s.dtype==="complex64"){let r=Yh({inputs:{input:s},backend:n}),a=Jm({inputs:{x:r},backend:n}),o=U2({inputs:{input:s},backend:n}),i=Jm({inputs:{x:o},backend:n}),l=kd({inputs:{real:a,imag:i},backend:n});return n.disposeData(r.dataId),n.disposeData(a.dataId),n.disposeData(o.dataId),n.disposeData(i.dataId),l}else return vu({attrs:{shape:s.shape,dtype:s.dtype,value:s.dtype==="string"?"":0},backend:n})}var gye={kernelName:ou,backendName:"webgpu",kernelFunc:Jm};function MT(e){let{inputs:t,backend:n}=e,{x:s}=t;if(s.dtype==="string")throw new Error("onesLike is not supported under string dtype");if(s.dtype==="complex64"){let r=Yh({inputs:{input:s},backend:n}),a=MT({inputs:{x:r},backend:n}),o=U2({inputs:{input:s},backend:n}),i=Jm({inputs:{x:o},backend:n}),l=kd({inputs:{real:a,imag:i},backend:n});return n.disposeData(r.dataId),n.disposeData(a.dataId),n.disposeData(o.dataId),n.disposeData(i.dataId),l}else return vu({attrs:{shape:s.shape,dtype:s.dtype,value:1},backend:n})}var yye={kernelName:Vl,backendName:"webgpu",kernelFunc:MT};function Aye(e){let{inputs:t,backend:n,attrs:s}=e,{axis:r}=s;if(t.length===1)return Fy({inputs:{input:t[0]},backend:n,attrs:{dim:r}});let a=t[0].shape,o=t[0].dtype;t.forEach(c=>{v.assertShapesMatch(a,c.shape,"All tensors passed to stack must have matching shapes"),v.assert(o===c.dtype,()=>"All tensors passed to stack must have matching dtypes")});let i=[],l=t.map(c=>{let p=Fy({inputs:{input:c},backend:n,attrs:{dim:r}});return i.push(p),p}),u=ET({inputs:l,backend:n,attrs:{axis:r}});return i.forEach(c=>n.disposeData(c.dataId)),u}var xye={kernelName:Gl,backendName:"webgpu",kernelFunc:Aye},bye=class{constructor(e,t){this.variableNames=["x"],this.uniforms="constantValue : f32,",this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=t.map((n,s)=>n[0]+e[s]+n[1]),this.dispatchLayout=lt(this.outputShape),this.dispatch=je(this.dispatchLayout,this.outputShape,this.workGroupSize),t.map((n,s)=>{this.uniforms+=` pad${s} : vec2<i32>,`}),this.xShape=e,this.shaderKey="pad"}getUserCode(){let e=this.xShape.length,t=Mn(e),n=this.xShape.map((c,p)=>`uniforms.pad${p}[0]`).join(","),s=this.xShape.map((c,p)=>`uniforms.pad${p}[0] + uniforms.xShape${e>1?`[${p}]`:""}`).join(","),r=e>1?`${t}(${n})`:`${n}`,a=e>1?`${t}(${s})`:`${s}`,o=e>1?"any(outC < start)":"outC < start",i=e>1?"any(outC >= end)":"outC >= end",l=e>1?["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,e):"coords";return`
+      ${nt("index")} {
         if (index < uniforms.size) {
           let start = ${r};
           let end = ${a};
@@ -6594,8 +6594,8 @@ return a / b;`,Nle=`
           }
         }
       }
-    `}},DT=e=>{let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{paddings:a,constantValue:o}=s;if(a.every(u=>v.arraysEqual(u,[0,0])))return tr({inputs:{x:r},backend:n});if(v.sizeFromShape(r.shape)===0){let u=a.map((c,p)=>c[0]+r.shape[p]+c[1]);return yu({backend:n,attrs:{shape:u,value:o,dtype:r.dtype}})}let i=[{type:"float32",data:[o]}];a.map(u=>i.push({type:"int32",data:[u[0],u[1]]}));let l=new lye(r.shape,a);return n.runWebGPUProgram(l,[r],r.dtype,i)},uye={kernelName:Ho,backendName:"webgpu",kernelFunc:DT},cye=jn({opType:Xe.POW}),dye={kernelName:jo,backendName:"webgpu",kernelFunc:cye};function pye(e){let{inputs:t,backend:n}=e,{x:s,alpha:r}=t,a=new Ey(Xe.PRELU,s.shape,r.shape);return n.runWebGPUProgram(a,[s,r],"float32")}var hye={kernelName:qo,backendName:"webgpu",kernelFunc:pye};function fye(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:o}=s;return Kh(r,a,o,"prod",n)}var mye={kernelName:Xo,backendName:"webgpu",kernelFunc:fye},gye=e=>{let{backend:t,attrs:n}=e,{start:s,stop:r,step:a,dtype:o}=n,i=y1e(s,r,a,o);return t.makeTensorInfo([i.length],o,i)},yye={kernelName:Wc,backendName:"webgpu",kernelFunc:gye},$T=jn({opType:Xe.DIV}),Aye={kernelName:To,backendName:"webgpu",kernelFunc:$T},xye=vn({opType:Oe.RECIPROCAL}),bye={kernelName:Wl,backendName:"webgpu",kernelFunc:xye},vye=vn({opType:Oe.RELU}),wye={kernelName:Ko,backendName:"webgpu",kernelFunc:vye},kye=vn({opType:Oe.RELU6}),Sye={kernelName:Jo,backendName:"webgpu",kernelFunc:kye},Iye=class{constructor(e,t,n){this.variableNames=["x"],this.uniforms="adjustHeightWidth : vec2<f32>, halfPixelCenters : f32,",this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=[e[0],t,n,e[3]],this.dispatchLayout=it(this.outputShape),this.dispatch=He(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey="resizeBilinear"}getUserCode(){return`
-      ${Je("index")} {
+    `}},zT=e=>{let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{paddings:a,constantValue:o}=s;if(a.every(u=>v.arraysEqual(u,[0,0])))return sr({inputs:{x:r},backend:n});if(v.sizeFromShape(r.shape)===0){let u=a.map((c,p)=>c[0]+r.shape[p]+c[1]);return vu({backend:n,attrs:{shape:u,value:o,dtype:r.dtype}})}let i=[{type:"float32",data:[o]}];a.map(u=>i.push({type:"int32",data:[u[0],u[1]]}));let l=new bye(r.shape,a);return n.runWebGPUProgram(l,[r],r.dtype,i)},vye={kernelName:Ko,backendName:"webgpu",kernelFunc:zT},wye=qn({opType:Ye.POW}),kye={kernelName:Zo,backendName:"webgpu",kernelFunc:wye};function Sye(e){let{inputs:t,backend:n}=e,{x:s,alpha:r}=t,a=new Dy(Ye.PRELU,s.shape,r.shape);return n.runWebGPUProgram(a,[s,r],"float32")}var Iye={kernelName:Yo,backendName:"webgpu",kernelFunc:Sye};function Cye(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:o}=s;return Zh(r,a,o,"prod",n)}var Tye={kernelName:Jo,backendName:"webgpu",kernelFunc:Cye},Nye=e=>{let{backend:t,attrs:n}=e,{start:s,stop:r,step:a,dtype:o}=n,i=E1e(s,r,a,o);return t.makeTensorInfo([i.length],o,i)},Eye={kernelName:Hc,backendName:"webgpu",kernelFunc:Nye},LT=qn({opType:Ye.DIV}),Rye={kernelName:_o,backendName:"webgpu",kernelFunc:LT},_ye=kn({opType:Oe.RECIPROCAL}),Dye={kernelName:Hl,backendName:"webgpu",kernelFunc:_ye},$ye=kn({opType:Oe.RELU}),Pye={kernelName:Qo,backendName:"webgpu",kernelFunc:$ye},Fye=kn({opType:Oe.RELU6}),Oye={kernelName:ni,backendName:"webgpu",kernelFunc:Fye},Mye=class{constructor(e,t,n){this.variableNames=["x"],this.uniforms="adjustHeightWidth : vec2<f32>, halfPixelCenters : f32,",this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=[e[0],t,n,e[3]],this.dispatchLayout=lt(this.outputShape),this.dispatch=je(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey="resizeBilinear"}getUserCode(){return`
+      ${nt("index")} {
         if (index < uniforms.size) {
         let coords = getCoordsFromIndex(index);
           let b = coords[0];
@@ -6637,8 +6637,8 @@ return a / b;`,Nle=`
           setOutputAtIndex(index, newValue);
         }
       }
-    `}};function Cye(e){let{inputs:t,backend:n,attrs:s}=e,{images:r}=t,{alignCorners:a,size:o,halfPixelCenters:i}=s,[l,u]=o,c=a&&l>1?1:0,p=a&&u>1?1:0,h=[{type:"float32",data:[c,p]},{type:"float32",data:[i?.5:0]}],f=new Iye(r.shape,l,u);return n.runWebGPUProgram(f,[r],"float32",h)}var Tye={kernelName:Yo,backendName:"webgpu",kernelFunc:Cye},Nye=class{constructor(e,t,n,s){this.variableNames=["x"],this.uniforms="adjustHeightWidth : vec2<f32>, roundBase : f32,",this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=[e[0],t,n,e[3]],this.dispatchLayout=it(this.outputShape),this.dispatch=He(this.dispatchLayout,this.outputShape,this.workGroupSize),this.halfPixelCenters=s,this.shaderKey=`resizeNearest_${s}`}getUserCode(){let e;return this.halfPixelCenters?e="max((vec2<f32>(rc) + vec2<f32>(0.5)) * effectiveInputOverOutputRatioRC, vec2<f32>(0.0))":e="vec2<f32>(rc) * effectiveInputOverOutputRatioRC",`
-      ${Je("index")} {
+    `}};function zye(e){let{inputs:t,backend:n,attrs:s}=e,{images:r}=t,{alignCorners:a,size:o,halfPixelCenters:i}=s,[l,u]=o,c=a&&l>1?1:0,p=a&&u>1?1:0,h=[{type:"float32",data:[c,p]},{type:"float32",data:[i?.5:0]}],f=new Mye(r.shape,l,u);return n.runWebGPUProgram(f,[r],"float32",h)}var Lye={kernelName:ti,backendName:"webgpu",kernelFunc:zye},Bye=class{constructor(e,t,n,s){this.variableNames=["x"],this.uniforms="adjustHeightWidth : vec2<f32>, roundBase : f32,",this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=[e[0],t,n,e[3]],this.dispatchLayout=lt(this.outputShape),this.dispatch=je(this.dispatchLayout,this.outputShape,this.workGroupSize),this.halfPixelCenters=s,this.shaderKey=`resizeNearest_${s}`}getUserCode(){let e;return this.halfPixelCenters?e="max((vec2<f32>(rc) + vec2<f32>(0.5)) * effectiveInputOverOutputRatioRC, vec2<f32>(0.0))":e="vec2<f32>(rc) * effectiveInputOverOutputRatioRC",`
+      ${nt("index")} {
         if (index < uniforms.size) {
           let coords = getCoordsFromIndex(index);
           let b = coords[0];
@@ -6668,9 +6668,9 @@ return a / b;`,Nle=`
           setOutputAtIndex(index, newValue);
         }
       }
-    `}};function Eye(e){let{inputs:t,backend:n,attrs:s}=e,{images:r}=t,{alignCorners:a,halfPixelCenters:o,size:i}=s,[l,u]=i,c=a&&l>1?1:0,p=a&&u>1?1:0,h=[{type:"float32",data:[c,p]},{type:"float32",data:[a?.5:0]}],f=new Nye(r.shape,l,u,o);return n.runWebGPUProgram(f,[r],r.dtype,h)}var Rye={kernelName:Zo,backendName:"webgpu",kernelFunc:Eye},_ye=class{constructor(e,t){this.outputShape=[],this.variableNames=["x"],this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=it(this.outputShape),this.dispatch=He(this.dispatchLayout,this.outputShape,this.workGroupSize),this.uniforms=`centerX : f32, centerY : f32, sinRadians : f32,
+    `}};function Wye(e){let{inputs:t,backend:n,attrs:s}=e,{images:r}=t,{alignCorners:a,halfPixelCenters:o,size:i}=s,[l,u]=i,c=a&&l>1?1:0,p=a&&u>1?1:0,h=[{type:"float32",data:[c,p]},{type:"float32",data:[a?.5:0]}],f=new Bye(r.shape,l,u,o);return n.runWebGPUProgram(f,[r],r.dtype,h)}var Vye={kernelName:ei,backendName:"webgpu",kernelFunc:Wye},Uye=class{constructor(e,t){this.outputShape=[],this.variableNames=["x"],this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=lt(this.outputShape),this.dispatch=je(this.dispatchLayout,this.outputShape,this.workGroupSize),this.uniforms=`centerX : f32, centerY : f32, sinRadians : f32,
           cosRadians : f32,`,this.shaderKey="rotate",this.outputShape=e,typeof t=="number"?(this.uniforms+=" fillValue : f32,",this.fillSnippet="var outputValue = uniforms.fillValue;",this.shaderKey+="_float"):(this.uniforms+=" fillValue : vec3<f32>,",this.fillSnippet="var outputValue = uniforms.fillValue[coords[3]];",this.shaderKey+="_vec3")}getUserCode(){return`
-        ${Je("index")} {
+        ${nt("index")} {
           if (index < uniforms.size) {
             let coords = getCoordsFromIndex(index);
             let coordXFloat = (f32(coords[2]) - uniforms.centerX) *
@@ -6689,7 +6689,7 @@ return a / b;`,Nle=`
             setOutputAtIndex(index, outputValue);
           }
         }
-      `}},Dye={kernelName:su,backendName:"webgpu",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{image:s}=e,{radians:r,fillValue:a,center:o}=t,i=n,l=new _ye(s.shape,a),[u,c]=T.getImageCenter(o,s.shape[1],s.shape[2]),p=[{type:"float32",data:[u]},{type:"float32",data:[c]},{type:"float32",data:[Math.sin(r)]},{type:"float32",data:[Math.cos(r)]}];return typeof a=="number"?p.push({type:"float32",data:[Number.parseFloat(a.toFixed(2))]}):p.push({type:"float32",data:a}),i.runWebGPUProgram(l,[s],s.dtype,p)}},$ye=vn({opType:Oe.RSQRT,cpuKernelImpl:A1e}),Pye={kernelName:Qo,backendName:"webgpu",kernelFunc:$ye},bm=class{constructor(e,t,n,s,r,a,o,i=!0){this.variableNames=["updates","indices"],this.workGroupSize=[64,1,1],this.atomic=!0,this.outputShape=a,this.type=o,this.sumDupeIndices=i,this.dispatchLayout=it(e),this.dispatch=He(this.dispatchLayout,e,this.workGroupSize),this.sliceDimGreaterThanOne=t>1,this.shaderKey=`scatter_${n}_${s}_${this.sliceDimGreaterThanOne}_${o}_${i}`;let l=Fn(r.length);this.uniforms=`sliceDim : i32, strides: ${l}, size: i32,`,this.updatesRank=s,this.indicesRank=n}getUserCode(){let e="";this.indicesRank===1?e="coords[0]":this.indicesRank===2&&(e="coords[0], j");let t=`getIndices(${e})`,n=this.sliceDimGreaterThanOne?"uniforms.strides[j]":"uniforms.strides",s="",r="";this.dispatchLayout.x.length===1?(s="flattenedIndex",r=`
+      `}},Gye={kernelName:iu,backendName:"webgpu",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{image:s}=e,{radians:r,fillValue:a,center:o}=t,i=n,l=new Uye(s.shape,a),[u,c]=T.getImageCenter(o,s.shape[1],s.shape[2]),p=[{type:"float32",data:[u]},{type:"float32",data:[c]},{type:"float32",data:[Math.sin(r)]},{type:"float32",data:[Math.cos(r)]}];return typeof a=="number"?p.push({type:"float32",data:[Number.parseFloat(a.toFixed(2))]}):p.push({type:"float32",data:a}),i.runWebGPUProgram(l,[s],s.dtype,p)}},Hye=kn({opType:Oe.RSQRT,cpuKernelImpl:R1e}),jye={kernelName:si,backendName:"webgpu",kernelFunc:Hye},bm=class{constructor(e,t,n,s,r,a,o,i=!0){this.variableNames=["updates","indices"],this.workGroupSize=[64,1,1],this.atomic=!0,this.outputShape=a,this.type=o,this.sumDupeIndices=i,this.dispatchLayout=lt(e),this.dispatch=je(this.dispatchLayout,e,this.workGroupSize),this.sliceDimGreaterThanOne=t>1,this.shaderKey=`scatter_${n}_${s}_${this.sliceDimGreaterThanOne}_${o}_${i}`;let l=Mn(r.length);this.uniforms=`sliceDim : i32, strides: ${l}, size: i32,`,this.updatesRank=s,this.indicesRank=n}getUserCode(){let e="";this.indicesRank===1?e="coords[0]":this.indicesRank===2&&(e="coords[0], j");let t=`getIndices(${e})`,n=this.sliceDimGreaterThanOne?"uniforms.strides[j]":"uniforms.strides",s="",r="";this.dispatchLayout.x.length===1?(s="flattenedIndex",r=`
       fn getUpdatesCoordsFromFlatIndex(index : i32) -> i32 {
         return index;
       }
@@ -6722,7 +6722,7 @@ return a / b;`,Nle=`
         `);let d=`atomicStore(${u}, bitcast<i32>(${c}));`;return this.sumDupeIndices?p:d};return`
     ${r}
 
-      ${Je("index")} {
+      ${nt("index")} {
         if (index < uniforms.size) {
           let coords = getUpdatesCoordsFromFlatIndex(index);
           var flattenedIndex = 0;
@@ -6731,13 +6731,13 @@ return a / b;`,Nle=`
             flattenedIndex = flattenedIndex + indexInside * ${n};
           }
           let updateValue =
-              ${wp(this.type,!1)}(${o});
+              ${kp(this.type,!1)}(${o});
           let flatIndex = getOutputIndexFromCoords(${s});
 
           ${i("&result[flatIndex]","updateValue")};
         }
-      }`}};function Fye(e){let{inputs:t,backend:n,attrs:s}=e,{indices:r,updates:a}=t,{shape:o}=s,{sliceRank:i,numUpdates:l,sliceSize:u,strides:c,outputSize:p}=T.calculateShapes(a,r,o),d=[p/u,u];if(p===0)return n.makeTensorInfo(o,r.dtype);let h=Ge({inputs:{x:r},backend:n,attrs:{shape:[l,i]}}),f=Ge({inputs:{x:a},backend:n,attrs:{shape:[l,u]}}),m=f.dtype,g=yu({backend:n,attrs:{shape:d,value:0,dtype:m}}),y=v.sizeFromShape(f.shape),x=[{type:"int32",data:[i]},{type:"int32",data:c},{type:"int32",data:[y]}],A=new bm(f.shape,i,h.shape.length,f.shape.length,c,d,m),b=n.runWebGPUProgram(A,[f,h],m,x,g),w=Ge({inputs:{x:b},backend:n,attrs:{shape:o}});return n.disposeData(h.dataId),n.disposeData(f.dataId),n.disposeData(b.dataId),w}var Oye={kernelName:Hl,backendName:"webgpu",kernelFunc:Fye},Mye=class{constructor(e,t,n){this.variableNames=["c","a","b"],this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.dispatchLayout=it(this.outputShape),this.dispatch=He(this.dispatchLayout,this.outputShape,this.workGroupSize),this.cRank=e,this.rank=n,this.shaderKey="select"}getUserCode(){let e,t;if(this.rank>4)throw Error(`Where for rank ${this.rank} is not yet supported`);if(this.rank===1)t="resRC",e="resRC";else{let s=["resRC.x","resRC.y","resRC.z","resRC.w"],r=[],a=[];for(let o=0;o<this.outputShape.length;o++)a.push(`${s[o]}`),o<this.cRank&&r.push(`${s[o]}`);e=r.join(),t=a.join()}return`
-      ${Je("index")} {
+      }`}};function qye(e){let{inputs:t,backend:n,attrs:s}=e,{indices:r,updates:a}=t,{shape:o}=s,{sliceRank:i,numUpdates:l,sliceSize:u,strides:c,outputSize:p}=T.calculateShapes(a,r,o),d=[p/u,u];if(p===0)return n.makeTensorInfo(o,r.dtype);let h=He({inputs:{x:r},backend:n,attrs:{shape:[l,i]}}),f=He({inputs:{x:a},backend:n,attrs:{shape:[l,u]}}),m=f.dtype,g=vu({backend:n,attrs:{shape:d,value:0,dtype:m}}),y=v.sizeFromShape(f.shape),x=[{type:"int32",data:[i]},{type:"int32",data:c},{type:"int32",data:[y]}],A=new bm(f.shape,i,h.shape.length,f.shape.length,c,d,m),b=n.runWebGPUProgram(A,[f,h],m,x,g),w=He({inputs:{x:b},backend:n,attrs:{shape:o}});return n.disposeData(h.dataId),n.disposeData(f.dataId),n.disposeData(b.dataId),w}var Xye={kernelName:Kl,backendName:"webgpu",kernelFunc:qye},Kye=class{constructor(e,t,n){this.variableNames=["c","a","b"],this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.dispatchLayout=lt(this.outputShape),this.dispatch=je(this.dispatchLayout,this.outputShape,this.workGroupSize),this.cRank=e,this.rank=n,this.shaderKey="select"}getUserCode(){let e,t;if(this.rank>4)throw Error(`Where for rank ${this.rank} is not yet supported`);if(this.rank===1)t="resRC",e="resRC";else{let s=["resRC.x","resRC.y","resRC.z","resRC.w"],r=[],a=[];for(let o=0;o<this.outputShape.length;o++)a.push(`${s[o]}`),o<this.cRank&&r.push(`${s[o]}`);e=r.join(),t=a.join()}return`
+      ${nt("index")} {
         if (index < uniforms.size) {
           let resRC = getCoordsFromIndex(index);
           let cVal = getC(${e});
@@ -6748,23 +6748,23 @@ return a / b;`,Nle=`
           }
         }
       }
-    `}};function zye(e){let{inputs:t,backend:n}=e,{condition:s,t:r,e:a}=t,o=new Mye(s.shape.length,r.shape,r.shape.length);return n.runWebGPUProgram(o,[s,r,a],Gn(r.dtype,a.dtype))}var Lye={kernelName:jl,backendName:"webgpu",kernelFunc:zye},Bye=vn({opType:Oe.SIGMOID}),Wye={kernelName:ti,backendName:"webgpu",kernelFunc:Bye},Vye=vn({opType:Oe.SIN}),Uye={kernelName:ei,backendName:"webgpu",kernelFunc:Vye},Gye=vn({opType:Oe.SINH}),Hye={kernelName:Xl,backendName:"webgpu",kernelFunc:Gye},PT=jn({opType:Xe.SUB,cpuKernelImpl:S1e,supportsComplex:!0}),jye={kernelName:oi,backendName:"webgpu",kernelFunc:PT};function qye(e){let{inputs:t,backend:n,attrs:s}=e,{logits:r}=t,{dim:a}=s,o=v.parseAxisParam([a],r.shape),i=wb({inputs:{x:r},backend:n,attrs:{reductionIndices:o,keepDims:!1}}),l=T.expandShapeToKeepDim(i.shape,o),u=Ge({inputs:{x:i},backend:n,attrs:{shape:l}}),c=PT({inputs:{a:r,b:u},backend:n}),p=ET({inputs:{x:c},backend:n}),d=kb({inputs:{x:p},backend:n,attrs:{axis:o,keepDims:!1}}),h=Ge({inputs:{x:d},backend:n,attrs:{shape:l}}),f=$T({inputs:{a:p,b:h},backend:n});return n.disposeData(i.dataId),n.disposeData(u.dataId),n.disposeData(c.dataId),n.disposeData(p.dataId),n.disposeData(d.dataId),n.disposeData(h.dataId),f}var Xye={kernelName:ri,backendName:"webgpu",kernelFunc:qye},Kye=e=>{let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockShape:a,paddings:o}=s;v.assert(r.shape.length<=4,()=>"spaceToBatchND for rank > 4 with a WebGPU backend not implemented yet");let i=a.reduce((y,x)=>y*x),l=[[0,0]];l.push(...o);for(let y=1+a.length;y<r.shape.length;++y)l.push([0,0]);let u=[],c=DT({inputs:{x:r},backend:n,attrs:{paddings:l,constantValue:0}}),p=T.getReshaped(c.shape,a,i,!1),d=T.getPermuted(p.length,a.length,!1),h=T.getReshapedPermuted(c.shape,a,i,!1),f=Ge({inputs:{x:c},backend:n,attrs:{shape:p}}),m=Ta({inputs:{x:f},backend:n,attrs:{perm:d}}),g=Ge({inputs:{x:m},backend:n,attrs:{shape:h}});return u.push(c),u.push(f),u.push(m),u.forEach(y=>n.disposeData(y.dataId)),g},Zye={kernelName:Kl,backendName:"webgpu",kernelFunc:Kye},Yye=class{constructor(e,t){this.variableNames=["A"],this.workGroupSize=[64,1,1],this.size=!0;let n=new Array(e.length);for(let s=0;s<n.length;s++)n[s]=e[s]*t[s];this.outputShape=n,this.dispatchLayout=it(this.outputShape),this.dispatch=He(this.dispatchLayout,this.outputShape,this.workGroupSize),this.rank=this.outputShape.length,this.shaderKey="tile"}getUserCode(){let e=Jye(this.rank,"uniforms.");return`
-      ${Je("index")} {
+    `}};function Zye(e){let{inputs:t,backend:n}=e,{condition:s,t:r,e:a}=t,o=new Kye(s.shape.length,r.shape,r.shape.length);return n.runWebGPUProgram(o,[s,r,a],Hn(r.dtype,a.dtype))}var Yye={kernelName:Zl,backendName:"webgpu",kernelFunc:Zye},Jye=kn({opType:Oe.SIGMOID}),Qye={kernelName:ai,backendName:"webgpu",kernelFunc:Jye},eAe=kn({opType:Oe.SIN}),tAe={kernelName:ri,backendName:"webgpu",kernelFunc:eAe},nAe=kn({opType:Oe.SINH}),sAe={kernelName:Jl,backendName:"webgpu",kernelFunc:nAe},BT=qn({opType:Ye.SUB,cpuKernelImpl:O1e,supportsComplex:!0}),rAe={kernelName:ci,backendName:"webgpu",kernelFunc:BT};function aAe(e){let{inputs:t,backend:n,attrs:s}=e,{logits:r}=t,{dim:a}=s,o=v.parseAxisParam([a],r.shape),i=Ib({inputs:{x:r},backend:n,attrs:{reductionIndices:o,keepDims:!1}}),l=T.expandShapeToKeepDim(i.shape,o),u=He({inputs:{x:i},backend:n,attrs:{shape:l}}),c=BT({inputs:{a:r,b:u},backend:n}),p=FT({inputs:{x:c},backend:n}),d=Cb({inputs:{x:p},backend:n,attrs:{axis:o,keepDims:!1}}),h=He({inputs:{x:d},backend:n,attrs:{shape:l}}),f=LT({inputs:{a:p,b:h},backend:n});return n.disposeData(i.dataId),n.disposeData(u.dataId),n.disposeData(c.dataId),n.disposeData(p.dataId),n.disposeData(d.dataId),n.disposeData(h.dataId),f}var oAe={kernelName:li,backendName:"webgpu",kernelFunc:aAe},iAe=e=>{let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockShape:a,paddings:o}=s;v.assert(r.shape.length<=4,()=>"spaceToBatchND for rank > 4 with a WebGPU backend not implemented yet");let i=a.reduce((y,x)=>y*x),l=[[0,0]];l.push(...o);for(let y=1+a.length;y<r.shape.length;++y)l.push([0,0]);let u=[],c=zT({inputs:{x:r},backend:n,attrs:{paddings:l,constantValue:0}}),p=T.getReshaped(c.shape,a,i,!1),d=T.getPermuted(p.length,a.length,!1),h=T.getReshapedPermuted(c.shape,a,i,!1),f=He({inputs:{x:c},backend:n,attrs:{shape:p}}),m=_a({inputs:{x:f},backend:n,attrs:{perm:d}}),g=He({inputs:{x:m},backend:n,attrs:{shape:h}});return u.push(c),u.push(f),u.push(m),u.forEach(y=>n.disposeData(y.dataId)),g},lAe={kernelName:Ql,backendName:"webgpu",kernelFunc:iAe},uAe=class{constructor(e,t){this.variableNames=["A"],this.workGroupSize=[64,1,1],this.size=!0;let n=new Array(e.length);for(let s=0;s<n.length;s++)n[s]=e[s]*t[s];this.outputShape=n,this.dispatchLayout=lt(this.outputShape),this.dispatch=je(this.dispatchLayout,this.outputShape,this.workGroupSize),this.rank=this.outputShape.length,this.shaderKey="tile"}getUserCode(){let e=cAe(this.rank,"uniforms.");return`
+      ${nt("index")} {
         if (index < uniforms.size) {
           let resRC = getCoordsFromIndex(index);
           setOutputAtIndex(index, getA(${e}));
         }
       }
-    `}};function Jye(e,t=""){if(e>=5)throw Error(`Tile for rank ${e} is not yet supported`);if(e===1)return`(resRC % ${t}aShape)`;let n=["resRC.x","resRC.y","resRC.z","resRC.w"],s=[];for(let r=0;r<e;r++)s.push(`(${n[r]} % ${t}aShape[${r}])`);return s.join()}function FT(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{reps:a}=s;if(n.shouldExecuteOnCPU([r])||r.dtype==="string"||r.shape.length>=5){let l=n.readSync(r.dataId),u=r.dtype==="string"?l.map(d=>v.decodeString(d)):l,c=Ve(r.shape,r.dtype,u),p=I1e(c,a);return n.makeTensorInfo(p.shape,p.dtype,p.values)}let o=new Yye(r.shape,a);return n.runWebGPUProgram(o,[r],r.dtype)}var Qye={kernelName:Ra,backendName:"webgpu",kernelFunc:FT};function eAe(e){let{inputs:t,backend:n,attrs:s}=e,{sparseIndices:r,sparseValues:a,defaultValue:o}=t,{outputShape:i}=s,{sliceRank:l,numUpdates:u,sliceSize:c,strides:p,outputSize:d}=T.calculateShapes(a,r,i),h=!1;if(a.dtype==="string"){let E=n.bufferSync(r),_=n.bufferSync(a),$=v.decodeString(n.readSync(o.dataId)[0]),R=x1e(E,_,i,d,c,u,l,p,$,h);return n.makeTensorInfo(i,R.dtype,R.values)}let f=[d/c,c],m=Ge({inputs:{x:r},backend:n,attrs:{shape:[u,l]}}),g=a.shape.length?Ge({inputs:{x:a},backend:n,attrs:{shape:[u,c]}}):tr({inputs:{x:a},backend:n}),y=g.dtype,x=n.makeTensorInfo([],y,v.makeZerosTypedArray(1,y)),A=Ge({inputs:{x:o},backend:n,attrs:{shape:Array(f.length).fill(1)}}),b=FT({inputs:{x:A},backend:n,attrs:{reps:f}}),w=v.sizeFromShape([u,c]),k=[{type:"int32",data:[l]},{type:"int32",data:p},{type:"int32",data:[w]}];switch(u){case 0:break;case 1:{let E=new bm([u,c],l,m.shape.length,g.shape.length,p,f,y,h);n.runWebGPUProgram(E,[g,m],y,k,b)}break;default:{let E=new bm([u,c],l,m.shape.length,x.shape.length,p,f,y,h);n.runWebGPUProgram(E,[x,m],y,k,b)}{let E=new bm([u,c],l,m.shape.length,g.shape.length,p,f,y);n.runWebGPUProgram(E,[g,m],y,k,b)}}let C=Ge({inputs:{x:b},backend:n,attrs:{shape:i}});return n.disposeData(m.dataId),n.disposeData(g.dataId),n.disposeData(A.dataId),n.disposeData(x.dataId),n.disposeData(b.dataId),C}var tAe={kernelName:sh,backendName:"webgpu",kernelFunc:eAe};function nAe(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{numOrSizeSplits:a,axis:o}=s,i=v.parseAxisParam(o,r.shape)[0],l=T.prepareSplitSize(r,a,i),u=r.shape.length,c=new Array(u).fill(0),p=r.shape.slice();return l.map(d=>{let h=[...p];h[i]=d;let f=vd({inputs:{x:r},backend:n,attrs:{begin:c,size:h}});return c[i]+=d,f})}var sAe={kernelName:Zl,backendName:"webgpu",kernelFunc:nAe},rAe=vn({opType:Oe.SQRT}),aAe={kernelName:ni,backendName:"webgpu",kernelFunc:rAe},oAe={kernelName:jc,backendName:"webgpu",kernelFunc:({inputs:e,backend:t})=>{let{x:n}=e,s=t,r=new Xh(n.shape,Oe.SQUARE);return s.runWebGPUProgram(r,[n],n.dtype)}},iAe=jn({opType:Xe.SQUARED_DIFFERENCE}),lAe={kernelName:ai,backendName:"webgpu",kernelFunc:iAe},uAe=class{constructor(e){this.variableNames=["x"],this.workPerThread=1,this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=it(this.outputShape),this.dispatch=He(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]);let t=Fn(this.outputShape.length);this.uniforms=`begin : ${t},  strides : ${t}, `,this.shaderKey="stridedSlice"}getUserCode(){let e=this.outputShape.length,t="";if(e===1)t="coords * uniforms.strides + uniforms.begin";else{let s=0;t=this.outputShape.map((r,a)=>(s++,this.outputShape.length===1?`coords * uniforms.strides[${a}] + uniforms.begin[${a}]`:`coords[${s-1}] * uniforms.strides[${a}] + uniforms.begin[${a}]`)).join(",")}return`
-       ${Je("index")} {
+    `}};function cAe(e,t=""){if(e>=5)throw Error(`Tile for rank ${e} is not yet supported`);if(e===1)return`(resRC % ${t}aShape)`;let n=["resRC.x","resRC.y","resRC.z","resRC.w"],s=[];for(let r=0;r<e;r++)s.push(`(${n[r]} % ${t}aShape[${r}])`);return s.join()}function WT(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{reps:a}=s;if(n.shouldExecuteOnCPU([r])||r.dtype==="string"||r.shape.length>=5){let l=n.readSync(r.dataId),u=r.dtype==="string"?l.map(d=>v.decodeString(d)):l,c=Ue(r.shape,r.dtype,u),p=M1e(c,a);return n.makeTensorInfo(p.shape,p.dtype,p.values)}let o=new uAe(r.shape,a);return n.runWebGPUProgram(o,[r],r.dtype)}var dAe={kernelName:Pa,backendName:"webgpu",kernelFunc:WT};function pAe(e){let{inputs:t,backend:n,attrs:s}=e,{sparseIndices:r,sparseValues:a,defaultValue:o}=t,{outputShape:i}=s,{sliceRank:l,numUpdates:u,sliceSize:c,strides:p,outputSize:d}=T.calculateShapes(a,r,i),h=!1;if(a.dtype==="string"){let E=n.bufferSync(r),_=n.bufferSync(a),$=v.decodeString(n.readSync(o.dataId)[0]),R=_1e(E,_,i,d,c,u,l,p,$,h);return n.makeTensorInfo(i,R.dtype,R.values)}let f=[d/c,c],m=He({inputs:{x:r},backend:n,attrs:{shape:[u,l]}}),g=a.shape.length?He({inputs:{x:a},backend:n,attrs:{shape:[u,c]}}):sr({inputs:{x:a},backend:n}),y=g.dtype,x=n.makeTensorInfo([],y,v.makeZerosTypedArray(1,y)),A=He({inputs:{x:o},backend:n,attrs:{shape:Array(f.length).fill(1)}}),b=WT({inputs:{x:A},backend:n,attrs:{reps:f}}),w=v.sizeFromShape([u,c]),k=[{type:"int32",data:[l]},{type:"int32",data:p},{type:"int32",data:[w]}];switch(u){case 0:break;case 1:{let E=new bm([u,c],l,m.shape.length,g.shape.length,p,f,y,h);n.runWebGPUProgram(E,[g,m],y,k,b)}break;default:{let E=new bm([u,c],l,m.shape.length,x.shape.length,p,f,y,h);n.runWebGPUProgram(E,[x,m],y,k,b)}{let E=new bm([u,c],l,m.shape.length,g.shape.length,p,f,y);n.runWebGPUProgram(E,[g,m],y,k,b)}}let C=He({inputs:{x:b},backend:n,attrs:{shape:i}});return n.disposeData(m.dataId),n.disposeData(g.dataId),n.disposeData(A.dataId),n.disposeData(x.dataId),n.disposeData(b.dataId),C}var hAe={kernelName:rh,backendName:"webgpu",kernelFunc:pAe};function fAe(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{numOrSizeSplits:a,axis:o}=s,i=v.parseAxisParam(o,r.shape)[0],l=T.prepareSplitSize(r,a,i),u=r.shape.length,c=new Array(u).fill(0),p=r.shape.slice();return l.map(d=>{let h=[...p];h[i]=d;let f=Sd({inputs:{x:r},backend:n,attrs:{begin:c,size:h}});return c[i]+=d,f})}var mAe={kernelName:eu,backendName:"webgpu",kernelFunc:fAe},gAe=kn({opType:Oe.SQRT}),yAe={kernelName:oi,backendName:"webgpu",kernelFunc:gAe},AAe={kernelName:Zc,backendName:"webgpu",kernelFunc:({inputs:e,backend:t})=>{let{x:n}=e,s=t,r=new Kh(n.shape,Oe.SQUARE);return s.runWebGPUProgram(r,[n],n.dtype)}},xAe=qn({opType:Ye.SQUARED_DIFFERENCE}),bAe={kernelName:ui,backendName:"webgpu",kernelFunc:xAe},vAe=class{constructor(e){this.variableNames=["x"],this.workPerThread=1,this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=lt(this.outputShape),this.dispatch=je(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]);let t=Mn(this.outputShape.length);this.uniforms=`begin : ${t},  strides : ${t}, `,this.shaderKey="stridedSlice"}getUserCode(){let e=this.outputShape.length,t="";if(e===1)t="coords * uniforms.strides + uniforms.begin";else{let s=0;t=this.outputShape.map((r,a)=>(s++,this.outputShape.length===1?`coords * uniforms.strides[${a}] + uniforms.begin[${a}]`:`coords[${s-1}] * uniforms.strides[${a}] + uniforms.begin[${a}]`)).join(",")}return`
+       ${nt("index")} {
          if (index < uniforms.size) {
            let coords = getCoordsFromIndex(index);
            setOutputAtIndex(index, getX(${t}));
          }
        }
-     `}};function cAe(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{begin:a,end:o,strides:i,beginMask:l,endMask:u,ellipsisMask:c,newAxisMask:p,shrinkAxisMask:d}=s,{finalShapeSparse:h,finalShape:f,isIdentity:m,sliceDim0:g,isSimpleSlice:y,begin:x,end:A,strides:b}=Ht.sliceInfo(r.shape,a,o,i,l,u,c,p,d),w;if(m)w=Ge({inputs:{x:r},backend:n,attrs:{shape:f}});else if(g||y){v.assert(r.shape.length>=1,()=>`Input must have rank at least 1, got: ${r.shape.length}`);let k=Ht.computeOutShape(x,A,b),C=vd({inputs:{x:r},backend:n,attrs:{begin:x,size:k}});w=Ge({inputs:{x:C},backend:n,attrs:{shape:f}}),n.disposeData(C.dataId)}else if(n.shouldExecuteOnCPU([r])){let C=n.readSync(r.dataId),E=Ve(r.shape,r.dtype,C),_=w1e(h,E,b,x);w=n.makeTensorInfo(f,r.dtype,_.values)}else{let C=new uAe(h),E=[{type:"int32",data:x},{type:"int32",data:b}],_=n.runWebGPUProgram(C,[r],r.dtype,E);w=Ge({inputs:{x:_},backend:n,attrs:{shape:f}}),n.disposeData(_.dataId)}return w}var dAe={kernelName:Yl,backendName:"webgpu",kernelFunc:cAe};function pAe(e){let{inputs:t,backend:n,attrs:s}=e,{separator:r,nGramWidths:a,leftPad:o,rightPad:i,padWidth:l,preserveShortSequences:u}=s,{data:c,dataSplits:p}=t,d=n.readSync(c.dataId),h=n.readSync(p.dataId),[f,m]=k1e(d,h,r,a,o,i,l,u);return[n.makeTensorInfo([f.length],"string",f),n.makeTensorInfo(p.shape,"int32",m)]}var hAe={kernelName:qc,backendName:"webgpu",kernelFunc:pAe},fAe=vn({opType:Oe.TANH}),mAe={kernelName:ii,backendName:"webgpu",kernelFunc:fAe},gAe=class{constructor(e){this.variableNames=["x","indices"],this.workGroupSize=[256,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=it(this.outputShape),this.dispatch=He(this.dispatchLayout,this.outputShape,this.workGroupSize),this.uniforms=`inputSize : i32, firstPass : i32, negativeInf : f32,
+     `}};function wAe(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{begin:a,end:o,strides:i,beginMask:l,endMask:u,ellipsisMask:c,newAxisMask:p,shrinkAxisMask:d}=s,{finalShapeSparse:h,finalShape:f,isIdentity:m,sliceDim0:g,isSimpleSlice:y,begin:x,end:A,strides:b}=jt.sliceInfo(r.shape,a,o,i,l,u,c,p,d),w;if(m)w=He({inputs:{x:r},backend:n,attrs:{shape:f}});else if(g||y){v.assert(r.shape.length>=1,()=>`Input must have rank at least 1, got: ${r.shape.length}`);let k=jt.computeOutShape(x,A,b),C=Sd({inputs:{x:r},backend:n,attrs:{begin:x,size:k}});w=He({inputs:{x:C},backend:n,attrs:{shape:f}}),n.disposeData(C.dataId)}else if(n.shouldExecuteOnCPU([r])){let C=n.readSync(r.dataId),E=Ue(r.shape,r.dtype,C),_=P1e(h,E,b,x);w=n.makeTensorInfo(f,r.dtype,_.values)}else{let C=new vAe(h),E=[{type:"int32",data:x},{type:"int32",data:b}],_=n.runWebGPUProgram(C,[r],r.dtype,E);w=He({inputs:{x:_},backend:n,attrs:{shape:f}}),n.disposeData(_.dataId)}return w}var kAe={kernelName:tu,backendName:"webgpu",kernelFunc:wAe};function SAe(e){let{inputs:t,backend:n,attrs:s}=e,{separator:r,nGramWidths:a,leftPad:o,rightPad:i,padWidth:l,preserveShortSequences:u}=s,{data:c,dataSplits:p}=t,d=n.readSync(c.dataId),h=n.readSync(p.dataId),[f,m]=F1e(d,h,r,a,o,i,l,u);return[n.makeTensorInfo([f.length],"string",f),n.makeTensorInfo(p.shape,"int32",m)]}var IAe={kernelName:Yc,backendName:"webgpu",kernelFunc:SAe},CAe=kn({opType:Oe.TANH}),TAe={kernelName:di,backendName:"webgpu",kernelFunc:CAe},NAe=class{constructor(e){this.variableNames=["x","indices"],this.workGroupSize=[256,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=lt(this.outputShape),this.dispatch=je(this.dispatchLayout,this.outputShape,this.workGroupSize),this.uniforms=`inputSize : i32, firstPass : i32, negativeInf : f32,
         dir : i32, inc : i32,`,this.shaderKey="swap"}getUserCode(){return`
-        ${Je("index")} {
+        ${nt("index")} {
           if (index < uniforms.size) {
             let outC = getCoordsFromIndex(index);
             let batch = outC[0];
@@ -6830,8 +6830,8 @@ return a / b;`,Nle=`
             }
           }
         }
-      `}},yAe=class{constructor(e){this.variableNames=["x","indices"],this.workGroupSize=[256,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=it(this.outputShape),this.dispatch=He(this.dispatchLayout,this.outputShape,this.workGroupSize),this.uniforms="inputSize : i32, firstPass : i32, k : i32,",this.shaderKey="merge"}getUserCode(){return`
-        ${Je("index")} {
+      `}},EAe=class{constructor(e){this.variableNames=["x","indices"],this.workGroupSize=[256,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=lt(this.outputShape),this.dispatch=je(this.dispatchLayout,this.outputShape,this.workGroupSize),this.uniforms="inputSize : i32, firstPass : i32, k : i32,",this.shaderKey="merge"}getUserCode(){return`
+        ${nt("index")} {
           if (index < uniforms.size) {
             let outC = getCoordsFromIndex(index);
             let batch = outC[0];
@@ -6889,7 +6889,7 @@ return a / b;`,Nle=`
             }
           }
         }
-      `}};function Zu(e,t){t!==null&&e.disposeData(t.dataId)}function h6(e){let t=1;for(;t<e;)t*=2;return t}function AAe(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{k:a,sorted:o}=s,i=r.shape,l=i[i.length-1];if(n.shouldExecuteOnCPU([r])){let w=n.readSync(r.dataId),[k,C]=C1e(w,i,r.dtype,a,o);return[n.makeTensorInfo(k.shape,k.dtype,k.values),n.makeTensorInfo(C.shape,C.dtype,C.values)]}if(a===0)return i[i.length-1]=0,[n.makeTensorInfo(i,r.dtype,[]),n.makeTensorInfo(i,"int32",[])];if(l===1)return[r,yu({attrs:{shape:i,dtype:"int32",value:0},backend:n})];let c=v.sizeFromShape(i)/l,p=Ge({inputs:{x:r},attrs:{shape:[c,l]},backend:n}),d=h6(a),h=h6(l),f=null,m=()=>f===null?[p,p]:[p,f],g=(w,k,C)=>{let E=m(),_=new gAe(C),R=[{type:"int32",data:[l]},{type:"int32",data:[f===null?1:0]},{type:"float32",data:[Number.NEGATIVE_INFINITY]},{type:"int32",data:[w]},{type:"int32",data:[k]}],P=f;f=n.runWebGPUProgram(_,E,"int32",R),Zu(n,P)};for(let w=1;w<d;w*=2){let k=w*2;for(let C=w;C>=1;C/=2)g(k,C,[c,h])}for(let w=h;w>d;w/=2){let k=m(),C=new yAe([c,w/2]),_=[{type:"int32",data:[l]},{type:"int32",data:[f===null?1:0]},{type:"int32",data:[d]}],$=f;f=n.runWebGPUProgram(C,k,"int32",_),Zu(n,$);let R=d/2,P=R*2;for(let S=R;S>=1;S/=2)g(P,S,f.shape)}let y=f;f=vd({inputs:{x:f},backend:n,attrs:{begin:0,size:[c,a]}}),Zu(n,y);let x=RT({inputs:{x:p,indices:f},backend:n,attrs:{axis:1,batchDims:1}});Zu(n,p);let A=i.slice(0,-1);A.push(a),y=f,f=Ge({inputs:{x:f},attrs:{shape:A},backend:n}),Zu(n,y);let b=x;return x=Ge({inputs:{x},attrs:{shape:A},backend:n}),Zu(n,b),[x,f]}var xAe={kernelName:Ql,backendName:"webgpu",kernelFunc:AAe},bAe=class{constructor(e){this.variableNames=["Image","Transforms"],this.uniforms="interpolationModeId : i32, fillModeId : i32, fillValue : f32,",this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=it(this.outputShape),this.dispatch=He(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey="transform"}getUserCode(){return`
+      `}};function ec(e,t){t!==null&&e.disposeData(t.dataId)}function x6(e){let t=1;for(;t<e;)t*=2;return t}function RAe(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{k:a,sorted:o}=s,i=r.shape,l=i[i.length-1];if(n.shouldExecuteOnCPU([r])){let w=n.readSync(r.dataId),[k,C]=z1e(w,i,r.dtype,a,o);return[n.makeTensorInfo(k.shape,k.dtype,k.values),n.makeTensorInfo(C.shape,C.dtype,C.values)]}if(a===0)return i[i.length-1]=0,[n.makeTensorInfo(i,r.dtype,[]),n.makeTensorInfo(i,"int32",[])];if(l===1)return[r,vu({attrs:{shape:i,dtype:"int32",value:0},backend:n})];let c=v.sizeFromShape(i)/l,p=He({inputs:{x:r},attrs:{shape:[c,l]},backend:n}),d=x6(a),h=x6(l),f=null,m=()=>f===null?[p,p]:[p,f],g=(w,k,C)=>{let E=m(),_=new NAe(C),R=[{type:"int32",data:[l]},{type:"int32",data:[f===null?1:0]},{type:"float32",data:[Number.NEGATIVE_INFINITY]},{type:"int32",data:[w]},{type:"int32",data:[k]}],P=f;f=n.runWebGPUProgram(_,E,"int32",R),ec(n,P)};for(let w=1;w<d;w*=2){let k=w*2;for(let C=w;C>=1;C/=2)g(k,C,[c,h])}for(let w=h;w>d;w/=2){let k=m(),C=new EAe([c,w/2]),_=[{type:"int32",data:[l]},{type:"int32",data:[f===null?1:0]},{type:"int32",data:[d]}],$=f;f=n.runWebGPUProgram(C,k,"int32",_),ec(n,$);let R=d/2,P=R*2;for(let S=R;S>=1;S/=2)g(P,S,f.shape)}let y=f;f=Sd({inputs:{x:f},backend:n,attrs:{begin:0,size:[c,a]}}),ec(n,y);let x=OT({inputs:{x:p,indices:f},backend:n,attrs:{axis:1,batchDims:1}});ec(n,p);let A=i.slice(0,-1);A.push(a),y=f,f=He({inputs:{x:f},attrs:{shape:A},backend:n}),ec(n,y);let b=x;return x=He({inputs:{x},attrs:{shape:A},backend:n}),ec(n,b),[x,f]}var _Ae={kernelName:su,backendName:"webgpu",kernelFunc:RAe},DAe=class{constructor(e){this.variableNames=["Image","Transforms"],this.uniforms="interpolationModeId : i32, fillModeId : i32, fillValue : f32,",this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=lt(this.outputShape),this.dispatch=je(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey="transform"}getUserCode(){return`
           fn mapCoord(outCoord : f32, len : f32) -> f32{
             var inCoord = outCoord;
             if(uniforms.fillModeId == 2) {
@@ -6953,7 +6953,7 @@ return a / b;`,Nle=`
             return outputValue;
           }
 
-          ${Je("index")} {
+          ${nt("index")} {
             if (index < uniforms.size) {
               let coords = getCoordsFromIndex(index);
               var outputValue : f32;
@@ -7005,7 +7005,7 @@ return a / b;`,Nle=`
               setOutputAtIndex(index, outputValue);
             }
           }
-        `}};function vAe(e){let{inputs:t,backend:n,attrs:s}=e,{image:r,transforms:a}=t,{interpolation:o,fillMode:i,fillValue:l,outputShape:u}=s,[c,p,d,h]=r.shape,[f,m]=u!=null?u:[p,d],g=[c,f,m,h],y=new bAe(g),x=o==="nearest"?1:2,A;switch(i){case"constant":A=1;break;case"reflect":A=2;break;case"wrap":A=3;break;case"nearest":A=4;break;default:A=1;break}let b=[{type:"int32",data:[x]},{type:"int32",data:[A]},{type:"float32",data:[l]}];return n.runWebGPUProgram(y,[r,a],"float32",b)}var wAe={kernelName:eu,backendName:"webgpu",kernelFunc:vAe};function kAe(e){let{inputs:t,backend:n,attrs:s}=e,{value:r}=t,{axis:a}=s;a<0&&(a+=r.shape.length);let o=r,i=o.shape.length,l=r.shape[a],u=new Array(i-1),c=0;for(let m=0;m<i;m++)m!==a&&(u[c++]=o.shape[m]);let p=[],d=new Array(i).fill(0),h=o.shape.slice();h[a]=1;let f=new Array(l);for(let m=0;m<f.length;m++){d[a]=m;let g=vd({inputs:{x:o},backend:n,attrs:{begin:d,size:h}}),y=Ge({inputs:{x:g},backend:n,attrs:{shape:u}});f[m]=y,p.push(g)}return p.forEach(m=>n.disposeData(m.dataId)),f}var SAe={kernelName:tu,backendName:"webgpu",kernelFunc:kAe},IAe=[j2e,E1e,_1e,P1e,B1e,V1e,G1e,Z1e,J1e,sge,ige,uge,hge,X2e,yge,wge,Tge,Ege,_ge,Pge,Oge,zge,Wge,Gge,Xge,Zge,Jge,Qge,e3e,n3e,U2e,r3e,c3e,o3e,l3e,h3e,m3e,y3e,b3e,k3e,I3e,T3e,q2e,mge,E3e,_3e,$3e,F3e,M3e,L3e,W3e,q1e,U3e,H3e,X1e,q3e,K3e,Y3e,Hge,Q3e,tye,sye,rge,aye,iye,uye,dye,hye,mye,yye,age,Aye,bye,wye,Sye,G2e,Tye,Rye,Dye,Pye,Oye,Lye,Wye,Uye,Hye,tge,dAe,hAe,Xye,Zye,tAe,sAe,aAe,oAe,lAe,jye,jge,mAe,Qye,xAe,wAe,z1e,SAe,rye];for(let e of IAe)nr(e);var CAe="3.20.0",TAe="3.20.0",NAe="3.20.0",EAe="3.20.0",RAe="3.20.0",_Ae="3.20.0",DAe="3.20.0",Yh={tfjs:CAe,"tfjs-core":TAe,"tfjs-data":NAe,"tfjs-layers":EAe,"tfjs-converter":RAe,"tfjs-backend-webgl":_Ae,"tfjs-backend-wasm":DAe};var OT=`
+        `}};function $Ae(e){let{inputs:t,backend:n,attrs:s}=e,{image:r,transforms:a}=t,{interpolation:o,fillMode:i,fillValue:l,outputShape:u}=s,[c,p,d,h]=r.shape,[f,m]=u!=null?u:[p,d],g=[c,f,m,h],y=new DAe(g),x=o==="nearest"?1:2,A;switch(i){case"constant":A=1;break;case"reflect":A=2;break;case"wrap":A=3;break;case"nearest":A=4;break;default:A=1;break}let b=[{type:"int32",data:[x]},{type:"int32",data:[A]},{type:"float32",data:[l]}];return n.runWebGPUProgram(y,[r,a],"float32",b)}var PAe={kernelName:ru,backendName:"webgpu",kernelFunc:$Ae};function FAe(e){let{inputs:t,backend:n,attrs:s}=e,{value:r}=t,{axis:a}=s;a<0&&(a+=r.shape.length);let o=r,i=o.shape.length,l=r.shape[a],u=new Array(i-1),c=0;for(let m=0;m<i;m++)m!==a&&(u[c++]=o.shape[m]);let p=[],d=new Array(i).fill(0),h=o.shape.slice();h[a]=1;let f=new Array(l);for(let m=0;m<f.length;m++){d[a]=m;let g=Sd({inputs:{x:o},backend:n,attrs:{begin:d,size:h}}),y=He({inputs:{x:g},backend:n,attrs:{shape:u}});f[m]=y,p.push(g)}return p.forEach(m=>n.disposeData(m.dataId)),f}var OAe={kernelName:au,backendName:"webgpu",kernelFunc:FAe},MAe=[r1e,W1e,U1e,j1e,J1e,ege,nge,lge,cge,mge,xge,vge,Ige,o1e,Ege,Pge,Lge,Wge,Uge,jge,Xge,Zge,Qge,n3e,o3e,l3e,c3e,d3e,p3e,f3e,t1e,g3e,w3e,A3e,b3e,I3e,T3e,E3e,D3e,F3e,M3e,L3e,a1e,Tge,W3e,U3e,H3e,q3e,K3e,Y3e,Q3e,age,tye,sye,oge,aye,iye,uye,s3e,dye,hye,mye,gge,yye,xye,vye,kye,Iye,Tye,Eye,yge,Rye,Dye,Pye,Oye,n1e,Lye,Vye,Gye,jye,Xye,Yye,Qye,tAe,sAe,hge,kAe,IAe,oAe,lAe,hAe,mAe,yAe,AAe,bAe,rAe,r3e,TAe,dAe,_Ae,PAe,Z1e,OAe,gye];for(let e of MAe)rr(e);var zAe="3.20.0",LAe="3.20.0",BAe="3.20.0",WAe="3.20.0",VAe="3.20.0",UAe="3.20.0",GAe="3.20.0",Jh={tfjs:zAe,"tfjs-core":LAe,"tfjs-data":BAe,"tfjs-layers":WAe,"tfjs-converter":VAe,"tfjs-backend-webgl":UAe,"tfjs-backend-wasm":GAe};var VT=`
   precision highp float;
   attribute vec2 pos;
   attribute vec2 uv;
@@ -7015,7 +7015,7 @@ return a / b;`,Nle=`
     vUv = uv;
     gl_Position = vec4(pos.x, pos.y*flipY, 0.0, 1.);
   }
-`;var MT=`
+`;var UT=`
   precision highp float;
   varying vec2 vUv;
   uniform sampler2D texture;
@@ -7027,7 +7027,7 @@ return a / b;`,Nle=`
     gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[13] * c.a + m[14];
     gl_FragColor.a = m[15] * c.r + m[16] * c.g + m[17] * c.b + m[18] * c.a + m[19];
   }
-`,zT=`
+`,GT=`
   precision highp float;
   varying vec2 vUv;
   uniform sampler2D texture;
@@ -7039,7 +7039,7 @@ return a / b;`,Nle=`
     gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[14];
     gl_FragColor.a = c.a;
   }
-`,LT=`
+`,HT=`
   precision highp float;
   varying vec2 vUv;
   uniform vec2 size;
@@ -7052,7 +7052,7 @@ return a / b;`,Nle=`
     vec2 coord = pixelate(vUv, size);
     gl_FragColor += texture2D(texture, coord);
   }
-`,BT=`
+`,jT=`
   precision highp float;
   varying vec2 vUv;
   uniform sampler2D texture;
@@ -7075,7 +7075,7 @@ return a / b;`,Nle=`
     gl_FragColor += texture2D(texture, vUv + vec2( 6.0*px.x,  6.0*px.y))*0.00895781211794;
     gl_FragColor += texture2D(texture, vUv + vec2( 7.0*px.x,  7.0*px.y))*0.0044299121055113265;
   }
-`,WT=`
+`,qT=`
   precision highp float;
   varying vec2 vUv;
   uniform sampler2D texture;
@@ -7097,7 +7097,7 @@ return a / b;`,Nle=`
     c31 * m[6] + c32 * m[7] + c33 * m[8];
     gl_FragColor.a = c22.a;
   }
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n={audio:!1,video:{facingMode:this.config.mode==="front"?"user":"environment",resizeMode:this.config.crop?"crop-and-scale":"none",width:{ideal:this.config.width>0?this.config.width:window.innerWidth},height:{ideal:this.config.height>0?this.config.height:window.innerHeight}}};if(this.element.addEventListener("play",()=>{this.config.debug&&ne("webcam","play")}),this.element.addEventListener("pause",()=>{this.config.debug&&ne("webcam","pause")}),this.element.addEventListener("click",async()=>{!this.element||!this.stream||(this.element.paused?await this.element.play():this.element.pause())}),!(navigator!=null&&navigator.mediaDevices)){this.config.debug&&ne("webcam","no devices");return}try{this.stream=await navigator.mediaDevices.getUserMedia(n)}catch(r){ne("webcam",r);return}if(!this.stream){this.config.debug&&ne("webcam","no stream");return}this.element.srcObject=this.stream,await new Promise(r=>{this.element?this.element.onloadeddata=()=>r(!0):r(!1)}),await this.element.play(),this.config.debug&&ne("webcam",{width:this.width,height:this.height,label:this.label,stream:this.stream,track:this.track,settings:this.settings,constraints:this.constraints,capabilities:this.capabilities})});fe(this,"pause",()=>{this.element&&this.element.pause()});fe(this,"play",async()=>{this.element&&await this.element.play()});fe(this,"stop",()=>{this.config.debug&&ne("webcam","stop"),this.track&&this.track.stop()});this.config={element:void 0,debug:!0,mode:"front",crop:!1,width:0,height:0}}get track(){if(!!this.stream)return this.stream.getVideoTracks()[0]}get capabilities(){if(!!this.track)return this.track.getCapabilities?this.track.getCapabilities():void 0}get constraints(){if(!!this.track)return this.track.getConstraints?this.track.getConstraints():void 0}get settings(){if(!this.stream)return;let t=this.stream.getVideoTracks()[0];return t.getSettings?t.getSettings():void 0}get label(){return this.track?this.track.label:""}get paused(){var 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Ws,X2=[],ZT=0,YT=0,_b=Number.MAX_SAFE_INTEGER;async function JT(e){return me.initial&&(Ws=null),Ws?e.debug&&ne("cached model:",Ws.modelUrl):Ws=await qe(e.face.ssrnet.modelPathAge),Ws}async function Db(e,t,n,s){var o,i,l,u;if(!Ws)return{age:0};let r=_b<(((o=t.face.ssrnet)==null?void 0:o.skipFrames)||0),a=(((i=t.face.ssrnet)==null?void 0:i.skipTime)||0)>ue()-YT;return t.skipAllowed&&r&&a&&ZT===s&&((l=X2[n])==null?void 0:l.age)&&((u=X2[n])==null?void 0:u.age)>0?(_b++,X2[n]):(_b=0,new Promise(async c=>{var h;if(!(Ws!=null&&Ws.inputs)||!Ws.inputs[0]||!Ws.inputs[0].shape)return;let p={};p.resize=Ce.resizeBilinear(e,[Ws.inputs[0].shape[2],Ws.inputs[0].shape[1]],!1),p.enhance=z(p.resize,ot.tf255);let d={age:0};if((h=t.face.ssrnet)!=null&&h.enabled&&(p.age=Ws.execute(p.enhance)),p.age){let f=await p.age.data();d.age=Math.trunc(10*f[0])/10}Object.keys(p).forEach(f=>ee(p[f])),X2[n]=d,ZT=s,YT=ue(),c(d)}))}var vr,K2=[],eN=0,tN=0,$b=Number.MAX_SAFE_INTEGER,Pb=[.2989,.587,.114];async function nN(e){var t;return me.initial&&(vr=null),vr?e.debug&&ne("cached model:",vr.modelUrl):vr=await qe((t=e.face.ssrnet)==null?void 0:t.modelPathGender),vr}async function Fb(e,t,n,s){var o,i,l,u;if(!vr)return{gender:"unknown",genderScore:0};let r=$b<(((o=t.face.ssrnet)==null?void 0:o.skipFrames)||0),a=(((i=t.face.ssrnet)==null?void 0:i.skipTime)||0)>ue()-tN;return t.skipAllowed&&r&&a&&eN===s&&((l=K2[n])==null?void 0:l.gender)&&((u=K2[n])==null?void 0:u.genderScore)>0?($b++,K2[n]):($b=0,new Promise(async c=>{var f;if(!(vr!=null&&vr.inputs[0].shape))return;let p={};p.resize=Ce.resizeBilinear(e,[vr.inputs[0].shape[2],vr.inputs[0].shape[1]],!1),p.enhance=Y(()=>{let[m,g,y]=Qt(p.resize,3,3),x=z(m,Pb[0]),A=z(g,Pb[1]),b=z(y,Pb[2]),w=N0([x,A,b]);return z(ye(w,ot.tf05),2)});let d={gender:"unknown",genderScore:0};(f=t.face.ssrnet)!=null&&f.enabled&&(p.gender=vr.execute(p.enhance));let h=await 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Sd=e=>[Math.abs(e.endPoint[0]-e.startPoint[0]),Math.abs(e.endPoint[1]-e.startPoint[1])],Y2=e=>[e.startPoint[0]+(e.endPoint[0]-e.startPoint[0])/2,e.startPoint[1]+(e.endPoint[1]-e.startPoint[1])/2,1],J2=(e,t)=>e?[Math.trunc(Math.max(0,e.startPoint[0])),Math.trunc(Math.max(0,e.startPoint[1])),Math.trunc(Math.min(t.shape[2]||0,e.endPoint[0])-Math.max(0,e.startPoint[0])),Math.trunc(Math.min(t.shape[1]||0,e.endPoint[1])-Math.max(0,e.startPoint[1]))]:[0,0,0,0],Q2=(e,t)=>e?[e.startPoint[0]/(t.shape[2]||0),e.startPoint[1]/(t.shape[1]||0),(e.endPoint[0]-e.startPoint[0])/(t.shape[2]||0),(e.endPoint[1]-e.startPoint[1])/(t.shape[1]||0)]:[0,0,0,0],cN=(e,t)=>{let n=[e.startPoint[0]*t[0],e.startPoint[1]*t[1]],s=[e.endPoint[0]*t[0],e.endPoint[1]*t[1]];return{startPoint:n,endPoint:s,landmarks:e.landmarks,confidence:e.confidence}},Wb=(e,t,n)=>{let s=t.shape[1],r=t.shape[2],a=[e.startPoint[1]/s,e.startPoint[0]/r,e.endPoint[1]/s,e.endPoint[0]/r],o=Ce.cropAndResize(t,[a],[0],n),i=ge(o,ot.tf255);return ee(o),i},e1=(e,t)=>{let n=Y2(e),s=Sd(e),r=[t*s[0]/2,t*s[1]/2];return{startPoint:[n[0]-r[0],n[1]-r[1]],endPoint:[n[0]+r[0],n[1]+r[1]],landmarks:e.landmarks,confidence:e.confidence}},t1=e=>{let t=Y2(e),n=Sd(e),s=Math.max(...n)/2;return{startPoint:[Math.round(t[0]-s),Math.round(t[1]-s)],endPoint:[Math.round(t[0]+s),Math.round(t[1]+s)],landmarks:e.landmarks,confidence:e.confidence}},dN=e=>{let t=e.map(s=>s[0]),n=e.map(s=>s[1]);return{startPoint:[Math.min(...t),Math.min(...n)],endPoint:[Math.max(...t),Math.max(...n)],landmarks:e}},Vb=[[1,0,0],[0,1,0],[0,0,1]],exe=e=>e-2*Math.PI*Math.floor((e+Math.PI)/(2*Math.PI)),txe=(e,t)=>exe(Math.PI/2-Math.atan2(-(t[1]-e[1]),t[0]-e[0]));var lN=(e,t)=>[[1,0,e],[0,1,t],[0,0,1]],bu=(e,t)=>{let n=0;for(let s=0;s<e.length;s++)n+=e[s]*t[s];return n},nxe=(e,t)=>{let n=[];for(let s=0;s<e.length;s++)n.push(e[s][t]);return n},uN=(e,t)=>{let n=[],s=e.length;for(let r=0;r<s;r++){n.push([]);for(let a=0;a<s;a++)n[r].push(bu(e[r],nxe(t,a)))}return n},pN=(e,t)=>{let n=Math.cos(e),s=Math.sin(e),r=[[n,-s,0],[s,n,0],[0,0,1]],a=lN(t[0],t[1]),o=uN(a,r),i=lN(-t[0],-t[1]);return uN(o,i)},sxe=e=>{let t=[[e[0][0],e[1][0]],[e[0][1],e[1][1]]],n=[e[0][2],e[1][2]],s=[-bu(t[0],n),-bu(t[1],n)];return[t[0].concat(s[0]),t[1].concat(s[1]),[0,0,1]]},rxe=(e,t)=>[bu(e,t[0]),bu(e,t[1])];function hN(e){let t=e===192?{strides:[4],anchors:[1]}:{strides:[e/16,e/8],anchors:[2,6]},n=[];for(let s=0;s<t.strides.length;s++){let r=t.strides[s],a=Math.floor((e+r-1)/r),o=Math.floor((e+r-1)/r),i=t.anchors[s];for(let l=0;l<a;l++){let u=r*(l+.5);for(let c=0;c<o;c++){let p=r*(c+.5);for(let d=0;d<i;d++)n.push([p,u])}}}return n}function fN(e,t,n,s,r){let a=Sd(t),o=e.map(h=>[a[0]/r*(h[0]-r/2),a[1]/r*(h[1]-r/2),h[2]||0]),i=n&&n!==0&&Math.abs(n)>.2,l=i?pN(n,[0,0]):Vb,u=i?o.map(h=>[...rxe(h,l),h[2]]):o,c=i?sxe(s):Vb,p=Y2(t),d=[bu(p,c[0]),bu(p,c[1])];return u.map(h=>[Math.trunc(h[0]+d[0]),Math.trunc(h[1]+d[1]),Math.trunc(h[2]||0)])}function mN(e,t,n,s){let r=t.landmarks.length>=zb.count?zb.symmetryLine:Au.symmetryLine,a=0,o=Vb,i;if(e&&me.kernels.includes("rotatewithoffset"))if(a=txe(t.landmarks[r[0]],t.landmarks[r[1]]),a&&a!==0&&Math.abs(a)>.2){let u=Y2(t),c=[u[0]/n.shape[2],u[1]/n.shape[1]],p=Ce.rotateWithOffset(n,a,0,c);o=pN(-a,u),i=Wb(t,p,[s,s]),ee(p)}else i=Wb(t,n,[s,s]);else i=Wb(t,n,[s,s]);return[a,o,i]}var axe=e=>{let t=e.map(s=>s[0]),n=e.map(s=>s[1]);return[Math.min(...t)+(Math.max(...t)-Math.min(...t))/2,Math.min(...n)+(Math.max(...n)-Math.min(...n))/2]},gN=(e,t)=>{let n=axe(e),s=Sd(t);return{startPoint:[n[0]-s[0]/2,n[1]-s[1]/2],endPoint:[n[0]+s[0]/2,n[1]+s[1]/2]}};var yN=6,oxe=1.4,Gr,AN=null,bi=0,tf=null,Id=()=>bi;async function xN(e){var t;return me.initial&&(Gr=null),Gr?e.debug&&ne("cached model:",Gr.modelUrl):Gr=await qe((t=e.face.detector)==null?void 0:t.modelPath),bi=Gr.executor&&Gr.inputs[0].shape?Gr.inputs[0].shape[2]:256,tf=Te(bi,"int32"),AN=fr(hN(bi)),Gr}function ixe(e){let t={};t.boxStarts=Le(e,[0,1],[-1,2]),t.centers=de(t.boxStarts,AN),t.boxSizes=Le(e,[0,3],[-1,2]),t.boxSizesNormalized=ge(t.boxSizes,tf),t.centersNormalized=ge(t.centers,tf),t.halfBoxSize=ge(t.boxSizesNormalized,ot.tf2),t.starts=ye(t.centersNormalized,t.halfBoxSize),t.ends=de(t.centersNormalized,t.halfBoxSize),t.startNormalized=z(t.starts,tf),t.endNormalized=z(t.ends,tf);let n=ou([t.startNormalized,t.endNormalized],1);return Object.keys(t).forEach(s=>ee(t[s])),n}async function bN(e,t){var i,l,u,c;if(!e||e.isDisposedInternal||e.shape.length!==4||e.shape[1]<1||e.shape[2]<1)return[];let n={};n.resized=Ce.resizeBilinear(e,[bi,bi]),n.div=ge(n.resized,ot.tf127),n.normalized=ye(n.div,ot.tf05);let s=Gr==null?void 0:Gr.execute(n.normalized);if(Array.isArray(s)&&s.length>2){let p=s.sort((d,h)=>d.size-h.size);n.concat384=Ct([p[0],p[2]],2),n.concat512=Ct([p[1],p[3]],2),n.concat=Ct([n.concat512,n.concat384],1),n.batch=at(n.concat,0)}else Array.isArray(s)?n.batch=at(s[0]):n.batch=at(s);ee(s),n.boxes=ixe(n.batch),n.logits=Le(n.batch,[0,0],[-1,1]),n.sigmoid=Pn(n.logits),n.scores=at(n.sigmoid),n.nms=await Ce.nonMaxSuppressionAsync(n.boxes,n.scores,((i=t.face.detector)==null?void 0:i.maxDetected)||0,((l=t.face.detector)==null?void 0:l.iouThreshold)||0,((u=t.face.detector)==null?void 0:u.minConfidence)||0);let r=await n.nms.array(),a=[],o=await n.scores.data();for(let p=0;p<r.length;p++){let d=o[r[p]];if(d>(((c=t.face.detector)==null?void 0:c.minConfidence)||0)){let h={};h.bbox=Le(n.boxes,[r[p],0],[1,-1]),h.slice=Le(n.batch,[r[p],yN-1],[1,-1]),h.squeeze=at(h.slice),h.landmarks=V(h.squeeze,[yN,-1]);let f=await h.bbox.data(),m={startPoint:[f[0],f[1]],endPoint:[f[2],f[3]],landmarks:await h.landmarks.array(),confidence:d},g=cN(m,[(e.shape[2]||0)/bi,(e.shape[1]||0)/bi]),y=e1(g,t.face.scale||oxe),x=t1(y);a.push(x),Object.keys(h).forEach(A=>ee(h[A]))}}return Object.keys(n).forEach(p=>ee(n[p])),a}var n1={};ma(n1,{connected:()=>Hb,kpt:()=>Gb});var Gb=["nose","leftEyeInside","leftEye","leftEyeOutside","rightEyeInside","rightEye","rightEyeOutside","leftEar","rightEar","leftMouth","rightMouth","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftPinky","rightPinky","leftIndex","rightIndex","leftThumb","rightThumb","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle","leftHeel","rightHeel","leftFoot","rightFoot","bodyCenter","bodyTop","leftPalm","leftHand","rightPalm","rightHand"],Hb={shoulders:["leftShoulder","rightShoulder"],hips:["rightHip","leftHip"],mouth:["leftMouth","rightMouth"],leftLegUpper:["leftHip","leftKnee"],leftLegLower:["leftKnee","leftAnkle"],leftFoot:["leftAnkle","leftHeel","leftFoot"],leftTorso:["leftShoulder","leftHip"],leftArmUpper:["leftShoulder","leftElbow"],leftArmLower:["leftElbow","leftWrist"],leftHand:["leftWrist","leftPalm"],leftHandPinky:["leftPalm","leftPinky"],leftHandIndex:["leftPalm","leftIndex"],leftHandThumb:["leftPalm","leftThumb"],leftEyeOutline:["leftEyeInside","leftEyeOutside"],rightLegUpper:["rightHip","rightKnee"],rightLegLower:["rightKnee","rightAnkle"],rightFoot:["rightAnkle","rightHeel","rightFoot"],rightTorso:["rightShoulder","rightHip"],rightArmUpper:["rightShoulder","rightElbow"],rightArmLower:["rightElbow","rightWrist"],rightHand:["rightWrist","rightPalm"],rightHandPinky:["rightPalm","rightPinky"],rightHandIndex:["rightPalm","rightIndex"],rightHandThumb:["rightPalm","rightThumb"],rightEyeOutline:["rightEyeInside","rightEyeOutside"]};var wN=224,lxe,uxe=5,s1=[8,16,32,32,32];function kN(){let e=[],t=0;for(;t<uxe;){let n=0,s=t;for(;s<s1.length&&s1[s]===s1[t];)n+=2,s++;let r=s1[t],a=Math.ceil(wN/r),o=Math.ceil(wN/r);for(let i=0;i<a;++i)for(let l=0;l<o;++l)for(let u=0;u<n;++u)e.push({x:(l+.5)/o,y:(i+.5)/a});t=s}lxe={x:Ft(e.map(n=>n.x)),y:Ft(e.map(n=>n.y))}}function Pa(e,t=[1,1]){let n=[e.map(i=>i[0]),e.map(i=>i[1])],s=[Math.min(...n[0]),Math.min(...n[1])],r=[Math.max(...n[0]),Math.max(...n[1])],a=[s[0],s[1],r[0]-s[0],r[1]-s[1]],o=[a[0]/t[0],a[1]/t[1],a[2]/t[0],a[3]/t[1]];return{box:a,boxRaw:o}}function SN(e,t=[1,1]){let n=[e.map(u=>u[0]),e.map(u=>u[1])],s=[Math.min(...n[0]),Math.min(...n[1])],r=[Math.max(...n[0]),Math.max(...n[1])],a=[(s[0]+r[0])/2,(s[1]+r[1])/2],o=Math.max(a[0]-s[0],a[1]-s[1],-a[0]+r[0],-a[1]+r[1]),i=[Math.trunc(a[0]-o),Math.trunc(a[1]-o),Math.trunc(2*o),Math.trunc(2*o)],l=[i[0]/t[0],i[1]/t[1],i[2]/t[0],i[3]/t[1]];return{box:i,boxRaw:l}}function r1(e,t){let n=[e[2]*t,e[3]*t];return[e[0]-(n[0]-e[2])/2,e[1]-(n[1]-e[3])/2,n[0],n[1]]}var TN={initial:!0},qn={detector:null,landmarks:null},Cd={detector:[224,224],landmarks:[256,256]},jb=Number.MAX_SAFE_INTEGER,dxe={landmarks:["ld_3d","activation_segmentation","activation_heatmap","world_3d","output_poseflag"],detector:[]},o1=null,nf,vi=[[0,0],[0,0],[0,0],[0,0]],IN=0,CN=e=>1-1/(1+Math.exp(e));async function NN(e){var t;if(TN.initial&&(qn.detector=null),!qn.detector&&e.body.detector&&e.body.detector.modelPath){qn.detector=await qe(e.body.detector.modelPath);let n=(t=qn.detector)!=null&&t.executor?Object.values(qn.detector.modelSignature.inputs):void 0;Cd.detector[0]=Array.isArray(n)?parseInt(n[0].tensorShape.dim[1].size):0,Cd.detector[1]=Array.isArray(n)?parseInt(n[0].tensorShape.dim[2].size):0}else e.debug&&qn.detector&&ne("cached model:",qn.detector.modelUrl);return kN(),qn.detector}async function EN(e){var t;if(TN.initial&&(qn.landmarks=null),qn.landmarks)e.debug&&ne("cached model:",qn.landmarks.modelUrl);else{qn.landmarks=await qe(e.body.modelPath);let n=(t=qn.landmarks)!=null&&t.executor?Object.values(qn.landmarks.modelSignature.inputs):void 0;Cd.landmarks[0]=Array.isArray(n)?parseInt(n[0].tensorShape.dim[1].size):0,Cd.landmarks[1]=Array.isArray(n)?parseInt(n[0].tensorShape.dim[2].size):0}return qn.landmarks}function pxe(e,t){var r,a;let n={};if(!((r=e==null?void 0:e.shape)!=null&&r[1])||!((a=e==null?void 0:e.shape)!=null&&a[2]))return e;let s;if(nf&&(n.cropped=Ce.cropAndResize(e,[nf],[0],[e.shape[1],e.shape[2]])),e.shape[1]!==e.shape[2]){let o=[e.shape[2]>e.shape[1]?Math.trunc((e.shape[2]-e.shape[1])/2):0,e.shape[2]>e.shape[1]?Math.trunc((e.shape[2]-e.shape[1])/2):0],i=[e.shape[1]>e.shape[2]?Math.trunc((e.shape[1]-e.shape[2])/2):0,e.shape[1]>e.shape[2]?Math.trunc((e.shape[1]-e.shape[2])/2):0];vi=[[0,0],o,i,[0,0]],n.pad=rr(n.cropped||e,vi),n.resize=Ce.resizeBilinear(n.pad,[t,t]),s=ge(n.resize,ot.tf255)}else e.shape[1]!==t?(n.resize=Ce.resizeBilinear(n.cropped||e,[t,t]),s=ge(n.resize,ot.tf255)):s=ge(n.cropped||e,ot.tf255);return Object.keys(n).forEach(o=>ee(n[o])),s}function hxe(e,t){for(let n of e)n.position=[Math.trunc(n.position[0]*(t[0]+vi[2][0]+vi[2][1])/t[0]-vi[2][0]),Math.trunc(n.position[1]*(t[1]+vi[1][0]+vi[1][1])/t[1]-vi[1][0]),n.position[2]],n.positionRaw=[n.position[0]/t[0],n.position[1]/t[1],2*n.position[2]/(t[0]+t[1])];if(nf)for(let n of e)n.positionRaw=[n.positionRaw[0]+nf[1],n.positionRaw[1]+nf[0],n.positionRaw[2]],n.position=[Math.trunc(n.positionRaw[0]*t[0]),Math.trunc(n.positionRaw[1]*t[1]),n.positionRaw[2]];return e}function fxe(e){let t=e.find(i=>i.part==="leftPalm"),n=e.find(i=>i.part==="leftWrist"),s=e.find(i=>i.part==="leftIndex");t.position[2]=((n.position[2]||0)+(s.position[2]||0))/2;let r=e.find(i=>i.part==="rightPalm"),a=e.find(i=>i.part==="rightWrist"),o=e.find(i=>i.part==="rightIndex");r.position[2]=((a.position[2]||0)+(o.position[2]||0))/2}async function mxe(e,t,n){var f,m;if(!((f=qn.landmarks)!=null&&f.executor))return null;let s={};[s.ld,s.segmentation,s.heatmap,s.world,s.poseflag]=(m=qn.landmarks)==null?void 0:m.execute(e,dxe.landmarks);let r=(await s.poseflag.data())[0],a=await s.ld.data(),o=await s.world.data();Object.keys(s).forEach(g=>ee(s[g]));let i=[],l=5;for(let g=0;g<a.length/l;g++){let y=CN(a[l*g+3]),x=CN(a[l*g+4]),A=Math.trunc(100*y*x*r)/100,b=[a[l*g+0]/Cd.landmarks[0],a[l*g+1]/Cd.landmarks[1],a[l*g+2]+0],w=[Math.trunc(n[0]*b[0]),Math.trunc(n[1]*b[1]),b[2]],k=[o[l*g+0],o[l*g+1],o[l*g+2]+0];i.push({part:Gb[g],positionRaw:b,position:w,distance:k,score:A})}if(r<(t.body.minConfidence||0))return null;fxe(i);let u=hxe(i,n),c=u.map(g=>g.position),p=Pa(c,[n[0],n[1]]),d={};for(let[g,y]of Object.entries(Hb)){let x=[];for(let A=0;A<y.length-1;A++){let b=u.find(k=>k.part===y[A]),w=u.find(k=>k.part===y[A+1]);b&&w&&x.push([b.position,w.position])}d[g]=x}return{id:0,score:Math.trunc(100*r)/100,box:p.box,boxRaw:p.boxRaw,keypoints:u,annotations:d}}async function qb(e,t){let n=[e.shape[2]||0,e.shape[1]||0],s=(t.body.skipTime||0)>ue()-IN,r=jb<(t.body.skipFrames||0);if(t.skipAllowed&&s&&r&&o1!==null)jb++;else{let a={};a.landmarks=pxe(e,256),o1=await mxe(a.landmarks,t,n),Object.keys(a).forEach(o=>ee(a[o])),IN=ue(),jb=0}return o1?[o1]:[]}var Td=[{class:1,label:"person"},{class:2,label:"bicycle"},{class:3,label:"car"},{class:4,label:"motorcycle"},{class:5,label:"airplane"},{class:6,label:"bus"},{class:7,label:"train"},{class:8,label:"truck"},{class:9,label:"boat"},{class:10,label:"traffic light"},{class:11,label:"fire hydrant"},{class:12,label:"stop sign"},{class:13,label:"parking meter"},{class:14,label:"bench"},{class:15,label:"bird"},{class:16,label:"cat"},{class:17,label:"dog"},{class:18,label:"horse"},{class:19,label:"sheep"},{class:20,label:"cow"},{class:21,label:"elephant"},{class:22,label:"bear"},{class:23,label:"zebra"},{class:24,label:"giraffe"},{class:25,label:"backpack"},{class:26,label:"umbrella"},{class:27,label:"handbag"},{class:28,label:"tie"},{class:29,label:"suitcase"},{class:30,label:"frisbee"},{class:31,label:"skis"},{class:32,label:"snowboard"},{class:33,label:"sports ball"},{class:34,label:"kite"},{class:35,label:"baseball bat"},{class:36,label:"baseball glove"},{class:37,label:"skateboard"},{class:38,label:"surfboard"},{class:39,label:"tennis racket"},{class:40,label:"bottle"},{class:41,label:"wine 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e4(e,t){if(!(kn!=null&&kn.executor))return[];let n=(t.body.skipTime||0)>ue()-PN,s=Qb<(t.body.skipFrames||0);return t.skipAllowed&&n&&s&&Object.keys(hs.keypoints).length>0?(Qb++,[hs]):(Qb=0,new Promise(async r=>{let a=Y(()=>{if(!(kn!=null&&kn.inputs[0].shape))return null;let p=Ce.resizeBilinear(e,[kn.inputs[0].shape[2],kn.inputs[0].shape[1]],!1),d=z(p,ot.tf2);return ye(d,ot.tf1)}),o;if(t.body.enabled&&(o=kn==null?void 0:kn.execute(a)),PN=ue(),ee(a),o){hs.keypoints.length=0;let p=at(o);ee(o);let d=Mn(p,2);ee(p);for(let h=0;h<d.length;h++){let[f,m,g]=await yxe(d[h],t.body.minConfidence);g>(t.body.minConfidence||0)&&hs.keypoints.push({score:Math.round(100*g)/100,part:Yb[h],positionRaw:[f/kn.inputs[0].shape[2],m/kn.inputs[0].shape[1]],position:[Math.round(e.shape[2]*f/kn.inputs[0].shape[2]),Math.round(e.shape[1]*m/kn.inputs[0].shape[1])]})}d.forEach(h=>ee(h))}hs.score=hs.keypoints.reduce((p,d)=>d.score>p?d.score:p,0);let i=hs.keypoints.map(p=>p.position[0]),l=hs.keypoints.map(p=>p.position[1]);hs.box=[Math.min(...i),Math.min(...l),Math.max(...i)-Math.min(...i),Math.max(...l)-Math.min(...l)];let u=hs.keypoints.map(p=>p.positionRaw[0]),c=hs.keypoints.map(p=>p.positionRaw[1]);hs.boxRaw=[Math.min(...u),Math.min(...c),Math.max(...u)-Math.min(...u),Math.max(...c)-Math.min(...c)];for(let[p,d]of Object.entries(Jb)){let h=[];for(let f=0;f<d.length-1;f++){let m=hs.keypoints.find(y=>y.part===d[f]),g=hs.keypoints.find(y=>y.part===d[f+1]);m&&g&&m.score>(t.body.minConfidence||0)&&g.score>(t.body.minConfidence||0)&&h.push([m.position,g.position])}hs.annotations[p]=h}r([hs])}))}var Axe=["angry","disgust","fear","happy","sad","surprise","neutral"],ir,l1=[],MN=0,zN=0,t4=Number.MAX_SAFE_INTEGER;async function LN(e){var t;return me.initial&&(ir=null),ir?e.debug&&ne("cached model:",ir.modelUrl):ir=await qe((t=e.face.emotion)==null?void 0:t.modelPath),ir}async function n4(e,t,n,s){var o,i;if(!ir)return[];let r=t4<(((o=t.face.emotion)==null?void 0:o.skipFrames)||0),a=(((i=t.face.emotion)==null?void 0:i.skipTime)||0)>ue()-zN;return t.skipAllowed&&a&&r&&MN===s&&l1[n]&&l1[n].length>0?(t4++,l1[n]):(t4=0,new Promise(async l=>{var c;let u=[];if((c=t.face.emotion)!=null&&c.enabled){let p={},d=ir!=null&&ir.inputs[0].shape?ir.inputs[0].shape[2]:0;p.resize=Ce.resizeBilinear(e,[d,d],!1),p.channels=z(p.resize,ot.rgb),p.grayscale=Se(p.channels,3,!0),p.grayscaleSub=ye(p.grayscale,ot.tf05),p.grayscaleMul=z(p.grayscaleSub,ot.tf2),p.emotion=ir==null?void 0:ir.execute(p.grayscaleMul),zN=ue();let h=await p.emotion.data();for(let f=0;f<h.length;f++)h[f]>(t.face.emotion.minConfidence||0)&&u.push({score:Math.min(.99,Math.trunc(100*h[f])/100),emotion:Axe[f]});u.sort((f,m)=>m.score-f.score),Object.keys(p).forEach(f=>ee(p[f]))}l1[n]=u,MN=s,l(u)}))}var Vs,s4=[],WN=0,VN=0,UN=Number.MAX_SAFE_INTEGER;async function GN(e){var t;return me.initial&&(Vs=null),Vs?e.debug&&ne("cached model:",Vs.modelUrl):Vs=await 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r=0;r<Lb.length;r++){let{key:a,indices:o}=Lb[r],i=wr[`${n}${a}`];if(!s||s.includes(a))for(let l=0;l<o.length;l++){let u=o[l];e[i[l]]=[t[u][0],t[u][1],(t[u][2]+e[i[l]][2])/2]}}}var bxe=e=>{let t=e[Nd.leftBounds[0]][2],n=e[Nd.rightBounds[0]][2];return t-n},YN=(e,t,n,s,r,a=!1)=>{let o=t1(e1(dN([e[n],e[s]]),xxe)),i=Sd(o),l=Ce.cropAndResize(t,[[o.startPoint[1]/r,o.startPoint[0]/r,o.endPoint[1]/r,o.endPoint[0]/r]],[0],[wi,wi]);if(a&&me.kernels.includes("flipleftright")){let u=Ce.flipLeftRight(l);ee(l),l=u}return{box:o,boxSize:i,crop:l}},JN=(e,t,n,s=!1)=>{let r=[];for(let a=0;a<Ed.numCoordinates;a++){let o=e[a*3],i=e[a*3+1],l=e[a*3+2];r.push([(s?1-o/wi:o/wi)*n[0]+t.startPoint[0],i/wi*n[1]+t.startPoint[1],l])}return{rawCoords:r,iris:r.slice(Ed.index)}},QN=(e,t,n)=>{let s=e[wr[`${n}EyeUpper0`][Ed.upperCenter]][2],r=e[wr[`${n}EyeLower0`][Ed.lowerCenter]][2],a=(s+r)/2;return t.map((o,i)=>{let l=a;return i===2?l=s:i===4&&(l=r),[o[0],o[1],l]})};async function tE(e,t,n){if(!(Gs!=null&&Gs.executor))return e;let{box:s,boxSize:r,crop:a}=YN(e,t,Nd.leftBounds[0],Nd.leftBounds[1],n,!0),{box:o,boxSize:i,crop:l}=YN(e,t,Nd.rightBounds[0],Nd.rightBounds[1],n,!0),u=Ct([a,l]);ee(a),ee(l);let c=Gs.execute(u);ee(u);let p=await c.data();ee(c);let d=p.slice(0,Ed.numCoordinates*3),{rawCoords:h,iris:f}=JN(d,s,r,!0),m=p.slice(Ed.numCoordinates*3),{rawCoords:g,iris:y}=JN(m,o,i,!1),x=bxe(e);Math.abs(x)<30?(u1(e,h,"left",null),u1(e,g,"right",null)):x<1?u1(e,h,"left",["EyeUpper0","EyeLower0"]):u1(e,g,"right",["EyeUpper0","EyeLower0"]);let A=QN(e,f,"left"),b=QN(e,y,"right");return e.concat(A).concat(b)}var vxe=[[61,146],[146,91],[91,181],[181,84],[84,17],[17,314],[314,405],[405,321],[321,375],[375,291],[61,185],[185,40],[40,39],[39,37],[37,0],[0,267],[267,269],[269,270],[270,409],[409,291],[78,95],[95,88],[88,178],[178,87],[87,14],[14,317],[317,402],[402,318],[318,324],[324,308],[78,191],[191,80],[80,81],[81,82],[82,13],[13,312],[312,311],[311,310],[310,415],[415,308]],wxe=[[263,249],[249,390],[390,373],[373,374],[374,380],[380,381],[381,382],[382,362],[263,466],[466,388],[388,387],[387,386],[386,385],[385,384],[384,398],[398,362]],kxe=[[276,283],[283,282],[282,295],[295,285],[300,293],[293,334],[334,296],[296,336]],Sxe=[[474,475],[475,476],[476,477],[477,474]],Ixe=[[33,7],[7,163],[163,144],[144,145],[145,153],[153,154],[154,155],[155,133],[33,246],[246,161],[161,160],[160,159],[159,158],[158,157],[157,173],[173,133]],Cxe=[[46,53],[53,52],[52,65],[65,55],[70,63],[63,105],[105,66],[66,107]],Txe=[[469,470],[470,471],[471,472],[472,469]],Nxe=[[10,338],[338,297],[297,332],[332,284],[284,251],[251,389],[389,356],[356,454],[454,323],[323,361],[361,288],[288,397],[397,365],[365,379],[379,378],[378,400],[400,377],[377,152],[152,148],[148,176],[176,149],[149,150],[150,136],[136,172],[172,58],[58,132],[132,93],[93,234],[234,127],[127,162],[162,21],[21,54],[54,103],[103,67],[67,109],[109,10]];function ki(e){let t=e.map(n=>n[0]);return t.push(e[e.length-1][1]),t}var Exe={lips:ki(vxe),leftEye:ki(wxe),leftEyebrow:ki(kxe),leftIris:ki(Sxe),rightEye:ki(Ixe),rightEyebrow:ki(Cxe),rightIris:ki(Txe),faceOval:ki(Nxe)},Rxe=Object.entries(Exe).map(([e,t])=>t.map(n=>[n,e])).flat(),PSe=new Map(Rxe),sf=[61,146,91,181,84,17,314,405,321,375,291,185,40,39,37,0,267,269,270,409,78,95,88,178,87,14,317,402,318,324,308,191,80,81,82,13,312,311,310,415,76,77,90,180,85,16,315,404,320,307,306,184,74,73,72,11,302,303,304,408,62,96,89,179,86,15,316,403,319,325,292,183,42,41,38,12,268,271,272,407],wu=[33,7,163,144,145,153,154,155,133,246,161,160,159,158,157,173,130,25,110,24,23,22,26,112,243,247,30,29,27,28,56,190,226,31,228,229,230,231,232,233,244,113,225,224,223,222,221,189,35,124,46,53,52,65,143,111,117,118,119,120,121,128,245,156,70,63,105,66,107,55,193],ku=[263,249,390,373,374,380,381,382,362,466,388,387,386,385,384,398,359,255,339,254,253,252,256,341,463,467,260,259,257,258,286,414,446,261,448,449,450,451,452,453,464,342,445,444,443,442,441,413,265,353,276,283,282,295,372,340,346,347,348,349,350,357,465,383,300,293,334,296,336,285,417];async function rE(e,t){var a,o,i,l,u,c,p,d,h,f;let n={lips:await((o=(a=t.filter(m=>m.size===160))==null?void 0:a[0])==null?void 0:o.data()),irisL:await((l=(i=t.filter(m=>m.size===10))==null?void 0:i[0])==null?void 0:l.data()),eyeL:await((c=(u=t.filter(m=>m.size===142))==null?void 0:u[0])==null?void 0:c.data()),irisR:await((d=(p=t.filter(m=>m.size===10))==null?void 0:p[1])==null?void 0:d.data()),eyeR:await((f=(h=t.filter(m=>m.size===142))==null?void 0:h[1])==null?void 0:f.data())};for(let m of Object.values(n))if(!m)return e;let s=wu.reduce((m,g)=>m+=e[g][2],0)/wu.length;for(let m=0;m<n.irisL.length/2;m++)e.push([n.irisL[2*m+0],n.irisL[2*m+1],s]);let r=ku.reduce((m,g)=>m+=e[g][2],0)/ku.length;for(let m=0;m<n.irisR.length/2;m++)e.push([n.irisR[2*m+0],n.irisR[2*m+1],r]);for(let m=0;m<n.eyeL.length/2;m++)e[wu[m]]=[n.eyeL[2*m+0],n.eyeL[2*m+1],e[wu[m]][2]];for(let m=0;m<n.eyeR.length/2;m++)e[ku[m]]=[n.eyeR[2*m+0],n.eyeR[2*m+1],e[ku[m]][2]];for(let m=0;m<n.lips.length/2;m++)e[sf[m]]=[n.lips[2*m+0],n.lips[2*m+1],e[sf[m]][2]];return e}var ua={boxes:[],skipped:Number.MAX_SAFE_INTEGER,timestamp:0},zt=null,rf=0;async function aE(e,t){var l,u,c,p,d,h,f,m,g,y;if(!(zt!=null&&zt.executor))return[];let n=(((l=t.face.detector)==null?void 0:l.skipTime)||0)>ue()-ua.timestamp,s=ua.skipped<(((u=t.face.detector)==null?void 0:u.skipFrames)||0);!t.skipAllowed||!n||!s||ua.boxes.length===0?(ua.boxes=await bN(e,t),ua.timestamp=ue(),ua.skipped=0):ua.skipped++;let r=[],a=[],o=0,i=rf;for(let x=0;x<ua.boxes.length;x++){let A=ua.boxes[x],b=0,w,k={id:o++,mesh:[],meshRaw:[],box:[0,0,0,0],boxRaw:[0,0,0,0],score:0,boxScore:0,faceScore:0,annotations:{}};if([b,w,k.tensor]=mN((c=t.face.detector)==null?void 0:c.rotation,A,e,(p=t.face.mesh)!=null&&p.enabled?rf:Id()),t.filter.equalization){let C=k.tensor?await G2(k.tensor):void 0;ee(k.tensor),C&&(k.tensor=C)}if(k.boxScore=Math.round(100*A.confidence)/100,(d=t.face.mesh)!=null&&d.enabled)if(!zt)t.debug&&ne("face mesh detection requested, but model is not loaded");else{if(((h=t.face.attention)==null?void 0:h.enabled)&&!me.kernels.includes("atan2"))return t.face.attention.enabled=!1,ee(k.tensor),r;let C=zt.execute(k.tensor),_=await C.find($=>$.shape[$.shape.length-1]===1).data();if(k.faceScore=Math.round(100*_[0])/100,k.faceScore<(((f=t.face.detector)==null?void 0:f.minConfidence)||1)){if(A.confidence=k.faceScore,t.face.mesh.keepInvalid){k.box=J2(A,e),k.boxRaw=Q2(A,e),k.score=k.boxScore,k.mesh=A.landmarks.map($=>[(A.startPoint[0]+A.endPoint[0])/2+(A.endPoint[0]+A.startPoint[0])*$[0]/Id(),(A.startPoint[1]+A.endPoint[1])/2+(A.endPoint[1]+A.startPoint[1])*$[1]/Id()]),k.meshRaw=k.mesh.map($=>[$[0]/(e.shape[2]||1),$[1]/(e.shape[1]||1),($[2]||0)/i]);for(let $ of Object.keys(Au))k.annotations[$]=[k.mesh[Au[$]]]}}else{let $=C.find(M=>M.shape[M.shape.length-1]===1404),R=V($,[-1,3]),P=await R.array();ee(R),(m=t.face.attention)!=null&&m.enabled?P=await rE(P,C):(g=t.face.iris)!=null&&g.enabled&&(P=await tE(P,k.tensor,rf)),k.mesh=fN(P,A,b,w,rf),k.meshRaw=k.mesh.map(M=>[M[0]/(e.shape[2]||0),M[1]/(e.shape[1]||0),(M[2]||0)/i]);for(let M of 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Object.keys(n).forEach(r=>ee(n[r])),s}normalizeLandmarks(t,n){let s={};s.reshape=V(t,[-1,7,2]),s.div=ge(s.reshape,this.inputSizeTensor),s.landmarks=de(s.div,this.anchors[n]?this.anchors[n]:0);let r=z(s.landmarks,this.inputSizeTensor);return Object.keys(s).forEach(a=>ee(s[a])),r}async predict(t,n){var i;let s={};s.resize=Ce.resizeBilinear(t,[this.inputSize,this.inputSize]),s.div=ge(s.resize,ot.tf127),s.image=ye(s.div,ot.tf1),s.batched=this.model.execute(s.image),s.predictions=at(s.batched),s.slice=Le(s.predictions,[0,0],[-1,1]),s.sigmoid=Pn(s.slice),s.scores=at(s.sigmoid);let r=await s.scores.data();s.boxes=Le(s.predictions,[0,1],[-1,4]),s.norm=this.normalizeBoxes(s.boxes),s.nms=await Ce.nonMaxSuppressionAsync(s.norm,s.scores,3*(((i=n.hand)==null?void 0:i.maxDetected)||1),n.hand.iouThreshold,n.hand.minConfidence);let a=await s.nms.array(),o=[];for(let l of a){let u={};u.box=Le(s.norm,[l,0],[1,-1]),u.slice=Le(s.predictions,[l,5],[1,14]),u.norm=this.normalizeLandmarks(u.slice,l),u.palmLandmarks=V(u.norm,[-1,2]);let c=await u.box.data(),p=c.slice(0,2),d=c.slice(2,4),h=await u.palmLandmarks.array(),f={startPoint:p,endPoint:d,palmLandmarks:h,confidence:r[l]},m=mE(f,[(t.shape[2]||1)/this.inputSize,(t.shape[1]||0)/this.inputSize]);o.push(m),Object.keys(u).forEach(g=>ee(u[g]))}return Object.keys(s).forEach(l=>ee(s[l])),o}};var Oxe=5,bE=1.65,vE=[0,5,9,13,17,1,2],Mxe=0,zxe=2,wE=0,f1=class{constructor(t,n){fe(this,"handDetector");fe(this,"handPoseModel");fe(this,"inputSize");fe(this,"storedBoxes");fe(this,"skipped");fe(this,"detectedHands");var s,r,a;this.handDetector=t,this.handPoseModel=n,this.inputSize=((a=(r=(s=this.handPoseModel)==null?void 0:s.inputs)==null?void 0:r[0].shape)==null?void 0:a[2])||0,this.storedBoxes=[],this.skipped=Number.MAX_SAFE_INTEGER,this.detectedHands=0}calculateLandmarksBoundingBox(t){let n=t.map(o=>o[0]),s=t.map(o=>o[1]),r=[Math.min(...n),Math.min(...s)],a=[Math.max(...n),Math.max(...s)];return{startPoint:r,endPoint:a}}getBoxForPalmLandmarks(t,n){let s=t.map(a=>m4([...a,1],n)),r=this.calculateLandmarksBoundingBox(s);return d1(p1(r),Oxe)}getBoxForHandLandmarks(t){let n=this.calculateLandmarksBoundingBox(t),s=d1(p1(n),bE);s.palmLandmarks=[];for(let r=0;r<vE.length;r++)s.palmLandmarks.push(t[vE[r]].slice(0,2));return s}transformRawCoords(t,n,s,r){let a=c1(n),o=[a[0]/this.inputSize,a[1]/this.inputSize,(a[0]+a[1])/this.inputSize/2],i=t.map(h=>[o[0]*(h[0]-this.inputSize/2),o[1]*(h[1]-this.inputSize/2),o[2]*h[2]]),l=f4(s,[0,0]),u=i.map(h=>[...m4(h,l),h[2]]),c=yE(r),p=[...af(n),1],d=[Ii(p,c[0]),Ii(p,c[1])];return u.map(h=>[Math.trunc(h[0]+d[0]),Math.trunc(h[1]+d[1]),Math.trunc(h[2])])}async estimateHands(t,n){let s=!1,r,a=(n.hand.skipTime||0)>ue()-wE,o=this.skipped<(n.hand.skipFrames||0);n.skipAllowed&&a&&o&&(r=await 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_={landmarks:C,confidence:b,boxConfidence:u.confidence,fingerConfidence:b,box:{topLeft:E.startPoint,bottomRight:E.endPoint}};i.push(_)}else this.storedBoxes[l]=null;ee(A)}else{let c=d1(p1(u),bE),p={confidence:u.confidence,boxConfidence:u.confidence,fingerConfidence:0,box:{topLeft:c.startPoint,bottomRight:c.endPoint},landmarks:[]};i.push(p)}}return this.storedBoxes=this.storedBoxes.filter(l=>l!==null),this.detectedHands=i.length,i.length>n.hand.maxDetected&&(i.length=n.hand.maxDetected),i}};var fs={thumb:0,index:1,middle:2,ring:3,pinky:4,all:[0,1,2,3,4],nameMapping:{0:"thumb",1:"index",2:"middle",3:"ring",4:"pinky"},pointsMapping:{0:[[0,1],[1,2],[2,3],[3,4]],1:[[0,5],[5,6],[6,7],[7,8]],2:[[0,9],[9,10],[10,11],[11,12]],3:[[0,13],[13,14],[14,15],[15,16]],4:[[0,17],[17,18],[18,19],[19,20]]},getName:e=>fs.nameMapping[e],getPoints:e=>fs.pointsMapping[e]},Ti={none:0,half:1,full:2,nameMapping:{0:"none",1:"half",2:"full"},getName:e=>Ti.nameMapping[e]},Xt={verticalUp:0,verticalDown:1,horizontalLeft:2,horizontalRight:3,diagonalUpRight:4,diagonalUpLeft:5,diagonalDownRight:6,diagonalDownLeft:7,nameMapping:{0:"verticalUp",1:"verticalDown",2:"horizontalLeft",3:"horizontalRight",4:"diagonalUpRight",5:"diagonalUpLeft",6:"diagonalDownRight",7:"diagonalDownLeft"},getName:e=>Xt.nameMapping[e]},Ci=class{constructor(t){fe(this,"name");fe(this,"curls");fe(this,"directions");fe(this,"weights");fe(this,"weightsRelative");this.name=t,this.curls={},this.directions={},this.weights=[1,1,1,1,1],this.weightsRelative=[1,1,1,1,1]}curl(t,n,s){typeof 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backend:",e.config.backend,r),!1}e.config.debug&&(OE=JSON.parse(JSON.stringify(H().flags)))}if((dn()==="humangl"||dn()==="webgl")&&(H().flagRegistry.WEBGL_USE_SHAPES_UNIFORMS&&H().set("WEBGL_USE_SHAPES_UNIFORMS",!0),H().flagRegistry.WEBGL_EXP_CONV&&H().set("WEBGL_EXP_CONV",!0),e.config.debug&&typeof e.config.deallocate!="undefined"&&e.config.deallocate&&(ne("changing webgl: WEBGL_DELETE_TEXTURE_THRESHOLD:",!0),H().set("WEBGL_DELETE_TEXTURE_THRESHOLD",0))),dn(),e.config.debug){let s=H().flags,r={};for(let a of Object.keys(s))OE[a]!==s[a]&&(r[a]=s[a]);e.config.debug&&Object.keys(r).length>0&&ne("backend:",dn(),"flags:",r)}if(e.config.flags&&Object.keys(e.config.flags).length>0){e.config.debug&&ne("flags:",e.config.flags);for(let[s,r]of Object.entries(e.config.flags))H().set(s,r)}Ky(),KT(),e.performance.initBackend=Math.trunc(ue()-n),e.config.backend=dn(),await me.updateBackend(),Xxe(e.config),me.initial=!1}return!0}function y1(e,t){for(let n of e){let s={kernelName:n,backendName:t.backend,kernelFunc:()=>{t.debug&&ne("kernelFunc",n,t.backend)}};nr(s)}me.kernels=na(dn()).map(n=>n.kernelName.toLowerCase())}var nn=[null,null],Kxe=["StatefulPartitionedCall/Postprocessor/Slice","StatefulPartitionedCall/Postprocessor/ExpandDims_1"],_i=[[0,0],[0,0]],Zxe=["hand","fist","pinch","point","face","tip","pinchtip"],zE=4,LE=1.6,Yxe=512,Jxe=1.4,A1=Number.MAX_SAFE_INTEGER,b4=0,Ma=[0,0],tn={boxes:[],hands:[]},BE={thumb:[1,2,3,4],index:[5,6,7,8],middle:[9,10,11,12],ring:[13,14,15,16],pinky:[17,18,19,20],base:[0],palm:[0,17,13,9,5,1,0]};async function WE(e){var t;if(me.initial&&(nn[0]=null),nn[0])e.debug&&ne("cached model:",nn[0].modelUrl);else{y1(["tensorlistreserve","enter","tensorlistfromtensor","merge","loopcond","switch","exit","tensorliststack","nextiteration","tensorlistsetitem","tensorlistgetitem","reciprocal","shape","split","where"],e),nn[0]=await qe((t=e.hand.detector)==null?void 0:t.modelPath);let n=nn[0].executor?Object.values(nn[0].modelSignature.inputs):void 0;_i[0][0]=Array.isArray(n)?parseInt(n[0].tensorShape.dim[1].size):0,_i[0][1]=Array.isArray(n)?parseInt(n[0].tensorShape.dim[2].size):0}return nn[0]}async function VE(e){var t;if(me.initial&&(nn[1]=null),nn[1])e.debug&&ne("cached model:",nn[1].modelUrl);else{nn[1]=await qe((t=e.hand.skeleton)==null?void 0:t.modelPath);let n=nn[1].executor?Object.values(nn[1].modelSignature.inputs):void 0;_i[1][0]=Array.isArray(n)?parseInt(n[0].tensorShape.dim[1].size):0,_i[1][1]=Array.isArray(n)?parseInt(n[0].tensorShape.dim[2].size):0}return nn[1]}async function Qxe(e,t){let n=[];if(!e||!nn[0])return n;let s={},r=(e.shape[2]||1)/(e.shape[1]||1),a=Math.min(Math.round((e.shape[1]||0)/8)*8,Yxe),o=Math.round(a*r/8)*8;s.resize=Ce.resizeBilinear(e,[a,o]),s.cast=Ae(s.resize,"int32"),[s.rawScores,s.rawBoxes]=await nn[0].executeAsync(s.cast,Kxe),s.boxes=at(s.rawBoxes,[0,2]),s.scores=at(s.rawScores,[0]);let i=Mn(s.scores,1);ee(i[zE]),i.splice(zE,1),s.filtered=un(i,1),ee(i),s.max=An(s.filtered,1),s.argmax=Ps(s.filtered,1);let l=0;s.nms=await Ce.nonMaxSuppressionAsync(s.boxes,s.max,(t.hand.maxDetected||0)+1,t.hand.iouThreshold||0,t.hand.minConfidence||1);let u=await s.nms.data(),c=await s.max.data(),p=await s.argmax.data();for(let d of Array.from(u)){let h=Le(s.boxes,d,1),f=await h.data();ee(h);let m=[f[1],f[0],f[3]-f[1],f[2]-f[0]],g=r1(m,Jxe),y=[Math.trunc(m[0]*Ma[0]),Math.trunc(m[1]*Ma[1]),Math.trunc(m[2]*Ma[0]),Math.trunc(m[3]*Ma[1])],x=c[d],A=Zxe[p[d]],b={id:l++,score:x,box:y,boxRaw:g,label:A};n.push(b)}return Object.keys(s).forEach(d=>ee(s[d])),n.sort((d,h)=>h.score-d.score),n.length>(t.hand.maxDetected||1)&&(n.length=t.hand.maxDetected||1),n}async function v4(e,t,n){let s={id:t.id,score:Math.round(100*t.score)/100,boxScore:Math.round(100*t.score)/100,fingerScore:0,box:t.box,boxRaw:t.boxRaw,label:t.label,keypoints:[],landmarks:{},annotations:{}};if(e&&nn[1]&&n.hand.landmarks&&t.score>(n.hand.minConfidence||0)){let r={},a=[t.boxRaw[1],t.boxRaw[0],t.boxRaw[3]+t.boxRaw[1],t.boxRaw[2]+t.boxRaw[0]];r.crop=Ce.cropAndResize(e,[a],[0],[_i[1][0],_i[1][1]],"bilinear"),r.div=ge(r.crop,ot.tf255),[r.score,r.keypoints]=nn[1].execute(r.div,["Identity_1","Identity"]);let o=(await r.score.data())[0],i=(100-Math.trunc(100/(1+Math.exp(o))))/100;if(i>=(n.hand.minConfidence||0)){s.fingerScore=i,r.reshaped=V(r.keypoints,[-1,3]);let c=(await r.reshaped.array()).map(p=>[p[0]/_i[1][1],p[1]/_i[1][0],p[2]||0]).map(p=>[p[0]*t.boxRaw[2],p[1]*t.boxRaw[3],p[2]||0]);s.keypoints=c.map(p=>[Ma[0]*(p[0]+t.boxRaw[0]),Ma[1]*(p[1]+t.boxRaw[1]),p[2]||0]),s.landmarks=m1(s.keypoints);for(let p of Object.keys(BE))s.annotations[p]=BE[p].map(d=>s.landmarks&&s.keypoints[d]?s.keypoints[d]:null)}Object.keys(r).forEach(l=>ee(r[l]))}return s}async function w4(e,t){var r,a;if(!((r=nn[0])!=null&&r.executor)||!((a=nn[1])!=null&&a.executor)||!nn[0].inputs[0].shape||!nn[1].inputs[0].shape)return[];Ma=[e.shape[2]||0,e.shape[1]||0],A1++;let n=(t.hand.skipTime||0)>ue()-b4,s=A1<(t.hand.skipFrames||0);return t.skipAllowed&&n&&s?tn.hands:new Promise(async o=>{let i=3*(t.hand.skipTime||0)>ue()-b4,l=A1<3*(t.hand.skipFrames||0);t.skipAllowed&&tn.hands.length===t.hand.maxDetected?tn.hands=await Promise.all(tn.boxes.map(c=>v4(e,c,t))):t.skipAllowed&&i&&l&&tn.hands.length>0?tn.hands=await Promise.all(tn.boxes.map(c=>v4(e,c,t))):(tn.boxes=await Qxe(e,t),b4=ue(),tn.hands=await Promise.all(tn.boxes.map(c=>v4(e,c,t))),A1=0);let u=[...tn.boxes];if(tn.boxes.length=0,t.cacheSensitivity>0)for(let c=0;c<tn.hands.length;c++){let p=SN(tn.hands[c].keypoints,Ma);if(p.box[2]/(e.shape[2]||1)>.05&&p.box[3]/(e.shape[1]||1)>.05&&tn.hands[c].fingerScore&&tn.hands[c].fingerScore>(t.hand.minConfidence||0)){let d=r1(p.box,LE),h=r1(p.boxRaw,LE);tn.boxes.push({...u[c],box:d,boxRaw:h})}}for(let c=0;c<tn.hands.length;c++){let p=Pa(tn.hands[c].keypoints,Ma);tn.hands[c].box=p.box,tn.hands[c].boxRaw=p.boxRaw}o(tn.hands)})}var Dn,x1=[],k4=Number.MAX_SAFE_INTEGER,GE=0,HE=0;async function jE(e){var t;return me.initial&&(Dn=null),Dn?e.debug&&ne("cached model:",Dn.modelUrl):Dn=await qe((t=e.face.liveness)==null?void 0:t.modelPath),Dn}async function S4(e,t,n,s){var o,i;if(!(Dn!=null&&Dn.executor))return 0;let r=(((o=t.face.liveness)==null?void 0:o.skipTime)||0)>ue()-HE,a=k4<(((i=t.face.liveness)==null?void 0:i.skipFrames)||0);return t.skipAllowed&&r&&a&&GE===s&&x1[n]?(k4++,x1[n]):(k4=0,new Promise(async l=>{let u=Ce.resizeBilinear(e,[Dn!=null&&Dn.inputs[0].shape?Dn.inputs[0].shape[2]:0,Dn!=null&&Dn.inputs[0].shape?Dn.inputs[0].shape[1]:0],!1),c=Dn==null?void 0:Dn.execute(u),p=(await c.data())[0];x1[n]=Math.round(100*p)/100,GE=s,HE=ue(),ee([u,c]),l(x1[n])}))}var lf={};ma(lf,{connected:()=>v1,horizontal:()=>I4,kpt:()=>b1,relative:()=>T4,vertical:()=>C4});var b1=["nose","leftEye","rightEye","leftEar","rightEar","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle"],I4=[["leftEye","rightEye"],["leftEar","rightEar"],["leftShoulder","rightShoulder"],["leftElbow","rightElbow"],["leftWrist","rightWrist"],["leftHip","rightHip"],["leftKnee","rightKnee"],["leftAnkle","rightAnkle"]],C4=[["leftKnee","leftShoulder"],["rightKnee","rightShoulder"],["leftAnkle","leftKnee"],["rightAnkle","rightKnee"]],T4=[[["leftHip","rightHip"],["leftShoulder","rightShoulder"]],[["leftElbow","rightElbow"],["leftShoulder","rightShoulder"]]],v1={leftLeg:["leftHip","leftKnee","leftAnkle"],rightLeg:["rightHip","rightKnee","rightAnkle"],torso:["leftShoulder","rightShoulder","rightHip","leftHip","leftShoulder"],leftArm:["leftShoulder","leftElbow","leftWrist"],rightArm:["rightShoulder","rightElbow","rightWrist"],head:[]};var XE=.005,Hs={keypoints:[],padding:[[0,0],[0,0],[0,0],[0,0]]};function N4(e){for(let t of I4){let n=e.keypoints.findIndex(r=>r.part===t[0]),s=e.keypoints.findIndex(r=>r.part===t[1]);if(e.keypoints[n]&&e.keypoints[s]&&e.keypoints[n].position[0]<e.keypoints[s].position[0]){let r=e.keypoints[n];e.keypoints[n]=e.keypoints[s],e.keypoints[s]=r}}for(let t of C4){let n=e.keypoints.findIndex(r=>r&&r.part===t[0]),s=e.keypoints.findIndex(r=>r&&r.part===t[1]);e.keypoints[n]&&e.keypoints[s]&&e.keypoints[n].position[1]<e.keypoints[s].position[1]&&e.keypoints.splice(n,1)}for(let[t,n]of T4){let s=e.keypoints.findIndex(u=>u&&u.part===t[0]),r=e.keypoints.findIndex(u=>u&&u.part===t[1]),a=e.keypoints.findIndex(u=>u&&u.part===n[0]),o=e.keypoints.findIndex(u=>u&&u.part===n[1]);if(!e.keypoints[a]||!e.keypoints[o])continue;let i=e.keypoints[s]?[Math.abs(e.keypoints[a].position[0]-e.keypoints[s].position[0]),Math.abs(e.keypoints[o].position[0]-e.keypoints[s].position[0])]:[0,0],l=e.keypoints[r]?[Math.abs(e.keypoints[o].position[0]-e.keypoints[r].position[0]),Math.abs(e.keypoints[a].position[0]-e.keypoints[r].position[0])]:[0,0];if(i[0]>i[1]||l[0]>l[1]){let u=e.keypoints[s];e.keypoints[s]=e.keypoints[r],e.keypoints[r]=u}}}function KE(e){for(let t=0;t<e.length;t++)if(e[t]&&Hs.keypoints[t]){let n=[Math.abs(e[t].positionRaw[0]-Hs.keypoints[t].positionRaw[0]),Math.abs(e[t].positionRaw[1]-Hs.keypoints[t].positionRaw[1])];n[0]<XE&&n[1]<XE?e[t]=Hs.keypoints[t]:Hs.keypoints[t]=e[t]}else Hs.keypoints[t]=e[t];return e}function ZE(e,t){var r,a;let n={};if(!((r=e==null?void 0:e.shape)!=null&&r[1])||!((a=e==null?void 0:e.shape)!=null&&a[2]))return e;Hs.padding=[[0,0],[e.shape[2]>e.shape[1]?Math.trunc((e.shape[2]-e.shape[1])/2):0,e.shape[2]>e.shape[1]?Math.trunc((e.shape[2]-e.shape[1])/2):0],[e.shape[1]>e.shape[2]?Math.trunc((e.shape[1]-e.shape[2])/2):0,e.shape[1]>e.shape[2]?Math.trunc((e.shape[1]-e.shape[2])/2):0],[0,0]],n.pad=rr(e,Hs.padding),n.resize=Ce.resizeBilinear(n.pad,[t,t]);let s=Ae(n.resize,"int32");return Object.keys(n).forEach(o=>ee(n[o])),s}function YE(e,t){e.keypoints=e.keypoints.filter(s=>s==null?void 0:s.position);for(let s of e.keypoints)s.position=[s.position[0]*(t[0]+Hs.padding[2][0]+Hs.padding[2][1])/t[0]-Hs.padding[2][0],s.position[1]*(t[1]+Hs.padding[1][0]+Hs.padding[1][1])/t[1]-Hs.padding[1][0]],s.positionRaw=[s.position[0]/t[0],s.position[1]/t[1]];let n=Pa(e.keypoints.map(s=>s.position),t);return e.box=n.box,e.boxRaw=n.boxRaw,e}var fn,w1=0,E4=Number.MAX_SAFE_INTEGER,Eu={boxes:[],bodies:[],last:0};async function JE(e){var t;return me.initial&&(fn=null),fn?e.debug&&ne("cached model:",fn.modelUrl):(y1(["size"],e),fn=await qe(e.body.modelPath)),w1=(fn==null?void 0:fn.executor)&&((t=fn==null?void 0:fn.inputs)==null?void 0:t[0].shape)?fn.inputs[0].shape[2]:0,w1<64&&(w1=256),fn}function tbe(e,t,n){let s=e[0][0],r=[],a=0;for(let c=0;c<s.length;c++)if(a=s[c][2],a>t.body.minConfidence){let p=[s[c][1],s[c][0]];r.push({score:Math.round(100*a)/100,part:b1[c],positionRaw:p,position:[Math.round((n.shape[2]||0)*p[0]),Math.round((n.shape[1]||0)*p[1])]})}a=r.reduce((c,p)=>p.score>c?p.score:c,0);let o=[],i=Pa(r.map(c=>c.position),[n.shape[2],n.shape[1]]),l={};for(let[c,p]of Object.entries(v1)){let d=[];for(let h=0;h<p.length-1;h++){let f=r.find(g=>g.part===p[h]),m=r.find(g=>g.part===p[h+1]);f&&m&&f.score>(t.body.minConfidence||0)&&m.score>(t.body.minConfidence||0)&&d.push([f.position,m.position])}l[c]=d}let u={id:0,score:a,box:i.box,boxRaw:i.boxRaw,keypoints:r,annotations:l};return N4(u),o.push(u),o}function nbe(e,t,n){let s=[];for(let r=0;r<e[0].length;r++){let a=e[0][r],o=Math.round(100*a[51+4])/100;if(o>t.body.minConfidence){let i=[];for(let p=0;p<17;p++){let d=a[3*p+2];if(d>t.body.minConfidence){let h=[a[3*p+1],a[3*p+0]];i.push({part:b1[p],score:Math.round(100*d)/100,positionRaw:h,position:[Math.round((n.shape[2]||0)*h[0]),Math.round((n.shape[1]||0)*h[1])]})}}let l=Pa(i.map(p=>p.position),[n.shape[2],n.shape[1]]),u={};for(let[p,d]of Object.entries(v1)){let h=[];for(let f=0;f<d.length-1;f++){let m=i.find(y=>y.part===d[f]),g=i.find(y=>y.part===d[f+1]);m&&g&&m.score>(t.body.minConfidence||0)&&g.score>(t.body.minConfidence||0)&&h.push([m.position,g.position])}u[p]=h}let c={id:r,score:o,box:l.box,boxRaw:l.boxRaw,keypoints:[...i],annotations:u};N4(c),s.push(c)}}return s.sort((r,a)=>a.score-r.score),s.length>t.body.maxDetected&&(s.length=t.body.maxDetected),s}async function R4(e,t){var r;if(!(fn!=null&&fn.executor)||!((r=fn==null?void 0:fn.inputs)!=null&&r[0].shape))return[];t.skipAllowed||(Eu.boxes.length=0),E4++;let n=(t.body.skipTime||0)>ue()-Eu.last,s=E4<(t.body.skipFrames||0);return t.skipAllowed&&n&&s?Eu.bodies:new Promise(async a=>{let o={};E4=0,o.input=ZE(e,w1),o.res=fn==null?void 0:fn.execute(o.input),Eu.last=ue();let i=await o.res.array();Eu.bodies=o.res.shape[2]===17?tbe(i,t,e):nbe(i,t,e);for(let l of Eu.bodies)YE(l,[e.shape[2]||1,e.shape[1]||1]),KE(l.keypoints);Object.keys(o).forEach(l=>ee(o[l])),a(Eu.bodies)})}var kr,k1=[],eR=0,_4=Number.MAX_SAFE_INTEGER,I1=0,S1=2.5;async function tR(e){if(!kr||me.initial){kr=await qe(e.object.modelPath);let t=kr!=null&&kr.executor?Object.values(kr.modelSignature.inputs):void 0;I1=Array.isArray(t)?parseInt(t[0].tensorShape.dim[2].size):416}else e.debug&&ne("cached model:",kr.modelUrl);return kr}async function sbe(e,t,n){let s=0,r=[],a=I1;for(let u of[1,2,4]){let c=u*13,p=at(e.find(y=>y.shape[1]===c**2&&(y.shape[2]||0)===Td.length)),d=await p.array(),h=at(e.find(y=>y.shape[1]===c**2&&(y.shape[2]||0)<Td.length)),f=h.reshape([-1,4,h.shape[1]/4]),m=f.argMax(2),g=await m.array();for(let y=0;y<p.shape[0];y++)for(let x=0;x<p.shape[1];x++){let A=d[y][x];if(A>(n.object.minConfidence||0)&&x!==61){let b=(.5+Math.trunc(y%c))/c,w=(.5+Math.trunc(y/c))/c,k=g[y].map(M=>M*(c/u/a)),[C,E]=[b-S1/u*k[0],w-S1/u*k[1]],[_,$]=[b+S1/u*k[2]-C,w+S1/u*k[3]-E],R=[C,E,_,$];R=R.map(M=>Math.max(0,Math.min(M,1)));let P=[R[0]*t[0],R[1]*t[1],R[2]*t[0],R[3]*t[1]],S={id:s++,score:Math.round(100*A)/100,class:x+1,label:Td[x].label,box:P.map(M=>Math.trunc(M)),boxRaw:R};r.push(S)}}ee([p,h,f,m])}let o=r.map(u=>[u.boxRaw[1],u.boxRaw[0],u.boxRaw[3],u.boxRaw[2]]),i=r.map(u=>u.score),l=[];if(o&&o.length>0){let u=await Ce.nonMaxSuppressionAsync(o,i,n.object.maxDetected,n.object.iouThreshold,n.object.minConfidence);l=await u.data(),ee(u)}return r=r.filter((u,c)=>l.includes(c)).sort((u,c)=>c.score-u.score),r}async function 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Ze={tf255:255,tf1:1,tf2:2,tf05:.5,tf127:127.5,rgb:[.2989,.587,.114]};function nN(){Ze.tf255=Te(255,"float32"),Ze.tf1=Te(1,"float32"),Ze.tf2=Te(2,"float32"),Ze.tf05=Te(.5,"float32"),Ze.tf127=Te(127.5,"float32"),Ze.rgb=Ot([.2989,.587,.114],"float32")}var Cd=e=>[Math.abs(e.endPoint[0]-e.startPoint[0]),Math.abs(e.endPoint[1]-e.startPoint[1])],Y2=e=>[e.startPoint[0]+(e.endPoint[0]-e.startPoint[0])/2,e.startPoint[1]+(e.endPoint[1]-e.startPoint[1])/2,1],J2=(e,t)=>e?[Math.trunc(Math.max(0,e.startPoint[0])),Math.trunc(Math.max(0,e.startPoint[1])),Math.trunc(Math.min(t.shape[2]||0,e.endPoint[0])-Math.max(0,e.startPoint[0])),Math.trunc(Math.min(t.shape[1]||0,e.endPoint[1])-Math.max(0,e.startPoint[1]))]:[0,0,0,0],Q2=(e,t)=>e?[e.startPoint[0]/(t.shape[2]||0),e.startPoint[1]/(t.shape[1]||0),(e.endPoint[0]-e.startPoint[0])/(t.shape[2]||0),(e.endPoint[1]-e.startPoint[1])/(t.shape[1]||0)]:[0,0,0,0],aN=(e,t)=>{let n=[e.startPoint[0]*t[0],e.startPoint[1]*t[1]],s=[e.endPoint[0]*t[0],e.endPoint[1]*t[1]];return{startPoint:n,endPoint:s,landmarks:e.landmarks,confidence:e.confidence}},Mb=(e,t,n)=>{let s=t.shape[1],r=t.shape[2],a=[e.startPoint[1]/s,e.startPoint[0]/r,e.endPoint[1]/s,e.endPoint[0]/r],o=Ce.cropAndResize(t,[a],[0],n),i=ye(o,Ze.tf255);return Q(o),i},e1=(e,t)=>{let n=Y2(e),s=Cd(e),r=[t*s[0]/2,t*s[1]/2];return{startPoint:[n[0]-r[0],n[1]-r[1]],endPoint:[n[0]+r[0],n[1]+r[1]],landmarks:e.landmarks,confidence:e.confidence}},t1=e=>{let t=Y2(e),n=Cd(e),s=Math.max(...n)/2;return{startPoint:[Math.round(t[0]-s),Math.round(t[1]-s)],endPoint:[Math.round(t[0]+s),Math.round(t[1]+s)],landmarks:e.landmarks,confidence:e.confidence}},oN=e=>{let 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p=0;p<r.length;p++){let d=o[r[p]];if(d>(((c=t.face.detector)==null?void 0:c.minConfidence)||0)){let h={};h.bbox=Le(n.boxes,[r[p],0],[1,-1]),h.slice=Le(n.batch,[r[p],pN-1],[1,-1]),h.squeeze=Ke(h.slice),h.landmarks=V(h.squeeze,[pN,-1]);let f=await h.bbox.data(),m={startPoint:[f[0],f[1]],endPoint:[f[2],f[3]],landmarks:await h.landmarks.array(),confidence:d},g=aN(m,[(e.shape[2]||0)/Si,(e.shape[1]||0)/Si]),y=e1(g,t.face.scale||xxe),x=t1(y);a.push(x),Object.keys(h).forEach(A=>Q(h[A]))}}return Object.keys(n).forEach(p=>Q(n[p])),a}var n1={};xa(n1,{connected:()=>Wb,kpt:()=>Bb});var 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yN=224,vxe,wxe=5,s1=[8,16,32,32,32];function AN(){let e=[],t=0;for(;t<wxe;){let n=0,s=t;for(;s<s1.length&&s1[s]===s1[t];)n+=2,s++;let r=s1[t],a=Math.ceil(yN/r),o=Math.ceil(yN/r);for(let i=0;i<a;++i)for(let l=0;l<o;++l)for(let u=0;u<n;++u)e.push({x:(l+.5)/o,y:(i+.5)/a});t=s}vxe={x:Ot(e.map(n=>n.x)),y:Ot(e.map(n=>n.y))}}function za(e,t=[1,1]){let n=[e.map(i=>i[0]),e.map(i=>i[1])],s=[Math.min(...n[0]),Math.min(...n[1])],r=[Math.max(...n[0]),Math.max(...n[1])],a=[s[0],s[1],r[0]-s[0],r[1]-s[1]],o=[a[0]/t[0],a[1]/t[1],a[2]/t[0],a[3]/t[1]];return{box:a,boxRaw:o}}function xN(e,t=[1,1]){let n=[e.map(u=>u[0]),e.map(u=>u[1])],s=[Math.min(...n[0]),Math.min(...n[1])],r=[Math.max(...n[0]),Math.max(...n[1])],a=[(s[0]+r[0])/2,(s[1]+r[1])/2],o=Math.max(a[0]-s[0],a[1]-s[1],-a[0]+r[0],-a[1]+r[1]),i=[Math.trunc(a[0]-o),Math.trunc(a[1]-o),Math.trunc(2*o),Math.trunc(2*o)],l=[i[0]/t[0],i[1]/t[1],i[2]/t[0],i[3]/t[1]];return{box:i,boxRaw:l}}function r1(e,t){let n=[e[2]*t,e[3]*t];return[e[0]-(n[0]-e[2])/2,e[1]-(n[1]-e[3])/2,n[0],n[1]]}var wN={initial:!0},Xn={detector:null,landmarks:null},Nd={detector:[224,224],landmarks:[256,256]},Vb=Number.MAX_SAFE_INTEGER,Sxe={landmarks:["ld_3d","activation_segmentation","activation_heatmap","world_3d","output_poseflag"],detector:[]},o1=null,nf,Ii=[[0,0],[0,0],[0,0],[0,0]],bN=0,vN=e=>1-1/(1+Math.exp(e));async function kN(e){var t;if(wN.initial&&(Xn.detector=null),!Xn.detector&&e.body.detector&&e.body.detector.modelPath){Xn.detector=await Ve(e.body.detector.modelPath);let n=(t=Xn.detector)!=null&&t.executor?Object.values(Xn.detector.modelSignature.inputs):void 0;Nd.detector[0]=Array.isArray(n)?parseInt(n[0].tensorShape.dim[1].size):0,Nd.detector[1]=Array.isArray(n)?parseInt(n[0].tensorShape.dim[2].size):0}else e.debug&&Xn.detector&&ne("cached model:",Xn.detector.modelUrl);return AN(),Xn.detector}async function SN(e){var t;if(wN.initial&&(Xn.landmarks=null),Xn.landmarks)e.debug&&ne("cached 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Nxe(e,t,n){var f,m;if(!((f=Xn.landmarks)!=null&&f.executor))return null;let s={};[s.ld,s.segmentation,s.heatmap,s.world,s.poseflag]=(m=Xn.landmarks)==null?void 0:m.execute(e,Sxe.landmarks);let r=(await s.poseflag.data())[0],a=await s.ld.data(),o=await s.world.data();Object.keys(s).forEach(g=>Q(s[g]));let i=[],l=5;for(let g=0;g<a.length/l;g++){let y=vN(a[l*g+3]),x=vN(a[l*g+4]),A=Math.trunc(100*y*x*r)/100,b=[a[l*g+0]/Nd.landmarks[0],a[l*g+1]/Nd.landmarks[1],a[l*g+2]+0],w=[Math.trunc(n[0]*b[0]),Math.trunc(n[1]*b[1]),b[2]],k=[o[l*g+0],o[l*g+1],o[l*g+2]+0];i.push({part:Bb[g],positionRaw:b,position:w,distance:k,score:A})}if(r<(t.body.minConfidence||0))return null;Txe(i);let u=Cxe(i,n),c=u.map(g=>g.position),p=za(c,[n[0],n[1]]),d={};for(let[g,y]of Object.entries(Wb)){let x=[];for(let A=0;A<y.length-1;A++){let 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qb=["head","neck","rightShoulder","rightElbow","rightWrist","chest","leftShoulder","leftElbow","leftWrist","bodyCenter","rightHip","rightKnee","rightAnkle","leftHip","leftKnee","leftAnkle"],Xb={leftLeg:["leftHip","leftKnee","leftAnkle"],rightLeg:["rightHip","rightKnee","rightAnkle"],torso:["leftShoulder","rightShoulder","rightHip","leftHip","leftShoulder"],leftArm:["leftShoulder","leftElbow","leftWrist"],rightArm:["rightShoulder","rightElbow","rightWrist"],head:[]};var In,EN=0,gs={id:0,keypoints:[],box:[0,0,0,0],boxRaw:[0,0,0,0],score:0,annotations:{}},Kb=Number.MAX_SAFE_INTEGER;async function RN(e){return me.initial&&(In=null),In?e.debug&&ne("cached model:",In.modelUrl):In=await Ve(e.body.modelPath),In}async function Rxe(e,t){let[n,s]=e.shape,r=V(e,[s*n]),a=xn(r,0),o=(await a.data())[0];if(o>t){let i=Ms(r,0),l=pu(i,n),u=(await l.data())[0],c=ye(i,n),p=(await c.data())[0];return Q([r,a,i,l,c]),[u,p,o]}return Q([r,a]),[0,0,o]}async function 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d=p.slice(0,_d.numCoordinates*3),{rawCoords:h,iris:f}=MN(d,s,r,!0),m=p.slice(_d.numCoordinates*3),{rawCoords:g,iris:y}=MN(m,o,i,!1),x=$xe(e);Math.abs(x)<30?(u1(e,h,"left",null),u1(e,g,"right",null)):x<1?u1(e,h,"left",["EyeUpper0","EyeLower0"]):u1(e,g,"right",["EyeUpper0","EyeLower0"]);let A=zN(e,f,"left"),b=zN(e,y,"right");return e.concat(A).concat(b)}var 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Ti(e){let t=e.map(n=>n[0]);return t.push(e[e.length-1][1]),t}var Vxe={lips:Ti(Pxe),leftEye:Ti(Fxe),leftEyebrow:Ti(Oxe),leftIris:Ti(Mxe),rightEye:Ti(zxe),rightEyebrow:Ti(Lxe),rightIris:Ti(Bxe),faceOval:Ti(Wxe)},Uxe=Object.entries(Vxe).map(([e,t])=>t.map(n=>[n,e])).flat(),$Se=new 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function GN(e,t){var l,u,c,p,d,h,f,m,g,y;if(!(Lt!=null&&Lt.executor))return[];let n=(((l=t.face.detector)==null?void 0:l.skipTime)||0)>ue()-ha.timestamp,s=ha.skipped<(((u=t.face.detector)==null?void 0:u.skipFrames)||0);!t.skipAllowed||!n||!s||ha.boxes.length===0?(ha.boxes=await mN(e,t),ha.timestamp=ue(),ha.skipped=0):ha.skipped++;let r=[],a=[],o=0,i=rf;for(let x=0;x<ha.boxes.length;x++){let A=ha.boxes[x],b=0,w,k={id:o++,mesh:[],meshRaw:[],box:[0,0,0,0],boxRaw:[0,0,0,0],score:0,boxScore:0,faceScore:0,annotations:{}};if([b,w,k.tensor]=cN((c=t.face.detector)==null?void 0:c.rotation,A,e,(p=t.face.mesh)!=null&&p.enabled?rf:Td()),t.filter.equalization){let C=k.tensor?await G2(k.tensor):void 0;Q(k.tensor),C&&(k.tensor=C)}if(k.boxScore=Math.round(100*A.confidence)/100,(d=t.face.mesh)!=null&&d.enabled)if(!Lt)t.debug&&ne("face mesh detection requested, but model is not loaded");else{if(((h=t.face.attention)==null?void 0:h.enabled)&&!me.kernels.includes("atan2"))return t.face.attention.enabled=!1,Q(k.tensor),r;let C=Lt.execute(k.tensor),_=await C.find($=>$.shape[$.shape.length-1]===1).data();if(k.faceScore=Math.round(100*_[0])/100,k.faceScore<(((f=t.face.detector)==null?void 0:f.minConfidence)||1)){if(A.confidence=k.faceScore,t.face.mesh.keepInvalid){k.box=J2(A,e),k.boxRaw=Q2(A,e),k.score=k.boxScore,k.mesh=A.landmarks.map($=>[(A.startPoint[0]+A.endPoint[0])/2+(A.endPoint[0]+A.startPoint[0])*$[0]/Td(),(A.startPoint[1]+A.endPoint[1])/2+(A.endPoint[1]+A.startPoint[1])*$[1]/Td()]),k.meshRaw=k.mesh.map($=>[$[0]/(e.shape[2]||1),$[1]/(e.shape[1]||1),($[2]||0)/i]);for(let $ of Object.keys(wu))k.annotations[$]=[k.mesh[wu[$]]]}}else{let $=C.find(M=>M.shape[M.shape.length-1]===1404),R=V($,[-1,3]),P=await R.array();Q(R),(m=t.face.attention)!=null&&m.enabled?P=await UN(P,C):(g=t.face.iris)!=null&&g.enabled&&(P=await BN(P,k.tensor,rf)),k.mesh=uN(P,A,b,w,rf),k.meshRaw=k.mesh.map(M=>[M[0]/(e.shape[2]||0),M[1]/(e.shape[1]||0),(M[2]||0)/i]);for(let M of Object.keys(kr))k.annotations[M]=kr[M].map(L=>k.mesh[L]);k.score=k.faceScore;let S={...dN(k.mesh,A),confidence:A.confidence,landmarks:A.landmarks};k.box=J2(S,e),k.boxRaw=Q2(S,e),a.push(S)}Q(C)}else{k.box=J2(A,e),k.boxRaw=Q2(A,e),k.score=k.boxScore,k.mesh=A.landmarks.map(C=>[(A.startPoint[0]+A.endPoint[0])/2+(A.endPoint[0]+A.startPoint[0])*C[0]/Td(),(A.startPoint[1]+A.endPoint[1])/2+(A.endPoint[1]+A.startPoint[1])*C[1]/Td()]),k.meshRaw=k.mesh.map(C=>[C[0]/(e.shape[2]||0),C[1]/(e.shape[1]||0),(C[2]||0)/i]);for(let C of Object.keys(wu))k.annotations[C]=[k.mesh[wu[C]]]}k.score>(((y=t.face.detector)==null?void 0:y.minConfidence)||1)?r.push(k):Q(k.tensor)}return ha.boxes=a,r}async function HN(e){var t,n,s,r,a,o;return me.initial&&(Lt=null),((t=e.face.attention)==null?void 0:t.enabled)&&(Lt==null?void 0:Lt.signature)&&Object.keys(((n=Lt==null?void 0:Lt.signature)==null?void 0:n.outputs)||{}).length<6&&(Lt=null),Lt?e.debug&&ne("cached model:",Lt.modelUrl):(s=e.face.attention)!=null&&s.enabled?Lt=await Ve(e.face.attention.modelPath):Lt=await Ve((r=e.face.mesh)==null?void 0:r.modelPath),rf=Lt.executor&&((a=Lt==null?void 0:Lt.inputs)==null?void 0:a[0].shape)?(o=Lt==null?void 0:Lt.inputs)==null?void 0:o[0].shape[2]:256,Lt}var jN=ku,qN=ef;var Kn,Ni=[],XN=0,KN=0,n4=Number.MAX_SAFE_INTEGER;async function ZN(e){var t;return me.initial&&(Kn=null),Kn?e.debug&&ne("cached model:",Kn.modelUrl):Kn=await Ve((t=e.face.description)==null?void 0:t.modelPath),Kn}function s4(e){let t=e.image||e.tensor||e;if(!(Kn!=null&&Kn.inputs[0].shape))return t;let n=Ce.resizeBilinear(t,[Kn.inputs[0].shape[2],Kn.inputs[0].shape[1]],!1),s=z(n,Ze.tf255);return Q(n),s}async function r4(e,t,n,s){var i,l,u,c;let r={age:0,gender:"unknown",genderScore:0,descriptor:[]};if(!(Kn!=null&&Kn.executor))return r;let a=n4<(((i=t.face.description)==null?void 0:i.skipFrames)||0),o=(((l=t.face.description)==null?void 0:l.skipTime)||0)>ue()-XN;return 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Object.keys(n).forEach(r=>Q(n[r])),s}normalizeLandmarks(t,n){let s={};s.reshape=V(t,[-1,7,2]),s.div=ye(s.reshape,this.inputSizeTensor),s.landmarks=de(s.div,this.anchors[n]?this.anchors[n]:0);let r=z(s.landmarks,this.inputSizeTensor);return Object.keys(s).forEach(a=>Q(s[a])),r}async predict(t,n){var i;let s={};s.resize=Ce.resizeBilinear(t,[this.inputSize,this.inputSize]),s.div=ye(s.resize,Ze.tf127),s.image=Ae(s.div,Ze.tf1),s.batched=this.model.execute(s.image),s.predictions=Ke(s.batched),s.slice=Le(s.predictions,[0,0],[-1,1]),s.sigmoid=On(s.slice),s.scores=Ke(s.sigmoid);let r=await s.scores.data();s.boxes=Le(s.predictions,[0,1],[-1,4]),s.norm=this.normalizeBoxes(s.boxes),s.nms=await Ce.nonMaxSuppressionAsync(s.norm,s.scores,3*(((i=n.hand)==null?void 0:i.maxDetected)||1),n.hand.iouThreshold,n.hand.minConfidence);let a=await s.nms.array(),o=[];for(let l of a){let u={};u.box=Le(s.norm,[l,0],[1,-1]),u.slice=Le(s.predictions,[l,5],[1,14]),u.norm=this.normalizeLandmarks(u.slice,l),u.palmLandmarks=V(u.norm,[-1,2]);let c=await u.box.data(),p=c.slice(0,2),d=c.slice(2,4),h=await u.palmLandmarks.array(),f={startPoint:p,endPoint:d,palmLandmarks:h,confidence:r[l]},m=rE(f,[(t.shape[2]||1)/this.inputSize,(t.shape[1]||0)/this.inputSize]);o.push(m),Object.keys(u).forEach(g=>Q(u[g]))}return Object.keys(s).forEach(l=>Q(s[l])),o}};var Yxe=5,uE=1.65,cE=[0,5,9,13,17,1,2],Jxe=0,Qxe=2,dE=0,f1=class{constructor(t,n){fe(this,"handDetector");fe(this,"handPoseModel");fe(this,"inputSize");fe(this,"storedBoxes");fe(this,"skipped");fe(this,"detectedHands");var s,r,a;this.handDetector=t,this.handPoseModel=n,this.inputSize=((a=(r=(s=this.handPoseModel)==null?void 0:s.inputs)==null?void 0:r[0].shape)==null?void 0:a[2])||0,this.storedBoxes=[],this.skipped=Number.MAX_SAFE_INTEGER,this.detectedHands=0}calculateLandmarksBoundingBox(t){let n=t.map(o=>o[0]),s=t.map(o=>o[1]),r=[Math.min(...n),Math.min(...s)],a=[Math.max(...n),Math.max(...s)];return{startPoint:r,endPoint:a}}getBoxForPalmLandmarks(t,n){let s=t.map(a=>c4([...a,1],n)),r=this.calculateLandmarksBoundingBox(s);return d1(p1(r),Yxe)}getBoxForHandLandmarks(t){let n=this.calculateLandmarksBoundingBox(t),s=d1(p1(n),uE);s.palmLandmarks=[];for(let r=0;r<cE.length;r++)s.palmLandmarks.push(t[cE[r]].slice(0,2));return s}transformRawCoords(t,n,s,r){let a=c1(n),o=[a[0]/this.inputSize,a[1]/this.inputSize,(a[0]+a[1])/this.inputSize/2],i=t.map(h=>[o[0]*(h[0]-this.inputSize/2),o[1]*(h[1]-this.inputSize/2),o[2]*h[2]]),l=u4(s,[0,0]),u=i.map(h=>[...c4(h,l),h[2]]),c=oE(r),p=[...af(n),1],d=[Ei(p,c[0]),Ei(p,c[1])];return u.map(h=>[Math.trunc(h[0]+d[0]),Math.trunc(h[1]+d[1]),Math.trunc(h[2])])}async estimateHands(t,n){let s=!1,r,a=(n.hand.skipTime||0)>ue()-dE,o=this.skipped<(n.hand.skipFrames||0);n.skipAllowed&&a&&o&&(r=await this.handDetector.predict(t,n),this.skipped=0),n.skipAllowed&&this.skipped++,r&&r.length>0&&(r.length!==this.detectedHands&&this.detectedHands!==n.hand.maxDetected||!n.hand.landmarks)&&(this.detectedHands=0,this.storedBoxes=[...r],this.storedBoxes.length>0&&(s=!0));let i=[];for(let l=0;l<this.storedBoxes.length;l++){let u=this.storedBoxes[l];if(!!u)if(n.hand.landmarks){let c=n.hand.rotation?aE(u.palmLandmarks[Jxe],u.palmLandmarks[Qxe]):0,p=af(u),d=[p[0]/t.shape[2],p[1]/t.shape[1]],h=n.hand.rotation&&me.kernels.includes("rotatewithoffset")?Ce.rotateWithOffset(t,c,0,d):t.clone(),f=u4(-c,p),m=s?this.getBoxForPalmLandmarks(u.palmLandmarks,f):u,g=sE(m,h,[this.inputSize,this.inputSize]),y=ye(g,Ze.tf255);Q(g),Q(h);let[x,A]=this.handPoseModel.execute(y);dE=ue(),Q(y);let b=(await x.data())[0];if(Q(x),b>=n.hand.minConfidence/4){let w=V(A,[-1,3]),k=await w.array();Q(A),Q(w);let C=this.transformRawCoords(k,m,c,f),E=this.getBoxForHandLandmarks(C);this.storedBoxes[l]={...E,confidence:b};let _={landmarks:C,confidence:b,boxConfidence:u.confidence,fingerConfidence:b,box:{topLeft:E.startPoint,bottomRight:E.endPoint}};i.push(_)}else this.storedBoxes[l]=null;Q(A)}else{let c=d1(p1(u),uE),p={confidence:u.confidence,boxConfidence:u.confidence,fingerConfidence:0,box:{topLeft:c.startPoint,bottomRight:c.endPoint},landmarks:[]};i.push(p)}}return this.storedBoxes=this.storedBoxes.filter(l=>l!==null),this.detectedHands=i.length,i.length>n.hand.maxDetected&&(i.length=n.hand.maxDetected),i}};var ys={thumb:0,index:1,middle:2,ring:3,pinky:4,all:[0,1,2,3,4],nameMapping:{0:"thumb",1:"index",2:"middle",3:"ring",4:"pinky"},pointsMapping:{0:[[0,1],[1,2],[2,3],[3,4]],1:[[0,5],[5,6],[6,7],[7,8]],2:[[0,9],[9,10],[10,11],[11,12]],3:[[0,13],[13,14],[14,15],[15,16]],4:[[0,17],[17,18],[18,19],[19,20]]},getName:e=>ys.nameMapping[e],getPoints:e=>ys.pointsMapping[e]},_i={none:0,half:1,full:2,nameMapping:{0:"none",1:"half",2:"full"},getName:e=>_i.nameMapping[e]},Zt={verticalUp:0,verticalDown:1,horizontalLeft:2,horizontalRight:3,diagonalUpRight:4,diagonalUpLeft:5,diagonalDownRight:6,diagonalDownLeft:7,nameMapping:{0:"verticalUp",1:"verticalDown",2:"horizontalLeft",3:"horizontalRight",4:"diagonalUpRight",5:"diagonalUpLeft",6:"diagonalDownRight",7:"diagonalDownLeft"},getName:e=>Zt.nameMapping[e]},Ri=class{constructor(t){fe(this,"name");fe(this,"curls");fe(this,"directions");fe(this,"weights");fe(this,"weightsRelative");this.name=t,this.curls={},this.directions={},this.weights=[1,1,1,1,1],this.weightsRelative=[1,1,1,1,1]}curl(t,n,s){typeof this.curls[t]=="undefined"&&(this.curls[t]=[]),this.curls[t].push([n,s])}direction(t,n,s){this.directions[t]||(this.directions[t]=[]),this.directions[t].push([n,s])}weight(t,n){this.weights[t]=n;let s=this.weights.reduce((r,a)=>r+a,0);this.weightsRelative=this.weights.map(r=>r*5/s)}matchAgainst(t,n){let s=0;for(let r in t){let a=t[r],o=this.curls[r];if(typeof o=="undefined"){s+=this.weightsRelative[r];continue}for(let[i,l]of o)if(a===i){s+=l*this.weightsRelative[r];break}}for(let r in n){let a=n[r],o=this.directions[r];if(typeof o=="undefined"){s+=this.weightsRelative[r];continue}for(let[i,l]of o)if(a===i){s+=l*this.weightsRelative[r];break}}return s/10}};var{thumb:qr,index:La,middle:Ba,ring:Nu,pinky:Eu}=ys,{none:Xr,half:tbe,full:Kr}=_i,{verticalUp:Dd,verticalDown:eIe,horizontalLeft:d4,horizontalRight:nbe,diagonalUpRight:sbe,diagonalUpLeft:$d,diagonalDownRight:tIe,diagonalDownLeft:nIe}=Zt,Di=new Ri("thumbs up");Di.curl(qr,Xr,1);Di.direction(qr,Dd,1);Di.direction(qr,$d,.25);Di.direction(qr,sbe,.25);for(let e of[ys.index,ys.middle,ys.ring,ys.pinky])Di.curl(e,Kr,1),Di.direction(e,d4,1),Di.direction(e,nbe,1);var hn=new Ri("victory");hn.curl(qr,tbe,.5);hn.curl(qr,Xr,.5);hn.direction(qr,Dd,1);hn.direction(qr,$d,1);hn.curl(La,Xr,1);hn.direction(La,Dd,.75);hn.direction(La,$d,1);hn.curl(Ba,Xr,1);hn.direction(Ba,Dd,1);hn.direction(Ba,$d,.75);hn.curl(Nu,Kr,1);hn.direction(Nu,Dd,.2);hn.direction(Nu,$d,1);hn.direction(Nu,d4,.2);hn.curl(Eu,Kr,1);hn.direction(Eu,Dd,.2);hn.direction(Eu,$d,1);hn.direction(Eu,d4,.2);hn.weight(La,2);hn.weight(Ba,2);var $i=new Ri("point");$i.curl(qr,Kr,1);$i.curl(La,Xr,.5);$i.curl(Ba,Kr,.5);$i.curl(Nu,Kr,.5);$i.curl(Eu,Kr,.5);$i.weight(La,2);$i.weight(Ba,2);var Pi=new Ri("middle finger");Pi.curl(qr,Xr,1);Pi.curl(La,Kr,.5);Pi.curl(Ba,Kr,.5);Pi.curl(Nu,Kr,.5);Pi.curl(Eu,Kr,.5);Pi.weight(La,2);Pi.weight(Ba,2);var Pd=new Ri("open palm");Pd.curl(qr,Xr,.75);Pd.curl(La,Xr,.75);Pd.curl(Ba,Xr,.75);Pd.curl(Nu,Xr,.75);Pd.curl(Eu,Xr,.75);var pE=[Di,hn,$i,Pi,Pd];var rbe=.7,Ru={HALF_CURL_START_LIMIT:60,NO_CURL_START_LIMIT:130,DISTANCE_VOTE_POWER:1.1,SINGLE_ANGLE_VOTE_POWER:.9,TOTAL_ANGLE_VOTE_POWER:1.6};function hE(e,t,n,s){let r=(t-s)/(e-n),a=Math.atan(r)*180/Math.PI;return a<=0?a=-a:a>0&&(a=180-a),a}function mE(e,t){if(!e||!t)return[0,0];let n=hE(e[0],e[1],t[0],t[1]);if(e.length===2)return n;let s=hE(e[1],e[2],t[1],t[2]);return[n,s]}function fE(e,t=1){let n=0,s=0,r=0;return e>=75&&e<=105?n=1*t:e>=25&&e<=155?s=1*t:r=1*t,[n,s,r]}function abe(e,t,n){let s=e[0]-t[0],r=e[0]-n[0],a=t[0]-n[0],o=e[1]-t[1],i=e[1]-n[1],l=t[1]-n[1],u=e[2]-t[2],c=e[2]-n[2],p=t[2]-n[2],d=Math.sqrt(s*s+o*o+u*u),h=Math.sqrt(r*r+i*i+c*c),f=Math.sqrt(a*a+l*l+p*p),m=(f*f+d*d-h*h)/(2*f*d);m>1?m=1:m<-1&&(m=-1);let g=Math.acos(m);g=57.2958*g%180;let y;return g>Ru.NO_CURL_START_LIMIT?y=_i.none:g>Ru.HALF_CURL_START_LIMIT?y=_i.half:y=_i.full,y}function gE(e,t,n,s){let r;return s===Math.abs(e)?e>0?r=Zt.horizontalLeft:r=Zt.horizontalRight:s===Math.abs(t)?t>0?r=Zt.horizontalLeft:r=Zt.horizontalRight:n>0?r=Zt.horizontalLeft:r=Zt.horizontalRight,r}function yE(e,t,n,s){let r;return s===Math.abs(e)?e<0?r=Zt.verticalDown:r=Zt.verticalUp:s===Math.abs(t)?t<0?r=Zt.verticalDown:r=Zt.verticalUp:n<0?r=Zt.verticalDown:r=Zt.verticalUp,r}function obe(e,t,n,s,r,a,o,i){let l,u=yE(e,t,n,s),c=gE(r,a,o,i);return u===Zt.verticalUp?c===Zt.horizontalLeft?l=Zt.diagonalUpLeft:l=Zt.diagonalUpRight:c===Zt.horizontalLeft?l=Zt.diagonalDownLeft:l=Zt.diagonalDownRight,l}function ibe(e,t,n,s){let r=e[0]-t[0],a=e[0]-n[0],o=t[0]-n[0],i=e[1]-t[1],l=e[1]-n[1],u=t[1]-n[1],c=Math.max(Math.abs(r),Math.abs(a),Math.abs(o)),p=Math.max(Math.abs(i),Math.abs(l),Math.abs(u)),d=0,h=0,f=0,m=p/(c+1e-5);m>1.5?d+=Ru.DISTANCE_VOTE_POWER:m>.66?h+=Ru.DISTANCE_VOTE_POWER:f+=Ru.DISTANCE_VOTE_POWER;let g=Math.sqrt(r*r+i*i),y=Math.sqrt(a*a+l*l),x=Math.sqrt(o*o+u*u),A=Math.max(g,y,x),b=e[0],w=e[1],k=n[0],C=n[1];A===g?(k=n[0],C=n[1]):A===x&&(b=t[0],w=t[1]);let $=mE([b,w],[k,C]),R=fE($,Ru.TOTAL_ANGLE_VOTE_POWER);d+=R[0],h+=R[1],f+=R[2];for(let S of s){let M=fE(S,Ru.SINGLE_ANGLE_VOTE_POWER);d+=M[0],h+=M[1],f+=M[2]}let P;return d===Math.max(d,h,f)?P=yE(l,i,u,p):f===Math.max(h,f)?P=gE(a,r,o,c):P=obe(l,i,u,p,a,r,o,c),P}function AE(e){let t=[],n=[],s=[],r=[];if(!e)return{curls:s,directions:r};for(let a of ys.all){let o=ys.getPoints(a),i=[],l=[];for(let u of o){let c=e[u[0]],p=e[u[1]],d=mE(c,p),h=d[0],f=d[1];i.push(h),l.push(f)}t.push(i),n.push(l)}for(let a of ys.all){let o=a===ys.thumb?1:0,i=ys.getPoints(a),l=e[i[o][0]],u=e[i[o+1][1]],c=e[i[3][1]],p=abe(l,u,c),d=ibe(l,u,c,t[a].slice(o));s[a]=p,r[a]=d}return{curls:s,directions:r}}function m1(e){if(!e||e.length===0)return null;let t=AE(e),n={};for(let s of ys.all)n[ys.getName(s)]={curl:_i.getName(t.curls[s]),direction:Zt.getName(t.directions[s])};return n}function xE(e){let t=[];if(!e||e.length===0)return t;let n=AE(e);for(let s of pE){let r=s.matchAgainst(n.curls,n.directions);r>=rbe&&t.push({name:s.name,confidence:r})}return t}var bE={thumb:[1,2,3,4],index:[5,6,7,8],middle:[9,10,11,12],ring:[13,14,15,16],pinky:[17,18,19,20],palm:[0]},_u,Du,vE;async function h4(e,t){let n=await vE.estimateHands(e,t);if(!n)return[];let s=[];for(let r=0;r<n.length;r++){let a={};if(n[r].landmarks)for(let c of Object.keys(bE))a[c]=bE[c].map(p=>n[r].landmarks[p]);let o=n[r].landmarks,i=[Number.MAX_SAFE_INTEGER,Number.MAX_SAFE_INTEGER,0,0],l=[0,0,0,0];if(o&&o.length>0){for(let c of o)c[0]<i[0]&&(i[0]=c[0]),c[1]<i[1]&&(i[1]=c[1]),c[0]>i[2]&&(i[2]=c[0]),c[1]>i[3]&&(i[3]=c[1]);i[2]-=i[0],i[3]-=i[1],l=[i[0]/(e.shape[2]||0),i[1]/(e.shape[1]||0),i[2]/(e.shape[2]||0),i[3]/(e.shape[1]||0)]}else i=n[r].box?[Math.trunc(Math.max(0,n[r].box.topLeft[0])),Math.trunc(Math.max(0,n[r].box.topLeft[1])),Math.trunc(Math.min(e.shape[2]||0,n[r].box.bottomRight[0])-Math.max(0,n[r].box.topLeft[0])),Math.trunc(Math.min(e.shape[1]||0,n[r].box.bottomRight[1])-Math.max(0,n[r].box.topLeft[1]))]:[0,0,0,0],l=[n[r].box.topLeft[0]/(e.shape[2]||0),n[r].box.topLeft[1]/(e.shape[1]||0),(n[r].box.bottomRight[0]-n[r].box.topLeft[0])/(e.shape[2]||0),(n[r].box.bottomRight[1]-n[r].box.topLeft[1])/(e.shape[1]||0)];let u=m1(o);s.push({id:r,score:Math.round(100*n[r].confidence)/100,boxScore:Math.round(100*n[r].boxConfidence)/100,fingerScore:Math.round(100*n[r].fingerConfidence)/100,label:"hand",box:i,boxRaw:l,keypoints:o,annotations:a,landmarks:u})}return s}async function f4(e){var n,s;me.initial&&(_u=null,Du=null),!_u||!Du?[_u,Du]=await Promise.all([e.hand.enabled?Ve((n=e.hand.detector)==null?void 0:n.modelPath):null,e.hand.landmarks?Ve((s=e.hand.skeleton)==null?void 0:s.modelPath):null]):(e.debug&&ne("cached model:",_u.modelUrl),e.debug&&ne("cached model:",Du.modelUrl));let t=_u?new h1(_u):void 0;return t&&Du&&(vE=new f1(t,Du)),[_u,Du]}var nn=[null,null],lbe=["StatefulPartitionedCall/Postprocessor/Slice","StatefulPartitionedCall/Postprocessor/ExpandDims_1"],Fi=[[0,0],[0,0]],ube=["hand","fist","pinch","point","face","tip","pinchtip"],kE=4,SE=1.6,cbe=512,dbe=1.4,g1=Number.MAX_SAFE_INTEGER,m4=0,Wa=[0,0],tn={boxes:[],hands:[]},IE={thumb:[1,2,3,4],index:[5,6,7,8],middle:[9,10,11,12],ring:[13,14,15,16],pinky:[17,18,19,20],base:[0],palm:[0,17,13,9,5,1,0]};async function CE(e){var t;if(me.initial&&(nn[0]=null),nn[0])e.debug&&ne("cached model:",nn[0].modelUrl);else{y1(["tensorlistreserve","enter","tensorlistfromtensor","merge","loopcond","switch","exit","tensorliststack","nextiteration","tensorlistsetitem","tensorlistgetitem","reciprocal","shape","split","where"],e),nn[0]=await Ve((t=e.hand.detector)==null?void 0:t.modelPath);let n=nn[0].executor?Object.values(nn[0].modelSignature.inputs):void 0;Fi[0][0]=Array.isArray(n)?parseInt(n[0].tensorShape.dim[1].size):0,Fi[0][1]=Array.isArray(n)?parseInt(n[0].tensorShape.dim[2].size):0}return nn[0]}async function TE(e){var t;if(me.initial&&(nn[1]=null),nn[1])e.debug&&ne("cached model:",nn[1].modelUrl);else{nn[1]=await Ve((t=e.hand.skeleton)==null?void 0:t.modelPath);let n=nn[1].executor?Object.values(nn[1].modelSignature.inputs):void 0;Fi[1][0]=Array.isArray(n)?parseInt(n[0].tensorShape.dim[1].size):0,Fi[1][1]=Array.isArray(n)?parseInt(n[0].tensorShape.dim[2].size):0}return nn[1]}async function pbe(e,t){let n=[];if(!e||!nn[0])return n;let s={},r=(e.shape[2]||1)/(e.shape[1]||1),a=Math.min(Math.round((e.shape[1]||0)/8)*8,cbe),o=Math.round(a*r/8)*8;s.resize=Ce.resizeBilinear(e,[a,o]),s.cast=ge(s.resize,"int32"),[s.rawScores,s.rawBoxes]=await nn[0].executeAsync(s.cast,lbe),s.boxes=Ke(s.rawBoxes,[0,2]),s.scores=Ke(s.rawScores,[0]);let i=wn(s.scores,1);Q(i[kE]),i.splice(kE,1),s.filtered=un(i,1),Q(i),s.max=xn(s.filtered,1),s.argmax=Ms(s.filtered,1);let l=0;s.nms=await Ce.nonMaxSuppressionAsync(s.boxes,s.max,(t.hand.maxDetected||0)+1,t.hand.iouThreshold||0,t.hand.minConfidence||1);let u=await s.nms.data(),c=await s.max.data(),p=await s.argmax.data();for(let d of Array.from(u)){let h=Le(s.boxes,d,1),f=await h.data();Q(h);let m=[f[1],f[0],f[3]-f[1],f[2]-f[0]],g=r1(m,dbe),y=[Math.trunc(m[0]*Wa[0]),Math.trunc(m[1]*Wa[1]),Math.trunc(m[2]*Wa[0]),Math.trunc(m[3]*Wa[1])],x=c[d],A=ube[p[d]],b={id:l++,score:x,box:y,boxRaw:g,label:A};n.push(b)}return Object.keys(s).forEach(d=>Q(s[d])),n.sort((d,h)=>h.score-d.score),n.length>(t.hand.maxDetected||1)&&(n.length=t.hand.maxDetected||1),n}async function g4(e,t,n){let s={id:t.id,score:Math.round(100*t.score)/100,boxScore:Math.round(100*t.score)/100,fingerScore:0,box:t.box,boxRaw:t.boxRaw,label:t.label,keypoints:[],landmarks:{},annotations:{}};if(e&&nn[1]&&n.hand.landmarks&&t.score>(n.hand.minConfidence||0)){let r={},a=[t.boxRaw[1],t.boxRaw[0],t.boxRaw[3]+t.boxRaw[1],t.boxRaw[2]+t.boxRaw[0]];r.crop=Ce.cropAndResize(e,[a],[0],[Fi[1][0],Fi[1][1]],"bilinear"),r.div=ye(r.crop,Ze.tf255),[r.score,r.keypoints]=nn[1].execute(r.div,["Identity_1","Identity"]);let o=(await r.score.data())[0],i=(100-Math.trunc(100/(1+Math.exp(o))))/100;if(i>=(n.hand.minConfidence||0)){s.fingerScore=i,r.reshaped=V(r.keypoints,[-1,3]);let c=(await r.reshaped.array()).map(p=>[p[0]/Fi[1][1],p[1]/Fi[1][0],p[2]||0]).map(p=>[p[0]*t.boxRaw[2],p[1]*t.boxRaw[3],p[2]||0]);s.keypoints=c.map(p=>[Wa[0]*(p[0]+t.boxRaw[0]),Wa[1]*(p[1]+t.boxRaw[1]),p[2]||0]),s.landmarks=m1(s.keypoints);for(let p of Object.keys(IE))s.annotations[p]=IE[p].map(d=>s.landmarks&&s.keypoints[d]?s.keypoints[d]:null)}Object.keys(r).forEach(l=>Q(r[l]))}return s}async function y4(e,t){var r,a;if(!((r=nn[0])!=null&&r.executor)||!((a=nn[1])!=null&&a.executor)||!nn[0].inputs[0].shape||!nn[1].inputs[0].shape)return[];Wa=[e.shape[2]||0,e.shape[1]||0],g1++;let n=(t.hand.skipTime||0)>ue()-m4,s=g1<(t.hand.skipFrames||0);return t.skipAllowed&&n&&s?tn.hands:new Promise(async o=>{let i=3*(t.hand.skipTime||0)>ue()-m4,l=g1<3*(t.hand.skipFrames||0);t.skipAllowed&&tn.hands.length===t.hand.maxDetected?tn.hands=await Promise.all(tn.boxes.map(c=>g4(e,c,t))):t.skipAllowed&&i&&l&&tn.hands.length>0?tn.hands=await Promise.all(tn.boxes.map(c=>g4(e,c,t))):(tn.boxes=await pbe(e,t),m4=ue(),tn.hands=await Promise.all(tn.boxes.map(c=>g4(e,c,t))),g1=0);let u=[...tn.boxes];if(tn.boxes.length=0,t.cacheSensitivity>0)for(let c=0;c<tn.hands.length;c++){let 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n=(t.object.skipTime||0)>ue()-YE,s=_4<(t.object.skipFrames||0);return t.skipAllowed&&n&&s&&w1.length>0?(_4++,w1):(_4=0,!me.kernels.includes("mod")||!me.kernels.includes("sparsetodense")?w1:new Promise(async r=>{let a=[e.shape[2]||0,e.shape[1]||0],o=Ce.resizeBilinear(e,[S1,S1],!1),i=ye(o,Ze.tf255),l=at(i,[0,3,1,2]),u;t.object.enabled&&(u=Ir.execute(l)),YE=ue();let c=await gbe(u,a,t);w1=c,Q([o,i,l,...u]),r(c)}))}var uf=["nose","leftEye","rightEye","leftEar","rightEar","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle"],ybe=uf.length,lf=uf.reduce((e,t,n)=>(e[t]=n,e),{}),Abe=[["leftHip","leftShoulder"],["leftElbow","leftShoulder"],["leftElbow","leftWrist"],["leftHip","leftKnee"],["leftKnee","leftAnkle"],["rightHip","rightShoulder"],["rightElbow","rightShoulder"],["rightElbow","rightWrist"],["rightHip","rightKnee"],["rightKnee","rightAnkle"],["leftShoulder","rightShoulder"],["leftHip","rightHip"]],OIe=Abe.map(([e,t])=>[lf[e],lf[t]]),eR=[["nose","leftEye"],["leftEye","leftEar"],["nose","rightEye"],["rightEye","rightEar"],["nose","leftShoulder"],["leftShoulder","leftElbow"],["leftElbow","leftWrist"],["leftShoulder","leftHip"],["leftHip","leftKnee"],["leftKnee","leftAnkle"],["nose","rightShoulder"],["rightShoulder","rightElbow"],["rightElbow","rightWrist"],["rightShoulder","rightHip"],["rightHip","rightKnee"],["rightKnee","rightAnkle"]];function 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0:r.rightEyeIris)&&((a=e.annotations)==null?void 0:a.rightEyeIris[0])){t.strokeStyle=gt.useDepth?"rgba(255, 200, 255, 0.3)":gt.color,t.beginPath();let o=Math.abs(e.annotations.rightEyeIris[3][0]-e.annotations.rightEyeIris[1][0])/2,i=Math.abs(e.annotations.rightEyeIris[4][1]-e.annotations.rightEyeIris[2][1])/2;t.ellipse(e.annotations.rightEyeIris[0][0],e.annotations.rightEyeIris[0][1],o,i,0,0,2*Math.PI),t.stroke(),gt.fillPolygons&&(t.fillStyle=gt.useDepth?"rgba(255, 255, 200, 0.3)":gt.color,t.fill())}}function $be(e,t){var n;if(gt.drawGaze&&((n=e.rotation)==null?void 0:n.angle)&&typeof Path2D!="undefined"){t.strokeStyle="pink";let s=e.box[0]+e.box[2]/2-e.box[3]*Pu(e.rotation.angle.yaw)/90,r=e.box[1]+e.box[3]/2+e.box[2]*Pu(e.rotation.angle.pitch)/90,a=new Path2D(`
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u=Math.min(u,e.boxRaw[2]/2,e.boxRaw[3]/2),{bearing:(t([0,0],l)+Math.PI/2)%Math.PI,strength:u}},gR=(e,t)=>{let n=m=>{let g=Math.sqrt(m[0]*m[0]+m[1]*m[1]+m[2]*m[2]);return m[0]/=g,m[1]/=g,m[2]/=g,m},s=(m,g)=>{let y=m[0]-g[0],x=m[1]-g[1],A=m[2]-g[2];return[y,x,A]},r=(m,g)=>{let y=m[1]*g[2]-m[2]*g[1],x=m[2]*g[0]-m[0]*g[2],A=m[0]*g[1]-m[1]*g[0];return[y,x,A]},a=m=>{let[g,y,x,A,b,w,k,C,E]=m,_,$,R;return A<1?A>-1?(R=Math.asin(A),$=Math.atan2(-k,g),_=Math.atan2(-w,b)):(R=-Math.PI/2,$=-Math.atan2(C,E),_=0):(R=Math.PI/2,$=Math.atan2(C,E),_=0),Number.isNaN(_)&&(_=0),Number.isNaN($)&&($=0),Number.isNaN(R)&&(R=0),{pitch:2*-_,yaw:2*-$,roll:2*-R}},o=e.meshRaw;if(!o||o.length<300)return{angle:{pitch:0,yaw:0,roll:0},matrix:[1,0,0,0,1,0,0,0,1],gaze:{bearing:0,strength:0}};let i=Math.max(e.boxRaw[2]*t[0],e.boxRaw[3]*t[1])/1.5,l=[o[10],o[152],o[234],o[454]].map(m=>[m[0]*t[0]/i,m[1]*t[1]/i,m[2]]),u=n(s(l[1],l[0])),c=n(s(l[3],l[2])),p=n(r(c,u));c=r(u,p);let d=[c[0],c[1],c[2],u[0],u[1],u[2],p[0],p[1],p[2]],h=a(d),f=o.length===478?Cbe(e):{bearing:0,strength:0};return{angle:h,matrix:d,gaze:f}};var Y4=async(e,t)=>{var f,m,g,y,x,A,b,w,k,C,E,_,$,R,P,S,M,L,U,K,q,Z,J,Q,le,ae,pe,ce,xe;let n=ue(),s,r,a,o,i,l,u,c,p,d=[];e.state="run:face";let h=await aE(t,e.config);if(e.performance.face=me.perfadd?(e.performance.face||0)+Math.trunc(ue()-n):Math.trunc(ue()-n),!t.shape||t.shape.length!==4)return[];if(!h)return[];for(let ie=0;ie<h.length;ie++){if(e.analyze("Get Face"),!h[ie].tensor||h[ie].tensor.isDisposedInternal){ne("Face object is disposed:",h[ie].tensor);continue}if((f=e.config.face.detector)!=null&&f.mask){let lt=await mR(h[ie]);ee(h[ie].tensor),lt&&(h[ie].tensor=lt)}let _e=h[ie].mesh&&h[ie].mesh.length>200?gR(h[ie],[t.shape[2],t.shape[1]]):null;e.analyze("Start Emotion:"),e.config.async?o=(m=e.config.face.emotion)!=null&&m.enabled?n4(h[ie].tensor||dt([]),e.config,ie,h.length):[]:(e.state="run:emotion",n=ue(),o=(g=e.config.face.emotion)!=null&&g.enabled?await n4(h[ie].tensor||dt([]),e.config,ie,h.length):[],e.performance.emotion=me.perfadd?(e.performance.emotion||0)+Math.trunc(ue()-n):Math.trunc(ue()-n)),e.analyze("End Emotion:"),e.analyze("Start AntiSpoof:"),e.config.async?u=(y=e.config.face.antispoof)!=null&&y.enabled?Mb(h[ie].tensor||dt([]),e.config,ie,h.length):0:(e.state="run:antispoof",n=ue(),u=(x=e.config.face.antispoof)!=null&&x.enabled?await Mb(h[ie].tensor||dt([]),e.config,ie,h.length):0,e.performance.antispoof=me.perfadd?(e.performance.antispoof||0)+Math.trunc(ue()-n):Math.trunc(ue()-n)),e.analyze("End AntiSpoof:"),e.analyze("Start Liveness:"),e.config.async?c=(A=e.config.face.liveness)!=null&&A.enabled?S4(h[ie].tensor||dt([]),e.config,ie,h.length):0:(e.state="run:liveness",n=ue(),c=(b=e.config.face.liveness)!=null&&b.enabled?await S4(h[ie].tensor||dt([]),e.config,ie,h.length):0,e.performance.liveness=me.perfadd?(e.performance.antispoof||0)+Math.trunc(ue()-n):Math.trunc(ue()-n)),e.analyze("End Liveness:"),e.analyze("Start GEAR:"),e.config.async?r=(w=e.config.face.gear)!=null&&w.enabled?Rb(h[ie].tensor||dt([]),e.config,ie,h.length):null:(e.state="run:gear",n=ue(),r=(k=e.config.face.gear)!=null&&k.enabled?await Rb(h[ie].tensor||dt([]),e.config,ie,h.length):null,e.performance.gear=Math.trunc(ue()-n)),e.analyze("End GEAR:"),e.analyze("Start SSRNet:"),e.config.async?(s=(C=e.config.face.ssrnet)!=null&&C.enabled?Db(h[ie].tensor||dt([]),e.config,ie,h.length):null,a=(E=e.config.face.ssrnet)!=null&&E.enabled?Fb(h[ie].tensor||dt([]),e.config,ie,h.length):null):(e.state="run:ssrnet",n=ue(),s=(_=e.config.face.ssrnet)!=null&&_.enabled?await Db(h[ie].tensor||dt([]),e.config,ie,h.length):null,a=($=e.config.face.ssrnet)!=null&&$.enabled?await Fb(h[ie].tensor||dt([]),e.config,ie,h.length):null,e.performance.ssrnet=Math.trunc(ue()-n)),e.analyze("End SSRNet:"),e.analyze("Start MobileFaceNet:"),e.config.async?i=(R=e.config.face.mobilefacenet)!=null&&R.enabled?r4(h[ie].tensor||dt([]),e.config,ie,h.length):null:(e.state="run:mobilefacenet",n=ue(),i=(P=e.config.face.mobilefacenet)!=null&&P.enabled?await r4(h[ie].tensor||dt([]),e.config,ie,h.length):null,e.performance.mobilefacenet=Math.trunc(ue()-n)),e.analyze("End MobileFaceNet:"),e.analyze("Start InsightFace:"),e.config.async?l=(S=e.config.face.insightface)!=null&&S.enabled?o4(h[ie].tensor||dt([]),e.config,ie,h.length):null:(e.state="run:mobilefacenet",n=ue(),l=(M=e.config.face.insightface)!=null&&M.enabled?await o4(h[ie].tensor||dt([]),e.config,ie,h.length):null,e.performance.mobilefacenet=Math.trunc(ue()-n)),e.analyze("End InsightFace:"),e.analyze("Start Description:"),e.config.async?p=p4(h[ie].tensor||dt([]),e.config,ie,h.length):(e.state="run:description",n=ue(),p=await p4(h[ie].tensor||dt([]),e.config,ie,h.length),e.performance.description=me.perfadd?(e.performance.description||0)+Math.trunc(ue()-n):Math.trunc(ue()-n)),e.analyze("End Description:"),e.config.async&&([s,a,o,i,l,p,r,u,c]=await Promise.all([s,a,o,i,l,p,r,u,c])),e.analyze("Finish Face:"),((L=e.config.face.ssrnet)==null?void 0:L.enabled)&&s&&a&&(p={...p,age:s.age,gender:a.gender,genderScore:a.genderScore}),((U=e.config.face.gear)==null?void 0:U.enabled)&&r&&(p={...p,age:r.age,gender:r.gender,genderScore:r.genderScore,race:r.race}),((K=e.config.face.mobilefacenet)==null?void 0:K.enabled)&&i&&(p.descriptor=i),((q=e.config.face.insightface)==null?void 0:q.enabled)&&l&&(p.descriptor=l),(Z=e.config.face.iris)!=null&&Z.enabled;let De=((le=(Q=(J=h[ie])==null?void 0:J.annotations)==null?void 0:Q.leftEyeIris)==null?void 0:le[0])&&((ce=(pe=(ae=h[ie])==null?void 0:ae.annotations)==null?void 0:pe.rightEyeIris)==null?void 0:ce[0])&&h[ie].annotations.leftEyeIris.length>0&&h[ie].annotations.rightEyeIris.length>0&&h[ie].annotations.leftEyeIris[0]!==null&&h[ie].annotations.rightEyeIris[0]!==null?Math.max(Math.abs(h[ie].annotations.leftEyeIris[3][0]-h[ie].annotations.leftEyeIris[1][0]),Math.abs(h[ie].annotations.rightEyeIris[4][1]-h[ie].annotations.rightEyeIris[2][1]))/t.shape[2]:0,Ue=(xe=e.config.face.detector)!=null&&xe.return?at(h[ie].tensor):null;ee(h[ie].tensor),h[ie].tensor&&delete h[ie].tensor;let ze={...h[ie],id:ie};p.age&&(ze.age=p.age),p.gender&&(ze.gender=p.gender),p.genderScore&&(ze.genderScore=p.genderScore),p.descriptor&&(ze.embedding=p.descriptor),p.race&&(ze.race=p.race),o&&(ze.emotion=o),u&&(ze.real=u),c&&(ze.live=c),De&&De!==0&&(ze.iris=Math.trunc(500/De/11.7)/100),_e&&(ze.rotation=_e),Ue&&(ze.tensor=Ue),d.push(ze),e.analyze("End Face")}return e.analyze("End FaceMesh:"),e.config.async&&(e.performance.face&&delete e.performance.face,e.performance.age&&delete e.performance.age,e.performance.gender&&delete e.performance.gender,e.performance.emotion&&delete e.performance.emotion),d};var yR=e=>{if(!e)return[];let t=[];for(let n=0;n<e.length;n++){let s=e[n].keypoints.find(l=>l.part==="leftWrist"),r=e[n].keypoints.find(l=>l.part==="rightWrist"),a=e[n].keypoints.find(l=>l.part==="nose");a&&s&&r&&s.position[1]<a.position[1]&&r.position[1]<a.position[1]?t.push({body:n,gesture:"i give up"}):a&&s&&s.position[1]<a.position[1]?t.push({body:n,gesture:"raise left hand"}):a&&r&&r.position[1]<a.position[1]&&t.push({body:n,gesture:"raise right hand"});let o=e[n].keypoints.find(l=>l.part==="leftShoulder"),i=e[n].keypoints.find(l=>l.part==="rightShoulder");o&&i&&Math.abs(o.positionRaw[1]-i.positionRaw[1])>.1&&t.push({body:n,gesture:`leaning ${o.position[1]>i.position[1]?"left":"right"}`})}return t},AR=e=>{if(!e)return[];let t=[];for(let n=0;n<e.length;n++)if(e[n].mesh&&e[n].mesh.length>450){let s=(e[n].mesh[33][2]||0)-(e[n].mesh[263][2]||0),r=e[n].mesh[33][0]-e[n].mesh[263][0];Math.abs(s/r)<=.15?t.push({face:n,gesture:"facing center"}):t.push({face:n,gesture:`facing ${s<0?"left":"right"}`}),Math.abs(e[n].mesh[374][1]-e[n].mesh[386][1])/Math.abs(e[n].mesh[443][1]-e[n].mesh[450][1])<.2&&t.push({face:n,gesture:"blink left eye"}),Math.abs(e[n].mesh[145][1]-e[n].mesh[159][1])/Math.abs(e[n].mesh[223][1]-e[n].mesh[230][1])<.2&&t.push({face:n,gesture:"blink right eye"});let i=Math.min(100,500*Math.abs(e[n].mesh[13][1]-e[n].mesh[14][1])/Math.abs(e[n].mesh[10][1]-e[n].mesh[152][1]));i>10&&t.push({face:n,gesture:`mouth ${Math.trunc(i)}% open`});let l=e[n].mesh[152][2]||0;Math.abs(l)>10&&t.push({face:n,gesture:`head ${l<0?"up":"down"}`})}return t},xR=e=>{var n,s,r,a;if(!e)return[];let t=[];for(let o=0;o<e.length;o++){if(!((s=(n=e[o].annotations)==null?void 0:n.leftEyeIris)!=null&&s[0])||!((a=(r=e[o].annotations)==null?void 0:r.rightEyeIris)!=null&&a[0]))continue;let i=e[o].annotations.leftEyeIris[3][0]-e[o].annotations.leftEyeIris[1][0],l=e[o].annotations.leftEyeIris[4][1]-e[o].annotations.leftEyeIris[2][1],u=Math.abs(i*l),c=e[o].annotations.rightEyeIris[3][0]-e[o].annotations.rightEyeIris[1][0],p=e[o].annotations.rightEyeIris[4][1]-e[o].annotations.rightEyeIris[2][1],d=Math.abs(c*p),h=!1;Math.abs(u-d)/Math.max(u,d)<.25&&(h=!0,t.push({iris:o,gesture:"facing center"}));let m=Math.abs(e[o].mesh[263][0]-e[o].annotations.leftEyeIris[0][0])/e[o].box[2],g=Math.abs(e[o].mesh[33][0]-e[o].annotations.rightEyeIris[0][0])/e[o].box[2];(m>.06||g>.06)&&(h=!1),m>g?m>.05&&t.push({iris:o,gesture:"looking right"}):g>.05&&t.push({iris:o,gesture:"looking left"});let y=Math.abs(e[o].mesh[145][1]-e[o].annotations.rightEyeIris[0][1])/e[o].box[3],x=Math.abs(e[o].mesh[374][1]-e[o].annotations.leftEyeIris[0][1])/e[o].box[3];(x<.01||y<.01||x>.022||y>.022)&&(h=!1),(x<.01||y<.01)&&t.push({iris:o,gesture:"looking down"}),(x>.022||y>.022)&&t.push({iris:o,gesture:"looking up"}),h&&t.push({iris:o,gesture:"looking center"})}return t},bR=e=>{if(!e)return[];let t=[];for(let n=0;n<e.length;n++){let s=[];if(e[n].annotations)for(let[r,a]of Object.entries(e[n].annotations))r!=="palmBase"&&Array.isArray(a)&&a[0]&&s.push({name:r.toLowerCase(),position:a[0]});if(s&&s.length>0){let r=s.reduce((o,i)=>(o.position[2]||0)<(i.position[2]||0)?o:i);t.push({hand:n,gesture:`${r.name} forward`});let a=s.reduce((o,i)=>o.position[1]<i.position[1]?o:i);t.push({hand:n,gesture:`${a.name} up`})}if(e[n].keypoints){let r=RE(e[n].keypoints);for(let a of r)t.push({hand:n,gesture:a.name})}}return t};var Re={face:[],body:[],hand:[],gesture:[],object:[],persons:[],performance:{},timestamp:0,error:null},J4=0;function vR(e,t){var o,i,l,u,c,p,d,h,f,m,g,y,x,A,b,w,k,C,E,_,$,R,P;let n=ue();if(!e)return{face:[],body:[],hand:[],gesture:[],object:[],persons:[],performance:{},timestamp:0,error:null};let s=Date.now()-e.timestamp,r=s<1e3?8-Math.log(s+1):1;if(e.canvas&&(Re.canvas=e.canvas),e.error&&(Re.error=e.error),!Re.body||e.body.length!==Re.body.length)Re.body=JSON.parse(JSON.stringify(e.body));else for(let S=0;S<e.body.length;S++){let M=e.body[S].box.map((Z,J)=>((r-1)*Re.body[S].box[J]+Z)/r),L=e.body[S].boxRaw.map((Z,J)=>((r-1)*Re.body[S].boxRaw[J]+Z)/r),U=e.body[S].keypoints.map((Z,J)=>{var 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u=Math.min(u,e.boxRaw[2]/2,e.boxRaw[3]/2),{bearing:(t([0,0],l)+Math.PI/2)%Math.PI,strength:u}},NR=(e,t)=>{let n=m=>{let g=Math.sqrt(m[0]*m[0]+m[1]*m[1]+m[2]*m[2]);return m[0]/=g,m[1]/=g,m[2]/=g,m},s=(m,g)=>{let y=m[0]-g[0],x=m[1]-g[1],A=m[2]-g[2];return[y,x,A]},r=(m,g)=>{let y=m[1]*g[2]-m[2]*g[1],x=m[2]*g[0]-m[0]*g[2],A=m[0]*g[1]-m[1]*g[0];return[y,x,A]},a=m=>{let[g,y,x,A,b,w,k,C,E]=m,_,$,R;return A<1?A>-1?(R=Math.asin(A),$=Math.atan2(-k,g),_=Math.atan2(-w,b)):(R=-Math.PI/2,$=-Math.atan2(C,E),_=0):(R=Math.PI/2,$=Math.atan2(C,E),_=0),Number.isNaN(_)&&(_=0),Number.isNaN($)&&($=0),Number.isNaN(R)&&(R=0),{pitch:2*-_,yaw:2*-$,roll:2*-R}},o=e.meshRaw;if(!o||o.length<300)return{angle:{pitch:0,yaw:0,roll:0},matrix:[1,0,0,0,1,0,0,0,1],gaze:{bearing:0,strength:0}};let i=Math.max(e.boxRaw[2]*t[0],e.boxRaw[3]*t[1])/1.5,l=[o[10],o[152],o[234],o[454]].map(m=>[m[0]*t[0]/i,m[1]*t[1]/i,m[2]]),u=n(s(l[1],l[0])),c=n(s(l[3],l[2])),p=n(r(c,u));c=r(u,p);let d=[c[0],c[1],c[2],u[0],u[1],u[2],p[0],p[1],p[2]],h=a(d),f=o.length===478?Bbe(e):{bearing:0,strength:0};return{angle:h,matrix:d,gaze:f}};var sv=async(e,t)=>{var f,m,g,y,x,A,b,w,k,C,E,_,$,R,P,S,M,L,U,K,q,Z,J,te,le,ae,pe,ce,xe;let n=ue(),s,r,a,o,i,l,u,c,p,d=[];e.state="run:face";let h=await GN(t,e.config);if(e.performance.face=me.perfadd?(e.performance.face||0)+Math.trunc(ue()-n):Math.trunc(ue()-n),!t.shape||t.shape.length!==4)return[];if(!h)return[];for(let ie=0;ie<h.length;ie++){if(e.analyze("Get Face"),!h[ie].tensor||h[ie].tensor.isDisposedInternal){ne("Face object is disposed:",h[ie].tensor);continue}if((f=e.config.face.detector)!=null&&f.mask){let ut=await TR(h[ie]);Q(h[ie].tensor),ut&&(h[ie].tensor=ut)}let _e=h[ie].mesh&&h[ie].mesh.length>200?NR(h[ie],[t.shape[2],t.shape[1]]):null;e.analyze("Start Emotion:"),e.config.async?o=(m=e.config.face.emotion)!=null&&m.enabled?Jb(h[ie].tensor||Xe([]),e.config,ie,h.length):[]:(e.state="run:emotion",n=ue(),o=(g=e.config.face.emotion)!=null&&g.enabled?await Jb(h[ie].tensor||Xe([]),e.config,ie,h.length):[],e.performance.emotion=me.perfadd?(e.performance.emotion||0)+Math.trunc(ue()-n):Math.trunc(ue()-n)),e.analyze("End Emotion:"),e.analyze("Start AntiSpoof:"),e.config.async?u=(y=e.config.face.antispoof)!=null&&y.enabled?$b(h[ie].tensor||Xe([]),e.config,ie,h.length):0:(e.state="run:antispoof",n=ue(),u=(x=e.config.face.antispoof)!=null&&x.enabled?await $b(h[ie].tensor||Xe([]),e.config,ie,h.length):0,e.performance.antispoof=me.perfadd?(e.performance.antispoof||0)+Math.trunc(ue()-n):Math.trunc(ue()-n)),e.analyze("End AntiSpoof:"),e.analyze("Start Liveness:"),e.config.async?c=(A=e.config.face.liveness)!=null&&A.enabled?v4(h[ie].tensor||Xe([]),e.config,ie,h.length):0:(e.state="run:liveness",n=ue(),c=(b=e.config.face.liveness)!=null&&b.enabled?await v4(h[ie].tensor||Xe([]),e.config,ie,h.length):0,e.performance.liveness=me.perfadd?(e.performance.antispoof||0)+Math.trunc(ue()-n):Math.trunc(ue()-n)),e.analyze("End Liveness:"),e.analyze("Start GEAR:"),e.config.async?r=(w=e.config.face.gear)!=null&&w.enabled?l4(h[ie].tensor||Xe([]),e.config,ie,h.length):null:(e.state="run:gear",n=ue(),r=(k=e.config.face.gear)!=null&&k.enabled?await l4(h[ie].tensor||Xe([]),e.config,ie,h.length):null,e.performance.gear=Math.trunc(ue()-n)),e.analyze("End GEAR:"),e.analyze("Start SSRNet:"),e.config.async?(s=(C=e.config.face.ssrnet)!=null&&C.enabled?U4(h[ie].tensor||Xe([]),e.config,ie,h.length):null,a=(E=e.config.face.ssrnet)!=null&&E.enabled?j4(h[ie].tensor||Xe([]),e.config,ie,h.length):null):(e.state="run:ssrnet",n=ue(),s=(_=e.config.face.ssrnet)!=null&&_.enabled?await U4(h[ie].tensor||Xe([]),e.config,ie,h.length):null,a=($=e.config.face.ssrnet)!=null&&$.enabled?await j4(h[ie].tensor||Xe([]),e.config,ie,h.length):null,e.performance.ssrnet=Math.trunc(ue()-n)),e.analyze("End SSRNet:"),e.analyze("Start MobileFaceNet:"),e.config.async?i=(R=e.config.face.mobilefacenet)!=null&&R.enabled?S4(h[ie].tensor||Xe([]),e.config,ie,h.length):null:(e.state="run:mobilefacenet",n=ue(),i=(P=e.config.face.mobilefacenet)!=null&&P.enabled?await S4(h[ie].tensor||Xe([]),e.config,ie,h.length):null,e.performance.mobilefacenet=Math.trunc(ue()-n)),e.analyze("End MobileFaceNet:"),e.analyze("Start InsightFace:"),e.config.async?l=(S=e.config.face.insightface)!=null&&S.enabled?x4(h[ie].tensor||Xe([]),e.config,ie,h.length):null:(e.state="run:mobilefacenet",n=ue(),l=(M=e.config.face.insightface)!=null&&M.enabled?await x4(h[ie].tensor||Xe([]),e.config,ie,h.length):null,e.performance.mobilefacenet=Math.trunc(ue()-n)),e.analyze("End InsightFace:"),e.analyze("Start Description:"),e.config.async?p=r4(h[ie].tensor||Xe([]),e.config,ie,h.length):(e.state="run:description",n=ue(),p=await r4(h[ie].tensor||Xe([]),e.config,ie,h.length),e.performance.description=me.perfadd?(e.performance.description||0)+Math.trunc(ue()-n):Math.trunc(ue()-n)),e.analyze("End Description:"),e.config.async&&([s,a,o,i,l,p,r,u,c]=await Promise.all([s,a,o,i,l,p,r,u,c])),e.analyze("Finish Face:"),((L=e.config.face.ssrnet)==null?void 0:L.enabled)&&s&&a&&(p={...p,age:s.age,gender:a.gender,genderScore:a.genderScore}),((U=e.config.face.gear)==null?void 0:U.enabled)&&r&&(p={...p,age:r.age,gender:r.gender,genderScore:r.genderScore,race:r.race}),((K=e.config.face.mobilefacenet)==null?void 0:K.enabled)&&i&&(p.descriptor=i),((q=e.config.face.insightface)==null?void 0:q.enabled)&&l&&(p.descriptor=l),(Z=e.config.face.iris)!=null&&Z.enabled;let De=((le=(te=(J=h[ie])==null?void 0:J.annotations)==null?void 0:te.leftEyeIris)==null?void 0:le[0])&&((ce=(pe=(ae=h[ie])==null?void 0:ae.annotations)==null?void 0:pe.rightEyeIris)==null?void 0:ce[0])&&h[ie].annotations.leftEyeIris.length>0&&h[ie].annotations.rightEyeIris.length>0&&h[ie].annotations.leftEyeIris[0]!==null&&h[ie].annotations.rightEyeIris[0]!==null?Math.max(Math.abs(h[ie].annotations.leftEyeIris[3][0]-h[ie].annotations.leftEyeIris[1][0]),Math.abs(h[ie].annotations.rightEyeIris[4][1]-h[ie].annotations.rightEyeIris[2][1]))/t.shape[2]:0,Ge=(xe=e.config.face.detector)!=null&&xe.return?Ke(h[ie].tensor):null;Q(h[ie].tensor),h[ie].tensor&&delete h[ie].tensor;let ze={...h[ie],id:ie};p.age&&(ze.age=p.age),p.gender&&(ze.gender=p.gender),p.genderScore&&(ze.genderScore=p.genderScore),p.descriptor&&(ze.embedding=p.descriptor),p.race&&(ze.race=p.race),o&&(ze.emotion=o),u&&(ze.real=u),c&&(ze.live=c),De&&De!==0&&(ze.iris=Math.trunc(500/De/11.7)/100),_e&&(ze.rotation=_e),Ge&&(ze.tensor=Ge),d.push(ze),e.analyze("End Face")}return e.analyze("End FaceMesh:"),e.config.async&&(e.performance.face&&delete e.performance.face,e.performance.age&&delete e.performance.age,e.performance.gender&&delete e.performance.gender,e.performance.emotion&&delete e.performance.emotion),d};var ER=e=>{if(!e)return[];let t=[];for(let n=0;n<e.length;n++){let s=e[n].keypoints.find(l=>l.part==="leftWrist"),r=e[n].keypoints.find(l=>l.part==="rightWrist"),a=e[n].keypoints.find(l=>l.part==="nose");a&&s&&r&&s.position[1]<a.position[1]&&r.position[1]<a.position[1]?t.push({body:n,gesture:"i give up"}):a&&s&&s.position[1]<a.position[1]?t.push({body:n,gesture:"raise left hand"}):a&&r&&r.position[1]<a.position[1]&&t.push({body:n,gesture:"raise right hand"});let o=e[n].keypoints.find(l=>l.part==="leftShoulder"),i=e[n].keypoints.find(l=>l.part==="rightShoulder");o&&i&&Math.abs(o.positionRaw[1]-i.positionRaw[1])>.1&&t.push({body:n,gesture:`leaning ${o.position[1]>i.position[1]?"left":"right"}`})}return t},RR=e=>{if(!e)return[];let t=[];for(let n=0;n<e.length;n++)if(e[n].mesh&&e[n].mesh.length>450){let s=(e[n].mesh[33][2]||0)-(e[n].mesh[263][2]||0),r=e[n].mesh[33][0]-e[n].mesh[263][0];Math.abs(s/r)<=.15?t.push({face:n,gesture:"facing center"}):t.push({face:n,gesture:`facing ${s<0?"left":"right"}`}),Math.abs(e[n].mesh[374][1]-e[n].mesh[386][1])/Math.abs(e[n].mesh[443][1]-e[n].mesh[450][1])<.2&&t.push({face:n,gesture:"blink left eye"}),Math.abs(e[n].mesh[145][1]-e[n].mesh[159][1])/Math.abs(e[n].mesh[223][1]-e[n].mesh[230][1])<.2&&t.push({face:n,gesture:"blink right eye"});let i=Math.min(100,500*Math.abs(e[n].mesh[13][1]-e[n].mesh[14][1])/Math.abs(e[n].mesh[10][1]-e[n].mesh[152][1]));i>10&&t.push({face:n,gesture:`mouth ${Math.trunc(i)}% open`});let l=e[n].mesh[152][2]||0;Math.abs(l)>10&&t.push({face:n,gesture:`head ${l<0?"up":"down"}`})}return t},_R=e=>{var n,s,r,a;if(!e)return[];let t=[];for(let o=0;o<e.length;o++){if(!((s=(n=e[o].annotations)==null?void 0:n.leftEyeIris)!=null&&s[0])||!((a=(r=e[o].annotations)==null?void 0:r.rightEyeIris)!=null&&a[0]))continue;let i=e[o].annotations.leftEyeIris[3][0]-e[o].annotations.leftEyeIris[1][0],l=e[o].annotations.leftEyeIris[4][1]-e[o].annotations.leftEyeIris[2][1],u=Math.abs(i*l),c=e[o].annotations.rightEyeIris[3][0]-e[o].annotations.rightEyeIris[1][0],p=e[o].annotations.rightEyeIris[4][1]-e[o].annotations.rightEyeIris[2][1],d=Math.abs(c*p),h=!1;Math.abs(u-d)/Math.max(u,d)<.25&&(h=!0,t.push({iris:o,gesture:"facing center"}));let m=Math.abs(e[o].mesh[263][0]-e[o].annotations.leftEyeIris[0][0])/e[o].box[2],g=Math.abs(e[o].mesh[33][0]-e[o].annotations.rightEyeIris[0][0])/e[o].box[2];(m>.06||g>.06)&&(h=!1),m>g?m>.05&&t.push({iris:o,gesture:"looking right"}):g>.05&&t.push({iris:o,gesture:"looking left"});let y=Math.abs(e[o].mesh[145][1]-e[o].annotations.rightEyeIris[0][1])/e[o].box[3],x=Math.abs(e[o].mesh[374][1]-e[o].annotations.leftEyeIris[0][1])/e[o].box[3];(x<.01||y<.01||x>.022||y>.022)&&(h=!1),(x<.01||y<.01)&&t.push({iris:o,gesture:"looking down"}),(x>.022||y>.022)&&t.push({iris:o,gesture:"looking up"}),h&&t.push({iris:o,gesture:"looking center"})}return t},DR=e=>{if(!e)return[];let t=[];for(let n=0;n<e.length;n++){let s=[];if(e[n].annotations)for(let[r,a]of Object.entries(e[n].annotations))r!=="palmBase"&&Array.isArray(a)&&a[0]&&s.push({name:r.toLowerCase(),position:a[0]});if(s&&s.length>0){let r=s.reduce((o,i)=>(o.position[2]||0)<(i.position[2]||0)?o:i);t.push({hand:n,gesture:`${r.name} forward`});let a=s.reduce((o,i)=>o.position[1]<i.position[1]?o:i);t.push({hand:n,gesture:`${a.name} up`})}if(e[n].keypoints){let r=xE(e[n].keypoints);for(let a of r)t.push({hand:n,gesture:a.name})}}return t};var Re={face:[],body:[],hand:[],gesture:[],object:[],persons:[],performance:{},timestamp:0,error:null},rv=0;function $R(e,t){var o,i,l,u,c,p,d,h,f,m,g,y,x,A,b,w,k,C,E,_,$,R,P;let n=ue();if(!e)return{face:[],body:[],hand:[],gesture:[],object:[],persons:[],performance:{},timestamp:0,error:null};let s=Date.now()-e.timestamp,r=s<1e3?8-Math.log(s+1):1;if(e.canvas&&(Re.canvas=e.canvas),e.error&&(Re.error=e.error),!Re.body||e.body.length!==Re.body.length)Re.body=JSON.parse(JSON.stringify(e.body));else for(let S=0;S<e.body.length;S++){let M=e.body[S].box.map((Z,J)=>((r-1)*Re.body[S].box[J]+Z)/r),L=e.body[S].boxRaw.map((Z,J)=>((r-1)*Re.body[S].boxRaw[J]+Z)/r),U=e.body[S].keypoints.map((Z,J)=>{var te,le,ae,pe,ce,xe,ie,_e,De;return{score:Z.score,part:Z.part,position:[Re.body[S].keypoints[J]?((r-1)*(Re.body[S].keypoints[J].position[0]||0)+(Z.position[0]||0))/r:Z.position[0],Re.body[S].keypoints[J]?((r-1)*(Re.body[S].keypoints[J].position[1]||0)+(Z.position[1]||0))/r:Z.position[1],Re.body[S].keypoints[J]?((r-1)*(Re.body[S].keypoints[J].position[2]||0)+(Z.position[2]||0))/r:Z.position[2]],positionRaw:[Re.body[S].keypoints[J]?((r-1)*(Re.body[S].keypoints[J].positionRaw[0]||0)+(Z.positionRaw[0]||0))/r:Z.positionRaw[0],Re.body[S].keypoints[J]?((r-1)*(Re.body[S].keypoints[J].positionRaw[1]||0)+(Z.positionRaw[1]||0))/r:Z.positionRaw[1],Re.body[S].keypoints[J]?((r-1)*(Re.body[S].keypoints[J].positionRaw[2]||0)+(Z.positionRaw[2]||0))/r:Z.positionRaw[2]],distance:[Re.body[S].keypoints[J]?((r-1)*(((te=Re.body[S].keypoints[J].distance)==null?void 0:te[0])||0)+(((le=Z.distance)==null?void 0:le[0])||0))/r:(ae=Z.distance)==null?void 0:ae[0],Re.body[S].keypoints[J]?((r-1)*(((pe=Re.body[S].keypoints[J].distance)==null?void 0:pe[1])||0)+(((ce=Z.distance)==null?void 0:ce[1])||0))/r:(xe=Z.distance)==null?void 0:xe[1],Re.body[S].keypoints[J]?((r-1)*(((ie=Re.body[S].keypoints[J].distance)==null?void 0:ie[2])||0)+(((_e=Z.distance)==null?void 0:_e[2])||0))/r:(De=Z.distance)==null?void 0:De[2]]}}),K={},q={connected:{}};(o=t.body.modelPath)!=null&&o.includes("efficientpose")?q=i1:(i=t.body.modelPath)!=null&&i.includes("blazepose")?q=n1:(l=t.body.modelPath)!=null&&l.includes("movenet")&&(q=of);for(let[Z,J]of Object.entries(q.connected)){let te=[];for(let le=0;le<J.length-1;le++){let ae=U.find(ce=>ce.part===J[le]),pe=U.find(ce=>ce.part===J[le+1]);ae&&pe&&te.push([ae.position,pe.position])}K[Z]=te}Re.body[S]={...e.body[S],box:M,boxRaw:L,keypoints:U,annotations:K}}if(!Re.hand||e.hand.length!==Re.hand.length)Re.hand=JSON.parse(JSON.stringify(e.hand));else for(let S=0;S<e.hand.length;S++){let M=e.hand[S].box.map((q,Z)=>((r-1)*Re.hand[S].box[Z]+q)/r),L=e.hand[S].boxRaw.map((q,Z)=>((r-1)*Re.hand[S].boxRaw[Z]+q)/r);Re.hand[S].keypoints.length!==e.hand[S].keypoints.length&&(Re.hand[S].keypoints=e.hand[S].keypoints);let U=e.hand[S].keypoints&&e.hand[S].keypoints.length>0?e.hand[S].keypoints.map((q,Z)=>q.map((J,te)=>((r-1)*(Re.hand[S].keypoints[Z][te]||1)+(J||0))/r)):[],K={};if(Object.keys(Re.hand[S].annotations).length!==Object.keys(e.hand[S].annotations).length)Re.hand[S].annotations=e.hand[S].annotations,K=Re.hand[S].annotations;else if(e.hand[S].annotations)for(let q of Object.keys(e.hand[S].annotations))K[q]=(p=(c=(u=e.hand[S])==null?void 0:u.annotations)==null?void 0:c[q])!=null&&p[0]?e.hand[S].annotations[q].map((Z,J)=>Z.map((te,le)=>((r-1)*Re.hand[S].annotations[q][J][le]+te)/r)):null;Re.hand[S]={...e.hand[S],box:M,boxRaw:L,keypoints:U,annotations:K}}if(!Re.face||e.face.length!==Re.face.length)Re.face=JSON.parse(JSON.stringify(e.face));else for(let S=0;S<e.face.length;S++){let M=e.face[S].box.map((U,K)=>((r-1)*Re.face[S].box[K]+U)/r),L=e.face[S].boxRaw.map((U,K)=>((r-1)*Re.face[S].boxRaw[K]+U)/r);if(e.face[S].rotation){let U={matrix:[0,0,0,0,0,0,0,0,0],angle:{roll:0,yaw:0,pitch:0},gaze:{bearing:0,strength:0}};U.matrix=(d=e.face[S].rotation)==null?void 0:d.matrix,U.angle={roll:((r-1)*(((f=(h=Re.face[S].rotation)==null?void 0:h.angle)==null?void 0:f.roll)||0)+(((g=(m=e.face[S].rotation)==null?void 0:m.angle)==null?void 0:g.roll)||0))/r,yaw:((r-1)*(((x=(y=Re.face[S].rotation)==null?void 0:y.angle)==null?void 0:x.yaw)||0)+(((b=(A=e.face[S].rotation)==null?void 0:A.angle)==null?void 0:b.yaw)||0))/r,pitch:((r-1)*(((k=(w=Re.face[S].rotation)==null?void 0:w.angle)==null?void 0:k.pitch)||0)+(((E=(C=e.face[S].rotation)==null?void 0:C.angle)==null?void 0:E.pitch)||0))/r},U.gaze={bearing:((r-1)*(((_=Re.face[S].rotation)==null?void 0:_.gaze.bearing)||0)+((($=e.face[S].rotation)==null?void 0:$.gaze.bearing)||0))/r,strength:((r-1)*(((R=Re.face[S].rotation)==null?void 0:R.gaze.strength)||0)+(((P=e.face[S].rotation)==null?void 0:P.gaze.strength)||0))/r},Re.face[S]={...e.face[S],rotation:U,box:M,boxRaw:L}}else Re.face[S]={...e.face[S],box:M,boxRaw:L}}if(!Re.object||e.object.length!==Re.object.length)Re.object=JSON.parse(JSON.stringify(e.object));else for(let S=0;S<e.object.length;S++){let M=e.object[S].box.map((U,K)=>((r-1)*Re.object[S].box[K]+U)/r),L=e.object[S].boxRaw.map((U,K)=>((r-1)*Re.object[S].boxRaw[K]+U)/r);Re.object[S]={...e.object[S],box:M,boxRaw:L}}if(e.persons){let S=e.persons;if(!Re.persons||S.length!==Re.persons.length)Re.persons=JSON.parse(JSON.stringify(S));else for(let M=0;M<S.length;M++)Re.persons[M].box=S[M].box.map((L,U)=>((r-1)*Re.persons[M].box[U]+L)/r)}e.gesture&&(Re.gesture=e.gesture);let a=ue();return rv=me.perfadd?rv+Math.round(a-n):Math.round(a-n),e.performance&&(Re.performance={...e.performance,interpolate:rv}),Re}var 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Qe&&dn()==="tensorflow"){let r=(void 0).decodeJpeg(n),a=Wt(r,0);e.tf.dispose(r),s=await e.detect(a,e.config),e.tf.dispose(a)}else e.config.debug&&ne("Warmup tfjs-node not loaded");return s}async function Fbe(e){let t;return typeof createImageBitmap=="function"?t=await Dbe(e):typeof Image!="undefined"||me.Canvas!==void 0?t=await $be(e):t=await Pbe(e),t}async function Obe(e){var i,l,u,c;if(!H().flagRegistry.ENGINE_COMPILE_ONLY)return;let t=dn(),n=Bs();if(t!=="webgl"&&t!=="humangl"||!(n!=null&&n.checkCompileCompletion))return;H().set("ENGINE_COMPILE_ONLY",!0);let s=Jt().state.numTensors,r=[];for(let[p,d]of Object.entries(e.models).filter(([h,f])=>h!==null&&f!==null)){let h=(l=(i=d.inputs)==null?void 0:i[0])!=null&&l.shape?[...d.inputs[0].shape]:[1,64,64,3],f=(c=(u=d.inputs)==null?void 0:u[0])!=null&&c.dtype?d.inputs[0].dtype:"float32";for(let g=0;g<h.length;g++)h[g]===-1&&(h[g]=g===0?1:64);let m=Ut(h,f);try{let g=d.execute(m);r.push(p),Array.isArray(g)?g.forEach(y=>ee(y)):ee(g)}catch(g){ne("compile fail model:",p)}ee(m)}let a=await n.checkCompileCompletionAsync();n.getUniformLocations(),e.config.debug&&ne("compile pass:",{models:r,kernels:a.length}),H().set("ENGINE_COMPILE_ONLY",!1);let o=Jt().state.numTensors;o-s>0&&ne("tensor leak:",o-s)}async function SR(e,t){await of(e,!1);let n=ue();return e.state="warmup",t&&(e.config=Kt(e.config,t)),!e.config.warmup||e.config.warmup.length===0||e.config.warmup==="none"?{face:[],body:[],hand:[],gesture:[],object:[],performance:e.performance,timestamp:ue(),persons:[],error:null}:new Promise(async s=>{await $d.load(e),await Obe(e);let r=await Fbe(e),a=ue();e.config.debug&&ne("warmup",e.config.warmup,Math.round(a-n),"ms"),e.emit("warmup"),s(r)})}var Vd,hf,ff,_1,Di,nv=class{constructor(t){fe(this,"version");fe(this,"config");fe(this,"result");fe(this,"state");fe(this,"process");fe(this,"tf");fe(this,"env");fe(this,"draw");fe(this,"models");fe(this,"events");fe(this,"faceTriangulation");fe(this,"faceUVMap");fe(this,"performance");Hu(this,Vd,void 0);Hu(this,hf,void 0);Hu(this,ff,void 0);fe(this,"gl");fe(this,"analyze",(...t)=>{if(!Xr(this,hf))return;let n=this.tf.engine().state.numTensors,s=Xr(this,Vd);np(this,Vd,n);let r=n-s;r!==0&&ne(...t,r)});Hu(this,_1,t=>{if(!Xr(this,ff))return null;if(!t)return"input is not defined";if(this.env.node&&!(t instanceof rt))return"input must be a tensor";try{this.tf.getBackend()}catch(n){return"backend not loaded"}return null});fe(this,"similarity",Q4);fe(this,"distance",pf);fe(this,"match",ev);fe(this,"webcam",new q2);fe(this,"emit",t=>{var n;(n=this.events)!=null&&n.dispatchEvent&&this.events.dispatchEvent(new Event(t))});Hu(this,Di,{});this.env=me;let n=(Yh.tfjs||aA).replace(/-(.*)/,"");Ga.wasmPath=`https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-backend-wasm@${n}/dist/`,Ga.modelBasePath=me.browser?"../models/":"file://models/",Ga.backend=me.browser?"webgl":"tensorflow",this.version=V4,Object.defineProperty(this,"version",{value:V4}),this.config=JSON.parse(JSON.stringify(Ga)),Object.seal(this.config),this.config.cacheModels=typeof indexedDB!="undefined",t&&(this.config=Kt(this.config,t)),hR(this.config),this.tf=Qe,this.state="idle",np(this,Vd,0),np(this,hf,!1),np(this,ff,!1),this.performance={},this.events=typeof EventTarget!="undefined"?new EventTarget:void 0,this.models=new 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vR(t,this.config)}getModelStats(){return B4(this)}async warmup(t){let n=ue(),s=await SR(this,t),r=ue();return this.performance.warmup=Math.trunc(r-n),s}async profile(t,n){let s=await this.tf.profile(()=>this.detect(t,n)),r={},a=0;for(let i of s.kernels)r[i.name]?r[i.name]+=i.kernelTimeMs:r[i.name]=i.kernelTimeMs,a+=i.kernelTimeMs;let o=[];Object.entries(r).forEach(i=>o.push({kernel:i[0],time:i[1],perc:0}));for(let i of o)i.perc=Math.round(1e3*i.time/a)/1e3,i.time=Math.round(1e3*i.time)/1e3;return o.sort((i,l)=>l.time-i.time),o.length=20,o}async detect(t,n){return this.state="detect",new Promise(async s=>{var g,y,x,A,b,w,k,C,E,_,$,R,P,S,M,L,U,K,q,Z,J;this.state="config";let r;this.config=Kt(this.config,n),this.state="check";let a=Xr(this,_1).call(this,t);a&&(ne(a,t),this.emit("error"),s({face:[],body:[],hand:[],gesture:[],object:[],performance:this.performance,timestamp:ue(),persons:[],error:a}));let o=ue();await this.load(),r=ue(),this.state="image";let i=await kd(t,this.config);if(this.process=i,this.performance.inputProcess=this.env.perfadd?(this.performance.inputProcess||0)+Math.trunc(ue()-r):Math.trunc(ue()-r),this.analyze("Get Image:"),!i.tensor){this.config.debug&&ne("could not convert input to tensor"),this.emit("error"),s({face:[],body:[],hand:[],gesture:[],object:[],performance:this.performance,timestamp:ue(),persons:[],error:"could not convert input to tensor"});return}this.emit("image"),r=ue(),this.config.skipAllowed=await UT(this.config,i.tensor),this.performance.totalFrames||(this.performance.totalFrames=0),this.performance.cachedFrames||(this.performance.cachedFrames=0),this.performance.totalFrames++,this.config.skipAllowed&&this.performance.cachedFrames++,this.performance.cacheCheck=this.env.perfadd?(this.performance.cacheCheck||0)+Math.trunc(ue()-r):Math.trunc(ue()-r),this.analyze("Check Changed:");let l=[],u=[],c=[],p=[];this.state="detect:face",this.config.async?(l=this.config.face.enabled?Y4(this,i.tensor):[],this.performance.face&&delete this.performance.face):(r=ue(),l=this.config.face.enabled?await Y4(this,i.tensor):[],this.performance.face=this.env.perfadd?(this.performance.face||0)+Math.trunc(ue()-r):Math.trunc(ue()-r)),this.config.async&&(this.config.body.maxDetected===-1||this.config.hand.maxDetected===-1)&&(l=await l),this.analyze("Start Body:"),this.state="detect:body";let d=this.config.body.maxDetected===-1?Kt(this.config,{body:{maxDetected:this.config.face.enabled?1*l.length:1}}):this.config;this.config.async?((g=this.config.body.modelPath)!=null&&g.includes("posenet")?u=this.config.body.enabled?M4(i.tensor,d):[]:(y=this.config.body.modelPath)!=null&&y.includes("blazepose")?u=this.config.body.enabled?qb(i.tensor,d):[]:(x=this.config.body.modelPath)!=null&&x.includes("efficientpose")?u=this.config.body.enabled?e4(i.tensor,d):[]:(A=this.config.body.modelPath)!=null&&A.includes("movenet")&&(u=this.config.body.enabled?R4(i.tensor,d):[]),this.performance.body&&delete this.performance.body):(r=ue(),(b=this.config.body.modelPath)!=null&&b.includes("posenet")?u=this.config.body.enabled?await M4(i.tensor,d):[]:(w=this.config.body.modelPath)!=null&&w.includes("blazepose")?u=this.config.body.enabled?await qb(i.tensor,d):[]:(k=this.config.body.modelPath)!=null&&k.includes("efficientpose")?u=this.config.body.enabled?await e4(i.tensor,d):[]:(C=this.config.body.modelPath)!=null&&C.includes("movenet")&&(u=this.config.body.enabled?await R4(i.tensor,d):[]),this.performance.body=this.env.perfadd?(this.performance.body||0)+Math.trunc(ue()-r):Math.trunc(ue()-r)),this.analyze("End Body:"),this.analyze("Start Hand:"),this.state="detect:hand";let h=this.config.hand.maxDetected===-1?Kt(this.config,{hand:{maxDetected:this.config.face.enabled?2*l.length:1}}):this.config;this.config.async?((_=(E=this.config.hand.detector)==null?void 0:E.modelPath)!=null&&_.includes("handdetect")?c=this.config.hand.enabled?A4(i.tensor,h):[]:(R=($=this.config.hand.detector)==null?void 0:$.modelPath)!=null&&R.includes("handtrack")&&(c=this.config.hand.enabled?w4(i.tensor,h):[]),this.performance.hand&&delete this.performance.hand):(r=ue(),(S=(P=this.config.hand.detector)==null?void 0:P.modelPath)!=null&&S.includes("handdetect")?c=this.config.hand.enabled?await A4(i.tensor,h):[]:(L=(M=this.config.hand.detector)==null?void 0:M.modelPath)!=null&&L.includes("handtrack")&&(c=this.config.hand.enabled?await w4(i.tensor,h):[]),this.performance.hand=this.env.perfadd?(this.performance.hand||0)+Math.trunc(ue()-r):Math.trunc(ue()-r)),this.analyze("End Hand:"),this.analyze("Start Object:"),this.state="detect:object",this.config.async?((U=this.config.object.modelPath)!=null&&U.includes("nanodet")?p=this.config.object.enabled?D4(i.tensor,this.config):[]:(K=this.config.object.modelPath)!=null&&K.includes("centernet")&&(p=this.config.object.enabled?Zb(i.tensor,this.config):[]),this.performance.object&&delete this.performance.object):(r=ue(),(q=this.config.object.modelPath)!=null&&q.includes("nanodet")?p=this.config.object.enabled?await D4(i.tensor,this.config):[]:(Z=this.config.object.modelPath)!=null&&Z.includes("centernet")&&(p=this.config.object.enabled?await Zb(i.tensor,this.config):[]),this.performance.object=this.env.perfadd?(this.performance.object||0)+Math.trunc(ue()-r):Math.trunc(ue()-r)),this.analyze("End Object:"),this.state="detect:await",this.config.async&&([l,u,c,p]=await Promise.all([l,u,c,p])),this.state="detect:gesture";let f=[];this.config.gesture.enabled&&(r=ue(),f=[...AR(l),...yR(u),...bR(c),...xR(l)],this.config.async?this.performance.gesture&&delete this.performance.gesture:this.performance.gesture=this.env.perfadd?(this.performance.gesture||0)+Math.trunc(ue()-r):Math.trunc(ue()-r)),this.performance.total=this.env.perfadd?(this.performance.total||0)+Math.trunc(ue()-o):Math.trunc(ue()-o);let m=((J=this.process.tensor)==null?void 0:J.shape)||[];this.result={face:l,body:u,hand:c,gesture:f,object:p,performance:this.performance,canvas:this.process.canvas,timestamp:Date.now(),error:null,get persons(){return kR(l,u,c,f,m)}},ee(i.tensor),this.emit("detect"),this.state="idle",s(this.result)})}async sleep(t){return new Promise(n=>{setTimeout(n,t)})}async video(t,n=!0,s=0){n?(Xr(this,Di)[t.id]||(this.config.debug&&ne("video start",t.id),Xr(this,Di)[t.id]=!0),!t.paused&&Xr(this,Di)[t.id]&&t.readyState>=2&&await this.detect(t),s>0&&await this.sleep(s),Xr(this,Di)[t.id]&&requestAnimationFrame(()=>this.video(t,n,s))):(this.config.debug&&ne("video stop",t.id),Xr(this,Di)[t.id]=!1)}};Vd=new WeakMap,hf=new WeakMap,ff=new WeakMap,_1=new WeakMap,Di=new WeakMap;return k_(zbe);})();
+2Q==`;async function jbe(e){let t=(r,a="application/octet-stream")=>fetch(`data:${a};base64,${r}`).then(o=>o.blob()),n,s;switch(e.config.warmup){case"face":n=await t(D1);break;case"body":case"full":n=await t($1);break;default:n=null}if(n){let r=await createImageBitmap(n);s=await e.detect(r,e.config),r.close()}return s}async function qbe(e){return new Promise(t=>{let n;switch(e.config.warmup){case"face":n="data:image/jpeg;base64,"+D1;break;case"full":case"body":n="data:image/jpeg;base64,"+$1;break;default:n=""}let s;if(typeof Image!="undefined")s=new Image;else if(me.Image)s=new me.Image;else return;s.onload=async()=>{let r=vr(s.naturalWidth,s.naturalHeight);if(!r)ne("Warmup: Canvas not found"),t(void 0);else{let a=r.getContext("2d");a&&a.drawImage(s,0,0);let o=await e.image(r),i=o.tensor?await e.detect(o.tensor,e.config):void 0;t(i)}},n?s.src=n:t(void 0)})}async function Xbe(e){let t=r=>Buffer.from(r,"base64"),n;e.config.warmup==="face"?n=t(D1):n=t($1);let s;if("node"in Qe&&dn()==="tensorflow"){let r=(void 0).decodeJpeg(n),a=Ft(r,0);e.tf.dispose(r),s=await e.detect(a,e.config),e.tf.dispose(a)}else e.config.debug&&ne("Warmup tfjs-node not loaded");return s}async function Kbe(e){let t;return typeof createImageBitmap=="function"?t=await jbe(e):typeof Image!="undefined"||me.Canvas!==void 0?t=await qbe(e):t=await Xbe(e),t}async function Zbe(e){var i,l,u,c;if(!H().flagRegistry.ENGINE_COMPILE_ONLY)return;let t=dn(),n=Us();if(t!=="webgl"&&t!=="humangl"||!(n!=null&&n.checkCompileCompletion))return;H().set("ENGINE_COMPILE_ONLY",!0);let s=Qt().state.numTensors,r=[];for(let[p,d]of Object.entries(e.models).filter(([h,f])=>h!==null&&f!==null)){let h=(l=(i=d.inputs)==null?void 0:i[0])!=null&&l.shape?[...d.inputs[0].shape]:[1,64,64,3],f=(c=(u=d.inputs)==null?void 0:u[0])!=null&&c.dtype?d.inputs[0].dtype:"float32";for(let g=0;g<h.length;g++)h[g]===-1&&(h[g]=g===0?1:64);let m=Gt(h,f);try{let g=d.execute(m);r.push(p),Array.isArray(g)?g.forEach(y=>Q(y)):Q(g)}catch(g){e.config.debug&&ne("compile fail model:",p)}Q(m)}let a=await n.checkCompileCompletionAsync();n.getUniformLocations(),e.config.debug&&ne("compile pass:",{models:r,kernels:a.length}),H().set("ENGINE_COMPILE_ONLY",!1);let o=Qt().state.numTensors;o-s>0&&ne("tensor leak:",o-s)}async function OR(e,t){await df(e,!1);let n=ue();return e.state="warmup",t&&(e.config=Vt(e.config,t)),!e.config.warmup||e.config.warmup.length===0||e.config.warmup==="none"?{face:[],body:[],hand:[],gesture:[],object:[],performance:e.performance,timestamp:ue(),persons:[],error:null}:new Promise(async s=>{await Od.load(e),await Zbe(e);let r=await Kbe(e),a=ue();e.config.debug&&ne("warmup",e.config.warmup,Math.round(a-n),"ms"),e.emit("warmup"),s(r)})}var Ud,hf,ff,P1,Oi,lv=class{constructor(t){fe(this,"version");fe(this,"config");fe(this,"result");fe(this,"state");fe(this,"process");fe(this,"tf");fe(this,"env");fe(this,"draw");fe(this,"models");fe(this,"events");fe(this,"faceTriangulation");fe(this,"faceUVMap");fe(this,"performance");Ku(this,Ud,void 0);Ku(this,hf,void 0);Ku(this,ff,void 0);fe(this,"gl");fe(this,"analyze",(...t)=>{if(!Zr(this,hf))return;let n=this.tf.engine().state.numTensors,s=Zr(this,Ud);sp(this,Ud,n);let r=n-s;r!==0&&ne(...t,r)});Ku(this,P1,t=>{if(!Zr(this,ff))return null;if(!t)return"input is not defined";if(this.env.node&&!(t instanceof it))return"input must be a tensor";try{this.tf.getBackend()}catch(n){return"backend not loaded"}return null});fe(this,"similarity",av);fe(this,"distance",pf);fe(this,"match",ov);fe(this,"webcam",new K2);fe(this,"emit",t=>{var n;(n=this.events)!=null&&n.dispatchEvent&&this.events.dispatchEvent(new Event(t))});Ku(this,Oi,{});this.env=me;let n=(Jh.tfjs||lA).replace(/-(.*)/,"");Xa.wasmPath=`https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-backend-wasm@${n}/dist/`,Xa.modelBasePath=me.browser?"../models/":"file://models/",Xa.backend=me.browser?"webgl":"tensorflow",this.version=_b,Object.defineProperty(this,"version",{value:_b}),this.config=JSON.parse(JSON.stringify(Xa)),Object.seal(this.config),this.config.cacheModels=typeof indexedDB!="undefined",t&&(this.config=Vt(this.config,t)),YT(this.config),this.tf=Qe,this.state="idle",sp(this,Ud,0),sp(this,hf,!1),sp(this,ff,!1),this.performance={},this.events=typeof EventTarget!="undefined"?new EventTarget:void 0,this.models=new cf,this.draw={options:Jn,canvas:(r,a)=>Q4(r,a),face:(r,a,o)=>Md(r,a,o),body:(r,a,o)=>zd(r,a,o),hand:(r,a,o)=>Ld(r,a,o),gesture:(r,a,o)=>Wd(r,a,o),object:(r,a,o)=>Bd(r,a,o),person:(r,a,o)=>J4(r,a,o),all:(r,a,o)=>ev(r,a,o)},this.result={face:[],body:[],hand:[],gesture:[],object:[],performance:{},timestamp:0,persons:[],error:null},this.process={tensor:null,canvas:null},this.faceTriangulation=jN,this.faceUVMap=qN,this.gl=Dt,R1(this,null,""),this.emit("create"),(this.config.debug||this.env.browser)&&ne(`version: ${this.version}`),this.config.debug&&ne(`tfjs version: ${this.tf.version["tfjs-core"]}`);let s=JSON.parse(JSON.stringify(this.env));delete s.kernels,delete s.initial,delete s.perfadd,this.config.debug&&ne("environment:",s)}reset(){let t=this.config.backend;this.config=JSON.parse(JSON.stringify(Xa)),this.config.backend=t,Eb(),me.initial=!0}validate(t){let n=g3(Xa,t||this.config);return n.length===0&&(this.config=Vt(this.config,t)),n}check(){return _1(this)}now(){return ue()}image(t,n=!0){return q2(t,this.config,n)}async segmentation(t,n){var a,o,i;if(n&&(this.config=Vt(this.config,n)),!this.config.segmentation.enabled)return null;let s=await q2(t,this.config);if(!s.tensor)return null;let r=null;return(a=this.config.segmentation.modelPath)!=null&&a.includes("rvm")&&(r=await cR(s.tensor,this.config)),(o=this.config.segmentation.modelPath)!=null&&o.includes("meet")&&(r=await zE(s.tensor,this.config)),(i=this.config.segmentation.modelPath)!=null&&i.includes("selfie")&&(r=await pR(s.tensor,this.config)),Q(s.tensor),r}enhance(t){return s4(t)}compare(t,n){return ZT(this.config,t,n)}async init(){await df(this,!0),await this.tf.ready(),Eb()}async load(t){this.state="load";let n=ue(),s=Object.values(this.models).filter(o=>o).length;t&&(this.config=Vt(this.config,t)),this.env.initial&&(await df(this,!1)||ne("error: backend check failed"),await fh(),this.env.browser&&(this.config.debug&&ne("configuration:",this.config),this.config.debug&&ne("tf flags:",this.tf.ENV.flags))),await X4(this),this.env.initial&&this.config.debug&&ne("tf engine state:",this.tf.engine().state.numBytes,"bytes",this.tf.engine().state.numTensors,"tensors"),this.env.initial=!1,Object.values(this.models).filter(o=>o).length!==s&&(_1(this),this.emit("load"));let a=Math.trunc(ue()-n);a>(this.performance.loadModels||0)&&(this.performance.loadModels=this.env.perfadd?(this.performance.loadModels||0)+a:a)}next(t=this.result){return $R(t,this.config)}getModelStats(){return q4(this)}async warmup(t){let n=ue(),s=await OR(this,t),r=ue();return this.performance.warmup=Math.trunc(r-n),s}async profile(t,n){let s=await this.tf.profile(()=>this.detect(t,n)),r={},a=0;for(let i of s.kernels)r[i.name]?r[i.name]+=i.kernelTimeMs:r[i.name]=i.kernelTimeMs,a+=i.kernelTimeMs;let o=[];Object.entries(r).forEach(i=>o.push({kernel:i[0],time:i[1],perc:0}));for(let i of o)i.perc=Math.round(1e3*i.time/a)/1e3,i.time=Math.round(1e3*i.time)/1e3;return o.sort((i,l)=>l.time-i.time),o.length=20,o}async detect(t,n){return this.state="detect",new Promise(async s=>{var g,y,x,A,b,w,k,C,E,_,$,R,P,S,M,L,U,K,q,Z,J;this.state="config";let r;this.config=Vt(this.config,n),this.state="check";let a=Zr(this,P1).call(this,t);a&&(ne(a,t),this.emit("error"),s({face:[],body:[],hand:[],gesture:[],object:[],performance:this.performance,timestamp:ue(),persons:[],error:a}));let o=ue();await this.load(),r=ue(),this.state="image";let i=await q2(t,this.config);if(this.process=i,this.performance.inputProcess=this.env.perfadd?(this.performance.inputProcess||0)+Math.trunc(ue()-r):Math.trunc(ue()-r),this.analyze("Get Image:"),!i.tensor){this.config.debug&&ne("could not convert input to tensor"),this.emit("error"),s({face:[],body:[],hand:[],gesture:[],object:[],performance:this.performance,timestamp:ue(),persons:[],error:"could not convert input to tensor"});return}this.emit("image"),r=ue(),this.config.skipAllowed=await KT(this.config,i.tensor),this.performance.totalFrames||(this.performance.totalFrames=0),this.performance.cachedFrames||(this.performance.cachedFrames=0),this.performance.totalFrames++,this.config.skipAllowed&&this.performance.cachedFrames++,this.performance.cacheCheck=this.env.perfadd?(this.performance.cacheCheck||0)+Math.trunc(ue()-r):Math.trunc(ue()-r),this.analyze("Check Changed:");let l=[],u=[],c=[],p=[];this.state="detect:face",this.config.async?(l=this.config.face.enabled?sv(this,i.tensor):[],this.performance.face&&delete this.performance.face):(r=ue(),l=this.config.face.enabled?await sv(this,i.tensor):[],this.performance.face=this.env.perfadd?(this.performance.face||0)+Math.trunc(ue()-r):Math.trunc(ue()-r)),this.config.async&&(this.config.body.maxDetected===-1||this.config.hand.maxDetected===-1)&&(l=await l),this.analyze("Start Body:"),this.state="detect:body";let d=this.config.body.maxDetected===-1?Vt(this.config,{body:{maxDetected:this.config.face.enabled?1*l.length:1}}):this.config;this.config.async?((g=this.config.body.modelPath)!=null&&g.includes("posenet")?u=this.config.body.enabled?M4(i.tensor,d):[]:(y=this.config.body.modelPath)!=null&&y.includes("blazepose")?u=this.config.body.enabled?Ub(i.tensor,d):[]:(x=this.config.body.modelPath)!=null&&x.includes("efficientpose")?u=this.config.body.enabled?Zb(i.tensor,d):[]:(A=this.config.body.modelPath)!=null&&A.includes("movenet")&&(u=this.config.body.enabled?R4(i.tensor,d):[]),this.performance.body&&delete this.performance.body):(r=ue(),(b=this.config.body.modelPath)!=null&&b.includes("posenet")?u=this.config.body.enabled?await M4(i.tensor,d):[]:(w=this.config.body.modelPath)!=null&&w.includes("blazepose")?u=this.config.body.enabled?await Ub(i.tensor,d):[]:(k=this.config.body.modelPath)!=null&&k.includes("efficientpose")?u=this.config.body.enabled?await Zb(i.tensor,d):[]:(C=this.config.body.modelPath)!=null&&C.includes("movenet")&&(u=this.config.body.enabled?await R4(i.tensor,d):[]),this.performance.body=this.env.perfadd?(this.performance.body||0)+Math.trunc(ue()-r):Math.trunc(ue()-r)),this.analyze("End Body:"),this.analyze("Start Hand:"),this.state="detect:hand";let h=this.config.hand.maxDetected===-1?Vt(this.config,{hand:{maxDetected:this.config.face.enabled?2*l.length:1}}):this.config;this.config.async?((_=(E=this.config.hand.detector)==null?void 0:E.modelPath)!=null&&_.includes("handdetect")?c=this.config.hand.enabled?h4(i.tensor,h):[]:(R=($=this.config.hand.detector)==null?void 0:$.modelPath)!=null&&R.includes("handtrack")&&(c=this.config.hand.enabled?y4(i.tensor,h):[]),this.performance.hand&&delete this.performance.hand):(r=ue(),(S=(P=this.config.hand.detector)==null?void 0:P.modelPath)!=null&&S.includes("handdetect")?c=this.config.hand.enabled?await h4(i.tensor,h):[]:(L=(M=this.config.hand.detector)==null?void 0:M.modelPath)!=null&&L.includes("handtrack")&&(c=this.config.hand.enabled?await y4(i.tensor,h):[]),this.performance.hand=this.env.perfadd?(this.performance.hand||0)+Math.trunc(ue()-r):Math.trunc(ue()-r)),this.analyze("End Hand:"),this.analyze("Start Object:"),this.state="detect:object",this.config.async?((U=this.config.object.modelPath)!=null&&U.includes("nanodet")?p=this.config.object.enabled?D4(i.tensor,this.config):[]:(K=this.config.object.modelPath)!=null&&K.includes("centernet")&&(p=this.config.object.enabled?jb(i.tensor,this.config):[]),this.performance.object&&delete this.performance.object):(r=ue(),(q=this.config.object.modelPath)!=null&&q.includes("nanodet")?p=this.config.object.enabled?await D4(i.tensor,this.config):[]:(Z=this.config.object.modelPath)!=null&&Z.includes("centernet")&&(p=this.config.object.enabled?await jb(i.tensor,this.config):[]),this.performance.object=this.env.perfadd?(this.performance.object||0)+Math.trunc(ue()-r):Math.trunc(ue()-r)),this.analyze("End Object:"),this.state="detect:await",this.config.async&&([l,u,c,p]=await Promise.all([l,u,c,p])),this.state="detect:gesture";let f=[];this.config.gesture.enabled&&(r=ue(),f=[...RR(l),...ER(u),...DR(c),..._R(l)],this.config.async?this.performance.gesture&&delete this.performance.gesture:this.performance.gesture=this.env.perfadd?(this.performance.gesture||0)+Math.trunc(ue()-r):Math.trunc(ue()-r)),this.performance.total=this.env.perfadd?(this.performance.total||0)+Math.trunc(ue()-o):Math.trunc(ue()-o);let m=((J=this.process.tensor)==null?void 0:J.shape)||[];this.result={face:l,body:u,hand:c,gesture:f,object:p,performance:this.performance,canvas:this.process.canvas,timestamp:Date.now(),error:null,get persons(){return FR(l,u,c,f,m)}},Q(i.tensor),this.emit("detect"),this.state="idle",s(this.result)})}async sleep(t){return new Promise(n=>{setTimeout(n,t)})}async video(t,n=!0,s=0){n?(Zr(this,Oi)[t.id]||(this.config.debug&&ne("video start",t.id),Zr(this,Oi)[t.id]=!0),!t.paused&&Zr(this,Oi)[t.id]&&t.readyState>=2&&await this.detect(t),s>0&&await this.sleep(s),Zr(this,Oi)[t.id]&&requestAnimationFrame(()=>this.video(t,n,s))):(this.config.debug&&ne("video stop",t.id),Zr(this,Oi)[t.id]=!1)}};Ud=new WeakMap,hf=new WeakMap,ff=new WeakMap,P1=new WeakMap,Oi=new WeakMap;return F_(Jbe);})();
diff --git a/dist/human.node-gpu.js b/dist/human.node-gpu.js
index ccda90d1..a44cc772 100644
--- a/dist/human.node-gpu.js
+++ b/dist/human.node-gpu.js
@@ -4,7 +4,291 @@
   author: <https://github.com/vladmandic>'
 */
 
-"use strict";var Mo=Object.create;var w2=Object.defineProperty;var vo=Object.getOwnPropertyDescriptor;var Po=Object.getOwnPropertyNames;var To=Object.getPrototypeOf,Ro=Object.prototype.hasOwnProperty;var ko=(e,t,n)=>t in e?w2(e,t,{enumerable:!0,configurable:!0,writable:!0,value:n}):e[t]=n;var wo=(e,t)=>()=>(t||e((t={exports:{}}).exports,t),t.exports),ve=(e,t)=>{for(var n in t)w2(e,n,{get:t[n],enumerable:!0})},v1=(e,t,n,o)=>{if(t&&typeof t=="object"||typeof t=="function")for(let r of Po(t))!Ro.call(e,r)&&r!==n&&w2(e,r,{get:()=>t[r],enumerable:!(o=vo(t,r))||o.enumerable});return e};var D=(e,t,n)=>(n=e!=null?Mo(To(e)):{},v1(t||!e||!e.__esModule?w2(n,"default",{value:e,enumerable:!0}):n,e)),Eo=e=>v1(w2({},"__esModule",{value:!0}),e);var R=(e,t,n)=>(ko(e,typeof t!="symbol"?t+"":t,n),n),P1=(e,t,n)=>{if(!t.has(e))throw TypeError("Cannot "+n)};var ie=(e,t,n)=>(P1(e,t,"read from private field"),n?n.call(e):t.get(e)),t2=(e,t,n)=>{if(t.has(e))throw TypeError("Cannot add the same private member more than once");t instanceof WeakSet?t.add(e):t.set(e,n)},E2=(e,t,n,o)=>(P1(e,t,"write to private field"),o?o.call(e,n):t.set(e,n),n);var H=wo((Ds,B5)=>{"use strict";var R1=Object.defineProperty,zo=Object.getOwnPropertyDescriptor,So=Object.getOwnPropertyNames,jo=Object.prototype.hasOwnProperty,G5=(e,t,n,o)=>{if(t&&typeof t=="object"||typeof t=="function")for(let r of So(t))!jo.call(e,r)&&r!==n&&R1(e,r,{get:()=>t[r],enumerable:!(o=zo(t,r))||o.enumerable});return e},No=(e,t,n)=>(G5(e,t,"default"),n&&G5(n,t,"default")),Io=e=>G5(R1({},"__esModule",{value:!0}),e),k1={};B5.exports=Io(k1);No(k1,require("@tensorflow/tfjs-node-gpu"),B5.exports)});var Gs={};ve(Gs,{Env:()=>S2,Human:()=>b1,default:()=>b1,defaults:()=>Pe,draw:()=>x1,env:()=>k,match:()=>h1,models:()=>f2});module.exports=Eo(Gs);function u(...e){let t=new Date,n=`${t.getHours().toString().padStart(2,"0")}:${t.getMinutes().toString().padStart(2,"0")}:${t.getSeconds().toString().padStart(2,"0")}.${t.getMilliseconds().toString().padStart(3,"0")}`;e&&console.log(n,"Human:",...e)}function T1(e,t){let n=e.endsWith("/")?"":"/",r=t.startsWith(".")||t.startsWith("/")||t.startsWith("http:")||t.startsWith("https:")||t.startsWith("file:")?`${t}`:`${e}${n}${t}`;if(!r.toLocaleLowerCase().includes(".json"))throw new Error(`modelpath error: expecting json file: ${r}`);return r}var v=()=>typeof performance!="undefined"?performance.now():parseInt((Number(process.hrtime.bigint())/1e3/1e3).toString());function F5(e,t,n="config",o=[]){for(let r of Object.keys(t))if(typeof t[r]=="object")F5(e[r],t[r],r,o);else{let A=e&&typeof e[r]!="undefined";A||o.push({reason:"unknown property",where:`${n}.${r} = ${t[r]}`});let s=e&&typeof e[r]==typeof t[r];A&&!s&&o.push({reason:"property type mismatch",where:`${n}.${r} = ${t[r]}`,expected:typeof e[r]})}return t.debug&&n==="config"&&o.length>0&&u("invalid configuration",o),o}function A0(...e){let t=n=>n&&typeof n=="object";return e.reduce((n,o)=>(Object.keys(o||{}).forEach(r=>{let A=n[r],s=o[r];Array.isArray(A)&&Array.isArray(s)?n[r]=A.concat(...s):t(A)&&t(s)?n[r]=A0(A,s):n[r]=s}),n),{})}var Pe={backend:"",modelBasePath:"",cacheModels:!0,validateModels:!0,wasmPath:"",wasmPlatformFetch:!1,debug:!1,async:!0,warmup:"full",cacheSensitivity:.7,skipAllowed:!1,deallocate:!1,flags:{},softwareKernels:!1,filter:{enabled:!0,equalization:!1,width:0,height:0,flip:!1,return:!0,brightness:0,contrast:0,sharpness:0,blur:0,saturation:0,hue:0,negative:!1,sepia:!1,vintage:!1,kodachrome:!1,technicolor:!1,polaroid:!1,pixelate:0},gesture:{enabled:!0},face:{enabled:!0,detector:{modelPath:"blazeface.json",rotation:!0,maxDetected:1,skipFrames:99,skipTime:2500,minConfidence:.2,iouThreshold:.1,mask:!1,return:!1},mesh:{enabled:!0,modelPath:"facemesh.json",keepInvalid:!1},attention:{enabled:!1,modelPath:"facemesh-attention.json"},iris:{enabled:!0,modelPath:"iris.json"},emotion:{enabled:!0,minConfidence:.1,skipFrames:99,skipTime:1500,modelPath:"emotion.json"},description:{enabled:!0,modelPath:"faceres.json",skipFrames:99,skipTime:3e3,minConfidence:.1},antispoof:{enabled:!1,skipFrames:99,skipTime:4e3,modelPath:"antispoof.json"},liveness:{enabled:!1,skipFrames:99,skipTime:4e3,modelPath:"liveness.json"}},body:{enabled:!0,modelPath:"movenet-lightning.json",maxDetected:-1,minConfidence:.3,skipFrames:1,skipTime:200},hand:{enabled:!0,rotation:!0,skipFrames:99,skipTime:1e3,minConfidence:.5,iouThreshold:.2,maxDetected:-1,landmarks:!0,detector:{modelPath:"handtrack.json"},skeleton:{modelPath:"handlandmark-full.json"}},object:{enabled:!1,modelPath:"mb3-centernet.json",minConfidence:.2,iouThreshold:.4,maxDetected:10,skipFrames:99,skipTime:2e3},segmentation:{enabled:!1,modelPath:"selfie.json",blur:8}};var x0=D(H());var O=D(H());var w1=`
+"use strict";
+var __create = Object.create;
+var __defProp = Object.defineProperty;
+var __getOwnPropDesc = Object.getOwnPropertyDescriptor;
+var __getOwnPropNames = Object.getOwnPropertyNames;
+var __getProtoOf = Object.getPrototypeOf;
+var __hasOwnProp = Object.prototype.hasOwnProperty;
+var __defNormalProp = (obj, key, value) => key in obj ? __defProp(obj, key, { enumerable: true, configurable: true, writable: true, value }) : obj[key] = value;
+var __commonJS = (cb, mod3) => function __require() {
+  return mod3 || (0, cb[__getOwnPropNames(cb)[0]])((mod3 = { exports: {} }).exports, mod3), mod3.exports;
+};
+var __export = (target, all2) => {
+  for (var name in all2)
+    __defProp(target, name, { get: all2[name], enumerable: true });
+};
+var __copyProps = (to, from, except, desc) => {
+  if (from && typeof from === "object" || typeof from === "function") {
+    for (let key of __getOwnPropNames(from))
+      if (!__hasOwnProp.call(to, key) && key !== except)
+        __defProp(to, key, { get: () => from[key], enumerable: !(desc = __getOwnPropDesc(from, key)) || desc.enumerable });
+  }
+  return to;
+};
+var __toESM = (mod3, isNodeMode, target) => (target = mod3 != null ? __create(__getProtoOf(mod3)) : {}, __copyProps(
+  isNodeMode || !mod3 || !mod3.__esModule ? __defProp(target, "default", { value: mod3, enumerable: true }) : target,
+  mod3
+));
+var __toCommonJS = (mod3) => __copyProps(__defProp({}, "__esModule", { value: true }), mod3);
+var __publicField = (obj, key, value) => {
+  __defNormalProp(obj, typeof key !== "symbol" ? key + "" : key, value);
+  return value;
+};
+var __accessCheck = (obj, member, msg) => {
+  if (!member.has(obj))
+    throw TypeError("Cannot " + msg);
+};
+var __privateGet = (obj, member, getter) => {
+  __accessCheck(obj, member, "read from private field");
+  return getter ? getter.call(obj) : member.get(obj);
+};
+var __privateAdd = (obj, member, value) => {
+  if (member.has(obj))
+    throw TypeError("Cannot add the same private member more than once");
+  member instanceof WeakSet ? member.add(obj) : member.set(obj, value);
+};
+var __privateSet = (obj, member, value, setter) => {
+  __accessCheck(obj, member, "write to private field");
+  setter ? setter.call(obj, value) : member.set(obj, value);
+  return value;
+};
+
+// dist/tfjs.esm.js
+var require_tfjs_esm = __commonJS({
+  "dist/tfjs.esm.js"(exports, module2) {
+    "use strict";
+    var __defProp2 = Object.defineProperty;
+    var __getOwnPropDesc2 = Object.getOwnPropertyDescriptor;
+    var __getOwnPropNames2 = Object.getOwnPropertyNames;
+    var __hasOwnProp2 = Object.prototype.hasOwnProperty;
+    var __copyProps2 = (to, from, except, desc) => {
+      if (from && typeof from === "object" || typeof from === "function") {
+        for (let key of __getOwnPropNames2(from))
+          if (!__hasOwnProp2.call(to, key) && key !== except)
+            __defProp2(to, key, { get: () => from[key], enumerable: !(desc = __getOwnPropDesc2(from, key)) || desc.enumerable });
+      }
+      return to;
+    };
+    var __reExport = (target, mod3, secondTarget) => (__copyProps2(target, mod3, "default"), secondTarget && __copyProps2(secondTarget, mod3, "default"));
+    var __toCommonJS2 = (mod3) => __copyProps2(__defProp2({}, "__esModule", { value: true }), mod3);
+    var tf_node_gpu_exports = {};
+    module2.exports = __toCommonJS2(tf_node_gpu_exports);
+    __reExport(tf_node_gpu_exports, require("@tensorflow/tfjs-node-gpu"), module2.exports);
+  }
+});
+
+// src/human.ts
+var human_exports = {};
+__export(human_exports, {
+  Env: () => Env,
+  Human: () => Human2,
+  default: () => Human2,
+  defaults: () => config,
+  draw: () => draw_exports,
+  env: () => env,
+  match: () => match_exports,
+  models: () => models_exports2
+});
+module.exports = __toCommonJS(human_exports);
+
+// src/util/util.ts
+function log(...msg) {
+  const dt = new Date();
+  const ts = `${dt.getHours().toString().padStart(2, "0")}:${dt.getMinutes().toString().padStart(2, "0")}:${dt.getSeconds().toString().padStart(2, "0")}.${dt.getMilliseconds().toString().padStart(3, "0")}`;
+  if (msg)
+    console.log(ts, "Human:", ...msg);
+}
+function join(folder, file) {
+  const separator = folder.endsWith("/") ? "" : "/";
+  const skipJoin = file.startsWith(".") || file.startsWith("/") || file.startsWith("http:") || file.startsWith("https:") || file.startsWith("file:");
+  const path = skipJoin ? `${file}` : `${folder}${separator}${file}`;
+  if (!path.toLocaleLowerCase().includes(".json"))
+    throw new Error(`modelpath error: expecting json file: ${path}`);
+  return path;
+}
+var now = () => {
+  if (typeof performance !== "undefined")
+    return performance.now();
+  return parseInt((Number(process.hrtime.bigint()) / 1e3 / 1e3).toString());
+};
+function validate(defaults, config3, parent = "config", msgs = []) {
+  for (const key of Object.keys(config3)) {
+    if (typeof config3[key] === "object") {
+      validate(defaults[key], config3[key], key, msgs);
+    } else {
+      const defined = defaults && typeof defaults[key] !== "undefined";
+      if (!defined)
+        msgs.push({ reason: "unknown property", where: `${parent}.${key} = ${config3[key]}` });
+      const same = defaults && typeof defaults[key] === typeof config3[key];
+      if (defined && !same)
+        msgs.push({ reason: "property type mismatch", where: `${parent}.${key} = ${config3[key]}`, expected: typeof defaults[key] });
+    }
+  }
+  if (config3.debug && parent === "config" && msgs.length > 0)
+    log("invalid configuration", msgs);
+  return msgs;
+}
+function mergeDeep(...objects) {
+  const isObject = (obj) => obj && typeof obj === "object";
+  return objects.reduce((prev, obj) => {
+    Object.keys(obj || {}).forEach((key) => {
+      const pVal = prev[key];
+      const oVal = obj[key];
+      if (Array.isArray(pVal) && Array.isArray(oVal))
+        prev[key] = pVal.concat(...oVal);
+      else if (isObject(pVal) && isObject(oVal))
+        prev[key] = mergeDeep(pVal, oVal);
+      else
+        prev[key] = oVal;
+    });
+    return prev;
+  }, {});
+}
+
+// src/config.ts
+var config = {
+  backend: "",
+  modelBasePath: "",
+  cacheModels: true,
+  validateModels: true,
+  wasmPath: "",
+  wasmPlatformFetch: false,
+  debug: false,
+  async: true,
+  warmup: "full",
+  cacheSensitivity: 0.7,
+  skipAllowed: false,
+  deallocate: false,
+  flags: {},
+  softwareKernels: false,
+  filter: {
+    enabled: true,
+    equalization: false,
+    width: 0,
+    height: 0,
+    flip: false,
+    return: true,
+    brightness: 0,
+    contrast: 0,
+    sharpness: 0,
+    blur: 0,
+    saturation: 0,
+    hue: 0,
+    negative: false,
+    sepia: false,
+    vintage: false,
+    kodachrome: false,
+    technicolor: false,
+    polaroid: false,
+    pixelate: 0
+  },
+  gesture: {
+    enabled: true
+  },
+  face: {
+    enabled: true,
+    detector: {
+      modelPath: "blazeface.json",
+      rotation: true,
+      maxDetected: 1,
+      skipFrames: 99,
+      skipTime: 2500,
+      minConfidence: 0.2,
+      iouThreshold: 0.1,
+      mask: false,
+      return: false
+    },
+    mesh: {
+      enabled: true,
+      modelPath: "facemesh.json",
+      keepInvalid: false
+    },
+    attention: {
+      enabled: false,
+      modelPath: "facemesh-attention.json"
+    },
+    iris: {
+      enabled: true,
+      modelPath: "iris.json"
+    },
+    emotion: {
+      enabled: true,
+      minConfidence: 0.1,
+      skipFrames: 99,
+      skipTime: 1500,
+      modelPath: "emotion.json"
+    },
+    description: {
+      enabled: true,
+      modelPath: "faceres.json",
+      skipFrames: 99,
+      skipTime: 3e3,
+      minConfidence: 0.1
+    },
+    antispoof: {
+      enabled: false,
+      skipFrames: 99,
+      skipTime: 4e3,
+      modelPath: "antispoof.json"
+    },
+    liveness: {
+      enabled: false,
+      skipFrames: 99,
+      skipTime: 4e3,
+      modelPath: "liveness.json"
+    }
+  },
+  body: {
+    enabled: true,
+    modelPath: "movenet-lightning.json",
+    maxDetected: -1,
+    minConfidence: 0.3,
+    skipFrames: 1,
+    skipTime: 200
+  },
+  hand: {
+    enabled: true,
+    rotation: true,
+    skipFrames: 99,
+    skipTime: 1e3,
+    minConfidence: 0.5,
+    iouThreshold: 0.2,
+    maxDetected: -1,
+    landmarks: true,
+    detector: {
+      modelPath: "handtrack.json"
+    },
+    skeleton: {
+      modelPath: "handlandmark-full.json"
+    }
+  },
+  object: {
+    enabled: false,
+    modelPath: "mb3-centernet.json",
+    minConfidence: 0.2,
+    iouThreshold: 0.4,
+    maxDetected: 10,
+    skipFrames: 99,
+    skipTime: 2e3
+  },
+  segmentation: {
+    enabled: false,
+    modelPath: "rvm.json",
+    ratio: 0.5,
+    mode: "default"
+  }
+};
+
+// src/util/env.ts
+var tf3 = __toESM(require_tfjs_esm());
+
+// src/image/image.ts
+var tf2 = __toESM(require_tfjs_esm());
+
+// src/image/imagefxshaders.ts
+var vertexIdentity = `
   precision highp float;
   attribute vec2 pos;
   attribute vec2 uv;
@@ -14,7 +298,8 @@
     vUv = uv;
     gl_Position = vec4(pos.x, pos.y*flipY, 0.0, 1.);
   }
-`;var E1=`
+`;
+var colorMatrixWithAlpha = `
   precision highp float;
   varying vec2 vUv;
   uniform sampler2D texture;
@@ -26,7 +311,8 @@
     gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[13] * c.a + m[14];
     gl_FragColor.a = m[15] * c.r + m[16] * c.g + m[17] * c.b + m[18] * c.a + m[19];
   }
-`,z1=`
+`;
+var colorMatrixWithoutAlpha = `
   precision highp float;
   varying vec2 vUv;
   uniform sampler2D texture;
@@ -38,7 +324,8 @@
     gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[14];
     gl_FragColor.a = c.a;
   }
-`,S1=`
+`;
+var pixelate = `
   precision highp float;
   varying vec2 vUv;
   uniform vec2 size;
@@ -51,7 +338,8 @@
     vec2 coord = pixelate(vUv, size);
     gl_FragColor += texture2D(texture, coord);
   }
-`,j1=`
+`;
+var blur = `
   precision highp float;
   varying vec2 vUv;
   uniform sampler2D texture;
@@ -74,7 +362,8 @@
     gl_FragColor += texture2D(texture, vUv + vec2( 6.0*px.x,  6.0*px.y))*0.00895781211794;
     gl_FragColor += texture2D(texture, vUv + vec2( 7.0*px.x,  7.0*px.y))*0.0044299121055113265;
   }
-`,N1=`
+`;
+var convolution = `
   precision highp float;
   varying vec2 vUv;
   uniform sampler2D texture;
@@ -96,19 +385,13456 @@
     c31 * m[6] + c32 * m[7] + c33 * m[8];
     gl_FragColor.a = c22.a;
   }
-`;var H5=(e,t,n)=>{let o=new RegExp("\\b"+t+" \\w+ (\\w+)","ig");e.replace(o,(r,A)=>(n[A]=0,r))},V5=class{constructor(t,n,o){R(this,"uniform",{});R(this,"attribute",{});R(this,"gl");R(this,"id");R(this,"compile",(t,n)=>{let o=this.gl.createShader(n);return o?(this.gl.shaderSource(o,t),this.gl.compileShader(o),this.gl.getShaderParameter(o,this.gl.COMPILE_STATUS)?o:(u(`filter: gl compile failed: ${this.gl.getShaderInfoLog(o)||"unknown"}`),null)):(u("filter: could not create shader"),null)});this.gl=t;let r=this.compile(n,this.gl.VERTEX_SHADER),A=this.compile(o,this.gl.FRAGMENT_SHADER);if(this.id=this.gl.createProgram(),!(!r||!A)){if(!this.id){u("filter: could not create webgl program");return}if(this.gl.attachShader(this.id,r),this.gl.attachShader(this.id,A),this.gl.linkProgram(this.id),!this.gl.getProgramParameter(this.id,this.gl.LINK_STATUS)){u(`filter: gl link failed: ${this.gl.getProgramInfoLog(this.id)||"unknown"}`);return}this.gl.useProgram(this.id),H5(n,"attribute",this.attribute);for(let s in this.attribute)this.attribute[s]=this.gl.getAttribLocation(this.id,s);H5(n,"uniform",this.uniform),H5(o,"uniform",this.uniform);for(let s in this.uniform)this.uniform[s]=this.gl.getUniformLocation(this.id,s)}}};function I1(){let e=0,t=null,n=!1,o=-1,r=[null,null],A=[],s=null,a=null,l=k0(100,100),c={},x={INTERMEDIATE:1},i=l.getContext("webgl");if(!i){u("filter: cannot get webgl context");return}this.gl=i;function y(P,m){if(!(P===l.width&&m===l.height)){if(l.width=P,l.height=m,!s){let b=new Float32Array([-1,-1,0,1,1,-1,1,1,-1,1,0,0,-1,1,0,0,1,-1,1,1,1,1,1,0]);s=i.createBuffer(),i.bindBuffer(i.ARRAY_BUFFER,s),i.bufferData(i.ARRAY_BUFFER,b,i.STATIC_DRAW),i.pixelStorei(i.UNPACK_PREMULTIPLY_ALPHA_WEBGL,!0)}i.viewport(0,0,l.width,l.height),r=[null,null]}}function d(P,m){let b=i.createFramebuffer();i.bindFramebuffer(i.FRAMEBUFFER,b);let z=i.createRenderbuffer();i.bindRenderbuffer(i.RENDERBUFFER,z);let w=i.createTexture();return i.bindTexture(i.TEXTURE_2D,w),i.texImage2D(i.TEXTURE_2D,0,i.RGBA,P,m,0,i.RGBA,i.UNSIGNED_BYTE,null),i.texParameteri(i.TEXTURE_2D,i.TEXTURE_MAG_FILTER,i.LINEAR),i.texParameteri(i.TEXTURE_2D,i.TEXTURE_MIN_FILTER,i.LINEAR),i.texParameteri(i.TEXTURE_2D,i.TEXTURE_WRAP_S,i.CLAMP_TO_EDGE),i.texParameteri(i.TEXTURE_2D,i.TEXTURE_WRAP_T,i.CLAMP_TO_EDGE),i.framebufferTexture2D(i.FRAMEBUFFER,i.COLOR_ATTACHMENT0,i.TEXTURE_2D,w,0),i.bindTexture(i.TEXTURE_2D,null),i.bindFramebuffer(i.FRAMEBUFFER,null),{fbo:b,texture:w}}function f(P){return r[P]=r[P]||d(l.width,l.height),r[P]}function p(P=0){if(!a)return;let m=null,b=null,z=!1;e===0?m=t:m=f(o).texture||null,e++,n&&!(P&x.INTERMEDIATE)?(b=null,z=e%2===0):(o=(o+1)%2,b=f(o).fbo||null),i.bindTexture(i.TEXTURE_2D,m),i.bindFramebuffer(i.FRAMEBUFFER,b),i.uniform1f(a.uniform.flipY,z?-1:1),i.drawArrays(i.TRIANGLES,0,6)}function g(P){if(c[P])return a=c[P],i.useProgram((a?a.id:null)||null),a;if(a=new V5(i,w1,P),!a)return u("filter: could not get webgl program"),null;let m=Float32Array.BYTES_PER_ELEMENT,b=4*m;return i.enableVertexAttribArray(a.attribute.pos),i.vertexAttribPointer(a.attribute.pos,2,i.FLOAT,!1,b,0*m),i.enableVertexAttribArray(a.attribute.uv),i.vertexAttribPointer(a.attribute.uv,2,i.FLOAT,!1,b,2*m),c[P]=a,a}let M={colorMatrix:P=>{let m=new Float32Array(P);m[4]/=255,m[9]/=255,m[14]/=255,m[19]/=255;let b=m[18]===1&&m[3]===0&&m[8]===0&&m[13]===0&&m[15]===0&&m[16]===0&&m[17]===0&&m[19]===0?z1:E1,z=g(b);!z||(i.uniform1fv(z.uniform.m,m),p())},brightness:P=>{let m=(P||0)+1;M.colorMatrix([m,0,0,0,0,0,m,0,0,0,0,0,m,0,0,0,0,0,1,0])},saturation:P=>{let m=(P||0)*2/3+1,b=(m-1)*-.5;M.colorMatrix([m,b,b,0,0,b,m,b,0,0,b,b,m,0,0,0,0,0,1,0])},desaturate:()=>{M.saturation(-1)},contrast:P=>{let m=(P||0)+1,b=-128*(m-1);M.colorMatrix([m,0,0,0,b,0,m,0,0,b,0,0,m,0,b,0,0,0,1,0])},negative:()=>{M.contrast(-2)},hue:P=>{P=(P||0)/180*Math.PI;let m=Math.cos(P),b=Math.sin(P),z=.213,w=.715,I=.072;M.colorMatrix([z+m*(1-z)+b*-z,w+m*-w+b*-w,I+m*-I+b*(1-I),0,0,z+m*-z+b*.143,w+m*(1-w)+b*.14,I+m*-I+b*-.283,0,0,z+m*-z+b*-(1-z),w+m*-w+b*w,I+m*(1-I)+b*I,0,0,0,0,0,1,0])},desaturateLuminance:()=>{M.colorMatrix([.2764723,.929708,.0938197,0,-37.1,.2764723,.929708,.0938197,0,-37.1,.2764723,.929708,.0938197,0,-37.1,0,0,0,1,0])},sepia:()=>{M.colorMatrix([.393,.7689999,.18899999,0,0,.349,.6859999,.16799999,0,0,.272,.5339999,.13099999,0,0,0,0,0,1,0])},brownie:()=>{M.colorMatrix([.5997023498159715,.34553243048391263,-.2708298674538042,0,47.43192855600873,-.037703249837783157,.8609577587992641,.15059552388459913,0,-36.96841498319127,.24113635128153335,-.07441037908422492,.44972182064877153,0,-7.562075277591283,0,0,0,1,0])},vintagePinhole:()=>{M.colorMatrix([.6279345635605994,.3202183420819367,-.03965408211312453,0,9.651285835294123,.02578397704808868,.6441188644374771,.03259127616149294,0,7.462829176470591,.0466055556782719,-.0851232987247891,.5241648018700465,0,5.159190588235296,0,0,0,1,0])},kodachrome:()=>{M.colorMatrix([1.1285582396593525,-.3967382283601348,-.03992559172921793,0,63.72958762196502,-.16404339962244616,1.0835251566291304,-.05498805115633132,0,24.732407896706203,-.16786010706155763,-.5603416277695248,1.6014850761964943,0,35.62982807460946,0,0,0,1,0])},technicolor:()=>{M.colorMatrix([1.9125277891456083,-.8545344976951645,-.09155508482755585,0,11.793603434377337,-.3087833385928097,1.7658908555458428,-.10601743074722245,0,-70.35205161461398,-.231103377548616,-.7501899197440212,1.847597816108189,0,30.950940869491138,0,0,0,1,0])},polaroid:()=>{M.colorMatrix([1.438,-.062,-.062,0,0,-.122,1.378,-.122,0,0,-.016,-.016,1.483,0,0,0,0,0,1,0])},shiftToBGR:()=>{M.colorMatrix([0,0,1,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,1,0])},convolution:P=>{let m=new Float32Array(P),b=1/l.width,z=1/l.height,w=g(N1);!w||(i.uniform1fv(w.uniform.m,m),i.uniform2f(w.uniform.px,b,z),p())},detectEdges:()=>{M.convolution.call(this,[0,1,0,1,-4,1,0,1,0])},sobelX:()=>{M.convolution.call(this,[-1,0,1,-2,0,2,-1,0,1])},sobelY:()=>{M.convolution.call(this,[-1,-2,-1,0,0,0,1,2,1])},sharpen:P=>{let m=P||1;M.convolution.call(this,[0,-1*m,0,-1*m,1+4*m,-1*m,0,-1*m,0])},emboss:P=>{let m=P||1;M.convolution.call(this,[-2*m,-1*m,0,-1*m,1,1*m,0,1*m,2*m])},blur:P=>{let m=P/7/l.width,b=P/7/l.height,z=g(j1);!z||(i.uniform2f(z.uniform.px,0,b),p(x.INTERMEDIATE),i.uniform2f(z.uniform.px,m,0),p())},pixelate:P=>{let m=P/l.width,b=P/l.height,z=g(S1);!z||(i.uniform2f(z.uniform.size,m,b),p())}};this.add=function(P){let m=Array.prototype.slice.call(arguments,1),b=M[P];A.push({func:b,args:m})},this.reset=function(){A=[]},this.get=function(){return A},this.apply=function(P){y(P.width,P.height),e=0,t||(t=i.createTexture()),i.bindTexture(i.TEXTURE_2D,t),i.texParameteri(i.TEXTURE_2D,i.TEXTURE_WRAP_S,i.CLAMP_TO_EDGE),i.texParameteri(i.TEXTURE_2D,i.TEXTURE_WRAP_T,i.CLAMP_TO_EDGE),i.texParameteri(i.TEXTURE_2D,i.TEXTURE_MIN_FILTER,i.NEAREST),i.texParameteri(i.TEXTURE_2D,i.TEXTURE_MAG_FILTER,i.NEAREST),i.texImage2D(i.TEXTURE_2D,0,i.RGBA,i.RGBA,i.UNSIGNED_BYTE,P);for(let m=0;m<A.length;m++){n=m===A.length-1;let b=A[m];b.func.apply(this,b.args||[])}return l},this.draw=function(P){return this.add("brightness",0),this.apply(P)}}var U=D(H());async function X2(e){let t=e.shape.length===4?U.squeeze(e):e,n=U.split(t,3,2),o=[U.min(n[0]),U.min(n[1]),U.min(n[2])],r=[U.max(n[0]),U.max(n[1]),U.max(n[2])],A=await Promise.all(r.map(d=>d.data())),s=.99*Math.max(A[0][0],A[1][0],A[2][0]),a=[U.sub(n[0],o[0]),U.sub(n[1],o[1]),U.sub(n[2],o[2])],l=[U.sub(r[0],o[0]),U.sub(r[1],o[1]),U.sub(r[2],o[2])],c=[U.div(s,l[0]),U.div(s,l[1]),U.div(s,l[2])],x=[U.mul(a[0],c[0]),U.mul(a[1],c[1]),U.mul(a[2],c[2])],i=U.stack([x[0],x[1],x[2]],2),y=U.reshape(i,[1,t.shape[0],t.shape[1],3]);return U.dispose([...n,...o,...r,...a,...l,...c,...x,i,t]),y}var q2=3840,h0=null,b0=null,n2=null,Q,D0={inputSum:0,cacheDiff:1,sumMethod:0,inputTensor:void 0};function D5(){D0.inputSum=0,D0.cacheDiff=1,D0.sumMethod=0,D0.inputTensor=void 0}function k0(e,t){let n;if(k.browser)if(k.worker){if(typeof OffscreenCanvas=="undefined")throw new Error("canvas error: attempted to run in web worker but OffscreenCanvas is not supported");n=new OffscreenCanvas(e,t)}else{if(typeof document=="undefined")throw new Error("canvas error: attempted to run in browser but DOM is not defined");n=document.createElement("canvas"),n.width=e,n.height=t}else typeof k.Canvas!="undefined"?n=new k.Canvas(e,t):typeof globalThis.Canvas!="undefined"&&(n=new globalThis.Canvas(e,t));return n}function U2(e,t){let n=t||k0(e.width,e.height);return n.getContext("2d").drawImage(e,0,0),n}async function o2(e,t,n=!0){var y,d;if(!e)return t.debug&&u("input error: input is missing"),{tensor:null,canvas:null};if(!(e instanceof O.Tensor)&&!(typeof Image!="undefined"&&e instanceof Image)&&!(typeof k.Canvas!="undefined"&&e instanceof k.Canvas)&&!(typeof globalThis.Canvas!="undefined"&&e instanceof globalThis.Canvas)&&!(typeof ImageData!="undefined"&&e instanceof ImageData)&&!(typeof ImageBitmap!="undefined"&&e instanceof ImageBitmap)&&!(typeof HTMLImageElement!="undefined"&&e instanceof HTMLImageElement)&&!(typeof HTMLMediaElement!="undefined"&&e instanceof HTMLMediaElement)&&!(typeof HTMLVideoElement!="undefined"&&e instanceof HTMLVideoElement)&&!(typeof HTMLCanvasElement!="undefined"&&e instanceof HTMLCanvasElement)&&!(typeof OffscreenCanvas!="undefined"&&e instanceof OffscreenCanvas))throw new Error("input error: type is not recognized");if(e instanceof O.Tensor){let f=null;if(e.isDisposedInternal)throw new Error("input error: attempted to use tensor but it is disposed");if(!e.shape)throw new Error("input error: attempted to use tensor without a shape");if(e.shape.length===3){if(e.shape[2]===3)f=O.expandDims(e,0);else if(e.shape[2]===4){let p=O.slice3d(e,[0,0,0],[-1,-1,3]);f=O.expandDims(p,0),O.dispose(p)}}else e.shape.length===4&&(e.shape[3]===3?f=O.clone(e):e.shape[3]===4&&(f=O.slice4d(e,[0,0,0,0],[-1,-1,-1,3])));if(f==null||f.shape.length!==4||f.shape[0]!==1||f.shape[3]!==3)throw new Error(`input error: attempted to use tensor with unrecognized shape: ${e.shape.toString()}`);if(f.dtype==="int32"){let p=O.cast(f,"float32");O.dispose(f),f=p}return{tensor:f,canvas:t.filter.return?b0:null}}if(typeof e.readyState!="undefined"&&e.readyState<=2)return t.debug&&u("input stream is not ready"),{tensor:null,canvas:h0};let o=e.naturalWidth||e.videoWidth||e.width||e.shape&&e.shape[1]>0,r=e.naturalHeight||e.videoHeight||e.height||e.shape&&e.shape[2]>0;if(!o||!r)return t.debug&&u("cannot determine input dimensions"),{tensor:null,canvas:h0};let A=o,s=r;if(A>q2&&(A=q2,s=Math.trunc(A*r/o)),s>q2&&(s=q2,A=Math.trunc(s*o/r)),(((y=t.filter)==null?void 0:y.width)||0)>0?A=t.filter.width:(((d=t.filter)==null?void 0:d.height)||0)>0&&(A=o*((t.filter.height||0)/r)),(t.filter.height||0)>0?s=t.filter.height:(t.filter.width||0)>0&&(s=r*((t.filter.width||0)/o)),!A||!s)throw new Error("input error: cannot determine dimension");(!h0||h0.width!==A||h0.height!==s)&&(h0=k0(A,s));let a=h0.getContext("2d");if(typeof ImageData!="undefined"&&e instanceof ImageData?a.putImageData(e,0,0):t.filter.flip&&typeof a.translate!="undefined"?(a.translate(o,0),a.scale(-1,1),a.drawImage(e,0,0,o,r,0,0,h0.width,h0.height),a.setTransform(1,0,0,1,0,0)):a.drawImage(e,0,0,o,r,0,0,h0.width,h0.height),(!b0||h0.width!==b0.width||h0.height!==b0.height)&&(b0=k0(h0.width,h0.height)),t.filter.enabled&&k.webgl.supported?(Q||(Q=k.browser?new I1:null),k.filter=!!Q,Q!=null&&Q.add?(Q.reset(),t.filter.brightness!==0&&Q.add("brightness",t.filter.brightness),t.filter.contrast!==0&&Q.add("contrast",t.filter.contrast),t.filter.sharpness!==0&&Q.add("sharpen",t.filter.sharpness),t.filter.blur!==0&&Q.add("blur",t.filter.blur),t.filter.saturation!==0&&Q.add("saturation",t.filter.saturation),t.filter.hue!==0&&Q.add("hue",t.filter.hue),t.filter.negative&&Q.add("negative"),t.filter.sepia&&Q.add("sepia"),t.filter.vintage&&Q.add("brownie"),t.filter.sepia&&Q.add("sepia"),t.filter.kodachrome&&Q.add("kodachrome"),t.filter.technicolor&&Q.add("technicolor"),t.filter.polaroid&&Q.add("polaroid"),t.filter.pixelate!==0&&Q.add("pixelate",t.filter.pixelate),Q.get()>0?b0=Q.apply(h0):b0=Q.draw(h0)):(t.debug&&u("input process error: cannot initialize filters"),k.webgl.supported=!1,t.filter.enabled=!1,U2(h0,b0))):(U2(h0,b0),Q&&(Q=null),k.filter=!!Q),!n)return{tensor:null,canvas:b0};if(!b0)throw new Error("canvas error: cannot create output");let l,c=3;if(typeof ImageData!="undefined"&&e instanceof ImageData||e.data&&e.width&&e.height)if(k.browser&&O.browser)l=O.browser?O.browser.fromPixels(e):null;else{c=e.data.length/e.height/e.width;let f=new Uint8Array(e.data.buffer);l=O.tensor(f,[e.height,e.width,c],"int32")}else if((!n2||b0.width!==n2.width||b0.height!==n2.height)&&(n2=k0(b0.width,b0.height)),O.browser&&k.browser)t.backend==="webgl"||t.backend==="humangl"||t.backend==="webgpu"?l=O.browser.fromPixels(b0):(n2=U2(b0),l=O.browser.fromPixels(n2));else{let g=U2(b0).getContext("2d").getImageData(0,0,A,s);c=g.data.length/A/s;let M=new Uint8Array(g.data.buffer);l=O.tensor(M,[A,s,c])}if(c===4){let f=O.slice3d(l,[0,0,0],[-1,-1,3]);O.dispose(l),l=f}if(!l)throw new Error("input error: cannot create tensor");let x=O.cast(l,"float32"),i=t.filter.equalization?await X2(x):O.expandDims(x,0);return O.dispose([l,x]),{tensor:i,canvas:t.filter.return?b0:null}}async function O1(e,t){let n=!1;if(e.cacheSensitivity===0||!t.shape||t.shape.length!==4||t.shape[1]>2048||t.shape[2]>2048)return n;if(!D0.inputTensor)D0.inputTensor=O.clone(t);else if(D0.inputTensor.shape[1]!==t.shape[1]||D0.inputTensor.shape[2]!==t.shape[2])O.dispose(D0.inputTensor),D0.inputTensor=O.clone(t);else{let o={};o.diff=O.sub(t,D0.inputTensor),o.squared=O.mul(o.diff,o.diff),o.sum=O.sum(o.squared);let A=(await o.sum.data())[0]/(t.shape[1]||1)/(t.shape[2]||1)/255/3;O.dispose([D0.inputTensor,o.diff,o.squared,o.sum]),D0.inputTensor=O.clone(t),n=A<=(e.cacheSensitivity||0)}return n}async function C1(e,t,n){let o={};if(!t||!n||t.shape.length!==4||t.shape.length!==n.shape.length)return e.debug||u("invalid input tensor or tensor shapes do not match:",t.shape,n.shape),0;if(t.shape[0]!==1||n.shape[0]!==1||t.shape[3]!==3||n.shape[3]!==3)return e.debug||u("input tensors must be of shape [1, height, width, 3]:",t.shape,n.shape),0;o.input1=O.clone(t),o.input2=t.shape[1]!==n.shape[1]||t.shape[2]!==n.shape[2]?O.image.resizeBilinear(n,[t.shape[1],t.shape[2]]):O.clone(n),o.diff=O.sub(o.input1,o.input2),o.squared=O.mul(o.diff,o.diff),o.sum=O.sum(o.squared);let A=(await o.sum.data())[0]/(t.shape[1]||1)/(t.shape[2]||1)/255/3;return O.dispose([o.input1,o.input2,o.diff,o.squared,o.sum]),A}var S2=class{constructor(){R(this,"browser");R(this,"node");R(this,"worker");R(this,"platform","");R(this,"agent","");R(this,"backends",[]);R(this,"initial");R(this,"filter");R(this,"tfjs");R(this,"offscreen");R(this,"perfadd",!1);R(this,"tensorflow",{version:void 0,gpu:void 0});R(this,"wasm",{supported:void 0,backend:void 0,simd:void 0,multithread:void 0});R(this,"webgl",{supported:void 0,backend:void 0,version:void 0,renderer:void 0});R(this,"webgpu",{supported:void 0,backend:void 0,adapter:void 0});R(this,"cpu",{model:void 0,flags:[]});R(this,"kernels",[]);R(this,"Canvas");R(this,"Image");R(this,"ImageData");if(this.browser=typeof navigator!="undefined",this.node=typeof process!="undefined"&&typeof process.versions!="undefined"&&typeof process.versions.node!="undefined",this.tfjs={version:x0.version["tfjs-core"]},this.offscreen=typeof OffscreenCanvas!="undefined",this.initial=!0,this.worker=this.browser&&this.offscreen?typeof WorkerGlobalScope!="undefined":void 0,typeof navigator!="undefined"){let t=navigator.userAgent.match(/\(([^()]+)\)/g);if(t!=null&&t[0]){let n=t[0].match(/\(([^()]+)\)/g);this.platform=n!=null&&n[0]?n[0].replace(/\(|\)/g,""):"",this.agent=navigator.userAgent.replace(t[0],""),this.platform[1]&&(this.agent=this.agent.replace(t[1],"")),this.agent=this.agent.replace(/  /g," ")}}else typeof process!="undefined"&&(this.platform=`${process.platform} ${process.arch}`,this.agent=`NodeJS ${process.version}`)}async updateBackend(){this.backends=Object.keys(x0.engine().registryFactory),this.tensorflow={version:x0.backend().binding?x0.backend().binding.TF_Version:void 0,gpu:x0.backend().binding?x0.backend().binding.isUsingGpuDevice():void 0},this.wasm.supported=typeof WebAssembly!="undefined",this.wasm.backend=this.backends.includes("wasm"),this.wasm.supported&&this.wasm.backend&&x0.getBackend()==="wasm"&&(this.wasm.simd=x0.env().get("WASM_HAS_SIMD_SUPPORT"),this.wasm.multithread=x0.env().get("WASM_HAS_MULTITHREAD_SUPPORT"));let t=k0(100,100),n=t?t.getContext("webgl2"):void 0;if(this.webgl.supported=typeof n!="undefined",this.webgl.backend=this.backends.includes("webgl"),this.webgl.supported&&this.webgl.backend&&(x0.getBackend()==="webgl"||x0.getBackend()==="humangl")){let o=x0.backend().gpgpu!=="undefined"?await x0.backend().getGPGPUContext().gl:null;o&&(this.webgl.version=o.getParameter(o.VERSION),this.webgl.renderer=o.getParameter(o.RENDERER))}this.webgpu.supported=this.browser&&typeof navigator.gpu!="undefined",this.webgpu.backend=this.backends.includes("webgpu");try{if(this.webgpu.supported){let o=await navigator.gpu.requestAdapter();this.webgpu.adapter=o?o.name:void 0}}catch(o){this.webgpu.supported=!1}try{this.kernels=x0.getKernelsForBackend(x0.getBackend()).map(o=>o.kernelName.toLowerCase())}catch(o){}}updateCPU(){let t={model:"",flags:[]};this.node&&this.platform.startsWith("linux"),this.cpu?this.cpu=t:Object.defineProperty(this,"cpu",{value:t})}},k=new S2;var Y2=class{constructor(){R(this,"config");R(this,"element");R(this,"stream");R(this,"start",async t=>{if(t!=null&&t.debug&&(this.config.debug=t==null?void 0:t.debug),t!=null&&t.crop&&(this.config.crop=t==null?void 0:t.crop),t!=null&&t.mode&&(this.config.mode=t==null?void 0:t.mode),t!=null&&t.width&&(this.config.width=t==null?void 0:t.width),t!=null&&t.height&&(this.config.height=t==null?void 0:t.height),t!=null&&t.element)if(typeof t.element=="string"){let r=document.getElementById(t.element);if(r&&r instanceof HTMLVideoElement)this.element=r;else{this.config.debug&&u("webcam","cannot get dom element",t.element);return}}else if(t.element instanceof HTMLVideoElement)this.element=t.element;else{this.config.debug&&u("webcam","unknown dom element",t.element);return}else this.element=document.createElement("video");let n={audio:!1,video:{facingMode:this.config.mode==="front"?"user":"environment",resizeMode:this.config.crop?"crop-and-scale":"none",width:{ideal:this.config.width>0?this.config.width:window.innerWidth},height:{ideal:this.config.height>0?this.config.height:window.innerHeight}}};if(this.element.addEventListener("play",()=>{this.config.debug&&u("webcam","play")}),this.element.addEventListener("pause",()=>{this.config.debug&&u("webcam","pause")}),this.element.addEventListener("click",async()=>{!this.element||!this.stream||(this.element.paused?await this.element.play():this.element.pause())}),!(navigator!=null&&navigator.mediaDevices)){this.config.debug&&u("webcam","no 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Zr=[127,234,132,58,172,150,149,148,152,377,378,379,397,288,361,454,356,70,63,105,66,107,336,296,334,293,300,168,6,195,4,98,97,2,326,327,33,160,158,133,153,144,362,385,387,263,373,380,57,40,37,0,267,270,287,321,314,17,84,91,78,81,13,311,308,402,14,178],Xr=[33,133,362,263,1,62,308,159,145,386,374,6,102,331,2,13,14,70,105,107,336,334,300,54,10,284,50,280,234,454,58,288,152],qr=[33,133,362,263,1,78,308],f7=Zr.map(e=>j2[e]),m7=Xr.map(e=>j2[e]),p7=qr.map(e=>j2[e]);function Re(e){let t=e.map(n=>n[0]);return t.push(e[e.length-1][1]),t}var 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A2=e=>[Math.abs(e.endPoint[0]-e.startPoint[0]),Math.abs(e.endPoint[1]-e.startPoint[1])],e5=e=>[e.startPoint[0]+(e.endPoint[0]-e.startPoint[0])/2,e.startPoint[1]+(e.endPoint[1]-e.startPoint[1])/2,1],t5=(e,t)=>e?[Math.trunc(Math.max(0,e.startPoint[0])),Math.trunc(Math.max(0,e.startPoint[1])),Math.trunc(Math.min(t.shape[2]||0,e.endPoint[0])-Math.max(0,e.startPoint[0])),Math.trunc(Math.min(t.shape[1]||0,e.endPoint[1])-Math.max(0,e.startPoint[1]))]:[0,0,0,0],n5=(e,t)=>e?[e.startPoint[0]/(t.shape[2]||0),e.startPoint[1]/(t.shape[1]||0),(e.endPoint[0]-e.startPoint[0])/(t.shape[2]||0),(e.endPoint[1]-e.startPoint[1])/(t.shape[1]||0)]:[0,0,0,0],t3=(e,t)=>{let n=[e.startPoint[0]*t[0],e.startPoint[1]*t[1]],o=[e.endPoint[0]*t[0],e.endPoint[1]*t[1]];return{startPoint:n,endPoint:o,landmarks:e.landmarks,confidence:e.confidence}},rt=(e,t,n)=>{let o=t.shape[1],r=t.shape[2],A=[e.startPoint[1]/o,e.startPoint[0]/r,e.endPoint[1]/o,e.endPoint[0]/r],s=ye.image.cropAndResize(t,[A],[0],n),a=ye.div(s,B.tf255);return ye.dispose(s),a},o5=(e,t)=>{let n=e5(e),o=A2(e),r=[t*o[0]/2,t*o[1]/2];return{startPoint:[n[0]-r[0],n[1]-r[1]],endPoint:[n[0]+r[0],n[1]+r[1]],landmarks:e.landmarks,confidence:e.confidence}},r5=e=>{let t=e5(e),n=A2(e),o=Math.max(...n)/2;return{startPoint:[Math.round(t[0]-o),Math.round(t[1]-o)],endPoint:[Math.round(t[0]+o),Math.round(t[1]+o)],landmarks:e.landmarks,confidence:e.confidence}},n3=e=>{let t=e.map(o=>o[0]),n=e.map(o=>o[1]);return{startPoint:[Math.min(...t),Math.min(...n)],endPoint:[Math.max(...t),Math.max(...n)],landmarks:e}},At=[[1,0,0],[0,1,0],[0,0,1]],tA=e=>e-2*Math.PI*Math.floor((e+Math.PI)/(2*Math.PI)),nA=(e,t)=>tA(Math.PI/2-Math.atan2(-(t[1]-e[1]),t[0]-e[0]));var $1=(e,t)=>[[1,0,e],[0,1,t],[0,0,1]],De=(e,t)=>{let n=0;for(let o=0;o<e.length;o++)n+=e[o]*t[o];return n},oA=(e,t)=>{let n=[];for(let o=0;o<e.length;o++)n.push(e[o][t]);return n},e3=(e,t)=>{let n=[],o=e.length;for(let r=0;r<o;r++){n.push([]);for(let A=0;A<o;A++)n[r].push(De(e[r],oA(t,A)))}return n},o3=(e,t)=>{let n=Math.cos(e),o=Math.sin(e),r=[[n,-o,0],[o,n,0],[0,0,1]],A=$1(t[0],t[1]),s=e3(A,r),a=$1(-t[0],-t[1]);return e3(s,a)},rA=e=>{let t=[[e[0][0],e[1][0]],[e[0][1],e[1][1]]],n=[e[0][2],e[1][2]],o=[-De(t[0],n),-De(t[1],n)];return[t[0].concat(o[0]),t[1].concat(o[1]),[0,0,1]]},AA=(e,t)=>[De(e,t[0]),De(e,t[1])];function r3(e){let t=e===192?{strides:[4],anchors:[1]}:{strides:[e/16,e/8],anchors:[2,6]},n=[];for(let o=0;o<t.strides.length;o++){let r=t.strides[o],A=Math.floor((e+r-1)/r),s=Math.floor((e+r-1)/r),a=t.anchors[o];for(let l=0;l<A;l++){let c=r*(l+.5);for(let x=0;x<s;x++){let i=r*(x+.5);for(let y=0;y<a;y++)n.push([i,c])}}}return n}function A3(e,t,n,o,r){let A=A2(t),s=e.map(d=>[A[0]/r*(d[0]-r/2),A[1]/r*(d[1]-r/2),d[2]||0]),a=n&&n!==0&&Math.abs(n)>.2,l=a?o3(n,[0,0]):At,c=a?s.map(d=>[...AA(d,l),d[2]]):s,x=a?rA(o):At,i=e5(t),y=[De(i,x[0]),De(i,x[1])];return c.map(d=>[Math.trunc(d[0]+y[0]),Math.trunc(d[1]+y[1]),Math.trunc(d[2]||0)])}function s3(e,t,n,o){let r=t.landmarks.length>=tt.count?tt.symmetryLine:He.symmetryLine,A=0,s=At,a;if(e&&k.kernels.includes("rotatewithoffset"))if(A=nA(t.landmarks[r[0]],t.landmarks[r[1]]),A&&A!==0&&Math.abs(A)>.2){let c=e5(t),x=[c[0]/n.shape[2],c[1]/n.shape[1]],i=ye.image.rotateWithOffset(n,A,0,x);s=o3(-A,c),a=rt(t,i,[o,o]),ye.dispose(i)}else a=rt(t,n,[o,o]);else a=rt(t,n,[o,o]);return[A,s,a]}var sA=e=>{let t=e.map(o=>o[0]),n=e.map(o=>o[1]);return[Math.min(...t)+(Math.max(...t)-Math.min(...t))/2,Math.min(...n)+(Math.max(...n)-Math.min(...n))/2]},a3=(e,t)=>{let n=sA(e),o=A2(t);return{startPoint:[n[0]-o[0]/2,n[1]-o[1]/2],endPoint:[n[0]+o[0]/2,n[1]+o[1]/2]}};var i3=6,aA=1.4,ee,l3=null,ke=0,N2=null,s2=()=>ke;async function c3(e){var t;return k.initial&&(ee=null),ee?e.debug&&u("cached model:",ee.modelUrl):ee=await L((t=e.face.detector)==null?void 0:t.modelPath),ke=ee.executor&&ee.inputs[0].shape?ee.inputs[0].shape[2]:256,N2=C.scalar(ke,"int32"),l3=C.tensor2d(r3(ke)),ee}function iA(e){let t={};t.boxStarts=C.slice(e,[0,1],[-1,2]),t.centers=C.add(t.boxStarts,l3),t.boxSizes=C.slice(e,[0,3],[-1,2]),t.boxSizesNormalized=C.div(t.boxSizes,N2),t.centersNormalized=C.div(t.centers,N2),t.halfBoxSize=C.div(t.boxSizesNormalized,B.tf2),t.starts=C.sub(t.centersNormalized,t.halfBoxSize),t.ends=C.add(t.centersNormalized,t.halfBoxSize),t.startNormalized=C.mul(t.starts,N2),t.endNormalized=C.mul(t.ends,N2);let n=C.concat2d([t.startNormalized,t.endNormalized],1);return Object.keys(t).forEach(o=>C.dispose(t[o])),n}async function d3(e,t){var a,l,c,x;if(!e||e.isDisposedInternal||e.shape.length!==4||e.shape[1]<1||e.shape[2]<1)return[];let n={};n.resized=C.image.resizeBilinear(e,[ke,ke]),n.div=C.div(n.resized,B.tf127),n.normalized=C.sub(n.div,B.tf05);let o=ee==null?void 0:ee.execute(n.normalized);if(Array.isArray(o)&&o.length>2){let i=o.sort((y,d)=>y.size-d.size);n.concat384=C.concat([i[0],i[2]],2),n.concat512=C.concat([i[1],i[3]],2),n.concat=C.concat([n.concat512,n.concat384],1),n.batch=C.squeeze(n.concat,0)}else Array.isArray(o)?n.batch=C.squeeze(o[0]):n.batch=C.squeeze(o);C.dispose(o),n.boxes=iA(n.batch),n.logits=C.slice(n.batch,[0,0],[-1,1]),n.sigmoid=C.sigmoid(n.logits),n.scores=C.squeeze(n.sigmoid),n.nms=await C.image.nonMaxSuppressionAsync(n.boxes,n.scores,((a=t.face.detector)==null?void 0:a.maxDetected)||0,((l=t.face.detector)==null?void 0:l.iouThreshold)||0,((c=t.face.detector)==null?void 0:c.minConfidence)||0);let r=await n.nms.array(),A=[],s=await n.scores.data();for(let i=0;i<r.length;i++){let y=s[r[i]];if(y>(((x=t.face.detector)==null?void 0:x.minConfidence)||0)){let d={};d.bbox=C.slice(n.boxes,[r[i],0],[1,-1]),d.slice=C.slice(n.batch,[r[i],i3-1],[1,-1]),d.squeeze=C.squeeze(d.slice),d.landmarks=C.reshape(d.squeeze,[i3,-1]);let f=await d.bbox.data(),p={startPoint:[f[0],f[1]],endPoint:[f[2],f[3]],landmarks:await d.landmarks.array(),confidence:y},g=t3(p,[(e.shape[2]||0)/ke,(e.shape[1]||0)/ke]),M=o5(g,t.face.scale||aA),P=r5(M);A.push(P),Object.keys(d).forEach(m=>C.dispose(d[m]))}}return Object.keys(n).forEach(i=>C.dispose(n[i])),A}var C0=D(H());var A5={};ve(A5,{connected:()=>it,kpt:()=>at});var at=["nose","leftEyeInside","leftEye","leftEyeOutside","rightEyeInside","rightEye","rightEyeOutside","leftEar","rightEar","leftMouth","rightMouth","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftPinky","rightPinky","leftIndex","rightIndex","leftThumb","rightThumb","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle","leftHeel","rightHeel","leftFoot","rightFoot","bodyCenter","bodyTop","leftPalm","leftHand","rightPalm","rightHand"],it={shoulders:["leftShoulder","rightShoulder"],hips:["rightHip","leftHip"],mouth:["leftMouth","rightMouth"],leftLegUpper:["leftHip","leftKnee"],leftLegLower:["leftKnee","leftAnkle"],leftFoot:["leftAnkle","leftHeel","leftFoot"],leftTorso:["leftShoulder","leftHip"],leftArmUpper:["leftShoulder","leftElbow"],leftArmLower:["leftElbow","leftWrist"],leftHand:["leftWrist","leftPalm"],leftHandPinky:["leftPalm","leftPinky"],leftHandIndex:["leftPalm","leftIndex"],leftHandThumb:["leftPalm","leftThumb"],leftEyeOutline:["leftEyeInside","leftEyeOutside"],rightLegUpper:["rightHip","rightKnee"],rightLegLower:["rightKnee","rightAnkle"],rightFoot:["rightAnkle","rightHeel","rightFoot"],rightTorso:["rightShoulder","rightHip"],rightArmUpper:["rightShoulder","rightElbow"],rightArmLower:["rightElbow","rightWrist"],rightHand:["rightWrist","rightPalm"],rightHandPinky:["rightPalm","rightPinky"],rightHandIndex:["rightPalm","rightIndex"],rightHandThumb:["rightPalm","rightThumb"],rightEyeOutline:["rightEyeInside","rightEyeOutside"]};var O0=D(H()),y3=224,lA,cA=5,s5=[8,16,32,32,32];function f3(){let e=[],t=0;for(;t<cA;){let n=0,o=t;for(;o<s5.length&&s5[o]===s5[t];)n+=2,o++;let r=s5[t],A=Math.ceil(y3/r),s=Math.ceil(y3/r);for(let a=0;a<A;++a)for(let l=0;l<s;++l)for(let c=0;c<n;++c)e.push({x:(l+.5)/s,y:(a+.5)/A});t=o}lA={x:O0.tensor1d(e.map(n=>n.x)),y:O0.tensor1d(e.map(n=>n.y))}}function fe(e,t=[1,1]){let n=[e.map(a=>a[0]),e.map(a=>a[1])],o=[Math.min(...n[0]),Math.min(...n[1])],r=[Math.max(...n[0]),Math.max(...n[1])],A=[o[0],o[1],r[0]-o[0],r[1]-o[1]],s=[A[0]/t[0],A[1]/t[1],A[2]/t[0],A[3]/t[1]];return{box:A,boxRaw:s}}function m3(e,t=[1,1]){let n=[e.map(c=>c[0]),e.map(c=>c[1])],o=[Math.min(...n[0]),Math.min(...n[1])],r=[Math.max(...n[0]),Math.max(...n[1])],A=[(o[0]+r[0])/2,(o[1]+r[1])/2],s=Math.max(A[0]-o[0],A[1]-o[1],-A[0]+r[0],-A[1]+r[1]),a=[Math.trunc(A[0]-s),Math.trunc(A[1]-s),Math.trunc(2*s),Math.trunc(2*s)],l=[a[0]/t[0],a[1]/t[1],a[2]/t[0],a[3]/t[1]];return{box:a,boxRaw:l}}function a5(e,t){let n=[e[2]*t,e[3]*t];return[e[0]-(n[0]-e[2])/2,e[1]-(n[1]-e[3])/2,n[0],n[1]]}var h3={initial:!0},P0={detector:null,landmarks:null},a2={detector:[224,224],landmarks:[256,256]},lt=Number.MAX_SAFE_INTEGER,xA={landmarks:["ld_3d","activation_segmentation","activation_heatmap","world_3d","output_poseflag"],detector:[]},l5=null,I2,we=[[0,0],[0,0],[0,0],[0,0]],p3=0,u3=e=>1-1/(1+Math.exp(e));async function b3(e){var t;if(h3.initial&&(P0.detector=null),!P0.detector&&e.body.detector&&e.body.detector.modelPath){P0.detector=await L(e.body.detector.modelPath);let n=(t=P0.detector)!=null&&t.executor?Object.values(P0.detector.modelSignature.inputs):void 0;a2.detector[0]=Array.isArray(n)?parseInt(n[0].tensorShape.dim[1].size):0,a2.detector[1]=Array.isArray(n)?parseInt(n[0].tensorShape.dim[2].size):0}else e.debug&&P0.detector&&u("cached model:",P0.detector.modelUrl);return f3(),P0.detector}async function g3(e){var t;if(h3.initial&&(P0.landmarks=null),P0.landmarks)e.debug&&u("cached model:",P0.landmarks.modelUrl);else{P0.landmarks=await L(e.body.modelPath);let n=(t=P0.landmarks)!=null&&t.executor?Object.values(P0.landmarks.modelSignature.inputs):void 0;a2.landmarks[0]=Array.isArray(n)?parseInt(n[0].tensorShape.dim[1].size):0,a2.landmarks[1]=Array.isArray(n)?parseInt(n[0].tensorShape.dim[2].size):0}return P0.landmarks}function yA(e,t){var r,A;let n={};if(!((r=e==null?void 0:e.shape)!=null&&r[1])||!((A=e==null?void 0:e.shape)!=null&&A[2]))return e;let o;if(I2&&(n.cropped=C0.image.cropAndResize(e,[I2],[0],[e.shape[1],e.shape[2]])),e.shape[1]!==e.shape[2]){let s=[e.shape[2]>e.shape[1]?Math.trunc((e.shape[2]-e.shape[1])/2):0,e.shape[2]>e.shape[1]?Math.trunc((e.shape[2]-e.shape[1])/2):0],a=[e.shape[1]>e.shape[2]?Math.trunc((e.shape[1]-e.shape[2])/2):0,e.shape[1]>e.shape[2]?Math.trunc((e.shape[1]-e.shape[2])/2):0];we=[[0,0],s,a,[0,0]],n.pad=C0.pad(n.cropped||e,we),n.resize=C0.image.resizeBilinear(n.pad,[t,t]),o=C0.div(n.resize,B.tf255)}else e.shape[1]!==t?(n.resize=C0.image.resizeBilinear(n.cropped||e,[t,t]),o=C0.div(n.resize,B.tf255)):o=C0.div(n.cropped||e,B.tf255);return Object.keys(n).forEach(s=>C0.dispose(n[s])),o}function fA(e,t){for(let n of e)n.position=[Math.trunc(n.position[0]*(t[0]+we[2][0]+we[2][1])/t[0]-we[2][0]),Math.trunc(n.position[1]*(t[1]+we[1][0]+we[1][1])/t[1]-we[1][0]),n.position[2]],n.positionRaw=[n.position[0]/t[0],n.position[1]/t[1],2*n.position[2]/(t[0]+t[1])];if(I2)for(let n of e)n.positionRaw=[n.positionRaw[0]+I2[1],n.positionRaw[1]+I2[0],n.positionRaw[2]],n.position=[Math.trunc(n.positionRaw[0]*t[0]),Math.trunc(n.positionRaw[1]*t[1]),n.positionRaw[2]];return e}function mA(e){let t=e.find(a=>a.part==="leftPalm"),n=e.find(a=>a.part==="leftWrist"),o=e.find(a=>a.part==="leftIndex");t.position[2]=((n.position[2]||0)+(o.position[2]||0))/2;let r=e.find(a=>a.part==="rightPalm"),A=e.find(a=>a.part==="rightWrist"),s=e.find(a=>a.part==="rightIndex");r.position[2]=((A.position[2]||0)+(s.position[2]||0))/2}async function pA(e,t,n){var f,p;if(!((f=P0.landmarks)!=null&&f.executor))return null;let o={};[o.ld,o.segmentation,o.heatmap,o.world,o.poseflag]=(p=P0.landmarks)==null?void 0:p.execute(e,xA.landmarks);let r=(await o.poseflag.data())[0],A=await o.ld.data(),s=await o.world.data();Object.keys(o).forEach(g=>C0.dispose(o[g]));let a=[],l=5;for(let g=0;g<A.length/l;g++){let M=u3(A[l*g+3]),P=u3(A[l*g+4]),m=Math.trunc(100*M*P*r)/100,b=[A[l*g+0]/a2.landmarks[0],A[l*g+1]/a2.landmarks[1],A[l*g+2]+0],z=[Math.trunc(n[0]*b[0]),Math.trunc(n[1]*b[1]),b[2]],w=[s[l*g+0],s[l*g+1],s[l*g+2]+0];a.push({part:at[g],positionRaw:b,position:z,distance:w,score:m})}if(r<(t.body.minConfidence||0))return null;mA(a);let c=fA(a,n),x=c.map(g=>g.position),i=fe(x,[n[0],n[1]]),y={};for(let[g,M]of Object.entries(it)){let P=[];for(let m=0;m<M.length-1;m++){let b=c.find(w=>w.part===M[m]),z=c.find(w=>w.part===M[m+1]);b&&z&&P.push([b.position,z.position])}y[g]=P}return{id:0,score:Math.trunc(100*r)/100,box:i.box,boxRaw:i.boxRaw,keypoints:c,annotations:y}}async function ct(e,t){let n=[e.shape[2]||0,e.shape[1]||0],o=(t.body.skipTime||0)>v()-p3,r=lt<(t.body.skipFrames||0);if(t.skipAllowed&&o&&r&&l5!==null)lt++;else{let A={};A.landmarks=yA(e,256),l5=await pA(A.landmarks,t,n),Object.keys(A).forEach(s=>C0.dispose(A[s])),p3=v(),lt=0}return l5?[l5]:[]}var w0=D(H());var i2=[{class:1,label:"person"},{class:2,label:"bicycle"},{class:3,label:"car"},{class:4,label:"motorcycle"},{class:5,label:"airplane"},{class:6,label:"bus"},{class:7,label:"train"},{class:8,label:"truck"},{class:9,label:"boat"},{class:10,label:"traffic light"},{class:11,label:"fire hydrant"},{class:12,label:"stop sign"},{class:13,label:"parking meter"},{class:14,label:"bench"},{class:15,label:"bird"},{class:16,label:"cat"},{class:17,label:"dog"},{class:18,label:"horse"},{class:19,label:"sheep"},{class:20,label:"cow"},{class:21,label:"elephant"},{class:22,label:"bear"},{class:23,label:"zebra"},{class:24,label:"giraffe"},{class:25,label:"backpack"},{class:26,label:"umbrella"},{class:27,label:"handbag"},{class:28,label:"tie"},{class:29,label:"suitcase"},{class:30,label:"frisbee"},{class:31,label:"skis"},{class:32,label:"snowboard"},{class:33,label:"sports ball"},{class:34,label:"kite"},{class:35,label:"baseball bat"},{class:36,label:"baseball glove"},{class:37,label:"skateboard"},{class:38,label:"surfboard"},{class:39,label:"tennis racket"},{class:40,label:"bottle"},{class:41,label:"wine 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drier"},{class:80,label:"toothbrush"}];var N0,Ze=0,dt=[],v3=0,xt=Number.MAX_SAFE_INTEGER;async function P3(e){if(k.initial&&(N0=null),N0)e.debug&&u("cached model:",N0.modelUrl);else{N0=await L(e.object.modelPath);let t=N0!=null&&N0.executor?Object.values(N0.modelSignature.inputs):void 0;Ze=Array.isArray(t)?parseInt(t[0].tensorShape.dim[2].size):0}return N0}async function uA(e,t,n){if(!e)return[];let o={},r=[],A=await e.array();o.squeeze=w0.squeeze(e);let s=w0.split(o.squeeze,6,1);o.stack=w0.stack([s[1],s[0],s[3],s[2]],1),o.boxes=w0.squeeze(o.stack),o.scores=w0.squeeze(s[4]),o.classes=w0.squeeze(s[5]),w0.dispose([e,...s]),o.nms=await w0.image.nonMaxSuppressionAsync(o.boxes,o.scores,n.object.maxDetected,n.object.iouThreshold,n.object.minConfidence||0);let a=await o.nms.data(),l=0;for(let c of Array.from(a)){let x=Math.trunc(100*A[0][c][4])/100,i=A[0][c][5];if(Number.isNaN(i))continue;let y=i2[i].label,[d,f]=[A[0][c][0]/Ze,A[0][c][1]/Ze],p=[d,f,A[0][c][2]/Ze-d,A[0][c][3]/Ze-f],g=[Math.trunc(p[0]*t[0]),Math.trunc(p[1]*t[1]),Math.trunc(p[2]*t[0]),Math.trunc(p[3]*t[1])];r.push({id:l++,score:x,class:i,label:y,box:g,boxRaw:p})}return Object.keys(o).forEach(c=>w0.dispose(o[c])),r}async function yt(e,t){if(!(N0!=null&&N0.executor))return[];let n=(t.object.skipTime||0)>v()-v3,o=xt<(t.object.skipFrames||0);return t.skipAllowed&&n&&o&&dt.length>0?(xt++,dt):(xt=0,new Promise(async r=>{let A=[e.shape[2]||0,e.shape[1]||0],s=w0.image.resizeBilinear(e,[Ze,Ze]),a=t.object.enabled?N0==null?void 0:N0.execute(s,["tower_0/detections"]):null;v3=v(),w0.dispose(s);let l=await uA(a,A,t);dt=l,r(l)}))}var K=D(H());var c5={};ve(c5,{connected:()=>mt,kpt:()=>ft});var ft=["head","neck","rightShoulder","rightElbow","rightWrist","chest","leftShoulder","leftElbow","leftWrist","bodyCenter","rightHip","rightKnee","rightAnkle","leftHip","leftKnee","leftAnkle"],mt={leftLeg:["leftHip","leftKnee","leftAnkle"],rightLeg:["rightHip","rightKnee","rightAnkle"],torso:["leftShoulder","rightShoulder","rightHip","leftHip","leftShoulder"],leftArm:["leftShoulder","leftElbow","leftWrist"],rightArm:["rightShoulder","rightElbow","rightWrist"],head:[]};var u0,R3=0,E0={id:0,keypoints:[],box:[0,0,0,0],boxRaw:[0,0,0,0],score:0,annotations:{}},pt=Number.MAX_SAFE_INTEGER;async function k3(e){return k.initial&&(u0=null),u0?e.debug&&u("cached model:",u0.modelUrl):u0=await L(e.body.modelPath),u0}async function hA(e,t){let[n,o]=e.shape,r=K.reshape(e,[o*n]),A=K.max(r,0),s=(await A.data())[0];if(s>t){let a=K.argMax(r,0),l=K.mod(a,n),c=(await l.data())[0],x=K.div(a,n),i=(await x.data())[0];return K.dispose([r,A,a,l,x]),[c,i,s]}return K.dispose([r,A]),[0,0,s]}async function ut(e,t){if(!(u0!=null&&u0.executor))return[];let n=(t.body.skipTime||0)>v()-R3,o=pt<(t.body.skipFrames||0);return t.skipAllowed&&n&&o&&Object.keys(E0.keypoints).length>0?(pt++,[E0]):(pt=0,new Promise(async r=>{let A=K.tidy(()=>{if(!(u0!=null&&u0.inputs[0].shape))return null;let i=K.image.resizeBilinear(e,[u0.inputs[0].shape[2],u0.inputs[0].shape[1]],!1),y=K.mul(i,B.tf2);return K.sub(y,B.tf1)}),s;if(t.body.enabled&&(s=u0==null?void 0:u0.execute(A)),R3=v(),K.dispose(A),s){E0.keypoints.length=0;let i=K.squeeze(s);K.dispose(s);let y=K.unstack(i,2);K.dispose(i);for(let d=0;d<y.length;d++){let[f,p,g]=await hA(y[d],t.body.minConfidence);g>(t.body.minConfidence||0)&&E0.keypoints.push({score:Math.round(100*g)/100,part:ft[d],positionRaw:[f/u0.inputs[0].shape[2],p/u0.inputs[0].shape[1]],position:[Math.round(e.shape[2]*f/u0.inputs[0].shape[2]),Math.round(e.shape[1]*p/u0.inputs[0].shape[1])]})}y.forEach(d=>K.dispose(d))}E0.score=E0.keypoints.reduce((i,y)=>y.score>i?y.score:i,0);let a=E0.keypoints.map(i=>i.position[0]),l=E0.keypoints.map(i=>i.position[1]);E0.box=[Math.min(...a),Math.min(...l),Math.max(...a)-Math.min(...a),Math.max(...l)-Math.min(...l)];let c=E0.keypoints.map(i=>i.positionRaw[0]),x=E0.keypoints.map(i=>i.positionRaw[1]);E0.boxRaw=[Math.min(...c),Math.min(...x),Math.max(...c)-Math.min(...c),Math.max(...x)-Math.min(...x)];for(let[i,y]of Object.entries(mt)){let d=[];for(let f=0;f<y.length-1;f++){let p=E0.keypoints.find(M=>M.part===y[f]),g=E0.keypoints.find(M=>M.part===y[f+1]);p&&g&&p.score>(t.body.minConfidence||0)&&g.score>(t.body.minConfidence||0)&&d.push([p.position,g.position])}E0.annotations[i]=d}r([E0])}))}var te=D(H());var bA=["angry","disgust","fear","happy","sad","surprise","neutral"],Z0,d5=[],E3=0,z3=0,ht=Number.MAX_SAFE_INTEGER;async function S3(e){var t;return k.initial&&(Z0=null),Z0?e.debug&&u("cached model:",Z0.modelUrl):Z0=await L((t=e.face.emotion)==null?void 0:t.modelPath),Z0}async function bt(e,t,n,o){var s,a;if(!Z0)return[];let r=ht<(((s=t.face.emotion)==null?void 0:s.skipFrames)||0),A=(((a=t.face.emotion)==null?void 0:a.skipTime)||0)>v()-z3;return t.skipAllowed&&A&&r&&E3===o&&d5[n]&&d5[n].length>0?(ht++,d5[n]):(ht=0,new Promise(async l=>{var x;let c=[];if((x=t.face.emotion)!=null&&x.enabled){let i={},y=Z0!=null&&Z0.inputs[0].shape?Z0.inputs[0].shape[2]:0;i.resize=te.image.resizeBilinear(e,[y,y],!1),i.channels=te.mul(i.resize,B.rgb),i.grayscale=te.sum(i.channels,3,!0),i.grayscaleSub=te.sub(i.grayscale,B.tf05),i.grayscaleMul=te.mul(i.grayscaleSub,B.tf2),i.emotion=Z0==null?void 0:Z0.execute(i.grayscaleMul),z3=v();let d=await i.emotion.data();for(let f=0;f<d.length;f++)d[f]>(t.face.emotion.minConfidence||0)&&c.push({score:Math.min(.99,Math.trunc(100*d[f])/100),emotion:bA[f]});c.sort((f,p)=>p.score-f.score),Object.keys(i).forEach(f=>te.dispose(i[f]))}d5[n]=c,E3=o,l(c)}))}var x5=D(H());var L0,gt=[],N3=0,I3=0,O3=Number.MAX_SAFE_INTEGER;async function C3(e){var t;return k.initial&&(L0=null),L0?e.debug&&u("cached 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0:n[0].shape)?F0.inputs[0].shape[2]:0,Ee===-1&&(Ee=64),F0}function f5(e,t,n,o){for(let r=0;r<nt.length;r++){let{key:A,indices:s}=nt[r],a=Q0[`${n}${A}`];if(!o||o.includes(A))for(let l=0;l<s.length;l++){let c=s[l];e[a[l]]=[t[c][0],t[c][1],(t[c][2]+e[a[l]][2])/2]}}}var MA=e=>{let t=e[l2.leftBounds[0]][2],n=e[l2.rightBounds[0]][2];return t-n},V3=(e,t,n,o,r,A=!1)=>{let s=r5(o5(n3([e[n],e[o]]),gA)),a=A2(s),l=ne.image.cropAndResize(t,[[s.startPoint[1]/r,s.startPoint[0]/r,s.endPoint[1]/r,s.endPoint[0]/r]],[0],[Ee,Ee]);if(A&&k.kernels.includes("flipleftright")){let c=ne.image.flipLeftRight(l);ne.dispose(l),l=c}return{box:s,boxSize:a,crop:l}},D3=(e,t,n,o=!1)=>{let r=[];for(let A=0;A<c2.numCoordinates;A++){let s=e[A*3],a=e[A*3+1],l=e[A*3+2];r.push([(o?1-s/Ee:s/Ee)*n[0]+t.startPoint[0],a/Ee*n[1]+t.startPoint[1],l])}return{rawCoords:r,iris:r.slice(c2.index)}},Z3=(e,t,n)=>{let o=e[Q0[`${n}EyeUpper0`][c2.upperCenter]][2],r=e[Q0[`${n}EyeLower0`][c2.lowerCenter]][2],A=(o+r)/2;return t.map((s,a)=>{let 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vA=[[61,146],[146,91],[91,181],[181,84],[84,17],[17,314],[314,405],[405,321],[321,375],[375,291],[61,185],[185,40],[40,39],[39,37],[37,0],[0,267],[267,269],[269,270],[270,409],[409,291],[78,95],[95,88],[88,178],[178,87],[87,14],[14,317],[317,402],[402,318],[318,324],[324,308],[78,191],[191,80],[80,81],[81,82],[82,13],[13,312],[312,311],[311,310],[310,415],[415,308]],PA=[[263,249],[249,390],[390,373],[373,374],[374,380],[380,381],[381,382],[382,362],[263,466],[466,388],[388,387],[387,386],[386,385],[385,384],[384,398],[398,362]],TA=[[276,283],[283,282],[282,295],[295,285],[300,293],[293,334],[334,296],[296,336]],RA=[[474,475],[475,476],[476,477],[477,474]],kA=[[33,7],[7,163],[163,144],[144,145],[145,153],[153,154],[154,155],[155,133],[33,246],[246,161],[161,160],[160,159],[159,158],[158,157],[157,173],[173,133]],wA=[[46,53],[53,52],[52,65],[65,55],[70,63],[63,105],[105,66],[66,107]],EA=[[469,470],[470,471],[471,472],[472,469]],zA=[[10,338],[338,297],[297,332],[332,284],[284,251],[251,389],[389,356],[356,454],[454,323],[323,361],[361,288],[288,397],[397,365],[365,379],[379,378],[378,400],[400,377],[377,152],[152,148],[148,176],[176,149],[149,150],[150,136],[136,172],[172,58],[58,132],[132,93],[93,234],[234,127],[127,162],[162,21],[21,54],[54,103],[103,67],[67,109],[109,10]];function ze(e){let t=e.map(n=>n[0]);return t.push(e[e.length-1][1]),t}var SA={lips:ze(vA),leftEye:ze(PA),leftEyebrow:ze(TA),leftIris:ze(RA),rightEye:ze(kA),rightEyebrow:ze(wA),rightIris:ze(EA),faceOval:ze(zA)},jA=Object.entries(SA).map(([e,t])=>t.map(n=>[n,e])).flat(),K7=new Map(jA),O2=[61,146,91,181,84,17,314,405,321,375,291,185,40,39,37,0,267,269,270,409,78,95,88,178,87,14,317,402,318,324,308,191,80,81,82,13,312,311,310,415,76,77,90,180,85,16,315,404,320,307,306,184,74,73,72,11,302,303,304,408,62,96,89,179,86,15,316,403,319,325,292,183,42,41,38,12,268,271,272,407],Xe=[33,7,163,144,145,153,154,155,133,246,161,160,159,158,157,173,130,25,110,24,23,22,26,112,243,247,30,29,27,28,56,190,226,31,228,229,230,231,232,233,244,113,225,224,223,222,221,189,35,124,46,53,52,65,143,111,117,118,119,120,121,128,245,156,70,63,105,66,107,55,193],qe=[263,249,390,373,374,380,381,382,362,466,388,387,386,385,384,398,359,255,339,254,253,252,256,341,463,467,260,259,257,258,286,414,446,261,448,449,450,451,452,453,464,342,445,444,443,442,441,413,265,353,276,283,282,295,372,340,346,347,348,349,350,357,465,383,300,293,334,296,336,285,417];async function K3(e,t){var A,s,a,l,c,x,i,y,d,f;let n={lips:await((s=(A=t.filter(p=>p.size===160))==null?void 0:A[0])==null?void 0:s.data()),irisL:await((l=(a=t.filter(p=>p.size===10))==null?void 0:a[0])==null?void 0:l.data()),eyeL:await((x=(c=t.filter(p=>p.size===142))==null?void 0:c[0])==null?void 0:x.data()),irisR:await((y=(i=t.filter(p=>p.size===10))==null?void 0:i[1])==null?void 0:y.data()),eyeR:await((f=(d=t.filter(p=>p.size===142))==null?void 0:d[1])==null?void 0:f.data())};for(let p of Object.values(n))if(!p)return e;let o=Xe.reduce((p,g)=>p+=e[g][2],0)/Xe.length;for(let p=0;p<n.irisL.length/2;p++)e.push([n.irisL[2*p+0],n.irisL[2*p+1],o]);let r=qe.reduce((p,g)=>p+=e[g][2],0)/qe.length;for(let p=0;p<n.irisR.length/2;p++)e.push([n.irisR[2*p+0],n.irisR[2*p+1],r]);for(let p=0;p<n.eyeL.length/2;p++)e[Xe[p]]=[n.eyeL[2*p+0],n.eyeL[2*p+1],e[Xe[p]][2]];for(let p=0;p<n.eyeR.length/2;p++)e[qe[p]]=[n.eyeR[2*p+0],n.eyeR[2*p+1],e[qe[p]][2]];for(let p=0;p<n.lips.length/2;p++)e[O2[p]]=[n.lips[2*p+0],n.lips[2*p+1],e[O2[p]][2]];return e}var le={boxes:[],skipped:Number.MAX_SAFE_INTEGER,timestamp:0},t0=null,C2=0;async function J3(e,t){var l,c,x,i,y,d,f,p,g,M;if(!(t0!=null&&t0.executor))return[];let n=(((l=t.face.detector)==null?void 0:l.skipTime)||0)>v()-le.timestamp,o=le.skipped<(((c=t.face.detector)==null?void 0:c.skipFrames)||0);!t.skipAllowed||!n||!o||le.boxes.length===0?(le.boxes=await d3(e,t),le.timestamp=v(),le.skipped=0):le.skipped++;let r=[],A=[],s=0,a=C2;for(let P=0;P<le.boxes.length;P++){let m=le.boxes[P],b=0,z,w={id:s++,mesh:[],meshRaw:[],box:[0,0,0,0],boxRaw:[0,0,0,0],score:0,boxScore:0,faceScore:0,annotations:{}};if([b,z,w.tensor]=s3((x=t.face.detector)==null?void 0:x.rotation,m,e,(i=t.face.mesh)!=null&&i.enabled?C2:s2()),t.filter.equalization){let I=w.tensor?await X2(w.tensor):void 0;Se.dispose(w.tensor),I&&(w.tensor=I)}if(w.boxScore=Math.round(100*m.confidence)/100,(y=t.face.mesh)!=null&&y.enabled)if(!t0)t.debug&&u("face mesh detection requested, but model is not loaded");else{if(((d=t.face.attention)==null?void 0:d.enabled)&&!k.kernels.includes("atan2"))return 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o=W.concat2d([n.startPoints,n.endPoints],1);return Object.keys(n).forEach(r=>W.dispose(n[r])),o}normalizeLandmarks(t,n){let o={};o.reshape=W.reshape(t,[-1,7,2]),o.div=W.div(o.reshape,this.inputSizeTensor),o.landmarks=W.add(o.div,this.anchors[n]?this.anchors[n]:0);let r=W.mul(o.landmarks,this.inputSizeTensor);return Object.keys(o).forEach(A=>W.dispose(o[A])),r}async predict(t,n){var a;let o={};o.resize=W.image.resizeBilinear(t,[this.inputSize,this.inputSize]),o.div=W.div(o.resize,B.tf127),o.image=W.sub(o.div,B.tf1),o.batched=this.model.execute(o.image),o.predictions=W.squeeze(o.batched),o.slice=W.slice(o.predictions,[0,0],[-1,1]),o.sigmoid=W.sigmoid(o.slice),o.scores=W.squeeze(o.sigmoid);let r=await o.scores.data();o.boxes=W.slice(o.predictions,[0,1],[-1,4]),o.norm=this.normalizeBoxes(o.boxes),o.nms=await W.image.nonMaxSuppressionAsync(o.norm,o.scores,3*(((a=n.hand)==null?void 0:a.maxDetected)||1),n.hand.iouThreshold,n.hand.minConfidence);let A=await o.nms.array(),s=[];for(let l of A){let c={};c.box=W.slice(o.norm,[l,0],[1,-1]),c.slice=W.slice(o.predictions,[l,5],[1,14]),c.norm=this.normalizeLandmarks(c.slice,l),c.palmLandmarks=W.reshape(c.norm,[-1,2]);let x=await c.box.data(),i=x.slice(0,2),y=x.slice(2,4),d=await c.palmLandmarks.array(),f={startPoint:i,endPoint:y,palmLandmarks:d,confidence:r[l]},p=an(f,[(t.shape[2]||1)/this.inputSize,(t.shape[1]||0)/this.inputSize]);s.push(p),Object.keys(c).forEach(g=>W.dispose(c[g]))}return Object.keys(o).forEach(l=>W.dispose(o[l])),s}};var G0=D(H());var WA=5,yn=1.65,fn=[0,5,9,13,17,1,2],FA=0,GA=2,mn=0,b5=class{constructor(t,n){R(this,"handDetector");R(this,"handPoseModel");R(this,"inputSize");R(this,"storedBoxes");R(this,"skipped");R(this,"detectedHands");var o,r,A;this.handDetector=t,this.handPoseModel=n,this.inputSize=((A=(r=(o=this.handPoseModel)==null?void 0:o.inputs)==null?void 0:r[0].shape)==null?void 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o=Object.keys(S.engine().registryFactory);if(e.config.backend==="humangl"&&!o.includes("humangl")&&(wn(e),o=Object.keys(S.engine().registryFactory)),e.config.debug&&u("available backends:",o),o.includes(e.config.backend)||(u(`error: backend ${e.config.backend} not found in registry`),e.config.backend=k.node?"tensorflow":"webgl",e.config.debug&&u(`override: setting backend ${e.config.backend}`)),e.config.debug&&u("setting backend:",[e.config.backend]),e.config.backend==="wasm"){if(S.env().flagRegistry.CANVAS2D_WILL_READ_FREQUENTLY&&S.env().set("CANVAS2D_WILL_READ_FREQUENTLY",!0),e.config.debug&&u("wasm path:",e.config.wasmPath),typeof S.setWasmPaths!="undefined")S.setWasmPaths(e.config.wasmPath,e.config.wasmPlatformFetch);else throw new Error("backend error: attempting to use wasm backend but wasm path is not set");let r=!1,A=!1;try{r=await S.env().getAsync("WASM_HAS_MULTITHREAD_SUPPORT"),A=await S.env().getAsync("WASM_HAS_SIMD_SUPPORT"),e.config.debug&&u(`wasm execution: ${A?"simd":"no 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Object.keys(o))zn[A]!==o[A]&&(r[A]=o[A]);e.config.debug&&Object.keys(r).length>0&&u("backend:",S.getBackend(),"flags:",r)}if(e.config.flags&&Object.keys(e.config.flags).length>0){e.config.debug&&u("flags:",e.config.flags);for(let[o,r]of Object.entries(e.config.flags))S.env().set(o,r)}S.enableProdMode(),B1(),e.performance.initBackend=Math.trunc(v()-n),e.config.backend=S.getBackend(),await k.updateBackend(),KA(e.config),k.initial=!1}return!0}function v5(e,t){for(let n of e){let o={kernelName:n,backendName:t.backend,kernelFunc:()=>{t.debug&&u("kernelFunc",n,t.backend)}};S.registerKernel(o)}k.kernels=S.getKernelsForBackend(S.getBackend()).map(n=>n.kernelName.toLowerCase())}var l0=[null,null],JA=["StatefulPartitionedCall/Postprocessor/Slice","StatefulPartitionedCall/Postprocessor/ExpandDims_1"],Fe=[[0,0],[0,0]],QA=["hand","fist","pinch","point","face","tip","pinchtip"],jn=4,Nn=1.6,_A=512,$A=1.4,P5=Number.MAX_SAFE_INTEGER,Wt=0,ue=[0,0],i0={boxes:[],hands:[]},In={thumb:[1,2,3,4],index:[5,6,7,8],middle:[9,10,11,12],ring:[13,14,15,16],pinky:[17,18,19,20],base:[0],palm:[0,17,13,9,5,1,0]};async function On(e){var t;if(k.initial&&(l0[0]=null),l0[0])e.debug&&u("cached model:",l0[0].modelUrl);else{v5(["tensorlistreserve","enter","tensorlistfromtensor","merge","loopcond","switch","exit","tensorliststack","nextiteration","tensorlistsetitem","tensorlistgetitem","reciprocal","shape","split","where"],e),l0[0]=await L((t=e.hand.detector)==null?void 0:t.modelPath);let n=l0[0].executor?Object.values(l0[0].modelSignature.inputs):void 0;Fe[0][0]=Array.isArray(n)?parseInt(n[0].tensorShape.dim[1].size):0,Fe[0][1]=Array.isArray(n)?parseInt(n[0].tensorShape.dim[2].size):0}return l0[0]}async function Cn(e){var t;if(k.initial&&(l0[1]=null),l0[1])e.debug&&u("cached model:",l0[1].modelUrl);else{l0[1]=await L((t=e.hand.skeleton)==null?void 0:t.modelPath);let n=l0[1].executor?Object.values(l0[1].modelSignature.inputs):void 0;Fe[1][0]=Array.isArray(n)?parseInt(n[0].tensorShape.dim[1].size):0,Fe[1][1]=Array.isArray(n)?parseInt(n[0].tensorShape.dim[2].size):0}return l0[1]}async function es(e,t){let n=[];if(!e||!l0[0])return n;let o={},r=(e.shape[2]||1)/(e.shape[1]||1),A=Math.min(Math.round((e.shape[1]||0)/8)*8,_A),s=Math.round(A*r/8)*8;o.resize=J.image.resizeBilinear(e,[A,s]),o.cast=J.cast(o.resize,"int32"),[o.rawScores,o.rawBoxes]=await l0[0].executeAsync(o.cast,JA),o.boxes=J.squeeze(o.rawBoxes,[0,2]),o.scores=J.squeeze(o.rawScores,[0]);let a=J.unstack(o.scores,1);J.dispose(a[jn]),a.splice(jn,1),o.filtered=J.stack(a,1),J.dispose(a),o.max=J.max(o.filtered,1),o.argmax=J.argMax(o.filtered,1);let l=0;o.nms=await J.image.nonMaxSuppressionAsync(o.boxes,o.max,(t.hand.maxDetected||0)+1,t.hand.iouThreshold||0,t.hand.minConfidence||1);let c=await o.nms.data(),x=await o.max.data(),i=await o.argmax.data();for(let y of Array.from(c)){let d=J.slice(o.boxes,y,1),f=await d.data();J.dispose(d);let p=[f[1],f[0],f[3]-f[1],f[2]-f[0]],g=a5(p,$A),M=[Math.trunc(p[0]*ue[0]),Math.trunc(p[1]*ue[1]),Math.trunc(p[2]*ue[0]),Math.trunc(p[3]*ue[1])],P=x[y],m=QA[i[y]],b={id:l++,score:P,box:M,boxRaw:g,label:m};n.push(b)}return Object.keys(o).forEach(y=>J.dispose(o[y])),n.sort((y,d)=>d.score-y.score),n.length>(t.hand.maxDetected||1)&&(n.length=t.hand.maxDetected||1),n}async function Ft(e,t,n){let o={id:t.id,score:Math.round(100*t.score)/100,boxScore:Math.round(100*t.score)/100,fingerScore:0,box:t.box,boxRaw:t.boxRaw,label:t.label,keypoints:[],landmarks:{},annotations:{}};if(e&&l0[1]&&n.hand.landmarks&&t.score>(n.hand.minConfidence||0)){let r={},A=[t.boxRaw[1],t.boxRaw[0],t.boxRaw[3]+t.boxRaw[1],t.boxRaw[2]+t.boxRaw[0]];r.crop=J.image.cropAndResize(e,[A],[0],[Fe[1][0],Fe[1][1]],"bilinear"),r.div=J.div(r.crop,B.tf255),[r.score,r.keypoints]=l0[1].execute(r.div,["Identity_1","Identity"]);let s=(await r.score.data())[0],a=(100-Math.trunc(100/(1+Math.exp(s))))/100;if(a>=(n.hand.minConfidence||0)){o.fingerScore=a,r.reshaped=J.reshape(r.keypoints,[-1,3]);let x=(await r.reshaped.array()).map(i=>[i[0]/Fe[1][1],i[1]/Fe[1][0],i[2]||0]).map(i=>[i[0]*t.boxRaw[2],i[1]*t.boxRaw[3],i[2]||0]);o.keypoints=x.map(i=>[ue[0]*(i[0]+t.boxRaw[0]),ue[1]*(i[1]+t.boxRaw[1]),i[2]||0]),o.landmarks=g5(o.keypoints);for(let i of Object.keys(In))o.annotations[i]=In[i].map(y=>o.landmarks&&o.keypoints[y]?o.keypoints[y]:null)}Object.keys(r).forEach(l=>J.dispose(r[l]))}return o}async function Gt(e,t){var r,A;if(!((r=l0[0])!=null&&r.executor)||!((A=l0[1])!=null&&A.executor)||!l0[0].inputs[0].shape||!l0[1].inputs[0].shape)return[];ue=[e.shape[2]||0,e.shape[1]||0],P5++;let n=(t.hand.skipTime||0)>v()-Wt,o=P5<(t.hand.skipFrames||0);return t.skipAllowed&&n&&o?i0.hands:new Promise(async s=>{let a=3*(t.hand.skipTime||0)>v()-Wt,l=P5<3*(t.hand.skipFrames||0);t.skipAllowed&&i0.hands.length===t.hand.maxDetected?i0.hands=await Promise.all(i0.boxes.map(x=>Ft(e,x,t))):t.skipAllowed&&a&&l&&i0.hands.length>0?i0.hands=await Promise.all(i0.boxes.map(x=>Ft(e,x,t))):(i0.boxes=await es(e,t),Wt=v(),i0.hands=await Promise.all(i0.boxes.map(x=>Ft(e,x,t))),P5=0);let c=[...i0.boxes];if(i0.boxes.length=0,t.cacheSensitivity>0)for(let x=0;x<i0.hands.length;x++){let i=m3(i0.hands[x].keypoints,ue);if(i.box[2]/(e.shape[2]||1)>.05&&i.box[3]/(e.shape[1]||1)>.05&&i0.hands[x].fingerScore&&i0.hands[x].fingerScore>(t.hand.minConfidence||0)){let y=a5(i.box,Nn),d=a5(i.boxRaw,Nn);i0.boxes.push({...c[x],box:y,boxRaw:d})}}for(let x=0;x<i0.hands.length;x++){let i=fe(i0.hands[x].keypoints,ue);i0.hands[x].box=i.box,i0.hands[x].boxRaw=i.boxRaw}s(i0.hands)})}var R5=D(H());var g0,T5=[],Bt=Number.MAX_SAFE_INTEGER,Wn=0,Fn=0;async function Gn(e){var t;return k.initial&&(g0=null),g0?e.debug&&u("cached model:",g0.modelUrl):g0=await L((t=e.face.liveness)==null?void 0:t.modelPath),g0}async function Ht(e,t,n,o){var s,a;if(!(g0!=null&&g0.executor))return 0;let r=(((s=t.face.liveness)==null?void 0:s.skipTime)||0)>v()-Fn,A=Bt<(((a=t.face.liveness)==null?void 0:a.skipFrames)||0);return t.skipAllowed&&r&&A&&Wn===o&&T5[n]?(Bt++,T5[n]):(Bt=0,new Promise(async l=>{let c=R5.image.resizeBilinear(e,[g0!=null&&g0.inputs[0].shape?g0.inputs[0].shape[2]:0,g0!=null&&g0.inputs[0].shape?g0.inputs[0].shape[1]:0],!1),x=g0==null?void 0:g0.execute(c),i=(await x.data())[0];T5[n]=Math.round(100*i)/100,Wn=o,Fn=v(),R5.dispose([c,x]),l(T5[n])}))}var Xn=D(H());var F2={};ve(F2,{connected:()=>w5,horizontal:()=>Vt,kpt:()=>k5,relative:()=>Zt,vertical:()=>Dt});var k5=["nose","leftEye","rightEye","leftEar","rightEar","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle"],Vt=[["leftEye","rightEye"],["leftEar","rightEar"],["leftShoulder","rightShoulder"],["leftElbow","rightElbow"],["leftWrist","rightWrist"],["leftHip","rightHip"],["leftKnee","rightKnee"],["leftAnkle","rightAnkle"]],Dt=[["leftKnee","leftShoulder"],["rightKnee","rightShoulder"],["leftAnkle","leftKnee"],["rightAnkle","rightKnee"]],Zt=[[["leftHip","rightHip"],["leftShoulder","rightShoulder"]],[["leftElbow","rightElbow"],["leftShoulder","rightShoulder"]]],w5={leftLeg:["leftHip","leftKnee","leftAnkle"],rightLeg:["rightHip","rightKnee","rightAnkle"],torso:["leftShoulder","rightShoulder","rightHip","leftHip","leftShoulder"],leftArm:["leftShoulder","leftElbow","leftWrist"],rightArm:["rightShoulder","rightElbow","rightWrist"],head:[]};var Ge=D(H()),Hn=.005,B0={keypoints:[],padding:[[0,0],[0,0],[0,0],[0,0]]};function Xt(e){for(let t of Vt){let n=e.keypoints.findIndex(r=>r.part===t[0]),o=e.keypoints.findIndex(r=>r.part===t[1]);if(e.keypoints[n]&&e.keypoints[o]&&e.keypoints[n].position[0]<e.keypoints[o].position[0]){let r=e.keypoints[n];e.keypoints[n]=e.keypoints[o],e.keypoints[o]=r}}for(let t of Dt){let n=e.keypoints.findIndex(r=>r&&r.part===t[0]),o=e.keypoints.findIndex(r=>r&&r.part===t[1]);e.keypoints[n]&&e.keypoints[o]&&e.keypoints[n].position[1]<e.keypoints[o].position[1]&&e.keypoints.splice(n,1)}for(let[t,n]of Zt){let o=e.keypoints.findIndex(c=>c&&c.part===t[0]),r=e.keypoints.findIndex(c=>c&&c.part===t[1]),A=e.keypoints.findIndex(c=>c&&c.part===n[0]),s=e.keypoints.findIndex(c=>c&&c.part===n[1]);if(!e.keypoints[A]||!e.keypoints[s])continue;let a=e.keypoints[o]?[Math.abs(e.keypoints[A].position[0]-e.keypoints[o].position[0]),Math.abs(e.keypoints[s].position[0]-e.keypoints[o].position[0])]:[0,0],l=e.keypoints[r]?[Math.abs(e.keypoints[s].position[0]-e.keypoints[r].position[0]),Math.abs(e.keypoints[A].position[0]-e.keypoints[r].position[0])]:[0,0];if(a[0]>a[1]||l[0]>l[1]){let c=e.keypoints[o];e.keypoints[o]=e.keypoints[r],e.keypoints[r]=c}}}function Vn(e){for(let t=0;t<e.length;t++)if(e[t]&&B0.keypoints[t]){let n=[Math.abs(e[t].positionRaw[0]-B0.keypoints[t].positionRaw[0]),Math.abs(e[t].positionRaw[1]-B0.keypoints[t].positionRaw[1])];n[0]<Hn&&n[1]<Hn?e[t]=B0.keypoints[t]:B0.keypoints[t]=e[t]}else B0.keypoints[t]=e[t];return e}function Dn(e,t){var r,A;let n={};if(!((r=e==null?void 0:e.shape)!=null&&r[1])||!((A=e==null?void 0:e.shape)!=null&&A[2]))return e;B0.padding=[[0,0],[e.shape[2]>e.shape[1]?Math.trunc((e.shape[2]-e.shape[1])/2):0,e.shape[2]>e.shape[1]?Math.trunc((e.shape[2]-e.shape[1])/2):0],[e.shape[1]>e.shape[2]?Math.trunc((e.shape[1]-e.shape[2])/2):0,e.shape[1]>e.shape[2]?Math.trunc((e.shape[1]-e.shape[2])/2):0],[0,0]],n.pad=Ge.pad(e,B0.padding),n.resize=Ge.image.resizeBilinear(n.pad,[t,t]);let o=Ge.cast(n.resize,"int32");return Object.keys(n).forEach(s=>Ge.dispose(n[s])),o}function Zn(e,t){e.keypoints=e.keypoints.filter(o=>o==null?void 0:o.position);for(let o of e.keypoints)o.position=[o.position[0]*(t[0]+B0.padding[2][0]+B0.padding[2][1])/t[0]-B0.padding[2][0],o.position[1]*(t[1]+B0.padding[1][0]+B0.padding[1][1])/t[1]-B0.padding[1][0]],o.positionRaw=[o.position[0]/t[0],o.position[1]/t[1]];let n=fe(e.keypoints.map(o=>o.position),t);return e.box=n.box,e.boxRaw=n.boxRaw,e}var f0,E5=0,qt=Number.MAX_SAFE_INTEGER,_e={boxes:[],bodies:[],last:0};async function qn(e){var t;return k.initial&&(f0=null),f0?e.debug&&u("cached 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A=e[0][r],s=Math.round(100*A[51+4])/100;if(s>t.body.minConfidence){let a=[];for(let i=0;i<17;i++){let y=A[3*i+2];if(y>t.body.minConfidence){let d=[A[3*i+1],A[3*i+0]];a.push({part:k5[i],score:Math.round(100*y)/100,positionRaw:d,position:[Math.round((n.shape[2]||0)*d[0]),Math.round((n.shape[1]||0)*d[1])]})}}let l=fe(a.map(i=>i.position),[n.shape[2],n.shape[1]]),c={};for(let[i,y]of Object.entries(w5)){let d=[];for(let f=0;f<y.length-1;f++){let p=a.find(M=>M.part===y[f]),g=a.find(M=>M.part===y[f+1]);p&&g&&p.score>(t.body.minConfidence||0)&&g.score>(t.body.minConfidence||0)&&d.push([p.position,g.position])}c[i]=d}let x={id:r,score:s,box:l.box,boxRaw:l.boxRaw,keypoints:[...a],annotations:c};Xt(x),o.push(x)}}return o.sort((r,A)=>A.score-r.score),o.length>t.body.maxDetected&&(o.length=t.body.maxDetected),o}async function Ut(e,t){var r;if(!(f0!=null&&f0.executor)||!((r=f0==null?void 0:f0.inputs)!=null&&r[0].shape))return[];t.skipAllowed||(_e.boxes.length=0),qt++;let n=(t.body.skipTime||0)>v()-_e.last,o=qt<(t.body.skipFrames||0);return t.skipAllowed&&n&&o?_e.bodies:new Promise(async A=>{let s={};qt=0,s.input=Dn(e,E5),s.res=f0==null?void 0:f0.execute(s.input),_e.last=v();let a=await s.res.array();_e.bodies=s.res.shape[2]===17?ns(a,t,e):os(a,t,e);for(let l of _e.bodies)Zn(l,[e.shape[2]||1,e.shape[1]||1]),Vn(l.keypoints);Object.keys(s).forEach(l=>Xn.dispose(s[l])),A(_e.bodies)})}var H0=D(H());var _0,z5=[],Yn=0,Yt=Number.MAX_SAFE_INTEGER,j5=0,S5=2.5;async function Kn(e){if(!_0||k.initial){_0=await L(e.object.modelPath);let t=_0!=null&&_0.executor?Object.values(_0.modelSignature.inputs):void 0;j5=Array.isArray(t)?parseInt(t[0].tensorShape.dim[2].size):416}else e.debug&&u("cached model:",_0.modelUrl);return _0}async function rs(e,t,n){let o=0,r=[],A=j5;for(let c of[1,2,4]){let x=c*13,i=H0.squeeze(e.find(M=>M.shape[1]===x**2&&(M.shape[2]||0)===i2.length)),y=await 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A1="2.11.0";var x1={};ve(x1,{all:()=>d1,body:()=>b2,canvas:()=>c1,face:()=>h2,gesture:()=>v2,hand:()=>g2,object:()=>M2,options:()=>R0,person:()=>l1});var X0=e=>{if(!e)u("draw error: invalid canvas");else if(!e.getContext)u("draw error: canvas context not defined");else{let t=e.getContext("2d");if(!t)u("draw error: cannot get canvas context");else return t}return null},$e=e=>Math.round(e*180/Math.PI),he=(e,t)=>{if(!t.useDepth||typeof e=="undefined")return t.color;let n=Uint8ClampedArray.from([127+2*e,127-2*e,255]);return`rgba(${n[0]}, ${n[1]}, ${n[2]}, ${t.alpha})`};function be(e,t,n,o,r){e.fillStyle=he(o,r),e.beginPath(),e.arc(t,n,r.pointSize,0,2*Math.PI),e.fill()}function de(e,t,n,o,r,A){if(e.beginPath(),e.lineWidth=A.lineWidth,A.useCurves){let s=(t+t+o)/2,a=(n+n+r)/2;e.ellipse(s,a,o/2,r/2,0,0,2*Math.PI)}else e.moveTo(t+A.roundRect,n),e.lineTo(t+o-A.roundRect,n),e.quadraticCurveTo(t+o,n,t+o,n+A.roundRect),e.lineTo(t+o,n+r-A.roundRect),e.quadraticCurveTo(t+o,n+r,t+o-A.roundRect,n+r),e.lineTo(t+A.roundRect,n+r),e.quadraticCurveTo(t,n+r,t,n+r-A.roundRect),e.lineTo(t,n+A.roundRect),e.quadraticCurveTo(t,n,t+A.roundRect,n),e.closePath();e.stroke()}function s1(e,t,n){if(!(t.length<2)){e.beginPath(),e.moveTo(t[0][0],t[0][1]);for(let o of t)e.strokeStyle=he(o[2]||0,n),e.lineTo(Math.trunc(o[0]),Math.trunc(o[1]));e.stroke(),n.fillPolygons&&(e.closePath(),e.fill())}}function io(e,t,n){if(!(t.length<2)){if(e.lineWidth=n.lineWidth,!n.useCurves||t.length<=2){s1(e,t,n);return}e.moveTo(t[0][0],t[0][1]);for(let o=0;o<t.length-2;o++){let r=(t[o][0]+t[o+1][0])/2,A=(t[o][1]+t[o+1][1])/2;e.quadraticCurveTo(t[o][0],t[o][1],r,A)}e.quadraticCurveTo(t[t.length-2][0],t[t.length-2][1],t[t.length-1][0],t[t.length-1][1]),e.stroke(),n.fillPolygons&&(e.closePath(),e.fill())}}function a1(e,t,n,o=5){let r,A,s;e.beginPath(),e.moveTo(t[0],t[1]),e.lineTo(n[0],n[1]),r=Math.atan2(n[1]-t[1],n[0]-t[0]),A=o*Math.cos(r)+n[0],s=o*Math.sin(r)+n[1],e.moveTo(A,s),r+=1/3*(2*Math.PI),A=o*Math.cos(r)+n[0],s=o*Math.sin(r)+n[1],e.lineTo(A,s),r+=1/3*(2*Math.PI),A=o*Math.cos(r)+n[0],s=o*Math.sin(r)+n[1],e.lineTo(A,s),e.closePath(),e.stroke(),e.fill()}var R0={color:"rgba(173, 216, 230, 0.6)",labelColor:"rgba(173, 216, 230, 1)",shadowColor:"black",alpha:.5,font:'small-caps 16px "Segoe UI"',lineHeight:18,lineWidth:4,pointSize:2,roundRect:8,drawPoints:!1,drawLabels:!0,drawBoxes:!0,drawAttention:!0,drawGestures:!0,drawPolygons:!0,drawGaze:!0,fillPolygons:!1,useDepth:!0,useCurves:!1};var Y;function hs(e,t){var n,o;if(Y.drawLabels){let r=[];if(r.push(`face: ${Math.trunc(100*e.score)}%`),e.genderScore&&r.push(`${e.gender||""} ${Math.trunc(100*e.genderScore)}%`),e.age&&r.push(`age: ${e.age||""}`),e.iris&&r.push(`distance: ${e.iris}`),e.real&&r.push(`real: ${Math.trunc(100*e.real)}%`),e.live&&r.push(`live: ${Math.trunc(100*e.live)}%`),e.emotion&&e.emotion.length>0){let A=e.emotion.map(s=>`${Math.trunc(100*s.score)}% ${s.emotion}`);A.length>3&&(A.length=3),r.push(A.join(" "))}((n=e.rotation)==null?void 0:n.angle)&&((o=e.rotation)==null?void 0:o.gaze)&&(e.rotation.angle.roll&&r.push(`roll: ${$e(e.rotation.angle.roll)}\xB0 yaw:${$e(e.rotation.angle.yaw)}\xB0 pitch:${$e(e.rotation.angle.pitch)}\xB0`),e.rotation.gaze.bearing&&r.push(`gaze: ${$e(e.rotation.gaze.bearing)}\xB0`)),r.length===0&&r.push("face"),t.fillStyle=Y.color;for(let A=r.length-1;A>=0;A--){let s=Math.max(e.box[0],0),a=A*Y.lineHeight+e.box[1];Y.shadowColor&&Y.shadowColor!==""&&(t.fillStyle=Y.shadowColor,t.fillText(r[A],s+5,a+16)),t.fillStyle=Y.labelColor,t.fillText(r[A],s+4,a+15)}}}function bs(e,t){var n,o,r,A;if(((n=e.annotations)==null?void 0:n.leftEyeIris)&&((o=e.annotations)==null?void 0:o.leftEyeIris[0])){t.strokeStyle=Y.useDepth?"rgba(255, 200, 255, 0.3)":Y.color,t.beginPath();let s=Math.abs(e.annotations.leftEyeIris[3][0]-e.annotations.leftEyeIris[1][0])/2,a=Math.abs(e.annotations.leftEyeIris[4][1]-e.annotations.leftEyeIris[2][1])/2;t.ellipse(e.annotations.leftEyeIris[0][0],e.annotations.leftEyeIris[0][1],s,a,0,0,2*Math.PI),t.stroke(),Y.fillPolygons&&(t.fillStyle=Y.useDepth?"rgba(255, 255, 200, 0.3)":Y.color,t.fill())}if(((r=e.annotations)==null?void 0:r.rightEyeIris)&&((A=e.annotations)==null?void 0:A.rightEyeIris[0])){t.strokeStyle=Y.useDepth?"rgba(255, 200, 255, 0.3)":Y.color,t.beginPath();let s=Math.abs(e.annotations.rightEyeIris[3][0]-e.annotations.rightEyeIris[1][0])/2,a=Math.abs(e.annotations.rightEyeIris[4][1]-e.annotations.rightEyeIris[2][1])/2;t.ellipse(e.annotations.rightEyeIris[0][0],e.annotations.rightEyeIris[0][1],s,a,0,0,2*Math.PI),t.stroke(),Y.fillPolygons&&(t.fillStyle=Y.useDepth?"rgba(255, 255, 200, 0.3)":Y.color,t.fill())}}function gs(e,t){var n;if(Y.drawGaze&&((n=e.rotation)==null?void 0:n.angle)&&typeof Path2D!="undefined"){t.strokeStyle="pink";let o=e.box[0]+e.box[2]/2-e.box[3]*$e(e.rotation.angle.yaw)/90,r=e.box[1]+e.box[3]/2+e.box[2]*$e(e.rotation.angle.pitch)/90,A=new Path2D(`
-      M ${e.box[0]+e.box[2]/2} ${e.box[1]}
+`;
+
+// src/image/imagefx.ts
+var collect = (source, prefix, collection) => {
+  const r = new RegExp("\\b" + prefix + " \\w+ (\\w+)", "ig");
+  source.replace(r, (match3, name) => {
+    collection[name] = 0;
+    return match3;
+  });
+};
+var GLProgram = class {
+  constructor(gl, vertexSource, fragmentSource) {
+    __publicField(this, "uniform", {});
+    __publicField(this, "attribute", {});
+    __publicField(this, "gl");
+    __publicField(this, "id");
+    __publicField(this, "compile", (source, type) => {
+      const shader = this.gl.createShader(type);
+      if (!shader) {
+        log("filter: could not create shader");
+        return null;
+      }
+      this.gl.shaderSource(shader, source);
+      this.gl.compileShader(shader);
+      if (!this.gl.getShaderParameter(shader, this.gl.COMPILE_STATUS)) {
+        log(`filter: gl compile failed: ${this.gl.getShaderInfoLog(shader) || "unknown"}`);
+        return null;
+      }
+      return shader;
+    });
+    this.gl = gl;
+    const vertexShader = this.compile(vertexSource, this.gl.VERTEX_SHADER);
+    const fragmentShader = this.compile(fragmentSource, this.gl.FRAGMENT_SHADER);
+    this.id = this.gl.createProgram();
+    if (!vertexShader || !fragmentShader)
+      return;
+    if (!this.id) {
+      log("filter: could not create webgl program");
+      return;
+    }
+    this.gl.attachShader(this.id, vertexShader);
+    this.gl.attachShader(this.id, fragmentShader);
+    this.gl.linkProgram(this.id);
+    if (!this.gl.getProgramParameter(this.id, this.gl.LINK_STATUS)) {
+      log(`filter: gl link failed: ${this.gl.getProgramInfoLog(this.id) || "unknown"}`);
+      return;
+    }
+    this.gl.useProgram(this.id);
+    collect(vertexSource, "attribute", this.attribute);
+    for (const a in this.attribute)
+      this.attribute[a] = this.gl.getAttribLocation(this.id, a);
+    collect(vertexSource, "uniform", this.uniform);
+    collect(fragmentSource, "uniform", this.uniform);
+    for (const u in this.uniform)
+      this.uniform[u] = this.gl.getUniformLocation(this.id, u);
+  }
+};
+function GLImageFilter() {
+  let drawCount = 0;
+  let sourceTexture = null;
+  let lastInChain = false;
+  let currentFramebufferIndex = -1;
+  let tempFramebuffers = [null, null];
+  let filterChain = [];
+  let vertexBuffer = null;
+  let currentProgram = null;
+  const fxcanvas = canvas(100, 100);
+  const shaderProgramCache = {};
+  const DRAW = { INTERMEDIATE: 1 };
+  const gl = fxcanvas.getContext("webgl");
+  if (!gl) {
+    log("filter: cannot get webgl context");
+    return;
+  }
+  this.gl = gl;
+  function resize(width, height) {
+    if (width === fxcanvas.width && height === fxcanvas.height)
+      return;
+    fxcanvas.width = width;
+    fxcanvas.height = height;
+    if (!vertexBuffer) {
+      const vertices = new Float32Array([-1, -1, 0, 1, 1, -1, 1, 1, -1, 1, 0, 0, -1, 1, 0, 0, 1, -1, 1, 1, 1, 1, 1, 0]);
+      vertexBuffer = gl.createBuffer();
+      gl.bindBuffer(gl.ARRAY_BUFFER, vertexBuffer);
+      gl.bufferData(gl.ARRAY_BUFFER, vertices, gl.STATIC_DRAW);
+      gl.pixelStorei(gl.UNPACK_PREMULTIPLY_ALPHA_WEBGL, true);
+    }
+    gl.viewport(0, 0, fxcanvas.width, fxcanvas.height);
+    tempFramebuffers = [null, null];
+  }
+  function createFramebufferTexture(width, height) {
+    const fbo = gl.createFramebuffer();
+    gl.bindFramebuffer(gl.FRAMEBUFFER, fbo);
+    const renderbuffer = gl.createRenderbuffer();
+    gl.bindRenderbuffer(gl.RENDERBUFFER, renderbuffer);
+    const texture = gl.createTexture();
+    gl.bindTexture(gl.TEXTURE_2D, texture);
+    gl.texImage2D(gl.TEXTURE_2D, 0, gl.RGBA, width, height, 0, gl.RGBA, gl.UNSIGNED_BYTE, null);
+    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MAG_FILTER, gl.LINEAR);
+    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MIN_FILTER, gl.LINEAR);
+    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_S, gl.CLAMP_TO_EDGE);
+    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_T, gl.CLAMP_TO_EDGE);
+    gl.framebufferTexture2D(gl.FRAMEBUFFER, gl.COLOR_ATTACHMENT0, gl.TEXTURE_2D, texture, 0);
+    gl.bindTexture(gl.TEXTURE_2D, null);
+    gl.bindFramebuffer(gl.FRAMEBUFFER, null);
+    return { fbo, texture };
+  }
+  function getTempFramebuffer(index2) {
+    tempFramebuffers[index2] = tempFramebuffers[index2] || createFramebufferTexture(fxcanvas.width, fxcanvas.height);
+    return tempFramebuffers[index2];
+  }
+  function draw(flags = 0) {
+    if (!currentProgram)
+      return;
+    let source = null;
+    let target = null;
+    let flipY = false;
+    if (drawCount === 0)
+      source = sourceTexture;
+    else
+      source = getTempFramebuffer(currentFramebufferIndex).texture || null;
+    drawCount++;
+    if (lastInChain && !(flags & DRAW.INTERMEDIATE)) {
+      target = null;
+      flipY = drawCount % 2 === 0;
+    } else {
+      currentFramebufferIndex = (currentFramebufferIndex + 1) % 2;
+      target = getTempFramebuffer(currentFramebufferIndex).fbo || null;
+    }
+    gl.bindTexture(gl.TEXTURE_2D, source);
+    gl.bindFramebuffer(gl.FRAMEBUFFER, target);
+    gl.uniform1f(currentProgram.uniform["flipY"], flipY ? -1 : 1);
+    gl.drawArrays(gl.TRIANGLES, 0, 6);
+  }
+  function compileShader(fragmentSource) {
+    if (shaderProgramCache[fragmentSource]) {
+      currentProgram = shaderProgramCache[fragmentSource];
+      gl.useProgram((currentProgram ? currentProgram.id : null) || null);
+      return currentProgram;
+    }
+    currentProgram = new GLProgram(gl, vertexIdentity, fragmentSource);
+    if (!currentProgram) {
+      log("filter: could not get webgl program");
+      return null;
+    }
+    const floatSize = Float32Array.BYTES_PER_ELEMENT;
+    const vertSize = 4 * floatSize;
+    gl.enableVertexAttribArray(currentProgram.attribute["pos"]);
+    gl.vertexAttribPointer(currentProgram.attribute["pos"], 2, gl.FLOAT, false, vertSize, 0 * floatSize);
+    gl.enableVertexAttribArray(currentProgram.attribute["uv"]);
+    gl.vertexAttribPointer(currentProgram.attribute["uv"], 2, gl.FLOAT, false, vertSize, 2 * floatSize);
+    shaderProgramCache[fragmentSource] = currentProgram;
+    return currentProgram;
+  }
+  const filter = {
+    colorMatrix: (matrix) => {
+      const m = new Float32Array(matrix);
+      m[4] /= 255;
+      m[9] /= 255;
+      m[14] /= 255;
+      m[19] /= 255;
+      const shader = m[18] === 1 && m[3] === 0 && m[8] === 0 && m[13] === 0 && m[15] === 0 && m[16] === 0 && m[17] === 0 && m[19] === 0 ? colorMatrixWithoutAlpha : colorMatrixWithAlpha;
+      const program = compileShader(shader);
+      if (!program)
+        return;
+      gl.uniform1fv(program.uniform["m"], m);
+      draw();
+    },
+    brightness: (brightness) => {
+      const b = (brightness || 0) + 1;
+      filter.colorMatrix([
+        b,
+        0,
+        0,
+        0,
+        0,
+        0,
+        b,
+        0,
+        0,
+        0,
+        0,
+        0,
+        b,
+        0,
+        0,
+        0,
+        0,
+        0,
+        1,
+        0
+      ]);
+    },
+    saturation: (amount) => {
+      const x = (amount || 0) * 2 / 3 + 1;
+      const y = (x - 1) * -0.5;
+      filter.colorMatrix([
+        x,
+        y,
+        y,
+        0,
+        0,
+        y,
+        x,
+        y,
+        0,
+        0,
+        y,
+        y,
+        x,
+        0,
+        0,
+        0,
+        0,
+        0,
+        1,
+        0
+      ]);
+    },
+    desaturate: () => {
+      filter.saturation(-1);
+    },
+    contrast: (amount) => {
+      const v = (amount || 0) + 1;
+      const o = -128 * (v - 1);
+      filter.colorMatrix([
+        v,
+        0,
+        0,
+        0,
+        o,
+        0,
+        v,
+        0,
+        0,
+        o,
+        0,
+        0,
+        v,
+        0,
+        o,
+        0,
+        0,
+        0,
+        1,
+        0
+      ]);
+    },
+    negative: () => {
+      filter.contrast(-2);
+    },
+    hue: (rotation) => {
+      rotation = (rotation || 0) / 180 * Math.PI;
+      const cos = Math.cos(rotation);
+      const sin = Math.sin(rotation);
+      const lumR = 0.213;
+      const lumG = 0.715;
+      const lumB = 0.072;
+      filter.colorMatrix([
+        lumR + cos * (1 - lumR) + sin * -lumR,
+        lumG + cos * -lumG + sin * -lumG,
+        lumB + cos * -lumB + sin * (1 - lumB),
+        0,
+        0,
+        lumR + cos * -lumR + sin * 0.143,
+        lumG + cos * (1 - lumG) + sin * 0.14,
+        lumB + cos * -lumB + sin * -0.283,
+        0,
+        0,
+        lumR + cos * -lumR + sin * -(1 - lumR),
+        lumG + cos * -lumG + sin * lumG,
+        lumB + cos * (1 - lumB) + sin * lumB,
+        0,
+        0,
+        0,
+        0,
+        0,
+        1,
+        0
+      ]);
+    },
+    desaturateLuminance: () => {
+      filter.colorMatrix([
+        0.2764723,
+        0.929708,
+        0.0938197,
+        0,
+        -37.1,
+        0.2764723,
+        0.929708,
+        0.0938197,
+        0,
+        -37.1,
+        0.2764723,
+        0.929708,
+        0.0938197,
+        0,
+        -37.1,
+        0,
+        0,
+        0,
+        1,
+        0
+      ]);
+    },
+    sepia: () => {
+      filter.colorMatrix([
+        0.393,
+        0.7689999,
+        0.18899999,
+        0,
+        0,
+        0.349,
+        0.6859999,
+        0.16799999,
+        0,
+        0,
+        0.272,
+        0.5339999,
+        0.13099999,
+        0,
+        0,
+        0,
+        0,
+        0,
+        1,
+        0
+      ]);
+    },
+    brownie: () => {
+      filter.colorMatrix([
+        0.5997023498159715,
+        0.34553243048391263,
+        -0.2708298674538042,
+        0,
+        47.43192855600873,
+        -0.037703249837783157,
+        0.8609577587992641,
+        0.15059552388459913,
+        0,
+        -36.96841498319127,
+        0.24113635128153335,
+        -0.07441037908422492,
+        0.44972182064877153,
+        0,
+        -7.562075277591283,
+        0,
+        0,
+        0,
+        1,
+        0
+      ]);
+    },
+    vintagePinhole: () => {
+      filter.colorMatrix([
+        0.6279345635605994,
+        0.3202183420819367,
+        -0.03965408211312453,
+        0,
+        9.651285835294123,
+        0.02578397704808868,
+        0.6441188644374771,
+        0.03259127616149294,
+        0,
+        7.462829176470591,
+        0.0466055556782719,
+        -0.0851232987247891,
+        0.5241648018700465,
+        0,
+        5.159190588235296,
+        0,
+        0,
+        0,
+        1,
+        0
+      ]);
+    },
+    kodachrome: () => {
+      filter.colorMatrix([
+        1.1285582396593525,
+        -0.3967382283601348,
+        -0.03992559172921793,
+        0,
+        63.72958762196502,
+        -0.16404339962244616,
+        1.0835251566291304,
+        -0.05498805115633132,
+        0,
+        24.732407896706203,
+        -0.16786010706155763,
+        -0.5603416277695248,
+        1.6014850761964943,
+        0,
+        35.62982807460946,
+        0,
+        0,
+        0,
+        1,
+        0
+      ]);
+    },
+    technicolor: () => {
+      filter.colorMatrix([
+        1.9125277891456083,
+        -0.8545344976951645,
+        -0.09155508482755585,
+        0,
+        11.793603434377337,
+        -0.3087833385928097,
+        1.7658908555458428,
+        -0.10601743074722245,
+        0,
+        -70.35205161461398,
+        -0.231103377548616,
+        -0.7501899197440212,
+        1.847597816108189,
+        0,
+        30.950940869491138,
+        0,
+        0,
+        0,
+        1,
+        0
+      ]);
+    },
+    polaroid: () => {
+      filter.colorMatrix([
+        1.438,
+        -0.062,
+        -0.062,
+        0,
+        0,
+        -0.122,
+        1.378,
+        -0.122,
+        0,
+        0,
+        -0.016,
+        -0.016,
+        1.483,
+        0,
+        0,
+        0,
+        0,
+        0,
+        1,
+        0
+      ]);
+    },
+    shiftToBGR: () => {
+      filter.colorMatrix([
+        0,
+        0,
+        1,
+        0,
+        0,
+        0,
+        1,
+        0,
+        0,
+        0,
+        1,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        1,
+        0
+      ]);
+    },
+    convolution: (matrix) => {
+      const m = new Float32Array(matrix);
+      const pixelSizeX = 1 / fxcanvas.width;
+      const pixelSizeY = 1 / fxcanvas.height;
+      const program = compileShader(convolution);
+      if (!program)
+        return;
+      gl.uniform1fv(program.uniform["m"], m);
+      gl.uniform2f(program.uniform["px"], pixelSizeX, pixelSizeY);
+      draw();
+    },
+    detectEdges: () => {
+      filter.convolution.call(this, [
+        0,
+        1,
+        0,
+        1,
+        -4,
+        1,
+        0,
+        1,
+        0
+      ]);
+    },
+    sobelX: () => {
+      filter.convolution.call(this, [
+        -1,
+        0,
+        1,
+        -2,
+        0,
+        2,
+        -1,
+        0,
+        1
+      ]);
+    },
+    sobelY: () => {
+      filter.convolution.call(this, [
+        -1,
+        -2,
+        -1,
+        0,
+        0,
+        0,
+        1,
+        2,
+        1
+      ]);
+    },
+    sharpen: (amount) => {
+      const a = amount || 1;
+      filter.convolution.call(this, [
+        0,
+        -1 * a,
+        0,
+        -1 * a,
+        1 + 4 * a,
+        -1 * a,
+        0,
+        -1 * a,
+        0
+      ]);
+    },
+    emboss: (size2) => {
+      const s = size2 || 1;
+      filter.convolution.call(this, [
+        -2 * s,
+        -1 * s,
+        0,
+        -1 * s,
+        1,
+        1 * s,
+        0,
+        1 * s,
+        2 * s
+      ]);
+    },
+    blur: (size2) => {
+      const blurSizeX = size2 / 7 / fxcanvas.width;
+      const blurSizeY = size2 / 7 / fxcanvas.height;
+      const program = compileShader(blur);
+      if (!program)
+        return;
+      gl.uniform2f(program.uniform["px"], 0, blurSizeY);
+      draw(DRAW.INTERMEDIATE);
+      gl.uniform2f(program.uniform["px"], blurSizeX, 0);
+      draw();
+    },
+    pixelate: (size2) => {
+      const blurSizeX = size2 / fxcanvas.width;
+      const blurSizeY = size2 / fxcanvas.height;
+      const program = compileShader(pixelate);
+      if (!program)
+        return;
+      gl.uniform2f(program.uniform["size"], blurSizeX, blurSizeY);
+      draw();
+    }
+  };
+  this.add = function(name) {
+    const args = Array.prototype.slice.call(arguments, 1);
+    const func = filter[name];
+    filterChain.push({ func, args });
+  };
+  this.reset = function() {
+    filterChain = [];
+  };
+  this.get = function() {
+    return filterChain;
+  };
+  this.apply = function(image27) {
+    resize(image27.width, image27.height);
+    drawCount = 0;
+    if (!sourceTexture)
+      sourceTexture = gl.createTexture();
+    gl.bindTexture(gl.TEXTURE_2D, sourceTexture);
+    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_S, gl.CLAMP_TO_EDGE);
+    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_T, gl.CLAMP_TO_EDGE);
+    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MIN_FILTER, gl.NEAREST);
+    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MAG_FILTER, gl.NEAREST);
+    gl.texImage2D(gl.TEXTURE_2D, 0, gl.RGBA, gl.RGBA, gl.UNSIGNED_BYTE, image27);
+    for (let i = 0; i < filterChain.length; i++) {
+      lastInChain = i === filterChain.length - 1;
+      const f = filterChain[i];
+      f.func.apply(this, f.args || []);
+    }
+    return fxcanvas;
+  };
+  this.draw = function(image27) {
+    this.add("brightness", 0);
+    return this.apply(image27);
+  };
+}
+
+// src/image/enhance.ts
+var tf = __toESM(require_tfjs_esm());
+async function histogramEqualization(inputImage) {
+  const squeeze14 = inputImage.shape.length === 4 ? tf.squeeze(inputImage) : inputImage;
+  const channels = tf.split(squeeze14, 3, 2);
+  const min2 = [tf.min(channels[0]), tf.min(channels[1]), tf.min(channels[2])];
+  const max4 = [tf.max(channels[0]), tf.max(channels[1]), tf.max(channels[2])];
+  const absMax = await Promise.all(max4.map((channel) => channel.data()));
+  const maxValue = 0.99 * Math.max(absMax[0][0], absMax[1][0], absMax[2][0]);
+  const sub11 = [tf.sub(channels[0], min2[0]), tf.sub(channels[1], min2[1]), tf.sub(channels[2], min2[2])];
+  const range = [tf.sub(max4[0], min2[0]), tf.sub(max4[1], min2[1]), tf.sub(max4[2], min2[2])];
+  const fact = [tf.div(maxValue, range[0]), tf.div(maxValue, range[1]), tf.div(maxValue, range[2])];
+  const enh = [tf.mul(sub11[0], fact[0]), tf.mul(sub11[1], fact[1]), tf.mul(sub11[2], fact[2])];
+  const rgb2 = tf.stack([enh[0], enh[1], enh[2]], 2);
+  const reshape8 = tf.reshape(rgb2, [1, squeeze14.shape[0], squeeze14.shape[1], 3]);
+  tf.dispose([...channels, ...min2, ...max4, ...sub11, ...range, ...fact, ...enh, rgb2, squeeze14]);
+  return reshape8;
+}
+
+// src/image/image.ts
+var maxSize = 3840;
+var inCanvas = null;
+var outCanvas = null;
+var tmpCanvas = null;
+var fx;
+var last = {
+  inputSum: 0,
+  cacheDiff: 1,
+  sumMethod: 0,
+  inputTensor: void 0
+};
+function reset() {
+  last.inputSum = 0;
+  last.cacheDiff = 1;
+  last.sumMethod = 0;
+  last.inputTensor = void 0;
+}
+function canvas(width, height) {
+  let c;
+  if (env.browser) {
+    if (env.worker) {
+      if (typeof OffscreenCanvas === "undefined")
+        throw new Error("canvas error: attempted to run in web worker but OffscreenCanvas is not supported");
+      c = new OffscreenCanvas(width, height);
+    } else {
+      if (typeof document === "undefined")
+        throw new Error("canvas error: attempted to run in browser but DOM is not defined");
+      c = document.createElement("canvas");
+      c.width = width;
+      c.height = height;
+    }
+  } else {
+    if (typeof env.Canvas !== "undefined")
+      c = new env.Canvas(width, height);
+    else if (typeof globalThis.Canvas !== "undefined")
+      c = new globalThis.Canvas(width, height);
+  }
+  return c;
+}
+function copy(input, output) {
+  const outputCanvas = output || canvas(input.width, input.height);
+  const ctx = outputCanvas.getContext("2d");
+  ctx.drawImage(input, 0, 0);
+  return outputCanvas;
+}
+async function process2(input, config3, getTensor = true) {
+  var _a, _b;
+  if (!input) {
+    if (config3.debug)
+      log("input error: input is missing");
+    return { tensor: null, canvas: null };
+  }
+  if (!(input instanceof tf2.Tensor) && !(typeof Image !== "undefined" && input instanceof Image) && !(typeof env.Canvas !== "undefined" && input instanceof env.Canvas) && !(typeof globalThis.Canvas !== "undefined" && input instanceof globalThis.Canvas) && !(typeof ImageData !== "undefined" && input instanceof ImageData) && !(typeof ImageBitmap !== "undefined" && input instanceof ImageBitmap) && !(typeof HTMLImageElement !== "undefined" && input instanceof HTMLImageElement) && !(typeof HTMLMediaElement !== "undefined" && input instanceof HTMLMediaElement) && !(typeof HTMLVideoElement !== "undefined" && input instanceof HTMLVideoElement) && !(typeof HTMLCanvasElement !== "undefined" && input instanceof HTMLCanvasElement) && !(typeof OffscreenCanvas !== "undefined" && input instanceof OffscreenCanvas)) {
+    throw new Error("input error: type is not recognized");
+  }
+  if (input instanceof tf2.Tensor) {
+    let tensor7 = null;
+    if (input["isDisposedInternal"])
+      throw new Error("input error: attempted to use tensor but it is disposed");
+    if (!input.shape)
+      throw new Error("input error: attempted to use tensor without a shape");
+    if (input.shape.length === 3) {
+      if (input.shape[2] === 3) {
+        tensor7 = tf2.expandDims(input, 0);
+      } else if (input.shape[2] === 4) {
+        const rgb2 = tf2.slice3d(input, [0, 0, 0], [-1, -1, 3]);
+        tensor7 = tf2.expandDims(rgb2, 0);
+        tf2.dispose(rgb2);
+      }
+    } else if (input.shape.length === 4) {
+      if (input.shape[3] === 3) {
+        tensor7 = tf2.clone(input);
+      } else if (input.shape[3] === 4) {
+        tensor7 = tf2.slice4d(input, [0, 0, 0, 0], [-1, -1, -1, 3]);
+      }
+    }
+    if (tensor7 == null || tensor7.shape.length !== 4 || tensor7.shape[0] !== 1 || tensor7.shape[3] !== 3)
+      throw new Error(`input error: attempted to use tensor with unrecognized shape: ${input.shape.toString()}`);
+    if (tensor7.dtype === "int32") {
+      const cast8 = tf2.cast(tensor7, "float32");
+      tf2.dispose(tensor7);
+      tensor7 = cast8;
+    }
+    return { tensor: tensor7, canvas: config3.filter.return ? outCanvas : null };
+  }
+  if (typeof input["readyState"] !== "undefined" && input.readyState <= 2) {
+    if (config3.debug)
+      log("input stream is not ready");
+    return { tensor: null, canvas: inCanvas };
+  }
+  const originalWidth = input["naturalWidth"] || input["videoWidth"] || input["width"] || input["shape"] && input["shape"][1] > 0;
+  const originalHeight = input["naturalHeight"] || input["videoHeight"] || input["height"] || input["shape"] && input["shape"][2] > 0;
+  if (!originalWidth || !originalHeight) {
+    if (config3.debug)
+      log("cannot determine input dimensions");
+    return { tensor: null, canvas: inCanvas };
+  }
+  let targetWidth = originalWidth;
+  let targetHeight = originalHeight;
+  if (targetWidth > maxSize) {
+    targetWidth = maxSize;
+    targetHeight = Math.trunc(targetWidth * originalHeight / originalWidth);
+  }
+  if (targetHeight > maxSize) {
+    targetHeight = maxSize;
+    targetWidth = Math.trunc(targetHeight * originalWidth / originalHeight);
+  }
+  if ((((_a = config3.filter) == null ? void 0 : _a.width) || 0) > 0)
+    targetWidth = config3.filter.width;
+  else if ((((_b = config3.filter) == null ? void 0 : _b.height) || 0) > 0)
+    targetWidth = originalWidth * ((config3.filter.height || 0) / originalHeight);
+  if ((config3.filter.height || 0) > 0)
+    targetHeight = config3.filter.height;
+  else if ((config3.filter.width || 0) > 0)
+    targetHeight = originalHeight * ((config3.filter.width || 0) / originalWidth);
+  if (!targetWidth || !targetHeight)
+    throw new Error("input error: cannot determine dimension");
+  if (!inCanvas || inCanvas.width !== targetWidth || inCanvas.height !== targetHeight)
+    inCanvas = canvas(targetWidth, targetHeight);
+  const inCtx = inCanvas.getContext("2d");
+  if (typeof ImageData !== "undefined" && input instanceof ImageData) {
+    inCtx.putImageData(input, 0, 0);
+  } else {
+    if (config3.filter.flip && typeof inCtx.translate !== "undefined") {
+      inCtx.translate(originalWidth, 0);
+      inCtx.scale(-1, 1);
+      inCtx.drawImage(input, 0, 0, originalWidth, originalHeight, 0, 0, inCanvas.width, inCanvas.height);
+      inCtx.setTransform(1, 0, 0, 1, 0, 0);
+    } else {
+      inCtx.drawImage(input, 0, 0, originalWidth, originalHeight, 0, 0, inCanvas.width, inCanvas.height);
+    }
+  }
+  if (!outCanvas || inCanvas.width !== outCanvas.width || inCanvas.height !== outCanvas.height)
+    outCanvas = canvas(inCanvas.width, inCanvas.height);
+  if (config3.filter.enabled && env.webgl.supported) {
+    if (!fx)
+      fx = env.browser ? new GLImageFilter() : null;
+    env.filter = !!fx;
+    if (!(fx == null ? void 0 : fx.add)) {
+      if (config3.debug)
+        log("input process error: cannot initialize filters");
+      env.webgl.supported = false;
+      config3.filter.enabled = false;
+      copy(inCanvas, outCanvas);
+    } else {
+      fx.reset();
+      if (config3.filter.brightness !== 0)
+        fx.add("brightness", config3.filter.brightness);
+      if (config3.filter.contrast !== 0)
+        fx.add("contrast", config3.filter.contrast);
+      if (config3.filter.sharpness !== 0)
+        fx.add("sharpen", config3.filter.sharpness);
+      if (config3.filter.blur !== 0)
+        fx.add("blur", config3.filter.blur);
+      if (config3.filter.saturation !== 0)
+        fx.add("saturation", config3.filter.saturation);
+      if (config3.filter.hue !== 0)
+        fx.add("hue", config3.filter.hue);
+      if (config3.filter.negative)
+        fx.add("negative");
+      if (config3.filter.sepia)
+        fx.add("sepia");
+      if (config3.filter.vintage)
+        fx.add("brownie");
+      if (config3.filter.sepia)
+        fx.add("sepia");
+      if (config3.filter.kodachrome)
+        fx.add("kodachrome");
+      if (config3.filter.technicolor)
+        fx.add("technicolor");
+      if (config3.filter.polaroid)
+        fx.add("polaroid");
+      if (config3.filter.pixelate !== 0)
+        fx.add("pixelate", config3.filter.pixelate);
+      if (fx.get() > 0)
+        outCanvas = fx.apply(inCanvas);
+      else
+        outCanvas = fx.draw(inCanvas);
+    }
+  } else {
+    copy(inCanvas, outCanvas);
+    if (fx)
+      fx = null;
+    env.filter = !!fx;
+  }
+  if (!getTensor)
+    return { tensor: null, canvas: outCanvas };
+  if (!outCanvas)
+    throw new Error("canvas error: cannot create output");
+  let pixels;
+  let depth = 3;
+  if (typeof ImageData !== "undefined" && input instanceof ImageData || input.data && input.width && input.height) {
+    if (env.browser && tf2.browser) {
+      pixels = tf2.browser ? tf2.browser.fromPixels(input) : null;
+    } else {
+      depth = input.data.length / input.height / input.width;
+      const arr = new Uint8Array(input.data.buffer);
+      pixels = tf2.tensor(arr, [input.height, input.width, depth], "int32");
+    }
+  } else {
+    if (!tmpCanvas || outCanvas.width !== tmpCanvas.width || outCanvas.height !== tmpCanvas.height)
+      tmpCanvas = canvas(outCanvas.width, outCanvas.height);
+    if (tf2.browser && env.browser) {
+      if (config3.backend === "webgl" || config3.backend === "humangl" || config3.backend === "webgpu") {
+        pixels = tf2.browser.fromPixels(outCanvas);
+      } else {
+        tmpCanvas = copy(outCanvas);
+        pixels = tf2.browser.fromPixels(tmpCanvas);
+      }
+    } else {
+      const tempCanvas = copy(outCanvas);
+      const tempCtx = tempCanvas.getContext("2d");
+      const tempData = tempCtx.getImageData(0, 0, targetWidth, targetHeight);
+      depth = tempData.data.length / targetWidth / targetHeight;
+      const arr = new Uint8Array(tempData.data.buffer);
+      pixels = tf2.tensor(arr, [targetWidth, targetHeight, depth]);
+    }
+  }
+  if (depth === 4) {
+    const rgb2 = tf2.slice3d(pixels, [0, 0, 0], [-1, -1, 3]);
+    tf2.dispose(pixels);
+    pixels = rgb2;
+  }
+  if (!pixels)
+    throw new Error("input error: cannot create tensor");
+  const casted = tf2.cast(pixels, "float32");
+  const tensor6 = config3.filter.equalization ? await histogramEqualization(casted) : tf2.expandDims(casted, 0);
+  tf2.dispose([pixels, casted]);
+  return { tensor: tensor6, canvas: config3.filter.return ? outCanvas : null };
+}
+async function skip(config3, input) {
+  let skipFrame = false;
+  if (config3.cacheSensitivity === 0 || !input.shape || input.shape.length !== 4 || input.shape[1] > 2048 || input.shape[2] > 2048)
+    return skipFrame;
+  if (!last.inputTensor) {
+    last.inputTensor = tf2.clone(input);
+  } else if (last.inputTensor.shape[1] !== input.shape[1] || last.inputTensor.shape[2] !== input.shape[2]) {
+    tf2.dispose(last.inputTensor);
+    last.inputTensor = tf2.clone(input);
+  } else {
+    const t2 = {};
+    t2.diff = tf2.sub(input, last.inputTensor);
+    t2.squared = tf2.mul(t2.diff, t2.diff);
+    t2.sum = tf2.sum(t2.squared);
+    const diffSum = await t2.sum.data();
+    const diffRelative = diffSum[0] / (input.shape[1] || 1) / (input.shape[2] || 1) / 255 / 3;
+    tf2.dispose([last.inputTensor, t2.diff, t2.squared, t2.sum]);
+    last.inputTensor = tf2.clone(input);
+    skipFrame = diffRelative <= (config3.cacheSensitivity || 0);
+  }
+  return skipFrame;
+}
+async function compare(config3, input1, input2) {
+  const t2 = {};
+  if (!input1 || !input2 || input1.shape.length !== 4 || input1.shape.length !== input2.shape.length) {
+    if (!config3.debug)
+      log("invalid input tensor or tensor shapes do not match:", input1.shape, input2.shape);
+    return 0;
+  }
+  if (input1.shape[0] !== 1 || input2.shape[0] !== 1 || input1.shape[3] !== 3 || input2.shape[3] !== 3) {
+    if (!config3.debug)
+      log("input tensors must be of shape [1, height, width, 3]:", input1.shape, input2.shape);
+    return 0;
+  }
+  t2.input1 = tf2.clone(input1);
+  t2.input2 = input1.shape[1] !== input2.shape[1] || input1.shape[2] !== input2.shape[2] ? tf2.image.resizeBilinear(input2, [input1.shape[1], input1.shape[2]]) : tf2.clone(input2);
+  t2.diff = tf2.sub(t2.input1, t2.input2);
+  t2.squared = tf2.mul(t2.diff, t2.diff);
+  t2.sum = tf2.sum(t2.squared);
+  const diffSum = await t2.sum.data();
+  const diffRelative = diffSum[0] / (input1.shape[1] || 1) / (input1.shape[2] || 1) / 255 / 3;
+  tf2.dispose([t2.input1, t2.input2, t2.diff, t2.squared, t2.sum]);
+  return diffRelative;
+}
+
+// src/util/env.ts
+var Env = class {
+  constructor() {
+    __publicField(this, "browser");
+    __publicField(this, "node");
+    __publicField(this, "worker");
+    __publicField(this, "platform", "");
+    __publicField(this, "agent", "");
+    __publicField(this, "backends", []);
+    __publicField(this, "initial");
+    __publicField(this, "filter");
+    __publicField(this, "tfjs");
+    __publicField(this, "offscreen");
+    __publicField(this, "perfadd", false);
+    __publicField(this, "tensorflow", {
+      version: void 0,
+      gpu: void 0
+    });
+    __publicField(this, "wasm", {
+      supported: void 0,
+      backend: void 0,
+      simd: void 0,
+      multithread: void 0
+    });
+    __publicField(this, "webgl", {
+      supported: void 0,
+      backend: void 0,
+      version: void 0,
+      renderer: void 0
+    });
+    __publicField(this, "webgpu", {
+      supported: void 0,
+      backend: void 0,
+      adapter: void 0
+    });
+    __publicField(this, "cpu", {
+      model: void 0,
+      flags: []
+    });
+    __publicField(this, "kernels", []);
+    __publicField(this, "Canvas");
+    __publicField(this, "Image");
+    __publicField(this, "ImageData");
+    this.browser = typeof navigator !== "undefined";
+    this.node = typeof process !== "undefined" && typeof process.versions !== "undefined" && typeof process.versions.node !== "undefined";
+    this.tfjs = { version: tf3.version["tfjs-core"] };
+    this.offscreen = typeof OffscreenCanvas !== "undefined";
+    this.initial = true;
+    this.worker = this.browser && this.offscreen ? typeof WorkerGlobalScope !== "undefined" : void 0;
+    if (typeof navigator !== "undefined") {
+      const raw = navigator.userAgent.match(/\(([^()]+)\)/g);
+      if (raw == null ? void 0 : raw[0]) {
+        const platformMatch = raw[0].match(/\(([^()]+)\)/g);
+        this.platform = (platformMatch == null ? void 0 : platformMatch[0]) ? platformMatch[0].replace(/\(|\)/g, "") : "";
+        this.agent = navigator.userAgent.replace(raw[0], "");
+        if (this.platform[1])
+          this.agent = this.agent.replace(raw[1], "");
+        this.agent = this.agent.replace(/  /g, " ");
+      }
+    } else if (typeof process !== "undefined") {
+      this.platform = `${process.platform} ${process.arch}`;
+      this.agent = `NodeJS ${process.version}`;
+    }
+  }
+  async updateBackend() {
+    this.backends = Object.keys(tf3.engine().registryFactory);
+    this.tensorflow = {
+      version: tf3.backend().binding ? tf3.backend().binding.TF_Version : void 0,
+      gpu: tf3.backend().binding ? tf3.backend().binding.isUsingGpuDevice() : void 0
+    };
+    this.wasm.supported = typeof WebAssembly !== "undefined";
+    this.wasm.backend = this.backends.includes("wasm");
+    if (this.wasm.supported && this.wasm.backend && tf3.getBackend() === "wasm") {
+      this.wasm.simd = tf3.env().get("WASM_HAS_SIMD_SUPPORT");
+      this.wasm.multithread = tf3.env().get("WASM_HAS_MULTITHREAD_SUPPORT");
+    }
+    const c = canvas(100, 100);
+    const ctx = c ? c.getContext("webgl2") : void 0;
+    this.webgl.supported = typeof ctx !== "undefined";
+    this.webgl.backend = this.backends.includes("webgl");
+    if (this.webgl.supported && this.webgl.backend && (tf3.getBackend() === "webgl" || tf3.getBackend() === "humangl")) {
+      const gl = tf3.backend().gpgpu !== "undefined" ? await tf3.backend().getGPGPUContext().gl : null;
+      if (gl) {
+        this.webgl.version = gl.getParameter(gl.VERSION);
+        this.webgl.renderer = gl.getParameter(gl.RENDERER);
+      }
+    }
+    this.webgpu.supported = this.browser && typeof navigator.gpu !== "undefined";
+    this.webgpu.backend = this.backends.includes("webgpu");
+    try {
+      if (this.webgpu.supported) {
+        const adapter = await navigator.gpu.requestAdapter();
+        this.webgpu.adapter = adapter ? adapter.name : void 0;
+      }
+    } catch (e) {
+      this.webgpu.supported = false;
+    }
+    try {
+      this.kernels = tf3.getKernelsForBackend(tf3.getBackend()).map((kernel) => kernel.kernelName.toLowerCase());
+    } catch (e) {
+    }
+  }
+  updateCPU() {
+    const cpu = { model: "", flags: [] };
+    if (this.node && this.platform.startsWith("linux")) {
+    }
+    if (!this.cpu)
+      Object.defineProperty(this, "cpu", { value: cpu });
+    else
+      this.cpu = cpu;
+  }
+};
+var env = new Env();
+
+// src/util/webcam.ts
+var WebCam = class {
+  constructor() {
+    __publicField(this, "config");
+    __publicField(this, "element");
+    __publicField(this, "stream");
+    __publicField(this, "start", async (webcamConfig) => {
+      if (webcamConfig == null ? void 0 : webcamConfig.debug)
+        this.config.debug = webcamConfig == null ? void 0 : webcamConfig.debug;
+      if (webcamConfig == null ? void 0 : webcamConfig.crop)
+        this.config.crop = webcamConfig == null ? void 0 : webcamConfig.crop;
+      if (webcamConfig == null ? void 0 : webcamConfig.mode)
+        this.config.mode = webcamConfig == null ? void 0 : webcamConfig.mode;
+      if (webcamConfig == null ? void 0 : webcamConfig.width)
+        this.config.width = webcamConfig == null ? void 0 : webcamConfig.width;
+      if (webcamConfig == null ? void 0 : webcamConfig.height)
+        this.config.height = webcamConfig == null ? void 0 : webcamConfig.height;
+      if (webcamConfig == null ? void 0 : webcamConfig.element) {
+        if (typeof webcamConfig.element === "string") {
+          const el = document.getElementById(webcamConfig.element);
+          if (el && el instanceof HTMLVideoElement) {
+            this.element = el;
+          } else {
+            if (this.config.debug)
+              log("webcam", "cannot get dom element", webcamConfig.element);
+            return;
+          }
+        } else if (webcamConfig.element instanceof HTMLVideoElement) {
+          this.element = webcamConfig.element;
+        } else {
+          if (this.config.debug)
+            log("webcam", "unknown dom element", webcamConfig.element);
+          return;
+        }
+      } else {
+        this.element = document.createElement("video");
+      }
+      const requestedConstraints = {
+        audio: false,
+        video: {
+          facingMode: this.config.mode === "front" ? "user" : "environment",
+          resizeMode: this.config.crop ? "crop-and-scale" : "none",
+          width: { ideal: this.config.width > 0 ? this.config.width : window.innerWidth },
+          height: { ideal: this.config.height > 0 ? this.config.height : window.innerHeight }
+        }
+      };
+      this.element.addEventListener("play", () => {
+        if (this.config.debug)
+          log("webcam", "play");
+      });
+      this.element.addEventListener("pause", () => {
+        if (this.config.debug)
+          log("webcam", "pause");
+      });
+      this.element.addEventListener("click", async () => {
+        if (!this.element || !this.stream)
+          return;
+        if (this.element.paused)
+          await this.element.play();
+        else
+          this.element.pause();
+      });
+      if (!(navigator == null ? void 0 : navigator.mediaDevices)) {
+        if (this.config.debug)
+          log("webcam", "no devices");
+        return;
+      }
+      try {
+        this.stream = await navigator.mediaDevices.getUserMedia(requestedConstraints);
+      } catch (err) {
+        log("webcam", err);
+        return;
+      }
+      if (!this.stream) {
+        if (this.config.debug)
+          log("webcam", "no stream");
+        return;
+      }
+      this.element.srcObject = this.stream;
+      const ready3 = new Promise((resolve) => {
+        if (!this.element)
+          resolve(false);
+        else
+          this.element.onloadeddata = () => resolve(true);
+      });
+      await ready3;
+      await this.element.play();
+      if (this.config.debug) {
+        log("webcam", {
+          width: this.width,
+          height: this.height,
+          label: this.label,
+          stream: this.stream,
+          track: this.track,
+          settings: this.settings,
+          constraints: this.constraints,
+          capabilities: this.capabilities
+        });
+      }
+    });
+    __publicField(this, "pause", () => {
+      if (this.element)
+        this.element.pause();
+    });
+    __publicField(this, "play", async () => {
+      if (this.element)
+        await this.element.play();
+    });
+    __publicField(this, "stop", () => {
+      if (this.config.debug)
+        log("webcam", "stop");
+      if (this.track)
+        this.track.stop();
+    });
+    this.config = {
+      element: void 0,
+      debug: true,
+      mode: "front",
+      crop: false,
+      width: 0,
+      height: 0
+    };
+  }
+  get track() {
+    if (!this.stream)
+      return void 0;
+    return this.stream.getVideoTracks()[0];
+  }
+  get capabilities() {
+    if (!this.track)
+      return void 0;
+    return this.track.getCapabilities ? this.track.getCapabilities() : void 0;
+  }
+  get constraints() {
+    if (!this.track)
+      return void 0;
+    return this.track.getConstraints ? this.track.getConstraints() : void 0;
+  }
+  get settings() {
+    if (!this.stream)
+      return void 0;
+    const track = this.stream.getVideoTracks()[0];
+    return track.getSettings ? track.getSettings() : void 0;
+  }
+  get label() {
+    if (!this.track)
+      return "";
+    return this.track.label;
+  }
+  get paused() {
+    var _a;
+    return ((_a = this.element) == null ? void 0 : _a.paused) || false;
+  }
+  get width() {
+    var _a;
+    return ((_a = this.element) == null ? void 0 : _a.videoWidth) || 0;
+  }
+  get height() {
+    var _a;
+    return ((_a = this.element) == null ? void 0 : _a.videoHeight) || 0;
+  }
+};
+
+// src/tfjs/load.ts
+var tf4 = __toESM(require_tfjs_esm());
+
+// models/models.json
+var models_exports = {};
+__export(models_exports, {
+  age: () => age,
+  "anti-spoofing": () => anti_spoofing,
+  antispoof: () => antispoof,
+  blazeface: () => blazeface,
+  "blazeface-back": () => blazeface_back,
+  "blazeface-front": () => blazeface_front,
+  "blazepose-detect": () => blazepose_detect,
+  "blazepose-detector2d": () => blazepose_detector2d,
+  "blazepose-detector3d": () => blazepose_detector3d,
+  "blazepose-full": () => blazepose_full,
+  "blazepose-heavy": () => blazepose_heavy,
+  "blazepose-lite": () => blazepose_lite,
+  default: () => models_default,
+  efficientpose: () => efficientpose,
+  "efficientpose-i-lite": () => efficientpose_i_lite,
+  "efficientpose-ii-lite": () => efficientpose_ii_lite,
+  "efficientpose-iv": () => efficientpose_iv,
+  emotion: () => emotion,
+  faceboxes: () => faceboxes,
+  facemesh: () => facemesh,
+  "facemesh-attention": () => facemesh_attention,
+  "facemesh-attention-alt": () => facemesh_attention_alt,
+  "facemesh-detection-full": () => facemesh_detection_full,
+  "facemesh-detection-short": () => facemesh_detection_short,
+  "facemesh-orig": () => facemesh_orig,
+  faceres: () => faceres,
+  "faceres-deep": () => faceres_deep,
+  gear: () => gear,
+  gender: () => gender,
+  "gender-ssrnet-imdb": () => gender_ssrnet_imdb,
+  handdetect: () => handdetect,
+  "handlandmark-full": () => handlandmark_full,
+  "handlandmark-lite": () => handlandmark_lite,
+  "handlandmark-sparse": () => handlandmark_sparse,
+  handskeleton: () => handskeleton,
+  handtrack: () => handtrack,
+  "insightface-efficientnet-b0": () => insightface_efficientnet_b0,
+  "insightface-ghostnet-strides1": () => insightface_ghostnet_strides1,
+  "insightface-ghostnet-strides2": () => insightface_ghostnet_strides2,
+  "insightface-mobilenet-emore": () => insightface_mobilenet_emore,
+  "insightface-mobilenet-swish": () => insightface_mobilenet_swish,
+  iris: () => iris,
+  liveness: () => liveness,
+  "mb3-centernet": () => mb3_centernet,
+  meet: () => meet,
+  mobileface: () => mobileface,
+  mobilefacenet: () => mobilefacenet,
+  models: () => models,
+  "movenet-lightning": () => movenet_lightning,
+  "movenet-multipose": () => movenet_multipose,
+  "movenet-thunder": () => movenet_thunder,
+  nanodet: () => nanodet,
+  "nanodet-e": () => nanodet_e,
+  "nanodet-g": () => nanodet_g,
+  "nanodet-m": () => nanodet_m,
+  "nanodet-t": () => nanodet_t,
+  posenet: () => posenet,
+  selfie: () => selfie
+});
+var antispoof = 853098;
+var blazeface = 538928;
+var emotion = 820516;
+var facemesh = 1477958;
+var faceres = 6978814;
+var handlandmark_full = 5431368;
+var handtrack = 2964837;
+var iris = 2599092;
+var liveness = 592976;
+var mb3_centernet = 4030290;
+var models = 0;
+var movenet_lightning = 4650216;
+var selfie = 212886;
+var age = 161240;
+var blazeface_back = 538928;
+var blazeface_front = 402048;
+var blazepose_detector2d = 7499400;
+var blazepose_detector3d = 5928856;
+var blazepose_full = 6338290;
+var blazepose_heavy = 27501554;
+var blazepose_lite = 2725490;
+var efficientpose = 5651240;
+var faceboxes = 2013002;
+var facemesh_attention_alt = 2387598;
+var facemesh_attention = 2382414;
+var facemesh_detection_full = 1026192;
+var facemesh_detection_short = 201268;
+var facemesh_orig = 2955780;
+var faceres_deep = 13957620;
+var gear = 1498916;
+var gender_ssrnet_imdb = 161236;
+var gender = 201808;
+var handdetect = 3515612;
+var handlandmark_lite = 2023432;
+var handlandmark_sparse = 5286322;
+var handskeleton = 5502280;
+var meet = 372228;
+var mobileface = 2183192;
+var mobilefacenet = 5171976;
+var movenet_multipose = 9448838;
+var movenet_thunder = 12477112;
+var nanodet = 7574558;
+var posenet = 5032780;
+var blazepose_detect = 5928804;
+var anti_spoofing = 853098;
+var efficientpose_i_lite = 2269064;
+var efficientpose_ii_lite = 5651240;
+var efficientpose_iv = 25643252;
+var insightface_efficientnet_b0 = 13013224;
+var insightface_ghostnet_strides1 = 8093408;
+var insightface_ghostnet_strides2 = 8049584;
+var insightface_mobilenet_emore = 6938536;
+var insightface_mobilenet_swish = 12168584;
+var nanodet_e = 12319156;
+var nanodet_g = 7574558;
+var nanodet_m = 1887474;
+var nanodet_t = 5294216;
+var models_default = {
+  antispoof,
+  blazeface,
+  emotion,
+  facemesh,
+  faceres,
+  "handlandmark-full": handlandmark_full,
+  handtrack,
+  iris,
+  liveness,
+  "mb3-centernet": mb3_centernet,
+  models,
+  "movenet-lightning": movenet_lightning,
+  selfie,
+  age,
+  "blazeface-back": blazeface_back,
+  "blazeface-front": blazeface_front,
+  "blazepose-detector2d": blazepose_detector2d,
+  "blazepose-detector3d": blazepose_detector3d,
+  "blazepose-full": blazepose_full,
+  "blazepose-heavy": blazepose_heavy,
+  "blazepose-lite": blazepose_lite,
+  efficientpose,
+  faceboxes,
+  "facemesh-attention-alt": facemesh_attention_alt,
+  "facemesh-attention": facemesh_attention,
+  "facemesh-detection-full": facemesh_detection_full,
+  "facemesh-detection-short": facemesh_detection_short,
+  "facemesh-orig": facemesh_orig,
+  "faceres-deep": faceres_deep,
+  gear,
+  "gender-ssrnet-imdb": gender_ssrnet_imdb,
+  gender,
+  handdetect,
+  "handlandmark-lite": handlandmark_lite,
+  "handlandmark-sparse": handlandmark_sparse,
+  handskeleton,
+  meet,
+  mobileface,
+  mobilefacenet,
+  "movenet-multipose": movenet_multipose,
+  "movenet-thunder": movenet_thunder,
+  nanodet,
+  posenet,
+  "blazepose-detect": blazepose_detect,
+  "anti-spoofing": anti_spoofing,
+  "efficientpose-i-lite": efficientpose_i_lite,
+  "efficientpose-ii-lite": efficientpose_ii_lite,
+  "efficientpose-iv": efficientpose_iv,
+  "insightface-efficientnet-b0": insightface_efficientnet_b0,
+  "insightface-ghostnet-strides1": insightface_ghostnet_strides1,
+  "insightface-ghostnet-strides2": insightface_ghostnet_strides2,
+  "insightface-mobilenet-emore": insightface_mobilenet_emore,
+  "insightface-mobilenet-swish": insightface_mobilenet_swish,
+  "nanodet-e": nanodet_e,
+  "nanodet-g": nanodet_g,
+  "nanodet-m": nanodet_m,
+  "nanodet-t": nanodet_t
+};
+
+// src/tfjs/load.ts
+var options = {
+  cacheModels: true,
+  cacheSupported: true,
+  verbose: true,
+  debug: false,
+  modelBasePath: ""
+};
+var modelStats = {};
+async function httpHandler(url, init3) {
+  if (options.debug)
+    log("load model fetch:", url, init3);
+  return fetch(url, init3);
+}
+function setModelLoadOptions(config3) {
+  options.cacheModels = config3.cacheModels;
+  options.verbose = config3.debug;
+  options.modelBasePath = config3.modelBasePath;
+}
+async function loadModel(modelPath) {
+  var _a, _b, _c, _d;
+  let modelUrl = join(options.modelBasePath, modelPath || "");
+  if (!modelUrl.toLowerCase().endsWith(".json"))
+    modelUrl += ".json";
+  const modelPathSegments = modelUrl.includes("/") ? modelUrl.split("/") : modelUrl.split("\\");
+  const shortModelName = modelPathSegments[modelPathSegments.length - 1].replace(".json", "");
+  const cachedModelName = "indexeddb://" + shortModelName;
+  modelStats[shortModelName] = {
+    name: shortModelName,
+    sizeFromManifest: 0,
+    sizeLoadedWeights: 0,
+    sizeDesired: models_exports[shortModelName],
+    inCache: false
+  };
+  options.cacheSupported = typeof indexedDB !== "undefined";
+  let cachedModels = {};
+  try {
+    cachedModels = options.cacheSupported && options.cacheModels ? await tf4.io.listModels() : {};
+  } catch (e) {
+    options.cacheSupported = false;
+  }
+  modelStats[shortModelName].inCache = options.cacheSupported && options.cacheModels && Object.keys(cachedModels).includes(cachedModelName);
+  const tfLoadOptions = typeof fetch === "undefined" ? {} : { fetchFunc: (url, init3) => httpHandler(url, init3) };
+  let model21 = new tf4.GraphModel(modelStats[shortModelName].inCache ? cachedModelName : modelUrl, tfLoadOptions);
+  let loaded = false;
+  try {
+    model21.findIOHandler();
+    if (options.debug)
+      log("model load handler:", model21["handler"]);
+  } catch (err) {
+    log("error finding model i/o handler:", modelUrl, err);
+  }
+  try {
+    const artifacts = await ((_a = model21.handler) == null ? void 0 : _a.load()) || null;
+    modelStats[shortModelName].sizeFromManifest = ((_b = artifacts == null ? void 0 : artifacts.weightData) == null ? void 0 : _b.byteLength) || 0;
+    if (artifacts)
+      model21.loadSync(artifacts);
+    else
+      model21 = await tf4.loadGraphModel(modelStats[shortModelName].inCache ? cachedModelName : modelUrl, tfLoadOptions);
+    modelStats[shortModelName].sizeLoadedWeights = ((_d = (_c = model21.artifacts) == null ? void 0 : _c.weightData) == null ? void 0 : _d.byteLength) || 0;
+    if (options.verbose)
+      log("load:", { model: shortModelName, url: model21["modelUrl"], bytes: modelStats[shortModelName].sizeLoadedWeights });
+    loaded = true;
+  } catch (err) {
+    log("error loading model:", modelUrl, err);
+  }
+  if (loaded && options.cacheModels && options.cacheSupported && !modelStats[shortModelName].inCache) {
+    try {
+      const saveResult = await model21.save(cachedModelName);
+      if (options.debug)
+        log("model saved:", cachedModelName, saveResult);
+    } catch (err) {
+      log("error saving model:", modelUrl, err);
+    }
+  }
+  return model21;
+}
+
+// src/human.ts
+var tf39 = __toESM(require_tfjs_esm());
+
+// package.json
+var version2 = "2.11.0";
+
+// src/tfjs/humangl.ts
+var tf34 = __toESM(require_tfjs_esm());
+
+// src/models.ts
+var models_exports2 = {};
+__export(models_exports2, {
+  Models: () => Models,
+  getModelStats: () => getModelStats,
+  load: () => load22,
+  reset: () => reset2,
+  validate: () => validate2,
+  validateModel: () => validateModel
+});
+
+// src/face/antispoof.ts
+var tf5 = __toESM(require_tfjs_esm());
+var model;
+var cached = [];
+var skipped = Number.MAX_SAFE_INTEGER;
+var lastCount = 0;
+var lastTime = 0;
+async function load(config3) {
+  var _a;
+  if (env.initial)
+    model = null;
+  if (!model)
+    model = await loadModel((_a = config3.face.antispoof) == null ? void 0 : _a.modelPath);
+  else if (config3.debug)
+    log("cached model:", model["modelUrl"]);
+  return model;
+}
+async function predict(image27, config3, idx, count2) {
+  var _a, _b;
+  if (!model || !(model == null ? void 0 : model["executor"]))
+    return 0;
+  const skipTime = (((_a = config3.face.antispoof) == null ? void 0 : _a.skipTime) || 0) > now() - lastTime;
+  const skipFrame = skipped < (((_b = config3.face.antispoof) == null ? void 0 : _b.skipFrames) || 0);
+  if (config3.skipAllowed && skipTime && skipFrame && lastCount === count2 && cached[idx]) {
+    skipped++;
+    return cached[idx];
+  }
+  skipped = 0;
+  return new Promise(async (resolve) => {
+    const resize = tf5.image.resizeBilinear(image27, [(model == null ? void 0 : model.inputs[0].shape) ? model.inputs[0].shape[2] : 0, (model == null ? void 0 : model.inputs[0].shape) ? model.inputs[0].shape[1] : 0], false);
+    const res = model == null ? void 0 : model.execute(resize);
+    const num = (await res.data())[0];
+    cached[idx] = Math.round(100 * num) / 100;
+    lastCount = count2;
+    lastTime = now();
+    tf5.dispose([resize, res]);
+    resolve(cached[idx]);
+  });
+}
+
+// src/face/blazeface.ts
+var tf8 = __toESM(require_tfjs_esm());
+
+// src/face/facemeshutil.ts
+var tf7 = __toESM(require_tfjs_esm());
+
+// src/face/facemeshcoords.ts
+var meshAnnotations = {
+  silhouette: [
+    10,
+    338,
+    297,
+    332,
+    284,
+    251,
+    389,
+    356,
+    454,
+    323,
+    361,
+    288,
+    397,
+    365,
+    379,
+    378,
+    400,
+    377,
+    152,
+    148,
+    176,
+    149,
+    150,
+    136,
+    172,
+    58,
+    132,
+    93,
+    234,
+    127,
+    162,
+    21,
+    54,
+    103,
+    67,
+    109
+  ],
+  lipsUpperOuter: [185, 40, 39, 37, 0, 267, 269, 270, 409],
+  lipsLowerOuter: [61, 146, 91, 181, 84, 17, 314, 405, 321, 375, 291],
+  lipsUpperInner: [191, 80, 81, 82, 13, 312, 311, 310, 415],
+  lipsLowerInner: [78, 95, 88, 178, 87, 14, 317, 402, 318, 324, 308],
+  lipsLowerSemiOuter: [76, 77, 90, 180, 85, 16, 315, 404, 320, 307, 306],
+  lipsUpperSemiOuter: [184, 74, 73, 72, 11, 302, 303, 304, 408],
+  lipsLowerSemiInner: [62, 96, 89, 179, 86, 15, 316, 403, 319, 325, 292],
+  lipsUpperSemiInner: [183, 42, 41, 38, 12, 268, 271, 272, 407],
+  rightEyeUpper0: [246, 161, 160, 159, 158, 157, 173],
+  rightEyeLower0: [33, 7, 163, 144, 145, 153, 154, 155, 133],
+  rightEyeUpper1: [247, 30, 29, 27, 28, 56, 190],
+  rightEyeLower1: [130, 25, 110, 24, 23, 22, 26, 112, 243],
+  rightEyeUpper2: [113, 225, 224, 223, 222, 221, 189],
+  rightEyeLower2: [226, 31, 228, 229, 230, 231, 232, 233, 244],
+  rightEyeLower3: [143, 111, 117, 118, 119, 120, 121, 128, 245],
+  rightEyebrowUpper: [156, 70, 63, 105, 66, 107, 55, 193],
+  rightEyebrowLower: [35, 124, 46, 53, 52, 65],
+  rightEyeIris: [473, 474, 475, 476, 477],
+  leftEyeUpper0: [466, 388, 387, 386, 385, 384, 398],
+  leftEyeLower0: [263, 249, 390, 373, 374, 380, 381, 382, 362],
+  leftEyeUpper1: [467, 260, 259, 257, 258, 286, 414],
+  leftEyeLower1: [359, 255, 339, 254, 253, 252, 256, 341, 463],
+  leftEyeUpper2: [342, 445, 444, 443, 442, 441, 413],
+  leftEyeLower2: [446, 261, 448, 449, 450, 451, 452, 453, 464],
+  leftEyeLower3: [372, 340, 346, 347, 348, 349, 350, 357, 465],
+  leftEyebrowUpper: [383, 300, 293, 334, 296, 336, 285, 417],
+  leftEyebrowLower: [265, 353, 276, 283, 282, 295],
+  leftEyeIris: [468, 469, 470, 471, 472],
+  midwayBetweenEyes: [168],
+  noseTip: [1],
+  noseBottom: [2],
+  noseRightCorner: [98],
+  noseLeftCorner: [327],
+  rightCheek: [205],
+  leftCheek: [425]
+};
+var meshLandmarks = {
+  count: 468,
+  mouth: 13,
+  symmetryLine: [13, meshAnnotations.midwayBetweenEyes[0]]
+};
+var blazeFaceLandmarks = {
+  leftEye: 0,
+  rightEye: 1,
+  nose: 2,
+  mouth: 3,
+  leftEar: 4,
+  rightEar: 5,
+  symmetryLine: [3, 2]
+};
+var irisIndices = [
+  { key: "EyeUpper0", indices: [9, 10, 11, 12, 13, 14, 15] },
+  { key: "EyeUpper1", indices: [25, 26, 27, 28, 29, 30, 31] },
+  { key: "EyeUpper2", indices: [41, 42, 43, 44, 45, 46, 47] },
+  { key: "EyeLower0", indices: [0, 1, 2, 3, 4, 5, 6, 7, 8] },
+  { key: "EyeLower1", indices: [16, 17, 18, 19, 20, 21, 22, 23, 24] },
+  { key: "EyeLower2", indices: [32, 33, 34, 35, 36, 37, 38, 39, 40] },
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+var VTX68 = [
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+  132,
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+  91,
+  78,
+  81,
+  13,
+  311,
+  308,
+  402,
+  14,
+  178
+];
+var VTX33 = [33, 133, 362, 263, 1, 62, 308, 159, 145, 386, 374, 6, 102, 331, 2, 13, 14, 70, 105, 107, 336, 334, 300, 54, 10, 284, 50, 280, 234, 454, 58, 288, 152];
+var VTX7 = [33, 133, 362, 263, 1, 78, 308];
+var UV68 = VTX68.map((x) => UV468[x]);
+var UV33 = VTX33.map((x) => UV468[x]);
+var UV7 = VTX7.map((x) => UV468[x]);
+function connectionsToIndices(connections) {
+  const indices = connections.map((connection) => connection[0]);
+  indices.push(connections[connections.length - 1][1]);
+  return indices;
+}
+var pairsLips = [
+  [61, 146],
+  [146, 91],
+  [91, 181],
+  [181, 84],
+  [84, 17],
+  [17, 314],
+  [314, 405],
+  [405, 321],
+  [321, 375],
+  [375, 291],
+  [61, 185],
+  [185, 40],
+  [40, 39],
+  [39, 37],
+  [37, 0],
+  [0, 267],
+  [267, 269],
+  [269, 270],
+  [270, 409],
+  [409, 291],
+  [78, 95],
+  [95, 88],
+  [88, 178],
+  [178, 87],
+  [87, 14],
+  [14, 317],
+  [317, 402],
+  [402, 318],
+  [318, 324],
+  [324, 308],
+  [78, 191],
+  [191, 80],
+  [80, 81],
+  [81, 82],
+  [82, 13],
+  [13, 312],
+  [312, 311],
+  [311, 310],
+  [310, 415],
+  [415, 308]
+];
+var pairsLeftEye = [[263, 249], [249, 390], [390, 373], [373, 374], [374, 380], [380, 381], [381, 382], [382, 362], [263, 466], [466, 388], [388, 387], [387, 386], [386, 385], [385, 384], [384, 398], [398, 362]];
+var pairsLeftEyebrow = [[276, 283], [283, 282], [282, 295], [295, 285], [300, 293], [293, 334], [334, 296], [296, 336]];
+var pairsLeftIris = [[474, 475], [475, 476], [476, 477], [477, 474]];
+var pairsRightEye = [[33, 7], [7, 163], [163, 144], [144, 145], [145, 153], [153, 154], [154, 155], [155, 133], [33, 246], [246, 161], [161, 160], [160, 159], [159, 158], [158, 157], [157, 173], [173, 133]];
+var pairsRightEyebrow = [[46, 53], [53, 52], [52, 65], [65, 55], [70, 63], [63, 105], [105, 66], [66, 107]];
+var pairsRightIris = [[469, 470], [470, 471], [471, 472], [472, 469]];
+var pairsFaceContour = [
+  [10, 338],
+  [338, 297],
+  [297, 332],
+  [332, 284],
+  [284, 251],
+  [251, 389],
+  [389, 356],
+  [356, 454],
+  [454, 323],
+  [323, 361],
+  [361, 288],
+  [288, 397],
+  [397, 365],
+  [365, 379],
+  [379, 378],
+  [378, 400],
+  [400, 377],
+  [377, 152],
+  [152, 148],
+  [148, 176],
+  [176, 149],
+  [149, 150],
+  [150, 136],
+  [136, 172],
+  [172, 58],
+  [58, 132],
+  [132, 93],
+  [93, 234],
+  [234, 127],
+  [127, 162],
+  [162, 21],
+  [21, 54],
+  [54, 103],
+  [103, 67],
+  [67, 109],
+  [109, 10]
+];
+var contourKeypoints = {
+  lips: connectionsToIndices(pairsLips),
+  leftEye: connectionsToIndices(pairsLeftEye),
+  leftEyebrow: connectionsToIndices(pairsLeftEyebrow),
+  leftIris: connectionsToIndices(pairsLeftIris),
+  rightEye: connectionsToIndices(pairsRightEye),
+  rightEyebrow: connectionsToIndices(pairsRightEyebrow),
+  rightIris: connectionsToIndices(pairsRightIris),
+  faceOval: connectionsToIndices(pairsFaceContour)
+};
+
+// src/tfjs/constants.ts
+var tf6 = __toESM(require_tfjs_esm());
+var constants = {
+  tf255: 255,
+  tf1: 1,
+  tf2: 2,
+  tf05: 0.5,
+  tf127: 127.5,
+  rgb: [0.2989, 0.587, 0.114]
+};
+function init() {
+  constants.tf255 = tf6.scalar(255, "float32");
+  constants.tf1 = tf6.scalar(1, "float32");
+  constants.tf2 = tf6.scalar(2, "float32");
+  constants.tf05 = tf6.scalar(0.5, "float32");
+  constants.tf127 = tf6.scalar(127.5, "float32");
+  constants.rgb = tf6.tensor1d([0.2989, 0.587, 0.114], "float32");
+}
+
+// src/face/facemeshutil.ts
+var getBoxSize = (box) => [Math.abs(box.endPoint[0] - box.startPoint[0]), Math.abs(box.endPoint[1] - box.startPoint[1])];
+var getBoxCenter = (box) => [box.startPoint[0] + (box.endPoint[0] - box.startPoint[0]) / 2, box.startPoint[1] + (box.endPoint[1] - box.startPoint[1]) / 2, 1];
+var clampBox = (box, input) => box ? [
+  Math.trunc(Math.max(0, box.startPoint[0])),
+  Math.trunc(Math.max(0, box.startPoint[1])),
+  Math.trunc(Math.min(input.shape[2] || 0, box.endPoint[0]) - Math.max(0, box.startPoint[0])),
+  Math.trunc(Math.min(input.shape[1] || 0, box.endPoint[1]) - Math.max(0, box.startPoint[1]))
+] : [0, 0, 0, 0];
+var getRawBox = (box, input) => box ? [
+  box.startPoint[0] / (input.shape[2] || 0),
+  box.startPoint[1] / (input.shape[1] || 0),
+  (box.endPoint[0] - box.startPoint[0]) / (input.shape[2] || 0),
+  (box.endPoint[1] - box.startPoint[1]) / (input.shape[1] || 0)
+] : [0, 0, 0, 0];
+var scaleBoxCoordinates = (box, factor) => {
+  const startPoint = [box.startPoint[0] * factor[0], box.startPoint[1] * factor[1]];
+  const endPoint = [box.endPoint[0] * factor[0], box.endPoint[1] * factor[1]];
+  return { startPoint, endPoint, landmarks: box.landmarks, confidence: box.confidence };
+};
+var cutAndResize = (box, image27, cropSize) => {
+  const h = image27.shape[1];
+  const w = image27.shape[2];
+  const cutBox = [box.startPoint[1] / h, box.startPoint[0] / w, box.endPoint[1] / h, box.endPoint[0] / w];
+  const crop = tf7.image.cropAndResize(image27, [cutBox], [0], cropSize);
+  const norm = tf7.div(crop, constants.tf255);
+  tf7.dispose(crop);
+  return norm;
+};
+var enlargeBox = (box, factor) => {
+  const center = getBoxCenter(box);
+  const size2 = getBoxSize(box);
+  const halfSize = [factor * size2[0] / 2, factor * size2[1] / 2];
+  return { startPoint: [center[0] - halfSize[0], center[1] - halfSize[1]], endPoint: [center[0] + halfSize[0], center[1] + halfSize[1]], landmarks: box.landmarks, confidence: box.confidence };
+};
+var squarifyBox = (box) => {
+  const centers = getBoxCenter(box);
+  const size2 = getBoxSize(box);
+  const halfSize = Math.max(...size2) / 2;
+  return { startPoint: [Math.round(centers[0] - halfSize), Math.round(centers[1] - halfSize)], endPoint: [Math.round(centers[0] + halfSize), Math.round(centers[1] + halfSize)], landmarks: box.landmarks, confidence: box.confidence };
+};
+var calculateLandmarksBoundingBox = (landmarks) => {
+  const x = landmarks.map((d) => d[0]);
+  const y = landmarks.map((d) => d[1]);
+  return { startPoint: [Math.min(...x), Math.min(...y)], endPoint: [Math.max(...x), Math.max(...y)], landmarks };
+};
+var fixedRotationMatrix = [[1, 0, 0], [0, 1, 0], [0, 0, 1]];
+var normalizeRadians = (angle) => angle - 2 * Math.PI * Math.floor((angle + Math.PI) / (2 * Math.PI));
+var computeRotation = (point1, point2) => normalizeRadians(Math.PI / 2 - Math.atan2(-(point2[1] - point1[1]), point2[0] - point1[0]));
+var buildTranslationMatrix = (x, y) => [[1, 0, x], [0, 1, y], [0, 0, 1]];
+var dot = (v1, v2) => {
+  let product = 0;
+  for (let i = 0; i < v1.length; i++)
+    product += v1[i] * v2[i];
+  return product;
+};
+var getColumnFrom2DArr = (arr, columnIndex) => {
+  const column = [];
+  for (let i = 0; i < arr.length; i++)
+    column.push(arr[i][columnIndex]);
+  return column;
+};
+var multiplyTransformMatrices = (mat1, mat2) => {
+  const product = [];
+  const size2 = mat1.length;
+  for (let row = 0; row < size2; row++) {
+    product.push([]);
+    for (let col = 0; col < size2; col++)
+      product[row].push(dot(mat1[row], getColumnFrom2DArr(mat2, col)));
+  }
+  return product;
+};
+var buildRotationMatrix = (rotation, center) => {
+  const cosA = Math.cos(rotation);
+  const sinA = Math.sin(rotation);
+  const rotationMatrix = [[cosA, -sinA, 0], [sinA, cosA, 0], [0, 0, 1]];
+  const translationMatrix = buildTranslationMatrix(center[0], center[1]);
+  const translationTimesRotation = multiplyTransformMatrices(translationMatrix, rotationMatrix);
+  const negativeTranslationMatrix = buildTranslationMatrix(-center[0], -center[1]);
+  return multiplyTransformMatrices(translationTimesRotation, negativeTranslationMatrix);
+};
+var invertTransformMatrix = (matrix) => {
+  const rotationComponent = [[matrix[0][0], matrix[1][0]], [matrix[0][1], matrix[1][1]]];
+  const translationComponent = [matrix[0][2], matrix[1][2]];
+  const invertedTranslation = [-dot(rotationComponent[0], translationComponent), -dot(rotationComponent[1], translationComponent)];
+  return [rotationComponent[0].concat(invertedTranslation[0]), rotationComponent[1].concat(invertedTranslation[1]), [0, 0, 1]];
+};
+var rotatePoint = (homogeneousCoordinate, rotationMatrix) => [dot(homogeneousCoordinate, rotationMatrix[0]), dot(homogeneousCoordinate, rotationMatrix[1])];
+function generateAnchors(inputSize10) {
+  const spec = inputSize10 === 192 ? { strides: [4], anchors: [1] } : { strides: [inputSize10 / 16, inputSize10 / 8], anchors: [2, 6] };
+  const anchors3 = [];
+  for (let i = 0; i < spec.strides.length; i++) {
+    const stride = spec.strides[i];
+    const gridRows = Math.floor((inputSize10 + stride - 1) / stride);
+    const gridCols = Math.floor((inputSize10 + stride - 1) / stride);
+    const anchorsNum = spec.anchors[i];
+    for (let gridY = 0; gridY < gridRows; gridY++) {
+      const anchorY = stride * (gridY + 0.5);
+      for (let gridX = 0; gridX < gridCols; gridX++) {
+        const anchorX = stride * (gridX + 0.5);
+        for (let n = 0; n < anchorsNum; n++)
+          anchors3.push([anchorX, anchorY]);
+      }
+    }
+  }
+  return anchors3;
+}
+function transformRawCoords(coordsRaw, box, angle, rotationMatrix, inputSize10) {
+  const boxSize = getBoxSize(box);
+  const coordsScaled = coordsRaw.map((coord) => [
+    boxSize[0] / inputSize10 * (coord[0] - inputSize10 / 2),
+    boxSize[1] / inputSize10 * (coord[1] - inputSize10 / 2),
+    coord[2] || 0
+  ]);
+  const largeAngle = angle && angle !== 0 && Math.abs(angle) > 0.2;
+  const coordsRotationMatrix = largeAngle ? buildRotationMatrix(angle, [0, 0]) : fixedRotationMatrix;
+  const coordsRotated = largeAngle ? coordsScaled.map((coord) => [...rotatePoint(coord, coordsRotationMatrix), coord[2]]) : coordsScaled;
+  const inverseRotationMatrix = largeAngle ? invertTransformMatrix(rotationMatrix) : fixedRotationMatrix;
+  const boxCenter = getBoxCenter(box);
+  const offsets = [dot(boxCenter, inverseRotationMatrix[0]), dot(boxCenter, inverseRotationMatrix[1])];
+  return coordsRotated.map((coord) => [
+    Math.trunc(coord[0] + offsets[0]),
+    Math.trunc(coord[1] + offsets[1]),
+    Math.trunc(coord[2] || 0)
+  ]);
+}
+function correctFaceRotation(rotate, box, input, inputSize10) {
+  const symmetryLine = box.landmarks.length >= meshLandmarks.count ? meshLandmarks.symmetryLine : blazeFaceLandmarks.symmetryLine;
+  let angle = 0;
+  let rotationMatrix = fixedRotationMatrix;
+  let face4;
+  if (rotate && env.kernels.includes("rotatewithoffset")) {
+    angle = computeRotation(box.landmarks[symmetryLine[0]], box.landmarks[symmetryLine[1]]);
+    const largeAngle = angle && angle !== 0 && Math.abs(angle) > 0.2;
+    if (largeAngle) {
+      const center = getBoxCenter(box);
+      const centerRaw = [center[0] / input.shape[2], center[1] / input.shape[1]];
+      const rotated = tf7.image.rotateWithOffset(input, angle, 0, centerRaw);
+      rotationMatrix = buildRotationMatrix(-angle, center);
+      face4 = cutAndResize(box, rotated, [inputSize10, inputSize10]);
+      tf7.dispose(rotated);
+    } else {
+      face4 = cutAndResize(box, input, [inputSize10, inputSize10]);
+    }
+  } else {
+    face4 = cutAndResize(box, input, [inputSize10, inputSize10]);
+  }
+  return [angle, rotationMatrix, face4];
+}
+var findFaceCenter = (mesh) => {
+  const x = mesh.map((m) => m[0]);
+  const y = mesh.map((m) => m[1]);
+  return [Math.min(...x) + (Math.max(...x) - Math.min(...x)) / 2, Math.min(...y) + (Math.max(...y) - Math.min(...y)) / 2];
+};
+var calculateFaceBox = (mesh, previousBox) => {
+  const center = findFaceCenter(mesh);
+  const boxSize = getBoxSize(previousBox);
+  const calculatedBox = {
+    startPoint: [center[0] - boxSize[0] / 2, center[1] - boxSize[1] / 2],
+    endPoint: [center[0] + boxSize[0] / 2, center[1] + boxSize[1] / 2]
+  };
+  return calculatedBox;
+};
+
+// src/face/blazeface.ts
+var keypointsCount = 6;
+var faceBoxScaleFactor = 1.4;
+var model2;
+var anchors = null;
+var inputSize = 0;
+var inputSizeT = null;
+var size = () => inputSize;
+async function load2(config3) {
+  var _a;
+  if (env.initial)
+    model2 = null;
+  if (!model2)
+    model2 = await loadModel((_a = config3.face.detector) == null ? void 0 : _a.modelPath);
+  else if (config3.debug)
+    log("cached model:", model2["modelUrl"]);
+  inputSize = model2["executor"] && model2.inputs[0].shape ? model2.inputs[0].shape[2] : 256;
+  inputSizeT = tf8.scalar(inputSize, "int32");
+  anchors = tf8.tensor2d(generateAnchors(inputSize));
+  return model2;
+}
+function decodeBoxes(boxOutputs) {
+  const t2 = {};
+  t2.boxStarts = tf8.slice(boxOutputs, [0, 1], [-1, 2]);
+  t2.centers = tf8.add(t2.boxStarts, anchors);
+  t2.boxSizes = tf8.slice(boxOutputs, [0, 3], [-1, 2]);
+  t2.boxSizesNormalized = tf8.div(t2.boxSizes, inputSizeT);
+  t2.centersNormalized = tf8.div(t2.centers, inputSizeT);
+  t2.halfBoxSize = tf8.div(t2.boxSizesNormalized, constants.tf2);
+  t2.starts = tf8.sub(t2.centersNormalized, t2.halfBoxSize);
+  t2.ends = tf8.add(t2.centersNormalized, t2.halfBoxSize);
+  t2.startNormalized = tf8.mul(t2.starts, inputSizeT);
+  t2.endNormalized = tf8.mul(t2.ends, inputSizeT);
+  const boxes = tf8.concat2d([t2.startNormalized, t2.endNormalized], 1);
+  Object.keys(t2).forEach((tensor6) => tf8.dispose(t2[tensor6]));
+  return boxes;
+}
+async function getBoxes(inputImage, config3) {
+  var _a, _b, _c, _d;
+  if (!inputImage || inputImage["isDisposedInternal"] || inputImage.shape.length !== 4 || inputImage.shape[1] < 1 || inputImage.shape[2] < 1)
+    return [];
+  const t2 = {};
+  t2.resized = tf8.image.resizeBilinear(inputImage, [inputSize, inputSize]);
+  t2.div = tf8.div(t2.resized, constants.tf127);
+  t2.normalized = tf8.sub(t2.div, constants.tf05);
+  const res = model2 == null ? void 0 : model2.execute(t2.normalized);
+  if (Array.isArray(res) && res.length > 2) {
+    const sorted = res.sort((a, b) => a.size - b.size);
+    t2.concat384 = tf8.concat([sorted[0], sorted[2]], 2);
+    t2.concat512 = tf8.concat([sorted[1], sorted[3]], 2);
+    t2.concat = tf8.concat([t2.concat512, t2.concat384], 1);
+    t2.batch = tf8.squeeze(t2.concat, 0);
+  } else if (Array.isArray(res)) {
+    t2.batch = tf8.squeeze(res[0]);
+  } else {
+    t2.batch = tf8.squeeze(res);
+  }
+  tf8.dispose(res);
+  t2.boxes = decodeBoxes(t2.batch);
+  t2.logits = tf8.slice(t2.batch, [0, 0], [-1, 1]);
+  t2.sigmoid = tf8.sigmoid(t2.logits);
+  t2.scores = tf8.squeeze(t2.sigmoid);
+  t2.nms = await tf8.image.nonMaxSuppressionAsync(t2.boxes, t2.scores, ((_a = config3.face.detector) == null ? void 0 : _a.maxDetected) || 0, ((_b = config3.face.detector) == null ? void 0 : _b.iouThreshold) || 0, ((_c = config3.face.detector) == null ? void 0 : _c.minConfidence) || 0);
+  const nms = await t2.nms.array();
+  const boxes = [];
+  const scores = await t2.scores.data();
+  for (let i = 0; i < nms.length; i++) {
+    const confidence = scores[nms[i]];
+    if (confidence > (((_d = config3.face.detector) == null ? void 0 : _d.minConfidence) || 0)) {
+      const b = {};
+      b.bbox = tf8.slice(t2.boxes, [nms[i], 0], [1, -1]);
+      b.slice = tf8.slice(t2.batch, [nms[i], keypointsCount - 1], [1, -1]);
+      b.squeeze = tf8.squeeze(b.slice);
+      b.landmarks = tf8.reshape(b.squeeze, [keypointsCount, -1]);
+      const points = await b.bbox.data();
+      const rawBox = {
+        startPoint: [points[0], points[1]],
+        endPoint: [points[2], points[3]],
+        landmarks: await b.landmarks.array(),
+        confidence
+      };
+      const scaledBox = scaleBoxCoordinates(rawBox, [(inputImage.shape[2] || 0) / inputSize, (inputImage.shape[1] || 0) / inputSize]);
+      const enlargedBox = enlargeBox(scaledBox, config3.face["scale"] || faceBoxScaleFactor);
+      const squaredBox = squarifyBox(enlargedBox);
+      boxes.push(squaredBox);
+      Object.keys(b).forEach((tensor6) => tf8.dispose(b[tensor6]));
+    }
+  }
+  Object.keys(t2).forEach((tensor6) => tf8.dispose(t2[tensor6]));
+  return boxes;
+}
+
+// src/body/blazepose.ts
+var tf10 = __toESM(require_tfjs_esm());
+
+// src/body/blazeposecoords.ts
+var blazeposecoords_exports = {};
+__export(blazeposecoords_exports, {
+  connected: () => connected,
+  kpt: () => kpt
+});
+var kpt = [
+  "nose",
+  "leftEyeInside",
+  "leftEye",
+  "leftEyeOutside",
+  "rightEyeInside",
+  "rightEye",
+  "rightEyeOutside",
+  "leftEar",
+  "rightEar",
+  "leftMouth",
+  "rightMouth",
+  "leftShoulder",
+  "rightShoulder",
+  "leftElbow",
+  "rightElbow",
+  "leftWrist",
+  "rightWrist",
+  "leftPinky",
+  "rightPinky",
+  "leftIndex",
+  "rightIndex",
+  "leftThumb",
+  "rightThumb",
+  "leftHip",
+  "rightHip",
+  "leftKnee",
+  "rightKnee",
+  "leftAnkle",
+  "rightAnkle",
+  "leftHeel",
+  "rightHeel",
+  "leftFoot",
+  "rightFoot",
+  "bodyCenter",
+  "bodyTop",
+  "leftPalm",
+  "leftHand",
+  "rightPalm",
+  "rightHand"
+];
+var connected = {
+  shoulders: ["leftShoulder", "rightShoulder"],
+  hips: ["rightHip", "leftHip"],
+  mouth: ["leftMouth", "rightMouth"],
+  leftLegUpper: ["leftHip", "leftKnee"],
+  leftLegLower: ["leftKnee", "leftAnkle"],
+  leftFoot: ["leftAnkle", "leftHeel", "leftFoot"],
+  leftTorso: ["leftShoulder", "leftHip"],
+  leftArmUpper: ["leftShoulder", "leftElbow"],
+  leftArmLower: ["leftElbow", "leftWrist"],
+  leftHand: ["leftWrist", "leftPalm"],
+  leftHandPinky: ["leftPalm", "leftPinky"],
+  leftHandIndex: ["leftPalm", "leftIndex"],
+  leftHandThumb: ["leftPalm", "leftThumb"],
+  leftEyeOutline: ["leftEyeInside", "leftEyeOutside"],
+  rightLegUpper: ["rightHip", "rightKnee"],
+  rightLegLower: ["rightKnee", "rightAnkle"],
+  rightFoot: ["rightAnkle", "rightHeel", "rightFoot"],
+  rightTorso: ["rightShoulder", "rightHip"],
+  rightArmUpper: ["rightShoulder", "rightElbow"],
+  rightArmLower: ["rightElbow", "rightWrist"],
+  rightHand: ["rightWrist", "rightPalm"],
+  rightHandPinky: ["rightPalm", "rightPinky"],
+  rightHandIndex: ["rightPalm", "rightIndex"],
+  rightHandThumb: ["rightPalm", "rightThumb"],
+  rightEyeOutline: ["rightEyeInside", "rightEyeOutside"]
+};
+
+// src/body/blazeposedetector.ts
+var tf9 = __toESM(require_tfjs_esm());
+var inputSize2 = 224;
+var anchorTensor;
+var numLayers = 5;
+var strides = [8, 16, 32, 32, 32];
+function createAnchors() {
+  const anchors3 = [];
+  let layerId = 0;
+  while (layerId < numLayers) {
+    let anchorCount = 0;
+    let lastSameStrideLayer = layerId;
+    while (lastSameStrideLayer < strides.length && strides[lastSameStrideLayer] === strides[layerId]) {
+      anchorCount += 2;
+      lastSameStrideLayer++;
+    }
+    const stride = strides[layerId];
+    const featureMapHeight = Math.ceil(inputSize2 / stride);
+    const featureMapWidth = Math.ceil(inputSize2 / stride);
+    for (let y = 0; y < featureMapHeight; ++y) {
+      for (let x = 0; x < featureMapWidth; ++x) {
+        for (let anchorId = 0; anchorId < anchorCount; ++anchorId) {
+          anchors3.push({ x: (x + 0.5) / featureMapWidth, y: (y + 0.5) / featureMapHeight });
+        }
+      }
+    }
+    layerId = lastSameStrideLayer;
+  }
+  anchorTensor = { x: tf9.tensor1d(anchors3.map((a) => a.x)), y: tf9.tensor1d(anchors3.map((a) => a.y)) };
+}
+
+// src/util/box.ts
+function calc(keypoints, outputSize2 = [1, 1]) {
+  const coords = [keypoints.map((pt) => pt[0]), keypoints.map((pt) => pt[1])];
+  const min2 = [Math.min(...coords[0]), Math.min(...coords[1])];
+  const max4 = [Math.max(...coords[0]), Math.max(...coords[1])];
+  const box = [min2[0], min2[1], max4[0] - min2[0], max4[1] - min2[1]];
+  const boxRaw = [box[0] / outputSize2[0], box[1] / outputSize2[1], box[2] / outputSize2[0], box[3] / outputSize2[1]];
+  return { box, boxRaw };
+}
+function square(keypoints, outputSize2 = [1, 1]) {
+  const coords = [keypoints.map((pt) => pt[0]), keypoints.map((pt) => pt[1])];
+  const min2 = [Math.min(...coords[0]), Math.min(...coords[1])];
+  const max4 = [Math.max(...coords[0]), Math.max(...coords[1])];
+  const center = [(min2[0] + max4[0]) / 2, (min2[1] + max4[1]) / 2];
+  const dist = Math.max(center[0] - min2[0], center[1] - min2[1], -center[0] + max4[0], -center[1] + max4[1]);
+  const box = [Math.trunc(center[0] - dist), Math.trunc(center[1] - dist), Math.trunc(2 * dist), Math.trunc(2 * dist)];
+  const boxRaw = [box[0] / outputSize2[0], box[1] / outputSize2[1], box[2] / outputSize2[0], box[3] / outputSize2[1]];
+  return { box, boxRaw };
+}
+function scale(box, scaleFact) {
+  const dist = [box[2] * scaleFact, box[3] * scaleFact];
+  const newBox = [
+    box[0] - (dist[0] - box[2]) / 2,
+    box[1] - (dist[1] - box[3]) / 2,
+    dist[0],
+    dist[1]
+  ];
+  return newBox;
+}
+
+// src/body/blazepose.ts
+var env3 = { initial: true };
+var models2 = { detector: null, landmarks: null };
+var inputSize3 = { detector: [224, 224], landmarks: [256, 256] };
+var skipped2 = Number.MAX_SAFE_INTEGER;
+var outputNodes = {
+  landmarks: ["ld_3d", "activation_segmentation", "activation_heatmap", "world_3d", "output_poseflag"],
+  detector: []
+};
+var cache = null;
+var cropBox;
+var padding = [[0, 0], [0, 0], [0, 0], [0, 0]];
+var lastTime2 = 0;
+var sigmoid3 = (x) => 1 - 1 / (1 + Math.exp(x));
+async function loadDetect(config3) {
+  var _a;
+  if (env3.initial)
+    models2.detector = null;
+  if (!models2.detector && config3.body["detector"] && config3.body["detector"].modelPath || "") {
+    models2.detector = await loadModel(config3.body["detector"].modelPath);
+    const inputs = ((_a = models2.detector) == null ? void 0 : _a["executor"]) ? Object.values(models2.detector.modelSignature["inputs"]) : void 0;
+    inputSize3.detector[0] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[1].size) : 0;
+    inputSize3.detector[1] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 0;
+  } else if (config3.debug && models2.detector)
+    log("cached model:", models2.detector["modelUrl"]);
+  createAnchors();
+  return models2.detector;
+}
+async function loadPose(config3) {
+  var _a;
+  if (env3.initial)
+    models2.landmarks = null;
+  if (!models2.landmarks) {
+    models2.landmarks = await loadModel(config3.body.modelPath);
+    const inputs = ((_a = models2.landmarks) == null ? void 0 : _a["executor"]) ? Object.values(models2.landmarks.modelSignature["inputs"]) : void 0;
+    inputSize3.landmarks[0] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[1].size) : 0;
+    inputSize3.landmarks[1] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 0;
+  } else if (config3.debug)
+    log("cached model:", models2.landmarks["modelUrl"]);
+  return models2.landmarks;
+}
+function prepareImage(input, size2) {
+  var _a, _b;
+  const t2 = {};
+  if (!((_a = input == null ? void 0 : input.shape) == null ? void 0 : _a[1]) || !((_b = input == null ? void 0 : input.shape) == null ? void 0 : _b[2]))
+    return input;
+  let final;
+  if (cropBox) {
+    t2.cropped = tf10.image.cropAndResize(input, [cropBox], [0], [input.shape[1], input.shape[2]]);
+  }
+  if (input.shape[1] !== input.shape[2]) {
+    const height = [
+      input.shape[2] > input.shape[1] ? Math.trunc((input.shape[2] - input.shape[1]) / 2) : 0,
+      input.shape[2] > input.shape[1] ? Math.trunc((input.shape[2] - input.shape[1]) / 2) : 0
+    ];
+    const width = [
+      input.shape[1] > input.shape[2] ? Math.trunc((input.shape[1] - input.shape[2]) / 2) : 0,
+      input.shape[1] > input.shape[2] ? Math.trunc((input.shape[1] - input.shape[2]) / 2) : 0
+    ];
+    padding = [
+      [0, 0],
+      height,
+      width,
+      [0, 0]
+    ];
+    t2.pad = tf10.pad(t2.cropped || input, padding);
+    t2.resize = tf10.image.resizeBilinear(t2.pad, [size2, size2]);
+    final = tf10.div(t2.resize, constants.tf255);
+  } else if (input.shape[1] !== size2) {
+    t2.resize = tf10.image.resizeBilinear(t2.cropped || input, [size2, size2]);
+    final = tf10.div(t2.resize, constants.tf255);
+  } else {
+    final = tf10.div(t2.cropped || input, constants.tf255);
+  }
+  Object.keys(t2).forEach((tensor6) => tf10.dispose(t2[tensor6]));
+  return final;
+}
+function rescaleKeypoints(keypoints, outputSize2) {
+  for (const kpt4 of keypoints) {
+    kpt4.position = [
+      Math.trunc(kpt4.position[0] * (outputSize2[0] + padding[2][0] + padding[2][1]) / outputSize2[0] - padding[2][0]),
+      Math.trunc(kpt4.position[1] * (outputSize2[1] + padding[1][0] + padding[1][1]) / outputSize2[1] - padding[1][0]),
+      kpt4.position[2]
+    ];
+    kpt4.positionRaw = [kpt4.position[0] / outputSize2[0], kpt4.position[1] / outputSize2[1], 2 * kpt4.position[2] / (outputSize2[0] + outputSize2[1])];
+  }
+  if (cropBox) {
+    for (const kpt4 of keypoints) {
+      kpt4.positionRaw = [
+        kpt4.positionRaw[0] + cropBox[1],
+        kpt4.positionRaw[1] + cropBox[0],
+        kpt4.positionRaw[2]
+      ];
+      kpt4.position = [
+        Math.trunc(kpt4.positionRaw[0] * outputSize2[0]),
+        Math.trunc(kpt4.positionRaw[1] * outputSize2[1]),
+        kpt4.positionRaw[2]
+      ];
+    }
+  }
+  return keypoints;
+}
+function fixKeypoints(keypoints) {
+  const leftPalm = keypoints.find((k) => k.part === "leftPalm");
+  const leftWrist = keypoints.find((k) => k.part === "leftWrist");
+  const leftIndex = keypoints.find((k) => k.part === "leftIndex");
+  leftPalm.position[2] = ((leftWrist.position[2] || 0) + (leftIndex.position[2] || 0)) / 2;
+  const rightPalm = keypoints.find((k) => k.part === "rightPalm");
+  const rightWrist = keypoints.find((k) => k.part === "rightWrist");
+  const rightIndex = keypoints.find((k) => k.part === "rightIndex");
+  rightPalm.position[2] = ((rightWrist.position[2] || 0) + (rightIndex.position[2] || 0)) / 2;
+}
+async function detectLandmarks(input, config3, outputSize2) {
+  var _a, _b;
+  if (!((_a = models2.landmarks) == null ? void 0 : _a["executor"]))
+    return null;
+  const t2 = {};
+  [t2.ld, t2.segmentation, t2.heatmap, t2.world, t2.poseflag] = (_b = models2.landmarks) == null ? void 0 : _b.execute(input, outputNodes.landmarks);
+  const poseScore = (await t2.poseflag.data())[0];
+  const points = await t2.ld.data();
+  const distances = await t2.world.data();
+  Object.keys(t2).forEach((tensor6) => tf10.dispose(t2[tensor6]));
+  const keypointsRelative = [];
+  const depth = 5;
+  for (let i = 0; i < points.length / depth; i++) {
+    const score = sigmoid3(points[depth * i + 3]);
+    const presence = sigmoid3(points[depth * i + 4]);
+    const adjScore = Math.trunc(100 * score * presence * poseScore) / 100;
+    const positionRaw = [points[depth * i + 0] / inputSize3.landmarks[0], points[depth * i + 1] / inputSize3.landmarks[1], points[depth * i + 2] + 0];
+    const position = [Math.trunc(outputSize2[0] * positionRaw[0]), Math.trunc(outputSize2[1] * positionRaw[1]), positionRaw[2]];
+    const distance2 = [distances[depth * i + 0], distances[depth * i + 1], distances[depth * i + 2] + 0];
+    keypointsRelative.push({ part: kpt[i], positionRaw, position, distance: distance2, score: adjScore });
+  }
+  if (poseScore < (config3.body.minConfidence || 0))
+    return null;
+  fixKeypoints(keypointsRelative);
+  const keypoints = rescaleKeypoints(keypointsRelative, outputSize2);
+  const kpts = keypoints.map((k) => k.position);
+  const boxes = calc(kpts, [outputSize2[0], outputSize2[1]]);
+  const annotations2 = {};
+  for (const [name, indexes] of Object.entries(connected)) {
+    const pt = [];
+    for (let i = 0; i < indexes.length - 1; i++) {
+      const pt0 = keypoints.find((kpt4) => kpt4.part === indexes[i]);
+      const pt1 = keypoints.find((kpt4) => kpt4.part === indexes[i + 1]);
+      if (pt0 && pt1)
+        pt.push([pt0.position, pt1.position]);
+    }
+    annotations2[name] = pt;
+  }
+  const body4 = { id: 0, score: Math.trunc(100 * poseScore) / 100, box: boxes.box, boxRaw: boxes.boxRaw, keypoints, annotations: annotations2 };
+  return body4;
+}
+async function predict2(input, config3) {
+  const outputSize2 = [input.shape[2] || 0, input.shape[1] || 0];
+  const skipTime = (config3.body.skipTime || 0) > now() - lastTime2;
+  const skipFrame = skipped2 < (config3.body.skipFrames || 0);
+  if (config3.skipAllowed && skipTime && skipFrame && cache !== null) {
+    skipped2++;
+  } else {
+    const t2 = {};
+    t2.landmarks = prepareImage(input, 256);
+    cache = await detectLandmarks(t2.landmarks, config3, outputSize2);
+    Object.keys(t2).forEach((tensor6) => tf10.dispose(t2[tensor6]));
+    lastTime2 = now();
+    skipped2 = 0;
+  }
+  return cache ? [cache] : [];
+}
+
+// src/object/centernet.ts
+var tf11 = __toESM(require_tfjs_esm());
+
+// src/object/labels.ts
+var labels = [
+  { class: 1, label: "person" },
+  { class: 2, label: "bicycle" },
+  { class: 3, label: "car" },
+  { class: 4, label: "motorcycle" },
+  { class: 5, label: "airplane" },
+  { class: 6, label: "bus" },
+  { class: 7, label: "train" },
+  { class: 8, label: "truck" },
+  { class: 9, label: "boat" },
+  { class: 10, label: "traffic light" },
+  { class: 11, label: "fire hydrant" },
+  { class: 12, label: "stop sign" },
+  { class: 13, label: "parking meter" },
+  { class: 14, label: "bench" },
+  { class: 15, label: "bird" },
+  { class: 16, label: "cat" },
+  { class: 17, label: "dog" },
+  { class: 18, label: "horse" },
+  { class: 19, label: "sheep" },
+  { class: 20, label: "cow" },
+  { class: 21, label: "elephant" },
+  { class: 22, label: "bear" },
+  { class: 23, label: "zebra" },
+  { class: 24, label: "giraffe" },
+  { class: 25, label: "backpack" },
+  { class: 26, label: "umbrella" },
+  { class: 27, label: "handbag" },
+  { class: 28, label: "tie" },
+  { class: 29, label: "suitcase" },
+  { class: 30, label: "frisbee" },
+  { class: 31, label: "skis" },
+  { class: 32, label: "snowboard" },
+  { class: 33, label: "sports ball" },
+  { class: 34, label: "kite" },
+  { class: 35, label: "baseball bat" },
+  { class: 36, label: "baseball glove" },
+  { class: 37, label: "skateboard" },
+  { class: 38, label: "surfboard" },
+  { class: 39, label: "tennis racket" },
+  { class: 40, label: "bottle" },
+  { class: 41, label: "wine glass" },
+  { class: 42, label: "cup" },
+  { class: 43, label: "fork" },
+  { class: 44, label: "knife" },
+  { class: 45, label: "spoon" },
+  { class: 46, label: "bowl" },
+  { class: 47, label: "banana" },
+  { class: 48, label: "apple" },
+  { class: 49, label: "sandwich" },
+  { class: 50, label: "orange" },
+  { class: 51, label: "broccoli" },
+  { class: 52, label: "carrot" },
+  { class: 53, label: "hot dog" },
+  { class: 54, label: "pizza" },
+  { class: 55, label: "donut" },
+  { class: 56, label: "cake" },
+  { class: 57, label: "chair" },
+  { class: 58, label: "couch" },
+  { class: 59, label: "potted plant" },
+  { class: 60, label: "bed" },
+  { class: 61, label: "dining table" },
+  { class: 62, label: "toilet" },
+  { class: 63, label: "tv" },
+  { class: 64, label: "laptop" },
+  { class: 65, label: "mouse" },
+  { class: 66, label: "remote" },
+  { class: 67, label: "keyboard" },
+  { class: 68, label: "cell phone" },
+  { class: 69, label: "microwave" },
+  { class: 70, label: "oven" },
+  { class: 71, label: "toaster" },
+  { class: 72, label: "sink" },
+  { class: 73, label: "refrigerator" },
+  { class: 74, label: "book" },
+  { class: 75, label: "clock" },
+  { class: 76, label: "vase" },
+  { class: 77, label: "scissors" },
+  { class: 78, label: "teddy bear" },
+  { class: 79, label: "hair drier" },
+  { class: 80, label: "toothbrush" }
+];
+
+// src/object/centernet.ts
+var model3;
+var inputSize4 = 0;
+var last2 = [];
+var lastTime3 = 0;
+var skipped3 = Number.MAX_SAFE_INTEGER;
+async function load3(config3) {
+  if (env.initial)
+    model3 = null;
+  if (!model3) {
+    model3 = await loadModel(config3.object.modelPath);
+    const inputs = (model3 == null ? void 0 : model3["executor"]) ? Object.values(model3.modelSignature["inputs"]) : void 0;
+    inputSize4 = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 0;
+  } else if (config3.debug)
+    log("cached model:", model3["modelUrl"]);
+  return model3;
+}
+async function process3(res, outputShape, config3) {
+  if (!res)
+    return [];
+  const t2 = {};
+  const results = [];
+  const detections = await res.array();
+  t2.squeeze = tf11.squeeze(res);
+  const arr = tf11.split(t2.squeeze, 6, 1);
+  t2.stack = tf11.stack([arr[1], arr[0], arr[3], arr[2]], 1);
+  t2.boxes = tf11.squeeze(t2.stack);
+  t2.scores = tf11.squeeze(arr[4]);
+  t2.classes = tf11.squeeze(arr[5]);
+  tf11.dispose([res, ...arr]);
+  t2.nms = await tf11.image.nonMaxSuppressionAsync(t2.boxes, t2.scores, config3.object.maxDetected, config3.object.iouThreshold, config3.object.minConfidence || 0);
+  const nms = await t2.nms.data();
+  let i = 0;
+  for (const id of Array.from(nms)) {
+    const score = Math.trunc(100 * detections[0][id][4]) / 100;
+    const classVal = detections[0][id][5];
+    if (Number.isNaN(classVal))
+      continue;
+    const label = labels[classVal].label;
+    const [x, y] = [
+      detections[0][id][0] / inputSize4,
+      detections[0][id][1] / inputSize4
+    ];
+    const boxRaw = [
+      x,
+      y,
+      detections[0][id][2] / inputSize4 - x,
+      detections[0][id][3] / inputSize4 - y
+    ];
+    const box = [
+      Math.trunc(boxRaw[0] * outputShape[0]),
+      Math.trunc(boxRaw[1] * outputShape[1]),
+      Math.trunc(boxRaw[2] * outputShape[0]),
+      Math.trunc(boxRaw[3] * outputShape[1])
+    ];
+    results.push({ id: i++, score, class: classVal, label, box, boxRaw });
+  }
+  Object.keys(t2).forEach((tensor6) => tf11.dispose(t2[tensor6]));
+  return results;
+}
+async function predict3(input, config3) {
+  if (!(model3 == null ? void 0 : model3["executor"]))
+    return [];
+  const skipTime = (config3.object.skipTime || 0) > now() - lastTime3;
+  const skipFrame = skipped3 < (config3.object.skipFrames || 0);
+  if (config3.skipAllowed && skipTime && skipFrame && last2.length > 0) {
+    skipped3++;
+    return last2;
+  }
+  skipped3 = 0;
+  return new Promise(async (resolve) => {
+    const outputSize2 = [input.shape[2] || 0, input.shape[1] || 0];
+    const resize = tf11.image.resizeBilinear(input, [inputSize4, inputSize4]);
+    const objectT = config3.object.enabled ? model3 == null ? void 0 : model3.execute(resize, ["tower_0/detections"]) : null;
+    lastTime3 = now();
+    tf11.dispose(resize);
+    const obj = await process3(objectT, outputSize2, config3);
+    last2 = obj;
+    resolve(obj);
+  });
+}
+
+// src/body/efficientpose.ts
+var tf12 = __toESM(require_tfjs_esm());
+
+// src/body/efficientposecoords.ts
+var efficientposecoords_exports = {};
+__export(efficientposecoords_exports, {
+  connected: () => connected2,
+  kpt: () => kpt2
+});
+var kpt2 = [
+  "head",
+  "neck",
+  "rightShoulder",
+  "rightElbow",
+  "rightWrist",
+  "chest",
+  "leftShoulder",
+  "leftElbow",
+  "leftWrist",
+  "bodyCenter",
+  "rightHip",
+  "rightKnee",
+  "rightAnkle",
+  "leftHip",
+  "leftKnee",
+  "leftAnkle"
+];
+var connected2 = {
+  leftLeg: ["leftHip", "leftKnee", "leftAnkle"],
+  rightLeg: ["rightHip", "rightKnee", "rightAnkle"],
+  torso: ["leftShoulder", "rightShoulder", "rightHip", "leftHip", "leftShoulder"],
+  leftArm: ["leftShoulder", "leftElbow", "leftWrist"],
+  rightArm: ["rightShoulder", "rightElbow", "rightWrist"],
+  head: []
+};
+
+// src/body/efficientpose.ts
+var model4;
+var lastTime4 = 0;
+var cache2 = { id: 0, keypoints: [], box: [0, 0, 0, 0], boxRaw: [0, 0, 0, 0], score: 0, annotations: {} };
+var skipped4 = Number.MAX_SAFE_INTEGER;
+async function load4(config3) {
+  if (env.initial)
+    model4 = null;
+  if (!model4)
+    model4 = await loadModel(config3.body.modelPath);
+  else if (config3.debug)
+    log("cached model:", model4["modelUrl"]);
+  return model4;
+}
+async function max2d(inputs, minScore) {
+  const [width, height] = inputs.shape;
+  const reshaped = tf12.reshape(inputs, [height * width]);
+  const max4 = tf12.max(reshaped, 0);
+  const newScore = (await max4.data())[0];
+  if (newScore > minScore) {
+    const coordinates = tf12.argMax(reshaped, 0);
+    const mod3 = tf12.mod(coordinates, width);
+    const x = (await mod3.data())[0];
+    const div16 = tf12.div(coordinates, width);
+    const y = (await div16.data())[0];
+    tf12.dispose([reshaped, max4, coordinates, mod3, div16]);
+    return [x, y, newScore];
+  }
+  tf12.dispose([reshaped, max4]);
+  return [0, 0, newScore];
+}
+async function predict4(image27, config3) {
+  if (!(model4 == null ? void 0 : model4["executor"]))
+    return [];
+  const skipTime = (config3.body.skipTime || 0) > now() - lastTime4;
+  const skipFrame = skipped4 < (config3.body.skipFrames || 0);
+  if (config3.skipAllowed && skipTime && skipFrame && Object.keys(cache2.keypoints).length > 0) {
+    skipped4++;
+    return [cache2];
+  }
+  skipped4 = 0;
+  return new Promise(async (resolve) => {
+    const tensor6 = tf12.tidy(() => {
+      if (!(model4 == null ? void 0 : model4.inputs[0].shape))
+        return null;
+      const resize = tf12.image.resizeBilinear(image27, [model4.inputs[0].shape[2], model4.inputs[0].shape[1]], false);
+      const enhance2 = tf12.mul(resize, constants.tf2);
+      const norm = tf12.sub(enhance2, constants.tf1);
+      return norm;
+    });
+    let resT;
+    if (config3.body.enabled)
+      resT = model4 == null ? void 0 : model4.execute(tensor6);
+    lastTime4 = now();
+    tf12.dispose(tensor6);
+    if (resT) {
+      cache2.keypoints.length = 0;
+      const squeeze14 = tf12.squeeze(resT);
+      tf12.dispose(resT);
+      const stack5 = tf12.unstack(squeeze14, 2);
+      tf12.dispose(squeeze14);
+      for (let id = 0; id < stack5.length; id++) {
+        const [x2, y2, partScore] = await max2d(stack5[id], config3.body.minConfidence);
+        if (partScore > (config3.body.minConfidence || 0)) {
+          cache2.keypoints.push({
+            score: Math.round(100 * partScore) / 100,
+            part: kpt2[id],
+            positionRaw: [
+              x2 / model4.inputs[0].shape[2],
+              y2 / model4.inputs[0].shape[1]
+            ],
+            position: [
+              Math.round(image27.shape[2] * x2 / model4.inputs[0].shape[2]),
+              Math.round(image27.shape[1] * y2 / model4.inputs[0].shape[1])
+            ]
+          });
+        }
+      }
+      stack5.forEach((s) => tf12.dispose(s));
+    }
+    cache2.score = cache2.keypoints.reduce((prev, curr) => curr.score > prev ? curr.score : prev, 0);
+    const x = cache2.keypoints.map((a) => a.position[0]);
+    const y = cache2.keypoints.map((a) => a.position[1]);
+    cache2.box = [
+      Math.min(...x),
+      Math.min(...y),
+      Math.max(...x) - Math.min(...x),
+      Math.max(...y) - Math.min(...y)
+    ];
+    const xRaw = cache2.keypoints.map((a) => a.positionRaw[0]);
+    const yRaw = cache2.keypoints.map((a) => a.positionRaw[1]);
+    cache2.boxRaw = [
+      Math.min(...xRaw),
+      Math.min(...yRaw),
+      Math.max(...xRaw) - Math.min(...xRaw),
+      Math.max(...yRaw) - Math.min(...yRaw)
+    ];
+    for (const [name, indexes] of Object.entries(connected2)) {
+      const pt = [];
+      for (let i = 0; i < indexes.length - 1; i++) {
+        const pt0 = cache2.keypoints.find((kpt4) => kpt4.part === indexes[i]);
+        const pt1 = cache2.keypoints.find((kpt4) => kpt4.part === indexes[i + 1]);
+        if (pt0 && pt1 && pt0.score > (config3.body.minConfidence || 0) && pt1.score > (config3.body.minConfidence || 0))
+          pt.push([pt0.position, pt1.position]);
+      }
+      cache2.annotations[name] = pt;
+    }
+    resolve([cache2]);
+  });
+}
+
+// src/gear/emotion.ts
+var tf13 = __toESM(require_tfjs_esm());
+var annotations = ["angry", "disgust", "fear", "happy", "sad", "surprise", "neutral"];
+var model5;
+var last3 = [];
+var lastCount2 = 0;
+var lastTime5 = 0;
+var skipped5 = Number.MAX_SAFE_INTEGER;
+async function load5(config3) {
+  var _a;
+  if (env.initial)
+    model5 = null;
+  if (!model5)
+    model5 = await loadModel((_a = config3.face.emotion) == null ? void 0 : _a.modelPath);
+  else if (config3.debug)
+    log("cached model:", model5["modelUrl"]);
+  return model5;
+}
+async function predict5(image27, config3, idx, count2) {
+  var _a, _b;
+  if (!model5)
+    return [];
+  const skipFrame = skipped5 < (((_a = config3.face.emotion) == null ? void 0 : _a.skipFrames) || 0);
+  const skipTime = (((_b = config3.face.emotion) == null ? void 0 : _b.skipTime) || 0) > now() - lastTime5;
+  if (config3.skipAllowed && skipTime && skipFrame && lastCount2 === count2 && last3[idx] && last3[idx].length > 0) {
+    skipped5++;
+    return last3[idx];
+  }
+  skipped5 = 0;
+  return new Promise(async (resolve) => {
+    var _a2;
+    const obj = [];
+    if ((_a2 = config3.face.emotion) == null ? void 0 : _a2.enabled) {
+      const t2 = {};
+      const inputSize10 = (model5 == null ? void 0 : model5.inputs[0].shape) ? model5.inputs[0].shape[2] : 0;
+      t2.resize = tf13.image.resizeBilinear(image27, [inputSize10, inputSize10], false);
+      t2.channels = tf13.mul(t2.resize, constants.rgb);
+      t2.grayscale = tf13.sum(t2.channels, 3, true);
+      t2.grayscaleSub = tf13.sub(t2.grayscale, constants.tf05);
+      t2.grayscaleMul = tf13.mul(t2.grayscaleSub, constants.tf2);
+      t2.emotion = model5 == null ? void 0 : model5.execute(t2.grayscaleMul);
+      lastTime5 = now();
+      const data = await t2.emotion.data();
+      for (let i = 0; i < data.length; i++) {
+        if (data[i] > (config3.face.emotion.minConfidence || 0))
+          obj.push({ score: Math.min(0.99, Math.trunc(100 * data[i]) / 100), emotion: annotations[i] });
+      }
+      obj.sort((a, b) => b.score - a.score);
+      Object.keys(t2).forEach((tensor6) => tf13.dispose(t2[tensor6]));
+    }
+    last3[idx] = obj;
+    lastCount2 = count2;
+    resolve(obj);
+  });
+}
+
+// src/face/facemesh.ts
+var tf15 = __toESM(require_tfjs_esm());
+
+// src/face/iris.ts
+var tf14 = __toESM(require_tfjs_esm());
+var model6;
+var inputSize5 = 0;
+var irisEnlarge = 2.3;
+var leftOutline = meshAnnotations.leftEyeLower0;
+var rightOutline = meshAnnotations.rightEyeLower0;
+var eyeLandmarks = {
+  leftBounds: [leftOutline[0], leftOutline[leftOutline.length - 1]],
+  rightBounds: [rightOutline[0], rightOutline[rightOutline.length - 1]]
+};
+var irisLandmarks = {
+  upperCenter: 3,
+  lowerCenter: 4,
+  index: 71,
+  numCoordinates: 76
+};
+async function load6(config3) {
+  var _a, _b;
+  if (env.initial)
+    model6 = null;
+  if (!model6)
+    model6 = await loadModel((_a = config3.face.iris) == null ? void 0 : _a.modelPath);
+  else if (config3.debug)
+    log("cached model:", model6["modelUrl"]);
+  inputSize5 = (model6 == null ? void 0 : model6["executor"]) && ((_b = model6.inputs) == null ? void 0 : _b[0].shape) ? model6.inputs[0].shape[2] : 0;
+  if (inputSize5 === -1)
+    inputSize5 = 64;
+  return model6;
+}
+function replaceIrisCoords(rawCoords, newCoords, prefix, keys) {
+  for (let i = 0; i < irisIndices.length; i++) {
+    const { key, indices } = irisIndices[i];
+    const originalIndices = meshAnnotations[`${prefix}${key}`];
+    if (!keys || keys.includes(key)) {
+      for (let j = 0; j < indices.length; j++) {
+        const index2 = indices[j];
+        rawCoords[originalIndices[j]] = [
+          newCoords[index2][0],
+          newCoords[index2][1],
+          (newCoords[index2][2] + rawCoords[originalIndices[j]][2]) / 2
+        ];
+      }
+    }
+  }
+}
+var getLeftToRightEyeDepthDifference = (rawCoords) => {
+  const leftEyeZ = rawCoords[eyeLandmarks.leftBounds[0]][2];
+  const rightEyeZ = rawCoords[eyeLandmarks.rightBounds[0]][2];
+  return leftEyeZ - rightEyeZ;
+};
+var getEyeBox = (rawCoords, face4, eyeInnerCornerIndex, eyeOuterCornerIndex, meshSize, flip = false) => {
+  const box = squarifyBox(enlargeBox(calculateLandmarksBoundingBox([rawCoords[eyeInnerCornerIndex], rawCoords[eyeOuterCornerIndex]]), irisEnlarge));
+  const boxSize = getBoxSize(box);
+  let crop = tf14.image.cropAndResize(face4, [[
+    box.startPoint[1] / meshSize,
+    box.startPoint[0] / meshSize,
+    box.endPoint[1] / meshSize,
+    box.endPoint[0] / meshSize
+  ]], [0], [inputSize5, inputSize5]);
+  if (flip && env.kernels.includes("flipleftright")) {
+    const flipped = tf14.image.flipLeftRight(crop);
+    tf14.dispose(crop);
+    crop = flipped;
+  }
+  return { box, boxSize, crop };
+};
+var getEyeCoords = (eyeData, eyeBox, eyeBoxSize, flip = false) => {
+  const eyeRawCoords = [];
+  for (let i = 0; i < irisLandmarks.numCoordinates; i++) {
+    const x = eyeData[i * 3];
+    const y = eyeData[i * 3 + 1];
+    const z = eyeData[i * 3 + 2];
+    eyeRawCoords.push([
+      (flip ? 1 - x / inputSize5 : x / inputSize5) * eyeBoxSize[0] + eyeBox.startPoint[0],
+      y / inputSize5 * eyeBoxSize[1] + eyeBox.startPoint[1],
+      z
+    ]);
+  }
+  return { rawCoords: eyeRawCoords, iris: eyeRawCoords.slice(irisLandmarks.index) };
+};
+var getAdjustedIrisCoords = (rawCoords, irisCoords, direction) => {
+  const upperCenterZ = rawCoords[meshAnnotations[`${direction}EyeUpper0`][irisLandmarks.upperCenter]][2];
+  const lowerCenterZ = rawCoords[meshAnnotations[`${direction}EyeLower0`][irisLandmarks.lowerCenter]][2];
+  const averageZ = (upperCenterZ + lowerCenterZ) / 2;
+  return irisCoords.map((coord, i) => {
+    let z = averageZ;
+    if (i === 2) {
+      z = upperCenterZ;
+    } else if (i === 4) {
+      z = lowerCenterZ;
+    }
+    return [coord[0], coord[1], z];
+  });
+};
+async function augmentIris(rawCoords, face4, meshSize) {
+  if (!(model6 == null ? void 0 : model6["executor"]))
+    return rawCoords;
+  const { box: leftEyeBox, boxSize: leftEyeBoxSize, crop: leftEyeCrop } = getEyeBox(rawCoords, face4, eyeLandmarks.leftBounds[0], eyeLandmarks.leftBounds[1], meshSize, true);
+  const { box: rightEyeBox, boxSize: rightEyeBoxSize, crop: rightEyeCrop } = getEyeBox(rawCoords, face4, eyeLandmarks.rightBounds[0], eyeLandmarks.rightBounds[1], meshSize, true);
+  const combined = tf14.concat([leftEyeCrop, rightEyeCrop]);
+  tf14.dispose(leftEyeCrop);
+  tf14.dispose(rightEyeCrop);
+  const eyePredictions = model6.execute(combined);
+  tf14.dispose(combined);
+  const eyePredictionsData = await eyePredictions.data();
+  tf14.dispose(eyePredictions);
+  const leftEyeData = eyePredictionsData.slice(0, irisLandmarks.numCoordinates * 3);
+  const { rawCoords: leftEyeRawCoords, iris: leftIrisRawCoords } = getEyeCoords(leftEyeData, leftEyeBox, leftEyeBoxSize, true);
+  const rightEyeData = eyePredictionsData.slice(irisLandmarks.numCoordinates * 3);
+  const { rawCoords: rightEyeRawCoords, iris: rightIrisRawCoords } = getEyeCoords(rightEyeData, rightEyeBox, rightEyeBoxSize, false);
+  const leftToRightEyeDepthDifference = getLeftToRightEyeDepthDifference(rawCoords);
+  if (Math.abs(leftToRightEyeDepthDifference) < 30) {
+    replaceIrisCoords(rawCoords, leftEyeRawCoords, "left", null);
+    replaceIrisCoords(rawCoords, rightEyeRawCoords, "right", null);
+  } else if (leftToRightEyeDepthDifference < 1) {
+    replaceIrisCoords(rawCoords, leftEyeRawCoords, "left", ["EyeUpper0", "EyeLower0"]);
+  } else {
+    replaceIrisCoords(rawCoords, rightEyeRawCoords, "right", ["EyeUpper0", "EyeLower0"]);
+  }
+  const adjustedLeftIrisCoords = getAdjustedIrisCoords(rawCoords, leftIrisRawCoords, "left");
+  const adjustedRightIrisCoords = getAdjustedIrisCoords(rawCoords, rightIrisRawCoords, "right");
+  const newCoords = rawCoords.concat(adjustedLeftIrisCoords).concat(adjustedRightIrisCoords);
+  return newCoords;
+}
+
+// src/face/constants.ts
+var LIPS_CONNECTIONS = [
+  [61, 146],
+  [146, 91],
+  [91, 181],
+  [181, 84],
+  [84, 17],
+  [17, 314],
+  [314, 405],
+  [405, 321],
+  [321, 375],
+  [375, 291],
+  [61, 185],
+  [185, 40],
+  [40, 39],
+  [39, 37],
+  [37, 0],
+  [0, 267],
+  [267, 269],
+  [269, 270],
+  [270, 409],
+  [409, 291],
+  [78, 95],
+  [95, 88],
+  [88, 178],
+  [178, 87],
+  [87, 14],
+  [14, 317],
+  [317, 402],
+  [402, 318],
+  [318, 324],
+  [324, 308],
+  [78, 191],
+  [191, 80],
+  [80, 81],
+  [81, 82],
+  [82, 13],
+  [13, 312],
+  [312, 311],
+  [311, 310],
+  [310, 415],
+  [415, 308]
+];
+var LEFT_EYE_CONNECTIONS = [[263, 249], [249, 390], [390, 373], [373, 374], [374, 380], [380, 381], [381, 382], [382, 362], [263, 466], [466, 388], [388, 387], [387, 386], [386, 385], [385, 384], [384, 398], [398, 362]];
+var LEFT_EYEBROW_CONNECTIONS = [[276, 283], [283, 282], [282, 295], [295, 285], [300, 293], [293, 334], [334, 296], [296, 336]];
+var LEFT_IRIS_CONNECTIONS = [[474, 475], [475, 476], [476, 477], [477, 474]];
+var RIGHT_EYE_CONNECTIONS = [[33, 7], [7, 163], [163, 144], [144, 145], [145, 153], [153, 154], [154, 155], [155, 133], [33, 246], [246, 161], [161, 160], [160, 159], [159, 158], [158, 157], [157, 173], [173, 133]];
+var RIGHT_EYEBROW_CONNECTIONS = [[46, 53], [53, 52], [52, 65], [65, 55], [70, 63], [63, 105], [105, 66], [66, 107]];
+var RIGHT_IRIS_CONNECTIONS = [[469, 470], [470, 471], [471, 472], [472, 469]];
+var FACE_OVAL_CONNECTIONS = [
+  [10, 338],
+  [338, 297],
+  [297, 332],
+  [332, 284],
+  [284, 251],
+  [251, 389],
+  [389, 356],
+  [356, 454],
+  [454, 323],
+  [323, 361],
+  [361, 288],
+  [288, 397],
+  [397, 365],
+  [365, 379],
+  [379, 378],
+  [378, 400],
+  [400, 377],
+  [377, 152],
+  [152, 148],
+  [148, 176],
+  [176, 149],
+  [149, 150],
+  [150, 136],
+  [136, 172],
+  [172, 58],
+  [58, 132],
+  [132, 93],
+  [93, 234],
+  [234, 127],
+  [127, 162],
+  [162, 21],
+  [21, 54],
+  [54, 103],
+  [103, 67],
+  [67, 109],
+  [109, 10]
+];
+function connectionsToIndices2(connections) {
+  const indices = connections.map((connection) => connection[0]);
+  indices.push(connections[connections.length - 1][1]);
+  return indices;
+}
+var MEDIAPIPE_FACE_MESH_KEYPOINTS_BY_CONTOUR = {
+  lips: connectionsToIndices2(LIPS_CONNECTIONS),
+  leftEye: connectionsToIndices2(LEFT_EYE_CONNECTIONS),
+  leftEyebrow: connectionsToIndices2(LEFT_EYEBROW_CONNECTIONS),
+  leftIris: connectionsToIndices2(LEFT_IRIS_CONNECTIONS),
+  rightEye: connectionsToIndices2(RIGHT_EYE_CONNECTIONS),
+  rightEyebrow: connectionsToIndices2(RIGHT_EYEBROW_CONNECTIONS),
+  rightIris: connectionsToIndices2(RIGHT_IRIS_CONNECTIONS),
+  faceOval: connectionsToIndices2(FACE_OVAL_CONNECTIONS)
+};
+var indexLabelPairs = Object.entries(MEDIAPIPE_FACE_MESH_KEYPOINTS_BY_CONTOUR).map(([label, indices]) => indices.map((index2) => [index2, label])).flat();
+var MEDIAPIPE_FACE_MESH_KEYPOINTS = new Map(indexLabelPairs);
+var LANDMARKS_REFINEMENT_LIPS_CONFIG = [
+  61,
+  146,
+  91,
+  181,
+  84,
+  17,
+  314,
+  405,
+  321,
+  375,
+  291,
+  185,
+  40,
+  39,
+  37,
+  0,
+  267,
+  269,
+  270,
+  409,
+  78,
+  95,
+  88,
+  178,
+  87,
+  14,
+  317,
+  402,
+  318,
+  324,
+  308,
+  191,
+  80,
+  81,
+  82,
+  13,
+  312,
+  311,
+  310,
+  415,
+  76,
+  77,
+  90,
+  180,
+  85,
+  16,
+  315,
+  404,
+  320,
+  307,
+  306,
+  184,
+  74,
+  73,
+  72,
+  11,
+  302,
+  303,
+  304,
+  408,
+  62,
+  96,
+  89,
+  179,
+  86,
+  15,
+  316,
+  403,
+  319,
+  325,
+  292,
+  183,
+  42,
+  41,
+  38,
+  12,
+  268,
+  271,
+  272,
+  407
+];
+var LANDMARKS_REFINEMENT_LEFT_EYE_CONFIG = [
+  33,
+  7,
+  163,
+  144,
+  145,
+  153,
+  154,
+  155,
+  133,
+  246,
+  161,
+  160,
+  159,
+  158,
+  157,
+  173,
+  130,
+  25,
+  110,
+  24,
+  23,
+  22,
+  26,
+  112,
+  243,
+  247,
+  30,
+  29,
+  27,
+  28,
+  56,
+  190,
+  226,
+  31,
+  228,
+  229,
+  230,
+  231,
+  232,
+  233,
+  244,
+  113,
+  225,
+  224,
+  223,
+  222,
+  221,
+  189,
+  35,
+  124,
+  46,
+  53,
+  52,
+  65,
+  143,
+  111,
+  117,
+  118,
+  119,
+  120,
+  121,
+  128,
+  245,
+  156,
+  70,
+  63,
+  105,
+  66,
+  107,
+  55,
+  193
+];
+var LANDMARKS_REFINEMENT_RIGHT_EYE_CONFIG = [
+  263,
+  249,
+  390,
+  373,
+  374,
+  380,
+  381,
+  382,
+  362,
+  466,
+  388,
+  387,
+  386,
+  385,
+  384,
+  398,
+  359,
+  255,
+  339,
+  254,
+  253,
+  252,
+  256,
+  341,
+  463,
+  467,
+  260,
+  259,
+  257,
+  258,
+  286,
+  414,
+  446,
+  261,
+  448,
+  449,
+  450,
+  451,
+  452,
+  453,
+  464,
+  342,
+  445,
+  444,
+  443,
+  442,
+  441,
+  413,
+  265,
+  353,
+  276,
+  283,
+  282,
+  295,
+  372,
+  340,
+  346,
+  347,
+  348,
+  349,
+  350,
+  357,
+  465,
+  383,
+  300,
+  293,
+  334,
+  296,
+  336,
+  285,
+  417
+];
+
+// src/face/attention.ts
+async function augment(rawCoords, results) {
+  var _a, _b, _c, _d, _e, _f, _g, _h, _i, _j;
+  const t2 = {
+    lips: await ((_b = (_a = results.filter((r) => r.size === 160)) == null ? void 0 : _a[0]) == null ? void 0 : _b.data()),
+    irisL: await ((_d = (_c = results.filter((r) => r.size === 10)) == null ? void 0 : _c[0]) == null ? void 0 : _d.data()),
+    eyeL: await ((_f = (_e = results.filter((r) => r.size === 142)) == null ? void 0 : _e[0]) == null ? void 0 : _f.data()),
+    irisR: await ((_h = (_g = results.filter((r) => r.size === 10)) == null ? void 0 : _g[1]) == null ? void 0 : _h.data()),
+    eyeR: await ((_j = (_i = results.filter((r) => r.size === 142)) == null ? void 0 : _i[1]) == null ? void 0 : _j.data())
+  };
+  for (const val of Object.values(t2)) {
+    if (!val)
+      return rawCoords;
+  }
+  const irisLDepth = LANDMARKS_REFINEMENT_LEFT_EYE_CONFIG.reduce((prev, curr) => prev += rawCoords[curr][2], 0) / LANDMARKS_REFINEMENT_LEFT_EYE_CONFIG.length;
+  for (let i = 0; i < t2.irisL.length / 2; i++)
+    rawCoords.push([t2.irisL[2 * i + 0], t2.irisL[2 * i + 1], irisLDepth]);
+  const irisRDepth = LANDMARKS_REFINEMENT_RIGHT_EYE_CONFIG.reduce((prev, curr) => prev += rawCoords[curr][2], 0) / LANDMARKS_REFINEMENT_RIGHT_EYE_CONFIG.length;
+  for (let i = 0; i < t2.irisR.length / 2; i++)
+    rawCoords.push([t2.irisR[2 * i + 0], t2.irisR[2 * i + 1], irisRDepth]);
+  for (let i = 0; i < t2.eyeL.length / 2; i++)
+    rawCoords[LANDMARKS_REFINEMENT_LEFT_EYE_CONFIG[i]] = [t2.eyeL[2 * i + 0], t2.eyeL[2 * i + 1], rawCoords[LANDMARKS_REFINEMENT_LEFT_EYE_CONFIG[i]][2]];
+  for (let i = 0; i < t2.eyeR.length / 2; i++)
+    rawCoords[LANDMARKS_REFINEMENT_RIGHT_EYE_CONFIG[i]] = [t2.eyeR[2 * i + 0], t2.eyeR[2 * i + 1], rawCoords[LANDMARKS_REFINEMENT_RIGHT_EYE_CONFIG[i]][2]];
+  for (let i = 0; i < t2.lips.length / 2; i++)
+    rawCoords[LANDMARKS_REFINEMENT_LIPS_CONFIG[i]] = [t2.lips[2 * i + 0], t2.lips[2 * i + 1], rawCoords[LANDMARKS_REFINEMENT_LIPS_CONFIG[i]][2]];
+  return rawCoords;
+}
+
+// src/face/facemesh.ts
+var cache3 = {
+  boxes: [],
+  skipped: Number.MAX_SAFE_INTEGER,
+  timestamp: 0
+};
+var model7 = null;
+var inputSize6 = 0;
+async function predict6(input, config3) {
+  var _a, _b, _c, _d, _e, _f, _g, _h, _i, _j;
+  if (!(model7 == null ? void 0 : model7["executor"]))
+    return [];
+  const skipTime = (((_a = config3.face.detector) == null ? void 0 : _a.skipTime) || 0) > now() - cache3.timestamp;
+  const skipFrame = cache3.skipped < (((_b = config3.face.detector) == null ? void 0 : _b.skipFrames) || 0);
+  if (!config3.skipAllowed || !skipTime || !skipFrame || cache3.boxes.length === 0) {
+    cache3.boxes = await getBoxes(input, config3);
+    cache3.timestamp = now();
+    cache3.skipped = 0;
+  } else {
+    cache3.skipped++;
+  }
+  const faces = [];
+  const newCache = [];
+  let id = 0;
+  const size2 = inputSize6;
+  for (let i = 0; i < cache3.boxes.length; i++) {
+    const box = cache3.boxes[i];
+    let angle = 0;
+    let rotationMatrix;
+    const face4 = {
+      id: id++,
+      mesh: [],
+      meshRaw: [],
+      box: [0, 0, 0, 0],
+      boxRaw: [0, 0, 0, 0],
+      score: 0,
+      boxScore: 0,
+      faceScore: 0,
+      annotations: {}
+    };
+    [angle, rotationMatrix, face4.tensor] = correctFaceRotation((_c = config3.face.detector) == null ? void 0 : _c.rotation, box, input, ((_d = config3.face.mesh) == null ? void 0 : _d.enabled) ? inputSize6 : size());
+    if (config3.filter.equalization) {
+      const equilized = face4.tensor ? await histogramEqualization(face4.tensor) : void 0;
+      tf15.dispose(face4.tensor);
+      if (equilized)
+        face4.tensor = equilized;
+    }
+    face4.boxScore = Math.round(100 * box.confidence) / 100;
+    if (!((_e = config3.face.mesh) == null ? void 0 : _e.enabled)) {
+      face4.box = clampBox(box, input);
+      face4.boxRaw = getRawBox(box, input);
+      face4.score = face4.boxScore;
+      face4.mesh = box.landmarks.map((pt) => [
+        (box.startPoint[0] + box.endPoint[0]) / 2 + (box.endPoint[0] + box.startPoint[0]) * pt[0] / size(),
+        (box.startPoint[1] + box.endPoint[1]) / 2 + (box.endPoint[1] + box.startPoint[1]) * pt[1] / size()
+      ]);
+      face4.meshRaw = face4.mesh.map((pt) => [pt[0] / (input.shape[2] || 0), pt[1] / (input.shape[1] || 0), (pt[2] || 0) / size2]);
+      for (const key of Object.keys(blazeFaceLandmarks)) {
+        face4.annotations[key] = [face4.mesh[blazeFaceLandmarks[key]]];
+      }
+    } else if (!model7) {
+      if (config3.debug)
+        log("face mesh detection requested, but model is not loaded");
+    } else {
+      if (((_f = config3.face.attention) == null ? void 0 : _f.enabled) && !env.kernels.includes("atan2")) {
+        config3.face.attention.enabled = false;
+        tf15.dispose(face4.tensor);
+        return faces;
+      }
+      const results = model7.execute(face4.tensor);
+      const confidenceT = results.find((t2) => t2.shape[t2.shape.length - 1] === 1);
+      const faceConfidence = await confidenceT.data();
+      face4.faceScore = Math.round(100 * faceConfidence[0]) / 100;
+      if (face4.faceScore < (((_g = config3.face.detector) == null ? void 0 : _g.minConfidence) || 1)) {
+        box.confidence = face4.faceScore;
+        if (config3.face.mesh.keepInvalid) {
+          face4.box = clampBox(box, input);
+          face4.boxRaw = getRawBox(box, input);
+          face4.score = face4.boxScore;
+          face4.mesh = box.landmarks.map((pt) => [
+            (box.startPoint[0] + box.endPoint[0]) / 2 + (box.endPoint[0] + box.startPoint[0]) * pt[0] / size(),
+            (box.startPoint[1] + box.endPoint[1]) / 2 + (box.endPoint[1] + box.startPoint[1]) * pt[1] / size()
+          ]);
+          face4.meshRaw = face4.mesh.map((pt) => [pt[0] / (input.shape[2] || 1), pt[1] / (input.shape[1] || 1), (pt[2] || 0) / size2]);
+          for (const key of Object.keys(blazeFaceLandmarks)) {
+            face4.annotations[key] = [face4.mesh[blazeFaceLandmarks[key]]];
+          }
+        }
+      } else {
+        const meshT = results.find((t2) => t2.shape[t2.shape.length - 1] === 1404);
+        const coordsReshaped = tf15.reshape(meshT, [-1, 3]);
+        let rawCoords = await coordsReshaped.array();
+        tf15.dispose(coordsReshaped);
+        if ((_h = config3.face.attention) == null ? void 0 : _h.enabled) {
+          rawCoords = await augment(rawCoords, results);
+        } else if ((_i = config3.face.iris) == null ? void 0 : _i.enabled) {
+          rawCoords = await augmentIris(rawCoords, face4.tensor, inputSize6);
+        }
+        face4.mesh = transformRawCoords(rawCoords, box, angle, rotationMatrix, inputSize6);
+        face4.meshRaw = face4.mesh.map((pt) => [pt[0] / (input.shape[2] || 0), pt[1] / (input.shape[1] || 0), (pt[2] || 0) / size2]);
+        for (const key of Object.keys(meshAnnotations))
+          face4.annotations[key] = meshAnnotations[key].map((index2) => face4.mesh[index2]);
+        face4.score = face4.faceScore;
+        const calculatedBox = { ...calculateFaceBox(face4.mesh, box), confidence: box.confidence, landmarks: box.landmarks };
+        face4.box = clampBox(calculatedBox, input);
+        face4.boxRaw = getRawBox(calculatedBox, input);
+        newCache.push(calculatedBox);
+      }
+      tf15.dispose(results);
+    }
+    if (face4.score > (((_j = config3.face.detector) == null ? void 0 : _j.minConfidence) || 1))
+      faces.push(face4);
+    else
+      tf15.dispose(face4.tensor);
+  }
+  cache3.boxes = newCache;
+  return faces;
+}
+async function load7(config3) {
+  var _a, _b, _c, _d, _e, _f;
+  if (env.initial)
+    model7 = null;
+  if (((_a = config3.face.attention) == null ? void 0 : _a.enabled) && (model7 == null ? void 0 : model7["signature"])) {
+    if (Object.keys(((_b = model7 == null ? void 0 : model7["signature"]) == null ? void 0 : _b.outputs) || {}).length < 6)
+      model7 = null;
+  }
+  if (!model7) {
+    if ((_c = config3.face.attention) == null ? void 0 : _c.enabled)
+      model7 = await loadModel(config3.face.attention.modelPath);
+    else
+      model7 = await loadModel((_d = config3.face.mesh) == null ? void 0 : _d.modelPath);
+  } else if (config3.debug) {
+    log("cached model:", model7["modelUrl"]);
+  }
+  inputSize6 = model7["executor"] && ((_e = model7 == null ? void 0 : model7.inputs) == null ? void 0 : _e[0].shape) ? (_f = model7 == null ? void 0 : model7.inputs) == null ? void 0 : _f[0].shape[2] : 256;
+  return model7;
+}
+var triangulation = TRI468;
+var uvmap = UV468;
+
+// src/face/faceres.ts
+var tf16 = __toESM(require_tfjs_esm());
+var model8;
+var last4 = [];
+var lastTime6 = 0;
+var lastCount3 = 0;
+var skipped6 = Number.MAX_SAFE_INTEGER;
+async function load8(config3) {
+  var _a;
+  if (env.initial)
+    model8 = null;
+  if (!model8)
+    model8 = await loadModel((_a = config3.face.description) == null ? void 0 : _a.modelPath);
+  else if (config3.debug)
+    log("cached model:", model8["modelUrl"]);
+  return model8;
+}
+function enhance(input) {
+  const tensor6 = input.image || input.tensor || input;
+  if (!(model8 == null ? void 0 : model8.inputs[0].shape))
+    return tensor6;
+  const crop = tf16.image.resizeBilinear(tensor6, [model8.inputs[0].shape[2], model8.inputs[0].shape[1]], false);
+  const norm = tf16.mul(crop, constants.tf255);
+  tf16.dispose(crop);
+  return norm;
+}
+async function predict7(image27, config3, idx, count2) {
+  var _a, _b, _c, _d;
+  const obj = {
+    age: 0,
+    gender: "unknown",
+    genderScore: 0,
+    descriptor: []
+  };
+  if (!(model8 == null ? void 0 : model8["executor"]))
+    return obj;
+  const skipFrame = skipped6 < (((_a = config3.face.description) == null ? void 0 : _a.skipFrames) || 0);
+  const skipTime = (((_b = config3.face.description) == null ? void 0 : _b.skipTime) || 0) > now() - lastTime6;
+  if (config3.skipAllowed && skipFrame && skipTime && lastCount3 === count2 && ((_c = last4 == null ? void 0 : last4[idx]) == null ? void 0 : _c.age) > 0 && ((_d = last4 == null ? void 0 : last4[idx]) == null ? void 0 : _d.genderScore) > 0) {
+    skipped6++;
+    return last4[idx];
+  }
+  skipped6 = 0;
+  return new Promise(async (resolve) => {
+    var _a2;
+    if ((_a2 = config3.face.description) == null ? void 0 : _a2.enabled) {
+      const enhanced = enhance(image27);
+      const resT = model8 == null ? void 0 : model8.execute(enhanced);
+      lastTime6 = now();
+      tf16.dispose(enhanced);
+      const genderT = resT.find((t2) => t2.shape[1] === 1);
+      const gender2 = await genderT.data();
+      const confidence = Math.trunc(200 * Math.abs(gender2[0] - 0.5)) / 100;
+      if (confidence > (config3.face.description.minConfidence || 0)) {
+        obj.gender = gender2[0] <= 0.5 ? "female" : "male";
+        obj.genderScore = Math.min(0.99, confidence);
+      }
+      const argmax = tf16.argMax(resT.find((t2) => t2.shape[1] === 100), 1);
+      const ageIdx = (await argmax.data())[0];
+      tf16.dispose(argmax);
+      const ageT = resT.find((t2) => t2.shape[1] === 100);
+      const all2 = await ageT.data();
+      obj.age = Math.round(all2[ageIdx - 1] > all2[ageIdx + 1] ? 10 * ageIdx - 100 * all2[ageIdx - 1] : 10 * ageIdx + 100 * all2[ageIdx + 1]) / 10;
+      if (Number.isNaN(gender2[0]) || Number.isNaN(all2[0]))
+        log("faceres error:", { model: model8, result: resT });
+      const desc = resT.find((t2) => t2.shape[1] === 1024);
+      const descriptor = desc ? await desc.data() : [];
+      obj.descriptor = Array.from(descriptor);
+      resT.forEach((t2) => tf16.dispose(t2));
+    }
+    last4[idx] = obj;
+    lastCount3 = count2;
+    resolve(obj);
+  });
+}
+
+// src/gear/gear.ts
+var tf17 = __toESM(require_tfjs_esm());
+var model9;
+var last5 = [];
+var raceNames = ["white", "black", "asian", "indian", "other"];
+var ageWeights = [15, 23, 28, 35.5, 45.5, 55.5, 65];
+var lastCount4 = 0;
+var lastTime7 = 0;
+var skipped7 = Number.MAX_SAFE_INTEGER;
+async function load9(config3) {
+  var _a;
+  if (env.initial)
+    model9 = null;
+  if (!model9)
+    model9 = await loadModel((_a = config3.face.gear) == null ? void 0 : _a.modelPath);
+  else if (config3.debug)
+    log("cached model:", model9["modelUrl"]);
+  return model9;
+}
+async function predict8(image27, config3, idx, count2) {
+  var _a, _b;
+  if (!model9)
+    return { age: 0, gender: "unknown", genderScore: 0, race: [] };
+  const skipFrame = skipped7 < (((_a = config3.face.gear) == null ? void 0 : _a.skipFrames) || 0);
+  const skipTime = (((_b = config3.face.gear) == null ? void 0 : _b.skipTime) || 0) > now() - lastTime7;
+  if (config3.skipAllowed && skipTime && skipFrame && lastCount4 === count2 && last5[idx]) {
+    skipped7++;
+    return last5[idx];
+  }
+  skipped7 = 0;
+  return new Promise(async (resolve) => {
+    var _a2, _b2;
+    if (!(model9 == null ? void 0 : model9.inputs[0].shape))
+      return;
+    const t2 = {};
+    const box = [[0, 0.1, 0.9, 0.9]];
+    t2.resize = tf17.image.cropAndResize(image27, box, [0], [model9.inputs[0].shape[2], model9.inputs[0].shape[1]]);
+    const obj = { age: 0, gender: "unknown", genderScore: 0, race: [] };
+    if ((_a2 = config3.face.gear) == null ? void 0 : _a2.enabled)
+      [t2.age, t2.gender, t2.race] = model9.execute(t2.resize, ["age_output", "gender_output", "race_output"]);
+    const gender2 = await t2.gender.data();
+    obj.gender = gender2[0] > gender2[1] ? "male" : "female";
+    obj.genderScore = Math.round(100 * (gender2[0] > gender2[1] ? gender2[0] : gender2[1])) / 100;
+    const race = await t2.race.data();
+    for (let i = 0; i < race.length; i++) {
+      if (race[i] > (((_b2 = config3.face.gear) == null ? void 0 : _b2.minConfidence) || 0.2))
+        obj.race.push({ score: Math.round(100 * race[i]) / 100, race: raceNames[i] });
+    }
+    obj.race.sort((a, b) => b.score - a.score);
+    const ageDistribution = Array.from(await t2.age.data());
+    const ageSorted = ageDistribution.map((a, i) => [ageWeights[i], a]).sort((a, b) => b[1] - a[1]);
+    let age2 = ageSorted[0][0];
+    for (let i = 1; i < ageSorted.length; i++)
+      age2 += ageSorted[i][1] * (ageSorted[i][0] - age2);
+    obj.age = Math.round(10 * age2) / 10;
+    Object.keys(t2).forEach((tensor6) => tf17.dispose(t2[tensor6]));
+    last5[idx] = obj;
+    lastCount4 = count2;
+    lastTime7 = now();
+    resolve(obj);
+  });
+}
+
+// src/hand/handposedetector.ts
+var tf19 = __toESM(require_tfjs_esm());
+
+// src/hand/handposeutil.ts
+var tf18 = __toESM(require_tfjs_esm());
+function getBoxSize2(box) {
+  return [
+    Math.abs(box.endPoint[0] - box.startPoint[0]),
+    Math.abs(box.endPoint[1] - box.startPoint[1])
+  ];
+}
+function getBoxCenter2(box) {
+  return [
+    box.startPoint[0] + (box.endPoint[0] - box.startPoint[0]) / 2,
+    box.startPoint[1] + (box.endPoint[1] - box.startPoint[1]) / 2
+  ];
+}
+function cutBoxFromImageAndResize(box, image27, cropSize) {
+  const h = image27.shape[1];
+  const w = image27.shape[2];
+  const boxes = [[
+    box.startPoint[1] / h,
+    box.startPoint[0] / w,
+    box.endPoint[1] / h,
+    box.endPoint[0] / w
+  ]];
+  return tf18.image.cropAndResize(image27, boxes, [0], cropSize);
+}
+function scaleBoxCoordinates2(box, factor) {
+  const startPoint = [box.startPoint[0] * factor[0], box.startPoint[1] * factor[1]];
+  const endPoint = [box.endPoint[0] * factor[0], box.endPoint[1] * factor[1]];
+  const palmLandmarks = box.palmLandmarks.map((coord) => {
+    const scaledCoord = [coord[0] * factor[0], coord[1] * factor[1]];
+    return scaledCoord;
+  });
+  return { startPoint, endPoint, palmLandmarks, confidence: box.confidence };
+}
+function enlargeBox2(box, factor = 1.5) {
+  const center = getBoxCenter2(box);
+  const size2 = getBoxSize2(box);
+  const newHalfSize = [factor * size2[0] / 2, factor * size2[1] / 2];
+  const startPoint = [center[0] - newHalfSize[0], center[1] - newHalfSize[1]];
+  const endPoint = [center[0] + newHalfSize[0], center[1] + newHalfSize[1]];
+  return { startPoint, endPoint, palmLandmarks: box.palmLandmarks };
+}
+function squarifyBox2(box) {
+  const centers = getBoxCenter2(box);
+  const size2 = getBoxSize2(box);
+  const maxEdge = Math.max(...size2);
+  const halfSize = maxEdge / 2;
+  const startPoint = [centers[0] - halfSize, centers[1] - halfSize];
+  const endPoint = [centers[0] + halfSize, centers[1] + halfSize];
+  return { startPoint, endPoint, palmLandmarks: box.palmLandmarks };
+}
+function normalizeRadians2(angle) {
+  return angle - 2 * Math.PI * Math.floor((angle + Math.PI) / (2 * Math.PI));
+}
+function computeRotation2(point1, point2) {
+  const radians = Math.PI / 2 - Math.atan2(-(point2[1] - point1[1]), point2[0] - point1[0]);
+  return normalizeRadians2(radians);
+}
+var buildTranslationMatrix2 = (x, y) => [[1, 0, x], [0, 1, y], [0, 0, 1]];
+function dot2(v1, v2) {
+  let product = 0;
+  for (let i = 0; i < v1.length; i++) {
+    product += v1[i] * v2[i];
+  }
+  return product;
+}
+function getColumnFrom2DArr2(arr, columnIndex) {
+  const column = [];
+  for (let i = 0; i < arr.length; i++) {
+    column.push(arr[i][columnIndex]);
+  }
+  return column;
+}
+function multiplyTransformMatrices2(mat1, mat2) {
+  const product = [];
+  const size2 = mat1.length;
+  for (let row = 0; row < size2; row++) {
+    product.push([]);
+    for (let col = 0; col < size2; col++) {
+      product[row].push(dot2(mat1[row], getColumnFrom2DArr2(mat2, col)));
+    }
+  }
+  return product;
+}
+function buildRotationMatrix2(rotation, center) {
+  const cosA = Math.cos(rotation);
+  const sinA = Math.sin(rotation);
+  const rotationMatrix = [[cosA, -sinA, 0], [sinA, cosA, 0], [0, 0, 1]];
+  const translationMatrix = buildTranslationMatrix2(center[0], center[1]);
+  const translationTimesRotation = multiplyTransformMatrices2(translationMatrix, rotationMatrix);
+  const negativeTranslationMatrix = buildTranslationMatrix2(-center[0], -center[1]);
+  return multiplyTransformMatrices2(translationTimesRotation, negativeTranslationMatrix);
+}
+function invertTransformMatrix2(matrix) {
+  const rotationComponent = [[matrix[0][0], matrix[1][0]], [matrix[0][1], matrix[1][1]]];
+  const translationComponent = [matrix[0][2], matrix[1][2]];
+  const invertedTranslation = [
+    -dot2(rotationComponent[0], translationComponent),
+    -dot2(rotationComponent[1], translationComponent)
+  ];
+  return [
+    rotationComponent[0].concat(invertedTranslation[0]),
+    rotationComponent[1].concat(invertedTranslation[1]),
+    [0, 0, 1]
+  ];
+}
+function rotatePoint2(homogeneousCoordinate, rotationMatrix) {
+  return [
+    dot2(homogeneousCoordinate, rotationMatrix[0]),
+    dot2(homogeneousCoordinate, rotationMatrix[1])
+  ];
+}
+
+// src/hand/handposeanchors.ts
+var anchors2 = [
+  { x: 0.015625, y: 0.015625 },
+  { x: 0.015625, y: 0.015625 },
+  { x: 0.046875, y: 0.015625 },
+  { x: 0.046875, y: 0.015625 },
+  { x: 0.078125, y: 0.015625 },
+  { x: 0.078125, y: 0.015625 },
+  { x: 0.109375, y: 0.015625 },
+  { x: 0.109375, y: 0.015625 },
+  { x: 0.140625, y: 0.015625 },
+  { x: 0.140625, y: 0.015625 },
+  { x: 0.171875, y: 0.015625 },
+  { x: 0.171875, y: 0.015625 },
+  { x: 0.203125, y: 0.015625 },
+  { x: 0.203125, y: 0.015625 },
+  { x: 0.234375, y: 0.015625 },
+  { x: 0.234375, y: 0.015625 },
+  { x: 0.265625, y: 0.015625 },
+  { x: 0.265625, y: 0.015625 },
+  { x: 0.296875, y: 0.015625 },
+  { x: 0.296875, y: 0.015625 },
+  { x: 0.328125, y: 0.015625 },
+  { x: 0.328125, y: 0.015625 },
+  { x: 0.359375, y: 0.015625 },
+  { x: 0.359375, y: 0.015625 },
+  { x: 0.390625, y: 0.015625 },
+  { x: 0.390625, y: 0.015625 },
+  { x: 0.421875, y: 0.015625 },
+  { x: 0.421875, y: 0.015625 },
+  { x: 0.453125, y: 0.015625 },
+  { x: 0.453125, y: 0.015625 },
+  { x: 0.484375, y: 0.015625 },
+  { x: 0.484375, y: 0.015625 },
+  { x: 0.515625, y: 0.015625 },
+  { x: 0.515625, y: 0.015625 },
+  { x: 0.546875, y: 0.015625 },
+  { x: 0.546875, y: 0.015625 },
+  { x: 0.578125, y: 0.015625 },
+  { x: 0.578125, y: 0.015625 },
+  { x: 0.609375, y: 0.015625 },
+  { x: 0.609375, y: 0.015625 },
+  { x: 0.640625, y: 0.015625 },
+  { x: 0.640625, y: 0.015625 },
+  { x: 0.671875, y: 0.015625 },
+  { x: 0.671875, y: 0.015625 },
+  { x: 0.703125, y: 0.015625 },
+  { x: 0.703125, y: 0.015625 },
+  { x: 0.734375, y: 0.015625 },
+  { x: 0.734375, y: 0.015625 },
+  { x: 0.765625, y: 0.015625 },
+  { x: 0.765625, y: 0.015625 },
+  { x: 0.796875, y: 0.015625 },
+  { x: 0.796875, y: 0.015625 },
+  { x: 0.828125, y: 0.015625 },
+  { x: 0.828125, y: 0.015625 },
+  { x: 0.859375, y: 0.015625 },
+  { x: 0.859375, y: 0.015625 },
+  { x: 0.890625, y: 0.015625 },
+  { x: 0.890625, y: 0.015625 },
+  { x: 0.921875, y: 0.015625 },
+  { x: 0.921875, y: 0.015625 },
+  { x: 0.953125, y: 0.015625 },
+  { x: 0.953125, y: 0.015625 },
+  { x: 0.984375, y: 0.015625 },
+  { x: 0.984375, y: 0.015625 },
+  { x: 0.015625, y: 0.046875 },
+  { x: 0.015625, y: 0.046875 },
+  { x: 0.046875, y: 0.046875 },
+  { x: 0.046875, y: 0.046875 },
+  { x: 0.078125, y: 0.046875 },
+  { x: 0.078125, y: 0.046875 },
+  { x: 0.109375, y: 0.046875 },
+  { x: 0.109375, y: 0.046875 },
+  { x: 0.140625, y: 0.046875 },
+  { x: 0.140625, y: 0.046875 },
+  { x: 0.171875, y: 0.046875 },
+  { x: 0.171875, y: 0.046875 },
+  { x: 0.203125, y: 0.046875 },
+  { x: 0.203125, y: 0.046875 },
+  { x: 0.234375, y: 0.046875 },
+  { x: 0.234375, y: 0.046875 },
+  { x: 0.265625, y: 0.046875 },
+  { x: 0.265625, y: 0.046875 },
+  { x: 0.296875, y: 0.046875 },
+  { x: 0.296875, y: 0.046875 },
+  { x: 0.328125, y: 0.046875 },
+  { x: 0.328125, y: 0.046875 },
+  { x: 0.359375, y: 0.046875 },
+  { x: 0.359375, y: 0.046875 },
+  { x: 0.390625, y: 0.046875 },
+  { x: 0.390625, y: 0.046875 },
+  { x: 0.421875, y: 0.046875 },
+  { x: 0.421875, y: 0.046875 },
+  { x: 0.453125, y: 0.046875 },
+  { x: 0.453125, y: 0.046875 },
+  { x: 0.484375, y: 0.046875 },
+  { x: 0.484375, y: 0.046875 },
+  { x: 0.515625, y: 0.046875 },
+  { x: 0.515625, y: 0.046875 },
+  { x: 0.546875, y: 0.046875 },
+  { x: 0.546875, y: 0.046875 },
+  { x: 0.578125, y: 0.046875 },
+  { x: 0.578125, y: 0.046875 },
+  { x: 0.609375, y: 0.046875 },
+  { x: 0.609375, y: 0.046875 },
+  { x: 0.640625, y: 0.046875 },
+  { x: 0.640625, y: 0.046875 },
+  { x: 0.671875, y: 0.046875 },
+  { x: 0.671875, y: 0.046875 },
+  { x: 0.703125, y: 0.046875 },
+  { x: 0.703125, y: 0.046875 },
+  { x: 0.734375, y: 0.046875 },
+  { x: 0.734375, y: 0.046875 },
+  { x: 0.765625, y: 0.046875 },
+  { x: 0.765625, y: 0.046875 },
+  { x: 0.796875, y: 0.046875 },
+  { x: 0.796875, y: 0.046875 },
+  { x: 0.828125, y: 0.046875 },
+  { x: 0.828125, y: 0.046875 },
+  { x: 0.859375, y: 0.046875 },
+  { x: 0.859375, y: 0.046875 },
+  { x: 0.890625, y: 0.046875 },
+  { x: 0.890625, y: 0.046875 },
+  { x: 0.921875, y: 0.046875 },
+  { x: 0.921875, y: 0.046875 },
+  { x: 0.953125, y: 0.046875 },
+  { x: 0.953125, y: 0.046875 },
+  { x: 0.984375, y: 0.046875 },
+  { x: 0.984375, y: 0.046875 },
+  { x: 0.015625, y: 0.078125 },
+  { x: 0.015625, y: 0.078125 },
+  { x: 0.046875, y: 0.078125 },
+  { x: 0.046875, y: 0.078125 },
+  { x: 0.078125, y: 0.078125 },
+  { x: 0.078125, y: 0.078125 },
+  { x: 0.109375, y: 0.078125 },
+  { x: 0.109375, y: 0.078125 },
+  { x: 0.140625, y: 0.078125 },
+  { x: 0.140625, y: 0.078125 },
+  { x: 0.171875, y: 0.078125 },
+  { x: 0.171875, y: 0.078125 },
+  { x: 0.203125, y: 0.078125 },
+  { x: 0.203125, y: 0.078125 },
+  { x: 0.234375, y: 0.078125 },
+  { x: 0.234375, y: 0.078125 },
+  { x: 0.265625, y: 0.078125 },
+  { x: 0.265625, y: 0.078125 },
+  { x: 0.296875, y: 0.078125 },
+  { x: 0.296875, y: 0.078125 },
+  { x: 0.328125, y: 0.078125 },
+  { x: 0.328125, y: 0.078125 },
+  { x: 0.359375, y: 0.078125 },
+  { x: 0.359375, y: 0.078125 },
+  { x: 0.390625, y: 0.078125 },
+  { x: 0.390625, y: 0.078125 },
+  { x: 0.421875, y: 0.078125 },
+  { x: 0.421875, y: 0.078125 },
+  { x: 0.453125, y: 0.078125 },
+  { x: 0.453125, y: 0.078125 },
+  { x: 0.484375, y: 0.078125 },
+  { x: 0.484375, y: 0.078125 },
+  { x: 0.515625, y: 0.078125 },
+  { x: 0.515625, y: 0.078125 },
+  { x: 0.546875, y: 0.078125 },
+  { x: 0.546875, y: 0.078125 },
+  { x: 0.578125, y: 0.078125 },
+  { x: 0.578125, y: 0.078125 },
+  { x: 0.609375, y: 0.078125 },
+  { x: 0.609375, y: 0.078125 },
+  { x: 0.640625, y: 0.078125 },
+  { x: 0.640625, y: 0.078125 },
+  { x: 0.671875, y: 0.078125 },
+  { x: 0.671875, y: 0.078125 },
+  { x: 0.703125, y: 0.078125 },
+  { x: 0.703125, y: 0.078125 },
+  { x: 0.734375, y: 0.078125 },
+  { x: 0.734375, y: 0.078125 },
+  { x: 0.765625, y: 0.078125 },
+  { x: 0.765625, y: 0.078125 },
+  { x: 0.796875, y: 0.078125 },
+  { x: 0.796875, y: 0.078125 },
+  { x: 0.828125, y: 0.078125 },
+  { x: 0.828125, y: 0.078125 },
+  { x: 0.859375, y: 0.078125 },
+  { x: 0.859375, y: 0.078125 },
+  { x: 0.890625, y: 0.078125 },
+  { x: 0.890625, y: 0.078125 },
+  { x: 0.921875, y: 0.078125 },
+  { x: 0.921875, y: 0.078125 },
+  { x: 0.953125, y: 0.078125 },
+  { x: 0.953125, y: 0.078125 },
+  { x: 0.984375, y: 0.078125 },
+  { x: 0.984375, y: 0.078125 },
+  { x: 0.015625, y: 0.109375 },
+  { x: 0.015625, y: 0.109375 },
+  { x: 0.046875, y: 0.109375 },
+  { x: 0.046875, y: 0.109375 },
+  { x: 0.078125, y: 0.109375 },
+  { x: 0.078125, y: 0.109375 },
+  { x: 0.109375, y: 0.109375 },
+  { x: 0.109375, y: 0.109375 },
+  { x: 0.140625, y: 0.109375 },
+  { x: 0.140625, y: 0.109375 },
+  { x: 0.171875, y: 0.109375 },
+  { x: 0.171875, y: 0.109375 },
+  { x: 0.203125, y: 0.109375 },
+  { x: 0.203125, y: 0.109375 },
+  { x: 0.234375, y: 0.109375 },
+  { x: 0.234375, y: 0.109375 },
+  { x: 0.265625, y: 0.109375 },
+  { x: 0.265625, y: 0.109375 },
+  { x: 0.296875, y: 0.109375 },
+  { x: 0.296875, y: 0.109375 },
+  { x: 0.328125, y: 0.109375 },
+  { x: 0.328125, y: 0.109375 },
+  { x: 0.359375, y: 0.109375 },
+  { x: 0.359375, y: 0.109375 },
+  { x: 0.390625, y: 0.109375 },
+  { x: 0.390625, y: 0.109375 },
+  { x: 0.421875, y: 0.109375 },
+  { x: 0.421875, y: 0.109375 },
+  { x: 0.453125, y: 0.109375 },
+  { x: 0.453125, y: 0.109375 },
+  { x: 0.484375, y: 0.109375 },
+  { x: 0.484375, y: 0.109375 },
+  { x: 0.515625, y: 0.109375 },
+  { x: 0.515625, y: 0.109375 },
+  { x: 0.546875, y: 0.109375 },
+  { x: 0.546875, y: 0.109375 },
+  { x: 0.578125, y: 0.109375 },
+  { x: 0.578125, y: 0.109375 },
+  { x: 0.609375, y: 0.109375 },
+  { x: 0.609375, y: 0.109375 },
+  { x: 0.640625, y: 0.109375 },
+  { x: 0.640625, y: 0.109375 },
+  { x: 0.671875, y: 0.109375 },
+  { x: 0.671875, y: 0.109375 },
+  { x: 0.703125, y: 0.109375 },
+  { x: 0.703125, y: 0.109375 },
+  { x: 0.734375, y: 0.109375 },
+  { x: 0.734375, y: 0.109375 },
+  { x: 0.765625, y: 0.109375 },
+  { x: 0.765625, y: 0.109375 },
+  { x: 0.796875, y: 0.109375 },
+  { x: 0.796875, y: 0.109375 },
+  { x: 0.828125, y: 0.109375 },
+  { x: 0.828125, y: 0.109375 },
+  { x: 0.859375, y: 0.109375 },
+  { x: 0.859375, y: 0.109375 },
+  { x: 0.890625, y: 0.109375 },
+  { x: 0.890625, y: 0.109375 },
+  { x: 0.921875, y: 0.109375 },
+  { x: 0.921875, y: 0.109375 },
+  { x: 0.953125, y: 0.109375 },
+  { x: 0.953125, y: 0.109375 },
+  { x: 0.984375, y: 0.109375 },
+  { x: 0.984375, y: 0.109375 },
+  { x: 0.015625, y: 0.140625 },
+  { x: 0.015625, y: 0.140625 },
+  { x: 0.046875, y: 0.140625 },
+  { x: 0.046875, y: 0.140625 },
+  { x: 0.078125, y: 0.140625 },
+  { x: 0.078125, y: 0.140625 },
+  { x: 0.109375, y: 0.140625 },
+  { x: 0.109375, y: 0.140625 },
+  { x: 0.140625, y: 0.140625 },
+  { x: 0.140625, y: 0.140625 },
+  { x: 0.171875, y: 0.140625 },
+  { x: 0.171875, y: 0.140625 },
+  { x: 0.203125, y: 0.140625 },
+  { x: 0.203125, y: 0.140625 },
+  { x: 0.234375, y: 0.140625 },
+  { x: 0.234375, y: 0.140625 },
+  { x: 0.265625, y: 0.140625 },
+  { x: 0.265625, y: 0.140625 },
+  { x: 0.296875, y: 0.140625 },
+  { x: 0.296875, y: 0.140625 },
+  { x: 0.328125, y: 0.140625 },
+  { x: 0.328125, y: 0.140625 },
+  { x: 0.359375, y: 0.140625 },
+  { x: 0.359375, y: 0.140625 },
+  { x: 0.390625, y: 0.140625 },
+  { x: 0.390625, y: 0.140625 },
+  { x: 0.421875, y: 0.140625 },
+  { x: 0.421875, y: 0.140625 },
+  { x: 0.453125, y: 0.140625 },
+  { x: 0.453125, y: 0.140625 },
+  { x: 0.484375, y: 0.140625 },
+  { x: 0.484375, y: 0.140625 },
+  { x: 0.515625, y: 0.140625 },
+  { x: 0.515625, y: 0.140625 },
+  { x: 0.546875, y: 0.140625 },
+  { x: 0.546875, y: 0.140625 },
+  { x: 0.578125, y: 0.140625 },
+  { x: 0.578125, y: 0.140625 },
+  { x: 0.609375, y: 0.140625 },
+  { x: 0.609375, y: 0.140625 },
+  { x: 0.640625, y: 0.140625 },
+  { x: 0.640625, y: 0.140625 },
+  { x: 0.671875, y: 0.140625 },
+  { x: 0.671875, y: 0.140625 },
+  { x: 0.703125, y: 0.140625 },
+  { x: 0.703125, y: 0.140625 },
+  { x: 0.734375, y: 0.140625 },
+  { x: 0.734375, y: 0.140625 },
+  { x: 0.765625, y: 0.140625 },
+  { x: 0.765625, y: 0.140625 },
+  { x: 0.796875, y: 0.140625 },
+  { x: 0.796875, y: 0.140625 },
+  { x: 0.828125, y: 0.140625 },
+  { x: 0.828125, y: 0.140625 },
+  { x: 0.859375, y: 0.140625 },
+  { x: 0.859375, y: 0.140625 },
+  { x: 0.890625, y: 0.140625 },
+  { x: 0.890625, y: 0.140625 },
+  { x: 0.921875, y: 0.140625 },
+  { x: 0.921875, y: 0.140625 },
+  { x: 0.953125, y: 0.140625 },
+  { x: 0.953125, y: 0.140625 },
+  { x: 0.984375, y: 0.140625 },
+  { x: 0.984375, y: 0.140625 },
+  { x: 0.015625, y: 0.171875 },
+  { x: 0.015625, y: 0.171875 },
+  { x: 0.046875, y: 0.171875 },
+  { x: 0.046875, y: 0.171875 },
+  { x: 0.078125, y: 0.171875 },
+  { x: 0.078125, y: 0.171875 },
+  { x: 0.109375, y: 0.171875 },
+  { x: 0.109375, y: 0.171875 },
+  { x: 0.140625, y: 0.171875 },
+  { x: 0.140625, y: 0.171875 },
+  { x: 0.171875, y: 0.171875 },
+  { x: 0.171875, y: 0.171875 },
+  { x: 0.203125, y: 0.171875 },
+  { x: 0.203125, y: 0.171875 },
+  { x: 0.234375, y: 0.171875 },
+  { x: 0.234375, y: 0.171875 },
+  { x: 0.265625, y: 0.171875 },
+  { x: 0.265625, y: 0.171875 },
+  { x: 0.296875, y: 0.171875 },
+  { x: 0.296875, y: 0.171875 },
+  { x: 0.328125, y: 0.171875 },
+  { x: 0.328125, y: 0.171875 },
+  { x: 0.359375, y: 0.171875 },
+  { x: 0.359375, y: 0.171875 },
+  { x: 0.390625, y: 0.171875 },
+  { x: 0.390625, y: 0.171875 },
+  { x: 0.421875, y: 0.171875 },
+  { x: 0.421875, y: 0.171875 },
+  { x: 0.453125, y: 0.171875 },
+  { x: 0.453125, y: 0.171875 },
+  { x: 0.484375, y: 0.171875 },
+  { x: 0.484375, y: 0.171875 },
+  { x: 0.515625, y: 0.171875 },
+  { x: 0.515625, y: 0.171875 },
+  { x: 0.546875, y: 0.171875 },
+  { x: 0.546875, y: 0.171875 },
+  { x: 0.578125, y: 0.171875 },
+  { x: 0.578125, y: 0.171875 },
+  { x: 0.609375, y: 0.171875 },
+  { x: 0.609375, y: 0.171875 },
+  { x: 0.640625, y: 0.171875 },
+  { x: 0.640625, y: 0.171875 },
+  { x: 0.671875, y: 0.171875 },
+  { x: 0.671875, y: 0.171875 },
+  { x: 0.703125, y: 0.171875 },
+  { x: 0.703125, y: 0.171875 },
+  { x: 0.734375, y: 0.171875 },
+  { x: 0.734375, y: 0.171875 },
+  { x: 0.765625, y: 0.171875 },
+  { x: 0.765625, y: 0.171875 },
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+  { x: 0.6875, y: 0.6875 },
+  { x: 0.6875, y: 0.6875 },
+  { x: 0.6875, y: 0.6875 },
+  { x: 0.8125, y: 0.6875 },
+  { x: 0.8125, y: 0.6875 },
+  { x: 0.8125, y: 0.6875 },
+  { x: 0.8125, y: 0.6875 },
+  { x: 0.8125, y: 0.6875 },
+  { x: 0.8125, y: 0.6875 },
+  { x: 0.9375, y: 0.6875 },
+  { x: 0.9375, y: 0.6875 },
+  { x: 0.9375, y: 0.6875 },
+  { x: 0.9375, y: 0.6875 },
+  { x: 0.9375, y: 0.6875 },
+  { x: 0.9375, y: 0.6875 },
+  { x: 0.0625, y: 0.8125 },
+  { x: 0.0625, y: 0.8125 },
+  { x: 0.0625, y: 0.8125 },
+  { x: 0.0625, y: 0.8125 },
+  { x: 0.0625, y: 0.8125 },
+  { x: 0.0625, y: 0.8125 },
+  { x: 0.1875, y: 0.8125 },
+  { x: 0.1875, y: 0.8125 },
+  { x: 0.1875, y: 0.8125 },
+  { x: 0.1875, y: 0.8125 },
+  { x: 0.1875, y: 0.8125 },
+  { x: 0.1875, y: 0.8125 },
+  { x: 0.3125, y: 0.8125 },
+  { x: 0.3125, y: 0.8125 },
+  { x: 0.3125, y: 0.8125 },
+  { x: 0.3125, y: 0.8125 },
+  { x: 0.3125, y: 0.8125 },
+  { x: 0.3125, y: 0.8125 },
+  { x: 0.4375, y: 0.8125 },
+  { x: 0.4375, y: 0.8125 },
+  { x: 0.4375, y: 0.8125 },
+  { x: 0.4375, y: 0.8125 },
+  { x: 0.4375, y: 0.8125 },
+  { x: 0.4375, y: 0.8125 },
+  { x: 0.5625, y: 0.8125 },
+  { x: 0.5625, y: 0.8125 },
+  { x: 0.5625, y: 0.8125 },
+  { x: 0.5625, y: 0.8125 },
+  { x: 0.5625, y: 0.8125 },
+  { x: 0.5625, y: 0.8125 },
+  { x: 0.6875, y: 0.8125 },
+  { x: 0.6875, y: 0.8125 },
+  { x: 0.6875, y: 0.8125 },
+  { x: 0.6875, y: 0.8125 },
+  { x: 0.6875, y: 0.8125 },
+  { x: 0.6875, y: 0.8125 },
+  { x: 0.8125, y: 0.8125 },
+  { x: 0.8125, y: 0.8125 },
+  { x: 0.8125, y: 0.8125 },
+  { x: 0.8125, y: 0.8125 },
+  { x: 0.8125, y: 0.8125 },
+  { x: 0.8125, y: 0.8125 },
+  { x: 0.9375, y: 0.8125 },
+  { x: 0.9375, y: 0.8125 },
+  { x: 0.9375, y: 0.8125 },
+  { x: 0.9375, y: 0.8125 },
+  { x: 0.9375, y: 0.8125 },
+  { x: 0.9375, y: 0.8125 },
+  { x: 0.0625, y: 0.9375 },
+  { x: 0.0625, y: 0.9375 },
+  { x: 0.0625, y: 0.9375 },
+  { x: 0.0625, y: 0.9375 },
+  { x: 0.0625, y: 0.9375 },
+  { x: 0.0625, y: 0.9375 },
+  { x: 0.1875, y: 0.9375 },
+  { x: 0.1875, y: 0.9375 },
+  { x: 0.1875, y: 0.9375 },
+  { x: 0.1875, y: 0.9375 },
+  { x: 0.1875, y: 0.9375 },
+  { x: 0.1875, y: 0.9375 },
+  { x: 0.3125, y: 0.9375 },
+  { x: 0.3125, y: 0.9375 },
+  { x: 0.3125, y: 0.9375 },
+  { x: 0.3125, y: 0.9375 },
+  { x: 0.3125, y: 0.9375 },
+  { x: 0.3125, y: 0.9375 },
+  { x: 0.4375, y: 0.9375 },
+  { x: 0.4375, y: 0.9375 },
+  { x: 0.4375, y: 0.9375 },
+  { x: 0.4375, y: 0.9375 },
+  { x: 0.4375, y: 0.9375 },
+  { x: 0.4375, y: 0.9375 },
+  { x: 0.5625, y: 0.9375 },
+  { x: 0.5625, y: 0.9375 },
+  { x: 0.5625, y: 0.9375 },
+  { x: 0.5625, y: 0.9375 },
+  { x: 0.5625, y: 0.9375 },
+  { x: 0.5625, y: 0.9375 },
+  { x: 0.6875, y: 0.9375 },
+  { x: 0.6875, y: 0.9375 },
+  { x: 0.6875, y: 0.9375 },
+  { x: 0.6875, y: 0.9375 },
+  { x: 0.6875, y: 0.9375 },
+  { x: 0.6875, y: 0.9375 },
+  { x: 0.8125, y: 0.9375 },
+  { x: 0.8125, y: 0.9375 },
+  { x: 0.8125, y: 0.9375 },
+  { x: 0.8125, y: 0.9375 },
+  { x: 0.8125, y: 0.9375 },
+  { x: 0.8125, y: 0.9375 },
+  { x: 0.9375, y: 0.9375 },
+  { x: 0.9375, y: 0.9375 },
+  { x: 0.9375, y: 0.9375 },
+  { x: 0.9375, y: 0.9375 },
+  { x: 0.9375, y: 0.9375 },
+  { x: 0.9375, y: 0.9375 }
+];
+
+// src/hand/handposedetector.ts
+var HandDetector = class {
+  constructor(model21) {
+    __publicField(this, "model");
+    __publicField(this, "anchors");
+    __publicField(this, "anchorsTensor");
+    __publicField(this, "inputSize");
+    __publicField(this, "inputSizeTensor");
+    __publicField(this, "doubleInputSizeTensor");
+    var _a, _b, _c, _d;
+    this.model = model21;
+    this.anchors = anchors2.map((anchor) => [anchor.x, anchor.y]);
+    this.anchorsTensor = tf19.tensor2d(this.anchors);
+    this.inputSize = ((_d = (_c = (_b = (_a = this == null ? void 0 : this.model) == null ? void 0 : _a.inputs) == null ? void 0 : _b[0]) == null ? void 0 : _c.shape) == null ? void 0 : _d[2]) || 0;
+    this.inputSizeTensor = tf19.tensor1d([this.inputSize, this.inputSize]);
+    this.doubleInputSizeTensor = tf19.tensor1d([this.inputSize * 2, this.inputSize * 2]);
+  }
+  normalizeBoxes(boxes) {
+    const t2 = {};
+    t2.boxOffsets = tf19.slice(boxes, [0, 0], [-1, 2]);
+    t2.boxSizes = tf19.slice(boxes, [0, 2], [-1, 2]);
+    t2.div = tf19.div(t2.boxOffsets, this.inputSizeTensor);
+    t2.boxCenterPoints = tf19.add(t2.div, this.anchorsTensor);
+    t2.halfBoxSizes = tf19.div(t2.boxSizes, this.doubleInputSizeTensor);
+    t2.sub = tf19.sub(t2.boxCenterPoints, t2.halfBoxSizes);
+    t2.startPoints = tf19.mul(t2.sub, this.inputSizeTensor);
+    t2.add = tf19.add(t2.boxCenterPoints, t2.halfBoxSizes);
+    t2.endPoints = tf19.mul(t2.add, this.inputSizeTensor);
+    const res = tf19.concat2d([t2.startPoints, t2.endPoints], 1);
+    Object.keys(t2).forEach((tensor6) => tf19.dispose(t2[tensor6]));
+    return res;
+  }
+  normalizeLandmarks(rawPalmLandmarks, index2) {
+    const t2 = {};
+    t2.reshape = tf19.reshape(rawPalmLandmarks, [-1, 7, 2]);
+    t2.div = tf19.div(t2.reshape, this.inputSizeTensor);
+    t2.landmarks = tf19.add(t2.div, this.anchors[index2] ? this.anchors[index2] : 0);
+    const res = tf19.mul(t2.landmarks, this.inputSizeTensor);
+    Object.keys(t2).forEach((tensor6) => tf19.dispose(t2[tensor6]));
+    return res;
+  }
+  async predict(input, config3) {
+    var _a;
+    const t2 = {};
+    t2.resize = tf19.image.resizeBilinear(input, [this.inputSize, this.inputSize]);
+    t2.div = tf19.div(t2.resize, constants.tf127);
+    t2.image = tf19.sub(t2.div, constants.tf1);
+    t2.batched = this.model.execute(t2.image);
+    t2.predictions = tf19.squeeze(t2.batched);
+    t2.slice = tf19.slice(t2.predictions, [0, 0], [-1, 1]);
+    t2.sigmoid = tf19.sigmoid(t2.slice);
+    t2.scores = tf19.squeeze(t2.sigmoid);
+    const scores = await t2.scores.data();
+    t2.boxes = tf19.slice(t2.predictions, [0, 1], [-1, 4]);
+    t2.norm = this.normalizeBoxes(t2.boxes);
+    t2.nms = await tf19.image.nonMaxSuppressionAsync(t2.norm, t2.scores, 3 * (((_a = config3.hand) == null ? void 0 : _a.maxDetected) || 1), config3.hand.iouThreshold, config3.hand.minConfidence);
+    const nms = await t2.nms.array();
+    const hands = [];
+    for (const index2 of nms) {
+      const p = {};
+      p.box = tf19.slice(t2.norm, [index2, 0], [1, -1]);
+      p.slice = tf19.slice(t2.predictions, [index2, 5], [1, 14]);
+      p.norm = this.normalizeLandmarks(p.slice, index2);
+      p.palmLandmarks = tf19.reshape(p.norm, [-1, 2]);
+      const box = await p.box.data();
+      const startPoint = box.slice(0, 2);
+      const endPoint = box.slice(2, 4);
+      const palmLandmarks = await p.palmLandmarks.array();
+      const hand3 = { startPoint, endPoint, palmLandmarks, confidence: scores[index2] };
+      const scaled = scaleBoxCoordinates2(hand3, [(input.shape[2] || 1) / this.inputSize, (input.shape[1] || 0) / this.inputSize]);
+      hands.push(scaled);
+      Object.keys(p).forEach((tensor6) => tf19.dispose(p[tensor6]));
+    }
+    Object.keys(t2).forEach((tensor6) => tf19.dispose(t2[tensor6]));
+    return hands;
+  }
+};
+
+// src/hand/handposepipeline.ts
+var tf20 = __toESM(require_tfjs_esm());
+var palmBoxEnlargeFactor = 5;
+var handBoxEnlargeFactor = 1.65;
+var palmLandmarkIds = [0, 5, 9, 13, 17, 1, 2];
+var palmLandmarksPalmBase = 0;
+var palmLandmarksMiddleFingerBase = 2;
+var lastTime8 = 0;
+var HandPipeline = class {
+  constructor(handDetector, handPoseModel2) {
+    __publicField(this, "handDetector");
+    __publicField(this, "handPoseModel");
+    __publicField(this, "inputSize");
+    __publicField(this, "storedBoxes");
+    __publicField(this, "skipped");
+    __publicField(this, "detectedHands");
+    var _a, _b, _c;
+    this.handDetector = handDetector;
+    this.handPoseModel = handPoseModel2;
+    this.inputSize = ((_c = (_b = (_a = this.handPoseModel) == null ? void 0 : _a.inputs) == null ? void 0 : _b[0].shape) == null ? void 0 : _c[2]) || 0;
+    this.storedBoxes = [];
+    this.skipped = Number.MAX_SAFE_INTEGER;
+    this.detectedHands = 0;
+  }
+  calculateLandmarksBoundingBox(landmarks) {
+    const xs = landmarks.map((d) => d[0]);
+    const ys = landmarks.map((d) => d[1]);
+    const startPoint = [Math.min(...xs), Math.min(...ys)];
+    const endPoint = [Math.max(...xs), Math.max(...ys)];
+    return { startPoint, endPoint };
+  }
+  getBoxForPalmLandmarks(palmLandmarks, rotationMatrix) {
+    const rotatedPalmLandmarks = palmLandmarks.map((coord) => rotatePoint2([...coord, 1], rotationMatrix));
+    const boxAroundPalm = this.calculateLandmarksBoundingBox(rotatedPalmLandmarks);
+    return enlargeBox2(squarifyBox2(boxAroundPalm), palmBoxEnlargeFactor);
+  }
+  getBoxForHandLandmarks(landmarks) {
+    const boundingBox = this.calculateLandmarksBoundingBox(landmarks);
+    const boxAroundHand = enlargeBox2(squarifyBox2(boundingBox), handBoxEnlargeFactor);
+    boxAroundHand.palmLandmarks = [];
+    for (let i = 0; i < palmLandmarkIds.length; i++) {
+      boxAroundHand.palmLandmarks.push(landmarks[palmLandmarkIds[i]].slice(0, 2));
+    }
+    return boxAroundHand;
+  }
+  transformRawCoords(rawCoords, box2, angle, rotationMatrix) {
+    const boxSize = getBoxSize2(box2);
+    const scaleFactor = [boxSize[0] / this.inputSize, boxSize[1] / this.inputSize, (boxSize[0] + boxSize[1]) / this.inputSize / 2];
+    const coordsScaled = rawCoords.map((coord) => [
+      scaleFactor[0] * (coord[0] - this.inputSize / 2),
+      scaleFactor[1] * (coord[1] - this.inputSize / 2),
+      scaleFactor[2] * coord[2]
+    ]);
+    const coordsRotationMatrix = buildRotationMatrix2(angle, [0, 0]);
+    const coordsRotated = coordsScaled.map((coord) => {
+      const rotated = rotatePoint2(coord, coordsRotationMatrix);
+      return [...rotated, coord[2]];
+    });
+    const inverseRotationMatrix = invertTransformMatrix2(rotationMatrix);
+    const boxCenter = [...getBoxCenter2(box2), 1];
+    const originalBoxCenter = [
+      dot2(boxCenter, inverseRotationMatrix[0]),
+      dot2(boxCenter, inverseRotationMatrix[1])
+    ];
+    return coordsRotated.map((coord) => [
+      Math.trunc(coord[0] + originalBoxCenter[0]),
+      Math.trunc(coord[1] + originalBoxCenter[1]),
+      Math.trunc(coord[2])
+    ]);
+  }
+  async estimateHands(image27, config3) {
+    let useFreshBox = false;
+    let boxes;
+    const skipTime = (config3.hand.skipTime || 0) > now() - lastTime8;
+    const skipFrame = this.skipped < (config3.hand.skipFrames || 0);
+    if (config3.skipAllowed && skipTime && skipFrame) {
+      boxes = await this.handDetector.predict(image27, config3);
+      this.skipped = 0;
+    }
+    if (config3.skipAllowed)
+      this.skipped++;
+    if (boxes && boxes.length > 0 && (boxes.length !== this.detectedHands && this.detectedHands !== config3.hand.maxDetected || !config3.hand.landmarks)) {
+      this.detectedHands = 0;
+      this.storedBoxes = [...boxes];
+      if (this.storedBoxes.length > 0)
+        useFreshBox = true;
+    }
+    const hands = [];
+    for (let i = 0; i < this.storedBoxes.length; i++) {
+      const currentBox = this.storedBoxes[i];
+      if (!currentBox)
+        continue;
+      if (config3.hand.landmarks) {
+        const angle = config3.hand.rotation ? computeRotation2(currentBox.palmLandmarks[palmLandmarksPalmBase], currentBox.palmLandmarks[palmLandmarksMiddleFingerBase]) : 0;
+        const palmCenter = getBoxCenter2(currentBox);
+        const palmCenterNormalized = [palmCenter[0] / image27.shape[2], palmCenter[1] / image27.shape[1]];
+        const rotatedImage = config3.hand.rotation && env.kernels.includes("rotatewithoffset") ? tf20.image.rotateWithOffset(image27, angle, 0, palmCenterNormalized) : image27.clone();
+        const rotationMatrix = buildRotationMatrix2(-angle, palmCenter);
+        const newBox = useFreshBox ? this.getBoxForPalmLandmarks(currentBox.palmLandmarks, rotationMatrix) : currentBox;
+        const croppedInput = cutBoxFromImageAndResize(newBox, rotatedImage, [this.inputSize, this.inputSize]);
+        const handImage = tf20.div(croppedInput, constants.tf255);
+        tf20.dispose(croppedInput);
+        tf20.dispose(rotatedImage);
+        const [confidenceT, keypoints] = this.handPoseModel.execute(handImage);
+        lastTime8 = now();
+        tf20.dispose(handImage);
+        const confidence = (await confidenceT.data())[0];
+        tf20.dispose(confidenceT);
+        if (confidence >= config3.hand.minConfidence / 4) {
+          const keypointsReshaped = tf20.reshape(keypoints, [-1, 3]);
+          const rawCoords = await keypointsReshaped.array();
+          tf20.dispose(keypoints);
+          tf20.dispose(keypointsReshaped);
+          const coords = this.transformRawCoords(rawCoords, newBox, angle, rotationMatrix);
+          const nextBoundingBox = this.getBoxForHandLandmarks(coords);
+          this.storedBoxes[i] = { ...nextBoundingBox, confidence };
+          const result = {
+            landmarks: coords,
+            confidence,
+            boxConfidence: currentBox.confidence,
+            fingerConfidence: confidence,
+            box: { topLeft: nextBoundingBox.startPoint, bottomRight: nextBoundingBox.endPoint }
+          };
+          hands.push(result);
+        } else {
+          this.storedBoxes[i] = null;
+        }
+        tf20.dispose(keypoints);
+      } else {
+        const enlarged = enlargeBox2(squarifyBox2(currentBox), handBoxEnlargeFactor);
+        const result = {
+          confidence: currentBox.confidence,
+          boxConfidence: currentBox.confidence,
+          fingerConfidence: 0,
+          box: { topLeft: enlarged.startPoint, bottomRight: enlarged.endPoint },
+          landmarks: []
+        };
+        hands.push(result);
+      }
+    }
+    this.storedBoxes = this.storedBoxes.filter((a) => a !== null);
+    this.detectedHands = hands.length;
+    if (hands.length > config3.hand.maxDetected)
+      hands.length = config3.hand.maxDetected;
+    return hands;
+  }
+};
+
+// src/hand/fingerdef.ts
+var Finger = {
+  thumb: 0,
+  index: 1,
+  middle: 2,
+  ring: 3,
+  pinky: 4,
+  all: [0, 1, 2, 3, 4],
+  nameMapping: { 0: "thumb", 1: "index", 2: "middle", 3: "ring", 4: "pinky" },
+  pointsMapping: {
+    0: [[0, 1], [1, 2], [2, 3], [3, 4]],
+    1: [[0, 5], [5, 6], [6, 7], [7, 8]],
+    2: [[0, 9], [9, 10], [10, 11], [11, 12]],
+    3: [[0, 13], [13, 14], [14, 15], [15, 16]],
+    4: [[0, 17], [17, 18], [18, 19], [19, 20]]
+  },
+  getName: (value) => Finger.nameMapping[value],
+  getPoints: (value) => Finger.pointsMapping[value]
+};
+var FingerCurl = {
+  none: 0,
+  half: 1,
+  full: 2,
+  nameMapping: { 0: "none", 1: "half", 2: "full" },
+  getName: (value) => FingerCurl.nameMapping[value]
+};
+var FingerDirection = {
+  verticalUp: 0,
+  verticalDown: 1,
+  horizontalLeft: 2,
+  horizontalRight: 3,
+  diagonalUpRight: 4,
+  diagonalUpLeft: 5,
+  diagonalDownRight: 6,
+  diagonalDownLeft: 7,
+  nameMapping: { 0: "verticalUp", 1: "verticalDown", 2: "horizontalLeft", 3: "horizontalRight", 4: "diagonalUpRight", 5: "diagonalUpLeft", 6: "diagonalDownRight", 7: "diagonalDownLeft" },
+  getName: (value) => FingerDirection.nameMapping[value]
+};
+var FingerGesture = class {
+  constructor(name) {
+    __publicField(this, "name");
+    __publicField(this, "curls");
+    __publicField(this, "directions");
+    __publicField(this, "weights");
+    __publicField(this, "weightsRelative");
+    this.name = name;
+    this.curls = {};
+    this.directions = {};
+    this.weights = [1, 1, 1, 1, 1];
+    this.weightsRelative = [1, 1, 1, 1, 1];
+  }
+  curl(finger, curl, confidence) {
+    if (typeof this.curls[finger] === "undefined")
+      this.curls[finger] = [];
+    this.curls[finger].push([curl, confidence]);
+  }
+  direction(finger, position, confidence) {
+    if (!this.directions[finger])
+      this.directions[finger] = [];
+    this.directions[finger].push([position, confidence]);
+  }
+  weight(finger, weight) {
+    this.weights[finger] = weight;
+    const total = this.weights.reduce((a, b) => a + b, 0);
+    this.weightsRelative = this.weights.map((el) => el * 5 / total);
+  }
+  matchAgainst(detectedCurls, detectedDirections) {
+    let confidence = 0;
+    for (const fingerIdx in detectedCurls) {
+      const detectedCurl = detectedCurls[fingerIdx];
+      const expectedCurls = this.curls[fingerIdx];
+      if (typeof expectedCurls === "undefined") {
+        confidence += this.weightsRelative[fingerIdx];
+        continue;
+      }
+      for (const [expectedCurl, score] of expectedCurls) {
+        if (detectedCurl === expectedCurl) {
+          confidence += score * this.weightsRelative[fingerIdx];
+          break;
+        }
+      }
+    }
+    for (const fingerIdx in detectedDirections) {
+      const detectedDirection = detectedDirections[fingerIdx];
+      const expectedDirections = this.directions[fingerIdx];
+      if (typeof expectedDirections === "undefined") {
+        confidence += this.weightsRelative[fingerIdx];
+        continue;
+      }
+      for (const [expectedDirection, score] of expectedDirections) {
+        if (detectedDirection === expectedDirection) {
+          confidence += score * this.weightsRelative[fingerIdx];
+          break;
+        }
+      }
+    }
+    return confidence / 10;
+  }
+};
+
+// src/hand/fingergesture.ts
+var { thumb, index, middle, ring, pinky } = Finger;
+var { none, half, full } = FingerCurl;
+var { verticalUp, verticalDown, horizontalLeft, horizontalRight, diagonalUpRight, diagonalUpLeft, diagonalDownRight, diagonalDownLeft } = FingerDirection;
+var ThumbsUp = new FingerGesture("thumbs up");
+ThumbsUp.curl(thumb, none, 1);
+ThumbsUp.direction(thumb, verticalUp, 1);
+ThumbsUp.direction(thumb, diagonalUpLeft, 0.25);
+ThumbsUp.direction(thumb, diagonalUpRight, 0.25);
+for (const finger of [Finger.index, Finger.middle, Finger.ring, Finger.pinky]) {
+  ThumbsUp.curl(finger, full, 1);
+  ThumbsUp.direction(finger, horizontalLeft, 1);
+  ThumbsUp.direction(finger, horizontalRight, 1);
+}
+var Victory = new FingerGesture("victory");
+Victory.curl(thumb, half, 0.5);
+Victory.curl(thumb, none, 0.5);
+Victory.direction(thumb, verticalUp, 1);
+Victory.direction(thumb, diagonalUpLeft, 1);
+Victory.curl(index, none, 1);
+Victory.direction(index, verticalUp, 0.75);
+Victory.direction(index, diagonalUpLeft, 1);
+Victory.curl(middle, none, 1);
+Victory.direction(middle, verticalUp, 1);
+Victory.direction(middle, diagonalUpLeft, 0.75);
+Victory.curl(ring, full, 1);
+Victory.direction(ring, verticalUp, 0.2);
+Victory.direction(ring, diagonalUpLeft, 1);
+Victory.direction(ring, horizontalLeft, 0.2);
+Victory.curl(pinky, full, 1);
+Victory.direction(pinky, verticalUp, 0.2);
+Victory.direction(pinky, diagonalUpLeft, 1);
+Victory.direction(pinky, horizontalLeft, 0.2);
+Victory.weight(index, 2);
+Victory.weight(middle, 2);
+var Point = new FingerGesture("point");
+Point.curl(thumb, full, 1);
+Point.curl(index, none, 0.5);
+Point.curl(middle, full, 0.5);
+Point.curl(ring, full, 0.5);
+Point.curl(pinky, full, 0.5);
+Point.weight(index, 2);
+Point.weight(middle, 2);
+var MiddleFinger = new FingerGesture("middle finger");
+MiddleFinger.curl(thumb, none, 1);
+MiddleFinger.curl(index, full, 0.5);
+MiddleFinger.curl(middle, full, 0.5);
+MiddleFinger.curl(ring, full, 0.5);
+MiddleFinger.curl(pinky, full, 0.5);
+MiddleFinger.weight(index, 2);
+MiddleFinger.weight(middle, 2);
+var OpenPalm = new FingerGesture("open palm");
+OpenPalm.curl(thumb, none, 0.75);
+OpenPalm.curl(index, none, 0.75);
+OpenPalm.curl(middle, none, 0.75);
+OpenPalm.curl(ring, none, 0.75);
+OpenPalm.curl(pinky, none, 0.75);
+var fingergesture_default = [ThumbsUp, Victory, Point, MiddleFinger, OpenPalm];
+
+// src/hand/fingerpose.ts
+var minConfidence = 0.7;
+var options2 = {
+  HALF_CURL_START_LIMIT: 60,
+  NO_CURL_START_LIMIT: 130,
+  DISTANCE_VOTE_POWER: 1.1,
+  SINGLE_ANGLE_VOTE_POWER: 0.9,
+  TOTAL_ANGLE_VOTE_POWER: 1.6
+};
+function calculateSlope(point1x, point1y, point2x, point2y) {
+  const value = (point1y - point2y) / (point1x - point2x);
+  let slope = Math.atan(value) * 180 / Math.PI;
+  if (slope <= 0)
+    slope = -slope;
+  else if (slope > 0)
+    slope = 180 - slope;
+  return slope;
+}
+function getSlopes(point1, point2) {
+  if (!point1 || !point2)
+    return [0, 0];
+  const slopeXY = calculateSlope(point1[0], point1[1], point2[0], point2[1]);
+  if (point1.length === 2)
+    return slopeXY;
+  const slopeYZ = calculateSlope(point1[1], point1[2], point2[1], point2[2]);
+  return [slopeXY, slopeYZ];
+}
+function angleOrientationAt(angle, weightageAt = 1) {
+  let isVertical = 0;
+  let isDiagonal = 0;
+  let isHorizontal = 0;
+  if (angle >= 75 && angle <= 105)
+    isVertical = 1 * weightageAt;
+  else if (angle >= 25 && angle <= 155)
+    isDiagonal = 1 * weightageAt;
+  else
+    isHorizontal = 1 * weightageAt;
+  return [isVertical, isDiagonal, isHorizontal];
+}
+function estimateFingerCurl(startPoint, midPoint, endPoint) {
+  const start_mid_x_dist = startPoint[0] - midPoint[0];
+  const start_end_x_dist = startPoint[0] - endPoint[0];
+  const mid_end_x_dist = midPoint[0] - endPoint[0];
+  const start_mid_y_dist = startPoint[1] - midPoint[1];
+  const start_end_y_dist = startPoint[1] - endPoint[1];
+  const mid_end_y_dist = midPoint[1] - endPoint[1];
+  const start_mid_z_dist = startPoint[2] - midPoint[2];
+  const start_end_z_dist = startPoint[2] - endPoint[2];
+  const mid_end_z_dist = midPoint[2] - endPoint[2];
+  const start_mid_dist = Math.sqrt(start_mid_x_dist * start_mid_x_dist + start_mid_y_dist * start_mid_y_dist + start_mid_z_dist * start_mid_z_dist);
+  const start_end_dist = Math.sqrt(start_end_x_dist * start_end_x_dist + start_end_y_dist * start_end_y_dist + start_end_z_dist * start_end_z_dist);
+  const mid_end_dist = Math.sqrt(mid_end_x_dist * mid_end_x_dist + mid_end_y_dist * mid_end_y_dist + mid_end_z_dist * mid_end_z_dist);
+  let cos_in = (mid_end_dist * mid_end_dist + start_mid_dist * start_mid_dist - start_end_dist * start_end_dist) / (2 * mid_end_dist * start_mid_dist);
+  if (cos_in > 1)
+    cos_in = 1;
+  else if (cos_in < -1)
+    cos_in = -1;
+  let angleOfCurve = Math.acos(cos_in);
+  angleOfCurve = 57.2958 * angleOfCurve % 180;
+  let fingerCurl;
+  if (angleOfCurve > options2.NO_CURL_START_LIMIT)
+    fingerCurl = FingerCurl.none;
+  else if (angleOfCurve > options2.HALF_CURL_START_LIMIT)
+    fingerCurl = FingerCurl.half;
+  else
+    fingerCurl = FingerCurl.full;
+  return fingerCurl;
+}
+function estimateHorizontalDirection(start_end_x_dist, start_mid_x_dist, mid_end_x_dist, max_dist_x) {
+  let estimatedDirection;
+  if (max_dist_x === Math.abs(start_end_x_dist)) {
+    if (start_end_x_dist > 0)
+      estimatedDirection = FingerDirection.horizontalLeft;
+    else
+      estimatedDirection = FingerDirection.horizontalRight;
+  } else if (max_dist_x === Math.abs(start_mid_x_dist)) {
+    if (start_mid_x_dist > 0)
+      estimatedDirection = FingerDirection.horizontalLeft;
+    else
+      estimatedDirection = FingerDirection.horizontalRight;
+  } else {
+    if (mid_end_x_dist > 0)
+      estimatedDirection = FingerDirection.horizontalLeft;
+    else
+      estimatedDirection = FingerDirection.horizontalRight;
+  }
+  return estimatedDirection;
+}
+function estimateVerticalDirection(start_end_y_dist, start_mid_y_dist, mid_end_y_dist, max_dist_y) {
+  let estimatedDirection;
+  if (max_dist_y === Math.abs(start_end_y_dist)) {
+    if (start_end_y_dist < 0)
+      estimatedDirection = FingerDirection.verticalDown;
+    else
+      estimatedDirection = FingerDirection.verticalUp;
+  } else if (max_dist_y === Math.abs(start_mid_y_dist)) {
+    if (start_mid_y_dist < 0)
+      estimatedDirection = FingerDirection.verticalDown;
+    else
+      estimatedDirection = FingerDirection.verticalUp;
+  } else {
+    if (mid_end_y_dist < 0)
+      estimatedDirection = FingerDirection.verticalDown;
+    else
+      estimatedDirection = FingerDirection.verticalUp;
+  }
+  return estimatedDirection;
+}
+function estimateDiagonalDirection(start_end_y_dist, start_mid_y_dist, mid_end_y_dist, max_dist_y, start_end_x_dist, start_mid_x_dist, mid_end_x_dist, max_dist_x) {
+  let estimatedDirection;
+  const reqd_vertical_direction = estimateVerticalDirection(start_end_y_dist, start_mid_y_dist, mid_end_y_dist, max_dist_y);
+  const reqd_horizontal_direction = estimateHorizontalDirection(start_end_x_dist, start_mid_x_dist, mid_end_x_dist, max_dist_x);
+  if (reqd_vertical_direction === FingerDirection.verticalUp) {
+    if (reqd_horizontal_direction === FingerDirection.horizontalLeft)
+      estimatedDirection = FingerDirection.diagonalUpLeft;
+    else
+      estimatedDirection = FingerDirection.diagonalUpRight;
+  } else {
+    if (reqd_horizontal_direction === FingerDirection.horizontalLeft)
+      estimatedDirection = FingerDirection.diagonalDownLeft;
+    else
+      estimatedDirection = FingerDirection.diagonalDownRight;
+  }
+  return estimatedDirection;
+}
+function calculateFingerDirection(startPoint, midPoint, endPoint, fingerSlopes) {
+  const start_mid_x_dist = startPoint[0] - midPoint[0];
+  const start_end_x_dist = startPoint[0] - endPoint[0];
+  const mid_end_x_dist = midPoint[0] - endPoint[0];
+  const start_mid_y_dist = startPoint[1] - midPoint[1];
+  const start_end_y_dist = startPoint[1] - endPoint[1];
+  const mid_end_y_dist = midPoint[1] - endPoint[1];
+  const max_dist_x = Math.max(Math.abs(start_mid_x_dist), Math.abs(start_end_x_dist), Math.abs(mid_end_x_dist));
+  const max_dist_y = Math.max(Math.abs(start_mid_y_dist), Math.abs(start_end_y_dist), Math.abs(mid_end_y_dist));
+  let voteVertical = 0;
+  let voteDiagonal = 0;
+  let voteHorizontal = 0;
+  const start_end_x_y_dist_ratio = max_dist_y / (max_dist_x + 1e-5);
+  if (start_end_x_y_dist_ratio > 1.5)
+    voteVertical += options2.DISTANCE_VOTE_POWER;
+  else if (start_end_x_y_dist_ratio > 0.66)
+    voteDiagonal += options2.DISTANCE_VOTE_POWER;
+  else
+    voteHorizontal += options2.DISTANCE_VOTE_POWER;
+  const start_mid_dist = Math.sqrt(start_mid_x_dist * start_mid_x_dist + start_mid_y_dist * start_mid_y_dist);
+  const start_end_dist = Math.sqrt(start_end_x_dist * start_end_x_dist + start_end_y_dist * start_end_y_dist);
+  const mid_end_dist = Math.sqrt(mid_end_x_dist * mid_end_x_dist + mid_end_y_dist * mid_end_y_dist);
+  const max_dist = Math.max(start_mid_dist, start_end_dist, mid_end_dist);
+  let calc_start_point_x = startPoint[0];
+  let calc_start_point_y = startPoint[1];
+  let calc_end_point_x = endPoint[0];
+  let calc_end_point_y = endPoint[1];
+  if (max_dist === start_mid_dist) {
+    calc_end_point_x = endPoint[0];
+    calc_end_point_y = endPoint[1];
+  } else if (max_dist === mid_end_dist) {
+    calc_start_point_x = midPoint[0];
+    calc_start_point_y = midPoint[1];
+  }
+  const calcStartPoint = [calc_start_point_x, calc_start_point_y];
+  const calcEndPoint = [calc_end_point_x, calc_end_point_y];
+  const totalAngle = getSlopes(calcStartPoint, calcEndPoint);
+  const votes = angleOrientationAt(totalAngle, options2.TOTAL_ANGLE_VOTE_POWER);
+  voteVertical += votes[0];
+  voteDiagonal += votes[1];
+  voteHorizontal += votes[2];
+  for (const fingerSlope of fingerSlopes) {
+    const fingerVotes = angleOrientationAt(fingerSlope, options2.SINGLE_ANGLE_VOTE_POWER);
+    voteVertical += fingerVotes[0];
+    voteDiagonal += fingerVotes[1];
+    voteHorizontal += fingerVotes[2];
+  }
+  let estimatedDirection;
+  if (voteVertical === Math.max(voteVertical, voteDiagonal, voteHorizontal)) {
+    estimatedDirection = estimateVerticalDirection(start_end_y_dist, start_mid_y_dist, mid_end_y_dist, max_dist_y);
+  } else if (voteHorizontal === Math.max(voteDiagonal, voteHorizontal)) {
+    estimatedDirection = estimateHorizontalDirection(start_end_x_dist, start_mid_x_dist, mid_end_x_dist, max_dist_x);
+  } else {
+    estimatedDirection = estimateDiagonalDirection(start_end_y_dist, start_mid_y_dist, mid_end_y_dist, max_dist_y, start_end_x_dist, start_mid_x_dist, mid_end_x_dist, max_dist_x);
+  }
+  return estimatedDirection;
+}
+function estimate(landmarks) {
+  const slopesXY = [];
+  const slopesYZ = [];
+  const fingerCurls = [];
+  const fingerDirections = [];
+  if (!landmarks)
+    return { curls: fingerCurls, directions: fingerDirections };
+  for (const finger of Finger.all) {
+    const points = Finger.getPoints(finger);
+    const slopeAtXY = [];
+    const slopeAtYZ = [];
+    for (const point2 of points) {
+      const point1 = landmarks[point2[0]];
+      const point22 = landmarks[point2[1]];
+      const slopes = getSlopes(point1, point22);
+      const slopeXY = slopes[0];
+      const slopeYZ = slopes[1];
+      slopeAtXY.push(slopeXY);
+      slopeAtYZ.push(slopeYZ);
+    }
+    slopesXY.push(slopeAtXY);
+    slopesYZ.push(slopeAtYZ);
+  }
+  for (const finger of Finger.all) {
+    const pointIndexAt = finger === Finger.thumb ? 1 : 0;
+    const fingerPointsAt = Finger.getPoints(finger);
+    const startPoint = landmarks[fingerPointsAt[pointIndexAt][0]];
+    const midPoint = landmarks[fingerPointsAt[pointIndexAt + 1][1]];
+    const endPoint = landmarks[fingerPointsAt[3][1]];
+    const fingerCurled = estimateFingerCurl(startPoint, midPoint, endPoint);
+    const fingerPosition = calculateFingerDirection(startPoint, midPoint, endPoint, slopesXY[finger].slice(pointIndexAt));
+    fingerCurls[finger] = fingerCurled;
+    fingerDirections[finger] = fingerPosition;
+  }
+  return { curls: fingerCurls, directions: fingerDirections };
+}
+function analyze(keypoints) {
+  if (!keypoints || keypoints.length === 0)
+    return null;
+  const estimatorRes = estimate(keypoints);
+  const landmarks = {};
+  for (const fingerIdx of Finger.all) {
+    landmarks[Finger.getName(fingerIdx)] = {
+      curl: FingerCurl.getName(estimatorRes.curls[fingerIdx]),
+      direction: FingerDirection.getName(estimatorRes.directions[fingerIdx])
+    };
+  }
+  return landmarks;
+}
+function match(keypoints) {
+  const poses = [];
+  if (!keypoints || keypoints.length === 0)
+    return poses;
+  const estimatorRes = estimate(keypoints);
+  for (const gesture2 of fingergesture_default) {
+    const confidence = gesture2.matchAgainst(estimatorRes.curls, estimatorRes.directions);
+    if (confidence >= minConfidence)
+      poses.push({ name: gesture2.name, confidence });
+  }
+  return poses;
+}
+
+// src/hand/handpose.ts
+var meshAnnotations2 = {
+  thumb: [1, 2, 3, 4],
+  index: [5, 6, 7, 8],
+  middle: [9, 10, 11, 12],
+  ring: [13, 14, 15, 16],
+  pinky: [17, 18, 19, 20],
+  palm: [0]
+};
+var handDetectorModel;
+var handPoseModel;
+var handPipeline;
+async function predict9(input, config3) {
+  const predictions = await handPipeline.estimateHands(input, config3);
+  if (!predictions)
+    return [];
+  const hands = [];
+  for (let i = 0; i < predictions.length; i++) {
+    const annotations2 = {};
+    if (predictions[i].landmarks) {
+      for (const key of Object.keys(meshAnnotations2)) {
+        annotations2[key] = meshAnnotations2[key].map((index2) => predictions[i].landmarks[index2]);
+      }
+    }
+    const keypoints = predictions[i].landmarks;
+    let box = [Number.MAX_SAFE_INTEGER, Number.MAX_SAFE_INTEGER, 0, 0];
+    let boxRaw = [0, 0, 0, 0];
+    if (keypoints && keypoints.length > 0) {
+      for (const pt of keypoints) {
+        if (pt[0] < box[0])
+          box[0] = pt[0];
+        if (pt[1] < box[1])
+          box[1] = pt[1];
+        if (pt[0] > box[2])
+          box[2] = pt[0];
+        if (pt[1] > box[3])
+          box[3] = pt[1];
+      }
+      box[2] -= box[0];
+      box[3] -= box[1];
+      boxRaw = [box[0] / (input.shape[2] || 0), box[1] / (input.shape[1] || 0), box[2] / (input.shape[2] || 0), box[3] / (input.shape[1] || 0)];
+    } else {
+      box = predictions[i].box ? [
+        Math.trunc(Math.max(0, predictions[i].box.topLeft[0])),
+        Math.trunc(Math.max(0, predictions[i].box.topLeft[1])),
+        Math.trunc(Math.min(input.shape[2] || 0, predictions[i].box.bottomRight[0]) - Math.max(0, predictions[i].box.topLeft[0])),
+        Math.trunc(Math.min(input.shape[1] || 0, predictions[i].box.bottomRight[1]) - Math.max(0, predictions[i].box.topLeft[1]))
+      ] : [0, 0, 0, 0];
+      boxRaw = [
+        predictions[i].box.topLeft[0] / (input.shape[2] || 0),
+        predictions[i].box.topLeft[1] / (input.shape[1] || 0),
+        (predictions[i].box.bottomRight[0] - predictions[i].box.topLeft[0]) / (input.shape[2] || 0),
+        (predictions[i].box.bottomRight[1] - predictions[i].box.topLeft[1]) / (input.shape[1] || 0)
+      ];
+    }
+    const landmarks = analyze(keypoints);
+    hands.push({
+      id: i,
+      score: Math.round(100 * predictions[i].confidence) / 100,
+      boxScore: Math.round(100 * predictions[i].boxConfidence) / 100,
+      fingerScore: Math.round(100 * predictions[i].fingerConfidence) / 100,
+      label: "hand",
+      box,
+      boxRaw,
+      keypoints,
+      annotations: annotations2,
+      landmarks
+    });
+  }
+  return hands;
+}
+async function load10(config3) {
+  var _a, _b;
+  if (env.initial) {
+    handDetectorModel = null;
+    handPoseModel = null;
+  }
+  if (!handDetectorModel || !handPoseModel) {
+    [handDetectorModel, handPoseModel] = await Promise.all([
+      config3.hand.enabled ? loadModel((_a = config3.hand.detector) == null ? void 0 : _a.modelPath) : null,
+      config3.hand.landmarks ? loadModel((_b = config3.hand.skeleton) == null ? void 0 : _b.modelPath) : null
+    ]);
+  } else {
+    if (config3.debug)
+      log("cached model:", handDetectorModel["modelUrl"]);
+    if (config3.debug)
+      log("cached model:", handPoseModel["modelUrl"]);
+  }
+  const handDetector = handDetectorModel ? new HandDetector(handDetectorModel) : void 0;
+  if (handDetector && handPoseModel)
+    handPipeline = new HandPipeline(handDetector, handPoseModel);
+  return [handDetectorModel, handPoseModel];
+}
+
+// src/hand/handtrack.ts
+var tf21 = __toESM(require_tfjs_esm());
+var models3 = [null, null];
+var modelOutputNodes = ["StatefulPartitionedCall/Postprocessor/Slice", "StatefulPartitionedCall/Postprocessor/ExpandDims_1"];
+var inputSize7 = [[0, 0], [0, 0]];
+var classes = ["hand", "fist", "pinch", "point", "face", "tip", "pinchtip"];
+var faceIndex = 4;
+var boxExpandFact = 1.6;
+var maxDetectorResolution = 512;
+var detectorExpandFact = 1.4;
+var skipped8 = Number.MAX_SAFE_INTEGER;
+var lastTime9 = 0;
+var outputSize = [0, 0];
+var cache4 = {
+  boxes: [],
+  hands: []
+};
+var fingerMap = {
+  thumb: [1, 2, 3, 4],
+  index: [5, 6, 7, 8],
+  middle: [9, 10, 11, 12],
+  ring: [13, 14, 15, 16],
+  pinky: [17, 18, 19, 20],
+  base: [0],
+  palm: [0, 17, 13, 9, 5, 1, 0]
+};
+async function loadDetect2(config3) {
+  var _a;
+  if (env.initial)
+    models3[0] = null;
+  if (!models3[0]) {
+    fakeOps(["tensorlistreserve", "enter", "tensorlistfromtensor", "merge", "loopcond", "switch", "exit", "tensorliststack", "nextiteration", "tensorlistsetitem", "tensorlistgetitem", "reciprocal", "shape", "split", "where"], config3);
+    models3[0] = await loadModel((_a = config3.hand.detector) == null ? void 0 : _a.modelPath);
+    const inputs = models3[0]["executor"] ? Object.values(models3[0].modelSignature["inputs"]) : void 0;
+    inputSize7[0][0] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[1].size) : 0;
+    inputSize7[0][1] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 0;
+  } else if (config3.debug)
+    log("cached model:", models3[0]["modelUrl"]);
+  return models3[0];
+}
+async function loadSkeleton(config3) {
+  var _a;
+  if (env.initial)
+    models3[1] = null;
+  if (!models3[1]) {
+    models3[1] = await loadModel((_a = config3.hand.skeleton) == null ? void 0 : _a.modelPath);
+    const inputs = models3[1]["executor"] ? Object.values(models3[1].modelSignature["inputs"]) : void 0;
+    inputSize7[1][0] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[1].size) : 0;
+    inputSize7[1][1] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 0;
+  } else if (config3.debug)
+    log("cached model:", models3[1]["modelUrl"]);
+  return models3[1];
+}
+async function detectHands(input, config3) {
+  const hands = [];
+  if (!input || !models3[0])
+    return hands;
+  const t2 = {};
+  const ratio2 = (input.shape[2] || 1) / (input.shape[1] || 1);
+  const height = Math.min(Math.round((input.shape[1] || 0) / 8) * 8, maxDetectorResolution);
+  const width = Math.round(height * ratio2 / 8) * 8;
+  t2.resize = tf21.image.resizeBilinear(input, [height, width]);
+  t2.cast = tf21.cast(t2.resize, "int32");
+  [t2.rawScores, t2.rawBoxes] = await models3[0].executeAsync(t2.cast, modelOutputNodes);
+  t2.boxes = tf21.squeeze(t2.rawBoxes, [0, 2]);
+  t2.scores = tf21.squeeze(t2.rawScores, [0]);
+  const classScores = tf21.unstack(t2.scores, 1);
+  tf21.dispose(classScores[faceIndex]);
+  classScores.splice(faceIndex, 1);
+  t2.filtered = tf21.stack(classScores, 1);
+  tf21.dispose(classScores);
+  t2.max = tf21.max(t2.filtered, 1);
+  t2.argmax = tf21.argMax(t2.filtered, 1);
+  let id = 0;
+  t2.nms = await tf21.image.nonMaxSuppressionAsync(t2.boxes, t2.max, (config3.hand.maxDetected || 0) + 1, config3.hand.iouThreshold || 0, config3.hand.minConfidence || 1);
+  const nms = await t2.nms.data();
+  const scores = await t2.max.data();
+  const classNum = await t2.argmax.data();
+  for (const nmsIndex of Array.from(nms)) {
+    const boxSlice = tf21.slice(t2.boxes, nmsIndex, 1);
+    const boxYX = await boxSlice.data();
+    tf21.dispose(boxSlice);
+    const boxData = [boxYX[1], boxYX[0], boxYX[3] - boxYX[1], boxYX[2] - boxYX[0]];
+    const boxRaw = scale(boxData, detectorExpandFact);
+    const boxFull = [Math.trunc(boxData[0] * outputSize[0]), Math.trunc(boxData[1] * outputSize[1]), Math.trunc(boxData[2] * outputSize[0]), Math.trunc(boxData[3] * outputSize[1])];
+    const score = scores[nmsIndex];
+    const label = classes[classNum[nmsIndex]];
+    const hand3 = { id: id++, score, box: boxFull, boxRaw, label };
+    hands.push(hand3);
+  }
+  Object.keys(t2).forEach((tensor6) => tf21.dispose(t2[tensor6]));
+  hands.sort((a, b) => b.score - a.score);
+  if (hands.length > (config3.hand.maxDetected || 1))
+    hands.length = config3.hand.maxDetected || 1;
+  return hands;
+}
+async function detectFingers(input, h, config3) {
+  const hand3 = {
+    id: h.id,
+    score: Math.round(100 * h.score) / 100,
+    boxScore: Math.round(100 * h.score) / 100,
+    fingerScore: 0,
+    box: h.box,
+    boxRaw: h.boxRaw,
+    label: h.label,
+    keypoints: [],
+    landmarks: {},
+    annotations: {}
+  };
+  if (input && models3[1] && config3.hand.landmarks && h.score > (config3.hand.minConfidence || 0)) {
+    const t2 = {};
+    const boxCrop = [h.boxRaw[1], h.boxRaw[0], h.boxRaw[3] + h.boxRaw[1], h.boxRaw[2] + h.boxRaw[0]];
+    t2.crop = tf21.image.cropAndResize(input, [boxCrop], [0], [inputSize7[1][0], inputSize7[1][1]], "bilinear");
+    t2.div = tf21.div(t2.crop, constants.tf255);
+    [t2.score, t2.keypoints] = models3[1].execute(t2.div, ["Identity_1", "Identity"]);
+    const rawScore = (await t2.score.data())[0];
+    const score = (100 - Math.trunc(100 / (1 + Math.exp(rawScore)))) / 100;
+    if (score >= (config3.hand.minConfidence || 0)) {
+      hand3.fingerScore = score;
+      t2.reshaped = tf21.reshape(t2.keypoints, [-1, 3]);
+      const coordsData = await t2.reshaped.array();
+      const coordsRaw = coordsData.map((kpt4) => [kpt4[0] / inputSize7[1][1], kpt4[1] / inputSize7[1][0], kpt4[2] || 0]);
+      const coordsNorm = coordsRaw.map((kpt4) => [kpt4[0] * h.boxRaw[2], kpt4[1] * h.boxRaw[3], kpt4[2] || 0]);
+      hand3.keypoints = coordsNorm.map((kpt4) => [outputSize[0] * (kpt4[0] + h.boxRaw[0]), outputSize[1] * (kpt4[1] + h.boxRaw[1]), kpt4[2] || 0]);
+      hand3.landmarks = analyze(hand3.keypoints);
+      for (const key of Object.keys(fingerMap)) {
+        hand3.annotations[key] = fingerMap[key].map((index2) => hand3.landmarks && hand3.keypoints[index2] ? hand3.keypoints[index2] : null);
+      }
+    }
+    Object.keys(t2).forEach((tensor6) => tf21.dispose(t2[tensor6]));
+  }
+  return hand3;
+}
+async function predict10(input, config3) {
+  var _a, _b;
+  if (!((_a = models3[0]) == null ? void 0 : _a["executor"]) || !((_b = models3[1]) == null ? void 0 : _b["executor"]) || !models3[0].inputs[0].shape || !models3[1].inputs[0].shape)
+    return [];
+  outputSize = [input.shape[2] || 0, input.shape[1] || 0];
+  skipped8++;
+  const skipTime = (config3.hand.skipTime || 0) > now() - lastTime9;
+  const skipFrame = skipped8 < (config3.hand.skipFrames || 0);
+  if (config3.skipAllowed && skipTime && skipFrame) {
+    return cache4.hands;
+  }
+  return new Promise(async (resolve) => {
+    const skipTimeExtended = 3 * (config3.hand.skipTime || 0) > now() - lastTime9;
+    const skipFrameExtended = skipped8 < 3 * (config3.hand.skipFrames || 0);
+    if (config3.skipAllowed && cache4.hands.length === config3.hand.maxDetected) {
+      cache4.hands = await Promise.all(cache4.boxes.map((handBox) => detectFingers(input, handBox, config3)));
+    } else if (config3.skipAllowed && skipTimeExtended && skipFrameExtended && cache4.hands.length > 0) {
+      cache4.hands = await Promise.all(cache4.boxes.map((handBox) => detectFingers(input, handBox, config3)));
+    } else {
+      cache4.boxes = await detectHands(input, config3);
+      lastTime9 = now();
+      cache4.hands = await Promise.all(cache4.boxes.map((handBox) => detectFingers(input, handBox, config3)));
+      skipped8 = 0;
+    }
+    const oldCache = [...cache4.boxes];
+    cache4.boxes.length = 0;
+    if (config3.cacheSensitivity > 0) {
+      for (let i = 0; i < cache4.hands.length; i++) {
+        const boxKpt = square(cache4.hands[i].keypoints, outputSize);
+        if (boxKpt.box[2] / (input.shape[2] || 1) > 0.05 && boxKpt.box[3] / (input.shape[1] || 1) > 0.05 && cache4.hands[i].fingerScore && cache4.hands[i].fingerScore > (config3.hand.minConfidence || 0)) {
+          const boxScale = scale(boxKpt.box, boxExpandFact);
+          const boxScaleRaw = scale(boxKpt.boxRaw, boxExpandFact);
+          cache4.boxes.push({ ...oldCache[i], box: boxScale, boxRaw: boxScaleRaw });
+        }
+      }
+    }
+    for (let i = 0; i < cache4.hands.length; i++) {
+      const bbox = calc(cache4.hands[i].keypoints, outputSize);
+      cache4.hands[i].box = bbox.box;
+      cache4.hands[i].boxRaw = bbox.boxRaw;
+    }
+    resolve(cache4.hands);
+  });
+}
+
+// src/face/insightface.ts
+var tf22 = __toESM(require_tfjs_esm());
+var model10;
+var last6 = [];
+var lastCount5 = 0;
+var lastTime10 = 0;
+var skipped9 = Number.MAX_SAFE_INTEGER;
+async function load11(config3) {
+  if (env.initial)
+    model10 = null;
+  if (!model10)
+    model10 = await loadModel(config3.face["insightface"].modelPath);
+  else if (config3.debug)
+    log("cached model:", model10["modelUrl"]);
+  return model10;
+}
+async function predict11(input, config3, idx, count2) {
+  var _a, _b;
+  if (!(model10 == null ? void 0 : model10["executor"]))
+    return [];
+  const skipFrame = skipped9 < (((_a = config3.face["insightface"]) == null ? void 0 : _a.skipFrames) || 0);
+  const skipTime = (((_b = config3.face["insightface"]) == null ? void 0 : _b.skipTime) || 0) > now() - lastTime10;
+  if (config3.skipAllowed && skipTime && skipFrame && lastCount5 === count2 && last6[idx]) {
+    skipped9++;
+    return last6[idx];
+  }
+  return new Promise(async (resolve) => {
+    var _a2;
+    let data = [];
+    if (((_a2 = config3.face["insightface"]) == null ? void 0 : _a2.enabled) && (model10 == null ? void 0 : model10.inputs[0].shape)) {
+      const t2 = {};
+      t2.crop = tf22.image.resizeBilinear(input, [model10.inputs[0].shape[2], model10.inputs[0].shape[1]], false);
+      t2.data = model10.execute(t2.crop);
+      const output = await t2.data.data();
+      data = Array.from(output);
+      Object.keys(t2).forEach((tensor6) => tf22.dispose(t2[tensor6]));
+    }
+    last6[idx] = data;
+    lastCount5 = count2;
+    lastTime10 = now();
+    resolve(data);
+  });
+}
+
+// src/face/liveness.ts
+var tf23 = __toESM(require_tfjs_esm());
+var model11;
+var cached2 = [];
+var skipped10 = Number.MAX_SAFE_INTEGER;
+var lastCount6 = 0;
+var lastTime11 = 0;
+async function load12(config3) {
+  var _a;
+  if (env.initial)
+    model11 = null;
+  if (!model11)
+    model11 = await loadModel((_a = config3.face.liveness) == null ? void 0 : _a.modelPath);
+  else if (config3.debug)
+    log("cached model:", model11["modelUrl"]);
+  return model11;
+}
+async function predict12(image27, config3, idx, count2) {
+  var _a, _b;
+  if (!(model11 == null ? void 0 : model11["executor"]))
+    return 0;
+  const skipTime = (((_a = config3.face.liveness) == null ? void 0 : _a.skipTime) || 0) > now() - lastTime11;
+  const skipFrame = skipped10 < (((_b = config3.face.liveness) == null ? void 0 : _b.skipFrames) || 0);
+  if (config3.skipAllowed && skipTime && skipFrame && lastCount6 === count2 && cached2[idx]) {
+    skipped10++;
+    return cached2[idx];
+  }
+  skipped10 = 0;
+  return new Promise(async (resolve) => {
+    const resize = tf23.image.resizeBilinear(image27, [(model11 == null ? void 0 : model11.inputs[0].shape) ? model11.inputs[0].shape[2] : 0, (model11 == null ? void 0 : model11.inputs[0].shape) ? model11.inputs[0].shape[1] : 0], false);
+    const res = model11 == null ? void 0 : model11.execute(resize);
+    const num = (await res.data())[0];
+    cached2[idx] = Math.round(100 * num) / 100;
+    lastCount6 = count2;
+    lastTime11 = now();
+    tf23.dispose([resize, res]);
+    resolve(cached2[idx]);
+  });
+}
+
+// src/segmentation/meet.ts
+var tf24 = __toESM(require_tfjs_esm());
+var model12;
+async function load13(config3) {
+  if (!model12 || env.initial)
+    model12 = await loadModel(config3.segmentation.modelPath);
+  else if (config3.debug)
+    log("cached model:", model12["modelUrl"]);
+  return model12;
+}
+async function predict13(input, config3) {
+  var _a;
+  if (!model12)
+    model12 = await load13(config3);
+  if (!(model12 == null ? void 0 : model12["executor"]) || !((_a = model12 == null ? void 0 : model12.inputs) == null ? void 0 : _a[0].shape))
+    return null;
+  const t2 = {};
+  t2.resize = tf24.image.resizeBilinear(input, [model12.inputs[0].shape ? model12.inputs[0].shape[1] : 0, model12.inputs[0].shape ? model12.inputs[0].shape[2] : 0], false);
+  t2.norm = tf24.div(t2.resize, constants.tf255);
+  t2.res = model12.execute(t2.norm);
+  t2.squeeze = tf24.squeeze(t2.res, 0);
+  [t2.bgRaw, t2.fgRaw] = tf24.unstack(t2.squeeze, 2);
+  t2.fg = tf24.softmax(t2.fgRaw);
+  t2.mul = tf24.mul(t2.fg, constants.tf255);
+  t2.expand = tf24.expandDims(t2.mul, 2);
+  t2.output = tf24.image.resizeBilinear(t2.expand, [input.shape[1], input.shape[2]]);
+  let rgba;
+  switch (config3.segmentation.mode || "default") {
+    case "default":
+      t2.input = tf24.squeeze(input);
+      t2.concat = tf24.concat([t2.input, t2.output], -1);
+      rgba = tf24.cast(t2.concat, "int32");
+      break;
+    case "alpha":
+      rgba = tf24.cast(t2.output, "int32");
+      break;
+    default:
+      rgba = tf24.tensor(0);
+  }
+  Object.keys(t2).forEach((tensor6) => tf24.dispose(t2[tensor6]));
+  return rgba;
+}
+
+// src/face/mobilefacenet.ts
+var tf25 = __toESM(require_tfjs_esm());
+var model13;
+var last7 = [];
+var lastCount7 = 0;
+var lastTime12 = 0;
+var skipped11 = Number.MAX_SAFE_INTEGER;
+async function load14(config3) {
+  var _a;
+  if (env.initial)
+    model13 = null;
+  if (!model13)
+    model13 = await loadModel((_a = config3.face["mobilefacenet"]) == null ? void 0 : _a.modelPath);
+  else if (config3.debug)
+    log("cached model:", model13["modelUrl"]);
+  return model13;
+}
+async function predict14(input, config3, idx, count2) {
+  var _a, _b;
+  if (!(model13 == null ? void 0 : model13["executor"]))
+    return [];
+  const skipFrame = skipped11 < (((_a = config3.face["mobilefacenet"]) == null ? void 0 : _a.skipFrames) || 0);
+  const skipTime = (((_b = config3.face["mobilefacenet"]) == null ? void 0 : _b.skipTime) || 0) > now() - lastTime12;
+  if (config3.skipAllowed && skipTime && skipFrame && lastCount7 === count2 && last7[idx]) {
+    skipped11++;
+    return last7[idx];
+  }
+  return new Promise(async (resolve) => {
+    var _a2;
+    let data = [];
+    if (((_a2 = config3.face["mobilefacenet"]) == null ? void 0 : _a2.enabled) && (model13 == null ? void 0 : model13.inputs[0].shape)) {
+      const t2 = {};
+      t2.crop = tf25.image.resizeBilinear(input, [model13.inputs[0].shape[2], model13.inputs[0].shape[1]], false);
+      t2.data = model13.execute(t2.crop);
+      const output = await t2.data.data();
+      data = Array.from(output);
+      Object.keys(t2).forEach((tensor6) => tf25.dispose(t2[tensor6]));
+    }
+    last7[idx] = data;
+    lastCount7 = count2;
+    lastTime12 = now();
+    resolve(data);
+  });
+}
+
+// src/body/movenet.ts
+var tf27 = __toESM(require_tfjs_esm());
+
+// src/body/movenetcoords.ts
+var movenetcoords_exports = {};
+__export(movenetcoords_exports, {
+  connected: () => connected3,
+  horizontal: () => horizontal,
+  kpt: () => kpt3,
+  relative: () => relative,
+  vertical: () => vertical
+});
+var kpt3 = [
+  "nose",
+  "leftEye",
+  "rightEye",
+  "leftEar",
+  "rightEar",
+  "leftShoulder",
+  "rightShoulder",
+  "leftElbow",
+  "rightElbow",
+  "leftWrist",
+  "rightWrist",
+  "leftHip",
+  "rightHip",
+  "leftKnee",
+  "rightKnee",
+  "leftAnkle",
+  "rightAnkle"
+];
+var horizontal = [
+  ["leftEye", "rightEye"],
+  ["leftEar", "rightEar"],
+  ["leftShoulder", "rightShoulder"],
+  ["leftElbow", "rightElbow"],
+  ["leftWrist", "rightWrist"],
+  ["leftHip", "rightHip"],
+  ["leftKnee", "rightKnee"],
+  ["leftAnkle", "rightAnkle"]
+];
+var vertical = [
+  ["leftKnee", "leftShoulder"],
+  ["rightKnee", "rightShoulder"],
+  ["leftAnkle", "leftKnee"],
+  ["rightAnkle", "rightKnee"]
+];
+var relative = [
+  [["leftHip", "rightHip"], ["leftShoulder", "rightShoulder"]],
+  [["leftElbow", "rightElbow"], ["leftShoulder", "rightShoulder"]]
+];
+var connected3 = {
+  leftLeg: ["leftHip", "leftKnee", "leftAnkle"],
+  rightLeg: ["rightHip", "rightKnee", "rightAnkle"],
+  torso: ["leftShoulder", "rightShoulder", "rightHip", "leftHip", "leftShoulder"],
+  leftArm: ["leftShoulder", "leftElbow", "leftWrist"],
+  rightArm: ["rightShoulder", "rightElbow", "rightWrist"],
+  head: []
+};
+
+// src/body/movenetfix.ts
+var tf26 = __toESM(require_tfjs_esm());
+var maxJitter = 5e-3;
+var cache5 = {
+  keypoints: [],
+  padding: [[0, 0], [0, 0], [0, 0], [0, 0]]
+};
+function bodyParts(body4) {
+  for (const pair of horizontal) {
+    const left = body4.keypoints.findIndex((kp) => kp.part === pair[0]);
+    const right = body4.keypoints.findIndex((kp) => kp.part === pair[1]);
+    if (body4.keypoints[left] && body4.keypoints[right]) {
+      if (body4.keypoints[left].position[0] < body4.keypoints[right].position[0]) {
+        const tmp = body4.keypoints[left];
+        body4.keypoints[left] = body4.keypoints[right];
+        body4.keypoints[right] = tmp;
+      }
+    }
+  }
+  for (const pair of vertical) {
+    const lower = body4.keypoints.findIndex((kp) => kp && kp.part === pair[0]);
+    const higher = body4.keypoints.findIndex((kp) => kp && kp.part === pair[1]);
+    if (body4.keypoints[lower] && body4.keypoints[higher]) {
+      if (body4.keypoints[lower].position[1] < body4.keypoints[higher].position[1]) {
+        body4.keypoints.splice(lower, 1);
+      }
+    }
+  }
+  for (const [pair, compare2] of relative) {
+    const left = body4.keypoints.findIndex((kp) => kp && kp.part === pair[0]);
+    const right = body4.keypoints.findIndex((kp) => kp && kp.part === pair[1]);
+    const leftTo = body4.keypoints.findIndex((kp) => kp && kp.part === compare2[0]);
+    const rightTo = body4.keypoints.findIndex((kp) => kp && kp.part === compare2[1]);
+    if (!body4.keypoints[leftTo] || !body4.keypoints[rightTo])
+      continue;
+    const distanceLeft = body4.keypoints[left] ? [
+      Math.abs(body4.keypoints[leftTo].position[0] - body4.keypoints[left].position[0]),
+      Math.abs(body4.keypoints[rightTo].position[0] - body4.keypoints[left].position[0])
+    ] : [0, 0];
+    const distanceRight = body4.keypoints[right] ? [
+      Math.abs(body4.keypoints[rightTo].position[0] - body4.keypoints[right].position[0]),
+      Math.abs(body4.keypoints[leftTo].position[0] - body4.keypoints[right].position[0])
+    ] : [0, 0];
+    if (distanceLeft[0] > distanceLeft[1] || distanceRight[0] > distanceRight[1]) {
+      const tmp = body4.keypoints[left];
+      body4.keypoints[left] = body4.keypoints[right];
+      body4.keypoints[right] = tmp;
+    }
+  }
+}
+function jitter(keypoints) {
+  for (let i = 0; i < keypoints.length; i++) {
+    if (keypoints[i] && cache5.keypoints[i]) {
+      const diff = [Math.abs(keypoints[i].positionRaw[0] - cache5.keypoints[i].positionRaw[0]), Math.abs(keypoints[i].positionRaw[1] - cache5.keypoints[i].positionRaw[1])];
+      if (diff[0] < maxJitter && diff[1] < maxJitter) {
+        keypoints[i] = cache5.keypoints[i];
+      } else {
+        cache5.keypoints[i] = keypoints[i];
+      }
+    } else {
+      cache5.keypoints[i] = keypoints[i];
+    }
+  }
+  return keypoints;
+}
+function padInput(input, inputSize10) {
+  var _a, _b;
+  const t2 = {};
+  if (!((_a = input == null ? void 0 : input.shape) == null ? void 0 : _a[1]) || !((_b = input == null ? void 0 : input.shape) == null ? void 0 : _b[2]))
+    return input;
+  cache5.padding = [
+    [0, 0],
+    [input.shape[2] > input.shape[1] ? Math.trunc((input.shape[2] - input.shape[1]) / 2) : 0, input.shape[2] > input.shape[1] ? Math.trunc((input.shape[2] - input.shape[1]) / 2) : 0],
+    [input.shape[1] > input.shape[2] ? Math.trunc((input.shape[1] - input.shape[2]) / 2) : 0, input.shape[1] > input.shape[2] ? Math.trunc((input.shape[1] - input.shape[2]) / 2) : 0],
+    [0, 0]
+  ];
+  t2.pad = tf26.pad(input, cache5.padding);
+  t2.resize = tf26.image.resizeBilinear(t2.pad, [inputSize10, inputSize10]);
+  const final = tf26.cast(t2.resize, "int32");
+  Object.keys(t2).forEach((tensor6) => tf26.dispose(t2[tensor6]));
+  return final;
+}
+function rescaleBody(body4, outputSize2) {
+  body4.keypoints = body4.keypoints.filter((kpt4) => kpt4 == null ? void 0 : kpt4.position);
+  for (const kpt4 of body4.keypoints) {
+    kpt4.position = [
+      kpt4.position[0] * (outputSize2[0] + cache5.padding[2][0] + cache5.padding[2][1]) / outputSize2[0] - cache5.padding[2][0],
+      kpt4.position[1] * (outputSize2[1] + cache5.padding[1][0] + cache5.padding[1][1]) / outputSize2[1] - cache5.padding[1][0]
+    ];
+    kpt4.positionRaw = [
+      kpt4.position[0] / outputSize2[0],
+      kpt4.position[1] / outputSize2[1]
+    ];
+  }
+  const rescaledBoxes = calc(body4.keypoints.map((pt) => pt.position), outputSize2);
+  body4.box = rescaledBoxes.box;
+  body4.boxRaw = rescaledBoxes.boxRaw;
+  return body4;
+}
+
+// src/body/movenet.ts
+var model14;
+var inputSize8 = 0;
+var skipped12 = Number.MAX_SAFE_INTEGER;
+var cache6 = {
+  boxes: [],
+  bodies: [],
+  last: 0
+};
+async function load15(config3) {
+  var _a;
+  if (env.initial)
+    model14 = null;
+  if (!model14) {
+    fakeOps(["size"], config3);
+    model14 = await loadModel(config3.body.modelPath);
+  } else if (config3.debug)
+    log("cached model:", model14["modelUrl"]);
+  inputSize8 = (model14 == null ? void 0 : model14["executor"]) && ((_a = model14 == null ? void 0 : model14.inputs) == null ? void 0 : _a[0].shape) ? model14.inputs[0].shape[2] : 0;
+  if (inputSize8 < 64)
+    inputSize8 = 256;
+  return model14;
+}
+function parseSinglePose(res, config3, image27) {
+  const kpt4 = res[0][0];
+  const keypoints = [];
+  let score = 0;
+  for (let id = 0; id < kpt4.length; id++) {
+    score = kpt4[id][2];
+    if (score > config3.body.minConfidence) {
+      const positionRaw = [kpt4[id][1], kpt4[id][0]];
+      keypoints.push({
+        score: Math.round(100 * score) / 100,
+        part: kpt3[id],
+        positionRaw,
+        position: [
+          Math.round((image27.shape[2] || 0) * positionRaw[0]),
+          Math.round((image27.shape[1] || 0) * positionRaw[1])
+        ]
+      });
+    }
+  }
+  score = keypoints.reduce((prev, curr) => curr.score > prev ? curr.score : prev, 0);
+  const bodies = [];
+  const newBox = calc(keypoints.map((pt) => pt.position), [image27.shape[2], image27.shape[1]]);
+  const annotations2 = {};
+  for (const [name, indexes] of Object.entries(connected3)) {
+    const pt = [];
+    for (let i = 0; i < indexes.length - 1; i++) {
+      const pt0 = keypoints.find((kp) => kp.part === indexes[i]);
+      const pt1 = keypoints.find((kp) => kp.part === indexes[i + 1]);
+      if (pt0 && pt1 && pt0.score > (config3.body.minConfidence || 0) && pt1.score > (config3.body.minConfidence || 0))
+        pt.push([pt0.position, pt1.position]);
+    }
+    annotations2[name] = pt;
+  }
+  const body4 = { id: 0, score, box: newBox.box, boxRaw: newBox.boxRaw, keypoints, annotations: annotations2 };
+  bodyParts(body4);
+  bodies.push(body4);
+  return bodies;
+}
+function parseMultiPose(res, config3, image27) {
+  const bodies = [];
+  for (let id = 0; id < res[0].length; id++) {
+    const kpt4 = res[0][id];
+    const totalScore = Math.round(100 * kpt4[51 + 4]) / 100;
+    if (totalScore > config3.body.minConfidence) {
+      const keypoints = [];
+      for (let i = 0; i < 17; i++) {
+        const score = kpt4[3 * i + 2];
+        if (score > config3.body.minConfidence) {
+          const positionRaw = [kpt4[3 * i + 1], kpt4[3 * i + 0]];
+          keypoints.push({
+            part: kpt3[i],
+            score: Math.round(100 * score) / 100,
+            positionRaw,
+            position: [Math.round((image27.shape[2] || 0) * positionRaw[0]), Math.round((image27.shape[1] || 0) * positionRaw[1])]
+          });
+        }
+      }
+      const newBox = calc(keypoints.map((pt) => pt.position), [image27.shape[2], image27.shape[1]]);
+      const annotations2 = {};
+      for (const [name, indexes] of Object.entries(connected3)) {
+        const pt = [];
+        for (let i = 0; i < indexes.length - 1; i++) {
+          const pt0 = keypoints.find((kp) => kp.part === indexes[i]);
+          const pt1 = keypoints.find((kp) => kp.part === indexes[i + 1]);
+          if (pt0 && pt1 && pt0.score > (config3.body.minConfidence || 0) && pt1.score > (config3.body.minConfidence || 0))
+            pt.push([pt0.position, pt1.position]);
+        }
+        annotations2[name] = pt;
+      }
+      const body4 = { id, score: totalScore, box: newBox.box, boxRaw: newBox.boxRaw, keypoints: [...keypoints], annotations: annotations2 };
+      bodyParts(body4);
+      bodies.push(body4);
+    }
+  }
+  bodies.sort((a, b) => b.score - a.score);
+  if (bodies.length > config3.body.maxDetected)
+    bodies.length = config3.body.maxDetected;
+  return bodies;
+}
+async function predict15(input, config3) {
+  var _a;
+  if (!(model14 == null ? void 0 : model14["executor"]) || !((_a = model14 == null ? void 0 : model14.inputs) == null ? void 0 : _a[0].shape))
+    return [];
+  if (!config3.skipAllowed)
+    cache6.boxes.length = 0;
+  skipped12++;
+  const skipTime = (config3.body.skipTime || 0) > now() - cache6.last;
+  const skipFrame = skipped12 < (config3.body.skipFrames || 0);
+  if (config3.skipAllowed && skipTime && skipFrame) {
+    return cache6.bodies;
+  }
+  return new Promise(async (resolve) => {
+    const t2 = {};
+    skipped12 = 0;
+    t2.input = padInput(input, inputSize8);
+    t2.res = model14 == null ? void 0 : model14.execute(t2.input);
+    cache6.last = now();
+    const res = await t2.res.array();
+    cache6.bodies = t2.res.shape[2] === 17 ? parseSinglePose(res, config3, input) : parseMultiPose(res, config3, input);
+    for (const body4 of cache6.bodies) {
+      rescaleBody(body4, [input.shape[2] || 1, input.shape[1] || 1]);
+      jitter(body4.keypoints);
+    }
+    Object.keys(t2).forEach((tensor6) => tf27.dispose(t2[tensor6]));
+    resolve(cache6.bodies);
+  });
+}
+
+// src/object/nanodet.ts
+var tf28 = __toESM(require_tfjs_esm());
+var model15;
+var last8 = [];
+var lastTime13 = 0;
+var skipped13 = Number.MAX_SAFE_INTEGER;
+var inputSize9 = 0;
+var scaleBox = 2.5;
+async function load16(config3) {
+  if (!model15 || env.initial) {
+    model15 = await loadModel(config3.object.modelPath);
+    const inputs = (model15 == null ? void 0 : model15["executor"]) ? Object.values(model15.modelSignature["inputs"]) : void 0;
+    inputSize9 = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 416;
+  } else if (config3.debug)
+    log("cached model:", model15["modelUrl"]);
+  return model15;
+}
+async function process4(res, outputShape, config3) {
+  let id = 0;
+  let results = [];
+  const size2 = inputSize9;
+  for (const strideSize of [1, 2, 4]) {
+    const baseSize = strideSize * 13;
+    const scoresT = tf28.squeeze(res.find((a) => a.shape[1] === baseSize ** 2 && (a.shape[2] || 0) === labels.length));
+    const scores = await scoresT.array();
+    const featuresT = tf28.squeeze(res.find((a) => a.shape[1] === baseSize ** 2 && (a.shape[2] || 0) < labels.length));
+    const boxesMaxT = featuresT.reshape([-1, 4, featuresT.shape[1] / 4]);
+    const boxIdxT = boxesMaxT.argMax(2);
+    const boxIdx = await boxIdxT.array();
+    for (let i = 0; i < scoresT.shape[0]; i++) {
+      for (let j = 0; j < scoresT.shape[1]; j++) {
+        const score = scores[i][j];
+        if (score > (config3.object.minConfidence || 0) && j !== 61) {
+          const cx = (0.5 + Math.trunc(i % baseSize)) / baseSize;
+          const cy = (0.5 + Math.trunc(i / baseSize)) / baseSize;
+          const boxOffset = boxIdx[i].map((a) => a * (baseSize / strideSize / size2));
+          const [x, y] = [
+            cx - scaleBox / strideSize * boxOffset[0],
+            cy - scaleBox / strideSize * boxOffset[1]
+          ];
+          const [w, h] = [
+            cx + scaleBox / strideSize * boxOffset[2] - x,
+            cy + scaleBox / strideSize * boxOffset[3] - y
+          ];
+          let boxRaw = [x, y, w, h];
+          boxRaw = boxRaw.map((a) => Math.max(0, Math.min(a, 1)));
+          const box = [
+            boxRaw[0] * outputShape[0],
+            boxRaw[1] * outputShape[1],
+            boxRaw[2] * outputShape[0],
+            boxRaw[3] * outputShape[1]
+          ];
+          const result = {
+            id: id++,
+            score: Math.round(100 * score) / 100,
+            class: j + 1,
+            label: labels[j].label,
+            box: box.map((a) => Math.trunc(a)),
+            boxRaw
+          };
+          results.push(result);
+        }
+      }
+    }
+    tf28.dispose([scoresT, featuresT, boxesMaxT, boxIdxT]);
+  }
+  const nmsBoxes = results.map((a) => [a.boxRaw[1], a.boxRaw[0], a.boxRaw[3], a.boxRaw[2]]);
+  const nmsScores = results.map((a) => a.score);
+  let nmsIdx = [];
+  if (nmsBoxes && nmsBoxes.length > 0) {
+    const nms = await tf28.image.nonMaxSuppressionAsync(nmsBoxes, nmsScores, config3.object.maxDetected, config3.object.iouThreshold, config3.object.minConfidence);
+    nmsIdx = await nms.data();
+    tf28.dispose(nms);
+  }
+  results = results.filter((_val, idx) => nmsIdx.includes(idx)).sort((a, b) => b.score - a.score);
+  return results;
+}
+async function predict16(image27, config3) {
+  if (!(model15 == null ? void 0 : model15["executor"]))
+    return [];
+  const skipTime = (config3.object.skipTime || 0) > now() - lastTime13;
+  const skipFrame = skipped13 < (config3.object.skipFrames || 0);
+  if (config3.skipAllowed && skipTime && skipFrame && last8.length > 0) {
+    skipped13++;
+    return last8;
+  }
+  skipped13 = 0;
+  if (!env.kernels.includes("mod") || !env.kernels.includes("sparsetodense"))
+    return last8;
+  return new Promise(async (resolve) => {
+    const outputSize2 = [image27.shape[2] || 0, image27.shape[1] || 0];
+    const resizeT = tf28.image.resizeBilinear(image27, [inputSize9, inputSize9], false);
+    const normT = tf28.div(resizeT, constants.tf255);
+    const transposeT = tf28.transpose(normT, [0, 3, 1, 2]);
+    let objectT;
+    if (config3.object.enabled)
+      objectT = model15.execute(transposeT);
+    lastTime13 = now();
+    const obj = await process4(objectT, outputSize2, config3);
+    last8 = obj;
+    tf28.dispose([resizeT, normT, transposeT, ...objectT]);
+    resolve(obj);
+  });
+}
+
+// src/body/posenet.ts
+var tf29 = __toESM(require_tfjs_esm());
+
+// src/body/posenetutils.ts
+var partNames = [
+  "nose",
+  "leftEye",
+  "rightEye",
+  "leftEar",
+  "rightEar",
+  "leftShoulder",
+  "rightShoulder",
+  "leftElbow",
+  "rightElbow",
+  "leftWrist",
+  "rightWrist",
+  "leftHip",
+  "rightHip",
+  "leftKnee",
+  "rightKnee",
+  "leftAnkle",
+  "rightAnkle"
+];
+var count = partNames.length;
+var partIds = partNames.reduce((result, jointName, i) => {
+  result[jointName] = i;
+  return result;
+}, {});
+var connectedPartNames = [
+  ["leftHip", "leftShoulder"],
+  ["leftElbow", "leftShoulder"],
+  ["leftElbow", "leftWrist"],
+  ["leftHip", "leftKnee"],
+  ["leftKnee", "leftAnkle"],
+  ["rightHip", "rightShoulder"],
+  ["rightElbow", "rightShoulder"],
+  ["rightElbow", "rightWrist"],
+  ["rightHip", "rightKnee"],
+  ["rightKnee", "rightAnkle"],
+  ["leftShoulder", "rightShoulder"],
+  ["leftHip", "rightHip"]
+];
+var connectedPartIndices = connectedPartNames.map(([jointNameA, jointNameB]) => [partIds[jointNameA], partIds[jointNameB]]);
+var poseChain = [
+  ["nose", "leftEye"],
+  ["leftEye", "leftEar"],
+  ["nose", "rightEye"],
+  ["rightEye", "rightEar"],
+  ["nose", "leftShoulder"],
+  ["leftShoulder", "leftElbow"],
+  ["leftElbow", "leftWrist"],
+  ["leftShoulder", "leftHip"],
+  ["leftHip", "leftKnee"],
+  ["leftKnee", "leftAnkle"],
+  ["nose", "rightShoulder"],
+  ["rightShoulder", "rightElbow"],
+  ["rightElbow", "rightWrist"],
+  ["rightShoulder", "rightHip"],
+  ["rightHip", "rightKnee"],
+  ["rightKnee", "rightAnkle"]
+];
+function getBoundingBox(keypoints) {
+  const coord = keypoints.reduce(({ maxX, maxY, minX, minY }, { position: { x, y } }) => ({
+    maxX: Math.max(maxX, x),
+    maxY: Math.max(maxY, y),
+    minX: Math.min(minX, x),
+    minY: Math.min(minY, y)
+  }), {
+    maxX: Number.NEGATIVE_INFINITY,
+    maxY: Number.NEGATIVE_INFINITY,
+    minX: Number.POSITIVE_INFINITY,
+    minY: Number.POSITIVE_INFINITY
+  });
+  return [coord.minX, coord.minY, coord.maxX - coord.minX, coord.maxY - coord.minY];
+}
+function scalePoses(poses, [height, width], [inputResolutionHeight, inputResolutionWidth]) {
+  const scaleY = height / inputResolutionHeight;
+  const scaleX = width / inputResolutionWidth;
+  const scalePose = (pose, i) => ({
+    id: i,
+    score: pose.score,
+    boxRaw: [pose.box[0] / inputResolutionWidth, pose.box[1] / inputResolutionHeight, pose.box[2] / inputResolutionWidth, pose.box[3] / inputResolutionHeight],
+    box: [Math.trunc(pose.box[0] * scaleX), Math.trunc(pose.box[1] * scaleY), Math.trunc(pose.box[2] * scaleX), Math.trunc(pose.box[3] * scaleY)],
+    keypoints: pose.keypoints.map(({ score, part, position }) => ({
+      score,
+      part,
+      position: [Math.trunc(position.x * scaleX), Math.trunc(position.y * scaleY)],
+      positionRaw: [position.x / inputResolutionHeight, position.y / inputResolutionHeight]
+    })),
+    annotations: {}
+  });
+  const scaledPoses = poses.map((pose, i) => scalePose(pose, i));
+  return scaledPoses;
+}
+var MaxHeap = class {
+  constructor(maxSize2, getElementValue) {
+    __publicField(this, "priorityQueue");
+    __publicField(this, "numberOfElements");
+    __publicField(this, "getElementValue");
+    this.priorityQueue = new Array(maxSize2);
+    this.numberOfElements = -1;
+    this.getElementValue = getElementValue;
+  }
+  enqueue(x) {
+    this.priorityQueue[++this.numberOfElements] = x;
+    this.swim(this.numberOfElements);
+  }
+  dequeue() {
+    const max4 = this.priorityQueue[0];
+    this.exchange(0, this.numberOfElements--);
+    this.sink(0);
+    this.priorityQueue[this.numberOfElements + 1] = null;
+    return max4;
+  }
+  empty() {
+    return this.numberOfElements === -1;
+  }
+  size() {
+    return this.numberOfElements + 1;
+  }
+  all() {
+    return this.priorityQueue.slice(0, this.numberOfElements + 1);
+  }
+  max() {
+    return this.priorityQueue[0];
+  }
+  swim(k) {
+    while (k > 0 && this.less(Math.floor(k / 2), k)) {
+      this.exchange(k, Math.floor(k / 2));
+      k = Math.floor(k / 2);
+    }
+  }
+  sink(k) {
+    while (2 * k <= this.numberOfElements) {
+      let j = 2 * k;
+      if (j < this.numberOfElements && this.less(j, j + 1))
+        j++;
+      if (!this.less(k, j))
+        break;
+      this.exchange(k, j);
+      k = j;
+    }
+  }
+  getValueAt(i) {
+    return this.getElementValue(this.priorityQueue[i]);
+  }
+  less(i, j) {
+    return this.getValueAt(i) < this.getValueAt(j);
+  }
+  exchange(i, j) {
+    const t2 = this.priorityQueue[i];
+    this.priorityQueue[i] = this.priorityQueue[j];
+    this.priorityQueue[j] = t2;
+  }
+};
+function getOffsetPoint(y, x, keypoint, offsets) {
+  return {
+    y: offsets.get(y, x, keypoint),
+    x: offsets.get(y, x, keypoint + count)
+  };
+}
+function getImageCoords(part, outputStride2, offsets) {
+  const { heatmapY, heatmapX, id: keypoint } = part;
+  const { y, x } = getOffsetPoint(heatmapY, heatmapX, keypoint, offsets);
+  return {
+    x: part.heatmapX * outputStride2 + x,
+    y: part.heatmapY * outputStride2 + y
+  };
+}
+function clamp(a, min2, max4) {
+  if (a < min2)
+    return min2;
+  if (a > max4)
+    return max4;
+  return a;
+}
+function squaredDistance(y1, x1, y2, x2) {
+  const dy = y2 - y1;
+  const dx = x2 - x1;
+  return dy * dy + dx * dx;
+}
+function addVectors(a, b) {
+  return { x: a.x + b.x, y: a.y + b.y };
+}
+
+// src/body/posenet.ts
+var model16;
+var poseNetOutputs = ["MobilenetV1/offset_2/BiasAdd", "MobilenetV1/heatmap_2/BiasAdd", "MobilenetV1/displacement_fwd_2/BiasAdd", "MobilenetV1/displacement_bwd_2/BiasAdd"];
+var localMaximumRadius = 1;
+var outputStride = 16;
+var squaredNmsRadius = 50 ** 2;
+function traverse(edgeId, sourceKeypoint, targetId, scores, offsets, displacements, offsetRefineStep = 2) {
+  const getDisplacement = (point2) => ({
+    y: displacements.get(point2.y, point2.x, edgeId),
+    x: displacements.get(point2.y, point2.x, displacements.shape[2] / 2 + edgeId)
+  });
+  const getStridedIndexNearPoint = (point2, height2, width2) => ({
+    y: clamp(Math.round(point2.y / outputStride), 0, height2 - 1),
+    x: clamp(Math.round(point2.x / outputStride), 0, width2 - 1)
+  });
+  const [height, width] = scores.shape;
+  const sourceKeypointIndices = getStridedIndexNearPoint(sourceKeypoint.position, height, width);
+  const displacement = getDisplacement(sourceKeypointIndices);
+  const displacedPoint = addVectors(sourceKeypoint.position, displacement);
+  let targetKeypoint = displacedPoint;
+  for (let i = 0; i < offsetRefineStep; i++) {
+    const targetKeypointIndices = getStridedIndexNearPoint(targetKeypoint, height, width);
+    const offsetPoint = getOffsetPoint(targetKeypointIndices.y, targetKeypointIndices.x, targetId, offsets);
+    targetKeypoint = addVectors(
+      { x: targetKeypointIndices.x * outputStride, y: targetKeypointIndices.y * outputStride },
+      { x: offsetPoint.x, y: offsetPoint.y }
+    );
+  }
+  const targetKeyPointIndices = getStridedIndexNearPoint(targetKeypoint, height, width);
+  const score = scores.get(targetKeyPointIndices.y, targetKeyPointIndices.x, targetId);
+  return { position: targetKeypoint, part: partNames[targetId], score };
+}
+function decodePose(root, scores, offsets, displacementsFwd, displacementsBwd) {
+  const tuples = poseChain.map(([parentJoinName, childJoinName]) => [partIds[parentJoinName], partIds[childJoinName]]);
+  const edgesFwd = tuples.map(([, childJointId]) => childJointId);
+  const edgesBwd = tuples.map(([parentJointId]) => parentJointId);
+  const numParts = scores.shape[2];
+  const numEdges = edgesFwd.length;
+  const keypoints = new Array(numParts);
+  const rootPoint = getImageCoords(root.part, outputStride, offsets);
+  keypoints[root.part.id] = {
+    score: root.score,
+    part: partNames[root.part.id],
+    position: rootPoint
+  };
+  for (let edge = numEdges - 1; edge >= 0; --edge) {
+    const sourceId = edgesFwd[edge];
+    const targetId = edgesBwd[edge];
+    if (keypoints[sourceId] && !keypoints[targetId]) {
+      keypoints[targetId] = traverse(edge, keypoints[sourceId], targetId, scores, offsets, displacementsBwd);
+    }
+  }
+  for (let edge = 0; edge < numEdges; ++edge) {
+    const sourceId = edgesBwd[edge];
+    const targetId = edgesFwd[edge];
+    if (keypoints[sourceId] && !keypoints[targetId]) {
+      keypoints[targetId] = traverse(edge, keypoints[sourceId], targetId, scores, offsets, displacementsFwd);
+    }
+  }
+  return keypoints;
+}
+function scoreIsMaximumInLocalWindow(keypointId, score, heatmapY, heatmapX, scores) {
+  const [height, width] = scores.shape;
+  let localMaximum = true;
+  const yStart = Math.max(heatmapY - localMaximumRadius, 0);
+  const yEnd = Math.min(heatmapY + localMaximumRadius + 1, height);
+  for (let yCurrent = yStart; yCurrent < yEnd; ++yCurrent) {
+    const xStart = Math.max(heatmapX - localMaximumRadius, 0);
+    const xEnd = Math.min(heatmapX + localMaximumRadius + 1, width);
+    for (let xCurrent = xStart; xCurrent < xEnd; ++xCurrent) {
+      if (scores.get(yCurrent, xCurrent, keypointId) > score) {
+        localMaximum = false;
+        break;
+      }
+    }
+    if (!localMaximum)
+      break;
+  }
+  return localMaximum;
+}
+function buildPartWithScoreQueue(minConfidence2, scores) {
+  const [height, width, numKeypoints] = scores.shape;
+  const queue = new MaxHeap(height * width * numKeypoints, ({ score }) => score);
+  for (let heatmapY = 0; heatmapY < height; ++heatmapY) {
+    for (let heatmapX = 0; heatmapX < width; ++heatmapX) {
+      for (let keypointId = 0; keypointId < numKeypoints; ++keypointId) {
+        const score = scores.get(heatmapY, heatmapX, keypointId);
+        if (score < minConfidence2)
+          continue;
+        if (scoreIsMaximumInLocalWindow(keypointId, score, heatmapY, heatmapX, scores))
+          queue.enqueue({ score, part: { heatmapY, heatmapX, id: keypointId } });
+      }
+    }
+  }
+  return queue;
+}
+function withinRadius(poses, { x, y }, keypointId) {
+  return poses.some(({ keypoints }) => {
+    var _a;
+    const correspondingKeypoint = (_a = keypoints[keypointId]) == null ? void 0 : _a.position;
+    if (!correspondingKeypoint)
+      return false;
+    return squaredDistance(y, x, correspondingKeypoint.y, correspondingKeypoint.x) <= squaredNmsRadius;
+  });
+}
+function getInstanceScore(existingPoses, keypoints) {
+  const notOverlappedKeypointScores = keypoints.reduce((result, { position, score }, keypointId) => {
+    if (!withinRadius(existingPoses, position, keypointId))
+      result += score;
+    return result;
+  }, 0);
+  return notOverlappedKeypointScores / keypoints.length;
+}
+function decode(offsets, scores, displacementsFwd, displacementsBwd, maxDetected, minConfidence2) {
+  const poses = [];
+  const queue = buildPartWithScoreQueue(minConfidence2, scores);
+  while (poses.length < maxDetected && !queue.empty()) {
+    const root = queue.dequeue();
+    const rootImageCoords = getImageCoords(root.part, outputStride, offsets);
+    if (withinRadius(poses, rootImageCoords, root.part.id))
+      continue;
+    let keypoints = decodePose(root, scores, offsets, displacementsFwd, displacementsBwd);
+    keypoints = keypoints.filter((a) => a.score > minConfidence2);
+    const score = getInstanceScore(poses, keypoints);
+    const box = getBoundingBox(keypoints);
+    if (score > minConfidence2)
+      poses.push({ keypoints, box, score: Math.round(100 * score) / 100 });
+  }
+  return poses;
+}
+async function predict17(input, config3) {
+  if (!(model16 == null ? void 0 : model16["executor"]))
+    return [];
+  const res = tf29.tidy(() => {
+    if (!model16.inputs[0].shape)
+      return [];
+    const resized = tf29.image.resizeBilinear(input, [model16.inputs[0].shape[2], model16.inputs[0].shape[1]]);
+    const normalized = tf29.sub(tf29.div(tf29.cast(resized, "float32"), 127.5), 1);
+    const results = model16.execute(normalized, poseNetOutputs);
+    const results3d = results.map((y) => tf29.squeeze(y, [0]));
+    results3d[1] = tf29.sigmoid(results3d[1]);
+    return results3d;
+  });
+  const buffers = await Promise.all(res.map((tensor6) => tensor6.buffer()));
+  for (const t2 of res)
+    tf29.dispose(t2);
+  const decoded = decode(buffers[0], buffers[1], buffers[2], buffers[3], config3.body.maxDetected, config3.body.minConfidence);
+  if (!model16.inputs[0].shape)
+    return [];
+  const scaled = scalePoses(decoded, [input.shape[1], input.shape[2]], [model16.inputs[0].shape[2], model16.inputs[0].shape[1]]);
+  return scaled;
+}
+async function load17(config3) {
+  if (!model16 || env.initial)
+    model16 = await loadModel(config3.body.modelPath);
+  else if (config3.debug)
+    log("cached model:", model16["modelUrl"]);
+  return model16;
+}
+
+// src/segmentation/rvm.ts
+var tf30 = __toESM(require_tfjs_esm());
+var model17;
+var outputNodes2 = ["fgr", "pha", "r1o", "r2o", "r3o", "r4o"];
+var t = {};
+var ratio = 0;
+function init2(config3) {
+  tf30.dispose([t.r1i, t.r2i, t.r3i, t.r4i, t.downsample_ratio]);
+  t.r1i = tf30.tensor(0);
+  t.r2i = tf30.tensor(0);
+  t.r3i = tf30.tensor(0);
+  t.r4i = tf30.tensor(0);
+  ratio = config3.segmentation.ratio || 0.5;
+  t.downsample_ratio = tf30.tensor(ratio);
+}
+async function load18(config3) {
+  if (!model17 || env.initial)
+    model17 = await loadModel(config3.segmentation.modelPath);
+  else if (config3.debug)
+    log("cached model:", model17["modelUrl"]);
+  init2(config3);
+  return model17;
+}
+var normalize = (r) => tf30.tidy(() => {
+  const squeeze14 = tf30.squeeze(r, [0]);
+  const mul15 = tf30.mul(squeeze14, constants.tf255);
+  const cast8 = tf30.cast(mul15, "int32");
+  return cast8;
+});
+function getRGBA(fgr, pha) {
+  const rgb2 = fgr ? normalize(fgr) : tf30.fill([pha.shape[1] || 0, pha.shape[2] || 0, 3], 255, "int32");
+  const a = pha ? normalize(pha) : tf30.fill([fgr.shape[1] || 0, fgr.shape[2] || 0, 1], 255, "int32");
+  const rgba = tf30.concat([rgb2, a], -1);
+  tf30.dispose([rgb2, a]);
+  return rgba;
+}
+function getState(state) {
+  return tf30.tidy(() => {
+    const r = {};
+    r.unstack = tf30.unstack(state, -1);
+    r.concat = tf30.concat(r.unstack, 1);
+    r.split = tf30.split(r.concat, 4, 1);
+    r.stack = tf30.concat(r.split, 2);
+    r.squeeze = tf30.squeeze(r.stack, [0]);
+    r.expand = tf30.expandDims(r.squeeze, -1);
+    r.add = tf30.add(r.expand, 1);
+    r.mul = tf30.mul(r.add, 127.5);
+    r.cast = tf30.cast(r.mul, "int32");
+    r.tile = tf30.tile(r.cast, [1, 1, 3]);
+    r.alpha = tf30.fill([r.tile.shape[0] || 0, r.tile.shape[1] || 0, 1], 255, "int32");
+    return tf30.concat([r.tile, r.alpha], -1);
+  });
+}
+async function predict18(input, config3) {
+  if (!model17)
+    model17 = await load18(config3);
+  if (!(model17 == null ? void 0 : model17["executor"]))
+    return null;
+  t.src = tf30.div(input, 255);
+  if (ratio !== config3.segmentation.ratio)
+    init2(config3);
+  const [fgr, pha, r1o, r2o, r3o, r4o] = await model17.executeAsync(t, outputNodes2);
+  let rgba;
+  switch (config3.segmentation.mode || "default") {
+    case "default":
+      rgba = getRGBA(fgr, pha);
+      break;
+    case "alpha":
+      rgba = getRGBA(null, pha);
+      break;
+    case "foreground":
+      rgba = getRGBA(fgr, null);
+      break;
+    case "state":
+      rgba = getState(r1o);
+      break;
+    default:
+      rgba = tf30.tensor(0);
+  }
+  tf30.dispose([t.src, fgr, pha, t.r1i, t.r2i, t.r3i, t.r4i]);
+  [t.r1i, t.r2i, t.r3i, t.r4i] = [r1o, r2o, r3o, r4o];
+  return rgba;
+}
+
+// src/segmentation/selfie.ts
+var tf31 = __toESM(require_tfjs_esm());
+var model18;
+async function load19(config3) {
+  if (!model18 || env.initial)
+    model18 = await loadModel(config3.segmentation.modelPath);
+  else if (config3.debug)
+    log("cached model:", model18["modelUrl"]);
+  return model18;
+}
+async function predict19(input, config3) {
+  var _a;
+  if (!model18)
+    model18 = await load19(config3);
+  if (!(model18 == null ? void 0 : model18["executor"]) || !((_a = model18 == null ? void 0 : model18.inputs) == null ? void 0 : _a[0].shape))
+    return null;
+  const t2 = {};
+  t2.resize = tf31.image.resizeBilinear(input, [model18.inputs[0].shape ? model18.inputs[0].shape[1] : 0, model18.inputs[0].shape ? model18.inputs[0].shape[2] : 0], false);
+  t2.norm = tf31.div(t2.resize, constants.tf255);
+  t2.res = model18.execute(t2.norm);
+  t2.squeeze = tf31.squeeze(t2.res, 0);
+  t2.alpha = tf31.image.resizeBilinear(t2.squeeze, [input.shape[1], input.shape[2]]);
+  t2.mul = tf31.mul(t2.alpha, constants.tf255);
+  let rgba;
+  switch (config3.segmentation.mode || "default") {
+    case "default":
+      t2.input = tf31.squeeze(input);
+      t2.concat = tf31.concat([t2.input, t2.mul], -1);
+      rgba = tf31.cast(t2.concat, "int32");
+      break;
+    case "alpha":
+      rgba = tf31.cast(t2.mul, "int32");
+      break;
+    default:
+      rgba = tf31.tensor(0);
+  }
+  Object.keys(t2).forEach((tensor6) => tf31.dispose(t2[tensor6]));
+  return rgba;
+}
+
+// src/gear/ssrnet-age.ts
+var tf32 = __toESM(require_tfjs_esm());
+var model19;
+var last9 = [];
+var lastCount8 = 0;
+var lastTime14 = 0;
+var skipped14 = Number.MAX_SAFE_INTEGER;
+async function load20(config3) {
+  if (env.initial)
+    model19 = null;
+  if (!model19)
+    model19 = await loadModel(config3.face["ssrnet"].modelPathAge);
+  else if (config3.debug)
+    log("cached model:", model19["modelUrl"]);
+  return model19;
+}
+async function predict20(image27, config3, idx, count2) {
+  var _a, _b, _c, _d;
+  if (!model19)
+    return { age: 0 };
+  const skipFrame = skipped14 < (((_a = config3.face["ssrnet"]) == null ? void 0 : _a.skipFrames) || 0);
+  const skipTime = (((_b = config3.face["ssrnet"]) == null ? void 0 : _b.skipTime) || 0) > now() - lastTime14;
+  if (config3.skipAllowed && skipFrame && skipTime && lastCount8 === count2 && ((_c = last9[idx]) == null ? void 0 : _c.age) && ((_d = last9[idx]) == null ? void 0 : _d.age) > 0) {
+    skipped14++;
+    return last9[idx];
+  }
+  skipped14 = 0;
+  return new Promise(async (resolve) => {
+    var _a2;
+    if (!(model19 == null ? void 0 : model19.inputs) || !model19.inputs[0] || !model19.inputs[0].shape)
+      return;
+    const t2 = {};
+    t2.resize = tf32.image.resizeBilinear(image27, [model19.inputs[0].shape[2], model19.inputs[0].shape[1]], false);
+    t2.enhance = tf32.mul(t2.resize, constants.tf255);
+    const obj = { age: 0 };
+    if ((_a2 = config3.face["ssrnet"]) == null ? void 0 : _a2.enabled)
+      t2.age = model19.execute(t2.enhance);
+    if (t2.age) {
+      const data = await t2.age.data();
+      obj.age = Math.trunc(10 * data[0]) / 10;
+    }
+    Object.keys(t2).forEach((tensor6) => tf32.dispose(t2[tensor6]));
+    last9[idx] = obj;
+    lastCount8 = count2;
+    lastTime14 = now();
+    resolve(obj);
+  });
+}
+
+// src/gear/ssrnet-gender.ts
+var tf33 = __toESM(require_tfjs_esm());
+var model20;
+var last10 = [];
+var lastCount9 = 0;
+var lastTime15 = 0;
+var skipped15 = Number.MAX_SAFE_INTEGER;
+var rgb = [0.2989, 0.587, 0.114];
+async function load21(config3) {
+  var _a;
+  if (env.initial)
+    model20 = null;
+  if (!model20)
+    model20 = await loadModel((_a = config3.face["ssrnet"]) == null ? void 0 : _a.modelPathGender);
+  else if (config3.debug)
+    log("cached model:", model20["modelUrl"]);
+  return model20;
+}
+async function predict21(image27, config3, idx, count2) {
+  var _a, _b, _c, _d;
+  if (!model20)
+    return { gender: "unknown", genderScore: 0 };
+  const skipFrame = skipped15 < (((_a = config3.face["ssrnet"]) == null ? void 0 : _a.skipFrames) || 0);
+  const skipTime = (((_b = config3.face["ssrnet"]) == null ? void 0 : _b.skipTime) || 0) > now() - lastTime15;
+  if (config3.skipAllowed && skipFrame && skipTime && lastCount9 === count2 && ((_c = last10[idx]) == null ? void 0 : _c.gender) && ((_d = last10[idx]) == null ? void 0 : _d.genderScore) > 0) {
+    skipped15++;
+    return last10[idx];
+  }
+  skipped15 = 0;
+  return new Promise(async (resolve) => {
+    var _a2;
+    if (!(model20 == null ? void 0 : model20.inputs[0].shape))
+      return;
+    const t2 = {};
+    t2.resize = tf33.image.resizeBilinear(image27, [model20.inputs[0].shape[2], model20.inputs[0].shape[1]], false);
+    t2.enhance = tf33.tidy(() => {
+      const [red, green, blue] = tf33.split(t2.resize, 3, 3);
+      const redNorm = tf33.mul(red, rgb[0]);
+      const greenNorm = tf33.mul(green, rgb[1]);
+      const blueNorm = tf33.mul(blue, rgb[2]);
+      const grayscale = tf33.addN([redNorm, greenNorm, blueNorm]);
+      const normalize2 = tf33.mul(tf33.sub(grayscale, constants.tf05), 2);
+      return normalize2;
+    });
+    const obj = { gender: "unknown", genderScore: 0 };
+    if ((_a2 = config3.face["ssrnet"]) == null ? void 0 : _a2.enabled)
+      t2.gender = model20.execute(t2.enhance);
+    const data = await t2.gender.data();
+    obj.gender = data[0] > data[1] ? "female" : "male";
+    obj.genderScore = data[0] > data[1] ? Math.trunc(100 * data[0]) / 100 : Math.trunc(100 * data[1]) / 100;
+    Object.keys(t2).forEach((tensor6) => tf33.dispose(t2[tensor6]));
+    last10[idx] = obj;
+    lastCount9 = count2;
+    lastTime15 = now();
+    resolve(obj);
+  });
+}
+
+// src/models.ts
+var Models = class {
+  constructor() {
+    __publicField(this, "ssrnetage", null);
+    __publicField(this, "gear", null);
+    __publicField(this, "blazeposedetect", null);
+    __publicField(this, "blazepose", null);
+    __publicField(this, "centernet", null);
+    __publicField(this, "efficientpose", null);
+    __publicField(this, "mobilefacenet", null);
+    __publicField(this, "insightface", null);
+    __publicField(this, "emotion", null);
+    __publicField(this, "facedetect", null);
+    __publicField(this, "faceiris", null);
+    __publicField(this, "facemesh", null);
+    __publicField(this, "faceres", null);
+    __publicField(this, "ssrnetgender", null);
+    __publicField(this, "handpose", null);
+    __publicField(this, "handskeleton", null);
+    __publicField(this, "handtrack", null);
+    __publicField(this, "liveness", null);
+    __publicField(this, "meet", null);
+    __publicField(this, "movenet", null);
+    __publicField(this, "nanodet", null);
+    __publicField(this, "posenet", null);
+    __publicField(this, "selfie", null);
+    __publicField(this, "rvm", null);
+    __publicField(this, "antispoof", null);
+  }
+};
+var instance;
+var getModelStats = (currentInstance) => {
+  if (currentInstance)
+    instance = currentInstance;
+  if (!instance)
+    log("instance not registred");
+  let totalSizeFromManifest = 0;
+  let totalSizeWeights = 0;
+  let totalSizeLoading = 0;
+  for (const m of Object.values(modelStats)) {
+    totalSizeFromManifest += m.sizeFromManifest;
+    totalSizeWeights += m.sizeLoadedWeights;
+    totalSizeLoading += m.sizeDesired;
+  }
+  const percentageLoaded = totalSizeLoading > 0 ? totalSizeWeights / totalSizeLoading : 0;
+  return {
+    numLoadedModels: Object.values(modelStats).length,
+    numDefinedModels: Object.keys(instance.models).length,
+    percentageLoaded,
+    totalSizeFromManifest,
+    totalSizeWeights,
+    totalSizeLoading,
+    totalSizeEnabled: void 0,
+    modelStats: Object.values(modelStats)
+  };
+};
+function reset2(currentInstance) {
+  if (currentInstance)
+    instance = currentInstance;
+  for (const model21 of Object.keys(instance.models))
+    instance.models[model21] = null;
+}
+async function load22(currentInstance) {
+  var _a, _b, _c, _d, _e, _f, _g, _h, _i, _j, _k, _l, _m, _n, _o, _p, _q, _r, _s, _t, _u, _v, _w, _x, _y, _z, _A, _B, _C;
+  if (currentInstance)
+    instance = currentInstance;
+  if (!instance)
+    log("instance not registred");
+  if (env.initial)
+    reset2(instance);
+  if (instance.config.hand.enabled) {
+    if (!instance.models.handpose && ((_b = (_a = instance.config.hand.detector) == null ? void 0 : _a.modelPath) == null ? void 0 : _b.includes("handdetect"))) {
+      [instance.models.handpose, instance.models.handskeleton] = await load10(instance.config);
+    }
+    if (!instance.models.handskeleton && instance.config.hand.landmarks && ((_d = (_c = instance.config.hand.detector) == null ? void 0 : _c.modelPath) == null ? void 0 : _d.includes("handdetect"))) {
+      [instance.models.handpose, instance.models.handskeleton] = await load10(instance.config);
+    }
+  }
+  if (instance.config.body.enabled && !instance.models.blazepose && ((_e = instance.config.body.modelPath) == null ? void 0 : _e.includes("blazepose")))
+    instance.models.blazepose = loadPose(instance.config);
+  if (instance.config.body.enabled && !instance.models.blazeposedetect && instance.config.body["detector"] && instance.config.body["detector"].modelPath)
+    instance.models.blazeposedetect = loadDetect(instance.config);
+  if (instance.config.body.enabled && !instance.models.efficientpose && ((_f = instance.config.body.modelPath) == null ? void 0 : _f.includes("efficientpose")))
+    instance.models.efficientpose = load4(instance.config);
+  if (instance.config.body.enabled && !instance.models.movenet && ((_g = instance.config.body.modelPath) == null ? void 0 : _g.includes("movenet")))
+    instance.models.movenet = load15(instance.config);
+  if (instance.config.body.enabled && !instance.models.posenet && ((_h = instance.config.body.modelPath) == null ? void 0 : _h.includes("posenet")))
+    instance.models.posenet = load17(instance.config);
+  if (instance.config.face.enabled && !instance.models.facedetect)
+    instance.models.facedetect = load2(instance.config);
+  if (instance.config.face.enabled && ((_i = instance.config.face.antispoof) == null ? void 0 : _i.enabled) && !instance.models.antispoof)
+    instance.models.antispoof = load(instance.config);
+  if (instance.config.face.enabled && ((_j = instance.config.face.liveness) == null ? void 0 : _j.enabled) && !instance.models.liveness)
+    instance.models.liveness = load12(instance.config);
+  if (instance.config.face.enabled && ((_k = instance.config.face.description) == null ? void 0 : _k.enabled) && !instance.models.faceres)
+    instance.models.faceres = load8(instance.config);
+  if (instance.config.face.enabled && ((_l = instance.config.face.emotion) == null ? void 0 : _l.enabled) && !instance.models.emotion)
+    instance.models.emotion = load5(instance.config);
+  if (instance.config.face.enabled && ((_m = instance.config.face.iris) == null ? void 0 : _m.enabled) && !((_n = instance.config.face.attention) == null ? void 0 : _n.enabled) && !instance.models.faceiris)
+    instance.models.faceiris = load6(instance.config);
+  if (instance.config.face.enabled && ((_o = instance.config.face.mesh) == null ? void 0 : _o.enabled) && !instance.models.facemesh)
+    instance.models.facemesh = load7(instance.config);
+  if (instance.config.face.enabled && ((_p = instance.config.face["gear"]) == null ? void 0 : _p.enabled) && !instance.models.gear)
+    instance.models.gear = load9(instance.config);
+  if (instance.config.face.enabled && ((_q = instance.config.face["ssrnet"]) == null ? void 0 : _q.enabled) && !instance.models.ssrnetage)
+    instance.models.ssrnetage = load20(instance.config);
+  if (instance.config.face.enabled && ((_r = instance.config.face["ssrnet"]) == null ? void 0 : _r.enabled) && !instance.models.ssrnetgender)
+    instance.models.ssrnetgender = load21(instance.config);
+  if (instance.config.face.enabled && ((_s = instance.config.face["mobilefacenet"]) == null ? void 0 : _s.enabled) && !instance.models.mobilefacenet)
+    instance.models.mobilefacenet = load14(instance.config);
+  if (instance.config.face.enabled && ((_t = instance.config.face["insightface"]) == null ? void 0 : _t.enabled) && !instance.models.insightface)
+    instance.models.insightface = load11(instance.config);
+  if (instance.config.hand.enabled && !instance.models.handtrack && ((_v = (_u = instance.config.hand.detector) == null ? void 0 : _u.modelPath) == null ? void 0 : _v.includes("handtrack")))
+    instance.models.handtrack = loadDetect2(instance.config);
+  if (instance.config.hand.enabled && instance.config.hand.landmarks && !instance.models.handskeleton && ((_x = (_w = instance.config.hand.detector) == null ? void 0 : _w.modelPath) == null ? void 0 : _x.includes("handtrack")))
+    instance.models.handskeleton = loadSkeleton(instance.config);
+  if (instance.config.object.enabled && !instance.models.centernet && ((_y = instance.config.object.modelPath) == null ? void 0 : _y.includes("centernet")))
+    instance.models.centernet = load3(instance.config);
+  if (instance.config.object.enabled && !instance.models.nanodet && ((_z = instance.config.object.modelPath) == null ? void 0 : _z.includes("nanodet")))
+    instance.models.nanodet = load16(instance.config);
+  if (instance.config.segmentation.enabled && !instance.models.selfie && ((_A = instance.config.segmentation.modelPath) == null ? void 0 : _A.includes("selfie")))
+    instance.models.selfie = load19(instance.config);
+  if (instance.config.segmentation.enabled && !instance.models.meet && ((_B = instance.config.segmentation.modelPath) == null ? void 0 : _B.includes("meet")))
+    instance.models.meet = load13(instance.config);
+  if (instance.config.segmentation.enabled && !instance.models.rvm && ((_C = instance.config.segmentation.modelPath) == null ? void 0 : _C.includes("rvm")))
+    instance.models.rvm = load18(instance.config);
+  for await (const model21 of Object.keys(instance.models)) {
+    if (instance.models[model21] && typeof instance.models[model21] !== "undefined") {
+      instance.models[model21] = await instance.models[model21];
+    }
+  }
+}
+function validateModel(currentInstance, model21, name) {
+  var _a, _b;
+  if (!model21)
+    return null;
+  if (currentInstance)
+    instance = currentInstance;
+  if (!instance)
+    log("instance not registred");
+  if (!((_a = instance == null ? void 0 : instance.config) == null ? void 0 : _a.validateModels))
+    return null;
+  const simpleOps = ["const", "placeholder", "noop", "pad", "squeeze", "add", "sub", "mul", "div"];
+  const ignoreOps = ["biasadd", "fusedbatchnormv3", "matmul", "switch", "shape", "merge", "split", "broadcastto"];
+  const ops = [];
+  const missing = [];
+  const url = model21["modelUrl"];
+  const executor = model21["executor"];
+  if ((_b = executor == null ? void 0 : executor.graph) == null ? void 0 : _b.nodes) {
+    for (const kernel of Object.values(executor.graph.nodes)) {
+      const op = kernel.op.toLowerCase();
+      if (!ops.includes(op))
+        ops.push(op);
+    }
+  } else {
+    if (!executor && instance.config.debug) {
+      log("model not loaded", name);
+    }
+  }
+  for (const op of ops) {
+    if (!simpleOps.includes(op) && !ignoreOps.includes(op) && !instance.env.kernels.includes(op) && !instance.env.kernels.includes(op.replace("_", "")) && !instance.env.kernels.includes(op.replace("native", "")) && !instance.env.kernels.includes(op.replace("v2", ""))) {
+      missing.push(op);
+    }
+  }
+  if (instance.config.debug && missing.length > 0)
+    log("model validation failed:", name, missing);
+  return missing.length > 0 ? { name, missing, ops, url } : null;
+}
+function validate2(currentInstance) {
+  if (currentInstance)
+    instance = currentInstance;
+  if (!instance)
+    log("instance not registred");
+  const missing = [];
+  for (const defined of Object.keys(currentInstance.models)) {
+    const model21 = currentInstance.models[defined];
+    if (!model21)
+      continue;
+    const res = validateModel(currentInstance, model21, defined);
+    if (res)
+      missing.push(res);
+  }
+  return missing;
+}
+
+// src/tfjs/humangl.ts
+var config2 = {
+  name: "humangl",
+  priority: 999,
+  canvas: null,
+  gl: null,
+  extensions: [],
+  webGLattr: {
+    alpha: false,
+    antialias: false,
+    premultipliedAlpha: false,
+    preserveDrawingBuffer: false,
+    depth: false,
+    stencil: false,
+    failIfMajorPerformanceCaveat: false,
+    desynchronized: true
+  }
+};
+function extensions() {
+  const gl = config2.gl;
+  if (!gl)
+    return;
+  config2.extensions = gl.getSupportedExtensions();
+}
+function register(instance2) {
+  var _a;
+  if (instance2.config.backend !== "humangl")
+    return;
+  if (config2.name in tf34.engine().registry && !((_a = config2 == null ? void 0 : config2.gl) == null ? void 0 : _a.getParameter(config2.gl.VERSION))) {
+    log("humangl error: backend invalid context");
+    reset2(instance2);
+  }
+  if (!tf34.findBackend(config2.name)) {
+    try {
+      config2.canvas = canvas(100, 100);
+    } catch (err) {
+      log("humangl error: cannot create canvas:", err);
+      return;
+    }
+    try {
+      config2.gl = config2.canvas.getContext("webgl2", config2.webGLattr);
+      if (!config2.gl) {
+        log("humangl error: cannot get webgl context");
+        return;
+      }
+      const glv2 = config2.gl.getParameter(config2.gl.VERSION).includes("2.0");
+      if (!glv2) {
+        log("backend override: using fallback webgl backend as webgl 2.0 is not detected");
+        instance2.config.backend = "webgl";
+        return;
+      }
+      if (config2.canvas) {
+        config2.canvas.addEventListener("webglcontextlost", (e) => {
+          log("humangl error:", e.type);
+          log("possible browser memory leak using webgl or conflict with multiple backend registrations");
+          instance2.emit("error");
+          throw new Error("backend error: webgl context lost");
+        });
+        config2.canvas.addEventListener("webglcontextrestored", (e) => {
+          log("humangl error: context restored:", e);
+        });
+        config2.canvas.addEventListener("webglcontextcreationerror", (e) => {
+          log("humangl error: context create:", e);
+        });
+      }
+    } catch (err) {
+      log("humangl error: cannot get webgl context:", err);
+      return;
+    }
+    try {
+      tf34.setWebGLContext(2, config2.gl);
+    } catch (err) {
+      log("humangl error: cannot set webgl context:", err);
+      return;
+    }
+    try {
+      const ctx = new tf34.GPGPUContext(config2.gl);
+      tf34.registerBackend(config2.name, () => new tf34.MathBackendWebGL(ctx), config2.priority);
+    } catch (err) {
+      log("humangl error: cannot register webgl backend:", err);
+      return;
+    }
+    try {
+      const kernels = tf34.getKernelsForBackend("webgl");
+      kernels.forEach((kernelConfig) => {
+        const newKernelConfig = { ...kernelConfig, backendName: config2.name };
+        tf34.registerKernel(newKernelConfig);
+      });
+    } catch (err) {
+      log("humangl error: cannot update webgl backend registration:", err);
+      return;
+    }
+    try {
+      if (tf34.env().flagRegistry.WEBGL_VERSION)
+        tf34.env().set("WEBGL_VERSION", 2);
+    } catch (err) {
+      log("humangl error: cannot set WebGL backend flags:", err);
+      return;
+    }
+    extensions();
+    const current = tf34.backend().getGPGPUContext ? tf34.backend().getGPGPUContext().gl : null;
+    if (current) {
+      if (instance2.config.debug)
+        log("humangl backend registered:", { webgl: current.getParameter(current.VERSION), renderer: current.getParameter(current.RENDERER) });
+    } else {
+      log("humangl error: no current gl context:", current, config2.gl);
+    }
+  }
+}
+
+// src/tfjs/backend.ts
+var tf35 = __toESM(require_tfjs_esm());
+function registerCustomOps(config3) {
+  const newKernels = [];
+  if (!env.kernels.includes("mod")) {
+    const kernelMod = {
+      kernelName: "Mod",
+      backendName: tf35.getBackend(),
+      kernelFunc: (op) => tf35.tidy(() => tf35.sub(op.inputs.a, tf35.mul(tf35.div(op.inputs.a, op.inputs.b), op.inputs.b)))
+    };
+    tf35.registerKernel(kernelMod);
+    env.kernels.push("mod");
+    newKernels.push("mod");
+  }
+  if (!env.kernels.includes("floormod")) {
+    const kernelFloorMod = {
+      kernelName: "FloorMod",
+      backendName: tf35.getBackend(),
+      kernelFunc: (op) => tf35.tidy(() => tf35.add(tf35.mul(tf35.floorDiv(op.inputs.a / op.inputs.b), op.inputs.b), tf35.mod(op.inputs.a, op.inputs.b)))
+    };
+    tf35.registerKernel(kernelFloorMod);
+    env.kernels.push("floormod");
+    newKernels.push("floormod");
+  }
+  if (!env.kernels.includes("rotatewithoffset") && config3.softwareKernels) {
+    const kernelRotateWithOffset = {
+      kernelName: "RotateWithOffset",
+      backendName: tf35.getBackend(),
+      kernelFunc: (op) => tf35.tidy(() => {
+        const backend4 = tf35.getBackend();
+        tf35.setBackend("cpu");
+        const t2 = tf35.image.rotateWithOffset(op.inputs.image, op.attrs.radians, op.attrs.fillValue, op.attrs.center);
+        tf35.setBackend(backend4);
+        return t2;
+      })
+    };
+    tf35.registerKernel(kernelRotateWithOffset);
+    env.kernels.push("rotatewithoffset");
+    newKernels.push("rotatewithoffset");
+  }
+  if (newKernels.length > 0 && config3.debug)
+    log("registered kernels:", newKernels);
+}
+var defaultFlags = {};
+async function check(instance2, force = false) {
+  instance2.state = "backend";
+  if (force || env.initial || instance2.config.backend && instance2.config.backend.length > 0 && tf35.getBackend() !== instance2.config.backend) {
+    const timeStamp = now();
+    if (instance2.config.backend && instance2.config.backend.length > 0) {
+      if (typeof window === "undefined" && typeof WorkerGlobalScope !== "undefined" && instance2.config.debug) {
+        if (instance2.config.debug)
+          log("running inside web worker");
+      }
+      if (env.browser && instance2.config.backend === "tensorflow") {
+        if (instance2.config.debug)
+          log("override: backend set to tensorflow while running in browser");
+        instance2.config.backend = "webgl";
+      }
+      if (env.node && (instance2.config.backend === "webgl" || instance2.config.backend === "humangl")) {
+        if (instance2.config.debug)
+          log(`override: backend set to ${instance2.config.backend} while running in nodejs`);
+        instance2.config.backend = "tensorflow";
+      }
+      if (env.browser && instance2.config.backend === "webgpu") {
+        if (typeof navigator === "undefined" || typeof navigator.gpu === "undefined") {
+          log("override: backend set to webgpu but browser does not support webgpu");
+          instance2.config.backend = "webgl";
+        } else {
+          const adapter = await navigator.gpu.requestAdapter();
+          if (instance2.config.debug)
+            log("enumerated webgpu adapter:", adapter);
+          if (!adapter) {
+            log("override: backend set to webgpu but browser reports no available gpu");
+            instance2.config.backend = "webgl";
+          } else {
+            const adapterInfo = "requestAdapterInfo" in adapter ? await adapter.requestAdapterInfo() : void 0;
+            log("webgpu adapter info:", adapterInfo);
+          }
+        }
+      }
+      let available = Object.keys(tf35.engine().registryFactory);
+      if (instance2.config.backend === "humangl" && !available.includes("humangl")) {
+        register(instance2);
+        available = Object.keys(tf35.engine().registryFactory);
+      }
+      if (instance2.config.debug)
+        log("available backends:", available);
+      if (!available.includes(instance2.config.backend)) {
+        log(`error: backend ${instance2.config.backend} not found in registry`);
+        instance2.config.backend = env.node ? "tensorflow" : "webgl";
+        if (instance2.config.debug)
+          log(`override: setting backend ${instance2.config.backend}`);
+      }
+      if (instance2.config.debug)
+        log("setting backend:", [instance2.config.backend]);
+      if (instance2.config.backend === "wasm") {
+        if (tf35.env().flagRegistry.CANVAS2D_WILL_READ_FREQUENTLY)
+          tf35.env().set("CANVAS2D_WILL_READ_FREQUENTLY", true);
+        if (instance2.config.debug)
+          log("wasm path:", instance2.config.wasmPath);
+        if (typeof tf35.setWasmPaths !== "undefined")
+          tf35.setWasmPaths(instance2.config.wasmPath, instance2.config.wasmPlatformFetch);
+        else
+          throw new Error("backend error: attempting to use wasm backend but wasm path is not set");
+        let mt = false;
+        let simd = false;
+        try {
+          mt = await tf35.env().getAsync("WASM_HAS_MULTITHREAD_SUPPORT");
+          simd = await tf35.env().getAsync("WASM_HAS_SIMD_SUPPORT");
+          if (instance2.config.debug)
+            log(`wasm execution: ${simd ? "simd" : "no simd"} ${mt ? "multithreaded" : "singlethreaded"}`);
+          if (instance2.config.debug && !simd)
+            log("warning: wasm simd support is not enabled");
+        } catch (e) {
+          log("wasm detection failed");
+        }
+      }
+      try {
+        await tf35.setBackend(instance2.config.backend);
+        await tf35.ready();
+      } catch (err) {
+        log("error: cannot set backend:", instance2.config.backend, err);
+        return false;
+      }
+      if (instance2.config.debug)
+        defaultFlags = JSON.parse(JSON.stringify(tf35.env().flags));
+    }
+    if (tf35.getBackend() === "humangl" || tf35.getBackend() === "webgl") {
+      if (tf35.env().flagRegistry.WEBGL_USE_SHAPES_UNIFORMS)
+        tf35.env().set("WEBGL_USE_SHAPES_UNIFORMS", true);
+      if (tf35.env().flagRegistry.WEBGL_EXP_CONV)
+        tf35.env().set("WEBGL_EXP_CONV", true);
+      if (instance2.config.debug && typeof instance2.config.deallocate !== "undefined" && instance2.config.deallocate) {
+        log("changing webgl: WEBGL_DELETE_TEXTURE_THRESHOLD:", true);
+        tf35.env().set("WEBGL_DELETE_TEXTURE_THRESHOLD", 0);
+      }
+    }
+    if (tf35.getBackend() === "webgpu") {
+    }
+    if (instance2.config.debug) {
+      const newFlags = tf35.env().flags;
+      const updatedFlags = {};
+      for (const key of Object.keys(newFlags)) {
+        if (defaultFlags[key] === newFlags[key])
+          continue;
+        updatedFlags[key] = newFlags[key];
+      }
+      if (instance2.config.debug && Object.keys(updatedFlags).length > 0)
+        log("backend:", tf35.getBackend(), "flags:", updatedFlags);
+    }
+    if (instance2.config.flags && Object.keys(instance2.config.flags).length > 0) {
+      if (instance2.config.debug)
+        log("flags:", instance2.config["flags"]);
+      for (const [key, val] of Object.entries(instance2.config.flags)) {
+        tf35.env().set(key, val);
+      }
+    }
+    tf35.enableProdMode();
+    init();
+    instance2.performance.initBackend = Math.trunc(now() - timeStamp);
+    instance2.config.backend = tf35.getBackend();
+    await env.updateBackend();
+    registerCustomOps(instance2.config);
+    env.initial = false;
+  }
+  return true;
+}
+function fakeOps(kernelNames, config3) {
+  for (const kernelName of kernelNames) {
+    const kernelConfig = {
+      kernelName,
+      backendName: config3.backend,
+      kernelFunc: () => {
+        if (config3.debug)
+          log("kernelFunc", kernelName, config3.backend);
+      }
+    };
+    tf35.registerKernel(kernelConfig);
+  }
+  env.kernels = tf35.getKernelsForBackend(tf35.getBackend()).map((kernel) => kernel.kernelName.toLowerCase());
+}
+
+// src/draw/draw.ts
+var draw_exports = {};
+__export(draw_exports, {
+  all: () => all,
+  body: () => body,
+  canvas: () => canvas2,
+  face: () => face,
+  gesture: () => gesture,
+  hand: () => hand,
+  object: () => object,
+  options: () => options3,
+  person: () => person
+});
+
+// src/draw/primitives.ts
+var getCanvasContext = (input) => {
+  if (!input)
+    log("draw error: invalid canvas");
+  else if (!input.getContext)
+    log("draw error: canvas context not defined");
+  else {
+    const ctx = input.getContext("2d");
+    if (!ctx)
+      log("draw error: cannot get canvas context");
+    else
+      return ctx;
+  }
+  return null;
+};
+var rad2deg = (theta) => Math.round(theta * 180 / Math.PI);
+var colorDepth = (z, opt2) => {
+  if (!opt2.useDepth || typeof z === "undefined")
+    return opt2.color;
+  const rgb2 = Uint8ClampedArray.from([127 + 2 * z, 127 - 2 * z, 255]);
+  return `rgba(${rgb2[0]}, ${rgb2[1]}, ${rgb2[2]}, ${opt2.alpha})`;
+};
+function point(ctx, x, y, z, localOptions) {
+  ctx.fillStyle = colorDepth(z, localOptions);
+  ctx.beginPath();
+  ctx.arc(x, y, localOptions.pointSize, 0, 2 * Math.PI);
+  ctx.fill();
+}
+function rect(ctx, x, y, width, height, localOptions) {
+  ctx.beginPath();
+  ctx.lineWidth = localOptions.lineWidth;
+  if (localOptions.useCurves) {
+    const cx = (x + x + width) / 2;
+    const cy = (y + y + height) / 2;
+    ctx.ellipse(cx, cy, width / 2, height / 2, 0, 0, 2 * Math.PI);
+  } else {
+    ctx.moveTo(x + localOptions.roundRect, y);
+    ctx.lineTo(x + width - localOptions.roundRect, y);
+    ctx.quadraticCurveTo(x + width, y, x + width, y + localOptions.roundRect);
+    ctx.lineTo(x + width, y + height - localOptions.roundRect);
+    ctx.quadraticCurveTo(x + width, y + height, x + width - localOptions.roundRect, y + height);
+    ctx.lineTo(x + localOptions.roundRect, y + height);
+    ctx.quadraticCurveTo(x, y + height, x, y + height - localOptions.roundRect);
+    ctx.lineTo(x, y + localOptions.roundRect);
+    ctx.quadraticCurveTo(x, y, x + localOptions.roundRect, y);
+    ctx.closePath();
+  }
+  ctx.stroke();
+}
+function lines(ctx, points, localOptions) {
+  if (points.length < 2)
+    return;
+  ctx.beginPath();
+  ctx.moveTo(points[0][0], points[0][1]);
+  for (const pt of points) {
+    ctx.strokeStyle = colorDepth(pt[2] || 0, localOptions);
+    ctx.lineTo(Math.trunc(pt[0]), Math.trunc(pt[1]));
+  }
+  ctx.stroke();
+  if (localOptions.fillPolygons) {
+    ctx.closePath();
+    ctx.fill();
+  }
+}
+function curves(ctx, points, localOptions) {
+  if (points.length < 2)
+    return;
+  ctx.lineWidth = localOptions.lineWidth;
+  if (!localOptions.useCurves || points.length <= 2) {
+    lines(ctx, points, localOptions);
+    return;
+  }
+  ctx.moveTo(points[0][0], points[0][1]);
+  for (let i = 0; i < points.length - 2; i++) {
+    const xc = (points[i][0] + points[i + 1][0]) / 2;
+    const yc = (points[i][1] + points[i + 1][1]) / 2;
+    ctx.quadraticCurveTo(points[i][0], points[i][1], xc, yc);
+  }
+  ctx.quadraticCurveTo(points[points.length - 2][0], points[points.length - 2][1], points[points.length - 1][0], points[points.length - 1][1]);
+  ctx.stroke();
+  if (localOptions.fillPolygons) {
+    ctx.closePath();
+    ctx.fill();
+  }
+}
+function arrow(ctx, from, to, radius = 5) {
+  let angle;
+  let x;
+  let y;
+  ctx.beginPath();
+  ctx.moveTo(from[0], from[1]);
+  ctx.lineTo(to[0], to[1]);
+  angle = Math.atan2(to[1] - from[1], to[0] - from[0]);
+  x = radius * Math.cos(angle) + to[0];
+  y = radius * Math.sin(angle) + to[1];
+  ctx.moveTo(x, y);
+  angle += 1 / 3 * (2 * Math.PI);
+  x = radius * Math.cos(angle) + to[0];
+  y = radius * Math.sin(angle) + to[1];
+  ctx.lineTo(x, y);
+  angle += 1 / 3 * (2 * Math.PI);
+  x = radius * Math.cos(angle) + to[0];
+  y = radius * Math.sin(angle) + to[1];
+  ctx.lineTo(x, y);
+  ctx.closePath();
+  ctx.stroke();
+  ctx.fill();
+}
+
+// src/draw/options.ts
+var options3 = {
+  color: "rgba(173, 216, 230, 0.6)",
+  labelColor: "rgba(173, 216, 230, 1)",
+  shadowColor: "black",
+  alpha: 0.5,
+  font: 'small-caps 16px "Segoe UI"',
+  lineHeight: 18,
+  lineWidth: 4,
+  pointSize: 2,
+  roundRect: 8,
+  drawPoints: false,
+  drawLabels: true,
+  drawBoxes: true,
+  drawAttention: true,
+  drawGestures: true,
+  drawPolygons: true,
+  drawGaze: true,
+  fillPolygons: false,
+  useDepth: true,
+  useCurves: false
+};
+
+// src/draw/face.ts
+var opt;
+function drawLabels(f, ctx) {
+  var _a, _b;
+  if (opt.drawLabels) {
+    const labels2 = [];
+    labels2.push(`face: ${Math.trunc(100 * f.score)}%`);
+    if (f.genderScore)
+      labels2.push(`${f.gender || ""} ${Math.trunc(100 * f.genderScore)}%`);
+    if (f.age)
+      labels2.push(`age: ${f.age || ""}`);
+    if (f.iris)
+      labels2.push(`distance: ${f.iris}`);
+    if (f.real)
+      labels2.push(`real: ${Math.trunc(100 * f.real)}%`);
+    if (f.live)
+      labels2.push(`live: ${Math.trunc(100 * f.live)}%`);
+    if (f.emotion && f.emotion.length > 0) {
+      const emotion2 = f.emotion.map((a) => `${Math.trunc(100 * a.score)}% ${a.emotion}`);
+      if (emotion2.length > 3)
+        emotion2.length = 3;
+      labels2.push(emotion2.join(" "));
+    }
+    if (((_a = f.rotation) == null ? void 0 : _a.angle) && ((_b = f.rotation) == null ? void 0 : _b.gaze)) {
+      if (f.rotation.angle.roll)
+        labels2.push(`roll: ${rad2deg(f.rotation.angle.roll)}\xB0 yaw:${rad2deg(f.rotation.angle.yaw)}\xB0 pitch:${rad2deg(f.rotation.angle.pitch)}\xB0`);
+      if (f.rotation.gaze.bearing)
+        labels2.push(`gaze: ${rad2deg(f.rotation.gaze.bearing)}\xB0`);
+    }
+    if (labels2.length === 0)
+      labels2.push("face");
+    ctx.fillStyle = opt.color;
+    for (let i = labels2.length - 1; i >= 0; i--) {
+      const x = Math.max(f.box[0], 0);
+      const y = i * opt.lineHeight + f.box[1];
+      if (opt.shadowColor && opt.shadowColor !== "") {
+        ctx.fillStyle = opt.shadowColor;
+        ctx.fillText(labels2[i], x + 5, y + 16);
+      }
+      ctx.fillStyle = opt.labelColor;
+      ctx.fillText(labels2[i], x + 4, y + 15);
+    }
+  }
+}
+function drawIrisElipse(f, ctx) {
+  var _a, _b, _c, _d;
+  if (((_a = f.annotations) == null ? void 0 : _a.leftEyeIris) && ((_b = f.annotations) == null ? void 0 : _b.leftEyeIris[0])) {
+    ctx.strokeStyle = opt.useDepth ? "rgba(255, 200, 255, 0.3)" : opt.color;
+    ctx.beginPath();
+    const sizeX = Math.abs(f.annotations.leftEyeIris[3][0] - f.annotations.leftEyeIris[1][0]) / 2;
+    const sizeY = Math.abs(f.annotations.leftEyeIris[4][1] - f.annotations.leftEyeIris[2][1]) / 2;
+    ctx.ellipse(f.annotations.leftEyeIris[0][0], f.annotations.leftEyeIris[0][1], sizeX, sizeY, 0, 0, 2 * Math.PI);
+    ctx.stroke();
+    if (opt.fillPolygons) {
+      ctx.fillStyle = opt.useDepth ? "rgba(255, 255, 200, 0.3)" : opt.color;
+      ctx.fill();
+    }
+  }
+  if (((_c = f.annotations) == null ? void 0 : _c.rightEyeIris) && ((_d = f.annotations) == null ? void 0 : _d.rightEyeIris[0])) {
+    ctx.strokeStyle = opt.useDepth ? "rgba(255, 200, 255, 0.3)" : opt.color;
+    ctx.beginPath();
+    const sizeX = Math.abs(f.annotations.rightEyeIris[3][0] - f.annotations.rightEyeIris[1][0]) / 2;
+    const sizeY = Math.abs(f.annotations.rightEyeIris[4][1] - f.annotations.rightEyeIris[2][1]) / 2;
+    ctx.ellipse(f.annotations.rightEyeIris[0][0], f.annotations.rightEyeIris[0][1], sizeX, sizeY, 0, 0, 2 * Math.PI);
+    ctx.stroke();
+    if (opt.fillPolygons) {
+      ctx.fillStyle = opt.useDepth ? "rgba(255, 255, 200, 0.3)" : opt.color;
+      ctx.fill();
+    }
+  }
+}
+function drawGazeSpheres(f, ctx) {
+  var _a;
+  if (opt.drawGaze && ((_a = f.rotation) == null ? void 0 : _a.angle) && typeof Path2D !== "undefined") {
+    ctx.strokeStyle = "pink";
+    const valX = f.box[0] + f.box[2] / 2 - f.box[3] * rad2deg(f.rotation.angle.yaw) / 90;
+    const valY = f.box[1] + f.box[3] / 2 + f.box[2] * rad2deg(f.rotation.angle.pitch) / 90;
+    const pathV = new Path2D(`
+      M ${f.box[0] + f.box[2] / 2} ${f.box[1]}
       C
-        ${o} ${e.box[1]},
-        ${o} ${e.box[1]+e.box[3]},
-        ${e.box[0]+e.box[2]/2} ${e.box[1]+e.box[3]}
-    `),s=new Path2D(`
-      M ${e.box[0]} ${e.box[1]+e.box[3]/2}
+        ${valX} ${f.box[1]},
+        ${valX} ${f.box[1] + f.box[3]},
+        ${f.box[0] + f.box[2] / 2} ${f.box[1] + f.box[3]}
+    `);
+    const pathH = new Path2D(`
+      M ${f.box[0]} ${f.box[1] + f.box[3] / 2}
       C 
-        ${e.box[0]} ${r},
-        ${e.box[0]+e.box[2]} ${r},
-        ${e.box[0]+e.box[2]} ${e.box[1]+e.box[3]/2}
-    `);t.stroke(s),t.stroke(A)}}function Ms(e,t){var n;if(Y.drawGaze&&((n=e.rotation)==null?void 0:n.gaze.strength)&&e.rotation.gaze.bearing&&e.annotations.leftEyeIris&&e.annotations.rightEyeIris&&e.annotations.leftEyeIris[0]&&e.annotations.rightEyeIris[0]){t.strokeStyle="pink",t.fillStyle="pink";let o=[e.annotations.leftEyeIris[0][0]+Math.sin(e.rotation.gaze.bearing)*e.rotation.gaze.strength*e.box[3],e.annotations.leftEyeIris[0][1]+Math.cos(e.rotation.gaze.bearing)*e.rotation.gaze.strength*e.box[2]];a1(t,[e.annotations.leftEyeIris[0][0],e.annotations.leftEyeIris[0][1]],[o[0],o[1]],4);let r=[e.annotations.rightEyeIris[0][0]+Math.sin(e.rotation.gaze.bearing)*e.rotation.gaze.strength*e.box[3],e.annotations.rightEyeIris[0][1]+Math.cos(e.rotation.gaze.bearing)*e.rotation.gaze.strength*e.box[2]];a1(t,[e.annotations.rightEyeIris[0][0],e.annotations.rightEyeIris[0][1]],[r[0],r[1]],4)}}function vs(e,t){if(Y.drawPolygons&&e.mesh.length>=468){t.lineWidth=1;for(let n=0;n<Ve.length/3;n++){let o=[Ve[n*3+0],Ve[n*3+1],Ve[n*3+2]].map(r=>e.mesh[r]);s1(t,o,Y)}bs(e,t)}}function Ps(e,t){if(Y.drawPoints&&e.mesh.length>=468)for(let n=0;n<e.mesh.length;n++)be(t,e.mesh[n][0],e.mesh[n][1],e.mesh[n][2],Y),Y.drawAttention&&(O2.includes(n)&&be(t,e.mesh[n][0],e.mesh[n][1],e.mesh[n][2]+127,Y),Xe.includes(n)&&be(t,e.mesh[n][0],e.mesh[n][1],e.mesh[n][2]-127,Y),qe.includes(n)&&be(t,e.mesh[n][0],e.mesh[n][1],e.mesh[n][2]-127,Y))}function Ts(e,t){Y.drawBoxes&&de(t,e.box[0],e.box[1],e.box[2],e.box[3],Y)}function h2(e,t,n){if(Y=A0(R0,n),!t||!e)return;let o=X0(e);if(!!o){o.font=Y.font,o.strokeStyle=Y.color,o.fillStyle=Y.color;for(let r of t)Ts(r,o),hs(r,o),r.mesh&&r.mesh.length>0&&(Ps(r,o),vs(r,o),gs(r,o),Ms(r,o))}}function b2(e,t,n){let o=A0(R0,n);if(!t||!e)return;let r=X0(e);if(!!r){r.lineJoin="round";for(let A=0;A<t.length;A++){if(r.strokeStyle=o.color,r.fillStyle=o.color,r.lineWidth=o.lineWidth,r.font=o.font,o.drawBoxes&&t[A].box&&t[A].box.length===4&&(de(r,t[A].box[0],t[A].box[1],t[A].box[2],t[A].box[3],o),o.drawLabels&&(o.shadowColor&&o.shadowColor!==""&&(r.fillStyle=o.shadowColor,r.fillText(`body ${100*t[A].score}%`,t[A].box[0]+3,1+t[A].box[1]+o.lineHeight,t[A].box[2])),r.fillStyle=o.labelColor,r.fillText(`body ${100*t[A].score}%`,t[A].box[0]+2,0+t[A].box[1]+o.lineHeight,t[A].box[2]))),o.drawPoints&&t[A].keypoints)for(let s=0;s<t[A].keypoints.length;s++)!t[A].keypoints[s].score||t[A].keypoints[s].score===0||(r.fillStyle=he(t[A].keypoints[s].position[2],o),be(r,t[A].keypoints[s].position[0],t[A].keypoints[s].position[1],0,o));if(o.drawLabels&&t[A].keypoints){r.font=o.font;for(let s of t[A].keypoints)!s.score||s.score===0||(r.fillStyle=he(s.position[2],o),r.fillText(`${s.part} ${Math.trunc(100*s.score)}%`,s.position[0]+4,s.position[1]+4))}if(o.drawPolygons&&t[A].keypoints&&t[A].annotations)for(let s of Object.values(t[A].annotations))for(let a of s)io(r,a,o)}}}function g2(e,t,n){let o=A0(R0,n);if(!t||!e)return;let r=X0(e);if(!!r){r.lineJoin="round",r.font=o.font;for(let A of t){if(o.drawBoxes&&(r.strokeStyle=o.color,r.fillStyle=o.color,de(r,A.box[0],A.box[1],A.box[2],A.box[3],o),o.drawLabels&&(o.shadowColor&&o.shadowColor!==""&&(r.fillStyle=o.shadowColor,r.fillText(`hand:${Math.trunc(100*A.score)}%`,A.box[0]+3,1+A.box[1]+o.lineHeight,A.box[2])),r.fillStyle=o.labelColor,r.fillText(`hand:${Math.trunc(100*A.score)}%`,A.box[0]+2,0+A.box[1]+o.lineHeight,A.box[2])),r.stroke()),o.drawPoints&&A.keypoints&&A.keypoints.length>0)for(let s of A.keypoints)r.fillStyle=he(s[2],o),be(r,s[0],s[1],0,o);if(o.drawLabels&&A.annotations){let s=(a,l)=>{if(!a||a.length===0||!a[0])return;let 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InsightFace:"),e.config.async?l=(T=e.config.face.insightface)!=null&&T.enabled?Pt(d[E].tensor||r0.tensor([]),e.config,E,d.length):null:(e.state="run:mobilefacenet",n=v(),l=(F=e.config.face.insightface)!=null&&F.enabled?await Pt(d[E].tensor||r0.tensor([]),e.config,E,d.length):null,e.performance.mobilefacenet=Math.trunc(v()-n)),e.analyze("End InsightFace:"),e.analyze("Start Description:"),e.config.async?i=zt(d[E].tensor||r0.tensor([]),e.config,E,d.length):(e.state="run:description",n=v(),i=await zt(d[E].tensor||r0.tensor([]),e.config,E,d.length),e.performance.description=k.perfadd?(e.performance.description||0)+Math.trunc(v()-n):Math.trunc(v()-n)),e.analyze("End Description:"),e.config.async&&([o,A,s,a,l,i,r,c,x]=await Promise.all([o,A,s,a,l,i,r,c,x])),e.analyze("Finish Face:"),((M0=e.config.face.ssrnet)==null?void 0:M0.enabled)&&o&&A&&(i={...i,age:o.age,gender:A.gender,genderScore:A.genderScore}),(($=e.config.face.gear)==null?void 0:$.enabled)&&r&&(i={...i,age:r.age,gender:r.gender,genderScore:r.genderScore,race:r.race}),((m0=e.config.face.mobilefacenet)==null?void 0:m0.enabled)&&a&&(i.descriptor=a),((d0=e.config.face.insightface)==null?void 0:d0.enabled)&&l&&(i.descriptor=l),(G=e.config.face.iris)!=null&&G.enabled;let e2=((U0=(q0=(Z=d[E])==null?void 0:Z.annotations)==null?void 0:q0.leftEyeIris)==null?void 0:U0[0])&&((xe=(Me=(ge=d[E])==null?void 0:ge.annotations)==null?void 0:Me.rightEyeIris)==null?void 0:xe[0])&&d[E].annotations.leftEyeIris.length>0&&d[E].annotations.rightEyeIris.length>0&&d[E].annotations.leftEyeIris[0]!==null&&d[E].annotations.rightEyeIris[0]!==null?Math.max(Math.abs(d[E].annotations.leftEyeIris[3][0]-d[E].annotations.leftEyeIris[1][0]),Math.abs(d[E].annotations.rightEyeIris[4][1]-d[E].annotations.rightEyeIris[2][1]))/t.shape[2]:0,g1=(R2=e.config.face.detector)!=null&&R2.return?r0.squeeze(d[E].tensor):null;r0.dispose(d[E].tensor),d[E].tensor&&delete d[E].tensor;let Y0={...d[E],id:E};i.age&&(Y0.age=i.age),i.gender&&(Y0.gender=i.gender),i.genderScore&&(Y0.genderScore=i.genderScore),i.descriptor&&(Y0.embedding=i.descriptor),i.race&&(Y0.race=i.race),s&&(Y0.emotion=s),c&&(Y0.real=c),x&&(Y0.live=x),e2&&e2!==0&&(Y0.iris=Math.trunc(500/e2/11.7)/100),k2&&(Y0.rotation=k2),g1&&(Y0.tensor=g1),y.push(Y0),e.analyze("End Face")}return e.analyze("End FaceMesh:"),e.config.async&&(e.performance.face&&delete e.performance.face,e.performance.age&&delete e.performance.age,e.performance.gender&&delete e.performance.gender,e.performance.emotion&&delete e.performance.emotion),y};var yo=e=>{if(!e)return[];let t=[];for(let n=0;n<e.length;n++){let o=e[n].keypoints.find(l=>l.part==="leftWrist"),r=e[n].keypoints.find(l=>l.part==="rightWrist"),A=e[n].keypoints.find(l=>l.part==="nose");A&&o&&r&&o.position[1]<A.position[1]&&r.position[1]<A.position[1]?t.push({body:n,gesture:"i give up"}):A&&o&&o.position[1]<A.position[1]?t.push({body:n,gesture:"raise left 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a=Math.min(100,500*Math.abs(e[n].mesh[13][1]-e[n].mesh[14][1])/Math.abs(e[n].mesh[10][1]-e[n].mesh[152][1]));a>10&&t.push({face:n,gesture:`mouth ${Math.trunc(a)}% open`});let l=e[n].mesh[152][2]||0;Math.abs(l)>10&&t.push({face:n,gesture:`head ${l<0?"up":"down"}`})}return t},mo=e=>{var n,o,r,A;if(!e)return[];let t=[];for(let s=0;s<e.length;s++){if(!((o=(n=e[s].annotations)==null?void 0:n.leftEyeIris)!=null&&o[0])||!((A=(r=e[s].annotations)==null?void 0:r.rightEyeIris)!=null&&A[0]))continue;let a=e[s].annotations.leftEyeIris[3][0]-e[s].annotations.leftEyeIris[1][0],l=e[s].annotations.leftEyeIris[4][1]-e[s].annotations.leftEyeIris[2][1],c=Math.abs(a*l),x=e[s].annotations.rightEyeIris[3][0]-e[s].annotations.rightEyeIris[1][0],i=e[s].annotations.rightEyeIris[4][1]-e[s].annotations.rightEyeIris[2][1],y=Math.abs(x*i),d=!1;Math.abs(c-y)/Math.max(c,y)<.25&&(d=!0,t.push({iris:s,gesture:"facing center"}));let p=Math.abs(e[s].mesh[263][0]-e[s].annotations.leftEyeIris[0][0])/e[s].box[2],g=Math.abs(e[s].mesh[33][0]-e[s].annotations.rightEyeIris[0][0])/e[s].box[2];(p>.06||g>.06)&&(d=!1),p>g?p>.05&&t.push({iris:s,gesture:"looking right"}):g>.05&&t.push({iris:s,gesture:"looking left"});let M=Math.abs(e[s].mesh[145][1]-e[s].annotations.rightEyeIris[0][1])/e[s].box[3],P=Math.abs(e[s].mesh[374][1]-e[s].annotations.leftEyeIris[0][1])/e[s].box[3];(P<.01||M<.01||P>.022||M>.022)&&(d=!1),(P<.01||M<.01)&&t.push({iris:s,gesture:"looking down"}),(P>.022||M>.022)&&t.push({iris:s,gesture:"looking up"}),d&&t.push({iris:s,gesture:"looking center"})}return t},po=e=>{if(!e)return[];let t=[];for(let n=0;n<e.length;n++){let o=[];if(e[n].annotations)for(let[r,A]of Object.entries(e[n].annotations))r!=="palmBase"&&Array.isArray(A)&&A[0]&&o.push({name:r.toLowerCase(),position:A[0]});if(o&&o.length>0){let r=o.reduce((s,a)=>(s.position[2]||0)<(a.position[2]||0)?s:a);t.push({hand:n,gesture:`${r.name} forward`});let A=o.reduce((s,a)=>s.position[1]<a.position[1]?s:a);t.push({hand:n,gesture:`${A.name} up`})}if(e[n].keypoints){let r=Pn(e[n].keypoints);for(let A of r)t.push({hand:n,gesture:A.name})}}return t};var j={face:[],body:[],hand:[],gesture:[],object:[],persons:[],performance:{},timestamp:0,error:null},m1=0;function uo(e,t){var s,a,l,c,x,i,y,d,f,p,g,M,P,m,b,z,w,I,q,e0,V,X,c0;let n=v();if(!e)return{face:[],body:[],hand:[],gesture:[],object:[],persons:[],performance:{},timestamp:0,error:null};let o=Date.now()-e.timestamp,r=o<1e3?8-Math.log(o+1):1;if(e.canvas&&(j.canvas=e.canvas),e.error&&(j.error=e.error),!j.body||e.body.length!==j.body.length)j.body=JSON.parse(JSON.stringify(e.body));else for(let T=0;T<e.body.length;T++){let F=e.body[T].box.map((G,Z)=>((r-1)*j.body[T].box[Z]+G)/r),M0=e.body[T].boxRaw.map((G,Z)=>((r-1)*j.body[T].boxRaw[Z]+G)/r),$=e.body[T].keypoints.map((G,Z)=>{var q0,U0,ge,Me,xe,R2,E,k2,e2;return{score:G.score,part:G.part,position:[j.body[T].keypoints[Z]?((r-1)*(j.body[T].keypoints[Z].position[0]||0)+(G.position[0]||0))/r:G.position[0],j.body[T].keypoints[Z]?((r-1)*(j.body[T].keypoints[Z].position[1]||0)+(G.position[1]||0))/r:G.position[1],j.body[T].keypoints[Z]?((r-1)*(j.body[T].keypoints[Z].position[2]||0)+(G.position[2]||0))/r:G.position[2]],positionRaw:[j.body[T].keypoints[Z]?((r-1)*(j.body[T].keypoints[Z].positionRaw[0]||0)+(G.positionRaw[0]||0))/r:G.positionRaw[0],j.body[T].keypoints[Z]?((r-1)*(j.body[T].keypoints[Z].positionRaw[1]||0)+(G.positionRaw[1]||0))/r:G.positionRaw[1],j.body[T].keypoints[Z]?((r-1)*(j.body[T].keypoints[Z].positionRaw[2]||0)+(G.positionRaw[2]||0))/r:G.positionRaw[2]],distance:[j.body[T].keypoints[Z]?((r-1)*(((q0=j.body[T].keypoints[Z].distance)==null?void 0:q0[0])||0)+(((U0=G.distance)==null?void 0:U0[0])||0))/r:(ge=G.distance)==null?void 0:ge[0],j.body[T].keypoints[Z]?((r-1)*(((Me=j.body[T].keypoints[Z].distance)==null?void 0:Me[1])||0)+(((xe=G.distance)==null?void 0:xe[1])||0))/r:(R2=G.distance)==null?void 0:R2[1],j.body[T].keypoints[Z]?((r-1)*(((E=j.body[T].keypoints[Z].distance)==null?void 0:E[2])||0)+(((k2=G.distance)==null?void 0:k2[2])||0))/r:(e2=G.distance)==null?void 0:e2[2]]}}),m0={},d0={connected:{}};(s=t.body.modelPath)!=null&&s.includes("efficientpose")?d0=c5:(a=t.body.modelPath)!=null&&a.includes("blazepose")?d0=A5:(l=t.body.modelPath)!=null&&l.includes("movenet")&&(d0=F2);for(let[G,Z]of Object.entries(d0.connected)){let q0=[];for(let U0=0;U0<Z.length-1;U0++){let ge=$.find(xe=>xe.part===Z[U0]),Me=$.find(xe=>xe.part===Z[U0+1]);ge&&Me&&q0.push([ge.position,Me.position])}m0[G]=q0}j.body[T]={...e.body[T],box:F,boxRaw:M0,keypoints:$,annotations:m0}}if(!j.hand||e.hand.length!==j.hand.length)j.hand=JSON.parse(JSON.stringify(e.hand));else for(let T=0;T<e.hand.length;T++){let F=e.hand[T].box.map((d0,G)=>((r-1)*j.hand[T].box[G]+d0)/r),M0=e.hand[T].boxRaw.map((d0,G)=>((r-1)*j.hand[T].boxRaw[G]+d0)/r);j.hand[T].keypoints.length!==e.hand[T].keypoints.length&&(j.hand[T].keypoints=e.hand[T].keypoints);let $=e.hand[T].keypoints&&e.hand[T].keypoints.length>0?e.hand[T].keypoints.map((d0,G)=>d0.map((Z,q0)=>((r-1)*(j.hand[T].keypoints[G][q0]||1)+(Z||0))/r)):[],m0={};if(Object.keys(j.hand[T].annotations).length!==Object.keys(e.hand[T].annotations).length)j.hand[T].annotations=e.hand[T].annotations,m0=j.hand[T].annotations;else if(e.hand[T].annotations)for(let d0 of Object.keys(e.hand[T].annotations))m0[d0]=(i=(x=(c=e.hand[T])==null?void 0:c.annotations)==null?void 0:x[d0])!=null&&i[0]?e.hand[T].annotations[d0].map((G,Z)=>G.map((q0,U0)=>((r-1)*j.hand[T].annotations[d0][Z][U0]+q0)/r)):null;j.hand[T]={...e.hand[T],box:F,boxRaw:M0,keypoints:$,annotations:m0}}if(!j.face||e.face.length!==j.face.length)j.face=JSON.parse(JSON.stringify(e.face));else for(let T=0;T<e.face.length;T++){let F=e.face[T].box.map(($,m0)=>((r-1)*j.face[T].box[m0]+$)/r),M0=e.face[T].boxRaw.map(($,m0)=>((r-1)*j.face[T].boxRaw[m0]+$)/r);if(e.face[T].rotation){let $={matrix:[0,0,0,0,0,0,0,0,0],angle:{roll:0,yaw:0,pitch:0},gaze:{bearing:0,strength:0}};$.matrix=(y=e.face[T].rotation)==null?void 0:y.matrix,$.angle={roll:((r-1)*(((f=(d=j.face[T].rotation)==null?void 0:d.angle)==null?void 0:f.roll)||0)+(((g=(p=e.face[T].rotation)==null?void 0:p.angle)==null?void 0:g.roll)||0))/r,yaw:((r-1)*(((P=(M=j.face[T].rotation)==null?void 0:M.angle)==null?void 0:P.yaw)||0)+(((b=(m=e.face[T].rotation)==null?void 0:m.angle)==null?void 0:b.yaw)||0))/r,pitch:((r-1)*(((w=(z=j.face[T].rotation)==null?void 0:z.angle)==null?void 0:w.pitch)||0)+(((q=(I=e.face[T].rotation)==null?void 0:I.angle)==null?void 0:q.pitch)||0))/r},$.gaze={bearing:((r-1)*(((e0=j.face[T].rotation)==null?void 0:e0.gaze.bearing)||0)+(((V=e.face[T].rotation)==null?void 0:V.gaze.bearing)||0))/r,strength:((r-1)*(((X=j.face[T].rotation)==null?void 0:X.gaze.strength)||0)+(((c0=e.face[T].rotation)==null?void 0:c0.gaze.strength)||0))/r},j.face[T]={...e.face[T],rotation:$,box:F,boxRaw:M0}}else j.face[T]={...e.face[T],box:F,boxRaw:M0}}if(!j.object||e.object.length!==j.object.length)j.object=JSON.parse(JSON.stringify(e.object));else for(let T=0;T<e.object.length;T++){let F=e.object[T].box.map(($,m0)=>((r-1)*j.object[T].box[m0]+$)/r),M0=e.object[T].boxRaw.map(($,m0)=>((r-1)*j.object[T].boxRaw[m0]+$)/r);j.object[T]={...e.object[T],box:F,boxRaw:M0}}if(e.persons){let T=e.persons;if(!j.persons||T.length!==j.persons.length)j.persons=JSON.parse(JSON.stringify(T));else for(let F=0;F<T.length;F++)j.persons[F].box=T[F].box.map((M0,$)=>((r-1)*j.persons[F].box[$]+M0)/r)}e.gesture&&(j.gesture=e.gesture);let A=v();return m1=k.perfadd?m1+Math.round(A-n):Math.round(A-n),e.performance&&(j.performance={...e.performance,interpolate:m1}),j}var h1={};ve(h1,{distance:()=>V2,match:()=>u1,similarity:()=>p1});function V2(e,t,n={order:2,multiplier:25}){if(!e||!e)return Number.MAX_SAFE_INTEGER;let o=0;for(let r=0;r<e.length;r++){let A=!n.order||n.order===2?e[r]-t[r]:Math.abs(e[r]-t[r]);o+=!n.order||n.order===2?A*A:A**n.order}return(n.multiplier||20)*o}var ho=(e,t,n,o)=>{if(e===0)return 1;let r=t===2?Math.sqrt(e):e**(1/t),A=(1-r/100-n)/(o-n);return Math.max(Math.min(A,1),0)};function p1(e,t,n={order:2,multiplier:25,min:.2,max:.8}){let o=V2(e,t,n);return ho(o,n.order||2,n.min||0,n.max||1)}function u1(e,t,n={order:2,multiplier:25,threshold:0,min:.2,max:.8}){if(!Array.isArray(e)||!Array.isArray(t)||e.length<64||t.length===0)return{index:-1,distance:Number.POSITIVE_INFINITY,similarity:0};let o=Number.MAX_SAFE_INTEGER,r=-1;for(let s=0;s<t.length;s++){let a=t[s].length===e.length?V2(e,t[s],n):Number.MAX_SAFE_INTEGER;if(a<o&&(o=a,r=s),o<(n.threshold||0))break}let A=ho(o,n.order||2,n.min||0,n.max||1);return{index:r,distance:o,similarity:A}}function bo(e,t,n,o,r){var a,l,c,x,i,y;let A=0,s=[];for(let d of e){let f={id:A++,face:d,body:null,hands:{left:null,right:null},gestures:[],box:[0,0,0,0]};for(let b of t)d.box[0]>b.box[0]&&d.box[0]<b.box[0]+b.box[2]&&d.box[1]+d.box[3]>b.box[1]&&d.box[1]+d.box[3]<b.box[1]+b.box[3]&&(f.body=b);if(f.body)for(let b of n)b.box[0]+b.box[2]>f.body.box[0]&&b.box[0]+b.box[2]<f.body.box[0]+f.body.box[2]&&b.box[1]+b.box[3]>f.body.box[1]&&b.box[1]+b.box[3]<f.body.box[1]+f.body.box[3]&&f.hands&&(f.hands.left=b),b.box[0]<f.body.box[0]+f.body.box[2]&&b.box[0]>f.body.box[0]&&b.box[1]+b.box[3]>f.body.box[1]&&b.box[1]+b.box[3]<f.body.box[1]+f.body.box[3]&&f.hands&&(f.hands.right=b);for(let b of o)(b.face!==void 0&&b.face===d.id||b.iris!==void 0&&b.iris===d.id||b.body!==void 0&&b.body===((a=f.body)==null?void 0:a.id)||b.hand!==void 0&&b.hand===((l=f.hands.left)==null?void 0:l.id)||b.hand!==void 0&&b.hand===((c=f.hands.right)==null?void 0:c.id))&&f.gestures.push(b);let p=[],g=[],M=b=>{b&&b.length===4&&(p.push(b[0],b[0]+b[2]),g.push(b[1],b[1]+b[3]))};M(f.face.box),M((x=f.body)==null?void 0:x.box),M((i=f.hands.left)==null?void 0:i.box),M((y=f.hands.right)==null?void 0:y.box);let P=Math.min(...p),m=Math.min(...g);f.box=[P,m,Math.max(...p)-P,Math.max(...g)-m],(r==null?void 0:r[1])&&(r==null?void 0:r[2])&&(f.boxRaw=[f.box[0]/r[2],f.box[1]/r[1],f.box[2]/r[2],f.box[3]/r[1]]),s.push(f)}return s}var C5=`
+        ${f.box[0]} ${valY},
+        ${f.box[0] + f.box[2]} ${valY},
+        ${f.box[0] + f.box[2]} ${f.box[1] + f.box[3] / 2}
+    `);
+    ctx.stroke(pathH);
+    ctx.stroke(pathV);
+  }
+}
+function drawGazeArrows(f, ctx) {
+  var _a;
+  if (opt.drawGaze && ((_a = f.rotation) == null ? void 0 : _a.gaze.strength) && f.rotation.gaze.bearing && f.annotations.leftEyeIris && f.annotations.rightEyeIris && f.annotations.leftEyeIris[0] && f.annotations.rightEyeIris[0]) {
+    ctx.strokeStyle = "pink";
+    ctx.fillStyle = "pink";
+    const leftGaze = [
+      f.annotations.leftEyeIris[0][0] + Math.sin(f.rotation.gaze.bearing) * f.rotation.gaze.strength * f.box[3],
+      f.annotations.leftEyeIris[0][1] + Math.cos(f.rotation.gaze.bearing) * f.rotation.gaze.strength * f.box[2]
+    ];
+    arrow(ctx, [f.annotations.leftEyeIris[0][0], f.annotations.leftEyeIris[0][1]], [leftGaze[0], leftGaze[1]], 4);
+    const rightGaze = [
+      f.annotations.rightEyeIris[0][0] + Math.sin(f.rotation.gaze.bearing) * f.rotation.gaze.strength * f.box[3],
+      f.annotations.rightEyeIris[0][1] + Math.cos(f.rotation.gaze.bearing) * f.rotation.gaze.strength * f.box[2]
+    ];
+    arrow(ctx, [f.annotations.rightEyeIris[0][0], f.annotations.rightEyeIris[0][1]], [rightGaze[0], rightGaze[1]], 4);
+  }
+}
+function drawFacePolygons(f, ctx) {
+  if (opt.drawPolygons && f.mesh.length >= 468) {
+    ctx.lineWidth = 1;
+    for (let i = 0; i < TRI468.length / 3; i++) {
+      const points = [TRI468[i * 3 + 0], TRI468[i * 3 + 1], TRI468[i * 3 + 2]].map((index2) => f.mesh[index2]);
+      lines(ctx, points, opt);
+    }
+    drawIrisElipse(f, ctx);
+  }
+}
+function drawFacePoints(f, ctx) {
+  if (opt.drawPoints && f.mesh.length >= 468) {
+    for (let i = 0; i < f.mesh.length; i++) {
+      point(ctx, f.mesh[i][0], f.mesh[i][1], f.mesh[i][2], opt);
+      if (opt.drawAttention) {
+        if (LANDMARKS_REFINEMENT_LIPS_CONFIG.includes(i))
+          point(ctx, f.mesh[i][0], f.mesh[i][1], f.mesh[i][2] + 127, opt);
+        if (LANDMARKS_REFINEMENT_LEFT_EYE_CONFIG.includes(i))
+          point(ctx, f.mesh[i][0], f.mesh[i][1], f.mesh[i][2] - 127, opt);
+        if (LANDMARKS_REFINEMENT_RIGHT_EYE_CONFIG.includes(i))
+          point(ctx, f.mesh[i][0], f.mesh[i][1], f.mesh[i][2] - 127, opt);
+      }
+    }
+  }
+}
+function drawFaceBoxes(f, ctx) {
+  if (opt.drawBoxes) {
+    rect(ctx, f.box[0], f.box[1], f.box[2], f.box[3], opt);
+  }
+}
+function face(inCanvas2, result, drawOptions) {
+  opt = mergeDeep(options3, drawOptions);
+  if (!result || !inCanvas2)
+    return;
+  const ctx = getCanvasContext(inCanvas2);
+  if (!ctx)
+    return;
+  ctx.font = opt.font;
+  ctx.strokeStyle = opt.color;
+  ctx.fillStyle = opt.color;
+  for (const f of result) {
+    drawFaceBoxes(f, ctx);
+    drawLabels(f, ctx);
+    if (f.mesh && f.mesh.length > 0) {
+      drawFacePoints(f, ctx);
+      drawFacePolygons(f, ctx);
+      drawGazeSpheres(f, ctx);
+      drawGazeArrows(f, ctx);
+    }
+  }
+}
+
+// src/draw/body.ts
+function body(inCanvas2, result, drawOptions) {
+  const localOptions = mergeDeep(options3, drawOptions);
+  if (!result || !inCanvas2)
+    return;
+  const ctx = getCanvasContext(inCanvas2);
+  if (!ctx)
+    return;
+  ctx.lineJoin = "round";
+  for (let i = 0; i < result.length; i++) {
+    ctx.strokeStyle = localOptions.color;
+    ctx.fillStyle = localOptions.color;
+    ctx.lineWidth = localOptions.lineWidth;
+    ctx.font = localOptions.font;
+    if (localOptions.drawBoxes && result[i].box && result[i].box.length === 4) {
+      rect(ctx, result[i].box[0], result[i].box[1], result[i].box[2], result[i].box[3], localOptions);
+      if (localOptions.drawLabels) {
+        if (localOptions.shadowColor && localOptions.shadowColor !== "") {
+          ctx.fillStyle = localOptions.shadowColor;
+          ctx.fillText(`body ${100 * result[i].score}%`, result[i].box[0] + 3, 1 + result[i].box[1] + localOptions.lineHeight, result[i].box[2]);
+        }
+        ctx.fillStyle = localOptions.labelColor;
+        ctx.fillText(`body ${100 * result[i].score}%`, result[i].box[0] + 2, 0 + result[i].box[1] + localOptions.lineHeight, result[i].box[2]);
+      }
+    }
+    if (localOptions.drawPoints && result[i].keypoints) {
+      for (let pt = 0; pt < result[i].keypoints.length; pt++) {
+        if (!result[i].keypoints[pt].score || result[i].keypoints[pt].score === 0)
+          continue;
+        ctx.fillStyle = colorDepth(result[i].keypoints[pt].position[2], localOptions);
+        point(ctx, result[i].keypoints[pt].position[0], result[i].keypoints[pt].position[1], 0, localOptions);
+      }
+    }
+    if (localOptions.drawLabels && result[i].keypoints) {
+      ctx.font = localOptions.font;
+      for (const pt of result[i].keypoints) {
+        if (!pt.score || pt.score === 0)
+          continue;
+        ctx.fillStyle = colorDepth(pt.position[2], localOptions);
+        ctx.fillText(`${pt.part} ${Math.trunc(100 * pt.score)}%`, pt.position[0] + 4, pt.position[1] + 4);
+      }
+    }
+    if (localOptions.drawPolygons && result[i].keypoints && result[i].annotations) {
+      for (const part of Object.values(result[i].annotations)) {
+        for (const connected4 of part)
+          curves(ctx, connected4, localOptions);
+      }
+    }
+  }
+}
+
+// src/draw/hand.ts
+function hand(inCanvas2, result, drawOptions) {
+  const localOptions = mergeDeep(options3, drawOptions);
+  if (!result || !inCanvas2)
+    return;
+  const ctx = getCanvasContext(inCanvas2);
+  if (!ctx)
+    return;
+  ctx.lineJoin = "round";
+  ctx.font = localOptions.font;
+  for (const h of result) {
+    if (localOptions.drawBoxes) {
+      ctx.strokeStyle = localOptions.color;
+      ctx.fillStyle = localOptions.color;
+      rect(ctx, h.box[0], h.box[1], h.box[2], h.box[3], localOptions);
+      if (localOptions.drawLabels) {
+        if (localOptions.shadowColor && localOptions.shadowColor !== "") {
+          ctx.fillStyle = localOptions.shadowColor;
+          ctx.fillText(`hand:${Math.trunc(100 * h.score)}%`, h.box[0] + 3, 1 + h.box[1] + localOptions.lineHeight, h.box[2]);
+        }
+        ctx.fillStyle = localOptions.labelColor;
+        ctx.fillText(`hand:${Math.trunc(100 * h.score)}%`, h.box[0] + 2, 0 + h.box[1] + localOptions.lineHeight, h.box[2]);
+      }
+      ctx.stroke();
+    }
+    if (localOptions.drawPoints) {
+      if (h.keypoints && h.keypoints.length > 0) {
+        for (const pt of h.keypoints) {
+          ctx.fillStyle = colorDepth(pt[2], localOptions);
+          point(ctx, pt[0], pt[1], 0, localOptions);
+        }
+      }
+    }
+    if (localOptions.drawLabels && h.annotations) {
+      const addHandLabel = (part, title) => {
+        if (!part || part.length === 0 || !part[0])
+          return;
+        const z = part[part.length - 1][2] || -256;
+        ctx.fillStyle = colorDepth(z, localOptions);
+        ctx.fillText(title, part[part.length - 1][0] + 4, part[part.length - 1][1] + 4);
+      };
+      ctx.font = localOptions.font;
+      addHandLabel(h.annotations.index, "index");
+      addHandLabel(h.annotations.middle, "middle");
+      addHandLabel(h.annotations.ring, "ring");
+      addHandLabel(h.annotations.pinky, "pinky");
+      addHandLabel(h.annotations.thumb, "thumb");
+      addHandLabel(h.annotations.palm, "palm");
+    }
+    if (localOptions.drawPolygons && h.annotations) {
+      const addHandLine = (part) => {
+        if (!part || part.length === 0 || !part[0])
+          return;
+        for (let i = 0; i < part.length; i++) {
+          ctx.beginPath();
+          const z = part[i][2] || 0;
+          ctx.strokeStyle = colorDepth(i * z, localOptions);
+          ctx.moveTo(part[i > 0 ? i - 1 : 0][0], part[i > 0 ? i - 1 : 0][1]);
+          ctx.lineTo(part[i][0], part[i][1]);
+          ctx.stroke();
+        }
+      };
+      ctx.lineWidth = localOptions.lineWidth;
+      addHandLine(h.annotations.index);
+      addHandLine(h.annotations.middle);
+      addHandLine(h.annotations.ring);
+      addHandLine(h.annotations.pinky);
+      addHandLine(h.annotations.thumb);
+    }
+  }
+}
+
+// src/draw/object.ts
+function object(inCanvas2, result, drawOptions) {
+  const localOptions = mergeDeep(options3, drawOptions);
+  if (!result || !inCanvas2)
+    return;
+  const ctx = getCanvasContext(inCanvas2);
+  if (!ctx)
+    return;
+  ctx.lineJoin = "round";
+  ctx.font = localOptions.font;
+  for (const h of result) {
+    if (localOptions.drawBoxes) {
+      ctx.strokeStyle = localOptions.color;
+      ctx.fillStyle = localOptions.color;
+      rect(ctx, h.box[0], h.box[1], h.box[2], h.box[3], localOptions);
+      if (localOptions.drawLabels) {
+        const label = `${h.label} ${Math.round(100 * h.score)}%`;
+        if (localOptions.shadowColor && localOptions.shadowColor !== "") {
+          ctx.fillStyle = localOptions.shadowColor;
+          ctx.fillText(label, h.box[0] + 3, 1 + h.box[1] + localOptions.lineHeight, h.box[2]);
+        }
+        ctx.fillStyle = localOptions.labelColor;
+        ctx.fillText(label, h.box[0] + 2, 0 + h.box[1] + localOptions.lineHeight, h.box[2]);
+      }
+      ctx.stroke();
+    }
+  }
+}
+
+// src/draw/gesture.ts
+function gesture(inCanvas2, result, drawOptions) {
+  const localOptions = mergeDeep(options3, drawOptions);
+  if (!result || !inCanvas2)
+    return;
+  if (localOptions.drawGestures) {
+    const ctx = getCanvasContext(inCanvas2);
+    if (!ctx)
+      return;
+    ctx.font = localOptions.font;
+    ctx.fillStyle = localOptions.color;
+    let i = 1;
+    for (let j = 0; j < result.length; j++) {
+      let where = [];
+      let what = [];
+      [where, what] = Object.entries(result[j]);
+      if (what.length > 1 && what[1].length > 0) {
+        const who = where[1] > 0 ? `#${where[1]}` : "";
+        const label = `${where[0]} ${who}: ${what[1]}`;
+        if (localOptions.shadowColor && localOptions.shadowColor !== "") {
+          ctx.fillStyle = localOptions.shadowColor;
+          ctx.fillText(label, 8, 2 + i * localOptions.lineHeight);
+        }
+        ctx.fillStyle = localOptions.labelColor;
+        ctx.fillText(label, 6, 0 + i * localOptions.lineHeight);
+        i += 1;
+      }
+    }
+  }
+}
+
+// src/draw/draw.ts
+var drawTime = 0;
+function person(inCanvas2, result, drawOptions) {
+  const localOptions = mergeDeep(options3, drawOptions);
+  if (!result || !inCanvas2)
+    return;
+  const ctx = getCanvasContext(inCanvas2);
+  if (!ctx)
+    return;
+  ctx.lineJoin = "round";
+  ctx.font = localOptions.font;
+  for (let i = 0; i < result.length; i++) {
+    if (localOptions.drawBoxes) {
+      ctx.strokeStyle = localOptions.color;
+      ctx.fillStyle = localOptions.color;
+      rect(ctx, result[i].box[0], result[i].box[1], result[i].box[2], result[i].box[3], localOptions);
+      if (localOptions.drawLabels) {
+        const label = `person #${i}`;
+        if (localOptions.shadowColor && localOptions.shadowColor !== "") {
+          ctx.fillStyle = localOptions.shadowColor;
+          ctx.fillText(label, result[i].box[0] + 3, 1 + result[i].box[1] + localOptions.lineHeight, result[i].box[2]);
+        }
+        ctx.fillStyle = localOptions.labelColor;
+        ctx.fillText(label, result[i].box[0] + 2, 0 + result[i].box[1] + localOptions.lineHeight, result[i].box[2]);
+      }
+      ctx.stroke();
+    }
+  }
+}
+function canvas2(input, output) {
+  if (!input || !output)
+    return;
+  const ctx = getCanvasContext(output);
+  if (!ctx)
+    return;
+  ctx.drawImage(input, 0, 0);
+}
+async function all(inCanvas2, result, drawOptions) {
+  if (!(result == null ? void 0 : result.performance) || !inCanvas2)
+    return null;
+  const timeStamp = now();
+  const localOptions = mergeDeep(options3, drawOptions);
+  const promise = Promise.all([
+    face(inCanvas2, result.face, localOptions),
+    body(inCanvas2, result.body, localOptions),
+    hand(inCanvas2, result.hand, localOptions),
+    object(inCanvas2, result.object, localOptions),
+    gesture(inCanvas2, result.gesture, localOptions)
+  ]);
+  drawTime = env.perfadd ? drawTime + Math.round(now() - timeStamp) : Math.round(now() - timeStamp);
+  result.performance.draw = drawTime;
+  return promise;
+}
+
+// src/face/face.ts
+var tf37 = __toESM(require_tfjs_esm());
+
+// src/face/mask.ts
+var tf36 = __toESM(require_tfjs_esm());
+var expandFact = 0.1;
+var alpha = 0.5;
+function insidePoly(x, y, polygon) {
+  let inside = false;
+  let j = polygon.length - 1;
+  for (let i = 0; i < polygon.length; j = i++) {
+    if (polygon[i].y > y !== polygon[j].y > y && x < (polygon[j].x - polygon[i].x) * (y - polygon[i].y) / (polygon[j].y - polygon[i].y) + polygon[i].x)
+      inside = !inside;
+  }
+  return inside;
+}
+async function mask(face4) {
+  if (!face4.tensor)
+    return face4.tensor;
+  if (!face4.mesh || face4.mesh.length < 100)
+    return face4.tensor;
+  const width = face4.tensor.shape[2] || 0;
+  const height = face4.tensor.shape[1] || 0;
+  const buffer = await face4.tensor.buffer();
+  let silhouette = [];
+  for (const pt of meshAnnotations.silhouette)
+    silhouette.push({ x: (face4.mesh[pt][0] - face4.box[0]) / face4.box[2], y: (face4.mesh[pt][1] - face4.box[1]) / face4.box[3] });
+  if (expandFact && expandFact > 0)
+    silhouette = silhouette.map((pt) => ({ x: pt.x > 0.5 ? pt.x + expandFact : pt.x - expandFact, y: pt.y > 0.5 ? pt.y + expandFact : pt.y - expandFact }));
+  for (let x = 0; x < width; x++) {
+    for (let y = 0; y < height; y++) {
+      const inside = insidePoly(x / width, y / width, silhouette);
+      if (!inside) {
+        buffer.set(alpha * buffer.get(0, y, x, 0), 0, y, x, 0);
+        buffer.set(alpha * buffer.get(0, y, x, 1), 0, y, x, 1);
+        buffer.set(alpha * buffer.get(0, y, x, 2), 0, y, x, 2);
+      }
+    }
+  }
+  const output = buffer.toTensor();
+  tf36.dispose(buffer);
+  return output;
+}
+
+// src/face/angles.ts
+var calculateGaze = (face4) => {
+  const radians = (pt1, pt2) => Math.atan2(pt1[1] - pt2[1], pt1[0] - pt2[0]);
+  if (!face4.annotations.rightEyeIris || !face4.annotations.leftEyeIris)
+    return { bearing: 0, strength: 0 };
+  const offsetIris = [0, -0.1];
+  const eyeRatio = 1;
+  const left = (face4.mesh[33][2] || 0) > (face4.mesh[263][2] || 0);
+  const irisCenter = left ? face4.mesh[473] : face4.mesh[468];
+  const eyeCenter = left ? [(face4.mesh[133][0] + face4.mesh[33][0]) / 2, (face4.mesh[133][1] + face4.mesh[33][1]) / 2] : [(face4.mesh[263][0] + face4.mesh[362][0]) / 2, (face4.mesh[263][1] + face4.mesh[362][1]) / 2];
+  const eyeSize = left ? [face4.mesh[133][0] - face4.mesh[33][0], face4.mesh[23][1] - face4.mesh[27][1]] : [face4.mesh[263][0] - face4.mesh[362][0], face4.mesh[253][1] - face4.mesh[257][1]];
+  const eyeDiff = [
+    (eyeCenter[0] - irisCenter[0]) / eyeSize[0] - offsetIris[0],
+    eyeRatio * (irisCenter[1] - eyeCenter[1]) / eyeSize[1] - offsetIris[1]
+  ];
+  let strength = Math.sqrt(eyeDiff[0] * eyeDiff[0] + eyeDiff[1] * eyeDiff[1]);
+  strength = Math.min(strength, face4.boxRaw[2] / 2, face4.boxRaw[3] / 2);
+  const bearing = (radians([0, 0], eyeDiff) + Math.PI / 2) % Math.PI;
+  return { bearing, strength };
+};
+var calculateFaceAngle = (face4, imageSize) => {
+  const normalize2 = (v) => {
+    const length = Math.sqrt(v[0] * v[0] + v[1] * v[1] + v[2] * v[2]);
+    v[0] /= length;
+    v[1] /= length;
+    v[2] /= length;
+    return v;
+  };
+  const subVectors = (a, b) => {
+    const x = a[0] - b[0];
+    const y = a[1] - b[1];
+    const z = a[2] - b[2];
+    return [x, y, z];
+  };
+  const crossVectors = (a, b) => {
+    const x = a[1] * b[2] - a[2] * b[1];
+    const y = a[2] * b[0] - a[0] * b[2];
+    const z = a[0] * b[1] - a[1] * b[0];
+    return [x, y, z];
+  };
+  const rotationMatrixToEulerAngle = (r) => {
+    const [r00, _r01, _r02, r10, r11, r12, r20, r21, r22] = r;
+    let thetaX;
+    let thetaY;
+    let thetaZ;
+    if (r10 < 1) {
+      if (r10 > -1) {
+        thetaZ = Math.asin(r10);
+        thetaY = Math.atan2(-r20, r00);
+        thetaX = Math.atan2(-r12, r11);
+      } else {
+        thetaZ = -Math.PI / 2;
+        thetaY = -Math.atan2(r21, r22);
+        thetaX = 0;
+      }
+    } else {
+      thetaZ = Math.PI / 2;
+      thetaY = Math.atan2(r21, r22);
+      thetaX = 0;
+    }
+    if (Number.isNaN(thetaX))
+      thetaX = 0;
+    if (Number.isNaN(thetaY))
+      thetaY = 0;
+    if (Number.isNaN(thetaZ))
+      thetaZ = 0;
+    return { pitch: 2 * -thetaX, yaw: 2 * -thetaY, roll: 2 * -thetaZ };
+  };
+  const mesh = face4.meshRaw;
+  if (!mesh || mesh.length < 300)
+    return { angle: { pitch: 0, yaw: 0, roll: 0 }, matrix: [1, 0, 0, 0, 1, 0, 0, 0, 1], gaze: { bearing: 0, strength: 0 } };
+  const size2 = Math.max(face4.boxRaw[2] * imageSize[0], face4.boxRaw[3] * imageSize[1]) / 1.5;
+  const pts = [mesh[10], mesh[152], mesh[234], mesh[454]].map((pt) => [pt[0] * imageSize[0] / size2, pt[1] * imageSize[1] / size2, pt[2]]);
+  const yAxis = normalize2(subVectors(pts[1], pts[0]));
+  let xAxis = normalize2(subVectors(pts[3], pts[2]));
+  const zAxis = normalize2(crossVectors(xAxis, yAxis));
+  xAxis = crossVectors(yAxis, zAxis);
+  const matrix = [
+    xAxis[0],
+    xAxis[1],
+    xAxis[2],
+    yAxis[0],
+    yAxis[1],
+    yAxis[2],
+    zAxis[0],
+    zAxis[1],
+    zAxis[2]
+  ];
+  const angle = rotationMatrixToEulerAngle(matrix);
+  const gaze = mesh.length === 478 ? calculateGaze(face4) : { bearing: 0, strength: 0 };
+  return { angle, matrix, gaze };
+};
+
+// src/face/face.ts
+var detectFace = async (instance2, input) => {
+  var _a, _b, _c, _d, _e, _f, _g, _h, _i, _j, _k, _l, _m, _n, _o, _p, _q, _r, _s, _t, _u, _v, _w, _x, _y, _z, _A, _B, _C;
+  let timeStamp = now();
+  let ageRes;
+  let gearRes;
+  let genderRes;
+  let emotionRes;
+  let mobilefacenetRes;
+  let insightfaceRes;
+  let antispoofRes;
+  let livenessRes;
+  let descRes;
+  const faceRes = [];
+  instance2.state = "run:face";
+  const faces = await predict6(input, instance2.config);
+  instance2.performance.face = env.perfadd ? (instance2.performance.face || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);
+  if (!input.shape || input.shape.length !== 4)
+    return [];
+  if (!faces)
+    return [];
+  for (let i = 0; i < faces.length; i++) {
+    instance2.analyze("Get Face");
+    if (!faces[i].tensor || faces[i].tensor.isDisposedInternal) {
+      log("Face object is disposed:", faces[i].tensor);
+      continue;
+    }
+    if ((_a = instance2.config.face.detector) == null ? void 0 : _a.mask) {
+      const masked = await mask(faces[i]);
+      tf37.dispose(faces[i].tensor);
+      if (masked)
+        faces[i].tensor = masked;
+    }
+    const rotation = faces[i].mesh && faces[i].mesh.length > 200 ? calculateFaceAngle(faces[i], [input.shape[2], input.shape[1]]) : null;
+    instance2.analyze("Start Emotion:");
+    if (instance2.config.async) {
+      emotionRes = ((_b = instance2.config.face.emotion) == null ? void 0 : _b.enabled) ? predict5(faces[i].tensor || tf37.tensor([]), instance2.config, i, faces.length) : [];
+    } else {
+      instance2.state = "run:emotion";
+      timeStamp = now();
+      emotionRes = ((_c = instance2.config.face.emotion) == null ? void 0 : _c.enabled) ? await predict5(faces[i].tensor || tf37.tensor([]), instance2.config, i, faces.length) : [];
+      instance2.performance.emotion = env.perfadd ? (instance2.performance.emotion || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);
+    }
+    instance2.analyze("End Emotion:");
+    instance2.analyze("Start AntiSpoof:");
+    if (instance2.config.async) {
+      antispoofRes = ((_d = instance2.config.face.antispoof) == null ? void 0 : _d.enabled) ? predict(faces[i].tensor || tf37.tensor([]), instance2.config, i, faces.length) : 0;
+    } else {
+      instance2.state = "run:antispoof";
+      timeStamp = now();
+      antispoofRes = ((_e = instance2.config.face.antispoof) == null ? void 0 : _e.enabled) ? await predict(faces[i].tensor || tf37.tensor([]), instance2.config, i, faces.length) : 0;
+      instance2.performance.antispoof = env.perfadd ? (instance2.performance.antispoof || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);
+    }
+    instance2.analyze("End AntiSpoof:");
+    instance2.analyze("Start Liveness:");
+    if (instance2.config.async) {
+      livenessRes = ((_f = instance2.config.face.liveness) == null ? void 0 : _f.enabled) ? predict12(faces[i].tensor || tf37.tensor([]), instance2.config, i, faces.length) : 0;
+    } else {
+      instance2.state = "run:liveness";
+      timeStamp = now();
+      livenessRes = ((_g = instance2.config.face.liveness) == null ? void 0 : _g.enabled) ? await predict12(faces[i].tensor || tf37.tensor([]), instance2.config, i, faces.length) : 0;
+      instance2.performance.liveness = env.perfadd ? (instance2.performance.antispoof || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);
+    }
+    instance2.analyze("End Liveness:");
+    instance2.analyze("Start GEAR:");
+    if (instance2.config.async) {
+      gearRes = ((_h = instance2.config.face.gear) == null ? void 0 : _h.enabled) ? predict8(faces[i].tensor || tf37.tensor([]), instance2.config, i, faces.length) : null;
+    } else {
+      instance2.state = "run:gear";
+      timeStamp = now();
+      gearRes = ((_i = instance2.config.face.gear) == null ? void 0 : _i.enabled) ? await predict8(faces[i].tensor || tf37.tensor([]), instance2.config, i, faces.length) : null;
+      instance2.performance.gear = Math.trunc(now() - timeStamp);
+    }
+    instance2.analyze("End GEAR:");
+    instance2.analyze("Start SSRNet:");
+    if (instance2.config.async) {
+      ageRes = ((_j = instance2.config.face["ssrnet"]) == null ? void 0 : _j.enabled) ? predict20(faces[i].tensor || tf37.tensor([]), instance2.config, i, faces.length) : null;
+      genderRes = ((_k = instance2.config.face["ssrnet"]) == null ? void 0 : _k.enabled) ? predict21(faces[i].tensor || tf37.tensor([]), instance2.config, i, faces.length) : null;
+    } else {
+      instance2.state = "run:ssrnet";
+      timeStamp = now();
+      ageRes = ((_l = instance2.config.face["ssrnet"]) == null ? void 0 : _l.enabled) ? await predict20(faces[i].tensor || tf37.tensor([]), instance2.config, i, faces.length) : null;
+      genderRes = ((_m = instance2.config.face["ssrnet"]) == null ? void 0 : _m.enabled) ? await predict21(faces[i].tensor || tf37.tensor([]), instance2.config, i, faces.length) : null;
+      instance2.performance.ssrnet = Math.trunc(now() - timeStamp);
+    }
+    instance2.analyze("End SSRNet:");
+    instance2.analyze("Start MobileFaceNet:");
+    if (instance2.config.async) {
+      mobilefacenetRes = ((_n = instance2.config.face["mobilefacenet"]) == null ? void 0 : _n.enabled) ? predict14(faces[i].tensor || tf37.tensor([]), instance2.config, i, faces.length) : null;
+    } else {
+      instance2.state = "run:mobilefacenet";
+      timeStamp = now();
+      mobilefacenetRes = ((_o = instance2.config.face["mobilefacenet"]) == null ? void 0 : _o.enabled) ? await predict14(faces[i].tensor || tf37.tensor([]), instance2.config, i, faces.length) : null;
+      instance2.performance.mobilefacenet = Math.trunc(now() - timeStamp);
+    }
+    instance2.analyze("End MobileFaceNet:");
+    instance2.analyze("Start InsightFace:");
+    if (instance2.config.async) {
+      insightfaceRes = ((_p = instance2.config.face["insightface"]) == null ? void 0 : _p.enabled) ? predict11(faces[i].tensor || tf37.tensor([]), instance2.config, i, faces.length) : null;
+    } else {
+      instance2.state = "run:mobilefacenet";
+      timeStamp = now();
+      insightfaceRes = ((_q = instance2.config.face["insightface"]) == null ? void 0 : _q.enabled) ? await predict11(faces[i].tensor || tf37.tensor([]), instance2.config, i, faces.length) : null;
+      instance2.performance.mobilefacenet = Math.trunc(now() - timeStamp);
+    }
+    instance2.analyze("End InsightFace:");
+    instance2.analyze("Start Description:");
+    if (instance2.config.async) {
+      descRes = predict7(faces[i].tensor || tf37.tensor([]), instance2.config, i, faces.length);
+    } else {
+      instance2.state = "run:description";
+      timeStamp = now();
+      descRes = await predict7(faces[i].tensor || tf37.tensor([]), instance2.config, i, faces.length);
+      instance2.performance.description = env.perfadd ? (instance2.performance.description || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);
+    }
+    instance2.analyze("End Description:");
+    if (instance2.config.async) {
+      [ageRes, genderRes, emotionRes, mobilefacenetRes, insightfaceRes, descRes, gearRes, antispoofRes, livenessRes] = await Promise.all([ageRes, genderRes, emotionRes, mobilefacenetRes, insightfaceRes, descRes, gearRes, antispoofRes, livenessRes]);
+    }
+    instance2.analyze("Finish Face:");
+    if (((_r = instance2.config.face["ssrnet"]) == null ? void 0 : _r.enabled) && ageRes && genderRes) {
+      descRes = {
+        ...descRes,
+        age: ageRes.age,
+        gender: genderRes.gender,
+        genderScore: genderRes.genderScore
+      };
+    }
+    if (((_s = instance2.config.face.gear) == null ? void 0 : _s.enabled) && gearRes) {
+      descRes = {
+        ...descRes,
+        age: gearRes.age,
+        gender: gearRes.gender,
+        genderScore: gearRes.genderScore,
+        race: gearRes.race
+      };
+    }
+    if (((_t = instance2.config.face["mobilefacenet"]) == null ? void 0 : _t.enabled) && mobilefacenetRes) {
+      descRes.descriptor = mobilefacenetRes;
+    }
+    if (((_u = instance2.config.face["insightface"]) == null ? void 0 : _u.enabled) && insightfaceRes) {
+      descRes.descriptor = insightfaceRes;
+    }
+    if (!((_v = instance2.config.face.iris) == null ? void 0 : _v.enabled)) {
+    }
+    const irisSize = ((_y = (_x = (_w = faces[i]) == null ? void 0 : _w.annotations) == null ? void 0 : _x.leftEyeIris) == null ? void 0 : _y[0]) && ((_B = (_A = (_z = faces[i]) == null ? void 0 : _z.annotations) == null ? void 0 : _A.rightEyeIris) == null ? void 0 : _B[0]) && faces[i].annotations.leftEyeIris.length > 0 && faces[i].annotations.rightEyeIris.length > 0 && faces[i].annotations.leftEyeIris[0] !== null && faces[i].annotations.rightEyeIris[0] !== null ? Math.max(Math.abs(faces[i].annotations.leftEyeIris[3][0] - faces[i].annotations.leftEyeIris[1][0]), Math.abs(faces[i].annotations.rightEyeIris[4][1] - faces[i].annotations.rightEyeIris[2][1])) / input.shape[2] : 0;
+    const tensor6 = ((_C = instance2.config.face.detector) == null ? void 0 : _C.return) ? tf37.squeeze(faces[i].tensor) : null;
+    tf37.dispose(faces[i].tensor);
+    if (faces[i].tensor)
+      delete faces[i].tensor;
+    const res = {
+      ...faces[i],
+      id: i
+    };
+    if (descRes.age)
+      res.age = descRes.age;
+    if (descRes.gender)
+      res.gender = descRes.gender;
+    if (descRes.genderScore)
+      res.genderScore = descRes.genderScore;
+    if (descRes.descriptor)
+      res.embedding = descRes.descriptor;
+    if (descRes.race)
+      res.race = descRes.race;
+    if (emotionRes)
+      res.emotion = emotionRes;
+    if (antispoofRes)
+      res.real = antispoofRes;
+    if (livenessRes)
+      res.live = livenessRes;
+    if (irisSize && irisSize !== 0)
+      res.iris = Math.trunc(500 / irisSize / 11.7) / 100;
+    if (rotation)
+      res.rotation = rotation;
+    if (tensor6)
+      res.tensor = tensor6;
+    faceRes.push(res);
+    instance2.analyze("End Face");
+  }
+  instance2.analyze("End FaceMesh:");
+  if (instance2.config.async) {
+    if (instance2.performance.face)
+      delete instance2.performance.face;
+    if (instance2.performance.age)
+      delete instance2.performance.age;
+    if (instance2.performance.gender)
+      delete instance2.performance.gender;
+    if (instance2.performance.emotion)
+      delete instance2.performance.emotion;
+  }
+  return faceRes;
+};
+
+// src/gesture/gesture.ts
+var body2 = (res) => {
+  if (!res)
+    return [];
+  const gestures = [];
+  for (let i = 0; i < res.length; i++) {
+    const leftWrist = res[i].keypoints.find((a) => a.part === "leftWrist");
+    const rightWrist = res[i].keypoints.find((a) => a.part === "rightWrist");
+    const nose = res[i].keypoints.find((a) => a.part === "nose");
+    if (nose && leftWrist && rightWrist && leftWrist.position[1] < nose.position[1] && rightWrist.position[1] < nose.position[1])
+      gestures.push({ body: i, gesture: "i give up" });
+    else if (nose && leftWrist && leftWrist.position[1] < nose.position[1])
+      gestures.push({ body: i, gesture: "raise left hand" });
+    else if (nose && rightWrist && rightWrist.position[1] < nose.position[1])
+      gestures.push({ body: i, gesture: "raise right hand" });
+    const leftShoulder = res[i].keypoints.find((a) => a.part === "leftShoulder");
+    const rightShoulder = res[i].keypoints.find((a) => a.part === "rightShoulder");
+    if (leftShoulder && rightShoulder && Math.abs(leftShoulder.positionRaw[1] - rightShoulder.positionRaw[1]) > 0.1) {
+      gestures.push({ body: i, gesture: `leaning ${leftShoulder.position[1] > rightShoulder.position[1] ? "left" : "right"}` });
+    }
+  }
+  return gestures;
+};
+var face2 = (res) => {
+  if (!res)
+    return [];
+  const gestures = [];
+  for (let i = 0; i < res.length; i++) {
+    if (res[i].mesh && res[i].mesh.length > 450) {
+      const zDiff = (res[i].mesh[33][2] || 0) - (res[i].mesh[263][2] || 0);
+      const xDiff = res[i].mesh[33][0] - res[i].mesh[263][0];
+      if (Math.abs(zDiff / xDiff) <= 0.15)
+        gestures.push({ face: i, gesture: "facing center" });
+      else
+        gestures.push({ face: i, gesture: `facing ${zDiff < 0 ? "left" : "right"}` });
+      const openLeft = Math.abs(res[i].mesh[374][1] - res[i].mesh[386][1]) / Math.abs(res[i].mesh[443][1] - res[i].mesh[450][1]);
+      if (openLeft < 0.2)
+        gestures.push({ face: i, gesture: "blink left eye" });
+      const openRight = Math.abs(res[i].mesh[145][1] - res[i].mesh[159][1]) / Math.abs(res[i].mesh[223][1] - res[i].mesh[230][1]);
+      if (openRight < 0.2)
+        gestures.push({ face: i, gesture: "blink right eye" });
+      const mouthOpen = Math.min(100, 500 * Math.abs(res[i].mesh[13][1] - res[i].mesh[14][1]) / Math.abs(res[i].mesh[10][1] - res[i].mesh[152][1]));
+      if (mouthOpen > 10)
+        gestures.push({ face: i, gesture: `mouth ${Math.trunc(mouthOpen)}% open` });
+      const chinDepth = res[i].mesh[152][2] || 0;
+      if (Math.abs(chinDepth) > 10)
+        gestures.push({ face: i, gesture: `head ${chinDepth < 0 ? "up" : "down"}` });
+    }
+  }
+  return gestures;
+};
+var iris2 = (res) => {
+  var _a, _b, _c, _d;
+  if (!res)
+    return [];
+  const gestures = [];
+  for (let i = 0; i < res.length; i++) {
+    if (!((_b = (_a = res[i].annotations) == null ? void 0 : _a.leftEyeIris) == null ? void 0 : _b[0]) || !((_d = (_c = res[i].annotations) == null ? void 0 : _c.rightEyeIris) == null ? void 0 : _d[0]))
+      continue;
+    const sizeXLeft = res[i].annotations.leftEyeIris[3][0] - res[i].annotations.leftEyeIris[1][0];
+    const sizeYLeft = res[i].annotations.leftEyeIris[4][1] - res[i].annotations.leftEyeIris[2][1];
+    const areaLeft = Math.abs(sizeXLeft * sizeYLeft);
+    const sizeXRight = res[i].annotations.rightEyeIris[3][0] - res[i].annotations.rightEyeIris[1][0];
+    const sizeYRight = res[i].annotations.rightEyeIris[4][1] - res[i].annotations.rightEyeIris[2][1];
+    const areaRight = Math.abs(sizeXRight * sizeYRight);
+    let center = false;
+    const difference = Math.abs(areaLeft - areaRight) / Math.max(areaLeft, areaRight);
+    if (difference < 0.25) {
+      center = true;
+      gestures.push({ iris: i, gesture: "facing center" });
+    }
+    const leftIrisCenterX = Math.abs(res[i].mesh[263][0] - res[i].annotations.leftEyeIris[0][0]) / res[i].box[2];
+    const rightIrisCenterX = Math.abs(res[i].mesh[33][0] - res[i].annotations.rightEyeIris[0][0]) / res[i].box[2];
+    if (leftIrisCenterX > 0.06 || rightIrisCenterX > 0.06)
+      center = false;
+    if (leftIrisCenterX > rightIrisCenterX) {
+      if (leftIrisCenterX > 0.05)
+        gestures.push({ iris: i, gesture: "looking right" });
+    } else {
+      if (rightIrisCenterX > 0.05)
+        gestures.push({ iris: i, gesture: "looking left" });
+    }
+    const rightIrisCenterY = Math.abs(res[i].mesh[145][1] - res[i].annotations.rightEyeIris[0][1]) / res[i].box[3];
+    const leftIrisCenterY = Math.abs(res[i].mesh[374][1] - res[i].annotations.leftEyeIris[0][1]) / res[i].box[3];
+    if (leftIrisCenterY < 0.01 || rightIrisCenterY < 0.01 || leftIrisCenterY > 0.022 || rightIrisCenterY > 0.022)
+      center = false;
+    if (leftIrisCenterY < 0.01 || rightIrisCenterY < 0.01)
+      gestures.push({ iris: i, gesture: "looking down" });
+    if (leftIrisCenterY > 0.022 || rightIrisCenterY > 0.022)
+      gestures.push({ iris: i, gesture: "looking up" });
+    if (center)
+      gestures.push({ iris: i, gesture: "looking center" });
+  }
+  return gestures;
+};
+var hand2 = (res) => {
+  if (!res)
+    return [];
+  const gestures = [];
+  for (let i = 0; i < res.length; i++) {
+    const fingers = [];
+    if (res[i].annotations) {
+      for (const [finger, pos] of Object.entries(res[i].annotations)) {
+        if (finger !== "palmBase" && Array.isArray(pos) && pos[0])
+          fingers.push({ name: finger.toLowerCase(), position: pos[0] });
+      }
+    }
+    if (fingers && fingers.length > 0) {
+      const closest = fingers.reduce((best, a) => (best.position[2] || 0) < (a.position[2] || 0) ? best : a);
+      gestures.push({ hand: i, gesture: `${closest.name} forward` });
+      const highest = fingers.reduce((best, a) => best.position[1] < a.position[1] ? best : a);
+      gestures.push({ hand: i, gesture: `${highest.name} up` });
+    }
+    if (res[i].keypoints) {
+      const poses = match(res[i].keypoints);
+      for (const pose of poses)
+        gestures.push({ hand: i, gesture: pose.name });
+    }
+  }
+  return gestures;
+};
+
+// src/util/interpolate.ts
+var bufferedResult = { face: [], body: [], hand: [], gesture: [], object: [], persons: [], performance: {}, timestamp: 0, error: null };
+var interpolateTime = 0;
+function calc2(newResult, config3) {
+  var _a, _b, _c, _d, _e, _f, _g, _h, _i, _j, _k, _l, _m, _n, _o, _p, _q, _r, _s, _t, _u, _v, _w;
+  const t0 = now();
+  if (!newResult)
+    return { face: [], body: [], hand: [], gesture: [], object: [], persons: [], performance: {}, timestamp: 0, error: null };
+  const elapsed = Date.now() - newResult.timestamp;
+  const bufferedFactor = elapsed < 1e3 ? 8 - Math.log(elapsed + 1) : 1;
+  if (newResult.canvas)
+    bufferedResult.canvas = newResult.canvas;
+  if (newResult.error)
+    bufferedResult.error = newResult.error;
+  if (!bufferedResult.body || newResult.body.length !== bufferedResult.body.length) {
+    bufferedResult.body = JSON.parse(JSON.stringify(newResult.body));
+  } else {
+    for (let i = 0; i < newResult.body.length; i++) {
+      const box = newResult.body[i].box.map((newBoxCoord, j) => ((bufferedFactor - 1) * bufferedResult.body[i].box[j] + newBoxCoord) / bufferedFactor);
+      const boxRaw = newResult.body[i].boxRaw.map((newBoxCoord, j) => ((bufferedFactor - 1) * bufferedResult.body[i].boxRaw[j] + newBoxCoord) / bufferedFactor);
+      const keypoints = newResult.body[i].keypoints.map((newKpt, j) => {
+        var _a2, _b2, _c2, _d2, _e2, _f2, _g2, _h2, _i2;
+        return {
+          score: newKpt.score,
+          part: newKpt.part,
+          position: [
+            bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].position[0] || 0) + (newKpt.position[0] || 0)) / bufferedFactor : newKpt.position[0],
+            bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].position[1] || 0) + (newKpt.position[1] || 0)) / bufferedFactor : newKpt.position[1],
+            bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].position[2] || 0) + (newKpt.position[2] || 0)) / bufferedFactor : newKpt.position[2]
+          ],
+          positionRaw: [
+            bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].positionRaw[0] || 0) + (newKpt.positionRaw[0] || 0)) / bufferedFactor : newKpt.positionRaw[0],
+            bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].positionRaw[1] || 0) + (newKpt.positionRaw[1] || 0)) / bufferedFactor : newKpt.positionRaw[1],
+            bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].positionRaw[2] || 0) + (newKpt.positionRaw[2] || 0)) / bufferedFactor : newKpt.positionRaw[2]
+          ],
+          distance: [
+            bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (((_a2 = bufferedResult.body[i].keypoints[j].distance) == null ? void 0 : _a2[0]) || 0) + (((_b2 = newKpt.distance) == null ? void 0 : _b2[0]) || 0)) / bufferedFactor : (_c2 = newKpt.distance) == null ? void 0 : _c2[0],
+            bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (((_d2 = bufferedResult.body[i].keypoints[j].distance) == null ? void 0 : _d2[1]) || 0) + (((_e2 = newKpt.distance) == null ? void 0 : _e2[1]) || 0)) / bufferedFactor : (_f2 = newKpt.distance) == null ? void 0 : _f2[1],
+            bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (((_g2 = bufferedResult.body[i].keypoints[j].distance) == null ? void 0 : _g2[2]) || 0) + (((_h2 = newKpt.distance) == null ? void 0 : _h2[2]) || 0)) / bufferedFactor : (_i2 = newKpt.distance) == null ? void 0 : _i2[2]
+          ]
+        };
+      });
+      const annotations2 = {};
+      let coords = { connected: {} };
+      if ((_a = config3.body.modelPath) == null ? void 0 : _a.includes("efficientpose"))
+        coords = efficientposecoords_exports;
+      else if ((_b = config3.body.modelPath) == null ? void 0 : _b.includes("blazepose"))
+        coords = blazeposecoords_exports;
+      else if ((_c = config3.body.modelPath) == null ? void 0 : _c.includes("movenet"))
+        coords = movenetcoords_exports;
+      for (const [name, indexes] of Object.entries(coords.connected)) {
+        const pt = [];
+        for (let j = 0; j < indexes.length - 1; j++) {
+          const pt0 = keypoints.find((kp) => kp.part === indexes[j]);
+          const pt1 = keypoints.find((kp) => kp.part === indexes[j + 1]);
+          if (pt0 && pt1)
+            pt.push([pt0.position, pt1.position]);
+        }
+        annotations2[name] = pt;
+      }
+      bufferedResult.body[i] = { ...newResult.body[i], box, boxRaw, keypoints, annotations: annotations2 };
+    }
+  }
+  if (!bufferedResult.hand || newResult.hand.length !== bufferedResult.hand.length) {
+    bufferedResult.hand = JSON.parse(JSON.stringify(newResult.hand));
+  } else {
+    for (let i = 0; i < newResult.hand.length; i++) {
+      const box = newResult.hand[i].box.map((b, j) => ((bufferedFactor - 1) * bufferedResult.hand[i].box[j] + b) / bufferedFactor);
+      const boxRaw = newResult.hand[i].boxRaw.map((b, j) => ((bufferedFactor - 1) * bufferedResult.hand[i].boxRaw[j] + b) / bufferedFactor);
+      if (bufferedResult.hand[i].keypoints.length !== newResult.hand[i].keypoints.length)
+        bufferedResult.hand[i].keypoints = newResult.hand[i].keypoints;
+      const keypoints = newResult.hand[i].keypoints && newResult.hand[i].keypoints.length > 0 ? newResult.hand[i].keypoints.map((landmark, j) => landmark.map((coord, k) => ((bufferedFactor - 1) * (bufferedResult.hand[i].keypoints[j][k] || 1) + (coord || 0)) / bufferedFactor)) : [];
+      let annotations2 = {};
+      if (Object.keys(bufferedResult.hand[i].annotations).length !== Object.keys(newResult.hand[i].annotations).length) {
+        bufferedResult.hand[i].annotations = newResult.hand[i].annotations;
+        annotations2 = bufferedResult.hand[i].annotations;
+      } else if (newResult.hand[i].annotations) {
+        for (const key of Object.keys(newResult.hand[i].annotations)) {
+          annotations2[key] = ((_f = (_e = (_d = newResult.hand[i]) == null ? void 0 : _d.annotations) == null ? void 0 : _e[key]) == null ? void 0 : _f[0]) ? newResult.hand[i].annotations[key].map((val, j) => val.map((coord, k) => ((bufferedFactor - 1) * bufferedResult.hand[i].annotations[key][j][k] + coord) / bufferedFactor)) : null;
+        }
+      }
+      bufferedResult.hand[i] = { ...newResult.hand[i], box, boxRaw, keypoints, annotations: annotations2 };
+    }
+  }
+  if (!bufferedResult.face || newResult.face.length !== bufferedResult.face.length) {
+    bufferedResult.face = JSON.parse(JSON.stringify(newResult.face));
+  } else {
+    for (let i = 0; i < newResult.face.length; i++) {
+      const box = newResult.face[i].box.map((b, j) => ((bufferedFactor - 1) * bufferedResult.face[i].box[j] + b) / bufferedFactor);
+      const boxRaw = newResult.face[i].boxRaw.map((b, j) => ((bufferedFactor - 1) * bufferedResult.face[i].boxRaw[j] + b) / bufferedFactor);
+      if (newResult.face[i].rotation) {
+        const rotation = { matrix: [0, 0, 0, 0, 0, 0, 0, 0, 0], angle: { roll: 0, yaw: 0, pitch: 0 }, gaze: { bearing: 0, strength: 0 } };
+        rotation.matrix = (_g = newResult.face[i].rotation) == null ? void 0 : _g.matrix;
+        rotation.angle = {
+          roll: ((bufferedFactor - 1) * (((_i = (_h = bufferedResult.face[i].rotation) == null ? void 0 : _h.angle) == null ? void 0 : _i.roll) || 0) + (((_k = (_j = newResult.face[i].rotation) == null ? void 0 : _j.angle) == null ? void 0 : _k.roll) || 0)) / bufferedFactor,
+          yaw: ((bufferedFactor - 1) * (((_m = (_l = bufferedResult.face[i].rotation) == null ? void 0 : _l.angle) == null ? void 0 : _m.yaw) || 0) + (((_o = (_n = newResult.face[i].rotation) == null ? void 0 : _n.angle) == null ? void 0 : _o.yaw) || 0)) / bufferedFactor,
+          pitch: ((bufferedFactor - 1) * (((_q = (_p = bufferedResult.face[i].rotation) == null ? void 0 : _p.angle) == null ? void 0 : _q.pitch) || 0) + (((_s = (_r = newResult.face[i].rotation) == null ? void 0 : _r.angle) == null ? void 0 : _s.pitch) || 0)) / bufferedFactor
+        };
+        rotation.gaze = {
+          bearing: ((bufferedFactor - 1) * (((_t = bufferedResult.face[i].rotation) == null ? void 0 : _t.gaze.bearing) || 0) + (((_u = newResult.face[i].rotation) == null ? void 0 : _u.gaze.bearing) || 0)) / bufferedFactor,
+          strength: ((bufferedFactor - 1) * (((_v = bufferedResult.face[i].rotation) == null ? void 0 : _v.gaze.strength) || 0) + (((_w = newResult.face[i].rotation) == null ? void 0 : _w.gaze.strength) || 0)) / bufferedFactor
+        };
+        bufferedResult.face[i] = { ...newResult.face[i], rotation, box, boxRaw };
+      } else {
+        bufferedResult.face[i] = { ...newResult.face[i], box, boxRaw };
+      }
+    }
+  }
+  if (!bufferedResult.object || newResult.object.length !== bufferedResult.object.length) {
+    bufferedResult.object = JSON.parse(JSON.stringify(newResult.object));
+  } else {
+    for (let i = 0; i < newResult.object.length; i++) {
+      const box = newResult.object[i].box.map((b, j) => ((bufferedFactor - 1) * bufferedResult.object[i].box[j] + b) / bufferedFactor);
+      const boxRaw = newResult.object[i].boxRaw.map((b, j) => ((bufferedFactor - 1) * bufferedResult.object[i].boxRaw[j] + b) / bufferedFactor);
+      bufferedResult.object[i] = { ...newResult.object[i], box, boxRaw };
+    }
+  }
+  if (newResult.persons) {
+    const newPersons = newResult.persons;
+    if (!bufferedResult.persons || newPersons.length !== bufferedResult.persons.length) {
+      bufferedResult.persons = JSON.parse(JSON.stringify(newPersons));
+    } else {
+      for (let i = 0; i < newPersons.length; i++) {
+        bufferedResult.persons[i].box = newPersons[i].box.map((box, j) => ((bufferedFactor - 1) * bufferedResult.persons[i].box[j] + box) / bufferedFactor);
+      }
+    }
+  }
+  if (newResult.gesture)
+    bufferedResult.gesture = newResult.gesture;
+  const t1 = now();
+  interpolateTime = env.perfadd ? interpolateTime + Math.round(t1 - t0) : Math.round(t1 - t0);
+  if (newResult.performance)
+    bufferedResult.performance = { ...newResult.performance, interpolate: interpolateTime };
+  return bufferedResult;
+}
+
+// src/face/match.ts
+var match_exports = {};
+__export(match_exports, {
+  distance: () => distance,
+  match: () => match2,
+  similarity: () => similarity
+});
+function distance(descriptor1, descriptor2, options4 = { order: 2, multiplier: 25 }) {
+  if (!descriptor1 || !descriptor1)
+    return Number.MAX_SAFE_INTEGER;
+  let sum3 = 0;
+  for (let i = 0; i < descriptor1.length; i++) {
+    const diff = !options4.order || options4.order === 2 ? descriptor1[i] - descriptor2[i] : Math.abs(descriptor1[i] - descriptor2[i]);
+    sum3 += !options4.order || options4.order === 2 ? diff * diff : diff ** options4.order;
+  }
+  return (options4.multiplier || 20) * sum3;
+}
+var normalizeDistance = (dist, order, min2, max4) => {
+  if (dist === 0)
+    return 1;
+  const root = order === 2 ? Math.sqrt(dist) : dist ** (1 / order);
+  const norm = (1 - root / 100 - min2) / (max4 - min2);
+  const clamp2 = Math.max(Math.min(norm, 1), 0);
+  return clamp2;
+};
+function similarity(descriptor1, descriptor2, options4 = { order: 2, multiplier: 25, min: 0.2, max: 0.8 }) {
+  const dist = distance(descriptor1, descriptor2, options4);
+  return normalizeDistance(dist, options4.order || 2, options4.min || 0, options4.max || 1);
+}
+function match2(descriptor, descriptors, options4 = { order: 2, multiplier: 25, threshold: 0, min: 0.2, max: 0.8 }) {
+  if (!Array.isArray(descriptor) || !Array.isArray(descriptors) || descriptor.length < 64 || descriptors.length === 0) {
+    return { index: -1, distance: Number.POSITIVE_INFINITY, similarity: 0 };
+  }
+  let lowestDistance = Number.MAX_SAFE_INTEGER;
+  let index2 = -1;
+  for (let i = 0; i < descriptors.length; i++) {
+    const res = descriptors[i].length === descriptor.length ? distance(descriptor, descriptors[i], options4) : Number.MAX_SAFE_INTEGER;
+    if (res < lowestDistance) {
+      lowestDistance = res;
+      index2 = i;
+    }
+    if (lowestDistance < (options4.threshold || 0))
+      break;
+  }
+  const normalizedSimilarity = normalizeDistance(lowestDistance, options4.order || 2, options4.min || 0, options4.max || 1);
+  return { index: index2, distance: lowestDistance, similarity: normalizedSimilarity };
+}
+
+// src/util/persons.ts
+function join2(faces, bodies, hands, gestures, shape) {
+  var _a, _b, _c, _d, _e, _f;
+  let id = 0;
+  const persons = [];
+  for (const face4 of faces) {
+    const person2 = { id: id++, face: face4, body: null, hands: { left: null, right: null }, gestures: [], box: [0, 0, 0, 0] };
+    for (const body4 of bodies) {
+      if (face4.box[0] > body4.box[0] && face4.box[0] < body4.box[0] + body4.box[2] && face4.box[1] + face4.box[3] > body4.box[1] && face4.box[1] + face4.box[3] < body4.box[1] + body4.box[3]) {
+        person2.body = body4;
+      }
+    }
+    if (person2.body) {
+      for (const hand3 of hands) {
+        if (hand3.box[0] + hand3.box[2] > person2.body.box[0] && hand3.box[0] + hand3.box[2] < person2.body.box[0] + person2.body.box[2] && hand3.box[1] + hand3.box[3] > person2.body.box[1] && hand3.box[1] + hand3.box[3] < person2.body.box[1] + person2.body.box[3]) {
+          if (person2.hands)
+            person2.hands.left = hand3;
+        }
+        if (hand3.box[0] < person2.body.box[0] + person2.body.box[2] && hand3.box[0] > person2.body.box[0] && hand3.box[1] + hand3.box[3] > person2.body.box[1] && hand3.box[1] + hand3.box[3] < person2.body.box[1] + person2.body.box[3]) {
+          if (person2.hands)
+            person2.hands.right = hand3;
+        }
+      }
+    }
+    for (const gesture2 of gestures) {
+      if (gesture2["face"] !== void 0 && gesture2["face"] === face4.id)
+        person2.gestures.push(gesture2);
+      else if (gesture2["iris"] !== void 0 && gesture2["iris"] === face4.id)
+        person2.gestures.push(gesture2);
+      else if (gesture2["body"] !== void 0 && gesture2["body"] === ((_a = person2.body) == null ? void 0 : _a.id))
+        person2.gestures.push(gesture2);
+      else if (gesture2["hand"] !== void 0 && gesture2["hand"] === ((_b = person2.hands.left) == null ? void 0 : _b.id))
+        person2.gestures.push(gesture2);
+      else if (gesture2["hand"] !== void 0 && gesture2["hand"] === ((_c = person2.hands.right) == null ? void 0 : _c.id))
+        person2.gestures.push(gesture2);
+    }
+    const x = [];
+    const y = [];
+    const extractXY = (box) => {
+      if (box && box.length === 4) {
+        x.push(box[0], box[0] + box[2]);
+        y.push(box[1], box[1] + box[3]);
+      }
+    };
+    extractXY(person2.face.box);
+    extractXY((_d = person2.body) == null ? void 0 : _d.box);
+    extractXY((_e = person2.hands.left) == null ? void 0 : _e.box);
+    extractXY((_f = person2.hands.right) == null ? void 0 : _f.box);
+    const minX = Math.min(...x);
+    const minY = Math.min(...y);
+    person2.box = [minX, minY, Math.max(...x) - minX, Math.max(...y) - minY];
+    if ((shape == null ? void 0 : shape[1]) && (shape == null ? void 0 : shape[2]))
+      person2.boxRaw = [person2.box[0] / shape[2], person2.box[1] / shape[1], person2.box[2] / shape[2], person2.box[3] / shape[1]];
+    persons.push(person2);
+  }
+  return persons;
+}
+
+// src/sample.ts
+var face3 = `
 /9j/4AAQSkZJRgABAQEAYABgAAD/4QBoRXhpZgAATU0AKgAAAAgABAEaAAUAAAABAAAAPgEbAAUA
 AAABAAAARgEoAAMAAAABAAIAAAExAAIAAAARAAAATgAAAAAAAABgAAAAAQAAAGAAAAABcGFpbnQu
 bmV0IDQuMi4xMwAA/9sAQwAGBAUGBQQGBgUGBwcGCAoQCgoJCQoUDg8MEBcUGBgXFBYWGh0lHxob
@@ -259,7 +13985,8 @@ PQ4GJ+ashuK0MhWaoWcA0AaOmASMK7jRNPWYBmHyiuepO2x10qfcv6vYxCzYqoGK4HVYVTJrmb5l
 c6oaM5TUJ8EgGsG4kLNUHT0M64OaqMMikSRsuKbnFMRLG3zVehOaGNE445NNlnVFpDMu6uie9Vo1
 8z5mOAOST2pDK91cNN+5tsrH3PrW54a06KxT7fdrlh/q1Pc+tJ6IUdZGvHPLezMcnBOWbsPap5r3
 ylFtbdT1xUWNWzU0/Zbwlgfmx8zGsHWtRHmMqE59aAMyNifvHPc1f0gtPdqkY5JosJHeNci2tktY
-euPnNY+oXWZEVJNrZ9aun8SIq/CzodHuriIokhDIR1ronbKZr0o6o8ipoz//2Q==`,L5=`
+euPnNY+oXWZEVJNrZ9aun8SIq/CzodHuriIokhDIR1ronbKZr0o6o8ipoz//2Q==`;
+var body3 = `
 /9j/4AAQSkZJRgABAQAAAQABAAD/2wBDAAsICAoIBwsKCQoNDAsNERwSEQ8PESIZGhQcKSQrKigk
 JyctMkA3LTA9MCcnOEw5PUNFSElIKzZPVU5GVEBHSEX/2wBDAQwNDREPESESEiFFLicuRUVFRUVF
 RUVFRUVFRUVFRUVFRUVFRUVFRUVFRUVFRUVFRUVFRUVFRUVFRUVFRUVFRUX/wAARCASwBLADASIA
@@ -827,4 +14554,580 @@ AAAAAAJAAAAAAAAAAAAAABAJEAAAAAAAAAAAAAAAIEoBKAAAAAAAAAAAAAAABAlAAAAAAAIAAAAA
 BAkBAkBAkBAlACEgMZjdjbFW8bWrEx8YWANb6Fp+bfwab+vLDKMFK9qxH5L0bAr8OPRPKz2AY7J2
 SbAjYZAI2E7AIEgIEgIEgMdkSy2NgY7MdlmyNoBXsxmFuyNgVTVjNV3KjlBRNTlXTVHKCrlIqt5T
 lBhEMohlFerLlBjEMohMVTEARDKCITsAk2AEgAAAkAAAAAAAAAAAAAAAAAAAAAAAASAAAAAAAAD/
-2Q==`;var s0=D(H());async function Is(e){let t=(r,A="application/octet-stream")=>fetch(`data:${A};base64,${r}`).then(s=>s.blob()),n,o;switch(e.config.warmup){case"face":n=await t(C5);break;case"body":case"full":n=await t(L5);break;default:n=null}if(n){let r=await createImageBitmap(n);o=await e.detect(r,e.config),r.close()}return o}async function Os(e){return new Promise(t=>{let n;switch(e.config.warmup){case"face":n="data:image/jpeg;base64,"+C5;break;case"full":case"body":n="data:image/jpeg;base64,"+L5;break;default:n=""}let o;if(typeof Image!="undefined")o=new Image;else if(k.Image)o=new k.Image;else return;o.onload=async()=>{let r=k0(o.naturalWidth,o.naturalHeight);if(!r)u("Warmup: Canvas not found"),t(void 0);else{let A=r.getContext("2d");A&&A.drawImage(o,0,0);let s=await e.image(r),a=s.tensor?await e.detect(s.tensor,e.config):void 0;t(a)}},n?o.src=n:t(void 0)})}async function Cs(e){let t=r=>Buffer.from(r,"base64"),n;e.config.warmup==="face"?n=t(C5):n=t(L5);let o;if("node"in s0&&s0.getBackend()==="tensorflow"){let r=s0.node.decodeJpeg(n),A=s0.expandDims(r,0);e.tf.dispose(r),o=await e.detect(A,e.config),e.tf.dispose(A)}else e.config.debug&&u("Warmup tfjs-node not loaded");return o}async function Ls(e){let t;return typeof createImageBitmap=="function"?t=await Is(e):typeof Image!="undefined"||k.Canvas!==void 0?t=await Os(e):t=await Cs(e),t}async function Ws(e){var a,l,c,x;if(!s0.env().flagRegistry.ENGINE_COMPILE_ONLY)return;let t=s0.getBackend(),n=s0.backend();if(t!=="webgl"&&t!=="humangl"||!(n!=null&&n.checkCompileCompletion))return;s0.env().set("ENGINE_COMPILE_ONLY",!0);let o=s0.engine().state.numTensors,r=[];for(let[i,y]of Object.entries(e.models).filter(([d,f])=>d!==null&&f!==null)){let d=(l=(a=y.inputs)==null?void 0:a[0])!=null&&l.shape?[...y.inputs[0].shape]:[1,64,64,3],f=(x=(c=y.inputs)==null?void 0:c[0])!=null&&x.dtype?y.inputs[0].dtype:"float32";for(let g=0;g<d.length;g++)d[g]===-1&&(d[g]=g===0?1:64);let p=s0.zeros(d,f);try{let g=y.execute(p);r.push(i),Array.isArray(g)?g.forEach(M=>s0.dispose(M)):s0.dispose(g)}catch(g){u("compile fail model:",i)}s0.dispose(p)}let A=await n.checkCompileCompletionAsync();n.getUniformLocations(),e.config.debug&&u("compile pass:",{models:r,kernels:A.length}),s0.env().set("ENGINE_COMPILE_ONLY",!1);let s=s0.engine().state.numTensors;s-o>0&&u("tensor leak:",s-o)}async function go(e,t){await W2(e,!1);let n=v();return e.state="warmup",t&&(e.config=A0(e.config,t)),!e.config.warmup||e.config.warmup.length===0||e.config.warmup==="none"?{face:[],body:[],hand:[],gesture:[],object:[],performance:e.performance,timestamp:v(),persons:[],error:null}:new Promise(async o=>{await f2.load(e),await Ws(e);let r=await Ls(e),A=v();e.config.debug&&u("warmup",e.config.warmup,Math.round(A-n),"ms"),e.emit("warmup"),o(r)})}var T2,D2,Z2,W5,Be,b1=class{constructor(t){R(this,"version");R(this,"config");R(this,"result");R(this,"state");R(this,"process");R(this,"tf");R(this,"env");R(this,"draw");R(this,"models");R(this,"events");R(this,"faceTriangulation");R(this,"faceUVMap");R(this,"performance");t2(this,T2,void 0);t2(this,D2,void 0);t2(this,Z2,void 0);R(this,"gl");R(this,"analyze",(...t)=>{if(!ie(this,D2))return;let n=this.tf.engine().state.numTensors,o=ie(this,T2);E2(this,T2,n);let r=n-o;r!==0&&u(...t,r)});t2(this,W5,t=>{if(!ie(this,Z2))return null;if(!t)return"input is not defined";if(this.env.node&&!(t instanceof ae.Tensor))return"input must be a tensor";try{this.tf.getBackend()}catch(n){return"backend not loaded"}return null});R(this,"similarity",p1);R(this,"distance",V2);R(this,"match",u1);R(this,"webcam",new Y2);R(this,"emit",t=>{var n;(n=this.events)!=null&&n.dispatchEvent&&this.events.dispatchEvent(new Event(t))});t2(this,Be,{});this.env=k;let n=(ae.version.tfjs||ae.version_core).replace(/-(.*)/,"");Pe.wasmPath=`https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-backend-wasm@${n}/dist/`,Pe.modelBasePath=k.browser?"../models/":"file://models/",Pe.backend=k.browser?"webgl":"tensorflow",this.version=A1,Object.defineProperty(this,"version",{value:A1}),this.config=JSON.parse(JSON.stringify(Pe)),Object.seal(this.config),this.config.cacheModels=typeof indexedDB!="undefined",t&&(this.config=A0(this.config,t)),ao(this.config),this.tf=ae,this.state="idle",E2(this,T2,0),E2(this,D2,!1),E2(this,Z2,!1),this.performance={},this.events=typeof EventTarget!="undefined"?new EventTarget:void 0,this.models=new H2,this.draw={options:R0,canvas:(r,A)=>c1(r,A),face:(r,A,s)=>h2(r,A,s),body:(r,A,s)=>b2(r,A,s),hand:(r,A,s)=>g2(r,A,s),gesture:(r,A,s)=>v2(r,A,s),object:(r,A,s)=>M2(r,A,s),person:(r,A,s)=>l1(r,A,s),all:(r,A,s)=>d1(r,A,s)},this.result={face:[],body:[],hand:[],gesture:[],object:[],performance:{},timestamp:0,persons:[],error:null},this.process={tensor:null,canvas:null},this.faceTriangulation=_3,this.faceUVMap=$3,this.gl=_,p2(this,null,""),this.emit("create"),(this.config.debug||this.env.browser)&&u(`version: ${this.version}`),this.config.debug&&u(`tfjs version: ${this.tf.version["tfjs-core"]}`);let o=JSON.parse(JSON.stringify(this.env));delete o.kernels,delete o.initial,delete o.perfadd,this.config.debug&&u("environment:",o)}reset(){let t=this.config.backend;this.config=JSON.parse(JSON.stringify(Pe)),this.config.backend=t,D5(),k.initial=!0}validate(t){let n=F5(Pe,t||this.config);return n.length===0&&(this.config=A0(this.config,t)),n}check(){return O5(this)}now(){return v()}image(t,n=!0){return o2(t,this.config,n)}async segmentation(t,n){return Ao(t,n,this.config)}enhance(t){return Et(t)}compare(t,n){return C1(this.config,t,n)}async init(){await W2(this,!0),await this.tf.ready(),D5()}async load(t){this.state="load";let n=v(),o=Object.values(this.models).filter(s=>s).length;t&&(this.config=A0(this.config,t)),this.env.initial&&(await W2(this,!1)||u("error: backend check failed"),await ae.ready(),this.env.browser&&(this.config.debug&&u("configuration:",this.config),this.config.debug&&u("tf flags:",this.tf.ENV.flags))),await r1(this),this.env.initial&&this.config.debug&&u("tf engine state:",this.tf.engine().state.numBytes,"bytes",this.tf.engine().state.numTensors,"tensors"),this.env.initial=!1,Object.values(this.models).filter(s=>s).length!==o&&(O5(this),this.emit("load"));let A=Math.trunc(v()-n);A>(this.performance.loadModels||0)&&(this.performance.loadModels=this.env.perfadd?(this.performance.loadModels||0)+A:A)}next(t=this.result){return uo(t,this.config)}getModelStats(){return o1(this)}async warmup(t){let n=v(),o=await go(this,t),r=v();return this.performance.warmup=Math.trunc(r-n),o}async profile(t,n){let o=await this.tf.profile(()=>this.detect(t,n)),r={},A=0;for(let a of o.kernels)r[a.name]?r[a.name]+=a.kernelTimeMs:r[a.name]=a.kernelTimeMs,A+=a.kernelTimeMs;let s=[];Object.entries(r).forEach(a=>s.push({kernel:a[0],time:a[1],perc:0}));for(let a of s)a.perc=Math.round(1e3*a.time/A)/1e3,a.time=Math.round(1e3*a.time)/1e3;return s.sort((a,l)=>l.time-a.time),s.length=20,s}async detect(t,n){return this.state="detect",new Promise(async o=>{var g,M,P,m,b,z,w,I,q,e0,V,X,c0,T,F,M0,$,m0,d0,G,Z;this.state="config";let r;this.config=A0(this.config,n),this.state="check";let A=ie(this,W5).call(this,t);A&&(u(A,t),this.emit("error"),o({face:[],body:[],hand:[],gesture:[],object:[],performance:this.performance,timestamp:v(),persons:[],error:A}));let s=v();await this.load(),r=v(),this.state="image";let a=await o2(t,this.config);if(this.process=a,this.performance.inputProcess=this.env.perfadd?(this.performance.inputProcess||0)+Math.trunc(v()-r):Math.trunc(v()-r),this.analyze("Get Image:"),!a.tensor){this.config.debug&&u("could not convert input to tensor"),this.emit("error"),o({face:[],body:[],hand:[],gesture:[],object:[],performance:this.performance,timestamp:v(),persons:[],error:"could not convert input to tensor"});return}this.emit("image"),r=v(),this.config.skipAllowed=await O1(this.config,a.tensor),this.performance.totalFrames||(this.performance.totalFrames=0),this.performance.cachedFrames||(this.performance.cachedFrames=0),this.performance.totalFrames++,this.config.skipAllowed&&this.performance.cachedFrames++,this.performance.cacheCheck=this.env.perfadd?(this.performance.cacheCheck||0)+Math.trunc(v()-r):Math.trunc(v()-r),this.analyze("Check Changed:");let l=[],c=[],x=[],i=[];this.state="detect:face",this.config.async?(l=this.config.face.enabled?f1(this,a.tensor):[],this.performance.face&&delete this.performance.face):(r=v(),l=this.config.face.enabled?await f1(this,a.tensor):[],this.performance.face=this.env.perfadd?(this.performance.face||0)+Math.trunc(v()-r):Math.trunc(v()-r)),this.config.async&&(this.config.body.maxDetected===-1||this.config.hand.maxDetected===-1)&&(l=await l),this.analyze("Start Body:"),this.state="detect:body";let y=this.config.body.maxDetected===-1?A0(this.config,{body:{maxDetected:this.config.face.enabled?1*l.length:1}}):this.config;this.config.async?((g=this.config.body.modelPath)!=null&&g.includes("posenet")?c=this.config.body.enabled?e1(a.tensor,y):[]:(M=this.config.body.modelPath)!=null&&M.includes("blazepose")?c=this.config.body.enabled?ct(a.tensor,y):[]:(P=this.config.body.modelPath)!=null&&P.includes("efficientpose")?c=this.config.body.enabled?ut(a.tensor,y):[]:(m=this.config.body.modelPath)!=null&&m.includes("movenet")&&(c=this.config.body.enabled?Ut(a.tensor,y):[]),this.performance.body&&delete this.performance.body):(r=v(),(b=this.config.body.modelPath)!=null&&b.includes("posenet")?c=this.config.body.enabled?await e1(a.tensor,y):[]:(z=this.config.body.modelPath)!=null&&z.includes("blazepose")?c=this.config.body.enabled?await ct(a.tensor,y):[]:(w=this.config.body.modelPath)!=null&&w.includes("efficientpose")?c=this.config.body.enabled?await ut(a.tensor,y):[]:(I=this.config.body.modelPath)!=null&&I.includes("movenet")&&(c=this.config.body.enabled?await Ut(a.tensor,y):[]),this.performance.body=this.env.perfadd?(this.performance.body||0)+Math.trunc(v()-r):Math.trunc(v()-r)),this.analyze("End Body:"),this.analyze("Start Hand:"),this.state="detect:hand";let d=this.config.hand.maxDetected===-1?A0(this.config,{hand:{maxDetected:this.config.face.enabled?2*l.length:1}}):this.config;this.config.async?((e0=(q=this.config.hand.detector)==null?void 0:q.modelPath)!=null&&e0.includes("handdetect")?x=this.config.hand.enabled?Ct(a.tensor,d):[]:(X=(V=this.config.hand.detector)==null?void 0:V.modelPath)!=null&&X.includes("handtrack")&&(x=this.config.hand.enabled?Gt(a.tensor,d):[]),this.performance.hand&&delete this.performance.hand):(r=v(),(T=(c0=this.config.hand.detector)==null?void 0:c0.modelPath)!=null&&T.includes("handdetect")?x=this.config.hand.enabled?await Ct(a.tensor,d):[]:(M0=(F=this.config.hand.detector)==null?void 0:F.modelPath)!=null&&M0.includes("handtrack")&&(x=this.config.hand.enabled?await Gt(a.tensor,d):[]),this.performance.hand=this.env.perfadd?(this.performance.hand||0)+Math.trunc(v()-r):Math.trunc(v()-r)),this.analyze("End Hand:"),this.analyze("Start Object:"),this.state="detect:object",this.config.async?(($=this.config.object.modelPath)!=null&&$.includes("nanodet")?i=this.config.object.enabled?Kt(a.tensor,this.config):[]:(m0=this.config.object.modelPath)!=null&&m0.includes("centernet")&&(i=this.config.object.enabled?yt(a.tensor,this.config):[]),this.performance.object&&delete this.performance.object):(r=v(),(d0=this.config.object.modelPath)!=null&&d0.includes("nanodet")?i=this.config.object.enabled?await Kt(a.tensor,this.config):[]:(G=this.config.object.modelPath)!=null&&G.includes("centernet")&&(i=this.config.object.enabled?await yt(a.tensor,this.config):[]),this.performance.object=this.env.perfadd?(this.performance.object||0)+Math.trunc(v()-r):Math.trunc(v()-r)),this.analyze("End Object:"),this.state="detect:await",this.config.async&&([l,c,x,i]=await Promise.all([l,c,x,i])),this.state="detect:gesture";let f=[];this.config.gesture.enabled&&(r=v(),f=[...fo(l),...yo(c),...po(x),...mo(l)],this.config.async?this.performance.gesture&&delete this.performance.gesture:this.performance.gesture=this.env.perfadd?(this.performance.gesture||0)+Math.trunc(v()-r):Math.trunc(v()-r)),this.performance.total=this.env.perfadd?(this.performance.total||0)+Math.trunc(v()-s):Math.trunc(v()-s);let p=((Z=this.process.tensor)==null?void 0:Z.shape)||[];this.result={face:l,body:c,hand:x,gesture:f,object:i,performance:this.performance,canvas:this.process.canvas,timestamp:Date.now(),error:null,get persons(){return bo(l,c,x,f,p)}},ae.dispose(a.tensor),this.emit("detect"),this.state="idle",o(this.result)})}async sleep(t){return new Promise(n=>{setTimeout(n,t)})}async video(t,n=!0,o=0){n?(ie(this,Be)[t.id]||(this.config.debug&&u("video start",t.id),ie(this,Be)[t.id]=!0),!t.paused&&ie(this,Be)[t.id]&&t.readyState>=2&&await this.detect(t),o>0&&await this.sleep(o),ie(this,Be)[t.id]&&requestAnimationFrame(()=>this.video(t,n,o))):(this.config.debug&&u("video stop",t.id),ie(this,Be)[t.id]=!1)}};T2=new WeakMap,D2=new WeakMap,Z2=new WeakMap,W5=new WeakMap,Be=new WeakMap;0&&(module.exports={Env,Human,defaults,draw,env,match,models});
+2Q==`;
+
+// src/warmup.ts
+var tf38 = __toESM(require_tfjs_esm());
+async function warmupBitmap(instance2) {
+  const b64toBlob = (base64, type = "application/octet-stream") => fetch(`data:${type};base64,${base64}`).then((res2) => res2.blob());
+  let blob;
+  let res;
+  switch (instance2.config.warmup) {
+    case "face":
+      blob = await b64toBlob(face3);
+      break;
+    case "body":
+    case "full":
+      blob = await b64toBlob(body3);
+      break;
+    default:
+      blob = null;
+  }
+  if (blob) {
+    const bitmap = await createImageBitmap(blob);
+    res = await instance2.detect(bitmap, instance2.config);
+    bitmap.close();
+  }
+  return res;
+}
+async function warmupCanvas(instance2) {
+  return new Promise((resolve) => {
+    let src;
+    switch (instance2.config.warmup) {
+      case "face":
+        src = "data:image/jpeg;base64," + face3;
+        break;
+      case "full":
+      case "body":
+        src = "data:image/jpeg;base64," + body3;
+        break;
+      default:
+        src = "";
+    }
+    let img;
+    if (typeof Image !== "undefined")
+      img = new Image();
+    else if (env.Image)
+      img = new env.Image();
+    else
+      return;
+    img.onload = async () => {
+      const canvas3 = canvas(img.naturalWidth, img.naturalHeight);
+      if (!canvas3) {
+        log("Warmup: Canvas not found");
+        resolve(void 0);
+      } else {
+        const ctx = canvas3.getContext("2d");
+        if (ctx)
+          ctx.drawImage(img, 0, 0);
+        const tensor6 = await instance2.image(canvas3);
+        const res = tensor6.tensor ? await instance2.detect(tensor6.tensor, instance2.config) : void 0;
+        resolve(res);
+      }
+    };
+    if (src)
+      img.src = src;
+    else
+      resolve(void 0);
+  });
+}
+async function warmupNode(instance2) {
+  const atob = (str) => Buffer.from(str, "base64");
+  let img;
+  if (instance2.config.warmup === "face")
+    img = atob(face3);
+  else
+    img = atob(body3);
+  let res;
+  if ("node" in tf38 && tf38.getBackend() === "tensorflow") {
+    const data = tf38["node"].decodeJpeg(img);
+    const expanded = tf38.expandDims(data, 0);
+    instance2.tf.dispose(data);
+    res = await instance2.detect(expanded, instance2.config);
+    instance2.tf.dispose(expanded);
+  } else {
+    if (instance2.config.debug)
+      log("Warmup tfjs-node not loaded");
+  }
+  return res;
+}
+async function runInference(instance2) {
+  let res;
+  if (typeof createImageBitmap === "function")
+    res = await warmupBitmap(instance2);
+  else if (typeof Image !== "undefined" || env.Canvas !== void 0)
+    res = await warmupCanvas(instance2);
+  else
+    res = await warmupNode(instance2);
+  return res;
+}
+async function runCompile(instance2) {
+  var _a, _b, _c, _d;
+  if (!tf38.env().flagRegistry.ENGINE_COMPILE_ONLY)
+    return;
+  const backendType = tf38.getBackend();
+  const webGLBackend = tf38.backend();
+  if (backendType !== "webgl" && backendType !== "humangl" || !(webGLBackend == null ? void 0 : webGLBackend.checkCompileCompletion)) {
+    return;
+  }
+  tf38.env().set("ENGINE_COMPILE_ONLY", true);
+  const numTensorsStart = tf38.engine().state.numTensors;
+  const compiledModels = [];
+  for (const [modelName, model21] of Object.entries(instance2.models).filter(([key, val]) => key !== null && val !== null)) {
+    const shape = ((_b = (_a = model21.inputs) == null ? void 0 : _a[0]) == null ? void 0 : _b.shape) ? [...model21.inputs[0].shape] : [1, 64, 64, 3];
+    const dtype = ((_d = (_c = model21.inputs) == null ? void 0 : _c[0]) == null ? void 0 : _d.dtype) ? model21.inputs[0].dtype : "float32";
+    for (let dim = 0; dim < shape.length; dim++) {
+      if (shape[dim] === -1)
+        shape[dim] = dim === 0 ? 1 : 64;
+    }
+    const tensor6 = tf38.zeros(shape, dtype);
+    try {
+      const res = model21.execute(tensor6);
+      compiledModels.push(modelName);
+      if (Array.isArray(res))
+        res.forEach((t2) => tf38.dispose(t2));
+      else
+        tf38.dispose(res);
+    } catch (e) {
+      if (instance2.config.debug)
+        log("compile fail model:", modelName);
+    }
+    tf38.dispose(tensor6);
+  }
+  const kernels = await webGLBackend.checkCompileCompletionAsync();
+  webGLBackend.getUniformLocations();
+  if (instance2.config.debug)
+    log("compile pass:", { models: compiledModels, kernels: kernels.length });
+  tf38.env().set("ENGINE_COMPILE_ONLY", false);
+  const numTensorsEnd = tf38.engine().state.numTensors;
+  if (numTensorsEnd - numTensorsStart > 0)
+    log("tensor leak:", numTensorsEnd - numTensorsStart);
+}
+async function warmup(instance2, userConfig) {
+  await check(instance2, false);
+  const t0 = now();
+  instance2.state = "warmup";
+  if (userConfig)
+    instance2.config = mergeDeep(instance2.config, userConfig);
+  if (!instance2.config.warmup || instance2.config.warmup.length === 0 || instance2.config.warmup === "none") {
+    return { face: [], body: [], hand: [], gesture: [], object: [], performance: instance2.performance, timestamp: now(), persons: [], error: null };
+  }
+  return new Promise(async (resolve) => {
+    await models_exports2.load(instance2);
+    await runCompile(instance2);
+    const res = await runInference(instance2);
+    const t1 = now();
+    if (instance2.config.debug)
+      log("warmup", instance2.config.warmup, Math.round(t1 - t0), "ms");
+    instance2.emit("warmup");
+    resolve(res);
+  });
+}
+
+// src/human.ts
+var _numTensors, _analyzeMemoryLeaks, _checkSanity, _sanity, _loops;
+var Human2 = class {
+  constructor(userConfig) {
+    __publicField(this, "version");
+    __publicField(this, "config");
+    __publicField(this, "result");
+    __publicField(this, "state");
+    __publicField(this, "process");
+    __publicField(this, "tf");
+    __publicField(this, "env");
+    __publicField(this, "draw");
+    __publicField(this, "models");
+    __publicField(this, "events");
+    __publicField(this, "faceTriangulation");
+    __publicField(this, "faceUVMap");
+    __publicField(this, "performance");
+    __privateAdd(this, _numTensors, void 0);
+    __privateAdd(this, _analyzeMemoryLeaks, void 0);
+    __privateAdd(this, _checkSanity, void 0);
+    __publicField(this, "gl");
+    __publicField(this, "analyze", (...msg) => {
+      if (!__privateGet(this, _analyzeMemoryLeaks))
+        return;
+      const currentTensors = this.tf.engine().state.numTensors;
+      const previousTensors = __privateGet(this, _numTensors);
+      __privateSet(this, _numTensors, currentTensors);
+      const leaked = currentTensors - previousTensors;
+      if (leaked !== 0)
+        log(...msg, leaked);
+    });
+    __privateAdd(this, _sanity, (input) => {
+      if (!__privateGet(this, _checkSanity))
+        return null;
+      if (!input)
+        return "input is not defined";
+      if (this.env.node && !(input instanceof tf39.Tensor))
+        return "input must be a tensor";
+      try {
+        this.tf.getBackend();
+      } catch (e) {
+        return "backend not loaded";
+      }
+      return null;
+    });
+    __publicField(this, "similarity", similarity);
+    __publicField(this, "distance", distance);
+    __publicField(this, "match", match2);
+    __publicField(this, "webcam", new WebCam());
+    __publicField(this, "emit", (event) => {
+      var _a;
+      if ((_a = this.events) == null ? void 0 : _a.dispatchEvent)
+        this.events.dispatchEvent(new Event(event));
+    });
+    __privateAdd(this, _loops, {});
+    this.env = env;
+    const tfVersion = (tf39.version.tfjs || tf39.version_core).replace(/-(.*)/, "");
+    config.wasmPath = `https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-backend-wasm@${tfVersion}/dist/`;
+    config.modelBasePath = env.browser ? "../models/" : "file://models/";
+    config.backend = env.browser ? "webgl" : "tensorflow";
+    this.version = version2;
+    Object.defineProperty(this, "version", { value: version2 });
+    this.config = JSON.parse(JSON.stringify(config));
+    Object.seal(this.config);
+    this.config.cacheModels = typeof indexedDB !== "undefined";
+    if (userConfig)
+      this.config = mergeDeep(this.config, userConfig);
+    setModelLoadOptions(this.config);
+    this.tf = tf39;
+    this.state = "idle";
+    __privateSet(this, _numTensors, 0);
+    __privateSet(this, _analyzeMemoryLeaks, false);
+    __privateSet(this, _checkSanity, false);
+    this.performance = {};
+    this.events = typeof EventTarget !== "undefined" ? new EventTarget() : void 0;
+    this.models = new Models();
+    this.draw = {
+      options: options3,
+      canvas: (input, output) => canvas2(input, output),
+      face: (output, result, options4) => face(output, result, options4),
+      body: (output, result, options4) => body(output, result, options4),
+      hand: (output, result, options4) => hand(output, result, options4),
+      gesture: (output, result, options4) => gesture(output, result, options4),
+      object: (output, result, options4) => object(output, result, options4),
+      person: (output, result, options4) => person(output, result, options4),
+      all: (output, result, options4) => all(output, result, options4)
+    };
+    this.result = { face: [], body: [], hand: [], gesture: [], object: [], performance: {}, timestamp: 0, persons: [], error: null };
+    this.process = { tensor: null, canvas: null };
+    this.faceTriangulation = triangulation;
+    this.faceUVMap = uvmap;
+    this.gl = config2;
+    validateModel(this, null, "");
+    this.emit("create");
+    if (this.config.debug || this.env.browser)
+      log(`version: ${this.version}`);
+    if (this.config.debug)
+      log(`tfjs version: ${this.tf.version["tfjs-core"]}`);
+    const envTemp = JSON.parse(JSON.stringify(this.env));
+    delete envTemp.kernels;
+    delete envTemp.initial;
+    delete envTemp.perfadd;
+    if (this.config.debug)
+      log("environment:", envTemp);
+  }
+  reset() {
+    const currentBackend = this.config.backend;
+    this.config = JSON.parse(JSON.stringify(config));
+    this.config.backend = currentBackend;
+    reset();
+    env.initial = true;
+  }
+  validate(userConfig) {
+    const msgs = validate(config, userConfig || this.config);
+    if (msgs.length === 0)
+      this.config = mergeDeep(this.config, userConfig);
+    return msgs;
+  }
+  check() {
+    return validate2(this);
+  }
+  now() {
+    return now();
+  }
+  image(input, getTensor = true) {
+    return process2(input, this.config, getTensor);
+  }
+  async segmentation(input, userConfig) {
+    var _a, _b, _c;
+    if (userConfig)
+      this.config = mergeDeep(this.config, userConfig);
+    if (!this.config.segmentation.enabled)
+      return null;
+    const processed = await process2(input, this.config);
+    if (!processed.tensor)
+      return null;
+    let tensor6 = null;
+    if ((_a = this.config.segmentation.modelPath) == null ? void 0 : _a.includes("rvm"))
+      tensor6 = await predict18(processed.tensor, this.config);
+    if ((_b = this.config.segmentation.modelPath) == null ? void 0 : _b.includes("meet"))
+      tensor6 = await predict13(processed.tensor, this.config);
+    if ((_c = this.config.segmentation.modelPath) == null ? void 0 : _c.includes("selfie"))
+      tensor6 = await predict19(processed.tensor, this.config);
+    tf39.dispose(processed.tensor);
+    return tensor6;
+  }
+  enhance(input) {
+    return enhance(input);
+  }
+  compare(firstImageTensor, secondImageTensor) {
+    return compare(this.config, firstImageTensor, secondImageTensor);
+  }
+  async init() {
+    await check(this, true);
+    await this.tf.ready();
+    reset();
+  }
+  async load(userConfig) {
+    this.state = "load";
+    const timeStamp = now();
+    const count2 = Object.values(this.models).filter((model21) => model21).length;
+    if (userConfig)
+      this.config = mergeDeep(this.config, userConfig);
+    if (this.env.initial) {
+      if (!await check(this, false))
+        log("error: backend check failed");
+      await tf39.ready();
+      if (this.env.browser) {
+        if (this.config.debug)
+          log("configuration:", this.config);
+        if (this.config.debug)
+          log("tf flags:", this.tf.ENV.flags);
+      }
+    }
+    await load22(this);
+    if (this.env.initial && this.config.debug)
+      log("tf engine state:", this.tf.engine().state.numBytes, "bytes", this.tf.engine().state.numTensors, "tensors");
+    this.env.initial = false;
+    const loaded = Object.values(this.models).filter((model21) => model21).length;
+    if (loaded !== count2) {
+      validate2(this);
+      this.emit("load");
+    }
+    const current = Math.trunc(now() - timeStamp);
+    if (current > (this.performance.loadModels || 0))
+      this.performance.loadModels = this.env.perfadd ? (this.performance.loadModels || 0) + current : current;
+  }
+  next(result = this.result) {
+    return calc2(result, this.config);
+  }
+  getModelStats() {
+    return getModelStats(this);
+  }
+  async warmup(userConfig) {
+    const t0 = now();
+    const res = await warmup(this, userConfig);
+    const t1 = now();
+    this.performance.warmup = Math.trunc(t1 - t0);
+    return res;
+  }
+  async profile(input, userConfig) {
+    const profile = await this.tf.profile(() => this.detect(input, userConfig));
+    const kernels = {};
+    let total = 0;
+    for (const kernel of profile.kernels) {
+      if (kernels[kernel.name])
+        kernels[kernel.name] += kernel.kernelTimeMs;
+      else
+        kernels[kernel.name] = kernel.kernelTimeMs;
+      total += kernel.kernelTimeMs;
+    }
+    const kernelArr = [];
+    Object.entries(kernels).forEach((key) => kernelArr.push({ kernel: key[0], time: key[1], perc: 0 }));
+    for (const kernel of kernelArr) {
+      kernel.perc = Math.round(1e3 * kernel.time / total) / 1e3;
+      kernel.time = Math.round(1e3 * kernel.time) / 1e3;
+    }
+    kernelArr.sort((a, b) => b.time - a.time);
+    kernelArr.length = 20;
+    return kernelArr;
+  }
+  async detect(input, userConfig) {
+    this.state = "detect";
+    return new Promise(async (resolve) => {
+      var _a, _b, _c, _d, _e, _f, _g, _h, _i, _j, _k, _l, _m, _n, _o, _p, _q, _r, _s, _t, _u;
+      this.state = "config";
+      let timeStamp;
+      this.config = mergeDeep(this.config, userConfig);
+      this.state = "check";
+      const error = __privateGet(this, _sanity).call(this, input);
+      if (error) {
+        log(error, input);
+        this.emit("error");
+        resolve({ face: [], body: [], hand: [], gesture: [], object: [], performance: this.performance, timestamp: now(), persons: [], error });
+      }
+      const timeStart = now();
+      await this.load();
+      timeStamp = now();
+      this.state = "image";
+      const img = await process2(input, this.config);
+      this.process = img;
+      this.performance.inputProcess = this.env.perfadd ? (this.performance.inputProcess || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);
+      this.analyze("Get Image:");
+      if (!img.tensor) {
+        if (this.config.debug)
+          log("could not convert input to tensor");
+        this.emit("error");
+        resolve({ face: [], body: [], hand: [], gesture: [], object: [], performance: this.performance, timestamp: now(), persons: [], error: "could not convert input to tensor" });
+        return;
+      }
+      this.emit("image");
+      timeStamp = now();
+      this.config.skipAllowed = await skip(this.config, img.tensor);
+      if (!this.performance.totalFrames)
+        this.performance.totalFrames = 0;
+      if (!this.performance.cachedFrames)
+        this.performance.cachedFrames = 0;
+      this.performance.totalFrames++;
+      if (this.config.skipAllowed)
+        this.performance.cachedFrames++;
+      this.performance.cacheCheck = this.env.perfadd ? (this.performance.cacheCheck || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);
+      this.analyze("Check Changed:");
+      let faceRes = [];
+      let bodyRes = [];
+      let handRes = [];
+      let objectRes = [];
+      this.state = "detect:face";
+      if (this.config.async) {
+        faceRes = this.config.face.enabled ? detectFace(this, img.tensor) : [];
+        if (this.performance.face)
+          delete this.performance.face;
+      } else {
+        timeStamp = now();
+        faceRes = this.config.face.enabled ? await detectFace(this, img.tensor) : [];
+        this.performance.face = this.env.perfadd ? (this.performance.face || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);
+      }
+      if (this.config.async && (this.config.body.maxDetected === -1 || this.config.hand.maxDetected === -1))
+        faceRes = await faceRes;
+      this.analyze("Start Body:");
+      this.state = "detect:body";
+      const bodyConfig = this.config.body.maxDetected === -1 ? mergeDeep(this.config, { body: { maxDetected: this.config.face.enabled ? 1 * faceRes.length : 1 } }) : this.config;
+      if (this.config.async) {
+        if ((_a = this.config.body.modelPath) == null ? void 0 : _a.includes("posenet"))
+          bodyRes = this.config.body.enabled ? predict17(img.tensor, bodyConfig) : [];
+        else if ((_b = this.config.body.modelPath) == null ? void 0 : _b.includes("blazepose"))
+          bodyRes = this.config.body.enabled ? predict2(img.tensor, bodyConfig) : [];
+        else if ((_c = this.config.body.modelPath) == null ? void 0 : _c.includes("efficientpose"))
+          bodyRes = this.config.body.enabled ? predict4(img.tensor, bodyConfig) : [];
+        else if ((_d = this.config.body.modelPath) == null ? void 0 : _d.includes("movenet"))
+          bodyRes = this.config.body.enabled ? predict15(img.tensor, bodyConfig) : [];
+        if (this.performance.body)
+          delete this.performance.body;
+      } else {
+        timeStamp = now();
+        if ((_e = this.config.body.modelPath) == null ? void 0 : _e.includes("posenet"))
+          bodyRes = this.config.body.enabled ? await predict17(img.tensor, bodyConfig) : [];
+        else if ((_f = this.config.body.modelPath) == null ? void 0 : _f.includes("blazepose"))
+          bodyRes = this.config.body.enabled ? await predict2(img.tensor, bodyConfig) : [];
+        else if ((_g = this.config.body.modelPath) == null ? void 0 : _g.includes("efficientpose"))
+          bodyRes = this.config.body.enabled ? await predict4(img.tensor, bodyConfig) : [];
+        else if ((_h = this.config.body.modelPath) == null ? void 0 : _h.includes("movenet"))
+          bodyRes = this.config.body.enabled ? await predict15(img.tensor, bodyConfig) : [];
+        this.performance.body = this.env.perfadd ? (this.performance.body || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);
+      }
+      this.analyze("End Body:");
+      this.analyze("Start Hand:");
+      this.state = "detect:hand";
+      const handConfig = this.config.hand.maxDetected === -1 ? mergeDeep(this.config, { hand: { maxDetected: this.config.face.enabled ? 2 * faceRes.length : 1 } }) : this.config;
+      if (this.config.async) {
+        if ((_j = (_i = this.config.hand.detector) == null ? void 0 : _i.modelPath) == null ? void 0 : _j.includes("handdetect"))
+          handRes = this.config.hand.enabled ? predict9(img.tensor, handConfig) : [];
+        else if ((_l = (_k = this.config.hand.detector) == null ? void 0 : _k.modelPath) == null ? void 0 : _l.includes("handtrack"))
+          handRes = this.config.hand.enabled ? predict10(img.tensor, handConfig) : [];
+        if (this.performance.hand)
+          delete this.performance.hand;
+      } else {
+        timeStamp = now();
+        if ((_n = (_m = this.config.hand.detector) == null ? void 0 : _m.modelPath) == null ? void 0 : _n.includes("handdetect"))
+          handRes = this.config.hand.enabled ? await predict9(img.tensor, handConfig) : [];
+        else if ((_p = (_o = this.config.hand.detector) == null ? void 0 : _o.modelPath) == null ? void 0 : _p.includes("handtrack"))
+          handRes = this.config.hand.enabled ? await predict10(img.tensor, handConfig) : [];
+        this.performance.hand = this.env.perfadd ? (this.performance.hand || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);
+      }
+      this.analyze("End Hand:");
+      this.analyze("Start Object:");
+      this.state = "detect:object";
+      if (this.config.async) {
+        if ((_q = this.config.object.modelPath) == null ? void 0 : _q.includes("nanodet"))
+          objectRes = this.config.object.enabled ? predict16(img.tensor, this.config) : [];
+        else if ((_r = this.config.object.modelPath) == null ? void 0 : _r.includes("centernet"))
+          objectRes = this.config.object.enabled ? predict3(img.tensor, this.config) : [];
+        if (this.performance.object)
+          delete this.performance.object;
+      } else {
+        timeStamp = now();
+        if ((_s = this.config.object.modelPath) == null ? void 0 : _s.includes("nanodet"))
+          objectRes = this.config.object.enabled ? await predict16(img.tensor, this.config) : [];
+        else if ((_t = this.config.object.modelPath) == null ? void 0 : _t.includes("centernet"))
+          objectRes = this.config.object.enabled ? await predict3(img.tensor, this.config) : [];
+        this.performance.object = this.env.perfadd ? (this.performance.object || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);
+      }
+      this.analyze("End Object:");
+      this.state = "detect:await";
+      if (this.config.async)
+        [faceRes, bodyRes, handRes, objectRes] = await Promise.all([faceRes, bodyRes, handRes, objectRes]);
+      this.state = "detect:gesture";
+      let gestureRes = [];
+      if (this.config.gesture.enabled) {
+        timeStamp = now();
+        gestureRes = [...face2(faceRes), ...body2(bodyRes), ...hand2(handRes), ...iris2(faceRes)];
+        if (!this.config.async)
+          this.performance.gesture = this.env.perfadd ? (this.performance.gesture || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);
+        else if (this.performance.gesture)
+          delete this.performance.gesture;
+      }
+      this.performance.total = this.env.perfadd ? (this.performance.total || 0) + Math.trunc(now() - timeStart) : Math.trunc(now() - timeStart);
+      const shape = ((_u = this.process.tensor) == null ? void 0 : _u.shape) || [];
+      this.result = {
+        face: faceRes,
+        body: bodyRes,
+        hand: handRes,
+        gesture: gestureRes,
+        object: objectRes,
+        performance: this.performance,
+        canvas: this.process.canvas,
+        timestamp: Date.now(),
+        error: null,
+        get persons() {
+          return join2(faceRes, bodyRes, handRes, gestureRes, shape);
+        }
+      };
+      tf39.dispose(img.tensor);
+      this.emit("detect");
+      this.state = "idle";
+      resolve(this.result);
+    });
+  }
+  async sleep(ms) {
+    return new Promise((resolve) => {
+      setTimeout(resolve, ms);
+    });
+  }
+  async video(element, run = true, delay = 0) {
+    if (run) {
+      if (!__privateGet(this, _loops)[element.id]) {
+        if (this.config.debug)
+          log("video start", element.id);
+        __privateGet(this, _loops)[element.id] = true;
+      }
+      if (!element.paused && __privateGet(this, _loops)[element.id] && element.readyState >= 2)
+        await this.detect(element);
+      if (delay > 0)
+        await this.sleep(delay);
+      if (__privateGet(this, _loops)[element.id])
+        requestAnimationFrame(() => this.video(element, run, delay));
+    } else {
+      if (this.config.debug)
+        log("video stop", element.id);
+      __privateGet(this, _loops)[element.id] = false;
+    }
+  }
+};
+_numTensors = new WeakMap();
+_analyzeMemoryLeaks = new WeakMap();
+_checkSanity = new WeakMap();
+_sanity = new WeakMap();
+_loops = new WeakMap();
+// Annotate the CommonJS export names for ESM import in node:
+0 && (module.exports = {
+  Env,
+  Human,
+  defaults,
+  draw,
+  env,
+  match,
+  models
+});
diff --git a/dist/human.node-wasm.js b/dist/human.node-wasm.js
index 194429ae..b78a6a17 100644
--- a/dist/human.node-wasm.js
+++ b/dist/human.node-wasm.js
@@ -4,7 +4,292 @@
   author: <https://github.com/vladmandic>'
 */
 
-"use strict";var vo=Object.create;var w2=Object.defineProperty;var Po=Object.getOwnPropertyDescriptor;var To=Object.getOwnPropertyNames;var Ro=Object.getPrototypeOf,ko=Object.prototype.hasOwnProperty;var wo=(e,t,n)=>t in e?w2(e,t,{enumerable:!0,configurable:!0,writable:!0,value:n}):e[t]=n;var Eo=(e,t)=>()=>(t||e((t={exports:{}}).exports,t),t.exports),ve=(e,t)=>{for(var n in t)w2(e,n,{get:t[n],enumerable:!0})},P1=(e,t,n,o)=>{if(t&&typeof t=="object"||typeof t=="function")for(let r of To(t))!ko.call(e,r)&&r!==n&&w2(e,r,{get:()=>t[r],enumerable:!(o=Po(t,r))||o.enumerable});return e};var D=(e,t,n)=>(n=e!=null?vo(Ro(e)):{},P1(t||!e||!e.__esModule?w2(n,"default",{value:e,enumerable:!0}):n,e)),zo=e=>P1(w2({},"__esModule",{value:!0}),e);var R=(e,t,n)=>(wo(e,typeof t!="symbol"?t+"":t,n),n),T1=(e,t,n)=>{if(!t.has(e))throw TypeError("Cannot "+n)};var ie=(e,t,n)=>(T1(e,t,"read from private field"),n?n.call(e):t.get(e)),t2=(e,t,n)=>{if(t.has(e))throw TypeError("Cannot add the same private member more than once");t instanceof WeakSet?t.add(e):t.set(e,n)},E2=(e,t,n,o)=>(T1(e,t,"write to private field"),o?o.call(e,n):t.set(e,n),n);var H=Eo((Ds,X2)=>{"use strict";var k1=Object.defineProperty,So=Object.getOwnPropertyDescriptor,jo=Object.getOwnPropertyNames,No=Object.prototype.hasOwnProperty,B5=(e,t,n,o)=>{if(t&&typeof t=="object"||typeof t=="function")for(let r of jo(t))!No.call(e,r)&&r!==n&&k1(e,r,{get:()=>t[r],enumerable:!(o=So(t,r))||o.enumerable});return e},w1=(e,t,n)=>(B5(e,t,"default"),n&&B5(n,t,"default")),Io=e=>B5(k1({},"__esModule",{value:!0}),e),H5={};X2.exports=Io(H5);w1(H5,require("@tensorflow/tfjs"),X2.exports);w1(H5,require("@tensorflow/tfjs-backend-wasm"),X2.exports)});var Gs={};ve(Gs,{Env:()=>S2,Human:()=>g1,default:()=>g1,defaults:()=>Pe,draw:()=>y1,env:()=>k,match:()=>b1,models:()=>f2});module.exports=zo(Gs);function u(...e){let t=new Date,n=`${t.getHours().toString().padStart(2,"0")}:${t.getMinutes().toString().padStart(2,"0")}:${t.getSeconds().toString().padStart(2,"0")}.${t.getMilliseconds().toString().padStart(3,"0")}`;e&&console.log(n,"Human:",...e)}function R1(e,t){let n=e.endsWith("/")?"":"/",r=t.startsWith(".")||t.startsWith("/")||t.startsWith("http:")||t.startsWith("https:")||t.startsWith("file:")?`${t}`:`${e}${n}${t}`;if(!r.toLocaleLowerCase().includes(".json"))throw new Error(`modelpath error: expecting json file: ${r}`);return r}var v=()=>typeof performance!="undefined"?performance.now():parseInt((Number(process.hrtime.bigint())/1e3/1e3).toString());function G5(e,t,n="config",o=[]){for(let r of Object.keys(t))if(typeof t[r]=="object")G5(e[r],t[r],r,o);else{let A=e&&typeof e[r]!="undefined";A||o.push({reason:"unknown property",where:`${n}.${r} = ${t[r]}`});let s=e&&typeof e[r]==typeof t[r];A&&!s&&o.push({reason:"property type mismatch",where:`${n}.${r} = ${t[r]}`,expected:typeof e[r]})}return t.debug&&n==="config"&&o.length>0&&u("invalid configuration",o),o}function A0(...e){let t=n=>n&&typeof n=="object";return e.reduce((n,o)=>(Object.keys(o||{}).forEach(r=>{let A=n[r],s=o[r];Array.isArray(A)&&Array.isArray(s)?n[r]=A.concat(...s):t(A)&&t(s)?n[r]=A0(A,s):n[r]=s}),n),{})}var Pe={backend:"",modelBasePath:"",cacheModels:!0,validateModels:!0,wasmPath:"",wasmPlatformFetch:!1,debug:!1,async:!0,warmup:"full",cacheSensitivity:.7,skipAllowed:!1,deallocate:!1,flags:{},softwareKernels:!1,filter:{enabled:!0,equalization:!1,width:0,height:0,flip:!1,return:!0,brightness:0,contrast:0,sharpness:0,blur:0,saturation:0,hue:0,negative:!1,sepia:!1,vintage:!1,kodachrome:!1,technicolor:!1,polaroid:!1,pixelate:0},gesture:{enabled:!0},face:{enabled:!0,detector:{modelPath:"blazeface.json",rotation:!0,maxDetected:1,skipFrames:99,skipTime:2500,minConfidence:.2,iouThreshold:.1,mask:!1,return:!1},mesh:{enabled:!0,modelPath:"facemesh.json",keepInvalid:!1},attention:{enabled:!1,modelPath:"facemesh-attention.json"},iris:{enabled:!0,modelPath:"iris.json"},emotion:{enabled:!0,minConfidence:.1,skipFrames:99,skipTime:1500,modelPath:"emotion.json"},description:{enabled:!0,modelPath:"faceres.json",skipFrames:99,skipTime:3e3,minConfidence:.1},antispoof:{enabled:!1,skipFrames:99,skipTime:4e3,modelPath:"antispoof.json"},liveness:{enabled:!1,skipFrames:99,skipTime:4e3,modelPath:"liveness.json"}},body:{enabled:!0,modelPath:"movenet-lightning.json",maxDetected:-1,minConfidence:.3,skipFrames:1,skipTime:200},hand:{enabled:!0,rotation:!0,skipFrames:99,skipTime:1e3,minConfidence:.5,iouThreshold:.2,maxDetected:-1,landmarks:!0,detector:{modelPath:"handtrack.json"},skeleton:{modelPath:"handlandmark-full.json"}},object:{enabled:!1,modelPath:"mb3-centernet.json",minConfidence:.2,iouThreshold:.4,maxDetected:10,skipFrames:99,skipTime:2e3},segmentation:{enabled:!1,modelPath:"selfie.json",blur:8}};var x0=D(H());var O=D(H());var E1=`
+"use strict";
+var __create = Object.create;
+var __defProp = Object.defineProperty;
+var __getOwnPropDesc = Object.getOwnPropertyDescriptor;
+var __getOwnPropNames = Object.getOwnPropertyNames;
+var __getProtoOf = Object.getPrototypeOf;
+var __hasOwnProp = Object.prototype.hasOwnProperty;
+var __defNormalProp = (obj, key, value) => key in obj ? __defProp(obj, key, { enumerable: true, configurable: true, writable: true, value }) : obj[key] = value;
+var __commonJS = (cb, mod3) => function __require() {
+  return mod3 || (0, cb[__getOwnPropNames(cb)[0]])((mod3 = { exports: {} }).exports, mod3), mod3.exports;
+};
+var __export = (target, all2) => {
+  for (var name in all2)
+    __defProp(target, name, { get: all2[name], enumerable: true });
+};
+var __copyProps = (to, from, except, desc) => {
+  if (from && typeof from === "object" || typeof from === "function") {
+    for (let key of __getOwnPropNames(from))
+      if (!__hasOwnProp.call(to, key) && key !== except)
+        __defProp(to, key, { get: () => from[key], enumerable: !(desc = __getOwnPropDesc(from, key)) || desc.enumerable });
+  }
+  return to;
+};
+var __toESM = (mod3, isNodeMode, target) => (target = mod3 != null ? __create(__getProtoOf(mod3)) : {}, __copyProps(
+  isNodeMode || !mod3 || !mod3.__esModule ? __defProp(target, "default", { value: mod3, enumerable: true }) : target,
+  mod3
+));
+var __toCommonJS = (mod3) => __copyProps(__defProp({}, "__esModule", { value: true }), mod3);
+var __publicField = (obj, key, value) => {
+  __defNormalProp(obj, typeof key !== "symbol" ? key + "" : key, value);
+  return value;
+};
+var __accessCheck = (obj, member, msg) => {
+  if (!member.has(obj))
+    throw TypeError("Cannot " + msg);
+};
+var __privateGet = (obj, member, getter) => {
+  __accessCheck(obj, member, "read from private field");
+  return getter ? getter.call(obj) : member.get(obj);
+};
+var __privateAdd = (obj, member, value) => {
+  if (member.has(obj))
+    throw TypeError("Cannot add the same private member more than once");
+  member instanceof WeakSet ? member.add(obj) : member.set(obj, value);
+};
+var __privateSet = (obj, member, value, setter) => {
+  __accessCheck(obj, member, "write to private field");
+  setter ? setter.call(obj, value) : member.set(obj, value);
+  return value;
+};
+
+// dist/tfjs.esm.js
+var require_tfjs_esm = __commonJS({
+  "dist/tfjs.esm.js"(exports, module2) {
+    "use strict";
+    var __defProp2 = Object.defineProperty;
+    var __getOwnPropDesc2 = Object.getOwnPropertyDescriptor;
+    var __getOwnPropNames2 = Object.getOwnPropertyNames;
+    var __hasOwnProp2 = Object.prototype.hasOwnProperty;
+    var __copyProps2 = (to, from, except, desc) => {
+      if (from && typeof from === "object" || typeof from === "function") {
+        for (let key of __getOwnPropNames2(from))
+          if (!__hasOwnProp2.call(to, key) && key !== except)
+            __defProp2(to, key, { get: () => from[key], enumerable: !(desc = __getOwnPropDesc2(from, key)) || desc.enumerable });
+      }
+      return to;
+    };
+    var __reExport = (target, mod3, secondTarget) => (__copyProps2(target, mod3, "default"), secondTarget && __copyProps2(secondTarget, mod3, "default"));
+    var __toCommonJS2 = (mod3) => __copyProps2(__defProp2({}, "__esModule", { value: true }), mod3);
+    var tf_node_wasm_exports = {};
+    module2.exports = __toCommonJS2(tf_node_wasm_exports);
+    __reExport(tf_node_wasm_exports, require("@tensorflow/tfjs"), module2.exports);
+    __reExport(tf_node_wasm_exports, require("@tensorflow/tfjs-backend-wasm"), module2.exports);
+  }
+});
+
+// src/human.ts
+var human_exports = {};
+__export(human_exports, {
+  Env: () => Env,
+  Human: () => Human2,
+  default: () => Human2,
+  defaults: () => config,
+  draw: () => draw_exports,
+  env: () => env,
+  match: () => match_exports,
+  models: () => models_exports2
+});
+module.exports = __toCommonJS(human_exports);
+
+// src/util/util.ts
+function log(...msg) {
+  const dt = new Date();
+  const ts = `${dt.getHours().toString().padStart(2, "0")}:${dt.getMinutes().toString().padStart(2, "0")}:${dt.getSeconds().toString().padStart(2, "0")}.${dt.getMilliseconds().toString().padStart(3, "0")}`;
+  if (msg)
+    console.log(ts, "Human:", ...msg);
+}
+function join(folder, file) {
+  const separator = folder.endsWith("/") ? "" : "/";
+  const skipJoin = file.startsWith(".") || file.startsWith("/") || file.startsWith("http:") || file.startsWith("https:") || file.startsWith("file:");
+  const path = skipJoin ? `${file}` : `${folder}${separator}${file}`;
+  if (!path.toLocaleLowerCase().includes(".json"))
+    throw new Error(`modelpath error: expecting json file: ${path}`);
+  return path;
+}
+var now = () => {
+  if (typeof performance !== "undefined")
+    return performance.now();
+  return parseInt((Number(process.hrtime.bigint()) / 1e3 / 1e3).toString());
+};
+function validate(defaults, config3, parent = "config", msgs = []) {
+  for (const key of Object.keys(config3)) {
+    if (typeof config3[key] === "object") {
+      validate(defaults[key], config3[key], key, msgs);
+    } else {
+      const defined = defaults && typeof defaults[key] !== "undefined";
+      if (!defined)
+        msgs.push({ reason: "unknown property", where: `${parent}.${key} = ${config3[key]}` });
+      const same = defaults && typeof defaults[key] === typeof config3[key];
+      if (defined && !same)
+        msgs.push({ reason: "property type mismatch", where: `${parent}.${key} = ${config3[key]}`, expected: typeof defaults[key] });
+    }
+  }
+  if (config3.debug && parent === "config" && msgs.length > 0)
+    log("invalid configuration", msgs);
+  return msgs;
+}
+function mergeDeep(...objects) {
+  const isObject = (obj) => obj && typeof obj === "object";
+  return objects.reduce((prev, obj) => {
+    Object.keys(obj || {}).forEach((key) => {
+      const pVal = prev[key];
+      const oVal = obj[key];
+      if (Array.isArray(pVal) && Array.isArray(oVal))
+        prev[key] = pVal.concat(...oVal);
+      else if (isObject(pVal) && isObject(oVal))
+        prev[key] = mergeDeep(pVal, oVal);
+      else
+        prev[key] = oVal;
+    });
+    return prev;
+  }, {});
+}
+
+// src/config.ts
+var config = {
+  backend: "",
+  modelBasePath: "",
+  cacheModels: true,
+  validateModels: true,
+  wasmPath: "",
+  wasmPlatformFetch: false,
+  debug: false,
+  async: true,
+  warmup: "full",
+  cacheSensitivity: 0.7,
+  skipAllowed: false,
+  deallocate: false,
+  flags: {},
+  softwareKernels: false,
+  filter: {
+    enabled: true,
+    equalization: false,
+    width: 0,
+    height: 0,
+    flip: false,
+    return: true,
+    brightness: 0,
+    contrast: 0,
+    sharpness: 0,
+    blur: 0,
+    saturation: 0,
+    hue: 0,
+    negative: false,
+    sepia: false,
+    vintage: false,
+    kodachrome: false,
+    technicolor: false,
+    polaroid: false,
+    pixelate: 0
+  },
+  gesture: {
+    enabled: true
+  },
+  face: {
+    enabled: true,
+    detector: {
+      modelPath: "blazeface.json",
+      rotation: true,
+      maxDetected: 1,
+      skipFrames: 99,
+      skipTime: 2500,
+      minConfidence: 0.2,
+      iouThreshold: 0.1,
+      mask: false,
+      return: false
+    },
+    mesh: {
+      enabled: true,
+      modelPath: "facemesh.json",
+      keepInvalid: false
+    },
+    attention: {
+      enabled: false,
+      modelPath: "facemesh-attention.json"
+    },
+    iris: {
+      enabled: true,
+      modelPath: "iris.json"
+    },
+    emotion: {
+      enabled: true,
+      minConfidence: 0.1,
+      skipFrames: 99,
+      skipTime: 1500,
+      modelPath: "emotion.json"
+    },
+    description: {
+      enabled: true,
+      modelPath: "faceres.json",
+      skipFrames: 99,
+      skipTime: 3e3,
+      minConfidence: 0.1
+    },
+    antispoof: {
+      enabled: false,
+      skipFrames: 99,
+      skipTime: 4e3,
+      modelPath: "antispoof.json"
+    },
+    liveness: {
+      enabled: false,
+      skipFrames: 99,
+      skipTime: 4e3,
+      modelPath: "liveness.json"
+    }
+  },
+  body: {
+    enabled: true,
+    modelPath: "movenet-lightning.json",
+    maxDetected: -1,
+    minConfidence: 0.3,
+    skipFrames: 1,
+    skipTime: 200
+  },
+  hand: {
+    enabled: true,
+    rotation: true,
+    skipFrames: 99,
+    skipTime: 1e3,
+    minConfidence: 0.5,
+    iouThreshold: 0.2,
+    maxDetected: -1,
+    landmarks: true,
+    detector: {
+      modelPath: "handtrack.json"
+    },
+    skeleton: {
+      modelPath: "handlandmark-full.json"
+    }
+  },
+  object: {
+    enabled: false,
+    modelPath: "mb3-centernet.json",
+    minConfidence: 0.2,
+    iouThreshold: 0.4,
+    maxDetected: 10,
+    skipFrames: 99,
+    skipTime: 2e3
+  },
+  segmentation: {
+    enabled: false,
+    modelPath: "rvm.json",
+    ratio: 0.5,
+    mode: "default"
+  }
+};
+
+// src/util/env.ts
+var tf3 = __toESM(require_tfjs_esm());
+
+// src/image/image.ts
+var tf2 = __toESM(require_tfjs_esm());
+
+// src/image/imagefxshaders.ts
+var vertexIdentity = `
   precision highp float;
   attribute vec2 pos;
   attribute vec2 uv;
@@ -14,7 +299,8 @@
     vUv = uv;
     gl_Position = vec4(pos.x, pos.y*flipY, 0.0, 1.);
   }
-`;var z1=`
+`;
+var colorMatrixWithAlpha = `
   precision highp float;
   varying vec2 vUv;
   uniform sampler2D texture;
@@ -26,7 +312,8 @@
     gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[13] * c.a + m[14];
     gl_FragColor.a = m[15] * c.r + m[16] * c.g + m[17] * c.b + m[18] * c.a + m[19];
   }
-`,S1=`
+`;
+var colorMatrixWithoutAlpha = `
   precision highp float;
   varying vec2 vUv;
   uniform sampler2D texture;
@@ -38,7 +325,8 @@
     gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[14];
     gl_FragColor.a = c.a;
   }
-`,j1=`
+`;
+var pixelate = `
   precision highp float;
   varying vec2 vUv;
   uniform vec2 size;
@@ -51,7 +339,8 @@
     vec2 coord = pixelate(vUv, size);
     gl_FragColor += texture2D(texture, coord);
   }
-`,N1=`
+`;
+var blur = `
   precision highp float;
   varying vec2 vUv;
   uniform sampler2D texture;
@@ -74,7 +363,8 @@
     gl_FragColor += texture2D(texture, vUv + vec2( 6.0*px.x,  6.0*px.y))*0.00895781211794;
     gl_FragColor += texture2D(texture, vUv + vec2( 7.0*px.x,  7.0*px.y))*0.0044299121055113265;
   }
-`,I1=`
+`;
+var convolution = `
   precision highp float;
   varying vec2 vUv;
   uniform sampler2D texture;
@@ -96,19 +386,13456 @@
     c31 * m[6] + c32 * m[7] + c33 * m[8];
     gl_FragColor.a = c22.a;
   }
-`;var V5=(e,t,n)=>{let o=new RegExp("\\b"+t+" \\w+ (\\w+)","ig");e.replace(o,(r,A)=>(n[A]=0,r))},D5=class{constructor(t,n,o){R(this,"uniform",{});R(this,"attribute",{});R(this,"gl");R(this,"id");R(this,"compile",(t,n)=>{let o=this.gl.createShader(n);return o?(this.gl.shaderSource(o,t),this.gl.compileShader(o),this.gl.getShaderParameter(o,this.gl.COMPILE_STATUS)?o:(u(`filter: gl compile failed: ${this.gl.getShaderInfoLog(o)||"unknown"}`),null)):(u("filter: could not create shader"),null)});this.gl=t;let r=this.compile(n,this.gl.VERTEX_SHADER),A=this.compile(o,this.gl.FRAGMENT_SHADER);if(this.id=this.gl.createProgram(),!(!r||!A)){if(!this.id){u("filter: could not create webgl program");return}if(this.gl.attachShader(this.id,r),this.gl.attachShader(this.id,A),this.gl.linkProgram(this.id),!this.gl.getProgramParameter(this.id,this.gl.LINK_STATUS)){u(`filter: gl link failed: ${this.gl.getProgramInfoLog(this.id)||"unknown"}`);return}this.gl.useProgram(this.id),V5(n,"attribute",this.attribute);for(let s in this.attribute)this.attribute[s]=this.gl.getAttribLocation(this.id,s);V5(n,"uniform",this.uniform),V5(o,"uniform",this.uniform);for(let s in this.uniform)this.uniform[s]=this.gl.getUniformLocation(this.id,s)}}};function O1(){let e=0,t=null,n=!1,o=-1,r=[null,null],A=[],s=null,a=null,l=k0(100,100),c={},x={INTERMEDIATE:1},i=l.getContext("webgl");if(!i){u("filter: cannot get webgl context");return}this.gl=i;function y(P,m){if(!(P===l.width&&m===l.height)){if(l.width=P,l.height=m,!s){let b=new Float32Array([-1,-1,0,1,1,-1,1,1,-1,1,0,0,-1,1,0,0,1,-1,1,1,1,1,1,0]);s=i.createBuffer(),i.bindBuffer(i.ARRAY_BUFFER,s),i.bufferData(i.ARRAY_BUFFER,b,i.STATIC_DRAW),i.pixelStorei(i.UNPACK_PREMULTIPLY_ALPHA_WEBGL,!0)}i.viewport(0,0,l.width,l.height),r=[null,null]}}function d(P,m){let b=i.createFramebuffer();i.bindFramebuffer(i.FRAMEBUFFER,b);let z=i.createRenderbuffer();i.bindRenderbuffer(i.RENDERBUFFER,z);let w=i.createTexture();return i.bindTexture(i.TEXTURE_2D,w),i.texImage2D(i.TEXTURE_2D,0,i.RGBA,P,m,0,i.RGBA,i.UNSIGNED_BYTE,null),i.texParameteri(i.TEXTURE_2D,i.TEXTURE_MAG_FILTER,i.LINEAR),i.texParameteri(i.TEXTURE_2D,i.TEXTURE_MIN_FILTER,i.LINEAR),i.texParameteri(i.TEXTURE_2D,i.TEXTURE_WRAP_S,i.CLAMP_TO_EDGE),i.texParameteri(i.TEXTURE_2D,i.TEXTURE_WRAP_T,i.CLAMP_TO_EDGE),i.framebufferTexture2D(i.FRAMEBUFFER,i.COLOR_ATTACHMENT0,i.TEXTURE_2D,w,0),i.bindTexture(i.TEXTURE_2D,null),i.bindFramebuffer(i.FRAMEBUFFER,null),{fbo:b,texture:w}}function f(P){return r[P]=r[P]||d(l.width,l.height),r[P]}function p(P=0){if(!a)return;let m=null,b=null,z=!1;e===0?m=t:m=f(o).texture||null,e++,n&&!(P&x.INTERMEDIATE)?(b=null,z=e%2===0):(o=(o+1)%2,b=f(o).fbo||null),i.bindTexture(i.TEXTURE_2D,m),i.bindFramebuffer(i.FRAMEBUFFER,b),i.uniform1f(a.uniform.flipY,z?-1:1),i.drawArrays(i.TRIANGLES,0,6)}function g(P){if(c[P])return 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m=new Float32Array(P),b=1/l.width,z=1/l.height,w=g(I1);!w||(i.uniform1fv(w.uniform.m,m),i.uniform2f(w.uniform.px,b,z),p())},detectEdges:()=>{M.convolution.call(this,[0,1,0,1,-4,1,0,1,0])},sobelX:()=>{M.convolution.call(this,[-1,0,1,-2,0,2,-1,0,1])},sobelY:()=>{M.convolution.call(this,[-1,-2,-1,0,0,0,1,2,1])},sharpen:P=>{let m=P||1;M.convolution.call(this,[0,-1*m,0,-1*m,1+4*m,-1*m,0,-1*m,0])},emboss:P=>{let m=P||1;M.convolution.call(this,[-2*m,-1*m,0,-1*m,1,1*m,0,1*m,2*m])},blur:P=>{let m=P/7/l.width,b=P/7/l.height,z=g(N1);!z||(i.uniform2f(z.uniform.px,0,b),p(x.INTERMEDIATE),i.uniform2f(z.uniform.px,m,0),p())},pixelate:P=>{let m=P/l.width,b=P/l.height,z=g(j1);!z||(i.uniform2f(z.uniform.size,m,b),p())}};this.add=function(P){let m=Array.prototype.slice.call(arguments,1),b=M[P];A.push({func:b,args:m})},this.reset=function(){A=[]},this.get=function(){return A},this.apply=function(P){y(P.width,P.height),e=0,t||(t=i.createTexture()),i.bindTexture(i.TEXTURE_2D,t),i.texParameteri(i.TEXTURE_2D,i.TEXTURE_WRAP_S,i.CLAMP_TO_EDGE),i.texParameteri(i.TEXTURE_2D,i.TEXTURE_WRAP_T,i.CLAMP_TO_EDGE),i.texParameteri(i.TEXTURE_2D,i.TEXTURE_MIN_FILTER,i.NEAREST),i.texParameteri(i.TEXTURE_2D,i.TEXTURE_MAG_FILTER,i.NEAREST),i.texImage2D(i.TEXTURE_2D,0,i.RGBA,i.RGBA,i.UNSIGNED_BYTE,P);for(let m=0;m<A.length;m++){n=m===A.length-1;let b=A[m];b.func.apply(this,b.args||[])}return l},this.draw=function(P){return this.add("brightness",0),this.apply(P)}}var U=D(H());async function q2(e){let t=e.shape.length===4?U.squeeze(e):e,n=U.split(t,3,2),o=[U.min(n[0]),U.min(n[1]),U.min(n[2])],r=[U.max(n[0]),U.max(n[1]),U.max(n[2])],A=await Promise.all(r.map(d=>d.data())),s=.99*Math.max(A[0][0],A[1][0],A[2][0]),a=[U.sub(n[0],o[0]),U.sub(n[1],o[1]),U.sub(n[2],o[2])],l=[U.sub(r[0],o[0]),U.sub(r[1],o[1]),U.sub(r[2],o[2])],c=[U.div(s,l[0]),U.div(s,l[1]),U.div(s,l[2])],x=[U.mul(a[0],c[0]),U.mul(a[1],c[1]),U.mul(a[2],c[2])],i=U.stack([x[0],x[1],x[2]],2),y=U.reshape(i,[1,t.shape[0],t.shape[1],3]);return U.dispose([...n,...o,...r,...a,...l,...c,...x,i,t]),y}var U2=3840,h0=null,b0=null,n2=null,Q,D0={inputSum:0,cacheDiff:1,sumMethod:0,inputTensor:void 0};function Z5(){D0.inputSum=0,D0.cacheDiff=1,D0.sumMethod=0,D0.inputTensor=void 0}function k0(e,t){let n;if(k.browser)if(k.worker){if(typeof OffscreenCanvas=="undefined")throw new Error("canvas error: attempted to run in web worker but OffscreenCanvas is not supported");n=new OffscreenCanvas(e,t)}else{if(typeof document=="undefined")throw new Error("canvas error: attempted to run in browser but DOM is not defined");n=document.createElement("canvas"),n.width=e,n.height=t}else typeof k.Canvas!="undefined"?n=new k.Canvas(e,t):typeof globalThis.Canvas!="undefined"&&(n=new globalThis.Canvas(e,t));return n}function Y2(e,t){let n=t||k0(e.width,e.height);return n.getContext("2d").drawImage(e,0,0),n}async function o2(e,t,n=!0){var y,d;if(!e)return t.debug&&u("input error: input is missing"),{tensor:null,canvas:null};if(!(e instanceof O.Tensor)&&!(typeof Image!="undefined"&&e instanceof Image)&&!(typeof k.Canvas!="undefined"&&e instanceof k.Canvas)&&!(typeof globalThis.Canvas!="undefined"&&e instanceof globalThis.Canvas)&&!(typeof ImageData!="undefined"&&e instanceof ImageData)&&!(typeof ImageBitmap!="undefined"&&e instanceof ImageBitmap)&&!(typeof HTMLImageElement!="undefined"&&e instanceof HTMLImageElement)&&!(typeof HTMLMediaElement!="undefined"&&e instanceof HTMLMediaElement)&&!(typeof HTMLVideoElement!="undefined"&&e instanceof HTMLVideoElement)&&!(typeof HTMLCanvasElement!="undefined"&&e instanceof HTMLCanvasElement)&&!(typeof OffscreenCanvas!="undefined"&&e 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o=e.naturalWidth||e.videoWidth||e.width||e.shape&&e.shape[1]>0,r=e.naturalHeight||e.videoHeight||e.height||e.shape&&e.shape[2]>0;if(!o||!r)return t.debug&&u("cannot determine input dimensions"),{tensor:null,canvas:h0};let A=o,s=r;if(A>U2&&(A=U2,s=Math.trunc(A*r/o)),s>U2&&(s=U2,A=Math.trunc(s*o/r)),(((y=t.filter)==null?void 0:y.width)||0)>0?A=t.filter.width:(((d=t.filter)==null?void 0:d.height)||0)>0&&(A=o*((t.filter.height||0)/r)),(t.filter.height||0)>0?s=t.filter.height:(t.filter.width||0)>0&&(s=r*((t.filter.width||0)/o)),!A||!s)throw new Error("input error: cannot determine dimension");(!h0||h0.width!==A||h0.height!==s)&&(h0=k0(A,s));let a=h0.getContext("2d");if(typeof ImageData!="undefined"&&e instanceof ImageData?a.putImageData(e,0,0):t.filter.flip&&typeof 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l,c=3;if(typeof ImageData!="undefined"&&e instanceof ImageData||e.data&&e.width&&e.height)if(k.browser&&O.browser)l=O.browser?O.browser.fromPixels(e):null;else{c=e.data.length/e.height/e.width;let f=new Uint8Array(e.data.buffer);l=O.tensor(f,[e.height,e.width,c],"int32")}else if((!n2||b0.width!==n2.width||b0.height!==n2.height)&&(n2=k0(b0.width,b0.height)),O.browser&&k.browser)t.backend==="webgl"||t.backend==="humangl"||t.backend==="webgpu"?l=O.browser.fromPixels(b0):(n2=Y2(b0),l=O.browser.fromPixels(n2));else{let g=Y2(b0).getContext("2d").getImageData(0,0,A,s);c=g.data.length/A/s;let M=new Uint8Array(g.data.buffer);l=O.tensor(M,[A,s,c])}if(c===4){let f=O.slice3d(l,[0,0,0],[-1,-1,3]);O.dispose(l),l=f}if(!l)throw new Error("input error: cannot create tensor");let x=O.cast(l,"float32"),i=t.filter.equalization?await q2(x):O.expandDims(x,0);return O.dispose([l,x]),{tensor:i,canvas:t.filter.return?b0:null}}async function C1(e,t){let n=!1;if(e.cacheSensitivity===0||!t.shape||t.shape.length!==4||t.shape[1]>2048||t.shape[2]>2048)return n;if(!D0.inputTensor)D0.inputTensor=O.clone(t);else if(D0.inputTensor.shape[1]!==t.shape[1]||D0.inputTensor.shape[2]!==t.shape[2])O.dispose(D0.inputTensor),D0.inputTensor=O.clone(t);else{let o={};o.diff=O.sub(t,D0.inputTensor),o.squared=O.mul(o.diff,o.diff),o.sum=O.sum(o.squared);let A=(await o.sum.data())[0]/(t.shape[1]||1)/(t.shape[2]||1)/255/3;O.dispose([D0.inputTensor,o.diff,o.squared,o.sum]),D0.inputTensor=O.clone(t),n=A<=(e.cacheSensitivity||0)}return n}async function L1(e,t,n){let o={};if(!t||!n||t.shape.length!==4||t.shape.length!==n.shape.length)return e.debug||u("invalid input tensor or tensor shapes do not match:",t.shape,n.shape),0;if(t.shape[0]!==1||n.shape[0]!==1||t.shape[3]!==3||n.shape[3]!==3)return e.debug||u("input tensors must be of shape [1, height, width, 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0,flags:[]});R(this,"kernels",[]);R(this,"Canvas");R(this,"Image");R(this,"ImageData");if(this.browser=typeof navigator!="undefined",this.node=typeof process!="undefined"&&typeof process.versions!="undefined"&&typeof process.versions.node!="undefined",this.tfjs={version:x0.version["tfjs-core"]},this.offscreen=typeof OffscreenCanvas!="undefined",this.initial=!0,this.worker=this.browser&&this.offscreen?typeof WorkerGlobalScope!="undefined":void 0,typeof navigator!="undefined"){let t=navigator.userAgent.match(/\(([^()]+)\)/g);if(t!=null&&t[0]){let n=t[0].match(/\(([^()]+)\)/g);this.platform=n!=null&&n[0]?n[0].replace(/\(|\)/g,""):"",this.agent=navigator.userAgent.replace(t[0],""),this.platform[1]&&(this.agent=this.agent.replace(t[1],"")),this.agent=this.agent.replace(/  /g," ")}}else typeof process!="undefined"&&(this.platform=`${process.platform} ${process.arch}`,this.agent=`NodeJS ${process.version}`)}async updateBackend(){this.backends=Object.keys(x0.engine().registryFactory),this.tensorflow={version:x0.backend().binding?x0.backend().binding.TF_Version:void 0,gpu:x0.backend().binding?x0.backend().binding.isUsingGpuDevice():void 0},this.wasm.supported=typeof WebAssembly!="undefined",this.wasm.backend=this.backends.includes("wasm"),this.wasm.supported&&this.wasm.backend&&x0.getBackend()==="wasm"&&(this.wasm.simd=x0.env().get("WASM_HAS_SIMD_SUPPORT"),this.wasm.multithread=x0.env().get("WASM_HAS_MULTITHREAD_SUPPORT"));let t=k0(100,100),n=t?t.getContext("webgl2"):void 0;if(this.webgl.supported=typeof n!="undefined",this.webgl.backend=this.backends.includes("webgl"),this.webgl.supported&&this.webgl.backend&&(x0.getBackend()==="webgl"||x0.getBackend()==="humangl")){let o=x0.backend().gpgpu!=="undefined"?await x0.backend().getGPGPUContext().gl:null;o&&(this.webgl.version=o.getParameter(o.VERSION),this.webgl.renderer=o.getParameter(o.RENDERER))}this.webgpu.supported=this.browser&&typeof navigator.gpu!="undefined",this.webgpu.backend=this.backends.includes("webgpu");try{if(this.webgpu.supported){let o=await navigator.gpu.requestAdapter();this.webgpu.adapter=o?o.name:void 0}}catch(o){this.webgpu.supported=!1}try{this.kernels=x0.getKernelsForBackend(x0.getBackend()).map(o=>o.kernelName.toLowerCase())}catch(o){}}updateCPU(){let t={model:"",flags:[]};this.node&&this.platform.startsWith("linux"),this.cpu?this.cpu=t:Object.defineProperty(this,"cpu",{value:t})}},k=new S2;var K2=class{constructor(){R(this,"config");R(this,"element");R(this,"stream");R(this,"start",async t=>{if(t!=null&&t.debug&&(this.config.debug=t==null?void 0:t.debug),t!=null&&t.crop&&(this.config.crop=t==null?void 0:t.crop),t!=null&&t.mode&&(this.config.mode=t==null?void 0:t.mode),t!=null&&t.width&&(this.config.width=t==null?void 0:t.width),t!=null&&t.height&&(this.config.height=t==null?void 0:t.height),t!=null&&t.element)if(typeof t.element=="string"){let r=document.getElementById(t.element);if(r&&r instanceof HTMLVideoElement)this.element=r;else{this.config.debug&&u("webcam","cannot get dom element",t.element);return}}else if(t.element instanceof HTMLVideoElement)this.element=t.element;else{this.config.debug&&u("webcam","unknown dom element",t.element);return}else this.element=document.createElement("video");let n={audio:!1,video:{facingMode:this.config.mode==="front"?"user":"environment",resizeMode:this.config.crop?"crop-and-scale":"none",width:{ideal:this.config.width>0?this.config.width:window.innerWidth},height:{ideal:this.config.height>0?this.config.height:window.innerHeight}}};if(this.element.addEventListener("play",()=>{this.config.debug&&u("webcam","play")}),this.element.addEventListener("pause",()=>{this.config.debug&&u("webcam","pause")}),this.element.addEventListener("click",async()=>{!this.element||!this.stream||(this.element.paused?await this.element.play():this.element.pause())}),!(navigator!=null&&navigator.mediaDevices)){this.config.debug&&u("webcam","no 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f2={};ve(f2,{Models:()=>H2,getModelStats:()=>r1,load:()=>A1,reset:()=>v5,validate:()=>C5,validateModel:()=>p2});var J2=D(H());var K0,q5=[],Hr=["white","black","asian","indian","other"],Vr=[15,23,28,35.5,45.5,55.5,65],W1=0,F1=0,U5=Number.MAX_SAFE_INTEGER;async function G1(e){var t;return k.initial&&(K0=null),K0?e.debug&&u("cached model:",K0.modelUrl):K0=await L((t=e.face.gear)==null?void 0:t.modelPath),K0}async function Y5(e,t,n,o){var s,a;if(!K0)return{age:0,gender:"unknown",genderScore:0,race:[]};let r=U5<(((s=t.face.gear)==null?void 0:s.skipFrames)||0),A=(((a=t.face.gear)==null?void 0:a.skipTime)||0)>v()-F1;return t.skipAllowed&&A&&r&&W1===o&&q5[n]?(U5++,q5[n]):(U5=0,new Promise(async l=>{var M,P;if(!(K0!=null&&K0.inputs[0].shape))return;let c={},x=[[0,.1,.9,.9]];c.resize=J2.image.cropAndResize(e,x,[0],[K0.inputs[0].shape[2],K0.inputs[0].shape[1]]);let 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y={age:0};if((d=t.face.ssrnet)!=null&&d.enabled&&(i.age=I0.execute(i.enhance)),i.age){let f=await i.age.data();y.age=Math.trunc(10*f[0])/10}Object.keys(i).forEach(f=>r2.dispose(i[f])),Q2[n]=y,V1=o,D1=v(),x(y)}))}var v0=D(H());var J0,_2=[],q1=0,U1=0,Q5=Number.MAX_SAFE_INTEGER,_5=[.2989,.587,.114];async function Y1(e){var t;return k.initial&&(J0=null),J0?e.debug&&u("cached model:",J0.modelUrl):J0=await L((t=e.face.ssrnet)==null?void 0:t.modelPathGender),J0}async function $5(e,t,n,o){var s,a,l,c;if(!J0)return{gender:"unknown",genderScore:0};let r=Q5<(((s=t.face.ssrnet)==null?void 0:s.skipFrames)||0),A=(((a=t.face.ssrnet)==null?void 0:a.skipTime)||0)>v()-U1;return t.skipAllowed&&r&&A&&q1===o&&((l=_2[n])==null?void 0:l.gender)&&((c=_2[n])==null?void 0:c.genderScore)>0?(Q5++,_2[n]):(Q5=0,new Promise(async x=>{var f;if(!(J0!=null&&J0.inputs[0].shape))return;let 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A2=e=>[Math.abs(e.endPoint[0]-e.startPoint[0]),Math.abs(e.endPoint[1]-e.startPoint[1])],t5=e=>[e.startPoint[0]+(e.endPoint[0]-e.startPoint[0])/2,e.startPoint[1]+(e.endPoint[1]-e.startPoint[1])/2,1],n5=(e,t)=>e?[Math.trunc(Math.max(0,e.startPoint[0])),Math.trunc(Math.max(0,e.startPoint[1])),Math.trunc(Math.min(t.shape[2]||0,e.endPoint[0])-Math.max(0,e.startPoint[0])),Math.trunc(Math.min(t.shape[1]||0,e.endPoint[1])-Math.max(0,e.startPoint[1]))]:[0,0,0,0],o5=(e,t)=>e?[e.startPoint[0]/(t.shape[2]||0),e.startPoint[1]/(t.shape[1]||0),(e.endPoint[0]-e.startPoint[0])/(t.shape[2]||0),(e.endPoint[1]-e.startPoint[1])/(t.shape[1]||0)]:[0,0,0,0],n3=(e,t)=>{let n=[e.startPoint[0]*t[0],e.startPoint[1]*t[1]],o=[e.endPoint[0]*t[0],e.endPoint[1]*t[1]];return{startPoint:n,endPoint:o,landmarks:e.landmarks,confidence:e.confidence}},At=(e,t,n)=>{let o=t.shape[1],r=t.shape[2],A=[e.startPoint[1]/o,e.startPoint[0]/r,e.endPoint[1]/o,e.endPoint[0]/r],s=ye.image.cropAndResize(t,[A],[0],n),a=ye.div(s,B.tf255);return ye.dispose(s),a},r5=(e,t)=>{let n=t5(e),o=A2(e),r=[t*o[0]/2,t*o[1]/2];return{startPoint:[n[0]-r[0],n[1]-r[1]],endPoint:[n[0]+r[0],n[1]+r[1]],landmarks:e.landmarks,confidence:e.confidence}},A5=e=>{let t=t5(e),n=A2(e),o=Math.max(...n)/2;return{startPoint:[Math.round(t[0]-o),Math.round(t[1]-o)],endPoint:[Math.round(t[0]+o),Math.round(t[1]+o)],landmarks:e.landmarks,confidence:e.confidence}},o3=e=>{let t=e.map(o=>o[0]),n=e.map(o=>o[1]);return{startPoint:[Math.min(...t),Math.min(...n)],endPoint:[Math.max(...t),Math.max(...n)],landmarks:e}},st=[[1,0,0],[0,1,0],[0,0,1]],tA=e=>e-2*Math.PI*Math.floor((e+Math.PI)/(2*Math.PI)),nA=(e,t)=>tA(Math.PI/2-Math.atan2(-(t[1]-e[1]),t[0]-e[0]));var e3=(e,t)=>[[1,0,e],[0,1,t],[0,0,1]],De=(e,t)=>{let n=0;for(let o=0;o<e.length;o++)n+=e[o]*t[o];return n},oA=(e,t)=>{let n=[];for(let o=0;o<e.length;o++)n.push(e[o][t]);return n},t3=(e,t)=>{let n=[],o=e.length;for(let r=0;r<o;r++){n.push([]);for(let A=0;A<o;A++)n[r].push(De(e[r],oA(t,A)))}return n},r3=(e,t)=>{let n=Math.cos(e),o=Math.sin(e),r=[[n,-o,0],[o,n,0],[0,0,1]],A=e3(t[0],t[1]),s=t3(A,r),a=e3(-t[0],-t[1]);return t3(s,a)},rA=e=>{let t=[[e[0][0],e[1][0]],[e[0][1],e[1][1]]],n=[e[0][2],e[1][2]],o=[-De(t[0],n),-De(t[1],n)];return[t[0].concat(o[0]),t[1].concat(o[1]),[0,0,1]]},AA=(e,t)=>[De(e,t[0]),De(e,t[1])];function A3(e){let t=e===192?{strides:[4],anchors:[1]}:{strides:[e/16,e/8],anchors:[2,6]},n=[];for(let o=0;o<t.strides.length;o++){let r=t.strides[o],A=Math.floor((e+r-1)/r),s=Math.floor((e+r-1)/r),a=t.anchors[o];for(let l=0;l<A;l++){let c=r*(l+.5);for(let x=0;x<s;x++){let i=r*(x+.5);for(let y=0;y<a;y++)n.push([i,c])}}}return n}function s3(e,t,n,o,r){let A=A2(t),s=e.map(d=>[A[0]/r*(d[0]-r/2),A[1]/r*(d[1]-r/2),d[2]||0]),a=n&&n!==0&&Math.abs(n)>.2,l=a?r3(n,[0,0]):st,c=a?s.map(d=>[...AA(d,l),d[2]]):s,x=a?rA(o):st,i=t5(t),y=[De(i,x[0]),De(i,x[1])];return c.map(d=>[Math.trunc(d[0]+y[0]),Math.trunc(d[1]+y[1]),Math.trunc(d[2]||0)])}function a3(e,t,n,o){let r=t.landmarks.length>=nt.count?nt.symmetryLine:He.symmetryLine,A=0,s=st,a;if(e&&k.kernels.includes("rotatewithoffset"))if(A=nA(t.landmarks[r[0]],t.landmarks[r[1]]),A&&A!==0&&Math.abs(A)>.2){let c=t5(t),x=[c[0]/n.shape[2],c[1]/n.shape[1]],i=ye.image.rotateWithOffset(n,A,0,x);s=r3(-A,c),a=At(t,i,[o,o]),ye.dispose(i)}else a=At(t,n,[o,o]);else a=At(t,n,[o,o]);return[A,s,a]}var sA=e=>{let t=e.map(o=>o[0]),n=e.map(o=>o[1]);return[Math.min(...t)+(Math.max(...t)-Math.min(...t))/2,Math.min(...n)+(Math.max(...n)-Math.min(...n))/2]},i3=(e,t)=>{let n=sA(e),o=A2(t);return{startPoint:[n[0]-o[0]/2,n[1]-o[1]/2],endPoint:[n[0]+o[0]/2,n[1]+o[1]/2]}};var l3=6,aA=1.4,ee,c3=null,ke=0,N2=null,s2=()=>ke;async function d3(e){var t;return k.initial&&(ee=null),ee?e.debug&&u("cached model:",ee.modelUrl):ee=await L((t=e.face.detector)==null?void 0:t.modelPath),ke=ee.executor&&ee.inputs[0].shape?ee.inputs[0].shape[2]:256,N2=C.scalar(ke,"int32"),c3=C.tensor2d(A3(ke)),ee}function iA(e){let t={};t.boxStarts=C.slice(e,[0,1],[-1,2]),t.centers=C.add(t.boxStarts,c3),t.boxSizes=C.slice(e,[0,3],[-1,2]),t.boxSizesNormalized=C.div(t.boxSizes,N2),t.centersNormalized=C.div(t.centers,N2),t.halfBoxSize=C.div(t.boxSizesNormalized,B.tf2),t.starts=C.sub(t.centersNormalized,t.halfBoxSize),t.ends=C.add(t.centersNormalized,t.halfBoxSize),t.startNormalized=C.mul(t.starts,N2),t.endNormalized=C.mul(t.ends,N2);let n=C.concat2d([t.startNormalized,t.endNormalized],1);return Object.keys(t).forEach(o=>C.dispose(t[o])),n}async function x3(e,t){var a,l,c,x;if(!e||e.isDisposedInternal||e.shape.length!==4||e.shape[1]<1||e.shape[2]<1)return[];let n={};n.resized=C.image.resizeBilinear(e,[ke,ke]),n.div=C.div(n.resized,B.tf127),n.normalized=C.sub(n.div,B.tf05);let o=ee==null?void 0:ee.execute(n.normalized);if(Array.isArray(o)&&o.length>2){let i=o.sort((y,d)=>y.size-d.size);n.concat384=C.concat([i[0],i[2]],2),n.concat512=C.concat([i[1],i[3]],2),n.concat=C.concat([n.concat512,n.concat384],1),n.batch=C.squeeze(n.concat,0)}else Array.isArray(o)?n.batch=C.squeeze(o[0]):n.batch=C.squeeze(o);C.dispose(o),n.boxes=iA(n.batch),n.logits=C.slice(n.batch,[0,0],[-1,1]),n.sigmoid=C.sigmoid(n.logits),n.scores=C.squeeze(n.sigmoid),n.nms=await C.image.nonMaxSuppressionAsync(n.boxes,n.scores,((a=t.face.detector)==null?void 0:a.maxDetected)||0,((l=t.face.detector)==null?void 0:l.iouThreshold)||0,((c=t.face.detector)==null?void 0:c.minConfidence)||0);let r=await n.nms.array(),A=[],s=await n.scores.data();for(let i=0;i<r.length;i++){let y=s[r[i]];if(y>(((x=t.face.detector)==null?void 0:x.minConfidence)||0)){let d={};d.bbox=C.slice(n.boxes,[r[i],0],[1,-1]),d.slice=C.slice(n.batch,[r[i],l3-1],[1,-1]),d.squeeze=C.squeeze(d.slice),d.landmarks=C.reshape(d.squeeze,[l3,-1]);let f=await d.bbox.data(),p={startPoint:[f[0],f[1]],endPoint:[f[2],f[3]],landmarks:await d.landmarks.array(),confidence:y},g=n3(p,[(e.shape[2]||0)/ke,(e.shape[1]||0)/ke]),M=r5(g,t.face.scale||aA),P=A5(M);A.push(P),Object.keys(d).forEach(m=>C.dispose(d[m]))}}return Object.keys(n).forEach(i=>C.dispose(n[i])),A}var C0=D(H());var s5={};ve(s5,{connected:()=>lt,kpt:()=>it});var it=["nose","leftEyeInside","leftEye","leftEyeOutside","rightEyeInside","rightEye","rightEyeOutside","leftEar","rightEar","leftMouth","rightMouth","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftPinky","rightPinky","leftIndex","rightIndex","leftThumb","rightThumb","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle","leftHeel","rightHeel","leftFoot","rightFoot","bodyCenter","bodyTop","leftPalm","leftHand","rightPalm","rightHand"],lt={shoulders:["leftShoulder","rightShoulder"],hips:["rightHip","leftHip"],mouth:["leftMouth","rightMouth"],leftLegUpper:["leftHip","leftKnee"],leftLegLower:["leftKnee","leftAnkle"],leftFoot:["leftAnkle","leftHeel","leftFoot"],leftTorso:["leftShoulder","leftHip"],leftArmUpper:["leftShoulder","leftElbow"],leftArmLower:["leftElbow","leftWrist"],leftHand:["leftWrist","leftPalm"],leftHandPinky:["leftPalm","leftPinky"],leftHandIndex:["leftPalm","leftIndex"],leftHandThumb:["leftPalm","leftThumb"],leftEyeOutline:["leftEyeInside","leftEyeOutside"],rightLegUpper:["rightHip","rightKnee"],rightLegLower:["rightKnee","rightAnkle"],rightFoot:["rightAnkle","rightHeel","rightFoot"],rightTorso:["rightShoulder","rightHip"],rightArmUpper:["rightShoulder","rightElbow"],rightArmLower:["rightElbow","rightWrist"],rightHand:["rightWrist","rightPalm"],rightHandPinky:["rightPalm","rightPinky"],rightHandIndex:["rightPalm","rightIndex"],rightHandThumb:["rightPalm","rightThumb"],rightEyeOutline:["rightEyeInside","rightEyeOutside"]};var O0=D(H()),f3=224,lA,cA=5,a5=[8,16,32,32,32];function m3(){let e=[],t=0;for(;t<cA;){let n=0,o=t;for(;o<a5.length&&a5[o]===a5[t];)n+=2,o++;let r=a5[t],A=Math.ceil(f3/r),s=Math.ceil(f3/r);for(let a=0;a<A;++a)for(let l=0;l<s;++l)for(let c=0;c<n;++c)e.push({x:(l+.5)/s,y:(a+.5)/A});t=o}lA={x:O0.tensor1d(e.map(n=>n.x)),y:O0.tensor1d(e.map(n=>n.y))}}function fe(e,t=[1,1]){let n=[e.map(a=>a[0]),e.map(a=>a[1])],o=[Math.min(...n[0]),Math.min(...n[1])],r=[Math.max(...n[0]),Math.max(...n[1])],A=[o[0],o[1],r[0]-o[0],r[1]-o[1]],s=[A[0]/t[0],A[1]/t[1],A[2]/t[0],A[3]/t[1]];return{box:A,boxRaw:s}}function p3(e,t=[1,1]){let n=[e.map(c=>c[0]),e.map(c=>c[1])],o=[Math.min(...n[0]),Math.min(...n[1])],r=[Math.max(...n[0]),Math.max(...n[1])],A=[(o[0]+r[0])/2,(o[1]+r[1])/2],s=Math.max(A[0]-o[0],A[1]-o[1],-A[0]+r[0],-A[1]+r[1]),a=[Math.trunc(A[0]-s),Math.trunc(A[1]-s),Math.trunc(2*s),Math.trunc(2*s)],l=[a[0]/t[0],a[1]/t[1],a[2]/t[0],a[3]/t[1]];return{box:a,boxRaw:l}}function i5(e,t){let n=[e[2]*t,e[3]*t];return[e[0]-(n[0]-e[2])/2,e[1]-(n[1]-e[3])/2,n[0],n[1]]}var b3={initial:!0},P0={detector:null,landmarks:null},a2={detector:[224,224],landmarks:[256,256]},ct=Number.MAX_SAFE_INTEGER,xA={landmarks:["ld_3d","activation_segmentation","activation_heatmap","world_3d","output_poseflag"],detector:[]},c5=null,I2,we=[[0,0],[0,0],[0,0],[0,0]],u3=0,h3=e=>1-1/(1+Math.exp(e));async function g3(e){var t;if(b3.initial&&(P0.detector=null),!P0.detector&&e.body.detector&&e.body.detector.modelPath){P0.detector=await L(e.body.detector.modelPath);let n=(t=P0.detector)!=null&&t.executor?Object.values(P0.detector.modelSignature.inputs):void 0;a2.detector[0]=Array.isArray(n)?parseInt(n[0].tensorShape.dim[1].size):0,a2.detector[1]=Array.isArray(n)?parseInt(n[0].tensorShape.dim[2].size):0}else e.debug&&P0.detector&&u("cached model:",P0.detector.modelUrl);return m3(),P0.detector}async function M3(e){var t;if(b3.initial&&(P0.landmarks=null),P0.landmarks)e.debug&&u("cached model:",P0.landmarks.modelUrl);else{P0.landmarks=await L(e.body.modelPath);let n=(t=P0.landmarks)!=null&&t.executor?Object.values(P0.landmarks.modelSignature.inputs):void 0;a2.landmarks[0]=Array.isArray(n)?parseInt(n[0].tensorShape.dim[1].size):0,a2.landmarks[1]=Array.isArray(n)?parseInt(n[0].tensorShape.dim[2].size):0}return P0.landmarks}function yA(e,t){var r,A;let n={};if(!((r=e==null?void 0:e.shape)!=null&&r[1])||!((A=e==null?void 0:e.shape)!=null&&A[2]))return e;let o;if(I2&&(n.cropped=C0.image.cropAndResize(e,[I2],[0],[e.shape[1],e.shape[2]])),e.shape[1]!==e.shape[2]){let s=[e.shape[2]>e.shape[1]?Math.trunc((e.shape[2]-e.shape[1])/2):0,e.shape[2]>e.shape[1]?Math.trunc((e.shape[2]-e.shape[1])/2):0],a=[e.shape[1]>e.shape[2]?Math.trunc((e.shape[1]-e.shape[2])/2):0,e.shape[1]>e.shape[2]?Math.trunc((e.shape[1]-e.shape[2])/2):0];we=[[0,0],s,a,[0,0]],n.pad=C0.pad(n.cropped||e,we),n.resize=C0.image.resizeBilinear(n.pad,[t,t]),o=C0.div(n.resize,B.tf255)}else e.shape[1]!==t?(n.resize=C0.image.resizeBilinear(n.cropped||e,[t,t]),o=C0.div(n.resize,B.tf255)):o=C0.div(n.cropped||e,B.tf255);return Object.keys(n).forEach(s=>C0.dispose(n[s])),o}function fA(e,t){for(let n of e)n.position=[Math.trunc(n.position[0]*(t[0]+we[2][0]+we[2][1])/t[0]-we[2][0]),Math.trunc(n.position[1]*(t[1]+we[1][0]+we[1][1])/t[1]-we[1][0]),n.position[2]],n.positionRaw=[n.position[0]/t[0],n.position[1]/t[1],2*n.position[2]/(t[0]+t[1])];if(I2)for(let n of e)n.positionRaw=[n.positionRaw[0]+I2[1],n.positionRaw[1]+I2[0],n.positionRaw[2]],n.position=[Math.trunc(n.positionRaw[0]*t[0]),Math.trunc(n.positionRaw[1]*t[1]),n.positionRaw[2]];return e}function mA(e){let t=e.find(a=>a.part==="leftPalm"),n=e.find(a=>a.part==="leftWrist"),o=e.find(a=>a.part==="leftIndex");t.position[2]=((n.position[2]||0)+(o.position[2]||0))/2;let r=e.find(a=>a.part==="rightPalm"),A=e.find(a=>a.part==="rightWrist"),s=e.find(a=>a.part==="rightIndex");r.position[2]=((A.position[2]||0)+(s.position[2]||0))/2}async function pA(e,t,n){var f,p;if(!((f=P0.landmarks)!=null&&f.executor))return null;let o={};[o.ld,o.segmentation,o.heatmap,o.world,o.poseflag]=(p=P0.landmarks)==null?void 0:p.execute(e,xA.landmarks);let r=(await o.poseflag.data())[0],A=await o.ld.data(),s=await o.world.data();Object.keys(o).forEach(g=>C0.dispose(o[g]));let a=[],l=5;for(let g=0;g<A.length/l;g++){let M=h3(A[l*g+3]),P=h3(A[l*g+4]),m=Math.trunc(100*M*P*r)/100,b=[A[l*g+0]/a2.landmarks[0],A[l*g+1]/a2.landmarks[1],A[l*g+2]+0],z=[Math.trunc(n[0]*b[0]),Math.trunc(n[1]*b[1]),b[2]],w=[s[l*g+0],s[l*g+1],s[l*g+2]+0];a.push({part:it[g],positionRaw:b,position:z,distance:w,score:m})}if(r<(t.body.minConfidence||0))return null;mA(a);let c=fA(a,n),x=c.map(g=>g.position),i=fe(x,[n[0],n[1]]),y={};for(let[g,M]of Object.entries(lt)){let P=[];for(let m=0;m<M.length-1;m++){let b=c.find(w=>w.part===M[m]),z=c.find(w=>w.part===M[m+1]);b&&z&&P.push([b.position,z.position])}y[g]=P}return{id:0,score:Math.trunc(100*r)/100,box:i.box,boxRaw:i.boxRaw,keypoints:c,annotations:y}}async function dt(e,t){let n=[e.shape[2]||0,e.shape[1]||0],o=(t.body.skipTime||0)>v()-u3,r=ct<(t.body.skipFrames||0);if(t.skipAllowed&&o&&r&&c5!==null)ct++;else{let A={};A.landmarks=yA(e,256),c5=await pA(A.landmarks,t,n),Object.keys(A).forEach(s=>C0.dispose(A[s])),u3=v(),ct=0}return c5?[c5]:[]}var w0=D(H());var i2=[{class:1,label:"person"},{class:2,label:"bicycle"},{class:3,label:"car"},{class:4,label:"motorcycle"},{class:5,label:"airplane"},{class:6,label:"bus"},{class:7,label:"train"},{class:8,label:"truck"},{class:9,label:"boat"},{class:10,label:"traffic light"},{class:11,label:"fire hydrant"},{class:12,label:"stop sign"},{class:13,label:"parking 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drier"},{class:80,label:"toothbrush"}];var N0,Ze=0,xt=[],P3=0,yt=Number.MAX_SAFE_INTEGER;async function T3(e){if(k.initial&&(N0=null),N0)e.debug&&u("cached model:",N0.modelUrl);else{N0=await L(e.object.modelPath);let t=N0!=null&&N0.executor?Object.values(N0.modelSignature.inputs):void 0;Ze=Array.isArray(t)?parseInt(t[0].tensorShape.dim[2].size):0}return N0}async function uA(e,t,n){if(!e)return[];let o={},r=[],A=await e.array();o.squeeze=w0.squeeze(e);let s=w0.split(o.squeeze,6,1);o.stack=w0.stack([s[1],s[0],s[3],s[2]],1),o.boxes=w0.squeeze(o.stack),o.scores=w0.squeeze(s[4]),o.classes=w0.squeeze(s[5]),w0.dispose([e,...s]),o.nms=await w0.image.nonMaxSuppressionAsync(o.boxes,o.scores,n.object.maxDetected,n.object.iouThreshold,n.object.minConfidence||0);let a=await o.nms.data(),l=0;for(let c of Array.from(a)){let x=Math.trunc(100*A[0][c][4])/100,i=A[0][c][5];if(Number.isNaN(i))continue;let y=i2[i].label,[d,f]=[A[0][c][0]/Ze,A[0][c][1]/Ze],p=[d,f,A[0][c][2]/Ze-d,A[0][c][3]/Ze-f],g=[Math.trunc(p[0]*t[0]),Math.trunc(p[1]*t[1]),Math.trunc(p[2]*t[0]),Math.trunc(p[3]*t[1])];r.push({id:l++,score:x,class:i,label:y,box:g,boxRaw:p})}return Object.keys(o).forEach(c=>w0.dispose(o[c])),r}async function ft(e,t){if(!(N0!=null&&N0.executor))return[];let n=(t.object.skipTime||0)>v()-P3,o=yt<(t.object.skipFrames||0);return t.skipAllowed&&n&&o&&xt.length>0?(yt++,xt):(yt=0,new Promise(async r=>{let A=[e.shape[2]||0,e.shape[1]||0],s=w0.image.resizeBilinear(e,[Ze,Ze]),a=t.object.enabled?N0==null?void 0:N0.execute(s,["tower_0/detections"]):null;P3=v(),w0.dispose(s);let l=await uA(a,A,t);xt=l,r(l)}))}var K=D(H());var d5={};ve(d5,{connected:()=>pt,kpt:()=>mt});var mt=["head","neck","rightShoulder","rightElbow","rightWrist","chest","leftShoulder","leftElbow","leftWrist","bodyCenter","rightHip","rightKnee","rightAnkle","leftHip","leftKnee","leftAnkle"],pt={leftLeg:["leftHip","leftKnee","leftAnkle"],rightLeg:["rightHip","rightKnee","rightAnkle"],torso:["leftShoulder","rightShoulder","rightHip","leftHip","leftShoulder"],leftArm:["leftShoulder","leftElbow","leftWrist"],rightArm:["rightShoulder","rightElbow","rightWrist"],head:[]};var u0,k3=0,E0={id:0,keypoints:[],box:[0,0,0,0],boxRaw:[0,0,0,0],score:0,annotations:{}},ut=Number.MAX_SAFE_INTEGER;async function w3(e){return k.initial&&(u0=null),u0?e.debug&&u("cached model:",u0.modelUrl):u0=await L(e.body.modelPath),u0}async function hA(e,t){let[n,o]=e.shape,r=K.reshape(e,[o*n]),A=K.max(r,0),s=(await A.data())[0];if(s>t){let a=K.argMax(r,0),l=K.mod(a,n),c=(await l.data())[0],x=K.div(a,n),i=(await x.data())[0];return K.dispose([r,A,a,l,x]),[c,i,s]}return K.dispose([r,A]),[0,0,s]}async function ht(e,t){if(!(u0!=null&&u0.executor))return[];let n=(t.body.skipTime||0)>v()-k3,o=ut<(t.body.skipFrames||0);return t.skipAllowed&&n&&o&&Object.keys(E0.keypoints).length>0?(ut++,[E0]):(ut=0,new Promise(async r=>{let A=K.tidy(()=>{if(!(u0!=null&&u0.inputs[0].shape))return null;let i=K.image.resizeBilinear(e,[u0.inputs[0].shape[2],u0.inputs[0].shape[1]],!1),y=K.mul(i,B.tf2);return K.sub(y,B.tf1)}),s;if(t.body.enabled&&(s=u0==null?void 0:u0.execute(A)),k3=v(),K.dispose(A),s){E0.keypoints.length=0;let i=K.squeeze(s);K.dispose(s);let y=K.unstack(i,2);K.dispose(i);for(let d=0;d<y.length;d++){let[f,p,g]=await hA(y[d],t.body.minConfidence);g>(t.body.minConfidence||0)&&E0.keypoints.push({score:Math.round(100*g)/100,part:mt[d],positionRaw:[f/u0.inputs[0].shape[2],p/u0.inputs[0].shape[1]],position:[Math.round(e.shape[2]*f/u0.inputs[0].shape[2]),Math.round(e.shape[1]*p/u0.inputs[0].shape[1])]})}y.forEach(d=>K.dispose(d))}E0.score=E0.keypoints.reduce((i,y)=>y.score>i?y.score:i,0);let a=E0.keypoints.map(i=>i.position[0]),l=E0.keypoints.map(i=>i.position[1]);E0.box=[Math.min(...a),Math.min(...l),Math.max(...a)-Math.min(...a),Math.max(...l)-Math.min(...l)];let c=E0.keypoints.map(i=>i.positionRaw[0]),x=E0.keypoints.map(i=>i.positionRaw[1]);E0.boxRaw=[Math.min(...c),Math.min(...x),Math.max(...c)-Math.min(...c),Math.max(...x)-Math.min(...x)];for(let[i,y]of Object.entries(pt)){let d=[];for(let f=0;f<y.length-1;f++){let p=E0.keypoints.find(M=>M.part===y[f]),g=E0.keypoints.find(M=>M.part===y[f+1]);p&&g&&p.score>(t.body.minConfidence||0)&&g.score>(t.body.minConfidence||0)&&d.push([p.position,g.position])}E0.annotations[i]=d}r([E0])}))}var te=D(H());var bA=["angry","disgust","fear","happy","sad","surprise","neutral"],Z0,x5=[],z3=0,S3=0,bt=Number.MAX_SAFE_INTEGER;async function j3(e){var t;return k.initial&&(Z0=null),Z0?e.debug&&u("cached model:",Z0.modelUrl):Z0=await L((t=e.face.emotion)==null?void 0:t.modelPath),Z0}async function gt(e,t,n,o){var s,a;if(!Z0)return[];let r=bt<(((s=t.face.emotion)==null?void 0:s.skipFrames)||0),A=(((a=t.face.emotion)==null?void 0:a.skipTime)||0)>v()-S3;return t.skipAllowed&&A&&r&&z3===o&&x5[n]&&x5[n].length>0?(bt++,x5[n]):(bt=0,new Promise(async l=>{var x;let c=[];if((x=t.face.emotion)!=null&&x.enabled){let i={},y=Z0!=null&&Z0.inputs[0].shape?Z0.inputs[0].shape[2]:0;i.resize=te.image.resizeBilinear(e,[y,y],!1),i.channels=te.mul(i.resize,B.rgb),i.grayscale=te.sum(i.channels,3,!0),i.grayscaleSub=te.sub(i.grayscale,B.tf05),i.grayscaleMul=te.mul(i.grayscaleSub,B.tf2),i.emotion=Z0==null?void 0:Z0.execute(i.grayscaleMul),S3=v();let d=await i.emotion.data();for(let f=0;f<d.length;f++)d[f]>(t.face.emotion.minConfidence||0)&&c.push({score:Math.min(.99,Math.trunc(100*d[f])/100),emotion:bA[f]});c.sort((f,p)=>p.score-f.score),Object.keys(i).forEach(f=>te.dispose(i[f]))}x5[n]=c,z3=o,l(c)}))}var y5=D(H());var L0,Mt=[],I3=0,O3=0,C3=Number.MAX_SAFE_INTEGER;async function L3(e){var t;return k.initial&&(L0=null),L0?e.debug&&u("cached 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r=B3<(((s=t.face.insightface)==null?void 0:s.skipFrames)||0),A=(((a=t.face.insightface)==null?void 0:a.skipTime)||0)>v()-G3;return t.skipAllowed&&A&&r&&F3===o&&Pt[n]?(B3++,Pt[n]):new Promise(async l=>{var x;let c=[];if(((x=t.face.insightface)==null?void 0:x.enabled)&&(W0==null?void 0:W0.inputs[0].shape)){let i={};i.crop=f5.image.resizeBilinear(e,[W0.inputs[0].shape[2],W0.inputs[0].shape[1]],!1),i.data=W0.execute(i.crop);let y=await i.data.data();c=Array.from(y),Object.keys(i).forEach(d=>f5.dispose(i[d]))}Pt[n]=c,F3=o,G3=v(),l(c)})}var Se=D(H());var ne=D(H());var F0,Ee=0,gA=2.3,Rt=Q0.leftEyeLower0,kt=Q0.rightEyeLower0,l2={leftBounds:[Rt[0],Rt[Rt.length-1]],rightBounds:[kt[0],kt[kt.length-1]]},c2={upperCenter:3,lowerCenter:4,index:71,numCoordinates:76};async function q3(e){var t,n;return k.initial&&(F0=null),F0?e.debug&&u("cached model:",F0.modelUrl):F0=await L((t=e.face.iris)==null?void 0:t.modelPath),Ee=(F0==null?void 0:F0.executor)&&((n=F0.inputs)==null?void 0:n[0].shape)?F0.inputs[0].shape[2]:0,Ee===-1&&(Ee=64),F0}function m5(e,t,n,o){for(let r=0;r<ot.length;r++){let{key:A,indices:s}=ot[r],a=Q0[`${n}${A}`];if(!o||o.includes(A))for(let l=0;l<s.length;l++){let c=s[l];e[a[l]]=[t[c][0],t[c][1],(t[c][2]+e[a[l]][2])/2]}}}var MA=e=>{let t=e[l2.leftBounds[0]][2],n=e[l2.rightBounds[0]][2];return t-n},D3=(e,t,n,o,r,A=!1)=>{let s=A5(r5(o3([e[n],e[o]]),gA)),a=A2(s),l=ne.image.cropAndResize(t,[[s.startPoint[1]/r,s.startPoint[0]/r,s.endPoint[1]/r,s.endPoint[0]/r]],[0],[Ee,Ee]);if(A&&k.kernels.includes("flipleftright")){let c=ne.image.flipLeftRight(l);ne.dispose(l),l=c}return{box:s,boxSize:a,crop:l}},Z3=(e,t,n,o=!1)=>{let r=[];for(let A=0;A<c2.numCoordinates;A++){let s=e[A*3],a=e[A*3+1],l=e[A*3+2];r.push([(o?1-s/Ee:s/Ee)*n[0]+t.startPoint[0],a/Ee*n[1]+t.startPoint[1],l])}return{rawCoords:r,iris:r.slice(c2.index)}},X3=(e,t,n)=>{let o=e[Q0[`${n}EyeUpper0`][c2.upperCenter]][2],r=e[Q0[`${n}EyeLower0`][c2.lowerCenter]][2],A=(o+r)/2;return t.map((s,a)=>{let 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vA=[[61,146],[146,91],[91,181],[181,84],[84,17],[17,314],[314,405],[405,321],[321,375],[375,291],[61,185],[185,40],[40,39],[39,37],[37,0],[0,267],[267,269],[269,270],[270,409],[409,291],[78,95],[95,88],[88,178],[178,87],[87,14],[14,317],[317,402],[402,318],[318,324],[324,308],[78,191],[191,80],[80,81],[81,82],[82,13],[13,312],[312,311],[311,310],[310,415],[415,308]],PA=[[263,249],[249,390],[390,373],[373,374],[374,380],[380,381],[381,382],[382,362],[263,466],[466,388],[388,387],[387,386],[386,385],[385,384],[384,398],[398,362]],TA=[[276,283],[283,282],[282,295],[295,285],[300,293],[293,334],[334,296],[296,336]],RA=[[474,475],[475,476],[476,477],[477,474]],kA=[[33,7],[7,163],[163,144],[144,145],[145,153],[153,154],[154,155],[155,133],[33,246],[246,161],[161,160],[160,159],[159,158],[158,157],[157,173],[173,133]],wA=[[46,53],[53,52],[52,65],[65,55],[70,63],[63,105],[105,66],[66,107]],EA=[[469,470],[470,471],[471,472],[472,469]],zA=[[10,338],[338,297],[297,332],[332,284],[284,251],[251,389],[389,356],[356,454],[454,323],[323,361],[361,288],[288,397],[397,365],[365,379],[379,378],[378,400],[400,377],[377,152],[152,148],[148,176],[176,149],[149,150],[150,136],[136,172],[172,58],[58,132],[132,93],[93,234],[234,127],[127,162],[162,21],[21,54],[54,103],[103,67],[67,109],[109,10]];function 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0:s.data()),irisL:await((l=(a=t.filter(p=>p.size===10))==null?void 0:a[0])==null?void 0:l.data()),eyeL:await((x=(c=t.filter(p=>p.size===142))==null?void 0:c[0])==null?void 0:x.data()),irisR:await((y=(i=t.filter(p=>p.size===10))==null?void 0:i[1])==null?void 0:y.data()),eyeR:await((f=(d=t.filter(p=>p.size===142))==null?void 0:d[1])==null?void 0:f.data())};for(let p of Object.values(n))if(!p)return e;let o=Xe.reduce((p,g)=>p+=e[g][2],0)/Xe.length;for(let p=0;p<n.irisL.length/2;p++)e.push([n.irisL[2*p+0],n.irisL[2*p+1],o]);let r=qe.reduce((p,g)=>p+=e[g][2],0)/qe.length;for(let p=0;p<n.irisR.length/2;p++)e.push([n.irisR[2*p+0],n.irisR[2*p+1],r]);for(let p=0;p<n.eyeL.length/2;p++)e[Xe[p]]=[n.eyeL[2*p+0],n.eyeL[2*p+1],e[Xe[p]][2]];for(let p=0;p<n.eyeR.length/2;p++)e[qe[p]]=[n.eyeR[2*p+0],n.eyeR[2*p+1],e[qe[p]][2]];for(let p=0;p<n.lips.length/2;p++)e[O2[p]]=[n.lips[2*p+0],n.lips[2*p+1],e[O2[p]][2]];return e}var le={boxes:[],skipped:Number.MAX_SAFE_INTEGER,timestamp:0},t0=null,C2=0;async function Q3(e,t){var l,c,x,i,y,d,f,p,g,M;if(!(t0!=null&&t0.executor))return[];let n=(((l=t.face.detector)==null?void 0:l.skipTime)||0)>v()-le.timestamp,o=le.skipped<(((c=t.face.detector)==null?void 0:c.skipFrames)||0);!t.skipAllowed||!n||!o||le.boxes.length===0?(le.boxes=await x3(e,t),le.timestamp=v(),le.skipped=0):le.skipped++;let r=[],A=[],s=0,a=C2;for(let P=0;P<le.boxes.length;P++){let m=le.boxes[P],b=0,z,w={id:s++,mesh:[],meshRaw:[],box:[0,0,0,0],boxRaw:[0,0,0,0],score:0,boxScore:0,faceScore:0,annotations:{}};if([b,z,w.tensor]=a3((x=t.face.detector)==null?void 0:x.rotation,m,e,(i=t.face.mesh)!=null&&i.enabled?C2:s2()),t.filter.equalization){let I=w.tensor?await q2(w.tensor):void 0;Se.dispose(w.tensor),I&&(w.tensor=I)}if(w.boxScore=Math.round(100*m.confidence)/100,(y=t.face.mesh)!=null&&y.enabled)if(!t0)t.debug&&u("face mesh detection requested, but model is not loaded");else{if(((d=t.face.attention)==null?void 0:d.enabled)&&!k.kernels.includes("atan2"))return 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o=W.concat2d([n.startPoints,n.endPoints],1);return Object.keys(n).forEach(r=>W.dispose(n[r])),o}normalizeLandmarks(t,n){let o={};o.reshape=W.reshape(t,[-1,7,2]),o.div=W.div(o.reshape,this.inputSizeTensor),o.landmarks=W.add(o.div,this.anchors[n]?this.anchors[n]:0);let r=W.mul(o.landmarks,this.inputSizeTensor);return Object.keys(o).forEach(A=>W.dispose(o[A])),r}async predict(t,n){var a;let o={};o.resize=W.image.resizeBilinear(t,[this.inputSize,this.inputSize]),o.div=W.div(o.resize,B.tf127),o.image=W.sub(o.div,B.tf1),o.batched=this.model.execute(o.image),o.predictions=W.squeeze(o.batched),o.slice=W.slice(o.predictions,[0,0],[-1,1]),o.sigmoid=W.sigmoid(o.slice),o.scores=W.squeeze(o.sigmoid);let r=await o.scores.data();o.boxes=W.slice(o.predictions,[0,1],[-1,4]),o.norm=this.normalizeBoxes(o.boxes),o.nms=await W.image.nonMaxSuppressionAsync(o.norm,o.scores,3*(((a=n.hand)==null?void 0:a.maxDetected)||1),n.hand.iouThreshold,n.hand.minConfidence);let A=await o.nms.array(),s=[];for(let l of A){let c={};c.box=W.slice(o.norm,[l,0],[1,-1]),c.slice=W.slice(o.predictions,[l,5],[1,14]),c.norm=this.normalizeLandmarks(c.slice,l),c.palmLandmarks=W.reshape(c.norm,[-1,2]);let x=await c.box.data(),i=x.slice(0,2),y=x.slice(2,4),d=await c.palmLandmarks.array(),f={startPoint:i,endPoint:y,palmLandmarks:d,confidence:r[l]},p=ln(f,[(t.shape[2]||1)/this.inputSize,(t.shape[1]||0)/this.inputSize]);s.push(p),Object.keys(c).forEach(g=>W.dispose(c[g]))}return Object.keys(o).forEach(l=>W.dispose(o[l])),s}};var G0=D(H());var WA=5,fn=1.65,mn=[0,5,9,13,17,1,2],FA=0,GA=2,pn=0,g5=class{constructor(t,n){R(this,"handDetector");R(this,"handPoseModel");R(this,"inputSize");R(this,"storedBoxes");R(this,"skipped");R(this,"detectedHands");var o,r,A;this.handDetector=t,this.handPoseModel=n,this.inputSize=((A=(r=(o=this.handPoseModel)==null?void 0:o.inputs)==null?void 0:r[0].shape)==null?void 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o=Object.keys(S.engine().registryFactory);if(e.config.backend==="humangl"&&!o.includes("humangl")&&(En(e),o=Object.keys(S.engine().registryFactory)),e.config.debug&&u("available backends:",o),o.includes(e.config.backend)||(u(`error: backend ${e.config.backend} not found in registry`),e.config.backend=k.node?"tensorflow":"webgl",e.config.debug&&u(`override: setting backend ${e.config.backend}`)),e.config.debug&&u("setting backend:",[e.config.backend]),e.config.backend==="wasm"){if(S.env().flagRegistry.CANVAS2D_WILL_READ_FREQUENTLY&&S.env().set("CANVAS2D_WILL_READ_FREQUENTLY",!0),e.config.debug&&u("wasm path:",e.config.wasmPath),typeof S.setWasmPaths!="undefined")S.setWasmPaths(e.config.wasmPath,e.config.wasmPlatformFetch);else throw new Error("backend error: attempting to use wasm backend but wasm path is not set");let r=!1,A=!1;try{r=await S.env().getAsync("WASM_HAS_MULTITHREAD_SUPPORT"),A=await S.env().getAsync("WASM_HAS_SIMD_SUPPORT"),e.config.debug&&u(`wasm execution: ${A?"simd":"no 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Object.keys(o))Sn[A]!==o[A]&&(r[A]=o[A]);e.config.debug&&Object.keys(r).length>0&&u("backend:",S.getBackend(),"flags:",r)}if(e.config.flags&&Object.keys(e.config.flags).length>0){e.config.debug&&u("flags:",e.config.flags);for(let[o,r]of Object.entries(e.config.flags))S.env().set(o,r)}S.enableProdMode(),H1(),e.performance.initBackend=Math.trunc(v()-n),e.config.backend=S.getBackend(),await k.updateBackend(),KA(e.config),k.initial=!1}return!0}function P5(e,t){for(let n of e){let o={kernelName:n,backendName:t.backend,kernelFunc:()=>{t.debug&&u("kernelFunc",n,t.backend)}};S.registerKernel(o)}k.kernels=S.getKernelsForBackend(S.getBackend()).map(n=>n.kernelName.toLowerCase())}var l0=[null,null],JA=["StatefulPartitionedCall/Postprocessor/Slice","StatefulPartitionedCall/Postprocessor/ExpandDims_1"],Fe=[[0,0],[0,0]],QA=["hand","fist","pinch","point","face","tip","pinchtip"],Nn=4,In=1.6,_A=512,$A=1.4,T5=Number.MAX_SAFE_INTEGER,Ft=0,ue=[0,0],i0={boxes:[],hands:[]},On={thumb:[1,2,3,4],index:[5,6,7,8],middle:[9,10,11,12],ring:[13,14,15,16],pinky:[17,18,19,20],base:[0],palm:[0,17,13,9,5,1,0]};async function Cn(e){var t;if(k.initial&&(l0[0]=null),l0[0])e.debug&&u("cached model:",l0[0].modelUrl);else{P5(["tensorlistreserve","enter","tensorlistfromtensor","merge","loopcond","switch","exit","tensorliststack","nextiteration","tensorlistsetitem","tensorlistgetitem","reciprocal","shape","split","where"],e),l0[0]=await L((t=e.hand.detector)==null?void 0:t.modelPath);let n=l0[0].executor?Object.values(l0[0].modelSignature.inputs):void 0;Fe[0][0]=Array.isArray(n)?parseInt(n[0].tensorShape.dim[1].size):0,Fe[0][1]=Array.isArray(n)?parseInt(n[0].tensorShape.dim[2].size):0}return l0[0]}async function Ln(e){var t;if(k.initial&&(l0[1]=null),l0[1])e.debug&&u("cached model:",l0[1].modelUrl);else{l0[1]=await L((t=e.hand.skeleton)==null?void 0:t.modelPath);let n=l0[1].executor?Object.values(l0[1].modelSignature.inputs):void 0;Fe[1][0]=Array.isArray(n)?parseInt(n[0].tensorShape.dim[1].size):0,Fe[1][1]=Array.isArray(n)?parseInt(n[0].tensorShape.dim[2].size):0}return l0[1]}async function es(e,t){let n=[];if(!e||!l0[0])return n;let o={},r=(e.shape[2]||1)/(e.shape[1]||1),A=Math.min(Math.round((e.shape[1]||0)/8)*8,_A),s=Math.round(A*r/8)*8;o.resize=J.image.resizeBilinear(e,[A,s]),o.cast=J.cast(o.resize,"int32"),[o.rawScores,o.rawBoxes]=await l0[0].executeAsync(o.cast,JA),o.boxes=J.squeeze(o.rawBoxes,[0,2]),o.scores=J.squeeze(o.rawScores,[0]);let a=J.unstack(o.scores,1);J.dispose(a[Nn]),a.splice(Nn,1),o.filtered=J.stack(a,1),J.dispose(a),o.max=J.max(o.filtered,1),o.argmax=J.argMax(o.filtered,1);let l=0;o.nms=await J.image.nonMaxSuppressionAsync(o.boxes,o.max,(t.hand.maxDetected||0)+1,t.hand.iouThreshold||0,t.hand.minConfidence||1);let c=await o.nms.data(),x=await o.max.data(),i=await o.argmax.data();for(let y of Array.from(c)){let d=J.slice(o.boxes,y,1),f=await d.data();J.dispose(d);let p=[f[1],f[0],f[3]-f[1],f[2]-f[0]],g=i5(p,$A),M=[Math.trunc(p[0]*ue[0]),Math.trunc(p[1]*ue[1]),Math.trunc(p[2]*ue[0]),Math.trunc(p[3]*ue[1])],P=x[y],m=QA[i[y]],b={id:l++,score:P,box:M,boxRaw:g,label:m};n.push(b)}return Object.keys(o).forEach(y=>J.dispose(o[y])),n.sort((y,d)=>d.score-y.score),n.length>(t.hand.maxDetected||1)&&(n.length=t.hand.maxDetected||1),n}async function Gt(e,t,n){let o={id:t.id,score:Math.round(100*t.score)/100,boxScore:Math.round(100*t.score)/100,fingerScore:0,box:t.box,boxRaw:t.boxRaw,label:t.label,keypoints:[],landmarks:{},annotations:{}};if(e&&l0[1]&&n.hand.landmarks&&t.score>(n.hand.minConfidence||0)){let r={},A=[t.boxRaw[1],t.boxRaw[0],t.boxRaw[3]+t.boxRaw[1],t.boxRaw[2]+t.boxRaw[0]];r.crop=J.image.cropAndResize(e,[A],[0],[Fe[1][0],Fe[1][1]],"bilinear"),r.div=J.div(r.crop,B.tf255),[r.score,r.keypoints]=l0[1].execute(r.div,["Identity_1","Identity"]);let s=(await r.score.data())[0],a=(100-Math.trunc(100/(1+Math.exp(s))))/100;if(a>=(n.hand.minConfidence||0)){o.fingerScore=a,r.reshaped=J.reshape(r.keypoints,[-1,3]);let x=(await r.reshaped.array()).map(i=>[i[0]/Fe[1][1],i[1]/Fe[1][0],i[2]||0]).map(i=>[i[0]*t.boxRaw[2],i[1]*t.boxRaw[3],i[2]||0]);o.keypoints=x.map(i=>[ue[0]*(i[0]+t.boxRaw[0]),ue[1]*(i[1]+t.boxRaw[1]),i[2]||0]),o.landmarks=M5(o.keypoints);for(let i of Object.keys(On))o.annotations[i]=On[i].map(y=>o.landmarks&&o.keypoints[y]?o.keypoints[y]:null)}Object.keys(r).forEach(l=>J.dispose(r[l]))}return o}async function Bt(e,t){var r,A;if(!((r=l0[0])!=null&&r.executor)||!((A=l0[1])!=null&&A.executor)||!l0[0].inputs[0].shape||!l0[1].inputs[0].shape)return[];ue=[e.shape[2]||0,e.shape[1]||0],T5++;let n=(t.hand.skipTime||0)>v()-Ft,o=T5<(t.hand.skipFrames||0);return t.skipAllowed&&n&&o?i0.hands:new Promise(async s=>{let a=3*(t.hand.skipTime||0)>v()-Ft,l=T5<3*(t.hand.skipFrames||0);t.skipAllowed&&i0.hands.length===t.hand.maxDetected?i0.hands=await Promise.all(i0.boxes.map(x=>Gt(e,x,t))):t.skipAllowed&&a&&l&&i0.hands.length>0?i0.hands=await Promise.all(i0.boxes.map(x=>Gt(e,x,t))):(i0.boxes=await es(e,t),Ft=v(),i0.hands=await Promise.all(i0.boxes.map(x=>Gt(e,x,t))),T5=0);let c=[...i0.boxes];if(i0.boxes.length=0,t.cacheSensitivity>0)for(let x=0;x<i0.hands.length;x++){let i=p3(i0.hands[x].keypoints,ue);if(i.box[2]/(e.shape[2]||1)>.05&&i.box[3]/(e.shape[1]||1)>.05&&i0.hands[x].fingerScore&&i0.hands[x].fingerScore>(t.hand.minConfidence||0)){let y=i5(i.box,In),d=i5(i.boxRaw,In);i0.boxes.push({...c[x],box:y,boxRaw:d})}}for(let x=0;x<i0.hands.length;x++){let i=fe(i0.hands[x].keypoints,ue);i0.hands[x].box=i.box,i0.hands[x].boxRaw=i.boxRaw}s(i0.hands)})}var k5=D(H());var g0,R5=[],Ht=Number.MAX_SAFE_INTEGER,Fn=0,Gn=0;async function Bn(e){var t;return k.initial&&(g0=null),g0?e.debug&&u("cached model:",g0.modelUrl):g0=await L((t=e.face.liveness)==null?void 0:t.modelPath),g0}async function Vt(e,t,n,o){var s,a;if(!(g0!=null&&g0.executor))return 0;let r=(((s=t.face.liveness)==null?void 0:s.skipTime)||0)>v()-Gn,A=Ht<(((a=t.face.liveness)==null?void 0:a.skipFrames)||0);return t.skipAllowed&&r&&A&&Fn===o&&R5[n]?(Ht++,R5[n]):(Ht=0,new Promise(async l=>{let c=k5.image.resizeBilinear(e,[g0!=null&&g0.inputs[0].shape?g0.inputs[0].shape[2]:0,g0!=null&&g0.inputs[0].shape?g0.inputs[0].shape[1]:0],!1),x=g0==null?void 0:g0.execute(c),i=(await x.data())[0];R5[n]=Math.round(100*i)/100,Fn=o,Gn=v(),k5.dispose([c,x]),l(R5[n])}))}var qn=D(H());var F2={};ve(F2,{connected:()=>E5,horizontal:()=>Dt,kpt:()=>w5,relative:()=>Xt,vertical:()=>Zt});var w5=["nose","leftEye","rightEye","leftEar","rightEar","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle"],Dt=[["leftEye","rightEye"],["leftEar","rightEar"],["leftShoulder","rightShoulder"],["leftElbow","rightElbow"],["leftWrist","rightWrist"],["leftHip","rightHip"],["leftKnee","rightKnee"],["leftAnkle","rightAnkle"]],Zt=[["leftKnee","leftShoulder"],["rightKnee","rightShoulder"],["leftAnkle","leftKnee"],["rightAnkle","rightKnee"]],Xt=[[["leftHip","rightHip"],["leftShoulder","rightShoulder"]],[["leftElbow","rightElbow"],["leftShoulder","rightShoulder"]]],E5={leftLeg:["leftHip","leftKnee","leftAnkle"],rightLeg:["rightHip","rightKnee","rightAnkle"],torso:["leftShoulder","rightShoulder","rightHip","leftHip","leftShoulder"],leftArm:["leftShoulder","leftElbow","leftWrist"],rightArm:["rightShoulder","rightElbow","rightWrist"],head:[]};var Ge=D(H()),Vn=.005,B0={keypoints:[],padding:[[0,0],[0,0],[0,0],[0,0]]};function qt(e){for(let t of Dt){let n=e.keypoints.findIndex(r=>r.part===t[0]),o=e.keypoints.findIndex(r=>r.part===t[1]);if(e.keypoints[n]&&e.keypoints[o]&&e.keypoints[n].position[0]<e.keypoints[o].position[0]){let r=e.keypoints[n];e.keypoints[n]=e.keypoints[o],e.keypoints[o]=r}}for(let t of Zt){let n=e.keypoints.findIndex(r=>r&&r.part===t[0]),o=e.keypoints.findIndex(r=>r&&r.part===t[1]);e.keypoints[n]&&e.keypoints[o]&&e.keypoints[n].position[1]<e.keypoints[o].position[1]&&e.keypoints.splice(n,1)}for(let[t,n]of Xt){let o=e.keypoints.findIndex(c=>c&&c.part===t[0]),r=e.keypoints.findIndex(c=>c&&c.part===t[1]),A=e.keypoints.findIndex(c=>c&&c.part===n[0]),s=e.keypoints.findIndex(c=>c&&c.part===n[1]);if(!e.keypoints[A]||!e.keypoints[s])continue;let a=e.keypoints[o]?[Math.abs(e.keypoints[A].position[0]-e.keypoints[o].position[0]),Math.abs(e.keypoints[s].position[0]-e.keypoints[o].position[0])]:[0,0],l=e.keypoints[r]?[Math.abs(e.keypoints[s].position[0]-e.keypoints[r].position[0]),Math.abs(e.keypoints[A].position[0]-e.keypoints[r].position[0])]:[0,0];if(a[0]>a[1]||l[0]>l[1]){let c=e.keypoints[o];e.keypoints[o]=e.keypoints[r],e.keypoints[r]=c}}}function Dn(e){for(let t=0;t<e.length;t++)if(e[t]&&B0.keypoints[t]){let n=[Math.abs(e[t].positionRaw[0]-B0.keypoints[t].positionRaw[0]),Math.abs(e[t].positionRaw[1]-B0.keypoints[t].positionRaw[1])];n[0]<Vn&&n[1]<Vn?e[t]=B0.keypoints[t]:B0.keypoints[t]=e[t]}else B0.keypoints[t]=e[t];return e}function Zn(e,t){var r,A;let n={};if(!((r=e==null?void 0:e.shape)!=null&&r[1])||!((A=e==null?void 0:e.shape)!=null&&A[2]))return e;B0.padding=[[0,0],[e.shape[2]>e.shape[1]?Math.trunc((e.shape[2]-e.shape[1])/2):0,e.shape[2]>e.shape[1]?Math.trunc((e.shape[2]-e.shape[1])/2):0],[e.shape[1]>e.shape[2]?Math.trunc((e.shape[1]-e.shape[2])/2):0,e.shape[1]>e.shape[2]?Math.trunc((e.shape[1]-e.shape[2])/2):0],[0,0]],n.pad=Ge.pad(e,B0.padding),n.resize=Ge.image.resizeBilinear(n.pad,[t,t]);let o=Ge.cast(n.resize,"int32");return Object.keys(n).forEach(s=>Ge.dispose(n[s])),o}function Xn(e,t){e.keypoints=e.keypoints.filter(o=>o==null?void 0:o.position);for(let o of e.keypoints)o.position=[o.position[0]*(t[0]+B0.padding[2][0]+B0.padding[2][1])/t[0]-B0.padding[2][0],o.position[1]*(t[1]+B0.padding[1][0]+B0.padding[1][1])/t[1]-B0.padding[1][0]],o.positionRaw=[o.position[0]/t[0],o.position[1]/t[1]];let n=fe(e.keypoints.map(o=>o.position),t);return e.box=n.box,e.boxRaw=n.boxRaw,e}var f0,z5=0,Ut=Number.MAX_SAFE_INTEGER,_e={boxes:[],bodies:[],last:0};async function Un(e){var t;return k.initial&&(f0=null),f0?e.debug&&u("cached 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A=e[0][r],s=Math.round(100*A[51+4])/100;if(s>t.body.minConfidence){let a=[];for(let i=0;i<17;i++){let y=A[3*i+2];if(y>t.body.minConfidence){let d=[A[3*i+1],A[3*i+0]];a.push({part:w5[i],score:Math.round(100*y)/100,positionRaw:d,position:[Math.round((n.shape[2]||0)*d[0]),Math.round((n.shape[1]||0)*d[1])]})}}let l=fe(a.map(i=>i.position),[n.shape[2],n.shape[1]]),c={};for(let[i,y]of Object.entries(E5)){let d=[];for(let f=0;f<y.length-1;f++){let p=a.find(M=>M.part===y[f]),g=a.find(M=>M.part===y[f+1]);p&&g&&p.score>(t.body.minConfidence||0)&&g.score>(t.body.minConfidence||0)&&d.push([p.position,g.position])}c[i]=d}let x={id:r,score:s,box:l.box,boxRaw:l.boxRaw,keypoints:[...a],annotations:c};qt(x),o.push(x)}}return o.sort((r,A)=>A.score-r.score),o.length>t.body.maxDetected&&(o.length=t.body.maxDetected),o}async function Yt(e,t){var r;if(!(f0!=null&&f0.executor)||!((r=f0==null?void 0:f0.inputs)!=null&&r[0].shape))return[];t.skipAllowed||(_e.boxes.length=0),Ut++;let n=(t.body.skipTime||0)>v()-_e.last,o=Ut<(t.body.skipFrames||0);return t.skipAllowed&&n&&o?_e.bodies:new Promise(async A=>{let s={};Ut=0,s.input=Zn(e,z5),s.res=f0==null?void 0:f0.execute(s.input),_e.last=v();let a=await s.res.array();_e.bodies=s.res.shape[2]===17?ns(a,t,e):os(a,t,e);for(let l of _e.bodies)Xn(l,[e.shape[2]||1,e.shape[1]||1]),Dn(l.keypoints);Object.keys(s).forEach(l=>qn.dispose(s[l])),A(_e.bodies)})}var H0=D(H());var _0,S5=[],Kn=0,Kt=Number.MAX_SAFE_INTEGER,N5=0,j5=2.5;async function Jn(e){if(!_0||k.initial){_0=await L(e.object.modelPath);let t=_0!=null&&_0.executor?Object.values(_0.modelSignature.inputs):void 0;N5=Array.isArray(t)?parseInt(t[0].tensorShape.dim[2].size):416}else e.debug&&u("cached model:",_0.modelUrl);return _0}async function rs(e,t,n){let o=0,r=[],A=N5;for(let c of[1,2,4]){let x=c*13,i=H0.squeeze(e.find(M=>M.shape[1]===x**2&&(M.shape[2]||0)===i2.length)),y=await 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s1="2.11.0";var y1={};ve(y1,{all:()=>x1,body:()=>b2,canvas:()=>d1,face:()=>h2,gesture:()=>v2,hand:()=>g2,object:()=>M2,options:()=>R0,person:()=>c1});var X0=e=>{if(!e)u("draw error: invalid canvas");else if(!e.getContext)u("draw error: canvas context not defined");else{let t=e.getContext("2d");if(!t)u("draw error: cannot get canvas context");else return t}return null},$e=e=>Math.round(e*180/Math.PI),he=(e,t)=>{if(!t.useDepth||typeof e=="undefined")return t.color;let n=Uint8ClampedArray.from([127+2*e,127-2*e,255]);return`rgba(${n[0]}, ${n[1]}, ${n[2]}, ${t.alpha})`};function be(e,t,n,o,r){e.fillStyle=he(o,r),e.beginPath(),e.arc(t,n,r.pointSize,0,2*Math.PI),e.fill()}function de(e,t,n,o,r,A){if(e.beginPath(),e.lineWidth=A.lineWidth,A.useCurves){let s=(t+t+o)/2,a=(n+n+r)/2;e.ellipse(s,a,o/2,r/2,0,0,2*Math.PI)}else e.moveTo(t+A.roundRect,n),e.lineTo(t+o-A.roundRect,n),e.quadraticCurveTo(t+o,n,t+o,n+A.roundRect),e.lineTo(t+o,n+r-A.roundRect),e.quadraticCurveTo(t+o,n+r,t+o-A.roundRect,n+r),e.lineTo(t+A.roundRect,n+r),e.quadraticCurveTo(t,n+r,t,n+r-A.roundRect),e.lineTo(t,n+A.roundRect),e.quadraticCurveTo(t,n,t+A.roundRect,n),e.closePath();e.stroke()}function a1(e,t,n){if(!(t.length<2)){e.beginPath(),e.moveTo(t[0][0],t[0][1]);for(let o of t)e.strokeStyle=he(o[2]||0,n),e.lineTo(Math.trunc(o[0]),Math.trunc(o[1]));e.stroke(),n.fillPolygons&&(e.closePath(),e.fill())}}function lo(e,t,n){if(!(t.length<2)){if(e.lineWidth=n.lineWidth,!n.useCurves||t.length<=2){a1(e,t,n);return}e.moveTo(t[0][0],t[0][1]);for(let o=0;o<t.length-2;o++){let r=(t[o][0]+t[o+1][0])/2,A=(t[o][1]+t[o+1][1])/2;e.quadraticCurveTo(t[o][0],t[o][1],r,A)}e.quadraticCurveTo(t[t.length-2][0],t[t.length-2][1],t[t.length-1][0],t[t.length-1][1]),e.stroke(),n.fillPolygons&&(e.closePath(),e.fill())}}function i1(e,t,n,o=5){let r,A,s;e.beginPath(),e.moveTo(t[0],t[1]),e.lineTo(n[0],n[1]),r=Math.atan2(n[1]-t[1],n[0]-t[0]),A=o*Math.cos(r)+n[0],s=o*Math.sin(r)+n[1],e.moveTo(A,s),r+=1/3*(2*Math.PI),A=o*Math.cos(r)+n[0],s=o*Math.sin(r)+n[1],e.lineTo(A,s),r+=1/3*(2*Math.PI),A=o*Math.cos(r)+n[0],s=o*Math.sin(r)+n[1],e.lineTo(A,s),e.closePath(),e.stroke(),e.fill()}var R0={color:"rgba(173, 216, 230, 0.6)",labelColor:"rgba(173, 216, 230, 1)",shadowColor:"black",alpha:.5,font:'small-caps 16px "Segoe UI"',lineHeight:18,lineWidth:4,pointSize:2,roundRect:8,drawPoints:!1,drawLabels:!0,drawBoxes:!0,drawAttention:!0,drawGestures:!0,drawPolygons:!0,drawGaze:!0,fillPolygons:!1,useDepth:!0,useCurves:!1};var Y;function hs(e,t){var n,o;if(Y.drawLabels){let r=[];if(r.push(`face: ${Math.trunc(100*e.score)}%`),e.genderScore&&r.push(`${e.gender||""} ${Math.trunc(100*e.genderScore)}%`),e.age&&r.push(`age: ${e.age||""}`),e.iris&&r.push(`distance: ${e.iris}`),e.real&&r.push(`real: ${Math.trunc(100*e.real)}%`),e.live&&r.push(`live: ${Math.trunc(100*e.live)}%`),e.emotion&&e.emotion.length>0){let A=e.emotion.map(s=>`${Math.trunc(100*s.score)}% ${s.emotion}`);A.length>3&&(A.length=3),r.push(A.join(" "))}((n=e.rotation)==null?void 0:n.angle)&&((o=e.rotation)==null?void 0:o.gaze)&&(e.rotation.angle.roll&&r.push(`roll: ${$e(e.rotation.angle.roll)}\xB0 yaw:${$e(e.rotation.angle.yaw)}\xB0 pitch:${$e(e.rotation.angle.pitch)}\xB0`),e.rotation.gaze.bearing&&r.push(`gaze: ${$e(e.rotation.gaze.bearing)}\xB0`)),r.length===0&&r.push("face"),t.fillStyle=Y.color;for(let A=r.length-1;A>=0;A--){let s=Math.max(e.box[0],0),a=A*Y.lineHeight+e.box[1];Y.shadowColor&&Y.shadowColor!==""&&(t.fillStyle=Y.shadowColor,t.fillText(r[A],s+5,a+16)),t.fillStyle=Y.labelColor,t.fillText(r[A],s+4,a+15)}}}function bs(e,t){var n,o,r,A;if(((n=e.annotations)==null?void 0:n.leftEyeIris)&&((o=e.annotations)==null?void 0:o.leftEyeIris[0])){t.strokeStyle=Y.useDepth?"rgba(255, 200, 255, 0.3)":Y.color,t.beginPath();let s=Math.abs(e.annotations.leftEyeIris[3][0]-e.annotations.leftEyeIris[1][0])/2,a=Math.abs(e.annotations.leftEyeIris[4][1]-e.annotations.leftEyeIris[2][1])/2;t.ellipse(e.annotations.leftEyeIris[0][0],e.annotations.leftEyeIris[0][1],s,a,0,0,2*Math.PI),t.stroke(),Y.fillPolygons&&(t.fillStyle=Y.useDepth?"rgba(255, 255, 200, 0.3)":Y.color,t.fill())}if(((r=e.annotations)==null?void 0:r.rightEyeIris)&&((A=e.annotations)==null?void 0:A.rightEyeIris[0])){t.strokeStyle=Y.useDepth?"rgba(255, 200, 255, 0.3)":Y.color,t.beginPath();let s=Math.abs(e.annotations.rightEyeIris[3][0]-e.annotations.rightEyeIris[1][0])/2,a=Math.abs(e.annotations.rightEyeIris[4][1]-e.annotations.rightEyeIris[2][1])/2;t.ellipse(e.annotations.rightEyeIris[0][0],e.annotations.rightEyeIris[0][1],s,a,0,0,2*Math.PI),t.stroke(),Y.fillPolygons&&(t.fillStyle=Y.useDepth?"rgba(255, 255, 200, 0.3)":Y.color,t.fill())}}function gs(e,t){var n;if(Y.drawGaze&&((n=e.rotation)==null?void 0:n.angle)&&typeof Path2D!="undefined"){t.strokeStyle="pink";let o=e.box[0]+e.box[2]/2-e.box[3]*$e(e.rotation.angle.yaw)/90,r=e.box[1]+e.box[3]/2+e.box[2]*$e(e.rotation.angle.pitch)/90,A=new Path2D(`
-      M ${e.box[0]+e.box[2]/2} ${e.box[1]}
+`;
+
+// src/image/imagefx.ts
+var collect = (source, prefix, collection) => {
+  const r = new RegExp("\\b" + prefix + " \\w+ (\\w+)", "ig");
+  source.replace(r, (match3, name) => {
+    collection[name] = 0;
+    return match3;
+  });
+};
+var GLProgram = class {
+  constructor(gl, vertexSource, fragmentSource) {
+    __publicField(this, "uniform", {});
+    __publicField(this, "attribute", {});
+    __publicField(this, "gl");
+    __publicField(this, "id");
+    __publicField(this, "compile", (source, type) => {
+      const shader = this.gl.createShader(type);
+      if (!shader) {
+        log("filter: could not create shader");
+        return null;
+      }
+      this.gl.shaderSource(shader, source);
+      this.gl.compileShader(shader);
+      if (!this.gl.getShaderParameter(shader, this.gl.COMPILE_STATUS)) {
+        log(`filter: gl compile failed: ${this.gl.getShaderInfoLog(shader) || "unknown"}`);
+        return null;
+      }
+      return shader;
+    });
+    this.gl = gl;
+    const vertexShader = this.compile(vertexSource, this.gl.VERTEX_SHADER);
+    const fragmentShader = this.compile(fragmentSource, this.gl.FRAGMENT_SHADER);
+    this.id = this.gl.createProgram();
+    if (!vertexShader || !fragmentShader)
+      return;
+    if (!this.id) {
+      log("filter: could not create webgl program");
+      return;
+    }
+    this.gl.attachShader(this.id, vertexShader);
+    this.gl.attachShader(this.id, fragmentShader);
+    this.gl.linkProgram(this.id);
+    if (!this.gl.getProgramParameter(this.id, this.gl.LINK_STATUS)) {
+      log(`filter: gl link failed: ${this.gl.getProgramInfoLog(this.id) || "unknown"}`);
+      return;
+    }
+    this.gl.useProgram(this.id);
+    collect(vertexSource, "attribute", this.attribute);
+    for (const a in this.attribute)
+      this.attribute[a] = this.gl.getAttribLocation(this.id, a);
+    collect(vertexSource, "uniform", this.uniform);
+    collect(fragmentSource, "uniform", this.uniform);
+    for (const u in this.uniform)
+      this.uniform[u] = this.gl.getUniformLocation(this.id, u);
+  }
+};
+function GLImageFilter() {
+  let drawCount = 0;
+  let sourceTexture = null;
+  let lastInChain = false;
+  let currentFramebufferIndex = -1;
+  let tempFramebuffers = [null, null];
+  let filterChain = [];
+  let vertexBuffer = null;
+  let currentProgram = null;
+  const fxcanvas = canvas(100, 100);
+  const shaderProgramCache = {};
+  const DRAW = { INTERMEDIATE: 1 };
+  const gl = fxcanvas.getContext("webgl");
+  if (!gl) {
+    log("filter: cannot get webgl context");
+    return;
+  }
+  this.gl = gl;
+  function resize(width, height) {
+    if (width === fxcanvas.width && height === fxcanvas.height)
+      return;
+    fxcanvas.width = width;
+    fxcanvas.height = height;
+    if (!vertexBuffer) {
+      const vertices = new Float32Array([-1, -1, 0, 1, 1, -1, 1, 1, -1, 1, 0, 0, -1, 1, 0, 0, 1, -1, 1, 1, 1, 1, 1, 0]);
+      vertexBuffer = gl.createBuffer();
+      gl.bindBuffer(gl.ARRAY_BUFFER, vertexBuffer);
+      gl.bufferData(gl.ARRAY_BUFFER, vertices, gl.STATIC_DRAW);
+      gl.pixelStorei(gl.UNPACK_PREMULTIPLY_ALPHA_WEBGL, true);
+    }
+    gl.viewport(0, 0, fxcanvas.width, fxcanvas.height);
+    tempFramebuffers = [null, null];
+  }
+  function createFramebufferTexture(width, height) {
+    const fbo = gl.createFramebuffer();
+    gl.bindFramebuffer(gl.FRAMEBUFFER, fbo);
+    const renderbuffer = gl.createRenderbuffer();
+    gl.bindRenderbuffer(gl.RENDERBUFFER, renderbuffer);
+    const texture = gl.createTexture();
+    gl.bindTexture(gl.TEXTURE_2D, texture);
+    gl.texImage2D(gl.TEXTURE_2D, 0, gl.RGBA, width, height, 0, gl.RGBA, gl.UNSIGNED_BYTE, null);
+    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MAG_FILTER, gl.LINEAR);
+    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MIN_FILTER, gl.LINEAR);
+    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_S, gl.CLAMP_TO_EDGE);
+    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_T, gl.CLAMP_TO_EDGE);
+    gl.framebufferTexture2D(gl.FRAMEBUFFER, gl.COLOR_ATTACHMENT0, gl.TEXTURE_2D, texture, 0);
+    gl.bindTexture(gl.TEXTURE_2D, null);
+    gl.bindFramebuffer(gl.FRAMEBUFFER, null);
+    return { fbo, texture };
+  }
+  function getTempFramebuffer(index2) {
+    tempFramebuffers[index2] = tempFramebuffers[index2] || createFramebufferTexture(fxcanvas.width, fxcanvas.height);
+    return tempFramebuffers[index2];
+  }
+  function draw(flags = 0) {
+    if (!currentProgram)
+      return;
+    let source = null;
+    let target = null;
+    let flipY = false;
+    if (drawCount === 0)
+      source = sourceTexture;
+    else
+      source = getTempFramebuffer(currentFramebufferIndex).texture || null;
+    drawCount++;
+    if (lastInChain && !(flags & DRAW.INTERMEDIATE)) {
+      target = null;
+      flipY = drawCount % 2 === 0;
+    } else {
+      currentFramebufferIndex = (currentFramebufferIndex + 1) % 2;
+      target = getTempFramebuffer(currentFramebufferIndex).fbo || null;
+    }
+    gl.bindTexture(gl.TEXTURE_2D, source);
+    gl.bindFramebuffer(gl.FRAMEBUFFER, target);
+    gl.uniform1f(currentProgram.uniform["flipY"], flipY ? -1 : 1);
+    gl.drawArrays(gl.TRIANGLES, 0, 6);
+  }
+  function compileShader(fragmentSource) {
+    if (shaderProgramCache[fragmentSource]) {
+      currentProgram = shaderProgramCache[fragmentSource];
+      gl.useProgram((currentProgram ? currentProgram.id : null) || null);
+      return currentProgram;
+    }
+    currentProgram = new GLProgram(gl, vertexIdentity, fragmentSource);
+    if (!currentProgram) {
+      log("filter: could not get webgl program");
+      return null;
+    }
+    const floatSize = Float32Array.BYTES_PER_ELEMENT;
+    const vertSize = 4 * floatSize;
+    gl.enableVertexAttribArray(currentProgram.attribute["pos"]);
+    gl.vertexAttribPointer(currentProgram.attribute["pos"], 2, gl.FLOAT, false, vertSize, 0 * floatSize);
+    gl.enableVertexAttribArray(currentProgram.attribute["uv"]);
+    gl.vertexAttribPointer(currentProgram.attribute["uv"], 2, gl.FLOAT, false, vertSize, 2 * floatSize);
+    shaderProgramCache[fragmentSource] = currentProgram;
+    return currentProgram;
+  }
+  const filter = {
+    colorMatrix: (matrix) => {
+      const m = new Float32Array(matrix);
+      m[4] /= 255;
+      m[9] /= 255;
+      m[14] /= 255;
+      m[19] /= 255;
+      const shader = m[18] === 1 && m[3] === 0 && m[8] === 0 && m[13] === 0 && m[15] === 0 && m[16] === 0 && m[17] === 0 && m[19] === 0 ? colorMatrixWithoutAlpha : colorMatrixWithAlpha;
+      const program = compileShader(shader);
+      if (!program)
+        return;
+      gl.uniform1fv(program.uniform["m"], m);
+      draw();
+    },
+    brightness: (brightness) => {
+      const b = (brightness || 0) + 1;
+      filter.colorMatrix([
+        b,
+        0,
+        0,
+        0,
+        0,
+        0,
+        b,
+        0,
+        0,
+        0,
+        0,
+        0,
+        b,
+        0,
+        0,
+        0,
+        0,
+        0,
+        1,
+        0
+      ]);
+    },
+    saturation: (amount) => {
+      const x = (amount || 0) * 2 / 3 + 1;
+      const y = (x - 1) * -0.5;
+      filter.colorMatrix([
+        x,
+        y,
+        y,
+        0,
+        0,
+        y,
+        x,
+        y,
+        0,
+        0,
+        y,
+        y,
+        x,
+        0,
+        0,
+        0,
+        0,
+        0,
+        1,
+        0
+      ]);
+    },
+    desaturate: () => {
+      filter.saturation(-1);
+    },
+    contrast: (amount) => {
+      const v = (amount || 0) + 1;
+      const o = -128 * (v - 1);
+      filter.colorMatrix([
+        v,
+        0,
+        0,
+        0,
+        o,
+        0,
+        v,
+        0,
+        0,
+        o,
+        0,
+        0,
+        v,
+        0,
+        o,
+        0,
+        0,
+        0,
+        1,
+        0
+      ]);
+    },
+    negative: () => {
+      filter.contrast(-2);
+    },
+    hue: (rotation) => {
+      rotation = (rotation || 0) / 180 * Math.PI;
+      const cos = Math.cos(rotation);
+      const sin = Math.sin(rotation);
+      const lumR = 0.213;
+      const lumG = 0.715;
+      const lumB = 0.072;
+      filter.colorMatrix([
+        lumR + cos * (1 - lumR) + sin * -lumR,
+        lumG + cos * -lumG + sin * -lumG,
+        lumB + cos * -lumB + sin * (1 - lumB),
+        0,
+        0,
+        lumR + cos * -lumR + sin * 0.143,
+        lumG + cos * (1 - lumG) + sin * 0.14,
+        lumB + cos * -lumB + sin * -0.283,
+        0,
+        0,
+        lumR + cos * -lumR + sin * -(1 - lumR),
+        lumG + cos * -lumG + sin * lumG,
+        lumB + cos * (1 - lumB) + sin * lumB,
+        0,
+        0,
+        0,
+        0,
+        0,
+        1,
+        0
+      ]);
+    },
+    desaturateLuminance: () => {
+      filter.colorMatrix([
+        0.2764723,
+        0.929708,
+        0.0938197,
+        0,
+        -37.1,
+        0.2764723,
+        0.929708,
+        0.0938197,
+        0,
+        -37.1,
+        0.2764723,
+        0.929708,
+        0.0938197,
+        0,
+        -37.1,
+        0,
+        0,
+        0,
+        1,
+        0
+      ]);
+    },
+    sepia: () => {
+      filter.colorMatrix([
+        0.393,
+        0.7689999,
+        0.18899999,
+        0,
+        0,
+        0.349,
+        0.6859999,
+        0.16799999,
+        0,
+        0,
+        0.272,
+        0.5339999,
+        0.13099999,
+        0,
+        0,
+        0,
+        0,
+        0,
+        1,
+        0
+      ]);
+    },
+    brownie: () => {
+      filter.colorMatrix([
+        0.5997023498159715,
+        0.34553243048391263,
+        -0.2708298674538042,
+        0,
+        47.43192855600873,
+        -0.037703249837783157,
+        0.8609577587992641,
+        0.15059552388459913,
+        0,
+        -36.96841498319127,
+        0.24113635128153335,
+        -0.07441037908422492,
+        0.44972182064877153,
+        0,
+        -7.562075277591283,
+        0,
+        0,
+        0,
+        1,
+        0
+      ]);
+    },
+    vintagePinhole: () => {
+      filter.colorMatrix([
+        0.6279345635605994,
+        0.3202183420819367,
+        -0.03965408211312453,
+        0,
+        9.651285835294123,
+        0.02578397704808868,
+        0.6441188644374771,
+        0.03259127616149294,
+        0,
+        7.462829176470591,
+        0.0466055556782719,
+        -0.0851232987247891,
+        0.5241648018700465,
+        0,
+        5.159190588235296,
+        0,
+        0,
+        0,
+        1,
+        0
+      ]);
+    },
+    kodachrome: () => {
+      filter.colorMatrix([
+        1.1285582396593525,
+        -0.3967382283601348,
+        -0.03992559172921793,
+        0,
+        63.72958762196502,
+        -0.16404339962244616,
+        1.0835251566291304,
+        -0.05498805115633132,
+        0,
+        24.732407896706203,
+        -0.16786010706155763,
+        -0.5603416277695248,
+        1.6014850761964943,
+        0,
+        35.62982807460946,
+        0,
+        0,
+        0,
+        1,
+        0
+      ]);
+    },
+    technicolor: () => {
+      filter.colorMatrix([
+        1.9125277891456083,
+        -0.8545344976951645,
+        -0.09155508482755585,
+        0,
+        11.793603434377337,
+        -0.3087833385928097,
+        1.7658908555458428,
+        -0.10601743074722245,
+        0,
+        -70.35205161461398,
+        -0.231103377548616,
+        -0.7501899197440212,
+        1.847597816108189,
+        0,
+        30.950940869491138,
+        0,
+        0,
+        0,
+        1,
+        0
+      ]);
+    },
+    polaroid: () => {
+      filter.colorMatrix([
+        1.438,
+        -0.062,
+        -0.062,
+        0,
+        0,
+        -0.122,
+        1.378,
+        -0.122,
+        0,
+        0,
+        -0.016,
+        -0.016,
+        1.483,
+        0,
+        0,
+        0,
+        0,
+        0,
+        1,
+        0
+      ]);
+    },
+    shiftToBGR: () => {
+      filter.colorMatrix([
+        0,
+        0,
+        1,
+        0,
+        0,
+        0,
+        1,
+        0,
+        0,
+        0,
+        1,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        1,
+        0
+      ]);
+    },
+    convolution: (matrix) => {
+      const m = new Float32Array(matrix);
+      const pixelSizeX = 1 / fxcanvas.width;
+      const pixelSizeY = 1 / fxcanvas.height;
+      const program = compileShader(convolution);
+      if (!program)
+        return;
+      gl.uniform1fv(program.uniform["m"], m);
+      gl.uniform2f(program.uniform["px"], pixelSizeX, pixelSizeY);
+      draw();
+    },
+    detectEdges: () => {
+      filter.convolution.call(this, [
+        0,
+        1,
+        0,
+        1,
+        -4,
+        1,
+        0,
+        1,
+        0
+      ]);
+    },
+    sobelX: () => {
+      filter.convolution.call(this, [
+        -1,
+        0,
+        1,
+        -2,
+        0,
+        2,
+        -1,
+        0,
+        1
+      ]);
+    },
+    sobelY: () => {
+      filter.convolution.call(this, [
+        -1,
+        -2,
+        -1,
+        0,
+        0,
+        0,
+        1,
+        2,
+        1
+      ]);
+    },
+    sharpen: (amount) => {
+      const a = amount || 1;
+      filter.convolution.call(this, [
+        0,
+        -1 * a,
+        0,
+        -1 * a,
+        1 + 4 * a,
+        -1 * a,
+        0,
+        -1 * a,
+        0
+      ]);
+    },
+    emboss: (size2) => {
+      const s = size2 || 1;
+      filter.convolution.call(this, [
+        -2 * s,
+        -1 * s,
+        0,
+        -1 * s,
+        1,
+        1 * s,
+        0,
+        1 * s,
+        2 * s
+      ]);
+    },
+    blur: (size2) => {
+      const blurSizeX = size2 / 7 / fxcanvas.width;
+      const blurSizeY = size2 / 7 / fxcanvas.height;
+      const program = compileShader(blur);
+      if (!program)
+        return;
+      gl.uniform2f(program.uniform["px"], 0, blurSizeY);
+      draw(DRAW.INTERMEDIATE);
+      gl.uniform2f(program.uniform["px"], blurSizeX, 0);
+      draw();
+    },
+    pixelate: (size2) => {
+      const blurSizeX = size2 / fxcanvas.width;
+      const blurSizeY = size2 / fxcanvas.height;
+      const program = compileShader(pixelate);
+      if (!program)
+        return;
+      gl.uniform2f(program.uniform["size"], blurSizeX, blurSizeY);
+      draw();
+    }
+  };
+  this.add = function(name) {
+    const args = Array.prototype.slice.call(arguments, 1);
+    const func = filter[name];
+    filterChain.push({ func, args });
+  };
+  this.reset = function() {
+    filterChain = [];
+  };
+  this.get = function() {
+    return filterChain;
+  };
+  this.apply = function(image27) {
+    resize(image27.width, image27.height);
+    drawCount = 0;
+    if (!sourceTexture)
+      sourceTexture = gl.createTexture();
+    gl.bindTexture(gl.TEXTURE_2D, sourceTexture);
+    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_S, gl.CLAMP_TO_EDGE);
+    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_T, gl.CLAMP_TO_EDGE);
+    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MIN_FILTER, gl.NEAREST);
+    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MAG_FILTER, gl.NEAREST);
+    gl.texImage2D(gl.TEXTURE_2D, 0, gl.RGBA, gl.RGBA, gl.UNSIGNED_BYTE, image27);
+    for (let i = 0; i < filterChain.length; i++) {
+      lastInChain = i === filterChain.length - 1;
+      const f = filterChain[i];
+      f.func.apply(this, f.args || []);
+    }
+    return fxcanvas;
+  };
+  this.draw = function(image27) {
+    this.add("brightness", 0);
+    return this.apply(image27);
+  };
+}
+
+// src/image/enhance.ts
+var tf = __toESM(require_tfjs_esm());
+async function histogramEqualization(inputImage) {
+  const squeeze14 = inputImage.shape.length === 4 ? tf.squeeze(inputImage) : inputImage;
+  const channels = tf.split(squeeze14, 3, 2);
+  const min2 = [tf.min(channels[0]), tf.min(channels[1]), tf.min(channels[2])];
+  const max4 = [tf.max(channels[0]), tf.max(channels[1]), tf.max(channels[2])];
+  const absMax = await Promise.all(max4.map((channel) => channel.data()));
+  const maxValue = 0.99 * Math.max(absMax[0][0], absMax[1][0], absMax[2][0]);
+  const sub11 = [tf.sub(channels[0], min2[0]), tf.sub(channels[1], min2[1]), tf.sub(channels[2], min2[2])];
+  const range = [tf.sub(max4[0], min2[0]), tf.sub(max4[1], min2[1]), tf.sub(max4[2], min2[2])];
+  const fact = [tf.div(maxValue, range[0]), tf.div(maxValue, range[1]), tf.div(maxValue, range[2])];
+  const enh = [tf.mul(sub11[0], fact[0]), tf.mul(sub11[1], fact[1]), tf.mul(sub11[2], fact[2])];
+  const rgb2 = tf.stack([enh[0], enh[1], enh[2]], 2);
+  const reshape8 = tf.reshape(rgb2, [1, squeeze14.shape[0], squeeze14.shape[1], 3]);
+  tf.dispose([...channels, ...min2, ...max4, ...sub11, ...range, ...fact, ...enh, rgb2, squeeze14]);
+  return reshape8;
+}
+
+// src/image/image.ts
+var maxSize = 3840;
+var inCanvas = null;
+var outCanvas = null;
+var tmpCanvas = null;
+var fx;
+var last = {
+  inputSum: 0,
+  cacheDiff: 1,
+  sumMethod: 0,
+  inputTensor: void 0
+};
+function reset() {
+  last.inputSum = 0;
+  last.cacheDiff = 1;
+  last.sumMethod = 0;
+  last.inputTensor = void 0;
+}
+function canvas(width, height) {
+  let c;
+  if (env.browser) {
+    if (env.worker) {
+      if (typeof OffscreenCanvas === "undefined")
+        throw new Error("canvas error: attempted to run in web worker but OffscreenCanvas is not supported");
+      c = new OffscreenCanvas(width, height);
+    } else {
+      if (typeof document === "undefined")
+        throw new Error("canvas error: attempted to run in browser but DOM is not defined");
+      c = document.createElement("canvas");
+      c.width = width;
+      c.height = height;
+    }
+  } else {
+    if (typeof env.Canvas !== "undefined")
+      c = new env.Canvas(width, height);
+    else if (typeof globalThis.Canvas !== "undefined")
+      c = new globalThis.Canvas(width, height);
+  }
+  return c;
+}
+function copy(input, output) {
+  const outputCanvas = output || canvas(input.width, input.height);
+  const ctx = outputCanvas.getContext("2d");
+  ctx.drawImage(input, 0, 0);
+  return outputCanvas;
+}
+async function process2(input, config3, getTensor = true) {
+  var _a, _b;
+  if (!input) {
+    if (config3.debug)
+      log("input error: input is missing");
+    return { tensor: null, canvas: null };
+  }
+  if (!(input instanceof tf2.Tensor) && !(typeof Image !== "undefined" && input instanceof Image) && !(typeof env.Canvas !== "undefined" && input instanceof env.Canvas) && !(typeof globalThis.Canvas !== "undefined" && input instanceof globalThis.Canvas) && !(typeof ImageData !== "undefined" && input instanceof ImageData) && !(typeof ImageBitmap !== "undefined" && input instanceof ImageBitmap) && !(typeof HTMLImageElement !== "undefined" && input instanceof HTMLImageElement) && !(typeof HTMLMediaElement !== "undefined" && input instanceof HTMLMediaElement) && !(typeof HTMLVideoElement !== "undefined" && input instanceof HTMLVideoElement) && !(typeof HTMLCanvasElement !== "undefined" && input instanceof HTMLCanvasElement) && !(typeof OffscreenCanvas !== "undefined" && input instanceof OffscreenCanvas)) {
+    throw new Error("input error: type is not recognized");
+  }
+  if (input instanceof tf2.Tensor) {
+    let tensor7 = null;
+    if (input["isDisposedInternal"])
+      throw new Error("input error: attempted to use tensor but it is disposed");
+    if (!input.shape)
+      throw new Error("input error: attempted to use tensor without a shape");
+    if (input.shape.length === 3) {
+      if (input.shape[2] === 3) {
+        tensor7 = tf2.expandDims(input, 0);
+      } else if (input.shape[2] === 4) {
+        const rgb2 = tf2.slice3d(input, [0, 0, 0], [-1, -1, 3]);
+        tensor7 = tf2.expandDims(rgb2, 0);
+        tf2.dispose(rgb2);
+      }
+    } else if (input.shape.length === 4) {
+      if (input.shape[3] === 3) {
+        tensor7 = tf2.clone(input);
+      } else if (input.shape[3] === 4) {
+        tensor7 = tf2.slice4d(input, [0, 0, 0, 0], [-1, -1, -1, 3]);
+      }
+    }
+    if (tensor7 == null || tensor7.shape.length !== 4 || tensor7.shape[0] !== 1 || tensor7.shape[3] !== 3)
+      throw new Error(`input error: attempted to use tensor with unrecognized shape: ${input.shape.toString()}`);
+    if (tensor7.dtype === "int32") {
+      const cast8 = tf2.cast(tensor7, "float32");
+      tf2.dispose(tensor7);
+      tensor7 = cast8;
+    }
+    return { tensor: tensor7, canvas: config3.filter.return ? outCanvas : null };
+  }
+  if (typeof input["readyState"] !== "undefined" && input.readyState <= 2) {
+    if (config3.debug)
+      log("input stream is not ready");
+    return { tensor: null, canvas: inCanvas };
+  }
+  const originalWidth = input["naturalWidth"] || input["videoWidth"] || input["width"] || input["shape"] && input["shape"][1] > 0;
+  const originalHeight = input["naturalHeight"] || input["videoHeight"] || input["height"] || input["shape"] && input["shape"][2] > 0;
+  if (!originalWidth || !originalHeight) {
+    if (config3.debug)
+      log("cannot determine input dimensions");
+    return { tensor: null, canvas: inCanvas };
+  }
+  let targetWidth = originalWidth;
+  let targetHeight = originalHeight;
+  if (targetWidth > maxSize) {
+    targetWidth = maxSize;
+    targetHeight = Math.trunc(targetWidth * originalHeight / originalWidth);
+  }
+  if (targetHeight > maxSize) {
+    targetHeight = maxSize;
+    targetWidth = Math.trunc(targetHeight * originalWidth / originalHeight);
+  }
+  if ((((_a = config3.filter) == null ? void 0 : _a.width) || 0) > 0)
+    targetWidth = config3.filter.width;
+  else if ((((_b = config3.filter) == null ? void 0 : _b.height) || 0) > 0)
+    targetWidth = originalWidth * ((config3.filter.height || 0) / originalHeight);
+  if ((config3.filter.height || 0) > 0)
+    targetHeight = config3.filter.height;
+  else if ((config3.filter.width || 0) > 0)
+    targetHeight = originalHeight * ((config3.filter.width || 0) / originalWidth);
+  if (!targetWidth || !targetHeight)
+    throw new Error("input error: cannot determine dimension");
+  if (!inCanvas || inCanvas.width !== targetWidth || inCanvas.height !== targetHeight)
+    inCanvas = canvas(targetWidth, targetHeight);
+  const inCtx = inCanvas.getContext("2d");
+  if (typeof ImageData !== "undefined" && input instanceof ImageData) {
+    inCtx.putImageData(input, 0, 0);
+  } else {
+    if (config3.filter.flip && typeof inCtx.translate !== "undefined") {
+      inCtx.translate(originalWidth, 0);
+      inCtx.scale(-1, 1);
+      inCtx.drawImage(input, 0, 0, originalWidth, originalHeight, 0, 0, inCanvas.width, inCanvas.height);
+      inCtx.setTransform(1, 0, 0, 1, 0, 0);
+    } else {
+      inCtx.drawImage(input, 0, 0, originalWidth, originalHeight, 0, 0, inCanvas.width, inCanvas.height);
+    }
+  }
+  if (!outCanvas || inCanvas.width !== outCanvas.width || inCanvas.height !== outCanvas.height)
+    outCanvas = canvas(inCanvas.width, inCanvas.height);
+  if (config3.filter.enabled && env.webgl.supported) {
+    if (!fx)
+      fx = env.browser ? new GLImageFilter() : null;
+    env.filter = !!fx;
+    if (!(fx == null ? void 0 : fx.add)) {
+      if (config3.debug)
+        log("input process error: cannot initialize filters");
+      env.webgl.supported = false;
+      config3.filter.enabled = false;
+      copy(inCanvas, outCanvas);
+    } else {
+      fx.reset();
+      if (config3.filter.brightness !== 0)
+        fx.add("brightness", config3.filter.brightness);
+      if (config3.filter.contrast !== 0)
+        fx.add("contrast", config3.filter.contrast);
+      if (config3.filter.sharpness !== 0)
+        fx.add("sharpen", config3.filter.sharpness);
+      if (config3.filter.blur !== 0)
+        fx.add("blur", config3.filter.blur);
+      if (config3.filter.saturation !== 0)
+        fx.add("saturation", config3.filter.saturation);
+      if (config3.filter.hue !== 0)
+        fx.add("hue", config3.filter.hue);
+      if (config3.filter.negative)
+        fx.add("negative");
+      if (config3.filter.sepia)
+        fx.add("sepia");
+      if (config3.filter.vintage)
+        fx.add("brownie");
+      if (config3.filter.sepia)
+        fx.add("sepia");
+      if (config3.filter.kodachrome)
+        fx.add("kodachrome");
+      if (config3.filter.technicolor)
+        fx.add("technicolor");
+      if (config3.filter.polaroid)
+        fx.add("polaroid");
+      if (config3.filter.pixelate !== 0)
+        fx.add("pixelate", config3.filter.pixelate);
+      if (fx.get() > 0)
+        outCanvas = fx.apply(inCanvas);
+      else
+        outCanvas = fx.draw(inCanvas);
+    }
+  } else {
+    copy(inCanvas, outCanvas);
+    if (fx)
+      fx = null;
+    env.filter = !!fx;
+  }
+  if (!getTensor)
+    return { tensor: null, canvas: outCanvas };
+  if (!outCanvas)
+    throw new Error("canvas error: cannot create output");
+  let pixels;
+  let depth = 3;
+  if (typeof ImageData !== "undefined" && input instanceof ImageData || input.data && input.width && input.height) {
+    if (env.browser && tf2.browser) {
+      pixels = tf2.browser ? tf2.browser.fromPixels(input) : null;
+    } else {
+      depth = input.data.length / input.height / input.width;
+      const arr = new Uint8Array(input.data.buffer);
+      pixels = tf2.tensor(arr, [input.height, input.width, depth], "int32");
+    }
+  } else {
+    if (!tmpCanvas || outCanvas.width !== tmpCanvas.width || outCanvas.height !== tmpCanvas.height)
+      tmpCanvas = canvas(outCanvas.width, outCanvas.height);
+    if (tf2.browser && env.browser) {
+      if (config3.backend === "webgl" || config3.backend === "humangl" || config3.backend === "webgpu") {
+        pixels = tf2.browser.fromPixels(outCanvas);
+      } else {
+        tmpCanvas = copy(outCanvas);
+        pixels = tf2.browser.fromPixels(tmpCanvas);
+      }
+    } else {
+      const tempCanvas = copy(outCanvas);
+      const tempCtx = tempCanvas.getContext("2d");
+      const tempData = tempCtx.getImageData(0, 0, targetWidth, targetHeight);
+      depth = tempData.data.length / targetWidth / targetHeight;
+      const arr = new Uint8Array(tempData.data.buffer);
+      pixels = tf2.tensor(arr, [targetWidth, targetHeight, depth]);
+    }
+  }
+  if (depth === 4) {
+    const rgb2 = tf2.slice3d(pixels, [0, 0, 0], [-1, -1, 3]);
+    tf2.dispose(pixels);
+    pixels = rgb2;
+  }
+  if (!pixels)
+    throw new Error("input error: cannot create tensor");
+  const casted = tf2.cast(pixels, "float32");
+  const tensor6 = config3.filter.equalization ? await histogramEqualization(casted) : tf2.expandDims(casted, 0);
+  tf2.dispose([pixels, casted]);
+  return { tensor: tensor6, canvas: config3.filter.return ? outCanvas : null };
+}
+async function skip(config3, input) {
+  let skipFrame = false;
+  if (config3.cacheSensitivity === 0 || !input.shape || input.shape.length !== 4 || input.shape[1] > 2048 || input.shape[2] > 2048)
+    return skipFrame;
+  if (!last.inputTensor) {
+    last.inputTensor = tf2.clone(input);
+  } else if (last.inputTensor.shape[1] !== input.shape[1] || last.inputTensor.shape[2] !== input.shape[2]) {
+    tf2.dispose(last.inputTensor);
+    last.inputTensor = tf2.clone(input);
+  } else {
+    const t2 = {};
+    t2.diff = tf2.sub(input, last.inputTensor);
+    t2.squared = tf2.mul(t2.diff, t2.diff);
+    t2.sum = tf2.sum(t2.squared);
+    const diffSum = await t2.sum.data();
+    const diffRelative = diffSum[0] / (input.shape[1] || 1) / (input.shape[2] || 1) / 255 / 3;
+    tf2.dispose([last.inputTensor, t2.diff, t2.squared, t2.sum]);
+    last.inputTensor = tf2.clone(input);
+    skipFrame = diffRelative <= (config3.cacheSensitivity || 0);
+  }
+  return skipFrame;
+}
+async function compare(config3, input1, input2) {
+  const t2 = {};
+  if (!input1 || !input2 || input1.shape.length !== 4 || input1.shape.length !== input2.shape.length) {
+    if (!config3.debug)
+      log("invalid input tensor or tensor shapes do not match:", input1.shape, input2.shape);
+    return 0;
+  }
+  if (input1.shape[0] !== 1 || input2.shape[0] !== 1 || input1.shape[3] !== 3 || input2.shape[3] !== 3) {
+    if (!config3.debug)
+      log("input tensors must be of shape [1, height, width, 3]:", input1.shape, input2.shape);
+    return 0;
+  }
+  t2.input1 = tf2.clone(input1);
+  t2.input2 = input1.shape[1] !== input2.shape[1] || input1.shape[2] !== input2.shape[2] ? tf2.image.resizeBilinear(input2, [input1.shape[1], input1.shape[2]]) : tf2.clone(input2);
+  t2.diff = tf2.sub(t2.input1, t2.input2);
+  t2.squared = tf2.mul(t2.diff, t2.diff);
+  t2.sum = tf2.sum(t2.squared);
+  const diffSum = await t2.sum.data();
+  const diffRelative = diffSum[0] / (input1.shape[1] || 1) / (input1.shape[2] || 1) / 255 / 3;
+  tf2.dispose([t2.input1, t2.input2, t2.diff, t2.squared, t2.sum]);
+  return diffRelative;
+}
+
+// src/util/env.ts
+var Env = class {
+  constructor() {
+    __publicField(this, "browser");
+    __publicField(this, "node");
+    __publicField(this, "worker");
+    __publicField(this, "platform", "");
+    __publicField(this, "agent", "");
+    __publicField(this, "backends", []);
+    __publicField(this, "initial");
+    __publicField(this, "filter");
+    __publicField(this, "tfjs");
+    __publicField(this, "offscreen");
+    __publicField(this, "perfadd", false);
+    __publicField(this, "tensorflow", {
+      version: void 0,
+      gpu: void 0
+    });
+    __publicField(this, "wasm", {
+      supported: void 0,
+      backend: void 0,
+      simd: void 0,
+      multithread: void 0
+    });
+    __publicField(this, "webgl", {
+      supported: void 0,
+      backend: void 0,
+      version: void 0,
+      renderer: void 0
+    });
+    __publicField(this, "webgpu", {
+      supported: void 0,
+      backend: void 0,
+      adapter: void 0
+    });
+    __publicField(this, "cpu", {
+      model: void 0,
+      flags: []
+    });
+    __publicField(this, "kernels", []);
+    __publicField(this, "Canvas");
+    __publicField(this, "Image");
+    __publicField(this, "ImageData");
+    this.browser = typeof navigator !== "undefined";
+    this.node = typeof process !== "undefined" && typeof process.versions !== "undefined" && typeof process.versions.node !== "undefined";
+    this.tfjs = { version: tf3.version["tfjs-core"] };
+    this.offscreen = typeof OffscreenCanvas !== "undefined";
+    this.initial = true;
+    this.worker = this.browser && this.offscreen ? typeof WorkerGlobalScope !== "undefined" : void 0;
+    if (typeof navigator !== "undefined") {
+      const raw = navigator.userAgent.match(/\(([^()]+)\)/g);
+      if (raw == null ? void 0 : raw[0]) {
+        const platformMatch = raw[0].match(/\(([^()]+)\)/g);
+        this.platform = (platformMatch == null ? void 0 : platformMatch[0]) ? platformMatch[0].replace(/\(|\)/g, "") : "";
+        this.agent = navigator.userAgent.replace(raw[0], "");
+        if (this.platform[1])
+          this.agent = this.agent.replace(raw[1], "");
+        this.agent = this.agent.replace(/  /g, " ");
+      }
+    } else if (typeof process !== "undefined") {
+      this.platform = `${process.platform} ${process.arch}`;
+      this.agent = `NodeJS ${process.version}`;
+    }
+  }
+  async updateBackend() {
+    this.backends = Object.keys(tf3.engine().registryFactory);
+    this.tensorflow = {
+      version: tf3.backend().binding ? tf3.backend().binding.TF_Version : void 0,
+      gpu: tf3.backend().binding ? tf3.backend().binding.isUsingGpuDevice() : void 0
+    };
+    this.wasm.supported = typeof WebAssembly !== "undefined";
+    this.wasm.backend = this.backends.includes("wasm");
+    if (this.wasm.supported && this.wasm.backend && tf3.getBackend() === "wasm") {
+      this.wasm.simd = tf3.env().get("WASM_HAS_SIMD_SUPPORT");
+      this.wasm.multithread = tf3.env().get("WASM_HAS_MULTITHREAD_SUPPORT");
+    }
+    const c = canvas(100, 100);
+    const ctx = c ? c.getContext("webgl2") : void 0;
+    this.webgl.supported = typeof ctx !== "undefined";
+    this.webgl.backend = this.backends.includes("webgl");
+    if (this.webgl.supported && this.webgl.backend && (tf3.getBackend() === "webgl" || tf3.getBackend() === "humangl")) {
+      const gl = tf3.backend().gpgpu !== "undefined" ? await tf3.backend().getGPGPUContext().gl : null;
+      if (gl) {
+        this.webgl.version = gl.getParameter(gl.VERSION);
+        this.webgl.renderer = gl.getParameter(gl.RENDERER);
+      }
+    }
+    this.webgpu.supported = this.browser && typeof navigator.gpu !== "undefined";
+    this.webgpu.backend = this.backends.includes("webgpu");
+    try {
+      if (this.webgpu.supported) {
+        const adapter = await navigator.gpu.requestAdapter();
+        this.webgpu.adapter = adapter ? adapter.name : void 0;
+      }
+    } catch (e) {
+      this.webgpu.supported = false;
+    }
+    try {
+      this.kernels = tf3.getKernelsForBackend(tf3.getBackend()).map((kernel) => kernel.kernelName.toLowerCase());
+    } catch (e) {
+    }
+  }
+  updateCPU() {
+    const cpu = { model: "", flags: [] };
+    if (this.node && this.platform.startsWith("linux")) {
+    }
+    if (!this.cpu)
+      Object.defineProperty(this, "cpu", { value: cpu });
+    else
+      this.cpu = cpu;
+  }
+};
+var env = new Env();
+
+// src/util/webcam.ts
+var WebCam = class {
+  constructor() {
+    __publicField(this, "config");
+    __publicField(this, "element");
+    __publicField(this, "stream");
+    __publicField(this, "start", async (webcamConfig) => {
+      if (webcamConfig == null ? void 0 : webcamConfig.debug)
+        this.config.debug = webcamConfig == null ? void 0 : webcamConfig.debug;
+      if (webcamConfig == null ? void 0 : webcamConfig.crop)
+        this.config.crop = webcamConfig == null ? void 0 : webcamConfig.crop;
+      if (webcamConfig == null ? void 0 : webcamConfig.mode)
+        this.config.mode = webcamConfig == null ? void 0 : webcamConfig.mode;
+      if (webcamConfig == null ? void 0 : webcamConfig.width)
+        this.config.width = webcamConfig == null ? void 0 : webcamConfig.width;
+      if (webcamConfig == null ? void 0 : webcamConfig.height)
+        this.config.height = webcamConfig == null ? void 0 : webcamConfig.height;
+      if (webcamConfig == null ? void 0 : webcamConfig.element) {
+        if (typeof webcamConfig.element === "string") {
+          const el = document.getElementById(webcamConfig.element);
+          if (el && el instanceof HTMLVideoElement) {
+            this.element = el;
+          } else {
+            if (this.config.debug)
+              log("webcam", "cannot get dom element", webcamConfig.element);
+            return;
+          }
+        } else if (webcamConfig.element instanceof HTMLVideoElement) {
+          this.element = webcamConfig.element;
+        } else {
+          if (this.config.debug)
+            log("webcam", "unknown dom element", webcamConfig.element);
+          return;
+        }
+      } else {
+        this.element = document.createElement("video");
+      }
+      const requestedConstraints = {
+        audio: false,
+        video: {
+          facingMode: this.config.mode === "front" ? "user" : "environment",
+          resizeMode: this.config.crop ? "crop-and-scale" : "none",
+          width: { ideal: this.config.width > 0 ? this.config.width : window.innerWidth },
+          height: { ideal: this.config.height > 0 ? this.config.height : window.innerHeight }
+        }
+      };
+      this.element.addEventListener("play", () => {
+        if (this.config.debug)
+          log("webcam", "play");
+      });
+      this.element.addEventListener("pause", () => {
+        if (this.config.debug)
+          log("webcam", "pause");
+      });
+      this.element.addEventListener("click", async () => {
+        if (!this.element || !this.stream)
+          return;
+        if (this.element.paused)
+          await this.element.play();
+        else
+          this.element.pause();
+      });
+      if (!(navigator == null ? void 0 : navigator.mediaDevices)) {
+        if (this.config.debug)
+          log("webcam", "no devices");
+        return;
+      }
+      try {
+        this.stream = await navigator.mediaDevices.getUserMedia(requestedConstraints);
+      } catch (err) {
+        log("webcam", err);
+        return;
+      }
+      if (!this.stream) {
+        if (this.config.debug)
+          log("webcam", "no stream");
+        return;
+      }
+      this.element.srcObject = this.stream;
+      const ready3 = new Promise((resolve) => {
+        if (!this.element)
+          resolve(false);
+        else
+          this.element.onloadeddata = () => resolve(true);
+      });
+      await ready3;
+      await this.element.play();
+      if (this.config.debug) {
+        log("webcam", {
+          width: this.width,
+          height: this.height,
+          label: this.label,
+          stream: this.stream,
+          track: this.track,
+          settings: this.settings,
+          constraints: this.constraints,
+          capabilities: this.capabilities
+        });
+      }
+    });
+    __publicField(this, "pause", () => {
+      if (this.element)
+        this.element.pause();
+    });
+    __publicField(this, "play", async () => {
+      if (this.element)
+        await this.element.play();
+    });
+    __publicField(this, "stop", () => {
+      if (this.config.debug)
+        log("webcam", "stop");
+      if (this.track)
+        this.track.stop();
+    });
+    this.config = {
+      element: void 0,
+      debug: true,
+      mode: "front",
+      crop: false,
+      width: 0,
+      height: 0
+    };
+  }
+  get track() {
+    if (!this.stream)
+      return void 0;
+    return this.stream.getVideoTracks()[0];
+  }
+  get capabilities() {
+    if (!this.track)
+      return void 0;
+    return this.track.getCapabilities ? this.track.getCapabilities() : void 0;
+  }
+  get constraints() {
+    if (!this.track)
+      return void 0;
+    return this.track.getConstraints ? this.track.getConstraints() : void 0;
+  }
+  get settings() {
+    if (!this.stream)
+      return void 0;
+    const track = this.stream.getVideoTracks()[0];
+    return track.getSettings ? track.getSettings() : void 0;
+  }
+  get label() {
+    if (!this.track)
+      return "";
+    return this.track.label;
+  }
+  get paused() {
+    var _a;
+    return ((_a = this.element) == null ? void 0 : _a.paused) || false;
+  }
+  get width() {
+    var _a;
+    return ((_a = this.element) == null ? void 0 : _a.videoWidth) || 0;
+  }
+  get height() {
+    var _a;
+    return ((_a = this.element) == null ? void 0 : _a.videoHeight) || 0;
+  }
+};
+
+// src/tfjs/load.ts
+var tf4 = __toESM(require_tfjs_esm());
+
+// models/models.json
+var models_exports = {};
+__export(models_exports, {
+  age: () => age,
+  "anti-spoofing": () => anti_spoofing,
+  antispoof: () => antispoof,
+  blazeface: () => blazeface,
+  "blazeface-back": () => blazeface_back,
+  "blazeface-front": () => blazeface_front,
+  "blazepose-detect": () => blazepose_detect,
+  "blazepose-detector2d": () => blazepose_detector2d,
+  "blazepose-detector3d": () => blazepose_detector3d,
+  "blazepose-full": () => blazepose_full,
+  "blazepose-heavy": () => blazepose_heavy,
+  "blazepose-lite": () => blazepose_lite,
+  default: () => models_default,
+  efficientpose: () => efficientpose,
+  "efficientpose-i-lite": () => efficientpose_i_lite,
+  "efficientpose-ii-lite": () => efficientpose_ii_lite,
+  "efficientpose-iv": () => efficientpose_iv,
+  emotion: () => emotion,
+  faceboxes: () => faceboxes,
+  facemesh: () => facemesh,
+  "facemesh-attention": () => facemesh_attention,
+  "facemesh-attention-alt": () => facemesh_attention_alt,
+  "facemesh-detection-full": () => facemesh_detection_full,
+  "facemesh-detection-short": () => facemesh_detection_short,
+  "facemesh-orig": () => facemesh_orig,
+  faceres: () => faceres,
+  "faceres-deep": () => faceres_deep,
+  gear: () => gear,
+  gender: () => gender,
+  "gender-ssrnet-imdb": () => gender_ssrnet_imdb,
+  handdetect: () => handdetect,
+  "handlandmark-full": () => handlandmark_full,
+  "handlandmark-lite": () => handlandmark_lite,
+  "handlandmark-sparse": () => handlandmark_sparse,
+  handskeleton: () => handskeleton,
+  handtrack: () => handtrack,
+  "insightface-efficientnet-b0": () => insightface_efficientnet_b0,
+  "insightface-ghostnet-strides1": () => insightface_ghostnet_strides1,
+  "insightface-ghostnet-strides2": () => insightface_ghostnet_strides2,
+  "insightface-mobilenet-emore": () => insightface_mobilenet_emore,
+  "insightface-mobilenet-swish": () => insightface_mobilenet_swish,
+  iris: () => iris,
+  liveness: () => liveness,
+  "mb3-centernet": () => mb3_centernet,
+  meet: () => meet,
+  mobileface: () => mobileface,
+  mobilefacenet: () => mobilefacenet,
+  models: () => models,
+  "movenet-lightning": () => movenet_lightning,
+  "movenet-multipose": () => movenet_multipose,
+  "movenet-thunder": () => movenet_thunder,
+  nanodet: () => nanodet,
+  "nanodet-e": () => nanodet_e,
+  "nanodet-g": () => nanodet_g,
+  "nanodet-m": () => nanodet_m,
+  "nanodet-t": () => nanodet_t,
+  posenet: () => posenet,
+  selfie: () => selfie
+});
+var antispoof = 853098;
+var blazeface = 538928;
+var emotion = 820516;
+var facemesh = 1477958;
+var faceres = 6978814;
+var handlandmark_full = 5431368;
+var handtrack = 2964837;
+var iris = 2599092;
+var liveness = 592976;
+var mb3_centernet = 4030290;
+var models = 0;
+var movenet_lightning = 4650216;
+var selfie = 212886;
+var age = 161240;
+var blazeface_back = 538928;
+var blazeface_front = 402048;
+var blazepose_detector2d = 7499400;
+var blazepose_detector3d = 5928856;
+var blazepose_full = 6338290;
+var blazepose_heavy = 27501554;
+var blazepose_lite = 2725490;
+var efficientpose = 5651240;
+var faceboxes = 2013002;
+var facemesh_attention_alt = 2387598;
+var facemesh_attention = 2382414;
+var facemesh_detection_full = 1026192;
+var facemesh_detection_short = 201268;
+var facemesh_orig = 2955780;
+var faceres_deep = 13957620;
+var gear = 1498916;
+var gender_ssrnet_imdb = 161236;
+var gender = 201808;
+var handdetect = 3515612;
+var handlandmark_lite = 2023432;
+var handlandmark_sparse = 5286322;
+var handskeleton = 5502280;
+var meet = 372228;
+var mobileface = 2183192;
+var mobilefacenet = 5171976;
+var movenet_multipose = 9448838;
+var movenet_thunder = 12477112;
+var nanodet = 7574558;
+var posenet = 5032780;
+var blazepose_detect = 5928804;
+var anti_spoofing = 853098;
+var efficientpose_i_lite = 2269064;
+var efficientpose_ii_lite = 5651240;
+var efficientpose_iv = 25643252;
+var insightface_efficientnet_b0 = 13013224;
+var insightface_ghostnet_strides1 = 8093408;
+var insightface_ghostnet_strides2 = 8049584;
+var insightface_mobilenet_emore = 6938536;
+var insightface_mobilenet_swish = 12168584;
+var nanodet_e = 12319156;
+var nanodet_g = 7574558;
+var nanodet_m = 1887474;
+var nanodet_t = 5294216;
+var models_default = {
+  antispoof,
+  blazeface,
+  emotion,
+  facemesh,
+  faceres,
+  "handlandmark-full": handlandmark_full,
+  handtrack,
+  iris,
+  liveness,
+  "mb3-centernet": mb3_centernet,
+  models,
+  "movenet-lightning": movenet_lightning,
+  selfie,
+  age,
+  "blazeface-back": blazeface_back,
+  "blazeface-front": blazeface_front,
+  "blazepose-detector2d": blazepose_detector2d,
+  "blazepose-detector3d": blazepose_detector3d,
+  "blazepose-full": blazepose_full,
+  "blazepose-heavy": blazepose_heavy,
+  "blazepose-lite": blazepose_lite,
+  efficientpose,
+  faceboxes,
+  "facemesh-attention-alt": facemesh_attention_alt,
+  "facemesh-attention": facemesh_attention,
+  "facemesh-detection-full": facemesh_detection_full,
+  "facemesh-detection-short": facemesh_detection_short,
+  "facemesh-orig": facemesh_orig,
+  "faceres-deep": faceres_deep,
+  gear,
+  "gender-ssrnet-imdb": gender_ssrnet_imdb,
+  gender,
+  handdetect,
+  "handlandmark-lite": handlandmark_lite,
+  "handlandmark-sparse": handlandmark_sparse,
+  handskeleton,
+  meet,
+  mobileface,
+  mobilefacenet,
+  "movenet-multipose": movenet_multipose,
+  "movenet-thunder": movenet_thunder,
+  nanodet,
+  posenet,
+  "blazepose-detect": blazepose_detect,
+  "anti-spoofing": anti_spoofing,
+  "efficientpose-i-lite": efficientpose_i_lite,
+  "efficientpose-ii-lite": efficientpose_ii_lite,
+  "efficientpose-iv": efficientpose_iv,
+  "insightface-efficientnet-b0": insightface_efficientnet_b0,
+  "insightface-ghostnet-strides1": insightface_ghostnet_strides1,
+  "insightface-ghostnet-strides2": insightface_ghostnet_strides2,
+  "insightface-mobilenet-emore": insightface_mobilenet_emore,
+  "insightface-mobilenet-swish": insightface_mobilenet_swish,
+  "nanodet-e": nanodet_e,
+  "nanodet-g": nanodet_g,
+  "nanodet-m": nanodet_m,
+  "nanodet-t": nanodet_t
+};
+
+// src/tfjs/load.ts
+var options = {
+  cacheModels: true,
+  cacheSupported: true,
+  verbose: true,
+  debug: false,
+  modelBasePath: ""
+};
+var modelStats = {};
+async function httpHandler(url, init3) {
+  if (options.debug)
+    log("load model fetch:", url, init3);
+  return fetch(url, init3);
+}
+function setModelLoadOptions(config3) {
+  options.cacheModels = config3.cacheModels;
+  options.verbose = config3.debug;
+  options.modelBasePath = config3.modelBasePath;
+}
+async function loadModel(modelPath) {
+  var _a, _b, _c, _d;
+  let modelUrl = join(options.modelBasePath, modelPath || "");
+  if (!modelUrl.toLowerCase().endsWith(".json"))
+    modelUrl += ".json";
+  const modelPathSegments = modelUrl.includes("/") ? modelUrl.split("/") : modelUrl.split("\\");
+  const shortModelName = modelPathSegments[modelPathSegments.length - 1].replace(".json", "");
+  const cachedModelName = "indexeddb://" + shortModelName;
+  modelStats[shortModelName] = {
+    name: shortModelName,
+    sizeFromManifest: 0,
+    sizeLoadedWeights: 0,
+    sizeDesired: models_exports[shortModelName],
+    inCache: false
+  };
+  options.cacheSupported = typeof indexedDB !== "undefined";
+  let cachedModels = {};
+  try {
+    cachedModels = options.cacheSupported && options.cacheModels ? await tf4.io.listModels() : {};
+  } catch (e) {
+    options.cacheSupported = false;
+  }
+  modelStats[shortModelName].inCache = options.cacheSupported && options.cacheModels && Object.keys(cachedModels).includes(cachedModelName);
+  const tfLoadOptions = typeof fetch === "undefined" ? {} : { fetchFunc: (url, init3) => httpHandler(url, init3) };
+  let model21 = new tf4.GraphModel(modelStats[shortModelName].inCache ? cachedModelName : modelUrl, tfLoadOptions);
+  let loaded = false;
+  try {
+    model21.findIOHandler();
+    if (options.debug)
+      log("model load handler:", model21["handler"]);
+  } catch (err) {
+    log("error finding model i/o handler:", modelUrl, err);
+  }
+  try {
+    const artifacts = await ((_a = model21.handler) == null ? void 0 : _a.load()) || null;
+    modelStats[shortModelName].sizeFromManifest = ((_b = artifacts == null ? void 0 : artifacts.weightData) == null ? void 0 : _b.byteLength) || 0;
+    if (artifacts)
+      model21.loadSync(artifacts);
+    else
+      model21 = await tf4.loadGraphModel(modelStats[shortModelName].inCache ? cachedModelName : modelUrl, tfLoadOptions);
+    modelStats[shortModelName].sizeLoadedWeights = ((_d = (_c = model21.artifacts) == null ? void 0 : _c.weightData) == null ? void 0 : _d.byteLength) || 0;
+    if (options.verbose)
+      log("load:", { model: shortModelName, url: model21["modelUrl"], bytes: modelStats[shortModelName].sizeLoadedWeights });
+    loaded = true;
+  } catch (err) {
+    log("error loading model:", modelUrl, err);
+  }
+  if (loaded && options.cacheModels && options.cacheSupported && !modelStats[shortModelName].inCache) {
+    try {
+      const saveResult = await model21.save(cachedModelName);
+      if (options.debug)
+        log("model saved:", cachedModelName, saveResult);
+    } catch (err) {
+      log("error saving model:", modelUrl, err);
+    }
+  }
+  return model21;
+}
+
+// src/human.ts
+var tf39 = __toESM(require_tfjs_esm());
+
+// package.json
+var version2 = "2.11.0";
+
+// src/tfjs/humangl.ts
+var tf34 = __toESM(require_tfjs_esm());
+
+// src/models.ts
+var models_exports2 = {};
+__export(models_exports2, {
+  Models: () => Models,
+  getModelStats: () => getModelStats,
+  load: () => load22,
+  reset: () => reset2,
+  validate: () => validate2,
+  validateModel: () => validateModel
+});
+
+// src/face/antispoof.ts
+var tf5 = __toESM(require_tfjs_esm());
+var model;
+var cached = [];
+var skipped = Number.MAX_SAFE_INTEGER;
+var lastCount = 0;
+var lastTime = 0;
+async function load(config3) {
+  var _a;
+  if (env.initial)
+    model = null;
+  if (!model)
+    model = await loadModel((_a = config3.face.antispoof) == null ? void 0 : _a.modelPath);
+  else if (config3.debug)
+    log("cached model:", model["modelUrl"]);
+  return model;
+}
+async function predict(image27, config3, idx, count2) {
+  var _a, _b;
+  if (!model || !(model == null ? void 0 : model["executor"]))
+    return 0;
+  const skipTime = (((_a = config3.face.antispoof) == null ? void 0 : _a.skipTime) || 0) > now() - lastTime;
+  const skipFrame = skipped < (((_b = config3.face.antispoof) == null ? void 0 : _b.skipFrames) || 0);
+  if (config3.skipAllowed && skipTime && skipFrame && lastCount === count2 && cached[idx]) {
+    skipped++;
+    return cached[idx];
+  }
+  skipped = 0;
+  return new Promise(async (resolve) => {
+    const resize = tf5.image.resizeBilinear(image27, [(model == null ? void 0 : model.inputs[0].shape) ? model.inputs[0].shape[2] : 0, (model == null ? void 0 : model.inputs[0].shape) ? model.inputs[0].shape[1] : 0], false);
+    const res = model == null ? void 0 : model.execute(resize);
+    const num = (await res.data())[0];
+    cached[idx] = Math.round(100 * num) / 100;
+    lastCount = count2;
+    lastTime = now();
+    tf5.dispose([resize, res]);
+    resolve(cached[idx]);
+  });
+}
+
+// src/face/blazeface.ts
+var tf8 = __toESM(require_tfjs_esm());
+
+// src/face/facemeshutil.ts
+var tf7 = __toESM(require_tfjs_esm());
+
+// src/face/facemeshcoords.ts
+var meshAnnotations = {
+  silhouette: [
+    10,
+    338,
+    297,
+    332,
+    284,
+    251,
+    389,
+    356,
+    454,
+    323,
+    361,
+    288,
+    397,
+    365,
+    379,
+    378,
+    400,
+    377,
+    152,
+    148,
+    176,
+    149,
+    150,
+    136,
+    172,
+    58,
+    132,
+    93,
+    234,
+    127,
+    162,
+    21,
+    54,
+    103,
+    67,
+    109
+  ],
+  lipsUpperOuter: [185, 40, 39, 37, 0, 267, 269, 270, 409],
+  lipsLowerOuter: [61, 146, 91, 181, 84, 17, 314, 405, 321, 375, 291],
+  lipsUpperInner: [191, 80, 81, 82, 13, 312, 311, 310, 415],
+  lipsLowerInner: [78, 95, 88, 178, 87, 14, 317, 402, 318, 324, 308],
+  lipsLowerSemiOuter: [76, 77, 90, 180, 85, 16, 315, 404, 320, 307, 306],
+  lipsUpperSemiOuter: [184, 74, 73, 72, 11, 302, 303, 304, 408],
+  lipsLowerSemiInner: [62, 96, 89, 179, 86, 15, 316, 403, 319, 325, 292],
+  lipsUpperSemiInner: [183, 42, 41, 38, 12, 268, 271, 272, 407],
+  rightEyeUpper0: [246, 161, 160, 159, 158, 157, 173],
+  rightEyeLower0: [33, 7, 163, 144, 145, 153, 154, 155, 133],
+  rightEyeUpper1: [247, 30, 29, 27, 28, 56, 190],
+  rightEyeLower1: [130, 25, 110, 24, 23, 22, 26, 112, 243],
+  rightEyeUpper2: [113, 225, 224, 223, 222, 221, 189],
+  rightEyeLower2: [226, 31, 228, 229, 230, 231, 232, 233, 244],
+  rightEyeLower3: [143, 111, 117, 118, 119, 120, 121, 128, 245],
+  rightEyebrowUpper: [156, 70, 63, 105, 66, 107, 55, 193],
+  rightEyebrowLower: [35, 124, 46, 53, 52, 65],
+  rightEyeIris: [473, 474, 475, 476, 477],
+  leftEyeUpper0: [466, 388, 387, 386, 385, 384, 398],
+  leftEyeLower0: [263, 249, 390, 373, 374, 380, 381, 382, 362],
+  leftEyeUpper1: [467, 260, 259, 257, 258, 286, 414],
+  leftEyeLower1: [359, 255, 339, 254, 253, 252, 256, 341, 463],
+  leftEyeUpper2: [342, 445, 444, 443, 442, 441, 413],
+  leftEyeLower2: [446, 261, 448, 449, 450, 451, 452, 453, 464],
+  leftEyeLower3: [372, 340, 346, 347, 348, 349, 350, 357, 465],
+  leftEyebrowUpper: [383, 300, 293, 334, 296, 336, 285, 417],
+  leftEyebrowLower: [265, 353, 276, 283, 282, 295],
+  leftEyeIris: [468, 469, 470, 471, 472],
+  midwayBetweenEyes: [168],
+  noseTip: [1],
+  noseBottom: [2],
+  noseRightCorner: [98],
+  noseLeftCorner: [327],
+  rightCheek: [205],
+  leftCheek: [425]
+};
+var meshLandmarks = {
+  count: 468,
+  mouth: 13,
+  symmetryLine: [13, meshAnnotations.midwayBetweenEyes[0]]
+};
+var blazeFaceLandmarks = {
+  leftEye: 0,
+  rightEye: 1,
+  nose: 2,
+  mouth: 3,
+  leftEar: 4,
+  rightEar: 5,
+  symmetryLine: [3, 2]
+};
+var irisIndices = [
+  { key: "EyeUpper0", indices: [9, 10, 11, 12, 13, 14, 15] },
+  { key: "EyeUpper1", indices: [25, 26, 27, 28, 29, 30, 31] },
+  { key: "EyeUpper2", indices: [41, 42, 43, 44, 45, 46, 47] },
+  { key: "EyeLower0", indices: [0, 1, 2, 3, 4, 5, 6, 7, 8] },
+  { key: "EyeLower1", indices: [16, 17, 18, 19, 20, 21, 22, 23, 24] },
+  { key: "EyeLower2", indices: [32, 33, 34, 35, 36, 37, 38, 39, 40] },
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+var VTX68 = [
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+  132,
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+  91,
+  78,
+  81,
+  13,
+  311,
+  308,
+  402,
+  14,
+  178
+];
+var VTX33 = [33, 133, 362, 263, 1, 62, 308, 159, 145, 386, 374, 6, 102, 331, 2, 13, 14, 70, 105, 107, 336, 334, 300, 54, 10, 284, 50, 280, 234, 454, 58, 288, 152];
+var VTX7 = [33, 133, 362, 263, 1, 78, 308];
+var UV68 = VTX68.map((x) => UV468[x]);
+var UV33 = VTX33.map((x) => UV468[x]);
+var UV7 = VTX7.map((x) => UV468[x]);
+function connectionsToIndices(connections) {
+  const indices = connections.map((connection) => connection[0]);
+  indices.push(connections[connections.length - 1][1]);
+  return indices;
+}
+var pairsLips = [
+  [61, 146],
+  [146, 91],
+  [91, 181],
+  [181, 84],
+  [84, 17],
+  [17, 314],
+  [314, 405],
+  [405, 321],
+  [321, 375],
+  [375, 291],
+  [61, 185],
+  [185, 40],
+  [40, 39],
+  [39, 37],
+  [37, 0],
+  [0, 267],
+  [267, 269],
+  [269, 270],
+  [270, 409],
+  [409, 291],
+  [78, 95],
+  [95, 88],
+  [88, 178],
+  [178, 87],
+  [87, 14],
+  [14, 317],
+  [317, 402],
+  [402, 318],
+  [318, 324],
+  [324, 308],
+  [78, 191],
+  [191, 80],
+  [80, 81],
+  [81, 82],
+  [82, 13],
+  [13, 312],
+  [312, 311],
+  [311, 310],
+  [310, 415],
+  [415, 308]
+];
+var pairsLeftEye = [[263, 249], [249, 390], [390, 373], [373, 374], [374, 380], [380, 381], [381, 382], [382, 362], [263, 466], [466, 388], [388, 387], [387, 386], [386, 385], [385, 384], [384, 398], [398, 362]];
+var pairsLeftEyebrow = [[276, 283], [283, 282], [282, 295], [295, 285], [300, 293], [293, 334], [334, 296], [296, 336]];
+var pairsLeftIris = [[474, 475], [475, 476], [476, 477], [477, 474]];
+var pairsRightEye = [[33, 7], [7, 163], [163, 144], [144, 145], [145, 153], [153, 154], [154, 155], [155, 133], [33, 246], [246, 161], [161, 160], [160, 159], [159, 158], [158, 157], [157, 173], [173, 133]];
+var pairsRightEyebrow = [[46, 53], [53, 52], [52, 65], [65, 55], [70, 63], [63, 105], [105, 66], [66, 107]];
+var pairsRightIris = [[469, 470], [470, 471], [471, 472], [472, 469]];
+var pairsFaceContour = [
+  [10, 338],
+  [338, 297],
+  [297, 332],
+  [332, 284],
+  [284, 251],
+  [251, 389],
+  [389, 356],
+  [356, 454],
+  [454, 323],
+  [323, 361],
+  [361, 288],
+  [288, 397],
+  [397, 365],
+  [365, 379],
+  [379, 378],
+  [378, 400],
+  [400, 377],
+  [377, 152],
+  [152, 148],
+  [148, 176],
+  [176, 149],
+  [149, 150],
+  [150, 136],
+  [136, 172],
+  [172, 58],
+  [58, 132],
+  [132, 93],
+  [93, 234],
+  [234, 127],
+  [127, 162],
+  [162, 21],
+  [21, 54],
+  [54, 103],
+  [103, 67],
+  [67, 109],
+  [109, 10]
+];
+var contourKeypoints = {
+  lips: connectionsToIndices(pairsLips),
+  leftEye: connectionsToIndices(pairsLeftEye),
+  leftEyebrow: connectionsToIndices(pairsLeftEyebrow),
+  leftIris: connectionsToIndices(pairsLeftIris),
+  rightEye: connectionsToIndices(pairsRightEye),
+  rightEyebrow: connectionsToIndices(pairsRightEyebrow),
+  rightIris: connectionsToIndices(pairsRightIris),
+  faceOval: connectionsToIndices(pairsFaceContour)
+};
+
+// src/tfjs/constants.ts
+var tf6 = __toESM(require_tfjs_esm());
+var constants = {
+  tf255: 255,
+  tf1: 1,
+  tf2: 2,
+  tf05: 0.5,
+  tf127: 127.5,
+  rgb: [0.2989, 0.587, 0.114]
+};
+function init() {
+  constants.tf255 = tf6.scalar(255, "float32");
+  constants.tf1 = tf6.scalar(1, "float32");
+  constants.tf2 = tf6.scalar(2, "float32");
+  constants.tf05 = tf6.scalar(0.5, "float32");
+  constants.tf127 = tf6.scalar(127.5, "float32");
+  constants.rgb = tf6.tensor1d([0.2989, 0.587, 0.114], "float32");
+}
+
+// src/face/facemeshutil.ts
+var getBoxSize = (box) => [Math.abs(box.endPoint[0] - box.startPoint[0]), Math.abs(box.endPoint[1] - box.startPoint[1])];
+var getBoxCenter = (box) => [box.startPoint[0] + (box.endPoint[0] - box.startPoint[0]) / 2, box.startPoint[1] + (box.endPoint[1] - box.startPoint[1]) / 2, 1];
+var clampBox = (box, input) => box ? [
+  Math.trunc(Math.max(0, box.startPoint[0])),
+  Math.trunc(Math.max(0, box.startPoint[1])),
+  Math.trunc(Math.min(input.shape[2] || 0, box.endPoint[0]) - Math.max(0, box.startPoint[0])),
+  Math.trunc(Math.min(input.shape[1] || 0, box.endPoint[1]) - Math.max(0, box.startPoint[1]))
+] : [0, 0, 0, 0];
+var getRawBox = (box, input) => box ? [
+  box.startPoint[0] / (input.shape[2] || 0),
+  box.startPoint[1] / (input.shape[1] || 0),
+  (box.endPoint[0] - box.startPoint[0]) / (input.shape[2] || 0),
+  (box.endPoint[1] - box.startPoint[1]) / (input.shape[1] || 0)
+] : [0, 0, 0, 0];
+var scaleBoxCoordinates = (box, factor) => {
+  const startPoint = [box.startPoint[0] * factor[0], box.startPoint[1] * factor[1]];
+  const endPoint = [box.endPoint[0] * factor[0], box.endPoint[1] * factor[1]];
+  return { startPoint, endPoint, landmarks: box.landmarks, confidence: box.confidence };
+};
+var cutAndResize = (box, image27, cropSize) => {
+  const h = image27.shape[1];
+  const w = image27.shape[2];
+  const cutBox = [box.startPoint[1] / h, box.startPoint[0] / w, box.endPoint[1] / h, box.endPoint[0] / w];
+  const crop = tf7.image.cropAndResize(image27, [cutBox], [0], cropSize);
+  const norm = tf7.div(crop, constants.tf255);
+  tf7.dispose(crop);
+  return norm;
+};
+var enlargeBox = (box, factor) => {
+  const center = getBoxCenter(box);
+  const size2 = getBoxSize(box);
+  const halfSize = [factor * size2[0] / 2, factor * size2[1] / 2];
+  return { startPoint: [center[0] - halfSize[0], center[1] - halfSize[1]], endPoint: [center[0] + halfSize[0], center[1] + halfSize[1]], landmarks: box.landmarks, confidence: box.confidence };
+};
+var squarifyBox = (box) => {
+  const centers = getBoxCenter(box);
+  const size2 = getBoxSize(box);
+  const halfSize = Math.max(...size2) / 2;
+  return { startPoint: [Math.round(centers[0] - halfSize), Math.round(centers[1] - halfSize)], endPoint: [Math.round(centers[0] + halfSize), Math.round(centers[1] + halfSize)], landmarks: box.landmarks, confidence: box.confidence };
+};
+var calculateLandmarksBoundingBox = (landmarks) => {
+  const x = landmarks.map((d) => d[0]);
+  const y = landmarks.map((d) => d[1]);
+  return { startPoint: [Math.min(...x), Math.min(...y)], endPoint: [Math.max(...x), Math.max(...y)], landmarks };
+};
+var fixedRotationMatrix = [[1, 0, 0], [0, 1, 0], [0, 0, 1]];
+var normalizeRadians = (angle) => angle - 2 * Math.PI * Math.floor((angle + Math.PI) / (2 * Math.PI));
+var computeRotation = (point1, point2) => normalizeRadians(Math.PI / 2 - Math.atan2(-(point2[1] - point1[1]), point2[0] - point1[0]));
+var buildTranslationMatrix = (x, y) => [[1, 0, x], [0, 1, y], [0, 0, 1]];
+var dot = (v1, v2) => {
+  let product = 0;
+  for (let i = 0; i < v1.length; i++)
+    product += v1[i] * v2[i];
+  return product;
+};
+var getColumnFrom2DArr = (arr, columnIndex) => {
+  const column = [];
+  for (let i = 0; i < arr.length; i++)
+    column.push(arr[i][columnIndex]);
+  return column;
+};
+var multiplyTransformMatrices = (mat1, mat2) => {
+  const product = [];
+  const size2 = mat1.length;
+  for (let row = 0; row < size2; row++) {
+    product.push([]);
+    for (let col = 0; col < size2; col++)
+      product[row].push(dot(mat1[row], getColumnFrom2DArr(mat2, col)));
+  }
+  return product;
+};
+var buildRotationMatrix = (rotation, center) => {
+  const cosA = Math.cos(rotation);
+  const sinA = Math.sin(rotation);
+  const rotationMatrix = [[cosA, -sinA, 0], [sinA, cosA, 0], [0, 0, 1]];
+  const translationMatrix = buildTranslationMatrix(center[0], center[1]);
+  const translationTimesRotation = multiplyTransformMatrices(translationMatrix, rotationMatrix);
+  const negativeTranslationMatrix = buildTranslationMatrix(-center[0], -center[1]);
+  return multiplyTransformMatrices(translationTimesRotation, negativeTranslationMatrix);
+};
+var invertTransformMatrix = (matrix) => {
+  const rotationComponent = [[matrix[0][0], matrix[1][0]], [matrix[0][1], matrix[1][1]]];
+  const translationComponent = [matrix[0][2], matrix[1][2]];
+  const invertedTranslation = [-dot(rotationComponent[0], translationComponent), -dot(rotationComponent[1], translationComponent)];
+  return [rotationComponent[0].concat(invertedTranslation[0]), rotationComponent[1].concat(invertedTranslation[1]), [0, 0, 1]];
+};
+var rotatePoint = (homogeneousCoordinate, rotationMatrix) => [dot(homogeneousCoordinate, rotationMatrix[0]), dot(homogeneousCoordinate, rotationMatrix[1])];
+function generateAnchors(inputSize10) {
+  const spec = inputSize10 === 192 ? { strides: [4], anchors: [1] } : { strides: [inputSize10 / 16, inputSize10 / 8], anchors: [2, 6] };
+  const anchors3 = [];
+  for (let i = 0; i < spec.strides.length; i++) {
+    const stride = spec.strides[i];
+    const gridRows = Math.floor((inputSize10 + stride - 1) / stride);
+    const gridCols = Math.floor((inputSize10 + stride - 1) / stride);
+    const anchorsNum = spec.anchors[i];
+    for (let gridY = 0; gridY < gridRows; gridY++) {
+      const anchorY = stride * (gridY + 0.5);
+      for (let gridX = 0; gridX < gridCols; gridX++) {
+        const anchorX = stride * (gridX + 0.5);
+        for (let n = 0; n < anchorsNum; n++)
+          anchors3.push([anchorX, anchorY]);
+      }
+    }
+  }
+  return anchors3;
+}
+function transformRawCoords(coordsRaw, box, angle, rotationMatrix, inputSize10) {
+  const boxSize = getBoxSize(box);
+  const coordsScaled = coordsRaw.map((coord) => [
+    boxSize[0] / inputSize10 * (coord[0] - inputSize10 / 2),
+    boxSize[1] / inputSize10 * (coord[1] - inputSize10 / 2),
+    coord[2] || 0
+  ]);
+  const largeAngle = angle && angle !== 0 && Math.abs(angle) > 0.2;
+  const coordsRotationMatrix = largeAngle ? buildRotationMatrix(angle, [0, 0]) : fixedRotationMatrix;
+  const coordsRotated = largeAngle ? coordsScaled.map((coord) => [...rotatePoint(coord, coordsRotationMatrix), coord[2]]) : coordsScaled;
+  const inverseRotationMatrix = largeAngle ? invertTransformMatrix(rotationMatrix) : fixedRotationMatrix;
+  const boxCenter = getBoxCenter(box);
+  const offsets = [dot(boxCenter, inverseRotationMatrix[0]), dot(boxCenter, inverseRotationMatrix[1])];
+  return coordsRotated.map((coord) => [
+    Math.trunc(coord[0] + offsets[0]),
+    Math.trunc(coord[1] + offsets[1]),
+    Math.trunc(coord[2] || 0)
+  ]);
+}
+function correctFaceRotation(rotate, box, input, inputSize10) {
+  const symmetryLine = box.landmarks.length >= meshLandmarks.count ? meshLandmarks.symmetryLine : blazeFaceLandmarks.symmetryLine;
+  let angle = 0;
+  let rotationMatrix = fixedRotationMatrix;
+  let face4;
+  if (rotate && env.kernels.includes("rotatewithoffset")) {
+    angle = computeRotation(box.landmarks[symmetryLine[0]], box.landmarks[symmetryLine[1]]);
+    const largeAngle = angle && angle !== 0 && Math.abs(angle) > 0.2;
+    if (largeAngle) {
+      const center = getBoxCenter(box);
+      const centerRaw = [center[0] / input.shape[2], center[1] / input.shape[1]];
+      const rotated = tf7.image.rotateWithOffset(input, angle, 0, centerRaw);
+      rotationMatrix = buildRotationMatrix(-angle, center);
+      face4 = cutAndResize(box, rotated, [inputSize10, inputSize10]);
+      tf7.dispose(rotated);
+    } else {
+      face4 = cutAndResize(box, input, [inputSize10, inputSize10]);
+    }
+  } else {
+    face4 = cutAndResize(box, input, [inputSize10, inputSize10]);
+  }
+  return [angle, rotationMatrix, face4];
+}
+var findFaceCenter = (mesh) => {
+  const x = mesh.map((m) => m[0]);
+  const y = mesh.map((m) => m[1]);
+  return [Math.min(...x) + (Math.max(...x) - Math.min(...x)) / 2, Math.min(...y) + (Math.max(...y) - Math.min(...y)) / 2];
+};
+var calculateFaceBox = (mesh, previousBox) => {
+  const center = findFaceCenter(mesh);
+  const boxSize = getBoxSize(previousBox);
+  const calculatedBox = {
+    startPoint: [center[0] - boxSize[0] / 2, center[1] - boxSize[1] / 2],
+    endPoint: [center[0] + boxSize[0] / 2, center[1] + boxSize[1] / 2]
+  };
+  return calculatedBox;
+};
+
+// src/face/blazeface.ts
+var keypointsCount = 6;
+var faceBoxScaleFactor = 1.4;
+var model2;
+var anchors = null;
+var inputSize = 0;
+var inputSizeT = null;
+var size = () => inputSize;
+async function load2(config3) {
+  var _a;
+  if (env.initial)
+    model2 = null;
+  if (!model2)
+    model2 = await loadModel((_a = config3.face.detector) == null ? void 0 : _a.modelPath);
+  else if (config3.debug)
+    log("cached model:", model2["modelUrl"]);
+  inputSize = model2["executor"] && model2.inputs[0].shape ? model2.inputs[0].shape[2] : 256;
+  inputSizeT = tf8.scalar(inputSize, "int32");
+  anchors = tf8.tensor2d(generateAnchors(inputSize));
+  return model2;
+}
+function decodeBoxes(boxOutputs) {
+  const t2 = {};
+  t2.boxStarts = tf8.slice(boxOutputs, [0, 1], [-1, 2]);
+  t2.centers = tf8.add(t2.boxStarts, anchors);
+  t2.boxSizes = tf8.slice(boxOutputs, [0, 3], [-1, 2]);
+  t2.boxSizesNormalized = tf8.div(t2.boxSizes, inputSizeT);
+  t2.centersNormalized = tf8.div(t2.centers, inputSizeT);
+  t2.halfBoxSize = tf8.div(t2.boxSizesNormalized, constants.tf2);
+  t2.starts = tf8.sub(t2.centersNormalized, t2.halfBoxSize);
+  t2.ends = tf8.add(t2.centersNormalized, t2.halfBoxSize);
+  t2.startNormalized = tf8.mul(t2.starts, inputSizeT);
+  t2.endNormalized = tf8.mul(t2.ends, inputSizeT);
+  const boxes = tf8.concat2d([t2.startNormalized, t2.endNormalized], 1);
+  Object.keys(t2).forEach((tensor6) => tf8.dispose(t2[tensor6]));
+  return boxes;
+}
+async function getBoxes(inputImage, config3) {
+  var _a, _b, _c, _d;
+  if (!inputImage || inputImage["isDisposedInternal"] || inputImage.shape.length !== 4 || inputImage.shape[1] < 1 || inputImage.shape[2] < 1)
+    return [];
+  const t2 = {};
+  t2.resized = tf8.image.resizeBilinear(inputImage, [inputSize, inputSize]);
+  t2.div = tf8.div(t2.resized, constants.tf127);
+  t2.normalized = tf8.sub(t2.div, constants.tf05);
+  const res = model2 == null ? void 0 : model2.execute(t2.normalized);
+  if (Array.isArray(res) && res.length > 2) {
+    const sorted = res.sort((a, b) => a.size - b.size);
+    t2.concat384 = tf8.concat([sorted[0], sorted[2]], 2);
+    t2.concat512 = tf8.concat([sorted[1], sorted[3]], 2);
+    t2.concat = tf8.concat([t2.concat512, t2.concat384], 1);
+    t2.batch = tf8.squeeze(t2.concat, 0);
+  } else if (Array.isArray(res)) {
+    t2.batch = tf8.squeeze(res[0]);
+  } else {
+    t2.batch = tf8.squeeze(res);
+  }
+  tf8.dispose(res);
+  t2.boxes = decodeBoxes(t2.batch);
+  t2.logits = tf8.slice(t2.batch, [0, 0], [-1, 1]);
+  t2.sigmoid = tf8.sigmoid(t2.logits);
+  t2.scores = tf8.squeeze(t2.sigmoid);
+  t2.nms = await tf8.image.nonMaxSuppressionAsync(t2.boxes, t2.scores, ((_a = config3.face.detector) == null ? void 0 : _a.maxDetected) || 0, ((_b = config3.face.detector) == null ? void 0 : _b.iouThreshold) || 0, ((_c = config3.face.detector) == null ? void 0 : _c.minConfidence) || 0);
+  const nms = await t2.nms.array();
+  const boxes = [];
+  const scores = await t2.scores.data();
+  for (let i = 0; i < nms.length; i++) {
+    const confidence = scores[nms[i]];
+    if (confidence > (((_d = config3.face.detector) == null ? void 0 : _d.minConfidence) || 0)) {
+      const b = {};
+      b.bbox = tf8.slice(t2.boxes, [nms[i], 0], [1, -1]);
+      b.slice = tf8.slice(t2.batch, [nms[i], keypointsCount - 1], [1, -1]);
+      b.squeeze = tf8.squeeze(b.slice);
+      b.landmarks = tf8.reshape(b.squeeze, [keypointsCount, -1]);
+      const points = await b.bbox.data();
+      const rawBox = {
+        startPoint: [points[0], points[1]],
+        endPoint: [points[2], points[3]],
+        landmarks: await b.landmarks.array(),
+        confidence
+      };
+      const scaledBox = scaleBoxCoordinates(rawBox, [(inputImage.shape[2] || 0) / inputSize, (inputImage.shape[1] || 0) / inputSize]);
+      const enlargedBox = enlargeBox(scaledBox, config3.face["scale"] || faceBoxScaleFactor);
+      const squaredBox = squarifyBox(enlargedBox);
+      boxes.push(squaredBox);
+      Object.keys(b).forEach((tensor6) => tf8.dispose(b[tensor6]));
+    }
+  }
+  Object.keys(t2).forEach((tensor6) => tf8.dispose(t2[tensor6]));
+  return boxes;
+}
+
+// src/body/blazepose.ts
+var tf10 = __toESM(require_tfjs_esm());
+
+// src/body/blazeposecoords.ts
+var blazeposecoords_exports = {};
+__export(blazeposecoords_exports, {
+  connected: () => connected,
+  kpt: () => kpt
+});
+var kpt = [
+  "nose",
+  "leftEyeInside",
+  "leftEye",
+  "leftEyeOutside",
+  "rightEyeInside",
+  "rightEye",
+  "rightEyeOutside",
+  "leftEar",
+  "rightEar",
+  "leftMouth",
+  "rightMouth",
+  "leftShoulder",
+  "rightShoulder",
+  "leftElbow",
+  "rightElbow",
+  "leftWrist",
+  "rightWrist",
+  "leftPinky",
+  "rightPinky",
+  "leftIndex",
+  "rightIndex",
+  "leftThumb",
+  "rightThumb",
+  "leftHip",
+  "rightHip",
+  "leftKnee",
+  "rightKnee",
+  "leftAnkle",
+  "rightAnkle",
+  "leftHeel",
+  "rightHeel",
+  "leftFoot",
+  "rightFoot",
+  "bodyCenter",
+  "bodyTop",
+  "leftPalm",
+  "leftHand",
+  "rightPalm",
+  "rightHand"
+];
+var connected = {
+  shoulders: ["leftShoulder", "rightShoulder"],
+  hips: ["rightHip", "leftHip"],
+  mouth: ["leftMouth", "rightMouth"],
+  leftLegUpper: ["leftHip", "leftKnee"],
+  leftLegLower: ["leftKnee", "leftAnkle"],
+  leftFoot: ["leftAnkle", "leftHeel", "leftFoot"],
+  leftTorso: ["leftShoulder", "leftHip"],
+  leftArmUpper: ["leftShoulder", "leftElbow"],
+  leftArmLower: ["leftElbow", "leftWrist"],
+  leftHand: ["leftWrist", "leftPalm"],
+  leftHandPinky: ["leftPalm", "leftPinky"],
+  leftHandIndex: ["leftPalm", "leftIndex"],
+  leftHandThumb: ["leftPalm", "leftThumb"],
+  leftEyeOutline: ["leftEyeInside", "leftEyeOutside"],
+  rightLegUpper: ["rightHip", "rightKnee"],
+  rightLegLower: ["rightKnee", "rightAnkle"],
+  rightFoot: ["rightAnkle", "rightHeel", "rightFoot"],
+  rightTorso: ["rightShoulder", "rightHip"],
+  rightArmUpper: ["rightShoulder", "rightElbow"],
+  rightArmLower: ["rightElbow", "rightWrist"],
+  rightHand: ["rightWrist", "rightPalm"],
+  rightHandPinky: ["rightPalm", "rightPinky"],
+  rightHandIndex: ["rightPalm", "rightIndex"],
+  rightHandThumb: ["rightPalm", "rightThumb"],
+  rightEyeOutline: ["rightEyeInside", "rightEyeOutside"]
+};
+
+// src/body/blazeposedetector.ts
+var tf9 = __toESM(require_tfjs_esm());
+var inputSize2 = 224;
+var anchorTensor;
+var numLayers = 5;
+var strides = [8, 16, 32, 32, 32];
+function createAnchors() {
+  const anchors3 = [];
+  let layerId = 0;
+  while (layerId < numLayers) {
+    let anchorCount = 0;
+    let lastSameStrideLayer = layerId;
+    while (lastSameStrideLayer < strides.length && strides[lastSameStrideLayer] === strides[layerId]) {
+      anchorCount += 2;
+      lastSameStrideLayer++;
+    }
+    const stride = strides[layerId];
+    const featureMapHeight = Math.ceil(inputSize2 / stride);
+    const featureMapWidth = Math.ceil(inputSize2 / stride);
+    for (let y = 0; y < featureMapHeight; ++y) {
+      for (let x = 0; x < featureMapWidth; ++x) {
+        for (let anchorId = 0; anchorId < anchorCount; ++anchorId) {
+          anchors3.push({ x: (x + 0.5) / featureMapWidth, y: (y + 0.5) / featureMapHeight });
+        }
+      }
+    }
+    layerId = lastSameStrideLayer;
+  }
+  anchorTensor = { x: tf9.tensor1d(anchors3.map((a) => a.x)), y: tf9.tensor1d(anchors3.map((a) => a.y)) };
+}
+
+// src/util/box.ts
+function calc(keypoints, outputSize2 = [1, 1]) {
+  const coords = [keypoints.map((pt) => pt[0]), keypoints.map((pt) => pt[1])];
+  const min2 = [Math.min(...coords[0]), Math.min(...coords[1])];
+  const max4 = [Math.max(...coords[0]), Math.max(...coords[1])];
+  const box = [min2[0], min2[1], max4[0] - min2[0], max4[1] - min2[1]];
+  const boxRaw = [box[0] / outputSize2[0], box[1] / outputSize2[1], box[2] / outputSize2[0], box[3] / outputSize2[1]];
+  return { box, boxRaw };
+}
+function square(keypoints, outputSize2 = [1, 1]) {
+  const coords = [keypoints.map((pt) => pt[0]), keypoints.map((pt) => pt[1])];
+  const min2 = [Math.min(...coords[0]), Math.min(...coords[1])];
+  const max4 = [Math.max(...coords[0]), Math.max(...coords[1])];
+  const center = [(min2[0] + max4[0]) / 2, (min2[1] + max4[1]) / 2];
+  const dist = Math.max(center[0] - min2[0], center[1] - min2[1], -center[0] + max4[0], -center[1] + max4[1]);
+  const box = [Math.trunc(center[0] - dist), Math.trunc(center[1] - dist), Math.trunc(2 * dist), Math.trunc(2 * dist)];
+  const boxRaw = [box[0] / outputSize2[0], box[1] / outputSize2[1], box[2] / outputSize2[0], box[3] / outputSize2[1]];
+  return { box, boxRaw };
+}
+function scale(box, scaleFact) {
+  const dist = [box[2] * scaleFact, box[3] * scaleFact];
+  const newBox = [
+    box[0] - (dist[0] - box[2]) / 2,
+    box[1] - (dist[1] - box[3]) / 2,
+    dist[0],
+    dist[1]
+  ];
+  return newBox;
+}
+
+// src/body/blazepose.ts
+var env3 = { initial: true };
+var models2 = { detector: null, landmarks: null };
+var inputSize3 = { detector: [224, 224], landmarks: [256, 256] };
+var skipped2 = Number.MAX_SAFE_INTEGER;
+var outputNodes = {
+  landmarks: ["ld_3d", "activation_segmentation", "activation_heatmap", "world_3d", "output_poseflag"],
+  detector: []
+};
+var cache = null;
+var cropBox;
+var padding = [[0, 0], [0, 0], [0, 0], [0, 0]];
+var lastTime2 = 0;
+var sigmoid3 = (x) => 1 - 1 / (1 + Math.exp(x));
+async function loadDetect(config3) {
+  var _a;
+  if (env3.initial)
+    models2.detector = null;
+  if (!models2.detector && config3.body["detector"] && config3.body["detector"].modelPath || "") {
+    models2.detector = await loadModel(config3.body["detector"].modelPath);
+    const inputs = ((_a = models2.detector) == null ? void 0 : _a["executor"]) ? Object.values(models2.detector.modelSignature["inputs"]) : void 0;
+    inputSize3.detector[0] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[1].size) : 0;
+    inputSize3.detector[1] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 0;
+  } else if (config3.debug && models2.detector)
+    log("cached model:", models2.detector["modelUrl"]);
+  createAnchors();
+  return models2.detector;
+}
+async function loadPose(config3) {
+  var _a;
+  if (env3.initial)
+    models2.landmarks = null;
+  if (!models2.landmarks) {
+    models2.landmarks = await loadModel(config3.body.modelPath);
+    const inputs = ((_a = models2.landmarks) == null ? void 0 : _a["executor"]) ? Object.values(models2.landmarks.modelSignature["inputs"]) : void 0;
+    inputSize3.landmarks[0] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[1].size) : 0;
+    inputSize3.landmarks[1] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 0;
+  } else if (config3.debug)
+    log("cached model:", models2.landmarks["modelUrl"]);
+  return models2.landmarks;
+}
+function prepareImage(input, size2) {
+  var _a, _b;
+  const t2 = {};
+  if (!((_a = input == null ? void 0 : input.shape) == null ? void 0 : _a[1]) || !((_b = input == null ? void 0 : input.shape) == null ? void 0 : _b[2]))
+    return input;
+  let final;
+  if (cropBox) {
+    t2.cropped = tf10.image.cropAndResize(input, [cropBox], [0], [input.shape[1], input.shape[2]]);
+  }
+  if (input.shape[1] !== input.shape[2]) {
+    const height = [
+      input.shape[2] > input.shape[1] ? Math.trunc((input.shape[2] - input.shape[1]) / 2) : 0,
+      input.shape[2] > input.shape[1] ? Math.trunc((input.shape[2] - input.shape[1]) / 2) : 0
+    ];
+    const width = [
+      input.shape[1] > input.shape[2] ? Math.trunc((input.shape[1] - input.shape[2]) / 2) : 0,
+      input.shape[1] > input.shape[2] ? Math.trunc((input.shape[1] - input.shape[2]) / 2) : 0
+    ];
+    padding = [
+      [0, 0],
+      height,
+      width,
+      [0, 0]
+    ];
+    t2.pad = tf10.pad(t2.cropped || input, padding);
+    t2.resize = tf10.image.resizeBilinear(t2.pad, [size2, size2]);
+    final = tf10.div(t2.resize, constants.tf255);
+  } else if (input.shape[1] !== size2) {
+    t2.resize = tf10.image.resizeBilinear(t2.cropped || input, [size2, size2]);
+    final = tf10.div(t2.resize, constants.tf255);
+  } else {
+    final = tf10.div(t2.cropped || input, constants.tf255);
+  }
+  Object.keys(t2).forEach((tensor6) => tf10.dispose(t2[tensor6]));
+  return final;
+}
+function rescaleKeypoints(keypoints, outputSize2) {
+  for (const kpt4 of keypoints) {
+    kpt4.position = [
+      Math.trunc(kpt4.position[0] * (outputSize2[0] + padding[2][0] + padding[2][1]) / outputSize2[0] - padding[2][0]),
+      Math.trunc(kpt4.position[1] * (outputSize2[1] + padding[1][0] + padding[1][1]) / outputSize2[1] - padding[1][0]),
+      kpt4.position[2]
+    ];
+    kpt4.positionRaw = [kpt4.position[0] / outputSize2[0], kpt4.position[1] / outputSize2[1], 2 * kpt4.position[2] / (outputSize2[0] + outputSize2[1])];
+  }
+  if (cropBox) {
+    for (const kpt4 of keypoints) {
+      kpt4.positionRaw = [
+        kpt4.positionRaw[0] + cropBox[1],
+        kpt4.positionRaw[1] + cropBox[0],
+        kpt4.positionRaw[2]
+      ];
+      kpt4.position = [
+        Math.trunc(kpt4.positionRaw[0] * outputSize2[0]),
+        Math.trunc(kpt4.positionRaw[1] * outputSize2[1]),
+        kpt4.positionRaw[2]
+      ];
+    }
+  }
+  return keypoints;
+}
+function fixKeypoints(keypoints) {
+  const leftPalm = keypoints.find((k) => k.part === "leftPalm");
+  const leftWrist = keypoints.find((k) => k.part === "leftWrist");
+  const leftIndex = keypoints.find((k) => k.part === "leftIndex");
+  leftPalm.position[2] = ((leftWrist.position[2] || 0) + (leftIndex.position[2] || 0)) / 2;
+  const rightPalm = keypoints.find((k) => k.part === "rightPalm");
+  const rightWrist = keypoints.find((k) => k.part === "rightWrist");
+  const rightIndex = keypoints.find((k) => k.part === "rightIndex");
+  rightPalm.position[2] = ((rightWrist.position[2] || 0) + (rightIndex.position[2] || 0)) / 2;
+}
+async function detectLandmarks(input, config3, outputSize2) {
+  var _a, _b;
+  if (!((_a = models2.landmarks) == null ? void 0 : _a["executor"]))
+    return null;
+  const t2 = {};
+  [t2.ld, t2.segmentation, t2.heatmap, t2.world, t2.poseflag] = (_b = models2.landmarks) == null ? void 0 : _b.execute(input, outputNodes.landmarks);
+  const poseScore = (await t2.poseflag.data())[0];
+  const points = await t2.ld.data();
+  const distances = await t2.world.data();
+  Object.keys(t2).forEach((tensor6) => tf10.dispose(t2[tensor6]));
+  const keypointsRelative = [];
+  const depth = 5;
+  for (let i = 0; i < points.length / depth; i++) {
+    const score = sigmoid3(points[depth * i + 3]);
+    const presence = sigmoid3(points[depth * i + 4]);
+    const adjScore = Math.trunc(100 * score * presence * poseScore) / 100;
+    const positionRaw = [points[depth * i + 0] / inputSize3.landmarks[0], points[depth * i + 1] / inputSize3.landmarks[1], points[depth * i + 2] + 0];
+    const position = [Math.trunc(outputSize2[0] * positionRaw[0]), Math.trunc(outputSize2[1] * positionRaw[1]), positionRaw[2]];
+    const distance2 = [distances[depth * i + 0], distances[depth * i + 1], distances[depth * i + 2] + 0];
+    keypointsRelative.push({ part: kpt[i], positionRaw, position, distance: distance2, score: adjScore });
+  }
+  if (poseScore < (config3.body.minConfidence || 0))
+    return null;
+  fixKeypoints(keypointsRelative);
+  const keypoints = rescaleKeypoints(keypointsRelative, outputSize2);
+  const kpts = keypoints.map((k) => k.position);
+  const boxes = calc(kpts, [outputSize2[0], outputSize2[1]]);
+  const annotations2 = {};
+  for (const [name, indexes] of Object.entries(connected)) {
+    const pt = [];
+    for (let i = 0; i < indexes.length - 1; i++) {
+      const pt0 = keypoints.find((kpt4) => kpt4.part === indexes[i]);
+      const pt1 = keypoints.find((kpt4) => kpt4.part === indexes[i + 1]);
+      if (pt0 && pt1)
+        pt.push([pt0.position, pt1.position]);
+    }
+    annotations2[name] = pt;
+  }
+  const body4 = { id: 0, score: Math.trunc(100 * poseScore) / 100, box: boxes.box, boxRaw: boxes.boxRaw, keypoints, annotations: annotations2 };
+  return body4;
+}
+async function predict2(input, config3) {
+  const outputSize2 = [input.shape[2] || 0, input.shape[1] || 0];
+  const skipTime = (config3.body.skipTime || 0) > now() - lastTime2;
+  const skipFrame = skipped2 < (config3.body.skipFrames || 0);
+  if (config3.skipAllowed && skipTime && skipFrame && cache !== null) {
+    skipped2++;
+  } else {
+    const t2 = {};
+    t2.landmarks = prepareImage(input, 256);
+    cache = await detectLandmarks(t2.landmarks, config3, outputSize2);
+    Object.keys(t2).forEach((tensor6) => tf10.dispose(t2[tensor6]));
+    lastTime2 = now();
+    skipped2 = 0;
+  }
+  return cache ? [cache] : [];
+}
+
+// src/object/centernet.ts
+var tf11 = __toESM(require_tfjs_esm());
+
+// src/object/labels.ts
+var labels = [
+  { class: 1, label: "person" },
+  { class: 2, label: "bicycle" },
+  { class: 3, label: "car" },
+  { class: 4, label: "motorcycle" },
+  { class: 5, label: "airplane" },
+  { class: 6, label: "bus" },
+  { class: 7, label: "train" },
+  { class: 8, label: "truck" },
+  { class: 9, label: "boat" },
+  { class: 10, label: "traffic light" },
+  { class: 11, label: "fire hydrant" },
+  { class: 12, label: "stop sign" },
+  { class: 13, label: "parking meter" },
+  { class: 14, label: "bench" },
+  { class: 15, label: "bird" },
+  { class: 16, label: "cat" },
+  { class: 17, label: "dog" },
+  { class: 18, label: "horse" },
+  { class: 19, label: "sheep" },
+  { class: 20, label: "cow" },
+  { class: 21, label: "elephant" },
+  { class: 22, label: "bear" },
+  { class: 23, label: "zebra" },
+  { class: 24, label: "giraffe" },
+  { class: 25, label: "backpack" },
+  { class: 26, label: "umbrella" },
+  { class: 27, label: "handbag" },
+  { class: 28, label: "tie" },
+  { class: 29, label: "suitcase" },
+  { class: 30, label: "frisbee" },
+  { class: 31, label: "skis" },
+  { class: 32, label: "snowboard" },
+  { class: 33, label: "sports ball" },
+  { class: 34, label: "kite" },
+  { class: 35, label: "baseball bat" },
+  { class: 36, label: "baseball glove" },
+  { class: 37, label: "skateboard" },
+  { class: 38, label: "surfboard" },
+  { class: 39, label: "tennis racket" },
+  { class: 40, label: "bottle" },
+  { class: 41, label: "wine glass" },
+  { class: 42, label: "cup" },
+  { class: 43, label: "fork" },
+  { class: 44, label: "knife" },
+  { class: 45, label: "spoon" },
+  { class: 46, label: "bowl" },
+  { class: 47, label: "banana" },
+  { class: 48, label: "apple" },
+  { class: 49, label: "sandwich" },
+  { class: 50, label: "orange" },
+  { class: 51, label: "broccoli" },
+  { class: 52, label: "carrot" },
+  { class: 53, label: "hot dog" },
+  { class: 54, label: "pizza" },
+  { class: 55, label: "donut" },
+  { class: 56, label: "cake" },
+  { class: 57, label: "chair" },
+  { class: 58, label: "couch" },
+  { class: 59, label: "potted plant" },
+  { class: 60, label: "bed" },
+  { class: 61, label: "dining table" },
+  { class: 62, label: "toilet" },
+  { class: 63, label: "tv" },
+  { class: 64, label: "laptop" },
+  { class: 65, label: "mouse" },
+  { class: 66, label: "remote" },
+  { class: 67, label: "keyboard" },
+  { class: 68, label: "cell phone" },
+  { class: 69, label: "microwave" },
+  { class: 70, label: "oven" },
+  { class: 71, label: "toaster" },
+  { class: 72, label: "sink" },
+  { class: 73, label: "refrigerator" },
+  { class: 74, label: "book" },
+  { class: 75, label: "clock" },
+  { class: 76, label: "vase" },
+  { class: 77, label: "scissors" },
+  { class: 78, label: "teddy bear" },
+  { class: 79, label: "hair drier" },
+  { class: 80, label: "toothbrush" }
+];
+
+// src/object/centernet.ts
+var model3;
+var inputSize4 = 0;
+var last2 = [];
+var lastTime3 = 0;
+var skipped3 = Number.MAX_SAFE_INTEGER;
+async function load3(config3) {
+  if (env.initial)
+    model3 = null;
+  if (!model3) {
+    model3 = await loadModel(config3.object.modelPath);
+    const inputs = (model3 == null ? void 0 : model3["executor"]) ? Object.values(model3.modelSignature["inputs"]) : void 0;
+    inputSize4 = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 0;
+  } else if (config3.debug)
+    log("cached model:", model3["modelUrl"]);
+  return model3;
+}
+async function process3(res, outputShape, config3) {
+  if (!res)
+    return [];
+  const t2 = {};
+  const results = [];
+  const detections = await res.array();
+  t2.squeeze = tf11.squeeze(res);
+  const arr = tf11.split(t2.squeeze, 6, 1);
+  t2.stack = tf11.stack([arr[1], arr[0], arr[3], arr[2]], 1);
+  t2.boxes = tf11.squeeze(t2.stack);
+  t2.scores = tf11.squeeze(arr[4]);
+  t2.classes = tf11.squeeze(arr[5]);
+  tf11.dispose([res, ...arr]);
+  t2.nms = await tf11.image.nonMaxSuppressionAsync(t2.boxes, t2.scores, config3.object.maxDetected, config3.object.iouThreshold, config3.object.minConfidence || 0);
+  const nms = await t2.nms.data();
+  let i = 0;
+  for (const id of Array.from(nms)) {
+    const score = Math.trunc(100 * detections[0][id][4]) / 100;
+    const classVal = detections[0][id][5];
+    if (Number.isNaN(classVal))
+      continue;
+    const label = labels[classVal].label;
+    const [x, y] = [
+      detections[0][id][0] / inputSize4,
+      detections[0][id][1] / inputSize4
+    ];
+    const boxRaw = [
+      x,
+      y,
+      detections[0][id][2] / inputSize4 - x,
+      detections[0][id][3] / inputSize4 - y
+    ];
+    const box = [
+      Math.trunc(boxRaw[0] * outputShape[0]),
+      Math.trunc(boxRaw[1] * outputShape[1]),
+      Math.trunc(boxRaw[2] * outputShape[0]),
+      Math.trunc(boxRaw[3] * outputShape[1])
+    ];
+    results.push({ id: i++, score, class: classVal, label, box, boxRaw });
+  }
+  Object.keys(t2).forEach((tensor6) => tf11.dispose(t2[tensor6]));
+  return results;
+}
+async function predict3(input, config3) {
+  if (!(model3 == null ? void 0 : model3["executor"]))
+    return [];
+  const skipTime = (config3.object.skipTime || 0) > now() - lastTime3;
+  const skipFrame = skipped3 < (config3.object.skipFrames || 0);
+  if (config3.skipAllowed && skipTime && skipFrame && last2.length > 0) {
+    skipped3++;
+    return last2;
+  }
+  skipped3 = 0;
+  return new Promise(async (resolve) => {
+    const outputSize2 = [input.shape[2] || 0, input.shape[1] || 0];
+    const resize = tf11.image.resizeBilinear(input, [inputSize4, inputSize4]);
+    const objectT = config3.object.enabled ? model3 == null ? void 0 : model3.execute(resize, ["tower_0/detections"]) : null;
+    lastTime3 = now();
+    tf11.dispose(resize);
+    const obj = await process3(objectT, outputSize2, config3);
+    last2 = obj;
+    resolve(obj);
+  });
+}
+
+// src/body/efficientpose.ts
+var tf12 = __toESM(require_tfjs_esm());
+
+// src/body/efficientposecoords.ts
+var efficientposecoords_exports = {};
+__export(efficientposecoords_exports, {
+  connected: () => connected2,
+  kpt: () => kpt2
+});
+var kpt2 = [
+  "head",
+  "neck",
+  "rightShoulder",
+  "rightElbow",
+  "rightWrist",
+  "chest",
+  "leftShoulder",
+  "leftElbow",
+  "leftWrist",
+  "bodyCenter",
+  "rightHip",
+  "rightKnee",
+  "rightAnkle",
+  "leftHip",
+  "leftKnee",
+  "leftAnkle"
+];
+var connected2 = {
+  leftLeg: ["leftHip", "leftKnee", "leftAnkle"],
+  rightLeg: ["rightHip", "rightKnee", "rightAnkle"],
+  torso: ["leftShoulder", "rightShoulder", "rightHip", "leftHip", "leftShoulder"],
+  leftArm: ["leftShoulder", "leftElbow", "leftWrist"],
+  rightArm: ["rightShoulder", "rightElbow", "rightWrist"],
+  head: []
+};
+
+// src/body/efficientpose.ts
+var model4;
+var lastTime4 = 0;
+var cache2 = { id: 0, keypoints: [], box: [0, 0, 0, 0], boxRaw: [0, 0, 0, 0], score: 0, annotations: {} };
+var skipped4 = Number.MAX_SAFE_INTEGER;
+async function load4(config3) {
+  if (env.initial)
+    model4 = null;
+  if (!model4)
+    model4 = await loadModel(config3.body.modelPath);
+  else if (config3.debug)
+    log("cached model:", model4["modelUrl"]);
+  return model4;
+}
+async function max2d(inputs, minScore) {
+  const [width, height] = inputs.shape;
+  const reshaped = tf12.reshape(inputs, [height * width]);
+  const max4 = tf12.max(reshaped, 0);
+  const newScore = (await max4.data())[0];
+  if (newScore > minScore) {
+    const coordinates = tf12.argMax(reshaped, 0);
+    const mod3 = tf12.mod(coordinates, width);
+    const x = (await mod3.data())[0];
+    const div16 = tf12.div(coordinates, width);
+    const y = (await div16.data())[0];
+    tf12.dispose([reshaped, max4, coordinates, mod3, div16]);
+    return [x, y, newScore];
+  }
+  tf12.dispose([reshaped, max4]);
+  return [0, 0, newScore];
+}
+async function predict4(image27, config3) {
+  if (!(model4 == null ? void 0 : model4["executor"]))
+    return [];
+  const skipTime = (config3.body.skipTime || 0) > now() - lastTime4;
+  const skipFrame = skipped4 < (config3.body.skipFrames || 0);
+  if (config3.skipAllowed && skipTime && skipFrame && Object.keys(cache2.keypoints).length > 0) {
+    skipped4++;
+    return [cache2];
+  }
+  skipped4 = 0;
+  return new Promise(async (resolve) => {
+    const tensor6 = tf12.tidy(() => {
+      if (!(model4 == null ? void 0 : model4.inputs[0].shape))
+        return null;
+      const resize = tf12.image.resizeBilinear(image27, [model4.inputs[0].shape[2], model4.inputs[0].shape[1]], false);
+      const enhance2 = tf12.mul(resize, constants.tf2);
+      const norm = tf12.sub(enhance2, constants.tf1);
+      return norm;
+    });
+    let resT;
+    if (config3.body.enabled)
+      resT = model4 == null ? void 0 : model4.execute(tensor6);
+    lastTime4 = now();
+    tf12.dispose(tensor6);
+    if (resT) {
+      cache2.keypoints.length = 0;
+      const squeeze14 = tf12.squeeze(resT);
+      tf12.dispose(resT);
+      const stack5 = tf12.unstack(squeeze14, 2);
+      tf12.dispose(squeeze14);
+      for (let id = 0; id < stack5.length; id++) {
+        const [x2, y2, partScore] = await max2d(stack5[id], config3.body.minConfidence);
+        if (partScore > (config3.body.minConfidence || 0)) {
+          cache2.keypoints.push({
+            score: Math.round(100 * partScore) / 100,
+            part: kpt2[id],
+            positionRaw: [
+              x2 / model4.inputs[0].shape[2],
+              y2 / model4.inputs[0].shape[1]
+            ],
+            position: [
+              Math.round(image27.shape[2] * x2 / model4.inputs[0].shape[2]),
+              Math.round(image27.shape[1] * y2 / model4.inputs[0].shape[1])
+            ]
+          });
+        }
+      }
+      stack5.forEach((s) => tf12.dispose(s));
+    }
+    cache2.score = cache2.keypoints.reduce((prev, curr) => curr.score > prev ? curr.score : prev, 0);
+    const x = cache2.keypoints.map((a) => a.position[0]);
+    const y = cache2.keypoints.map((a) => a.position[1]);
+    cache2.box = [
+      Math.min(...x),
+      Math.min(...y),
+      Math.max(...x) - Math.min(...x),
+      Math.max(...y) - Math.min(...y)
+    ];
+    const xRaw = cache2.keypoints.map((a) => a.positionRaw[0]);
+    const yRaw = cache2.keypoints.map((a) => a.positionRaw[1]);
+    cache2.boxRaw = [
+      Math.min(...xRaw),
+      Math.min(...yRaw),
+      Math.max(...xRaw) - Math.min(...xRaw),
+      Math.max(...yRaw) - Math.min(...yRaw)
+    ];
+    for (const [name, indexes] of Object.entries(connected2)) {
+      const pt = [];
+      for (let i = 0; i < indexes.length - 1; i++) {
+        const pt0 = cache2.keypoints.find((kpt4) => kpt4.part === indexes[i]);
+        const pt1 = cache2.keypoints.find((kpt4) => kpt4.part === indexes[i + 1]);
+        if (pt0 && pt1 && pt0.score > (config3.body.minConfidence || 0) && pt1.score > (config3.body.minConfidence || 0))
+          pt.push([pt0.position, pt1.position]);
+      }
+      cache2.annotations[name] = pt;
+    }
+    resolve([cache2]);
+  });
+}
+
+// src/gear/emotion.ts
+var tf13 = __toESM(require_tfjs_esm());
+var annotations = ["angry", "disgust", "fear", "happy", "sad", "surprise", "neutral"];
+var model5;
+var last3 = [];
+var lastCount2 = 0;
+var lastTime5 = 0;
+var skipped5 = Number.MAX_SAFE_INTEGER;
+async function load5(config3) {
+  var _a;
+  if (env.initial)
+    model5 = null;
+  if (!model5)
+    model5 = await loadModel((_a = config3.face.emotion) == null ? void 0 : _a.modelPath);
+  else if (config3.debug)
+    log("cached model:", model5["modelUrl"]);
+  return model5;
+}
+async function predict5(image27, config3, idx, count2) {
+  var _a, _b;
+  if (!model5)
+    return [];
+  const skipFrame = skipped5 < (((_a = config3.face.emotion) == null ? void 0 : _a.skipFrames) || 0);
+  const skipTime = (((_b = config3.face.emotion) == null ? void 0 : _b.skipTime) || 0) > now() - lastTime5;
+  if (config3.skipAllowed && skipTime && skipFrame && lastCount2 === count2 && last3[idx] && last3[idx].length > 0) {
+    skipped5++;
+    return last3[idx];
+  }
+  skipped5 = 0;
+  return new Promise(async (resolve) => {
+    var _a2;
+    const obj = [];
+    if ((_a2 = config3.face.emotion) == null ? void 0 : _a2.enabled) {
+      const t2 = {};
+      const inputSize10 = (model5 == null ? void 0 : model5.inputs[0].shape) ? model5.inputs[0].shape[2] : 0;
+      t2.resize = tf13.image.resizeBilinear(image27, [inputSize10, inputSize10], false);
+      t2.channels = tf13.mul(t2.resize, constants.rgb);
+      t2.grayscale = tf13.sum(t2.channels, 3, true);
+      t2.grayscaleSub = tf13.sub(t2.grayscale, constants.tf05);
+      t2.grayscaleMul = tf13.mul(t2.grayscaleSub, constants.tf2);
+      t2.emotion = model5 == null ? void 0 : model5.execute(t2.grayscaleMul);
+      lastTime5 = now();
+      const data = await t2.emotion.data();
+      for (let i = 0; i < data.length; i++) {
+        if (data[i] > (config3.face.emotion.minConfidence || 0))
+          obj.push({ score: Math.min(0.99, Math.trunc(100 * data[i]) / 100), emotion: annotations[i] });
+      }
+      obj.sort((a, b) => b.score - a.score);
+      Object.keys(t2).forEach((tensor6) => tf13.dispose(t2[tensor6]));
+    }
+    last3[idx] = obj;
+    lastCount2 = count2;
+    resolve(obj);
+  });
+}
+
+// src/face/facemesh.ts
+var tf15 = __toESM(require_tfjs_esm());
+
+// src/face/iris.ts
+var tf14 = __toESM(require_tfjs_esm());
+var model6;
+var inputSize5 = 0;
+var irisEnlarge = 2.3;
+var leftOutline = meshAnnotations.leftEyeLower0;
+var rightOutline = meshAnnotations.rightEyeLower0;
+var eyeLandmarks = {
+  leftBounds: [leftOutline[0], leftOutline[leftOutline.length - 1]],
+  rightBounds: [rightOutline[0], rightOutline[rightOutline.length - 1]]
+};
+var irisLandmarks = {
+  upperCenter: 3,
+  lowerCenter: 4,
+  index: 71,
+  numCoordinates: 76
+};
+async function load6(config3) {
+  var _a, _b;
+  if (env.initial)
+    model6 = null;
+  if (!model6)
+    model6 = await loadModel((_a = config3.face.iris) == null ? void 0 : _a.modelPath);
+  else if (config3.debug)
+    log("cached model:", model6["modelUrl"]);
+  inputSize5 = (model6 == null ? void 0 : model6["executor"]) && ((_b = model6.inputs) == null ? void 0 : _b[0].shape) ? model6.inputs[0].shape[2] : 0;
+  if (inputSize5 === -1)
+    inputSize5 = 64;
+  return model6;
+}
+function replaceIrisCoords(rawCoords, newCoords, prefix, keys) {
+  for (let i = 0; i < irisIndices.length; i++) {
+    const { key, indices } = irisIndices[i];
+    const originalIndices = meshAnnotations[`${prefix}${key}`];
+    if (!keys || keys.includes(key)) {
+      for (let j = 0; j < indices.length; j++) {
+        const index2 = indices[j];
+        rawCoords[originalIndices[j]] = [
+          newCoords[index2][0],
+          newCoords[index2][1],
+          (newCoords[index2][2] + rawCoords[originalIndices[j]][2]) / 2
+        ];
+      }
+    }
+  }
+}
+var getLeftToRightEyeDepthDifference = (rawCoords) => {
+  const leftEyeZ = rawCoords[eyeLandmarks.leftBounds[0]][2];
+  const rightEyeZ = rawCoords[eyeLandmarks.rightBounds[0]][2];
+  return leftEyeZ - rightEyeZ;
+};
+var getEyeBox = (rawCoords, face4, eyeInnerCornerIndex, eyeOuterCornerIndex, meshSize, flip = false) => {
+  const box = squarifyBox(enlargeBox(calculateLandmarksBoundingBox([rawCoords[eyeInnerCornerIndex], rawCoords[eyeOuterCornerIndex]]), irisEnlarge));
+  const boxSize = getBoxSize(box);
+  let crop = tf14.image.cropAndResize(face4, [[
+    box.startPoint[1] / meshSize,
+    box.startPoint[0] / meshSize,
+    box.endPoint[1] / meshSize,
+    box.endPoint[0] / meshSize
+  ]], [0], [inputSize5, inputSize5]);
+  if (flip && env.kernels.includes("flipleftright")) {
+    const flipped = tf14.image.flipLeftRight(crop);
+    tf14.dispose(crop);
+    crop = flipped;
+  }
+  return { box, boxSize, crop };
+};
+var getEyeCoords = (eyeData, eyeBox, eyeBoxSize, flip = false) => {
+  const eyeRawCoords = [];
+  for (let i = 0; i < irisLandmarks.numCoordinates; i++) {
+    const x = eyeData[i * 3];
+    const y = eyeData[i * 3 + 1];
+    const z = eyeData[i * 3 + 2];
+    eyeRawCoords.push([
+      (flip ? 1 - x / inputSize5 : x / inputSize5) * eyeBoxSize[0] + eyeBox.startPoint[0],
+      y / inputSize5 * eyeBoxSize[1] + eyeBox.startPoint[1],
+      z
+    ]);
+  }
+  return { rawCoords: eyeRawCoords, iris: eyeRawCoords.slice(irisLandmarks.index) };
+};
+var getAdjustedIrisCoords = (rawCoords, irisCoords, direction) => {
+  const upperCenterZ = rawCoords[meshAnnotations[`${direction}EyeUpper0`][irisLandmarks.upperCenter]][2];
+  const lowerCenterZ = rawCoords[meshAnnotations[`${direction}EyeLower0`][irisLandmarks.lowerCenter]][2];
+  const averageZ = (upperCenterZ + lowerCenterZ) / 2;
+  return irisCoords.map((coord, i) => {
+    let z = averageZ;
+    if (i === 2) {
+      z = upperCenterZ;
+    } else if (i === 4) {
+      z = lowerCenterZ;
+    }
+    return [coord[0], coord[1], z];
+  });
+};
+async function augmentIris(rawCoords, face4, meshSize) {
+  if (!(model6 == null ? void 0 : model6["executor"]))
+    return rawCoords;
+  const { box: leftEyeBox, boxSize: leftEyeBoxSize, crop: leftEyeCrop } = getEyeBox(rawCoords, face4, eyeLandmarks.leftBounds[0], eyeLandmarks.leftBounds[1], meshSize, true);
+  const { box: rightEyeBox, boxSize: rightEyeBoxSize, crop: rightEyeCrop } = getEyeBox(rawCoords, face4, eyeLandmarks.rightBounds[0], eyeLandmarks.rightBounds[1], meshSize, true);
+  const combined = tf14.concat([leftEyeCrop, rightEyeCrop]);
+  tf14.dispose(leftEyeCrop);
+  tf14.dispose(rightEyeCrop);
+  const eyePredictions = model6.execute(combined);
+  tf14.dispose(combined);
+  const eyePredictionsData = await eyePredictions.data();
+  tf14.dispose(eyePredictions);
+  const leftEyeData = eyePredictionsData.slice(0, irisLandmarks.numCoordinates * 3);
+  const { rawCoords: leftEyeRawCoords, iris: leftIrisRawCoords } = getEyeCoords(leftEyeData, leftEyeBox, leftEyeBoxSize, true);
+  const rightEyeData = eyePredictionsData.slice(irisLandmarks.numCoordinates * 3);
+  const { rawCoords: rightEyeRawCoords, iris: rightIrisRawCoords } = getEyeCoords(rightEyeData, rightEyeBox, rightEyeBoxSize, false);
+  const leftToRightEyeDepthDifference = getLeftToRightEyeDepthDifference(rawCoords);
+  if (Math.abs(leftToRightEyeDepthDifference) < 30) {
+    replaceIrisCoords(rawCoords, leftEyeRawCoords, "left", null);
+    replaceIrisCoords(rawCoords, rightEyeRawCoords, "right", null);
+  } else if (leftToRightEyeDepthDifference < 1) {
+    replaceIrisCoords(rawCoords, leftEyeRawCoords, "left", ["EyeUpper0", "EyeLower0"]);
+  } else {
+    replaceIrisCoords(rawCoords, rightEyeRawCoords, "right", ["EyeUpper0", "EyeLower0"]);
+  }
+  const adjustedLeftIrisCoords = getAdjustedIrisCoords(rawCoords, leftIrisRawCoords, "left");
+  const adjustedRightIrisCoords = getAdjustedIrisCoords(rawCoords, rightIrisRawCoords, "right");
+  const newCoords = rawCoords.concat(adjustedLeftIrisCoords).concat(adjustedRightIrisCoords);
+  return newCoords;
+}
+
+// src/face/constants.ts
+var LIPS_CONNECTIONS = [
+  [61, 146],
+  [146, 91],
+  [91, 181],
+  [181, 84],
+  [84, 17],
+  [17, 314],
+  [314, 405],
+  [405, 321],
+  [321, 375],
+  [375, 291],
+  [61, 185],
+  [185, 40],
+  [40, 39],
+  [39, 37],
+  [37, 0],
+  [0, 267],
+  [267, 269],
+  [269, 270],
+  [270, 409],
+  [409, 291],
+  [78, 95],
+  [95, 88],
+  [88, 178],
+  [178, 87],
+  [87, 14],
+  [14, 317],
+  [317, 402],
+  [402, 318],
+  [318, 324],
+  [324, 308],
+  [78, 191],
+  [191, 80],
+  [80, 81],
+  [81, 82],
+  [82, 13],
+  [13, 312],
+  [312, 311],
+  [311, 310],
+  [310, 415],
+  [415, 308]
+];
+var LEFT_EYE_CONNECTIONS = [[263, 249], [249, 390], [390, 373], [373, 374], [374, 380], [380, 381], [381, 382], [382, 362], [263, 466], [466, 388], [388, 387], [387, 386], [386, 385], [385, 384], [384, 398], [398, 362]];
+var LEFT_EYEBROW_CONNECTIONS = [[276, 283], [283, 282], [282, 295], [295, 285], [300, 293], [293, 334], [334, 296], [296, 336]];
+var LEFT_IRIS_CONNECTIONS = [[474, 475], [475, 476], [476, 477], [477, 474]];
+var RIGHT_EYE_CONNECTIONS = [[33, 7], [7, 163], [163, 144], [144, 145], [145, 153], [153, 154], [154, 155], [155, 133], [33, 246], [246, 161], [161, 160], [160, 159], [159, 158], [158, 157], [157, 173], [173, 133]];
+var RIGHT_EYEBROW_CONNECTIONS = [[46, 53], [53, 52], [52, 65], [65, 55], [70, 63], [63, 105], [105, 66], [66, 107]];
+var RIGHT_IRIS_CONNECTIONS = [[469, 470], [470, 471], [471, 472], [472, 469]];
+var FACE_OVAL_CONNECTIONS = [
+  [10, 338],
+  [338, 297],
+  [297, 332],
+  [332, 284],
+  [284, 251],
+  [251, 389],
+  [389, 356],
+  [356, 454],
+  [454, 323],
+  [323, 361],
+  [361, 288],
+  [288, 397],
+  [397, 365],
+  [365, 379],
+  [379, 378],
+  [378, 400],
+  [400, 377],
+  [377, 152],
+  [152, 148],
+  [148, 176],
+  [176, 149],
+  [149, 150],
+  [150, 136],
+  [136, 172],
+  [172, 58],
+  [58, 132],
+  [132, 93],
+  [93, 234],
+  [234, 127],
+  [127, 162],
+  [162, 21],
+  [21, 54],
+  [54, 103],
+  [103, 67],
+  [67, 109],
+  [109, 10]
+];
+function connectionsToIndices2(connections) {
+  const indices = connections.map((connection) => connection[0]);
+  indices.push(connections[connections.length - 1][1]);
+  return indices;
+}
+var MEDIAPIPE_FACE_MESH_KEYPOINTS_BY_CONTOUR = {
+  lips: connectionsToIndices2(LIPS_CONNECTIONS),
+  leftEye: connectionsToIndices2(LEFT_EYE_CONNECTIONS),
+  leftEyebrow: connectionsToIndices2(LEFT_EYEBROW_CONNECTIONS),
+  leftIris: connectionsToIndices2(LEFT_IRIS_CONNECTIONS),
+  rightEye: connectionsToIndices2(RIGHT_EYE_CONNECTIONS),
+  rightEyebrow: connectionsToIndices2(RIGHT_EYEBROW_CONNECTIONS),
+  rightIris: connectionsToIndices2(RIGHT_IRIS_CONNECTIONS),
+  faceOval: connectionsToIndices2(FACE_OVAL_CONNECTIONS)
+};
+var indexLabelPairs = Object.entries(MEDIAPIPE_FACE_MESH_KEYPOINTS_BY_CONTOUR).map(([label, indices]) => indices.map((index2) => [index2, label])).flat();
+var MEDIAPIPE_FACE_MESH_KEYPOINTS = new Map(indexLabelPairs);
+var LANDMARKS_REFINEMENT_LIPS_CONFIG = [
+  61,
+  146,
+  91,
+  181,
+  84,
+  17,
+  314,
+  405,
+  321,
+  375,
+  291,
+  185,
+  40,
+  39,
+  37,
+  0,
+  267,
+  269,
+  270,
+  409,
+  78,
+  95,
+  88,
+  178,
+  87,
+  14,
+  317,
+  402,
+  318,
+  324,
+  308,
+  191,
+  80,
+  81,
+  82,
+  13,
+  312,
+  311,
+  310,
+  415,
+  76,
+  77,
+  90,
+  180,
+  85,
+  16,
+  315,
+  404,
+  320,
+  307,
+  306,
+  184,
+  74,
+  73,
+  72,
+  11,
+  302,
+  303,
+  304,
+  408,
+  62,
+  96,
+  89,
+  179,
+  86,
+  15,
+  316,
+  403,
+  319,
+  325,
+  292,
+  183,
+  42,
+  41,
+  38,
+  12,
+  268,
+  271,
+  272,
+  407
+];
+var LANDMARKS_REFINEMENT_LEFT_EYE_CONFIG = [
+  33,
+  7,
+  163,
+  144,
+  145,
+  153,
+  154,
+  155,
+  133,
+  246,
+  161,
+  160,
+  159,
+  158,
+  157,
+  173,
+  130,
+  25,
+  110,
+  24,
+  23,
+  22,
+  26,
+  112,
+  243,
+  247,
+  30,
+  29,
+  27,
+  28,
+  56,
+  190,
+  226,
+  31,
+  228,
+  229,
+  230,
+  231,
+  232,
+  233,
+  244,
+  113,
+  225,
+  224,
+  223,
+  222,
+  221,
+  189,
+  35,
+  124,
+  46,
+  53,
+  52,
+  65,
+  143,
+  111,
+  117,
+  118,
+  119,
+  120,
+  121,
+  128,
+  245,
+  156,
+  70,
+  63,
+  105,
+  66,
+  107,
+  55,
+  193
+];
+var LANDMARKS_REFINEMENT_RIGHT_EYE_CONFIG = [
+  263,
+  249,
+  390,
+  373,
+  374,
+  380,
+  381,
+  382,
+  362,
+  466,
+  388,
+  387,
+  386,
+  385,
+  384,
+  398,
+  359,
+  255,
+  339,
+  254,
+  253,
+  252,
+  256,
+  341,
+  463,
+  467,
+  260,
+  259,
+  257,
+  258,
+  286,
+  414,
+  446,
+  261,
+  448,
+  449,
+  450,
+  451,
+  452,
+  453,
+  464,
+  342,
+  445,
+  444,
+  443,
+  442,
+  441,
+  413,
+  265,
+  353,
+  276,
+  283,
+  282,
+  295,
+  372,
+  340,
+  346,
+  347,
+  348,
+  349,
+  350,
+  357,
+  465,
+  383,
+  300,
+  293,
+  334,
+  296,
+  336,
+  285,
+  417
+];
+
+// src/face/attention.ts
+async function augment(rawCoords, results) {
+  var _a, _b, _c, _d, _e, _f, _g, _h, _i, _j;
+  const t2 = {
+    lips: await ((_b = (_a = results.filter((r) => r.size === 160)) == null ? void 0 : _a[0]) == null ? void 0 : _b.data()),
+    irisL: await ((_d = (_c = results.filter((r) => r.size === 10)) == null ? void 0 : _c[0]) == null ? void 0 : _d.data()),
+    eyeL: await ((_f = (_e = results.filter((r) => r.size === 142)) == null ? void 0 : _e[0]) == null ? void 0 : _f.data()),
+    irisR: await ((_h = (_g = results.filter((r) => r.size === 10)) == null ? void 0 : _g[1]) == null ? void 0 : _h.data()),
+    eyeR: await ((_j = (_i = results.filter((r) => r.size === 142)) == null ? void 0 : _i[1]) == null ? void 0 : _j.data())
+  };
+  for (const val of Object.values(t2)) {
+    if (!val)
+      return rawCoords;
+  }
+  const irisLDepth = LANDMARKS_REFINEMENT_LEFT_EYE_CONFIG.reduce((prev, curr) => prev += rawCoords[curr][2], 0) / LANDMARKS_REFINEMENT_LEFT_EYE_CONFIG.length;
+  for (let i = 0; i < t2.irisL.length / 2; i++)
+    rawCoords.push([t2.irisL[2 * i + 0], t2.irisL[2 * i + 1], irisLDepth]);
+  const irisRDepth = LANDMARKS_REFINEMENT_RIGHT_EYE_CONFIG.reduce((prev, curr) => prev += rawCoords[curr][2], 0) / LANDMARKS_REFINEMENT_RIGHT_EYE_CONFIG.length;
+  for (let i = 0; i < t2.irisR.length / 2; i++)
+    rawCoords.push([t2.irisR[2 * i + 0], t2.irisR[2 * i + 1], irisRDepth]);
+  for (let i = 0; i < t2.eyeL.length / 2; i++)
+    rawCoords[LANDMARKS_REFINEMENT_LEFT_EYE_CONFIG[i]] = [t2.eyeL[2 * i + 0], t2.eyeL[2 * i + 1], rawCoords[LANDMARKS_REFINEMENT_LEFT_EYE_CONFIG[i]][2]];
+  for (let i = 0; i < t2.eyeR.length / 2; i++)
+    rawCoords[LANDMARKS_REFINEMENT_RIGHT_EYE_CONFIG[i]] = [t2.eyeR[2 * i + 0], t2.eyeR[2 * i + 1], rawCoords[LANDMARKS_REFINEMENT_RIGHT_EYE_CONFIG[i]][2]];
+  for (let i = 0; i < t2.lips.length / 2; i++)
+    rawCoords[LANDMARKS_REFINEMENT_LIPS_CONFIG[i]] = [t2.lips[2 * i + 0], t2.lips[2 * i + 1], rawCoords[LANDMARKS_REFINEMENT_LIPS_CONFIG[i]][2]];
+  return rawCoords;
+}
+
+// src/face/facemesh.ts
+var cache3 = {
+  boxes: [],
+  skipped: Number.MAX_SAFE_INTEGER,
+  timestamp: 0
+};
+var model7 = null;
+var inputSize6 = 0;
+async function predict6(input, config3) {
+  var _a, _b, _c, _d, _e, _f, _g, _h, _i, _j;
+  if (!(model7 == null ? void 0 : model7["executor"]))
+    return [];
+  const skipTime = (((_a = config3.face.detector) == null ? void 0 : _a.skipTime) || 0) > now() - cache3.timestamp;
+  const skipFrame = cache3.skipped < (((_b = config3.face.detector) == null ? void 0 : _b.skipFrames) || 0);
+  if (!config3.skipAllowed || !skipTime || !skipFrame || cache3.boxes.length === 0) {
+    cache3.boxes = await getBoxes(input, config3);
+    cache3.timestamp = now();
+    cache3.skipped = 0;
+  } else {
+    cache3.skipped++;
+  }
+  const faces = [];
+  const newCache = [];
+  let id = 0;
+  const size2 = inputSize6;
+  for (let i = 0; i < cache3.boxes.length; i++) {
+    const box = cache3.boxes[i];
+    let angle = 0;
+    let rotationMatrix;
+    const face4 = {
+      id: id++,
+      mesh: [],
+      meshRaw: [],
+      box: [0, 0, 0, 0],
+      boxRaw: [0, 0, 0, 0],
+      score: 0,
+      boxScore: 0,
+      faceScore: 0,
+      annotations: {}
+    };
+    [angle, rotationMatrix, face4.tensor] = correctFaceRotation((_c = config3.face.detector) == null ? void 0 : _c.rotation, box, input, ((_d = config3.face.mesh) == null ? void 0 : _d.enabled) ? inputSize6 : size());
+    if (config3.filter.equalization) {
+      const equilized = face4.tensor ? await histogramEqualization(face4.tensor) : void 0;
+      tf15.dispose(face4.tensor);
+      if (equilized)
+        face4.tensor = equilized;
+    }
+    face4.boxScore = Math.round(100 * box.confidence) / 100;
+    if (!((_e = config3.face.mesh) == null ? void 0 : _e.enabled)) {
+      face4.box = clampBox(box, input);
+      face4.boxRaw = getRawBox(box, input);
+      face4.score = face4.boxScore;
+      face4.mesh = box.landmarks.map((pt) => [
+        (box.startPoint[0] + box.endPoint[0]) / 2 + (box.endPoint[0] + box.startPoint[0]) * pt[0] / size(),
+        (box.startPoint[1] + box.endPoint[1]) / 2 + (box.endPoint[1] + box.startPoint[1]) * pt[1] / size()
+      ]);
+      face4.meshRaw = face4.mesh.map((pt) => [pt[0] / (input.shape[2] || 0), pt[1] / (input.shape[1] || 0), (pt[2] || 0) / size2]);
+      for (const key of Object.keys(blazeFaceLandmarks)) {
+        face4.annotations[key] = [face4.mesh[blazeFaceLandmarks[key]]];
+      }
+    } else if (!model7) {
+      if (config3.debug)
+        log("face mesh detection requested, but model is not loaded");
+    } else {
+      if (((_f = config3.face.attention) == null ? void 0 : _f.enabled) && !env.kernels.includes("atan2")) {
+        config3.face.attention.enabled = false;
+        tf15.dispose(face4.tensor);
+        return faces;
+      }
+      const results = model7.execute(face4.tensor);
+      const confidenceT = results.find((t2) => t2.shape[t2.shape.length - 1] === 1);
+      const faceConfidence = await confidenceT.data();
+      face4.faceScore = Math.round(100 * faceConfidence[0]) / 100;
+      if (face4.faceScore < (((_g = config3.face.detector) == null ? void 0 : _g.minConfidence) || 1)) {
+        box.confidence = face4.faceScore;
+        if (config3.face.mesh.keepInvalid) {
+          face4.box = clampBox(box, input);
+          face4.boxRaw = getRawBox(box, input);
+          face4.score = face4.boxScore;
+          face4.mesh = box.landmarks.map((pt) => [
+            (box.startPoint[0] + box.endPoint[0]) / 2 + (box.endPoint[0] + box.startPoint[0]) * pt[0] / size(),
+            (box.startPoint[1] + box.endPoint[1]) / 2 + (box.endPoint[1] + box.startPoint[1]) * pt[1] / size()
+          ]);
+          face4.meshRaw = face4.mesh.map((pt) => [pt[0] / (input.shape[2] || 1), pt[1] / (input.shape[1] || 1), (pt[2] || 0) / size2]);
+          for (const key of Object.keys(blazeFaceLandmarks)) {
+            face4.annotations[key] = [face4.mesh[blazeFaceLandmarks[key]]];
+          }
+        }
+      } else {
+        const meshT = results.find((t2) => t2.shape[t2.shape.length - 1] === 1404);
+        const coordsReshaped = tf15.reshape(meshT, [-1, 3]);
+        let rawCoords = await coordsReshaped.array();
+        tf15.dispose(coordsReshaped);
+        if ((_h = config3.face.attention) == null ? void 0 : _h.enabled) {
+          rawCoords = await augment(rawCoords, results);
+        } else if ((_i = config3.face.iris) == null ? void 0 : _i.enabled) {
+          rawCoords = await augmentIris(rawCoords, face4.tensor, inputSize6);
+        }
+        face4.mesh = transformRawCoords(rawCoords, box, angle, rotationMatrix, inputSize6);
+        face4.meshRaw = face4.mesh.map((pt) => [pt[0] / (input.shape[2] || 0), pt[1] / (input.shape[1] || 0), (pt[2] || 0) / size2]);
+        for (const key of Object.keys(meshAnnotations))
+          face4.annotations[key] = meshAnnotations[key].map((index2) => face4.mesh[index2]);
+        face4.score = face4.faceScore;
+        const calculatedBox = { ...calculateFaceBox(face4.mesh, box), confidence: box.confidence, landmarks: box.landmarks };
+        face4.box = clampBox(calculatedBox, input);
+        face4.boxRaw = getRawBox(calculatedBox, input);
+        newCache.push(calculatedBox);
+      }
+      tf15.dispose(results);
+    }
+    if (face4.score > (((_j = config3.face.detector) == null ? void 0 : _j.minConfidence) || 1))
+      faces.push(face4);
+    else
+      tf15.dispose(face4.tensor);
+  }
+  cache3.boxes = newCache;
+  return faces;
+}
+async function load7(config3) {
+  var _a, _b, _c, _d, _e, _f;
+  if (env.initial)
+    model7 = null;
+  if (((_a = config3.face.attention) == null ? void 0 : _a.enabled) && (model7 == null ? void 0 : model7["signature"])) {
+    if (Object.keys(((_b = model7 == null ? void 0 : model7["signature"]) == null ? void 0 : _b.outputs) || {}).length < 6)
+      model7 = null;
+  }
+  if (!model7) {
+    if ((_c = config3.face.attention) == null ? void 0 : _c.enabled)
+      model7 = await loadModel(config3.face.attention.modelPath);
+    else
+      model7 = await loadModel((_d = config3.face.mesh) == null ? void 0 : _d.modelPath);
+  } else if (config3.debug) {
+    log("cached model:", model7["modelUrl"]);
+  }
+  inputSize6 = model7["executor"] && ((_e = model7 == null ? void 0 : model7.inputs) == null ? void 0 : _e[0].shape) ? (_f = model7 == null ? void 0 : model7.inputs) == null ? void 0 : _f[0].shape[2] : 256;
+  return model7;
+}
+var triangulation = TRI468;
+var uvmap = UV468;
+
+// src/face/faceres.ts
+var tf16 = __toESM(require_tfjs_esm());
+var model8;
+var last4 = [];
+var lastTime6 = 0;
+var lastCount3 = 0;
+var skipped6 = Number.MAX_SAFE_INTEGER;
+async function load8(config3) {
+  var _a;
+  if (env.initial)
+    model8 = null;
+  if (!model8)
+    model8 = await loadModel((_a = config3.face.description) == null ? void 0 : _a.modelPath);
+  else if (config3.debug)
+    log("cached model:", model8["modelUrl"]);
+  return model8;
+}
+function enhance(input) {
+  const tensor6 = input.image || input.tensor || input;
+  if (!(model8 == null ? void 0 : model8.inputs[0].shape))
+    return tensor6;
+  const crop = tf16.image.resizeBilinear(tensor6, [model8.inputs[0].shape[2], model8.inputs[0].shape[1]], false);
+  const norm = tf16.mul(crop, constants.tf255);
+  tf16.dispose(crop);
+  return norm;
+}
+async function predict7(image27, config3, idx, count2) {
+  var _a, _b, _c, _d;
+  const obj = {
+    age: 0,
+    gender: "unknown",
+    genderScore: 0,
+    descriptor: []
+  };
+  if (!(model8 == null ? void 0 : model8["executor"]))
+    return obj;
+  const skipFrame = skipped6 < (((_a = config3.face.description) == null ? void 0 : _a.skipFrames) || 0);
+  const skipTime = (((_b = config3.face.description) == null ? void 0 : _b.skipTime) || 0) > now() - lastTime6;
+  if (config3.skipAllowed && skipFrame && skipTime && lastCount3 === count2 && ((_c = last4 == null ? void 0 : last4[idx]) == null ? void 0 : _c.age) > 0 && ((_d = last4 == null ? void 0 : last4[idx]) == null ? void 0 : _d.genderScore) > 0) {
+    skipped6++;
+    return last4[idx];
+  }
+  skipped6 = 0;
+  return new Promise(async (resolve) => {
+    var _a2;
+    if ((_a2 = config3.face.description) == null ? void 0 : _a2.enabled) {
+      const enhanced = enhance(image27);
+      const resT = model8 == null ? void 0 : model8.execute(enhanced);
+      lastTime6 = now();
+      tf16.dispose(enhanced);
+      const genderT = resT.find((t2) => t2.shape[1] === 1);
+      const gender2 = await genderT.data();
+      const confidence = Math.trunc(200 * Math.abs(gender2[0] - 0.5)) / 100;
+      if (confidence > (config3.face.description.minConfidence || 0)) {
+        obj.gender = gender2[0] <= 0.5 ? "female" : "male";
+        obj.genderScore = Math.min(0.99, confidence);
+      }
+      const argmax = tf16.argMax(resT.find((t2) => t2.shape[1] === 100), 1);
+      const ageIdx = (await argmax.data())[0];
+      tf16.dispose(argmax);
+      const ageT = resT.find((t2) => t2.shape[1] === 100);
+      const all2 = await ageT.data();
+      obj.age = Math.round(all2[ageIdx - 1] > all2[ageIdx + 1] ? 10 * ageIdx - 100 * all2[ageIdx - 1] : 10 * ageIdx + 100 * all2[ageIdx + 1]) / 10;
+      if (Number.isNaN(gender2[0]) || Number.isNaN(all2[0]))
+        log("faceres error:", { model: model8, result: resT });
+      const desc = resT.find((t2) => t2.shape[1] === 1024);
+      const descriptor = desc ? await desc.data() : [];
+      obj.descriptor = Array.from(descriptor);
+      resT.forEach((t2) => tf16.dispose(t2));
+    }
+    last4[idx] = obj;
+    lastCount3 = count2;
+    resolve(obj);
+  });
+}
+
+// src/gear/gear.ts
+var tf17 = __toESM(require_tfjs_esm());
+var model9;
+var last5 = [];
+var raceNames = ["white", "black", "asian", "indian", "other"];
+var ageWeights = [15, 23, 28, 35.5, 45.5, 55.5, 65];
+var lastCount4 = 0;
+var lastTime7 = 0;
+var skipped7 = Number.MAX_SAFE_INTEGER;
+async function load9(config3) {
+  var _a;
+  if (env.initial)
+    model9 = null;
+  if (!model9)
+    model9 = await loadModel((_a = config3.face.gear) == null ? void 0 : _a.modelPath);
+  else if (config3.debug)
+    log("cached model:", model9["modelUrl"]);
+  return model9;
+}
+async function predict8(image27, config3, idx, count2) {
+  var _a, _b;
+  if (!model9)
+    return { age: 0, gender: "unknown", genderScore: 0, race: [] };
+  const skipFrame = skipped7 < (((_a = config3.face.gear) == null ? void 0 : _a.skipFrames) || 0);
+  const skipTime = (((_b = config3.face.gear) == null ? void 0 : _b.skipTime) || 0) > now() - lastTime7;
+  if (config3.skipAllowed && skipTime && skipFrame && lastCount4 === count2 && last5[idx]) {
+    skipped7++;
+    return last5[idx];
+  }
+  skipped7 = 0;
+  return new Promise(async (resolve) => {
+    var _a2, _b2;
+    if (!(model9 == null ? void 0 : model9.inputs[0].shape))
+      return;
+    const t2 = {};
+    const box = [[0, 0.1, 0.9, 0.9]];
+    t2.resize = tf17.image.cropAndResize(image27, box, [0], [model9.inputs[0].shape[2], model9.inputs[0].shape[1]]);
+    const obj = { age: 0, gender: "unknown", genderScore: 0, race: [] };
+    if ((_a2 = config3.face.gear) == null ? void 0 : _a2.enabled)
+      [t2.age, t2.gender, t2.race] = model9.execute(t2.resize, ["age_output", "gender_output", "race_output"]);
+    const gender2 = await t2.gender.data();
+    obj.gender = gender2[0] > gender2[1] ? "male" : "female";
+    obj.genderScore = Math.round(100 * (gender2[0] > gender2[1] ? gender2[0] : gender2[1])) / 100;
+    const race = await t2.race.data();
+    for (let i = 0; i < race.length; i++) {
+      if (race[i] > (((_b2 = config3.face.gear) == null ? void 0 : _b2.minConfidence) || 0.2))
+        obj.race.push({ score: Math.round(100 * race[i]) / 100, race: raceNames[i] });
+    }
+    obj.race.sort((a, b) => b.score - a.score);
+    const ageDistribution = Array.from(await t2.age.data());
+    const ageSorted = ageDistribution.map((a, i) => [ageWeights[i], a]).sort((a, b) => b[1] - a[1]);
+    let age2 = ageSorted[0][0];
+    for (let i = 1; i < ageSorted.length; i++)
+      age2 += ageSorted[i][1] * (ageSorted[i][0] - age2);
+    obj.age = Math.round(10 * age2) / 10;
+    Object.keys(t2).forEach((tensor6) => tf17.dispose(t2[tensor6]));
+    last5[idx] = obj;
+    lastCount4 = count2;
+    lastTime7 = now();
+    resolve(obj);
+  });
+}
+
+// src/hand/handposedetector.ts
+var tf19 = __toESM(require_tfjs_esm());
+
+// src/hand/handposeutil.ts
+var tf18 = __toESM(require_tfjs_esm());
+function getBoxSize2(box) {
+  return [
+    Math.abs(box.endPoint[0] - box.startPoint[0]),
+    Math.abs(box.endPoint[1] - box.startPoint[1])
+  ];
+}
+function getBoxCenter2(box) {
+  return [
+    box.startPoint[0] + (box.endPoint[0] - box.startPoint[0]) / 2,
+    box.startPoint[1] + (box.endPoint[1] - box.startPoint[1]) / 2
+  ];
+}
+function cutBoxFromImageAndResize(box, image27, cropSize) {
+  const h = image27.shape[1];
+  const w = image27.shape[2];
+  const boxes = [[
+    box.startPoint[1] / h,
+    box.startPoint[0] / w,
+    box.endPoint[1] / h,
+    box.endPoint[0] / w
+  ]];
+  return tf18.image.cropAndResize(image27, boxes, [0], cropSize);
+}
+function scaleBoxCoordinates2(box, factor) {
+  const startPoint = [box.startPoint[0] * factor[0], box.startPoint[1] * factor[1]];
+  const endPoint = [box.endPoint[0] * factor[0], box.endPoint[1] * factor[1]];
+  const palmLandmarks = box.palmLandmarks.map((coord) => {
+    const scaledCoord = [coord[0] * factor[0], coord[1] * factor[1]];
+    return scaledCoord;
+  });
+  return { startPoint, endPoint, palmLandmarks, confidence: box.confidence };
+}
+function enlargeBox2(box, factor = 1.5) {
+  const center = getBoxCenter2(box);
+  const size2 = getBoxSize2(box);
+  const newHalfSize = [factor * size2[0] / 2, factor * size2[1] / 2];
+  const startPoint = [center[0] - newHalfSize[0], center[1] - newHalfSize[1]];
+  const endPoint = [center[0] + newHalfSize[0], center[1] + newHalfSize[1]];
+  return { startPoint, endPoint, palmLandmarks: box.palmLandmarks };
+}
+function squarifyBox2(box) {
+  const centers = getBoxCenter2(box);
+  const size2 = getBoxSize2(box);
+  const maxEdge = Math.max(...size2);
+  const halfSize = maxEdge / 2;
+  const startPoint = [centers[0] - halfSize, centers[1] - halfSize];
+  const endPoint = [centers[0] + halfSize, centers[1] + halfSize];
+  return { startPoint, endPoint, palmLandmarks: box.palmLandmarks };
+}
+function normalizeRadians2(angle) {
+  return angle - 2 * Math.PI * Math.floor((angle + Math.PI) / (2 * Math.PI));
+}
+function computeRotation2(point1, point2) {
+  const radians = Math.PI / 2 - Math.atan2(-(point2[1] - point1[1]), point2[0] - point1[0]);
+  return normalizeRadians2(radians);
+}
+var buildTranslationMatrix2 = (x, y) => [[1, 0, x], [0, 1, y], [0, 0, 1]];
+function dot2(v1, v2) {
+  let product = 0;
+  for (let i = 0; i < v1.length; i++) {
+    product += v1[i] * v2[i];
+  }
+  return product;
+}
+function getColumnFrom2DArr2(arr, columnIndex) {
+  const column = [];
+  for (let i = 0; i < arr.length; i++) {
+    column.push(arr[i][columnIndex]);
+  }
+  return column;
+}
+function multiplyTransformMatrices2(mat1, mat2) {
+  const product = [];
+  const size2 = mat1.length;
+  for (let row = 0; row < size2; row++) {
+    product.push([]);
+    for (let col = 0; col < size2; col++) {
+      product[row].push(dot2(mat1[row], getColumnFrom2DArr2(mat2, col)));
+    }
+  }
+  return product;
+}
+function buildRotationMatrix2(rotation, center) {
+  const cosA = Math.cos(rotation);
+  const sinA = Math.sin(rotation);
+  const rotationMatrix = [[cosA, -sinA, 0], [sinA, cosA, 0], [0, 0, 1]];
+  const translationMatrix = buildTranslationMatrix2(center[0], center[1]);
+  const translationTimesRotation = multiplyTransformMatrices2(translationMatrix, rotationMatrix);
+  const negativeTranslationMatrix = buildTranslationMatrix2(-center[0], -center[1]);
+  return multiplyTransformMatrices2(translationTimesRotation, negativeTranslationMatrix);
+}
+function invertTransformMatrix2(matrix) {
+  const rotationComponent = [[matrix[0][0], matrix[1][0]], [matrix[0][1], matrix[1][1]]];
+  const translationComponent = [matrix[0][2], matrix[1][2]];
+  const invertedTranslation = [
+    -dot2(rotationComponent[0], translationComponent),
+    -dot2(rotationComponent[1], translationComponent)
+  ];
+  return [
+    rotationComponent[0].concat(invertedTranslation[0]),
+    rotationComponent[1].concat(invertedTranslation[1]),
+    [0, 0, 1]
+  ];
+}
+function rotatePoint2(homogeneousCoordinate, rotationMatrix) {
+  return [
+    dot2(homogeneousCoordinate, rotationMatrix[0]),
+    dot2(homogeneousCoordinate, rotationMatrix[1])
+  ];
+}
+
+// src/hand/handposeanchors.ts
+var anchors2 = [
+  { x: 0.015625, y: 0.015625 },
+  { x: 0.015625, y: 0.015625 },
+  { x: 0.046875, y: 0.015625 },
+  { x: 0.046875, y: 0.015625 },
+  { x: 0.078125, y: 0.015625 },
+  { x: 0.078125, y: 0.015625 },
+  { x: 0.109375, y: 0.015625 },
+  { x: 0.109375, y: 0.015625 },
+  { x: 0.140625, y: 0.015625 },
+  { x: 0.140625, y: 0.015625 },
+  { x: 0.171875, y: 0.015625 },
+  { x: 0.171875, y: 0.015625 },
+  { x: 0.203125, y: 0.015625 },
+  { x: 0.203125, y: 0.015625 },
+  { x: 0.234375, y: 0.015625 },
+  { x: 0.234375, y: 0.015625 },
+  { x: 0.265625, y: 0.015625 },
+  { x: 0.265625, y: 0.015625 },
+  { x: 0.296875, y: 0.015625 },
+  { x: 0.296875, y: 0.015625 },
+  { x: 0.328125, y: 0.015625 },
+  { x: 0.328125, y: 0.015625 },
+  { x: 0.359375, y: 0.015625 },
+  { x: 0.359375, y: 0.015625 },
+  { x: 0.390625, y: 0.015625 },
+  { x: 0.390625, y: 0.015625 },
+  { x: 0.421875, y: 0.015625 },
+  { x: 0.421875, y: 0.015625 },
+  { x: 0.453125, y: 0.015625 },
+  { x: 0.453125, y: 0.015625 },
+  { x: 0.484375, y: 0.015625 },
+  { x: 0.484375, y: 0.015625 },
+  { x: 0.515625, y: 0.015625 },
+  { x: 0.515625, y: 0.015625 },
+  { x: 0.546875, y: 0.015625 },
+  { x: 0.546875, y: 0.015625 },
+  { x: 0.578125, y: 0.015625 },
+  { x: 0.578125, y: 0.015625 },
+  { x: 0.609375, y: 0.015625 },
+  { x: 0.609375, y: 0.015625 },
+  { x: 0.640625, y: 0.015625 },
+  { x: 0.640625, y: 0.015625 },
+  { x: 0.671875, y: 0.015625 },
+  { x: 0.671875, y: 0.015625 },
+  { x: 0.703125, y: 0.015625 },
+  { x: 0.703125, y: 0.015625 },
+  { x: 0.734375, y: 0.015625 },
+  { x: 0.734375, y: 0.015625 },
+  { x: 0.765625, y: 0.015625 },
+  { x: 0.765625, y: 0.015625 },
+  { x: 0.796875, y: 0.015625 },
+  { x: 0.796875, y: 0.015625 },
+  { x: 0.828125, y: 0.015625 },
+  { x: 0.828125, y: 0.015625 },
+  { x: 0.859375, y: 0.015625 },
+  { x: 0.859375, y: 0.015625 },
+  { x: 0.890625, y: 0.015625 },
+  { x: 0.890625, y: 0.015625 },
+  { x: 0.921875, y: 0.015625 },
+  { x: 0.921875, y: 0.015625 },
+  { x: 0.953125, y: 0.015625 },
+  { x: 0.953125, y: 0.015625 },
+  { x: 0.984375, y: 0.015625 },
+  { x: 0.984375, y: 0.015625 },
+  { x: 0.015625, y: 0.046875 },
+  { x: 0.015625, y: 0.046875 },
+  { x: 0.046875, y: 0.046875 },
+  { x: 0.046875, y: 0.046875 },
+  { x: 0.078125, y: 0.046875 },
+  { x: 0.078125, y: 0.046875 },
+  { x: 0.109375, y: 0.046875 },
+  { x: 0.109375, y: 0.046875 },
+  { x: 0.140625, y: 0.046875 },
+  { x: 0.140625, y: 0.046875 },
+  { x: 0.171875, y: 0.046875 },
+  { x: 0.171875, y: 0.046875 },
+  { x: 0.203125, y: 0.046875 },
+  { x: 0.203125, y: 0.046875 },
+  { x: 0.234375, y: 0.046875 },
+  { x: 0.234375, y: 0.046875 },
+  { x: 0.265625, y: 0.046875 },
+  { x: 0.265625, y: 0.046875 },
+  { x: 0.296875, y: 0.046875 },
+  { x: 0.296875, y: 0.046875 },
+  { x: 0.328125, y: 0.046875 },
+  { x: 0.328125, y: 0.046875 },
+  { x: 0.359375, y: 0.046875 },
+  { x: 0.359375, y: 0.046875 },
+  { x: 0.390625, y: 0.046875 },
+  { x: 0.390625, y: 0.046875 },
+  { x: 0.421875, y: 0.046875 },
+  { x: 0.421875, y: 0.046875 },
+  { x: 0.453125, y: 0.046875 },
+  { x: 0.453125, y: 0.046875 },
+  { x: 0.484375, y: 0.046875 },
+  { x: 0.484375, y: 0.046875 },
+  { x: 0.515625, y: 0.046875 },
+  { x: 0.515625, y: 0.046875 },
+  { x: 0.546875, y: 0.046875 },
+  { x: 0.546875, y: 0.046875 },
+  { x: 0.578125, y: 0.046875 },
+  { x: 0.578125, y: 0.046875 },
+  { x: 0.609375, y: 0.046875 },
+  { x: 0.609375, y: 0.046875 },
+  { x: 0.640625, y: 0.046875 },
+  { x: 0.640625, y: 0.046875 },
+  { x: 0.671875, y: 0.046875 },
+  { x: 0.671875, y: 0.046875 },
+  { x: 0.703125, y: 0.046875 },
+  { x: 0.703125, y: 0.046875 },
+  { x: 0.734375, y: 0.046875 },
+  { x: 0.734375, y: 0.046875 },
+  { x: 0.765625, y: 0.046875 },
+  { x: 0.765625, y: 0.046875 },
+  { x: 0.796875, y: 0.046875 },
+  { x: 0.796875, y: 0.046875 },
+  { x: 0.828125, y: 0.046875 },
+  { x: 0.828125, y: 0.046875 },
+  { x: 0.859375, y: 0.046875 },
+  { x: 0.859375, y: 0.046875 },
+  { x: 0.890625, y: 0.046875 },
+  { x: 0.890625, y: 0.046875 },
+  { x: 0.921875, y: 0.046875 },
+  { x: 0.921875, y: 0.046875 },
+  { x: 0.953125, y: 0.046875 },
+  { x: 0.953125, y: 0.046875 },
+  { x: 0.984375, y: 0.046875 },
+  { x: 0.984375, y: 0.046875 },
+  { x: 0.015625, y: 0.078125 },
+  { x: 0.015625, y: 0.078125 },
+  { x: 0.046875, y: 0.078125 },
+  { x: 0.046875, y: 0.078125 },
+  { x: 0.078125, y: 0.078125 },
+  { x: 0.078125, y: 0.078125 },
+  { x: 0.109375, y: 0.078125 },
+  { x: 0.109375, y: 0.078125 },
+  { x: 0.140625, y: 0.078125 },
+  { x: 0.140625, y: 0.078125 },
+  { x: 0.171875, y: 0.078125 },
+  { x: 0.171875, y: 0.078125 },
+  { x: 0.203125, y: 0.078125 },
+  { x: 0.203125, y: 0.078125 },
+  { x: 0.234375, y: 0.078125 },
+  { x: 0.234375, y: 0.078125 },
+  { x: 0.265625, y: 0.078125 },
+  { x: 0.265625, y: 0.078125 },
+  { x: 0.296875, y: 0.078125 },
+  { x: 0.296875, y: 0.078125 },
+  { x: 0.328125, y: 0.078125 },
+  { x: 0.328125, y: 0.078125 },
+  { x: 0.359375, y: 0.078125 },
+  { x: 0.359375, y: 0.078125 },
+  { x: 0.390625, y: 0.078125 },
+  { x: 0.390625, y: 0.078125 },
+  { x: 0.421875, y: 0.078125 },
+  { x: 0.421875, y: 0.078125 },
+  { x: 0.453125, y: 0.078125 },
+  { x: 0.453125, y: 0.078125 },
+  { x: 0.484375, y: 0.078125 },
+  { x: 0.484375, y: 0.078125 },
+  { x: 0.515625, y: 0.078125 },
+  { x: 0.515625, y: 0.078125 },
+  { x: 0.546875, y: 0.078125 },
+  { x: 0.546875, y: 0.078125 },
+  { x: 0.578125, y: 0.078125 },
+  { x: 0.578125, y: 0.078125 },
+  { x: 0.609375, y: 0.078125 },
+  { x: 0.609375, y: 0.078125 },
+  { x: 0.640625, y: 0.078125 },
+  { x: 0.640625, y: 0.078125 },
+  { x: 0.671875, y: 0.078125 },
+  { x: 0.671875, y: 0.078125 },
+  { x: 0.703125, y: 0.078125 },
+  { x: 0.703125, y: 0.078125 },
+  { x: 0.734375, y: 0.078125 },
+  { x: 0.734375, y: 0.078125 },
+  { x: 0.765625, y: 0.078125 },
+  { x: 0.765625, y: 0.078125 },
+  { x: 0.796875, y: 0.078125 },
+  { x: 0.796875, y: 0.078125 },
+  { x: 0.828125, y: 0.078125 },
+  { x: 0.828125, y: 0.078125 },
+  { x: 0.859375, y: 0.078125 },
+  { x: 0.859375, y: 0.078125 },
+  { x: 0.890625, y: 0.078125 },
+  { x: 0.890625, y: 0.078125 },
+  { x: 0.921875, y: 0.078125 },
+  { x: 0.921875, y: 0.078125 },
+  { x: 0.953125, y: 0.078125 },
+  { x: 0.953125, y: 0.078125 },
+  { x: 0.984375, y: 0.078125 },
+  { x: 0.984375, y: 0.078125 },
+  { x: 0.015625, y: 0.109375 },
+  { x: 0.015625, y: 0.109375 },
+  { x: 0.046875, y: 0.109375 },
+  { x: 0.046875, y: 0.109375 },
+  { x: 0.078125, y: 0.109375 },
+  { x: 0.078125, y: 0.109375 },
+  { x: 0.109375, y: 0.109375 },
+  { x: 0.109375, y: 0.109375 },
+  { x: 0.140625, y: 0.109375 },
+  { x: 0.140625, y: 0.109375 },
+  { x: 0.171875, y: 0.109375 },
+  { x: 0.171875, y: 0.109375 },
+  { x: 0.203125, y: 0.109375 },
+  { x: 0.203125, y: 0.109375 },
+  { x: 0.234375, y: 0.109375 },
+  { x: 0.234375, y: 0.109375 },
+  { x: 0.265625, y: 0.109375 },
+  { x: 0.265625, y: 0.109375 },
+  { x: 0.296875, y: 0.109375 },
+  { x: 0.296875, y: 0.109375 },
+  { x: 0.328125, y: 0.109375 },
+  { x: 0.328125, y: 0.109375 },
+  { x: 0.359375, y: 0.109375 },
+  { x: 0.359375, y: 0.109375 },
+  { x: 0.390625, y: 0.109375 },
+  { x: 0.390625, y: 0.109375 },
+  { x: 0.421875, y: 0.109375 },
+  { x: 0.421875, y: 0.109375 },
+  { x: 0.453125, y: 0.109375 },
+  { x: 0.453125, y: 0.109375 },
+  { x: 0.484375, y: 0.109375 },
+  { x: 0.484375, y: 0.109375 },
+  { x: 0.515625, y: 0.109375 },
+  { x: 0.515625, y: 0.109375 },
+  { x: 0.546875, y: 0.109375 },
+  { x: 0.546875, y: 0.109375 },
+  { x: 0.578125, y: 0.109375 },
+  { x: 0.578125, y: 0.109375 },
+  { x: 0.609375, y: 0.109375 },
+  { x: 0.609375, y: 0.109375 },
+  { x: 0.640625, y: 0.109375 },
+  { x: 0.640625, y: 0.109375 },
+  { x: 0.671875, y: 0.109375 },
+  { x: 0.671875, y: 0.109375 },
+  { x: 0.703125, y: 0.109375 },
+  { x: 0.703125, y: 0.109375 },
+  { x: 0.734375, y: 0.109375 },
+  { x: 0.734375, y: 0.109375 },
+  { x: 0.765625, y: 0.109375 },
+  { x: 0.765625, y: 0.109375 },
+  { x: 0.796875, y: 0.109375 },
+  { x: 0.796875, y: 0.109375 },
+  { x: 0.828125, y: 0.109375 },
+  { x: 0.828125, y: 0.109375 },
+  { x: 0.859375, y: 0.109375 },
+  { x: 0.859375, y: 0.109375 },
+  { x: 0.890625, y: 0.109375 },
+  { x: 0.890625, y: 0.109375 },
+  { x: 0.921875, y: 0.109375 },
+  { x: 0.921875, y: 0.109375 },
+  { x: 0.953125, y: 0.109375 },
+  { x: 0.953125, y: 0.109375 },
+  { x: 0.984375, y: 0.109375 },
+  { x: 0.984375, y: 0.109375 },
+  { x: 0.015625, y: 0.140625 },
+  { x: 0.015625, y: 0.140625 },
+  { x: 0.046875, y: 0.140625 },
+  { x: 0.046875, y: 0.140625 },
+  { x: 0.078125, y: 0.140625 },
+  { x: 0.078125, y: 0.140625 },
+  { x: 0.109375, y: 0.140625 },
+  { x: 0.109375, y: 0.140625 },
+  { x: 0.140625, y: 0.140625 },
+  { x: 0.140625, y: 0.140625 },
+  { x: 0.171875, y: 0.140625 },
+  { x: 0.171875, y: 0.140625 },
+  { x: 0.203125, y: 0.140625 },
+  { x: 0.203125, y: 0.140625 },
+  { x: 0.234375, y: 0.140625 },
+  { x: 0.234375, y: 0.140625 },
+  { x: 0.265625, y: 0.140625 },
+  { x: 0.265625, y: 0.140625 },
+  { x: 0.296875, y: 0.140625 },
+  { x: 0.296875, y: 0.140625 },
+  { x: 0.328125, y: 0.140625 },
+  { x: 0.328125, y: 0.140625 },
+  { x: 0.359375, y: 0.140625 },
+  { x: 0.359375, y: 0.140625 },
+  { x: 0.390625, y: 0.140625 },
+  { x: 0.390625, y: 0.140625 },
+  { x: 0.421875, y: 0.140625 },
+  { x: 0.421875, y: 0.140625 },
+  { x: 0.453125, y: 0.140625 },
+  { x: 0.453125, y: 0.140625 },
+  { x: 0.484375, y: 0.140625 },
+  { x: 0.484375, y: 0.140625 },
+  { x: 0.515625, y: 0.140625 },
+  { x: 0.515625, y: 0.140625 },
+  { x: 0.546875, y: 0.140625 },
+  { x: 0.546875, y: 0.140625 },
+  { x: 0.578125, y: 0.140625 },
+  { x: 0.578125, y: 0.140625 },
+  { x: 0.609375, y: 0.140625 },
+  { x: 0.609375, y: 0.140625 },
+  { x: 0.640625, y: 0.140625 },
+  { x: 0.640625, y: 0.140625 },
+  { x: 0.671875, y: 0.140625 },
+  { x: 0.671875, y: 0.140625 },
+  { x: 0.703125, y: 0.140625 },
+  { x: 0.703125, y: 0.140625 },
+  { x: 0.734375, y: 0.140625 },
+  { x: 0.734375, y: 0.140625 },
+  { x: 0.765625, y: 0.140625 },
+  { x: 0.765625, y: 0.140625 },
+  { x: 0.796875, y: 0.140625 },
+  { x: 0.796875, y: 0.140625 },
+  { x: 0.828125, y: 0.140625 },
+  { x: 0.828125, y: 0.140625 },
+  { x: 0.859375, y: 0.140625 },
+  { x: 0.859375, y: 0.140625 },
+  { x: 0.890625, y: 0.140625 },
+  { x: 0.890625, y: 0.140625 },
+  { x: 0.921875, y: 0.140625 },
+  { x: 0.921875, y: 0.140625 },
+  { x: 0.953125, y: 0.140625 },
+  { x: 0.953125, y: 0.140625 },
+  { x: 0.984375, y: 0.140625 },
+  { x: 0.984375, y: 0.140625 },
+  { x: 0.015625, y: 0.171875 },
+  { x: 0.015625, y: 0.171875 },
+  { x: 0.046875, y: 0.171875 },
+  { x: 0.046875, y: 0.171875 },
+  { x: 0.078125, y: 0.171875 },
+  { x: 0.078125, y: 0.171875 },
+  { x: 0.109375, y: 0.171875 },
+  { x: 0.109375, y: 0.171875 },
+  { x: 0.140625, y: 0.171875 },
+  { x: 0.140625, y: 0.171875 },
+  { x: 0.171875, y: 0.171875 },
+  { x: 0.171875, y: 0.171875 },
+  { x: 0.203125, y: 0.171875 },
+  { x: 0.203125, y: 0.171875 },
+  { x: 0.234375, y: 0.171875 },
+  { x: 0.234375, y: 0.171875 },
+  { x: 0.265625, y: 0.171875 },
+  { x: 0.265625, y: 0.171875 },
+  { x: 0.296875, y: 0.171875 },
+  { x: 0.296875, y: 0.171875 },
+  { x: 0.328125, y: 0.171875 },
+  { x: 0.328125, y: 0.171875 },
+  { x: 0.359375, y: 0.171875 },
+  { x: 0.359375, y: 0.171875 },
+  { x: 0.390625, y: 0.171875 },
+  { x: 0.390625, y: 0.171875 },
+  { x: 0.421875, y: 0.171875 },
+  { x: 0.421875, y: 0.171875 },
+  { x: 0.453125, y: 0.171875 },
+  { x: 0.453125, y: 0.171875 },
+  { x: 0.484375, y: 0.171875 },
+  { x: 0.484375, y: 0.171875 },
+  { x: 0.515625, y: 0.171875 },
+  { x: 0.515625, y: 0.171875 },
+  { x: 0.546875, y: 0.171875 },
+  { x: 0.546875, y: 0.171875 },
+  { x: 0.578125, y: 0.171875 },
+  { x: 0.578125, y: 0.171875 },
+  { x: 0.609375, y: 0.171875 },
+  { x: 0.609375, y: 0.171875 },
+  { x: 0.640625, y: 0.171875 },
+  { x: 0.640625, y: 0.171875 },
+  { x: 0.671875, y: 0.171875 },
+  { x: 0.671875, y: 0.171875 },
+  { x: 0.703125, y: 0.171875 },
+  { x: 0.703125, y: 0.171875 },
+  { x: 0.734375, y: 0.171875 },
+  { x: 0.734375, y: 0.171875 },
+  { x: 0.765625, y: 0.171875 },
+  { x: 0.765625, y: 0.171875 },
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+  { x: 0.6875, y: 0.6875 },
+  { x: 0.6875, y: 0.6875 },
+  { x: 0.6875, y: 0.6875 },
+  { x: 0.8125, y: 0.6875 },
+  { x: 0.8125, y: 0.6875 },
+  { x: 0.8125, y: 0.6875 },
+  { x: 0.8125, y: 0.6875 },
+  { x: 0.8125, y: 0.6875 },
+  { x: 0.8125, y: 0.6875 },
+  { x: 0.9375, y: 0.6875 },
+  { x: 0.9375, y: 0.6875 },
+  { x: 0.9375, y: 0.6875 },
+  { x: 0.9375, y: 0.6875 },
+  { x: 0.9375, y: 0.6875 },
+  { x: 0.9375, y: 0.6875 },
+  { x: 0.0625, y: 0.8125 },
+  { x: 0.0625, y: 0.8125 },
+  { x: 0.0625, y: 0.8125 },
+  { x: 0.0625, y: 0.8125 },
+  { x: 0.0625, y: 0.8125 },
+  { x: 0.0625, y: 0.8125 },
+  { x: 0.1875, y: 0.8125 },
+  { x: 0.1875, y: 0.8125 },
+  { x: 0.1875, y: 0.8125 },
+  { x: 0.1875, y: 0.8125 },
+  { x: 0.1875, y: 0.8125 },
+  { x: 0.1875, y: 0.8125 },
+  { x: 0.3125, y: 0.8125 },
+  { x: 0.3125, y: 0.8125 },
+  { x: 0.3125, y: 0.8125 },
+  { x: 0.3125, y: 0.8125 },
+  { x: 0.3125, y: 0.8125 },
+  { x: 0.3125, y: 0.8125 },
+  { x: 0.4375, y: 0.8125 },
+  { x: 0.4375, y: 0.8125 },
+  { x: 0.4375, y: 0.8125 },
+  { x: 0.4375, y: 0.8125 },
+  { x: 0.4375, y: 0.8125 },
+  { x: 0.4375, y: 0.8125 },
+  { x: 0.5625, y: 0.8125 },
+  { x: 0.5625, y: 0.8125 },
+  { x: 0.5625, y: 0.8125 },
+  { x: 0.5625, y: 0.8125 },
+  { x: 0.5625, y: 0.8125 },
+  { x: 0.5625, y: 0.8125 },
+  { x: 0.6875, y: 0.8125 },
+  { x: 0.6875, y: 0.8125 },
+  { x: 0.6875, y: 0.8125 },
+  { x: 0.6875, y: 0.8125 },
+  { x: 0.6875, y: 0.8125 },
+  { x: 0.6875, y: 0.8125 },
+  { x: 0.8125, y: 0.8125 },
+  { x: 0.8125, y: 0.8125 },
+  { x: 0.8125, y: 0.8125 },
+  { x: 0.8125, y: 0.8125 },
+  { x: 0.8125, y: 0.8125 },
+  { x: 0.8125, y: 0.8125 },
+  { x: 0.9375, y: 0.8125 },
+  { x: 0.9375, y: 0.8125 },
+  { x: 0.9375, y: 0.8125 },
+  { x: 0.9375, y: 0.8125 },
+  { x: 0.9375, y: 0.8125 },
+  { x: 0.9375, y: 0.8125 },
+  { x: 0.0625, y: 0.9375 },
+  { x: 0.0625, y: 0.9375 },
+  { x: 0.0625, y: 0.9375 },
+  { x: 0.0625, y: 0.9375 },
+  { x: 0.0625, y: 0.9375 },
+  { x: 0.0625, y: 0.9375 },
+  { x: 0.1875, y: 0.9375 },
+  { x: 0.1875, y: 0.9375 },
+  { x: 0.1875, y: 0.9375 },
+  { x: 0.1875, y: 0.9375 },
+  { x: 0.1875, y: 0.9375 },
+  { x: 0.1875, y: 0.9375 },
+  { x: 0.3125, y: 0.9375 },
+  { x: 0.3125, y: 0.9375 },
+  { x: 0.3125, y: 0.9375 },
+  { x: 0.3125, y: 0.9375 },
+  { x: 0.3125, y: 0.9375 },
+  { x: 0.3125, y: 0.9375 },
+  { x: 0.4375, y: 0.9375 },
+  { x: 0.4375, y: 0.9375 },
+  { x: 0.4375, y: 0.9375 },
+  { x: 0.4375, y: 0.9375 },
+  { x: 0.4375, y: 0.9375 },
+  { x: 0.4375, y: 0.9375 },
+  { x: 0.5625, y: 0.9375 },
+  { x: 0.5625, y: 0.9375 },
+  { x: 0.5625, y: 0.9375 },
+  { x: 0.5625, y: 0.9375 },
+  { x: 0.5625, y: 0.9375 },
+  { x: 0.5625, y: 0.9375 },
+  { x: 0.6875, y: 0.9375 },
+  { x: 0.6875, y: 0.9375 },
+  { x: 0.6875, y: 0.9375 },
+  { x: 0.6875, y: 0.9375 },
+  { x: 0.6875, y: 0.9375 },
+  { x: 0.6875, y: 0.9375 },
+  { x: 0.8125, y: 0.9375 },
+  { x: 0.8125, y: 0.9375 },
+  { x: 0.8125, y: 0.9375 },
+  { x: 0.8125, y: 0.9375 },
+  { x: 0.8125, y: 0.9375 },
+  { x: 0.8125, y: 0.9375 },
+  { x: 0.9375, y: 0.9375 },
+  { x: 0.9375, y: 0.9375 },
+  { x: 0.9375, y: 0.9375 },
+  { x: 0.9375, y: 0.9375 },
+  { x: 0.9375, y: 0.9375 },
+  { x: 0.9375, y: 0.9375 }
+];
+
+// src/hand/handposedetector.ts
+var HandDetector = class {
+  constructor(model21) {
+    __publicField(this, "model");
+    __publicField(this, "anchors");
+    __publicField(this, "anchorsTensor");
+    __publicField(this, "inputSize");
+    __publicField(this, "inputSizeTensor");
+    __publicField(this, "doubleInputSizeTensor");
+    var _a, _b, _c, _d;
+    this.model = model21;
+    this.anchors = anchors2.map((anchor) => [anchor.x, anchor.y]);
+    this.anchorsTensor = tf19.tensor2d(this.anchors);
+    this.inputSize = ((_d = (_c = (_b = (_a = this == null ? void 0 : this.model) == null ? void 0 : _a.inputs) == null ? void 0 : _b[0]) == null ? void 0 : _c.shape) == null ? void 0 : _d[2]) || 0;
+    this.inputSizeTensor = tf19.tensor1d([this.inputSize, this.inputSize]);
+    this.doubleInputSizeTensor = tf19.tensor1d([this.inputSize * 2, this.inputSize * 2]);
+  }
+  normalizeBoxes(boxes) {
+    const t2 = {};
+    t2.boxOffsets = tf19.slice(boxes, [0, 0], [-1, 2]);
+    t2.boxSizes = tf19.slice(boxes, [0, 2], [-1, 2]);
+    t2.div = tf19.div(t2.boxOffsets, this.inputSizeTensor);
+    t2.boxCenterPoints = tf19.add(t2.div, this.anchorsTensor);
+    t2.halfBoxSizes = tf19.div(t2.boxSizes, this.doubleInputSizeTensor);
+    t2.sub = tf19.sub(t2.boxCenterPoints, t2.halfBoxSizes);
+    t2.startPoints = tf19.mul(t2.sub, this.inputSizeTensor);
+    t2.add = tf19.add(t2.boxCenterPoints, t2.halfBoxSizes);
+    t2.endPoints = tf19.mul(t2.add, this.inputSizeTensor);
+    const res = tf19.concat2d([t2.startPoints, t2.endPoints], 1);
+    Object.keys(t2).forEach((tensor6) => tf19.dispose(t2[tensor6]));
+    return res;
+  }
+  normalizeLandmarks(rawPalmLandmarks, index2) {
+    const t2 = {};
+    t2.reshape = tf19.reshape(rawPalmLandmarks, [-1, 7, 2]);
+    t2.div = tf19.div(t2.reshape, this.inputSizeTensor);
+    t2.landmarks = tf19.add(t2.div, this.anchors[index2] ? this.anchors[index2] : 0);
+    const res = tf19.mul(t2.landmarks, this.inputSizeTensor);
+    Object.keys(t2).forEach((tensor6) => tf19.dispose(t2[tensor6]));
+    return res;
+  }
+  async predict(input, config3) {
+    var _a;
+    const t2 = {};
+    t2.resize = tf19.image.resizeBilinear(input, [this.inputSize, this.inputSize]);
+    t2.div = tf19.div(t2.resize, constants.tf127);
+    t2.image = tf19.sub(t2.div, constants.tf1);
+    t2.batched = this.model.execute(t2.image);
+    t2.predictions = tf19.squeeze(t2.batched);
+    t2.slice = tf19.slice(t2.predictions, [0, 0], [-1, 1]);
+    t2.sigmoid = tf19.sigmoid(t2.slice);
+    t2.scores = tf19.squeeze(t2.sigmoid);
+    const scores = await t2.scores.data();
+    t2.boxes = tf19.slice(t2.predictions, [0, 1], [-1, 4]);
+    t2.norm = this.normalizeBoxes(t2.boxes);
+    t2.nms = await tf19.image.nonMaxSuppressionAsync(t2.norm, t2.scores, 3 * (((_a = config3.hand) == null ? void 0 : _a.maxDetected) || 1), config3.hand.iouThreshold, config3.hand.minConfidence);
+    const nms = await t2.nms.array();
+    const hands = [];
+    for (const index2 of nms) {
+      const p = {};
+      p.box = tf19.slice(t2.norm, [index2, 0], [1, -1]);
+      p.slice = tf19.slice(t2.predictions, [index2, 5], [1, 14]);
+      p.norm = this.normalizeLandmarks(p.slice, index2);
+      p.palmLandmarks = tf19.reshape(p.norm, [-1, 2]);
+      const box = await p.box.data();
+      const startPoint = box.slice(0, 2);
+      const endPoint = box.slice(2, 4);
+      const palmLandmarks = await p.palmLandmarks.array();
+      const hand3 = { startPoint, endPoint, palmLandmarks, confidence: scores[index2] };
+      const scaled = scaleBoxCoordinates2(hand3, [(input.shape[2] || 1) / this.inputSize, (input.shape[1] || 0) / this.inputSize]);
+      hands.push(scaled);
+      Object.keys(p).forEach((tensor6) => tf19.dispose(p[tensor6]));
+    }
+    Object.keys(t2).forEach((tensor6) => tf19.dispose(t2[tensor6]));
+    return hands;
+  }
+};
+
+// src/hand/handposepipeline.ts
+var tf20 = __toESM(require_tfjs_esm());
+var palmBoxEnlargeFactor = 5;
+var handBoxEnlargeFactor = 1.65;
+var palmLandmarkIds = [0, 5, 9, 13, 17, 1, 2];
+var palmLandmarksPalmBase = 0;
+var palmLandmarksMiddleFingerBase = 2;
+var lastTime8 = 0;
+var HandPipeline = class {
+  constructor(handDetector, handPoseModel2) {
+    __publicField(this, "handDetector");
+    __publicField(this, "handPoseModel");
+    __publicField(this, "inputSize");
+    __publicField(this, "storedBoxes");
+    __publicField(this, "skipped");
+    __publicField(this, "detectedHands");
+    var _a, _b, _c;
+    this.handDetector = handDetector;
+    this.handPoseModel = handPoseModel2;
+    this.inputSize = ((_c = (_b = (_a = this.handPoseModel) == null ? void 0 : _a.inputs) == null ? void 0 : _b[0].shape) == null ? void 0 : _c[2]) || 0;
+    this.storedBoxes = [];
+    this.skipped = Number.MAX_SAFE_INTEGER;
+    this.detectedHands = 0;
+  }
+  calculateLandmarksBoundingBox(landmarks) {
+    const xs = landmarks.map((d) => d[0]);
+    const ys = landmarks.map((d) => d[1]);
+    const startPoint = [Math.min(...xs), Math.min(...ys)];
+    const endPoint = [Math.max(...xs), Math.max(...ys)];
+    return { startPoint, endPoint };
+  }
+  getBoxForPalmLandmarks(palmLandmarks, rotationMatrix) {
+    const rotatedPalmLandmarks = palmLandmarks.map((coord) => rotatePoint2([...coord, 1], rotationMatrix));
+    const boxAroundPalm = this.calculateLandmarksBoundingBox(rotatedPalmLandmarks);
+    return enlargeBox2(squarifyBox2(boxAroundPalm), palmBoxEnlargeFactor);
+  }
+  getBoxForHandLandmarks(landmarks) {
+    const boundingBox = this.calculateLandmarksBoundingBox(landmarks);
+    const boxAroundHand = enlargeBox2(squarifyBox2(boundingBox), handBoxEnlargeFactor);
+    boxAroundHand.palmLandmarks = [];
+    for (let i = 0; i < palmLandmarkIds.length; i++) {
+      boxAroundHand.palmLandmarks.push(landmarks[palmLandmarkIds[i]].slice(0, 2));
+    }
+    return boxAroundHand;
+  }
+  transformRawCoords(rawCoords, box2, angle, rotationMatrix) {
+    const boxSize = getBoxSize2(box2);
+    const scaleFactor = [boxSize[0] / this.inputSize, boxSize[1] / this.inputSize, (boxSize[0] + boxSize[1]) / this.inputSize / 2];
+    const coordsScaled = rawCoords.map((coord) => [
+      scaleFactor[0] * (coord[0] - this.inputSize / 2),
+      scaleFactor[1] * (coord[1] - this.inputSize / 2),
+      scaleFactor[2] * coord[2]
+    ]);
+    const coordsRotationMatrix = buildRotationMatrix2(angle, [0, 0]);
+    const coordsRotated = coordsScaled.map((coord) => {
+      const rotated = rotatePoint2(coord, coordsRotationMatrix);
+      return [...rotated, coord[2]];
+    });
+    const inverseRotationMatrix = invertTransformMatrix2(rotationMatrix);
+    const boxCenter = [...getBoxCenter2(box2), 1];
+    const originalBoxCenter = [
+      dot2(boxCenter, inverseRotationMatrix[0]),
+      dot2(boxCenter, inverseRotationMatrix[1])
+    ];
+    return coordsRotated.map((coord) => [
+      Math.trunc(coord[0] + originalBoxCenter[0]),
+      Math.trunc(coord[1] + originalBoxCenter[1]),
+      Math.trunc(coord[2])
+    ]);
+  }
+  async estimateHands(image27, config3) {
+    let useFreshBox = false;
+    let boxes;
+    const skipTime = (config3.hand.skipTime || 0) > now() - lastTime8;
+    const skipFrame = this.skipped < (config3.hand.skipFrames || 0);
+    if (config3.skipAllowed && skipTime && skipFrame) {
+      boxes = await this.handDetector.predict(image27, config3);
+      this.skipped = 0;
+    }
+    if (config3.skipAllowed)
+      this.skipped++;
+    if (boxes && boxes.length > 0 && (boxes.length !== this.detectedHands && this.detectedHands !== config3.hand.maxDetected || !config3.hand.landmarks)) {
+      this.detectedHands = 0;
+      this.storedBoxes = [...boxes];
+      if (this.storedBoxes.length > 0)
+        useFreshBox = true;
+    }
+    const hands = [];
+    for (let i = 0; i < this.storedBoxes.length; i++) {
+      const currentBox = this.storedBoxes[i];
+      if (!currentBox)
+        continue;
+      if (config3.hand.landmarks) {
+        const angle = config3.hand.rotation ? computeRotation2(currentBox.palmLandmarks[palmLandmarksPalmBase], currentBox.palmLandmarks[palmLandmarksMiddleFingerBase]) : 0;
+        const palmCenter = getBoxCenter2(currentBox);
+        const palmCenterNormalized = [palmCenter[0] / image27.shape[2], palmCenter[1] / image27.shape[1]];
+        const rotatedImage = config3.hand.rotation && env.kernels.includes("rotatewithoffset") ? tf20.image.rotateWithOffset(image27, angle, 0, palmCenterNormalized) : image27.clone();
+        const rotationMatrix = buildRotationMatrix2(-angle, palmCenter);
+        const newBox = useFreshBox ? this.getBoxForPalmLandmarks(currentBox.palmLandmarks, rotationMatrix) : currentBox;
+        const croppedInput = cutBoxFromImageAndResize(newBox, rotatedImage, [this.inputSize, this.inputSize]);
+        const handImage = tf20.div(croppedInput, constants.tf255);
+        tf20.dispose(croppedInput);
+        tf20.dispose(rotatedImage);
+        const [confidenceT, keypoints] = this.handPoseModel.execute(handImage);
+        lastTime8 = now();
+        tf20.dispose(handImage);
+        const confidence = (await confidenceT.data())[0];
+        tf20.dispose(confidenceT);
+        if (confidence >= config3.hand.minConfidence / 4) {
+          const keypointsReshaped = tf20.reshape(keypoints, [-1, 3]);
+          const rawCoords = await keypointsReshaped.array();
+          tf20.dispose(keypoints);
+          tf20.dispose(keypointsReshaped);
+          const coords = this.transformRawCoords(rawCoords, newBox, angle, rotationMatrix);
+          const nextBoundingBox = this.getBoxForHandLandmarks(coords);
+          this.storedBoxes[i] = { ...nextBoundingBox, confidence };
+          const result = {
+            landmarks: coords,
+            confidence,
+            boxConfidence: currentBox.confidence,
+            fingerConfidence: confidence,
+            box: { topLeft: nextBoundingBox.startPoint, bottomRight: nextBoundingBox.endPoint }
+          };
+          hands.push(result);
+        } else {
+          this.storedBoxes[i] = null;
+        }
+        tf20.dispose(keypoints);
+      } else {
+        const enlarged = enlargeBox2(squarifyBox2(currentBox), handBoxEnlargeFactor);
+        const result = {
+          confidence: currentBox.confidence,
+          boxConfidence: currentBox.confidence,
+          fingerConfidence: 0,
+          box: { topLeft: enlarged.startPoint, bottomRight: enlarged.endPoint },
+          landmarks: []
+        };
+        hands.push(result);
+      }
+    }
+    this.storedBoxes = this.storedBoxes.filter((a) => a !== null);
+    this.detectedHands = hands.length;
+    if (hands.length > config3.hand.maxDetected)
+      hands.length = config3.hand.maxDetected;
+    return hands;
+  }
+};
+
+// src/hand/fingerdef.ts
+var Finger = {
+  thumb: 0,
+  index: 1,
+  middle: 2,
+  ring: 3,
+  pinky: 4,
+  all: [0, 1, 2, 3, 4],
+  nameMapping: { 0: "thumb", 1: "index", 2: "middle", 3: "ring", 4: "pinky" },
+  pointsMapping: {
+    0: [[0, 1], [1, 2], [2, 3], [3, 4]],
+    1: [[0, 5], [5, 6], [6, 7], [7, 8]],
+    2: [[0, 9], [9, 10], [10, 11], [11, 12]],
+    3: [[0, 13], [13, 14], [14, 15], [15, 16]],
+    4: [[0, 17], [17, 18], [18, 19], [19, 20]]
+  },
+  getName: (value) => Finger.nameMapping[value],
+  getPoints: (value) => Finger.pointsMapping[value]
+};
+var FingerCurl = {
+  none: 0,
+  half: 1,
+  full: 2,
+  nameMapping: { 0: "none", 1: "half", 2: "full" },
+  getName: (value) => FingerCurl.nameMapping[value]
+};
+var FingerDirection = {
+  verticalUp: 0,
+  verticalDown: 1,
+  horizontalLeft: 2,
+  horizontalRight: 3,
+  diagonalUpRight: 4,
+  diagonalUpLeft: 5,
+  diagonalDownRight: 6,
+  diagonalDownLeft: 7,
+  nameMapping: { 0: "verticalUp", 1: "verticalDown", 2: "horizontalLeft", 3: "horizontalRight", 4: "diagonalUpRight", 5: "diagonalUpLeft", 6: "diagonalDownRight", 7: "diagonalDownLeft" },
+  getName: (value) => FingerDirection.nameMapping[value]
+};
+var FingerGesture = class {
+  constructor(name) {
+    __publicField(this, "name");
+    __publicField(this, "curls");
+    __publicField(this, "directions");
+    __publicField(this, "weights");
+    __publicField(this, "weightsRelative");
+    this.name = name;
+    this.curls = {};
+    this.directions = {};
+    this.weights = [1, 1, 1, 1, 1];
+    this.weightsRelative = [1, 1, 1, 1, 1];
+  }
+  curl(finger, curl, confidence) {
+    if (typeof this.curls[finger] === "undefined")
+      this.curls[finger] = [];
+    this.curls[finger].push([curl, confidence]);
+  }
+  direction(finger, position, confidence) {
+    if (!this.directions[finger])
+      this.directions[finger] = [];
+    this.directions[finger].push([position, confidence]);
+  }
+  weight(finger, weight) {
+    this.weights[finger] = weight;
+    const total = this.weights.reduce((a, b) => a + b, 0);
+    this.weightsRelative = this.weights.map((el) => el * 5 / total);
+  }
+  matchAgainst(detectedCurls, detectedDirections) {
+    let confidence = 0;
+    for (const fingerIdx in detectedCurls) {
+      const detectedCurl = detectedCurls[fingerIdx];
+      const expectedCurls = this.curls[fingerIdx];
+      if (typeof expectedCurls === "undefined") {
+        confidence += this.weightsRelative[fingerIdx];
+        continue;
+      }
+      for (const [expectedCurl, score] of expectedCurls) {
+        if (detectedCurl === expectedCurl) {
+          confidence += score * this.weightsRelative[fingerIdx];
+          break;
+        }
+      }
+    }
+    for (const fingerIdx in detectedDirections) {
+      const detectedDirection = detectedDirections[fingerIdx];
+      const expectedDirections = this.directions[fingerIdx];
+      if (typeof expectedDirections === "undefined") {
+        confidence += this.weightsRelative[fingerIdx];
+        continue;
+      }
+      for (const [expectedDirection, score] of expectedDirections) {
+        if (detectedDirection === expectedDirection) {
+          confidence += score * this.weightsRelative[fingerIdx];
+          break;
+        }
+      }
+    }
+    return confidence / 10;
+  }
+};
+
+// src/hand/fingergesture.ts
+var { thumb, index, middle, ring, pinky } = Finger;
+var { none, half, full } = FingerCurl;
+var { verticalUp, verticalDown, horizontalLeft, horizontalRight, diagonalUpRight, diagonalUpLeft, diagonalDownRight, diagonalDownLeft } = FingerDirection;
+var ThumbsUp = new FingerGesture("thumbs up");
+ThumbsUp.curl(thumb, none, 1);
+ThumbsUp.direction(thumb, verticalUp, 1);
+ThumbsUp.direction(thumb, diagonalUpLeft, 0.25);
+ThumbsUp.direction(thumb, diagonalUpRight, 0.25);
+for (const finger of [Finger.index, Finger.middle, Finger.ring, Finger.pinky]) {
+  ThumbsUp.curl(finger, full, 1);
+  ThumbsUp.direction(finger, horizontalLeft, 1);
+  ThumbsUp.direction(finger, horizontalRight, 1);
+}
+var Victory = new FingerGesture("victory");
+Victory.curl(thumb, half, 0.5);
+Victory.curl(thumb, none, 0.5);
+Victory.direction(thumb, verticalUp, 1);
+Victory.direction(thumb, diagonalUpLeft, 1);
+Victory.curl(index, none, 1);
+Victory.direction(index, verticalUp, 0.75);
+Victory.direction(index, diagonalUpLeft, 1);
+Victory.curl(middle, none, 1);
+Victory.direction(middle, verticalUp, 1);
+Victory.direction(middle, diagonalUpLeft, 0.75);
+Victory.curl(ring, full, 1);
+Victory.direction(ring, verticalUp, 0.2);
+Victory.direction(ring, diagonalUpLeft, 1);
+Victory.direction(ring, horizontalLeft, 0.2);
+Victory.curl(pinky, full, 1);
+Victory.direction(pinky, verticalUp, 0.2);
+Victory.direction(pinky, diagonalUpLeft, 1);
+Victory.direction(pinky, horizontalLeft, 0.2);
+Victory.weight(index, 2);
+Victory.weight(middle, 2);
+var Point = new FingerGesture("point");
+Point.curl(thumb, full, 1);
+Point.curl(index, none, 0.5);
+Point.curl(middle, full, 0.5);
+Point.curl(ring, full, 0.5);
+Point.curl(pinky, full, 0.5);
+Point.weight(index, 2);
+Point.weight(middle, 2);
+var MiddleFinger = new FingerGesture("middle finger");
+MiddleFinger.curl(thumb, none, 1);
+MiddleFinger.curl(index, full, 0.5);
+MiddleFinger.curl(middle, full, 0.5);
+MiddleFinger.curl(ring, full, 0.5);
+MiddleFinger.curl(pinky, full, 0.5);
+MiddleFinger.weight(index, 2);
+MiddleFinger.weight(middle, 2);
+var OpenPalm = new FingerGesture("open palm");
+OpenPalm.curl(thumb, none, 0.75);
+OpenPalm.curl(index, none, 0.75);
+OpenPalm.curl(middle, none, 0.75);
+OpenPalm.curl(ring, none, 0.75);
+OpenPalm.curl(pinky, none, 0.75);
+var fingergesture_default = [ThumbsUp, Victory, Point, MiddleFinger, OpenPalm];
+
+// src/hand/fingerpose.ts
+var minConfidence = 0.7;
+var options2 = {
+  HALF_CURL_START_LIMIT: 60,
+  NO_CURL_START_LIMIT: 130,
+  DISTANCE_VOTE_POWER: 1.1,
+  SINGLE_ANGLE_VOTE_POWER: 0.9,
+  TOTAL_ANGLE_VOTE_POWER: 1.6
+};
+function calculateSlope(point1x, point1y, point2x, point2y) {
+  const value = (point1y - point2y) / (point1x - point2x);
+  let slope = Math.atan(value) * 180 / Math.PI;
+  if (slope <= 0)
+    slope = -slope;
+  else if (slope > 0)
+    slope = 180 - slope;
+  return slope;
+}
+function getSlopes(point1, point2) {
+  if (!point1 || !point2)
+    return [0, 0];
+  const slopeXY = calculateSlope(point1[0], point1[1], point2[0], point2[1]);
+  if (point1.length === 2)
+    return slopeXY;
+  const slopeYZ = calculateSlope(point1[1], point1[2], point2[1], point2[2]);
+  return [slopeXY, slopeYZ];
+}
+function angleOrientationAt(angle, weightageAt = 1) {
+  let isVertical = 0;
+  let isDiagonal = 0;
+  let isHorizontal = 0;
+  if (angle >= 75 && angle <= 105)
+    isVertical = 1 * weightageAt;
+  else if (angle >= 25 && angle <= 155)
+    isDiagonal = 1 * weightageAt;
+  else
+    isHorizontal = 1 * weightageAt;
+  return [isVertical, isDiagonal, isHorizontal];
+}
+function estimateFingerCurl(startPoint, midPoint, endPoint) {
+  const start_mid_x_dist = startPoint[0] - midPoint[0];
+  const start_end_x_dist = startPoint[0] - endPoint[0];
+  const mid_end_x_dist = midPoint[0] - endPoint[0];
+  const start_mid_y_dist = startPoint[1] - midPoint[1];
+  const start_end_y_dist = startPoint[1] - endPoint[1];
+  const mid_end_y_dist = midPoint[1] - endPoint[1];
+  const start_mid_z_dist = startPoint[2] - midPoint[2];
+  const start_end_z_dist = startPoint[2] - endPoint[2];
+  const mid_end_z_dist = midPoint[2] - endPoint[2];
+  const start_mid_dist = Math.sqrt(start_mid_x_dist * start_mid_x_dist + start_mid_y_dist * start_mid_y_dist + start_mid_z_dist * start_mid_z_dist);
+  const start_end_dist = Math.sqrt(start_end_x_dist * start_end_x_dist + start_end_y_dist * start_end_y_dist + start_end_z_dist * start_end_z_dist);
+  const mid_end_dist = Math.sqrt(mid_end_x_dist * mid_end_x_dist + mid_end_y_dist * mid_end_y_dist + mid_end_z_dist * mid_end_z_dist);
+  let cos_in = (mid_end_dist * mid_end_dist + start_mid_dist * start_mid_dist - start_end_dist * start_end_dist) / (2 * mid_end_dist * start_mid_dist);
+  if (cos_in > 1)
+    cos_in = 1;
+  else if (cos_in < -1)
+    cos_in = -1;
+  let angleOfCurve = Math.acos(cos_in);
+  angleOfCurve = 57.2958 * angleOfCurve % 180;
+  let fingerCurl;
+  if (angleOfCurve > options2.NO_CURL_START_LIMIT)
+    fingerCurl = FingerCurl.none;
+  else if (angleOfCurve > options2.HALF_CURL_START_LIMIT)
+    fingerCurl = FingerCurl.half;
+  else
+    fingerCurl = FingerCurl.full;
+  return fingerCurl;
+}
+function estimateHorizontalDirection(start_end_x_dist, start_mid_x_dist, mid_end_x_dist, max_dist_x) {
+  let estimatedDirection;
+  if (max_dist_x === Math.abs(start_end_x_dist)) {
+    if (start_end_x_dist > 0)
+      estimatedDirection = FingerDirection.horizontalLeft;
+    else
+      estimatedDirection = FingerDirection.horizontalRight;
+  } else if (max_dist_x === Math.abs(start_mid_x_dist)) {
+    if (start_mid_x_dist > 0)
+      estimatedDirection = FingerDirection.horizontalLeft;
+    else
+      estimatedDirection = FingerDirection.horizontalRight;
+  } else {
+    if (mid_end_x_dist > 0)
+      estimatedDirection = FingerDirection.horizontalLeft;
+    else
+      estimatedDirection = FingerDirection.horizontalRight;
+  }
+  return estimatedDirection;
+}
+function estimateVerticalDirection(start_end_y_dist, start_mid_y_dist, mid_end_y_dist, max_dist_y) {
+  let estimatedDirection;
+  if (max_dist_y === Math.abs(start_end_y_dist)) {
+    if (start_end_y_dist < 0)
+      estimatedDirection = FingerDirection.verticalDown;
+    else
+      estimatedDirection = FingerDirection.verticalUp;
+  } else if (max_dist_y === Math.abs(start_mid_y_dist)) {
+    if (start_mid_y_dist < 0)
+      estimatedDirection = FingerDirection.verticalDown;
+    else
+      estimatedDirection = FingerDirection.verticalUp;
+  } else {
+    if (mid_end_y_dist < 0)
+      estimatedDirection = FingerDirection.verticalDown;
+    else
+      estimatedDirection = FingerDirection.verticalUp;
+  }
+  return estimatedDirection;
+}
+function estimateDiagonalDirection(start_end_y_dist, start_mid_y_dist, mid_end_y_dist, max_dist_y, start_end_x_dist, start_mid_x_dist, mid_end_x_dist, max_dist_x) {
+  let estimatedDirection;
+  const reqd_vertical_direction = estimateVerticalDirection(start_end_y_dist, start_mid_y_dist, mid_end_y_dist, max_dist_y);
+  const reqd_horizontal_direction = estimateHorizontalDirection(start_end_x_dist, start_mid_x_dist, mid_end_x_dist, max_dist_x);
+  if (reqd_vertical_direction === FingerDirection.verticalUp) {
+    if (reqd_horizontal_direction === FingerDirection.horizontalLeft)
+      estimatedDirection = FingerDirection.diagonalUpLeft;
+    else
+      estimatedDirection = FingerDirection.diagonalUpRight;
+  } else {
+    if (reqd_horizontal_direction === FingerDirection.horizontalLeft)
+      estimatedDirection = FingerDirection.diagonalDownLeft;
+    else
+      estimatedDirection = FingerDirection.diagonalDownRight;
+  }
+  return estimatedDirection;
+}
+function calculateFingerDirection(startPoint, midPoint, endPoint, fingerSlopes) {
+  const start_mid_x_dist = startPoint[0] - midPoint[0];
+  const start_end_x_dist = startPoint[0] - endPoint[0];
+  const mid_end_x_dist = midPoint[0] - endPoint[0];
+  const start_mid_y_dist = startPoint[1] - midPoint[1];
+  const start_end_y_dist = startPoint[1] - endPoint[1];
+  const mid_end_y_dist = midPoint[1] - endPoint[1];
+  const max_dist_x = Math.max(Math.abs(start_mid_x_dist), Math.abs(start_end_x_dist), Math.abs(mid_end_x_dist));
+  const max_dist_y = Math.max(Math.abs(start_mid_y_dist), Math.abs(start_end_y_dist), Math.abs(mid_end_y_dist));
+  let voteVertical = 0;
+  let voteDiagonal = 0;
+  let voteHorizontal = 0;
+  const start_end_x_y_dist_ratio = max_dist_y / (max_dist_x + 1e-5);
+  if (start_end_x_y_dist_ratio > 1.5)
+    voteVertical += options2.DISTANCE_VOTE_POWER;
+  else if (start_end_x_y_dist_ratio > 0.66)
+    voteDiagonal += options2.DISTANCE_VOTE_POWER;
+  else
+    voteHorizontal += options2.DISTANCE_VOTE_POWER;
+  const start_mid_dist = Math.sqrt(start_mid_x_dist * start_mid_x_dist + start_mid_y_dist * start_mid_y_dist);
+  const start_end_dist = Math.sqrt(start_end_x_dist * start_end_x_dist + start_end_y_dist * start_end_y_dist);
+  const mid_end_dist = Math.sqrt(mid_end_x_dist * mid_end_x_dist + mid_end_y_dist * mid_end_y_dist);
+  const max_dist = Math.max(start_mid_dist, start_end_dist, mid_end_dist);
+  let calc_start_point_x = startPoint[0];
+  let calc_start_point_y = startPoint[1];
+  let calc_end_point_x = endPoint[0];
+  let calc_end_point_y = endPoint[1];
+  if (max_dist === start_mid_dist) {
+    calc_end_point_x = endPoint[0];
+    calc_end_point_y = endPoint[1];
+  } else if (max_dist === mid_end_dist) {
+    calc_start_point_x = midPoint[0];
+    calc_start_point_y = midPoint[1];
+  }
+  const calcStartPoint = [calc_start_point_x, calc_start_point_y];
+  const calcEndPoint = [calc_end_point_x, calc_end_point_y];
+  const totalAngle = getSlopes(calcStartPoint, calcEndPoint);
+  const votes = angleOrientationAt(totalAngle, options2.TOTAL_ANGLE_VOTE_POWER);
+  voteVertical += votes[0];
+  voteDiagonal += votes[1];
+  voteHorizontal += votes[2];
+  for (const fingerSlope of fingerSlopes) {
+    const fingerVotes = angleOrientationAt(fingerSlope, options2.SINGLE_ANGLE_VOTE_POWER);
+    voteVertical += fingerVotes[0];
+    voteDiagonal += fingerVotes[1];
+    voteHorizontal += fingerVotes[2];
+  }
+  let estimatedDirection;
+  if (voteVertical === Math.max(voteVertical, voteDiagonal, voteHorizontal)) {
+    estimatedDirection = estimateVerticalDirection(start_end_y_dist, start_mid_y_dist, mid_end_y_dist, max_dist_y);
+  } else if (voteHorizontal === Math.max(voteDiagonal, voteHorizontal)) {
+    estimatedDirection = estimateHorizontalDirection(start_end_x_dist, start_mid_x_dist, mid_end_x_dist, max_dist_x);
+  } else {
+    estimatedDirection = estimateDiagonalDirection(start_end_y_dist, start_mid_y_dist, mid_end_y_dist, max_dist_y, start_end_x_dist, start_mid_x_dist, mid_end_x_dist, max_dist_x);
+  }
+  return estimatedDirection;
+}
+function estimate(landmarks) {
+  const slopesXY = [];
+  const slopesYZ = [];
+  const fingerCurls = [];
+  const fingerDirections = [];
+  if (!landmarks)
+    return { curls: fingerCurls, directions: fingerDirections };
+  for (const finger of Finger.all) {
+    const points = Finger.getPoints(finger);
+    const slopeAtXY = [];
+    const slopeAtYZ = [];
+    for (const point2 of points) {
+      const point1 = landmarks[point2[0]];
+      const point22 = landmarks[point2[1]];
+      const slopes = getSlopes(point1, point22);
+      const slopeXY = slopes[0];
+      const slopeYZ = slopes[1];
+      slopeAtXY.push(slopeXY);
+      slopeAtYZ.push(slopeYZ);
+    }
+    slopesXY.push(slopeAtXY);
+    slopesYZ.push(slopeAtYZ);
+  }
+  for (const finger of Finger.all) {
+    const pointIndexAt = finger === Finger.thumb ? 1 : 0;
+    const fingerPointsAt = Finger.getPoints(finger);
+    const startPoint = landmarks[fingerPointsAt[pointIndexAt][0]];
+    const midPoint = landmarks[fingerPointsAt[pointIndexAt + 1][1]];
+    const endPoint = landmarks[fingerPointsAt[3][1]];
+    const fingerCurled = estimateFingerCurl(startPoint, midPoint, endPoint);
+    const fingerPosition = calculateFingerDirection(startPoint, midPoint, endPoint, slopesXY[finger].slice(pointIndexAt));
+    fingerCurls[finger] = fingerCurled;
+    fingerDirections[finger] = fingerPosition;
+  }
+  return { curls: fingerCurls, directions: fingerDirections };
+}
+function analyze(keypoints) {
+  if (!keypoints || keypoints.length === 0)
+    return null;
+  const estimatorRes = estimate(keypoints);
+  const landmarks = {};
+  for (const fingerIdx of Finger.all) {
+    landmarks[Finger.getName(fingerIdx)] = {
+      curl: FingerCurl.getName(estimatorRes.curls[fingerIdx]),
+      direction: FingerDirection.getName(estimatorRes.directions[fingerIdx])
+    };
+  }
+  return landmarks;
+}
+function match(keypoints) {
+  const poses = [];
+  if (!keypoints || keypoints.length === 0)
+    return poses;
+  const estimatorRes = estimate(keypoints);
+  for (const gesture2 of fingergesture_default) {
+    const confidence = gesture2.matchAgainst(estimatorRes.curls, estimatorRes.directions);
+    if (confidence >= minConfidence)
+      poses.push({ name: gesture2.name, confidence });
+  }
+  return poses;
+}
+
+// src/hand/handpose.ts
+var meshAnnotations2 = {
+  thumb: [1, 2, 3, 4],
+  index: [5, 6, 7, 8],
+  middle: [9, 10, 11, 12],
+  ring: [13, 14, 15, 16],
+  pinky: [17, 18, 19, 20],
+  palm: [0]
+};
+var handDetectorModel;
+var handPoseModel;
+var handPipeline;
+async function predict9(input, config3) {
+  const predictions = await handPipeline.estimateHands(input, config3);
+  if (!predictions)
+    return [];
+  const hands = [];
+  for (let i = 0; i < predictions.length; i++) {
+    const annotations2 = {};
+    if (predictions[i].landmarks) {
+      for (const key of Object.keys(meshAnnotations2)) {
+        annotations2[key] = meshAnnotations2[key].map((index2) => predictions[i].landmarks[index2]);
+      }
+    }
+    const keypoints = predictions[i].landmarks;
+    let box = [Number.MAX_SAFE_INTEGER, Number.MAX_SAFE_INTEGER, 0, 0];
+    let boxRaw = [0, 0, 0, 0];
+    if (keypoints && keypoints.length > 0) {
+      for (const pt of keypoints) {
+        if (pt[0] < box[0])
+          box[0] = pt[0];
+        if (pt[1] < box[1])
+          box[1] = pt[1];
+        if (pt[0] > box[2])
+          box[2] = pt[0];
+        if (pt[1] > box[3])
+          box[3] = pt[1];
+      }
+      box[2] -= box[0];
+      box[3] -= box[1];
+      boxRaw = [box[0] / (input.shape[2] || 0), box[1] / (input.shape[1] || 0), box[2] / (input.shape[2] || 0), box[3] / (input.shape[1] || 0)];
+    } else {
+      box = predictions[i].box ? [
+        Math.trunc(Math.max(0, predictions[i].box.topLeft[0])),
+        Math.trunc(Math.max(0, predictions[i].box.topLeft[1])),
+        Math.trunc(Math.min(input.shape[2] || 0, predictions[i].box.bottomRight[0]) - Math.max(0, predictions[i].box.topLeft[0])),
+        Math.trunc(Math.min(input.shape[1] || 0, predictions[i].box.bottomRight[1]) - Math.max(0, predictions[i].box.topLeft[1]))
+      ] : [0, 0, 0, 0];
+      boxRaw = [
+        predictions[i].box.topLeft[0] / (input.shape[2] || 0),
+        predictions[i].box.topLeft[1] / (input.shape[1] || 0),
+        (predictions[i].box.bottomRight[0] - predictions[i].box.topLeft[0]) / (input.shape[2] || 0),
+        (predictions[i].box.bottomRight[1] - predictions[i].box.topLeft[1]) / (input.shape[1] || 0)
+      ];
+    }
+    const landmarks = analyze(keypoints);
+    hands.push({
+      id: i,
+      score: Math.round(100 * predictions[i].confidence) / 100,
+      boxScore: Math.round(100 * predictions[i].boxConfidence) / 100,
+      fingerScore: Math.round(100 * predictions[i].fingerConfidence) / 100,
+      label: "hand",
+      box,
+      boxRaw,
+      keypoints,
+      annotations: annotations2,
+      landmarks
+    });
+  }
+  return hands;
+}
+async function load10(config3) {
+  var _a, _b;
+  if (env.initial) {
+    handDetectorModel = null;
+    handPoseModel = null;
+  }
+  if (!handDetectorModel || !handPoseModel) {
+    [handDetectorModel, handPoseModel] = await Promise.all([
+      config3.hand.enabled ? loadModel((_a = config3.hand.detector) == null ? void 0 : _a.modelPath) : null,
+      config3.hand.landmarks ? loadModel((_b = config3.hand.skeleton) == null ? void 0 : _b.modelPath) : null
+    ]);
+  } else {
+    if (config3.debug)
+      log("cached model:", handDetectorModel["modelUrl"]);
+    if (config3.debug)
+      log("cached model:", handPoseModel["modelUrl"]);
+  }
+  const handDetector = handDetectorModel ? new HandDetector(handDetectorModel) : void 0;
+  if (handDetector && handPoseModel)
+    handPipeline = new HandPipeline(handDetector, handPoseModel);
+  return [handDetectorModel, handPoseModel];
+}
+
+// src/hand/handtrack.ts
+var tf21 = __toESM(require_tfjs_esm());
+var models3 = [null, null];
+var modelOutputNodes = ["StatefulPartitionedCall/Postprocessor/Slice", "StatefulPartitionedCall/Postprocessor/ExpandDims_1"];
+var inputSize7 = [[0, 0], [0, 0]];
+var classes = ["hand", "fist", "pinch", "point", "face", "tip", "pinchtip"];
+var faceIndex = 4;
+var boxExpandFact = 1.6;
+var maxDetectorResolution = 512;
+var detectorExpandFact = 1.4;
+var skipped8 = Number.MAX_SAFE_INTEGER;
+var lastTime9 = 0;
+var outputSize = [0, 0];
+var cache4 = {
+  boxes: [],
+  hands: []
+};
+var fingerMap = {
+  thumb: [1, 2, 3, 4],
+  index: [5, 6, 7, 8],
+  middle: [9, 10, 11, 12],
+  ring: [13, 14, 15, 16],
+  pinky: [17, 18, 19, 20],
+  base: [0],
+  palm: [0, 17, 13, 9, 5, 1, 0]
+};
+async function loadDetect2(config3) {
+  var _a;
+  if (env.initial)
+    models3[0] = null;
+  if (!models3[0]) {
+    fakeOps(["tensorlistreserve", "enter", "tensorlistfromtensor", "merge", "loopcond", "switch", "exit", "tensorliststack", "nextiteration", "tensorlistsetitem", "tensorlistgetitem", "reciprocal", "shape", "split", "where"], config3);
+    models3[0] = await loadModel((_a = config3.hand.detector) == null ? void 0 : _a.modelPath);
+    const inputs = models3[0]["executor"] ? Object.values(models3[0].modelSignature["inputs"]) : void 0;
+    inputSize7[0][0] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[1].size) : 0;
+    inputSize7[0][1] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 0;
+  } else if (config3.debug)
+    log("cached model:", models3[0]["modelUrl"]);
+  return models3[0];
+}
+async function loadSkeleton(config3) {
+  var _a;
+  if (env.initial)
+    models3[1] = null;
+  if (!models3[1]) {
+    models3[1] = await loadModel((_a = config3.hand.skeleton) == null ? void 0 : _a.modelPath);
+    const inputs = models3[1]["executor"] ? Object.values(models3[1].modelSignature["inputs"]) : void 0;
+    inputSize7[1][0] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[1].size) : 0;
+    inputSize7[1][1] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 0;
+  } else if (config3.debug)
+    log("cached model:", models3[1]["modelUrl"]);
+  return models3[1];
+}
+async function detectHands(input, config3) {
+  const hands = [];
+  if (!input || !models3[0])
+    return hands;
+  const t2 = {};
+  const ratio2 = (input.shape[2] || 1) / (input.shape[1] || 1);
+  const height = Math.min(Math.round((input.shape[1] || 0) / 8) * 8, maxDetectorResolution);
+  const width = Math.round(height * ratio2 / 8) * 8;
+  t2.resize = tf21.image.resizeBilinear(input, [height, width]);
+  t2.cast = tf21.cast(t2.resize, "int32");
+  [t2.rawScores, t2.rawBoxes] = await models3[0].executeAsync(t2.cast, modelOutputNodes);
+  t2.boxes = tf21.squeeze(t2.rawBoxes, [0, 2]);
+  t2.scores = tf21.squeeze(t2.rawScores, [0]);
+  const classScores = tf21.unstack(t2.scores, 1);
+  tf21.dispose(classScores[faceIndex]);
+  classScores.splice(faceIndex, 1);
+  t2.filtered = tf21.stack(classScores, 1);
+  tf21.dispose(classScores);
+  t2.max = tf21.max(t2.filtered, 1);
+  t2.argmax = tf21.argMax(t2.filtered, 1);
+  let id = 0;
+  t2.nms = await tf21.image.nonMaxSuppressionAsync(t2.boxes, t2.max, (config3.hand.maxDetected || 0) + 1, config3.hand.iouThreshold || 0, config3.hand.minConfidence || 1);
+  const nms = await t2.nms.data();
+  const scores = await t2.max.data();
+  const classNum = await t2.argmax.data();
+  for (const nmsIndex of Array.from(nms)) {
+    const boxSlice = tf21.slice(t2.boxes, nmsIndex, 1);
+    const boxYX = await boxSlice.data();
+    tf21.dispose(boxSlice);
+    const boxData = [boxYX[1], boxYX[0], boxYX[3] - boxYX[1], boxYX[2] - boxYX[0]];
+    const boxRaw = scale(boxData, detectorExpandFact);
+    const boxFull = [Math.trunc(boxData[0] * outputSize[0]), Math.trunc(boxData[1] * outputSize[1]), Math.trunc(boxData[2] * outputSize[0]), Math.trunc(boxData[3] * outputSize[1])];
+    const score = scores[nmsIndex];
+    const label = classes[classNum[nmsIndex]];
+    const hand3 = { id: id++, score, box: boxFull, boxRaw, label };
+    hands.push(hand3);
+  }
+  Object.keys(t2).forEach((tensor6) => tf21.dispose(t2[tensor6]));
+  hands.sort((a, b) => b.score - a.score);
+  if (hands.length > (config3.hand.maxDetected || 1))
+    hands.length = config3.hand.maxDetected || 1;
+  return hands;
+}
+async function detectFingers(input, h, config3) {
+  const hand3 = {
+    id: h.id,
+    score: Math.round(100 * h.score) / 100,
+    boxScore: Math.round(100 * h.score) / 100,
+    fingerScore: 0,
+    box: h.box,
+    boxRaw: h.boxRaw,
+    label: h.label,
+    keypoints: [],
+    landmarks: {},
+    annotations: {}
+  };
+  if (input && models3[1] && config3.hand.landmarks && h.score > (config3.hand.minConfidence || 0)) {
+    const t2 = {};
+    const boxCrop = [h.boxRaw[1], h.boxRaw[0], h.boxRaw[3] + h.boxRaw[1], h.boxRaw[2] + h.boxRaw[0]];
+    t2.crop = tf21.image.cropAndResize(input, [boxCrop], [0], [inputSize7[1][0], inputSize7[1][1]], "bilinear");
+    t2.div = tf21.div(t2.crop, constants.tf255);
+    [t2.score, t2.keypoints] = models3[1].execute(t2.div, ["Identity_1", "Identity"]);
+    const rawScore = (await t2.score.data())[0];
+    const score = (100 - Math.trunc(100 / (1 + Math.exp(rawScore)))) / 100;
+    if (score >= (config3.hand.minConfidence || 0)) {
+      hand3.fingerScore = score;
+      t2.reshaped = tf21.reshape(t2.keypoints, [-1, 3]);
+      const coordsData = await t2.reshaped.array();
+      const coordsRaw = coordsData.map((kpt4) => [kpt4[0] / inputSize7[1][1], kpt4[1] / inputSize7[1][0], kpt4[2] || 0]);
+      const coordsNorm = coordsRaw.map((kpt4) => [kpt4[0] * h.boxRaw[2], kpt4[1] * h.boxRaw[3], kpt4[2] || 0]);
+      hand3.keypoints = coordsNorm.map((kpt4) => [outputSize[0] * (kpt4[0] + h.boxRaw[0]), outputSize[1] * (kpt4[1] + h.boxRaw[1]), kpt4[2] || 0]);
+      hand3.landmarks = analyze(hand3.keypoints);
+      for (const key of Object.keys(fingerMap)) {
+        hand3.annotations[key] = fingerMap[key].map((index2) => hand3.landmarks && hand3.keypoints[index2] ? hand3.keypoints[index2] : null);
+      }
+    }
+    Object.keys(t2).forEach((tensor6) => tf21.dispose(t2[tensor6]));
+  }
+  return hand3;
+}
+async function predict10(input, config3) {
+  var _a, _b;
+  if (!((_a = models3[0]) == null ? void 0 : _a["executor"]) || !((_b = models3[1]) == null ? void 0 : _b["executor"]) || !models3[0].inputs[0].shape || !models3[1].inputs[0].shape)
+    return [];
+  outputSize = [input.shape[2] || 0, input.shape[1] || 0];
+  skipped8++;
+  const skipTime = (config3.hand.skipTime || 0) > now() - lastTime9;
+  const skipFrame = skipped8 < (config3.hand.skipFrames || 0);
+  if (config3.skipAllowed && skipTime && skipFrame) {
+    return cache4.hands;
+  }
+  return new Promise(async (resolve) => {
+    const skipTimeExtended = 3 * (config3.hand.skipTime || 0) > now() - lastTime9;
+    const skipFrameExtended = skipped8 < 3 * (config3.hand.skipFrames || 0);
+    if (config3.skipAllowed && cache4.hands.length === config3.hand.maxDetected) {
+      cache4.hands = await Promise.all(cache4.boxes.map((handBox) => detectFingers(input, handBox, config3)));
+    } else if (config3.skipAllowed && skipTimeExtended && skipFrameExtended && cache4.hands.length > 0) {
+      cache4.hands = await Promise.all(cache4.boxes.map((handBox) => detectFingers(input, handBox, config3)));
+    } else {
+      cache4.boxes = await detectHands(input, config3);
+      lastTime9 = now();
+      cache4.hands = await Promise.all(cache4.boxes.map((handBox) => detectFingers(input, handBox, config3)));
+      skipped8 = 0;
+    }
+    const oldCache = [...cache4.boxes];
+    cache4.boxes.length = 0;
+    if (config3.cacheSensitivity > 0) {
+      for (let i = 0; i < cache4.hands.length; i++) {
+        const boxKpt = square(cache4.hands[i].keypoints, outputSize);
+        if (boxKpt.box[2] / (input.shape[2] || 1) > 0.05 && boxKpt.box[3] / (input.shape[1] || 1) > 0.05 && cache4.hands[i].fingerScore && cache4.hands[i].fingerScore > (config3.hand.minConfidence || 0)) {
+          const boxScale = scale(boxKpt.box, boxExpandFact);
+          const boxScaleRaw = scale(boxKpt.boxRaw, boxExpandFact);
+          cache4.boxes.push({ ...oldCache[i], box: boxScale, boxRaw: boxScaleRaw });
+        }
+      }
+    }
+    for (let i = 0; i < cache4.hands.length; i++) {
+      const bbox = calc(cache4.hands[i].keypoints, outputSize);
+      cache4.hands[i].box = bbox.box;
+      cache4.hands[i].boxRaw = bbox.boxRaw;
+    }
+    resolve(cache4.hands);
+  });
+}
+
+// src/face/insightface.ts
+var tf22 = __toESM(require_tfjs_esm());
+var model10;
+var last6 = [];
+var lastCount5 = 0;
+var lastTime10 = 0;
+var skipped9 = Number.MAX_SAFE_INTEGER;
+async function load11(config3) {
+  if (env.initial)
+    model10 = null;
+  if (!model10)
+    model10 = await loadModel(config3.face["insightface"].modelPath);
+  else if (config3.debug)
+    log("cached model:", model10["modelUrl"]);
+  return model10;
+}
+async function predict11(input, config3, idx, count2) {
+  var _a, _b;
+  if (!(model10 == null ? void 0 : model10["executor"]))
+    return [];
+  const skipFrame = skipped9 < (((_a = config3.face["insightface"]) == null ? void 0 : _a.skipFrames) || 0);
+  const skipTime = (((_b = config3.face["insightface"]) == null ? void 0 : _b.skipTime) || 0) > now() - lastTime10;
+  if (config3.skipAllowed && skipTime && skipFrame && lastCount5 === count2 && last6[idx]) {
+    skipped9++;
+    return last6[idx];
+  }
+  return new Promise(async (resolve) => {
+    var _a2;
+    let data = [];
+    if (((_a2 = config3.face["insightface"]) == null ? void 0 : _a2.enabled) && (model10 == null ? void 0 : model10.inputs[0].shape)) {
+      const t2 = {};
+      t2.crop = tf22.image.resizeBilinear(input, [model10.inputs[0].shape[2], model10.inputs[0].shape[1]], false);
+      t2.data = model10.execute(t2.crop);
+      const output = await t2.data.data();
+      data = Array.from(output);
+      Object.keys(t2).forEach((tensor6) => tf22.dispose(t2[tensor6]));
+    }
+    last6[idx] = data;
+    lastCount5 = count2;
+    lastTime10 = now();
+    resolve(data);
+  });
+}
+
+// src/face/liveness.ts
+var tf23 = __toESM(require_tfjs_esm());
+var model11;
+var cached2 = [];
+var skipped10 = Number.MAX_SAFE_INTEGER;
+var lastCount6 = 0;
+var lastTime11 = 0;
+async function load12(config3) {
+  var _a;
+  if (env.initial)
+    model11 = null;
+  if (!model11)
+    model11 = await loadModel((_a = config3.face.liveness) == null ? void 0 : _a.modelPath);
+  else if (config3.debug)
+    log("cached model:", model11["modelUrl"]);
+  return model11;
+}
+async function predict12(image27, config3, idx, count2) {
+  var _a, _b;
+  if (!(model11 == null ? void 0 : model11["executor"]))
+    return 0;
+  const skipTime = (((_a = config3.face.liveness) == null ? void 0 : _a.skipTime) || 0) > now() - lastTime11;
+  const skipFrame = skipped10 < (((_b = config3.face.liveness) == null ? void 0 : _b.skipFrames) || 0);
+  if (config3.skipAllowed && skipTime && skipFrame && lastCount6 === count2 && cached2[idx]) {
+    skipped10++;
+    return cached2[idx];
+  }
+  skipped10 = 0;
+  return new Promise(async (resolve) => {
+    const resize = tf23.image.resizeBilinear(image27, [(model11 == null ? void 0 : model11.inputs[0].shape) ? model11.inputs[0].shape[2] : 0, (model11 == null ? void 0 : model11.inputs[0].shape) ? model11.inputs[0].shape[1] : 0], false);
+    const res = model11 == null ? void 0 : model11.execute(resize);
+    const num = (await res.data())[0];
+    cached2[idx] = Math.round(100 * num) / 100;
+    lastCount6 = count2;
+    lastTime11 = now();
+    tf23.dispose([resize, res]);
+    resolve(cached2[idx]);
+  });
+}
+
+// src/segmentation/meet.ts
+var tf24 = __toESM(require_tfjs_esm());
+var model12;
+async function load13(config3) {
+  if (!model12 || env.initial)
+    model12 = await loadModel(config3.segmentation.modelPath);
+  else if (config3.debug)
+    log("cached model:", model12["modelUrl"]);
+  return model12;
+}
+async function predict13(input, config3) {
+  var _a;
+  if (!model12)
+    model12 = await load13(config3);
+  if (!(model12 == null ? void 0 : model12["executor"]) || !((_a = model12 == null ? void 0 : model12.inputs) == null ? void 0 : _a[0].shape))
+    return null;
+  const t2 = {};
+  t2.resize = tf24.image.resizeBilinear(input, [model12.inputs[0].shape ? model12.inputs[0].shape[1] : 0, model12.inputs[0].shape ? model12.inputs[0].shape[2] : 0], false);
+  t2.norm = tf24.div(t2.resize, constants.tf255);
+  t2.res = model12.execute(t2.norm);
+  t2.squeeze = tf24.squeeze(t2.res, 0);
+  [t2.bgRaw, t2.fgRaw] = tf24.unstack(t2.squeeze, 2);
+  t2.fg = tf24.softmax(t2.fgRaw);
+  t2.mul = tf24.mul(t2.fg, constants.tf255);
+  t2.expand = tf24.expandDims(t2.mul, 2);
+  t2.output = tf24.image.resizeBilinear(t2.expand, [input.shape[1], input.shape[2]]);
+  let rgba;
+  switch (config3.segmentation.mode || "default") {
+    case "default":
+      t2.input = tf24.squeeze(input);
+      t2.concat = tf24.concat([t2.input, t2.output], -1);
+      rgba = tf24.cast(t2.concat, "int32");
+      break;
+    case "alpha":
+      rgba = tf24.cast(t2.output, "int32");
+      break;
+    default:
+      rgba = tf24.tensor(0);
+  }
+  Object.keys(t2).forEach((tensor6) => tf24.dispose(t2[tensor6]));
+  return rgba;
+}
+
+// src/face/mobilefacenet.ts
+var tf25 = __toESM(require_tfjs_esm());
+var model13;
+var last7 = [];
+var lastCount7 = 0;
+var lastTime12 = 0;
+var skipped11 = Number.MAX_SAFE_INTEGER;
+async function load14(config3) {
+  var _a;
+  if (env.initial)
+    model13 = null;
+  if (!model13)
+    model13 = await loadModel((_a = config3.face["mobilefacenet"]) == null ? void 0 : _a.modelPath);
+  else if (config3.debug)
+    log("cached model:", model13["modelUrl"]);
+  return model13;
+}
+async function predict14(input, config3, idx, count2) {
+  var _a, _b;
+  if (!(model13 == null ? void 0 : model13["executor"]))
+    return [];
+  const skipFrame = skipped11 < (((_a = config3.face["mobilefacenet"]) == null ? void 0 : _a.skipFrames) || 0);
+  const skipTime = (((_b = config3.face["mobilefacenet"]) == null ? void 0 : _b.skipTime) || 0) > now() - lastTime12;
+  if (config3.skipAllowed && skipTime && skipFrame && lastCount7 === count2 && last7[idx]) {
+    skipped11++;
+    return last7[idx];
+  }
+  return new Promise(async (resolve) => {
+    var _a2;
+    let data = [];
+    if (((_a2 = config3.face["mobilefacenet"]) == null ? void 0 : _a2.enabled) && (model13 == null ? void 0 : model13.inputs[0].shape)) {
+      const t2 = {};
+      t2.crop = tf25.image.resizeBilinear(input, [model13.inputs[0].shape[2], model13.inputs[0].shape[1]], false);
+      t2.data = model13.execute(t2.crop);
+      const output = await t2.data.data();
+      data = Array.from(output);
+      Object.keys(t2).forEach((tensor6) => tf25.dispose(t2[tensor6]));
+    }
+    last7[idx] = data;
+    lastCount7 = count2;
+    lastTime12 = now();
+    resolve(data);
+  });
+}
+
+// src/body/movenet.ts
+var tf27 = __toESM(require_tfjs_esm());
+
+// src/body/movenetcoords.ts
+var movenetcoords_exports = {};
+__export(movenetcoords_exports, {
+  connected: () => connected3,
+  horizontal: () => horizontal,
+  kpt: () => kpt3,
+  relative: () => relative,
+  vertical: () => vertical
+});
+var kpt3 = [
+  "nose",
+  "leftEye",
+  "rightEye",
+  "leftEar",
+  "rightEar",
+  "leftShoulder",
+  "rightShoulder",
+  "leftElbow",
+  "rightElbow",
+  "leftWrist",
+  "rightWrist",
+  "leftHip",
+  "rightHip",
+  "leftKnee",
+  "rightKnee",
+  "leftAnkle",
+  "rightAnkle"
+];
+var horizontal = [
+  ["leftEye", "rightEye"],
+  ["leftEar", "rightEar"],
+  ["leftShoulder", "rightShoulder"],
+  ["leftElbow", "rightElbow"],
+  ["leftWrist", "rightWrist"],
+  ["leftHip", "rightHip"],
+  ["leftKnee", "rightKnee"],
+  ["leftAnkle", "rightAnkle"]
+];
+var vertical = [
+  ["leftKnee", "leftShoulder"],
+  ["rightKnee", "rightShoulder"],
+  ["leftAnkle", "leftKnee"],
+  ["rightAnkle", "rightKnee"]
+];
+var relative = [
+  [["leftHip", "rightHip"], ["leftShoulder", "rightShoulder"]],
+  [["leftElbow", "rightElbow"], ["leftShoulder", "rightShoulder"]]
+];
+var connected3 = {
+  leftLeg: ["leftHip", "leftKnee", "leftAnkle"],
+  rightLeg: ["rightHip", "rightKnee", "rightAnkle"],
+  torso: ["leftShoulder", "rightShoulder", "rightHip", "leftHip", "leftShoulder"],
+  leftArm: ["leftShoulder", "leftElbow", "leftWrist"],
+  rightArm: ["rightShoulder", "rightElbow", "rightWrist"],
+  head: []
+};
+
+// src/body/movenetfix.ts
+var tf26 = __toESM(require_tfjs_esm());
+var maxJitter = 5e-3;
+var cache5 = {
+  keypoints: [],
+  padding: [[0, 0], [0, 0], [0, 0], [0, 0]]
+};
+function bodyParts(body4) {
+  for (const pair of horizontal) {
+    const left = body4.keypoints.findIndex((kp) => kp.part === pair[0]);
+    const right = body4.keypoints.findIndex((kp) => kp.part === pair[1]);
+    if (body4.keypoints[left] && body4.keypoints[right]) {
+      if (body4.keypoints[left].position[0] < body4.keypoints[right].position[0]) {
+        const tmp = body4.keypoints[left];
+        body4.keypoints[left] = body4.keypoints[right];
+        body4.keypoints[right] = tmp;
+      }
+    }
+  }
+  for (const pair of vertical) {
+    const lower = body4.keypoints.findIndex((kp) => kp && kp.part === pair[0]);
+    const higher = body4.keypoints.findIndex((kp) => kp && kp.part === pair[1]);
+    if (body4.keypoints[lower] && body4.keypoints[higher]) {
+      if (body4.keypoints[lower].position[1] < body4.keypoints[higher].position[1]) {
+        body4.keypoints.splice(lower, 1);
+      }
+    }
+  }
+  for (const [pair, compare2] of relative) {
+    const left = body4.keypoints.findIndex((kp) => kp && kp.part === pair[0]);
+    const right = body4.keypoints.findIndex((kp) => kp && kp.part === pair[1]);
+    const leftTo = body4.keypoints.findIndex((kp) => kp && kp.part === compare2[0]);
+    const rightTo = body4.keypoints.findIndex((kp) => kp && kp.part === compare2[1]);
+    if (!body4.keypoints[leftTo] || !body4.keypoints[rightTo])
+      continue;
+    const distanceLeft = body4.keypoints[left] ? [
+      Math.abs(body4.keypoints[leftTo].position[0] - body4.keypoints[left].position[0]),
+      Math.abs(body4.keypoints[rightTo].position[0] - body4.keypoints[left].position[0])
+    ] : [0, 0];
+    const distanceRight = body4.keypoints[right] ? [
+      Math.abs(body4.keypoints[rightTo].position[0] - body4.keypoints[right].position[0]),
+      Math.abs(body4.keypoints[leftTo].position[0] - body4.keypoints[right].position[0])
+    ] : [0, 0];
+    if (distanceLeft[0] > distanceLeft[1] || distanceRight[0] > distanceRight[1]) {
+      const tmp = body4.keypoints[left];
+      body4.keypoints[left] = body4.keypoints[right];
+      body4.keypoints[right] = tmp;
+    }
+  }
+}
+function jitter(keypoints) {
+  for (let i = 0; i < keypoints.length; i++) {
+    if (keypoints[i] && cache5.keypoints[i]) {
+      const diff = [Math.abs(keypoints[i].positionRaw[0] - cache5.keypoints[i].positionRaw[0]), Math.abs(keypoints[i].positionRaw[1] - cache5.keypoints[i].positionRaw[1])];
+      if (diff[0] < maxJitter && diff[1] < maxJitter) {
+        keypoints[i] = cache5.keypoints[i];
+      } else {
+        cache5.keypoints[i] = keypoints[i];
+      }
+    } else {
+      cache5.keypoints[i] = keypoints[i];
+    }
+  }
+  return keypoints;
+}
+function padInput(input, inputSize10) {
+  var _a, _b;
+  const t2 = {};
+  if (!((_a = input == null ? void 0 : input.shape) == null ? void 0 : _a[1]) || !((_b = input == null ? void 0 : input.shape) == null ? void 0 : _b[2]))
+    return input;
+  cache5.padding = [
+    [0, 0],
+    [input.shape[2] > input.shape[1] ? Math.trunc((input.shape[2] - input.shape[1]) / 2) : 0, input.shape[2] > input.shape[1] ? Math.trunc((input.shape[2] - input.shape[1]) / 2) : 0],
+    [input.shape[1] > input.shape[2] ? Math.trunc((input.shape[1] - input.shape[2]) / 2) : 0, input.shape[1] > input.shape[2] ? Math.trunc((input.shape[1] - input.shape[2]) / 2) : 0],
+    [0, 0]
+  ];
+  t2.pad = tf26.pad(input, cache5.padding);
+  t2.resize = tf26.image.resizeBilinear(t2.pad, [inputSize10, inputSize10]);
+  const final = tf26.cast(t2.resize, "int32");
+  Object.keys(t2).forEach((tensor6) => tf26.dispose(t2[tensor6]));
+  return final;
+}
+function rescaleBody(body4, outputSize2) {
+  body4.keypoints = body4.keypoints.filter((kpt4) => kpt4 == null ? void 0 : kpt4.position);
+  for (const kpt4 of body4.keypoints) {
+    kpt4.position = [
+      kpt4.position[0] * (outputSize2[0] + cache5.padding[2][0] + cache5.padding[2][1]) / outputSize2[0] - cache5.padding[2][0],
+      kpt4.position[1] * (outputSize2[1] + cache5.padding[1][0] + cache5.padding[1][1]) / outputSize2[1] - cache5.padding[1][0]
+    ];
+    kpt4.positionRaw = [
+      kpt4.position[0] / outputSize2[0],
+      kpt4.position[1] / outputSize2[1]
+    ];
+  }
+  const rescaledBoxes = calc(body4.keypoints.map((pt) => pt.position), outputSize2);
+  body4.box = rescaledBoxes.box;
+  body4.boxRaw = rescaledBoxes.boxRaw;
+  return body4;
+}
+
+// src/body/movenet.ts
+var model14;
+var inputSize8 = 0;
+var skipped12 = Number.MAX_SAFE_INTEGER;
+var cache6 = {
+  boxes: [],
+  bodies: [],
+  last: 0
+};
+async function load15(config3) {
+  var _a;
+  if (env.initial)
+    model14 = null;
+  if (!model14) {
+    fakeOps(["size"], config3);
+    model14 = await loadModel(config3.body.modelPath);
+  } else if (config3.debug)
+    log("cached model:", model14["modelUrl"]);
+  inputSize8 = (model14 == null ? void 0 : model14["executor"]) && ((_a = model14 == null ? void 0 : model14.inputs) == null ? void 0 : _a[0].shape) ? model14.inputs[0].shape[2] : 0;
+  if (inputSize8 < 64)
+    inputSize8 = 256;
+  return model14;
+}
+function parseSinglePose(res, config3, image27) {
+  const kpt4 = res[0][0];
+  const keypoints = [];
+  let score = 0;
+  for (let id = 0; id < kpt4.length; id++) {
+    score = kpt4[id][2];
+    if (score > config3.body.minConfidence) {
+      const positionRaw = [kpt4[id][1], kpt4[id][0]];
+      keypoints.push({
+        score: Math.round(100 * score) / 100,
+        part: kpt3[id],
+        positionRaw,
+        position: [
+          Math.round((image27.shape[2] || 0) * positionRaw[0]),
+          Math.round((image27.shape[1] || 0) * positionRaw[1])
+        ]
+      });
+    }
+  }
+  score = keypoints.reduce((prev, curr) => curr.score > prev ? curr.score : prev, 0);
+  const bodies = [];
+  const newBox = calc(keypoints.map((pt) => pt.position), [image27.shape[2], image27.shape[1]]);
+  const annotations2 = {};
+  for (const [name, indexes] of Object.entries(connected3)) {
+    const pt = [];
+    for (let i = 0; i < indexes.length - 1; i++) {
+      const pt0 = keypoints.find((kp) => kp.part === indexes[i]);
+      const pt1 = keypoints.find((kp) => kp.part === indexes[i + 1]);
+      if (pt0 && pt1 && pt0.score > (config3.body.minConfidence || 0) && pt1.score > (config3.body.minConfidence || 0))
+        pt.push([pt0.position, pt1.position]);
+    }
+    annotations2[name] = pt;
+  }
+  const body4 = { id: 0, score, box: newBox.box, boxRaw: newBox.boxRaw, keypoints, annotations: annotations2 };
+  bodyParts(body4);
+  bodies.push(body4);
+  return bodies;
+}
+function parseMultiPose(res, config3, image27) {
+  const bodies = [];
+  for (let id = 0; id < res[0].length; id++) {
+    const kpt4 = res[0][id];
+    const totalScore = Math.round(100 * kpt4[51 + 4]) / 100;
+    if (totalScore > config3.body.minConfidence) {
+      const keypoints = [];
+      for (let i = 0; i < 17; i++) {
+        const score = kpt4[3 * i + 2];
+        if (score > config3.body.minConfidence) {
+          const positionRaw = [kpt4[3 * i + 1], kpt4[3 * i + 0]];
+          keypoints.push({
+            part: kpt3[i],
+            score: Math.round(100 * score) / 100,
+            positionRaw,
+            position: [Math.round((image27.shape[2] || 0) * positionRaw[0]), Math.round((image27.shape[1] || 0) * positionRaw[1])]
+          });
+        }
+      }
+      const newBox = calc(keypoints.map((pt) => pt.position), [image27.shape[2], image27.shape[1]]);
+      const annotations2 = {};
+      for (const [name, indexes] of Object.entries(connected3)) {
+        const pt = [];
+        for (let i = 0; i < indexes.length - 1; i++) {
+          const pt0 = keypoints.find((kp) => kp.part === indexes[i]);
+          const pt1 = keypoints.find((kp) => kp.part === indexes[i + 1]);
+          if (pt0 && pt1 && pt0.score > (config3.body.minConfidence || 0) && pt1.score > (config3.body.minConfidence || 0))
+            pt.push([pt0.position, pt1.position]);
+        }
+        annotations2[name] = pt;
+      }
+      const body4 = { id, score: totalScore, box: newBox.box, boxRaw: newBox.boxRaw, keypoints: [...keypoints], annotations: annotations2 };
+      bodyParts(body4);
+      bodies.push(body4);
+    }
+  }
+  bodies.sort((a, b) => b.score - a.score);
+  if (bodies.length > config3.body.maxDetected)
+    bodies.length = config3.body.maxDetected;
+  return bodies;
+}
+async function predict15(input, config3) {
+  var _a;
+  if (!(model14 == null ? void 0 : model14["executor"]) || !((_a = model14 == null ? void 0 : model14.inputs) == null ? void 0 : _a[0].shape))
+    return [];
+  if (!config3.skipAllowed)
+    cache6.boxes.length = 0;
+  skipped12++;
+  const skipTime = (config3.body.skipTime || 0) > now() - cache6.last;
+  const skipFrame = skipped12 < (config3.body.skipFrames || 0);
+  if (config3.skipAllowed && skipTime && skipFrame) {
+    return cache6.bodies;
+  }
+  return new Promise(async (resolve) => {
+    const t2 = {};
+    skipped12 = 0;
+    t2.input = padInput(input, inputSize8);
+    t2.res = model14 == null ? void 0 : model14.execute(t2.input);
+    cache6.last = now();
+    const res = await t2.res.array();
+    cache6.bodies = t2.res.shape[2] === 17 ? parseSinglePose(res, config3, input) : parseMultiPose(res, config3, input);
+    for (const body4 of cache6.bodies) {
+      rescaleBody(body4, [input.shape[2] || 1, input.shape[1] || 1]);
+      jitter(body4.keypoints);
+    }
+    Object.keys(t2).forEach((tensor6) => tf27.dispose(t2[tensor6]));
+    resolve(cache6.bodies);
+  });
+}
+
+// src/object/nanodet.ts
+var tf28 = __toESM(require_tfjs_esm());
+var model15;
+var last8 = [];
+var lastTime13 = 0;
+var skipped13 = Number.MAX_SAFE_INTEGER;
+var inputSize9 = 0;
+var scaleBox = 2.5;
+async function load16(config3) {
+  if (!model15 || env.initial) {
+    model15 = await loadModel(config3.object.modelPath);
+    const inputs = (model15 == null ? void 0 : model15["executor"]) ? Object.values(model15.modelSignature["inputs"]) : void 0;
+    inputSize9 = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 416;
+  } else if (config3.debug)
+    log("cached model:", model15["modelUrl"]);
+  return model15;
+}
+async function process4(res, outputShape, config3) {
+  let id = 0;
+  let results = [];
+  const size2 = inputSize9;
+  for (const strideSize of [1, 2, 4]) {
+    const baseSize = strideSize * 13;
+    const scoresT = tf28.squeeze(res.find((a) => a.shape[1] === baseSize ** 2 && (a.shape[2] || 0) === labels.length));
+    const scores = await scoresT.array();
+    const featuresT = tf28.squeeze(res.find((a) => a.shape[1] === baseSize ** 2 && (a.shape[2] || 0) < labels.length));
+    const boxesMaxT = featuresT.reshape([-1, 4, featuresT.shape[1] / 4]);
+    const boxIdxT = boxesMaxT.argMax(2);
+    const boxIdx = await boxIdxT.array();
+    for (let i = 0; i < scoresT.shape[0]; i++) {
+      for (let j = 0; j < scoresT.shape[1]; j++) {
+        const score = scores[i][j];
+        if (score > (config3.object.minConfidence || 0) && j !== 61) {
+          const cx = (0.5 + Math.trunc(i % baseSize)) / baseSize;
+          const cy = (0.5 + Math.trunc(i / baseSize)) / baseSize;
+          const boxOffset = boxIdx[i].map((a) => a * (baseSize / strideSize / size2));
+          const [x, y] = [
+            cx - scaleBox / strideSize * boxOffset[0],
+            cy - scaleBox / strideSize * boxOffset[1]
+          ];
+          const [w, h] = [
+            cx + scaleBox / strideSize * boxOffset[2] - x,
+            cy + scaleBox / strideSize * boxOffset[3] - y
+          ];
+          let boxRaw = [x, y, w, h];
+          boxRaw = boxRaw.map((a) => Math.max(0, Math.min(a, 1)));
+          const box = [
+            boxRaw[0] * outputShape[0],
+            boxRaw[1] * outputShape[1],
+            boxRaw[2] * outputShape[0],
+            boxRaw[3] * outputShape[1]
+          ];
+          const result = {
+            id: id++,
+            score: Math.round(100 * score) / 100,
+            class: j + 1,
+            label: labels[j].label,
+            box: box.map((a) => Math.trunc(a)),
+            boxRaw
+          };
+          results.push(result);
+        }
+      }
+    }
+    tf28.dispose([scoresT, featuresT, boxesMaxT, boxIdxT]);
+  }
+  const nmsBoxes = results.map((a) => [a.boxRaw[1], a.boxRaw[0], a.boxRaw[3], a.boxRaw[2]]);
+  const nmsScores = results.map((a) => a.score);
+  let nmsIdx = [];
+  if (nmsBoxes && nmsBoxes.length > 0) {
+    const nms = await tf28.image.nonMaxSuppressionAsync(nmsBoxes, nmsScores, config3.object.maxDetected, config3.object.iouThreshold, config3.object.minConfidence);
+    nmsIdx = await nms.data();
+    tf28.dispose(nms);
+  }
+  results = results.filter((_val, idx) => nmsIdx.includes(idx)).sort((a, b) => b.score - a.score);
+  return results;
+}
+async function predict16(image27, config3) {
+  if (!(model15 == null ? void 0 : model15["executor"]))
+    return [];
+  const skipTime = (config3.object.skipTime || 0) > now() - lastTime13;
+  const skipFrame = skipped13 < (config3.object.skipFrames || 0);
+  if (config3.skipAllowed && skipTime && skipFrame && last8.length > 0) {
+    skipped13++;
+    return last8;
+  }
+  skipped13 = 0;
+  if (!env.kernels.includes("mod") || !env.kernels.includes("sparsetodense"))
+    return last8;
+  return new Promise(async (resolve) => {
+    const outputSize2 = [image27.shape[2] || 0, image27.shape[1] || 0];
+    const resizeT = tf28.image.resizeBilinear(image27, [inputSize9, inputSize9], false);
+    const normT = tf28.div(resizeT, constants.tf255);
+    const transposeT = tf28.transpose(normT, [0, 3, 1, 2]);
+    let objectT;
+    if (config3.object.enabled)
+      objectT = model15.execute(transposeT);
+    lastTime13 = now();
+    const obj = await process4(objectT, outputSize2, config3);
+    last8 = obj;
+    tf28.dispose([resizeT, normT, transposeT, ...objectT]);
+    resolve(obj);
+  });
+}
+
+// src/body/posenet.ts
+var tf29 = __toESM(require_tfjs_esm());
+
+// src/body/posenetutils.ts
+var partNames = [
+  "nose",
+  "leftEye",
+  "rightEye",
+  "leftEar",
+  "rightEar",
+  "leftShoulder",
+  "rightShoulder",
+  "leftElbow",
+  "rightElbow",
+  "leftWrist",
+  "rightWrist",
+  "leftHip",
+  "rightHip",
+  "leftKnee",
+  "rightKnee",
+  "leftAnkle",
+  "rightAnkle"
+];
+var count = partNames.length;
+var partIds = partNames.reduce((result, jointName, i) => {
+  result[jointName] = i;
+  return result;
+}, {});
+var connectedPartNames = [
+  ["leftHip", "leftShoulder"],
+  ["leftElbow", "leftShoulder"],
+  ["leftElbow", "leftWrist"],
+  ["leftHip", "leftKnee"],
+  ["leftKnee", "leftAnkle"],
+  ["rightHip", "rightShoulder"],
+  ["rightElbow", "rightShoulder"],
+  ["rightElbow", "rightWrist"],
+  ["rightHip", "rightKnee"],
+  ["rightKnee", "rightAnkle"],
+  ["leftShoulder", "rightShoulder"],
+  ["leftHip", "rightHip"]
+];
+var connectedPartIndices = connectedPartNames.map(([jointNameA, jointNameB]) => [partIds[jointNameA], partIds[jointNameB]]);
+var poseChain = [
+  ["nose", "leftEye"],
+  ["leftEye", "leftEar"],
+  ["nose", "rightEye"],
+  ["rightEye", "rightEar"],
+  ["nose", "leftShoulder"],
+  ["leftShoulder", "leftElbow"],
+  ["leftElbow", "leftWrist"],
+  ["leftShoulder", "leftHip"],
+  ["leftHip", "leftKnee"],
+  ["leftKnee", "leftAnkle"],
+  ["nose", "rightShoulder"],
+  ["rightShoulder", "rightElbow"],
+  ["rightElbow", "rightWrist"],
+  ["rightShoulder", "rightHip"],
+  ["rightHip", "rightKnee"],
+  ["rightKnee", "rightAnkle"]
+];
+function getBoundingBox(keypoints) {
+  const coord = keypoints.reduce(({ maxX, maxY, minX, minY }, { position: { x, y } }) => ({
+    maxX: Math.max(maxX, x),
+    maxY: Math.max(maxY, y),
+    minX: Math.min(minX, x),
+    minY: Math.min(minY, y)
+  }), {
+    maxX: Number.NEGATIVE_INFINITY,
+    maxY: Number.NEGATIVE_INFINITY,
+    minX: Number.POSITIVE_INFINITY,
+    minY: Number.POSITIVE_INFINITY
+  });
+  return [coord.minX, coord.minY, coord.maxX - coord.minX, coord.maxY - coord.minY];
+}
+function scalePoses(poses, [height, width], [inputResolutionHeight, inputResolutionWidth]) {
+  const scaleY = height / inputResolutionHeight;
+  const scaleX = width / inputResolutionWidth;
+  const scalePose = (pose, i) => ({
+    id: i,
+    score: pose.score,
+    boxRaw: [pose.box[0] / inputResolutionWidth, pose.box[1] / inputResolutionHeight, pose.box[2] / inputResolutionWidth, pose.box[3] / inputResolutionHeight],
+    box: [Math.trunc(pose.box[0] * scaleX), Math.trunc(pose.box[1] * scaleY), Math.trunc(pose.box[2] * scaleX), Math.trunc(pose.box[3] * scaleY)],
+    keypoints: pose.keypoints.map(({ score, part, position }) => ({
+      score,
+      part,
+      position: [Math.trunc(position.x * scaleX), Math.trunc(position.y * scaleY)],
+      positionRaw: [position.x / inputResolutionHeight, position.y / inputResolutionHeight]
+    })),
+    annotations: {}
+  });
+  const scaledPoses = poses.map((pose, i) => scalePose(pose, i));
+  return scaledPoses;
+}
+var MaxHeap = class {
+  constructor(maxSize2, getElementValue) {
+    __publicField(this, "priorityQueue");
+    __publicField(this, "numberOfElements");
+    __publicField(this, "getElementValue");
+    this.priorityQueue = new Array(maxSize2);
+    this.numberOfElements = -1;
+    this.getElementValue = getElementValue;
+  }
+  enqueue(x) {
+    this.priorityQueue[++this.numberOfElements] = x;
+    this.swim(this.numberOfElements);
+  }
+  dequeue() {
+    const max4 = this.priorityQueue[0];
+    this.exchange(0, this.numberOfElements--);
+    this.sink(0);
+    this.priorityQueue[this.numberOfElements + 1] = null;
+    return max4;
+  }
+  empty() {
+    return this.numberOfElements === -1;
+  }
+  size() {
+    return this.numberOfElements + 1;
+  }
+  all() {
+    return this.priorityQueue.slice(0, this.numberOfElements + 1);
+  }
+  max() {
+    return this.priorityQueue[0];
+  }
+  swim(k) {
+    while (k > 0 && this.less(Math.floor(k / 2), k)) {
+      this.exchange(k, Math.floor(k / 2));
+      k = Math.floor(k / 2);
+    }
+  }
+  sink(k) {
+    while (2 * k <= this.numberOfElements) {
+      let j = 2 * k;
+      if (j < this.numberOfElements && this.less(j, j + 1))
+        j++;
+      if (!this.less(k, j))
+        break;
+      this.exchange(k, j);
+      k = j;
+    }
+  }
+  getValueAt(i) {
+    return this.getElementValue(this.priorityQueue[i]);
+  }
+  less(i, j) {
+    return this.getValueAt(i) < this.getValueAt(j);
+  }
+  exchange(i, j) {
+    const t2 = this.priorityQueue[i];
+    this.priorityQueue[i] = this.priorityQueue[j];
+    this.priorityQueue[j] = t2;
+  }
+};
+function getOffsetPoint(y, x, keypoint, offsets) {
+  return {
+    y: offsets.get(y, x, keypoint),
+    x: offsets.get(y, x, keypoint + count)
+  };
+}
+function getImageCoords(part, outputStride2, offsets) {
+  const { heatmapY, heatmapX, id: keypoint } = part;
+  const { y, x } = getOffsetPoint(heatmapY, heatmapX, keypoint, offsets);
+  return {
+    x: part.heatmapX * outputStride2 + x,
+    y: part.heatmapY * outputStride2 + y
+  };
+}
+function clamp(a, min2, max4) {
+  if (a < min2)
+    return min2;
+  if (a > max4)
+    return max4;
+  return a;
+}
+function squaredDistance(y1, x1, y2, x2) {
+  const dy = y2 - y1;
+  const dx = x2 - x1;
+  return dy * dy + dx * dx;
+}
+function addVectors(a, b) {
+  return { x: a.x + b.x, y: a.y + b.y };
+}
+
+// src/body/posenet.ts
+var model16;
+var poseNetOutputs = ["MobilenetV1/offset_2/BiasAdd", "MobilenetV1/heatmap_2/BiasAdd", "MobilenetV1/displacement_fwd_2/BiasAdd", "MobilenetV1/displacement_bwd_2/BiasAdd"];
+var localMaximumRadius = 1;
+var outputStride = 16;
+var squaredNmsRadius = 50 ** 2;
+function traverse(edgeId, sourceKeypoint, targetId, scores, offsets, displacements, offsetRefineStep = 2) {
+  const getDisplacement = (point2) => ({
+    y: displacements.get(point2.y, point2.x, edgeId),
+    x: displacements.get(point2.y, point2.x, displacements.shape[2] / 2 + edgeId)
+  });
+  const getStridedIndexNearPoint = (point2, height2, width2) => ({
+    y: clamp(Math.round(point2.y / outputStride), 0, height2 - 1),
+    x: clamp(Math.round(point2.x / outputStride), 0, width2 - 1)
+  });
+  const [height, width] = scores.shape;
+  const sourceKeypointIndices = getStridedIndexNearPoint(sourceKeypoint.position, height, width);
+  const displacement = getDisplacement(sourceKeypointIndices);
+  const displacedPoint = addVectors(sourceKeypoint.position, displacement);
+  let targetKeypoint = displacedPoint;
+  for (let i = 0; i < offsetRefineStep; i++) {
+    const targetKeypointIndices = getStridedIndexNearPoint(targetKeypoint, height, width);
+    const offsetPoint = getOffsetPoint(targetKeypointIndices.y, targetKeypointIndices.x, targetId, offsets);
+    targetKeypoint = addVectors(
+      { x: targetKeypointIndices.x * outputStride, y: targetKeypointIndices.y * outputStride },
+      { x: offsetPoint.x, y: offsetPoint.y }
+    );
+  }
+  const targetKeyPointIndices = getStridedIndexNearPoint(targetKeypoint, height, width);
+  const score = scores.get(targetKeyPointIndices.y, targetKeyPointIndices.x, targetId);
+  return { position: targetKeypoint, part: partNames[targetId], score };
+}
+function decodePose(root, scores, offsets, displacementsFwd, displacementsBwd) {
+  const tuples = poseChain.map(([parentJoinName, childJoinName]) => [partIds[parentJoinName], partIds[childJoinName]]);
+  const edgesFwd = tuples.map(([, childJointId]) => childJointId);
+  const edgesBwd = tuples.map(([parentJointId]) => parentJointId);
+  const numParts = scores.shape[2];
+  const numEdges = edgesFwd.length;
+  const keypoints = new Array(numParts);
+  const rootPoint = getImageCoords(root.part, outputStride, offsets);
+  keypoints[root.part.id] = {
+    score: root.score,
+    part: partNames[root.part.id],
+    position: rootPoint
+  };
+  for (let edge = numEdges - 1; edge >= 0; --edge) {
+    const sourceId = edgesFwd[edge];
+    const targetId = edgesBwd[edge];
+    if (keypoints[sourceId] && !keypoints[targetId]) {
+      keypoints[targetId] = traverse(edge, keypoints[sourceId], targetId, scores, offsets, displacementsBwd);
+    }
+  }
+  for (let edge = 0; edge < numEdges; ++edge) {
+    const sourceId = edgesBwd[edge];
+    const targetId = edgesFwd[edge];
+    if (keypoints[sourceId] && !keypoints[targetId]) {
+      keypoints[targetId] = traverse(edge, keypoints[sourceId], targetId, scores, offsets, displacementsFwd);
+    }
+  }
+  return keypoints;
+}
+function scoreIsMaximumInLocalWindow(keypointId, score, heatmapY, heatmapX, scores) {
+  const [height, width] = scores.shape;
+  let localMaximum = true;
+  const yStart = Math.max(heatmapY - localMaximumRadius, 0);
+  const yEnd = Math.min(heatmapY + localMaximumRadius + 1, height);
+  for (let yCurrent = yStart; yCurrent < yEnd; ++yCurrent) {
+    const xStart = Math.max(heatmapX - localMaximumRadius, 0);
+    const xEnd = Math.min(heatmapX + localMaximumRadius + 1, width);
+    for (let xCurrent = xStart; xCurrent < xEnd; ++xCurrent) {
+      if (scores.get(yCurrent, xCurrent, keypointId) > score) {
+        localMaximum = false;
+        break;
+      }
+    }
+    if (!localMaximum)
+      break;
+  }
+  return localMaximum;
+}
+function buildPartWithScoreQueue(minConfidence2, scores) {
+  const [height, width, numKeypoints] = scores.shape;
+  const queue = new MaxHeap(height * width * numKeypoints, ({ score }) => score);
+  for (let heatmapY = 0; heatmapY < height; ++heatmapY) {
+    for (let heatmapX = 0; heatmapX < width; ++heatmapX) {
+      for (let keypointId = 0; keypointId < numKeypoints; ++keypointId) {
+        const score = scores.get(heatmapY, heatmapX, keypointId);
+        if (score < minConfidence2)
+          continue;
+        if (scoreIsMaximumInLocalWindow(keypointId, score, heatmapY, heatmapX, scores))
+          queue.enqueue({ score, part: { heatmapY, heatmapX, id: keypointId } });
+      }
+    }
+  }
+  return queue;
+}
+function withinRadius(poses, { x, y }, keypointId) {
+  return poses.some(({ keypoints }) => {
+    var _a;
+    const correspondingKeypoint = (_a = keypoints[keypointId]) == null ? void 0 : _a.position;
+    if (!correspondingKeypoint)
+      return false;
+    return squaredDistance(y, x, correspondingKeypoint.y, correspondingKeypoint.x) <= squaredNmsRadius;
+  });
+}
+function getInstanceScore(existingPoses, keypoints) {
+  const notOverlappedKeypointScores = keypoints.reduce((result, { position, score }, keypointId) => {
+    if (!withinRadius(existingPoses, position, keypointId))
+      result += score;
+    return result;
+  }, 0);
+  return notOverlappedKeypointScores / keypoints.length;
+}
+function decode(offsets, scores, displacementsFwd, displacementsBwd, maxDetected, minConfidence2) {
+  const poses = [];
+  const queue = buildPartWithScoreQueue(minConfidence2, scores);
+  while (poses.length < maxDetected && !queue.empty()) {
+    const root = queue.dequeue();
+    const rootImageCoords = getImageCoords(root.part, outputStride, offsets);
+    if (withinRadius(poses, rootImageCoords, root.part.id))
+      continue;
+    let keypoints = decodePose(root, scores, offsets, displacementsFwd, displacementsBwd);
+    keypoints = keypoints.filter((a) => a.score > minConfidence2);
+    const score = getInstanceScore(poses, keypoints);
+    const box = getBoundingBox(keypoints);
+    if (score > minConfidence2)
+      poses.push({ keypoints, box, score: Math.round(100 * score) / 100 });
+  }
+  return poses;
+}
+async function predict17(input, config3) {
+  if (!(model16 == null ? void 0 : model16["executor"]))
+    return [];
+  const res = tf29.tidy(() => {
+    if (!model16.inputs[0].shape)
+      return [];
+    const resized = tf29.image.resizeBilinear(input, [model16.inputs[0].shape[2], model16.inputs[0].shape[1]]);
+    const normalized = tf29.sub(tf29.div(tf29.cast(resized, "float32"), 127.5), 1);
+    const results = model16.execute(normalized, poseNetOutputs);
+    const results3d = results.map((y) => tf29.squeeze(y, [0]));
+    results3d[1] = tf29.sigmoid(results3d[1]);
+    return results3d;
+  });
+  const buffers = await Promise.all(res.map((tensor6) => tensor6.buffer()));
+  for (const t2 of res)
+    tf29.dispose(t2);
+  const decoded = decode(buffers[0], buffers[1], buffers[2], buffers[3], config3.body.maxDetected, config3.body.minConfidence);
+  if (!model16.inputs[0].shape)
+    return [];
+  const scaled = scalePoses(decoded, [input.shape[1], input.shape[2]], [model16.inputs[0].shape[2], model16.inputs[0].shape[1]]);
+  return scaled;
+}
+async function load17(config3) {
+  if (!model16 || env.initial)
+    model16 = await loadModel(config3.body.modelPath);
+  else if (config3.debug)
+    log("cached model:", model16["modelUrl"]);
+  return model16;
+}
+
+// src/segmentation/rvm.ts
+var tf30 = __toESM(require_tfjs_esm());
+var model17;
+var outputNodes2 = ["fgr", "pha", "r1o", "r2o", "r3o", "r4o"];
+var t = {};
+var ratio = 0;
+function init2(config3) {
+  tf30.dispose([t.r1i, t.r2i, t.r3i, t.r4i, t.downsample_ratio]);
+  t.r1i = tf30.tensor(0);
+  t.r2i = tf30.tensor(0);
+  t.r3i = tf30.tensor(0);
+  t.r4i = tf30.tensor(0);
+  ratio = config3.segmentation.ratio || 0.5;
+  t.downsample_ratio = tf30.tensor(ratio);
+}
+async function load18(config3) {
+  if (!model17 || env.initial)
+    model17 = await loadModel(config3.segmentation.modelPath);
+  else if (config3.debug)
+    log("cached model:", model17["modelUrl"]);
+  init2(config3);
+  return model17;
+}
+var normalize = (r) => tf30.tidy(() => {
+  const squeeze14 = tf30.squeeze(r, [0]);
+  const mul15 = tf30.mul(squeeze14, constants.tf255);
+  const cast8 = tf30.cast(mul15, "int32");
+  return cast8;
+});
+function getRGBA(fgr, pha) {
+  const rgb2 = fgr ? normalize(fgr) : tf30.fill([pha.shape[1] || 0, pha.shape[2] || 0, 3], 255, "int32");
+  const a = pha ? normalize(pha) : tf30.fill([fgr.shape[1] || 0, fgr.shape[2] || 0, 1], 255, "int32");
+  const rgba = tf30.concat([rgb2, a], -1);
+  tf30.dispose([rgb2, a]);
+  return rgba;
+}
+function getState(state) {
+  return tf30.tidy(() => {
+    const r = {};
+    r.unstack = tf30.unstack(state, -1);
+    r.concat = tf30.concat(r.unstack, 1);
+    r.split = tf30.split(r.concat, 4, 1);
+    r.stack = tf30.concat(r.split, 2);
+    r.squeeze = tf30.squeeze(r.stack, [0]);
+    r.expand = tf30.expandDims(r.squeeze, -1);
+    r.add = tf30.add(r.expand, 1);
+    r.mul = tf30.mul(r.add, 127.5);
+    r.cast = tf30.cast(r.mul, "int32");
+    r.tile = tf30.tile(r.cast, [1, 1, 3]);
+    r.alpha = tf30.fill([r.tile.shape[0] || 0, r.tile.shape[1] || 0, 1], 255, "int32");
+    return tf30.concat([r.tile, r.alpha], -1);
+  });
+}
+async function predict18(input, config3) {
+  if (!model17)
+    model17 = await load18(config3);
+  if (!(model17 == null ? void 0 : model17["executor"]))
+    return null;
+  t.src = tf30.div(input, 255);
+  if (ratio !== config3.segmentation.ratio)
+    init2(config3);
+  const [fgr, pha, r1o, r2o, r3o, r4o] = await model17.executeAsync(t, outputNodes2);
+  let rgba;
+  switch (config3.segmentation.mode || "default") {
+    case "default":
+      rgba = getRGBA(fgr, pha);
+      break;
+    case "alpha":
+      rgba = getRGBA(null, pha);
+      break;
+    case "foreground":
+      rgba = getRGBA(fgr, null);
+      break;
+    case "state":
+      rgba = getState(r1o);
+      break;
+    default:
+      rgba = tf30.tensor(0);
+  }
+  tf30.dispose([t.src, fgr, pha, t.r1i, t.r2i, t.r3i, t.r4i]);
+  [t.r1i, t.r2i, t.r3i, t.r4i] = [r1o, r2o, r3o, r4o];
+  return rgba;
+}
+
+// src/segmentation/selfie.ts
+var tf31 = __toESM(require_tfjs_esm());
+var model18;
+async function load19(config3) {
+  if (!model18 || env.initial)
+    model18 = await loadModel(config3.segmentation.modelPath);
+  else if (config3.debug)
+    log("cached model:", model18["modelUrl"]);
+  return model18;
+}
+async function predict19(input, config3) {
+  var _a;
+  if (!model18)
+    model18 = await load19(config3);
+  if (!(model18 == null ? void 0 : model18["executor"]) || !((_a = model18 == null ? void 0 : model18.inputs) == null ? void 0 : _a[0].shape))
+    return null;
+  const t2 = {};
+  t2.resize = tf31.image.resizeBilinear(input, [model18.inputs[0].shape ? model18.inputs[0].shape[1] : 0, model18.inputs[0].shape ? model18.inputs[0].shape[2] : 0], false);
+  t2.norm = tf31.div(t2.resize, constants.tf255);
+  t2.res = model18.execute(t2.norm);
+  t2.squeeze = tf31.squeeze(t2.res, 0);
+  t2.alpha = tf31.image.resizeBilinear(t2.squeeze, [input.shape[1], input.shape[2]]);
+  t2.mul = tf31.mul(t2.alpha, constants.tf255);
+  let rgba;
+  switch (config3.segmentation.mode || "default") {
+    case "default":
+      t2.input = tf31.squeeze(input);
+      t2.concat = tf31.concat([t2.input, t2.mul], -1);
+      rgba = tf31.cast(t2.concat, "int32");
+      break;
+    case "alpha":
+      rgba = tf31.cast(t2.mul, "int32");
+      break;
+    default:
+      rgba = tf31.tensor(0);
+  }
+  Object.keys(t2).forEach((tensor6) => tf31.dispose(t2[tensor6]));
+  return rgba;
+}
+
+// src/gear/ssrnet-age.ts
+var tf32 = __toESM(require_tfjs_esm());
+var model19;
+var last9 = [];
+var lastCount8 = 0;
+var lastTime14 = 0;
+var skipped14 = Number.MAX_SAFE_INTEGER;
+async function load20(config3) {
+  if (env.initial)
+    model19 = null;
+  if (!model19)
+    model19 = await loadModel(config3.face["ssrnet"].modelPathAge);
+  else if (config3.debug)
+    log("cached model:", model19["modelUrl"]);
+  return model19;
+}
+async function predict20(image27, config3, idx, count2) {
+  var _a, _b, _c, _d;
+  if (!model19)
+    return { age: 0 };
+  const skipFrame = skipped14 < (((_a = config3.face["ssrnet"]) == null ? void 0 : _a.skipFrames) || 0);
+  const skipTime = (((_b = config3.face["ssrnet"]) == null ? void 0 : _b.skipTime) || 0) > now() - lastTime14;
+  if (config3.skipAllowed && skipFrame && skipTime && lastCount8 === count2 && ((_c = last9[idx]) == null ? void 0 : _c.age) && ((_d = last9[idx]) == null ? void 0 : _d.age) > 0) {
+    skipped14++;
+    return last9[idx];
+  }
+  skipped14 = 0;
+  return new Promise(async (resolve) => {
+    var _a2;
+    if (!(model19 == null ? void 0 : model19.inputs) || !model19.inputs[0] || !model19.inputs[0].shape)
+      return;
+    const t2 = {};
+    t2.resize = tf32.image.resizeBilinear(image27, [model19.inputs[0].shape[2], model19.inputs[0].shape[1]], false);
+    t2.enhance = tf32.mul(t2.resize, constants.tf255);
+    const obj = { age: 0 };
+    if ((_a2 = config3.face["ssrnet"]) == null ? void 0 : _a2.enabled)
+      t2.age = model19.execute(t2.enhance);
+    if (t2.age) {
+      const data = await t2.age.data();
+      obj.age = Math.trunc(10 * data[0]) / 10;
+    }
+    Object.keys(t2).forEach((tensor6) => tf32.dispose(t2[tensor6]));
+    last9[idx] = obj;
+    lastCount8 = count2;
+    lastTime14 = now();
+    resolve(obj);
+  });
+}
+
+// src/gear/ssrnet-gender.ts
+var tf33 = __toESM(require_tfjs_esm());
+var model20;
+var last10 = [];
+var lastCount9 = 0;
+var lastTime15 = 0;
+var skipped15 = Number.MAX_SAFE_INTEGER;
+var rgb = [0.2989, 0.587, 0.114];
+async function load21(config3) {
+  var _a;
+  if (env.initial)
+    model20 = null;
+  if (!model20)
+    model20 = await loadModel((_a = config3.face["ssrnet"]) == null ? void 0 : _a.modelPathGender);
+  else if (config3.debug)
+    log("cached model:", model20["modelUrl"]);
+  return model20;
+}
+async function predict21(image27, config3, idx, count2) {
+  var _a, _b, _c, _d;
+  if (!model20)
+    return { gender: "unknown", genderScore: 0 };
+  const skipFrame = skipped15 < (((_a = config3.face["ssrnet"]) == null ? void 0 : _a.skipFrames) || 0);
+  const skipTime = (((_b = config3.face["ssrnet"]) == null ? void 0 : _b.skipTime) || 0) > now() - lastTime15;
+  if (config3.skipAllowed && skipFrame && skipTime && lastCount9 === count2 && ((_c = last10[idx]) == null ? void 0 : _c.gender) && ((_d = last10[idx]) == null ? void 0 : _d.genderScore) > 0) {
+    skipped15++;
+    return last10[idx];
+  }
+  skipped15 = 0;
+  return new Promise(async (resolve) => {
+    var _a2;
+    if (!(model20 == null ? void 0 : model20.inputs[0].shape))
+      return;
+    const t2 = {};
+    t2.resize = tf33.image.resizeBilinear(image27, [model20.inputs[0].shape[2], model20.inputs[0].shape[1]], false);
+    t2.enhance = tf33.tidy(() => {
+      const [red, green, blue] = tf33.split(t2.resize, 3, 3);
+      const redNorm = tf33.mul(red, rgb[0]);
+      const greenNorm = tf33.mul(green, rgb[1]);
+      const blueNorm = tf33.mul(blue, rgb[2]);
+      const grayscale = tf33.addN([redNorm, greenNorm, blueNorm]);
+      const normalize2 = tf33.mul(tf33.sub(grayscale, constants.tf05), 2);
+      return normalize2;
+    });
+    const obj = { gender: "unknown", genderScore: 0 };
+    if ((_a2 = config3.face["ssrnet"]) == null ? void 0 : _a2.enabled)
+      t2.gender = model20.execute(t2.enhance);
+    const data = await t2.gender.data();
+    obj.gender = data[0] > data[1] ? "female" : "male";
+    obj.genderScore = data[0] > data[1] ? Math.trunc(100 * data[0]) / 100 : Math.trunc(100 * data[1]) / 100;
+    Object.keys(t2).forEach((tensor6) => tf33.dispose(t2[tensor6]));
+    last10[idx] = obj;
+    lastCount9 = count2;
+    lastTime15 = now();
+    resolve(obj);
+  });
+}
+
+// src/models.ts
+var Models = class {
+  constructor() {
+    __publicField(this, "ssrnetage", null);
+    __publicField(this, "gear", null);
+    __publicField(this, "blazeposedetect", null);
+    __publicField(this, "blazepose", null);
+    __publicField(this, "centernet", null);
+    __publicField(this, "efficientpose", null);
+    __publicField(this, "mobilefacenet", null);
+    __publicField(this, "insightface", null);
+    __publicField(this, "emotion", null);
+    __publicField(this, "facedetect", null);
+    __publicField(this, "faceiris", null);
+    __publicField(this, "facemesh", null);
+    __publicField(this, "faceres", null);
+    __publicField(this, "ssrnetgender", null);
+    __publicField(this, "handpose", null);
+    __publicField(this, "handskeleton", null);
+    __publicField(this, "handtrack", null);
+    __publicField(this, "liveness", null);
+    __publicField(this, "meet", null);
+    __publicField(this, "movenet", null);
+    __publicField(this, "nanodet", null);
+    __publicField(this, "posenet", null);
+    __publicField(this, "selfie", null);
+    __publicField(this, "rvm", null);
+    __publicField(this, "antispoof", null);
+  }
+};
+var instance;
+var getModelStats = (currentInstance) => {
+  if (currentInstance)
+    instance = currentInstance;
+  if (!instance)
+    log("instance not registred");
+  let totalSizeFromManifest = 0;
+  let totalSizeWeights = 0;
+  let totalSizeLoading = 0;
+  for (const m of Object.values(modelStats)) {
+    totalSizeFromManifest += m.sizeFromManifest;
+    totalSizeWeights += m.sizeLoadedWeights;
+    totalSizeLoading += m.sizeDesired;
+  }
+  const percentageLoaded = totalSizeLoading > 0 ? totalSizeWeights / totalSizeLoading : 0;
+  return {
+    numLoadedModels: Object.values(modelStats).length,
+    numDefinedModels: Object.keys(instance.models).length,
+    percentageLoaded,
+    totalSizeFromManifest,
+    totalSizeWeights,
+    totalSizeLoading,
+    totalSizeEnabled: void 0,
+    modelStats: Object.values(modelStats)
+  };
+};
+function reset2(currentInstance) {
+  if (currentInstance)
+    instance = currentInstance;
+  for (const model21 of Object.keys(instance.models))
+    instance.models[model21] = null;
+}
+async function load22(currentInstance) {
+  var _a, _b, _c, _d, _e, _f, _g, _h, _i, _j, _k, _l, _m, _n, _o, _p, _q, _r, _s, _t, _u, _v, _w, _x, _y, _z, _A, _B, _C;
+  if (currentInstance)
+    instance = currentInstance;
+  if (!instance)
+    log("instance not registred");
+  if (env.initial)
+    reset2(instance);
+  if (instance.config.hand.enabled) {
+    if (!instance.models.handpose && ((_b = (_a = instance.config.hand.detector) == null ? void 0 : _a.modelPath) == null ? void 0 : _b.includes("handdetect"))) {
+      [instance.models.handpose, instance.models.handskeleton] = await load10(instance.config);
+    }
+    if (!instance.models.handskeleton && instance.config.hand.landmarks && ((_d = (_c = instance.config.hand.detector) == null ? void 0 : _c.modelPath) == null ? void 0 : _d.includes("handdetect"))) {
+      [instance.models.handpose, instance.models.handskeleton] = await load10(instance.config);
+    }
+  }
+  if (instance.config.body.enabled && !instance.models.blazepose && ((_e = instance.config.body.modelPath) == null ? void 0 : _e.includes("blazepose")))
+    instance.models.blazepose = loadPose(instance.config);
+  if (instance.config.body.enabled && !instance.models.blazeposedetect && instance.config.body["detector"] && instance.config.body["detector"].modelPath)
+    instance.models.blazeposedetect = loadDetect(instance.config);
+  if (instance.config.body.enabled && !instance.models.efficientpose && ((_f = instance.config.body.modelPath) == null ? void 0 : _f.includes("efficientpose")))
+    instance.models.efficientpose = load4(instance.config);
+  if (instance.config.body.enabled && !instance.models.movenet && ((_g = instance.config.body.modelPath) == null ? void 0 : _g.includes("movenet")))
+    instance.models.movenet = load15(instance.config);
+  if (instance.config.body.enabled && !instance.models.posenet && ((_h = instance.config.body.modelPath) == null ? void 0 : _h.includes("posenet")))
+    instance.models.posenet = load17(instance.config);
+  if (instance.config.face.enabled && !instance.models.facedetect)
+    instance.models.facedetect = load2(instance.config);
+  if (instance.config.face.enabled && ((_i = instance.config.face.antispoof) == null ? void 0 : _i.enabled) && !instance.models.antispoof)
+    instance.models.antispoof = load(instance.config);
+  if (instance.config.face.enabled && ((_j = instance.config.face.liveness) == null ? void 0 : _j.enabled) && !instance.models.liveness)
+    instance.models.liveness = load12(instance.config);
+  if (instance.config.face.enabled && ((_k = instance.config.face.description) == null ? void 0 : _k.enabled) && !instance.models.faceres)
+    instance.models.faceres = load8(instance.config);
+  if (instance.config.face.enabled && ((_l = instance.config.face.emotion) == null ? void 0 : _l.enabled) && !instance.models.emotion)
+    instance.models.emotion = load5(instance.config);
+  if (instance.config.face.enabled && ((_m = instance.config.face.iris) == null ? void 0 : _m.enabled) && !((_n = instance.config.face.attention) == null ? void 0 : _n.enabled) && !instance.models.faceiris)
+    instance.models.faceiris = load6(instance.config);
+  if (instance.config.face.enabled && ((_o = instance.config.face.mesh) == null ? void 0 : _o.enabled) && !instance.models.facemesh)
+    instance.models.facemesh = load7(instance.config);
+  if (instance.config.face.enabled && ((_p = instance.config.face["gear"]) == null ? void 0 : _p.enabled) && !instance.models.gear)
+    instance.models.gear = load9(instance.config);
+  if (instance.config.face.enabled && ((_q = instance.config.face["ssrnet"]) == null ? void 0 : _q.enabled) && !instance.models.ssrnetage)
+    instance.models.ssrnetage = load20(instance.config);
+  if (instance.config.face.enabled && ((_r = instance.config.face["ssrnet"]) == null ? void 0 : _r.enabled) && !instance.models.ssrnetgender)
+    instance.models.ssrnetgender = load21(instance.config);
+  if (instance.config.face.enabled && ((_s = instance.config.face["mobilefacenet"]) == null ? void 0 : _s.enabled) && !instance.models.mobilefacenet)
+    instance.models.mobilefacenet = load14(instance.config);
+  if (instance.config.face.enabled && ((_t = instance.config.face["insightface"]) == null ? void 0 : _t.enabled) && !instance.models.insightface)
+    instance.models.insightface = load11(instance.config);
+  if (instance.config.hand.enabled && !instance.models.handtrack && ((_v = (_u = instance.config.hand.detector) == null ? void 0 : _u.modelPath) == null ? void 0 : _v.includes("handtrack")))
+    instance.models.handtrack = loadDetect2(instance.config);
+  if (instance.config.hand.enabled && instance.config.hand.landmarks && !instance.models.handskeleton && ((_x = (_w = instance.config.hand.detector) == null ? void 0 : _w.modelPath) == null ? void 0 : _x.includes("handtrack")))
+    instance.models.handskeleton = loadSkeleton(instance.config);
+  if (instance.config.object.enabled && !instance.models.centernet && ((_y = instance.config.object.modelPath) == null ? void 0 : _y.includes("centernet")))
+    instance.models.centernet = load3(instance.config);
+  if (instance.config.object.enabled && !instance.models.nanodet && ((_z = instance.config.object.modelPath) == null ? void 0 : _z.includes("nanodet")))
+    instance.models.nanodet = load16(instance.config);
+  if (instance.config.segmentation.enabled && !instance.models.selfie && ((_A = instance.config.segmentation.modelPath) == null ? void 0 : _A.includes("selfie")))
+    instance.models.selfie = load19(instance.config);
+  if (instance.config.segmentation.enabled && !instance.models.meet && ((_B = instance.config.segmentation.modelPath) == null ? void 0 : _B.includes("meet")))
+    instance.models.meet = load13(instance.config);
+  if (instance.config.segmentation.enabled && !instance.models.rvm && ((_C = instance.config.segmentation.modelPath) == null ? void 0 : _C.includes("rvm")))
+    instance.models.rvm = load18(instance.config);
+  for await (const model21 of Object.keys(instance.models)) {
+    if (instance.models[model21] && typeof instance.models[model21] !== "undefined") {
+      instance.models[model21] = await instance.models[model21];
+    }
+  }
+}
+function validateModel(currentInstance, model21, name) {
+  var _a, _b;
+  if (!model21)
+    return null;
+  if (currentInstance)
+    instance = currentInstance;
+  if (!instance)
+    log("instance not registred");
+  if (!((_a = instance == null ? void 0 : instance.config) == null ? void 0 : _a.validateModels))
+    return null;
+  const simpleOps = ["const", "placeholder", "noop", "pad", "squeeze", "add", "sub", "mul", "div"];
+  const ignoreOps = ["biasadd", "fusedbatchnormv3", "matmul", "switch", "shape", "merge", "split", "broadcastto"];
+  const ops = [];
+  const missing = [];
+  const url = model21["modelUrl"];
+  const executor = model21["executor"];
+  if ((_b = executor == null ? void 0 : executor.graph) == null ? void 0 : _b.nodes) {
+    for (const kernel of Object.values(executor.graph.nodes)) {
+      const op = kernel.op.toLowerCase();
+      if (!ops.includes(op))
+        ops.push(op);
+    }
+  } else {
+    if (!executor && instance.config.debug) {
+      log("model not loaded", name);
+    }
+  }
+  for (const op of ops) {
+    if (!simpleOps.includes(op) && !ignoreOps.includes(op) && !instance.env.kernels.includes(op) && !instance.env.kernels.includes(op.replace("_", "")) && !instance.env.kernels.includes(op.replace("native", "")) && !instance.env.kernels.includes(op.replace("v2", ""))) {
+      missing.push(op);
+    }
+  }
+  if (instance.config.debug && missing.length > 0)
+    log("model validation failed:", name, missing);
+  return missing.length > 0 ? { name, missing, ops, url } : null;
+}
+function validate2(currentInstance) {
+  if (currentInstance)
+    instance = currentInstance;
+  if (!instance)
+    log("instance not registred");
+  const missing = [];
+  for (const defined of Object.keys(currentInstance.models)) {
+    const model21 = currentInstance.models[defined];
+    if (!model21)
+      continue;
+    const res = validateModel(currentInstance, model21, defined);
+    if (res)
+      missing.push(res);
+  }
+  return missing;
+}
+
+// src/tfjs/humangl.ts
+var config2 = {
+  name: "humangl",
+  priority: 999,
+  canvas: null,
+  gl: null,
+  extensions: [],
+  webGLattr: {
+    alpha: false,
+    antialias: false,
+    premultipliedAlpha: false,
+    preserveDrawingBuffer: false,
+    depth: false,
+    stencil: false,
+    failIfMajorPerformanceCaveat: false,
+    desynchronized: true
+  }
+};
+function extensions() {
+  const gl = config2.gl;
+  if (!gl)
+    return;
+  config2.extensions = gl.getSupportedExtensions();
+}
+function register(instance2) {
+  var _a;
+  if (instance2.config.backend !== "humangl")
+    return;
+  if (config2.name in tf34.engine().registry && !((_a = config2 == null ? void 0 : config2.gl) == null ? void 0 : _a.getParameter(config2.gl.VERSION))) {
+    log("humangl error: backend invalid context");
+    reset2(instance2);
+  }
+  if (!tf34.findBackend(config2.name)) {
+    try {
+      config2.canvas = canvas(100, 100);
+    } catch (err) {
+      log("humangl error: cannot create canvas:", err);
+      return;
+    }
+    try {
+      config2.gl = config2.canvas.getContext("webgl2", config2.webGLattr);
+      if (!config2.gl) {
+        log("humangl error: cannot get webgl context");
+        return;
+      }
+      const glv2 = config2.gl.getParameter(config2.gl.VERSION).includes("2.0");
+      if (!glv2) {
+        log("backend override: using fallback webgl backend as webgl 2.0 is not detected");
+        instance2.config.backend = "webgl";
+        return;
+      }
+      if (config2.canvas) {
+        config2.canvas.addEventListener("webglcontextlost", (e) => {
+          log("humangl error:", e.type);
+          log("possible browser memory leak using webgl or conflict with multiple backend registrations");
+          instance2.emit("error");
+          throw new Error("backend error: webgl context lost");
+        });
+        config2.canvas.addEventListener("webglcontextrestored", (e) => {
+          log("humangl error: context restored:", e);
+        });
+        config2.canvas.addEventListener("webglcontextcreationerror", (e) => {
+          log("humangl error: context create:", e);
+        });
+      }
+    } catch (err) {
+      log("humangl error: cannot get webgl context:", err);
+      return;
+    }
+    try {
+      tf34.setWebGLContext(2, config2.gl);
+    } catch (err) {
+      log("humangl error: cannot set webgl context:", err);
+      return;
+    }
+    try {
+      const ctx = new tf34.GPGPUContext(config2.gl);
+      tf34.registerBackend(config2.name, () => new tf34.MathBackendWebGL(ctx), config2.priority);
+    } catch (err) {
+      log("humangl error: cannot register webgl backend:", err);
+      return;
+    }
+    try {
+      const kernels = tf34.getKernelsForBackend("webgl");
+      kernels.forEach((kernelConfig) => {
+        const newKernelConfig = { ...kernelConfig, backendName: config2.name };
+        tf34.registerKernel(newKernelConfig);
+      });
+    } catch (err) {
+      log("humangl error: cannot update webgl backend registration:", err);
+      return;
+    }
+    try {
+      if (tf34.env().flagRegistry.WEBGL_VERSION)
+        tf34.env().set("WEBGL_VERSION", 2);
+    } catch (err) {
+      log("humangl error: cannot set WebGL backend flags:", err);
+      return;
+    }
+    extensions();
+    const current = tf34.backend().getGPGPUContext ? tf34.backend().getGPGPUContext().gl : null;
+    if (current) {
+      if (instance2.config.debug)
+        log("humangl backend registered:", { webgl: current.getParameter(current.VERSION), renderer: current.getParameter(current.RENDERER) });
+    } else {
+      log("humangl error: no current gl context:", current, config2.gl);
+    }
+  }
+}
+
+// src/tfjs/backend.ts
+var tf35 = __toESM(require_tfjs_esm());
+function registerCustomOps(config3) {
+  const newKernels = [];
+  if (!env.kernels.includes("mod")) {
+    const kernelMod = {
+      kernelName: "Mod",
+      backendName: tf35.getBackend(),
+      kernelFunc: (op) => tf35.tidy(() => tf35.sub(op.inputs.a, tf35.mul(tf35.div(op.inputs.a, op.inputs.b), op.inputs.b)))
+    };
+    tf35.registerKernel(kernelMod);
+    env.kernels.push("mod");
+    newKernels.push("mod");
+  }
+  if (!env.kernels.includes("floormod")) {
+    const kernelFloorMod = {
+      kernelName: "FloorMod",
+      backendName: tf35.getBackend(),
+      kernelFunc: (op) => tf35.tidy(() => tf35.add(tf35.mul(tf35.floorDiv(op.inputs.a / op.inputs.b), op.inputs.b), tf35.mod(op.inputs.a, op.inputs.b)))
+    };
+    tf35.registerKernel(kernelFloorMod);
+    env.kernels.push("floormod");
+    newKernels.push("floormod");
+  }
+  if (!env.kernels.includes("rotatewithoffset") && config3.softwareKernels) {
+    const kernelRotateWithOffset = {
+      kernelName: "RotateWithOffset",
+      backendName: tf35.getBackend(),
+      kernelFunc: (op) => tf35.tidy(() => {
+        const backend4 = tf35.getBackend();
+        tf35.setBackend("cpu");
+        const t2 = tf35.image.rotateWithOffset(op.inputs.image, op.attrs.radians, op.attrs.fillValue, op.attrs.center);
+        tf35.setBackend(backend4);
+        return t2;
+      })
+    };
+    tf35.registerKernel(kernelRotateWithOffset);
+    env.kernels.push("rotatewithoffset");
+    newKernels.push("rotatewithoffset");
+  }
+  if (newKernels.length > 0 && config3.debug)
+    log("registered kernels:", newKernels);
+}
+var defaultFlags = {};
+async function check(instance2, force = false) {
+  instance2.state = "backend";
+  if (force || env.initial || instance2.config.backend && instance2.config.backend.length > 0 && tf35.getBackend() !== instance2.config.backend) {
+    const timeStamp = now();
+    if (instance2.config.backend && instance2.config.backend.length > 0) {
+      if (typeof window === "undefined" && typeof WorkerGlobalScope !== "undefined" && instance2.config.debug) {
+        if (instance2.config.debug)
+          log("running inside web worker");
+      }
+      if (env.browser && instance2.config.backend === "tensorflow") {
+        if (instance2.config.debug)
+          log("override: backend set to tensorflow while running in browser");
+        instance2.config.backend = "webgl";
+      }
+      if (env.node && (instance2.config.backend === "webgl" || instance2.config.backend === "humangl")) {
+        if (instance2.config.debug)
+          log(`override: backend set to ${instance2.config.backend} while running in nodejs`);
+        instance2.config.backend = "tensorflow";
+      }
+      if (env.browser && instance2.config.backend === "webgpu") {
+        if (typeof navigator === "undefined" || typeof navigator.gpu === "undefined") {
+          log("override: backend set to webgpu but browser does not support webgpu");
+          instance2.config.backend = "webgl";
+        } else {
+          const adapter = await navigator.gpu.requestAdapter();
+          if (instance2.config.debug)
+            log("enumerated webgpu adapter:", adapter);
+          if (!adapter) {
+            log("override: backend set to webgpu but browser reports no available gpu");
+            instance2.config.backend = "webgl";
+          } else {
+            const adapterInfo = "requestAdapterInfo" in adapter ? await adapter.requestAdapterInfo() : void 0;
+            log("webgpu adapter info:", adapterInfo);
+          }
+        }
+      }
+      let available = Object.keys(tf35.engine().registryFactory);
+      if (instance2.config.backend === "humangl" && !available.includes("humangl")) {
+        register(instance2);
+        available = Object.keys(tf35.engine().registryFactory);
+      }
+      if (instance2.config.debug)
+        log("available backends:", available);
+      if (!available.includes(instance2.config.backend)) {
+        log(`error: backend ${instance2.config.backend} not found in registry`);
+        instance2.config.backend = env.node ? "tensorflow" : "webgl";
+        if (instance2.config.debug)
+          log(`override: setting backend ${instance2.config.backend}`);
+      }
+      if (instance2.config.debug)
+        log("setting backend:", [instance2.config.backend]);
+      if (instance2.config.backend === "wasm") {
+        if (tf35.env().flagRegistry.CANVAS2D_WILL_READ_FREQUENTLY)
+          tf35.env().set("CANVAS2D_WILL_READ_FREQUENTLY", true);
+        if (instance2.config.debug)
+          log("wasm path:", instance2.config.wasmPath);
+        if (typeof tf35.setWasmPaths !== "undefined")
+          tf35.setWasmPaths(instance2.config.wasmPath, instance2.config.wasmPlatformFetch);
+        else
+          throw new Error("backend error: attempting to use wasm backend but wasm path is not set");
+        let mt = false;
+        let simd = false;
+        try {
+          mt = await tf35.env().getAsync("WASM_HAS_MULTITHREAD_SUPPORT");
+          simd = await tf35.env().getAsync("WASM_HAS_SIMD_SUPPORT");
+          if (instance2.config.debug)
+            log(`wasm execution: ${simd ? "simd" : "no simd"} ${mt ? "multithreaded" : "singlethreaded"}`);
+          if (instance2.config.debug && !simd)
+            log("warning: wasm simd support is not enabled");
+        } catch (e) {
+          log("wasm detection failed");
+        }
+      }
+      try {
+        await tf35.setBackend(instance2.config.backend);
+        await tf35.ready();
+      } catch (err) {
+        log("error: cannot set backend:", instance2.config.backend, err);
+        return false;
+      }
+      if (instance2.config.debug)
+        defaultFlags = JSON.parse(JSON.stringify(tf35.env().flags));
+    }
+    if (tf35.getBackend() === "humangl" || tf35.getBackend() === "webgl") {
+      if (tf35.env().flagRegistry.WEBGL_USE_SHAPES_UNIFORMS)
+        tf35.env().set("WEBGL_USE_SHAPES_UNIFORMS", true);
+      if (tf35.env().flagRegistry.WEBGL_EXP_CONV)
+        tf35.env().set("WEBGL_EXP_CONV", true);
+      if (instance2.config.debug && typeof instance2.config.deallocate !== "undefined" && instance2.config.deallocate) {
+        log("changing webgl: WEBGL_DELETE_TEXTURE_THRESHOLD:", true);
+        tf35.env().set("WEBGL_DELETE_TEXTURE_THRESHOLD", 0);
+      }
+    }
+    if (tf35.getBackend() === "webgpu") {
+    }
+    if (instance2.config.debug) {
+      const newFlags = tf35.env().flags;
+      const updatedFlags = {};
+      for (const key of Object.keys(newFlags)) {
+        if (defaultFlags[key] === newFlags[key])
+          continue;
+        updatedFlags[key] = newFlags[key];
+      }
+      if (instance2.config.debug && Object.keys(updatedFlags).length > 0)
+        log("backend:", tf35.getBackend(), "flags:", updatedFlags);
+    }
+    if (instance2.config.flags && Object.keys(instance2.config.flags).length > 0) {
+      if (instance2.config.debug)
+        log("flags:", instance2.config["flags"]);
+      for (const [key, val] of Object.entries(instance2.config.flags)) {
+        tf35.env().set(key, val);
+      }
+    }
+    tf35.enableProdMode();
+    init();
+    instance2.performance.initBackend = Math.trunc(now() - timeStamp);
+    instance2.config.backend = tf35.getBackend();
+    await env.updateBackend();
+    registerCustomOps(instance2.config);
+    env.initial = false;
+  }
+  return true;
+}
+function fakeOps(kernelNames, config3) {
+  for (const kernelName of kernelNames) {
+    const kernelConfig = {
+      kernelName,
+      backendName: config3.backend,
+      kernelFunc: () => {
+        if (config3.debug)
+          log("kernelFunc", kernelName, config3.backend);
+      }
+    };
+    tf35.registerKernel(kernelConfig);
+  }
+  env.kernels = tf35.getKernelsForBackend(tf35.getBackend()).map((kernel) => kernel.kernelName.toLowerCase());
+}
+
+// src/draw/draw.ts
+var draw_exports = {};
+__export(draw_exports, {
+  all: () => all,
+  body: () => body,
+  canvas: () => canvas2,
+  face: () => face,
+  gesture: () => gesture,
+  hand: () => hand,
+  object: () => object,
+  options: () => options3,
+  person: () => person
+});
+
+// src/draw/primitives.ts
+var getCanvasContext = (input) => {
+  if (!input)
+    log("draw error: invalid canvas");
+  else if (!input.getContext)
+    log("draw error: canvas context not defined");
+  else {
+    const ctx = input.getContext("2d");
+    if (!ctx)
+      log("draw error: cannot get canvas context");
+    else
+      return ctx;
+  }
+  return null;
+};
+var rad2deg = (theta) => Math.round(theta * 180 / Math.PI);
+var colorDepth = (z, opt2) => {
+  if (!opt2.useDepth || typeof z === "undefined")
+    return opt2.color;
+  const rgb2 = Uint8ClampedArray.from([127 + 2 * z, 127 - 2 * z, 255]);
+  return `rgba(${rgb2[0]}, ${rgb2[1]}, ${rgb2[2]}, ${opt2.alpha})`;
+};
+function point(ctx, x, y, z, localOptions) {
+  ctx.fillStyle = colorDepth(z, localOptions);
+  ctx.beginPath();
+  ctx.arc(x, y, localOptions.pointSize, 0, 2 * Math.PI);
+  ctx.fill();
+}
+function rect(ctx, x, y, width, height, localOptions) {
+  ctx.beginPath();
+  ctx.lineWidth = localOptions.lineWidth;
+  if (localOptions.useCurves) {
+    const cx = (x + x + width) / 2;
+    const cy = (y + y + height) / 2;
+    ctx.ellipse(cx, cy, width / 2, height / 2, 0, 0, 2 * Math.PI);
+  } else {
+    ctx.moveTo(x + localOptions.roundRect, y);
+    ctx.lineTo(x + width - localOptions.roundRect, y);
+    ctx.quadraticCurveTo(x + width, y, x + width, y + localOptions.roundRect);
+    ctx.lineTo(x + width, y + height - localOptions.roundRect);
+    ctx.quadraticCurveTo(x + width, y + height, x + width - localOptions.roundRect, y + height);
+    ctx.lineTo(x + localOptions.roundRect, y + height);
+    ctx.quadraticCurveTo(x, y + height, x, y + height - localOptions.roundRect);
+    ctx.lineTo(x, y + localOptions.roundRect);
+    ctx.quadraticCurveTo(x, y, x + localOptions.roundRect, y);
+    ctx.closePath();
+  }
+  ctx.stroke();
+}
+function lines(ctx, points, localOptions) {
+  if (points.length < 2)
+    return;
+  ctx.beginPath();
+  ctx.moveTo(points[0][0], points[0][1]);
+  for (const pt of points) {
+    ctx.strokeStyle = colorDepth(pt[2] || 0, localOptions);
+    ctx.lineTo(Math.trunc(pt[0]), Math.trunc(pt[1]));
+  }
+  ctx.stroke();
+  if (localOptions.fillPolygons) {
+    ctx.closePath();
+    ctx.fill();
+  }
+}
+function curves(ctx, points, localOptions) {
+  if (points.length < 2)
+    return;
+  ctx.lineWidth = localOptions.lineWidth;
+  if (!localOptions.useCurves || points.length <= 2) {
+    lines(ctx, points, localOptions);
+    return;
+  }
+  ctx.moveTo(points[0][0], points[0][1]);
+  for (let i = 0; i < points.length - 2; i++) {
+    const xc = (points[i][0] + points[i + 1][0]) / 2;
+    const yc = (points[i][1] + points[i + 1][1]) / 2;
+    ctx.quadraticCurveTo(points[i][0], points[i][1], xc, yc);
+  }
+  ctx.quadraticCurveTo(points[points.length - 2][0], points[points.length - 2][1], points[points.length - 1][0], points[points.length - 1][1]);
+  ctx.stroke();
+  if (localOptions.fillPolygons) {
+    ctx.closePath();
+    ctx.fill();
+  }
+}
+function arrow(ctx, from, to, radius = 5) {
+  let angle;
+  let x;
+  let y;
+  ctx.beginPath();
+  ctx.moveTo(from[0], from[1]);
+  ctx.lineTo(to[0], to[1]);
+  angle = Math.atan2(to[1] - from[1], to[0] - from[0]);
+  x = radius * Math.cos(angle) + to[0];
+  y = radius * Math.sin(angle) + to[1];
+  ctx.moveTo(x, y);
+  angle += 1 / 3 * (2 * Math.PI);
+  x = radius * Math.cos(angle) + to[0];
+  y = radius * Math.sin(angle) + to[1];
+  ctx.lineTo(x, y);
+  angle += 1 / 3 * (2 * Math.PI);
+  x = radius * Math.cos(angle) + to[0];
+  y = radius * Math.sin(angle) + to[1];
+  ctx.lineTo(x, y);
+  ctx.closePath();
+  ctx.stroke();
+  ctx.fill();
+}
+
+// src/draw/options.ts
+var options3 = {
+  color: "rgba(173, 216, 230, 0.6)",
+  labelColor: "rgba(173, 216, 230, 1)",
+  shadowColor: "black",
+  alpha: 0.5,
+  font: 'small-caps 16px "Segoe UI"',
+  lineHeight: 18,
+  lineWidth: 4,
+  pointSize: 2,
+  roundRect: 8,
+  drawPoints: false,
+  drawLabels: true,
+  drawBoxes: true,
+  drawAttention: true,
+  drawGestures: true,
+  drawPolygons: true,
+  drawGaze: true,
+  fillPolygons: false,
+  useDepth: true,
+  useCurves: false
+};
+
+// src/draw/face.ts
+var opt;
+function drawLabels(f, ctx) {
+  var _a, _b;
+  if (opt.drawLabels) {
+    const labels2 = [];
+    labels2.push(`face: ${Math.trunc(100 * f.score)}%`);
+    if (f.genderScore)
+      labels2.push(`${f.gender || ""} ${Math.trunc(100 * f.genderScore)}%`);
+    if (f.age)
+      labels2.push(`age: ${f.age || ""}`);
+    if (f.iris)
+      labels2.push(`distance: ${f.iris}`);
+    if (f.real)
+      labels2.push(`real: ${Math.trunc(100 * f.real)}%`);
+    if (f.live)
+      labels2.push(`live: ${Math.trunc(100 * f.live)}%`);
+    if (f.emotion && f.emotion.length > 0) {
+      const emotion2 = f.emotion.map((a) => `${Math.trunc(100 * a.score)}% ${a.emotion}`);
+      if (emotion2.length > 3)
+        emotion2.length = 3;
+      labels2.push(emotion2.join(" "));
+    }
+    if (((_a = f.rotation) == null ? void 0 : _a.angle) && ((_b = f.rotation) == null ? void 0 : _b.gaze)) {
+      if (f.rotation.angle.roll)
+        labels2.push(`roll: ${rad2deg(f.rotation.angle.roll)}\xB0 yaw:${rad2deg(f.rotation.angle.yaw)}\xB0 pitch:${rad2deg(f.rotation.angle.pitch)}\xB0`);
+      if (f.rotation.gaze.bearing)
+        labels2.push(`gaze: ${rad2deg(f.rotation.gaze.bearing)}\xB0`);
+    }
+    if (labels2.length === 0)
+      labels2.push("face");
+    ctx.fillStyle = opt.color;
+    for (let i = labels2.length - 1; i >= 0; i--) {
+      const x = Math.max(f.box[0], 0);
+      const y = i * opt.lineHeight + f.box[1];
+      if (opt.shadowColor && opt.shadowColor !== "") {
+        ctx.fillStyle = opt.shadowColor;
+        ctx.fillText(labels2[i], x + 5, y + 16);
+      }
+      ctx.fillStyle = opt.labelColor;
+      ctx.fillText(labels2[i], x + 4, y + 15);
+    }
+  }
+}
+function drawIrisElipse(f, ctx) {
+  var _a, _b, _c, _d;
+  if (((_a = f.annotations) == null ? void 0 : _a.leftEyeIris) && ((_b = f.annotations) == null ? void 0 : _b.leftEyeIris[0])) {
+    ctx.strokeStyle = opt.useDepth ? "rgba(255, 200, 255, 0.3)" : opt.color;
+    ctx.beginPath();
+    const sizeX = Math.abs(f.annotations.leftEyeIris[3][0] - f.annotations.leftEyeIris[1][0]) / 2;
+    const sizeY = Math.abs(f.annotations.leftEyeIris[4][1] - f.annotations.leftEyeIris[2][1]) / 2;
+    ctx.ellipse(f.annotations.leftEyeIris[0][0], f.annotations.leftEyeIris[0][1], sizeX, sizeY, 0, 0, 2 * Math.PI);
+    ctx.stroke();
+    if (opt.fillPolygons) {
+      ctx.fillStyle = opt.useDepth ? "rgba(255, 255, 200, 0.3)" : opt.color;
+      ctx.fill();
+    }
+  }
+  if (((_c = f.annotations) == null ? void 0 : _c.rightEyeIris) && ((_d = f.annotations) == null ? void 0 : _d.rightEyeIris[0])) {
+    ctx.strokeStyle = opt.useDepth ? "rgba(255, 200, 255, 0.3)" : opt.color;
+    ctx.beginPath();
+    const sizeX = Math.abs(f.annotations.rightEyeIris[3][0] - f.annotations.rightEyeIris[1][0]) / 2;
+    const sizeY = Math.abs(f.annotations.rightEyeIris[4][1] - f.annotations.rightEyeIris[2][1]) / 2;
+    ctx.ellipse(f.annotations.rightEyeIris[0][0], f.annotations.rightEyeIris[0][1], sizeX, sizeY, 0, 0, 2 * Math.PI);
+    ctx.stroke();
+    if (opt.fillPolygons) {
+      ctx.fillStyle = opt.useDepth ? "rgba(255, 255, 200, 0.3)" : opt.color;
+      ctx.fill();
+    }
+  }
+}
+function drawGazeSpheres(f, ctx) {
+  var _a;
+  if (opt.drawGaze && ((_a = f.rotation) == null ? void 0 : _a.angle) && typeof Path2D !== "undefined") {
+    ctx.strokeStyle = "pink";
+    const valX = f.box[0] + f.box[2] / 2 - f.box[3] * rad2deg(f.rotation.angle.yaw) / 90;
+    const valY = f.box[1] + f.box[3] / 2 + f.box[2] * rad2deg(f.rotation.angle.pitch) / 90;
+    const pathV = new Path2D(`
+      M ${f.box[0] + f.box[2] / 2} ${f.box[1]}
       C
-        ${o} ${e.box[1]},
-        ${o} ${e.box[1]+e.box[3]},
-        ${e.box[0]+e.box[2]/2} ${e.box[1]+e.box[3]}
-    `),s=new Path2D(`
-      M ${e.box[0]} ${e.box[1]+e.box[3]/2}
+        ${valX} ${f.box[1]},
+        ${valX} ${f.box[1] + f.box[3]},
+        ${f.box[0] + f.box[2] / 2} ${f.box[1] + f.box[3]}
+    `);
+    const pathH = new Path2D(`
+      M ${f.box[0]} ${f.box[1] + f.box[3] / 2}
       C 
-        ${e.box[0]} ${r},
-        ${e.box[0]+e.box[2]} ${r},
-        ${e.box[0]+e.box[2]} ${e.box[1]+e.box[3]/2}
-    `);t.stroke(s),t.stroke(A)}}function Ms(e,t){var n;if(Y.drawGaze&&((n=e.rotation)==null?void 0:n.gaze.strength)&&e.rotation.gaze.bearing&&e.annotations.leftEyeIris&&e.annotations.rightEyeIris&&e.annotations.leftEyeIris[0]&&e.annotations.rightEyeIris[0]){t.strokeStyle="pink",t.fillStyle="pink";let o=[e.annotations.leftEyeIris[0][0]+Math.sin(e.rotation.gaze.bearing)*e.rotation.gaze.strength*e.box[3],e.annotations.leftEyeIris[0][1]+Math.cos(e.rotation.gaze.bearing)*e.rotation.gaze.strength*e.box[2]];i1(t,[e.annotations.leftEyeIris[0][0],e.annotations.leftEyeIris[0][1]],[o[0],o[1]],4);let r=[e.annotations.rightEyeIris[0][0]+Math.sin(e.rotation.gaze.bearing)*e.rotation.gaze.strength*e.box[3],e.annotations.rightEyeIris[0][1]+Math.cos(e.rotation.gaze.bearing)*e.rotation.gaze.strength*e.box[2]];i1(t,[e.annotations.rightEyeIris[0][0],e.annotations.rightEyeIris[0][1]],[r[0],r[1]],4)}}function vs(e,t){if(Y.drawPolygons&&e.mesh.length>=468){t.lineWidth=1;for(let n=0;n<Ve.length/3;n++){let o=[Ve[n*3+0],Ve[n*3+1],Ve[n*3+2]].map(r=>e.mesh[r]);a1(t,o,Y)}bs(e,t)}}function Ps(e,t){if(Y.drawPoints&&e.mesh.length>=468)for(let n=0;n<e.mesh.length;n++)be(t,e.mesh[n][0],e.mesh[n][1],e.mesh[n][2],Y),Y.drawAttention&&(O2.includes(n)&&be(t,e.mesh[n][0],e.mesh[n][1],e.mesh[n][2]+127,Y),Xe.includes(n)&&be(t,e.mesh[n][0],e.mesh[n][1],e.mesh[n][2]-127,Y),qe.includes(n)&&be(t,e.mesh[n][0],e.mesh[n][1],e.mesh[n][2]-127,Y))}function Ts(e,t){Y.drawBoxes&&de(t,e.box[0],e.box[1],e.box[2],e.box[3],Y)}function h2(e,t,n){if(Y=A0(R0,n),!t||!e)return;let o=X0(e);if(!!o){o.font=Y.font,o.strokeStyle=Y.color,o.fillStyle=Y.color;for(let r of t)Ts(r,o),hs(r,o),r.mesh&&r.mesh.length>0&&(Ps(r,o),vs(r,o),gs(r,o),Ms(r,o))}}function b2(e,t,n){let o=A0(R0,n);if(!t||!e)return;let r=X0(e);if(!!r){r.lineJoin="round";for(let A=0;A<t.length;A++){if(r.strokeStyle=o.color,r.fillStyle=o.color,r.lineWidth=o.lineWidth,r.font=o.font,o.drawBoxes&&t[A].box&&t[A].box.length===4&&(de(r,t[A].box[0],t[A].box[1],t[A].box[2],t[A].box[3],o),o.drawLabels&&(o.shadowColor&&o.shadowColor!==""&&(r.fillStyle=o.shadowColor,r.fillText(`body ${100*t[A].score}%`,t[A].box[0]+3,1+t[A].box[1]+o.lineHeight,t[A].box[2])),r.fillStyle=o.labelColor,r.fillText(`body ${100*t[A].score}%`,t[A].box[0]+2,0+t[A].box[1]+o.lineHeight,t[A].box[2]))),o.drawPoints&&t[A].keypoints)for(let s=0;s<t[A].keypoints.length;s++)!t[A].keypoints[s].score||t[A].keypoints[s].score===0||(r.fillStyle=he(t[A].keypoints[s].position[2],o),be(r,t[A].keypoints[s].position[0],t[A].keypoints[s].position[1],0,o));if(o.drawLabels&&t[A].keypoints){r.font=o.font;for(let s of t[A].keypoints)!s.score||s.score===0||(r.fillStyle=he(s.position[2],o),r.fillText(`${s.part} ${Math.trunc(100*s.score)}%`,s.position[0]+4,s.position[1]+4))}if(o.drawPolygons&&t[A].keypoints&&t[A].annotations)for(let s of Object.values(t[A].annotations))for(let a of s)lo(r,a,o)}}}function g2(e,t,n){let o=A0(R0,n);if(!t||!e)return;let r=X0(e);if(!!r){r.lineJoin="round",r.font=o.font;for(let A of t){if(o.drawBoxes&&(r.strokeStyle=o.color,r.fillStyle=o.color,de(r,A.box[0],A.box[1],A.box[2],A.box[3],o),o.drawLabels&&(o.shadowColor&&o.shadowColor!==""&&(r.fillStyle=o.shadowColor,r.fillText(`hand:${Math.trunc(100*A.score)}%`,A.box[0]+3,1+A.box[1]+o.lineHeight,A.box[2])),r.fillStyle=o.labelColor,r.fillText(`hand:${Math.trunc(100*A.score)}%`,A.box[0]+2,0+A.box[1]+o.lineHeight,A.box[2])),r.stroke()),o.drawPoints&&A.keypoints&&A.keypoints.length>0)for(let s of A.keypoints)r.fillStyle=he(s[2],o),be(r,s[0],s[1],0,o);if(o.drawLabels&&A.annotations){let s=(a,l)=>{if(!a||a.length===0||!a[0])return;let 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0:$.enabled)&&r&&(i={...i,age:r.age,gender:r.gender,genderScore:r.genderScore,race:r.race}),((m0=e.config.face.mobilefacenet)==null?void 0:m0.enabled)&&a&&(i.descriptor=a),((d0=e.config.face.insightface)==null?void 0:d0.enabled)&&l&&(i.descriptor=l),(G=e.config.face.iris)!=null&&G.enabled;let e2=((U0=(q0=(Z=d[E])==null?void 0:Z.annotations)==null?void 0:q0.leftEyeIris)==null?void 0:U0[0])&&((xe=(Me=(ge=d[E])==null?void 0:ge.annotations)==null?void 0:Me.rightEyeIris)==null?void 0:xe[0])&&d[E].annotations.leftEyeIris.length>0&&d[E].annotations.rightEyeIris.length>0&&d[E].annotations.leftEyeIris[0]!==null&&d[E].annotations.rightEyeIris[0]!==null?Math.max(Math.abs(d[E].annotations.leftEyeIris[3][0]-d[E].annotations.leftEyeIris[1][0]),Math.abs(d[E].annotations.rightEyeIris[4][1]-d[E].annotations.rightEyeIris[2][1]))/t.shape[2]:0,M1=(R2=e.config.face.detector)!=null&&R2.return?r0.squeeze(d[E].tensor):null;r0.dispose(d[E].tensor),d[E].tensor&&delete d[E].tensor;let Y0={...d[E],id:E};i.age&&(Y0.age=i.age),i.gender&&(Y0.gender=i.gender),i.genderScore&&(Y0.genderScore=i.genderScore),i.descriptor&&(Y0.embedding=i.descriptor),i.race&&(Y0.race=i.race),s&&(Y0.emotion=s),c&&(Y0.real=c),x&&(Y0.live=x),e2&&e2!==0&&(Y0.iris=Math.trunc(500/e2/11.7)/100),k2&&(Y0.rotation=k2),M1&&(Y0.tensor=M1),y.push(Y0),e.analyze("End Face")}return e.analyze("End FaceMesh:"),e.config.async&&(e.performance.face&&delete e.performance.face,e.performance.age&&delete e.performance.age,e.performance.gender&&delete e.performance.gender,e.performance.emotion&&delete e.performance.emotion),y};var fo=e=>{if(!e)return[];let t=[];for(let n=0;n<e.length;n++){let o=e[n].keypoints.find(l=>l.part==="leftWrist"),r=e[n].keypoints.find(l=>l.part==="rightWrist"),A=e[n].keypoints.find(l=>l.part==="nose");A&&o&&r&&o.position[1]<A.position[1]&&r.position[1]<A.position[1]?t.push({body:n,gesture:"i give up"}):A&&o&&o.position[1]<A.position[1]?t.push({body:n,gesture:"raise left 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a=Math.min(100,500*Math.abs(e[n].mesh[13][1]-e[n].mesh[14][1])/Math.abs(e[n].mesh[10][1]-e[n].mesh[152][1]));a>10&&t.push({face:n,gesture:`mouth ${Math.trunc(a)}% open`});let l=e[n].mesh[152][2]||0;Math.abs(l)>10&&t.push({face:n,gesture:`head ${l<0?"up":"down"}`})}return t},po=e=>{var n,o,r,A;if(!e)return[];let t=[];for(let s=0;s<e.length;s++){if(!((o=(n=e[s].annotations)==null?void 0:n.leftEyeIris)!=null&&o[0])||!((A=(r=e[s].annotations)==null?void 0:r.rightEyeIris)!=null&&A[0]))continue;let a=e[s].annotations.leftEyeIris[3][0]-e[s].annotations.leftEyeIris[1][0],l=e[s].annotations.leftEyeIris[4][1]-e[s].annotations.leftEyeIris[2][1],c=Math.abs(a*l),x=e[s].annotations.rightEyeIris[3][0]-e[s].annotations.rightEyeIris[1][0],i=e[s].annotations.rightEyeIris[4][1]-e[s].annotations.rightEyeIris[2][1],y=Math.abs(x*i),d=!1;Math.abs(c-y)/Math.max(c,y)<.25&&(d=!0,t.push({iris:s,gesture:"facing center"}));let p=Math.abs(e[s].mesh[263][0]-e[s].annotations.leftEyeIris[0][0])/e[s].box[2],g=Math.abs(e[s].mesh[33][0]-e[s].annotations.rightEyeIris[0][0])/e[s].box[2];(p>.06||g>.06)&&(d=!1),p>g?p>.05&&t.push({iris:s,gesture:"looking right"}):g>.05&&t.push({iris:s,gesture:"looking left"});let M=Math.abs(e[s].mesh[145][1]-e[s].annotations.rightEyeIris[0][1])/e[s].box[3],P=Math.abs(e[s].mesh[374][1]-e[s].annotations.leftEyeIris[0][1])/e[s].box[3];(P<.01||M<.01||P>.022||M>.022)&&(d=!1),(P<.01||M<.01)&&t.push({iris:s,gesture:"looking down"}),(P>.022||M>.022)&&t.push({iris:s,gesture:"looking up"}),d&&t.push({iris:s,gesture:"looking center"})}return t},uo=e=>{if(!e)return[];let t=[];for(let n=0;n<e.length;n++){let o=[];if(e[n].annotations)for(let[r,A]of Object.entries(e[n].annotations))r!=="palmBase"&&Array.isArray(A)&&A[0]&&o.push({name:r.toLowerCase(),position:A[0]});if(o&&o.length>0){let r=o.reduce((s,a)=>(s.position[2]||0)<(a.position[2]||0)?s:a);t.push({hand:n,gesture:`${r.name} forward`});let A=o.reduce((s,a)=>s.position[1]<a.position[1]?s:a);t.push({hand:n,gesture:`${A.name} up`})}if(e[n].keypoints){let r=Tn(e[n].keypoints);for(let A of r)t.push({hand:n,gesture:A.name})}}return t};var j={face:[],body:[],hand:[],gesture:[],object:[],persons:[],performance:{},timestamp:0,error:null},p1=0;function ho(e,t){var s,a,l,c,x,i,y,d,f,p,g,M,P,m,b,z,w,I,q,e0,V,X,c0;let n=v();if(!e)return{face:[],body:[],hand:[],gesture:[],object:[],persons:[],performance:{},timestamp:0,error:null};let o=Date.now()-e.timestamp,r=o<1e3?8-Math.log(o+1):1;if(e.canvas&&(j.canvas=e.canvas),e.error&&(j.error=e.error),!j.body||e.body.length!==j.body.length)j.body=JSON.parse(JSON.stringify(e.body));else for(let T=0;T<e.body.length;T++){let F=e.body[T].box.map((G,Z)=>((r-1)*j.body[T].box[Z]+G)/r),M0=e.body[T].boxRaw.map((G,Z)=>((r-1)*j.body[T].boxRaw[Z]+G)/r),$=e.body[T].keypoints.map((G,Z)=>{var q0,U0,ge,Me,xe,R2,E,k2,e2;return{score:G.score,part:G.part,position:[j.body[T].keypoints[Z]?((r-1)*(j.body[T].keypoints[Z].position[0]||0)+(G.position[0]||0))/r:G.position[0],j.body[T].keypoints[Z]?((r-1)*(j.body[T].keypoints[Z].position[1]||0)+(G.position[1]||0))/r:G.position[1],j.body[T].keypoints[Z]?((r-1)*(j.body[T].keypoints[Z].position[2]||0)+(G.position[2]||0))/r:G.position[2]],positionRaw:[j.body[T].keypoints[Z]?((r-1)*(j.body[T].keypoints[Z].positionRaw[0]||0)+(G.positionRaw[0]||0))/r:G.positionRaw[0],j.body[T].keypoints[Z]?((r-1)*(j.body[T].keypoints[Z].positionRaw[1]||0)+(G.positionRaw[1]||0))/r:G.positionRaw[1],j.body[T].keypoints[Z]?((r-1)*(j.body[T].keypoints[Z].positionRaw[2]||0)+(G.positionRaw[2]||0))/r:G.positionRaw[2]],distance:[j.body[T].keypoints[Z]?((r-1)*(((q0=j.body[T].keypoints[Z].distance)==null?void 0:q0[0])||0)+(((U0=G.distance)==null?void 0:U0[0])||0))/r:(ge=G.distance)==null?void 0:ge[0],j.body[T].keypoints[Z]?((r-1)*(((Me=j.body[T].keypoints[Z].distance)==null?void 0:Me[1])||0)+(((xe=G.distance)==null?void 0:xe[1])||0))/r:(R2=G.distance)==null?void 0:R2[1],j.body[T].keypoints[Z]?((r-1)*(((E=j.body[T].keypoints[Z].distance)==null?void 0:E[2])||0)+(((k2=G.distance)==null?void 0:k2[2])||0))/r:(e2=G.distance)==null?void 0:e2[2]]}}),m0={},d0={connected:{}};(s=t.body.modelPath)!=null&&s.includes("efficientpose")?d0=d5:(a=t.body.modelPath)!=null&&a.includes("blazepose")?d0=s5:(l=t.body.modelPath)!=null&&l.includes("movenet")&&(d0=F2);for(let[G,Z]of Object.entries(d0.connected)){let q0=[];for(let U0=0;U0<Z.length-1;U0++){let ge=$.find(xe=>xe.part===Z[U0]),Me=$.find(xe=>xe.part===Z[U0+1]);ge&&Me&&q0.push([ge.position,Me.position])}m0[G]=q0}j.body[T]={...e.body[T],box:F,boxRaw:M0,keypoints:$,annotations:m0}}if(!j.hand||e.hand.length!==j.hand.length)j.hand=JSON.parse(JSON.stringify(e.hand));else for(let T=0;T<e.hand.length;T++){let F=e.hand[T].box.map((d0,G)=>((r-1)*j.hand[T].box[G]+d0)/r),M0=e.hand[T].boxRaw.map((d0,G)=>((r-1)*j.hand[T].boxRaw[G]+d0)/r);j.hand[T].keypoints.length!==e.hand[T].keypoints.length&&(j.hand[T].keypoints=e.hand[T].keypoints);let $=e.hand[T].keypoints&&e.hand[T].keypoints.length>0?e.hand[T].keypoints.map((d0,G)=>d0.map((Z,q0)=>((r-1)*(j.hand[T].keypoints[G][q0]||1)+(Z||0))/r)):[],m0={};if(Object.keys(j.hand[T].annotations).length!==Object.keys(e.hand[T].annotations).length)j.hand[T].annotations=e.hand[T].annotations,m0=j.hand[T].annotations;else if(e.hand[T].annotations)for(let d0 of Object.keys(e.hand[T].annotations))m0[d0]=(i=(x=(c=e.hand[T])==null?void 0:c.annotations)==null?void 0:x[d0])!=null&&i[0]?e.hand[T].annotations[d0].map((G,Z)=>G.map((q0,U0)=>((r-1)*j.hand[T].annotations[d0][Z][U0]+q0)/r)):null;j.hand[T]={...e.hand[T],box:F,boxRaw:M0,keypoints:$,annotations:m0}}if(!j.face||e.face.length!==j.face.length)j.face=JSON.parse(JSON.stringify(e.face));else for(let T=0;T<e.face.length;T++){let F=e.face[T].box.map(($,m0)=>((r-1)*j.face[T].box[m0]+$)/r),M0=e.face[T].boxRaw.map(($,m0)=>((r-1)*j.face[T].boxRaw[m0]+$)/r);if(e.face[T].rotation){let $={matrix:[0,0,0,0,0,0,0,0,0],angle:{roll:0,yaw:0,pitch:0},gaze:{bearing:0,strength:0}};$.matrix=(y=e.face[T].rotation)==null?void 0:y.matrix,$.angle={roll:((r-1)*(((f=(d=j.face[T].rotation)==null?void 0:d.angle)==null?void 0:f.roll)||0)+(((g=(p=e.face[T].rotation)==null?void 0:p.angle)==null?void 0:g.roll)||0))/r,yaw:((r-1)*(((P=(M=j.face[T].rotation)==null?void 0:M.angle)==null?void 0:P.yaw)||0)+(((b=(m=e.face[T].rotation)==null?void 0:m.angle)==null?void 0:b.yaw)||0))/r,pitch:((r-1)*(((w=(z=j.face[T].rotation)==null?void 0:z.angle)==null?void 0:w.pitch)||0)+(((q=(I=e.face[T].rotation)==null?void 0:I.angle)==null?void 0:q.pitch)||0))/r},$.gaze={bearing:((r-1)*(((e0=j.face[T].rotation)==null?void 0:e0.gaze.bearing)||0)+(((V=e.face[T].rotation)==null?void 0:V.gaze.bearing)||0))/r,strength:((r-1)*(((X=j.face[T].rotation)==null?void 0:X.gaze.strength)||0)+(((c0=e.face[T].rotation)==null?void 0:c0.gaze.strength)||0))/r},j.face[T]={...e.face[T],rotation:$,box:F,boxRaw:M0}}else j.face[T]={...e.face[T],box:F,boxRaw:M0}}if(!j.object||e.object.length!==j.object.length)j.object=JSON.parse(JSON.stringify(e.object));else for(let T=0;T<e.object.length;T++){let F=e.object[T].box.map(($,m0)=>((r-1)*j.object[T].box[m0]+$)/r),M0=e.object[T].boxRaw.map(($,m0)=>((r-1)*j.object[T].boxRaw[m0]+$)/r);j.object[T]={...e.object[T],box:F,boxRaw:M0}}if(e.persons){let T=e.persons;if(!j.persons||T.length!==j.persons.length)j.persons=JSON.parse(JSON.stringify(T));else for(let F=0;F<T.length;F++)j.persons[F].box=T[F].box.map((M0,$)=>((r-1)*j.persons[F].box[$]+M0)/r)}e.gesture&&(j.gesture=e.gesture);let A=v();return p1=k.perfadd?p1+Math.round(A-n):Math.round(A-n),e.performance&&(j.performance={...e.performance,interpolate:p1}),j}var b1={};ve(b1,{distance:()=>V2,match:()=>h1,similarity:()=>u1});function V2(e,t,n={order:2,multiplier:25}){if(!e||!e)return Number.MAX_SAFE_INTEGER;let o=0;for(let r=0;r<e.length;r++){let A=!n.order||n.order===2?e[r]-t[r]:Math.abs(e[r]-t[r]);o+=!n.order||n.order===2?A*A:A**n.order}return(n.multiplier||20)*o}var bo=(e,t,n,o)=>{if(e===0)return 1;let r=t===2?Math.sqrt(e):e**(1/t),A=(1-r/100-n)/(o-n);return Math.max(Math.min(A,1),0)};function u1(e,t,n={order:2,multiplier:25,min:.2,max:.8}){let o=V2(e,t,n);return bo(o,n.order||2,n.min||0,n.max||1)}function h1(e,t,n={order:2,multiplier:25,threshold:0,min:.2,max:.8}){if(!Array.isArray(e)||!Array.isArray(t)||e.length<64||t.length===0)return{index:-1,distance:Number.POSITIVE_INFINITY,similarity:0};let o=Number.MAX_SAFE_INTEGER,r=-1;for(let s=0;s<t.length;s++){let a=t[s].length===e.length?V2(e,t[s],n):Number.MAX_SAFE_INTEGER;if(a<o&&(o=a,r=s),o<(n.threshold||0))break}let A=bo(o,n.order||2,n.min||0,n.max||1);return{index:r,distance:o,similarity:A}}function go(e,t,n,o,r){var a,l,c,x,i,y;let A=0,s=[];for(let d of e){let f={id:A++,face:d,body:null,hands:{left:null,right:null},gestures:[],box:[0,0,0,0]};for(let b of t)d.box[0]>b.box[0]&&d.box[0]<b.box[0]+b.box[2]&&d.box[1]+d.box[3]>b.box[1]&&d.box[1]+d.box[3]<b.box[1]+b.box[3]&&(f.body=b);if(f.body)for(let b of n)b.box[0]+b.box[2]>f.body.box[0]&&b.box[0]+b.box[2]<f.body.box[0]+f.body.box[2]&&b.box[1]+b.box[3]>f.body.box[1]&&b.box[1]+b.box[3]<f.body.box[1]+f.body.box[3]&&f.hands&&(f.hands.left=b),b.box[0]<f.body.box[0]+f.body.box[2]&&b.box[0]>f.body.box[0]&&b.box[1]+b.box[3]>f.body.box[1]&&b.box[1]+b.box[3]<f.body.box[1]+f.body.box[3]&&f.hands&&(f.hands.right=b);for(let b of o)(b.face!==void 0&&b.face===d.id||b.iris!==void 0&&b.iris===d.id||b.body!==void 0&&b.body===((a=f.body)==null?void 0:a.id)||b.hand!==void 0&&b.hand===((l=f.hands.left)==null?void 0:l.id)||b.hand!==void 0&&b.hand===((c=f.hands.right)==null?void 0:c.id))&&f.gestures.push(b);let p=[],g=[],M=b=>{b&&b.length===4&&(p.push(b[0],b[0]+b[2]),g.push(b[1],b[1]+b[3]))};M(f.face.box),M((x=f.body)==null?void 0:x.box),M((i=f.hands.left)==null?void 0:i.box),M((y=f.hands.right)==null?void 0:y.box);let P=Math.min(...p),m=Math.min(...g);f.box=[P,m,Math.max(...p)-P,Math.max(...g)-m],(r==null?void 0:r[1])&&(r==null?void 0:r[2])&&(f.boxRaw=[f.box[0]/r[2],f.box[1]/r[1],f.box[2]/r[2],f.box[3]/r[1]]),s.push(f)}return s}var L5=`
+        ${f.box[0]} ${valY},
+        ${f.box[0] + f.box[2]} ${valY},
+        ${f.box[0] + f.box[2]} ${f.box[1] + f.box[3] / 2}
+    `);
+    ctx.stroke(pathH);
+    ctx.stroke(pathV);
+  }
+}
+function drawGazeArrows(f, ctx) {
+  var _a;
+  if (opt.drawGaze && ((_a = f.rotation) == null ? void 0 : _a.gaze.strength) && f.rotation.gaze.bearing && f.annotations.leftEyeIris && f.annotations.rightEyeIris && f.annotations.leftEyeIris[0] && f.annotations.rightEyeIris[0]) {
+    ctx.strokeStyle = "pink";
+    ctx.fillStyle = "pink";
+    const leftGaze = [
+      f.annotations.leftEyeIris[0][0] + Math.sin(f.rotation.gaze.bearing) * f.rotation.gaze.strength * f.box[3],
+      f.annotations.leftEyeIris[0][1] + Math.cos(f.rotation.gaze.bearing) * f.rotation.gaze.strength * f.box[2]
+    ];
+    arrow(ctx, [f.annotations.leftEyeIris[0][0], f.annotations.leftEyeIris[0][1]], [leftGaze[0], leftGaze[1]], 4);
+    const rightGaze = [
+      f.annotations.rightEyeIris[0][0] + Math.sin(f.rotation.gaze.bearing) * f.rotation.gaze.strength * f.box[3],
+      f.annotations.rightEyeIris[0][1] + Math.cos(f.rotation.gaze.bearing) * f.rotation.gaze.strength * f.box[2]
+    ];
+    arrow(ctx, [f.annotations.rightEyeIris[0][0], f.annotations.rightEyeIris[0][1]], [rightGaze[0], rightGaze[1]], 4);
+  }
+}
+function drawFacePolygons(f, ctx) {
+  if (opt.drawPolygons && f.mesh.length >= 468) {
+    ctx.lineWidth = 1;
+    for (let i = 0; i < TRI468.length / 3; i++) {
+      const points = [TRI468[i * 3 + 0], TRI468[i * 3 + 1], TRI468[i * 3 + 2]].map((index2) => f.mesh[index2]);
+      lines(ctx, points, opt);
+    }
+    drawIrisElipse(f, ctx);
+  }
+}
+function drawFacePoints(f, ctx) {
+  if (opt.drawPoints && f.mesh.length >= 468) {
+    for (let i = 0; i < f.mesh.length; i++) {
+      point(ctx, f.mesh[i][0], f.mesh[i][1], f.mesh[i][2], opt);
+      if (opt.drawAttention) {
+        if (LANDMARKS_REFINEMENT_LIPS_CONFIG.includes(i))
+          point(ctx, f.mesh[i][0], f.mesh[i][1], f.mesh[i][2] + 127, opt);
+        if (LANDMARKS_REFINEMENT_LEFT_EYE_CONFIG.includes(i))
+          point(ctx, f.mesh[i][0], f.mesh[i][1], f.mesh[i][2] - 127, opt);
+        if (LANDMARKS_REFINEMENT_RIGHT_EYE_CONFIG.includes(i))
+          point(ctx, f.mesh[i][0], f.mesh[i][1], f.mesh[i][2] - 127, opt);
+      }
+    }
+  }
+}
+function drawFaceBoxes(f, ctx) {
+  if (opt.drawBoxes) {
+    rect(ctx, f.box[0], f.box[1], f.box[2], f.box[3], opt);
+  }
+}
+function face(inCanvas2, result, drawOptions) {
+  opt = mergeDeep(options3, drawOptions);
+  if (!result || !inCanvas2)
+    return;
+  const ctx = getCanvasContext(inCanvas2);
+  if (!ctx)
+    return;
+  ctx.font = opt.font;
+  ctx.strokeStyle = opt.color;
+  ctx.fillStyle = opt.color;
+  for (const f of result) {
+    drawFaceBoxes(f, ctx);
+    drawLabels(f, ctx);
+    if (f.mesh && f.mesh.length > 0) {
+      drawFacePoints(f, ctx);
+      drawFacePolygons(f, ctx);
+      drawGazeSpheres(f, ctx);
+      drawGazeArrows(f, ctx);
+    }
+  }
+}
+
+// src/draw/body.ts
+function body(inCanvas2, result, drawOptions) {
+  const localOptions = mergeDeep(options3, drawOptions);
+  if (!result || !inCanvas2)
+    return;
+  const ctx = getCanvasContext(inCanvas2);
+  if (!ctx)
+    return;
+  ctx.lineJoin = "round";
+  for (let i = 0; i < result.length; i++) {
+    ctx.strokeStyle = localOptions.color;
+    ctx.fillStyle = localOptions.color;
+    ctx.lineWidth = localOptions.lineWidth;
+    ctx.font = localOptions.font;
+    if (localOptions.drawBoxes && result[i].box && result[i].box.length === 4) {
+      rect(ctx, result[i].box[0], result[i].box[1], result[i].box[2], result[i].box[3], localOptions);
+      if (localOptions.drawLabels) {
+        if (localOptions.shadowColor && localOptions.shadowColor !== "") {
+          ctx.fillStyle = localOptions.shadowColor;
+          ctx.fillText(`body ${100 * result[i].score}%`, result[i].box[0] + 3, 1 + result[i].box[1] + localOptions.lineHeight, result[i].box[2]);
+        }
+        ctx.fillStyle = localOptions.labelColor;
+        ctx.fillText(`body ${100 * result[i].score}%`, result[i].box[0] + 2, 0 + result[i].box[1] + localOptions.lineHeight, result[i].box[2]);
+      }
+    }
+    if (localOptions.drawPoints && result[i].keypoints) {
+      for (let pt = 0; pt < result[i].keypoints.length; pt++) {
+        if (!result[i].keypoints[pt].score || result[i].keypoints[pt].score === 0)
+          continue;
+        ctx.fillStyle = colorDepth(result[i].keypoints[pt].position[2], localOptions);
+        point(ctx, result[i].keypoints[pt].position[0], result[i].keypoints[pt].position[1], 0, localOptions);
+      }
+    }
+    if (localOptions.drawLabels && result[i].keypoints) {
+      ctx.font = localOptions.font;
+      for (const pt of result[i].keypoints) {
+        if (!pt.score || pt.score === 0)
+          continue;
+        ctx.fillStyle = colorDepth(pt.position[2], localOptions);
+        ctx.fillText(`${pt.part} ${Math.trunc(100 * pt.score)}%`, pt.position[0] + 4, pt.position[1] + 4);
+      }
+    }
+    if (localOptions.drawPolygons && result[i].keypoints && result[i].annotations) {
+      for (const part of Object.values(result[i].annotations)) {
+        for (const connected4 of part)
+          curves(ctx, connected4, localOptions);
+      }
+    }
+  }
+}
+
+// src/draw/hand.ts
+function hand(inCanvas2, result, drawOptions) {
+  const localOptions = mergeDeep(options3, drawOptions);
+  if (!result || !inCanvas2)
+    return;
+  const ctx = getCanvasContext(inCanvas2);
+  if (!ctx)
+    return;
+  ctx.lineJoin = "round";
+  ctx.font = localOptions.font;
+  for (const h of result) {
+    if (localOptions.drawBoxes) {
+      ctx.strokeStyle = localOptions.color;
+      ctx.fillStyle = localOptions.color;
+      rect(ctx, h.box[0], h.box[1], h.box[2], h.box[3], localOptions);
+      if (localOptions.drawLabels) {
+        if (localOptions.shadowColor && localOptions.shadowColor !== "") {
+          ctx.fillStyle = localOptions.shadowColor;
+          ctx.fillText(`hand:${Math.trunc(100 * h.score)}%`, h.box[0] + 3, 1 + h.box[1] + localOptions.lineHeight, h.box[2]);
+        }
+        ctx.fillStyle = localOptions.labelColor;
+        ctx.fillText(`hand:${Math.trunc(100 * h.score)}%`, h.box[0] + 2, 0 + h.box[1] + localOptions.lineHeight, h.box[2]);
+      }
+      ctx.stroke();
+    }
+    if (localOptions.drawPoints) {
+      if (h.keypoints && h.keypoints.length > 0) {
+        for (const pt of h.keypoints) {
+          ctx.fillStyle = colorDepth(pt[2], localOptions);
+          point(ctx, pt[0], pt[1], 0, localOptions);
+        }
+      }
+    }
+    if (localOptions.drawLabels && h.annotations) {
+      const addHandLabel = (part, title) => {
+        if (!part || part.length === 0 || !part[0])
+          return;
+        const z = part[part.length - 1][2] || -256;
+        ctx.fillStyle = colorDepth(z, localOptions);
+        ctx.fillText(title, part[part.length - 1][0] + 4, part[part.length - 1][1] + 4);
+      };
+      ctx.font = localOptions.font;
+      addHandLabel(h.annotations.index, "index");
+      addHandLabel(h.annotations.middle, "middle");
+      addHandLabel(h.annotations.ring, "ring");
+      addHandLabel(h.annotations.pinky, "pinky");
+      addHandLabel(h.annotations.thumb, "thumb");
+      addHandLabel(h.annotations.palm, "palm");
+    }
+    if (localOptions.drawPolygons && h.annotations) {
+      const addHandLine = (part) => {
+        if (!part || part.length === 0 || !part[0])
+          return;
+        for (let i = 0; i < part.length; i++) {
+          ctx.beginPath();
+          const z = part[i][2] || 0;
+          ctx.strokeStyle = colorDepth(i * z, localOptions);
+          ctx.moveTo(part[i > 0 ? i - 1 : 0][0], part[i > 0 ? i - 1 : 0][1]);
+          ctx.lineTo(part[i][0], part[i][1]);
+          ctx.stroke();
+        }
+      };
+      ctx.lineWidth = localOptions.lineWidth;
+      addHandLine(h.annotations.index);
+      addHandLine(h.annotations.middle);
+      addHandLine(h.annotations.ring);
+      addHandLine(h.annotations.pinky);
+      addHandLine(h.annotations.thumb);
+    }
+  }
+}
+
+// src/draw/object.ts
+function object(inCanvas2, result, drawOptions) {
+  const localOptions = mergeDeep(options3, drawOptions);
+  if (!result || !inCanvas2)
+    return;
+  const ctx = getCanvasContext(inCanvas2);
+  if (!ctx)
+    return;
+  ctx.lineJoin = "round";
+  ctx.font = localOptions.font;
+  for (const h of result) {
+    if (localOptions.drawBoxes) {
+      ctx.strokeStyle = localOptions.color;
+      ctx.fillStyle = localOptions.color;
+      rect(ctx, h.box[0], h.box[1], h.box[2], h.box[3], localOptions);
+      if (localOptions.drawLabels) {
+        const label = `${h.label} ${Math.round(100 * h.score)}%`;
+        if (localOptions.shadowColor && localOptions.shadowColor !== "") {
+          ctx.fillStyle = localOptions.shadowColor;
+          ctx.fillText(label, h.box[0] + 3, 1 + h.box[1] + localOptions.lineHeight, h.box[2]);
+        }
+        ctx.fillStyle = localOptions.labelColor;
+        ctx.fillText(label, h.box[0] + 2, 0 + h.box[1] + localOptions.lineHeight, h.box[2]);
+      }
+      ctx.stroke();
+    }
+  }
+}
+
+// src/draw/gesture.ts
+function gesture(inCanvas2, result, drawOptions) {
+  const localOptions = mergeDeep(options3, drawOptions);
+  if (!result || !inCanvas2)
+    return;
+  if (localOptions.drawGestures) {
+    const ctx = getCanvasContext(inCanvas2);
+    if (!ctx)
+      return;
+    ctx.font = localOptions.font;
+    ctx.fillStyle = localOptions.color;
+    let i = 1;
+    for (let j = 0; j < result.length; j++) {
+      let where = [];
+      let what = [];
+      [where, what] = Object.entries(result[j]);
+      if (what.length > 1 && what[1].length > 0) {
+        const who = where[1] > 0 ? `#${where[1]}` : "";
+        const label = `${where[0]} ${who}: ${what[1]}`;
+        if (localOptions.shadowColor && localOptions.shadowColor !== "") {
+          ctx.fillStyle = localOptions.shadowColor;
+          ctx.fillText(label, 8, 2 + i * localOptions.lineHeight);
+        }
+        ctx.fillStyle = localOptions.labelColor;
+        ctx.fillText(label, 6, 0 + i * localOptions.lineHeight);
+        i += 1;
+      }
+    }
+  }
+}
+
+// src/draw/draw.ts
+var drawTime = 0;
+function person(inCanvas2, result, drawOptions) {
+  const localOptions = mergeDeep(options3, drawOptions);
+  if (!result || !inCanvas2)
+    return;
+  const ctx = getCanvasContext(inCanvas2);
+  if (!ctx)
+    return;
+  ctx.lineJoin = "round";
+  ctx.font = localOptions.font;
+  for (let i = 0; i < result.length; i++) {
+    if (localOptions.drawBoxes) {
+      ctx.strokeStyle = localOptions.color;
+      ctx.fillStyle = localOptions.color;
+      rect(ctx, result[i].box[0], result[i].box[1], result[i].box[2], result[i].box[3], localOptions);
+      if (localOptions.drawLabels) {
+        const label = `person #${i}`;
+        if (localOptions.shadowColor && localOptions.shadowColor !== "") {
+          ctx.fillStyle = localOptions.shadowColor;
+          ctx.fillText(label, result[i].box[0] + 3, 1 + result[i].box[1] + localOptions.lineHeight, result[i].box[2]);
+        }
+        ctx.fillStyle = localOptions.labelColor;
+        ctx.fillText(label, result[i].box[0] + 2, 0 + result[i].box[1] + localOptions.lineHeight, result[i].box[2]);
+      }
+      ctx.stroke();
+    }
+  }
+}
+function canvas2(input, output) {
+  if (!input || !output)
+    return;
+  const ctx = getCanvasContext(output);
+  if (!ctx)
+    return;
+  ctx.drawImage(input, 0, 0);
+}
+async function all(inCanvas2, result, drawOptions) {
+  if (!(result == null ? void 0 : result.performance) || !inCanvas2)
+    return null;
+  const timeStamp = now();
+  const localOptions = mergeDeep(options3, drawOptions);
+  const promise = Promise.all([
+    face(inCanvas2, result.face, localOptions),
+    body(inCanvas2, result.body, localOptions),
+    hand(inCanvas2, result.hand, localOptions),
+    object(inCanvas2, result.object, localOptions),
+    gesture(inCanvas2, result.gesture, localOptions)
+  ]);
+  drawTime = env.perfadd ? drawTime + Math.round(now() - timeStamp) : Math.round(now() - timeStamp);
+  result.performance.draw = drawTime;
+  return promise;
+}
+
+// src/face/face.ts
+var tf37 = __toESM(require_tfjs_esm());
+
+// src/face/mask.ts
+var tf36 = __toESM(require_tfjs_esm());
+var expandFact = 0.1;
+var alpha = 0.5;
+function insidePoly(x, y, polygon) {
+  let inside = false;
+  let j = polygon.length - 1;
+  for (let i = 0; i < polygon.length; j = i++) {
+    if (polygon[i].y > y !== polygon[j].y > y && x < (polygon[j].x - polygon[i].x) * (y - polygon[i].y) / (polygon[j].y - polygon[i].y) + polygon[i].x)
+      inside = !inside;
+  }
+  return inside;
+}
+async function mask(face4) {
+  if (!face4.tensor)
+    return face4.tensor;
+  if (!face4.mesh || face4.mesh.length < 100)
+    return face4.tensor;
+  const width = face4.tensor.shape[2] || 0;
+  const height = face4.tensor.shape[1] || 0;
+  const buffer = await face4.tensor.buffer();
+  let silhouette = [];
+  for (const pt of meshAnnotations.silhouette)
+    silhouette.push({ x: (face4.mesh[pt][0] - face4.box[0]) / face4.box[2], y: (face4.mesh[pt][1] - face4.box[1]) / face4.box[3] });
+  if (expandFact && expandFact > 0)
+    silhouette = silhouette.map((pt) => ({ x: pt.x > 0.5 ? pt.x + expandFact : pt.x - expandFact, y: pt.y > 0.5 ? pt.y + expandFact : pt.y - expandFact }));
+  for (let x = 0; x < width; x++) {
+    for (let y = 0; y < height; y++) {
+      const inside = insidePoly(x / width, y / width, silhouette);
+      if (!inside) {
+        buffer.set(alpha * buffer.get(0, y, x, 0), 0, y, x, 0);
+        buffer.set(alpha * buffer.get(0, y, x, 1), 0, y, x, 1);
+        buffer.set(alpha * buffer.get(0, y, x, 2), 0, y, x, 2);
+      }
+    }
+  }
+  const output = buffer.toTensor();
+  tf36.dispose(buffer);
+  return output;
+}
+
+// src/face/angles.ts
+var calculateGaze = (face4) => {
+  const radians = (pt1, pt2) => Math.atan2(pt1[1] - pt2[1], pt1[0] - pt2[0]);
+  if (!face4.annotations.rightEyeIris || !face4.annotations.leftEyeIris)
+    return { bearing: 0, strength: 0 };
+  const offsetIris = [0, -0.1];
+  const eyeRatio = 1;
+  const left = (face4.mesh[33][2] || 0) > (face4.mesh[263][2] || 0);
+  const irisCenter = left ? face4.mesh[473] : face4.mesh[468];
+  const eyeCenter = left ? [(face4.mesh[133][0] + face4.mesh[33][0]) / 2, (face4.mesh[133][1] + face4.mesh[33][1]) / 2] : [(face4.mesh[263][0] + face4.mesh[362][0]) / 2, (face4.mesh[263][1] + face4.mesh[362][1]) / 2];
+  const eyeSize = left ? [face4.mesh[133][0] - face4.mesh[33][0], face4.mesh[23][1] - face4.mesh[27][1]] : [face4.mesh[263][0] - face4.mesh[362][0], face4.mesh[253][1] - face4.mesh[257][1]];
+  const eyeDiff = [
+    (eyeCenter[0] - irisCenter[0]) / eyeSize[0] - offsetIris[0],
+    eyeRatio * (irisCenter[1] - eyeCenter[1]) / eyeSize[1] - offsetIris[1]
+  ];
+  let strength = Math.sqrt(eyeDiff[0] * eyeDiff[0] + eyeDiff[1] * eyeDiff[1]);
+  strength = Math.min(strength, face4.boxRaw[2] / 2, face4.boxRaw[3] / 2);
+  const bearing = (radians([0, 0], eyeDiff) + Math.PI / 2) % Math.PI;
+  return { bearing, strength };
+};
+var calculateFaceAngle = (face4, imageSize) => {
+  const normalize2 = (v) => {
+    const length = Math.sqrt(v[0] * v[0] + v[1] * v[1] + v[2] * v[2]);
+    v[0] /= length;
+    v[1] /= length;
+    v[2] /= length;
+    return v;
+  };
+  const subVectors = (a, b) => {
+    const x = a[0] - b[0];
+    const y = a[1] - b[1];
+    const z = a[2] - b[2];
+    return [x, y, z];
+  };
+  const crossVectors = (a, b) => {
+    const x = a[1] * b[2] - a[2] * b[1];
+    const y = a[2] * b[0] - a[0] * b[2];
+    const z = a[0] * b[1] - a[1] * b[0];
+    return [x, y, z];
+  };
+  const rotationMatrixToEulerAngle = (r) => {
+    const [r00, _r01, _r02, r10, r11, r12, r20, r21, r22] = r;
+    let thetaX;
+    let thetaY;
+    let thetaZ;
+    if (r10 < 1) {
+      if (r10 > -1) {
+        thetaZ = Math.asin(r10);
+        thetaY = Math.atan2(-r20, r00);
+        thetaX = Math.atan2(-r12, r11);
+      } else {
+        thetaZ = -Math.PI / 2;
+        thetaY = -Math.atan2(r21, r22);
+        thetaX = 0;
+      }
+    } else {
+      thetaZ = Math.PI / 2;
+      thetaY = Math.atan2(r21, r22);
+      thetaX = 0;
+    }
+    if (Number.isNaN(thetaX))
+      thetaX = 0;
+    if (Number.isNaN(thetaY))
+      thetaY = 0;
+    if (Number.isNaN(thetaZ))
+      thetaZ = 0;
+    return { pitch: 2 * -thetaX, yaw: 2 * -thetaY, roll: 2 * -thetaZ };
+  };
+  const mesh = face4.meshRaw;
+  if (!mesh || mesh.length < 300)
+    return { angle: { pitch: 0, yaw: 0, roll: 0 }, matrix: [1, 0, 0, 0, 1, 0, 0, 0, 1], gaze: { bearing: 0, strength: 0 } };
+  const size2 = Math.max(face4.boxRaw[2] * imageSize[0], face4.boxRaw[3] * imageSize[1]) / 1.5;
+  const pts = [mesh[10], mesh[152], mesh[234], mesh[454]].map((pt) => [pt[0] * imageSize[0] / size2, pt[1] * imageSize[1] / size2, pt[2]]);
+  const yAxis = normalize2(subVectors(pts[1], pts[0]));
+  let xAxis = normalize2(subVectors(pts[3], pts[2]));
+  const zAxis = normalize2(crossVectors(xAxis, yAxis));
+  xAxis = crossVectors(yAxis, zAxis);
+  const matrix = [
+    xAxis[0],
+    xAxis[1],
+    xAxis[2],
+    yAxis[0],
+    yAxis[1],
+    yAxis[2],
+    zAxis[0],
+    zAxis[1],
+    zAxis[2]
+  ];
+  const angle = rotationMatrixToEulerAngle(matrix);
+  const gaze = mesh.length === 478 ? calculateGaze(face4) : { bearing: 0, strength: 0 };
+  return { angle, matrix, gaze };
+};
+
+// src/face/face.ts
+var detectFace = async (instance2, input) => {
+  var _a, _b, _c, _d, _e, _f, _g, _h, _i, _j, _k, _l, _m, _n, _o, _p, _q, _r, _s, _t, _u, _v, _w, _x, _y, _z, _A, _B, _C;
+  let timeStamp = now();
+  let ageRes;
+  let gearRes;
+  let genderRes;
+  let emotionRes;
+  let mobilefacenetRes;
+  let insightfaceRes;
+  let antispoofRes;
+  let livenessRes;
+  let descRes;
+  const faceRes = [];
+  instance2.state = "run:face";
+  const faces = await predict6(input, instance2.config);
+  instance2.performance.face = env.perfadd ? (instance2.performance.face || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);
+  if (!input.shape || input.shape.length !== 4)
+    return [];
+  if (!faces)
+    return [];
+  for (let i = 0; i < faces.length; i++) {
+    instance2.analyze("Get Face");
+    if (!faces[i].tensor || faces[i].tensor.isDisposedInternal) {
+      log("Face object is disposed:", faces[i].tensor);
+      continue;
+    }
+    if ((_a = instance2.config.face.detector) == null ? void 0 : _a.mask) {
+      const masked = await mask(faces[i]);
+      tf37.dispose(faces[i].tensor);
+      if (masked)
+        faces[i].tensor = masked;
+    }
+    const rotation = faces[i].mesh && faces[i].mesh.length > 200 ? calculateFaceAngle(faces[i], [input.shape[2], input.shape[1]]) : null;
+    instance2.analyze("Start Emotion:");
+    if (instance2.config.async) {
+      emotionRes = ((_b = instance2.config.face.emotion) == null ? void 0 : _b.enabled) ? predict5(faces[i].tensor || tf37.tensor([]), instance2.config, i, faces.length) : [];
+    } else {
+      instance2.state = "run:emotion";
+      timeStamp = now();
+      emotionRes = ((_c = instance2.config.face.emotion) == null ? void 0 : _c.enabled) ? await predict5(faces[i].tensor || tf37.tensor([]), instance2.config, i, faces.length) : [];
+      instance2.performance.emotion = env.perfadd ? (instance2.performance.emotion || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);
+    }
+    instance2.analyze("End Emotion:");
+    instance2.analyze("Start AntiSpoof:");
+    if (instance2.config.async) {
+      antispoofRes = ((_d = instance2.config.face.antispoof) == null ? void 0 : _d.enabled) ? predict(faces[i].tensor || tf37.tensor([]), instance2.config, i, faces.length) : 0;
+    } else {
+      instance2.state = "run:antispoof";
+      timeStamp = now();
+      antispoofRes = ((_e = instance2.config.face.antispoof) == null ? void 0 : _e.enabled) ? await predict(faces[i].tensor || tf37.tensor([]), instance2.config, i, faces.length) : 0;
+      instance2.performance.antispoof = env.perfadd ? (instance2.performance.antispoof || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);
+    }
+    instance2.analyze("End AntiSpoof:");
+    instance2.analyze("Start Liveness:");
+    if (instance2.config.async) {
+      livenessRes = ((_f = instance2.config.face.liveness) == null ? void 0 : _f.enabled) ? predict12(faces[i].tensor || tf37.tensor([]), instance2.config, i, faces.length) : 0;
+    } else {
+      instance2.state = "run:liveness";
+      timeStamp = now();
+      livenessRes = ((_g = instance2.config.face.liveness) == null ? void 0 : _g.enabled) ? await predict12(faces[i].tensor || tf37.tensor([]), instance2.config, i, faces.length) : 0;
+      instance2.performance.liveness = env.perfadd ? (instance2.performance.antispoof || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);
+    }
+    instance2.analyze("End Liveness:");
+    instance2.analyze("Start GEAR:");
+    if (instance2.config.async) {
+      gearRes = ((_h = instance2.config.face.gear) == null ? void 0 : _h.enabled) ? predict8(faces[i].tensor || tf37.tensor([]), instance2.config, i, faces.length) : null;
+    } else {
+      instance2.state = "run:gear";
+      timeStamp = now();
+      gearRes = ((_i = instance2.config.face.gear) == null ? void 0 : _i.enabled) ? await predict8(faces[i].tensor || tf37.tensor([]), instance2.config, i, faces.length) : null;
+      instance2.performance.gear = Math.trunc(now() - timeStamp);
+    }
+    instance2.analyze("End GEAR:");
+    instance2.analyze("Start SSRNet:");
+    if (instance2.config.async) {
+      ageRes = ((_j = instance2.config.face["ssrnet"]) == null ? void 0 : _j.enabled) ? predict20(faces[i].tensor || tf37.tensor([]), instance2.config, i, faces.length) : null;
+      genderRes = ((_k = instance2.config.face["ssrnet"]) == null ? void 0 : _k.enabled) ? predict21(faces[i].tensor || tf37.tensor([]), instance2.config, i, faces.length) : null;
+    } else {
+      instance2.state = "run:ssrnet";
+      timeStamp = now();
+      ageRes = ((_l = instance2.config.face["ssrnet"]) == null ? void 0 : _l.enabled) ? await predict20(faces[i].tensor || tf37.tensor([]), instance2.config, i, faces.length) : null;
+      genderRes = ((_m = instance2.config.face["ssrnet"]) == null ? void 0 : _m.enabled) ? await predict21(faces[i].tensor || tf37.tensor([]), instance2.config, i, faces.length) : null;
+      instance2.performance.ssrnet = Math.trunc(now() - timeStamp);
+    }
+    instance2.analyze("End SSRNet:");
+    instance2.analyze("Start MobileFaceNet:");
+    if (instance2.config.async) {
+      mobilefacenetRes = ((_n = instance2.config.face["mobilefacenet"]) == null ? void 0 : _n.enabled) ? predict14(faces[i].tensor || tf37.tensor([]), instance2.config, i, faces.length) : null;
+    } else {
+      instance2.state = "run:mobilefacenet";
+      timeStamp = now();
+      mobilefacenetRes = ((_o = instance2.config.face["mobilefacenet"]) == null ? void 0 : _o.enabled) ? await predict14(faces[i].tensor || tf37.tensor([]), instance2.config, i, faces.length) : null;
+      instance2.performance.mobilefacenet = Math.trunc(now() - timeStamp);
+    }
+    instance2.analyze("End MobileFaceNet:");
+    instance2.analyze("Start InsightFace:");
+    if (instance2.config.async) {
+      insightfaceRes = ((_p = instance2.config.face["insightface"]) == null ? void 0 : _p.enabled) ? predict11(faces[i].tensor || tf37.tensor([]), instance2.config, i, faces.length) : null;
+    } else {
+      instance2.state = "run:mobilefacenet";
+      timeStamp = now();
+      insightfaceRes = ((_q = instance2.config.face["insightface"]) == null ? void 0 : _q.enabled) ? await predict11(faces[i].tensor || tf37.tensor([]), instance2.config, i, faces.length) : null;
+      instance2.performance.mobilefacenet = Math.trunc(now() - timeStamp);
+    }
+    instance2.analyze("End InsightFace:");
+    instance2.analyze("Start Description:");
+    if (instance2.config.async) {
+      descRes = predict7(faces[i].tensor || tf37.tensor([]), instance2.config, i, faces.length);
+    } else {
+      instance2.state = "run:description";
+      timeStamp = now();
+      descRes = await predict7(faces[i].tensor || tf37.tensor([]), instance2.config, i, faces.length);
+      instance2.performance.description = env.perfadd ? (instance2.performance.description || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);
+    }
+    instance2.analyze("End Description:");
+    if (instance2.config.async) {
+      [ageRes, genderRes, emotionRes, mobilefacenetRes, insightfaceRes, descRes, gearRes, antispoofRes, livenessRes] = await Promise.all([ageRes, genderRes, emotionRes, mobilefacenetRes, insightfaceRes, descRes, gearRes, antispoofRes, livenessRes]);
+    }
+    instance2.analyze("Finish Face:");
+    if (((_r = instance2.config.face["ssrnet"]) == null ? void 0 : _r.enabled) && ageRes && genderRes) {
+      descRes = {
+        ...descRes,
+        age: ageRes.age,
+        gender: genderRes.gender,
+        genderScore: genderRes.genderScore
+      };
+    }
+    if (((_s = instance2.config.face.gear) == null ? void 0 : _s.enabled) && gearRes) {
+      descRes = {
+        ...descRes,
+        age: gearRes.age,
+        gender: gearRes.gender,
+        genderScore: gearRes.genderScore,
+        race: gearRes.race
+      };
+    }
+    if (((_t = instance2.config.face["mobilefacenet"]) == null ? void 0 : _t.enabled) && mobilefacenetRes) {
+      descRes.descriptor = mobilefacenetRes;
+    }
+    if (((_u = instance2.config.face["insightface"]) == null ? void 0 : _u.enabled) && insightfaceRes) {
+      descRes.descriptor = insightfaceRes;
+    }
+    if (!((_v = instance2.config.face.iris) == null ? void 0 : _v.enabled)) {
+    }
+    const irisSize = ((_y = (_x = (_w = faces[i]) == null ? void 0 : _w.annotations) == null ? void 0 : _x.leftEyeIris) == null ? void 0 : _y[0]) && ((_B = (_A = (_z = faces[i]) == null ? void 0 : _z.annotations) == null ? void 0 : _A.rightEyeIris) == null ? void 0 : _B[0]) && faces[i].annotations.leftEyeIris.length > 0 && faces[i].annotations.rightEyeIris.length > 0 && faces[i].annotations.leftEyeIris[0] !== null && faces[i].annotations.rightEyeIris[0] !== null ? Math.max(Math.abs(faces[i].annotations.leftEyeIris[3][0] - faces[i].annotations.leftEyeIris[1][0]), Math.abs(faces[i].annotations.rightEyeIris[4][1] - faces[i].annotations.rightEyeIris[2][1])) / input.shape[2] : 0;
+    const tensor6 = ((_C = instance2.config.face.detector) == null ? void 0 : _C.return) ? tf37.squeeze(faces[i].tensor) : null;
+    tf37.dispose(faces[i].tensor);
+    if (faces[i].tensor)
+      delete faces[i].tensor;
+    const res = {
+      ...faces[i],
+      id: i
+    };
+    if (descRes.age)
+      res.age = descRes.age;
+    if (descRes.gender)
+      res.gender = descRes.gender;
+    if (descRes.genderScore)
+      res.genderScore = descRes.genderScore;
+    if (descRes.descriptor)
+      res.embedding = descRes.descriptor;
+    if (descRes.race)
+      res.race = descRes.race;
+    if (emotionRes)
+      res.emotion = emotionRes;
+    if (antispoofRes)
+      res.real = antispoofRes;
+    if (livenessRes)
+      res.live = livenessRes;
+    if (irisSize && irisSize !== 0)
+      res.iris = Math.trunc(500 / irisSize / 11.7) / 100;
+    if (rotation)
+      res.rotation = rotation;
+    if (tensor6)
+      res.tensor = tensor6;
+    faceRes.push(res);
+    instance2.analyze("End Face");
+  }
+  instance2.analyze("End FaceMesh:");
+  if (instance2.config.async) {
+    if (instance2.performance.face)
+      delete instance2.performance.face;
+    if (instance2.performance.age)
+      delete instance2.performance.age;
+    if (instance2.performance.gender)
+      delete instance2.performance.gender;
+    if (instance2.performance.emotion)
+      delete instance2.performance.emotion;
+  }
+  return faceRes;
+};
+
+// src/gesture/gesture.ts
+var body2 = (res) => {
+  if (!res)
+    return [];
+  const gestures = [];
+  for (let i = 0; i < res.length; i++) {
+    const leftWrist = res[i].keypoints.find((a) => a.part === "leftWrist");
+    const rightWrist = res[i].keypoints.find((a) => a.part === "rightWrist");
+    const nose = res[i].keypoints.find((a) => a.part === "nose");
+    if (nose && leftWrist && rightWrist && leftWrist.position[1] < nose.position[1] && rightWrist.position[1] < nose.position[1])
+      gestures.push({ body: i, gesture: "i give up" });
+    else if (nose && leftWrist && leftWrist.position[1] < nose.position[1])
+      gestures.push({ body: i, gesture: "raise left hand" });
+    else if (nose && rightWrist && rightWrist.position[1] < nose.position[1])
+      gestures.push({ body: i, gesture: "raise right hand" });
+    const leftShoulder = res[i].keypoints.find((a) => a.part === "leftShoulder");
+    const rightShoulder = res[i].keypoints.find((a) => a.part === "rightShoulder");
+    if (leftShoulder && rightShoulder && Math.abs(leftShoulder.positionRaw[1] - rightShoulder.positionRaw[1]) > 0.1) {
+      gestures.push({ body: i, gesture: `leaning ${leftShoulder.position[1] > rightShoulder.position[1] ? "left" : "right"}` });
+    }
+  }
+  return gestures;
+};
+var face2 = (res) => {
+  if (!res)
+    return [];
+  const gestures = [];
+  for (let i = 0; i < res.length; i++) {
+    if (res[i].mesh && res[i].mesh.length > 450) {
+      const zDiff = (res[i].mesh[33][2] || 0) - (res[i].mesh[263][2] || 0);
+      const xDiff = res[i].mesh[33][0] - res[i].mesh[263][0];
+      if (Math.abs(zDiff / xDiff) <= 0.15)
+        gestures.push({ face: i, gesture: "facing center" });
+      else
+        gestures.push({ face: i, gesture: `facing ${zDiff < 0 ? "left" : "right"}` });
+      const openLeft = Math.abs(res[i].mesh[374][1] - res[i].mesh[386][1]) / Math.abs(res[i].mesh[443][1] - res[i].mesh[450][1]);
+      if (openLeft < 0.2)
+        gestures.push({ face: i, gesture: "blink left eye" });
+      const openRight = Math.abs(res[i].mesh[145][1] - res[i].mesh[159][1]) / Math.abs(res[i].mesh[223][1] - res[i].mesh[230][1]);
+      if (openRight < 0.2)
+        gestures.push({ face: i, gesture: "blink right eye" });
+      const mouthOpen = Math.min(100, 500 * Math.abs(res[i].mesh[13][1] - res[i].mesh[14][1]) / Math.abs(res[i].mesh[10][1] - res[i].mesh[152][1]));
+      if (mouthOpen > 10)
+        gestures.push({ face: i, gesture: `mouth ${Math.trunc(mouthOpen)}% open` });
+      const chinDepth = res[i].mesh[152][2] || 0;
+      if (Math.abs(chinDepth) > 10)
+        gestures.push({ face: i, gesture: `head ${chinDepth < 0 ? "up" : "down"}` });
+    }
+  }
+  return gestures;
+};
+var iris2 = (res) => {
+  var _a, _b, _c, _d;
+  if (!res)
+    return [];
+  const gestures = [];
+  for (let i = 0; i < res.length; i++) {
+    if (!((_b = (_a = res[i].annotations) == null ? void 0 : _a.leftEyeIris) == null ? void 0 : _b[0]) || !((_d = (_c = res[i].annotations) == null ? void 0 : _c.rightEyeIris) == null ? void 0 : _d[0]))
+      continue;
+    const sizeXLeft = res[i].annotations.leftEyeIris[3][0] - res[i].annotations.leftEyeIris[1][0];
+    const sizeYLeft = res[i].annotations.leftEyeIris[4][1] - res[i].annotations.leftEyeIris[2][1];
+    const areaLeft = Math.abs(sizeXLeft * sizeYLeft);
+    const sizeXRight = res[i].annotations.rightEyeIris[3][0] - res[i].annotations.rightEyeIris[1][0];
+    const sizeYRight = res[i].annotations.rightEyeIris[4][1] - res[i].annotations.rightEyeIris[2][1];
+    const areaRight = Math.abs(sizeXRight * sizeYRight);
+    let center = false;
+    const difference = Math.abs(areaLeft - areaRight) / Math.max(areaLeft, areaRight);
+    if (difference < 0.25) {
+      center = true;
+      gestures.push({ iris: i, gesture: "facing center" });
+    }
+    const leftIrisCenterX = Math.abs(res[i].mesh[263][0] - res[i].annotations.leftEyeIris[0][0]) / res[i].box[2];
+    const rightIrisCenterX = Math.abs(res[i].mesh[33][0] - res[i].annotations.rightEyeIris[0][0]) / res[i].box[2];
+    if (leftIrisCenterX > 0.06 || rightIrisCenterX > 0.06)
+      center = false;
+    if (leftIrisCenterX > rightIrisCenterX) {
+      if (leftIrisCenterX > 0.05)
+        gestures.push({ iris: i, gesture: "looking right" });
+    } else {
+      if (rightIrisCenterX > 0.05)
+        gestures.push({ iris: i, gesture: "looking left" });
+    }
+    const rightIrisCenterY = Math.abs(res[i].mesh[145][1] - res[i].annotations.rightEyeIris[0][1]) / res[i].box[3];
+    const leftIrisCenterY = Math.abs(res[i].mesh[374][1] - res[i].annotations.leftEyeIris[0][1]) / res[i].box[3];
+    if (leftIrisCenterY < 0.01 || rightIrisCenterY < 0.01 || leftIrisCenterY > 0.022 || rightIrisCenterY > 0.022)
+      center = false;
+    if (leftIrisCenterY < 0.01 || rightIrisCenterY < 0.01)
+      gestures.push({ iris: i, gesture: "looking down" });
+    if (leftIrisCenterY > 0.022 || rightIrisCenterY > 0.022)
+      gestures.push({ iris: i, gesture: "looking up" });
+    if (center)
+      gestures.push({ iris: i, gesture: "looking center" });
+  }
+  return gestures;
+};
+var hand2 = (res) => {
+  if (!res)
+    return [];
+  const gestures = [];
+  for (let i = 0; i < res.length; i++) {
+    const fingers = [];
+    if (res[i].annotations) {
+      for (const [finger, pos] of Object.entries(res[i].annotations)) {
+        if (finger !== "palmBase" && Array.isArray(pos) && pos[0])
+          fingers.push({ name: finger.toLowerCase(), position: pos[0] });
+      }
+    }
+    if (fingers && fingers.length > 0) {
+      const closest = fingers.reduce((best, a) => (best.position[2] || 0) < (a.position[2] || 0) ? best : a);
+      gestures.push({ hand: i, gesture: `${closest.name} forward` });
+      const highest = fingers.reduce((best, a) => best.position[1] < a.position[1] ? best : a);
+      gestures.push({ hand: i, gesture: `${highest.name} up` });
+    }
+    if (res[i].keypoints) {
+      const poses = match(res[i].keypoints);
+      for (const pose of poses)
+        gestures.push({ hand: i, gesture: pose.name });
+    }
+  }
+  return gestures;
+};
+
+// src/util/interpolate.ts
+var bufferedResult = { face: [], body: [], hand: [], gesture: [], object: [], persons: [], performance: {}, timestamp: 0, error: null };
+var interpolateTime = 0;
+function calc2(newResult, config3) {
+  var _a, _b, _c, _d, _e, _f, _g, _h, _i, _j, _k, _l, _m, _n, _o, _p, _q, _r, _s, _t, _u, _v, _w;
+  const t0 = now();
+  if (!newResult)
+    return { face: [], body: [], hand: [], gesture: [], object: [], persons: [], performance: {}, timestamp: 0, error: null };
+  const elapsed = Date.now() - newResult.timestamp;
+  const bufferedFactor = elapsed < 1e3 ? 8 - Math.log(elapsed + 1) : 1;
+  if (newResult.canvas)
+    bufferedResult.canvas = newResult.canvas;
+  if (newResult.error)
+    bufferedResult.error = newResult.error;
+  if (!bufferedResult.body || newResult.body.length !== bufferedResult.body.length) {
+    bufferedResult.body = JSON.parse(JSON.stringify(newResult.body));
+  } else {
+    for (let i = 0; i < newResult.body.length; i++) {
+      const box = newResult.body[i].box.map((newBoxCoord, j) => ((bufferedFactor - 1) * bufferedResult.body[i].box[j] + newBoxCoord) / bufferedFactor);
+      const boxRaw = newResult.body[i].boxRaw.map((newBoxCoord, j) => ((bufferedFactor - 1) * bufferedResult.body[i].boxRaw[j] + newBoxCoord) / bufferedFactor);
+      const keypoints = newResult.body[i].keypoints.map((newKpt, j) => {
+        var _a2, _b2, _c2, _d2, _e2, _f2, _g2, _h2, _i2;
+        return {
+          score: newKpt.score,
+          part: newKpt.part,
+          position: [
+            bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].position[0] || 0) + (newKpt.position[0] || 0)) / bufferedFactor : newKpt.position[0],
+            bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].position[1] || 0) + (newKpt.position[1] || 0)) / bufferedFactor : newKpt.position[1],
+            bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].position[2] || 0) + (newKpt.position[2] || 0)) / bufferedFactor : newKpt.position[2]
+          ],
+          positionRaw: [
+            bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].positionRaw[0] || 0) + (newKpt.positionRaw[0] || 0)) / bufferedFactor : newKpt.positionRaw[0],
+            bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].positionRaw[1] || 0) + (newKpt.positionRaw[1] || 0)) / bufferedFactor : newKpt.positionRaw[1],
+            bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].positionRaw[2] || 0) + (newKpt.positionRaw[2] || 0)) / bufferedFactor : newKpt.positionRaw[2]
+          ],
+          distance: [
+            bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (((_a2 = bufferedResult.body[i].keypoints[j].distance) == null ? void 0 : _a2[0]) || 0) + (((_b2 = newKpt.distance) == null ? void 0 : _b2[0]) || 0)) / bufferedFactor : (_c2 = newKpt.distance) == null ? void 0 : _c2[0],
+            bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (((_d2 = bufferedResult.body[i].keypoints[j].distance) == null ? void 0 : _d2[1]) || 0) + (((_e2 = newKpt.distance) == null ? void 0 : _e2[1]) || 0)) / bufferedFactor : (_f2 = newKpt.distance) == null ? void 0 : _f2[1],
+            bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (((_g2 = bufferedResult.body[i].keypoints[j].distance) == null ? void 0 : _g2[2]) || 0) + (((_h2 = newKpt.distance) == null ? void 0 : _h2[2]) || 0)) / bufferedFactor : (_i2 = newKpt.distance) == null ? void 0 : _i2[2]
+          ]
+        };
+      });
+      const annotations2 = {};
+      let coords = { connected: {} };
+      if ((_a = config3.body.modelPath) == null ? void 0 : _a.includes("efficientpose"))
+        coords = efficientposecoords_exports;
+      else if ((_b = config3.body.modelPath) == null ? void 0 : _b.includes("blazepose"))
+        coords = blazeposecoords_exports;
+      else if ((_c = config3.body.modelPath) == null ? void 0 : _c.includes("movenet"))
+        coords = movenetcoords_exports;
+      for (const [name, indexes] of Object.entries(coords.connected)) {
+        const pt = [];
+        for (let j = 0; j < indexes.length - 1; j++) {
+          const pt0 = keypoints.find((kp) => kp.part === indexes[j]);
+          const pt1 = keypoints.find((kp) => kp.part === indexes[j + 1]);
+          if (pt0 && pt1)
+            pt.push([pt0.position, pt1.position]);
+        }
+        annotations2[name] = pt;
+      }
+      bufferedResult.body[i] = { ...newResult.body[i], box, boxRaw, keypoints, annotations: annotations2 };
+    }
+  }
+  if (!bufferedResult.hand || newResult.hand.length !== bufferedResult.hand.length) {
+    bufferedResult.hand = JSON.parse(JSON.stringify(newResult.hand));
+  } else {
+    for (let i = 0; i < newResult.hand.length; i++) {
+      const box = newResult.hand[i].box.map((b, j) => ((bufferedFactor - 1) * bufferedResult.hand[i].box[j] + b) / bufferedFactor);
+      const boxRaw = newResult.hand[i].boxRaw.map((b, j) => ((bufferedFactor - 1) * bufferedResult.hand[i].boxRaw[j] + b) / bufferedFactor);
+      if (bufferedResult.hand[i].keypoints.length !== newResult.hand[i].keypoints.length)
+        bufferedResult.hand[i].keypoints = newResult.hand[i].keypoints;
+      const keypoints = newResult.hand[i].keypoints && newResult.hand[i].keypoints.length > 0 ? newResult.hand[i].keypoints.map((landmark, j) => landmark.map((coord, k) => ((bufferedFactor - 1) * (bufferedResult.hand[i].keypoints[j][k] || 1) + (coord || 0)) / bufferedFactor)) : [];
+      let annotations2 = {};
+      if (Object.keys(bufferedResult.hand[i].annotations).length !== Object.keys(newResult.hand[i].annotations).length) {
+        bufferedResult.hand[i].annotations = newResult.hand[i].annotations;
+        annotations2 = bufferedResult.hand[i].annotations;
+      } else if (newResult.hand[i].annotations) {
+        for (const key of Object.keys(newResult.hand[i].annotations)) {
+          annotations2[key] = ((_f = (_e = (_d = newResult.hand[i]) == null ? void 0 : _d.annotations) == null ? void 0 : _e[key]) == null ? void 0 : _f[0]) ? newResult.hand[i].annotations[key].map((val, j) => val.map((coord, k) => ((bufferedFactor - 1) * bufferedResult.hand[i].annotations[key][j][k] + coord) / bufferedFactor)) : null;
+        }
+      }
+      bufferedResult.hand[i] = { ...newResult.hand[i], box, boxRaw, keypoints, annotations: annotations2 };
+    }
+  }
+  if (!bufferedResult.face || newResult.face.length !== bufferedResult.face.length) {
+    bufferedResult.face = JSON.parse(JSON.stringify(newResult.face));
+  } else {
+    for (let i = 0; i < newResult.face.length; i++) {
+      const box = newResult.face[i].box.map((b, j) => ((bufferedFactor - 1) * bufferedResult.face[i].box[j] + b) / bufferedFactor);
+      const boxRaw = newResult.face[i].boxRaw.map((b, j) => ((bufferedFactor - 1) * bufferedResult.face[i].boxRaw[j] + b) / bufferedFactor);
+      if (newResult.face[i].rotation) {
+        const rotation = { matrix: [0, 0, 0, 0, 0, 0, 0, 0, 0], angle: { roll: 0, yaw: 0, pitch: 0 }, gaze: { bearing: 0, strength: 0 } };
+        rotation.matrix = (_g = newResult.face[i].rotation) == null ? void 0 : _g.matrix;
+        rotation.angle = {
+          roll: ((bufferedFactor - 1) * (((_i = (_h = bufferedResult.face[i].rotation) == null ? void 0 : _h.angle) == null ? void 0 : _i.roll) || 0) + (((_k = (_j = newResult.face[i].rotation) == null ? void 0 : _j.angle) == null ? void 0 : _k.roll) || 0)) / bufferedFactor,
+          yaw: ((bufferedFactor - 1) * (((_m = (_l = bufferedResult.face[i].rotation) == null ? void 0 : _l.angle) == null ? void 0 : _m.yaw) || 0) + (((_o = (_n = newResult.face[i].rotation) == null ? void 0 : _n.angle) == null ? void 0 : _o.yaw) || 0)) / bufferedFactor,
+          pitch: ((bufferedFactor - 1) * (((_q = (_p = bufferedResult.face[i].rotation) == null ? void 0 : _p.angle) == null ? void 0 : _q.pitch) || 0) + (((_s = (_r = newResult.face[i].rotation) == null ? void 0 : _r.angle) == null ? void 0 : _s.pitch) || 0)) / bufferedFactor
+        };
+        rotation.gaze = {
+          bearing: ((bufferedFactor - 1) * (((_t = bufferedResult.face[i].rotation) == null ? void 0 : _t.gaze.bearing) || 0) + (((_u = newResult.face[i].rotation) == null ? void 0 : _u.gaze.bearing) || 0)) / bufferedFactor,
+          strength: ((bufferedFactor - 1) * (((_v = bufferedResult.face[i].rotation) == null ? void 0 : _v.gaze.strength) || 0) + (((_w = newResult.face[i].rotation) == null ? void 0 : _w.gaze.strength) || 0)) / bufferedFactor
+        };
+        bufferedResult.face[i] = { ...newResult.face[i], rotation, box, boxRaw };
+      } else {
+        bufferedResult.face[i] = { ...newResult.face[i], box, boxRaw };
+      }
+    }
+  }
+  if (!bufferedResult.object || newResult.object.length !== bufferedResult.object.length) {
+    bufferedResult.object = JSON.parse(JSON.stringify(newResult.object));
+  } else {
+    for (let i = 0; i < newResult.object.length; i++) {
+      const box = newResult.object[i].box.map((b, j) => ((bufferedFactor - 1) * bufferedResult.object[i].box[j] + b) / bufferedFactor);
+      const boxRaw = newResult.object[i].boxRaw.map((b, j) => ((bufferedFactor - 1) * bufferedResult.object[i].boxRaw[j] + b) / bufferedFactor);
+      bufferedResult.object[i] = { ...newResult.object[i], box, boxRaw };
+    }
+  }
+  if (newResult.persons) {
+    const newPersons = newResult.persons;
+    if (!bufferedResult.persons || newPersons.length !== bufferedResult.persons.length) {
+      bufferedResult.persons = JSON.parse(JSON.stringify(newPersons));
+    } else {
+      for (let i = 0; i < newPersons.length; i++) {
+        bufferedResult.persons[i].box = newPersons[i].box.map((box, j) => ((bufferedFactor - 1) * bufferedResult.persons[i].box[j] + box) / bufferedFactor);
+      }
+    }
+  }
+  if (newResult.gesture)
+    bufferedResult.gesture = newResult.gesture;
+  const t1 = now();
+  interpolateTime = env.perfadd ? interpolateTime + Math.round(t1 - t0) : Math.round(t1 - t0);
+  if (newResult.performance)
+    bufferedResult.performance = { ...newResult.performance, interpolate: interpolateTime };
+  return bufferedResult;
+}
+
+// src/face/match.ts
+var match_exports = {};
+__export(match_exports, {
+  distance: () => distance,
+  match: () => match2,
+  similarity: () => similarity
+});
+function distance(descriptor1, descriptor2, options4 = { order: 2, multiplier: 25 }) {
+  if (!descriptor1 || !descriptor1)
+    return Number.MAX_SAFE_INTEGER;
+  let sum3 = 0;
+  for (let i = 0; i < descriptor1.length; i++) {
+    const diff = !options4.order || options4.order === 2 ? descriptor1[i] - descriptor2[i] : Math.abs(descriptor1[i] - descriptor2[i]);
+    sum3 += !options4.order || options4.order === 2 ? diff * diff : diff ** options4.order;
+  }
+  return (options4.multiplier || 20) * sum3;
+}
+var normalizeDistance = (dist, order, min2, max4) => {
+  if (dist === 0)
+    return 1;
+  const root = order === 2 ? Math.sqrt(dist) : dist ** (1 / order);
+  const norm = (1 - root / 100 - min2) / (max4 - min2);
+  const clamp2 = Math.max(Math.min(norm, 1), 0);
+  return clamp2;
+};
+function similarity(descriptor1, descriptor2, options4 = { order: 2, multiplier: 25, min: 0.2, max: 0.8 }) {
+  const dist = distance(descriptor1, descriptor2, options4);
+  return normalizeDistance(dist, options4.order || 2, options4.min || 0, options4.max || 1);
+}
+function match2(descriptor, descriptors, options4 = { order: 2, multiplier: 25, threshold: 0, min: 0.2, max: 0.8 }) {
+  if (!Array.isArray(descriptor) || !Array.isArray(descriptors) || descriptor.length < 64 || descriptors.length === 0) {
+    return { index: -1, distance: Number.POSITIVE_INFINITY, similarity: 0 };
+  }
+  let lowestDistance = Number.MAX_SAFE_INTEGER;
+  let index2 = -1;
+  for (let i = 0; i < descriptors.length; i++) {
+    const res = descriptors[i].length === descriptor.length ? distance(descriptor, descriptors[i], options4) : Number.MAX_SAFE_INTEGER;
+    if (res < lowestDistance) {
+      lowestDistance = res;
+      index2 = i;
+    }
+    if (lowestDistance < (options4.threshold || 0))
+      break;
+  }
+  const normalizedSimilarity = normalizeDistance(lowestDistance, options4.order || 2, options4.min || 0, options4.max || 1);
+  return { index: index2, distance: lowestDistance, similarity: normalizedSimilarity };
+}
+
+// src/util/persons.ts
+function join2(faces, bodies, hands, gestures, shape) {
+  var _a, _b, _c, _d, _e, _f;
+  let id = 0;
+  const persons = [];
+  for (const face4 of faces) {
+    const person2 = { id: id++, face: face4, body: null, hands: { left: null, right: null }, gestures: [], box: [0, 0, 0, 0] };
+    for (const body4 of bodies) {
+      if (face4.box[0] > body4.box[0] && face4.box[0] < body4.box[0] + body4.box[2] && face4.box[1] + face4.box[3] > body4.box[1] && face4.box[1] + face4.box[3] < body4.box[1] + body4.box[3]) {
+        person2.body = body4;
+      }
+    }
+    if (person2.body) {
+      for (const hand3 of hands) {
+        if (hand3.box[0] + hand3.box[2] > person2.body.box[0] && hand3.box[0] + hand3.box[2] < person2.body.box[0] + person2.body.box[2] && hand3.box[1] + hand3.box[3] > person2.body.box[1] && hand3.box[1] + hand3.box[3] < person2.body.box[1] + person2.body.box[3]) {
+          if (person2.hands)
+            person2.hands.left = hand3;
+        }
+        if (hand3.box[0] < person2.body.box[0] + person2.body.box[2] && hand3.box[0] > person2.body.box[0] && hand3.box[1] + hand3.box[3] > person2.body.box[1] && hand3.box[1] + hand3.box[3] < person2.body.box[1] + person2.body.box[3]) {
+          if (person2.hands)
+            person2.hands.right = hand3;
+        }
+      }
+    }
+    for (const gesture2 of gestures) {
+      if (gesture2["face"] !== void 0 && gesture2["face"] === face4.id)
+        person2.gestures.push(gesture2);
+      else if (gesture2["iris"] !== void 0 && gesture2["iris"] === face4.id)
+        person2.gestures.push(gesture2);
+      else if (gesture2["body"] !== void 0 && gesture2["body"] === ((_a = person2.body) == null ? void 0 : _a.id))
+        person2.gestures.push(gesture2);
+      else if (gesture2["hand"] !== void 0 && gesture2["hand"] === ((_b = person2.hands.left) == null ? void 0 : _b.id))
+        person2.gestures.push(gesture2);
+      else if (gesture2["hand"] !== void 0 && gesture2["hand"] === ((_c = person2.hands.right) == null ? void 0 : _c.id))
+        person2.gestures.push(gesture2);
+    }
+    const x = [];
+    const y = [];
+    const extractXY = (box) => {
+      if (box && box.length === 4) {
+        x.push(box[0], box[0] + box[2]);
+        y.push(box[1], box[1] + box[3]);
+      }
+    };
+    extractXY(person2.face.box);
+    extractXY((_d = person2.body) == null ? void 0 : _d.box);
+    extractXY((_e = person2.hands.left) == null ? void 0 : _e.box);
+    extractXY((_f = person2.hands.right) == null ? void 0 : _f.box);
+    const minX = Math.min(...x);
+    const minY = Math.min(...y);
+    person2.box = [minX, minY, Math.max(...x) - minX, Math.max(...y) - minY];
+    if ((shape == null ? void 0 : shape[1]) && (shape == null ? void 0 : shape[2]))
+      person2.boxRaw = [person2.box[0] / shape[2], person2.box[1] / shape[1], person2.box[2] / shape[2], person2.box[3] / shape[1]];
+    persons.push(person2);
+  }
+  return persons;
+}
+
+// src/sample.ts
+var face3 = `
 /9j/4AAQSkZJRgABAQEAYABgAAD/4QBoRXhpZgAATU0AKgAAAAgABAEaAAUAAAABAAAAPgEbAAUA
 AAABAAAARgEoAAMAAAABAAIAAAExAAIAAAARAAAATgAAAAAAAABgAAAAAQAAAGAAAAABcGFpbnQu
 bmV0IDQuMi4xMwAA/9sAQwAGBAUGBQQGBgUGBwcGCAoQCgoJCQoUDg8MEBcUGBgXFBYWGh0lHxob
@@ -259,7 +13986,8 @@ PQ4GJ+ashuK0MhWaoWcA0AaOmASMK7jRNPWYBmHyiuepO2x10qfcv6vYxCzYqoGK4HVYVTJrmb5l
 c6oaM5TUJ8EgGsG4kLNUHT0M64OaqMMikSRsuKbnFMRLG3zVehOaGNE445NNlnVFpDMu6uie9Vo1
 8z5mOAOST2pDK91cNN+5tsrH3PrW54a06KxT7fdrlh/q1Pc+tJ6IUdZGvHPLezMcnBOWbsPap5r3
 ylFtbdT1xUWNWzU0/Zbwlgfmx8zGsHWtRHmMqE59aAMyNifvHPc1f0gtPdqkY5JosJHeNci2tktY
-euPnNY+oXWZEVJNrZ9aun8SIq/CzodHuriIokhDIR1ronbKZr0o6o8ipoz//2Q==`,W5=`
+euPnNY+oXWZEVJNrZ9aun8SIq/CzodHuriIokhDIR1ronbKZr0o6o8ipoz//2Q==`;
+var body3 = `
 /9j/4AAQSkZJRgABAQAAAQABAAD/2wBDAAsICAoIBwsKCQoNDAsNERwSEQ8PESIZGhQcKSQrKigk
 JyctMkA3LTA9MCcnOEw5PUNFSElIKzZPVU5GVEBHSEX/2wBDAQwNDREPESESEiFFLicuRUVFRUVF
 RUVFRUVFRUVFRUVFRUVFRUVFRUVFRUVFRUVFRUVFRUVFRUVFRUVFRUVFRUX/wAARCASwBLADASIA
@@ -827,4 +14555,580 @@ AAAAAAJAAAAAAAAAAAAAABAJEAAAAAAAAAAAAAAAIEoBKAAAAAAAAAAAAAAABAlAAAAAAAIAAAAA
 BAkBAkBAkBAlACEgMZjdjbFW8bWrEx8YWANb6Fp+bfwab+vLDKMFK9qxH5L0bAr8OPRPKz2AY7J2
 SbAjYZAI2E7AIEgIEgIEgMdkSy2NgY7MdlmyNoBXsxmFuyNgVTVjNV3KjlBRNTlXTVHKCrlIqt5T
 lBhEMohlFerLlBjEMohMVTEARDKCITsAk2AEgAAAkAAAAAAAAAAAAAAAAAAAAAAAASAAAAAAAAD/
-2Q==`;var s0=D(H());async function Is(e){let t=(r,A="application/octet-stream")=>fetch(`data:${A};base64,${r}`).then(s=>s.blob()),n,o;switch(e.config.warmup){case"face":n=await t(L5);break;case"body":case"full":n=await t(W5);break;default:n=null}if(n){let r=await createImageBitmap(n);o=await e.detect(r,e.config),r.close()}return o}async function Os(e){return new Promise(t=>{let n;switch(e.config.warmup){case"face":n="data:image/jpeg;base64,"+L5;break;case"full":case"body":n="data:image/jpeg;base64,"+W5;break;default:n=""}let o;if(typeof Image!="undefined")o=new Image;else if(k.Image)o=new k.Image;else return;o.onload=async()=>{let r=k0(o.naturalWidth,o.naturalHeight);if(!r)u("Warmup: Canvas not found"),t(void 0);else{let A=r.getContext("2d");A&&A.drawImage(o,0,0);let s=await e.image(r),a=s.tensor?await e.detect(s.tensor,e.config):void 0;t(a)}},n?o.src=n:t(void 0)})}async function Cs(e){let t=r=>Buffer.from(r,"base64"),n;e.config.warmup==="face"?n=t(L5):n=t(W5);let o;if("node"in s0&&s0.getBackend()==="tensorflow"){let r=s0.node.decodeJpeg(n),A=s0.expandDims(r,0);e.tf.dispose(r),o=await e.detect(A,e.config),e.tf.dispose(A)}else e.config.debug&&u("Warmup tfjs-node not loaded");return o}async function Ls(e){let t;return typeof createImageBitmap=="function"?t=await Is(e):typeof Image!="undefined"||k.Canvas!==void 0?t=await Os(e):t=await Cs(e),t}async function Ws(e){var a,l,c,x;if(!s0.env().flagRegistry.ENGINE_COMPILE_ONLY)return;let t=s0.getBackend(),n=s0.backend();if(t!=="webgl"&&t!=="humangl"||!(n!=null&&n.checkCompileCompletion))return;s0.env().set("ENGINE_COMPILE_ONLY",!0);let o=s0.engine().state.numTensors,r=[];for(let[i,y]of Object.entries(e.models).filter(([d,f])=>d!==null&&f!==null)){let d=(l=(a=y.inputs)==null?void 0:a[0])!=null&&l.shape?[...y.inputs[0].shape]:[1,64,64,3],f=(x=(c=y.inputs)==null?void 0:c[0])!=null&&x.dtype?y.inputs[0].dtype:"float32";for(let g=0;g<d.length;g++)d[g]===-1&&(d[g]=g===0?1:64);let p=s0.zeros(d,f);try{let g=y.execute(p);r.push(i),Array.isArray(g)?g.forEach(M=>s0.dispose(M)):s0.dispose(g)}catch(g){u("compile fail model:",i)}s0.dispose(p)}let A=await n.checkCompileCompletionAsync();n.getUniformLocations(),e.config.debug&&u("compile pass:",{models:r,kernels:A.length}),s0.env().set("ENGINE_COMPILE_ONLY",!1);let s=s0.engine().state.numTensors;s-o>0&&u("tensor leak:",s-o)}async function Mo(e,t){await W2(e,!1);let n=v();return e.state="warmup",t&&(e.config=A0(e.config,t)),!e.config.warmup||e.config.warmup.length===0||e.config.warmup==="none"?{face:[],body:[],hand:[],gesture:[],object:[],performance:e.performance,timestamp:v(),persons:[],error:null}:new Promise(async o=>{await f2.load(e),await Ws(e);let r=await Ls(e),A=v();e.config.debug&&u("warmup",e.config.warmup,Math.round(A-n),"ms"),e.emit("warmup"),o(r)})}var T2,D2,Z2,F5,Be,g1=class{constructor(t){R(this,"version");R(this,"config");R(this,"result");R(this,"state");R(this,"process");R(this,"tf");R(this,"env");R(this,"draw");R(this,"models");R(this,"events");R(this,"faceTriangulation");R(this,"faceUVMap");R(this,"performance");t2(this,T2,void 0);t2(this,D2,void 0);t2(this,Z2,void 0);R(this,"gl");R(this,"analyze",(...t)=>{if(!ie(this,D2))return;let n=this.tf.engine().state.numTensors,o=ie(this,T2);E2(this,T2,n);let r=n-o;r!==0&&u(...t,r)});t2(this,F5,t=>{if(!ie(this,Z2))return null;if(!t)return"input is not defined";if(this.env.node&&!(t instanceof ae.Tensor))return"input must be a tensor";try{this.tf.getBackend()}catch(n){return"backend not loaded"}return null});R(this,"similarity",u1);R(this,"distance",V2);R(this,"match",h1);R(this,"webcam",new K2);R(this,"emit",t=>{var n;(n=this.events)!=null&&n.dispatchEvent&&this.events.dispatchEvent(new Event(t))});t2(this,Be,{});this.env=k;let n=(ae.version.tfjs||ae.version_core).replace(/-(.*)/,"");Pe.wasmPath=`https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-backend-wasm@${n}/dist/`,Pe.modelBasePath=k.browser?"../models/":"file://models/",Pe.backend=k.browser?"webgl":"tensorflow",this.version=s1,Object.defineProperty(this,"version",{value:s1}),this.config=JSON.parse(JSON.stringify(Pe)),Object.seal(this.config),this.config.cacheModels=typeof indexedDB!="undefined",t&&(this.config=A0(this.config,t)),io(this.config),this.tf=ae,this.state="idle",E2(this,T2,0),E2(this,D2,!1),E2(this,Z2,!1),this.performance={},this.events=typeof EventTarget!="undefined"?new EventTarget:void 0,this.models=new H2,this.draw={options:R0,canvas:(r,A)=>d1(r,A),face:(r,A,s)=>h2(r,A,s),body:(r,A,s)=>b2(r,A,s),hand:(r,A,s)=>g2(r,A,s),gesture:(r,A,s)=>v2(r,A,s),object:(r,A,s)=>M2(r,A,s),person:(r,A,s)=>c1(r,A,s),all:(r,A,s)=>x1(r,A,s)},this.result={face:[],body:[],hand:[],gesture:[],object:[],performance:{},timestamp:0,persons:[],error:null},this.process={tensor:null,canvas:null},this.faceTriangulation=$3,this.faceUVMap=en,this.gl=_,p2(this,null,""),this.emit("create"),(this.config.debug||this.env.browser)&&u(`version: ${this.version}`),this.config.debug&&u(`tfjs version: ${this.tf.version["tfjs-core"]}`);let o=JSON.parse(JSON.stringify(this.env));delete o.kernels,delete o.initial,delete o.perfadd,this.config.debug&&u("environment:",o)}reset(){let t=this.config.backend;this.config=JSON.parse(JSON.stringify(Pe)),this.config.backend=t,Z5(),k.initial=!0}validate(t){let n=G5(Pe,t||this.config);return n.length===0&&(this.config=A0(this.config,t)),n}check(){return C5(this)}now(){return v()}image(t,n=!0){return o2(t,this.config,n)}async segmentation(t,n){return so(t,n,this.config)}enhance(t){return zt(t)}compare(t,n){return L1(this.config,t,n)}async init(){await W2(this,!0),await this.tf.ready(),Z5()}async load(t){this.state="load";let n=v(),o=Object.values(this.models).filter(s=>s).length;t&&(this.config=A0(this.config,t)),this.env.initial&&(await W2(this,!1)||u("error: backend check failed"),await ae.ready(),this.env.browser&&(this.config.debug&&u("configuration:",this.config),this.config.debug&&u("tf flags:",this.tf.ENV.flags))),await A1(this),this.env.initial&&this.config.debug&&u("tf engine state:",this.tf.engine().state.numBytes,"bytes",this.tf.engine().state.numTensors,"tensors"),this.env.initial=!1,Object.values(this.models).filter(s=>s).length!==o&&(C5(this),this.emit("load"));let A=Math.trunc(v()-n);A>(this.performance.loadModels||0)&&(this.performance.loadModels=this.env.perfadd?(this.performance.loadModels||0)+A:A)}next(t=this.result){return ho(t,this.config)}getModelStats(){return r1(this)}async warmup(t){let n=v(),o=await Mo(this,t),r=v();return this.performance.warmup=Math.trunc(r-n),o}async profile(t,n){let o=await this.tf.profile(()=>this.detect(t,n)),r={},A=0;for(let a of o.kernels)r[a.name]?r[a.name]+=a.kernelTimeMs:r[a.name]=a.kernelTimeMs,A+=a.kernelTimeMs;let s=[];Object.entries(r).forEach(a=>s.push({kernel:a[0],time:a[1],perc:0}));for(let a of s)a.perc=Math.round(1e3*a.time/A)/1e3,a.time=Math.round(1e3*a.time)/1e3;return s.sort((a,l)=>l.time-a.time),s.length=20,s}async detect(t,n){return this.state="detect",new Promise(async o=>{var g,M,P,m,b,z,w,I,q,e0,V,X,c0,T,F,M0,$,m0,d0,G,Z;this.state="config";let r;this.config=A0(this.config,n),this.state="check";let A=ie(this,F5).call(this,t);A&&(u(A,t),this.emit("error"),o({face:[],body:[],hand:[],gesture:[],object:[],performance:this.performance,timestamp:v(),persons:[],error:A}));let s=v();await this.load(),r=v(),this.state="image";let a=await o2(t,this.config);if(this.process=a,this.performance.inputProcess=this.env.perfadd?(this.performance.inputProcess||0)+Math.trunc(v()-r):Math.trunc(v()-r),this.analyze("Get Image:"),!a.tensor){this.config.debug&&u("could not convert input to tensor"),this.emit("error"),o({face:[],body:[],hand:[],gesture:[],object:[],performance:this.performance,timestamp:v(),persons:[],error:"could not convert input to tensor"});return}this.emit("image"),r=v(),this.config.skipAllowed=await C1(this.config,a.tensor),this.performance.totalFrames||(this.performance.totalFrames=0),this.performance.cachedFrames||(this.performance.cachedFrames=0),this.performance.totalFrames++,this.config.skipAllowed&&this.performance.cachedFrames++,this.performance.cacheCheck=this.env.perfadd?(this.performance.cacheCheck||0)+Math.trunc(v()-r):Math.trunc(v()-r),this.analyze("Check Changed:");let l=[],c=[],x=[],i=[];this.state="detect:face",this.config.async?(l=this.config.face.enabled?m1(this,a.tensor):[],this.performance.face&&delete this.performance.face):(r=v(),l=this.config.face.enabled?await m1(this,a.tensor):[],this.performance.face=this.env.perfadd?(this.performance.face||0)+Math.trunc(v()-r):Math.trunc(v()-r)),this.config.async&&(this.config.body.maxDetected===-1||this.config.hand.maxDetected===-1)&&(l=await l),this.analyze("Start Body:"),this.state="detect:body";let y=this.config.body.maxDetected===-1?A0(this.config,{body:{maxDetected:this.config.face.enabled?1*l.length:1}}):this.config;this.config.async?((g=this.config.body.modelPath)!=null&&g.includes("posenet")?c=this.config.body.enabled?t1(a.tensor,y):[]:(M=this.config.body.modelPath)!=null&&M.includes("blazepose")?c=this.config.body.enabled?dt(a.tensor,y):[]:(P=this.config.body.modelPath)!=null&&P.includes("efficientpose")?c=this.config.body.enabled?ht(a.tensor,y):[]:(m=this.config.body.modelPath)!=null&&m.includes("movenet")&&(c=this.config.body.enabled?Yt(a.tensor,y):[]),this.performance.body&&delete this.performance.body):(r=v(),(b=this.config.body.modelPath)!=null&&b.includes("posenet")?c=this.config.body.enabled?await t1(a.tensor,y):[]:(z=this.config.body.modelPath)!=null&&z.includes("blazepose")?c=this.config.body.enabled?await dt(a.tensor,y):[]:(w=this.config.body.modelPath)!=null&&w.includes("efficientpose")?c=this.config.body.enabled?await ht(a.tensor,y):[]:(I=this.config.body.modelPath)!=null&&I.includes("movenet")&&(c=this.config.body.enabled?await Yt(a.tensor,y):[]),this.performance.body=this.env.perfadd?(this.performance.body||0)+Math.trunc(v()-r):Math.trunc(v()-r)),this.analyze("End Body:"),this.analyze("Start Hand:"),this.state="detect:hand";let d=this.config.hand.maxDetected===-1?A0(this.config,{hand:{maxDetected:this.config.face.enabled?2*l.length:1}}):this.config;this.config.async?((e0=(q=this.config.hand.detector)==null?void 0:q.modelPath)!=null&&e0.includes("handdetect")?x=this.config.hand.enabled?Lt(a.tensor,d):[]:(X=(V=this.config.hand.detector)==null?void 0:V.modelPath)!=null&&X.includes("handtrack")&&(x=this.config.hand.enabled?Bt(a.tensor,d):[]),this.performance.hand&&delete this.performance.hand):(r=v(),(T=(c0=this.config.hand.detector)==null?void 0:c0.modelPath)!=null&&T.includes("handdetect")?x=this.config.hand.enabled?await Lt(a.tensor,d):[]:(M0=(F=this.config.hand.detector)==null?void 0:F.modelPath)!=null&&M0.includes("handtrack")&&(x=this.config.hand.enabled?await Bt(a.tensor,d):[]),this.performance.hand=this.env.perfadd?(this.performance.hand||0)+Math.trunc(v()-r):Math.trunc(v()-r)),this.analyze("End Hand:"),this.analyze("Start Object:"),this.state="detect:object",this.config.async?(($=this.config.object.modelPath)!=null&&$.includes("nanodet")?i=this.config.object.enabled?Jt(a.tensor,this.config):[]:(m0=this.config.object.modelPath)!=null&&m0.includes("centernet")&&(i=this.config.object.enabled?ft(a.tensor,this.config):[]),this.performance.object&&delete this.performance.object):(r=v(),(d0=this.config.object.modelPath)!=null&&d0.includes("nanodet")?i=this.config.object.enabled?await Jt(a.tensor,this.config):[]:(G=this.config.object.modelPath)!=null&&G.includes("centernet")&&(i=this.config.object.enabled?await ft(a.tensor,this.config):[]),this.performance.object=this.env.perfadd?(this.performance.object||0)+Math.trunc(v()-r):Math.trunc(v()-r)),this.analyze("End Object:"),this.state="detect:await",this.config.async&&([l,c,x,i]=await Promise.all([l,c,x,i])),this.state="detect:gesture";let f=[];this.config.gesture.enabled&&(r=v(),f=[...mo(l),...fo(c),...uo(x),...po(l)],this.config.async?this.performance.gesture&&delete this.performance.gesture:this.performance.gesture=this.env.perfadd?(this.performance.gesture||0)+Math.trunc(v()-r):Math.trunc(v()-r)),this.performance.total=this.env.perfadd?(this.performance.total||0)+Math.trunc(v()-s):Math.trunc(v()-s);let p=((Z=this.process.tensor)==null?void 0:Z.shape)||[];this.result={face:l,body:c,hand:x,gesture:f,object:i,performance:this.performance,canvas:this.process.canvas,timestamp:Date.now(),error:null,get persons(){return go(l,c,x,f,p)}},ae.dispose(a.tensor),this.emit("detect"),this.state="idle",o(this.result)})}async sleep(t){return new Promise(n=>{setTimeout(n,t)})}async video(t,n=!0,o=0){n?(ie(this,Be)[t.id]||(this.config.debug&&u("video start",t.id),ie(this,Be)[t.id]=!0),!t.paused&&ie(this,Be)[t.id]&&t.readyState>=2&&await this.detect(t),o>0&&await this.sleep(o),ie(this,Be)[t.id]&&requestAnimationFrame(()=>this.video(t,n,o))):(this.config.debug&&u("video stop",t.id),ie(this,Be)[t.id]=!1)}};T2=new WeakMap,D2=new WeakMap,Z2=new WeakMap,F5=new WeakMap,Be=new WeakMap;0&&(module.exports={Env,Human,defaults,draw,env,match,models});
+2Q==`;
+
+// src/warmup.ts
+var tf38 = __toESM(require_tfjs_esm());
+async function warmupBitmap(instance2) {
+  const b64toBlob = (base64, type = "application/octet-stream") => fetch(`data:${type};base64,${base64}`).then((res2) => res2.blob());
+  let blob;
+  let res;
+  switch (instance2.config.warmup) {
+    case "face":
+      blob = await b64toBlob(face3);
+      break;
+    case "body":
+    case "full":
+      blob = await b64toBlob(body3);
+      break;
+    default:
+      blob = null;
+  }
+  if (blob) {
+    const bitmap = await createImageBitmap(blob);
+    res = await instance2.detect(bitmap, instance2.config);
+    bitmap.close();
+  }
+  return res;
+}
+async function warmupCanvas(instance2) {
+  return new Promise((resolve) => {
+    let src;
+    switch (instance2.config.warmup) {
+      case "face":
+        src = "data:image/jpeg;base64," + face3;
+        break;
+      case "full":
+      case "body":
+        src = "data:image/jpeg;base64," + body3;
+        break;
+      default:
+        src = "";
+    }
+    let img;
+    if (typeof Image !== "undefined")
+      img = new Image();
+    else if (env.Image)
+      img = new env.Image();
+    else
+      return;
+    img.onload = async () => {
+      const canvas3 = canvas(img.naturalWidth, img.naturalHeight);
+      if (!canvas3) {
+        log("Warmup: Canvas not found");
+        resolve(void 0);
+      } else {
+        const ctx = canvas3.getContext("2d");
+        if (ctx)
+          ctx.drawImage(img, 0, 0);
+        const tensor6 = await instance2.image(canvas3);
+        const res = tensor6.tensor ? await instance2.detect(tensor6.tensor, instance2.config) : void 0;
+        resolve(res);
+      }
+    };
+    if (src)
+      img.src = src;
+    else
+      resolve(void 0);
+  });
+}
+async function warmupNode(instance2) {
+  const atob = (str) => Buffer.from(str, "base64");
+  let img;
+  if (instance2.config.warmup === "face")
+    img = atob(face3);
+  else
+    img = atob(body3);
+  let res;
+  if ("node" in tf38 && tf38.getBackend() === "tensorflow") {
+    const data = tf38["node"].decodeJpeg(img);
+    const expanded = tf38.expandDims(data, 0);
+    instance2.tf.dispose(data);
+    res = await instance2.detect(expanded, instance2.config);
+    instance2.tf.dispose(expanded);
+  } else {
+    if (instance2.config.debug)
+      log("Warmup tfjs-node not loaded");
+  }
+  return res;
+}
+async function runInference(instance2) {
+  let res;
+  if (typeof createImageBitmap === "function")
+    res = await warmupBitmap(instance2);
+  else if (typeof Image !== "undefined" || env.Canvas !== void 0)
+    res = await warmupCanvas(instance2);
+  else
+    res = await warmupNode(instance2);
+  return res;
+}
+async function runCompile(instance2) {
+  var _a, _b, _c, _d;
+  if (!tf38.env().flagRegistry.ENGINE_COMPILE_ONLY)
+    return;
+  const backendType = tf38.getBackend();
+  const webGLBackend = tf38.backend();
+  if (backendType !== "webgl" && backendType !== "humangl" || !(webGLBackend == null ? void 0 : webGLBackend.checkCompileCompletion)) {
+    return;
+  }
+  tf38.env().set("ENGINE_COMPILE_ONLY", true);
+  const numTensorsStart = tf38.engine().state.numTensors;
+  const compiledModels = [];
+  for (const [modelName, model21] of Object.entries(instance2.models).filter(([key, val]) => key !== null && val !== null)) {
+    const shape = ((_b = (_a = model21.inputs) == null ? void 0 : _a[0]) == null ? void 0 : _b.shape) ? [...model21.inputs[0].shape] : [1, 64, 64, 3];
+    const dtype = ((_d = (_c = model21.inputs) == null ? void 0 : _c[0]) == null ? void 0 : _d.dtype) ? model21.inputs[0].dtype : "float32";
+    for (let dim = 0; dim < shape.length; dim++) {
+      if (shape[dim] === -1)
+        shape[dim] = dim === 0 ? 1 : 64;
+    }
+    const tensor6 = tf38.zeros(shape, dtype);
+    try {
+      const res = model21.execute(tensor6);
+      compiledModels.push(modelName);
+      if (Array.isArray(res))
+        res.forEach((t2) => tf38.dispose(t2));
+      else
+        tf38.dispose(res);
+    } catch (e) {
+      if (instance2.config.debug)
+        log("compile fail model:", modelName);
+    }
+    tf38.dispose(tensor6);
+  }
+  const kernels = await webGLBackend.checkCompileCompletionAsync();
+  webGLBackend.getUniformLocations();
+  if (instance2.config.debug)
+    log("compile pass:", { models: compiledModels, kernels: kernels.length });
+  tf38.env().set("ENGINE_COMPILE_ONLY", false);
+  const numTensorsEnd = tf38.engine().state.numTensors;
+  if (numTensorsEnd - numTensorsStart > 0)
+    log("tensor leak:", numTensorsEnd - numTensorsStart);
+}
+async function warmup(instance2, userConfig) {
+  await check(instance2, false);
+  const t0 = now();
+  instance2.state = "warmup";
+  if (userConfig)
+    instance2.config = mergeDeep(instance2.config, userConfig);
+  if (!instance2.config.warmup || instance2.config.warmup.length === 0 || instance2.config.warmup === "none") {
+    return { face: [], body: [], hand: [], gesture: [], object: [], performance: instance2.performance, timestamp: now(), persons: [], error: null };
+  }
+  return new Promise(async (resolve) => {
+    await models_exports2.load(instance2);
+    await runCompile(instance2);
+    const res = await runInference(instance2);
+    const t1 = now();
+    if (instance2.config.debug)
+      log("warmup", instance2.config.warmup, Math.round(t1 - t0), "ms");
+    instance2.emit("warmup");
+    resolve(res);
+  });
+}
+
+// src/human.ts
+var _numTensors, _analyzeMemoryLeaks, _checkSanity, _sanity, _loops;
+var Human2 = class {
+  constructor(userConfig) {
+    __publicField(this, "version");
+    __publicField(this, "config");
+    __publicField(this, "result");
+    __publicField(this, "state");
+    __publicField(this, "process");
+    __publicField(this, "tf");
+    __publicField(this, "env");
+    __publicField(this, "draw");
+    __publicField(this, "models");
+    __publicField(this, "events");
+    __publicField(this, "faceTriangulation");
+    __publicField(this, "faceUVMap");
+    __publicField(this, "performance");
+    __privateAdd(this, _numTensors, void 0);
+    __privateAdd(this, _analyzeMemoryLeaks, void 0);
+    __privateAdd(this, _checkSanity, void 0);
+    __publicField(this, "gl");
+    __publicField(this, "analyze", (...msg) => {
+      if (!__privateGet(this, _analyzeMemoryLeaks))
+        return;
+      const currentTensors = this.tf.engine().state.numTensors;
+      const previousTensors = __privateGet(this, _numTensors);
+      __privateSet(this, _numTensors, currentTensors);
+      const leaked = currentTensors - previousTensors;
+      if (leaked !== 0)
+        log(...msg, leaked);
+    });
+    __privateAdd(this, _sanity, (input) => {
+      if (!__privateGet(this, _checkSanity))
+        return null;
+      if (!input)
+        return "input is not defined";
+      if (this.env.node && !(input instanceof tf39.Tensor))
+        return "input must be a tensor";
+      try {
+        this.tf.getBackend();
+      } catch (e) {
+        return "backend not loaded";
+      }
+      return null;
+    });
+    __publicField(this, "similarity", similarity);
+    __publicField(this, "distance", distance);
+    __publicField(this, "match", match2);
+    __publicField(this, "webcam", new WebCam());
+    __publicField(this, "emit", (event) => {
+      var _a;
+      if ((_a = this.events) == null ? void 0 : _a.dispatchEvent)
+        this.events.dispatchEvent(new Event(event));
+    });
+    __privateAdd(this, _loops, {});
+    this.env = env;
+    const tfVersion = (tf39.version.tfjs || tf39.version_core).replace(/-(.*)/, "");
+    config.wasmPath = `https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-backend-wasm@${tfVersion}/dist/`;
+    config.modelBasePath = env.browser ? "../models/" : "file://models/";
+    config.backend = env.browser ? "webgl" : "tensorflow";
+    this.version = version2;
+    Object.defineProperty(this, "version", { value: version2 });
+    this.config = JSON.parse(JSON.stringify(config));
+    Object.seal(this.config);
+    this.config.cacheModels = typeof indexedDB !== "undefined";
+    if (userConfig)
+      this.config = mergeDeep(this.config, userConfig);
+    setModelLoadOptions(this.config);
+    this.tf = tf39;
+    this.state = "idle";
+    __privateSet(this, _numTensors, 0);
+    __privateSet(this, _analyzeMemoryLeaks, false);
+    __privateSet(this, _checkSanity, false);
+    this.performance = {};
+    this.events = typeof EventTarget !== "undefined" ? new EventTarget() : void 0;
+    this.models = new Models();
+    this.draw = {
+      options: options3,
+      canvas: (input, output) => canvas2(input, output),
+      face: (output, result, options4) => face(output, result, options4),
+      body: (output, result, options4) => body(output, result, options4),
+      hand: (output, result, options4) => hand(output, result, options4),
+      gesture: (output, result, options4) => gesture(output, result, options4),
+      object: (output, result, options4) => object(output, result, options4),
+      person: (output, result, options4) => person(output, result, options4),
+      all: (output, result, options4) => all(output, result, options4)
+    };
+    this.result = { face: [], body: [], hand: [], gesture: [], object: [], performance: {}, timestamp: 0, persons: [], error: null };
+    this.process = { tensor: null, canvas: null };
+    this.faceTriangulation = triangulation;
+    this.faceUVMap = uvmap;
+    this.gl = config2;
+    validateModel(this, null, "");
+    this.emit("create");
+    if (this.config.debug || this.env.browser)
+      log(`version: ${this.version}`);
+    if (this.config.debug)
+      log(`tfjs version: ${this.tf.version["tfjs-core"]}`);
+    const envTemp = JSON.parse(JSON.stringify(this.env));
+    delete envTemp.kernels;
+    delete envTemp.initial;
+    delete envTemp.perfadd;
+    if (this.config.debug)
+      log("environment:", envTemp);
+  }
+  reset() {
+    const currentBackend = this.config.backend;
+    this.config = JSON.parse(JSON.stringify(config));
+    this.config.backend = currentBackend;
+    reset();
+    env.initial = true;
+  }
+  validate(userConfig) {
+    const msgs = validate(config, userConfig || this.config);
+    if (msgs.length === 0)
+      this.config = mergeDeep(this.config, userConfig);
+    return msgs;
+  }
+  check() {
+    return validate2(this);
+  }
+  now() {
+    return now();
+  }
+  image(input, getTensor = true) {
+    return process2(input, this.config, getTensor);
+  }
+  async segmentation(input, userConfig) {
+    var _a, _b, _c;
+    if (userConfig)
+      this.config = mergeDeep(this.config, userConfig);
+    if (!this.config.segmentation.enabled)
+      return null;
+    const processed = await process2(input, this.config);
+    if (!processed.tensor)
+      return null;
+    let tensor6 = null;
+    if ((_a = this.config.segmentation.modelPath) == null ? void 0 : _a.includes("rvm"))
+      tensor6 = await predict18(processed.tensor, this.config);
+    if ((_b = this.config.segmentation.modelPath) == null ? void 0 : _b.includes("meet"))
+      tensor6 = await predict13(processed.tensor, this.config);
+    if ((_c = this.config.segmentation.modelPath) == null ? void 0 : _c.includes("selfie"))
+      tensor6 = await predict19(processed.tensor, this.config);
+    tf39.dispose(processed.tensor);
+    return tensor6;
+  }
+  enhance(input) {
+    return enhance(input);
+  }
+  compare(firstImageTensor, secondImageTensor) {
+    return compare(this.config, firstImageTensor, secondImageTensor);
+  }
+  async init() {
+    await check(this, true);
+    await this.tf.ready();
+    reset();
+  }
+  async load(userConfig) {
+    this.state = "load";
+    const timeStamp = now();
+    const count2 = Object.values(this.models).filter((model21) => model21).length;
+    if (userConfig)
+      this.config = mergeDeep(this.config, userConfig);
+    if (this.env.initial) {
+      if (!await check(this, false))
+        log("error: backend check failed");
+      await tf39.ready();
+      if (this.env.browser) {
+        if (this.config.debug)
+          log("configuration:", this.config);
+        if (this.config.debug)
+          log("tf flags:", this.tf.ENV.flags);
+      }
+    }
+    await load22(this);
+    if (this.env.initial && this.config.debug)
+      log("tf engine state:", this.tf.engine().state.numBytes, "bytes", this.tf.engine().state.numTensors, "tensors");
+    this.env.initial = false;
+    const loaded = Object.values(this.models).filter((model21) => model21).length;
+    if (loaded !== count2) {
+      validate2(this);
+      this.emit("load");
+    }
+    const current = Math.trunc(now() - timeStamp);
+    if (current > (this.performance.loadModels || 0))
+      this.performance.loadModels = this.env.perfadd ? (this.performance.loadModels || 0) + current : current;
+  }
+  next(result = this.result) {
+    return calc2(result, this.config);
+  }
+  getModelStats() {
+    return getModelStats(this);
+  }
+  async warmup(userConfig) {
+    const t0 = now();
+    const res = await warmup(this, userConfig);
+    const t1 = now();
+    this.performance.warmup = Math.trunc(t1 - t0);
+    return res;
+  }
+  async profile(input, userConfig) {
+    const profile = await this.tf.profile(() => this.detect(input, userConfig));
+    const kernels = {};
+    let total = 0;
+    for (const kernel of profile.kernels) {
+      if (kernels[kernel.name])
+        kernels[kernel.name] += kernel.kernelTimeMs;
+      else
+        kernels[kernel.name] = kernel.kernelTimeMs;
+      total += kernel.kernelTimeMs;
+    }
+    const kernelArr = [];
+    Object.entries(kernels).forEach((key) => kernelArr.push({ kernel: key[0], time: key[1], perc: 0 }));
+    for (const kernel of kernelArr) {
+      kernel.perc = Math.round(1e3 * kernel.time / total) / 1e3;
+      kernel.time = Math.round(1e3 * kernel.time) / 1e3;
+    }
+    kernelArr.sort((a, b) => b.time - a.time);
+    kernelArr.length = 20;
+    return kernelArr;
+  }
+  async detect(input, userConfig) {
+    this.state = "detect";
+    return new Promise(async (resolve) => {
+      var _a, _b, _c, _d, _e, _f, _g, _h, _i, _j, _k, _l, _m, _n, _o, _p, _q, _r, _s, _t, _u;
+      this.state = "config";
+      let timeStamp;
+      this.config = mergeDeep(this.config, userConfig);
+      this.state = "check";
+      const error = __privateGet(this, _sanity).call(this, input);
+      if (error) {
+        log(error, input);
+        this.emit("error");
+        resolve({ face: [], body: [], hand: [], gesture: [], object: [], performance: this.performance, timestamp: now(), persons: [], error });
+      }
+      const timeStart = now();
+      await this.load();
+      timeStamp = now();
+      this.state = "image";
+      const img = await process2(input, this.config);
+      this.process = img;
+      this.performance.inputProcess = this.env.perfadd ? (this.performance.inputProcess || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);
+      this.analyze("Get Image:");
+      if (!img.tensor) {
+        if (this.config.debug)
+          log("could not convert input to tensor");
+        this.emit("error");
+        resolve({ face: [], body: [], hand: [], gesture: [], object: [], performance: this.performance, timestamp: now(), persons: [], error: "could not convert input to tensor" });
+        return;
+      }
+      this.emit("image");
+      timeStamp = now();
+      this.config.skipAllowed = await skip(this.config, img.tensor);
+      if (!this.performance.totalFrames)
+        this.performance.totalFrames = 0;
+      if (!this.performance.cachedFrames)
+        this.performance.cachedFrames = 0;
+      this.performance.totalFrames++;
+      if (this.config.skipAllowed)
+        this.performance.cachedFrames++;
+      this.performance.cacheCheck = this.env.perfadd ? (this.performance.cacheCheck || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);
+      this.analyze("Check Changed:");
+      let faceRes = [];
+      let bodyRes = [];
+      let handRes = [];
+      let objectRes = [];
+      this.state = "detect:face";
+      if (this.config.async) {
+        faceRes = this.config.face.enabled ? detectFace(this, img.tensor) : [];
+        if (this.performance.face)
+          delete this.performance.face;
+      } else {
+        timeStamp = now();
+        faceRes = this.config.face.enabled ? await detectFace(this, img.tensor) : [];
+        this.performance.face = this.env.perfadd ? (this.performance.face || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);
+      }
+      if (this.config.async && (this.config.body.maxDetected === -1 || this.config.hand.maxDetected === -1))
+        faceRes = await faceRes;
+      this.analyze("Start Body:");
+      this.state = "detect:body";
+      const bodyConfig = this.config.body.maxDetected === -1 ? mergeDeep(this.config, { body: { maxDetected: this.config.face.enabled ? 1 * faceRes.length : 1 } }) : this.config;
+      if (this.config.async) {
+        if ((_a = this.config.body.modelPath) == null ? void 0 : _a.includes("posenet"))
+          bodyRes = this.config.body.enabled ? predict17(img.tensor, bodyConfig) : [];
+        else if ((_b = this.config.body.modelPath) == null ? void 0 : _b.includes("blazepose"))
+          bodyRes = this.config.body.enabled ? predict2(img.tensor, bodyConfig) : [];
+        else if ((_c = this.config.body.modelPath) == null ? void 0 : _c.includes("efficientpose"))
+          bodyRes = this.config.body.enabled ? predict4(img.tensor, bodyConfig) : [];
+        else if ((_d = this.config.body.modelPath) == null ? void 0 : _d.includes("movenet"))
+          bodyRes = this.config.body.enabled ? predict15(img.tensor, bodyConfig) : [];
+        if (this.performance.body)
+          delete this.performance.body;
+      } else {
+        timeStamp = now();
+        if ((_e = this.config.body.modelPath) == null ? void 0 : _e.includes("posenet"))
+          bodyRes = this.config.body.enabled ? await predict17(img.tensor, bodyConfig) : [];
+        else if ((_f = this.config.body.modelPath) == null ? void 0 : _f.includes("blazepose"))
+          bodyRes = this.config.body.enabled ? await predict2(img.tensor, bodyConfig) : [];
+        else if ((_g = this.config.body.modelPath) == null ? void 0 : _g.includes("efficientpose"))
+          bodyRes = this.config.body.enabled ? await predict4(img.tensor, bodyConfig) : [];
+        else if ((_h = this.config.body.modelPath) == null ? void 0 : _h.includes("movenet"))
+          bodyRes = this.config.body.enabled ? await predict15(img.tensor, bodyConfig) : [];
+        this.performance.body = this.env.perfadd ? (this.performance.body || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);
+      }
+      this.analyze("End Body:");
+      this.analyze("Start Hand:");
+      this.state = "detect:hand";
+      const handConfig = this.config.hand.maxDetected === -1 ? mergeDeep(this.config, { hand: { maxDetected: this.config.face.enabled ? 2 * faceRes.length : 1 } }) : this.config;
+      if (this.config.async) {
+        if ((_j = (_i = this.config.hand.detector) == null ? void 0 : _i.modelPath) == null ? void 0 : _j.includes("handdetect"))
+          handRes = this.config.hand.enabled ? predict9(img.tensor, handConfig) : [];
+        else if ((_l = (_k = this.config.hand.detector) == null ? void 0 : _k.modelPath) == null ? void 0 : _l.includes("handtrack"))
+          handRes = this.config.hand.enabled ? predict10(img.tensor, handConfig) : [];
+        if (this.performance.hand)
+          delete this.performance.hand;
+      } else {
+        timeStamp = now();
+        if ((_n = (_m = this.config.hand.detector) == null ? void 0 : _m.modelPath) == null ? void 0 : _n.includes("handdetect"))
+          handRes = this.config.hand.enabled ? await predict9(img.tensor, handConfig) : [];
+        else if ((_p = (_o = this.config.hand.detector) == null ? void 0 : _o.modelPath) == null ? void 0 : _p.includes("handtrack"))
+          handRes = this.config.hand.enabled ? await predict10(img.tensor, handConfig) : [];
+        this.performance.hand = this.env.perfadd ? (this.performance.hand || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);
+      }
+      this.analyze("End Hand:");
+      this.analyze("Start Object:");
+      this.state = "detect:object";
+      if (this.config.async) {
+        if ((_q = this.config.object.modelPath) == null ? void 0 : _q.includes("nanodet"))
+          objectRes = this.config.object.enabled ? predict16(img.tensor, this.config) : [];
+        else if ((_r = this.config.object.modelPath) == null ? void 0 : _r.includes("centernet"))
+          objectRes = this.config.object.enabled ? predict3(img.tensor, this.config) : [];
+        if (this.performance.object)
+          delete this.performance.object;
+      } else {
+        timeStamp = now();
+        if ((_s = this.config.object.modelPath) == null ? void 0 : _s.includes("nanodet"))
+          objectRes = this.config.object.enabled ? await predict16(img.tensor, this.config) : [];
+        else if ((_t = this.config.object.modelPath) == null ? void 0 : _t.includes("centernet"))
+          objectRes = this.config.object.enabled ? await predict3(img.tensor, this.config) : [];
+        this.performance.object = this.env.perfadd ? (this.performance.object || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);
+      }
+      this.analyze("End Object:");
+      this.state = "detect:await";
+      if (this.config.async)
+        [faceRes, bodyRes, handRes, objectRes] = await Promise.all([faceRes, bodyRes, handRes, objectRes]);
+      this.state = "detect:gesture";
+      let gestureRes = [];
+      if (this.config.gesture.enabled) {
+        timeStamp = now();
+        gestureRes = [...face2(faceRes), ...body2(bodyRes), ...hand2(handRes), ...iris2(faceRes)];
+        if (!this.config.async)
+          this.performance.gesture = this.env.perfadd ? (this.performance.gesture || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);
+        else if (this.performance.gesture)
+          delete this.performance.gesture;
+      }
+      this.performance.total = this.env.perfadd ? (this.performance.total || 0) + Math.trunc(now() - timeStart) : Math.trunc(now() - timeStart);
+      const shape = ((_u = this.process.tensor) == null ? void 0 : _u.shape) || [];
+      this.result = {
+        face: faceRes,
+        body: bodyRes,
+        hand: handRes,
+        gesture: gestureRes,
+        object: objectRes,
+        performance: this.performance,
+        canvas: this.process.canvas,
+        timestamp: Date.now(),
+        error: null,
+        get persons() {
+          return join2(faceRes, bodyRes, handRes, gestureRes, shape);
+        }
+      };
+      tf39.dispose(img.tensor);
+      this.emit("detect");
+      this.state = "idle";
+      resolve(this.result);
+    });
+  }
+  async sleep(ms) {
+    return new Promise((resolve) => {
+      setTimeout(resolve, ms);
+    });
+  }
+  async video(element, run = true, delay = 0) {
+    if (run) {
+      if (!__privateGet(this, _loops)[element.id]) {
+        if (this.config.debug)
+          log("video start", element.id);
+        __privateGet(this, _loops)[element.id] = true;
+      }
+      if (!element.paused && __privateGet(this, _loops)[element.id] && element.readyState >= 2)
+        await this.detect(element);
+      if (delay > 0)
+        await this.sleep(delay);
+      if (__privateGet(this, _loops)[element.id])
+        requestAnimationFrame(() => this.video(element, run, delay));
+    } else {
+      if (this.config.debug)
+        log("video stop", element.id);
+      __privateGet(this, _loops)[element.id] = false;
+    }
+  }
+};
+_numTensors = new WeakMap();
+_analyzeMemoryLeaks = new WeakMap();
+_checkSanity = new WeakMap();
+_sanity = new WeakMap();
+_loops = new WeakMap();
+// Annotate the CommonJS export names for ESM import in node:
+0 && (module.exports = {
+  Env,
+  Human,
+  defaults,
+  draw,
+  env,
+  match,
+  models
+});
diff --git a/dist/human.node.js b/dist/human.node.js
index b62e966d..964a11cc 100644
--- a/dist/human.node.js
+++ b/dist/human.node.js
@@ -4,7 +4,291 @@
   author: <https://github.com/vladmandic>'
 */
 
-"use strict";var Mo=Object.create;var w2=Object.defineProperty;var vo=Object.getOwnPropertyDescriptor;var Po=Object.getOwnPropertyNames;var To=Object.getPrototypeOf,Ro=Object.prototype.hasOwnProperty;var ko=(e,t,n)=>t in e?w2(e,t,{enumerable:!0,configurable:!0,writable:!0,value:n}):e[t]=n;var wo=(e,t)=>()=>(t||e((t={exports:{}}).exports,t),t.exports),ve=(e,t)=>{for(var n in t)w2(e,n,{get:t[n],enumerable:!0})},v1=(e,t,n,o)=>{if(t&&typeof t=="object"||typeof t=="function")for(let r of Po(t))!Ro.call(e,r)&&r!==n&&w2(e,r,{get:()=>t[r],enumerable:!(o=vo(t,r))||o.enumerable});return e};var D=(e,t,n)=>(n=e!=null?Mo(To(e)):{},v1(t||!e||!e.__esModule?w2(n,"default",{value:e,enumerable:!0}):n,e)),Eo=e=>v1(w2({},"__esModule",{value:!0}),e);var R=(e,t,n)=>(ko(e,typeof t!="symbol"?t+"":t,n),n),P1=(e,t,n)=>{if(!t.has(e))throw TypeError("Cannot "+n)};var ie=(e,t,n)=>(P1(e,t,"read from private field"),n?n.call(e):t.get(e)),t2=(e,t,n)=>{if(t.has(e))throw TypeError("Cannot add the same private member more than once");t instanceof WeakSet?t.add(e):t.set(e,n)},E2=(e,t,n,o)=>(P1(e,t,"write to private field"),o?o.call(e,n):t.set(e,n),n);var H=wo((Ds,B5)=>{"use strict";var R1=Object.defineProperty,zo=Object.getOwnPropertyDescriptor,So=Object.getOwnPropertyNames,jo=Object.prototype.hasOwnProperty,G5=(e,t,n,o)=>{if(t&&typeof t=="object"||typeof t=="function")for(let r of So(t))!jo.call(e,r)&&r!==n&&R1(e,r,{get:()=>t[r],enumerable:!(o=zo(t,r))||o.enumerable});return e},No=(e,t,n)=>(G5(e,t,"default"),n&&G5(n,t,"default")),Io=e=>G5(R1({},"__esModule",{value:!0}),e),k1={};B5.exports=Io(k1);No(k1,require("@tensorflow/tfjs-node"),B5.exports)});var Gs={};ve(Gs,{Env:()=>S2,Human:()=>b1,default:()=>b1,defaults:()=>Pe,draw:()=>x1,env:()=>k,match:()=>h1,models:()=>f2});module.exports=Eo(Gs);function u(...e){let t=new Date,n=`${t.getHours().toString().padStart(2,"0")}:${t.getMinutes().toString().padStart(2,"0")}:${t.getSeconds().toString().padStart(2,"0")}.${t.getMilliseconds().toString().padStart(3,"0")}`;e&&console.log(n,"Human:",...e)}function T1(e,t){let n=e.endsWith("/")?"":"/",r=t.startsWith(".")||t.startsWith("/")||t.startsWith("http:")||t.startsWith("https:")||t.startsWith("file:")?`${t}`:`${e}${n}${t}`;if(!r.toLocaleLowerCase().includes(".json"))throw new Error(`modelpath error: expecting json file: ${r}`);return r}var v=()=>typeof performance!="undefined"?performance.now():parseInt((Number(process.hrtime.bigint())/1e3/1e3).toString());function F5(e,t,n="config",o=[]){for(let r of Object.keys(t))if(typeof t[r]=="object")F5(e[r],t[r],r,o);else{let A=e&&typeof e[r]!="undefined";A||o.push({reason:"unknown property",where:`${n}.${r} = ${t[r]}`});let s=e&&typeof e[r]==typeof t[r];A&&!s&&o.push({reason:"property type mismatch",where:`${n}.${r} = ${t[r]}`,expected:typeof e[r]})}return t.debug&&n==="config"&&o.length>0&&u("invalid configuration",o),o}function A0(...e){let t=n=>n&&typeof n=="object";return e.reduce((n,o)=>(Object.keys(o||{}).forEach(r=>{let A=n[r],s=o[r];Array.isArray(A)&&Array.isArray(s)?n[r]=A.concat(...s):t(A)&&t(s)?n[r]=A0(A,s):n[r]=s}),n),{})}var Pe={backend:"",modelBasePath:"",cacheModels:!0,validateModels:!0,wasmPath:"",wasmPlatformFetch:!1,debug:!1,async:!0,warmup:"full",cacheSensitivity:.7,skipAllowed:!1,deallocate:!1,flags:{},softwareKernels:!1,filter:{enabled:!0,equalization:!1,width:0,height:0,flip:!1,return:!0,brightness:0,contrast:0,sharpness:0,blur:0,saturation:0,hue:0,negative:!1,sepia:!1,vintage:!1,kodachrome:!1,technicolor:!1,polaroid:!1,pixelate:0},gesture:{enabled:!0},face:{enabled:!0,detector:{modelPath:"blazeface.json",rotation:!0,maxDetected:1,skipFrames:99,skipTime:2500,minConfidence:.2,iouThreshold:.1,mask:!1,return:!1},mesh:{enabled:!0,modelPath:"facemesh.json",keepInvalid:!1},attention:{enabled:!1,modelPath:"facemesh-attention.json"},iris:{enabled:!0,modelPath:"iris.json"},emotion:{enabled:!0,minConfidence:.1,skipFrames:99,skipTime:1500,modelPath:"emotion.json"},description:{enabled:!0,modelPath:"faceres.json",skipFrames:99,skipTime:3e3,minConfidence:.1},antispoof:{enabled:!1,skipFrames:99,skipTime:4e3,modelPath:"antispoof.json"},liveness:{enabled:!1,skipFrames:99,skipTime:4e3,modelPath:"liveness.json"}},body:{enabled:!0,modelPath:"movenet-lightning.json",maxDetected:-1,minConfidence:.3,skipFrames:1,skipTime:200},hand:{enabled:!0,rotation:!0,skipFrames:99,skipTime:1e3,minConfidence:.5,iouThreshold:.2,maxDetected:-1,landmarks:!0,detector:{modelPath:"handtrack.json"},skeleton:{modelPath:"handlandmark-full.json"}},object:{enabled:!1,modelPath:"mb3-centernet.json",minConfidence:.2,iouThreshold:.4,maxDetected:10,skipFrames:99,skipTime:2e3},segmentation:{enabled:!1,modelPath:"selfie.json",blur:8}};var x0=D(H());var O=D(H());var w1=`
+"use strict";
+var __create = Object.create;
+var __defProp = Object.defineProperty;
+var __getOwnPropDesc = Object.getOwnPropertyDescriptor;
+var __getOwnPropNames = Object.getOwnPropertyNames;
+var __getProtoOf = Object.getPrototypeOf;
+var __hasOwnProp = Object.prototype.hasOwnProperty;
+var __defNormalProp = (obj, key, value) => key in obj ? __defProp(obj, key, { enumerable: true, configurable: true, writable: true, value }) : obj[key] = value;
+var __commonJS = (cb, mod3) => function __require() {
+  return mod3 || (0, cb[__getOwnPropNames(cb)[0]])((mod3 = { exports: {} }).exports, mod3), mod3.exports;
+};
+var __export = (target, all2) => {
+  for (var name in all2)
+    __defProp(target, name, { get: all2[name], enumerable: true });
+};
+var __copyProps = (to, from, except, desc) => {
+  if (from && typeof from === "object" || typeof from === "function") {
+    for (let key of __getOwnPropNames(from))
+      if (!__hasOwnProp.call(to, key) && key !== except)
+        __defProp(to, key, { get: () => from[key], enumerable: !(desc = __getOwnPropDesc(from, key)) || desc.enumerable });
+  }
+  return to;
+};
+var __toESM = (mod3, isNodeMode, target) => (target = mod3 != null ? __create(__getProtoOf(mod3)) : {}, __copyProps(
+  isNodeMode || !mod3 || !mod3.__esModule ? __defProp(target, "default", { value: mod3, enumerable: true }) : target,
+  mod3
+));
+var __toCommonJS = (mod3) => __copyProps(__defProp({}, "__esModule", { value: true }), mod3);
+var __publicField = (obj, key, value) => {
+  __defNormalProp(obj, typeof key !== "symbol" ? key + "" : key, value);
+  return value;
+};
+var __accessCheck = (obj, member, msg) => {
+  if (!member.has(obj))
+    throw TypeError("Cannot " + msg);
+};
+var __privateGet = (obj, member, getter) => {
+  __accessCheck(obj, member, "read from private field");
+  return getter ? getter.call(obj) : member.get(obj);
+};
+var __privateAdd = (obj, member, value) => {
+  if (member.has(obj))
+    throw TypeError("Cannot add the same private member more than once");
+  member instanceof WeakSet ? member.add(obj) : member.set(obj, value);
+};
+var __privateSet = (obj, member, value, setter) => {
+  __accessCheck(obj, member, "write to private field");
+  setter ? setter.call(obj, value) : member.set(obj, value);
+  return value;
+};
+
+// dist/tfjs.esm.js
+var require_tfjs_esm = __commonJS({
+  "dist/tfjs.esm.js"(exports, module2) {
+    "use strict";
+    var __defProp2 = Object.defineProperty;
+    var __getOwnPropDesc2 = Object.getOwnPropertyDescriptor;
+    var __getOwnPropNames2 = Object.getOwnPropertyNames;
+    var __hasOwnProp2 = Object.prototype.hasOwnProperty;
+    var __copyProps2 = (to, from, except, desc) => {
+      if (from && typeof from === "object" || typeof from === "function") {
+        for (let key of __getOwnPropNames2(from))
+          if (!__hasOwnProp2.call(to, key) && key !== except)
+            __defProp2(to, key, { get: () => from[key], enumerable: !(desc = __getOwnPropDesc2(from, key)) || desc.enumerable });
+      }
+      return to;
+    };
+    var __reExport = (target, mod3, secondTarget) => (__copyProps2(target, mod3, "default"), secondTarget && __copyProps2(secondTarget, mod3, "default"));
+    var __toCommonJS2 = (mod3) => __copyProps2(__defProp2({}, "__esModule", { value: true }), mod3);
+    var tf_node_exports = {};
+    module2.exports = __toCommonJS2(tf_node_exports);
+    __reExport(tf_node_exports, require("@tensorflow/tfjs-node"), module2.exports);
+  }
+});
+
+// src/human.ts
+var human_exports = {};
+__export(human_exports, {
+  Env: () => Env,
+  Human: () => Human2,
+  default: () => Human2,
+  defaults: () => config,
+  draw: () => draw_exports,
+  env: () => env,
+  match: () => match_exports,
+  models: () => models_exports2
+});
+module.exports = __toCommonJS(human_exports);
+
+// src/util/util.ts
+function log(...msg) {
+  const dt = new Date();
+  const ts = `${dt.getHours().toString().padStart(2, "0")}:${dt.getMinutes().toString().padStart(2, "0")}:${dt.getSeconds().toString().padStart(2, "0")}.${dt.getMilliseconds().toString().padStart(3, "0")}`;
+  if (msg)
+    console.log(ts, "Human:", ...msg);
+}
+function join(folder, file) {
+  const separator = folder.endsWith("/") ? "" : "/";
+  const skipJoin = file.startsWith(".") || file.startsWith("/") || file.startsWith("http:") || file.startsWith("https:") || file.startsWith("file:");
+  const path = skipJoin ? `${file}` : `${folder}${separator}${file}`;
+  if (!path.toLocaleLowerCase().includes(".json"))
+    throw new Error(`modelpath error: expecting json file: ${path}`);
+  return path;
+}
+var now = () => {
+  if (typeof performance !== "undefined")
+    return performance.now();
+  return parseInt((Number(process.hrtime.bigint()) / 1e3 / 1e3).toString());
+};
+function validate(defaults, config3, parent = "config", msgs = []) {
+  for (const key of Object.keys(config3)) {
+    if (typeof config3[key] === "object") {
+      validate(defaults[key], config3[key], key, msgs);
+    } else {
+      const defined = defaults && typeof defaults[key] !== "undefined";
+      if (!defined)
+        msgs.push({ reason: "unknown property", where: `${parent}.${key} = ${config3[key]}` });
+      const same = defaults && typeof defaults[key] === typeof config3[key];
+      if (defined && !same)
+        msgs.push({ reason: "property type mismatch", where: `${parent}.${key} = ${config3[key]}`, expected: typeof defaults[key] });
+    }
+  }
+  if (config3.debug && parent === "config" && msgs.length > 0)
+    log("invalid configuration", msgs);
+  return msgs;
+}
+function mergeDeep(...objects) {
+  const isObject = (obj) => obj && typeof obj === "object";
+  return objects.reduce((prev, obj) => {
+    Object.keys(obj || {}).forEach((key) => {
+      const pVal = prev[key];
+      const oVal = obj[key];
+      if (Array.isArray(pVal) && Array.isArray(oVal))
+        prev[key] = pVal.concat(...oVal);
+      else if (isObject(pVal) && isObject(oVal))
+        prev[key] = mergeDeep(pVal, oVal);
+      else
+        prev[key] = oVal;
+    });
+    return prev;
+  }, {});
+}
+
+// src/config.ts
+var config = {
+  backend: "",
+  modelBasePath: "",
+  cacheModels: true,
+  validateModels: true,
+  wasmPath: "",
+  wasmPlatformFetch: false,
+  debug: false,
+  async: true,
+  warmup: "full",
+  cacheSensitivity: 0.7,
+  skipAllowed: false,
+  deallocate: false,
+  flags: {},
+  softwareKernels: false,
+  filter: {
+    enabled: true,
+    equalization: false,
+    width: 0,
+    height: 0,
+    flip: false,
+    return: true,
+    brightness: 0,
+    contrast: 0,
+    sharpness: 0,
+    blur: 0,
+    saturation: 0,
+    hue: 0,
+    negative: false,
+    sepia: false,
+    vintage: false,
+    kodachrome: false,
+    technicolor: false,
+    polaroid: false,
+    pixelate: 0
+  },
+  gesture: {
+    enabled: true
+  },
+  face: {
+    enabled: true,
+    detector: {
+      modelPath: "blazeface.json",
+      rotation: true,
+      maxDetected: 1,
+      skipFrames: 99,
+      skipTime: 2500,
+      minConfidence: 0.2,
+      iouThreshold: 0.1,
+      mask: false,
+      return: false
+    },
+    mesh: {
+      enabled: true,
+      modelPath: "facemesh.json",
+      keepInvalid: false
+    },
+    attention: {
+      enabled: false,
+      modelPath: "facemesh-attention.json"
+    },
+    iris: {
+      enabled: true,
+      modelPath: "iris.json"
+    },
+    emotion: {
+      enabled: true,
+      minConfidence: 0.1,
+      skipFrames: 99,
+      skipTime: 1500,
+      modelPath: "emotion.json"
+    },
+    description: {
+      enabled: true,
+      modelPath: "faceres.json",
+      skipFrames: 99,
+      skipTime: 3e3,
+      minConfidence: 0.1
+    },
+    antispoof: {
+      enabled: false,
+      skipFrames: 99,
+      skipTime: 4e3,
+      modelPath: "antispoof.json"
+    },
+    liveness: {
+      enabled: false,
+      skipFrames: 99,
+      skipTime: 4e3,
+      modelPath: "liveness.json"
+    }
+  },
+  body: {
+    enabled: true,
+    modelPath: "movenet-lightning.json",
+    maxDetected: -1,
+    minConfidence: 0.3,
+    skipFrames: 1,
+    skipTime: 200
+  },
+  hand: {
+    enabled: true,
+    rotation: true,
+    skipFrames: 99,
+    skipTime: 1e3,
+    minConfidence: 0.5,
+    iouThreshold: 0.2,
+    maxDetected: -1,
+    landmarks: true,
+    detector: {
+      modelPath: "handtrack.json"
+    },
+    skeleton: {
+      modelPath: "handlandmark-full.json"
+    }
+  },
+  object: {
+    enabled: false,
+    modelPath: "mb3-centernet.json",
+    minConfidence: 0.2,
+    iouThreshold: 0.4,
+    maxDetected: 10,
+    skipFrames: 99,
+    skipTime: 2e3
+  },
+  segmentation: {
+    enabled: false,
+    modelPath: "rvm.json",
+    ratio: 0.5,
+    mode: "default"
+  }
+};
+
+// src/util/env.ts
+var tf3 = __toESM(require_tfjs_esm());
+
+// src/image/image.ts
+var tf2 = __toESM(require_tfjs_esm());
+
+// src/image/imagefxshaders.ts
+var vertexIdentity = `
   precision highp float;
   attribute vec2 pos;
   attribute vec2 uv;
@@ -14,7 +298,8 @@
     vUv = uv;
     gl_Position = vec4(pos.x, pos.y*flipY, 0.0, 1.);
   }
-`;var E1=`
+`;
+var colorMatrixWithAlpha = `
   precision highp float;
   varying vec2 vUv;
   uniform sampler2D texture;
@@ -26,7 +311,8 @@
     gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[13] * c.a + m[14];
     gl_FragColor.a = m[15] * c.r + m[16] * c.g + m[17] * c.b + m[18] * c.a + m[19];
   }
-`,z1=`
+`;
+var colorMatrixWithoutAlpha = `
   precision highp float;
   varying vec2 vUv;
   uniform sampler2D texture;
@@ -38,7 +324,8 @@
     gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[14];
     gl_FragColor.a = c.a;
   }
-`,S1=`
+`;
+var pixelate = `
   precision highp float;
   varying vec2 vUv;
   uniform vec2 size;
@@ -51,7 +338,8 @@
     vec2 coord = pixelate(vUv, size);
     gl_FragColor += texture2D(texture, coord);
   }
-`,j1=`
+`;
+var blur = `
   precision highp float;
   varying vec2 vUv;
   uniform sampler2D texture;
@@ -74,7 +362,8 @@
     gl_FragColor += texture2D(texture, vUv + vec2( 6.0*px.x,  6.0*px.y))*0.00895781211794;
     gl_FragColor += texture2D(texture, vUv + vec2( 7.0*px.x,  7.0*px.y))*0.0044299121055113265;
   }
-`,N1=`
+`;
+var convolution = `
   precision highp float;
   varying vec2 vUv;
   uniform sampler2D texture;
@@ -96,19 +385,13456 @@
     c31 * m[6] + c32 * m[7] + c33 * m[8];
     gl_FragColor.a = c22.a;
   }
-`;var H5=(e,t,n)=>{let o=new RegExp("\\b"+t+" \\w+ (\\w+)","ig");e.replace(o,(r,A)=>(n[A]=0,r))},V5=class{constructor(t,n,o){R(this,"uniform",{});R(this,"attribute",{});R(this,"gl");R(this,"id");R(this,"compile",(t,n)=>{let o=this.gl.createShader(n);return o?(this.gl.shaderSource(o,t),this.gl.compileShader(o),this.gl.getShaderParameter(o,this.gl.COMPILE_STATUS)?o:(u(`filter: gl compile failed: ${this.gl.getShaderInfoLog(o)||"unknown"}`),null)):(u("filter: could not create shader"),null)});this.gl=t;let r=this.compile(n,this.gl.VERTEX_SHADER),A=this.compile(o,this.gl.FRAGMENT_SHADER);if(this.id=this.gl.createProgram(),!(!r||!A)){if(!this.id){u("filter: could not create webgl program");return}if(this.gl.attachShader(this.id,r),this.gl.attachShader(this.id,A),this.gl.linkProgram(this.id),!this.gl.getProgramParameter(this.id,this.gl.LINK_STATUS)){u(`filter: gl link failed: ${this.gl.getProgramInfoLog(this.id)||"unknown"}`);return}this.gl.useProgram(this.id),H5(n,"attribute",this.attribute);for(let s in this.attribute)this.attribute[s]=this.gl.getAttribLocation(this.id,s);H5(n,"uniform",this.uniform),H5(o,"uniform",this.uniform);for(let s in this.uniform)this.uniform[s]=this.gl.getUniformLocation(this.id,s)}}};function I1(){let e=0,t=null,n=!1,o=-1,r=[null,null],A=[],s=null,a=null,l=k0(100,100),c={},x={INTERMEDIATE:1},i=l.getContext("webgl");if(!i){u("filter: cannot get webgl context");return}this.gl=i;function y(P,m){if(!(P===l.width&&m===l.height)){if(l.width=P,l.height=m,!s){let b=new Float32Array([-1,-1,0,1,1,-1,1,1,-1,1,0,0,-1,1,0,0,1,-1,1,1,1,1,1,0]);s=i.createBuffer(),i.bindBuffer(i.ARRAY_BUFFER,s),i.bufferData(i.ARRAY_BUFFER,b,i.STATIC_DRAW),i.pixelStorei(i.UNPACK_PREMULTIPLY_ALPHA_WEBGL,!0)}i.viewport(0,0,l.width,l.height),r=[null,null]}}function d(P,m){let b=i.createFramebuffer();i.bindFramebuffer(i.FRAMEBUFFER,b);let z=i.createRenderbuffer();i.bindRenderbuffer(i.RENDERBUFFER,z);let w=i.createTexture();return i.bindTexture(i.TEXTURE_2D,w),i.texImage2D(i.TEXTURE_2D,0,i.RGBA,P,m,0,i.RGBA,i.UNSIGNED_BYTE,null),i.texParameteri(i.TEXTURE_2D,i.TEXTURE_MAG_FILTER,i.LINEAR),i.texParameteri(i.TEXTURE_2D,i.TEXTURE_MIN_FILTER,i.LINEAR),i.texParameteri(i.TEXTURE_2D,i.TEXTURE_WRAP_S,i.CLAMP_TO_EDGE),i.texParameteri(i.TEXTURE_2D,i.TEXTURE_WRAP_T,i.CLAMP_TO_EDGE),i.framebufferTexture2D(i.FRAMEBUFFER,i.COLOR_ATTACHMENT0,i.TEXTURE_2D,w,0),i.bindTexture(i.TEXTURE_2D,null),i.bindFramebuffer(i.FRAMEBUFFER,null),{fbo:b,texture:w}}function f(P){return r[P]=r[P]||d(l.width,l.height),r[P]}function p(P=0){if(!a)return;let m=null,b=null,z=!1;e===0?m=t:m=f(o).texture||null,e++,n&&!(P&x.INTERMEDIATE)?(b=null,z=e%2===0):(o=(o+1)%2,b=f(o).fbo||null),i.bindTexture(i.TEXTURE_2D,m),i.bindFramebuffer(i.FRAMEBUFFER,b),i.uniform1f(a.uniform.flipY,z?-1:1),i.drawArrays(i.TRIANGLES,0,6)}function g(P){if(c[P])return 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m=Math.cos(P),b=Math.sin(P),z=.213,w=.715,I=.072;M.colorMatrix([z+m*(1-z)+b*-z,w+m*-w+b*-w,I+m*-I+b*(1-I),0,0,z+m*-z+b*.143,w+m*(1-w)+b*.14,I+m*-I+b*-.283,0,0,z+m*-z+b*-(1-z),w+m*-w+b*w,I+m*(1-I)+b*I,0,0,0,0,0,1,0])},desaturateLuminance:()=>{M.colorMatrix([.2764723,.929708,.0938197,0,-37.1,.2764723,.929708,.0938197,0,-37.1,.2764723,.929708,.0938197,0,-37.1,0,0,0,1,0])},sepia:()=>{M.colorMatrix([.393,.7689999,.18899999,0,0,.349,.6859999,.16799999,0,0,.272,.5339999,.13099999,0,0,0,0,0,1,0])},brownie:()=>{M.colorMatrix([.5997023498159715,.34553243048391263,-.2708298674538042,0,47.43192855600873,-.037703249837783157,.8609577587992641,.15059552388459913,0,-36.96841498319127,.24113635128153335,-.07441037908422492,.44972182064877153,0,-7.562075277591283,0,0,0,1,0])},vintagePinhole:()=>{M.colorMatrix([.6279345635605994,.3202183420819367,-.03965408211312453,0,9.651285835294123,.02578397704808868,.6441188644374771,.03259127616149294,0,7.462829176470591,.0466055556782719,-.0851232987247891,.5241648018700465,0,5.159190588235296,0,0,0,1,0])},kodachrome:()=>{M.colorMatrix([1.1285582396593525,-.3967382283601348,-.03992559172921793,0,63.72958762196502,-.16404339962244616,1.0835251566291304,-.05498805115633132,0,24.732407896706203,-.16786010706155763,-.5603416277695248,1.6014850761964943,0,35.62982807460946,0,0,0,1,0])},technicolor:()=>{M.colorMatrix([1.9125277891456083,-.8545344976951645,-.09155508482755585,0,11.793603434377337,-.3087833385928097,1.7658908555458428,-.10601743074722245,0,-70.35205161461398,-.231103377548616,-.7501899197440212,1.847597816108189,0,30.950940869491138,0,0,0,1,0])},polaroid:()=>{M.colorMatrix([1.438,-.062,-.062,0,0,-.122,1.378,-.122,0,0,-.016,-.016,1.483,0,0,0,0,0,1,0])},shiftToBGR:()=>{M.colorMatrix([0,0,1,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,1,0])},convolution:P=>{let m=new Float32Array(P),b=1/l.width,z=1/l.height,w=g(N1);!w||(i.uniform1fv(w.uniform.m,m),i.uniform2f(w.uniform.px,b,z),p())},detectEdges:()=>{M.convolution.call(this,[0,1,0,1,-4,1,0,1,0])},sobelX:()=>{M.convolution.call(this,[-1,0,1,-2,0,2,-1,0,1])},sobelY:()=>{M.convolution.call(this,[-1,-2,-1,0,0,0,1,2,1])},sharpen:P=>{let m=P||1;M.convolution.call(this,[0,-1*m,0,-1*m,1+4*m,-1*m,0,-1*m,0])},emboss:P=>{let m=P||1;M.convolution.call(this,[-2*m,-1*m,0,-1*m,1,1*m,0,1*m,2*m])},blur:P=>{let m=P/7/l.width,b=P/7/l.height,z=g(j1);!z||(i.uniform2f(z.uniform.px,0,b),p(x.INTERMEDIATE),i.uniform2f(z.uniform.px,m,0),p())},pixelate:P=>{let m=P/l.width,b=P/l.height,z=g(S1);!z||(i.uniform2f(z.uniform.size,m,b),p())}};this.add=function(P){let m=Array.prototype.slice.call(arguments,1),b=M[P];A.push({func:b,args:m})},this.reset=function(){A=[]},this.get=function(){return A},this.apply=function(P){y(P.width,P.height),e=0,t||(t=i.createTexture()),i.bindTexture(i.TEXTURE_2D,t),i.texParameteri(i.TEXTURE_2D,i.TEXTURE_WRAP_S,i.CLAMP_TO_EDGE),i.texParameteri(i.TEXTURE_2D,i.TEXTURE_WRAP_T,i.CLAMP_TO_EDGE),i.texParameteri(i.TEXTURE_2D,i.TEXTURE_MIN_FILTER,i.NEAREST),i.texParameteri(i.TEXTURE_2D,i.TEXTURE_MAG_FILTER,i.NEAREST),i.texImage2D(i.TEXTURE_2D,0,i.RGBA,i.RGBA,i.UNSIGNED_BYTE,P);for(let m=0;m<A.length;m++){n=m===A.length-1;let b=A[m];b.func.apply(this,b.args||[])}return l},this.draw=function(P){return this.add("brightness",0),this.apply(P)}}var U=D(H());async function X2(e){let t=e.shape.length===4?U.squeeze(e):e,n=U.split(t,3,2),o=[U.min(n[0]),U.min(n[1]),U.min(n[2])],r=[U.max(n[0]),U.max(n[1]),U.max(n[2])],A=await Promise.all(r.map(d=>d.data())),s=.99*Math.max(A[0][0],A[1][0],A[2][0]),a=[U.sub(n[0],o[0]),U.sub(n[1],o[1]),U.sub(n[2],o[2])],l=[U.sub(r[0],o[0]),U.sub(r[1],o[1]),U.sub(r[2],o[2])],c=[U.div(s,l[0]),U.div(s,l[1]),U.div(s,l[2])],x=[U.mul(a[0],c[0]),U.mul(a[1],c[1]),U.mul(a[2],c[2])],i=U.stack([x[0],x[1],x[2]],2),y=U.reshape(i,[1,t.shape[0],t.shape[1],3]);return U.dispose([...n,...o,...r,...a,...l,...c,...x,i,t]),y}var q2=3840,h0=null,b0=null,n2=null,Q,D0={inputSum:0,cacheDiff:1,sumMethod:0,inputTensor:void 0};function D5(){D0.inputSum=0,D0.cacheDiff=1,D0.sumMethod=0,D0.inputTensor=void 0}function k0(e,t){let n;if(k.browser)if(k.worker){if(typeof OffscreenCanvas=="undefined")throw new Error("canvas error: attempted to run in web worker but OffscreenCanvas is not supported");n=new OffscreenCanvas(e,t)}else{if(typeof document=="undefined")throw new Error("canvas error: attempted to run in browser but DOM is not defined");n=document.createElement("canvas"),n.width=e,n.height=t}else typeof k.Canvas!="undefined"?n=new k.Canvas(e,t):typeof globalThis.Canvas!="undefined"&&(n=new globalThis.Canvas(e,t));return n}function U2(e,t){let n=t||k0(e.width,e.height);return n.getContext("2d").drawImage(e,0,0),n}async function o2(e,t,n=!0){var y,d;if(!e)return t.debug&&u("input error: input is missing"),{tensor:null,canvas:null};if(!(e instanceof O.Tensor)&&!(typeof Image!="undefined"&&e instanceof Image)&&!(typeof k.Canvas!="undefined"&&e instanceof k.Canvas)&&!(typeof globalThis.Canvas!="undefined"&&e instanceof globalThis.Canvas)&&!(typeof ImageData!="undefined"&&e instanceof ImageData)&&!(typeof ImageBitmap!="undefined"&&e instanceof ImageBitmap)&&!(typeof HTMLImageElement!="undefined"&&e instanceof HTMLImageElement)&&!(typeof HTMLMediaElement!="undefined"&&e instanceof HTMLMediaElement)&&!(typeof HTMLVideoElement!="undefined"&&e instanceof HTMLVideoElement)&&!(typeof HTMLCanvasElement!="undefined"&&e instanceof HTMLCanvasElement)&&!(typeof OffscreenCanvas!="undefined"&&e instanceof OffscreenCanvas))throw new Error("input error: type is not recognized");if(e instanceof O.Tensor){let f=null;if(e.isDisposedInternal)throw new Error("input error: attempted to use tensor but it is disposed");if(!e.shape)throw new Error("input error: attempted to use tensor without a shape");if(e.shape.length===3){if(e.shape[2]===3)f=O.expandDims(e,0);else if(e.shape[2]===4){let p=O.slice3d(e,[0,0,0],[-1,-1,3]);f=O.expandDims(p,0),O.dispose(p)}}else e.shape.length===4&&(e.shape[3]===3?f=O.clone(e):e.shape[3]===4&&(f=O.slice4d(e,[0,0,0,0],[-1,-1,-1,3])));if(f==null||f.shape.length!==4||f.shape[0]!==1||f.shape[3]!==3)throw new Error(`input error: attempted to use tensor with unrecognized shape: ${e.shape.toString()}`);if(f.dtype==="int32"){let p=O.cast(f,"float32");O.dispose(f),f=p}return{tensor:f,canvas:t.filter.return?b0:null}}if(typeof e.readyState!="undefined"&&e.readyState<=2)return t.debug&&u("input stream is not ready"),{tensor:null,canvas:h0};let o=e.naturalWidth||e.videoWidth||e.width||e.shape&&e.shape[1]>0,r=e.naturalHeight||e.videoHeight||e.height||e.shape&&e.shape[2]>0;if(!o||!r)return t.debug&&u("cannot determine input dimensions"),{tensor:null,canvas:h0};let A=o,s=r;if(A>q2&&(A=q2,s=Math.trunc(A*r/o)),s>q2&&(s=q2,A=Math.trunc(s*o/r)),(((y=t.filter)==null?void 0:y.width)||0)>0?A=t.filter.width:(((d=t.filter)==null?void 0:d.height)||0)>0&&(A=o*((t.filter.height||0)/r)),(t.filter.height||0)>0?s=t.filter.height:(t.filter.width||0)>0&&(s=r*((t.filter.width||0)/o)),!A||!s)throw new Error("input error: cannot determine dimension");(!h0||h0.width!==A||h0.height!==s)&&(h0=k0(A,s));let a=h0.getContext("2d");if(typeof ImageData!="undefined"&&e instanceof ImageData?a.putImageData(e,0,0):t.filter.flip&&typeof a.translate!="undefined"?(a.translate(o,0),a.scale(-1,1),a.drawImage(e,0,0,o,r,0,0,h0.width,h0.height),a.setTransform(1,0,0,1,0,0)):a.drawImage(e,0,0,o,r,0,0,h0.width,h0.height),(!b0||h0.width!==b0.width||h0.height!==b0.height)&&(b0=k0(h0.width,h0.height)),t.filter.enabled&&k.webgl.supported?(Q||(Q=k.browser?new I1:null),k.filter=!!Q,Q!=null&&Q.add?(Q.reset(),t.filter.brightness!==0&&Q.add("brightness",t.filter.brightness),t.filter.contrast!==0&&Q.add("contrast",t.filter.contrast),t.filter.sharpness!==0&&Q.add("sharpen",t.filter.sharpness),t.filter.blur!==0&&Q.add("blur",t.filter.blur),t.filter.saturation!==0&&Q.add("saturation",t.filter.saturation),t.filter.hue!==0&&Q.add("hue",t.filter.hue),t.filter.negative&&Q.add("negative"),t.filter.sepia&&Q.add("sepia"),t.filter.vintage&&Q.add("brownie"),t.filter.sepia&&Q.add("sepia"),t.filter.kodachrome&&Q.add("kodachrome"),t.filter.technicolor&&Q.add("technicolor"),t.filter.polaroid&&Q.add("polaroid"),t.filter.pixelate!==0&&Q.add("pixelate",t.filter.pixelate),Q.get()>0?b0=Q.apply(h0):b0=Q.draw(h0)):(t.debug&&u("input process error: cannot initialize filters"),k.webgl.supported=!1,t.filter.enabled=!1,U2(h0,b0))):(U2(h0,b0),Q&&(Q=null),k.filter=!!Q),!n)return{tensor:null,canvas:b0};if(!b0)throw new Error("canvas error: cannot create output");let l,c=3;if(typeof ImageData!="undefined"&&e instanceof ImageData||e.data&&e.width&&e.height)if(k.browser&&O.browser)l=O.browser?O.browser.fromPixels(e):null;else{c=e.data.length/e.height/e.width;let f=new Uint8Array(e.data.buffer);l=O.tensor(f,[e.height,e.width,c],"int32")}else if((!n2||b0.width!==n2.width||b0.height!==n2.height)&&(n2=k0(b0.width,b0.height)),O.browser&&k.browser)t.backend==="webgl"||t.backend==="humangl"||t.backend==="webgpu"?l=O.browser.fromPixels(b0):(n2=U2(b0),l=O.browser.fromPixels(n2));else{let g=U2(b0).getContext("2d").getImageData(0,0,A,s);c=g.data.length/A/s;let M=new Uint8Array(g.data.buffer);l=O.tensor(M,[A,s,c])}if(c===4){let f=O.slice3d(l,[0,0,0],[-1,-1,3]);O.dispose(l),l=f}if(!l)throw new Error("input error: cannot create tensor");let x=O.cast(l,"float32"),i=t.filter.equalization?await X2(x):O.expandDims(x,0);return O.dispose([l,x]),{tensor:i,canvas:t.filter.return?b0:null}}async function O1(e,t){let n=!1;if(e.cacheSensitivity===0||!t.shape||t.shape.length!==4||t.shape[1]>2048||t.shape[2]>2048)return n;if(!D0.inputTensor)D0.inputTensor=O.clone(t);else if(D0.inputTensor.shape[1]!==t.shape[1]||D0.inputTensor.shape[2]!==t.shape[2])O.dispose(D0.inputTensor),D0.inputTensor=O.clone(t);else{let o={};o.diff=O.sub(t,D0.inputTensor),o.squared=O.mul(o.diff,o.diff),o.sum=O.sum(o.squared);let A=(await o.sum.data())[0]/(t.shape[1]||1)/(t.shape[2]||1)/255/3;O.dispose([D0.inputTensor,o.diff,o.squared,o.sum]),D0.inputTensor=O.clone(t),n=A<=(e.cacheSensitivity||0)}return n}async function C1(e,t,n){let o={};if(!t||!n||t.shape.length!==4||t.shape.length!==n.shape.length)return e.debug||u("invalid input tensor or tensor shapes do not match:",t.shape,n.shape),0;if(t.shape[0]!==1||n.shape[0]!==1||t.shape[3]!==3||n.shape[3]!==3)return e.debug||u("input tensors must be of shape [1, height, width, 3]:",t.shape,n.shape),0;o.input1=O.clone(t),o.input2=t.shape[1]!==n.shape[1]||t.shape[2]!==n.shape[2]?O.image.resizeBilinear(n,[t.shape[1],t.shape[2]]):O.clone(n),o.diff=O.sub(o.input1,o.input2),o.squared=O.mul(o.diff,o.diff),o.sum=O.sum(o.squared);let A=(await o.sum.data())[0]/(t.shape[1]||1)/(t.shape[2]||1)/255/3;return O.dispose([o.input1,o.input2,o.diff,o.squared,o.sum]),A}var S2=class{constructor(){R(this,"browser");R(this,"node");R(this,"worker");R(this,"platform","");R(this,"agent","");R(this,"backends",[]);R(this,"initial");R(this,"filter");R(this,"tfjs");R(this,"offscreen");R(this,"perfadd",!1);R(this,"tensorflow",{version:void 0,gpu:void 0});R(this,"wasm",{supported:void 0,backend:void 0,simd:void 0,multithread:void 0});R(this,"webgl",{supported:void 0,backend:void 0,version:void 0,renderer:void 0});R(this,"webgpu",{supported:void 0,backend:void 0,adapter:void 0});R(this,"cpu",{model:void 0,flags:[]});R(this,"kernels",[]);R(this,"Canvas");R(this,"Image");R(this,"ImageData");if(this.browser=typeof navigator!="undefined",this.node=typeof process!="undefined"&&typeof process.versions!="undefined"&&typeof process.versions.node!="undefined",this.tfjs={version:x0.version["tfjs-core"]},this.offscreen=typeof OffscreenCanvas!="undefined",this.initial=!0,this.worker=this.browser&&this.offscreen?typeof WorkerGlobalScope!="undefined":void 0,typeof navigator!="undefined"){let t=navigator.userAgent.match(/\(([^()]+)\)/g);if(t!=null&&t[0]){let n=t[0].match(/\(([^()]+)\)/g);this.platform=n!=null&&n[0]?n[0].replace(/\(|\)/g,""):"",this.agent=navigator.userAgent.replace(t[0],""),this.platform[1]&&(this.agent=this.agent.replace(t[1],"")),this.agent=this.agent.replace(/  /g," ")}}else typeof process!="undefined"&&(this.platform=`${process.platform} ${process.arch}`,this.agent=`NodeJS ${process.version}`)}async updateBackend(){this.backends=Object.keys(x0.engine().registryFactory),this.tensorflow={version:x0.backend().binding?x0.backend().binding.TF_Version:void 0,gpu:x0.backend().binding?x0.backend().binding.isUsingGpuDevice():void 0},this.wasm.supported=typeof WebAssembly!="undefined",this.wasm.backend=this.backends.includes("wasm"),this.wasm.supported&&this.wasm.backend&&x0.getBackend()==="wasm"&&(this.wasm.simd=x0.env().get("WASM_HAS_SIMD_SUPPORT"),this.wasm.multithread=x0.env().get("WASM_HAS_MULTITHREAD_SUPPORT"));let t=k0(100,100),n=t?t.getContext("webgl2"):void 0;if(this.webgl.supported=typeof n!="undefined",this.webgl.backend=this.backends.includes("webgl"),this.webgl.supported&&this.webgl.backend&&(x0.getBackend()==="webgl"||x0.getBackend()==="humangl")){let o=x0.backend().gpgpu!=="undefined"?await x0.backend().getGPGPUContext().gl:null;o&&(this.webgl.version=o.getParameter(o.VERSION),this.webgl.renderer=o.getParameter(o.RENDERER))}this.webgpu.supported=this.browser&&typeof navigator.gpu!="undefined",this.webgpu.backend=this.backends.includes("webgpu");try{if(this.webgpu.supported){let o=await navigator.gpu.requestAdapter();this.webgpu.adapter=o?o.name:void 0}}catch(o){this.webgpu.supported=!1}try{this.kernels=x0.getKernelsForBackend(x0.getBackend()).map(o=>o.kernelName.toLowerCase())}catch(o){}}updateCPU(){let t={model:"",flags:[]};this.node&&this.platform.startsWith("linux"),this.cpu?this.cpu=t:Object.defineProperty(this,"cpu",{value:t})}},k=new S2;var Y2=class{constructor(){R(this,"config");R(this,"element");R(this,"stream");R(this,"start",async t=>{if(t!=null&&t.debug&&(this.config.debug=t==null?void 0:t.debug),t!=null&&t.crop&&(this.config.crop=t==null?void 0:t.crop),t!=null&&t.mode&&(this.config.mode=t==null?void 0:t.mode),t!=null&&t.width&&(this.config.width=t==null?void 0:t.width),t!=null&&t.height&&(this.config.height=t==null?void 0:t.height),t!=null&&t.element)if(typeof t.element=="string"){let r=document.getElementById(t.element);if(r&&r instanceof HTMLVideoElement)this.element=r;else{this.config.debug&&u("webcam","cannot get dom element",t.element);return}}else if(t.element instanceof HTMLVideoElement)this.element=t.element;else{this.config.debug&&u("webcam","unknown dom element",t.element);return}else this.element=document.createElement("video");let n={audio:!1,video:{facingMode:this.config.mode==="front"?"user":"environment",resizeMode:this.config.crop?"crop-and-scale":"none",width:{ideal:this.config.width>0?this.config.width:window.innerWidth},height:{ideal:this.config.height>0?this.config.height:window.innerHeight}}};if(this.element.addEventListener("play",()=>{this.config.debug&&u("webcam","play")}),this.element.addEventListener("pause",()=>{this.config.debug&&u("webcam","pause")}),this.element.addEventListener("click",async()=>{!this.element||!this.stream||(this.element.paused?await this.element.play():this.element.pause())}),!(navigator!=null&&navigator.mediaDevices)){this.config.debug&&u("webcam","no 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Zr=[127,234,132,58,172,150,149,148,152,377,378,379,397,288,361,454,356,70,63,105,66,107,336,296,334,293,300,168,6,195,4,98,97,2,326,327,33,160,158,133,153,144,362,385,387,263,373,380,57,40,37,0,267,270,287,321,314,17,84,91,78,81,13,311,308,402,14,178],Xr=[33,133,362,263,1,62,308,159,145,386,374,6,102,331,2,13,14,70,105,107,336,334,300,54,10,284,50,280,234,454,58,288,152],qr=[33,133,362,263,1,78,308],f7=Zr.map(e=>j2[e]),m7=Xr.map(e=>j2[e]),p7=qr.map(e=>j2[e]);function Re(e){let t=e.map(n=>n[0]);return t.push(e[e.length-1][1]),t}var 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A2=e=>[Math.abs(e.endPoint[0]-e.startPoint[0]),Math.abs(e.endPoint[1]-e.startPoint[1])],e5=e=>[e.startPoint[0]+(e.endPoint[0]-e.startPoint[0])/2,e.startPoint[1]+(e.endPoint[1]-e.startPoint[1])/2,1],t5=(e,t)=>e?[Math.trunc(Math.max(0,e.startPoint[0])),Math.trunc(Math.max(0,e.startPoint[1])),Math.trunc(Math.min(t.shape[2]||0,e.endPoint[0])-Math.max(0,e.startPoint[0])),Math.trunc(Math.min(t.shape[1]||0,e.endPoint[1])-Math.max(0,e.startPoint[1]))]:[0,0,0,0],n5=(e,t)=>e?[e.startPoint[0]/(t.shape[2]||0),e.startPoint[1]/(t.shape[1]||0),(e.endPoint[0]-e.startPoint[0])/(t.shape[2]||0),(e.endPoint[1]-e.startPoint[1])/(t.shape[1]||0)]:[0,0,0,0],t3=(e,t)=>{let n=[e.startPoint[0]*t[0],e.startPoint[1]*t[1]],o=[e.endPoint[0]*t[0],e.endPoint[1]*t[1]];return{startPoint:n,endPoint:o,landmarks:e.landmarks,confidence:e.confidence}},rt=(e,t,n)=>{let o=t.shape[1],r=t.shape[2],A=[e.startPoint[1]/o,e.startPoint[0]/r,e.endPoint[1]/o,e.endPoint[0]/r],s=ye.image.cropAndResize(t,[A],[0],n),a=ye.div(s,B.tf255);return ye.dispose(s),a},o5=(e,t)=>{let n=e5(e),o=A2(e),r=[t*o[0]/2,t*o[1]/2];return{startPoint:[n[0]-r[0],n[1]-r[1]],endPoint:[n[0]+r[0],n[1]+r[1]],landmarks:e.landmarks,confidence:e.confidence}},r5=e=>{let t=e5(e),n=A2(e),o=Math.max(...n)/2;return{startPoint:[Math.round(t[0]-o),Math.round(t[1]-o)],endPoint:[Math.round(t[0]+o),Math.round(t[1]+o)],landmarks:e.landmarks,confidence:e.confidence}},n3=e=>{let t=e.map(o=>o[0]),n=e.map(o=>o[1]);return{startPoint:[Math.min(...t),Math.min(...n)],endPoint:[Math.max(...t),Math.max(...n)],landmarks:e}},At=[[1,0,0],[0,1,0],[0,0,1]],tA=e=>e-2*Math.PI*Math.floor((e+Math.PI)/(2*Math.PI)),nA=(e,t)=>tA(Math.PI/2-Math.atan2(-(t[1]-e[1]),t[0]-e[0]));var $1=(e,t)=>[[1,0,e],[0,1,t],[0,0,1]],De=(e,t)=>{let n=0;for(let o=0;o<e.length;o++)n+=e[o]*t[o];return n},oA=(e,t)=>{let n=[];for(let o=0;o<e.length;o++)n.push(e[o][t]);return n},e3=(e,t)=>{let n=[],o=e.length;for(let r=0;r<o;r++){n.push([]);for(let A=0;A<o;A++)n[r].push(De(e[r],oA(t,A)))}return n},o3=(e,t)=>{let n=Math.cos(e),o=Math.sin(e),r=[[n,-o,0],[o,n,0],[0,0,1]],A=$1(t[0],t[1]),s=e3(A,r),a=$1(-t[0],-t[1]);return e3(s,a)},rA=e=>{let t=[[e[0][0],e[1][0]],[e[0][1],e[1][1]]],n=[e[0][2],e[1][2]],o=[-De(t[0],n),-De(t[1],n)];return[t[0].concat(o[0]),t[1].concat(o[1]),[0,0,1]]},AA=(e,t)=>[De(e,t[0]),De(e,t[1])];function r3(e){let t=e===192?{strides:[4],anchors:[1]}:{strides:[e/16,e/8],anchors:[2,6]},n=[];for(let o=0;o<t.strides.length;o++){let r=t.strides[o],A=Math.floor((e+r-1)/r),s=Math.floor((e+r-1)/r),a=t.anchors[o];for(let l=0;l<A;l++){let c=r*(l+.5);for(let x=0;x<s;x++){let i=r*(x+.5);for(let y=0;y<a;y++)n.push([i,c])}}}return n}function A3(e,t,n,o,r){let A=A2(t),s=e.map(d=>[A[0]/r*(d[0]-r/2),A[1]/r*(d[1]-r/2),d[2]||0]),a=n&&n!==0&&Math.abs(n)>.2,l=a?o3(n,[0,0]):At,c=a?s.map(d=>[...AA(d,l),d[2]]):s,x=a?rA(o):At,i=e5(t),y=[De(i,x[0]),De(i,x[1])];return c.map(d=>[Math.trunc(d[0]+y[0]),Math.trunc(d[1]+y[1]),Math.trunc(d[2]||0)])}function s3(e,t,n,o){let r=t.landmarks.length>=tt.count?tt.symmetryLine:He.symmetryLine,A=0,s=At,a;if(e&&k.kernels.includes("rotatewithoffset"))if(A=nA(t.landmarks[r[0]],t.landmarks[r[1]]),A&&A!==0&&Math.abs(A)>.2){let c=e5(t),x=[c[0]/n.shape[2],c[1]/n.shape[1]],i=ye.image.rotateWithOffset(n,A,0,x);s=o3(-A,c),a=rt(t,i,[o,o]),ye.dispose(i)}else a=rt(t,n,[o,o]);else a=rt(t,n,[o,o]);return[A,s,a]}var sA=e=>{let t=e.map(o=>o[0]),n=e.map(o=>o[1]);return[Math.min(...t)+(Math.max(...t)-Math.min(...t))/2,Math.min(...n)+(Math.max(...n)-Math.min(...n))/2]},a3=(e,t)=>{let n=sA(e),o=A2(t);return{startPoint:[n[0]-o[0]/2,n[1]-o[1]/2],endPoint:[n[0]+o[0]/2,n[1]+o[1]/2]}};var i3=6,aA=1.4,ee,l3=null,ke=0,N2=null,s2=()=>ke;async function c3(e){var t;return k.initial&&(ee=null),ee?e.debug&&u("cached model:",ee.modelUrl):ee=await L((t=e.face.detector)==null?void 0:t.modelPath),ke=ee.executor&&ee.inputs[0].shape?ee.inputs[0].shape[2]:256,N2=C.scalar(ke,"int32"),l3=C.tensor2d(r3(ke)),ee}function iA(e){let t={};t.boxStarts=C.slice(e,[0,1],[-1,2]),t.centers=C.add(t.boxStarts,l3),t.boxSizes=C.slice(e,[0,3],[-1,2]),t.boxSizesNormalized=C.div(t.boxSizes,N2),t.centersNormalized=C.div(t.centers,N2),t.halfBoxSize=C.div(t.boxSizesNormalized,B.tf2),t.starts=C.sub(t.centersNormalized,t.halfBoxSize),t.ends=C.add(t.centersNormalized,t.halfBoxSize),t.startNormalized=C.mul(t.starts,N2),t.endNormalized=C.mul(t.ends,N2);let n=C.concat2d([t.startNormalized,t.endNormalized],1);return Object.keys(t).forEach(o=>C.dispose(t[o])),n}async function d3(e,t){var a,l,c,x;if(!e||e.isDisposedInternal||e.shape.length!==4||e.shape[1]<1||e.shape[2]<1)return[];let n={};n.resized=C.image.resizeBilinear(e,[ke,ke]),n.div=C.div(n.resized,B.tf127),n.normalized=C.sub(n.div,B.tf05);let o=ee==null?void 0:ee.execute(n.normalized);if(Array.isArray(o)&&o.length>2){let i=o.sort((y,d)=>y.size-d.size);n.concat384=C.concat([i[0],i[2]],2),n.concat512=C.concat([i[1],i[3]],2),n.concat=C.concat([n.concat512,n.concat384],1),n.batch=C.squeeze(n.concat,0)}else Array.isArray(o)?n.batch=C.squeeze(o[0]):n.batch=C.squeeze(o);C.dispose(o),n.boxes=iA(n.batch),n.logits=C.slice(n.batch,[0,0],[-1,1]),n.sigmoid=C.sigmoid(n.logits),n.scores=C.squeeze(n.sigmoid),n.nms=await C.image.nonMaxSuppressionAsync(n.boxes,n.scores,((a=t.face.detector)==null?void 0:a.maxDetected)||0,((l=t.face.detector)==null?void 0:l.iouThreshold)||0,((c=t.face.detector)==null?void 0:c.minConfidence)||0);let r=await n.nms.array(),A=[],s=await n.scores.data();for(let i=0;i<r.length;i++){let y=s[r[i]];if(y>(((x=t.face.detector)==null?void 0:x.minConfidence)||0)){let d={};d.bbox=C.slice(n.boxes,[r[i],0],[1,-1]),d.slice=C.slice(n.batch,[r[i],i3-1],[1,-1]),d.squeeze=C.squeeze(d.slice),d.landmarks=C.reshape(d.squeeze,[i3,-1]);let f=await d.bbox.data(),p={startPoint:[f[0],f[1]],endPoint:[f[2],f[3]],landmarks:await d.landmarks.array(),confidence:y},g=t3(p,[(e.shape[2]||0)/ke,(e.shape[1]||0)/ke]),M=o5(g,t.face.scale||aA),P=r5(M);A.push(P),Object.keys(d).forEach(m=>C.dispose(d[m]))}}return Object.keys(n).forEach(i=>C.dispose(n[i])),A}var C0=D(H());var A5={};ve(A5,{connected:()=>it,kpt:()=>at});var at=["nose","leftEyeInside","leftEye","leftEyeOutside","rightEyeInside","rightEye","rightEyeOutside","leftEar","rightEar","leftMouth","rightMouth","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftPinky","rightPinky","leftIndex","rightIndex","leftThumb","rightThumb","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle","leftHeel","rightHeel","leftFoot","rightFoot","bodyCenter","bodyTop","leftPalm","leftHand","rightPalm","rightHand"],it={shoulders:["leftShoulder","rightShoulder"],hips:["rightHip","leftHip"],mouth:["leftMouth","rightMouth"],leftLegUpper:["leftHip","leftKnee"],leftLegLower:["leftKnee","leftAnkle"],leftFoot:["leftAnkle","leftHeel","leftFoot"],leftTorso:["leftShoulder","leftHip"],leftArmUpper:["leftShoulder","leftElbow"],leftArmLower:["leftElbow","leftWrist"],leftHand:["leftWrist","leftPalm"],leftHandPinky:["leftPalm","leftPinky"],leftHandIndex:["leftPalm","leftIndex"],leftHandThumb:["leftPalm","leftThumb"],leftEyeOutline:["leftEyeInside","leftEyeOutside"],rightLegUpper:["rightHip","rightKnee"],rightLegLower:["rightKnee","rightAnkle"],rightFoot:["rightAnkle","rightHeel","rightFoot"],rightTorso:["rightShoulder","rightHip"],rightArmUpper:["rightShoulder","rightElbow"],rightArmLower:["rightElbow","rightWrist"],rightHand:["rightWrist","rightPalm"],rightHandPinky:["rightPalm","rightPinky"],rightHandIndex:["rightPalm","rightIndex"],rightHandThumb:["rightPalm","rightThumb"],rightEyeOutline:["rightEyeInside","rightEyeOutside"]};var O0=D(H()),y3=224,lA,cA=5,s5=[8,16,32,32,32];function f3(){let e=[],t=0;for(;t<cA;){let n=0,o=t;for(;o<s5.length&&s5[o]===s5[t];)n+=2,o++;let r=s5[t],A=Math.ceil(y3/r),s=Math.ceil(y3/r);for(let a=0;a<A;++a)for(let l=0;l<s;++l)for(let c=0;c<n;++c)e.push({x:(l+.5)/s,y:(a+.5)/A});t=o}lA={x:O0.tensor1d(e.map(n=>n.x)),y:O0.tensor1d(e.map(n=>n.y))}}function fe(e,t=[1,1]){let n=[e.map(a=>a[0]),e.map(a=>a[1])],o=[Math.min(...n[0]),Math.min(...n[1])],r=[Math.max(...n[0]),Math.max(...n[1])],A=[o[0],o[1],r[0]-o[0],r[1]-o[1]],s=[A[0]/t[0],A[1]/t[1],A[2]/t[0],A[3]/t[1]];return{box:A,boxRaw:s}}function m3(e,t=[1,1]){let n=[e.map(c=>c[0]),e.map(c=>c[1])],o=[Math.min(...n[0]),Math.min(...n[1])],r=[Math.max(...n[0]),Math.max(...n[1])],A=[(o[0]+r[0])/2,(o[1]+r[1])/2],s=Math.max(A[0]-o[0],A[1]-o[1],-A[0]+r[0],-A[1]+r[1]),a=[Math.trunc(A[0]-s),Math.trunc(A[1]-s),Math.trunc(2*s),Math.trunc(2*s)],l=[a[0]/t[0],a[1]/t[1],a[2]/t[0],a[3]/t[1]];return{box:a,boxRaw:l}}function a5(e,t){let n=[e[2]*t,e[3]*t];return[e[0]-(n[0]-e[2])/2,e[1]-(n[1]-e[3])/2,n[0],n[1]]}var h3={initial:!0},P0={detector:null,landmarks:null},a2={detector:[224,224],landmarks:[256,256]},lt=Number.MAX_SAFE_INTEGER,xA={landmarks:["ld_3d","activation_segmentation","activation_heatmap","world_3d","output_poseflag"],detector:[]},l5=null,I2,we=[[0,0],[0,0],[0,0],[0,0]],p3=0,u3=e=>1-1/(1+Math.exp(e));async function b3(e){var t;if(h3.initial&&(P0.detector=null),!P0.detector&&e.body.detector&&e.body.detector.modelPath){P0.detector=await L(e.body.detector.modelPath);let n=(t=P0.detector)!=null&&t.executor?Object.values(P0.detector.modelSignature.inputs):void 0;a2.detector[0]=Array.isArray(n)?parseInt(n[0].tensorShape.dim[1].size):0,a2.detector[1]=Array.isArray(n)?parseInt(n[0].tensorShape.dim[2].size):0}else e.debug&&P0.detector&&u("cached model:",P0.detector.modelUrl);return f3(),P0.detector}async function g3(e){var t;if(h3.initial&&(P0.landmarks=null),P0.landmarks)e.debug&&u("cached model:",P0.landmarks.modelUrl);else{P0.landmarks=await L(e.body.modelPath);let n=(t=P0.landmarks)!=null&&t.executor?Object.values(P0.landmarks.modelSignature.inputs):void 0;a2.landmarks[0]=Array.isArray(n)?parseInt(n[0].tensorShape.dim[1].size):0,a2.landmarks[1]=Array.isArray(n)?parseInt(n[0].tensorShape.dim[2].size):0}return P0.landmarks}function yA(e,t){var r,A;let n={};if(!((r=e==null?void 0:e.shape)!=null&&r[1])||!((A=e==null?void 0:e.shape)!=null&&A[2]))return e;let o;if(I2&&(n.cropped=C0.image.cropAndResize(e,[I2],[0],[e.shape[1],e.shape[2]])),e.shape[1]!==e.shape[2]){let s=[e.shape[2]>e.shape[1]?Math.trunc((e.shape[2]-e.shape[1])/2):0,e.shape[2]>e.shape[1]?Math.trunc((e.shape[2]-e.shape[1])/2):0],a=[e.shape[1]>e.shape[2]?Math.trunc((e.shape[1]-e.shape[2])/2):0,e.shape[1]>e.shape[2]?Math.trunc((e.shape[1]-e.shape[2])/2):0];we=[[0,0],s,a,[0,0]],n.pad=C0.pad(n.cropped||e,we),n.resize=C0.image.resizeBilinear(n.pad,[t,t]),o=C0.div(n.resize,B.tf255)}else e.shape[1]!==t?(n.resize=C0.image.resizeBilinear(n.cropped||e,[t,t]),o=C0.div(n.resize,B.tf255)):o=C0.div(n.cropped||e,B.tf255);return Object.keys(n).forEach(s=>C0.dispose(n[s])),o}function fA(e,t){for(let n of e)n.position=[Math.trunc(n.position[0]*(t[0]+we[2][0]+we[2][1])/t[0]-we[2][0]),Math.trunc(n.position[1]*(t[1]+we[1][0]+we[1][1])/t[1]-we[1][0]),n.position[2]],n.positionRaw=[n.position[0]/t[0],n.position[1]/t[1],2*n.position[2]/(t[0]+t[1])];if(I2)for(let n of e)n.positionRaw=[n.positionRaw[0]+I2[1],n.positionRaw[1]+I2[0],n.positionRaw[2]],n.position=[Math.trunc(n.positionRaw[0]*t[0]),Math.trunc(n.positionRaw[1]*t[1]),n.positionRaw[2]];return e}function mA(e){let t=e.find(a=>a.part==="leftPalm"),n=e.find(a=>a.part==="leftWrist"),o=e.find(a=>a.part==="leftIndex");t.position[2]=((n.position[2]||0)+(o.position[2]||0))/2;let r=e.find(a=>a.part==="rightPalm"),A=e.find(a=>a.part==="rightWrist"),s=e.find(a=>a.part==="rightIndex");r.position[2]=((A.position[2]||0)+(s.position[2]||0))/2}async function pA(e,t,n){var f,p;if(!((f=P0.landmarks)!=null&&f.executor))return null;let o={};[o.ld,o.segmentation,o.heatmap,o.world,o.poseflag]=(p=P0.landmarks)==null?void 0:p.execute(e,xA.landmarks);let r=(await o.poseflag.data())[0],A=await o.ld.data(),s=await o.world.data();Object.keys(o).forEach(g=>C0.dispose(o[g]));let a=[],l=5;for(let g=0;g<A.length/l;g++){let M=u3(A[l*g+3]),P=u3(A[l*g+4]),m=Math.trunc(100*M*P*r)/100,b=[A[l*g+0]/a2.landmarks[0],A[l*g+1]/a2.landmarks[1],A[l*g+2]+0],z=[Math.trunc(n[0]*b[0]),Math.trunc(n[1]*b[1]),b[2]],w=[s[l*g+0],s[l*g+1],s[l*g+2]+0];a.push({part:at[g],positionRaw:b,position:z,distance:w,score:m})}if(r<(t.body.minConfidence||0))return null;mA(a);let c=fA(a,n),x=c.map(g=>g.position),i=fe(x,[n[0],n[1]]),y={};for(let[g,M]of Object.entries(it)){let P=[];for(let m=0;m<M.length-1;m++){let b=c.find(w=>w.part===M[m]),z=c.find(w=>w.part===M[m+1]);b&&z&&P.push([b.position,z.position])}y[g]=P}return{id:0,score:Math.trunc(100*r)/100,box:i.box,boxRaw:i.boxRaw,keypoints:c,annotations:y}}async function ct(e,t){let n=[e.shape[2]||0,e.shape[1]||0],o=(t.body.skipTime||0)>v()-p3,r=lt<(t.body.skipFrames||0);if(t.skipAllowed&&o&&r&&l5!==null)lt++;else{let A={};A.landmarks=yA(e,256),l5=await pA(A.landmarks,t,n),Object.keys(A).forEach(s=>C0.dispose(A[s])),p3=v(),lt=0}return l5?[l5]:[]}var w0=D(H());var i2=[{class:1,label:"person"},{class:2,label:"bicycle"},{class:3,label:"car"},{class:4,label:"motorcycle"},{class:5,label:"airplane"},{class:6,label:"bus"},{class:7,label:"train"},{class:8,label:"truck"},{class:9,label:"boat"},{class:10,label:"traffic light"},{class:11,label:"fire hydrant"},{class:12,label:"stop sign"},{class:13,label:"parking meter"},{class:14,label:"bench"},{class:15,label:"bird"},{class:16,label:"cat"},{class:17,label:"dog"},{class:18,label:"horse"},{class:19,label:"sheep"},{class:20,label:"cow"},{class:21,label:"elephant"},{class:22,label:"bear"},{class:23,label:"zebra"},{class:24,label:"giraffe"},{class:25,label:"backpack"},{class:26,label:"umbrella"},{class:27,label:"handbag"},{class:28,label:"tie"},{class:29,label:"suitcase"},{class:30,label:"frisbee"},{class:31,label:"skis"},{class:32,label:"snowboard"},{class:33,label:"sports ball"},{class:34,label:"kite"},{class:35,label:"baseball bat"},{class:36,label:"baseball glove"},{class:37,label:"skateboard"},{class:38,label:"surfboard"},{class:39,label:"tennis racket"},{class:40,label:"bottle"},{class:41,label:"wine 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drier"},{class:80,label:"toothbrush"}];var N0,Ze=0,dt=[],v3=0,xt=Number.MAX_SAFE_INTEGER;async function P3(e){if(k.initial&&(N0=null),N0)e.debug&&u("cached model:",N0.modelUrl);else{N0=await L(e.object.modelPath);let t=N0!=null&&N0.executor?Object.values(N0.modelSignature.inputs):void 0;Ze=Array.isArray(t)?parseInt(t[0].tensorShape.dim[2].size):0}return N0}async function uA(e,t,n){if(!e)return[];let o={},r=[],A=await e.array();o.squeeze=w0.squeeze(e);let s=w0.split(o.squeeze,6,1);o.stack=w0.stack([s[1],s[0],s[3],s[2]],1),o.boxes=w0.squeeze(o.stack),o.scores=w0.squeeze(s[4]),o.classes=w0.squeeze(s[5]),w0.dispose([e,...s]),o.nms=await w0.image.nonMaxSuppressionAsync(o.boxes,o.scores,n.object.maxDetected,n.object.iouThreshold,n.object.minConfidence||0);let a=await o.nms.data(),l=0;for(let c of Array.from(a)){let x=Math.trunc(100*A[0][c][4])/100,i=A[0][c][5];if(Number.isNaN(i))continue;let y=i2[i].label,[d,f]=[A[0][c][0]/Ze,A[0][c][1]/Ze],p=[d,f,A[0][c][2]/Ze-d,A[0][c][3]/Ze-f],g=[Math.trunc(p[0]*t[0]),Math.trunc(p[1]*t[1]),Math.trunc(p[2]*t[0]),Math.trunc(p[3]*t[1])];r.push({id:l++,score:x,class:i,label:y,box:g,boxRaw:p})}return Object.keys(o).forEach(c=>w0.dispose(o[c])),r}async function yt(e,t){if(!(N0!=null&&N0.executor))return[];let n=(t.object.skipTime||0)>v()-v3,o=xt<(t.object.skipFrames||0);return t.skipAllowed&&n&&o&&dt.length>0?(xt++,dt):(xt=0,new Promise(async r=>{let A=[e.shape[2]||0,e.shape[1]||0],s=w0.image.resizeBilinear(e,[Ze,Ze]),a=t.object.enabled?N0==null?void 0:N0.execute(s,["tower_0/detections"]):null;v3=v(),w0.dispose(s);let l=await uA(a,A,t);dt=l,r(l)}))}var K=D(H());var c5={};ve(c5,{connected:()=>mt,kpt:()=>ft});var ft=["head","neck","rightShoulder","rightElbow","rightWrist","chest","leftShoulder","leftElbow","leftWrist","bodyCenter","rightHip","rightKnee","rightAnkle","leftHip","leftKnee","leftAnkle"],mt={leftLeg:["leftHip","leftKnee","leftAnkle"],rightLeg:["rightHip","rightKnee","rightAnkle"],torso:["leftShoulder","rightShoulder","rightHip","leftHip","leftShoulder"],leftArm:["leftShoulder","leftElbow","leftWrist"],rightArm:["rightShoulder","rightElbow","rightWrist"],head:[]};var u0,R3=0,E0={id:0,keypoints:[],box:[0,0,0,0],boxRaw:[0,0,0,0],score:0,annotations:{}},pt=Number.MAX_SAFE_INTEGER;async function k3(e){return k.initial&&(u0=null),u0?e.debug&&u("cached model:",u0.modelUrl):u0=await L(e.body.modelPath),u0}async function hA(e,t){let[n,o]=e.shape,r=K.reshape(e,[o*n]),A=K.max(r,0),s=(await A.data())[0];if(s>t){let a=K.argMax(r,0),l=K.mod(a,n),c=(await l.data())[0],x=K.div(a,n),i=(await x.data())[0];return K.dispose([r,A,a,l,x]),[c,i,s]}return K.dispose([r,A]),[0,0,s]}async function ut(e,t){if(!(u0!=null&&u0.executor))return[];let n=(t.body.skipTime||0)>v()-R3,o=pt<(t.body.skipFrames||0);return t.skipAllowed&&n&&o&&Object.keys(E0.keypoints).length>0?(pt++,[E0]):(pt=0,new Promise(async r=>{let A=K.tidy(()=>{if(!(u0!=null&&u0.inputs[0].shape))return null;let i=K.image.resizeBilinear(e,[u0.inputs[0].shape[2],u0.inputs[0].shape[1]],!1),y=K.mul(i,B.tf2);return K.sub(y,B.tf1)}),s;if(t.body.enabled&&(s=u0==null?void 0:u0.execute(A)),R3=v(),K.dispose(A),s){E0.keypoints.length=0;let i=K.squeeze(s);K.dispose(s);let y=K.unstack(i,2);K.dispose(i);for(let d=0;d<y.length;d++){let[f,p,g]=await hA(y[d],t.body.minConfidence);g>(t.body.minConfidence||0)&&E0.keypoints.push({score:Math.round(100*g)/100,part:ft[d],positionRaw:[f/u0.inputs[0].shape[2],p/u0.inputs[0].shape[1]],position:[Math.round(e.shape[2]*f/u0.inputs[0].shape[2]),Math.round(e.shape[1]*p/u0.inputs[0].shape[1])]})}y.forEach(d=>K.dispose(d))}E0.score=E0.keypoints.reduce((i,y)=>y.score>i?y.score:i,0);let a=E0.keypoints.map(i=>i.position[0]),l=E0.keypoints.map(i=>i.position[1]);E0.box=[Math.min(...a),Math.min(...l),Math.max(...a)-Math.min(...a),Math.max(...l)-Math.min(...l)];let c=E0.keypoints.map(i=>i.positionRaw[0]),x=E0.keypoints.map(i=>i.positionRaw[1]);E0.boxRaw=[Math.min(...c),Math.min(...x),Math.max(...c)-Math.min(...c),Math.max(...x)-Math.min(...x)];for(let[i,y]of Object.entries(mt)){let d=[];for(let f=0;f<y.length-1;f++){let p=E0.keypoints.find(M=>M.part===y[f]),g=E0.keypoints.find(M=>M.part===y[f+1]);p&&g&&p.score>(t.body.minConfidence||0)&&g.score>(t.body.minConfidence||0)&&d.push([p.position,g.position])}E0.annotations[i]=d}r([E0])}))}var te=D(H());var bA=["angry","disgust","fear","happy","sad","surprise","neutral"],Z0,d5=[],E3=0,z3=0,ht=Number.MAX_SAFE_INTEGER;async function S3(e){var t;return k.initial&&(Z0=null),Z0?e.debug&&u("cached model:",Z0.modelUrl):Z0=await L((t=e.face.emotion)==null?void 0:t.modelPath),Z0}async function bt(e,t,n,o){var s,a;if(!Z0)return[];let r=ht<(((s=t.face.emotion)==null?void 0:s.skipFrames)||0),A=(((a=t.face.emotion)==null?void 0:a.skipTime)||0)>v()-z3;return t.skipAllowed&&A&&r&&E3===o&&d5[n]&&d5[n].length>0?(ht++,d5[n]):(ht=0,new Promise(async l=>{var x;let c=[];if((x=t.face.emotion)!=null&&x.enabled){let i={},y=Z0!=null&&Z0.inputs[0].shape?Z0.inputs[0].shape[2]:0;i.resize=te.image.resizeBilinear(e,[y,y],!1),i.channels=te.mul(i.resize,B.rgb),i.grayscale=te.sum(i.channels,3,!0),i.grayscaleSub=te.sub(i.grayscale,B.tf05),i.grayscaleMul=te.mul(i.grayscaleSub,B.tf2),i.emotion=Z0==null?void 0:Z0.execute(i.grayscaleMul),z3=v();let d=await i.emotion.data();for(let f=0;f<d.length;f++)d[f]>(t.face.emotion.minConfidence||0)&&c.push({score:Math.min(.99,Math.trunc(100*d[f])/100),emotion:bA[f]});c.sort((f,p)=>p.score-f.score),Object.keys(i).forEach(f=>te.dispose(i[f]))}d5[n]=c,E3=o,l(c)}))}var x5=D(H());var L0,gt=[],N3=0,I3=0,O3=Number.MAX_SAFE_INTEGER;async function C3(e){var t;return k.initial&&(L0=null),L0?e.debug&&u("cached 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0:n[0].shape)?F0.inputs[0].shape[2]:0,Ee===-1&&(Ee=64),F0}function f5(e,t,n,o){for(let r=0;r<nt.length;r++){let{key:A,indices:s}=nt[r],a=Q0[`${n}${A}`];if(!o||o.includes(A))for(let l=0;l<s.length;l++){let c=s[l];e[a[l]]=[t[c][0],t[c][1],(t[c][2]+e[a[l]][2])/2]}}}var MA=e=>{let t=e[l2.leftBounds[0]][2],n=e[l2.rightBounds[0]][2];return t-n},V3=(e,t,n,o,r,A=!1)=>{let s=r5(o5(n3([e[n],e[o]]),gA)),a=A2(s),l=ne.image.cropAndResize(t,[[s.startPoint[1]/r,s.startPoint[0]/r,s.endPoint[1]/r,s.endPoint[0]/r]],[0],[Ee,Ee]);if(A&&k.kernels.includes("flipleftright")){let c=ne.image.flipLeftRight(l);ne.dispose(l),l=c}return{box:s,boxSize:a,crop:l}},D3=(e,t,n,o=!1)=>{let r=[];for(let A=0;A<c2.numCoordinates;A++){let s=e[A*3],a=e[A*3+1],l=e[A*3+2];r.push([(o?1-s/Ee:s/Ee)*n[0]+t.startPoint[0],a/Ee*n[1]+t.startPoint[1],l])}return{rawCoords:r,iris:r.slice(c2.index)}},Z3=(e,t,n)=>{let o=e[Q0[`${n}EyeUpper0`][c2.upperCenter]][2],r=e[Q0[`${n}EyeLower0`][c2.lowerCenter]][2],A=(o+r)/2;return t.map((s,a)=>{let 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vA=[[61,146],[146,91],[91,181],[181,84],[84,17],[17,314],[314,405],[405,321],[321,375],[375,291],[61,185],[185,40],[40,39],[39,37],[37,0],[0,267],[267,269],[269,270],[270,409],[409,291],[78,95],[95,88],[88,178],[178,87],[87,14],[14,317],[317,402],[402,318],[318,324],[324,308],[78,191],[191,80],[80,81],[81,82],[82,13],[13,312],[312,311],[311,310],[310,415],[415,308]],PA=[[263,249],[249,390],[390,373],[373,374],[374,380],[380,381],[381,382],[382,362],[263,466],[466,388],[388,387],[387,386],[386,385],[385,384],[384,398],[398,362]],TA=[[276,283],[283,282],[282,295],[295,285],[300,293],[293,334],[334,296],[296,336]],RA=[[474,475],[475,476],[476,477],[477,474]],kA=[[33,7],[7,163],[163,144],[144,145],[145,153],[153,154],[154,155],[155,133],[33,246],[246,161],[161,160],[160,159],[159,158],[158,157],[157,173],[173,133]],wA=[[46,53],[53,52],[52,65],[65,55],[70,63],[63,105],[105,66],[66,107]],EA=[[469,470],[470,471],[471,472],[472,469]],zA=[[10,338],[338,297],[297,332],[332,284],[284,251],[251,389],[389,356],[356,454],[454,323],[323,361],[361,288],[288,397],[397,365],[365,379],[379,378],[378,400],[400,377],[377,152],[152,148],[148,176],[176,149],[149,150],[150,136],[136,172],[172,58],[58,132],[132,93],[93,234],[234,127],[127,162],[162,21],[21,54],[54,103],[103,67],[67,109],[109,10]];function ze(e){let t=e.map(n=>n[0]);return t.push(e[e.length-1][1]),t}var SA={lips:ze(vA),leftEye:ze(PA),leftEyebrow:ze(TA),leftIris:ze(RA),rightEye:ze(kA),rightEyebrow:ze(wA),rightIris:ze(EA),faceOval:ze(zA)},jA=Object.entries(SA).map(([e,t])=>t.map(n=>[n,e])).flat(),K7=new Map(jA),O2=[61,146,91,181,84,17,314,405,321,375,291,185,40,39,37,0,267,269,270,409,78,95,88,178,87,14,317,402,318,324,308,191,80,81,82,13,312,311,310,415,76,77,90,180,85,16,315,404,320,307,306,184,74,73,72,11,302,303,304,408,62,96,89,179,86,15,316,403,319,325,292,183,42,41,38,12,268,271,272,407],Xe=[33,7,163,144,145,153,154,155,133,246,161,160,159,158,157,173,130,25,110,24,23,22,26,112,243,247,30,29,27,28,56,190,226,31,228,229,230,231,232,233,244,113,225,224,223,222,221,189,35,124,46,53,52,65,143,111,117,118,119,120,121,128,245,156,70,63,105,66,107,55,193],qe=[263,249,390,373,374,380,381,382,362,466,388,387,386,385,384,398,359,255,339,254,253,252,256,341,463,467,260,259,257,258,286,414,446,261,448,449,450,451,452,453,464,342,445,444,443,442,441,413,265,353,276,283,282,295,372,340,346,347,348,349,350,357,465,383,300,293,334,296,336,285,417];async function K3(e,t){var A,s,a,l,c,x,i,y,d,f;let n={lips:await((s=(A=t.filter(p=>p.size===160))==null?void 0:A[0])==null?void 0:s.data()),irisL:await((l=(a=t.filter(p=>p.size===10))==null?void 0:a[0])==null?void 0:l.data()),eyeL:await((x=(c=t.filter(p=>p.size===142))==null?void 0:c[0])==null?void 0:x.data()),irisR:await((y=(i=t.filter(p=>p.size===10))==null?void 0:i[1])==null?void 0:y.data()),eyeR:await((f=(d=t.filter(p=>p.size===142))==null?void 0:d[1])==null?void 0:f.data())};for(let p of Object.values(n))if(!p)return e;let o=Xe.reduce((p,g)=>p+=e[g][2],0)/Xe.length;for(let p=0;p<n.irisL.length/2;p++)e.push([n.irisL[2*p+0],n.irisL[2*p+1],o]);let r=qe.reduce((p,g)=>p+=e[g][2],0)/qe.length;for(let p=0;p<n.irisR.length/2;p++)e.push([n.irisR[2*p+0],n.irisR[2*p+1],r]);for(let p=0;p<n.eyeL.length/2;p++)e[Xe[p]]=[n.eyeL[2*p+0],n.eyeL[2*p+1],e[Xe[p]][2]];for(let p=0;p<n.eyeR.length/2;p++)e[qe[p]]=[n.eyeR[2*p+0],n.eyeR[2*p+1],e[qe[p]][2]];for(let p=0;p<n.lips.length/2;p++)e[O2[p]]=[n.lips[2*p+0],n.lips[2*p+1],e[O2[p]][2]];return e}var le={boxes:[],skipped:Number.MAX_SAFE_INTEGER,timestamp:0},t0=null,C2=0;async function J3(e,t){var l,c,x,i,y,d,f,p,g,M;if(!(t0!=null&&t0.executor))return[];let n=(((l=t.face.detector)==null?void 0:l.skipTime)||0)>v()-le.timestamp,o=le.skipped<(((c=t.face.detector)==null?void 0:c.skipFrames)||0);!t.skipAllowed||!n||!o||le.boxes.length===0?(le.boxes=await d3(e,t),le.timestamp=v(),le.skipped=0):le.skipped++;let r=[],A=[],s=0,a=C2;for(let P=0;P<le.boxes.length;P++){let m=le.boxes[P],b=0,z,w={id:s++,mesh:[],meshRaw:[],box:[0,0,0,0],boxRaw:[0,0,0,0],score:0,boxScore:0,faceScore:0,annotations:{}};if([b,z,w.tensor]=s3((x=t.face.detector)==null?void 0:x.rotation,m,e,(i=t.face.mesh)!=null&&i.enabled?C2:s2()),t.filter.equalization){let I=w.tensor?await X2(w.tensor):void 0;Se.dispose(w.tensor),I&&(w.tensor=I)}if(w.boxScore=Math.round(100*m.confidence)/100,(y=t.face.mesh)!=null&&y.enabled)if(!t0)t.debug&&u("face mesh detection requested, but model is not loaded");else{if(((d=t.face.attention)==null?void 0:d.enabled)&&!k.kernels.includes("atan2"))return 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o=W.concat2d([n.startPoints,n.endPoints],1);return Object.keys(n).forEach(r=>W.dispose(n[r])),o}normalizeLandmarks(t,n){let o={};o.reshape=W.reshape(t,[-1,7,2]),o.div=W.div(o.reshape,this.inputSizeTensor),o.landmarks=W.add(o.div,this.anchors[n]?this.anchors[n]:0);let r=W.mul(o.landmarks,this.inputSizeTensor);return Object.keys(o).forEach(A=>W.dispose(o[A])),r}async predict(t,n){var a;let o={};o.resize=W.image.resizeBilinear(t,[this.inputSize,this.inputSize]),o.div=W.div(o.resize,B.tf127),o.image=W.sub(o.div,B.tf1),o.batched=this.model.execute(o.image),o.predictions=W.squeeze(o.batched),o.slice=W.slice(o.predictions,[0,0],[-1,1]),o.sigmoid=W.sigmoid(o.slice),o.scores=W.squeeze(o.sigmoid);let r=await o.scores.data();o.boxes=W.slice(o.predictions,[0,1],[-1,4]),o.norm=this.normalizeBoxes(o.boxes),o.nms=await W.image.nonMaxSuppressionAsync(o.norm,o.scores,3*(((a=n.hand)==null?void 0:a.maxDetected)||1),n.hand.iouThreshold,n.hand.minConfidence);let A=await o.nms.array(),s=[];for(let l of A){let c={};c.box=W.slice(o.norm,[l,0],[1,-1]),c.slice=W.slice(o.predictions,[l,5],[1,14]),c.norm=this.normalizeLandmarks(c.slice,l),c.palmLandmarks=W.reshape(c.norm,[-1,2]);let x=await c.box.data(),i=x.slice(0,2),y=x.slice(2,4),d=await c.palmLandmarks.array(),f={startPoint:i,endPoint:y,palmLandmarks:d,confidence:r[l]},p=an(f,[(t.shape[2]||1)/this.inputSize,(t.shape[1]||0)/this.inputSize]);s.push(p),Object.keys(c).forEach(g=>W.dispose(c[g]))}return Object.keys(o).forEach(l=>W.dispose(o[l])),s}};var G0=D(H());var WA=5,yn=1.65,fn=[0,5,9,13,17,1,2],FA=0,GA=2,mn=0,b5=class{constructor(t,n){R(this,"handDetector");R(this,"handPoseModel");R(this,"inputSize");R(this,"storedBoxes");R(this,"skipped");R(this,"detectedHands");var o,r,A;this.handDetector=t,this.handPoseModel=n,this.inputSize=((A=(r=(o=this.handPoseModel)==null?void 0:o.inputs)==null?void 0:r[0].shape)==null?void 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o=Object.keys(S.engine().registryFactory);if(e.config.backend==="humangl"&&!o.includes("humangl")&&(wn(e),o=Object.keys(S.engine().registryFactory)),e.config.debug&&u("available backends:",o),o.includes(e.config.backend)||(u(`error: backend ${e.config.backend} not found in registry`),e.config.backend=k.node?"tensorflow":"webgl",e.config.debug&&u(`override: setting backend ${e.config.backend}`)),e.config.debug&&u("setting backend:",[e.config.backend]),e.config.backend==="wasm"){if(S.env().flagRegistry.CANVAS2D_WILL_READ_FREQUENTLY&&S.env().set("CANVAS2D_WILL_READ_FREQUENTLY",!0),e.config.debug&&u("wasm path:",e.config.wasmPath),typeof S.setWasmPaths!="undefined")S.setWasmPaths(e.config.wasmPath,e.config.wasmPlatformFetch);else throw new Error("backend error: attempting to use wasm backend but wasm path is not set");let r=!1,A=!1;try{r=await S.env().getAsync("WASM_HAS_MULTITHREAD_SUPPORT"),A=await S.env().getAsync("WASM_HAS_SIMD_SUPPORT"),e.config.debug&&u(`wasm execution: ${A?"simd":"no 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Object.keys(o))zn[A]!==o[A]&&(r[A]=o[A]);e.config.debug&&Object.keys(r).length>0&&u("backend:",S.getBackend(),"flags:",r)}if(e.config.flags&&Object.keys(e.config.flags).length>0){e.config.debug&&u("flags:",e.config.flags);for(let[o,r]of Object.entries(e.config.flags))S.env().set(o,r)}S.enableProdMode(),B1(),e.performance.initBackend=Math.trunc(v()-n),e.config.backend=S.getBackend(),await k.updateBackend(),KA(e.config),k.initial=!1}return!0}function v5(e,t){for(let n of e){let o={kernelName:n,backendName:t.backend,kernelFunc:()=>{t.debug&&u("kernelFunc",n,t.backend)}};S.registerKernel(o)}k.kernels=S.getKernelsForBackend(S.getBackend()).map(n=>n.kernelName.toLowerCase())}var l0=[null,null],JA=["StatefulPartitionedCall/Postprocessor/Slice","StatefulPartitionedCall/Postprocessor/ExpandDims_1"],Fe=[[0,0],[0,0]],QA=["hand","fist","pinch","point","face","tip","pinchtip"],jn=4,Nn=1.6,_A=512,$A=1.4,P5=Number.MAX_SAFE_INTEGER,Wt=0,ue=[0,0],i0={boxes:[],hands:[]},In={thumb:[1,2,3,4],index:[5,6,7,8],middle:[9,10,11,12],ring:[13,14,15,16],pinky:[17,18,19,20],base:[0],palm:[0,17,13,9,5,1,0]};async function On(e){var t;if(k.initial&&(l0[0]=null),l0[0])e.debug&&u("cached model:",l0[0].modelUrl);else{v5(["tensorlistreserve","enter","tensorlistfromtensor","merge","loopcond","switch","exit","tensorliststack","nextiteration","tensorlistsetitem","tensorlistgetitem","reciprocal","shape","split","where"],e),l0[0]=await L((t=e.hand.detector)==null?void 0:t.modelPath);let n=l0[0].executor?Object.values(l0[0].modelSignature.inputs):void 0;Fe[0][0]=Array.isArray(n)?parseInt(n[0].tensorShape.dim[1].size):0,Fe[0][1]=Array.isArray(n)?parseInt(n[0].tensorShape.dim[2].size):0}return l0[0]}async function Cn(e){var t;if(k.initial&&(l0[1]=null),l0[1])e.debug&&u("cached model:",l0[1].modelUrl);else{l0[1]=await L((t=e.hand.skeleton)==null?void 0:t.modelPath);let n=l0[1].executor?Object.values(l0[1].modelSignature.inputs):void 0;Fe[1][0]=Array.isArray(n)?parseInt(n[0].tensorShape.dim[1].size):0,Fe[1][1]=Array.isArray(n)?parseInt(n[0].tensorShape.dim[2].size):0}return l0[1]}async function es(e,t){let n=[];if(!e||!l0[0])return n;let o={},r=(e.shape[2]||1)/(e.shape[1]||1),A=Math.min(Math.round((e.shape[1]||0)/8)*8,_A),s=Math.round(A*r/8)*8;o.resize=J.image.resizeBilinear(e,[A,s]),o.cast=J.cast(o.resize,"int32"),[o.rawScores,o.rawBoxes]=await l0[0].executeAsync(o.cast,JA),o.boxes=J.squeeze(o.rawBoxes,[0,2]),o.scores=J.squeeze(o.rawScores,[0]);let a=J.unstack(o.scores,1);J.dispose(a[jn]),a.splice(jn,1),o.filtered=J.stack(a,1),J.dispose(a),o.max=J.max(o.filtered,1),o.argmax=J.argMax(o.filtered,1);let l=0;o.nms=await J.image.nonMaxSuppressionAsync(o.boxes,o.max,(t.hand.maxDetected||0)+1,t.hand.iouThreshold||0,t.hand.minConfidence||1);let c=await o.nms.data(),x=await o.max.data(),i=await o.argmax.data();for(let y of Array.from(c)){let d=J.slice(o.boxes,y,1),f=await d.data();J.dispose(d);let p=[f[1],f[0],f[3]-f[1],f[2]-f[0]],g=a5(p,$A),M=[Math.trunc(p[0]*ue[0]),Math.trunc(p[1]*ue[1]),Math.trunc(p[2]*ue[0]),Math.trunc(p[3]*ue[1])],P=x[y],m=QA[i[y]],b={id:l++,score:P,box:M,boxRaw:g,label:m};n.push(b)}return Object.keys(o).forEach(y=>J.dispose(o[y])),n.sort((y,d)=>d.score-y.score),n.length>(t.hand.maxDetected||1)&&(n.length=t.hand.maxDetected||1),n}async function Ft(e,t,n){let o={id:t.id,score:Math.round(100*t.score)/100,boxScore:Math.round(100*t.score)/100,fingerScore:0,box:t.box,boxRaw:t.boxRaw,label:t.label,keypoints:[],landmarks:{},annotations:{}};if(e&&l0[1]&&n.hand.landmarks&&t.score>(n.hand.minConfidence||0)){let r={},A=[t.boxRaw[1],t.boxRaw[0],t.boxRaw[3]+t.boxRaw[1],t.boxRaw[2]+t.boxRaw[0]];r.crop=J.image.cropAndResize(e,[A],[0],[Fe[1][0],Fe[1][1]],"bilinear"),r.div=J.div(r.crop,B.tf255),[r.score,r.keypoints]=l0[1].execute(r.div,["Identity_1","Identity"]);let s=(await r.score.data())[0],a=(100-Math.trunc(100/(1+Math.exp(s))))/100;if(a>=(n.hand.minConfidence||0)){o.fingerScore=a,r.reshaped=J.reshape(r.keypoints,[-1,3]);let x=(await r.reshaped.array()).map(i=>[i[0]/Fe[1][1],i[1]/Fe[1][0],i[2]||0]).map(i=>[i[0]*t.boxRaw[2],i[1]*t.boxRaw[3],i[2]||0]);o.keypoints=x.map(i=>[ue[0]*(i[0]+t.boxRaw[0]),ue[1]*(i[1]+t.boxRaw[1]),i[2]||0]),o.landmarks=g5(o.keypoints);for(let i of Object.keys(In))o.annotations[i]=In[i].map(y=>o.landmarks&&o.keypoints[y]?o.keypoints[y]:null)}Object.keys(r).forEach(l=>J.dispose(r[l]))}return o}async function Gt(e,t){var r,A;if(!((r=l0[0])!=null&&r.executor)||!((A=l0[1])!=null&&A.executor)||!l0[0].inputs[0].shape||!l0[1].inputs[0].shape)return[];ue=[e.shape[2]||0,e.shape[1]||0],P5++;let n=(t.hand.skipTime||0)>v()-Wt,o=P5<(t.hand.skipFrames||0);return t.skipAllowed&&n&&o?i0.hands:new Promise(async s=>{let a=3*(t.hand.skipTime||0)>v()-Wt,l=P5<3*(t.hand.skipFrames||0);t.skipAllowed&&i0.hands.length===t.hand.maxDetected?i0.hands=await Promise.all(i0.boxes.map(x=>Ft(e,x,t))):t.skipAllowed&&a&&l&&i0.hands.length>0?i0.hands=await Promise.all(i0.boxes.map(x=>Ft(e,x,t))):(i0.boxes=await es(e,t),Wt=v(),i0.hands=await Promise.all(i0.boxes.map(x=>Ft(e,x,t))),P5=0);let c=[...i0.boxes];if(i0.boxes.length=0,t.cacheSensitivity>0)for(let x=0;x<i0.hands.length;x++){let i=m3(i0.hands[x].keypoints,ue);if(i.box[2]/(e.shape[2]||1)>.05&&i.box[3]/(e.shape[1]||1)>.05&&i0.hands[x].fingerScore&&i0.hands[x].fingerScore>(t.hand.minConfidence||0)){let y=a5(i.box,Nn),d=a5(i.boxRaw,Nn);i0.boxes.push({...c[x],box:y,boxRaw:d})}}for(let x=0;x<i0.hands.length;x++){let i=fe(i0.hands[x].keypoints,ue);i0.hands[x].box=i.box,i0.hands[x].boxRaw=i.boxRaw}s(i0.hands)})}var R5=D(H());var g0,T5=[],Bt=Number.MAX_SAFE_INTEGER,Wn=0,Fn=0;async function Gn(e){var t;return k.initial&&(g0=null),g0?e.debug&&u("cached model:",g0.modelUrl):g0=await L((t=e.face.liveness)==null?void 0:t.modelPath),g0}async function Ht(e,t,n,o){var s,a;if(!(g0!=null&&g0.executor))return 0;let r=(((s=t.face.liveness)==null?void 0:s.skipTime)||0)>v()-Fn,A=Bt<(((a=t.face.liveness)==null?void 0:a.skipFrames)||0);return t.skipAllowed&&r&&A&&Wn===o&&T5[n]?(Bt++,T5[n]):(Bt=0,new Promise(async l=>{let c=R5.image.resizeBilinear(e,[g0!=null&&g0.inputs[0].shape?g0.inputs[0].shape[2]:0,g0!=null&&g0.inputs[0].shape?g0.inputs[0].shape[1]:0],!1),x=g0==null?void 0:g0.execute(c),i=(await x.data())[0];T5[n]=Math.round(100*i)/100,Wn=o,Fn=v(),R5.dispose([c,x]),l(T5[n])}))}var Xn=D(H());var F2={};ve(F2,{connected:()=>w5,horizontal:()=>Vt,kpt:()=>k5,relative:()=>Zt,vertical:()=>Dt});var k5=["nose","leftEye","rightEye","leftEar","rightEar","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle"],Vt=[["leftEye","rightEye"],["leftEar","rightEar"],["leftShoulder","rightShoulder"],["leftElbow","rightElbow"],["leftWrist","rightWrist"],["leftHip","rightHip"],["leftKnee","rightKnee"],["leftAnkle","rightAnkle"]],Dt=[["leftKnee","leftShoulder"],["rightKnee","rightShoulder"],["leftAnkle","leftKnee"],["rightAnkle","rightKnee"]],Zt=[[["leftHip","rightHip"],["leftShoulder","rightShoulder"]],[["leftElbow","rightElbow"],["leftShoulder","rightShoulder"]]],w5={leftLeg:["leftHip","leftKnee","leftAnkle"],rightLeg:["rightHip","rightKnee","rightAnkle"],torso:["leftShoulder","rightShoulder","rightHip","leftHip","leftShoulder"],leftArm:["leftShoulder","leftElbow","leftWrist"],rightArm:["rightShoulder","rightElbow","rightWrist"],head:[]};var Ge=D(H()),Hn=.005,B0={keypoints:[],padding:[[0,0],[0,0],[0,0],[0,0]]};function Xt(e){for(let t of Vt){let n=e.keypoints.findIndex(r=>r.part===t[0]),o=e.keypoints.findIndex(r=>r.part===t[1]);if(e.keypoints[n]&&e.keypoints[o]&&e.keypoints[n].position[0]<e.keypoints[o].position[0]){let r=e.keypoints[n];e.keypoints[n]=e.keypoints[o],e.keypoints[o]=r}}for(let t of Dt){let n=e.keypoints.findIndex(r=>r&&r.part===t[0]),o=e.keypoints.findIndex(r=>r&&r.part===t[1]);e.keypoints[n]&&e.keypoints[o]&&e.keypoints[n].position[1]<e.keypoints[o].position[1]&&e.keypoints.splice(n,1)}for(let[t,n]of Zt){let o=e.keypoints.findIndex(c=>c&&c.part===t[0]),r=e.keypoints.findIndex(c=>c&&c.part===t[1]),A=e.keypoints.findIndex(c=>c&&c.part===n[0]),s=e.keypoints.findIndex(c=>c&&c.part===n[1]);if(!e.keypoints[A]||!e.keypoints[s])continue;let a=e.keypoints[o]?[Math.abs(e.keypoints[A].position[0]-e.keypoints[o].position[0]),Math.abs(e.keypoints[s].position[0]-e.keypoints[o].position[0])]:[0,0],l=e.keypoints[r]?[Math.abs(e.keypoints[s].position[0]-e.keypoints[r].position[0]),Math.abs(e.keypoints[A].position[0]-e.keypoints[r].position[0])]:[0,0];if(a[0]>a[1]||l[0]>l[1]){let c=e.keypoints[o];e.keypoints[o]=e.keypoints[r],e.keypoints[r]=c}}}function Vn(e){for(let t=0;t<e.length;t++)if(e[t]&&B0.keypoints[t]){let n=[Math.abs(e[t].positionRaw[0]-B0.keypoints[t].positionRaw[0]),Math.abs(e[t].positionRaw[1]-B0.keypoints[t].positionRaw[1])];n[0]<Hn&&n[1]<Hn?e[t]=B0.keypoints[t]:B0.keypoints[t]=e[t]}else B0.keypoints[t]=e[t];return e}function Dn(e,t){var r,A;let n={};if(!((r=e==null?void 0:e.shape)!=null&&r[1])||!((A=e==null?void 0:e.shape)!=null&&A[2]))return e;B0.padding=[[0,0],[e.shape[2]>e.shape[1]?Math.trunc((e.shape[2]-e.shape[1])/2):0,e.shape[2]>e.shape[1]?Math.trunc((e.shape[2]-e.shape[1])/2):0],[e.shape[1]>e.shape[2]?Math.trunc((e.shape[1]-e.shape[2])/2):0,e.shape[1]>e.shape[2]?Math.trunc((e.shape[1]-e.shape[2])/2):0],[0,0]],n.pad=Ge.pad(e,B0.padding),n.resize=Ge.image.resizeBilinear(n.pad,[t,t]);let o=Ge.cast(n.resize,"int32");return Object.keys(n).forEach(s=>Ge.dispose(n[s])),o}function Zn(e,t){e.keypoints=e.keypoints.filter(o=>o==null?void 0:o.position);for(let o of e.keypoints)o.position=[o.position[0]*(t[0]+B0.padding[2][0]+B0.padding[2][1])/t[0]-B0.padding[2][0],o.position[1]*(t[1]+B0.padding[1][0]+B0.padding[1][1])/t[1]-B0.padding[1][0]],o.positionRaw=[o.position[0]/t[0],o.position[1]/t[1]];let n=fe(e.keypoints.map(o=>o.position),t);return e.box=n.box,e.boxRaw=n.boxRaw,e}var f0,E5=0,qt=Number.MAX_SAFE_INTEGER,_e={boxes:[],bodies:[],last:0};async function qn(e){var t;return k.initial&&(f0=null),f0?e.debug&&u("cached 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A=e[0][r],s=Math.round(100*A[51+4])/100;if(s>t.body.minConfidence){let a=[];for(let i=0;i<17;i++){let y=A[3*i+2];if(y>t.body.minConfidence){let d=[A[3*i+1],A[3*i+0]];a.push({part:k5[i],score:Math.round(100*y)/100,positionRaw:d,position:[Math.round((n.shape[2]||0)*d[0]),Math.round((n.shape[1]||0)*d[1])]})}}let l=fe(a.map(i=>i.position),[n.shape[2],n.shape[1]]),c={};for(let[i,y]of Object.entries(w5)){let d=[];for(let f=0;f<y.length-1;f++){let p=a.find(M=>M.part===y[f]),g=a.find(M=>M.part===y[f+1]);p&&g&&p.score>(t.body.minConfidence||0)&&g.score>(t.body.minConfidence||0)&&d.push([p.position,g.position])}c[i]=d}let x={id:r,score:s,box:l.box,boxRaw:l.boxRaw,keypoints:[...a],annotations:c};Xt(x),o.push(x)}}return o.sort((r,A)=>A.score-r.score),o.length>t.body.maxDetected&&(o.length=t.body.maxDetected),o}async function Ut(e,t){var r;if(!(f0!=null&&f0.executor)||!((r=f0==null?void 0:f0.inputs)!=null&&r[0].shape))return[];t.skipAllowed||(_e.boxes.length=0),qt++;let n=(t.body.skipTime||0)>v()-_e.last,o=qt<(t.body.skipFrames||0);return t.skipAllowed&&n&&o?_e.bodies:new Promise(async A=>{let s={};qt=0,s.input=Dn(e,E5),s.res=f0==null?void 0:f0.execute(s.input),_e.last=v();let a=await s.res.array();_e.bodies=s.res.shape[2]===17?ns(a,t,e):os(a,t,e);for(let l of _e.bodies)Zn(l,[e.shape[2]||1,e.shape[1]||1]),Vn(l.keypoints);Object.keys(s).forEach(l=>Xn.dispose(s[l])),A(_e.bodies)})}var H0=D(H());var _0,z5=[],Yn=0,Yt=Number.MAX_SAFE_INTEGER,j5=0,S5=2.5;async function Kn(e){if(!_0||k.initial){_0=await L(e.object.modelPath);let t=_0!=null&&_0.executor?Object.values(_0.modelSignature.inputs):void 0;j5=Array.isArray(t)?parseInt(t[0].tensorShape.dim[2].size):416}else e.debug&&u("cached model:",_0.modelUrl);return _0}async function rs(e,t,n){let o=0,r=[],A=j5;for(let c of[1,2,4]){let x=c*13,i=H0.squeeze(e.find(M=>M.shape[1]===x**2&&(M.shape[2]||0)===i2.length)),y=await 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A1="2.11.0";var x1={};ve(x1,{all:()=>d1,body:()=>b2,canvas:()=>c1,face:()=>h2,gesture:()=>v2,hand:()=>g2,object:()=>M2,options:()=>R0,person:()=>l1});var X0=e=>{if(!e)u("draw error: invalid canvas");else if(!e.getContext)u("draw error: canvas context not defined");else{let t=e.getContext("2d");if(!t)u("draw error: cannot get canvas context");else return t}return null},$e=e=>Math.round(e*180/Math.PI),he=(e,t)=>{if(!t.useDepth||typeof e=="undefined")return t.color;let n=Uint8ClampedArray.from([127+2*e,127-2*e,255]);return`rgba(${n[0]}, ${n[1]}, ${n[2]}, ${t.alpha})`};function be(e,t,n,o,r){e.fillStyle=he(o,r),e.beginPath(),e.arc(t,n,r.pointSize,0,2*Math.PI),e.fill()}function de(e,t,n,o,r,A){if(e.beginPath(),e.lineWidth=A.lineWidth,A.useCurves){let s=(t+t+o)/2,a=(n+n+r)/2;e.ellipse(s,a,o/2,r/2,0,0,2*Math.PI)}else e.moveTo(t+A.roundRect,n),e.lineTo(t+o-A.roundRect,n),e.quadraticCurveTo(t+o,n,t+o,n+A.roundRect),e.lineTo(t+o,n+r-A.roundRect),e.quadraticCurveTo(t+o,n+r,t+o-A.roundRect,n+r),e.lineTo(t+A.roundRect,n+r),e.quadraticCurveTo(t,n+r,t,n+r-A.roundRect),e.lineTo(t,n+A.roundRect),e.quadraticCurveTo(t,n,t+A.roundRect,n),e.closePath();e.stroke()}function s1(e,t,n){if(!(t.length<2)){e.beginPath(),e.moveTo(t[0][0],t[0][1]);for(let o of t)e.strokeStyle=he(o[2]||0,n),e.lineTo(Math.trunc(o[0]),Math.trunc(o[1]));e.stroke(),n.fillPolygons&&(e.closePath(),e.fill())}}function io(e,t,n){if(!(t.length<2)){if(e.lineWidth=n.lineWidth,!n.useCurves||t.length<=2){s1(e,t,n);return}e.moveTo(t[0][0],t[0][1]);for(let o=0;o<t.length-2;o++){let r=(t[o][0]+t[o+1][0])/2,A=(t[o][1]+t[o+1][1])/2;e.quadraticCurveTo(t[o][0],t[o][1],r,A)}e.quadraticCurveTo(t[t.length-2][0],t[t.length-2][1],t[t.length-1][0],t[t.length-1][1]),e.stroke(),n.fillPolygons&&(e.closePath(),e.fill())}}function a1(e,t,n,o=5){let r,A,s;e.beginPath(),e.moveTo(t[0],t[1]),e.lineTo(n[0],n[1]),r=Math.atan2(n[1]-t[1],n[0]-t[0]),A=o*Math.cos(r)+n[0],s=o*Math.sin(r)+n[1],e.moveTo(A,s),r+=1/3*(2*Math.PI),A=o*Math.cos(r)+n[0],s=o*Math.sin(r)+n[1],e.lineTo(A,s),r+=1/3*(2*Math.PI),A=o*Math.cos(r)+n[0],s=o*Math.sin(r)+n[1],e.lineTo(A,s),e.closePath(),e.stroke(),e.fill()}var R0={color:"rgba(173, 216, 230, 0.6)",labelColor:"rgba(173, 216, 230, 1)",shadowColor:"black",alpha:.5,font:'small-caps 16px "Segoe UI"',lineHeight:18,lineWidth:4,pointSize:2,roundRect:8,drawPoints:!1,drawLabels:!0,drawBoxes:!0,drawAttention:!0,drawGestures:!0,drawPolygons:!0,drawGaze:!0,fillPolygons:!1,useDepth:!0,useCurves:!1};var Y;function hs(e,t){var n,o;if(Y.drawLabels){let r=[];if(r.push(`face: ${Math.trunc(100*e.score)}%`),e.genderScore&&r.push(`${e.gender||""} ${Math.trunc(100*e.genderScore)}%`),e.age&&r.push(`age: ${e.age||""}`),e.iris&&r.push(`distance: ${e.iris}`),e.real&&r.push(`real: ${Math.trunc(100*e.real)}%`),e.live&&r.push(`live: ${Math.trunc(100*e.live)}%`),e.emotion&&e.emotion.length>0){let A=e.emotion.map(s=>`${Math.trunc(100*s.score)}% ${s.emotion}`);A.length>3&&(A.length=3),r.push(A.join(" "))}((n=e.rotation)==null?void 0:n.angle)&&((o=e.rotation)==null?void 0:o.gaze)&&(e.rotation.angle.roll&&r.push(`roll: ${$e(e.rotation.angle.roll)}\xB0 yaw:${$e(e.rotation.angle.yaw)}\xB0 pitch:${$e(e.rotation.angle.pitch)}\xB0`),e.rotation.gaze.bearing&&r.push(`gaze: ${$e(e.rotation.gaze.bearing)}\xB0`)),r.length===0&&r.push("face"),t.fillStyle=Y.color;for(let A=r.length-1;A>=0;A--){let s=Math.max(e.box[0],0),a=A*Y.lineHeight+e.box[1];Y.shadowColor&&Y.shadowColor!==""&&(t.fillStyle=Y.shadowColor,t.fillText(r[A],s+5,a+16)),t.fillStyle=Y.labelColor,t.fillText(r[A],s+4,a+15)}}}function bs(e,t){var n,o,r,A;if(((n=e.annotations)==null?void 0:n.leftEyeIris)&&((o=e.annotations)==null?void 0:o.leftEyeIris[0])){t.strokeStyle=Y.useDepth?"rgba(255, 200, 255, 0.3)":Y.color,t.beginPath();let s=Math.abs(e.annotations.leftEyeIris[3][0]-e.annotations.leftEyeIris[1][0])/2,a=Math.abs(e.annotations.leftEyeIris[4][1]-e.annotations.leftEyeIris[2][1])/2;t.ellipse(e.annotations.leftEyeIris[0][0],e.annotations.leftEyeIris[0][1],s,a,0,0,2*Math.PI),t.stroke(),Y.fillPolygons&&(t.fillStyle=Y.useDepth?"rgba(255, 255, 200, 0.3)":Y.color,t.fill())}if(((r=e.annotations)==null?void 0:r.rightEyeIris)&&((A=e.annotations)==null?void 0:A.rightEyeIris[0])){t.strokeStyle=Y.useDepth?"rgba(255, 200, 255, 0.3)":Y.color,t.beginPath();let s=Math.abs(e.annotations.rightEyeIris[3][0]-e.annotations.rightEyeIris[1][0])/2,a=Math.abs(e.annotations.rightEyeIris[4][1]-e.annotations.rightEyeIris[2][1])/2;t.ellipse(e.annotations.rightEyeIris[0][0],e.annotations.rightEyeIris[0][1],s,a,0,0,2*Math.PI),t.stroke(),Y.fillPolygons&&(t.fillStyle=Y.useDepth?"rgba(255, 255, 200, 0.3)":Y.color,t.fill())}}function gs(e,t){var n;if(Y.drawGaze&&((n=e.rotation)==null?void 0:n.angle)&&typeof Path2D!="undefined"){t.strokeStyle="pink";let o=e.box[0]+e.box[2]/2-e.box[3]*$e(e.rotation.angle.yaw)/90,r=e.box[1]+e.box[3]/2+e.box[2]*$e(e.rotation.angle.pitch)/90,A=new Path2D(`
-      M ${e.box[0]+e.box[2]/2} ${e.box[1]}
+`;
+
+// src/image/imagefx.ts
+var collect = (source, prefix, collection) => {
+  const r = new RegExp("\\b" + prefix + " \\w+ (\\w+)", "ig");
+  source.replace(r, (match3, name) => {
+    collection[name] = 0;
+    return match3;
+  });
+};
+var GLProgram = class {
+  constructor(gl, vertexSource, fragmentSource) {
+    __publicField(this, "uniform", {});
+    __publicField(this, "attribute", {});
+    __publicField(this, "gl");
+    __publicField(this, "id");
+    __publicField(this, "compile", (source, type) => {
+      const shader = this.gl.createShader(type);
+      if (!shader) {
+        log("filter: could not create shader");
+        return null;
+      }
+      this.gl.shaderSource(shader, source);
+      this.gl.compileShader(shader);
+      if (!this.gl.getShaderParameter(shader, this.gl.COMPILE_STATUS)) {
+        log(`filter: gl compile failed: ${this.gl.getShaderInfoLog(shader) || "unknown"}`);
+        return null;
+      }
+      return shader;
+    });
+    this.gl = gl;
+    const vertexShader = this.compile(vertexSource, this.gl.VERTEX_SHADER);
+    const fragmentShader = this.compile(fragmentSource, this.gl.FRAGMENT_SHADER);
+    this.id = this.gl.createProgram();
+    if (!vertexShader || !fragmentShader)
+      return;
+    if (!this.id) {
+      log("filter: could not create webgl program");
+      return;
+    }
+    this.gl.attachShader(this.id, vertexShader);
+    this.gl.attachShader(this.id, fragmentShader);
+    this.gl.linkProgram(this.id);
+    if (!this.gl.getProgramParameter(this.id, this.gl.LINK_STATUS)) {
+      log(`filter: gl link failed: ${this.gl.getProgramInfoLog(this.id) || "unknown"}`);
+      return;
+    }
+    this.gl.useProgram(this.id);
+    collect(vertexSource, "attribute", this.attribute);
+    for (const a in this.attribute)
+      this.attribute[a] = this.gl.getAttribLocation(this.id, a);
+    collect(vertexSource, "uniform", this.uniform);
+    collect(fragmentSource, "uniform", this.uniform);
+    for (const u in this.uniform)
+      this.uniform[u] = this.gl.getUniformLocation(this.id, u);
+  }
+};
+function GLImageFilter() {
+  let drawCount = 0;
+  let sourceTexture = null;
+  let lastInChain = false;
+  let currentFramebufferIndex = -1;
+  let tempFramebuffers = [null, null];
+  let filterChain = [];
+  let vertexBuffer = null;
+  let currentProgram = null;
+  const fxcanvas = canvas(100, 100);
+  const shaderProgramCache = {};
+  const DRAW = { INTERMEDIATE: 1 };
+  const gl = fxcanvas.getContext("webgl");
+  if (!gl) {
+    log("filter: cannot get webgl context");
+    return;
+  }
+  this.gl = gl;
+  function resize(width, height) {
+    if (width === fxcanvas.width && height === fxcanvas.height)
+      return;
+    fxcanvas.width = width;
+    fxcanvas.height = height;
+    if (!vertexBuffer) {
+      const vertices = new Float32Array([-1, -1, 0, 1, 1, -1, 1, 1, -1, 1, 0, 0, -1, 1, 0, 0, 1, -1, 1, 1, 1, 1, 1, 0]);
+      vertexBuffer = gl.createBuffer();
+      gl.bindBuffer(gl.ARRAY_BUFFER, vertexBuffer);
+      gl.bufferData(gl.ARRAY_BUFFER, vertices, gl.STATIC_DRAW);
+      gl.pixelStorei(gl.UNPACK_PREMULTIPLY_ALPHA_WEBGL, true);
+    }
+    gl.viewport(0, 0, fxcanvas.width, fxcanvas.height);
+    tempFramebuffers = [null, null];
+  }
+  function createFramebufferTexture(width, height) {
+    const fbo = gl.createFramebuffer();
+    gl.bindFramebuffer(gl.FRAMEBUFFER, fbo);
+    const renderbuffer = gl.createRenderbuffer();
+    gl.bindRenderbuffer(gl.RENDERBUFFER, renderbuffer);
+    const texture = gl.createTexture();
+    gl.bindTexture(gl.TEXTURE_2D, texture);
+    gl.texImage2D(gl.TEXTURE_2D, 0, gl.RGBA, width, height, 0, gl.RGBA, gl.UNSIGNED_BYTE, null);
+    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MAG_FILTER, gl.LINEAR);
+    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MIN_FILTER, gl.LINEAR);
+    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_S, gl.CLAMP_TO_EDGE);
+    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_T, gl.CLAMP_TO_EDGE);
+    gl.framebufferTexture2D(gl.FRAMEBUFFER, gl.COLOR_ATTACHMENT0, gl.TEXTURE_2D, texture, 0);
+    gl.bindTexture(gl.TEXTURE_2D, null);
+    gl.bindFramebuffer(gl.FRAMEBUFFER, null);
+    return { fbo, texture };
+  }
+  function getTempFramebuffer(index2) {
+    tempFramebuffers[index2] = tempFramebuffers[index2] || createFramebufferTexture(fxcanvas.width, fxcanvas.height);
+    return tempFramebuffers[index2];
+  }
+  function draw(flags = 0) {
+    if (!currentProgram)
+      return;
+    let source = null;
+    let target = null;
+    let flipY = false;
+    if (drawCount === 0)
+      source = sourceTexture;
+    else
+      source = getTempFramebuffer(currentFramebufferIndex).texture || null;
+    drawCount++;
+    if (lastInChain && !(flags & DRAW.INTERMEDIATE)) {
+      target = null;
+      flipY = drawCount % 2 === 0;
+    } else {
+      currentFramebufferIndex = (currentFramebufferIndex + 1) % 2;
+      target = getTempFramebuffer(currentFramebufferIndex).fbo || null;
+    }
+    gl.bindTexture(gl.TEXTURE_2D, source);
+    gl.bindFramebuffer(gl.FRAMEBUFFER, target);
+    gl.uniform1f(currentProgram.uniform["flipY"], flipY ? -1 : 1);
+    gl.drawArrays(gl.TRIANGLES, 0, 6);
+  }
+  function compileShader(fragmentSource) {
+    if (shaderProgramCache[fragmentSource]) {
+      currentProgram = shaderProgramCache[fragmentSource];
+      gl.useProgram((currentProgram ? currentProgram.id : null) || null);
+      return currentProgram;
+    }
+    currentProgram = new GLProgram(gl, vertexIdentity, fragmentSource);
+    if (!currentProgram) {
+      log("filter: could not get webgl program");
+      return null;
+    }
+    const floatSize = Float32Array.BYTES_PER_ELEMENT;
+    const vertSize = 4 * floatSize;
+    gl.enableVertexAttribArray(currentProgram.attribute["pos"]);
+    gl.vertexAttribPointer(currentProgram.attribute["pos"], 2, gl.FLOAT, false, vertSize, 0 * floatSize);
+    gl.enableVertexAttribArray(currentProgram.attribute["uv"]);
+    gl.vertexAttribPointer(currentProgram.attribute["uv"], 2, gl.FLOAT, false, vertSize, 2 * floatSize);
+    shaderProgramCache[fragmentSource] = currentProgram;
+    return currentProgram;
+  }
+  const filter = {
+    colorMatrix: (matrix) => {
+      const m = new Float32Array(matrix);
+      m[4] /= 255;
+      m[9] /= 255;
+      m[14] /= 255;
+      m[19] /= 255;
+      const shader = m[18] === 1 && m[3] === 0 && m[8] === 0 && m[13] === 0 && m[15] === 0 && m[16] === 0 && m[17] === 0 && m[19] === 0 ? colorMatrixWithoutAlpha : colorMatrixWithAlpha;
+      const program = compileShader(shader);
+      if (!program)
+        return;
+      gl.uniform1fv(program.uniform["m"], m);
+      draw();
+    },
+    brightness: (brightness) => {
+      const b = (brightness || 0) + 1;
+      filter.colorMatrix([
+        b,
+        0,
+        0,
+        0,
+        0,
+        0,
+        b,
+        0,
+        0,
+        0,
+        0,
+        0,
+        b,
+        0,
+        0,
+        0,
+        0,
+        0,
+        1,
+        0
+      ]);
+    },
+    saturation: (amount) => {
+      const x = (amount || 0) * 2 / 3 + 1;
+      const y = (x - 1) * -0.5;
+      filter.colorMatrix([
+        x,
+        y,
+        y,
+        0,
+        0,
+        y,
+        x,
+        y,
+        0,
+        0,
+        y,
+        y,
+        x,
+        0,
+        0,
+        0,
+        0,
+        0,
+        1,
+        0
+      ]);
+    },
+    desaturate: () => {
+      filter.saturation(-1);
+    },
+    contrast: (amount) => {
+      const v = (amount || 0) + 1;
+      const o = -128 * (v - 1);
+      filter.colorMatrix([
+        v,
+        0,
+        0,
+        0,
+        o,
+        0,
+        v,
+        0,
+        0,
+        o,
+        0,
+        0,
+        v,
+        0,
+        o,
+        0,
+        0,
+        0,
+        1,
+        0
+      ]);
+    },
+    negative: () => {
+      filter.contrast(-2);
+    },
+    hue: (rotation) => {
+      rotation = (rotation || 0) / 180 * Math.PI;
+      const cos = Math.cos(rotation);
+      const sin = Math.sin(rotation);
+      const lumR = 0.213;
+      const lumG = 0.715;
+      const lumB = 0.072;
+      filter.colorMatrix([
+        lumR + cos * (1 - lumR) + sin * -lumR,
+        lumG + cos * -lumG + sin * -lumG,
+        lumB + cos * -lumB + sin * (1 - lumB),
+        0,
+        0,
+        lumR + cos * -lumR + sin * 0.143,
+        lumG + cos * (1 - lumG) + sin * 0.14,
+        lumB + cos * -lumB + sin * -0.283,
+        0,
+        0,
+        lumR + cos * -lumR + sin * -(1 - lumR),
+        lumG + cos * -lumG + sin * lumG,
+        lumB + cos * (1 - lumB) + sin * lumB,
+        0,
+        0,
+        0,
+        0,
+        0,
+        1,
+        0
+      ]);
+    },
+    desaturateLuminance: () => {
+      filter.colorMatrix([
+        0.2764723,
+        0.929708,
+        0.0938197,
+        0,
+        -37.1,
+        0.2764723,
+        0.929708,
+        0.0938197,
+        0,
+        -37.1,
+        0.2764723,
+        0.929708,
+        0.0938197,
+        0,
+        -37.1,
+        0,
+        0,
+        0,
+        1,
+        0
+      ]);
+    },
+    sepia: () => {
+      filter.colorMatrix([
+        0.393,
+        0.7689999,
+        0.18899999,
+        0,
+        0,
+        0.349,
+        0.6859999,
+        0.16799999,
+        0,
+        0,
+        0.272,
+        0.5339999,
+        0.13099999,
+        0,
+        0,
+        0,
+        0,
+        0,
+        1,
+        0
+      ]);
+    },
+    brownie: () => {
+      filter.colorMatrix([
+        0.5997023498159715,
+        0.34553243048391263,
+        -0.2708298674538042,
+        0,
+        47.43192855600873,
+        -0.037703249837783157,
+        0.8609577587992641,
+        0.15059552388459913,
+        0,
+        -36.96841498319127,
+        0.24113635128153335,
+        -0.07441037908422492,
+        0.44972182064877153,
+        0,
+        -7.562075277591283,
+        0,
+        0,
+        0,
+        1,
+        0
+      ]);
+    },
+    vintagePinhole: () => {
+      filter.colorMatrix([
+        0.6279345635605994,
+        0.3202183420819367,
+        -0.03965408211312453,
+        0,
+        9.651285835294123,
+        0.02578397704808868,
+        0.6441188644374771,
+        0.03259127616149294,
+        0,
+        7.462829176470591,
+        0.0466055556782719,
+        -0.0851232987247891,
+        0.5241648018700465,
+        0,
+        5.159190588235296,
+        0,
+        0,
+        0,
+        1,
+        0
+      ]);
+    },
+    kodachrome: () => {
+      filter.colorMatrix([
+        1.1285582396593525,
+        -0.3967382283601348,
+        -0.03992559172921793,
+        0,
+        63.72958762196502,
+        -0.16404339962244616,
+        1.0835251566291304,
+        -0.05498805115633132,
+        0,
+        24.732407896706203,
+        -0.16786010706155763,
+        -0.5603416277695248,
+        1.6014850761964943,
+        0,
+        35.62982807460946,
+        0,
+        0,
+        0,
+        1,
+        0
+      ]);
+    },
+    technicolor: () => {
+      filter.colorMatrix([
+        1.9125277891456083,
+        -0.8545344976951645,
+        -0.09155508482755585,
+        0,
+        11.793603434377337,
+        -0.3087833385928097,
+        1.7658908555458428,
+        -0.10601743074722245,
+        0,
+        -70.35205161461398,
+        -0.231103377548616,
+        -0.7501899197440212,
+        1.847597816108189,
+        0,
+        30.950940869491138,
+        0,
+        0,
+        0,
+        1,
+        0
+      ]);
+    },
+    polaroid: () => {
+      filter.colorMatrix([
+        1.438,
+        -0.062,
+        -0.062,
+        0,
+        0,
+        -0.122,
+        1.378,
+        -0.122,
+        0,
+        0,
+        -0.016,
+        -0.016,
+        1.483,
+        0,
+        0,
+        0,
+        0,
+        0,
+        1,
+        0
+      ]);
+    },
+    shiftToBGR: () => {
+      filter.colorMatrix([
+        0,
+        0,
+        1,
+        0,
+        0,
+        0,
+        1,
+        0,
+        0,
+        0,
+        1,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        0,
+        1,
+        0
+      ]);
+    },
+    convolution: (matrix) => {
+      const m = new Float32Array(matrix);
+      const pixelSizeX = 1 / fxcanvas.width;
+      const pixelSizeY = 1 / fxcanvas.height;
+      const program = compileShader(convolution);
+      if (!program)
+        return;
+      gl.uniform1fv(program.uniform["m"], m);
+      gl.uniform2f(program.uniform["px"], pixelSizeX, pixelSizeY);
+      draw();
+    },
+    detectEdges: () => {
+      filter.convolution.call(this, [
+        0,
+        1,
+        0,
+        1,
+        -4,
+        1,
+        0,
+        1,
+        0
+      ]);
+    },
+    sobelX: () => {
+      filter.convolution.call(this, [
+        -1,
+        0,
+        1,
+        -2,
+        0,
+        2,
+        -1,
+        0,
+        1
+      ]);
+    },
+    sobelY: () => {
+      filter.convolution.call(this, [
+        -1,
+        -2,
+        -1,
+        0,
+        0,
+        0,
+        1,
+        2,
+        1
+      ]);
+    },
+    sharpen: (amount) => {
+      const a = amount || 1;
+      filter.convolution.call(this, [
+        0,
+        -1 * a,
+        0,
+        -1 * a,
+        1 + 4 * a,
+        -1 * a,
+        0,
+        -1 * a,
+        0
+      ]);
+    },
+    emboss: (size2) => {
+      const s = size2 || 1;
+      filter.convolution.call(this, [
+        -2 * s,
+        -1 * s,
+        0,
+        -1 * s,
+        1,
+        1 * s,
+        0,
+        1 * s,
+        2 * s
+      ]);
+    },
+    blur: (size2) => {
+      const blurSizeX = size2 / 7 / fxcanvas.width;
+      const blurSizeY = size2 / 7 / fxcanvas.height;
+      const program = compileShader(blur);
+      if (!program)
+        return;
+      gl.uniform2f(program.uniform["px"], 0, blurSizeY);
+      draw(DRAW.INTERMEDIATE);
+      gl.uniform2f(program.uniform["px"], blurSizeX, 0);
+      draw();
+    },
+    pixelate: (size2) => {
+      const blurSizeX = size2 / fxcanvas.width;
+      const blurSizeY = size2 / fxcanvas.height;
+      const program = compileShader(pixelate);
+      if (!program)
+        return;
+      gl.uniform2f(program.uniform["size"], blurSizeX, blurSizeY);
+      draw();
+    }
+  };
+  this.add = function(name) {
+    const args = Array.prototype.slice.call(arguments, 1);
+    const func = filter[name];
+    filterChain.push({ func, args });
+  };
+  this.reset = function() {
+    filterChain = [];
+  };
+  this.get = function() {
+    return filterChain;
+  };
+  this.apply = function(image27) {
+    resize(image27.width, image27.height);
+    drawCount = 0;
+    if (!sourceTexture)
+      sourceTexture = gl.createTexture();
+    gl.bindTexture(gl.TEXTURE_2D, sourceTexture);
+    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_S, gl.CLAMP_TO_EDGE);
+    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_T, gl.CLAMP_TO_EDGE);
+    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MIN_FILTER, gl.NEAREST);
+    gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MAG_FILTER, gl.NEAREST);
+    gl.texImage2D(gl.TEXTURE_2D, 0, gl.RGBA, gl.RGBA, gl.UNSIGNED_BYTE, image27);
+    for (let i = 0; i < filterChain.length; i++) {
+      lastInChain = i === filterChain.length - 1;
+      const f = filterChain[i];
+      f.func.apply(this, f.args || []);
+    }
+    return fxcanvas;
+  };
+  this.draw = function(image27) {
+    this.add("brightness", 0);
+    return this.apply(image27);
+  };
+}
+
+// src/image/enhance.ts
+var tf = __toESM(require_tfjs_esm());
+async function histogramEqualization(inputImage) {
+  const squeeze14 = inputImage.shape.length === 4 ? tf.squeeze(inputImage) : inputImage;
+  const channels = tf.split(squeeze14, 3, 2);
+  const min2 = [tf.min(channels[0]), tf.min(channels[1]), tf.min(channels[2])];
+  const max4 = [tf.max(channels[0]), tf.max(channels[1]), tf.max(channels[2])];
+  const absMax = await Promise.all(max4.map((channel) => channel.data()));
+  const maxValue = 0.99 * Math.max(absMax[0][0], absMax[1][0], absMax[2][0]);
+  const sub11 = [tf.sub(channels[0], min2[0]), tf.sub(channels[1], min2[1]), tf.sub(channels[2], min2[2])];
+  const range = [tf.sub(max4[0], min2[0]), tf.sub(max4[1], min2[1]), tf.sub(max4[2], min2[2])];
+  const fact = [tf.div(maxValue, range[0]), tf.div(maxValue, range[1]), tf.div(maxValue, range[2])];
+  const enh = [tf.mul(sub11[0], fact[0]), tf.mul(sub11[1], fact[1]), tf.mul(sub11[2], fact[2])];
+  const rgb2 = tf.stack([enh[0], enh[1], enh[2]], 2);
+  const reshape8 = tf.reshape(rgb2, [1, squeeze14.shape[0], squeeze14.shape[1], 3]);
+  tf.dispose([...channels, ...min2, ...max4, ...sub11, ...range, ...fact, ...enh, rgb2, squeeze14]);
+  return reshape8;
+}
+
+// src/image/image.ts
+var maxSize = 3840;
+var inCanvas = null;
+var outCanvas = null;
+var tmpCanvas = null;
+var fx;
+var last = {
+  inputSum: 0,
+  cacheDiff: 1,
+  sumMethod: 0,
+  inputTensor: void 0
+};
+function reset() {
+  last.inputSum = 0;
+  last.cacheDiff = 1;
+  last.sumMethod = 0;
+  last.inputTensor = void 0;
+}
+function canvas(width, height) {
+  let c;
+  if (env.browser) {
+    if (env.worker) {
+      if (typeof OffscreenCanvas === "undefined")
+        throw new Error("canvas error: attempted to run in web worker but OffscreenCanvas is not supported");
+      c = new OffscreenCanvas(width, height);
+    } else {
+      if (typeof document === "undefined")
+        throw new Error("canvas error: attempted to run in browser but DOM is not defined");
+      c = document.createElement("canvas");
+      c.width = width;
+      c.height = height;
+    }
+  } else {
+    if (typeof env.Canvas !== "undefined")
+      c = new env.Canvas(width, height);
+    else if (typeof globalThis.Canvas !== "undefined")
+      c = new globalThis.Canvas(width, height);
+  }
+  return c;
+}
+function copy(input, output) {
+  const outputCanvas = output || canvas(input.width, input.height);
+  const ctx = outputCanvas.getContext("2d");
+  ctx.drawImage(input, 0, 0);
+  return outputCanvas;
+}
+async function process2(input, config3, getTensor = true) {
+  var _a, _b;
+  if (!input) {
+    if (config3.debug)
+      log("input error: input is missing");
+    return { tensor: null, canvas: null };
+  }
+  if (!(input instanceof tf2.Tensor) && !(typeof Image !== "undefined" && input instanceof Image) && !(typeof env.Canvas !== "undefined" && input instanceof env.Canvas) && !(typeof globalThis.Canvas !== "undefined" && input instanceof globalThis.Canvas) && !(typeof ImageData !== "undefined" && input instanceof ImageData) && !(typeof ImageBitmap !== "undefined" && input instanceof ImageBitmap) && !(typeof HTMLImageElement !== "undefined" && input instanceof HTMLImageElement) && !(typeof HTMLMediaElement !== "undefined" && input instanceof HTMLMediaElement) && !(typeof HTMLVideoElement !== "undefined" && input instanceof HTMLVideoElement) && !(typeof HTMLCanvasElement !== "undefined" && input instanceof HTMLCanvasElement) && !(typeof OffscreenCanvas !== "undefined" && input instanceof OffscreenCanvas)) {
+    throw new Error("input error: type is not recognized");
+  }
+  if (input instanceof tf2.Tensor) {
+    let tensor7 = null;
+    if (input["isDisposedInternal"])
+      throw new Error("input error: attempted to use tensor but it is disposed");
+    if (!input.shape)
+      throw new Error("input error: attempted to use tensor without a shape");
+    if (input.shape.length === 3) {
+      if (input.shape[2] === 3) {
+        tensor7 = tf2.expandDims(input, 0);
+      } else if (input.shape[2] === 4) {
+        const rgb2 = tf2.slice3d(input, [0, 0, 0], [-1, -1, 3]);
+        tensor7 = tf2.expandDims(rgb2, 0);
+        tf2.dispose(rgb2);
+      }
+    } else if (input.shape.length === 4) {
+      if (input.shape[3] === 3) {
+        tensor7 = tf2.clone(input);
+      } else if (input.shape[3] === 4) {
+        tensor7 = tf2.slice4d(input, [0, 0, 0, 0], [-1, -1, -1, 3]);
+      }
+    }
+    if (tensor7 == null || tensor7.shape.length !== 4 || tensor7.shape[0] !== 1 || tensor7.shape[3] !== 3)
+      throw new Error(`input error: attempted to use tensor with unrecognized shape: ${input.shape.toString()}`);
+    if (tensor7.dtype === "int32") {
+      const cast8 = tf2.cast(tensor7, "float32");
+      tf2.dispose(tensor7);
+      tensor7 = cast8;
+    }
+    return { tensor: tensor7, canvas: config3.filter.return ? outCanvas : null };
+  }
+  if (typeof input["readyState"] !== "undefined" && input.readyState <= 2) {
+    if (config3.debug)
+      log("input stream is not ready");
+    return { tensor: null, canvas: inCanvas };
+  }
+  const originalWidth = input["naturalWidth"] || input["videoWidth"] || input["width"] || input["shape"] && input["shape"][1] > 0;
+  const originalHeight = input["naturalHeight"] || input["videoHeight"] || input["height"] || input["shape"] && input["shape"][2] > 0;
+  if (!originalWidth || !originalHeight) {
+    if (config3.debug)
+      log("cannot determine input dimensions");
+    return { tensor: null, canvas: inCanvas };
+  }
+  let targetWidth = originalWidth;
+  let targetHeight = originalHeight;
+  if (targetWidth > maxSize) {
+    targetWidth = maxSize;
+    targetHeight = Math.trunc(targetWidth * originalHeight / originalWidth);
+  }
+  if (targetHeight > maxSize) {
+    targetHeight = maxSize;
+    targetWidth = Math.trunc(targetHeight * originalWidth / originalHeight);
+  }
+  if ((((_a = config3.filter) == null ? void 0 : _a.width) || 0) > 0)
+    targetWidth = config3.filter.width;
+  else if ((((_b = config3.filter) == null ? void 0 : _b.height) || 0) > 0)
+    targetWidth = originalWidth * ((config3.filter.height || 0) / originalHeight);
+  if ((config3.filter.height || 0) > 0)
+    targetHeight = config3.filter.height;
+  else if ((config3.filter.width || 0) > 0)
+    targetHeight = originalHeight * ((config3.filter.width || 0) / originalWidth);
+  if (!targetWidth || !targetHeight)
+    throw new Error("input error: cannot determine dimension");
+  if (!inCanvas || inCanvas.width !== targetWidth || inCanvas.height !== targetHeight)
+    inCanvas = canvas(targetWidth, targetHeight);
+  const inCtx = inCanvas.getContext("2d");
+  if (typeof ImageData !== "undefined" && input instanceof ImageData) {
+    inCtx.putImageData(input, 0, 0);
+  } else {
+    if (config3.filter.flip && typeof inCtx.translate !== "undefined") {
+      inCtx.translate(originalWidth, 0);
+      inCtx.scale(-1, 1);
+      inCtx.drawImage(input, 0, 0, originalWidth, originalHeight, 0, 0, inCanvas.width, inCanvas.height);
+      inCtx.setTransform(1, 0, 0, 1, 0, 0);
+    } else {
+      inCtx.drawImage(input, 0, 0, originalWidth, originalHeight, 0, 0, inCanvas.width, inCanvas.height);
+    }
+  }
+  if (!outCanvas || inCanvas.width !== outCanvas.width || inCanvas.height !== outCanvas.height)
+    outCanvas = canvas(inCanvas.width, inCanvas.height);
+  if (config3.filter.enabled && env.webgl.supported) {
+    if (!fx)
+      fx = env.browser ? new GLImageFilter() : null;
+    env.filter = !!fx;
+    if (!(fx == null ? void 0 : fx.add)) {
+      if (config3.debug)
+        log("input process error: cannot initialize filters");
+      env.webgl.supported = false;
+      config3.filter.enabled = false;
+      copy(inCanvas, outCanvas);
+    } else {
+      fx.reset();
+      if (config3.filter.brightness !== 0)
+        fx.add("brightness", config3.filter.brightness);
+      if (config3.filter.contrast !== 0)
+        fx.add("contrast", config3.filter.contrast);
+      if (config3.filter.sharpness !== 0)
+        fx.add("sharpen", config3.filter.sharpness);
+      if (config3.filter.blur !== 0)
+        fx.add("blur", config3.filter.blur);
+      if (config3.filter.saturation !== 0)
+        fx.add("saturation", config3.filter.saturation);
+      if (config3.filter.hue !== 0)
+        fx.add("hue", config3.filter.hue);
+      if (config3.filter.negative)
+        fx.add("negative");
+      if (config3.filter.sepia)
+        fx.add("sepia");
+      if (config3.filter.vintage)
+        fx.add("brownie");
+      if (config3.filter.sepia)
+        fx.add("sepia");
+      if (config3.filter.kodachrome)
+        fx.add("kodachrome");
+      if (config3.filter.technicolor)
+        fx.add("technicolor");
+      if (config3.filter.polaroid)
+        fx.add("polaroid");
+      if (config3.filter.pixelate !== 0)
+        fx.add("pixelate", config3.filter.pixelate);
+      if (fx.get() > 0)
+        outCanvas = fx.apply(inCanvas);
+      else
+        outCanvas = fx.draw(inCanvas);
+    }
+  } else {
+    copy(inCanvas, outCanvas);
+    if (fx)
+      fx = null;
+    env.filter = !!fx;
+  }
+  if (!getTensor)
+    return { tensor: null, canvas: outCanvas };
+  if (!outCanvas)
+    throw new Error("canvas error: cannot create output");
+  let pixels;
+  let depth = 3;
+  if (typeof ImageData !== "undefined" && input instanceof ImageData || input.data && input.width && input.height) {
+    if (env.browser && tf2.browser) {
+      pixels = tf2.browser ? tf2.browser.fromPixels(input) : null;
+    } else {
+      depth = input.data.length / input.height / input.width;
+      const arr = new Uint8Array(input.data.buffer);
+      pixels = tf2.tensor(arr, [input.height, input.width, depth], "int32");
+    }
+  } else {
+    if (!tmpCanvas || outCanvas.width !== tmpCanvas.width || outCanvas.height !== tmpCanvas.height)
+      tmpCanvas = canvas(outCanvas.width, outCanvas.height);
+    if (tf2.browser && env.browser) {
+      if (config3.backend === "webgl" || config3.backend === "humangl" || config3.backend === "webgpu") {
+        pixels = tf2.browser.fromPixels(outCanvas);
+      } else {
+        tmpCanvas = copy(outCanvas);
+        pixels = tf2.browser.fromPixels(tmpCanvas);
+      }
+    } else {
+      const tempCanvas = copy(outCanvas);
+      const tempCtx = tempCanvas.getContext("2d");
+      const tempData = tempCtx.getImageData(0, 0, targetWidth, targetHeight);
+      depth = tempData.data.length / targetWidth / targetHeight;
+      const arr = new Uint8Array(tempData.data.buffer);
+      pixels = tf2.tensor(arr, [targetWidth, targetHeight, depth]);
+    }
+  }
+  if (depth === 4) {
+    const rgb2 = tf2.slice3d(pixels, [0, 0, 0], [-1, -1, 3]);
+    tf2.dispose(pixels);
+    pixels = rgb2;
+  }
+  if (!pixels)
+    throw new Error("input error: cannot create tensor");
+  const casted = tf2.cast(pixels, "float32");
+  const tensor6 = config3.filter.equalization ? await histogramEqualization(casted) : tf2.expandDims(casted, 0);
+  tf2.dispose([pixels, casted]);
+  return { tensor: tensor6, canvas: config3.filter.return ? outCanvas : null };
+}
+async function skip(config3, input) {
+  let skipFrame = false;
+  if (config3.cacheSensitivity === 0 || !input.shape || input.shape.length !== 4 || input.shape[1] > 2048 || input.shape[2] > 2048)
+    return skipFrame;
+  if (!last.inputTensor) {
+    last.inputTensor = tf2.clone(input);
+  } else if (last.inputTensor.shape[1] !== input.shape[1] || last.inputTensor.shape[2] !== input.shape[2]) {
+    tf2.dispose(last.inputTensor);
+    last.inputTensor = tf2.clone(input);
+  } else {
+    const t2 = {};
+    t2.diff = tf2.sub(input, last.inputTensor);
+    t2.squared = tf2.mul(t2.diff, t2.diff);
+    t2.sum = tf2.sum(t2.squared);
+    const diffSum = await t2.sum.data();
+    const diffRelative = diffSum[0] / (input.shape[1] || 1) / (input.shape[2] || 1) / 255 / 3;
+    tf2.dispose([last.inputTensor, t2.diff, t2.squared, t2.sum]);
+    last.inputTensor = tf2.clone(input);
+    skipFrame = diffRelative <= (config3.cacheSensitivity || 0);
+  }
+  return skipFrame;
+}
+async function compare(config3, input1, input2) {
+  const t2 = {};
+  if (!input1 || !input2 || input1.shape.length !== 4 || input1.shape.length !== input2.shape.length) {
+    if (!config3.debug)
+      log("invalid input tensor or tensor shapes do not match:", input1.shape, input2.shape);
+    return 0;
+  }
+  if (input1.shape[0] !== 1 || input2.shape[0] !== 1 || input1.shape[3] !== 3 || input2.shape[3] !== 3) {
+    if (!config3.debug)
+      log("input tensors must be of shape [1, height, width, 3]:", input1.shape, input2.shape);
+    return 0;
+  }
+  t2.input1 = tf2.clone(input1);
+  t2.input2 = input1.shape[1] !== input2.shape[1] || input1.shape[2] !== input2.shape[2] ? tf2.image.resizeBilinear(input2, [input1.shape[1], input1.shape[2]]) : tf2.clone(input2);
+  t2.diff = tf2.sub(t2.input1, t2.input2);
+  t2.squared = tf2.mul(t2.diff, t2.diff);
+  t2.sum = tf2.sum(t2.squared);
+  const diffSum = await t2.sum.data();
+  const diffRelative = diffSum[0] / (input1.shape[1] || 1) / (input1.shape[2] || 1) / 255 / 3;
+  tf2.dispose([t2.input1, t2.input2, t2.diff, t2.squared, t2.sum]);
+  return diffRelative;
+}
+
+// src/util/env.ts
+var Env = class {
+  constructor() {
+    __publicField(this, "browser");
+    __publicField(this, "node");
+    __publicField(this, "worker");
+    __publicField(this, "platform", "");
+    __publicField(this, "agent", "");
+    __publicField(this, "backends", []);
+    __publicField(this, "initial");
+    __publicField(this, "filter");
+    __publicField(this, "tfjs");
+    __publicField(this, "offscreen");
+    __publicField(this, "perfadd", false);
+    __publicField(this, "tensorflow", {
+      version: void 0,
+      gpu: void 0
+    });
+    __publicField(this, "wasm", {
+      supported: void 0,
+      backend: void 0,
+      simd: void 0,
+      multithread: void 0
+    });
+    __publicField(this, "webgl", {
+      supported: void 0,
+      backend: void 0,
+      version: void 0,
+      renderer: void 0
+    });
+    __publicField(this, "webgpu", {
+      supported: void 0,
+      backend: void 0,
+      adapter: void 0
+    });
+    __publicField(this, "cpu", {
+      model: void 0,
+      flags: []
+    });
+    __publicField(this, "kernels", []);
+    __publicField(this, "Canvas");
+    __publicField(this, "Image");
+    __publicField(this, "ImageData");
+    this.browser = typeof navigator !== "undefined";
+    this.node = typeof process !== "undefined" && typeof process.versions !== "undefined" && typeof process.versions.node !== "undefined";
+    this.tfjs = { version: tf3.version["tfjs-core"] };
+    this.offscreen = typeof OffscreenCanvas !== "undefined";
+    this.initial = true;
+    this.worker = this.browser && this.offscreen ? typeof WorkerGlobalScope !== "undefined" : void 0;
+    if (typeof navigator !== "undefined") {
+      const raw = navigator.userAgent.match(/\(([^()]+)\)/g);
+      if (raw == null ? void 0 : raw[0]) {
+        const platformMatch = raw[0].match(/\(([^()]+)\)/g);
+        this.platform = (platformMatch == null ? void 0 : platformMatch[0]) ? platformMatch[0].replace(/\(|\)/g, "") : "";
+        this.agent = navigator.userAgent.replace(raw[0], "");
+        if (this.platform[1])
+          this.agent = this.agent.replace(raw[1], "");
+        this.agent = this.agent.replace(/  /g, " ");
+      }
+    } else if (typeof process !== "undefined") {
+      this.platform = `${process.platform} ${process.arch}`;
+      this.agent = `NodeJS ${process.version}`;
+    }
+  }
+  async updateBackend() {
+    this.backends = Object.keys(tf3.engine().registryFactory);
+    this.tensorflow = {
+      version: tf3.backend().binding ? tf3.backend().binding.TF_Version : void 0,
+      gpu: tf3.backend().binding ? tf3.backend().binding.isUsingGpuDevice() : void 0
+    };
+    this.wasm.supported = typeof WebAssembly !== "undefined";
+    this.wasm.backend = this.backends.includes("wasm");
+    if (this.wasm.supported && this.wasm.backend && tf3.getBackend() === "wasm") {
+      this.wasm.simd = tf3.env().get("WASM_HAS_SIMD_SUPPORT");
+      this.wasm.multithread = tf3.env().get("WASM_HAS_MULTITHREAD_SUPPORT");
+    }
+    const c = canvas(100, 100);
+    const ctx = c ? c.getContext("webgl2") : void 0;
+    this.webgl.supported = typeof ctx !== "undefined";
+    this.webgl.backend = this.backends.includes("webgl");
+    if (this.webgl.supported && this.webgl.backend && (tf3.getBackend() === "webgl" || tf3.getBackend() === "humangl")) {
+      const gl = tf3.backend().gpgpu !== "undefined" ? await tf3.backend().getGPGPUContext().gl : null;
+      if (gl) {
+        this.webgl.version = gl.getParameter(gl.VERSION);
+        this.webgl.renderer = gl.getParameter(gl.RENDERER);
+      }
+    }
+    this.webgpu.supported = this.browser && typeof navigator.gpu !== "undefined";
+    this.webgpu.backend = this.backends.includes("webgpu");
+    try {
+      if (this.webgpu.supported) {
+        const adapter = await navigator.gpu.requestAdapter();
+        this.webgpu.adapter = adapter ? adapter.name : void 0;
+      }
+    } catch (e) {
+      this.webgpu.supported = false;
+    }
+    try {
+      this.kernels = tf3.getKernelsForBackend(tf3.getBackend()).map((kernel) => kernel.kernelName.toLowerCase());
+    } catch (e) {
+    }
+  }
+  updateCPU() {
+    const cpu = { model: "", flags: [] };
+    if (this.node && this.platform.startsWith("linux")) {
+    }
+    if (!this.cpu)
+      Object.defineProperty(this, "cpu", { value: cpu });
+    else
+      this.cpu = cpu;
+  }
+};
+var env = new Env();
+
+// src/util/webcam.ts
+var WebCam = class {
+  constructor() {
+    __publicField(this, "config");
+    __publicField(this, "element");
+    __publicField(this, "stream");
+    __publicField(this, "start", async (webcamConfig) => {
+      if (webcamConfig == null ? void 0 : webcamConfig.debug)
+        this.config.debug = webcamConfig == null ? void 0 : webcamConfig.debug;
+      if (webcamConfig == null ? void 0 : webcamConfig.crop)
+        this.config.crop = webcamConfig == null ? void 0 : webcamConfig.crop;
+      if (webcamConfig == null ? void 0 : webcamConfig.mode)
+        this.config.mode = webcamConfig == null ? void 0 : webcamConfig.mode;
+      if (webcamConfig == null ? void 0 : webcamConfig.width)
+        this.config.width = webcamConfig == null ? void 0 : webcamConfig.width;
+      if (webcamConfig == null ? void 0 : webcamConfig.height)
+        this.config.height = webcamConfig == null ? void 0 : webcamConfig.height;
+      if (webcamConfig == null ? void 0 : webcamConfig.element) {
+        if (typeof webcamConfig.element === "string") {
+          const el = document.getElementById(webcamConfig.element);
+          if (el && el instanceof HTMLVideoElement) {
+            this.element = el;
+          } else {
+            if (this.config.debug)
+              log("webcam", "cannot get dom element", webcamConfig.element);
+            return;
+          }
+        } else if (webcamConfig.element instanceof HTMLVideoElement) {
+          this.element = webcamConfig.element;
+        } else {
+          if (this.config.debug)
+            log("webcam", "unknown dom element", webcamConfig.element);
+          return;
+        }
+      } else {
+        this.element = document.createElement("video");
+      }
+      const requestedConstraints = {
+        audio: false,
+        video: {
+          facingMode: this.config.mode === "front" ? "user" : "environment",
+          resizeMode: this.config.crop ? "crop-and-scale" : "none",
+          width: { ideal: this.config.width > 0 ? this.config.width : window.innerWidth },
+          height: { ideal: this.config.height > 0 ? this.config.height : window.innerHeight }
+        }
+      };
+      this.element.addEventListener("play", () => {
+        if (this.config.debug)
+          log("webcam", "play");
+      });
+      this.element.addEventListener("pause", () => {
+        if (this.config.debug)
+          log("webcam", "pause");
+      });
+      this.element.addEventListener("click", async () => {
+        if (!this.element || !this.stream)
+          return;
+        if (this.element.paused)
+          await this.element.play();
+        else
+          this.element.pause();
+      });
+      if (!(navigator == null ? void 0 : navigator.mediaDevices)) {
+        if (this.config.debug)
+          log("webcam", "no devices");
+        return;
+      }
+      try {
+        this.stream = await navigator.mediaDevices.getUserMedia(requestedConstraints);
+      } catch (err) {
+        log("webcam", err);
+        return;
+      }
+      if (!this.stream) {
+        if (this.config.debug)
+          log("webcam", "no stream");
+        return;
+      }
+      this.element.srcObject = this.stream;
+      const ready3 = new Promise((resolve) => {
+        if (!this.element)
+          resolve(false);
+        else
+          this.element.onloadeddata = () => resolve(true);
+      });
+      await ready3;
+      await this.element.play();
+      if (this.config.debug) {
+        log("webcam", {
+          width: this.width,
+          height: this.height,
+          label: this.label,
+          stream: this.stream,
+          track: this.track,
+          settings: this.settings,
+          constraints: this.constraints,
+          capabilities: this.capabilities
+        });
+      }
+    });
+    __publicField(this, "pause", () => {
+      if (this.element)
+        this.element.pause();
+    });
+    __publicField(this, "play", async () => {
+      if (this.element)
+        await this.element.play();
+    });
+    __publicField(this, "stop", () => {
+      if (this.config.debug)
+        log("webcam", "stop");
+      if (this.track)
+        this.track.stop();
+    });
+    this.config = {
+      element: void 0,
+      debug: true,
+      mode: "front",
+      crop: false,
+      width: 0,
+      height: 0
+    };
+  }
+  get track() {
+    if (!this.stream)
+      return void 0;
+    return this.stream.getVideoTracks()[0];
+  }
+  get capabilities() {
+    if (!this.track)
+      return void 0;
+    return this.track.getCapabilities ? this.track.getCapabilities() : void 0;
+  }
+  get constraints() {
+    if (!this.track)
+      return void 0;
+    return this.track.getConstraints ? this.track.getConstraints() : void 0;
+  }
+  get settings() {
+    if (!this.stream)
+      return void 0;
+    const track = this.stream.getVideoTracks()[0];
+    return track.getSettings ? track.getSettings() : void 0;
+  }
+  get label() {
+    if (!this.track)
+      return "";
+    return this.track.label;
+  }
+  get paused() {
+    var _a;
+    return ((_a = this.element) == null ? void 0 : _a.paused) || false;
+  }
+  get width() {
+    var _a;
+    return ((_a = this.element) == null ? void 0 : _a.videoWidth) || 0;
+  }
+  get height() {
+    var _a;
+    return ((_a = this.element) == null ? void 0 : _a.videoHeight) || 0;
+  }
+};
+
+// src/tfjs/load.ts
+var tf4 = __toESM(require_tfjs_esm());
+
+// models/models.json
+var models_exports = {};
+__export(models_exports, {
+  age: () => age,
+  "anti-spoofing": () => anti_spoofing,
+  antispoof: () => antispoof,
+  blazeface: () => blazeface,
+  "blazeface-back": () => blazeface_back,
+  "blazeface-front": () => blazeface_front,
+  "blazepose-detect": () => blazepose_detect,
+  "blazepose-detector2d": () => blazepose_detector2d,
+  "blazepose-detector3d": () => blazepose_detector3d,
+  "blazepose-full": () => blazepose_full,
+  "blazepose-heavy": () => blazepose_heavy,
+  "blazepose-lite": () => blazepose_lite,
+  default: () => models_default,
+  efficientpose: () => efficientpose,
+  "efficientpose-i-lite": () => efficientpose_i_lite,
+  "efficientpose-ii-lite": () => efficientpose_ii_lite,
+  "efficientpose-iv": () => efficientpose_iv,
+  emotion: () => emotion,
+  faceboxes: () => faceboxes,
+  facemesh: () => facemesh,
+  "facemesh-attention": () => facemesh_attention,
+  "facemesh-attention-alt": () => facemesh_attention_alt,
+  "facemesh-detection-full": () => facemesh_detection_full,
+  "facemesh-detection-short": () => facemesh_detection_short,
+  "facemesh-orig": () => facemesh_orig,
+  faceres: () => faceres,
+  "faceres-deep": () => faceres_deep,
+  gear: () => gear,
+  gender: () => gender,
+  "gender-ssrnet-imdb": () => gender_ssrnet_imdb,
+  handdetect: () => handdetect,
+  "handlandmark-full": () => handlandmark_full,
+  "handlandmark-lite": () => handlandmark_lite,
+  "handlandmark-sparse": () => handlandmark_sparse,
+  handskeleton: () => handskeleton,
+  handtrack: () => handtrack,
+  "insightface-efficientnet-b0": () => insightface_efficientnet_b0,
+  "insightface-ghostnet-strides1": () => insightface_ghostnet_strides1,
+  "insightface-ghostnet-strides2": () => insightface_ghostnet_strides2,
+  "insightface-mobilenet-emore": () => insightface_mobilenet_emore,
+  "insightface-mobilenet-swish": () => insightface_mobilenet_swish,
+  iris: () => iris,
+  liveness: () => liveness,
+  "mb3-centernet": () => mb3_centernet,
+  meet: () => meet,
+  mobileface: () => mobileface,
+  mobilefacenet: () => mobilefacenet,
+  models: () => models,
+  "movenet-lightning": () => movenet_lightning,
+  "movenet-multipose": () => movenet_multipose,
+  "movenet-thunder": () => movenet_thunder,
+  nanodet: () => nanodet,
+  "nanodet-e": () => nanodet_e,
+  "nanodet-g": () => nanodet_g,
+  "nanodet-m": () => nanodet_m,
+  "nanodet-t": () => nanodet_t,
+  posenet: () => posenet,
+  selfie: () => selfie
+});
+var antispoof = 853098;
+var blazeface = 538928;
+var emotion = 820516;
+var facemesh = 1477958;
+var faceres = 6978814;
+var handlandmark_full = 5431368;
+var handtrack = 2964837;
+var iris = 2599092;
+var liveness = 592976;
+var mb3_centernet = 4030290;
+var models = 0;
+var movenet_lightning = 4650216;
+var selfie = 212886;
+var age = 161240;
+var blazeface_back = 538928;
+var blazeface_front = 402048;
+var blazepose_detector2d = 7499400;
+var blazepose_detector3d = 5928856;
+var blazepose_full = 6338290;
+var blazepose_heavy = 27501554;
+var blazepose_lite = 2725490;
+var efficientpose = 5651240;
+var faceboxes = 2013002;
+var facemesh_attention_alt = 2387598;
+var facemesh_attention = 2382414;
+var facemesh_detection_full = 1026192;
+var facemesh_detection_short = 201268;
+var facemesh_orig = 2955780;
+var faceres_deep = 13957620;
+var gear = 1498916;
+var gender_ssrnet_imdb = 161236;
+var gender = 201808;
+var handdetect = 3515612;
+var handlandmark_lite = 2023432;
+var handlandmark_sparse = 5286322;
+var handskeleton = 5502280;
+var meet = 372228;
+var mobileface = 2183192;
+var mobilefacenet = 5171976;
+var movenet_multipose = 9448838;
+var movenet_thunder = 12477112;
+var nanodet = 7574558;
+var posenet = 5032780;
+var blazepose_detect = 5928804;
+var anti_spoofing = 853098;
+var efficientpose_i_lite = 2269064;
+var efficientpose_ii_lite = 5651240;
+var efficientpose_iv = 25643252;
+var insightface_efficientnet_b0 = 13013224;
+var insightface_ghostnet_strides1 = 8093408;
+var insightface_ghostnet_strides2 = 8049584;
+var insightface_mobilenet_emore = 6938536;
+var insightface_mobilenet_swish = 12168584;
+var nanodet_e = 12319156;
+var nanodet_g = 7574558;
+var nanodet_m = 1887474;
+var nanodet_t = 5294216;
+var models_default = {
+  antispoof,
+  blazeface,
+  emotion,
+  facemesh,
+  faceres,
+  "handlandmark-full": handlandmark_full,
+  handtrack,
+  iris,
+  liveness,
+  "mb3-centernet": mb3_centernet,
+  models,
+  "movenet-lightning": movenet_lightning,
+  selfie,
+  age,
+  "blazeface-back": blazeface_back,
+  "blazeface-front": blazeface_front,
+  "blazepose-detector2d": blazepose_detector2d,
+  "blazepose-detector3d": blazepose_detector3d,
+  "blazepose-full": blazepose_full,
+  "blazepose-heavy": blazepose_heavy,
+  "blazepose-lite": blazepose_lite,
+  efficientpose,
+  faceboxes,
+  "facemesh-attention-alt": facemesh_attention_alt,
+  "facemesh-attention": facemesh_attention,
+  "facemesh-detection-full": facemesh_detection_full,
+  "facemesh-detection-short": facemesh_detection_short,
+  "facemesh-orig": facemesh_orig,
+  "faceres-deep": faceres_deep,
+  gear,
+  "gender-ssrnet-imdb": gender_ssrnet_imdb,
+  gender,
+  handdetect,
+  "handlandmark-lite": handlandmark_lite,
+  "handlandmark-sparse": handlandmark_sparse,
+  handskeleton,
+  meet,
+  mobileface,
+  mobilefacenet,
+  "movenet-multipose": movenet_multipose,
+  "movenet-thunder": movenet_thunder,
+  nanodet,
+  posenet,
+  "blazepose-detect": blazepose_detect,
+  "anti-spoofing": anti_spoofing,
+  "efficientpose-i-lite": efficientpose_i_lite,
+  "efficientpose-ii-lite": efficientpose_ii_lite,
+  "efficientpose-iv": efficientpose_iv,
+  "insightface-efficientnet-b0": insightface_efficientnet_b0,
+  "insightface-ghostnet-strides1": insightface_ghostnet_strides1,
+  "insightface-ghostnet-strides2": insightface_ghostnet_strides2,
+  "insightface-mobilenet-emore": insightface_mobilenet_emore,
+  "insightface-mobilenet-swish": insightface_mobilenet_swish,
+  "nanodet-e": nanodet_e,
+  "nanodet-g": nanodet_g,
+  "nanodet-m": nanodet_m,
+  "nanodet-t": nanodet_t
+};
+
+// src/tfjs/load.ts
+var options = {
+  cacheModels: true,
+  cacheSupported: true,
+  verbose: true,
+  debug: false,
+  modelBasePath: ""
+};
+var modelStats = {};
+async function httpHandler(url, init3) {
+  if (options.debug)
+    log("load model fetch:", url, init3);
+  return fetch(url, init3);
+}
+function setModelLoadOptions(config3) {
+  options.cacheModels = config3.cacheModels;
+  options.verbose = config3.debug;
+  options.modelBasePath = config3.modelBasePath;
+}
+async function loadModel(modelPath) {
+  var _a, _b, _c, _d;
+  let modelUrl = join(options.modelBasePath, modelPath || "");
+  if (!modelUrl.toLowerCase().endsWith(".json"))
+    modelUrl += ".json";
+  const modelPathSegments = modelUrl.includes("/") ? modelUrl.split("/") : modelUrl.split("\\");
+  const shortModelName = modelPathSegments[modelPathSegments.length - 1].replace(".json", "");
+  const cachedModelName = "indexeddb://" + shortModelName;
+  modelStats[shortModelName] = {
+    name: shortModelName,
+    sizeFromManifest: 0,
+    sizeLoadedWeights: 0,
+    sizeDesired: models_exports[shortModelName],
+    inCache: false
+  };
+  options.cacheSupported = typeof indexedDB !== "undefined";
+  let cachedModels = {};
+  try {
+    cachedModels = options.cacheSupported && options.cacheModels ? await tf4.io.listModels() : {};
+  } catch (e) {
+    options.cacheSupported = false;
+  }
+  modelStats[shortModelName].inCache = options.cacheSupported && options.cacheModels && Object.keys(cachedModels).includes(cachedModelName);
+  const tfLoadOptions = typeof fetch === "undefined" ? {} : { fetchFunc: (url, init3) => httpHandler(url, init3) };
+  let model21 = new tf4.GraphModel(modelStats[shortModelName].inCache ? cachedModelName : modelUrl, tfLoadOptions);
+  let loaded = false;
+  try {
+    model21.findIOHandler();
+    if (options.debug)
+      log("model load handler:", model21["handler"]);
+  } catch (err) {
+    log("error finding model i/o handler:", modelUrl, err);
+  }
+  try {
+    const artifacts = await ((_a = model21.handler) == null ? void 0 : _a.load()) || null;
+    modelStats[shortModelName].sizeFromManifest = ((_b = artifacts == null ? void 0 : artifacts.weightData) == null ? void 0 : _b.byteLength) || 0;
+    if (artifacts)
+      model21.loadSync(artifacts);
+    else
+      model21 = await tf4.loadGraphModel(modelStats[shortModelName].inCache ? cachedModelName : modelUrl, tfLoadOptions);
+    modelStats[shortModelName].sizeLoadedWeights = ((_d = (_c = model21.artifacts) == null ? void 0 : _c.weightData) == null ? void 0 : _d.byteLength) || 0;
+    if (options.verbose)
+      log("load:", { model: shortModelName, url: model21["modelUrl"], bytes: modelStats[shortModelName].sizeLoadedWeights });
+    loaded = true;
+  } catch (err) {
+    log("error loading model:", modelUrl, err);
+  }
+  if (loaded && options.cacheModels && options.cacheSupported && !modelStats[shortModelName].inCache) {
+    try {
+      const saveResult = await model21.save(cachedModelName);
+      if (options.debug)
+        log("model saved:", cachedModelName, saveResult);
+    } catch (err) {
+      log("error saving model:", modelUrl, err);
+    }
+  }
+  return model21;
+}
+
+// src/human.ts
+var tf39 = __toESM(require_tfjs_esm());
+
+// package.json
+var version2 = "2.11.0";
+
+// src/tfjs/humangl.ts
+var tf34 = __toESM(require_tfjs_esm());
+
+// src/models.ts
+var models_exports2 = {};
+__export(models_exports2, {
+  Models: () => Models,
+  getModelStats: () => getModelStats,
+  load: () => load22,
+  reset: () => reset2,
+  validate: () => validate2,
+  validateModel: () => validateModel
+});
+
+// src/face/antispoof.ts
+var tf5 = __toESM(require_tfjs_esm());
+var model;
+var cached = [];
+var skipped = Number.MAX_SAFE_INTEGER;
+var lastCount = 0;
+var lastTime = 0;
+async function load(config3) {
+  var _a;
+  if (env.initial)
+    model = null;
+  if (!model)
+    model = await loadModel((_a = config3.face.antispoof) == null ? void 0 : _a.modelPath);
+  else if (config3.debug)
+    log("cached model:", model["modelUrl"]);
+  return model;
+}
+async function predict(image27, config3, idx, count2) {
+  var _a, _b;
+  if (!model || !(model == null ? void 0 : model["executor"]))
+    return 0;
+  const skipTime = (((_a = config3.face.antispoof) == null ? void 0 : _a.skipTime) || 0) > now() - lastTime;
+  const skipFrame = skipped < (((_b = config3.face.antispoof) == null ? void 0 : _b.skipFrames) || 0);
+  if (config3.skipAllowed && skipTime && skipFrame && lastCount === count2 && cached[idx]) {
+    skipped++;
+    return cached[idx];
+  }
+  skipped = 0;
+  return new Promise(async (resolve) => {
+    const resize = tf5.image.resizeBilinear(image27, [(model == null ? void 0 : model.inputs[0].shape) ? model.inputs[0].shape[2] : 0, (model == null ? void 0 : model.inputs[0].shape) ? model.inputs[0].shape[1] : 0], false);
+    const res = model == null ? void 0 : model.execute(resize);
+    const num = (await res.data())[0];
+    cached[idx] = Math.round(100 * num) / 100;
+    lastCount = count2;
+    lastTime = now();
+    tf5.dispose([resize, res]);
+    resolve(cached[idx]);
+  });
+}
+
+// src/face/blazeface.ts
+var tf8 = __toESM(require_tfjs_esm());
+
+// src/face/facemeshutil.ts
+var tf7 = __toESM(require_tfjs_esm());
+
+// src/face/facemeshcoords.ts
+var meshAnnotations = {
+  silhouette: [
+    10,
+    338,
+    297,
+    332,
+    284,
+    251,
+    389,
+    356,
+    454,
+    323,
+    361,
+    288,
+    397,
+    365,
+    379,
+    378,
+    400,
+    377,
+    152,
+    148,
+    176,
+    149,
+    150,
+    136,
+    172,
+    58,
+    132,
+    93,
+    234,
+    127,
+    162,
+    21,
+    54,
+    103,
+    67,
+    109
+  ],
+  lipsUpperOuter: [185, 40, 39, 37, 0, 267, 269, 270, 409],
+  lipsLowerOuter: [61, 146, 91, 181, 84, 17, 314, 405, 321, 375, 291],
+  lipsUpperInner: [191, 80, 81, 82, 13, 312, 311, 310, 415],
+  lipsLowerInner: [78, 95, 88, 178, 87, 14, 317, 402, 318, 324, 308],
+  lipsLowerSemiOuter: [76, 77, 90, 180, 85, 16, 315, 404, 320, 307, 306],
+  lipsUpperSemiOuter: [184, 74, 73, 72, 11, 302, 303, 304, 408],
+  lipsLowerSemiInner: [62, 96, 89, 179, 86, 15, 316, 403, 319, 325, 292],
+  lipsUpperSemiInner: [183, 42, 41, 38, 12, 268, 271, 272, 407],
+  rightEyeUpper0: [246, 161, 160, 159, 158, 157, 173],
+  rightEyeLower0: [33, 7, 163, 144, 145, 153, 154, 155, 133],
+  rightEyeUpper1: [247, 30, 29, 27, 28, 56, 190],
+  rightEyeLower1: [130, 25, 110, 24, 23, 22, 26, 112, 243],
+  rightEyeUpper2: [113, 225, 224, 223, 222, 221, 189],
+  rightEyeLower2: [226, 31, 228, 229, 230, 231, 232, 233, 244],
+  rightEyeLower3: [143, 111, 117, 118, 119, 120, 121, 128, 245],
+  rightEyebrowUpper: [156, 70, 63, 105, 66, 107, 55, 193],
+  rightEyebrowLower: [35, 124, 46, 53, 52, 65],
+  rightEyeIris: [473, 474, 475, 476, 477],
+  leftEyeUpper0: [466, 388, 387, 386, 385, 384, 398],
+  leftEyeLower0: [263, 249, 390, 373, 374, 380, 381, 382, 362],
+  leftEyeUpper1: [467, 260, 259, 257, 258, 286, 414],
+  leftEyeLower1: [359, 255, 339, 254, 253, 252, 256, 341, 463],
+  leftEyeUpper2: [342, 445, 444, 443, 442, 441, 413],
+  leftEyeLower2: [446, 261, 448, 449, 450, 451, 452, 453, 464],
+  leftEyeLower3: [372, 340, 346, 347, 348, 349, 350, 357, 465],
+  leftEyebrowUpper: [383, 300, 293, 334, 296, 336, 285, 417],
+  leftEyebrowLower: [265, 353, 276, 283, 282, 295],
+  leftEyeIris: [468, 469, 470, 471, 472],
+  midwayBetweenEyes: [168],
+  noseTip: [1],
+  noseBottom: [2],
+  noseRightCorner: [98],
+  noseLeftCorner: [327],
+  rightCheek: [205],
+  leftCheek: [425]
+};
+var meshLandmarks = {
+  count: 468,
+  mouth: 13,
+  symmetryLine: [13, meshAnnotations.midwayBetweenEyes[0]]
+};
+var blazeFaceLandmarks = {
+  leftEye: 0,
+  rightEye: 1,
+  nose: 2,
+  mouth: 3,
+  leftEar: 4,
+  rightEar: 5,
+  symmetryLine: [3, 2]
+};
+var irisIndices = [
+  { key: "EyeUpper0", indices: [9, 10, 11, 12, 13, 14, 15] },
+  { key: "EyeUpper1", indices: [25, 26, 27, 28, 29, 30, 31] },
+  { key: "EyeUpper2", indices: [41, 42, 43, 44, 45, 46, 47] },
+  { key: "EyeLower0", indices: [0, 1, 2, 3, 4, 5, 6, 7, 8] },
+  { key: "EyeLower1", indices: [16, 17, 18, 19, 20, 21, 22, 23, 24] },
+  { key: "EyeLower2", indices: [32, 33, 34, 35, 36, 37, 38, 39, 40] },
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+var VTX68 = [
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+  132,
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+  91,
+  78,
+  81,
+  13,
+  311,
+  308,
+  402,
+  14,
+  178
+];
+var VTX33 = [33, 133, 362, 263, 1, 62, 308, 159, 145, 386, 374, 6, 102, 331, 2, 13, 14, 70, 105, 107, 336, 334, 300, 54, 10, 284, 50, 280, 234, 454, 58, 288, 152];
+var VTX7 = [33, 133, 362, 263, 1, 78, 308];
+var UV68 = VTX68.map((x) => UV468[x]);
+var UV33 = VTX33.map((x) => UV468[x]);
+var UV7 = VTX7.map((x) => UV468[x]);
+function connectionsToIndices(connections) {
+  const indices = connections.map((connection) => connection[0]);
+  indices.push(connections[connections.length - 1][1]);
+  return indices;
+}
+var pairsLips = [
+  [61, 146],
+  [146, 91],
+  [91, 181],
+  [181, 84],
+  [84, 17],
+  [17, 314],
+  [314, 405],
+  [405, 321],
+  [321, 375],
+  [375, 291],
+  [61, 185],
+  [185, 40],
+  [40, 39],
+  [39, 37],
+  [37, 0],
+  [0, 267],
+  [267, 269],
+  [269, 270],
+  [270, 409],
+  [409, 291],
+  [78, 95],
+  [95, 88],
+  [88, 178],
+  [178, 87],
+  [87, 14],
+  [14, 317],
+  [317, 402],
+  [402, 318],
+  [318, 324],
+  [324, 308],
+  [78, 191],
+  [191, 80],
+  [80, 81],
+  [81, 82],
+  [82, 13],
+  [13, 312],
+  [312, 311],
+  [311, 310],
+  [310, 415],
+  [415, 308]
+];
+var pairsLeftEye = [[263, 249], [249, 390], [390, 373], [373, 374], [374, 380], [380, 381], [381, 382], [382, 362], [263, 466], [466, 388], [388, 387], [387, 386], [386, 385], [385, 384], [384, 398], [398, 362]];
+var pairsLeftEyebrow = [[276, 283], [283, 282], [282, 295], [295, 285], [300, 293], [293, 334], [334, 296], [296, 336]];
+var pairsLeftIris = [[474, 475], [475, 476], [476, 477], [477, 474]];
+var pairsRightEye = [[33, 7], [7, 163], [163, 144], [144, 145], [145, 153], [153, 154], [154, 155], [155, 133], [33, 246], [246, 161], [161, 160], [160, 159], [159, 158], [158, 157], [157, 173], [173, 133]];
+var pairsRightEyebrow = [[46, 53], [53, 52], [52, 65], [65, 55], [70, 63], [63, 105], [105, 66], [66, 107]];
+var pairsRightIris = [[469, 470], [470, 471], [471, 472], [472, 469]];
+var pairsFaceContour = [
+  [10, 338],
+  [338, 297],
+  [297, 332],
+  [332, 284],
+  [284, 251],
+  [251, 389],
+  [389, 356],
+  [356, 454],
+  [454, 323],
+  [323, 361],
+  [361, 288],
+  [288, 397],
+  [397, 365],
+  [365, 379],
+  [379, 378],
+  [378, 400],
+  [400, 377],
+  [377, 152],
+  [152, 148],
+  [148, 176],
+  [176, 149],
+  [149, 150],
+  [150, 136],
+  [136, 172],
+  [172, 58],
+  [58, 132],
+  [132, 93],
+  [93, 234],
+  [234, 127],
+  [127, 162],
+  [162, 21],
+  [21, 54],
+  [54, 103],
+  [103, 67],
+  [67, 109],
+  [109, 10]
+];
+var contourKeypoints = {
+  lips: connectionsToIndices(pairsLips),
+  leftEye: connectionsToIndices(pairsLeftEye),
+  leftEyebrow: connectionsToIndices(pairsLeftEyebrow),
+  leftIris: connectionsToIndices(pairsLeftIris),
+  rightEye: connectionsToIndices(pairsRightEye),
+  rightEyebrow: connectionsToIndices(pairsRightEyebrow),
+  rightIris: connectionsToIndices(pairsRightIris),
+  faceOval: connectionsToIndices(pairsFaceContour)
+};
+
+// src/tfjs/constants.ts
+var tf6 = __toESM(require_tfjs_esm());
+var constants = {
+  tf255: 255,
+  tf1: 1,
+  tf2: 2,
+  tf05: 0.5,
+  tf127: 127.5,
+  rgb: [0.2989, 0.587, 0.114]
+};
+function init() {
+  constants.tf255 = tf6.scalar(255, "float32");
+  constants.tf1 = tf6.scalar(1, "float32");
+  constants.tf2 = tf6.scalar(2, "float32");
+  constants.tf05 = tf6.scalar(0.5, "float32");
+  constants.tf127 = tf6.scalar(127.5, "float32");
+  constants.rgb = tf6.tensor1d([0.2989, 0.587, 0.114], "float32");
+}
+
+// src/face/facemeshutil.ts
+var getBoxSize = (box) => [Math.abs(box.endPoint[0] - box.startPoint[0]), Math.abs(box.endPoint[1] - box.startPoint[1])];
+var getBoxCenter = (box) => [box.startPoint[0] + (box.endPoint[0] - box.startPoint[0]) / 2, box.startPoint[1] + (box.endPoint[1] - box.startPoint[1]) / 2, 1];
+var clampBox = (box, input) => box ? [
+  Math.trunc(Math.max(0, box.startPoint[0])),
+  Math.trunc(Math.max(0, box.startPoint[1])),
+  Math.trunc(Math.min(input.shape[2] || 0, box.endPoint[0]) - Math.max(0, box.startPoint[0])),
+  Math.trunc(Math.min(input.shape[1] || 0, box.endPoint[1]) - Math.max(0, box.startPoint[1]))
+] : [0, 0, 0, 0];
+var getRawBox = (box, input) => box ? [
+  box.startPoint[0] / (input.shape[2] || 0),
+  box.startPoint[1] / (input.shape[1] || 0),
+  (box.endPoint[0] - box.startPoint[0]) / (input.shape[2] || 0),
+  (box.endPoint[1] - box.startPoint[1]) / (input.shape[1] || 0)
+] : [0, 0, 0, 0];
+var scaleBoxCoordinates = (box, factor) => {
+  const startPoint = [box.startPoint[0] * factor[0], box.startPoint[1] * factor[1]];
+  const endPoint = [box.endPoint[0] * factor[0], box.endPoint[1] * factor[1]];
+  return { startPoint, endPoint, landmarks: box.landmarks, confidence: box.confidence };
+};
+var cutAndResize = (box, image27, cropSize) => {
+  const h = image27.shape[1];
+  const w = image27.shape[2];
+  const cutBox = [box.startPoint[1] / h, box.startPoint[0] / w, box.endPoint[1] / h, box.endPoint[0] / w];
+  const crop = tf7.image.cropAndResize(image27, [cutBox], [0], cropSize);
+  const norm = tf7.div(crop, constants.tf255);
+  tf7.dispose(crop);
+  return norm;
+};
+var enlargeBox = (box, factor) => {
+  const center = getBoxCenter(box);
+  const size2 = getBoxSize(box);
+  const halfSize = [factor * size2[0] / 2, factor * size2[1] / 2];
+  return { startPoint: [center[0] - halfSize[0], center[1] - halfSize[1]], endPoint: [center[0] + halfSize[0], center[1] + halfSize[1]], landmarks: box.landmarks, confidence: box.confidence };
+};
+var squarifyBox = (box) => {
+  const centers = getBoxCenter(box);
+  const size2 = getBoxSize(box);
+  const halfSize = Math.max(...size2) / 2;
+  return { startPoint: [Math.round(centers[0] - halfSize), Math.round(centers[1] - halfSize)], endPoint: [Math.round(centers[0] + halfSize), Math.round(centers[1] + halfSize)], landmarks: box.landmarks, confidence: box.confidence };
+};
+var calculateLandmarksBoundingBox = (landmarks) => {
+  const x = landmarks.map((d) => d[0]);
+  const y = landmarks.map((d) => d[1]);
+  return { startPoint: [Math.min(...x), Math.min(...y)], endPoint: [Math.max(...x), Math.max(...y)], landmarks };
+};
+var fixedRotationMatrix = [[1, 0, 0], [0, 1, 0], [0, 0, 1]];
+var normalizeRadians = (angle) => angle - 2 * Math.PI * Math.floor((angle + Math.PI) / (2 * Math.PI));
+var computeRotation = (point1, point2) => normalizeRadians(Math.PI / 2 - Math.atan2(-(point2[1] - point1[1]), point2[0] - point1[0]));
+var buildTranslationMatrix = (x, y) => [[1, 0, x], [0, 1, y], [0, 0, 1]];
+var dot = (v1, v2) => {
+  let product = 0;
+  for (let i = 0; i < v1.length; i++)
+    product += v1[i] * v2[i];
+  return product;
+};
+var getColumnFrom2DArr = (arr, columnIndex) => {
+  const column = [];
+  for (let i = 0; i < arr.length; i++)
+    column.push(arr[i][columnIndex]);
+  return column;
+};
+var multiplyTransformMatrices = (mat1, mat2) => {
+  const product = [];
+  const size2 = mat1.length;
+  for (let row = 0; row < size2; row++) {
+    product.push([]);
+    for (let col = 0; col < size2; col++)
+      product[row].push(dot(mat1[row], getColumnFrom2DArr(mat2, col)));
+  }
+  return product;
+};
+var buildRotationMatrix = (rotation, center) => {
+  const cosA = Math.cos(rotation);
+  const sinA = Math.sin(rotation);
+  const rotationMatrix = [[cosA, -sinA, 0], [sinA, cosA, 0], [0, 0, 1]];
+  const translationMatrix = buildTranslationMatrix(center[0], center[1]);
+  const translationTimesRotation = multiplyTransformMatrices(translationMatrix, rotationMatrix);
+  const negativeTranslationMatrix = buildTranslationMatrix(-center[0], -center[1]);
+  return multiplyTransformMatrices(translationTimesRotation, negativeTranslationMatrix);
+};
+var invertTransformMatrix = (matrix) => {
+  const rotationComponent = [[matrix[0][0], matrix[1][0]], [matrix[0][1], matrix[1][1]]];
+  const translationComponent = [matrix[0][2], matrix[1][2]];
+  const invertedTranslation = [-dot(rotationComponent[0], translationComponent), -dot(rotationComponent[1], translationComponent)];
+  return [rotationComponent[0].concat(invertedTranslation[0]), rotationComponent[1].concat(invertedTranslation[1]), [0, 0, 1]];
+};
+var rotatePoint = (homogeneousCoordinate, rotationMatrix) => [dot(homogeneousCoordinate, rotationMatrix[0]), dot(homogeneousCoordinate, rotationMatrix[1])];
+function generateAnchors(inputSize10) {
+  const spec = inputSize10 === 192 ? { strides: [4], anchors: [1] } : { strides: [inputSize10 / 16, inputSize10 / 8], anchors: [2, 6] };
+  const anchors3 = [];
+  for (let i = 0; i < spec.strides.length; i++) {
+    const stride = spec.strides[i];
+    const gridRows = Math.floor((inputSize10 + stride - 1) / stride);
+    const gridCols = Math.floor((inputSize10 + stride - 1) / stride);
+    const anchorsNum = spec.anchors[i];
+    for (let gridY = 0; gridY < gridRows; gridY++) {
+      const anchorY = stride * (gridY + 0.5);
+      for (let gridX = 0; gridX < gridCols; gridX++) {
+        const anchorX = stride * (gridX + 0.5);
+        for (let n = 0; n < anchorsNum; n++)
+          anchors3.push([anchorX, anchorY]);
+      }
+    }
+  }
+  return anchors3;
+}
+function transformRawCoords(coordsRaw, box, angle, rotationMatrix, inputSize10) {
+  const boxSize = getBoxSize(box);
+  const coordsScaled = coordsRaw.map((coord) => [
+    boxSize[0] / inputSize10 * (coord[0] - inputSize10 / 2),
+    boxSize[1] / inputSize10 * (coord[1] - inputSize10 / 2),
+    coord[2] || 0
+  ]);
+  const largeAngle = angle && angle !== 0 && Math.abs(angle) > 0.2;
+  const coordsRotationMatrix = largeAngle ? buildRotationMatrix(angle, [0, 0]) : fixedRotationMatrix;
+  const coordsRotated = largeAngle ? coordsScaled.map((coord) => [...rotatePoint(coord, coordsRotationMatrix), coord[2]]) : coordsScaled;
+  const inverseRotationMatrix = largeAngle ? invertTransformMatrix(rotationMatrix) : fixedRotationMatrix;
+  const boxCenter = getBoxCenter(box);
+  const offsets = [dot(boxCenter, inverseRotationMatrix[0]), dot(boxCenter, inverseRotationMatrix[1])];
+  return coordsRotated.map((coord) => [
+    Math.trunc(coord[0] + offsets[0]),
+    Math.trunc(coord[1] + offsets[1]),
+    Math.trunc(coord[2] || 0)
+  ]);
+}
+function correctFaceRotation(rotate, box, input, inputSize10) {
+  const symmetryLine = box.landmarks.length >= meshLandmarks.count ? meshLandmarks.symmetryLine : blazeFaceLandmarks.symmetryLine;
+  let angle = 0;
+  let rotationMatrix = fixedRotationMatrix;
+  let face4;
+  if (rotate && env.kernels.includes("rotatewithoffset")) {
+    angle = computeRotation(box.landmarks[symmetryLine[0]], box.landmarks[symmetryLine[1]]);
+    const largeAngle = angle && angle !== 0 && Math.abs(angle) > 0.2;
+    if (largeAngle) {
+      const center = getBoxCenter(box);
+      const centerRaw = [center[0] / input.shape[2], center[1] / input.shape[1]];
+      const rotated = tf7.image.rotateWithOffset(input, angle, 0, centerRaw);
+      rotationMatrix = buildRotationMatrix(-angle, center);
+      face4 = cutAndResize(box, rotated, [inputSize10, inputSize10]);
+      tf7.dispose(rotated);
+    } else {
+      face4 = cutAndResize(box, input, [inputSize10, inputSize10]);
+    }
+  } else {
+    face4 = cutAndResize(box, input, [inputSize10, inputSize10]);
+  }
+  return [angle, rotationMatrix, face4];
+}
+var findFaceCenter = (mesh) => {
+  const x = mesh.map((m) => m[0]);
+  const y = mesh.map((m) => m[1]);
+  return [Math.min(...x) + (Math.max(...x) - Math.min(...x)) / 2, Math.min(...y) + (Math.max(...y) - Math.min(...y)) / 2];
+};
+var calculateFaceBox = (mesh, previousBox) => {
+  const center = findFaceCenter(mesh);
+  const boxSize = getBoxSize(previousBox);
+  const calculatedBox = {
+    startPoint: [center[0] - boxSize[0] / 2, center[1] - boxSize[1] / 2],
+    endPoint: [center[0] + boxSize[0] / 2, center[1] + boxSize[1] / 2]
+  };
+  return calculatedBox;
+};
+
+// src/face/blazeface.ts
+var keypointsCount = 6;
+var faceBoxScaleFactor = 1.4;
+var model2;
+var anchors = null;
+var inputSize = 0;
+var inputSizeT = null;
+var size = () => inputSize;
+async function load2(config3) {
+  var _a;
+  if (env.initial)
+    model2 = null;
+  if (!model2)
+    model2 = await loadModel((_a = config3.face.detector) == null ? void 0 : _a.modelPath);
+  else if (config3.debug)
+    log("cached model:", model2["modelUrl"]);
+  inputSize = model2["executor"] && model2.inputs[0].shape ? model2.inputs[0].shape[2] : 256;
+  inputSizeT = tf8.scalar(inputSize, "int32");
+  anchors = tf8.tensor2d(generateAnchors(inputSize));
+  return model2;
+}
+function decodeBoxes(boxOutputs) {
+  const t2 = {};
+  t2.boxStarts = tf8.slice(boxOutputs, [0, 1], [-1, 2]);
+  t2.centers = tf8.add(t2.boxStarts, anchors);
+  t2.boxSizes = tf8.slice(boxOutputs, [0, 3], [-1, 2]);
+  t2.boxSizesNormalized = tf8.div(t2.boxSizes, inputSizeT);
+  t2.centersNormalized = tf8.div(t2.centers, inputSizeT);
+  t2.halfBoxSize = tf8.div(t2.boxSizesNormalized, constants.tf2);
+  t2.starts = tf8.sub(t2.centersNormalized, t2.halfBoxSize);
+  t2.ends = tf8.add(t2.centersNormalized, t2.halfBoxSize);
+  t2.startNormalized = tf8.mul(t2.starts, inputSizeT);
+  t2.endNormalized = tf8.mul(t2.ends, inputSizeT);
+  const boxes = tf8.concat2d([t2.startNormalized, t2.endNormalized], 1);
+  Object.keys(t2).forEach((tensor6) => tf8.dispose(t2[tensor6]));
+  return boxes;
+}
+async function getBoxes(inputImage, config3) {
+  var _a, _b, _c, _d;
+  if (!inputImage || inputImage["isDisposedInternal"] || inputImage.shape.length !== 4 || inputImage.shape[1] < 1 || inputImage.shape[2] < 1)
+    return [];
+  const t2 = {};
+  t2.resized = tf8.image.resizeBilinear(inputImage, [inputSize, inputSize]);
+  t2.div = tf8.div(t2.resized, constants.tf127);
+  t2.normalized = tf8.sub(t2.div, constants.tf05);
+  const res = model2 == null ? void 0 : model2.execute(t2.normalized);
+  if (Array.isArray(res) && res.length > 2) {
+    const sorted = res.sort((a, b) => a.size - b.size);
+    t2.concat384 = tf8.concat([sorted[0], sorted[2]], 2);
+    t2.concat512 = tf8.concat([sorted[1], sorted[3]], 2);
+    t2.concat = tf8.concat([t2.concat512, t2.concat384], 1);
+    t2.batch = tf8.squeeze(t2.concat, 0);
+  } else if (Array.isArray(res)) {
+    t2.batch = tf8.squeeze(res[0]);
+  } else {
+    t2.batch = tf8.squeeze(res);
+  }
+  tf8.dispose(res);
+  t2.boxes = decodeBoxes(t2.batch);
+  t2.logits = tf8.slice(t2.batch, [0, 0], [-1, 1]);
+  t2.sigmoid = tf8.sigmoid(t2.logits);
+  t2.scores = tf8.squeeze(t2.sigmoid);
+  t2.nms = await tf8.image.nonMaxSuppressionAsync(t2.boxes, t2.scores, ((_a = config3.face.detector) == null ? void 0 : _a.maxDetected) || 0, ((_b = config3.face.detector) == null ? void 0 : _b.iouThreshold) || 0, ((_c = config3.face.detector) == null ? void 0 : _c.minConfidence) || 0);
+  const nms = await t2.nms.array();
+  const boxes = [];
+  const scores = await t2.scores.data();
+  for (let i = 0; i < nms.length; i++) {
+    const confidence = scores[nms[i]];
+    if (confidence > (((_d = config3.face.detector) == null ? void 0 : _d.minConfidence) || 0)) {
+      const b = {};
+      b.bbox = tf8.slice(t2.boxes, [nms[i], 0], [1, -1]);
+      b.slice = tf8.slice(t2.batch, [nms[i], keypointsCount - 1], [1, -1]);
+      b.squeeze = tf8.squeeze(b.slice);
+      b.landmarks = tf8.reshape(b.squeeze, [keypointsCount, -1]);
+      const points = await b.bbox.data();
+      const rawBox = {
+        startPoint: [points[0], points[1]],
+        endPoint: [points[2], points[3]],
+        landmarks: await b.landmarks.array(),
+        confidence
+      };
+      const scaledBox = scaleBoxCoordinates(rawBox, [(inputImage.shape[2] || 0) / inputSize, (inputImage.shape[1] || 0) / inputSize]);
+      const enlargedBox = enlargeBox(scaledBox, config3.face["scale"] || faceBoxScaleFactor);
+      const squaredBox = squarifyBox(enlargedBox);
+      boxes.push(squaredBox);
+      Object.keys(b).forEach((tensor6) => tf8.dispose(b[tensor6]));
+    }
+  }
+  Object.keys(t2).forEach((tensor6) => tf8.dispose(t2[tensor6]));
+  return boxes;
+}
+
+// src/body/blazepose.ts
+var tf10 = __toESM(require_tfjs_esm());
+
+// src/body/blazeposecoords.ts
+var blazeposecoords_exports = {};
+__export(blazeposecoords_exports, {
+  connected: () => connected,
+  kpt: () => kpt
+});
+var kpt = [
+  "nose",
+  "leftEyeInside",
+  "leftEye",
+  "leftEyeOutside",
+  "rightEyeInside",
+  "rightEye",
+  "rightEyeOutside",
+  "leftEar",
+  "rightEar",
+  "leftMouth",
+  "rightMouth",
+  "leftShoulder",
+  "rightShoulder",
+  "leftElbow",
+  "rightElbow",
+  "leftWrist",
+  "rightWrist",
+  "leftPinky",
+  "rightPinky",
+  "leftIndex",
+  "rightIndex",
+  "leftThumb",
+  "rightThumb",
+  "leftHip",
+  "rightHip",
+  "leftKnee",
+  "rightKnee",
+  "leftAnkle",
+  "rightAnkle",
+  "leftHeel",
+  "rightHeel",
+  "leftFoot",
+  "rightFoot",
+  "bodyCenter",
+  "bodyTop",
+  "leftPalm",
+  "leftHand",
+  "rightPalm",
+  "rightHand"
+];
+var connected = {
+  shoulders: ["leftShoulder", "rightShoulder"],
+  hips: ["rightHip", "leftHip"],
+  mouth: ["leftMouth", "rightMouth"],
+  leftLegUpper: ["leftHip", "leftKnee"],
+  leftLegLower: ["leftKnee", "leftAnkle"],
+  leftFoot: ["leftAnkle", "leftHeel", "leftFoot"],
+  leftTorso: ["leftShoulder", "leftHip"],
+  leftArmUpper: ["leftShoulder", "leftElbow"],
+  leftArmLower: ["leftElbow", "leftWrist"],
+  leftHand: ["leftWrist", "leftPalm"],
+  leftHandPinky: ["leftPalm", "leftPinky"],
+  leftHandIndex: ["leftPalm", "leftIndex"],
+  leftHandThumb: ["leftPalm", "leftThumb"],
+  leftEyeOutline: ["leftEyeInside", "leftEyeOutside"],
+  rightLegUpper: ["rightHip", "rightKnee"],
+  rightLegLower: ["rightKnee", "rightAnkle"],
+  rightFoot: ["rightAnkle", "rightHeel", "rightFoot"],
+  rightTorso: ["rightShoulder", "rightHip"],
+  rightArmUpper: ["rightShoulder", "rightElbow"],
+  rightArmLower: ["rightElbow", "rightWrist"],
+  rightHand: ["rightWrist", "rightPalm"],
+  rightHandPinky: ["rightPalm", "rightPinky"],
+  rightHandIndex: ["rightPalm", "rightIndex"],
+  rightHandThumb: ["rightPalm", "rightThumb"],
+  rightEyeOutline: ["rightEyeInside", "rightEyeOutside"]
+};
+
+// src/body/blazeposedetector.ts
+var tf9 = __toESM(require_tfjs_esm());
+var inputSize2 = 224;
+var anchorTensor;
+var numLayers = 5;
+var strides = [8, 16, 32, 32, 32];
+function createAnchors() {
+  const anchors3 = [];
+  let layerId = 0;
+  while (layerId < numLayers) {
+    let anchorCount = 0;
+    let lastSameStrideLayer = layerId;
+    while (lastSameStrideLayer < strides.length && strides[lastSameStrideLayer] === strides[layerId]) {
+      anchorCount += 2;
+      lastSameStrideLayer++;
+    }
+    const stride = strides[layerId];
+    const featureMapHeight = Math.ceil(inputSize2 / stride);
+    const featureMapWidth = Math.ceil(inputSize2 / stride);
+    for (let y = 0; y < featureMapHeight; ++y) {
+      for (let x = 0; x < featureMapWidth; ++x) {
+        for (let anchorId = 0; anchorId < anchorCount; ++anchorId) {
+          anchors3.push({ x: (x + 0.5) / featureMapWidth, y: (y + 0.5) / featureMapHeight });
+        }
+      }
+    }
+    layerId = lastSameStrideLayer;
+  }
+  anchorTensor = { x: tf9.tensor1d(anchors3.map((a) => a.x)), y: tf9.tensor1d(anchors3.map((a) => a.y)) };
+}
+
+// src/util/box.ts
+function calc(keypoints, outputSize2 = [1, 1]) {
+  const coords = [keypoints.map((pt) => pt[0]), keypoints.map((pt) => pt[1])];
+  const min2 = [Math.min(...coords[0]), Math.min(...coords[1])];
+  const max4 = [Math.max(...coords[0]), Math.max(...coords[1])];
+  const box = [min2[0], min2[1], max4[0] - min2[0], max4[1] - min2[1]];
+  const boxRaw = [box[0] / outputSize2[0], box[1] / outputSize2[1], box[2] / outputSize2[0], box[3] / outputSize2[1]];
+  return { box, boxRaw };
+}
+function square(keypoints, outputSize2 = [1, 1]) {
+  const coords = [keypoints.map((pt) => pt[0]), keypoints.map((pt) => pt[1])];
+  const min2 = [Math.min(...coords[0]), Math.min(...coords[1])];
+  const max4 = [Math.max(...coords[0]), Math.max(...coords[1])];
+  const center = [(min2[0] + max4[0]) / 2, (min2[1] + max4[1]) / 2];
+  const dist = Math.max(center[0] - min2[0], center[1] - min2[1], -center[0] + max4[0], -center[1] + max4[1]);
+  const box = [Math.trunc(center[0] - dist), Math.trunc(center[1] - dist), Math.trunc(2 * dist), Math.trunc(2 * dist)];
+  const boxRaw = [box[0] / outputSize2[0], box[1] / outputSize2[1], box[2] / outputSize2[0], box[3] / outputSize2[1]];
+  return { box, boxRaw };
+}
+function scale(box, scaleFact) {
+  const dist = [box[2] * scaleFact, box[3] * scaleFact];
+  const newBox = [
+    box[0] - (dist[0] - box[2]) / 2,
+    box[1] - (dist[1] - box[3]) / 2,
+    dist[0],
+    dist[1]
+  ];
+  return newBox;
+}
+
+// src/body/blazepose.ts
+var env3 = { initial: true };
+var models2 = { detector: null, landmarks: null };
+var inputSize3 = { detector: [224, 224], landmarks: [256, 256] };
+var skipped2 = Number.MAX_SAFE_INTEGER;
+var outputNodes = {
+  landmarks: ["ld_3d", "activation_segmentation", "activation_heatmap", "world_3d", "output_poseflag"],
+  detector: []
+};
+var cache = null;
+var cropBox;
+var padding = [[0, 0], [0, 0], [0, 0], [0, 0]];
+var lastTime2 = 0;
+var sigmoid3 = (x) => 1 - 1 / (1 + Math.exp(x));
+async function loadDetect(config3) {
+  var _a;
+  if (env3.initial)
+    models2.detector = null;
+  if (!models2.detector && config3.body["detector"] && config3.body["detector"].modelPath || "") {
+    models2.detector = await loadModel(config3.body["detector"].modelPath);
+    const inputs = ((_a = models2.detector) == null ? void 0 : _a["executor"]) ? Object.values(models2.detector.modelSignature["inputs"]) : void 0;
+    inputSize3.detector[0] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[1].size) : 0;
+    inputSize3.detector[1] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 0;
+  } else if (config3.debug && models2.detector)
+    log("cached model:", models2.detector["modelUrl"]);
+  createAnchors();
+  return models2.detector;
+}
+async function loadPose(config3) {
+  var _a;
+  if (env3.initial)
+    models2.landmarks = null;
+  if (!models2.landmarks) {
+    models2.landmarks = await loadModel(config3.body.modelPath);
+    const inputs = ((_a = models2.landmarks) == null ? void 0 : _a["executor"]) ? Object.values(models2.landmarks.modelSignature["inputs"]) : void 0;
+    inputSize3.landmarks[0] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[1].size) : 0;
+    inputSize3.landmarks[1] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 0;
+  } else if (config3.debug)
+    log("cached model:", models2.landmarks["modelUrl"]);
+  return models2.landmarks;
+}
+function prepareImage(input, size2) {
+  var _a, _b;
+  const t2 = {};
+  if (!((_a = input == null ? void 0 : input.shape) == null ? void 0 : _a[1]) || !((_b = input == null ? void 0 : input.shape) == null ? void 0 : _b[2]))
+    return input;
+  let final;
+  if (cropBox) {
+    t2.cropped = tf10.image.cropAndResize(input, [cropBox], [0], [input.shape[1], input.shape[2]]);
+  }
+  if (input.shape[1] !== input.shape[2]) {
+    const height = [
+      input.shape[2] > input.shape[1] ? Math.trunc((input.shape[2] - input.shape[1]) / 2) : 0,
+      input.shape[2] > input.shape[1] ? Math.trunc((input.shape[2] - input.shape[1]) / 2) : 0
+    ];
+    const width = [
+      input.shape[1] > input.shape[2] ? Math.trunc((input.shape[1] - input.shape[2]) / 2) : 0,
+      input.shape[1] > input.shape[2] ? Math.trunc((input.shape[1] - input.shape[2]) / 2) : 0
+    ];
+    padding = [
+      [0, 0],
+      height,
+      width,
+      [0, 0]
+    ];
+    t2.pad = tf10.pad(t2.cropped || input, padding);
+    t2.resize = tf10.image.resizeBilinear(t2.pad, [size2, size2]);
+    final = tf10.div(t2.resize, constants.tf255);
+  } else if (input.shape[1] !== size2) {
+    t2.resize = tf10.image.resizeBilinear(t2.cropped || input, [size2, size2]);
+    final = tf10.div(t2.resize, constants.tf255);
+  } else {
+    final = tf10.div(t2.cropped || input, constants.tf255);
+  }
+  Object.keys(t2).forEach((tensor6) => tf10.dispose(t2[tensor6]));
+  return final;
+}
+function rescaleKeypoints(keypoints, outputSize2) {
+  for (const kpt4 of keypoints) {
+    kpt4.position = [
+      Math.trunc(kpt4.position[0] * (outputSize2[0] + padding[2][0] + padding[2][1]) / outputSize2[0] - padding[2][0]),
+      Math.trunc(kpt4.position[1] * (outputSize2[1] + padding[1][0] + padding[1][1]) / outputSize2[1] - padding[1][0]),
+      kpt4.position[2]
+    ];
+    kpt4.positionRaw = [kpt4.position[0] / outputSize2[0], kpt4.position[1] / outputSize2[1], 2 * kpt4.position[2] / (outputSize2[0] + outputSize2[1])];
+  }
+  if (cropBox) {
+    for (const kpt4 of keypoints) {
+      kpt4.positionRaw = [
+        kpt4.positionRaw[0] + cropBox[1],
+        kpt4.positionRaw[1] + cropBox[0],
+        kpt4.positionRaw[2]
+      ];
+      kpt4.position = [
+        Math.trunc(kpt4.positionRaw[0] * outputSize2[0]),
+        Math.trunc(kpt4.positionRaw[1] * outputSize2[1]),
+        kpt4.positionRaw[2]
+      ];
+    }
+  }
+  return keypoints;
+}
+function fixKeypoints(keypoints) {
+  const leftPalm = keypoints.find((k) => k.part === "leftPalm");
+  const leftWrist = keypoints.find((k) => k.part === "leftWrist");
+  const leftIndex = keypoints.find((k) => k.part === "leftIndex");
+  leftPalm.position[2] = ((leftWrist.position[2] || 0) + (leftIndex.position[2] || 0)) / 2;
+  const rightPalm = keypoints.find((k) => k.part === "rightPalm");
+  const rightWrist = keypoints.find((k) => k.part === "rightWrist");
+  const rightIndex = keypoints.find((k) => k.part === "rightIndex");
+  rightPalm.position[2] = ((rightWrist.position[2] || 0) + (rightIndex.position[2] || 0)) / 2;
+}
+async function detectLandmarks(input, config3, outputSize2) {
+  var _a, _b;
+  if (!((_a = models2.landmarks) == null ? void 0 : _a["executor"]))
+    return null;
+  const t2 = {};
+  [t2.ld, t2.segmentation, t2.heatmap, t2.world, t2.poseflag] = (_b = models2.landmarks) == null ? void 0 : _b.execute(input, outputNodes.landmarks);
+  const poseScore = (await t2.poseflag.data())[0];
+  const points = await t2.ld.data();
+  const distances = await t2.world.data();
+  Object.keys(t2).forEach((tensor6) => tf10.dispose(t2[tensor6]));
+  const keypointsRelative = [];
+  const depth = 5;
+  for (let i = 0; i < points.length / depth; i++) {
+    const score = sigmoid3(points[depth * i + 3]);
+    const presence = sigmoid3(points[depth * i + 4]);
+    const adjScore = Math.trunc(100 * score * presence * poseScore) / 100;
+    const positionRaw = [points[depth * i + 0] / inputSize3.landmarks[0], points[depth * i + 1] / inputSize3.landmarks[1], points[depth * i + 2] + 0];
+    const position = [Math.trunc(outputSize2[0] * positionRaw[0]), Math.trunc(outputSize2[1] * positionRaw[1]), positionRaw[2]];
+    const distance2 = [distances[depth * i + 0], distances[depth * i + 1], distances[depth * i + 2] + 0];
+    keypointsRelative.push({ part: kpt[i], positionRaw, position, distance: distance2, score: adjScore });
+  }
+  if (poseScore < (config3.body.minConfidence || 0))
+    return null;
+  fixKeypoints(keypointsRelative);
+  const keypoints = rescaleKeypoints(keypointsRelative, outputSize2);
+  const kpts = keypoints.map((k) => k.position);
+  const boxes = calc(kpts, [outputSize2[0], outputSize2[1]]);
+  const annotations2 = {};
+  for (const [name, indexes] of Object.entries(connected)) {
+    const pt = [];
+    for (let i = 0; i < indexes.length - 1; i++) {
+      const pt0 = keypoints.find((kpt4) => kpt4.part === indexes[i]);
+      const pt1 = keypoints.find((kpt4) => kpt4.part === indexes[i + 1]);
+      if (pt0 && pt1)
+        pt.push([pt0.position, pt1.position]);
+    }
+    annotations2[name] = pt;
+  }
+  const body4 = { id: 0, score: Math.trunc(100 * poseScore) / 100, box: boxes.box, boxRaw: boxes.boxRaw, keypoints, annotations: annotations2 };
+  return body4;
+}
+async function predict2(input, config3) {
+  const outputSize2 = [input.shape[2] || 0, input.shape[1] || 0];
+  const skipTime = (config3.body.skipTime || 0) > now() - lastTime2;
+  const skipFrame = skipped2 < (config3.body.skipFrames || 0);
+  if (config3.skipAllowed && skipTime && skipFrame && cache !== null) {
+    skipped2++;
+  } else {
+    const t2 = {};
+    t2.landmarks = prepareImage(input, 256);
+    cache = await detectLandmarks(t2.landmarks, config3, outputSize2);
+    Object.keys(t2).forEach((tensor6) => tf10.dispose(t2[tensor6]));
+    lastTime2 = now();
+    skipped2 = 0;
+  }
+  return cache ? [cache] : [];
+}
+
+// src/object/centernet.ts
+var tf11 = __toESM(require_tfjs_esm());
+
+// src/object/labels.ts
+var labels = [
+  { class: 1, label: "person" },
+  { class: 2, label: "bicycle" },
+  { class: 3, label: "car" },
+  { class: 4, label: "motorcycle" },
+  { class: 5, label: "airplane" },
+  { class: 6, label: "bus" },
+  { class: 7, label: "train" },
+  { class: 8, label: "truck" },
+  { class: 9, label: "boat" },
+  { class: 10, label: "traffic light" },
+  { class: 11, label: "fire hydrant" },
+  { class: 12, label: "stop sign" },
+  { class: 13, label: "parking meter" },
+  { class: 14, label: "bench" },
+  { class: 15, label: "bird" },
+  { class: 16, label: "cat" },
+  { class: 17, label: "dog" },
+  { class: 18, label: "horse" },
+  { class: 19, label: "sheep" },
+  { class: 20, label: "cow" },
+  { class: 21, label: "elephant" },
+  { class: 22, label: "bear" },
+  { class: 23, label: "zebra" },
+  { class: 24, label: "giraffe" },
+  { class: 25, label: "backpack" },
+  { class: 26, label: "umbrella" },
+  { class: 27, label: "handbag" },
+  { class: 28, label: "tie" },
+  { class: 29, label: "suitcase" },
+  { class: 30, label: "frisbee" },
+  { class: 31, label: "skis" },
+  { class: 32, label: "snowboard" },
+  { class: 33, label: "sports ball" },
+  { class: 34, label: "kite" },
+  { class: 35, label: "baseball bat" },
+  { class: 36, label: "baseball glove" },
+  { class: 37, label: "skateboard" },
+  { class: 38, label: "surfboard" },
+  { class: 39, label: "tennis racket" },
+  { class: 40, label: "bottle" },
+  { class: 41, label: "wine glass" },
+  { class: 42, label: "cup" },
+  { class: 43, label: "fork" },
+  { class: 44, label: "knife" },
+  { class: 45, label: "spoon" },
+  { class: 46, label: "bowl" },
+  { class: 47, label: "banana" },
+  { class: 48, label: "apple" },
+  { class: 49, label: "sandwich" },
+  { class: 50, label: "orange" },
+  { class: 51, label: "broccoli" },
+  { class: 52, label: "carrot" },
+  { class: 53, label: "hot dog" },
+  { class: 54, label: "pizza" },
+  { class: 55, label: "donut" },
+  { class: 56, label: "cake" },
+  { class: 57, label: "chair" },
+  { class: 58, label: "couch" },
+  { class: 59, label: "potted plant" },
+  { class: 60, label: "bed" },
+  { class: 61, label: "dining table" },
+  { class: 62, label: "toilet" },
+  { class: 63, label: "tv" },
+  { class: 64, label: "laptop" },
+  { class: 65, label: "mouse" },
+  { class: 66, label: "remote" },
+  { class: 67, label: "keyboard" },
+  { class: 68, label: "cell phone" },
+  { class: 69, label: "microwave" },
+  { class: 70, label: "oven" },
+  { class: 71, label: "toaster" },
+  { class: 72, label: "sink" },
+  { class: 73, label: "refrigerator" },
+  { class: 74, label: "book" },
+  { class: 75, label: "clock" },
+  { class: 76, label: "vase" },
+  { class: 77, label: "scissors" },
+  { class: 78, label: "teddy bear" },
+  { class: 79, label: "hair drier" },
+  { class: 80, label: "toothbrush" }
+];
+
+// src/object/centernet.ts
+var model3;
+var inputSize4 = 0;
+var last2 = [];
+var lastTime3 = 0;
+var skipped3 = Number.MAX_SAFE_INTEGER;
+async function load3(config3) {
+  if (env.initial)
+    model3 = null;
+  if (!model3) {
+    model3 = await loadModel(config3.object.modelPath);
+    const inputs = (model3 == null ? void 0 : model3["executor"]) ? Object.values(model3.modelSignature["inputs"]) : void 0;
+    inputSize4 = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 0;
+  } else if (config3.debug)
+    log("cached model:", model3["modelUrl"]);
+  return model3;
+}
+async function process3(res, outputShape, config3) {
+  if (!res)
+    return [];
+  const t2 = {};
+  const results = [];
+  const detections = await res.array();
+  t2.squeeze = tf11.squeeze(res);
+  const arr = tf11.split(t2.squeeze, 6, 1);
+  t2.stack = tf11.stack([arr[1], arr[0], arr[3], arr[2]], 1);
+  t2.boxes = tf11.squeeze(t2.stack);
+  t2.scores = tf11.squeeze(arr[4]);
+  t2.classes = tf11.squeeze(arr[5]);
+  tf11.dispose([res, ...arr]);
+  t2.nms = await tf11.image.nonMaxSuppressionAsync(t2.boxes, t2.scores, config3.object.maxDetected, config3.object.iouThreshold, config3.object.minConfidence || 0);
+  const nms = await t2.nms.data();
+  let i = 0;
+  for (const id of Array.from(nms)) {
+    const score = Math.trunc(100 * detections[0][id][4]) / 100;
+    const classVal = detections[0][id][5];
+    if (Number.isNaN(classVal))
+      continue;
+    const label = labels[classVal].label;
+    const [x, y] = [
+      detections[0][id][0] / inputSize4,
+      detections[0][id][1] / inputSize4
+    ];
+    const boxRaw = [
+      x,
+      y,
+      detections[0][id][2] / inputSize4 - x,
+      detections[0][id][3] / inputSize4 - y
+    ];
+    const box = [
+      Math.trunc(boxRaw[0] * outputShape[0]),
+      Math.trunc(boxRaw[1] * outputShape[1]),
+      Math.trunc(boxRaw[2] * outputShape[0]),
+      Math.trunc(boxRaw[3] * outputShape[1])
+    ];
+    results.push({ id: i++, score, class: classVal, label, box, boxRaw });
+  }
+  Object.keys(t2).forEach((tensor6) => tf11.dispose(t2[tensor6]));
+  return results;
+}
+async function predict3(input, config3) {
+  if (!(model3 == null ? void 0 : model3["executor"]))
+    return [];
+  const skipTime = (config3.object.skipTime || 0) > now() - lastTime3;
+  const skipFrame = skipped3 < (config3.object.skipFrames || 0);
+  if (config3.skipAllowed && skipTime && skipFrame && last2.length > 0) {
+    skipped3++;
+    return last2;
+  }
+  skipped3 = 0;
+  return new Promise(async (resolve) => {
+    const outputSize2 = [input.shape[2] || 0, input.shape[1] || 0];
+    const resize = tf11.image.resizeBilinear(input, [inputSize4, inputSize4]);
+    const objectT = config3.object.enabled ? model3 == null ? void 0 : model3.execute(resize, ["tower_0/detections"]) : null;
+    lastTime3 = now();
+    tf11.dispose(resize);
+    const obj = await process3(objectT, outputSize2, config3);
+    last2 = obj;
+    resolve(obj);
+  });
+}
+
+// src/body/efficientpose.ts
+var tf12 = __toESM(require_tfjs_esm());
+
+// src/body/efficientposecoords.ts
+var efficientposecoords_exports = {};
+__export(efficientposecoords_exports, {
+  connected: () => connected2,
+  kpt: () => kpt2
+});
+var kpt2 = [
+  "head",
+  "neck",
+  "rightShoulder",
+  "rightElbow",
+  "rightWrist",
+  "chest",
+  "leftShoulder",
+  "leftElbow",
+  "leftWrist",
+  "bodyCenter",
+  "rightHip",
+  "rightKnee",
+  "rightAnkle",
+  "leftHip",
+  "leftKnee",
+  "leftAnkle"
+];
+var connected2 = {
+  leftLeg: ["leftHip", "leftKnee", "leftAnkle"],
+  rightLeg: ["rightHip", "rightKnee", "rightAnkle"],
+  torso: ["leftShoulder", "rightShoulder", "rightHip", "leftHip", "leftShoulder"],
+  leftArm: ["leftShoulder", "leftElbow", "leftWrist"],
+  rightArm: ["rightShoulder", "rightElbow", "rightWrist"],
+  head: []
+};
+
+// src/body/efficientpose.ts
+var model4;
+var lastTime4 = 0;
+var cache2 = { id: 0, keypoints: [], box: [0, 0, 0, 0], boxRaw: [0, 0, 0, 0], score: 0, annotations: {} };
+var skipped4 = Number.MAX_SAFE_INTEGER;
+async function load4(config3) {
+  if (env.initial)
+    model4 = null;
+  if (!model4)
+    model4 = await loadModel(config3.body.modelPath);
+  else if (config3.debug)
+    log("cached model:", model4["modelUrl"]);
+  return model4;
+}
+async function max2d(inputs, minScore) {
+  const [width, height] = inputs.shape;
+  const reshaped = tf12.reshape(inputs, [height * width]);
+  const max4 = tf12.max(reshaped, 0);
+  const newScore = (await max4.data())[0];
+  if (newScore > minScore) {
+    const coordinates = tf12.argMax(reshaped, 0);
+    const mod3 = tf12.mod(coordinates, width);
+    const x = (await mod3.data())[0];
+    const div16 = tf12.div(coordinates, width);
+    const y = (await div16.data())[0];
+    tf12.dispose([reshaped, max4, coordinates, mod3, div16]);
+    return [x, y, newScore];
+  }
+  tf12.dispose([reshaped, max4]);
+  return [0, 0, newScore];
+}
+async function predict4(image27, config3) {
+  if (!(model4 == null ? void 0 : model4["executor"]))
+    return [];
+  const skipTime = (config3.body.skipTime || 0) > now() - lastTime4;
+  const skipFrame = skipped4 < (config3.body.skipFrames || 0);
+  if (config3.skipAllowed && skipTime && skipFrame && Object.keys(cache2.keypoints).length > 0) {
+    skipped4++;
+    return [cache2];
+  }
+  skipped4 = 0;
+  return new Promise(async (resolve) => {
+    const tensor6 = tf12.tidy(() => {
+      if (!(model4 == null ? void 0 : model4.inputs[0].shape))
+        return null;
+      const resize = tf12.image.resizeBilinear(image27, [model4.inputs[0].shape[2], model4.inputs[0].shape[1]], false);
+      const enhance2 = tf12.mul(resize, constants.tf2);
+      const norm = tf12.sub(enhance2, constants.tf1);
+      return norm;
+    });
+    let resT;
+    if (config3.body.enabled)
+      resT = model4 == null ? void 0 : model4.execute(tensor6);
+    lastTime4 = now();
+    tf12.dispose(tensor6);
+    if (resT) {
+      cache2.keypoints.length = 0;
+      const squeeze14 = tf12.squeeze(resT);
+      tf12.dispose(resT);
+      const stack5 = tf12.unstack(squeeze14, 2);
+      tf12.dispose(squeeze14);
+      for (let id = 0; id < stack5.length; id++) {
+        const [x2, y2, partScore] = await max2d(stack5[id], config3.body.minConfidence);
+        if (partScore > (config3.body.minConfidence || 0)) {
+          cache2.keypoints.push({
+            score: Math.round(100 * partScore) / 100,
+            part: kpt2[id],
+            positionRaw: [
+              x2 / model4.inputs[0].shape[2],
+              y2 / model4.inputs[0].shape[1]
+            ],
+            position: [
+              Math.round(image27.shape[2] * x2 / model4.inputs[0].shape[2]),
+              Math.round(image27.shape[1] * y2 / model4.inputs[0].shape[1])
+            ]
+          });
+        }
+      }
+      stack5.forEach((s) => tf12.dispose(s));
+    }
+    cache2.score = cache2.keypoints.reduce((prev, curr) => curr.score > prev ? curr.score : prev, 0);
+    const x = cache2.keypoints.map((a) => a.position[0]);
+    const y = cache2.keypoints.map((a) => a.position[1]);
+    cache2.box = [
+      Math.min(...x),
+      Math.min(...y),
+      Math.max(...x) - Math.min(...x),
+      Math.max(...y) - Math.min(...y)
+    ];
+    const xRaw = cache2.keypoints.map((a) => a.positionRaw[0]);
+    const yRaw = cache2.keypoints.map((a) => a.positionRaw[1]);
+    cache2.boxRaw = [
+      Math.min(...xRaw),
+      Math.min(...yRaw),
+      Math.max(...xRaw) - Math.min(...xRaw),
+      Math.max(...yRaw) - Math.min(...yRaw)
+    ];
+    for (const [name, indexes] of Object.entries(connected2)) {
+      const pt = [];
+      for (let i = 0; i < indexes.length - 1; i++) {
+        const pt0 = cache2.keypoints.find((kpt4) => kpt4.part === indexes[i]);
+        const pt1 = cache2.keypoints.find((kpt4) => kpt4.part === indexes[i + 1]);
+        if (pt0 && pt1 && pt0.score > (config3.body.minConfidence || 0) && pt1.score > (config3.body.minConfidence || 0))
+          pt.push([pt0.position, pt1.position]);
+      }
+      cache2.annotations[name] = pt;
+    }
+    resolve([cache2]);
+  });
+}
+
+// src/gear/emotion.ts
+var tf13 = __toESM(require_tfjs_esm());
+var annotations = ["angry", "disgust", "fear", "happy", "sad", "surprise", "neutral"];
+var model5;
+var last3 = [];
+var lastCount2 = 0;
+var lastTime5 = 0;
+var skipped5 = Number.MAX_SAFE_INTEGER;
+async function load5(config3) {
+  var _a;
+  if (env.initial)
+    model5 = null;
+  if (!model5)
+    model5 = await loadModel((_a = config3.face.emotion) == null ? void 0 : _a.modelPath);
+  else if (config3.debug)
+    log("cached model:", model5["modelUrl"]);
+  return model5;
+}
+async function predict5(image27, config3, idx, count2) {
+  var _a, _b;
+  if (!model5)
+    return [];
+  const skipFrame = skipped5 < (((_a = config3.face.emotion) == null ? void 0 : _a.skipFrames) || 0);
+  const skipTime = (((_b = config3.face.emotion) == null ? void 0 : _b.skipTime) || 0) > now() - lastTime5;
+  if (config3.skipAllowed && skipTime && skipFrame && lastCount2 === count2 && last3[idx] && last3[idx].length > 0) {
+    skipped5++;
+    return last3[idx];
+  }
+  skipped5 = 0;
+  return new Promise(async (resolve) => {
+    var _a2;
+    const obj = [];
+    if ((_a2 = config3.face.emotion) == null ? void 0 : _a2.enabled) {
+      const t2 = {};
+      const inputSize10 = (model5 == null ? void 0 : model5.inputs[0].shape) ? model5.inputs[0].shape[2] : 0;
+      t2.resize = tf13.image.resizeBilinear(image27, [inputSize10, inputSize10], false);
+      t2.channels = tf13.mul(t2.resize, constants.rgb);
+      t2.grayscale = tf13.sum(t2.channels, 3, true);
+      t2.grayscaleSub = tf13.sub(t2.grayscale, constants.tf05);
+      t2.grayscaleMul = tf13.mul(t2.grayscaleSub, constants.tf2);
+      t2.emotion = model5 == null ? void 0 : model5.execute(t2.grayscaleMul);
+      lastTime5 = now();
+      const data = await t2.emotion.data();
+      for (let i = 0; i < data.length; i++) {
+        if (data[i] > (config3.face.emotion.minConfidence || 0))
+          obj.push({ score: Math.min(0.99, Math.trunc(100 * data[i]) / 100), emotion: annotations[i] });
+      }
+      obj.sort((a, b) => b.score - a.score);
+      Object.keys(t2).forEach((tensor6) => tf13.dispose(t2[tensor6]));
+    }
+    last3[idx] = obj;
+    lastCount2 = count2;
+    resolve(obj);
+  });
+}
+
+// src/face/facemesh.ts
+var tf15 = __toESM(require_tfjs_esm());
+
+// src/face/iris.ts
+var tf14 = __toESM(require_tfjs_esm());
+var model6;
+var inputSize5 = 0;
+var irisEnlarge = 2.3;
+var leftOutline = meshAnnotations.leftEyeLower0;
+var rightOutline = meshAnnotations.rightEyeLower0;
+var eyeLandmarks = {
+  leftBounds: [leftOutline[0], leftOutline[leftOutline.length - 1]],
+  rightBounds: [rightOutline[0], rightOutline[rightOutline.length - 1]]
+};
+var irisLandmarks = {
+  upperCenter: 3,
+  lowerCenter: 4,
+  index: 71,
+  numCoordinates: 76
+};
+async function load6(config3) {
+  var _a, _b;
+  if (env.initial)
+    model6 = null;
+  if (!model6)
+    model6 = await loadModel((_a = config3.face.iris) == null ? void 0 : _a.modelPath);
+  else if (config3.debug)
+    log("cached model:", model6["modelUrl"]);
+  inputSize5 = (model6 == null ? void 0 : model6["executor"]) && ((_b = model6.inputs) == null ? void 0 : _b[0].shape) ? model6.inputs[0].shape[2] : 0;
+  if (inputSize5 === -1)
+    inputSize5 = 64;
+  return model6;
+}
+function replaceIrisCoords(rawCoords, newCoords, prefix, keys) {
+  for (let i = 0; i < irisIndices.length; i++) {
+    const { key, indices } = irisIndices[i];
+    const originalIndices = meshAnnotations[`${prefix}${key}`];
+    if (!keys || keys.includes(key)) {
+      for (let j = 0; j < indices.length; j++) {
+        const index2 = indices[j];
+        rawCoords[originalIndices[j]] = [
+          newCoords[index2][0],
+          newCoords[index2][1],
+          (newCoords[index2][2] + rawCoords[originalIndices[j]][2]) / 2
+        ];
+      }
+    }
+  }
+}
+var getLeftToRightEyeDepthDifference = (rawCoords) => {
+  const leftEyeZ = rawCoords[eyeLandmarks.leftBounds[0]][2];
+  const rightEyeZ = rawCoords[eyeLandmarks.rightBounds[0]][2];
+  return leftEyeZ - rightEyeZ;
+};
+var getEyeBox = (rawCoords, face4, eyeInnerCornerIndex, eyeOuterCornerIndex, meshSize, flip = false) => {
+  const box = squarifyBox(enlargeBox(calculateLandmarksBoundingBox([rawCoords[eyeInnerCornerIndex], rawCoords[eyeOuterCornerIndex]]), irisEnlarge));
+  const boxSize = getBoxSize(box);
+  let crop = tf14.image.cropAndResize(face4, [[
+    box.startPoint[1] / meshSize,
+    box.startPoint[0] / meshSize,
+    box.endPoint[1] / meshSize,
+    box.endPoint[0] / meshSize
+  ]], [0], [inputSize5, inputSize5]);
+  if (flip && env.kernels.includes("flipleftright")) {
+    const flipped = tf14.image.flipLeftRight(crop);
+    tf14.dispose(crop);
+    crop = flipped;
+  }
+  return { box, boxSize, crop };
+};
+var getEyeCoords = (eyeData, eyeBox, eyeBoxSize, flip = false) => {
+  const eyeRawCoords = [];
+  for (let i = 0; i < irisLandmarks.numCoordinates; i++) {
+    const x = eyeData[i * 3];
+    const y = eyeData[i * 3 + 1];
+    const z = eyeData[i * 3 + 2];
+    eyeRawCoords.push([
+      (flip ? 1 - x / inputSize5 : x / inputSize5) * eyeBoxSize[0] + eyeBox.startPoint[0],
+      y / inputSize5 * eyeBoxSize[1] + eyeBox.startPoint[1],
+      z
+    ]);
+  }
+  return { rawCoords: eyeRawCoords, iris: eyeRawCoords.slice(irisLandmarks.index) };
+};
+var getAdjustedIrisCoords = (rawCoords, irisCoords, direction) => {
+  const upperCenterZ = rawCoords[meshAnnotations[`${direction}EyeUpper0`][irisLandmarks.upperCenter]][2];
+  const lowerCenterZ = rawCoords[meshAnnotations[`${direction}EyeLower0`][irisLandmarks.lowerCenter]][2];
+  const averageZ = (upperCenterZ + lowerCenterZ) / 2;
+  return irisCoords.map((coord, i) => {
+    let z = averageZ;
+    if (i === 2) {
+      z = upperCenterZ;
+    } else if (i === 4) {
+      z = lowerCenterZ;
+    }
+    return [coord[0], coord[1], z];
+  });
+};
+async function augmentIris(rawCoords, face4, meshSize) {
+  if (!(model6 == null ? void 0 : model6["executor"]))
+    return rawCoords;
+  const { box: leftEyeBox, boxSize: leftEyeBoxSize, crop: leftEyeCrop } = getEyeBox(rawCoords, face4, eyeLandmarks.leftBounds[0], eyeLandmarks.leftBounds[1], meshSize, true);
+  const { box: rightEyeBox, boxSize: rightEyeBoxSize, crop: rightEyeCrop } = getEyeBox(rawCoords, face4, eyeLandmarks.rightBounds[0], eyeLandmarks.rightBounds[1], meshSize, true);
+  const combined = tf14.concat([leftEyeCrop, rightEyeCrop]);
+  tf14.dispose(leftEyeCrop);
+  tf14.dispose(rightEyeCrop);
+  const eyePredictions = model6.execute(combined);
+  tf14.dispose(combined);
+  const eyePredictionsData = await eyePredictions.data();
+  tf14.dispose(eyePredictions);
+  const leftEyeData = eyePredictionsData.slice(0, irisLandmarks.numCoordinates * 3);
+  const { rawCoords: leftEyeRawCoords, iris: leftIrisRawCoords } = getEyeCoords(leftEyeData, leftEyeBox, leftEyeBoxSize, true);
+  const rightEyeData = eyePredictionsData.slice(irisLandmarks.numCoordinates * 3);
+  const { rawCoords: rightEyeRawCoords, iris: rightIrisRawCoords } = getEyeCoords(rightEyeData, rightEyeBox, rightEyeBoxSize, false);
+  const leftToRightEyeDepthDifference = getLeftToRightEyeDepthDifference(rawCoords);
+  if (Math.abs(leftToRightEyeDepthDifference) < 30) {
+    replaceIrisCoords(rawCoords, leftEyeRawCoords, "left", null);
+    replaceIrisCoords(rawCoords, rightEyeRawCoords, "right", null);
+  } else if (leftToRightEyeDepthDifference < 1) {
+    replaceIrisCoords(rawCoords, leftEyeRawCoords, "left", ["EyeUpper0", "EyeLower0"]);
+  } else {
+    replaceIrisCoords(rawCoords, rightEyeRawCoords, "right", ["EyeUpper0", "EyeLower0"]);
+  }
+  const adjustedLeftIrisCoords = getAdjustedIrisCoords(rawCoords, leftIrisRawCoords, "left");
+  const adjustedRightIrisCoords = getAdjustedIrisCoords(rawCoords, rightIrisRawCoords, "right");
+  const newCoords = rawCoords.concat(adjustedLeftIrisCoords).concat(adjustedRightIrisCoords);
+  return newCoords;
+}
+
+// src/face/constants.ts
+var LIPS_CONNECTIONS = [
+  [61, 146],
+  [146, 91],
+  [91, 181],
+  [181, 84],
+  [84, 17],
+  [17, 314],
+  [314, 405],
+  [405, 321],
+  [321, 375],
+  [375, 291],
+  [61, 185],
+  [185, 40],
+  [40, 39],
+  [39, 37],
+  [37, 0],
+  [0, 267],
+  [267, 269],
+  [269, 270],
+  [270, 409],
+  [409, 291],
+  [78, 95],
+  [95, 88],
+  [88, 178],
+  [178, 87],
+  [87, 14],
+  [14, 317],
+  [317, 402],
+  [402, 318],
+  [318, 324],
+  [324, 308],
+  [78, 191],
+  [191, 80],
+  [80, 81],
+  [81, 82],
+  [82, 13],
+  [13, 312],
+  [312, 311],
+  [311, 310],
+  [310, 415],
+  [415, 308]
+];
+var LEFT_EYE_CONNECTIONS = [[263, 249], [249, 390], [390, 373], [373, 374], [374, 380], [380, 381], [381, 382], [382, 362], [263, 466], [466, 388], [388, 387], [387, 386], [386, 385], [385, 384], [384, 398], [398, 362]];
+var LEFT_EYEBROW_CONNECTIONS = [[276, 283], [283, 282], [282, 295], [295, 285], [300, 293], [293, 334], [334, 296], [296, 336]];
+var LEFT_IRIS_CONNECTIONS = [[474, 475], [475, 476], [476, 477], [477, 474]];
+var RIGHT_EYE_CONNECTIONS = [[33, 7], [7, 163], [163, 144], [144, 145], [145, 153], [153, 154], [154, 155], [155, 133], [33, 246], [246, 161], [161, 160], [160, 159], [159, 158], [158, 157], [157, 173], [173, 133]];
+var RIGHT_EYEBROW_CONNECTIONS = [[46, 53], [53, 52], [52, 65], [65, 55], [70, 63], [63, 105], [105, 66], [66, 107]];
+var RIGHT_IRIS_CONNECTIONS = [[469, 470], [470, 471], [471, 472], [472, 469]];
+var FACE_OVAL_CONNECTIONS = [
+  [10, 338],
+  [338, 297],
+  [297, 332],
+  [332, 284],
+  [284, 251],
+  [251, 389],
+  [389, 356],
+  [356, 454],
+  [454, 323],
+  [323, 361],
+  [361, 288],
+  [288, 397],
+  [397, 365],
+  [365, 379],
+  [379, 378],
+  [378, 400],
+  [400, 377],
+  [377, 152],
+  [152, 148],
+  [148, 176],
+  [176, 149],
+  [149, 150],
+  [150, 136],
+  [136, 172],
+  [172, 58],
+  [58, 132],
+  [132, 93],
+  [93, 234],
+  [234, 127],
+  [127, 162],
+  [162, 21],
+  [21, 54],
+  [54, 103],
+  [103, 67],
+  [67, 109],
+  [109, 10]
+];
+function connectionsToIndices2(connections) {
+  const indices = connections.map((connection) => connection[0]);
+  indices.push(connections[connections.length - 1][1]);
+  return indices;
+}
+var MEDIAPIPE_FACE_MESH_KEYPOINTS_BY_CONTOUR = {
+  lips: connectionsToIndices2(LIPS_CONNECTIONS),
+  leftEye: connectionsToIndices2(LEFT_EYE_CONNECTIONS),
+  leftEyebrow: connectionsToIndices2(LEFT_EYEBROW_CONNECTIONS),
+  leftIris: connectionsToIndices2(LEFT_IRIS_CONNECTIONS),
+  rightEye: connectionsToIndices2(RIGHT_EYE_CONNECTIONS),
+  rightEyebrow: connectionsToIndices2(RIGHT_EYEBROW_CONNECTIONS),
+  rightIris: connectionsToIndices2(RIGHT_IRIS_CONNECTIONS),
+  faceOval: connectionsToIndices2(FACE_OVAL_CONNECTIONS)
+};
+var indexLabelPairs = Object.entries(MEDIAPIPE_FACE_MESH_KEYPOINTS_BY_CONTOUR).map(([label, indices]) => indices.map((index2) => [index2, label])).flat();
+var MEDIAPIPE_FACE_MESH_KEYPOINTS = new Map(indexLabelPairs);
+var LANDMARKS_REFINEMENT_LIPS_CONFIG = [
+  61,
+  146,
+  91,
+  181,
+  84,
+  17,
+  314,
+  405,
+  321,
+  375,
+  291,
+  185,
+  40,
+  39,
+  37,
+  0,
+  267,
+  269,
+  270,
+  409,
+  78,
+  95,
+  88,
+  178,
+  87,
+  14,
+  317,
+  402,
+  318,
+  324,
+  308,
+  191,
+  80,
+  81,
+  82,
+  13,
+  312,
+  311,
+  310,
+  415,
+  76,
+  77,
+  90,
+  180,
+  85,
+  16,
+  315,
+  404,
+  320,
+  307,
+  306,
+  184,
+  74,
+  73,
+  72,
+  11,
+  302,
+  303,
+  304,
+  408,
+  62,
+  96,
+  89,
+  179,
+  86,
+  15,
+  316,
+  403,
+  319,
+  325,
+  292,
+  183,
+  42,
+  41,
+  38,
+  12,
+  268,
+  271,
+  272,
+  407
+];
+var LANDMARKS_REFINEMENT_LEFT_EYE_CONFIG = [
+  33,
+  7,
+  163,
+  144,
+  145,
+  153,
+  154,
+  155,
+  133,
+  246,
+  161,
+  160,
+  159,
+  158,
+  157,
+  173,
+  130,
+  25,
+  110,
+  24,
+  23,
+  22,
+  26,
+  112,
+  243,
+  247,
+  30,
+  29,
+  27,
+  28,
+  56,
+  190,
+  226,
+  31,
+  228,
+  229,
+  230,
+  231,
+  232,
+  233,
+  244,
+  113,
+  225,
+  224,
+  223,
+  222,
+  221,
+  189,
+  35,
+  124,
+  46,
+  53,
+  52,
+  65,
+  143,
+  111,
+  117,
+  118,
+  119,
+  120,
+  121,
+  128,
+  245,
+  156,
+  70,
+  63,
+  105,
+  66,
+  107,
+  55,
+  193
+];
+var LANDMARKS_REFINEMENT_RIGHT_EYE_CONFIG = [
+  263,
+  249,
+  390,
+  373,
+  374,
+  380,
+  381,
+  382,
+  362,
+  466,
+  388,
+  387,
+  386,
+  385,
+  384,
+  398,
+  359,
+  255,
+  339,
+  254,
+  253,
+  252,
+  256,
+  341,
+  463,
+  467,
+  260,
+  259,
+  257,
+  258,
+  286,
+  414,
+  446,
+  261,
+  448,
+  449,
+  450,
+  451,
+  452,
+  453,
+  464,
+  342,
+  445,
+  444,
+  443,
+  442,
+  441,
+  413,
+  265,
+  353,
+  276,
+  283,
+  282,
+  295,
+  372,
+  340,
+  346,
+  347,
+  348,
+  349,
+  350,
+  357,
+  465,
+  383,
+  300,
+  293,
+  334,
+  296,
+  336,
+  285,
+  417
+];
+
+// src/face/attention.ts
+async function augment(rawCoords, results) {
+  var _a, _b, _c, _d, _e, _f, _g, _h, _i, _j;
+  const t2 = {
+    lips: await ((_b = (_a = results.filter((r) => r.size === 160)) == null ? void 0 : _a[0]) == null ? void 0 : _b.data()),
+    irisL: await ((_d = (_c = results.filter((r) => r.size === 10)) == null ? void 0 : _c[0]) == null ? void 0 : _d.data()),
+    eyeL: await ((_f = (_e = results.filter((r) => r.size === 142)) == null ? void 0 : _e[0]) == null ? void 0 : _f.data()),
+    irisR: await ((_h = (_g = results.filter((r) => r.size === 10)) == null ? void 0 : _g[1]) == null ? void 0 : _h.data()),
+    eyeR: await ((_j = (_i = results.filter((r) => r.size === 142)) == null ? void 0 : _i[1]) == null ? void 0 : _j.data())
+  };
+  for (const val of Object.values(t2)) {
+    if (!val)
+      return rawCoords;
+  }
+  const irisLDepth = LANDMARKS_REFINEMENT_LEFT_EYE_CONFIG.reduce((prev, curr) => prev += rawCoords[curr][2], 0) / LANDMARKS_REFINEMENT_LEFT_EYE_CONFIG.length;
+  for (let i = 0; i < t2.irisL.length / 2; i++)
+    rawCoords.push([t2.irisL[2 * i + 0], t2.irisL[2 * i + 1], irisLDepth]);
+  const irisRDepth = LANDMARKS_REFINEMENT_RIGHT_EYE_CONFIG.reduce((prev, curr) => prev += rawCoords[curr][2], 0) / LANDMARKS_REFINEMENT_RIGHT_EYE_CONFIG.length;
+  for (let i = 0; i < t2.irisR.length / 2; i++)
+    rawCoords.push([t2.irisR[2 * i + 0], t2.irisR[2 * i + 1], irisRDepth]);
+  for (let i = 0; i < t2.eyeL.length / 2; i++)
+    rawCoords[LANDMARKS_REFINEMENT_LEFT_EYE_CONFIG[i]] = [t2.eyeL[2 * i + 0], t2.eyeL[2 * i + 1], rawCoords[LANDMARKS_REFINEMENT_LEFT_EYE_CONFIG[i]][2]];
+  for (let i = 0; i < t2.eyeR.length / 2; i++)
+    rawCoords[LANDMARKS_REFINEMENT_RIGHT_EYE_CONFIG[i]] = [t2.eyeR[2 * i + 0], t2.eyeR[2 * i + 1], rawCoords[LANDMARKS_REFINEMENT_RIGHT_EYE_CONFIG[i]][2]];
+  for (let i = 0; i < t2.lips.length / 2; i++)
+    rawCoords[LANDMARKS_REFINEMENT_LIPS_CONFIG[i]] = [t2.lips[2 * i + 0], t2.lips[2 * i + 1], rawCoords[LANDMARKS_REFINEMENT_LIPS_CONFIG[i]][2]];
+  return rawCoords;
+}
+
+// src/face/facemesh.ts
+var cache3 = {
+  boxes: [],
+  skipped: Number.MAX_SAFE_INTEGER,
+  timestamp: 0
+};
+var model7 = null;
+var inputSize6 = 0;
+async function predict6(input, config3) {
+  var _a, _b, _c, _d, _e, _f, _g, _h, _i, _j;
+  if (!(model7 == null ? void 0 : model7["executor"]))
+    return [];
+  const skipTime = (((_a = config3.face.detector) == null ? void 0 : _a.skipTime) || 0) > now() - cache3.timestamp;
+  const skipFrame = cache3.skipped < (((_b = config3.face.detector) == null ? void 0 : _b.skipFrames) || 0);
+  if (!config3.skipAllowed || !skipTime || !skipFrame || cache3.boxes.length === 0) {
+    cache3.boxes = await getBoxes(input, config3);
+    cache3.timestamp = now();
+    cache3.skipped = 0;
+  } else {
+    cache3.skipped++;
+  }
+  const faces = [];
+  const newCache = [];
+  let id = 0;
+  const size2 = inputSize6;
+  for (let i = 0; i < cache3.boxes.length; i++) {
+    const box = cache3.boxes[i];
+    let angle = 0;
+    let rotationMatrix;
+    const face4 = {
+      id: id++,
+      mesh: [],
+      meshRaw: [],
+      box: [0, 0, 0, 0],
+      boxRaw: [0, 0, 0, 0],
+      score: 0,
+      boxScore: 0,
+      faceScore: 0,
+      annotations: {}
+    };
+    [angle, rotationMatrix, face4.tensor] = correctFaceRotation((_c = config3.face.detector) == null ? void 0 : _c.rotation, box, input, ((_d = config3.face.mesh) == null ? void 0 : _d.enabled) ? inputSize6 : size());
+    if (config3.filter.equalization) {
+      const equilized = face4.tensor ? await histogramEqualization(face4.tensor) : void 0;
+      tf15.dispose(face4.tensor);
+      if (equilized)
+        face4.tensor = equilized;
+    }
+    face4.boxScore = Math.round(100 * box.confidence) / 100;
+    if (!((_e = config3.face.mesh) == null ? void 0 : _e.enabled)) {
+      face4.box = clampBox(box, input);
+      face4.boxRaw = getRawBox(box, input);
+      face4.score = face4.boxScore;
+      face4.mesh = box.landmarks.map((pt) => [
+        (box.startPoint[0] + box.endPoint[0]) / 2 + (box.endPoint[0] + box.startPoint[0]) * pt[0] / size(),
+        (box.startPoint[1] + box.endPoint[1]) / 2 + (box.endPoint[1] + box.startPoint[1]) * pt[1] / size()
+      ]);
+      face4.meshRaw = face4.mesh.map((pt) => [pt[0] / (input.shape[2] || 0), pt[1] / (input.shape[1] || 0), (pt[2] || 0) / size2]);
+      for (const key of Object.keys(blazeFaceLandmarks)) {
+        face4.annotations[key] = [face4.mesh[blazeFaceLandmarks[key]]];
+      }
+    } else if (!model7) {
+      if (config3.debug)
+        log("face mesh detection requested, but model is not loaded");
+    } else {
+      if (((_f = config3.face.attention) == null ? void 0 : _f.enabled) && !env.kernels.includes("atan2")) {
+        config3.face.attention.enabled = false;
+        tf15.dispose(face4.tensor);
+        return faces;
+      }
+      const results = model7.execute(face4.tensor);
+      const confidenceT = results.find((t2) => t2.shape[t2.shape.length - 1] === 1);
+      const faceConfidence = await confidenceT.data();
+      face4.faceScore = Math.round(100 * faceConfidence[0]) / 100;
+      if (face4.faceScore < (((_g = config3.face.detector) == null ? void 0 : _g.minConfidence) || 1)) {
+        box.confidence = face4.faceScore;
+        if (config3.face.mesh.keepInvalid) {
+          face4.box = clampBox(box, input);
+          face4.boxRaw = getRawBox(box, input);
+          face4.score = face4.boxScore;
+          face4.mesh = box.landmarks.map((pt) => [
+            (box.startPoint[0] + box.endPoint[0]) / 2 + (box.endPoint[0] + box.startPoint[0]) * pt[0] / size(),
+            (box.startPoint[1] + box.endPoint[1]) / 2 + (box.endPoint[1] + box.startPoint[1]) * pt[1] / size()
+          ]);
+          face4.meshRaw = face4.mesh.map((pt) => [pt[0] / (input.shape[2] || 1), pt[1] / (input.shape[1] || 1), (pt[2] || 0) / size2]);
+          for (const key of Object.keys(blazeFaceLandmarks)) {
+            face4.annotations[key] = [face4.mesh[blazeFaceLandmarks[key]]];
+          }
+        }
+      } else {
+        const meshT = results.find((t2) => t2.shape[t2.shape.length - 1] === 1404);
+        const coordsReshaped = tf15.reshape(meshT, [-1, 3]);
+        let rawCoords = await coordsReshaped.array();
+        tf15.dispose(coordsReshaped);
+        if ((_h = config3.face.attention) == null ? void 0 : _h.enabled) {
+          rawCoords = await augment(rawCoords, results);
+        } else if ((_i = config3.face.iris) == null ? void 0 : _i.enabled) {
+          rawCoords = await augmentIris(rawCoords, face4.tensor, inputSize6);
+        }
+        face4.mesh = transformRawCoords(rawCoords, box, angle, rotationMatrix, inputSize6);
+        face4.meshRaw = face4.mesh.map((pt) => [pt[0] / (input.shape[2] || 0), pt[1] / (input.shape[1] || 0), (pt[2] || 0) / size2]);
+        for (const key of Object.keys(meshAnnotations))
+          face4.annotations[key] = meshAnnotations[key].map((index2) => face4.mesh[index2]);
+        face4.score = face4.faceScore;
+        const calculatedBox = { ...calculateFaceBox(face4.mesh, box), confidence: box.confidence, landmarks: box.landmarks };
+        face4.box = clampBox(calculatedBox, input);
+        face4.boxRaw = getRawBox(calculatedBox, input);
+        newCache.push(calculatedBox);
+      }
+      tf15.dispose(results);
+    }
+    if (face4.score > (((_j = config3.face.detector) == null ? void 0 : _j.minConfidence) || 1))
+      faces.push(face4);
+    else
+      tf15.dispose(face4.tensor);
+  }
+  cache3.boxes = newCache;
+  return faces;
+}
+async function load7(config3) {
+  var _a, _b, _c, _d, _e, _f;
+  if (env.initial)
+    model7 = null;
+  if (((_a = config3.face.attention) == null ? void 0 : _a.enabled) && (model7 == null ? void 0 : model7["signature"])) {
+    if (Object.keys(((_b = model7 == null ? void 0 : model7["signature"]) == null ? void 0 : _b.outputs) || {}).length < 6)
+      model7 = null;
+  }
+  if (!model7) {
+    if ((_c = config3.face.attention) == null ? void 0 : _c.enabled)
+      model7 = await loadModel(config3.face.attention.modelPath);
+    else
+      model7 = await loadModel((_d = config3.face.mesh) == null ? void 0 : _d.modelPath);
+  } else if (config3.debug) {
+    log("cached model:", model7["modelUrl"]);
+  }
+  inputSize6 = model7["executor"] && ((_e = model7 == null ? void 0 : model7.inputs) == null ? void 0 : _e[0].shape) ? (_f = model7 == null ? void 0 : model7.inputs) == null ? void 0 : _f[0].shape[2] : 256;
+  return model7;
+}
+var triangulation = TRI468;
+var uvmap = UV468;
+
+// src/face/faceres.ts
+var tf16 = __toESM(require_tfjs_esm());
+var model8;
+var last4 = [];
+var lastTime6 = 0;
+var lastCount3 = 0;
+var skipped6 = Number.MAX_SAFE_INTEGER;
+async function load8(config3) {
+  var _a;
+  if (env.initial)
+    model8 = null;
+  if (!model8)
+    model8 = await loadModel((_a = config3.face.description) == null ? void 0 : _a.modelPath);
+  else if (config3.debug)
+    log("cached model:", model8["modelUrl"]);
+  return model8;
+}
+function enhance(input) {
+  const tensor6 = input.image || input.tensor || input;
+  if (!(model8 == null ? void 0 : model8.inputs[0].shape))
+    return tensor6;
+  const crop = tf16.image.resizeBilinear(tensor6, [model8.inputs[0].shape[2], model8.inputs[0].shape[1]], false);
+  const norm = tf16.mul(crop, constants.tf255);
+  tf16.dispose(crop);
+  return norm;
+}
+async function predict7(image27, config3, idx, count2) {
+  var _a, _b, _c, _d;
+  const obj = {
+    age: 0,
+    gender: "unknown",
+    genderScore: 0,
+    descriptor: []
+  };
+  if (!(model8 == null ? void 0 : model8["executor"]))
+    return obj;
+  const skipFrame = skipped6 < (((_a = config3.face.description) == null ? void 0 : _a.skipFrames) || 0);
+  const skipTime = (((_b = config3.face.description) == null ? void 0 : _b.skipTime) || 0) > now() - lastTime6;
+  if (config3.skipAllowed && skipFrame && skipTime && lastCount3 === count2 && ((_c = last4 == null ? void 0 : last4[idx]) == null ? void 0 : _c.age) > 0 && ((_d = last4 == null ? void 0 : last4[idx]) == null ? void 0 : _d.genderScore) > 0) {
+    skipped6++;
+    return last4[idx];
+  }
+  skipped6 = 0;
+  return new Promise(async (resolve) => {
+    var _a2;
+    if ((_a2 = config3.face.description) == null ? void 0 : _a2.enabled) {
+      const enhanced = enhance(image27);
+      const resT = model8 == null ? void 0 : model8.execute(enhanced);
+      lastTime6 = now();
+      tf16.dispose(enhanced);
+      const genderT = resT.find((t2) => t2.shape[1] === 1);
+      const gender2 = await genderT.data();
+      const confidence = Math.trunc(200 * Math.abs(gender2[0] - 0.5)) / 100;
+      if (confidence > (config3.face.description.minConfidence || 0)) {
+        obj.gender = gender2[0] <= 0.5 ? "female" : "male";
+        obj.genderScore = Math.min(0.99, confidence);
+      }
+      const argmax = tf16.argMax(resT.find((t2) => t2.shape[1] === 100), 1);
+      const ageIdx = (await argmax.data())[0];
+      tf16.dispose(argmax);
+      const ageT = resT.find((t2) => t2.shape[1] === 100);
+      const all2 = await ageT.data();
+      obj.age = Math.round(all2[ageIdx - 1] > all2[ageIdx + 1] ? 10 * ageIdx - 100 * all2[ageIdx - 1] : 10 * ageIdx + 100 * all2[ageIdx + 1]) / 10;
+      if (Number.isNaN(gender2[0]) || Number.isNaN(all2[0]))
+        log("faceres error:", { model: model8, result: resT });
+      const desc = resT.find((t2) => t2.shape[1] === 1024);
+      const descriptor = desc ? await desc.data() : [];
+      obj.descriptor = Array.from(descriptor);
+      resT.forEach((t2) => tf16.dispose(t2));
+    }
+    last4[idx] = obj;
+    lastCount3 = count2;
+    resolve(obj);
+  });
+}
+
+// src/gear/gear.ts
+var tf17 = __toESM(require_tfjs_esm());
+var model9;
+var last5 = [];
+var raceNames = ["white", "black", "asian", "indian", "other"];
+var ageWeights = [15, 23, 28, 35.5, 45.5, 55.5, 65];
+var lastCount4 = 0;
+var lastTime7 = 0;
+var skipped7 = Number.MAX_SAFE_INTEGER;
+async function load9(config3) {
+  var _a;
+  if (env.initial)
+    model9 = null;
+  if (!model9)
+    model9 = await loadModel((_a = config3.face.gear) == null ? void 0 : _a.modelPath);
+  else if (config3.debug)
+    log("cached model:", model9["modelUrl"]);
+  return model9;
+}
+async function predict8(image27, config3, idx, count2) {
+  var _a, _b;
+  if (!model9)
+    return { age: 0, gender: "unknown", genderScore: 0, race: [] };
+  const skipFrame = skipped7 < (((_a = config3.face.gear) == null ? void 0 : _a.skipFrames) || 0);
+  const skipTime = (((_b = config3.face.gear) == null ? void 0 : _b.skipTime) || 0) > now() - lastTime7;
+  if (config3.skipAllowed && skipTime && skipFrame && lastCount4 === count2 && last5[idx]) {
+    skipped7++;
+    return last5[idx];
+  }
+  skipped7 = 0;
+  return new Promise(async (resolve) => {
+    var _a2, _b2;
+    if (!(model9 == null ? void 0 : model9.inputs[0].shape))
+      return;
+    const t2 = {};
+    const box = [[0, 0.1, 0.9, 0.9]];
+    t2.resize = tf17.image.cropAndResize(image27, box, [0], [model9.inputs[0].shape[2], model9.inputs[0].shape[1]]);
+    const obj = { age: 0, gender: "unknown", genderScore: 0, race: [] };
+    if ((_a2 = config3.face.gear) == null ? void 0 : _a2.enabled)
+      [t2.age, t2.gender, t2.race] = model9.execute(t2.resize, ["age_output", "gender_output", "race_output"]);
+    const gender2 = await t2.gender.data();
+    obj.gender = gender2[0] > gender2[1] ? "male" : "female";
+    obj.genderScore = Math.round(100 * (gender2[0] > gender2[1] ? gender2[0] : gender2[1])) / 100;
+    const race = await t2.race.data();
+    for (let i = 0; i < race.length; i++) {
+      if (race[i] > (((_b2 = config3.face.gear) == null ? void 0 : _b2.minConfidence) || 0.2))
+        obj.race.push({ score: Math.round(100 * race[i]) / 100, race: raceNames[i] });
+    }
+    obj.race.sort((a, b) => b.score - a.score);
+    const ageDistribution = Array.from(await t2.age.data());
+    const ageSorted = ageDistribution.map((a, i) => [ageWeights[i], a]).sort((a, b) => b[1] - a[1]);
+    let age2 = ageSorted[0][0];
+    for (let i = 1; i < ageSorted.length; i++)
+      age2 += ageSorted[i][1] * (ageSorted[i][0] - age2);
+    obj.age = Math.round(10 * age2) / 10;
+    Object.keys(t2).forEach((tensor6) => tf17.dispose(t2[tensor6]));
+    last5[idx] = obj;
+    lastCount4 = count2;
+    lastTime7 = now();
+    resolve(obj);
+  });
+}
+
+// src/hand/handposedetector.ts
+var tf19 = __toESM(require_tfjs_esm());
+
+// src/hand/handposeutil.ts
+var tf18 = __toESM(require_tfjs_esm());
+function getBoxSize2(box) {
+  return [
+    Math.abs(box.endPoint[0] - box.startPoint[0]),
+    Math.abs(box.endPoint[1] - box.startPoint[1])
+  ];
+}
+function getBoxCenter2(box) {
+  return [
+    box.startPoint[0] + (box.endPoint[0] - box.startPoint[0]) / 2,
+    box.startPoint[1] + (box.endPoint[1] - box.startPoint[1]) / 2
+  ];
+}
+function cutBoxFromImageAndResize(box, image27, cropSize) {
+  const h = image27.shape[1];
+  const w = image27.shape[2];
+  const boxes = [[
+    box.startPoint[1] / h,
+    box.startPoint[0] / w,
+    box.endPoint[1] / h,
+    box.endPoint[0] / w
+  ]];
+  return tf18.image.cropAndResize(image27, boxes, [0], cropSize);
+}
+function scaleBoxCoordinates2(box, factor) {
+  const startPoint = [box.startPoint[0] * factor[0], box.startPoint[1] * factor[1]];
+  const endPoint = [box.endPoint[0] * factor[0], box.endPoint[1] * factor[1]];
+  const palmLandmarks = box.palmLandmarks.map((coord) => {
+    const scaledCoord = [coord[0] * factor[0], coord[1] * factor[1]];
+    return scaledCoord;
+  });
+  return { startPoint, endPoint, palmLandmarks, confidence: box.confidence };
+}
+function enlargeBox2(box, factor = 1.5) {
+  const center = getBoxCenter2(box);
+  const size2 = getBoxSize2(box);
+  const newHalfSize = [factor * size2[0] / 2, factor * size2[1] / 2];
+  const startPoint = [center[0] - newHalfSize[0], center[1] - newHalfSize[1]];
+  const endPoint = [center[0] + newHalfSize[0], center[1] + newHalfSize[1]];
+  return { startPoint, endPoint, palmLandmarks: box.palmLandmarks };
+}
+function squarifyBox2(box) {
+  const centers = getBoxCenter2(box);
+  const size2 = getBoxSize2(box);
+  const maxEdge = Math.max(...size2);
+  const halfSize = maxEdge / 2;
+  const startPoint = [centers[0] - halfSize, centers[1] - halfSize];
+  const endPoint = [centers[0] + halfSize, centers[1] + halfSize];
+  return { startPoint, endPoint, palmLandmarks: box.palmLandmarks };
+}
+function normalizeRadians2(angle) {
+  return angle - 2 * Math.PI * Math.floor((angle + Math.PI) / (2 * Math.PI));
+}
+function computeRotation2(point1, point2) {
+  const radians = Math.PI / 2 - Math.atan2(-(point2[1] - point1[1]), point2[0] - point1[0]);
+  return normalizeRadians2(radians);
+}
+var buildTranslationMatrix2 = (x, y) => [[1, 0, x], [0, 1, y], [0, 0, 1]];
+function dot2(v1, v2) {
+  let product = 0;
+  for (let i = 0; i < v1.length; i++) {
+    product += v1[i] * v2[i];
+  }
+  return product;
+}
+function getColumnFrom2DArr2(arr, columnIndex) {
+  const column = [];
+  for (let i = 0; i < arr.length; i++) {
+    column.push(arr[i][columnIndex]);
+  }
+  return column;
+}
+function multiplyTransformMatrices2(mat1, mat2) {
+  const product = [];
+  const size2 = mat1.length;
+  for (let row = 0; row < size2; row++) {
+    product.push([]);
+    for (let col = 0; col < size2; col++) {
+      product[row].push(dot2(mat1[row], getColumnFrom2DArr2(mat2, col)));
+    }
+  }
+  return product;
+}
+function buildRotationMatrix2(rotation, center) {
+  const cosA = Math.cos(rotation);
+  const sinA = Math.sin(rotation);
+  const rotationMatrix = [[cosA, -sinA, 0], [sinA, cosA, 0], [0, 0, 1]];
+  const translationMatrix = buildTranslationMatrix2(center[0], center[1]);
+  const translationTimesRotation = multiplyTransformMatrices2(translationMatrix, rotationMatrix);
+  const negativeTranslationMatrix = buildTranslationMatrix2(-center[0], -center[1]);
+  return multiplyTransformMatrices2(translationTimesRotation, negativeTranslationMatrix);
+}
+function invertTransformMatrix2(matrix) {
+  const rotationComponent = [[matrix[0][0], matrix[1][0]], [matrix[0][1], matrix[1][1]]];
+  const translationComponent = [matrix[0][2], matrix[1][2]];
+  const invertedTranslation = [
+    -dot2(rotationComponent[0], translationComponent),
+    -dot2(rotationComponent[1], translationComponent)
+  ];
+  return [
+    rotationComponent[0].concat(invertedTranslation[0]),
+    rotationComponent[1].concat(invertedTranslation[1]),
+    [0, 0, 1]
+  ];
+}
+function rotatePoint2(homogeneousCoordinate, rotationMatrix) {
+  return [
+    dot2(homogeneousCoordinate, rotationMatrix[0]),
+    dot2(homogeneousCoordinate, rotationMatrix[1])
+  ];
+}
+
+// src/hand/handposeanchors.ts
+var anchors2 = [
+  { x: 0.015625, y: 0.015625 },
+  { x: 0.015625, y: 0.015625 },
+  { x: 0.046875, y: 0.015625 },
+  { x: 0.046875, y: 0.015625 },
+  { x: 0.078125, y: 0.015625 },
+  { x: 0.078125, y: 0.015625 },
+  { x: 0.109375, y: 0.015625 },
+  { x: 0.109375, y: 0.015625 },
+  { x: 0.140625, y: 0.015625 },
+  { x: 0.140625, y: 0.015625 },
+  { x: 0.171875, y: 0.015625 },
+  { x: 0.171875, y: 0.015625 },
+  { x: 0.203125, y: 0.015625 },
+  { x: 0.203125, y: 0.015625 },
+  { x: 0.234375, y: 0.015625 },
+  { x: 0.234375, y: 0.015625 },
+  { x: 0.265625, y: 0.015625 },
+  { x: 0.265625, y: 0.015625 },
+  { x: 0.296875, y: 0.015625 },
+  { x: 0.296875, y: 0.015625 },
+  { x: 0.328125, y: 0.015625 },
+  { x: 0.328125, y: 0.015625 },
+  { x: 0.359375, y: 0.015625 },
+  { x: 0.359375, y: 0.015625 },
+  { x: 0.390625, y: 0.015625 },
+  { x: 0.390625, y: 0.015625 },
+  { x: 0.421875, y: 0.015625 },
+  { x: 0.421875, y: 0.015625 },
+  { x: 0.453125, y: 0.015625 },
+  { x: 0.453125, y: 0.015625 },
+  { x: 0.484375, y: 0.015625 },
+  { x: 0.484375, y: 0.015625 },
+  { x: 0.515625, y: 0.015625 },
+  { x: 0.515625, y: 0.015625 },
+  { x: 0.546875, y: 0.015625 },
+  { x: 0.546875, y: 0.015625 },
+  { x: 0.578125, y: 0.015625 },
+  { x: 0.578125, y: 0.015625 },
+  { x: 0.609375, y: 0.015625 },
+  { x: 0.609375, y: 0.015625 },
+  { x: 0.640625, y: 0.015625 },
+  { x: 0.640625, y: 0.015625 },
+  { x: 0.671875, y: 0.015625 },
+  { x: 0.671875, y: 0.015625 },
+  { x: 0.703125, y: 0.015625 },
+  { x: 0.703125, y: 0.015625 },
+  { x: 0.734375, y: 0.015625 },
+  { x: 0.734375, y: 0.015625 },
+  { x: 0.765625, y: 0.015625 },
+  { x: 0.765625, y: 0.015625 },
+  { x: 0.796875, y: 0.015625 },
+  { x: 0.796875, y: 0.015625 },
+  { x: 0.828125, y: 0.015625 },
+  { x: 0.828125, y: 0.015625 },
+  { x: 0.859375, y: 0.015625 },
+  { x: 0.859375, y: 0.015625 },
+  { x: 0.890625, y: 0.015625 },
+  { x: 0.890625, y: 0.015625 },
+  { x: 0.921875, y: 0.015625 },
+  { x: 0.921875, y: 0.015625 },
+  { x: 0.953125, y: 0.015625 },
+  { x: 0.953125, y: 0.015625 },
+  { x: 0.984375, y: 0.015625 },
+  { x: 0.984375, y: 0.015625 },
+  { x: 0.015625, y: 0.046875 },
+  { x: 0.015625, y: 0.046875 },
+  { x: 0.046875, y: 0.046875 },
+  { x: 0.046875, y: 0.046875 },
+  { x: 0.078125, y: 0.046875 },
+  { x: 0.078125, y: 0.046875 },
+  { x: 0.109375, y: 0.046875 },
+  { x: 0.109375, y: 0.046875 },
+  { x: 0.140625, y: 0.046875 },
+  { x: 0.140625, y: 0.046875 },
+  { x: 0.171875, y: 0.046875 },
+  { x: 0.171875, y: 0.046875 },
+  { x: 0.203125, y: 0.046875 },
+  { x: 0.203125, y: 0.046875 },
+  { x: 0.234375, y: 0.046875 },
+  { x: 0.234375, y: 0.046875 },
+  { x: 0.265625, y: 0.046875 },
+  { x: 0.265625, y: 0.046875 },
+  { x: 0.296875, y: 0.046875 },
+  { x: 0.296875, y: 0.046875 },
+  { x: 0.328125, y: 0.046875 },
+  { x: 0.328125, y: 0.046875 },
+  { x: 0.359375, y: 0.046875 },
+  { x: 0.359375, y: 0.046875 },
+  { x: 0.390625, y: 0.046875 },
+  { x: 0.390625, y: 0.046875 },
+  { x: 0.421875, y: 0.046875 },
+  { x: 0.421875, y: 0.046875 },
+  { x: 0.453125, y: 0.046875 },
+  { x: 0.453125, y: 0.046875 },
+  { x: 0.484375, y: 0.046875 },
+  { x: 0.484375, y: 0.046875 },
+  { x: 0.515625, y: 0.046875 },
+  { x: 0.515625, y: 0.046875 },
+  { x: 0.546875, y: 0.046875 },
+  { x: 0.546875, y: 0.046875 },
+  { x: 0.578125, y: 0.046875 },
+  { x: 0.578125, y: 0.046875 },
+  { x: 0.609375, y: 0.046875 },
+  { x: 0.609375, y: 0.046875 },
+  { x: 0.640625, y: 0.046875 },
+  { x: 0.640625, y: 0.046875 },
+  { x: 0.671875, y: 0.046875 },
+  { x: 0.671875, y: 0.046875 },
+  { x: 0.703125, y: 0.046875 },
+  { x: 0.703125, y: 0.046875 },
+  { x: 0.734375, y: 0.046875 },
+  { x: 0.734375, y: 0.046875 },
+  { x: 0.765625, y: 0.046875 },
+  { x: 0.765625, y: 0.046875 },
+  { x: 0.796875, y: 0.046875 },
+  { x: 0.796875, y: 0.046875 },
+  { x: 0.828125, y: 0.046875 },
+  { x: 0.828125, y: 0.046875 },
+  { x: 0.859375, y: 0.046875 },
+  { x: 0.859375, y: 0.046875 },
+  { x: 0.890625, y: 0.046875 },
+  { x: 0.890625, y: 0.046875 },
+  { x: 0.921875, y: 0.046875 },
+  { x: 0.921875, y: 0.046875 },
+  { x: 0.953125, y: 0.046875 },
+  { x: 0.953125, y: 0.046875 },
+  { x: 0.984375, y: 0.046875 },
+  { x: 0.984375, y: 0.046875 },
+  { x: 0.015625, y: 0.078125 },
+  { x: 0.015625, y: 0.078125 },
+  { x: 0.046875, y: 0.078125 },
+  { x: 0.046875, y: 0.078125 },
+  { x: 0.078125, y: 0.078125 },
+  { x: 0.078125, y: 0.078125 },
+  { x: 0.109375, y: 0.078125 },
+  { x: 0.109375, y: 0.078125 },
+  { x: 0.140625, y: 0.078125 },
+  { x: 0.140625, y: 0.078125 },
+  { x: 0.171875, y: 0.078125 },
+  { x: 0.171875, y: 0.078125 },
+  { x: 0.203125, y: 0.078125 },
+  { x: 0.203125, y: 0.078125 },
+  { x: 0.234375, y: 0.078125 },
+  { x: 0.234375, y: 0.078125 },
+  { x: 0.265625, y: 0.078125 },
+  { x: 0.265625, y: 0.078125 },
+  { x: 0.296875, y: 0.078125 },
+  { x: 0.296875, y: 0.078125 },
+  { x: 0.328125, y: 0.078125 },
+  { x: 0.328125, y: 0.078125 },
+  { x: 0.359375, y: 0.078125 },
+  { x: 0.359375, y: 0.078125 },
+  { x: 0.390625, y: 0.078125 },
+  { x: 0.390625, y: 0.078125 },
+  { x: 0.421875, y: 0.078125 },
+  { x: 0.421875, y: 0.078125 },
+  { x: 0.453125, y: 0.078125 },
+  { x: 0.453125, y: 0.078125 },
+  { x: 0.484375, y: 0.078125 },
+  { x: 0.484375, y: 0.078125 },
+  { x: 0.515625, y: 0.078125 },
+  { x: 0.515625, y: 0.078125 },
+  { x: 0.546875, y: 0.078125 },
+  { x: 0.546875, y: 0.078125 },
+  { x: 0.578125, y: 0.078125 },
+  { x: 0.578125, y: 0.078125 },
+  { x: 0.609375, y: 0.078125 },
+  { x: 0.609375, y: 0.078125 },
+  { x: 0.640625, y: 0.078125 },
+  { x: 0.640625, y: 0.078125 },
+  { x: 0.671875, y: 0.078125 },
+  { x: 0.671875, y: 0.078125 },
+  { x: 0.703125, y: 0.078125 },
+  { x: 0.703125, y: 0.078125 },
+  { x: 0.734375, y: 0.078125 },
+  { x: 0.734375, y: 0.078125 },
+  { x: 0.765625, y: 0.078125 },
+  { x: 0.765625, y: 0.078125 },
+  { x: 0.796875, y: 0.078125 },
+  { x: 0.796875, y: 0.078125 },
+  { x: 0.828125, y: 0.078125 },
+  { x: 0.828125, y: 0.078125 },
+  { x: 0.859375, y: 0.078125 },
+  { x: 0.859375, y: 0.078125 },
+  { x: 0.890625, y: 0.078125 },
+  { x: 0.890625, y: 0.078125 },
+  { x: 0.921875, y: 0.078125 },
+  { x: 0.921875, y: 0.078125 },
+  { x: 0.953125, y: 0.078125 },
+  { x: 0.953125, y: 0.078125 },
+  { x: 0.984375, y: 0.078125 },
+  { x: 0.984375, y: 0.078125 },
+  { x: 0.015625, y: 0.109375 },
+  { x: 0.015625, y: 0.109375 },
+  { x: 0.046875, y: 0.109375 },
+  { x: 0.046875, y: 0.109375 },
+  { x: 0.078125, y: 0.109375 },
+  { x: 0.078125, y: 0.109375 },
+  { x: 0.109375, y: 0.109375 },
+  { x: 0.109375, y: 0.109375 },
+  { x: 0.140625, y: 0.109375 },
+  { x: 0.140625, y: 0.109375 },
+  { x: 0.171875, y: 0.109375 },
+  { x: 0.171875, y: 0.109375 },
+  { x: 0.203125, y: 0.109375 },
+  { x: 0.203125, y: 0.109375 },
+  { x: 0.234375, y: 0.109375 },
+  { x: 0.234375, y: 0.109375 },
+  { x: 0.265625, y: 0.109375 },
+  { x: 0.265625, y: 0.109375 },
+  { x: 0.296875, y: 0.109375 },
+  { x: 0.296875, y: 0.109375 },
+  { x: 0.328125, y: 0.109375 },
+  { x: 0.328125, y: 0.109375 },
+  { x: 0.359375, y: 0.109375 },
+  { x: 0.359375, y: 0.109375 },
+  { x: 0.390625, y: 0.109375 },
+  { x: 0.390625, y: 0.109375 },
+  { x: 0.421875, y: 0.109375 },
+  { x: 0.421875, y: 0.109375 },
+  { x: 0.453125, y: 0.109375 },
+  { x: 0.453125, y: 0.109375 },
+  { x: 0.484375, y: 0.109375 },
+  { x: 0.484375, y: 0.109375 },
+  { x: 0.515625, y: 0.109375 },
+  { x: 0.515625, y: 0.109375 },
+  { x: 0.546875, y: 0.109375 },
+  { x: 0.546875, y: 0.109375 },
+  { x: 0.578125, y: 0.109375 },
+  { x: 0.578125, y: 0.109375 },
+  { x: 0.609375, y: 0.109375 },
+  { x: 0.609375, y: 0.109375 },
+  { x: 0.640625, y: 0.109375 },
+  { x: 0.640625, y: 0.109375 },
+  { x: 0.671875, y: 0.109375 },
+  { x: 0.671875, y: 0.109375 },
+  { x: 0.703125, y: 0.109375 },
+  { x: 0.703125, y: 0.109375 },
+  { x: 0.734375, y: 0.109375 },
+  { x: 0.734375, y: 0.109375 },
+  { x: 0.765625, y: 0.109375 },
+  { x: 0.765625, y: 0.109375 },
+  { x: 0.796875, y: 0.109375 },
+  { x: 0.796875, y: 0.109375 },
+  { x: 0.828125, y: 0.109375 },
+  { x: 0.828125, y: 0.109375 },
+  { x: 0.859375, y: 0.109375 },
+  { x: 0.859375, y: 0.109375 },
+  { x: 0.890625, y: 0.109375 },
+  { x: 0.890625, y: 0.109375 },
+  { x: 0.921875, y: 0.109375 },
+  { x: 0.921875, y: 0.109375 },
+  { x: 0.953125, y: 0.109375 },
+  { x: 0.953125, y: 0.109375 },
+  { x: 0.984375, y: 0.109375 },
+  { x: 0.984375, y: 0.109375 },
+  { x: 0.015625, y: 0.140625 },
+  { x: 0.015625, y: 0.140625 },
+  { x: 0.046875, y: 0.140625 },
+  { x: 0.046875, y: 0.140625 },
+  { x: 0.078125, y: 0.140625 },
+  { x: 0.078125, y: 0.140625 },
+  { x: 0.109375, y: 0.140625 },
+  { x: 0.109375, y: 0.140625 },
+  { x: 0.140625, y: 0.140625 },
+  { x: 0.140625, y: 0.140625 },
+  { x: 0.171875, y: 0.140625 },
+  { x: 0.171875, y: 0.140625 },
+  { x: 0.203125, y: 0.140625 },
+  { x: 0.203125, y: 0.140625 },
+  { x: 0.234375, y: 0.140625 },
+  { x: 0.234375, y: 0.140625 },
+  { x: 0.265625, y: 0.140625 },
+  { x: 0.265625, y: 0.140625 },
+  { x: 0.296875, y: 0.140625 },
+  { x: 0.296875, y: 0.140625 },
+  { x: 0.328125, y: 0.140625 },
+  { x: 0.328125, y: 0.140625 },
+  { x: 0.359375, y: 0.140625 },
+  { x: 0.359375, y: 0.140625 },
+  { x: 0.390625, y: 0.140625 },
+  { x: 0.390625, y: 0.140625 },
+  { x: 0.421875, y: 0.140625 },
+  { x: 0.421875, y: 0.140625 },
+  { x: 0.453125, y: 0.140625 },
+  { x: 0.453125, y: 0.140625 },
+  { x: 0.484375, y: 0.140625 },
+  { x: 0.484375, y: 0.140625 },
+  { x: 0.515625, y: 0.140625 },
+  { x: 0.515625, y: 0.140625 },
+  { x: 0.546875, y: 0.140625 },
+  { x: 0.546875, y: 0.140625 },
+  { x: 0.578125, y: 0.140625 },
+  { x: 0.578125, y: 0.140625 },
+  { x: 0.609375, y: 0.140625 },
+  { x: 0.609375, y: 0.140625 },
+  { x: 0.640625, y: 0.140625 },
+  { x: 0.640625, y: 0.140625 },
+  { x: 0.671875, y: 0.140625 },
+  { x: 0.671875, y: 0.140625 },
+  { x: 0.703125, y: 0.140625 },
+  { x: 0.703125, y: 0.140625 },
+  { x: 0.734375, y: 0.140625 },
+  { x: 0.734375, y: 0.140625 },
+  { x: 0.765625, y: 0.140625 },
+  { x: 0.765625, y: 0.140625 },
+  { x: 0.796875, y: 0.140625 },
+  { x: 0.796875, y: 0.140625 },
+  { x: 0.828125, y: 0.140625 },
+  { x: 0.828125, y: 0.140625 },
+  { x: 0.859375, y: 0.140625 },
+  { x: 0.859375, y: 0.140625 },
+  { x: 0.890625, y: 0.140625 },
+  { x: 0.890625, y: 0.140625 },
+  { x: 0.921875, y: 0.140625 },
+  { x: 0.921875, y: 0.140625 },
+  { x: 0.953125, y: 0.140625 },
+  { x: 0.953125, y: 0.140625 },
+  { x: 0.984375, y: 0.140625 },
+  { x: 0.984375, y: 0.140625 },
+  { x: 0.015625, y: 0.171875 },
+  { x: 0.015625, y: 0.171875 },
+  { x: 0.046875, y: 0.171875 },
+  { x: 0.046875, y: 0.171875 },
+  { x: 0.078125, y: 0.171875 },
+  { x: 0.078125, y: 0.171875 },
+  { x: 0.109375, y: 0.171875 },
+  { x: 0.109375, y: 0.171875 },
+  { x: 0.140625, y: 0.171875 },
+  { x: 0.140625, y: 0.171875 },
+  { x: 0.171875, y: 0.171875 },
+  { x: 0.171875, y: 0.171875 },
+  { x: 0.203125, y: 0.171875 },
+  { x: 0.203125, y: 0.171875 },
+  { x: 0.234375, y: 0.171875 },
+  { x: 0.234375, y: 0.171875 },
+  { x: 0.265625, y: 0.171875 },
+  { x: 0.265625, y: 0.171875 },
+  { x: 0.296875, y: 0.171875 },
+  { x: 0.296875, y: 0.171875 },
+  { x: 0.328125, y: 0.171875 },
+  { x: 0.328125, y: 0.171875 },
+  { x: 0.359375, y: 0.171875 },
+  { x: 0.359375, y: 0.171875 },
+  { x: 0.390625, y: 0.171875 },
+  { x: 0.390625, y: 0.171875 },
+  { x: 0.421875, y: 0.171875 },
+  { x: 0.421875, y: 0.171875 },
+  { x: 0.453125, y: 0.171875 },
+  { x: 0.453125, y: 0.171875 },
+  { x: 0.484375, y: 0.171875 },
+  { x: 0.484375, y: 0.171875 },
+  { x: 0.515625, y: 0.171875 },
+  { x: 0.515625, y: 0.171875 },
+  { x: 0.546875, y: 0.171875 },
+  { x: 0.546875, y: 0.171875 },
+  { x: 0.578125, y: 0.171875 },
+  { x: 0.578125, y: 0.171875 },
+  { x: 0.609375, y: 0.171875 },
+  { x: 0.609375, y: 0.171875 },
+  { x: 0.640625, y: 0.171875 },
+  { x: 0.640625, y: 0.171875 },
+  { x: 0.671875, y: 0.171875 },
+  { x: 0.671875, y: 0.171875 },
+  { x: 0.703125, y: 0.171875 },
+  { x: 0.703125, y: 0.171875 },
+  { x: 0.734375, y: 0.171875 },
+  { x: 0.734375, y: 0.171875 },
+  { x: 0.765625, y: 0.171875 },
+  { x: 0.765625, y: 0.171875 },
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+  { x: 0.6875, y: 0.6875 },
+  { x: 0.6875, y: 0.6875 },
+  { x: 0.6875, y: 0.6875 },
+  { x: 0.8125, y: 0.6875 },
+  { x: 0.8125, y: 0.6875 },
+  { x: 0.8125, y: 0.6875 },
+  { x: 0.8125, y: 0.6875 },
+  { x: 0.8125, y: 0.6875 },
+  { x: 0.8125, y: 0.6875 },
+  { x: 0.9375, y: 0.6875 },
+  { x: 0.9375, y: 0.6875 },
+  { x: 0.9375, y: 0.6875 },
+  { x: 0.9375, y: 0.6875 },
+  { x: 0.9375, y: 0.6875 },
+  { x: 0.9375, y: 0.6875 },
+  { x: 0.0625, y: 0.8125 },
+  { x: 0.0625, y: 0.8125 },
+  { x: 0.0625, y: 0.8125 },
+  { x: 0.0625, y: 0.8125 },
+  { x: 0.0625, y: 0.8125 },
+  { x: 0.0625, y: 0.8125 },
+  { x: 0.1875, y: 0.8125 },
+  { x: 0.1875, y: 0.8125 },
+  { x: 0.1875, y: 0.8125 },
+  { x: 0.1875, y: 0.8125 },
+  { x: 0.1875, y: 0.8125 },
+  { x: 0.1875, y: 0.8125 },
+  { x: 0.3125, y: 0.8125 },
+  { x: 0.3125, y: 0.8125 },
+  { x: 0.3125, y: 0.8125 },
+  { x: 0.3125, y: 0.8125 },
+  { x: 0.3125, y: 0.8125 },
+  { x: 0.3125, y: 0.8125 },
+  { x: 0.4375, y: 0.8125 },
+  { x: 0.4375, y: 0.8125 },
+  { x: 0.4375, y: 0.8125 },
+  { x: 0.4375, y: 0.8125 },
+  { x: 0.4375, y: 0.8125 },
+  { x: 0.4375, y: 0.8125 },
+  { x: 0.5625, y: 0.8125 },
+  { x: 0.5625, y: 0.8125 },
+  { x: 0.5625, y: 0.8125 },
+  { x: 0.5625, y: 0.8125 },
+  { x: 0.5625, y: 0.8125 },
+  { x: 0.5625, y: 0.8125 },
+  { x: 0.6875, y: 0.8125 },
+  { x: 0.6875, y: 0.8125 },
+  { x: 0.6875, y: 0.8125 },
+  { x: 0.6875, y: 0.8125 },
+  { x: 0.6875, y: 0.8125 },
+  { x: 0.6875, y: 0.8125 },
+  { x: 0.8125, y: 0.8125 },
+  { x: 0.8125, y: 0.8125 },
+  { x: 0.8125, y: 0.8125 },
+  { x: 0.8125, y: 0.8125 },
+  { x: 0.8125, y: 0.8125 },
+  { x: 0.8125, y: 0.8125 },
+  { x: 0.9375, y: 0.8125 },
+  { x: 0.9375, y: 0.8125 },
+  { x: 0.9375, y: 0.8125 },
+  { x: 0.9375, y: 0.8125 },
+  { x: 0.9375, y: 0.8125 },
+  { x: 0.9375, y: 0.8125 },
+  { x: 0.0625, y: 0.9375 },
+  { x: 0.0625, y: 0.9375 },
+  { x: 0.0625, y: 0.9375 },
+  { x: 0.0625, y: 0.9375 },
+  { x: 0.0625, y: 0.9375 },
+  { x: 0.0625, y: 0.9375 },
+  { x: 0.1875, y: 0.9375 },
+  { x: 0.1875, y: 0.9375 },
+  { x: 0.1875, y: 0.9375 },
+  { x: 0.1875, y: 0.9375 },
+  { x: 0.1875, y: 0.9375 },
+  { x: 0.1875, y: 0.9375 },
+  { x: 0.3125, y: 0.9375 },
+  { x: 0.3125, y: 0.9375 },
+  { x: 0.3125, y: 0.9375 },
+  { x: 0.3125, y: 0.9375 },
+  { x: 0.3125, y: 0.9375 },
+  { x: 0.3125, y: 0.9375 },
+  { x: 0.4375, y: 0.9375 },
+  { x: 0.4375, y: 0.9375 },
+  { x: 0.4375, y: 0.9375 },
+  { x: 0.4375, y: 0.9375 },
+  { x: 0.4375, y: 0.9375 },
+  { x: 0.4375, y: 0.9375 },
+  { x: 0.5625, y: 0.9375 },
+  { x: 0.5625, y: 0.9375 },
+  { x: 0.5625, y: 0.9375 },
+  { x: 0.5625, y: 0.9375 },
+  { x: 0.5625, y: 0.9375 },
+  { x: 0.5625, y: 0.9375 },
+  { x: 0.6875, y: 0.9375 },
+  { x: 0.6875, y: 0.9375 },
+  { x: 0.6875, y: 0.9375 },
+  { x: 0.6875, y: 0.9375 },
+  { x: 0.6875, y: 0.9375 },
+  { x: 0.6875, y: 0.9375 },
+  { x: 0.8125, y: 0.9375 },
+  { x: 0.8125, y: 0.9375 },
+  { x: 0.8125, y: 0.9375 },
+  { x: 0.8125, y: 0.9375 },
+  { x: 0.8125, y: 0.9375 },
+  { x: 0.8125, y: 0.9375 },
+  { x: 0.9375, y: 0.9375 },
+  { x: 0.9375, y: 0.9375 },
+  { x: 0.9375, y: 0.9375 },
+  { x: 0.9375, y: 0.9375 },
+  { x: 0.9375, y: 0.9375 },
+  { x: 0.9375, y: 0.9375 }
+];
+
+// src/hand/handposedetector.ts
+var HandDetector = class {
+  constructor(model21) {
+    __publicField(this, "model");
+    __publicField(this, "anchors");
+    __publicField(this, "anchorsTensor");
+    __publicField(this, "inputSize");
+    __publicField(this, "inputSizeTensor");
+    __publicField(this, "doubleInputSizeTensor");
+    var _a, _b, _c, _d;
+    this.model = model21;
+    this.anchors = anchors2.map((anchor) => [anchor.x, anchor.y]);
+    this.anchorsTensor = tf19.tensor2d(this.anchors);
+    this.inputSize = ((_d = (_c = (_b = (_a = this == null ? void 0 : this.model) == null ? void 0 : _a.inputs) == null ? void 0 : _b[0]) == null ? void 0 : _c.shape) == null ? void 0 : _d[2]) || 0;
+    this.inputSizeTensor = tf19.tensor1d([this.inputSize, this.inputSize]);
+    this.doubleInputSizeTensor = tf19.tensor1d([this.inputSize * 2, this.inputSize * 2]);
+  }
+  normalizeBoxes(boxes) {
+    const t2 = {};
+    t2.boxOffsets = tf19.slice(boxes, [0, 0], [-1, 2]);
+    t2.boxSizes = tf19.slice(boxes, [0, 2], [-1, 2]);
+    t2.div = tf19.div(t2.boxOffsets, this.inputSizeTensor);
+    t2.boxCenterPoints = tf19.add(t2.div, this.anchorsTensor);
+    t2.halfBoxSizes = tf19.div(t2.boxSizes, this.doubleInputSizeTensor);
+    t2.sub = tf19.sub(t2.boxCenterPoints, t2.halfBoxSizes);
+    t2.startPoints = tf19.mul(t2.sub, this.inputSizeTensor);
+    t2.add = tf19.add(t2.boxCenterPoints, t2.halfBoxSizes);
+    t2.endPoints = tf19.mul(t2.add, this.inputSizeTensor);
+    const res = tf19.concat2d([t2.startPoints, t2.endPoints], 1);
+    Object.keys(t2).forEach((tensor6) => tf19.dispose(t2[tensor6]));
+    return res;
+  }
+  normalizeLandmarks(rawPalmLandmarks, index2) {
+    const t2 = {};
+    t2.reshape = tf19.reshape(rawPalmLandmarks, [-1, 7, 2]);
+    t2.div = tf19.div(t2.reshape, this.inputSizeTensor);
+    t2.landmarks = tf19.add(t2.div, this.anchors[index2] ? this.anchors[index2] : 0);
+    const res = tf19.mul(t2.landmarks, this.inputSizeTensor);
+    Object.keys(t2).forEach((tensor6) => tf19.dispose(t2[tensor6]));
+    return res;
+  }
+  async predict(input, config3) {
+    var _a;
+    const t2 = {};
+    t2.resize = tf19.image.resizeBilinear(input, [this.inputSize, this.inputSize]);
+    t2.div = tf19.div(t2.resize, constants.tf127);
+    t2.image = tf19.sub(t2.div, constants.tf1);
+    t2.batched = this.model.execute(t2.image);
+    t2.predictions = tf19.squeeze(t2.batched);
+    t2.slice = tf19.slice(t2.predictions, [0, 0], [-1, 1]);
+    t2.sigmoid = tf19.sigmoid(t2.slice);
+    t2.scores = tf19.squeeze(t2.sigmoid);
+    const scores = await t2.scores.data();
+    t2.boxes = tf19.slice(t2.predictions, [0, 1], [-1, 4]);
+    t2.norm = this.normalizeBoxes(t2.boxes);
+    t2.nms = await tf19.image.nonMaxSuppressionAsync(t2.norm, t2.scores, 3 * (((_a = config3.hand) == null ? void 0 : _a.maxDetected) || 1), config3.hand.iouThreshold, config3.hand.minConfidence);
+    const nms = await t2.nms.array();
+    const hands = [];
+    for (const index2 of nms) {
+      const p = {};
+      p.box = tf19.slice(t2.norm, [index2, 0], [1, -1]);
+      p.slice = tf19.slice(t2.predictions, [index2, 5], [1, 14]);
+      p.norm = this.normalizeLandmarks(p.slice, index2);
+      p.palmLandmarks = tf19.reshape(p.norm, [-1, 2]);
+      const box = await p.box.data();
+      const startPoint = box.slice(0, 2);
+      const endPoint = box.slice(2, 4);
+      const palmLandmarks = await p.palmLandmarks.array();
+      const hand3 = { startPoint, endPoint, palmLandmarks, confidence: scores[index2] };
+      const scaled = scaleBoxCoordinates2(hand3, [(input.shape[2] || 1) / this.inputSize, (input.shape[1] || 0) / this.inputSize]);
+      hands.push(scaled);
+      Object.keys(p).forEach((tensor6) => tf19.dispose(p[tensor6]));
+    }
+    Object.keys(t2).forEach((tensor6) => tf19.dispose(t2[tensor6]));
+    return hands;
+  }
+};
+
+// src/hand/handposepipeline.ts
+var tf20 = __toESM(require_tfjs_esm());
+var palmBoxEnlargeFactor = 5;
+var handBoxEnlargeFactor = 1.65;
+var palmLandmarkIds = [0, 5, 9, 13, 17, 1, 2];
+var palmLandmarksPalmBase = 0;
+var palmLandmarksMiddleFingerBase = 2;
+var lastTime8 = 0;
+var HandPipeline = class {
+  constructor(handDetector, handPoseModel2) {
+    __publicField(this, "handDetector");
+    __publicField(this, "handPoseModel");
+    __publicField(this, "inputSize");
+    __publicField(this, "storedBoxes");
+    __publicField(this, "skipped");
+    __publicField(this, "detectedHands");
+    var _a, _b, _c;
+    this.handDetector = handDetector;
+    this.handPoseModel = handPoseModel2;
+    this.inputSize = ((_c = (_b = (_a = this.handPoseModel) == null ? void 0 : _a.inputs) == null ? void 0 : _b[0].shape) == null ? void 0 : _c[2]) || 0;
+    this.storedBoxes = [];
+    this.skipped = Number.MAX_SAFE_INTEGER;
+    this.detectedHands = 0;
+  }
+  calculateLandmarksBoundingBox(landmarks) {
+    const xs = landmarks.map((d) => d[0]);
+    const ys = landmarks.map((d) => d[1]);
+    const startPoint = [Math.min(...xs), Math.min(...ys)];
+    const endPoint = [Math.max(...xs), Math.max(...ys)];
+    return { startPoint, endPoint };
+  }
+  getBoxForPalmLandmarks(palmLandmarks, rotationMatrix) {
+    const rotatedPalmLandmarks = palmLandmarks.map((coord) => rotatePoint2([...coord, 1], rotationMatrix));
+    const boxAroundPalm = this.calculateLandmarksBoundingBox(rotatedPalmLandmarks);
+    return enlargeBox2(squarifyBox2(boxAroundPalm), palmBoxEnlargeFactor);
+  }
+  getBoxForHandLandmarks(landmarks) {
+    const boundingBox = this.calculateLandmarksBoundingBox(landmarks);
+    const boxAroundHand = enlargeBox2(squarifyBox2(boundingBox), handBoxEnlargeFactor);
+    boxAroundHand.palmLandmarks = [];
+    for (let i = 0; i < palmLandmarkIds.length; i++) {
+      boxAroundHand.palmLandmarks.push(landmarks[palmLandmarkIds[i]].slice(0, 2));
+    }
+    return boxAroundHand;
+  }
+  transformRawCoords(rawCoords, box2, angle, rotationMatrix) {
+    const boxSize = getBoxSize2(box2);
+    const scaleFactor = [boxSize[0] / this.inputSize, boxSize[1] / this.inputSize, (boxSize[0] + boxSize[1]) / this.inputSize / 2];
+    const coordsScaled = rawCoords.map((coord) => [
+      scaleFactor[0] * (coord[0] - this.inputSize / 2),
+      scaleFactor[1] * (coord[1] - this.inputSize / 2),
+      scaleFactor[2] * coord[2]
+    ]);
+    const coordsRotationMatrix = buildRotationMatrix2(angle, [0, 0]);
+    const coordsRotated = coordsScaled.map((coord) => {
+      const rotated = rotatePoint2(coord, coordsRotationMatrix);
+      return [...rotated, coord[2]];
+    });
+    const inverseRotationMatrix = invertTransformMatrix2(rotationMatrix);
+    const boxCenter = [...getBoxCenter2(box2), 1];
+    const originalBoxCenter = [
+      dot2(boxCenter, inverseRotationMatrix[0]),
+      dot2(boxCenter, inverseRotationMatrix[1])
+    ];
+    return coordsRotated.map((coord) => [
+      Math.trunc(coord[0] + originalBoxCenter[0]),
+      Math.trunc(coord[1] + originalBoxCenter[1]),
+      Math.trunc(coord[2])
+    ]);
+  }
+  async estimateHands(image27, config3) {
+    let useFreshBox = false;
+    let boxes;
+    const skipTime = (config3.hand.skipTime || 0) > now() - lastTime8;
+    const skipFrame = this.skipped < (config3.hand.skipFrames || 0);
+    if (config3.skipAllowed && skipTime && skipFrame) {
+      boxes = await this.handDetector.predict(image27, config3);
+      this.skipped = 0;
+    }
+    if (config3.skipAllowed)
+      this.skipped++;
+    if (boxes && boxes.length > 0 && (boxes.length !== this.detectedHands && this.detectedHands !== config3.hand.maxDetected || !config3.hand.landmarks)) {
+      this.detectedHands = 0;
+      this.storedBoxes = [...boxes];
+      if (this.storedBoxes.length > 0)
+        useFreshBox = true;
+    }
+    const hands = [];
+    for (let i = 0; i < this.storedBoxes.length; i++) {
+      const currentBox = this.storedBoxes[i];
+      if (!currentBox)
+        continue;
+      if (config3.hand.landmarks) {
+        const angle = config3.hand.rotation ? computeRotation2(currentBox.palmLandmarks[palmLandmarksPalmBase], currentBox.palmLandmarks[palmLandmarksMiddleFingerBase]) : 0;
+        const palmCenter = getBoxCenter2(currentBox);
+        const palmCenterNormalized = [palmCenter[0] / image27.shape[2], palmCenter[1] / image27.shape[1]];
+        const rotatedImage = config3.hand.rotation && env.kernels.includes("rotatewithoffset") ? tf20.image.rotateWithOffset(image27, angle, 0, palmCenterNormalized) : image27.clone();
+        const rotationMatrix = buildRotationMatrix2(-angle, palmCenter);
+        const newBox = useFreshBox ? this.getBoxForPalmLandmarks(currentBox.palmLandmarks, rotationMatrix) : currentBox;
+        const croppedInput = cutBoxFromImageAndResize(newBox, rotatedImage, [this.inputSize, this.inputSize]);
+        const handImage = tf20.div(croppedInput, constants.tf255);
+        tf20.dispose(croppedInput);
+        tf20.dispose(rotatedImage);
+        const [confidenceT, keypoints] = this.handPoseModel.execute(handImage);
+        lastTime8 = now();
+        tf20.dispose(handImage);
+        const confidence = (await confidenceT.data())[0];
+        tf20.dispose(confidenceT);
+        if (confidence >= config3.hand.minConfidence / 4) {
+          const keypointsReshaped = tf20.reshape(keypoints, [-1, 3]);
+          const rawCoords = await keypointsReshaped.array();
+          tf20.dispose(keypoints);
+          tf20.dispose(keypointsReshaped);
+          const coords = this.transformRawCoords(rawCoords, newBox, angle, rotationMatrix);
+          const nextBoundingBox = this.getBoxForHandLandmarks(coords);
+          this.storedBoxes[i] = { ...nextBoundingBox, confidence };
+          const result = {
+            landmarks: coords,
+            confidence,
+            boxConfidence: currentBox.confidence,
+            fingerConfidence: confidence,
+            box: { topLeft: nextBoundingBox.startPoint, bottomRight: nextBoundingBox.endPoint }
+          };
+          hands.push(result);
+        } else {
+          this.storedBoxes[i] = null;
+        }
+        tf20.dispose(keypoints);
+      } else {
+        const enlarged = enlargeBox2(squarifyBox2(currentBox), handBoxEnlargeFactor);
+        const result = {
+          confidence: currentBox.confidence,
+          boxConfidence: currentBox.confidence,
+          fingerConfidence: 0,
+          box: { topLeft: enlarged.startPoint, bottomRight: enlarged.endPoint },
+          landmarks: []
+        };
+        hands.push(result);
+      }
+    }
+    this.storedBoxes = this.storedBoxes.filter((a) => a !== null);
+    this.detectedHands = hands.length;
+    if (hands.length > config3.hand.maxDetected)
+      hands.length = config3.hand.maxDetected;
+    return hands;
+  }
+};
+
+// src/hand/fingerdef.ts
+var Finger = {
+  thumb: 0,
+  index: 1,
+  middle: 2,
+  ring: 3,
+  pinky: 4,
+  all: [0, 1, 2, 3, 4],
+  nameMapping: { 0: "thumb", 1: "index", 2: "middle", 3: "ring", 4: "pinky" },
+  pointsMapping: {
+    0: [[0, 1], [1, 2], [2, 3], [3, 4]],
+    1: [[0, 5], [5, 6], [6, 7], [7, 8]],
+    2: [[0, 9], [9, 10], [10, 11], [11, 12]],
+    3: [[0, 13], [13, 14], [14, 15], [15, 16]],
+    4: [[0, 17], [17, 18], [18, 19], [19, 20]]
+  },
+  getName: (value) => Finger.nameMapping[value],
+  getPoints: (value) => Finger.pointsMapping[value]
+};
+var FingerCurl = {
+  none: 0,
+  half: 1,
+  full: 2,
+  nameMapping: { 0: "none", 1: "half", 2: "full" },
+  getName: (value) => FingerCurl.nameMapping[value]
+};
+var FingerDirection = {
+  verticalUp: 0,
+  verticalDown: 1,
+  horizontalLeft: 2,
+  horizontalRight: 3,
+  diagonalUpRight: 4,
+  diagonalUpLeft: 5,
+  diagonalDownRight: 6,
+  diagonalDownLeft: 7,
+  nameMapping: { 0: "verticalUp", 1: "verticalDown", 2: "horizontalLeft", 3: "horizontalRight", 4: "diagonalUpRight", 5: "diagonalUpLeft", 6: "diagonalDownRight", 7: "diagonalDownLeft" },
+  getName: (value) => FingerDirection.nameMapping[value]
+};
+var FingerGesture = class {
+  constructor(name) {
+    __publicField(this, "name");
+    __publicField(this, "curls");
+    __publicField(this, "directions");
+    __publicField(this, "weights");
+    __publicField(this, "weightsRelative");
+    this.name = name;
+    this.curls = {};
+    this.directions = {};
+    this.weights = [1, 1, 1, 1, 1];
+    this.weightsRelative = [1, 1, 1, 1, 1];
+  }
+  curl(finger, curl, confidence) {
+    if (typeof this.curls[finger] === "undefined")
+      this.curls[finger] = [];
+    this.curls[finger].push([curl, confidence]);
+  }
+  direction(finger, position, confidence) {
+    if (!this.directions[finger])
+      this.directions[finger] = [];
+    this.directions[finger].push([position, confidence]);
+  }
+  weight(finger, weight) {
+    this.weights[finger] = weight;
+    const total = this.weights.reduce((a, b) => a + b, 0);
+    this.weightsRelative = this.weights.map((el) => el * 5 / total);
+  }
+  matchAgainst(detectedCurls, detectedDirections) {
+    let confidence = 0;
+    for (const fingerIdx in detectedCurls) {
+      const detectedCurl = detectedCurls[fingerIdx];
+      const expectedCurls = this.curls[fingerIdx];
+      if (typeof expectedCurls === "undefined") {
+        confidence += this.weightsRelative[fingerIdx];
+        continue;
+      }
+      for (const [expectedCurl, score] of expectedCurls) {
+        if (detectedCurl === expectedCurl) {
+          confidence += score * this.weightsRelative[fingerIdx];
+          break;
+        }
+      }
+    }
+    for (const fingerIdx in detectedDirections) {
+      const detectedDirection = detectedDirections[fingerIdx];
+      const expectedDirections = this.directions[fingerIdx];
+      if (typeof expectedDirections === "undefined") {
+        confidence += this.weightsRelative[fingerIdx];
+        continue;
+      }
+      for (const [expectedDirection, score] of expectedDirections) {
+        if (detectedDirection === expectedDirection) {
+          confidence += score * this.weightsRelative[fingerIdx];
+          break;
+        }
+      }
+    }
+    return confidence / 10;
+  }
+};
+
+// src/hand/fingergesture.ts
+var { thumb, index, middle, ring, pinky } = Finger;
+var { none, half, full } = FingerCurl;
+var { verticalUp, verticalDown, horizontalLeft, horizontalRight, diagonalUpRight, diagonalUpLeft, diagonalDownRight, diagonalDownLeft } = FingerDirection;
+var ThumbsUp = new FingerGesture("thumbs up");
+ThumbsUp.curl(thumb, none, 1);
+ThumbsUp.direction(thumb, verticalUp, 1);
+ThumbsUp.direction(thumb, diagonalUpLeft, 0.25);
+ThumbsUp.direction(thumb, diagonalUpRight, 0.25);
+for (const finger of [Finger.index, Finger.middle, Finger.ring, Finger.pinky]) {
+  ThumbsUp.curl(finger, full, 1);
+  ThumbsUp.direction(finger, horizontalLeft, 1);
+  ThumbsUp.direction(finger, horizontalRight, 1);
+}
+var Victory = new FingerGesture("victory");
+Victory.curl(thumb, half, 0.5);
+Victory.curl(thumb, none, 0.5);
+Victory.direction(thumb, verticalUp, 1);
+Victory.direction(thumb, diagonalUpLeft, 1);
+Victory.curl(index, none, 1);
+Victory.direction(index, verticalUp, 0.75);
+Victory.direction(index, diagonalUpLeft, 1);
+Victory.curl(middle, none, 1);
+Victory.direction(middle, verticalUp, 1);
+Victory.direction(middle, diagonalUpLeft, 0.75);
+Victory.curl(ring, full, 1);
+Victory.direction(ring, verticalUp, 0.2);
+Victory.direction(ring, diagonalUpLeft, 1);
+Victory.direction(ring, horizontalLeft, 0.2);
+Victory.curl(pinky, full, 1);
+Victory.direction(pinky, verticalUp, 0.2);
+Victory.direction(pinky, diagonalUpLeft, 1);
+Victory.direction(pinky, horizontalLeft, 0.2);
+Victory.weight(index, 2);
+Victory.weight(middle, 2);
+var Point = new FingerGesture("point");
+Point.curl(thumb, full, 1);
+Point.curl(index, none, 0.5);
+Point.curl(middle, full, 0.5);
+Point.curl(ring, full, 0.5);
+Point.curl(pinky, full, 0.5);
+Point.weight(index, 2);
+Point.weight(middle, 2);
+var MiddleFinger = new FingerGesture("middle finger");
+MiddleFinger.curl(thumb, none, 1);
+MiddleFinger.curl(index, full, 0.5);
+MiddleFinger.curl(middle, full, 0.5);
+MiddleFinger.curl(ring, full, 0.5);
+MiddleFinger.curl(pinky, full, 0.5);
+MiddleFinger.weight(index, 2);
+MiddleFinger.weight(middle, 2);
+var OpenPalm = new FingerGesture("open palm");
+OpenPalm.curl(thumb, none, 0.75);
+OpenPalm.curl(index, none, 0.75);
+OpenPalm.curl(middle, none, 0.75);
+OpenPalm.curl(ring, none, 0.75);
+OpenPalm.curl(pinky, none, 0.75);
+var fingergesture_default = [ThumbsUp, Victory, Point, MiddleFinger, OpenPalm];
+
+// src/hand/fingerpose.ts
+var minConfidence = 0.7;
+var options2 = {
+  HALF_CURL_START_LIMIT: 60,
+  NO_CURL_START_LIMIT: 130,
+  DISTANCE_VOTE_POWER: 1.1,
+  SINGLE_ANGLE_VOTE_POWER: 0.9,
+  TOTAL_ANGLE_VOTE_POWER: 1.6
+};
+function calculateSlope(point1x, point1y, point2x, point2y) {
+  const value = (point1y - point2y) / (point1x - point2x);
+  let slope = Math.atan(value) * 180 / Math.PI;
+  if (slope <= 0)
+    slope = -slope;
+  else if (slope > 0)
+    slope = 180 - slope;
+  return slope;
+}
+function getSlopes(point1, point2) {
+  if (!point1 || !point2)
+    return [0, 0];
+  const slopeXY = calculateSlope(point1[0], point1[1], point2[0], point2[1]);
+  if (point1.length === 2)
+    return slopeXY;
+  const slopeYZ = calculateSlope(point1[1], point1[2], point2[1], point2[2]);
+  return [slopeXY, slopeYZ];
+}
+function angleOrientationAt(angle, weightageAt = 1) {
+  let isVertical = 0;
+  let isDiagonal = 0;
+  let isHorizontal = 0;
+  if (angle >= 75 && angle <= 105)
+    isVertical = 1 * weightageAt;
+  else if (angle >= 25 && angle <= 155)
+    isDiagonal = 1 * weightageAt;
+  else
+    isHorizontal = 1 * weightageAt;
+  return [isVertical, isDiagonal, isHorizontal];
+}
+function estimateFingerCurl(startPoint, midPoint, endPoint) {
+  const start_mid_x_dist = startPoint[0] - midPoint[0];
+  const start_end_x_dist = startPoint[0] - endPoint[0];
+  const mid_end_x_dist = midPoint[0] - endPoint[0];
+  const start_mid_y_dist = startPoint[1] - midPoint[1];
+  const start_end_y_dist = startPoint[1] - endPoint[1];
+  const mid_end_y_dist = midPoint[1] - endPoint[1];
+  const start_mid_z_dist = startPoint[2] - midPoint[2];
+  const start_end_z_dist = startPoint[2] - endPoint[2];
+  const mid_end_z_dist = midPoint[2] - endPoint[2];
+  const start_mid_dist = Math.sqrt(start_mid_x_dist * start_mid_x_dist + start_mid_y_dist * start_mid_y_dist + start_mid_z_dist * start_mid_z_dist);
+  const start_end_dist = Math.sqrt(start_end_x_dist * start_end_x_dist + start_end_y_dist * start_end_y_dist + start_end_z_dist * start_end_z_dist);
+  const mid_end_dist = Math.sqrt(mid_end_x_dist * mid_end_x_dist + mid_end_y_dist * mid_end_y_dist + mid_end_z_dist * mid_end_z_dist);
+  let cos_in = (mid_end_dist * mid_end_dist + start_mid_dist * start_mid_dist - start_end_dist * start_end_dist) / (2 * mid_end_dist * start_mid_dist);
+  if (cos_in > 1)
+    cos_in = 1;
+  else if (cos_in < -1)
+    cos_in = -1;
+  let angleOfCurve = Math.acos(cos_in);
+  angleOfCurve = 57.2958 * angleOfCurve % 180;
+  let fingerCurl;
+  if (angleOfCurve > options2.NO_CURL_START_LIMIT)
+    fingerCurl = FingerCurl.none;
+  else if (angleOfCurve > options2.HALF_CURL_START_LIMIT)
+    fingerCurl = FingerCurl.half;
+  else
+    fingerCurl = FingerCurl.full;
+  return fingerCurl;
+}
+function estimateHorizontalDirection(start_end_x_dist, start_mid_x_dist, mid_end_x_dist, max_dist_x) {
+  let estimatedDirection;
+  if (max_dist_x === Math.abs(start_end_x_dist)) {
+    if (start_end_x_dist > 0)
+      estimatedDirection = FingerDirection.horizontalLeft;
+    else
+      estimatedDirection = FingerDirection.horizontalRight;
+  } else if (max_dist_x === Math.abs(start_mid_x_dist)) {
+    if (start_mid_x_dist > 0)
+      estimatedDirection = FingerDirection.horizontalLeft;
+    else
+      estimatedDirection = FingerDirection.horizontalRight;
+  } else {
+    if (mid_end_x_dist > 0)
+      estimatedDirection = FingerDirection.horizontalLeft;
+    else
+      estimatedDirection = FingerDirection.horizontalRight;
+  }
+  return estimatedDirection;
+}
+function estimateVerticalDirection(start_end_y_dist, start_mid_y_dist, mid_end_y_dist, max_dist_y) {
+  let estimatedDirection;
+  if (max_dist_y === Math.abs(start_end_y_dist)) {
+    if (start_end_y_dist < 0)
+      estimatedDirection = FingerDirection.verticalDown;
+    else
+      estimatedDirection = FingerDirection.verticalUp;
+  } else if (max_dist_y === Math.abs(start_mid_y_dist)) {
+    if (start_mid_y_dist < 0)
+      estimatedDirection = FingerDirection.verticalDown;
+    else
+      estimatedDirection = FingerDirection.verticalUp;
+  } else {
+    if (mid_end_y_dist < 0)
+      estimatedDirection = FingerDirection.verticalDown;
+    else
+      estimatedDirection = FingerDirection.verticalUp;
+  }
+  return estimatedDirection;
+}
+function estimateDiagonalDirection(start_end_y_dist, start_mid_y_dist, mid_end_y_dist, max_dist_y, start_end_x_dist, start_mid_x_dist, mid_end_x_dist, max_dist_x) {
+  let estimatedDirection;
+  const reqd_vertical_direction = estimateVerticalDirection(start_end_y_dist, start_mid_y_dist, mid_end_y_dist, max_dist_y);
+  const reqd_horizontal_direction = estimateHorizontalDirection(start_end_x_dist, start_mid_x_dist, mid_end_x_dist, max_dist_x);
+  if (reqd_vertical_direction === FingerDirection.verticalUp) {
+    if (reqd_horizontal_direction === FingerDirection.horizontalLeft)
+      estimatedDirection = FingerDirection.diagonalUpLeft;
+    else
+      estimatedDirection = FingerDirection.diagonalUpRight;
+  } else {
+    if (reqd_horizontal_direction === FingerDirection.horizontalLeft)
+      estimatedDirection = FingerDirection.diagonalDownLeft;
+    else
+      estimatedDirection = FingerDirection.diagonalDownRight;
+  }
+  return estimatedDirection;
+}
+function calculateFingerDirection(startPoint, midPoint, endPoint, fingerSlopes) {
+  const start_mid_x_dist = startPoint[0] - midPoint[0];
+  const start_end_x_dist = startPoint[0] - endPoint[0];
+  const mid_end_x_dist = midPoint[0] - endPoint[0];
+  const start_mid_y_dist = startPoint[1] - midPoint[1];
+  const start_end_y_dist = startPoint[1] - endPoint[1];
+  const mid_end_y_dist = midPoint[1] - endPoint[1];
+  const max_dist_x = Math.max(Math.abs(start_mid_x_dist), Math.abs(start_end_x_dist), Math.abs(mid_end_x_dist));
+  const max_dist_y = Math.max(Math.abs(start_mid_y_dist), Math.abs(start_end_y_dist), Math.abs(mid_end_y_dist));
+  let voteVertical = 0;
+  let voteDiagonal = 0;
+  let voteHorizontal = 0;
+  const start_end_x_y_dist_ratio = max_dist_y / (max_dist_x + 1e-5);
+  if (start_end_x_y_dist_ratio > 1.5)
+    voteVertical += options2.DISTANCE_VOTE_POWER;
+  else if (start_end_x_y_dist_ratio > 0.66)
+    voteDiagonal += options2.DISTANCE_VOTE_POWER;
+  else
+    voteHorizontal += options2.DISTANCE_VOTE_POWER;
+  const start_mid_dist = Math.sqrt(start_mid_x_dist * start_mid_x_dist + start_mid_y_dist * start_mid_y_dist);
+  const start_end_dist = Math.sqrt(start_end_x_dist * start_end_x_dist + start_end_y_dist * start_end_y_dist);
+  const mid_end_dist = Math.sqrt(mid_end_x_dist * mid_end_x_dist + mid_end_y_dist * mid_end_y_dist);
+  const max_dist = Math.max(start_mid_dist, start_end_dist, mid_end_dist);
+  let calc_start_point_x = startPoint[0];
+  let calc_start_point_y = startPoint[1];
+  let calc_end_point_x = endPoint[0];
+  let calc_end_point_y = endPoint[1];
+  if (max_dist === start_mid_dist) {
+    calc_end_point_x = endPoint[0];
+    calc_end_point_y = endPoint[1];
+  } else if (max_dist === mid_end_dist) {
+    calc_start_point_x = midPoint[0];
+    calc_start_point_y = midPoint[1];
+  }
+  const calcStartPoint = [calc_start_point_x, calc_start_point_y];
+  const calcEndPoint = [calc_end_point_x, calc_end_point_y];
+  const totalAngle = getSlopes(calcStartPoint, calcEndPoint);
+  const votes = angleOrientationAt(totalAngle, options2.TOTAL_ANGLE_VOTE_POWER);
+  voteVertical += votes[0];
+  voteDiagonal += votes[1];
+  voteHorizontal += votes[2];
+  for (const fingerSlope of fingerSlopes) {
+    const fingerVotes = angleOrientationAt(fingerSlope, options2.SINGLE_ANGLE_VOTE_POWER);
+    voteVertical += fingerVotes[0];
+    voteDiagonal += fingerVotes[1];
+    voteHorizontal += fingerVotes[2];
+  }
+  let estimatedDirection;
+  if (voteVertical === Math.max(voteVertical, voteDiagonal, voteHorizontal)) {
+    estimatedDirection = estimateVerticalDirection(start_end_y_dist, start_mid_y_dist, mid_end_y_dist, max_dist_y);
+  } else if (voteHorizontal === Math.max(voteDiagonal, voteHorizontal)) {
+    estimatedDirection = estimateHorizontalDirection(start_end_x_dist, start_mid_x_dist, mid_end_x_dist, max_dist_x);
+  } else {
+    estimatedDirection = estimateDiagonalDirection(start_end_y_dist, start_mid_y_dist, mid_end_y_dist, max_dist_y, start_end_x_dist, start_mid_x_dist, mid_end_x_dist, max_dist_x);
+  }
+  return estimatedDirection;
+}
+function estimate(landmarks) {
+  const slopesXY = [];
+  const slopesYZ = [];
+  const fingerCurls = [];
+  const fingerDirections = [];
+  if (!landmarks)
+    return { curls: fingerCurls, directions: fingerDirections };
+  for (const finger of Finger.all) {
+    const points = Finger.getPoints(finger);
+    const slopeAtXY = [];
+    const slopeAtYZ = [];
+    for (const point2 of points) {
+      const point1 = landmarks[point2[0]];
+      const point22 = landmarks[point2[1]];
+      const slopes = getSlopes(point1, point22);
+      const slopeXY = slopes[0];
+      const slopeYZ = slopes[1];
+      slopeAtXY.push(slopeXY);
+      slopeAtYZ.push(slopeYZ);
+    }
+    slopesXY.push(slopeAtXY);
+    slopesYZ.push(slopeAtYZ);
+  }
+  for (const finger of Finger.all) {
+    const pointIndexAt = finger === Finger.thumb ? 1 : 0;
+    const fingerPointsAt = Finger.getPoints(finger);
+    const startPoint = landmarks[fingerPointsAt[pointIndexAt][0]];
+    const midPoint = landmarks[fingerPointsAt[pointIndexAt + 1][1]];
+    const endPoint = landmarks[fingerPointsAt[3][1]];
+    const fingerCurled = estimateFingerCurl(startPoint, midPoint, endPoint);
+    const fingerPosition = calculateFingerDirection(startPoint, midPoint, endPoint, slopesXY[finger].slice(pointIndexAt));
+    fingerCurls[finger] = fingerCurled;
+    fingerDirections[finger] = fingerPosition;
+  }
+  return { curls: fingerCurls, directions: fingerDirections };
+}
+function analyze(keypoints) {
+  if (!keypoints || keypoints.length === 0)
+    return null;
+  const estimatorRes = estimate(keypoints);
+  const landmarks = {};
+  for (const fingerIdx of Finger.all) {
+    landmarks[Finger.getName(fingerIdx)] = {
+      curl: FingerCurl.getName(estimatorRes.curls[fingerIdx]),
+      direction: FingerDirection.getName(estimatorRes.directions[fingerIdx])
+    };
+  }
+  return landmarks;
+}
+function match(keypoints) {
+  const poses = [];
+  if (!keypoints || keypoints.length === 0)
+    return poses;
+  const estimatorRes = estimate(keypoints);
+  for (const gesture2 of fingergesture_default) {
+    const confidence = gesture2.matchAgainst(estimatorRes.curls, estimatorRes.directions);
+    if (confidence >= minConfidence)
+      poses.push({ name: gesture2.name, confidence });
+  }
+  return poses;
+}
+
+// src/hand/handpose.ts
+var meshAnnotations2 = {
+  thumb: [1, 2, 3, 4],
+  index: [5, 6, 7, 8],
+  middle: [9, 10, 11, 12],
+  ring: [13, 14, 15, 16],
+  pinky: [17, 18, 19, 20],
+  palm: [0]
+};
+var handDetectorModel;
+var handPoseModel;
+var handPipeline;
+async function predict9(input, config3) {
+  const predictions = await handPipeline.estimateHands(input, config3);
+  if (!predictions)
+    return [];
+  const hands = [];
+  for (let i = 0; i < predictions.length; i++) {
+    const annotations2 = {};
+    if (predictions[i].landmarks) {
+      for (const key of Object.keys(meshAnnotations2)) {
+        annotations2[key] = meshAnnotations2[key].map((index2) => predictions[i].landmarks[index2]);
+      }
+    }
+    const keypoints = predictions[i].landmarks;
+    let box = [Number.MAX_SAFE_INTEGER, Number.MAX_SAFE_INTEGER, 0, 0];
+    let boxRaw = [0, 0, 0, 0];
+    if (keypoints && keypoints.length > 0) {
+      for (const pt of keypoints) {
+        if (pt[0] < box[0])
+          box[0] = pt[0];
+        if (pt[1] < box[1])
+          box[1] = pt[1];
+        if (pt[0] > box[2])
+          box[2] = pt[0];
+        if (pt[1] > box[3])
+          box[3] = pt[1];
+      }
+      box[2] -= box[0];
+      box[3] -= box[1];
+      boxRaw = [box[0] / (input.shape[2] || 0), box[1] / (input.shape[1] || 0), box[2] / (input.shape[2] || 0), box[3] / (input.shape[1] || 0)];
+    } else {
+      box = predictions[i].box ? [
+        Math.trunc(Math.max(0, predictions[i].box.topLeft[0])),
+        Math.trunc(Math.max(0, predictions[i].box.topLeft[1])),
+        Math.trunc(Math.min(input.shape[2] || 0, predictions[i].box.bottomRight[0]) - Math.max(0, predictions[i].box.topLeft[0])),
+        Math.trunc(Math.min(input.shape[1] || 0, predictions[i].box.bottomRight[1]) - Math.max(0, predictions[i].box.topLeft[1]))
+      ] : [0, 0, 0, 0];
+      boxRaw = [
+        predictions[i].box.topLeft[0] / (input.shape[2] || 0),
+        predictions[i].box.topLeft[1] / (input.shape[1] || 0),
+        (predictions[i].box.bottomRight[0] - predictions[i].box.topLeft[0]) / (input.shape[2] || 0),
+        (predictions[i].box.bottomRight[1] - predictions[i].box.topLeft[1]) / (input.shape[1] || 0)
+      ];
+    }
+    const landmarks = analyze(keypoints);
+    hands.push({
+      id: i,
+      score: Math.round(100 * predictions[i].confidence) / 100,
+      boxScore: Math.round(100 * predictions[i].boxConfidence) / 100,
+      fingerScore: Math.round(100 * predictions[i].fingerConfidence) / 100,
+      label: "hand",
+      box,
+      boxRaw,
+      keypoints,
+      annotations: annotations2,
+      landmarks
+    });
+  }
+  return hands;
+}
+async function load10(config3) {
+  var _a, _b;
+  if (env.initial) {
+    handDetectorModel = null;
+    handPoseModel = null;
+  }
+  if (!handDetectorModel || !handPoseModel) {
+    [handDetectorModel, handPoseModel] = await Promise.all([
+      config3.hand.enabled ? loadModel((_a = config3.hand.detector) == null ? void 0 : _a.modelPath) : null,
+      config3.hand.landmarks ? loadModel((_b = config3.hand.skeleton) == null ? void 0 : _b.modelPath) : null
+    ]);
+  } else {
+    if (config3.debug)
+      log("cached model:", handDetectorModel["modelUrl"]);
+    if (config3.debug)
+      log("cached model:", handPoseModel["modelUrl"]);
+  }
+  const handDetector = handDetectorModel ? new HandDetector(handDetectorModel) : void 0;
+  if (handDetector && handPoseModel)
+    handPipeline = new HandPipeline(handDetector, handPoseModel);
+  return [handDetectorModel, handPoseModel];
+}
+
+// src/hand/handtrack.ts
+var tf21 = __toESM(require_tfjs_esm());
+var models3 = [null, null];
+var modelOutputNodes = ["StatefulPartitionedCall/Postprocessor/Slice", "StatefulPartitionedCall/Postprocessor/ExpandDims_1"];
+var inputSize7 = [[0, 0], [0, 0]];
+var classes = ["hand", "fist", "pinch", "point", "face", "tip", "pinchtip"];
+var faceIndex = 4;
+var boxExpandFact = 1.6;
+var maxDetectorResolution = 512;
+var detectorExpandFact = 1.4;
+var skipped8 = Number.MAX_SAFE_INTEGER;
+var lastTime9 = 0;
+var outputSize = [0, 0];
+var cache4 = {
+  boxes: [],
+  hands: []
+};
+var fingerMap = {
+  thumb: [1, 2, 3, 4],
+  index: [5, 6, 7, 8],
+  middle: [9, 10, 11, 12],
+  ring: [13, 14, 15, 16],
+  pinky: [17, 18, 19, 20],
+  base: [0],
+  palm: [0, 17, 13, 9, 5, 1, 0]
+};
+async function loadDetect2(config3) {
+  var _a;
+  if (env.initial)
+    models3[0] = null;
+  if (!models3[0]) {
+    fakeOps(["tensorlistreserve", "enter", "tensorlistfromtensor", "merge", "loopcond", "switch", "exit", "tensorliststack", "nextiteration", "tensorlistsetitem", "tensorlistgetitem", "reciprocal", "shape", "split", "where"], config3);
+    models3[0] = await loadModel((_a = config3.hand.detector) == null ? void 0 : _a.modelPath);
+    const inputs = models3[0]["executor"] ? Object.values(models3[0].modelSignature["inputs"]) : void 0;
+    inputSize7[0][0] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[1].size) : 0;
+    inputSize7[0][1] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 0;
+  } else if (config3.debug)
+    log("cached model:", models3[0]["modelUrl"]);
+  return models3[0];
+}
+async function loadSkeleton(config3) {
+  var _a;
+  if (env.initial)
+    models3[1] = null;
+  if (!models3[1]) {
+    models3[1] = await loadModel((_a = config3.hand.skeleton) == null ? void 0 : _a.modelPath);
+    const inputs = models3[1]["executor"] ? Object.values(models3[1].modelSignature["inputs"]) : void 0;
+    inputSize7[1][0] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[1].size) : 0;
+    inputSize7[1][1] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 0;
+  } else if (config3.debug)
+    log("cached model:", models3[1]["modelUrl"]);
+  return models3[1];
+}
+async function detectHands(input, config3) {
+  const hands = [];
+  if (!input || !models3[0])
+    return hands;
+  const t2 = {};
+  const ratio2 = (input.shape[2] || 1) / (input.shape[1] || 1);
+  const height = Math.min(Math.round((input.shape[1] || 0) / 8) * 8, maxDetectorResolution);
+  const width = Math.round(height * ratio2 / 8) * 8;
+  t2.resize = tf21.image.resizeBilinear(input, [height, width]);
+  t2.cast = tf21.cast(t2.resize, "int32");
+  [t2.rawScores, t2.rawBoxes] = await models3[0].executeAsync(t2.cast, modelOutputNodes);
+  t2.boxes = tf21.squeeze(t2.rawBoxes, [0, 2]);
+  t2.scores = tf21.squeeze(t2.rawScores, [0]);
+  const classScores = tf21.unstack(t2.scores, 1);
+  tf21.dispose(classScores[faceIndex]);
+  classScores.splice(faceIndex, 1);
+  t2.filtered = tf21.stack(classScores, 1);
+  tf21.dispose(classScores);
+  t2.max = tf21.max(t2.filtered, 1);
+  t2.argmax = tf21.argMax(t2.filtered, 1);
+  let id = 0;
+  t2.nms = await tf21.image.nonMaxSuppressionAsync(t2.boxes, t2.max, (config3.hand.maxDetected || 0) + 1, config3.hand.iouThreshold || 0, config3.hand.minConfidence || 1);
+  const nms = await t2.nms.data();
+  const scores = await t2.max.data();
+  const classNum = await t2.argmax.data();
+  for (const nmsIndex of Array.from(nms)) {
+    const boxSlice = tf21.slice(t2.boxes, nmsIndex, 1);
+    const boxYX = await boxSlice.data();
+    tf21.dispose(boxSlice);
+    const boxData = [boxYX[1], boxYX[0], boxYX[3] - boxYX[1], boxYX[2] - boxYX[0]];
+    const boxRaw = scale(boxData, detectorExpandFact);
+    const boxFull = [Math.trunc(boxData[0] * outputSize[0]), Math.trunc(boxData[1] * outputSize[1]), Math.trunc(boxData[2] * outputSize[0]), Math.trunc(boxData[3] * outputSize[1])];
+    const score = scores[nmsIndex];
+    const label = classes[classNum[nmsIndex]];
+    const hand3 = { id: id++, score, box: boxFull, boxRaw, label };
+    hands.push(hand3);
+  }
+  Object.keys(t2).forEach((tensor6) => tf21.dispose(t2[tensor6]));
+  hands.sort((a, b) => b.score - a.score);
+  if (hands.length > (config3.hand.maxDetected || 1))
+    hands.length = config3.hand.maxDetected || 1;
+  return hands;
+}
+async function detectFingers(input, h, config3) {
+  const hand3 = {
+    id: h.id,
+    score: Math.round(100 * h.score) / 100,
+    boxScore: Math.round(100 * h.score) / 100,
+    fingerScore: 0,
+    box: h.box,
+    boxRaw: h.boxRaw,
+    label: h.label,
+    keypoints: [],
+    landmarks: {},
+    annotations: {}
+  };
+  if (input && models3[1] && config3.hand.landmarks && h.score > (config3.hand.minConfidence || 0)) {
+    const t2 = {};
+    const boxCrop = [h.boxRaw[1], h.boxRaw[0], h.boxRaw[3] + h.boxRaw[1], h.boxRaw[2] + h.boxRaw[0]];
+    t2.crop = tf21.image.cropAndResize(input, [boxCrop], [0], [inputSize7[1][0], inputSize7[1][1]], "bilinear");
+    t2.div = tf21.div(t2.crop, constants.tf255);
+    [t2.score, t2.keypoints] = models3[1].execute(t2.div, ["Identity_1", "Identity"]);
+    const rawScore = (await t2.score.data())[0];
+    const score = (100 - Math.trunc(100 / (1 + Math.exp(rawScore)))) / 100;
+    if (score >= (config3.hand.minConfidence || 0)) {
+      hand3.fingerScore = score;
+      t2.reshaped = tf21.reshape(t2.keypoints, [-1, 3]);
+      const coordsData = await t2.reshaped.array();
+      const coordsRaw = coordsData.map((kpt4) => [kpt4[0] / inputSize7[1][1], kpt4[1] / inputSize7[1][0], kpt4[2] || 0]);
+      const coordsNorm = coordsRaw.map((kpt4) => [kpt4[0] * h.boxRaw[2], kpt4[1] * h.boxRaw[3], kpt4[2] || 0]);
+      hand3.keypoints = coordsNorm.map((kpt4) => [outputSize[0] * (kpt4[0] + h.boxRaw[0]), outputSize[1] * (kpt4[1] + h.boxRaw[1]), kpt4[2] || 0]);
+      hand3.landmarks = analyze(hand3.keypoints);
+      for (const key of Object.keys(fingerMap)) {
+        hand3.annotations[key] = fingerMap[key].map((index2) => hand3.landmarks && hand3.keypoints[index2] ? hand3.keypoints[index2] : null);
+      }
+    }
+    Object.keys(t2).forEach((tensor6) => tf21.dispose(t2[tensor6]));
+  }
+  return hand3;
+}
+async function predict10(input, config3) {
+  var _a, _b;
+  if (!((_a = models3[0]) == null ? void 0 : _a["executor"]) || !((_b = models3[1]) == null ? void 0 : _b["executor"]) || !models3[0].inputs[0].shape || !models3[1].inputs[0].shape)
+    return [];
+  outputSize = [input.shape[2] || 0, input.shape[1] || 0];
+  skipped8++;
+  const skipTime = (config3.hand.skipTime || 0) > now() - lastTime9;
+  const skipFrame = skipped8 < (config3.hand.skipFrames || 0);
+  if (config3.skipAllowed && skipTime && skipFrame) {
+    return cache4.hands;
+  }
+  return new Promise(async (resolve) => {
+    const skipTimeExtended = 3 * (config3.hand.skipTime || 0) > now() - lastTime9;
+    const skipFrameExtended = skipped8 < 3 * (config3.hand.skipFrames || 0);
+    if (config3.skipAllowed && cache4.hands.length === config3.hand.maxDetected) {
+      cache4.hands = await Promise.all(cache4.boxes.map((handBox) => detectFingers(input, handBox, config3)));
+    } else if (config3.skipAllowed && skipTimeExtended && skipFrameExtended && cache4.hands.length > 0) {
+      cache4.hands = await Promise.all(cache4.boxes.map((handBox) => detectFingers(input, handBox, config3)));
+    } else {
+      cache4.boxes = await detectHands(input, config3);
+      lastTime9 = now();
+      cache4.hands = await Promise.all(cache4.boxes.map((handBox) => detectFingers(input, handBox, config3)));
+      skipped8 = 0;
+    }
+    const oldCache = [...cache4.boxes];
+    cache4.boxes.length = 0;
+    if (config3.cacheSensitivity > 0) {
+      for (let i = 0; i < cache4.hands.length; i++) {
+        const boxKpt = square(cache4.hands[i].keypoints, outputSize);
+        if (boxKpt.box[2] / (input.shape[2] || 1) > 0.05 && boxKpt.box[3] / (input.shape[1] || 1) > 0.05 && cache4.hands[i].fingerScore && cache4.hands[i].fingerScore > (config3.hand.minConfidence || 0)) {
+          const boxScale = scale(boxKpt.box, boxExpandFact);
+          const boxScaleRaw = scale(boxKpt.boxRaw, boxExpandFact);
+          cache4.boxes.push({ ...oldCache[i], box: boxScale, boxRaw: boxScaleRaw });
+        }
+      }
+    }
+    for (let i = 0; i < cache4.hands.length; i++) {
+      const bbox = calc(cache4.hands[i].keypoints, outputSize);
+      cache4.hands[i].box = bbox.box;
+      cache4.hands[i].boxRaw = bbox.boxRaw;
+    }
+    resolve(cache4.hands);
+  });
+}
+
+// src/face/insightface.ts
+var tf22 = __toESM(require_tfjs_esm());
+var model10;
+var last6 = [];
+var lastCount5 = 0;
+var lastTime10 = 0;
+var skipped9 = Number.MAX_SAFE_INTEGER;
+async function load11(config3) {
+  if (env.initial)
+    model10 = null;
+  if (!model10)
+    model10 = await loadModel(config3.face["insightface"].modelPath);
+  else if (config3.debug)
+    log("cached model:", model10["modelUrl"]);
+  return model10;
+}
+async function predict11(input, config3, idx, count2) {
+  var _a, _b;
+  if (!(model10 == null ? void 0 : model10["executor"]))
+    return [];
+  const skipFrame = skipped9 < (((_a = config3.face["insightface"]) == null ? void 0 : _a.skipFrames) || 0);
+  const skipTime = (((_b = config3.face["insightface"]) == null ? void 0 : _b.skipTime) || 0) > now() - lastTime10;
+  if (config3.skipAllowed && skipTime && skipFrame && lastCount5 === count2 && last6[idx]) {
+    skipped9++;
+    return last6[idx];
+  }
+  return new Promise(async (resolve) => {
+    var _a2;
+    let data = [];
+    if (((_a2 = config3.face["insightface"]) == null ? void 0 : _a2.enabled) && (model10 == null ? void 0 : model10.inputs[0].shape)) {
+      const t2 = {};
+      t2.crop = tf22.image.resizeBilinear(input, [model10.inputs[0].shape[2], model10.inputs[0].shape[1]], false);
+      t2.data = model10.execute(t2.crop);
+      const output = await t2.data.data();
+      data = Array.from(output);
+      Object.keys(t2).forEach((tensor6) => tf22.dispose(t2[tensor6]));
+    }
+    last6[idx] = data;
+    lastCount5 = count2;
+    lastTime10 = now();
+    resolve(data);
+  });
+}
+
+// src/face/liveness.ts
+var tf23 = __toESM(require_tfjs_esm());
+var model11;
+var cached2 = [];
+var skipped10 = Number.MAX_SAFE_INTEGER;
+var lastCount6 = 0;
+var lastTime11 = 0;
+async function load12(config3) {
+  var _a;
+  if (env.initial)
+    model11 = null;
+  if (!model11)
+    model11 = await loadModel((_a = config3.face.liveness) == null ? void 0 : _a.modelPath);
+  else if (config3.debug)
+    log("cached model:", model11["modelUrl"]);
+  return model11;
+}
+async function predict12(image27, config3, idx, count2) {
+  var _a, _b;
+  if (!(model11 == null ? void 0 : model11["executor"]))
+    return 0;
+  const skipTime = (((_a = config3.face.liveness) == null ? void 0 : _a.skipTime) || 0) > now() - lastTime11;
+  const skipFrame = skipped10 < (((_b = config3.face.liveness) == null ? void 0 : _b.skipFrames) || 0);
+  if (config3.skipAllowed && skipTime && skipFrame && lastCount6 === count2 && cached2[idx]) {
+    skipped10++;
+    return cached2[idx];
+  }
+  skipped10 = 0;
+  return new Promise(async (resolve) => {
+    const resize = tf23.image.resizeBilinear(image27, [(model11 == null ? void 0 : model11.inputs[0].shape) ? model11.inputs[0].shape[2] : 0, (model11 == null ? void 0 : model11.inputs[0].shape) ? model11.inputs[0].shape[1] : 0], false);
+    const res = model11 == null ? void 0 : model11.execute(resize);
+    const num = (await res.data())[0];
+    cached2[idx] = Math.round(100 * num) / 100;
+    lastCount6 = count2;
+    lastTime11 = now();
+    tf23.dispose([resize, res]);
+    resolve(cached2[idx]);
+  });
+}
+
+// src/segmentation/meet.ts
+var tf24 = __toESM(require_tfjs_esm());
+var model12;
+async function load13(config3) {
+  if (!model12 || env.initial)
+    model12 = await loadModel(config3.segmentation.modelPath);
+  else if (config3.debug)
+    log("cached model:", model12["modelUrl"]);
+  return model12;
+}
+async function predict13(input, config3) {
+  var _a;
+  if (!model12)
+    model12 = await load13(config3);
+  if (!(model12 == null ? void 0 : model12["executor"]) || !((_a = model12 == null ? void 0 : model12.inputs) == null ? void 0 : _a[0].shape))
+    return null;
+  const t2 = {};
+  t2.resize = tf24.image.resizeBilinear(input, [model12.inputs[0].shape ? model12.inputs[0].shape[1] : 0, model12.inputs[0].shape ? model12.inputs[0].shape[2] : 0], false);
+  t2.norm = tf24.div(t2.resize, constants.tf255);
+  t2.res = model12.execute(t2.norm);
+  t2.squeeze = tf24.squeeze(t2.res, 0);
+  [t2.bgRaw, t2.fgRaw] = tf24.unstack(t2.squeeze, 2);
+  t2.fg = tf24.softmax(t2.fgRaw);
+  t2.mul = tf24.mul(t2.fg, constants.tf255);
+  t2.expand = tf24.expandDims(t2.mul, 2);
+  t2.output = tf24.image.resizeBilinear(t2.expand, [input.shape[1], input.shape[2]]);
+  let rgba;
+  switch (config3.segmentation.mode || "default") {
+    case "default":
+      t2.input = tf24.squeeze(input);
+      t2.concat = tf24.concat([t2.input, t2.output], -1);
+      rgba = tf24.cast(t2.concat, "int32");
+      break;
+    case "alpha":
+      rgba = tf24.cast(t2.output, "int32");
+      break;
+    default:
+      rgba = tf24.tensor(0);
+  }
+  Object.keys(t2).forEach((tensor6) => tf24.dispose(t2[tensor6]));
+  return rgba;
+}
+
+// src/face/mobilefacenet.ts
+var tf25 = __toESM(require_tfjs_esm());
+var model13;
+var last7 = [];
+var lastCount7 = 0;
+var lastTime12 = 0;
+var skipped11 = Number.MAX_SAFE_INTEGER;
+async function load14(config3) {
+  var _a;
+  if (env.initial)
+    model13 = null;
+  if (!model13)
+    model13 = await loadModel((_a = config3.face["mobilefacenet"]) == null ? void 0 : _a.modelPath);
+  else if (config3.debug)
+    log("cached model:", model13["modelUrl"]);
+  return model13;
+}
+async function predict14(input, config3, idx, count2) {
+  var _a, _b;
+  if (!(model13 == null ? void 0 : model13["executor"]))
+    return [];
+  const skipFrame = skipped11 < (((_a = config3.face["mobilefacenet"]) == null ? void 0 : _a.skipFrames) || 0);
+  const skipTime = (((_b = config3.face["mobilefacenet"]) == null ? void 0 : _b.skipTime) || 0) > now() - lastTime12;
+  if (config3.skipAllowed && skipTime && skipFrame && lastCount7 === count2 && last7[idx]) {
+    skipped11++;
+    return last7[idx];
+  }
+  return new Promise(async (resolve) => {
+    var _a2;
+    let data = [];
+    if (((_a2 = config3.face["mobilefacenet"]) == null ? void 0 : _a2.enabled) && (model13 == null ? void 0 : model13.inputs[0].shape)) {
+      const t2 = {};
+      t2.crop = tf25.image.resizeBilinear(input, [model13.inputs[0].shape[2], model13.inputs[0].shape[1]], false);
+      t2.data = model13.execute(t2.crop);
+      const output = await t2.data.data();
+      data = Array.from(output);
+      Object.keys(t2).forEach((tensor6) => tf25.dispose(t2[tensor6]));
+    }
+    last7[idx] = data;
+    lastCount7 = count2;
+    lastTime12 = now();
+    resolve(data);
+  });
+}
+
+// src/body/movenet.ts
+var tf27 = __toESM(require_tfjs_esm());
+
+// src/body/movenetcoords.ts
+var movenetcoords_exports = {};
+__export(movenetcoords_exports, {
+  connected: () => connected3,
+  horizontal: () => horizontal,
+  kpt: () => kpt3,
+  relative: () => relative,
+  vertical: () => vertical
+});
+var kpt3 = [
+  "nose",
+  "leftEye",
+  "rightEye",
+  "leftEar",
+  "rightEar",
+  "leftShoulder",
+  "rightShoulder",
+  "leftElbow",
+  "rightElbow",
+  "leftWrist",
+  "rightWrist",
+  "leftHip",
+  "rightHip",
+  "leftKnee",
+  "rightKnee",
+  "leftAnkle",
+  "rightAnkle"
+];
+var horizontal = [
+  ["leftEye", "rightEye"],
+  ["leftEar", "rightEar"],
+  ["leftShoulder", "rightShoulder"],
+  ["leftElbow", "rightElbow"],
+  ["leftWrist", "rightWrist"],
+  ["leftHip", "rightHip"],
+  ["leftKnee", "rightKnee"],
+  ["leftAnkle", "rightAnkle"]
+];
+var vertical = [
+  ["leftKnee", "leftShoulder"],
+  ["rightKnee", "rightShoulder"],
+  ["leftAnkle", "leftKnee"],
+  ["rightAnkle", "rightKnee"]
+];
+var relative = [
+  [["leftHip", "rightHip"], ["leftShoulder", "rightShoulder"]],
+  [["leftElbow", "rightElbow"], ["leftShoulder", "rightShoulder"]]
+];
+var connected3 = {
+  leftLeg: ["leftHip", "leftKnee", "leftAnkle"],
+  rightLeg: ["rightHip", "rightKnee", "rightAnkle"],
+  torso: ["leftShoulder", "rightShoulder", "rightHip", "leftHip", "leftShoulder"],
+  leftArm: ["leftShoulder", "leftElbow", "leftWrist"],
+  rightArm: ["rightShoulder", "rightElbow", "rightWrist"],
+  head: []
+};
+
+// src/body/movenetfix.ts
+var tf26 = __toESM(require_tfjs_esm());
+var maxJitter = 5e-3;
+var cache5 = {
+  keypoints: [],
+  padding: [[0, 0], [0, 0], [0, 0], [0, 0]]
+};
+function bodyParts(body4) {
+  for (const pair of horizontal) {
+    const left = body4.keypoints.findIndex((kp) => kp.part === pair[0]);
+    const right = body4.keypoints.findIndex((kp) => kp.part === pair[1]);
+    if (body4.keypoints[left] && body4.keypoints[right]) {
+      if (body4.keypoints[left].position[0] < body4.keypoints[right].position[0]) {
+        const tmp = body4.keypoints[left];
+        body4.keypoints[left] = body4.keypoints[right];
+        body4.keypoints[right] = tmp;
+      }
+    }
+  }
+  for (const pair of vertical) {
+    const lower = body4.keypoints.findIndex((kp) => kp && kp.part === pair[0]);
+    const higher = body4.keypoints.findIndex((kp) => kp && kp.part === pair[1]);
+    if (body4.keypoints[lower] && body4.keypoints[higher]) {
+      if (body4.keypoints[lower].position[1] < body4.keypoints[higher].position[1]) {
+        body4.keypoints.splice(lower, 1);
+      }
+    }
+  }
+  for (const [pair, compare2] of relative) {
+    const left = body4.keypoints.findIndex((kp) => kp && kp.part === pair[0]);
+    const right = body4.keypoints.findIndex((kp) => kp && kp.part === pair[1]);
+    const leftTo = body4.keypoints.findIndex((kp) => kp && kp.part === compare2[0]);
+    const rightTo = body4.keypoints.findIndex((kp) => kp && kp.part === compare2[1]);
+    if (!body4.keypoints[leftTo] || !body4.keypoints[rightTo])
+      continue;
+    const distanceLeft = body4.keypoints[left] ? [
+      Math.abs(body4.keypoints[leftTo].position[0] - body4.keypoints[left].position[0]),
+      Math.abs(body4.keypoints[rightTo].position[0] - body4.keypoints[left].position[0])
+    ] : [0, 0];
+    const distanceRight = body4.keypoints[right] ? [
+      Math.abs(body4.keypoints[rightTo].position[0] - body4.keypoints[right].position[0]),
+      Math.abs(body4.keypoints[leftTo].position[0] - body4.keypoints[right].position[0])
+    ] : [0, 0];
+    if (distanceLeft[0] > distanceLeft[1] || distanceRight[0] > distanceRight[1]) {
+      const tmp = body4.keypoints[left];
+      body4.keypoints[left] = body4.keypoints[right];
+      body4.keypoints[right] = tmp;
+    }
+  }
+}
+function jitter(keypoints) {
+  for (let i = 0; i < keypoints.length; i++) {
+    if (keypoints[i] && cache5.keypoints[i]) {
+      const diff = [Math.abs(keypoints[i].positionRaw[0] - cache5.keypoints[i].positionRaw[0]), Math.abs(keypoints[i].positionRaw[1] - cache5.keypoints[i].positionRaw[1])];
+      if (diff[0] < maxJitter && diff[1] < maxJitter) {
+        keypoints[i] = cache5.keypoints[i];
+      } else {
+        cache5.keypoints[i] = keypoints[i];
+      }
+    } else {
+      cache5.keypoints[i] = keypoints[i];
+    }
+  }
+  return keypoints;
+}
+function padInput(input, inputSize10) {
+  var _a, _b;
+  const t2 = {};
+  if (!((_a = input == null ? void 0 : input.shape) == null ? void 0 : _a[1]) || !((_b = input == null ? void 0 : input.shape) == null ? void 0 : _b[2]))
+    return input;
+  cache5.padding = [
+    [0, 0],
+    [input.shape[2] > input.shape[1] ? Math.trunc((input.shape[2] - input.shape[1]) / 2) : 0, input.shape[2] > input.shape[1] ? Math.trunc((input.shape[2] - input.shape[1]) / 2) : 0],
+    [input.shape[1] > input.shape[2] ? Math.trunc((input.shape[1] - input.shape[2]) / 2) : 0, input.shape[1] > input.shape[2] ? Math.trunc((input.shape[1] - input.shape[2]) / 2) : 0],
+    [0, 0]
+  ];
+  t2.pad = tf26.pad(input, cache5.padding);
+  t2.resize = tf26.image.resizeBilinear(t2.pad, [inputSize10, inputSize10]);
+  const final = tf26.cast(t2.resize, "int32");
+  Object.keys(t2).forEach((tensor6) => tf26.dispose(t2[tensor6]));
+  return final;
+}
+function rescaleBody(body4, outputSize2) {
+  body4.keypoints = body4.keypoints.filter((kpt4) => kpt4 == null ? void 0 : kpt4.position);
+  for (const kpt4 of body4.keypoints) {
+    kpt4.position = [
+      kpt4.position[0] * (outputSize2[0] + cache5.padding[2][0] + cache5.padding[2][1]) / outputSize2[0] - cache5.padding[2][0],
+      kpt4.position[1] * (outputSize2[1] + cache5.padding[1][0] + cache5.padding[1][1]) / outputSize2[1] - cache5.padding[1][0]
+    ];
+    kpt4.positionRaw = [
+      kpt4.position[0] / outputSize2[0],
+      kpt4.position[1] / outputSize2[1]
+    ];
+  }
+  const rescaledBoxes = calc(body4.keypoints.map((pt) => pt.position), outputSize2);
+  body4.box = rescaledBoxes.box;
+  body4.boxRaw = rescaledBoxes.boxRaw;
+  return body4;
+}
+
+// src/body/movenet.ts
+var model14;
+var inputSize8 = 0;
+var skipped12 = Number.MAX_SAFE_INTEGER;
+var cache6 = {
+  boxes: [],
+  bodies: [],
+  last: 0
+};
+async function load15(config3) {
+  var _a;
+  if (env.initial)
+    model14 = null;
+  if (!model14) {
+    fakeOps(["size"], config3);
+    model14 = await loadModel(config3.body.modelPath);
+  } else if (config3.debug)
+    log("cached model:", model14["modelUrl"]);
+  inputSize8 = (model14 == null ? void 0 : model14["executor"]) && ((_a = model14 == null ? void 0 : model14.inputs) == null ? void 0 : _a[0].shape) ? model14.inputs[0].shape[2] : 0;
+  if (inputSize8 < 64)
+    inputSize8 = 256;
+  return model14;
+}
+function parseSinglePose(res, config3, image27) {
+  const kpt4 = res[0][0];
+  const keypoints = [];
+  let score = 0;
+  for (let id = 0; id < kpt4.length; id++) {
+    score = kpt4[id][2];
+    if (score > config3.body.minConfidence) {
+      const positionRaw = [kpt4[id][1], kpt4[id][0]];
+      keypoints.push({
+        score: Math.round(100 * score) / 100,
+        part: kpt3[id],
+        positionRaw,
+        position: [
+          Math.round((image27.shape[2] || 0) * positionRaw[0]),
+          Math.round((image27.shape[1] || 0) * positionRaw[1])
+        ]
+      });
+    }
+  }
+  score = keypoints.reduce((prev, curr) => curr.score > prev ? curr.score : prev, 0);
+  const bodies = [];
+  const newBox = calc(keypoints.map((pt) => pt.position), [image27.shape[2], image27.shape[1]]);
+  const annotations2 = {};
+  for (const [name, indexes] of Object.entries(connected3)) {
+    const pt = [];
+    for (let i = 0; i < indexes.length - 1; i++) {
+      const pt0 = keypoints.find((kp) => kp.part === indexes[i]);
+      const pt1 = keypoints.find((kp) => kp.part === indexes[i + 1]);
+      if (pt0 && pt1 && pt0.score > (config3.body.minConfidence || 0) && pt1.score > (config3.body.minConfidence || 0))
+        pt.push([pt0.position, pt1.position]);
+    }
+    annotations2[name] = pt;
+  }
+  const body4 = { id: 0, score, box: newBox.box, boxRaw: newBox.boxRaw, keypoints, annotations: annotations2 };
+  bodyParts(body4);
+  bodies.push(body4);
+  return bodies;
+}
+function parseMultiPose(res, config3, image27) {
+  const bodies = [];
+  for (let id = 0; id < res[0].length; id++) {
+    const kpt4 = res[0][id];
+    const totalScore = Math.round(100 * kpt4[51 + 4]) / 100;
+    if (totalScore > config3.body.minConfidence) {
+      const keypoints = [];
+      for (let i = 0; i < 17; i++) {
+        const score = kpt4[3 * i + 2];
+        if (score > config3.body.minConfidence) {
+          const positionRaw = [kpt4[3 * i + 1], kpt4[3 * i + 0]];
+          keypoints.push({
+            part: kpt3[i],
+            score: Math.round(100 * score) / 100,
+            positionRaw,
+            position: [Math.round((image27.shape[2] || 0) * positionRaw[0]), Math.round((image27.shape[1] || 0) * positionRaw[1])]
+          });
+        }
+      }
+      const newBox = calc(keypoints.map((pt) => pt.position), [image27.shape[2], image27.shape[1]]);
+      const annotations2 = {};
+      for (const [name, indexes] of Object.entries(connected3)) {
+        const pt = [];
+        for (let i = 0; i < indexes.length - 1; i++) {
+          const pt0 = keypoints.find((kp) => kp.part === indexes[i]);
+          const pt1 = keypoints.find((kp) => kp.part === indexes[i + 1]);
+          if (pt0 && pt1 && pt0.score > (config3.body.minConfidence || 0) && pt1.score > (config3.body.minConfidence || 0))
+            pt.push([pt0.position, pt1.position]);
+        }
+        annotations2[name] = pt;
+      }
+      const body4 = { id, score: totalScore, box: newBox.box, boxRaw: newBox.boxRaw, keypoints: [...keypoints], annotations: annotations2 };
+      bodyParts(body4);
+      bodies.push(body4);
+    }
+  }
+  bodies.sort((a, b) => b.score - a.score);
+  if (bodies.length > config3.body.maxDetected)
+    bodies.length = config3.body.maxDetected;
+  return bodies;
+}
+async function predict15(input, config3) {
+  var _a;
+  if (!(model14 == null ? void 0 : model14["executor"]) || !((_a = model14 == null ? void 0 : model14.inputs) == null ? void 0 : _a[0].shape))
+    return [];
+  if (!config3.skipAllowed)
+    cache6.boxes.length = 0;
+  skipped12++;
+  const skipTime = (config3.body.skipTime || 0) > now() - cache6.last;
+  const skipFrame = skipped12 < (config3.body.skipFrames || 0);
+  if (config3.skipAllowed && skipTime && skipFrame) {
+    return cache6.bodies;
+  }
+  return new Promise(async (resolve) => {
+    const t2 = {};
+    skipped12 = 0;
+    t2.input = padInput(input, inputSize8);
+    t2.res = model14 == null ? void 0 : model14.execute(t2.input);
+    cache6.last = now();
+    const res = await t2.res.array();
+    cache6.bodies = t2.res.shape[2] === 17 ? parseSinglePose(res, config3, input) : parseMultiPose(res, config3, input);
+    for (const body4 of cache6.bodies) {
+      rescaleBody(body4, [input.shape[2] || 1, input.shape[1] || 1]);
+      jitter(body4.keypoints);
+    }
+    Object.keys(t2).forEach((tensor6) => tf27.dispose(t2[tensor6]));
+    resolve(cache6.bodies);
+  });
+}
+
+// src/object/nanodet.ts
+var tf28 = __toESM(require_tfjs_esm());
+var model15;
+var last8 = [];
+var lastTime13 = 0;
+var skipped13 = Number.MAX_SAFE_INTEGER;
+var inputSize9 = 0;
+var scaleBox = 2.5;
+async function load16(config3) {
+  if (!model15 || env.initial) {
+    model15 = await loadModel(config3.object.modelPath);
+    const inputs = (model15 == null ? void 0 : model15["executor"]) ? Object.values(model15.modelSignature["inputs"]) : void 0;
+    inputSize9 = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 416;
+  } else if (config3.debug)
+    log("cached model:", model15["modelUrl"]);
+  return model15;
+}
+async function process4(res, outputShape, config3) {
+  let id = 0;
+  let results = [];
+  const size2 = inputSize9;
+  for (const strideSize of [1, 2, 4]) {
+    const baseSize = strideSize * 13;
+    const scoresT = tf28.squeeze(res.find((a) => a.shape[1] === baseSize ** 2 && (a.shape[2] || 0) === labels.length));
+    const scores = await scoresT.array();
+    const featuresT = tf28.squeeze(res.find((a) => a.shape[1] === baseSize ** 2 && (a.shape[2] || 0) < labels.length));
+    const boxesMaxT = featuresT.reshape([-1, 4, featuresT.shape[1] / 4]);
+    const boxIdxT = boxesMaxT.argMax(2);
+    const boxIdx = await boxIdxT.array();
+    for (let i = 0; i < scoresT.shape[0]; i++) {
+      for (let j = 0; j < scoresT.shape[1]; j++) {
+        const score = scores[i][j];
+        if (score > (config3.object.minConfidence || 0) && j !== 61) {
+          const cx = (0.5 + Math.trunc(i % baseSize)) / baseSize;
+          const cy = (0.5 + Math.trunc(i / baseSize)) / baseSize;
+          const boxOffset = boxIdx[i].map((a) => a * (baseSize / strideSize / size2));
+          const [x, y] = [
+            cx - scaleBox / strideSize * boxOffset[0],
+            cy - scaleBox / strideSize * boxOffset[1]
+          ];
+          const [w, h] = [
+            cx + scaleBox / strideSize * boxOffset[2] - x,
+            cy + scaleBox / strideSize * boxOffset[3] - y
+          ];
+          let boxRaw = [x, y, w, h];
+          boxRaw = boxRaw.map((a) => Math.max(0, Math.min(a, 1)));
+          const box = [
+            boxRaw[0] * outputShape[0],
+            boxRaw[1] * outputShape[1],
+            boxRaw[2] * outputShape[0],
+            boxRaw[3] * outputShape[1]
+          ];
+          const result = {
+            id: id++,
+            score: Math.round(100 * score) / 100,
+            class: j + 1,
+            label: labels[j].label,
+            box: box.map((a) => Math.trunc(a)),
+            boxRaw
+          };
+          results.push(result);
+        }
+      }
+    }
+    tf28.dispose([scoresT, featuresT, boxesMaxT, boxIdxT]);
+  }
+  const nmsBoxes = results.map((a) => [a.boxRaw[1], a.boxRaw[0], a.boxRaw[3], a.boxRaw[2]]);
+  const nmsScores = results.map((a) => a.score);
+  let nmsIdx = [];
+  if (nmsBoxes && nmsBoxes.length > 0) {
+    const nms = await tf28.image.nonMaxSuppressionAsync(nmsBoxes, nmsScores, config3.object.maxDetected, config3.object.iouThreshold, config3.object.minConfidence);
+    nmsIdx = await nms.data();
+    tf28.dispose(nms);
+  }
+  results = results.filter((_val, idx) => nmsIdx.includes(idx)).sort((a, b) => b.score - a.score);
+  return results;
+}
+async function predict16(image27, config3) {
+  if (!(model15 == null ? void 0 : model15["executor"]))
+    return [];
+  const skipTime = (config3.object.skipTime || 0) > now() - lastTime13;
+  const skipFrame = skipped13 < (config3.object.skipFrames || 0);
+  if (config3.skipAllowed && skipTime && skipFrame && last8.length > 0) {
+    skipped13++;
+    return last8;
+  }
+  skipped13 = 0;
+  if (!env.kernels.includes("mod") || !env.kernels.includes("sparsetodense"))
+    return last8;
+  return new Promise(async (resolve) => {
+    const outputSize2 = [image27.shape[2] || 0, image27.shape[1] || 0];
+    const resizeT = tf28.image.resizeBilinear(image27, [inputSize9, inputSize9], false);
+    const normT = tf28.div(resizeT, constants.tf255);
+    const transposeT = tf28.transpose(normT, [0, 3, 1, 2]);
+    let objectT;
+    if (config3.object.enabled)
+      objectT = model15.execute(transposeT);
+    lastTime13 = now();
+    const obj = await process4(objectT, outputSize2, config3);
+    last8 = obj;
+    tf28.dispose([resizeT, normT, transposeT, ...objectT]);
+    resolve(obj);
+  });
+}
+
+// src/body/posenet.ts
+var tf29 = __toESM(require_tfjs_esm());
+
+// src/body/posenetutils.ts
+var partNames = [
+  "nose",
+  "leftEye",
+  "rightEye",
+  "leftEar",
+  "rightEar",
+  "leftShoulder",
+  "rightShoulder",
+  "leftElbow",
+  "rightElbow",
+  "leftWrist",
+  "rightWrist",
+  "leftHip",
+  "rightHip",
+  "leftKnee",
+  "rightKnee",
+  "leftAnkle",
+  "rightAnkle"
+];
+var count = partNames.length;
+var partIds = partNames.reduce((result, jointName, i) => {
+  result[jointName] = i;
+  return result;
+}, {});
+var connectedPartNames = [
+  ["leftHip", "leftShoulder"],
+  ["leftElbow", "leftShoulder"],
+  ["leftElbow", "leftWrist"],
+  ["leftHip", "leftKnee"],
+  ["leftKnee", "leftAnkle"],
+  ["rightHip", "rightShoulder"],
+  ["rightElbow", "rightShoulder"],
+  ["rightElbow", "rightWrist"],
+  ["rightHip", "rightKnee"],
+  ["rightKnee", "rightAnkle"],
+  ["leftShoulder", "rightShoulder"],
+  ["leftHip", "rightHip"]
+];
+var connectedPartIndices = connectedPartNames.map(([jointNameA, jointNameB]) => [partIds[jointNameA], partIds[jointNameB]]);
+var poseChain = [
+  ["nose", "leftEye"],
+  ["leftEye", "leftEar"],
+  ["nose", "rightEye"],
+  ["rightEye", "rightEar"],
+  ["nose", "leftShoulder"],
+  ["leftShoulder", "leftElbow"],
+  ["leftElbow", "leftWrist"],
+  ["leftShoulder", "leftHip"],
+  ["leftHip", "leftKnee"],
+  ["leftKnee", "leftAnkle"],
+  ["nose", "rightShoulder"],
+  ["rightShoulder", "rightElbow"],
+  ["rightElbow", "rightWrist"],
+  ["rightShoulder", "rightHip"],
+  ["rightHip", "rightKnee"],
+  ["rightKnee", "rightAnkle"]
+];
+function getBoundingBox(keypoints) {
+  const coord = keypoints.reduce(({ maxX, maxY, minX, minY }, { position: { x, y } }) => ({
+    maxX: Math.max(maxX, x),
+    maxY: Math.max(maxY, y),
+    minX: Math.min(minX, x),
+    minY: Math.min(minY, y)
+  }), {
+    maxX: Number.NEGATIVE_INFINITY,
+    maxY: Number.NEGATIVE_INFINITY,
+    minX: Number.POSITIVE_INFINITY,
+    minY: Number.POSITIVE_INFINITY
+  });
+  return [coord.minX, coord.minY, coord.maxX - coord.minX, coord.maxY - coord.minY];
+}
+function scalePoses(poses, [height, width], [inputResolutionHeight, inputResolutionWidth]) {
+  const scaleY = height / inputResolutionHeight;
+  const scaleX = width / inputResolutionWidth;
+  const scalePose = (pose, i) => ({
+    id: i,
+    score: pose.score,
+    boxRaw: [pose.box[0] / inputResolutionWidth, pose.box[1] / inputResolutionHeight, pose.box[2] / inputResolutionWidth, pose.box[3] / inputResolutionHeight],
+    box: [Math.trunc(pose.box[0] * scaleX), Math.trunc(pose.box[1] * scaleY), Math.trunc(pose.box[2] * scaleX), Math.trunc(pose.box[3] * scaleY)],
+    keypoints: pose.keypoints.map(({ score, part, position }) => ({
+      score,
+      part,
+      position: [Math.trunc(position.x * scaleX), Math.trunc(position.y * scaleY)],
+      positionRaw: [position.x / inputResolutionHeight, position.y / inputResolutionHeight]
+    })),
+    annotations: {}
+  });
+  const scaledPoses = poses.map((pose, i) => scalePose(pose, i));
+  return scaledPoses;
+}
+var MaxHeap = class {
+  constructor(maxSize2, getElementValue) {
+    __publicField(this, "priorityQueue");
+    __publicField(this, "numberOfElements");
+    __publicField(this, "getElementValue");
+    this.priorityQueue = new Array(maxSize2);
+    this.numberOfElements = -1;
+    this.getElementValue = getElementValue;
+  }
+  enqueue(x) {
+    this.priorityQueue[++this.numberOfElements] = x;
+    this.swim(this.numberOfElements);
+  }
+  dequeue() {
+    const max4 = this.priorityQueue[0];
+    this.exchange(0, this.numberOfElements--);
+    this.sink(0);
+    this.priorityQueue[this.numberOfElements + 1] = null;
+    return max4;
+  }
+  empty() {
+    return this.numberOfElements === -1;
+  }
+  size() {
+    return this.numberOfElements + 1;
+  }
+  all() {
+    return this.priorityQueue.slice(0, this.numberOfElements + 1);
+  }
+  max() {
+    return this.priorityQueue[0];
+  }
+  swim(k) {
+    while (k > 0 && this.less(Math.floor(k / 2), k)) {
+      this.exchange(k, Math.floor(k / 2));
+      k = Math.floor(k / 2);
+    }
+  }
+  sink(k) {
+    while (2 * k <= this.numberOfElements) {
+      let j = 2 * k;
+      if (j < this.numberOfElements && this.less(j, j + 1))
+        j++;
+      if (!this.less(k, j))
+        break;
+      this.exchange(k, j);
+      k = j;
+    }
+  }
+  getValueAt(i) {
+    return this.getElementValue(this.priorityQueue[i]);
+  }
+  less(i, j) {
+    return this.getValueAt(i) < this.getValueAt(j);
+  }
+  exchange(i, j) {
+    const t2 = this.priorityQueue[i];
+    this.priorityQueue[i] = this.priorityQueue[j];
+    this.priorityQueue[j] = t2;
+  }
+};
+function getOffsetPoint(y, x, keypoint, offsets) {
+  return {
+    y: offsets.get(y, x, keypoint),
+    x: offsets.get(y, x, keypoint + count)
+  };
+}
+function getImageCoords(part, outputStride2, offsets) {
+  const { heatmapY, heatmapX, id: keypoint } = part;
+  const { y, x } = getOffsetPoint(heatmapY, heatmapX, keypoint, offsets);
+  return {
+    x: part.heatmapX * outputStride2 + x,
+    y: part.heatmapY * outputStride2 + y
+  };
+}
+function clamp(a, min2, max4) {
+  if (a < min2)
+    return min2;
+  if (a > max4)
+    return max4;
+  return a;
+}
+function squaredDistance(y1, x1, y2, x2) {
+  const dy = y2 - y1;
+  const dx = x2 - x1;
+  return dy * dy + dx * dx;
+}
+function addVectors(a, b) {
+  return { x: a.x + b.x, y: a.y + b.y };
+}
+
+// src/body/posenet.ts
+var model16;
+var poseNetOutputs = ["MobilenetV1/offset_2/BiasAdd", "MobilenetV1/heatmap_2/BiasAdd", "MobilenetV1/displacement_fwd_2/BiasAdd", "MobilenetV1/displacement_bwd_2/BiasAdd"];
+var localMaximumRadius = 1;
+var outputStride = 16;
+var squaredNmsRadius = 50 ** 2;
+function traverse(edgeId, sourceKeypoint, targetId, scores, offsets, displacements, offsetRefineStep = 2) {
+  const getDisplacement = (point2) => ({
+    y: displacements.get(point2.y, point2.x, edgeId),
+    x: displacements.get(point2.y, point2.x, displacements.shape[2] / 2 + edgeId)
+  });
+  const getStridedIndexNearPoint = (point2, height2, width2) => ({
+    y: clamp(Math.round(point2.y / outputStride), 0, height2 - 1),
+    x: clamp(Math.round(point2.x / outputStride), 0, width2 - 1)
+  });
+  const [height, width] = scores.shape;
+  const sourceKeypointIndices = getStridedIndexNearPoint(sourceKeypoint.position, height, width);
+  const displacement = getDisplacement(sourceKeypointIndices);
+  const displacedPoint = addVectors(sourceKeypoint.position, displacement);
+  let targetKeypoint = displacedPoint;
+  for (let i = 0; i < offsetRefineStep; i++) {
+    const targetKeypointIndices = getStridedIndexNearPoint(targetKeypoint, height, width);
+    const offsetPoint = getOffsetPoint(targetKeypointIndices.y, targetKeypointIndices.x, targetId, offsets);
+    targetKeypoint = addVectors(
+      { x: targetKeypointIndices.x * outputStride, y: targetKeypointIndices.y * outputStride },
+      { x: offsetPoint.x, y: offsetPoint.y }
+    );
+  }
+  const targetKeyPointIndices = getStridedIndexNearPoint(targetKeypoint, height, width);
+  const score = scores.get(targetKeyPointIndices.y, targetKeyPointIndices.x, targetId);
+  return { position: targetKeypoint, part: partNames[targetId], score };
+}
+function decodePose(root, scores, offsets, displacementsFwd, displacementsBwd) {
+  const tuples = poseChain.map(([parentJoinName, childJoinName]) => [partIds[parentJoinName], partIds[childJoinName]]);
+  const edgesFwd = tuples.map(([, childJointId]) => childJointId);
+  const edgesBwd = tuples.map(([parentJointId]) => parentJointId);
+  const numParts = scores.shape[2];
+  const numEdges = edgesFwd.length;
+  const keypoints = new Array(numParts);
+  const rootPoint = getImageCoords(root.part, outputStride, offsets);
+  keypoints[root.part.id] = {
+    score: root.score,
+    part: partNames[root.part.id],
+    position: rootPoint
+  };
+  for (let edge = numEdges - 1; edge >= 0; --edge) {
+    const sourceId = edgesFwd[edge];
+    const targetId = edgesBwd[edge];
+    if (keypoints[sourceId] && !keypoints[targetId]) {
+      keypoints[targetId] = traverse(edge, keypoints[sourceId], targetId, scores, offsets, displacementsBwd);
+    }
+  }
+  for (let edge = 0; edge < numEdges; ++edge) {
+    const sourceId = edgesBwd[edge];
+    const targetId = edgesFwd[edge];
+    if (keypoints[sourceId] && !keypoints[targetId]) {
+      keypoints[targetId] = traverse(edge, keypoints[sourceId], targetId, scores, offsets, displacementsFwd);
+    }
+  }
+  return keypoints;
+}
+function scoreIsMaximumInLocalWindow(keypointId, score, heatmapY, heatmapX, scores) {
+  const [height, width] = scores.shape;
+  let localMaximum = true;
+  const yStart = Math.max(heatmapY - localMaximumRadius, 0);
+  const yEnd = Math.min(heatmapY + localMaximumRadius + 1, height);
+  for (let yCurrent = yStart; yCurrent < yEnd; ++yCurrent) {
+    const xStart = Math.max(heatmapX - localMaximumRadius, 0);
+    const xEnd = Math.min(heatmapX + localMaximumRadius + 1, width);
+    for (let xCurrent = xStart; xCurrent < xEnd; ++xCurrent) {
+      if (scores.get(yCurrent, xCurrent, keypointId) > score) {
+        localMaximum = false;
+        break;
+      }
+    }
+    if (!localMaximum)
+      break;
+  }
+  return localMaximum;
+}
+function buildPartWithScoreQueue(minConfidence2, scores) {
+  const [height, width, numKeypoints] = scores.shape;
+  const queue = new MaxHeap(height * width * numKeypoints, ({ score }) => score);
+  for (let heatmapY = 0; heatmapY < height; ++heatmapY) {
+    for (let heatmapX = 0; heatmapX < width; ++heatmapX) {
+      for (let keypointId = 0; keypointId < numKeypoints; ++keypointId) {
+        const score = scores.get(heatmapY, heatmapX, keypointId);
+        if (score < minConfidence2)
+          continue;
+        if (scoreIsMaximumInLocalWindow(keypointId, score, heatmapY, heatmapX, scores))
+          queue.enqueue({ score, part: { heatmapY, heatmapX, id: keypointId } });
+      }
+    }
+  }
+  return queue;
+}
+function withinRadius(poses, { x, y }, keypointId) {
+  return poses.some(({ keypoints }) => {
+    var _a;
+    const correspondingKeypoint = (_a = keypoints[keypointId]) == null ? void 0 : _a.position;
+    if (!correspondingKeypoint)
+      return false;
+    return squaredDistance(y, x, correspondingKeypoint.y, correspondingKeypoint.x) <= squaredNmsRadius;
+  });
+}
+function getInstanceScore(existingPoses, keypoints) {
+  const notOverlappedKeypointScores = keypoints.reduce((result, { position, score }, keypointId) => {
+    if (!withinRadius(existingPoses, position, keypointId))
+      result += score;
+    return result;
+  }, 0);
+  return notOverlappedKeypointScores / keypoints.length;
+}
+function decode(offsets, scores, displacementsFwd, displacementsBwd, maxDetected, minConfidence2) {
+  const poses = [];
+  const queue = buildPartWithScoreQueue(minConfidence2, scores);
+  while (poses.length < maxDetected && !queue.empty()) {
+    const root = queue.dequeue();
+    const rootImageCoords = getImageCoords(root.part, outputStride, offsets);
+    if (withinRadius(poses, rootImageCoords, root.part.id))
+      continue;
+    let keypoints = decodePose(root, scores, offsets, displacementsFwd, displacementsBwd);
+    keypoints = keypoints.filter((a) => a.score > minConfidence2);
+    const score = getInstanceScore(poses, keypoints);
+    const box = getBoundingBox(keypoints);
+    if (score > minConfidence2)
+      poses.push({ keypoints, box, score: Math.round(100 * score) / 100 });
+  }
+  return poses;
+}
+async function predict17(input, config3) {
+  if (!(model16 == null ? void 0 : model16["executor"]))
+    return [];
+  const res = tf29.tidy(() => {
+    if (!model16.inputs[0].shape)
+      return [];
+    const resized = tf29.image.resizeBilinear(input, [model16.inputs[0].shape[2], model16.inputs[0].shape[1]]);
+    const normalized = tf29.sub(tf29.div(tf29.cast(resized, "float32"), 127.5), 1);
+    const results = model16.execute(normalized, poseNetOutputs);
+    const results3d = results.map((y) => tf29.squeeze(y, [0]));
+    results3d[1] = tf29.sigmoid(results3d[1]);
+    return results3d;
+  });
+  const buffers = await Promise.all(res.map((tensor6) => tensor6.buffer()));
+  for (const t2 of res)
+    tf29.dispose(t2);
+  const decoded = decode(buffers[0], buffers[1], buffers[2], buffers[3], config3.body.maxDetected, config3.body.minConfidence);
+  if (!model16.inputs[0].shape)
+    return [];
+  const scaled = scalePoses(decoded, [input.shape[1], input.shape[2]], [model16.inputs[0].shape[2], model16.inputs[0].shape[1]]);
+  return scaled;
+}
+async function load17(config3) {
+  if (!model16 || env.initial)
+    model16 = await loadModel(config3.body.modelPath);
+  else if (config3.debug)
+    log("cached model:", model16["modelUrl"]);
+  return model16;
+}
+
+// src/segmentation/rvm.ts
+var tf30 = __toESM(require_tfjs_esm());
+var model17;
+var outputNodes2 = ["fgr", "pha", "r1o", "r2o", "r3o", "r4o"];
+var t = {};
+var ratio = 0;
+function init2(config3) {
+  tf30.dispose([t.r1i, t.r2i, t.r3i, t.r4i, t.downsample_ratio]);
+  t.r1i = tf30.tensor(0);
+  t.r2i = tf30.tensor(0);
+  t.r3i = tf30.tensor(0);
+  t.r4i = tf30.tensor(0);
+  ratio = config3.segmentation.ratio || 0.5;
+  t.downsample_ratio = tf30.tensor(ratio);
+}
+async function load18(config3) {
+  if (!model17 || env.initial)
+    model17 = await loadModel(config3.segmentation.modelPath);
+  else if (config3.debug)
+    log("cached model:", model17["modelUrl"]);
+  init2(config3);
+  return model17;
+}
+var normalize = (r) => tf30.tidy(() => {
+  const squeeze14 = tf30.squeeze(r, [0]);
+  const mul15 = tf30.mul(squeeze14, constants.tf255);
+  const cast8 = tf30.cast(mul15, "int32");
+  return cast8;
+});
+function getRGBA(fgr, pha) {
+  const rgb2 = fgr ? normalize(fgr) : tf30.fill([pha.shape[1] || 0, pha.shape[2] || 0, 3], 255, "int32");
+  const a = pha ? normalize(pha) : tf30.fill([fgr.shape[1] || 0, fgr.shape[2] || 0, 1], 255, "int32");
+  const rgba = tf30.concat([rgb2, a], -1);
+  tf30.dispose([rgb2, a]);
+  return rgba;
+}
+function getState(state) {
+  return tf30.tidy(() => {
+    const r = {};
+    r.unstack = tf30.unstack(state, -1);
+    r.concat = tf30.concat(r.unstack, 1);
+    r.split = tf30.split(r.concat, 4, 1);
+    r.stack = tf30.concat(r.split, 2);
+    r.squeeze = tf30.squeeze(r.stack, [0]);
+    r.expand = tf30.expandDims(r.squeeze, -1);
+    r.add = tf30.add(r.expand, 1);
+    r.mul = tf30.mul(r.add, 127.5);
+    r.cast = tf30.cast(r.mul, "int32");
+    r.tile = tf30.tile(r.cast, [1, 1, 3]);
+    r.alpha = tf30.fill([r.tile.shape[0] || 0, r.tile.shape[1] || 0, 1], 255, "int32");
+    return tf30.concat([r.tile, r.alpha], -1);
+  });
+}
+async function predict18(input, config3) {
+  if (!model17)
+    model17 = await load18(config3);
+  if (!(model17 == null ? void 0 : model17["executor"]))
+    return null;
+  t.src = tf30.div(input, 255);
+  if (ratio !== config3.segmentation.ratio)
+    init2(config3);
+  const [fgr, pha, r1o, r2o, r3o, r4o] = await model17.executeAsync(t, outputNodes2);
+  let rgba;
+  switch (config3.segmentation.mode || "default") {
+    case "default":
+      rgba = getRGBA(fgr, pha);
+      break;
+    case "alpha":
+      rgba = getRGBA(null, pha);
+      break;
+    case "foreground":
+      rgba = getRGBA(fgr, null);
+      break;
+    case "state":
+      rgba = getState(r1o);
+      break;
+    default:
+      rgba = tf30.tensor(0);
+  }
+  tf30.dispose([t.src, fgr, pha, t.r1i, t.r2i, t.r3i, t.r4i]);
+  [t.r1i, t.r2i, t.r3i, t.r4i] = [r1o, r2o, r3o, r4o];
+  return rgba;
+}
+
+// src/segmentation/selfie.ts
+var tf31 = __toESM(require_tfjs_esm());
+var model18;
+async function load19(config3) {
+  if (!model18 || env.initial)
+    model18 = await loadModel(config3.segmentation.modelPath);
+  else if (config3.debug)
+    log("cached model:", model18["modelUrl"]);
+  return model18;
+}
+async function predict19(input, config3) {
+  var _a;
+  if (!model18)
+    model18 = await load19(config3);
+  if (!(model18 == null ? void 0 : model18["executor"]) || !((_a = model18 == null ? void 0 : model18.inputs) == null ? void 0 : _a[0].shape))
+    return null;
+  const t2 = {};
+  t2.resize = tf31.image.resizeBilinear(input, [model18.inputs[0].shape ? model18.inputs[0].shape[1] : 0, model18.inputs[0].shape ? model18.inputs[0].shape[2] : 0], false);
+  t2.norm = tf31.div(t2.resize, constants.tf255);
+  t2.res = model18.execute(t2.norm);
+  t2.squeeze = tf31.squeeze(t2.res, 0);
+  t2.alpha = tf31.image.resizeBilinear(t2.squeeze, [input.shape[1], input.shape[2]]);
+  t2.mul = tf31.mul(t2.alpha, constants.tf255);
+  let rgba;
+  switch (config3.segmentation.mode || "default") {
+    case "default":
+      t2.input = tf31.squeeze(input);
+      t2.concat = tf31.concat([t2.input, t2.mul], -1);
+      rgba = tf31.cast(t2.concat, "int32");
+      break;
+    case "alpha":
+      rgba = tf31.cast(t2.mul, "int32");
+      break;
+    default:
+      rgba = tf31.tensor(0);
+  }
+  Object.keys(t2).forEach((tensor6) => tf31.dispose(t2[tensor6]));
+  return rgba;
+}
+
+// src/gear/ssrnet-age.ts
+var tf32 = __toESM(require_tfjs_esm());
+var model19;
+var last9 = [];
+var lastCount8 = 0;
+var lastTime14 = 0;
+var skipped14 = Number.MAX_SAFE_INTEGER;
+async function load20(config3) {
+  if (env.initial)
+    model19 = null;
+  if (!model19)
+    model19 = await loadModel(config3.face["ssrnet"].modelPathAge);
+  else if (config3.debug)
+    log("cached model:", model19["modelUrl"]);
+  return model19;
+}
+async function predict20(image27, config3, idx, count2) {
+  var _a, _b, _c, _d;
+  if (!model19)
+    return { age: 0 };
+  const skipFrame = skipped14 < (((_a = config3.face["ssrnet"]) == null ? void 0 : _a.skipFrames) || 0);
+  const skipTime = (((_b = config3.face["ssrnet"]) == null ? void 0 : _b.skipTime) || 0) > now() - lastTime14;
+  if (config3.skipAllowed && skipFrame && skipTime && lastCount8 === count2 && ((_c = last9[idx]) == null ? void 0 : _c.age) && ((_d = last9[idx]) == null ? void 0 : _d.age) > 0) {
+    skipped14++;
+    return last9[idx];
+  }
+  skipped14 = 0;
+  return new Promise(async (resolve) => {
+    var _a2;
+    if (!(model19 == null ? void 0 : model19.inputs) || !model19.inputs[0] || !model19.inputs[0].shape)
+      return;
+    const t2 = {};
+    t2.resize = tf32.image.resizeBilinear(image27, [model19.inputs[0].shape[2], model19.inputs[0].shape[1]], false);
+    t2.enhance = tf32.mul(t2.resize, constants.tf255);
+    const obj = { age: 0 };
+    if ((_a2 = config3.face["ssrnet"]) == null ? void 0 : _a2.enabled)
+      t2.age = model19.execute(t2.enhance);
+    if (t2.age) {
+      const data = await t2.age.data();
+      obj.age = Math.trunc(10 * data[0]) / 10;
+    }
+    Object.keys(t2).forEach((tensor6) => tf32.dispose(t2[tensor6]));
+    last9[idx] = obj;
+    lastCount8 = count2;
+    lastTime14 = now();
+    resolve(obj);
+  });
+}
+
+// src/gear/ssrnet-gender.ts
+var tf33 = __toESM(require_tfjs_esm());
+var model20;
+var last10 = [];
+var lastCount9 = 0;
+var lastTime15 = 0;
+var skipped15 = Number.MAX_SAFE_INTEGER;
+var rgb = [0.2989, 0.587, 0.114];
+async function load21(config3) {
+  var _a;
+  if (env.initial)
+    model20 = null;
+  if (!model20)
+    model20 = await loadModel((_a = config3.face["ssrnet"]) == null ? void 0 : _a.modelPathGender);
+  else if (config3.debug)
+    log("cached model:", model20["modelUrl"]);
+  return model20;
+}
+async function predict21(image27, config3, idx, count2) {
+  var _a, _b, _c, _d;
+  if (!model20)
+    return { gender: "unknown", genderScore: 0 };
+  const skipFrame = skipped15 < (((_a = config3.face["ssrnet"]) == null ? void 0 : _a.skipFrames) || 0);
+  const skipTime = (((_b = config3.face["ssrnet"]) == null ? void 0 : _b.skipTime) || 0) > now() - lastTime15;
+  if (config3.skipAllowed && skipFrame && skipTime && lastCount9 === count2 && ((_c = last10[idx]) == null ? void 0 : _c.gender) && ((_d = last10[idx]) == null ? void 0 : _d.genderScore) > 0) {
+    skipped15++;
+    return last10[idx];
+  }
+  skipped15 = 0;
+  return new Promise(async (resolve) => {
+    var _a2;
+    if (!(model20 == null ? void 0 : model20.inputs[0].shape))
+      return;
+    const t2 = {};
+    t2.resize = tf33.image.resizeBilinear(image27, [model20.inputs[0].shape[2], model20.inputs[0].shape[1]], false);
+    t2.enhance = tf33.tidy(() => {
+      const [red, green, blue] = tf33.split(t2.resize, 3, 3);
+      const redNorm = tf33.mul(red, rgb[0]);
+      const greenNorm = tf33.mul(green, rgb[1]);
+      const blueNorm = tf33.mul(blue, rgb[2]);
+      const grayscale = tf33.addN([redNorm, greenNorm, blueNorm]);
+      const normalize2 = tf33.mul(tf33.sub(grayscale, constants.tf05), 2);
+      return normalize2;
+    });
+    const obj = { gender: "unknown", genderScore: 0 };
+    if ((_a2 = config3.face["ssrnet"]) == null ? void 0 : _a2.enabled)
+      t2.gender = model20.execute(t2.enhance);
+    const data = await t2.gender.data();
+    obj.gender = data[0] > data[1] ? "female" : "male";
+    obj.genderScore = data[0] > data[1] ? Math.trunc(100 * data[0]) / 100 : Math.trunc(100 * data[1]) / 100;
+    Object.keys(t2).forEach((tensor6) => tf33.dispose(t2[tensor6]));
+    last10[idx] = obj;
+    lastCount9 = count2;
+    lastTime15 = now();
+    resolve(obj);
+  });
+}
+
+// src/models.ts
+var Models = class {
+  constructor() {
+    __publicField(this, "ssrnetage", null);
+    __publicField(this, "gear", null);
+    __publicField(this, "blazeposedetect", null);
+    __publicField(this, "blazepose", null);
+    __publicField(this, "centernet", null);
+    __publicField(this, "efficientpose", null);
+    __publicField(this, "mobilefacenet", null);
+    __publicField(this, "insightface", null);
+    __publicField(this, "emotion", null);
+    __publicField(this, "facedetect", null);
+    __publicField(this, "faceiris", null);
+    __publicField(this, "facemesh", null);
+    __publicField(this, "faceres", null);
+    __publicField(this, "ssrnetgender", null);
+    __publicField(this, "handpose", null);
+    __publicField(this, "handskeleton", null);
+    __publicField(this, "handtrack", null);
+    __publicField(this, "liveness", null);
+    __publicField(this, "meet", null);
+    __publicField(this, "movenet", null);
+    __publicField(this, "nanodet", null);
+    __publicField(this, "posenet", null);
+    __publicField(this, "selfie", null);
+    __publicField(this, "rvm", null);
+    __publicField(this, "antispoof", null);
+  }
+};
+var instance;
+var getModelStats = (currentInstance) => {
+  if (currentInstance)
+    instance = currentInstance;
+  if (!instance)
+    log("instance not registred");
+  let totalSizeFromManifest = 0;
+  let totalSizeWeights = 0;
+  let totalSizeLoading = 0;
+  for (const m of Object.values(modelStats)) {
+    totalSizeFromManifest += m.sizeFromManifest;
+    totalSizeWeights += m.sizeLoadedWeights;
+    totalSizeLoading += m.sizeDesired;
+  }
+  const percentageLoaded = totalSizeLoading > 0 ? totalSizeWeights / totalSizeLoading : 0;
+  return {
+    numLoadedModels: Object.values(modelStats).length,
+    numDefinedModels: Object.keys(instance.models).length,
+    percentageLoaded,
+    totalSizeFromManifest,
+    totalSizeWeights,
+    totalSizeLoading,
+    totalSizeEnabled: void 0,
+    modelStats: Object.values(modelStats)
+  };
+};
+function reset2(currentInstance) {
+  if (currentInstance)
+    instance = currentInstance;
+  for (const model21 of Object.keys(instance.models))
+    instance.models[model21] = null;
+}
+async function load22(currentInstance) {
+  var _a, _b, _c, _d, _e, _f, _g, _h, _i, _j, _k, _l, _m, _n, _o, _p, _q, _r, _s, _t, _u, _v, _w, _x, _y, _z, _A, _B, _C;
+  if (currentInstance)
+    instance = currentInstance;
+  if (!instance)
+    log("instance not registred");
+  if (env.initial)
+    reset2(instance);
+  if (instance.config.hand.enabled) {
+    if (!instance.models.handpose && ((_b = (_a = instance.config.hand.detector) == null ? void 0 : _a.modelPath) == null ? void 0 : _b.includes("handdetect"))) {
+      [instance.models.handpose, instance.models.handskeleton] = await load10(instance.config);
+    }
+    if (!instance.models.handskeleton && instance.config.hand.landmarks && ((_d = (_c = instance.config.hand.detector) == null ? void 0 : _c.modelPath) == null ? void 0 : _d.includes("handdetect"))) {
+      [instance.models.handpose, instance.models.handskeleton] = await load10(instance.config);
+    }
+  }
+  if (instance.config.body.enabled && !instance.models.blazepose && ((_e = instance.config.body.modelPath) == null ? void 0 : _e.includes("blazepose")))
+    instance.models.blazepose = loadPose(instance.config);
+  if (instance.config.body.enabled && !instance.models.blazeposedetect && instance.config.body["detector"] && instance.config.body["detector"].modelPath)
+    instance.models.blazeposedetect = loadDetect(instance.config);
+  if (instance.config.body.enabled && !instance.models.efficientpose && ((_f = instance.config.body.modelPath) == null ? void 0 : _f.includes("efficientpose")))
+    instance.models.efficientpose = load4(instance.config);
+  if (instance.config.body.enabled && !instance.models.movenet && ((_g = instance.config.body.modelPath) == null ? void 0 : _g.includes("movenet")))
+    instance.models.movenet = load15(instance.config);
+  if (instance.config.body.enabled && !instance.models.posenet && ((_h = instance.config.body.modelPath) == null ? void 0 : _h.includes("posenet")))
+    instance.models.posenet = load17(instance.config);
+  if (instance.config.face.enabled && !instance.models.facedetect)
+    instance.models.facedetect = load2(instance.config);
+  if (instance.config.face.enabled && ((_i = instance.config.face.antispoof) == null ? void 0 : _i.enabled) && !instance.models.antispoof)
+    instance.models.antispoof = load(instance.config);
+  if (instance.config.face.enabled && ((_j = instance.config.face.liveness) == null ? void 0 : _j.enabled) && !instance.models.liveness)
+    instance.models.liveness = load12(instance.config);
+  if (instance.config.face.enabled && ((_k = instance.config.face.description) == null ? void 0 : _k.enabled) && !instance.models.faceres)
+    instance.models.faceres = load8(instance.config);
+  if (instance.config.face.enabled && ((_l = instance.config.face.emotion) == null ? void 0 : _l.enabled) && !instance.models.emotion)
+    instance.models.emotion = load5(instance.config);
+  if (instance.config.face.enabled && ((_m = instance.config.face.iris) == null ? void 0 : _m.enabled) && !((_n = instance.config.face.attention) == null ? void 0 : _n.enabled) && !instance.models.faceiris)
+    instance.models.faceiris = load6(instance.config);
+  if (instance.config.face.enabled && ((_o = instance.config.face.mesh) == null ? void 0 : _o.enabled) && !instance.models.facemesh)
+    instance.models.facemesh = load7(instance.config);
+  if (instance.config.face.enabled && ((_p = instance.config.face["gear"]) == null ? void 0 : _p.enabled) && !instance.models.gear)
+    instance.models.gear = load9(instance.config);
+  if (instance.config.face.enabled && ((_q = instance.config.face["ssrnet"]) == null ? void 0 : _q.enabled) && !instance.models.ssrnetage)
+    instance.models.ssrnetage = load20(instance.config);
+  if (instance.config.face.enabled && ((_r = instance.config.face["ssrnet"]) == null ? void 0 : _r.enabled) && !instance.models.ssrnetgender)
+    instance.models.ssrnetgender = load21(instance.config);
+  if (instance.config.face.enabled && ((_s = instance.config.face["mobilefacenet"]) == null ? void 0 : _s.enabled) && !instance.models.mobilefacenet)
+    instance.models.mobilefacenet = load14(instance.config);
+  if (instance.config.face.enabled && ((_t = instance.config.face["insightface"]) == null ? void 0 : _t.enabled) && !instance.models.insightface)
+    instance.models.insightface = load11(instance.config);
+  if (instance.config.hand.enabled && !instance.models.handtrack && ((_v = (_u = instance.config.hand.detector) == null ? void 0 : _u.modelPath) == null ? void 0 : _v.includes("handtrack")))
+    instance.models.handtrack = loadDetect2(instance.config);
+  if (instance.config.hand.enabled && instance.config.hand.landmarks && !instance.models.handskeleton && ((_x = (_w = instance.config.hand.detector) == null ? void 0 : _w.modelPath) == null ? void 0 : _x.includes("handtrack")))
+    instance.models.handskeleton = loadSkeleton(instance.config);
+  if (instance.config.object.enabled && !instance.models.centernet && ((_y = instance.config.object.modelPath) == null ? void 0 : _y.includes("centernet")))
+    instance.models.centernet = load3(instance.config);
+  if (instance.config.object.enabled && !instance.models.nanodet && ((_z = instance.config.object.modelPath) == null ? void 0 : _z.includes("nanodet")))
+    instance.models.nanodet = load16(instance.config);
+  if (instance.config.segmentation.enabled && !instance.models.selfie && ((_A = instance.config.segmentation.modelPath) == null ? void 0 : _A.includes("selfie")))
+    instance.models.selfie = load19(instance.config);
+  if (instance.config.segmentation.enabled && !instance.models.meet && ((_B = instance.config.segmentation.modelPath) == null ? void 0 : _B.includes("meet")))
+    instance.models.meet = load13(instance.config);
+  if (instance.config.segmentation.enabled && !instance.models.rvm && ((_C = instance.config.segmentation.modelPath) == null ? void 0 : _C.includes("rvm")))
+    instance.models.rvm = load18(instance.config);
+  for await (const model21 of Object.keys(instance.models)) {
+    if (instance.models[model21] && typeof instance.models[model21] !== "undefined") {
+      instance.models[model21] = await instance.models[model21];
+    }
+  }
+}
+function validateModel(currentInstance, model21, name) {
+  var _a, _b;
+  if (!model21)
+    return null;
+  if (currentInstance)
+    instance = currentInstance;
+  if (!instance)
+    log("instance not registred");
+  if (!((_a = instance == null ? void 0 : instance.config) == null ? void 0 : _a.validateModels))
+    return null;
+  const simpleOps = ["const", "placeholder", "noop", "pad", "squeeze", "add", "sub", "mul", "div"];
+  const ignoreOps = ["biasadd", "fusedbatchnormv3", "matmul", "switch", "shape", "merge", "split", "broadcastto"];
+  const ops = [];
+  const missing = [];
+  const url = model21["modelUrl"];
+  const executor = model21["executor"];
+  if ((_b = executor == null ? void 0 : executor.graph) == null ? void 0 : _b.nodes) {
+    for (const kernel of Object.values(executor.graph.nodes)) {
+      const op = kernel.op.toLowerCase();
+      if (!ops.includes(op))
+        ops.push(op);
+    }
+  } else {
+    if (!executor && instance.config.debug) {
+      log("model not loaded", name);
+    }
+  }
+  for (const op of ops) {
+    if (!simpleOps.includes(op) && !ignoreOps.includes(op) && !instance.env.kernels.includes(op) && !instance.env.kernels.includes(op.replace("_", "")) && !instance.env.kernels.includes(op.replace("native", "")) && !instance.env.kernels.includes(op.replace("v2", ""))) {
+      missing.push(op);
+    }
+  }
+  if (instance.config.debug && missing.length > 0)
+    log("model validation failed:", name, missing);
+  return missing.length > 0 ? { name, missing, ops, url } : null;
+}
+function validate2(currentInstance) {
+  if (currentInstance)
+    instance = currentInstance;
+  if (!instance)
+    log("instance not registred");
+  const missing = [];
+  for (const defined of Object.keys(currentInstance.models)) {
+    const model21 = currentInstance.models[defined];
+    if (!model21)
+      continue;
+    const res = validateModel(currentInstance, model21, defined);
+    if (res)
+      missing.push(res);
+  }
+  return missing;
+}
+
+// src/tfjs/humangl.ts
+var config2 = {
+  name: "humangl",
+  priority: 999,
+  canvas: null,
+  gl: null,
+  extensions: [],
+  webGLattr: {
+    alpha: false,
+    antialias: false,
+    premultipliedAlpha: false,
+    preserveDrawingBuffer: false,
+    depth: false,
+    stencil: false,
+    failIfMajorPerformanceCaveat: false,
+    desynchronized: true
+  }
+};
+function extensions() {
+  const gl = config2.gl;
+  if (!gl)
+    return;
+  config2.extensions = gl.getSupportedExtensions();
+}
+function register(instance2) {
+  var _a;
+  if (instance2.config.backend !== "humangl")
+    return;
+  if (config2.name in tf34.engine().registry && !((_a = config2 == null ? void 0 : config2.gl) == null ? void 0 : _a.getParameter(config2.gl.VERSION))) {
+    log("humangl error: backend invalid context");
+    reset2(instance2);
+  }
+  if (!tf34.findBackend(config2.name)) {
+    try {
+      config2.canvas = canvas(100, 100);
+    } catch (err) {
+      log("humangl error: cannot create canvas:", err);
+      return;
+    }
+    try {
+      config2.gl = config2.canvas.getContext("webgl2", config2.webGLattr);
+      if (!config2.gl) {
+        log("humangl error: cannot get webgl context");
+        return;
+      }
+      const glv2 = config2.gl.getParameter(config2.gl.VERSION).includes("2.0");
+      if (!glv2) {
+        log("backend override: using fallback webgl backend as webgl 2.0 is not detected");
+        instance2.config.backend = "webgl";
+        return;
+      }
+      if (config2.canvas) {
+        config2.canvas.addEventListener("webglcontextlost", (e) => {
+          log("humangl error:", e.type);
+          log("possible browser memory leak using webgl or conflict with multiple backend registrations");
+          instance2.emit("error");
+          throw new Error("backend error: webgl context lost");
+        });
+        config2.canvas.addEventListener("webglcontextrestored", (e) => {
+          log("humangl error: context restored:", e);
+        });
+        config2.canvas.addEventListener("webglcontextcreationerror", (e) => {
+          log("humangl error: context create:", e);
+        });
+      }
+    } catch (err) {
+      log("humangl error: cannot get webgl context:", err);
+      return;
+    }
+    try {
+      tf34.setWebGLContext(2, config2.gl);
+    } catch (err) {
+      log("humangl error: cannot set webgl context:", err);
+      return;
+    }
+    try {
+      const ctx = new tf34.GPGPUContext(config2.gl);
+      tf34.registerBackend(config2.name, () => new tf34.MathBackendWebGL(ctx), config2.priority);
+    } catch (err) {
+      log("humangl error: cannot register webgl backend:", err);
+      return;
+    }
+    try {
+      const kernels = tf34.getKernelsForBackend("webgl");
+      kernels.forEach((kernelConfig) => {
+        const newKernelConfig = { ...kernelConfig, backendName: config2.name };
+        tf34.registerKernel(newKernelConfig);
+      });
+    } catch (err) {
+      log("humangl error: cannot update webgl backend registration:", err);
+      return;
+    }
+    try {
+      if (tf34.env().flagRegistry.WEBGL_VERSION)
+        tf34.env().set("WEBGL_VERSION", 2);
+    } catch (err) {
+      log("humangl error: cannot set WebGL backend flags:", err);
+      return;
+    }
+    extensions();
+    const current = tf34.backend().getGPGPUContext ? tf34.backend().getGPGPUContext().gl : null;
+    if (current) {
+      if (instance2.config.debug)
+        log("humangl backend registered:", { webgl: current.getParameter(current.VERSION), renderer: current.getParameter(current.RENDERER) });
+    } else {
+      log("humangl error: no current gl context:", current, config2.gl);
+    }
+  }
+}
+
+// src/tfjs/backend.ts
+var tf35 = __toESM(require_tfjs_esm());
+function registerCustomOps(config3) {
+  const newKernels = [];
+  if (!env.kernels.includes("mod")) {
+    const kernelMod = {
+      kernelName: "Mod",
+      backendName: tf35.getBackend(),
+      kernelFunc: (op) => tf35.tidy(() => tf35.sub(op.inputs.a, tf35.mul(tf35.div(op.inputs.a, op.inputs.b), op.inputs.b)))
+    };
+    tf35.registerKernel(kernelMod);
+    env.kernels.push("mod");
+    newKernels.push("mod");
+  }
+  if (!env.kernels.includes("floormod")) {
+    const kernelFloorMod = {
+      kernelName: "FloorMod",
+      backendName: tf35.getBackend(),
+      kernelFunc: (op) => tf35.tidy(() => tf35.add(tf35.mul(tf35.floorDiv(op.inputs.a / op.inputs.b), op.inputs.b), tf35.mod(op.inputs.a, op.inputs.b)))
+    };
+    tf35.registerKernel(kernelFloorMod);
+    env.kernels.push("floormod");
+    newKernels.push("floormod");
+  }
+  if (!env.kernels.includes("rotatewithoffset") && config3.softwareKernels) {
+    const kernelRotateWithOffset = {
+      kernelName: "RotateWithOffset",
+      backendName: tf35.getBackend(),
+      kernelFunc: (op) => tf35.tidy(() => {
+        const backend4 = tf35.getBackend();
+        tf35.setBackend("cpu");
+        const t2 = tf35.image.rotateWithOffset(op.inputs.image, op.attrs.radians, op.attrs.fillValue, op.attrs.center);
+        tf35.setBackend(backend4);
+        return t2;
+      })
+    };
+    tf35.registerKernel(kernelRotateWithOffset);
+    env.kernels.push("rotatewithoffset");
+    newKernels.push("rotatewithoffset");
+  }
+  if (newKernels.length > 0 && config3.debug)
+    log("registered kernels:", newKernels);
+}
+var defaultFlags = {};
+async function check(instance2, force = false) {
+  instance2.state = "backend";
+  if (force || env.initial || instance2.config.backend && instance2.config.backend.length > 0 && tf35.getBackend() !== instance2.config.backend) {
+    const timeStamp = now();
+    if (instance2.config.backend && instance2.config.backend.length > 0) {
+      if (typeof window === "undefined" && typeof WorkerGlobalScope !== "undefined" && instance2.config.debug) {
+        if (instance2.config.debug)
+          log("running inside web worker");
+      }
+      if (env.browser && instance2.config.backend === "tensorflow") {
+        if (instance2.config.debug)
+          log("override: backend set to tensorflow while running in browser");
+        instance2.config.backend = "webgl";
+      }
+      if (env.node && (instance2.config.backend === "webgl" || instance2.config.backend === "humangl")) {
+        if (instance2.config.debug)
+          log(`override: backend set to ${instance2.config.backend} while running in nodejs`);
+        instance2.config.backend = "tensorflow";
+      }
+      if (env.browser && instance2.config.backend === "webgpu") {
+        if (typeof navigator === "undefined" || typeof navigator.gpu === "undefined") {
+          log("override: backend set to webgpu but browser does not support webgpu");
+          instance2.config.backend = "webgl";
+        } else {
+          const adapter = await navigator.gpu.requestAdapter();
+          if (instance2.config.debug)
+            log("enumerated webgpu adapter:", adapter);
+          if (!adapter) {
+            log("override: backend set to webgpu but browser reports no available gpu");
+            instance2.config.backend = "webgl";
+          } else {
+            const adapterInfo = "requestAdapterInfo" in adapter ? await adapter.requestAdapterInfo() : void 0;
+            log("webgpu adapter info:", adapterInfo);
+          }
+        }
+      }
+      let available = Object.keys(tf35.engine().registryFactory);
+      if (instance2.config.backend === "humangl" && !available.includes("humangl")) {
+        register(instance2);
+        available = Object.keys(tf35.engine().registryFactory);
+      }
+      if (instance2.config.debug)
+        log("available backends:", available);
+      if (!available.includes(instance2.config.backend)) {
+        log(`error: backend ${instance2.config.backend} not found in registry`);
+        instance2.config.backend = env.node ? "tensorflow" : "webgl";
+        if (instance2.config.debug)
+          log(`override: setting backend ${instance2.config.backend}`);
+      }
+      if (instance2.config.debug)
+        log("setting backend:", [instance2.config.backend]);
+      if (instance2.config.backend === "wasm") {
+        if (tf35.env().flagRegistry.CANVAS2D_WILL_READ_FREQUENTLY)
+          tf35.env().set("CANVAS2D_WILL_READ_FREQUENTLY", true);
+        if (instance2.config.debug)
+          log("wasm path:", instance2.config.wasmPath);
+        if (typeof tf35.setWasmPaths !== "undefined")
+          tf35.setWasmPaths(instance2.config.wasmPath, instance2.config.wasmPlatformFetch);
+        else
+          throw new Error("backend error: attempting to use wasm backend but wasm path is not set");
+        let mt = false;
+        let simd = false;
+        try {
+          mt = await tf35.env().getAsync("WASM_HAS_MULTITHREAD_SUPPORT");
+          simd = await tf35.env().getAsync("WASM_HAS_SIMD_SUPPORT");
+          if (instance2.config.debug)
+            log(`wasm execution: ${simd ? "simd" : "no simd"} ${mt ? "multithreaded" : "singlethreaded"}`);
+          if (instance2.config.debug && !simd)
+            log("warning: wasm simd support is not enabled");
+        } catch (e) {
+          log("wasm detection failed");
+        }
+      }
+      try {
+        await tf35.setBackend(instance2.config.backend);
+        await tf35.ready();
+      } catch (err) {
+        log("error: cannot set backend:", instance2.config.backend, err);
+        return false;
+      }
+      if (instance2.config.debug)
+        defaultFlags = JSON.parse(JSON.stringify(tf35.env().flags));
+    }
+    if (tf35.getBackend() === "humangl" || tf35.getBackend() === "webgl") {
+      if (tf35.env().flagRegistry.WEBGL_USE_SHAPES_UNIFORMS)
+        tf35.env().set("WEBGL_USE_SHAPES_UNIFORMS", true);
+      if (tf35.env().flagRegistry.WEBGL_EXP_CONV)
+        tf35.env().set("WEBGL_EXP_CONV", true);
+      if (instance2.config.debug && typeof instance2.config.deallocate !== "undefined" && instance2.config.deallocate) {
+        log("changing webgl: WEBGL_DELETE_TEXTURE_THRESHOLD:", true);
+        tf35.env().set("WEBGL_DELETE_TEXTURE_THRESHOLD", 0);
+      }
+    }
+    if (tf35.getBackend() === "webgpu") {
+    }
+    if (instance2.config.debug) {
+      const newFlags = tf35.env().flags;
+      const updatedFlags = {};
+      for (const key of Object.keys(newFlags)) {
+        if (defaultFlags[key] === newFlags[key])
+          continue;
+        updatedFlags[key] = newFlags[key];
+      }
+      if (instance2.config.debug && Object.keys(updatedFlags).length > 0)
+        log("backend:", tf35.getBackend(), "flags:", updatedFlags);
+    }
+    if (instance2.config.flags && Object.keys(instance2.config.flags).length > 0) {
+      if (instance2.config.debug)
+        log("flags:", instance2.config["flags"]);
+      for (const [key, val] of Object.entries(instance2.config.flags)) {
+        tf35.env().set(key, val);
+      }
+    }
+    tf35.enableProdMode();
+    init();
+    instance2.performance.initBackend = Math.trunc(now() - timeStamp);
+    instance2.config.backend = tf35.getBackend();
+    await env.updateBackend();
+    registerCustomOps(instance2.config);
+    env.initial = false;
+  }
+  return true;
+}
+function fakeOps(kernelNames, config3) {
+  for (const kernelName of kernelNames) {
+    const kernelConfig = {
+      kernelName,
+      backendName: config3.backend,
+      kernelFunc: () => {
+        if (config3.debug)
+          log("kernelFunc", kernelName, config3.backend);
+      }
+    };
+    tf35.registerKernel(kernelConfig);
+  }
+  env.kernels = tf35.getKernelsForBackend(tf35.getBackend()).map((kernel) => kernel.kernelName.toLowerCase());
+}
+
+// src/draw/draw.ts
+var draw_exports = {};
+__export(draw_exports, {
+  all: () => all,
+  body: () => body,
+  canvas: () => canvas2,
+  face: () => face,
+  gesture: () => gesture,
+  hand: () => hand,
+  object: () => object,
+  options: () => options3,
+  person: () => person
+});
+
+// src/draw/primitives.ts
+var getCanvasContext = (input) => {
+  if (!input)
+    log("draw error: invalid canvas");
+  else if (!input.getContext)
+    log("draw error: canvas context not defined");
+  else {
+    const ctx = input.getContext("2d");
+    if (!ctx)
+      log("draw error: cannot get canvas context");
+    else
+      return ctx;
+  }
+  return null;
+};
+var rad2deg = (theta) => Math.round(theta * 180 / Math.PI);
+var colorDepth = (z, opt2) => {
+  if (!opt2.useDepth || typeof z === "undefined")
+    return opt2.color;
+  const rgb2 = Uint8ClampedArray.from([127 + 2 * z, 127 - 2 * z, 255]);
+  return `rgba(${rgb2[0]}, ${rgb2[1]}, ${rgb2[2]}, ${opt2.alpha})`;
+};
+function point(ctx, x, y, z, localOptions) {
+  ctx.fillStyle = colorDepth(z, localOptions);
+  ctx.beginPath();
+  ctx.arc(x, y, localOptions.pointSize, 0, 2 * Math.PI);
+  ctx.fill();
+}
+function rect(ctx, x, y, width, height, localOptions) {
+  ctx.beginPath();
+  ctx.lineWidth = localOptions.lineWidth;
+  if (localOptions.useCurves) {
+    const cx = (x + x + width) / 2;
+    const cy = (y + y + height) / 2;
+    ctx.ellipse(cx, cy, width / 2, height / 2, 0, 0, 2 * Math.PI);
+  } else {
+    ctx.moveTo(x + localOptions.roundRect, y);
+    ctx.lineTo(x + width - localOptions.roundRect, y);
+    ctx.quadraticCurveTo(x + width, y, x + width, y + localOptions.roundRect);
+    ctx.lineTo(x + width, y + height - localOptions.roundRect);
+    ctx.quadraticCurveTo(x + width, y + height, x + width - localOptions.roundRect, y + height);
+    ctx.lineTo(x + localOptions.roundRect, y + height);
+    ctx.quadraticCurveTo(x, y + height, x, y + height - localOptions.roundRect);
+    ctx.lineTo(x, y + localOptions.roundRect);
+    ctx.quadraticCurveTo(x, y, x + localOptions.roundRect, y);
+    ctx.closePath();
+  }
+  ctx.stroke();
+}
+function lines(ctx, points, localOptions) {
+  if (points.length < 2)
+    return;
+  ctx.beginPath();
+  ctx.moveTo(points[0][0], points[0][1]);
+  for (const pt of points) {
+    ctx.strokeStyle = colorDepth(pt[2] || 0, localOptions);
+    ctx.lineTo(Math.trunc(pt[0]), Math.trunc(pt[1]));
+  }
+  ctx.stroke();
+  if (localOptions.fillPolygons) {
+    ctx.closePath();
+    ctx.fill();
+  }
+}
+function curves(ctx, points, localOptions) {
+  if (points.length < 2)
+    return;
+  ctx.lineWidth = localOptions.lineWidth;
+  if (!localOptions.useCurves || points.length <= 2) {
+    lines(ctx, points, localOptions);
+    return;
+  }
+  ctx.moveTo(points[0][0], points[0][1]);
+  for (let i = 0; i < points.length - 2; i++) {
+    const xc = (points[i][0] + points[i + 1][0]) / 2;
+    const yc = (points[i][1] + points[i + 1][1]) / 2;
+    ctx.quadraticCurveTo(points[i][0], points[i][1], xc, yc);
+  }
+  ctx.quadraticCurveTo(points[points.length - 2][0], points[points.length - 2][1], points[points.length - 1][0], points[points.length - 1][1]);
+  ctx.stroke();
+  if (localOptions.fillPolygons) {
+    ctx.closePath();
+    ctx.fill();
+  }
+}
+function arrow(ctx, from, to, radius = 5) {
+  let angle;
+  let x;
+  let y;
+  ctx.beginPath();
+  ctx.moveTo(from[0], from[1]);
+  ctx.lineTo(to[0], to[1]);
+  angle = Math.atan2(to[1] - from[1], to[0] - from[0]);
+  x = radius * Math.cos(angle) + to[0];
+  y = radius * Math.sin(angle) + to[1];
+  ctx.moveTo(x, y);
+  angle += 1 / 3 * (2 * Math.PI);
+  x = radius * Math.cos(angle) + to[0];
+  y = radius * Math.sin(angle) + to[1];
+  ctx.lineTo(x, y);
+  angle += 1 / 3 * (2 * Math.PI);
+  x = radius * Math.cos(angle) + to[0];
+  y = radius * Math.sin(angle) + to[1];
+  ctx.lineTo(x, y);
+  ctx.closePath();
+  ctx.stroke();
+  ctx.fill();
+}
+
+// src/draw/options.ts
+var options3 = {
+  color: "rgba(173, 216, 230, 0.6)",
+  labelColor: "rgba(173, 216, 230, 1)",
+  shadowColor: "black",
+  alpha: 0.5,
+  font: 'small-caps 16px "Segoe UI"',
+  lineHeight: 18,
+  lineWidth: 4,
+  pointSize: 2,
+  roundRect: 8,
+  drawPoints: false,
+  drawLabels: true,
+  drawBoxes: true,
+  drawAttention: true,
+  drawGestures: true,
+  drawPolygons: true,
+  drawGaze: true,
+  fillPolygons: false,
+  useDepth: true,
+  useCurves: false
+};
+
+// src/draw/face.ts
+var opt;
+function drawLabels(f, ctx) {
+  var _a, _b;
+  if (opt.drawLabels) {
+    const labels2 = [];
+    labels2.push(`face: ${Math.trunc(100 * f.score)}%`);
+    if (f.genderScore)
+      labels2.push(`${f.gender || ""} ${Math.trunc(100 * f.genderScore)}%`);
+    if (f.age)
+      labels2.push(`age: ${f.age || ""}`);
+    if (f.iris)
+      labels2.push(`distance: ${f.iris}`);
+    if (f.real)
+      labels2.push(`real: ${Math.trunc(100 * f.real)}%`);
+    if (f.live)
+      labels2.push(`live: ${Math.trunc(100 * f.live)}%`);
+    if (f.emotion && f.emotion.length > 0) {
+      const emotion2 = f.emotion.map((a) => `${Math.trunc(100 * a.score)}% ${a.emotion}`);
+      if (emotion2.length > 3)
+        emotion2.length = 3;
+      labels2.push(emotion2.join(" "));
+    }
+    if (((_a = f.rotation) == null ? void 0 : _a.angle) && ((_b = f.rotation) == null ? void 0 : _b.gaze)) {
+      if (f.rotation.angle.roll)
+        labels2.push(`roll: ${rad2deg(f.rotation.angle.roll)}\xB0 yaw:${rad2deg(f.rotation.angle.yaw)}\xB0 pitch:${rad2deg(f.rotation.angle.pitch)}\xB0`);
+      if (f.rotation.gaze.bearing)
+        labels2.push(`gaze: ${rad2deg(f.rotation.gaze.bearing)}\xB0`);
+    }
+    if (labels2.length === 0)
+      labels2.push("face");
+    ctx.fillStyle = opt.color;
+    for (let i = labels2.length - 1; i >= 0; i--) {
+      const x = Math.max(f.box[0], 0);
+      const y = i * opt.lineHeight + f.box[1];
+      if (opt.shadowColor && opt.shadowColor !== "") {
+        ctx.fillStyle = opt.shadowColor;
+        ctx.fillText(labels2[i], x + 5, y + 16);
+      }
+      ctx.fillStyle = opt.labelColor;
+      ctx.fillText(labels2[i], x + 4, y + 15);
+    }
+  }
+}
+function drawIrisElipse(f, ctx) {
+  var _a, _b, _c, _d;
+  if (((_a = f.annotations) == null ? void 0 : _a.leftEyeIris) && ((_b = f.annotations) == null ? void 0 : _b.leftEyeIris[0])) {
+    ctx.strokeStyle = opt.useDepth ? "rgba(255, 200, 255, 0.3)" : opt.color;
+    ctx.beginPath();
+    const sizeX = Math.abs(f.annotations.leftEyeIris[3][0] - f.annotations.leftEyeIris[1][0]) / 2;
+    const sizeY = Math.abs(f.annotations.leftEyeIris[4][1] - f.annotations.leftEyeIris[2][1]) / 2;
+    ctx.ellipse(f.annotations.leftEyeIris[0][0], f.annotations.leftEyeIris[0][1], sizeX, sizeY, 0, 0, 2 * Math.PI);
+    ctx.stroke();
+    if (opt.fillPolygons) {
+      ctx.fillStyle = opt.useDepth ? "rgba(255, 255, 200, 0.3)" : opt.color;
+      ctx.fill();
+    }
+  }
+  if (((_c = f.annotations) == null ? void 0 : _c.rightEyeIris) && ((_d = f.annotations) == null ? void 0 : _d.rightEyeIris[0])) {
+    ctx.strokeStyle = opt.useDepth ? "rgba(255, 200, 255, 0.3)" : opt.color;
+    ctx.beginPath();
+    const sizeX = Math.abs(f.annotations.rightEyeIris[3][0] - f.annotations.rightEyeIris[1][0]) / 2;
+    const sizeY = Math.abs(f.annotations.rightEyeIris[4][1] - f.annotations.rightEyeIris[2][1]) / 2;
+    ctx.ellipse(f.annotations.rightEyeIris[0][0], f.annotations.rightEyeIris[0][1], sizeX, sizeY, 0, 0, 2 * Math.PI);
+    ctx.stroke();
+    if (opt.fillPolygons) {
+      ctx.fillStyle = opt.useDepth ? "rgba(255, 255, 200, 0.3)" : opt.color;
+      ctx.fill();
+    }
+  }
+}
+function drawGazeSpheres(f, ctx) {
+  var _a;
+  if (opt.drawGaze && ((_a = f.rotation) == null ? void 0 : _a.angle) && typeof Path2D !== "undefined") {
+    ctx.strokeStyle = "pink";
+    const valX = f.box[0] + f.box[2] / 2 - f.box[3] * rad2deg(f.rotation.angle.yaw) / 90;
+    const valY = f.box[1] + f.box[3] / 2 + f.box[2] * rad2deg(f.rotation.angle.pitch) / 90;
+    const pathV = new Path2D(`
+      M ${f.box[0] + f.box[2] / 2} ${f.box[1]}
       C
-        ${o} ${e.box[1]},
-        ${o} ${e.box[1]+e.box[3]},
-        ${e.box[0]+e.box[2]/2} ${e.box[1]+e.box[3]}
-    `),s=new Path2D(`
-      M ${e.box[0]} ${e.box[1]+e.box[3]/2}
+        ${valX} ${f.box[1]},
+        ${valX} ${f.box[1] + f.box[3]},
+        ${f.box[0] + f.box[2] / 2} ${f.box[1] + f.box[3]}
+    `);
+    const pathH = new Path2D(`
+      M ${f.box[0]} ${f.box[1] + f.box[3] / 2}
       C 
-        ${e.box[0]} ${r},
-        ${e.box[0]+e.box[2]} ${r},
-        ${e.box[0]+e.box[2]} ${e.box[1]+e.box[3]/2}
-    `);t.stroke(s),t.stroke(A)}}function Ms(e,t){var n;if(Y.drawGaze&&((n=e.rotation)==null?void 0:n.gaze.strength)&&e.rotation.gaze.bearing&&e.annotations.leftEyeIris&&e.annotations.rightEyeIris&&e.annotations.leftEyeIris[0]&&e.annotations.rightEyeIris[0]){t.strokeStyle="pink",t.fillStyle="pink";let o=[e.annotations.leftEyeIris[0][0]+Math.sin(e.rotation.gaze.bearing)*e.rotation.gaze.strength*e.box[3],e.annotations.leftEyeIris[0][1]+Math.cos(e.rotation.gaze.bearing)*e.rotation.gaze.strength*e.box[2]];a1(t,[e.annotations.leftEyeIris[0][0],e.annotations.leftEyeIris[0][1]],[o[0],o[1]],4);let r=[e.annotations.rightEyeIris[0][0]+Math.sin(e.rotation.gaze.bearing)*e.rotation.gaze.strength*e.box[3],e.annotations.rightEyeIris[0][1]+Math.cos(e.rotation.gaze.bearing)*e.rotation.gaze.strength*e.box[2]];a1(t,[e.annotations.rightEyeIris[0][0],e.annotations.rightEyeIris[0][1]],[r[0],r[1]],4)}}function vs(e,t){if(Y.drawPolygons&&e.mesh.length>=468){t.lineWidth=1;for(let n=0;n<Ve.length/3;n++){let o=[Ve[n*3+0],Ve[n*3+1],Ve[n*3+2]].map(r=>e.mesh[r]);s1(t,o,Y)}bs(e,t)}}function Ps(e,t){if(Y.drawPoints&&e.mesh.length>=468)for(let n=0;n<e.mesh.length;n++)be(t,e.mesh[n][0],e.mesh[n][1],e.mesh[n][2],Y),Y.drawAttention&&(O2.includes(n)&&be(t,e.mesh[n][0],e.mesh[n][1],e.mesh[n][2]+127,Y),Xe.includes(n)&&be(t,e.mesh[n][0],e.mesh[n][1],e.mesh[n][2]-127,Y),qe.includes(n)&&be(t,e.mesh[n][0],e.mesh[n][1],e.mesh[n][2]-127,Y))}function Ts(e,t){Y.drawBoxes&&de(t,e.box[0],e.box[1],e.box[2],e.box[3],Y)}function h2(e,t,n){if(Y=A0(R0,n),!t||!e)return;let o=X0(e);if(!!o){o.font=Y.font,o.strokeStyle=Y.color,o.fillStyle=Y.color;for(let r of t)Ts(r,o),hs(r,o),r.mesh&&r.mesh.length>0&&(Ps(r,o),vs(r,o),gs(r,o),Ms(r,o))}}function b2(e,t,n){let o=A0(R0,n);if(!t||!e)return;let r=X0(e);if(!!r){r.lineJoin="round";for(let A=0;A<t.length;A++){if(r.strokeStyle=o.color,r.fillStyle=o.color,r.lineWidth=o.lineWidth,r.font=o.font,o.drawBoxes&&t[A].box&&t[A].box.length===4&&(de(r,t[A].box[0],t[A].box[1],t[A].box[2],t[A].box[3],o),o.drawLabels&&(o.shadowColor&&o.shadowColor!==""&&(r.fillStyle=o.shadowColor,r.fillText(`body ${100*t[A].score}%`,t[A].box[0]+3,1+t[A].box[1]+o.lineHeight,t[A].box[2])),r.fillStyle=o.labelColor,r.fillText(`body ${100*t[A].score}%`,t[A].box[0]+2,0+t[A].box[1]+o.lineHeight,t[A].box[2]))),o.drawPoints&&t[A].keypoints)for(let s=0;s<t[A].keypoints.length;s++)!t[A].keypoints[s].score||t[A].keypoints[s].score===0||(r.fillStyle=he(t[A].keypoints[s].position[2],o),be(r,t[A].keypoints[s].position[0],t[A].keypoints[s].position[1],0,o));if(o.drawLabels&&t[A].keypoints){r.font=o.font;for(let s of t[A].keypoints)!s.score||s.score===0||(r.fillStyle=he(s.position[2],o),r.fillText(`${s.part} ${Math.trunc(100*s.score)}%`,s.position[0]+4,s.position[1]+4))}if(o.drawPolygons&&t[A].keypoints&&t[A].annotations)for(let s of Object.values(t[A].annotations))for(let a of s)io(r,a,o)}}}function g2(e,t,n){let o=A0(R0,n);if(!t||!e)return;let r=X0(e);if(!!r){r.lineJoin="round",r.font=o.font;for(let A of t){if(o.drawBoxes&&(r.strokeStyle=o.color,r.fillStyle=o.color,de(r,A.box[0],A.box[1],A.box[2],A.box[3],o),o.drawLabels&&(o.shadowColor&&o.shadowColor!==""&&(r.fillStyle=o.shadowColor,r.fillText(`hand:${Math.trunc(100*A.score)}%`,A.box[0]+3,1+A.box[1]+o.lineHeight,A.box[2])),r.fillStyle=o.labelColor,r.fillText(`hand:${Math.trunc(100*A.score)}%`,A.box[0]+2,0+A.box[1]+o.lineHeight,A.box[2])),r.stroke()),o.drawPoints&&A.keypoints&&A.keypoints.length>0)for(let s of A.keypoints)r.fillStyle=he(s[2],o),be(r,s[0],s[1],0,o);if(o.drawLabels&&A.annotations){let s=(a,l)=>{if(!a||a.length===0||!a[0])return;let 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InsightFace:"),e.config.async?l=(T=e.config.face.insightface)!=null&&T.enabled?Pt(d[E].tensor||r0.tensor([]),e.config,E,d.length):null:(e.state="run:mobilefacenet",n=v(),l=(F=e.config.face.insightface)!=null&&F.enabled?await Pt(d[E].tensor||r0.tensor([]),e.config,E,d.length):null,e.performance.mobilefacenet=Math.trunc(v()-n)),e.analyze("End InsightFace:"),e.analyze("Start Description:"),e.config.async?i=zt(d[E].tensor||r0.tensor([]),e.config,E,d.length):(e.state="run:description",n=v(),i=await zt(d[E].tensor||r0.tensor([]),e.config,E,d.length),e.performance.description=k.perfadd?(e.performance.description||0)+Math.trunc(v()-n):Math.trunc(v()-n)),e.analyze("End Description:"),e.config.async&&([o,A,s,a,l,i,r,c,x]=await Promise.all([o,A,s,a,l,i,r,c,x])),e.analyze("Finish Face:"),((M0=e.config.face.ssrnet)==null?void 0:M0.enabled)&&o&&A&&(i={...i,age:o.age,gender:A.gender,genderScore:A.genderScore}),(($=e.config.face.gear)==null?void 0:$.enabled)&&r&&(i={...i,age:r.age,gender:r.gender,genderScore:r.genderScore,race:r.race}),((m0=e.config.face.mobilefacenet)==null?void 0:m0.enabled)&&a&&(i.descriptor=a),((d0=e.config.face.insightface)==null?void 0:d0.enabled)&&l&&(i.descriptor=l),(G=e.config.face.iris)!=null&&G.enabled;let e2=((U0=(q0=(Z=d[E])==null?void 0:Z.annotations)==null?void 0:q0.leftEyeIris)==null?void 0:U0[0])&&((xe=(Me=(ge=d[E])==null?void 0:ge.annotations)==null?void 0:Me.rightEyeIris)==null?void 0:xe[0])&&d[E].annotations.leftEyeIris.length>0&&d[E].annotations.rightEyeIris.length>0&&d[E].annotations.leftEyeIris[0]!==null&&d[E].annotations.rightEyeIris[0]!==null?Math.max(Math.abs(d[E].annotations.leftEyeIris[3][0]-d[E].annotations.leftEyeIris[1][0]),Math.abs(d[E].annotations.rightEyeIris[4][1]-d[E].annotations.rightEyeIris[2][1]))/t.shape[2]:0,g1=(R2=e.config.face.detector)!=null&&R2.return?r0.squeeze(d[E].tensor):null;r0.dispose(d[E].tensor),d[E].tensor&&delete d[E].tensor;let Y0={...d[E],id:E};i.age&&(Y0.age=i.age),i.gender&&(Y0.gender=i.gender),i.genderScore&&(Y0.genderScore=i.genderScore),i.descriptor&&(Y0.embedding=i.descriptor),i.race&&(Y0.race=i.race),s&&(Y0.emotion=s),c&&(Y0.real=c),x&&(Y0.live=x),e2&&e2!==0&&(Y0.iris=Math.trunc(500/e2/11.7)/100),k2&&(Y0.rotation=k2),g1&&(Y0.tensor=g1),y.push(Y0),e.analyze("End Face")}return e.analyze("End FaceMesh:"),e.config.async&&(e.performance.face&&delete e.performance.face,e.performance.age&&delete e.performance.age,e.performance.gender&&delete e.performance.gender,e.performance.emotion&&delete e.performance.emotion),y};var yo=e=>{if(!e)return[];let t=[];for(let n=0;n<e.length;n++){let o=e[n].keypoints.find(l=>l.part==="leftWrist"),r=e[n].keypoints.find(l=>l.part==="rightWrist"),A=e[n].keypoints.find(l=>l.part==="nose");A&&o&&r&&o.position[1]<A.position[1]&&r.position[1]<A.position[1]?t.push({body:n,gesture:"i give up"}):A&&o&&o.position[1]<A.position[1]?t.push({body:n,gesture:"raise left 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a=Math.min(100,500*Math.abs(e[n].mesh[13][1]-e[n].mesh[14][1])/Math.abs(e[n].mesh[10][1]-e[n].mesh[152][1]));a>10&&t.push({face:n,gesture:`mouth ${Math.trunc(a)}% open`});let l=e[n].mesh[152][2]||0;Math.abs(l)>10&&t.push({face:n,gesture:`head ${l<0?"up":"down"}`})}return t},mo=e=>{var n,o,r,A;if(!e)return[];let t=[];for(let s=0;s<e.length;s++){if(!((o=(n=e[s].annotations)==null?void 0:n.leftEyeIris)!=null&&o[0])||!((A=(r=e[s].annotations)==null?void 0:r.rightEyeIris)!=null&&A[0]))continue;let a=e[s].annotations.leftEyeIris[3][0]-e[s].annotations.leftEyeIris[1][0],l=e[s].annotations.leftEyeIris[4][1]-e[s].annotations.leftEyeIris[2][1],c=Math.abs(a*l),x=e[s].annotations.rightEyeIris[3][0]-e[s].annotations.rightEyeIris[1][0],i=e[s].annotations.rightEyeIris[4][1]-e[s].annotations.rightEyeIris[2][1],y=Math.abs(x*i),d=!1;Math.abs(c-y)/Math.max(c,y)<.25&&(d=!0,t.push({iris:s,gesture:"facing center"}));let p=Math.abs(e[s].mesh[263][0]-e[s].annotations.leftEyeIris[0][0])/e[s].box[2],g=Math.abs(e[s].mesh[33][0]-e[s].annotations.rightEyeIris[0][0])/e[s].box[2];(p>.06||g>.06)&&(d=!1),p>g?p>.05&&t.push({iris:s,gesture:"looking right"}):g>.05&&t.push({iris:s,gesture:"looking left"});let M=Math.abs(e[s].mesh[145][1]-e[s].annotations.rightEyeIris[0][1])/e[s].box[3],P=Math.abs(e[s].mesh[374][1]-e[s].annotations.leftEyeIris[0][1])/e[s].box[3];(P<.01||M<.01||P>.022||M>.022)&&(d=!1),(P<.01||M<.01)&&t.push({iris:s,gesture:"looking down"}),(P>.022||M>.022)&&t.push({iris:s,gesture:"looking up"}),d&&t.push({iris:s,gesture:"looking center"})}return t},po=e=>{if(!e)return[];let t=[];for(let n=0;n<e.length;n++){let o=[];if(e[n].annotations)for(let[r,A]of Object.entries(e[n].annotations))r!=="palmBase"&&Array.isArray(A)&&A[0]&&o.push({name:r.toLowerCase(),position:A[0]});if(o&&o.length>0){let r=o.reduce((s,a)=>(s.position[2]||0)<(a.position[2]||0)?s:a);t.push({hand:n,gesture:`${r.name} forward`});let A=o.reduce((s,a)=>s.position[1]<a.position[1]?s:a);t.push({hand:n,gesture:`${A.name} up`})}if(e[n].keypoints){let r=Pn(e[n].keypoints);for(let A of r)t.push({hand:n,gesture:A.name})}}return t};var j={face:[],body:[],hand:[],gesture:[],object:[],persons:[],performance:{},timestamp:0,error:null},m1=0;function uo(e,t){var s,a,l,c,x,i,y,d,f,p,g,M,P,m,b,z,w,I,q,e0,V,X,c0;let n=v();if(!e)return{face:[],body:[],hand:[],gesture:[],object:[],persons:[],performance:{},timestamp:0,error:null};let o=Date.now()-e.timestamp,r=o<1e3?8-Math.log(o+1):1;if(e.canvas&&(j.canvas=e.canvas),e.error&&(j.error=e.error),!j.body||e.body.length!==j.body.length)j.body=JSON.parse(JSON.stringify(e.body));else for(let T=0;T<e.body.length;T++){let F=e.body[T].box.map((G,Z)=>((r-1)*j.body[T].box[Z]+G)/r),M0=e.body[T].boxRaw.map((G,Z)=>((r-1)*j.body[T].boxRaw[Z]+G)/r),$=e.body[T].keypoints.map((G,Z)=>{var q0,U0,ge,Me,xe,R2,E,k2,e2;return{score:G.score,part:G.part,position:[j.body[T].keypoints[Z]?((r-1)*(j.body[T].keypoints[Z].position[0]||0)+(G.position[0]||0))/r:G.position[0],j.body[T].keypoints[Z]?((r-1)*(j.body[T].keypoints[Z].position[1]||0)+(G.position[1]||0))/r:G.position[1],j.body[T].keypoints[Z]?((r-1)*(j.body[T].keypoints[Z].position[2]||0)+(G.position[2]||0))/r:G.position[2]],positionRaw:[j.body[T].keypoints[Z]?((r-1)*(j.body[T].keypoints[Z].positionRaw[0]||0)+(G.positionRaw[0]||0))/r:G.positionRaw[0],j.body[T].keypoints[Z]?((r-1)*(j.body[T].keypoints[Z].positionRaw[1]||0)+(G.positionRaw[1]||0))/r:G.positionRaw[1],j.body[T].keypoints[Z]?((r-1)*(j.body[T].keypoints[Z].positionRaw[2]||0)+(G.positionRaw[2]||0))/r:G.positionRaw[2]],distance:[j.body[T].keypoints[Z]?((r-1)*(((q0=j.body[T].keypoints[Z].distance)==null?void 0:q0[0])||0)+(((U0=G.distance)==null?void 0:U0[0])||0))/r:(ge=G.distance)==null?void 0:ge[0],j.body[T].keypoints[Z]?((r-1)*(((Me=j.body[T].keypoints[Z].distance)==null?void 0:Me[1])||0)+(((xe=G.distance)==null?void 0:xe[1])||0))/r:(R2=G.distance)==null?void 0:R2[1],j.body[T].keypoints[Z]?((r-1)*(((E=j.body[T].keypoints[Z].distance)==null?void 0:E[2])||0)+(((k2=G.distance)==null?void 0:k2[2])||0))/r:(e2=G.distance)==null?void 0:e2[2]]}}),m0={},d0={connected:{}};(s=t.body.modelPath)!=null&&s.includes("efficientpose")?d0=c5:(a=t.body.modelPath)!=null&&a.includes("blazepose")?d0=A5:(l=t.body.modelPath)!=null&&l.includes("movenet")&&(d0=F2);for(let[G,Z]of Object.entries(d0.connected)){let q0=[];for(let U0=0;U0<Z.length-1;U0++){let ge=$.find(xe=>xe.part===Z[U0]),Me=$.find(xe=>xe.part===Z[U0+1]);ge&&Me&&q0.push([ge.position,Me.position])}m0[G]=q0}j.body[T]={...e.body[T],box:F,boxRaw:M0,keypoints:$,annotations:m0}}if(!j.hand||e.hand.length!==j.hand.length)j.hand=JSON.parse(JSON.stringify(e.hand));else for(let T=0;T<e.hand.length;T++){let F=e.hand[T].box.map((d0,G)=>((r-1)*j.hand[T].box[G]+d0)/r),M0=e.hand[T].boxRaw.map((d0,G)=>((r-1)*j.hand[T].boxRaw[G]+d0)/r);j.hand[T].keypoints.length!==e.hand[T].keypoints.length&&(j.hand[T].keypoints=e.hand[T].keypoints);let $=e.hand[T].keypoints&&e.hand[T].keypoints.length>0?e.hand[T].keypoints.map((d0,G)=>d0.map((Z,q0)=>((r-1)*(j.hand[T].keypoints[G][q0]||1)+(Z||0))/r)):[],m0={};if(Object.keys(j.hand[T].annotations).length!==Object.keys(e.hand[T].annotations).length)j.hand[T].annotations=e.hand[T].annotations,m0=j.hand[T].annotations;else if(e.hand[T].annotations)for(let d0 of Object.keys(e.hand[T].annotations))m0[d0]=(i=(x=(c=e.hand[T])==null?void 0:c.annotations)==null?void 0:x[d0])!=null&&i[0]?e.hand[T].annotations[d0].map((G,Z)=>G.map((q0,U0)=>((r-1)*j.hand[T].annotations[d0][Z][U0]+q0)/r)):null;j.hand[T]={...e.hand[T],box:F,boxRaw:M0,keypoints:$,annotations:m0}}if(!j.face||e.face.length!==j.face.length)j.face=JSON.parse(JSON.stringify(e.face));else for(let T=0;T<e.face.length;T++){let F=e.face[T].box.map(($,m0)=>((r-1)*j.face[T].box[m0]+$)/r),M0=e.face[T].boxRaw.map(($,m0)=>((r-1)*j.face[T].boxRaw[m0]+$)/r);if(e.face[T].rotation){let $={matrix:[0,0,0,0,0,0,0,0,0],angle:{roll:0,yaw:0,pitch:0},gaze:{bearing:0,strength:0}};$.matrix=(y=e.face[T].rotation)==null?void 0:y.matrix,$.angle={roll:((r-1)*(((f=(d=j.face[T].rotation)==null?void 0:d.angle)==null?void 0:f.roll)||0)+(((g=(p=e.face[T].rotation)==null?void 0:p.angle)==null?void 0:g.roll)||0))/r,yaw:((r-1)*(((P=(M=j.face[T].rotation)==null?void 0:M.angle)==null?void 0:P.yaw)||0)+(((b=(m=e.face[T].rotation)==null?void 0:m.angle)==null?void 0:b.yaw)||0))/r,pitch:((r-1)*(((w=(z=j.face[T].rotation)==null?void 0:z.angle)==null?void 0:w.pitch)||0)+(((q=(I=e.face[T].rotation)==null?void 0:I.angle)==null?void 0:q.pitch)||0))/r},$.gaze={bearing:((r-1)*(((e0=j.face[T].rotation)==null?void 0:e0.gaze.bearing)||0)+(((V=e.face[T].rotation)==null?void 0:V.gaze.bearing)||0))/r,strength:((r-1)*(((X=j.face[T].rotation)==null?void 0:X.gaze.strength)||0)+(((c0=e.face[T].rotation)==null?void 0:c0.gaze.strength)||0))/r},j.face[T]={...e.face[T],rotation:$,box:F,boxRaw:M0}}else j.face[T]={...e.face[T],box:F,boxRaw:M0}}if(!j.object||e.object.length!==j.object.length)j.object=JSON.parse(JSON.stringify(e.object));else for(let T=0;T<e.object.length;T++){let F=e.object[T].box.map(($,m0)=>((r-1)*j.object[T].box[m0]+$)/r),M0=e.object[T].boxRaw.map(($,m0)=>((r-1)*j.object[T].boxRaw[m0]+$)/r);j.object[T]={...e.object[T],box:F,boxRaw:M0}}if(e.persons){let T=e.persons;if(!j.persons||T.length!==j.persons.length)j.persons=JSON.parse(JSON.stringify(T));else for(let F=0;F<T.length;F++)j.persons[F].box=T[F].box.map((M0,$)=>((r-1)*j.persons[F].box[$]+M0)/r)}e.gesture&&(j.gesture=e.gesture);let A=v();return m1=k.perfadd?m1+Math.round(A-n):Math.round(A-n),e.performance&&(j.performance={...e.performance,interpolate:m1}),j}var h1={};ve(h1,{distance:()=>V2,match:()=>u1,similarity:()=>p1});function V2(e,t,n={order:2,multiplier:25}){if(!e||!e)return Number.MAX_SAFE_INTEGER;let o=0;for(let r=0;r<e.length;r++){let A=!n.order||n.order===2?e[r]-t[r]:Math.abs(e[r]-t[r]);o+=!n.order||n.order===2?A*A:A**n.order}return(n.multiplier||20)*o}var ho=(e,t,n,o)=>{if(e===0)return 1;let r=t===2?Math.sqrt(e):e**(1/t),A=(1-r/100-n)/(o-n);return Math.max(Math.min(A,1),0)};function p1(e,t,n={order:2,multiplier:25,min:.2,max:.8}){let o=V2(e,t,n);return ho(o,n.order||2,n.min||0,n.max||1)}function u1(e,t,n={order:2,multiplier:25,threshold:0,min:.2,max:.8}){if(!Array.isArray(e)||!Array.isArray(t)||e.length<64||t.length===0)return{index:-1,distance:Number.POSITIVE_INFINITY,similarity:0};let o=Number.MAX_SAFE_INTEGER,r=-1;for(let s=0;s<t.length;s++){let a=t[s].length===e.length?V2(e,t[s],n):Number.MAX_SAFE_INTEGER;if(a<o&&(o=a,r=s),o<(n.threshold||0))break}let A=ho(o,n.order||2,n.min||0,n.max||1);return{index:r,distance:o,similarity:A}}function bo(e,t,n,o,r){var a,l,c,x,i,y;let A=0,s=[];for(let d of e){let f={id:A++,face:d,body:null,hands:{left:null,right:null},gestures:[],box:[0,0,0,0]};for(let b of t)d.box[0]>b.box[0]&&d.box[0]<b.box[0]+b.box[2]&&d.box[1]+d.box[3]>b.box[1]&&d.box[1]+d.box[3]<b.box[1]+b.box[3]&&(f.body=b);if(f.body)for(let b of n)b.box[0]+b.box[2]>f.body.box[0]&&b.box[0]+b.box[2]<f.body.box[0]+f.body.box[2]&&b.box[1]+b.box[3]>f.body.box[1]&&b.box[1]+b.box[3]<f.body.box[1]+f.body.box[3]&&f.hands&&(f.hands.left=b),b.box[0]<f.body.box[0]+f.body.box[2]&&b.box[0]>f.body.box[0]&&b.box[1]+b.box[3]>f.body.box[1]&&b.box[1]+b.box[3]<f.body.box[1]+f.body.box[3]&&f.hands&&(f.hands.right=b);for(let b of o)(b.face!==void 0&&b.face===d.id||b.iris!==void 0&&b.iris===d.id||b.body!==void 0&&b.body===((a=f.body)==null?void 0:a.id)||b.hand!==void 0&&b.hand===((l=f.hands.left)==null?void 0:l.id)||b.hand!==void 0&&b.hand===((c=f.hands.right)==null?void 0:c.id))&&f.gestures.push(b);let p=[],g=[],M=b=>{b&&b.length===4&&(p.push(b[0],b[0]+b[2]),g.push(b[1],b[1]+b[3]))};M(f.face.box),M((x=f.body)==null?void 0:x.box),M((i=f.hands.left)==null?void 0:i.box),M((y=f.hands.right)==null?void 0:y.box);let P=Math.min(...p),m=Math.min(...g);f.box=[P,m,Math.max(...p)-P,Math.max(...g)-m],(r==null?void 0:r[1])&&(r==null?void 0:r[2])&&(f.boxRaw=[f.box[0]/r[2],f.box[1]/r[1],f.box[2]/r[2],f.box[3]/r[1]]),s.push(f)}return s}var C5=`
+        ${f.box[0]} ${valY},
+        ${f.box[0] + f.box[2]} ${valY},
+        ${f.box[0] + f.box[2]} ${f.box[1] + f.box[3] / 2}
+    `);
+    ctx.stroke(pathH);
+    ctx.stroke(pathV);
+  }
+}
+function drawGazeArrows(f, ctx) {
+  var _a;
+  if (opt.drawGaze && ((_a = f.rotation) == null ? void 0 : _a.gaze.strength) && f.rotation.gaze.bearing && f.annotations.leftEyeIris && f.annotations.rightEyeIris && f.annotations.leftEyeIris[0] && f.annotations.rightEyeIris[0]) {
+    ctx.strokeStyle = "pink";
+    ctx.fillStyle = "pink";
+    const leftGaze = [
+      f.annotations.leftEyeIris[0][0] + Math.sin(f.rotation.gaze.bearing) * f.rotation.gaze.strength * f.box[3],
+      f.annotations.leftEyeIris[0][1] + Math.cos(f.rotation.gaze.bearing) * f.rotation.gaze.strength * f.box[2]
+    ];
+    arrow(ctx, [f.annotations.leftEyeIris[0][0], f.annotations.leftEyeIris[0][1]], [leftGaze[0], leftGaze[1]], 4);
+    const rightGaze = [
+      f.annotations.rightEyeIris[0][0] + Math.sin(f.rotation.gaze.bearing) * f.rotation.gaze.strength * f.box[3],
+      f.annotations.rightEyeIris[0][1] + Math.cos(f.rotation.gaze.bearing) * f.rotation.gaze.strength * f.box[2]
+    ];
+    arrow(ctx, [f.annotations.rightEyeIris[0][0], f.annotations.rightEyeIris[0][1]], [rightGaze[0], rightGaze[1]], 4);
+  }
+}
+function drawFacePolygons(f, ctx) {
+  if (opt.drawPolygons && f.mesh.length >= 468) {
+    ctx.lineWidth = 1;
+    for (let i = 0; i < TRI468.length / 3; i++) {
+      const points = [TRI468[i * 3 + 0], TRI468[i * 3 + 1], TRI468[i * 3 + 2]].map((index2) => f.mesh[index2]);
+      lines(ctx, points, opt);
+    }
+    drawIrisElipse(f, ctx);
+  }
+}
+function drawFacePoints(f, ctx) {
+  if (opt.drawPoints && f.mesh.length >= 468) {
+    for (let i = 0; i < f.mesh.length; i++) {
+      point(ctx, f.mesh[i][0], f.mesh[i][1], f.mesh[i][2], opt);
+      if (opt.drawAttention) {
+        if (LANDMARKS_REFINEMENT_LIPS_CONFIG.includes(i))
+          point(ctx, f.mesh[i][0], f.mesh[i][1], f.mesh[i][2] + 127, opt);
+        if (LANDMARKS_REFINEMENT_LEFT_EYE_CONFIG.includes(i))
+          point(ctx, f.mesh[i][0], f.mesh[i][1], f.mesh[i][2] - 127, opt);
+        if (LANDMARKS_REFINEMENT_RIGHT_EYE_CONFIG.includes(i))
+          point(ctx, f.mesh[i][0], f.mesh[i][1], f.mesh[i][2] - 127, opt);
+      }
+    }
+  }
+}
+function drawFaceBoxes(f, ctx) {
+  if (opt.drawBoxes) {
+    rect(ctx, f.box[0], f.box[1], f.box[2], f.box[3], opt);
+  }
+}
+function face(inCanvas2, result, drawOptions) {
+  opt = mergeDeep(options3, drawOptions);
+  if (!result || !inCanvas2)
+    return;
+  const ctx = getCanvasContext(inCanvas2);
+  if (!ctx)
+    return;
+  ctx.font = opt.font;
+  ctx.strokeStyle = opt.color;
+  ctx.fillStyle = opt.color;
+  for (const f of result) {
+    drawFaceBoxes(f, ctx);
+    drawLabels(f, ctx);
+    if (f.mesh && f.mesh.length > 0) {
+      drawFacePoints(f, ctx);
+      drawFacePolygons(f, ctx);
+      drawGazeSpheres(f, ctx);
+      drawGazeArrows(f, ctx);
+    }
+  }
+}
+
+// src/draw/body.ts
+function body(inCanvas2, result, drawOptions) {
+  const localOptions = mergeDeep(options3, drawOptions);
+  if (!result || !inCanvas2)
+    return;
+  const ctx = getCanvasContext(inCanvas2);
+  if (!ctx)
+    return;
+  ctx.lineJoin = "round";
+  for (let i = 0; i < result.length; i++) {
+    ctx.strokeStyle = localOptions.color;
+    ctx.fillStyle = localOptions.color;
+    ctx.lineWidth = localOptions.lineWidth;
+    ctx.font = localOptions.font;
+    if (localOptions.drawBoxes && result[i].box && result[i].box.length === 4) {
+      rect(ctx, result[i].box[0], result[i].box[1], result[i].box[2], result[i].box[3], localOptions);
+      if (localOptions.drawLabels) {
+        if (localOptions.shadowColor && localOptions.shadowColor !== "") {
+          ctx.fillStyle = localOptions.shadowColor;
+          ctx.fillText(`body ${100 * result[i].score}%`, result[i].box[0] + 3, 1 + result[i].box[1] + localOptions.lineHeight, result[i].box[2]);
+        }
+        ctx.fillStyle = localOptions.labelColor;
+        ctx.fillText(`body ${100 * result[i].score}%`, result[i].box[0] + 2, 0 + result[i].box[1] + localOptions.lineHeight, result[i].box[2]);
+      }
+    }
+    if (localOptions.drawPoints && result[i].keypoints) {
+      for (let pt = 0; pt < result[i].keypoints.length; pt++) {
+        if (!result[i].keypoints[pt].score || result[i].keypoints[pt].score === 0)
+          continue;
+        ctx.fillStyle = colorDepth(result[i].keypoints[pt].position[2], localOptions);
+        point(ctx, result[i].keypoints[pt].position[0], result[i].keypoints[pt].position[1], 0, localOptions);
+      }
+    }
+    if (localOptions.drawLabels && result[i].keypoints) {
+      ctx.font = localOptions.font;
+      for (const pt of result[i].keypoints) {
+        if (!pt.score || pt.score === 0)
+          continue;
+        ctx.fillStyle = colorDepth(pt.position[2], localOptions);
+        ctx.fillText(`${pt.part} ${Math.trunc(100 * pt.score)}%`, pt.position[0] + 4, pt.position[1] + 4);
+      }
+    }
+    if (localOptions.drawPolygons && result[i].keypoints && result[i].annotations) {
+      for (const part of Object.values(result[i].annotations)) {
+        for (const connected4 of part)
+          curves(ctx, connected4, localOptions);
+      }
+    }
+  }
+}
+
+// src/draw/hand.ts
+function hand(inCanvas2, result, drawOptions) {
+  const localOptions = mergeDeep(options3, drawOptions);
+  if (!result || !inCanvas2)
+    return;
+  const ctx = getCanvasContext(inCanvas2);
+  if (!ctx)
+    return;
+  ctx.lineJoin = "round";
+  ctx.font = localOptions.font;
+  for (const h of result) {
+    if (localOptions.drawBoxes) {
+      ctx.strokeStyle = localOptions.color;
+      ctx.fillStyle = localOptions.color;
+      rect(ctx, h.box[0], h.box[1], h.box[2], h.box[3], localOptions);
+      if (localOptions.drawLabels) {
+        if (localOptions.shadowColor && localOptions.shadowColor !== "") {
+          ctx.fillStyle = localOptions.shadowColor;
+          ctx.fillText(`hand:${Math.trunc(100 * h.score)}%`, h.box[0] + 3, 1 + h.box[1] + localOptions.lineHeight, h.box[2]);
+        }
+        ctx.fillStyle = localOptions.labelColor;
+        ctx.fillText(`hand:${Math.trunc(100 * h.score)}%`, h.box[0] + 2, 0 + h.box[1] + localOptions.lineHeight, h.box[2]);
+      }
+      ctx.stroke();
+    }
+    if (localOptions.drawPoints) {
+      if (h.keypoints && h.keypoints.length > 0) {
+        for (const pt of h.keypoints) {
+          ctx.fillStyle = colorDepth(pt[2], localOptions);
+          point(ctx, pt[0], pt[1], 0, localOptions);
+        }
+      }
+    }
+    if (localOptions.drawLabels && h.annotations) {
+      const addHandLabel = (part, title) => {
+        if (!part || part.length === 0 || !part[0])
+          return;
+        const z = part[part.length - 1][2] || -256;
+        ctx.fillStyle = colorDepth(z, localOptions);
+        ctx.fillText(title, part[part.length - 1][0] + 4, part[part.length - 1][1] + 4);
+      };
+      ctx.font = localOptions.font;
+      addHandLabel(h.annotations.index, "index");
+      addHandLabel(h.annotations.middle, "middle");
+      addHandLabel(h.annotations.ring, "ring");
+      addHandLabel(h.annotations.pinky, "pinky");
+      addHandLabel(h.annotations.thumb, "thumb");
+      addHandLabel(h.annotations.palm, "palm");
+    }
+    if (localOptions.drawPolygons && h.annotations) {
+      const addHandLine = (part) => {
+        if (!part || part.length === 0 || !part[0])
+          return;
+        for (let i = 0; i < part.length; i++) {
+          ctx.beginPath();
+          const z = part[i][2] || 0;
+          ctx.strokeStyle = colorDepth(i * z, localOptions);
+          ctx.moveTo(part[i > 0 ? i - 1 : 0][0], part[i > 0 ? i - 1 : 0][1]);
+          ctx.lineTo(part[i][0], part[i][1]);
+          ctx.stroke();
+        }
+      };
+      ctx.lineWidth = localOptions.lineWidth;
+      addHandLine(h.annotations.index);
+      addHandLine(h.annotations.middle);
+      addHandLine(h.annotations.ring);
+      addHandLine(h.annotations.pinky);
+      addHandLine(h.annotations.thumb);
+    }
+  }
+}
+
+// src/draw/object.ts
+function object(inCanvas2, result, drawOptions) {
+  const localOptions = mergeDeep(options3, drawOptions);
+  if (!result || !inCanvas2)
+    return;
+  const ctx = getCanvasContext(inCanvas2);
+  if (!ctx)
+    return;
+  ctx.lineJoin = "round";
+  ctx.font = localOptions.font;
+  for (const h of result) {
+    if (localOptions.drawBoxes) {
+      ctx.strokeStyle = localOptions.color;
+      ctx.fillStyle = localOptions.color;
+      rect(ctx, h.box[0], h.box[1], h.box[2], h.box[3], localOptions);
+      if (localOptions.drawLabels) {
+        const label = `${h.label} ${Math.round(100 * h.score)}%`;
+        if (localOptions.shadowColor && localOptions.shadowColor !== "") {
+          ctx.fillStyle = localOptions.shadowColor;
+          ctx.fillText(label, h.box[0] + 3, 1 + h.box[1] + localOptions.lineHeight, h.box[2]);
+        }
+        ctx.fillStyle = localOptions.labelColor;
+        ctx.fillText(label, h.box[0] + 2, 0 + h.box[1] + localOptions.lineHeight, h.box[2]);
+      }
+      ctx.stroke();
+    }
+  }
+}
+
+// src/draw/gesture.ts
+function gesture(inCanvas2, result, drawOptions) {
+  const localOptions = mergeDeep(options3, drawOptions);
+  if (!result || !inCanvas2)
+    return;
+  if (localOptions.drawGestures) {
+    const ctx = getCanvasContext(inCanvas2);
+    if (!ctx)
+      return;
+    ctx.font = localOptions.font;
+    ctx.fillStyle = localOptions.color;
+    let i = 1;
+    for (let j = 0; j < result.length; j++) {
+      let where = [];
+      let what = [];
+      [where, what] = Object.entries(result[j]);
+      if (what.length > 1 && what[1].length > 0) {
+        const who = where[1] > 0 ? `#${where[1]}` : "";
+        const label = `${where[0]} ${who}: ${what[1]}`;
+        if (localOptions.shadowColor && localOptions.shadowColor !== "") {
+          ctx.fillStyle = localOptions.shadowColor;
+          ctx.fillText(label, 8, 2 + i * localOptions.lineHeight);
+        }
+        ctx.fillStyle = localOptions.labelColor;
+        ctx.fillText(label, 6, 0 + i * localOptions.lineHeight);
+        i += 1;
+      }
+    }
+  }
+}
+
+// src/draw/draw.ts
+var drawTime = 0;
+function person(inCanvas2, result, drawOptions) {
+  const localOptions = mergeDeep(options3, drawOptions);
+  if (!result || !inCanvas2)
+    return;
+  const ctx = getCanvasContext(inCanvas2);
+  if (!ctx)
+    return;
+  ctx.lineJoin = "round";
+  ctx.font = localOptions.font;
+  for (let i = 0; i < result.length; i++) {
+    if (localOptions.drawBoxes) {
+      ctx.strokeStyle = localOptions.color;
+      ctx.fillStyle = localOptions.color;
+      rect(ctx, result[i].box[0], result[i].box[1], result[i].box[2], result[i].box[3], localOptions);
+      if (localOptions.drawLabels) {
+        const label = `person #${i}`;
+        if (localOptions.shadowColor && localOptions.shadowColor !== "") {
+          ctx.fillStyle = localOptions.shadowColor;
+          ctx.fillText(label, result[i].box[0] + 3, 1 + result[i].box[1] + localOptions.lineHeight, result[i].box[2]);
+        }
+        ctx.fillStyle = localOptions.labelColor;
+        ctx.fillText(label, result[i].box[0] + 2, 0 + result[i].box[1] + localOptions.lineHeight, result[i].box[2]);
+      }
+      ctx.stroke();
+    }
+  }
+}
+function canvas2(input, output) {
+  if (!input || !output)
+    return;
+  const ctx = getCanvasContext(output);
+  if (!ctx)
+    return;
+  ctx.drawImage(input, 0, 0);
+}
+async function all(inCanvas2, result, drawOptions) {
+  if (!(result == null ? void 0 : result.performance) || !inCanvas2)
+    return null;
+  const timeStamp = now();
+  const localOptions = mergeDeep(options3, drawOptions);
+  const promise = Promise.all([
+    face(inCanvas2, result.face, localOptions),
+    body(inCanvas2, result.body, localOptions),
+    hand(inCanvas2, result.hand, localOptions),
+    object(inCanvas2, result.object, localOptions),
+    gesture(inCanvas2, result.gesture, localOptions)
+  ]);
+  drawTime = env.perfadd ? drawTime + Math.round(now() - timeStamp) : Math.round(now() - timeStamp);
+  result.performance.draw = drawTime;
+  return promise;
+}
+
+// src/face/face.ts
+var tf37 = __toESM(require_tfjs_esm());
+
+// src/face/mask.ts
+var tf36 = __toESM(require_tfjs_esm());
+var expandFact = 0.1;
+var alpha = 0.5;
+function insidePoly(x, y, polygon) {
+  let inside = false;
+  let j = polygon.length - 1;
+  for (let i = 0; i < polygon.length; j = i++) {
+    if (polygon[i].y > y !== polygon[j].y > y && x < (polygon[j].x - polygon[i].x) * (y - polygon[i].y) / (polygon[j].y - polygon[i].y) + polygon[i].x)
+      inside = !inside;
+  }
+  return inside;
+}
+async function mask(face4) {
+  if (!face4.tensor)
+    return face4.tensor;
+  if (!face4.mesh || face4.mesh.length < 100)
+    return face4.tensor;
+  const width = face4.tensor.shape[2] || 0;
+  const height = face4.tensor.shape[1] || 0;
+  const buffer = await face4.tensor.buffer();
+  let silhouette = [];
+  for (const pt of meshAnnotations.silhouette)
+    silhouette.push({ x: (face4.mesh[pt][0] - face4.box[0]) / face4.box[2], y: (face4.mesh[pt][1] - face4.box[1]) / face4.box[3] });
+  if (expandFact && expandFact > 0)
+    silhouette = silhouette.map((pt) => ({ x: pt.x > 0.5 ? pt.x + expandFact : pt.x - expandFact, y: pt.y > 0.5 ? pt.y + expandFact : pt.y - expandFact }));
+  for (let x = 0; x < width; x++) {
+    for (let y = 0; y < height; y++) {
+      const inside = insidePoly(x / width, y / width, silhouette);
+      if (!inside) {
+        buffer.set(alpha * buffer.get(0, y, x, 0), 0, y, x, 0);
+        buffer.set(alpha * buffer.get(0, y, x, 1), 0, y, x, 1);
+        buffer.set(alpha * buffer.get(0, y, x, 2), 0, y, x, 2);
+      }
+    }
+  }
+  const output = buffer.toTensor();
+  tf36.dispose(buffer);
+  return output;
+}
+
+// src/face/angles.ts
+var calculateGaze = (face4) => {
+  const radians = (pt1, pt2) => Math.atan2(pt1[1] - pt2[1], pt1[0] - pt2[0]);
+  if (!face4.annotations.rightEyeIris || !face4.annotations.leftEyeIris)
+    return { bearing: 0, strength: 0 };
+  const offsetIris = [0, -0.1];
+  const eyeRatio = 1;
+  const left = (face4.mesh[33][2] || 0) > (face4.mesh[263][2] || 0);
+  const irisCenter = left ? face4.mesh[473] : face4.mesh[468];
+  const eyeCenter = left ? [(face4.mesh[133][0] + face4.mesh[33][0]) / 2, (face4.mesh[133][1] + face4.mesh[33][1]) / 2] : [(face4.mesh[263][0] + face4.mesh[362][0]) / 2, (face4.mesh[263][1] + face4.mesh[362][1]) / 2];
+  const eyeSize = left ? [face4.mesh[133][0] - face4.mesh[33][0], face4.mesh[23][1] - face4.mesh[27][1]] : [face4.mesh[263][0] - face4.mesh[362][0], face4.mesh[253][1] - face4.mesh[257][1]];
+  const eyeDiff = [
+    (eyeCenter[0] - irisCenter[0]) / eyeSize[0] - offsetIris[0],
+    eyeRatio * (irisCenter[1] - eyeCenter[1]) / eyeSize[1] - offsetIris[1]
+  ];
+  let strength = Math.sqrt(eyeDiff[0] * eyeDiff[0] + eyeDiff[1] * eyeDiff[1]);
+  strength = Math.min(strength, face4.boxRaw[2] / 2, face4.boxRaw[3] / 2);
+  const bearing = (radians([0, 0], eyeDiff) + Math.PI / 2) % Math.PI;
+  return { bearing, strength };
+};
+var calculateFaceAngle = (face4, imageSize) => {
+  const normalize2 = (v) => {
+    const length = Math.sqrt(v[0] * v[0] + v[1] * v[1] + v[2] * v[2]);
+    v[0] /= length;
+    v[1] /= length;
+    v[2] /= length;
+    return v;
+  };
+  const subVectors = (a, b) => {
+    const x = a[0] - b[0];
+    const y = a[1] - b[1];
+    const z = a[2] - b[2];
+    return [x, y, z];
+  };
+  const crossVectors = (a, b) => {
+    const x = a[1] * b[2] - a[2] * b[1];
+    const y = a[2] * b[0] - a[0] * b[2];
+    const z = a[0] * b[1] - a[1] * b[0];
+    return [x, y, z];
+  };
+  const rotationMatrixToEulerAngle = (r) => {
+    const [r00, _r01, _r02, r10, r11, r12, r20, r21, r22] = r;
+    let thetaX;
+    let thetaY;
+    let thetaZ;
+    if (r10 < 1) {
+      if (r10 > -1) {
+        thetaZ = Math.asin(r10);
+        thetaY = Math.atan2(-r20, r00);
+        thetaX = Math.atan2(-r12, r11);
+      } else {
+        thetaZ = -Math.PI / 2;
+        thetaY = -Math.atan2(r21, r22);
+        thetaX = 0;
+      }
+    } else {
+      thetaZ = Math.PI / 2;
+      thetaY = Math.atan2(r21, r22);
+      thetaX = 0;
+    }
+    if (Number.isNaN(thetaX))
+      thetaX = 0;
+    if (Number.isNaN(thetaY))
+      thetaY = 0;
+    if (Number.isNaN(thetaZ))
+      thetaZ = 0;
+    return { pitch: 2 * -thetaX, yaw: 2 * -thetaY, roll: 2 * -thetaZ };
+  };
+  const mesh = face4.meshRaw;
+  if (!mesh || mesh.length < 300)
+    return { angle: { pitch: 0, yaw: 0, roll: 0 }, matrix: [1, 0, 0, 0, 1, 0, 0, 0, 1], gaze: { bearing: 0, strength: 0 } };
+  const size2 = Math.max(face4.boxRaw[2] * imageSize[0], face4.boxRaw[3] * imageSize[1]) / 1.5;
+  const pts = [mesh[10], mesh[152], mesh[234], mesh[454]].map((pt) => [pt[0] * imageSize[0] / size2, pt[1] * imageSize[1] / size2, pt[2]]);
+  const yAxis = normalize2(subVectors(pts[1], pts[0]));
+  let xAxis = normalize2(subVectors(pts[3], pts[2]));
+  const zAxis = normalize2(crossVectors(xAxis, yAxis));
+  xAxis = crossVectors(yAxis, zAxis);
+  const matrix = [
+    xAxis[0],
+    xAxis[1],
+    xAxis[2],
+    yAxis[0],
+    yAxis[1],
+    yAxis[2],
+    zAxis[0],
+    zAxis[1],
+    zAxis[2]
+  ];
+  const angle = rotationMatrixToEulerAngle(matrix);
+  const gaze = mesh.length === 478 ? calculateGaze(face4) : { bearing: 0, strength: 0 };
+  return { angle, matrix, gaze };
+};
+
+// src/face/face.ts
+var detectFace = async (instance2, input) => {
+  var _a, _b, _c, _d, _e, _f, _g, _h, _i, _j, _k, _l, _m, _n, _o, _p, _q, _r, _s, _t, _u, _v, _w, _x, _y, _z, _A, _B, _C;
+  let timeStamp = now();
+  let ageRes;
+  let gearRes;
+  let genderRes;
+  let emotionRes;
+  let mobilefacenetRes;
+  let insightfaceRes;
+  let antispoofRes;
+  let livenessRes;
+  let descRes;
+  const faceRes = [];
+  instance2.state = "run:face";
+  const faces = await predict6(input, instance2.config);
+  instance2.performance.face = env.perfadd ? (instance2.performance.face || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);
+  if (!input.shape || input.shape.length !== 4)
+    return [];
+  if (!faces)
+    return [];
+  for (let i = 0; i < faces.length; i++) {
+    instance2.analyze("Get Face");
+    if (!faces[i].tensor || faces[i].tensor.isDisposedInternal) {
+      log("Face object is disposed:", faces[i].tensor);
+      continue;
+    }
+    if ((_a = instance2.config.face.detector) == null ? void 0 : _a.mask) {
+      const masked = await mask(faces[i]);
+      tf37.dispose(faces[i].tensor);
+      if (masked)
+        faces[i].tensor = masked;
+    }
+    const rotation = faces[i].mesh && faces[i].mesh.length > 200 ? calculateFaceAngle(faces[i], [input.shape[2], input.shape[1]]) : null;
+    instance2.analyze("Start Emotion:");
+    if (instance2.config.async) {
+      emotionRes = ((_b = instance2.config.face.emotion) == null ? void 0 : _b.enabled) ? predict5(faces[i].tensor || tf37.tensor([]), instance2.config, i, faces.length) : [];
+    } else {
+      instance2.state = "run:emotion";
+      timeStamp = now();
+      emotionRes = ((_c = instance2.config.face.emotion) == null ? void 0 : _c.enabled) ? await predict5(faces[i].tensor || tf37.tensor([]), instance2.config, i, faces.length) : [];
+      instance2.performance.emotion = env.perfadd ? (instance2.performance.emotion || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);
+    }
+    instance2.analyze("End Emotion:");
+    instance2.analyze("Start AntiSpoof:");
+    if (instance2.config.async) {
+      antispoofRes = ((_d = instance2.config.face.antispoof) == null ? void 0 : _d.enabled) ? predict(faces[i].tensor || tf37.tensor([]), instance2.config, i, faces.length) : 0;
+    } else {
+      instance2.state = "run:antispoof";
+      timeStamp = now();
+      antispoofRes = ((_e = instance2.config.face.antispoof) == null ? void 0 : _e.enabled) ? await predict(faces[i].tensor || tf37.tensor([]), instance2.config, i, faces.length) : 0;
+      instance2.performance.antispoof = env.perfadd ? (instance2.performance.antispoof || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);
+    }
+    instance2.analyze("End AntiSpoof:");
+    instance2.analyze("Start Liveness:");
+    if (instance2.config.async) {
+      livenessRes = ((_f = instance2.config.face.liveness) == null ? void 0 : _f.enabled) ? predict12(faces[i].tensor || tf37.tensor([]), instance2.config, i, faces.length) : 0;
+    } else {
+      instance2.state = "run:liveness";
+      timeStamp = now();
+      livenessRes = ((_g = instance2.config.face.liveness) == null ? void 0 : _g.enabled) ? await predict12(faces[i].tensor || tf37.tensor([]), instance2.config, i, faces.length) : 0;
+      instance2.performance.liveness = env.perfadd ? (instance2.performance.antispoof || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);
+    }
+    instance2.analyze("End Liveness:");
+    instance2.analyze("Start GEAR:");
+    if (instance2.config.async) {
+      gearRes = ((_h = instance2.config.face.gear) == null ? void 0 : _h.enabled) ? predict8(faces[i].tensor || tf37.tensor([]), instance2.config, i, faces.length) : null;
+    } else {
+      instance2.state = "run:gear";
+      timeStamp = now();
+      gearRes = ((_i = instance2.config.face.gear) == null ? void 0 : _i.enabled) ? await predict8(faces[i].tensor || tf37.tensor([]), instance2.config, i, faces.length) : null;
+      instance2.performance.gear = Math.trunc(now() - timeStamp);
+    }
+    instance2.analyze("End GEAR:");
+    instance2.analyze("Start SSRNet:");
+    if (instance2.config.async) {
+      ageRes = ((_j = instance2.config.face["ssrnet"]) == null ? void 0 : _j.enabled) ? predict20(faces[i].tensor || tf37.tensor([]), instance2.config, i, faces.length) : null;
+      genderRes = ((_k = instance2.config.face["ssrnet"]) == null ? void 0 : _k.enabled) ? predict21(faces[i].tensor || tf37.tensor([]), instance2.config, i, faces.length) : null;
+    } else {
+      instance2.state = "run:ssrnet";
+      timeStamp = now();
+      ageRes = ((_l = instance2.config.face["ssrnet"]) == null ? void 0 : _l.enabled) ? await predict20(faces[i].tensor || tf37.tensor([]), instance2.config, i, faces.length) : null;
+      genderRes = ((_m = instance2.config.face["ssrnet"]) == null ? void 0 : _m.enabled) ? await predict21(faces[i].tensor || tf37.tensor([]), instance2.config, i, faces.length) : null;
+      instance2.performance.ssrnet = Math.trunc(now() - timeStamp);
+    }
+    instance2.analyze("End SSRNet:");
+    instance2.analyze("Start MobileFaceNet:");
+    if (instance2.config.async) {
+      mobilefacenetRes = ((_n = instance2.config.face["mobilefacenet"]) == null ? void 0 : _n.enabled) ? predict14(faces[i].tensor || tf37.tensor([]), instance2.config, i, faces.length) : null;
+    } else {
+      instance2.state = "run:mobilefacenet";
+      timeStamp = now();
+      mobilefacenetRes = ((_o = instance2.config.face["mobilefacenet"]) == null ? void 0 : _o.enabled) ? await predict14(faces[i].tensor || tf37.tensor([]), instance2.config, i, faces.length) : null;
+      instance2.performance.mobilefacenet = Math.trunc(now() - timeStamp);
+    }
+    instance2.analyze("End MobileFaceNet:");
+    instance2.analyze("Start InsightFace:");
+    if (instance2.config.async) {
+      insightfaceRes = ((_p = instance2.config.face["insightface"]) == null ? void 0 : _p.enabled) ? predict11(faces[i].tensor || tf37.tensor([]), instance2.config, i, faces.length) : null;
+    } else {
+      instance2.state = "run:mobilefacenet";
+      timeStamp = now();
+      insightfaceRes = ((_q = instance2.config.face["insightface"]) == null ? void 0 : _q.enabled) ? await predict11(faces[i].tensor || tf37.tensor([]), instance2.config, i, faces.length) : null;
+      instance2.performance.mobilefacenet = Math.trunc(now() - timeStamp);
+    }
+    instance2.analyze("End InsightFace:");
+    instance2.analyze("Start Description:");
+    if (instance2.config.async) {
+      descRes = predict7(faces[i].tensor || tf37.tensor([]), instance2.config, i, faces.length);
+    } else {
+      instance2.state = "run:description";
+      timeStamp = now();
+      descRes = await predict7(faces[i].tensor || tf37.tensor([]), instance2.config, i, faces.length);
+      instance2.performance.description = env.perfadd ? (instance2.performance.description || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);
+    }
+    instance2.analyze("End Description:");
+    if (instance2.config.async) {
+      [ageRes, genderRes, emotionRes, mobilefacenetRes, insightfaceRes, descRes, gearRes, antispoofRes, livenessRes] = await Promise.all([ageRes, genderRes, emotionRes, mobilefacenetRes, insightfaceRes, descRes, gearRes, antispoofRes, livenessRes]);
+    }
+    instance2.analyze("Finish Face:");
+    if (((_r = instance2.config.face["ssrnet"]) == null ? void 0 : _r.enabled) && ageRes && genderRes) {
+      descRes = {
+        ...descRes,
+        age: ageRes.age,
+        gender: genderRes.gender,
+        genderScore: genderRes.genderScore
+      };
+    }
+    if (((_s = instance2.config.face.gear) == null ? void 0 : _s.enabled) && gearRes) {
+      descRes = {
+        ...descRes,
+        age: gearRes.age,
+        gender: gearRes.gender,
+        genderScore: gearRes.genderScore,
+        race: gearRes.race
+      };
+    }
+    if (((_t = instance2.config.face["mobilefacenet"]) == null ? void 0 : _t.enabled) && mobilefacenetRes) {
+      descRes.descriptor = mobilefacenetRes;
+    }
+    if (((_u = instance2.config.face["insightface"]) == null ? void 0 : _u.enabled) && insightfaceRes) {
+      descRes.descriptor = insightfaceRes;
+    }
+    if (!((_v = instance2.config.face.iris) == null ? void 0 : _v.enabled)) {
+    }
+    const irisSize = ((_y = (_x = (_w = faces[i]) == null ? void 0 : _w.annotations) == null ? void 0 : _x.leftEyeIris) == null ? void 0 : _y[0]) && ((_B = (_A = (_z = faces[i]) == null ? void 0 : _z.annotations) == null ? void 0 : _A.rightEyeIris) == null ? void 0 : _B[0]) && faces[i].annotations.leftEyeIris.length > 0 && faces[i].annotations.rightEyeIris.length > 0 && faces[i].annotations.leftEyeIris[0] !== null && faces[i].annotations.rightEyeIris[0] !== null ? Math.max(Math.abs(faces[i].annotations.leftEyeIris[3][0] - faces[i].annotations.leftEyeIris[1][0]), Math.abs(faces[i].annotations.rightEyeIris[4][1] - faces[i].annotations.rightEyeIris[2][1])) / input.shape[2] : 0;
+    const tensor6 = ((_C = instance2.config.face.detector) == null ? void 0 : _C.return) ? tf37.squeeze(faces[i].tensor) : null;
+    tf37.dispose(faces[i].tensor);
+    if (faces[i].tensor)
+      delete faces[i].tensor;
+    const res = {
+      ...faces[i],
+      id: i
+    };
+    if (descRes.age)
+      res.age = descRes.age;
+    if (descRes.gender)
+      res.gender = descRes.gender;
+    if (descRes.genderScore)
+      res.genderScore = descRes.genderScore;
+    if (descRes.descriptor)
+      res.embedding = descRes.descriptor;
+    if (descRes.race)
+      res.race = descRes.race;
+    if (emotionRes)
+      res.emotion = emotionRes;
+    if (antispoofRes)
+      res.real = antispoofRes;
+    if (livenessRes)
+      res.live = livenessRes;
+    if (irisSize && irisSize !== 0)
+      res.iris = Math.trunc(500 / irisSize / 11.7) / 100;
+    if (rotation)
+      res.rotation = rotation;
+    if (tensor6)
+      res.tensor = tensor6;
+    faceRes.push(res);
+    instance2.analyze("End Face");
+  }
+  instance2.analyze("End FaceMesh:");
+  if (instance2.config.async) {
+    if (instance2.performance.face)
+      delete instance2.performance.face;
+    if (instance2.performance.age)
+      delete instance2.performance.age;
+    if (instance2.performance.gender)
+      delete instance2.performance.gender;
+    if (instance2.performance.emotion)
+      delete instance2.performance.emotion;
+  }
+  return faceRes;
+};
+
+// src/gesture/gesture.ts
+var body2 = (res) => {
+  if (!res)
+    return [];
+  const gestures = [];
+  for (let i = 0; i < res.length; i++) {
+    const leftWrist = res[i].keypoints.find((a) => a.part === "leftWrist");
+    const rightWrist = res[i].keypoints.find((a) => a.part === "rightWrist");
+    const nose = res[i].keypoints.find((a) => a.part === "nose");
+    if (nose && leftWrist && rightWrist && leftWrist.position[1] < nose.position[1] && rightWrist.position[1] < nose.position[1])
+      gestures.push({ body: i, gesture: "i give up" });
+    else if (nose && leftWrist && leftWrist.position[1] < nose.position[1])
+      gestures.push({ body: i, gesture: "raise left hand" });
+    else if (nose && rightWrist && rightWrist.position[1] < nose.position[1])
+      gestures.push({ body: i, gesture: "raise right hand" });
+    const leftShoulder = res[i].keypoints.find((a) => a.part === "leftShoulder");
+    const rightShoulder = res[i].keypoints.find((a) => a.part === "rightShoulder");
+    if (leftShoulder && rightShoulder && Math.abs(leftShoulder.positionRaw[1] - rightShoulder.positionRaw[1]) > 0.1) {
+      gestures.push({ body: i, gesture: `leaning ${leftShoulder.position[1] > rightShoulder.position[1] ? "left" : "right"}` });
+    }
+  }
+  return gestures;
+};
+var face2 = (res) => {
+  if (!res)
+    return [];
+  const gestures = [];
+  for (let i = 0; i < res.length; i++) {
+    if (res[i].mesh && res[i].mesh.length > 450) {
+      const zDiff = (res[i].mesh[33][2] || 0) - (res[i].mesh[263][2] || 0);
+      const xDiff = res[i].mesh[33][0] - res[i].mesh[263][0];
+      if (Math.abs(zDiff / xDiff) <= 0.15)
+        gestures.push({ face: i, gesture: "facing center" });
+      else
+        gestures.push({ face: i, gesture: `facing ${zDiff < 0 ? "left" : "right"}` });
+      const openLeft = Math.abs(res[i].mesh[374][1] - res[i].mesh[386][1]) / Math.abs(res[i].mesh[443][1] - res[i].mesh[450][1]);
+      if (openLeft < 0.2)
+        gestures.push({ face: i, gesture: "blink left eye" });
+      const openRight = Math.abs(res[i].mesh[145][1] - res[i].mesh[159][1]) / Math.abs(res[i].mesh[223][1] - res[i].mesh[230][1]);
+      if (openRight < 0.2)
+        gestures.push({ face: i, gesture: "blink right eye" });
+      const mouthOpen = Math.min(100, 500 * Math.abs(res[i].mesh[13][1] - res[i].mesh[14][1]) / Math.abs(res[i].mesh[10][1] - res[i].mesh[152][1]));
+      if (mouthOpen > 10)
+        gestures.push({ face: i, gesture: `mouth ${Math.trunc(mouthOpen)}% open` });
+      const chinDepth = res[i].mesh[152][2] || 0;
+      if (Math.abs(chinDepth) > 10)
+        gestures.push({ face: i, gesture: `head ${chinDepth < 0 ? "up" : "down"}` });
+    }
+  }
+  return gestures;
+};
+var iris2 = (res) => {
+  var _a, _b, _c, _d;
+  if (!res)
+    return [];
+  const gestures = [];
+  for (let i = 0; i < res.length; i++) {
+    if (!((_b = (_a = res[i].annotations) == null ? void 0 : _a.leftEyeIris) == null ? void 0 : _b[0]) || !((_d = (_c = res[i].annotations) == null ? void 0 : _c.rightEyeIris) == null ? void 0 : _d[0]))
+      continue;
+    const sizeXLeft = res[i].annotations.leftEyeIris[3][0] - res[i].annotations.leftEyeIris[1][0];
+    const sizeYLeft = res[i].annotations.leftEyeIris[4][1] - res[i].annotations.leftEyeIris[2][1];
+    const areaLeft = Math.abs(sizeXLeft * sizeYLeft);
+    const sizeXRight = res[i].annotations.rightEyeIris[3][0] - res[i].annotations.rightEyeIris[1][0];
+    const sizeYRight = res[i].annotations.rightEyeIris[4][1] - res[i].annotations.rightEyeIris[2][1];
+    const areaRight = Math.abs(sizeXRight * sizeYRight);
+    let center = false;
+    const difference = Math.abs(areaLeft - areaRight) / Math.max(areaLeft, areaRight);
+    if (difference < 0.25) {
+      center = true;
+      gestures.push({ iris: i, gesture: "facing center" });
+    }
+    const leftIrisCenterX = Math.abs(res[i].mesh[263][0] - res[i].annotations.leftEyeIris[0][0]) / res[i].box[2];
+    const rightIrisCenterX = Math.abs(res[i].mesh[33][0] - res[i].annotations.rightEyeIris[0][0]) / res[i].box[2];
+    if (leftIrisCenterX > 0.06 || rightIrisCenterX > 0.06)
+      center = false;
+    if (leftIrisCenterX > rightIrisCenterX) {
+      if (leftIrisCenterX > 0.05)
+        gestures.push({ iris: i, gesture: "looking right" });
+    } else {
+      if (rightIrisCenterX > 0.05)
+        gestures.push({ iris: i, gesture: "looking left" });
+    }
+    const rightIrisCenterY = Math.abs(res[i].mesh[145][1] - res[i].annotations.rightEyeIris[0][1]) / res[i].box[3];
+    const leftIrisCenterY = Math.abs(res[i].mesh[374][1] - res[i].annotations.leftEyeIris[0][1]) / res[i].box[3];
+    if (leftIrisCenterY < 0.01 || rightIrisCenterY < 0.01 || leftIrisCenterY > 0.022 || rightIrisCenterY > 0.022)
+      center = false;
+    if (leftIrisCenterY < 0.01 || rightIrisCenterY < 0.01)
+      gestures.push({ iris: i, gesture: "looking down" });
+    if (leftIrisCenterY > 0.022 || rightIrisCenterY > 0.022)
+      gestures.push({ iris: i, gesture: "looking up" });
+    if (center)
+      gestures.push({ iris: i, gesture: "looking center" });
+  }
+  return gestures;
+};
+var hand2 = (res) => {
+  if (!res)
+    return [];
+  const gestures = [];
+  for (let i = 0; i < res.length; i++) {
+    const fingers = [];
+    if (res[i].annotations) {
+      for (const [finger, pos] of Object.entries(res[i].annotations)) {
+        if (finger !== "palmBase" && Array.isArray(pos) && pos[0])
+          fingers.push({ name: finger.toLowerCase(), position: pos[0] });
+      }
+    }
+    if (fingers && fingers.length > 0) {
+      const closest = fingers.reduce((best, a) => (best.position[2] || 0) < (a.position[2] || 0) ? best : a);
+      gestures.push({ hand: i, gesture: `${closest.name} forward` });
+      const highest = fingers.reduce((best, a) => best.position[1] < a.position[1] ? best : a);
+      gestures.push({ hand: i, gesture: `${highest.name} up` });
+    }
+    if (res[i].keypoints) {
+      const poses = match(res[i].keypoints);
+      for (const pose of poses)
+        gestures.push({ hand: i, gesture: pose.name });
+    }
+  }
+  return gestures;
+};
+
+// src/util/interpolate.ts
+var bufferedResult = { face: [], body: [], hand: [], gesture: [], object: [], persons: [], performance: {}, timestamp: 0, error: null };
+var interpolateTime = 0;
+function calc2(newResult, config3) {
+  var _a, _b, _c, _d, _e, _f, _g, _h, _i, _j, _k, _l, _m, _n, _o, _p, _q, _r, _s, _t, _u, _v, _w;
+  const t0 = now();
+  if (!newResult)
+    return { face: [], body: [], hand: [], gesture: [], object: [], persons: [], performance: {}, timestamp: 0, error: null };
+  const elapsed = Date.now() - newResult.timestamp;
+  const bufferedFactor = elapsed < 1e3 ? 8 - Math.log(elapsed + 1) : 1;
+  if (newResult.canvas)
+    bufferedResult.canvas = newResult.canvas;
+  if (newResult.error)
+    bufferedResult.error = newResult.error;
+  if (!bufferedResult.body || newResult.body.length !== bufferedResult.body.length) {
+    bufferedResult.body = JSON.parse(JSON.stringify(newResult.body));
+  } else {
+    for (let i = 0; i < newResult.body.length; i++) {
+      const box = newResult.body[i].box.map((newBoxCoord, j) => ((bufferedFactor - 1) * bufferedResult.body[i].box[j] + newBoxCoord) / bufferedFactor);
+      const boxRaw = newResult.body[i].boxRaw.map((newBoxCoord, j) => ((bufferedFactor - 1) * bufferedResult.body[i].boxRaw[j] + newBoxCoord) / bufferedFactor);
+      const keypoints = newResult.body[i].keypoints.map((newKpt, j) => {
+        var _a2, _b2, _c2, _d2, _e2, _f2, _g2, _h2, _i2;
+        return {
+          score: newKpt.score,
+          part: newKpt.part,
+          position: [
+            bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].position[0] || 0) + (newKpt.position[0] || 0)) / bufferedFactor : newKpt.position[0],
+            bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].position[1] || 0) + (newKpt.position[1] || 0)) / bufferedFactor : newKpt.position[1],
+            bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].position[2] || 0) + (newKpt.position[2] || 0)) / bufferedFactor : newKpt.position[2]
+          ],
+          positionRaw: [
+            bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].positionRaw[0] || 0) + (newKpt.positionRaw[0] || 0)) / bufferedFactor : newKpt.positionRaw[0],
+            bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].positionRaw[1] || 0) + (newKpt.positionRaw[1] || 0)) / bufferedFactor : newKpt.positionRaw[1],
+            bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].positionRaw[2] || 0) + (newKpt.positionRaw[2] || 0)) / bufferedFactor : newKpt.positionRaw[2]
+          ],
+          distance: [
+            bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (((_a2 = bufferedResult.body[i].keypoints[j].distance) == null ? void 0 : _a2[0]) || 0) + (((_b2 = newKpt.distance) == null ? void 0 : _b2[0]) || 0)) / bufferedFactor : (_c2 = newKpt.distance) == null ? void 0 : _c2[0],
+            bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (((_d2 = bufferedResult.body[i].keypoints[j].distance) == null ? void 0 : _d2[1]) || 0) + (((_e2 = newKpt.distance) == null ? void 0 : _e2[1]) || 0)) / bufferedFactor : (_f2 = newKpt.distance) == null ? void 0 : _f2[1],
+            bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (((_g2 = bufferedResult.body[i].keypoints[j].distance) == null ? void 0 : _g2[2]) || 0) + (((_h2 = newKpt.distance) == null ? void 0 : _h2[2]) || 0)) / bufferedFactor : (_i2 = newKpt.distance) == null ? void 0 : _i2[2]
+          ]
+        };
+      });
+      const annotations2 = {};
+      let coords = { connected: {} };
+      if ((_a = config3.body.modelPath) == null ? void 0 : _a.includes("efficientpose"))
+        coords = efficientposecoords_exports;
+      else if ((_b = config3.body.modelPath) == null ? void 0 : _b.includes("blazepose"))
+        coords = blazeposecoords_exports;
+      else if ((_c = config3.body.modelPath) == null ? void 0 : _c.includes("movenet"))
+        coords = movenetcoords_exports;
+      for (const [name, indexes] of Object.entries(coords.connected)) {
+        const pt = [];
+        for (let j = 0; j < indexes.length - 1; j++) {
+          const pt0 = keypoints.find((kp) => kp.part === indexes[j]);
+          const pt1 = keypoints.find((kp) => kp.part === indexes[j + 1]);
+          if (pt0 && pt1)
+            pt.push([pt0.position, pt1.position]);
+        }
+        annotations2[name] = pt;
+      }
+      bufferedResult.body[i] = { ...newResult.body[i], box, boxRaw, keypoints, annotations: annotations2 };
+    }
+  }
+  if (!bufferedResult.hand || newResult.hand.length !== bufferedResult.hand.length) {
+    bufferedResult.hand = JSON.parse(JSON.stringify(newResult.hand));
+  } else {
+    for (let i = 0; i < newResult.hand.length; i++) {
+      const box = newResult.hand[i].box.map((b, j) => ((bufferedFactor - 1) * bufferedResult.hand[i].box[j] + b) / bufferedFactor);
+      const boxRaw = newResult.hand[i].boxRaw.map((b, j) => ((bufferedFactor - 1) * bufferedResult.hand[i].boxRaw[j] + b) / bufferedFactor);
+      if (bufferedResult.hand[i].keypoints.length !== newResult.hand[i].keypoints.length)
+        bufferedResult.hand[i].keypoints = newResult.hand[i].keypoints;
+      const keypoints = newResult.hand[i].keypoints && newResult.hand[i].keypoints.length > 0 ? newResult.hand[i].keypoints.map((landmark, j) => landmark.map((coord, k) => ((bufferedFactor - 1) * (bufferedResult.hand[i].keypoints[j][k] || 1) + (coord || 0)) / bufferedFactor)) : [];
+      let annotations2 = {};
+      if (Object.keys(bufferedResult.hand[i].annotations).length !== Object.keys(newResult.hand[i].annotations).length) {
+        bufferedResult.hand[i].annotations = newResult.hand[i].annotations;
+        annotations2 = bufferedResult.hand[i].annotations;
+      } else if (newResult.hand[i].annotations) {
+        for (const key of Object.keys(newResult.hand[i].annotations)) {
+          annotations2[key] = ((_f = (_e = (_d = newResult.hand[i]) == null ? void 0 : _d.annotations) == null ? void 0 : _e[key]) == null ? void 0 : _f[0]) ? newResult.hand[i].annotations[key].map((val, j) => val.map((coord, k) => ((bufferedFactor - 1) * bufferedResult.hand[i].annotations[key][j][k] + coord) / bufferedFactor)) : null;
+        }
+      }
+      bufferedResult.hand[i] = { ...newResult.hand[i], box, boxRaw, keypoints, annotations: annotations2 };
+    }
+  }
+  if (!bufferedResult.face || newResult.face.length !== bufferedResult.face.length) {
+    bufferedResult.face = JSON.parse(JSON.stringify(newResult.face));
+  } else {
+    for (let i = 0; i < newResult.face.length; i++) {
+      const box = newResult.face[i].box.map((b, j) => ((bufferedFactor - 1) * bufferedResult.face[i].box[j] + b) / bufferedFactor);
+      const boxRaw = newResult.face[i].boxRaw.map((b, j) => ((bufferedFactor - 1) * bufferedResult.face[i].boxRaw[j] + b) / bufferedFactor);
+      if (newResult.face[i].rotation) {
+        const rotation = { matrix: [0, 0, 0, 0, 0, 0, 0, 0, 0], angle: { roll: 0, yaw: 0, pitch: 0 }, gaze: { bearing: 0, strength: 0 } };
+        rotation.matrix = (_g = newResult.face[i].rotation) == null ? void 0 : _g.matrix;
+        rotation.angle = {
+          roll: ((bufferedFactor - 1) * (((_i = (_h = bufferedResult.face[i].rotation) == null ? void 0 : _h.angle) == null ? void 0 : _i.roll) || 0) + (((_k = (_j = newResult.face[i].rotation) == null ? void 0 : _j.angle) == null ? void 0 : _k.roll) || 0)) / bufferedFactor,
+          yaw: ((bufferedFactor - 1) * (((_m = (_l = bufferedResult.face[i].rotation) == null ? void 0 : _l.angle) == null ? void 0 : _m.yaw) || 0) + (((_o = (_n = newResult.face[i].rotation) == null ? void 0 : _n.angle) == null ? void 0 : _o.yaw) || 0)) / bufferedFactor,
+          pitch: ((bufferedFactor - 1) * (((_q = (_p = bufferedResult.face[i].rotation) == null ? void 0 : _p.angle) == null ? void 0 : _q.pitch) || 0) + (((_s = (_r = newResult.face[i].rotation) == null ? void 0 : _r.angle) == null ? void 0 : _s.pitch) || 0)) / bufferedFactor
+        };
+        rotation.gaze = {
+          bearing: ((bufferedFactor - 1) * (((_t = bufferedResult.face[i].rotation) == null ? void 0 : _t.gaze.bearing) || 0) + (((_u = newResult.face[i].rotation) == null ? void 0 : _u.gaze.bearing) || 0)) / bufferedFactor,
+          strength: ((bufferedFactor - 1) * (((_v = bufferedResult.face[i].rotation) == null ? void 0 : _v.gaze.strength) || 0) + (((_w = newResult.face[i].rotation) == null ? void 0 : _w.gaze.strength) || 0)) / bufferedFactor
+        };
+        bufferedResult.face[i] = { ...newResult.face[i], rotation, box, boxRaw };
+      } else {
+        bufferedResult.face[i] = { ...newResult.face[i], box, boxRaw };
+      }
+    }
+  }
+  if (!bufferedResult.object || newResult.object.length !== bufferedResult.object.length) {
+    bufferedResult.object = JSON.parse(JSON.stringify(newResult.object));
+  } else {
+    for (let i = 0; i < newResult.object.length; i++) {
+      const box = newResult.object[i].box.map((b, j) => ((bufferedFactor - 1) * bufferedResult.object[i].box[j] + b) / bufferedFactor);
+      const boxRaw = newResult.object[i].boxRaw.map((b, j) => ((bufferedFactor - 1) * bufferedResult.object[i].boxRaw[j] + b) / bufferedFactor);
+      bufferedResult.object[i] = { ...newResult.object[i], box, boxRaw };
+    }
+  }
+  if (newResult.persons) {
+    const newPersons = newResult.persons;
+    if (!bufferedResult.persons || newPersons.length !== bufferedResult.persons.length) {
+      bufferedResult.persons = JSON.parse(JSON.stringify(newPersons));
+    } else {
+      for (let i = 0; i < newPersons.length; i++) {
+        bufferedResult.persons[i].box = newPersons[i].box.map((box, j) => ((bufferedFactor - 1) * bufferedResult.persons[i].box[j] + box) / bufferedFactor);
+      }
+    }
+  }
+  if (newResult.gesture)
+    bufferedResult.gesture = newResult.gesture;
+  const t1 = now();
+  interpolateTime = env.perfadd ? interpolateTime + Math.round(t1 - t0) : Math.round(t1 - t0);
+  if (newResult.performance)
+    bufferedResult.performance = { ...newResult.performance, interpolate: interpolateTime };
+  return bufferedResult;
+}
+
+// src/face/match.ts
+var match_exports = {};
+__export(match_exports, {
+  distance: () => distance,
+  match: () => match2,
+  similarity: () => similarity
+});
+function distance(descriptor1, descriptor2, options4 = { order: 2, multiplier: 25 }) {
+  if (!descriptor1 || !descriptor1)
+    return Number.MAX_SAFE_INTEGER;
+  let sum3 = 0;
+  for (let i = 0; i < descriptor1.length; i++) {
+    const diff = !options4.order || options4.order === 2 ? descriptor1[i] - descriptor2[i] : Math.abs(descriptor1[i] - descriptor2[i]);
+    sum3 += !options4.order || options4.order === 2 ? diff * diff : diff ** options4.order;
+  }
+  return (options4.multiplier || 20) * sum3;
+}
+var normalizeDistance = (dist, order, min2, max4) => {
+  if (dist === 0)
+    return 1;
+  const root = order === 2 ? Math.sqrt(dist) : dist ** (1 / order);
+  const norm = (1 - root / 100 - min2) / (max4 - min2);
+  const clamp2 = Math.max(Math.min(norm, 1), 0);
+  return clamp2;
+};
+function similarity(descriptor1, descriptor2, options4 = { order: 2, multiplier: 25, min: 0.2, max: 0.8 }) {
+  const dist = distance(descriptor1, descriptor2, options4);
+  return normalizeDistance(dist, options4.order || 2, options4.min || 0, options4.max || 1);
+}
+function match2(descriptor, descriptors, options4 = { order: 2, multiplier: 25, threshold: 0, min: 0.2, max: 0.8 }) {
+  if (!Array.isArray(descriptor) || !Array.isArray(descriptors) || descriptor.length < 64 || descriptors.length === 0) {
+    return { index: -1, distance: Number.POSITIVE_INFINITY, similarity: 0 };
+  }
+  let lowestDistance = Number.MAX_SAFE_INTEGER;
+  let index2 = -1;
+  for (let i = 0; i < descriptors.length; i++) {
+    const res = descriptors[i].length === descriptor.length ? distance(descriptor, descriptors[i], options4) : Number.MAX_SAFE_INTEGER;
+    if (res < lowestDistance) {
+      lowestDistance = res;
+      index2 = i;
+    }
+    if (lowestDistance < (options4.threshold || 0))
+      break;
+  }
+  const normalizedSimilarity = normalizeDistance(lowestDistance, options4.order || 2, options4.min || 0, options4.max || 1);
+  return { index: index2, distance: lowestDistance, similarity: normalizedSimilarity };
+}
+
+// src/util/persons.ts
+function join2(faces, bodies, hands, gestures, shape) {
+  var _a, _b, _c, _d, _e, _f;
+  let id = 0;
+  const persons = [];
+  for (const face4 of faces) {
+    const person2 = { id: id++, face: face4, body: null, hands: { left: null, right: null }, gestures: [], box: [0, 0, 0, 0] };
+    for (const body4 of bodies) {
+      if (face4.box[0] > body4.box[0] && face4.box[0] < body4.box[0] + body4.box[2] && face4.box[1] + face4.box[3] > body4.box[1] && face4.box[1] + face4.box[3] < body4.box[1] + body4.box[3]) {
+        person2.body = body4;
+      }
+    }
+    if (person2.body) {
+      for (const hand3 of hands) {
+        if (hand3.box[0] + hand3.box[2] > person2.body.box[0] && hand3.box[0] + hand3.box[2] < person2.body.box[0] + person2.body.box[2] && hand3.box[1] + hand3.box[3] > person2.body.box[1] && hand3.box[1] + hand3.box[3] < person2.body.box[1] + person2.body.box[3]) {
+          if (person2.hands)
+            person2.hands.left = hand3;
+        }
+        if (hand3.box[0] < person2.body.box[0] + person2.body.box[2] && hand3.box[0] > person2.body.box[0] && hand3.box[1] + hand3.box[3] > person2.body.box[1] && hand3.box[1] + hand3.box[3] < person2.body.box[1] + person2.body.box[3]) {
+          if (person2.hands)
+            person2.hands.right = hand3;
+        }
+      }
+    }
+    for (const gesture2 of gestures) {
+      if (gesture2["face"] !== void 0 && gesture2["face"] === face4.id)
+        person2.gestures.push(gesture2);
+      else if (gesture2["iris"] !== void 0 && gesture2["iris"] === face4.id)
+        person2.gestures.push(gesture2);
+      else if (gesture2["body"] !== void 0 && gesture2["body"] === ((_a = person2.body) == null ? void 0 : _a.id))
+        person2.gestures.push(gesture2);
+      else if (gesture2["hand"] !== void 0 && gesture2["hand"] === ((_b = person2.hands.left) == null ? void 0 : _b.id))
+        person2.gestures.push(gesture2);
+      else if (gesture2["hand"] !== void 0 && gesture2["hand"] === ((_c = person2.hands.right) == null ? void 0 : _c.id))
+        person2.gestures.push(gesture2);
+    }
+    const x = [];
+    const y = [];
+    const extractXY = (box) => {
+      if (box && box.length === 4) {
+        x.push(box[0], box[0] + box[2]);
+        y.push(box[1], box[1] + box[3]);
+      }
+    };
+    extractXY(person2.face.box);
+    extractXY((_d = person2.body) == null ? void 0 : _d.box);
+    extractXY((_e = person2.hands.left) == null ? void 0 : _e.box);
+    extractXY((_f = person2.hands.right) == null ? void 0 : _f.box);
+    const minX = Math.min(...x);
+    const minY = Math.min(...y);
+    person2.box = [minX, minY, Math.max(...x) - minX, Math.max(...y) - minY];
+    if ((shape == null ? void 0 : shape[1]) && (shape == null ? void 0 : shape[2]))
+      person2.boxRaw = [person2.box[0] / shape[2], person2.box[1] / shape[1], person2.box[2] / shape[2], person2.box[3] / shape[1]];
+    persons.push(person2);
+  }
+  return persons;
+}
+
+// src/sample.ts
+var face3 = `
 /9j/4AAQSkZJRgABAQEAYABgAAD/4QBoRXhpZgAATU0AKgAAAAgABAEaAAUAAAABAAAAPgEbAAUA
 AAABAAAARgEoAAMAAAABAAIAAAExAAIAAAARAAAATgAAAAAAAABgAAAAAQAAAGAAAAABcGFpbnQu
 bmV0IDQuMi4xMwAA/9sAQwAGBAUGBQQGBgUGBwcGCAoQCgoJCQoUDg8MEBcUGBgXFBYWGh0lHxob
@@ -259,7 +13985,8 @@ PQ4GJ+ashuK0MhWaoWcA0AaOmASMK7jRNPWYBmHyiuepO2x10qfcv6vYxCzYqoGK4HVYVTJrmb5l
 c6oaM5TUJ8EgGsG4kLNUHT0M64OaqMMikSRsuKbnFMRLG3zVehOaGNE445NNlnVFpDMu6uie9Vo1
 8z5mOAOST2pDK91cNN+5tsrH3PrW54a06KxT7fdrlh/q1Pc+tJ6IUdZGvHPLezMcnBOWbsPap5r3
 ylFtbdT1xUWNWzU0/Zbwlgfmx8zGsHWtRHmMqE59aAMyNifvHPc1f0gtPdqkY5JosJHeNci2tktY
-euPnNY+oXWZEVJNrZ9aun8SIq/CzodHuriIokhDIR1ronbKZr0o6o8ipoz//2Q==`,L5=`
+euPnNY+oXWZEVJNrZ9aun8SIq/CzodHuriIokhDIR1ronbKZr0o6o8ipoz//2Q==`;
+var body3 = `
 /9j/4AAQSkZJRgABAQAAAQABAAD/2wBDAAsICAoIBwsKCQoNDAsNERwSEQ8PESIZGhQcKSQrKigk
 JyctMkA3LTA9MCcnOEw5PUNFSElIKzZPVU5GVEBHSEX/2wBDAQwNDREPESESEiFFLicuRUVFRUVF
 RUVFRUVFRUVFRUVFRUVFRUVFRUVFRUVFRUVFRUVFRUVFRUVFRUVFRUVFRUX/wAARCASwBLADASIA
@@ -827,4 +14554,580 @@ AAAAAAJAAAAAAAAAAAAAABAJEAAAAAAAAAAAAAAAIEoBKAAAAAAAAAAAAAAABAlAAAAAAAIAAAAA
 BAkBAkBAkBAlACEgMZjdjbFW8bWrEx8YWANb6Fp+bfwab+vLDKMFK9qxH5L0bAr8OPRPKz2AY7J2
 SbAjYZAI2E7AIEgIEgIEgMdkSy2NgY7MdlmyNoBXsxmFuyNgVTVjNV3KjlBRNTlXTVHKCrlIqt5T
 lBhEMohlFerLlBjEMohMVTEARDKCITsAk2AEgAAAkAAAAAAAAAAAAAAAAAAAAAAAASAAAAAAAAD/
-2Q==`;var s0=D(H());async function Is(e){let t=(r,A="application/octet-stream")=>fetch(`data:${A};base64,${r}`).then(s=>s.blob()),n,o;switch(e.config.warmup){case"face":n=await t(C5);break;case"body":case"full":n=await t(L5);break;default:n=null}if(n){let r=await createImageBitmap(n);o=await e.detect(r,e.config),r.close()}return o}async function Os(e){return new Promise(t=>{let n;switch(e.config.warmup){case"face":n="data:image/jpeg;base64,"+C5;break;case"full":case"body":n="data:image/jpeg;base64,"+L5;break;default:n=""}let o;if(typeof Image!="undefined")o=new Image;else if(k.Image)o=new k.Image;else return;o.onload=async()=>{let r=k0(o.naturalWidth,o.naturalHeight);if(!r)u("Warmup: Canvas not found"),t(void 0);else{let A=r.getContext("2d");A&&A.drawImage(o,0,0);let s=await e.image(r),a=s.tensor?await e.detect(s.tensor,e.config):void 0;t(a)}},n?o.src=n:t(void 0)})}async function Cs(e){let t=r=>Buffer.from(r,"base64"),n;e.config.warmup==="face"?n=t(C5):n=t(L5);let o;if("node"in s0&&s0.getBackend()==="tensorflow"){let r=s0.node.decodeJpeg(n),A=s0.expandDims(r,0);e.tf.dispose(r),o=await e.detect(A,e.config),e.tf.dispose(A)}else e.config.debug&&u("Warmup tfjs-node not loaded");return o}async function Ls(e){let t;return typeof createImageBitmap=="function"?t=await Is(e):typeof Image!="undefined"||k.Canvas!==void 0?t=await Os(e):t=await Cs(e),t}async function Ws(e){var a,l,c,x;if(!s0.env().flagRegistry.ENGINE_COMPILE_ONLY)return;let t=s0.getBackend(),n=s0.backend();if(t!=="webgl"&&t!=="humangl"||!(n!=null&&n.checkCompileCompletion))return;s0.env().set("ENGINE_COMPILE_ONLY",!0);let o=s0.engine().state.numTensors,r=[];for(let[i,y]of Object.entries(e.models).filter(([d,f])=>d!==null&&f!==null)){let d=(l=(a=y.inputs)==null?void 0:a[0])!=null&&l.shape?[...y.inputs[0].shape]:[1,64,64,3],f=(x=(c=y.inputs)==null?void 0:c[0])!=null&&x.dtype?y.inputs[0].dtype:"float32";for(let g=0;g<d.length;g++)d[g]===-1&&(d[g]=g===0?1:64);let p=s0.zeros(d,f);try{let g=y.execute(p);r.push(i),Array.isArray(g)?g.forEach(M=>s0.dispose(M)):s0.dispose(g)}catch(g){u("compile fail model:",i)}s0.dispose(p)}let A=await n.checkCompileCompletionAsync();n.getUniformLocations(),e.config.debug&&u("compile pass:",{models:r,kernels:A.length}),s0.env().set("ENGINE_COMPILE_ONLY",!1);let s=s0.engine().state.numTensors;s-o>0&&u("tensor leak:",s-o)}async function go(e,t){await W2(e,!1);let n=v();return e.state="warmup",t&&(e.config=A0(e.config,t)),!e.config.warmup||e.config.warmup.length===0||e.config.warmup==="none"?{face:[],body:[],hand:[],gesture:[],object:[],performance:e.performance,timestamp:v(),persons:[],error:null}:new Promise(async o=>{await f2.load(e),await Ws(e);let r=await Ls(e),A=v();e.config.debug&&u("warmup",e.config.warmup,Math.round(A-n),"ms"),e.emit("warmup"),o(r)})}var T2,D2,Z2,W5,Be,b1=class{constructor(t){R(this,"version");R(this,"config");R(this,"result");R(this,"state");R(this,"process");R(this,"tf");R(this,"env");R(this,"draw");R(this,"models");R(this,"events");R(this,"faceTriangulation");R(this,"faceUVMap");R(this,"performance");t2(this,T2,void 0);t2(this,D2,void 0);t2(this,Z2,void 0);R(this,"gl");R(this,"analyze",(...t)=>{if(!ie(this,D2))return;let n=this.tf.engine().state.numTensors,o=ie(this,T2);E2(this,T2,n);let r=n-o;r!==0&&u(...t,r)});t2(this,W5,t=>{if(!ie(this,Z2))return null;if(!t)return"input is not defined";if(this.env.node&&!(t instanceof ae.Tensor))return"input must be a tensor";try{this.tf.getBackend()}catch(n){return"backend not loaded"}return null});R(this,"similarity",p1);R(this,"distance",V2);R(this,"match",u1);R(this,"webcam",new Y2);R(this,"emit",t=>{var n;(n=this.events)!=null&&n.dispatchEvent&&this.events.dispatchEvent(new Event(t))});t2(this,Be,{});this.env=k;let n=(ae.version.tfjs||ae.version_core).replace(/-(.*)/,"");Pe.wasmPath=`https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-backend-wasm@${n}/dist/`,Pe.modelBasePath=k.browser?"../models/":"file://models/",Pe.backend=k.browser?"webgl":"tensorflow",this.version=A1,Object.defineProperty(this,"version",{value:A1}),this.config=JSON.parse(JSON.stringify(Pe)),Object.seal(this.config),this.config.cacheModels=typeof indexedDB!="undefined",t&&(this.config=A0(this.config,t)),ao(this.config),this.tf=ae,this.state="idle",E2(this,T2,0),E2(this,D2,!1),E2(this,Z2,!1),this.performance={},this.events=typeof EventTarget!="undefined"?new EventTarget:void 0,this.models=new H2,this.draw={options:R0,canvas:(r,A)=>c1(r,A),face:(r,A,s)=>h2(r,A,s),body:(r,A,s)=>b2(r,A,s),hand:(r,A,s)=>g2(r,A,s),gesture:(r,A,s)=>v2(r,A,s),object:(r,A,s)=>M2(r,A,s),person:(r,A,s)=>l1(r,A,s),all:(r,A,s)=>d1(r,A,s)},this.result={face:[],body:[],hand:[],gesture:[],object:[],performance:{},timestamp:0,persons:[],error:null},this.process={tensor:null,canvas:null},this.faceTriangulation=_3,this.faceUVMap=$3,this.gl=_,p2(this,null,""),this.emit("create"),(this.config.debug||this.env.browser)&&u(`version: ${this.version}`),this.config.debug&&u(`tfjs version: ${this.tf.version["tfjs-core"]}`);let o=JSON.parse(JSON.stringify(this.env));delete o.kernels,delete o.initial,delete o.perfadd,this.config.debug&&u("environment:",o)}reset(){let t=this.config.backend;this.config=JSON.parse(JSON.stringify(Pe)),this.config.backend=t,D5(),k.initial=!0}validate(t){let n=F5(Pe,t||this.config);return n.length===0&&(this.config=A0(this.config,t)),n}check(){return O5(this)}now(){return v()}image(t,n=!0){return o2(t,this.config,n)}async segmentation(t,n){return Ao(t,n,this.config)}enhance(t){return Et(t)}compare(t,n){return C1(this.config,t,n)}async init(){await W2(this,!0),await this.tf.ready(),D5()}async load(t){this.state="load";let n=v(),o=Object.values(this.models).filter(s=>s).length;t&&(this.config=A0(this.config,t)),this.env.initial&&(await W2(this,!1)||u("error: backend check failed"),await ae.ready(),this.env.browser&&(this.config.debug&&u("configuration:",this.config),this.config.debug&&u("tf flags:",this.tf.ENV.flags))),await r1(this),this.env.initial&&this.config.debug&&u("tf engine state:",this.tf.engine().state.numBytes,"bytes",this.tf.engine().state.numTensors,"tensors"),this.env.initial=!1,Object.values(this.models).filter(s=>s).length!==o&&(O5(this),this.emit("load"));let A=Math.trunc(v()-n);A>(this.performance.loadModels||0)&&(this.performance.loadModels=this.env.perfadd?(this.performance.loadModels||0)+A:A)}next(t=this.result){return uo(t,this.config)}getModelStats(){return o1(this)}async warmup(t){let n=v(),o=await go(this,t),r=v();return this.performance.warmup=Math.trunc(r-n),o}async profile(t,n){let o=await this.tf.profile(()=>this.detect(t,n)),r={},A=0;for(let a of o.kernels)r[a.name]?r[a.name]+=a.kernelTimeMs:r[a.name]=a.kernelTimeMs,A+=a.kernelTimeMs;let s=[];Object.entries(r).forEach(a=>s.push({kernel:a[0],time:a[1],perc:0}));for(let a of s)a.perc=Math.round(1e3*a.time/A)/1e3,a.time=Math.round(1e3*a.time)/1e3;return s.sort((a,l)=>l.time-a.time),s.length=20,s}async detect(t,n){return this.state="detect",new Promise(async o=>{var g,M,P,m,b,z,w,I,q,e0,V,X,c0,T,F,M0,$,m0,d0,G,Z;this.state="config";let r;this.config=A0(this.config,n),this.state="check";let A=ie(this,W5).call(this,t);A&&(u(A,t),this.emit("error"),o({face:[],body:[],hand:[],gesture:[],object:[],performance:this.performance,timestamp:v(),persons:[],error:A}));let s=v();await this.load(),r=v(),this.state="image";let a=await o2(t,this.config);if(this.process=a,this.performance.inputProcess=this.env.perfadd?(this.performance.inputProcess||0)+Math.trunc(v()-r):Math.trunc(v()-r),this.analyze("Get Image:"),!a.tensor){this.config.debug&&u("could not convert input to tensor"),this.emit("error"),o({face:[],body:[],hand:[],gesture:[],object:[],performance:this.performance,timestamp:v(),persons:[],error:"could not convert input to tensor"});return}this.emit("image"),r=v(),this.config.skipAllowed=await O1(this.config,a.tensor),this.performance.totalFrames||(this.performance.totalFrames=0),this.performance.cachedFrames||(this.performance.cachedFrames=0),this.performance.totalFrames++,this.config.skipAllowed&&this.performance.cachedFrames++,this.performance.cacheCheck=this.env.perfadd?(this.performance.cacheCheck||0)+Math.trunc(v()-r):Math.trunc(v()-r),this.analyze("Check Changed:");let l=[],c=[],x=[],i=[];this.state="detect:face",this.config.async?(l=this.config.face.enabled?f1(this,a.tensor):[],this.performance.face&&delete this.performance.face):(r=v(),l=this.config.face.enabled?await f1(this,a.tensor):[],this.performance.face=this.env.perfadd?(this.performance.face||0)+Math.trunc(v()-r):Math.trunc(v()-r)),this.config.async&&(this.config.body.maxDetected===-1||this.config.hand.maxDetected===-1)&&(l=await l),this.analyze("Start Body:"),this.state="detect:body";let y=this.config.body.maxDetected===-1?A0(this.config,{body:{maxDetected:this.config.face.enabled?1*l.length:1}}):this.config;this.config.async?((g=this.config.body.modelPath)!=null&&g.includes("posenet")?c=this.config.body.enabled?e1(a.tensor,y):[]:(M=this.config.body.modelPath)!=null&&M.includes("blazepose")?c=this.config.body.enabled?ct(a.tensor,y):[]:(P=this.config.body.modelPath)!=null&&P.includes("efficientpose")?c=this.config.body.enabled?ut(a.tensor,y):[]:(m=this.config.body.modelPath)!=null&&m.includes("movenet")&&(c=this.config.body.enabled?Ut(a.tensor,y):[]),this.performance.body&&delete this.performance.body):(r=v(),(b=this.config.body.modelPath)!=null&&b.includes("posenet")?c=this.config.body.enabled?await e1(a.tensor,y):[]:(z=this.config.body.modelPath)!=null&&z.includes("blazepose")?c=this.config.body.enabled?await ct(a.tensor,y):[]:(w=this.config.body.modelPath)!=null&&w.includes("efficientpose")?c=this.config.body.enabled?await ut(a.tensor,y):[]:(I=this.config.body.modelPath)!=null&&I.includes("movenet")&&(c=this.config.body.enabled?await Ut(a.tensor,y):[]),this.performance.body=this.env.perfadd?(this.performance.body||0)+Math.trunc(v()-r):Math.trunc(v()-r)),this.analyze("End Body:"),this.analyze("Start Hand:"),this.state="detect:hand";let d=this.config.hand.maxDetected===-1?A0(this.config,{hand:{maxDetected:this.config.face.enabled?2*l.length:1}}):this.config;this.config.async?((e0=(q=this.config.hand.detector)==null?void 0:q.modelPath)!=null&&e0.includes("handdetect")?x=this.config.hand.enabled?Ct(a.tensor,d):[]:(X=(V=this.config.hand.detector)==null?void 0:V.modelPath)!=null&&X.includes("handtrack")&&(x=this.config.hand.enabled?Gt(a.tensor,d):[]),this.performance.hand&&delete this.performance.hand):(r=v(),(T=(c0=this.config.hand.detector)==null?void 0:c0.modelPath)!=null&&T.includes("handdetect")?x=this.config.hand.enabled?await Ct(a.tensor,d):[]:(M0=(F=this.config.hand.detector)==null?void 0:F.modelPath)!=null&&M0.includes("handtrack")&&(x=this.config.hand.enabled?await Gt(a.tensor,d):[]),this.performance.hand=this.env.perfadd?(this.performance.hand||0)+Math.trunc(v()-r):Math.trunc(v()-r)),this.analyze("End Hand:"),this.analyze("Start Object:"),this.state="detect:object",this.config.async?(($=this.config.object.modelPath)!=null&&$.includes("nanodet")?i=this.config.object.enabled?Kt(a.tensor,this.config):[]:(m0=this.config.object.modelPath)!=null&&m0.includes("centernet")&&(i=this.config.object.enabled?yt(a.tensor,this.config):[]),this.performance.object&&delete this.performance.object):(r=v(),(d0=this.config.object.modelPath)!=null&&d0.includes("nanodet")?i=this.config.object.enabled?await Kt(a.tensor,this.config):[]:(G=this.config.object.modelPath)!=null&&G.includes("centernet")&&(i=this.config.object.enabled?await yt(a.tensor,this.config):[]),this.performance.object=this.env.perfadd?(this.performance.object||0)+Math.trunc(v()-r):Math.trunc(v()-r)),this.analyze("End Object:"),this.state="detect:await",this.config.async&&([l,c,x,i]=await Promise.all([l,c,x,i])),this.state="detect:gesture";let f=[];this.config.gesture.enabled&&(r=v(),f=[...fo(l),...yo(c),...po(x),...mo(l)],this.config.async?this.performance.gesture&&delete this.performance.gesture:this.performance.gesture=this.env.perfadd?(this.performance.gesture||0)+Math.trunc(v()-r):Math.trunc(v()-r)),this.performance.total=this.env.perfadd?(this.performance.total||0)+Math.trunc(v()-s):Math.trunc(v()-s);let p=((Z=this.process.tensor)==null?void 0:Z.shape)||[];this.result={face:l,body:c,hand:x,gesture:f,object:i,performance:this.performance,canvas:this.process.canvas,timestamp:Date.now(),error:null,get persons(){return bo(l,c,x,f,p)}},ae.dispose(a.tensor),this.emit("detect"),this.state="idle",o(this.result)})}async sleep(t){return new Promise(n=>{setTimeout(n,t)})}async video(t,n=!0,o=0){n?(ie(this,Be)[t.id]||(this.config.debug&&u("video start",t.id),ie(this,Be)[t.id]=!0),!t.paused&&ie(this,Be)[t.id]&&t.readyState>=2&&await this.detect(t),o>0&&await this.sleep(o),ie(this,Be)[t.id]&&requestAnimationFrame(()=>this.video(t,n,o))):(this.config.debug&&u("video stop",t.id),ie(this,Be)[t.id]=!1)}};T2=new WeakMap,D2=new WeakMap,Z2=new WeakMap,W5=new WeakMap,Be=new WeakMap;0&&(module.exports={Env,Human,defaults,draw,env,match,models});
+2Q==`;
+
+// src/warmup.ts
+var tf38 = __toESM(require_tfjs_esm());
+async function warmupBitmap(instance2) {
+  const b64toBlob = (base64, type = "application/octet-stream") => fetch(`data:${type};base64,${base64}`).then((res2) => res2.blob());
+  let blob;
+  let res;
+  switch (instance2.config.warmup) {
+    case "face":
+      blob = await b64toBlob(face3);
+      break;
+    case "body":
+    case "full":
+      blob = await b64toBlob(body3);
+      break;
+    default:
+      blob = null;
+  }
+  if (blob) {
+    const bitmap = await createImageBitmap(blob);
+    res = await instance2.detect(bitmap, instance2.config);
+    bitmap.close();
+  }
+  return res;
+}
+async function warmupCanvas(instance2) {
+  return new Promise((resolve) => {
+    let src;
+    switch (instance2.config.warmup) {
+      case "face":
+        src = "data:image/jpeg;base64," + face3;
+        break;
+      case "full":
+      case "body":
+        src = "data:image/jpeg;base64," + body3;
+        break;
+      default:
+        src = "";
+    }
+    let img;
+    if (typeof Image !== "undefined")
+      img = new Image();
+    else if (env.Image)
+      img = new env.Image();
+    else
+      return;
+    img.onload = async () => {
+      const canvas3 = canvas(img.naturalWidth, img.naturalHeight);
+      if (!canvas3) {
+        log("Warmup: Canvas not found");
+        resolve(void 0);
+      } else {
+        const ctx = canvas3.getContext("2d");
+        if (ctx)
+          ctx.drawImage(img, 0, 0);
+        const tensor6 = await instance2.image(canvas3);
+        const res = tensor6.tensor ? await instance2.detect(tensor6.tensor, instance2.config) : void 0;
+        resolve(res);
+      }
+    };
+    if (src)
+      img.src = src;
+    else
+      resolve(void 0);
+  });
+}
+async function warmupNode(instance2) {
+  const atob = (str) => Buffer.from(str, "base64");
+  let img;
+  if (instance2.config.warmup === "face")
+    img = atob(face3);
+  else
+    img = atob(body3);
+  let res;
+  if ("node" in tf38 && tf38.getBackend() === "tensorflow") {
+    const data = tf38["node"].decodeJpeg(img);
+    const expanded = tf38.expandDims(data, 0);
+    instance2.tf.dispose(data);
+    res = await instance2.detect(expanded, instance2.config);
+    instance2.tf.dispose(expanded);
+  } else {
+    if (instance2.config.debug)
+      log("Warmup tfjs-node not loaded");
+  }
+  return res;
+}
+async function runInference(instance2) {
+  let res;
+  if (typeof createImageBitmap === "function")
+    res = await warmupBitmap(instance2);
+  else if (typeof Image !== "undefined" || env.Canvas !== void 0)
+    res = await warmupCanvas(instance2);
+  else
+    res = await warmupNode(instance2);
+  return res;
+}
+async function runCompile(instance2) {
+  var _a, _b, _c, _d;
+  if (!tf38.env().flagRegistry.ENGINE_COMPILE_ONLY)
+    return;
+  const backendType = tf38.getBackend();
+  const webGLBackend = tf38.backend();
+  if (backendType !== "webgl" && backendType !== "humangl" || !(webGLBackend == null ? void 0 : webGLBackend.checkCompileCompletion)) {
+    return;
+  }
+  tf38.env().set("ENGINE_COMPILE_ONLY", true);
+  const numTensorsStart = tf38.engine().state.numTensors;
+  const compiledModels = [];
+  for (const [modelName, model21] of Object.entries(instance2.models).filter(([key, val]) => key !== null && val !== null)) {
+    const shape = ((_b = (_a = model21.inputs) == null ? void 0 : _a[0]) == null ? void 0 : _b.shape) ? [...model21.inputs[0].shape] : [1, 64, 64, 3];
+    const dtype = ((_d = (_c = model21.inputs) == null ? void 0 : _c[0]) == null ? void 0 : _d.dtype) ? model21.inputs[0].dtype : "float32";
+    for (let dim = 0; dim < shape.length; dim++) {
+      if (shape[dim] === -1)
+        shape[dim] = dim === 0 ? 1 : 64;
+    }
+    const tensor6 = tf38.zeros(shape, dtype);
+    try {
+      const res = model21.execute(tensor6);
+      compiledModels.push(modelName);
+      if (Array.isArray(res))
+        res.forEach((t2) => tf38.dispose(t2));
+      else
+        tf38.dispose(res);
+    } catch (e) {
+      if (instance2.config.debug)
+        log("compile fail model:", modelName);
+    }
+    tf38.dispose(tensor6);
+  }
+  const kernels = await webGLBackend.checkCompileCompletionAsync();
+  webGLBackend.getUniformLocations();
+  if (instance2.config.debug)
+    log("compile pass:", { models: compiledModels, kernels: kernels.length });
+  tf38.env().set("ENGINE_COMPILE_ONLY", false);
+  const numTensorsEnd = tf38.engine().state.numTensors;
+  if (numTensorsEnd - numTensorsStart > 0)
+    log("tensor leak:", numTensorsEnd - numTensorsStart);
+}
+async function warmup(instance2, userConfig) {
+  await check(instance2, false);
+  const t0 = now();
+  instance2.state = "warmup";
+  if (userConfig)
+    instance2.config = mergeDeep(instance2.config, userConfig);
+  if (!instance2.config.warmup || instance2.config.warmup.length === 0 || instance2.config.warmup === "none") {
+    return { face: [], body: [], hand: [], gesture: [], object: [], performance: instance2.performance, timestamp: now(), persons: [], error: null };
+  }
+  return new Promise(async (resolve) => {
+    await models_exports2.load(instance2);
+    await runCompile(instance2);
+    const res = await runInference(instance2);
+    const t1 = now();
+    if (instance2.config.debug)
+      log("warmup", instance2.config.warmup, Math.round(t1 - t0), "ms");
+    instance2.emit("warmup");
+    resolve(res);
+  });
+}
+
+// src/human.ts
+var _numTensors, _analyzeMemoryLeaks, _checkSanity, _sanity, _loops;
+var Human2 = class {
+  constructor(userConfig) {
+    __publicField(this, "version");
+    __publicField(this, "config");
+    __publicField(this, "result");
+    __publicField(this, "state");
+    __publicField(this, "process");
+    __publicField(this, "tf");
+    __publicField(this, "env");
+    __publicField(this, "draw");
+    __publicField(this, "models");
+    __publicField(this, "events");
+    __publicField(this, "faceTriangulation");
+    __publicField(this, "faceUVMap");
+    __publicField(this, "performance");
+    __privateAdd(this, _numTensors, void 0);
+    __privateAdd(this, _analyzeMemoryLeaks, void 0);
+    __privateAdd(this, _checkSanity, void 0);
+    __publicField(this, "gl");
+    __publicField(this, "analyze", (...msg) => {
+      if (!__privateGet(this, _analyzeMemoryLeaks))
+        return;
+      const currentTensors = this.tf.engine().state.numTensors;
+      const previousTensors = __privateGet(this, _numTensors);
+      __privateSet(this, _numTensors, currentTensors);
+      const leaked = currentTensors - previousTensors;
+      if (leaked !== 0)
+        log(...msg, leaked);
+    });
+    __privateAdd(this, _sanity, (input) => {
+      if (!__privateGet(this, _checkSanity))
+        return null;
+      if (!input)
+        return "input is not defined";
+      if (this.env.node && !(input instanceof tf39.Tensor))
+        return "input must be a tensor";
+      try {
+        this.tf.getBackend();
+      } catch (e) {
+        return "backend not loaded";
+      }
+      return null;
+    });
+    __publicField(this, "similarity", similarity);
+    __publicField(this, "distance", distance);
+    __publicField(this, "match", match2);
+    __publicField(this, "webcam", new WebCam());
+    __publicField(this, "emit", (event) => {
+      var _a;
+      if ((_a = this.events) == null ? void 0 : _a.dispatchEvent)
+        this.events.dispatchEvent(new Event(event));
+    });
+    __privateAdd(this, _loops, {});
+    this.env = env;
+    const tfVersion = (tf39.version.tfjs || tf39.version_core).replace(/-(.*)/, "");
+    config.wasmPath = `https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-backend-wasm@${tfVersion}/dist/`;
+    config.modelBasePath = env.browser ? "../models/" : "file://models/";
+    config.backend = env.browser ? "webgl" : "tensorflow";
+    this.version = version2;
+    Object.defineProperty(this, "version", { value: version2 });
+    this.config = JSON.parse(JSON.stringify(config));
+    Object.seal(this.config);
+    this.config.cacheModels = typeof indexedDB !== "undefined";
+    if (userConfig)
+      this.config = mergeDeep(this.config, userConfig);
+    setModelLoadOptions(this.config);
+    this.tf = tf39;
+    this.state = "idle";
+    __privateSet(this, _numTensors, 0);
+    __privateSet(this, _analyzeMemoryLeaks, false);
+    __privateSet(this, _checkSanity, false);
+    this.performance = {};
+    this.events = typeof EventTarget !== "undefined" ? new EventTarget() : void 0;
+    this.models = new Models();
+    this.draw = {
+      options: options3,
+      canvas: (input, output) => canvas2(input, output),
+      face: (output, result, options4) => face(output, result, options4),
+      body: (output, result, options4) => body(output, result, options4),
+      hand: (output, result, options4) => hand(output, result, options4),
+      gesture: (output, result, options4) => gesture(output, result, options4),
+      object: (output, result, options4) => object(output, result, options4),
+      person: (output, result, options4) => person(output, result, options4),
+      all: (output, result, options4) => all(output, result, options4)
+    };
+    this.result = { face: [], body: [], hand: [], gesture: [], object: [], performance: {}, timestamp: 0, persons: [], error: null };
+    this.process = { tensor: null, canvas: null };
+    this.faceTriangulation = triangulation;
+    this.faceUVMap = uvmap;
+    this.gl = config2;
+    validateModel(this, null, "");
+    this.emit("create");
+    if (this.config.debug || this.env.browser)
+      log(`version: ${this.version}`);
+    if (this.config.debug)
+      log(`tfjs version: ${this.tf.version["tfjs-core"]}`);
+    const envTemp = JSON.parse(JSON.stringify(this.env));
+    delete envTemp.kernels;
+    delete envTemp.initial;
+    delete envTemp.perfadd;
+    if (this.config.debug)
+      log("environment:", envTemp);
+  }
+  reset() {
+    const currentBackend = this.config.backend;
+    this.config = JSON.parse(JSON.stringify(config));
+    this.config.backend = currentBackend;
+    reset();
+    env.initial = true;
+  }
+  validate(userConfig) {
+    const msgs = validate(config, userConfig || this.config);
+    if (msgs.length === 0)
+      this.config = mergeDeep(this.config, userConfig);
+    return msgs;
+  }
+  check() {
+    return validate2(this);
+  }
+  now() {
+    return now();
+  }
+  image(input, getTensor = true) {
+    return process2(input, this.config, getTensor);
+  }
+  async segmentation(input, userConfig) {
+    var _a, _b, _c;
+    if (userConfig)
+      this.config = mergeDeep(this.config, userConfig);
+    if (!this.config.segmentation.enabled)
+      return null;
+    const processed = await process2(input, this.config);
+    if (!processed.tensor)
+      return null;
+    let tensor6 = null;
+    if ((_a = this.config.segmentation.modelPath) == null ? void 0 : _a.includes("rvm"))
+      tensor6 = await predict18(processed.tensor, this.config);
+    if ((_b = this.config.segmentation.modelPath) == null ? void 0 : _b.includes("meet"))
+      tensor6 = await predict13(processed.tensor, this.config);
+    if ((_c = this.config.segmentation.modelPath) == null ? void 0 : _c.includes("selfie"))
+      tensor6 = await predict19(processed.tensor, this.config);
+    tf39.dispose(processed.tensor);
+    return tensor6;
+  }
+  enhance(input) {
+    return enhance(input);
+  }
+  compare(firstImageTensor, secondImageTensor) {
+    return compare(this.config, firstImageTensor, secondImageTensor);
+  }
+  async init() {
+    await check(this, true);
+    await this.tf.ready();
+    reset();
+  }
+  async load(userConfig) {
+    this.state = "load";
+    const timeStamp = now();
+    const count2 = Object.values(this.models).filter((model21) => model21).length;
+    if (userConfig)
+      this.config = mergeDeep(this.config, userConfig);
+    if (this.env.initial) {
+      if (!await check(this, false))
+        log("error: backend check failed");
+      await tf39.ready();
+      if (this.env.browser) {
+        if (this.config.debug)
+          log("configuration:", this.config);
+        if (this.config.debug)
+          log("tf flags:", this.tf.ENV.flags);
+      }
+    }
+    await load22(this);
+    if (this.env.initial && this.config.debug)
+      log("tf engine state:", this.tf.engine().state.numBytes, "bytes", this.tf.engine().state.numTensors, "tensors");
+    this.env.initial = false;
+    const loaded = Object.values(this.models).filter((model21) => model21).length;
+    if (loaded !== count2) {
+      validate2(this);
+      this.emit("load");
+    }
+    const current = Math.trunc(now() - timeStamp);
+    if (current > (this.performance.loadModels || 0))
+      this.performance.loadModels = this.env.perfadd ? (this.performance.loadModels || 0) + current : current;
+  }
+  next(result = this.result) {
+    return calc2(result, this.config);
+  }
+  getModelStats() {
+    return getModelStats(this);
+  }
+  async warmup(userConfig) {
+    const t0 = now();
+    const res = await warmup(this, userConfig);
+    const t1 = now();
+    this.performance.warmup = Math.trunc(t1 - t0);
+    return res;
+  }
+  async profile(input, userConfig) {
+    const profile = await this.tf.profile(() => this.detect(input, userConfig));
+    const kernels = {};
+    let total = 0;
+    for (const kernel of profile.kernels) {
+      if (kernels[kernel.name])
+        kernels[kernel.name] += kernel.kernelTimeMs;
+      else
+        kernels[kernel.name] = kernel.kernelTimeMs;
+      total += kernel.kernelTimeMs;
+    }
+    const kernelArr = [];
+    Object.entries(kernels).forEach((key) => kernelArr.push({ kernel: key[0], time: key[1], perc: 0 }));
+    for (const kernel of kernelArr) {
+      kernel.perc = Math.round(1e3 * kernel.time / total) / 1e3;
+      kernel.time = Math.round(1e3 * kernel.time) / 1e3;
+    }
+    kernelArr.sort((a, b) => b.time - a.time);
+    kernelArr.length = 20;
+    return kernelArr;
+  }
+  async detect(input, userConfig) {
+    this.state = "detect";
+    return new Promise(async (resolve) => {
+      var _a, _b, _c, _d, _e, _f, _g, _h, _i, _j, _k, _l, _m, _n, _o, _p, _q, _r, _s, _t, _u;
+      this.state = "config";
+      let timeStamp;
+      this.config = mergeDeep(this.config, userConfig);
+      this.state = "check";
+      const error = __privateGet(this, _sanity).call(this, input);
+      if (error) {
+        log(error, input);
+        this.emit("error");
+        resolve({ face: [], body: [], hand: [], gesture: [], object: [], performance: this.performance, timestamp: now(), persons: [], error });
+      }
+      const timeStart = now();
+      await this.load();
+      timeStamp = now();
+      this.state = "image";
+      const img = await process2(input, this.config);
+      this.process = img;
+      this.performance.inputProcess = this.env.perfadd ? (this.performance.inputProcess || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);
+      this.analyze("Get Image:");
+      if (!img.tensor) {
+        if (this.config.debug)
+          log("could not convert input to tensor");
+        this.emit("error");
+        resolve({ face: [], body: [], hand: [], gesture: [], object: [], performance: this.performance, timestamp: now(), persons: [], error: "could not convert input to tensor" });
+        return;
+      }
+      this.emit("image");
+      timeStamp = now();
+      this.config.skipAllowed = await skip(this.config, img.tensor);
+      if (!this.performance.totalFrames)
+        this.performance.totalFrames = 0;
+      if (!this.performance.cachedFrames)
+        this.performance.cachedFrames = 0;
+      this.performance.totalFrames++;
+      if (this.config.skipAllowed)
+        this.performance.cachedFrames++;
+      this.performance.cacheCheck = this.env.perfadd ? (this.performance.cacheCheck || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);
+      this.analyze("Check Changed:");
+      let faceRes = [];
+      let bodyRes = [];
+      let handRes = [];
+      let objectRes = [];
+      this.state = "detect:face";
+      if (this.config.async) {
+        faceRes = this.config.face.enabled ? detectFace(this, img.tensor) : [];
+        if (this.performance.face)
+          delete this.performance.face;
+      } else {
+        timeStamp = now();
+        faceRes = this.config.face.enabled ? await detectFace(this, img.tensor) : [];
+        this.performance.face = this.env.perfadd ? (this.performance.face || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);
+      }
+      if (this.config.async && (this.config.body.maxDetected === -1 || this.config.hand.maxDetected === -1))
+        faceRes = await faceRes;
+      this.analyze("Start Body:");
+      this.state = "detect:body";
+      const bodyConfig = this.config.body.maxDetected === -1 ? mergeDeep(this.config, { body: { maxDetected: this.config.face.enabled ? 1 * faceRes.length : 1 } }) : this.config;
+      if (this.config.async) {
+        if ((_a = this.config.body.modelPath) == null ? void 0 : _a.includes("posenet"))
+          bodyRes = this.config.body.enabled ? predict17(img.tensor, bodyConfig) : [];
+        else if ((_b = this.config.body.modelPath) == null ? void 0 : _b.includes("blazepose"))
+          bodyRes = this.config.body.enabled ? predict2(img.tensor, bodyConfig) : [];
+        else if ((_c = this.config.body.modelPath) == null ? void 0 : _c.includes("efficientpose"))
+          bodyRes = this.config.body.enabled ? predict4(img.tensor, bodyConfig) : [];
+        else if ((_d = this.config.body.modelPath) == null ? void 0 : _d.includes("movenet"))
+          bodyRes = this.config.body.enabled ? predict15(img.tensor, bodyConfig) : [];
+        if (this.performance.body)
+          delete this.performance.body;
+      } else {
+        timeStamp = now();
+        if ((_e = this.config.body.modelPath) == null ? void 0 : _e.includes("posenet"))
+          bodyRes = this.config.body.enabled ? await predict17(img.tensor, bodyConfig) : [];
+        else if ((_f = this.config.body.modelPath) == null ? void 0 : _f.includes("blazepose"))
+          bodyRes = this.config.body.enabled ? await predict2(img.tensor, bodyConfig) : [];
+        else if ((_g = this.config.body.modelPath) == null ? void 0 : _g.includes("efficientpose"))
+          bodyRes = this.config.body.enabled ? await predict4(img.tensor, bodyConfig) : [];
+        else if ((_h = this.config.body.modelPath) == null ? void 0 : _h.includes("movenet"))
+          bodyRes = this.config.body.enabled ? await predict15(img.tensor, bodyConfig) : [];
+        this.performance.body = this.env.perfadd ? (this.performance.body || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);
+      }
+      this.analyze("End Body:");
+      this.analyze("Start Hand:");
+      this.state = "detect:hand";
+      const handConfig = this.config.hand.maxDetected === -1 ? mergeDeep(this.config, { hand: { maxDetected: this.config.face.enabled ? 2 * faceRes.length : 1 } }) : this.config;
+      if (this.config.async) {
+        if ((_j = (_i = this.config.hand.detector) == null ? void 0 : _i.modelPath) == null ? void 0 : _j.includes("handdetect"))
+          handRes = this.config.hand.enabled ? predict9(img.tensor, handConfig) : [];
+        else if ((_l = (_k = this.config.hand.detector) == null ? void 0 : _k.modelPath) == null ? void 0 : _l.includes("handtrack"))
+          handRes = this.config.hand.enabled ? predict10(img.tensor, handConfig) : [];
+        if (this.performance.hand)
+          delete this.performance.hand;
+      } else {
+        timeStamp = now();
+        if ((_n = (_m = this.config.hand.detector) == null ? void 0 : _m.modelPath) == null ? void 0 : _n.includes("handdetect"))
+          handRes = this.config.hand.enabled ? await predict9(img.tensor, handConfig) : [];
+        else if ((_p = (_o = this.config.hand.detector) == null ? void 0 : _o.modelPath) == null ? void 0 : _p.includes("handtrack"))
+          handRes = this.config.hand.enabled ? await predict10(img.tensor, handConfig) : [];
+        this.performance.hand = this.env.perfadd ? (this.performance.hand || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);
+      }
+      this.analyze("End Hand:");
+      this.analyze("Start Object:");
+      this.state = "detect:object";
+      if (this.config.async) {
+        if ((_q = this.config.object.modelPath) == null ? void 0 : _q.includes("nanodet"))
+          objectRes = this.config.object.enabled ? predict16(img.tensor, this.config) : [];
+        else if ((_r = this.config.object.modelPath) == null ? void 0 : _r.includes("centernet"))
+          objectRes = this.config.object.enabled ? predict3(img.tensor, this.config) : [];
+        if (this.performance.object)
+          delete this.performance.object;
+      } else {
+        timeStamp = now();
+        if ((_s = this.config.object.modelPath) == null ? void 0 : _s.includes("nanodet"))
+          objectRes = this.config.object.enabled ? await predict16(img.tensor, this.config) : [];
+        else if ((_t = this.config.object.modelPath) == null ? void 0 : _t.includes("centernet"))
+          objectRes = this.config.object.enabled ? await predict3(img.tensor, this.config) : [];
+        this.performance.object = this.env.perfadd ? (this.performance.object || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);
+      }
+      this.analyze("End Object:");
+      this.state = "detect:await";
+      if (this.config.async)
+        [faceRes, bodyRes, handRes, objectRes] = await Promise.all([faceRes, bodyRes, handRes, objectRes]);
+      this.state = "detect:gesture";
+      let gestureRes = [];
+      if (this.config.gesture.enabled) {
+        timeStamp = now();
+        gestureRes = [...face2(faceRes), ...body2(bodyRes), ...hand2(handRes), ...iris2(faceRes)];
+        if (!this.config.async)
+          this.performance.gesture = this.env.perfadd ? (this.performance.gesture || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);
+        else if (this.performance.gesture)
+          delete this.performance.gesture;
+      }
+      this.performance.total = this.env.perfadd ? (this.performance.total || 0) + Math.trunc(now() - timeStart) : Math.trunc(now() - timeStart);
+      const shape = ((_u = this.process.tensor) == null ? void 0 : _u.shape) || [];
+      this.result = {
+        face: faceRes,
+        body: bodyRes,
+        hand: handRes,
+        gesture: gestureRes,
+        object: objectRes,
+        performance: this.performance,
+        canvas: this.process.canvas,
+        timestamp: Date.now(),
+        error: null,
+        get persons() {
+          return join2(faceRes, bodyRes, handRes, gestureRes, shape);
+        }
+      };
+      tf39.dispose(img.tensor);
+      this.emit("detect");
+      this.state = "idle";
+      resolve(this.result);
+    });
+  }
+  async sleep(ms) {
+    return new Promise((resolve) => {
+      setTimeout(resolve, ms);
+    });
+  }
+  async video(element, run = true, delay = 0) {
+    if (run) {
+      if (!__privateGet(this, _loops)[element.id]) {
+        if (this.config.debug)
+          log("video start", element.id);
+        __privateGet(this, _loops)[element.id] = true;
+      }
+      if (!element.paused && __privateGet(this, _loops)[element.id] && element.readyState >= 2)
+        await this.detect(element);
+      if (delay > 0)
+        await this.sleep(delay);
+      if (__privateGet(this, _loops)[element.id])
+        requestAnimationFrame(() => this.video(element, run, delay));
+    } else {
+      if (this.config.debug)
+        log("video stop", element.id);
+      __privateGet(this, _loops)[element.id] = false;
+    }
+  }
+};
+_numTensors = new WeakMap();
+_analyzeMemoryLeaks = new WeakMap();
+_checkSanity = new WeakMap();
+_sanity = new WeakMap();
+_loops = new WeakMap();
+// Annotate the CommonJS export names for ESM import in node:
+0 && (module.exports = {
+  Env,
+  Human,
+  defaults,
+  draw,
+  env,
+  match,
+  models
+});
diff --git a/dist/tfjs.esm.js b/dist/tfjs.esm.js
index dcb26c5f..44d200bc 100644
--- a/dist/tfjs.esm.js
+++ b/dist/tfjs.esm.js
@@ -324,7 +324,7 @@ var require_long = __commonJS({
         167,
         11
       ])), {}).exports;
-    } catch (e2) {
+    } catch (e) {
     }
     function Long2(low, high, unsigned) {
       this.low = low | 0;
@@ -411,8 +411,8 @@ var require_long = __commonJS({
       }
       var radixToPower = fromNumber(pow_dbl(radix, 8));
       var result = ZERO;
-      for (var i2 = 0; i2 < str.length; i2 += 8) {
-        var size = Math.min(8, str.length - i2), value = parseInt(str.substring(i2, i2 + size), radix);
+      for (var i = 0; i < str.length; i += 8) {
+        var size = Math.min(8, str.length - i), value = parseInt(str.substring(i, i + size), radix);
         if (size < 8) {
           var power = fromNumber(pow_dbl(radix, size));
           result = result.mul(power).add(fromNumber(value));
@@ -897,10 +897,10 @@ var require_alea = __commonJS({
       function Alea(seed) {
         var me = this, mash = Mash();
         me.next = function() {
-          var t2 = 2091639 * me.s0 + me.c * 23283064365386963e-26;
+          var t = 2091639 * me.s0 + me.c * 23283064365386963e-26;
           me.s0 = me.s1;
           me.s1 = me.s2;
-          return me.s2 = t2 - (me.c = t2 | 0);
+          return me.s2 = t - (me.c = t | 0);
         };
         me.c = 1;
         me.s0 = mash(" ");
@@ -920,12 +920,12 @@ var require_alea = __commonJS({
         }
         mash = null;
       }
-      function copy(f, t2) {
-        t2.c = f.c;
-        t2.s0 = f.s0;
-        t2.s1 = f.s1;
-        t2.s2 = f.s2;
-        return t2;
+      function copy(f, t) {
+        t.c = f.c;
+        t.s0 = f.s0;
+        t.s1 = f.s1;
+        t.s2 = f.s2;
+        return t;
       }
       function impl(seed, opts) {
         var xg = new Alea(seed), state = opts && opts.state, prng = xg.next;
@@ -946,20 +946,20 @@ var require_alea = __commonJS({
         return prng;
       }
       function Mash() {
-        var n2 = 4022871197;
+        var n = 4022871197;
         var mash = function(data) {
           data = String(data);
-          for (var i2 = 0; i2 < data.length; i2++) {
-            n2 += data.charCodeAt(i2);
-            var h = 0.02519603282416938 * n2;
-            n2 = h >>> 0;
-            h -= n2;
-            h *= n2;
-            n2 = h >>> 0;
-            h -= n2;
-            n2 += h * 4294967296;
+          for (var i = 0; i < data.length; i++) {
+            n += data.charCodeAt(i);
+            var h = 0.02519603282416938 * n;
+            n = h >>> 0;
+            h -= n;
+            h *= n;
+            n = h >>> 0;
+            h -= n;
+            n += h * 4294967296;
           }
-          return (n2 >>> 0) * 23283064365386963e-26;
+          return (n >>> 0) * 23283064365386963e-26;
         };
         return mash;
       }
@@ -991,11 +991,11 @@ var require_xor128 = __commonJS({
         me.z = 0;
         me.w = 0;
         me.next = function() {
-          var t2 = me.x ^ me.x << 11;
+          var t = me.x ^ me.x << 11;
           me.x = me.y;
           me.y = me.z;
           me.z = me.w;
-          return me.w ^= me.w >>> 19 ^ t2 ^ t2 >>> 8;
+          return me.w ^= me.w >>> 19 ^ t ^ t >>> 8;
         };
         if (seed === (seed | 0)) {
           me.x = seed;
@@ -1007,12 +1007,12 @@ var require_xor128 = __commonJS({
           me.next();
         }
       }
-      function copy(f, t2) {
-        t2.x = f.x;
-        t2.y = f.y;
-        t2.z = f.z;
-        t2.w = f.w;
-        return t2;
+      function copy(f, t) {
+        t.x = f.x;
+        t.y = f.y;
+        t.z = f.z;
+        t.w = f.w;
+        return t;
       }
       function impl(seed, opts) {
         var xg = new XorGen(seed), state = opts && opts.state, prng = function() {
@@ -1059,12 +1059,12 @@ var require_xorwow = __commonJS({
       function XorGen(seed) {
         var me = this, strseed = "";
         me.next = function() {
-          var t2 = me.x ^ me.x >>> 2;
+          var t = me.x ^ me.x >>> 2;
           me.x = me.y;
           me.y = me.z;
           me.z = me.w;
           me.w = me.v;
-          return (me.d = me.d + 362437 | 0) + (me.v = me.v ^ me.v << 4 ^ (t2 ^ t2 << 1)) | 0;
+          return (me.d = me.d + 362437 | 0) + (me.v = me.v ^ me.v << 4 ^ (t ^ t << 1)) | 0;
         };
         me.x = 0;
         me.y = 0;
@@ -1084,14 +1084,14 @@ var require_xorwow = __commonJS({
           me.next();
         }
       }
-      function copy(f, t2) {
-        t2.x = f.x;
-        t2.y = f.y;
-        t2.z = f.z;
-        t2.w = f.w;
-        t2.v = f.v;
-        t2.d = f.d;
-        return t2;
+      function copy(f, t) {
+        t.x = f.x;
+        t.y = f.y;
+        t.z = f.z;
+        t.w = f.w;
+        t.v = f.v;
+        t.d = f.d;
+        return t;
       }
       function impl(seed, opts) {
         var xg = new XorGen(seed), state = opts && opts.state, prng = function() {
@@ -1138,21 +1138,21 @@ var require_xorshift7 = __commonJS({
       function XorGen(seed) {
         var me = this;
         me.next = function() {
-          var X = me.x, i2 = me.i, t2, v, w;
-          t2 = X[i2];
-          t2 ^= t2 >>> 7;
-          v = t2 ^ t2 << 24;
-          t2 = X[i2 + 1 & 7];
-          v ^= t2 ^ t2 >>> 10;
-          t2 = X[i2 + 3 & 7];
-          v ^= t2 ^ t2 >>> 3;
-          t2 = X[i2 + 4 & 7];
-          v ^= t2 ^ t2 << 7;
-          t2 = X[i2 + 7 & 7];
-          t2 = t2 ^ t2 << 13;
-          v ^= t2 ^ t2 << 9;
-          X[i2] = v;
-          me.i = i2 + 1 & 7;
+          var X = me.x, i = me.i, t, v, w;
+          t = X[i];
+          t ^= t >>> 7;
+          v = t ^ t << 24;
+          t = X[i + 1 & 7];
+          v ^= t ^ t >>> 10;
+          t = X[i + 3 & 7];
+          v ^= t ^ t >>> 3;
+          t = X[i + 4 & 7];
+          v ^= t ^ t << 7;
+          t = X[i + 7 & 7];
+          t = t ^ t << 13;
+          v ^= t ^ t << 9;
+          X[i] = v;
+          me.i = i + 1 & 7;
           return v;
         };
         function init2(me2, seed2) {
@@ -1181,10 +1181,10 @@ var require_xorshift7 = __commonJS({
         }
         init2(me, seed);
       }
-      function copy(f, t2) {
-        t2.x = f.x.slice();
-        t2.i = f.i;
-        return t2;
+      function copy(f, t) {
+        t.x = f.x.slice();
+        t.i = f.i;
+        return t;
       }
       function impl(seed, opts) {
         if (seed == null)
@@ -1233,20 +1233,20 @@ var require_xor4096 = __commonJS({
       function XorGen(seed) {
         var me = this;
         me.next = function() {
-          var w = me.w, X = me.X, i2 = me.i, t2, v;
+          var w = me.w, X = me.X, i = me.i, t, v;
           me.w = w = w + 1640531527 | 0;
-          v = X[i2 + 34 & 127];
-          t2 = X[i2 = i2 + 1 & 127];
+          v = X[i + 34 & 127];
+          t = X[i = i + 1 & 127];
           v ^= v << 13;
-          t2 ^= t2 << 17;
+          t ^= t << 17;
           v ^= v >>> 15;
-          t2 ^= t2 >>> 12;
-          v = X[i2] = v ^ t2;
-          me.i = i2;
+          t ^= t >>> 12;
+          v = X[i] = v ^ t;
+          me.i = i;
           return v + (w ^ w >>> 16) | 0;
         };
         function init2(me2, seed2) {
-          var t2, v, i2, j, w, X = [], limit = 128;
+          var t, v, i, j, w, X = [], limit = 128;
           if (seed2 === (seed2 | 0)) {
             v = seed2;
             seed2 = null;
@@ -1255,7 +1255,7 @@ var require_xor4096 = __commonJS({
             v = 0;
             limit = Math.max(limit, seed2.length);
           }
-          for (i2 = 0, j = -32; j < limit; ++j) {
+          for (i = 0, j = -32; j < limit; ++j) {
             if (seed2)
               v ^= seed2.charCodeAt((j + 32) % seed2.length);
             if (j === 0)
@@ -1266,34 +1266,34 @@ var require_xor4096 = __commonJS({
             v ^= v >>> 13;
             if (j >= 0) {
               w = w + 1640531527 | 0;
-              t2 = X[j & 127] ^= v + w;
-              i2 = 0 == t2 ? i2 + 1 : 0;
+              t = X[j & 127] ^= v + w;
+              i = 0 == t ? i + 1 : 0;
             }
           }
-          if (i2 >= 128) {
+          if (i >= 128) {
             X[(seed2 && seed2.length || 0) & 127] = -1;
           }
-          i2 = 127;
+          i = 127;
           for (j = 4 * 128; j > 0; --j) {
-            v = X[i2 + 34 & 127];
-            t2 = X[i2 = i2 + 1 & 127];
+            v = X[i + 34 & 127];
+            t = X[i = i + 1 & 127];
             v ^= v << 13;
-            t2 ^= t2 << 17;
+            t ^= t << 17;
             v ^= v >>> 15;
-            t2 ^= t2 >>> 12;
-            X[i2] = v ^ t2;
+            t ^= t >>> 12;
+            X[i] = v ^ t;
           }
           me2.w = w;
           me2.X = X;
-          me2.i = i2;
+          me2.i = i;
         }
         init2(me, seed);
       }
-      function copy(f, t2) {
-        t2.i = f.i;
-        t2.w = f.w;
-        t2.X = f.X.slice();
-        return t2;
+      function copy(f, t) {
+        t.i = f.i;
+        t.w = f.w;
+        t.X = f.X.slice();
+        return t;
       }
       ;
       function impl(seed, opts) {
@@ -1368,12 +1368,12 @@ var require_tychei = __commonJS({
           me.next();
         }
       }
-      function copy(f, t2) {
-        t2.a = f.a;
-        t2.b = f.b;
-        t2.c = f.c;
-        t2.d = f.d;
-        return t2;
+      function copy(f, t) {
+        t.a = f.a;
+        t.b = f.b;
+        t.c = f.c;
+        t.d = f.d;
+        return t;
       }
       ;
       function impl(seed, opts) {
@@ -1434,18 +1434,18 @@ var require_seedrandom = __commonJS({
         ), key);
         var arc4 = new ARC4(key);
         var prng = function() {
-          var n2 = arc4.g(chunks), d = startdenom, x = 0;
-          while (n2 < significance) {
-            n2 = (n2 + x) * width;
+          var n = arc4.g(chunks), d = startdenom, x = 0;
+          while (n < significance) {
+            n = (n + x) * width;
             d *= width;
             x = arc4.g(1);
           }
-          while (n2 >= overflow) {
-            n2 /= 2;
+          while (n >= overflow) {
+            n /= 2;
             d /= 2;
             x >>>= 1;
           }
-          return (n2 + x) / d;
+          return (n + x) / d;
         };
         prng.int32 = function() {
           return arc4.g(4) | 0;
@@ -1477,33 +1477,33 @@ var require_seedrandom = __commonJS({
         );
       }
       function ARC4(key) {
-        var t2, keylen = key.length, me = this, i2 = 0, j = me.i = me.j = 0, s2 = me.S = [];
+        var t, keylen = key.length, me = this, i = 0, j = me.i = me.j = 0, s = me.S = [];
         if (!keylen) {
           key = [keylen++];
         }
-        while (i2 < width) {
-          s2[i2] = i2++;
+        while (i < width) {
+          s[i] = i++;
         }
-        for (i2 = 0; i2 < width; i2++) {
-          s2[i2] = s2[j = mask & j + key[i2 % keylen] + (t2 = s2[i2])];
-          s2[j] = t2;
+        for (i = 0; i < width; i++) {
+          s[i] = s[j = mask & j + key[i % keylen] + (t = s[i])];
+          s[j] = t;
         }
         (me.g = function(count2) {
-          var t3, r2 = 0, i3 = me.i, j2 = me.j, s3 = me.S;
+          var t2, r = 0, i2 = me.i, j2 = me.j, s2 = me.S;
           while (count2--) {
-            t3 = s3[i3 = mask & i3 + 1];
-            r2 = r2 * width + s3[mask & (s3[i3] = s3[j2 = mask & j2 + t3]) + (s3[j2] = t3)];
+            t2 = s2[i2 = mask & i2 + 1];
+            r = r * width + s2[mask & (s2[i2] = s2[j2 = mask & j2 + t2]) + (s2[j2] = t2)];
           }
-          me.i = i3;
+          me.i = i2;
           me.j = j2;
-          return r2;
+          return r;
         })(width);
       }
-      function copy(f, t2) {
-        t2.i = f.i;
-        t2.j = f.j;
-        t2.S = f.S.slice();
-        return t2;
+      function copy(f, t) {
+        t.i = f.i;
+        t.j = f.j;
+        t.S = f.S.slice();
+        return t;
       }
       ;
       function flatten4(obj, depth) {
@@ -1512,7 +1512,7 @@ var require_seedrandom = __commonJS({
           for (prop in obj) {
             try {
               result.push(flatten4(obj[prop], depth - 1));
-            } catch (e2) {
+            } catch (e) {
             }
           }
         }
@@ -1535,7 +1535,7 @@ var require_seedrandom = __commonJS({
             (global2.crypto || global2.msCrypto).getRandomValues(out);
           }
           return tostring(out);
-        } catch (e2) {
+        } catch (e) {
           var browser = global2.navigator, plugins = browser && browser.plugins;
           return [+new Date(), global2, plugins, global2.screen, tostring(pool3)];
         }
@@ -1700,10 +1700,10 @@ var require_tfjs_backend_wasm_threaded_simd = __commonJS({
           return scriptDirectory + path;
         }
         var read_, readAsync, readBinary, setWindowTitle;
-        function logExceptionOnExit(e2) {
-          if (e2 instanceof ExitStatus)
+        function logExceptionOnExit(e) {
+          if (e instanceof ExitStatus)
             return;
-          let toLog = e2;
+          let toLog = e;
           err("exiting due to exception: " + toLog);
         }
         var fs;
@@ -1769,9 +1769,9 @@ var require_tfjs_backend_wasm_threaded_simd = __commonJS({
           let nodeWorkerThreads;
           try {
             nodeWorkerThreads = require_worker_threads();
-          } catch (e2) {
+          } catch (e) {
             console.error('The "worker_threads" module is not supported in this node.js build - perhaps a newer version is needed?');
-            throw e2;
+            throw e;
           }
           global.Worker = nodeWorkerThreads.Worker;
         } else if (ENVIRONMENT_IS_WEB || ENVIRONMENT_IS_WORKER) {
@@ -1857,8 +1857,8 @@ var require_tfjs_backend_wasm_threaded_simd = __commonJS({
           if (typeof WebAssembly.Function === "function") {
             var typeNames = { "i": "i32", "j": "i64", "f": "f32", "d": "f64" };
             var type = { parameters: [], results: sig[0] == "v" ? [] : [typeNames[sig[0]]] };
-            for (var i2 = 1; i2 < sig.length; ++i2) {
-              type.parameters.push(typeNames[sig[i2]]);
+            for (var i = 1; i < sig.length; ++i) {
+              type.parameters.push(typeNames[sig[i]]);
             }
             return new WebAssembly.Function(type, func2);
           }
@@ -1867,8 +1867,8 @@ var require_tfjs_backend_wasm_threaded_simd = __commonJS({
           var sigParam = sig.slice(1);
           var typeCodes = { "i": 127, "j": 126, "f": 125, "d": 124 };
           typeSection.push(sigParam.length);
-          for (var i2 = 0; i2 < sigParam.length; ++i2) {
-            typeSection.push(typeCodes[sigParam[i2]]);
+          for (var i = 0; i < sigParam.length; ++i) {
+            typeSection.push(typeCodes[sigParam[i]]);
           }
           if (sigRet == "v") {
             typeSection.push(0);
@@ -1899,10 +1899,10 @@ var require_tfjs_backend_wasm_threaded_simd = __commonJS({
           return wasmTable.length - 1;
         }
         function updateTableMap(offset, count2) {
-          for (var i2 = offset; i2 < offset + count2; i2++) {
-            var item = getWasmTableEntry(i2);
+          for (var i = offset; i < offset + count2; i++) {
+            var item = getWasmTableEntry(i);
             if (item) {
-              functionsInTableMap.set(item, i2);
+              functionsInTableMap.set(item, i);
             }
           }
         }
@@ -1958,14 +1958,14 @@ var require_tfjs_backend_wasm_threaded_simd = __commonJS({
           var cArgs = [];
           var stack2 = 0;
           if (args) {
-            for (var i2 = 0; i2 < args.length; i2++) {
-              var converter = toC[argTypes[i2]];
+            for (var i = 0; i < args.length; i++) {
+              var converter = toC[argTypes[i]];
               if (converter) {
                 if (stack2 === 0)
                   stack2 = stackSave();
-                cArgs[i2] = converter(args[i2]);
+                cArgs[i] = converter(args[i]);
               } else {
-                cArgs[i2] = args[i2];
+                cArgs[i] = args[i];
               }
             }
           }
@@ -2046,10 +2046,10 @@ var require_tfjs_backend_wasm_threaded_simd = __commonJS({
             return 0;
           var startIdx = outIdx;
           var endIdx = outIdx + maxBytesToWrite - 1;
-          for (var i2 = 0; i2 < str.length; ++i2) {
-            var u = str.charCodeAt(i2);
+          for (var i = 0; i < str.length; ++i) {
+            var u = str.charCodeAt(i);
             if (u >= 55296 && u <= 57343) {
-              var u1 = str.charCodeAt(++i2);
+              var u1 = str.charCodeAt(++i);
               u = 65536 + ((u & 1023) << 10) | u1 & 1023;
             }
             if (u <= 127) {
@@ -2084,10 +2084,10 @@ var require_tfjs_backend_wasm_threaded_simd = __commonJS({
         }
         function lengthBytesUTF8(str) {
           var len = 0;
-          for (var i2 = 0; i2 < str.length; ++i2) {
-            var u = str.charCodeAt(i2);
+          for (var i = 0; i < str.length; ++i) {
+            var u = str.charCodeAt(i);
             if (u >= 55296 && u <= 57343)
-              u = 65536 + ((u & 1023) << 10) | str.charCodeAt(++i2) & 1023;
+              u = 65536 + ((u & 1023) << 10) | str.charCodeAt(++i) & 1023;
             if (u <= 127)
               ++len;
             else if (u <= 2047)
@@ -2104,8 +2104,8 @@ var require_tfjs_backend_wasm_threaded_simd = __commonJS({
           GROWABLE_HEAP_I8().set(array2, buffer3);
         }
         function writeAsciiToMemory(str, buffer3, dontAddNull) {
-          for (var i2 = 0; i2 < str.length; ++i2) {
-            GROWABLE_HEAP_I8()[buffer3++ >> 0] = str.charCodeAt(i2);
+          for (var i = 0; i < str.length; ++i) {
+            GROWABLE_HEAP_I8()[buffer3++ >> 0] = str.charCodeAt(i);
           }
           if (!dontAddNull)
             GROWABLE_HEAP_I8()[buffer3 >> 0] = 0;
@@ -2249,9 +2249,9 @@ var require_tfjs_backend_wasm_threaded_simd = __commonJS({
           ABORT = true;
           EXITSTATUS = 1;
           what += ". Build with -s ASSERTIONS=1 for more info.";
-          var e2 = new WebAssembly.RuntimeError(what);
-          readyPromiseReject(e2);
-          throw e2;
+          var e = new WebAssembly.RuntimeError(what);
+          readyPromiseReject(e);
+          throw e;
         }
         var dataURIPrefix = "data:application/octet-stream;base64,";
         function isDataURI(filename) {
@@ -2357,8 +2357,8 @@ var require_tfjs_backend_wasm_threaded_simd = __commonJS({
             try {
               var exports2 = Module["instantiateWasm"](info, receiveInstance);
               return exports2;
-            } catch (e2) {
-              err("Module.instantiateWasm callback failed with error: " + e2);
+            } catch (e) {
+              err("Module.instantiateWasm callback failed with error: " + e);
               return false;
             }
           }
@@ -2427,11 +2427,11 @@ var require_tfjs_backend_wasm_threaded_simd = __commonJS({
         function _exit(status) {
           exit(status);
         }
-        function handleException(e2) {
-          if (e2 instanceof ExitStatus || e2 == "unwind") {
+        function handleException(e) {
+          if (e instanceof ExitStatus || e == "unwind") {
             return EXITSTATUS;
           }
-          quit_(1, e2);
+          quit_(1, e);
         }
         var PThread = { unusedWorkers: [], runningWorkers: [], tlsInitFunctions: [], init: function() {
           if (ENVIRONMENT_IS_PTHREAD) {
@@ -2441,7 +2441,7 @@ var require_tfjs_backend_wasm_threaded_simd = __commonJS({
           }
         }, initMainThread: function() {
           var pthreadPoolSize = 8;
-          for (var i2 = 0; i2 < pthreadPoolSize; ++i2) {
+          for (var i = 0; i < pthreadPoolSize; ++i) {
             PThread.allocateUnusedWorker();
           }
         }, initWorker: function() {
@@ -2449,14 +2449,14 @@ var require_tfjs_backend_wasm_threaded_simd = __commonJS({
         }, pthreads: {}, setExitStatus: function(status) {
           EXITSTATUS = status;
         }, terminateAllThreads: function() {
-          for (var t2 in PThread.pthreads) {
-            var pthread = PThread.pthreads[t2];
+          for (var t in PThread.pthreads) {
+            var pthread = PThread.pthreads[t];
             if (pthread && pthread.worker) {
               PThread.returnWorkerToPool(pthread.worker);
             }
           }
-          for (var i2 = 0; i2 < PThread.unusedWorkers.length; ++i2) {
-            var worker = PThread.unusedWorkers[i2];
+          for (var i = 0; i < PThread.unusedWorkers.length; ++i) {
+            var worker = PThread.unusedWorkers[i];
             worker.terminate();
           }
           PThread.unusedWorkers = [];
@@ -2477,12 +2477,12 @@ var require_tfjs_backend_wasm_threaded_simd = __commonJS({
           }
         }, receiveObjectTransfer: function(data) {
         }, threadInit: function() {
-          for (var i2 in PThread.tlsInitFunctions) {
-            PThread.tlsInitFunctions[i2]();
+          for (var i in PThread.tlsInitFunctions) {
+            PThread.tlsInitFunctions[i]();
           }
         }, loadWasmModuleToWorker: function(worker, onFinishedLoading) {
-          worker.onmessage = (e2) => {
-            var d = e2["data"];
+          worker.onmessage = (e) => {
+            var d = e["data"];
             var cmd = d["cmd"];
             if (worker.pthread)
               PThread.currentProxiedOperationCallerThread = worker.pthread.threadInfoStruct;
@@ -2531,17 +2531,17 @@ var require_tfjs_backend_wasm_threaded_simd = __commonJS({
             }
             PThread.currentProxiedOperationCallerThread = void 0;
           };
-          worker.onerror = (e2) => {
+          worker.onerror = (e) => {
             var message = "worker sent an error!";
-            err(message + " " + e2.filename + ":" + e2.lineno + ": " + e2.message);
-            throw e2;
+            err(message + " " + e.filename + ":" + e.lineno + ": " + e.message);
+            throw e;
           };
           if (ENVIRONMENT_IS_NODE) {
             worker.on("message", function(data) {
               worker.onmessage({ data });
             });
-            worker.on("error", function(e2) {
-              worker.onerror(e2);
+            worker.on("error", function(e) {
+              worker.onerror(e);
             });
             worker.on("detachedExit", function() {
             });
@@ -2571,8 +2571,8 @@ var require_tfjs_backend_wasm_threaded_simd = __commonJS({
             return _emscripten_proxy_to_main_thread_js(1, 0, returnCode);
           try {
             _exit(returnCode);
-          } catch (e2) {
-            handleException(e2);
+          } catch (e) {
+            handleException(e);
           }
         }
         var wasmTableMirror = [];
@@ -2594,8 +2594,8 @@ var require_tfjs_backend_wasm_threaded_simd = __commonJS({
           if (!error.stack) {
             try {
               throw new Error();
-            } catch (e2) {
-              error = e2;
+            } catch (e) {
+              error = e;
             }
             if (!error.stack) {
               return "(no stack trace available)";
@@ -2613,8 +2613,8 @@ var require_tfjs_backend_wasm_threaded_simd = __commonJS({
         var _emscripten_get_now;
         if (ENVIRONMENT_IS_NODE) {
           _emscripten_get_now = () => {
-            var t2 = process["hrtime"]();
-            return t2[0] * 1e3 + t2[1] / 1e6;
+            var t = process["hrtime"]();
+            return t[0] * 1e3 + t[1] / 1e6;
           };
         } else if (ENVIRONMENT_IS_PTHREAD) {
           _emscripten_get_now = () => performance.now() - Module["__performance_now_clock_drift"];
@@ -2737,9 +2737,9 @@ var require_tfjs_backend_wasm_threaded_simd = __commonJS({
             var serializedNumCallArgs = numCallArgs;
             var args = stackAlloc(serializedNumCallArgs * 8);
             var b = args >> 3;
-            for (var i2 = 0; i2 < numCallArgs; i2++) {
-              var arg = outerArgs[2 + i2];
-              GROWABLE_HEAP_F64()[b + i2] = arg;
+            for (var i = 0; i < numCallArgs; i++) {
+              var arg = outerArgs[2 + i];
+              GROWABLE_HEAP_F64()[b + i] = arg;
             }
             return _emscripten_run_in_main_runtime_thread_js(index, serializedNumCallArgs, args, sync);
           });
@@ -2748,8 +2748,8 @@ var require_tfjs_backend_wasm_threaded_simd = __commonJS({
         function _emscripten_receive_on_main_thread_js(index, numCallArgs, args) {
           _emscripten_receive_on_main_thread_js_callArgs.length = numCallArgs;
           var b = args >> 3;
-          for (var i2 = 0; i2 < numCallArgs; i2++) {
-            _emscripten_receive_on_main_thread_js_callArgs[i2] = GROWABLE_HEAP_F64()[b + i2];
+          for (var i = 0; i < numCallArgs; i++) {
+            _emscripten_receive_on_main_thread_js_callArgs[i] = GROWABLE_HEAP_F64()[b + i];
           }
           var isEmAsmConst = index < 0;
           var func2 = !isEmAsmConst ? proxiedFunctionTable[index] : ASM_CONSTS[-index - 1];
@@ -2760,7 +2760,7 @@ var require_tfjs_backend_wasm_threaded_simd = __commonJS({
             wasmMemory.grow(size - buffer2.byteLength + 65535 >>> 16);
             updateGlobalBufferAndViews(wasmMemory.buffer);
             return 1;
-          } catch (e2) {
+          } catch (e) {
           }
         }
         function _emscripten_resize_heap(requestedSize) {
@@ -2785,8 +2785,8 @@ var require_tfjs_backend_wasm_threaded_simd = __commonJS({
           return false;
         }
         var JSEvents = { inEventHandler: 0, removeAllEventListeners: function() {
-          for (var i2 = JSEvents.eventHandlers.length - 1; i2 >= 0; --i2) {
-            JSEvents._removeHandler(i2);
+          for (var i = JSEvents.eventHandlers.length - 1; i >= 0; --i) {
+            JSEvents._removeHandler(i);
           }
           JSEvents.eventHandlers = [];
           JSEvents.deferredCalls = [];
@@ -2799,14 +2799,14 @@ var require_tfjs_backend_wasm_threaded_simd = __commonJS({
           function arraysHaveEqualContent(arrA, arrB) {
             if (arrA.length != arrB.length)
               return false;
-            for (var i3 in arrA) {
-              if (arrA[i3] != arrB[i3])
+            for (var i2 in arrA) {
+              if (arrA[i2] != arrB[i2])
                 return false;
             }
             return true;
           }
-          for (var i2 in JSEvents.deferredCalls) {
-            var call = JSEvents.deferredCalls[i2];
+          for (var i in JSEvents.deferredCalls) {
+            var call = JSEvents.deferredCalls[i];
             if (call.targetFunction == targetFunction && arraysHaveEqualContent(call.argsList, argsList)) {
               return;
             }
@@ -2816,10 +2816,10 @@ var require_tfjs_backend_wasm_threaded_simd = __commonJS({
             return x.precedence < y.precedence;
           });
         }, removeDeferredCalls: function(targetFunction) {
-          for (var i2 = 0; i2 < JSEvents.deferredCalls.length; ++i2) {
-            if (JSEvents.deferredCalls[i2].targetFunction == targetFunction) {
-              JSEvents.deferredCalls.splice(i2, 1);
-              --i2;
+          for (var i = 0; i < JSEvents.deferredCalls.length; ++i) {
+            if (JSEvents.deferredCalls[i].targetFunction == targetFunction) {
+              JSEvents.deferredCalls.splice(i, 1);
+              --i;
             }
           }
         }, canPerformEventHandlerRequests: function() {
@@ -2828,22 +2828,22 @@ var require_tfjs_backend_wasm_threaded_simd = __commonJS({
           if (!JSEvents.canPerformEventHandlerRequests()) {
             return;
           }
-          for (var i2 = 0; i2 < JSEvents.deferredCalls.length; ++i2) {
-            var call = JSEvents.deferredCalls[i2];
-            JSEvents.deferredCalls.splice(i2, 1);
-            --i2;
+          for (var i = 0; i < JSEvents.deferredCalls.length; ++i) {
+            var call = JSEvents.deferredCalls[i];
+            JSEvents.deferredCalls.splice(i, 1);
+            --i;
             call.targetFunction.apply(null, call.argsList);
           }
         }, eventHandlers: [], removeAllHandlersOnTarget: function(target, eventTypeString) {
-          for (var i2 = 0; i2 < JSEvents.eventHandlers.length; ++i2) {
-            if (JSEvents.eventHandlers[i2].target == target && (!eventTypeString || eventTypeString == JSEvents.eventHandlers[i2].eventTypeString)) {
-              JSEvents._removeHandler(i2--);
+          for (var i = 0; i < JSEvents.eventHandlers.length; ++i) {
+            if (JSEvents.eventHandlers[i].target == target && (!eventTypeString || eventTypeString == JSEvents.eventHandlers[i].eventTypeString)) {
+              JSEvents._removeHandler(i--);
             }
           }
-        }, _removeHandler: function(i2) {
-          var h = JSEvents.eventHandlers[i2];
+        }, _removeHandler: function(i) {
+          var h = JSEvents.eventHandlers[i];
           h.target.removeEventListener(h.eventTypeString, h.eventListenerFunc, h.useCapture);
-          JSEvents.eventHandlers.splice(i2, 1);
+          JSEvents.eventHandlers.splice(i, 1);
         }, registerOrRemoveHandler: function(eventHandler) {
           var jsEventHandler = function jsEventHandler2(event) {
             ++JSEvents.inEventHandler;
@@ -2859,9 +2859,9 @@ var require_tfjs_backend_wasm_threaded_simd = __commonJS({
             JSEvents.eventHandlers.push(eventHandler);
             JSEvents.registerRemoveEventListeners();
           } else {
-            for (var i2 = 0; i2 < JSEvents.eventHandlers.length; ++i2) {
-              if (JSEvents.eventHandlers[i2].target == eventHandler.target && JSEvents.eventHandlers[i2].eventTypeString == eventHandler.eventTypeString) {
-                JSEvents._removeHandler(i2--);
+            for (var i = 0; i < JSEvents.eventHandlers.length; ++i) {
+              if (JSEvents.eventHandlers[i].target == eventHandler.target && JSEvents.eventHandlers[i].eventTypeString == eventHandler.eventTypeString) {
+                JSEvents._removeHandler(i--);
               }
             }
           }
@@ -3010,8 +3010,8 @@ var require_tfjs_backend_wasm_threaded_simd = __commonJS({
         function __webgl_enable_WEBGL_draw_buffers(ctx) {
           var ext = ctx.getExtension("WEBGL_draw_buffers");
           if (ext) {
-            ctx["drawBuffers"] = function(n2, bufs) {
-              ext["drawBuffersWEBGL"](n2, bufs);
+            ctx["drawBuffers"] = function(n, bufs) {
+              ext["drawBuffersWEBGL"](n, bufs);
             };
             return 1;
           }
@@ -3025,15 +3025,15 @@ var require_tfjs_backend_wasm_threaded_simd = __commonJS({
           }
         }, getNewId: function(table) {
           var ret = GL.counter++;
-          for (var i2 = table.length; i2 < ret; i2++) {
-            table[i2] = null;
+          for (var i = table.length; i < ret; i++) {
+            table[i] = null;
           }
           return ret;
         }, getSource: function(shader, count2, string2, length) {
           var source = "";
-          for (var i2 = 0; i2 < count2; ++i2) {
-            var len = length ? GROWABLE_HEAP_I32()[length + i2 * 4 >> 2] : -1;
-            source += UTF8ToString(GROWABLE_HEAP_I32()[string2 + i2 * 4 >> 2], len < 0 ? void 0 : len);
+          for (var i = 0; i < count2; ++i) {
+            var len = length ? GROWABLE_HEAP_I32()[length + i * 4 >> 2] : -1;
+            source += UTF8ToString(GROWABLE_HEAP_I32()[string2 + i * 4 >> 2], len < 0 ? void 0 : len);
           }
           return source;
         }, createContext: function(canvas, webGLContextAttributes) {
@@ -3145,7 +3145,7 @@ var require_tfjs_backend_wasm_threaded_simd = __commonJS({
           if (ENVIRONMENT_IS_PTHREAD)
             return _emscripten_proxy_to_main_thread_js(5, 1, fd, iov, iovcnt, pnum);
           var num = 0;
-          for (var i2 = 0; i2 < iovcnt; i2++) {
+          for (var i = 0; i < iovcnt; i++) {
             var ptr = GROWABLE_HEAP_I32()[iov >> 2];
             var len = GROWABLE_HEAP_I32()[iov + 4 >> 2];
             iov += 8;
@@ -3702,10 +3702,10 @@ var require_tfjs_backend_wasm = __commonJS({
           return scriptDirectory + path;
         }
         var read_, readAsync, readBinary, setWindowTitle;
-        function logExceptionOnExit(e2) {
-          if (e2 instanceof ExitStatus)
+        function logExceptionOnExit(e) {
+          if (e instanceof ExitStatus)
             return;
-          let toLog = e2;
+          let toLog = e;
           err("exiting due to exception: " + toLog);
         }
         var fs;
@@ -3839,8 +3839,8 @@ var require_tfjs_backend_wasm = __commonJS({
           if (typeof WebAssembly.Function === "function") {
             var typeNames = { "i": "i32", "j": "i64", "f": "f32", "d": "f64" };
             var type = { parameters: [], results: sig[0] == "v" ? [] : [typeNames[sig[0]]] };
-            for (var i2 = 1; i2 < sig.length; ++i2) {
-              type.parameters.push(typeNames[sig[i2]]);
+            for (var i = 1; i < sig.length; ++i) {
+              type.parameters.push(typeNames[sig[i]]);
             }
             return new WebAssembly.Function(type, func2);
           }
@@ -3849,8 +3849,8 @@ var require_tfjs_backend_wasm = __commonJS({
           var sigParam = sig.slice(1);
           var typeCodes = { "i": 127, "j": 126, "f": 125, "d": 124 };
           typeSection.push(sigParam.length);
-          for (var i2 = 0; i2 < sigParam.length; ++i2) {
-            typeSection.push(typeCodes[sigParam[i2]]);
+          for (var i = 0; i < sigParam.length; ++i) {
+            typeSection.push(typeCodes[sigParam[i]]);
           }
           if (sigRet == "v") {
             typeSection.push(0);
@@ -3881,10 +3881,10 @@ var require_tfjs_backend_wasm = __commonJS({
           return wasmTable.length - 1;
         }
         function updateTableMap(offset, count2) {
-          for (var i2 = offset; i2 < offset + count2; i2++) {
-            var item = getWasmTableEntry(i2);
+          for (var i = offset; i < offset + count2; i++) {
+            var item = getWasmTableEntry(i);
             if (item) {
-              functionsInTableMap.set(item, i2);
+              functionsInTableMap.set(item, i);
             }
           }
         }
@@ -3936,14 +3936,14 @@ var require_tfjs_backend_wasm = __commonJS({
           var cArgs = [];
           var stack2 = 0;
           if (args) {
-            for (var i2 = 0; i2 < args.length; i2++) {
-              var converter = toC[argTypes[i2]];
+            for (var i = 0; i < args.length; i++) {
+              var converter = toC[argTypes[i]];
               if (converter) {
                 if (stack2 === 0)
                   stack2 = stackSave();
-                cArgs[i2] = converter(args[i2]);
+                cArgs[i] = converter(args[i]);
               } else {
-                cArgs[i2] = args[i2];
+                cArgs[i] = args[i];
               }
             }
           }
@@ -4015,10 +4015,10 @@ var require_tfjs_backend_wasm = __commonJS({
             return 0;
           var startIdx = outIdx;
           var endIdx = outIdx + maxBytesToWrite - 1;
-          for (var i2 = 0; i2 < str.length; ++i2) {
-            var u = str.charCodeAt(i2);
+          for (var i = 0; i < str.length; ++i) {
+            var u = str.charCodeAt(i);
             if (u >= 55296 && u <= 57343) {
-              var u1 = str.charCodeAt(++i2);
+              var u1 = str.charCodeAt(++i);
               u = 65536 + ((u & 1023) << 10) | u1 & 1023;
             }
             if (u <= 127) {
@@ -4053,10 +4053,10 @@ var require_tfjs_backend_wasm = __commonJS({
         }
         function lengthBytesUTF8(str) {
           var len = 0;
-          for (var i2 = 0; i2 < str.length; ++i2) {
-            var u = str.charCodeAt(i2);
+          for (var i = 0; i < str.length; ++i) {
+            var u = str.charCodeAt(i);
             if (u >= 55296 && u <= 57343)
-              u = 65536 + ((u & 1023) << 10) | str.charCodeAt(++i2) & 1023;
+              u = 65536 + ((u & 1023) << 10) | str.charCodeAt(++i) & 1023;
             if (u <= 127)
               ++len;
             else if (u <= 2047)
@@ -4073,8 +4073,8 @@ var require_tfjs_backend_wasm = __commonJS({
           HEAP8.set(array2, buffer3);
         }
         function writeAsciiToMemory(str, buffer3, dontAddNull) {
-          for (var i2 = 0; i2 < str.length; ++i2) {
-            HEAP8[buffer3++ >> 0] = str.charCodeAt(i2);
+          for (var i = 0; i < str.length; ++i) {
+            HEAP8[buffer3++ >> 0] = str.charCodeAt(i);
           }
           if (!dontAddNull)
             HEAP8[buffer3 >> 0] = 0;
@@ -4183,9 +4183,9 @@ var require_tfjs_backend_wasm = __commonJS({
           ABORT = true;
           EXITSTATUS = 1;
           what += ". Build with -s ASSERTIONS=1 for more info.";
-          var e2 = new WebAssembly.RuntimeError(what);
-          readyPromiseReject(e2);
-          throw e2;
+          var e = new WebAssembly.RuntimeError(what);
+          readyPromiseReject(e);
+          throw e;
         }
         var dataURIPrefix = "data:application/octet-stream;base64,";
         function isDataURI(filename) {
@@ -4281,8 +4281,8 @@ var require_tfjs_backend_wasm = __commonJS({
             try {
               var exports2 = Module["instantiateWasm"](info, receiveInstance);
               return exports2;
-            } catch (e2) {
-              err("Module.instantiateWasm callback failed with error: " + e2);
+            } catch (e) {
+              err("Module.instantiateWasm callback failed with error: " + e);
               return false;
             }
           }
@@ -4335,8 +4335,8 @@ var require_tfjs_backend_wasm = __commonJS({
           if (!error.stack) {
             try {
               throw new Error();
-            } catch (e2) {
-              error = e2;
+            } catch (e) {
+              error = e;
             }
             if (!error.stack) {
               return "(no stack trace available)";
@@ -4362,7 +4362,7 @@ var require_tfjs_backend_wasm = __commonJS({
             wasmMemory.grow(size - buffer2.byteLength + 65535 >>> 16);
             updateGlobalBufferAndViews(wasmMemory.buffer);
             return 1;
-          } catch (e2) {
+          } catch (e) {
           }
         }
         function _emscripten_resize_heap(requestedSize) {
@@ -4408,7 +4408,7 @@ var require_tfjs_backend_wasm = __commonJS({
         }
         function _fd_write(fd, iov, iovcnt, pnum) {
           var num = 0;
-          for (var i2 = 0; i2 < iovcnt; i2++) {
+          for (var i = 0; i < iovcnt; i++) {
             var ptr = HEAP32[iov >> 2];
             var len = HEAP32[iov + 4 >> 2];
             iov += 8;
@@ -4966,19 +4966,19 @@ function swap(object, left, right) {
 }
 function sum(arr) {
   let sum7 = 0;
-  for (let i2 = 0; i2 < arr.length; i2++) {
-    sum7 += arr[i2];
+  for (let i = 0; i < arr.length; i++) {
+    sum7 += arr[i];
   }
   return sum7;
 }
 function randUniform(a, b) {
-  const r2 = Math.random();
-  return b * r2 + (1 - r2) * a;
+  const r = Math.random();
+  return b * r + (1 - r) * a;
 }
 function distSquared(a, b) {
   let result = 0;
-  for (let i2 = 0; i2 < a.length; i2++) {
-    const diff = Number(a[i2]) - Number(b[i2]);
+  for (let i = 0; i < a.length; i++) {
+    const diff = Number(a[i]) - Number(b[i]);
     result += diff * diff;
   }
   return result;
@@ -4999,8 +4999,8 @@ function flatten(arr, result = [], skipTypedArray = false) {
     result = [];
   }
   if (Array.isArray(arr) || isTypedArray(arr) && !skipTypedArray) {
-    for (let i2 = 0; i2 < arr.length; ++i2) {
-      flatten(arr[i2], result, skipTypedArray);
+    for (let i = 0; i < arr.length; ++i) {
+      flatten(arr[i], result, skipTypedArray);
     }
   } else {
     result.push(arr);
@@ -5012,8 +5012,8 @@ function sizeFromShape(shape) {
     return 1;
   }
   let size = shape[0];
-  for (let i2 = 1; i2 < shape.length; i2++) {
-    size *= shape[i2];
+  for (let i = 1; i < shape.length; i++) {
+    size *= shape[i];
   }
   return size;
 }
@@ -5030,8 +5030,8 @@ function arraysEqual(n1, n2) {
   if (n1.length !== n2.length) {
     return false;
   }
-  for (let i2 = 0; i2 < n1.length; i2++) {
-    if (n1[i2] !== n2[i2]) {
+  for (let i = 0; i < n1.length; i++) {
+    if (n1[i] !== n2[i]) {
       return false;
     }
   }
@@ -5057,10 +5057,10 @@ function sizeToSquarishShape(size) {
   const width = Math.ceil(Math.sqrt(size));
   return [width, Math.ceil(size / width)];
 }
-function createShuffledIndices(n2) {
-  const shuffledIndices = new Uint32Array(n2);
-  for (let i2 = 0; i2 < n2; ++i2) {
-    shuffledIndices[i2] = i2;
+function createShuffledIndices(n) {
+  const shuffledIndices = new Uint32Array(n);
+  for (let i = 0; i < n; ++i) {
+    shuffledIndices[i] = i;
   }
   shuffle(shuffledIndices);
   return shuffledIndices;
@@ -5093,16 +5093,16 @@ function repeatedTry(checkFn, delayFn = (counter) => 0, maxCounter) {
 function inferFromImplicitShape(shape, size) {
   let shapeProd = 1;
   let implicitIdx = -1;
-  for (let i2 = 0; i2 < shape.length; ++i2) {
-    if (shape[i2] >= 0) {
-      shapeProd *= shape[i2];
-    } else if (shape[i2] === -1) {
+  for (let i = 0; i < shape.length; ++i) {
+    if (shape[i] >= 0) {
+      shapeProd *= shape[i];
+    } else if (shape[i] === -1) {
       if (implicitIdx !== -1) {
-        throw Error(`Shapes can only have 1 implicit size. Found -1 at dim ${implicitIdx} and dim ${i2}`);
+        throw Error(`Shapes can only have 1 implicit size. Found -1 at dim ${implicitIdx} and dim ${i}`);
       }
-      implicitIdx = i2;
-    } else if (shape[i2] < 0) {
-      throw Error(`Shapes can not be < 0. Found ${shape[i2]} at dim ${i2}`);
+      implicitIdx = i;
+    } else if (shape[i] < 0) {
+      throw Error(`Shapes can not be < 0. Found ${shape[i]} at dim ${i}`);
     }
   }
   if (implicitIdx === -1) {
@@ -5123,7 +5123,7 @@ function inferFromImplicitShape(shape, size) {
 }
 function parseAxisParam(axis, shape) {
   const rank = shape.length;
-  axis = axis == null ? shape.map((s2, i2) => i2) : [].concat(axis);
+  axis = axis == null ? shape.map((s, i) => i) : [].concat(axis);
   assert(axis.every((ax) => ax >= -rank && ax < rank), () => `All values in axis param must be in range [-${rank}, ${rank}) but got axis ${axis}`);
   assert(axis.every((ax) => isInt(ax)), () => `All values in axis param must be integers but got axis ${axis}`);
   return axis.map((a) => a < 0 ? rank + a : a);
@@ -5134,22 +5134,22 @@ function squeezeShape(shape, axis) {
   const isEmptyArray = axis != null && Array.isArray(axis) && axis.length === 0;
   const axes = axis == null || isEmptyArray ? null : parseAxisParam(axis, shape).sort();
   let j = 0;
-  for (let i2 = 0; i2 < shape.length; ++i2) {
+  for (let i = 0; i < shape.length; ++i) {
     if (axes != null) {
-      if (axes[j] === i2 && shape[i2] !== 1) {
-        throw new Error(`Can't squeeze axis ${i2} since its dim '${shape[i2]}' is not 1`);
+      if (axes[j] === i && shape[i] !== 1) {
+        throw new Error(`Can't squeeze axis ${i} since its dim '${shape[i]}' is not 1`);
       }
-      if ((axes[j] == null || axes[j] > i2) && shape[i2] === 1) {
-        newShape.push(shape[i2]);
-        keptDims.push(i2);
+      if ((axes[j] == null || axes[j] > i) && shape[i] === 1) {
+        newShape.push(shape[i]);
+        keptDims.push(i);
       }
-      if (axes[j] <= i2) {
+      if (axes[j] <= i) {
         j++;
       }
     }
-    if (shape[i2] !== 1) {
-      newShape.push(shape[i2]);
-      keptDims.push(i2);
+    if (shape[i] !== 1) {
+      newShape.push(shape[i]);
+      keptDims.push(i);
     }
   }
   return { newShape, keptDims };
@@ -5183,8 +5183,8 @@ function getArrayFromDType(dtype, size) {
   return values;
 }
 function checkConversionForErrors(vals, dtype) {
-  for (let i2 = 0; i2 < vals.length; i2++) {
-    const num = vals[i2];
+  for (let i = 0; i < vals.length; i++) {
+    const num = vals[i];
     if (isNaN(num) || !isFinite(num)) {
       throw Error(`A tensor of type ${dtype} being uploaded contains ${num}.`);
     }
@@ -5260,9 +5260,9 @@ function isFunction(f) {
   return !!(f && f.constructor && f.call && f.apply);
 }
 function nearestDivisor(size, start) {
-  for (let i2 = start; i2 < size; ++i2) {
-    if (size % i2 === 0) {
-      return i2;
+  for (let i = start; i < size; ++i) {
+    if (size % i === 0) {
+      return i;
     }
   }
   return size;
@@ -5274,8 +5274,8 @@ function computeStrides(shape) {
   }
   const strides = new Array(rank - 1);
   strides[rank - 2] = shape[rank - 1];
-  for (let i2 = rank - 3; i2 >= 0; --i2) {
-    strides[i2] = strides[i2 + 1] * shape[i2 + 1];
+  for (let i = rank - 3; i >= 0; --i) {
+    strides[i] = strides[i + 1] * shape[i + 1];
   }
   return strides;
 }
@@ -5283,15 +5283,15 @@ function createNestedArray(offset, shape, a, isComplex = false) {
   const ret = new Array();
   if (shape.length === 1) {
     const d = shape[0] * (isComplex ? 2 : 1);
-    for (let i2 = 0; i2 < d; i2++) {
-      ret[i2] = a[offset + i2];
+    for (let i = 0; i < d; i++) {
+      ret[i] = a[offset + i];
     }
   } else {
     const d = shape[0];
     const rest = shape.slice(1);
     const len = rest.reduce((acc, c) => acc * c) * (isComplex ? 2 : 1);
-    for (let i2 = 0; i2 < d; i2++) {
-      ret[i2] = createNestedArray(offset + i2 * len, rest, a, isComplex);
+    for (let i = 0; i < d; i++) {
+      ret[i] = createNestedArray(offset + i * len, rest, a, isComplex);
     }
   }
   return ret;
@@ -5311,8 +5311,8 @@ function toNestedArray(shape, a, isComplex = false) {
 }
 function makeOnesTypedArray(size, dtype) {
   const array2 = makeZerosTypedArray(size, dtype);
-  for (let i2 = 0; i2 < array2.length; i2++) {
-    array2[i2] = 1;
+  for (let i = 0; i < array2.length; i++) {
+    array2[i] = 1;
   }
   return array2;
 }
@@ -5351,8 +5351,8 @@ function locToIndex(locs, rank, strides) {
     return locs[0];
   }
   let index = locs[locs.length - 1];
-  for (let i2 = 0; i2 < locs.length - 1; ++i2) {
-    index += strides[i2] * locs[i2];
+  for (let i = 0; i < locs.length - 1; ++i) {
+    index += strides[i] * locs[i];
   }
   return index;
 }
@@ -5363,9 +5363,9 @@ function indexToLoc(index, rank, strides) {
     return [index];
   }
   const locs = new Array(rank);
-  for (let i2 = 0; i2 < locs.length - 1; ++i2) {
-    locs[i2] = Math.floor(index / strides[i2]);
-    index -= locs[i2] * strides[i2];
+  for (let i = 0; i < locs.length - 1; ++i) {
+    locs[i] = Math.floor(index / strides[i]);
+    index -= locs[i] * strides[i];
   }
   locs[locs.length - 1] = index;
   return locs;
@@ -5473,9 +5473,9 @@ var Environment = class {
 };
 function getQueryParams(queryString) {
   const params = {};
-  queryString.replace(/[?&]([^=?&]+)(?:=([^&]*))?/g, (s2, ...t2) => {
-    decodeParam(params, t2[0], t2[1]);
-    return t2.join("=");
+  queryString.replace(/[?&]([^=?&]+)(?:=([^&]*))?/g, (s, ...t) => {
+    decodeParam(params, t[0], t[1]);
+    return t.join("=");
   });
   return params;
 }
@@ -5863,15 +5863,15 @@ var k2 = hexToLong("9ae16a3b2f90404f");
 function shiftMix(val) {
   return val.xor(val.shru(47));
 }
-function fetch2(s2, offset, numBytes) {
-  const bytes = s2.slice(offset, offset + numBytes);
+function fetch2(s, offset, numBytes) {
+  const bytes = s.slice(offset, offset + numBytes);
   return Long.fromBytes(Array.from(bytes), true, true);
 }
-function fetch64(s2, offset) {
-  return fetch2(s2, offset, 8);
+function fetch64(s, offset) {
+  return fetch2(s, offset, 8);
 }
-function fetch32(s2, offset) {
-  return fetch2(s2, offset, 4);
+function fetch32(s, offset) {
+  return fetch2(s, offset, 4);
 }
 function rotate64(val, shift) {
   return shift === 0 ? val : val.shru(shift).or(val.shl(64 - shift));
@@ -5893,83 +5893,83 @@ function weakHashLen32WithSeeds(w, x, y, z, a, b) {
   b = b.add(rotate64(a, 44));
   return [a.add(z), b.add(c)];
 }
-function weakHashLen32WithSeedsStr(s2, offset, a, b) {
-  return weakHashLen32WithSeeds(fetch64(s2, offset), fetch64(s2, offset + 8), fetch64(s2, offset + 16), fetch64(s2, offset + 24), a, b);
+function weakHashLen32WithSeedsStr(s, offset, a, b) {
+  return weakHashLen32WithSeeds(fetch64(s, offset), fetch64(s, offset + 8), fetch64(s, offset + 16), fetch64(s, offset + 24), a, b);
 }
-function hashLen0to16(s2, len = s2.length) {
+function hashLen0to16(s, len = s.length) {
   if (len >= 8) {
     const mul2 = k2.add(len * 2);
-    const a = fetch64(s2, 0).add(k2);
-    const b = fetch64(s2, len - 8);
+    const a = fetch64(s, 0).add(k2);
+    const b = fetch64(s, len - 8);
     const c = rotate64(b, 37).mul(mul2).add(a);
     const d = rotate64(a, 25).add(b).mul(mul2);
     return hashLen16(c, d, mul2);
   }
   if (len >= 4) {
     const mul2 = k2.add(len * 2);
-    const a = fetch32(s2, 0);
-    return hashLen16(a.shl(3).add(len), fetch32(s2, len - 4), mul2);
+    const a = fetch32(s, 0);
+    return hashLen16(a.shl(3).add(len), fetch32(s, len - 4), mul2);
   }
   if (len > 0) {
-    const a = s2[0];
-    const b = s2[len >> 1];
-    const c = s2[len - 1];
+    const a = s[0];
+    const b = s[len >> 1];
+    const c = s[len - 1];
     const y = a + (b << 8);
     const z = len + (c << 2);
     return shiftMix(k2.mul(y).xor(k0.mul(z))).mul(k2);
   }
   return k2;
 }
-function hashLen17to32(s2, len = s2.length) {
+function hashLen17to32(s, len = s.length) {
   const mul2 = k2.add(len * 2);
-  const a = fetch64(s2, 0).mul(k1);
-  const b = fetch64(s2, 8);
-  const c = fetch64(s2, len - 8).mul(mul2);
-  const d = fetch64(s2, len - 16).mul(k2);
+  const a = fetch64(s, 0).mul(k1);
+  const b = fetch64(s, 8);
+  const c = fetch64(s, len - 8).mul(mul2);
+  const d = fetch64(s, len - 16).mul(k2);
   return hashLen16(rotate64(a.add(b), 43).add(rotate64(c, 30)).add(d), a.add(rotate64(b.add(k2), 18)).add(c), mul2);
 }
-function hashLen33to64(s2, len = s2.length) {
+function hashLen33to64(s, len = s.length) {
   const mul2 = k2.add(len * 2);
-  const a = fetch64(s2, 0).mul(k2);
-  const b = fetch64(s2, 8);
-  const c = fetch64(s2, len - 8).mul(mul2);
-  const d = fetch64(s2, len - 16).mul(k2);
+  const a = fetch64(s, 0).mul(k2);
+  const b = fetch64(s, 8);
+  const c = fetch64(s, len - 8).mul(mul2);
+  const d = fetch64(s, len - 16).mul(k2);
   const y = rotate64(a.add(b), 43).add(rotate64(c, 30)).add(d);
   const z = hashLen16(y, a.add(rotate64(b.add(k2), 18)).add(c), mul2);
-  const e2 = fetch64(s2, 16).mul(mul2);
-  const f = fetch64(s2, 24);
-  const g = y.add(fetch64(s2, len - 32)).mul(mul2);
-  const h = z.add(fetch64(s2, len - 24)).mul(mul2);
-  return hashLen16(rotate64(e2.add(f), 43).add(rotate64(g, 30)).add(h), e2.add(rotate64(f.add(a), 18)).add(g), mul2);
+  const e = fetch64(s, 16).mul(mul2);
+  const f = fetch64(s, 24);
+  const g = y.add(fetch64(s, len - 32)).mul(mul2);
+  const h = z.add(fetch64(s, len - 24)).mul(mul2);
+  return hashLen16(rotate64(e.add(f), 43).add(rotate64(g, 30)).add(h), e.add(rotate64(f.add(a), 18)).add(g), mul2);
 }
-function fingerPrint64(s2, len = s2.length) {
+function fingerPrint64(s, len = s.length) {
   const seed = Long.fromNumber(81, true);
   if (len <= 32) {
     if (len <= 16) {
-      return hashLen0to16(s2, len);
+      return hashLen0to16(s, len);
     } else {
-      return hashLen17to32(s2, len);
+      return hashLen17to32(s, len);
     }
   } else if (len <= 64) {
-    return hashLen33to64(s2, len);
+    return hashLen33to64(s, len);
   }
   let x = seed;
   let y = seed.mul(k1).add(113);
   let z = shiftMix(y.mul(k2).add(113)).mul(k2);
   let v = [Long.UZERO, Long.UZERO];
   let w = [Long.UZERO, Long.UZERO];
-  x = x.mul(k2).add(fetch64(s2, 0));
+  x = x.mul(k2).add(fetch64(s, 0));
   let offset = 0;
   const end = (len - 1 >> 6) * 64;
   const last64 = end + (len - 1 & 63) - 63;
   do {
-    x = rotate64(x.add(y).add(v[0]).add(fetch64(s2, offset + 8)), 37).mul(k1);
-    y = rotate64(y.add(v[1]).add(fetch64(s2, offset + 48)), 42).mul(k1);
+    x = rotate64(x.add(y).add(v[0]).add(fetch64(s, offset + 8)), 37).mul(k1);
+    y = rotate64(y.add(v[1]).add(fetch64(s, offset + 48)), 42).mul(k1);
     x = x.xor(w[1]);
-    y = y.add(v[0]).add(fetch64(s2, offset + 40));
+    y = y.add(v[0]).add(fetch64(s, offset + 40));
     z = rotate64(z.add(w[0]), 33).mul(k1);
-    v = weakHashLen32WithSeedsStr(s2, offset, v[1].mul(k1), x.add(w[0]));
-    w = weakHashLen32WithSeedsStr(s2, offset + 32, z.add(w[1]), y.add(fetch64(s2, offset + 16)));
+    v = weakHashLen32WithSeedsStr(s, offset, v[1].mul(k1), x.add(w[0]));
+    w = weakHashLen32WithSeedsStr(s, offset + 32, z.add(w[1]), y.add(fetch64(s, offset + 16)));
     [z, x] = [x, z];
     offset += 64;
   } while (offset !== end);
@@ -5978,13 +5978,13 @@ function fingerPrint64(s2, len = s2.length) {
   w[0] = w[0].add(len - 1 & 63);
   v[0] = v[0].add(w[0]);
   w[0] = w[0].add(v[0]);
-  x = rotate64(x.add(y).add(v[0]).add(fetch64(s2, offset + 8)), 37).mul(mul2);
-  y = rotate64(y.add(v[1]).add(fetch64(s2, offset + 48)), 42).mul(mul2);
+  x = rotate64(x.add(y).add(v[0]).add(fetch64(s, offset + 8)), 37).mul(mul2);
+  y = rotate64(y.add(v[1]).add(fetch64(s, offset + 48)), 42).mul(mul2);
   x = x.xor(w[1].mul(9));
-  y = y.add(v[0].mul(9).add(fetch64(s2, offset + 40)));
+  y = y.add(v[0].mul(9).add(fetch64(s, offset + 40)));
   z = rotate64(z.add(w[0]), 33).mul(mul2);
-  v = weakHashLen32WithSeedsStr(s2, offset, v[1].mul(mul2), x.add(w[0]));
-  w = weakHashLen32WithSeedsStr(s2, offset + 32, z.add(w[1]), y.add(fetch64(s2, offset + 16)));
+  v = weakHashLen32WithSeedsStr(s, offset, v[1].mul(mul2), x.add(w[0]));
+  w = weakHashLen32WithSeedsStr(s, offset + 32, z.add(w[1]), y.add(fetch64(s, offset + 16)));
   [z, x] = [x, z];
   return hashLen16(hashLen16(v[0], w[0], mul2).add(shiftMix(y).mul(k0)).add(z), hashLen16(v[1], w[1], mul2).add(x), mul2);
 }
@@ -6018,9 +6018,9 @@ function toTypedArray(a, dtype) {
     return new Int32Array(a);
   } else if (dtype === "bool") {
     const bool = new Uint8Array(a.length);
-    for (let i2 = 0; i2 < bool.length; ++i2) {
-      if (Math.round(a[i2]) !== 0) {
-        bool[i2] = 1;
+    for (let i = 0; i < bool.length; ++i) {
+      if (Math.round(a[i]) !== 0) {
+        bool[i] = 1;
       }
     }
     return bool;
@@ -6034,9 +6034,9 @@ function now() {
 function fetch3(path, requestInits) {
   return env().platform.fetch(path, requestInits);
 }
-function encodeString(s2, encoding = "utf-8") {
+function encodeString(s, encoding = "utf-8") {
   encoding = encoding || "utf-8";
-  return env().platform.encode(s2, encoding);
+  return env().platform.encode(s, encoding);
 }
 function decodeString(bytes, encoding = "utf-8") {
   encoding = encoding || "utf-8";
@@ -6069,8 +6069,8 @@ var Profiler = class {
       timer = Promise.resolve({ kernelMs: now() - start });
     }
     if (env().getBool("CHECK_COMPUTATION_FOR_ERRORS")) {
-      for (let i2 = 0; i2 < outputs.length; i2++) {
-        const output = outputs[i2];
+      for (let i = 0; i < outputs.length; i++) {
+        const output = outputs[i];
         output.data().then((tensorVals) => {
           checkComputationForErrors(tensorVals, output.dtype, kernelName);
         });
@@ -6098,8 +6098,8 @@ function checkComputationForErrors(vals, dtype, kernelName) {
   if (dtype !== "float32") {
     return false;
   }
-  for (let i2 = 0; i2 < vals.length; i2++) {
-    const num = vals[i2];
+  for (let i = 0; i < vals.length; i++) {
+    const num = vals[i];
     if (isNaN(num) || !isFinite(num)) {
       console.warn(`Found ${num} in the result of '${kernelName}'`);
       return true;
@@ -6131,11 +6131,11 @@ var Logger = class {
 function getFilteredNodesXToY(tape, xs, y) {
   const tensorsFromX = {};
   const nodesFromX = {};
-  for (let i2 = 0; i2 < xs.length; i2++) {
-    tensorsFromX[xs[i2].id] = true;
+  for (let i = 0; i < xs.length; i++) {
+    tensorsFromX[xs[i].id] = true;
   }
-  for (let i2 = 0; i2 < tape.length; i2++) {
-    const node = tape[i2];
+  for (let i = 0; i < tape.length; i++) {
+    const node = tape[i];
     const nodeInputs = node.inputs;
     for (const inputName in nodeInputs) {
       const input2 = nodeInputs[inputName];
@@ -6156,8 +6156,8 @@ function getFilteredNodesXToY(tape, xs, y) {
   const tensorsLeadToY = {};
   tensorsLeadToY[y.id] = true;
   const nodesToY = {};
-  for (let i2 = tape.length - 1; i2 >= 0; i2--) {
-    const node = tape[i2];
+  for (let i = tape.length - 1; i >= 0; i--) {
+    const node = tape[i];
     const nodeInputs = node.inputs;
     for (let j = 0; j < node.outputs.length; j++) {
       if (tensorsLeadToY[node.outputs[j].id]) {
@@ -6170,8 +6170,8 @@ function getFilteredNodesXToY(tape, xs, y) {
     }
   }
   const filteredTape = [];
-  for (let i2 = 0; i2 < tape.length; i2++) {
-    const node = tape[i2];
+  for (let i = 0; i < tape.length; i++) {
+    const node = tape[i];
     if (nodesFromX[node.id] && nodesToY[node.id]) {
       const prunedInputs = {};
       for (const inputName in node.inputs) {
@@ -6189,8 +6189,8 @@ function getFilteredNodesXToY(tape, xs, y) {
   return filteredTape;
 }
 function backpropagateGradients(tensorAccumulatedGradientMap, filteredTape, tidy2, add5) {
-  for (let i2 = filteredTape.length - 1; i2 >= 0; i2--) {
-    const node = filteredTape[i2];
+  for (let i = filteredTape.length - 1; i >= 0; i--) {
+    const node = filteredTape[i];
     const dys = [];
     node.outputs.forEach((o) => {
       const gradTensor = tensorAccumulatedGradientMap[o.id];
@@ -6243,17 +6243,17 @@ function tensorToString(vals, shape, dtype, verbose) {
     lines.push(`  shape: [${shape}]`);
     lines.push(`  values:`);
   }
-  lines.push(valsLines.map((l3) => "    " + l3).join("\n"));
+  lines.push(valsLines.map((l) => "    " + l).join("\n"));
   return lines.join("\n");
 }
 function computeMaxSizePerColumn(vals, shape, dtype, strides) {
-  const n2 = sizeFromShape(shape);
+  const n = sizeFromShape(shape);
   const numCols = strides[strides.length - 1];
   const padPerCol = new Array(numCols).fill(0);
   const rank = shape.length;
   const valuesOrTuples = dtype === "complex64" ? createComplexTuples(vals) : vals;
   if (rank > 1) {
-    for (let row = 0; row < n2 / numCols; row++) {
+    for (let row = 0; row < n / numCols; row++) {
       const offset = row * numCols;
       for (let j = 0; j < numCols; j++) {
         padPerCol[j] = Math.max(padPerCol[j], valToString(valuesOrTuples[offset + j], 0, dtype).length);
@@ -6302,12 +6302,12 @@ function subTensorToString(vals, shape, dtype, strides, padPerCol, isLast = true
         lastVals = createComplexTuples(lastVals);
       }
       return [
-        "[" + firstVals.map((x, i2) => valToString(x, padPerCol[i2], dtype)).join(", ") + ", ..., " + lastVals.map((x, i2) => valToString(x, padPerCol[size - FORMAT_NUM_FIRST_LAST_VALS + i2], dtype)).join(", ") + "]"
+        "[" + firstVals.map((x, i) => valToString(x, padPerCol[i], dtype)).join(", ") + ", ..., " + lastVals.map((x, i) => valToString(x, padPerCol[size - FORMAT_NUM_FIRST_LAST_VALS + i], dtype)).join(", ") + "]"
       ];
     }
     const displayVals = dtype === "complex64" ? createComplexTuples(vals) : Array.from(vals);
     return [
-      "[" + displayVals.map((x, i2) => valToString(x, padPerCol[i2], dtype)).join(", ") + "]"
+      "[" + displayVals.map((x, i) => valToString(x, padPerCol[i], dtype)).join(", ") + "]"
     ];
   }
   const subshape = shape.slice(1);
@@ -6315,31 +6315,31 @@ function subTensorToString(vals, shape, dtype, strides, padPerCol, isLast = true
   const stride = strides[0] * storagePerElement;
   const lines = [];
   if (size > FORMAT_LIMIT_NUM_VALS) {
-    for (let i2 = 0; i2 < FORMAT_NUM_FIRST_LAST_VALS; i2++) {
-      const start = i2 * stride;
+    for (let i = 0; i < FORMAT_NUM_FIRST_LAST_VALS; i++) {
+      const start = i * stride;
       const end = start + stride;
       lines.push(...subTensorToString(vals.slice(start, end), subshape, dtype, substrides, padPerCol, false));
     }
     lines.push("...");
-    for (let i2 = size - FORMAT_NUM_FIRST_LAST_VALS; i2 < size; i2++) {
-      const start = i2 * stride;
+    for (let i = size - FORMAT_NUM_FIRST_LAST_VALS; i < size; i++) {
+      const start = i * stride;
       const end = start + stride;
-      lines.push(...subTensorToString(vals.slice(start, end), subshape, dtype, substrides, padPerCol, i2 === size - 1));
+      lines.push(...subTensorToString(vals.slice(start, end), subshape, dtype, substrides, padPerCol, i === size - 1));
     }
   } else {
-    for (let i2 = 0; i2 < size; i2++) {
-      const start = i2 * stride;
+    for (let i = 0; i < size; i++) {
+      const start = i * stride;
       const end = start + stride;
-      lines.push(...subTensorToString(vals.slice(start, end), subshape, dtype, substrides, padPerCol, i2 === size - 1));
+      lines.push(...subTensorToString(vals.slice(start, end), subshape, dtype, substrides, padPerCol, i === size - 1));
     }
   }
   const sep = rank === 2 ? "," : "";
   lines[0] = "[" + lines[0] + sep;
-  for (let i2 = 1; i2 < lines.length - 1; i2++) {
-    lines[i2] = " " + lines[i2] + sep;
+  for (let i = 1; i < lines.length - 1; i++) {
+    lines[i] = " " + lines[i] + sep;
   }
   let newLineSep = ",\n";
-  for (let i2 = 2; i2 < rank; i2++) {
+  for (let i = 2; i < rank; i++) {
     newLineSep += "\n";
   }
   lines[lines.length - 1] = " " + lines[lines.length - 1] + "]" + (isLast ? "" : newLineSep);
@@ -6347,8 +6347,8 @@ function subTensorToString(vals, shape, dtype, strides, padPerCol, isLast = true
 }
 function createComplexTuples(vals) {
   const complexTuples = [];
-  for (let i2 = 0; i2 < vals.length; i2 += 2) {
-    complexTuples.push([vals[i2], vals[i2 + 1]]);
+  for (let i = 0; i < vals.length; i += 2) {
+    complexTuples.push([vals[i], vals[i + 1]]);
   }
   return complexTuples;
 }
@@ -6360,8 +6360,8 @@ var TensorBuffer = class {
     this.shape = shape.slice();
     this.size = sizeFromShape(shape);
     if (values != null) {
-      const n2 = values.length;
-      assert(n2 === this.size, () => `Length of values '${n2}' does not match the size inferred by the shape '${this.size}'.`);
+      const n = values.length;
+      assert(n === this.size, () => `Length of values '${n}' does not match the size inferred by the shape '${this.size}'.`);
     }
     if (dtype === "complex64") {
       throw new Error(`complex64 dtype TensorBuffers are not supported. Please create a TensorBuffer for the real and imaginary parts separately and call tf.complex(real, imag).`);
@@ -6381,17 +6381,17 @@ var TensorBuffer = class {
     if (locs.length === 0) {
       locs = [0];
     }
-    let i2 = 0;
+    let i = 0;
     for (const loc of locs) {
-      if (loc < 0 || loc >= this.shape[i2]) {
+      if (loc < 0 || loc >= this.shape[i]) {
         const msg = `Requested out of range element at ${locs}.   Buffer shape=${this.shape}`;
         throw new Error(msg);
       }
-      i2++;
+      i++;
     }
     let index = locs[locs.length - 1];
-    for (let i3 = 0; i3 < locs.length - 1; ++i3) {
-      index += this.strides[i3] * locs[i3];
+    for (let i2 = 0; i2 < locs.length - 1; ++i2) {
+      index += this.strides[i2] * locs[i2];
     }
     return this.values[index];
   }
@@ -6402,8 +6402,8 @@ var TensorBuffer = class {
       return locs[0];
     }
     let index = locs[locs.length - 1];
-    for (let i2 = 0; i2 < locs.length - 1; ++i2) {
-      index += this.strides[i2] * locs[i2];
+    for (let i = 0; i < locs.length - 1; ++i) {
+      index += this.strides[i] * locs[i];
     }
     return index;
   }
@@ -6414,9 +6414,9 @@ var TensorBuffer = class {
       return [index];
     }
     const locs = new Array(this.shape.length);
-    for (let i2 = 0; i2 < locs.length - 1; ++i2) {
-      locs[i2] = Math.floor(index / this.strides[i2]);
-      index -= locs[i2] * this.strides[i2];
+    for (let i = 0; i < locs.length - 1; ++i) {
+      locs[i] = Math.floor(index / this.strides[i]);
+      index -= locs[i] * this.strides[i];
     }
     locs[locs.length - 1] = index;
     return locs;
@@ -6745,8 +6745,8 @@ var Engine = class {
       return;
     }
     const sortedBackends = this.getSortedBackends();
-    for (let i2 = 0; i2 < sortedBackends.length; i2++) {
-      const backendName = sortedBackends[i2];
+    for (let i = 0; i < sortedBackends.length; i++) {
+      const backendName = sortedBackends[i];
       const success = await this.initializeBackend(backendName).success;
       if (success) {
         await this.setBackend(backendName);
@@ -6898,8 +6898,8 @@ var Engine = class {
   }
   initializeBackendsAndReturnBest() {
     const sortedBackends = this.getSortedBackends();
-    for (let i2 = 0; i2 < sortedBackends.length; i2++) {
-      const backendName = sortedBackends[i2];
+    for (let i = 0; i < sortedBackends.length; i++) {
+      const backendName = sortedBackends[i];
       const { success, asyncInit } = this.initializeBackend(backendName);
       if (asyncInit || success) {
         return { name: backendName, asyncInit };
@@ -7116,7 +7116,7 @@ var Engine = class {
       } else {
         inputTensorsToSave = inputsToSave.map((inputName) => inputs[inputName]);
       }
-      const outputTensorsToSave = outputs.filter((_, i2) => outputsToSave[i2]);
+      const outputTensorsToSave = outputs.filter((_, i) => outputsToSave[i]);
       return inputTensorsToSave.concat(outputTensorsToSave);
     }
     return [];
@@ -7132,15 +7132,15 @@ var Engine = class {
       backendVals = values.map((d) => encodeString(d));
     }
     const dataId = backend2.write(backendVals, shape, dtype);
-    const t2 = new Tensor(shape, dtype, dataId, this.nextTensorId());
-    this.trackTensor(t2, backend2);
+    const t = new Tensor(shape, dtype, dataId, this.nextTensorId());
+    this.trackTensor(t, backend2);
     if (dtype === "string") {
       const info = this.state.tensorInfo.get(dataId);
       const newBytes = bytesFromStringArray(backendVals);
       this.state.numBytes += newBytes - info.bytes;
       info.bytes = newBytes;
     }
-    return t2;
+    return t;
   }
   makeTensorFromDataId(dataId, shape, dtype, backend2) {
     dtype = dtype || "float32";
@@ -7149,9 +7149,9 @@ var Engine = class {
   }
   makeTensorFromTensorInfo(tensorInfo, backend2) {
     const { dataId, shape, dtype } = tensorInfo;
-    const t2 = new Tensor(shape, dtype, dataId, this.nextTensorId());
-    this.trackTensor(t2, backend2);
-    return t2;
+    const t = new Tensor(shape, dtype, dataId, this.nextTensorId());
+    this.trackTensor(t, backend2);
+    return t;
   }
   makeVariable(initialValue, trainable = true, name, dtype) {
     name = name || this.nextVariableId().toString();
@@ -7270,9 +7270,9 @@ var Engine = class {
     }
     if (gradientsFunc != null) {
       tapeNode.gradient = (dys) => {
-        dys = dys.map((dy, i2) => {
+        dys = dys.map((dy, i) => {
           if (dy == null) {
-            const output = outputs[i2];
+            const output = outputs[i];
             const vals = makeZerosTypedArray(output.size, output.dtype);
             return this.makeTensor(vals, output.shape, output.dtype);
           }
@@ -7310,9 +7310,9 @@ var Engine = class {
   }
   endScope(result) {
     const tensorsToTrackInParent = getTensorsInContainer(result);
-    const tensorsToTrackInParentSet = new Set(tensorsToTrackInParent.map((t2) => t2.id));
-    for (let i2 = 0; i2 < this.state.activeScope.track.length; i2++) {
-      const tensor2 = this.state.activeScope.track[i2];
+    const tensorsToTrackInParentSet = new Set(tensorsToTrackInParent.map((t) => t.id));
+    for (let i = 0; i < this.state.activeScope.track.length; i++) {
+      const tensor2 = this.state.activeScope.track[i];
       if (!tensor2.kept && !tensorsToTrackInParentSet.has(tensor2.id)) {
         tensor2.dispose();
       }
@@ -7360,11 +7360,11 @@ var Engine = class {
   customGrad(f) {
     assert(isFunction(f), () => "The f passed in customGrad(f) must be a function.");
     return (...inputs) => {
-      assert(inputs.every((t2) => t2 instanceof Tensor), () => "The args passed in customGrad(f)(x1, x2,...) must all be tensors");
+      assert(inputs.every((t) => t instanceof Tensor), () => "The args passed in customGrad(f)(x1, x2,...) must all be tensors");
       let res;
       const inputMap = {};
-      inputs.forEach((input2, i2) => {
-        inputMap[i2] = input2;
+      inputs.forEach((input2, i) => {
+        inputMap[i] = input2;
       });
       const forwardFunc = (_, save) => {
         res = f(...[...inputs, save]);
@@ -7376,10 +7376,10 @@ var Engine = class {
         const gradRes = res.gradFunc(dy, saved);
         const grads2 = Array.isArray(gradRes) ? gradRes : [gradRes];
         assert(grads2.length === inputs.length, () => "The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function that returns the same number of tensors as inputs passed to f(...).");
-        assert(grads2.every((t2) => t2 instanceof Tensor), () => "The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function that returns a list of only tensors.");
+        assert(grads2.every((t) => t instanceof Tensor), () => "The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function that returns a list of only tensors.");
         const gradMap = {};
-        grads2.forEach((grad2, i2) => {
-          gradMap[i2] = () => grad2;
+        grads2.forEach((grad2, i) => {
+          gradMap[i] = () => grad2;
         });
         return gradMap;
       };
@@ -7540,8 +7540,8 @@ function deepAssertShapeConsistency(val, shape, indices) {
   assert(shape.length > 0, () => `Element arr[${indices.join("][")}] should be a primitive, but is an array of ${val.length} elements`);
   assert(val.length === shape[0], () => `Element arr[${indices.join("][")}] should have ${shape[0]} elements, but has ${val.length} elements`);
   const subShape = shape.slice(1);
-  for (let i2 = 0; i2 < val.length; ++i2) {
-    deepAssertShapeConsistency(val[i2], subShape, indices.concat(i2));
+  for (let i = 0; i < val.length; ++i) {
+    deepAssertShapeConsistency(val[i], subShape, indices.concat(i));
   }
 }
 function assertDtype(expectedDtype, actualDType, argName, functionName) {
@@ -7582,7 +7582,7 @@ function convertToTensorArray(arg, argName, functionName, parseAsDtype = "numeri
     throw new Error(`Argument ${argName} passed to ${functionName} must be a \`Tensor[]\` or \`TensorLike[]\``);
   }
   const tensors = arg;
-  return tensors.map((t2, i2) => convertToTensor(t2, `${argName}[${i2}]`, functionName, parseAsDtype));
+  return tensors.map((t, i) => convertToTensor(t, `${argName}[${i}]`, functionName, parseAsDtype));
 }
 
 // node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/operation.js
@@ -7642,10 +7642,10 @@ function makeTensor(values, shape, inferredShape, dtype) {
     const providedSize = sizeFromShape(shape);
     const inferredSize = sizeFromShape(inferredShape);
     assert(providedSize === inferredSize, () => `Based on the provided shape, [${shape}], the tensor should have ${providedSize} values but has ${inferredSize}`);
-    for (let i2 = 0; i2 < inferredShape.length; ++i2) {
-      const inferred = inferredShape[i2];
-      const flatDimsDontMatch = i2 === inferredShape.length - 1 ? inferred !== sizeFromShape(shape.slice(i2)) : true;
-      assert(inferredShape[i2] === shape[i2] || !flatDimsDontMatch, () => `Error creating a new Tensor. Inferred shape (${inferredShape}) does not match the provided shape (${shape}). `);
+    for (let i = 0; i < inferredShape.length; ++i) {
+      const inferred = inferredShape[i];
+      const flatDimsDontMatch = i === inferredShape.length - 1 ? inferred !== sizeFromShape(shape.slice(i)) : true;
+      assert(inferredShape[i] === shape[i] || !flatDimsDontMatch, () => `Error creating a new Tensor. Inferred shape (${inferredShape}) does not match the provided shape (${shape}). `);
     }
   }
   if (!isTypedArray(values) && !Array.isArray(values)) {
@@ -7679,21 +7679,21 @@ async function encodeWeights(tensors, group) {
   const specs = [];
   const dataPromises = [];
   const names = Array.isArray(tensors) ? tensors.map((tensor2) => tensor2.name) : Object.keys(tensors);
-  for (let i2 = 0; i2 < names.length; ++i2) {
-    const name = names[i2];
-    const t2 = Array.isArray(tensors) ? tensors[i2].tensor : tensors[name];
-    if (t2.dtype !== "float32" && t2.dtype !== "int32" && t2.dtype !== "bool" && t2.dtype !== "string" && t2.dtype !== "complex64") {
-      throw new Error(`Unsupported dtype in weight '${name}': ${t2.dtype}`);
+  for (let i = 0; i < names.length; ++i) {
+    const name = names[i];
+    const t = Array.isArray(tensors) ? tensors[i].tensor : tensors[name];
+    if (t.dtype !== "float32" && t.dtype !== "int32" && t.dtype !== "bool" && t.dtype !== "string" && t.dtype !== "complex64") {
+      throw new Error(`Unsupported dtype in weight '${name}': ${t.dtype}`);
     }
-    const spec = { name, shape: t2.shape, dtype: t2.dtype };
-    if (t2.dtype === "string") {
+    const spec = { name, shape: t.shape, dtype: t.dtype };
+    if (t.dtype === "string") {
       const utf8bytes = new Promise(async (resolve) => {
-        const vals = await t2.bytes();
+        const vals = await t.bytes();
         const totalNumBytes = vals.reduce((p2, c) => p2 + c.length, 0) + NUM_BYTES_STRING_LENGTH * vals.length;
         const bytes = new Uint8Array(totalNumBytes);
         let offset = 0;
-        for (let i3 = 0; i3 < vals.length; i3++) {
-          const val = vals[i3];
+        for (let i2 = 0; i2 < vals.length; i2++) {
+          const val = vals[i2];
           const bytesOfLength = new Uint8Array(new Uint32Array([val.length]).buffer);
           bytes.set(bytesOfLength, offset);
           offset += NUM_BYTES_STRING_LENGTH;
@@ -7704,7 +7704,7 @@ async function encodeWeights(tensors, group) {
       });
       dataPromises.push(utf8bytes);
     } else {
-      dataPromises.push(t2.data());
+      dataPromises.push(t.data());
     }
     if (group != null) {
       spec.group = group;
@@ -7743,9 +7743,9 @@ function decodeWeights(buffer2, specs) {
       if (dtype === "float32") {
         if (quantization.dtype === "uint8" || quantization.dtype === "uint16") {
           values = new Float32Array(quantizedArray.length);
-          for (let i2 = 0; i2 < quantizedArray.length; i2++) {
-            const v = quantizedArray[i2];
-            values[i2] = v * quantization.scale + quantization.min;
+          for (let i = 0; i < quantizedArray.length; i++) {
+            const v = quantizedArray[i];
+            values[i] = v * quantization.scale + quantization.min;
           }
         } else if (quantization.dtype === "float16") {
           if (float16Decode === void 0) {
@@ -7760,9 +7760,9 @@ function decodeWeights(buffer2, specs) {
           throw new Error(`Unsupported quantization type ${quantization.dtype} for weight type int32.`);
         }
         values = new Int32Array(quantizedArray.length);
-        for (let i2 = 0; i2 < quantizedArray.length; i2++) {
-          const v = quantizedArray[i2];
-          values[i2] = Math.round(v * quantization.scale + quantization.min);
+        for (let i = 0; i < quantizedArray.length; i++) {
+          const v = quantizedArray[i];
+          values[i] = Math.round(v * quantization.scale + quantization.min);
         }
       } else {
         throw new Error(`Unsupported dtype in weight '${name}': ${dtype}`);
@@ -7771,7 +7771,7 @@ function decodeWeights(buffer2, specs) {
     } else if (dtype === "string") {
       const size2 = sizeFromShape(spec.shape);
       values = [];
-      for (let i2 = 0; i2 < size2; i2++) {
+      for (let i = 0; i < size2; i++) {
         const byteLength = new Uint32Array(buffer2.slice(offset, offset + NUM_BYTES_STRING_LENGTH))[0];
         offset += NUM_BYTES_STRING_LENGTH;
         const bytes = new Uint8Array(buffer2.slice(offset, offset + byteLength));
@@ -7791,9 +7791,9 @@ function decodeWeights(buffer2, specs) {
         values = new Float32Array(byteBuffer);
         const real5 = new Float32Array(values.length / 2);
         const image2 = new Float32Array(values.length / 2);
-        for (let i2 = 0; i2 < real5.length; i2++) {
-          real5[i2] = values[i2 * 2];
-          image2[i2] = values[i2 * 2 + 1];
+        for (let i = 0; i < real5.length; i++) {
+          real5[i] = values[i * 2];
+          image2[i] = values[i * 2 + 1];
         }
         const realTensor = tensor(real5, shape, "float32");
         const imageTensor = tensor(image2, shape, "float32");
@@ -7844,21 +7844,21 @@ function arrayBufferToBase64String(buffer2) {
     return Buffer.from(buffer2).toString("base64");
   }
   const buf = new Uint8Array(buffer2);
-  let s2 = "";
-  for (let i2 = 0, l3 = buf.length; i2 < l3; i2++) {
-    s2 += String.fromCharCode(buf[i2]);
+  let s = "";
+  for (let i = 0, l = buf.length; i < l; i++) {
+    s += String.fromCharCode(buf[i]);
   }
-  return btoa(s2);
+  return btoa(s);
 }
 function base64StringToArrayBuffer(str) {
   if (useNodeBuffer) {
     const buf = Buffer.from(str, "base64");
     return buf.buffer.slice(buf.byteOffset, buf.byteOffset + buf.byteLength);
   }
-  const s2 = atob(str);
-  const buffer2 = new Uint8Array(s2.length);
-  for (let i2 = 0; i2 < s2.length; ++i2) {
-    buffer2.set([s2.charCodeAt(i2)], i2);
+  const s = atob(str);
+  const buffer2 = new Uint8Array(s.length);
+  for (let i = 0; i < s.length; ++i) {
+    buffer2.set([s.charCodeAt(i)], i);
   }
   return buffer2.buffer;
 }
@@ -7948,24 +7948,24 @@ function getModelArtifactsInfoForJSON(modelArtifacts) {
   };
 }
 function computeFloat16MantisaTable() {
-  const convertMantissa = (i2) => {
-    let m = i2 << 13;
-    let e2 = 0;
+  const convertMantissa = (i) => {
+    let m = i << 13;
+    let e = 0;
     while ((m & 8388608) === 0) {
-      e2 -= 8388608;
+      e -= 8388608;
       m <<= 1;
     }
     m &= ~8388608;
-    e2 += 947912704;
-    return m | e2;
+    e += 947912704;
+    return m | e;
   };
   const mantisaTable = new Uint32Array(2048);
   mantisaTable[0] = 0;
-  for (let i2 = 1; i2 < 1024; i2++) {
-    mantisaTable[i2] = convertMantissa(i2);
+  for (let i = 1; i < 1024; i++) {
+    mantisaTable[i] = convertMantissa(i);
   }
-  for (let i2 = 1024; i2 < 2048; i2++) {
-    mantisaTable[i2] = 939524096 + (i2 - 1024 << 13);
+  for (let i = 1024; i < 2048; i++) {
+    mantisaTable[i] = 939524096 + (i - 1024 << 13);
   }
   return mantisaTable;
 }
@@ -7975,18 +7975,18 @@ function computeFloat16ExponentTable() {
   exponentTable[31] = 1199570944;
   exponentTable[32] = 2147483648;
   exponentTable[63] = 3347054592;
-  for (let i2 = 1; i2 < 31; i2++) {
-    exponentTable[i2] = i2 << 23;
+  for (let i = 1; i < 31; i++) {
+    exponentTable[i] = i << 23;
   }
-  for (let i2 = 33; i2 < 63; i2++) {
-    exponentTable[i2] = 2147483648 + (i2 - 32 << 23);
+  for (let i = 33; i < 63; i++) {
+    exponentTable[i] = 2147483648 + (i - 32 << 23);
   }
   return exponentTable;
 }
 function computeFloat16OffsetTable() {
   const offsetTable = new Uint32Array(64);
-  for (let i2 = 0; i2 < 64; i2++) {
-    offsetTable[i2] = 1024;
+  for (let i = 0; i < 64; i++) {
+    offsetTable[i] = 1024;
   }
   offsetTable[0] = offsetTable[32] = 0;
   return offsetTable;
@@ -8398,8 +8398,8 @@ var BrowserLocalStorageManager = class {
     const out = {};
     const prefix = PATH_PREFIX + PATH_SEPARATOR;
     const suffix = PATH_SEPARATOR + INFO_SUFFIX;
-    for (let i2 = 0; i2 < this.LS.length; ++i2) {
-      const key = this.LS.key(i2);
+    for (let i = 0; i < this.LS.length; ++i) {
+      const key = this.LS.key(i);
       if (key.startsWith(prefix) && key.endsWith(suffix)) {
         const modelPath = getModelPathFromKey(key);
         out[modelPath] = JSON.parse(this.LS.getItem(key));
@@ -8889,19 +8889,19 @@ function weightsLoaderFactory(fetchWeightsFunction) {
       });
     });
     if (!weightsFound.every((found) => found)) {
-      const weightsNotFound = weightNames.filter((_, i2) => !weightsFound[i2]);
+      const weightsNotFound = weightNames.filter((_, i) => !weightsFound[i]);
       throw new Error(`Could not find weights in manifest with names: ${weightsNotFound.join(", ")}. 
 Manifest JSON has weights with names: ${allManifestWeightNames.join(", ")}.`);
     }
-    const groupIndicesToFetch = groupIndicesToFetchMap.reduce((accumulator, shouldFetch, i2) => {
+    const groupIndicesToFetch = groupIndicesToFetchMap.reduce((accumulator, shouldFetch, i) => {
       if (shouldFetch) {
-        accumulator.push(i2);
+        accumulator.push(i);
       }
       return accumulator;
     }, []);
     const fetchUrls = [];
-    groupIndicesToFetch.forEach((i2) => {
-      manifest[i2].paths.forEach((filepath) => {
+    groupIndicesToFetch.forEach((i) => {
+      manifest[i].paths.forEach((filepath) => {
         const fetchUrl = filePathPrefix + (!filePathPrefix.endsWith("/") ? "/" : "") + filepath;
         fetchUrls.push(fetchUrl);
       });
@@ -8909,21 +8909,21 @@ Manifest JSON has weights with names: ${allManifestWeightNames.join(", ")}.`);
     const buffers = await fetchWeightsFunction(fetchUrls);
     const weightsTensorMap = {};
     let bufferIndexOffset = 0;
-    groupIndicesToFetch.forEach((i2) => {
-      const numBuffers = manifest[i2].paths.length;
+    groupIndicesToFetch.forEach((i) => {
+      const numBuffers = manifest[i].paths.length;
       let groupBytes = 0;
-      for (let i3 = 0; i3 < numBuffers; i3++) {
-        groupBytes += buffers[bufferIndexOffset + i3].byteLength;
+      for (let i2 = 0; i2 < numBuffers; i2++) {
+        groupBytes += buffers[bufferIndexOffset + i2].byteLength;
       }
       const groupBuffer = new ArrayBuffer(groupBytes);
       const groupByteBuffer = new Uint8Array(groupBuffer);
       let groupBufferOffset = 0;
-      for (let i3 = 0; i3 < numBuffers; i3++) {
-        const buffer2 = new Uint8Array(buffers[bufferIndexOffset + i3]);
+      for (let i2 = 0; i2 < numBuffers; i2++) {
+        const buffer2 = new Uint8Array(buffers[bufferIndexOffset + i2]);
         groupByteBuffer.set(buffer2, groupBufferOffset);
         groupBufferOffset += buffer2.byteLength;
       }
-      const weightsEntries = groupWeightsToFetch[i2];
+      const weightsEntries = groupWeightsToFetch[i];
       weightsEntries.forEach((weightsEntry) => {
         const byteBuffer = groupBuffer.slice(weightsEntry.groupOffset, weightsEntry.groupOffset + weightsEntry.sizeBytes);
         const nameToTensorMap = decodeWeights(byteBuffer, [weightsEntry.manifestEntry]);
@@ -8998,7 +8998,7 @@ var HTTPRequest = class {
     let modelJSON;
     try {
       modelJSON = await modelConfigRequest.json();
-    } catch (e2) {
+    } catch (e) {
       let message = `Failed to parse model JSON of response from ${this.path}.`;
       if (this.path.endsWith(".pb")) {
         message += " Your path contains a .pb file extension. Support for .pb models have been removed in TensorFlow.js 1.0 in favor of .json models. You can re-convert your Python TensorFlow model using the TensorFlow.js 1.0 conversion scripts or you can convert your.pb models with the 'pb2json'NPM script in the tensorflow/tfjs-converter repository.";
@@ -9264,7 +9264,7 @@ var real = op({ real_ });
 function transpose_(x, perm, conjugate) {
   const $x = convertToTensor(x, "x", "transpose");
   if (perm == null) {
-    perm = $x.shape.map((s2, i2) => i2).reverse();
+    perm = $x.shape.map((s, i) => i).reverse();
   }
   assert($x.rank === perm.length, () => `Error in transpose: rank of input ${$x.rank} must match length of perm ${perm}.`);
   perm.forEach((axis) => {
@@ -9318,10 +9318,10 @@ __export(broadcast_util_exports, {
 function getBroadcastDims(inShape, outShape) {
   const inRank = inShape.length;
   const dims = [];
-  for (let i2 = 0; i2 < inRank; i2++) {
-    const dim = inRank - 1 - i2;
+  for (let i = 0; i < inRank; i++) {
+    const dim = inRank - 1 - i;
     const a = inShape[dim] || 1;
-    const b = outShape[outShape.length - 1 - i2] || 1;
+    const b = outShape[outShape.length - 1 - i] || 1;
     if (b > 1 && a === 1) {
       dims.unshift(dim);
     }
@@ -9330,9 +9330,9 @@ function getBroadcastDims(inShape, outShape) {
 }
 function getReductionAxes(inShape, outShape) {
   const result = [];
-  for (let i2 = 0; i2 < outShape.length; i2++) {
-    const inDim = inShape[inShape.length - i2 - 1];
-    const outAxis = outShape.length - i2 - 1;
+  for (let i = 0; i < outShape.length; i++) {
+    const inDim = inShape[inShape.length - i - 1];
+    const outAxis = outShape.length - i - 1;
     const outDim = outShape[outAxis];
     if (inDim == null || inDim === 1 && outDim > 1) {
       result.unshift(outAxis);
@@ -9342,13 +9342,13 @@ function getReductionAxes(inShape, outShape) {
 }
 function assertAndGetBroadcastShape(shapeA, shapeB) {
   const result = [];
-  const l3 = Math.max(shapeA.length, shapeB.length);
-  for (let i2 = 0; i2 < l3; i2++) {
-    let a = shapeA[shapeA.length - i2 - 1];
+  const l = Math.max(shapeA.length, shapeB.length);
+  for (let i = 0; i < l; i++) {
+    let a = shapeA[shapeA.length - i - 1];
     if (a == null) {
       a = 1;
     }
-    let b = shapeB[shapeB.length - i2 - 1];
+    let b = shapeB[shapeB.length - i - 1];
     if (b == null) {
       b = 1;
     }
@@ -9458,9 +9458,9 @@ function fromPixels_(pixels, numChannels = 3) {
   } else {
     const numPixels = width * height;
     values = new Int32Array(numPixels * numChannels);
-    for (let i2 = 0; i2 < numPixels; i2++) {
+    for (let i = 0; i < numPixels; i++) {
       for (let channel = 0; channel < numChannels; ++channel) {
-        values[i2 * numChannels + channel] = vals[i2 * 4 + channel];
+        values[i * numChannels + channel] = vals[i * 4 + channel];
       }
     }
   }
@@ -9485,7 +9485,7 @@ async function fromPixelsAsync(pixels, numChannels = 3) {
     let imageBitmap;
     try {
       imageBitmap = await createImageBitmap(pixels, { premultiplyAlpha: "none" });
-    } catch (e2) {
+    } catch (e) {
       imageBitmap = null;
     }
     if (imageBitmap != null && imageBitmap.width === pixels.width && imageBitmap.height === pixels.height) {
@@ -9519,10 +9519,10 @@ async function toPixels(img, canvas) {
   const data = await $img.data();
   const multiplier = $img.dtype === "float32" ? 255 : 1;
   const bytes = new Uint8ClampedArray(width * height * 4);
-  for (let i2 = 0; i2 < height * width; ++i2) {
+  for (let i = 0; i < height * width; ++i) {
     const rgba = [0, 0, 0, 255];
     for (let d = 0; d < depth; d++) {
-      const value = data[i2 * depth + d];
+      const value = data[i * depth + d];
       if ($img.dtype === "float32") {
         if (value < 0 || value > 1) {
           throw new Error(`Tensor values for a float32 Tensor must be in the range [0 - 1] but encountered ${value}.`);
@@ -9540,7 +9540,7 @@ async function toPixels(img, canvas) {
         rgba[d] = value * multiplier;
       }
     }
-    const j = i2 * 4;
+    const j = i * 4;
     bytes[j + 0] = Math.round(rgba[0]);
     bytes[j + 1] = Math.round(rgba[1]);
     bytes[j + 2] = Math.round(rgba[2]);
@@ -9586,16 +9586,16 @@ function prepareAndValidate(tensor2, indices) {
   const indicesShape = indices.shape;
   const sliceRank = indicesShape[indicesShape.length - 1];
   let nResult = 1;
-  for (let i2 = 0; i2 < indicesShape.length - 1; ++i2) {
-    nResult *= indicesShape[i2];
+  for (let i = 0; i < indicesShape.length - 1; ++i) {
+    nResult *= indicesShape[i];
   }
   const inputShape = tensor2.shape;
   const resultShape = indicesShape.slice();
   resultShape.pop();
   let sliceSize = 1;
-  for (let i2 = sliceRank; i2 < tensorRank; ++i2) {
-    sliceSize *= inputShape[i2];
-    resultShape.push(inputShape[i2]);
+  for (let i = sliceRank; i < tensorRank; ++i) {
+    sliceSize *= inputShape[i];
+    resultShape.push(inputShape[i]);
   }
   const strides = [
     ...computeStrides(tensor2.shape).map((stride) => stride / sliceSize),
@@ -9663,8 +9663,8 @@ function calculateShapes(updates, indices, shape) {
   const sliceRank = indicesRank > 1 ? indices.shape[indicesRank - 1] : 1;
   const totalNd = shape.length;
   let sliceSize = 1;
-  for (let i2 = sliceRank; i2 < totalNd; ++i2) {
-    sliceSize *= shape[i2];
+  for (let i = sliceRank; i < totalNd; ++i) {
+    sliceSize *= shape[i];
   }
   const safeSliceDim = sliceRank < 1 ? 1 : sliceRank;
   const numUpdates = sizeFromShape(indices.shape) / safeSliceDim;
@@ -9697,8 +9697,8 @@ function assertParamsValid(input2, begin, size) {
   const inputRank = input2.shape.length;
   assert(inputRank === begin.length, () => `Error in slice${inputRank}D: Length of begin ${begin} must match the rank of the array (${inputRank}).`);
   assert(inputRank === size.length, () => `Error in slice${inputRank}D: Length of size ${size} must match the rank of the array (${inputRank}).`);
-  for (let i2 = 0; i2 < inputRank; ++i2) {
-    assert(begin[i2] + size[i2] <= input2.shape[i2], () => `Error in slice${inputRank}D: begin[${i2}] + size[${i2}] (${begin[i2] + size[i2]}) would overflow input.shape[${i2}] (${input2.shape[i2]})`);
+  for (let i = 0; i < inputRank; ++i) {
+    assert(begin[i] + size[i] <= input2.shape[i], () => `Error in slice${inputRank}D: begin[${i}] + size[${i}] (${begin[i] + size[i]}) would overflow input.shape[${i}] (${input2.shape[i]})`);
   }
 }
 function maskToAxes(mask) {
@@ -9722,11 +9722,11 @@ function computeOutShape(begin, end, strides) {
 }
 function stridesWithElidedDims(strides, ellipsisInsertionIndex, numElidedAxes, inputShape) {
   const newStrides = [...strides];
-  for (let i2 = newStrides.length; i2 < inputShape.length; i2++) {
+  for (let i = newStrides.length; i < inputShape.length; i++) {
     newStrides.push(1);
   }
-  for (let i2 = 0; i2 < numElidedAxes; i2++) {
-    if (i2 === 0) {
+  for (let i = 0; i < numElidedAxes; i++) {
+    if (i === 0) {
       newStrides[ellipsisInsertionIndex] = 1;
     } else {
       newStrides.splice(ellipsisInsertionIndex, 0, 1);
@@ -9743,8 +9743,8 @@ function unnormalizeAxis(ellipsisInsertionIndex, numElidedAxes, normalizedAxis)
 }
 function getElidedAxes(numElidedAxes, ellipsisInsertionIndex) {
   const elidedAxes = [];
-  for (let i2 = 0; i2 < numElidedAxes; i2++) {
-    elidedAxes.push(ellipsisInsertionIndex + i2);
+  for (let i = 0; i < numElidedAxes; i++) {
+    elidedAxes.push(ellipsisInsertionIndex + i);
   }
   return elidedAxes;
 }
@@ -9802,12 +9802,12 @@ function stopIndicesWithElidedDims(endMask, ellipsisInsertionIndex, numElidedAxe
       newIndices[axis] = originalValue;
     }
   }
-  for (let i2 = 0; i2 < newIndices.length; i2++) {
-    const axisSize = inputShape[i2];
-    if (newIndices[i2] < 0) {
-      newIndices[i2] += axisSize;
+  for (let i = 0; i < newIndices.length; i++) {
+    const axisSize = inputShape[i];
+    if (newIndices[i] < 0) {
+      newIndices[i] += axisSize;
     }
-    newIndices[i2] = clamp(0, newIndices[i2], inputShape[i2]);
+    newIndices[i] = clamp(0, newIndices[i], inputShape[i]);
   }
   return newIndices;
 }
@@ -9858,14 +9858,14 @@ function stopForAxis(endMask, stopIndices, strides, inputShape, axis, ellipsisMa
 }
 function isSliceContinous(shape, begin, size) {
   let firstNonOneAxis = size.length;
-  for (let i2 = 0; i2 < size.length; i2++) {
-    if (size[i2] > 1) {
-      firstNonOneAxis = i2;
+  for (let i = 0; i < size.length; i++) {
+    if (size[i] > 1) {
+      firstNonOneAxis = i;
       break;
     }
   }
-  for (let i2 = firstNonOneAxis + 1; i2 < size.length; i2++) {
-    if (begin[i2] > 0 || size[i2] !== shape[i2]) {
+  for (let i = firstNonOneAxis + 1; i < size.length; i++) {
+    if (begin[i] > 0 || size[i] !== shape[i]) {
       return false;
     }
   }
@@ -9873,8 +9873,8 @@ function isSliceContinous(shape, begin, size) {
 }
 function computeFlatOffset(begin, strides) {
   let flatOffset = begin.length > 0 ? begin[begin.length - 1] : 1;
-  for (let i2 = 0; i2 < begin.length - 1; i2++) {
-    flatOffset += begin[i2] * strides[i2];
+  for (let i = 0; i < begin.length - 1; i++) {
+    flatOffset += begin[i] * strides[i];
   }
   return flatOffset;
 }
@@ -9901,12 +9901,12 @@ function parseSliceParams(x, begin, size) {
   } else {
     size_ = size;
   }
-  size_ = size_.map((d, i2) => {
+  size_ = size_.map((d, i) => {
     if (d >= 0) {
       return d;
     } else {
-      assert(d === -1, () => `Negative size values should be exactly -1 but got ${d} for the slice() size at index ${i2}.`);
-      return x.shape[i2] - begin_[i2];
+      assert(d === -1, () => `Negative size values should be exactly -1 but got ${d} for the slice() size at index ${i}.`);
+      return x.shape[i] - begin_[i];
     }
   });
   return [begin_, size_];
@@ -9935,11 +9935,11 @@ function sliceInfo(xShape, begin, end, strides, beginMask, endMask, ellipsisMask
     newAxisMask,
     shrinkAxisMask
   };
-  for (let i2 = 0; i2 < sparseSpec.dims; i2++) {
-    if (ellipsisSeen && (1 << i2 & newAxisMask) !== 0) {
+  for (let i = 0; i < sparseSpec.dims; i++) {
+    if (ellipsisSeen && (1 << i & newAxisMask) !== 0) {
       sparseSpec.numAddAxisAfterEllipsis++;
     }
-    if (1 << i2 & ellipsisMask) {
+    if (1 << i & ellipsisMask) {
       ellipsisSeen = true;
     }
   }
@@ -9960,56 +9960,56 @@ function sliceInfo(xShape, begin, end, strides, beginMask, endMask, ellipsisMask
   let isSimpleSlice = true;
   const processingShape = [];
   const finalShape = [];
-  for (let i2 = 0; i2 < xShape.length; ++i2) {
-    if (denseSpec.strides[i2] === 0) {
-      throw Error(`strides[${i2}] must be non-zero`);
+  for (let i = 0; i < xShape.length; ++i) {
+    if (denseSpec.strides[i] === 0) {
+      throw Error(`strides[${i}] must be non-zero`);
     }
-    const shrinkI = !!(denseSpec.shrinkAxisMask & 1 << i2);
-    const dimI = xShape[i2];
+    const shrinkI = !!(denseSpec.shrinkAxisMask & 1 << i);
+    const dimI = xShape[i];
     if (dimI === -1) {
       processingShape.push(shrinkI ? 1 : -1);
       continue;
     }
-    const masks = [denseSpec.beginMask & 1 << i2, denseSpec.endMask & 1 << i2];
+    const masks = [denseSpec.beginMask & 1 << i, denseSpec.endMask & 1 << i];
     const validRange = [
-      denseSpec.strides[i2] > 0 ? 0 : -1,
-      denseSpec.strides[i2] > 0 ? dimI : dimI - 1
+      denseSpec.strides[i] > 0 ? 0 : -1,
+      denseSpec.strides[i] > 0 ? dimI : dimI - 1
     ];
-    if (shrinkI && denseSpec.strides[i2] <= 0) {
+    if (shrinkI && denseSpec.strides[i] <= 0) {
       throw Error("only stride 1 allowed on non-range indexing.");
     }
-    isSimpleSlice = isSimpleSlice && denseSpec.strides[i2] === 1;
-    const beginAndEndMasked = !!(denseSpec.beginMask & 1 << i2 && denseSpec.endMask & 1 << i2);
+    isSimpleSlice = isSimpleSlice && denseSpec.strides[i] === 1;
+    const beginAndEndMasked = !!(denseSpec.beginMask & 1 << i && denseSpec.endMask & 1 << i);
     if (denseSpec.beginValid && denseSpec.endValid) {
       if (shrinkI) {
-        const xFwd = denseSpec.begin[i2] < 0 ? dimI + denseSpec.begin[i2] : denseSpec.begin[i2];
-        denseSpec.begin[i2] = xFwd;
-        denseSpec.end[i2] = denseSpec.begin[i2] + 1;
+        const xFwd = denseSpec.begin[i] < 0 ? dimI + denseSpec.begin[i] : denseSpec.begin[i];
+        denseSpec.begin[i] = xFwd;
+        denseSpec.end[i] = denseSpec.begin[i] + 1;
         if (xFwd < 0 || xFwd >= dimI) {
-          throw Error(`slice index ${denseSpec.begin[i2]} of dimension ${i2} out of bounds.`);
+          throw Error(`slice index ${denseSpec.begin[i]} of dimension ${i} out of bounds.`);
         }
       } else {
-        denseSpec.begin[i2] = canonical(denseSpec.begin[i2], 0, denseSpec.strides[i2], dimI, masks, validRange);
-        denseSpec.end[i2] = canonical(denseSpec.end[i2], 1, denseSpec.strides[i2], dimI, masks, validRange);
+        denseSpec.begin[i] = canonical(denseSpec.begin[i], 0, denseSpec.strides[i], dimI, masks, validRange);
+        denseSpec.end[i] = canonical(denseSpec.end[i], 1, denseSpec.strides[i], dimI, masks, validRange);
       }
-      const takeAllInDimension = denseSpec.strides[i2] === 1 && denseSpec.begin[i2] === 0 && denseSpec.end[i2] === dimI;
+      const takeAllInDimension = denseSpec.strides[i] === 1 && denseSpec.begin[i] === 0 && denseSpec.end[i] === dimI;
       isIdentity = isIdentity && takeAllInDimension;
-      sliceDim0 = sliceDim0 && (i2 === 0 && denseSpec.strides[i2] === 1 || takeAllInDimension);
+      sliceDim0 = sliceDim0 && (i === 0 && denseSpec.strides[i] === 1 || takeAllInDimension);
     } else {
-      isIdentity = isIdentity && (denseSpec.strides[i2] === 1 && beginAndEndMasked);
-      sliceDim0 = sliceDim0 && (i2 === 0 && denseSpec.strides[i2] === 1 || beginAndEndMasked);
+      isIdentity = isIdentity && (denseSpec.strides[i] === 1 && beginAndEndMasked);
+      sliceDim0 = sliceDim0 && (i === 0 && denseSpec.strides[i] === 1 || beginAndEndMasked);
     }
     let intervalLength;
     let knownInterval = false;
     if (denseSpec.beginValid && denseSpec.endValid) {
-      intervalLength = denseSpec.end[i2] - denseSpec.begin[i2];
+      intervalLength = denseSpec.end[i] - denseSpec.begin[i];
       knownInterval = true;
     } else if (shrinkI) {
       intervalLength = 1;
       knownInterval = true;
     } else if (beginAndEndMasked) {
       if (dimI >= 0) {
-        if (denseSpec.strides[i2] < 0) {
+        if (denseSpec.strides[i] < 0) {
           intervalLength = -dimI;
         } else {
           intervalLength = dimI;
@@ -10019,10 +10019,10 @@ function sliceInfo(xShape, begin, end, strides, beginMask, endMask, ellipsisMask
     }
     if (knownInterval) {
       let sizeI;
-      if (intervalLength === 0 || intervalLength < 0 !== denseSpec.strides[i2] < 0) {
+      if (intervalLength === 0 || intervalLength < 0 !== denseSpec.strides[i] < 0) {
         sizeI = 0;
       } else {
-        sizeI = Math.trunc(intervalLength / denseSpec.strides[i2]) + (intervalLength % denseSpec.strides[i2] !== 0 ? 1 : 0);
+        sizeI = Math.trunc(intervalLength / denseSpec.strides[i]) + (intervalLength % denseSpec.strides[i] !== 0 ? 1 : 0);
       }
       processingShape.push(sizeI);
     } else {
@@ -10037,7 +10037,7 @@ function sliceInfo(xShape, begin, end, strides, beginMask, endMask, ellipsisMask
       finalShape.push(1);
     }
   }
-  const finalShapeSparse = finalShape.filter((dim, i2) => denseSpec.finalShapeGatherIndices[i2] !== NEW_AXIS);
+  const finalShapeSparse = finalShape.filter((dim, i) => denseSpec.finalShapeGatherIndices[i] !== NEW_AXIS);
   return {
     finalShapeSparse,
     finalShape,
@@ -10062,9 +10062,9 @@ function buildDenseSpec(sparse2, dense2) {
   dense2.finalShapeGatherIndices = [];
   dense2.finalShapeGatherIndicesSparse = [];
   dense2.inputShapeGatherIndicesSparse = new Array(dense2.dims);
-  for (let i2 = 0; i2 < sparse2.dims; i2++) {
-    if (1 << i2 & sparse2.ellipsisMask) {
-      const nextIndex = Math.min(dense2.dims - (sparse2.dims - i2) + 1 + sparse2.numAddAxisAfterEllipsis, dense2.dims);
+  for (let i = 0; i < sparse2.dims; i++) {
+    if (1 << i & sparse2.ellipsisMask) {
+      const nextIndex = Math.min(dense2.dims - (sparse2.dims - i) + 1 + sparse2.numAddAxisAfterEllipsis, dense2.dims);
       for (; fullIndex < nextIndex; fullIndex++) {
         dense2.begin[fullIndex] = 0;
         dense2.end[fullIndex] = 0;
@@ -10073,9 +10073,9 @@ function buildDenseSpec(sparse2, dense2) {
         dense2.endMask |= 1 << fullIndex;
         dense2.finalShapeGatherIndices.push(fullIndex);
         dense2.finalShapeGatherIndicesSparse.push(-1);
-        dense2.inputShapeGatherIndicesSparse[fullIndex] = i2;
+        dense2.inputShapeGatherIndicesSparse[fullIndex] = i;
       }
-    } else if (1 << i2 & sparse2.newAxisMask) {
+    } else if (1 << i & sparse2.newAxisMask) {
       dense2.finalShapeGatherIndices.push(NEW_AXIS);
       dense2.finalShapeGatherIndicesSparse.push(-1);
     } else {
@@ -10083,27 +10083,27 @@ function buildDenseSpec(sparse2, dense2) {
         throw Error(`Index out of range using input dim ${fullIndex}; input has only ${dense2.dims} dims, ${dense2.begin.length}.`);
       }
       if (sparse2.begin != null) {
-        dense2.begin[fullIndex] = sparse2.begin[i2];
+        dense2.begin[fullIndex] = sparse2.begin[i];
       }
       if (sparse2.end != null) {
-        dense2.end[fullIndex] = sparse2.end[i2];
+        dense2.end[fullIndex] = sparse2.end[i];
       }
-      dense2.strides[fullIndex] = sparse2.strides[i2];
-      if (sparse2.beginMask & 1 << i2) {
+      dense2.strides[fullIndex] = sparse2.strides[i];
+      if (sparse2.beginMask & 1 << i) {
         dense2.beginMask |= 1 << fullIndex;
       }
-      if (sparse2.endMask & 1 << i2) {
+      if (sparse2.endMask & 1 << i) {
         dense2.endMask |= 1 << fullIndex;
       }
-      if (sparse2.shrinkAxisMask & 1 << i2) {
+      if (sparse2.shrinkAxisMask & 1 << i) {
         dense2.finalShapeGatherIndices.push(SHRINK_AXIS);
         dense2.finalShapeGatherIndicesSparse.push(-1);
         dense2.shrinkAxisMask |= 1 << fullIndex;
       } else {
         dense2.finalShapeGatherIndices.push(fullIndex);
-        dense2.finalShapeGatherIndicesSparse.push(i2);
+        dense2.finalShapeGatherIndicesSparse.push(i);
       }
-      dense2.inputShapeGatherIndicesSparse[fullIndex] = i2;
+      dense2.inputShapeGatherIndicesSparse[fullIndex] = i;
       fullIndex++;
     }
   }
@@ -10208,11 +10208,11 @@ function expectArraysPredicate(actual, expected, predicate) {
 Actual:   ${actualFlat}.
 Expected: ${expectedFlat}.`);
   }
-  for (let i2 = 0; i2 < expectedFlat.length; ++i2) {
-    const a = actualFlat[i2];
-    const e2 = expectedFlat[i2];
-    if (!predicate(a, e2)) {
-      throw new Error(`Arrays differ: actual[${i2}] = ${a}, expected[${i2}] = ${e2}.
+  for (let i = 0; i < expectedFlat.length; ++i) {
+    const a = actualFlat[i];
+    const e = expectedFlat[i];
+    if (!predicate(a, e)) {
+      throw new Error(`Arrays differ: actual[${i}] = ${a}, expected[${i}] = ${e}.
 Actual:   ${actualFlat}.
 Expected: ${expectedFlat}.`);
     }
@@ -10234,30 +10234,30 @@ function expectArraysEqual(actual, expected) {
   }
   return expectArraysPredicate(actual, expected, (a, b) => areClose(a, b, 0));
 }
-function expectNumbersClose(a, e2, epsilon3) {
+function expectNumbersClose(a, e, epsilon3) {
   if (epsilon3 == null) {
     epsilon3 = testEpsilon();
   }
-  if (!areClose(a, e2, epsilon3)) {
-    throw new Error(`Numbers differ: actual === ${a}, expected === ${e2}`);
+  if (!areClose(a, e, epsilon3)) {
+    throw new Error(`Numbers differ: actual === ${a}, expected === ${e}`);
   }
   if (typeof expect !== "undefined") {
     expect().nothing();
   }
 }
-function areClose(a, e2, epsilon3) {
-  if (!isFinite(a) && !isFinite(e2)) {
+function areClose(a, e, epsilon3) {
+  if (!isFinite(a) && !isFinite(e)) {
     return true;
   }
-  if (isNaN(a) || isNaN(e2) || Math.abs(a - e2) > epsilon3) {
+  if (isNaN(a) || isNaN(e) || Math.abs(a - e) > epsilon3) {
     return false;
   }
   return true;
 }
 function expectValuesInRange(actual, low, high) {
-  for (let i2 = 0; i2 < actual.length; i2++) {
-    if (actual[i2] < low || actual[i2] > high) {
-      throw new Error(`Value out of range:${actual[i2]} low: ${low}, high: ${high}`);
+  for (let i = 0; i < actual.length; i++) {
+    if (actual[i] < low || actual[i] > high) {
+      throw new Error(`Value out of range:${actual[i]} low: ${low}, high: ${high}`);
     }
   }
 }
@@ -10267,19 +10267,19 @@ function expectArrayBuffersEqual(actual, expected) {
   if (actualArray.length !== expectedArray.length) {
     throw new Error(`Expected ArrayBuffer to be of length ${expectedArray.length}, but it was ${actualArray.length}`);
   }
-  for (let i2 = 0; i2 < expectedArray.length; i2++) {
-    if (actualArray[i2] !== expectedArray[i2]) {
-      throw new Error(`Expected ArrayBuffer value at ${i2} to be ${expectedArray[i2]} but got ${actualArray[i2]} instead`);
+  for (let i = 0; i < expectedArray.length; i++) {
+    if (actualArray[i] !== expectedArray[i]) {
+      throw new Error(`Expected ArrayBuffer value at ${i} to be ${expectedArray[i]} but got ${actualArray[i]} instead`);
     }
   }
 }
 function encodeStrings(a) {
-  for (let i2 = 0; i2 < a.length; i2++) {
-    const val = a[i2];
+  for (let i = 0; i < a.length; i++) {
+    const val = a[i];
     if (Array.isArray(val)) {
       encodeStrings(val);
     } else {
-      a[i2] = encodeString(val);
+      a[i] = encodeString(val);
     }
   }
   return a;
@@ -10390,15 +10390,15 @@ var acosh = op({ acosh_ });
 function addN_(tensors) {
   assert(Array.isArray(tensors), () => "The argument passed to tf.addN() must be a list of tensors");
   assert(tensors.length >= 1, () => `Must pass at least one tensor to tf.addN(), but got ${tensors.length}`);
-  const $tensors = tensors.map((t2, i2) => convertToTensor(t2, `tensors${i2}`, "addN"));
+  const $tensors = tensors.map((t, i) => convertToTensor(t, `tensors${i}`, "addN"));
   const firstTensor = $tensors[0];
-  $tensors.forEach((t2) => {
-    if (t2.dtype !== firstTensor.dtype) {
+  $tensors.forEach((t) => {
+    if (t.dtype !== firstTensor.dtype) {
       throw new Error("All tensors passed to tf.addN() must have the same dtype");
     }
   });
-  $tensors.forEach((t2) => {
-    if (!arraysEqual(t2.shape, firstTensor.shape)) {
+  $tensors.forEach((t) => {
+    if (!arraysEqual(t.shape, firstTensor.shape)) {
       throw new Error("All tensors passed to tf.addN() must have the same shape");
     }
   });
@@ -10918,11 +10918,11 @@ function basicLSTMCell_(forgetBias, lstmKernel, lstmBias, data, c, h) {
   const batchSize = res.shape[0];
   const sliceCols = res.shape[1] / 4;
   const sliceSize = [batchSize, sliceCols];
-  const i2 = slice(res, [0, 0], sliceSize);
+  const i = slice(res, [0, 0], sliceSize);
   const j = slice(res, [0, sliceCols], sliceSize);
   const f = slice(res, [0, sliceCols * 2], sliceSize);
   const o = slice(res, [0, sliceCols * 3], sliceSize);
-  const newC = add2(mul(sigmoid(i2), tanh2(j)), mul($c, sigmoid(add2($forgetBias, f))));
+  const newC = add2(mul(sigmoid(i), tanh2(j)), mul($c, sigmoid(add2($forgetBias, f))));
   const newH = mul(tanh2(newC), sigmoid(o));
   return [newC, newH];
 }
@@ -11114,14 +11114,14 @@ function broadcastTo_(x, shape) {
   }
   const inputShape = input2.shape;
   const reps = Array.from(shape);
-  for (let i2 = shape.length - 1; i2 >= 0; i2--) {
-    if (inputShape[i2] === shape[i2]) {
-      reps[i2] = 1;
-    } else if (input2.shape[i2] !== 1) {
+  for (let i = shape.length - 1; i >= 0; i--) {
+    if (inputShape[i] === shape[i]) {
+      reps[i] = 1;
+    } else if (input2.shape[i] !== 1) {
       throw new Error(`broadcastTo(): [${xShape}] cannot be broadcast to [${shape}].`);
     }
   }
-  const axes = reps.map((n2, i2) => n2 > 1 ? i2 : -1).filter((i2) => i2 >= 0);
+  const axes = reps.map((n, i) => n > 1 ? i : -1).filter((i) => i >= 0);
   if (axes.length === 0) {
     return clone(input2);
   }
@@ -11531,7 +11531,7 @@ var dot = op({ dot_ });
 
 // node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/einsum.js
 function einsum_(equation, ...tensors) {
-  const $tensors = tensors.map((t2, i2) => convertToTensor(t2, `tensors${i2}`, "einsum"));
+  const $tensors = tensors.map((t, i) => convertToTensor(t, `tensors${i}`, "einsum"));
   const attrs = { equation };
   return ENGINE.runKernel(Einsum, $tensors, attrs);
 }
@@ -11559,8 +11559,8 @@ var erf = op({ erf_ });
 
 // node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/axis_util.js
 function axesAreInnerMostDims(axes, rank) {
-  for (let i2 = 0; i2 < axes.length; ++i2) {
-    if (axes[axes.length - i2 - 1] !== rank - 1 - i2) {
+  for (let i = 0; i < axes.length; ++i) {
+    if (axes[axes.length - i - 1] !== rank - 1 - i) {
       return false;
     }
   }
@@ -11603,21 +11603,21 @@ function getAxesPermutation(axes, rank) {
     return null;
   }
   const result = [];
-  for (let i2 = 0; i2 < rank; ++i2) {
-    if (axes.indexOf(i2) === -1) {
-      result.push(i2);
+  for (let i = 0; i < rank; ++i) {
+    if (axes.indexOf(i) === -1) {
+      result.push(i);
     }
   }
   axes.forEach((axis) => result.push(axis));
   return result;
 }
 function getUndoAxesPermutation(axes) {
-  return axes.map((axis, i2) => [i2, axis]).sort((a, b) => a[1] - b[1]).map((x) => x[0]);
+  return axes.map((axis, i) => [i, axis]).sort((a, b) => a[1] - b[1]).map((x) => x[0]);
 }
 function getInnerMostAxes(numAxes, rank) {
   const res = [];
-  for (let i2 = rank - numAxes; i2 < rank; ++i2) {
-    res.push(i2);
+  for (let i = rank - numAxes; i < rank; ++i) {
+    res.push(i);
   }
   return res;
 }
@@ -11791,9 +11791,9 @@ function eye_(numRows, numColumns, batchShape, dtype = "float32") {
     numColumns = numRows;
   }
   const buff = buffer([numRows, numColumns], dtype);
-  const n2 = numRows <= numColumns ? numRows : numColumns;
-  for (let i2 = 0; i2 < n2; ++i2) {
-    buff.set(1, i2, i2);
+  const n = numRows <= numColumns ? numRows : numColumns;
+  for (let i = 0; i < n; ++i) {
+    buff.set(1, i, i);
   }
   const out = reshape(buff.toTensor(), [numRows, numColumns]);
   if (batchShape == null) {
@@ -12041,9 +12041,9 @@ function variableGrads(f, varList) {
   assert(grads2.some((g) => g != null), () => "Cannot find a connection between any variable and the result of the loss function y=f(x). Please make sure the operations that use variables are inside the function f passed to minimize().");
   assert(value.rank === 0, () => `The f passed in variableGrads(f) must return a scalar, but it returned a rank-${value.rank} tensor`);
   const namedGrads = {};
-  varList.forEach((v, i2) => {
-    if (grads2[i2] != null) {
-      namedGrads[v.name] = grads2[i2];
+  varList.forEach((v, i) => {
+    if (grads2[i] != null) {
+      namedGrads[v.name] = grads2[i];
     }
   });
   if (specifiedNonTrainable != null) {
@@ -12368,9 +12368,9 @@ function mirrorPad_(x, paddings, mode) {
   }
   assert(paddings.length === $x.rank, () => `Padding doesn't match input. Must be ${$x.rank}. Got ${paddings.length}.`);
   const shapeOffset = mode === "reflect" ? 1 : 0;
-  for (let i2 = 0; i2 < $x.rank; i2++) {
-    assert(paddings[i2].length === 2, () => `Invalid number of paddings. Must be length of 2 each.`);
-    assert(paddings[i2][0] >= 0 && paddings[i2][0] <= $x.shape[i2] - shapeOffset && paddings[i2][1] >= 0 && paddings[i2][1] <= $x.shape[i2] - shapeOffset, () => `Padding in dimension ${i2} cannot be greater than or equal to ${$x.shape[i2] - shapeOffset} or less than 0 for input of shape ${$x.shape}`);
+  for (let i = 0; i < $x.rank; i++) {
+    assert(paddings[i].length === 2, () => `Invalid number of paddings. Must be length of 2 each.`);
+    assert(paddings[i][0] >= 0 && paddings[i][0] <= $x.shape[i] - shapeOffset && paddings[i][1] >= 0 && paddings[i][1] <= $x.shape[i] - shapeOffset, () => `Padding in dimension ${i} cannot be greater than or equal to ${$x.shape[i] - shapeOffset} or less than 0 for input of shape ${$x.shape}`);
   }
   const attrs = { paddings, mode };
   const inputs = { x: $x };
@@ -12410,17 +12410,17 @@ function multiRNNCell_(lstmCells, data, c, h) {
   const $h = convertToTensorArray(h, "h", "multiRNNCell");
   let input2 = $data;
   const newStates = [];
-  for (let i2 = 0; i2 < lstmCells.length; i2++) {
-    const output = lstmCells[i2](input2, $c[i2], $h[i2]);
+  for (let i = 0; i < lstmCells.length; i++) {
+    const output = lstmCells[i](input2, $c[i], $h[i]);
     newStates.push(output[0]);
     newStates.push(output[1]);
     input2 = output[1];
   }
   const newC = [];
   const newH = [];
-  for (let i2 = 0; i2 < newStates.length; i2 += 2) {
-    newC.push(newStates[i2]);
-    newH.push(newStates[i2 + 1]);
+  for (let i = 0; i < newStates.length; i += 2) {
+    newC.push(newStates[i]);
+    newH.push(newStates[i + 1]);
   }
   return [newC, newH];
 }
@@ -12521,9 +12521,9 @@ function spaceToBatchND_(x, blockShape, paddings) {
   const $x = convertToTensor(x, "x", "spaceToBatchND");
   assert($x.rank >= 1 + blockShape.length, () => `input rank ${$x.rank} should be > than [blockShape] ${blockShape.length}`);
   assert(paddings.length === blockShape.length, () => `paddings.shape[0] ${paddings.length} must be equal to [blockShape] ${blockShape.length}`);
-  assert($x.shape.reduce((a, b, i2) => {
-    if (i2 > 0 && i2 <= blockShape.length) {
-      return a && (b + paddings[i2 - 1][0] + paddings[i2 - 1][1]) % blockShape[i2 - 1] === 0;
+  assert($x.shape.reduce((a, b, i) => {
+    if (i > 0 && i <= blockShape.length) {
+      return a && (b + paddings[i - 1][0] + paddings[i - 1][1]) % blockShape[i - 1] === 0;
     }
     return a;
   }, true), () => `input spatial dimensions ${$x.shape.slice(1)} with paddings ${paddings.toString()} must be divisible by blockShapes ${blockShape.toString()}`);
@@ -12576,21 +12576,21 @@ function requiredSpaceToBatchPaddings(inputShape, blockShape, basePadding) {
   const padStart = basePadding.map((b) => b[0]);
   const origPadEnd = basePadding.map((b) => b[1]);
   const fullInputShape = inputShape.concat(padStart, origPadEnd);
-  const padEndExtra = blockShape.map((b, i2) => (b - fullInputShape[i2] % b) % b);
-  const padEnd = origPadEnd.map((s2, i2) => s2 + padEndExtra[i2]);
-  const paddings = blockShape.map((_, i2) => [padStart[i2], padEnd[i2]]);
-  const crops = blockShape.map((_, i2) => [0, padEndExtra[i2]]);
+  const padEndExtra = blockShape.map((b, i) => (b - fullInputShape[i] % b) % b);
+  const padEnd = origPadEnd.map((s, i) => s + padEndExtra[i]);
+  const paddings = blockShape.map((_, i) => [padStart[i], padEnd[i]]);
+  const crops = blockShape.map((_, i) => [0, padEndExtra[i]]);
   return [paddings, crops];
 }
 function withSpaceToBatchBasePaddings(filterShape, dilation) {
-  const dilatedFilterShape = filterShape.map((s2, i2) => {
-    return s2 + (s2 - 1) * (dilation[i2] - 1);
+  const dilatedFilterShape = filterShape.map((s, i) => {
+    return s + (s - 1) * (dilation[i] - 1);
   });
-  const padExtraShape = dilatedFilterShape.map((s2) => s2 - 1);
-  const padExtraStart = padExtraShape.map((s2) => Math.floor(s2 / 2));
-  const padExtraEnd = padExtraShape.map((s2, i2) => s2 - padExtraStart[i2]);
-  return padExtraShape.map((_, i2) => {
-    return [padExtraStart[i2], padExtraEnd[i2]];
+  const padExtraShape = dilatedFilterShape.map((s) => s - 1);
+  const padExtraStart = padExtraShape.map((s) => Math.floor(s / 2));
+  const padExtraEnd = padExtraShape.map((s, i) => s - padExtraStart[i]);
+  return padExtraShape.map((_, i) => {
+    return [padExtraStart[i], padExtraEnd[i]];
   });
 }
 var pool = op({ pool_ });
@@ -12621,7 +12621,7 @@ function raggedTensorToTensor_(shape, values, defaultValue, rowPartitionTensors,
   const $shape = convertToTensor(shape, "shape", "raggedTensorToTensor", "int32");
   const $values = convertToTensor(values, "values", "raggedTensorToTensor");
   const $defaultValue = convertToTensor(defaultValue, "defaultValue", "raggedTensorToTensor", $values.dtype);
-  const $rowPartitionTensors = rowPartitionTensors.map((t2, i2) => convertToTensor(t2, `tensors${i2}`, "raggedTensorToTensor", "int32"));
+  const $rowPartitionTensors = rowPartitionTensors.map((t, i) => convertToTensor(t, `tensors${i}`, "raggedTensorToTensor", "int32"));
   const inputs = {
     shape: $shape,
     values: $values,
@@ -12646,8 +12646,8 @@ function rand_(shape, randFunction, dtype) {
   } else {
     throw new Error(`Unknown data type ${dtype}`);
   }
-  for (let i2 = 0; i2 < size; i2++) {
-    values[i2] = randFunction();
+  for (let i = 0; i < size; i++) {
+    values[i] = randFunction();
   }
   return ENGINE.makeTensor(values, shape, dtype);
 }
@@ -12678,13 +12678,13 @@ var MPRandGauss = class {
     let resultX, resultY;
     let isValid = false;
     while (!isValid) {
-      let v1, v2, s2;
+      let v1, v2, s;
       do {
         v1 = 2 * this.random() - 1;
         v2 = 2 * this.random() - 1;
-        s2 = v1 * v1 + v2 * v2;
-      } while (s2 >= 1 || s2 === 0);
-      const mul2 = Math.sqrt(-2 * Math.log(s2) / s2);
+        s = v1 * v1 + v2 * v2;
+      } while (s >= 1 || s === 0);
+      const mul2 = Math.sqrt(-2 * Math.log(s) / s);
       resultX = this.mean + this.stdDev * v1 * mul2;
       resultY = this.mean + this.stdDev * v2 * mul2;
       if (!this.truncated || this.isValidTruncated(resultX)) {
@@ -12791,8 +12791,8 @@ function randomGamma_(shape, alpha, beta = 1, dtype = "float32", seed) {
   }
   const rgamma = new RandGamma(alpha, beta, dtype, seed);
   const res = buffer(shape, dtype);
-  for (let i2 = 0; i2 < res.values.length; i2++) {
-    res.values[i2] = rgamma.nextValue();
+  for (let i = 0; i < res.values.length; i++) {
+    res.values[i] = rgamma.nextValue();
   }
   return res.toTensor();
 }
@@ -12805,8 +12805,8 @@ function randomNormal_(shape, mean5 = 0, stdDev = 1, dtype, seed) {
   }
   const randGauss = new MPRandGauss(mean5, stdDev, dtype, false, seed);
   const res = buffer(shape, dtype);
-  for (let i2 = 0; i2 < res.values.length; i2++) {
-    res.values[i2] = randGauss.nextValue();
+  for (let i = 0; i < res.values.length; i++) {
+    res.values[i] = randGauss.nextValue();
   }
   return res.toTensor();
 }
@@ -12825,8 +12825,8 @@ var randomStandardNormal = op({ randomStandardNormal_ });
 function randomUniform_(shape, minval = 0, maxval = 1, dtype = "float32", seed) {
   const res = buffer(shape, dtype);
   const random = new UniformRandom(minval, maxval, null, seed);
-  for (let i2 = 0; i2 < res.values.length; i2++) {
-    res.values[i2] = random.nextValue();
+  for (let i = 0; i < res.values.length; i++) {
+    res.values[i] = random.nextValue();
   }
   return res.toTensor();
 }
@@ -12973,17 +12973,17 @@ async function setdiff1dAsync_(x, y) {
   const yVals = await $y.data();
   const ySet = new Set(yVals);
   let outputSize = 0;
-  for (let i2 = 0; i2 < xVals.length; i2++) {
-    if (!ySet.has(xVals[i2])) {
+  for (let i = 0; i < xVals.length; i++) {
+    if (!ySet.has(xVals[i])) {
       outputSize++;
     }
   }
   const buffer2 = new TensorBuffer([outputSize], $x.dtype);
   const indices = new TensorBuffer([outputSize], "int32");
-  for (let i2 = 0, p2 = 0; i2 < xVals.length; i2++) {
-    if (!ySet.has(xVals[i2])) {
-      buffer2.values[p2] = xVals[i2];
-      indices.values[p2] = i2;
+  for (let i = 0, p2 = 0; i < xVals.length; i++) {
+    if (!ySet.has(xVals[i])) {
+      buffer2.values[p2] = xVals[i];
+      indices.values[p2] = i;
       p2++;
     }
   }
@@ -13092,9 +13092,9 @@ function irfft_(input2) {
     const imagInput = reshape(imag(input2), [batch, innerDimensionSize]);
     const realConjugate = reverse(slice(realInput, [0, 1], [batch, innerDimensionSize - 2]), 1);
     const imagConjugate = mul(reverse(slice(imagInput, [0, 1], [batch, innerDimensionSize - 2]), 1), scalar(-1));
-    const r2 = concat([realInput, realConjugate], 1);
-    const i2 = concat([imagInput, imagConjugate], 1);
-    const complexInput = reshape(complex(r2, i2), [outputShape[0], outputShape[1]]);
+    const r = concat([realInput, realConjugate], 1);
+    const i = concat([imagInput, imagConjugate], 1);
+    const complexInput = reshape(complex(r, i), [outputShape[0], outputShape[1]]);
     ret = ifft(complexInput);
   }
   ret = real(ret);
@@ -13321,8 +13321,8 @@ function truncatedNormal_(shape, mean5 = 0, stdDev = 1, dtype, seed) {
   }
   const randGauss = new MPRandGauss(mean5, stdDev, dtype, true, seed);
   const res = buffer(shape, dtype);
-  for (let i2 = 0; i2 < res.values.length; i2++) {
-    res.values[i2] = randGauss.nextValue();
+  for (let i = 0; i < res.values.length; i++) {
+    res.values[i] = randGauss.nextValue();
   }
   return res.toTensor();
 }
@@ -13373,16 +13373,16 @@ function variable(initialValue, trainable = true, name, dtype) {
 // node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/backends/where_impl.js
 function whereImpl(condShape, condVals) {
   const indices = [];
-  for (let i2 = 0; i2 < condVals.length; i2++) {
-    if (condVals[i2]) {
-      indices.push(i2);
+  for (let i = 0; i < condVals.length; i++) {
+    if (condVals[i]) {
+      indices.push(i);
     }
   }
   const inBuffer = buffer(condShape, "int32");
   const out = buffer([indices.length, condShape.length], "int32");
-  for (let i2 = 0; i2 < indices.length; i2++) {
-    const loc = inBuffer.indexToLoc(indices[i2]);
-    const offset = i2 * condShape.length;
+  for (let i = 0; i < indices.length; i++) {
+    const loc = inBuffer.indexToLoc(indices[i]);
+    const offset = i * condShape.length;
     out.values.set(loc, offset);
   }
   return out.toTensor();
@@ -13410,8 +13410,8 @@ async function booleanMaskAsync_(tensor2, mask, axis) {
   assert(maskDim > 0, () => "mask cannot be scalar");
   assertShapesMatch(tensorShape.slice(axisFrom, axisFrom + maskDim), $mask.shape, `mask's shape must match the first K dimensions of tensor's shape,`);
   let leadingSize = 1;
-  for (let i2 = axisFrom; i2 < axisFrom + maskDim; i2++) {
-    leadingSize *= tensorShape[i2];
+  for (let i = axisFrom; i < axisFrom + maskDim; i++) {
+    leadingSize *= tensorShape[i];
   }
   const targetTensorShape = tensorShape.slice(0, axisFrom).concat([leadingSize], tensorShape.slice(axisFrom + maskDim));
   const reshapedTensor = reshape($tensor, targetTensorShape);
@@ -13520,11 +13520,11 @@ function getNoiseShape(x, noiseShape) {
   }
   if (x.shape.length === noiseShape.length) {
     const newDimension = [];
-    for (let i2 = 0; i2 < x.shape.length; i2++) {
-      if (noiseShape[i2] == null && x.shape[i2] != null) {
-        newDimension.push(x.shape[i2]);
+    for (let i = 0; i < x.shape.length; i++) {
+      if (noiseShape[i] == null && x.shape[i] != null) {
+        newDimension.push(x.shape[i]);
       } else {
-        newDimension.push(noiseShape[i2]);
+        newDimension.push(noiseShape[i]);
       }
     }
     return newDimension;
@@ -13554,9 +13554,9 @@ function enclosingPowerOfTwo(value) {
 function cosineWindow(windowLength, a, b) {
   const even = 1 - windowLength % 2;
   const newValues = new Float32Array(windowLength);
-  for (let i2 = 0; i2 < windowLength; ++i2) {
-    const cosArg = 2 * Math.PI * i2 / (windowLength + even - 1);
-    newValues[i2] = a - b * Math.cos(cosArg);
+  for (let i = 0; i < windowLength; ++i) {
+    const cosArg = 2 * Math.PI * i / (windowLength + even - 1);
+    newValues[i] = a - b * Math.cos(cosArg);
   }
   return tensor1d(newValues, "float32");
 }
@@ -13578,13 +13578,13 @@ async function inTopKAsync_(predictions, targets, k = 1) {
     const offset = b * size;
     const vals = predictionsVals.subarray(offset, offset + size);
     const valAndInd = [];
-    for (let i2 = 0; i2 < vals.length; i2++) {
-      valAndInd.push({ value: vals[i2], index: i2 });
+    for (let i = 0; i < vals.length; i++) {
+      valAndInd.push({ value: vals[i], index: i });
     }
     valAndInd.sort((a, b2) => b2.value - a.value);
     precision3[b] = 0;
-    for (let i2 = 0; i2 < k; i2++) {
-      if (valAndInd[i2].index === targetsVals[b]) {
+    for (let i = 0; i < k; i++) {
+      if (valAndInd[i].index === targetsVals[b]) {
         precision3[b] = 1;
         break;
       }
@@ -13719,7 +13719,7 @@ function fusedConv2d_({ x, filter, strides, pad: pad3, dataFormat = "NHWC", dila
     } else if (alphaShape.length === 3) {
       try {
         assertAndGetBroadcastShape(alphaShape, convInfo.outShape);
-      } catch (e2) {
+      } catch (e) {
         const errMsg = `Error in fused conv2d: PReLU activation weights (${alphaShape}) is not compatible with the output shape of the conv2d (${convInfo.outShape}).`;
         throw Error(errMsg);
       }
@@ -14175,9 +14175,9 @@ function nonMaxSuppressionV5Impl(boxes, scores, maxOutputSize, iouThreshold, sco
 }
 function nonMaxSuppressionImpl_(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma, returnScoresTensor = false, padToMaxOutputSize = false, returnValidOutputs = false) {
   const candidates = [];
-  for (let i2 = 0; i2 < scores.length; i2++) {
-    if (scores[i2] > scoreThreshold) {
-      candidates.push({ score: scores[i2], boxIndex: i2, suppressBeginIndex: 0 });
+  for (let i = 0; i < scores.length; i++) {
+    if (scores[i] > scoreThreshold) {
+      candidates.push({ score: scores[i], boxIndex: i, suppressBeginIndex: 0 });
     }
   }
   candidates.sort(ascendingComparator);
@@ -14227,8 +14227,8 @@ function nonMaxSuppressionImpl_(boxes, scores, maxOutputSize, iouThreshold, scor
   }
   return result;
 }
-function intersectionOverUnion(boxes, i2, j) {
-  const iCoord = boxes.subarray(i2 * 4, i2 * 4 + 4);
+function intersectionOverUnion(boxes, i, j) {
+  const iCoord = boxes.subarray(i * 4, i * 4 + 4);
   const jCoord = boxes.subarray(j * 4, j * 4 + 4);
   const yminI = Math.min(iCoord[0], iCoord[2]);
   const xminI = Math.min(iCoord[1], iCoord[3]);
@@ -14420,14 +14420,14 @@ function threshold_(image2, method = "binary", inverted = false, threshValue = 0
   const BLUE_INTENCITY_COEF = 0.114;
   const totalPixelsInImage = $image.shape[0] * $image.shape[1];
   let $threshold = mul(tensor1d([threshValue]), 255);
-  let r2, g, b, grayscale;
+  let r, g, b, grayscale;
   assert($image.rank === 3, () => `Error in threshold: image must be rank 3,but got rank ${$image.rank}.`);
   assert($image.shape[2] === 3 || $image.shape[2] === 1, () => `Error in threshold: image color channel must be equal to 3 or 1but got ${$image.shape[2]}.`);
   assert($image.dtype === "int32" || $image.dtype === "float32", () => `Error in dtype: image dtype must be int32 or float32,but got dtype ${$image.dtype}.`);
   assert(method === "otsu" || method === "binary", () => `Method must be binary or otsu, but was ${method}`);
   if ($image.shape[2] === 3) {
-    [r2, g, b] = split($image, [1, 1, 1], -1);
-    const $r = mul(r2, RED_INTENCITY_COEF);
+    [r, g, b] = split($image, [1, 1, 1], -1);
+    const $r = mul(r, RED_INTENCITY_COEF);
     const $g = mul(g, GREEN_INTENCITY_COEF);
     const $b = mul(b, BLUE_INTENCITY_COEF);
     grayscale = add2(add2($r, $g), $b);
@@ -14503,9 +14503,9 @@ function bandPart_(a, numLower, numUpper) {
   if (numUpper < 0) {
     numUpper = N;
   }
-  const i2 = reshape(range(0, M, 1, "int32"), [-1, 1]);
+  const i = reshape(range(0, M, 1, "int32"), [-1, 1]);
   const j = range(0, N, 1, "int32");
-  const ij = sub(i2, j);
+  const ij = sub(i, j);
   const inBand = logicalAnd(lessEqual(ij, scalar(+numLower, "int32")), greaterEqual(ij, scalar(-numUpper, "int32")));
   const zero = zeros([M, N], $a.dtype);
   return reshape(stack(unstack(reshape($a, [-1, M, N])).map((mat) => where(inBand, mat, zero))), shape);
@@ -14519,8 +14519,8 @@ function gramSchmidt_(xs) {
     inputIsTensor2D = false;
     assert(xs != null && xs.length > 0, () => "Gram-Schmidt process: input must not be null, undefined, or empty");
     const dim = xs[0].shape[0];
-    for (let i2 = 1; i2 < xs.length; ++i2) {
-      assert(xs[i2].shape[0] === dim, () => `Gram-Schmidt: Non-unique lengths found in the input vectors: (${xs[i2].shape[0]} vs. ${dim})`);
+    for (let i = 1; i < xs.length; ++i) {
+      assert(xs[i].shape[0] === dim, () => `Gram-Schmidt: Non-unique lengths found in the input vectors: (${xs[i].shape[0]} vs. ${dim})`);
     }
   } else {
     inputIsTensor2D = true;
@@ -14529,11 +14529,11 @@ function gramSchmidt_(xs) {
   assert(xs.length <= xs[0].shape[0], () => `Gram-Schmidt: Number of vectors (${xs.length}) exceeds number of dimensions (${xs[0].shape[0]}).`);
   const ys = [];
   const xs1d = xs;
-  for (let i2 = 0; i2 < xs.length; ++i2) {
+  for (let i = 0; i < xs.length; ++i) {
     ys.push(ENGINE.tidy(() => {
-      let x = xs1d[i2];
-      if (i2 > 0) {
-        for (let j = 0; j < i2; ++j) {
+      let x = xs1d[i];
+      if (i > 0) {
+        for (let j = 0; j < i; ++j) {
           const proj = mul(sum2(mul(ys[j], x)), ys[j]);
           x = sub(x, proj);
         }
@@ -14569,30 +14569,30 @@ function qr_(x, fullMatrices = false) {
       r2ds.push(r2d);
     });
     const q = reshape(stack(q2ds, 0), x.shape);
-    const r2 = reshape(stack(r2ds, 0), x.shape);
-    return [q, r2];
+    const r = reshape(stack(r2ds, 0), x.shape);
+    return [q, r];
   }
 }
 function qr2d(x, fullMatrices = false) {
   return ENGINE.tidy(() => {
     assert(x.shape.length === 2, () => `qr2d() requires a 2D Tensor, but got a ${x.shape.length}D Tensor.`);
     const m = x.shape[0];
-    const n2 = x.shape[1];
+    const n = x.shape[1];
     let q = eye(m);
-    let r2 = clone(x);
+    let r = clone(x);
     const one2D = tensor2d([[1]], [1, 1]);
     let w = clone(one2D);
-    const iters = m >= n2 ? n2 : m;
+    const iters = m >= n ? n : m;
     for (let j = 0; j < iters; ++j) {
-      const rTemp = r2;
+      const rTemp = r;
       const wTemp = w;
       const qTemp = q;
-      [w, r2, q] = ENGINE.tidy(() => {
-        const rjEnd1 = slice(r2, [j, j], [m - j, 1]);
+      [w, r, q] = ENGINE.tidy(() => {
+        const rjEnd1 = slice(r, [j, j], [m - j, 1]);
         const normX = norm(rjEnd1);
-        const rjj = slice(r2, [j, j], [1, 1]);
-        const s2 = where(greater(rjj, 0), tensor2d([[-1]]), tensor2d([[1]]));
-        const u1 = sub(rjj, mul(s2, normX));
+        const rjj = slice(r, [j, j], [1, 1]);
+        const s = where(greater(rjj, 0), tensor2d([[-1]]), tensor2d([[1]]));
+        const u1 = sub(rjj, mul(s, normX));
         const wPre = div(rjEnd1, u1);
         if (wPre.shape[0] === 1) {
           w = clone(one2D);
@@ -14602,15 +14602,15 @@ function qr2d(x, fullMatrices = false) {
             slice(wPre, [1, 0], [wPre.shape[0] - 1, wPre.shape[1]])
           ], 0);
         }
-        const tau = neg(div(matMul(s2, u1), normX));
-        const rjEndAll = slice(r2, [j, 0], [m - j, n2]);
+        const tau = neg(div(matMul(s, u1), normX));
+        const rjEndAll = slice(r, [j, 0], [m - j, n]);
         const tauTimesW = mul(tau, w);
         const wT = transpose(w);
         if (j === 0) {
-          r2 = sub(rjEndAll, matMul(tauTimesW, matMul(wT, rjEndAll)));
+          r = sub(rjEndAll, matMul(tauTimesW, matMul(wT, rjEndAll)));
         } else {
           const rTimesTau = sub(rjEndAll, matMul(tauTimesW, matMul(wT, rjEndAll)));
-          r2 = concat([slice(r2, [0, 0], [j, n2]), rTimesTau], 0);
+          r = concat([slice(r, [0, 0], [j, n]), rTimesTau], 0);
         }
         const tawTimesWT = transpose(tauTimesW);
         const qAllJEnd = slice(q, [0, j], [m, q.shape[1] - j]);
@@ -14620,15 +14620,15 @@ function qr2d(x, fullMatrices = false) {
           const qTimesTau = sub(qAllJEnd, matMul(matMul(qAllJEnd, w), tawTimesWT));
           q = concat([slice(q, [0, 0], [m, j]), qTimesTau], 1);
         }
-        return [w, r2, q];
+        return [w, r, q];
       });
       dispose([rTemp, wTemp, qTemp]);
     }
-    if (!fullMatrices && m > n2) {
-      q = slice(q, [0, 0], [m, n2]);
-      r2 = slice(r2, [0, 0], [n2, n2]);
+    if (!fullMatrices && m > n) {
+      q = slice(q, [0, 0], [m, n]);
+      r = slice(r, [0, 0], [n, n]);
     }
-    return [q, r2];
+    return [q, r];
   });
 }
 var qr = op({ qr_ });
@@ -15147,27 +15147,27 @@ var AdadeltaOptimizer = class extends Optimizer {
   }
   applyGradients(variableGradients) {
     const variableNames = Array.isArray(variableGradients) ? variableGradients.map((item) => item.name) : Object.keys(variableGradients);
-    variableNames.forEach((name, i2) => {
+    variableNames.forEach((name, i) => {
       const value = ENGINE.registeredVariables[name];
       const trainable = false;
-      if (this.accumulatedGrads[i2] == null) {
-        this.accumulatedGrads[i2] = {
+      if (this.accumulatedGrads[i] == null) {
+        this.accumulatedGrads[i] = {
           originalName: `${name}/accum_grad`,
           variable: tidy(() => zerosLike(value).variable(trainable))
         };
       }
-      if (this.accumulatedUpdates[i2] == null) {
-        this.accumulatedUpdates[i2] = {
+      if (this.accumulatedUpdates[i] == null) {
+        this.accumulatedUpdates[i] = {
           originalName: `${name}/accum_var`,
           variable: tidy(() => zerosLike(value).variable(trainable))
         };
       }
-      const gradient = Array.isArray(variableGradients) ? variableGradients[i2].tensor : variableGradients[name];
+      const gradient = Array.isArray(variableGradients) ? variableGradients[i].tensor : variableGradients[name];
       if (gradient == null) {
         return;
       }
-      const accumulatedGrad = this.accumulatedGrads[i2].variable;
-      const accumulatedUpdate = this.accumulatedUpdates[i2].variable;
+      const accumulatedGrad = this.accumulatedGrads[i].variable;
+      const accumulatedUpdate = this.accumulatedUpdates[i].variable;
       tidy(() => {
         const newAccumulatedGrad = add2(mul(accumulatedGrad, this.rho), mul(square(gradient), 1 - this.rho));
         const updates = mul(div(sqrt(add2(accumulatedUpdate, this.epsilon)), sqrt(add2(accumulatedGrad, this.epsilon))), gradient);
@@ -15227,20 +15227,20 @@ var AdagradOptimizer = class extends Optimizer {
   }
   applyGradients(variableGradients) {
     const variableNames = Array.isArray(variableGradients) ? variableGradients.map((item) => item.name) : Object.keys(variableGradients);
-    variableNames.forEach((name, i2) => {
+    variableNames.forEach((name, i) => {
       const value = ENGINE.registeredVariables[name];
-      if (this.accumulatedGrads[i2] == null) {
+      if (this.accumulatedGrads[i] == null) {
         const trainable = false;
-        this.accumulatedGrads[i2] = {
+        this.accumulatedGrads[i] = {
           originalName: `${name}/accumulator`,
           variable: tidy(() => fill(value.shape, this.initialAccumulatorValue).variable(trainable))
         };
       }
-      const gradient = Array.isArray(variableGradients) ? variableGradients[i2].tensor : variableGradients[name];
+      const gradient = Array.isArray(variableGradients) ? variableGradients[i].tensor : variableGradients[name];
       if (gradient == null) {
         return;
       }
-      const accumulatedGrad = this.accumulatedGrads[i2].variable;
+      const accumulatedGrad = this.accumulatedGrads[i].variable;
       tidy(() => {
         const newAccumulatedGrad = add2(accumulatedGrad, square(gradient));
         accumulatedGrad.assign(newAccumulatedGrad);
@@ -15299,27 +15299,27 @@ var AdamOptimizer = class extends Optimizer {
     tidy(() => {
       const oneMinusAccBeta1 = sub(1, this.accBeta1);
       const oneMinusAccBeta2 = sub(1, this.accBeta2);
-      varNames.forEach((name, i2) => {
+      varNames.forEach((name, i) => {
         const value = ENGINE.registeredVariables[name];
         const trainable = false;
-        if (this.accumulatedFirstMoment[i2] == null) {
-          this.accumulatedFirstMoment[i2] = {
+        if (this.accumulatedFirstMoment[i] == null) {
+          this.accumulatedFirstMoment[i] = {
             originalName: `${name}/m`,
             variable: tidy(() => zerosLike(value).variable(trainable))
           };
         }
-        if (this.accumulatedSecondMoment[i2] == null) {
-          this.accumulatedSecondMoment[i2] = {
+        if (this.accumulatedSecondMoment[i] == null) {
+          this.accumulatedSecondMoment[i] = {
             originalName: `${name}/v`,
             variable: tidy(() => zerosLike(value).variable(trainable))
           };
         }
-        const gradient = Array.isArray(variableGradients) ? variableGradients[i2].tensor : variableGradients[name];
+        const gradient = Array.isArray(variableGradients) ? variableGradients[i].tensor : variableGradients[name];
         if (gradient == null) {
           return;
         }
-        const firstMoment = this.accumulatedFirstMoment[i2].variable;
-        const secondMoment = this.accumulatedSecondMoment[i2].variable;
+        const firstMoment = this.accumulatedFirstMoment[i].variable;
+        const secondMoment = this.accumulatedSecondMoment[i].variable;
         const newFirstMoment = add2(mul(firstMoment, this.beta1), mul(gradient, 1 - this.beta1));
         const newSecondMoment = add2(mul(secondMoment, this.beta2), mul(square(gradient), 1 - this.beta2));
         const biasCorrectedFirstMoment = div(newFirstMoment, oneMinusAccBeta1);
@@ -15404,27 +15404,27 @@ var AdamaxOptimizer = class extends Optimizer {
     tidy(() => {
       const oneMinusAccBeta1 = sub(1, this.accBeta1);
       const lr = div(-this.learningRate, add2(mul(this.iteration, this.decay), 1));
-      variableNames.forEach((name, i2) => {
+      variableNames.forEach((name, i) => {
         const value = ENGINE.registeredVariables[name];
         const trainable = false;
-        if (this.accumulatedFirstMoment[i2] == null) {
-          this.accumulatedFirstMoment[i2] = {
+        if (this.accumulatedFirstMoment[i] == null) {
+          this.accumulatedFirstMoment[i] = {
             originalName: `${name}/m`,
             variable: zerosLike(value).variable(trainable)
           };
         }
-        if (this.accumulatedWeightedInfNorm[i2] == null) {
-          this.accumulatedWeightedInfNorm[i2] = {
+        if (this.accumulatedWeightedInfNorm[i] == null) {
+          this.accumulatedWeightedInfNorm[i] = {
             originalName: `${name}/v`,
             variable: zerosLike(value).variable(trainable)
           };
         }
-        const gradient = Array.isArray(variableGradients) ? variableGradients[i2].tensor : variableGradients[name];
+        const gradient = Array.isArray(variableGradients) ? variableGradients[i].tensor : variableGradients[name];
         if (gradient == null) {
           return;
         }
-        const firstMoment = this.accumulatedFirstMoment[i2].variable;
-        const weightedInfNorm = this.accumulatedWeightedInfNorm[i2].variable;
+        const firstMoment = this.accumulatedFirstMoment[i].variable;
+        const weightedInfNorm = this.accumulatedWeightedInfNorm[i].variable;
         const newFirstMoment = add2(mul(firstMoment, this.beta1), mul(gradient, 1 - this.beta1));
         const ut0 = mul(weightedInfNorm, this.beta2);
         const ut1 = abs(gradient);
@@ -15480,8 +15480,8 @@ var SGDOptimizer = class extends Optimizer {
   }
   applyGradients(variableGradients) {
     const varNames = Array.isArray(variableGradients) ? variableGradients.map((v) => v.name) : Object.keys(variableGradients);
-    varNames.forEach((name, i2) => {
-      const gradient = Array.isArray(variableGradients) ? variableGradients[i2].tensor : variableGradients[name];
+    varNames.forEach((name, i) => {
+      const gradient = Array.isArray(variableGradients) ? variableGradients[i].tensor : variableGradients[name];
       if (gradient == null) {
         return;
       }
@@ -15534,17 +15534,17 @@ var MomentumOptimizer = class extends SGDOptimizer {
   }
   applyGradients(variableGradients) {
     const variableNames = Array.isArray(variableGradients) ? variableGradients.map((item) => item.name) : Object.keys(variableGradients);
-    variableNames.forEach((name, i2) => {
+    variableNames.forEach((name, i) => {
       const value = ENGINE.registeredVariables[name];
-      if (this.accumulations[i2] == null) {
+      if (this.accumulations[i] == null) {
         const trainable = false;
-        this.accumulations[i2] = {
+        this.accumulations[i] = {
           originalName: `${name}/momentum`,
           variable: tidy(() => zerosLike(value).variable(trainable))
         };
       }
-      const accumulation = this.accumulations[i2].variable;
-      const gradient = Array.isArray(variableGradients) ? variableGradients[i2].tensor : variableGradients[name];
+      const accumulation = this.accumulations[i].variable;
+      const gradient = Array.isArray(variableGradients) ? variableGradients[i].tensor : variableGradients[name];
       if (gradient == null) {
         return;
       }
@@ -15614,37 +15614,37 @@ var RMSPropOptimizer = class extends Optimizer {
   }
   applyGradients(variableGradients) {
     const variableNames = Array.isArray(variableGradients) ? variableGradients.map((item) => item.name) : Object.keys(variableGradients);
-    variableNames.forEach((name, i2) => {
+    variableNames.forEach((name, i) => {
       const value = ENGINE.registeredVariables[name];
       const trainable = false;
-      if (this.accumulatedMeanSquares[i2] == null) {
-        this.accumulatedMeanSquares[i2] = {
+      if (this.accumulatedMeanSquares[i] == null) {
+        this.accumulatedMeanSquares[i] = {
           originalName: `${name}/rms`,
           variable: tidy(() => zerosLike(value).variable(trainable))
         };
       }
-      if (this.accumulatedMoments[i2] == null) {
-        this.accumulatedMoments[i2] = {
+      if (this.accumulatedMoments[i] == null) {
+        this.accumulatedMoments[i] = {
           originalName: `${name}/momentum`,
           variable: tidy(() => zerosLike(value).variable(trainable))
         };
       }
-      if (this.accumulatedMeanGrads[i2] == null && this.centered) {
-        this.accumulatedMeanGrads[i2] = {
+      if (this.accumulatedMeanGrads[i] == null && this.centered) {
+        this.accumulatedMeanGrads[i] = {
           originalName: `${name}/mg`,
           variable: tidy(() => zerosLike(value).variable(trainable))
         };
       }
-      const gradient = Array.isArray(variableGradients) ? variableGradients[i2].tensor : variableGradients[name];
+      const gradient = Array.isArray(variableGradients) ? variableGradients[i].tensor : variableGradients[name];
       if (gradient == null) {
         return;
       }
-      const accumulatedMeanSquare = this.accumulatedMeanSquares[i2].variable;
-      const accumulatedMoments = this.accumulatedMoments[i2].variable;
+      const accumulatedMeanSquare = this.accumulatedMeanSquares[i].variable;
+      const accumulatedMoments = this.accumulatedMoments[i].variable;
       tidy(() => {
         const newAccumulatedMeanSquare = add2(mul(accumulatedMeanSquare, this.decay), mul(square(gradient), 1 - this.decay));
         if (this.centered) {
-          const accumulatedMeanGrad = this.accumulatedMeanGrads[i2].variable;
+          const accumulatedMeanGrad = this.accumulatedMeanGrads[i].variable;
           const newAccumulatedMeanGrad = add2(mul(accumulatedMeanGrad, this.decay), mul(gradient, 1 - this.decay));
           const gradContribution = div(mul(gradient, this.learningRate), sqrt(sub(newAccumulatedMeanSquare, add2(square(newAccumulatedMeanGrad), this.epsilon))));
           const newAccumulatedMoments = add2(mul(accumulatedMoments, this.momentum), gradContribution);
@@ -15860,21 +15860,21 @@ __export(backend_util_exports, {
 // node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/ops/concat_util.js
 function assertParamsConsistent(shapes, axis) {
   const rank = shapes[0].length;
-  shapes.forEach((shape, i2) => {
-    assert(shape.length === rank, () => `Error in concat${rank}D: rank of tensors[${i2}] must be the same as the rank of the rest (${rank})`);
+  shapes.forEach((shape, i) => {
+    assert(shape.length === rank, () => `Error in concat${rank}D: rank of tensors[${i}] must be the same as the rank of the rest (${rank})`);
   });
   assert(axis >= 0 && axis < rank, () => `Error in concat${rank}D: axis must be between 0 and ${rank - 1}.`);
   const firstShape = shapes[0];
-  shapes.forEach((shape, i2) => {
-    for (let r2 = 0; r2 < rank; r2++) {
-      assert(r2 === axis || shape[r2] === firstShape[r2], () => `Error in concat${rank}D: Shape of tensors[${i2}] (${shape}) does not match the shape of the rest (${firstShape}) along the non-concatenated axis ${i2}.`);
+  shapes.forEach((shape, i) => {
+    for (let r = 0; r < rank; r++) {
+      assert(r === axis || shape[r] === firstShape[r], () => `Error in concat${rank}D: Shape of tensors[${i}] (${shape}) does not match the shape of the rest (${firstShape}) along the non-concatenated axis ${i}.`);
     }
   });
 }
 function computeOutShape2(shapes, axis) {
   const outputShape = shapes[0].slice();
-  for (let i2 = 1; i2 < shapes.length; i2++) {
-    outputShape[axis] += shapes[i2][axis];
+  for (let i = 1; i < shapes.length; i++) {
+    outputShape[axis] += shapes[i][axis];
   }
   return outputShape;
 }
@@ -15907,14 +15907,14 @@ function combineRaggedTensorToTensorShapes(raggedRank, shape, valueShape) {
   if (raggedRank + valueShape.length !== outputShape.length) {
     throw new Error(`rt input.shape and shape=${shape} are incompatible: rt input.rank = ${raggedRank + valueShape.length}, but shape.rank = ${outputShape.length}`);
   }
-  for (let i2 = 1; i2 < valueShape.length; ++i2) {
-    const valueDim = valueShape[i2];
-    const outputShapeDimIndex = outputShape[outputShape.length - valueShape.length + i2];
+  for (let i = 1; i < valueShape.length; ++i) {
+    const valueDim = valueShape[i];
+    const outputShapeDimIndex = outputShape[outputShape.length - valueShape.length + i];
     const outputShapeDim = outputShape[outputShapeDimIndex];
     if (valueDim >= 0) {
       if (outputShapeDim >= 0) {
         if (outputShapeDim !== valueDim) {
-          throw new Error(`rt input.shape and shape=${shape} are incompatible: rt input.shape[${i2 + raggedRank}] = ${valueDim} but shape[${i2 + raggedRank}] = ${outputShapeDim}`);
+          throw new Error(`rt input.shape and shape=${shape} are incompatible: rt input.shape[${i + raggedRank}] = ${valueDim} but shape[${i + raggedRank}] = ${outputShapeDim}`);
         }
       } else {
         outputShape[outputShapeDimIndex] = valueDim;
@@ -15960,11 +15960,11 @@ function validateDefaultValueShape(defaultValueShape, valueShape) {
   if (defaultNDims >= valuesNDims) {
     throw new Error(`defaultValue.shape=${defaultValueShape} and ragged tensor flatValues.shape=${valueShape}, are incompatible: defaultValue.rank = ${defaultNDims} must be less than ragged tensor input flatValues.rank = ${valuesNDims})`);
   }
-  for (let i2 = 0; i2 < Math.min(defaultNDims, valuesNDims - 1); ++i2) {
-    const defaultDim = defaultValueShape[i2];
-    const valueDim = valueShape[i2 + 1];
+  for (let i = 0; i < Math.min(defaultNDims, valuesNDims - 1); ++i) {
+    const defaultDim = defaultValueShape[i];
+    const valueDim = valueShape[i + 1];
     if (defaultDim >= 0 && valueDim >= 0 && defaultDim !== 1 && defaultDim !== valueDim) {
-      throw new Error(`defaultValue.shape=${defaultValueShape}, and ragged tensor input flatValues.shape=${valueShape} are incompatible: defaultValue.shape[${i2 - defaultValueShape.length}] = ${defaultDim} but ragged tensor input.flatValues.shape[${i2 - defaultValueShape.length}] = ${valueDim}`);
+      throw new Error(`defaultValue.shape=${defaultValueShape}, and ragged tensor input flatValues.shape=${valueShape} are incompatible: defaultValue.shape[${i - defaultValueShape.length}] = ${defaultDim} but ragged tensor input.flatValues.shape[${i - defaultValueShape.length}] = ${valueDim}`);
     }
   }
 }
@@ -15995,8 +15995,8 @@ function getReshaped(inputShape, blockShape, prod6, batchToSpace = true) {
   } else {
     reshaped = reshaped.concat(inputShape[0]);
     const spatialLength = blockShape.length;
-    for (let i2 = 0; i2 < spatialLength; ++i2) {
-      reshaped = reshaped.concat([inputShape[i2 + 1] / blockShape[i2], blockShape[i2]]);
+    for (let i = 0; i < spatialLength; ++i) {
+      reshaped = reshaped.concat([inputShape[i + 1] / blockShape[i], blockShape[i]]);
     }
     reshaped = reshaped.concat(inputShape.slice(spatialLength + 1));
   }
@@ -16006,22 +16006,22 @@ function getPermuted(reshapedRank, blockShapeRank, batchToSpace = true) {
   const permuted = [];
   if (batchToSpace) {
     permuted.push(blockShapeRank);
-    for (let i2 = blockShapeRank + 1; i2 < reshapedRank; ++i2) {
-      if (i2 <= 2 * blockShapeRank) {
-        permuted.push(i2);
-        permuted.push(i2 - (blockShapeRank + 1));
+    for (let i = blockShapeRank + 1; i < reshapedRank; ++i) {
+      if (i <= 2 * blockShapeRank) {
+        permuted.push(i);
+        permuted.push(i - (blockShapeRank + 1));
       } else {
-        permuted.push(i2);
+        permuted.push(i);
       }
     }
   } else {
     const permutedBeforeBatch = [];
     const permutedAfterBatch = [];
-    for (let i2 = 1; i2 < reshapedRank; ++i2) {
-      if (i2 >= blockShapeRank * 2 + 1 || i2 % 2 === 1) {
-        permutedAfterBatch.push(i2);
+    for (let i = 1; i < reshapedRank; ++i) {
+      if (i >= blockShapeRank * 2 + 1 || i % 2 === 1) {
+        permutedAfterBatch.push(i);
       } else {
-        permutedBeforeBatch.push(i2);
+        permutedBeforeBatch.push(i);
       }
     }
     permuted.push(...permutedBeforeBatch);
@@ -16037,30 +16037,30 @@ function getReshapedPermuted(inputShape, blockShape, prod6, batchToSpace = true)
   } else {
     reshapedPermuted.push(inputShape[0] * prod6);
   }
-  for (let i2 = 1; i2 < inputShape.length; ++i2) {
-    if (i2 <= blockShape.length) {
+  for (let i = 1; i < inputShape.length; ++i) {
+    if (i <= blockShape.length) {
       if (batchToSpace) {
-        reshapedPermuted.push(blockShape[i2 - 1] * inputShape[i2]);
+        reshapedPermuted.push(blockShape[i - 1] * inputShape[i]);
       } else {
-        reshapedPermuted.push(inputShape[i2] / blockShape[i2 - 1]);
+        reshapedPermuted.push(inputShape[i] / blockShape[i - 1]);
       }
     } else {
-      reshapedPermuted.push(inputShape[i2]);
+      reshapedPermuted.push(inputShape[i]);
     }
   }
   return reshapedPermuted;
 }
 function getSliceBeginCoords(crops, blockShape) {
   const sliceBeginCoords = [0];
-  for (let i2 = 0; i2 < blockShape; ++i2) {
-    sliceBeginCoords.push(crops[i2][0]);
+  for (let i = 0; i < blockShape; ++i) {
+    sliceBeginCoords.push(crops[i][0]);
   }
   return sliceBeginCoords;
 }
 function getSliceSize(uncroppedShape, crops, blockShape) {
   const sliceSize = uncroppedShape.slice(0, 1);
-  for (let i2 = 0; i2 < blockShape; ++i2) {
-    sliceSize.push(uncroppedShape[i2 + 1] - crops[i2][0] - crops[i2][1]);
+  for (let i = 0; i < blockShape; ++i) {
+    sliceSize.push(uncroppedShape[i + 1] - crops[i][0] - crops[i][1]);
   }
   return sliceSize;
 }
@@ -16083,18 +16083,18 @@ function mergeRealAndImagArrays(real5, imag5) {
     throw new Error(`Cannot merge real and imag arrays of different lengths. real:${real5.length}, imag: ${imag5.length}.`);
   }
   const result = new Float32Array(real5.length * 2);
-  for (let i2 = 0; i2 < result.length; i2 += 2) {
-    result[i2] = real5[i2 / 2];
-    result[i2 + 1] = imag5[i2 / 2];
+  for (let i = 0; i < result.length; i += 2) {
+    result[i] = real5[i / 2];
+    result[i + 1] = imag5[i / 2];
   }
   return result;
 }
 function splitRealAndImagArrays(complex5) {
   const real5 = new Float32Array(complex5.length / 2);
   const imag5 = new Float32Array(complex5.length / 2);
-  for (let i2 = 0; i2 < complex5.length; i2 += 2) {
-    real5[i2 / 2] = complex5[i2];
-    imag5[i2 / 2] = complex5[i2 + 1];
+  for (let i = 0; i < complex5.length; i += 2) {
+    real5[i / 2] = complex5[i];
+    imag5[i / 2] = complex5[i + 1];
   }
   return { real: real5, imag: imag5 };
 }
@@ -16102,9 +16102,9 @@ function complexWithEvenIndex(complex5) {
   const len = Math.ceil(complex5.length / 4);
   const real5 = new Float32Array(len);
   const imag5 = new Float32Array(len);
-  for (let i2 = 0; i2 < complex5.length; i2 += 4) {
-    real5[Math.floor(i2 / 4)] = complex5[i2];
-    imag5[Math.floor(i2 / 4)] = complex5[i2 + 1];
+  for (let i = 0; i < complex5.length; i += 4) {
+    real5[Math.floor(i / 4)] = complex5[i];
+    imag5[Math.floor(i / 4)] = complex5[i + 1];
   }
   return { real: real5, imag: imag5 };
 }
@@ -16112,9 +16112,9 @@ function complexWithOddIndex(complex5) {
   const len = Math.floor(complex5.length / 4);
   const real5 = new Float32Array(len);
   const imag5 = new Float32Array(len);
-  for (let i2 = 2; i2 < complex5.length; i2 += 4) {
-    real5[Math.floor(i2 / 4)] = complex5[i2];
-    imag5[Math.floor(i2 / 4)] = complex5[i2 + 1];
+  for (let i = 2; i < complex5.length; i += 4) {
+    real5[Math.floor(i / 4)] = complex5[i];
+    imag5[Math.floor(i / 4)] = complex5[i + 1];
   }
   return { real: real5, imag: imag5 };
 }
@@ -16127,18 +16127,18 @@ function assignToTypedArray(data, real5, imag5, index) {
   data[index * 2] = real5;
   data[index * 2 + 1] = imag5;
 }
-function exponents(n2, inverse) {
-  const real5 = new Float32Array(n2 / 2);
-  const imag5 = new Float32Array(n2 / 2);
-  for (let i2 = 0; i2 < Math.ceil(n2 / 2); i2++) {
-    const x = (inverse ? 2 : -2) * Math.PI * (i2 / n2);
-    real5[i2] = Math.cos(x);
-    imag5[i2] = Math.sin(x);
+function exponents(n, inverse) {
+  const real5 = new Float32Array(n / 2);
+  const imag5 = new Float32Array(n / 2);
+  for (let i = 0; i < Math.ceil(n / 2); i++) {
+    const x = (inverse ? 2 : -2) * Math.PI * (i / n);
+    real5[i] = Math.cos(x);
+    imag5[i] = Math.sin(x);
   }
   return { real: real5, imag: imag5 };
 }
-function exponent(k, n2, inverse) {
-  const x = (inverse ? 2 : -2) * Math.PI * (k / n2);
+function exponent(k, n, inverse) {
+  const x = (inverse ? 2 : -2) * Math.PI * (k / n);
   const real5 = Math.cos(x);
   const imag5 = Math.sin(x);
   return { real: real5, imag: imag5 };
@@ -16168,8 +16168,8 @@ function decodeEinsumEquation(equation, numTensors) {
     throw new Error("Support for more than 2 input tensors is not implemented yet.");
   }
   const allDims = [];
-  for (let i2 = 0; i2 < outputString.length; ++i2) {
-    const dimName = outputString[i2];
+  for (let i = 0; i < outputString.length; ++i) {
+    const dimName = outputString[i];
     if (!inputTerms.some((inputTerm) => inputTerm.indexOf(dimName) !== -1)) {
       throw new Error(`Output subscripts contain the label ${dimName} not present in the input subscripts.`);
     }
@@ -16177,40 +16177,40 @@ function decodeEinsumEquation(equation, numTensors) {
       allDims.push(dimName);
     }
   }
-  for (let i2 = 0; i2 < inputString.length; ++i2) {
-    const dimName = inputString[i2];
+  for (let i = 0; i < inputString.length; ++i) {
+    const dimName = inputString[i];
     if (allDims.indexOf(dimName) === -1 && dimName !== COMMA) {
       allDims.push(dimName);
     }
   }
   const idDims = new Array(inputTerms.length);
-  for (let i2 = 0; i2 < numInputs; ++i2) {
-    if (new Set(inputTerms[i2].split("")).size !== inputTerms[i2].length) {
-      throw new Error(`Found duplicate axes in input component ${inputTerms[i2]}. Support for duplicate axes in input is not implemented yet.`);
+  for (let i = 0; i < numInputs; ++i) {
+    if (new Set(inputTerms[i].split("")).size !== inputTerms[i].length) {
+      throw new Error(`Found duplicate axes in input component ${inputTerms[i]}. Support for duplicate axes in input is not implemented yet.`);
     }
-    idDims[i2] = [];
-    for (let j = 0; j < inputTerms[i2].length; ++j) {
-      idDims[i2].push(allDims.indexOf(inputTerms[i2][j]));
+    idDims[i] = [];
+    for (let j = 0; j < inputTerms[i].length; ++j) {
+      idDims[i].push(allDims.indexOf(inputTerms[i][j]));
     }
   }
   const numDims = allDims.length;
   const numOutDims = outputString.length;
   const summedDims = [];
-  for (let i2 = numOutDims; i2 < numDims; ++i2) {
-    summedDims.push(i2);
+  for (let i = numOutDims; i < numDims; ++i) {
+    summedDims.push(i);
   }
   return { allDims, summedDims, idDims };
 }
 function getEinsumPermutation(nDims, idDims) {
   let permutationIndices = new Array(nDims);
   permutationIndices.fill(-1);
-  for (let i2 = 0; i2 < idDims.length; ++i2) {
-    permutationIndices[idDims[i2]] = i2;
+  for (let i = 0; i < idDims.length; ++i) {
+    permutationIndices[idDims[i]] = i;
   }
   const expandDims7 = [];
-  for (let i2 = 0; i2 < nDims; ++i2) {
-    if (permutationIndices[i2] === -1) {
-      expandDims7.push(i2);
+  for (let i = 0; i < nDims; ++i) {
+    if (permutationIndices[i] === -1) {
+      expandDims7.push(i);
     }
   }
   permutationIndices = permutationIndices.filter((d) => d !== -1);
@@ -16218,13 +16218,13 @@ function getEinsumPermutation(nDims, idDims) {
 }
 function checkEinsumDimSizes(nDims, idDims, tensors) {
   const dimSizes = new Array(nDims);
-  for (let i2 = 0; i2 < tensors.length; ++i2) {
-    const shape = tensors[i2].shape;
-    for (let j = 0; j < idDims[i2].length; ++j) {
-      if (dimSizes[idDims[i2][j]] === void 0) {
-        dimSizes[idDims[i2][j]] = shape[j];
+  for (let i = 0; i < tensors.length; ++i) {
+    const shape = tensors[i].shape;
+    for (let j = 0; j < idDims[i].length; ++j) {
+      if (dimSizes[idDims[i][j]] === void 0) {
+        dimSizes[idDims[i][j]] = shape[j];
       } else {
-        assert(dimSizes[idDims[i2][j]] === shape[j], () => `Expected dimension ${dimSizes[idDims[i2][j]]} at axis ${j} of input shaped ${JSON.stringify(shape)}, but got dimension ${shape[j]}`);
+        assert(dimSizes[idDims[i][j]] === shape[j], () => `Expected dimension ${dimSizes[idDims[i][j]]} at axis ${j} of input shaped ${JSON.stringify(shape)}, but got dimension ${shape[j]}`);
       }
     }
   }
@@ -16237,16 +16237,16 @@ function getEinsumComputePath(summedDims, idDims) {
     path.push(-1);
   }
   nSteps = summedDims.length + 1;
-  for (let i2 = 0; i2 < nSteps; ++i2) {
+  for (let i = 0; i < nSteps; ++i) {
     steps.push([]);
   }
   const computedTermIndices = [];
-  for (let i2 = 0; i2 < path.length; ++i2) {
-    const summedDim = path[i2];
+  for (let i = 0; i < path.length; ++i) {
+    const summedDim = path[i];
     const termIndices = findTermsWithDim(idDims, summedDim);
     for (const termIndex of termIndices) {
       if (computedTermIndices.indexOf(termIndex) === -1) {
-        steps[i2].push(termIndex);
+        steps[i].push(termIndex);
         computedTermIndices.push(termIndex);
       }
     }
@@ -16258,9 +16258,9 @@ function isIdentityPermutation(perm) {
 }
 function findTermsWithDim(idDims, dim) {
   const termIndices = [];
-  for (let i2 = 0; i2 < idDims.length; ++i2) {
-    if (idDims[i2].length === 0 || idDims[i2].indexOf(dim) !== -1 || dim === -1) {
-      termIndices.push(i2);
+  for (let i = 0; i < idDims.length; ++i) {
+    if (idDims[i].length === 0 || idDims[i].indexOf(dim) !== -1 || dim === -1) {
+      termIndices.push(i);
     }
   }
   return termIndices;
@@ -16394,9 +16394,9 @@ function collectGatherOpShapeInfo(x, indices, axis, batchDims) {
   if (axis < batchDims) {
     throw new Error(`batchDims (${batchDims}) must be less than or equal to axis (${axis}).`);
   }
-  for (let i2 = 0; i2 < batchDims; ++i2) {
-    if (x.shape[i2] !== indices.shape[i2]) {
-      throw new Error(`x.shape[${i2}]: ${x.shape[i2]} should be equal to indices.shape[${i2}]: ${indices.shape[i2]}.`);
+  for (let i = 0; i < batchDims; ++i) {
+    if (x.shape[i] !== indices.shape[i]) {
+      throw new Error(`x.shape[${i}]: ${x.shape[i]} should be equal to indices.shape[${i}]: ${indices.shape[i]}.`);
     }
   }
   const dimSize = x.shape[axis];
@@ -16404,20 +16404,20 @@ function collectGatherOpShapeInfo(x, indices, axis, batchDims) {
   let batchSize = 1;
   let outerSize = 1;
   let sliceSize = 1;
-  for (let i2 = 0; i2 < batchDims; ++i2) {
-    outputShape.push(x.shape[i2]);
-    batchSize *= x.shape[i2];
+  for (let i = 0; i < batchDims; ++i) {
+    outputShape.push(x.shape[i]);
+    batchSize *= x.shape[i];
   }
-  for (let i2 = batchDims; i2 < axis; i2++) {
-    outputShape.push(x.shape[i2]);
-    outerSize *= x.shape[i2];
+  for (let i = batchDims; i < axis; i++) {
+    outputShape.push(x.shape[i]);
+    outerSize *= x.shape[i];
   }
-  for (let i2 = batchDims; i2 < indicesRank; i2++) {
-    outputShape.push(indices.shape[i2]);
+  for (let i = batchDims; i < indicesRank; i++) {
+    outputShape.push(indices.shape[i]);
   }
-  for (let i2 = axis + 1; i2 < xRank; i2++) {
-    outputShape.push(x.shape[i2]);
-    sliceSize *= x.shape[i2];
+  for (let i = axis + 1; i < xRank; i++) {
+    outputShape.push(x.shape[i]);
+    sliceSize *= x.shape[i];
   }
   return { batchSize, sliceSize, outerSize, dimSize, outputShape };
 }
@@ -16431,7 +16431,7 @@ function fromUint8ToStringArray(vals) {
   }
 }
 function fromStringArrayToUint8(strings) {
-  return strings.map((s2) => encodeString(s2));
+  return strings.map((s) => encodeString(s));
 }
 
 // node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/dist/backends/kernel_impls.js
@@ -16517,8 +16517,8 @@ var addNGradConfig = {
   saveAllInputs: true,
   gradFunc: (dy, saved) => {
     const ders = {};
-    saved.forEach((_, i2) => {
-      ders[i2] = () => dy.clone();
+    saved.forEach((_, i) => {
+      ders[i] = () => dy.clone();
     });
     return ders;
   }
@@ -16746,17 +16746,17 @@ var broadcastToGradConfig = {
     const inputShape = broadCastToAttrs.inputShape;
     const outputShape = broadCastToAttrs.shape;
     const reps = Array.from(outputShape);
-    for (let i2 = inputShape.length - 1; i2 >= 0; i2--) {
-      if (inputShape[i2] === outputShape[i2]) {
-        reps[i2] = 1;
-      } else if (inputShape[i2] !== 1) {
+    for (let i = inputShape.length - 1; i >= 0; i--) {
+      if (inputShape[i] === outputShape[i]) {
+        reps[i] = 1;
+      } else if (inputShape[i] !== 1) {
         throw new Error(`broadcastTo(): [${inputShape}] cannot be broadcast to [${outputShape}].`);
       }
     }
     const axes = [];
-    for (let i2 = 0; i2 < reps.length; i2++) {
-      if (reps[i2] > 1) {
-        axes.push(i2);
+    for (let i = 0; i < reps.length; i++) {
+      if (reps[i] > 1) {
+        axes.push(i);
       }
     }
     return { x: () => sum2(dy, axes, true) };
@@ -16804,12 +16804,12 @@ var concatGradConfig = {
   kernelName: Concat,
   saveAllInputs: true,
   gradFunc: (dy, saved, attrs) => {
-    const shapes = saved.map((t2) => t2.shape);
+    const shapes = saved.map((t) => t.shape);
     const { axis } = attrs;
     const $axis = parseAxisParam(axis, saved[0].shape)[0];
-    const sizeSplits = shapes.map((s2) => s2[$axis]);
+    const sizeSplits = shapes.map((s) => s[$axis]);
     const derTensors = split(dy, sizeSplits, $axis);
-    return derTensors.map((t2) => () => t2);
+    return derTensors.map((t) => () => t);
   }
 };
 
@@ -17053,8 +17053,8 @@ var fusedBatchNormGradConfig = {
     const reductionAxes = getReductionAxes(mean5.shape, x.shape);
     const tileShape = [];
     if (mean5.rank === 1) {
-      for (let i2 = 0; i2 < x.shape.length - 1; ++i2) {
-        tileShape.push(x.shape[i2]);
+      for (let i = 0; i < x.shape.length - 1; ++i) {
+        tileShape.push(x.shape[i]);
       }
       tileShape.push(1);
     }
@@ -17140,16 +17140,16 @@ var gatherGradConfig = {
 };
 function arrayRange(start, stop) {
   const result = [];
-  for (let i2 = start; i2 < stop; ++i2) {
-    result.push(i2);
+  for (let i = start; i < stop; ++i) {
+    result.push(i);
   }
   return result;
 }
 function arrayConcat(arrays) {
   const result = [];
-  for (let i2 = 0; i2 < arrays.length; ++i2) {
-    for (let j = 0; j < arrays[i2].length; ++j) {
-      result.push(arrays[i2][j]);
+  for (let i = 0; i < arrays.length; ++i) {
+    for (let j = 0; j < arrays[i].length; ++j) {
+      result.push(arrays[i][j]);
     }
   }
   return result;
@@ -17554,7 +17554,7 @@ var packGradConfig = {
   gradFunc: (dy, saved, attrs) => {
     const { axis } = attrs;
     const derTensors = unstack(dy, axis);
-    return derTensors.map((t2) => () => t2);
+    return derTensors.map((t) => () => t);
   }
 };
 
@@ -17663,7 +17663,7 @@ var prodGradConfig = {
     const { axis } = attrs;
     let axisArr = [];
     if (axis === void 0 || axis === null) {
-      axisArr = x.shape.map((_, i2) => i2);
+      axisArr = x.shape.map((_, i) => i);
     } else if (typeof axis === "number") {
       axisArr = [axis];
     } else {
@@ -17875,8 +17875,8 @@ var sliceGradConfig = {
     const inputShape = x.shape;
     const [begin_, size_] = parseSliceParams(x, begin, size);
     const paddings = [];
-    for (let i2 = 0; i2 < dy.rank; i2++) {
-      paddings.push([begin_[i2], inputShape[i2] - begin_[i2] - size_[i2]]);
+    for (let i = 0; i < dy.rank; i++) {
+      paddings.push([begin_[i], inputShape[i] - begin_[i] - size_[i]]);
     }
     return { x: () => pad(dy, paddings) };
   }
@@ -18041,36 +18041,36 @@ var tileGradConfig = {
     const derX = () => {
       let xGrad = zerosLike(x);
       if (x.rank === 1) {
-        for (let i2 = 0; i2 < reps[0]; ++i2) {
-          xGrad = add2(xGrad, slice(dy, [i2 * x.shape[0]], [x.shape[0]]));
+        for (let i = 0; i < reps[0]; ++i) {
+          xGrad = add2(xGrad, slice(dy, [i * x.shape[0]], [x.shape[0]]));
         }
       } else if (x.rank === 2) {
-        for (let i2 = 0; i2 < reps[0]; ++i2) {
+        for (let i = 0; i < reps[0]; ++i) {
           for (let j = 0; j < reps[1]; ++j) {
-            xGrad = add2(xGrad, slice(dy, [i2 * x.shape[0], j * x.shape[1]], [
+            xGrad = add2(xGrad, slice(dy, [i * x.shape[0], j * x.shape[1]], [
               x.shape[0],
               x.shape[1]
             ]));
           }
         }
       } else if (x.rank === 3) {
-        for (let i2 = 0; i2 < reps[0]; ++i2) {
+        for (let i = 0; i < reps[0]; ++i) {
           for (let j = 0; j < reps[1]; ++j) {
             for (let k = 0; k < reps[2]; ++k) {
-              xGrad = add2(xGrad, slice(dy, [i2 * x.shape[0], j * x.shape[1], k * x.shape[2]], [x.shape[0], x.shape[1], x.shape[2]]));
+              xGrad = add2(xGrad, slice(dy, [i * x.shape[0], j * x.shape[1], k * x.shape[2]], [x.shape[0], x.shape[1], x.shape[2]]));
             }
           }
         }
       } else if (x.rank === 4) {
-        for (let i2 = 0; i2 < reps[0]; ++i2) {
+        for (let i = 0; i < reps[0]; ++i) {
           for (let j = 0; j < reps[1]; ++j) {
             for (let k = 0; k < reps[2]; ++k) {
-              for (let l3 = 0; l3 < reps[3]; ++l3) {
+              for (let l = 0; l < reps[3]; ++l) {
                 xGrad = add2(xGrad, slice(dy, [
-                  i2 * x.shape[0],
+                  i * x.shape[0],
                   j * x.shape[1],
                   k * x.shape[2],
-                  l3 * x.shape[3]
+                  l * x.shape[3]
                 ], [x.shape[0], x.shape[1], x.shape[2], x.shape[3]]));
               }
             }
@@ -18123,8 +18123,8 @@ function gatherDropNegatives(x, indices) {
   const gathered = gather(x, zeroClippedIndices);
   let isPositive = greaterEqual(indices, scalar(0, "int32"));
   const numIters = gathered.rank - isPositive.rank;
-  for (let i2 = 0; i2 < numIters; ++i2) {
-    isPositive = expandDims(isPositive, i2 + 1);
+  for (let i = 0; i < numIters; ++i) {
+    isPositive = expandDims(isPositive, i + 1);
   }
   isPositive = logicalAnd(isPositive, ones2(gathered.shape, "bool"));
   const zeroSlice = zerosLike(gathered);
@@ -19142,7 +19142,7 @@ var LruCache = class {
       throw new Error(`The maxEntries of LRU caches must be at least 0, but got ${maxEntries}.`);
     }
     if (this.maxEntries > maxEntries) {
-      for (let i2 = 0; i2 < this.maxEntries - maxEntries; i2++) {
+      for (let i = 0; i < this.maxEntries - maxEntries; i++) {
         const keyToDelete = this.cache.keys().next().value;
         this.cache.delete(keyToDelete);
       }
@@ -19155,7 +19155,7 @@ var LruCache = class {
 function pyListRepeat(value, numValues) {
   if (Array.isArray(value)) {
     let newArray = [];
-    for (let i2 = 0; i2 < numValues; i2++) {
+    for (let i = 0; i < numValues; i++) {
       newArray = newArray.concat(value);
     }
     return newArray;
@@ -19343,12 +19343,12 @@ function checkStringTypeUnionValue(values, label, value) {
 function checkArrayTypeAndLength(x, expectedType, minLength = 0, maxLength = Infinity) {
   assert2(minLength >= 0);
   assert2(maxLength >= minLength);
-  return Array.isArray(x) && x.length >= minLength && x.length <= maxLength && x.every((e2) => typeof e2 === expectedType);
+  return Array.isArray(x) && x.length >= minLength && x.length <= maxLength && x.every((e) => typeof e === expectedType);
 }
 function assertPositiveInteger(value, name) {
   if (Array.isArray(value)) {
     util_exports.assert(value.length > 0, () => `${name} is unexpectedly an empty array.`);
-    value.forEach((v, i2) => assertPositiveInteger(v, `element ${i2 + 1} of ${name}`));
+    value.forEach((v, i) => assertPositiveInteger(v, `element ${i + 1} of ${name}`));
   } else {
     util_exports.assert(Number.isInteger(value) && value > 0, () => `Expected ${name} to be a positive integer, but got ${formatAsFriendlyString(value)}.`);
   }
@@ -19434,9 +19434,9 @@ function nameScope(name, fn) {
     const val = fn();
     _nameScopeStack.pop();
     return val;
-  } catch (e2) {
+  } catch (e) {
     _nameScopeStack.pop();
-    throw e2;
+    throw e;
   }
 }
 function currentNameScopePrefix() {
@@ -19486,8 +19486,8 @@ function arrayProd(array2, begin, end) {
     end = array2.length;
   }
   let prod6 = 1;
-  for (let i2 = begin; i2 < end; ++i2) {
-    prod6 *= array2[i2];
+  for (let i = begin; i < end; ++i) {
+    prod6 *= array2[i];
   }
   return prod6;
 }
@@ -19496,8 +19496,8 @@ function min2(array2) {
     return Number.NaN;
   }
   let min7 = Number.POSITIVE_INFINITY;
-  for (let i2 = 0; i2 < array2.length; i2++) {
-    const value = array2[i2];
+  for (let i = 0; i < array2.length; i++) {
+    const value = array2[i];
     if (value < min7) {
       min7 = value;
     }
@@ -19509,8 +19509,8 @@ function max2(array2) {
     return Number.NaN;
   }
   let max7 = Number.NEGATIVE_INFINITY;
-  for (let i2 = 0; i2 < array2.length; i2++) {
-    const value = array2[i2];
+  for (let i = 0; i < array2.length; i++) {
+    const value = array2[i];
     if (value > max7) {
       max7 = value;
     }
@@ -19522,8 +19522,8 @@ function range2(begin, end) {
     throw new ValueError(`end (${end}) < begin (${begin}) is forbidden.`);
   }
   const out = [];
-  for (let i2 = begin; i2 < end; ++i2) {
-    out.push(i2);
+  for (let i = begin; i < end; ++i) {
+    out.push(i);
   }
   return out;
 }
@@ -19552,13 +19552,13 @@ function expandDims2(x, axis = -1) {
   outShape.splice(axis, 0, 1);
   return reshape(x, outShape);
 }
-function repeat(x, n2) {
+function repeat(x, n) {
   return tidy(() => {
     if (x.shape.length !== 2) {
       throw new ValueError(`repeat() expects a rank-2 tensor, but received a rank-${x.shape.length} tensor.`);
     }
     const y = expandDims2(x, 1);
-    return tile2(y, [1, n2, 1]);
+    return tile2(y, [1, n, 1]);
   });
 }
 function flatten2(x) {
@@ -19693,14 +19693,14 @@ function concatAlongFirstAxis(a, b) {
       throw new ValueError(`concatAlongFirstAxis() received an unsupported tensor rank: ${a.rank}`);
   }
 }
-function tile2(x, n2) {
-  if (!Array.isArray(n2)) {
-    n2 = [n2];
+function tile2(x, n) {
+  if (!Array.isArray(n)) {
+    n = [n];
   }
-  if (x.rank !== n2.length) {
-    throw new ValueError(`The length of input n (${n2.length}) does not match the number of dimensions in input x (${x.rank})`);
+  if (x.rank !== n.length) {
+    throw new ValueError(`The length of input n (${n.length}) does not match the number of dimensions in input x (${x.rank})`);
   }
-  return tile(x, n2);
+  return tile(x, n);
 }
 function randomNormal2(shape, mean5 = 0, stddev = 1, dtype, seed) {
   return randomNormal(shape, mean5, stddev, dtype, seed);
@@ -19735,13 +19735,13 @@ function dot2(a, b, activation2, bias) {
     const bLastDim = bShape.pop();
     const ySecondLastDim = bShape.pop();
     const yOtherDims = [...bShape, bLastDim];
-    const perm = Array.from({ length: b.rank }, (_, i2) => {
-      if (i2 === 0) {
+    const perm = Array.from({ length: b.rank }, (_, i) => {
+      if (i === 0) {
         return b.rank - 2;
-      } else if (i2 <= b.rank - 2) {
-        return i2 - 1;
+      } else if (i <= b.rank - 2) {
+        return i - 1;
       }
-      return i2;
+      return i;
     });
     b = reshape(transpose(b, perm), [ySecondLastDim, -1]);
     const outputShape = [...aFirstDims, ...yOtherDims];
@@ -20611,9 +20611,9 @@ var Layer = class extends serialization_exports.Serializable {
         }
       }
       if (spec.shape != null) {
-        for (let i2 = 0; i2 < spec.shape.length; ++i2) {
-          const specDim = spec.shape[i2];
-          const dim = x.shape[i2];
+        for (let i = 0; i < spec.shape.length; ++i) {
+          const specDim = spec.shape[i];
+          const dim = x.shape[i];
           if (specDim != null && dim != null) {
             if (specDim !== dim) {
               throw new ValueError(`Input ${inputIndex} is incompatible with layer ${this.name}: expected shape=${spec.shape}, found shape=${x.shape}.`);
@@ -20717,8 +20717,8 @@ var Layer = class extends serialization_exports.Serializable {
       console.warn(`The rank of the input tensor provided (shape: ${JSON.stringify(inputShape)}) does not match that of the batchInputShape (${JSON.stringify(this.batchInputShape)}) of the layer ${this.name}`);
     } else {
       let dimMismatch = false;
-      this.batchInputShape.forEach((dimension, i2) => {
-        if (dimension != null && inputShape[i2] != null && inputShape[i2] !== dimension) {
+      this.batchInputShape.forEach((dimension, i) => {
+        if (dimension != null && inputShape[i] != null && inputShape[i] !== dimension) {
           dimMismatch = true;
         }
       });
@@ -20772,10 +20772,10 @@ var Layer = class extends serialization_exports.Serializable {
       }
       const weightValueTuples = [];
       const paramValues = batchGetValue(params);
-      for (let i2 = 0; i2 < paramValues.length; ++i2) {
-        const pv = paramValues[i2];
-        const p2 = params[i2];
-        const w = weights[i2];
+      for (let i = 0; i < paramValues.length; ++i) {
+        const pv = paramValues[i];
+        const p2 = params[i];
+        const w = weights[i];
         if (!util_exports.arraysEqual(pv.shape, w.shape)) {
           throw new ValueError(`Layer weight shape ${pv.shape} not compatible with provided weight shape ${w.shape}`);
         }
@@ -20870,10 +20870,10 @@ var Layer = class extends serialization_exports.Serializable {
       inputShapes,
       outputShapes
     }, kwargs);
-    for (let i2 = 0; i2 < outputTensors.length; i2++) {
-      outputTensors[i2].sourceLayer = this;
-      outputTensors[i2].nodeIndex = this.inboundNodes.length - 1;
-      outputTensors[i2].tensorIndex = i2;
+    for (let i = 0; i < outputTensors.length; i++) {
+      outputTensors[i].sourceLayer = this;
+      outputTensors[i].nodeIndex = this.inboundNodes.length - 1;
+      outputTensors[i].tensorIndex = i;
     }
   }
   getConfig() {
@@ -20934,10 +20934,10 @@ function getSourceInputs(tensor2, layer, nodeIndex) {
       return node.inputTensors;
     } else {
       const sourceTensors = [];
-      for (let i2 = 0; i2 < node.inboundLayers.length; i2++) {
-        const x = node.inputTensors[i2];
-        const layer2 = node.inboundLayers[i2];
-        const nodeIndex2 = node.nodeIndices[i2];
+      for (let i = 0; i < node.inboundLayers.length; i++) {
+        const x = node.inputTensors[i];
+        const layer2 = node.inboundLayers[i];
+        const nodeIndex2 = node.nodeIndices[i];
         const previousSources = getSourceInputs(x, layer2, nodeIndex2);
         for (const x2 of previousSources) {
           if (sourceTensors.indexOf(x2) === -1) {
@@ -21146,7 +21146,7 @@ function execute(fetches, feedDict, kwargs, probe) {
   const training = kwargs == null ? false : kwargs["training"];
   const arrayFetches = Array.isArray(fetches);
   const fetchArray = arrayFetches ? fetches : [fetches];
-  const outputNames = fetchArray.map((t2) => t2.name);
+  const outputNames = fetchArray.map((t) => t.name);
   const finalOutputs = [];
   const feedNames = feedDict.names();
   for (const outputName of outputNames) {
@@ -21175,7 +21175,7 @@ function execute(fetches, feedDict, kwargs, probe) {
     Object.assign(recipientCounts, cachedRecipientCounts.get(fetchAndFeedKey));
   }
   const internalFeedDict = new FeedDict(feedDict);
-  for (let i2 = 0; i2 < sorted.length; ++i2) {
+  for (let i = 0; i < sorted.length; ++i) {
     if (probe != null) {
       const numTensors = memory().numTensors;
       if (numTensors > probe.maxNumTensors) {
@@ -21185,7 +21185,7 @@ function execute(fetches, feedDict, kwargs, probe) {
         probe.minNumTensors = numTensors;
       }
     }
-    const symbolic = sorted[i2];
+    const symbolic = sorted[i];
     const srcLayer = symbolic.sourceLayer;
     if (srcLayer instanceof InputLayer) {
       continue;
@@ -21220,13 +21220,13 @@ function execute(fetches, feedDict, kwargs, probe) {
     }
     const layerOutputs = getNodeOutputs(symbolic);
     const outputSymbolicTensors = Array.isArray(layerOutputs) ? layerOutputs : [layerOutputs];
-    for (let i3 = 0; i3 < outputSymbolicTensors.length; ++i3) {
-      if (!internalFeedDict.hasKey(outputSymbolicTensors[i3])) {
-        internalFeedDict.add(outputSymbolicTensors[i3], outputTensors[i3], Array.isArray(outputMask) ? outputMask[0] : outputMask);
+    for (let i2 = 0; i2 < outputSymbolicTensors.length; ++i2) {
+      if (!internalFeedDict.hasKey(outputSymbolicTensors[i2])) {
+        internalFeedDict.add(outputSymbolicTensors[i2], outputTensors[i2], Array.isArray(outputMask) ? outputMask[0] : outputMask);
       }
-      const index = outputNames.indexOf(outputSymbolicTensors[i3].name);
+      const index = outputNames.indexOf(outputSymbolicTensors[i2].name);
       if (index !== -1) {
-        finalOutputs[index] = outputTensors[i3];
+        finalOutputs[index] = outputTensors[i2];
       }
     }
     if (!training) {
@@ -21320,10 +21320,10 @@ function getNodeOutputs(fetch4) {
     layerOutputs = fetch4.sourceLayer.output;
   } else {
     let nodeIndex = null;
-    for (let i2 = 0; i2 < fetch4.sourceLayer.inboundNodes.length; ++i2) {
-      for (const outputTensor of fetch4.sourceLayer.inboundNodes[i2].outputTensors) {
+    for (let i = 0; i < fetch4.sourceLayer.inboundNodes.length; ++i) {
+      for (const outputTensor of fetch4.sourceLayer.inboundNodes[i].outputTensors) {
         if (outputTensor.id === fetch4.id) {
-          nodeIndex = i2;
+          nodeIndex = i;
           break;
         }
       }
@@ -21633,8 +21633,8 @@ async function resolveScalarsInLogs(logs) {
   }
   if (promises.length > 0) {
     const values = await Promise.all(promises);
-    for (let i2 = 0; i2 < values.length; ++i2) {
-      logs[keys[i2]] = values[i2][0];
+    for (let i = 0; i < values.length; ++i) {
+      logs[keys[i]] = values[i][0];
     }
     dispose(scalarsToDispose);
   }
@@ -21829,20 +21829,20 @@ var History = class extends BaseCallback {
     const indices = [];
     for (const key in this.history) {
       const valueArray = this.history[key];
-      for (let i2 = 0; i2 < valueArray.length; ++i2) {
-        if (typeof valueArray[i2] !== "number") {
-          const valueScalar = valueArray[i2];
+      for (let i = 0; i < valueArray.length; ++i) {
+        if (typeof valueArray[i] !== "number") {
+          const valueScalar = valueArray[i];
           promises.push(valueScalar.data());
           keys.push(key);
-          indices.push(i2);
+          indices.push(i);
         }
       }
     }
     const values = await Promise.all(promises);
-    for (let n2 = 0; n2 < values.length; ++n2) {
-      const tensorToDispose = this.history[keys[n2]][indices[n2]];
+    for (let n = 0; n < values.length; ++n) {
+      const tensorToDispose = this.history[keys[n]][indices[n]];
       tensorToDispose.dispose();
-      this.history[keys[n2]][indices[n2]] = values[n2][0];
+      this.history[keys[n]][indices[n]] = values[n][0];
     }
   }
 };
@@ -22369,13 +22369,13 @@ function printSummary(model2, lineLength, positions, printFn = console.log) {
   printRow(toDisplay, positions, printFn);
   printFn("=".repeat(lineLength));
   const layers = model2.layers;
-  for (let i2 = 0; i2 < layers.length; ++i2) {
+  for (let i = 0; i < layers.length; ++i) {
     if (sequentialLike) {
-      printLayerSummary(layers[i2], positions, printFn);
+      printLayerSummary(layers[i], positions, printFn);
     } else {
-      printLayerSummaryWithConnections(layers[i2], positions, relevantNodes, printFn);
+      printLayerSummaryWithConnections(layers[i], positions, relevantNodes, printFn);
     }
-    printFn((i2 === layers.length - 1 ? "=" : "_").repeat(lineLength));
+    printFn((i === layers.length - 1 ? "=" : "_").repeat(lineLength));
   }
   model2.checkTrainableWeightsConsistency();
   const trainableCount = countTrainableParams(model2);
@@ -22430,13 +22430,13 @@ function isModelSequentialLike(model2) {
 }
 function printRow(fields, positions, printFn = console.log) {
   let line = "";
-  for (let i2 = 0; i2 < fields.length; ++i2) {
-    if (i2 > 0) {
+  for (let i = 0; i < fields.length; ++i) {
+    if (i > 0) {
       line = line.slice(0, line.length - 1) + " ";
     }
-    line += fields[i2];
-    line = line.slice(0, positions[i2]);
-    line += " ".repeat(positions[i2] - line.length);
+    line += fields[i];
+    line = line.slice(0, positions[i]);
+    line += " ".repeat(positions[i] - line.length);
   }
   printFn(line);
 }
@@ -22481,10 +22481,10 @@ function printLayerSummaryWithConnections(layer, positions, relevantNodes, print
     if (relevantNodes != null && relevantNodes.length > 0 && relevantNodes.indexOf(node) === -1) {
       continue;
     }
-    for (let i2 = 0; i2 < node.inboundLayers.length; ++i2) {
-      const inboundLayer = node.inboundLayers[i2].name;
-      const inboundLayerIndex = node.nodeIndices[i2];
-      const inboundTensorIndex = node.tensorIndices[i2];
+    for (let i = 0; i < node.inboundLayers.length; ++i) {
+      const inboundLayer = node.inboundLayers[i].name;
+      const inboundLayerIndex = node.nodeIndices[i];
+      const inboundTensorIndex = node.tensorIndices[i];
       connections.push(`${inboundLayer}[${inboundLayerIndex}][${inboundTensorIndex}]`);
     }
   }
@@ -22499,8 +22499,8 @@ function printLayerSummaryWithConnections(layer, positions, relevantNodes, print
     firstConnection
   ];
   printRow(fields, positions, printFn);
-  for (let i2 = 1; i2 < connections.length; ++i2) {
-    printRow(["", "", "", "", connections[i2]], positions, printFn);
+  for (let i = 1; i < connections.length; ++i) {
+    printRow(["", "", "", "", connections[i]], positions, printFn);
   }
 }
 
@@ -22518,9 +22518,9 @@ function convertPythonicToTs(pythonicConfig, key) {
   } else if (pythonicConfig instanceof Array) {
     const tsArray = [];
     const arrayLength = pythonicConfig.length;
-    for (let i2 = 0; i2 < arrayLength; ++i2) {
-      const item = pythonicConfig[i2];
-      if (isArrayItemInputOrOutputName(key, i2, item)) {
+    for (let i = 0; i < arrayLength; ++i) {
+      const item = pythonicConfig[i];
+      if (isArrayItemInputOrOutputName(key, i, item)) {
         tsArray.push(item);
       } else {
         tsArray.push(convertPythonicToTs(item, key));
@@ -22551,9 +22551,9 @@ function convertTsToPythonic(tsConfig, key) {
   } else if (tsConfig instanceof Array) {
     const pyArray = [];
     const arrayLength = tsConfig.length;
-    for (let i2 = 0; i2 < arrayLength; ++i2) {
-      const item = tsConfig[i2];
-      if (isArrayItemInputOrOutputName(key, i2, item)) {
+    for (let i = 0; i < arrayLength; ++i) {
+      const item = tsConfig[i];
+      if (isArrayItemInputOrOutputName(key, i, item)) {
         pyArray.push(item);
       } else {
         pyArray.push(convertTsToPythonic(item, key));
@@ -22637,10 +22637,10 @@ var Container = class extends Layer {
     this.feedInputShapes = [];
     this.feedInputNames = [];
     this.feedOutputNames = [];
-    for (let i2 = 0; i2 < this.inputLayers.length; i2++) {
-      const layer = this.inputLayers[i2];
+    for (let i = 0; i < this.inputLayers.length; i++) {
+      const layer = this.inputLayers[i];
       if (!(layer instanceof InputLayer)) {
-        throw new TypeError(`Input layers to a LayersModel must be InputLayer objects. Received inputs: ${args.inputs}. Input ${i2} (0-based) originates from layer type ${layer.getClassName()}.`);
+        throw new TypeError(`Input layers to a LayersModel must be InputLayer objects. Received inputs: ${args.inputs}. Input ${i} (0-based) originates from layer type ${layer.getClassName()}.`);
       }
       this.inputNames.push(layer.name);
       this.feedInputShapes.push(layer.batchInputShape);
@@ -22678,11 +22678,11 @@ var Container = class extends Layer {
         nodesInProgress2.push(node);
       }
       const numInboundLayers = node.inboundLayers.length;
-      for (let i2 = 0; i2 < numInboundLayers; i2++) {
-        const x = node.inputTensors[i2];
-        const layer2 = node.inboundLayers[i2];
-        const nodeIndex2 = node.nodeIndices[i2];
-        const tensorIndex2 = node.tensorIndices[i2];
+      for (let i = 0; i < numInboundLayers; i++) {
+        const x = node.inputTensors[i];
+        const layer2 = node.inboundLayers[i];
+        const nodeIndex2 = node.nodeIndices[i];
+        const tensorIndex2 = node.tensorIndices[i];
         buildMapOfGraph(x, finishedNodes2, nodesInProgress2, layer2, nodeIndex2, tensorIndex2);
       }
       finishedNodes2.push(node);
@@ -22708,9 +22708,9 @@ var Container = class extends Layer {
       layersDepths[node.outboundLayer.id] = depth;
       layerIDToLayer[node.outboundLayer.id] = node.outboundLayer;
       nodesDepths[node.id] = depth;
-      for (let i2 = 0; i2 < node.inboundLayers.length; i2++) {
-        const inboundLayer = node.inboundLayers[i2];
-        const nodeIndex = node.nodeIndices[i2];
+      for (let i = 0; i < node.inboundLayers.length; i++) {
+        const inboundLayer = node.inboundLayers[i];
+        const nodeIndex = node.nodeIndices[i];
         const inboundNode = inboundLayer.inboundNodes[nodeIndex];
         const previousDepth2 = nodesDepths[inboundNode.id] == null ? 0 : nodesDepths[inboundNode.id];
         nodesDepths[inboundNode.id] = Math.max(depth + 1, previousDepth2);
@@ -22913,8 +22913,8 @@ var Container = class extends Layer {
     return tidy(() => {
       inputs = toList(inputs);
       const feedDict = new FeedDict();
-      for (let i2 = 0; i2 < this.inputs.length; ++i2) {
-        feedDict.add(this.inputs[i2], inputs[i2]);
+      for (let i = 0; i < this.inputs.length; ++i) {
+        feedDict.add(this.inputs[i], inputs[i]);
       }
       return execute(this.outputs, feedDict, kwargs);
     });
@@ -22937,9 +22937,9 @@ var Container = class extends Layer {
       throw new ValueError(`Invalid inputShape argument ${inputShape}: model has ${this.inputLayers.length} tensor inputs.`);
     }
     const layersToOutputShapes = {};
-    for (let i2 = 0; i2 < inputShapes.length; i2++) {
-      const layer = this.inputLayers[i2];
-      const inputShape2 = inputShapes[i2];
+    for (let i = 0; i < inputShapes.length; i++) {
+      const layer = this.inputLayers[i];
+      const inputShape2 = inputShapes[i];
       const shapeKey = layer.name + "_0_0";
       layersToOutputShapes[shapeKey] = inputShape2;
     }
@@ -22973,15 +22973,15 @@ var Container = class extends Layer {
     }
     const outputShapes = [];
     const outputShapeKeys = [];
-    for (let i2 = 0; i2 < this.outputLayers.length; i2++) {
-      const layer = this.outputLayers[i2];
-      const nodeIndex = this.outputLayersNodeIndices[i2];
-      const tensorIndex = this.outputLayersTensorIndices[i2];
+    for (let i = 0; i < this.outputLayers.length; i++) {
+      const layer = this.outputLayers[i];
+      const nodeIndex = this.outputLayersNodeIndices[i];
+      const tensorIndex = this.outputLayersTensorIndices[i];
       const shapeKey = `${layer.name}_${nodeIndex}_${tensorIndex}`;
       outputShapeKeys.push(shapeKey);
     }
-    for (let i2 = 0; i2 < outputShapeKeys.length; i2++) {
-      const key = outputShapeKeys[i2];
+    for (let i = 0; i < outputShapeKeys.length; i++) {
+      const key = outputShapeKeys[i];
       assert2(key in layersToOutputShapes);
       outputShapes.push(layersToOutputShapes[key]);
     }
@@ -22992,10 +22992,10 @@ var Container = class extends Layer {
       masks = pyListRepeat(null, inputs.length);
     }
     const tensorMap = {};
-    for (let i2 = 0; i2 < this.inputs.length; ++i2) {
-      const x = this.inputs[i2];
-      const y = inputs[i2];
-      const mask = masks[i2];
+    for (let i = 0; i < this.inputs.length; ++i) {
+      const x = this.inputs[i];
+      const y = inputs[i];
+      const mask = masks[i];
       tensorMap[x.id] = [y, mask];
     }
     const depthKeys = Object.keys(this.nodesByDepth).map((x) => parseInt(x, 10)).sort(reverseNumberCompare);
@@ -23041,10 +23041,10 @@ var Container = class extends Layer {
           if (layer.activityRegularizer) {
             throw new NotImplementedError("LayersModel invocation with concrete Tensor value(s) in the presence of activity regularizer(s) is not supported yet.");
           }
-          for (let i2 = 0; i2 < referenceOutputTensors.length; ++i2) {
-            const x = referenceOutputTensors[i2];
-            const y = outputTensors2[i2];
-            const mask = outputMasks2[i2];
+          for (let i = 0; i < referenceOutputTensors.length; ++i) {
+            const x = referenceOutputTensors[i];
+            const y = outputTensors2[i];
+            const mask = outputMasks2[i];
             tensorMap[x.id] = [y, mask];
           }
         }
@@ -23134,10 +23134,10 @@ var Container = class extends Layer {
           }
           if (node.inboundLayers.length > 0) {
             const nodeData = [];
-            for (let i2 = 0; i2 < node.inboundLayers.length; i2++) {
-              const inboundLayer = node.inboundLayers[i2];
-              const nodeIndex = node.nodeIndices[i2];
-              const tensorIndex = node.tensorIndices[i2];
+            for (let i = 0; i < node.inboundLayers.length; i++) {
+              const inboundLayer = node.inboundLayers[i];
+              const nodeIndex = node.nodeIndices[i];
+              const tensorIndex = node.tensorIndices[i];
               const nodeKey2 = Container.nodeKey(inboundLayer, nodeIndex);
               let newNodeIndex = nodeConversionMap[nodeKey2];
               if (newNodeIndex == null) {
@@ -23158,9 +23158,9 @@ var Container = class extends Layer {
     }
     config["layers"] = layerConfigs;
     const modelInputs = [];
-    for (let i2 = 0; i2 < this.inputLayers.length; i2++) {
-      const layer = this.inputLayers[i2];
-      const nodeIndex = this.inputLayersNodeIndices[i2];
+    for (let i = 0; i < this.inputLayers.length; i++) {
+      const layer = this.inputLayers[i];
+      const nodeIndex = this.inputLayersNodeIndices[i];
       const nodeKey = Container.nodeKey(layer, nodeIndex);
       if (!this.containerNodes.has(nodeKey)) {
         continue;
@@ -23169,14 +23169,14 @@ var Container = class extends Layer {
       if (newNodeIndex === null || newNodeIndex === void 0) {
         newNodeIndex = 0;
       }
-      const tensorIndex = this.inputLayersTensorIndices[i2];
+      const tensorIndex = this.inputLayersTensorIndices[i];
       modelInputs.push([layer.name, newNodeIndex, tensorIndex]);
     }
     config["inputLayers"] = modelInputs;
     const modelOutputs = [];
-    for (let i2 = 0; i2 < this.outputLayers.length; i2++) {
-      const layer = this.outputLayers[i2];
-      const nodeIndex = this.outputLayersNodeIndices[i2];
+    for (let i = 0; i < this.outputLayers.length; i++) {
+      const layer = this.outputLayers[i];
+      const nodeIndex = this.outputLayersNodeIndices[i];
       const nodeKey = Container.nodeKey(layer, nodeIndex);
       if (!this.containerNodes.has(nodeKey)) {
         continue;
@@ -23185,7 +23185,7 @@ var Container = class extends Layer {
       if (newNodeIndex === null || newNodeIndex === void 0) {
         newNodeIndex = 0;
       }
-      const tensorIndex = this.outputLayersTensorIndices[i2];
+      const tensorIndex = this.outputLayersTensorIndices[i];
       modelOutputs.push([layer.name, newNodeIndex, tensorIndex]);
     }
     config["outputLayers"] = modelOutputs;
@@ -23454,7 +23454,7 @@ async function fitDataset(model2, dataset, args) {
     const outLabels = model2.getDedupedMetricsNames();
     let callbackMetrics;
     if (doValidation) {
-      callbackMetrics = outLabels.slice().concat(outLabels.map((n2) => "val_" + n2));
+      callbackMetrics = outLabels.slice().concat(outLabels.map((n) => "val_" + n));
     } else {
       callbackMetrics = outLabels.slice();
     }
@@ -23500,16 +23500,16 @@ async function fitDataset(model2, dataset, args) {
           const sampleWeights = [];
           if (args.classWeight != null) {
             const standardClassWeights = standardizeClassWeights(args.classWeight, model2.outputNames);
-            for (let i2 = 0; i2 < standardClassWeights.length; ++i2) {
-              sampleWeights.push(await standardizeWeights(ys[i2], null, standardClassWeights[i2]));
+            for (let i = 0; i < standardClassWeights.length; ++i) {
+              sampleWeights.push(await standardizeWeights(ys[i], null, standardClassWeights[i]));
             }
           }
           const ins = xs.concat(ys).concat(sampleWeights);
           const outs = trainFunction(ins);
           dispose(ins);
-          for (let i2 = 0; i2 < outLabels.length; ++i2) {
-            const label = outLabels[i2];
-            const out = outs[i2];
+          for (let i = 0; i < outLabels.length; ++i) {
+            const label = outLabels[i];
+            const out = outs[i];
             batchLogs[label] = out;
             keep(out);
           }
@@ -23529,8 +23529,8 @@ async function fitDataset(model2, dataset, args) {
                 verbose: 0
               }));
             }
-            for (let i2 = 0; i2 < model2.metricsNames.length; ++i2) {
-              epochLogs[`val_${model2.metricsNames[i2]}`] = valOuts[i2];
+            for (let i = 0; i < model2.metricsNames.length; ++i) {
+              epochLogs[`val_${model2.metricsNames[i]}`] = valOuts[i];
             }
           }
           break;
@@ -23588,15 +23588,15 @@ async function evaluateDataset(model2, dataset, args) {
         const batchOuts = tidy(() => f(xsAndYs));
         dispose(xsAndYs);
         if (batch === 0) {
-          for (let i2 = 0; i2 < batchOuts.length; ++i2) {
+          for (let i = 0; i < batchOuts.length; ++i) {
             outs.push(scalar(0));
           }
         }
         const batchSize = xsAndYs[0].shape[0];
-        for (let i2 = 0; i2 < batchOuts.length; ++i2) {
-          const batchOut = batchOuts[i2];
-          const oldScalar = outs[i2];
-          outs[i2] = tidy(() => add2(outs[i2], mul(batchSize, batchOut)));
+        for (let i = 0; i < batchOuts.length; ++i) {
+          const batchOut = batchOuts[i];
+          const oldScalar = outs[i];
+          outs[i] = tidy(() => add2(outs[i], mul(batchSize, batchOut)));
           if (batch > 0) {
             dispose(oldScalar);
           }
@@ -23614,9 +23614,9 @@ async function evaluateDataset(model2, dataset, args) {
       break;
     }
   }
-  for (let i2 = 0; i2 < outs.length; ++i2) {
-    const oldScalar = outs[i2];
-    outs[i2] = div(outs[i2], numExamples);
+  for (let i = 0; i < outs.length; ++i) {
+    const oldScalar = outs[i];
+    outs[i] = div(outs[i], numExamples);
     dispose(oldScalar);
   }
   return singletonOrArray(outs);
@@ -23720,18 +23720,18 @@ async function fitLoop(model2, f, ins, outLabels, batchSize, epochs, verbose, ca
           batchLogs["size"] = batchEnd - batchStart;
           const insBatch = sliceArraysByIndices(ins, batchIds);
           const outs = f(insBatch);
-          for (let i2 = 0; i2 < outLabels.length; ++i2) {
-            const label = outLabels[i2];
-            const out = outs[i2];
+          for (let i = 0; i < outLabels.length; ++i) {
+            const label = outLabels[i];
+            const out = outs[i];
             batchLogs[label] = out;
             keep(out);
           }
           if (batchIndex === batches.length - 1) {
             if (doValidation) {
               const valOuts = model2.testLoop(valF, valIns, batchSize);
-              for (let i2 = 0; i2 < outLabels.length; ++i2) {
-                const label = outLabels[i2];
-                const out = valOuts[i2];
+              for (let i = 0; i < outLabels.length; ++i) {
+                const label = outLabels[i];
+                const out = valOuts[i];
                 keep(out);
                 epochLogs["val_" + label] = out;
               }
@@ -23817,7 +23817,7 @@ async function fitTensors(model2, x, y, args = {}) {
     if (doValidation) {
       model2.makeTestFunction();
       valFunction = model2.testFunction;
-      callbackMetrics = outLabels.slice().concat(outLabels.map((n2) => "val_" + n2));
+      callbackMetrics = outLabels.slice().concat(outLabels.map((n) => "val_" + n));
     } else {
       valFunction = null;
       valIns = [];
@@ -23844,8 +23844,8 @@ function ensureTensorsRank2OrHigher(tensors) {
   if (tensors instanceof Tensor) {
     tensors = [tensors];
   }
-  for (let i2 = 0; i2 < tensors.length; ++i2) {
-    const tensor2 = tensors[i2];
+  for (let i = 0; i < tensors.length; ++i) {
+    const tensor2 = tensors[i];
     if (tensor2.rank === 1) {
       outs.push(expandDims2(tensor2, 1));
     } else if (tensor2.rank === 0) {
@@ -23864,7 +23864,7 @@ function disposeNewTensors(tensors, refTensors) {
   if (refTensors instanceof Tensor) {
     oldTensorIds.push(refTensors.id);
   } else if (Array.isArray(refTensors)) {
-    refTensors.forEach((t2) => oldTensorIds.push(t2.id));
+    refTensors.forEach((t) => oldTensorIds.push(t.id));
   } else if (refTensors != null) {
     for (const name in refTensors) {
       const oldTensor = refTensors[name];
@@ -23877,9 +23877,9 @@ function disposeNewTensors(tensors, refTensors) {
       tensorsToDispose.push(tensors);
     }
   } else if (Array.isArray(tensors)) {
-    tensors.forEach((t2) => {
-      if (oldTensorIds.indexOf(t2.id) === -1) {
-        tensorsToDispose.push(t2);
+    tensors.forEach((t) => {
+      if (oldTensorIds.indexOf(t.id) === -1) {
+        tensorsToDispose.push(t);
       }
     });
   } else if (tensors != null) {
@@ -23890,9 +23890,9 @@ function disposeNewTensors(tensors, refTensors) {
       }
     }
   }
-  tensorsToDispose.forEach((t2) => {
-    if (!t2.isDisposed) {
-      t2.dispose();
+  tensorsToDispose.forEach((t) => {
+    if (!t.isDisposed) {
+      t.dispose();
     }
   });
 }
@@ -23957,22 +23957,22 @@ function standardizeInputData(data, names, shapes, checkBatchAxis = true, except
   }
   arrays = ensureTensorsRank2OrHigher(arrays);
   if (shapes != null) {
-    for (let i2 = 0; i2 < names.length; ++i2) {
-      if (shapes[i2] == null) {
+    for (let i = 0; i < names.length; ++i) {
+      if (shapes[i] == null) {
         continue;
       }
-      const array2 = arrays[i2];
-      if (array2.shape.length !== shapes[i2].length) {
-        throw new ValueError(`Error when checking ${exceptionPrefix}: expected ${names[i2]} to have ${shapes[i2].length} dimension(s). but got array with shape ${array2.shape}`);
+      const array2 = arrays[i];
+      if (array2.shape.length !== shapes[i].length) {
+        throw new ValueError(`Error when checking ${exceptionPrefix}: expected ${names[i]} to have ${shapes[i].length} dimension(s). but got array with shape ${array2.shape}`);
       }
-      for (let j = 0; j < shapes[i2].length; ++j) {
+      for (let j = 0; j < shapes[i].length; ++j) {
         if (j === 0 && !checkBatchAxis) {
           continue;
         }
         const dim = array2.shape[j];
-        const refDim = shapes[i2][j];
+        const refDim = shapes[i][j];
         if (refDim != null && refDim >= 0 && dim !== refDim) {
-          throw new ValueError(`${exceptionPrefix} expected a batch of elements where each example has shape [${shapes[i2].slice(1, shapes[i2].length)}] (i.e.,tensor shape [*,${shapes[i2].slice(1, shapes[i2].length)}]) but the ${exceptionPrefix} received an input with ${array2.shape[0]} examples, each with shape [${array2.shape.slice(1, array2.shape.length)}] (tensor shape [${array2.shape}])`);
+          throw new ValueError(`${exceptionPrefix} expected a batch of elements where each example has shape [${shapes[i].slice(1, shapes[i].length)}] (i.e.,tensor shape [*,${shapes[i].slice(1, shapes[i].length)}]) but the ${exceptionPrefix} received an input with ${array2.shape[0]} examples, each with shape [${array2.shape.slice(1, array2.shape.length)}] (tensor shape [${array2.shape}])`);
         }
       }
     }
@@ -24000,10 +24000,10 @@ function checkLossAndTargetCompatibility(targets, lossFns, outputShapes) {
     binaryCrossentropy,
     categoricalCrossentropy
   ];
-  for (let i2 = 0; i2 < targets.length; ++i2) {
-    const y = targets[i2];
-    const loss = lossFns[i2];
-    const shape = outputShapes[i2];
+  for (let i = 0; i < targets.length; ++i) {
+    const y = targets[i];
+    const loss = lossFns[i];
+    const shape = outputShapes[i];
     if (loss == null) {
       continue;
     }
@@ -24039,23 +24039,23 @@ function checkInputData(data, names, shapes, checkBatchAxis = true, exceptionPre
     arrays = [data];
   }
   if (shapes != null) {
-    for (let i2 = 0; i2 < names.length; ++i2) {
-      if (shapes[i2] == null) {
+    for (let i = 0; i < names.length; ++i) {
+      if (shapes[i] == null) {
         continue;
       }
-      const array2 = arrays[i2];
-      if (array2.shape.length !== shapes[i2].length) {
-        throw new ValueError(`Error when checking ${exceptionPrefix}: expected ${names[i2]} to have ${shapes[i2].length} dimension(s), but got array with shape ${JSON.stringify(array2.shape)}`);
+      const array2 = arrays[i];
+      if (array2.shape.length !== shapes[i].length) {
+        throw new ValueError(`Error when checking ${exceptionPrefix}: expected ${names[i]} to have ${shapes[i].length} dimension(s), but got array with shape ${JSON.stringify(array2.shape)}`);
       }
-      for (let j = 0; j < shapes[i2].length; ++j) {
+      for (let j = 0; j < shapes[i].length; ++j) {
         if (j === 0 && !checkBatchAxis) {
           continue;
         }
         const dim = array2.shape[j];
-        const refDim = shapes[i2][j];
+        const refDim = shapes[i][j];
         if (refDim != null) {
           if (refDim !== dim) {
-            throw new ValueError(`Error when checking ${exceptionPrefix}: expected ${names[i2]} to have shape ${JSON.stringify(shapes[i2])} but got array with shape ${JSON.stringify(array2.shape)}.`);
+            throw new ValueError(`Error when checking ${exceptionPrefix}: expected ${names[i]} to have shape ${JSON.stringify(shapes[i])} but got array with shape ${JSON.stringify(array2.shape)}.`);
           }
         }
       }
@@ -24134,7 +24134,7 @@ var LayersModel = class extends Container {
         throw new ValueError(`When passing an Array as loss, it should have one entry per model output. The model has ${this.outputs.length} output(s), but you passed loss=${args.loss}.`);
       }
       const theLosses = args.loss;
-      lossFunctions = theLosses.map((l3) => get(l3));
+      lossFunctions = theLosses.map((l) => get(l));
     } else {
       const lossFunction = get(args.loss);
       this.outputs.forEach((_) => {
@@ -24145,26 +24145,26 @@ var LayersModel = class extends Container {
     this.feedOutputNames = [];
     this.feedOutputShapes = [];
     this.feedLossFns = [];
-    for (let i2 = 0; i2 < this.outputs.length; ++i2) {
-      const shape = this.internalOutputShapes[i2];
-      const name = this.outputNames[i2];
+    for (let i = 0; i < this.outputs.length; ++i) {
+      const shape = this.internalOutputShapes[i];
+      const name = this.outputNames[i];
       this.feedOutputNames.push(name);
       this.feedOutputShapes.push(shape);
-      this.feedLossFns.push(this.lossFunctions[i2]);
+      this.feedLossFns.push(this.lossFunctions[i]);
     }
     const skipTargetIndices = [];
     this.metrics = args.metrics;
     this.metricsNames = ["loss"];
     this.metricsTensors = [];
     nameScope("loss", () => {
-      for (let i2 = 0; i2 < this.outputs.length; ++i2) {
-        if (skipTargetIndices.indexOf(i2) !== -1) {
+      for (let i = 0; i < this.outputs.length; ++i) {
+        if (skipTargetIndices.indexOf(i) !== -1) {
           continue;
         }
-        const weightedLoss = this.lossFunctions[i2];
+        const weightedLoss = this.lossFunctions[i];
         if (this.outputs.length > 1) {
-          this.metricsTensors.push([weightedLoss, i2]);
-          this.metricsNames.push(this.outputNames[i2] + "_loss");
+          this.metricsTensors.push([weightedLoss, i]);
+          this.metricsNames.push(this.outputNames[i] + "_loss");
         }
       }
     });
@@ -24177,11 +24177,11 @@ var LayersModel = class extends Container {
       this.metricsTensors.push([metricTensor, outputIndex]);
     };
     nameScope("metric", () => {
-      for (let i2 = 0; i2 < this.outputs.length; ++i2) {
-        if (skipTargetIndices.indexOf(i2) !== -1) {
+      for (let i = 0; i < this.outputs.length; ++i) {
+        if (skipTargetIndices.indexOf(i) !== -1) {
           continue;
         }
-        const outputMetrics = nestedMetrics[i2];
+        const outputMetrics = nestedMetrics[i];
         const handleMetrics = (metrics) => {
           const metricNamePrefix = "";
           let metricName;
@@ -24189,14 +24189,14 @@ var LayersModel = class extends Container {
           let weightedMetricFn;
           for (const metric of metrics) {
             if (typeof metric === "string" && ["accuracy", "acc", "crossentropy", "ce"].indexOf(metric) !== -1) {
-              const outputShape = this.internalOutputShapes[i2];
-              if (outputShape[outputShape.length - 1] === 1 || this.lossFunctions[i2] === binaryCrossentropy) {
+              const outputShape = this.internalOutputShapes[i];
+              if (outputShape[outputShape.length - 1] === 1 || this.lossFunctions[i] === binaryCrossentropy) {
                 if (["accuracy", "acc"].indexOf(metric) !== -1) {
                   accFn = binaryAccuracy;
                 } else if (["crossentropy", "ce"].indexOf(metric) !== -1) {
                   accFn = binaryCrossentropy2;
                 }
-              } else if (this.lossFunctions[i2] === sparseCategoricalCrossentropy) {
+              } else if (this.lossFunctions[i] === sparseCategoricalCrossentropy) {
                 if (["accuracy", "acc"].indexOf(metric) !== -1) {
                   accFn = sparseCategoricalAccuracy;
                 } else if (["crossentropy", "ce"].indexOf(metric) !== -1) {
@@ -24226,7 +24226,7 @@ var LayersModel = class extends Container {
             nameScope(metricName, () => {
               metricResult = weightedMetricFn;
             });
-            appendMetric(i2, metricName, metricResult);
+            appendMetric(i, metricName, metricResult);
           }
         };
         handleMetrics(outputMetrics);
@@ -24295,8 +24295,8 @@ var LayersModel = class extends Container {
       if (inputs.length !== this.inputs.length) {
         throw new ValueError(`The number of inputs provided (${inputs.length}) does not match the number of inputs of this model (${this.inputs.length}).`);
       }
-      for (let i2 = 0; i2 < this.inputs.length; ++i2) {
-        feedDict.add(this.inputs[i2], inputs[i2]);
+      for (let i = 0; i < this.inputs.length; ++i) {
+        feedDict.add(this.inputs[i], inputs[i]);
       }
     } else {
       for (const input2 of this.inputs) {
@@ -24316,10 +24316,10 @@ var LayersModel = class extends Container {
     for (const layer of this.layers) {
       const layerOutputs = Array.isArray(layer.output) ? layer.output : [layer.output];
       const layerOutputNames = layerOutputs.map((output) => output.name);
-      for (let i2 = 0; i2 < symbolicTensorNames.length; ++i2) {
-        const index = layerOutputNames.indexOf(symbolicTensorNames[i2]);
+      for (let i = 0; i < symbolicTensorNames.length; ++i) {
+        const index = layerOutputNames.indexOf(symbolicTensorNames[i]);
         if (index !== -1) {
-          outputSymbolicTensors[i2] = layerOutputs[index];
+          outputSymbolicTensors[i] = layerOutputs[index];
           outputsRemaining--;
         }
         if (outputsRemaining === 0) {
@@ -24332,9 +24332,9 @@ var LayersModel = class extends Container {
     }
     if (outputsRemaining > 0) {
       const remainingNames = [];
-      outputSymbolicTensors.forEach((tensor2, i2) => {
+      outputSymbolicTensors.forEach((tensor2, i) => {
         if (tensor2 == null) {
-          remainingNames.push(symbolicTensorNames[i2]);
+          remainingNames.push(symbolicTensorNames[i]);
         }
       });
       throw new ValueError(`Cannot find SymbolicTensors for output name(s): ${JSON.stringify(remainingNames)}`);
@@ -24356,8 +24356,8 @@ var LayersModel = class extends Container {
           const insBatch = sliceArrays(ins, batchStart, batchEnd);
           const feeds = [];
           if (Array.isArray(insBatch)) {
-            for (let i2 = 0; i2 < insBatch.length; ++i2) {
-              feeds.push({ key: this.inputs[i2], value: insBatch[i2] });
+            for (let i = 0; i < insBatch.length; ++i) {
+              feeds.push({ key: this.inputs[i], value: insBatch[i] });
             }
           } else {
             feeds.push({ key: this.inputs[0], value: insBatch });
@@ -24365,7 +24365,7 @@ var LayersModel = class extends Container {
           const feedDict = new FeedDict(feeds);
           return execute(this.outputs, feedDict);
         });
-        batchOuts.forEach((batchOut, i2) => outsBatches[i2].push(batchOut));
+        batchOuts.forEach((batchOut, i) => outsBatches[i].push(batchOut));
       }
       return singletonOrArray(outsBatches.map((batches2) => concat(batches2, 0)));
     });
@@ -24391,9 +24391,9 @@ var LayersModel = class extends Container {
       throw new RuntimeError("You must compile a model before training/testing. Use LayersModel.compile(modelCompileArgs).");
     }
     const outputShapes = [];
-    for (let i2 = 0; i2 < this.feedOutputShapes.length; ++i2) {
-      const outputShape = this.feedOutputShapes[i2];
-      const lossFn = this.feedLossFns[i2];
+    for (let i = 0; i < this.feedOutputShapes.length; ++i) {
+      const outputShape = this.feedOutputShapes[i];
+      const lossFn = this.feedLossFns[i];
       if (lossFn === sparseCategoricalCrossentropy) {
         outputShapes.push(outputShape.slice(0, outputShape.length - 1).concat([1]));
       } else {
@@ -24420,8 +24420,8 @@ var LayersModel = class extends Container {
     if (classWeight != null) {
       const classWeights = standardizeClassWeights(classWeight, this.outputNames);
       standardSampleWeights = [];
-      for (let i2 = 0; i2 < classWeights.length; ++i2) {
-        standardSampleWeights.push(await standardizeWeights(standardYs[i2], null, classWeights[i2]));
+      for (let i = 0; i < classWeights.length; ++i) {
+        standardSampleWeights.push(await standardizeWeights(standardYs[i], null, classWeights[i]));
       }
     }
     return [standardXs, standardYs, standardSampleWeights];
@@ -24445,17 +24445,17 @@ var LayersModel = class extends Container {
           const insBatch = sliceArraysByIndices(ins, batchIds);
           const batchOuts = f(insBatch);
           if (batchIndex === 0) {
-            for (let i2 = 0; i2 < batchOuts.length; ++i2) {
+            for (let i = 0; i < batchOuts.length; ++i) {
               outs.push(scalar(0));
             }
           }
-          for (let i2 = 0; i2 < batchOuts.length; ++i2) {
-            const batchOut = batchOuts[i2];
-            outs[i2] = add2(outs[i2], mul(batchEnd - batchStart, batchOut));
+          for (let i = 0; i < batchOuts.length; ++i) {
+            const batchOut = batchOuts[i];
+            outs[i] = add2(outs[i], mul(batchEnd - batchStart, batchOut));
           }
         }
-        for (let i2 = 0; i2 < outs.length; ++i2) {
-          outs[i2] = div(outs[i2], numSamples);
+        for (let i = 0; i < outs.length; ++i) {
+          outs[i] = div(outs[i], numSamples);
         }
       }
       return outs;
@@ -24464,11 +24464,11 @@ var LayersModel = class extends Container {
   getDedupedMetricsNames() {
     const outLabels = this.metricsNames;
     const dedupedOutLabels = [];
-    for (let i2 = 0; i2 < outLabels.length; ++i2) {
-      const label = outLabels[i2];
+    for (let i = 0; i < outLabels.length; ++i) {
+      const label = outLabels[i];
       let newLabel = label;
       if (count(outLabels, label) > 1) {
-        const dupIndex = count(outLabels.slice(0, i2), label);
+        const dupIndex = count(outLabels.slice(0, i), label);
         newLabel += `_${dupIndex}`;
       }
       dedupedOutLabels.push(newLabel);
@@ -24484,33 +24484,33 @@ var LayersModel = class extends Container {
       const metricsValues = [];
       const totalLossFunction = () => {
         const feeds = [];
-        for (let i2 = 0; i2 < this.inputs.length; ++i2) {
-          feeds.push({ key: this.inputs[i2], value: inputs[i2] });
+        for (let i = 0; i < this.inputs.length; ++i) {
+          feeds.push({ key: this.inputs[i], value: inputs[i] });
         }
         const feedDict = new FeedDict(feeds);
         const outputs = execute(this.outputs, feedDict, { "training": true });
         let totalLoss;
-        for (let i2 = 0; i2 < this.lossFunctions.length; ++i2) {
-          const lossFunction = this.lossFunctions[i2];
-          let loss = lossFunction(targets[i2], outputs[i2]);
-          if (sampleWeights[i2] != null) {
-            loss = computeWeightedLoss2(loss, sampleWeights[i2]);
+        for (let i = 0; i < this.lossFunctions.length; ++i) {
+          const lossFunction = this.lossFunctions[i];
+          let loss = lossFunction(targets[i], outputs[i]);
+          if (sampleWeights[i] != null) {
+            loss = computeWeightedLoss2(loss, sampleWeights[i]);
           }
           const meanLoss = mean(loss);
           lossValues.push(meanLoss);
-          if (i2 === 0) {
+          if (i === 0) {
             totalLoss = loss;
           } else {
             totalLoss = add2(totalLoss, loss);
           }
         }
-        for (let i2 = 0; i2 < this.metricsTensors.length; ++i2) {
+        for (let i = 0; i < this.metricsTensors.length; ++i) {
           let weightedMetric;
-          if (this.outputs.length > 1 && i2 < this.outputs.length) {
-            weightedMetric = lossValues[i2];
+          if (this.outputs.length > 1 && i < this.outputs.length) {
+            weightedMetric = lossValues[i];
           } else {
-            const metric = this.metricsTensors[i2][0];
-            const outputIndex = this.metricsTensors[i2][1];
+            const metric = this.metricsTensors[i][0];
+            const outputIndex = this.metricsTensors[i][1];
             weightedMetric = mean(metric(targets[outputIndex], outputs[outputIndex]));
           }
           keep(weightedMetric);
@@ -24536,24 +24536,24 @@ var LayersModel = class extends Container {
         const inputs = data.slice(0, this.inputs.length);
         const targets = data.slice(this.inputs.length, this.inputs.length + this.outputs.length);
         const feeds = [];
-        for (let i2 = 0; i2 < this.inputs.length; ++i2) {
-          feeds.push({ key: this.inputs[i2], value: inputs[i2] });
+        for (let i = 0; i < this.inputs.length; ++i) {
+          feeds.push({ key: this.inputs[i], value: inputs[i] });
         }
         const feedDict = new FeedDict(feeds);
         const outputs = execute(this.outputs, feedDict);
-        for (let i2 = 0; i2 < this.lossFunctions.length; ++i2) {
-          const lossFunction = this.lossFunctions[i2];
-          const loss = mean(lossFunction(targets[i2], outputs[i2]));
-          if (i2 === 0) {
+        for (let i = 0; i < this.lossFunctions.length; ++i) {
+          const lossFunction = this.lossFunctions[i];
+          const loss = mean(lossFunction(targets[i], outputs[i]));
+          if (i === 0) {
             totalLoss = loss;
           } else {
             totalLoss = add2(totalLoss, loss);
           }
           valOutputs.push(totalLoss);
         }
-        for (let i2 = 0; i2 < this.metricsTensors.length; ++i2) {
-          const metric = this.metricsTensors[i2][0];
-          const outputIndex = this.metricsTensors[i2][1];
+        for (let i = 0; i < this.metricsTensors.length; ++i) {
+          const metric = this.metricsTensors[i][0];
+          const outputIndex = this.metricsTensors[i][1];
           const meanMetric = mean(metric(targets[outputIndex], outputs[outputIndex]));
           valOutputs.push(meanMetric);
         }
@@ -24588,11 +24588,11 @@ var LayersModel = class extends Container {
     const trainableOnly = config != null && config.trainableOnly;
     const weights = trainableOnly ? this.trainableWeights : this.weights;
     const weightValues = this.getWeights(trainableOnly);
-    for (let i2 = 0; i2 < weights.length; ++i2) {
-      if (trainableOnly && !weights[i2].trainable) {
+    for (let i = 0; i < weights.length; ++i) {
+      if (trainableOnly && !weights[i].trainable) {
         continue;
       }
-      namedWeights.push({ name: weights[i2].originalName, tensor: weightValues[i2] });
+      namedWeights.push({ name: weights[i].originalName, tensor: weightValues[i] });
     }
     return namedWeights;
   }
@@ -25399,15 +25399,15 @@ var PReLU = class extends Layer {
     inputShape = getExactlyOneShape(inputShape);
     const paramShape = inputShape.slice(1);
     if (this.sharedAxes != null) {
-      for (const i2 of this.sharedAxes) {
-        paramShape[i2 - 1] = 1;
+      for (const i of this.sharedAxes) {
+        paramShape[i - 1] = 1;
       }
     }
     this.alpha = this.addWeight("alpha", paramShape, "float32", this.alphaInitializer, this.alphaRegularizer, true, this.alphaConstraint);
     const axes = {};
     if (this.sharedAxes != null) {
-      for (let i2 = 1; i2 < inputShape.length; ++i2) {
-        axes[i2] = inputShape[i2];
+      for (let i = 1; i < inputShape.length; ++i) {
+        axes[i] = inputShape[i];
       }
     }
     this.inputSpec = [new InputSpec({
@@ -25515,17 +25515,17 @@ Softmax3.className = "Softmax";
 serialization_exports.registerClass(Softmax3);
 
 // node_modules/.pnpm/@tensorflow+tfjs-layers@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-layers/dist/utils/conv_utils.js
-function normalizeArray(value, n2, name) {
+function normalizeArray(value, n, name) {
   if (typeof value === "number") {
-    return pyListRepeat(value, n2);
+    return pyListRepeat(value, n);
   } else {
-    if (value.length !== n2) {
-      throw new ValueError(`The ${name} argument must be an integer or tuple of ${n2} integers. Received: ${value.length} elements.`);
+    if (value.length !== n) {
+      throw new ValueError(`The ${name} argument must be an integer or tuple of ${n} integers. Received: ${value.length} elements.`);
     }
-    for (let i2 = 0; i2 < n2; ++i2) {
-      const singleValue = value[i2];
+    for (let i = 0; i < n; ++i) {
+      const singleValue = value[i];
       if (!isInteger(singleValue)) {
-        throw new ValueError(`The ${name} argument must be an integer or tuple of ${n2} integers. Received: ${JSON.stringify(value)} including a non-integer number ${singleValue}`);
+        throw new ValueError(`The ${name} argument must be an integer or tuple of ${n} integers. Received: ${JSON.stringify(value)} including a non-integer number ${singleValue}`);
       }
     }
     return value;
@@ -25786,8 +25786,8 @@ var Conv = class extends BaseConv {
     inputShape = getExactlyOneShape(inputShape);
     const newSpace = [];
     const space = this.dataFormat === "channelsLast" ? inputShape.slice(1, inputShape.length - 1) : inputShape.slice(2);
-    for (let i2 = 0; i2 < space.length; ++i2) {
-      const newDim = convOutputLength(space[i2], this.kernelSize[i2], this.padding, this.strides[i2], typeof this.dilationRate === "number" ? this.dilationRate : this.dilationRate[i2]);
+    for (let i = 0; i < space.length; ++i) {
+      const newDim = convOutputLength(space[i], this.kernelSize[i], this.padding, this.strides[i], typeof this.dilationRate === "number" ? this.dilationRate : this.dilationRate[i]);
       newSpace.push(newDim);
     }
     let outputShape = [inputShape[0]];
@@ -26104,7 +26104,7 @@ var SeparableConv = class extends Conv {
     const inputDim = inputShape[channelAxis];
     const depthwiseKernelShape = this.kernelSize.concat([inputDim, this.depthMultiplier]);
     const pointwiseKernelShape = [];
-    for (let i2 = 0; i2 < this.rank; ++i2) {
+    for (let i = 0; i < this.rank; ++i) {
       pointwiseKernelShape.push(1);
     }
     pointwiseKernelShape.push(inputDim * this.depthMultiplier, this.filters);
@@ -26447,19 +26447,19 @@ function rnn(stepFunction, inputs, initialStates, goBackwards = false, mask, con
     if (mask != null) {
       perStepMasks = unstack(mask);
     }
-    for (let t2 = 0; t2 < timeSteps; ++t2) {
-      const currentInput = perStepInputs[t2];
+    for (let t = 0; t < timeSteps; ++t) {
+      const currentInput = perStepInputs[t];
       const stepOutputs = tidy(() => stepFunction(currentInput, states));
       if (mask == null) {
         lastOutput = stepOutputs[0];
         states = stepOutputs[1];
       } else {
         const maskedOutputs = tidy(() => {
-          const stepMask = perStepMasks[t2];
+          const stepMask = perStepMasks[t];
           const negStepMask = sub(onesLike(stepMask), stepMask);
           const output = add2(mul(stepOutputs[0], stepMask), mul(states[0], negStepMask));
-          const newStates = states.map((state, i2) => {
-            return add2(mul(stepOutputs[1][i2], stepMask), mul(state, negStepMask));
+          const newStates = states.map((state, i) => {
+            return add2(mul(stepOutputs[1][i], stepMask), mul(state, negStepMask));
           });
           return { output, newStates };
         });
@@ -26549,7 +26549,7 @@ var RNN = class extends Layer {
       }
       const outputMask = this.returnSequences ? mask : null;
       if (this.returnState) {
-        const stateMask = this.states.map((s2) => null);
+        const stateMask = this.states.map((s) => null);
         return [outputMask].concat(stateMask);
       } else {
         return outputMask;
@@ -26560,7 +26560,7 @@ var RNN = class extends Layer {
     if (this.states_ == null) {
       const numStates = Array.isArray(this.cell.stateSize) ? this.cell.stateSize.length : 1;
       const output = [];
-      for (let i2 = 0; i2 < numStates; ++i2) {
+      for (let i = 0; i < numStates; ++i) {
         output.push(null);
       }
       return output;
@@ -26568,8 +26568,8 @@ var RNN = class extends Layer {
       return this.states_;
     }
   }
-  set states(s2) {
-    this.states_ = s2;
+  set states(s) {
+    this.states_ = s;
   }
   build(inputShape) {
     const constantShape = null;
@@ -27013,7 +27013,7 @@ var GRUCell = class extends RNNCell {
       const dpMask = this.dropoutMask;
       const recDpMask = this.recurrentDropoutMask;
       let z;
-      let r2;
+      let r;
       let hh;
       if (0 < this.dropout && this.dropout < 1) {
         inputs = mul(inputs, dpMask[0]);
@@ -27031,8 +27031,8 @@ var GRUCell = class extends RNNCell {
       const [xZ, xR, xH] = split(matrixX, 3, matrixX.rank - 1);
       const [recurrentZ, recurrentR] = split(matrixInner, 2, matrixInner.rank - 1);
       z = this.recurrentActivation.apply(add2(xZ, recurrentZ));
-      r2 = this.recurrentActivation.apply(add2(xR, recurrentR));
-      const recurrentH = dot2(mul(r2, hTMinus1), rk2);
+      r = this.recurrentActivation.apply(add2(xR, recurrentR));
+      const recurrentH = dot2(mul(r, hTMinus1), rk2);
       hh = this.activation.apply(add2(xH, recurrentH));
       const h = add2(mul(z, hTMinus1), mul(add2(1, neg(z)), hh));
       return [h, h];
@@ -27190,7 +27190,7 @@ var LSTMCell = class extends RNNCell {
       }
       const dpMask = this.dropoutMask;
       const recDpMask = this.recurrentDropoutMask;
-      let i2;
+      let i;
       let f;
       let c;
       let o;
@@ -27206,9 +27206,9 @@ var LSTMCell = class extends RNNCell {
         z = biasAdd(z, this.bias.read());
       }
       const [z0, z1, z2, z3] = split(z, 4, z.rank - 1);
-      i2 = this.recurrentActivation.apply(z0);
+      i = this.recurrentActivation.apply(z0);
       f = this.recurrentActivation.apply(z1);
-      c = add2(mul(f, cTMinus1), mul(i2, this.activation.apply(z2)));
+      c = add2(mul(f, cTMinus1), mul(i, this.activation.apply(z2)));
       o = this.recurrentActivation.apply(z3);
       const h = mul(o, this.activation.apply(c));
       return [h, h, c];
@@ -27305,10 +27305,10 @@ var StackedRNNCells = class extends RNNCell {
       nestedStates.reverse();
       const newNestedStates = [];
       let callInputs;
-      for (let i2 = 0; i2 < this.cells.length; ++i2) {
-        const cell = this.cells[i2];
-        states = nestedStates[i2];
-        if (i2 === 0) {
+      for (let i = 0; i < this.cells.length; ++i) {
+        const cell = this.cells[i];
+        states = nestedStates[i];
+        if (i === 0) {
           callInputs = [inputs[0]].concat(states);
         } else {
           callInputs = [callInputs[0]].concat(states);
@@ -27329,8 +27329,8 @@ var StackedRNNCells = class extends RNNCell {
     }
     inputShape = inputShape;
     let outputDim;
-    this.cells.forEach((cell, i2) => {
-      nameScope(`RNNCell_${i2}`, () => {
+    this.cells.forEach((cell, i) => {
+      nameScope(`RNNCell_${i}`, () => {
         cell.build(inputShape);
         if (Array.isArray(cell.stateSize)) {
           outputDim = cell.stateSize[0];
@@ -27397,8 +27397,8 @@ var StackedRNNCells = class extends RNNCell {
     for (const cell of this.cells) {
       const numParams = cell.weights.length;
       const inputWeights = weights.splice(numParams);
-      for (let i2 = 0; i2 < cell.weights.length; ++i2) {
-        tuples.push([cell.weights[i2], inputWeights[i2]]);
+      for (let i = 0; i < cell.weights.length; ++i) {
+        tuples.push([cell.weights[i], inputWeights[i]]);
       }
     }
     batchSetValue(tuples);
@@ -27418,17 +27418,17 @@ function generateDropoutMask(args) {
 }
 
 // node_modules/.pnpm/@tensorflow+tfjs-layers@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-layers/dist/layers/convolutional_recurrent.js
-var __rest = function(s2, e2) {
-  var t2 = {};
-  for (var p2 in s2)
-    if (Object.prototype.hasOwnProperty.call(s2, p2) && e2.indexOf(p2) < 0)
-      t2[p2] = s2[p2];
-  if (s2 != null && typeof Object.getOwnPropertySymbols === "function")
-    for (var i2 = 0, p2 = Object.getOwnPropertySymbols(s2); i2 < p2.length; i2++) {
-      if (e2.indexOf(p2[i2]) < 0 && Object.prototype.propertyIsEnumerable.call(s2, p2[i2]))
-        t2[p2[i2]] = s2[p2[i2]];
+var __rest = function(s, e) {
+  var t = {};
+  for (var p2 in s)
+    if (Object.prototype.hasOwnProperty.call(s, p2) && e.indexOf(p2) < 0)
+      t[p2] = s[p2];
+  if (s != null && typeof Object.getOwnPropertySymbols === "function")
+    for (var i = 0, p2 = Object.getOwnPropertySymbols(s); i < p2.length; i++) {
+      if (e.indexOf(p2[i]) < 0 && Object.prototype.propertyIsEnumerable.call(s, p2[i]))
+        t[p2[i]] = s[p2[i]];
     }
-  return t2;
+  return t;
 };
 var ConvRNN2D = class extends RNN {
   constructor(args) {
@@ -27657,9 +27657,9 @@ var ConvLSTM2DCell = class extends LSTMCell {
       hF = this.recurrentConv(hF, recKernelF);
       hC = this.recurrentConv(hC, recKernelC);
       hO = this.recurrentConv(hO, recKernelO);
-      const i2 = this.recurrentActivation.apply(add2(xI, hI));
+      const i = this.recurrentActivation.apply(add2(xI, hI));
       const f = this.recurrentActivation.apply(add2(xF, hF));
-      const c = add2(mul(f, cTMinus1), mul(i2, this.activation.apply(add2(xC, hC))));
+      const c = add2(mul(f, cTMinus1), mul(i, this.activation.apply(add2(xC, hC))));
       const h = mul(this.recurrentActivation.apply(add2(xO, hO)), this.activation.apply(c));
       return [h, h, c];
     });
@@ -27717,8 +27717,8 @@ var Dropout = class extends Layer {
     }
     const inputShape = input2.shape;
     const noiseShape = [];
-    for (let i2 = 0; i2 < this.noiseShape.length; ++i2) {
-      noiseShape.push(this.noiseShape[i2] == null ? inputShape[i2] : this.noiseShape[i2]);
+    for (let i = 0; i < this.noiseShape.length; ++i) {
+      noiseShape.push(this.noiseShape[i] == null ? inputShape[i] : this.noiseShape[i]);
     }
     return noiseShape;
   }
@@ -27875,8 +27875,8 @@ var Flatten = class extends Layer {
       let input2 = getExactlyOneTensor(inputs);
       if (this.dataFormat === "channelsFirst" && input2.rank > 1) {
         const permutation = [0];
-        for (let i2 = 2; i2 < input2.rank; ++i2) {
-          permutation.push(i2);
+        for (let i = 2; i < input2.rank; ++i) {
+          permutation.push(i);
         }
         permutation.push(1);
         input2 = transpose(input2, permutation);
@@ -27948,9 +27948,9 @@ var Reshape2 = class extends Layer {
   constructor(args) {
     super(args);
     this.targetShape = args.targetShape;
-    for (let i2 = 0; i2 < this.targetShape.length; ++i2) {
-      if (this.isUnknown(this.targetShape[i2])) {
-        this.targetShape[i2] = null;
+    for (let i = 0; i < this.targetShape.length; ++i) {
+      if (this.isUnknown(this.targetShape[i])) {
+        this.targetShape[i] = null;
       }
     }
   }
@@ -27962,11 +27962,11 @@ var Reshape2 = class extends Layer {
     const finalShape = outputShape.slice();
     let known = 1;
     let unknown = null;
-    for (let i2 = 0; i2 < finalShape.length; ++i2) {
-      const dim = finalShape[i2];
+    for (let i = 0; i < finalShape.length; ++i) {
+      const dim = finalShape[i];
       if (this.isUnknown(dim)) {
         if (unknown === null) {
-          unknown = i2;
+          unknown = i;
         } else {
           throw new ValueError("Can only specifiy one unknown dimension.");
         }
@@ -27987,8 +27987,8 @@ var Reshape2 = class extends Layer {
   }
   computeOutputShape(inputShape) {
     let anyUnknownDims = false;
-    for (let i2 = 0; i2 < inputShape.length; ++i2) {
-      if (this.isUnknown(inputShape[i2])) {
+    for (let i = 0; i < inputShape.length; ++i) {
+      if (this.isUnknown(inputShape[i])) {
         anyUnknownDims = true;
         break;
       }
@@ -28039,8 +28039,8 @@ var Permute = class extends Layer {
   computeOutputShape(inputShape) {
     inputShape = getExactlyOneShape(inputShape);
     const outputShape = inputShape.slice();
-    this.dims.forEach((dim, i2) => {
-      outputShape[i2 + 1] = inputShape[dim];
+    this.dims.forEach((dim, i) => {
+      outputShape[i + 1] = inputShape[dim];
     });
     return outputShape;
   }
@@ -28151,16 +28151,16 @@ var Embedding = class extends Layer {
     if (inLens.length !== inputShape.length - 1) {
       throw new ValueError(`"inputLength" is ${this.inputLength}, but received input shape has shape ${inputShape}`);
     } else {
-      let i2 = 0;
+      let i = 0;
       for (let k = 0; k < inLens.length; ++k) {
         const s1 = inLens[k];
         const s2 = inputShape[k + 1];
         if (s1 != null && s2 != null && s1 !== s2) {
           throw new ValueError(`"inputLength" is ${this.inputLength}, but received input shape has shape ${inputShape}`);
         } else if (s1 == null) {
-          inLens[i2] = s2;
+          inLens[i] = s2;
         }
-        i2++;
+        i++;
       }
     }
     return [inputShape[0], ...inLens, this.outputDim];
@@ -28214,19 +28214,19 @@ var Merge = class extends Layer {
     }
     const outputShape = shape1.slice(0, shape1.length - shape2.length);
     for (let k = 0; k < shape2.length; ++k) {
-      const i2 = shape1[shape1.length - shape2.length + k];
+      const i = shape1[shape1.length - shape2.length + k];
       const j = shape2[k];
-      if (i2 == null || j == null || i2 < 0 || j < 0) {
+      if (i == null || j == null || i < 0 || j < 0) {
         outputShape.push(null);
-      } else if (i2 === 1) {
+      } else if (i === 1) {
         outputShape.push(j);
       } else if (j === 1) {
-        outputShape.push(i2);
+        outputShape.push(i);
       } else {
-        if (i2 !== j) {
+        if (i !== j) {
           throw new ValueError("Operands could not be broadcast together with shapes " + JSON.stringify(shape1) + " " + JSON.stringify(shape2));
         }
-        outputShape.push(i2);
+        outputShape.push(i);
       }
     }
     return outputShape;
@@ -28250,8 +28250,8 @@ var Merge = class extends Layer {
       throw new ValueError(`Can not merge tensors with different batch sizes. Got tensors with shapes: ${JSON.stringify(inputShape)}.`);
     }
     let outputShape = inputShape[0] == null ? null : inputShape[0].slice(1);
-    for (let i2 = 1; i2 < inputShape.length; ++i2) {
-      const shape = inputShape[i2] == null ? null : inputShape[i2].slice(1);
+    for (let i = 1; i < inputShape.length; ++i) {
+      const shape = inputShape[i] == null ? null : inputShape[i].slice(1);
       outputShape = this.computeElementwiseOpOutputShape(outputShape, shape);
     }
     const allRanks = inputShape.map((shape) => shape.length);
@@ -28327,8 +28327,8 @@ var Merge = class extends Layer {
     } else {
       outputShape = inputShape[0].slice(1);
     }
-    for (let i2 = 1; i2 < inputShape.length; ++i2) {
-      const shape = inputShape[i2] == null ? null : inputShape[i2].slice(1);
+    for (let i = 1; i < inputShape.length; ++i) {
+      const shape = inputShape[i] == null ? null : inputShape[i].slice(1);
       outputShape = this.computeElementwiseOpOutputShape(outputShape, shape);
     }
     let batchSizes = [];
@@ -28364,8 +28364,8 @@ var Merge = class extends Layer {
       }
       mask = mask.map((m) => m == null ? m : expandDims(m, 0));
       let output = mask[0];
-      for (let i2 = 1; i2 < mask.length - 1; ++i2) {
-        output = logicalAnd(output, mask[i2]);
+      for (let i = 1; i < mask.length - 1; ++i) {
+        output = logicalAnd(output, mask[i]);
       }
       return output;
     });
@@ -28378,8 +28378,8 @@ var Add2 = class extends Merge {
   mergeFunction(inputs) {
     return tidy(() => {
       let output = inputs[0].clone();
-      for (let i2 = 1; i2 < inputs.length; ++i2) {
-        output = add2(output, inputs[i2]);
+      for (let i = 1; i < inputs.length; ++i) {
+        output = add2(output, inputs[i]);
       }
       return output;
     });
@@ -28394,8 +28394,8 @@ var Multiply2 = class extends Merge {
   mergeFunction(inputs) {
     return tidy(() => {
       let output = inputs[0].clone();
-      for (let i2 = 1; i2 < inputs.length; ++i2) {
-        output = mul(output, inputs[i2]);
+      for (let i = 1; i < inputs.length; ++i) {
+        output = mul(output, inputs[i]);
       }
       return output;
     });
@@ -28410,8 +28410,8 @@ var Average = class extends Merge {
   mergeFunction(inputs) {
     return tidy(() => {
       let output = inputs[0].clone();
-      for (let i2 = 1; i2 < inputs.length; ++i2) {
-        output = add2(output, inputs[i2]);
+      for (let i = 1; i < inputs.length; ++i) {
+        output = add2(output, inputs[i]);
       }
       return mul(1 / inputs.length, output);
     });
@@ -28426,8 +28426,8 @@ var Maximum2 = class extends Merge {
   mergeFunction(inputs) {
     return tidy(() => {
       let output = inputs[0];
-      for (let i2 = 1; i2 < inputs.length; ++i2) {
-        output = maximum(output, inputs[i2]);
+      for (let i = 1; i < inputs.length; ++i) {
+        output = maximum(output, inputs[i]);
       }
       return output;
     });
@@ -28442,8 +28442,8 @@ var Minimum2 = class extends Merge {
   mergeFunction(inputs) {
     return tidy(() => {
       let output = inputs[0];
-      for (let i2 = 1; i2 < inputs.length; ++i2) {
-        output = minimum(output, inputs[i2]);
+      for (let i = 1; i < inputs.length; ++i) {
+        output = minimum(output, inputs[i]);
       }
       return output;
     });
@@ -28478,8 +28478,8 @@ var Concatenate = class extends Merge {
       return;
     }
     const shapeSet = [];
-    for (let i2 = 0; i2 < inputShape.length; ++i2) {
-      const shapeWithoutConcatAxis = inputShape[i2].slice();
+    for (let i = 0; i < inputShape.length; ++i) {
+      const shapeWithoutConcatAxis = inputShape[i].slice();
       shapeWithoutConcatAxis.splice(this.axis, 1);
       let exists = false;
       for (const shape of shapeSet) {
@@ -28542,13 +28542,13 @@ var Concatenate = class extends Merge {
         return null;
       }
       const outputMasks = [];
-      for (let i2 = 0; i2 < inputs.length; ++i2) {
-        if (mask[i2] == null) {
-          outputMasks.push(cast(onesLike(inputs[i2]), "bool"));
-        } else if (mask[i2].rank < inputs[i2].rank) {
-          outputMasks.push(expandDims(mask[i2], -1));
+      for (let i = 0; i < inputs.length; ++i) {
+        if (mask[i] == null) {
+          outputMasks.push(cast(onesLike(inputs[i]), "bool"));
+        } else if (mask[i].rank < inputs[i].rank) {
+          outputMasks.push(expandDims(mask[i], -1));
         } else {
-          outputMasks.push(mask[i2]);
+          outputMasks.push(mask[i]);
         }
       }
       const concatenatedMasks = concat(outputMasks, this.axis);
@@ -28595,14 +28595,14 @@ function batchDot(x, y, axes) {
     if (xNDim > yNDim) {
       diff = xNDim - yNDim;
       const diffShape = [];
-      for (let i2 = 0; i2 < diff; ++i2) {
+      for (let i = 0; i < diff; ++i) {
         diffShape.push(1);
       }
       y = reshape(y, y.shape.concat(diffShape));
     } else if (yNDim > xNDim) {
       diff = yNDim - xNDim;
       const diffShape = [];
-      for (let i2 = 0; i2 < diff; ++i2) {
+      for (let i = 0; i < diff; ++i) {
         diffShape.push(1);
       }
       x = reshape(x, x.shape.concat(diffShape));
@@ -28629,8 +28629,8 @@ function batchDot(x, y, axes) {
         idx = xNDim - 1;
       }
       const squeezeAxes = [];
-      for (let i2 = idx; i2 < idx + diff; ++i2) {
-        squeezeAxes.push(i2);
+      for (let i = idx; i < idx + diff; ++i) {
+        squeezeAxes.push(i);
       }
       out = squeeze(out, squeezeAxes);
     }
@@ -28673,7 +28673,7 @@ var Dot = class extends Merge {
         interpretAxis(this.axes, x2.shape.length)
       ];
     } else {
-      axes = this.axes.map((axis, i2) => interpretAxis(axis, inputs[i2].shape.length));
+      axes = this.axes.map((axis, i) => interpretAxis(axis, inputs[i].shape.length));
     }
     if (this.normalize) {
       x1 = l2Normalize(x1, axes[0]);
@@ -29024,9 +29024,9 @@ var LayerNormalization = class extends Layer {
     if (typeof this.axis === "number") {
       this.axis = [this.axis];
     }
-    for (let i2 = 0; i2 < this.axis.length; ++i2) {
-      if (this.axis[i2] < 0) {
-        this.axis[i2] += nDims;
+    for (let i = 0; i < this.axis.length; ++i) {
+      if (this.axis[i] < 0) {
+        this.axis[i] += nDims;
       }
     }
     for (const axis of this.axis) {
@@ -29073,13 +29073,13 @@ var LayerNormalization = class extends Layer {
       let offset = this.center ? broadcast(this.beta.read()) : null;
       const momentsTiling = [];
       const scaleOffsetTiling = [];
-      for (let i2 = 0; i2 < nDims; ++i2) {
-        if (this.axis.indexOf(i2) !== -1) {
-          momentsTiling.push(inputShape[i2]);
+      for (let i = 0; i < nDims; ++i) {
+        if (this.axis.indexOf(i) !== -1) {
+          momentsTiling.push(inputShape[i]);
           scaleOffsetTiling.push(1);
         } else {
           momentsTiling.push(1);
-          scaleOffsetTiling.push(inputShape[i2]);
+          scaleOffsetTiling.push(inputShape[i]);
         }
       }
       mean5 = tile(mean5, momentsTiling);
@@ -30533,9 +30533,9 @@ function getPadding(node, tensorMap, context) {
   if (pad3 === "explicit") {
     pad3 = getParamValue("explicitPaddings", node, tensorMap, context);
     const explicitPadding = [[0, 0], [0, 0], [0, 0], [0, 0]];
-    for (let i2 = 0; i2 < 4; i2++) {
-      explicitPadding[i2][0] = pad3[i2 * 2];
-      explicitPadding[i2][1] = pad3[i2 * 2 + 1];
+    for (let i = 0; i < 4; i++) {
+      explicitPadding[i][0] = pad3[i * 2];
+      explicitPadding[i][1] = pad3[i * 2 + 1];
     }
     return explicitPadding;
   }
@@ -36946,8 +36946,8 @@ function decodeBase64(text) {
     throw new Error("Unable to decode base64 in this environment. Missing built-in atob() or Buffer()");
   }
 }
-function parseStringParam(s2, keepCase) {
-  const value = Array.isArray(s2) ? String.fromCharCode.apply(null, s2) : decodeBase64(s2);
+function parseStringParam(s, keepCase) {
+  const value = Array.isArray(s) ? String.fromCharCode.apply(null, s) : decodeBase64(s);
   return keepCase ? value : value.toLowerCase();
 }
 function getStringParam(attrs, name, def, keepCase = false) {
@@ -37490,9 +37490,9 @@ function assertShapesMatchAllowUndefinedSize(shapeA, shapeB, errorMessagePrefix
     return;
   }
   util_exports.assert(shapeA.length === shapeB.length, () => errorMessagePrefix + ` Shapes ${shapeA} and ${shapeB} must match`);
-  for (let i2 = 0; i2 < shapeA.length; i2++) {
-    const dim0 = shapeA[i2];
-    const dim1 = shapeB[i2];
+  for (let i = 0; i < shapeA.length; i++) {
+    const dim0 = shapeA[i];
+    const dim1 = shapeB[i];
     util_exports.assert(dim0 < 0 || dim1 < 0 || dim0 === dim1, () => errorMessagePrefix + ` Shapes ${shapeA} and ${shapeB} must match`);
   }
 }
@@ -37529,13 +37529,13 @@ function mergeElementShape(elementShapeA, elementShapeB) {
     throw new Error(`Incompatible ranks during merge: ${elementShapeA} vs. ${elementShapeB}`);
   }
   const result = [];
-  for (let i2 = 0; i2 < elementShapeA.length; ++i2) {
-    const dim0 = elementShapeA[i2];
-    const dim1 = elementShapeB[i2];
+  for (let i = 0; i < elementShapeA.length; ++i) {
+    const dim0 = elementShapeA[i];
+    const dim1 = elementShapeB[i];
     if (dim0 >= 0 && dim1 >= 0 && dim0 !== dim1) {
       throw new Error(`Incompatible shape during merge: ${elementShapeA} vs. ${elementShapeB}`);
     }
-    result[i2] = dim0 >= 0 ? dim0 : dim1;
+    result[i] = dim0 >= 0 ? dim0 : dim1;
   }
   return result;
 }
@@ -37601,7 +37601,7 @@ var TensorArray = class {
     if (index < 0 || !this.dynamicSize && index >= this.maxSize) {
       throw new Error(`Tried to write to index ${index}, but array is not resizeable and size is: ${this.maxSize}`);
     }
-    const t2 = this.tensors[index] || {};
+    const t = this.tensors[index] || {};
     if (tensor2.dtype !== this.dtype) {
       throw new Error(`TensorArray ${this.name}: Could not write to TensorArray index ${index},
           because the value dtype is ${tensor2.dtype}, but TensorArray dtype is ${this.dtype}.`);
@@ -37610,22 +37610,22 @@ var TensorArray = class {
       this.elementShape = tensor2.shape;
     }
     assertShapesMatchAllowUndefinedSize(this.elementShape, tensor2.shape, `TensorArray ${this.name}: Could not write to TensorArray index ${index}.`);
-    if (t2.read) {
+    if (t.read) {
       throw new Error(`TensorArray ${this.name}: Could not write to TensorArray index ${index}, because it has already been read.`);
     }
-    if (t2.written) {
+    if (t.written) {
       throw new Error(`TensorArray ${this.name}: Could not write to TensorArray index ${index}, because it has already been written.`);
     }
-    t2.tensor = tensor2;
+    t.tensor = tensor2;
     keep(tensor2);
-    t2.written = true;
-    this.tensors[index] = t2;
+    t.written = true;
+    this.tensors[index] = t;
   }
   writeMany(indices, tensors) {
     if (indices.length !== tensors.length) {
       throw new Error(`TensorArray ${this.name}: could not write multiple tensors,because the index size: ${indices.length} is not the same as tensors size: ${tensors.length}.`);
     }
-    indices.forEach((i2, index) => this.write(i2, tensors[index]));
+    indices.forEach((i, index) => this.write(i, tensors[index]));
   }
   gather(indices, dtype) {
     if (!!dtype && dtype !== this.dtype) {
@@ -37633,8 +37633,8 @@ var TensorArray = class {
     }
     if (!indices) {
       indices = [];
-      for (let i2 = 0; i2 < this.size(); i2++) {
-        indices.push(i2);
+      for (let i = 0; i < this.size(); i++) {
+        indices.push(i);
       }
     } else {
       indices = indices.slice(0, this.size());
@@ -37654,8 +37654,8 @@ var TensorArray = class {
       return tensor([], [0].concat(this.elementShape));
     }
     const indices = [];
-    for (let i2 = 0; i2 < this.size(); i2++) {
-      indices.push(i2);
+    for (let i = 0; i < this.size(); i++) {
+      indices.push(i);
     }
     const tensors = this.readMany(indices);
     assertShapesMatchAllowUndefinedSize(this.elementShape, tensors[0].shape, `TensorArray shape mismatch: tensor array shape (${this.elementShape}) vs first tensor shape (${tensors[0].shape})`);
@@ -37695,17 +37695,17 @@ var TensorArray = class {
     const tensors = [];
     tidy(() => {
       tensor2 = reshape(tensor2, [1, totalLength, elementPerRow]);
-      for (let i2 = 0; i2 < length.length; ++i2) {
-        const previousLength = i2 === 0 ? 0 : cumulativeLengths[i2 - 1];
+      for (let i = 0; i < length.length; ++i) {
+        const previousLength = i === 0 ? 0 : cumulativeLengths[i - 1];
         const indices2 = [0, previousLength, 0];
-        const sizes = [1, length[i2], elementPerRow];
-        tensors[i2] = reshape(slice(tensor2, indices2, sizes), this.elementShape);
+        const sizes = [1, length[i], elementPerRow];
+        tensors[i] = reshape(slice(tensor2, indices2, sizes), this.elementShape);
       }
       return tensors;
     });
     const indices = [];
-    for (let i2 = 0; i2 < length.length; i2++) {
-      indices[i2] = i2;
+    for (let i = 0; i < length.length; i++) {
+      indices[i] = i;
     }
     this.writeMany(indices, tensors);
   }
@@ -37795,8 +37795,8 @@ var TensorList = class {
     }
     const destTensorList = new TensorList([], this.elementShape, this.elementDtype, this.maxNumElements);
     destTensorList.tensors.length = size;
-    for (let i2 = 0; i2 < Math.min(this.tensors.length, size); ++i2) {
-      destTensorList.tensors[i2] = this.tensors[i2];
+    for (let i = 0; i < Math.min(this.tensors.length, size); ++i) {
+      destTensorList.tensors[i] = this.tensors[i];
     }
     return destTensorList;
   }
@@ -37839,7 +37839,7 @@ var TensorList = class {
       return tensor([], [0].concat(outputElementShape));
     }
     return tidy(() => {
-      const tensors = indices.map((i2) => reshape(this.tensors[i2], outputElementShape));
+      const tensors = indices.map((i) => reshape(this.tensors[i], outputElementShape));
       return stack(tensors, 0);
     });
   }
@@ -37853,7 +37853,7 @@ var TensorList = class {
       return tensor([], [0].concat(outputElementShape));
     }
     return tidy(() => {
-      const tensors = this.tensors.map((t2) => reshape(t2, outputElementShape));
+      const tensors = this.tensors.map((t) => reshape(t, outputElementShape));
       return concat(tensors, 0);
     });
   }
@@ -37906,18 +37906,18 @@ function split2(tensor2, length, elementShape) {
   const tensors = tidy(() => {
     const tensors2 = [];
     tensor2 = reshape(tensor2, [1, totalLength, elementPerRow]);
-    for (let i2 = 0; i2 < length.length; ++i2) {
-      const previousLength = i2 === 0 ? 0 : cumulativeLengths[i2 - 1];
+    for (let i = 0; i < length.length; ++i) {
+      const previousLength = i === 0 ? 0 : cumulativeLengths[i - 1];
       const indices = [0, previousLength, 0];
-      const sizes = [1, length[i2], elementPerRow];
-      tensors2[i2] = reshape(slice(tensor2, indices, sizes), outputElementShape);
+      const sizes = [1, length[i], elementPerRow];
+      tensors2[i] = reshape(slice(tensor2, indices, sizes), outputElementShape);
     }
     tensor2.dispose();
     return tensors2;
   });
   const list = new TensorList([], elementShape, tensor2.dtype, length.length);
-  for (let i2 = 0; i2 < tensors.length; i2++) {
-    list.setItem(i2, tensors[i2]);
+  for (let i = 0; i < tensors.length; i++) {
+    list.setItem(i, tensors[i]);
   }
   return list;
 }
@@ -38515,14 +38515,14 @@ var executeOp8 = (node, tensorMap, context, ops = ops_for_converter_exports) =>
       return [cloneTensor(data2)];
     }
     case "IdentityN":
-      return getParamValue("x", node, tensorMap, context).map((t2) => cloneTensor(t2));
+      return getParamValue("x", node, tensorMap, context).map((t) => cloneTensor(t));
     case "Snapshot":
       const snapshot = getParamValue("x", node, tensorMap, context);
       return [cloneTensor(snapshot)];
     case "Shape":
       return [ops.tensor1d(getParamValue("x", node, tensorMap, context).shape, "int32")];
     case "ShapeN":
-      return getParamValue("x", node, tensorMap, context).map((t2) => ops.tensor1d(t2.shape));
+      return getParamValue("x", node, tensorMap, context).map((t) => ops.tensor1d(t.shape));
     case "Size":
       return [ops.scalar(getParamValue("x", node, tensorMap, context).size, "int32")];
     case "Rank":
@@ -38536,8 +38536,8 @@ var executeOp8 = (node, tensorMap, context, ops = ops_for_converter_exports) =>
       const summarize = getParamValue("summarize", node, tensorMap, context);
       console.warn("The graph has a tf.print() operation,usually used for debugging, which slows down performance.");
       console.log(message);
-      for (let i2 = 0; i2 < data.length; i2++) {
-        console.log(Array.prototype.slice.call(data[i2].dataSync()).slice(0, summarize));
+      for (let i = 0; i < data.length; i++) {
+        console.log(Array.prototype.slice.call(data[i].dataSync()).slice(0, summarize));
       }
       return [input2];
     default:
@@ -38578,9 +38578,9 @@ var HashTable = class {
       const keysLength = $keys.length;
       const valuesLength = $values.length;
       util_exports.assert(keysLength === valuesLength, () => `The number of elements doesn't match, keys has ${keysLength} elements, the values has ${valuesLength} elements.`);
-      for (let i2 = 0; i2 < keysLength; i2++) {
-        const key = $keys[i2];
-        const value = $values[i2];
+      for (let i = 0; i < keysLength; i++) {
+        const key = $keys[i];
+        const value = $values[i];
         keep(value);
         this.tensorMap.set(key, value);
       }
@@ -38592,8 +38592,8 @@ var HashTable = class {
     const $keys = await keys.data();
     return tidy(() => {
       const result = [];
-      for (let i2 = 0; i2 < $keys.length; i2++) {
-        const key = $keys[i2];
+      for (let i = 0; i < $keys.length; i++) {
+        const key = $keys[i];
         const value = this.findWithDefault(key, defaultValue);
         result.push(value);
       }
@@ -38881,10 +38881,10 @@ var executeOp15 = (node, tensorMap, context, ops = ops_for_converter_exports) =>
   switch (node.op) {
     case "ConcatV2":
     case "Concat": {
-      const n2 = getParamValue("n", node, tensorMap, context);
+      const n = getParamValue("n", node, tensorMap, context);
       const axis = getParamValue("axis", node, tensorMap, context);
       let inputs = getParamValue("tensors", node, tensorMap, context);
-      inputs = inputs.slice(0, n2);
+      inputs = inputs.slice(0, n);
       return [ops.concat(inputs, axis)];
     }
     case "Gather": {
@@ -38902,9 +38902,9 @@ var executeOp15 = (node, tensorMap, context, ops = ops_for_converter_exports) =>
     case "Reverse": {
       const dims = getParamValue("dims", node, tensorMap, context);
       const axis = [];
-      for (let i2 = 0; i2 < dims.length; i2++) {
-        if (dims[i2]) {
-          axis.push(i2);
+      for (let i = 0; i < dims.length; i++) {
+        if (dims[i]) {
+          axis.push(i);
         }
       }
       const input2 = getParamValue("x", node, tensorMap, context);
@@ -39197,8 +39197,8 @@ var ExecutionContext = class {
   }
   generateCurrentContextIds() {
     const names = [];
-    for (let i2 = 0; i2 < this.contexts.length - 1; i2++) {
-      const contexts2 = this.contexts.slice(0, this.contexts.length - i2);
+    for (let i = 0; i < this.contexts.length - 1; i++) {
+      const contexts2 = this.contexts.slice(0, this.contexts.length - i);
       names.push(this.contextIdforContexts(contexts2));
     }
     names.push("");
@@ -39467,7 +39467,7 @@ var GraphExecutor = class {
       throw new Error(`This execution contains the node '${dynamicNode.name}', which has the dynamic op '${dynamicNode.op}'. Please use model.executeAsync() instead. Alternatively, to avoid the dynamic ops, specify the inputs [${syncInputs}]`);
     }
     if (missingInputs.length > 0) {
-      const outNames = outputs.map((n2) => n2.name);
+      const outNames = outputs.map((n) => n.name);
       const inNames = Object.keys(inputs);
       throw new Error(`Cannot compute the outputs [${outNames}] from the provided inputs [${inNames}]. Missing the following inputs: [${missingInputs}]`);
     }
@@ -39506,8 +39506,8 @@ var GraphExecutor = class {
       });
       const tensorsToKeep = this.getFrozenTensorIds(tensorsMap);
       const intermediateTensorConsumerCount = {};
-      for (let i2 = 0; i2 < orderedNodes.length; i2++) {
-        const node = orderedNodes[i2];
+      for (let i = 0; i < orderedNodes.length; i++) {
+        const node = orderedNodes[i];
         if (!tensorsMap[node.name]) {
           const tensors = executeOp20(node, tensorsMap, context, this._resourceManager);
           if (util_exports.isPromise(tensors)) {
@@ -39607,14 +39607,14 @@ var GraphExecutor = class {
     }
     try {
       this.keepTensorForDebug = env().getBool("KEEP_INTERMEDIATE_TENSORS");
-    } catch (e2) {
-      console.warn(e2.message);
+    } catch (e) {
+      console.warn(e.message);
     }
     this.resetIntermediateTensors();
     const context = new ExecutionContext(this.weightMap, tensorArrayMap, tensorListMap, this.functionExecutorMap);
     this.tensorsMap = await this.executeWithControlFlow(inputs, context, outputs, isFunctionExecution);
     const results = outputs.map((name) => getTensor(name, this.tensorsMap, context));
-    const outputIds = results.map((t2) => t2.id);
+    const outputIds = results.map((t) => t.id);
     const inputIds = Object.keys(inputs).map((name) => inputs[name].id);
     this.keepIds = /* @__PURE__ */ new Set([...outputIds, ...inputIds, ...this.weightIds]);
     if (!this.keepTensorForDebug) {
@@ -39691,12 +39691,12 @@ var GraphExecutor = class {
         }
         const currentContext = context.currentContext;
         if (util_exports.isPromise(tensors)) {
-          promises.push(tensors.then((t2) => {
-            tensorMap[nodeName] = t2;
+          promises.push(tensors.then((t) => {
+            tensorMap[nodeName] = t;
             context.currentContext = currentContext;
             this.checkTensorForDisposal(nodeName, item.node, tensorMap, context, tensorsToKeep, outputNames, intermediateTensorConsumerCount);
             this.processChildNodes(item.node, stack2, context, tensorMap, added, usedNodes);
-            return t2;
+            return t;
           }));
         } else {
           tensorMap[nodeName] = tensors;
@@ -39932,7 +39932,7 @@ var GraphModel = class {
     if (this.structuredOutputKeys) {
       const outputTensorsArray = outputTensors instanceof Tensor ? [outputTensors] : outputTensors;
       const outputTensorMap = {};
-      outputTensorsArray.forEach((outputTensor, i2) => outputTensorMap[this.structuredOutputKeys[i2]] = outputTensor);
+      outputTensorsArray.forEach((outputTensor, i) => outputTensorMap[this.structuredOutputKeys[i]] = outputTensor);
       return outputTensorMap;
     }
     return outputTensors;
@@ -39945,8 +39945,8 @@ var GraphModel = class {
     if (inputs.length !== this.inputNodes.length) {
       throw new Error(`Input tensor count mismatch,the graph model has ${this.inputNodes.length} placeholders, while there are ${inputs.length} input tensors.`);
     }
-    return this.inputNodes.reduce((map, inputName, i2) => {
-      map[inputName] = inputs[i2];
+    return this.inputNodes.reduce((map, inputName, i) => {
+      map[inputName] = inputs[i];
       return map;
     }, {});
   }
@@ -40286,8 +40286,8 @@ var GrowingRingBuffer = class extends RingBuffer {
     const newCapacity = this.capacity * 2;
     const newData = new Array(newCapacity);
     const len = this.length();
-    for (let i2 = 0; i2 < len; i2++) {
-      newData[i2] = this.get(this.wrap(this.begin + i2));
+    for (let i = 0; i < len; i++) {
+      newData[i] = this.get(this.wrap(this.begin + i));
     }
     this.data = newData;
     this.capacity = newCapacity;
@@ -40430,9 +40430,9 @@ var FunctionCallIterator = class extends LazyIterator {
   async next() {
     try {
       return this.nextFn();
-    } catch (e2) {
-      e2.message = `Error thrown while iterating through a dataset: ${e2.message}`;
-      throw e2;
+    } catch (e) {
+      e.message = `Error thrown while iterating through a dataset: ${e.message}`;
+      throw e;
     }
   }
 };
@@ -40567,9 +40567,9 @@ var MapIterator = class extends LazyIterator {
     const inputTensors = tensor_util_exports.getTensorsInContainer(item.value);
     const mapped = this.transform(item.value);
     const outputTensors = tensor_util_exports.getTensorsInContainer(mapped);
-    for (const t2 of inputTensors) {
-      if (!tensor_util_exports.isTensorInList(t2, outputTensors)) {
-        t2.dispose();
+    for (const t of inputTensors) {
+      if (!tensor_util_exports.isTensorInList(t, outputTensors)) {
+        t.dispose();
       }
     }
     return { value: mapped, done: false };
@@ -40594,8 +40594,8 @@ var ErrorHandlingLazyIterator = class extends LazyIterator {
     while (true) {
       try {
         return await this.upstream.next();
-      } catch (e2) {
-        if (!this.handler(e2)) {
+      } catch (e) {
+        if (!this.handler(e)) {
           return { value: null, done: true };
         }
       }
@@ -40619,9 +40619,9 @@ var AsyncMapIterator = class extends LazyIterator {
     const inputTensors = tensor_util_exports.getTensorsInContainer(item.value);
     const mapped = await this.transform(item.value);
     const outputTensors = tensor_util_exports.getTensorsInContainer(mapped);
-    for (const t2 of inputTensors) {
-      if (!tensor_util_exports.isTensorInList(t2, outputTensors)) {
-        t2.dispose();
+    for (const t of inputTensors) {
+      if (!tensor_util_exports.isTensorInList(t, outputTensors)) {
+        t.dispose();
       }
     }
     return { value: mapped, done: false };
@@ -40664,9 +40664,9 @@ var FlatmapIterator = class extends OneToManyIterator {
     const mappedArray = this.transform(item.value);
     const outputTensors = tensor_util_exports.getTensorsInContainer(mappedArray);
     this.outputQueue.pushAll(mappedArray);
-    for (const t2 of inputTensors) {
-      if (!tensor_util_exports.isTensorInList(t2, outputTensors)) {
-        t2.dispose();
+    for (const t of inputTensors) {
+      if (!tensor_util_exports.isTensorInList(t, outputTensors)) {
+        t.dispose();
       }
     }
     return true;
@@ -40987,8 +40987,8 @@ function zip(datasets) {
   }
   let size;
   if (Array.isArray(datasets)) {
-    for (let i2 = 0; i2 < datasets.length; i2++) {
-      size = size == null ? datasets[i2].size : Math.min(size, datasets[i2].size);
+    for (let i = 0; i < datasets.length; i++) {
+      size = size == null ? datasets[i].size : Math.min(size, datasets[i].size);
     }
   } else if (datasets instanceof Object) {
     for (const ds in datasets) {
@@ -41143,13 +41143,13 @@ var CSVDataset = class extends Dataset {
     const values = this.parseRow(line);
     const features = {};
     const labels = {};
-    for (let i2 = 0; i2 < this.fullColumnNames.length; i2++) {
-      const key = this.fullColumnNames[i2];
+    for (let i = 0; i < this.fullColumnNames.length; i++) {
+      const key = this.fullColumnNames[i];
       const config = this.columnConfigs ? this.columnConfigs[key] : null;
       if (this.configuredColumnsOnly && !config) {
         continue;
       } else {
-        const value = values[i2];
+        const value = values[i];
         let parsedValue = null;
         if (value === "") {
           if (config && config.default !== void 0) {
@@ -41206,16 +41206,16 @@ var CSVDataset = class extends Dataset {
     let readOffset = 0;
     const readLength = line.length;
     let currentState = STATE_OUT;
-    for (let i2 = 0; i2 < readLength; i2++) {
+    for (let i = 0; i < readLength; i++) {
       switch (currentState) {
         case STATE_OUT:
-          switch (line.charAt(i2)) {
+          switch (line.charAt(i)) {
             case CODE_QUOTE:
-              readOffset = i2 + 1;
+              readOffset = i + 1;
               currentState = STATE_QUOTE;
               break;
             case this.delimiter:
-              readOffset = i2 + 1;
+              readOffset = i + 1;
               if (this.delimiter === " " && this.delimWhitespace) {
                 break;
               }
@@ -41224,22 +41224,22 @@ var CSVDataset = class extends Dataset {
               break;
             default:
               currentState = STATE_FIELD;
-              readOffset = i2;
+              readOffset = i;
               break;
           }
           break;
         case STATE_FIELD:
-          switch (line.charAt(i2)) {
+          switch (line.charAt(i)) {
             case this.delimiter:
-              result.push(line.substring(readOffset, i2));
+              result.push(line.substring(readOffset, i));
               currentState = STATE_OUT;
-              readOffset = i2 + 1;
+              readOffset = i + 1;
               break;
             default:
           }
           break;
         case STATE_QUOTE:
-          switch (line.charAt(i2)) {
+          switch (line.charAt(i)) {
             case CODE_QUOTE:
               currentState = STATE_QUOTE_AFTER_QUOTE;
               break;
@@ -41247,11 +41247,11 @@ var CSVDataset = class extends Dataset {
           }
           break;
         case STATE_QUOTE_AFTER_QUOTE:
-          switch (line.charAt(i2)) {
+          switch (line.charAt(i)) {
             case this.delimiter:
-              result.push(line.substring(readOffset, i2 - 1));
+              result.push(line.substring(readOffset, i - 1));
               currentState = STATE_OUT;
-              readOffset = i2 + 1;
+              readOffset = i + 1;
               break;
             case CODE_QUOTE:
               currentState = STATE_QUOTE;
@@ -41262,7 +41262,7 @@ var CSVDataset = class extends Dataset {
           }
           break;
         case STATE_WITHIN_QUOTE_IN_QUOTE:
-          switch (line.charAt(i2)) {
+          switch (line.charAt(i)) {
             case CODE_QUOTE:
               currentState = STATE_QUOTE;
               break;
@@ -41323,8 +41323,8 @@ var MicrophoneIterator = class extends LazyIterator {
         audio: this.audioTrackConstraints == null ? true : this.audioTrackConstraints,
         video: false
       });
-    } catch (e2) {
-      throw new Error(`Error thrown while initializing video stream: ${e2.message}`);
+    } catch (e) {
+      throw new Error(`Error thrown while initializing video stream: ${e.message}`);
     }
     if (!this.stream) {
       throw new Error("Could not obtain audio from microphone.");
@@ -41411,7 +41411,7 @@ var MicrophoneIterator = class extends LazyIterator {
   flattenQueue(queue) {
     const frameSize = queue[0].length;
     const freqData = new Float32Array(queue.length * frameSize);
-    queue.forEach((data, i2) => freqData.set(data, i2 * frameSize));
+    queue.forEach((data, i) => freqData.set(data, i * frameSize));
     return freqData;
   }
   getTensorFromAudioDataArray(freqData, shape) {
@@ -41478,9 +41478,9 @@ var WebcamIterator = class extends LazyIterator {
           height: this.webcamVideoElement.height
         }
       });
-    } catch (e2) {
-      e2.message = `Error thrown while initializing video stream: ${e2.message}`;
-      throw e2;
+    } catch (e) {
+      e.message = `Error thrown while initializing video stream: ${e.message}`;
+      throw e;
     }
     if (!this.stream) {
       throw new Error("Could not obtain video from webcam.");
@@ -41506,14 +41506,14 @@ var WebcamIterator = class extends LazyIterator {
     let img;
     try {
       img = browser_exports.fromPixels(this.webcamVideoElement);
-    } catch (e2) {
-      throw new Error(`Error thrown converting video to pixels: ${JSON.stringify(e2)}`);
+    } catch (e) {
+      throw new Error(`Error thrown converting video to pixels: ${JSON.stringify(e)}`);
     }
     if (this.resize) {
       try {
         return { value: this.cropAndResizeFrame(img), done: false };
-      } catch (e2) {
-        throw new Error(`Error thrown cropping the video: ${e2.message}`);
+      } catch (e) {
+        throw new Error(`Error thrown cropping the video: ${e.message}`);
       } finally {
         img.dispose();
       }
@@ -41807,9 +41807,9 @@ function assertNotComplex(tensor2, opName) {
   if (!Array.isArray(tensor2)) {
     tensor2 = [tensor2];
   }
-  tensor2.forEach((t2) => {
-    if (t2 != null) {
-      util_exports.assert(t2.dtype !== "complex64", () => `${opName} does not support complex64 tensors in the CPU backend.`);
+  tensor2.forEach((t) => {
+    if (t != null) {
+      util_exports.assert(t.dtype !== "complex64", () => `${opName} does not support complex64 tensors in the CPU backend.`);
     }
   });
 }
@@ -41882,17 +41882,17 @@ var MathBackendCPU = class extends KernelBackend {
     }
     return this.data.get(dataId).values;
   }
-  bufferSync(t2) {
-    const data = this.readSync(t2.dataId);
-    if (t2.dtype === "string") {
+  bufferSync(t) {
+    const data = this.readSync(t.dataId);
+    if (t.dtype === "string") {
       try {
         const strings = data.map((d) => util_exports.decodeString(d));
-        return buffer(t2.shape, t2.dtype, strings);
+        return buffer(t.shape, t.dtype, strings);
       } catch (_a) {
         throw new Error("Failed to decode encoded string bytes into utf-8");
       }
     }
-    return buffer(t2.shape, t2.dtype, data);
+    return buffer(t.shape, t.dtype, data);
   }
   makeOutput(values, shape, dtype) {
     return engine().makeTensorFromTensorInfo(this.makeTensorInfo(shape, dtype, values), this);
@@ -41997,8 +41997,8 @@ __export(shared_exports, {
 // node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Abs.js
 function simpleAbsImpl(vals) {
   const resultValues = new Float32Array(vals.length);
-  for (let i2 = 0; i2 < vals.length; ++i2) {
-    resultValues[i2] = Math.abs(vals[i2]);
+  for (let i = 0; i < vals.length; ++i) {
+    resultValues[i] = Math.abs(vals[i]);
   }
   return resultValues;
 }
@@ -42032,19 +42032,19 @@ function createSimpleBinaryKernelImpl(op2) {
     const aBroadcastDims = backend_util_exports.getBroadcastDims(aShape, newShape);
     const bBroadcastDims = backend_util_exports.getBroadcastDims(bShape, newShape);
     if (aBroadcastDims.length + bBroadcastDims.length === 0) {
-      for (let i2 = 0; i2 < result.length; ++i2) {
-        result[i2] = op2(aVals[i2 % aVals.length], bVals[i2 % bVals.length]);
+      for (let i = 0; i < result.length; ++i) {
+        result[i] = op2(aVals[i % aVals.length], bVals[i % bVals.length]);
       }
     } else {
-      for (let i2 = 0; i2 < result.length; ++i2) {
-        const loc = util_exports.indexToLoc(i2, resultRank, resultStrides);
+      for (let i = 0; i < result.length; ++i) {
+        const loc = util_exports.indexToLoc(i, resultRank, resultStrides);
         const aLoc = loc.slice(-aRank);
         aBroadcastDims.forEach((d) => aLoc[d] = 0);
         const aIndex = util_exports.locToIndex(aLoc, aRank, aStrides);
         const bLoc = loc.slice(-bRank);
         bBroadcastDims.forEach((d) => bLoc[d] = 0);
         const bIndex = util_exports.locToIndex(bLoc, bRank, bStrides);
-        result[i2] = op2(aVals[aIndex], bVals[bIndex]);
+        result[i] = op2(aVals[aIndex], bVals[bIndex]);
       }
     }
     return [result, newShape];
@@ -42224,16 +42224,16 @@ function createComplexBinaryKernelImpl(op2) {
     const bRank = bShape.length;
     const bStrides = util_exports.computeStrides(bShape);
     if (aBroadcastDims.length + bBroadcastDims.length === 0) {
-      for (let i2 = 0; i2 < resultRealVals.length; i2++) {
-        const aIdx = i2 % aVals.length;
-        const bIdx = i2 % bVals.length;
+      for (let i = 0; i < resultRealVals.length; i++) {
+        const aIdx = i % aVals.length;
+        const bIdx = i % bVals.length;
         const result = op2(aVals[aIdx * 2], aVals[aIdx * 2 + 1], bVals[bIdx * 2], bVals[bIdx * 2 + 1]);
-        resultRealVals[i2] = result.real;
-        resultImagVals[i2] = result.imag;
+        resultRealVals[i] = result.real;
+        resultImagVals[i] = result.imag;
       }
     } else {
-      for (let i2 = 0; i2 < resultRealVals.length; i2++) {
-        const loc = util_exports.indexToLoc(i2, resultRank, resultStrides);
+      for (let i = 0; i < resultRealVals.length; i++) {
+        const loc = util_exports.indexToLoc(i, resultRank, resultStrides);
         const aLoc = loc.slice(-aRank);
         aBroadcastDims.forEach((d) => aLoc[d] = 0);
         const aIndex = util_exports.locToIndex(aLoc, aRank, aStrides);
@@ -42241,8 +42241,8 @@ function createComplexBinaryKernelImpl(op2) {
         bBroadcastDims.forEach((d) => bLoc[d] = 0);
         const bIndex = util_exports.locToIndex(bLoc, bRank, bStrides);
         const opResult = op2(aVals[aIndex * 2], aVals[aIndex * 2 + 1], bVals[bIndex * 2], bVals[bIndex * 2 + 1]);
-        resultRealVals[i2] = opResult.real;
-        resultImagVals[i2] = opResult.imag;
+        resultRealVals[i] = opResult.real;
+        resultImagVals[i] = opResult.imag;
       }
     }
     return [resultRealVals, resultImagVals, resultShape];
@@ -42265,8 +42265,8 @@ var addConfig = {
 function bincountImpl(xVals, weightsVals, weightsDtype, weightsShape, size) {
   const weightsSize = util_exports.sizeFromShape(weightsShape);
   const outVals = util_exports.makeZerosTypedArray(size, weightsDtype);
-  for (let i2 = 0; i2 < xVals.length; i2++) {
-    const value = xVals[i2];
+  for (let i = 0; i < xVals.length; i++) {
+    const value = xVals[i];
     if (value < 0) {
       throw new Error("Input x must be non-negative!");
     }
@@ -42274,7 +42274,7 @@ function bincountImpl(xVals, weightsVals, weightsDtype, weightsShape, size) {
       continue;
     }
     if (weightsSize > 0) {
-      outVals[value] += weightsVals[i2];
+      outVals[value] += weightsVals[i];
     } else {
       outVals[value] += 1;
     }
@@ -42285,9 +42285,9 @@ function bincountReduceImpl(xBuf, weightsBuf, size, binaryOutput = false) {
   const numRows = xBuf.shape[0];
   const numCols = xBuf.shape[1];
   const outBuf = buffer([numRows, size], weightsBuf.dtype);
-  for (let i2 = 0; i2 < numRows; i2++) {
+  for (let i = 0; i < numRows; i++) {
     for (let j = 0; j < numCols; j++) {
-      const value = xBuf.get(i2, j);
+      const value = xBuf.get(i, j);
       if (value < 0) {
         throw new Error("Input x must be non-negative!");
       }
@@ -42295,12 +42295,12 @@ function bincountReduceImpl(xBuf, weightsBuf, size, binaryOutput = false) {
         continue;
       }
       if (binaryOutput) {
-        outBuf.set(1, i2, value);
+        outBuf.set(1, i, value);
       } else {
         if (weightsBuf.size > 0) {
-          outBuf.set(outBuf.get(i2, value) + weightsBuf.get(i2, j), i2, value);
+          outBuf.set(outBuf.get(i, value) + weightsBuf.get(i, j), i, value);
         } else {
-          outBuf.set(outBuf.get(i2, value) + 1, i2, value);
+          outBuf.set(outBuf.get(i, value) + 1, i, value);
         }
       }
     }
@@ -42312,8 +42312,8 @@ function bincountReduceImpl(xBuf, weightsBuf, size, binaryOutput = false) {
 function createSimpleUnaryImpl(op2) {
   return (values, dtype, attrs) => {
     const newValues = util_exports.getTypedArrayFromDType(dtype, values.length);
-    for (let i2 = 0; i2 < values.length; ++i2) {
-      newValues[i2] = op2(values[i2], attrs);
+    for (let i = 0; i < values.length; ++i) {
+      newValues[i] = op2(values[i], attrs);
     }
     return newValues;
   };
@@ -42332,8 +42332,8 @@ function unaryKernelFunc(name, op2, dtype) {
     const xSize = util_exports.sizeFromShape(x.shape);
     const $dtype = dtype || x.dtype;
     const newValues = util_exports.getArrayFromDType($dtype, xSize);
-    for (let i2 = 0; i2 < xSize; ++i2) {
-      newValues[i2] = op2(values[i2], attrs);
+    for (let i = 0; i < xSize; ++i) {
+      newValues[i] = op2(values[i], attrs);
     }
     return cpuBackend.makeTensorInfo(x.shape, $dtype, newValues);
   };
@@ -42428,11 +42428,11 @@ var floorConfig = {
 // node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/GatherNd_Impl.js
 function gatherNdImpl(indicesData, paramsBuf, dtype, numSlices, sliceRank, sliceSize, strides, paramsShape, paramsSize) {
   const outBuf = buffer([numSlices, sliceSize], dtype);
-  for (let i2 = 0; i2 < numSlices; i2++) {
+  for (let i = 0; i < numSlices; i++) {
     const index = [];
     let flattenIndex = 0;
     for (let j = 0; j < sliceRank; j++) {
-      const dim = indicesData[i2 * sliceRank + j];
+      const dim = indicesData[i * sliceRank + j];
       flattenIndex += dim * strides[j];
       index.push(dim);
     }
@@ -42440,7 +42440,7 @@ function gatherNdImpl(indicesData, paramsBuf, dtype, numSlices, sliceRank, slice
       throw new Error(`Invalid indices: ${index} does not index into ${paramsShape}`);
     }
     for (let k = 0; k < sliceSize; k++) {
-      outBuf.values[i2 * sliceSize + k] = paramsBuf.get(...paramsBuf.indexToLoc(flattenIndex * sliceSize + k));
+      outBuf.values[i * sliceSize + k] = paramsBuf.get(...paramsBuf.indexToLoc(flattenIndex * sliceSize + k));
     }
   }
   return outBuf;
@@ -42449,8 +42449,8 @@ function gatherNdImpl(indicesData, paramsBuf, dtype, numSlices, sliceRank, slice
 // node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/GatherV2_impl.js
 function gatherV2Impl(xBuf, indicesBuf, flattenOutputShape) {
   const outBuf = buffer(flattenOutputShape, xBuf.dtype);
-  for (let i2 = 0; i2 < outBuf.size; ++i2) {
-    const newLoc = outBuf.indexToLoc(i2);
+  for (let i = 0; i < outBuf.size; ++i) {
+    const newLoc = outBuf.indexToLoc(i);
     const originalLoc = newLoc.slice();
     const batchIdx = originalLoc[0];
     const indicesIdx = originalLoc[2];
@@ -42458,7 +42458,7 @@ function gatherV2Impl(xBuf, indicesBuf, flattenOutputShape) {
     originalLoc[2] = indicesBuf.values[indicesIndex];
     const originalIndex = xBuf.locToIndex(originalLoc);
     if (0 <= originalIndex && originalIndex < xBuf.values.length) {
-      outBuf.values[i2] = xBuf.values[originalIndex];
+      outBuf.values[i] = xBuf.values[originalIndex];
     }
   }
   return outBuf;
@@ -42505,8 +42505,8 @@ function linSpaceImpl(start, stop, num) {
   const step5 = (stop - start) / (num - 1);
   const values = util_exports.makeZerosTypedArray(num, "float32");
   values[0] = start;
-  for (let i2 = 1; i2 < values.length; i2++) {
-    values[i2] = values[i2 - 1] + step5;
+  for (let i = 1; i < values.length; i++) {
+    values[i] = values[i - 1] + step5;
   }
   return values;
 }
@@ -42523,8 +42523,8 @@ var logConfig = {
 // node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Max_impl.js
 function maxImpl(aVals, reduceSize, outShape, dtype) {
   const vals = util_exports.getTypedArrayFromDType(dtype, util_exports.sizeFromShape(outShape));
-  for (let i2 = 0; i2 < vals.length; ++i2) {
-    const offset = i2 * reduceSize;
+  for (let i = 0; i < vals.length; ++i) {
+    const offset = i * reduceSize;
     let max7 = aVals[offset];
     for (let j = 0; j < reduceSize; ++j) {
       const value = aVals[offset + j];
@@ -42532,7 +42532,7 @@ function maxImpl(aVals, reduceSize, outShape, dtype) {
         max7 = value;
       }
     }
-    vals[i2] = max7;
+    vals[i] = max7;
   }
   return vals;
 }
@@ -42605,14 +42605,14 @@ function transposeImpl(xVals, xShape, dtype, perm, newShape) {
   const xStrides = util_exports.computeStrides(xShape);
   const newStrides = util_exports.computeStrides(newShape);
   const result = util_exports.getTypedArrayFromDType(dtype, util_exports.sizeFromShape(newShape));
-  for (let i2 = 0; i2 < xSize; ++i2) {
-    const loc = util_exports.indexToLoc(i2, xRank, xStrides);
+  for (let i = 0; i < xSize; ++i) {
+    const loc = util_exports.indexToLoc(i, xRank, xStrides);
     const newLoc = new Array(loc.length);
-    for (let i3 = 0; i3 < newLoc.length; i3++) {
-      newLoc[i3] = loc[perm[i3]];
+    for (let i2 = 0; i2 < newLoc.length; i2++) {
+      newLoc[i2] = loc[perm[i2]];
     }
     const newIndex = util_exports.locToIndex(newLoc, xRank, newStrides);
-    result[newIndex] = xVals[i2];
+    result[newIndex] = xVals[i];
   }
   return result;
 }
@@ -42625,8 +42625,8 @@ function transpose2(args) {
   assertNotComplex(x, "transpose");
   const xRank = x.shape.length;
   const newShape = new Array(xRank);
-  for (let i2 = 0; i2 < newShape.length; i2++) {
-    newShape[i2] = x.shape[perm[i2]];
+  for (let i = 0; i < newShape.length; i++) {
+    newShape[i] = x.shape[perm[i]];
   }
   const values = backend2.data.get(x.dataId).values;
   const result = transposeImpl(values, x.shape, x.dtype, perm, newShape);
@@ -42645,13 +42645,13 @@ function prodImpl(xShape, xDtype, xVals, reductionAxes) {
   const outDtype = upcastType(xDtype, "int32");
   const outVals = util_exports.makeZerosTypedArray(util_exports.sizeFromShape(outShape), outDtype);
   const reduceSize = util_exports.sizeFromShape(reduceShape);
-  for (let i2 = 0; i2 < outVals.length; ++i2) {
-    const offset = i2 * reduceSize;
+  for (let i = 0; i < outVals.length; ++i) {
+    const offset = i * reduceSize;
     let prod6 = 1;
     for (let j = 0; j < reduceSize; ++j) {
       prod6 *= xVals[offset + j];
     }
-    outVals[i2] = prod6;
+    outVals[i] = prod6;
   }
   return { outVals, outShape, outDtype };
 }
@@ -42677,7 +42677,7 @@ function prod2(args) {
   if (keepDims) {
     resultShape = backend_util_exports.expandShapeToKeepDim(outShape, axes);
   }
-  intermediateTensorInfos.forEach((t2) => backend2.disposeIntermediateTensorInfo(t2));
+  intermediateTensorInfos.forEach((t) => backend2.disposeIntermediateTensorInfo(t));
   return backend2.makeTensorInfo(resultShape, outDtype, outVals);
 }
 var prodConfig = {
@@ -42733,8 +42733,8 @@ var RaggedTensorToTensorOp = class {
       return 0;
     }
     let maxWidth = 0;
-    for (let i2 = 0; i2 < tensorLength - 1; ++i2) {
-      const currentWidth = rowSplit[i2 + 1] - rowSplit[i2];
+    for (let i = 0; i < tensorLength - 1; ++i) {
+      const currentWidth = rowSplit[i + 1] - rowSplit[i];
       if (currentWidth > maxWidth) {
         maxWidth = currentWidth;
       }
@@ -42749,24 +42749,24 @@ var RaggedTensorToTensorOp = class {
     let firstEqualIndex = 0;
     let firstEqualIndexValue = valueRowIds[0];
     let maxWidth = 0;
-    for (let i2 = 1; i2 < indexLength; ++i2) {
-      const value = valueRowIds[i2];
+    for (let i = 1; i < indexLength; ++i) {
+      const value = valueRowIds[i];
       if (value !== firstEqualIndexValue) {
         firstEqualIndexValue = value;
-        maxWidth = Math.max(i2 - firstEqualIndex, maxWidth);
-        firstEqualIndex = i2;
+        maxWidth = Math.max(i - firstEqualIndex, maxWidth);
+        firstEqualIndex = i;
       }
     }
     return Math.max(indexLength - firstEqualIndex, maxWidth);
   }
-  tensorShapeFromTensor(t2, tShape, isPartial = true) {
+  tensorShapeFromTensor(t, tShape, isPartial = true) {
     if (tShape.length === 0) {
-      if (t2[0] === -1) {
+      if (t[0] === -1) {
         return [];
       }
       throw new Error(`The only valid scalar shape tensor is the fully unknown shape specified as -1.`);
     }
-    return makeShape(t2, isPartial);
+    return makeShape(t, isPartial);
   }
   calculateOutputSize(firstDim) {
     const valueShape = this.valuesShape;
@@ -42778,9 +42778,9 @@ var RaggedTensorToTensorOp = class {
     if (result[0] < 0) {
       result[0] = firstDim;
     }
-    for (let i2 = 1; i2 <= this.raggedRank; ++i2) {
-      if (result[i2] < 0) {
-        result[i2] = this.getMaxWidth(i2);
+    for (let i = 1; i <= this.raggedRank; ++i) {
+      if (result[i] < 0) {
+        result[i] = this.getMaxWidth(i);
       }
     }
     return result;
@@ -42789,10 +42789,10 @@ var RaggedTensorToTensorOp = class {
     const minDimension = Math.min(firstDimension, firstDimensionOutput);
     const result = [];
     let currentOutputIndex = 0;
-    for (let i2 = 0; i2 < minDimension; ++i2, currentOutputIndex += outputIndexMultiplier) {
+    for (let i = 0; i < minDimension; ++i, currentOutputIndex += outputIndexMultiplier) {
       result.push(currentOutputIndex);
     }
-    for (let i2 = minDimension; i2 < firstDimension; ++i2) {
+    for (let i = minDimension; i < firstDimension; ++i) {
       result.push(-1);
     }
     util_exports.assert(result.length === firstDimension, () => "Final length of result must be equal to firstDimension.");
@@ -42801,10 +42801,10 @@ var RaggedTensorToTensorOp = class {
   calculateOutputIndexRowSplit(rowSplit, parentOutputIndex, outputIndexMultiplier, outputSize) {
     const rowSplitSize = rowSplit.length;
     const result = [];
-    for (let i2 = 0; i2 < rowSplitSize - 1; ++i2) {
-      const rowLength = rowSplit[i2 + 1] - rowSplit[i2];
+    for (let i = 0; i < rowSplitSize - 1; ++i) {
+      const rowLength = rowSplit[i + 1] - rowSplit[i];
       let realLength = Math.min(outputSize, rowLength);
-      let parentOutputIndexCurrent = parentOutputIndex[i2];
+      let parentOutputIndexCurrent = parentOutputIndex[i];
       if (parentOutputIndexCurrent === -1) {
         realLength = 0;
       }
@@ -42834,8 +42834,8 @@ var RaggedTensorToTensorOp = class {
     }
     let currentOutputIndex = parentOutputIndex[currentValueRowId];
     result.push(currentOutputIndex);
-    for (let i2 = 1; i2 < indexSize; ++i2) {
-      const nextValueRowId = valueRowIds[i2];
+    for (let i = 1; i < indexSize; ++i) {
+      const nextValueRowId = valueRowIds[i];
       if (nextValueRowId === currentValueRowId) {
         if (currentOutputIndex >= 0) {
           ++currentOutputColumn;
@@ -42901,16 +42901,16 @@ var RaggedTensorToTensorOp = class {
     const outputSize = this.calculateOutputSize(firstDimension);
     const multiplier = new Array(this.raggedRank + 1);
     multiplier[multiplier.length - 1] = 1;
-    for (let i2 = multiplier.length - 2; i2 >= 0; --i2) {
-      multiplier[i2] = multiplier[i2 + 1] * outputSize[i2 + 1];
+    for (let i = multiplier.length - 2; i >= 0; --i) {
+      multiplier[i] = multiplier[i + 1] * outputSize[i + 1];
     }
     const outputShape = makeShape(outputSize, false);
     const outputTensor = util_exports.getArrayFromDType(this.valuesDType, util_exports.sizeFromShape(outputShape));
     const fullSize = multiplier[0] * outputSize[0];
     if (fullSize > 0) {
       let outputIndex = this.calculateFirstParentOutputIndex(firstDimension, multiplier[0], outputSize[0]);
-      for (let i2 = 1; i2 <= this.raggedRank; ++i2) {
-        const newOutputIndex = this.calculateOutputIndex(i2 - 1, outputIndex, multiplier[i2], outputSize[i2]);
+      for (let i = 1; i <= this.raggedRank; ++i) {
+        const newOutputIndex = this.calculateOutputIndex(i - 1, outputIndex, multiplier[i], outputSize[i]);
         outputIndex = newOutputIndex;
       }
       this.setOutput(this.raggedRank, outputIndex, outputTensor, outputShape);
@@ -42979,8 +42979,8 @@ var RaggedTensorToTensorOp = class {
   }
 };
 function copyArray(dst, src, size) {
-  for (let i2 = 0; i2 < size; i2++) {
-    dst[i2] = src[i2];
+  for (let i = 0; i < size; i++) {
+    dst[i] = src[i];
   }
 }
 function makeShape(shape, isPartial) {
@@ -43017,8 +43017,8 @@ function rangeImpl(start, stop, step5, dtype) {
     step5 = -1;
   }
   values[0] = start;
-  for (let i2 = 1; i2 < values.length; i2++) {
-    values[i2] = values[i2 - 1] + step5;
+  for (let i = 1; i < values.length; i++) {
+    values[i] = values[i - 1] + step5;
   }
   return values;
 }
@@ -43048,11 +43048,11 @@ function scatterImpl(indices, updates, shape, outputSize, sliceSize, numUpdates,
   } else if (typeof defaultValue === "boolean") {
     outBuf.values.fill(+defaultValue);
   }
-  for (let i2 = 0; i2 < numUpdates; i2++) {
+  for (let i = 0; i < numUpdates; i++) {
     const index = [];
     let flattenIndex = 0;
     for (let j = 0; j < sliceRank; j++) {
-      const dim = indicesData[i2 * sliceRank + j];
+      const dim = indicesData[i * sliceRank + j];
       index.push(dim);
       flattenIndex += dim * strides[j];
     }
@@ -43061,9 +43061,9 @@ function scatterImpl(indices, updates, shape, outputSize, sliceSize, numUpdates,
     }
     for (let k = 0; k < sliceSize; k++) {
       if (sumDupeIndices) {
-        outBuf.values[flattenIndex * sliceSize + k] += updatesData[i2 * sliceSize + k];
+        outBuf.values[flattenIndex * sliceSize + k] += updatesData[i * sliceSize + k];
       } else {
-        outBuf.values[flattenIndex * sliceSize + k] = updates.rank === 0 ? updatesData[0] : updatesData[i2 * sliceSize + k];
+        outBuf.values[flattenIndex * sliceSize + k] = updates.rank === 0 ? updatesData[0] : updatesData[i * sliceSize + k];
       }
     }
   }
@@ -43094,8 +43094,8 @@ function sliceImpl(vals, begin, size, shape, dtype) {
   const decodedData = dtype === "string" ? backend_util_exports.fromUint8ToStringArray(vals) : vals;
   const inBuf = buffer(shape, dtype, decodedData);
   const outBuf = buffer(size, dtype);
-  for (let i2 = 0; i2 < outBuf.size; ++i2) {
-    const outLoc = outBuf.indexToLoc(i2);
+  for (let i = 0; i < outBuf.size; ++i) {
+    const outLoc = outBuf.indexToLoc(i);
     const inLoc = outLoc.map((idx, j) => idx + begin[j]);
     outBuf.set(inBuf.get(...inLoc), ...outLoc);
   }
@@ -43145,13 +43145,13 @@ function sparseFillEmptyRowsImpl(indices, indicesShape, indicesDType, values, va
   let rowsAreOrdered = true;
   let lastIndicesRow = 0;
   const csrOffset = new Array(denseRows).fill(0);
-  for (let i2 = 0; i2 < indicesCount; ++i2) {
-    const row = indices[i2 * rank];
+  for (let i = 0; i < indicesCount; ++i) {
+    const row = indices[i * rank];
     if (row < 0) {
-      throw new Error(backend_util_exports.getSparseFillEmptyRowsNegativeIndexErrorMessage(i2, row));
+      throw new Error(backend_util_exports.getSparseFillEmptyRowsNegativeIndexErrorMessage(i, row));
     }
     if (row >= denseRows) {
-      throw new Error(backend_util_exports.getSparseFillEmptyRowsOutOfRangeIndexErrorMessage(i2, row, denseRows));
+      throw new Error(backend_util_exports.getSparseFillEmptyRowsOutOfRangeIndexErrorMessage(i, row, denseRows));
     }
     ++csrOffset[row];
     rowsAreOrdered = rowsAreOrdered && row >= lastIndicesRow;
@@ -43170,8 +43170,8 @@ function sparseFillEmptyRowsImpl(indices, indicesShape, indicesDType, values, va
   if (allRowsFull && rowsAreOrdered) {
     const outputIndices = indices;
     const outputValues = values;
-    for (let i2 = 0; i2 < indicesCount; ++i2) {
-      reverseIndexMap[i2] = i2;
+    for (let i = 0; i < indicesCount; ++i) {
+      reverseIndexMap[i] = i;
     }
     return [
       outputIndices,
@@ -43185,16 +43185,16 @@ function sparseFillEmptyRowsImpl(indices, indicesShape, indicesDType, values, va
     const outputIndices = util_exports.getArrayFromDType(indicesDType, fullIndicesCount * rank);
     const outputValues = util_exports.getArrayFromDType(valuesDType, fullIndicesCount);
     const filledCount = new Array(denseRows).fill(0);
-    for (let i2 = 0; i2 < indicesCount; ++i2) {
-      const row = indices[i2 * rank];
+    for (let i = 0; i < indicesCount; ++i) {
+      const row = indices[i * rank];
       const offset = filledCount[row];
       const outputI = (row === 0 ? 0 : csrOffset[row - 1]) + offset;
       filledCount[row]++;
       for (let j = 0; j < rank; ++j) {
-        outputIndices[outputI * rank + j] = indices[i2 * rank + j];
+        outputIndices[outputI * rank + j] = indices[i * rank + j];
       }
-      outputValues[outputI] = values[i2];
-      reverseIndexMap[i2] = outputI;
+      outputValues[outputI] = values[i];
+      reverseIndexMap[i] = outputI;
     }
     for (let row = 0; row < denseRows; ++row) {
       const rowCount = filledCount[row];
@@ -43271,13 +43271,13 @@ function sparseReshapeImpl(inputIndices, inputIndicesShape, inputDType, inputSha
     }
   }
   const newIndices = util_exports.getArrayFromDType(inputDType, nnz * outputRank);
-  for (let i2 = 0; i2 < nnz; ++i2) {
+  for (let i = 0; i < nnz; ++i) {
     let id = 0;
     for (let j = 0; j < inputRank; ++j) {
-      id += inputIndices[i2 * inputRank + j] * inputStrides[j];
+      id += inputIndices[i * inputRank + j] * inputStrides[j];
     }
     for (let j = 0; j < outputRank; ++j) {
-      newIndices[i2 * outputRank + j] = Math.trunc(id / outputStrides[j]);
+      newIndices[i * outputRank + j] = Math.trunc(id / outputStrides[j]);
       id %= outputStrides[j];
     }
   }
@@ -43328,10 +43328,10 @@ function sparseSegmentReductionImpl(input2, inputShape, inputDType, indices, seg
     if (outIndex > uninitializedIndex) {
       output.fill(defaultValue, uninitializedIndex * numCol, outIndex * numCol);
     }
-    for (let i2 = start; i2 < end; ++i2) {
-      const index = indices[i2];
+    for (let i = start; i < end; ++i) {
+      const index = indices[i];
       if (index < 0 || index >= inputFlat[0]) {
-        throw new Error(backend_util_exports.getSparseSegmentReductionIndicesOutOfRangeErrorMessage(i2, indices[i2], inputFlat[0]));
+        throw new Error(backend_util_exports.getSparseSegmentReductionIndicesOutOfRangeErrorMessage(i, indices[i], inputFlat[0]));
       }
       for (let j = 0; j < numCol; j++) {
         output[outIndex * numCol + j] += input2[index * numCol + j];
@@ -43380,8 +43380,8 @@ var squaredDifferenceConfig = {
 // node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/StridedSlice_impl.js
 function stridedSliceImpl(outShape, xBuf, strides, begin) {
   const outBuf = buffer(outShape, xBuf.dtype);
-  for (let i2 = 0; i2 < outBuf.size; i2++) {
-    const loc = outBuf.indexToLoc(i2);
+  for (let i = 0; i < outBuf.size; i++) {
+    const loc = outBuf.indexToLoc(i);
     const newLoc = new Array(loc.length);
     for (let j = 0; j < newLoc.length; j++) {
       newLoc[j] = loc[j] * strides[j] + begin[j];
@@ -43417,8 +43417,8 @@ var StringNGramsOp = class {
       const dataStartIndex = splitIndex + (leftPadding > 0 ? 0 : nGramIndex - padWidth);
       let nGramSize = 0;
       nGramSize += leftPadding * this.leftPad.length;
-      for (let n2 = 0; n2 < numTokens; ++n2) {
-        nGramSize += data[dataStartIndex + n2].length;
+      for (let n = 0; n < numTokens; ++n) {
+        nGramSize += data[dataStartIndex + n].length;
       }
       nGramSize += rightPadding * this.rightPad.length;
       const numSeparators = leftPadding + rightPadding + numTokens - 1;
@@ -43427,22 +43427,22 @@ var StringNGramsOp = class {
       const nGram = output[outputStartIndex + nGramIndex];
       let nextNGramIndex = 0;
       const appendToNGram = (str) => str.forEach((value) => nGram[nextNGramIndex++] = value);
-      for (let n2 = 0; n2 < leftPadding; ++n2) {
+      for (let n = 0; n < leftPadding; ++n) {
         appendToNGram(this.leftPad);
         appendToNGram(this.separator);
       }
-      for (let n2 = 0; n2 < numTokens - 1; ++n2) {
-        appendToNGram(data[dataStartIndex + n2]);
+      for (let n = 0; n < numTokens - 1; ++n) {
+        appendToNGram(data[dataStartIndex + n]);
         appendToNGram(this.separator);
       }
       if (numTokens > 0) {
         appendToNGram(data[dataStartIndex + numTokens - 1]);
-        for (let n2 = 0; n2 < rightPadding; ++n2) {
+        for (let n = 0; n < rightPadding; ++n) {
           appendToNGram(this.separator);
           appendToNGram(this.rightPad);
         }
       } else {
-        for (let n2 = 0; n2 < rightPadding - 1; ++n2) {
+        for (let n = 0; n < rightPadding - 1; ++n) {
           appendToNGram(this.rightPad);
           appendToNGram(this.separator);
         }
@@ -43458,13 +43458,13 @@ var StringNGramsOp = class {
       if (prevSplit !== 0) {
         throw new Error(`First split value must be 0, got ${prevSplit}`);
       }
-      for (let i2 = 1; i2 < splitsSize; ++i2) {
-        let validSplits = splits[i2] >= prevSplit;
-        validSplits = validSplits && splits[i2] <= inputDataSize;
+      for (let i = 1; i < splitsSize; ++i) {
+        let validSplits = splits[i] >= prevSplit;
+        validSplits = validSplits && splits[i] <= inputDataSize;
         if (!validSplits) {
-          throw new Error(`Invalid split value ${splits[i2]}, must be in [${prevSplit}, ${inputDataSize}]`);
+          throw new Error(`Invalid split value ${splits[i]}, must be in [${prevSplit}, ${inputDataSize}]`);
         }
-        prevSplit = splits[i2];
+        prevSplit = splits[i];
       }
       if (prevSplit !== inputDataSize) {
         throw new Error(`Last split value must be data size. Expected ${inputDataSize}, got ${prevSplit}`);
@@ -43474,14 +43474,14 @@ var StringNGramsOp = class {
     const nGramsSplits = util_exports.getArrayFromDType("int32", splitsSize);
     if (inputDataSize === 0 || splitsSize === 0) {
       const empty = new Array(inputDataSize);
-      for (let i2 = 0; i2 <= numBatchItems; ++i2) {
-        nGramsSplits[i2] = 0;
+      for (let i = 0; i <= numBatchItems; ++i) {
+        nGramsSplits[i] = 0;
       }
       return [empty, nGramsSplits];
     }
     nGramsSplits[0] = 0;
-    for (let i2 = 1; i2 <= numBatchItems; ++i2) {
-      const length = splits[i2] - splits[i2 - 1];
+    for (let i = 1; i <= numBatchItems; ++i) {
+      const length = splits[i] - splits[i - 1];
       let numNGrams = 0;
       this.nGramWidths.forEach((nGramWidth) => {
         numNGrams += this.getNumNGrams(length, nGramWidth);
@@ -43489,20 +43489,20 @@ var StringNGramsOp = class {
       if (this.preserveShort && length > 0 && numNGrams === 0) {
         numNGrams = 1;
       }
-      nGramsSplits[i2] = nGramsSplits[i2 - 1] + numNGrams;
+      nGramsSplits[i] = nGramsSplits[i - 1] + numNGrams;
     }
     const nGrams = new Array(nGramsSplits[numBatchItems]);
-    for (let i2 = 0; i2 < numBatchItems; ++i2) {
-      const splitIndex = splits[i2];
-      let outputStartIdx = nGramsSplits[i2];
+    for (let i = 0; i < numBatchItems; ++i) {
+      const splitIndex = splits[i];
+      let outputStartIdx = nGramsSplits[i];
       this.nGramWidths.forEach((nGramWidth) => {
-        const length = splits[i2 + 1] - splits[i2];
+        const length = splits[i + 1] - splits[i];
         const numNGrams = this.getNumNGrams(length, nGramWidth);
         this.createNGrams(data, splitIndex, nGrams, outputStartIdx, numNGrams, nGramWidth);
         outputStartIdx += numNGrams;
       });
-      if (this.preserveShort && outputStartIdx === nGramsSplits[i2]) {
-        const dataLength = splits[i2 + 1] - splits[i2];
+      if (this.preserveShort && outputStartIdx === nGramsSplits[i]) {
+        const dataLength = splits[i + 1] - splits[i];
         if (dataLength === 0) {
           continue;
         }
@@ -43524,8 +43524,8 @@ function split3(str, delimiters, skipEmpty, result) {
     return;
   }
   if (delimiters.length === 0) {
-    for (let i2 = 0; i2 < str.length; ++i2) {
-      result.push(str.subarray(i2, i2 + 1));
+    for (let i = 0; i < str.length; ++i) {
+      result.push(str.subarray(i, i + 1));
     }
     return;
   }
@@ -43546,13 +43546,13 @@ function split3(str, delimiters, skipEmpty, result) {
     return;
   }
   let tokenStart = 0;
-  for (let i2 = 0; i2 < str.length + 1; i2++) {
-    if (i2 === str.length || delimiters.indexOf(str[i2]) !== -1) {
-      const token = str.subarray(tokenStart, i2);
+  for (let i = 0; i < str.length + 1; i++) {
+    if (i === str.length || delimiters.indexOf(str[i]) !== -1) {
+      const token = str.subarray(tokenStart, i);
       if (!skipEmpty || token.length !== 0) {
         result.push(token);
       }
-      tokenStart = i2 + 1;
+      tokenStart = i + 1;
     }
   }
 }
@@ -43562,11 +43562,11 @@ function stringSplitImpl(input2, delimiter, skipEmpty) {
   let outputSize = 0;
   let maxNumEntries = 0;
   const numIndices = new Array(batchSize);
-  for (let i2 = 0; i2 < batchSize; ++i2) {
+  for (let i = 0; i < batchSize; ++i) {
     const prevTokensLength = tokens.length;
-    split3(input2[i2], delimiter, skipEmpty, tokens);
+    split3(input2[i], delimiter, skipEmpty, tokens);
     const nEntries = tokens.length - prevTokensLength;
-    numIndices[i2] = nEntries;
+    numIndices[i] = nEntries;
     outputSize += nEntries;
     maxNumEntries = Math.max(maxNumEntries, nEntries);
   }
@@ -43574,9 +43574,9 @@ function stringSplitImpl(input2, delimiter, skipEmpty) {
   const values = new Array(outputSize);
   const shape = [batchSize, maxNumEntries];
   let c = 0;
-  for (let i2 = 0; i2 < batchSize; ++i2) {
-    for (let j = 0; j < numIndices[i2]; ++j) {
-      indices[c * 2] = i2;
+  for (let i = 0; i < batchSize; ++i) {
+    for (let j = 0; j < numIndices[i]; ++j) {
+      indices[c * 2] = i;
       indices[c * 2 + 1] = j;
       values[c] = tokens[c];
       ++c;
@@ -43588,8 +43588,8 @@ function stringSplitImpl(input2, delimiter, skipEmpty) {
 // node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/StringToHashBucketFast_impl.js
 function stringToHashBucketFastImpl(input2, numBuckets) {
   const output = util_exports.getArrayFromDType("int32", input2.length);
-  for (let i2 = 0; i2 < input2.length; ++i2) {
-    output[i2] = util_exports.fingerPrint64(input2[i2]).modulo(numBuckets).getLowBitsUnsigned();
+  for (let i = 0; i < input2.length; ++i) {
+    output[i] = util_exports.fingerPrint64(input2[i]).modulo(numBuckets).getLowBitsUnsigned();
   }
   return output;
 }
@@ -43609,18 +43609,18 @@ var subConfig = {
 // node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Tile_impl.js
 function tileImpl(xBuf, reps) {
   const newShape = new Array(xBuf.rank);
-  for (let i2 = 0; i2 < newShape.length; i2++) {
-    newShape[i2] = xBuf.shape[i2] * reps[i2];
+  for (let i = 0; i < newShape.length; i++) {
+    newShape[i] = xBuf.shape[i] * reps[i];
   }
   const result = buffer(newShape, xBuf.dtype);
-  for (let i2 = 0; i2 < result.values.length; ++i2) {
-    const newLoc = result.indexToLoc(i2);
+  for (let i = 0; i < result.values.length; ++i) {
+    const newLoc = result.indexToLoc(i);
     const originalLoc = new Array(xBuf.rank);
     for (let j = 0; j < originalLoc.length; j++) {
       originalLoc[j] = newLoc[j] % xBuf.shape[j];
     }
     const originalIndex = xBuf.locToIndex(originalLoc);
-    result.values[i2] = xBuf.values[originalIndex];
+    result.values[i] = xBuf.values[originalIndex];
   }
   return result;
 }
@@ -43633,34 +43633,34 @@ var comparePair = (a, b) => {
 function select(array2, k, left = 0, right = array2.length - 1) {
   while (right > left) {
     if (right - left > 600) {
-      const n2 = right - left + 1;
-      const i3 = k - left + 1;
-      const z = Math.log(n2);
-      const s2 = 0.5 * Math.exp(2 * z / 3);
-      const sd = 0.5 * Math.sqrt(z * s2 * (n2 - s2) / n2) * Math.sign(i3 - n2 / 2);
-      const newLeft = Math.max(left, Math.floor(k - i3 * s2 / n2 + sd));
-      const newRight = Math.min(right, Math.floor(k + (n2 - i3) * s2 / n2 + sd));
+      const n = right - left + 1;
+      const i2 = k - left + 1;
+      const z = Math.log(n);
+      const s = 0.5 * Math.exp(2 * z / 3);
+      const sd = 0.5 * Math.sqrt(z * s * (n - s) / n) * Math.sign(i2 - n / 2);
+      const newLeft = Math.max(left, Math.floor(k - i2 * s / n + sd));
+      const newRight = Math.min(right, Math.floor(k + (n - i2) * s / n + sd));
       select(array2, k, newLeft, newRight);
     }
-    const t2 = array2[k];
-    let i2 = left;
+    const t = array2[k];
+    let i = left;
     let j = right;
     util_exports.swap(array2, left, k);
-    if (comparePair(array2[right], t2) > 0) {
+    if (comparePair(array2[right], t) > 0) {
       util_exports.swap(array2, left, right);
     }
-    while (i2 < j) {
-      util_exports.swap(array2, i2, j);
-      i2++;
+    while (i < j) {
+      util_exports.swap(array2, i, j);
+      i++;
       j--;
-      while (comparePair(array2[i2], t2) < 0) {
-        i2 = i2 + 1;
+      while (comparePair(array2[i], t) < 0) {
+        i = i + 1;
       }
-      while (comparePair(array2[j], t2) > 0) {
+      while (comparePair(array2[j], t) > 0) {
         j = j - 1;
       }
     }
-    if (comparePair(array2[left], t2) === 0) {
+    if (comparePair(array2[left], t) === 0) {
       util_exports.swap(array2, left, j);
     } else {
       j = j + 1;
@@ -43694,9 +43694,9 @@ function topKImpl(x, xShape, xDtype, k, sorted) {
     const outOffset = b * k;
     const topKVals = allTopKVals.subarray(outOffset, outOffset + k);
     const topKIndices = allTopKIndices.subarray(outOffset, outOffset + k);
-    for (let i2 = 0; i2 < k; i2++) {
-      topKVals[i2] = valAndInd[i2].value;
-      topKIndices[i2] = valAndInd[i2].index;
+    for (let i = 0; i < k; i++) {
+      topKVals[i] = valAndInd[i].value;
+      topKIndices[i] = valAndInd[i].index;
     }
   }
   const outputShape = xShape.slice();
@@ -43711,47 +43711,47 @@ function topKImpl(x, xShape, xDtype, k, sorted) {
 function uniqueImpl(values, axis, shape, dtype) {
   const $axis = util_exports.parseAxisParam(axis, shape)[0];
   const newShape = [1, shape[0], 1];
-  for (let i2 = 0; i2 < $axis; i2++) {
-    newShape[0] *= shape[i2];
+  for (let i = 0; i < $axis; i++) {
+    newShape[0] *= shape[i];
   }
   newShape[1] = shape[$axis];
-  for (let i2 = $axis + 1; i2 < shape.length; i2++) {
-    newShape[2] *= shape[i2];
+  for (let i = $axis + 1; i < shape.length; i++) {
+    newShape[2] *= shape[i];
   }
   const uniqueElements = {};
   const indices = new Int32Array(shape[$axis]);
   const inputBuffer = new TensorBuffer(newShape, dtype, values);
   const uniqueIndices = [];
   const is1DTensor = newShape[0] === 1 && newShape[2] === 1;
-  for (let i2 = 0; i2 < shape[$axis]; i2++) {
+  for (let i = 0; i < shape[$axis]; i++) {
     let element;
     if (is1DTensor) {
-      element = values[i2].toString();
+      element = values[i].toString();
     } else {
       const axisValues = [];
       for (let m = 0; m < newShape[0]; m++) {
-        for (let n2 = 0; n2 < newShape[2]; n2++) {
-          axisValues.push(inputBuffer.get(m, i2, n2));
+        for (let n = 0; n < newShape[2]; n++) {
+          axisValues.push(inputBuffer.get(m, i, n));
         }
       }
       element = axisValues.join(",");
     }
     if (uniqueElements[element] !== void 0) {
-      indices[i2] = uniqueElements[element];
+      indices[i] = uniqueElements[element];
     } else {
       const uniqueIndex = Object.keys(uniqueElements).length;
       uniqueElements[element] = uniqueIndex;
-      indices[i2] = uniqueIndex;
-      uniqueIndices.push(i2);
+      indices[i] = uniqueIndex;
+      uniqueIndices.push(i);
     }
   }
   const outputTmpShape = newShape.slice();
   outputTmpShape[1] = Object.keys(uniqueElements).length;
   const outputBuffer = new TensorBuffer(outputTmpShape, dtype);
-  uniqueIndices.forEach((uniqueElementIndex, i2) => {
+  uniqueIndices.forEach((uniqueElementIndex, i) => {
     for (let m = 0; m < newShape[0]; m++) {
-      for (let n2 = 0; n2 < newShape[2]; n2++) {
-        outputBuffer.set(inputBuffer.get(m, uniqueElementIndex, n2), m, i2, n2);
+      for (let n = 0; n < newShape[2]; n++) {
+        outputBuffer.set(inputBuffer.get(m, uniqueElementIndex, n), m, i, n);
       }
     }
   });
@@ -43784,8 +43784,8 @@ function leakyRelu2(args) {
   const xSize = util_exports.sizeFromShape(x.shape);
   const xVals = backend2.data.get(x.dataId).values;
   const outVals = util_exports.getTypedArrayFromDType("float32", xSize);
-  for (let i2 = 0; i2 < xVals.length; i2++) {
-    outVals[i2] = xVals[i2] < 0 ? alpha * xVals[i2] : xVals[i2];
+  for (let i = 0; i < xVals.length; i++) {
+    outVals[i] = xVals[i] < 0 ? alpha * xVals[i] : xVals[i];
   }
   return backend2.makeTensorInfo(x.shape, "float32", outVals);
 }
@@ -43917,17 +43917,17 @@ function batchMatMul(args) {
           const iBlock = Math.min(i0 + blockSize, leftDim);
           const jBlock = Math.min(j0 + blockSize, rightDim);
           const kBlock = Math.min(k02 + blockSize, sharedDim);
-          for (let i2 = i0; i2 < iBlock; i2++) {
+          for (let i = i0; i < iBlock; i++) {
             for (let j = j0; j < jBlock; j++) {
               let sum7 = 0;
               for (let k = k02; k < kBlock; k++) {
                 const batchOffsetA = Math.min(bi, batchDimA - 1) * aBatch;
                 const batchOffsetB = Math.min(bi, batchDimB - 1) * bBatch;
-                const aVal = a3dValues[batchOffsetA + i2 * aOuterStep + k * aInnerStep];
+                const aVal = a3dValues[batchOffsetA + i * aOuterStep + k * aInnerStep];
                 const bVal = b3dValues[k * bInnerStep + j * bOuterStep + batchOffsetB];
                 sum7 += aVal * bVal;
               }
-              resVals[bi * size + (i2 * rightDim + j)] += sum7;
+              resVals[bi * size + (i * rightDim + j)] += sum7;
             }
           }
         }
@@ -43965,8 +43965,8 @@ function _fusedMatMul(args) {
     intermediates.push(current);
     current = activationRes;
   }
-  for (const i2 of intermediates) {
-    backend2.disposeIntermediateTensorInfo(i2);
+  for (const i of intermediates) {
+    backend2.disposeIntermediateTensorInfo(i);
   }
   return current;
 }
@@ -43997,11 +43997,11 @@ function addN2(args) {
   const { inputs, backend: backend2 } = args;
   const tensors = inputs;
   assertNotComplex(inputs, "addN");
-  const vals = tensors.map((t2) => backend2.data.get(t2.dataId).values);
+  const vals = tensors.map((t) => backend2.data.get(t.dataId).values);
   const outBuf = buffer(tensors[0].shape, tensors[0].dtype);
   const outVals = outBuf.values;
-  for (let i2 = 0; i2 < tensors.length; i2++) {
-    const currVals = vals[i2];
+  for (let i = 0; i < tensors.length; i++) {
+    const currVals = vals[i];
     for (let j = 0; j < outVals.length; j++) {
       outVals[j] += currVals[j];
     }
@@ -44033,14 +44033,14 @@ function all2(args) {
   const reduceSize = util_exports.sizeFromShape(reduceShape);
   const vals = util_exports.makeZerosTypedArray(util_exports.sizeFromShape(outShape), $x.dtype);
   const aVals = backend2.data.get($x.dataId).values;
-  for (let i2 = 0; i2 < vals.length; ++i2) {
-    const offset = i2 * reduceSize;
+  for (let i = 0; i < vals.length; ++i) {
+    const offset = i * reduceSize;
     let all5 = aVals[offset];
     for (let j = 0; j < reduceSize; ++j) {
       const value = aVals[offset + j];
       all5 = all5 && value;
     }
-    vals[i2] = all5;
+    vals[i] = all5;
   }
   if (permutedAxes != null) {
     backend2.disposeIntermediateTensorInfo($x);
@@ -44079,14 +44079,14 @@ function any2(args) {
   const reduceSize = util_exports.sizeFromShape(reduceShape);
   const vals = util_exports.makeZerosTypedArray(util_exports.sizeFromShape(outShape), $x.dtype);
   const aVals = backend2.data.get($x.dataId).values;
-  for (let i2 = 0; i2 < vals.length; ++i2) {
-    const offset = i2 * reduceSize;
+  for (let i = 0; i < vals.length; ++i) {
+    const offset = i * reduceSize;
     let anyVal = aVals[offset];
     for (let j = 0; j < reduceSize; ++j) {
       const value = aVals[offset + j];
       anyVal = anyVal || value;
     }
-    vals[i2] = anyVal;
+    vals[i] = anyVal;
   }
   if (permutedAxes != null) {
     backend2.disposeIntermediateTensorInfo($x);
@@ -44128,8 +44128,8 @@ function argMax2(args) {
   const vals = util_exports.makeZerosTypedArray(outSize, "int32");
   const reduceSize = util_exports.sizeFromShape(reduceShape);
   const aVals = backend2.data.get($x.dataId).values;
-  for (let i2 = 0; i2 < vals.length; ++i2) {
-    const offset = i2 * reduceSize;
+  for (let i = 0; i < vals.length; ++i) {
+    const offset = i * reduceSize;
     let max7 = aVals[offset];
     let maxIndex = 0;
     for (let j = 0; j < reduceSize; ++j) {
@@ -44139,9 +44139,9 @@ function argMax2(args) {
         maxIndex = j;
       }
     }
-    vals[i2] = maxIndex;
+    vals[i] = maxIndex;
   }
-  intermediateTensorInfos.forEach((t2) => backend2.disposeIntermediateTensorInfo(t2));
+  intermediateTensorInfos.forEach((t) => backend2.disposeIntermediateTensorInfo(t));
   return backend2.makeTensorInfo(outShape, "int32", vals);
 }
 var argMaxConfig = {
@@ -44172,8 +44172,8 @@ function argMin2(args) {
   const vals = util_exports.makeZerosTypedArray(outSize, "int32");
   const reduceSize = util_exports.sizeFromShape(reduceShape);
   const aVals = backend2.data.get($x.dataId).values;
-  for (let i2 = 0; i2 < vals.length; ++i2) {
-    const offset = i2 * reduceSize;
+  for (let i = 0; i < vals.length; ++i) {
+    const offset = i * reduceSize;
     let min7 = aVals[offset];
     let minIndex = 0;
     for (let j = 0; j < reduceSize; ++j) {
@@ -44183,9 +44183,9 @@ function argMin2(args) {
         minIndex = j;
       }
     }
-    vals[i2] = minIndex;
+    vals[i] = minIndex;
   }
-  intermediateTensorInfos.forEach((t2) => backend2.disposeIntermediateTensorInfo(t2));
+  intermediateTensorInfos.forEach((t) => backend2.disposeIntermediateTensorInfo(t));
   return backend2.makeTensorInfo(outShape, "int32", vals);
 }
 var argMinConfig = {
@@ -44682,8 +44682,8 @@ function batchNorm2(args) {
   let mi = 0;
   let si = 0;
   let vi = 0;
-  for (let i2 = 0; i2 < xVals.length; ++i2) {
-    outVals[i2] = offVals[offi++] + (xVals[i2] - mVals[mi++]) * sVals[si++] / Math.sqrt(varVals[vi++] + varianceEpsilon);
+  for (let i = 0; i < xVals.length; ++i) {
+    outVals[i] = offVals[offi++] + (xVals[i] - mVals[mi++]) * sVals[si++] / Math.sqrt(varVals[vi++] + varianceEpsilon);
     if (offi >= offValsLength) {
       offi = 0;
     }
@@ -44791,10 +44791,10 @@ var complexAbs = (args) => {
   const imag5 = complexVals.complexTensorInfos.imag;
   const realVals = cpuBackend.data.get(real5.dataId).values;
   const imagVals = cpuBackend.data.get(imag5.dataId).values;
-  for (let i2 = 0; i2 < realVals.length; i2++) {
-    const real6 = realVals[i2];
-    const imag6 = imagVals[i2];
-    resultValues[i2] = Math.hypot(real6, imag6);
+  for (let i = 0; i < realVals.length; i++) {
+    const real6 = realVals[i];
+    const imag6 = imagVals[i];
+    resultValues[i] = Math.hypot(real6, imag6);
   }
   return cpuBackend.makeOutput(resultValues, x.shape, "float32");
 };
@@ -44823,42 +44823,42 @@ function concat2(args) {
   const { inputs, backend: backend2, attrs } = args;
   const { axis } = attrs;
   const $axis = util_exports.parseAxisParam(axis, inputs[0].shape)[0];
-  let outShape = backend_util_exports.computeOutShape(inputs.map((t2) => t2.shape), $axis);
+  let outShape = backend_util_exports.computeOutShape(inputs.map((t) => t.shape), $axis);
   if (util_exports.sizeFromShape(outShape) === 0) {
     return backend2.makeTensorInfo(outShape, inputs[0].dtype, []);
   }
-  const $inputs = inputs.filter((t2) => util_exports.sizeFromShape(t2.shape) > 0);
+  const $inputs = inputs.filter((t) => util_exports.sizeFromShape(t.shape) > 0);
   if ($inputs.length === 1) {
     return identity2({ inputs: { x: $inputs[0] }, backend: backend2 });
   }
-  const shapes = $inputs.map((t2) => t2.shape);
+  const shapes = $inputs.map((t) => t.shape);
   backend_util_exports.assertParamsConsistent(shapes, $axis);
   if ($inputs[0].dtype === "complex64") {
-    const reals = $inputs.map((t2) => real2({ inputs: { input: t2 }, backend: backend2 }));
-    const imags = $inputs.map((t2) => imag2({ inputs: { input: t2 }, backend: backend2 }));
+    const reals = $inputs.map((t) => real2({ inputs: { input: t }, backend: backend2 }));
+    const imags = $inputs.map((t) => imag2({ inputs: { input: t }, backend: backend2 }));
     const realConcated = concat2({ inputs: reals, backend: backend2, attrs: { axis: $axis } });
     const imagConcated = concat2({ inputs: imags, backend: backend2, attrs: { axis: $axis } });
     const result = complex2({ inputs: { real: realConcated, imag: imagConcated }, backend: backend2 });
-    reals.forEach((r2) => backend2.disposeIntermediateTensorInfo(r2));
-    imags.forEach((i2) => backend2.disposeIntermediateTensorInfo(i2));
+    reals.forEach((r) => backend2.disposeIntermediateTensorInfo(r));
+    imags.forEach((i) => backend2.disposeIntermediateTensorInfo(i));
     backend2.disposeIntermediateTensorInfo(realConcated);
     backend2.disposeIntermediateTensorInfo(imagConcated);
     return result;
   }
-  const inputs2D = $inputs.map((t2) => {
-    const innerSize = util_exports.sizeFromShape(t2.shape.slice($axis));
+  const inputs2D = $inputs.map((t) => {
+    const innerSize = util_exports.sizeFromShape(t.shape.slice($axis));
     const shape = [-1, innerSize];
-    return reshape3({ inputs: { x: t2 }, backend: backend2, attrs: { shape } });
+    return reshape3({ inputs: { x: t }, backend: backend2, attrs: { shape } });
   });
-  const inputsValShapes = inputs2D.map((t2) => {
-    return { vals: backend2.data.get(t2.dataId).values, shape: t2.shape };
+  const inputsValShapes = inputs2D.map((t) => {
+    return { vals: backend2.data.get(t.dataId).values, shape: t.shape };
   });
-  outShape = backend_util_exports.computeOutShape(inputs2D.map((t2) => t2.shape), 1);
+  outShape = backend_util_exports.computeOutShape(inputs2D.map((t) => t.shape), 1);
   const simplyConcat = inputs2D[0].shape[0] === 1;
   const outVals = concatImpl(inputsValShapes, outShape, inputs[0].dtype, simplyConcat);
-  const finalOutShape = backend_util_exports.computeOutShape($inputs.map((t2) => t2.shape), $axis);
+  const finalOutShape = backend_util_exports.computeOutShape($inputs.map((t) => t.shape), $axis);
   const outInfo = backend2.makeTensorInfo(finalOutShape, inputs[0].dtype, outVals);
-  inputs2D.forEach((t2) => backend2.disposeIntermediateTensorInfo(t2));
+  inputs2D.forEach((t) => backend2.disposeIntermediateTensorInfo(t));
   return outInfo;
 }
 var concatConfig = {
@@ -45403,14 +45403,14 @@ function cumprod2(args) {
   const vals = util_exports.makeOnesTypedArray(util_exports.sizeFromShape($x.shape), resultDtype);
   const aVals = backend2.data.get($x.dataId).values;
   const finalDim = $x.shape[$x.shape.length - 1];
-  const indexAdjuster = reverse5 ? (i2, j) => i2 + finalDim - j - 1 : (i2, j) => i2 + j;
-  for (let i2 = 0; i2 < aVals.length; i2 += finalDim) {
+  const indexAdjuster = reverse5 ? (i, j) => i + finalDim - j - 1 : (i, j) => i + j;
+  for (let i = 0; i < aVals.length; i += finalDim) {
     for (let j = 0; j < finalDim; j++) {
-      const idx = indexAdjuster(i2, j);
+      const idx = indexAdjuster(i, j);
       if (j === 0) {
         vals[idx] = exclusive ? 1 : aVals[idx];
       } else {
-        const prevIdx = indexAdjuster(i2, j - 1);
+        const prevIdx = indexAdjuster(i, j - 1);
         vals[idx] = exclusive ? aVals[prevIdx] * vals[prevIdx] : aVals[idx] * vals[prevIdx];
       }
     }
@@ -45450,14 +45450,14 @@ function cumsum2(args) {
   const vals = util_exports.makeZerosTypedArray(util_exports.sizeFromShape($x.shape), resultDtype);
   const aVals = backend2.data.get($x.dataId).values;
   const finalDim = $x.shape[$x.shape.length - 1];
-  const indexAdjuster = reverse5 ? (i2, j) => i2 + finalDim - j - 1 : (i2, j) => i2 + j;
-  for (let i2 = 0; i2 < aVals.length; i2 += finalDim) {
+  const indexAdjuster = reverse5 ? (i, j) => i + finalDim - j - 1 : (i, j) => i + j;
+  for (let i = 0; i < aVals.length; i += finalDim) {
     for (let j = 0; j < finalDim; j++) {
-      const idx = indexAdjuster(i2, j);
+      const idx = indexAdjuster(i, j);
       if (j === 0) {
         vals[idx] = exclusive ? 0 : aVals[idx];
       } else {
-        const prevIdx = indexAdjuster(i2, j - 1);
+        const prevIdx = indexAdjuster(i, j - 1);
         vals[idx] = exclusive ? aVals[prevIdx] + vals[prevIdx] : aVals[idx] + vals[prevIdx];
       }
     }
@@ -45723,8 +45723,8 @@ function diag2(args) {
   const xVals = backend2.data.get(x.dataId).values;
   const outBuf = buffer([xSize, xSize], x.dtype);
   const vals = outBuf.values;
-  for (let i2 = 0; i2 < xVals.length; i2++) {
-    vals[i2 * xSize + i2] = xVals[i2];
+  for (let i = 0; i < xVals.length; i++) {
+    vals[i * xSize + i] = xVals[i];
   }
   const outShape = [...x.shape, ...x.shape];
   return backend2.makeTensorInfo(outShape, outBuf.dtype, outBuf.values);
@@ -45911,13 +45911,13 @@ function sum3(args) {
   const reduceSize = util_exports.sizeFromShape(reduceShape);
   const vals = backend2.data.get(result.dataId).values;
   const aVals = backend2.data.get(permutedX.dataId).values;
-  for (let i2 = 0; i2 < vals.length; ++i2) {
-    const offset = i2 * reduceSize;
+  for (let i = 0; i < vals.length; ++i) {
+    const offset = i * reduceSize;
     let sum7 = 0;
     for (let j = 0; j < reduceSize; ++j) {
       sum7 += aVals[offset + j];
     }
-    vals[i2] = sum7;
+    vals[i] = sum7;
   }
   if (keepDims) {
     const newShape = backend_util_exports.expandShapeToKeepDim(result.shape, axes);
@@ -45949,8 +45949,8 @@ function einsum2(args) {
   let out = null;
   let numDimsRemaining = allDims.length;
   const tensorsToDispose = [];
-  for (let i2 = 0; i2 < nSteps; ++i2) {
-    for (const idTerm of steps[i2]) {
+  for (let i = 0; i < nSteps; ++i) {
+    for (const idTerm of steps[i]) {
       const { permutationIndices: perm, expandDims: dimsToExpand } = backend_util_exports.getEinsumPermutation(numDimsRemaining, idDims[idTerm]);
       let x;
       if (backend_util_exports.isIdentityPermutation(perm)) {
@@ -45974,13 +45974,13 @@ function einsum2(args) {
         tensorsToDispose.push(out);
       }
     }
-    if (i2 < nSteps - 1) {
-      if (path[i2] >= 0) {
+    if (i < nSteps - 1) {
+      if (path[i] >= 0) {
         out = sum3({
           inputs: { x: out },
           backend: backend2,
           attrs: {
-            axis: path[i2] - (allDims.length - numDimsRemaining),
+            axis: path[i] - (allDims.length - numDimsRemaining),
             keepDims: false
           }
         });
@@ -46011,12 +46011,12 @@ function eluGrad(args) {
   const resultValues = new Float32Array(util_exports.sizeFromShape(y.shape));
   const values = backend2.data.get(y.dataId).values;
   const dyValues = backend2.data.get(dy.dataId).values;
-  for (let i2 = 0; i2 < values.length; ++i2) {
-    const v = values[i2];
+  for (let i = 0; i < values.length; ++i) {
+    const v = values[i];
     if (v >= 1) {
-      resultValues[i2] = dyValues[i2];
+      resultValues[i] = dyValues[i];
     } else {
-      resultValues[i2] = dyValues[i2] * (v + 1);
+      resultValues[i] = dyValues[i] * (v + 1);
     }
   }
   return backend2.makeTensorInfo(y.shape, "float32", resultValues);
@@ -46037,8 +46037,8 @@ var a5 = backend_util_exports.ERF_A5;
 var erf2 = unaryKernelFunc(Erf, (xi) => {
   const sign4 = Math.sign(xi);
   const v = Math.abs(xi);
-  const t2 = 1 / (1 + p * v);
-  return sign4 * (1 - ((((a5 * t2 + a4) * t2 + a3) * t2 + a2) * t2 + a1) * t2 * Math.exp(-v * v));
+  const t = 1 / (1 + p * v);
+  return sign4 * (1 - ((((a5 * t + a4) * t + a3) * t + a2) * t + a1) * t * Math.exp(-v * v));
 });
 var erfConfig = {
   kernelName: Erf,
@@ -46089,17 +46089,17 @@ function fftBatch(input2, inverse, cpuBackend) {
   const resultReal = util_exports.getTypedArrayFromDType("float32", resultSize);
   const resultImag = util_exports.getTypedArrayFromDType("float32", resultSize);
   for (let b = 0; b < batch; b++) {
-    const r2 = slice2({
+    const r = slice2({
       inputs: { x: real2D },
       backend: cpuBackend,
       attrs: { begin: [b, 0], size: [1, innerDim] }
     });
-    const i2 = slice2({
+    const i = slice2({
       inputs: { x: imag2D },
       backend: cpuBackend,
       attrs: { begin: [b, 0], size: [1, innerDim] }
     });
-    const input3 = complex2({ inputs: { real: r2, imag: i2 }, backend: cpuBackend });
+    const input3 = complex2({ inputs: { real: r, imag: i }, backend: cpuBackend });
     const { real: real5, imag: imag5 } = fftImpl(input3, inverse, cpuBackend);
     const res = backend_util_exports.mergeRealAndImagArrays(real5, imag5);
     for (let d = 0; d < innerDim; d++) {
@@ -46107,8 +46107,8 @@ function fftBatch(input2, inverse, cpuBackend) {
       resultReal[b * innerDim + d] = c.real;
       resultImag[b * innerDim + d] = c.imag;
     }
-    cpuBackend.disposeIntermediateTensorInfo(r2);
-    cpuBackend.disposeIntermediateTensorInfo(i2);
+    cpuBackend.disposeIntermediateTensorInfo(r);
+    cpuBackend.disposeIntermediateTensorInfo(i);
     cpuBackend.disposeIntermediateTensorInfo(input3);
   }
   const $realInfo = cpuBackend.makeTensorInfo(resultShape, "float32", resultReal);
@@ -46190,10 +46190,10 @@ function fftRadix2(realVals, imagVals, size, inverse, cpuBackend) {
   const $oddRealInfo = cpuBackend.makeTensorInfo($oddShape, "float32", $oddRealVals);
   const $oddImagInfo = cpuBackend.makeTensorInfo($oddShape, "float32", $oddImagVals);
   const $oddTensorInfo = complex2({ inputs: { real: $oddRealInfo, imag: $oddImagInfo }, backend: cpuBackend });
-  const e2 = backend_util_exports.exponents(size, inverse);
-  const eShape = [e2.real.length];
-  const eRealInfo = cpuBackend.makeTensorInfo(eShape, "float32", e2.real);
-  const eImagInfo = cpuBackend.makeTensorInfo(eShape, "float32", e2.imag);
+  const e = backend_util_exports.exponents(size, inverse);
+  const eShape = [e.real.length];
+  const eRealInfo = cpuBackend.makeTensorInfo(eShape, "float32", e.real);
+  const eImagInfo = cpuBackend.makeTensorInfo(eShape, "float32", e.imag);
   const complexInfo = complex2({ inputs: { real: eRealInfo, imag: eImagInfo }, backend: cpuBackend });
   const exponentInfo = multiply2({ inputs: { a: complexInfo, b: $oddTensorInfo }, backend: cpuBackend });
   const addPart = add4({
@@ -46248,20 +46248,20 @@ function fftRadix2(realVals, imagVals, size, inverse, cpuBackend) {
 }
 function fourierTransformByMatmul(data, size, inverse) {
   const ret = new Float32Array(size * 2);
-  for (let r2 = 0; r2 < size; r2++) {
+  for (let r = 0; r < size; r++) {
     let real5 = 0;
     let imag5 = 0;
     for (let c = 0; c < size; c++) {
-      const e2 = backend_util_exports.exponent(r2 * c, size, inverse);
+      const e = backend_util_exports.exponent(r * c, size, inverse);
       const term = backend_util_exports.getComplexWithIndex(data, c);
-      real5 += term.real * e2.real - term.imag * e2.imag;
-      imag5 += term.real * e2.imag + term.imag * e2.real;
+      real5 += term.real * e.real - term.imag * e.imag;
+      imag5 += term.real * e.imag + term.imag * e.real;
     }
     if (inverse) {
       real5 /= size;
       imag5 /= size;
     }
-    backend_util_exports.assignToTypedArray(ret, real5, imag5, r2);
+    backend_util_exports.assignToTypedArray(ret, real5, imag5, r);
   }
   return ret;
 }
@@ -46459,8 +46459,8 @@ function gatherV2(args) {
   const parsedAxis = util_exports.parseAxisParam(axis, x.shape)[0];
   const indicesVals = backend2.data.get(indices.dataId).values;
   const axisDim = x.shape[parsedAxis];
-  for (let i2 = 0; i2 < indicesVals.length; ++i2) {
-    const index = indicesVals[i2];
+  for (let i = 0; i < indicesVals.length; ++i) {
+    const index = indicesVals[i];
     util_exports.assert(index <= axisDim - 1 && index >= 0, () => `GatherV2: the index value ${index} is not in [0, ${axisDim - 1}]`);
   }
   let $batchDims = batchDims;
@@ -46688,8 +46688,8 @@ function max3(args) {
   let xVals = cpuBackend.data.get(x.dataId).values;
   if (permutedAxes != null) {
     const newShape = new Array(xRank);
-    for (let i2 = 0; i2 < newShape.length; i2++) {
-      newShape[i2] = xShape[permutedAxes[i2]];
+    for (let i = 0; i < newShape.length; i++) {
+      newShape[i] = xShape[permutedAxes[i]];
     }
     xVals = transposeImpl(xVals, xShape, x.dtype, permutedAxes, newShape);
     axes = backend_util_exports.getInnerMostAxes(axes.length, xRank);
@@ -46936,7 +46936,7 @@ function mean2(args) {
   const res = div2({ inputs: { a: $x, b: reduceSizeScalar }, backend: backend2 });
   toDispose.push(res);
   const result = sum3({ inputs: { x: res }, backend: backend2, attrs: { axis, keepDims } });
-  toDispose.forEach((t2) => backend2.disposeIntermediateTensorInfo(t2));
+  toDispose.forEach((t) => backend2.disposeIntermediateTensorInfo(t));
   return result;
 }
 var meanConfig = {
@@ -46964,8 +46964,8 @@ function min3(args) {
   const reduceSize = util_exports.sizeFromShape(reduceShape);
   const vals = util_exports.makeZerosTypedArray(util_exports.sizeFromShape(outShape), $x.dtype);
   const aVals = backend2.data.get($x.dataId).values;
-  for (let i2 = 0; i2 < vals.length; ++i2) {
-    const offset = i2 * reduceSize;
+  for (let i = 0; i < vals.length; ++i) {
+    const offset = i * reduceSize;
     let min7 = aVals[offset];
     for (let j = 0; j < reduceSize; ++j) {
       const value = aVals[offset + j];
@@ -46973,7 +46973,7 @@ function min3(args) {
         min7 = value;
       }
     }
-    vals[i2] = min7;
+    vals[i] = min7;
   }
   if (permutedAxes != null) {
     backend2.disposeIntermediateTensorInfo($x);
@@ -46999,9 +46999,9 @@ function mirrorPad2(args) {
   const { x } = inputs;
   const { paddings, mode } = attrs;
   assertNotComplex(x, "mirrorPad");
-  const outShape = paddings.map((p2, i2) => p2[0] + x.shape[i2] + p2[1]);
+  const outShape = paddings.map((p2, i) => p2[0] + x.shape[i] + p2[1]);
   const start = paddings.map((p2) => p2[0]);
-  const end = paddings.map((p2, i2) => p2[0] + x.shape[i2]);
+  const end = paddings.map((p2, i) => p2[0] + x.shape[i]);
   const offset = mode === "reflect" ? 0 : 1;
   const xVals = backend2.data.get(x.dataId).values;
   const xRank = x.shape.length;
@@ -47010,18 +47010,18 @@ function mirrorPad2(args) {
   const resultRank = outShape.length;
   const resultStrides = util_exports.computeStrides(outShape);
   const resVals = util_exports.getTypedArrayFromDType(x.dtype, resultSize);
-  for (let i2 = 0; i2 < resultSize; i2++) {
-    let coords3 = util_exports.indexToLoc(i2, resultRank, resultStrides);
-    for (let i3 = 0; i3 < resultRank; i3++) {
-      if (coords3[i3] < start[i3]) {
-        coords3[i3] = start[i3] * 2 - coords3[i3] - offset;
-      } else if (coords3[i3] >= end[i3]) {
-        coords3[i3] = (end[i3] - 1) * 2 - coords3[i3] + offset;
+  for (let i = 0; i < resultSize; i++) {
+    let coords3 = util_exports.indexToLoc(i, resultRank, resultStrides);
+    for (let i2 = 0; i2 < resultRank; i2++) {
+      if (coords3[i2] < start[i2]) {
+        coords3[i2] = start[i2] * 2 - coords3[i2] - offset;
+      } else if (coords3[i2] >= end[i2]) {
+        coords3[i2] = (end[i2] - 1) * 2 - coords3[i2] + offset;
       }
     }
-    coords3 = coords3.map((c, i3) => c - start[i3]);
+    coords3 = coords3.map((c, i2) => c - start[i2]);
     const inIndex = util_exports.locToIndex(coords3, xRank, xStrides);
-    resVals[i2] = xVals[inIndex];
+    resVals[i] = xVals[inIndex];
   }
   const outId = backend2.write(resVals, outShape, x.dtype);
   return { dataId: outId, shape: outShape, dtype: x.dtype };
@@ -47113,10 +47113,10 @@ function multinomial2(args) {
     const random = seedrandom4.alea(seed.toString());
     const outOffset = b * numSamples;
     for (let sampleId = 0; sampleId < numSamples; ++sampleId) {
-      const r2 = random();
+      const r = random();
       resVals[outOffset + sampleId] = cdf.length;
       for (let event = 0; event < cdf.length; event++) {
-        if (r2 < cdf[event]) {
+        if (r < cdf[event]) {
           resVals[outOffset + sampleId] = event;
           break;
         }
@@ -47229,14 +47229,14 @@ function zerosLike2(args) {
     throw new Error("zerosLike is not supported for string tensors");
   } else if (x.dtype === "complex64") {
     const realPart = real2({ inputs: { input: x }, backend: backend2 });
-    const r2 = zerosLike2({ inputs: { x: realPart }, backend: backend2 });
+    const r = zerosLike2({ inputs: { x: realPart }, backend: backend2 });
     const imagPart = imag2({ inputs: { input: x }, backend: backend2 });
-    const i2 = zerosLike2({ inputs: { x: imagPart }, backend: backend2 });
-    const result = complex2({ inputs: { real: r2, imag: i2 }, backend: backend2 });
+    const i = zerosLike2({ inputs: { x: imagPart }, backend: backend2 });
+    const result = complex2({ inputs: { real: r, imag: i }, backend: backend2 });
     backend2.disposeIntermediateTensorInfo(realPart);
-    backend2.disposeIntermediateTensorInfo(r2);
+    backend2.disposeIntermediateTensorInfo(r);
     backend2.disposeIntermediateTensorInfo(imagPart);
-    backend2.disposeIntermediateTensorInfo(i2);
+    backend2.disposeIntermediateTensorInfo(i);
     return result;
   } else {
     return fill2({ backend: backend2, attrs: { shape: x.shape, value: 0, dtype: x.dtype } });
@@ -47256,14 +47256,14 @@ function onesLike2(args) {
     throw new Error("onesLike is not supported for string tensors");
   } else if (x.dtype === "complex64") {
     const realPart = real2({ inputs: { input: x }, backend: backend2 });
-    const r2 = onesLike2({ inputs: { x: realPart }, backend: backend2 });
+    const r = onesLike2({ inputs: { x: realPart }, backend: backend2 });
     const imagPart = imag2({ inputs: { input: x }, backend: backend2 });
-    const i2 = zerosLike2({ inputs: { x: imagPart }, backend: backend2 });
-    const result = complex2({ inputs: { real: r2, imag: i2 }, backend: backend2 });
+    const i = zerosLike2({ inputs: { x: imagPart }, backend: backend2 });
+    const result = complex2({ inputs: { real: r, imag: i }, backend: backend2 });
     backend2.disposeIntermediateTensorInfo(realPart);
-    backend2.disposeIntermediateTensorInfo(r2);
+    backend2.disposeIntermediateTensorInfo(r);
     backend2.disposeIntermediateTensorInfo(imagPart);
-    backend2.disposeIntermediateTensorInfo(i2);
+    backend2.disposeIntermediateTensorInfo(i);
     return result;
   } else {
     return fill2({ backend: backend2, attrs: { shape: x.shape, value: 1, dtype: x.dtype } });
@@ -47284,18 +47284,18 @@ function pack(args) {
   }
   const shape = inputs[0].shape;
   const dtype = inputs[0].dtype;
-  inputs.forEach((t2) => {
-    util_exports.assertShapesMatch(shape, t2.shape, "All tensors passed to stack must have matching shapes");
-    util_exports.assert(dtype === t2.dtype, () => "All tensors passed to stack must have matching dtypes");
+  inputs.forEach((t) => {
+    util_exports.assertShapesMatch(shape, t.shape, "All tensors passed to stack must have matching shapes");
+    util_exports.assert(dtype === t.dtype, () => "All tensors passed to stack must have matching dtypes");
   });
   const intermediateTensorInfos = [];
-  const expandedTensors = inputs.map((t2) => {
-    const expandedT = expandDims3({ inputs: { input: t2 }, backend: backend2, attrs: { dim: axis } });
+  const expandedTensors = inputs.map((t) => {
+    const expandedT = expandDims3({ inputs: { input: t }, backend: backend2, attrs: { dim: axis } });
     intermediateTensorInfos.push(expandedT);
     return expandedT;
   });
   const result = concat2({ inputs: expandedTensors, backend: backend2, attrs: { axis } });
-  intermediateTensorInfos.forEach((t2) => backend2.disposeIntermediateTensorInfo(t2));
+  intermediateTensorInfos.forEach((t) => backend2.disposeIntermediateTensorInfo(t));
   return result;
 }
 var packConfig = {
@@ -47310,7 +47310,7 @@ function padV2(args) {
   const { x } = inputs;
   const { paddings, constantValue } = attrs;
   assertNotComplex(x, "pad");
-  const outShape = paddings.map((p2, i2) => p2[0] + x.shape[i2] + p2[1]);
+  const outShape = paddings.map((p2, i) => p2[0] + x.shape[i] + p2[1]);
   const start = paddings.map((p2) => p2[0]);
   const xVals = backend2.data.get(x.dataId).values;
   const xSize = util_exports.sizeFromShape(x.shape);
@@ -47323,11 +47323,11 @@ function padV2(args) {
   if (constantValue !== 0) {
     resVals.fill(constantValue);
   }
-  for (let i2 = 0; i2 < xSize; i2++) {
-    const coords3 = util_exports.indexToLoc(i2, xRank, xStrides);
-    const outCoords = coords3.map((c, i3) => c + start[i3]);
+  for (let i = 0; i < xSize; i++) {
+    const coords3 = util_exports.indexToLoc(i, xRank, xStrides);
+    const outCoords = coords3.map((c, i2) => c + start[i2]);
     const outIndex = util_exports.locToIndex(outCoords, resultRank, resultStrides);
-    resVals[outIndex] = xVals[i2];
+    resVals[outIndex] = xVals[i];
   }
   const outId = backend2.write(resVals, outShape, x.dtype);
   return { dataId: outId, shape: outShape, dtype: x.dtype };
@@ -47355,8 +47355,8 @@ function raggedTensorToTensor2(args) {
   const $shape = backend2.data.get(shape.dataId).values;
   const $values = backend2.data.get(values.dataId).values;
   const $defaultValue = backend2.data.get(defaultValue.dataId).values;
-  const $rowPartitionValues = rowPartitionTensors.map((t2) => backend2.data.get(t2.dataId).values);
-  const rowPartitionValuesShapes = rowPartitionTensors.map((t2) => t2.shape);
+  const $rowPartitionValues = rowPartitionTensors.map((t) => backend2.data.get(t.dataId).values);
+  const rowPartitionValuesShapes = rowPartitionTensors.map((t) => t.shape);
   const [outputShape, output] = raggedTensorToTensorImpl($shape, shape.shape, $values, values.shape, values.dtype, $defaultValue, defaultValue.shape, $rowPartitionValues, rowPartitionValuesShapes, rowPartitionTypes);
   return backend2.makeTensorInfo(outputShape, values.dtype, output);
 }
@@ -47410,12 +47410,12 @@ function resizeBilinear2(args) {
   const effectiveRowSizeRatio = effectiveInputSize[0] / effectiveOutputSize[0];
   const effectiveColSizeRatio = effectiveInputSize[1] / effectiveOutputSize[1];
   for (let b = 0; b < batch; b++) {
-    for (let r2 = 0; r2 < newHeight; r2++) {
+    for (let r = 0; r < newHeight; r++) {
       let sourceFracRow;
       if (halfPixelCenters) {
-        sourceFracRow = effectiveRowSizeRatio * (r2 + 0.5) - 0.5;
+        sourceFracRow = effectiveRowSizeRatio * (r + 0.5) - 0.5;
       } else {
-        sourceFracRow = effectiveRowSizeRatio * r2;
+        sourceFracRow = effectiveRowSizeRatio * r;
       }
       const sourceRowFloor = Math.max(0, Math.floor(sourceFracRow));
       const rowFrac = sourceFracRow - sourceRowFloor;
@@ -47481,8 +47481,8 @@ function resizeBilinearGrad(args) {
   let offset = 0;
   for (let b = 0; b < batch; b++) {
     const bOffset = b * imagesStrides[0];
-    for (let r2 = 0; r2 < yHeight; r2++) {
-      const dxR = r2 * heightScale;
+    for (let r = 0; r < yHeight; r++) {
+      const dxR = r * heightScale;
       const topDxRIndex = Math.floor(dxR);
       const bottomDxRIndex = Math.min(Math.ceil(dxR), xHeight - 1);
       const topDxROffset = bOffset + topDxRIndex * imagesStrides[1];
@@ -47545,8 +47545,8 @@ function resizeNearestNeighbor2(args) {
   let outputOffset = 0;
   for (let b = 0; b < batch; b++) {
     const batchOffset = b * imagesStrides[0];
-    for (let r2 = 0; r2 < newHeight; r2++) {
-      const sourceFracRow = halfPixelCenters ? effectiveRowSizeRatio * (r2 + 0.5) : effectiveRowSizeRatio * r2;
+    for (let r = 0; r < newHeight; r++) {
+      const sourceFracRow = halfPixelCenters ? effectiveRowSizeRatio * (r + 0.5) : effectiveRowSizeRatio * r;
       let sourceNearestRow = Math.min(oldHeight - 1, alignCorners ? Math.round(sourceFracRow) : Math.floor(sourceFracRow));
       if (halfPixelCenters) {
         sourceNearestRow = Math.max(0, sourceNearestRow);
@@ -47602,9 +47602,9 @@ function resizeNearestNeighborGrad(args) {
   const winWidth = Math.ceil(invWidthScale) * 2 + 2;
   for (let b = 0; b < batch; b++) {
     const batchOffset = b * imagesStrides[0];
-    for (let r2 = 0; r2 < xHeight; r2++) {
-      const rowOffset = batchOffset + r2 * imagesStrides[1];
-      const startRLerp = Math.floor(r2 * invHeightScale);
+    for (let r = 0; r < xHeight; r++) {
+      const rowOffset = batchOffset + r * imagesStrides[1];
+      const startRLerp = Math.floor(r * invHeightScale);
       const startDyR = Math.floor(startRLerp - winHeight / 2);
       for (let c = 0; c < xWidth; c++) {
         const colOffset = rowOffset + c * imagesStrides[2];
@@ -47620,7 +47620,7 @@ function resizeNearestNeighborGrad(args) {
             const dyROffset = batchOffset + dyR * dyStrides[1];
             const sourceFracRow = dyR * heightScale;
             const sourceNearestRow = Math.min(xHeight - 1, alignCorners ? Math.round(sourceFracRow) : Math.floor(sourceFracRow));
-            if (r2 !== sourceNearestRow) {
+            if (r !== sourceNearestRow) {
               continue;
             }
             for (let dyCIndex = 0; dyCIndex < winWidth; dyCIndex++) {
@@ -47662,8 +47662,8 @@ function reverse2(args) {
   }
   const outBuf = new TensorBuffer(x.shape, x.dtype);
   const xBuf = backend2.bufferSync(x);
-  for (let i2 = 0; i2 < outBuf.size; i2++) {
-    const outLoc = outBuf.indexToLoc(i2);
+  for (let i = 0; i < outBuf.size; i++) {
+    const outLoc = outBuf.indexToLoc(i);
     const inLoc = outLoc.slice();
     $dims.forEach((d) => inLoc[d] = x.shape[d] - 1 - inLoc[d]);
     outBuf.set(xBuf.get(...inLoc), ...outLoc);
@@ -47803,8 +47803,8 @@ function searchSortedImpl(sortedInputs, values, batchSize, numInputs, numValues,
   for (let b = 0; b < batchSize; ++b) {
     const sortedInputsSlice = sortedInputs.slice(b * numInputs, (b + 1) * numInputs);
     const outputOffset = b * numValues;
-    for (let i2 = 0; i2 < numValues; ++i2) {
-      output[outputOffset + i2] = side === "left" ? lowerBound2(sortedInputsSlice, values[i2 + outputOffset]) : upperBound2(sortedInputsSlice, values[i2 + outputOffset]);
+    for (let i = 0; i < numValues; ++i) {
+      output[outputOffset + i] = side === "left" ? lowerBound2(sortedInputsSlice, values[i + outputOffset]) : upperBound2(sortedInputsSlice, values[i + outputOffset]);
     }
   }
   return output;
@@ -47829,26 +47829,26 @@ var searchSortedConfig = {
 // node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Select.js
 function select2(args) {
   const { inputs, backend: backend2 } = args;
-  const { condition, t: t2, e: e2 } = inputs;
-  assertNotComplex([condition, t2, e2], "select");
+  const { condition, t, e } = inputs;
+  assertNotComplex([condition, t, e], "select");
   const conditionRank = condition.shape.length;
   const values = backend2.data.get(condition.dataId).values;
-  const tValues = backend2.data.get(t2.dataId).values;
-  const eValues = backend2.data.get(e2.dataId).values;
-  const resultDtype = upcastType(t2.dtype, e2.dtype);
-  const newValues = util_exports.makeZerosTypedArray(util_exports.sizeFromShape(t2.shape), resultDtype);
+  const tValues = backend2.data.get(t.dataId).values;
+  const eValues = backend2.data.get(e.dataId).values;
+  const resultDtype = upcastType(t.dtype, e.dtype);
+  const newValues = util_exports.makeZerosTypedArray(util_exports.sizeFromShape(t.shape), resultDtype);
   let index = 0;
-  const offset = conditionRank === 0 || conditionRank > 1 || t2.shape.length === 1 ? 1 : util_exports.sizeFromShape(t2.shape.slice(1));
-  for (let i2 = 0; i2 < values.length; i2++) {
+  const offset = conditionRank === 0 || conditionRank > 1 || t.shape.length === 1 ? 1 : util_exports.sizeFromShape(t.shape.slice(1));
+  for (let i = 0; i < values.length; i++) {
     for (let j = 0; j < offset; j++) {
-      if (values[i2] === 1) {
-        newValues[index++] = tValues[i2];
+      if (values[i] === 1) {
+        newValues[index++] = tValues[i];
       } else {
-        newValues[index++] = eValues[i2];
+        newValues[index++] = eValues[i];
       }
     }
   }
-  return backend2.makeTensorInfo(t2.shape, resultDtype, newValues);
+  return backend2.makeTensorInfo(t.shape, resultDtype, newValues);
 }
 var selectConfig = {
   kernelName: Select,
@@ -47936,7 +47936,7 @@ function spaceToBatchND2(args) {
   const prod6 = util_exports.sizeFromShape(blockShape);
   const completePaddings = [[0, 0]];
   completePaddings.push(...paddings);
-  for (let i2 = 1 + blockShape.length; i2 < x.shape.length; ++i2) {
+  for (let i = 1 + blockShape.length; i < x.shape.length; ++i) {
     completePaddings.push([0, 0]);
   }
   const paddedX = padV2Config.kernelFunc({
@@ -48149,11 +48149,11 @@ function splitV(args) {
   const splitSizes = backend_util_exports.prepareSplitSize(x, numOrSizeSplits, $axis);
   const begin = new Array(x.shape.length).fill(0);
   const size = x.shape.slice();
-  return splitSizes.map((s2) => {
+  return splitSizes.map((s) => {
     const sliceSize = [...size];
-    sliceSize[$axis] = s2;
+    sliceSize[$axis] = s;
     const sliceT = slice2({ inputs: { x }, backend: backend2, attrs: { begin, size: sliceSize } });
-    begin[$axis] += s2;
+    begin[$axis] += s;
     return sliceT;
   });
 }
@@ -48173,9 +48173,9 @@ var squareConfig = {
     assertNotComplex(x, "square");
     const values = cpuBackend.data.get(x.dataId).values;
     const newValues = new Float32Array(values.length);
-    for (let i2 = 0; i2 < values.length; ++i2) {
-      const value = values[i2];
-      newValues[i2] = value * value;
+    for (let i = 0; i < values.length; ++i) {
+      const value = values[i];
+      newValues[i] = value * value;
     }
     const dataId = cpuBackend.write(newValues, x.shape, x.dtype);
     return { dataId, shape: x.shape, dtype: x.dtype };
@@ -48522,19 +48522,19 @@ function unpack(args) {
   const num = value.shape[axis];
   const outShape = new Array(valueRank - 1);
   let outIndex = 0;
-  for (let i2 = 0; i2 < valueRank; i2++) {
-    if (i2 !== axis) {
-      outShape[outIndex++] = value.shape[i2];
+  for (let i = 0; i < valueRank; i++) {
+    if (i !== axis) {
+      outShape[outIndex++] = value.shape[i];
     }
   }
   const begin = new Array(valueRank).fill(0);
   const size = value.shape.slice();
   size[axis] = 1;
   const res = new Array(num);
-  for (let i2 = 0; i2 < res.length; i2++) {
-    begin[axis] = i2;
+  for (let i = 0; i < res.length; i++) {
+    begin[axis] = i;
     const tempRes = slice2({ inputs: { x: value }, backend: backend2, attrs: { begin, size } });
-    res[i2] = reshape3({ inputs: { x: tempRes }, backend: backend2, attrs: { shape: outShape } });
+    res[i] = reshape3({ inputs: { x: tempRes }, backend: backend2, attrs: { shape: outShape } });
     backend2.disposeIntermediateTensorInfo(tempRes);
   }
   return res;
@@ -48557,13 +48557,13 @@ function unsortedSegmentSum2(args) {
   const intermediates = [];
   const numIters = xRank - segmentIdsRank;
   let $segmentIds = segmentIds;
-  for (let i2 = 0; i2 < numIters; ++i2) {
-    const expanded = expandDims3({ inputs: { input: $segmentIds }, backend: backend2, attrs: { dim: i2 + 1 } });
+  for (let i = 0; i < numIters; ++i) {
+    const expanded = expandDims3({ inputs: { input: $segmentIds }, backend: backend2, attrs: { dim: i + 1 } });
     $segmentIds = expanded;
     intermediates.push(expanded);
   }
-  for (let i2 = 0; i2 < numSegments; ++i2) {
-    const scalarValue = util_exports.createScalarValue(i2, "int32");
+  for (let i = 0; i < numSegments; ++i) {
+    const scalarValue = util_exports.createScalarValue(i, "int32");
     const segmentId = backend2.makeTensorInfo([], "int32", scalarValue);
     const mask = equal2({ inputs: { a: segmentId, b: $segmentIds }, backend: backend2 });
     const maskCasted = cast3({ inputs: { x: mask }, backend: backend2, attrs: { dtype: "float32" } });
@@ -48577,7 +48577,7 @@ function unsortedSegmentSum2(args) {
     intermediates.push(sumTensorInfo);
   }
   const result = pack({ inputs: res, backend: backend2, attrs: { axis: 0 } });
-  intermediates.forEach((t2) => backend2.disposeIntermediateTensorInfo(t2));
+  intermediates.forEach((t) => backend2.disposeIntermediateTensorInfo(t));
   return result;
 }
 var unsortedSegmentSumConfig = {
@@ -49046,8 +49046,8 @@ function logShaderSourceAndInfoLog(shaderSource, shaderInfoLog) {
   const pad3 = shaderLines.length.toString().length + 2;
   const linesWithLineNumbers = shaderLines.map((line, lineNumber2) => util_exports.rightPad((lineNumber2 + 1).toString(), pad3) + line);
   let maxLineLength = 0;
-  for (let i2 = 0; i2 < linesWithLineNumbers.length; i2++) {
-    maxLineLength = Math.max(linesWithLineNumbers[i2].length, maxLineLength);
+  for (let i = 0; i < linesWithLineNumbers.length; i++) {
+    maxLineLength = Math.max(linesWithLineNumbers[i].length, maxLineLength);
   }
   const beforeErrorLines = linesWithLineNumbers.slice(0, lineNumber - 1);
   const errorLine = linesWithLineNumbers.slice(lineNumber - 1, lineNumber);
@@ -49215,7 +49215,7 @@ function getTextureShapeFromLogicalShape(logShape, isPacked = false) {
   let maxTexSize = env().getNumber("WEBGL_MAX_TEXTURE_SIZE");
   if (isPacked) {
     maxTexSize = maxTexSize * 2;
-    logShape = logShape.map((d, i2) => i2 >= logShape.length - 2 ? util_exports.nearestLargerEven(logShape[i2]) : logShape[i2]);
+    logShape = logShape.map((d, i) => i >= logShape.length - 2 ? util_exports.nearestLargerEven(logShape[i]) : logShape[i]);
     if (logShape.length === 1) {
       logShape = [2, logShape[0]];
     }
@@ -49250,8 +49250,8 @@ function getTextureShapeFromLogicalShape(logShape, isPacked = false) {
     return util_exports.sizeToSquarishShape(size);
   }
 }
-function isEven(n2) {
-  return n2 % 2 === 0;
+function isEven(n) {
+  return n % 2 === 0;
 }
 function isReshapeFree(shape1, shape2) {
   shape1 = shape1.slice(-2);
@@ -49324,8 +49324,8 @@ function isWebGLVersionEnabled(webGLVersion) {
     if (gl != null) {
       return true;
     }
-  } catch (e2) {
-    console.log("Error when getting WebGL context: ", e2);
+  } catch (e) {
+    console.log("Error when getting WebGL context: ", e);
     return false;
   }
   return false;
@@ -49419,9 +49419,9 @@ function assertNotComplex2(tensor2, opName) {
   if (!Array.isArray(tensor2)) {
     tensor2 = [tensor2];
   }
-  tensor2.forEach((t2) => {
-    if (t2 != null) {
-      util_exports.assert(t2.dtype !== "complex64", () => `${opName} does not support complex64 tensors in the WebGL backend.`);
+  tensor2.forEach((t) => {
+    if (t != null) {
+      util_exports.assert(t.dtype !== "complex64", () => `${opName} does not support complex64 tensors in the WebGL backend.`);
     }
   });
 }
@@ -49495,7 +49495,7 @@ ENV5.registerFlag("SOFTWARE_WEBGL_ENABLED", () => ENV5.getBool("IS_TEST"));
 
 // node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/glsl_version.js
 function getGlslDifferences() {
-  let version9;
+  let version10;
   let attribute;
   let varyingVs;
   let varyingFs;
@@ -49506,7 +49506,7 @@ function getGlslDifferences() {
   let defineSpecialInf;
   let defineRound;
   if (env().getNumber("WEBGL_VERSION") === 2) {
-    version9 = "#version 300 es";
+    version10 = "#version 300 es";
     attribute = "in";
     varyingVs = "out";
     varyingFs = "in";
@@ -49538,7 +49538,7 @@ function getGlslDifferences() {
       }
     `;
   } else {
-    version9 = "";
+    version10 = "";
     attribute = "attribute";
     varyingVs = "varying";
     varyingFs = "varying";
@@ -49575,7 +49575,7 @@ function getGlslDifferences() {
     `;
   }
   return {
-    version: version9,
+    version: version10,
     attribute,
     varyingVs,
     varyingFs,
@@ -49591,17 +49591,17 @@ function getGlslDifferences() {
 // node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/shader_compiler_util.js
 function getLogicalCoordinatesFromFlatIndex(coords3, shape, index = "index") {
   const strides = util_exports.computeStrides(shape);
-  return strides.map((stride, i2) => {
-    const line1 = `int ${coords3[i2]} = ${index} / ${stride}`;
-    const line2 = i2 === strides.length - 1 ? `int ${coords3[i2 + 1]} = ${index} - ${coords3[i2]} * ${stride}` : `index -= ${coords3[i2]} * ${stride}`;
+  return strides.map((stride, i) => {
+    const line1 = `int ${coords3[i]} = ${index} / ${stride}`;
+    const line2 = i === strides.length - 1 ? `int ${coords3[i + 1]} = ${index} - ${coords3[i]} * ${stride}` : `index -= ${coords3[i]} * ${stride}`;
     return `${line1}; ${line2};`;
   }).join("");
 }
 function getOutputLogicalCoordinatesFromFlatIndexByUniform(coords3, shape, index = "index") {
   const strides = util_exports.computeStrides(shape);
-  return strides.map((_, i2) => {
-    const line1 = `int ${coords3[i2]} = ${index} / outShapeStrides[${i2}]`;
-    const line2 = i2 === strides.length - 1 ? `int ${coords3[i2 + 1]} = ${index} - ${coords3[i2]} * outShapeStrides[${i2}]` : `index -= ${coords3[i2]} * outShapeStrides[${i2}]`;
+  return strides.map((_, i) => {
+    const line1 = `int ${coords3[i]} = ${index} / outShapeStrides[${i}]`;
+    const line2 = i === strides.length - 1 ? `int ${coords3[i + 1]} = ${index} - ${coords3[i]} * outShapeStrides[${i}]` : `index -= ${coords3[i]} * outShapeStrides[${i}]`;
     return `${line1}; ${line2};`;
   }).join("");
 }
@@ -49610,17 +49610,17 @@ function symbolicallyComputeStrides(indicesArr, variableName) {
   const shape = indicesArr.map((d) => `${variableName}[${d}]`);
   const strides = new Array(numCoords - 1);
   strides[numCoords - 2] = shape[numCoords - 1];
-  for (let i2 = numCoords - 3; i2 >= 0; --i2) {
-    strides[i2] = `(${strides[i2 + 1]} * ${shape[i2 + 1]})`;
+  for (let i = numCoords - 3; i >= 0; --i) {
+    strides[i] = `(${strides[i + 1]} * ${shape[i + 1]})`;
   }
   return strides;
 }
 function getLogicalCoordinatesFromFlatIndexByUniform(coords3, variableName, index = "index") {
-  const indicesArray = coords3.map((_, i2) => i2);
+  const indicesArray = coords3.map((_, i) => i);
   const strides = symbolicallyComputeStrides(indicesArray, variableName);
-  return strides.map((_, i2) => {
-    const line1 = `int ${coords3[i2]} = ${index} / ${strides[i2]}`;
-    const line2 = i2 === strides.length - 1 ? `int ${coords3[i2 + 1]} = ${index} - ${coords3[i2]} * ${strides[i2]}` : `index -= ${coords3[i2]} * ${strides[i2]}`;
+  return strides.map((_, i) => {
+    const line1 = `int ${coords3[i]} = ${index} / ${strides[i]}`;
+    const line2 = i === strides.length - 1 ? `int ${coords3[i + 1]} = ${index} - ${coords3[i]} * ${strides[i]}` : `index -= ${coords3[i]} * ${strides[i]}`;
     return `${line1}; ${line2};`;
   }).join("");
 }
@@ -51151,7 +51151,7 @@ function getPackedSamplerAtOutputCoords(inputInfo, outShapeInfo) {
   if (outRank < 2 && inRank > 0) {
     unpackedCoordsSnippet = "coords";
   } else {
-    unpackedCoordsSnippet = inputInfo.shapeInfo.logicalShape.map((s2, i2) => `coords.${fields[i2 + rankDiff]}`).join(", ");
+    unpackedCoordsSnippet = inputInfo.shapeInfo.logicalShape.map((s, i) => `coords.${fields[i + rankDiff]}`).join(", ");
   }
   let output = `return outputValue;`;
   const inSize = util_exports.sizeFromShape(inputInfo.shapeInfo.logicalShape);
@@ -51223,7 +51223,7 @@ function getSamplerAtOutputCoords(inputInfo, outShapeInfo) {
   if (outRank < 2 && inRank > 0) {
     unpackedCoordsSnippet = "coords";
   } else {
-    unpackedCoordsSnippet = inputInfo.shapeInfo.logicalShape.map((s2, i2) => `coords.${fields[i2 + rankDiff]}`).join(", ");
+    unpackedCoordsSnippet = inputInfo.shapeInfo.logicalShape.map((s, i) => `coords.${fields[i + rankDiff]}`).join(", ");
   }
   return `
     float ${funcName}() {
@@ -51270,7 +51270,7 @@ function getSqueezedParams(params, keptDims) {
 
 // node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/gpgpu_math.js
 function compileProgram(gpgpu, program, inputs, output) {
-  const inputInfos = inputs.map((input2, i2) => {
+  const inputInfos = inputs.map((input2, i) => {
     const shapeInfo = {
       logicalShape: input2.shape,
       texShape: input2.isUniform ? null : input2.texData.texShape,
@@ -51281,7 +51281,7 @@ function compileProgram(gpgpu, program, inputs, output) {
     if (input2.texData != null && input2.texData.slice != null && input2.texData.slice.flatOffset > 0) {
       shapeInfo.flatOffset = input2.texData.slice.flatOffset;
     }
-    return { name: program.variableNames[i2], shapeInfo };
+    return { name: program.variableNames[i], shapeInfo };
   });
   const inShapeInfos = inputInfos.map((x) => x.shapeInfo);
   const outShapeInfo = {
@@ -51338,8 +51338,8 @@ function getUniformLocations(gpgpu, program, webGLProgram) {
     infLoc = gpgpu.getUniformLocation(webGLProgram, "INFINITY", false);
   }
   const shouldThrow = false;
-  for (let i2 = 0; i2 < program.variableNames.length; i2++) {
-    const varName = program.variableNames[i2];
+  for (let i = 0; i < program.variableNames.length; i++) {
+    const varName = program.variableNames[i];
     uniformLocations[varName] = gpgpu.getUniformLocation(webGLProgram, varName, shouldThrow);
     uniformLocations[`offset${varName}`] = gpgpu.getUniformLocation(webGLProgram, `offset${varName}`, shouldThrow);
     if (program.enableShapeUniforms) {
@@ -51353,8 +51353,8 @@ function getUniformLocations(gpgpu, program, webGLProgram) {
     outTexShapeLocation = gpgpu.getUniformLocation(webGLProgram, "outTexShape", shouldThrow);
   }
   if (program.customUniforms) {
-    program.customUniforms.forEach((d, i2) => {
-      customUniformLocations[i2] = gpgpu.getUniformLocation(webGLProgram, d.name, shouldThrow);
+    program.customUniforms.forEach((d, i) => {
+      customUniformLocations[i] = gpgpu.getUniformLocation(webGLProgram, d.name, shouldThrow);
     });
   }
   return {
@@ -51373,17 +51373,17 @@ function validateBinaryAndProgram(shapeInfos, inputs) {
   if (shapeInfos.length !== inputs.length) {
     throw Error(`Binary was compiled with ${shapeInfos.length} inputs, but was executed with ${inputs.length} inputs`);
   }
-  shapeInfos.forEach((s2, i2) => {
-    const shapeA = s2.logicalShape;
-    const input2 = inputs[i2];
+  shapeInfos.forEach((s, i) => {
+    const shapeA = s.logicalShape;
+    const input2 = inputs[i];
     const shapeB = input2.shape;
     if (!util_exports.arraysEqual(shapeA, shapeB)) {
       throw Error(`Binary was compiled with different shapes than the current args. Shapes ${shapeA} and ${shapeB} must match`);
     }
-    if (s2.isUniform && input2.isUniform) {
+    if (s.isUniform && input2.isUniform) {
       return;
     }
-    const texShapeA = s2.texShape;
+    const texShapeA = s.texShape;
     const texShapeB = input2.isUniform ? null : input2.texData.texShape;
     if (!util_exports.arraysEqual(texShapeA, texShapeB)) {
       throw Error(`Binary was compiled with different texture shapes than the current args. Shape ${texShapeA} and ${texShapeB} must match`);
@@ -51411,8 +51411,8 @@ function runProgram(gpgpu, binary, inputs, output, customUniformValues) {
   if (binary.nanLoc !== null) {
     gpgpu.gl.uniform1f(binary.nanLoc, NaN);
   }
-  inputs.forEach((input2, i2) => {
-    const varName = binary.program.variableNames[i2];
+  inputs.forEach((input2, i) => {
+    const varName = binary.program.variableNames[i];
     const varLoc = binary.uniformLocations[varName];
     const varOffsetLoc = binary.uniformLocations[`offset${varName}`];
     const varShapeLoc = binary.inShapesLocations[`${varName}Shape`];
@@ -51457,7 +51457,7 @@ function runProgram(gpgpu, binary, inputs, output, customUniformValues) {
     if (input2.texData.slice != null && varOffsetLoc != null) {
       gpgpu.gl.uniform1i(varOffsetLoc, input2.texData.slice.flatOffset);
     }
-    gpgpu.setInputMatrixTexture(input2.texData.texture.texture, varLoc, i2);
+    gpgpu.setInputMatrixTexture(input2.texData.texture.texture, varLoc, i);
   });
   const outShapeLoc = binary.outShapeLocation;
   if (outShapeLoc) {
@@ -51498,9 +51498,9 @@ function runProgram(gpgpu, binary, inputs, output, customUniformValues) {
     gpgpu.gl.uniform2i(binary.outTexShapeLocation, output.texData.texShape[0], output.texData.texShape[1]);
   }
   if (binary.program.customUniforms && customUniformValues) {
-    binary.program.customUniforms.forEach((d, i2) => {
-      const customLoc = binary.customUniformLocations[i2];
-      const customValue = customUniformValues[i2];
+    binary.program.customUniforms.forEach((d, i) => {
+      const customLoc = binary.customUniformLocations[i];
+      const customValue = customUniformValues[i];
       if (d.type === "float") {
         gpgpu.gl.uniform1fv(customLoc, customValue);
       } else if (d.type === "vec2") {
@@ -52290,8 +52290,8 @@ var GPGPUContext = class {
   }
   pollItems() {
     const index = linearSearchLastTrue(this.itemsToPoll.map((x) => x.isDoneFn));
-    for (let i2 = 0; i2 <= index; ++i2) {
-      const { resolveFn } = this.itemsToPoll[i2];
+    for (let i = 0; i <= index; ++i) {
+      const { resolveFn } = this.itemsToPoll[i];
       resolveFn();
     }
     this.itemsToPoll = this.itemsToPoll.slice(index + 1);
@@ -52356,14 +52356,14 @@ var GPGPUContext = class {
   }
 };
 function linearSearchLastTrue(arr) {
-  let i2 = 0;
-  for (; i2 < arr.length; ++i2) {
-    const isDone = arr[i2]();
+  let i = 0;
+  for (; i < arr.length; ++i) {
+    const isDone = arr[i]();
     if (!isDone) {
       break;
     }
   }
-  return i2 - 1;
+  return i - 1;
 }
 
 // node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernel_utils/shared.js
@@ -52384,9 +52384,9 @@ function getSourceCoords(rank, dims) {
     return "rc";
   }
   let coords3 = "";
-  for (let i2 = 0; i2 < rank; i2++) {
-    coords3 += dims[i2];
-    if (i2 < rank - 1) {
+  for (let i = 0; i < rank; i++) {
+    coords3 += dims[i];
+    if (i < rank - 1) {
       coords3 += ",";
     }
   }
@@ -52447,9 +52447,9 @@ var PackProgram = class {
       return `rc > ${this.enableShapeUniforms ? "outShape" : this.outputShape[0]}`;
     }
     let cond = "";
-    for (let i2 = this.rank - 2; i2 < this.rank; i2++) {
-      cond += `${dims[i2]} >= ${this.enableShapeUniforms ? `outShape[${i2}]` : this.outputShape[i2]}`;
-      if (i2 < this.rank - 1) {
+    for (let i = this.rank - 2; i < this.rank; i++) {
+      cond += `${dims[i]} >= ${this.enableShapeUniforms ? `outShape[${i}]` : this.outputShape[i]}`;
+      if (i < this.rank - 1) {
         cond += "||";
       }
     }
@@ -52495,25 +52495,25 @@ var ReshapePackedProgram = class {
     this.outputShape = outputShape;
     this.enableShapeUniforms = useShapeUniforms(this.outputShape.length);
     let mainLoop = ``;
-    for (let i2 = 0; i2 < 4; i2++) {
+    for (let i = 0; i < 4; i++) {
       let thisRC = `thisRC = rc;`;
-      if (i2 % 2 === 1) {
+      if (i % 2 === 1) {
         thisRC += `thisRC.z += 1;`;
       }
-      if (i2 > 1) {
+      if (i > 1) {
         thisRC += `thisRC.y += 1;`;
       }
       mainLoop += `
         ${thisRC}
-        ${i2 > 0 ? `if(thisRC.y < rows && thisRC.z < cols){` : ""}
+        ${i > 0 ? `if(thisRC.y < rows && thisRC.z < cols){` : ""}
           int flatIndex = getFlatIndex(thisRC);
 
           ivec3 inputRC = inputCoordsFromReshapedOutCoords(flatIndex);
           vec2 inputRCInnerDims = vec2(float(inputRC.y),float(inputRC.z));
 
-          result[${i2}] =
+          result[${i}] =
             getChannel(getA(inputRC.x, inputRC.y, inputRC.z), inputRCInnerDims);
-        ${i2 > 0 ? "}" : ""}
+        ${i > 0 ? "}" : ""}
       `;
     }
     this.userCode = `
@@ -53100,24 +53100,24 @@ var MathBackendWebGL = class extends KernelBackend {
     const tmpData = this.texData.get(tmpTarget.dataId);
     return Object.assign({ tensorRef }, tmpData.texture);
   }
-  bufferSync(t2) {
-    const data = this.readSync(t2.dataId);
-    if (t2.dtype === "string") {
+  bufferSync(t) {
+    const data = this.readSync(t.dataId);
+    if (t.dtype === "string") {
       try {
         const strings = data.map((d) => util_exports.decodeString(d));
-        return buffer(t2.shape, t2.dtype, strings);
+        return buffer(t.shape, t.dtype, strings);
       } catch (_a) {
         throw new Error("Failed to decode encoded string bytes into utf-8");
       }
     }
-    return buffer(t2.shape, t2.dtype, data);
+    return buffer(t.shape, t.dtype, data);
   }
   checkNumericalProblems(values) {
     if (values == null) {
       return;
     }
-    for (let i2 = 0; i2 < values.length; i2++) {
-      const num = values[i2];
+    for (let i = 0; i < values.length; i++) {
+      const num = values[i];
       if (!canBeRepresented(num)) {
         if (env().getBool("WEBGL_RENDER_FLOAT32_CAPABLE")) {
           throw Error(`The value ${num} cannot be represented with your current settings. Consider enabling float32 rendering: 'tf.env().set('WEBGL_RENDER_FLOAT32_ENABLED', true);'`);
@@ -53176,7 +53176,7 @@ var MathBackendWebGL = class extends KernelBackend {
       if (env().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE") > 0) {
         const kernelMs = await Promise.all(flattenedActiveTimerQueries);
         res["kernelMs"] = util_exports.sum(kernelMs);
-        res["getExtraProfileInfo"] = () => kernelMs.map((d, i2) => ({ name: flattenedActiveTimerNames[i2], ms: d })).map((d) => `${d.name}: ${d.ms}`).join(", ");
+        res["getExtraProfileInfo"] = () => kernelMs.map((d, i) => ({ name: flattenedActiveTimerNames[i], ms: d })).map((d) => `${d.name}: ${d.ms}`).join(", ");
       } else {
         res["kernelMs"] = {
           error: "WebGL query timers are not supported in this environment."
@@ -53656,8 +53656,8 @@ function float32ToTypedArray(a, dtype) {
     return a;
   } else if (dtype === "int32" || dtype === "bool") {
     const result = dtype === "int32" ? new Int32Array(a.length) : new Uint8Array(a.length);
-    for (let i2 = 0; i2 < result.length; ++i2) {
-      result[i2] = Math.round(a[i2]);
+    for (let i = 0; i < result.length; ++i) {
+      result[i] = Math.round(a[i]);
     }
     return result;
   } else {
@@ -54434,12 +54434,12 @@ function getReductionStages(inShape) {
 function reduce(x, dtype, reductionType, backend2) {
   const reductionStages = getReductionStages(x.shape);
   let result = x;
-  for (let i2 = 0; i2 < reductionStages.length; i2++) {
-    const { inSize, windowSize, outSize } = reductionStages[i2];
+  for (let i = 0; i < reductionStages.length; i++) {
+    const { inSize, windowSize, outSize } = reductionStages[i];
     let program;
     let previousResult;
     if (reductionType === "mean") {
-      program = i2 === 0 ? new MeanProgram({ windowSize, inSize, batchSize: x.shape[0], outSize }, inSize) : new MeanProgram({ windowSize, inSize, batchSize: x.shape[0], outSize });
+      program = i === 0 ? new MeanProgram({ windowSize, inSize, batchSize: x.shape[0], outSize }, inSize) : new MeanProgram({ windowSize, inSize, batchSize: x.shape[0], outSize });
     } else {
       program = new ReduceProgram({ windowSize, inSize, batchSize: x.shape[0], outSize }, reductionType);
     }
@@ -54457,8 +54457,8 @@ var TransposeProgram = class {
   constructor(aShape, newDim) {
     this.variableNames = ["A"];
     const outputShape = new Array(aShape.length);
-    for (let i2 = 0; i2 < outputShape.length; i2++) {
-      outputShape[i2] = aShape[newDim[i2]];
+    for (let i = 0; i < outputShape.length; i++) {
+      outputShape[i] = aShape[newDim[i]];
     }
     this.outputShape = outputShape;
     this.rank = outputShape.length;
@@ -54479,8 +54479,8 @@ function getSwitchedCoords(newDim) {
   }
   const originalOrder = ["resRC.x", "resRC.y", "resRC.z", "resRC.w", "resRC.u", "resRC.v"];
   const switchedCoords = new Array(rank);
-  for (let i2 = 0; i2 < newDim.length; i2++) {
-    switchedCoords[newDim[i2]] = originalOrder[i2];
+  for (let i = 0; i < newDim.length; i++) {
+    switchedCoords[newDim[i]] = originalOrder[i];
   }
   return switchedCoords.join();
 }
@@ -54492,8 +54492,8 @@ var TransposePackedProgram = class {
     this.packedInputs = true;
     this.packedOutput = true;
     const outputShape = new Array(aShape.length);
-    for (let i2 = 0; i2 < outputShape.length; i2++) {
-      outputShape[i2] = aShape[newDim[i2]];
+    for (let i = 0; i < outputShape.length; i++) {
+      outputShape[i] = aShape[newDim[i]];
     }
     this.outputShape = outputShape;
     this.rank = outputShape.length;
@@ -54503,8 +54503,8 @@ var TransposePackedProgram = class {
     const dtype = getCoordsDataType(this.rank);
     const outputOrder = getVecChannels("rc", this.rank);
     const switchedOrder = new Array(this.rank);
-    for (let i2 = 0; i2 < newDim.length; i2++) {
-      switchedOrder[newDim[i2]] = outputOrder[i2];
+    for (let i = 0; i < newDim.length; i++) {
+      switchedOrder[newDim[i]] = outputOrder[i];
     }
     const innerDims = `vec2(${switchedOrder.slice(-2).join()})`;
     const nextColumn = `++${outputOrder[this.rank - 1]} < ${outputShape[this.rank - 1]}`;
@@ -54591,8 +54591,8 @@ function transpose3(args) {
   const webglBackend = backend2;
   const xRank = x.shape.length;
   const newShape = new Array(xRank);
-  for (let i2 = 0; i2 < newShape.length; i2++) {
-    newShape[i2] = x.shape[perm[i2]];
+  for (let i = 0; i < newShape.length; i++) {
+    newShape[i] = x.shape[perm[i]];
   }
   let out;
   if (webglBackend.shouldExecuteOnCPU([x])) {
@@ -54696,8 +54696,8 @@ function batchMatMulImpl({ a, b, transposeA, transposeB, backend: backend2, bias
   }
   const outReshaped = reshape4({ inputs: { x: out }, backend: backend2, attrs: { shape: outShape } });
   intermediates.push(out);
-  for (const i2 of intermediates) {
-    backend2.disposeIntermediateTensorInfo(i2);
+  for (const i of intermediates) {
+    backend2.disposeIntermediateTensorInfo(i);
   }
   return outReshaped;
 }
@@ -54793,7 +54793,7 @@ var AddNProgram = class {
   constructor(outputShape, shapes) {
     this.outputShape = [];
     this.outputShape = outputShape;
-    this.variableNames = shapes.map((_, i2) => `T${i2}`);
+    this.variableNames = shapes.map((_, i) => `T${i}`);
     const snippets = [];
     this.variableNames.forEach((variable2) => {
       snippets.push(`float v${variable2} = get${variable2}AtOutCoords();`);
@@ -54819,7 +54819,7 @@ var AddNPackedProgram = class {
     this.packedInputs = true;
     this.packedOutput = true;
     this.outputShape = outputShape;
-    this.variableNames = shapes.map((_, i2) => `T${i2}`);
+    this.variableNames = shapes.map((_, i) => `T${i}`);
     const snippets = [];
     this.variableNames.forEach((variable2) => {
       snippets.push(`vec4 v${variable2} = get${variable2}AtOutCoords();`);
@@ -54851,8 +54851,8 @@ function addN3(args) {
     const rightSide = addN3({ inputs: tensors.slice(midIndex), backend: backend2 });
     return addN3({ inputs: [leftSide, rightSide], backend: backend2 });
   }
-  const dtype = tensors.map((t2) => t2.dtype).reduce((d1, d2) => upcastType(d1, d2));
-  const shapes = tensors.map((t2) => t2.shape);
+  const dtype = tensors.map((t) => t.dtype).reduce((d1, d2) => upcastType(d1, d2));
+  const shapes = tensors.map((t) => t.shape);
   const usePackedOp = env().getBool("WEBGL_PACK");
   const program = usePackedOp ? new AddNPackedProgram(tensors[0].shape, shapes) : new AddNProgram(tensors[0].shape, shapes);
   return backend2.runWebGLProgram(program, tensors, dtype);
@@ -55139,7 +55139,7 @@ function argMinMaxReduce(backend2, x, axis, reduceType) {
     const reduced = argReduce(backend2, a2D, reduceType);
     intermediateTensorInfos.push(reduced);
     const reshaped = reshape4({ inputs: { x: reduced }, backend: backend2, attrs: { shape: outShape } });
-    intermediateTensorInfos.forEach((t2) => backend2.disposeIntermediateTensorInfo(t2));
+    intermediateTensorInfos.forEach((t) => backend2.disposeIntermediateTensorInfo(t));
     return reshaped;
   }
   return argReducePacked(backend2, x, reduceType);
@@ -55161,7 +55161,7 @@ function argMax3(args) {
   }
   backend_util_exports.assertAxesAreInnerMostDims("argMax", [axes[0]], $x.shape.length);
   const out = argMinMaxReduce(backend2, $x, axes[0], "max");
-  intermediateTensorInfos.forEach((t2) => backend2.disposeIntermediateTensorInfo(t2));
+  intermediateTensorInfos.forEach((t) => backend2.disposeIntermediateTensorInfo(t));
   return out;
 }
 var argMaxConfig2 = {
@@ -55186,7 +55186,7 @@ function argMin3(args) {
   }
   backend_util_exports.assertAxesAreInnerMostDims("argMin", [axes[0]], $x.shape.length);
   const out = argMinMaxReduce(backend2, $x, axes[0], "min");
-  intermediateTensorInfos.forEach((t2) => backend2.disposeIntermediateTensorInfo(t2));
+  intermediateTensorInfos.forEach((t) => backend2.disposeIntermediateTensorInfo(t));
   return out;
 }
 var argMinConfig2 = {
@@ -55981,8 +55981,8 @@ var SliceProgram = class {
     this.customUniforms = [{ name: "start", arrayIndex: this.rank, type: "int" }];
     const sourceCoords = getCoords(this.rank);
     let body;
-    const coordSum = destSize.map((_, i2) => {
-      return `sourceLoc.${coords[i2]} = start[${i2}] + coords.${coords[i2]};`;
+    const coordSum = destSize.map((_, i) => {
+      return `sourceLoc.${coords[i]} = start[${i}] + coords.${coords[i]};`;
     });
     body = `
         ${dtype} sourceLoc;
@@ -56042,7 +56042,7 @@ var SlicePackedProgram = class {
       }
     `;
     const sourceLocSetup = this.rank <= 4 ? `sourceLoc = coords +
-            ${dtype}(${destSize.map((_, i2) => `start[${i2}]`).join()});` : destSize.map((_, i2) => `${sourceLoc[i2]} = ${coords3[i2]} + start[${i2}];`).join("\n");
+            ${dtype}(${destSize.map((_, i) => `start[${i}]`).join()});` : destSize.map((_, i) => `${sourceLoc[i]} = ${coords3[i]} + start[${i}];`).join("\n");
     this.userCode = `
       void main() {
         ${dtype} coords = getOutputCoords();
@@ -56060,8 +56060,8 @@ var SlicePackedProgram = class {
 // node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Slice.js
 function shallowSlice(x, begin, size, backend2) {
   const xTexData = backend2.texData.get(x.dataId);
-  const t2 = backend2.makeTensorInfo(size, x.dtype);
-  const newTexData = backend2.texData.get(t2.dataId);
+  const t = backend2.makeTensorInfo(size, x.dtype);
+  const newTexData = backend2.texData.get(t.dataId);
   Object.assign(newTexData, xTexData);
   newTexData.refCount = 1;
   newTexData.shape = size;
@@ -56076,7 +56076,7 @@ function shallowSlice(x, begin, size, backend2) {
   };
   const refCount = backend2.dataRefCount.get(newTexData.slice.origDataId) || 1;
   backend2.dataRefCount.set(newTexData.slice.origDataId, refCount + 1);
-  return t2;
+  return t;
 }
 function slice3(args) {
   const { inputs, backend: backend2, attrs } = args;
@@ -56136,7 +56136,7 @@ var batchToSpaceND3 = (args) => {
   toDispose.push(reshapedIntermediate);
   toDispose.push(transposedIntermediate);
   toDispose.push(reshapedIntermediate2);
-  toDispose.forEach((t2) => backend2.disposeIntermediateTensorInfo(t2));
+  toDispose.forEach((t) => backend2.disposeIntermediateTensorInfo(t));
   return sliced;
 };
 var batchToSpaceNDConfig2 = {
@@ -56386,16 +56386,16 @@ var ConcatProgram = class {
   constructor(shapes) {
     this.outputShape = [];
     this.outputShape = backend_util_exports.computeOutShape(shapes, 1);
-    this.variableNames = shapes.map((_, i2) => `T${i2}`);
+    this.variableNames = shapes.map((_, i) => `T${i}`);
     const offsets = new Array(shapes.length - 1);
     offsets[0] = shapes[0][1];
-    for (let i2 = 1; i2 < offsets.length; i2++) {
-      offsets[i2] = offsets[i2 - 1] + shapes[i2][1];
+    for (let i = 1; i < offsets.length; i++) {
+      offsets[i] = offsets[i - 1] + shapes[i][1];
     }
     const snippets = [`if (yC < ${offsets[0]}) setOutput(getT0(yR, yC));`];
-    for (let i2 = 1; i2 < offsets.length; i2++) {
-      const shift = offsets[i2 - 1];
-      snippets.push(`else if (yC < ${offsets[i2]}) setOutput(getT${i2}(yR, yC-${shift}));`);
+    for (let i = 1; i < offsets.length; i++) {
+      const shift = offsets[i - 1];
+      snippets.push(`else if (yC < ${offsets[i]}) setOutput(getT${i}(yR, yC-${shift}));`);
     }
     const lastIndex = offsets.length;
     const lastShift = offsets[offsets.length - 1];
@@ -56424,11 +56424,11 @@ var ConcatPackedProgram = class {
     const dtype = getCoordsDataType(rank);
     const coords3 = getChannels("coords", rank);
     const channels = ["x", "y", "z", "w", "u", "v"].slice(0, rank);
-    this.variableNames = shapes.map((_, i2) => `T${i2}`);
+    this.variableNames = shapes.map((_, i) => `T${i}`);
     const offsets = new Array(shapes.length - 1);
     offsets[0] = shapes[0][axis];
-    for (let i2 = 1; i2 < offsets.length; i2++) {
-      offsets[i2] = offsets[i2 - 1] + shapes[i2][axis];
+    for (let i = 1; i < offsets.length; i++) {
+      offsets[i] = offsets[i - 1] + shapes[i][axis];
     }
     const channel = channels[axis];
     const lastChannels = channels.slice(-2);
@@ -56437,12 +56437,12 @@ var ConcatPackedProgram = class {
         return getChannel(
             getT0(${allChannels}), vec2(${lastChannels.join()}));
         }`;
-    for (let i2 = 1; i2 < offsets.length; i2++) {
-      const shift2 = offsets[i2 - 1];
+    for (let i = 1; i < offsets.length; i++) {
+      const shift2 = offsets[i - 1];
       getValueSnippet += `
-        if (${channel} < ${offsets[i2]}  && ${channel} >= ${offsets[i2 - 1]}) {
+        if (${channel} < ${offsets[i]}  && ${channel} >= ${offsets[i - 1]}) {
           return getChannel(
-            getT${i2}(${shiftedChannels(channels, channel, shift2)}),
+            getT${i}(${shiftedChannels(channels, channel, shift2)}),
             vec2(${shiftedChannels(lastChannels, channel, shift2)}));
         }`;
     }
@@ -56510,13 +56510,13 @@ var imagConfig2 = {
 function concatImpl2(inputs, axis, backend2) {
   const dtype = inputs[0].dtype;
   if (dtype === "complex64") {
-    const reals = inputs.map((t2) => real3({ inputs: { input: t2 }, backend: backend2 }));
-    const imags = inputs.map((t2) => imag3({ inputs: { input: t2 }, backend: backend2 }));
+    const reals = inputs.map((t) => real3({ inputs: { input: t }, backend: backend2 }));
+    const imags = inputs.map((t) => imag3({ inputs: { input: t }, backend: backend2 }));
     const realConcated = concatImpl2(reals, axis, backend2);
     const imagConcated = concatImpl2(imags, axis, backend2);
     const result2 = complex3({ inputs: { real: realConcated, imag: imagConcated }, backend: backend2 });
-    reals.forEach((r2) => backend2.disposeIntermediateTensorInfo(r2));
-    imags.forEach((i2) => backend2.disposeIntermediateTensorInfo(i2));
+    reals.forEach((r) => backend2.disposeIntermediateTensorInfo(r));
+    imags.forEach((i) => backend2.disposeIntermediateTensorInfo(i));
     backend2.disposeIntermediateTensorInfo(realConcated);
     backend2.disposeIntermediateTensorInfo(imagConcated);
     return result2;
@@ -56526,49 +56526,49 @@ function concatImpl2(inputs, axis, backend2) {
     runOnCpu = true;
   }
   if (runOnCpu) {
-    const tensors2D2 = inputs.map((t2) => {
-      const innerSize = util_exports.sizeFromShape(t2.shape.slice(axis));
+    const tensors2D2 = inputs.map((t) => {
+      const innerSize = util_exports.sizeFromShape(t.shape.slice(axis));
       const shape = [-1, innerSize];
-      return reshape4({ inputs: { x: t2 }, backend: backend2, attrs: { shape } });
+      return reshape4({ inputs: { x: t }, backend: backend2, attrs: { shape } });
     });
-    const inputsValShapes = tensors2D2.map((t2) => {
-      return { vals: backend2.readSync(t2.dataId), shape: t2.shape };
+    const inputsValShapes = tensors2D2.map((t) => {
+      return { vals: backend2.readSync(t.dataId), shape: t.shape };
     });
-    const outShape2 = backend_util_exports.computeOutShape(tensors2D2.map((t2) => t2.shape), 1);
+    const outShape2 = backend_util_exports.computeOutShape(tensors2D2.map((t) => t.shape), 1);
     const simplyConcat = tensors2D2[0].shape[0] === 1;
     const outVals = concatImplCPU(inputsValShapes, outShape2, dtype, simplyConcat);
-    const finalOutShape = backend_util_exports.computeOutShape(inputs.map((t2) => t2.shape), axis);
+    const finalOutShape = backend_util_exports.computeOutShape(inputs.map((t) => t.shape), axis);
     const outInfo = backend2.makeTensorInfo(finalOutShape, dtype, outVals);
-    tensors2D2.forEach((t2) => backend2.disposeIntermediateTensorInfo(t2));
+    tensors2D2.forEach((t) => backend2.disposeIntermediateTensorInfo(t));
     return outInfo;
   }
   const maxTexturesInShader = env().getNumber("WEBGL_MAX_TEXTURES_IN_SHADER");
   if (inputs.length > maxTexturesInShader) {
     const reducedInputs = [];
-    for (let i2 = 0; i2 < inputs.length; i2 += maxTexturesInShader) {
-      const subArray = inputs.slice(i2, i2 + maxTexturesInShader);
+    for (let i = 0; i < inputs.length; i += maxTexturesInShader) {
+      const subArray = inputs.slice(i, i + maxTexturesInShader);
       reducedInputs.push(concatImpl2(subArray, axis, backend2));
     }
     const result2 = concatImpl2(reducedInputs, axis, backend2);
-    for (const i2 of reducedInputs) {
-      backend2.disposeIntermediateTensorInfo(i2);
+    for (const i of reducedInputs) {
+      backend2.disposeIntermediateTensorInfo(i);
     }
     return result2;
   }
   if (env().getBool("WEBGL_PACK_ARRAY_OPERATIONS") && inputs[0].shape.length > 1) {
-    const program2 = new ConcatPackedProgram(inputs.map((t2) => t2.shape), axis);
+    const program2 = new ConcatPackedProgram(inputs.map((t) => t.shape), axis);
     return backend2.runWebGLProgram(program2, inputs, dtype);
   }
   const { tensors2D, outShape } = computeTensors2D(inputs, axis, backend2);
-  const program = new ConcatProgram(tensors2D.map((t2) => t2.shape));
+  const program = new ConcatProgram(tensors2D.map((t) => t.shape));
   const result = backend2.runWebGLProgram(program, tensors2D, dtype);
-  tensors2D.forEach((r2) => backend2.disposeIntermediateTensorInfo(r2));
+  tensors2D.forEach((r) => backend2.disposeIntermediateTensorInfo(r));
   const reshapedResult = reshape4({ inputs: { x: result }, attrs: { shape: outShape }, backend: backend2 });
   backend2.disposeIntermediateTensorInfo(result);
   return reshapedResult;
 }
 function computeTensors2D(inputs, axis, backend2) {
-  const outShape = backend_util_exports.computeOutShape(inputs.map((t2) => t2.shape), axis);
+  const outShape = backend_util_exports.computeOutShape(inputs.map((t) => t.shape), axis);
   const tensors2D = inputs.map((x) => reshape4({
     inputs: { x },
     attrs: { shape: [-1, util_exports.sizeFromShape(x.shape.slice(axis))] },
@@ -56582,15 +56582,15 @@ function concat3(args) {
   const { inputs, backend: backend2, attrs } = args;
   const { axis } = attrs;
   const $axis = util_exports.parseAxisParam(axis, inputs[0].shape)[0];
-  const outShape = backend_util_exports.computeOutShape(inputs.map((t2) => t2.shape), $axis);
+  const outShape = backend_util_exports.computeOutShape(inputs.map((t) => t.shape), $axis);
   if (util_exports.sizeFromShape(outShape) === 0) {
     return backend2.makeTensorInfo(outShape, inputs[0].dtype, []);
   }
-  const $inputs = inputs.filter((t2) => util_exports.sizeFromShape(t2.shape) > 0);
+  const $inputs = inputs.filter((t) => util_exports.sizeFromShape(t.shape) > 0);
   if ($inputs.length === 1) {
     return identity3({ inputs: { x: $inputs[0] }, backend: backend2 });
   }
-  const shapes = $inputs.map((t2) => t2.shape);
+  const shapes = $inputs.map((t) => t.shape);
   backend_util_exports.assertParamsConsistent(shapes, $axis);
   return concatImpl2($inputs, $axis, backend2);
 }
@@ -57390,8 +57390,8 @@ function conv2dByMatMul({ x, filter, convInfo, backend: backend2, bias = null, p
     intermediates.push(filterReshaped);
     intermediates.push(result);
   }
-  for (const i2 of intermediates) {
-    backend2.disposeIntermediateTensorInfo(i2);
+  for (const i of intermediates) {
+    backend2.disposeIntermediateTensorInfo(i);
   }
   return out;
 }
@@ -57462,8 +57462,8 @@ function conv2dWithIm2Row({ x, filter, convInfo, backend: backend2, bias = null,
   const product = backend2.runWebGLProgram(matmulProgram, inputs, "float32");
   const out = reshape4({ inputs: { x: product }, backend: backend2, attrs: { shape: convInfo.outShape } });
   intermediates.push(product);
-  for (const i2 of intermediates) {
-    backend2.disposeIntermediateTensorInfo(i2);
+  for (const i of intermediates) {
+    backend2.disposeIntermediateTensorInfo(i);
   }
   return out;
 }
@@ -58047,9 +58047,9 @@ function cumImpl(op2, x, backend2, axis, exclusive, reverse5) {
   }
   const size = permutedX.shape[permutedAxis];
   let result = identity3({ inputs: { x: permutedX }, backend: backend2 });
-  for (let i2 = 0; i2 <= Math.ceil(Math.log2(size)) - 1; i2++) {
+  for (let i = 0; i <= Math.ceil(Math.log2(size)) - 1; i++) {
     const program = new CumProgram(op2, permutedX.shape, false, reverse5);
-    const customValues = [[i2]];
+    const customValues = [[i]];
     const prevResult = result;
     result = backend2.runWebGLProgram(program, [result], result.dtype, customValues);
     backend2.disposeIntermediateTensorInfo(prevResult);
@@ -58886,8 +58886,8 @@ function einsum3(args) {
   let out = null;
   let numDimsRemaining = allDims.length;
   const tensorsToDispose = [];
-  for (let i2 = 0; i2 < nSteps; ++i2) {
-    for (const idTerm of steps[i2]) {
+  for (let i = 0; i < nSteps; ++i) {
+    for (const idTerm of steps[i]) {
       const { permutationIndices: perm, expandDims: dimsToExpand } = backend_util_exports.getEinsumPermutation(numDimsRemaining, idDims[idTerm]);
       let x;
       if (backend_util_exports.isIdentityPermutation(perm)) {
@@ -58911,13 +58911,13 @@ function einsum3(args) {
         tensorsToDispose.push(out);
       }
     }
-    if (i2 < nSteps - 1) {
-      if (path[i2] >= 0) {
+    if (i < nSteps - 1) {
+      if (path[i] >= 0) {
         out = sum4({
           inputs: { x: out },
           backend: backend2,
           attrs: {
-            axis: path[i2] - (allDims.length - numDimsRemaining),
+            axis: path[i] - (allDims.length - numDimsRemaining),
             keepDims: false
           }
         });
@@ -59498,7 +59498,7 @@ function fusedConv2d(args) {
   }
   const outReshaped = reshape4({ inputs: { x: out }, backend: backend2, attrs: { shape: convInfo.outShape } });
   intermediates.push(out);
-  intermediates.forEach((t2) => backend2.disposeIntermediateTensorInfo(t2));
+  intermediates.forEach((t) => backend2.disposeIntermediateTensorInfo(t));
   return outReshaped;
 }
 var fusedConv2DConfig2 = {
@@ -59549,7 +59549,7 @@ function fusedDepthwiseConv2D2(args) {
     [convInfo.inHeight, convInfo.inWidth]
   ];
   const result = backend2.runWebGLProgram(program, programInputs, "float32", customValues);
-  intermediates.forEach((t2) => backend2.disposeIntermediateTensorInfo(t2));
+  intermediates.forEach((t) => backend2.disposeIntermediateTensorInfo(t));
   return result;
 }
 var fusedDepthwiseConv2DConfig2 = {
@@ -59645,11 +59645,11 @@ var GatherProgram = class {
 function getSourceCoords2(aShape, axis) {
   const currentCoords = ["resRC.x", "resRC.y", "resRC.z", "resRC.w"];
   const sourceCoords = [];
-  for (let i2 = 0; i2 < aShape.length; i2++) {
-    if (i2 === 2) {
+  for (let i = 0; i < aShape.length; i++) {
+    if (i === 2) {
       sourceCoords.push("index");
     } else {
-      sourceCoords.push(`${currentCoords[i2]}`);
+      sourceCoords.push(`${currentCoords[i]}`);
     }
   }
   return sourceCoords.join();
@@ -59664,8 +59664,8 @@ function gatherV22(args) {
   if (env().get("DEBUG")) {
     const indicesVals = backend2.readSync(indices.dataId);
     const axisDim = x.shape[parsedAxis];
-    for (let i2 = 0; i2 < indicesVals.length; ++i2) {
-      const index = indicesVals[i2];
+    for (let i = 0; i < indicesVals.length; ++i) {
+      const index = indicesVals[i];
       util_exports.assert(index <= axisDim - 1 && index >= 0, () => `GatherV2: the index value ${index} is not in [0, ${axisDim - 1}]`);
     }
   }
@@ -59701,14 +59701,14 @@ function gatherV22(args) {
     const indicesBuf = backend2.bufferSync(flattenIndex);
     const xBuf = backend2.bufferSync(flattenX);
     const outBuf = gatherV2ImplCPU(xBuf, indicesBuf, flattenOutputShape);
-    toDispose.forEach((t2) => backend2.disposeIntermediateTensorInfo(t2));
+    toDispose.forEach((t) => backend2.disposeIntermediateTensorInfo(t));
     return backend2.makeTensorInfo(shapeInfo.outputShape, outBuf.dtype, outBuf.values);
   }
   const program = new GatherProgram(flattenX.shape, flattenOutputShape);
   const res = backend2.runWebGLProgram(program, [flattenX, flattenIndex], flattenX.dtype);
   toDispose.push(res);
   const reshaped = reshape4({ inputs: { x: res }, backend: backend2, attrs: { shape: shapeInfo.outputShape } });
-  toDispose.forEach((t2) => backend2.disposeIntermediateTensorInfo(t2));
+  toDispose.forEach((t) => backend2.disposeIntermediateTensorInfo(t));
   return reshaped;
 }
 var gatherV2Config2 = {
@@ -60163,8 +60163,8 @@ function max4(args) {
       const xTexData = backend2.texData.get(maxInput.dataId);
       const values = xTexData.values;
       const newShape = new Array(xRank);
-      for (let i2 = 0; i2 < newShape.length; i2++) {
-        newShape[i2] = x.shape[permutedAxes[i2]];
+      for (let i = 0; i < newShape.length; i++) {
+        newShape[i] = x.shape[permutedAxes[i]];
       }
       const maxInputValues = transposeImplCPU(values, x.shape, x.dtype, permutedAxes, newShape);
       maxInput = backend2.makeTensorInfo(newShape, x.dtype);
@@ -60511,8 +60511,8 @@ var meanConfig2 = {
         const xTexData = webglBackend.texData.get(meanInput.dataId);
         const values = xTexData.values;
         const newShape = new Array(xRank);
-        for (let i2 = 0; i2 < newShape.length; i2++) {
-          newShape[i2] = x.shape[permutedAxes[i2]];
+        for (let i = 0; i < newShape.length; i++) {
+          newShape[i] = x.shape[permutedAxes[i]];
         }
         const meanInputValues = transposeImplCPU(values, x.shape, x.dtype, permutedAxes, newShape);
         meanInput = webglBackend.makeTensorInfo(newShape, x.dtype);
@@ -60531,8 +60531,8 @@ var meanConfig2 = {
       outShape = backend_util_exports.expandShapeToKeepDim(meanOutShape, origAxes);
     }
     const out = meanImpl(meanInput, reduceShape, outShape, webglBackend);
-    for (const i2 of intermediates) {
-      webglBackend.disposeIntermediateTensorInfo(i2);
+    for (const i of intermediates) {
+      webglBackend.disposeIntermediateTensorInfo(i);
     }
     return out;
   }
@@ -60602,11 +60602,11 @@ var minimumConfig2 = {
 var MirrorPadProgram = class {
   constructor(xShape, paddings, mode) {
     this.variableNames = ["x"];
-    this.outputShape = paddings.map((p2, i2) => p2[0] + xShape[i2] + p2[1]);
+    this.outputShape = paddings.map((p2, i) => p2[0] + xShape[i] + p2[1]);
     const rank = xShape.length;
     const dtype = getCoordsDataType(rank);
     const start = paddings.map((p2) => p2[0]).join(",");
-    const end = paddings.map((p2, i2) => p2[0] + xShape[i2]).join(",");
+    const end = paddings.map((p2, i) => p2[0] + xShape[i]).join(",");
     const unpackedCoords = ["coords[0]", "coords[1]", "coords[2]", "coords[3]"].slice(0, rank);
     const offset = mode === "reflect" ? 0 : 1;
     if (rank === 1) {
@@ -60652,11 +60652,11 @@ var MirrorPadPackedProgram = class {
     this.variableNames = ["x"];
     this.packedInputs = true;
     this.packedOutput = true;
-    this.outputShape = paddings.map((p2, i2) => p2[0] + xShape[i2] + p2[1]);
+    this.outputShape = paddings.map((p2, i) => p2[0] + xShape[i] + p2[1]);
     const rank = xShape.length;
     const dtype = getCoordsDataType(rank);
     const start = paddings.map((p2) => p2[0]).join(",");
-    const end = paddings.map((p2, i2) => p2[0] + xShape[i2]).join(",");
+    const end = paddings.map((p2, i) => p2[0] + xShape[i]).join(",");
     const coords3 = getChannels("rc", rank);
     const source = getChannels("source", rank);
     const cLimit = `${coords3[rank - 1]} < ${this.outputShape[rank - 1]}`;
@@ -61037,14 +61037,14 @@ function zerosLike3(args) {
   const { x } = inputs;
   if (x.dtype === "complex64") {
     const realPart = real3({ inputs: { input: x }, backend: backend2 });
-    const r2 = zerosLike3({ inputs: { x: realPart }, backend: backend2 });
+    const r = zerosLike3({ inputs: { x: realPart }, backend: backend2 });
     const imagPart = imag3({ inputs: { input: x }, backend: backend2 });
-    const i2 = zerosLike3({ inputs: { x: imagPart }, backend: backend2 });
-    const result = complex3({ inputs: { real: r2, imag: i2 }, backend: backend2 });
+    const i = zerosLike3({ inputs: { x: imagPart }, backend: backend2 });
+    const result = complex3({ inputs: { real: r, imag: i }, backend: backend2 });
     backend2.disposeIntermediateTensorInfo(realPart);
-    backend2.disposeIntermediateTensorInfo(r2);
+    backend2.disposeIntermediateTensorInfo(r);
     backend2.disposeIntermediateTensorInfo(imagPart);
-    backend2.disposeIntermediateTensorInfo(i2);
+    backend2.disposeIntermediateTensorInfo(i);
     return result;
   } else {
     return fill3({
@@ -61071,14 +61071,14 @@ function onesLike3(args) {
     throw new Error("onesLike is not supported under string dtype");
   } else if (x.dtype === "complex64") {
     const realPart = real3({ inputs: { input: x }, backend: backend2 });
-    const r2 = onesLike3({ inputs: { x: realPart }, backend: backend2 });
+    const r = onesLike3({ inputs: { x: realPart }, backend: backend2 });
     const imagPart = imag3({ inputs: { input: x }, backend: backend2 });
-    const i2 = zerosLike3({ inputs: { x: imagPart }, backend: backend2 });
-    const result = complex3({ inputs: { real: r2, imag: i2 }, backend: backend2 });
+    const i = zerosLike3({ inputs: { x: imagPart }, backend: backend2 });
+    const result = complex3({ inputs: { real: r, imag: i }, backend: backend2 });
     backend2.disposeIntermediateTensorInfo(realPart);
-    backend2.disposeIntermediateTensorInfo(r2);
+    backend2.disposeIntermediateTensorInfo(r);
     backend2.disposeIntermediateTensorInfo(imagPart);
-    backend2.disposeIntermediateTensorInfo(i2);
+    backend2.disposeIntermediateTensorInfo(i);
     return result;
   } else {
     return fill3({ attrs: { shape: x.shape, dtype: x.dtype, value: 1 }, backend: backend2 });
@@ -61099,18 +61099,18 @@ function pack2(args) {
   }
   const shape = inputs[0].shape;
   const dtype = inputs[0].dtype;
-  inputs.forEach((t2) => {
-    util_exports.assertShapesMatch(shape, t2.shape, "All tensors passed to stack must have matching shapes");
-    util_exports.assert(dtype === t2.dtype, () => "All tensors passed to stack must have matching dtypes");
+  inputs.forEach((t) => {
+    util_exports.assertShapesMatch(shape, t.shape, "All tensors passed to stack must have matching shapes");
+    util_exports.assert(dtype === t.dtype, () => "All tensors passed to stack must have matching dtypes");
   });
   const intermediateTensorInfos = [];
-  const expandedTensors = inputs.map((t2) => {
-    const expandedT = expandDims4({ inputs: { input: t2 }, backend: backend2, attrs: { dim: axis } });
+  const expandedTensors = inputs.map((t) => {
+    const expandedT = expandDims4({ inputs: { input: t }, backend: backend2, attrs: { dim: axis } });
     intermediateTensorInfos.push(expandedT);
     return expandedT;
   });
   const result = concat3({ inputs: expandedTensors, backend: backend2, attrs: { axis } });
-  intermediateTensorInfos.forEach((t2) => backend2.disposeIntermediateTensorInfo(t2));
+  intermediateTensorInfos.forEach((t) => backend2.disposeIntermediateTensorInfo(t));
   return result;
 }
 var packConfig2 = {
@@ -61124,11 +61124,11 @@ var PadProgram = class {
   constructor(xShape, paddings, constantValue) {
     this.variableNames = ["x"];
     this.customUniforms = [{ name: "value", type: "float" }];
-    this.outputShape = paddings.map((p2, i2) => p2[0] + xShape[i2] + p2[1]);
+    this.outputShape = paddings.map((p2, i) => p2[0] + xShape[i] + p2[1]);
     const rank = xShape.length;
     const type = getCoordsDataType(rank);
     const start = paddings.map((p2) => p2[0]).join(",");
-    const end = paddings.map((p2, i2) => p2[0] + xShape[i2]).join(",");
+    const end = paddings.map((p2, i) => p2[0] + xShape[i]).join(",");
     const unpackedCoords = ["coords[0]", "coords[1]", "coords[2]", "coords[3]"].slice(0, rank);
     if (rank === 1) {
       this.userCode = `
@@ -61170,11 +61170,11 @@ var PadPackedProgram = class {
     this.packedInputs = true;
     this.packedOutput = true;
     this.customUniforms = [{ name: "value", type: "float" }];
-    this.outputShape = paddings.map((p2, i2) => p2[0] + xShape[i2] + p2[1]);
+    this.outputShape = paddings.map((p2, i) => p2[0] + xShape[i] + p2[1]);
     const rank = xShape.length;
     const dtype = getCoordsDataType(rank);
     const start = paddings.map((p2) => p2[0]).join(",");
-    const end = paddings.map((p2, i2) => p2[0] + xShape[i2]).join(",");
+    const end = paddings.map((p2, i) => p2[0] + xShape[i]).join(",");
     const coords3 = getChannels("rc", rank);
     const source = getChannels("source", rank);
     const cLimit = `${coords3[rank - 1]} < ${this.outputShape[rank - 1]}`;
@@ -61193,14 +61193,14 @@ var PadPackedProgram = class {
     ];
     const paddingArea = rank === 1 ? "rc < start || rc >= end" : "any(lessThan(rc, start)) || any(greaterThanEqual(rc, end))";
     let mainLoop = "";
-    for (let i2 = 0, j = rank === 1 ? 2 : 4; i2 < j; i2++) {
+    for (let i = 0, j = rank === 1 ? 2 : 4; i < j; i++) {
       mainLoop += `
-        ${componentSetup[i2]}
+        ${componentSetup[i]}
         if (${paddingArea}) {
-          result[${i2}] = float(value);
+          result[${i}] = float(value);
         } else {
           ${dtype} source = rc - start;
-          result[${i2}] = getChannel(getX(${source.join()}), ${innerDims});
+          result[${i}] = getChannel(getX(${source.join()}), ${innerDims});
         }
       `;
     }
@@ -61225,7 +61225,7 @@ var padV22 = (args) => {
   const { x } = inputs;
   const { paddings, constantValue } = attrs;
   if (util_exports.sizeFromShape(x.shape) === 0) {
-    const outputShape = paddings.map((p2, i2) => p2[0] + x.shape[i2] + p2[1]);
+    const outputShape = paddings.map((p2, i) => p2[0] + x.shape[i] + p2[1]);
     return fill3({
       backend: backend2,
       attrs: { shape: outputShape, value: constantValue, dtype: x.dtype }
@@ -61313,7 +61313,7 @@ function prod3(args) {
     const newShape = backend_util_exports.expandShapeToKeepDim(res.shape, origAxes);
     res = reshape4({ inputs: { x: res }, backend: backend2, attrs: { shape: newShape } });
   }
-  toDispose.forEach((t2) => backend2.disposeIntermediateTensorInfo(t2));
+  toDispose.forEach((t) => backend2.disposeIntermediateTensorInfo(t));
   return res;
 }
 var prodConfig2 = {
@@ -61330,8 +61330,8 @@ function raggedTensorToTensor3(args) {
   const $shape = backend2.readSync(shape.dataId);
   const $values = backend2.readSync(values.dataId);
   const $defaultValue = backend2.readSync(defaultValue.dataId);
-  const $rowPartitionValues = rowPartitionTensors.map((t2) => backend2.readSync(t2.dataId));
-  const rowPartitionValuesShapes = rowPartitionTensors.map((t2) => t2.shape);
+  const $rowPartitionValues = rowPartitionTensors.map((t) => backend2.readSync(t.dataId));
+  const rowPartitionValuesShapes = rowPartitionTensors.map((t) => t.shape);
   const [outputShape, output] = raggedTensorToTensorImplCPU($shape, shape.shape, $values, values.shape, values.dtype, $defaultValue, defaultValue.shape, $rowPartitionValues, rowPartitionValuesShapes, rowPartitionTypes);
   return backend2.makeTensorInfo(outputShape, values.dtype, output);
 }
@@ -61965,13 +61965,13 @@ var ReverseProgram = class {
       `;
       return;
     }
-    const getInCoord = (i2) => {
-      if (axis.indexOf(i2) !== -1 && xShape[i2] !== 1) {
-        return `${xShape[i2]} - coords[${i2}] - 1`;
+    const getInCoord = (i) => {
+      if (axis.indexOf(i) !== -1 && xShape[i] !== 1) {
+        return `${xShape[i]} - coords[${i}] - 1`;
       }
-      return `coords[${i2}]`;
+      return `coords[${i}]`;
     };
-    const inCoords = xShape.map((_, i2) => getInCoord(i2)).join(",");
+    const inCoords = xShape.map((_, i) => getInCoord(i)).join(",");
     const type = getCoordsDataType(rank);
     this.userCode = `
       void main() {
@@ -62047,16 +62047,16 @@ var ReversePackedProgram = class {
       return getChannel(channels2);
     }
     function getChannel(channels2) {
-      const inCoordsArray = xShape.map((_, i2) => getInCoord(i2, channels2));
+      const inCoordsArray = xShape.map((_, i) => getInCoord(i, channels2));
       const inCoords = inCoordsArray.join(",");
       const innerDims = inCoordsArray.slice(-2).join(",");
       return `getChannel(getX(${inCoords}), vec2(${innerDims}))`;
     }
-    function getInCoord(i2, channels1) {
-      if (axis.indexOf(i2) !== -1 && xShape[i2] !== 1) {
-        return `${xShape[i2]} - ${channels1[i2]} - 1`;
+    function getInCoord(i, channels1) {
+      if (axis.indexOf(i) !== -1 && xShape[i] !== 1) {
+        return `${xShape[i]} - ${channels1[i]} - 1`;
       } else {
-        return `${channels1[i2]}`;
+        return `${channels1[i]}`;
       }
     }
   }
@@ -62313,10 +62313,10 @@ var SelectProgram = class {
       const currentCoords = ["resRC.x", "resRC.y", "resRC.z", "resRC.w"];
       const cCoordVars = [];
       const abCoordVars = [];
-      for (let i2 = 0; i2 < shape.length; i2++) {
-        abCoordVars.push(`${currentCoords[i2]}`);
-        if (i2 < cRank) {
-          cCoordVars.push(`${currentCoords[i2]}`);
+      for (let i = 0; i < shape.length; i++) {
+        abCoordVars.push(`${currentCoords[i]}`);
+        if (i < cRank) {
+          cCoordVars.push(`${currentCoords[i]}`);
         }
       }
       cCoords = cCoordVars.join();
@@ -62340,9 +62340,9 @@ var SelectProgram = class {
 // node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Select.js
 function select3(args) {
   const { inputs, backend: backend2 } = args;
-  const { condition, t: t2, e: e2 } = inputs;
-  const program = new SelectProgram(condition.shape.length, t2.shape, t2.shape.length);
-  return backend2.runWebGLProgram(program, [condition, t2, e2], upcastType(t2.dtype, e2.dtype));
+  const { condition, t, e } = inputs;
+  const program = new SelectProgram(condition.shape.length, t.shape, t.shape.length);
+  return backend2.runWebGLProgram(program, [condition, t, e], upcastType(t.dtype, e.dtype));
 }
 var selectConfig2 = {
   kernelName: Select,
@@ -62464,7 +62464,7 @@ var spaceToBatchND3 = (args) => {
   const prod6 = blockShape.reduce((a, b) => a * b);
   const completePaddings = [[0, 0]];
   completePaddings.push(...paddings);
-  for (let i2 = 1 + blockShape.length; i2 < x.shape.length; ++i2) {
+  for (let i = 1 + blockShape.length; i < x.shape.length; ++i) {
     completePaddings.push([0, 0]);
   }
   const toDispose = [];
@@ -62486,7 +62486,7 @@ var spaceToBatchND3 = (args) => {
   toDispose.push(paddedX);
   toDispose.push(reshapedPaddedX);
   toDispose.push(paddedXT);
-  toDispose.forEach((t2) => backend2.disposeIntermediateTensorInfo(t2));
+  toDispose.forEach((t) => backend2.disposeIntermediateTensorInfo(t));
   return result;
 };
 var spaceToBatchNDConfig2 = {
@@ -62651,11 +62651,11 @@ function splitV2(args) {
   const xRank = x.shape.length;
   const begin = new Array(xRank).fill(0);
   const size = x.shape.slice();
-  return splitSizes.map((s2) => {
+  return splitSizes.map((s) => {
     const sliceSize = [...size];
-    sliceSize[$axis] = s2;
+    sliceSize[$axis] = s;
     const sliceT = slice3({ inputs: { x }, backend: backend2, attrs: { begin, size: sliceSize } });
-    begin[$axis] += s2;
+    begin[$axis] += s;
     return sliceT;
   });
 }
@@ -62720,9 +62720,9 @@ var StridedSliceProgram = class {
       newCoords = "coords * strides + begin";
     } else {
       let outputAxis = 0;
-      newCoords = size.map((_, i2) => {
+      newCoords = size.map((_, i) => {
         outputAxis++;
-        return size.length === 1 ? `coords * strides[${i2}] + begin[${i2}]` : `coords[${outputAxis - 1}] * strides[${i2}] + begin[${i2}]`;
+        return size.length === 1 ? `coords * strides[${i}] + begin[${i}]` : `coords[${outputAxis - 1}] * strides[${i}] + begin[${i}]`;
       }).join(",");
     }
     this.userCode = `
@@ -62870,8 +62870,8 @@ var TileProgram = class {
   constructor(aShape, reps) {
     this.variableNames = ["A"];
     const outputShape = new Array(aShape.length);
-    for (let i2 = 0; i2 < outputShape.length; i2++) {
-      outputShape[i2] = aShape[i2] * reps[i2];
+    for (let i = 0; i < outputShape.length; i++) {
+      outputShape[i] = aShape[i] * reps[i];
     }
     this.outputShape = outputShape;
     this.rank = outputShape.length;
@@ -62895,8 +62895,8 @@ function getSourceCoords3(aShape) {
   }
   const currentCoords = ["resRC.x", "resRC.y", "resRC.z", "resRC.w", "resRC.u"];
   const sourceCoords = [];
-  for (let i2 = 0; i2 < aShape.length; i2++) {
-    sourceCoords.push(`imod(${currentCoords[i2]}, ${aShape[i2]})`);
+  for (let i = 0; i < aShape.length; i++) {
+    sourceCoords.push(`imod(${currentCoords[i]}, ${aShape[i]})`);
   }
   return sourceCoords.join();
 }
@@ -63325,9 +63325,9 @@ function unpack2(args) {
   const num = value.shape[axis];
   const outShape = new Array(xRank - 1);
   let outIndex = 0;
-  for (let i2 = 0; i2 < xRank; i2++) {
-    if (i2 !== axis) {
-      outShape[outIndex++] = x.shape[i2];
+  for (let i = 0; i < xRank; i++) {
+    if (i !== axis) {
+      outShape[outIndex++] = x.shape[i];
     }
   }
   const toDispose = [];
@@ -63335,14 +63335,14 @@ function unpack2(args) {
   const size = x.shape.slice();
   size[axis] = 1;
   const res = new Array(num);
-  for (let i2 = 0; i2 < res.length; i2++) {
-    begin[axis] = i2;
+  for (let i = 0; i < res.length; i++) {
+    begin[axis] = i;
     const sliced = slice3({ inputs: { x }, backend: backend2, attrs: { begin, size } });
     const reshaped = reshape4({ inputs: { x: sliced }, backend: backend2, attrs: { shape: outShape } });
-    res[i2] = reshaped;
+    res[i] = reshaped;
     toDispose.push(sliced);
   }
-  toDispose.forEach((t2) => backend2.disposeIntermediateTensorInfo(t2));
+  toDispose.forEach((t) => backend2.disposeIntermediateTensorInfo(t));
   return res;
 }
 var unpackConfig2 = {
@@ -63537,7 +63537,7 @@ function unsortedSegmentSum3(args) {
     const perm = backend_util_exports.getUndoAxesPermutation(permutation);
     result = transpose3({ inputs: { x: result }, backend: backend2, attrs: { perm } });
   }
-  toDispose.forEach((t2) => backend2.disposeIntermediateTensorInfo(t2));
+  toDispose.forEach((t) => backend2.disposeIntermediateTensorInfo(t));
   return result;
 }
 var unsortedSegmentSumConfig2 = {
@@ -63924,8 +63924,8 @@ function transpose4(args) {
   const { inputs, backend: backend2, attrs } = args;
   const [reducedShape, perm] = removeOneSizeDims(inputs.x.shape, attrs.perm);
   let permIsNoOp = true;
-  for (let i2 = 0; i2 < perm.length; i2++) {
-    if (perm[i2] !== i2) {
+  for (let i = 0; i < perm.length; i++) {
+    if (perm[i] !== i) {
       permIsNoOp = false;
     }
   }
@@ -63950,30 +63950,30 @@ function transpose4(args) {
 }
 function computeOutShape4(inShape, perm) {
   const outShape = new Array(inShape.length);
-  for (let i2 = 0; i2 < outShape.length; i2++) {
-    outShape[i2] = inShape[perm[i2]];
+  for (let i = 0; i < outShape.length; i++) {
+    outShape[i] = inShape[perm[i]];
   }
   return outShape;
 }
 function removeOneSizeDims(shape, perm) {
   const newShape = [];
   const newPerm = [];
-  for (let i2 = 0; i2 < shape.length; ++i2) {
-    if (shape[i2] !== 1) {
-      newShape.push(shape[i2]);
+  for (let i = 0; i < shape.length; ++i) {
+    if (shape[i] !== 1) {
+      newShape.push(shape[i]);
     }
-    if (shape[perm[i2]] !== 1) {
-      newPerm.push(perm[i2]);
+    if (shape[perm[i]] !== 1) {
+      newPerm.push(perm[i]);
     }
   }
-  for (let i2 = 0; i2 < newPerm.length; ++i2) {
+  for (let i = 0; i < newPerm.length; ++i) {
     let minValIdx = -1;
     for (let j = 0; j < newPerm.length; ++j) {
-      if (newPerm[j] >= i2 && (minValIdx === -1 || newPerm[minValIdx] > newPerm[j])) {
+      if (newPerm[j] >= i && (minValIdx === -1 || newPerm[minValIdx] > newPerm[j])) {
         minValIdx = j;
       }
     }
-    newPerm[minValIdx] = i2;
+    newPerm[minValIdx] = i;
   }
   return [newShape, newPerm];
 }
@@ -63995,8 +63995,8 @@ function permuteAxesAndTranspose(x, axis, backend2) {
   let inputWasTransposed = false;
   if (permutedAxes != null) {
     const newShape = new Array(xRank);
-    for (let i2 = 0; i2 < newShape.length; i2++) {
-      newShape[i2] = xShape[permutedAxes[i2]];
+    for (let i = 0; i < newShape.length; i++) {
+      newShape[i] = xShape[permutedAxes[i]];
     }
     axes = backend_util_exports.getInnerMostAxes(axes.length, xRank);
     xTransposed = transpose4({ inputs: { x }, attrs: { perm: permutedAxes }, backend: backend2 });
@@ -64313,8 +64313,8 @@ function slice2d2(xVals, xStride, outVals, begin, size) {
   const beginI = begin[0];
   const beginJ = begin[1];
   const endI = beginI + size[0];
-  for (let i2 = beginI; i2 < endI; i2++) {
-    const xOffset = i2 * xStride + beginJ;
+  for (let i = beginI; i < endI; i++) {
+    const xOffset = i * xStride + beginJ;
     outVals.set(xVals.subarray(xOffset, xOffset + size[1]), outOffset);
     outOffset += size[1];
   }
@@ -64326,9 +64326,9 @@ function slice3d2(xVals, xStride1, xStride2, outVals, begin, size) {
   const beginK = begin[2];
   const endI = beginI + size[0];
   const endJ = beginJ + size[1];
-  for (let i2 = beginI; i2 < endI; i2++) {
+  for (let i = beginI; i < endI; i++) {
     for (let j = beginJ; j < endJ; j++) {
-      const xOffset = i2 * xStride1 + j * xStride2 + beginK;
+      const xOffset = i * xStride1 + j * xStride2 + beginK;
       outVals.set(xVals.subarray(xOffset, xOffset + size[2]), outOffset);
       outOffset += size[2];
     }
@@ -64343,10 +64343,10 @@ function slice4d2(xVals, xStride1, xStride2, xStride3, outVals, begin, size) {
   const endJ = beginJ + size[1];
   const endK = beginK + size[2];
   const beginL = begin[3];
-  for (let i2 = beginI; i2 < endI; i2++) {
+  for (let i = beginI; i < endI; i++) {
     for (let j = beginJ; j < endJ; j++) {
       for (let k = beginK; k < endK; k++) {
-        const xOffset = i2 * xStride1 + j * xStride2 + k * xStride3 + beginL;
+        const xOffset = i * xStride1 + j * xStride2 + k * xStride3 + beginL;
         outVals.set(xVals.subarray(xOffset, xOffset + size[3]), outOffset);
         outOffset += size[3];
       }
@@ -64438,8 +64438,8 @@ var clipByValueConfig3 = {
 function concat4(args) {
   const { inputs, backend: backend2 } = args;
   const axis = util_exports.parseAxisParam(args.attrs.axis, inputs[0].shape)[0];
-  let outShape = backend_util_exports.computeOutShape(inputs.map((t2) => t2.shape), axis);
-  const $inputs = inputs.filter((t2) => util_exports.sizeFromShape(t2.shape) > 0);
+  let outShape = backend_util_exports.computeOutShape(inputs.map((t) => t.shape), axis);
+  const $inputs = inputs.filter((t) => util_exports.sizeFromShape(t.shape) > 0);
   if ($inputs.length === 1) {
     return identity4({ inputs: { x: $inputs[0] }, backend: backend2 });
   }
@@ -64447,25 +64447,25 @@ function concat4(args) {
   if (util_exports.sizeFromShape(outShape) === 0) {
     return out;
   }
-  const shapes = $inputs.map((t2) => t2.shape);
+  const shapes = $inputs.map((t) => t.shape);
   backend_util_exports.assertParamsConsistent(shapes, axis);
   if ($inputs[0].dtype === "string") {
-    const inputs2D = $inputs.map((t2) => {
-      const innerSize = util_exports.sizeFromShape(t2.shape.slice(axis));
+    const inputs2D = $inputs.map((t) => {
+      const innerSize = util_exports.sizeFromShape(t.shape.slice(axis));
       const shape = [-1, innerSize];
-      return reshape5({ inputs: { x: t2 }, backend: backend2, attrs: { shape } });
+      return reshape5({ inputs: { x: t }, backend: backend2, attrs: { shape } });
     });
-    const inputsValShapes = inputs2D.map((t2) => {
-      return { vals: backend2.readSync(t2.dataId), shape: t2.shape };
+    const inputsValShapes = inputs2D.map((t) => {
+      return { vals: backend2.readSync(t.dataId), shape: t.shape };
     });
-    outShape = backend_util_exports.computeOutShape(inputs2D.map((t2) => t2.shape), 1);
+    outShape = backend_util_exports.computeOutShape(inputs2D.map((t) => t.shape), 1);
     const simplyConcat = inputs2D[0].shape[0] === 1;
     const outVals2 = concatImpl(inputsValShapes, outShape, inputs[0].dtype, simplyConcat);
-    const finalOutShape = backend_util_exports.computeOutShape($inputs.map((t2) => t2.shape), axis);
+    const finalOutShape = backend_util_exports.computeOutShape($inputs.map((t) => t.shape), axis);
     out.shape = finalOutShape;
     const outData = backend2.dataIdMap.get(out.dataId);
     outData.stringBytes = backend_util_exports.fromStringArrayToUint8(outVals2);
-    inputs2D.forEach((t2) => backend2.disposeData(t2.dataId));
+    inputs2D.forEach((t) => backend2.disposeData(t.dataId));
     return out;
   }
   const batchDim = util_exports.sizeFromShape($inputs[0].shape.slice(0, axis));
@@ -64479,10 +64479,10 @@ function concat4(args) {
   const outVals = backend2.typedArrayFromHeap(out);
   for (let b = 0; b < batchDim; b++) {
     let outOffset = b * sumInnerDims;
-    for (let i2 = 0; i2 < inVals.length; i2++) {
-      const innerDim = innerDims[i2];
+    for (let i = 0; i < inVals.length; i++) {
+      const innerDim = innerDims[i];
       const inOffset = b * innerDim;
-      const vals = inVals[i2].subarray(inOffset, inOffset + innerDim);
+      const vals = inVals[i].subarray(inOffset, inOffset + innerDim);
       outVals.set(vals, outOffset);
       outOffset += innerDim;
     }
@@ -65219,8 +65219,8 @@ function gatherV23(args) {
   const parsedAxis = util_exports.parseAxisParam(axis, x.shape)[0];
   const indicesVals = backend2.readSync(indices.dataId);
   const axisDim = x.shape[parsedAxis];
-  for (let i2 = 0; i2 < indicesVals.length; ++i2) {
-    const index = indicesVals[i2];
+  for (let i = 0; i < indicesVals.length; ++i) {
+    const index = indicesVals[i];
     util_exports.assert(index <= axisDim - 1 && index >= 0, () => `GatherV2: the index value ${index} is not in [0, ${axisDim - 1}]`);
   }
   const shapeInfo = backend_util_exports.segment_util.collectGatherOpShapeInfo(x, indices, parsedAxis, batchDims);
@@ -65572,7 +65572,7 @@ function setup26(backend2) {
 }
 function mirrorPad3(args) {
   const { inputs: { x }, backend: backend2, attrs: { paddings, mode } } = args;
-  const outShape = paddings.map((p2, i2) => p2[0] + x.shape[i2] + p2[1]);
+  const outShape = paddings.map((p2, i) => p2[0] + x.shape[i] + p2[1]);
   const xId = backend2.dataIdMap.get(x.dataId).id;
   const out = backend2.makeOutput(outShape, x.dtype);
   const outId = backend2.dataIdMap.get(out.dataId).id;
@@ -65772,18 +65772,18 @@ function pack3(args) {
   }
   const shape = inputs[0].shape;
   const dtype = inputs[0].dtype;
-  inputs.forEach((t2) => {
-    util_exports.assertShapesMatch(shape, t2.shape, "All tensors passed to stack must have matching shapes");
-    util_exports.assert(dtype === t2.dtype, () => "All tensors passed to stack must have matching dtypes");
+  inputs.forEach((t) => {
+    util_exports.assertShapesMatch(shape, t.shape, "All tensors passed to stack must have matching shapes");
+    util_exports.assert(dtype === t.dtype, () => "All tensors passed to stack must have matching dtypes");
   });
   const intermediateTensorInfos = [];
-  const expandedTensors = inputs.map((t2) => {
-    const expandedT = expandDims5({ inputs: { input: t2 }, backend: backend2, attrs: { dim: axis } });
+  const expandedTensors = inputs.map((t) => {
+    const expandedT = expandDims5({ inputs: { input: t }, backend: backend2, attrs: { dim: axis } });
     intermediateTensorInfos.push(expandedT);
     return expandedT;
   });
   const result = concat4({ inputs: expandedTensors, backend: backend2, attrs: { axis } });
-  intermediateTensorInfos.forEach((t2) => backend2.disposeData(t2.dataId));
+  intermediateTensorInfos.forEach((t) => backend2.disposeData(t.dataId));
   return result;
 }
 var packConfig3 = {
@@ -65808,7 +65808,7 @@ function setup31(backend2) {
 }
 function pad2(args) {
   const { inputs: { x }, backend: backend2, attrs: { paddings, constantValue } } = args;
-  const outShape = paddings.map((p2, i2) => p2[0] + x.shape[i2] + p2[1]);
+  const outShape = paddings.map((p2, i) => p2[0] + x.shape[i] + p2[1]);
   if (util_exports.sizeFromShape(x.shape) === 0) {
     return fill4({
       backend: backend2,
@@ -66187,15 +66187,15 @@ function setup39(backend2) {
 }
 function select4(args) {
   const { inputs, backend: backend2 } = args;
-  const { condition, t: t2, e: e2 } = inputs;
+  const { condition, t, e } = inputs;
   const conditionId = backend2.dataIdMap.get(condition.dataId).id;
-  const tId = backend2.dataIdMap.get(t2.dataId).id;
-  const eId = backend2.dataIdMap.get(e2.dataId).id;
-  const out = backend2.makeOutput(t2.shape, t2.dtype);
+  const tId = backend2.dataIdMap.get(t.dataId).id;
+  const eId = backend2.dataIdMap.get(e.dataId).id;
+  const out = backend2.makeOutput(t.shape, t.dtype);
   const outId = backend2.dataIdMap.get(out.dataId).id;
   const cRank = condition.shape.length;
-  const tRank = t2.shape.length;
-  const offset = cRank === 0 || cRank > 1 || tRank === 1 ? 1 : util_exports.sizeFromShape(t2.shape.slice(1));
+  const tRank = t.shape.length;
+  const offset = cRank === 0 || cRank > 1 || tRank === 1 ? 1 : util_exports.sizeFromShape(t.shape.slice(1));
   wasmSelect(conditionId, tId, eId, offset, outId);
   return out;
 }
@@ -66270,7 +66270,7 @@ function spaceToBatchND4(args) {
   const prod6 = util_exports.sizeFromShape(blockShape);
   const completePaddings = [[0, 0]];
   completePaddings.push(...paddings);
-  for (let i2 = 1 + blockShape.length; i2 < x.shape.length; ++i2) {
+  for (let i = 1 + blockShape.length; i < x.shape.length; ++i) {
     completePaddings.push([0, 0]);
   }
   const paddedX = padV2Config3.kernelFunc({
@@ -66565,11 +66565,11 @@ function splitV3(args) {
   const splitSizes = backend_util_exports.prepareSplitSize(x, numOrSizeSplits, $axis);
   const begin = new Array(x.shape.length).fill(0);
   const size = x.shape.slice();
-  return splitSizes.map((s2) => {
+  return splitSizes.map((s) => {
     const xSliceSize = [...size];
-    xSliceSize[$axis] = s2;
+    xSliceSize[$axis] = s;
     const xSlice = slice4({ inputs: { x }, attrs: { begin, size: xSliceSize }, backend: backend2 });
-    begin[$axis] += s2;
+    begin[$axis] += s;
     return xSlice;
   });
 }
@@ -66814,8 +66814,8 @@ function tile5(args) {
   const xId = backend2.dataIdMap.get(x.dataId).id;
   const { reps } = attrs;
   const newShape = new Array(x.shape.length);
-  for (let i2 = 0; i2 < newShape.length; i2++) {
-    newShape[i2] = x.shape[i2] * reps[i2];
+  for (let i = 0; i < newShape.length; i++) {
+    newShape[i] = x.shape[i] * reps[i];
   }
   const xShapeBytes = new Uint8Array(new Int32Array(x.shape).buffer);
   const newShapeBytes = new Uint8Array(new Int32Array(newShape).buffer);
@@ -66950,18 +66950,18 @@ function unpack3(args) {
   const rank = value.shape.length;
   const outShape = new Array(rank - 1);
   let outIndex = 0;
-  for (let i2 = 0; i2 < rank; i2++) {
-    if (i2 !== axis) {
-      outShape[outIndex++] = value.shape[i2];
+  for (let i = 0; i < rank; i++) {
+    if (i !== axis) {
+      outShape[outIndex++] = value.shape[i];
     }
   }
   const outs = new Array(numOutputs);
   const begin = new Array(rank).fill(0);
   const size = value.shape.slice();
   size[axis] = 1;
-  for (let i2 = 0; i2 < outs.length; i2++) {
-    begin[axis] = i2;
-    outs[i2] = slice4({ inputs: { x: value }, attrs: { begin, size }, backend: backend2 });
+  for (let i = 0; i < outs.length; i++) {
+    begin[axis] = i;
+    outs[i] = slice4({ inputs: { x: value }, attrs: { begin, size }, backend: backend2 });
   }
   return outs.map(({ dataId, dtype }) => ({ dataId, dtype, shape: outShape }));
 }
@@ -67181,7 +67181,7 @@ ENV6.registerFlag("WASM_HAS_MULTITHREAD_SUPPORT", async () => {
       26,
       11
     ]));
-  } catch (e2) {
+  } catch (e) {
     return false;
   }
 });
@@ -67682,8 +67682,8 @@ function symbolicallyComputeStrides2(indicesArr, variableName) {
   const shape = indicesArr.map((d) => `${variableName}[${d}]`);
   const strides = new Array(numCoords - 1);
   strides[numCoords - 2] = shape[numCoords - 1];
-  for (let i2 = numCoords - 3; i2 >= 0; --i2) {
-    strides[i2] = `(${strides[i2 + 1]} * ${shape[i2 + 1]})`;
+  for (let i = numCoords - 3; i >= 0; --i) {
+    strides[i] = `(${strides[i + 1]} * ${shape[i + 1]})`;
   }
   return strides;
 }
@@ -67821,8 +67821,8 @@ function makeShader2(inputInfo, outputData, program) {
     ].join("\n");
   }
   let uniformDeclaration = "struct Uniforms { NAN : f32, ";
-  program.variableNames.forEach((x, i2) => {
-    const perDataType = getCoordsDataType2(inputInfo[i2].shape.length);
+  program.variableNames.forEach((x, i) => {
+    const perDataType = getCoordsDataType2(inputInfo[i].shape.length);
     uniformDeclaration += `${x.charAt(0).toLowerCase() + x.slice(1)}Shape : ${perDataType}, `;
   });
   const outputDataType = getCoordsDataType2(outputData.shape.length);
@@ -67849,9 +67849,9 @@ function makeShader2(inputInfo, outputData, program) {
       @group(0) @binding(0) var<storage, read_write> result: array<${mapToWgslTypes(outputData.dtype, program.isVec4)}>;
     `);
   }
-  program.variableNames.forEach((x, i2) => {
+  program.variableNames.forEach((x, i) => {
     prefixSnippets.push(`
-      @group(0) @binding(${1 + i2}) var<storage, read> ${x}: array<${program.variableTypes ? program.variableTypes[i2] : mapToWgslTypes(inputInfo[i2].dtype, program.isVec4)}>;
+      @group(0) @binding(${1 + i}) var<storage, read> ${x}: array<${program.variableTypes ? program.variableTypes[i] : mapToWgslTypes(inputInfo[i].dtype, program.isVec4)}>;
         `);
   });
   if (uniformDeclaration !== "") {
@@ -67870,7 +67870,7 @@ function makeShader2(inputInfo, outputData, program) {
   if (!program.atomic) {
     sources.push(setOutputSnippet(outputData.shape, outputData.dtype, program.isVec4));
   }
-  const inputSnippet = inputInfo.map((x, i2) => getInputSnippet(x, outputData.shape, program.variableTypes ? program.variableTypes[i2] === "vec4<f32>" : program.isVec4, program.dispatchLayout.x.length === outputData.shape.length)).join("\n");
+  const inputSnippet = inputInfo.map((x, i) => getInputSnippet(x, outputData.shape, program.variableTypes ? program.variableTypes[i] === "vec4<f32>" : program.isVec4, program.dispatchLayout.x.length === outputData.shape.length)).join("\n");
   sources.push(inputSnippet);
   sources.push(program.getUserCode());
   const source = sources.join("\n");
@@ -67956,8 +67956,8 @@ function getCoordsFromIndexSnippet(shape) {
   const strides = util_exports.computeStrides(shape);
   const dtype = getCoordsDataType2(rank);
   const coords3 = [];
-  for (let i2 = 0; i2 < rank; i2++) {
-    coords3.push(`d${i2}`);
+  for (let i = 0; i < rank; i++) {
+    coords3.push(`d${i}`);
   }
   if (strides.length === 1) {
     return `    fn getCoordsFromIndex(index : i32) -> vec2<i32> {
@@ -67966,9 +67966,9 @@ function getCoordsFromIndexSnippet(shape) {
     }`;
   }
   let snippet;
-  snippet = "var index2 = index;" + strides.map((_, i2) => {
-    const line1 = `let ${coords3[i2]} = index2 / uniforms.outShapeStrides.${getCoordsXYZ(i2)}`;
-    const line2 = i2 === strides.length - 1 ? `let ${coords3[i2 + 1]} = index2 - ${coords3[i2]} * uniforms.outShapeStrides.${getCoordsXYZ(i2)}` : `index2 = index2 - ${coords3[i2]} * uniforms.outShapeStrides.${getCoordsXYZ(i2)}`;
+  snippet = "var index2 = index;" + strides.map((_, i) => {
+    const line1 = `let ${coords3[i]} = index2 / uniforms.outShapeStrides.${getCoordsXYZ(i)}`;
+    const line2 = i === strides.length - 1 ? `let ${coords3[i + 1]} = index2 - ${coords3[i]} * uniforms.outShapeStrides.${getCoordsXYZ(i)}` : `index2 = index2 - ${coords3[i]} * uniforms.outShapeStrides.${getCoordsXYZ(i)}`;
     return `${line1}; ${line2};`;
   }).join("");
   return `
@@ -68086,7 +68086,7 @@ function getInputByOutputSnippet(inputInfo, outShape, isVec4, isFlatDispatchLayo
   } else {
     if (outRank > 1) {
       const coordsType = getCoordsDataType2(inRank);
-      const coordsValues = inputInfo.shape.map((s2, i2) => `coords.${getCoordsXYZ(i2 + rankDiff)}`).join(", ");
+      const coordsValues = inputInfo.shape.map((s, i) => `coords.${getCoordsXYZ(i + rankDiff)}`).join(", ");
       unpackedCoordsSnippet = `${coordsType}(${coordsValues})`;
     } else {
       unpackedCoordsSnippet = "coords";
@@ -68146,30 +68146,30 @@ function getOutputCoordsSnippet(outShape, dispatchLayout) {
   let gatherDimensionsStr = "";
   const dims = [x, y, z];
   let rank = 0;
-  for (let i2 = 0; i2 < dims.length; i2++) {
-    const arr = dims[i2];
+  for (let i = 0; i < dims.length; i++) {
+    const arr = dims[i];
     if (arr.length === 0) {
       continue;
     }
     rank += arr.length;
     if (arr.length === 1) {
-      gatherDimensionsStr += `let d${arr[0]} = i32(globalId[${i2}]);`;
+      gatherDimensionsStr += `let d${arr[0]} = i32(globalId[${i}]);`;
     } else {
       const strides = symbolicallyComputeStrides2(arr, "uniforms.outShape");
-      gatherDimensionsStr += `var index${i2} = i32(globalId[${i2}]);`;
+      gatherDimensionsStr += `var index${i} = i32(globalId[${i}]);`;
       for (let j = 0; j < strides.length; j++) {
-        gatherDimensionsStr += `let d${arr[j]} = index${i2} / ${strides[j]};`;
+        gatherDimensionsStr += `let d${arr[j]} = index${i} / ${strides[j]};`;
         if (j === strides.length - 1) {
-          gatherDimensionsStr += `let d${arr[j + 1]} = index${i2} - d${arr[j]} * ${strides[j]};`;
+          gatherDimensionsStr += `let d${arr[j + 1]} = index${i} - d${arr[j]} * ${strides[j]};`;
         } else {
-          gatherDimensionsStr += `index${i2} = index${i2} - d${arr[j]} * ${strides[j]};`;
+          gatherDimensionsStr += `index${i} = index${i} - d${arr[j]} * ${strides[j]};`;
         }
       }
     }
   }
   const dimensions = [];
-  for (let i2 = 0; i2 < rank; i2++) {
-    dimensions.push(`d${i2}`);
+  for (let i = 0; i < rank; i++) {
+    dimensions.push(`d${i}`);
   }
   const dtype = getCoordsDataType2(rank);
   let snippet = `fn getOutputCoords() -> ${dtype} {
@@ -68333,8 +68333,8 @@ __export(webgpu_util_exports, {
 });
 var arrayProduct = (arr) => {
   let product = 1;
-  for (let i2 = 0; i2 < arr.length; i2++) {
-    product *= arr[i2];
+  for (let i = 0; i < arr.length; i++) {
+    product *= arr[i];
   }
   return product;
 };
@@ -68394,7 +68394,7 @@ function computeWorkPerThreadForConv2d(layout, outputShape, isVec4 = false) {
   return [2, 2, 1];
 }
 function flatDispatchLayout(shape) {
-  return { x: shape.map((d, i2) => i2) };
+  return { x: shape.map((d, i) => i) };
 }
 function GPUBytesPerElement(dtype) {
   if (dtype === "float32" || dtype === "int32" || dtype === "bool" || dtype === "string") {
@@ -68694,17 +68694,17 @@ var WebGPUBackend = class extends KernelBackend {
     tensorData.resourceInfo = { size, usage: this.defaultGpuBufferUsage(), buffer: buffer2 };
     return { tensorRef, buffer: buffer2, bufSize: size };
   }
-  bufferSync(t2) {
-    const data = this.readSync(t2.dataId);
-    if (t2.dtype === "string") {
+  bufferSync(t) {
+    const data = this.readSync(t.dataId);
+    if (t.dtype === "string") {
       try {
         const strings = data.map((d) => util_exports.decodeString(d));
-        return buffer(t2.shape, t2.dtype, strings);
+        return buffer(t.shape, t.dtype, strings);
       } catch (_a) {
         throw new Error("Failed to decode encoded string bytes into utf-8");
       }
     }
-    return buffer(t2.shape, t2.dtype, data);
+    return buffer(t.shape, t.dtype, data);
   }
   async time(f) {
     if (!this.supportTimeQuery) {
@@ -68735,7 +68735,7 @@ var WebGPUBackend = class extends KernelBackend {
     };
     const kernelMs = await Promise.all(flattenedActiveTimerQueries);
     res["kernelMs"] = util_exports.sum(kernelMs);
-    res["getExtraProfileInfo"] = () => kernelMs.map((d, i2) => ({ name: flattenedActiveTimerNames[i2], ms: d })).map((d) => `${d.name}: ${d.ms}`).join(", ");
+    res["getExtraProfileInfo"] = () => kernelMs.map((d, i) => ({ name: flattenedActiveTimerNames[i], ms: d })).map((d) => `${d.name}: ${d.ms}`).join(", ");
     this.uploadWaitMs = 0;
     this.downloadWaitMs = 0;
     return res;
@@ -68838,8 +68838,8 @@ var WebGPUBackend = class extends KernelBackend {
       currentOffset += d.data.length * 4;
     });
     const arrayBuffer = new ArrayBuffer(currentOffset);
-    programUniform.forEach((d, i2) => {
-      const offset = offsets[i2];
+    programUniform.forEach((d, i) => {
+      const offset = offsets[i];
       if (d.type === "int32") {
         new Int32Array(arrayBuffer, offset, d.data.length).set(d.data);
       } else if (d.type === "uint32") {
@@ -68884,7 +68884,7 @@ var WebGPUBackend = class extends KernelBackend {
         programUniform.push({ type: uniformsType, data: [program.isVec4 ? size / 4 : size] });
       }
     }
-    const inputsData = inputs.map((input2, i2) => {
+    const inputsData = inputs.map((input2, i) => {
       if (input2.dtype === "complex64") {
         throw new Error(`GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.`);
       }
@@ -68892,7 +68892,7 @@ var WebGPUBackend = class extends KernelBackend {
       return {
         dtype: this.tensorMap.get(input2.dataId).dtype,
         shape: input2.shape,
-        name: program.variableNames[i2]
+        name: program.variableNames[i]
       };
     });
     const key = makeShaderKey2(program, bufferShapes, inputsData, output);
@@ -68908,12 +68908,12 @@ var WebGPUBackend = class extends KernelBackend {
     }
     const bindings = [
       this.tensorToBinding(output),
-      ...inputs.map((t2) => this.tensorToBinding(t2)),
+      ...inputs.map((t) => this.tensorToBinding(t)),
       this.makeUniforms(programUniform)
     ];
     const bindGroup = this.device.createBindGroup({
       layout: pipeline.getBindGroupLayout(0),
-      entries: bindings.map((b, i2) => ({ binding: i2, resource: b }))
+      entries: bindings.map((b, i) => ({ binding: i, resource: b }))
     });
     this.ensureCommandEncoderReady();
     const pass = this.getComputePass();
@@ -70224,8 +70224,8 @@ function batchMatMulImpl2({ a, b, transposeA, transposeB, backend: backend2, bia
         intermediates.push(out);
         const outReshaped2 = reshape6({ inputs: { x: outActivated }, backend: backend2, attrs: { shape: outShape } });
         intermediates.push(outActivated);
-        for (const i2 of intermediates) {
-          backend2.disposeData(i2.dataId);
+        for (const i of intermediates) {
+          backend2.disposeData(i.dataId);
         }
         return outReshaped2;
       }
@@ -70253,8 +70253,8 @@ function batchMatMulImpl2({ a, b, transposeA, transposeB, backend: backend2, bia
   out = backend2.runWebGPUProgram(program, inputs, a.dtype, dimensions, out);
   const outReshaped = reshape6({ inputs: { x: out }, backend: backend2, attrs: { shape: outShape } });
   intermediates.push(out);
-  for (const i2 of intermediates) {
-    backend2.disposeData(i2.dataId);
+  for (const i of intermediates) {
+    backend2.disposeData(i.dataId);
   }
   return outReshaped;
 }
@@ -70580,7 +70580,7 @@ var AddNPackedProgram2 = class {
     this.workGroupSize = [64, 1, 1];
     this.size = true;
     this.outputShape = shapes[0];
-    this.variableNames = shapes.map((_, i2) => `T${i2}`);
+    this.variableNames = shapes.map((_, i) => `T${i}`);
     this.dispatchLayout = flatDispatchLayout(this.outputShape);
     this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, this.workGroupSize, [this.workPerThread, 1, 1]);
     this.shaderKey = "addN";
@@ -70616,8 +70616,8 @@ function addN4(args) {
   if (tensors.length === 1) {
     return identity5({ inputs: { x: tensors[0] }, backend: backend2 });
   }
-  const dtype = tensors.map((t2) => t2.dtype).reduce((d1, d2) => upcastType(d1, d2));
-  const shapes = tensors.map((t2) => t2.shape);
+  const dtype = tensors.map((t) => t.dtype).reduce((d1, d2) => upcastType(d1, d2));
+  const shapes = tensors.map((t) => t.shape);
   const program = new AddNPackedProgram2(shapes);
   return backend2.runWebGPUProgram(program, tensors, dtype);
 }
@@ -70664,8 +70664,8 @@ var ArgMinMaxProgram2 = class {
           snippet += "outputCoords,";
         }
       } else {
-        for (let i2 = 0; i2 < this.outputShape.length; i2++) {
-          snippet += `outputCoords.${getCoordsXYZ(i2)},`;
+        for (let i = 0; i < this.outputShape.length; i++) {
+          snippet += `outputCoords.${getCoordsXYZ(i)},`;
         }
       }
       return snippet;
@@ -70753,8 +70753,8 @@ var TransposeSharedProgram = class {
     this.variableNames = ["A"];
     this.workGroupSize = [16, 16, 1];
     const outputShape = new Array(aShape.length);
-    for (let i2 = 0; i2 < outputShape.length; i2++) {
-      outputShape[i2] = aShape[newDim[i2]];
+    for (let i = 0; i < outputShape.length; i++) {
+      outputShape[i] = aShape[newDim[i]];
     }
     this.outputShape = outputShape;
     this.dispatchLayout = { x: [0], y: [1] };
@@ -70797,8 +70797,8 @@ var TransposeProgram2 = class {
     this.workGroupSize = [64, 1, 1];
     this.size = true;
     const outputShape = new Array(aShape.length);
-    for (let i2 = 0; i2 < outputShape.length; i2++) {
-      outputShape[i2] = aShape[newDim[i2]];
+    for (let i = 0; i < outputShape.length; i++) {
+      outputShape[i] = aShape[newDim[i]];
     }
     this.outputShape = outputShape;
     this.dispatchLayout = flatDispatchLayout(this.outputShape);
@@ -70830,8 +70830,8 @@ function getSwitchedCoords2(newDim) {
     throw Error(`Transpose for rank ${rank} is not yet supported`);
   }
   const switchedCoords = new Array(rank);
-  for (let i2 = 0; i2 < newDim.length; i2++) {
-    switchedCoords[newDim[i2]] = `resRC.${getCoordsXYZ(i2)}`;
+  for (let i = 0; i < newDim.length; i++) {
+    switchedCoords[newDim[i]] = `resRC.${getCoordsXYZ(i)}`;
   }
   return switchedCoords.join();
 }
@@ -70844,8 +70844,8 @@ function transpose5(args) {
   const webgpuBackend = backend2;
   const xRank = x.shape.length;
   const newShape = new Array(xRank);
-  for (let i2 = 0; i2 < newShape.length; i2++) {
-    newShape[i2] = x.shape[perm[i2]];
+  for (let i = 0; i < newShape.length; i++) {
+    newShape[i] = x.shape[perm[i]];
   }
   if (backend2.shouldExecuteOnCPU([x])) {
     const xData = webgpuBackend.tensorMap.get(x.dataId);
@@ -70884,7 +70884,7 @@ function argMax4(args) {
   const program = new ArgMinMaxProgram2($x.shape, axes[0], "max");
   const uniformData = [{ type: "float32", data: [Number.NEGATIVE_INFINITY] }];
   const out = backend2.runWebGPUProgram(program, [$x], "int32", uniformData);
-  intermediateTensorInfos.forEach((t2) => backend2.disposeData(t2.dataId));
+  intermediateTensorInfos.forEach((t) => backend2.disposeData(t.dataId));
   return out;
 }
 var argMaxConfig4 = {
@@ -70911,7 +70911,7 @@ function argMin4(args) {
   const program = new ArgMinMaxProgram2($x.shape, axes[0], "min");
   const uniformData = [{ type: "float32", data: [Number.POSITIVE_INFINITY] }];
   const out = backend2.runWebGPUProgram(program, [$x], "int32", uniformData);
-  intermediateTensorInfos.forEach((t2) => backend2.disposeData(t2.dataId));
+  intermediateTensorInfos.forEach((t) => backend2.disposeData(t.dataId));
   return out;
 }
 var argMinConfig3 = {
@@ -71151,7 +71151,7 @@ function reduce2(x, axis, keepDims, reduceType, backend2) {
     toDispose.push(reduced);
     res = reshape6({ inputs: { x: reduced }, attrs: { shape: resOutShape }, backend: backend2 });
   }
-  toDispose.forEach((t2) => backend2.disposeData(t2.dataId));
+  toDispose.forEach((t) => backend2.disposeData(t.dataId));
   return res;
 }
 
@@ -71284,12 +71284,12 @@ var SliceProgram2 = class {
     const sourceCoords = getCoords3(this.rank);
     let coordSum;
     if (this.start.length === 1) {
-      coordSum = this.outputShape.map((_, i2) => {
+      coordSum = this.outputShape.map((_, i) => {
         return `sourceLoc = uniforms.start + coords;`;
       });
     } else {
-      coordSum = this.outputShape.map((_, i2) => {
-        return `sourceLoc.${coords2[i2]} = uniforms.start.${getCoordsXYZ(i2)} + coords.${coords2[i2]};`;
+      coordSum = this.outputShape.map((_, i) => {
+        return `sourceLoc.${coords2[i]} = uniforms.start.${getCoordsXYZ(i)} + coords.${coords2[i]};`;
       });
     }
     const userCode = `
@@ -71369,7 +71369,7 @@ var batchToSpaceND5 = (args) => {
   toDispose.push(reshapedIntermediate);
   toDispose.push(transposedIntermediate);
   toDispose.push(reshapedIntermediate2);
-  toDispose.forEach((t2) => backend2.disposeData(t2.dataId));
+  toDispose.forEach((t) => backend2.disposeData(t.dataId));
   return sliced;
 };
 var batchToSpaceNDConfig4 = {
@@ -71563,12 +71563,12 @@ var ConcatProgram2 = class {
     this.workGroupSize = [64, 1, 1];
     this.size = true;
     this.outputShape = backend_util_exports.computeOutShape(shapes, 1);
-    this.variableNames = shapes.map((_, i2) => `T${i2}`);
+    this.variableNames = shapes.map((_, i) => `T${i}`);
     this.dispatchLayout = flatDispatchLayout(this.outputShape);
     this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, this.workGroupSize, [this.workPerThread, 1, 1]);
     this.offsetLength = shapes.length - 1;
-    for (let i2 = 0; i2 < this.offsetLength; i2++) {
-      this.uniforms += `offset${i2} : i32,`;
+    for (let i = 0; i < this.offsetLength; i++) {
+      this.uniforms += `offset${i} : i32,`;
     }
     this.shaderKey = "concat";
   }
@@ -71576,8 +71576,8 @@ var ConcatProgram2 = class {
     const snippets = [];
     if (this.offsetLength > 0) {
       snippets.push(`if (yC < uniforms.offset0){ setOutputAtCoords(coords.x, coords.y, getT0(yR, yC)); }`);
-      for (let i2 = 1; i2 < this.offsetLength; i2++) {
-        snippets.push(`else if (yC < uniforms.offset${[i2]}){ setOutputAtCoords(coords.x, coords.y, getT${i2}(yR, yC - uniforms.offset${i2 - 1})); }`);
+      for (let i = 1; i < this.offsetLength; i++) {
+        snippets.push(`else if (yC < uniforms.offset${[i]}){ setOutputAtCoords(coords.x, coords.y, getT${i}(yR, yC - uniforms.offset${i - 1})); }`);
       }
       const lastIndex = this.offsetLength;
       const lastShiftIndex = this.offsetLength - 1;
@@ -71620,13 +71620,13 @@ var imagConfig3 = {
 function concatImpl3(inputs, axis, backend2) {
   const dtype = inputs[0].dtype;
   if (dtype === "complex64") {
-    const reals = inputs.map((t2) => real4({ inputs: { input: t2 }, backend: backend2 }));
-    const imags = inputs.map((t2) => imag4({ inputs: { input: t2 }, backend: backend2 }));
+    const reals = inputs.map((t) => real4({ inputs: { input: t }, backend: backend2 }));
+    const imags = inputs.map((t) => imag4({ inputs: { input: t }, backend: backend2 }));
     const realConcated = concatImpl3(reals, axis, backend2);
     const imagConcated = concatImpl3(imags, axis, backend2);
     const result = complex4({ inputs: { real: realConcated, imag: imagConcated }, backend: backend2 });
-    reals.forEach((r2) => backend2.disposeData(r2.dataId));
-    imags.forEach((i2) => backend2.disposeData(i2.dataId));
+    reals.forEach((r) => backend2.disposeData(r.dataId));
+    imags.forEach((i) => backend2.disposeData(i.dataId));
     backend2.disposeData(realConcated.dataId);
     backend2.disposeData(imagConcated.dataId);
     return result;
@@ -71636,63 +71636,63 @@ function concatImpl3(inputs, axis, backend2) {
     runOnCpu = true;
   }
   if (runOnCpu) {
-    const tensors2D2 = inputs.map((t2) => {
-      const innerSize = util_exports.sizeFromShape(t2.shape.slice(axis));
+    const tensors2D2 = inputs.map((t) => {
+      const innerSize = util_exports.sizeFromShape(t.shape.slice(axis));
       const shape = [-1, innerSize];
-      return reshape6({ inputs: { x: t2 }, backend: backend2, attrs: { shape } });
+      return reshape6({ inputs: { x: t }, backend: backend2, attrs: { shape } });
     });
-    const inputsValShapes = tensors2D2.map((t2) => {
-      return { vals: backend2.readSync(t2.dataId), shape: t2.shape };
+    const inputsValShapes = tensors2D2.map((t) => {
+      return { vals: backend2.readSync(t.dataId), shape: t.shape };
     });
-    const outShape2 = backend_util_exports.computeOutShape(tensors2D2.map((t2) => t2.shape), 1);
+    const outShape2 = backend_util_exports.computeOutShape(tensors2D2.map((t) => t.shape), 1);
     const simplyConcat = tensors2D2[0].shape[0] === 1;
     const outVals = concatImplCPU2(inputsValShapes, outShape2, dtype, simplyConcat);
-    const finalOutShape = backend_util_exports.computeOutShape(inputs.map((t2) => t2.shape), axis);
+    const finalOutShape = backend_util_exports.computeOutShape(inputs.map((t) => t.shape), axis);
     const outInfo = backend2.makeTensorInfo(finalOutShape, dtype, outVals);
-    tensors2D2.forEach((t2) => backend2.disposeData(t2.dataId));
+    tensors2D2.forEach((t) => backend2.disposeData(t.dataId));
     return outInfo;
   }
   const maxInputNum = backend2.device.limits.maxStorageBuffersPerShaderStage - 1;
   if (inputs.length > maxInputNum) {
     const reducedInputs = [];
-    for (let i2 = 0; i2 < inputs.length; i2 += maxInputNum) {
-      const subArray = inputs.slice(i2, i2 + maxInputNum);
+    for (let i = 0; i < inputs.length; i += maxInputNum) {
+      const subArray = inputs.slice(i, i + maxInputNum);
       reducedInputs.push(concatImpl3(subArray, axis, backend2));
     }
     const result = concatImpl3(reducedInputs, axis, backend2);
-    for (const i2 of reducedInputs) {
-      backend2.disposeData(i2.dataId);
+    for (const i of reducedInputs) {
+      backend2.disposeData(i.dataId);
     }
     return result;
   }
   const { tensors2D, outShape } = computeTensors2D2(inputs, axis, backend2);
-  const shapes = tensors2D.map((t2) => t2.shape);
+  const shapes = tensors2D.map((t) => t.shape);
   const program = new ConcatProgram2(shapes);
   const uniformData = [];
   const offsets = new Array(shapes.length - 1);
   if (offsets.length > 0) {
     offsets[0] = shapes[0][1];
     uniformData.push({ type: "int32", data: [offsets[0]] });
-    for (let i2 = 1; i2 < offsets.length; i2++) {
-      offsets[i2] = offsets[i2 - 1] + shapes[i2][1];
-      uniformData.push({ type: "int32", data: [offsets[i2]] });
+    for (let i = 1; i < offsets.length; i++) {
+      offsets[i] = offsets[i - 1] + shapes[i][1];
+      uniformData.push({ type: "int32", data: [offsets[i]] });
     }
   }
   const res = backend2.runWebGPUProgram(program, tensors2D, tensors2D[0].dtype, uniformData);
-  tensors2D.forEach((r2) => backend2.disposeData(r2.dataId));
+  tensors2D.forEach((r) => backend2.disposeData(r.dataId));
   const reshapedResult = reshape6({ inputs: { x: res }, backend: backend2, attrs: { shape: outShape } });
   backend2.disposeData(res.dataId);
   return reshapedResult;
 }
 function computeTensors2D2(inputs, axis, backend2) {
-  const outShape = backend_util_exports.computeOutShape(inputs.map((t2) => t2.shape), axis);
-  const tensors2D = inputs.map((t2) => reshape6({
-    inputs: { x: t2 },
+  const outShape = backend_util_exports.computeOutShape(inputs.map((t) => t.shape), axis);
+  const tensors2D = inputs.map((t) => reshape6({
+    inputs: { x: t },
     backend: backend2,
     attrs: {
       shape: [
-        util_exports.sizeFromShape(t2.shape.slice(0, axis)),
-        util_exports.sizeFromShape(t2.shape.slice(axis))
+        util_exports.sizeFromShape(t.shape.slice(0, axis)),
+        util_exports.sizeFromShape(t.shape.slice(axis))
       ]
     }
   }));
@@ -71704,15 +71704,15 @@ function concat5(args) {
   const { inputs, backend: backend2, attrs } = args;
   const { axis } = attrs;
   const $axis = util_exports.parseAxisParam(axis, inputs[0].shape)[0];
-  const outShape = backend_util_exports.computeOutShape(inputs.map((t2) => t2.shape), $axis);
+  const outShape = backend_util_exports.computeOutShape(inputs.map((t) => t.shape), $axis);
   if (util_exports.sizeFromShape(outShape) === 0) {
     return backend2.makeTensorInfo(outShape, inputs[0].dtype, []);
   }
-  const $inputs = inputs.filter((t2) => util_exports.sizeFromShape(t2.shape) > 0);
+  const $inputs = inputs.filter((t) => util_exports.sizeFromShape(t.shape) > 0);
   if ($inputs.length === 1) {
     return identity5({ inputs: { x: $inputs[0] }, backend: backend2 });
   }
-  const shapes = $inputs.map((t2) => t2.shape);
+  const shapes = $inputs.map((t) => t.shape);
   backend_util_exports.assertParamsConsistent(shapes, $axis);
   return concatImpl3($inputs, $axis, backend2);
 }
@@ -71982,8 +71982,8 @@ function conv2dByMatMul2({ x, filter, convInfo, backend: backend2, bias = null,
   });
   const out = reshape6({ inputs: { x: result }, backend: backend2, attrs: { shape: convInfo.outShape } });
   intermediates.push(result);
-  for (const i2 of intermediates) {
-    backend2.disposeData(i2.dataId);
+  for (const i of intermediates) {
+    backend2.disposeData(i.dataId);
   }
   return out;
 }
@@ -72043,8 +72043,8 @@ function conv2DImpl({ x, filter, convInfo, backend: backend2, bias = null, prelu
     program.uniforms += " alpha : f32,";
   }
   const out = backend2.runWebGPUProgram(program, inputVar, x.dtype, dimensions);
-  for (const i2 of intermediates) {
-    backend2.disposeData(i2.dataId);
+  for (const i of intermediates) {
+    backend2.disposeData(i.dataId);
   }
   return out;
 }
@@ -72508,10 +72508,10 @@ function cumImpl2(op2, x, backend2, axis, exclusive, reverse5) {
   }
   const size = permutedX.shape[permutedAxis];
   let result = identity5({ inputs: { x: permutedX }, backend: backend2 });
-  for (let i2 = 0; i2 <= Math.ceil(Math.log2(size)) - 1; i2++) {
+  for (let i = 0; i <= Math.ceil(Math.log2(size)) - 1; i++) {
     const program = new CumProgram2(op2, permutedX.shape, false, reverse5);
     const prevResult = result;
-    const uniformData = [{ type: "float32", data: [i2] }];
+    const uniformData = [{ type: "float32", data: [i] }];
     result = backend2.runWebGPUProgram(program, [result], result.dtype, uniformData);
     backend2.disposeData(prevResult.dataId);
   }
@@ -73010,8 +73010,8 @@ function einsum4(args) {
   let out = null;
   let numDimsRemaining = allDims.length;
   const tensorsToDispose = [];
-  for (let i2 = 0; i2 < nSteps; ++i2) {
-    for (const idTerm of steps[i2]) {
+  for (let i = 0; i < nSteps; ++i) {
+    for (const idTerm of steps[i]) {
       const { permutationIndices: perm, expandDims: dimsToExpand } = backend_util_exports.getEinsumPermutation(numDimsRemaining, idDims[idTerm]);
       let x;
       if (backend_util_exports.isIdentityPermutation(perm)) {
@@ -73035,13 +73035,13 @@ function einsum4(args) {
         tensorsToDispose.push(out);
       }
     }
-    if (i2 < nSteps - 1) {
-      if (path[i2] >= 0) {
+    if (i < nSteps - 1) {
+      if (path[i] >= 0) {
         out = sum6({
           inputs: { x: out },
           backend: backend2,
           attrs: {
-            axis: path[i2] - (allDims.length - numDimsRemaining),
+            axis: path[i] - (allDims.length - numDimsRemaining),
             keepDims: false
           }
         });
@@ -73290,9 +73290,9 @@ function fromPixels3(args) {
     pixelArray = new Uint8Array(pixels.width * pixels.height * numChannels);
     const dataLength = imageData.length;
     let j = 0;
-    for (let i2 = 0; i2 < dataLength; i2++) {
-      if (i2 % 4 < numChannels) {
-        pixelArray[j++] = imageData[i2];
+    for (let i = 0; i < dataLength; i++) {
+      if (i % 4 < numChannels) {
+        pixelArray[j++] = imageData[i];
       }
     }
   }
@@ -73553,11 +73553,11 @@ var GatherProgram2 = class {
 function getSourceCoords4(aShape) {
   const currentCoords = ["resRC.x", "resRC.y", "resRC.z", "resRC.w"];
   const sourceCoords = [];
-  for (let i2 = 0; i2 < aShape.length; i2++) {
-    if (i2 === 2) {
+  for (let i = 0; i < aShape.length; i++) {
+    if (i === 2) {
       sourceCoords.push("indexZ");
     } else {
-      sourceCoords.push(`${currentCoords[i2]}`);
+      sourceCoords.push(`${currentCoords[i]}`);
     }
   }
   return sourceCoords.join();
@@ -73605,14 +73605,14 @@ function gatherV24(args) {
     const xValues = xBufferInfo.values;
     const xBuf = buffer(flattenX.shape, flattenX.dtype, xValues);
     const outBuf = gatherV2ImplCPU2(xBuf, indicesBuf, flattenOutputShape);
-    toDispose.forEach((t2) => backend2.disposeData(t2.dataId));
+    toDispose.forEach((t) => backend2.disposeData(t.dataId));
     return backend2.makeTensorInfo(shapeInfo.outputShape, outBuf.dtype, outBuf.values);
   }
   const program = new GatherProgram2(flattenX.shape, flattenOutputShape);
   const res = backend2.runWebGPUProgram(program, [flattenX, flattenIndex], flattenX.dtype);
   toDispose.push(res);
   const reshaped = reshape6({ inputs: { x: res }, backend: backend2, attrs: { shape: shapeInfo.outputShape } });
-  toDispose.forEach((t2) => backend2.disposeData(t2.dataId));
+  toDispose.forEach((t) => backend2.disposeData(t.dataId));
   return reshaped;
 }
 var gatherV2Config4 = {
@@ -73770,20 +73770,20 @@ var MirrorPadProgram2 = class {
     this.variableNames = ["x"];
     this.workGroupSize = [64, 1, 1];
     this.size = true;
-    this.outputShape = paddings.map((p2, i2) => p2[0] + xShape[i2] + p2[1]);
+    this.outputShape = paddings.map((p2, i) => p2[0] + xShape[i] + p2[1]);
     this.dispatchLayout = flatDispatchLayout(this.outputShape);
     this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, this.workGroupSize);
     this.xShape = xShape;
-    paddings.map((_, i2) => {
-      this.uniforms += ` pad${i2} : vec2<i32>,`;
+    paddings.map((_, i) => {
+      this.uniforms += ` pad${i} : vec2<i32>,`;
     });
     this.offset = mode === "reflect" ? 0 : 1;
     this.shaderKey = `mirrorPad_${mode}`;
   }
   getUserCode() {
     const rank = this.xShape.length;
-    const start = this.xShape.map((_, i2) => `uniforms.pad${i2}[0]`).join(",");
-    const end = this.xShape.map((_, i2) => `uniforms.pad${i2}[0] + uniforms.xShape${rank > 1 ? `[${i2}]` : ""}`).join(",");
+    const start = this.xShape.map((_, i) => `uniforms.pad${i}[0]`).join(",");
+    const end = this.xShape.map((_, i) => `uniforms.pad${i}[0] + uniforms.xShape${rank > 1 ? `[${i}]` : ""}`).join(",");
     const shaderStart = rank === 1 ? "start" : "start[i]";
     const shaderEnd = rank === 1 ? "end" : "end[i]";
     const shaderOutC = rank === 1 ? "outC" : "outC[i]";
@@ -73892,14 +73892,14 @@ function zerosLike5(args) {
   const { x } = inputs;
   if (x.dtype === "complex64") {
     const realPart = real4({ inputs: { input: x }, backend: backend2 });
-    const r2 = zerosLike5({ inputs: { x: realPart }, backend: backend2 });
+    const r = zerosLike5({ inputs: { x: realPart }, backend: backend2 });
     const imagPart = imag4({ inputs: { input: x }, backend: backend2 });
-    const i2 = zerosLike5({ inputs: { x: imagPart }, backend: backend2 });
-    const result = complex4({ inputs: { real: r2, imag: i2 }, backend: backend2 });
+    const i = zerosLike5({ inputs: { x: imagPart }, backend: backend2 });
+    const result = complex4({ inputs: { real: r, imag: i }, backend: backend2 });
     backend2.disposeData(realPart.dataId);
-    backend2.disposeData(r2.dataId);
+    backend2.disposeData(r.dataId);
     backend2.disposeData(imagPart.dataId);
-    backend2.disposeData(i2.dataId);
+    backend2.disposeData(i.dataId);
     return result;
   } else {
     return fill5({
@@ -73926,14 +73926,14 @@ function onesLike5(args) {
     throw new Error("onesLike is not supported under string dtype");
   } else if (x.dtype === "complex64") {
     const realPart = real4({ inputs: { input: x }, backend: backend2 });
-    const r2 = onesLike5({ inputs: { x: realPart }, backend: backend2 });
+    const r = onesLike5({ inputs: { x: realPart }, backend: backend2 });
     const imagPart = imag4({ inputs: { input: x }, backend: backend2 });
-    const i2 = zerosLike5({ inputs: { x: imagPart }, backend: backend2 });
-    const result = complex4({ inputs: { real: r2, imag: i2 }, backend: backend2 });
+    const i = zerosLike5({ inputs: { x: imagPart }, backend: backend2 });
+    const result = complex4({ inputs: { real: r, imag: i }, backend: backend2 });
     backend2.disposeData(realPart.dataId);
-    backend2.disposeData(r2.dataId);
+    backend2.disposeData(r.dataId);
     backend2.disposeData(imagPart.dataId);
-    backend2.disposeData(i2.dataId);
+    backend2.disposeData(i.dataId);
     return result;
   } else {
     return fill5({ attrs: { shape: x.shape, dtype: x.dtype, value: 1 }, backend: backend2 });
@@ -73954,18 +73954,18 @@ function pack4(args) {
   }
   const shape = inputs[0].shape;
   const dtype = inputs[0].dtype;
-  inputs.forEach((t2) => {
-    util_exports.assertShapesMatch(shape, t2.shape, "All tensors passed to stack must have matching shapes");
-    util_exports.assert(dtype === t2.dtype, () => "All tensors passed to stack must have matching dtypes");
+  inputs.forEach((t) => {
+    util_exports.assertShapesMatch(shape, t.shape, "All tensors passed to stack must have matching shapes");
+    util_exports.assert(dtype === t.dtype, () => "All tensors passed to stack must have matching dtypes");
   });
   const intermediateTensorInfos = [];
-  const expandedTensors = inputs.map((t2) => {
-    const expandedT = expandDims6({ inputs: { input: t2 }, backend: backend2, attrs: { dim: axis } });
+  const expandedTensors = inputs.map((t) => {
+    const expandedT = expandDims6({ inputs: { input: t }, backend: backend2, attrs: { dim: axis } });
     intermediateTensorInfos.push(expandedT);
     return expandedT;
   });
   const result = concat5({ inputs: expandedTensors, backend: backend2, attrs: { axis } });
-  intermediateTensorInfos.forEach((t2) => backend2.disposeData(t2.dataId));
+  intermediateTensorInfos.forEach((t) => backend2.disposeData(t.dataId));
   return result;
 }
 var packConfig4 = {
@@ -73981,11 +73981,11 @@ var PadProgram2 = class {
     this.uniforms = "constantValue : f32,";
     this.workGroupSize = [64, 1, 1];
     this.size = true;
-    this.outputShape = paddings.map((p2, i2) => p2[0] + xShape[i2] + p2[1]);
+    this.outputShape = paddings.map((p2, i) => p2[0] + xShape[i] + p2[1]);
     this.dispatchLayout = flatDispatchLayout(this.outputShape);
     this.dispatch = computeDispatch(this.dispatchLayout, this.outputShape, this.workGroupSize);
-    paddings.map((_, i2) => {
-      this.uniforms += ` pad${i2} : vec2<i32>,`;
+    paddings.map((_, i) => {
+      this.uniforms += ` pad${i} : vec2<i32>,`;
     });
     this.xShape = xShape;
     this.shaderKey = "pad";
@@ -73993,8 +73993,8 @@ var PadProgram2 = class {
   getUserCode() {
     const rank = this.xShape.length;
     const type = getCoordsDataType2(rank);
-    const start = this.xShape.map((_, i2) => `uniforms.pad${i2}[0]`).join(",");
-    const end = this.xShape.map((_, i2) => `uniforms.pad${i2}[0] + uniforms.xShape${rank > 1 ? `[${i2}]` : ""}`).join(",");
+    const start = this.xShape.map((_, i) => `uniforms.pad${i}[0]`).join(",");
+    const end = this.xShape.map((_, i) => `uniforms.pad${i}[0] + uniforms.xShape${rank > 1 ? `[${i}]` : ""}`).join(",");
     const startValue = rank > 1 ? `${type}(${start})` : `${start}`;
     const endValue = rank > 1 ? `${type}(${end})` : `${end}`;
     const leftPadCondition = rank > 1 ? `any(outC < start)` : `outC < start`;
@@ -74029,7 +74029,7 @@ var padV23 = (args) => {
     return identity5({ inputs: { x }, backend: backend2 });
   }
   if (util_exports.sizeFromShape(x.shape) === 0) {
-    const outputShape = paddings.map((p2, i2) => p2[0] + x.shape[i2] + p2[1]);
+    const outputShape = paddings.map((p2, i) => p2[0] + x.shape[i] + p2[1]);
     return fill5({
       backend: backend2,
       attrs: { shape: outputShape, value: constantValue, dtype: x.dtype }
@@ -74530,10 +74530,10 @@ var SelectProgram2 = class {
       const currentCoords = ["resRC.x", "resRC.y", "resRC.z", "resRC.w"];
       const cCoordVars = [];
       const abCoordVars = [];
-      for (let i2 = 0; i2 < this.outputShape.length; i2++) {
-        abCoordVars.push(`${currentCoords[i2]}`);
-        if (i2 < this.cRank) {
-          cCoordVars.push(`${currentCoords[i2]}`);
+      for (let i = 0; i < this.outputShape.length; i++) {
+        abCoordVars.push(`${currentCoords[i]}`);
+        if (i < this.cRank) {
+          cCoordVars.push(`${currentCoords[i]}`);
         }
       }
       cCoords = cCoordVars.join();
@@ -74559,9 +74559,9 @@ var SelectProgram2 = class {
 // node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@0.0.1-alpha.13_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgpu/dist/kernels/Select.js
 function select5(args) {
   const { inputs, backend: backend2 } = args;
-  const { condition, t: t2, e: e2 } = inputs;
-  const program = new SelectProgram2(condition.shape.length, t2.shape, t2.shape.length);
-  return backend2.runWebGPUProgram(program, [condition, t2, e2], upcastType(t2.dtype, e2.dtype));
+  const { condition, t, e } = inputs;
+  const program = new SelectProgram2(condition.shape.length, t.shape, t.shape.length);
+  return backend2.runWebGPUProgram(program, [condition, t, e], upcastType(t.dtype, e.dtype));
 }
 var selectConfig4 = {
   kernelName: Select,
@@ -74642,7 +74642,7 @@ var spaceToBatchND5 = (args) => {
   const prod6 = blockShape.reduce((a, b) => a * b);
   const completePaddings = [[0, 0]];
   completePaddings.push(...paddings);
-  for (let i2 = 1 + blockShape.length; i2 < x.shape.length; ++i2) {
+  for (let i = 1 + blockShape.length; i < x.shape.length; ++i) {
     completePaddings.push([0, 0]);
   }
   const toDispose = [];
@@ -74664,7 +74664,7 @@ var spaceToBatchND5 = (args) => {
   toDispose.push(paddedX);
   toDispose.push(reshapedPaddedX);
   toDispose.push(paddedXT);
-  toDispose.forEach((t2) => backend2.disposeData(t2.dataId));
+  toDispose.forEach((t) => backend2.disposeData(t.dataId));
   return result;
 };
 var spaceToBatchNDConfig4 = {
@@ -74680,8 +74680,8 @@ var TileProgram2 = class {
     this.workGroupSize = [64, 1, 1];
     this.size = true;
     const outputShape = new Array(aShape.length);
-    for (let i2 = 0; i2 < outputShape.length; i2++) {
-      outputShape[i2] = aShape[i2] * reps[i2];
+    for (let i = 0; i < outputShape.length; i++) {
+      outputShape[i] = aShape[i] * reps[i];
     }
     this.outputShape = outputShape;
     this.dispatchLayout = flatDispatchLayout(this.outputShape);
@@ -74711,8 +74711,8 @@ function getSourceCoords5(rank, uniformPrefix = "") {
   }
   const currentCoords = ["resRC.x", "resRC.y", "resRC.z", "resRC.w"];
   const sourceCoords = [];
-  for (let i2 = 0; i2 < rank; i2++) {
-    sourceCoords.push(`(${currentCoords[i2]} % ${uniformPrefix}aShape[${i2}])`);
+  for (let i = 0; i < rank; i++) {
+    sourceCoords.push(`(${currentCoords[i]} % ${uniformPrefix}aShape[${i}])`);
   }
   return sourceCoords.join();
 }
@@ -74821,11 +74821,11 @@ function splitV4(args) {
   const xRank = x.shape.length;
   const begin = new Array(xRank).fill(0);
   const size = x.shape.slice();
-  return splitSizes.map((s2) => {
+  return splitSizes.map((s) => {
     const sliceSize = [...size];
-    sliceSize[$axis] = s2;
+    sliceSize[$axis] = s;
     const sliceT = slice5({ inputs: { x }, backend: backend2, attrs: { begin, size: sliceSize } });
-    begin[$axis] += s2;
+    begin[$axis] += s;
     return sliceT;
   });
 }
@@ -74886,9 +74886,9 @@ var StridedSliceProgram2 = class {
       newCoords = "coords * uniforms.strides + uniforms.begin";
     } else {
       let outputAxis = 0;
-      newCoords = this.outputShape.map((_, i2) => {
+      newCoords = this.outputShape.map((_, i) => {
         outputAxis++;
-        return this.outputShape.length === 1 ? `coords * uniforms.strides[${i2}] + uniforms.begin[${i2}]` : `coords[${outputAxis - 1}] * uniforms.strides[${i2}] + uniforms.begin[${i2}]`;
+        return this.outputShape.length === 1 ? `coords * uniforms.strides[${i}] + uniforms.begin[${i}]` : `coords[${outputAxis - 1}] * uniforms.strides[${i}] + uniforms.begin[${i}]`;
       }).join(",");
     }
     const userCode = `
@@ -75429,9 +75429,9 @@ function unpack4(args) {
   const num = value.shape[axis];
   const outShape = new Array(xRank - 1);
   let outIndex = 0;
-  for (let i2 = 0; i2 < xRank; i2++) {
-    if (i2 !== axis) {
-      outShape[outIndex++] = x.shape[i2];
+  for (let i = 0; i < xRank; i++) {
+    if (i !== axis) {
+      outShape[outIndex++] = x.shape[i];
     }
   }
   const toDispose = [];
@@ -75439,14 +75439,14 @@ function unpack4(args) {
   const size = x.shape.slice();
   size[axis] = 1;
   const res = new Array(num);
-  for (let i2 = 0; i2 < res.length; i2++) {
-    begin[axis] = i2;
+  for (let i = 0; i < res.length; i++) {
+    begin[axis] = i;
     const sliced = slice5({ inputs: { x }, backend: backend2, attrs: { begin, size } });
     const reshaped = reshape6({ inputs: { x: sliced }, backend: backend2, attrs: { shape: outShape } });
-    res[i2] = reshaped;
+    res[i] = reshaped;
     toDispose.push(sliced);
   }
-  toDispose.forEach((t2) => backend2.disposeData(t2.dataId));
+  toDispose.forEach((t) => backend2.disposeData(t.dataId));
   return res;
 }
 var unpackConfig4 = {
@@ -75567,14 +75567,22 @@ for (const kernelConfig of kernelConfigs4) {
 }
 
 // dist/tfjs.version.js
-var e = "3.20.0";
-var s = "3.20.0";
-var t = "3.20.0";
-var i = "3.20.0";
-var n = "3.20.0";
-var r = "3.20.0";
-var l = "3.20.0";
-var V = { tfjs: e, "tfjs-core": s, "tfjs-data": t, "tfjs-layers": i, "tfjs-converter": n, "tfjs-backend-webgl": r, "tfjs-backend-wasm": l };
+var version9 = "3.20.0";
+var version22 = "3.20.0";
+var version32 = "3.20.0";
+var version42 = "3.20.0";
+var version52 = "3.20.0";
+var version62 = "3.20.0";
+var version72 = "3.20.0";
+var version82 = {
+  tfjs: version9,
+  "tfjs-core": version22,
+  "tfjs-data": version32,
+  "tfjs-layers": version42,
+  "tfjs-converter": version52,
+  "tfjs-backend-webgl": version62,
+  "tfjs-backend-wasm": version72
+};
 export {
   Abs,
   Acos,
@@ -76068,7 +76076,7 @@ export {
   valueAndGrads,
   variable,
   variableGrads,
-  V as version,
+  version82 as version,
   version3 as version_converter,
   version as version_core,
   version2 as version_layers,
diff --git a/dist/tfjs.version.js b/dist/tfjs.version.js
index 38651394..567c36c8 100644
--- a/dist/tfjs.version.js
+++ b/dist/tfjs.version.js
@@ -4,4 +4,38 @@
   author: <https://github.com/vladmandic>'
 */
 
-var e="3.20.0";var s="3.20.0";var t="3.20.0";var i="3.20.0";var n="3.20.0";var r="3.20.0";var l="3.20.0";var V={tfjs:e,"tfjs-core":s,"tfjs-data":t,"tfjs-layers":i,"tfjs-converter":n,"tfjs-backend-webgl":r,"tfjs-backend-wasm":l};export{V as version};
+
+// node_modules/.pnpm/@tensorflow+tfjs@3.20.0_seedrandom@3.0.5/node_modules/@tensorflow/tfjs/package.json
+var version = "3.20.0";
+
+// node_modules/.pnpm/@tensorflow+tfjs-core@3.20.0/node_modules/@tensorflow/tfjs-core/package.json
+var version2 = "3.20.0";
+
+// node_modules/.pnpm/@tensorflow+tfjs-data@3.20.0_k7dauiu3y265wd6lcplf62oi7i/node_modules/@tensorflow/tfjs-data/package.json
+var version3 = "3.20.0";
+
+// node_modules/.pnpm/@tensorflow+tfjs-layers@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-layers/package.json
+var version4 = "3.20.0";
+
+// node_modules/.pnpm/@tensorflow+tfjs-converter@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-converter/package.json
+var version5 = "3.20.0";
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-webgl/package.json
+var version6 = "3.20.0";
+
+// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@3.20.0_au2niqrxqvhsnv4oetlud656gy/node_modules/@tensorflow/tfjs-backend-wasm/package.json
+var version7 = "3.20.0";
+
+// tfjs/tf-version.ts
+var version8 = {
+  tfjs: version,
+  "tfjs-core": version2,
+  "tfjs-data": version3,
+  "tfjs-layers": version4,
+  "tfjs-converter": version5,
+  "tfjs-backend-webgl": version6,
+  "tfjs-backend-wasm": version7
+};
+export {
+  version8 as version
+};
diff --git a/models/README.md b/models/README.md
index 84e90b89..4618f458 100644
--- a/models/README.md
+++ b/models/README.md
@@ -3,308 +3,3 @@
 For details see Wiki:
 
 - [**List of Models & Credits**](https://github.com/vladmandic/human/wiki/Models)
-
-## Model signatures:
-
-```js
-INFO:  graph model: /home/vlado/dev/human/models/iris.json
-INFO:  created on: 2020-10-12T18:46:47.060Z
-INFO:  metadata: { generatedBy: 'https://github.com/google/mediapipe', convertedBy: 'https://github.com/vladmandic', version: undefined }
-INFO:  model inputs based on signature
- { name: 'input_1:0', dtype: 'DT_FLOAT', shape: [ -1, 64, 64, 3 ] }
-INFO:  model outputs based on signature
- { id: 0, name: 'Identity:0', dytpe: 'DT_FLOAT', shape: [ -1, 1, 1, 228 ] }
-INFO:  tensors: 191
-DATA:  weights: {
-  files: [ 'iris.bin' ],
-  size: { disk: 2599092, memory: 2599092 },
-  count: { total: 191, float32: 189, int32: 2 },
-  quantized: { none: 191 },
-  values: { total: 649773, float32: 649764, int32: 9 }
-}
-DATA:  kernel ops: {
-  graph: [ 'Const', 'Placeholder', 'Identity' ],
-  convolution: [ '_FusedConv2D', 'DepthwiseConv2dNative', 'MaxPool' ],
-  arithmetic: [ 'AddV2' ],
-  basic_math: [ 'Prelu' ],
-  transformation: [ 'Pad' ],
-  slice_join: [ 'ConcatV2' ]
-}
-INFO:  graph model: /home/vlado/dev/human/models/facemesh.json
-INFO:  created on: 2020-10-12T18:46:46.944Z
-INFO:  metadata: { generatedBy: 'https://github.com/google/mediapipe', convertedBy: 'https://github.com/vladmandic', version: undefined }
-INFO:  model inputs based on signature
- { name: 'input_1:0', dtype: 'DT_FLOAT', shape: [ 1, 192, 192, 3 ] }
-INFO:  model outputs based on signature
- { id: 0, name: 'Identity_1:0', dytpe: 'DT_FLOAT', shape: [ 1, 266 ] }
- { id: 1, name: 'Identity_2:0', dytpe: 'DT_FLOAT', shape: [ 1, 1 ] }
- { id: 2, name: 'Identity:0', dytpe: 'DT_FLOAT', shape: [ 1, 1404 ] }
-INFO:  tensors: 118
-DATA:  weights: {
-  files: [ 'facemesh.bin' ],
-  size: { disk: 2955780, memory: 2955780 },
-  count: { total: 118, float32: 114, int32: 4 },
-  quantized: { none: 118 },
-  values: { total: 738945, float32: 738919, int32: 26 }
-}
-DATA:  kernel ops: {
-  graph: [ 'Placeholder', 'Const', 'NoOp', 'Identity' ],
-  convolution: [ '_FusedConv2D', 'DepthwiseConv2dNative', 'MaxPool' ],
-  arithmetic: [ 'AddV2' ],
-  basic_math: [ 'Prelu', 'Sigmoid' ],
-  transformation: [ 'Pad', 'Reshape' ]
-}
-INFO:  graph model: /home/vlado/dev/human/models/emotion.json
-INFO:  created on: 2020-11-05T20:11:29.740Z
-INFO:  metadata: { generatedBy: 'https://github.com/oarriaga/face_classification', convertedBy: 'https://github.com/vladmandic', version: undefined }
-INFO:  model inputs based on signature
- { name: 'input_1:0', dtype: 'DT_FLOAT', shape: [ -1, 64, 64, 1 ] }
-INFO:  model outputs based on signature
- { id: 0, name: 'Identity:0', dytpe: 'DT_FLOAT', shape: [ -1, 7 ] }
-INFO:  tensors: 23
-DATA:  weights: {
-  files: [ 'emotion.bin' ],
-  size: { disk: 820516, memory: 820516 },
-  count: { total: 23, float32: 22, int32: 1 },
-  quantized: { none: 23 },
-  values: { total: 205129, float32: 205127, int32: 2 }
-}
-DATA:  kernel ops: {
-  graph: [ 'Const', 'Placeholder', 'Identity' ],
-  convolution: [ '_FusedConv2D', 'DepthwiseConv2dNative', 'MaxPool' ],
-  arithmetic: [ 'AddV2' ],
-  basic_math: [ 'Relu' ],
-  reduction: [ 'Mean' ],
-  normalization: [ 'Softmax' ]
-}
-INFO:  graph model: /home/vlado/dev/human/models/faceres.json
-INFO:  created on: 2021-03-21T14:12:59.863Z
-INFO:  metadata: { generatedBy: 'https://github.com/HSE-asavchenko/HSE_FaceRec_tf', convertedBy: 'https://github.com/vladmandic', version: undefined }
-INFO:  model inputs based on signature
- { name: 'input_1', dtype: 'DT_FLOAT', shape: [ -1, 224, 224, 3 ] }
-INFO:  model outputs based on signature
- { id: 0, name: 'gender_pred/Sigmoid:0', dytpe: 'DT_FLOAT', shape: [ 1, 1 ] }
- { id: 1, name: 'global_pooling/Mean', dytpe: 'DT_FLOAT', shape: [ 1, 1024 ] }
- { id: 2, name: 'age_pred/Softmax:0', dytpe: 'DT_FLOAT', shape: [ 1, 100 ] }
-INFO:  tensors: 128
-DATA:  weights: {
-  files: [ 'faceres.bin' ],
-  size: { disk: 6978814, memory: 13957620 },
-  count: { total: 128, float32: 127, int32: 1 },
-  quantized: { float16: 127, none: 1 },
-  values: { total: 3489405, float32: 3489403, int32: 2 }
-}
-DATA:  kernel ops: {
-  graph: [ 'Const', 'Placeholder' ],
-  convolution: [ 'Conv2D', 'DepthwiseConv2dNative' ],
-  arithmetic: [ 'Add', 'Minimum', 'Maximum', 'Mul' ],
-  basic_math: [ 'Relu', 'Sigmoid' ],
-  reduction: [ 'Mean' ],
-  matrices: [ '_FusedMatMul' ],
-  normalization: [ 'Softmax' ]
-}
-INFO:  graph model: /home/vlado/dev/human/models/blazeface.json
-INFO:  created on: 2020-10-15T19:57:26.419Z
-INFO:  metadata: { generatedBy: 'https://github.com/google/mediapipe', convertedBy: 'https://github.com/vladmandic', version: undefined }
-INFO:  model inputs based on signature
- { name: 'input:0', dtype: 'DT_FLOAT', shape: [ 1, 256, 256, 3 ] }
-INFO:  model outputs based on signature
- { id: 0, name: 'Identity_3:0', dytpe: 'DT_FLOAT', shape: [ 1, 384, 16 ] }
- { id: 1, name: 'Identity:0', dytpe: 'DT_FLOAT', shape: [ 1, 512, 1 ] }
- { id: 2, name: 'Identity_1:0', dytpe: 'DT_FLOAT', shape: [ 1, 384, 1 ] }
- { id: 3, name: 'Identity_2:0', dytpe: 'DT_FLOAT', shape: [ 1, 512, 16 ] }
-INFO:  tensors: 112
-DATA:  weights: {
-  files: [ 'blazeface.bin' ],
-  size: { disk: 538928, memory: 538928 },
-  count: { total: 112, float32: 106, int32: 6 },
-  quantized: { none: 112 },
-  values: { total: 134732, float32: 134704, int32: 28 }
-}
-DATA:  kernel ops: {
-  graph: [ 'Const', 'Placeholder', 'Identity' ],
-  convolution: [ '_FusedConv2D', 'DepthwiseConv2dNative', 'MaxPool' ],
-  arithmetic: [ 'AddV2' ],
-  basic_math: [ 'Relu' ],
-  transformation: [ 'Pad', 'Reshape' ]
-}
-INFO:  graph model: /home/vlado/dev/human/models/mb3-centernet.json
-INFO:  created on: 2021-05-19T11:50:13.013Z
-INFO:  metadata: { generatedBy: 'https://github.com/610265158/mobilenetv3_centernet', convertedBy: 'https://github.com/vladmandic', version: undefined }
-INFO:  model inputs based on signature
- { name: 'tower_0/images', dtype: 'DT_FLOAT', shape: [ 1, 512, 512, 3 ] }
-INFO:  model outputs based on signature
- { id: 0, name: 'tower_0/wh', dytpe: 'DT_FLOAT', shape: [ 1, 128, 128, 4 ] }
- { id: 1, name: 'tower_0/keypoints', dytpe: 'DT_FLOAT', shape: [ 1, 128, 128, 80 ] }
- { id: 2, name: 'tower_0/detections', dytpe: 'DT_FLOAT', shape: [ 1, 100, 6 ] }
-INFO:  tensors: 267
-DATA:  weights: {
-  files: [ 'mb3-centernet.bin' ],
-  size: { disk: 4030290, memory: 8060260 },
-  count: { total: 267, float32: 227, int32: 40 },
-  quantized: { float16: 227, none: 40 },
-  values: { total: 2015065, float32: 2014985, int32: 80 }
-}
-DATA:  kernel ops: {
-  graph: [ 'Const', 'Placeholder', 'Identity' ],
-  convolution: [ '_FusedConv2D', 'FusedDepthwiseConv2dNative', 'DepthwiseConv2dNative', 'Conv2D', 'MaxPool' ],
-  arithmetic: [ 'Mul', 'Add', 'FloorDiv', 'FloorMod', 'Sub' ],
-  basic_math: [ 'Relu6', 'Relu', 'Sigmoid' ],
-  reduction: [ 'Mean' ],
-  image: [ 'ResizeBilinear' ],
-  slice_join: [ 'ConcatV2', 'GatherV2', 'StridedSlice' ],
-  transformation: [ 'Reshape', 'Cast', 'ExpandDims' ],
-  logical: [ 'Equal' ],
-  evaluation: [ 'TopKV2' ]
-}
-INFO:  graph model: /home/vlado/dev/human/models/movenet-lightning.json
-INFO:  created on: 2021-05-29T12:26:32.994Z
-INFO:  metadata: { generatedBy: 'https://tfhub.dev/google/movenet/singlepose/lightning/4', convertedBy: 'https://github.com/vladmandic', version: undefined }
-INFO:  model inputs based on signature
- { name: 'input:0', dtype: 'DT_INT32', shape: [ 1, 192, 192, 3 ] }
-INFO:  model outputs based on signature
- { id: 0, name: 'Identity:0', dytpe: 'DT_FLOAT', shape: [ 1, 1, 17, 3 ] }
-INFO:  tensors: 180
-DATA:  weights: {
-  files: [ 'movenet-lightning.bin' ],
-  size: { disk: 4650216, memory: 9300008 },
-  count: { total: 180, int32: 31, float32: 149 },
-  quantized: { none: 31, float16: 149 },
-  values: { total: 2325002, int32: 106, float32: 2324896 }
-}
-DATA:  kernel ops: {
-  graph: [ 'Const', 'Placeholder', 'Identity' ],
-  transformation: [ 'Cast', 'ExpandDims', 'Squeeze', 'Reshape' ],
-  slice_join: [ 'Unpack', 'Pack', 'GatherNd', 'ConcatV2' ],
-  arithmetic: [ 'Sub', 'Mul', 'AddV2', 'FloorDiv', 'SquaredDifference', 'RealDiv' ],
-  convolution: [ '_FusedConv2D', 'FusedDepthwiseConv2dNative', 'DepthwiseConv2dNative' ],
-  image: [ 'ResizeBilinear' ],
-  basic_math: [ 'Sigmoid', 'Sqrt' ],
-  reduction: [ 'ArgMax' ]
-}
-INFO:  graph model: /home/vlado/dev/human/models/selfie.json
-INFO:  created on: 2021-06-04T13:46:56.904Z
-INFO:  metadata: { generatedBy: 'https://github.com/PINTO0309/PINTO_model_zoo/tree/main/109_Selfie_Segmentation', convertedBy: 'https://github.com/vladmandic', version: '561.undefined' }
-INFO:  model inputs based on signature
- { name: 'input_1:0', dtype: 'DT_FLOAT', shape: [ 1, 256, 256, 3 ] }
-INFO:  model outputs based on signature
- { id: 0, name: 'activation_10:0', dytpe: 'DT_FLOAT', shape: [ 1, 256, 256, 1 ] }
-INFO:  tensors: 136
-DATA:  weights: {
-  files: [ 'selfie.bin' ],
-  size: { disk: 212886, memory: 425732 },
-  count: { total: 136, int32: 4, float32: 132 },
-  quantized: { none: 4, float16: 132 },
-  values: { total: 106433, int32: 10, float32: 106423 }
-}
-DATA:  kernel ops: {
-  graph: [ 'Const', 'Placeholder' ],
-  convolution: [ 'Conv2D', 'DepthwiseConv2dNative', 'AvgPool', 'Conv2DBackpropInput' ],
-  arithmetic: [ 'Add', 'Mul', 'AddV2', 'AddN' ],
-  basic_math: [ 'Relu6', 'Relu', 'Sigmoid' ],
-  image: [ 'ResizeBilinear' ]
-}
-INFO:  graph model: /home/vlado/dev/human/models/handtrack.json
-INFO:  created on: 2021-09-21T12:09:47.583Z
-INFO:  metadata: { generatedBy: 'https://github.com/victordibia/handtracking', convertedBy: 'https://github.com/vladmandic', version: '561.undefined' }
-INFO:  model inputs based on signature
- { name: 'input_tensor:0', dtype: 'DT_UINT8', shape: [ 1, 320, 320, 3 ] }
-INFO:  model outputs based on signature
- { id: 0, name: 'Identity_2:0', dytpe: 'DT_FLOAT', shape: [ 1, 100 ] }
- { id: 1, name: 'Identity_4:0', dytpe: 'DT_FLOAT', shape: [ 1, 100 ] }
- { id: 2, name: 'Identity_6:0', dytpe: 'DT_FLOAT', shape: [ 1, 12804, 4 ] }
- { id: 3, name: 'Identity_1:0', dytpe: 'DT_FLOAT', shape: [ 1, 100, 4 ] }
- { id: 4, name: 'Identity_3:0', dytpe: 'DT_FLOAT', shape: [ 1, 100, 8 ] }
- { id: 5, name: 'Identity_5:0', dytpe: 'DT_FLOAT', shape: [ 1 ] }
- { id: 6, name: 'Identity:0', dytpe: 'DT_FLOAT', shape: [ 1, 100 ] }
- { id: 7, name: 'Identity_7:0', dytpe: 'DT_FLOAT', shape: [ 1, 12804, 8 ] }
-INFO:  tensors: 619
-DATA:  weights: {
-  files: [ 'handtrack.bin' ],
-  size: { disk: 2964837, memory: 11846016 },
-  count: { total: 619, int32: 347, float32: 272 },
-  quantized: { none: 347, uint8: 272 },
-  values: { total: 2961504, int32: 1111, float32: 2960393 }
-}
-DATA:  kernel ops: {
-  graph: [ 'Const', 'Placeholder', 'Identity', 'Shape', 'NoOp' ],
-  control: [ 'TensorListReserve', 'Enter', 'TensorListFromTensor', 'Merge', 'LoopCond', 'Switch', 'Exit', 'TensorListStack', 'NextIteration', 'TensorListSetItem', 'TensorListGetItem' ],
-  logical: [ 'Less', 'LogicalAnd', 'Select', 'Greater', 'GreaterEqual' ],
-  convolution: [ '_FusedConv2D', 'FusedDepthwiseConv2dNative', 'DepthwiseConv2dNative' ],
-  arithmetic: [ 'AddV2', 'Mul', 'Sub', 'Minimum', 'Maximum' ],
-  transformation: [ 'Cast', 'ExpandDims', 'Squeeze', 'Reshape', 'Pad' ],
-  slice_join: [ 'Unpack', 'StridedSlice', 'Pack', 'ConcatV2', 'Slice', 'GatherV2', 'Split' ],
-  image: [ 'ResizeBilinear' ],
-  basic_math: [ 'Reciprocal', 'Sigmoid', 'Exp' ],
-  matrices: [ 'Transpose' ],
-  dynamic: [ 'NonMaxSuppressionV5', 'Where' ],
-  creation: [ 'Fill', 'Range' ],
-  evaluation: [ 'TopKV2' ],
-  reduction: [ 'Sum' ]
-}
-INFO:  graph model: /home/vlado/dev/human/models/antispoof.json
-INFO:  created on: 2021-10-13T14:20:27.100Z
-INFO:  metadata: { generatedBy: 'https://www.kaggle.com/anku420/fake-face-detection', convertedBy: 'https://github.com/vladmandic', version: '716.undefined' }
-INFO:  model inputs based on signature
- { name: 'conv2d_input', dtype: 'DT_FLOAT', shape: [ -1, 128, 128, 3 ] }
-INFO:  model outputs based on signature
- { id: 0, name: 'activation_4', dytpe: 'DT_FLOAT', shape: [ -1, 1 ] }
-INFO:  tensors: 11
-DATA:  weights: {
-  files: [ 'antispoof.bin' ],
-  size: { disk: 853098, memory: 1706188 },
-  count: { total: 11, float32: 10, int32: 1 },
-  quantized: { float16: 10, none: 1 },
-  values: { total: 426547, float32: 426545, int32: 2 }
-}
-DATA:  kernel ops: { graph: [ 'Const', 'Placeholder', 'Identity' ], convolution: [ '_FusedConv2D', 'MaxPool' ], basic_math: [ 'Relu', 'Sigmoid' ], transformation: [ 'Reshape' ], matrices: [ '_FusedMatMul' ] }
-INFO:  graph model: /home/vlado/dev/human/models/handlandmark-full.json
-INFO:  created on: 2021-10-31T12:27:49.343Z
-INFO:  metadata: { generatedBy: 'https://github.com/google/mediapipe', convertedBy: 'https://github.com/vladmandic', version: '808.undefined' }
-INFO:  model inputs based on signature
- { name: 'input_1', dtype: 'DT_FLOAT', shape: [ 1, 224, 224, 3 ] }
-INFO:  model outputs based on signature
- { id: 0, name: 'Identity_3:0', dytpe: 'DT_FLOAT', shape: [ 1, 63 ] }
- { id: 1, name: 'Identity:0', dytpe: 'DT_FLOAT', shape: [ 1, 63 ] }
- { id: 2, name: 'Identity_1:0', dytpe: 'DT_FLOAT', shape: [ 1, 1 ] }
- { id: 3, name: 'Identity_2:0', dytpe: 'DT_FLOAT', shape: [ 1, 1 ] }
-INFO:  tensors: 103
-DATA:  weights: {
-  files: [ 'handlandmark-full.bin' ],
-  size: { disk: 5431368, memory: 10862728 },
-  count: { total: 103, float32: 102, int32: 1 },
-  quantized: { float16: 102, none: 1 },
-  values: { total: 2715682, float32: 2715680, int32: 2 }
-}
-DATA:  kernel ops: {
-  graph: [ 'Const', 'Placeholder', 'Identity' ],
-  convolution: [ 'Conv2D', 'DepthwiseConv2dNative' ],
-  arithmetic: [ 'AddV2', 'AddN' ],
-  basic_math: [ 'Relu6', 'Sigmoid' ],
-  reduction: [ 'Mean' ],
-  matrices: [ '_FusedMatMul' ]
-}
-INFO:  graph model: /home/vlado/dev/human/models/liveness.json
-INFO:  created on: 2021-11-09T12:39:11.760Z
-INFO:  metadata: { generatedBy: 'https://github.com/leokwu/livenessnet', convertedBy: 'https://github.com/vladmandic', version: '808.undefined' }
-INFO:  model inputs based on signature
- { name: 'conv2d_1_input', dtype: 'DT_FLOAT', shape: [ -1, 32, 32, 3 ] }
-INFO:  model outputs based on signature
- { id: 0, name: 'activation_6', dytpe: 'DT_FLOAT', shape: [ -1, 2 ] }
-INFO:  tensors: 23
-DATA:  weights: {
-  files: [ 'liveness.bin' ],
-  size: { disk: 592976, memory: 592976 },
-  count: { total: 23, float32: 22, int32: 1 },
-  quantized: { none: 23 },
-  values: { total: 148244, float32: 148242, int32: 2 }
-}
-DATA:  kernel ops: {
-  graph: [ 'Const', 'Placeholder', 'Identity' ],
-  convolution: [ '_FusedConv2D', 'MaxPool' ],
-  arithmetic: [ 'Mul', 'Add', 'AddV2' ],
-  transformation: [ 'Reshape' ],
-  matrices: [ '_FusedMatMul' ],
-  normalization: [ 'Softmax' ]
-}
-```
diff --git a/models/selfie.bin b/models/selfie.bin
deleted file mode 100644
index 517c9e09..00000000
Binary files a/models/selfie.bin and /dev/null differ
diff --git a/models/selfie.json b/models/selfie.json
deleted file mode 100644
index 14b83960..00000000
--- a/models/selfie.json
+++ /dev/null
@@ -1,395 +0,0 @@
-{
-  "format": "graph-model",
-  "generatedBy": "https://github.com/PINTO0309/PINTO_model_zoo/tree/main/109_Selfie_Segmentation",
-  "convertedBy": "https://github.com/vladmandic",
-  "signature":
-  {
-      "inputs":
-      {
-          "input_1:0": {"name":"input_1:0","dtype":"DT_FLOAT","tensorShape":{"dim":[{"size":"1"},{"size":"256"},{"size":"256"},{"size":"3"}]}}
-      },
-      "outputs":
-      {
-          "activation_10:0": {"name":"activation_10:0","dtype":"DT_FLOAT","tensorShape":{"dim":[{"size":"1"},{"size":"256"},{"size":"256"},{"size":"1"}]}}
-      }
-  },
-  "modelTopology":
-  {
-      "node":
-      [
-          {"name":"segment/input_sizes","op":"Const","attr":{"dtype":{"type":"DT_INT32"},"value":{"tensor":{"dtype":"DT_INT32","tensorShape":{"dim":[{"size":"4"}]}}}}},
-          {"name":"segment/filter","op":"Const","attr":{"dtype":{"type":"DT_FLOAT"},"value":{"tensor":{"dtype":"DT_FLOAT","tensorShape":{"dim":[{"size":"2"},{"size":"2"},{"size":"1"},{"size":"16"}]}}}}},
-          {"name":"Conv2D_41/filter","op":"Const","attr":{"value":{"tensor":{"dtype":"DT_FLOAT","tensorShape":{"dim":[{"size":"1"},{"size":"1"},{"size":"16"},{"size":"16"}]}}},"dtype":{"type":"DT_FLOAT"}}},
-          {"name":"conv2d_40_1/y","op":"Const","attr":{"dtype":{"type":"DT_FLOAT"},"value":{"tensor":{"dtype":"DT_FLOAT","tensorShape":{"dim":[{"size":"16"}]}}}}},
-          {"name":"Conv2D_42/filter","op":"Const","attr":{"dtype":{"type":"DT_FLOAT"},"value":{"tensor":{"dtype":"DT_FLOAT","tensorShape":{"dim":[{"size":"1"},{"size":"1"},{"size":"16"},{"size":"16"}]}}}}},
-          {"name":"conv2d_41_1/y","op":"Const","attr":{"dtype":{"type":"DT_FLOAT"},"value":{"tensor":{"dtype":"DT_FLOAT","tensorShape":{"dim":[{"size":"16"}]}}}}},
-          {"name":"Conv2D_37/filter","op":"Const","attr":{"value":{"tensor":{"dtype":"DT_FLOAT","tensorShape":{"dim":[{"size":"1"},{"size":"1"},{"size":"16"},{"size":"16"}]}}},"dtype":{"type":"DT_FLOAT"}}},
-          {"name":"conv2d_36_1/y","op":"Const","attr":{"value":{"tensor":{"dtype":"DT_FLOAT","tensorShape":{"dim":[{"size":"16"}]}}},"dtype":{"type":"DT_FLOAT"}}},
-          {"name":"Conv2D_38/filter","op":"Const","attr":{"value":{"tensor":{"dtype":"DT_FLOAT","tensorShape":{"dim":[{"size":"1"},{"size":"1"},{"size":"16"},{"size":"16"}]}}},"dtype":{"type":"DT_FLOAT"}}},
-          {"name":"conv2d_37_1/y","op":"Const","attr":{"dtype":{"type":"DT_FLOAT"},"value":{"tensor":{"dtype":"DT_FLOAT","tensorShape":{"dim":[{"size":"16"}]}}}}},
-          {"name":"Conv2D_33/filter","op":"Const","attr":{"value":{"tensor":{"dtype":"DT_FLOAT","tensorShape":{"dim":[{"size":"1"},{"size":"1"},{"size":"24"},{"size":"24"}]}}},"dtype":{"type":"DT_FLOAT"}}},
-          {"name":"conv2d_32_1/y","op":"Const","attr":{"value":{"tensor":{"dtype":"DT_FLOAT","tensorShape":{"dim":[{"size":"24"}]}}},"dtype":{"type":"DT_FLOAT"}}},
-          {"name":"Conv2D_34/filter","op":"Const","attr":{"value":{"tensor":{"dtype":"DT_FLOAT","tensorShape":{"dim":[{"size":"1"},{"size":"1"},{"size":"24"},{"size":"24"}]}}},"dtype":{"type":"DT_FLOAT"}}},
-          {"name":"conv2d_33_1/y","op":"Const","attr":{"value":{"tensor":{"dtype":"DT_FLOAT","tensorShape":{"dim":[{"size":"24"}]}}},"dtype":{"type":"DT_FLOAT"}}},
-          {"name":"Conv2D_30/filter","op":"Const","attr":{"value":{"tensor":{"dtype":"DT_FLOAT","tensorShape":{"dim":[{"size":"1"},{"size":"1"},{"size":"32"},{"size":"128"}]}}},"dtype":{"type":"DT_FLOAT"}}},
-          {"name":"conv2d_29_1/y","op":"Const","attr":{"value":{"tensor":{"dtype":"DT_FLOAT","tensorShape":{"dim":[{"size":"128"}]}}},"dtype":{"type":"DT_FLOAT"}}},
-          {"name":"mul_21/y","op":"Const","attr":{"dtype":{"type":"DT_FLOAT"},"value":{"tensor":{"dtype":"DT_FLOAT","tensorShape":{}}}}},
-          {"name":"mul_19/y","op":"Const","attr":{"value":{"tensor":{"dtype":"DT_FLOAT","tensorShape":{}}},"dtype":{"type":"DT_FLOAT"}}},
-          {"name":"Conv2D_26/filter","op":"Const","attr":{"dtype":{"type":"DT_FLOAT"},"value":{"tensor":{"dtype":"DT_FLOAT","tensorShape":{"dim":[{"size":"1"},{"size":"1"},{"size":"32"},{"size":"96"}]}}}}},
-          {"name":"conv2d_25_1/y","op":"Const","attr":{"dtype":{"type":"DT_FLOAT"},"value":{"tensor":{"dtype":"DT_FLOAT","tensorShape":{"dim":[{"size":"96"}]}}}}},
-          {"name":"add_9/y","op":"Const","attr":{"dtype":{"type":"DT_FLOAT"},"value":{"tensor":{"dtype":"DT_FLOAT","tensorShape":{}}}}},
-          {"name":"depthwise_7/filter_in","op":"Const","attr":{"dtype":{"type":"DT_FLOAT"},"value":{"tensor":{"dtype":"DT_FLOAT","tensorShape":{"dim":[{"size":"5"},{"size":"5"},{"size":"96"},{"size":"1"}]}}}}},
-          {"name":"depthwise_conv2d_7/y","op":"Const","attr":{"value":{"tensor":{"dtype":"DT_FLOAT","tensorShape":{"dim":[{"size":"96"}]}}},"dtype":{"type":"DT_FLOAT"}}},
-          {"name":"add_10/y","op":"Const","attr":{"value":{"tensor":{"dtype":"DT_FLOAT","tensorShape":{}}},"dtype":{"type":"DT_FLOAT"}}},
-          {"name":"Conv2D_27/filter","op":"Const","attr":{"dtype":{"type":"DT_FLOAT"},"value":{"tensor":{"dtype":"DT_FLOAT","tensorShape":{"dim":[{"size":"1"},{"size":"1"},{"size":"96"},{"size":"24"}]}}}}},
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-          {"name":"Conv2D_29/filter","op":"Const","attr":{"dtype":{"type":"DT_FLOAT"},"value":{"tensor":{"dtype":"DT_FLOAT","tensorShape":{"dim":[{"size":"1"},{"size":"1"},{"size":"96"},{"size":"32"}]}}}}},
-          {"name":"conv2d_28_1/y","op":"Const","attr":{"value":{"tensor":{"dtype":"DT_FLOAT","tensorShape":{"dim":[{"size":"32"}]}}},"dtype":{"type":"DT_FLOAT"}}},
-          {"name":"mul_17/y","op":"Const","attr":{"dtype":{"type":"DT_FLOAT"},"value":{"tensor":{"dtype":"DT_FLOAT","tensorShape":{}}}}},
-          {"name":"mul_15/y","op":"Const","attr":{"dtype":{"type":"DT_FLOAT"},"value":{"tensor":{"dtype":"DT_FLOAT","tensorShape":{}}}}},
-          {"name":"Conv2D_22/filter","op":"Const","attr":{"value":{"tensor":{"dtype":"DT_FLOAT","tensorShape":{"dim":[{"size":"1"},{"size":"1"},{"size":"32"},{"size":"96"}]}}},"dtype":{"type":"DT_FLOAT"}}},
-          {"name":"conv2d_21_1/y","op":"Const","attr":{"dtype":{"type":"DT_FLOAT"},"value":{"tensor":{"dtype":"DT_FLOAT","tensorShape":{"dim":[{"size":"96"}]}}}}},
-          {"name":"add_7/y","op":"Const","attr":{"dtype":{"type":"DT_FLOAT"},"value":{"tensor":{"dtype":"DT_FLOAT","tensorShape":{}}}}},
-          {"name":"depthwise_6/filter_in","op":"Const","attr":{"dtype":{"type":"DT_FLOAT"},"value":{"tensor":{"dtype":"DT_FLOAT","tensorShape":{"dim":[{"size":"5"},{"size":"5"},{"size":"96"},{"size":"1"}]}}}}},
-          {"name":"depthwise_conv2d_6/y","op":"Const","attr":{"dtype":{"type":"DT_FLOAT"},"value":{"tensor":{"dtype":"DT_FLOAT","tensorShape":{"dim":[{"size":"96"}]}}}}},
-          {"name":"add_8/y","op":"Const","attr":{"dtype":{"type":"DT_FLOAT"},"value":{"tensor":{"dtype":"DT_FLOAT","tensorShape":{}}}}},
-          {"name":"Conv2D_23/filter","op":"Const","attr":{"value":{"tensor":{"dtype":"DT_FLOAT","tensorShape":{"dim":[{"size":"1"},{"size":"1"},{"size":"96"},{"size":"24"}]}}},"dtype":{"type":"DT_FLOAT"}}},
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-          {"name":"Conv2D_24/filter","op":"Const","attr":{"dtype":{"type":"DT_FLOAT"},"value":{"tensor":{"dtype":"DT_FLOAT","tensorShape":{"dim":[{"size":"1"},{"size":"1"},{"size":"24"},{"size":"96"}]}}}}},
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-          {"name":"Conv2D_25/filter","op":"Const","attr":{"dtype":{"type":"DT_FLOAT"},"value":{"tensor":{"dtype":"DT_FLOAT","tensorShape":{"dim":[{"size":"1"},{"size":"1"},{"size":"96"},{"size":"32"}]}}}}},
-          {"name":"conv2d_24_1/y","op":"Const","attr":{"value":{"tensor":{"dtype":"DT_FLOAT","tensorShape":{"dim":[{"size":"32"}]}}},"dtype":{"type":"DT_FLOAT"}}},
-          {"name":"mul_13/y","op":"Const","attr":{"value":{"tensor":{"dtype":"DT_FLOAT","tensorShape":{}}},"dtype":{"type":"DT_FLOAT"}}},
-          {"name":"mul_11/y","op":"Const","attr":{"value":{"tensor":{"dtype":"DT_FLOAT","tensorShape":{}}},"dtype":{"type":"DT_FLOAT"}}},
-          {"name":"Conv2D_18/filter","op":"Const","attr":{"dtype":{"type":"DT_FLOAT"},"value":{"tensor":{"dtype":"DT_FLOAT","tensorShape":{"dim":[{"size":"1"},{"size":"1"},{"size":"32"},{"size":"128"}]}}}}},
-          {"name":"conv2d_17_1/y","op":"Const","attr":{"dtype":{"type":"DT_FLOAT"},"value":{"tensor":{"dtype":"DT_FLOAT","tensorShape":{"dim":[{"size":"128"}]}}}}},
-          {"name":"add_5/y","op":"Const","attr":{"value":{"tensor":{"dtype":"DT_FLOAT","tensorShape":{}}},"dtype":{"type":"DT_FLOAT"}}},
-          {"name":"depthwise_5/filter_in","op":"Const","attr":{"value":{"tensor":{"dtype":"DT_FLOAT","tensorShape":{"dim":[{"size":"5"},{"size":"5"},{"size":"128"},{"size":"1"}]}}},"dtype":{"type":"DT_FLOAT"}}},
-          {"name":"depthwise_conv2d_5/y","op":"Const","attr":{"dtype":{"type":"DT_FLOAT"},"value":{"tensor":{"dtype":"DT_FLOAT","tensorShape":{"dim":[{"size":"128"}]}}}}},
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-          {"name":"Conv2D_19/filter","op":"Const","attr":{"value":{"tensor":{"dtype":"DT_FLOAT","tensorShape":{"dim":[{"size":"1"},{"size":"1"},{"size":"128"},{"size":"32"}]}}},"dtype":{"type":"DT_FLOAT"}}},
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-          {"name":"mul_7/y","op":"Const","attr":{"dtype":{"type":"DT_FLOAT"},"value":{"tensor":{"dtype":"DT_FLOAT","tensorShape":{}}}}},
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-          {"name":"depthwise_4/filter_in","op":"Const","attr":{"value":{"tensor":{"dtype":"DT_FLOAT","tensorShape":{"dim":[{"size":"5"},{"size":"5"},{"size":"128"},{"size":"1"}]}}},"dtype":{"type":"DT_FLOAT"}}},
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-          {"name":"Conv2D_17/filter","op":"Const","attr":{"value":{"tensor":{"dtype":"DT_FLOAT","tensorShape":{"dim":[{"size":"1"},{"size":"1"},{"size":"128"},{"size":"32"}]}}},"dtype":{"type":"DT_FLOAT"}}},
-          {"name":"conv2d_16_1/y","op":"Const","attr":{"value":{"tensor":{"dtype":"DT_FLOAT","tensorShape":{"dim":[{"size":"32"}]}}},"dtype":{"type":"DT_FLOAT"}}},
-          {"name":"mul_5/y","op":"Const","attr":{"dtype":{"type":"DT_FLOAT"},"value":{"tensor":{"dtype":"DT_FLOAT","tensorShape":{}}}}},
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-          {"name":"conv2d_8_1/y","op":"Const","attr":{"dtype":{"type":"DT_FLOAT"},"value":{"tensor":{"dtype":"DT_FLOAT","tensorShape":{"dim":[{"size":"24"}]}}}}},
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-          {"name":"conv2d_7_1/y","op":"Const","attr":{"dtype":{"type":"DT_FLOAT"},"value":{"tensor":{"dtype":"DT_FLOAT","tensorShape":{"dim":[{"size":"88"}]}}}}},
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-          {"name":"mul_1/y","op":"Const","attr":{"dtype":{"type":"DT_FLOAT"},"value":{"tensor":{"dtype":"DT_FLOAT","tensorShape":{}}}}},
-          {"name":"Conv2D/filter","op":"Const","attr":{"value":{"tensor":{"dtype":"DT_FLOAT","tensorShape":{"dim":[{"size":"3"},{"size":"3"},{"size":"3"},{"size":"16"}]}}},"dtype":{"type":"DT_FLOAT"}}},
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-          {"name":"Conv2D_2/filter","op":"Const","attr":{"value":{"tensor":{"dtype":"DT_FLOAT","tensorShape":{"dim":[{"size":"1"},{"size":"1"},{"size":"16"},{"size":"16"}]}}},"dtype":{"type":"DT_FLOAT"}}},
-          {"name":"conv2d_1_1/y","op":"Const","attr":{"dtype":{"type":"DT_FLOAT"},"value":{"tensor":{"dtype":"DT_FLOAT","tensorShape":{"dim":[{"size":"16"}]}}}}},
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-          {"name":"depthwise_conv2d_1/y","op":"Const","attr":{"dtype":{"type":"DT_FLOAT"},"value":{"tensor":{"dtype":"DT_FLOAT","tensorShape":{"dim":[{"size":"72"}]}}}}},
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ype":"float32","quantization":{"dtype":"float16","original_dtype":"float32"}},{"name":"Conv2D_2/filter","shape":[1,1,16,16],"dtype":"float32","quantization":{"dtype":"float16","original_dtype":"float32"}},{"name":"conv2d_1_1/y","shape":[16],"dtype":"float32","quantization":{"dtype":"float16","original_dtype":"float32"}},{"name":"depthwise/filter_in","shape":[3,3,16,1],"dtype":"float32","quantization":{"dtype":"float16","original_dtype":"float32"}},{"name":"depthwise_conv2d/y","shape":[16],"dtype":"float32","quantization":{"dtype":"float16","original_dtype":"float32"}},{"name":"Conv2D_3/filter","shape":[1,1,16,8],"dtype":"float32","quantization":{"dtype":"float16","original_dtype":"float32"}},{"name":"conv2d_2_1/y","shape":[8],"dtype":"float32","quantization":{"dtype":"float16","original_dtype":"float32"}},{"name":"Conv2D_4/filter","shape":[1,1,8,16],"dtype":"float32","quantization":{"dtype":"float16","original_dtype":"float32"}},{"name":"conv2d_3_1/y","shape":[16],"dtype":"float32","quantization":{"dtype":"float16","original_dtype":"float32"}},{"name":"Conv2D_5/filter","shape":[1,1,16,16],"dtype":"float32","quantization":{"dtype":"float16","original_dtype":"float32"}},{"name":"conv2d_4_1/y","shape":[16],"dtype":"float32","quantization":{"dtype":"float16","original_dtype":"float32"}},{"name":"Conv2D_6/filter","shape":[1,1,16,72],"dtype":"float32","quantization":{"dtype":"float16","original_dtype":"float32"}},{"name":"conv2d_5_1/y","shape":[72],"dtype":"float32","quantization":{"dtype":"float16","original_dtype":"float32"}},{"name":"depthwise_1/filter_in","shape":[3,3,72,1],"dtype":"float32","quantization":{"dtype":"float16","original_dtype":"float32"}},{"name":"depthwise_conv2d_1/y","shape":[72],"dtype":"float32","quantization":{"dtype":"float16","original_dtype":"float32"}},{"name":"Conv2D_7/filter","shape":[1,1,72,24],"dtype":"float32","quantization":{"dtype":"float16","original_dtype":"float32"}},{"name":"conv2d_6_1/y","shape":[24],"dtype":"float32","quantization":{"dtype":"float16","original_dtype":"float32"}},{"name":"Conv2D_10/filter","shape":[1,1,24,96],"dtype":"float32","quantization":{"dtype":"float16","original_dtype":"float32"}},{"name":"conv2d_9_1/y","shape":[96],"dtype":"float32","quantization":{"dtype":"float16","original_dtype":"float32"}},{"name":"add_1/y","shape":[],"dtype":"float32","quantization":{"dtype":"float16","original_dtype":"float32"}},{"name":"depthwise_3/filter_in","shape":[5,5,96,1],"dtype":"float32","quantization":{"dtype":"float16","original_dtype":"float32"}},{"name":"depthwise_conv2d_3/y","shape":[96],"dtype":"float32","quantization":{"dtype":"float16","original_dtype":"float32"}},{"name":"add_2/y","shape":[],"dtype":"float32","quantization":{"dtype":"float16","original_dtype":"float32"}},{"name":"Conv2D_11/filter","shape":[1,1,96,24],"dtype":"float32","quantization":{"dtype":"float16","original_dtype":"float32"}},{"name":"conv2d_10_1/y","shape":[24],"dtype":"float32","quantization":{"dtype":"float16","original_dtype":"float32"}},{"name":"Conv2D_12/filter","shape":[1,1,24,96],"dtype":"float32","quantization":{"dtype":"float16","original_dtype":"float32"}},{"name":"conv2d_11_1/y","shape":[96],"dtype":"float32","quantization":{"dtype":"float16","original_dtype":"float32"}},{"name":"Conv2D_13/filter","shape":[1,1,96,32],"dtype":"float32","quantization":{"dtype":"float16","original_dtype":"float32"}},{"name":"conv2d_12_1/y","shape":[32],"dtype":"float32","quantization":{"dtype":"float16","original_dtype":"float32"}},{"name":"Conv2D_31/filter","shape":[1,1,32,128],"dtype":"float32","quantization":{"dtype":"float16","original_dtype":"float32"}},{"name":"conv2d_30_1/y","shape":[128],"dtype":"float32","quantization":{"dtype":"float16","original_dtype":"float32"}},{"name":"up_sampling2d_lambda/resize/size","shape":[2],"dtype":"int32"},{"name":"Conv2D_32/filter","shape":[1,1,128,24],"dtype":"float32","quantization":{"dtype":"float16","original_dtype":"float32"}},{"name":"conv2d_31_1/y","shape":[24],"dtype":"float32","quantization":{"dtype":"float16","original_dtype":"float32"}},{"name":"Conv2D_35/filter","shape":[1,1,24,24],"dtype":"float32","quantization":{"dtype":"float16","original_dtype":"float32"}},{"name":"conv2d_34_1/y","shape":[24],"dtype":"float32","quantization":{"dtype":"float16","original_dtype":"float32"}},{"name":"depthwise_8/filter_in","shape":[3,3,24,1],"dtype":"float32","quantization":{"dtype":"float16","original_dtype":"float32"}},{"name":"depthwise_conv2d_8/y","shape":[24],"dtype":"float32","quantization":{"dtype":"float16","original_dtype":"float32"}},{"name":"up_sampling2d_1_lambda/resize/size","shape":[2],"dtype":"int32"},{"name":"Conv2D_36/filter","shape":[1,1,24,16],"dtype":"float32","quantization":{"dtype":"float16","original_dtype":"float32"}},{"name":"conv2d_35_1/y","shape":[16],"dtype":"float32","quantization":{"dtype":"float16","original_dtype":"float32"}},{"name":"Conv2D_39/filter","shape":[1,1,16,16],"dtype":"float32","quantization":{"dtype":"float16","original_dtype":"float32"}},{"name":"conv2d_38_1/y","shape":[16],"dtype":"float32","quantization":{"dtype":"float16","original_dtype":"float32"}},{"name":"depthwise_9/filter_in","shape":[3,3,16,1],"dtype":"float32","quantization":{"dtype":"float16","original_dtype":"float32"}},{"name":"depthwise_conv2d_9/y","shape":[16],"dtype":"float32","quantization":{"dtype":"float16","original_dtype":"float32"}},{"name":"up_sampling2d_2_lambda/resize/size","shape":[2],"dtype":"int32"},{"name":"Conv2D_40/filter","shape":[1,1,16,16],"dtype":"float32","quantization":{"dtype":"float16","original_dtype":"float32"}},{"name":"conv2d_39_1/y","shape":[16],"dtype":"float32","quantization":{"dtype":"float16","original_dtype":"float32"}},{"name":"Conv2D_43/filter","shape":[1,1,16,16],"dtype":"float32","quantization":{"dtype":"float16","original_dtype":"float32"}},{"name":"conv2d_42_1/y","shape":[16],"dtype":"float32","quantization":{"dtype":"float16","original_dtype":"float32"}},{"name":"depthwise_10/filter_in","shape":[3,3,16,1],"dtype":"float32","quantization":{"dtype":"float16","original_dtype":"float32"}},{"name":"depthwise_conv2d_10/y","shape":[16],"dtype":"float32","quantization":{"dtype":"float16","original_dtype":"float32"}},{"name":"segment_add/y","shape":[1],"dtype":"float32","quantization":{"dtype":"float16","original_dtype":"float32"}}]
-      }
-  ]
-}
\ No newline at end of file
diff --git a/src/config.ts b/src/config.ts
index b054b450..08ff32c8 100644
--- a/src/config.ts
+++ b/src/config.ts
@@ -1,5 +1,14 @@
 /* eslint-disable no-multi-spaces */
 
+/** Possible TensorFlow backends */
+export type BackendEnum = '' | 'cpu' | 'wasm' | 'webgl' | 'humangl' | 'tensorflow' | 'webgpu';
+
+/** Possible values for `human.warmup` */
+export type WarmupEnum = '' | 'none' | 'face' | 'full' | 'body';
+
+/** Possible segmentation model behavior */
+export type SegmentationEnum = 'default' | 'alpha' | 'foreground' | 'state'
+
 /** Generic config type inherited by all module types */
 export interface GenericConfig {
   /** is module enabled? */
@@ -144,8 +153,10 @@ export interface ObjectConfig extends GenericConfig {
  * remove background or replace it with user-provided background
 */
 export interface SegmentationConfig extends GenericConfig {
-  /** blur segmentation output by <number> pixels for more realistic image */
-  blur: number,
+  /** downsample ratio, adjust to reflect approximately how much of input is taken by body */
+  ratio: number,
+  /** possible rvm segmentation mode */
+  mode: SegmentationEnum,
 }
 
 /** Run input through image filters before inference
@@ -208,12 +219,6 @@ export interface GestureConfig {
   /** is gesture detection enabled? */
   enabled: boolean,
 }
-/** Possible TensorFlow backends */
-export type BackendEnum = '' | 'cpu' | 'wasm' | 'webgl' | 'humangl' | 'tensorflow' | 'webgpu';
-
-/** Possible values for `human.warmup` */
-export type WarmupEnum = '' | 'none' | 'face' | 'full' | 'body';
-
 /**
  * Configuration interface definition for **Human** library
  * Contains all configurable parameters
@@ -450,8 +455,9 @@ const config: Config = {
   },
   segmentation: {
     enabled: false,
-    modelPath: 'selfie.json',
-    blur: 8,
+    modelPath: 'rvm.json',
+    ratio: 0.5,
+    mode: 'default',
   },
 };
 
diff --git a/src/human.ts b/src/human.ts
index cf50c4ee..44293a25 100644
--- a/src/human.ts
+++ b/src/human.ts
@@ -16,9 +16,9 @@ import { setModelLoadOptions } from './tfjs/load';
 import * as tf from '../dist/tfjs.esm.js';
 import * as app from '../package.json';
 import * as backend from './tfjs/backend';
+import * as draw from './draw/draw';
 import * as blazepose from './body/blazepose';
 import * as centernet from './object/centernet';
-import * as draw from './draw/draw';
 import * as efficientpose from './body/efficientpose';
 import * as face from './face/face';
 import * as facemesh from './face/facemesh';
@@ -29,13 +29,15 @@ import * as handtrack from './hand/handtrack';
 import * as humangl from './tfjs/humangl';
 import * as image from './image/image';
 import * as interpolate from './util/interpolate';
+import * as meet from './segmentation/meet';
 import * as match from './face/match';
 import * as models from './models';
 import * as movenet from './body/movenet';
 import * as nanodet from './object/nanodet';
 import * as persons from './util/persons';
 import * as posenet from './body/posenet';
-import * as segmentation from './segmentation/segmentation';
+import * as rvm from './segmentation/rvm';
+import * as selfie from './segmentation/selfie';
 import * as warmups from './warmup';
 
 // type definitions
@@ -251,18 +253,23 @@ export class Human {
     return image.process(input, this.config, getTensor);
   }
 
-  /** Segmentation method takes any input and returns processed canvas with body segmentation
-   *  - Segmentation is not triggered as part of detect process
+  /** Segmentation method takes any input and returns RGBA tensor
+   * Note: Segmentation is not triggered as part of detect process
+   *
    * @param input - {@link Input}
-   * @param background - {@link Input}
-   *  - Optional parameter background is used to fill the background with specific input
-   *  Returns:
-   *  - `data` as raw data array with per-pixel segmentation values
-   *  - `canvas` as canvas which is input image filtered with segementation data and optionally merged with background image. canvas alpha values are set to segmentation values for easy merging
-   *  - `alpha` as grayscale canvas that represents segmentation alpha values
+   * Returns tensor which contains image data in RGBA format
    */
-  async segmentation(input: Input, background?: Input): Promise<{ data: number[] | Tensor, canvas: AnyCanvas | null, alpha: AnyCanvas | null }> {
-    return segmentation.process(input, background, this.config);
+  async segmentation(input: Input, userConfig?: Partial<Config>): Promise<Tensor | null> {
+    if (userConfig) this.config = mergeDeep(this.config, userConfig) as Config;
+    if (!this.config.segmentation.enabled) return null;
+    const processed = await image.process(input, this.config);
+    if (!processed.tensor) return null;
+    let tensor: Tensor | null = null;
+    if (this.config.segmentation.modelPath?.includes('rvm')) tensor = await rvm.predict(processed.tensor, this.config);
+    if (this.config.segmentation.modelPath?.includes('meet')) tensor = await meet.predict(processed.tensor, this.config);
+    if (this.config.segmentation.modelPath?.includes('selfie')) tensor = await selfie.predict(processed.tensor, this.config);
+    tf.dispose(processed.tensor);
+    return tensor;
   }
 
   /** Enhance method performs additional enhacements to face image previously detected for futher processing
diff --git a/src/models.ts b/src/models.ts
index 44bf8b12..b9ae9d0a 100644
--- a/src/models.ts
+++ b/src/models.ts
@@ -4,27 +4,29 @@
 
 import { env } from './util/env';
 import { log } from './util/util';
-import * as gear from './gear/gear';
-import * as ssrnetAge from './gear/ssrnet-age';
-import * as ssrnetGender from './gear/ssrnet-gender';
 import * as antispoof from './face/antispoof';
 import * as blazeface from './face/blazeface';
 import * as blazepose from './body/blazepose';
 import * as centernet from './object/centernet';
 import * as efficientpose from './body/efficientpose';
 import * as emotion from './gear/emotion';
-import * as mobilefacenet from './face/mobilefacenet';
-import * as insightface from './face/insightface';
 import * as facemesh from './face/facemesh';
 import * as faceres from './face/faceres';
+import * as gear from './gear/gear';
 import * as handpose from './hand/handpose';
 import * as handtrack from './hand/handtrack';
+import * as insightface from './face/insightface';
 import * as iris from './face/iris';
 import * as liveness from './face/liveness';
+import * as meet from './segmentation/meet';
+import * as mobilefacenet from './face/mobilefacenet';
 import * as movenet from './body/movenet';
 import * as nanodet from './object/nanodet';
 import * as posenet from './body/posenet';
-import * as segmentation from './segmentation/segmentation';
+import * as rvm from './segmentation/rvm';
+import * as selfie from './segmentation/selfie';
+import * as ssrnetAge from './gear/ssrnet-age';
+import * as ssrnetGender from './gear/ssrnet-gender';
 import { modelStats, ModelInfo } from './tfjs/load';
 import type { GraphModel } from './tfjs/types';
 import type { Human } from './human';
@@ -54,17 +56,18 @@ export class Models {
   handskeleton: null | GraphModel | Promise<GraphModel> = null;
   handtrack: null | GraphModel | Promise<GraphModel> = null;
   liveness: null | GraphModel | Promise<GraphModel> = null;
+  meet: null | GraphModel | Promise<GraphModel> = null;
   movenet: null | GraphModel | Promise<GraphModel> = null;
   nanodet: null | GraphModel | Promise<GraphModel> = null;
   posenet: null | GraphModel | Promise<GraphModel> = null;
-  segmentation: null | GraphModel | Promise<GraphModel> = null;
+  selfie: null | GraphModel | Promise<GraphModel> = null;
+  rvm: null | GraphModel | Promise<GraphModel> = null;
   antispoof: null | GraphModel | Promise<GraphModel> = null;
 }
 
 /** structure that holds global stats for currently loaded models */
 export interface ModelStats {
   numLoadedModels: number,
-  numEnabledModels: undefined,
   numDefinedModels: number,
   percentageLoaded: number,
   totalSizeFromManifest: number,
@@ -90,7 +93,6 @@ export const getModelStats = (currentInstance: Human): ModelStats => {
   const percentageLoaded = totalSizeLoading > 0 ? totalSizeWeights / totalSizeLoading : 0;
   return {
     numLoadedModels: Object.values(modelStats).length,
-    numEnabledModels: undefined,
     numDefinedModels: Object.keys(instance.models).length,
     percentageLoaded,
     totalSizeFromManifest,
@@ -141,7 +143,9 @@ export async function load(currentInstance: Human): Promise<void> {
   if (instance.config.hand.enabled && instance.config.hand.landmarks && !instance.models.handskeleton && instance.config.hand.detector?.modelPath?.includes('handtrack')) instance.models.handskeleton = handtrack.loadSkeleton(instance.config);
   if (instance.config.object.enabled && !instance.models.centernet && instance.config.object.modelPath?.includes('centernet')) instance.models.centernet = centernet.load(instance.config);
   if (instance.config.object.enabled && !instance.models.nanodet && instance.config.object.modelPath?.includes('nanodet')) instance.models.nanodet = nanodet.load(instance.config);
-  if (instance.config.segmentation.enabled && !instance.models.segmentation) instance.models.segmentation = segmentation.load(instance.config);
+  if (instance.config.segmentation.enabled && !instance.models.selfie && instance.config.segmentation.modelPath?.includes('selfie')) instance.models.selfie = selfie.load(instance.config);
+  if (instance.config.segmentation.enabled && !instance.models.meet && instance.config.segmentation.modelPath?.includes('meet')) instance.models.meet = meet.load(instance.config);
+  if (instance.config.segmentation.enabled && !instance.models.rvm && instance.config.segmentation.modelPath?.includes('rvm')) instance.models.rvm = rvm.load(instance.config);
 
   // models are loaded in parallel asynchronously so lets wait until they are actually loaded
   for await (const model of Object.keys(instance.models)) {
@@ -159,7 +163,7 @@ export function validateModel(currentInstance: Human | null, model: GraphModel |
   if (!instance) log('instance not registred');
   if (!instance?.config?.validateModels) return null;
   const simpleOps = ['const', 'placeholder', 'noop', 'pad', 'squeeze', 'add', 'sub', 'mul', 'div'];
-  const ignoreOps = ['biasadd', 'fusedbatchnormv3', 'matmul'];
+  const ignoreOps = ['biasadd', 'fusedbatchnormv3', 'matmul', 'switch', 'shape', 'merge', 'split', 'broadcastto'];
   const ops: string[] = [];
   const missing: string[] = [];
   interface Op { name: string, category: string, op: string }
diff --git a/src/segmentation/meet.ts b/src/segmentation/meet.ts
new file mode 100644
index 00000000..1a2e95ee
--- /dev/null
+++ b/src/segmentation/meet.ts
@@ -0,0 +1,54 @@
+/**
+ * Image segmentation for body detection model
+ *
+ * Based on:
+ * - [**MediaPipe Meet**](https://drive.google.com/file/d/1lnP1bRi9CSqQQXUHa13159vLELYDgDu0/preview)
+ */
+
+import { log } from '../util/util';
+import * as tf from '../../dist/tfjs.esm.js';
+import { loadModel } from '../tfjs/load';
+import { constants } from '../tfjs/constants';
+import type { GraphModel, Tensor } from '../tfjs/types';
+import type { Config } from '../config';
+import { env } from '../util/env';
+
+let model: GraphModel;
+
+export async function load(config: Config): Promise<GraphModel> {
+  if (!model || env.initial) model = await loadModel(config.segmentation.modelPath);
+  else if (config.debug) log('cached model:', model['modelUrl']);
+  return model;
+}
+
+export async function predict(input: Tensor, config: Config): Promise<Tensor | null> {
+  if (!model) model = await load(config);
+  if (!model?.['executor'] || !model?.inputs?.[0].shape) return null; // something is wrong with the model
+  const t: Record<string, Tensor> = {};
+  t.resize = tf.image.resizeBilinear(input, [model.inputs[0].shape ? model.inputs[0].shape[1] : 0, model.inputs[0].shape ? model.inputs[0].shape[2] : 0], false);
+  t.norm = tf.div(t.resize, constants.tf255);
+  t.res = model.execute(t.norm) as Tensor;
+  t.squeeze = tf.squeeze(t.res, 0);
+  // t.softmax = tf.softmax(t.squeeze); // model meet has two channels for fg and bg
+  [t.bgRaw, t.fgRaw] = tf.unstack(t.squeeze, 2);
+  // t.bg = tf.softmax(t.bgRaw); // we can ignore bg channel
+  t.fg = tf.softmax(t.fgRaw);
+  t.mul = tf.mul(t.fg, constants.tf255);
+  t.expand = tf.expandDims(t.mul, 2);
+  t.output = tf.image.resizeBilinear(t.expand, [input.shape[1], input.shape[2]]);
+  let rgba: Tensor;
+  switch (config.segmentation.mode || 'default') {
+    case 'default':
+      t.input = tf.squeeze(input);
+      t.concat = tf.concat([t.input, t.output], -1);
+      rgba = tf.cast(t.concat, 'int32'); // combined original with alpha
+      break;
+    case 'alpha':
+      rgba = tf.cast(t.output, 'int32'); // just get alpha value from model
+      break;
+    default:
+      rgba = tf.tensor(0);
+  }
+  Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));
+  return rgba;
+}
diff --git a/src/segmentation/rvm.ts b/src/segmentation/rvm.ts
new file mode 100644
index 00000000..b75891cd
--- /dev/null
+++ b/src/segmentation/rvm.ts
@@ -0,0 +1,104 @@
+/**
+ * Image segmentation for body detection model
+ *
+ * Based on:
+ * - [**Robust Video Matting**](https://github.com/PeterL1n/RobustVideoMatting)
+ */
+
+import { log } from '../util/util';
+import * as tf from '../../dist/tfjs.esm.js';
+import { loadModel } from '../tfjs/load';
+import { constants } from '../tfjs/constants';
+import type { GraphModel, Tensor } from '../tfjs/types';
+import type { Config } from '../config';
+import { env } from '../util/env';
+
+let model: GraphModel;
+
+// internal state varaibles
+const outputNodes = ['fgr', 'pha', 'r1o', 'r2o', 'r3o', 'r4o'];
+const t: Record<string, Tensor> = {}; // contains input tensor and recurrent states
+let ratio = 0;
+
+function init(config: Config) {
+  tf.dispose([t.r1i, t.r2i, t.r3i, t.r4i, t.downsample_ratio]);
+  t.r1i = tf.tensor(0.0);
+  t.r2i = tf.tensor(0.0);
+  t.r3i = tf.tensor(0.0);
+  t.r4i = tf.tensor(0.0);
+  ratio = config.segmentation.ratio || 0.5;
+  t.downsample_ratio = tf.tensor(ratio); // initialize downsample ratio
+}
+
+export async function load(config: Config): Promise<GraphModel> {
+  if (!model || env.initial) model = await loadModel(config.segmentation.modelPath);
+  else if (config.debug) log('cached model:', model['modelUrl']);
+  init(config);
+  return model;
+}
+
+const normalize = (r: Tensor) => tf.tidy(() => {
+  const squeeze = tf.squeeze(r, ([0]));
+  const mul = tf.mul(squeeze, constants.tf255);
+  const cast = tf.cast(mul, 'int32');
+  return cast as Tensor;
+});
+
+function getRGBA(fgr: Tensor | null, pha: Tensor | null): Tensor { // gets rgba // either fgr or pha must be present
+  const rgb = fgr
+    ? normalize(fgr) // normalize and use value
+    : tf.fill([pha!.shape[1] || 0, pha!.shape[2] || 0, 3], 255, 'int32'); // eslint-disable-line @typescript-eslint/no-non-null-assertion
+  const a = pha
+    ? normalize(pha) // normalize and use value
+    : tf.fill([fgr!.shape[1] || 0, fgr!.shape[2] || 0, 1], 255, 'int32'); // eslint-disable-line @typescript-eslint/no-non-null-assertion
+  const rgba = tf.concat([rgb, a], -1);
+  tf.dispose([rgb, a]);
+  return rgba;
+}
+
+function getState(state: Tensor): Tensor { // gets internal recurrent states
+  return tf.tidy(() => {
+    const r: Record<string, Tensor | Tensor[]> = {};
+    r.unstack = tf.unstack(state, -1);
+    r.concat = tf.concat(r.unstack, 1);
+    r.split = tf.split(r.concat, 4, 1);
+    r.stack = tf.concat(r.split, 2);
+    r.squeeze = tf.squeeze(r.stack, [0]);
+    r.expand = tf.expandDims(r.squeeze, -1);
+    r.add = tf.add(r.expand, 1);
+    r.mul = tf.mul(r.add, 127.5);
+    r.cast = tf.cast(r.mul, 'int32');
+    r.tile = tf.tile(r.cast, [1, 1, 3]) as Tensor;
+    r.alpha = tf.fill([r.tile.shape[0] || 0, r.tile.shape[1] || 0, 1], 255, 'int32');
+    return tf.concat([r.tile, r.alpha], -1) as Tensor;
+  });
+}
+
+export async function predict(input: Tensor, config: Config): Promise<Tensor | null> {
+  if (!model) model = await load(config);
+  if (!model?.['executor']) return null;
+  // const expand = tf.expandDims(input, 0);
+  t.src = tf.div(input, 255);
+  if (ratio !== config.segmentation.ratio) init(config); // reinitialize recurrent states if requested downsample ratio changed
+  const [fgr, pha, r1o, r2o, r3o, r4o] = await model.executeAsync(t, outputNodes) as Tensor[]; // execute model
+  let rgba: Tensor;
+  switch (config.segmentation.mode || 'default') {
+    case 'default':
+      rgba = getRGBA(fgr, pha);
+      break;
+    case 'alpha':
+      rgba = getRGBA(null, pha);
+      break;
+    case 'foreground':
+      rgba = getRGBA(fgr, null);
+      break;
+    case 'state':
+      rgba = getState(r1o); // can view any internal recurrent state r10, r20, r3o, r4o
+      break;
+    default:
+      rgba = tf.tensor(0);
+  }
+  tf.dispose([t.src, fgr, pha, t.r1i, t.r2i, t.r3i, t.r4i]);
+  [t.r1i, t.r2i, t.r3i, t.r4i] = [r1o, r2o, r3o, r4o]; // update recurrent states
+  return rgba;
+}
diff --git a/src/segmentation/segmentation.ts b/src/segmentation/segmentation.ts
deleted file mode 100644
index b940e499..00000000
--- a/src/segmentation/segmentation.ts
+++ /dev/null
@@ -1,98 +0,0 @@
-/**
- * Image segmentation for body detection model
- *
- * Based on:
- * - [**MediaPipe Meet**](https://drive.google.com/file/d/1lnP1bRi9CSqQQXUHa13159vLELYDgDu0/preview)
- * - [**MediaPipe Selfie**](https://drive.google.com/file/d/1dCfozqknMa068vVsO2j_1FgZkW_e3VWv/preview)
- */
-
-import { log } from '../util/util';
-import * as tf from '../../dist/tfjs.esm.js';
-import { loadModel } from '../tfjs/load';
-import * as image from '../image/image';
-import { constants } from '../tfjs/constants';
-import type { GraphModel, Tensor } from '../tfjs/types';
-import type { Config } from '../config';
-import { env } from '../util/env';
-import type { Input, AnyCanvas } from '../exports';
-
-let model: GraphModel;
-let busy = false;
-
-export async function load(config: Config): Promise<GraphModel> {
-  if (!model || env.initial) model = await loadModel(config.segmentation.modelPath);
-  else if (config.debug) log('cached model:', model['modelUrl']);
-  return model;
-}
-
-export async function process(input: Input, background: Input | undefined, config: Config)
-: Promise<{ data: number[] | Tensor, canvas: AnyCanvas | null, alpha: AnyCanvas | null }> {
-  if (busy) return { data: [], canvas: null, alpha: null };
-  busy = true;
-  if (!model) await load(config);
-  const inputImage = await image.process(input, config);
-  const width = inputImage.tensor?.shape[2] || 0;
-  const height = inputImage.tensor?.shape[1] || 0;
-  if (!inputImage.tensor) return { data: [], canvas: null, alpha: null };
-  const t: Record<string, Tensor> = {};
-
-  t.resize = tf.image.resizeBilinear(inputImage.tensor, [model.inputs[0].shape ? model.inputs[0].shape[1] : 0, model.inputs[0].shape ? model.inputs[0].shape[2] : 0], false);
-  tf.dispose(inputImage.tensor);
-  t.norm = tf.div(t.resize, constants.tf255);
-  t.res = model.execute(t.norm) as Tensor;
-
-  t.squeeze = tf.squeeze(t.res, 0); // meet.shape:[1,256,256,1], selfie.shape:[1,144,256,2]
-  if (t.squeeze.shape[2] === 2) {
-    t.softmax = tf.softmax(t.squeeze); // model meet has two channels for fg and bg
-    [t.bg, t.fg] = tf.unstack(t.softmax, 2);
-    t.expand = tf.expandDims(t.fg, 2);
-    t.pad = tf.expandDims(t.expand, 0);
-    t.crop = tf.image.cropAndResize(t.pad, [[0, 0, 0.5, 0.5]], [0], [width, height]);
-    // running sofmax before unstack creates 2x2 matrix so we only take upper-left quadrant
-    // otherwise run softmax after unstack and use standard resize
-    // resizeOutput = tf.image.resizeBilinear(expand, [input.tensor?.shape[1], input.tensor?.shape[2]]);
-    t.data = tf.squeeze(t.crop, 0);
-  } else {
-    t.data = tf.image.resizeBilinear(t.squeeze, [height, width]); // model selfie has a single channel that we can use directly
-  }
-  const data = Array.from(await t.data.data());
-
-  if (env.node && !env.Canvas && (typeof ImageData === 'undefined')) {
-    if (config.debug) log('canvas support missing');
-    Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));
-    return { data, canvas: null, alpha: null }; // running in nodejs so return alpha array as-is
-  }
-
-  const alphaCanvas = image.canvas(width, height);
-  if (tf.browser) await tf.browser.toPixels(t.data, alphaCanvas);
-  const alphaCtx = alphaCanvas.getContext('2d') as CanvasRenderingContext2D;
-  if (config.segmentation.blur && config.segmentation.blur > 0) alphaCtx.filter = `blur(${config.segmentation.blur}px)`; // use css filter for bluring, can be done with gaussian blur manually instead
-  const alphaData = alphaCtx.getImageData(0, 0, width, height);
-
-  const compositeCanvas = image.canvas(width, height);
-  const compositeCtx = compositeCanvas.getContext('2d') as CanvasRenderingContext2D;
-  if (inputImage.canvas) compositeCtx.drawImage(inputImage.canvas, 0, 0);
-  compositeCtx.globalCompositeOperation = 'darken'; // https://developer.mozilla.org/en-US/docs/Web/API/CanvasRenderingContext2D/globalCompositeOperation // best options are: darken, color-burn, multiply
-  if (config.segmentation.blur && config.segmentation.blur > 0) compositeCtx.filter = `blur(${config.segmentation.blur}px)`; // use css filter for bluring, can be done with gaussian blur manually instead
-  compositeCtx.drawImage(alphaCanvas, 0, 0);
-  compositeCtx.globalCompositeOperation = 'source-over'; // reset composite operation
-  compositeCtx.filter = 'none'; // reset css filter
-  const compositeData = compositeCtx.getImageData(0, 0, width, height);
-  for (let i = 0; i < width * height; i++) compositeData.data[4 * i + 3] = alphaData.data[4 * i + 0]; // copy original alpha value to new composite canvas
-  compositeCtx.putImageData(compositeData, 0, 0);
-
-  let mergedCanvas: AnyCanvas | null = null;
-  if (background && compositeCanvas) { // draw background with segmentation as overlay if background is present
-    mergedCanvas = image.canvas(width, height);
-    const bgImage = await image.process(background, config);
-    tf.dispose(bgImage.tensor);
-    const ctxMerge = mergedCanvas.getContext('2d') as CanvasRenderingContext2D;
-    ctxMerge.drawImage(bgImage.canvas as HTMLCanvasElement, 0, 0, mergedCanvas.width, mergedCanvas.height);
-    ctxMerge.drawImage(compositeCanvas, 0, 0);
-  }
-
-  Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));
-  busy = false;
-  // return { data, canvas: mergedCanvas || compositeCanvas, alpha: alphaCanvas };
-  return { data, canvas: compositeCanvas, alpha: alphaCanvas };
-}
diff --git a/src/segmentation/selfie.ts b/src/segmentation/selfie.ts
new file mode 100644
index 00000000..addd7e0f
--- /dev/null
+++ b/src/segmentation/selfie.ts
@@ -0,0 +1,49 @@
+/**
+ * Image segmentation for body detection model
+ *
+ * Based on:
+ * - [**MediaPipe Selfie**](https://drive.google.com/file/d/1dCfozqknMa068vVsO2j_1FgZkW_e3VWv/preview)
+ */
+
+import { log } from '../util/util';
+import * as tf from '../../dist/tfjs.esm.js';
+import { loadModel } from '../tfjs/load';
+import { constants } from '../tfjs/constants';
+import type { GraphModel, Tensor } from '../tfjs/types';
+import type { Config } from '../config';
+import { env } from '../util/env';
+
+let model: GraphModel;
+
+export async function load(config: Config): Promise<GraphModel> {
+  if (!model || env.initial) model = await loadModel(config.segmentation.modelPath);
+  else if (config.debug) log('cached model:', model['modelUrl']);
+  return model;
+}
+
+export async function predict(input: Tensor, config: Config): Promise<Tensor | null> {
+  if (!model) model = await load(config);
+  if (!model?.['executor'] || !model?.inputs?.[0].shape) return null; // something is wrong with the model
+  const t: Record<string, Tensor> = {};
+  t.resize = tf.image.resizeBilinear(input, [model.inputs[0].shape ? model.inputs[0].shape[1] : 0, model.inputs[0].shape ? model.inputs[0].shape[2] : 0], false);
+  t.norm = tf.div(t.resize, constants.tf255);
+  t.res = model.execute(t.norm) as Tensor;
+  t.squeeze = tf.squeeze(t.res, 0); // meet.shape:[1,256,256,1], selfie.shape:[1,144,256,2]
+  t.alpha = tf.image.resizeBilinear(t.squeeze, [input.shape[1], input.shape[2]]); // model selfie has a single channel that we can use directly
+  t.mul = tf.mul(t.alpha, constants.tf255);
+  let rgba: Tensor;
+  switch (config.segmentation.mode || 'default') {
+    case 'default':
+      t.input = tf.squeeze(input);
+      t.concat = tf.concat([t.input, t.mul], -1);
+      rgba = tf.cast(t.concat, 'int32'); // combined original with alpha
+      break;
+    case 'alpha':
+      rgba = tf.cast(t.mul, 'int32'); // just get alpha value from model
+      break;
+    default:
+      rgba = tf.tensor(0);
+  }
+  Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));
+  return rgba;
+}
diff --git a/src/tfjs/load.ts b/src/tfjs/load.ts
index 98421d80..db477cb0 100644
--- a/src/tfjs/load.ts
+++ b/src/tfjs/load.ts
@@ -3,7 +3,7 @@ import * as tf from '../../dist/tfjs.esm.js';
 import type { GraphModel } from './types';
 import type { Config } from '../config';
 import * as modelsDefs from '../../models/models.json';
-import { validateModel } from '../models';
+// import { validateModel } from '../models';
 
 const options = {
   cacheModels: true,
@@ -86,6 +86,6 @@ export async function loadModel(modelPath: string | undefined): Promise<GraphMod
       log('error saving model:', modelUrl, err);
     }
   }
-  validateModel(null, model, `${modelPath || ''}`);
+  // validateModel(null, model, `${modelPath || ''}`);
   return model;
 }
diff --git a/src/warmup.ts b/src/warmup.ts
index 7554949a..d8115824 100644
--- a/src/warmup.ts
+++ b/src/warmup.ts
@@ -133,7 +133,7 @@ export async function runCompile(instance: Human) {
       if (Array.isArray(res)) res.forEach((t) => tf.dispose(t));
       else tf.dispose(res);
     } catch {
-      log('compile fail model:', modelName);
+      if (instance.config.debug) log('compile fail model:', modelName);
     }
     tf.dispose(tensor);
   }
diff --git a/test/build.log b/test/build.log
index 6580d0fd..f7faa44e 100644
--- a/test/build.log
+++ b/test/build.log
@@ -38,3 +38,2690 @@
 2022-09-30 10:10:53 STATE: Models {"folder":"../nanodet/models","models":4} 
 2022-09-30 10:10:53 STATE: Models: {"count":57,"totalSize":383017442} 
 2022-09-30 10:10:53 INFO:  Human Build complete... {"logFile":"test/build.log"} 
+2022-10-02 11:25:38 INFO:  @vladmandic/human version 2.11.0 
+2022-10-02 11:25:38 INFO:  User: vlado Platform: linux Arch: x64 Node: v18.10.0 
+2022-10-02 11:25:38 INFO:  Application: {"name":"@vladmandic/human","version":"2.11.0"} 
+2022-10-02 11:25:38 INFO:  Environment: {"profile":"development","config":".build.json","package":"package.json","tsconfig":true,"eslintrc":true,"git":true} 
+2022-10-02 11:25:38 INFO:  Toolchain: {"build":"0.7.14","esbuild":"0.15.10","typescript":"4.8.4","typedoc":"0.23.15","eslint":"8.24.0"} 
+2022-10-02 11:25:38 INFO:  Build: {"profile":"development","steps":["serve","watch","compile"]} 
+2022-10-02 11:25:38 STATE: WebServer: {"ssl":false,"port":10030,"root":"."} 
+2022-10-02 11:25:38 STATE: WebServer: {"ssl":true,"port":10031,"root":".","sslKey":"node_modules/@vladmandic/build/cert/https.key","sslCrt":"node_modules/@vladmandic/build/cert/https.crt"} 
+2022-10-02 11:25:38 STATE: Watch: {"locations":["src/**/*","tfjs/**/*","demo/**/*.ts"]} 
+2022-10-02 11:25:38 STATE: Compile: {"name":"tfjs/nodejs/cpu","format":"cjs","platform":"node","input":"tfjs/tf-node.ts","output":"dist/tfjs.esm.js","files":1,"inputBytes":159,"outputBytes":1098} 
+2022-10-02 11:25:38 STATE: Compile: {"name":"human/nodejs/cpu","format":"cjs","platform":"node","input":"src/human.ts","output":"dist/human.node.js","files":76,"inputBytes":665733,"outputBytes":497798} 
+2022-10-02 11:25:38 STATE: Compile: {"name":"tfjs/nodejs/gpu","format":"cjs","platform":"node","input":"tfjs/tf-node-gpu.ts","output":"dist/tfjs.esm.js","files":1,"inputBytes":167,"outputBytes":1118} 
+2022-10-02 11:25:38 STATE: Compile: {"name":"human/nodejs/gpu","format":"cjs","platform":"node","input":"src/human.ts","output":"dist/human.node-gpu.js","files":76,"inputBytes":665753,"outputBytes":497814} 
+2022-10-02 11:25:38 STATE: Compile: {"name":"tfjs/nodejs/wasm","format":"cjs","platform":"node","input":"tfjs/tf-node-wasm.ts","output":"dist/tfjs.esm.js","files":1,"inputBytes":206,"outputBytes":1205} 
+2022-10-02 11:25:38 STATE: Compile: {"name":"human/nodejs/wasm","format":"cjs","platform":"node","input":"src/human.ts","output":"dist/human.node-wasm.js","files":76,"inputBytes":665840,"outputBytes":497905} 
+2022-10-02 11:25:38 STATE: Compile: {"name":"tfjs/browser/version","format":"esm","platform":"browser","input":"tfjs/tf-version.ts","output":"dist/tfjs.version.js","files":1,"inputBytes":1125,"outputBytes":1439} 
+2022-10-02 11:25:38 STATE: Compile: {"name":"tfjs/browser/esm/nobundle","format":"esm","platform":"browser","input":"tfjs/tf-browser.ts","output":"dist/tfjs.esm.js","files":2,"inputBytes":2169,"outputBytes":856} 
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+2022-10-02 12:06:01 ERROR: Compile: {"name":"human/browser/esm/nobundle","format":"esm","platform":"browser","input":"src/human.ts"} {"errors":[{"id":"","location":{"column":24,"file":"src/human.ts","length":23,"line":39,"lineText":"import * as selfie from './segmentation/selfie';","namespace":"","suggestion":""},"notes":[],"pluginName":"","text":"Could not resolve \"./segmentation/selfie\""},{"id":"","location":{"column":24,"file":"src/models.ts","length":23,"line":26,"lineText":"import * as selfie from './segmentation/selfie';","namespace":"","suggestion":""},"notes":[],"pluginName":"","text":"Could not resolve \"./segmentation/selfie\""}]} 
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+2022-10-02 14:58:24 STATE: Compile: {"name":"tfjs/browser/version","format":"esm","platform":"browser","input":"tfjs/tf-version.ts","output":"dist/tfjs.version.js","files":1,"inputBytes":1125,"outputBytes":1439} 
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+2022-10-02 14:58:24 STATE: Compile: {"name":"demo/faceid","format":"esm","platform":"browser","input":"demo/faceid/index.ts","output":"demo/faceid/index.js","files":2,"inputBytes":17155,"outputBytes":13538} 
+2022-10-02 14:58:36 INFO:  Watch: {"event":"modify","input":"src/segmentation/rvm.ts"} 
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+2022-10-02 14:58:36 STATE: Compile: {"name":"tfjs/browser/version","format":"esm","platform":"browser","input":"tfjs/tf-version.ts","output":"dist/tfjs.version.js","files":1,"inputBytes":1125,"outputBytes":1439} 
+2022-10-02 14:58:36 STATE: Compile: {"name":"tfjs/browser/esm/nobundle","format":"esm","platform":"browser","input":"tfjs/tf-browser.ts","output":"dist/tfjs.esm.js","files":2,"inputBytes":2169,"outputBytes":856} 
+2022-10-02 14:58:36 STATE: Compile: {"name":"human/browser/esm/nobundle","format":"esm","platform":"browser","input":"src/human.ts","output":"dist/human.esm-nobundle.js","files":78,"inputBytes":669655,"outputBytes":504749} 
+2022-10-02 14:58:36 STATE: Compile: {"name":"tfjs/browser/esm/custom","format":"esm","platform":"browser","input":"tfjs/tf-custom.ts","output":"dist/tfjs.esm.js","files":11,"inputBytes":2346,"outputBytes":2818325} 
+2022-10-02 14:58:36 STATE: Compile: {"name":"human/browser/iife/bundle","format":"iife","platform":"browser","input":"src/human.ts","output":"dist/human.js","files":78,"inputBytes":3487124,"outputBytes":1694471} 
+2022-10-02 14:58:36 STATE: Compile: {"name":"human/browser/esm/bundle","format":"esm","platform":"browser","input":"src/human.ts","output":"dist/human.esm.js","files":78,"inputBytes":3487124,"outputBytes":3116024} 
+2022-10-02 14:58:36 STATE: Compile: {"name":"demo/typescript","format":"esm","platform":"browser","input":"demo/typescript/index.ts","output":"demo/typescript/index.js","files":1,"inputBytes":5841,"outputBytes":3814} 
+2022-10-02 14:58:36 STATE: Compile: {"name":"demo/faceid","format":"esm","platform":"browser","input":"demo/faceid/index.ts","output":"demo/faceid/index.js","files":2,"inputBytes":17155,"outputBytes":13538} 
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+2022-10-02 14:59:08 STATE: Compile: {"name":"tfjs/browser/version","format":"esm","platform":"browser","input":"tfjs/tf-version.ts","output":"dist/tfjs.version.js","files":1,"inputBytes":1125,"outputBytes":1439} 
+2022-10-02 14:59:08 STATE: Compile: {"name":"tfjs/browser/esm/nobundle","format":"esm","platform":"browser","input":"tfjs/tf-browser.ts","output":"dist/tfjs.esm.js","files":2,"inputBytes":2169,"outputBytes":856} 
+2022-10-02 14:59:08 STATE: Compile: {"name":"human/browser/esm/nobundle","format":"esm","platform":"browser","input":"src/human.ts","output":"dist/human.esm-nobundle.js","files":78,"inputBytes":669702,"outputBytes":504824} 
+2022-10-02 14:59:08 STATE: Compile: {"name":"tfjs/browser/esm/custom","format":"esm","platform":"browser","input":"tfjs/tf-custom.ts","output":"dist/tfjs.esm.js","files":11,"inputBytes":2346,"outputBytes":2818325} 
+2022-10-02 14:59:08 STATE: Compile: {"name":"human/browser/iife/bundle","format":"iife","platform":"browser","input":"src/human.ts","output":"dist/human.js","files":78,"inputBytes":3487171,"outputBytes":1694519} 
+2022-10-02 14:59:08 STATE: Compile: {"name":"human/browser/esm/bundle","format":"esm","platform":"browser","input":"src/human.ts","output":"dist/human.esm.js","files":78,"inputBytes":3487171,"outputBytes":3116099} 
+2022-10-02 14:59:08 STATE: Compile: {"name":"demo/typescript","format":"esm","platform":"browser","input":"demo/typescript/index.ts","output":"demo/typescript/index.js","files":1,"inputBytes":5841,"outputBytes":3814} 
+2022-10-02 14:59:08 STATE: Compile: {"name":"demo/faceid","format":"esm","platform":"browser","input":"demo/faceid/index.ts","output":"demo/faceid/index.js","files":2,"inputBytes":17155,"outputBytes":13538} 
+2022-10-02 15:03:07 INFO:  Watch: {"event":"modify","input":"src/segmentation/rvm.ts"} 
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+2022-10-02 15:03:07 STATE: Compile: {"name":"human/nodejs/cpu","format":"cjs","platform":"node","input":"src/human.ts","output":"dist/human.node.js","files":78,"inputBytes":669502,"outputBytes":501672} 
+2022-10-02 15:03:07 STATE: Compile: {"name":"tfjs/nodejs/gpu","format":"cjs","platform":"node","input":"tfjs/tf-node-gpu.ts","output":"dist/tfjs.esm.js","files":1,"inputBytes":167,"outputBytes":1118} 
+2022-10-02 15:03:07 STATE: Compile: {"name":"human/nodejs/gpu","format":"cjs","platform":"node","input":"src/human.ts","output":"dist/human.node-gpu.js","files":78,"inputBytes":669522,"outputBytes":501688} 
+2022-10-02 15:03:07 STATE: Compile: {"name":"tfjs/nodejs/wasm","format":"cjs","platform":"node","input":"tfjs/tf-node-wasm.ts","output":"dist/tfjs.esm.js","files":1,"inputBytes":206,"outputBytes":1205} 
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+2022-10-02 15:03:07 STATE: Compile: {"name":"tfjs/browser/version","format":"esm","platform":"browser","input":"tfjs/tf-version.ts","output":"dist/tfjs.version.js","files":1,"inputBytes":1125,"outputBytes":1439} 
+2022-10-02 15:03:07 STATE: Compile: {"name":"tfjs/browser/esm/nobundle","format":"esm","platform":"browser","input":"tfjs/tf-browser.ts","output":"dist/tfjs.esm.js","files":2,"inputBytes":2169,"outputBytes":856} 
+2022-10-02 15:03:07 STATE: Compile: {"name":"human/browser/esm/nobundle","format":"esm","platform":"browser","input":"src/human.ts","output":"dist/human.esm-nobundle.js","files":78,"inputBytes":669260,"outputBytes":504265} 
+2022-10-02 15:03:07 STATE: Compile: {"name":"tfjs/browser/esm/custom","format":"esm","platform":"browser","input":"tfjs/tf-custom.ts","output":"dist/tfjs.esm.js","files":11,"inputBytes":2346,"outputBytes":2818325} 
+2022-10-02 15:03:07 STATE: Compile: {"name":"human/browser/iife/bundle","format":"iife","platform":"browser","input":"src/human.ts","output":"dist/human.js","files":78,"inputBytes":3486729,"outputBytes":1694086} 
+2022-10-02 15:03:07 STATE: Compile: {"name":"human/browser/esm/bundle","format":"esm","platform":"browser","input":"src/human.ts","output":"dist/human.esm.js","files":78,"inputBytes":3486729,"outputBytes":3115540} 
+2022-10-02 15:03:07 STATE: Compile: {"name":"demo/typescript","format":"esm","platform":"browser","input":"demo/typescript/index.ts","output":"demo/typescript/index.js","files":1,"inputBytes":5841,"outputBytes":3814} 
+2022-10-02 15:03:07 STATE: Compile: {"name":"demo/faceid","format":"esm","platform":"browser","input":"demo/faceid/index.ts","output":"demo/faceid/index.js","files":2,"inputBytes":17155,"outputBytes":13538} 
diff --git a/test/test-backend-node-gpu.js b/test/test-backend-node-gpu.js
index e817b972..0e2c07d9 100644
--- a/test/test-backend-node-gpu.js
+++ b/test/test-backend-node-gpu.js
@@ -21,7 +21,7 @@ const config = {
   hand: { enabled: true },
   body: { enabled: true },
   object: { enabled: true },
-  segmentation: { enabled: true },
+  segmentation: { enabled: false },
   filter: { enabled: false },
 };
 
diff --git a/test/test-backend-node-wasm.js b/test/test-backend-node-wasm.js
index cf259b28..da4cc623 100644
--- a/test/test-backend-node-wasm.js
+++ b/test/test-backend-node-wasm.js
@@ -10,7 +10,7 @@ H.env.Image = Image; // requires monkey-patch as wasm does not have tf.browser n
 
 const config = {
   cacheSensitivity: 0,
-  modelBasePath: 'https://vladmandic.github.io/human/models/',
+  modelBasePath: 'https://vladmandic.github.io/human-models/models/',
   backend: 'wasm',
   // wasmPath: 'node_modules/@tensorflow/tfjs-backend-wasm/dist/',
   wasmPath: `https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-backend-wasm@${tf.version_core}/dist/`,
@@ -30,7 +30,7 @@ const config = {
   hand: { enabled: true, rotation: false },
   body: { enabled: true },
   object: { enabled: true },
-  segmentation: { enabled: true },
+  segmentation: { enabled: false },
   filter: { enabled: false },
 };
 
diff --git a/test/test-backend-node.js b/test/test-backend-node.js
index 76a9f5c9..0bb67fa3 100644
--- a/test/test-backend-node.js
+++ b/test/test-backend-node.js
@@ -22,7 +22,7 @@ const config = {
   hand: { enabled: true },
   body: { enabled: true },
   object: { enabled: true },
-  segmentation: { enabled: true },
+  segmentation: { enabled: false },
   filter: { enabled: false },
 };
 
diff --git a/test/test-node-main.js b/test/test-node-main.js
index b68285a1..23c228f5 100644
--- a/test/test-node-main.js
+++ b/test/test-node-main.js
@@ -294,7 +294,7 @@ async function test(Human, inputConfig) {
   await human.load();
   const models = Object.keys(human.models).map((model) => ({ name: model, loaded: (human.models[model] !== null), url: human.models[model] ? human.models[model]['modelUrl'] : null }));
   const loaded = models.filter((model) => model.loaded);
-  if (models.length === 23 && loaded.length === 12) log('state', 'passed: models loaded', models.length, loaded.length, models);
+  if (models.length === 25 && loaded.length === 11) log('state', 'passed: models loaded', models.length, loaded.length, models);
   else log('error', 'failed: models loaded', models.length, loaded.length, models);
   log('info', 'memory:', { memory: human.tf.memory() });
   log('info', 'state:', { state: human.tf.engine().state });
@@ -539,14 +539,18 @@ async function test(Human, inputConfig) {
   const ctx = inputCanvas.getContext('2d');
   ctx.drawImage(inputImage, 0, 0); // draw input image onto canvas
   res = await human.detect(inputCanvas);
-  if (!res || res?.face?.length !== 1) log('error', 'failed: monkey patch');
+  if (res?.face?.length !== 1) log('error', 'failed: monkey patch');
   else log('state', 'passed: monkey patch');
 
   // test segmentation
-  res = await human.segmentation(inputCanvas, inputCanvas);
-  if (!res || !res.data || !res.canvas) log('error', 'failed: segmentation');
-  else log('state', 'passed: segmentation', [res.data.length]);
-  human.env.Canvas = undefined;
+  config.segmentation = { enabled: true, modelPath: 'https://vladmandic.github.io/human-models/models/rvm.json' };
+  res = await human.segmentation(inputCanvas, config);
+  if (res?.shape?.length !== 3) log('error', 'failed: segmentation');
+  else log('state', 'passed: segmentation', [res.size]);
+  human.tf.dispose(res);
+  config.segmentation = { enabled: false };
+
+  human.env.Canvas = null; // disable canvas monkey-patch
 
   // check if all instances reported same
   const tensors1 = human.tf.engine().state.numTensors;
diff --git a/test/test.log b/test/test.log
index ba3d682f..b6f57b2e 100644
--- a/test/test.log
+++ b/test/test.log
@@ -1,1002 +1,999 @@
-2022-09-30 10:04:19 INFO:  @vladmandic/human version 2.11.0 
-2022-09-30 10:04:19 INFO:  User: vlado Platform: linux Arch: x64 Node: v18.10.0 
-2022-09-30 10:04:19 INFO:  demos: [{"cmd":"../demo/nodejs/node.js","args":[]},{"cmd":"../demo/nodejs/node-simple.js","args":[]},{"cmd":"../demo/nodejs/node-fetch.js","args":[]},{"cmd":"../demo/nodejs/node-event.js","args":["samples/in/ai-body.jpg"]},{"cmd":"../demo/nodejs/node-similarity.js","args":["samples/in/ai-face.jpg","samples/in/ai-upper.jpg"]},{"cmd":"../demo/nodejs/node-canvas.js","args":["samples/in/ai-body.jpg","samples/out/ai-body.jpg"]},{"cmd":"../demo/nodejs/process-folder.js","args":["samples"]},{"cmd":"../demo/multithread/node-multiprocess.js","args":[]},{"cmd":"../demo/facematch/node-match.js","args":[]}] 
-2022-09-30 10:04:19 INFO:  {"cmd":"../demo/nodejs/node.js","args":[]} start 
-2022-09-30 10:04:20 INFO:  {"cmd":"../demo/nodejs/node-simple.js","args":[]} start 
-2022-09-30 10:04:21 INFO:  {"cmd":"../demo/nodejs/node-fetch.js","args":[]} start 
-2022-09-30 10:04:24 INFO:  {"cmd":"../demo/nodejs/node-event.js","args":["samples/in/ai-body.jpg"]} start 
-2022-09-30 10:04:24 INFO:  {"cmd":"../demo/nodejs/node-similarity.js","args":["samples/in/ai-face.jpg","samples/in/ai-upper.jpg"]} start 
-2022-09-30 10:04:25 INFO:  {"cmd":"../demo/nodejs/node-canvas.js","args":["samples/in/ai-body.jpg","samples/out/ai-body.jpg"]} start 
-2022-09-30 10:04:26 INFO:  {"cmd":"../demo/nodejs/process-folder.js","args":["samples"]} start 
-2022-09-30 10:04:27 INFO:  {"cmd":"../demo/multithread/node-multiprocess.js","args":[]} start 
-2022-09-30 10:04:38 INFO:  {"cmd":"../demo/facematch/node-match.js","args":[]} start 
-2022-09-30 10:04:39 INFO:  tests: ["test-node-load.js","test-node-gear.js","test-backend-node.js","test-backend-node-gpu.js","test-backend-node-wasm.js"] 
-2022-09-30 10:04:39 INFO:  
-2022-09-30 10:04:39 INFO:  test-node-load.js start 
-2022-09-30 10:04:40 INFO:  test-node-load.js load start {"human":"2.11.0","tf":"3.20.0","progress":0} 
-2022-09-30 10:04:40 DATA:  test-node-load.js load interval {"elapsed":1,"progress":0} 
-2022-09-30 10:04:40 DATA:  test-node-load.js load interval {"elapsed":11,"progress":0} 
-2022-09-30 10:04:40 DATA:  test-node-load.js load interval {"elapsed":23,"progress":0.05339166087267679} 
-2022-09-30 10:04:40 DATA:  test-node-load.js load interval {"elapsed":33,"progress":0.2135162934143239} 
-2022-09-30 10:04:40 DATA:  test-node-load.js load interval {"elapsed":60,"progress":0.3299591712723044} 
-2022-09-30 10:04:40 DATA:  test-node-load.js load interval {"elapsed":81,"progress":0.5125946867158943} 
-2022-09-30 10:04:40 DATA:  test-node-load.js load interval {"elapsed":91,"progress":0.7259096583739463} 
-2022-09-30 10:04:40 STATE: test-node-load.js passed {"progress":1} 
-2022-09-30 10:04:40 INFO:  test-node-load.js load final {"progress":1} 
-2022-09-30 10:04:40 DATA:  test-node-load.js load interval {"elapsed":366,"progress":1} 
-2022-09-30 10:04:40 INFO:  
-2022-09-30 10:04:40 INFO:  test-node-gear.js start 
-2022-09-30 10:04:40 DATA:  test-node-gear.js input: ["samples/in/ai-face.jpg"] 
-2022-09-30 10:04:42 STATE: test-node-gear.js passed: gear faceres samples/in/ai-face.jpg 
-2022-09-30 10:04:42 DATA:  test-node-gear.js results {"face":0,"model":"faceres","image":"samples/in/ai-face.jpg","age":23.5,"gender":"female","genderScore":0.92} 
-2022-09-30 10:04:42 STATE: test-node-gear.js passed: gear gear samples/in/ai-face.jpg 
-2022-09-30 10:04:42 DATA:  test-node-gear.js results {"face":0,"model":"gear","image":"samples/in/ai-face.jpg","age":23.3,"gender":"female","genderScore":0.51,"race":[{"score":0.93,"race":"white"}]} 
-2022-09-30 10:04:42 STATE: test-node-gear.js passed: gear ssrnet samples/in/ai-face.jpg 
-2022-09-30 10:04:42 DATA:  test-node-gear.js results {"face":0,"model":"ssrnet","image":"samples/in/ai-face.jpg","age":23.4,"gender":"female","genderScore":0.99} 
-2022-09-30 10:04:42 INFO:  
-2022-09-30 10:04:42 INFO:  test-backend-node.js start 
-2022-09-30 10:04:42 INFO:  test-backend-node.js test: configuration validation 
-2022-09-30 10:04:42 STATE: test-backend-node.js passed: configuration default validation [] 
-2022-09-30 10:04:42 STATE: test-backend-node.js passed: configuration invalid validation [{"reason":"unknown property","where":"config.invalid = true"}] 
-2022-09-30 10:04:42 INFO:  test-backend-node.js test: model load 
-2022-09-30 10:04:42 STATE: test-backend-node.js passed: models loaded 23 12 [{"name":"ssrnetage","loaded":false,"url":null},{"name":"gear","loaded":false,"url":null},{"name":"blazeposedetect","loaded":false,"url":null},{"name":"blazepose","loaded":false,"url":null},{"name":"centernet","loaded":true,"url":"file://models/mb3-centernet.json"},{"name":"efficientpose","loaded":false,"url":null},{"name":"mobilefacenet","loaded":false,"url":null},{"name":"insightface","loaded":false,"url":null},{"name":"emotion","loaded":true,"url":"file://models/emotion.json"},{"name":"facedetect","loaded":true,"url":"file://models/blazeface.json"},{"name":"faceiris","loaded":true,"url":"file://models/iris.json"},{"name":"facemesh","loaded":true,"url":"file://models/facemesh.json"},{"name":"faceres","loaded":true,"url":"file://models/faceres.json"},{"name":"ssrnetgender","loaded":false,"url":null},{"name":"handpose","loaded":false,"url":null},{"name":"handskeleton","loaded":true,"url":"file://models/handlandmark-full.json"},{"name":"handtrack","loaded":true,"url":"file://models/handtrack.json"},{"name":"liveness","loaded":true,"url":"file://models/liveness.json"},{"name":"movenet","loaded":true,"url":"file://models/movenet-lightning.json"},{"name":"nanodet","loaded":false,"url":null},{"name":"posenet","loaded":false,"url":null},{"name":"segmentation","loaded":true,"url":"file://models/selfie.json"},{"name":"antispoof","loaded":true,"url":"file://models/antispoof.json"}] 
-2022-09-30 10:04:42 INFO:  test-backend-node.js memory: {"memory":{"unreliable":true,"numTensors":1921,"numDataBuffers":1921,"numBytes":63673064}} 
-2022-09-30 10:04:42 INFO:  test-backend-node.js state: {"state":{"registeredVariables":{},"nextTapeNodeId":0,"numBytes":63673064,"numTensors":1921,"numStringTensors":0,"numDataBuffers":1921,"gradientDepth":0,"kernelDepth":0,"scopeStack":[],"numDataMovesStack":[],"nextScopeId":0,"tensorInfo":{},"profiling":false,"activeProfile":{"newBytes":0,"newTensors":0,"peakBytes":0,"kernels":[],"result":null,"kernelNames":[]}}} 
-2022-09-30 10:04:42 INFO:  test-backend-node.js test: warmup 
-2022-09-30 10:04:42 STATE: test-backend-node.js passed: create human 
-2022-09-30 10:04:42 INFO:  test-backend-node.js human version: 2.11.0 
-2022-09-30 10:04:42 INFO:  test-backend-node.js platform: linux x64 agent: NodeJS v18.10.0 
-2022-09-30 10:04:42 INFO:  test-backend-node.js tfjs version: 3.20.0 
-2022-09-30 10:04:42 INFO:  test-backend-node.js env: {"browser":false,"node":true,"platform":"linux x64","agent":"NodeJS v18.10.0","backends":["cpu","tensorflow"],"initial":false,"tfjs":{"version":"3.20.0"},"offscreen":false,"perfadd":false,"tensorflow":{"version":"2.7.3-dev20220521","gpu":false},"wasm":{"supported":true,"backend":false},"webgl":{"supported":false,"backend":false},"webgpu":{"supported":false,"backend":false},"cpu":{"flags":[]},"kernels":169} 
-2022-09-30 10:04:42 STATE: test-backend-node.js passed: set backend: tensorflow 
-2022-09-30 10:04:42 STATE: test-backend-node.js tensors 1921 
-2022-09-30 10:04:42 STATE: test-backend-node.js passed: load models 
-2022-09-30 10:04:42 STATE: test-backend-node.js  result: defined models: 23 loaded models: 12 
-2022-09-30 10:04:42 STATE: test-backend-node.js passed: warmup: none default 
-2022-09-30 10:04:42 DATA:  test-backend-node.js  result: face: 0 body: 0 hand: 0 gesture: 0 object: 0 person: 0 {} {} {} 
-2022-09-30 10:04:42 DATA:  test-backend-node.js  result: performance: load: null total: null 
-2022-09-30 10:04:42 STATE: test-backend-node.js passed: warmup none result match 
-2022-09-30 10:04:42 STATE: test-backend-node.js event: image 
-2022-09-30 10:04:42 STATE: test-backend-node.js event: detect 
-2022-09-30 10:04:42 STATE: test-backend-node.js event: warmup 
-2022-09-30 10:04:42 STATE: test-backend-node.js passed: warmup: face default 
-2022-09-30 10:04:42 DATA:  test-backend-node.js  result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.5,"gender":"female"} {"score":0.82,"class":"person"} {"score":0.42,"keypoints":4} 
-2022-09-30 10:04:42 DATA:  test-backend-node.js  result: performance: load: null total: 361 
-2022-09-30 10:04:42 STATE: test-backend-node.js passed: warmup face result match 
-2022-09-30 10:04:42 STATE: test-backend-node.js event: image 
-2022-09-30 10:04:43 STATE: test-backend-node.js event: detect 
-2022-09-30 10:04:43 STATE: test-backend-node.js event: warmup 
-2022-09-30 10:04:43 STATE: test-backend-node.js passed: warmup: body default 
-2022-09-30 10:04:43 DATA:  test-backend-node.js  result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
-2022-09-30 10:04:43 DATA:  test-backend-node.js  result: performance: load: null total: 253 
-2022-09-30 10:04:43 STATE: test-backend-node.js passed: warmup body result match 
-2022-09-30 10:04:43 STATE: test-backend-node.js details: {"face":{"boxScore":0.92,"faceScore":1,"age":23.7,"gender":"female","genderScore":0.97},"emotion":[{"score":0.63,"emotion":"angry"},{"score":0.22,"emotion":"fear"}],"body":{"score":0.92,"keypoints":17},"hand":{"boxScore":0.52,"fingerScore":0.73,"keypoints":21},"gestures":[{"face":0,"gesture":"facing right"},{"face":0,"gesture":"mouth 10% open"},{"hand":0,"gesture":"pinky forward"},{"hand":0,"gesture":"palm up"},{"hand":0,"gesture":"open palm"},{"iris":0,"gesture":"looking left"},{"iris":0,"gesture":"looking up"}]} 
-2022-09-30 10:04:43 INFO:  test-backend-node.js test: details verification 
-2022-09-30 10:04:43 STATE: test-backend-node.js start default 
-2022-09-30 10:04:43 STATE: test-backend-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} 
-2022-09-30 10:04:43 STATE: test-backend-node.js event: image 
-2022-09-30 10:04:43 STATE: test-backend-node.js event: detect 
-2022-09-30 10:04:43 STATE: test-backend-node.js passed: detect: samples/in/ai-body.jpg default 
-2022-09-30 10:04:43 DATA:  test-backend-node.js  result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
-2022-09-30 10:04:43 DATA:  test-backend-node.js  result: performance: load: null total: 246 
-2022-09-30 10:04:43 STATE: test-backend-node.js passed: details face length 1 
-2022-09-30 10:04:43 STATE: test-backend-node.js passed: details face score 1 0.93 1 
-2022-09-30 10:04:43 STATE: test-backend-node.js passed: details face age/gender 23.7 female 0.97 85.47 
-2022-09-30 10:04:43 STATE: test-backend-node.js passed: details face arrays 4 478 1024 
-2022-09-30 10:04:43 STATE: test-backend-node.js passed: details face emotion 2 {"score":0.59,"emotion":"angry"} 
-2022-09-30 10:04:43 STATE: test-backend-node.js passed: details face anti-spoofing 0.79 
-2022-09-30 10:04:43 STATE: test-backend-node.js passed: details face liveness 0.83 
-2022-09-30 10:04:43 STATE: test-backend-node.js passed: details body length 1 
-2022-09-30 10:04:43 STATE: test-backend-node.js passed: details body 0.92 17 6 
-2022-09-30 10:04:43 STATE: test-backend-node.js passed: details hand length 1 
-2022-09-30 10:04:43 STATE: test-backend-node.js passed: details hand 0.51 0.73 point 
-2022-09-30 10:04:43 STATE: test-backend-node.js passed: details hand arrays 21 5 7 
-2022-09-30 10:04:43 STATE: test-backend-node.js passed: details gesture length 7 
-2022-09-30 10:04:43 STATE: test-backend-node.js passed: details gesture first {"face":0,"gesture":"facing right"} 
-2022-09-30 10:04:43 STATE: test-backend-node.js passed: details object length 1 
-2022-09-30 10:04:43 STATE: test-backend-node.js passed: details object 0.72 person 
-2022-09-30 10:04:43 STATE: test-backend-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,4] {"checksum":1371996928} 
-2022-09-30 10:04:43 STATE: test-backend-node.js event: image 
-2022-09-30 10:04:44 STATE: test-backend-node.js event: detect 
-2022-09-30 10:04:44 STATE: test-backend-node.js passed: tensor shape: [1,1200,1200,4] dtype: float32 
-2022-09-30 10:04:44 STATE: test-backend-node.js passed: load image: samples/in/ai-body.jpg [1200,1200,4] {"checksum":1371996928} 
-2022-09-30 10:04:44 STATE: test-backend-node.js event: image 
-2022-09-30 10:04:44 STATE: test-backend-node.js event: detect 
-2022-09-30 10:04:44 STATE: test-backend-node.js passed: tensor shape: [1200,1200,4] dtype: float32 
-2022-09-30 10:04:44 STATE: test-backend-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} 
-2022-09-30 10:04:44 STATE: test-backend-node.js event: image 
-2022-09-30 10:04:44 STATE: test-backend-node.js event: detect 
-2022-09-30 10:04:44 STATE: test-backend-node.js passed: tensor shape: [1,1200,1200,3] dtype: float32 
-2022-09-30 10:04:44 STATE: test-backend-node.js passed: load image: samples/in/ai-body.jpg [1200,1200,3] {"checksum":1004796864} 
-2022-09-30 10:04:44 STATE: test-backend-node.js event: image 
-2022-09-30 10:04:45 STATE: test-backend-node.js event: detect 
-2022-09-30 10:04:45 STATE: test-backend-node.js passed: tensor shape: [1200,1200,3] dtype: float32 
-2022-09-30 10:04:45 STATE: test-backend-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,4] {"checksum":1371996871} 
-2022-09-30 10:04:45 STATE: test-backend-node.js event: image 
-2022-09-30 10:04:45 STATE: test-backend-node.js event: detect 
-2022-09-30 10:04:45 STATE: test-backend-node.js passed: tensor shape: [1,1200,1200,4] dtype: int32 
-2022-09-30 10:04:45 INFO:  test-backend-node.js test default 
-2022-09-30 10:04:45 STATE: test-backend-node.js start async 
-2022-09-30 10:04:45 STATE: test-backend-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} 
-2022-09-30 10:04:45 STATE: test-backend-node.js event: image 
-2022-09-30 10:04:46 STATE: test-backend-node.js event: detect 
-2022-09-30 10:04:46 STATE: test-backend-node.js passed: detect: samples/in/ai-body.jpg async 
-2022-09-30 10:04:46 DATA:  test-backend-node.js  result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
-2022-09-30 10:04:46 DATA:  test-backend-node.js  result: performance: load: null total: 232 
-2022-09-30 10:04:46 STATE: test-backend-node.js passed: default result face match 1 female 0.97 
-2022-09-30 10:04:46 INFO:  test-backend-node.js test sync 
-2022-09-30 10:04:46 STATE: test-backend-node.js start sync 
-2022-09-30 10:04:46 STATE: test-backend-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} 
-2022-09-30 10:04:46 STATE: test-backend-node.js event: image 
-2022-09-30 10:04:46 STATE: test-backend-node.js event: detect 
-2022-09-30 10:04:46 STATE: test-backend-node.js passed: detect: samples/in/ai-body.jpg sync 
-2022-09-30 10:04:46 DATA:  test-backend-node.js  result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
-2022-09-30 10:04:46 DATA:  test-backend-node.js  result: performance: load: null total: 217 
-2022-09-30 10:04:46 STATE: test-backend-node.js passed: default sync 1 female 0.97 
-2022-09-30 10:04:46 INFO:  test-backend-node.js test: image process 
-2022-09-30 10:04:46 STATE: test-backend-node.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} 
-2022-09-30 10:04:46 STATE: test-backend-node.js passed: image input null [1,256,256,3] 
-2022-09-30 10:04:46 INFO:  test-backend-node.js test: image null 
-2022-09-30 10:04:46 STATE: test-backend-node.js passed: invalid input could not convert input to tensor 
-2022-09-30 10:04:46 INFO:  test-backend-node.js test face similarity 
-2022-09-30 10:04:46 STATE: test-backend-node.js start face similarity 
-2022-09-30 10:04:46 STATE: test-backend-node.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} 
-2022-09-30 10:04:46 STATE: test-backend-node.js event: image 
-2022-09-30 10:04:46 STATE: test-backend-node.js event: detect 
-2022-09-30 10:04:46 STATE: test-backend-node.js passed: detect: samples/in/ai-face.jpg face similarity 
-2022-09-30 10:04:46 DATA:  test-backend-node.js  result: face: 1 body: 1 hand: 1 gesture: 6 object: 1 person: 1 {"score":1,"age":23.5,"gender":"female"} {"score":0.82,"class":"person"} {"score":0.47,"keypoints":3} 
-2022-09-30 10:04:46 DATA:  test-backend-node.js  result: performance: load: null total: 202 
-2022-09-30 10:04:46 STATE: test-backend-node.js start face similarity 
-2022-09-30 10:04:46 STATE: test-backend-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} 
-2022-09-30 10:04:46 STATE: test-backend-node.js event: image 
-2022-09-30 10:04:47 STATE: test-backend-node.js event: detect 
-2022-09-30 10:04:47 STATE: test-backend-node.js passed: detect: samples/in/ai-body.jpg face similarity 
-2022-09-30 10:04:47 DATA:  test-backend-node.js  result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
-2022-09-30 10:04:47 DATA:  test-backend-node.js  result: performance: load: null total: 214 
-2022-09-30 10:04:47 STATE: test-backend-node.js start face similarity 
-2022-09-30 10:04:47 STATE: test-backend-node.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151289024} 
-2022-09-30 10:04:47 STATE: test-backend-node.js event: image 
-2022-09-30 10:04:47 STATE: test-backend-node.js event: detect 
-2022-09-30 10:04:47 STATE: test-backend-node.js passed: detect: samples/in/ai-upper.jpg face similarity 
-2022-09-30 10:04:47 DATA:  test-backend-node.js  result: face: 1 body: 1 hand: 0 gesture: 4 object: 1 person: 1 {"score":1,"age":23.5,"gender":"female"} {"score":0.71,"class":"person"} {"score":0.75,"keypoints":7} 
-2022-09-30 10:04:47 DATA:  test-backend-node.js  result: performance: load: null total: 191 
-2022-09-30 10:04:47 STATE: test-backend-node.js passed: face descriptor 
-2022-09-30 10:04:47 STATE: test-backend-node.js passed: face similarity {"similarity":[1,0.44727452329649126,0.5567935850640406],"descriptors":[1024,1024,1024]} 
-2022-09-30 10:04:47 INFO:  test-backend-node.js test object 
-2022-09-30 10:04:47 STATE: test-backend-node.js start object 
-2022-09-30 10:04:47 STATE: test-backend-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} 
-2022-09-30 10:04:47 STATE: test-backend-node.js event: image 
-2022-09-30 10:04:47 STATE: test-backend-node.js event: detect 
-2022-09-30 10:04:47 STATE: test-backend-node.js passed: detect: samples/in/ai-body.jpg object 
-2022-09-30 10:04:47 DATA:  test-backend-node.js  result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
-2022-09-30 10:04:47 DATA:  test-backend-node.js  result: performance: load: null total: 231 
-2022-09-30 10:04:47 STATE: test-backend-node.js passed: centernet 
-2022-09-30 10:04:47 STATE: test-backend-node.js start object 
-2022-09-30 10:04:48 STATE: test-backend-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} 
-2022-09-30 10:04:48 STATE: test-backend-node.js event: image 
-2022-09-30 10:04:48 STATE: test-backend-node.js event: detect 
-2022-09-30 10:04:48 STATE: test-backend-node.js passed: detect: samples/in/ai-body.jpg object 
-2022-09-30 10:04:48 DATA:  test-backend-node.js  result: face: 1 body: 1 hand: 1 gesture: 7 object: 3 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.86,"class":"person"} {"score":0.92,"keypoints":17} 
-2022-09-30 10:04:48 DATA:  test-backend-node.js  result: performance: load: null total: 215 
-2022-09-30 10:04:48 STATE: test-backend-node.js passed: nanodet 
-2022-09-30 10:04:48 INFO:  test-backend-node.js test sensitive 
-2022-09-30 10:04:48 STATE: test-backend-node.js start sensitive 
-2022-09-30 10:04:49 STATE: test-backend-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} 
-2022-09-30 10:04:49 STATE: test-backend-node.js event: image 
-2022-09-30 10:04:49 STATE: test-backend-node.js event: detect 
-2022-09-30 10:04:49 STATE: test-backend-node.js passed: detect: samples/in/ai-body.jpg sensitive 
-2022-09-30 10:04:49 DATA:  test-backend-node.js  result: face: 1 body: 1 hand: 2 gesture: 9 object: 0 person: 1 {"score":1,"age":23.7,"gender":"female"} {} {"score":0.92,"keypoints":17} 
-2022-09-30 10:04:49 DATA:  test-backend-node.js  result: performance: load: null total: 171 
-2022-09-30 10:04:49 STATE: test-backend-node.js passed: sensitive result match 
-2022-09-30 10:04:49 STATE: test-backend-node.js passed: sensitive face result match 
-2022-09-30 10:04:49 STATE: test-backend-node.js passed: sensitive face emotion result [{"score":0.59,"emotion":"angry"},{"score":0.29,"emotion":"fear"}] 
-2022-09-30 10:04:49 STATE: test-backend-node.js passed: sensitive body result match 
-2022-09-30 10:04:49 STATE: test-backend-node.js passed: sensitive hand result match 
-2022-09-30 10:04:49 INFO:  test-backend-node.js test body 
-2022-09-30 10:04:49 STATE: test-backend-node.js start blazepose 
-2022-09-30 10:04:51 STATE: test-backend-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} 
-2022-09-30 10:04:51 STATE: test-backend-node.js event: image 
-2022-09-30 10:04:51 STATE: test-backend-node.js event: detect 
-2022-09-30 10:04:51 STATE: test-backend-node.js passed: detect: samples/in/ai-body.jpg blazepose 
-2022-09-30 10:04:51 DATA:  test-backend-node.js  result: face: 1 body: 1 hand: 2 gesture: 9 object: 0 person: 1 {"score":1,"age":23.7,"gender":"female"} {} {"score":0.99,"keypoints":39} 
-2022-09-30 10:04:51 DATA:  test-backend-node.js  result: performance: load: null total: 219 
-2022-09-30 10:04:51 STATE: test-backend-node.js passed: blazepose 
-2022-09-30 10:04:51 STATE: test-backend-node.js start efficientpose 
-2022-09-30 10:04:52 STATE: test-backend-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} 
-2022-09-30 10:04:52 STATE: test-backend-node.js event: image 
-2022-09-30 10:04:52 STATE: test-backend-node.js event: detect 
-2022-09-30 10:04:52 STATE: test-backend-node.js passed: detect: samples/in/ai-body.jpg efficientpose 
-2022-09-30 10:04:52 DATA:  test-backend-node.js  result: face: 1 body: 1 hand: 2 gesture: 9 object: 0 person: 1 {"score":1,"age":23.7,"gender":"female"} {} {"score":0.75,"keypoints":13} 
-2022-09-30 10:04:52 DATA:  test-backend-node.js  result: performance: load: null total: 236 
-2022-09-30 10:04:52 STATE: test-backend-node.js passed: efficientpose 
-2022-09-30 10:04:52 STATE: test-backend-node.js start posenet 
-2022-09-30 10:04:53 STATE: test-backend-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} 
-2022-09-30 10:04:53 STATE: test-backend-node.js event: image 
-2022-09-30 10:04:53 STATE: test-backend-node.js event: detect 
-2022-09-30 10:04:53 STATE: test-backend-node.js passed: detect: samples/in/ai-body.jpg posenet 
-2022-09-30 10:04:53 DATA:  test-backend-node.js  result: face: 1 body: 1 hand: 2 gesture: 9 object: 0 person: 1 {"score":1,"age":23.7,"gender":"female"} {} {"score":0.96,"keypoints":16} 
-2022-09-30 10:04:53 DATA:  test-backend-node.js  result: performance: load: null total: 169 
-2022-09-30 10:04:53 STATE: test-backend-node.js passed: posenet 
-2022-09-30 10:04:53 STATE: test-backend-node.js start movenet 
-2022-09-30 10:04:53 STATE: test-backend-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} 
-2022-09-30 10:04:53 STATE: test-backend-node.js event: image 
-2022-09-30 10:04:53 STATE: test-backend-node.js event: detect 
-2022-09-30 10:04:53 STATE: test-backend-node.js passed: detect: samples/in/ai-body.jpg movenet 
-2022-09-30 10:04:53 DATA:  test-backend-node.js  result: face: 1 body: 1 hand: 2 gesture: 9 object: 0 person: 1 {"score":1,"age":23.7,"gender":"female"} {} {"score":0.92,"keypoints":17} 
-2022-09-30 10:04:53 DATA:  test-backend-node.js  result: performance: load: null total: 170 
-2022-09-30 10:04:53 STATE: test-backend-node.js passed: movenet 
-2022-09-30 10:04:53 INFO:  test-backend-node.js test face matching 
-2022-09-30 10:04:53 STATE: test-backend-node.js passed: face database 40 
-2022-09-30 10:04:53 STATE: test-backend-node.js passed: face match {"first":{"index":4,"similarity":0.7827852615252829}} {"second":{"index":4,"similarity":0.5002052633015844}} {"third":{"index":4,"similarity":0.5401587887998899}} 
-2022-09-30 10:04:53 INFO:  test-backend-node.js test face similarity alternative 
-2022-09-30 10:04:53 STATE: test-backend-node.js start face embeddings 
-2022-09-30 10:04:54 STATE: test-backend-node.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} 
-2022-09-30 10:04:54 STATE: test-backend-node.js event: image 
-2022-09-30 10:04:54 STATE: test-backend-node.js event: detect 
-2022-09-30 10:04:54 STATE: test-backend-node.js passed: detect: samples/in/ai-face.jpg face embeddings 
-2022-09-30 10:04:54 DATA:  test-backend-node.js  result: face: 1 body: 1 hand: 2 gesture: 8 object: 0 person: 1 {"score":1,"age":23.5,"gender":"female"} {} {"score":0.47,"keypoints":3} 
-2022-09-30 10:04:54 DATA:  test-backend-node.js  result: performance: load: null total: 183 
-2022-09-30 10:04:54 STATE: test-backend-node.js passed: mobilefacenet {"embedding":192} 
-2022-09-30 10:04:54 STATE: test-backend-node.js start face embeddings 
-2022-09-30 10:04:55 STATE: test-backend-node.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} 
-2022-09-30 10:04:55 STATE: test-backend-node.js event: image 
-2022-09-30 10:04:55 STATE: test-backend-node.js event: detect 
-2022-09-30 10:04:55 STATE: test-backend-node.js passed: detect: samples/in/ai-face.jpg face embeddings 
-2022-09-30 10:04:55 DATA:  test-backend-node.js  result: face: 1 body: 1 hand: 2 gesture: 8 object: 0 person: 1 {"score":1,"age":23.5,"gender":"female"} {} {"score":0.47,"keypoints":3} 
-2022-09-30 10:04:55 DATA:  test-backend-node.js  result: performance: load: null total: 195 
-2022-09-30 10:04:55 STATE: test-backend-node.js passed: insightface {"embedding":512} 
-2022-09-30 10:04:55 INFO:  test-backend-node.js test face attention 
-2022-09-30 10:04:55 STATE: test-backend-node.js start face attention 
-2022-09-30 10:04:55 STATE: test-backend-node.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} 
-2022-09-30 10:04:56 STATE: test-backend-node.js event: image 
-2022-09-30 10:04:56 STATE: test-backend-node.js event: detect 
-2022-09-30 10:04:56 STATE: test-backend-node.js passed: detect: samples/in/ai-face.jpg face attention 
-2022-09-30 10:04:56 DATA:  test-backend-node.js  result: face: 1 body: 1 hand: 2 gesture: 8 object: 0 person: 1 {"score":1,"age":23.5,"gender":"female"} {} {"score":0.47,"keypoints":3} 
-2022-09-30 10:04:56 DATA:  test-backend-node.js  result: performance: load: null total: 175 
-2022-09-30 10:04:56 STATE: test-backend-node.js passed: face attention 
-2022-09-30 10:04:56 INFO:  test-backend-node.js test detectors 
-2022-09-30 10:04:56 STATE: test-backend-node.js start detectors 
-2022-09-30 10:04:56 STATE: test-backend-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} 
-2022-09-30 10:04:56 STATE: test-backend-node.js event: image 
-2022-09-30 10:04:56 STATE: test-backend-node.js event: detect 
-2022-09-30 10:04:56 STATE: test-backend-node.js passed: detect: samples/in/ai-body.jpg detectors 
-2022-09-30 10:04:56 DATA:  test-backend-node.js  result: face: 1 body: 1 hand: 1 gesture: 0 object: 0 person: 1 {"score":0.93,"gender":"unknown"} {} {"score":0.92,"keypoints":17} 
-2022-09-30 10:04:56 DATA:  test-backend-node.js  result: performance: load: null total: 127 
-2022-09-30 10:04:56 STATE: test-backend-node.js passed: detector result face match 
-2022-09-30 10:04:56 STATE: test-backend-node.js passed: detector result hand match 
-2022-09-30 10:04:56 INFO:  test-backend-node.js test: multi-instance 
-2022-09-30 10:04:56 STATE: test-backend-node.js start multi instance 
-2022-09-30 10:04:56 STATE: test-backend-node.js event: image 
-2022-09-30 10:04:56 STATE: test-backend-node.js event: detect 
-2022-09-30 10:04:56 STATE: test-backend-node.js passed: detect: random multi instance 
-2022-09-30 10:04:56 DATA:  test-backend-node.js  result: face: 0 body: 1 hand: 0 gesture: 0 object: 0 person: 0 {} {} {"score":0,"keypoints":0} 
-2022-09-30 10:04:56 DATA:  test-backend-node.js  result: performance: load: null total: 87 
-2022-09-30 10:04:56 INFO:  test-backend-node.js test: first instance 
-2022-09-30 10:04:56 STATE: test-backend-node.js start multi instance 
-2022-09-30 10:04:56 STATE: test-backend-node.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151289024} 
-2022-09-30 10:04:56 STATE: test-backend-node.js passed: detect: samples/in/ai-upper.jpg multi instance 
-2022-09-30 10:04:56 DATA:  test-backend-node.js  result: face: 1 body: 1 hand: 0 gesture: 0 object: 0 person: 1 {"score":0.96,"gender":"unknown"} {} {"score":0.75,"keypoints":7} 
-2022-09-30 10:04:56 DATA:  test-backend-node.js  result: performance: load: null total: 108 
-2022-09-30 10:04:56 INFO:  test-backend-node.js test: second instance 
-2022-09-30 10:04:56 STATE: test-backend-node.js start multi instance 
-2022-09-30 10:04:56 STATE: test-backend-node.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151289024} 
-2022-09-30 10:04:56 STATE: test-backend-node.js passed: detect: samples/in/ai-upper.jpg multi instance 
-2022-09-30 10:04:56 DATA:  test-backend-node.js  result: face: 1 body: 1 hand: 0 gesture: 0 object: 0 person: 1 {"score":0.96,"gender":"unknown"} {} {"score":0.75,"keypoints":7} 
-2022-09-30 10:04:56 DATA:  test-backend-node.js  result: performance: load: null total: 91 
-2022-09-30 10:04:56 INFO:  test-backend-node.js test: concurrent 
-2022-09-30 10:04:56 STATE: test-backend-node.js start concurrent 
-2022-09-30 10:04:56 STATE: test-backend-node.js start concurrent 
-2022-09-30 10:04:56 STATE: test-backend-node.js start concurrent 
-2022-09-30 10:04:56 STATE: test-backend-node.js start concurrent 
-2022-09-30 10:04:56 STATE: test-backend-node.js start concurrent 
-2022-09-30 10:04:56 STATE: test-backend-node.js start concurrent 
-2022-09-30 10:04:56 STATE: test-backend-node.js start concurrent 
-2022-09-30 10:04:56 STATE: test-backend-node.js start concurrent 
-2022-09-30 10:04:56 STATE: test-backend-node.js start concurrent 
-2022-09-30 10:04:56 STATE: test-backend-node.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} 
-2022-09-30 10:04:56 STATE: test-backend-node.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} 
-2022-09-30 10:04:57 STATE: test-backend-node.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} 
-2022-09-30 10:04:57 STATE: test-backend-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} 
-2022-09-30 10:04:57 STATE: test-backend-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} 
-2022-09-30 10:04:57 STATE: test-backend-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} 
-2022-09-30 10:04:57 STATE: test-backend-node.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151289024} 
-2022-09-30 10:04:57 STATE: test-backend-node.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151289024} 
-2022-09-30 10:04:57 STATE: test-backend-node.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151289024} 
-2022-09-30 10:04:57 STATE: test-backend-node.js event: image 
-2022-09-30 10:04:57 STATE: test-backend-node.js event: image 
-2022-09-30 10:04:57 STATE: test-backend-node.js event: image 
-2022-09-30 10:04:58 STATE: test-backend-node.js event: detect 
-2022-09-30 10:04:58 STATE: test-backend-node.js passed: detect: samples/in/ai-upper.jpg concurrent 
-2022-09-30 10:04:58 DATA:  test-backend-node.js  result: face: 1 body: 1 hand: 0 gesture: 0 object: 0 person: 1 {"score":0.96,"gender":"unknown"} {} {"score":0.75,"keypoints":7} 
-2022-09-30 10:04:58 DATA:  test-backend-node.js  result: performance: load: null total: 835 
-2022-09-30 10:04:58 STATE: test-backend-node.js passed: detect: samples/in/ai-upper.jpg concurrent 
-2022-09-30 10:04:58 DATA:  test-backend-node.js  result: face: 1 body: 1 hand: 0 gesture: 0 object: 0 person: 1 {"score":0.96,"gender":"unknown"} {} {"score":0.75,"keypoints":7} 
-2022-09-30 10:04:58 DATA:  test-backend-node.js  result: performance: load: null total: 835 
-2022-09-30 10:04:58 STATE: test-backend-node.js passed: detect: samples/in/ai-upper.jpg concurrent 
-2022-09-30 10:04:58 DATA:  test-backend-node.js  result: face: 1 body: 1 hand: 0 gesture: 0 object: 0 person: 1 {"score":0.96,"gender":"unknown"} {} {"score":0.75,"keypoints":7} 
-2022-09-30 10:04:58 DATA:  test-backend-node.js  result: performance: load: null total: 835 
-2022-09-30 10:04:58 STATE: test-backend-node.js event: detect 
-2022-09-30 10:04:58 STATE: test-backend-node.js event: detect 
-2022-09-30 10:04:58 STATE: test-backend-node.js passed: detect: samples/in/ai-face.jpg concurrent 
-2022-09-30 10:04:58 DATA:  test-backend-node.js  result: face: 1 body: 1 hand: 1 gesture: 0 object: 0 person: 1 {"score":0.91,"gender":"unknown"} {} {"score":0.47,"keypoints":3} 
-2022-09-30 10:04:58 DATA:  test-backend-node.js  result: performance: load: null total: 835 
-2022-09-30 10:04:58 STATE: test-backend-node.js passed: detect: samples/in/ai-face.jpg concurrent 
-2022-09-30 10:04:58 DATA:  test-backend-node.js  result: face: 1 body: 1 hand: 1 gesture: 0 object: 0 person: 1 {"score":0.91,"gender":"unknown"} {} {"score":0.47,"keypoints":3} 
-2022-09-30 10:04:58 DATA:  test-backend-node.js  result: performance: load: null total: 835 
-2022-09-30 10:04:58 STATE: test-backend-node.js passed: detect: samples/in/ai-face.jpg concurrent 
-2022-09-30 10:04:58 DATA:  test-backend-node.js  result: face: 1 body: 1 hand: 1 gesture: 0 object: 0 person: 1 {"score":0.91,"gender":"unknown"} {} {"score":0.47,"keypoints":3} 
-2022-09-30 10:04:58 DATA:  test-backend-node.js  result: performance: load: null total: 835 
-2022-09-30 10:04:58 STATE: test-backend-node.js passed: detect: samples/in/ai-body.jpg concurrent 
-2022-09-30 10:04:58 DATA:  test-backend-node.js  result: face: 1 body: 1 hand: 1 gesture: 0 object: 0 person: 1 {"score":0.93,"gender":"unknown"} {} {"score":0.92,"keypoints":17} 
-2022-09-30 10:04:58 DATA:  test-backend-node.js  result: performance: load: null total: 835 
-2022-09-30 10:04:58 STATE: test-backend-node.js passed: detect: samples/in/ai-body.jpg concurrent 
-2022-09-30 10:04:58 DATA:  test-backend-node.js  result: face: 1 body: 1 hand: 1 gesture: 0 object: 0 person: 1 {"score":0.93,"gender":"unknown"} {} {"score":0.92,"keypoints":17} 
-2022-09-30 10:04:58 DATA:  test-backend-node.js  result: performance: load: null total: 835 
-2022-09-30 10:04:58 STATE: test-backend-node.js passed: detect: samples/in/ai-body.jpg concurrent 
-2022-09-30 10:04:58 DATA:  test-backend-node.js  result: face: 1 body: 1 hand: 1 gesture: 0 object: 0 person: 1 {"score":0.93,"gender":"unknown"} {} {"score":0.92,"keypoints":17} 
-2022-09-30 10:04:58 DATA:  test-backend-node.js  result: performance: load: null total: 835 
-2022-09-30 10:04:58 INFO:  test-backend-node.js test: monkey-patch 
-2022-09-30 10:04:58 STATE: test-backend-node.js event: image 
-2022-09-30 10:04:58 STATE: test-backend-node.js event: detect 
-2022-09-30 10:04:58 STATE: test-backend-node.js passed: monkey patch 
-2022-09-30 10:04:58 STATE: test-backend-node.js passed: segmentation [65536] 
-2022-09-30 10:04:58 STATE: test-backend-node.js passeed: equal usage 
-2022-09-30 10:04:58 INFO:  test-backend-node.js test: input compare 
-2022-09-30 10:04:58 STATE: test-backend-node.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} 
-2022-09-30 10:04:58 STATE: test-backend-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} 
-2022-09-30 10:04:58 STATE: test-backend-node.js passed: image compare 0 23.275441687091504 
-2022-09-30 10:04:58 INFO:  test-backend-node.js events: {"image":29,"detect":29,"warmup":2} 
-2022-09-30 10:04:58 INFO:  test-backend-node.js tensors 4147 
-2022-09-30 10:04:58 INFO:  test-backend-node.js test complete: 16423 ms 
-2022-09-30 10:04:58 INFO:  
-2022-09-30 10:04:58 INFO:  test-backend-node-gpu.js start 
-2022-09-30 10:04:59 INFO:  test-backend-node-gpu.js test: configuration validation 
-2022-09-30 10:04:59 STATE: test-backend-node-gpu.js passed: configuration default validation [] 
-2022-09-30 10:04:59 STATE: test-backend-node-gpu.js passed: configuration invalid validation [{"reason":"unknown property","where":"config.invalid = true"}] 
-2022-09-30 10:04:59 INFO:  test-backend-node-gpu.js test: model load 
-2022-09-30 10:04:59 STATE: test-backend-node-gpu.js passed: models loaded 23 12 [{"name":"ssrnetage","loaded":false,"url":null},{"name":"gear","loaded":false,"url":null},{"name":"blazeposedetect","loaded":false,"url":null},{"name":"blazepose","loaded":false,"url":null},{"name":"centernet","loaded":true,"url":"file://models/mb3-centernet.json"},{"name":"efficientpose","loaded":false,"url":null},{"name":"mobilefacenet","loaded":false,"url":null},{"name":"insightface","loaded":false,"url":null},{"name":"emotion","loaded":true,"url":"file://models/emotion.json"},{"name":"facedetect","loaded":true,"url":"file://models/blazeface.json"},{"name":"faceiris","loaded":true,"url":"file://models/iris.json"},{"name":"facemesh","loaded":true,"url":"file://models/facemesh.json"},{"name":"faceres","loaded":true,"url":"file://models/faceres.json"},{"name":"ssrnetgender","loaded":false,"url":null},{"name":"handpose","loaded":false,"url":null},{"name":"handskeleton","loaded":true,"url":"file://models/handlandmark-full.json"},{"name":"handtrack","loaded":true,"url":"file://models/handtrack.json"},{"name":"liveness","loaded":true,"url":"file://models/liveness.json"},{"name":"movenet","loaded":true,"url":"file://models/movenet-lightning.json"},{"name":"nanodet","loaded":false,"url":null},{"name":"posenet","loaded":false,"url":null},{"name":"segmentation","loaded":true,"url":"file://models/selfie.json"},{"name":"antispoof","loaded":true,"url":"file://models/antispoof.json"}] 
-2022-09-30 10:04:59 INFO:  test-backend-node-gpu.js memory: {"memory":{"unreliable":true,"numTensors":1921,"numDataBuffers":1921,"numBytes":63673064}} 
-2022-09-30 10:04:59 INFO:  test-backend-node-gpu.js state: {"state":{"registeredVariables":{},"nextTapeNodeId":0,"numBytes":63673064,"numTensors":1921,"numStringTensors":0,"numDataBuffers":1921,"gradientDepth":0,"kernelDepth":0,"scopeStack":[],"numDataMovesStack":[],"nextScopeId":0,"tensorInfo":{},"profiling":false,"activeProfile":{"newBytes":0,"newTensors":0,"peakBytes":0,"kernels":[],"result":null,"kernelNames":[]}}} 
-2022-09-30 10:04:59 INFO:  test-backend-node-gpu.js test: warmup 
-2022-09-30 10:04:59 STATE: test-backend-node-gpu.js passed: create human 
-2022-09-30 10:04:59 INFO:  test-backend-node-gpu.js human version: 2.11.0 
-2022-09-30 10:04:59 INFO:  test-backend-node-gpu.js platform: linux x64 agent: NodeJS v18.10.0 
-2022-09-30 10:04:59 INFO:  test-backend-node-gpu.js tfjs version: 3.20.0 
-2022-09-30 10:04:59 INFO:  test-backend-node-gpu.js env: {"browser":false,"node":true,"platform":"linux x64","agent":"NodeJS v18.10.0","backends":["cpu","tensorflow"],"initial":false,"tfjs":{"version":"3.20.0"},"offscreen":false,"perfadd":false,"tensorflow":{"version":"2.7.3-dev20220521","gpu":true},"wasm":{"supported":true,"backend":false},"webgl":{"supported":false,"backend":false},"webgpu":{"supported":false,"backend":false},"cpu":{"flags":[]},"kernels":169} 
-2022-09-30 10:04:59 STATE: test-backend-node-gpu.js passed: set backend: tensorflow 
-2022-09-30 10:04:59 STATE: test-backend-node-gpu.js tensors 1921 
-2022-09-30 10:04:59 STATE: test-backend-node-gpu.js passed: load models 
-2022-09-30 10:04:59 STATE: test-backend-node-gpu.js  result: defined models: 23 loaded models: 12 
-2022-09-30 10:04:59 STATE: test-backend-node-gpu.js passed: warmup: none default 
-2022-09-30 10:04:59 DATA:  test-backend-node-gpu.js  result: face: 0 body: 0 hand: 0 gesture: 0 object: 0 person: 0 {} {} {} 
-2022-09-30 10:04:59 DATA:  test-backend-node-gpu.js  result: performance: load: null total: null 
-2022-09-30 10:04:59 STATE: test-backend-node-gpu.js passed: warmup none result match 
-2022-09-30 10:05:00 STATE: test-backend-node-gpu.js event: image 
-2022-09-30 10:05:03 STATE: test-backend-node-gpu.js event: detect 
-2022-09-30 10:05:03 STATE: test-backend-node-gpu.js event: warmup 
-2022-09-30 10:05:03 STATE: test-backend-node-gpu.js passed: warmup: face default 
-2022-09-30 10:05:03 DATA:  test-backend-node-gpu.js  result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.5,"gender":"female"} {"score":0.82,"class":"person"} {"score":0.42,"keypoints":4} 
-2022-09-30 10:05:03 DATA:  test-backend-node-gpu.js  result: performance: load: null total: 3145 
-2022-09-30 10:05:03 STATE: test-backend-node-gpu.js passed: warmup face result match 
-2022-09-30 10:05:03 STATE: test-backend-node-gpu.js event: image 
-2022-09-30 10:05:03 STATE: test-backend-node-gpu.js event: detect 
-2022-09-30 10:05:03 STATE: test-backend-node-gpu.js event: warmup 
-2022-09-30 10:05:03 STATE: test-backend-node-gpu.js passed: warmup: body default 
-2022-09-30 10:05:03 DATA:  test-backend-node-gpu.js  result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
-2022-09-30 10:05:03 DATA:  test-backend-node-gpu.js  result: performance: load: null total: 143 
-2022-09-30 10:05:03 STATE: test-backend-node-gpu.js passed: warmup body result match 
-2022-09-30 10:05:03 STATE: test-backend-node-gpu.js details: {"face":{"boxScore":0.92,"faceScore":1,"age":23.7,"gender":"female","genderScore":0.97},"emotion":[{"score":0.63,"emotion":"angry"},{"score":0.22,"emotion":"fear"}],"body":{"score":0.92,"keypoints":17},"hand":{"boxScore":0.52,"fingerScore":0.73,"keypoints":21},"gestures":[{"face":0,"gesture":"facing right"},{"face":0,"gesture":"mouth 10% open"},{"hand":0,"gesture":"pinky forward"},{"hand":0,"gesture":"palm up"},{"hand":0,"gesture":"open palm"},{"iris":0,"gesture":"looking left"},{"iris":0,"gesture":"looking up"}]} 
-2022-09-30 10:05:03 INFO:  test-backend-node-gpu.js test: details verification 
-2022-09-30 10:05:03 STATE: test-backend-node-gpu.js start default 
-2022-09-30 10:05:03 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796928} 
-2022-09-30 10:05:03 STATE: test-backend-node-gpu.js event: image 
-2022-09-30 10:05:03 STATE: test-backend-node-gpu.js event: detect 
-2022-09-30 10:05:03 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-body.jpg default 
-2022-09-30 10:05:03 DATA:  test-backend-node-gpu.js  result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
-2022-09-30 10:05:03 DATA:  test-backend-node-gpu.js  result: performance: load: null total: 132 
-2022-09-30 10:05:03 STATE: test-backend-node-gpu.js passed: details face length 1 
-2022-09-30 10:05:03 STATE: test-backend-node-gpu.js passed: details face score 1 0.93 1 
-2022-09-30 10:05:03 STATE: test-backend-node-gpu.js passed: details face age/gender 23.7 female 0.97 85.47 
-2022-09-30 10:05:03 STATE: test-backend-node-gpu.js passed: details face arrays 4 478 1024 
-2022-09-30 10:05:03 STATE: test-backend-node-gpu.js passed: details face emotion 2 {"score":0.59,"emotion":"angry"} 
-2022-09-30 10:05:03 STATE: test-backend-node-gpu.js passed: details face anti-spoofing 0.79 
-2022-09-30 10:05:03 STATE: test-backend-node-gpu.js passed: details face liveness 0.83 
-2022-09-30 10:05:03 STATE: test-backend-node-gpu.js passed: details body length 1 
-2022-09-30 10:05:03 STATE: test-backend-node-gpu.js passed: details body 0.92 17 6 
-2022-09-30 10:05:03 STATE: test-backend-node-gpu.js passed: details hand length 1 
-2022-09-30 10:05:03 STATE: test-backend-node-gpu.js passed: details hand 0.51 0.73 point 
-2022-09-30 10:05:03 STATE: test-backend-node-gpu.js passed: details hand arrays 21 5 7 
-2022-09-30 10:05:03 STATE: test-backend-node-gpu.js passed: details gesture length 7 
-2022-09-30 10:05:03 STATE: test-backend-node-gpu.js passed: details gesture first {"face":0,"gesture":"facing right"} 
-2022-09-30 10:05:03 STATE: test-backend-node-gpu.js passed: details object length 1 
-2022-09-30 10:05:03 STATE: test-backend-node-gpu.js passed: details object 0.72 person 
-2022-09-30 10:05:03 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,4] {"checksum":1371996928} 
-2022-09-30 10:05:03 STATE: test-backend-node-gpu.js event: image 
-2022-09-30 10:05:03 STATE: test-backend-node-gpu.js event: detect 
-2022-09-30 10:05:03 STATE: test-backend-node-gpu.js passed: tensor shape: [1,1200,1200,4] dtype: float32 
-2022-09-30 10:05:04 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-body.jpg [1200,1200,4] {"checksum":1371996928} 
-2022-09-30 10:05:04 STATE: test-backend-node-gpu.js event: image 
-2022-09-30 10:05:04 STATE: test-backend-node-gpu.js event: detect 
-2022-09-30 10:05:04 STATE: test-backend-node-gpu.js passed: tensor shape: [1200,1200,4] dtype: float32 
-2022-09-30 10:05:04 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796928} 
-2022-09-30 10:05:04 STATE: test-backend-node-gpu.js event: image 
-2022-09-30 10:05:04 STATE: test-backend-node-gpu.js event: detect 
-2022-09-30 10:05:04 STATE: test-backend-node-gpu.js passed: tensor shape: [1,1200,1200,3] dtype: float32 
-2022-09-30 10:05:04 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-body.jpg [1200,1200,3] {"checksum":1004796928} 
-2022-09-30 10:05:04 STATE: test-backend-node-gpu.js event: image 
-2022-09-30 10:05:04 STATE: test-backend-node-gpu.js event: detect 
-2022-09-30 10:05:04 STATE: test-backend-node-gpu.js passed: tensor shape: [1200,1200,3] dtype: float32 
-2022-09-30 10:05:05 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,4] {"checksum":1371996871} 
-2022-09-30 10:05:05 STATE: test-backend-node-gpu.js event: image 
-2022-09-30 10:05:05 STATE: test-backend-node-gpu.js event: detect 
-2022-09-30 10:05:05 STATE: test-backend-node-gpu.js passed: tensor shape: [1,1200,1200,4] dtype: int32 
-2022-09-30 10:05:05 INFO:  test-backend-node-gpu.js test default 
-2022-09-30 10:05:05 STATE: test-backend-node-gpu.js start async 
-2022-09-30 10:05:05 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796928} 
-2022-09-30 10:05:05 STATE: test-backend-node-gpu.js event: image 
-2022-09-30 10:05:05 STATE: test-backend-node-gpu.js event: detect 
-2022-09-30 10:05:05 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-body.jpg async 
-2022-09-30 10:05:05 DATA:  test-backend-node-gpu.js  result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
-2022-09-30 10:05:05 DATA:  test-backend-node-gpu.js  result: performance: load: null total: 121 
-2022-09-30 10:05:05 STATE: test-backend-node-gpu.js passed: default result face match 1 female 0.97 
-2022-09-30 10:05:05 INFO:  test-backend-node-gpu.js test sync 
-2022-09-30 10:05:05 STATE: test-backend-node-gpu.js start sync 
-2022-09-30 10:05:05 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796928} 
-2022-09-30 10:05:05 STATE: test-backend-node-gpu.js event: image 
-2022-09-30 10:05:05 STATE: test-backend-node-gpu.js event: detect 
-2022-09-30 10:05:05 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-body.jpg sync 
-2022-09-30 10:05:05 DATA:  test-backend-node-gpu.js  result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
-2022-09-30 10:05:05 DATA:  test-backend-node-gpu.js  result: performance: load: null total: 143 
-2022-09-30 10:05:05 STATE: test-backend-node-gpu.js passed: default sync 1 female 0.97 
-2022-09-30 10:05:05 INFO:  test-backend-node-gpu.js test: image process 
-2022-09-30 10:05:05 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} 
-2022-09-30 10:05:05 STATE: test-backend-node-gpu.js passed: image input null [1,256,256,3] 
-2022-09-30 10:05:05 INFO:  test-backend-node-gpu.js test: image null 
-2022-09-30 10:05:05 STATE: test-backend-node-gpu.js passed: invalid input could not convert input to tensor 
-2022-09-30 10:05:05 INFO:  test-backend-node-gpu.js test face similarity 
-2022-09-30 10:05:05 STATE: test-backend-node-gpu.js start face similarity 
-2022-09-30 10:05:05 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} 
-2022-09-30 10:05:05 STATE: test-backend-node-gpu.js event: image 
-2022-09-30 10:05:05 STATE: test-backend-node-gpu.js event: detect 
-2022-09-30 10:05:05 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-face.jpg face similarity 
-2022-09-30 10:05:05 DATA:  test-backend-node-gpu.js  result: face: 1 body: 1 hand: 1 gesture: 6 object: 1 person: 1 {"score":1,"age":23.5,"gender":"female"} {"score":0.82,"class":"person"} {"score":0.47,"keypoints":3} 
-2022-09-30 10:05:05 DATA:  test-backend-node-gpu.js  result: performance: load: null total: 128 
-2022-09-30 10:05:05 STATE: test-backend-node-gpu.js start face similarity 
-2022-09-30 10:05:06 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796928} 
-2022-09-30 10:05:06 STATE: test-backend-node-gpu.js event: image 
-2022-09-30 10:05:06 STATE: test-backend-node-gpu.js event: detect 
-2022-09-30 10:05:06 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-body.jpg face similarity 
-2022-09-30 10:05:06 DATA:  test-backend-node-gpu.js  result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
-2022-09-30 10:05:06 DATA:  test-backend-node-gpu.js  result: performance: load: null total: 139 
-2022-09-30 10:05:06 STATE: test-backend-node-gpu.js start face similarity 
-2022-09-30 10:05:06 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151289056} 
-2022-09-30 10:05:06 STATE: test-backend-node-gpu.js event: image 
-2022-09-30 10:05:06 STATE: test-backend-node-gpu.js event: detect 
-2022-09-30 10:05:06 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-upper.jpg face similarity 
-2022-09-30 10:05:06 DATA:  test-backend-node-gpu.js  result: face: 1 body: 1 hand: 0 gesture: 4 object: 1 person: 1 {"score":1,"age":23.5,"gender":"female"} {"score":0.71,"class":"person"} {"score":0.75,"keypoints":7} 
-2022-09-30 10:05:06 DATA:  test-backend-node-gpu.js  result: performance: load: null total: 129 
-2022-09-30 10:05:06 STATE: test-backend-node-gpu.js passed: face descriptor 
-2022-09-30 10:05:06 STATE: test-backend-node-gpu.js passed: face similarity {"similarity":[1,0.447238756461232,0.556914029877052],"descriptors":[1024,1024,1024]} 
-2022-09-30 10:05:06 INFO:  test-backend-node-gpu.js test object 
-2022-09-30 10:05:06 STATE: test-backend-node-gpu.js start object 
-2022-09-30 10:05:06 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796928} 
-2022-09-30 10:05:06 STATE: test-backend-node-gpu.js event: image 
-2022-09-30 10:05:06 STATE: test-backend-node-gpu.js event: detect 
-2022-09-30 10:05:06 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-body.jpg object 
-2022-09-30 10:05:06 DATA:  test-backend-node-gpu.js  result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
-2022-09-30 10:05:06 DATA:  test-backend-node-gpu.js  result: performance: load: null total: 130 
-2022-09-30 10:05:06 STATE: test-backend-node-gpu.js passed: centernet 
-2022-09-30 10:05:06 STATE: test-backend-node-gpu.js start object 
-2022-09-30 10:05:07 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796928} 
-2022-09-30 10:05:07 STATE: test-backend-node-gpu.js event: image 
-2022-09-30 10:05:08 STATE: test-backend-node-gpu.js event: detect 
-2022-09-30 10:05:08 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-body.jpg object 
-2022-09-30 10:05:08 DATA:  test-backend-node-gpu.js  result: face: 1 body: 1 hand: 1 gesture: 7 object: 3 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.86,"class":"person"} {"score":0.92,"keypoints":17} 
-2022-09-30 10:05:08 DATA:  test-backend-node-gpu.js  result: performance: load: null total: 560 
-2022-09-30 10:05:08 STATE: test-backend-node-gpu.js passed: nanodet 
-2022-09-30 10:05:08 INFO:  test-backend-node-gpu.js test sensitive 
-2022-09-30 10:05:08 STATE: test-backend-node-gpu.js start sensitive 
-2022-09-30 10:05:08 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796928} 
-2022-09-30 10:05:08 STATE: test-backend-node-gpu.js event: image 
-2022-09-30 10:05:08 STATE: test-backend-node-gpu.js event: detect 
-2022-09-30 10:05:08 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-body.jpg sensitive 
-2022-09-30 10:05:08 DATA:  test-backend-node-gpu.js  result: face: 1 body: 1 hand: 2 gesture: 9 object: 0 person: 1 {"score":1,"age":23.7,"gender":"female"} {} {"score":0.92,"keypoints":17} 
-2022-09-30 10:05:08 DATA:  test-backend-node-gpu.js  result: performance: load: null total: 115 
-2022-09-30 10:05:08 STATE: test-backend-node-gpu.js passed: sensitive result match 
-2022-09-30 10:05:08 STATE: test-backend-node-gpu.js passed: sensitive face result match 
-2022-09-30 10:05:08 STATE: test-backend-node-gpu.js passed: sensitive face emotion result [{"score":0.59,"emotion":"angry"},{"score":0.29,"emotion":"fear"}] 
-2022-09-30 10:05:08 STATE: test-backend-node-gpu.js passed: sensitive body result match 
-2022-09-30 10:05:08 STATE: test-backend-node-gpu.js passed: sensitive hand result match 
-2022-09-30 10:05:08 INFO:  test-backend-node-gpu.js test body 
-2022-09-30 10:05:08 STATE: test-backend-node-gpu.js start blazepose 
-2022-09-30 10:05:10 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796928} 
-2022-09-30 10:05:10 STATE: test-backend-node-gpu.js event: image 
-2022-09-30 10:05:10 STATE: test-backend-node-gpu.js event: detect 
-2022-09-30 10:05:10 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-body.jpg blazepose 
-2022-09-30 10:05:10 DATA:  test-backend-node-gpu.js  result: face: 1 body: 1 hand: 2 gesture: 9 object: 0 person: 1 {"score":1,"age":23.7,"gender":"female"} {} {"score":0.99,"keypoints":39} 
-2022-09-30 10:05:10 DATA:  test-backend-node-gpu.js  result: performance: load: null total: 265 
-2022-09-30 10:05:10 STATE: test-backend-node-gpu.js passed: blazepose 
-2022-09-30 10:05:10 STATE: test-backend-node-gpu.js start efficientpose 
-2022-09-30 10:05:11 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796928} 
-2022-09-30 10:05:11 STATE: test-backend-node-gpu.js event: image 
-2022-09-30 10:05:11 STATE: test-backend-node-gpu.js event: detect 
-2022-09-30 10:05:11 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-body.jpg efficientpose 
-2022-09-30 10:05:11 DATA:  test-backend-node-gpu.js  result: face: 1 body: 1 hand: 2 gesture: 9 object: 0 person: 1 {"score":1,"age":23.7,"gender":"female"} {} {"score":0.75,"keypoints":13} 
-2022-09-30 10:05:11 DATA:  test-backend-node-gpu.js  result: performance: load: null total: 740 
-2022-09-30 10:05:11 STATE: test-backend-node-gpu.js passed: efficientpose 
-2022-09-30 10:05:11 STATE: test-backend-node-gpu.js start posenet 
-2022-09-30 10:05:12 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796928} 
-2022-09-30 10:05:12 STATE: test-backend-node-gpu.js event: image 
-2022-09-30 10:05:12 STATE: test-backend-node-gpu.js event: detect 
-2022-09-30 10:05:12 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-body.jpg posenet 
-2022-09-30 10:05:12 DATA:  test-backend-node-gpu.js  result: face: 1 body: 1 hand: 2 gesture: 9 object: 0 person: 1 {"score":1,"age":23.7,"gender":"female"} {} {"score":0.96,"keypoints":16} 
-2022-09-30 10:05:12 DATA:  test-backend-node-gpu.js  result: performance: load: null total: 144 
-2022-09-30 10:05:12 STATE: test-backend-node-gpu.js passed: posenet 
-2022-09-30 10:05:12 STATE: test-backend-node-gpu.js start movenet 
-2022-09-30 10:05:12 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796928} 
-2022-09-30 10:05:12 STATE: test-backend-node-gpu.js event: image 
-2022-09-30 10:05:12 STATE: test-backend-node-gpu.js event: detect 
-2022-09-30 10:05:12 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-body.jpg movenet 
-2022-09-30 10:05:12 DATA:  test-backend-node-gpu.js  result: face: 1 body: 1 hand: 2 gesture: 9 object: 0 person: 1 {"score":1,"age":23.7,"gender":"female"} {} {"score":0.92,"keypoints":17} 
-2022-09-30 10:05:12 DATA:  test-backend-node-gpu.js  result: performance: load: null total: 131 
-2022-09-30 10:05:12 STATE: test-backend-node-gpu.js passed: movenet 
-2022-09-30 10:05:12 INFO:  test-backend-node-gpu.js test face matching 
-2022-09-30 10:05:12 STATE: test-backend-node-gpu.js passed: face database 40 
-2022-09-30 10:05:12 STATE: test-backend-node-gpu.js passed: face match {"first":{"index":4,"similarity":0.7828184453007331}} {"second":{"index":4,"similarity":0.5001334216773398}} {"third":{"index":4,"similarity":0.5403054967489764}} 
-2022-09-30 10:05:12 INFO:  test-backend-node-gpu.js test face similarity alternative 
-2022-09-30 10:05:12 STATE: test-backend-node-gpu.js start face embeddings 
-2022-09-30 10:05:13 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} 
-2022-09-30 10:05:13 STATE: test-backend-node-gpu.js event: image 
-2022-09-30 10:05:13 STATE: test-backend-node-gpu.js event: detect 
-2022-09-30 10:05:13 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-face.jpg face embeddings 
-2022-09-30 10:05:13 DATA:  test-backend-node-gpu.js  result: face: 1 body: 1 hand: 2 gesture: 8 object: 0 person: 1 {"score":1,"age":23.5,"gender":"female"} {} {"score":0.47,"keypoints":3} 
-2022-09-30 10:05:13 DATA:  test-backend-node-gpu.js  result: performance: load: null total: 164 
-2022-09-30 10:05:13 STATE: test-backend-node-gpu.js passed: mobilefacenet {"embedding":192} 
-2022-09-30 10:05:13 STATE: test-backend-node-gpu.js start face embeddings 
-2022-09-30 10:05:14 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} 
-2022-09-30 10:05:14 STATE: test-backend-node-gpu.js event: image 
-2022-09-30 10:05:14 STATE: test-backend-node-gpu.js event: detect 
-2022-09-30 10:05:14 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-face.jpg face embeddings 
-2022-09-30 10:05:14 DATA:  test-backend-node-gpu.js  result: face: 1 body: 1 hand: 2 gesture: 8 object: 0 person: 1 {"score":1,"age":23.5,"gender":"female"} {} {"score":0.47,"keypoints":3} 
-2022-09-30 10:05:14 DATA:  test-backend-node-gpu.js  result: performance: load: null total: 178 
-2022-09-30 10:05:14 STATE: test-backend-node-gpu.js passed: insightface {"embedding":512} 
-2022-09-30 10:05:14 INFO:  test-backend-node-gpu.js test face attention 
-2022-09-30 10:05:14 STATE: test-backend-node-gpu.js start face attention 
-2022-09-30 10:05:14 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} 
-2022-09-30 10:05:14 STATE: test-backend-node-gpu.js event: image 
-2022-09-30 10:05:14 STATE: test-backend-node-gpu.js event: detect 
-2022-09-30 10:05:14 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-face.jpg face attention 
-2022-09-30 10:05:14 DATA:  test-backend-node-gpu.js  result: face: 1 body: 1 hand: 2 gesture: 8 object: 0 person: 1 {"score":1,"age":23.5,"gender":"female"} {} {"score":0.47,"keypoints":3} 
-2022-09-30 10:05:14 DATA:  test-backend-node-gpu.js  result: performance: load: null total: 243 
-2022-09-30 10:05:14 STATE: test-backend-node-gpu.js passed: face attention 
-2022-09-30 10:05:14 INFO:  test-backend-node-gpu.js test detectors 
-2022-09-30 10:05:14 STATE: test-backend-node-gpu.js start detectors 
-2022-09-30 10:05:14 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796928} 
-2022-09-30 10:05:14 STATE: test-backend-node-gpu.js event: image 
-2022-09-30 10:05:14 STATE: test-backend-node-gpu.js event: detect 
-2022-09-30 10:05:14 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-body.jpg detectors 
-2022-09-30 10:05:14 DATA:  test-backend-node-gpu.js  result: face: 1 body: 1 hand: 1 gesture: 0 object: 0 person: 1 {"score":0.93,"gender":"unknown"} {} {"score":0.92,"keypoints":17} 
-2022-09-30 10:05:14 DATA:  test-backend-node-gpu.js  result: performance: load: null total: 104 
-2022-09-30 10:05:14 STATE: test-backend-node-gpu.js passed: detector result face match 
-2022-09-30 10:05:14 STATE: test-backend-node-gpu.js passed: detector result hand match 
-2022-09-30 10:05:14 INFO:  test-backend-node-gpu.js test: multi-instance 
-2022-09-30 10:05:14 STATE: test-backend-node-gpu.js start multi instance 
-2022-09-30 10:05:15 STATE: test-backend-node-gpu.js event: image 
-2022-09-30 10:05:15 STATE: test-backend-node-gpu.js event: detect 
-2022-09-30 10:05:15 STATE: test-backend-node-gpu.js passed: detect: random multi instance 
-2022-09-30 10:05:15 DATA:  test-backend-node-gpu.js  result: face: 0 body: 1 hand: 0 gesture: 0 object: 0 person: 0 {} {} {"score":0,"keypoints":0} 
-2022-09-30 10:05:15 DATA:  test-backend-node-gpu.js  result: performance: load: null total: 60 
-2022-09-30 10:05:15 INFO:  test-backend-node-gpu.js test: first instance 
-2022-09-30 10:05:15 STATE: test-backend-node-gpu.js start multi instance 
-2022-09-30 10:05:15 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151289056} 
-2022-09-30 10:05:15 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-upper.jpg multi instance 
-2022-09-30 10:05:15 DATA:  test-backend-node-gpu.js  result: face: 1 body: 1 hand: 0 gesture: 0 object: 0 person: 1 {"score":0.96,"gender":"unknown"} {} {"score":0.75,"keypoints":7} 
-2022-09-30 10:05:15 DATA:  test-backend-node-gpu.js  result: performance: load: null total: 90 
-2022-09-30 10:05:15 INFO:  test-backend-node-gpu.js test: second instance 
-2022-09-30 10:05:15 STATE: test-backend-node-gpu.js start multi instance 
-2022-09-30 10:05:15 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151289056} 
-2022-09-30 10:05:15 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-upper.jpg multi instance 
-2022-09-30 10:05:15 DATA:  test-backend-node-gpu.js  result: face: 1 body: 1 hand: 0 gesture: 0 object: 0 person: 1 {"score":0.96,"gender":"unknown"} {} {"score":0.75,"keypoints":7} 
-2022-09-30 10:05:15 DATA:  test-backend-node-gpu.js  result: performance: load: null total: 77 
-2022-09-30 10:05:15 INFO:  test-backend-node-gpu.js test: concurrent 
-2022-09-30 10:05:15 STATE: test-backend-node-gpu.js start concurrent 
-2022-09-30 10:05:15 STATE: test-backend-node-gpu.js start concurrent 
-2022-09-30 10:05:15 STATE: test-backend-node-gpu.js start concurrent 
-2022-09-30 10:05:15 STATE: test-backend-node-gpu.js start concurrent 
-2022-09-30 10:05:15 STATE: test-backend-node-gpu.js start concurrent 
-2022-09-30 10:05:15 STATE: test-backend-node-gpu.js start concurrent 
-2022-09-30 10:05:15 STATE: test-backend-node-gpu.js start concurrent 
-2022-09-30 10:05:15 STATE: test-backend-node-gpu.js start concurrent 
-2022-09-30 10:05:15 STATE: test-backend-node-gpu.js start concurrent 
-2022-09-30 10:05:15 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} 
-2022-09-30 10:05:15 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} 
-2022-09-30 10:05:15 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} 
-2022-09-30 10:05:15 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796928} 
-2022-09-30 10:05:15 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796928} 
-2022-09-30 10:05:15 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796928} 
-2022-09-30 10:05:16 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151289056} 
-2022-09-30 10:05:16 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151289056} 
-2022-09-30 10:05:16 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151289056} 
-2022-09-30 10:05:16 STATE: test-backend-node-gpu.js event: image 
-2022-09-30 10:05:16 STATE: test-backend-node-gpu.js event: image 
-2022-09-30 10:05:16 STATE: test-backend-node-gpu.js event: image 
-2022-09-30 10:05:16 STATE: test-backend-node-gpu.js event: detect 
-2022-09-30 10:05:16 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-upper.jpg concurrent 
-2022-09-30 10:05:16 DATA:  test-backend-node-gpu.js  result: face: 1 body: 1 hand: 0 gesture: 0 object: 0 person: 1 {"score":0.96,"gender":"unknown"} {} {"score":0.75,"keypoints":7} 
-2022-09-30 10:05:16 DATA:  test-backend-node-gpu.js  result: performance: load: null total: 656 
-2022-09-30 10:05:16 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-upper.jpg concurrent 
-2022-09-30 10:05:16 DATA:  test-backend-node-gpu.js  result: face: 1 body: 1 hand: 0 gesture: 0 object: 0 person: 1 {"score":0.96,"gender":"unknown"} {} {"score":0.75,"keypoints":7} 
-2022-09-30 10:05:16 DATA:  test-backend-node-gpu.js  result: performance: load: null total: 656 
-2022-09-30 10:05:16 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-upper.jpg concurrent 
-2022-09-30 10:05:16 DATA:  test-backend-node-gpu.js  result: face: 1 body: 1 hand: 0 gesture: 0 object: 0 person: 1 {"score":0.96,"gender":"unknown"} {} {"score":0.75,"keypoints":7} 
-2022-09-30 10:05:16 DATA:  test-backend-node-gpu.js  result: performance: load: null total: 656 
-2022-09-30 10:05:16 STATE: test-backend-node-gpu.js event: detect 
-2022-09-30 10:05:16 STATE: test-backend-node-gpu.js event: detect 
-2022-09-30 10:05:16 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-face.jpg concurrent 
-2022-09-30 10:05:16 DATA:  test-backend-node-gpu.js  result: face: 1 body: 1 hand: 1 gesture: 0 object: 0 person: 1 {"score":0.91,"gender":"unknown"} {} {"score":0.47,"keypoints":3} 
-2022-09-30 10:05:16 DATA:  test-backend-node-gpu.js  result: performance: load: null total: 657 
-2022-09-30 10:05:16 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-face.jpg concurrent 
-2022-09-30 10:05:16 DATA:  test-backend-node-gpu.js  result: face: 1 body: 1 hand: 1 gesture: 0 object: 0 person: 1 {"score":0.91,"gender":"unknown"} {} {"score":0.47,"keypoints":3} 
-2022-09-30 10:05:16 DATA:  test-backend-node-gpu.js  result: performance: load: null total: 657 
-2022-09-30 10:05:16 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-face.jpg concurrent 
-2022-09-30 10:05:16 DATA:  test-backend-node-gpu.js  result: face: 1 body: 1 hand: 1 gesture: 0 object: 0 person: 1 {"score":0.91,"gender":"unknown"} {} {"score":0.47,"keypoints":3} 
-2022-09-30 10:05:16 DATA:  test-backend-node-gpu.js  result: performance: load: null total: 656 
-2022-09-30 10:05:16 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-body.jpg concurrent 
-2022-09-30 10:05:16 DATA:  test-backend-node-gpu.js  result: face: 1 body: 1 hand: 1 gesture: 0 object: 0 person: 1 {"score":0.93,"gender":"unknown"} {} {"score":0.92,"keypoints":17} 
-2022-09-30 10:05:16 DATA:  test-backend-node-gpu.js  result: performance: load: null total: 657 
-2022-09-30 10:05:16 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-body.jpg concurrent 
-2022-09-30 10:05:16 DATA:  test-backend-node-gpu.js  result: face: 1 body: 1 hand: 1 gesture: 0 object: 0 person: 1 {"score":0.93,"gender":"unknown"} {} {"score":0.92,"keypoints":17} 
-2022-09-30 10:05:16 DATA:  test-backend-node-gpu.js  result: performance: load: null total: 657 
-2022-09-30 10:05:16 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-body.jpg concurrent 
-2022-09-30 10:05:16 DATA:  test-backend-node-gpu.js  result: face: 1 body: 1 hand: 1 gesture: 0 object: 0 person: 1 {"score":0.93,"gender":"unknown"} {} {"score":0.92,"keypoints":17} 
-2022-09-30 10:05:16 DATA:  test-backend-node-gpu.js  result: performance: load: null total: 656 
-2022-09-30 10:05:16 INFO:  test-backend-node-gpu.js test: monkey-patch 
-2022-09-30 10:05:16 STATE: test-backend-node-gpu.js event: image 
-2022-09-30 10:05:16 STATE: test-backend-node-gpu.js event: detect 
-2022-09-30 10:05:16 STATE: test-backend-node-gpu.js passed: monkey patch 
-2022-09-30 10:05:17 STATE: test-backend-node-gpu.js passed: segmentation [65536] 
-2022-09-30 10:05:17 STATE: test-backend-node-gpu.js passeed: equal usage 
-2022-09-30 10:05:17 INFO:  test-backend-node-gpu.js test: input compare 
-2022-09-30 10:05:17 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} 
-2022-09-30 10:05:17 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796928} 
-2022-09-30 10:05:17 STATE: test-backend-node-gpu.js passed: image compare 0 23.275441687091504 
-2022-09-30 10:05:17 INFO:  test-backend-node-gpu.js events: {"image":29,"detect":29,"warmup":2} 
-2022-09-30 10:05:17 INFO:  test-backend-node-gpu.js tensors 4147 
-2022-09-30 10:05:17 INFO:  test-backend-node-gpu.js test complete: 17677 ms 
-2022-09-30 10:05:17 INFO:  
-2022-09-30 10:05:17 INFO:  test-backend-node-wasm.js start 
-2022-09-30 10:05:18 DATA:  test-backend-node-wasm.js stdout: 2022-09-30 10:05:18 INFO:  { supported: true, backend: true, simd: true, multithread: false } https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-backend-wasm@3.20.0/dist/ 
-2022-09-30 10:05:18 STATE: test-backend-node-wasm.js passed: model server: https://vladmandic.github.io/human/models/ 
-2022-09-30 10:05:18 INFO:  test-backend-node-wasm.js test: configuration validation 
-2022-09-30 10:05:18 STATE: test-backend-node-wasm.js passed: configuration default validation [] 
-2022-09-30 10:05:18 STATE: test-backend-node-wasm.js passed: configuration invalid validation [{"reason":"unknown property","where":"config.invalid = true"}] 
-2022-09-30 10:05:18 INFO:  test-backend-node-wasm.js test: model load 
-2022-09-30 10:05:20 STATE: test-backend-node-wasm.js passed: models loaded 23 12 [{"name":"ssrnetage","loaded":false,"url":null},{"name":"gear","loaded":false,"url":null},{"name":"blazeposedetect","loaded":false,"url":null},{"name":"blazepose","loaded":false,"url":null},{"name":"centernet","loaded":true,"url":"https://vladmandic.github.io/human/models/mb3-centernet.json"},{"name":"efficientpose","loaded":false,"url":null},{"name":"mobilefacenet","loaded":false,"url":null},{"name":"insightface","loaded":false,"url":null},{"name":"emotion","loaded":true,"url":"https://vladmandic.github.io/human/models/emotion.json"},{"name":"facedetect","loaded":true,"url":"https://vladmandic.github.io/human/models/blazeface.json"},{"name":"faceiris","loaded":true,"url":"https://vladmandic.github.io/human/models/iris.json"},{"name":"facemesh","loaded":true,"url":"https://vladmandic.github.io/human/models/facemesh.json"},{"name":"faceres","loaded":true,"url":"https://vladmandic.github.io/human/models/faceres.json"},{"name":"ssrnetgender","loaded":false,"url":null},{"name":"handpose","loaded":false,"url":null},{"name":"handskeleton","loaded":true,"url":"https://vladmandic.github.io/human/models/handlandmark-full.json"},{"name":"handtrack","loaded":true,"url":"https://vladmandic.github.io/human/models/handtrack.json"},{"name":"liveness","loaded":true,"url":"https://vladmandic.github.io/human/models/liveness.json"},{"name":"movenet","loaded":true,"url":"https://vladmandic.github.io/human/models/movenet-lightning.json"},{"name":"nanodet","loaded":false,"url":null},{"name":"posenet","loaded":false,"url":null},{"name":"segmentation","loaded":true,"url":"https://vladmandic.github.io/human/models/selfie.json"},{"name":"antispoof","loaded":true,"url":"https://vladmandic.github.io/human/models/antispoof.json"}] 
-2022-09-30 10:05:20 INFO:  test-backend-node-wasm.js memory: {"memory":{"unreliable":false,"numTensors":1921,"numDataBuffers":1921,"numBytes":63673064}} 
-2022-09-30 10:05:20 INFO:  test-backend-node-wasm.js state: {"state":{"registeredVariables":{},"nextTapeNodeId":0,"numBytes":63673064,"numTensors":1921,"numStringTensors":0,"numDataBuffers":1921,"gradientDepth":0,"kernelDepth":0,"scopeStack":[],"numDataMovesStack":[],"nextScopeId":0,"tensorInfo":{},"profiling":false,"activeProfile":{"newBytes":0,"newTensors":0,"peakBytes":0,"kernels":[],"result":null,"kernelNames":[]}}} 
-2022-09-30 10:05:20 INFO:  test-backend-node-wasm.js test: warmup 
-2022-09-30 10:05:20 STATE: test-backend-node-wasm.js passed: create human 
-2022-09-30 10:05:20 INFO:  test-backend-node-wasm.js human version: 2.11.0 
-2022-09-30 10:05:20 INFO:  test-backend-node-wasm.js platform: linux x64 agent: NodeJS v18.10.0 
-2022-09-30 10:05:20 INFO:  test-backend-node-wasm.js tfjs version: 3.20.0 
-2022-09-30 10:05:20 INFO:  test-backend-node-wasm.js env: {"browser":false,"node":true,"platform":"linux x64","agent":"NodeJS v18.10.0","backends":["cpu","wasm"],"initial":false,"tfjs":{"version":"3.20.0"},"offscreen":false,"perfadd":false,"tensorflow":{},"wasm":{"supported":true,"backend":true,"simd":true,"multithread":false},"webgl":{"supported":false,"backend":false},"webgpu":{"supported":false,"backend":false},"cpu":{"flags":[]},"kernels":126} 
-2022-09-30 10:05:20 STATE: test-backend-node-wasm.js passed: set backend: wasm 
-2022-09-30 10:05:20 STATE: test-backend-node-wasm.js tensors 1921 
-2022-09-30 10:05:20 STATE: test-backend-node-wasm.js passed: load models 
-2022-09-30 10:05:20 STATE: test-backend-node-wasm.js  result: defined models: 23 loaded models: 12 
-2022-09-30 10:05:20 STATE: test-backend-node-wasm.js passed: warmup: none default 
-2022-09-30 10:05:20 DATA:  test-backend-node-wasm.js  result: face: 0 body: 0 hand: 0 gesture: 0 object: 0 person: 0 {} {} {} 
-2022-09-30 10:05:20 DATA:  test-backend-node-wasm.js  result: performance: load: null total: null 
-2022-09-30 10:05:20 STATE: test-backend-node-wasm.js passed: warmup none result match 
-2022-09-30 10:05:20 STATE: test-backend-node-wasm.js event: image 
-2022-09-30 10:05:20 STATE: test-backend-node-wasm.js event: detect 
-2022-09-30 10:05:20 STATE: test-backend-node-wasm.js event: warmup 
-2022-09-30 10:05:20 STATE: test-backend-node-wasm.js passed: warmup: face default 
-2022-09-30 10:05:20 DATA:  test-backend-node-wasm.js  result: face: 1 body: 1 hand: 1 gesture: 6 object: 1 person: 1 {"score":1,"age":23.5,"gender":"female"} {"score":0.82,"class":"person"} {"score":0.47,"keypoints":3} 
-2022-09-30 10:05:20 DATA:  test-backend-node-wasm.js  result: performance: load: null total: 523 
-2022-09-30 10:05:20 STATE: test-backend-node-wasm.js passed: warmup face result match 
-2022-09-30 10:05:20 STATE: test-backend-node-wasm.js event: image 
-2022-09-30 10:05:21 STATE: test-backend-node-wasm.js event: detect 
-2022-09-30 10:05:21 STATE: test-backend-node-wasm.js event: warmup 
-2022-09-30 10:05:21 STATE: test-backend-node-wasm.js passed: warmup: body default 
-2022-09-30 10:05:21 DATA:  test-backend-node-wasm.js  result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
-2022-09-30 10:05:21 DATA:  test-backend-node-wasm.js  result: performance: load: null total: 356 
-2022-09-30 10:05:21 STATE: test-backend-node-wasm.js passed: warmup body result match 
-2022-09-30 10:05:21 STATE: test-backend-node-wasm.js details: {"face":{"boxScore":0.93,"faceScore":1,"age":23.7,"gender":"female","genderScore":0.97},"emotion":[{"score":0.59,"emotion":"angry"},{"score":0.29,"emotion":"fear"}],"body":{"score":0.92,"keypoints":17},"hand":{"boxScore":0.51,"fingerScore":0.73,"keypoints":21},"gestures":[{"face":0,"gesture":"facing right"},{"face":0,"gesture":"mouth 21% open"},{"hand":0,"gesture":"pinky forward"},{"hand":0,"gesture":"palm up"},{"hand":0,"gesture":"open palm"},{"iris":0,"gesture":"looking left"},{"iris":0,"gesture":"looking up"}]} 
-2022-09-30 10:05:21 INFO:  test-backend-node-wasm.js test: details verification 
-2022-09-30 10:05:21 STATE: test-backend-node-wasm.js start default 
-2022-09-30 10:05:21 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} 
-2022-09-30 10:05:21 STATE: test-backend-node-wasm.js event: image 
-2022-09-30 10:05:21 STATE: test-backend-node-wasm.js event: detect 
-2022-09-30 10:05:21 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-body.jpg default 
-2022-09-30 10:05:21 DATA:  test-backend-node-wasm.js  result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
-2022-09-30 10:05:21 DATA:  test-backend-node-wasm.js  result: performance: load: null total: 316 
-2022-09-30 10:05:21 STATE: test-backend-node-wasm.js passed: details face length 1 
-2022-09-30 10:05:21 STATE: test-backend-node-wasm.js passed: details face score 1 0.93 1 
-2022-09-30 10:05:21 STATE: test-backend-node-wasm.js passed: details face age/gender 23.7 female 0.97 85.47 
-2022-09-30 10:05:21 STATE: test-backend-node-wasm.js passed: details face arrays 4 478 1024 
-2022-09-30 10:05:21 STATE: test-backend-node-wasm.js passed: details face emotion 2 {"score":0.59,"emotion":"angry"} 
-2022-09-30 10:05:21 STATE: test-backend-node-wasm.js passed: details face anti-spoofing 0.79 
-2022-09-30 10:05:21 STATE: test-backend-node-wasm.js passed: details face liveness 0.83 
-2022-09-30 10:05:21 STATE: test-backend-node-wasm.js passed: details body length 1 
-2022-09-30 10:05:21 STATE: test-backend-node-wasm.js passed: details body 0.92 17 6 
-2022-09-30 10:05:21 STATE: test-backend-node-wasm.js passed: details hand length 1 
-2022-09-30 10:05:21 STATE: test-backend-node-wasm.js passed: details hand 0.51 0.73 point 
-2022-09-30 10:05:21 STATE: test-backend-node-wasm.js passed: details hand arrays 21 5 7 
-2022-09-30 10:05:21 STATE: test-backend-node-wasm.js passed: details gesture length 7 
-2022-09-30 10:05:21 STATE: test-backend-node-wasm.js passed: details gesture first {"face":0,"gesture":"facing right"} 
-2022-09-30 10:05:21 STATE: test-backend-node-wasm.js passed: details object length 1 
-2022-09-30 10:05:21 STATE: test-backend-node-wasm.js passed: details object 0.72 person 
-2022-09-30 10:05:21 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,4] {"checksum":1413675264} 
-2022-09-30 10:05:21 STATE: test-backend-node-wasm.js event: image 
-2022-09-30 10:05:22 STATE: test-backend-node-wasm.js event: detect 
-2022-09-30 10:05:22 STATE: test-backend-node-wasm.js passed: tensor shape: [1,1200,1200,4] dtype: float32 
-2022-09-30 10:05:22 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-body.jpg [1200,1200,4] {"checksum":1413675264} 
-2022-09-30 10:05:22 STATE: test-backend-node-wasm.js event: image 
-2022-09-30 10:05:22 STATE: test-backend-node-wasm.js event: detect 
-2022-09-30 10:05:22 STATE: test-backend-node-wasm.js passed: tensor shape: [1200,1200,4] dtype: float32 
-2022-09-30 10:05:23 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} 
-2022-09-30 10:05:23 STATE: test-backend-node-wasm.js event: image 
-2022-09-30 10:05:23 STATE: test-backend-node-wasm.js event: detect 
-2022-09-30 10:05:23 STATE: test-backend-node-wasm.js passed: tensor shape: [1,1200,1200,3] dtype: float32 
-2022-09-30 10:05:23 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-body.jpg [1200,1200,3] {"checksum":1038921856} 
-2022-09-30 10:05:23 STATE: test-backend-node-wasm.js event: image 
-2022-09-30 10:05:24 STATE: test-backend-node-wasm.js event: detect 
-2022-09-30 10:05:24 STATE: test-backend-node-wasm.js passed: tensor shape: [1200,1200,3] dtype: float32 
-2022-09-30 10:05:24 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,4] {"checksum":1371996871} 
-2022-09-30 10:05:24 STATE: test-backend-node-wasm.js event: image 
-2022-09-30 10:05:24 STATE: test-backend-node-wasm.js event: detect 
-2022-09-30 10:05:24 STATE: test-backend-node-wasm.js passed: tensor shape: [1,1200,1200,4] dtype: int32 
-2022-09-30 10:05:24 INFO:  test-backend-node-wasm.js test default 
-2022-09-30 10:05:24 STATE: test-backend-node-wasm.js start async 
-2022-09-30 10:05:24 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} 
-2022-09-30 10:05:24 STATE: test-backend-node-wasm.js event: image 
-2022-09-30 10:05:25 STATE: test-backend-node-wasm.js event: detect 
-2022-09-30 10:05:25 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-body.jpg async 
-2022-09-30 10:05:25 DATA:  test-backend-node-wasm.js  result: face: 1 body: 1 hand: 1 gesture: 8 object: 1 person: 1 {"score":1,"age":29.6,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
-2022-09-30 10:05:25 DATA:  test-backend-node-wasm.js  result: performance: load: null total: 353 
-2022-09-30 10:05:25 STATE: test-backend-node-wasm.js passed: default result face match 1 female 0.97 
-2022-09-30 10:05:25 INFO:  test-backend-node-wasm.js test sync 
-2022-09-30 10:05:25 STATE: test-backend-node-wasm.js start sync 
-2022-09-30 10:05:25 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} 
-2022-09-30 10:05:25 STATE: test-backend-node-wasm.js event: image 
-2022-09-30 10:05:25 STATE: test-backend-node-wasm.js event: detect 
-2022-09-30 10:05:25 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-body.jpg sync 
-2022-09-30 10:05:25 DATA:  test-backend-node-wasm.js  result: face: 1 body: 1 hand: 1 gesture: 8 object: 1 person: 1 {"score":1,"age":29.6,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
-2022-09-30 10:05:25 DATA:  test-backend-node-wasm.js  result: performance: load: null total: 412 
-2022-09-30 10:05:25 STATE: test-backend-node-wasm.js passed: default sync 1 female 0.97 
-2022-09-30 10:05:25 INFO:  test-backend-node-wasm.js test: image process 
-2022-09-30 10:05:25 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34697856} 
-2022-09-30 10:05:25 STATE: test-backend-node-wasm.js passed: image input null [1,256,256,3] 
-2022-09-30 10:05:25 INFO:  test-backend-node-wasm.js test: image null 
-2022-09-30 10:05:25 STATE: test-backend-node-wasm.js passed: invalid input could not convert input to tensor 
-2022-09-30 10:05:25 INFO:  test-backend-node-wasm.js test face similarity 
-2022-09-30 10:05:25 STATE: test-backend-node-wasm.js start face similarity 
-2022-09-30 10:05:26 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34697856} 
-2022-09-30 10:05:26 STATE: test-backend-node-wasm.js event: image 
-2022-09-30 10:05:26 STATE: test-backend-node-wasm.js event: detect 
-2022-09-30 10:05:26 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-face.jpg face similarity 
-2022-09-30 10:05:26 DATA:  test-backend-node-wasm.js  result: face: 1 body: 1 hand: 1 gesture: 6 object: 1 person: 1 {"score":1,"age":23.5,"gender":"female"} {"score":0.82,"class":"person"} {"score":0.47,"keypoints":3} 
-2022-09-30 10:05:26 DATA:  test-backend-node-wasm.js  result: performance: load: null total: 303 
-2022-09-30 10:05:26 STATE: test-backend-node-wasm.js start face similarity 
-2022-09-30 10:05:26 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} 
-2022-09-30 10:05:26 STATE: test-backend-node-wasm.js event: image 
-2022-09-30 10:05:26 STATE: test-backend-node-wasm.js event: detect 
-2022-09-30 10:05:26 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-body.jpg face similarity 
-2022-09-30 10:05:26 DATA:  test-backend-node-wasm.js  result: face: 1 body: 1 hand: 1 gesture: 8 object: 1 person: 1 {"score":1,"age":29.6,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
-2022-09-30 10:05:26 DATA:  test-backend-node-wasm.js  result: performance: load: null total: 330 
-2022-09-30 10:05:26 STATE: test-backend-node-wasm.js start face similarity 
-2022-09-30 10:05:26 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151155104} 
-2022-09-30 10:05:26 STATE: test-backend-node-wasm.js event: image 
-2022-09-30 10:05:27 STATE: test-backend-node-wasm.js event: detect 
-2022-09-30 10:05:27 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-upper.jpg face similarity 
-2022-09-30 10:05:27 DATA:  test-backend-node-wasm.js  result: face: 1 body: 1 hand: 0 gesture: 4 object: 1 person: 1 {"score":1,"age":23.5,"gender":"female"} {"score":0.71,"class":"person"} {"score":0.75,"keypoints":7} 
-2022-09-30 10:05:27 DATA:  test-backend-node-wasm.js  result: performance: load: null total: 311 
-2022-09-30 10:05:27 STATE: test-backend-node-wasm.js passed: face descriptor 
-2022-09-30 10:05:27 STATE: test-backend-node-wasm.js passed: face similarity {"similarity":[1,0.5266119940661309,0.4858842904087851],"descriptors":[1024,1024,1024]} 
-2022-09-30 10:05:27 INFO:  test-backend-node-wasm.js test object 
-2022-09-30 10:05:27 STATE: test-backend-node-wasm.js start object 
-2022-09-30 10:05:27 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} 
-2022-09-30 10:05:27 STATE: test-backend-node-wasm.js event: image 
-2022-09-30 10:05:27 STATE: test-backend-node-wasm.js event: detect 
-2022-09-30 10:05:27 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-body.jpg object 
-2022-09-30 10:05:27 DATA:  test-backend-node-wasm.js  result: face: 1 body: 1 hand: 1 gesture: 8 object: 1 person: 1 {"score":1,"age":29.6,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
-2022-09-30 10:05:27 DATA:  test-backend-node-wasm.js  result: performance: load: null total: 324 
-2022-09-30 10:05:27 STATE: test-backend-node-wasm.js passed: centernet 
-2022-09-30 10:05:27 STATE: test-backend-node-wasm.js start object 
-2022-09-30 10:05:28 WARN:  test-backend-node-wasm.js missing kernel ops {"title":"object","model":"nanodet","url":"https://vladmandic.github.io/human-models/models/nanodet.json","missing":["sparsetodense"],"backkend":"wasm"} 
-2022-09-30 10:05:28 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} 
-2022-09-30 10:05:28 STATE: test-backend-node-wasm.js event: image 
-2022-09-30 10:05:29 STATE: test-backend-node-wasm.js event: detect 
-2022-09-30 10:05:29 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-body.jpg object 
-2022-09-30 10:05:29 DATA:  test-backend-node-wasm.js  result: face: 1 body: 1 hand: 1 gesture: 8 object: 0 person: 1 {"score":1,"age":29.6,"gender":"female"} {} {"score":0.92,"keypoints":17} 
-2022-09-30 10:05:29 DATA:  test-backend-node-wasm.js  result: performance: load: null total: 225 
-2022-09-30 10:05:29 ERROR: test-backend-node-wasm.js failed: nanodet [] 
-2022-09-30 10:05:29 INFO:  test-backend-node-wasm.js test sensitive 
-2022-09-30 10:05:29 STATE: test-backend-node-wasm.js start sensitive 
-2022-09-30 10:05:29 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} 
-2022-09-30 10:05:29 STATE: test-backend-node-wasm.js event: image 
-2022-09-30 10:05:29 STATE: test-backend-node-wasm.js event: detect 
-2022-09-30 10:05:29 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-body.jpg sensitive 
-2022-09-30 10:05:29 DATA:  test-backend-node-wasm.js  result: face: 1 body: 1 hand: 2 gesture: 10 object: 0 person: 1 {"score":1,"age":29.6,"gender":"female"} {} {"score":0.92,"keypoints":17} 
-2022-09-30 10:05:29 DATA:  test-backend-node-wasm.js  result: performance: load: null total: 243 
-2022-09-30 10:05:29 STATE: test-backend-node-wasm.js passed: sensitive result match 
-2022-09-30 10:05:29 STATE: test-backend-node-wasm.js passed: sensitive face result match 
-2022-09-30 10:05:29 STATE: test-backend-node-wasm.js passed: sensitive face emotion result [{"score":0.46,"emotion":"neutral"},{"score":0.24,"emotion":"fear"},{"score":0.17,"emotion":"sad"}] 
-2022-09-30 10:05:29 STATE: test-backend-node-wasm.js passed: sensitive body result match 
-2022-09-30 10:05:29 STATE: test-backend-node-wasm.js passed: sensitive hand result match 
-2022-09-30 10:05:29 INFO:  test-backend-node-wasm.js test body 
-2022-09-30 10:05:29 STATE: test-backend-node-wasm.js start blazepose 
-2022-09-30 10:05:32 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} 
-2022-09-30 10:05:32 STATE: test-backend-node-wasm.js event: image 
-2022-09-30 10:05:32 STATE: test-backend-node-wasm.js event: detect 
-2022-09-30 10:05:32 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-body.jpg blazepose 
-2022-09-30 10:05:32 DATA:  test-backend-node-wasm.js  result: face: 1 body: 1 hand: 2 gesture: 10 object: 0 person: 1 {"score":1,"age":29.6,"gender":"female"} {} {"score":0.99,"keypoints":39} 
-2022-09-30 10:05:32 DATA:  test-backend-node-wasm.js  result: performance: load: null total: 419 
-2022-09-30 10:05:32 STATE: test-backend-node-wasm.js passed: blazepose 
-2022-09-30 10:05:32 STATE: test-backend-node-wasm.js start efficientpose 
-2022-09-30 10:05:33 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} 
-2022-09-30 10:05:33 STATE: test-backend-node-wasm.js event: image 
-2022-09-30 10:05:34 STATE: test-backend-node-wasm.js event: detect 
-2022-09-30 10:05:34 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-body.jpg efficientpose 
-2022-09-30 10:05:34 DATA:  test-backend-node-wasm.js  result: face: 1 body: 1 hand: 2 gesture: 10 object: 0 person: 1 {"score":1,"age":29.6,"gender":"female"} {} {"score":0.75,"keypoints":13} 
-2022-09-30 10:05:34 DATA:  test-backend-node-wasm.js  result: performance: load: null total: 716 
-2022-09-30 10:05:34 STATE: test-backend-node-wasm.js passed: efficientpose 
-2022-09-30 10:05:34 STATE: test-backend-node-wasm.js start posenet 
-2022-09-30 10:05:35 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} 
-2022-09-30 10:05:35 STATE: test-backend-node-wasm.js event: image 
-2022-09-30 10:05:35 STATE: test-backend-node-wasm.js event: detect 
-2022-09-30 10:05:35 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-body.jpg posenet 
-2022-09-30 10:05:35 DATA:  test-backend-node-wasm.js  result: face: 1 body: 1 hand: 2 gesture: 10 object: 0 person: 1 {"score":1,"age":29.6,"gender":"female"} {} {"score":0.96,"keypoints":16} 
-2022-09-30 10:05:35 DATA:  test-backend-node-wasm.js  result: performance: load: null total: 289 
-2022-09-30 10:05:35 STATE: test-backend-node-wasm.js passed: posenet 
-2022-09-30 10:05:35 STATE: test-backend-node-wasm.js start movenet 
-2022-09-30 10:05:35 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} 
-2022-09-30 10:05:35 STATE: test-backend-node-wasm.js event: image 
-2022-09-30 10:05:35 STATE: test-backend-node-wasm.js event: detect 
-2022-09-30 10:05:35 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-body.jpg movenet 
-2022-09-30 10:05:35 DATA:  test-backend-node-wasm.js  result: face: 1 body: 1 hand: 2 gesture: 10 object: 0 person: 1 {"score":1,"age":29.6,"gender":"female"} {} {"score":0.92,"keypoints":17} 
-2022-09-30 10:05:35 DATA:  test-backend-node-wasm.js  result: performance: load: null total: 239 
-2022-09-30 10:05:35 STATE: test-backend-node-wasm.js passed: movenet 
-2022-09-30 10:05:35 INFO:  test-backend-node-wasm.js test face matching 
-2022-09-30 10:05:35 STATE: test-backend-node-wasm.js passed: face database 40 
-2022-09-30 10:05:35 STATE: test-backend-node-wasm.js passed: face match {"first":{"index":4,"similarity":0.7827852754786533}} {"second":{"index":4,"similarity":0.5660821189104794}} {"third":{"index":4,"similarity":0.45074189882665594}} 
-2022-09-30 10:05:35 INFO:  test-backend-node-wasm.js test face similarity alternative 
-2022-09-30 10:05:35 STATE: test-backend-node-wasm.js start face embeddings 
-2022-09-30 10:05:36 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34697856} 
-2022-09-30 10:05:36 STATE: test-backend-node-wasm.js event: image 
-2022-09-30 10:05:36 STATE: test-backend-node-wasm.js event: detect 
-2022-09-30 10:05:36 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-face.jpg face embeddings 
-2022-09-30 10:05:36 DATA:  test-backend-node-wasm.js  result: face: 1 body: 1 hand: 2 gesture: 8 object: 0 person: 1 {"score":1,"age":23.5,"gender":"female"} {} {"score":0.47,"keypoints":3} 
-2022-09-30 10:05:36 DATA:  test-backend-node-wasm.js  result: performance: load: null total: 243 
-2022-09-30 10:05:36 STATE: test-backend-node-wasm.js passed: mobilefacenet {"embedding":192} 
-2022-09-30 10:05:36 STATE: test-backend-node-wasm.js start face embeddings 
-2022-09-30 10:05:37 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34697856} 
-2022-09-30 10:05:37 STATE: test-backend-node-wasm.js event: image 
-2022-09-30 10:05:37 STATE: test-backend-node-wasm.js event: detect 
-2022-09-30 10:05:37 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-face.jpg face embeddings 
-2022-09-30 10:05:37 DATA:  test-backend-node-wasm.js  result: face: 1 body: 1 hand: 2 gesture: 8 object: 0 person: 1 {"score":1,"age":23.5,"gender":"female"} {} {"score":0.47,"keypoints":3} 
-2022-09-30 10:05:37 DATA:  test-backend-node-wasm.js  result: performance: load: null total: 288 
-2022-09-30 10:05:37 STATE: test-backend-node-wasm.js passed: insightface {"embedding":512} 
-2022-09-30 10:05:37 INFO:  test-backend-node-wasm.js test face attention 
-2022-09-30 10:05:37 STATE: test-backend-node-wasm.js start face attention 
-2022-09-30 10:05:38 WARN:  test-backend-node-wasm.js missing kernel ops {"title":"face attention","model":"facemesh","url":"https://vladmandic.github.io/human-models/models/facemesh-attention.json","missing":["atan2"],"backkend":"wasm"} 
-2022-09-30 10:05:38 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34697856} 
-2022-09-30 10:05:38 STATE: test-backend-node-wasm.js event: image 
-2022-09-30 10:05:38 STATE: test-backend-node-wasm.js event: detect 
-2022-09-30 10:05:38 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-face.jpg face attention 
-2022-09-30 10:05:38 DATA:  test-backend-node-wasm.js  result: face: 0 body: 1 hand: 1 gesture: 2 object: 0 person: 0 {} {} {"score":0.47,"keypoints":3} 
-2022-09-30 10:05:38 DATA:  test-backend-node-wasm.js  result: performance: load: null total: 117 
-2022-09-30 10:05:38 ERROR: test-backend-node-wasm.js failed: face attention {"annotations":0} 
-2022-09-30 10:05:38 INFO:  test-backend-node-wasm.js test detectors 
-2022-09-30 10:05:38 STATE: test-backend-node-wasm.js start detectors 
-2022-09-30 10:05:38 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} 
-2022-09-30 10:05:38 STATE: test-backend-node-wasm.js event: image 
-2022-09-30 10:05:38 STATE: test-backend-node-wasm.js event: detect 
-2022-09-30 10:05:38 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-body.jpg detectors 
-2022-09-30 10:05:38 DATA:  test-backend-node-wasm.js  result: face: 1 body: 1 hand: 1 gesture: 0 object: 0 person: 1 {"score":0.93,"gender":"unknown"} {} {"score":0.92,"keypoints":17} 
-2022-09-30 10:05:38 DATA:  test-backend-node-wasm.js  result: performance: load: null total: 118 
-2022-09-30 10:05:38 STATE: test-backend-node-wasm.js passed: detector result face match 
-2022-09-30 10:05:38 STATE: test-backend-node-wasm.js passed: detector result hand match 
-2022-09-30 10:05:38 INFO:  test-backend-node-wasm.js test: multi-instance 
-2022-09-30 10:05:38 STATE: test-backend-node-wasm.js start multi instance 
-2022-09-30 10:05:38 STATE: test-backend-node-wasm.js event: image 
-2022-09-30 10:05:38 STATE: test-backend-node-wasm.js event: detect 
-2022-09-30 10:05:38 STATE: test-backend-node-wasm.js passed: detect: random multi instance 
-2022-09-30 10:05:38 DATA:  test-backend-node-wasm.js  result: face: 0 body: 1 hand: 0 gesture: 0 object: 0 person: 0 {} {} {"score":0,"keypoints":0} 
-2022-09-30 10:05:38 DATA:  test-backend-node-wasm.js  result: performance: load: null total: 98 
-2022-09-30 10:05:38 INFO:  test-backend-node-wasm.js test: first instance 
-2022-09-30 10:05:38 STATE: test-backend-node-wasm.js start multi instance 
-2022-09-30 10:05:39 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151155104} 
-2022-09-30 10:05:39 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-upper.jpg multi instance 
-2022-09-30 10:05:39 DATA:  test-backend-node-wasm.js  result: face: 1 body: 1 hand: 0 gesture: 0 object: 0 person: 1 {"score":0.96,"gender":"unknown"} {} {"score":0.75,"keypoints":7} 
-2022-09-30 10:05:39 DATA:  test-backend-node-wasm.js  result: performance: load: null total: 111 
-2022-09-30 10:05:39 INFO:  test-backend-node-wasm.js test: second instance 
-2022-09-30 10:05:39 STATE: test-backend-node-wasm.js start multi instance 
-2022-09-30 10:05:39 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151155104} 
-2022-09-30 10:05:39 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-upper.jpg multi instance 
-2022-09-30 10:05:39 DATA:  test-backend-node-wasm.js  result: face: 1 body: 1 hand: 0 gesture: 0 object: 0 person: 1 {"score":0.96,"gender":"unknown"} {} {"score":0.75,"keypoints":7} 
-2022-09-30 10:05:39 DATA:  test-backend-node-wasm.js  result: performance: load: null total: 111 
-2022-09-30 10:05:39 INFO:  test-backend-node-wasm.js test: concurrent 
-2022-09-30 10:05:39 STATE: test-backend-node-wasm.js start concurrent 
-2022-09-30 10:05:39 STATE: test-backend-node-wasm.js start concurrent 
-2022-09-30 10:05:39 STATE: test-backend-node-wasm.js start concurrent 
-2022-09-30 10:05:39 STATE: test-backend-node-wasm.js start concurrent 
-2022-09-30 10:05:39 STATE: test-backend-node-wasm.js start concurrent 
-2022-09-30 10:05:39 STATE: test-backend-node-wasm.js start concurrent 
-2022-09-30 10:05:39 STATE: test-backend-node-wasm.js start concurrent 
-2022-09-30 10:05:39 STATE: test-backend-node-wasm.js start concurrent 
-2022-09-30 10:05:39 STATE: test-backend-node-wasm.js start concurrent 
-2022-09-30 10:05:39 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34697856} 
-2022-09-30 10:05:39 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34697856} 
-2022-09-30 10:05:39 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34697856} 
-2022-09-30 10:05:39 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} 
-2022-09-30 10:05:39 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} 
-2022-09-30 10:05:40 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} 
-2022-09-30 10:05:40 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151155104} 
-2022-09-30 10:05:40 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151155104} 
-2022-09-30 10:05:40 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151155104} 
-2022-09-30 10:05:40 STATE: test-backend-node-wasm.js event: image 
-2022-09-30 10:05:40 STATE: test-backend-node-wasm.js event: image 
-2022-09-30 10:05:40 STATE: test-backend-node-wasm.js event: image 
-2022-09-30 10:05:41 STATE: test-backend-node-wasm.js event: detect 
-2022-09-30 10:05:41 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-upper.jpg concurrent 
-2022-09-30 10:05:41 DATA:  test-backend-node-wasm.js  result: face: 1 body: 1 hand: 0 gesture: 0 object: 0 person: 1 {"score":0.96,"gender":"unknown"} {} {"score":0.75,"keypoints":7} 
-2022-09-30 10:05:41 DATA:  test-backend-node-wasm.js  result: performance: load: null total: 944 
-2022-09-30 10:05:41 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-upper.jpg concurrent 
-2022-09-30 10:05:41 DATA:  test-backend-node-wasm.js  result: face: 1 body: 1 hand: 0 gesture: 0 object: 0 person: 1 {"score":0.96,"gender":"unknown"} {} {"score":0.75,"keypoints":7} 
-2022-09-30 10:05:41 DATA:  test-backend-node-wasm.js  result: performance: load: null total: 944 
-2022-09-30 10:05:41 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-upper.jpg concurrent 
-2022-09-30 10:05:41 DATA:  test-backend-node-wasm.js  result: face: 1 body: 1 hand: 0 gesture: 0 object: 0 person: 1 {"score":0.96,"gender":"unknown"} {} {"score":0.75,"keypoints":7} 
-2022-09-30 10:05:41 DATA:  test-backend-node-wasm.js  result: performance: load: null total: 944 
-2022-09-30 10:05:41 STATE: test-backend-node-wasm.js event: detect 
-2022-09-30 10:05:41 STATE: test-backend-node-wasm.js event: detect 
-2022-09-30 10:05:41 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-face.jpg concurrent 
-2022-09-30 10:05:41 DATA:  test-backend-node-wasm.js  result: face: 1 body: 1 hand: 1 gesture: 0 object: 0 person: 1 {"score":0.91,"gender":"unknown"} {} {"score":0.47,"keypoints":3} 
-2022-09-30 10:05:41 DATA:  test-backend-node-wasm.js  result: performance: load: null total: 945 
-2022-09-30 10:05:41 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-face.jpg concurrent 
-2022-09-30 10:05:41 DATA:  test-backend-node-wasm.js  result: face: 1 body: 1 hand: 1 gesture: 0 object: 0 person: 1 {"score":0.91,"gender":"unknown"} {} {"score":0.47,"keypoints":3} 
-2022-09-30 10:05:41 DATA:  test-backend-node-wasm.js  result: performance: load: null total: 945 
-2022-09-30 10:05:41 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-face.jpg concurrent 
-2022-09-30 10:05:41 DATA:  test-backend-node-wasm.js  result: face: 1 body: 1 hand: 1 gesture: 0 object: 0 person: 1 {"score":0.91,"gender":"unknown"} {} {"score":0.47,"keypoints":3} 
-2022-09-30 10:05:41 DATA:  test-backend-node-wasm.js  result: performance: load: null total: 945 
-2022-09-30 10:05:41 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-body.jpg concurrent 
-2022-09-30 10:05:41 DATA:  test-backend-node-wasm.js  result: face: 1 body: 1 hand: 1 gesture: 0 object: 0 person: 1 {"score":0.93,"gender":"unknown"} {} {"score":0.92,"keypoints":17} 
-2022-09-30 10:05:41 DATA:  test-backend-node-wasm.js  result: performance: load: null total: 945 
-2022-09-30 10:05:41 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-body.jpg concurrent 
-2022-09-30 10:05:41 DATA:  test-backend-node-wasm.js  result: face: 1 body: 1 hand: 1 gesture: 0 object: 0 person: 1 {"score":0.93,"gender":"unknown"} {} {"score":0.92,"keypoints":17} 
-2022-09-30 10:05:41 DATA:  test-backend-node-wasm.js  result: performance: load: null total: 945 
-2022-09-30 10:05:41 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-body.jpg concurrent 
-2022-09-30 10:05:41 DATA:  test-backend-node-wasm.js  result: face: 1 body: 1 hand: 1 gesture: 0 object: 0 person: 1 {"score":0.93,"gender":"unknown"} {} {"score":0.92,"keypoints":17} 
-2022-09-30 10:05:41 DATA:  test-backend-node-wasm.js  result: performance: load: null total: 945 
-2022-09-30 10:05:41 INFO:  test-backend-node-wasm.js test: monkey-patch 
-2022-09-30 10:05:41 STATE: test-backend-node-wasm.js event: image 
-2022-09-30 10:05:41 STATE: test-backend-node-wasm.js event: detect 
-2022-09-30 10:05:41 STATE: test-backend-node-wasm.js passed: monkey patch 
-2022-09-30 10:05:41 STATE: test-backend-node-wasm.js passed: segmentation [65536] 
-2022-09-30 10:05:41 STATE: test-backend-node-wasm.js passeed: equal usage 
-2022-09-30 10:05:41 INFO:  test-backend-node-wasm.js test: input compare 
-2022-09-30 10:05:41 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34697856} 
-2022-09-30 10:05:41 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} 
-2022-09-30 10:05:41 STATE: test-backend-node-wasm.js passed: image compare 0 23.280073018790848 
-2022-09-30 10:05:41 INFO:  test-backend-node-wasm.js events: {"image":29,"detect":29,"warmup":2} 
-2022-09-30 10:05:41 INFO:  test-backend-node-wasm.js tensors 4149 
-2022-09-30 10:05:41 INFO:  test-backend-node-wasm.js test complete: 23460 ms 
-2022-09-30 10:05:41 STATE: all tests complete 
-2022-09-30 10:05:41 INFO:    status {"test":"../demo/nodejs/node.js","passed":1,"failed":0} 
-2022-09-30 10:05:41 INFO:    status {"test":"../demo/nodejs/node-simple.js","passed":1,"failed":0} 
-2022-09-30 10:05:41 INFO:    status {"test":"../demo/nodejs/node-fetch.js","passed":1,"failed":0} 
-2022-09-30 10:05:41 INFO:    status {"test":"../demo/nodejs/node-event.js","passed":1,"failed":0} 
-2022-09-30 10:05:41 INFO:    status {"test":"../demo/nodejs/node-similarity.js","passed":1,"failed":0} 
-2022-09-30 10:05:41 INFO:    status {"test":"../demo/nodejs/node-canvas.js","passed":1,"failed":0} 
-2022-09-30 10:05:41 INFO:    status {"test":"../demo/nodejs/process-folder.js","passed":1,"failed":0} 
-2022-09-30 10:05:41 INFO:    status {"test":"../demo/multithread/node-multiprocess.js","passed":1,"failed":0} 
-2022-09-30 10:05:41 INFO:    status {"test":"../demo/facematch/node-match.js","passed":1,"failed":0} 
-2022-09-30 10:05:41 INFO:    status {"test":"test-node-load.js","passed":1,"failed":0} 
-2022-09-30 10:05:41 INFO:    status {"test":"test-node-gear.js","passed":3,"failed":0} 
-2022-09-30 10:05:41 INFO:    status {"test":"test-backend-node.js","passed":125,"failed":0} 
-2022-09-30 10:05:41 INFO:    status {"test":"test-backend-node-gpu.js","passed":125,"failed":0} 
-2022-09-30 10:05:41 INFO:    status {"test":"test-backend-node-wasm.js","passed":124,"failed":2} 
-2022-09-30 10:05:41 INFO:  failures {"count":2} 
-2022-09-30 10:05:41 WARN:    failed {"test":"test-backend-node-wasm.js","message":["error",["failed: nanodet",[]]]} 
-2022-09-30 10:05:41 WARN:    failed {"test":"test-backend-node-wasm.js","message":["error",["failed: face attention",{"annotations":0}]]} 
+2022-10-02 15:05:46 INFO:  @vladmandic/human version 2.11.0 
+2022-10-02 15:05:46 INFO:  User: vlado Platform: linux Arch: x64 Node: v18.10.0 
+2022-10-02 15:05:46 INFO:  demos: [{"cmd":"../demo/nodejs/node.js","args":[]},{"cmd":"../demo/nodejs/node-simple.js","args":[]},{"cmd":"../demo/nodejs/node-fetch.js","args":[]},{"cmd":"../demo/nodejs/node-event.js","args":["samples/in/ai-body.jpg"]},{"cmd":"../demo/nodejs/node-similarity.js","args":["samples/in/ai-face.jpg","samples/in/ai-upper.jpg"]},{"cmd":"../demo/nodejs/node-canvas.js","args":["samples/in/ai-body.jpg","samples/out/ai-body.jpg"]},{"cmd":"../demo/nodejs/process-folder.js","args":["samples"]},{"cmd":"../demo/multithread/node-multiprocess.js","args":[]},{"cmd":"../demo/facematch/node-match.js","args":[]}] 
+2022-10-02 15:05:46 INFO:  {"cmd":"../demo/nodejs/node.js","args":[]} start 
+2022-10-02 15:05:47 INFO:  {"cmd":"../demo/nodejs/node-simple.js","args":[]} start 
+2022-10-02 15:05:47 INFO:  {"cmd":"../demo/nodejs/node-fetch.js","args":[]} start 
+2022-10-02 15:05:50 INFO:  {"cmd":"../demo/nodejs/node-event.js","args":["samples/in/ai-body.jpg"]} start 
+2022-10-02 15:05:50 INFO:  {"cmd":"../demo/nodejs/node-similarity.js","args":["samples/in/ai-face.jpg","samples/in/ai-upper.jpg"]} start 
+2022-10-02 15:05:51 INFO:  {"cmd":"../demo/nodejs/node-canvas.js","args":["samples/in/ai-body.jpg","samples/out/ai-body.jpg"]} start 
+2022-10-02 15:05:52 INFO:  {"cmd":"../demo/nodejs/process-folder.js","args":["samples"]} start 
+2022-10-02 15:05:53 INFO:  {"cmd":"../demo/multithread/node-multiprocess.js","args":[]} start 
+2022-10-02 15:06:03 INFO:  {"cmd":"../demo/facematch/node-match.js","args":[]} start 
+2022-10-02 15:06:05 INFO:  tests: ["test-node-load.js","test-node-gear.js","test-backend-node.js","test-backend-node-gpu.js","test-backend-node-wasm.js"] 
+2022-10-02 15:06:05 INFO:  
+2022-10-02 15:06:05 INFO:  test-node-load.js start 
+2022-10-02 15:06:05 INFO:  test-node-load.js load start {"human":"2.11.0","tf":"3.20.0","progress":0} 
+2022-10-02 15:06:05 DATA:  test-node-load.js load interval {"elapsed":0,"progress":0} 
+2022-10-02 15:06:05 DATA:  test-node-load.js load interval {"elapsed":10,"progress":0} 
+2022-10-02 15:06:05 DATA:  test-node-load.js load interval {"elapsed":21,"progress":0.05339166087267679} 
+2022-10-02 15:06:05 DATA:  test-node-load.js load interval {"elapsed":64,"progress":0.5125946867158943} 
+2022-10-02 15:06:05 STATE: test-node-load.js passed {"progress":1} 
+2022-10-02 15:06:05 INFO:  test-node-load.js load final {"progress":1} 
+2022-10-02 15:06:05 DATA:  test-node-load.js load interval {"elapsed":348,"progress":1} 
+2022-10-02 15:06:05 INFO:  
+2022-10-02 15:06:05 INFO:  test-node-gear.js start 
+2022-10-02 15:06:05 DATA:  test-node-gear.js input: ["samples/in/ai-face.jpg"] 
+2022-10-02 15:06:06 STATE: test-node-gear.js passed: gear faceres samples/in/ai-face.jpg 
+2022-10-02 15:06:06 DATA:  test-node-gear.js results {"face":0,"model":"faceres","image":"samples/in/ai-face.jpg","age":23.5,"gender":"female","genderScore":0.92} 
+2022-10-02 15:06:06 STATE: test-node-gear.js passed: gear gear samples/in/ai-face.jpg 
+2022-10-02 15:06:06 DATA:  test-node-gear.js results {"face":0,"model":"gear","image":"samples/in/ai-face.jpg","age":23.3,"gender":"female","genderScore":0.51,"race":[{"score":0.93,"race":"white"}]} 
+2022-10-02 15:06:07 STATE: test-node-gear.js passed: gear ssrnet samples/in/ai-face.jpg 
+2022-10-02 15:06:07 DATA:  test-node-gear.js results {"face":0,"model":"ssrnet","image":"samples/in/ai-face.jpg","age":23.4,"gender":"female","genderScore":0.99} 
+2022-10-02 15:06:07 INFO:  
+2022-10-02 15:06:07 INFO:  test-backend-node.js start 
+2022-10-02 15:06:07 INFO:  test-backend-node.js test: configuration validation 
+2022-10-02 15:06:07 STATE: test-backend-node.js passed: configuration default validation [] 
+2022-10-02 15:06:07 STATE: test-backend-node.js passed: configuration invalid validation [{"reason":"unknown property","where":"config.invalid = true"}] 
+2022-10-02 15:06:07 INFO:  test-backend-node.js test: model load 
+2022-10-02 15:06:07 STATE: test-backend-node.js passed: models loaded 25 11 [{"name":"ssrnetage","loaded":false,"url":null},{"name":"gear","loaded":false,"url":null},{"name":"blazeposedetect","loaded":false,"url":null},{"name":"blazepose","loaded":false,"url":null},{"name":"centernet","loaded":true,"url":"file://models/mb3-centernet.json"},{"name":"efficientpose","loaded":false,"url":null},{"name":"mobilefacenet","loaded":false,"url":null},{"name":"insightface","loaded":false,"url":null},{"name":"emotion","loaded":true,"url":"file://models/emotion.json"},{"name":"facedetect","loaded":true,"url":"file://models/blazeface.json"},{"name":"faceiris","loaded":true,"url":"file://models/iris.json"},{"name":"facemesh","loaded":true,"url":"file://models/facemesh.json"},{"name":"faceres","loaded":true,"url":"file://models/faceres.json"},{"name":"ssrnetgender","loaded":false,"url":null},{"name":"handpose","loaded":false,"url":null},{"name":"handskeleton","loaded":true,"url":"file://models/handlandmark-full.json"},{"name":"handtrack","loaded":true,"url":"file://models/handtrack.json"},{"name":"liveness","loaded":true,"url":"file://models/liveness.json"},{"name":"meet","loaded":false,"url":null},{"name":"movenet","loaded":true,"url":"file://models/movenet-lightning.json"},{"name":"nanodet","loaded":false,"url":null},{"name":"posenet","loaded":false,"url":null},{"name":"selfie","loaded":false,"url":null},{"name":"rvm","loaded":false,"url":null},{"name":"antispoof","loaded":true,"url":"file://models/antispoof.json"}] 
+2022-10-02 15:06:07 INFO:  test-backend-node.js memory: {"memory":{"unreliable":true,"numTensors":1785,"numDataBuffers":1785,"numBytes":63247332}} 
+2022-10-02 15:06:07 INFO:  test-backend-node.js state: {"state":{"registeredVariables":{},"nextTapeNodeId":0,"numBytes":63247332,"numTensors":1785,"numStringTensors":0,"numDataBuffers":1785,"gradientDepth":0,"kernelDepth":0,"scopeStack":[],"numDataMovesStack":[],"nextScopeId":0,"tensorInfo":{},"profiling":false,"activeProfile":{"newBytes":0,"newTensors":0,"peakBytes":0,"kernels":[],"result":null,"kernelNames":[]}}} 
+2022-10-02 15:06:07 INFO:  test-backend-node.js test: warmup 
+2022-10-02 15:06:07 STATE: test-backend-node.js passed: create human 
+2022-10-02 15:06:07 INFO:  test-backend-node.js human version: 2.11.0 
+2022-10-02 15:06:07 INFO:  test-backend-node.js platform: linux x64 agent: NodeJS v18.10.0 
+2022-10-02 15:06:07 INFO:  test-backend-node.js tfjs version: 3.20.0 
+2022-10-02 15:06:07 INFO:  test-backend-node.js env: {"browser":false,"node":true,"platform":"linux x64","agent":"NodeJS v18.10.0","backends":["cpu","tensorflow"],"initial":false,"tfjs":{"version":"3.20.0"},"offscreen":false,"perfadd":false,"tensorflow":{"version":"2.7.3-dev20220521","gpu":false},"wasm":{"supported":true,"backend":false},"webgl":{"supported":false,"backend":false},"webgpu":{"supported":false,"backend":false},"cpu":{"flags":[]},"kernels":169} 
+2022-10-02 15:06:07 STATE: test-backend-node.js passed: set backend: tensorflow 
+2022-10-02 15:06:07 STATE: test-backend-node.js tensors 1785 
+2022-10-02 15:06:07 STATE: test-backend-node.js passed: load models 
+2022-10-02 15:06:07 STATE: test-backend-node.js  result: defined models: 25 loaded models: 11 
+2022-10-02 15:06:07 STATE: test-backend-node.js passed: warmup: none default 
+2022-10-02 15:06:07 DATA:  test-backend-node.js  result: face: 0 body: 0 hand: 0 gesture: 0 object: 0 person: 0 {} {} {} 
+2022-10-02 15:06:07 DATA:  test-backend-node.js  result: performance: load: null total: null 
+2022-10-02 15:06:07 STATE: test-backend-node.js passed: warmup none result match 
+2022-10-02 15:06:07 STATE: test-backend-node.js event: image 
+2022-10-02 15:06:07 STATE: test-backend-node.js event: detect 
+2022-10-02 15:06:07 STATE: test-backend-node.js event: warmup 
+2022-10-02 15:06:07 STATE: test-backend-node.js passed: warmup: face default 
+2022-10-02 15:06:07 DATA:  test-backend-node.js  result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.5,"gender":"female"} {"score":0.82,"class":"person"} {"score":0.42,"keypoints":4} 
+2022-10-02 15:06:07 DATA:  test-backend-node.js  result: performance: load: null total: 368 
+2022-10-02 15:06:07 STATE: test-backend-node.js passed: warmup face result match 
+2022-10-02 15:06:07 STATE: test-backend-node.js event: image 
+2022-10-02 15:06:07 STATE: test-backend-node.js event: detect 
+2022-10-02 15:06:07 STATE: test-backend-node.js event: warmup 
+2022-10-02 15:06:07 STATE: test-backend-node.js passed: warmup: body default 
+2022-10-02 15:06:07 DATA:  test-backend-node.js  result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
+2022-10-02 15:06:07 DATA:  test-backend-node.js  result: performance: load: null total: 242 
+2022-10-02 15:06:07 STATE: test-backend-node.js passed: warmup body result match 
+2022-10-02 15:06:07 STATE: test-backend-node.js details: {"face":{"boxScore":0.92,"faceScore":1,"age":23.7,"gender":"female","genderScore":0.97},"emotion":[{"score":0.63,"emotion":"angry"},{"score":0.22,"emotion":"fear"}],"body":{"score":0.92,"keypoints":17},"hand":{"boxScore":0.52,"fingerScore":0.73,"keypoints":21},"gestures":[{"face":0,"gesture":"facing right"},{"face":0,"gesture":"mouth 10% open"},{"hand":0,"gesture":"pinky forward"},{"hand":0,"gesture":"palm up"},{"hand":0,"gesture":"open palm"},{"iris":0,"gesture":"looking left"},{"iris":0,"gesture":"looking up"}]} 
+2022-10-02 15:06:07 INFO:  test-backend-node.js test: details verification 
+2022-10-02 15:06:07 STATE: test-backend-node.js start default 
+2022-10-02 15:06:08 STATE: test-backend-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} 
+2022-10-02 15:06:08 STATE: test-backend-node.js event: image 
+2022-10-02 15:06:08 STATE: test-backend-node.js event: detect 
+2022-10-02 15:06:08 STATE: test-backend-node.js passed: detect: samples/in/ai-body.jpg default 
+2022-10-02 15:06:08 DATA:  test-backend-node.js  result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
+2022-10-02 15:06:08 DATA:  test-backend-node.js  result: performance: load: null total: 230 
+2022-10-02 15:06:08 STATE: test-backend-node.js passed: details face length 1 
+2022-10-02 15:06:08 STATE: test-backend-node.js passed: details face score 1 0.93 1 
+2022-10-02 15:06:08 STATE: test-backend-node.js passed: details face age/gender 23.7 female 0.97 85.47 
+2022-10-02 15:06:08 STATE: test-backend-node.js passed: details face arrays 4 478 1024 
+2022-10-02 15:06:08 STATE: test-backend-node.js passed: details face emotion 2 {"score":0.59,"emotion":"angry"} 
+2022-10-02 15:06:08 STATE: test-backend-node.js passed: details face anti-spoofing 0.79 
+2022-10-02 15:06:08 STATE: test-backend-node.js passed: details face liveness 0.83 
+2022-10-02 15:06:08 STATE: test-backend-node.js passed: details body length 1 
+2022-10-02 15:06:08 STATE: test-backend-node.js passed: details body 0.92 17 6 
+2022-10-02 15:06:08 STATE: test-backend-node.js passed: details hand length 1 
+2022-10-02 15:06:08 STATE: test-backend-node.js passed: details hand 0.51 0.73 point 
+2022-10-02 15:06:08 STATE: test-backend-node.js passed: details hand arrays 21 5 7 
+2022-10-02 15:06:08 STATE: test-backend-node.js passed: details gesture length 7 
+2022-10-02 15:06:08 STATE: test-backend-node.js passed: details gesture first {"face":0,"gesture":"facing right"} 
+2022-10-02 15:06:08 STATE: test-backend-node.js passed: details object length 1 
+2022-10-02 15:06:08 STATE: test-backend-node.js passed: details object 0.72 person 
+2022-10-02 15:06:08 STATE: test-backend-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,4] {"checksum":1371996928} 
+2022-10-02 15:06:08 STATE: test-backend-node.js event: image 
+2022-10-02 15:06:08 STATE: test-backend-node.js event: detect 
+2022-10-02 15:06:08 STATE: test-backend-node.js passed: tensor shape: [1,1200,1200,4] dtype: float32 
+2022-10-02 15:06:08 STATE: test-backend-node.js passed: load image: samples/in/ai-body.jpg [1200,1200,4] {"checksum":1371996928} 
+2022-10-02 15:06:08 STATE: test-backend-node.js event: image 
+2022-10-02 15:06:09 STATE: test-backend-node.js event: detect 
+2022-10-02 15:06:09 STATE: test-backend-node.js passed: tensor shape: [1200,1200,4] dtype: float32 
+2022-10-02 15:06:09 STATE: test-backend-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} 
+2022-10-02 15:06:09 STATE: test-backend-node.js event: image 
+2022-10-02 15:06:09 STATE: test-backend-node.js event: detect 
+2022-10-02 15:06:09 STATE: test-backend-node.js passed: tensor shape: [1,1200,1200,3] dtype: float32 
+2022-10-02 15:06:09 STATE: test-backend-node.js passed: load image: samples/in/ai-body.jpg [1200,1200,3] {"checksum":1004796864} 
+2022-10-02 15:06:09 STATE: test-backend-node.js event: image 
+2022-10-02 15:06:09 STATE: test-backend-node.js event: detect 
+2022-10-02 15:06:09 STATE: test-backend-node.js passed: tensor shape: [1200,1200,3] dtype: float32 
+2022-10-02 15:06:10 STATE: test-backend-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,4] {"checksum":1371996871} 
+2022-10-02 15:06:10 STATE: test-backend-node.js event: image 
+2022-10-02 15:06:10 STATE: test-backend-node.js event: detect 
+2022-10-02 15:06:10 STATE: test-backend-node.js passed: tensor shape: [1,1200,1200,4] dtype: int32 
+2022-10-02 15:06:10 INFO:  test-backend-node.js test default 
+2022-10-02 15:06:10 STATE: test-backend-node.js start async 
+2022-10-02 15:06:10 STATE: test-backend-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} 
+2022-10-02 15:06:10 STATE: test-backend-node.js event: image 
+2022-10-02 15:06:10 STATE: test-backend-node.js event: detect 
+2022-10-02 15:06:10 STATE: test-backend-node.js passed: detect: samples/in/ai-body.jpg async 
+2022-10-02 15:06:10 DATA:  test-backend-node.js  result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
+2022-10-02 15:06:10 DATA:  test-backend-node.js  result: performance: load: null total: 212 
+2022-10-02 15:06:10 STATE: test-backend-node.js passed: default result face match 1 female 0.97 
+2022-10-02 15:06:10 INFO:  test-backend-node.js test sync 
+2022-10-02 15:06:10 STATE: test-backend-node.js start sync 
+2022-10-02 15:06:10 STATE: test-backend-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} 
+2022-10-02 15:06:10 STATE: test-backend-node.js event: image 
+2022-10-02 15:06:11 STATE: test-backend-node.js event: detect 
+2022-10-02 15:06:11 STATE: test-backend-node.js passed: detect: samples/in/ai-body.jpg sync 
+2022-10-02 15:06:11 DATA:  test-backend-node.js  result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
+2022-10-02 15:06:11 DATA:  test-backend-node.js  result: performance: load: null total: 206 
+2022-10-02 15:06:11 STATE: test-backend-node.js passed: default sync 1 female 0.97 
+2022-10-02 15:06:11 INFO:  test-backend-node.js test: image process 
+2022-10-02 15:06:11 STATE: test-backend-node.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} 
+2022-10-02 15:06:11 STATE: test-backend-node.js passed: image input null [1,256,256,3] 
+2022-10-02 15:06:11 INFO:  test-backend-node.js test: image null 
+2022-10-02 15:06:11 STATE: test-backend-node.js passed: invalid input could not convert input to tensor 
+2022-10-02 15:06:11 INFO:  test-backend-node.js test face similarity 
+2022-10-02 15:06:11 STATE: test-backend-node.js start face similarity 
+2022-10-02 15:06:11 STATE: test-backend-node.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} 
+2022-10-02 15:06:11 STATE: test-backend-node.js event: image 
+2022-10-02 15:06:11 STATE: test-backend-node.js event: detect 
+2022-10-02 15:06:11 STATE: test-backend-node.js passed: detect: samples/in/ai-face.jpg face similarity 
+2022-10-02 15:06:11 DATA:  test-backend-node.js  result: face: 1 body: 1 hand: 1 gesture: 6 object: 1 person: 1 {"score":1,"age":23.5,"gender":"female"} {"score":0.82,"class":"person"} {"score":0.47,"keypoints":3} 
+2022-10-02 15:06:11 DATA:  test-backend-node.js  result: performance: load: null total: 204 
+2022-10-02 15:06:11 STATE: test-backend-node.js start face similarity 
+2022-10-02 15:06:11 STATE: test-backend-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} 
+2022-10-02 15:06:11 STATE: test-backend-node.js event: image 
+2022-10-02 15:06:11 STATE: test-backend-node.js event: detect 
+2022-10-02 15:06:11 STATE: test-backend-node.js passed: detect: samples/in/ai-body.jpg face similarity 
+2022-10-02 15:06:11 DATA:  test-backend-node.js  result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
+2022-10-02 15:06:11 DATA:  test-backend-node.js  result: performance: load: null total: 216 
+2022-10-02 15:06:11 STATE: test-backend-node.js start face similarity 
+2022-10-02 15:06:11 STATE: test-backend-node.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151289024} 
+2022-10-02 15:06:11 STATE: test-backend-node.js event: image 
+2022-10-02 15:06:11 STATE: test-backend-node.js event: detect 
+2022-10-02 15:06:11 STATE: test-backend-node.js passed: detect: samples/in/ai-upper.jpg face similarity 
+2022-10-02 15:06:11 DATA:  test-backend-node.js  result: face: 1 body: 1 hand: 0 gesture: 4 object: 1 person: 1 {"score":1,"age":23.5,"gender":"female"} {"score":0.71,"class":"person"} {"score":0.75,"keypoints":7} 
+2022-10-02 15:06:11 DATA:  test-backend-node.js  result: performance: load: null total: 197 
+2022-10-02 15:06:11 STATE: test-backend-node.js passed: face descriptor 
+2022-10-02 15:06:11 STATE: test-backend-node.js passed: face similarity {"similarity":[1,0.44727452329649126,0.5567935850640406],"descriptors":[1024,1024,1024]} 
+2022-10-02 15:06:11 INFO:  test-backend-node.js test object 
+2022-10-02 15:06:11 STATE: test-backend-node.js start object 
+2022-10-02 15:06:12 STATE: test-backend-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} 
+2022-10-02 15:06:12 STATE: test-backend-node.js event: image 
+2022-10-02 15:06:12 STATE: test-backend-node.js event: detect 
+2022-10-02 15:06:12 STATE: test-backend-node.js passed: detect: samples/in/ai-body.jpg object 
+2022-10-02 15:06:12 DATA:  test-backend-node.js  result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
+2022-10-02 15:06:12 DATA:  test-backend-node.js  result: performance: load: null total: 208 
+2022-10-02 15:06:12 STATE: test-backend-node.js passed: centernet 
+2022-10-02 15:06:12 STATE: test-backend-node.js start object 
+2022-10-02 15:06:13 STATE: test-backend-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} 
+2022-10-02 15:06:13 STATE: test-backend-node.js event: image 
+2022-10-02 15:06:13 STATE: test-backend-node.js event: detect 
+2022-10-02 15:06:13 STATE: test-backend-node.js passed: detect: samples/in/ai-body.jpg object 
+2022-10-02 15:06:13 DATA:  test-backend-node.js  result: face: 1 body: 1 hand: 1 gesture: 7 object: 3 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.86,"class":"person"} {"score":0.92,"keypoints":17} 
+2022-10-02 15:06:13 DATA:  test-backend-node.js  result: performance: load: null total: 219 
+2022-10-02 15:06:13 STATE: test-backend-node.js passed: nanodet 
+2022-10-02 15:06:13 INFO:  test-backend-node.js test sensitive 
+2022-10-02 15:06:13 STATE: test-backend-node.js start sensitive 
+2022-10-02 15:06:13 STATE: test-backend-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} 
+2022-10-02 15:06:13 STATE: test-backend-node.js event: image 
+2022-10-02 15:06:13 STATE: test-backend-node.js event: detect 
+2022-10-02 15:06:13 STATE: test-backend-node.js passed: detect: samples/in/ai-body.jpg sensitive 
+2022-10-02 15:06:13 DATA:  test-backend-node.js  result: face: 1 body: 1 hand: 2 gesture: 9 object: 0 person: 1 {"score":1,"age":23.7,"gender":"female"} {} {"score":0.92,"keypoints":17} 
+2022-10-02 15:06:13 DATA:  test-backend-node.js  result: performance: load: null total: 190 
+2022-10-02 15:06:13 STATE: test-backend-node.js passed: sensitive result match 
+2022-10-02 15:06:13 STATE: test-backend-node.js passed: sensitive face result match 
+2022-10-02 15:06:13 STATE: test-backend-node.js passed: sensitive face emotion result [{"score":0.59,"emotion":"angry"},{"score":0.29,"emotion":"fear"}] 
+2022-10-02 15:06:13 STATE: test-backend-node.js passed: sensitive body result match 
+2022-10-02 15:06:13 STATE: test-backend-node.js passed: sensitive hand result match 
+2022-10-02 15:06:13 INFO:  test-backend-node.js test body 
+2022-10-02 15:06:13 STATE: test-backend-node.js start blazepose 
+2022-10-02 15:06:15 STATE: test-backend-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} 
+2022-10-02 15:06:15 STATE: test-backend-node.js event: image 
+2022-10-02 15:06:15 STATE: test-backend-node.js event: detect 
+2022-10-02 15:06:15 STATE: test-backend-node.js passed: detect: samples/in/ai-body.jpg blazepose 
+2022-10-02 15:06:15 DATA:  test-backend-node.js  result: face: 1 body: 1 hand: 2 gesture: 9 object: 0 person: 1 {"score":1,"age":23.7,"gender":"female"} {} {"score":0.99,"keypoints":39} 
+2022-10-02 15:06:15 DATA:  test-backend-node.js  result: performance: load: null total: 229 
+2022-10-02 15:06:15 STATE: test-backend-node.js passed: blazepose 
+2022-10-02 15:06:15 STATE: test-backend-node.js start efficientpose 
+2022-10-02 15:06:16 STATE: test-backend-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} 
+2022-10-02 15:06:16 STATE: test-backend-node.js event: image 
+2022-10-02 15:06:16 STATE: test-backend-node.js event: detect 
+2022-10-02 15:06:16 STATE: test-backend-node.js passed: detect: samples/in/ai-body.jpg efficientpose 
+2022-10-02 15:06:16 DATA:  test-backend-node.js  result: face: 1 body: 1 hand: 2 gesture: 9 object: 0 person: 1 {"score":1,"age":23.7,"gender":"female"} {} {"score":0.75,"keypoints":13} 
+2022-10-02 15:06:16 DATA:  test-backend-node.js  result: performance: load: null total: 251 
+2022-10-02 15:06:16 STATE: test-backend-node.js passed: efficientpose 
+2022-10-02 15:06:16 STATE: test-backend-node.js start posenet 
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+2022-10-02 15:06:17 STATE: test-backend-node.js event: image 
+2022-10-02 15:06:17 STATE: test-backend-node.js event: detect 
+2022-10-02 15:06:17 STATE: test-backend-node.js passed: detect: samples/in/ai-body.jpg posenet 
+2022-10-02 15:06:17 DATA:  test-backend-node.js  result: face: 1 body: 1 hand: 2 gesture: 9 object: 0 person: 1 {"score":1,"age":23.7,"gender":"female"} {} {"score":0.96,"keypoints":16} 
+2022-10-02 15:06:17 DATA:  test-backend-node.js  result: performance: load: null total: 170 
+2022-10-02 15:06:17 STATE: test-backend-node.js passed: posenet 
+2022-10-02 15:06:17 STATE: test-backend-node.js start movenet 
+2022-10-02 15:06:17 STATE: test-backend-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} 
+2022-10-02 15:06:17 STATE: test-backend-node.js event: image 
+2022-10-02 15:06:17 STATE: test-backend-node.js event: detect 
+2022-10-02 15:06:17 STATE: test-backend-node.js passed: detect: samples/in/ai-body.jpg movenet 
+2022-10-02 15:06:17 DATA:  test-backend-node.js  result: face: 1 body: 1 hand: 2 gesture: 9 object: 0 person: 1 {"score":1,"age":23.7,"gender":"female"} {} {"score":0.92,"keypoints":17} 
+2022-10-02 15:06:17 DATA:  test-backend-node.js  result: performance: load: null total: 179 
+2022-10-02 15:06:17 STATE: test-backend-node.js passed: movenet 
+2022-10-02 15:06:17 INFO:  test-backend-node.js test face matching 
+2022-10-02 15:06:17 STATE: test-backend-node.js passed: face database 40 
+2022-10-02 15:06:17 STATE: test-backend-node.js passed: face match {"first":{"index":4,"similarity":0.7827852615252829}} {"second":{"index":4,"similarity":0.5002052633015844}} {"third":{"index":4,"similarity":0.5401587887998899}} 
+2022-10-02 15:06:17 INFO:  test-backend-node.js test face similarity alternative 
+2022-10-02 15:06:17 STATE: test-backend-node.js start face embeddings 
+2022-10-02 15:06:18 STATE: test-backend-node.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} 
+2022-10-02 15:06:18 STATE: test-backend-node.js event: image 
+2022-10-02 15:06:18 STATE: test-backend-node.js event: detect 
+2022-10-02 15:06:18 STATE: test-backend-node.js passed: detect: samples/in/ai-face.jpg face embeddings 
+2022-10-02 15:06:18 DATA:  test-backend-node.js  result: face: 1 body: 1 hand: 2 gesture: 8 object: 0 person: 1 {"score":1,"age":23.5,"gender":"female"} {} {"score":0.47,"keypoints":3} 
+2022-10-02 15:06:18 DATA:  test-backend-node.js  result: performance: load: null total: 185 
+2022-10-02 15:06:18 STATE: test-backend-node.js passed: mobilefacenet {"embedding":192} 
+2022-10-02 15:06:18 STATE: test-backend-node.js start face embeddings 
+2022-10-02 15:06:19 STATE: test-backend-node.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} 
+2022-10-02 15:06:19 STATE: test-backend-node.js event: image 
+2022-10-02 15:06:19 STATE: test-backend-node.js event: detect 
+2022-10-02 15:06:19 STATE: test-backend-node.js passed: detect: samples/in/ai-face.jpg face embeddings 
+2022-10-02 15:06:19 DATA:  test-backend-node.js  result: face: 1 body: 1 hand: 2 gesture: 8 object: 0 person: 1 {"score":1,"age":23.5,"gender":"female"} {} {"score":0.47,"keypoints":3} 
+2022-10-02 15:06:19 DATA:  test-backend-node.js  result: performance: load: null total: 193 
+2022-10-02 15:06:19 STATE: test-backend-node.js passed: insightface {"embedding":512} 
+2022-10-02 15:06:19 INFO:  test-backend-node.js test face attention 
+2022-10-02 15:06:19 STATE: test-backend-node.js start face attention 
+2022-10-02 15:06:19 STATE: test-backend-node.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} 
+2022-10-02 15:06:19 STATE: test-backend-node.js event: image 
+2022-10-02 15:06:19 STATE: test-backend-node.js event: detect 
+2022-10-02 15:06:19 STATE: test-backend-node.js passed: detect: samples/in/ai-face.jpg face attention 
+2022-10-02 15:06:19 DATA:  test-backend-node.js  result: face: 1 body: 1 hand: 2 gesture: 8 object: 0 person: 1 {"score":1,"age":23.5,"gender":"female"} {} {"score":0.47,"keypoints":3} 
+2022-10-02 15:06:19 DATA:  test-backend-node.js  result: performance: load: null total: 185 
+2022-10-02 15:06:19 STATE: test-backend-node.js passed: face attention 
+2022-10-02 15:06:19 INFO:  test-backend-node.js test detectors 
+2022-10-02 15:06:19 STATE: test-backend-node.js start detectors 
+2022-10-02 15:06:19 STATE: test-backend-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} 
+2022-10-02 15:06:19 STATE: test-backend-node.js event: image 
+2022-10-02 15:06:20 STATE: test-backend-node.js event: detect 
+2022-10-02 15:06:20 STATE: test-backend-node.js passed: detect: samples/in/ai-body.jpg detectors 
+2022-10-02 15:06:20 DATA:  test-backend-node.js  result: face: 1 body: 1 hand: 1 gesture: 0 object: 0 person: 1 {"score":0.93,"gender":"unknown"} {} {"score":0.92,"keypoints":17} 
+2022-10-02 15:06:20 DATA:  test-backend-node.js  result: performance: load: null total: 131 
+2022-10-02 15:06:20 STATE: test-backend-node.js passed: detector result face match 
+2022-10-02 15:06:20 STATE: test-backend-node.js passed: detector result hand match 
+2022-10-02 15:06:20 INFO:  test-backend-node.js test: multi-instance 
+2022-10-02 15:06:20 STATE: test-backend-node.js start multi instance 
+2022-10-02 15:06:20 STATE: test-backend-node.js event: image 
+2022-10-02 15:06:20 STATE: test-backend-node.js event: detect 
+2022-10-02 15:06:20 STATE: test-backend-node.js passed: detect: random multi instance 
+2022-10-02 15:06:20 DATA:  test-backend-node.js  result: face: 0 body: 1 hand: 0 gesture: 0 object: 0 person: 0 {} {} {"score":0,"keypoints":0} 
+2022-10-02 15:06:20 DATA:  test-backend-node.js  result: performance: load: null total: 88 
+2022-10-02 15:06:20 INFO:  test-backend-node.js test: first instance 
+2022-10-02 15:06:20 STATE: test-backend-node.js start multi instance 
+2022-10-02 15:06:20 STATE: test-backend-node.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151289024} 
+2022-10-02 15:06:20 STATE: test-backend-node.js passed: detect: samples/in/ai-upper.jpg multi instance 
+2022-10-02 15:06:20 DATA:  test-backend-node.js  result: face: 1 body: 1 hand: 0 gesture: 0 object: 0 person: 1 {"score":0.96,"gender":"unknown"} {} {"score":0.75,"keypoints":7} 
+2022-10-02 15:06:20 DATA:  test-backend-node.js  result: performance: load: null total: 107 
+2022-10-02 15:06:20 INFO:  test-backend-node.js test: second instance 
+2022-10-02 15:06:20 STATE: test-backend-node.js start multi instance 
+2022-10-02 15:06:20 STATE: test-backend-node.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151289024} 
+2022-10-02 15:06:20 STATE: test-backend-node.js passed: detect: samples/in/ai-upper.jpg multi instance 
+2022-10-02 15:06:20 DATA:  test-backend-node.js  result: face: 1 body: 1 hand: 0 gesture: 0 object: 0 person: 1 {"score":0.96,"gender":"unknown"} {} {"score":0.75,"keypoints":7} 
+2022-10-02 15:06:20 DATA:  test-backend-node.js  result: performance: load: null total: 94 
+2022-10-02 15:06:20 INFO:  test-backend-node.js test: concurrent 
+2022-10-02 15:06:20 STATE: test-backend-node.js start concurrent 
+2022-10-02 15:06:20 STATE: test-backend-node.js start concurrent 
+2022-10-02 15:06:20 STATE: test-backend-node.js start concurrent 
+2022-10-02 15:06:20 STATE: test-backend-node.js start concurrent 
+2022-10-02 15:06:20 STATE: test-backend-node.js start concurrent 
+2022-10-02 15:06:20 STATE: test-backend-node.js start concurrent 
+2022-10-02 15:06:20 STATE: test-backend-node.js start concurrent 
+2022-10-02 15:06:20 STATE: test-backend-node.js start concurrent 
+2022-10-02 15:06:20 STATE: test-backend-node.js start concurrent 
+2022-10-02 15:06:20 STATE: test-backend-node.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} 
+2022-10-02 15:06:20 STATE: test-backend-node.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} 
+2022-10-02 15:06:20 STATE: test-backend-node.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} 
+2022-10-02 15:06:20 STATE: test-backend-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} 
+2022-10-02 15:06:20 STATE: test-backend-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} 
+2022-10-02 15:06:21 STATE: test-backend-node.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796864} 
+2022-10-02 15:06:21 STATE: test-backend-node.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151289024} 
+2022-10-02 15:06:21 STATE: test-backend-node.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151289024} 
+2022-10-02 15:06:21 STATE: test-backend-node.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151289024} 
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+2022-10-02 15:06:22 DATA:  test-backend-node.js  result: performance: load: null total: 847 
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+2022-10-02 15:06:22 DATA:  test-backend-node.js  result: face: 1 body: 1 hand: 1 gesture: 0 object: 0 person: 1 {"score":0.91,"gender":"unknown"} {} {"score":0.47,"keypoints":3} 
+2022-10-02 15:06:22 DATA:  test-backend-node.js  result: performance: load: null total: 847 
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+2022-10-02 15:06:22 DATA:  test-backend-node.js  result: face: 1 body: 1 hand: 1 gesture: 0 object: 0 person: 1 {"score":0.93,"gender":"unknown"} {} {"score":0.92,"keypoints":17} 
+2022-10-02 15:06:22 DATA:  test-backend-node.js  result: performance: load: null total: 847 
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+2022-10-02 15:06:22 STATE: test-backend-node.js passed: monkey patch 
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+2022-10-02 15:06:22 INFO:  test-backend-node.js tensors 4441 
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+2022-10-02 15:06:23 INFO:  test-backend-node-gpu.js test: model load 
+2022-10-02 15:06:23 STATE: test-backend-node-gpu.js passed: models loaded 25 11 [{"name":"ssrnetage","loaded":false,"url":null},{"name":"gear","loaded":false,"url":null},{"name":"blazeposedetect","loaded":false,"url":null},{"name":"blazepose","loaded":false,"url":null},{"name":"centernet","loaded":true,"url":"file://models/mb3-centernet.json"},{"name":"efficientpose","loaded":false,"url":null},{"name":"mobilefacenet","loaded":false,"url":null},{"name":"insightface","loaded":false,"url":null},{"name":"emotion","loaded":true,"url":"file://models/emotion.json"},{"name":"facedetect","loaded":true,"url":"file://models/blazeface.json"},{"name":"faceiris","loaded":true,"url":"file://models/iris.json"},{"name":"facemesh","loaded":true,"url":"file://models/facemesh.json"},{"name":"faceres","loaded":true,"url":"file://models/faceres.json"},{"name":"ssrnetgender","loaded":false,"url":null},{"name":"handpose","loaded":false,"url":null},{"name":"handskeleton","loaded":true,"url":"file://models/handlandmark-full.json"},{"name":"handtrack","loaded":true,"url":"file://models/handtrack.json"},{"name":"liveness","loaded":true,"url":"file://models/liveness.json"},{"name":"meet","loaded":false,"url":null},{"name":"movenet","loaded":true,"url":"file://models/movenet-lightning.json"},{"name":"nanodet","loaded":false,"url":null},{"name":"posenet","loaded":false,"url":null},{"name":"selfie","loaded":false,"url":null},{"name":"rvm","loaded":false,"url":null},{"name":"antispoof","loaded":true,"url":"file://models/antispoof.json"}] 
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+2022-10-02 15:06:23 INFO:  test-backend-node-gpu.js test: warmup 
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+2022-10-02 15:06:23 INFO:  test-backend-node-gpu.js tfjs version: 3.20.0 
+2022-10-02 15:06:23 INFO:  test-backend-node-gpu.js env: {"browser":false,"node":true,"platform":"linux x64","agent":"NodeJS v18.10.0","backends":["cpu","tensorflow"],"initial":false,"tfjs":{"version":"3.20.0"},"offscreen":false,"perfadd":false,"tensorflow":{"version":"2.7.3-dev20220521","gpu":true},"wasm":{"supported":true,"backend":false},"webgl":{"supported":false,"backend":false},"webgpu":{"supported":false,"backend":false},"cpu":{"flags":[]},"kernels":169} 
+2022-10-02 15:06:23 STATE: test-backend-node-gpu.js passed: set backend: tensorflow 
+2022-10-02 15:06:23 STATE: test-backend-node-gpu.js tensors 1785 
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+2022-10-02 15:06:23 STATE: test-backend-node-gpu.js passed: warmup: none default 
+2022-10-02 15:06:23 DATA:  test-backend-node-gpu.js  result: face: 0 body: 0 hand: 0 gesture: 0 object: 0 person: 0 {} {} {} 
+2022-10-02 15:06:23 DATA:  test-backend-node-gpu.js  result: performance: load: null total: null 
+2022-10-02 15:06:23 STATE: test-backend-node-gpu.js passed: warmup none result match 
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+2022-10-02 15:06:25 STATE: test-backend-node-gpu.js passed: warmup: face default 
+2022-10-02 15:06:25 DATA:  test-backend-node-gpu.js  result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.5,"gender":"female"} {"score":0.82,"class":"person"} {"score":0.42,"keypoints":4} 
+2022-10-02 15:06:25 DATA:  test-backend-node-gpu.js  result: performance: load: null total: 1937 
+2022-10-02 15:06:25 STATE: test-backend-node-gpu.js passed: warmup face result match 
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+2022-10-02 15:06:26 DATA:  test-backend-node-gpu.js  result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
+2022-10-02 15:06:26 DATA:  test-backend-node-gpu.js  result: performance: load: null total: 198 
+2022-10-02 15:06:26 STATE: test-backend-node-gpu.js passed: warmup body result match 
+2022-10-02 15:06:26 STATE: test-backend-node-gpu.js details: {"face":{"boxScore":0.92,"faceScore":1,"age":23.7,"gender":"female","genderScore":0.97},"emotion":[{"score":0.63,"emotion":"angry"},{"score":0.22,"emotion":"fear"}],"body":{"score":0.92,"keypoints":17},"hand":{"boxScore":0.52,"fingerScore":0.73,"keypoints":21},"gestures":[{"face":0,"gesture":"facing right"},{"face":0,"gesture":"mouth 10% open"},{"hand":0,"gesture":"pinky forward"},{"hand":0,"gesture":"palm up"},{"hand":0,"gesture":"open palm"},{"iris":0,"gesture":"looking left"},{"iris":0,"gesture":"looking up"}]} 
+2022-10-02 15:06:26 INFO:  test-backend-node-gpu.js test: details verification 
+2022-10-02 15:06:26 STATE: test-backend-node-gpu.js start default 
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+2022-10-02 15:06:26 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-body.jpg default 
+2022-10-02 15:06:26 DATA:  test-backend-node-gpu.js  result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
+2022-10-02 15:06:26 DATA:  test-backend-node-gpu.js  result: performance: load: null total: 186 
+2022-10-02 15:06:26 STATE: test-backend-node-gpu.js passed: details face length 1 
+2022-10-02 15:06:26 STATE: test-backend-node-gpu.js passed: details face score 1 0.93 1 
+2022-10-02 15:06:26 STATE: test-backend-node-gpu.js passed: details face age/gender 23.7 female 0.97 85.47 
+2022-10-02 15:06:26 STATE: test-backend-node-gpu.js passed: details face arrays 4 478 1024 
+2022-10-02 15:06:26 STATE: test-backend-node-gpu.js passed: details face emotion 2 {"score":0.59,"emotion":"angry"} 
+2022-10-02 15:06:26 STATE: test-backend-node-gpu.js passed: details face anti-spoofing 0.79 
+2022-10-02 15:06:26 STATE: test-backend-node-gpu.js passed: details face liveness 0.83 
+2022-10-02 15:06:26 STATE: test-backend-node-gpu.js passed: details body length 1 
+2022-10-02 15:06:26 STATE: test-backend-node-gpu.js passed: details body 0.92 17 6 
+2022-10-02 15:06:26 STATE: test-backend-node-gpu.js passed: details hand length 1 
+2022-10-02 15:06:26 STATE: test-backend-node-gpu.js passed: details hand 0.51 0.73 point 
+2022-10-02 15:06:26 STATE: test-backend-node-gpu.js passed: details hand arrays 21 5 7 
+2022-10-02 15:06:26 STATE: test-backend-node-gpu.js passed: details gesture length 7 
+2022-10-02 15:06:26 STATE: test-backend-node-gpu.js passed: details gesture first {"face":0,"gesture":"facing right"} 
+2022-10-02 15:06:26 STATE: test-backend-node-gpu.js passed: details object length 1 
+2022-10-02 15:06:26 STATE: test-backend-node-gpu.js passed: details object 0.72 person 
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+2022-10-02 15:06:26 STATE: test-backend-node-gpu.js passed: tensor shape: [1,1200,1200,4] dtype: float32 
+2022-10-02 15:06:27 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-body.jpg [1200,1200,4] {"checksum":1371996928} 
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+2022-10-02 15:06:27 STATE: test-backend-node-gpu.js passed: tensor shape: [1200,1200,4] dtype: float32 
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+2022-10-02 15:06:27 STATE: test-backend-node-gpu.js event: image 
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+2022-10-02 15:06:27 STATE: test-backend-node-gpu.js passed: tensor shape: [1,1200,1200,3] dtype: float32 
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+2022-10-02 15:06:27 STATE: test-backend-node-gpu.js passed: tensor shape: [1200,1200,3] dtype: float32 
+2022-10-02 15:06:28 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,4] {"checksum":1371996871} 
+2022-10-02 15:06:28 STATE: test-backend-node-gpu.js event: image 
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+2022-10-02 15:06:28 STATE: test-backend-node-gpu.js passed: tensor shape: [1,1200,1200,4] dtype: int32 
+2022-10-02 15:06:28 INFO:  test-backend-node-gpu.js test default 
+2022-10-02 15:06:28 STATE: test-backend-node-gpu.js start async 
+2022-10-02 15:06:28 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796928} 
+2022-10-02 15:06:28 STATE: test-backend-node-gpu.js event: image 
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+2022-10-02 15:06:28 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-body.jpg async 
+2022-10-02 15:06:28 DATA:  test-backend-node-gpu.js  result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
+2022-10-02 15:06:28 DATA:  test-backend-node-gpu.js  result: performance: load: null total: 132 
+2022-10-02 15:06:28 STATE: test-backend-node-gpu.js passed: default result face match 1 female 0.97 
+2022-10-02 15:06:28 INFO:  test-backend-node-gpu.js test sync 
+2022-10-02 15:06:28 STATE: test-backend-node-gpu.js start sync 
+2022-10-02 15:06:28 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1004796928} 
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+2022-10-02 15:06:28 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-body.jpg sync 
+2022-10-02 15:06:28 DATA:  test-backend-node-gpu.js  result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
+2022-10-02 15:06:28 DATA:  test-backend-node-gpu.js  result: performance: load: null total: 157 
+2022-10-02 15:06:28 STATE: test-backend-node-gpu.js passed: default sync 1 female 0.97 
+2022-10-02 15:06:28 INFO:  test-backend-node-gpu.js test: image process 
+2022-10-02 15:06:28 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} 
+2022-10-02 15:06:28 STATE: test-backend-node-gpu.js passed: image input null [1,256,256,3] 
+2022-10-02 15:06:28 INFO:  test-backend-node-gpu.js test: image null 
+2022-10-02 15:06:28 STATE: test-backend-node-gpu.js passed: invalid input could not convert input to tensor 
+2022-10-02 15:06:28 INFO:  test-backend-node-gpu.js test face similarity 
+2022-10-02 15:06:28 STATE: test-backend-node-gpu.js start face similarity 
+2022-10-02 15:06:28 STATE: test-backend-node-gpu.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34696120} 
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+2022-10-02 15:06:29 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-face.jpg face similarity 
+2022-10-02 15:06:29 DATA:  test-backend-node-gpu.js  result: face: 1 body: 1 hand: 1 gesture: 6 object: 1 person: 1 {"score":1,"age":23.5,"gender":"female"} {"score":0.82,"class":"person"} {"score":0.47,"keypoints":3} 
+2022-10-02 15:06:29 DATA:  test-backend-node-gpu.js  result: performance: load: null total: 144 
+2022-10-02 15:06:29 STATE: test-backend-node-gpu.js start face similarity 
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+2022-10-02 15:06:29 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-body.jpg face similarity 
+2022-10-02 15:06:29 DATA:  test-backend-node-gpu.js  result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
+2022-10-02 15:06:29 DATA:  test-backend-node-gpu.js  result: performance: load: null total: 144 
+2022-10-02 15:06:29 STATE: test-backend-node-gpu.js start face similarity 
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+2022-10-02 15:06:29 STATE: test-backend-node-gpu.js passed: detect: samples/in/ai-upper.jpg face similarity 
+2022-10-02 15:06:29 DATA:  test-backend-node-gpu.js  result: face: 1 body: 1 hand: 0 gesture: 4 object: 1 person: 1 {"score":1,"age":23.5,"gender":"female"} {"score":0.71,"class":"person"} {"score":0.75,"keypoints":7} 
+2022-10-02 15:06:29 DATA:  test-backend-node-gpu.js  result: performance: load: null total: 121 
+2022-10-02 15:06:29 STATE: test-backend-node-gpu.js passed: face descriptor 
+2022-10-02 15:06:29 STATE: test-backend-node-gpu.js passed: face similarity {"similarity":[1,0.447238756461232,0.556914029877052],"descriptors":[1024,1024,1024]} 
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+2022-10-02 15:06:29 DATA:  test-backend-node-gpu.js  result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
+2022-10-02 15:06:29 DATA:  test-backend-node-gpu.js  result: performance: load: null total: 121 
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+2022-10-02 15:06:31 DATA:  test-backend-node-gpu.js  result: face: 1 body: 1 hand: 1 gesture: 7 object: 3 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.86,"class":"person"} {"score":0.92,"keypoints":17} 
+2022-10-02 15:06:31 DATA:  test-backend-node-gpu.js  result: performance: load: null total: 632 
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+2022-10-02 15:06:31 DATA:  test-backend-node-gpu.js  result: face: 1 body: 1 hand: 2 gesture: 9 object: 0 person: 1 {"score":1,"age":23.7,"gender":"female"} {} {"score":0.92,"keypoints":17} 
+2022-10-02 15:06:31 DATA:  test-backend-node-gpu.js  result: performance: load: null total: 110 
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+2022-10-02 15:06:31 STATE: test-backend-node-gpu.js passed: sensitive face emotion result [{"score":0.59,"emotion":"angry"},{"score":0.29,"emotion":"fear"}] 
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+2022-10-02 15:06:33 DATA:  test-backend-node-gpu.js  result: performance: load: null total: 288 
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+2022-10-02 15:06:34 DATA:  test-backend-node-gpu.js  result: performance: load: null total: 643 
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+2022-10-02 15:06:35 DATA:  test-backend-node-gpu.js  result: performance: load: null total: 111 
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+2022-10-02 15:06:35 INFO:  test-backend-node-gpu.js test face similarity alternative 
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+2022-10-02 15:06:36 DATA:  test-backend-node-gpu.js  result: performance: load: null total: 152 
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+2022-10-02 15:06:37 DATA:  test-backend-node-gpu.js  result: performance: load: null total: 245 
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+2022-10-02 15:06:37 DATA:  test-backend-node-gpu.js  result: performance: load: null total: 89 
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+2022-10-02 15:06:39 DATA:  test-backend-node-gpu.js  result: performance: load: null total: 606 
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+2022-10-02 15:06:40 STATE: test-backend-node-gpu.js passeed: equal usage 
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+2022-10-02 15:06:40 INFO:  test-backend-node-gpu.js tensors 4441 
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+2022-10-02 15:06:40 INFO:  
+2022-10-02 15:06:40 INFO:  test-backend-node-wasm.js start 
+2022-10-02 15:06:40 DATA:  test-backend-node-wasm.js stdout: 2022-10-02 15:06:40 INFO:  { supported: true, backend: true, simd: true, multithread: false } https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-backend-wasm@3.20.0/dist/ 
+2022-10-02 15:06:41 STATE: test-backend-node-wasm.js passed: model server: https://vladmandic.github.io/human-models/models/ 
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+2022-10-02 15:06:41 STATE: test-backend-node-wasm.js passed: configuration default validation [] 
+2022-10-02 15:06:41 STATE: test-backend-node-wasm.js passed: configuration invalid validation [{"reason":"unknown property","where":"config.invalid = true"}] 
+2022-10-02 15:06:41 INFO:  test-backend-node-wasm.js test: model load 
+2022-10-02 15:06:43 STATE: test-backend-node-wasm.js passed: models loaded 25 11 [{"name":"ssrnetage","loaded":false,"url":null},{"name":"gear","loaded":false,"url":null},{"name":"blazeposedetect","loaded":false,"url":null},{"name":"blazepose","loaded":false,"url":null},{"name":"centernet","loaded":true,"url":"https://vladmandic.github.io/human-models/models/mb3-centernet.json"},{"name":"efficientpose","loaded":false,"url":null},{"name":"mobilefacenet","loaded":false,"url":null},{"name":"insightface","loaded":false,"url":null},{"name":"emotion","loaded":true,"url":"https://vladmandic.github.io/human-models/models/emotion.json"},{"name":"facedetect","loaded":true,"url":"https://vladmandic.github.io/human-models/models/blazeface.json"},{"name":"faceiris","loaded":true,"url":"https://vladmandic.github.io/human-models/models/iris.json"},{"name":"facemesh","loaded":true,"url":"https://vladmandic.github.io/human-models/models/facemesh.json"},{"name":"faceres","loaded":true,"url":"https://vladmandic.github.io/human-models/models/faceres.json"},{"name":"ssrnetgender","loaded":false,"url":null},{"name":"handpose","loaded":false,"url":null},{"name":"handskeleton","loaded":true,"url":"https://vladmandic.github.io/human-models/models/handlandmark-full.json"},{"name":"handtrack","loaded":true,"url":"https://vladmandic.github.io/human-models/models/handtrack.json"},{"name":"liveness","loaded":true,"url":"https://vladmandic.github.io/human-models/models/liveness.json"},{"name":"meet","loaded":false,"url":null},{"name":"movenet","loaded":true,"url":"https://vladmandic.github.io/human-models/models/movenet-lightning.json"},{"name":"nanodet","loaded":false,"url":null},{"name":"posenet","loaded":false,"url":null},{"name":"selfie","loaded":false,"url":null},{"name":"rvm","loaded":false,"url":null},{"name":"antispoof","loaded":true,"url":"https://vladmandic.github.io/human-models/models/antispoof.json"}] 
+2022-10-02 15:06:43 INFO:  test-backend-node-wasm.js memory: {"memory":{"unreliable":false,"numTensors":1785,"numDataBuffers":1785,"numBytes":63247332}} 
+2022-10-02 15:06:43 INFO:  test-backend-node-wasm.js state: {"state":{"registeredVariables":{},"nextTapeNodeId":0,"numBytes":63247332,"numTensors":1785,"numStringTensors":0,"numDataBuffers":1785,"gradientDepth":0,"kernelDepth":0,"scopeStack":[],"numDataMovesStack":[],"nextScopeId":0,"tensorInfo":{},"profiling":false,"activeProfile":{"newBytes":0,"newTensors":0,"peakBytes":0,"kernels":[],"result":null,"kernelNames":[]}}} 
+2022-10-02 15:06:43 INFO:  test-backend-node-wasm.js test: warmup 
+2022-10-02 15:06:43 STATE: test-backend-node-wasm.js passed: create human 
+2022-10-02 15:06:43 INFO:  test-backend-node-wasm.js human version: 2.11.0 
+2022-10-02 15:06:43 INFO:  test-backend-node-wasm.js platform: linux x64 agent: NodeJS v18.10.0 
+2022-10-02 15:06:43 INFO:  test-backend-node-wasm.js tfjs version: 3.20.0 
+2022-10-02 15:06:43 INFO:  test-backend-node-wasm.js env: {"browser":false,"node":true,"platform":"linux x64","agent":"NodeJS v18.10.0","backends":["cpu","wasm"],"initial":false,"tfjs":{"version":"3.20.0"},"offscreen":false,"perfadd":false,"tensorflow":{},"wasm":{"supported":true,"backend":true,"simd":true,"multithread":false},"webgl":{"supported":false,"backend":false},"webgpu":{"supported":false,"backend":false},"cpu":{"flags":[]},"kernels":126} 
+2022-10-02 15:06:43 STATE: test-backend-node-wasm.js passed: set backend: wasm 
+2022-10-02 15:06:43 STATE: test-backend-node-wasm.js tensors 1785 
+2022-10-02 15:06:43 STATE: test-backend-node-wasm.js passed: load models 
+2022-10-02 15:06:43 STATE: test-backend-node-wasm.js  result: defined models: 25 loaded models: 11 
+2022-10-02 15:06:43 STATE: test-backend-node-wasm.js passed: warmup: none default 
+2022-10-02 15:06:43 DATA:  test-backend-node-wasm.js  result: face: 0 body: 0 hand: 0 gesture: 0 object: 0 person: 0 {} {} {} 
+2022-10-02 15:06:43 DATA:  test-backend-node-wasm.js  result: performance: load: null total: null 
+2022-10-02 15:06:43 STATE: test-backend-node-wasm.js passed: warmup none result match 
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+2022-10-02 15:06:43 DATA:  test-backend-node-wasm.js  result: face: 1 body: 1 hand: 1 gesture: 6 object: 1 person: 1 {"score":1,"age":23.5,"gender":"female"} {"score":0.82,"class":"person"} {"score":0.47,"keypoints":3} 
+2022-10-02 15:06:43 DATA:  test-backend-node-wasm.js  result: performance: load: null total: 565 
+2022-10-02 15:06:43 STATE: test-backend-node-wasm.js passed: warmup face result match 
+2022-10-02 15:06:43 STATE: test-backend-node-wasm.js event: image 
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+2022-10-02 15:06:44 STATE: test-backend-node-wasm.js passed: warmup: body default 
+2022-10-02 15:06:44 DATA:  test-backend-node-wasm.js  result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
+2022-10-02 15:06:44 DATA:  test-backend-node-wasm.js  result: performance: load: null total: 354 
+2022-10-02 15:06:44 STATE: test-backend-node-wasm.js passed: warmup body result match 
+2022-10-02 15:06:44 STATE: test-backend-node-wasm.js details: {"face":{"boxScore":0.93,"faceScore":1,"age":23.7,"gender":"female","genderScore":0.97},"emotion":[{"score":0.59,"emotion":"angry"},{"score":0.29,"emotion":"fear"}],"body":{"score":0.92,"keypoints":17},"hand":{"boxScore":0.51,"fingerScore":0.73,"keypoints":21},"gestures":[{"face":0,"gesture":"facing right"},{"face":0,"gesture":"mouth 21% open"},{"hand":0,"gesture":"pinky forward"},{"hand":0,"gesture":"palm up"},{"hand":0,"gesture":"open palm"},{"iris":0,"gesture":"looking left"},{"iris":0,"gesture":"looking up"}]} 
+2022-10-02 15:06:44 INFO:  test-backend-node-wasm.js test: details verification 
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+2022-10-02 15:06:44 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-body.jpg default 
+2022-10-02 15:06:44 DATA:  test-backend-node-wasm.js  result: face: 1 body: 1 hand: 1 gesture: 7 object: 1 person: 1 {"score":1,"age":23.7,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
+2022-10-02 15:06:44 DATA:  test-backend-node-wasm.js  result: performance: load: null total: 336 
+2022-10-02 15:06:44 STATE: test-backend-node-wasm.js passed: details face length 1 
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+2022-10-02 15:06:44 STATE: test-backend-node-wasm.js passed: details face age/gender 23.7 female 0.97 85.47 
+2022-10-02 15:06:44 STATE: test-backend-node-wasm.js passed: details face arrays 4 478 1024 
+2022-10-02 15:06:44 STATE: test-backend-node-wasm.js passed: details face emotion 2 {"score":0.59,"emotion":"angry"} 
+2022-10-02 15:06:44 STATE: test-backend-node-wasm.js passed: details face anti-spoofing 0.79 
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+2022-10-02 15:06:44 STATE: test-backend-node-wasm.js passed: details body length 1 
+2022-10-02 15:06:44 STATE: test-backend-node-wasm.js passed: details body 0.92 17 6 
+2022-10-02 15:06:44 STATE: test-backend-node-wasm.js passed: details hand length 1 
+2022-10-02 15:06:44 STATE: test-backend-node-wasm.js passed: details hand 0.51 0.73 point 
+2022-10-02 15:06:44 STATE: test-backend-node-wasm.js passed: details hand arrays 21 5 7 
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+2022-10-02 15:06:44 STATE: test-backend-node-wasm.js passed: details gesture first {"face":0,"gesture":"facing right"} 
+2022-10-02 15:06:44 STATE: test-backend-node-wasm.js passed: details object length 1 
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+2022-10-02 15:06:45 STATE: test-backend-node-wasm.js event: image 
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+2022-10-02 15:06:45 STATE: test-backend-node-wasm.js passed: tensor shape: [1200,1200,4] dtype: float32 
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+2022-10-02 15:06:45 STATE: test-backend-node-wasm.js event: image 
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+2022-10-02 15:06:46 STATE: test-backend-node-wasm.js passed: tensor shape: [1,1200,1200,3] dtype: float32 
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+2022-10-02 15:06:46 STATE: test-backend-node-wasm.js event: image 
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+2022-10-02 15:06:46 STATE: test-backend-node-wasm.js event: image 
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+2022-10-02 15:06:47 INFO:  test-backend-node-wasm.js test default 
+2022-10-02 15:06:47 STATE: test-backend-node-wasm.js start async 
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+2022-10-02 15:06:47 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-body.jpg async 
+2022-10-02 15:06:47 DATA:  test-backend-node-wasm.js  result: face: 1 body: 1 hand: 1 gesture: 8 object: 1 person: 1 {"score":1,"age":29.6,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
+2022-10-02 15:06:47 DATA:  test-backend-node-wasm.js  result: performance: load: null total: 309 
+2022-10-02 15:06:47 STATE: test-backend-node-wasm.js passed: default result face match 1 female 0.97 
+2022-10-02 15:06:47 INFO:  test-backend-node-wasm.js test sync 
+2022-10-02 15:06:47 STATE: test-backend-node-wasm.js start sync 
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+2022-10-02 15:06:48 STATE: test-backend-node-wasm.js event: image 
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+2022-10-02 15:06:48 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-body.jpg sync 
+2022-10-02 15:06:48 DATA:  test-backend-node-wasm.js  result: face: 1 body: 1 hand: 1 gesture: 8 object: 1 person: 1 {"score":1,"age":29.6,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
+2022-10-02 15:06:48 DATA:  test-backend-node-wasm.js  result: performance: load: null total: 326 
+2022-10-02 15:06:48 STATE: test-backend-node-wasm.js passed: default sync 1 female 0.97 
+2022-10-02 15:06:48 INFO:  test-backend-node-wasm.js test: image process 
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+2022-10-02 15:06:48 STATE: test-backend-node-wasm.js passed: image input null [1,256,256,3] 
+2022-10-02 15:06:48 INFO:  test-backend-node-wasm.js test: image null 
+2022-10-02 15:06:48 STATE: test-backend-node-wasm.js passed: invalid input could not convert input to tensor 
+2022-10-02 15:06:48 INFO:  test-backend-node-wasm.js test face similarity 
+2022-10-02 15:06:48 STATE: test-backend-node-wasm.js start face similarity 
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+2022-10-02 15:06:48 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-face.jpg face similarity 
+2022-10-02 15:06:48 DATA:  test-backend-node-wasm.js  result: face: 1 body: 1 hand: 1 gesture: 6 object: 1 person: 1 {"score":1,"age":23.5,"gender":"female"} {"score":0.82,"class":"person"} {"score":0.47,"keypoints":3} 
+2022-10-02 15:06:48 DATA:  test-backend-node-wasm.js  result: performance: load: null total: 299 
+2022-10-02 15:06:48 STATE: test-backend-node-wasm.js start face similarity 
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+2022-10-02 15:06:48 STATE: test-backend-node-wasm.js event: image 
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+2022-10-02 15:06:49 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-body.jpg face similarity 
+2022-10-02 15:06:49 DATA:  test-backend-node-wasm.js  result: face: 1 body: 1 hand: 1 gesture: 8 object: 1 person: 1 {"score":1,"age":29.6,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
+2022-10-02 15:06:49 DATA:  test-backend-node-wasm.js  result: performance: load: null total: 329 
+2022-10-02 15:06:49 STATE: test-backend-node-wasm.js start face similarity 
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+2022-10-02 15:06:49 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-upper.jpg face similarity 
+2022-10-02 15:06:49 DATA:  test-backend-node-wasm.js  result: face: 1 body: 1 hand: 0 gesture: 4 object: 1 person: 1 {"score":1,"age":23.5,"gender":"female"} {"score":0.71,"class":"person"} {"score":0.75,"keypoints":7} 
+2022-10-02 15:06:49 DATA:  test-backend-node-wasm.js  result: performance: load: null total: 295 
+2022-10-02 15:06:49 STATE: test-backend-node-wasm.js passed: face descriptor 
+2022-10-02 15:06:49 STATE: test-backend-node-wasm.js passed: face similarity {"similarity":[1,0.5266119940661309,0.4858842904087851],"descriptors":[1024,1024,1024]} 
+2022-10-02 15:06:49 INFO:  test-backend-node-wasm.js test object 
+2022-10-02 15:06:49 STATE: test-backend-node-wasm.js start object 
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+2022-10-02 15:06:50 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-body.jpg object 
+2022-10-02 15:06:50 DATA:  test-backend-node-wasm.js  result: face: 1 body: 1 hand: 1 gesture: 8 object: 1 person: 1 {"score":1,"age":29.6,"gender":"female"} {"score":0.72,"class":"person"} {"score":0.92,"keypoints":17} 
+2022-10-02 15:06:50 DATA:  test-backend-node-wasm.js  result: performance: load: null total: 349 
+2022-10-02 15:06:50 STATE: test-backend-node-wasm.js passed: centernet 
+2022-10-02 15:06:50 STATE: test-backend-node-wasm.js start object 
+2022-10-02 15:06:51 WARN:  test-backend-node-wasm.js missing kernel ops {"title":"object","model":"nanodet","url":"https://vladmandic.github.io/human-models/models/nanodet.json","missing":["sparsetodense"],"backkend":"wasm"} 
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+2022-10-02 15:06:52 STATE: test-backend-node-wasm.js event: image 
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+2022-10-02 15:06:52 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-body.jpg object 
+2022-10-02 15:06:52 DATA:  test-backend-node-wasm.js  result: face: 1 body: 1 hand: 1 gesture: 8 object: 0 person: 1 {"score":1,"age":29.6,"gender":"female"} {} {"score":0.92,"keypoints":17} 
+2022-10-02 15:06:52 DATA:  test-backend-node-wasm.js  result: performance: load: null total: 226 
+2022-10-02 15:06:52 ERROR: test-backend-node-wasm.js failed: nanodet [] 
+2022-10-02 15:06:52 INFO:  test-backend-node-wasm.js test sensitive 
+2022-10-02 15:06:52 STATE: test-backend-node-wasm.js start sensitive 
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+2022-10-02 15:06:52 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-body.jpg sensitive 
+2022-10-02 15:06:52 DATA:  test-backend-node-wasm.js  result: face: 1 body: 1 hand: 2 gesture: 10 object: 0 person: 1 {"score":1,"age":29.6,"gender":"female"} {} {"score":0.92,"keypoints":17} 
+2022-10-02 15:06:52 DATA:  test-backend-node-wasm.js  result: performance: load: null total: 241 
+2022-10-02 15:06:52 STATE: test-backend-node-wasm.js passed: sensitive result match 
+2022-10-02 15:06:52 STATE: test-backend-node-wasm.js passed: sensitive face result match 
+2022-10-02 15:06:52 STATE: test-backend-node-wasm.js passed: sensitive face emotion result [{"score":0.46,"emotion":"neutral"},{"score":0.24,"emotion":"fear"},{"score":0.17,"emotion":"sad"}] 
+2022-10-02 15:06:52 STATE: test-backend-node-wasm.js passed: sensitive body result match 
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+2022-10-02 15:06:52 INFO:  test-backend-node-wasm.js test body 
+2022-10-02 15:06:52 STATE: test-backend-node-wasm.js start blazepose 
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+2022-10-02 15:06:56 DATA:  test-backend-node-wasm.js  result: face: 1 body: 1 hand: 2 gesture: 10 object: 0 person: 1 {"score":1,"age":29.6,"gender":"female"} {} {"score":0.99,"keypoints":39} 
+2022-10-02 15:06:56 DATA:  test-backend-node-wasm.js  result: performance: load: null total: 378 
+2022-10-02 15:06:56 STATE: test-backend-node-wasm.js passed: blazepose 
+2022-10-02 15:06:56 STATE: test-backend-node-wasm.js start efficientpose 
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+2022-10-02 15:06:57 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-body.jpg efficientpose 
+2022-10-02 15:06:57 DATA:  test-backend-node-wasm.js  result: face: 1 body: 1 hand: 2 gesture: 10 object: 0 person: 1 {"score":1,"age":29.6,"gender":"female"} {} {"score":0.75,"keypoints":13} 
+2022-10-02 15:06:57 DATA:  test-backend-node-wasm.js  result: performance: load: null total: 636 
+2022-10-02 15:06:57 STATE: test-backend-node-wasm.js passed: efficientpose 
+2022-10-02 15:06:57 STATE: test-backend-node-wasm.js start posenet 
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+2022-10-02 15:06:58 STATE: test-backend-node-wasm.js event: image 
+2022-10-02 15:06:58 STATE: test-backend-node-wasm.js event: detect 
+2022-10-02 15:06:58 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-body.jpg posenet 
+2022-10-02 15:06:58 DATA:  test-backend-node-wasm.js  result: face: 1 body: 1 hand: 2 gesture: 10 object: 0 person: 1 {"score":1,"age":29.6,"gender":"female"} {} {"score":0.96,"keypoints":16} 
+2022-10-02 15:06:58 DATA:  test-backend-node-wasm.js  result: performance: load: null total: 287 
+2022-10-02 15:06:58 STATE: test-backend-node-wasm.js passed: posenet 
+2022-10-02 15:06:58 STATE: test-backend-node-wasm.js start movenet 
+2022-10-02 15:06:58 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} 
+2022-10-02 15:06:58 STATE: test-backend-node-wasm.js event: image 
+2022-10-02 15:06:59 STATE: test-backend-node-wasm.js event: detect 
+2022-10-02 15:06:59 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-body.jpg movenet 
+2022-10-02 15:06:59 DATA:  test-backend-node-wasm.js  result: face: 1 body: 1 hand: 2 gesture: 10 object: 0 person: 1 {"score":1,"age":29.6,"gender":"female"} {} {"score":0.92,"keypoints":17} 
+2022-10-02 15:06:59 DATA:  test-backend-node-wasm.js  result: performance: load: null total: 235 
+2022-10-02 15:06:59 STATE: test-backend-node-wasm.js passed: movenet 
+2022-10-02 15:06:59 INFO:  test-backend-node-wasm.js test face matching 
+2022-10-02 15:06:59 STATE: test-backend-node-wasm.js passed: face database 40 
+2022-10-02 15:06:59 STATE: test-backend-node-wasm.js passed: face match {"first":{"index":4,"similarity":0.7827852754786533}} {"second":{"index":4,"similarity":0.5660821189104794}} {"third":{"index":4,"similarity":0.45074189882665594}} 
+2022-10-02 15:06:59 INFO:  test-backend-node-wasm.js test face similarity alternative 
+2022-10-02 15:06:59 STATE: test-backend-node-wasm.js start face embeddings 
+2022-10-02 15:06:59 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34697856} 
+2022-10-02 15:06:59 STATE: test-backend-node-wasm.js event: image 
+2022-10-02 15:06:59 STATE: test-backend-node-wasm.js event: detect 
+2022-10-02 15:06:59 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-face.jpg face embeddings 
+2022-10-02 15:06:59 DATA:  test-backend-node-wasm.js  result: face: 1 body: 1 hand: 2 gesture: 8 object: 0 person: 1 {"score":1,"age":23.5,"gender":"female"} {} {"score":0.47,"keypoints":3} 
+2022-10-02 15:06:59 DATA:  test-backend-node-wasm.js  result: performance: load: null total: 237 
+2022-10-02 15:06:59 STATE: test-backend-node-wasm.js passed: mobilefacenet {"embedding":192} 
+2022-10-02 15:06:59 STATE: test-backend-node-wasm.js start face embeddings 
+2022-10-02 15:07:00 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34697856} 
+2022-10-02 15:07:00 STATE: test-backend-node-wasm.js event: image 
+2022-10-02 15:07:01 STATE: test-backend-node-wasm.js event: detect 
+2022-10-02 15:07:01 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-face.jpg face embeddings 
+2022-10-02 15:07:01 DATA:  test-backend-node-wasm.js  result: face: 1 body: 1 hand: 2 gesture: 8 object: 0 person: 1 {"score":1,"age":23.5,"gender":"female"} {} {"score":0.47,"keypoints":3} 
+2022-10-02 15:07:01 DATA:  test-backend-node-wasm.js  result: performance: load: null total: 296 
+2022-10-02 15:07:01 STATE: test-backend-node-wasm.js passed: insightface {"embedding":512} 
+2022-10-02 15:07:01 INFO:  test-backend-node-wasm.js test face attention 
+2022-10-02 15:07:01 STATE: test-backend-node-wasm.js start face attention 
+2022-10-02 15:07:01 WARN:  test-backend-node-wasm.js missing kernel ops {"title":"face attention","model":"facemesh","url":"https://vladmandic.github.io/human-models/models/facemesh-attention.json","missing":["atan2"],"backkend":"wasm"} 
+2022-10-02 15:07:01 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34697856} 
+2022-10-02 15:07:01 STATE: test-backend-node-wasm.js event: image 
+2022-10-02 15:07:01 STATE: test-backend-node-wasm.js event: detect 
+2022-10-02 15:07:01 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-face.jpg face attention 
+2022-10-02 15:07:01 DATA:  test-backend-node-wasm.js  result: face: 0 body: 1 hand: 1 gesture: 2 object: 0 person: 0 {} {} {"score":0.47,"keypoints":3} 
+2022-10-02 15:07:01 DATA:  test-backend-node-wasm.js  result: performance: load: null total: 127 
+2022-10-02 15:07:01 ERROR: test-backend-node-wasm.js failed: face attention {"annotations":0} 
+2022-10-02 15:07:01 INFO:  test-backend-node-wasm.js test detectors 
+2022-10-02 15:07:01 STATE: test-backend-node-wasm.js start detectors 
+2022-10-02 15:07:01 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} 
+2022-10-02 15:07:01 STATE: test-backend-node-wasm.js event: image 
+2022-10-02 15:07:01 STATE: test-backend-node-wasm.js event: detect 
+2022-10-02 15:07:01 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-body.jpg detectors 
+2022-10-02 15:07:01 DATA:  test-backend-node-wasm.js  result: face: 1 body: 1 hand: 1 gesture: 0 object: 0 person: 1 {"score":0.93,"gender":"unknown"} {} {"score":0.92,"keypoints":17} 
+2022-10-02 15:07:01 DATA:  test-backend-node-wasm.js  result: performance: load: null total: 117 
+2022-10-02 15:07:01 STATE: test-backend-node-wasm.js passed: detector result face match 
+2022-10-02 15:07:01 STATE: test-backend-node-wasm.js passed: detector result hand match 
+2022-10-02 15:07:01 INFO:  test-backend-node-wasm.js test: multi-instance 
+2022-10-02 15:07:01 STATE: test-backend-node-wasm.js start multi instance 
+2022-10-02 15:07:02 STATE: test-backend-node-wasm.js event: image 
+2022-10-02 15:07:02 STATE: test-backend-node-wasm.js event: detect 
+2022-10-02 15:07:02 STATE: test-backend-node-wasm.js passed: detect: random multi instance 
+2022-10-02 15:07:02 DATA:  test-backend-node-wasm.js  result: face: 0 body: 1 hand: 0 gesture: 0 object: 0 person: 0 {} {} {"score":0,"keypoints":0} 
+2022-10-02 15:07:02 DATA:  test-backend-node-wasm.js  result: performance: load: null total: 93 
+2022-10-02 15:07:02 INFO:  test-backend-node-wasm.js test: first instance 
+2022-10-02 15:07:02 STATE: test-backend-node-wasm.js start multi instance 
+2022-10-02 15:07:02 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151155104} 
+2022-10-02 15:07:02 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-upper.jpg multi instance 
+2022-10-02 15:07:02 DATA:  test-backend-node-wasm.js  result: face: 1 body: 1 hand: 0 gesture: 0 object: 0 person: 1 {"score":0.96,"gender":"unknown"} {} {"score":0.75,"keypoints":7} 
+2022-10-02 15:07:02 DATA:  test-backend-node-wasm.js  result: performance: load: null total: 106 
+2022-10-02 15:07:02 INFO:  test-backend-node-wasm.js test: second instance 
+2022-10-02 15:07:02 STATE: test-backend-node-wasm.js start multi instance 
+2022-10-02 15:07:02 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151155104} 
+2022-10-02 15:07:02 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-upper.jpg multi instance 
+2022-10-02 15:07:02 DATA:  test-backend-node-wasm.js  result: face: 1 body: 1 hand: 0 gesture: 0 object: 0 person: 1 {"score":0.96,"gender":"unknown"} {} {"score":0.75,"keypoints":7} 
+2022-10-02 15:07:02 DATA:  test-backend-node-wasm.js  result: performance: load: null total: 106 
+2022-10-02 15:07:02 INFO:  test-backend-node-wasm.js test: concurrent 
+2022-10-02 15:07:02 STATE: test-backend-node-wasm.js start concurrent 
+2022-10-02 15:07:02 STATE: test-backend-node-wasm.js start concurrent 
+2022-10-02 15:07:02 STATE: test-backend-node-wasm.js start concurrent 
+2022-10-02 15:07:02 STATE: test-backend-node-wasm.js start concurrent 
+2022-10-02 15:07:02 STATE: test-backend-node-wasm.js start concurrent 
+2022-10-02 15:07:02 STATE: test-backend-node-wasm.js start concurrent 
+2022-10-02 15:07:02 STATE: test-backend-node-wasm.js start concurrent 
+2022-10-02 15:07:02 STATE: test-backend-node-wasm.js start concurrent 
+2022-10-02 15:07:02 STATE: test-backend-node-wasm.js start concurrent 
+2022-10-02 15:07:02 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34697856} 
+2022-10-02 15:07:02 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34697856} 
+2022-10-02 15:07:02 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34697856} 
+2022-10-02 15:07:02 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} 
+2022-10-02 15:07:02 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} 
+2022-10-02 15:07:03 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} 
+2022-10-02 15:07:03 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151155104} 
+2022-10-02 15:07:03 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151155104} 
+2022-10-02 15:07:03 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-upper.jpg [1,720,688,3] {"checksum":151155104} 
+2022-10-02 15:07:03 STATE: test-backend-node-wasm.js event: image 
+2022-10-02 15:07:03 STATE: test-backend-node-wasm.js event: image 
+2022-10-02 15:07:03 STATE: test-backend-node-wasm.js event: image 
+2022-10-02 15:07:04 STATE: test-backend-node-wasm.js event: detect 
+2022-10-02 15:07:04 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-upper.jpg concurrent 
+2022-10-02 15:07:04 DATA:  test-backend-node-wasm.js  result: face: 1 body: 1 hand: 0 gesture: 0 object: 0 person: 1 {"score":0.96,"gender":"unknown"} {} {"score":0.75,"keypoints":7} 
+2022-10-02 15:07:04 DATA:  test-backend-node-wasm.js  result: performance: load: null total: 947 
+2022-10-02 15:07:04 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-upper.jpg concurrent 
+2022-10-02 15:07:04 DATA:  test-backend-node-wasm.js  result: face: 1 body: 1 hand: 0 gesture: 0 object: 0 person: 1 {"score":0.96,"gender":"unknown"} {} {"score":0.75,"keypoints":7} 
+2022-10-02 15:07:04 DATA:  test-backend-node-wasm.js  result: performance: load: null total: 947 
+2022-10-02 15:07:04 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-upper.jpg concurrent 
+2022-10-02 15:07:04 DATA:  test-backend-node-wasm.js  result: face: 1 body: 1 hand: 0 gesture: 0 object: 0 person: 1 {"score":0.96,"gender":"unknown"} {} {"score":0.75,"keypoints":7} 
+2022-10-02 15:07:04 DATA:  test-backend-node-wasm.js  result: performance: load: null total: 947 
+2022-10-02 15:07:04 STATE: test-backend-node-wasm.js event: detect 
+2022-10-02 15:07:04 STATE: test-backend-node-wasm.js event: detect 
+2022-10-02 15:07:04 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-face.jpg concurrent 
+2022-10-02 15:07:04 DATA:  test-backend-node-wasm.js  result: face: 1 body: 1 hand: 1 gesture: 0 object: 0 person: 1 {"score":0.91,"gender":"unknown"} {} {"score":0.47,"keypoints":3} 
+2022-10-02 15:07:04 DATA:  test-backend-node-wasm.js  result: performance: load: null total: 948 
+2022-10-02 15:07:04 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-face.jpg concurrent 
+2022-10-02 15:07:04 DATA:  test-backend-node-wasm.js  result: face: 1 body: 1 hand: 1 gesture: 0 object: 0 person: 1 {"score":0.91,"gender":"unknown"} {} {"score":0.47,"keypoints":3} 
+2022-10-02 15:07:04 DATA:  test-backend-node-wasm.js  result: performance: load: null total: 948 
+2022-10-02 15:07:04 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-face.jpg concurrent 
+2022-10-02 15:07:04 DATA:  test-backend-node-wasm.js  result: face: 1 body: 1 hand: 1 gesture: 0 object: 0 person: 1 {"score":0.91,"gender":"unknown"} {} {"score":0.47,"keypoints":3} 
+2022-10-02 15:07:04 DATA:  test-backend-node-wasm.js  result: performance: load: null total: 948 
+2022-10-02 15:07:04 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-body.jpg concurrent 
+2022-10-02 15:07:04 DATA:  test-backend-node-wasm.js  result: face: 1 body: 1 hand: 1 gesture: 0 object: 0 person: 1 {"score":0.93,"gender":"unknown"} {} {"score":0.92,"keypoints":17} 
+2022-10-02 15:07:04 DATA:  test-backend-node-wasm.js  result: performance: load: null total: 948 
+2022-10-02 15:07:04 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-body.jpg concurrent 
+2022-10-02 15:07:04 DATA:  test-backend-node-wasm.js  result: face: 1 body: 1 hand: 1 gesture: 0 object: 0 person: 1 {"score":0.93,"gender":"unknown"} {} {"score":0.92,"keypoints":17} 
+2022-10-02 15:07:04 DATA:  test-backend-node-wasm.js  result: performance: load: null total: 948 
+2022-10-02 15:07:04 STATE: test-backend-node-wasm.js passed: detect: samples/in/ai-body.jpg concurrent 
+2022-10-02 15:07:04 DATA:  test-backend-node-wasm.js  result: face: 1 body: 1 hand: 1 gesture: 0 object: 0 person: 1 {"score":0.93,"gender":"unknown"} {} {"score":0.92,"keypoints":17} 
+2022-10-02 15:07:04 DATA:  test-backend-node-wasm.js  result: performance: load: null total: 948 
+2022-10-02 15:07:04 INFO:  test-backend-node-wasm.js test: monkey-patch 
+2022-10-02 15:07:04 STATE: test-backend-node-wasm.js event: image 
+2022-10-02 15:07:04 STATE: test-backend-node-wasm.js event: detect 
+2022-10-02 15:07:04 STATE: test-backend-node-wasm.js passed: monkey patch 
+2022-10-02 15:07:04 STATE: test-backend-node-wasm.js passed: segmentation [262144] 
+2022-10-02 15:07:04 STATE: test-backend-node-wasm.js passeed: equal usage 
+2022-10-02 15:07:04 INFO:  test-backend-node-wasm.js test: input compare 
+2022-10-02 15:07:05 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-face.jpg [1,256,256,3] {"checksum":34697856} 
+2022-10-02 15:07:05 STATE: test-backend-node-wasm.js passed: load image: samples/in/ai-body.jpg [1,1200,1200,3] {"checksum":1038921856} 
+2022-10-02 15:07:05 STATE: test-backend-node-wasm.js passed: image compare 0 23.280073018790848 
+2022-10-02 15:07:05 INFO:  test-backend-node-wasm.js events: {"image":29,"detect":29,"warmup":2} 
+2022-10-02 15:07:05 INFO:  test-backend-node-wasm.js tensors 4443 
+2022-10-02 15:07:05 INFO:  test-backend-node-wasm.js test complete: 24175 ms 
+2022-10-02 15:07:05 STATE: all tests complete 
+2022-10-02 15:07:05 INFO:    status {"test":"../demo/nodejs/node.js","passed":1,"failed":0} 
+2022-10-02 15:07:05 INFO:    status {"test":"../demo/nodejs/node-simple.js","passed":1,"failed":0} 
+2022-10-02 15:07:05 INFO:    status {"test":"../demo/nodejs/node-fetch.js","passed":1,"failed":0} 
+2022-10-02 15:07:05 INFO:    status {"test":"../demo/nodejs/node-event.js","passed":1,"failed":0} 
+2022-10-02 15:07:05 INFO:    status {"test":"../demo/nodejs/node-similarity.js","passed":1,"failed":0} 
+2022-10-02 15:07:05 INFO:    status {"test":"../demo/nodejs/node-canvas.js","passed":1,"failed":0} 
+2022-10-02 15:07:05 INFO:    status {"test":"../demo/nodejs/process-folder.js","passed":1,"failed":0} 
+2022-10-02 15:07:05 INFO:    status {"test":"../demo/multithread/node-multiprocess.js","passed":1,"failed":0} 
+2022-10-02 15:07:05 INFO:    status {"test":"../demo/facematch/node-match.js","passed":1,"failed":0} 
+2022-10-02 15:07:05 INFO:    status {"test":"test-node-load.js","passed":1,"failed":0} 
+2022-10-02 15:07:05 INFO:    status {"test":"test-node-gear.js","passed":3,"failed":0} 
+2022-10-02 15:07:05 INFO:    status {"test":"test-backend-node.js","passed":125,"failed":0} 
+2022-10-02 15:07:05 INFO:    status {"test":"test-backend-node-gpu.js","passed":125,"failed":0} 
+2022-10-02 15:07:05 INFO:    status {"test":"test-backend-node-wasm.js","passed":124,"failed":2} 
+2022-10-02 15:07:05 INFO:  failures {"count":2} 
+2022-10-02 15:07:05 WARN:    failed {"test":"test-backend-node-wasm.js","message":["error",["failed: nanodet",[]]]} 
+2022-10-02 15:07:05 WARN:    failed {"test":"test-backend-node-wasm.js","message":["error",["failed: face attention",{"annotations":0}]]} 
diff --git a/tfjs/tf-custom.ts b/tfjs/tf-custom.ts
index 7b6f10ed..735b9b5b 100644
--- a/tfjs/tf-custom.ts
+++ b/tfjs/tf-custom.ts
@@ -1,15 +1,11 @@
 /** Creates tfjs bundle used by Human browser build target
  * @external
  */
-// export * from '@vladmandic/tfjs';
-
-/**
- * Creates tfjs bundle used by Human browser build target
- * @external
- */
 
 /* eslint-disable import/no-extraneous-dependencies */
 
+// export * from '@vladmandic/tfjs';
+
 // export all from build bundle
 export * from '@tensorflow/tfjs/dist/index.js';
 export * from '@tensorflow/tfjs-backend-webgl/dist/index.js';
diff --git a/tsconfig.json b/tsconfig.json
index bbde1847..81afeb82 100644
--- a/tsconfig.json
+++ b/tsconfig.json
@@ -52,7 +52,7 @@
     "tabSize": 2
   },
   "exclude": ["node_modules/", "types/", "dist/**/*.js"],
-  "include": ["src", "tfjs/*.ts", "types/human.d.ts", "test/**/*.ts", "demo/**/*.ts"],
+  "include": ["src", "tfjs/*.ts", "types/human.d.ts", "test/**/*.ts", "demo/**/*.ts", "demo/segmentation/index.js", "demo/index.js"],
   "typedocOptions": {
     "externalPattern": ["node_modules/", "tfjs/"]
   }
diff --git a/wiki b/wiki
index 7ea124ad..b6432fc4 160000
--- a/wiki
+++ b/wiki
@@ -1 +1 @@
-Subproject commit 7ea124ad02f27fa74241e5bfc6f34ceab1062de5
+Subproject commit b6432fc419f6ee9aaf36e94b6be21930edb50bc0